From be0ff221c0f3dac94e6c07fcd96e373c3edc7256 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Aug 2015 01:18:10 -0400
Subject: [PATCH 0001/2677] Setup async project; Setup basic queues;

---
 src/backend/cpu/CMakeLists.txt | 14 ++++++++++++++
 src/backend/cpu/copy.cpp       |  3 +++
 src/backend/cpu/platform.cpp   | 11 ++++++++++-
 src/backend/cpu/platform.hpp   |  4 ++++
 4 files changed, 31 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index ab1e0a685c..10a749b08d 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -47,12 +47,25 @@ IF(NOT UNIX)
     ADD_DEFINITIONS(-DAFDLL)
 ENDIF()
 
+INCLUDE(ExternalProject)
+ExternalProject_Add(
+    threads
+    PREFIX ${CMAKE_BINARY_DIR}/third_party/threads
+    GIT_REPOSITORY https://github.com/alltheflops/threads.git
+    CONFIGURE_COMMAND ""
+    BUILD_COMMAND ""
+    INSTALL_COMMAND ${CMAKE_COMMAND} -E copy_directory
+                    <SOURCE_DIR>/threads ${CMAKE_BINARY_DIR}/third_party/threads
+    LOG_DOWNLOAD ON
+    LOG_INSTALL ON
+    )
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
     "${CMAKE_SOURCE_DIR}/src/backend/cpu"
     ${FFTW_INCLUDES}
     ${CBLAS_INCLUDE_DIR}
     ${LAPACK_INCLUDE_DIR}
+    ${CMAKE_BINARY_DIR}/third_party/threads/src/threads
     )
 
 FILE(GLOB cpu_headers
@@ -148,6 +161,7 @@ TARGET_LINK_LIBRARIES(afcpu
                             PRIVATE ${CBLAS_LIBRARIES}
                             PRIVATE ${FFTW_LIBRARIES})
 
+ADD_DEPENDENCIES(afcpu threads)
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
   ADD_DEPENDENCIES(afcpu forge)
 ENDIF()
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index a2bb4ff912..35c1ebe23f 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -18,6 +18,8 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -46,6 +48,7 @@ namespace cpu
     template<typename T>
     void copyData(T *to, const Array<T> &from)
     {
+        getQueue().sync();
         if(from.isOwner()) {
             // FIXME: Check for errors / exceptions
             memcpy(to, from.get(), from.elements()*sizeof(T));
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index d6b4724c25..73bd5875d0 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -10,6 +10,8 @@
 #include <af/version.h>
 #include <platform.hpp>
 #include <sstream>
+#include <async_queue.hpp>
+#include <array>
 
 namespace cpu
 {
@@ -75,9 +77,16 @@ int getActiveDeviceId()
     return 0;
 }
 
+static const int MAX_QUEUES = 1;
+
+async_queue& getQueue(int idx) {
+    static std::array<async_queue, MAX_QUEUES> queues;
+    return queues[idx];
+}
+
 void sync(int device)
 {
-    // Nothing here
+    getQueue().sync();
 }
 
 }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index e899837b8c..2bf6bf2a93 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -9,6 +9,8 @@
 
 #include <string>
 
+class async_queue;
+
 namespace cpu {
     std::string getInfo();
 
@@ -23,4 +25,6 @@ namespace cpu {
     int getActiveDeviceId();
 
     void sync(int device);
+
+    async_queue& getQueue(int idx = 0);
 }

From b94c3df4e1c5288eb1bb985f372e3f8e5910fe3d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Aug 2015 01:19:34 -0400
Subject: [PATCH 0002/2677] Convert CPU blas to use async queues

---
 src/backend/cpu/blas.cpp | 73 ++++++++++++++++++++++------------------
 1 file changed, 40 insertions(+), 33 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 0bbd39970f..8887202064 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -13,6 +13,8 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <err_common.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -131,36 +133,38 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     int N = rDims[bColDim];
     int K = lDims[aColDim];
 
-    //FIXME: Leaks on errors.
-    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
-    auto alpha = getScale<T, 1>();
-    auto beta  = getScale<T, 0>();
-
-    dim4 lStrides = lhs.strides();
-    dim4 rStrides = rhs.strides();
     using BT  =       typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
-    if(rDims[bColDim] == 1) {
-        N = lDims[aColDim];
-        gemv_func<T>()(
-            CblasColMajor, lOpts,
-            lDims[0], lDims[1],
-            alpha,
-            reinterpret_cast<CBT*>(lhs.get()), lStrides[1],
-            reinterpret_cast<CBT*>(rhs.get()), rStrides[0],
-            beta,
-            reinterpret_cast<BT*>(out.get()), 1);
-    } else {
-        gemm_func<T>()(
-            CblasColMajor, lOpts, rOpts,
-            M, N, K,
-            alpha,
-            reinterpret_cast<CBT*>(lhs.get()), lStrides[1],
-            reinterpret_cast<CBT*>(rhs.get()), rStrides[1],
-            beta,
-            reinterpret_cast<BT*>(out.get()), out.dims()[0]);
-    }
+    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    auto func = [=] (Array<T> output, const Array<T> left, const Array<T> right) {
+        auto alpha = getScale<T, 1>();
+        auto beta  = getScale<T, 0>();
+
+        dim4 lStrides = left.strides();
+        dim4 rStrides = right.strides();
+
+        if(rDims[bColDim] == 1) {
+            gemv_func<T>()(
+                CblasColMajor, lOpts,
+                lDims[0], lDims[1],
+                alpha,
+                reinterpret_cast<CBT*>(left.get()), lStrides[1],
+                reinterpret_cast<CBT*>(right.get()), rStrides[0],
+                beta,
+                reinterpret_cast<BT*>(output.get()), 1);
+        } else {
+            gemm_func<T>()(
+                CblasColMajor, lOpts, rOpts,
+                M, N, K,
+                alpha,
+                reinterpret_cast<CBT*>(left.get()), lStrides[1],
+                reinterpret_cast<CBT*>(right.get()), rStrides[1],
+                beta,
+                reinterpret_cast<BT*>(output.get()), output.dims()[0]);
+        }
+    };
+    getQueue().enqueue(func, out, lhs, rhs);
 
     return out;
 }
@@ -172,7 +176,7 @@ template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
 template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
 
 template<typename T, bool conjugate, bool both_conjugate>
-Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
+void dot_(Array<T> output, const Array<T> &lhs, const Array<T> &rhs,
               af_mat_prop optLhs, af_mat_prop optRhs)
 {
     int N = lhs.dims()[0];
@@ -186,22 +190,25 @@ Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
 
     if(both_conjugate) out = cpu::conj(out);
 
-    return createValueArray(af::dim4(1), out);
+    *output.get() = out;
+
 }
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    Array<T> out = createEmptyArray<T>(af::dim4(1));
     if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        return dot_<T, false, true>(lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(dot_<T, false, true>, out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        return dot_<T, true, false>(lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(dot_<T, true, false>,out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        return dot_<T, true, false>(rhs, lhs, optRhs, optLhs);
+        getQueue().enqueue(dot_<T, true, false>,out, rhs, lhs, optRhs, optLhs);
     } else {
-        return dot_<T, false, false>(lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(dot_<T, false, false>,out, lhs, rhs, optLhs, optRhs);
     }
+    return out;
 }
 
 #undef BT

From 3188bdf56555ae36b24c41039d973a7d9835301f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Aug 2015 11:51:54 -0400
Subject: [PATCH 0003/2677] Async CPU approx1 and approx2

---
 src/backend/cpu/approx.cpp | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 69b943a6e5..735edd4fd2 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -12,6 +12,8 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -141,14 +143,14 @@ namespace cpu
 
         switch(method) {
             case AF_INTERP_NEAREST:
-                approx1_<Ty, Tp, AF_INTERP_NEAREST>
-                        (out.get(), out.dims(), out.elements(),
+                getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_NEAREST>,
+                        out.get(), out.dims(), out.elements(),
                          in.get(), in.dims(), in.elements(), pos.get(), pos.dims(),
                          out.strides(), in.strides(), pos.strides(), offGrid);
                 break;
             case AF_INTERP_LINEAR:
-                approx1_<Ty, Tp, AF_INTERP_LINEAR>
-                        (out.get(), out.dims(), out.elements(),
+                getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_LINEAR>,
+                        out.get(), out.dims(), out.elements(),
                          in.get(), in.dims(), in.elements(), pos.get(), pos.dims(),
                          out.strides(), in.strides(), pos.strides(), offGrid);
                 break;
@@ -304,16 +306,16 @@ namespace cpu
 
         switch(method) {
             case AF_INTERP_NEAREST:
-                approx2_<Ty, Tp, AF_INTERP_NEAREST>
-                        (out.get(), out.dims(), out.elements(),
+                getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_NEAREST>,
+                        out.get(), out.dims(), out.elements(),
                          in.get(), in.dims(), in.elements(),
                          pos0.get(), pos0.dims(), pos1.get(), pos1.dims(),
                          out.strides(), in.strides(), pos0.strides(), pos1.strides(),
                          offGrid);
                 break;
             case AF_INTERP_LINEAR:
-                approx2_<Ty, Tp, AF_INTERP_LINEAR>
-                        (out.get(), out.dims(), out.elements(),
+                getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_LINEAR>,
+                        out.get(), out.dims(), out.elements(),
                          in.get(), in.dims(), in.elements(),
                          pos0.get(), pos0.dims(), pos1.get(), pos1.dims(),
                          out.strides(), in.strides(), pos0.strides(), pos1.strides(),

From f797314daf6ac0ff8f630437ecf276b274b86a36 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Aug 2015 12:20:39 -0400
Subject: [PATCH 0004/2677] Async CPU Assign

---
 src/backend/cpu/assign.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index a8ac33ece0..c0a177f5e2 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -14,8 +14,11 @@
 #include <handle.hpp>
 #include <assign.hpp>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
+using std::ref;
 
 namespace cpu
 {
@@ -34,7 +37,7 @@ dim_t trimIndex(int idx, const dim_t &len)
 }
 
 template<typename T>
-void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
+void assign_(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 {
     bool isSeq[4];
     std::vector<af_seq> seqs(4, af_span);
@@ -111,6 +114,12 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
     }
 }
 
+template<typename T>
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
+{
+    getQueue().enqueue(assign_<T>, ref(out), idxrs, ref(rhs));
+}
+
 #define INSTANTIATE(T) \
     template void assign<T>(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
 

From c8ecdb92c5607b76cf108fb3dd7784ef8a696641 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Aug 2015 16:21:30 -0400
Subject: [PATCH 0005/2677] Async CPU Bilateral

---
 src/backend/cpu/bilateral.cpp | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index d8ef7c61cb..446b8a0c17 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -14,8 +14,11 @@
 #include <bilateral.hpp>
 #include <cmath>
 #include <algorithm>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
+using std::ref;
 
 namespace cpu
 {
@@ -35,12 +38,11 @@ static inline unsigned getIdx(const dim4 &strides,
 }
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
+void bilateral_(Array<outType> out, const Array<inType> &in, float s_sigma, float c_sigma)
 {
     const dim4 dims     = in.dims();
     const dim4 istrides = in.strides();
 
-    Array<outType> out = createEmptyArray<outType>(dims);
     const dim4 ostrides = out.strides();
 
     outType *outData    = out.get();
@@ -93,7 +95,14 @@ Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const fl
             inData  += istrides[2];
         }
     }
+}
 
+template<typename inType, typename outType, bool isColor>
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
+{
+    const dim4 dims     = in.dims();
+    Array<outType> out = createEmptyArray<outType>(dims);
+    getQueue().enqueue(bilateral_<inType, outType, isColor>, out, ref(in), s_sigma, c_sigma);
     return out;
 }
 

From 759b506fed2406bac094ec26b4fad293cd09f0e9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 10 Aug 2015 22:08:55 -0400
Subject: [PATCH 0006/2677] Async CPU Convolve

---
 src/backend/cpu/convolve.cpp | 47 +++++++++++++++++++++---------------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 33670d47cc..a5d7ded17c 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -14,6 +14,8 @@
 #include <convolve.hpp>
 #include <err_cpu.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -204,8 +206,8 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    convolve_nd<T, accT, baseDim, expand>(out.get(), signal.get(), filter.get(),
-            oDims, sDims, fDims, out.strides(), sStrides, filter.strides(), kind);
+    getQueue().enqueue(convolve_nd<T, accT, baseDim, expand>,out.get(), signal.get(), filter.get(),
+                                              oDims, sDims, fDims, out.strides(), sStrides, filter.strides(), kind);
 
     return out;
 }
@@ -271,32 +273,37 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
         oDims[1] += rflen - 1;
     }
 
-    Array<T> temp = createEmptyArray<T>(tDims);
     Array<T> out  = createEmptyArray<T>(oDims);
-    auto tStrides = temp.strides();
-    auto oStrides = out.strides();
 
-    for (dim_t b3=0; b3<oDims[3]; ++b3) {
+    auto func = [=] (Array<T> out) {
+        Array<T> temp = createEmptyArray<T>(tDims);
+        auto tStrides = temp.strides();
+        auto oStrides = out.strides();
 
-        dim_t i_b3Off = b3*sStrides[3];
-        dim_t t_b3Off = b3*tStrides[3];
-        dim_t o_b3Off = b3*oStrides[3];
+        for (dim_t b3=0; b3<oDims[3]; ++b3) {
 
-        for (dim_t b2=0; b2<oDims[2]; ++b2) {
+            dim_t i_b3Off = b3*sStrides[3];
+            dim_t t_b3Off = b3*tStrides[3];
+            dim_t o_b3Off = b3*oStrides[3];
 
-            T const *iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
-            T *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
-            T *optr = out.get()  + b2*oStrides[2] + o_b3Off;
+            for (dim_t b2=0; b2<oDims[2]; ++b2) {
 
-            convolve2_separable<T, accT, 0, expand>(tptr, iptr, c_filter.get(),
-                    tDims, sDims, sDims, cflen,
-                    tStrides, sStrides, c_filter.strides()[0]);
+                T const *iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
+                T *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
+                T *optr = out.get()  + b2*oStrides[2] + o_b3Off;
 
-            convolve2_separable<T, accT, 1, expand>(optr, tptr, r_filter.get(),
-                    oDims, tDims, sDims, rflen,
-                    oStrides, tStrides, r_filter.strides()[0]);
+                convolve2_separable<T, accT, 0, expand>(tptr, iptr, c_filter.get(),
+                        tDims, sDims, sDims, cflen,
+                        tStrides, sStrides, c_filter.strides()[0]);
+
+                convolve2_separable<T, accT, 1, expand>(optr, tptr, r_filter.get(),
+                        oDims, tDims, sDims, rflen,
+                        oStrides, tStrides, r_filter.strides()[0]);
+            }
         }
-    }
+    };
+
+    getQueue().enqueue(func, out);
 
     return out;
 }

From c399e751cf7c824e0df575cf266ea7bb13645905 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 10 Aug 2015 23:25:04 -0400
Subject: [PATCH 0007/2677] Async CPU diff1 and diff2

---
 src/backend/cpu/diff.cpp | 91 ++++++++++++++++++++++------------------
 1 file changed, 50 insertions(+), 41 deletions(-)

diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 907c111c0b..08c1a66ac2 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -11,6 +11,8 @@
 #include <diff.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -36,28 +38,31 @@ namespace cpu
         dims[dim]--;
 
         // Create output placeholder
-        Array<T> outArray = createValueArray(dims, (T)0);
-
-        // Get pointers to raw data
-        const T *inPtr = in.get();
-              T *outPtr = outArray.get();
-
-        // TODO: Improve this
-        for(dim_t l = 0; l < dims[3]; l++) {
-            for(dim_t k = 0; k < dims[2]; k++) {
-                for(dim_t j = 0; j < dims[1]; j++) {
-                    for(dim_t i = 0; i < dims[0]; i++) {
-                        // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                        int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                        int jdx = getIdx(in.strides(), in.offsets(),
-                                         i + is_dim0, j + is_dim1,
-                                         k + is_dim2, l + is_dim3);
-                        int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
-                        outPtr[odx] = inPtr[jdx] - inPtr[idx];
+        Array<T> outArray = createEmptyArray<T>(dims);
+
+        auto func = [=] (Array<T> outArray, Array<T> in) {
+            // Get pointers to raw data
+            const T *inPtr = in.get();
+                  T *outPtr = outArray.get();
+
+            // TODO: Improve this
+            for(dim_t l = 0; l < dims[3]; l++) {
+                for(dim_t k = 0; k < dims[2]; k++) {
+                    for(dim_t j = 0; j < dims[1]; j++) {
+                        for(dim_t i = 0; i < dims[0]; i++) {
+                            // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+                            int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
+                            int jdx = getIdx(in.strides(), in.offsets(),
+                                            i + is_dim0, j + is_dim1,
+                                            k + is_dim2, l + is_dim3);
+                            int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
+                            outPtr[odx] = inPtr[jdx] - inPtr[idx];
+                        }
                     }
                 }
             }
-        }
+        };
+        getQueue().enqueue(func, outArray, in);
 
         return outArray;
     }
@@ -76,31 +81,35 @@ namespace cpu
         dims[dim] -= 2;
 
         // Create output placeholder
-        Array<T> outArray = createValueArray(dims, (T)0);
-
-        // Get pointers to raw data
-        const T *inPtr = in.get();
-              T *outPtr = outArray.get();
-
-        // TODO: Improve this
-        for(dim_t l = 0; l < dims[3]; l++) {
-            for(dim_t k = 0; k < dims[2]; k++) {
-                for(dim_t j = 0; j < dims[1]; j++) {
-                    for(dim_t i = 0; i < dims[0]; i++) {
-                        // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                        int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                        int jdx = getIdx(in.strides(), in.offsets(),
-                                         i + is_dim0, j + is_dim1,
-                                         k + is_dim2, l + is_dim3);
-                        int kdx = getIdx(in.strides(), in.offsets(),
-                                         i + 2 * is_dim0, j + 2 * is_dim1,
-                                         k + 2 * is_dim2, l + 2 * is_dim3);
-                        int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
-                        outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
+        Array<T> outArray = createEmptyArray<T>(dims);
+
+        auto func = [=] (Array<T> outArray, Array<T> in) {
+            // Get pointers to raw data
+            const T *inPtr = in.get();
+                T *outPtr = outArray.get();
+
+            // TODO: Improve this
+            for(dim_t l = 0; l < dims[3]; l++) {
+                for(dim_t k = 0; k < dims[2]; k++) {
+                    for(dim_t j = 0; j < dims[1]; j++) {
+                        for(dim_t i = 0; i < dims[0]; i++) {
+                            // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+                            int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
+                            int jdx = getIdx(in.strides(), in.offsets(),
+                                            i + is_dim0, j + is_dim1,
+                                            k + is_dim2, l + is_dim3);
+                            int kdx = getIdx(in.strides(), in.offsets(),
+                                            i + 2 * is_dim0, j + 2 * is_dim1,
+                                            k + 2 * is_dim2, l + 2 * is_dim3);
+                            int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
+                            outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
+                        }
                     }
                 }
             }
-        }
+        };
+
+        getQueue().enqueue(func, outArray, in);
 
         return outArray;
     }

From 80903d062962fcbb215c3cc6ffb12b18457f73be Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 13 Aug 2015 17:24:13 -0400
Subject: [PATCH 0008/2677] Avoid sending references to queued lambdas

---
 src/backend/cpu/bilateral.cpp | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 446b8a0c17..c826ef67be 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -18,7 +18,6 @@
 #include <async_queue.hpp>
 
 using af::dim4;
-using std::ref;
 
 namespace cpu
 {
@@ -38,15 +37,15 @@ static inline unsigned getIdx(const dim4 &strides,
 }
 
 template<typename inType, typename outType, bool isColor>
-void bilateral_(Array<outType> out, const Array<inType> &in, float s_sigma, float c_sigma)
+void bilateral_(Array<outType> out, const Array<inType> in, float s_sigma, float c_sigma)
 {
     const dim4 dims     = in.dims();
     const dim4 istrides = in.strides();
 
     const dim4 ostrides = out.strides();
 
-    outType *outData    = out.get();
-    const inType * inData = in.get();
+          outType *outData = out.get();
+    const inType  *inData  = in.get();
 
     // clamp spatical and chromatic sigma's
     float space_          = std::min(11.5f, std::max(s_sigma, 0.f));
@@ -102,7 +101,7 @@ Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const fl
 {
     const dim4 dims     = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
-    getQueue().enqueue(bilateral_<inType, outType, isColor>, out, ref(in), s_sigma, c_sigma);
+    getQueue().enqueue(bilateral_<inType, outType, isColor>, out, in, s_sigma, c_sigma);
     return out;
 }
 

From 96c5602965334c5f36f33dc69ad81314fd6e6bd7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 13 Aug 2015 17:25:31 -0400
Subject: [PATCH 0009/2677] Async CPU Copy, Assign, and Index

---
 src/backend/cpu/assign.cpp |  9 ++++--
 src/backend/cpu/copy.cpp   |  6 ++--
 src/backend/cpu/index.cpp  | 56 ++++++++++++++++++++++----------------
 3 files changed, 42 insertions(+), 29 deletions(-)

diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index c0a177f5e2..589fa537f5 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -16,9 +16,12 @@
 #include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <array>
 
 using af::dim4;
 using std::ref;
+using std::copy;
+using std::array;
 
 namespace cpu
 {
@@ -37,7 +40,7 @@ dim_t trimIndex(int idx, const dim_t &len)
 }
 
 template<typename T>
-void assign_(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
+void assign_(Array<T> out, const array<af_index_t, 4> idxrs, const Array<T> rhs)
 {
     bool isSeq[4];
     std::vector<af_seq> seqs(4, af_span);
@@ -117,7 +120,9 @@ void assign_(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 {
-    getQueue().enqueue(assign_<T>, ref(out), idxrs, ref(rhs));
+    array<af_index_t, 4> idx;
+    copy(idxrs, idxrs+4, begin(idx));
+    getQueue().enqueue(assign_<T>, out, move(idx), rhs);
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 35c1ebe23f..433e7186bd 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -117,7 +117,7 @@ namespace cpu
     template<typename T>
     void multiply_inplace(Array<T> &in, double val)
     {
-        copy<T, T>(in, in, 0, val);
+        getQueue().enqueue(copy<T, T>,in, in, 0, val);
     }
 
     template<typename inType, typename outType>
@@ -126,14 +126,14 @@ namespace cpu
              outType default_value, double factor)
     {
         Array<outType> ret = createValueArray<outType>(dims, default_value);
-        copy<inType, outType>(ret, in, outType(default_value), factor);
+        getQueue().enqueue(copy<inType, outType>,ret, in, outType(default_value), factor);
         return ret;
     }
 
     template<typename inType, typename outType>
     void copyArray(Array<outType> &out, Array<inType> const &in)
     {
-        copy<inType, outType>(out, in, scalar<outType>(0), 1.0);
+        getQueue().enqueue(copy<inType, outType>,out, in, scalar<outType>(0), 1.0);
     }
 
 
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 162e67fb46..c6112fa6c8 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -15,6 +15,8 @@
 #include <handle.hpp>
 #include <err_cpu.hpp>
 #include <vector>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -68,43 +70,49 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     Array<T> out = createEmptyArray<T>(oDims);
     dim4 oStrides= out.strides();
 
-    const T *src = in.get();
-    T *dst = out.get();
 
-    const uint* ptr0 = idxArrs[0].get();
-    const uint* ptr1 = idxArrs[1].get();
-    const uint* ptr2 = idxArrs[2].get();
-    const uint* ptr3 = idxArrs[3].get();
+    auto func = [=] (Array<T> out, const Array<T> in) {
 
-    for (dim_t l=0; l<oDims[3]; ++l) {
+        const T *src = in.get();
+              T *dst = out.get();
 
-        dim_t lOff   = l*oStrides[3];
-        dim_t inIdx3 = trimIndex(isSeq[3] ? l+iOffs[3] : ptr3[l], iDims[3]);
-        dim_t inOff3 = inIdx3*iStrds[3];
+        const uint* ptr0 = idxArrs[0].get();
+        const uint* ptr1 = idxArrs[1].get();
+        const uint* ptr2 = idxArrs[2].get();
+        const uint* ptr3 = idxArrs[3].get();
 
-        for (dim_t k=0; k<oDims[2]; ++k) {
+        for (dim_t l=0; l<oDims[3]; ++l) {
 
-            dim_t kOff   = k*oStrides[2];
-            dim_t inIdx2 = trimIndex(isSeq[2] ? k+iOffs[2] : ptr2[k], iDims[2]);
-            dim_t inOff2 = inIdx2*iStrds[2];
+            dim_t lOff   = l*oStrides[3];
+            dim_t inIdx3 = trimIndex(isSeq[3] ? l+iOffs[3] : ptr3[l], iDims[3]);
+            dim_t inOff3 = inIdx3*iStrds[3];
 
-            for (dim_t j=0; j<oDims[1]; ++j) {
+            for (dim_t k=0; k<oDims[2]; ++k) {
 
-                dim_t jOff   = j*oStrides[1];
-                dim_t inIdx1 = trimIndex(isSeq[1] ? j+iOffs[1] : ptr1[j], iDims[1]);
-                dim_t inOff1 = inIdx1*iStrds[1];
+                dim_t kOff   = k*oStrides[2];
+                dim_t inIdx2 = trimIndex(isSeq[2] ? k+iOffs[2] : ptr2[k], iDims[2]);
+                dim_t inOff2 = inIdx2*iStrds[2];
 
-                for (dim_t i=0; i<oDims[0]; ++i) {
+                for (dim_t j=0; j<oDims[1]; ++j) {
 
-                    dim_t iOff   = i*oStrides[0];
-                    dim_t inIdx0 = trimIndex(isSeq[0] ? i+iOffs[0] : ptr0[i], iDims[0]);
-                    dim_t inOff0 = inIdx0*iStrds[0];
+                    dim_t jOff   = j*oStrides[1];
+                    dim_t inIdx1 = trimIndex(isSeq[1] ? j+iOffs[1] : ptr1[j], iDims[1]);
+                    dim_t inOff1 = inIdx1*iStrds[1];
 
-                    dst[lOff+kOff+jOff+iOff] = src[inOff3+inOff2+inOff1+inOff0];
+                    for (dim_t i=0; i<oDims[0]; ++i) {
+
+                        dim_t iOff   = i*oStrides[0];
+                        dim_t inIdx0 = trimIndex(isSeq[0] ? i+iOffs[0] : ptr0[i], iDims[0]);
+                        dim_t inOff0 = inIdx0*iStrds[0];
+
+                        dst[lOff+kOff+jOff+iOff] = src[inOff3+inOff2+inOff1+inOff0];
+                    }
                 }
             }
         }
-    }
+    };
+
+    getQueue().enqueue(func, out, in);
 
     return out;
 }

From 46beebc9080c07e788e3e663654e2c871dc8a1c0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 13 Aug 2015 17:26:08 -0400
Subject: [PATCH 0010/2677] Async CPU diagonal

---
 src/backend/cpu/diagonal.cpp | 55 +++++++++++++++++++++---------------
 1 file changed, 32 insertions(+), 23 deletions(-)

diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 2ae69a6901..c2e7e92e17 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -14,6 +14,8 @@
 #include <diagonal.hpp>
 #include <math.hpp>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -24,22 +26,25 @@ namespace cpu
         int batch = in.dims()[1];
         Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
-        const T *iptr = in.get();
-        T *optr = out.get();
+        auto func = [=] (Array<T> out, const Array<T> in) {
+            const T *iptr = in.get();
+                  T *optr = out.get();
 
-        for (int k = 0; k < batch; k++) {
-            for (int j = 0; j < size; j++) {
-                for (int i = 0; i < size; i++) {
-                    T val = scalar<T>(0);
-                    if (i == j - num) {
-                        val = (num > 0) ? iptr[i] : iptr[j];
+            for (int k = 0; k < batch; k++) {
+                for (int j = 0; j < size; j++) {
+                    for (int i = 0; i < size; i++) {
+                        T val = scalar<T>(0);
+                        if (i == j - num) {
+                            val = (num > 0) ? iptr[i] : iptr[j];
+                        }
+                        optr[i + j * out.strides()[1]] = val;
                     }
-                    optr[i + j * out.strides()[1]] = val;
                 }
+                optr += out.strides()[2];
+                iptr += in.strides()[1];
             }
-            optr += out.strides()[2];
-            iptr += in.strides()[1];
-        }
+        };
+        getQueue().enqueue(func, out, in);
 
         return out;
     }
@@ -51,23 +56,27 @@ namespace cpu
         dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
         Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-        const dim_t *odims = out.dims().get();
+        auto func = [=] (Array<T> out, const Array<T> in) {
+            const dim_t *odims = out.dims().get();
 
-        const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
+            const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
 
-        for (int l = 0; l < (int)odims[3]; l++) {
+            for (int l = 0; l < (int)odims[3]; l++) {
 
-            for (int k = 0; k < (int)odims[2]; k++) {
-                const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
-                T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
+                for (int k = 0; k < (int)odims[2]; k++) {
+                    const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
+                    T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
 
-                for (int i = 0; i < (int)odims[0]; i++) {
-                    T val = scalar<T>(0);
-                    if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
-                    optr[i] = val;
+                    for (int i = 0; i < (int)odims[0]; i++) {
+                        T val = scalar<T>(0);
+                        if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
+                        optr[i] = val;
+                    }
                 }
             }
-        }
+        };
+
+        getQueue().enqueue(func, out, in);
 
         return out;
     }

From b7c83e800e7e5ef714b9ac63e82b79f818e3b965 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Sep 2015 14:36:05 -0400
Subject: [PATCH 0011/2677] Async FFT for the CPU backend

---
 src/backend/cpu/Array.cpp   | 48 +++++++++++++++++++------------------
 src/backend/cpu/copy.cpp    | 10 ++++----
 src/backend/cpu/fft.cpp     | 39 ++++++++++++++++++++++--------
 src/backend/cpu/reorder.cpp | 22 ++++++++++-------
 4 files changed, 74 insertions(+), 45 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 15515fa7b5..d714fd9682 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -14,6 +14,7 @@
 #include <TNJ/ScalarNode.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
+#include <async_queue.hpp>
 #include <cstring>
 #include <cstddef>
 
@@ -46,7 +47,6 @@ namespace cpu
         }
     }
 
-
     template<typename T>
     Array<T>::Array(af::dim4 dims, TNJ::Node_ptr n) :
         info(-1, dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
@@ -67,40 +67,42 @@ namespace cpu
     template<typename T>
     void Array<T>::eval()
     {
-        if (isReady()) return;
+        auto func = [this] {
+            if (isReady()) return;
 
-        this->setId(getActiveDeviceId());
-        data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
-        T *ptr = data.get();
+            setId(getActiveDeviceId());
+            data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
+            T *ptr = data.get();
 
-        dim4 ostrs = strides();
-        dim4 odims = dims();
+            dim4 ostrs = strides();
+            dim4 odims = dims();
 
-        for (int w = 0; w < (int)odims[3]; w++) {
-            dim_t offw = w * ostrs[3];
+            for (int w = 0; w < (int)odims[3]; w++) {
+                dim_t offw = w * ostrs[3];
 
-            for (int z = 0; z < (int)odims[2]; z++) {
-                dim_t offz = z * ostrs[2] + offw;
+                for (int z = 0; z < (int)odims[2]; z++) {
+                    dim_t offz = z * ostrs[2] + offw;
 
-                for (int y = 0; y < (int)odims[1]; y++) {
-                    dim_t offy = y * ostrs[1] + offz;
+                    for (int y = 0; y < (int)odims[1]; y++) {
+                        dim_t offy = y * ostrs[1] + offz;
 
-                    for (int x = 0; x < (int)odims[0]; x++) {
-                        dim_t id = x + offy;
+                        for (int x = 0; x < (int)odims[0]; x++) {
+                            dim_t id = x + offy;
 
-                        ptr[id] = *(T *)node->calc(x, y, z, w);
+                            ptr[id] = *(T *)node->calc(x, y, z, w);
+                        }
                     }
                 }
             }
-        }
-
 
-        ready = true;
+            ready = true;
+            Node_ptr prev = node;
+            prev->reset();
+            // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
+            node.reset();
+        };
 
-        Node_ptr prev = node;
-        prev->reset();
-        // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-        node.reset();
+        getQueue().enqueue(func);
     }
 
     template<typename T>
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 433e7186bd..58773afbaa 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -67,7 +67,7 @@ namespace cpu
     }
 
     template<typename inType, typename outType>
-    static void copy(Array<outType> &dst, const Array<inType> &src, outType default_value, double factor)
+    static void copy(Array<outType> dst, const Array<inType> src, outType default_value, double factor)
     {
         dim4 src_dims       = src.dims();
         dim4 dst_dims       = dst.dims();
@@ -117,7 +117,7 @@ namespace cpu
     template<typename T>
     void multiply_inplace(Array<T> &in, double val)
     {
-        getQueue().enqueue(copy<T, T>,in, in, 0, val);
+        getQueue().enqueue(copy<T, T>, in, in, 0, val);
     }
 
     template<typename inType, typename outType>
@@ -126,14 +126,16 @@ namespace cpu
              outType default_value, double factor)
     {
         Array<outType> ret = createValueArray<outType>(dims, default_value);
-        getQueue().enqueue(copy<inType, outType>,ret, in, outType(default_value), factor);
+        ret.eval();
+        getQueue().sync();
+        getQueue().enqueue(copy<inType, outType>, ret, in, outType(default_value), factor);
         return ret;
     }
 
     template<typename inType, typename outType>
     void copyArray(Array<outType> &out, Array<inType> const &in)
     {
-        getQueue().enqueue(copy<inType, outType>,out, in, scalar<outType>(0), 1.0);
+        getQueue().enqueue(copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
     }
 
 
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index e41c8a1658..7262e6dd78 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -16,6 +16,8 @@
 #include <fftw3.h>
 #include <copy.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -52,7 +54,7 @@ TRANSFORM(fftwf, cfloat)
 TRANSFORM(fftw, cdouble)
 
 template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in)
+void fft_inplace_(Array<T> in)
 {
     int t_dims[rank];
     int in_embed[rank];
@@ -90,6 +92,12 @@ void fft_inplace(Array<T> &in)
     transform.destroy(plan);
 }
 
+template<typename T, int rank, bool direction>
+void fft_inplace(Array<T> &in)
+{
+    getQueue().enqueue(fft_inplace_<T, rank, direction>, in);
+}
+
 template<typename To, typename Ti>
 struct fftw_real_transform;
 
@@ -114,14 +122,9 @@ TRANSFORM_REAL(fftwf, float , cfloat , c2r)
 TRANSFORM_REAL(fftw , double, cdouble, c2r)
 
 template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in)
+void fft_r2c_(Array<Tc> out, const Array<Tr> in)
 {
     dim4 idims = in.dims();
-    dim4 odims = in.dims();
-
-    odims[0] = odims[0] / 2 + 1;
-
-    Array<Tc> out = createEmptyArray<Tc>(odims);
 
     int t_dims[rank];
     int in_embed[rank];
@@ -157,15 +160,23 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
 
     transform.execute(plan);
     transform.destroy(plan);
+}
+
+template<typename Tc, typename Tr, int rank>
+Array<Tc> fft_r2c(const Array<Tr> &in)
+{
+    dim4 odims = in.dims();
+    odims[0] = odims[0] / 2 + 1;
+    Array<Tc> out = createEmptyArray<Tc>(odims);
+
+    getQueue().enqueue(fft_r2c_<Tc, Tr, rank>, out, in);
 
     return out;
 }
 
 template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
+void fft_c2r_(Array<Tr> out, const Array<Tc> in, const dim4 odims)
 {
-    Array<Tr> out = createEmptyArray<Tr>(odims);
-
     int t_dims[rank];
     int in_embed[rank];
     int out_embed[rank];
@@ -200,6 +211,14 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
 
     transform.execute(plan);
     transform.destroy(plan);
+}
+
+template<typename Tr, typename Tc, int rank>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
+{
+    Array<Tr> out = createEmptyArray<Tr>(odims);
+    getQueue().enqueue(fft_c2r_<Tr, Tc, rank>, out, in, odims);
+
     return out;
 }
 
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 42da24e435..5e1cd8fbca 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -11,19 +11,14 @@
 #include <reorder.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
     template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
+    void reorder_(Array<T> out, const Array<T> in, const af::dim4 oDims, const af::dim4 rdims)
     {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims(0);
-        for(int i = 0; i < 4; i++)
-            oDims[i] = iDims[rdims[i]];
-
-        Array<T> out = createEmptyArray<T>(oDims);
-
         T* outPtr = out.get();
         const T* inPtr = in.get();
 
@@ -53,7 +48,18 @@ namespace cpu
                 }
             }
         }
+    }
 
+    template<typename T>
+    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
+    {
+        const af::dim4 iDims = in.dims();
+        af::dim4 oDims(0);
+        for(int i = 0; i < 4; i++)
+            oDims[i] = iDims[rdims[i]];
+
+        Array<T> out = createEmptyArray<T>(oDims);
+        getQueue().enqueue(reorder_<T>, out, in, oDims, rdims);
         return out;
     }
 

From 413eea8f8c4abe6850b561c59ebe1e1a0f361a6f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Sep 2015 16:53:06 -0400
Subject: [PATCH 0012/2677] Add eval to copyData

---
 src/backend/cpu/copy.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 58773afbaa..3be201b893 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -48,6 +48,7 @@ namespace cpu
     template<typename T>
     void copyData(T *to, const Array<T> &from)
     {
+        evalArray(from);
         getQueue().sync();
         if(from.isOwner()) {
             // FIXME: Check for errors / exceptions

From 49f0cce2f145385b1908dd95faf6b048a3327d01 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Sep 2015 16:56:21 -0400
Subject: [PATCH 0013/2677] Async random on CPU backend

---
 src/backend/cpu/random.cpp | 87 +++++++++++++++++++++++---------------
 1 file changed, 52 insertions(+), 35 deletions(-)

diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index 4c91b96fb1..7ecf272d41 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -18,6 +18,8 @@
 #include <af/defines.h>
 #include <Array.hpp>
 #include <random.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -74,7 +76,7 @@ static bool is_first = true;
 #define GLOBAL 1
 
 template<typename T>
-Array<T> randn(const af::dim4 &dims)
+void randn_(Array<T> out)
 {
     static unsigned long long my_seed = 0;
     if (is_first) {
@@ -89,16 +91,22 @@ Array<T> randn(const af::dim4 &dims)
         my_seed = gen_seed;
     }
 
-    Array<T> outArray = createEmptyArray<T>(dims);
-    T *outPtr = outArray.get();
-    for (int i = 0; i < (int)outArray.elements(); i++) {
+    T *outPtr = out.get();
+    for (int i = 0; i < (int)out.elements(); i++) {
         outPtr[i] = gen();
     }
+}
+
+template<typename T>
+Array<T> randn(const af::dim4 &dims)
+{
+    Array<T> outArray = createEmptyArray<T>(dims);
+    getQueue().enqueue(randn_<T>, outArray);
     return outArray;
 }
 
 template<typename T>
-Array<T> randu(const af::dim4 &dims)
+void randu_(Array<T> out)
 {
     static unsigned long long my_seed = 0;
     if (is_first) {
@@ -113,11 +121,39 @@ Array<T> randu(const af::dim4 &dims)
         my_seed = gen_seed;
     }
 
-    Array<T> outArray = createEmptyArray<T>(dims);
-    T *outPtr = outArray.get();
-    for (int i = 0; i < (int)outArray.elements(); i++) {
+    T *outPtr = out.get();
+    for (int i = 0; i < (int)out.elements(); i++) {
         outPtr[i] = gen();
     }
+}
+
+template<>
+void randu_(Array<char> out)
+{
+    static unsigned long long my_seed = 0;
+    if (is_first) {
+        setSeed(gen_seed);
+        my_seed = gen_seed;
+    }
+
+    static auto gen = urand<float>(generator);
+
+    if (my_seed != gen_seed) {
+        gen = urand<float>(generator);
+        my_seed = gen_seed;
+    }
+
+    char *outPtr = out.get();
+    for (int i = 0; i < (int)out.elements(); i++) {
+        outPtr[i] = gen() > 0.5;
+    }
+}
+
+template<typename T>
+Array<T> randu(const af::dim4 &dims)
+{
+    Array<T> outArray = createEmptyArray<T>(dims);
+    getQueue().enqueue(randu_<T>, outArray);
     return outArray;
 }
 
@@ -133,6 +169,7 @@ INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
 INSTANTIATE_UNIFORM(uintl)
 INSTANTIATE_UNIFORM(uchar)
+INSTANTIATE_UNIFORM(char)
 
 #define INSTANTIATE_NORMAL(T)                              \
     template Array<T>  randn<T>(const af::dim4 &dims);
@@ -143,39 +180,19 @@ INSTANTIATE_NORMAL(cfloat)
 INSTANTIATE_NORMAL(cdouble)
 
 
-template<>
-Array<char> randu(const af::dim4 &dims)
-{
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
-    }
-
-    static auto gen = urand<float>(generator);
-
-    if (my_seed != gen_seed) {
-        gen = urand<float>(generator);
-        my_seed = gen_seed;
-    }
-
-    Array<char> outArray = createEmptyArray<char>(dims);
-    char *outPtr = outArray.get();
-    for (int i = 0; i < (int)outArray.elements(); i++) {
-        outPtr[i] = gen() > 0.5;
-    }
-    return outArray;
-}
-
 void setSeed(const uintl seed)
 {
-    generator.seed(seed);
-    is_first = false;
-    gen_seed = seed;
+    auto f = [=](const uintl seed){
+        generator.seed(seed);
+        is_first = false;
+        gen_seed = seed;
+    };
+    getQueue().enqueue(f, seed);
 }
 
 uintl getSeed()
 {
+    getQueue().sync();
     return gen_seed;
 }
 

From fada8833549ff6833143ab79dcc8f4f35e5eaa17 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 22 Sep 2015 09:03:58 -0400
Subject: [PATCH 0014/2677] Async where on the CPU backe

---
 src/backend/cpu/where.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index c1ffd0f973..c5102c8c61 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -16,6 +16,8 @@
 #include <where.hpp>
 #include <ops.hpp>
 #include <vector>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -24,6 +26,9 @@ namespace cpu
     template<typename T>
     Array<uint> where(const Array<T> &in)
     {
+        evalArray(in);
+        getQueue().sync();
+
         const dim_t *dims    = in.dims().get();
         const dim_t *strides = in.strides().get();
         static const T zero = scalar<T>(0);

From 1a0802fb2fe22930e81613faa340038b68e0e2e2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 22 Sep 2015 13:05:17 -0400
Subject: [PATCH 0015/2677] Async CPU reduce and ireduce

---
 src/backend/cpu/ireduce.cpp | 42 +++++++++++++++----------------------
 src/backend/cpu/reduce.cpp  | 15 ++++++++-----
 2 files changed, 27 insertions(+), 30 deletions(-)

diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 199a0befb3..d3a76d92f3 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -14,6 +14,9 @@
 #include <Array.hpp>
 #include <ireduce.hpp>
 
+#include <platform.hpp>
+#include <async_queue.hpp>
+
 using af::dim4;
 
 namespace cpu
@@ -105,43 +108,32 @@ namespace cpu
         }
     };
 
+    template<af_op_t op, typename T>
+    using ireduce_dim_func = std::function<void(T *out, const dim4 ostrides, const dim4 odims,
+                                                uint *loc,
+                                                const T *in , const dim4 istrides, const dim4 idims,
+                                                const int dim)>;
+
     template<af_op_t op, typename T>
     void ireduce(Array<T> &out, Array<uint> &loc,
                  const Array<T> &in, const int dim)
     {
         dim4 odims = in.dims();
         odims[dim] = 1;
+        static const ireduce_dim_func<op, T> ireduce_funcs[] = { ireduce_dim<op, T, 1>()
+                                                               , ireduce_dim<op, T, 2>()
+                                                               , ireduce_dim<op, T, 3>()
+                                                               , ireduce_dim<op, T, 4>()};
 
-        switch (in.ndims()) {
-        case 1:
-            ireduce_dim<op, T, 1>()(out.get(), out.strides(), out.dims(),
-                                    loc.get(),
-                                    in.get(), in.strides(), in.dims(), dim);
-            break;
-
-        case 2:
-            ireduce_dim<op, T, 2>()(out.get(), out.strides(), out.dims(),
-                                    loc.get(),
-                                    in.get(), in.strides(), in.dims(), dim);
-            break;
-
-        case 3:
-            ireduce_dim<op, T, 3>()(out.get(), out.strides(), out.dims(),
-                                    loc.get(),
-                                    in.get(), in.strides(), in.dims(), dim);
-            break;
-
-        case 4:
-            ireduce_dim<op, T, 4>()(out.get(), out.strides(), out.dims(),
-                                    loc.get(),
-                                    in.get(), in.strides(), in.dims(), dim);
-            break;
-        }
+        getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out.get(), out.strides(), out.dims(),
+                           loc.get(), in.get(), in.strides(), in.dims(), dim);
     }
 
     template<af_op_t op, typename T>
     T ireduce_all(unsigned *loc, const Array<T> &in)
     {
+        evalArray(in);
+        getQueue().sync();
         af::dim4 dims = in.dims();
         af::dim4 strides = in.strides();
         const T *inPtr = in.get();
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 5724508be6..8ce7d0de28 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -16,6 +16,9 @@
 #include <functional>
 #include <complex>
 
+#include <platform.hpp>
+#include <async_queue.hpp>
+
 using af::dim4;
 
 namespace cpu
@@ -74,12 +77,12 @@ namespace cpu
         odims[dim] = 1;
 
         Array<To> out = createEmptyArray<To>(odims);
-        static reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { reduce_dim<op, Ti, To, 1>()
-                                                              , reduce_dim<op, Ti, To, 2>()
-                                                              , reduce_dim<op, Ti, To, 3>()
-                                                              , reduce_dim<op, Ti, To, 4>()};
+        static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { reduce_dim<op, Ti, To, 1>()
+                                                                    , reduce_dim<op, Ti, To, 2>()
+                                                                    , reduce_dim<op, Ti, To, 3>()
+                                                                    , reduce_dim<op, Ti, To, 4>()};
 
-        reduce_funcs[in.ndims() - 1](out.get(), out.strides(), out.dims(),
+        getQueue().enqueue(reduce_funcs[in.ndims() - 1],out.get(), out.strides(), out.dims(),
                                      in.get(), in.strides(), in.dims(), dim,
                                      change_nan, nanval);
 
@@ -89,6 +92,8 @@ namespace cpu
     template<af_op_t op, typename Ti, typename To>
     To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
     {
+        evalArray(in);
+        getQueue().sync();
         Transform<Ti, To, op> transform;
         Binary<To, op> reduce;
 

From 1842bcf50998ca83b9fc5a94f7823a1e6f5aade8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 22 Sep 2015 15:14:55 -0400
Subject: [PATCH 0016/2677] Async CPU Transpose. Fix bug in eval

---
 src/backend/cpu/Array.cpp     |  9 +++++----
 src/backend/cpu/transpose.cpp | 30 +++++++++++++++++++++---------
 2 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index d714fd9682..64aacf5fd6 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -67,11 +67,12 @@ namespace cpu
     template<typename T>
     void Array<T>::eval()
     {
-        auto func = [this] {
-            if (isReady()) return;
+        if (isReady()) return;
 
+        data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
+
+        auto func = [this] {
             setId(getActiveDeviceId());
-            data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
             T *ptr = data.get();
 
             dim4 ostrs = strides();
@@ -95,13 +96,13 @@ namespace cpu
                 }
             }
 
-            ready = true;
             Node_ptr prev = node;
             prev->reset();
             // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
             node.reset();
         };
 
+        ready = true;
         getQueue().enqueue(func);
     }
 
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index f820f9ea5d..4afbfaae8e 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -12,6 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <transpose.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 #include <utility>
 #include <cassert>
@@ -78,15 +80,8 @@ void transpose_(T *out, const T *in, const af::dim4 &odims, const af::dim4 &idim
 }
 
 template<typename T>
-Array<T> transpose(const Array<T> &in, const bool conjugate)
+void transpose_(Array<T> out, const Array<T> in, const bool conjugate)
 {
-    const dim4 inDims = in.dims();
-
-    dim4 outDims   = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
-
-    // create an array with first two dimensions swapped
-    Array<T> out  = createEmptyArray<T>(outDims);
-
     // get data pointers for input and output Arrays
     T* outData          = out.get();
     const T*   inData   = in.get();
@@ -98,7 +93,18 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
         transpose_<T, false>(outData, inData,
                              out.dims(), in.dims(), out.strides(), in.strides());
     }
+}
+
+template<typename T>
+Array<T> transpose(const Array<T> &in, const bool conjugate)
+{
+    const dim4 inDims = in.dims();
+
+    dim4 outDims   = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
 
+    // create an array with first two dimensions swapped
+    Array<T> out  = createEmptyArray<T>(outDims);
+    getQueue().enqueue(transpose_<T>, out, in, conjugate);
     return out;
 }
 
@@ -133,7 +139,7 @@ void transpose_inplace(T *in, const af::dim4 &idims, const af::dim4 &istrides)
 }
 
 template<typename T>
-void transpose_inplace(Array<T> &in, const bool conjugate)
+void transpose_inplace_(Array<T> in, const bool conjugate)
 {
     // get data pointers for input and output Arrays
     T* inData = in.get();
@@ -145,6 +151,12 @@ void transpose_inplace(Array<T> &in, const bool conjugate)
     }
 }
 
+template<typename T>
+void transpose_inplace(Array<T> &in, const bool conjugate)
+{
+    getQueue().enqueue(transpose_inplace_<T>, in, conjugate);
+}
+
 #define INSTANTIATE(T)                                                      \
     template Array<T> transpose(const Array<T> &in, const bool conjugate);  \
     template void transpose_inplace(Array<T> &in, const bool conjugate);

From f10075b694bb41a9bb3e270160360ccd50d6259a Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Fri, 13 Nov 2015 13:43:07 -0500
Subject: [PATCH 0017/2677] Fixed typos in documentation.

---
 docs/pages/getting_started.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index 6d1c7cdd3d..c1ae05e9d2 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -22,7 +22,7 @@ underlying data may be one of various [basic types](\ref af::af_dtype):
 
 Older devices may not support double precision operations.
 
-# Creating an populating an ArrayFire array {#getting_started_af_arrays}
+# Creating and populating an ArrayFire array {#getting_started_af_arrays}
 
 ArrayFire [array](\ref af::array)s always exist on the device. They
 may be populated with data using an ArrayFire function, or filled with data
@@ -45,7 +45,7 @@ For example ArrayFire can be populated directly by a call to `cudaMemcpy`
 
 \snippet test/getting_started.cpp ex_getting_started_dev_ptr
 
-# ArrayFire array contents, dimentions, and properties {#getting_started_array_properties}
+# ArrayFire array contents, dimensions, and properties {#getting_started_array_properties}
 
 The [af_print](\ref af::af_print) function can be used to print arrays that
 have already been generated or an expression involving arrays:

From ca0c7cc10ba044887a44b6b900da03311fb1d453 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 13 Nov 2015 17:48:49 -0500
Subject: [PATCH 0018/2677] Fixes for examples when used with installer

---
 examples/CMakeLists.txt    | 6 +++++-
 examples/unified/basic.cpp | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index a795916eb3..e20db4ed2a 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -84,7 +84,11 @@ endif()
 # Next we build each example using every backend.
 if(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
   MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
-  BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "")
+  IF(WIN32)
+    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "")
+  ELSE()
+    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "dl")
+  ENDIF()
 elseif(TARGET af)        # variable defined by the ArrayFire build tree
   MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
   IF(WIN32)
diff --git a/examples/unified/basic.cpp b/examples/unified/basic.cpp
index 31d1eacfca..791466a140 100644
--- a/examples/unified/basic.cpp
+++ b/examples/unified/basic.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <cstdio>
 #include <vector>
 #include <algorithm>
 

From 91f7a1ffe531a82e3f7a9af7fdff6efe3fb6c5f9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Nov 2015 17:50:00 -0500
Subject: [PATCH 0019/2677] async cpu::index function

---
 src/backend/cpu/Array.cpp |  2 --
 src/backend/cpu/index.cpp | 38 +++++++++++++++++++++-----------------
 2 files changed, 21 insertions(+), 19 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 8ea6104a55..b612c7b45c 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -72,8 +72,6 @@ namespace cpu
 
         this->setId(getActiveDeviceId());
 
-        if (isReady()) return;
-
         data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
         auto func = [] (Array<T> in) {
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index cad79f7d4e..39502e9683 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -17,6 +17,7 @@
 #include <vector>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <utility>
 
 using af::dim4;
 
@@ -50,12 +51,8 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
         isSeq[x] = idxrs[x].isSeq;
     }
 
-    // rettrieve
-    dim4 iDims = in.dims();
-    dim4 dDims = in.getDataDims();
-    dim4 oDims = toDims  (seqs, iDims);
-    dim4 iOffs = toOffset(seqs, dDims);
-    dim4 iStrds= toStride(seqs, dDims);
+    // retrieve
+    dim4 oDims = toDims(seqs, in.dims());
 
     std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
@@ -68,18 +65,25 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
-    dim4 oStrides= out.strides();
 
 
-    auto func = [=] (Array<T> out, const Array<T> in) {
-
-        const T *src = in.get();
-              T *dst = out.get();
-
-        const uint* ptr0 = idxArrs[0].get();
-        const uint* ptr1 = idxArrs[1].get();
-        const uint* ptr2 = idxArrs[2].get();
-        const uint* ptr3 = idxArrs[3].get();
+    auto func = [=] (Array<T> out, const Array<T> in,
+                     const bool isSeq[],
+                     const std::vector<af_seq> seqs,
+                     const std::vector< Array<uint> > idxArrs) {
+
+        const dim4 iDims    = in.dims();
+        const dim4 dDims    = in.getDataDims();
+        const dim4 iOffs    = toOffset(seqs, dDims);
+        const dim4 iStrds   = toStride(seqs, dDims);
+        const dim4 oDims    = out.dims();
+        const dim4 oStrides = out.strides();
+        const T *src        = in.get();
+              T *dst        = out.get();
+        const uint* ptr0    = idxArrs[0].get();
+        const uint* ptr1    = idxArrs[1].get();
+        const uint* ptr2    = idxArrs[2].get();
+        const uint* ptr3    = idxArrs[3].get();
 
         for (dim_t l=0; l<oDims[3]; ++l) {
 
@@ -112,7 +116,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
         }
     };
 
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(func, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
 
     return out;
 }

From 86dd6c71ac0fdcce41d36ef9308e52bb071f0554 Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Wed, 18 Nov 2015 11:19:31 +0000
Subject: [PATCH 0020/2677] Build and install documentation in a separate
 output folder.

---
 docs/CMakeLists.txt | 9 +++------
 docs/doxygen.mk     | 4 ++--
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index ec7f384fb3..fbc02e15cf 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -21,18 +21,15 @@ CONFIGURE_FILE(${AF_DOCS_LAYOUT} ${AF_DOCS_LAYOUT_OUT})
 ADD_CUSTOM_TARGET(docs
     ALL
     COMMAND ${DOXYGEN_EXECUTABLE} ${AF_DOCS_CONFIG_OUT}
-    COMMAND cmake -E copy_directory ${ASSETS_DIR} ${CMAKE_CURRENT_BINARY_DIR}
-    COMMAND cmake -E remove ${CMAKE_CURRENT_BINARY_DIR}/.git
+    COMMAND cmake -E copy_directory ${ASSETS_DIR} ${CMAKE_CURRENT_BINARY_DIR}/html
     WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
     COMMENT "Generating Documentation"
     VERBATIM)
 
 # Install Doxygen documentation
-INSTALL(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/ DESTINATION ${AF_INSTALL_DOC_DIR}
+INSTALL(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/html DESTINATION ${AF_INSTALL_DOC_DIR}
     COMPONENT documentation
-    PATTERN "*"
-    PATTERN "CMakeFiles" EXCLUDE
-    PATTERN "man" EXCLUDE
+    PATTERN ".git" EXCLUDE
 )
 
 # Install man pages
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index defb7fe330..46a1dc0861 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -58,7 +58,7 @@ PROJECT_LOGO           = ${ASSETS_DIR}/arrayfire_logo.png
 # entered, it will be relative to the location where doxygen was started. If
 # left blank the current directory will be used.
 
-OUTPUT_DIRECTORY       = .
+OUTPUT_DIRECTORY       = ${CMAKE_CURRENT_BINARY_DIR}
 
 # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
 # directories (in 2 levels) under the output directory of each output format and
@@ -1039,7 +1039,7 @@ GENERATE_HTML          = YES
 # The default directory is: html.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_OUTPUT            = .
+HTML_OUTPUT            = html
 
 # The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
 # generated HTML page (for example: .htm, .php, .asp).

From eb0e0a5b600d021506e2ef0924e82951f0e293a3 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 18 Nov 2015 13:31:22 -0500
Subject: [PATCH 0021/2677] fix code formatting in doxygen

---
 docs/pages/matrix_manipulation.md | 36 +++++++++++++++----------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/docs/pages/matrix_manipulation.md b/docs/pages/matrix_manipulation.md
index 35b2b9a61f..f0af0a77e9 100644
--- a/docs/pages/matrix_manipulation.md
+++ b/docs/pages/matrix_manipulation.md
@@ -15,7 +15,7 @@ Many different kinds of [matrix manipulation routines](\ref manip_mat) are avail
 
 ### flat()
 The __flat()__ function flattens an array to one dimension.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 3 1 1]
     1.0000     4.0000     7.0000
     2.0000     5.0000     8.0000
@@ -31,8 +31,8 @@ flat(a) [9 1 1 1]
     7.0000
     8.0000
     9.0000
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-```
 The flat function has the following overloads:
 * __array af::flat(const array& in)__ -- flatten an array
 * __af_err af_flat(af_array* out, const af_array in)__ -- C interface for flat() function
@@ -40,7 +40,7 @@ The flat function has the following overloads:
 
 ### flip()
 The __flip()__ function flips the contents of an array along a chosen dimension.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [5 2 1 1]
     1.0000     6.0000
     2.0000     7.0000
@@ -61,14 +61,14 @@ flip(a, 1) [5 2 1 1]
     8.0000     3.0000
     9.0000     4.0000
    10.0000     5.0000
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The flip function has the following overloads:
 * __array af::flip(const array &in, const unsigned dim)__ -- flips an array along a dimension 
 * __af_err af_flip(af_array *out, const af_array in, const unsigned dim)__ -- C interface for flip()
 
 ### join()
 The __join()__ function can join up to 4 arrays together.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [5 1 1 1]
     1.0000
     2.0000
@@ -94,7 +94,7 @@ join(1, a, a) [5 2 1 1]
     3.0000     3.0000
     4.0000     4.0000
     5.0000     5.0000
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The join function has several overloads:
 * __array af::join(const int dim, const array &first, const array &second)__ -- Joins 2 arrays along a dimension
 
@@ -108,7 +108,7 @@ The join function has several overloads:
 
 ### moddims()
 The __moddims()__ function changes the dimensions of an array without changing its data or order. It is important to remember that the function only modifies the _metadata_ associated with the array and does not actually modify the content of the array.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [8 1 1 1]
     1.0000
     2.0000
@@ -133,7 +133,7 @@ moddims(a, a.elements(), 1, 1, 1) [8 1 1 1]
     2.0000
     1.0000
     2.0000
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The moddims function has several overloads:
 * __array af::moddims(const array &in, const unsigned ndims, const dim_t *const dims)__ -- mods number of dimensions to match _ndims_ as specidied in the array _dims_
 * __array af::moddims(const array &in, const dim4 &dims)__ -- mods dimensions as specified by _dims_
@@ -142,7 +142,7 @@ The moddims function has several overloads:
 
 ### reorder()
 The __reorder()__ function changes the order of the dimensions within the array. This actually alters the underlying data of the array.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [2 2 3 1]
     1.0000     3.0000
     2.0000     4.0000
@@ -173,7 +173,7 @@ reorder(a, 2, 0, 1) [3 2 2 1]
     3.0000     4.0000
     3.0000     4.0000
     3.0000     4.0000
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The reorder function the following several overloads:
 * __array af::reorder(const array &in, const unsigned x, const unsigned y=1, const unsigned z=2, const unsigned w=3)__ -- Reorders dimensions of an array
 
@@ -181,7 +181,7 @@ The reorder function the following several overloads:
 
 ### shift()
 The __shift()__ function shifts data in a circular buffer fashion along a chosen dimension.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 5 1 1]
     0.0000     0.0000     0.0000     0.0000     0.0000
     3.0000     4.0000     5.0000     1.0000     2.0000
@@ -196,7 +196,7 @@ shift(a, -1, 2 ) [3 5 1 1]
     1.0000     2.0000     3.0000     4.0000     5.0000
     1.0000     2.0000     3.0000     4.0000     5.0000
     0.0000     0.0000     0.0000     0.0000     0.0000
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The shift function has the following overloads:
 * __array af::shift(const array &in, const int x, const int y=0, const int z=0, const int w=0)__ -- Shifts array along specified dimensions
 
@@ -204,7 +204,7 @@ The shift function has the following overloads:
 
 ### tile()
 The __tile()__ function repeats an array along a dimension
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 1 1 1]
     1.0000
     2.0000
@@ -239,8 +239,8 @@ tile(a, tile_dims) [3 2 3 1]
     1.0000     1.0000
     2.0000     2.0000
     3.0000     3.0000
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-```
 The tile function has several overloads:
 * __array af::tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)__  --  Tiles array along specified dimensions
 * __array af::tile(const array &in, const dim4 &dims)__  --  Tile an array according to a dim4 object
@@ -248,7 +248,7 @@ The tile function has several overloads:
 
 ### transpose()
 The __transpose()__ function performs a standard matrix transpose. The input array must have the dimensions of a 2D-matrix.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 3 1 1]
     1.0000     3.0000     3.0000
     2.0000     1.0000     3.0000
@@ -258,8 +258,8 @@ transpose(a) [3 3 1 1]
     1.0000     2.0000     2.0000
     3.0000     1.0000     2.0000
     3.0000     3.0000     1.0000
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-```
 The transpose function has several overloads:
 * __array af::transpose(const array &in, const bool conjugate=false)__ -- Transposes a matrix.
 
@@ -282,7 +282,7 @@ used to form the [matrix or vector transpose](\ref af::array::T) .
 
 ### Combining re-ordering functions to enumerate grid coordinates
 By using a combination of the array restructuring functions, we can quickly code complex manipulation patterns with a few lines of code. For example, consider generating _(x,y)_ coordinates for a grid where each axis goes from *1 to n*. Instead of using several loops to populate our arrays we can just use a small combination of the above functions.
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 unsigned n=3;
 af::array xy = join(1
                 tile(seq(1, n), n)
@@ -298,6 +298,6 @@ xy [9 2 1 1]
     1.0000     3.0000
     2.0000     3.0000
     3.0000     3.0000
-```
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 ### Conclusion
 Functions provided by arrayfire offer ease and flexibility for efficiently manipulating the structure of arrays. The provided functions can be used as building blocks to generate, shift, or prepare data to any form imaginable!

From 5955b14b2c3dc2ca70816b6f0f570838d0001d07 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 18 Nov 2015 13:33:01 -0500
Subject: [PATCH 0022/2677] initial vectorization tutorial

---
 docs/pages/vectorization.md | 115 ++++++++++++++++++++++++++++++++++++
 1 file changed, 115 insertions(+)
 create mode 100644 docs/pages/vectorization.md

diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
new file mode 100644
index 0000000000..b7e77044c4
--- /dev/null
+++ b/docs/pages/vectorization.md
@@ -0,0 +1,115 @@
+Vectorization {#vectorization}
+===================
+
+Programmers and Data Scientists want to take advantage of fast and parallel computational devices. Writing vectorized code is becoming a necessity to get the best performance out of the current generation parallel hardware and scientific computing software. However, writing vectorized code may not be intuitive immediately. Arrayfire provides many ways to vectorize a given code segment. In this tutorial, we will be presenting various ways to vectorize code using ArrayFire and the benefits and drawbacks associated with each method.
+
+### Generic/Default vectorization
+By its very nature, Arrayfire is a vectorized library. Most functions operate on arrays as a whole -- on all elements in parallel. Wherever possible, existing vectorized functions should be used opposed to manually indexing into arrays. For example, consider this valid, yet mislead code that attempts to increment each element of an array:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array a = af::seq(10); // [0,  9]
+for(int i=0; i<a.dims(0); ++i)
+{
+    a(i) = a(i) + 1;       // [1, 10]
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Instead, the existing vectorized Arrayfire overload of the + operator should have been used:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array a = af::seq(10);  // [0,  9]
+a = a + 1;                  // [1, 10]
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Some of the vectorized mathematical functions of Arrayfire include:  
+
+__Arithmetic operations:__
+ 	+, -, *, /, >>, <<
+
+__Complex operations:__
+ 	real, imag, conjugate, etc. 
+ 	 
+__Exponential and logarithmic functions:__
+ 	exp, log, expm1, log1p, etc. 
+ 
+__Hyperbolic functions:__
+ 	sinh, cosh, tanh, etc. 
+ 
+__Logical operations:__
+ 	&&, ||, |, &, <, >, <=, >=, ==, ! 
+ 
+__Numeric functions:__
+ 	floor, round, min, max, etc. 
+ 
+__Trigonometric functions:__
+ 	sin, cos, tan, etc. 
+  
+  
+### GFOR: Parallel for-loops
+Another novel method of vectorization present in Arrayfire is the GFOR loop replacement construct.
+GFOR allows launching all iterations of a loop in parallel on the GPU or device, as long as the iterations are independent. While the standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop performs each iteration at the same time (in parallel). ArrayFire does this by tiling out the values of all loop iterations and then performing computation on those tiles in one pass.  
+You can think of gfor as performing auto-vectorization of your code, e.g. you write a gfor-loop that increments every element of a vector but behind the scenes ArrayFire rewrites it to operate on the entire vector in parallel.  
+We can remedy our first example with GFOR:  
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array a = af::seq(10);
+gfor(seq i, n)
+    a(i) = a(i) + 1;
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+It is best to vectorize computation as much as possible to avoid the overhead in both for-loops and gfor-loops.
+
+To see another example, you could run an FFT on every 2D slice of a volume in a for-loop, or you could "vectorize" and simply do it all in one gfor-loop operation:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+for (int i = 0; i < N; ++i)
+   A(span,span,i) = fft2(A(span,span,i)); // runs each FFT in sequence
+gfor (seq i, N)
+   A(span,span,i) = fft2(A(span,span,i)); // runs N FFTs in parallel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+### GFOR: Usage
+There are three formats for instantiating gfor-loops:  
+
+ 1. gfor(var,n)-- Creates a sequence <B>{0, 1, ..., n-1}</B>
+ 2. gfor(var,first,last)-- Creates a sequence <B>{first, first+1, ..., last}</B>
+ 3. gfor(var,first,incr,last)-- Creates a sequence <B>{first, first+inc, first+2 * inc, ..., last}</B>
+
+
+All of the following represent the equivalent sequence: 0,1,2,3,4
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+gfor (seq i, 5)
+gfor (seq i, 0, 4)
+gfor (seq i, 0, 1, 4)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Using GFOR requires following several rules and multiple guidelines for optimal performance. The details of this vectorization method can be found in the <a href="page_gfor.htm">GFOR documentation.</a>
+
+### batchFunc()
+The batchFunc() function allows the broad application of existing Arrayfire functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire functions to execute in "batch processing" mode. In this mode, functions will find a dimension which contains "batches" of data to be processed and will parallelize the procedure. 
+Consider the following example:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array filter = randn(1, 5);
+af::array weights = randu(5, 5);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+We have a filter that we would like to apply to each of several weights vectors.
+The naive solution would be using a loop as we've seen before:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array filtered_weights = constant(0, 5, 5);
+for(int i=0; i<weights.dims(1); ++i){
+    filtered_weights.col(i) = filter * weights(i);
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+However we would like a vectorized solution. The following syntax begs to be used:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array filtered_weights = filter * weights; //fails due to dimension mismatch
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+but it fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if Arrayfire could figure out along which dimension we intend to apply the batch operation? That is exactly what batchFunc() does!
+The signature of the function is:  
+
+__AFAPI array batchFunc( const array &lhs, const array &rhs, batchFunc_t func );__  
+
+where __batchFunc_t__ is a function pointer of the form:  
+__typedef array (*batchFunc_t) ( const array &lhs, const array &rhs );__  
+  
+
+So, to use batchFunc(), we need to provide the function we will be applying as a batch operation. Our final batch call is not much more difficult than the ideal syntax we imagined. 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array filtered_weights = batchFunc(filter, weights, operator* );
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The batch function will work with many previously mentioned vectorized Arrayfire functions. It can even work with a combination of those functions if they are wrapped inside a helper function matching the __batchFunc_t__ signature. Unfortunately, the batch function cannot be used within a gfor() construct at this moment.
+

From 2796770f1689358892fde35c7303fa406f1ceb78 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Nov 2015 15:38:27 -0500
Subject: [PATCH 0023/2677] Fixed asynchronous indexing & assignment in cpu
 backend

---
 src/backend/cpu/assign.cpp | 100 ++++++++++++++++++-------------------
 src/backend/cpu/index.cpp  |  18 +++----
 2 files changed, 57 insertions(+), 61 deletions(-)

diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index b75b6d549c..b1578d49f6 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -16,12 +16,11 @@
 #include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
-#include <array>
 
 using af::dim4;
 using std::ref;
 using std::copy;
-using std::array;
+using std::vector;
 
 namespace cpu
 {
@@ -40,12 +39,11 @@ dim_t trimIndex(int idx, const dim_t &len)
 }
 
 template<typename T>
-void assign_(Array<T> out, const array<af_index_t, 4> idxrs, const Array<T> rhs)
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 {
-    bool isSeq[4];
-    std::vector<af_seq> seqs(4, af_span);
-    // create seq vector to retrieve output
-    // dimensions, offsets & offsets
+    vector<bool> isSeq(4);
+    vector<af_seq> seqs(4, af_span);
+    // create seq vector to retrieve output dimensions, offsets & offsets
     for (dim_t x=0; x<4; ++x) {
         if (idxrs[x].isSeq) {
             seqs[x] = idxrs[x].idx.seq;
@@ -53,17 +51,7 @@ void assign_(Array<T> out, const array<af_index_t, 4> idxrs, const Array<T> rhs)
         isSeq[x] = idxrs[x].isSeq;
     }
 
-    dim4 dDims = out.getDataDims();
-    dim4 pDims = out.dims();
-    // retrieve dimensions & strides for array
-    // to which rhs is being copied to
-    dim4 dst_offsets    = toOffset(seqs, dDims);
-    dim4 dst_strides    = toStride(seqs, dDims);
-    // retrieve rhs array dimenesions & strides
-    dim4 src_dims       = rhs.dims();
-    dim4 src_strides    = rhs.strides();
-
-    std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
     for (dim_t x=0; x<4; ++x) {
         if (!isSeq[x]) {
@@ -71,58 +59,66 @@ void assign_(Array<T> out, const array<af_index_t, 4> idxrs, const Array<T> rhs)
         }
     }
 
-    // declare pointers to af_array index data
-    const uint* ptr0 = idxArrs[0].get();
-    const uint* ptr1 = idxArrs[1].get();
-    const uint* ptr2 = idxArrs[2].get();
-    const uint* ptr3 = idxArrs[3].get();
+    auto func = [=] (Array<T> out, const Array<T> rhs,
+                     const vector<bool> isSeq,
+                     const vector<af_seq> seqs,
+                     const vector< Array<uint> > idxArrs) {
+
+        dim4 dDims = out.getDataDims();
+        dim4 pDims = out.dims();
+        // retrieve dimensions & strides for array to which rhs is being copied to
+        dim4 dst_offsets = toOffset(seqs, dDims);
+        dim4 dst_strides = toStride(seqs, dDims);
+        // retrieve rhs array dimenesions & strides
+        dim4 src_dims    = rhs.dims();
+        dim4 src_strides = rhs.strides();
+        // declare pointers to af_array index data
+        const uint* ptr0 = idxArrs[0].get();
+        const uint* ptr1 = idxArrs[1].get();
+        const uint* ptr2 = idxArrs[2].get();
+        const uint* ptr3 = idxArrs[3].get();
 
-    const T * src= rhs.get();
-    T * dst      = out.get();
+        const T * src= rhs.get();
+        T * dst      = out.get();
 
-    for(dim_t l=0; l<src_dims[3]; ++l) {
+        for(dim_t l=0; l<src_dims[3]; ++l) {
 
-        dim_t src_loff = l*src_strides[3];
+            dim_t src_loff = l*src_strides[3];
 
-        dim_t dst_lIdx = trimIndex(isSeq[3] ? l+dst_offsets[3] : ptr3[l], pDims[3]);
-        dim_t dst_loff = dst_lIdx * dst_strides[3];
+            dim_t dst_lIdx = trimIndex(isSeq[3] ? l+dst_offsets[3] : ptr3[l], pDims[3]);
+            dim_t dst_loff = dst_lIdx * dst_strides[3];
 
-        for(dim_t k=0; k<src_dims[2]; ++k) {
+            for(dim_t k=0; k<src_dims[2]; ++k) {
 
-            dim_t src_koff = k*src_strides[2];
+                dim_t src_koff = k*src_strides[2];
 
-            dim_t dst_kIdx = trimIndex(isSeq[2] ? k+dst_offsets[2] : ptr2[k], pDims[2]);
-            dim_t dst_koff = dst_kIdx * dst_strides[2];
+                dim_t dst_kIdx = trimIndex(isSeq[2] ? k+dst_offsets[2] : ptr2[k], pDims[2]);
+                dim_t dst_koff = dst_kIdx * dst_strides[2];
 
-            for(dim_t j=0; j<src_dims[1]; ++j) {
+                for(dim_t j=0; j<src_dims[1]; ++j) {
 
-                dim_t src_joff = j*src_strides[1];
+                    dim_t src_joff = j*src_strides[1];
 
-                dim_t dst_jIdx = trimIndex(isSeq[1] ? j+dst_offsets[1] : ptr1[j], pDims[1]);
-                dim_t dst_joff = dst_jIdx * dst_strides[1];
+                    dim_t dst_jIdx = trimIndex(isSeq[1] ? j+dst_offsets[1] : ptr1[j], pDims[1]);
+                    dim_t dst_joff = dst_jIdx * dst_strides[1];
 
-                for(dim_t i=0; i<src_dims[0]; ++i) {
+                    for(dim_t i=0; i<src_dims[0]; ++i) {
 
-                    dim_t src_ioff = i*src_strides[0];
-                    dim_t src_idx  = src_ioff + src_joff + src_koff + src_loff;
+                        dim_t src_ioff = i*src_strides[0];
+                        dim_t src_idx  = src_ioff + src_joff + src_koff + src_loff;
 
-                    dim_t dst_iIdx = trimIndex(isSeq[0] ? i+dst_offsets[0] : ptr0[i], pDims[0]);
-                    dim_t dst_ioff = dst_iIdx * dst_strides[0];
-                    dim_t dst_idx  = dst_ioff + dst_joff + dst_koff + dst_loff;
+                        dim_t dst_iIdx = trimIndex(isSeq[0] ? i+dst_offsets[0] : ptr0[i], pDims[0]);
+                        dim_t dst_ioff = dst_iIdx * dst_strides[0];
+                        dim_t dst_idx  = dst_ioff + dst_joff + dst_koff + dst_loff;
 
-                    dst[dst_idx] = src[src_idx];
+                        dst[dst_idx] = src[src_idx];
+                    }
                 }
             }
         }
-    }
-}
+    };
 
-template<typename T>
-void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
-{
-    array<af_index_t, 4> idx;
-    copy(idxrs, idxrs+4, begin(idx));
-    getQueue().enqueue(assign_<T>, out, move(idx), rhs);
+    getQueue().enqueue(func, out, rhs, std::move(isSeq), std::move(seqs), std::move(idxArrs));
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 39502e9683..c1beeea9c0 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -19,6 +19,7 @@
 #include <async_queue.hpp>
 #include <utility>
 
+using std::vector;
 using af::dim4;
 
 namespace cpu
@@ -40,11 +41,11 @@ dim_t trimIndex(dim_t idx, const dim_t &len)
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 {
-    bool isSeq[4];
-    std::vector<af_seq> seqs(4, af_span);
+    vector<bool> isSeq(4);
+    vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x=0; x<isSeq.size(); ++x) {
         if (idxrs[x].isSeq) {
             seqs[x] = idxrs[x].idx.seq;
         }
@@ -54,9 +55,9 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     // retrieve
     dim4 oDims = toDims(seqs, in.dims());
 
-    std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x=0; x<isSeq.size(); ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
             // set output array ith dimension value
@@ -66,11 +67,10 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-
     auto func = [=] (Array<T> out, const Array<T> in,
-                     const bool isSeq[],
-                     const std::vector<af_seq> seqs,
-                     const std::vector< Array<uint> > idxArrs) {
+                     const vector<bool> isSeq,
+                     const vector<af_seq> seqs,
+                     const vector< Array<uint> > idxArrs) {
 
         const dim4 iDims    = in.dims();
         const dim4 dDims    = in.getDataDims();

From 0aeed429006dcf95a2aa502bf7aad5e409b90a3b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Nov 2015 16:54:34 -0500
Subject: [PATCH 0024/2677] converted cpu tile to asychronous call

This fixed `Assign.LinearAssignSeq` unit test in assign unit tests.
---
 src/backend/cpu/tile.cpp | 58 +++++++++++++++++++++++-----------------
 1 file changed, 34 insertions(+), 24 deletions(-)

diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 77e72afd09..f7560121f4 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -11,25 +11,32 @@
 #include <tile.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    template<typename T>
-    Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
-        oDims *= tileDims;
 
-        if(iDims.elements() == 0 || oDims.elements() == 0) {
-            throw std::runtime_error("Elements are 0");
-        }
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
+{
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims = iDims;
+    oDims *= tileDims;
+
+    if(iDims.elements() == 0 || oDims.elements() == 0) {
+        throw std::runtime_error("Elements are 0");
+    }
 
-        Array<T> out = createEmptyArray<T>(oDims);
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    auto func = [=] (Array<T> out, const Array<T> in) {
 
         T* outPtr = out.get();
         const T* inPtr = in.get();
 
+        const af::dim4 iDims = in.dims();
+        const af::dim4 oDims = out.dims();
         const af::dim4 ist = in.strides();
         const af::dim4 ost = out.strides();
 
@@ -54,24 +61,27 @@ namespace cpu
                 }
             }
         }
+    };
 
-        return out;
-    }
+    getQueue().enqueue(func, out, in);
+
+    return out;
+}
 
 #define INSTANTIATE(T)                                                         \
     template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);  \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
 }

From 330ae1c3b7fc85c8794069a9a4fa09b43615493e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Nov 2015 17:06:40 -0500
Subject: [PATCH 0025/2677] converted sort_index cpu function to asynchronous
 call

This also fixed assign unit test: `ArrayAssign.CPP_ASSIGN_VECTOR_2D`
---
 src/backend/cpu/sort_index.cpp | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index eb6b4bee60..f07d585b41 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -14,16 +14,12 @@
 #include <err_cpu.hpp>
 #include <algorithm>
 #include <numeric>
-#include <queue>
-#include <future>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using std::greater;
 using std::less;
 using std::sort;
-using std::function;
-using std::queue;
-using std::future;
-using std::async;
 
 namespace cpu
 {
@@ -85,8 +81,7 @@ namespace cpu
         val = createEmptyArray<T>(in.dims());
         idx = createEmptyArray<uint>(in.dims());
         switch(dim) {
-            case 0: sort0_index<T, isAscending>(val, idx, in);
-                    break;
+            case 0: getQueue().enqueue(sort0_index<T, isAscending>, val, idx, in); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }

From 5eea071e457ca37c2a285365c42f9f1c1bd1d0c0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Nov 2015 18:11:16 -0500
Subject: [PATCH 0026/2677] converted triangle fn in cpu backend to async call

---
 src/backend/cpu/triangle.cpp | 56 ++++++++++++++++++++----------------
 1 file changed, 31 insertions(+), 25 deletions(-)

diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 6b0f326aad..ed7f348bad 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -12,6 +12,8 @@
 #include <Array.hpp>
 #include <triangle.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -19,42 +21,46 @@ namespace cpu
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in)
 {
-    T *o = out.get();
-    const T *i = in.get();
+    auto func = [=] (Array<T> out, const Array<T> in) {
+        T *o = out.get();
+        const T *i = in.get();
 
-    dim4 odm = out.dims();
+        dim4 odm = out.dims();
 
-    dim4 ost = out.strides();
-    dim4 ist = in.strides();
+        dim4 ost = out.strides();
+        dim4 ist = in.strides();
 
-    for(dim_t ow = 0; ow < odm[3]; ow++) {
-        const dim_t oW = ow * ost[3];
-        const dim_t iW = ow * ist[3];
+        for(dim_t ow = 0; ow < odm[3]; ow++) {
+            const dim_t oW = ow * ost[3];
+            const dim_t iW = ow * ist[3];
 
-        for(dim_t oz = 0; oz < odm[2]; oz++) {
-            const dim_t oZW = oW + oz * ost[2];
-            const dim_t iZW = iW + oz * ist[2];
+            for(dim_t oz = 0; oz < odm[2]; oz++) {
+                const dim_t oZW = oW + oz * ost[2];
+                const dim_t iZW = iW + oz * ist[2];
 
-            for(dim_t oy = 0; oy < odm[1]; oy++) {
-                const dim_t oYZW = oZW + oy * ost[1];
-                const dim_t iYZW = iZW + oy * ist[1];
+                for(dim_t oy = 0; oy < odm[1]; oy++) {
+                    const dim_t oYZW = oZW + oy * ost[1];
+                    const dim_t iYZW = iZW + oy * ist[1];
 
-                for(dim_t ox = 0; ox < odm[0]; ox++) {
-                    const dim_t oMem = oYZW + ox;
-                    const dim_t iMem = iYZW + ox;
+                    for(dim_t ox = 0; ox < odm[0]; ox++) {
+                        const dim_t oMem = oYZW + ox;
+                        const dim_t iMem = iYZW + ox;
 
-                    bool cond = is_upper ? (oy >= ox) : (oy <= ox);
-                    bool do_unit_diag = (is_unit_diag && ox == oy);
-                    if(cond) {
-                        o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
-                    } else {
-                        o[oMem] = scalar<T>(0);
-                    }
+                        bool cond = is_upper ? (oy >= ox) : (oy <= ox);
+                        bool do_unit_diag = (is_unit_diag && ox == oy);
+                        if(cond) {
+                            o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
+                        } else {
+                            o[oMem] = scalar<T>(0);
+                        }
 
+                    }
                 }
             }
         }
-    }
+    };
+
+    getQueue().enqueue(func, out, in);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>

From 551433e7aaf045d9c22ed4a2c107d7f9f1149b59 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Nov 2015 19:02:51 -0500
Subject: [PATCH 0027/2677] converted lu & cholesky decomposition functions to
 async calls

---
 src/backend/cpu/cholesky.cpp | 12 +++++++++---
 src/backend/cpu/lu.cpp       | 20 ++++++++++----------
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 57beaa4146..d0bd3c8787 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -18,8 +18,9 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <triangle.hpp>
-
 #include <lapack_helper.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -65,8 +66,13 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     if(is_upper)
         uplo = 'U';
 
-    int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo,
-                               N, in.get(), in.strides()[1]);
+    int info = 0;
+    auto func = [&] (int& info, Array<T>& in) {
+        info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(), in.strides()[1]);
+    };
+
+    getQueue().enqueue(func, info, in);
+    getQueue().sync();
 
     return info;
 }
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 0eefb16816..ed165cba8e 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -17,9 +17,10 @@
 #include <iostream>
 #include <cassert>
 #include <err_cpu.hpp>
-
 #include <range.hpp>
 #include <lapack_helper.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -128,23 +129,22 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
 
-    lu_split<T>(lower, upper, in_copy);
+    getQueue().enqueue(lu_split<T>, lower, upper, in_copy);
 }
 
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-
-    Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
+    Array<int> pivot = createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
 
-    getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    in.get(), in.strides()[1],
-                    pivot.get());
+    auto func = [=] (Array<T> in, Array<int> pivot, const bool convert_pivot) {
+        dim4 iDims = in.dims();
+        getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides()[1], pivot.get());
+        if(convert_pivot) convertPivot(pivot, iDims[0]);
+    };
 
-    if(convert_pivot) convertPivot(pivot, M);
+    getQueue().enqueue(func, in, pivot, convert_pivot);
 
     return pivot;
 }

From ed6d26da36df33e1469194d8b3d05e628a9fc26c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Nov 2015 19:13:54 -0500
Subject: [PATCH 0028/2677] svd cpu backend is async now

---
 src/backend/cpu/svd.cpp | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 461b9014aa..33bfab75aa 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -10,12 +10,13 @@
 #include <Array.hpp>
 #include <svd.hpp>
 #include <err_common.hpp>
-
 #include <err_cpu.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 #include <lapack_helper.hpp>
 #include <copy.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -67,18 +68,21 @@ namespace cpu
     template <typename T, typename Tr>
     void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
     {
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
+        auto func = [=] (Array<Tr> s, Array<T> u, Array<T> vt, Array<T> in) {
+            dim4 iDims = in.dims();
+            int M = iDims[0];
+            int N = iDims[1];
 
 #if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides()[1],
-                          s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1]);
+            svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides()[1],
+                    s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1]);
 #else
-        std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides()[1],
-                          s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
+            std::vector<Tr> superb(std::min(M, N));
+            svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides()[1],
+                    s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
 #endif
+        };
+        getQueue().enqueue(func, s, u, vt, in);
     }
 
     template <typename T, typename Tr>

From ed730cfcd174110a7483a9d8eca881672bab8e83 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 20 Nov 2015 13:14:14 -0500
Subject: [PATCH 0029/2677] adds scatter function

---
 include/af/defines.h          | 16 ++++++++++++++
 include/af/graphics.h         | 35 ++++++++++++++++++++++++++++++
 src/api/c/graphics_common.cpp |  7 +++---
 src/api/c/graphics_common.hpp |  2 +-
 src/api/c/plot.cpp            | 40 +++++++++++++++++++++++++++--------
 src/api/cpp/graphics.cpp      |  6 ++++++
 src/api/unified/graphics.cpp  |  6 ++++++
 7 files changed, 99 insertions(+), 13 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index a25d23996d..2b53baabed 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -378,6 +378,19 @@ typedef enum {
     AF_ID = 0
 } af_someenum_t;
 
+#if AF_API_VERSION >=32
+typedef enum {
+    AF_MARKER_NONE         = 0,
+    AF_MARKER_POINT        = 1,
+    AF_MARKER_CIRCLE       = 2,
+    AF_MARKER_SQUARE       = 3,
+    AF_MARKER_TRIANGLE     = 4,
+    AF_MARKER_CROSS        = 5,
+    AF_MARKER_PLUS         = 6,
+    AF_MARKER_STAR         = 7
+} af_marker_type;
+#endif
+
 #ifdef __cplusplus
 namespace af
 {
@@ -404,6 +417,9 @@ namespace af
 #if AF_API_VERSION >= 32
     typedef af_backend Backend;
 #endif
+#if AF_API_VERSION >= 32
+    typedef af_marker_type markerType;
+#endif
 }
 
 #endif
diff --git a/include/af/graphics.h b/include/af/graphics.h
index 5c143c721e..e4286e1ea7 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -180,6 +180,20 @@ class AFAPI Window {
 
         void plot(const array& X, const array& Y, const char* const title=NULL);
 
+        /**
+           Renders the input arrays as a 2D scatter-plot to the window
+
+           \param[in] X is an \ref array with the x-axis data points
+           \param[in] Y is an \ref array with the y-axis data points
+           \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note \p X and \p Y should be vectors.
+
+           \ingroup gfx_func_draw
+         */
+
+        void scatter(const array& X, const array& Y, const af::markerType marker=AF_MARKER_POINT, const char* const title=NULL);
         /**
            Renders the input array as a histogram to the window
 
@@ -371,6 +385,27 @@ AFAPI af_err af_draw_image(const af_window wind, const af_array in, const af_cel
 */
 AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props);
 
+#if AF_API_VERSION >= 32
+/**
+   C Interface wrapper for drawing an array as a plot
+
+   \param[in]   wind is the window handle
+   \param[in]   X is an \ref af_array with the x-axis data points
+   \param[in]   Y is an \ref af_array with the y-axis data points
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p X and \p Y should be vectors.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, const af_marker_type marker);
+#endif
+
 #if AF_API_VERSION >= 32
 /**
    C Interface wrapper for drawing an array as a plot
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 4b50bc046e..92346f59d1 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -161,7 +161,7 @@ fg::Image* ForgeManager::getImage(int w, int h, fg::ChannelFormat mode, fg::dtyp
     return mImgMap[key];
 }
 
-fg::Plot* ForgeManager::getPlot(int nPoints, fg::dtype type)
+fg::Plot* ForgeManager::getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype)
 {
     /* nPoints needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -169,11 +169,12 @@ fg::Plot* ForgeManager::getPlot(int nPoints, fg::dtype type)
      * is a limitation on how big of an plot graph can be rendered
      * using arrayfire graphics funtionality */
     assert(nPoints <= 2ll<<48);
-    long long key = ((nPoints & _48BIT) << 48) | (type & _16BIT);
+    long long key = ((nPoints & _48BIT) << 48);
+    key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
 
     PltMapIter iter = mPltMap.find(key);
     if (iter==mPltMap.end()) {
-        fg::Plot* temp = new fg::Plot(nPoints, type);
+        fg::Plot* temp = new fg::Plot(nPoints, dtype, ptype, mtype);
         mPltMap[key] = temp;
     }
 
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 39225e6a0c..caadb88cd9 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -82,7 +82,7 @@ class ForgeManager
         fg::Font* getFont(const bool dontCreate=false);
         fg::Window* getMainWindow(const bool dontCreate=false);
         fg::Image* getImage(int w, int h, fg::ChannelFormat mode, fg::dtype type);
-        fg::Plot* getPlot(int nPoints, fg::dtype type);
+        fg::Plot* getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype);
         fg::Plot3* getPlot3(int nPoints, fg::dtype type);
         fg::Histogram* getHistogram(int nBins, fg::dtype type);
         fg::Surface* getSurface(int nX, int nY, fg::dtype type);
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index b22e92850b..f2740305ea 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -27,7 +27,7 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg::Plot* setup_plot(const af_array X, const af_array Y)
+fg::Plot* setup_plot(const af_array X, const af_array Y, fg::PlotType type, fg::MarkerType marker)
 {
     Array<T> xIn = getArray<T>(X);
     Array<T> yIn = getArray<T>(Y);
@@ -46,7 +46,7 @@ fg::Plot* setup_plot(const af_array X, const af_array Y)
     af::dim4 X_dims = Xinfo.dims();
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Plot* plot = fgMngr.getPlot(X_dims.elements(), getGLType<T>());
+    fg::Plot* plot = fgMngr.getPlot(X_dims.elements(), getGLType<T>(), type, marker);
     plot->setColor(1.0, 0.0, 0.0);
     plot->setAxesLimits(xmax, xmin, ymax, ymin);
     plot->setAxesTitles("X Axis", "Y Axis");
@@ -57,7 +57,7 @@ fg::Plot* setup_plot(const af_array X, const af_array Y)
 }
 #endif
 
-af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
+af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, fg::PlotType type=fg::FG_LINE, fg::MarkerType marker=fg::FG_NONE)
 {
 #if defined(WITH_GRAPHICS)
     if(wind==0) {
@@ -85,12 +85,12 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
         fg::Plot* plot = NULL;
 
         switch(Xtype) {
-            case f32: plot = setup_plot<float  >(X, Y); break;
-            case s32: plot = setup_plot<int    >(X, Y); break;
-            case u32: plot = setup_plot<uint   >(X, Y); break;
-            case s16: plot = setup_plot<short  >(X, Y); break;
-            case u16: plot = setup_plot<ushort >(X, Y); break;
-            case u8 : plot = setup_plot<uchar  >(X, Y); break;
+            case f32: plot = setup_plot<float  >(X, Y, type, marker); break;
+            case s32: plot = setup_plot<int    >(X, Y, type, marker); break;
+            case u32: plot = setup_plot<uint   >(X, Y, type, marker); break;
+            case s16: plot = setup_plot<short  >(X, Y, type, marker); break;
+            case u16: plot = setup_plot<ushort >(X, Y, type, marker); break;
+            case u8 : plot = setup_plot<uchar  >(X, Y, type, marker); break;
             default:  TYPE_ERROR(1, Xtype);
         }
 
@@ -105,3 +105,25 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
     return AF_ERR_NO_GFX;
 #endif
 }
+
+af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
+{
+    return plotWrapper(wind, X, Y, props);
+}
+
+af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, const af::markerType af_marker)
+{
+    fg::MarkerType fg_marker;
+    switch(af_marker){
+        case AF_MARKER_NONE: fg_marker = fg::FG_NONE; break;
+        case AF_MARKER_POINT: fg_marker = fg::FG_POINT; break;
+        case AF_MARKER_CIRCLE: fg_marker = fg::FG_CIRCLE; break;
+        case AF_MARKER_SQUARE: fg_marker = fg::FG_SQUARE; break;
+        case AF_MARKER_TRIANGLE: fg_marker = fg::FG_TRIANGLE; break;
+        case AF_MARKER_CROSS: fg_marker = fg::FG_CROSS; break;
+        case AF_MARKER_PLUS: fg_marker = fg::FG_PLUS; break;
+        case AF_MARKER_STAR: fg_marker = fg::FG_STAR; break;
+        default: fg_marker = fg::FG_NONE; break;
+    }
+    return plotWrapper(wind, X, Y, props, fg::FG_SCATTER, fg_marker);
+}
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index b7480195dc..8d2d8cd4b6 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -79,6 +79,12 @@ void Window::plot(const array& X, const array& Y, const char* const title)
     AF_THROW(af_draw_plot(get(), X.get(), Y.get(), &temp));
 }
 
+void Window::scatter(const array& X, const array& Y, af::markerType marker, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_scatter(get(), X.get(), Y.get(), &temp, marker));
+}
+
 void Window::plot3(const array& P, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 81076f233c..596429318f 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -44,6 +44,12 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
     return CALL(wind, X, Y, props);
 }
 
+af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, const af_marker_type marker)
+{
+    CHECK_ARRAYS(X, Y);
+    return CALL(wind, X, Y, props, marker);
+}
+
 af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
 {
     CHECK_ARRAYS(P);

From 0a78b60dd57056353b3265a56428577db52f98a5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 20 Nov 2015 13:15:06 -0500
Subject: [PATCH 0030/2677] update plot2d example to include scatter plot

---
 examples/graphics/plot2d.cpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index 7e28d34ebd..c6d3452c3b 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -21,17 +21,21 @@ int main(int argc, char *argv[])
     try {
         // Initialize the kernel array just once
         af::info();
-        af::Window myWindow(512, 512, "2D Plot example: ArrayFire");
+        af::Window myWindow(1024, 512, "2D Plot example: ArrayFire");
 
         array Y;
         int sign = 1;
         array X = seq(-af::Pi, af::Pi, PRECISION);
 
+        myWindow.grid(1, 2);
         for (double val=-af::Pi; !myWindow.close(); ) {
 
             Y = sin(X);
 
-            myWindow.plot(X, Y);
+            myWindow(0,0).plot(X, Y);
+            myWindow(0,1).scatter(X, Y, AF_MARKER_POINT);
+
+            myWindow.show();
 
             X = X + PRECISION * float(sign);
             val += PRECISION * float(sign);

From e0d7c12d97a69d950623691b4c17c3755b1387f1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Nov 2015 13:58:35 -0500
Subject: [PATCH 0031/2677] converted qr & solve cpu functions to async calls

Fixed lu async function
---
 src/backend/cpu/lu.cpp    |  66 ++++++++------------
 src/backend/cpu/qr.cpp    |  56 +++++++----------
 src/backend/cpu/solve.cpp | 127 +++++++++++++++++---------------------
 3 files changed, 105 insertions(+), 144 deletions(-)

diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index ed165cba8e..ff0be438ee 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -11,7 +11,6 @@
 #include <err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
-
 #include <af/dim4.hpp>
 #include <handle.hpp>
 #include <iostream>
@@ -26,9 +25,7 @@ namespace cpu
 {
 
 template<typename T>
-using getrf_func_def = int (*)(ORDER_TYPE, int, int,
-                               T*, int,
-                               int*);
+using getrf_func_def = int (*)(ORDER_TYPE, int, int, T*, int, int*);
 
 #define LU_FUNC_DEF( FUNC )                                     \
 template<typename T> FUNC##_func_def<T> FUNC##_func();
@@ -45,7 +42,7 @@ LU_FUNC(getrf , cfloat , c)
 LU_FUNC(getrf , cdouble, z)
 
 template<typename T>
-void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
+void lu_split(Array<T> lower, Array<T> upper, const Array<T> in)
 {
     T *l = lower.get();
     T *u = upper.get();
@@ -54,7 +51,6 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
     dim4 ldm = lower.dims();
     dim4 udm = upper.dims();
     dim4 idm = in.dims();
-
     dim4 lst = lower.strides();
     dim4 ust = upper.strides();
     dim4 ist = in.strides();
@@ -79,20 +75,14 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
                     const dim_t uMem = uYZW + ox;
                     const dim_t iMem = iYZW + ox;
                     if(ox > oy) {
-                        if(oy < ldm[1])
-                            l[lMem] = i[iMem];
-                        if(ox < udm[0])
-                            u[uMem] = scalar<T>(0);
+                        if(oy < ldm[1]) l[lMem] = i[iMem];
+                        if(ox < udm[0]) u[uMem] = scalar<T>(0);
                     } else if (oy > ox) {
-                        if(oy < ldm[1])
-                            l[lMem] = scalar<T>(0);
-                        if(ox < udm[0])
-                            u[uMem] = i[iMem];
+                        if(oy < ldm[1]) l[lMem] = scalar<T>(0);
+                        if(ox < udm[0]) u[uMem] = i[iMem];
                     } else if(ox == oy) {
-                        if(oy < ldm[1])
-                            l[lMem] = scalar<T>(1.0);
-                        if(ox < udm[0])
-                            u[uMem] = i[iMem];
+                        if(oy < ldm[1]) l[lMem] = scalar<T>(1.0);
+                        if(ox < udm[0]) u[uMem] = i[iMem];
                     }
                 }
             }
@@ -100,17 +90,15 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
     }
 }
 
-void convertPivot(Array<int> &pivot, int out_sz)
+void convertPivot(Array<int> p, Array<int> pivot)
 {
-    Array<int> p = range<int>(dim4(out_sz), 0);
     int *d_pi = pivot.get();
     int *d_po = p.get();
-    dim_t d0 = pivot.dims()[0];
+    dim_t d0  = pivot.dims()[0];
     for(int j = 0; j < (int)d0; j++) {
         // 1 indexed in pivot
         std::swap(d_po[j], d_po[d_pi[j] - 1]);
     }
-    pivot = p;
 }
 
 template<typename T>
@@ -138,26 +126,21 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     dim4 iDims = in.dims();
     Array<int> pivot = createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
 
-    auto func = [=] (Array<T> in, Array<int> pivot, const bool convert_pivot) {
+    auto func = [=] (Array<T> in, Array<int> pivot) {
         dim4 iDims = in.dims();
         getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides()[1], pivot.get());
-        if(convert_pivot) convertPivot(pivot, iDims[0]);
     };
-
-    getQueue().enqueue(func, in, pivot, convert_pivot);
-
-    return pivot;
+    getQueue().enqueue(func, in, pivot);
+
+    if(convert_pivot) {
+        Array<int> p = range<int>(dim4(iDims[0]), 0);
+        getQueue().enqueue(convertPivot, p, pivot);
+        return p;
+    } else {
+        return pivot;
+    }
 }
 
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
-
-INSTANTIATE_LU(float)
-INSTANTIATE_LU(cfloat)
-INSTANTIATE_LU(double)
-INSTANTIATE_LU(cdouble)
-
 }
 
 #else
@@ -177,6 +160,12 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
+}
+
+#endif
+
+namespace cpu
+{
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
@@ -185,7 +174,4 @@ INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
-
 }
-
-#endif
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index d1c3e233af..b5f18064f5 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -11,28 +11,23 @@
 #include <err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
-
 #include <af/dim4.hpp>
 #include <handle.hpp>
-#include <iostream>
 #include <cassert>
 #include <err_cpu.hpp>
 #include <triangle.hpp>
-
 #include <lapack_helper.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
 
 template<typename T>
-using geqrf_func_def = int (*)(ORDER_TYPE, int, int,
-                               T*, int,
-                               T*);
+using geqrf_func_def = int (*)(ORDER_TYPE, int, int, T*, int, T*);
 
 template<typename T>
-using gqr_func_def = int (*)(ORDER_TYPE, int, int, int,
-                             T*, int,
-                             const T*);
+using gqr_func_def = int (*)(ORDER_TYPE, int, int, int, T*, int, const T*);
 
 #define QR_FUNC_DEF( FUNC )                                         \
 template<typename T> FUNC##_func_def<T> FUNC##_func();
@@ -65,8 +60,8 @@ template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     q = padArray<T, T>(in, dim4(M, max(M, N)));
     q.resetDims(iDims);
@@ -78,39 +73,29 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 
     triangle<T, true, false>(r, q);
 
-    gqr_func<T>()(AF_LAPACK_COL_MAJOR,
-                  M, M, min(M, N),
-                  q.get(), q.strides()[1],
-                  t.get());
-
+    auto func = [=] (Array<T> q, Array<T> t, int M, int N) {
+        gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(), q.strides()[1], t.get());
+    };
     q.resetDims(dim4(M, M));
+    getQueue().enqueue(func, q, t, M, N);
 }
 
 template<typename T>
 Array<T> qr_inplace(Array<T> &in)
 {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-
+    int M      = iDims[0];
+    int N      = iDims[1];
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
-    geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    in.get(), in.strides()[1],
-                    t.get());
+    auto func = [=] (Array<T> in, Array<T> t, int M, int N) {
+        geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides()[1], t.get());
+    };
+    getQueue().enqueue(func, in, t, M, N);
 
     return t;
 }
 
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
-
-INSTANTIATE_QR(float)
-INSTANTIATE_QR(cfloat)
-INSTANTIATE_QR(double)
-INSTANTIATE_QR(cdouble)
-
 }
 
 #else
@@ -130,6 +115,12 @@ Array<T> qr_inplace(Array<T> &in)
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
+}
+
+#endif
+
+namespace cpu
+{
 #define INSTANTIATE_QR(T)                                                                           \
     template Array<T> qr_inplace<T>(Array<T> &in);                                                \
     template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
@@ -138,7 +129,4 @@ INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
-
 }
-
-#endif
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 1e88e8d915..b279971c7b 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -11,52 +11,40 @@
 #include <err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
-
 #include <af/dim4.hpp>
 #include <handle.hpp>
-#include <range.hpp>
-#include <iostream>
 #include <cassert>
 #include <err_cpu.hpp>
-
 #include <lapack_helper.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
 
 template<typename T>
 using gesv_func_def = int (*)(ORDER_TYPE, int, int,
-                              T *, int,
-                              int *,
-                              T *, int);
+                              T *, int, int *, T *, int);
 
 template<typename T>
-using gels_func_def = int (*)(ORDER_TYPE, char,
-                              int, int, int,
-                              T *, int,
-                              T *, int);
+using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int,
+                              T *, int, T *, int);
 
 template<typename T>
-using getrs_func_def = int (*)(ORDER_TYPE, char,
-                               int, int,
-                               const T *, int,
-                               const int *,
-                               T *, int);
+using getrs_func_def = int (*)(ORDER_TYPE, char, int, int,
+                               const T *, int, const int *, T *, int);
 
 template<typename T>
-using trtrs_func_def = int (*)(ORDER_TYPE,
-                               char, char, char,
-                               int, int,
-                               const T *, int,
-                               T *, int);
+using trtrs_func_def = int (*)(ORDER_TYPE, char, char, char, int, int,
+                               const T *, int, T *, int);
 
 
-#define SOLVE_FUNC_DEF( FUNC )                                      \
+#define SOLVE_FUNC_DEF( FUNC )                                 \
 template<typename T> FUNC##_func_def<T> FUNC##_func();
 
 
-#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                            \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
+#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                       \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()       \
 { return & LAPACK_NAME(PREFIX##FUNC); }
 
 SOLVE_FUNC_DEF( gesv )
@@ -87,16 +75,16 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
-    int N = A.dims()[0];
-    int NRHS = b.dims()[1];
-
+    int N        = A.dims()[0];
+    int NRHS     = b.dims()[1];
     Array< T > B = copyArray<T>(b);
 
-    getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                    N, NRHS,
-                    A.get(), A.strides()[1],
-                    pivot.get(),
-                    B.get(), B.strides()[1]);
+    auto func = [=] (Array<T> A, Array<T> B, Array<int> pivot, int N, int NRHS) {
+        getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
+                        N, NRHS, A.get(), A.strides()[1],
+                        pivot.get(), B.get(), B.strides()[1]);
+    };
+    getQueue().enqueue(func, A, B, pivot, N, NRHS);
 
     return B;
 }
@@ -105,16 +93,20 @@ template<typename T>
 Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
 {
     Array<T> B = copyArray<T>(b);
-    int N = B.dims()[0];
-    int NRHS = B.dims()[1];
-
-    trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
-                    options & AF_MAT_UPPER ? 'U' : 'L',
-                    'N', // transpose flag
-                    options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
-                    N, NRHS,
-                    A.get(), A.strides()[1],
-                    B.get(), B.strides()[1]);
+    int N      = B.dims()[0];
+    int NRHS   = B.dims()[1];
+
+    auto func = [=] (Array<T> A, Array<T> B, int N, int NRHS, const af_mat_prop options) {
+        trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
+                        options & AF_MAT_UPPER ? 'U' : 'L',
+                        'N', // transpose flag
+                        options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
+                        N, NRHS,
+                        A.get(), A.strides()[1],
+                        B.get(), B.strides()[1]);
+    };
+    getQueue().enqueue(func, A, B, N, NRHS, options);
+
     return B;
 }
 
@@ -132,41 +124,34 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
     int N = a.dims()[1];
     int K = b.dims()[1];
 
-
     Array<T> A = copyArray<T>(a);
     Array<T> B = padArray<T, T>(b, dim4(max(M, N), K));
 
     if(M == N) {
         Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
-        gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K,
-                       A.get(), A.strides()[1],
-                       pivot.get(),
-                       B.get(), B.strides()[1]);
+
+        auto func = [=] (Array<T> A, Array<T> B, Array<int> pivot, int N, int K) {
+            gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides()[1],
+                           pivot.get(), B.get(), B.strides()[1]);
+        };
+        getQueue().enqueue(func, A, B, pivot, N, K);
     } else {
-        int sM = a.strides()[1];
-        int sN = a.strides()[2] / sM;
+        auto func = [=] (Array<T> A, Array<T> B, int M, int N, int K) {
+            int sM = A.strides()[1];
+            int sN = A.strides()[2] / sM;
 
-        gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                       M, N, K,
-                       A.get(), A.strides()[1],
-                       B.get(), max(sM, sN));
+            gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
+                    M, N, K,
+                    A.get(), A.strides()[1],
+                    B.get(), max(sM, sN));
+        };
         B.resetDims(dim4(N, K));
+        getQueue().enqueue(func, A, B, M, N, K);
     }
 
     return B;
 }
 
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
-    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
-
-INSTANTIATE_SOLVE(float)
-INSTANTIATE_SOLVE(cfloat)
-INSTANTIATE_SOLVE(double)
-INSTANTIATE_SOLVE(cdouble)
-
 }
 
 #else
@@ -178,17 +163,21 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
-    AF_ERROR("Linear Algebra is diabled on CPU",
-             AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is diabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 {
-    AF_ERROR("Linear Algebra is diabled on CPU",
-              AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is diabled on CPU", AF_ERR_NOT_CONFIGURED);
+}
+
 }
 
+#endif
+
+namespace cpu
+{
 #define INSTANTIATE_SOLVE(T)                                            \
     template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
                                const af_mat_prop options);              \
@@ -200,5 +189,3 @@ INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 }
-
-#endif

From d0223f980047dfee315569eaf359105377e978b7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Nov 2015 15:48:10 -0500
Subject: [PATCH 0032/2677] Converted wrap & unwrap cpu fns to async calls

---
 src/backend/cpu/unwrap.cpp | 173 +++++++++++++++++++------------------
 src/backend/cpu/wrap.cpp   | 171 ++++++++++++++++++------------------
 2 files changed, 175 insertions(+), 169 deletions(-)

diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index f9c25f9a9e..efb46be7f4 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -13,112 +13,115 @@
 #include <err_cpu.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    template<typename T, int d>
-    void unwrap_dim(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-                    const af::dim4 &ostrides, const af::dim4 &istrides,
-                    const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-                    const dim_t px, const dim_t py)
-    {
-        dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-
-        for(dim_t w = 0; w < odims[3]; w++) {
-            for(dim_t z = 0; z < odims[2]; z++) {
-
-                dim_t cOut = w * ostrides[3] + z * ostrides[2];
-                dim_t cIn  = w * istrides[3] + z * istrides[2];
-                const T* iptr = inPtr  + cIn;
-                T* optr_= outPtr + cOut;
-
-                for(dim_t col = 0; col < odims[d]; col++) {
-                    // Offset output ptr
-                    T* optr = optr_ + col * ostrides[d];
-
-                    // Calculate input window index
-                    dim_t winy = (col / nx);
-                    dim_t winx = (col % nx);
-
-                    dim_t startx = winx * sx;
-                    dim_t starty = winy * sy;
-
-                    dim_t spx = startx - px;
-                    dim_t spy = starty - py;
-
-                    // Short cut condition ensuring all values within input dimensions
-                    bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 && spy + wy < idims[1]);
-
-                    for(dim_t y = 0; y < wy; y++) {
-                        for(dim_t x = 0; x < wx; x++) {
-                            dim_t xpad = spx + x;
-                            dim_t ypad = spy + y;
-
-                            dim_t oloc = (y * wx + x);
-                            if (d == 0) oloc *= ostrides[1];
-
-                            if(cond || (xpad >= 0 && xpad < idims[0] && ypad >= 0 && ypad < idims[1])) {
-                                dim_t iloc = (ypad * istrides[1] + xpad * istrides[0]);
-                                optr[oloc] = iptr[iloc];
-                            } else {
-                                optr[oloc] = scalar<T>(0.0);
-                            }
+
+template<typename T, int d>
+void unwrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
+{
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
+
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    af::dim4 istrides = in.strides();
+    af::dim4 ostrides = out.strides();
+
+    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
+
+    for(dim_t w = 0; w < odims[3]; w++) {
+        for(dim_t z = 0; z < odims[2]; z++) {
+
+            dim_t cOut = w * ostrides[3] + z * ostrides[2];
+            dim_t cIn  = w * istrides[3] + z * istrides[2];
+            const T* iptr = inPtr  + cIn;
+            T* optr_= outPtr + cOut;
+
+            for(dim_t col = 0; col < odims[d]; col++) {
+                // Offset output ptr
+                T* optr = optr_ + col * ostrides[d];
+
+                // Calculate input window index
+                dim_t winy = (col / nx);
+                dim_t winx = (col % nx);
+
+                dim_t startx = winx * sx;
+                dim_t starty = winy * sy;
+
+                dim_t spx = startx - px;
+                dim_t spy = starty - py;
+
+                // Short cut condition ensuring all values within input dimensions
+                bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 && spy + wy < idims[1]);
+
+                for(dim_t y = 0; y < wy; y++) {
+                    for(dim_t x = 0; x < wx; x++) {
+                        dim_t xpad = spx + x;
+                        dim_t ypad = spy + y;
+
+                        dim_t oloc = (y * wx + x);
+                        if (d == 0) oloc *= ostrides[1];
+
+                        if(cond || (xpad >= 0 && xpad < idims[0] && ypad >= 0 && ypad < idims[1])) {
+                            dim_t iloc = (ypad * istrides[1] + xpad * istrides[0]);
+                            optr[oloc] = iptr[iloc];
+                        } else {
+                            optr[oloc] = scalar<T>(0.0);
                         }
                     }
                 }
             }
         }
     }
+}
 
-    template<typename T>
-    Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
-    {
-        af::dim4 idims = in.dims();
-
-        dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-        dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
-
-        af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
+{
+    af::dim4 idims = in.dims();
 
-        if (!is_column) {
-            std::swap(odims[0], odims[1]);
-        }
+    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
+    dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
 
-        // Create output placeholder
-        Array<T> outArray = createEmptyArray<T>(odims);
+    af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
 
-        // Get pointers to raw data
-        const T *inPtr = in.get();
-        T *outPtr = outArray.get();
+    if (!is_column) {
+        std::swap(odims[0], odims[1]);
+    }
 
-        af::dim4 ostrides = outArray.strides();
-        af::dim4 istrides = in.strides();
+    Array<T> outArray = createEmptyArray<T>(odims);
 
-        if (is_column) {
-            unwrap_dim<T, 1>(outPtr, inPtr, odims, idims, ostrides, istrides, wx, wy, sx, sy, px, py);
-        } else {
-            unwrap_dim<T, 0>(outPtr, inPtr, odims, idims, ostrides, istrides, wx, wy, sx, sy, px, py);
-        }
-        return outArray;
+    if (is_column) {
+        getQueue().enqueue(unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
+    } else {
+        getQueue().enqueue(unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
     }
 
+    return outArray;
+}
+
 
 #define INSTANTIATE(T)                                                                  \
     template Array<T> unwrap<T> (const Array<T> &in, const dim_t wx, const dim_t wy,    \
                     const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
 
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
 }
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index a04a6f5250..3ff54de640 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -13,92 +13,95 @@
 #include <err_cpu.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
 
-    template<typename T, int d>
-    void wrap_dim(T *outPtr, const T *inPtr,
-                  const af::dim4 &odims, const af::dim4 &idims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py)
-    {
-        dim_t nx = (odims[0] + 2 * px - wx) / sx + 1;
-
-        for(dim_t w = 0; w < idims[3]; w++) {
-            for(dim_t z = 0; z < idims[2]; z++) {
-
-                dim_t cIn  = w * istrides[3] + z * istrides[2];
-                dim_t cOut = w * ostrides[3] + z * ostrides[2];
-                const T* iptr_ = inPtr  + cIn;
-                T* optr= outPtr + cOut;
-
-                for(dim_t col = 0; col < idims[d]; col++) {
-                    // Offset output ptr
-                    const T* iptr = iptr_ + col * istrides[d];
-
-                    // Calculate input window index
-                    dim_t winy = (col / nx);
-                    dim_t winx = (col % nx);
-
-                    dim_t startx = winx * sx;
-                    dim_t starty = winy * sy;
-
-                    dim_t spx = startx - px;
-                    dim_t spy = starty - py;
-
-                    // Short cut condition ensuring all values within input dimensions
-                    bool cond = (spx >= 0 && spx + wx < odims[0] && spy >= 0 && spy + wy < odims[1]);
-
-                    for(dim_t y = 0; y < wy; y++) {
-                        for(dim_t x = 0; x < wx; x++) {
-                            dim_t xpad = spx + x;
-                            dim_t ypad = spy + y;
-
-                            dim_t iloc = (y * wx + x);
-                            if (d == 0) iloc *= istrides[1];
-
-                            if(cond || (xpad >= 0 && xpad < odims[0] && ypad >= 0 && ypad < odims[1])) {
-                                dim_t oloc = (ypad * ostrides[1] + xpad * ostrides[0]);
-                                // FIXME: When using threads, atomize this
-                                optr[oloc] += iptr[iloc];
-                            }
+template<typename T, int d>
+void wrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
+              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
+{
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
+
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    af::dim4 istrides = in.strides();
+    af::dim4 ostrides = out.strides();
+
+    dim_t nx = (odims[0] + 2 * px - wx) / sx + 1;
+
+    for(dim_t w = 0; w < idims[3]; w++) {
+        for(dim_t z = 0; z < idims[2]; z++) {
+
+            dim_t cIn  = w * istrides[3] + z * istrides[2];
+            dim_t cOut = w * ostrides[3] + z * ostrides[2];
+            const T* iptr_ = inPtr  + cIn;
+            T* optr= outPtr + cOut;
+
+            for(dim_t col = 0; col < idims[d]; col++) {
+                // Offset output ptr
+                const T* iptr = iptr_ + col * istrides[d];
+
+                // Calculate input window index
+                dim_t winy = (col / nx);
+                dim_t winx = (col % nx);
+
+                dim_t startx = winx * sx;
+                dim_t starty = winy * sy;
+
+                dim_t spx = startx - px;
+                dim_t spy = starty - py;
+
+                // Short cut condition ensuring all values within input dimensions
+                bool cond = (spx >= 0 && spx + wx < odims[0] && spy >= 0 && spy + wy < odims[1]);
+
+                for(dim_t y = 0; y < wy; y++) {
+                    for(dim_t x = 0; x < wx; x++) {
+                        dim_t xpad = spx + x;
+                        dim_t ypad = spy + y;
+
+                        dim_t iloc = (y * wx + x);
+                        if (d == 0) iloc *= istrides[1];
+
+                        if(cond || (xpad >= 0 && xpad < odims[0] && ypad >= 0 && ypad < odims[1])) {
+                            dim_t oloc = (ypad * ostrides[1] + xpad * ostrides[0]);
+                            // FIXME: When using threads, atomize this
+                            optr[oloc] += iptr[iloc];
                         }
                     }
                 }
             }
         }
     }
+}
 
-    template<typename T>
-    Array<T> wrap(const Array<T> &in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column)
-    {
-        af::dim4 idims = in.dims();
-        af::dim4 odims(ox, oy, idims[2], idims[3]);
-        Array<T> out = createValueArray<T>(odims, scalar<T>(0));
-
-        const T *inPtr = in.get();
-        T *outPtr = out.get();
-
-        af::dim4 istrides = in.strides();
-        af::dim4 ostrides = out.strides();
-
-        if (is_column) {
-            wrap_dim<T, true >(outPtr, inPtr, odims, idims, ostrides, istrides, wx, wy, sx, sy, px, py);
-        } else {
-            wrap_dim<T, false>(outPtr, inPtr, odims, idims, ostrides, istrides, wx, wy, sx, sy, px, py);
-        }
+template<typename T>
+Array<T> wrap(const Array<T> &in,
+              const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy,
+              const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py,
+              const bool is_column)
+{
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
+    out.eval();
+    in.eval();
 
-        return out;
+    if (is_column) {
+        getQueue().enqueue(wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
+    } else {
+        getQueue().enqueue(wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
     }
 
+    return out;
+}
+
 
 #define INSTANTIATE(T)                                          \
     template Array<T> wrap<T> (const Array<T> &in,              \
@@ -108,17 +111,17 @@ namespace cpu
                                const dim_t px, const dim_t py,  \
                                const bool is_column);
 
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
 }

From 1b0ef66cd28f9dba9230d9f3c160425e2996783c Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Thu, 19 Nov 2015 16:09:17 +0000
Subject: [PATCH 0033/2677] Add missing linkage with libdl

---
 src/api/unified/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 179293cabc..917c6dce42 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -55,6 +55,8 @@ IF(${BUILD_OPENCL})
     ADD_DEPENDENCIES(af afopencl)
 ENDIF()
 
+TARGET_LINK_LIBRARIES(af ${CMAKE_DL_LIBS})
+
 SET_TARGET_PROPERTIES(af PROPERTIES
                       VERSION "${AF_VERSION}"
                       SOVERSION "${AF_VERSION_MAJOR}")

From 32a65d8f390e2893ba91ef6742365f9fd8e7c3c4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Nov 2015 16:58:13 -0500
Subject: [PATCH 0034/2677] converted transform to async call

---
 src/backend/cpu/transform.cpp | 230 +++++++++++++++++-----------------
 1 file changed, 116 insertions(+), 114 deletions(-)

diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 68e8d96eba..f4a05148c5 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -12,136 +12,138 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include "transform_interp.hpp"
 
 namespace cpu
 {
-    template <typename T>
-    void calc_affine_inverse(T *txo, const T *txi)
-    {
-        T det = txi[0]*txi[4] - txi[1]*txi[3];
-
-        txo[0] = txi[4] / det;
-        txo[1] = txi[3] / det;
-        txo[3] = txi[1] / det;
-        txo[4] = txi[0] / det;
-
-        txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-        txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
-    }
 
-    template <typename T>
-    void calc_affine_inverse(T *tmat, const T *tmat_ptr, const bool inverse)
-    {
-        // The way kernel is structured, it expects an inverse
-        // transform matrix by default.
-        // If it is an forward transform, then we need its inverse
-        if(inverse) {
-            for(int i = 0; i < 6; i++)
-                tmat[i] = tmat_ptr[i];
-        } else {
-            calc_affine_inverse(tmat, tmat_ptr);
-        }
+template <typename T>
+void calc_affine_inverse(T *txo, const T *txi)
+{
+    T det = txi[0]*txi[4] - txi[1]*txi[3];
+
+    txo[0] = txi[4] / det;
+    txo[1] = txi[3] / det;
+    txo[3] = txi[1] / det;
+    txo[4] = txi[0] / det;
+
+    txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+    txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+}
+
+template <typename T>
+void calc_affine_inverse(T *tmat, const T *tmat_ptr, const bool inverse)
+{
+    // The way kernel is structured, it expects an inverse
+    // transform matrix by default.
+    // If it is an forward transform, then we need its inverse
+    if(inverse) {
+        for(int i = 0; i < 6; i++)
+            tmat[i] = tmat_ptr[i];
+    } else {
+        calc_affine_inverse(tmat, tmat_ptr);
     }
+}
 
-    template<typename T, af_interp_type method>
-    void transform_(T *out, const T *in, const float *tf,
-                    const af::dim4 &odims, const af::dim4 &idims,
-                    const af::dim4 &ostrides, const af::dim4 &istrides,
-                    const af::dim4 &tstrides, const bool inverse)
-    {
-        dim_t nimages     = idims[2];
-        // Multiplied in src/backend/transform.cpp
-        dim_t ntransforms = odims[2] / idims[2];
-
-        void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
-                     const af::dim4 &, const af::dim4 &,
-                     const dim_t, const dim_t, const dim_t, const dim_t);
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                t_fn = &transform_n;
-                break;
-            case AF_INTERP_BILINEAR:
-                t_fn = &transform_b;
-                break;
-            case AF_INTERP_LOWER:
-                t_fn = &transform_l;
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                break;
-        }
+template<typename T, af_interp_type method>
+void transform_(Array<T> output, const Array<T> input,
+                const Array<float> transform, const bool inverse)
+{
+    const af::dim4 idims    = input.dims();
+    const af::dim4 odims    = output.dims();
+    const af::dim4 istrides = input.strides();
+    const af::dim4 ostrides = output.strides();
+
+    T * out = output.get();
+    const T * in = input.get();
+    const float* tf = transform.get();
+
+    dim_t nimages     = idims[2];
+    // Multiplied in src/backend/transform.cpp
+    dim_t ntransforms = odims[2] / idims[2];
+
+    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
+                 const af::dim4 &, const af::dim4 &,
+                 const dim_t, const dim_t, const dim_t, const dim_t);
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            t_fn = &transform_n;
+            break;
+        case AF_INTERP_BILINEAR:
+            t_fn = &transform_b;
+            break;
+        case AF_INTERP_LOWER:
+            t_fn = &transform_l;
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+            break;
+    }
 
 
-        // For each transform channel
-        for(int t_idx = 0; t_idx < (int)ntransforms; t_idx++) {
-            // Compute inverse if required
-            const float *tmat_ptr = tf + t_idx * 6;
-            float tmat[6];
-            calc_affine_inverse(tmat, tmat_ptr, inverse);
+    // For each transform channel
+    for(int t_idx = 0; t_idx < (int)ntransforms; t_idx++) {
+        // Compute inverse if required
+        const float *tmat_ptr = tf + t_idx * 6;
+        float tmat[6];
+        calc_affine_inverse(tmat, tmat_ptr, inverse);
 
-            // Offset for output pointer
-            dim_t o_offset = t_idx * nimages * ostrides[2];
+        // Offset for output pointer
+        dim_t o_offset = t_idx * nimages * ostrides[2];
 
-            // Do transform for image
-            for(int yy = 0; yy < (int)odims[1]; yy++) {
-                for(int xx = 0; xx < (int)odims[0]; xx++) {
-                    t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy);
-                }
+        // Do transform for image
+        for(int yy = 0; yy < (int)odims[1]; yy++) {
+            for(int xx = 0; xx < (int)odims[0]; xx++) {
+                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy);
             }
         }
     }
+}
 
-    template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse)
-    {
-        const af::dim4 idims = in.dims();
-
-        Array<T> out = createEmptyArray<T>(odims);
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                transform_<T, AF_INTERP_NEAREST>
-                          (out.get(), in.get(), transform.get(), odims, idims,
-                           out.strides(), in.strides(), transform.strides(), inverse);
-                break;
-            case AF_INTERP_BILINEAR:
-                transform_<T, AF_INTERP_BILINEAR>
-                          (out.get(), in.get(), transform.get(), odims, idims,
-                           out.strides(), in.strides(), transform.strides(), inverse);
-                break;
-            case AF_INTERP_LOWER:
-                transform_<T, AF_INTERP_LOWER>
-                          (out.get(), in.get(), transform.get(), odims, idims,
-                           out.strides(), in.strides(), transform.strides(), inverse);
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                break;
-        }
-
-        return out;
+template<typename T>
+Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
+                    const af_interp_type method, const bool inverse)
+{
+    Array<T> out = createEmptyArray<T>(odims);
+    in.eval();
+
+    switch(method) {
+        case AF_INTERP_NEAREST :
+            getQueue().enqueue(transform_<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
+            break;
+        case AF_INTERP_BILINEAR:
+            getQueue().enqueue(transform_<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
+            break;
+        case AF_INTERP_LOWER   :
+            getQueue().enqueue(transform_<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
+            break;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
 
+    return out;
+}
+
+
+#define INSTANTIATE(T)                                                              \
+template Array<T> transform(const Array<T> &in, const Array<float> &transform,      \
+                            const af::dim4 &odims, const af_interp_type method,     \
+                            const bool inverse);
+
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-#define INSTANTIATE(T)                                                                  \
-    template Array<T> transform(const Array<T> &in, const Array<float> &transform,      \
-                                const af::dim4 &odims, const af_interp_type method,     \
-                                const bool inverse);
-
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
 }

From ef2e7d7bfe7a363c737ed93b3afaf917cba51ed0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 21 Nov 2015 12:11:40 -0500
Subject: [PATCH 0035/2677] Fix documentation when using older doxygen

* Eliminate dummy groups
* Remove repeats of operator() groups
---
 docs/details/array.dox | 21 -----------
 include/af/array.h     | 80 ++++++++++++++++++++++++++----------------
 2 files changed, 49 insertions(+), 52 deletions(-)

diff --git a/docs/details/array.dox b/docs/details/array.dox
index a58955c698..2f696a26a3 100644
--- a/docs/details/array.dox
+++ b/docs/details/array.dox
@@ -16,27 +16,6 @@ to \ref af::array objects.
 
 ===============================================================================
 
-\defgroup array_mem_operator_paren_one operator()
-
-This operator returns a reference of the original array at a given coordinate.
-You can pass \ref af::seq, \ref af::array, or an int as it's parameters. These
-references can be used for assignment or returning references
-to \ref af::array objects.
-
-If the \ref af::array is a multi-dimensional array then this coordinate
-will treated as the data as a linear array.
-
-===============================================================================
-
-\defgroup array_mem_operator_paren_many operator()
-
-This operator returns a reference of the original array at a given coordinate.
-You can pass \ref af::seq, \ref af::array, or an int as it's parameters. These
-references can be used for assignment or returning references
-to \ref af::array objects.
-
-===============================================================================
-
 \defgroup array_mem_row row/rows
 \ingroup index_mat
 
diff --git a/include/af/array.h b/include/af/array.h
index a5f39e7793..193cf8d14d 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -683,47 +683,65 @@ namespace af
         // INDEXING
         // Single arguments
 
+        /**
+            \brief This operator returns a reference of the original array at a given coordinate.
 
-        /// \ingroup array_mem_operator_paren
-        /// @{
-        ///
-        /// \brief Gets a reference to a set of linear elements
-        ///
-        /// \copydetails array_mem_operator_paren_one
-        ///
-        /// \param[in] s0   is sequence of linear indices
-        ///
-        /// \returns A reference to the array at the given index
-        ///
-              array::array_proxy operator()(const index &s0);
+            You can pass \ref af::seq, \ref af::array, or an int as it's parameters.
+            These references can be used for assignment or returning references
+            to \ref af::array objects.
+
+            If the \ref af::array is a multi-dimensional array then this coordinate
+            will treated as the data as a linear array.
+
+            \param[in] s0   is sequence of linear indices
 
-        /// \copydoc operator()(const index &)
+            \returns A reference to the array at the given index
+
+            \ingroup array_mem_operator_paren
+
+        */
+        array::array_proxy operator()(const index &s0);
+
+        /**
+            \copydoc operator()(const index &)
+
+            \ingroup array_mem_operator_paren
+        */
         const array::array_proxy operator()(const index &s0) const;
 
 
-        ///
-        /// \brief Gets a reference to a sub array
-        ///
-        /// \copydetails array_mem_operator_paren_many
-        ///
-        /// \param[in] s0   is sequence of indices along the first dimension
-        /// \param[in] s1   is sequence of indices along the second dimension
-        /// \param[in] s2   is sequence of indices along the third dimension
-        /// \param[in] s3   is sequence of indices along the fourth dimension
-        ///
-        /// \returns A reference to the array at the given index
-        ///
-              array::array_proxy operator()(const index &s0,
-                                            const index &s1,
-                                            const index &s2 = span,
-                                            const index &s3 = span);
+        /**
+            \brief This operator returns a reference of the original array at a
+            given coordinate.
+
+            You can pass \ref af::seq, \ref af::array, or an int as it's parameters.
+            These references can be used for assignment or returning references
+            to \ref af::array objects.
+
+            \param[in] s0   is sequence of indices along the first dimension
+            \param[in] s1   is sequence of indices along the second dimension
+            \param[in] s2   is sequence of indices along the third dimension
+            \param[in] s3   is sequence of indices along the fourth dimension
 
-        /// \copydoc operator()(const index &, const index &, const index &, const index &)
+            \returns A reference to the array at the given index
+
+            \ingroup array_mem_operator_paren
+        */
+        array::array_proxy operator()(const index &s0,
+                                      const index &s1,
+                                      const index &s2 = span,
+                                      const index &s3 = span);
+
+        /**
+            \copydoc operator()(const index &, const index &, const index &, const index &)
+
+            \ingroup array_mem_operator_paren
+        */
         const array::array_proxy operator()(const index &s0,
                                             const index &s1,
                                             const index &s2 = span,
                                             const index &s3 = span) const;
-        /// @}
+
 
         /// \ingroup array_mem_row
         /// @{

From 54ad0b34fcd8f86492b9f69c86a37e824a77d57e Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Sun, 22 Nov 2015 17:09:41 +0000
Subject: [PATCH 0036/2677] Fix examples target.

---
 examples/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index e20db4ed2a..49ce38ddb8 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -134,10 +134,10 @@ else()
   MESSAGE(STATUS "EXAMPLES: OPENCL backend is OFF. OPENCL was not found")
 endif()
 
-INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
+INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/"
     DESTINATION "${AF_INSTALL_EXAMPLE_DIR}"
     COMPONENT examples)
 
 INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../assets/examples"
-    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/examples/assets/"
+    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/assets/"
 )

From 3281b50c8e8e08021cc12baa4cedc4681eb12474 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 22 Nov 2015 22:12:20 -0500
Subject: [PATCH 0037/2677] Added missing symbol export for af_draw_surface

---
 include/af/graphics.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 5c143c721e..30cb287351 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -427,7 +427,7 @@ AFAPI af_err af_draw_hist(const af_window wind, const af_array X, const double m
 
    \ingroup gfx_func_draw
 */
-af_err af_draw_surface(const af_window wind, const af_array xVals, const af_array yVals, const af_array S, const af_cell* const props);
+AFAPI af_err af_draw_surface(const af_window wind, const af_array xVals, const af_array yVals, const af_array S, const af_cell* const props);
 #endif
 
 /**

From 743fb4a1e57dea918dc4465d4a25dc0279a286a4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 23 Nov 2015 13:28:55 -0500
Subject: [PATCH 0038/2677] converted susan fn in cpu backend to asynchronous
 call

---
 src/backend/cpu/susan.cpp | 59 +++++++++++++++++++++++----------------
 1 file changed, 35 insertions(+), 24 deletions(-)

diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 77493915c0..e2c908c378 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -11,18 +11,25 @@
 #include <Array.hpp>
 #include <cmath>
 #include <math.hpp>
+#include <memory>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::features;
+using std::shared_ptr;
 
 namespace cpu
 {
 
 template<typename T>
-void susan_responses(T* resp_out, const T* in,
+void susan_responses(Array<T> output, const Array<T> input,
                      const unsigned idim0, const unsigned idim1,
                      const int radius, const float t, const float g,
                      const unsigned border_len)
 {
+    T* resp_out = output.get();
+    const T* in = input.get();
+
     const unsigned r = border_len;
     const int rSqrd = radius*radius;
 
@@ -51,10 +58,16 @@ void susan_responses(T* resp_out, const T* in,
 }
 
 template<typename T>
-void non_maximal(float* x_out, float* y_out, float* resp_out,
-                 unsigned* count, const unsigned idim0, const unsigned idim1,
-                 const T* resp_in, const unsigned border_len, const unsigned max_corners)
+void non_maximal(Array<float> xcoords, Array<float> ycoords, Array<float> response,
+                 shared_ptr<unsigned> counter, const unsigned idim0, const unsigned idim1,
+                 const Array<T> input, const unsigned border_len, const unsigned max_corners)
 {
+    float* x_out    = xcoords.get();
+    float* y_out    = ycoords.get();
+    float* resp_out = response.get();
+    unsigned* count = counter.get();
+    const T* resp_in= input.get();
+
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
 
@@ -94,36 +107,34 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                const float feature_ratio, const unsigned edge)
 {
     dim4 idims = in.dims();
-
     const unsigned corner_lim = in.elements() * feature_ratio;
-    float* x_corners          = memAlloc<float>(corner_lim);
-    float* y_corners          = memAlloc<float>(corner_lim);
-    float* resp_corners       = memAlloc<float>(corner_lim);
 
-    T* resp = memAlloc<T>(in.elements());
-    unsigned corners_found = 0;
+    auto x_corners    = createEmptyArray<float>(dim4(corner_lim));
+    auto y_corners    = createEmptyArray<float>(dim4(corner_lim));
+    auto resp_corners = createEmptyArray<float>(dim4(corner_lim));
+    auto response     = createEmptyArray<T>(dim4(in.elements()));
+    auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1), memFree<unsigned>);
+    corners_found.get()[0] = 0;
 
-    susan_responses<T>(resp, in.get(), idims[0], idims[1], radius, diff_thr, geom_thr, edge);
+    getQueue().enqueue(susan_responses<T>, response, in, idims[0], idims[1],
+                       radius, diff_thr, geom_thr, edge);
+    getQueue().enqueue(non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
+                       idims[0], idims[1], response, edge, corner_lim);
+    getQueue().sync();
 
-    non_maximal<T>(x_corners, y_corners, resp_corners, &corners_found,
-                   idims[0], idims[1], resp, edge, corner_lim);
-
-    memFree(resp);
-
-    const unsigned corners_out = min(corners_found, corner_lim);
+    const unsigned corners_out = min((corners_found.get())[0], corner_lim);
     if (corners_out == 0) {
-        memFree(x_corners);
-        memFree(y_corners);
-        memFree(resp_corners);
         x_out    = createEmptyArray<float>(dim4());
         y_out    = createEmptyArray<float>(dim4());
         resp_out = createEmptyArray<float>(dim4());
         return 0;
     } else {
-
-        x_out = createDeviceDataArray<float>(dim4(corners_out), (void*)x_corners);
-        y_out = createDeviceDataArray<float>(dim4(corners_out), (void*)y_corners);
-        resp_out = createDeviceDataArray<float>(dim4(corners_out), (void*)resp_corners);
+        x_out = x_corners;
+        y_out = y_corners;
+        resp_out = resp_corners;
+        x_out.resetDims(dim4(corners_out));
+        y_out.resetDims(dim4(corners_out));
+        resp_out.resetDims(dim4(corners_out));
         return corners_out;
     }
 }

From 840af46e2ccce3ee3b0e23b65ac18f28231b9a1e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 23 Nov 2015 13:35:11 -0500
Subject: [PATCH 0039/2677] convert sort & sort_by_key cpu fns to async calls

---
 src/backend/cpu/sort.cpp        | 7 ++++---
 src/backend/cpu/sort_by_key.cpp | 9 ++++-----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 0b3fb9aabe..94d70a8e49 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -15,6 +15,8 @@
 #include <err_cpu.hpp>
 #include <algorithm>
 #include <functional>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using std::greater;
 using std::less;
@@ -29,7 +31,7 @@ namespace cpu
 
     // Based off of http://stackoverflow.com/a/12399290
     template<typename T, bool isAscending>
-    void sort0(Array<T> &val)
+    void sort0(Array<T> val)
     {
         // initialize original index locations
         T *val_ptr = val.get();
@@ -62,8 +64,7 @@ namespace cpu
     {
         Array<T> out = copyArray<T>(in);
         switch(dim) {
-            case 0: sort0<T, isAscending>(out);
-                    break;
+            case 0: getQueue().enqueue(sort0<T, isAscending>, out); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         return out;
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 4b0a092834..684b9bac58 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -15,14 +15,14 @@
 #include <algorithm>
 #include <numeric>
 #include <queue>
-#include <future>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using std::greater;
 using std::less;
 using std::sort;
 using std::function;
 using std::queue;
-using std::future;
 using std::async;
 
 namespace cpu
@@ -32,7 +32,7 @@ namespace cpu
     ///////////////////////////////////////////////////////////////////////////
 
     template<typename Tk, typename Tv, bool isAscending>
-    void sort0_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey, const Array<Tv> &ival)
+    void sort0_by_key(Array<Tk> okey, Array<Tv> oval, const Array<Tk> ikey, const Array<Tv> ival)
     {
         function<bool(Tk, Tk)> op = greater<Tk>();
         if(isAscending) { op = less<Tk>(); }
@@ -101,8 +101,7 @@ namespace cpu
         okey = createEmptyArray<Tk>(ikey.dims());
         oval = createEmptyArray<Tv>(ival.dims());
         switch(dim) {
-            case 0: sort0_by_key<Tk, Tv, isAscending>(okey, oval, ikey, ival);
-                    break;
+            case 0: getQueue().enqueue(sort0_by_key<Tk, Tv, isAscending>, okey, oval, ikey, ival); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }

From e0055579305bb4969f0f3bf5855968387ad2c7da Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 23 Nov 2015 13:43:08 -0500
Subject: [PATCH 0040/2677] sobel cpu fn is async fn after this change

---
 src/backend/cpu/sobel.cpp | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 3c6b1740d5..9f683fc450 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -14,7 +14,8 @@
 #include <sobel.hpp>
 #include <convolve.hpp>
 #include <err_cpu.hpp>
-#include <utility>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -22,8 +23,13 @@ namespace cpu
 {
 
 template<typename Ti, typename To, bool isDX>
-void derivative(To *optr, Ti const *iptr, dim4 const &dims, dim4 const &strides)
+void derivative(Array<To> output, const Array<Ti> input)
 {
+    const dim4 dims    = input.dims();
+    const dim4 strides = input.strides();
+          To* optr     = output.get();
+    const Ti* iptr     = input.get();
+
     for(dim_t b3=0; b3<dims[3]; ++b3) {
     for(dim_t b2=0; b2<dims[2]; ++b2) {
 
@@ -85,16 +91,18 @@ template<typename Ti, typename To>
 std::pair< Array<To>, Array<To> >
 sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
 {
+    // ket_size is for future proofing, this argument is not used
+    // currently
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
-    derivative<Ti, To, true >(dx.get(), img.get(), img.dims(), img.strides());
-    derivative<Ti, To, false>(dy.get(), img.get(), img.dims(), img.strides());
+    getQueue().enqueue(derivative<Ti, To, true >, dx, img);
+    getQueue().enqueue(derivative<Ti, To, false>, dy, img);
 
     return std::make_pair(dx, dy);
 }
 
-#define INSTANTIATE(Ti, To)                                                 \
+#define INSTANTIATE(Ti, To)                                               \
     template std::pair< Array<To>, Array<To> >                            \
     sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
 

From d39f9e88c26c46be422e0801a3c78bdbec95ad14 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 23 Nov 2015 13:52:13 -0500
Subject: [PATCH 0041/2677] Fix type in documentation

---
 docs/pages/INSTALL.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/INSTALL.md b/docs/pages/INSTALL.md
index dabb10b318..3565889571 100644
--- a/docs/pages/INSTALL.md
+++ b/docs/pages/INSTALL.md
@@ -162,7 +162,7 @@ not include MKL acceleration of linear algebra functions.
 
 After ArrayFire is installed, you can build the example programs as follows:
 
-    cp -r /usr/local/share/doc/arrayfire/examples .
+    cp -r /usr/local/share/ArrayFire/examples .
     cd examples
     mkdir build
     cd build

From 4ec314a225140e0d0078fc4542dab2ce65ce0c04 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 23 Nov 2015 15:08:49 -0500
Subject: [PATCH 0042/2677] cleanup and scatter example update

---
 examples/graphics/plot2d.cpp | 5 +++--
 src/api/cpp/graphics.cpp     | 1 -
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index c6d3452c3b..7593f8a602 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -13,7 +13,7 @@
 
 using namespace af;
 
-static const int ITERATIONS = 100;
+static const int ITERATIONS = 50;
 static const float PRECISION = 1.0f/ITERATIONS;
 
 int main(int argc, char *argv[])
@@ -26,6 +26,7 @@ int main(int argc, char *argv[])
         array Y;
         int sign = 1;
         array X = seq(-af::Pi, af::Pi, PRECISION);
+        array noise = randn(X.dims(0))/5.f;
 
         myWindow.grid(1, 2);
         for (double val=-af::Pi; !myWindow.close(); ) {
@@ -33,7 +34,7 @@ int main(int argc, char *argv[])
             Y = sin(X);
 
             myWindow(0,0).plot(X, Y);
-            myWindow(0,1).scatter(X, Y, AF_MARKER_POINT);
+            myWindow(0,1).scatter(X, Y + noise, AF_MARKER_POINT);
 
             myWindow.show();
 
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 8d2d8cd4b6..cb9b0803e7 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -99,7 +99,6 @@ void Window::hist(const array& X, const double minval, const double maxval, cons
 }
 
 void Window::surface(const array& S, const char* const title){
-    //TODO: fix offset on forge?
     af::array xVals = seq(0, S.dims(0)-1);
     af::array yVals = seq(0, S.dims(1)-1);
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};

From 6f52c36b5bd09657c1b40b16669f03b813916a17 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 23 Nov 2015 18:38:04 -0500
Subject: [PATCH 0043/2677] adds scatter3

---
 include/af/graphics.h         | 36 +++++++++++++++++++++++++++++++----
 src/api/c/graphics_common.cpp | 23 +++++++++++++++++++---
 src/api/c/graphics_common.hpp |  3 ++-
 src/api/c/plot.cpp            | 15 ++-------------
 src/api/c/plot3.cpp           | 29 +++++++++++++++++++---------
 src/api/cpp/graphics.cpp      |  8 +++++++-
 src/api/unified/graphics.cpp  | 10 ++++++++--
 7 files changed, 91 insertions(+), 33 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index e4286e1ea7..129b43949f 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -177,7 +177,6 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-
         void plot(const array& X, const array& Y, const char* const title=NULL);
 
         /**
@@ -192,8 +191,19 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-
         void scatter(const array& X, const array& Y, const af::markerType marker=AF_MARKER_POINT, const char* const title=NULL);
+
+        /**
+           Renders the input arrays as a 2D scatter-plot to the window
+
+           \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
+           \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \ingroup gfx_func_draw
+         */
+        void scatter3(const array& P, const af::markerType marker=AF_MARKER_POINT, const char* const title=NULL);
+
         /**
            Renders the input array as a histogram to the window
 
@@ -392,8 +402,8 @@ AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array
    \param[in]   wind is the window handle
    \param[in]   X is an \ref af_array with the x-axis data points
    \param[in]   Y is an \ref af_array with the y-axis data points
+   \param[in]   marker is an \ref markerType enum specifying which marker to use in the scatter plot
    \param[in]   props is structure \ref af_cell that has the properties that are used
-   \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
    for the current rendering.
 
    \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
@@ -403,9 +413,27 @@ AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array
 
    \ingroup gfx_func_draw
 */
-AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, const af_marker_type marker);
+AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type marker, const af_cell* const props);
 #endif
 
+#if AF_API_VERSION >= 32
+/**
+   C Interface wrapper for drawing an array as a plot
+
+   \param[in]   wind is the window handle
+   \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
+   \param[in]   marker is an \ref markerType enum specifying which marker to use in the scatter plot
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type marker, const af_cell* const props);
+
+#endif
 #if AF_API_VERSION >= 32
 /**
    C Interface wrapper for drawing an array as a plot
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 92346f59d1..a4132b55dd 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -19,6 +19,22 @@ using namespace std;
 template<typename T>
 GLenum getGLType() { return GL_FLOAT; }
 
+fg::MarkerType getFGMarker(const af_marker_type af_marker) {
+    fg::MarkerType fg_marker;
+    switch (af_marker) {
+        case AF_MARKER_NONE: fg_marker = fg::FG_NONE; break;
+        case AF_MARKER_POINT: fg_marker = fg::FG_POINT; break;
+        case AF_MARKER_CIRCLE: fg_marker = fg::FG_CIRCLE; break;
+        case AF_MARKER_SQUARE: fg_marker = fg::FG_SQUARE; break;
+        case AF_MARKER_TRIANGLE: fg_marker = fg::FG_TRIANGLE; break;
+        case AF_MARKER_CROSS: fg_marker = fg::FG_CROSS; break;
+        case AF_MARKER_PLUS: fg_marker = fg::FG_PLUS; break;
+        case AF_MARKER_STAR: fg_marker = fg::FG_STAR; break;
+        default: fg_marker = fg::FG_NONE; break;
+    }
+    return fg_marker;
+}
+
 #define INSTANTIATE_GET_FG_TYPE(T, ForgeEnum)\
     template<> fg::dtype getGLType<T>() { return ForgeEnum; }
 
@@ -181,7 +197,7 @@ fg::Plot* ForgeManager::getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype
     return mPltMap[key];
 }
 
-fg::Plot3* ForgeManager::getPlot3(int nPoints, fg::dtype type)
+fg::Plot3* ForgeManager::getPlot3(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype)
 {
     /* nPoints needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -189,11 +205,12 @@ fg::Plot3* ForgeManager::getPlot3(int nPoints, fg::dtype type)
      * is a limitation on how big of an plot graph can be rendered
      * using arrayfire graphics funtionality */
     assert(nPoints <= 2ll<<48);
-    long long key = ((nPoints & _48BIT) << 48) | (type & _16BIT);
+    long long key = ((nPoints & _48BIT) << 48);
+    key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
 
     Plt3MapIter iter = mPlt3Map.find(key);
     if (iter==mPlt3Map.end()) {
-        fg::Plot3* temp = new fg::Plot3(nPoints, type);
+        fg::Plot3* temp = new fg::Plot3(nPoints, dtype, ptype, mtype);
         mPlt3Map[key] = temp;
     }
 
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index caadb88cd9..8c7607f313 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -30,6 +30,7 @@ GLenum glForceErrorCheck(const char *msg, const char* file, int line);
 #define ForceCheckGL(msg) glForceErrorCheck(msg, __FILE__, __LINE__)
 #define CheckGLSkip(msg)  glErrorSkip      (msg, __FILE__, __LINE__)
 
+fg::MarkerType getFGMarker(const af_marker_type af_marker);
 namespace graphics
 {
 
@@ -83,7 +84,7 @@ class ForgeManager
         fg::Window* getMainWindow(const bool dontCreate=false);
         fg::Image* getImage(int w, int h, fg::ChannelFormat mode, fg::dtype type);
         fg::Plot* getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype);
-        fg::Plot3* getPlot3(int nPoints, fg::dtype type);
+        fg::Plot3* getPlot3(int nPoints, fg::dtype dtype,fg::PlotType ptype, fg::MarkerType mtype);
         fg::Histogram* getHistogram(int nBins, fg::dtype type);
         fg::Surface* getSurface(int nX, int nY, fg::dtype type);
 
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index f2740305ea..c58a894d31 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -111,19 +111,8 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
     return plotWrapper(wind, X, Y, props);
 }
 
-af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, const af::markerType af_marker)
+af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type af_marker, const af_cell* const props)
 {
-    fg::MarkerType fg_marker;
-    switch(af_marker){
-        case AF_MARKER_NONE: fg_marker = fg::FG_NONE; break;
-        case AF_MARKER_POINT: fg_marker = fg::FG_POINT; break;
-        case AF_MARKER_CIRCLE: fg_marker = fg::FG_CIRCLE; break;
-        case AF_MARKER_SQUARE: fg_marker = fg::FG_SQUARE; break;
-        case AF_MARKER_TRIANGLE: fg_marker = fg::FG_TRIANGLE; break;
-        case AF_MARKER_CROSS: fg_marker = fg::FG_CROSS; break;
-        case AF_MARKER_PLUS: fg_marker = fg::FG_PLUS; break;
-        case AF_MARKER_STAR: fg_marker = fg::FG_STAR; break;
-        default: fg_marker = fg::FG_NONE; break;
-    }
+    fg::MarkerType fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, fg::FG_SCATTER, fg_marker);
 }
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 473bce0b96..4d311058ba 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -30,7 +30,7 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg::Plot3* setup_plot3(const af_array P)
+fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtype)
 {
     Array<T> pIn = getArray<T>(P);
     ArrayInfo Pinfo = getInfo(P);
@@ -58,7 +58,7 @@ fg::Plot3* setup_plot3(const af_array P)
     }
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Plot3* plot3 = fgMngr.getPlot3(P_dims.elements()/3, getGLType<T>());
+    fg::Plot3* plot3 = fgMngr.getPlot3(P_dims.elements()/3, getGLType<T>(), ptype, mtype);
     plot3->setColor(1.0, 0.0, 0.0);
     plot3->setAxesLimits(max[0], min[0],
                          max[1], min[1],
@@ -74,7 +74,7 @@ fg::Plot3* setup_plot3(const af_array P)
 }
 #endif
 
-af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
+af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const props, const fg::PlotType type=fg::FG_LINE, const fg::MarkerType marker=fg::FG_NONE)
 {
 #if defined(WITH_GRAPHICS)
     if(wind==0) {
@@ -91,12 +91,12 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
         fg::Plot3* plot3 = NULL;
 
         switch(Ptype) {
-            case f32: plot3 = setup_plot3<float >(P); break;
-            case s32: plot3 = setup_plot3<int   >(P); break;
-            case u32: plot3 = setup_plot3<uint  >(P); break;
-            case s16: plot3 = setup_plot3<short >(P); break;
-            case u16: plot3 = setup_plot3<ushort>(P); break;
-            case u8 : plot3 = setup_plot3<uchar >(P); break;
+            case f32: plot3 = setup_plot3<float >(P, type, marker); break;
+            case s32: plot3 = setup_plot3<int   >(P, type, marker); break;
+            case u32: plot3 = setup_plot3<uint  >(P, type, marker); break;
+            case s16: plot3 = setup_plot3<short >(P, type, marker); break;
+            case u16: plot3 = setup_plot3<ushort>(P, type, marker); break;
+            case u8 : plot3 = setup_plot3<uchar >(P, type, marker); break;
             default:  TYPE_ERROR(1, Ptype);
         }
 
@@ -111,3 +111,14 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
     return AF_ERR_NO_GFX;
 #endif
 }
+
+af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
+{
+    return plot3Wrapper(wind, P, props);
+}
+
+af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type af_marker, const af_cell* const props)
+{
+    fg::MarkerType fg_marker = getFGMarker(af_marker);
+    return plot3Wrapper(wind, P, props, fg::FG_SCATTER, fg_marker);
+}
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index cb9b0803e7..162bacb4ab 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -82,7 +82,13 @@ void Window::plot(const array& X, const array& Y, const char* const title)
 void Window::scatter(const array& X, const array& Y, af::markerType marker, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_scatter(get(), X.get(), Y.get(), &temp, marker));
+    AF_THROW(af_draw_scatter(get(), X.get(), Y.get(), marker, &temp));
+}
+
+void Window::scatter3(const array& P, af::markerType marker, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_scatter3(get(), P.get(), marker, &temp));
 }
 
 void Window::plot3(const array& P, const char* const title)
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 596429318f..2895cc7afc 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -44,10 +44,16 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
     return CALL(wind, X, Y, props);
 }
 
-af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, const af_marker_type marker)
+af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type marker, const af_cell* const props)
 {
     CHECK_ARRAYS(X, Y);
-    return CALL(wind, X, Y, props, marker);
+    return CALL(wind, X, Y, marker, props);
+}
+
+af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type marker, const af_cell* const props)
+{
+    CHECK_ARRAYS(P);
+    return CALL(wind, P, marker, props);
 }
 
 af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)

From 14e9d3180ecc4dde44bf66dba743524408030ba7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 24 Nov 2015 13:29:57 -0500
Subject: [PATCH 0044/2677] Converted resize & shift cpu fns to async calls

---
 src/backend/cpu/resize.cpp | 348 ++++++++++++++++++-------------------
 src/backend/cpu/shift.cpp  |  60 ++++---
 2 files changed, 205 insertions(+), 203 deletions(-)

diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 8c4da58934..160ed46c0d 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -14,209 +14,205 @@
 #include <math.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    /**
-     * noop function for round to avoid compilation
-     * issues due to lack of this function in C90 based
-     * compilers, it is only present in C99 and C++11
-     *
-     * This is not a full fledged implementation, this function
-     * is to be used only for positive numbers, i m using it here
-     * for calculating dimensions of arrays
-     */
-    dim_t round2int(float value)
-    {
-        return (dim_t)(value+0.5f);
-    }
-
-    using std::conditional;
-    using std::is_same;
+/**
+ * noop function for round to avoid compilation
+ * issues due to lack of this function in C90 based
+ * compilers, it is only present in C99 and C++11
+ *
+ * This is not a full fledged implementation, this function
+ * is to be used only for positive numbers, i m using it here
+ * for calculating dimensions of arrays
+ */
+dim_t round2int(float value)
+{
+    return (dim_t)(value+0.5f);
+}
 
-    template<typename T>
-    using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+using std::conditional;
+using std::is_same;
 
-    template<typename T>
-    using vtype_t = typename conditional<is_complex<T>::value,
-                                         T, wtype_t<T>
-                                        >::type;
+template<typename T>
+using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
 
-    template<typename T, af_interp_type method>
-    struct resize_op
-    {
-        void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides,
-                  const dim_t x, const dim_t y)
-        {
-            return;
-        }
-    };
+template<typename T>
+using vtype_t = typename conditional<is_complex<T>::value,
+                                     T, wtype_t<T>
+                                    >::type;
 
-    template<typename T>
-    struct resize_op<T, AF_INTERP_NEAREST>
+template<typename T, af_interp_type method>
+struct resize_op
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const dim_t x, const dim_t y)
     {
-        void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-                const af::dim4 &ostrides, const af::dim4 &istrides,
-                const dim_t x, const dim_t y)
-        {
-            // Compute Indices
-            dim_t i_x = round2int((float)x / (odims[0] / (float)idims[0]));
-            dim_t i_y = round2int((float)y / (odims[1] / (float)idims[1]));
-
-            if (i_x >= idims[0]) i_x = idims[0] - 1;
-            if (i_y >= idims[1]) i_y = idims[1] - 1;
-
-            dim_t i_off = i_y * istrides[1] + i_x;
-            dim_t o_off =   y * ostrides[1] + x;
-            // Copy values from all channels
-            for(dim_t w = 0; w < odims[3]; w++) {
-                dim_t wost = w * ostrides[3];
-                dim_t wist = w * istrides[3];
-                for(dim_t z = 0; z < odims[2]; z++) {
-                    outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
-                }
-            }
-        }
-    };
+        return;
+    }
+};
 
-    template<typename T>
-    struct resize_op<T, AF_INTERP_BILINEAR>
+template<typename T>
+struct resize_op<T, AF_INTERP_NEAREST>
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+            const af::dim4 &ostrides, const af::dim4 &istrides,
+            const dim_t x, const dim_t y)
     {
-        void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-                const af::dim4 &ostrides, const af::dim4 &istrides,
-                const dim_t x, const dim_t y)
-        {
-            // Compute Indices
-            float f_x = (float)x / (odims[0] / (float)idims[0]);
-            float f_y = (float)y / (odims[1] / (float)idims[1]);
-
-            dim_t i1_x  = floor(f_x);
-            dim_t i1_y  = floor(f_y);
-
-            if (i1_x >= idims[0]) i1_x = idims[0] - 1;
-            if (i1_y >= idims[1]) i1_y = idims[1] - 1;
-
-            float b   = f_x - i1_x;
-            float a   = f_y - i1_y;
-
-            dim_t i2_x  = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
-            dim_t i2_y  = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
-
-            typedef typename dtype_traits<T>::base_type BT;
-            typedef wtype_t<BT> WT;
-            typedef vtype_t<T> VT;
-
-            dim_t o_off = y * ostrides[1] + x;
-            // Copy values from all channels
-            for(dim_t w = 0; w < odims[3]; w++) {
-                dim_t wst = w * istrides[3];
-                for(dim_t z = 0; z < odims[2]; z++) {
-                    dim_t zst = z * istrides[2];
-                    dim_t channel_off = zst + wst;
-                    VT p1 = inPtr[i1_y * istrides[1] + i1_x + channel_off];
-                    VT p2 = inPtr[i2_y * istrides[1] + i1_x + channel_off];
-                    VT p3 = inPtr[i1_y * istrides[1] + i2_x + channel_off];
-                    VT p4 = inPtr[i2_y * istrides[1] + i2_x + channel_off];
-
-                    outPtr[o_off + z * ostrides[2] + w * ostrides[3]] =
-                                    scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
-                                    scalar<WT>((    a   ) * (1.0f - b)) * p2 +
-                                    scalar<WT>((1.0f - a) * (    b   )) * p3 +
-                                    scalar<WT>((    a   ) * (    b   )) * p4;
-                }
+        // Compute Indices
+        dim_t i_x = round2int((float)x / (odims[0] / (float)idims[0]));
+        dim_t i_y = round2int((float)y / (odims[1] / (float)idims[1]));
+
+        if (i_x >= idims[0]) i_x = idims[0] - 1;
+        if (i_y >= idims[1]) i_y = idims[1] - 1;
+
+        dim_t i_off = i_y * istrides[1] + i_x;
+        dim_t o_off =   y * ostrides[1] + x;
+        // Copy values from all channels
+        for(dim_t w = 0; w < odims[3]; w++) {
+            dim_t wost = w * ostrides[3];
+            dim_t wist = w * istrides[3];
+            for(dim_t z = 0; z < odims[2]; z++) {
+                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
             }
         }
-    };
+    }
+};
 
-    template<typename T>
-    struct resize_op<T, AF_INTERP_LOWER>
+template<typename T>
+struct resize_op<T, AF_INTERP_BILINEAR>
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+            const af::dim4 &ostrides, const af::dim4 &istrides,
+            const dim_t x, const dim_t y)
     {
-        void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-                const af::dim4 &ostrides, const af::dim4 &istrides,
-                const dim_t x, const dim_t y)
-        {
-            // Compute Indices
-            dim_t i_x = floor((float)x / (odims[0] / (float)idims[0]));
-            dim_t i_y = floor((float)y / (odims[1] / (float)idims[1]));
-
-            if (i_x >= idims[0]) i_x = idims[0] - 1;
-            if (i_y >= idims[1]) i_y = idims[1] - 1;
-
-            dim_t i_off = i_y * istrides[1] + i_x;
-            dim_t o_off =   y * ostrides[1] + x;
-            // Copy values from all channels
-            for(dim_t w = 0; w < odims[3]; w++) {
-                dim_t wost = w * ostrides[3];
-                dim_t wist = w * istrides[3];
-                for(dim_t z = 0; z < odims[2]; z++) {
-                    outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
-                }
+        // Compute Indices
+        float f_x = (float)x / (odims[0] / (float)idims[0]);
+        float f_y = (float)y / (odims[1] / (float)idims[1]);
+
+        dim_t i1_x  = floor(f_x);
+        dim_t i1_y  = floor(f_y);
+
+        if (i1_x >= idims[0]) i1_x = idims[0] - 1;
+        if (i1_y >= idims[1]) i1_y = idims[1] - 1;
+
+        float b   = f_x - i1_x;
+        float a   = f_y - i1_y;
+
+        dim_t i2_x  = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
+        dim_t i2_y  = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
+
+        typedef typename dtype_traits<T>::base_type BT;
+        typedef wtype_t<BT> WT;
+        typedef vtype_t<T> VT;
+
+        dim_t o_off = y * ostrides[1] + x;
+        // Copy values from all channels
+        for(dim_t w = 0; w < odims[3]; w++) {
+            dim_t wst = w * istrides[3];
+            for(dim_t z = 0; z < odims[2]; z++) {
+                dim_t zst = z * istrides[2];
+                dim_t channel_off = zst + wst;
+                VT p1 = inPtr[i1_y * istrides[1] + i1_x + channel_off];
+                VT p2 = inPtr[i2_y * istrides[1] + i1_x + channel_off];
+                VT p3 = inPtr[i1_y * istrides[1] + i2_x + channel_off];
+                VT p4 = inPtr[i2_y * istrides[1] + i2_x + channel_off];
+
+                outPtr[o_off + z * ostrides[2] + w * ostrides[3]] =
+                                scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
+                                scalar<WT>((    a   ) * (1.0f - b)) * p2 +
+                                scalar<WT>((1.0f - a) * (    b   )) * p3 +
+                                scalar<WT>((    a   ) * (    b   )) * p4;
             }
         }
-    };
+    }
+};
 
-    template<typename T, af_interp_type method>
-    void resize_(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-                 const af::dim4 &ostrides, const af::dim4 &istrides)
+template<typename T>
+struct resize_op<T, AF_INTERP_LOWER>
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+            const af::dim4 &ostrides, const af::dim4 &istrides,
+            const dim_t x, const dim_t y)
     {
-        resize_op<T, method> op;
-        for(dim_t y = 0; y < odims[1]; y++) {
-            for(dim_t x = 0; x < odims[0]; x++) {
-                op(outPtr, inPtr, odims, idims, ostrides, istrides, x, y);
+        // Compute Indices
+        dim_t i_x = floor((float)x / (odims[0] / (float)idims[0]));
+        dim_t i_y = floor((float)y / (odims[1] / (float)idims[1]));
+
+        if (i_x >= idims[0]) i_x = idims[0] - 1;
+        if (i_y >= idims[1]) i_y = idims[1] - 1;
+
+        dim_t i_off = i_y * istrides[1] + i_x;
+        dim_t o_off =   y * ostrides[1] + x;
+        // Copy values from all channels
+        for(dim_t w = 0; w < odims[3]; w++) {
+            dim_t wost = w * ostrides[3];
+            dim_t wist = w * istrides[3];
+            for(dim_t z = 0; z < odims[2]; z++) {
+                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
             }
         }
     }
+};
 
-    template<typename T>
-    Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                    const af_interp_type method)
-    {
-        af::dim4 idims = in.dims();
-        af::dim4 odims(odim0, odim1, idims[2], idims[3]);
-
-        // Create output placeholder
-        Array<T> outArray = createValueArray(odims, (T)0);
-
-        // Get pointers to raw data
-        const T *inPtr = in.get();
-              T *outPtr = outArray.get();
-
-        af::dim4 ostrides = outArray.strides();
-        af::dim4 istrides = in.strides();
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                resize_<T, AF_INTERP_NEAREST>(outPtr, inPtr, odims, idims, ostrides, istrides);
-                break;
-            case AF_INTERP_BILINEAR:
-                resize_<T, AF_INTERP_BILINEAR>(outPtr, inPtr, odims, idims, ostrides, istrides);
-                break;
-            case AF_INTERP_LOWER:
-                resize_<T, AF_INTERP_LOWER>(outPtr, inPtr, odims, idims, ostrides, istrides);
-                break;
-            default:
-                break;
+template<typename T, af_interp_type method>
+void resize_(Array<T> out, const Array<T> in)
+{
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    const T *inPtr    = in.get();
+          T *outPtr   = out.get();
+    af::dim4 ostrides = out.strides();
+    af::dim4 istrides = in.strides();
+
+    resize_op<T, method> op;
+    for(dim_t y = 0; y < odims[1]; y++) {
+        for(dim_t x = 0; x < odims[0]; x++) {
+            op(outPtr, inPtr, odims, idims, ostrides, istrides, x, y);
         }
-        return outArray;
     }
+}
 
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method)
+{
+    af::dim4 idims = in.dims();
+    af::dim4 odims(odim0, odim1, idims[2], idims[3]);
+    // Create output placeholder
+    Array<T> out = createValueArray(odims, (T)0);
+    out.eval();
+    in.eval();
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            getQueue().enqueue(resize_<T, AF_INTERP_NEAREST>, out, in); break;
+        case AF_INTERP_BILINEAR:
+            getQueue().enqueue(resize_<T, AF_INTERP_BILINEAR>, out, in); break;
+        case AF_INTERP_LOWER:
+            getQueue().enqueue(resize_<T, AF_INTERP_LOWER>, out, in); break;
+        default: break;
+    }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                                            \
+#define INSTANTIATE(T)                                                                     \
     template Array<T> resize<T> (const Array<T> &in, const dim_t odim0, const dim_t odim1, \
                                  const af_interp_type method);
 
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 }
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index 05cac4c678..6a2b939cca 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -12,27 +12,32 @@
 #include <stdexcept>
 #include <err_cpu.hpp>
 #include <cassert>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    static inline dim_t simple_mod(const dim_t i, const dim_t dim)
-    {
-        return (i < dim) ? i : (i - dim);
-    }
+static inline dim_t simple_mod(const dim_t i, const dim_t dim)
+{
+    return (i < dim) ? i : (i - dim);
+}
 
-    template<typename T>
-    Array<T> shift(const Array<T> &in, const int sdims[4])
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4])
+{
+    Array<T> out = createEmptyArray<T>(in.dims());
+    out.eval();
+    in.eval();
+    const af::dim4 temp(sdims[0], sdims[1], sdims[2], sdims[3]);
 
-        Array<T> out = createEmptyArray<T>(oDims);
+    auto func = [=] (Array<T> out, const Array<T> in, const af::dim4 sdims) {
 
         T* outPtr = out.get();
         const T* inPtr = in.get();
 
-        const af::dim4 ist = in.strides();
-        const af::dim4 ost = out.strides();
+        const af::dim4 oDims = out.dims();
+        const af::dim4 ist   = in.strides();
+        const af::dim4 ost   = out.strides();
 
         int sdims_[4];
         // Need to do this because we are mapping output to input in the kernel
@@ -65,24 +70,25 @@ namespace cpu
                 }
             }
         }
+    };
+    getQueue().enqueue(func, out, in, temp);
 
-        return out;
-    }
+    return out;
+}
 
 #define INSTANTIATE(T)                                                  \
     template Array<T> shift<T>(const Array<T> &in, const int sdims[4]); \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 }

From 0d5913bdb9a313c006deb243e9f88c15b18b418a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 24 Nov 2015 13:26:38 -0500
Subject: [PATCH 0045/2677] Fixes for examples cmakelists for dl lib

* Formatting fixes included
---
 examples/CMakeLists.txt | 117 +++++++++++++++++++---------------------
 1 file changed, 55 insertions(+), 62 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index e20db4ed2a..793cb04fcf 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,5 +1,5 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
-PROJECT(arrayfire-examples)
+PROJECT(ArrayFire-Examples)
 
 # Find CUDA and OpenCL
 SET(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
@@ -8,10 +8,11 @@ FIND_PACKAGE(OpenCL QUIET)
 
 # If the examples are not being built at the same time as ArrayFire,
 # we need to first find the ArrayFire library
-if(TARGET afcpu OR TARGET afcuda OR TARGET afopencl)
+IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
     SET(ArrayFire_CPU_FOUND False)
     SET(ArrayFire_CUDA_FOUND False)
     SET(ArrayFire_OpenCL_FOUND False)
+    SET(ArrayFire_Unified_FOUND False)
     SET(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../assets")
     IF(NOT EXISTS "${ASSETS_DIR}/LICENSE")
         MESSAGE(STATUS "Assests submodule unavailable. Updating submodules.")
@@ -21,12 +22,12 @@ if(TARGET afcpu OR TARGET afcuda OR TARGET afopencl)
             OUTPUT_QUIET
         )
     ENDIF()
-else()
+ELSE()
     FIND_PACKAGE(ArrayFire REQUIRED)
     INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
 
     SET(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/assets")
-endif()
+ENDIF()
 
 IF(WIN32)
     # Deprecated Errors are Warning 4996 on VS2013.
@@ -71,68 +72,60 @@ FILE(GLOB FILES "*/*.cpp")
 ADD_DEFINITIONS("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
 # Next we build each example using every backend.
-if(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
-  MESSAGE(STATUS "EXAMPLES: CPU backend is ON.")
-  BUILD_ALL("${FILES}" cpu ${ArrayFire_CPU_LIBRARIES} "")
-elseif(TARGET afcpu)        # variable defined by the ArrayFire build tree
-  MESSAGE(STATUS "EXAMPLES: CPU backend is ON.")
-  BUILD_ALL("${FILES}" cpu afcpu "")
-else()
-  MESSAGE(STATUS "EXAMPLES: CPU backend is OFF. afcpu was not found.")
-endif()
+IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
+    MESSAGE(STATUS "EXAMPLES: CPU backend is ON.")
+    BUILD_ALL("${FILES}" cpu ${ArrayFire_CPU_LIBRARIES} "")
+ELSEIF(TARGET afcpu)        # variable defined by the ArrayFire build tree
+    MESSAGE(STATUS "EXAMPLES: CPU backend is ON.")
+    BUILD_ALL("${FILES}" cpu afcpu "")
+ELSE()
+    MESSAGE(STATUS "EXAMPLES: CPU backend is OFF. afcpu was not found.")
+ENDIF()
 
 # Next we build each example using every backend.
-if(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
-  MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
-  IF(WIN32)
-    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "")
-  ELSE()
-    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "dl")
-  ENDIF()
-elseif(TARGET af)        # variable defined by the ArrayFire build tree
-  MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
-  IF(WIN32)
-      BUILD_ALL("${FILES}" unified af "")
-  ELSE()
-      BUILD_ALL("${FILES}" unified af "dl")
-  ENDIF()
-else()
-  MESSAGE(STATUS "EXAMPLES: UNIFIED backend is OFF. af was not found.")
-endif()
+IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
+    MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
+    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} ${CMAKE_DL_LIBS})
+ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
+    MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
+    BUILD_ALL("${FILES}" unified af ${CMAKE_DL_LIBS})
+ELSE()
+    MESSAGE(STATUS "EXAMPLES: UNIFIED backend is OFF. af was not found.")
+ENDIF()
 
-if (${CUDA_FOUND})
-  if(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
-    FIND_LIBRARY( CUDA_NVVM_LIBRARY
-      NAMES "nvvm"
-      PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
-      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-      DOC "CUDA NVVM Library"
-      )
-    MESSAGE(STATUS "EXAMPLES: CUDA backend is ON.")
-    BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
-  elseif(TARGET afcuda)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "EXAMPLES: CUDA backend is ON.")
-    BUILD_ALL("${FILES}" cuda afcuda "")
-  else()
-    MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. afcuda was not found")
-  endif()
-else()
-  MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. CUDA was not found")
-endif()
+IF (${CUDA_FOUND})
+    IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
+        FIND_LIBRARY( CUDA_NVVM_LIBRARY
+          NAMES "nvvm"
+          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
+          PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+          DOC "CUDA NVVM Library"
+          )
+        MESSAGE(STATUS "EXAMPLES: CUDA backend is ON.")
+        BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+    ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
+        MESSAGE(STATUS "EXAMPLES: CUDA backend is ON.")
+        BUILD_ALL("${FILES}" cuda afcuda "")
+    ELSE()
+        MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. afcuda was not found")
+    ENDIF()
+ELSE()
+    MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. CUDA was not found")
+ENDIF()
 
-if (${OpenCL_FOUND})
-  if(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
-    MESSAGE(STATUS "EXAMPLES: OPENCL backend is ON.")
-    BUILD_ALL("${FILES}" opencl ${ArrayFire_OpenCL_LIBRARIES} "${OpenCL_LIBRARIES}")
-  elseif(TARGET afopencl)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "EXAMPLES: OPENCL backend is ON.")
-    BUILD_ALL("${FILES}" opencl afopencl ${OpenCL_LIBRARIES})
-  else()
-    MESSAGE(STATUS "EXAMPLES: OPENCL backend is OFF. afopencl was not found")
-  endif()
-else()
-  MESSAGE(STATUS "EXAMPLES: OPENCL backend is OFF. OPENCL was not found")
-endif()
+IF (${OpenCL_FOUND})
+    IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
+        MESSAGE(STATUS "EXAMPLES: OpenCL backend is ON.")
+        BUILD_ALL("${FILES}" opencl ${ArrayFire_OpenCL_LIBRARIES} "${OpenCL_LIBRARIES}")
+    ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
+        MESSAGE(STATUS "EXAMPLES: OpenCL backend is ON.")
+        BUILD_ALL("${FILES}" opencl afopencl ${OpenCL_LIBRARIES})
+    ELSE()
+        MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. afopencl was not found")
+    ENDIF()
+ELSE()
+    MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. OpenCL was not found")
+ENDIF()
 
 INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
     DESTINATION "${AF_INSTALL_EXAMPLE_DIR}"

From 3c2bc65b12fe04c455837790cbba27b2417bc9a1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 24 Nov 2015 14:37:14 -0500
Subject: [PATCH 0046/2677] convert select & rotate cpu fns to async calls

---
 src/backend/cpu/rotate.cpp | 23 ++++++++------
 src/backend/cpu/select.cpp | 64 ++++++++++++++++++++++++--------------
 2 files changed, 53 insertions(+), 34 deletions(-)

diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index a4af64b669..01ec96228c 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -12,15 +12,22 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include "transform_interp.hpp"
 
 namespace cpu
 {
     template<typename T, af_interp_type method>
-    void rotate_(T *out, const T *in, const float theta,
-                 const af::dim4 &odims, const af::dim4 &idims,
-                 const af::dim4 &ostrides, const af::dim4 &istrides)
+    void rotate_(Array<T> output, const Array<T> input, const float theta)
     {
+        const af::dim4 odims    = output.dims();
+        const af::dim4 idims    = input.dims();
+        const af::dim4 ostrides = output.strides();
+        const af::dim4 istrides = input.strides();
+
+        const T* in   = input.get();
+              T* out  = output.get();
         dim_t nimages = idims[2];
 
         void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
@@ -77,20 +84,16 @@ namespace cpu
                      const af_interp_type method)
     {
         Array<T> out = createEmptyArray<T>(odims);
-        const af::dim4 idims = in.dims();
 
         switch(method) {
             case AF_INTERP_NEAREST:
-                rotate_<T, AF_INTERP_NEAREST>
-                       (out.get(), in.get(), theta, odims, idims, out.strides(), in.strides());
+                getQueue().enqueue(rotate_<T, AF_INTERP_NEAREST>, out, in, theta);
                 break;
             case AF_INTERP_BILINEAR:
-                rotate_<T, AF_INTERP_BILINEAR>
-                       (out.get(), in.get(), theta, odims, idims, out.strides(), in.strides());
+                getQueue().enqueue(rotate_<T, AF_INTERP_BILINEAR>, out, in, theta);
                 break;
             case AF_INTERP_LOWER:
-                rotate_<T, AF_INTERP_LOWER>
-                       (out.get(), in.get(), theta, odims, idims, out.strides(), in.strides());
+                getQueue().enqueue(rotate_<T, AF_INTERP_LOWER>, out, in, theta);
                 break;
             default:
                 AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 7b2cc81735..4a219eda04 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -10,14 +10,22 @@
 #include <Array.hpp>
 #include <select.hpp>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b)
-    {
+
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b)
+{
+    out.eval();
+    cond.eval();
+    a.eval();
+    b.eval();
+    auto func = [=] (Array<T> out, const Array<char> cond, const Array<T> a, const Array<T> b) {
         dim4 adims = a.dims();
         dim4 astrides = a.strides();
         dim4 bdims = b.dims();
@@ -30,13 +38,13 @@ namespace cpu
         dim4 ostrides = out.strides();
 
         bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
-                            adims[2] == odims[2], adims[3] == odims[3]};
+            adims[2] == odims[2], adims[3] == odims[3]};
 
         bool is_b_same[] = {bdims[0] == odims[0], bdims[1] == odims[1],
-                            bdims[2] == odims[2], bdims[3] == odims[3]};
+            bdims[2] == odims[2], bdims[3] == odims[3]};
 
         bool is_c_same[] = {cdims[0] == odims[0], cdims[1] == odims[1],
-                            cdims[2] == odims[2], cdims[3] == odims[3]};
+            cdims[2] == odims[2], cdims[3] == odims[3]};
 
         const T *aptr = a.get();
         const T *bptr = b.get();
@@ -75,11 +83,17 @@ namespace cpu
                 }
             }
         }
-    }
+    };
+    getQueue().enqueue(func, out, cond, a, b);
+}
 
-    template<typename T, bool flip>
-    void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b)
-    {
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b)
+{
+    out.eval();
+    cond.eval();
+    a.eval();
+    auto func = [=] (Array<T> out, const Array<char> cond, const Array<T> a, const double b) {
         dim4 astrides = a.strides();
         dim4 cstrides = cond.strides();
 
@@ -115,8 +129,9 @@ namespace cpu
                 }
             }
         }
-    }
-
+    };
+    getQueue().enqueue(func, out, cond, a, b);
+}
 
 #define INSTANTIATE(T)                                              \
     template void select<T>(Array<T> &out, const Array<char> &cond, \
@@ -130,16 +145,17 @@ namespace cpu
                                           const Array<T> &a,        \
                                           const double &b);         \
 
-    INSTANTIATE(float  )
-    INSTANTIATE(double )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(char   )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+INSTANTIATE(float  )
+INSTANTIATE(double )
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
+INSTANTIATE(int    )
+INSTANTIATE(uint   )
+INSTANTIATE(intl   )
+INSTANTIATE(uintl  )
+INSTANTIATE(char   )
+INSTANTIATE(uchar  )
+INSTANTIATE(short  )
+INSTANTIATE(ushort )
+
 }

From 86c1d7d025ae21f48e7a86371a70d80ce95ad969 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 24 Nov 2015 14:14:37 -0500
Subject: [PATCH 0047/2677] Tests are now available as standalone

* Similar to examples.
* Use `cmake arrayfire_dir/test -DArrayFire_DIR=/path/to/af_install/share/ArrayFire/cmake`
to build as standalone.
* When not building with ArrayFire, it uses the ArrayFire_DIR option to find
ArrayFire installation
* Allows use of relative test dir
* Allows use of BUILD_NONFREE option
---
 test/CMakeLists.txt                           | 161 ++++++++++-----
 test/CMakeModules/FindOpenCL.cmake            | 190 ++++++++++++++++++
 .../CMakeModules}/build_gtest.cmake           |  15 +-
 3 files changed, 312 insertions(+), 54 deletions(-)
 create mode 100644 test/CMakeModules/FindOpenCL.cmake
 rename {CMakeModules => test/CMakeModules}/build_gtest.cmake (90%)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 30907d3390..28f650d7af 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -1,11 +1,30 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
+PROJECT(ArrayFire-Tests)
 
-REMOVE_DEFINITIONS(-std=c++11)
+# Find CUDA and OpenCL
+SET(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 FIND_PACKAGE(CUDA QUIET)
 FIND_PACKAGE(OpenCL QUIET)
 
+# If the tests are not being built at the same time as ArrayFire,
+# we need to first find the ArrayFire library
+IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
+    SET(ArrayFire_CPU_FOUND False)
+    SET(ArrayFire_CUDA_FOUND False)
+    SET(ArrayFire_OpenCL_FOUND False)
+    SET(ArrayFire_Unified_FOUND False)
+ELSE()
+    FIND_PACKAGE(ArrayFire REQUIRED)
+    INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
+    OPTION(BUILD_NONFREE "Build Tests for nonfree algorithms" OFF)
+    IF(${BUILD_NONFREE}) # Add definition. Not required when building with AF
+        ADD_DEFINITIONS(-DAF_BUILD_SIFT)
+    ENDIF(${BUILD_NONFREE})
+ENDIF()
+
+REMOVE_DEFINITIONS(-std=c++11)
 
-MACRO(CREATE_TESTS BACKEND LIBNAME GTEST_LIBS OTHER_LIBS)
+MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
     STRING(TOUPPER ${BACKEND} DEF_NAME)
 
     FOREACH(FILE ${FILES})
@@ -22,7 +41,7 @@ MACRO(CREATE_TESTS BACKEND LIBNAME GTEST_LIBS OTHER_LIBS)
 
         FILE(GLOB TEST_FILE "${FNAME}.cpp" "${FNAME}.c")
         ADD_EXECUTABLE(${TEST_NAME} ${TEST_FILE})
-        TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE  af${LIBNAME}
+        TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE ${AFLIBNAME}
                                             PRIVATE ${THREAD_LIB_FLAG}
                                             PRIVATE ${GTEST_LIBS}
                                             PRIVATE ${OTHER_LIBS})
@@ -45,10 +64,11 @@ ENDIF()
 OPTION(USE_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF)
 
 IF(${USE_RELATIVE_TEST_DIR})
-    SET(RELATIVE_TEST_DATA_DIR "./data" CACHE STRING "Relative Test Data Directory")
+    # RELATIVE_TEST_DATA_DIR is a User-visible option with default value of test/data directory
+    SET(RELATIVE_TEST_DATA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data" CACHE STRING "Relative Test Data Directory")
     SET(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
-ELSE(${USE_RELATIVE_TEST_DIR})
-    SET(TESTDATA_SOURCE_DIR "${CMAKE_SOURCE_DIR}/test/data")
+ELSE(${USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
+    SET(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
 ENDIF(${USE_RELATIVE_TEST_DIR})
 
 IF (${CMAKE_GENERATOR} STREQUAL "Xcode")
@@ -58,20 +78,20 @@ ELSE (${CMAKE_GENERATOR} STREQUAL "Xcode")
 ENDIF (${CMAKE_GENERATOR} STREQUAL "Xcode")
 
 IF(NOT ${USE_RELATIVE_TEST_DIR})
-# Check if data exists
-IF (EXISTS "${TESTDATA_SOURCE_DIR}" AND IS_DIRECTORY "${TESTDATA_SOURCE_DIR}"
-    AND EXISTS "${TESTDATA_SOURCE_DIR}/README.md")
-    # Test data is available
-    # Do Nothing
-ELSE (EXISTS "${TESTDATA_SOURCE_DIR}" AND IS_DIRECTORY "${TESTDATA_SOURCE_DIR}"
-    AND EXISTS "${TESTDATA_SOURCE_DIR}/README.md")
-    MESSAGE(STATUS "Test submodules unavailable. Updating submodules.")
-    EXECUTE_PROCESS(
-        COMMAND git submodule update --init --recursive
-        WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
-        OUTPUT_QUIET
-    )
-ENDIF()
+    # Check if data exists
+    IF (EXISTS "${TESTDATA_SOURCE_DIR}" AND IS_DIRECTORY "${TESTDATA_SOURCE_DIR}"
+        AND EXISTS "${TESTDATA_SOURCE_DIR}/README.md")
+        # Test data is available
+        # Do Nothing
+    ELSE (EXISTS "${TESTDATA_SOURCE_DIR}" AND IS_DIRECTORY "${TESTDATA_SOURCE_DIR}"
+        AND EXISTS "${TESTDATA_SOURCE_DIR}/README.md")
+        MESSAGE(STATUS "Test submodules unavailable. Updating submodules.")
+        EXECUTE_PROCESS(
+            COMMAND git submodule update --init --recursive
+            WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+            OUTPUT_QUIET
+        )
+    ENDIF()
 ENDIF(NOT ${USE_RELATIVE_TEST_DIR})
 
 OPTION(USE_SYSTEM_GTEST "Use GTEST from system libraries" OFF)
@@ -86,42 +106,89 @@ INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")
 
-IF(${BUILD_CPU})
-    MESSAGE(STATUS "TESTS: CPU backend is ON")
-    CREATE_TESTS(cpu cpu "${GTEST_LIBRARIES}" "")
+# Next we build each example using every backend.
+IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
+    MESSAGE(STATUS "TESTS: CPU backend is ON.")
+    CREATE_TESTS(cpu ${ArrayFire_CPU_LIBRARIES} "${GTEST_LIBRARIES}" "")
+ELSEIF(TARGET afcpu)        # variable defined by the ArrayFire build tree
+    MESSAGE(STATUS "TESTS: CPU backend is ON.")
+    CREATE_TESTS(cpu afcpu "${GTEST_LIBRARIES}" "")
 ELSE()
-    MESSAGE(STATUS "TESTS: CPU backend is OFF")
+    MESSAGE(STATUS "TESTS: CPU backend is OFF. afcpu was not found.")
 ENDIF()
 
-IF(${BUILD_CUDA} AND ${CUDA_FOUND})
-    MESSAGE(STATUS "TESTS: CUDA backend is ON")
-    IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-        CREATE_TESTS(cuda cuda "${GTEST_LIBRARIES_STDLIB}" "")
-        FOREACH(FILE ${FILES})
-            GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
-            SET(TEST_NAME ${FNAME}_cuda)
-            SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_FLAGS -stdlib=libstdc++)
-            SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS -stdlib=libstdc++)
-        ENDFOREACH()
-    ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-        CREATE_TESTS(cuda cuda "${GTEST_LIBRARIES}" "")
-    ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+# Next we build each example using every backend.
+IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
+    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
+    CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
+ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
+    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
+    CREATE_TESTS(unified af "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
 ELSE()
-    MESSAGE(STATUS "TESTS: CUDA backend is OFF")
+    MESSAGE(STATUS "TESTS: UNIFIED backend is OFF. af was not found.")
 ENDIF()
 
-IF(${BUILD_OPENCL} AND ${OpenCL_FOUND})
-    MESSAGE(STATUS "TESTS: OPENCL backend is ON")
-    CREATE_TESTS(opencl opencl "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
+IF (${CUDA_FOUND})
+    IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
+        FIND_LIBRARY( CUDA_NVVM_LIBRARY
+          NAMES "nvvm"
+          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
+          PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+          DOC "CUDA NVVM Library"
+          )
+        MESSAGE(STATUS "TESTS: CUDA backend is ON.")
+        # If OSX && CLANG && CUDA < 7
+        IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+            CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+
+            FOREACH(FILE ${FILES})
+                GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
+                SET(TEST_NAME ${FNAME}_cuda)
+                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_FLAGS -stdlib=libstdc++)
+                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS -stdlib=libstdc++)
+            ENDFOREACH()
+
+        # ELSE OSX && CLANG && CUDA < 7
+        ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+            CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+
+        ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+
+    ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
+        MESSAGE(STATUS "TESTS: CUDA backend is ON.")
+        # If OSX && CLANG && CUDA < 7
+        IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+            CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+
+            FOREACH(FILE ${FILES})
+                GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
+                SET(TEST_NAME ${FNAME}_cuda)
+                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_FLAGS -stdlib=libstdc++)
+                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS -stdlib=libstdc++)
+            ENDFOREACH()
+
+        # ELSE OSX && CLANG && CUDA < 7
+        ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+            CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+
+        ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
+    ELSE()
+        MESSAGE(STATUS "TESTS: CUDA backend is OFF. afcuda was not found")
+    ENDIF()
 ELSE()
-    MESSAGE(STATUS "TESTS: OPENCL backend is OFF")
+    MESSAGE(STATUS "TESTS: CUDA backend is OFF. CUDA was not found")
 ENDIF()
 
-IF(${BUILD_UNIFIED})
-    MESSAGE(STATUS "TESTS: Unified backends is ON")
-    IF(WIN32)
-        CREATE_TESTS(unified "" "${GTEST_LIBRARIES}" "")
+IF (${OpenCL_FOUND})
+    IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
+        MESSAGE(STATUS "TESTS: OpenCL backend is ON.")
+        CREATE_TESTS(opencl ${ArrayFire_OpenCL_LIBRARIES} "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
+    ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
+        MESSAGE(STATUS "TESTS: OpenCL backend is ON.")
+        CREATE_TESTS(opencl afopencl "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
     ELSE()
-        CREATE_TESTS(unified "" "${GTEST_LIBRARIES}" dl)
+        MESSAGE(STATUS "TESTS: OpenCL backend is OFF. afopencl was not found")
     ENDIF()
+ELSE()
+    MESSAGE(STATUS "TESTS: OpenCL backend is OFF. OpenCL was not found")
 ENDIF()
diff --git a/test/CMakeModules/FindOpenCL.cmake b/test/CMakeModules/FindOpenCL.cmake
new file mode 100644
index 0000000000..4d4ef57bc3
--- /dev/null
+++ b/test/CMakeModules/FindOpenCL.cmake
@@ -0,0 +1,190 @@
+#.rst:
+# FindOpenCL
+# ----------
+#
+# Try to find OpenCL
+#
+# Once done this will define::
+#
+#   OpenCL_FOUND          - True if OpenCL was found
+#   OpenCL_INCLUDE_DIRS   - include directories for OpenCL
+#   OpenCL_LIBRARIES      - link against this library to use OpenCL
+#   OpenCL_VERSION_STRING - Highest supported OpenCL version (eg. 1.2)
+#   OpenCL_VERSION_MAJOR  - The major version of the OpenCL implementation
+#   OpenCL_VERSION_MINOR  - The minor version of the OpenCL implementation
+#
+# The module will also define two cache variables::
+#
+#   OpenCL_INCLUDE_DIR    - the OpenCL include directory
+#   OpenCL_LIBRARY        - the path to the OpenCL library
+#
+
+#=============================================================================
+# From CMake 3.2
+# Copyright 2014 Matthaeus G. Chajdas
+#
+# Distributed under the OSI-approved BSD License (the "License");
+# see accompanying file Copyright.txt for details.
+#
+# This software is distributed WITHOUT ANY WARRANTY; without even the
+# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the License for more information.
+
+# CMake - Cross Platform Makefile Generator
+# Copyright 2000-2014 Kitware, Inc.
+# Copyright 2000-2011 Insight Software Consortium
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# * Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+#
+# * Neither the names of Kitware, Inc., the Insight Software Consortium,
+# nor the names of their contributors may be used to endorse or promote
+# products derived from this software without specific prior written
+# permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#=============================================================================
+
+function(_FIND_OPENCL_VERSION)
+  include(CheckSymbolExists)
+  include(CMakePushCheckState)
+  set(CMAKE_REQUIRED_QUIET ${OpenCL_FIND_QUIETLY})
+
+  CMAKE_PUSH_CHECK_STATE()
+  foreach(VERSION "2_0" "1_2" "1_1" "1_0")
+    set(CMAKE_REQUIRED_INCLUDES "${OpenCL_INCLUDE_DIR}")
+    if(APPLE)
+      CHECK_SYMBOL_EXISTS(
+        CL_VERSION_${VERSION}
+        "${OpenCL_INCLUDE_DIR}/OpenCL/cl.h"
+        OPENCL_VERSION_${VERSION})
+    else()
+      CHECK_SYMBOL_EXISTS(
+        CL_VERSION_${VERSION}
+        "${OpenCL_INCLUDE_DIR}/CL/cl.h"
+        OPENCL_VERSION_${VERSION})
+    endif()
+
+    if(OPENCL_VERSION_${VERSION})
+      string(REPLACE "_" "." VERSION "${VERSION}")
+      set(OpenCL_VERSION_STRING ${VERSION} PARENT_SCOPE)
+      string(REGEX MATCHALL "[0-9]+" version_components "${VERSION}")
+      list(GET version_components 0 major_version)
+      list(GET version_components 1 minor_version)
+      set(OpenCL_VERSION_MAJOR ${major_version} PARENT_SCOPE)
+      set(OpenCL_VERSION_MINOR ${minor_version} PARENT_SCOPE)
+      break()
+    endif()
+  endforeach()
+  CMAKE_POP_CHECK_STATE()
+endfunction()
+
+find_path(OpenCL_INCLUDE_DIR
+  NAMES
+    CL/cl.h OpenCL/cl.h
+  PATHS
+    ENV "PROGRAMFILES(X86)"
+    ENV NVSDKCOMPUTE_ROOT
+    ENV CUDA_PATH
+    ENV AMDAPPSDKROOT
+    ENV INTELOCLSDKROOT
+    ENV ATISTREAMSDKROOT
+  PATH_SUFFIXES
+    include
+    OpenCL/common/inc
+    "AMD APP/include")
+
+_FIND_OPENCL_VERSION()
+
+if(WIN32)
+  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+    find_library(OpenCL_LIBRARY
+      NAMES OpenCL
+      PATHS
+        ENV "PROGRAMFILES(X86)"
+        ENV CUDA_PATH
+        ENV NVSDKCOMPUTE_ROOT
+        ENV AMDAPPSDKROOT
+        ENV INTELOCLSDKROOT
+        ENV ATISTREAMSDKROOT
+      PATH_SUFFIXES
+        "AMD APP/lib/x86"
+        lib/x86
+        lib/Win32
+        OpenCL/common/lib/Win32)
+  elseif(CMAKE_SIZEOF_VOID_P EQUAL 8)
+    find_library(OpenCL_LIBRARY
+      NAMES OpenCL
+      PATHS
+        ENV "PROGRAMFILES(X86)"
+        ENV CUDA_PATH
+        ENV NVSDKCOMPUTE_ROOT
+        ENV AMDAPPSDKROOT
+        ENV INTELOCLSDKROOT
+        ENV ATISTREAMSDKROOT
+      PATH_SUFFIXES
+        "AMD APP/lib/x86_64"
+        lib/x86_64
+        lib/x64
+        OpenCL/common/lib/x64)
+  endif()
+else()
+  find_library(OpenCL_LIBRARY
+    NAMES OpenCL
+    PATHS
+        ENV LD_LIBRARY_PATH
+        ENV AMDAPPSDKROOT
+        ENV INTELOCLSDKROOT
+        ENV CUDA_PATH
+        ENV NVSDKCOMPUTE_ROOT
+        ENV ATISTREAMSDKROOT
+        /usr/lib64
+        /usr/lib
+        /usr/local/lib64
+        /usr/local/lib
+        /sw/lib
+        /opt/local/lib
+    PATH_SUFFIXES
+        "AMD APP/lib/x86_64"
+        lib/x86_64
+        lib/x64
+        lib/
+        lib64/
+        x86_64-linux-gnu
+        arm-linux-gnueabihf
+    )
+endif()
+
+set(OpenCL_LIBRARIES ${OpenCL_LIBRARY})
+set(OpenCL_INCLUDE_DIRS ${OpenCL_INCLUDE_DIR})
+
+#include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
+find_package_handle_standard_args(
+  OpenCL
+  FOUND_VAR OpenCL_FOUND
+  REQUIRED_VARS OpenCL_LIBRARY OpenCL_INCLUDE_DIR
+  VERSION_VAR OpenCL_VERSION_STRING)
+
+mark_as_advanced(
+  OpenCL_INCLUDE_DIR
+  OpenCL_LIBRARY)
+
diff --git a/CMakeModules/build_gtest.cmake b/test/CMakeModules/build_gtest.cmake
similarity index 90%
rename from CMakeModules/build_gtest.cmake
rename to test/CMakeModules/build_gtest.cmake
index 4d7fbbe055..eb4a0ad264 100644
--- a/CMakeModules/build_gtest.cmake
+++ b/test/CMakeModules/build_gtest.cmake
@@ -1,14 +1,15 @@
 # Build the gtest libraries
 
 # Check if Google Test exists
-SET(GTEST_SOURCE_DIR "${CMAKE_SOURCE_DIR}/test/gtest")
+SET(GTEST_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/gtest")
+MESSAGE(STATUS ${GTEST_SOURCE_DIR})
 IF(NOT EXISTS "${GTEST_SOURCE_DIR}/README")
-    MESSAGE(WARNING "GTest Source is not available. Tests will not build.")
-    MESSAGE("Did you miss the --recursive option when cloning?")
-    MESSAGE("Run the following commands to correct this:")
-    MESSAGE("git submodule init")
-    MESSAGE("git submodule update")
-    MESSAGE("git submodule foreach git pull origin master")
+    MESSAGE(STATUS "GTest submodules unavailable. Updating submodules.")
+    EXECUTE_PROCESS(
+        COMMAND git submodule update --init --recursive
+        WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+        OUTPUT_QUIET
+    )
 ENDIF()
 
 if(CMAKE_VERSION VERSION_LESS 3.2 AND CMAKE_GENERATOR MATCHES "Ninja")

From 258d57364178a49ce8b60add412b2efd99a1633a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 24 Nov 2015 14:52:12 -0500
Subject: [PATCH 0048/2677] Convert morph & range cpu fns to async calls

---
 src/backend/cpu/morph.cpp | 216 ++++++++++++++++++++------------------
 src/backend/cpu/range.cpp | 116 ++++++++++----------
 2 files changed, 172 insertions(+), 160 deletions(-)

diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index eb2e1de339..c64d09be30 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -13,6 +13,8 @@
 #include <Array.hpp>
 #include <morph.hpp>
 #include <algorithm>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -31,108 +33,41 @@ static inline unsigned getIdx(const dim4 &strides,
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask)
 {
-    const dim4 dims       = in.dims();
-    const dim4 window     = mask.dims();
-    const dim_t R0     = window[0]/2;
-    const dim_t R1     = window[1]/2;
-    const dim4 istrides   = in.strides();
-    const dim4 fstrides   = mask.strides();
-
-    Array<T> out         = createEmptyArray<T>(dims);
-    const dim4 ostrides   = out.strides();
-
-    T* outData            = out.get();
-    const T*   inData     = in.get();
-    const T*   filter     = mask.get();
-
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        for(dim_t b2=0; b2<dims[2]; ++b2) {
-            // either channels or batch is handled by outer most loop
-            for(dim_t j=0; j<dims[1]; ++j) {
-                // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
-                    // i steps along 1st dimension
-                    T filterResult = inData[ getIdx(istrides, i, j) ];
-
-                    // wj,wi steps along 2nd & 1st dimensions of filter window respectively
-                    for(dim_t wj=0; wj<window[1]; wj++) {
-                        for(dim_t wi=0; wi<window[0]; wi++) {
-
-                            dim_t offj = j+wj-R1;
-                            dim_t offi = i+wi-R0;
-
-                            T maskValue = filter[ getIdx(fstrides, wi, wj) ];
-
-                            if ((maskValue > (T)0) && offi>=0 && offj>=0 && offi<dims[0] && offj<dims[1]) {
-
-                                T inValue   = inData[ getIdx(istrides, offi, offj) ];
-
-                                if (isDilation)
-                                    filterResult = std::max(filterResult, inValue);
-                                else
-                                    filterResult = std::min(filterResult, inValue);
-                            }
-
-                        } // window 1st dimension loop ends here
-                    } // filter window loop ends here
-
-                    outData[ getIdx(ostrides, i, j) ] = filterResult;
-                } //1st dimension loop ends here
-            } // 2nd dimension loop ends here
-
-            // next iteration will be next batch if any
-            outData += ostrides[2];
-            inData  += istrides[2];
-        }
-    }
-
-    return out;
-}
-
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
-{
-    const dim4 dims       = in.dims();
-    const dim4 window     = mask.dims();
-    const dim_t R0     = window[0]/2;
-    const dim_t R1     = window[1]/2;
-    const dim_t R2     = window[2]/2;
-    const dim4 istrides   = in.strides();
-    const dim4 fstrides   = mask.strides();
-    const dim_t bCount = dims[3];
-
-    Array<T> out         = createEmptyArray<T>(dims);
-    const dim4 ostrides   = out.strides();
-
-    T* outData            = out.get();
-    const T*   inData     = in.get();
-    const T*   filter     = mask.get();
-
-    for(dim_t batchId=0; batchId<bCount; ++batchId) {
-        // either channels or batch is handled by outer most loop
-        for(dim_t k=0; k<dims[2]; ++k) {
-            // k steps along 3rd dimension
-            for(dim_t j=0; j<dims[1]; ++j) {
-                // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
-                    // i steps along 1st dimension
-                    T filterResult = inData[ getIdx(istrides, i, j, k) ];
-
-                    // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
-                    for(dim_t wk=0; wk<window[2]; wk++) {
+    Array<T> out = createEmptyArray<T>(in.dims());
+
+    auto func = [=] (Array<T> out, const Array<T> in, const Array<T> mask) {
+        const dim4 ostrides = out.strides();
+        const dim4 istrides = in.strides();
+        const dim4 fstrides = mask.strides();
+        const dim4 dims     = in.dims();
+        const dim4 window   = mask.dims();
+        T* outData          = out.get();
+        const T*   inData   = in.get();
+        const T*   filter   = mask.get();
+        const dim_t R0      = window[0]/2;
+        const dim_t R1      = window[1]/2;
+
+        for(dim_t b3=0; b3<dims[3]; ++b3) {
+            for(dim_t b2=0; b2<dims[2]; ++b2) {
+                // either channels or batch is handled by outer most loop
+                for(dim_t j=0; j<dims[1]; ++j) {
+                    // j steps along 2nd dimension
+                    for(dim_t i=0; i<dims[0]; ++i) {
+                        // i steps along 1st dimension
+                        T filterResult = inData[ getIdx(istrides, i, j) ];
+
+                        // wj,wi steps along 2nd & 1st dimensions of filter window respectively
                         for(dim_t wj=0; wj<window[1]; wj++) {
                             for(dim_t wi=0; wi<window[0]; wi++) {
 
-                                dim_t offk = k+wk-R2;
                                 dim_t offj = j+wj-R1;
                                 dim_t offi = i+wi-R0;
 
-                                T maskValue = filter[ getIdx(fstrides, wi, wj, wk) ];
+                                T maskValue = filter[ getIdx(fstrides, wi, wj) ];
 
-                                if ((maskValue > (T)0) && offi>=0 && offj>=0 && offk>=0 &&
-                                        offi<dims[0] && offj<dims[1] && offk<dims[2]) {
+                                if ((maskValue > (T)0) && offi>=0 && offj>=0 && offi<dims[0] && offj<dims[1]) {
 
-                                    T inValue   = inData[ getIdx(istrides, offi, offj, offk) ];
+                                    T inValue   = inData[ getIdx(istrides, offi, offj) ];
 
                                     if (isDilation)
                                         filterResult = std::max(filterResult, inValue);
@@ -141,17 +76,88 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
                                 }
 
                             } // window 1st dimension loop ends here
-                        }  // window 1st dimension loop ends here
-                    }// filter window loop ends here
-
-                    outData[ getIdx(ostrides, i, j, k) ] = filterResult;
-                } //1st dimension loop ends here
-            } // 2nd dimension loop ends here
-        } // 3rd dimension loop ends here
-        // next iteration will be next batch if any
-        outData += ostrides[3];
-        inData  += istrides[3];
-    }
+                        } // filter window loop ends here
+
+                        outData[ getIdx(ostrides, i, j) ] = filterResult;
+                    } //1st dimension loop ends here
+                } // 2nd dimension loop ends here
+
+                // next iteration will be next batch if any
+                outData += ostrides[2];
+                inData  += istrides[2];
+            }
+        }
+    };
+    getQueue().enqueue(func, out, in, mask);
+
+    return out;
+}
+
+template<typename T, bool isDilation>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
+{
+    Array<T> out = createEmptyArray<T>(in.dims());
+
+    auto func = [=] (Array<T> out, const Array<T> in, const Array<T> mask) {
+        const dim4 dims     = in.dims();
+        const dim4 window   = mask.dims();
+        const dim_t R0      = window[0]/2;
+        const dim_t R1      = window[1]/2;
+        const dim_t R2      = window[2]/2;
+        const dim4 istrides = in.strides();
+        const dim4 fstrides = mask.strides();
+        const dim_t bCount  = dims[3];
+        const dim4 ostrides = out.strides();
+        T* outData          = out.get();
+        const T*   inData   = in.get();
+        const T*   filter   = mask.get();
+
+        for(dim_t batchId=0; batchId<bCount; ++batchId) {
+            // either channels or batch is handled by outer most loop
+            for(dim_t k=0; k<dims[2]; ++k) {
+                // k steps along 3rd dimension
+                for(dim_t j=0; j<dims[1]; ++j) {
+                    // j steps along 2nd dimension
+                    for(dim_t i=0; i<dims[0]; ++i) {
+                        // i steps along 1st dimension
+                        T filterResult = inData[ getIdx(istrides, i, j, k) ];
+
+                        // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
+                        for(dim_t wk=0; wk<window[2]; wk++) {
+                            for(dim_t wj=0; wj<window[1]; wj++) {
+                                for(dim_t wi=0; wi<window[0]; wi++) {
+
+                                    dim_t offk = k+wk-R2;
+                                    dim_t offj = j+wj-R1;
+                                    dim_t offi = i+wi-R0;
+
+                                    T maskValue = filter[ getIdx(fstrides, wi, wj, wk) ];
+
+                                    if ((maskValue > (T)0) && offi>=0 && offj>=0 && offk>=0 &&
+                                            offi<dims[0] && offj<dims[1] && offk<dims[2]) {
+
+                                        T inValue   = inData[ getIdx(istrides, offi, offj, offk) ];
+
+                                        if (isDilation)
+                                            filterResult = std::max(filterResult, inValue);
+                                        else
+                                            filterResult = std::min(filterResult, inValue);
+                                    }
+
+                                } // window 1st dimension loop ends here
+                            }  // window 1st dimension loop ends here
+                        }// filter window loop ends here
+
+                        outData[ getIdx(ostrides, i, j, k) ] = filterResult;
+                    } //1st dimension loop ends here
+                } // 2nd dimension loop ends here
+            } // 3rd dimension loop ends here
+            // next iteration will be next batch if any
+            outData += ostrides[3];
+            inData  += istrides[3];
+        }
+    };
+    getQueue().enqueue(func, out, in, mask);
 
     return out;
 }
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index eabf3a1ee1..1fa46b2e89 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -14,74 +14,80 @@
 #include <err_cpu.hpp>
 #include <algorithm>
 #include <numeric>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    ///////////////////////////////////////////////////////////////////////////
-    // Kernel Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T, int dim>
-    void range(T *out, const dim4 &dims, const dim4 &strides)
-    {
-        for(dim_t w = 0; w < dims[3]; w++) {
-            dim_t offW = w * strides[3];
-            for(dim_t z = 0; z < dims[2]; z++) {
-                dim_t offWZ = offW + z * strides[2];
-                for(dim_t y = 0; y < dims[1]; y++) {
-                    dim_t offWZY = offWZ + y * strides[1];
-                    for(dim_t x = 0; x < dims[0]; x++) {
-                        dim_t id = offWZY + x;
-                        if(dim == 0) {
-                            out[id] = x;
-                        } else if(dim == 1) {
-                            out[id] = y;
-                        } else if(dim == 2) {
-                            out[id] = z;
-                        } else if(dim == 3) {
-                            out[id] = w;
-                        }
+///////////////////////////////////////////////////////////////////////////
+// Kernel Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, int dim>
+void range(Array<T> output)
+{
+    T* out = output.get();
+
+    const dim4 dims = output.dims();
+    const dim4 strides = output.strides();
+
+    for(dim_t w = 0; w < dims[3]; w++) {
+        dim_t offW = w * strides[3];
+        for(dim_t z = 0; z < dims[2]; z++) {
+            dim_t offWZ = offW + z * strides[2];
+            for(dim_t y = 0; y < dims[1]; y++) {
+                dim_t offWZY = offWZ + y * strides[1];
+                for(dim_t x = 0; x < dims[0]; x++) {
+                    dim_t id = offWZY + x;
+                    if(dim == 0) {
+                        out[id] = x;
+                    } else if(dim == 1) {
+                        out[id] = y;
+                    } else if(dim == 2) {
+                        out[id] = z;
+                    } else if(dim == 3) {
+                        out[id] = w;
                     }
                 }
             }
         }
     }
+}
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Wrapper Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T>
-    Array<T> range(const dim4& dims, const int seq_dim)
-    {
-        // Set dimension along which the sequence should be
-        // Other dimensions are simply tiled
-        int _seq_dim = seq_dim;
-        if(seq_dim < 0) {
-            _seq_dim = 0;   // column wise sequence
-        }
-
-        Array<T> out = createEmptyArray<T>(dims);
-        switch(_seq_dim) {
-            case 0: range<T, 0>(out.get(), out.dims(), out.strides()); break;
-            case 1: range<T, 1>(out.get(), out.dims(), out.strides()); break;
-            case 2: range<T, 2>(out.get(), out.dims(), out.strides()); break;
-            case 3: range<T, 3>(out.get(), out.dims(), out.strides()); break;
-            default : AF_ERROR("Invalid rep selection", AF_ERR_ARG);
-        }
-
+///////////////////////////////////////////////////////////////////////////
+// Wrapper Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+Array<T> range(const dim4& dims, const int seq_dim)
+{
+    // Set dimension along which the sequence should be
+    // Other dimensions are simply tiled
+    int _seq_dim = seq_dim;
+    if(seq_dim < 0) {
+        _seq_dim = 0;   // column wise sequence
+    }
 
-        return out;
+    Array<T> out = createEmptyArray<T>(dims);
+    switch(_seq_dim) {
+        case 0: getQueue().enqueue(range<T, 0>, out); break;
+        case 1: getQueue().enqueue(range<T, 1>, out); break;
+        case 2: getQueue().enqueue(range<T, 2>, out); break;
+        case 3: getQueue().enqueue(range<T, 3>, out); break;
+        default : AF_ERROR("Invalid rep selection", AF_ERR_ARG);
     }
 
+    return out;
+}
+
 #define INSTANTIATE(T)                                                      \
     template Array<T> range<T>(const af::dim4 &dims, const int seq_dims);   \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(ushort)
-    INSTANTIATE(short)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 }

From bdee78a64a665423d029afbcc9e9c1b7e6f049fd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 24 Nov 2015 15:10:06 -0500
Subject: [PATCH 0049/2677] Fix examples/cmakelist arguments for osx and
 windows

---
 examples/CMakeLists.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 793cb04fcf..bbadd46fee 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -85,10 +85,10 @@ ENDIF()
 # Next we build each example using every backend.
 IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
     MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
-    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} ${CMAKE_DL_LIBS})
+    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "${CMAKE_DL_LIBS}")
 ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
     MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
-    BUILD_ALL("${FILES}" unified af ${CMAKE_DL_LIBS})
+    BUILD_ALL("${FILES}" unified af "${CMAKE_DL_LIBS}")
 ELSE()
     MESSAGE(STATUS "EXAMPLES: UNIFIED backend is OFF. af was not found.")
 ENDIF()
@@ -119,7 +119,7 @@ IF (${OpenCL_FOUND})
         BUILD_ALL("${FILES}" opencl ${ArrayFire_OpenCL_LIBRARIES} "${OpenCL_LIBRARIES}")
     ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
         MESSAGE(STATUS "EXAMPLES: OpenCL backend is ON.")
-        BUILD_ALL("${FILES}" opencl afopencl ${OpenCL_LIBRARIES})
+        BUILD_ALL("${FILES}" opencl afopencl "${OpenCL_LIBRARIES}")
     ELSE()
         MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. afopencl was not found")
     ENDIF()

From aa076e96e8b7fa547d6ac400832eba994de067bc Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 19 Nov 2015 14:11:16 -0500
Subject: [PATCH 0050/2677] forge visualization tutorial

---
 assets                            |   2 +-
 docs/layout.xml                   |   2 +
 docs/pages/forge_visualization.md | 110 ++++++++++++++++++++++++++++++
 docs/pages/vectorization.md       |  67 ++++++++----------
 4 files changed, 141 insertions(+), 40 deletions(-)
 create mode 100644 docs/pages/forge_visualization.md

diff --git a/assets b/assets
index 7c2a12739a..8030a5c626 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit 7c2a12739ac0f5830d26334731e9ac96ba01e2d7
+Subproject commit 8030a5c626777a5b3f46b319dd4d1723eca4b0f9
diff --git a/docs/layout.xml b/docs/layout.xml
index 3a66b563e4..720e9d6743 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -10,6 +10,8 @@
       <tab type="user" url="\ref gettingstarted" visible="yes" title="Getting Started"/>
       <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
       <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Matrix Manipulation"/>
+      <tab type="user" url="\ref vectorization" visible="yes" title="Vectorization"/>
+      <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
       <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
       <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
       <tab type="user" url="\ref configuring_environment" visible="yes" title="Configuring ArrayFire Environment"/>
diff --git a/docs/pages/forge_visualization.md b/docs/pages/forge_visualization.md
new file mode 100644
index 0000000000..6107fb358d
--- /dev/null
+++ b/docs/pages/forge_visualization.md
@@ -0,0 +1,110 @@
+Visualizing af::arrays with Forge {#forge_visualization}
+===================
+Arrayfire as a library aims to provide a robust and easy to use platform for high-performance, parallel and GPU computing. The goal of Forge, an OpenGL visualization library, is to provide equally robust visualizations that are interoperable between Arrayfire data-structures and an OpenGL context. Instead of wasting time copying and reformatting data from the GPU to the host and back to the GPU, we can draw directly from GPU-data to GPU-framebuffers! Furthermore, Arrayfire provides wrapper functions that handle all of the interoperability for us and leave us with a simple interface to visualize af::arrays. Let's see exactly what visuals we can illuminate with forge and how Arrayfire anneals the data between the two libraries.
+
+# Setup
+Before we can call Forge functions, we need to set up the related "canvas" classes. Forge functions are tied to the af::Window class. First let's create a window:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+const static int WIDTH = 512, HEIGHT = 512;
+af::Window window(WIDTH, HEIGHT, "2D plot example title");
+
+do{
+
+//drawing functions here
+
+} while( !window.close() );
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We also added a drawing loop, so now we can use Forge's drawing functions to draw to the window.
+The drawing functions present in Forge are listed below.
+
+# af::Window::image
+The af::Window::image() function can be used to plot grayscale or color images. To plot a grayscale image a 2d array should be passed into the function. Let's see this on a static noise example:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array img = constant(0, WIDTH, HEIGHT); //make a black image
+array random = randu(WIDTH, HEIGHT);      //make random [0,1] distribution
+img(random > 0.5) = 1; //set all pixels where distribution > 0.5 to white
+
+window.image(img);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/noise.png" alt="Forge image plot of noise" width="20%" />
+Tweaking the previous example by giving our image a depth of 3 for the RGB values allows us to generate colorful noise:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array img = 255 * randu(WIDTH, HEIGHT, 3);      //make random [0, 255] distribution
+window.image( img.as(u8) );
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/color_noise.png" alt="Forge image plot of color noise" width="20%" />
+Notice Forge automatically handles any af::array type passed from Arrayfire. In the first example we passed in an image of floats in the range [0, 1]. In the last example we cast our array to an unsigned byte array with the range [0, 255]. The type-handling properties are consistent for all Forge drawing functions.
+
+# af::Window::plot
+The af::Window::plot() function visualizes an array as a 2d-line plot. Let's see a simple example:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array X = seq(-af::Pi, af::Pi, 0.01);
+array Y = sin(X);
+window.plot(X, Y);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+<img src="gfx_docs_images/sin_plot.png" alt="Forge 2d line plot of sin() function" width="30%" />
+The plot function has the signature:
+<br> **void plot( const array &X, const array &Y, const char * const title = NULL );**
+<br> Both the x and y coordinates of the points are required to plot. This allows for non-uniform, or parametric plots:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array t = seq(0, 100, 0.01);
+array X = sin(t) * (exp(cos(t)) - 2*cos(4*t) - pow(sin(t/12), 5)); array Y = cos(t) * (exp(cos(t)) - 2*cos(4*t) - pow(sin(t/12), 5));
+window.plot(X, Y);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+<img src="gfx_docs_images/butterfly_plot.png" alt="Forge 2d line plot of butterfly function" width="30%" />
+
+# af::Window::plot3
+The af::Window::plot3() function will plot a curve in 3d-space.
+Its signature is:
+<br> **void plot3 (const array &in, const char * title = NULL);**
+<br> The input array expects xyz-triplets in sequential order. The points can be in a flattened one dimensional <i>(3n x 1)</i> array, or in one of the <i>(3 x n),</i> <i>(n x 3)</i> matrix forms.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array Z = seq(0.1f, 10.f, 0.01);
+array Y = sin(10*Z) / Z;
+array X = cos(10*Z) / Z;
+
+array Pts = join(1, X, Y, Z);
+//Pts can be passed in as a matrix in the from n x 3, 3 x n
+//or in the flattened xyz-triplet array with size 3n x 1
+window.plot3(Pts);
+//both of the following are equally valid
+//window.plot3(transpose(Pts));
+//window.plot3(flat(Pts));
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/spiral_plot3.png" alt="Forge 3d line plot" width="40%" />
+
+# af::Window::hist
+The af::Window::hist() function renders an input array as a histogram. In our example, the input array will be created with Arrayfire's histogram() function, which actually counts and bins each sample. The output from histogram() can directly be fed into the af::Window::hist() rendering function.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+const int BINS = 128; SAMPLES = 9162;
+array norm = randn(SAMPLES);
+array hist_arr = histogram(norm, BINS);
+
+win.hist(hist_arr, 0, BINS);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+In addition to the histogram array with the number of samples in each bin, the af::Window::hist() function takes two additional parameters -- the minimum and maximum values of all datapoints in the histogram array. This effectively sets the range of the binned data. The full signature of af::Window::hist() is: **void hist(const array & X, const double minval, const double maxval, const char * const title = NULL);**
+<img src="gfx_docs_images/norm_histogram.png" alt="Forge 3d scatter plot" width="40%" />
+
+
+# af::Window::surface
+The af::Window::surface() function will plot af::arrays as a 3d surface.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array Z = randu(21, 21);
+window.surface(Z, "Random Surface");    //equal to next function call
+//window.surface( seq(-1, 1, 0.1), seq(-1, 1, 0.1), Z, "Random Surface");
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/rand_surface.png" alt="Forge random surface plot" width="30%" />
+There are two overloads for the **af::Window::surface()** function:
+* **void surface (const array & S, const char *const title )** -- accepts a 2d matrix with the z values of the surface
+* **void surface (const array &xVals, const array &yVals, const array &S, const char * const title)** -- accepts additional vectors that define the x,y coordinates for the surface points.
+
+The second overload has two options for the x, y coordinate vectors. Assuming a surface grid of size **m x n**:
+ 1. Short vectors defining the spacing along each axis. Vectors will have sizes **m x 1** and **n x 1**.
+ 2. Vectors containing the coordinates of each and every point. Each of the vectors will have length **mn x 1**. This can be used for completely non-uniform or parametric surfaces.
+
+# Conclusion
+There is a fairly comprehensive collection of methods to visualize data in Arrayfire. Thanks to the high-performance gpu plotting library Forge, the provided Arrayfire functions not only make visualizations as simple as possible, but keep them as robust as the rest of the Arrayfire library.
diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index b7e77044c4..2061172d5f 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -3,7 +3,7 @@ Vectorization {#vectorization}
 
 Programmers and Data Scientists want to take advantage of fast and parallel computational devices. Writing vectorized code is becoming a necessity to get the best performance out of the current generation parallel hardware and scientific computing software. However, writing vectorized code may not be intuitive immediately. Arrayfire provides many ways to vectorize a given code segment. In this tutorial, we will be presenting various ways to vectorize code using ArrayFire and the benefits and drawbacks associated with each method.
 
-### Generic/Default vectorization
+# Generic/Default vectorization
 By its very nature, Arrayfire is a vectorized library. Most functions operate on arrays as a whole -- on all elements in parallel. Wherever possible, existing vectorized functions should be used opposed to manually indexing into arrays. For example, consider this valid, yet mislead code that attempts to increment each element of an array:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::seq(10); // [0,  9]
@@ -19,35 +19,24 @@ af::array a = af::seq(10);  // [0,  9]
 a = a + 1;                  // [1, 10]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Some of the vectorized mathematical functions of Arrayfire include:  
-
-__Arithmetic operations:__
- 	+, -, *, /, >>, <<
-
-__Complex operations:__
- 	real, imag, conjugate, etc. 
- 	 
-__Exponential and logarithmic functions:__
- 	exp, log, expm1, log1p, etc. 
- 
-__Hyperbolic functions:__
- 	sinh, cosh, tanh, etc. 
- 
-__Logical operations:__
- 	&&, ||, |, &, <, >, <=, >=, ==, ! 
- 
-__Numeric functions:__
- 	floor, round, min, max, etc. 
- 
-__Trigonometric functions:__
- 	sin, cos, tan, etc. 
-  
-  
-### GFOR: Parallel for-loops
+Some of the vectorized mathematical functions of Arrayfire include:
+
+Operator Category                     | Functions
+--------------------------------------|--------------------------
+Arithmetic operations                 | operator+(), operator-(), operator*(), operator/(), operator>>(), operator<<()
+Complex operations                    | real(), imag(), conjugate(), etc.
+Exponential and logarithmic functions | exp(), log(), expm1(), log1p(), etc.
+Hyperbolic functions                  | sinh(), cosh(), tanh(), etc.
+Numeric functions                     | floor(), round(), min(), max(), etc.
+Trigonometric functions               | sin(), cos(), tan(), etc.
+Logical operations                    | &&, \|\|, \|, &, <, >, <=, >=, ==, !
+
+
+# GFOR: Parallel for-loops
 Another novel method of vectorization present in Arrayfire is the GFOR loop replacement construct.
-GFOR allows launching all iterations of a loop in parallel on the GPU or device, as long as the iterations are independent. While the standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop performs each iteration at the same time (in parallel). ArrayFire does this by tiling out the values of all loop iterations and then performing computation on those tiles in one pass.  
-You can think of gfor as performing auto-vectorization of your code, e.g. you write a gfor-loop that increments every element of a vector but behind the scenes ArrayFire rewrites it to operate on the entire vector in parallel.  
-We can remedy our first example with GFOR:  
+GFOR allows launching all iterations of a loop in parallel on the GPU or device, as long as the iterations are independent. While the standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop performs each iteration at the same time (in parallel). ArrayFire does this by tiling out the values of all loop iterations and then performing computation on those tiles in one pass.
+You can think of gfor as performing auto-vectorization of your code, e.g. you write a gfor-loop that increments every element of a vector but behind the scenes ArrayFire rewrites it to operate on the entire vector in parallel.
+We can remedy our first example with GFOR:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::seq(10);
 gfor(seq i, n)
@@ -62,8 +51,8 @@ for (int i = 0; i < N; ++i)
 gfor (seq i, N)
    A(span,span,i) = fft2(A(span,span,i)); // runs N FFTs in parallel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-### GFOR: Usage
-There are three formats for instantiating gfor-loops:  
+## GFOR: Usage
+There are three formats for instantiating gfor-loops:
 
  1. gfor(var,n)-- Creates a sequence <B>{0, 1, ..., n-1}</B>
  2. gfor(var,first,last)-- Creates a sequence <B>{first, first+1, ..., last}</B>
@@ -78,8 +67,8 @@ gfor (seq i, 0, 1, 4)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Using GFOR requires following several rules and multiple guidelines for optimal performance. The details of this vectorization method can be found in the <a href="page_gfor.htm">GFOR documentation.</a>
 
-### batchFunc()
-The batchFunc() function allows the broad application of existing Arrayfire functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire functions to execute in "batch processing" mode. In this mode, functions will find a dimension which contains "batches" of data to be processed and will parallelize the procedure. 
+# batchFunc()
+The batchFunc() function allows the broad application of existing Arrayfire functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire functions to execute in "batch processing" mode. In this mode, functions will find a dimension which contains "batches" of data to be processed and will parallelize the procedure.
 Consider the following example:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filter = randn(1, 5);
@@ -98,15 +87,15 @@ However we would like a vectorized solution. The following syntax begs to be use
 af::array filtered_weights = filter * weights; //fails due to dimension mismatch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 but it fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if Arrayfire could figure out along which dimension we intend to apply the batch operation? That is exactly what batchFunc() does!
-The signature of the function is:  
+The signature of the function is:
+
+__AFAPI array batchFunc( const array &lhs, const array &rhs, batchFunc_t func );__
 
-__AFAPI array batchFunc( const array &lhs, const array &rhs, batchFunc_t func );__  
+where __batchFunc_t__ is a function pointer of the form:
+__typedef array (*batchFunc_t) ( const array &lhs, const array &rhs );__
 
-where __batchFunc_t__ is a function pointer of the form:  
-__typedef array (*batchFunc_t) ( const array &lhs, const array &rhs );__  
-  
 
-So, to use batchFunc(), we need to provide the function we will be applying as a batch operation. Our final batch call is not much more difficult than the ideal syntax we imagined. 
+So, to use batchFunc(), we need to provide the function we will be applying as a batch operation. Our final batch call is not much more difficult than the ideal syntax we imagined.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = batchFunc(filter, weights, operator* );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From 1cca0deb7c839ac027726ad8767fdca2c3be16e4 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 25 Nov 2015 19:28:01 -0500
Subject: [PATCH 0051/2677] initial opencl, cuda interop tutorials

---
 docs/pages/interop_cuda.md   | 103 +++++++++++++++++++++++++++++++++++
 docs/pages/interop_opencl.md |  78 ++++++++++++++++++++++++++
 2 files changed, 181 insertions(+)
 create mode 100644 docs/pages/interop_cuda.md
 create mode 100644 docs/pages/interop_opencl.md

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
new file mode 100644
index 0000000000..822a8876a1
--- /dev/null
+++ b/docs/pages/interop_cuda.md
@@ -0,0 +1,103 @@
+Interoperability with CUDA {#interop_cuda}
+========
+
+As extensive as ArrayFire is, there are a few cases where you are still working with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels. For example, you may want to integrate ArrayFire into an existing code base for productivity or you may want to keep it around the old implementation for testing purposes. In this post we are going to talk about how to integrate your custom kernels into ArrayFire in a seamless fashion.
+
+# In and Out of Arrayfire
+
+First, let's consider the following code and then break it down bit by bit.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+int main() {
+    af::array x = randu(num);
+    af::array y = randu(num);
+
+    float *d_x = x.device<float>();
+    float *d_y = y.device<float>();
+
+    af::sync();
+
+    // Launch kernel to do the following operations
+    // y = sin(x)^2 + cos(x)^2
+    launch_simple_kernel(d_x, d_y, num);
+
+    x.unlock();
+    y.unlock();
+
+    // check for errors, should be 0,
+    // since sin(x)^2 + cos(x)^2 == 1
+    float err = af::sum(af::abs(y-1));
+    printf("Error: %f\n", err);
+    return 0;
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+## Breakdown
+Most kernels require an input. In this case, we created a random uniform array **x**.
+We also go ahead and prepare the output array. The necessary memory required is allocated in array **y** before the kernel launch.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    af::array x = randu(num);
+    af::array y = randu(num);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In this example, the output is the same size as in the input. Note that the actual output data type is not specified. For such cases, ArrayFire assumes the data type is single precision floating point ( af::f32 ). If necessary, the data type can be specified at the end of the array(..) constructor. Once you have the input and output arrays, you will need to extract the device pointers / objects using array::device() method in the following manner.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    float *d_x = x.device<float>();
+    float *d_y = y.device<float>();
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Before  launching your custom kernel, it is best to make sure that all ArrayFire computations have finished. This can be called by using af::sync(). The function ensures you are not unintentionally doing out of order executions.
+af::sync() is not strictly required if you are not using streams in CUDA.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    af::sync();
+
+    // Launch kernel to do the following operations
+    // y = sin(x)^2 + cos(x)^2
+    launch_simple_kernel(d_x, d_y, num);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The function **launch_simple_kernel** handles the launching of your custom kernel. We will have a look at how to do this in CUDA and OpenCL later in the post. Once you have finished your computations, you have to tell ArrayFire to take control of the memory objects.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    x.unlock();
+    y.unlock();
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+This is a very crucial step as ArrayFire believes the user is still in control of the pointer. This means that ArrayFire will not perform garbage collection on these objects resulting in memory leaks. You can now proceed with the rest of the program. In our particular example, we are just performing an error check and exiting.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    // check for errors, should be 0,
+    // since sin(x)^2 + cos(x)^2 == 1
+    float err = af::sum(af::abs(y-1));
+    printf("Error: %f\n", err);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Launching a CUDA kernel
+Integrating a CUDA kernel into your ArrayFire code base is a fairly straightforward process. You need to set the launch configuration parameters, launch the kernel and wait for the computations to finish. This is shown below.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+ 
+ __global__
+ static void simple_kernel(float *d_y,
+                           const float *d_x,
+                           const int num)
+{
+    const int id = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (id < num) {
+        float x = d_x[id];
+        float sin_x = sin(x);
+        float cos_x = cos(x);
+        d_y[id] = (sin_x * sin_x) + (cos_x * cos_x);
+    }
+}
+
+void inline launch_simple_kernel(float *d_y,
+                                 const float *d_x,
+                                 const int num)
+{
+    // Set launch configuration
+    const int threads = 256;
+    const int blocks = (num / threads) + ((num % threads) ? 1 : 0);
+    simple_kernel<<<blocks, threads>>>(d_y, d_x, num);
+    // Synchronize and check for error
+    cudaDeviceSynchronize();
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
new file mode 100644
index 0000000000..8a8acefb85
--- /dev/null
+++ b/docs/pages/interop_opencl.md
@@ -0,0 +1,78 @@
+Interoperability with OpenCL {#interop_cuda}
+========
+
+As extensive as ArrayFire is, there are a few cases where you are still working with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels. For example, you may want to integrate ArrayFire into an existing code base for productivity or you may want to keep it around the old implementation for testing purposes. In this post we are going to talk about how to integrate your custom kernels into ArrayFire in a seamless fashion.
+
+# In and Out of Arrayfire
+
+First, let's consider the following code and then break it down bit by bit.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+int main() {
+    af::array x = randu(num);
+    af::array y = randu(num);
+
+    float *d_x = x.device<float>();
+    float *d_y = y.device<float>();
+
+    af::sync();
+
+    // Launch kernel to do the following operations
+    // y = sin(x)^2 + cos(x)^2
+    launch_simple_kernel(d_x, d_y, num);
+
+    x.unlock();
+    y.unlock();
+
+    // check for errors, should be 0,
+    // since sin(x)^2 + cos(x)^2 == 1
+    float err = af::sum(af::abs(y-1));
+    printf("Error: %f\n", err);
+    return 0;
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+## Breakdown
+Most kernels require an input. In this case, we created a random uniform array **x**.
+We also go ahead and prepare the output array. The necessary memory required is allocated in array **y** before the kernel launch.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    af::array x = randu(num);
+    af::array y = randu(num);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In this example, the output is the same size as in the input. Note that the actual output data type is not specified. For such cases, ArrayFire assumes the data type is single precision floating point ( af::f32 ). If necessary, the data type can be specified at the end of the array(..) constructor. Once you have the input and output arrays, you will need to extract the device pointers / objects using array::device() method in the following manner.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    float *d_x = x.device<float>();
+    float *d_y = y.device<float>();
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Accesing the device pointer in this manner internally sets a flag prohibiting the arrayfire object from further managing the memory. Ownership will need to be returned to the af::array object once we are finished using it.
+
+Before  launching your custom kernel, it is best to make sure that all ArrayFire computations have finished. This can be called by using af::sync(). The function ensures you are not unintentionally doing out of order executions.
+af::sync() is not strictly required if you are not using streams in CUDA.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    af::sync();
+
+    // Launch kernel to do the following operations
+    // y = sin(x)^2 + cos(x)^2
+    launch_simple_kernel(d_x, d_y, num);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The function **launch_simple_kernel** handles the launching of your custom kernel. We will have a look at how to do this in CUDA and OpenCL later in the post. Once you have finished your computations, you have to tell ArrayFire to take control of the memory objects.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    x.unlock();
+    y.unlock();
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+This is a very crucial step as ArrayFire believes the user is still in control of the pointer. This means that ArrayFire will not perform garbage collection on these objects resulting in memory leaks. You can now proceed with the rest of the program. In our particular example, we are just performing an error check and exiting.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    // check for errors, should be 0,
+    // since sin(x)^2 + cos(x)^2 == 1
+    float err = af::sum(af::abs(y-1));
+    printf("Error: %f\n", err);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Launching an OpenCL kernel
+If you are integrating an OpenCL kernel into your ArrayFire code base, launching a kernel is slightly complicated. Since ArrayFire uses its own context internally, you need to get the context from a memory object. Once you have access to the same context ArrayFire is using, the rest of the process is exactly the same as launching a stand alone OpenCL context.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+

From 75c217280dc0188e73558a84935be296bb30f4e8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 29 Nov 2015 16:09:33 -0500
Subject: [PATCH 0052/2677] BUGFIX: GFOR assignment when other dimensions have
 step indices

---
 src/api/cpp/array.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 208f60ed68..03de1744ca 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -493,15 +493,16 @@ namespace af
         bool batch_assign = false;
         bool is_reordered = false;
         if (dim >= 0) {
+            //FIXME: Figure out a faster, cleaner way to do this
+            dim4 out_dims = seqToDims(impl->indices, this_dims, false);
+
             batch_assign = true;
             for (int i = 0; i < AF_MAX_DIMS; i++) {
                 if (this->impl->indices[i].isBatch) batch_assign &= (other_dims[i] == 1);
-                else                          batch_assign &= (other_dims[i] == this_dims[i]);
+                else                          batch_assign &= (other_dims[i] == out_dims[i]);
             }
 
             if (batch_assign) {
-                //FIXME: Figure out a faster, cleaner way to do this
-                dim4 out_dims = seqToDims(impl->indices, this_dims, false);
                 af_array out;
                 AF_THROW(af_tile(&out, other_arr,
                                  out_dims[0] / other_dims[0],
@@ -510,7 +511,7 @@ namespace af
                                  out_dims[3] / other_dims[3]));
                 other_arr = out;
 
-            } else if (this_dims != other_dims) {
+            } else if (out_dims != other_dims) {
                 // HACK: This is a quick check to see if other has been reordered inside gfor
                 // TODO: Figure out if this breaks and implement a cleaner method
                 other_arr = gforReorder(other_arr, dim);

From 7d06e9fded248df9c9cfef930f3cf65cb3c1f918 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 29 Nov 2015 17:59:42 -0500
Subject: [PATCH 0053/2677] BUGFIX: Issue with vector indexing when using spans

---
 src/api/cpp/array.cpp | 18 +-----------------
 1 file changed, 1 insertion(+), 17 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 03de1744ca..76b6e2e569 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -357,23 +357,7 @@ namespace af
 
     const array::array_proxy array::operator()(const index &s0, const index &s1, const index &s2, const index &s3) const
     {
-        if(isvector()   && s1.isspan()
-                        && s2.isspan()
-                        && s3.isspan()) {
-            int num_dims = numDims(this->arr);
-
-            switch(num_dims) {
-            case 1: return gen_indexing(*this, s0, s1, s2, s3);
-            case 2: return gen_indexing(*this, s1, s0, s2, s3);
-            case 3: return gen_indexing(*this, s1, s2, s0, s3);
-            case 4: return gen_indexing(*this, s1, s2, s3, s0);
-            default: THROW(AF_ERR_SIZE);
-            }
-        }
-        else {
-            return gen_indexing(*this, s0, s1, s2, s3);
-        }
-
+        return gen_indexing(*this, s0, s1, s2, s3);
     }
 
     const array::array_proxy array::row(int index) const

From 05e00d5b03be04d75e7d7d83750e89b35b16f997 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 29 Nov 2015 18:00:54 -0500
Subject: [PATCH 0054/2677] Do not perform copies in moddims if memory is
 contiguous

---
 src/api/c/moddims.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 7ccc38c3cb..bb156ffc2c 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -27,9 +27,10 @@ Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
 
     Array<T> Out = in;
 
-    if (!in.isOwner()) {
+    if (!in.isLinear()) {
         Out = copyArray<T>(in);
     }
+
     Out.modDims(newDims);
 
     return Out;

From ee8a1eed30bdcd0955bb64274ebfdc1bbc48106e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 30 Nov 2015 13:29:04 -0500
Subject: [PATCH 0055/2677] Documentation for seq class

---
 include/af/seq.h | 170 ++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 167 insertions(+), 3 deletions(-)

diff --git a/include/af/seq.h b/include/af/seq.h
index 5f952d4281..7ab36879a4 100644
--- a/include/af/seq.h
+++ b/include/af/seq.h
@@ -10,8 +10,21 @@
 #pragma once
 #include <af/defines.h>
 
+/**
+    \struct af_seq
+
+    \brief C-style struct to creating sequences for indexing
+
+    \ingroup index_mat
+*/
 typedef struct af_seq {
-    double begin, end;
+    /// Start position of the sequence
+    double begin;
+
+    /// End position of the sequence (inclusive)
+    double end;
+
+    /// Step size between sequence values
     double step;
 } af_seq;
 
@@ -22,31 +35,167 @@ namespace af
 {
 class array;
 
+/**
+    \class seq
+
+    \brief seq is used to create seq for indexing af::array
+
+    \ingroup index_mat
+*/
 class AFAPI seq
 {
 public:
+    ///
+    /// \brief Get the \ref af_seq C-style struct
+    ///
     af_seq s;
+
+    ///
+    /// \brief Get's the length of the sequence
+    ///
     size_t size;
+
+    ///
+    /// \brief Flag for gfor
+    ///
     bool m_gfor;
 
-    seq(double = 0);
+    /**
+        \brief Creates a sequence of size length as [0, 1, 2..., length - 1]
+
+        The sequence has begin as 0, end as length - 1 and step as 1.
+
+        \note When doing seq(-n), where n is > 0, then the sequence is generated as
+        0...-n but step remains +1. This is because when such a sequence is
+        used for indexing af::array, then -n represents n elements from the
+        end. That is, seq(-2) will imply indexing an array 0...dimSize - 2.
+
+        \code
+                            // [begin, end, step]
+        seq a(10);          // [0, 9, 1]    => 0, 1, 2....9
+        \endcode
+
+        \param[in] length is the size of the seq to be created.
+    */
+    seq(double length = 0);
+
+    /**
+        \brief Destructor
+    */
     ~seq();
 
-    // begin, end, step
+    /**
+        \brief Creates a sequence starting at begin,
+        ending at or before end (inclusive) with increments as step.
+
+        The sequence will be [begin, begin + step, begin + 2 * step...., begin + n * step]
+        where the begin + n * step <= end.
+
+        \code
+                            // [begin, end, step]
+        seq a(10, 20);      // [10, 20, 1]  => 10, 11, 12....20
+        seq b(10, 20, 2);   // [10, 20, 2]  => 10, 12, 14....20
+        seq c(-5, 5);       // [-5, 5, 1]   => -5, -4, -3....0, 1....5
+        seq d(-5, -15, -1); // [-5,-15, -1] => -5, -6, -7....-15
+        seq e(-15, -5, 1);  // [-15, -5, 1] => -15, -14, -13....-5
+        \endcode
+
+        \param[in] begin is the start of the sequence
+        \param[in] end is the maximum value a sequence can take (inclusive)
+        \param[in] step is the increment or decrement size (default is 1)
+    */
     seq(double begin, double end, double step = 1);
 
+    /**
+        \brief Copy constructor
+
+        Creates a copy seq from another sequence.
+
+        \param[in] afs seqence to be copies
+        \param[in] is_gfor is the gfor flag
+    */
     seq(seq afs, bool is_gfor);
 
+    /**
+        \brief Create a seq object from an \ref af_seq struct
+
+        \param[in] s_ is the \ref af_seq struct
+    */
     seq(const af_seq& s_);
 
+    /**
+        \brief Assignment operator to create a new sequence from an af_seq
+
+        This operator creates a new sequence using the begin, end and step
+        from the input sequence.
+
+        \param[in] s is the input sequence
+    */
     seq& operator=(const af_seq& s);
 
+    /**
+        \brief Negation operator creates a sequence with the signs negated
+
+        begin is changed to -begin
+        end is changed to -end
+        step is changed to -step
+
+        \code
+                        // [begin, end, step]
+        seq a(1, 10);   // [ 1, 10, 1] => 1, 2, 3....10
+        seq b = -a;     // [-1,-10,-1] => -1, -2, -3...-10
+        \endcode
+    */
     inline seq operator-()         { return seq(-s.begin, -s.end,  -s.step); }
 
+    /**
+        \brief Addition operator offsets the begin and end by x. There is no
+        change in step.
+
+        begin is changed to begin + x
+        end is changed to end + x
+
+        \code
+                            // [begin, end, step]
+        seq a(2, 20, 2);    // [2, 20, 2] => 2, 4, 6....20
+        seq b = a + 3;      // [5, 23, 2] => 5, 7, 9....23
+        \endcode
+    */
     inline seq operator+(double x) { return seq(s.begin + x, s.end + x, s.step); }
 
+    /**
+        \brief Subtraction operator offsets the begin and end by x. There is no
+        change in step.
+
+        begin is changed to begin - x
+        end is changed to end - x
+
+        \code
+                            // [begin, end, step]
+        seq a(10, 20, 2);   // [10, 20, 2] => 10, 12, 14....20
+        seq b(2, 10);       // [ 2, 10, 1] => 2, 3, 4....10
+        seq c = a - 3;      // [ 7, 17, 2] => 7, 9, 11....17
+        seq d = b - 3;      // [-1,  7, 2] => -1, 1, 3....7
+        \endcode
+    */
     inline seq operator-(double x) { return seq(s.begin - x, s.end - x, s.step); }
 
+    /**
+        \brief Multiplication operator spaces the sequence by a factor x.
+
+        begin is changed to begin * x
+        end is changed to end * x
+        step is changed to step * x
+
+        \code
+                            // [begin, end, step]
+        seq a(10, 20, 2);   // [10, 20, 2] => 10, 12, 14....20
+        seq b(-5, 5);       // [-5, 5, 1] => -5, -4, -3....0, 1....5
+        seq c = a * 3;      // [30, 60, 6] => 30, 36, 42....60
+        seq d = b * 3;      // [-15, 15, 3] => -15, -12, -9....0, 3....15
+        seq e = a * 0.5;    // [5, 10, 1] => 5, 6, 7....10
+        \endcode
+    */
     inline seq operator*(double x) { return seq(s.begin * x, s.end * x, s.step * x); }
 
     friend inline seq operator+(double x, seq y) { return  y + x; }
@@ -55,6 +204,21 @@ class AFAPI seq
 
     friend inline seq operator*(double x, seq y) { return  y * x; }
 
+    /**
+        \brief Implicit conversion operator from seq to af::array
+
+        Convertes a seq object into an af::array object. The contents of the
+        af:array will be the explicit values from the seq.
+
+        \note Do not use this to create arrays of sequences. Use \ref range.
+
+        \code
+                            // [begin, end, step]
+        seq s(10, 20, 2);   // [10, 20, 2] => 10, 12, 14....20
+        array arr = s;
+        af_print(arr);      // 10    12    14    16    18    20
+        \endcode
+    */
     operator array() const;
 
     private:

From 71442bb0f41d29949be702494c71e2adb720c3fa Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 30 Nov 2015 13:58:13 -0500
Subject: [PATCH 0056/2677] Fix possible divide by zero case in cpu info

---
 src/backend/cpu/platform.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index fc782eab76..2b99037496 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -118,7 +118,9 @@ CPUInfo::CPUInfo()
                         default: break;
                     }
             }
-            mNumCores = mNumLogCpus/mNumSMT;
+            // Fixes Possible divide by zero error
+            // TODO: Fix properly
+            mNumCores = mNumLogCpus/(mNumSMT == 0 ? 1 : mNumSMT);
         } else {
             if (HFS>=1) {
                 mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;

From b267ffd0fb0b7b401ad4a7507af8fb3df88d002c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 30 Nov 2015 14:23:52 -0500
Subject: [PATCH 0057/2677] Increment version for devel to 3.3

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 3a474d1755..236058d154 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -2,7 +2,7 @@
 # Make a version file that includes the ArrayFire version and git revision
 #
 SET(AF_VERSION_MAJOR "3")
-SET(AF_VERSION_MINOR "2")
+SET(AF_VERSION_MINOR "3")
 SET(AF_VERSION_PATCH "0")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")

From 65c7a23c76173b7e6b98593ec4a76ad1e1b94181 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 30 Nov 2015 14:24:06 -0500
Subject: [PATCH 0058/2677] Fixes for scatter

---
 include/af/graphics.h | 26 +++++++++++++++++---------
 src/api/c/plot.cpp    | 15 ++++++++++-----
 src/api/c/plot3.cpp   | 15 ++++++++++-----
 3 files changed, 37 insertions(+), 19 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 129b43949f..a8b4816d95 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -179,6 +179,7 @@ class AFAPI Window {
          */
         void plot(const array& X, const array& Y, const char* const title=NULL);
 
+#if AF_API_VERSION >= 33
         /**
            Renders the input arrays as a 2D scatter-plot to the window
 
@@ -191,18 +192,23 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-        void scatter(const array& X, const array& Y, const af::markerType marker=AF_MARKER_POINT, const char* const title=NULL);
+        void scatter(const array& X, const array& Y,
+                     const af::markerType marker = AF_MARKER_POINT, const char* const title = NULL);
+#endif
 
+#if AF_API_VERSION >= 33
         /**
-           Renders the input arrays as a 2D scatter-plot to the window
+           Renders the input arrays as a 3D scatter-plot to the window
 
-           \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
+           \param[in] P is an \ref af_array or matrix with the xyz-values of the points
            \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
            \param[in] title parameter is used when this function is called in grid mode
 
            \ingroup gfx_func_draw
          */
-        void scatter3(const array& P, const af::markerType marker=AF_MARKER_POINT, const char* const title=NULL);
+        void scatter3(const array& P, const af::markerType marker = AF_MARKER_POINT,
+                      const char* const title = NULL);
+#endif
 
         /**
            Renders the input array as a histogram to the window
@@ -395,7 +401,7 @@ AFAPI af_err af_draw_image(const af_window wind, const af_array in, const af_cel
 */
 AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props);
 
-#if AF_API_VERSION >= 32
+#if AF_API_VERSION >= 33
 /**
    C Interface wrapper for drawing an array as a plot
 
@@ -413,10 +419,11 @@ AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array
 
    \ingroup gfx_func_draw
 */
-AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type marker, const af_cell* const props);
+AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
+                             const af_marker_type marker, const af_cell* const props);
 #endif
 
-#if AF_API_VERSION >= 32
+#if AF_API_VERSION >= 33
 /**
    C Interface wrapper for drawing an array as a plot
 
@@ -431,9 +438,10 @@ AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_ar
 
    \ingroup gfx_func_draw
 */
-AFAPI af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type marker, const af_cell* const props);
-
+AFAPI af_err af_draw_scatter3(const af_window wind, const af_array P,
+                              const af_marker_type marker, const af_cell* const props);
 #endif
+
 #if AF_API_VERSION >= 32
 /**
    C Interface wrapper for drawing an array as a plot
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index c58a894d31..a2c026b39e 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -55,11 +55,9 @@ fg::Plot* setup_plot(const af_array X, const af_array Y, fg::PlotType type, fg::
 
     return plot;
 }
-#endif
 
 af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, fg::PlotType type=fg::FG_LINE, fg::MarkerType marker=fg::FG_NONE)
 {
-#if defined(WITH_GRAPHICS)
     if(wind==0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
@@ -101,18 +99,25 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    return AF_ERR_NO_GFX;
-#endif
 }
 
+#endif // WITH_GRAPHICS
+
 af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
 {
+#if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, props);
+#else
+    return AF_ERR_NO_GFX;
+#endif
 }
 
 af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type af_marker, const af_cell* const props)
 {
+#if defined(WITH_GRAPHICS)
     fg::MarkerType fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, fg::FG_SCATTER, fg_marker);
+#else
+    return AF_ERR_NO_GFX;
+#endif
 }
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 4d311058ba..d21a39905c 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -72,11 +72,9 @@ fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtyp
 
     return plot3;
 }
-#endif
 
 af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const props, const fg::PlotType type=fg::FG_LINE, const fg::MarkerType marker=fg::FG_NONE)
 {
-#if defined(WITH_GRAPHICS)
     if(wind==0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
@@ -107,18 +105,25 @@ af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    return AF_ERR_NO_GFX;
-#endif
 }
 
+#endif // WITH_GRAPHICS
+
 af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
 {
+#if defined(WITH_GRAPHICS)
     return plot3Wrapper(wind, P, props);
+#else
+    return AF_ERR_NO_GFX;
+#endif
 }
 
 af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type af_marker, const af_cell* const props)
 {
+#if defined(WITH_GRAPHICS)
     fg::MarkerType fg_marker = getFGMarker(af_marker);
     return plot3Wrapper(wind, P, props, fg::FG_SCATTER, fg_marker);
+#else
+    return AF_ERR_NO_GFX;
+#endif
 }

From d7b9f9a35d0b8375db5b29b027f65a4b365e4763 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 30 Nov 2015 15:14:40 -0500
Subject: [PATCH 0059/2677] TEST: Adding test for GFOR assign bug

---
 test/gfor.cpp | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/test/gfor.cpp b/test/gfor.cpp
index adf936b956..3aa1d14939 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -468,3 +468,37 @@ TEST(BatchFunc, 4D_2_3)
 
     gforSet(false);
 }
+
+TEST(ASSIGN, ISSUE_1127)
+{
+    using namespace af;
+    array orig = randu(512, 768, 3);
+    array vert = randu(512, 768, 3);
+    array horiz = randu(512, 768, 3);
+    array diag = randu(512, 768, 3);
+
+    array out0 = constant(0, orig.dims(0) * 2, orig.dims(1) * 2, orig.dims(2));
+    array out1 = constant(0, orig.dims(0) * 2, orig.dims(1) * 2, orig.dims(2));
+    int rows = out0.dims(0), cols = out0.dims(1);
+
+    gfor(seq chan, 3) {
+        out0(seq(0,rows-1,2), seq(0,cols-1,2), chan) = orig(span,span,chan);
+        out0(seq(1,rows-1,2), seq(0,cols-1,2), chan) = vert(span,span,chan);
+        out0(seq(0,rows-1,2), seq(1,cols-1,2), chan) = horiz(span,span,chan);
+        out0(seq(1,rows-1,2), seq(1,cols-1,2), chan) = diag(span,span,chan);
+    }
+    out1(seq(0,rows-1,2), seq(0,cols-1,2), span) = orig;
+    out1(seq(1,rows-1,2), seq(0,cols-1,2), span) = vert;
+    out1(seq(0,rows-1,2), seq(1,cols-1,2), span) = horiz;
+    out1(seq(1,rows-1,2), seq(1,cols-1,2), span) = diag;
+
+    std::vector<float> hout0(out0.elements());
+    std::vector<float> hout1(out1.elements());
+
+    out0.host(&hout0[0]);
+    out1.host(&hout1[0]);
+
+    for (int i = 0; i < out0.elements(); i++) {
+        ASSERT_EQ(hout0[i], hout1[i]);
+    }
+}

From 7fcf9bb24102d919744bd36bfae2639e57f52a4c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 30 Nov 2015 15:35:45 -0500
Subject: [PATCH 0060/2677] Add enable_testing to test/CMakeLists.txt

* When building with source, the ENABLE_TESTING is picked up from the
  arrayfire CMakeList.txt
---
 test/CMakeLists.txt | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 28f650d7af..69850ef4cd 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -20,6 +20,11 @@ ELSE()
     IF(${BUILD_NONFREE}) # Add definition. Not required when building with AF
         ADD_DEFINITIONS(-DAF_BUILD_SIFT)
     ENDIF(${BUILD_NONFREE})
+
+    # ENABLE_TESTING is required when building only tests
+    # When building from source, enable_testing is picked from from the main
+    # CMakeLists.txt
+    ENABLE_TESTING()
 ENDIF()
 
 REMOVE_DEFINITIONS(-std=c++11)

From 37b3c8c84f1da81fa7c37065d57c0152dc10948c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 30 Nov 2015 19:38:52 -0500
Subject: [PATCH 0061/2677] BUGFIX: Getting the device pointer performs memory
 copy when needed

- This includes when data is being accessed by other arrays
- Added necessary tests
---
 src/backend/cpu/Array.cpp    |  6 ++---
 src/backend/cpu/Array.hpp    | 20 +++++++++++++---
 src/backend/cuda/Array.cpp   | 10 +++++---
 src/backend/cuda/Array.hpp   | 20 +++++++++++++---
 src/backend/opencl/Array.cpp | 11 +++++++--
 src/backend/opencl/Array.hpp | 21 ++++++++++++++---
 test/array.cpp               | 45 ++++++++++++++++++++++++++++++++++++
 7 files changed, 116 insertions(+), 17 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 5321137cd5..3829a9a5d1 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -34,14 +34,14 @@ namespace cpu
     { }
 
     template<typename T>
-    Array<T>::Array(dim4 dims, const T * const in_data, bool is_device):
+    Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
         info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(is_device ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
+        data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
         node(), offset(0), ready(true), owner(true)
     {
         static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
         static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
-        if (!is_device) {
+        if (!is_device || copy_device) {
             std::copy(in_data, in_data + dims.elements(), data.get());
         }
     }
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 7f23bc8471..e9e40db54a 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -73,8 +73,9 @@ namespace cpu
     template<typename T>
     void *getDevicePtr(const Array<T>& arr)
     {
-        memPop((T *)arr.get());
-        return (void *)arr.get();
+        T *ptr = arr.device();
+        memPop(ptr);
+        return (void *)ptr;
     }
 
     // Array Array Implementation
@@ -95,7 +96,7 @@ namespace cpu
 
         Array() = default;
         Array(dim4 dims);
-        explicit Array(dim4 dims, const T * const in_data, bool is_device);
+        explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
         explicit Array(af::dim4 dims, TNJ::Node_ptr n);
 
@@ -159,6 +160,19 @@ namespace cpu
             return isOwner() ? info.dims() : data_dims;
         }
 
+        T* device()
+        {
+            if (!isOwner() || data.use_count() > 1) {
+                *this = Array(dims(), get(), true, true);
+            }
+            return this->data.get();
+        }
+
+        T* device() const
+        {
+            return const_cast<Array<T>*>(this)->device();
+        }
+
         T* get(bool withOffset = true)
         {
             return const_cast<T*>(static_cast<const Array<T>*>(this)->get(withOffset));
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index d7dbec56bc..8b05fc0e98 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -35,9 +35,9 @@ namespace cuda
     {}
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, const T * const in_data, bool is_device) :
+    Array<T>::Array(af::dim4 dims, const T * const in_data, bool is_device, bool copy_device) :
         info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data((is_device ? (T *)in_data : memAlloc<T>(dims.elements())), memFree<T>),
+        data(((is_device & !copy_device) ? (T *)in_data : memAlloc<T>(dims.elements())), memFree<T>),
         data_dims(dims),
         node(), offset(0), ready(true), owner(true)
     {
@@ -47,7 +47,11 @@ namespace cuda
 #endif
         if (!is_device) {
             CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                        cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                                       cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        } else if (copy_device) {
+            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
+                                       cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
         }
     }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 3616ebb219..3117675a80 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -88,8 +88,9 @@ namespace cuda
     template<typename T>
     void *getDevicePtr(const Array<T>& arr)
     {
-        memPop((T *)arr.get());
-        return (void *)arr.get();
+        T *ptr = arr.device();
+        memPop(ptr);
+        return (void *)ptr;
     }
 
     template<typename T>
@@ -105,7 +106,7 @@ namespace cuda
         bool owner;
 
         Array(af::dim4 dims);
-        explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false);
+        explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false, bool copy_device = false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
         Array(Param<T> &tmp);
         Array(af::dim4 dims, JIT::Node_ptr n);
@@ -168,6 +169,19 @@ namespace cuda
             return isOwner() ? dims() : data_dims;
         }
 
+        T* device()
+        {
+            if (!isOwner() || data.use_count() > 1) {
+                *this = Array(dims(), get(), true, true);
+            }
+            return this->data.get();
+        }
+
+        T* device() const
+        {
+            return const_cast<Array<T>*>(this)->device();
+        }
+
         T* get(bool withOffset = true)
         {
             if (!isReady()) eval();
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 466666fa4a..00635e136e 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -58,12 +58,19 @@ namespace opencl
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, cl_mem mem) :
+    Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy) :
         info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(new cl::Buffer(mem), bufferFree),
+        data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new cl::Buffer(mem), bufferFree),
         data_dims(dims),
         node(), offset(0), ready(true), owner(true)
     {
+        if (copy) {
+            clRetainMemObject(mem);
+            cl::Buffer src_buf = cl::Buffer((cl_mem)(mem));
+            getQueue().enqueueCopyBuffer(src_buf, *data.get(),
+                                         src_offset, 0,
+                                         sizeof(T) * info.elements());
+        }
     }
 
     template<typename T>
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index a4ecd2cacf..50da72e6bd 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -20,6 +20,7 @@
 #include <JIT/Node.hpp>
 #include <memory.hpp>
 #include <memory>
+#include <err_common.hpp>
 
 namespace opencl
 {
@@ -78,8 +79,9 @@ namespace opencl
     template<typename T>
     void *getDevicePtr(const Array<T>& arr)
     {
-        memPop((T *)arr.get());
-        return (void *)((*arr.get())());
+        cl::Buffer *buf = arr.device();
+        memPop((T *)buf);
+        return (void *)((*buf)());
     }
 
     template<typename T>
@@ -99,7 +101,7 @@ namespace opencl
         Array(Param &tmp);
         explicit Array(af::dim4 dims, JIT::Node_ptr n);
         explicit Array(af::dim4 dims, const T * const in_data);
-        explicit Array(af::dim4 dims, cl_mem mem);
+        explicit Array(af::dim4 dims, cl_mem mem, size_t offset = 0, bool copy = false);
 
     public:
 
@@ -149,6 +151,19 @@ namespace opencl
         void eval();
         void eval() const;
 
+        cl::Buffer* device()
+        {
+            if (!isOwner() || data.use_count() > 1) {
+                *this = Array(dims(), (*get())(), getOffset(), true);
+            }
+            return this->data.get();
+        }
+
+        cl::Buffer* device() const
+        {
+            return const_cast<Array<T>*>(this)->device();
+        }
+
         //FIXME: This should do a copy if it is not owner. You do not want to overwrite parents data
         cl::Buffer *get()
         {
diff --git a/test/array.cpp b/test/array.cpp
index e3cb6220cb..6c1f511410 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -409,3 +409,48 @@ TEST(Array, ISSUE_951)
     const af::array a = randu(100, 100);
     af::array b = a.cols(0, 20).rows(10, 20);
 }
+
+
+TEST(Device, simple)
+{
+    array a = randu(5,5);
+    {
+        float *ptr0 = a.device<float>();
+        float *ptr1 = a.device<float>();
+        ASSERT_EQ(ptr0, ptr1);
+    }
+
+    {
+        float *ptr0 = a.device<float>();
+        a.unlock();
+        float *ptr1 = a.device<float>();
+        ASSERT_EQ(ptr0, ptr1);
+    }
+}
+
+TEST(Device, index)
+{
+    array a = randu(5,5);
+    array b = a(span, 0);
+
+    ASSERT_NE(a.device<float>(), b.device<float>());
+}
+
+TEST(Device, unequal)
+{
+    {
+        array a = randu(5,5);
+        float *ptr = a.device<float>();
+        array b = a;
+        ASSERT_NE(ptr, b.device<float>());
+        ASSERT_EQ(ptr, a.device<float>());
+    }
+
+    {
+        array a = randu(5,5);
+        float *ptr = a.device<float>();
+        array b = a;
+        ASSERT_NE(ptr, a.device<float>());
+        ASSERT_EQ(ptr, b.device<float>());
+    }
+}

From 967545dc16cdc33bd88af6578546ee6098544487 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 30 Nov 2015 19:40:22 -0500
Subject: [PATCH 0062/2677] TEST: Adding tests to verify unnecessary copies
 aren't being done

---
 test/index.cpp | 95 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)

diff --git a/test/index.cpp b/test/index.cpp
index d6d1a64709..0bfd71a835 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1386,3 +1386,98 @@ TEST(Index, OutOfBounds)
     for(int i=0; i<7; ++i)
         ASSERT_EQ(gold[i], output[i]);
 }
+
+TEST(Index, ISSUE_1101_FULL)
+{
+    using namespace af;
+    deviceGC();
+    array a = randu(5,5);
+    std::vector<float> ha(a.elements());
+    a.host(&ha[0]);
+
+    size_t aby, abu, lby, lbu;
+    deviceMemInfo(&aby, &abu, &lby, &lbu);
+
+    array b = a(span, span);
+
+    size_t aby1, abu1, lby1, lbu1;
+    deviceMemInfo(&aby1, &abu1, &lby1, &lbu1);
+
+    ASSERT_EQ(aby, aby1);
+    ASSERT_EQ(abu, abu1);
+    ASSERT_EQ(lby, lby1);
+    ASSERT_EQ(lbu, lbu1);
+
+    std::vector<float> hb(b.elements());
+    b.host(&hb[0]);
+    for (int i = 0; i < b.elements(); i++) {
+        ASSERT_EQ(ha[i], hb[i]);
+    }
+}
+
+TEST(Index, ISSUE_1101_COL0)
+{
+    using namespace af;
+    deviceGC();
+    array a = randu(5,5);
+    std::vector<float> ha(a.elements());
+    a.host(&ha[0]);
+
+    size_t aby, abu, lby, lbu;
+    deviceMemInfo(&aby, &abu, &lby, &lbu);
+
+    array b = a(span, 0);
+
+    size_t aby1, abu1, lby1, lbu1;
+    deviceMemInfo(&aby1, &abu1, &lby1, &lbu1);
+
+    ASSERT_EQ(aby, aby1);
+    ASSERT_EQ(abu, abu1);
+    ASSERT_EQ(lby, lby1);
+    ASSERT_EQ(lbu, lbu1);
+
+    std::vector<float> hb(b.elements());
+    b.host(&hb[0]);
+    for (int i = 0; i < b.elements(); i++) {
+        ASSERT_EQ(ha[i], hb[i]);
+    }
+
+}
+
+TEST(Index, ISSUE_1101_MODDIMS)
+{
+    using namespace af;
+    deviceGC();
+    array a = randu(5,5);
+    std::vector<float> ha(a.elements());
+    a.host(&ha[0]);
+
+    size_t aby, abu, lby, lbu;
+    deviceMemInfo(&aby, &abu, &lby, &lbu);
+
+    int st = 0;
+    int en = 9;
+    int nx = 2;
+    int ny = 5;
+    array b = a(seq(st, en));
+    array c = moddims(b, nx, ny);
+    size_t aby1, abu1, lby1, lbu1;
+    deviceMemInfo(&aby1, &abu1, &lby1, &lbu1);
+
+    ASSERT_EQ(aby, aby1);
+    ASSERT_EQ(abu, abu1);
+    ASSERT_EQ(lby, lby1);
+    ASSERT_EQ(lbu, lbu1);
+
+    std::vector<float> hb(b.elements());
+    b.host(&hb[0]);
+    for (int i = 0; i < b.elements(); i++) {
+        ASSERT_EQ(ha[i + st], hb[i]);
+    }
+
+    std::vector<float> hc(c.elements());
+    c.host(&hc[0]);
+    for (int i = 0; i < c.elements(); i++) {
+        ASSERT_EQ(ha[i + st], hc[i]);
+    }
+}

From b6e75429e0fa5dc6672c89a4f7f55a59890b4cce Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 1 Dec 2015 12:30:34 -0500
Subject: [PATCH 0063/2677] initial interop tutorials

---
 docs/layout.xml              |   1 +
 docs/pages/interop_cuda.md   |  19 ++++-
 docs/pages/interop_opencl.md | 150 +++++++++++++++++++++++++++++++----
 3 files changed, 153 insertions(+), 17 deletions(-)

diff --git a/docs/layout.xml b/docs/layout.xml
index 720e9d6743..76b6bcc6e7 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -12,6 +12,7 @@
       <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Matrix Manipulation"/>
       <tab type="user" url="\ref vectorization" visible="yes" title="Vectorization"/>
       <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
+      <tab type="user" url="\ref interop_opencl" visible="yes" title="OpenCL Interoperability"/>
       <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
       <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
       <tab type="user" url="\ref configuring_environment" visible="yes" title="Configuring ArrayFire Environment"/>
diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index 822a8876a1..e20cf6682f 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -1,7 +1,7 @@
 Interoperability with CUDA {#interop_cuda}
 ========
 
-As extensive as ArrayFire is, there are a few cases where you are still working with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels. For example, you may want to integrate ArrayFire into an existing code base for productivity or you may want to keep it around the old implementation for testing purposes. In this post we are going to talk about how to integrate your custom kernels into ArrayFire in a seamless fashion.
+As extensive as ArrayFire is, there are a few cases where you are still working with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels. For example, you may want to integrate ArrayFire into an existing code base for productivity or you may want to keep it around the old implementation for testing purposes. Arrayfire provides a number of functions that allow it to work alongside native CUDA commands. In this tutorial we are going to talk about how to use native CUDA memory operations and integrate custom CUDA kernels into ArrayFire in a seamless fashion.
 
 # In and Out of Arrayfire
 
@@ -45,6 +45,7 @@ In this example, the output is the same size as in the input. Note that the actu
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Accesing the device pointer in this manner internally sets a flag prohibiting the arrayfire object from further managing the memory. Ownership will need to be returned to the af::array object once we are finished using it.
 
 Before  launching your custom kernel, it is best to make sure that all ArrayFire computations have finished. This can be called by using af::sync(). The function ensures you are not unintentionally doing out of order executions.
 af::sync() is not strictly required if you are not using streams in CUDA.
@@ -55,7 +56,9 @@ af::sync() is not strictly required if you are not using streams in CUDA.
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The function **launch_simple_kernel** handles the launching of your custom kernel. We will have a look at how to do this in CUDA and OpenCL later in the post. Once you have finished your computations, you have to tell ArrayFire to take control of the memory objects.
+The function **launch_simple_kernel** handles the launching of your custom kernel. We will have a look at how to do this in CUDA and OpenCL later in the post.
+
+Once you have finished your computations, you have to tell ArrayFire to take control of the memory objects.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     x.unlock();
     y.unlock();
@@ -101,3 +104,15 @@ void inline launch_simple_kernel(float *d_y,
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+# Additional interop functions and CUDA Streams
+
+Arrayfire provides a collection of CUDA interoperability functions for additional capabilities when working with custom CUDA code. To use them, we need to include the appropriate header.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+#include <af/cuda.h>
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The first thing these headers allow us to do are to get and set the active device using native CUDA device ids. This is achieved through the following functions:
+    **static int getNativeId (int id)** -- Get the native device id of the CUDA device with id in the ArrayFire context.
+    **static void setNativeId (int nativeId)**  -- Set the CUDA device with given native id as the active device for ArrayFire. 
+
+These functions are available within the afcu:: namespace and equal C variants can be fund in the full [cuda interop documentation.](group__cuda__mat.htm)
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 8a8acefb85..2b81972113 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -1,11 +1,18 @@
-Interoperability with OpenCL {#interop_cuda}
+Interoperability with OpenCL {#interop_opencl}
 ========
 
-As extensive as ArrayFire is, there are a few cases where you are still working with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels. For example, you may want to integrate ArrayFire into an existing code base for productivity or you may want to keep it around the old implementation for testing purposes. In this post we are going to talk about how to integrate your custom kernels into ArrayFire in a seamless fashion.
+As extensive as ArrayFire is, there are a few cases where you are still working
+with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels.
+For example, you may want to integrate ArrayFire into an existing code base for 
+productivity or you may want to keep it around the old implementation for testing
+purposes. Arrayfire provides a number of functions that allow it to work alongside 
+native OpenCL commands. In this tutorial we are going to talk about how to use 
+native OpenCL memory operations and custom OpenCL kernels alongside ArrayFire
+in a seamless fashion.
 
-# In and Out of Arrayfire
-
-First, let's consider the following code and then break it down bit by bit.
+# OpenCL Kernels with Arrayfire arrays
+First, we will see how custom OpenCL kernels can be integrated into Arrayfire code.
+Let's consider the following code and then break it down bit by bit.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 int main() {
@@ -26,7 +33,7 @@ int main() {
 
     // check for errors, should be 0,
     // since sin(x)^2 + cos(x)^2 == 1
-    float err = af::sum(af::abs(y-1));
+    float err = af::sum<float>(af::abs(y-1));
     printf("Error: %f\n", err);
     return 0;
 }
@@ -34,21 +41,30 @@ int main() {
 
 ## Breakdown
 Most kernels require an input. In this case, we created a random uniform array **x**.
-We also go ahead and prepare the output array. The necessary memory required is allocated in array **y** before the kernel launch.
+We also go ahead and prepare the output array. The necessary memory required is
+allocated in array **y** before the kernel launch.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     af::array x = randu(num);
     af::array y = randu(num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-In this example, the output is the same size as in the input. Note that the actual output data type is not specified. For such cases, ArrayFire assumes the data type is single precision floating point ( af::f32 ). If necessary, the data type can be specified at the end of the array(..) constructor. Once you have the input and output arrays, you will need to extract the device pointers / objects using array::device() method in the following manner.
+In this example, the output is the same size as in the input. Note that the actual
+output data type is not specified. For such cases, ArrayFire assumes the data type
+is single precision floating point ( af::f32 ). If necessary, the data type can
+be specified at the end of the array(..) constructor. Once you have the input and
+output arrays, you will need to extract the device pointers / objects using 
+array::device() method in the following manner.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Accesing the device pointer in this manner internally sets a flag prohibiting the arrayfire object from further managing the memory. Ownership will need to be returned to the af::array object once we are finished using it.
+Accesing the device pointer in this manner internally sets a flag prohibiting 
+the arrayfire object from further managing the memory. Ownership will need to be
+returned to the af::array object once we are finished using it.
 
-Before  launching your custom kernel, it is best to make sure that all ArrayFire computations have finished. This can be called by using af::sync(). The function ensures you are not unintentionally doing out of order executions.
-af::sync() is not strictly required if you are not using streams in CUDA.
+Before  launching your custom kernel, it is best to make sure that all ArrayFire
+computations have finished. This can be called by using af::sync(). The function
+ensures you are not unintentionally doing out of order executions.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     af::sync();
 
@@ -56,12 +72,20 @@ af::sync() is not strictly required if you are not using streams in CUDA.
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The function **launch_simple_kernel** handles the launching of your custom kernel. We will have a look at how to do this in CUDA and OpenCL later in the post. Once you have finished your computations, you have to tell ArrayFire to take control of the memory objects.
+The function **launch_simple_kernel** handles the launching of your custom kernel.
+We will have a look at the specific functions Arrayfire provides to interface with
+OpenCL later in the post. 
+
+Once you have finished your computations, you have to tell ArrayFire to take control
+of the memory objects.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     x.unlock();
     y.unlock();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-This is a very crucial step as ArrayFire believes the user is still in control of the pointer. This means that ArrayFire will not perform garbage collection on these objects resulting in memory leaks. You can now proceed with the rest of the program. In our particular example, we are just performing an error check and exiting.
+This is a very crucial step as ArrayFire believes the user is still in control
+of the pointer. This means that ArrayFire will not perform garbage collection 
+on these objects resulting in memory leaks. You can now proceed with the rest of
+the program. In our particular example, we are just performing an error check and exiting.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     // check for errors, should be 0,
@@ -70,9 +94,105 @@ This is a very crucial step as ArrayFire believes the user is still in control o
     printf("Error: %f\n", err);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-# Launching an OpenCL kernel
-If you are integrating an OpenCL kernel into your ArrayFire code base, launching a kernel is slightly complicated. Since ArrayFire uses its own context internally, you need to get the context from a memory object. Once you have access to the same context ArrayFire is using, the rest of the process is exactly the same as launching a stand alone OpenCL context.
+## Launching an OpenCL kernel
+If you are integrating an OpenCL kernel into your ArrayFire code base you will
+need several additional steps to access Arrayfire's internal OpenCL context. 
+Once you have access to the same context ArrayFire is using, the rest of the 
+process is exactly the same as launching a stand alone OpenCL context.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+void inline launch_simple_kernel(float *d_y,
+                                 const float *d_x,
+                                 const int num)
+{
+    std::string simple_kernel_str = CONST_KERNEL_STRING;
+
+    // Get OpenCL context from memory buffer and create a Queue
+    cl::Context context(afcl::getContext(true));
+    cl::CommandQueue queue(afcl::getQueue(true));
+
+    //Build program and get the required kernel
+    cl::Program prog = cl::Program(context, simple_kernel_str, true);
+    cl::Kernel  kern = cl::Kernel(prog, "simple_kernel");
+
+    //set global work dimensions
+    static const cl::NDRange global(num);
+
+    //prepare argumenst
+    kern.setArg(0, d_y);
+    kern.setArg(1, d_x);
+    kern.setArg(2, num);
+
+    //run kernel
+    queue.enqueueNDRangeKernel(kern, cl::NullRange, global);
+    queue.finish();
+
+    return;
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+First of all, to access to OpenCL and the interoperability functions we need to
+include the appropriate headers.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+#include <af/opencl.h>
+#include <CL/cl.hpp>
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The **opencl.h** header includes a number of functions for getting and setting
+the context, queue, and device ids used internally in Arrayfire. There are also
+a number of methods to construct an af::array from an OpenCL cl_mem buffer object.
+There are both C and C++ versions of these functions, and the C++ versions are
+wrapped inside the afcl:: namespace. See full datails of these functions in the
+[opencl interop documentation.] (\ref opencl_mat)
+
 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+cl::Context context(afcl::getContext(true));
+cl::CommandQueue queue(afcl::getQueue(true));
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+We start to use these functions by getting Arrayfire's context and queue. For the
+C++ api, a **true** flag must be passed for the retain parameter which calls the
+clRetainQueue() and clRetainContext() functions before returning. This allows us
+to use Arrayfire's internal OpenCL structures inside of the cl::Context and
+cl::CommandQueue objects from the C++ api.
+Once we have them, we can proceed to set up and enqueue the kernel like we would
+in any other OpenCL program. The kernel we are using is actually simple and can
+be seen below.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+std::string CONST_KERNEL_STRING = R"(
+__kernel
+void simple_kernel(__global float *d_y,
+                   __global const float *d_x,
+                   const int num)
+{
+    const int id = get_global_id(0);
+
+    if (id < num) {
+        float x = d_x[id];
+        float sin_x = sin(x);
+        float cos_x = cos(x);
+        d_y[id] = (sin_x * sin_x) + (cos_x * cos_x);
+    }
+}
+)";
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+# Reversing the workflow: Arrayfire arrays from OpenCL Memory
+
+Arrayfire's interoperability functions don't limit us to working with memory
+managed by Arrayfire. We could take the reverse route and start with completely
+custom OpenCL code, then transfer our results into an af::array object. This is
+done rather simply with a special set of construction functions.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+cl::Buffer my_cl_buffer(context, CL_MEM_READ_WRITE, sizeof(float) * SIZE);
+//work and computations with OpenCL buffer
+
+//kernel(my_cl_buffer, queue);
+
+//construct af::array from OpenCL buffer
+af::array my_array = afcl::array(SIZE, my_cl_buffer(), f32);
+af_print(my_array);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Please note: the \ref af::array constructors are not thread safe. 
+You may create and upload data to `cl_mem` objects from separate threads, 
+but the thread which instantiated ArrayFire must do the `cl_mem` to \ref af::array conversion.

From 1ada68e07dc1808fb91c45baf7d40187aeed380a Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 1 Dec 2015 14:01:19 -0500
Subject: [PATCH 0064/2677] doxygen formatting and reference fixes

---
 docs/pages/forge_visualization.md | 122 +++++++++++++++++++++---------
 docs/pages/matrix_manipulation.md | 112 ++++++++++++++++++---------
 docs/pages/vectorization.md       |  90 ++++++++++++++--------
 3 files changed, 221 insertions(+), 103 deletions(-)

diff --git a/docs/pages/forge_visualization.md b/docs/pages/forge_visualization.md
index 6107fb358d..72901dc681 100644
--- a/docs/pages/forge_visualization.md
+++ b/docs/pages/forge_visualization.md
@@ -1,12 +1,30 @@
-Visualizing af::arrays with Forge {#forge_visualization}
+Visualizing af::array with Forge {#forge_visualization}
 ===================
-Arrayfire as a library aims to provide a robust and easy to use platform for high-performance, parallel and GPU computing. The goal of Forge, an OpenGL visualization library, is to provide equally robust visualizations that are interoperable between Arrayfire data-structures and an OpenGL context. Instead of wasting time copying and reformatting data from the GPU to the host and back to the GPU, we can draw directly from GPU-data to GPU-framebuffers! Furthermore, Arrayfire provides wrapper functions that handle all of the interoperability for us and leave us with a simple interface to visualize af::arrays. Let's see exactly what visuals we can illuminate with forge and how Arrayfire anneals the data between the two libraries.
 
-# Setup
-Before we can call Forge functions, we need to set up the related "canvas" classes. Forge functions are tied to the af::Window class. First let's create a window:
+Arrayfire as a library aims to provide a robust and easy to use platform for
+high-performance, parallel and GPU computing.
+
+[TOC]
+
+The goal of [Forge](https://github.com/arrayfire/forge), an OpenGL visualization
+library, is to provide equally robust visualizations that are interoperable
+between Arrayfire data-structures and an OpenGL context.
+
+Arrayfire provides wrapper functions that are designed to be a simple interface
+to visualize af::arrays. These functions perform various interop tasks. One in
+particular is that instead of wasting time copying and reformatting data from
+the GPU to the host and back to the GPU, we can draw directly from GPU-data to
+GPU-framebuffers! This saves 2 memory copies.
+
+Let's see exactly what visuals we can illuminate with forge and how Arrayfire
+anneals the data between the two libraries.
+
+# Setup {#setup}
+Before we can call Forge functions, we need to set up the related "canvas" classes.
+Forge functions are tied to the af::Window class. First let's create a window:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-const static int WIDTH = 512, HEIGHT = 512;
-af::Window window(WIDTH, HEIGHT, "2D plot example title");
+const static int width = 512, height = 512;
+af::Window window(width, height, "2D plot example title");
 
 do{
 
@@ -15,29 +33,41 @@ do{
 } while( !window.close() );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-We also added a drawing loop, so now we can use Forge's drawing functions to draw to the window.
+We also added a drawing loop, so now we can use Forge's drawing functions to 
+draw to the window.
 The drawing functions present in Forge are listed below.
 
-# af::Window::image
-The af::Window::image() function can be used to plot grayscale or color images. To plot a grayscale image a 2d array should be passed into the function. Let's see this on a static noise example:
+# Rendering Functions {#render_func}
+
+Documentation for rendering functions can be found [here](\ref gfx_func_draw).
+
+## Image {#image}
+The af::Window::image() function can be used to plot grayscale or color images.
+To plot a grayscale image a 2d array should be passed into the function.
+Let's see this on a static noise example:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array img = constant(0, WIDTH, HEIGHT); //make a black image
-array random = randu(WIDTH, HEIGHT);      //make random [0,1] distribution
+array img = constant(0, width, height); //make a black image
+array random = randu(width, height);      //make random [0,1] distribution
 img(random > 0.5) = 1; //set all pixels where distribution > 0.5 to white
 
 window.image(img);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 <img src="gfx_docs_images/noise.png" alt="Forge image plot of noise" width="20%" />
-Tweaking the previous example by giving our image a depth of 3 for the RGB values allows us to generate colorful noise:
+Tweaking the previous example by giving our image a depth of 3 for the RGB values
+allows us to generate colorful noise:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array img = 255 * randu(WIDTH, HEIGHT, 3);      //make random [0, 255] distribution
+array img = 255 * randu(width, height, 3);      //make random [0, 255] distribution
 window.image( img.as(u8) );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 <img src="gfx_docs_images/color_noise.png" alt="Forge image plot of color noise" width="20%" />
-Notice Forge automatically handles any af::array type passed from Arrayfire. In the first example we passed in an image of floats in the range [0, 1]. In the last example we cast our array to an unsigned byte array with the range [0, 255]. The type-handling properties are consistent for all Forge drawing functions.
-
-# af::Window::plot
-The af::Window::plot() function visualizes an array as a 2d-line plot. Let's see a simple example:
+Note that Forge automatically handles any af::array type passed from Arrayfire.
+In the first example we passed in an image of floats in the range [0, 1].
+In the last example we cast our array to an unsigned byte array with the range
+[0, 255]. The type-handling properties are consistent for all Forge drawing functions.
+
+## Plot {#plot}
+The af::Window::plot() function visualizes an array as a 2d-line plot. Let's see
+a simple example:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 array X = seq(-af::Pi, af::Pi, 0.01);
 array Y = sin(X);
@@ -46,25 +76,30 @@ window.plot(X, Y);
 
 <img src="gfx_docs_images/sin_plot.png" alt="Forge 2d line plot of sin() function" width="30%" />
 The plot function has the signature:
-<br> **void plot( const array &X, const array &Y, const char * const title = NULL );**
-<br> Both the x and y coordinates of the points are required to plot. This allows for non-uniform, or parametric plots:
+
+> **void plot( const array &X, const array &Y, const char * const title = NULL );**
+
+Both the x and y coordinates of the points are required to plot. This allows for
+non-uniform, or parametric plots:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 array t = seq(0, 100, 0.01);
-array X = sin(t) * (exp(cos(t)) - 2*cos(4*t) - pow(sin(t/12), 5)); array Y = cos(t) * (exp(cos(t)) - 2*cos(4*t) - pow(sin(t/12), 5));
+array X = sin(t) * (exp(cos(t)) - 2 * cos(4 * t) - pow(sin(t / 12), 5));
+array Y = cos(t) * (exp(cos(t)) - 2 * cos(4 * t) - pow(sin(t / 12), 5));
 window.plot(X, Y);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 <img src="gfx_docs_images/butterfly_plot.png" alt="Forge 2d line plot of butterfly function" width="30%" />
 
-# af::Window::plot3
+## Plot3 {#plot3}
 The af::Window::plot3() function will plot a curve in 3d-space.
 Its signature is:
-<br> **void plot3 (const array &in, const char * title = NULL);**
-<br> The input array expects xyz-triplets in sequential order. The points can be in a flattened one dimensional <i>(3n x 1)</i> array, or in one of the <i>(3 x n),</i> <i>(n x 3)</i> matrix forms.
+> **void plot3 (const array &in, const char * title = NULL);**
+The input array expects xyz-triplets in sequential order. The points can be in a
+flattened one dimensional (*3n x 1*) array, or in one of the (*3 x n*), (*n x 3*) matrix forms.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 array Z = seq(0.1f, 10.f, 0.01);
-array Y = sin(10*Z) / Z;
-array X = cos(10*Z) / Z;
+array Y = sin(10 * Z) / Z;
+array X = cos(10 * Z) / Z;
 
 array Pts = join(1, X, Y, Z);
 //Pts can be passed in as a matrix in the from n x 3, 3 x n
@@ -76,8 +111,11 @@ window.plot3(Pts);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 <img src="gfx_docs_images/spiral_plot3.png" alt="Forge 3d line plot" width="40%" />
 
-# af::Window::hist
-The af::Window::hist() function renders an input array as a histogram. In our example, the input array will be created with Arrayfire's histogram() function, which actually counts and bins each sample. The output from histogram() can directly be fed into the af::Window::hist() rendering function.
+## Histogram {#histogram}
+The af::Window::hist() function renders an input array as a histogram.
+In our example, the input array will be created with Arrayfire's histogram()
+function, which actually counts and bins each sample. The output from histogram()
+can directly be fed into the af::Window::hist() rendering function.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 const int BINS = 128; SAMPLES = 9162;
@@ -86,11 +124,15 @@ array hist_arr = histogram(norm, BINS);
 
 win.hist(hist_arr, 0, BINS);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-In addition to the histogram array with the number of samples in each bin, the af::Window::hist() function takes two additional parameters -- the minimum and maximum values of all datapoints in the histogram array. This effectively sets the range of the binned data. The full signature of af::Window::hist() is: **void hist(const array & X, const double minval, const double maxval, const char * const title = NULL);**
+In addition to the histogram array with the number of samples in each bin, the
+af::Window::hist() function takes two additional parameters -- the minimum and
+maximum values of all datapoints in the histogram array. This effectively sets
+the range of the binned data. The full signature of af::Window::hist() is:
+> **void hist(const array & X, const double minval, const double maxval, const char * const title = NULL);**
 <img src="gfx_docs_images/norm_histogram.png" alt="Forge 3d scatter plot" width="40%" />
 
 
-# af::Window::surface
+## Surface {#surface}
 The af::Window::surface() function will plot af::arrays as a 3d surface.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 array Z = randu(21, 21);
@@ -98,13 +140,21 @@ window.surface(Z, "Random Surface");    //equal to next function call
 //window.surface( seq(-1, 1, 0.1), seq(-1, 1, 0.1), Z, "Random Surface");
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 <img src="gfx_docs_images/rand_surface.png" alt="Forge random surface plot" width="30%" />
-There are two overloads for the **af::Window::surface()** function:
-* **void surface (const array & S, const char *const title )** -- accepts a 2d matrix with the z values of the surface
-* **void surface (const array &xVals, const array &yVals, const array &S, const char * const title)** -- accepts additional vectors that define the x,y coordinates for the surface points.
+There are two overloads for the af::Window::surface() function:
+> **void surface (const array & S, const char * const title )**
+> // Accepts a 2d matrix with the z values of the surface
+
+> **void surface (const array &xVals, const array &yVals, const array &S, const char * const title)**
+> // accepts additional vectors that define the x,y coordinates for the surface points.
 
 The second overload has two options for the x, y coordinate vectors. Assuming a surface grid of size **m x n**:
  1. Short vectors defining the spacing along each axis. Vectors will have sizes **m x 1** and **n x 1**.
- 2. Vectors containing the coordinates of each and every point. Each of the vectors will have length **mn x 1**. This can be used for completely non-uniform or parametric surfaces.
-
-# Conclusion
-There is a fairly comprehensive collection of methods to visualize data in Arrayfire. Thanks to the high-performance gpu plotting library Forge, the provided Arrayfire functions not only make visualizations as simple as possible, but keep them as robust as the rest of the Arrayfire library.
+ 2. Vectors containing the coordinates of each and every point.
+ Each of the vectors will have length **mn x 1**.
+ This can be used for completely non-uniform or parametric surfaces.
+
+# Conclusion {#conclusion}
+There is a fairly comprehensive collection of methods to visualize data in Arrayfire.
+Thanks to the high-performance gpu plotting library Forge, the provided Arrayfire
+functions not only make visualizations as simple as possible, but keep them as 
+robust as the rest of the Arrayfire library.
diff --git a/docs/pages/matrix_manipulation.md b/docs/pages/matrix_manipulation.md
index f0af0a77e9..8fde4882d9 100644
--- a/docs/pages/matrix_manipulation.md
+++ b/docs/pages/matrix_manipulation.md
@@ -2,6 +2,7 @@ Matrix Manipulation {#matrixmanipulation}
 ===================
 
 Many different kinds of [matrix manipulation routines](\ref manip_mat) are available:
+
 * flat() - flatten an array to one dimension
 * flip() - flip an array along a dimension
 * join() - join up to 4 arrays
@@ -13,7 +14,7 @@ Many different kinds of [matrix manipulation routines](\ref manip_mat) are avail
 * [array()](\ref af::array) to adjust the dimensions of an array
 * [transpose](\ref af::array::T) a matrix or vector with shorthand notation
 
-### flat()
+## flat()
 The __flat()__ function flattens an array to one dimension.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 3 1 1]
@@ -34,11 +35,14 @@ flat(a) [9 1 1 1]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The flat function has the following overloads:
-* __array af::flat(const array& in)__ -- flatten an array
-* __af_err af_flat(af_array* out, const af_array in)__ -- C interface for flat() function
+> __array af::flat(const array& in)__
+> --  flatten an array
+
+> __af_err af_flat(af_array* out, const af_array in)__
+> --  C interface for flat() function
 
 
-### flip()
+## flip()
 The __flip()__ function flips the contents of an array along a chosen dimension.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [5 2 1 1]
@@ -63,10 +67,12 @@ flip(a, 1) [5 2 1 1]
    10.0000     5.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The flip function has the following overloads:
-* __array af::flip(const array &in, const unsigned dim)__ -- flips an array along a dimension 
-* __af_err af_flip(af_array *out, const af_array in, const unsigned dim)__ -- C interface for flip()
+> __array af::flip(const array &in, const unsigned dim)__
+> --  flips an array along a dimension 
+> __af_err af_flip(af_array *out, const af_array in, const unsigned dim)__
+> --  C interface for flip()
 
-### join()
+## join()
 The __join()__ function can join up to 4 arrays together.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [5 1 1 1]
@@ -96,17 +102,22 @@ join(1, a, a) [5 2 1 1]
     5.0000     5.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The join function has several overloads:
-* __array af::join(const int dim, const array &first, const array &second)__ -- Joins 2 arrays along a dimension
+> __array af::join(const int dim, const array &first, const array &second)__
+> --  Joins 2 arrays along a dimension
 
-* __array af::join(const int dim, const array &first, const array &second, const array &third)__ -- Joins 3 arrays along a dimension.
+> __array af::join(const int dim, const array &first, const array &second, const array &third)__
+> --  Joins 3 arrays along a dimension.
 
-* __array af::join(const int dim, const array &first, const array &second, const array &third, const array &fourth)__ -- Joins 4 arrays along a dimension
+> __array af::join(const int dim, const array &first, const array &second, const array &third, const array &fourth)__
+> --  Joins 4 arrays along a dimension
 
-* __af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)__ -- C interface function to join 2 arrays along a dimension
+> __af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)__
+> --  C interface function to join 2 arrays along a dimension
 
-* __af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs)__ -- C interface function to join up to 10 arrays along a dimension
+> __af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs)__
+> --  C interface function to join up to 10 arrays along a dimension
 
-### moddims()
+## moddims()
 The __moddims()__ function changes the dimensions of an array without changing its data or order. It is important to remember that the function only modifies the _metadata_ associated with the array and does not actually modify the content of the array.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [8 1 1 1]
@@ -135,13 +146,21 @@ moddims(a, a.elements(), 1, 1, 1) [8 1 1 1]
     2.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The moddims function has several overloads:
-* __array af::moddims(const array &in, const unsigned ndims, const dim_t *const dims)__ -- mods number of dimensions to match _ndims_ as specidied in the array _dims_
-* __array af::moddims(const array &in, const dim4 &dims)__ -- mods dimensions as specified by _dims_
-* __array af::moddims(const array &in, const dim_t d0, const dim_t d1=1, const dim_t d2=1, const dim_t d3=1)__ -- mods dimensions of an array
-* __af_err af_moddims(af_array *out, const af_array in, const unsigned ndims, const dim_t *const dims)__ -- C interface to mod dimensions of an array
+> __array af::moddims(const array &in, const unsigned ndims, const dim_t *const dims)__
+> --  mods number of dimensions to match _ndims_ as specidied in the array _dims_
+
+> __array af::moddims(const array &in, const dim4 &dims)__
+> --  mods dimensions as specified by _dims_
+
+> __array af::moddims(const array &in, const dim_t d0, const dim_t d1=1, const dim_t d2=1, const dim_t d3=1)__
+> --  mods dimensions of an array
 
-### reorder()
-The __reorder()__ function changes the order of the dimensions within the array. This actually alters the underlying data of the array.
+> __af_err af_moddims(af_array *out, const af_array in, const unsigned ndims, const dim_t *const dims)__
+> --  C interface to mod dimensions of an array
+
+## reorder()
+The __reorder()__ function changes the order of the dimensions within the array.
+This actually alters the underlying data of the array.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [2 2 3 1]
     1.0000     3.0000
@@ -175,11 +194,13 @@ reorder(a, 2, 0, 1) [3 2 2 1]
     3.0000     4.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The reorder function the following several overloads:
-* __array af::reorder(const array &in, const unsigned x, const unsigned y=1, const unsigned z=2, const unsigned w=3)__ -- Reorders dimensions of an array
+> __array af::reorder(const array &in, const unsigned x, const unsigned y=1, const unsigned z=2, const unsigned w=3)__
+> --  Reorders dimensions of an array
 
-* __af_err af_reorder(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__ -- C interface for reordering function
+> __af_err af_reorder(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__
+> --  C interface for reordering function
 
-### shift()
+## shift()
 The __shift()__ function shifts data in a circular buffer fashion along a chosen dimension.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 5 1 1]
@@ -198,11 +219,13 @@ shift(a, -1, 2 ) [3 5 1 1]
     0.0000     0.0000     0.0000     0.0000     0.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The shift function has the following overloads:
-* __array af::shift(const array &in, const int x, const int y=0, const int z=0, const int w=0)__ -- Shifts array along specified dimensions
+> __array af::shift(const array &in, const int x, const int y=0, const int z=0, const int w=0)__
+> --  Shifts array along specified dimensions
 
-* __af_err af_shift(af_array *out, const af_array in, const int x, const int y, const int z, const int w)__ -- C interface for shifting an array
+> __af_err af_shift(af_array *out, const af_array in, const int x, const int y, const int z, const int w)__
+> --  C interface for shifting an array
 
-### tile()
+## tile()
 The __tile()__ function repeats an array along a dimension
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 1 1 1]
@@ -242,11 +265,16 @@ tile(a, tile_dims) [3 2 3 1]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The tile function has several overloads:
-* __array af::tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)__  --  Tiles array along specified dimensions
-* __array af::tile(const array &in, const dim4 &dims)__  --  Tile an array according to a dim4 object
-* __af_err af_tile(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__  --  C interface for tiling an array
+> __array af::tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)__
+> --  Tiles array along specified dimensions
+
+> __array af::tile(const array &in, const dim4 &dims)__  
+> --  Tile an array according to a dim4 object
+
+> __af_err af_tile(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__
+> --  C interface for tiling an array
 
-### transpose()
+## transpose()
 The __transpose()__ function performs a standard matrix transpose. The input array must have the dimensions of a 2D-matrix.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 3 1 1]
@@ -261,13 +289,17 @@ transpose(a) [3 3 1 1]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The transpose function has several overloads:
-* __array af::transpose(const array &in, const bool conjugate=false)__ -- Transposes a matrix.
+> __array af::transpose(const array &in, const bool conjugate=false)__
+> --   Transposes a matrix.
 
-* __void af::transposeInPlace(array &in, const bool conjugate=false)__ -- Transposes a matrix in-place.
+> __void af::transposeInPlace(array &in, const bool conjugate=false)__
+> --   Transposes a matrix in-place.
 
-* __af_err af_transpose(af_array *out, af_array in, const bool conjugate)__ -- C interface to transpose a matrix.
+> __af_err af_transpose(af_array *out, af_array in, const bool conjugate)__
+> --   C interface to transpose a matrix.
 
-* __af_err af_transpose_inplace(af_array in, const bool conjugate)__ -- C interface to transpose a matrix in-place.
+> __af_err af_transpose_inplace(af_array in, const bool conjugate)__
+> --   C interface to transpose a matrix in-place.
 
 [array()](\ref af::array) can be used to create a (shallow) copy of a matrix
 with different dimensions.  The number of elements must remain the same as
@@ -280,8 +312,12 @@ used to form the [matrix or vector transpose](\ref af::array::T) .
 
 \snippet test/matrix_manipulation.cpp ex_matrix_manipulation_transpose
 
-### Combining re-ordering functions to enumerate grid coordinates
-By using a combination of the array restructuring functions, we can quickly code complex manipulation patterns with a few lines of code. For example, consider generating _(x,y)_ coordinates for a grid where each axis goes from *1 to n*. Instead of using several loops to populate our arrays we can just use a small combination of the above functions.
+# Combining re-ordering functions to enumerate grid coordinates
+By using a combination of the array restructuring functions, we can quickly code
+complex manipulation patterns with a few lines of code. For example, consider
+generating (*x,y*) coordinates for a grid where each axis goes from *1 to n*.
+Instead of using several loops to populate our arrays we can just use a small
+combination of the above functions.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 unsigned n=3;
 af::array xy = join(1
@@ -299,5 +335,7 @@ xy [9 2 1 1]
     2.0000     3.0000
     3.0000     3.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-### Conclusion
-Functions provided by arrayfire offer ease and flexibility for efficiently manipulating the structure of arrays. The provided functions can be used as building blocks to generate, shift, or prepare data to any form imaginable!
+# Conclusion
+Functions provided by arrayfire offer ease and flexibility for efficiently
+manipulating the structure of arrays. The provided functions can be used as 
+building blocks to generate, shift, or prepare data to any form imaginable!
diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index 2061172d5f..ad51e64bbe 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -1,50 +1,68 @@
 Vectorization {#vectorization}
 ===================
 
-Programmers and Data Scientists want to take advantage of fast and parallel computational devices. Writing vectorized code is becoming a necessity to get the best performance out of the current generation parallel hardware and scientific computing software. However, writing vectorized code may not be intuitive immediately. Arrayfire provides many ways to vectorize a given code segment. In this tutorial, we will be presenting various ways to vectorize code using ArrayFire and the benefits and drawbacks associated with each method.
+Programmers and Data Scientists want to take advantage of fast and parallel
+computational devices. Writing vectorized code is becoming a necessity to get
+the best performance out of the current generation parallel hardware and
+scientific computing software. However, writing vectorized code may not be
+intuitive immediately. Arrayfire provides many ways to vectorize a given code
+segment. In this tutorial, we will be presenting various ways to vectorize code
+using ArrayFire and the benefits and drawbacks associated with each method.
 
 # Generic/Default vectorization
-By its very nature, Arrayfire is a vectorized library. Most functions operate on arrays as a whole -- on all elements in parallel. Wherever possible, existing vectorized functions should be used opposed to manually indexing into arrays. For example, consider this valid, yet mislead code that attempts to increment each element of an array:
+By its very nature, Arrayfire is a vectorized library. Most functions operate on
+arrays as a whole -- on all elements in parallel. Wherever possible, existing
+vectorized functions should be used opposed to manually indexing into arrays.
+For example, consider this valid, yet mislead code that attempts to increment
+each element of an array:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array a = af::seq(10); // [0,  9]
-for(int i=0; i<a.dims(0); ++i)
+af::array a = af::range(10); // [0,  9]
+for(int i = 0; i < a.dims(0); ++i)
 {
-    a(i) = a(i) + 1;       // [1, 10]
+    a(i) = a(i) + 1;         // [1, 10]
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Instead, the existing vectorized Arrayfire overload of the + operator should have been used:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array a = af::seq(10);  // [0,  9]
-a = a + 1;                  // [1, 10]
+af::array a = af::range(10);  // [0,  9]
+a = a + 1;                    // [1, 10]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Some of the vectorized mathematical functions of Arrayfire include:
 
-Operator Category                     | Functions
---------------------------------------|--------------------------
-Arithmetic operations                 | operator+(), operator-(), operator*(), operator/(), operator>>(), operator<<()
-Complex operations                    | real(), imag(), conjugate(), etc.
-Exponential and logarithmic functions | exp(), log(), expm1(), log1p(), etc.
-Hyperbolic functions                  | sinh(), cosh(), tanh(), etc.
-Numeric functions                     | floor(), round(), min(), max(), etc.
-Trigonometric functions               | sin(), cos(), tan(), etc.
-Logical operations                    | &&, \|\|, \|, &, <, >, <=, >=, ==, !
+Operator Category                                           | Functions
+------------------------------------------------------------|--------------------------
+[Arithmetic operations](\ref arith_mat)                     | [+](\ref arith_func_add), [-](\ref arith_func_sub), [*](\ref arith_func_mul), [/](\ref arith_func_div), [%](\ref arith_func_mod), [>>](\ref arith_func_shiftr), [<<](\ref arith_func_shiftl)
+[Complex operations](\ref complex_mat)                      | real(), imag(), conj(), etc.
+[Exponential and logarithmic functions](\ref explog_mat)    | exp(), log(), expm1(), log1p(), etc.
+[Hyperbolic functions](\ref hyper_mat)                      | sinh(), cosh(), tanh(), etc.
+[Logical operations](\ref logic_mat)                        | [&&](\ref arith_func_and), [\|\|](\ref arith_func_or), [<](\ref arith_func_lt), [>](\ref arith_func_gt), [==](\ref arith_func_eq), [!=](\ref arith_func_neq) etc.
+[Numeric functions](\ref numeric_mat)                       | abs(), floor(), round(), min(), max(), etc.
+[Trigonometric functions](\ref trig_mat)                    | sin(), cos(), tan(), etc.
 
 
 # GFOR: Parallel for-loops
 Another novel method of vectorization present in Arrayfire is the GFOR loop replacement construct.
-GFOR allows launching all iterations of a loop in parallel on the GPU or device, as long as the iterations are independent. While the standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop performs each iteration at the same time (in parallel). ArrayFire does this by tiling out the values of all loop iterations and then performing computation on those tiles in one pass.
-You can think of gfor as performing auto-vectorization of your code, e.g. you write a gfor-loop that increments every element of a vector but behind the scenes ArrayFire rewrites it to operate on the entire vector in parallel.
+GFOR allows launching all iterations of a loop in parallel on the GPU or device,
+as long as the iterations are independent. While the standard for-loop performs
+each iteration sequentially, ArrayFire's gfor-loop performs each iteration at
+the same time (in parallel). ArrayFire does this by tiling out the values of all
+loop iterations and then performing computation on those tiles in one pass.
+You can think of gfor as performing auto-vectorization of your code, e.g. you
+write a gfor-loop that increments every element of a vector but behind the scenes
+ArrayFire rewrites it to operate on the entire vector in parallel.
 We can remedy our first example with GFOR:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array a = af::seq(10);
+af::array a = af::range(10);
 gfor(seq i, n)
     a(i) = a(i) + 1;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-It is best to vectorize computation as much as possible to avoid the overhead in both for-loops and gfor-loops.
+It is best to vectorize computation as much as possible to avoid the overhead in
+both for-loops and gfor-loops.
 
-To see another example, you could run an FFT on every 2D slice of a volume in a for-loop, or you could "vectorize" and simply do it all in one gfor-loop operation:
+To see another example, you could run an FFT on every 2D slice of a volume in a
+for-loop, or you could "vectorize" and simply do it all in one gfor-loop operation:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 for (int i = 0; i < N; ++i)
    A(span,span,i) = fft2(A(span,span,i)); // runs each FFT in sequence
@@ -65,10 +83,15 @@ gfor (seq i, 5)
 gfor (seq i, 0, 4)
 gfor (seq i, 0, 1, 4)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Using GFOR requires following several rules and multiple guidelines for optimal performance. The details of this vectorization method can be found in the <a href="page_gfor.htm">GFOR documentation.</a>
-
-# batchFunc()
-The batchFunc() function allows the broad application of existing Arrayfire functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire functions to execute in "batch processing" mode. In this mode, functions will find a dimension which contains "batches" of data to be processed and will parallelize the procedure.
+Using GFOR requires following several rules and multiple guidelines for optimal performance.
+The details of this vectorization method can be found in the [GFOR documentation](\ref gfor).
+
+# Batching
+The batchFunc() function allows the broad application of existing Arrayfire
+functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire
+functions to execute in "batch processing" mode. In this mode, functions will
+find a dimension which contains "batches" of data to be processed and will
+parallelize the procedure.
 Consider the following example:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filter = randn(1, 5);
@@ -86,19 +109,26 @@ However we would like a vectorized solution. The following syntax begs to be use
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = filter * weights; //fails due to dimension mismatch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-but it fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if Arrayfire could figure out along which dimension we intend to apply the batch operation? That is exactly what batchFunc() does!
+but it fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if
+Arrayfire could figure out along which dimension we intend to apply the batch
+operation? That is exactly what batchFunc() does!
 The signature of the function is:
 
-__AFAPI array batchFunc( const array &lhs, const array &rhs, batchFunc_t func );__
+> array batchFunc(const array &lhs, const array &rhs, batchFunc_t func);
 
 where __batchFunc_t__ is a function pointer of the form:
-__typedef array (*batchFunc_t) ( const array &lhs, const array &rhs );__
+`typedef array (*batchFunc_t) (const array &lhs, const array &rhs);`
 
 
-So, to use batchFunc(), we need to provide the function we will be applying as a batch operation. Our final batch call is not much more difficult than the ideal syntax we imagined.
+So, to use batchFunc(), we need to provide the function we will be applying as a
+batch operation. Our final batch call is not much more difficult than the ideal
+syntax we imagined.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = batchFunc(filter, weights, operator* );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The batch function will work with many previously mentioned vectorized Arrayfire functions. It can even work with a combination of those functions if they are wrapped inside a helper function matching the __batchFunc_t__ signature. Unfortunately, the batch function cannot be used within a gfor() construct at this moment.
+The batch function will work with many previously mentioned vectorized Arrayfire
+functions. It can even work with a combination of those functions if they are
+wrapped inside a helper function matching the __batchFunc_t__ signature. Unfortunately,
+the batch function cannot be used within a gfor() construct at this moment.
 

From 4d75c78dc7e141cad201bb99cf2f271d4f7026d7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 1 Dec 2015 12:15:21 -0500
Subject: [PATCH 0065/2677] Compile fixes for older compilers

---
 src/backend/cpu/Array.cpp    | 2 ++
 src/backend/cpu/Array.hpp    | 2 +-
 src/backend/cuda/Array.cpp   | 2 ++
 src/backend/cuda/Array.hpp   | 2 +-
 src/backend/opencl/Array.cpp | 3 ++-
 src/backend/opencl/Array.hpp | 4 ++--
 6 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 3829a9a5d1..8cf1b752f8 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -279,6 +279,8 @@ namespace cpu
     template       Array<T>  createNodeArray<T>       (const dim4 &size, TNJ::Node_ptr node); \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
+    template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
+                                   bool is_device, bool copy_device);   \
     template       TNJ::Node_ptr Array<T>::getNode() const;             \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index e9e40db54a..471a6741ea 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -163,7 +163,7 @@ namespace cpu
         T* device()
         {
             if (!isOwner() || data.use_count() > 1) {
-                *this = Array(dims(), get(), true, true);
+                *this = Array<T>(dims(), get(), true, true);
             }
             return this->data.get();
         }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 8b05fc0e98..275ea13a99 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -281,6 +281,8 @@ namespace cuda
     template       void      destroyArray<T>          (Array<T> *A);    \
     template       void      evalArray<T>             (const Array<T> &A); \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
+    template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
+                                   bool is_device, bool copy_device);   \
     template       Array<T>::~Array        ();                          \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 3117675a80..598fdfd35e 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -172,7 +172,7 @@ namespace cuda
         T* device()
         {
             if (!isOwner() || data.use_count() > 1) {
-                *this = Array(dims(), get(), true, true);
+                *this = Array<T>(dims(), get(), true, true);
             }
             return this->data.get();
         }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 00635e136e..0860098c9f 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -218,7 +218,7 @@ namespace opencl
     {
         verifyDoubleSupport<T>();
 
-        return Array<T>(size, (cl_mem)(data));
+        return Array<T>(size, (cl_mem)(data), 0, false);
     }
 
     template<typename T>
@@ -314,6 +314,7 @@ namespace opencl
     template       void      destroyArray<T>          (Array<T> *A);    \
     template       void      evalArray<T>             (const Array<T> &A); \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
+    template       Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
     template       Array<T>::~Array        ();                          \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 50da72e6bd..1db0ab6347 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -101,7 +101,7 @@ namespace opencl
         Array(Param &tmp);
         explicit Array(af::dim4 dims, JIT::Node_ptr n);
         explicit Array(af::dim4 dims, const T * const in_data);
-        explicit Array(af::dim4 dims, cl_mem mem, size_t offset = 0, bool copy = false);
+        explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
 
     public:
 
@@ -154,7 +154,7 @@ namespace opencl
         cl::Buffer* device()
         {
             if (!isOwner() || data.use_count() > 1) {
-                *this = Array(dims(), (*get())(), getOffset(), true);
+                *this = Array<T>(dims(), (*get())(), (size_t)getOffset(), true);
             }
             return this->data.get();
         }

From 1c673f94a3d73f7fa10bd79d82dc8050e257e565 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 1 Dec 2015 16:29:48 -0500
Subject: [PATCH 0066/2677] Fixed and improved OpenCL's homography

* Added missing barrier
* Removed need for some global memory buffers
---
 src/backend/opencl/homography.cpp        |  6 +-
 src/backend/opencl/kernel/homography.cl  | 87 ++++++++++++------------
 src/backend/opencl/kernel/homography.hpp |  8 +--
 3 files changed, 49 insertions(+), 52 deletions(-)

diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index dbce53b19b..97c5d21c9d 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -62,18 +62,16 @@ int homography(Array<T> &bestH,
     Array<float> rnd = arithOp<float, af_mul_t>(frnd, fctr, rdims);
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter_sz), (T)0);
-    Array<T> tmpA = createValueArray<T>(af::dim4(9, 9, iter_sz), (T)0);
-    Array<T> tmpV = createValueArray<T>(af::dim4(9, 9, iter_sz), (T)0);
 
     bestH = createValueArray<T>(af::dim4(3, 3), (T)0);
     switch (htype) {
     case AF_HOMOGRAPHY_RANSAC:
-        return kernel::computeH<T, AF_HOMOGRAPHY_RANSAC>(bestH, tmpH, tmpA, tmpV, err,
+        return kernel::computeH<T, AF_HOMOGRAPHY_RANSAC>(bestH, tmpH, err,
                                               x_src, y_src, x_dst, y_dst,
                                               rnd, iter, nsamples, inlier_thr);
         break;
     case AF_HOMOGRAPHY_LMEDS:
-        return kernel::computeH<T, AF_HOMOGRAPHY_LMEDS> (bestH, tmpH, tmpA, tmpV, err,
+        return kernel::computeH<T, AF_HOMOGRAPHY_LMEDS> (bestH, tmpH, err,
                                               x_src, y_src, x_dst, y_dst,
                                               rnd, iter, nsamples, inlier_thr);
         break;
diff --git a/src/backend/opencl/kernel/homography.cl b/src/backend/opencl/kernel/homography.cl
index 618cb28d7d..572cb23ab5 100644
--- a/src/backend/opencl/kernel/homography.cl
+++ b/src/backend/opencl/kernel/homography.cl
@@ -12,9 +12,8 @@ inline T sq(T a)
     return a * a;
 }
 
-inline void jacobi_svd(__global T* S, __global T* V, int m, int n,
-                       __local T* l_acc1, __local T* l_acc2, __local T* l_S,
-                       __local T* l_V, __local T* l_d)
+inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
+                       __local T* l_acc1, __local T* l_acc2, int m, int n)
 {
     const int iterations = 30;
 
@@ -23,12 +22,6 @@ inline void jacobi_svd(__global T* S, __global T* V, int m, int n,
     int tid_y = get_local_id(1);
     int gid_y = get_global_id(1);
 
-    for (int k = 0; k <= 4; k++)
-        l_S[tid_y * 81 + k*bsz_x + tid_x] = S[gid_y * 81 + k*bsz_x + tid_x];
-    if (tid_x == 0)
-        l_S[tid_y * 81 + 80] = S[gid_y * 81 + 80];
-    barrier(CLK_LOCAL_MEM_FENCE);
-
     // Copy first 80 elements
     T t = l_S[tid_y*81 + tid_x];
     l_acc1[tid_y*bsz_x + tid_x] = t*t;
@@ -145,12 +138,6 @@ inline void jacobi_svd(__global T* S, __global T* V, int m, int n,
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
-
-    for (int i = 0; i <= 4; i++)
-        V[gid_y * 81 + tid_x+i*bsz_x] = l_V[tid_y * 81 + tid_x+i*bsz_x];
-    if (tid_x == 0)
-        V[gid_y * 81 + 80] = l_V[tid_y * 81 + 80];
-    barrier(CLK_LOCAL_MEM_FENCE);
 }
 
 inline int compute_mean_scale(
@@ -210,15 +197,11 @@ inline int compute_mean_scale(
     return !bad;
 }
 
-#define APTR(Z, Y, X) (A[(Z) * AInfo.dims[0] * AInfo.dims[1] + (Y) * AInfo.dims[0] + (X)])
+#define LSPTR(Z, Y, X) (l_S[(Z) * 81 + (Y) * 9 + (X)])
 
 __kernel void compute_homography(
     __global T* H,
     KParam HInfo,
-    __global T* A,
-    KParam AInfo,
-    __global T* V,
-    KParam VInfo,
     __global const float* x_src,
     __global const float* y_src,
     __global const float* x_dst,
@@ -228,6 +211,7 @@ __kernel void compute_homography(
     const unsigned iterations)
 {
     unsigned i = get_global_id(1);
+    unsigned tid_y = get_local_id(1);
 
     float x_src_mean, y_src_mean;
     float x_dst_mean, y_dst_mean;
@@ -242,6 +226,13 @@ __kernel void compute_homography(
                        x_src, y_src, x_dst, y_dst,
                        rnd, rInfo, i);
 
+    __local T l_acc1[256];
+    __local T l_acc2[256];
+
+    __local T l_S[16*81];
+    __local T l_V[16*81];
+    __local T l_d[16*9];
+
     // Compute input matrix
     for (unsigned j = get_local_id(0); j < 4; j+=get_local_size(0)) {
         float srcx = (src_pt_x[j] - x_src_mean) * src_scale;
@@ -249,33 +240,45 @@ __kernel void compute_homography(
         float dstx = (dst_pt_x[j] - x_dst_mean) * dst_scale;
         float dsty = (dst_pt_y[j] - y_dst_mean) * dst_scale;
 
-        APTR(i, 3, j*2) = -srcx;
-        APTR(i, 4, j*2) = -srcy;
-        APTR(i, 5, j*2) = -1.0f;
-        APTR(i, 6, j*2) = dsty*srcx;
-        APTR(i, 7, j*2) = dsty*srcy;
-        APTR(i, 8, j*2) = dsty;
-
-        APTR(i, 0, j*2+1) = srcx;
-        APTR(i, 1, j*2+1) = srcy;
-        APTR(i, 2, j*2+1) = 1.0f;
-        APTR(i, 6, j*2+1) = -dstx*srcx;
-        APTR(i, 7, j*2+1) = -dstx*srcy;
-        APTR(i, 8, j*2+1) = -dstx;
+        LSPTR(tid_y, 0, j*2) = 0.0f;
+        LSPTR(tid_y, 1, j*2) = 0.0f;
+        LSPTR(tid_y, 2, j*2) = 0.0f;
+        LSPTR(tid_y, 3, j*2) = -srcx;
+        LSPTR(tid_y, 4, j*2) = -srcy;
+        LSPTR(tid_y, 5, j*2) = -1.0f;
+        LSPTR(tid_y, 6, j*2) = dsty*srcx;
+        LSPTR(tid_y, 7, j*2) = dsty*srcy;
+        LSPTR(tid_y, 8, j*2) = dsty;
+
+        LSPTR(tid_y, 0, j*2+1) = srcx;
+        LSPTR(tid_y, 1, j*2+1) = srcy;
+        LSPTR(tid_y, 2, j*2+1) = 1.0f;
+        LSPTR(tid_y, 3, j*2+1) = 0.0f;
+        LSPTR(tid_y, 4, j*2+1) = 0.0f;
+        LSPTR(tid_y, 5, j*2+1) = 0.0f;
+        LSPTR(tid_y, 6, j*2+1) = -dstx*srcx;
+        LSPTR(tid_y, 7, j*2+1) = -dstx*srcy;
+        LSPTR(tid_y, 8, j*2+1) = -dstx;
+
+        if (j == 4) {
+            LSPTR(tid_y, 0, 8) = 0.0f;
+            LSPTR(tid_y, 1, 8) = 0.0f;
+            LSPTR(tid_y, 2, 8) = 0.0f;
+            LSPTR(tid_y, 3, 8) = 0.0f;
+            LSPTR(tid_y, 4, 8) = 0.0f;
+            LSPTR(tid_y, 5, 8) = 0.0f;
+            LSPTR(tid_y, 6, 8) = 0.0f;
+            LSPTR(tid_y, 7, 8) = 0.0f;
+            LSPTR(tid_y, 8, 8) = 0.0f;
+        }
     }
+    barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local T l_acc1[256];
-    __local T l_acc2[256];
-
-    __local T l_S[16*81];
-    __local T l_V[16*81];
-    __local T l_d[16*9];
-
-    jacobi_svd(A, V, 9, 9, l_acc1, l_acc2, l_S, l_V, l_d);
+    jacobi_svd(l_V, l_S, l_d, l_acc1, l_acc2, 9, 9);
 
     T vH[9], H_tmp[9];
     for (unsigned j = 0; j < 9; j++)
-        vH[j] = V[i * VInfo.dims[0] * VInfo.dims[1] + 8 * VInfo.dims[0] + j];
+        vH[j] = l_V[tid_y * 81 + 8 * 9 + j];
 
     H_tmp[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
     H_tmp[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 714070353b..bd4896fc36 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -40,8 +40,6 @@ template<typename T, af_homography_type htype>
 int computeH(
     Param bestH,
     Param H,
-    Param A,
-    Param V,
     Param err,
     Param x_src,
     Param y_src,
@@ -96,14 +94,12 @@ int computeH(
         const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
 
         // Build linear system and solve SVD
-        auto chOp = make_kernel<Buffer, KParam, Buffer, KParam,
-                                Buffer, KParam,
+        auto chOp = make_kernel<Buffer, KParam,
                                 Buffer, Buffer, Buffer, Buffer,
                                 Buffer, KParam, unsigned>(*chKernel[device]);
 
         chOp(EnqueueArgs(getQueue(), global_ch, local_ch),
-             *H.data, H.info, *A.data, A.info,
-             *V.data, V.info,
+             *H.data, H.info,
              *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
              *rnd.data, rnd.info, iterations);
         CL_DEBUG_FINISH(getQueue());

From 9b0051147a2802866e8dcc07da83ce2d36633c8d Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 1 Dec 2015 16:31:08 -0500
Subject: [PATCH 0067/2677] Fixed and improved CUDA's homography

* Added missing __syncthreads()
* Removed need of some global memory arrays
---
 src/backend/cuda/homography.cu         |  4 +-
 src/backend/cuda/kernel/homography.hpp | 91 ++++++++++++++------------
 2 files changed, 51 insertions(+), 44 deletions(-)

diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index a7a993aa4f..e522e814f2 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -56,12 +56,10 @@ int homography(Array<T> &bestH,
     Array<float> rnd = arithOp<float, af_mul_t>(frnd, fctr, rdims);
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter), (T)0);
-    Array<T> tmpA = createValueArray<T>(af::dim4(9, 9, iter), (T)0);
-    Array<T> tmpV = createValueArray<T>(af::dim4(9, 9, iter), (T)0);
 
     bestH = createValueArray<T>(af::dim4(3, 3), (T)0);
 
-    return kernel::computeH<T>(bestH, tmpH, tmpA, tmpV, err,
+    return kernel::computeH<T>(bestH, tmpH, err,
                                x_src, y_src, x_dst, y_dst,
                                rnd, iter, nsamples, inlier_thr, htype);
 }
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 8dd179492c..65d880e59b 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -54,9 +54,10 @@ struct EPS<double>
 #define LMEDSConfidence 0.99f
 #define LMEDSOutlierRatio 0.4f
 
+extern __shared__ char sh[];
 
 template<typename T>
-__device__ void JacobiSVD(T* S, T* V, int m, int n)
+__device__ void JacobiSVD(int m, int n)
 {
     const int iterations = 30;
 
@@ -65,19 +66,13 @@ __device__ void JacobiSVD(T* S, T* V, int m, int n)
     int tid_y = threadIdx.y;
     int gid_y = blockIdx.y * blockDim.y + tid_y;
 
-    __shared__ T acc[512];
-    T* acc1 = acc;
-    T* acc2 = acc + 256;
+    __shared__ T acc1[256];
+    __shared__ T acc2[256];
 
-    __shared__ T s_S[16*81];
-    __shared__ T s_V[16*81];
     __shared__ T d[16*9];
 
-    for (int i = 0; i <= 4; i++)
-        s_S[tid_y * 81 + i*bsz_x + tid_x] = S[gid_y * 81 + i*bsz_x + tid_x];
-    if (tid_x == 0)
-        s_S[tid_y * 81 + 80] = S[gid_y * 81 + 80];
-    __syncthreads();
+    T* s_V = (T*)sh;
+    T* s_S = (T*)sh + 16*81;
 
     // Copy first 80 elements
     for (int i = 0; i <= 4; i++) {
@@ -194,12 +189,6 @@ __device__ void JacobiSVD(T* S, T* V, int m, int n)
         }
     }
     __syncthreads();
-
-    for (int i = 0; i <= 4; i++)
-        V[gid_y * 81 + tid_x+i*bsz_x] = s_V[tid_y * 81 + tid_x+i*bsz_x];
-    if (tid_x == 0)
-        V[gid_y * 81 + 80] = s_V[tid_y * 81 + 80];
-    __syncthreads();
 }
 
 __device__ bool computeMeanScale(
@@ -258,13 +247,11 @@ __device__ bool computeMeanScale(
     return !bad;
 }
 
-#define APTR(Z, Y, X) (A.ptr[(Z) * A.dims[0] * A.dims[1] + (Y) * A.dims[0] + (X)])
+#define SSPTR(Z, Y, X) (s_S[(Z) * 81 + (Y) * 9 + (X)])
 
 template<typename T>
 __global__ void buildLinearSystem(
     Param<T> H,
-    Param<T> A,
-    Param<T> V,
     CParam<float> x_src,
     CParam<float> y_src,
     CParam<float> x_dst,
@@ -272,7 +259,8 @@ __global__ void buildLinearSystem(
     CParam<float> rnd,
     const unsigned iterations)
 {
-    unsigned i = blockIdx.y * blockDim.y + threadIdx.y;
+    unsigned tid_y = threadIdx.y;
+    unsigned i = blockIdx.y * blockDim.y + tid_y;
 
     if (i < iterations) {
         float x_src_mean, y_src_mean;
@@ -288,6 +276,9 @@ __global__ void buildLinearSystem(
                          x_src, y_src, x_dst, y_dst,
                          rnd, i);
 
+        T* s_V = (T*)sh;
+        T* s_S = (T*)sh + 16*81;
+
         // Compute input matrix
         for (unsigned j = threadIdx.x; j < 4; j+=blockDim.x) {
             float srcx = (src_pt_x[j] - x_src_mean) * src_scale;
@@ -295,26 +286,45 @@ __global__ void buildLinearSystem(
             float dstx = (dst_pt_x[j] - x_dst_mean) * dst_scale;
             float dsty = (dst_pt_y[j] - y_dst_mean) * dst_scale;
 
-            APTR(i, 3, j*2) = -srcx;
-            APTR(i, 4, j*2) = -srcy;
-            APTR(i, 5, j*2) = -1.0f;
-            APTR(i, 6, j*2) = dsty*srcx;
-            APTR(i, 7, j*2) = dsty*srcy;
-            APTR(i, 8, j*2) = dsty;
-
-            APTR(i, 0, j*2+1) = srcx;
-            APTR(i, 1, j*2+1) = srcy;
-            APTR(i, 2, j*2+1) = 1.0f;
-            APTR(i, 6, j*2+1) = -dstx*srcx;
-            APTR(i, 7, j*2+1) = -dstx*srcy;
-            APTR(i, 8, j*2+1) = -dstx;
+            SSPTR(tid_y, 0, j*2) = 0.0f;
+            SSPTR(tid_y, 1, j*2) = 0.0f;
+            SSPTR(tid_y, 2, j*2) = 0.0f;
+            SSPTR(tid_y, 3, j*2) = -srcx;
+            SSPTR(tid_y, 4, j*2) = -srcy;
+            SSPTR(tid_y, 5, j*2) = -1.0f;
+            SSPTR(tid_y, 6, j*2) = dsty*srcx;
+            SSPTR(tid_y, 7, j*2) = dsty*srcy;
+            SSPTR(tid_y, 8, j*2) = dsty;
+
+            SSPTR(tid_y, 0, j*2+1) = srcx;
+            SSPTR(tid_y, 1, j*2+1) = srcy;
+            SSPTR(tid_y, 2, j*2+1) = 1.0f;
+            SSPTR(tid_y, 3, j*2+1) = 0.0f;
+            SSPTR(tid_y, 4, j*2+1) = 0.0f;
+            SSPTR(tid_y, 5, j*2+1) = 0.0f;
+            SSPTR(tid_y, 6, j*2+1) = -dstx*srcx;
+            SSPTR(tid_y, 7, j*2+1) = -dstx*srcy;
+            SSPTR(tid_y, 8, j*2+1) = -dstx;
+
+            if (j == 4) {
+                SSPTR(tid_y, 0, 8) = 0.0f;
+                SSPTR(tid_y, 1, 8) = 0.0f;
+                SSPTR(tid_y, 2, 8) = 0.0f;
+                SSPTR(tid_y, 3, 8) = 0.0f;
+                SSPTR(tid_y, 4, 8) = 0.0f;
+                SSPTR(tid_y, 5, 8) = 0.0f;
+                SSPTR(tid_y, 6, 8) = 0.0f;
+                SSPTR(tid_y, 7, 8) = 0.0f;
+                SSPTR(tid_y, 8, 8) = 0.0f;
+            }
         }
+        __syncthreads();
 
-        JacobiSVD<T>(A.ptr, V.ptr, 9, 9);
+        JacobiSVD<T>(9, 9);
 
         T vH[9], H_tmp[9];
         for (unsigned j = 0; j < 9; j++)
-            vH[j] = V.ptr[i * V.dims[0] * V.dims[1] + 8 * V.dims[0] + j];
+            vH[j] = s_V[tid_y * 81 + 8 * 9 + j];
 
         H_tmp[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
         H_tmp[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
@@ -337,7 +347,7 @@ __global__ void buildLinearSystem(
     }
 }
 
-#undef APTR
+#undef SSPTR
 
 // LMedS: http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
 template<typename T>
@@ -557,8 +567,6 @@ template<typename T>
 int computeH(
     Param<T> bestH,
     Param<T> H,
-    Param<T> A,
-    Param<T> V,
     Param<float> err,
     CParam<float> x_src,
     CParam<float> y_src,
@@ -574,8 +582,9 @@ int computeH(
     dim3 blocks(1, divup(iterations, threads.y));
 
     // Build linear system and solve SVD
-    CUDA_LAUNCH((buildLinearSystem<T>), blocks, threads,
-                H, A, V, x_src, y_src, x_dst, y_dst, rnd, iterations);
+    size_t ls_shared_sz = threads.x * 81 * 2 * sizeof(T);
+    CUDA_LAUNCH_SMEM((buildLinearSystem<T>), blocks, threads, ls_shared_sz,
+                H, x_src, y_src, x_dst, y_dst, rnd, iterations);
     POST_LAUNCH_CHECK();
 
     threads = dim3(256);

From 516c36e08bcd225b13f5d3568f5449ede1d9696e Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 1 Dec 2015 18:15:25 -0500
Subject: [PATCH 0068/2677] interop tweaks temporarily remove external opencl
 context from interop tutorial. add cuda stream topic

---
 docs/pages/interop_cuda.md   | 91 ++++++++++++++++++++++++------------
 docs/pages/interop_opencl.md | 36 ++++----------
 2 files changed, 70 insertions(+), 57 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index e20cf6682f..f2e4e3958c 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -1,7 +1,13 @@
 Interoperability with CUDA {#interop_cuda}
 ========
 
-As extensive as ArrayFire is, there are a few cases where you are still working with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels. For example, you may want to integrate ArrayFire into an existing code base for productivity or you may want to keep it around the old implementation for testing purposes. Arrayfire provides a number of functions that allow it to work alongside native CUDA commands. In this tutorial we are going to talk about how to use native CUDA memory operations and integrate custom CUDA kernels into ArrayFire in a seamless fashion.
+As extensive as ArrayFire is, there are a few cases where you are still working
+with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels.
+For example, you may want to integrate ArrayFire into an existing code base for
+productivity or you may want to keep it around the old implementation for testing
+purposes. Arrayfire provides a number of functions that allow it to work alongside
+native CUDA commands. In this tutorial we are going to talk about how to use native
+CUDA memory operations and integrate custom CUDA kernels into ArrayFire in a seamless fashion.
 
 # In and Out of Arrayfire
 
@@ -15,8 +21,6 @@ int main() {
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 
-    af::sync();
-
     // Launch kernel to do the following operations
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
@@ -34,36 +38,45 @@ int main() {
 
 ## Breakdown
 Most kernels require an input. In this case, we created a random uniform array **x**.
-We also go ahead and prepare the output array. The necessary memory required is allocated in array **y** before the kernel launch.
+We also go ahead and prepare the output array. 
+The necessary memory required is allocated in array **y** before the kernel launch.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     af::array x = randu(num);
     af::array y = randu(num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-In this example, the output is the same size as in the input. Note that the actual output data type is not specified. For such cases, ArrayFire assumes the data type is single precision floating point ( af::f32 ). If necessary, the data type can be specified at the end of the array(..) constructor. Once you have the input and output arrays, you will need to extract the device pointers / objects using array::device() method in the following manner.
+In this example, the output is the same size as in the input. Note that the actual
+output data type is not specified. For such cases, ArrayFire assumes the data type
+is single precision floating point ( af::f32 ). If necessary, the data type can
+be specified at the end of the array(..) constructor. Once you have the input and
+output arrays, you will need to extract the device pointers / objects using 
+array::device() method in the following manner.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Accesing the device pointer in this manner internally sets a flag prohibiting the arrayfire object from further managing the memory. Ownership will need to be returned to the af::array object once we are finished using it.
+Accesing the device pointer in this manner internally sets a flag prohibiting the
+arrayfire object from further managing the memory. Ownership will need to be
+returned to the af::array object once we are finished using it.
 
-Before  launching your custom kernel, it is best to make sure that all ArrayFire computations have finished. This can be called by using af::sync(). The function ensures you are not unintentionally doing out of order executions.
-af::sync() is not strictly required if you are not using streams in CUDA.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    af::sync();
-
     // Launch kernel to do the following operations
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The function **launch_simple_kernel** handles the launching of your custom kernel. We will have a look at how to do this in CUDA and OpenCL later in the post.
+The function **launch_simple_kernel** handles the launching of your custom kernel.
+We will have a look at how to do this in CUDA later in the post.
 
-Once you have finished your computations, you have to tell ArrayFire to take control of the memory objects.
+Once you have finished your computations, you have to tell ArrayFire to take 
+control of the memory objects.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     x.unlock();
     y.unlock();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-This is a very crucial step as ArrayFire believes the user is still in control of the pointer. This means that ArrayFire will not perform garbage collection on these objects resulting in memory leaks. You can now proceed with the rest of the program. In our particular example, we are just performing an error check and exiting.
+This is a very crucial step as ArrayFire believes the user is still in control 
+of the pointer. This means that ArrayFire will not perform garbage collection on
+these objects resulting in memory leaks. You can now proceed with the rest of the program.
+In our particular example, we are just performing an error check and exiting.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     // check for errors, should be 0,
@@ -73,7 +86,34 @@ This is a very crucial step as ArrayFire believes the user is still in control o
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 # Launching a CUDA kernel
-Integrating a CUDA kernel into your ArrayFire code base is a fairly straightforward process. You need to set the launch configuration parameters, launch the kernel and wait for the computations to finish. This is shown below.
+Arrayfire provides a collection of CUDA interoperability functions for additional
+capabilities when working with custom CUDA code. To use them, we need to include
+the appropriate header.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+#include <af/cuda.h>
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The first thing these headers allow us to do are to get and set the active device
+using native CUDA device ids. This is achieved through the following functions:
+> **static int getNativeId (int id)** 
+> -- Get the native device id of the CUDA device with id in the ArrayFire context.
+
+> **static void setNativeId (int nativeId)**  
+> -- Set the CUDA device with given native id as the active device for ArrayFire.
+The headers also allow us to retrieve the CUDA stream used internally inside Arrayfire.
+> **static cudaStream_t afcu::getStream(int id)**  
+> -- Get the stream for the CUDA device with id in ArrayFire context.
+These functions are available within the afcu:: namespace and equal C variants 
+can be fund in the full [cuda interop documentation.](\ref cuda_mat.htm)
+
+To integrate a CUDA kernel into an ArrayFire code base, we first need to get the
+CUDA stream associated with arrayfire. Once we have this stream, we need to make
+sure Arrayfire is done with all computation before we can call our custom kernel
+to avoid out of order execution. We can do this with some variant of 
+**cudaStreamQuery(af_stream)** or **cudaStreamSynchronize(af_stream)** or instead,
+we could add our kernel launch to Arrayfire's stream as shown below. Once we get
+the associated stream, all that is left is setting up the usual launch configuration
+parameters, launching the kernel and wait for the computations to finish:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
  
  __global__
@@ -95,24 +135,17 @@ void inline launch_simple_kernel(float *d_y,
                                  const float *d_x,
                                  const int num)
 {
+    // Get Arrayfire's internal CUDA stream
+    int af_id = af::getDevice();
+    cudaStream_t af_stream = afcu::getStream(af_id);
+
     // Set launch configuration
     const int threads = 256;
     const int blocks = (num / threads) + ((num % threads) ? 1 : 0);
-    simple_kernel<<<blocks, threads>>>(d_y, d_x, num);
-    // Synchronize and check for error
-    cudaDeviceSynchronize();
-}
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-# Additional interop functions and CUDA Streams
-
-Arrayfire provides a collection of CUDA interoperability functions for additional capabilities when working with custom CUDA code. To use them, we need to include the appropriate header.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-#include <af/cuda.h>
+    // execute kernel on Arrayfire's stream, 
+    // ensuring all previous arrayfire operations complete
+    simple_kernel<<<blocks, threads, 0, af_stream>>>(d_y, d_x, num);
+}
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The first thing these headers allow us to do are to get and set the active device using native CUDA device ids. This is achieved through the following functions:
-    **static int getNativeId (int id)** -- Get the native device id of the CUDA device with id in the ArrayFire context.
-    **static void setNativeId (int nativeId)**  -- Set the CUDA device with given native id as the active device for ArrayFire. 
-
-These functions are available within the afcu:: namespace and equal C variants can be fund in the full [cuda interop documentation.](group__cuda__mat.htm)
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 2b81972113..6e270a924b 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -22,8 +22,6 @@ int main() {
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 
-    af::sync();
-
     // Launch kernel to do the following operations
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
@@ -62,12 +60,7 @@ Accesing the device pointer in this manner internally sets a flag prohibiting
 the arrayfire object from further managing the memory. Ownership will need to be
 returned to the af::array object once we are finished using it.
 
-Before  launching your custom kernel, it is best to make sure that all ArrayFire
-computations have finished. This can be called by using af::sync(). The function
-ensures you are not unintentionally doing out of order executions.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    af::sync();
-
     // Launch kernel to do the following operations
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
@@ -152,10 +145,9 @@ We start to use these functions by getting Arrayfire's context and queue. For th
 C++ api, a **true** flag must be passed for the retain parameter which calls the
 clRetainQueue() and clRetainContext() functions before returning. This allows us
 to use Arrayfire's internal OpenCL structures inside of the cl::Context and
-cl::CommandQueue objects from the C++ api.
-Once we have them, we can proceed to set up and enqueue the kernel like we would
-in any other OpenCL program. The kernel we are using is actually simple and can
-be seen below.
+cl::CommandQueue objects from the C++ api. Once we have them, we can proceed to 
+set up and enqueue the kernel like we would in any other OpenCL program. 
+The kernel we are using is actually simple and can be seen below.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 std::string CONST_KERNEL_STRING = R"(
@@ -178,21 +170,9 @@ void simple_kernel(__global float *d_y,
 
 # Reversing the workflow: Arrayfire arrays from OpenCL Memory
 
-Arrayfire's interoperability functions don't limit us to working with memory
-managed by Arrayfire. We could take the reverse route and start with completely
-custom OpenCL code, then transfer our results into an af::array object. This is
-done rather simply with a special set of construction functions.
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-cl::Buffer my_cl_buffer(context, CL_MEM_READ_WRITE, sizeof(float) * SIZE);
-//work and computations with OpenCL buffer
-
-//kernel(my_cl_buffer, queue);
+Unfortunately, Arrayfire's interoperability functions don't yet allow us to work with
+external OpenCL contexts. This is currently an open issue and can be tracked here:
+https://github.com/arrayfire/arrayfire/issues/1002
+Once the issue is addressed, it will be possible to take the reverse route and start with
+completely custom OpenCL code, then transfer our results into af::array objects.
 
-//construct af::array from OpenCL buffer
-af::array my_array = afcl::array(SIZE, my_cl_buffer(), f32);
-af_print(my_array);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Please note: the \ref af::array constructors are not thread safe. 
-You may create and upload data to `cl_mem` objects from separate threads, 
-but the thread which instantiated ArrayFire must do the `cl_mem` to \ref af::array conversion.

From 9510fcb1e2554078070c28ea88c2e5078353f72d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 10:44:06 -0500
Subject: [PATCH 0069/2677] Converted cpu scan function to async call

Added `.eval()` calls on input Array objects inside the following
functions to ensure that the inputs are computed by the time `.get()`
is called on these objects to get the data values.

* reduce
* setUnique
* setIntersection
* setUnion
---
 src/backend/cpu/reduce.cpp |   1 +
 src/backend/cpu/scan.cpp   | 154 ++++++++++++++++++++-----------------
 src/backend/cpu/set.cpp    |  13 ++++
 3 files changed, 96 insertions(+), 72 deletions(-)

diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index ffe91851b1..e01f0c51f1 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -89,6 +89,7 @@ namespace cpu
     {
         dim4 odims = in.dims();
         odims[dim] = 1;
+        in.eval();
 
         Array<To> out = createEmptyArray<To>(odims);
         static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { reduce_dim<op, Ti, To, 1>()
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 2bdda210a2..39157ca9a1 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -14,102 +14,112 @@
 #include <Array.hpp>
 #include <scan.hpp>
 #include <ops.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<af_op_t op, typename Ti, typename To, int D>
-    struct scan_dim
-    {
-        void operator()(To *out, const dim4 ostrides, const dim4 odims,
-                        const Ti *in , const dim4 istrides, const dim4 idims,
-                        const int dim)
-        {
-            const int D1 = D - 1;
-            for (dim_t i = 0; i < odims[D1]; i++) {
-                scan_dim<op, Ti, To, D1>()(out + i * ostrides[D1],
-                                           ostrides, odims,
-                                           in  + i * istrides[D1],
-                                           istrides, idims,
-                                           dim);
-                if (D1 == dim) break;
-            }
-        }
-    };
 
-    template<af_op_t op, typename Ti, typename To>
-    struct scan_dim<op, Ti, To, 0>
+template<af_op_t op, typename Ti, typename To, int D>
+struct scan_dim
+{
+    void operator()(Array<To> out, dim_t outOffset,
+                    const Array<Ti> in, dim_t inOffset,
+                    const int dim) const
     {
-        void operator()(To *out, const dim4 ostrides, const dim4 odims,
-                        const Ti *in , const dim4 istrides, const dim4 idims,
-                        const int dim)
-        {
-
-            dim_t istride = istrides[dim];
-            dim_t ostride = ostrides[dim];
-
-            Transform<Ti, To, op> transform;
-            // FIXME: Change the name to something better
-            Binary<To, op> scan;
-
-            To out_val = scan.init();
-            for (dim_t i = 0; i < idims[dim]; i++) {
-                To in_val = transform(in[i * istride]);
-                out_val = scan(in_val, out_val);
-                out[i * ostride] = out_val;
-            }
+        const dim4 odims    = out.dims();
+        const dim4 ostrides = out.strides();
+        const dim4 istrides = in.strides();
+
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            scan_dim<op, Ti, To, D1> func;
+            getQueue().enqueue(func,
+                    out, outOffset + i * ostrides[D1],
+                    in, inOffset + i * istrides[D1], dim);
+            if (D1 == dim) break;
         }
-    };
+    }
+};
 
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim)
+template<af_op_t op, typename Ti, typename To>
+struct scan_dim<op, Ti, To, 0>
+{
+    void operator()(Array<To> output, dim_t outOffset,
+                    const Array<Ti> input,  dim_t inOffset,
+                    const int dim) const
     {
-        dim4 dims = in.dims();
+        const Ti* in = input.get() + inOffset;
+              To* out= output.get()+ outOffset;
 
-        Array<To> out = createValueArray<To>(dims, 0);
+        const dim4 ostrides = output.strides();
+        const dim4 istrides = input.strides();
+        const dim4 idims    = input.dims();
+
+        dim_t istride = istrides[dim];
+        dim_t ostride = ostrides[dim];
+
+        Transform<Ti, To, op> transform;
+        // FIXME: Change the name to something better
+        Binary<To, op> scan;
+
+        To out_val = scan.init();
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            To in_val = transform(in[i * istride]);
+            out_val = scan(in_val, out_val);
+            out[i * ostride] = out_val;
+        }
+    }
+};
 
-        switch (in.ndims()) {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim)
+{
+    dim4 dims     = in.dims();
+    Array<To> out = createValueArray<To>(dims, 0);
+    out.eval();
+    in.eval();
+
+    switch (in.ndims()) {
         case 1:
-            scan_dim<op, Ti, To, 1>()(out.get(), out.strides(), out.dims(),
-                                      in.get(), in.strides(), in.dims(), dim);
+            scan_dim<op, Ti, To, 1> func1;
+            getQueue().enqueue(func1, out, 0, in, 0, dim);
             break;
-
         case 2:
-            scan_dim<op, Ti, To, 2>()(out.get(), out.strides(), out.dims(),
-                                      in.get(), in.strides(), in.dims(), dim);
+            scan_dim<op, Ti, To, 2> func2;
+            getQueue().enqueue(func2, out, 0, in, 0, dim);
             break;
-
         case 3:
-            scan_dim<op, Ti, To, 3>()(out.get(), out.strides(), out.dims(),
-                                      in.get(), in.strides(), in.dims(), dim);
+            scan_dim<op, Ti, To, 3> func3;
+            getQueue().enqueue(func3, out, 0, in, 0, dim);
             break;
-
         case 4:
-            scan_dim<op, Ti, To, 4>()(out.get(), out.strides(), out.dims(),
-                                      in.get(), in.strides(), in.dims(), dim);
+            scan_dim<op, Ti, To, 4> func4;
+            getQueue().enqueue(func4, out, 0, in, 0, dim);
             break;
-        }
-
-        return out;
     }
 
+    return out;
+}
+
 #define INSTANTIATE(ROp, Ti, To)                                        \
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim); \
 
-    //accum
-    INSTANTIATE(af_add_t, float  , float  )
-    INSTANTIATE(af_add_t, double , double )
-    INSTANTIATE(af_add_t, cfloat , cfloat )
-    INSTANTIATE(af_add_t, cdouble, cdouble)
-    INSTANTIATE(af_add_t, int    , int    )
-    INSTANTIATE(af_add_t, uint   , uint   )
-    INSTANTIATE(af_add_t, intl   , intl   )
-    INSTANTIATE(af_add_t, uintl  , uintl  )
-    INSTANTIATE(af_add_t, char   , int    )
-    INSTANTIATE(af_add_t, uchar  , uint   )
-    INSTANTIATE(af_add_t, short  , int    )
-    INSTANTIATE(af_add_t, ushort , uint   )
-    INSTANTIATE(af_notzero_t, char  , uint   )
+//accum
+INSTANTIATE(af_add_t, float  , float  )
+INSTANTIATE(af_add_t, double , double )
+INSTANTIATE(af_add_t, cfloat , cfloat )
+INSTANTIATE(af_add_t, cdouble, cdouble)
+INSTANTIATE(af_add_t, int    , int    )
+INSTANTIATE(af_add_t, uint   , uint   )
+INSTANTIATE(af_add_t, intl   , intl   )
+INSTANTIATE(af_add_t, uintl  , uintl  )
+INSTANTIATE(af_add_t, char   , int    )
+INSTANTIATE(af_add_t, uchar  , uint   )
+INSTANTIATE(af_add_t, short  , int    )
+INSTANTIATE(af_add_t, ushort , uint   )
+INSTANTIATE(af_notzero_t, char  , uint)
 
 }
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 3215e6d5c2..d9ca0849c0 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -18,6 +18,8 @@
 #include <sort.hpp>
 #include <err_cpu.hpp>
 #include <vector>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -28,6 +30,9 @@ namespace cpu
     Array<T> setUnique(const Array<T> &in,
                         const bool is_sorted)
     {
+        in.eval();
+        getQueue().sync();
+
         Array<T> out = createEmptyArray<T>(af::dim4());
         if (is_sorted) out = copyArray<T>(in);
         else           out = sort<T, 1>(in, 0);
@@ -46,6 +51,10 @@ namespace cpu
                        const Array<T> &second,
                        const bool is_unique)
     {
+        first.eval();
+        second.eval();
+        getQueue().sync();
+
         Array<T> uFirst = first;
         Array<T> uSecond = second;
 
@@ -78,6 +87,10 @@ namespace cpu
                           const Array<T> &second,
                           const bool is_unique)
     {
+        first.eval();
+        second.eval();
+        getQueue().sync();
+
         Array<T> uFirst = first;
         Array<T> uSecond = second;
 

From 6fc636fead1e3e14b9da100375c8a5651e2c1089 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 11:40:47 -0500
Subject: [PATCH 0070/2677] fix for async sift cpu function

Added input evaluation for sift cpu backend function to ensure
the inputs have correct values before sift operation begins.
---
 src/backend/cpu/sift_nonfree.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cpu/sift_nonfree.hpp b/src/backend/cpu/sift_nonfree.hpp
index 514a134c7d..853f407f7f 100644
--- a/src/backend/cpu/sift_nonfree.hpp
+++ b/src/backend/cpu/sift_nonfree.hpp
@@ -968,6 +968,7 @@ namespace cpu
                        const float img_scale, const float feature_ratio,
                        const bool compute_GLOH)
     {
+        in.eval();
         af::dim4 idims = in.dims();
 
         const unsigned min_dim = (double_input) ? min(idims[0]*2, idims[1]*2)

From 48a9e581d7f2b0ce1eb48171d5f1ceaaf7b4c712 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 12:48:56 -0500
Subject: [PATCH 0071/2677] converted matchTemplate, meanShift & medfilt to
 async calls

---
 src/backend/cpu/match_template.cpp | 206 +++++++++++++++--------------
 src/backend/cpu/meanshift.cpp      | 174 ++++++++++++------------
 src/backend/cpu/medfilt.cpp        | 149 +++++++++++----------
 3 files changed, 275 insertions(+), 254 deletions(-)

diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 4d930145d5..02a4888864 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -13,6 +13,8 @@
 #include <Array.hpp>
 #include <match_template.hpp>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -22,122 +24,126 @@ namespace cpu
 template<typename inType, typename outType, af_match_type mType>
 Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg)
 {
-    const dim4 sDims = sImg.dims();
-    const dim4 tDims = tImg.dims();
-    const dim4 sStrides = sImg.strides();
-    const dim4 tStrides = tImg.strides();
-
-    const dim_t tDim0  = tDims[0];
-    const dim_t tDim1  = tDims[1];
-    const dim_t sDim0  = sDims[0];
-    const dim_t sDim1  = sDims[1];
-
-    Array<outType> out = createEmptyArray<outType>(sDims);
-    const dim4 oStrides = out.strides();
-
-    outType tImgMean = outType(0);
-    dim_t winNumElements = tImg.elements();
-    bool needMean = mType==AF_ZSAD || mType==AF_LSAD ||
-                    mType==AF_ZSSD || mType==AF_LSSD ||
-                    mType==AF_ZNCC;
-    const inType * tpl = tImg.get();
-
-    if (needMean) {
-        for(dim_t tj=0; tj<tDim1; tj++) {
-            dim_t tjStride = tj*tStrides[1];
-
-            for(dim_t ti=0; ti<tDim0; ti++) {
-                tImgMean += (outType)tpl[tjStride+ti*tStrides[0]];
+    Array<outType> out = createEmptyArray<outType>(sImg.dims());
+
+    auto func = [=](Array<outType> out, const Array<inType> sImg, const Array<inType> tImg) {
+        const dim4 sDims = sImg.dims();
+        const dim4 tDims = tImg.dims();
+        const dim4 sStrides = sImg.strides();
+        const dim4 tStrides = tImg.strides();
+
+        const dim_t tDim0  = tDims[0];
+        const dim_t tDim1  = tDims[1];
+        const dim_t sDim0  = sDims[0];
+        const dim_t sDim1  = sDims[1];
+
+        const dim4 oStrides = out.strides();
+
+        outType tImgMean = outType(0);
+        dim_t winNumElements = tImg.elements();
+        bool needMean = mType==AF_ZSAD || mType==AF_LSAD ||
+            mType==AF_ZSSD || mType==AF_LSSD ||
+            mType==AF_ZNCC;
+        const inType * tpl = tImg.get();
+
+        if (needMean) {
+            for(dim_t tj=0; tj<tDim1; tj++) {
+                dim_t tjStride = tj*tStrides[1];
+
+                for(dim_t ti=0; ti<tDim0; ti++) {
+                    tImgMean += (outType)tpl[tjStride+ti*tStrides[0]];
+                }
             }
+            tImgMean /= winNumElements;
         }
-        tImgMean /= winNumElements;
-    }
 
-    outType * dst      = out.get();
-    const inType * src = sImg.get();
+        outType * dst      = out.get();
+        const inType * src = sImg.get();
 
-    for(dim_t b3=0; b3<sDims[3]; ++b3) {
-    for(dim_t b2=0; b2<sDims[2]; ++b2) {
+        for(dim_t b3=0; b3<sDims[3]; ++b3) {
+            for(dim_t b2=0; b2<sDims[2]; ++b2) {
 
-        // slide through image window after window
-        for(dim_t sj=0; sj<sDim1; sj++) {
+                // slide through image window after window
+                for(dim_t sj=0; sj<sDim1; sj++) {
 
-            dim_t ojStride = sj*oStrides[1];
+                    dim_t ojStride = sj*oStrides[1];
 
-            for(dim_t si=0; si<sDim0; si++) {
-                outType disparity = outType(0);
+                    for(dim_t si=0; si<sDim0; si++) {
+                        outType disparity = outType(0);
 
-                // mean for window
-                // this variable will be used based on mType value
-                outType wImgMean = outType(0);
-                if (needMean) {
-                    for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
-                        dim_t jStride = j*sStrides[1];
+                        // mean for window
+                        // this variable will be used based on mType value
+                        outType wImgMean = outType(0);
+                        if (needMean) {
+                            for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
+                                dim_t jStride = j*sStrides[1];
 
-                        for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
-                            inType sVal = ((j<sDim1 && i<sDim0) ?
-                                    src[jStride + i*sStrides[0]] : inType(0));
-                            wImgMean += (outType)sVal;
+                                for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
+                                    inType sVal = ((j<sDim1 && i<sDim0) ?
+                                            src[jStride + i*sStrides[0]] : inType(0));
+                                    wImgMean += (outType)sVal;
+                                }
+                            }
+                            wImgMean /= winNumElements;
                         }
-                    }
-                    wImgMean /= winNumElements;
-                }
-
-                // run the window match metric
-                for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
-                    dim_t jStride = j*sStrides[1];
-                    dim_t tjStride = tj*tStrides[1];
 
-                    for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
-                        inType sVal = ((j<sDim1 && i<sDim0) ?
-                                            src[jStride + i*sStrides[0]] : inType(0));
-                        inType tVal = tpl[tjStride+ti*tStrides[0]];
-                        outType temp;
-                        switch(mType) {
-                            case AF_SAD:
-                                disparity += fabs((outType)sVal-(outType)tVal);
-                                break;
-                            case AF_ZSAD:
-                                disparity += fabs((outType)sVal - wImgMean -
-                                                  (outType)tVal + tImgMean);
-                                break;
-                            case AF_LSAD:
-                                disparity += fabs((outType)sVal-(wImgMean/tImgMean)*tVal);
-                                break;
-                            case AF_SSD:
-                                disparity += ((outType)sVal-(outType)tVal)*((outType)sVal-(outType)tVal);
-                                break;
-                            case AF_ZSSD:
-                                temp = ((outType)sVal - wImgMean - (outType)tVal + tImgMean);
-                                disparity += temp*temp;
-                                break;
-                            case AF_LSSD:
-                                temp = ((outType)sVal-(wImgMean/tImgMean)*tVal);
-                                disparity += temp*temp;
-                                break;
-                            case AF_NCC:
-                                //TODO: furture implementation
-                                break;
-                            case AF_ZNCC:
-                                //TODO: furture implementation
-                                break;
-                            case AF_SHD:
-                                //TODO: furture implementation
-                                break;
+                        // run the window match metric
+                        for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
+                            dim_t jStride = j*sStrides[1];
+                            dim_t tjStride = tj*tStrides[1];
+
+                            for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
+                                inType sVal = ((j<sDim1 && i<sDim0) ?
+                                        src[jStride + i*sStrides[0]] : inType(0));
+                                inType tVal = tpl[tjStride+ti*tStrides[0]];
+                                outType temp;
+                                switch(mType) {
+                                    case AF_SAD:
+                                        disparity += fabs((outType)sVal-(outType)tVal);
+                                        break;
+                                    case AF_ZSAD:
+                                        disparity += fabs((outType)sVal - wImgMean -
+                                                (outType)tVal + tImgMean);
+                                        break;
+                                    case AF_LSAD:
+                                        disparity += fabs((outType)sVal-(wImgMean/tImgMean)*tVal);
+                                        break;
+                                    case AF_SSD:
+                                        disparity += ((outType)sVal-(outType)tVal)*((outType)sVal-(outType)tVal);
+                                        break;
+                                    case AF_ZSSD:
+                                        temp = ((outType)sVal - wImgMean - (outType)tVal + tImgMean);
+                                        disparity += temp*temp;
+                                        break;
+                                    case AF_LSSD:
+                                        temp = ((outType)sVal-(wImgMean/tImgMean)*tVal);
+                                        disparity += temp*temp;
+                                        break;
+                                    case AF_NCC:
+                                        //TODO: furture implementation
+                                        break;
+                                    case AF_ZNCC:
+                                        //TODO: furture implementation
+                                        break;
+                                    case AF_SHD:
+                                        //TODO: furture implementation
+                                        break;
+                                }
+                            }
                         }
+                        // output is just created, hence not doing the
+                        // extra multiplication for 0th dim stride
+                        dst[ojStride + si] = disparity;
                     }
                 }
-                // output is just created, hence not doing the
-                // extra multiplication for 0th dim stride
-                dst[ojStride + si] = disparity;
+                src += sStrides[2];
+                dst += oStrides[2];
             }
+            src += sStrides[3];
+            dst += oStrides[3];
         }
-        src += sStrides[2];
-        dst += oStrides[2];
-    }
-        src += sStrides[3];
-        dst += oStrides[3];
-    }
+    };
+    getQueue().enqueue(func, out, sImg, tImg);
 
     return out;
 }
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index b52eaf9387..3f99d15b0c 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -16,6 +16,8 @@
 #include <algorithm>
 #include <err_cpu.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 using std::vector;
@@ -31,117 +33,123 @@ inline dim_t clamp(dim_t a, dim_t mn, dim_t mx)
 template<typename T, bool is_color>
 Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
 {
-    const dim4 dims     = in.dims();
-    const dim4 istrides = in.strides();
-    Array<T> out        = createEmptyArray<T>(dims);
-    const dim4 ostrides = out.strides();
+    Array<T> out = createEmptyArray<T>(in.dims());
 
-    const dim_t bCount   = (is_color ? 1 : dims[2]);
-    const dim_t channels = (is_color ? dims[2] : 1);
+    auto func = [=] (Array<T> out, const Array<T> in, const float s_sigma,
+                     const float c_sigma, const unsigned iter) {
+        const dim4 dims     = in.dims();
+        const dim4 istrides = in.strides();
+        const dim4 ostrides = out.strides();
 
-    // clamp spatical and chromatic sigma's
-    float space_          = std::min(11.5f, s_sigma);
-    const dim_t radius = std::max((int)(space_ * 1.5f), 1);
-    const float cvar      = c_sigma*c_sigma;
+        const dim_t bCount   = (is_color ? 1 : dims[2]);
+        const dim_t channels = (is_color ? dims[2] : 1);
 
-    vector<float> means;
-    vector<float> centers;
-    vector<float> tmpclrs;
-    means.reserve(channels);
-    centers.reserve(channels);
-    tmpclrs.reserve(channels);
+        // clamp spatical and chromatic sigma's
+        float space_          = std::min(11.5f, s_sigma);
+        const dim_t radius = std::max((int)(space_ * 1.5f), 1);
+        const float cvar      = c_sigma*c_sigma;
 
-    T *outData       = out.get();
-    const T * inData = in.get();
+        vector<float> means;
+        vector<float> centers;
+        vector<float> tmpclrs;
+        means.reserve(channels);
+        centers.reserve(channels);
+        tmpclrs.reserve(channels);
 
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        for(dim_t b2=0; b2<bCount; ++b2) {
+        T *outData       = out.get();
+        const T * inData = in.get();
 
-            for(dim_t j=0; j<dims[1]; ++j) {
+        for(dim_t b3=0; b3<dims[3]; ++b3) {
+            for(dim_t b2=0; b2<bCount; ++b2) {
 
-                dim_t j_in_off  = j*istrides[1];
-                dim_t j_out_off = j*ostrides[1];
+                for(dim_t j=0; j<dims[1]; ++j) {
 
-                for(dim_t i=0; i<dims[0]; ++i) {
+                    dim_t j_in_off  = j*istrides[1];
+                    dim_t j_out_off = j*ostrides[1];
 
-                    dim_t i_in_off  = i*istrides[0];
-                    dim_t i_out_off = i*ostrides[0];
+                    for(dim_t i=0; i<dims[0]; ++i) {
 
-                    // clear means and centers for this pixel
-                    for(dim_t ch=0; ch<channels; ++ch) {
-                        means[ch] = 0.0f;
-                        // the expression ch*istrides[2] will only effect when ch>1
-                        // i.e for color images where batch is along fourth dimension
-                        centers[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
-                    }
+                        dim_t i_in_off  = i*istrides[0];
+                        dim_t i_out_off = i*ostrides[0];
 
-                    // scope of meanshift iterationd begin
-                    for(unsigned it=0; it<iter; ++it) {
+                        // clear means and centers for this pixel
+                        for(dim_t ch=0; ch<channels; ++ch) {
+                            means[ch] = 0.0f;
+                            // the expression ch*istrides[2] will only effect when ch>1
+                            // i.e for color images where batch is along fourth dimension
+                            centers[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
+                        }
 
-                        int count   = 0;
-                        int shift_x = 0;
-                        int shift_y = 0;
+                        // scope of meanshift iterationd begin
+                        for(unsigned it=0; it<iter; ++it) {
 
-                        for(dim_t wj=-radius; wj<=radius; ++wj) {
+                            int count   = 0;
+                            int shift_x = 0;
+                            int shift_y = 0;
 
-                            int hit_count = 0;
+                            for(dim_t wj=-radius; wj<=radius; ++wj) {
 
-                            for(dim_t wi=-radius; wi<=radius; ++wi) {
+                                int hit_count = 0;
 
-                                dim_t tj = j + wj;
-                                dim_t ti = i + wi;
+                                for(dim_t wi=-radius; wi<=radius; ++wi) {
 
-                                // clamps offsets
-                                tj = clamp(tj, 0ll, dims[1]-1);
-                                ti = clamp(ti, 0ll, dims[0]-1);
+                                    dim_t tj = j + wj;
+                                    dim_t ti = i + wi;
 
-                                // proceed
-                                float norm = 0.0f;
-                                for(dim_t ch=0; ch<channels; ++ch) {
-                                    tmpclrs[ch] = inData[ tj*istrides[1] + ti*istrides[0] + ch*istrides[2]];
-                                    norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
-                                }
+                                    // clamps offsets
+                                    tj = clamp(tj, 0ll, dims[1]-1);
+                                    ti = clamp(ti, 0ll, dims[0]-1);
 
-                                if (norm<= cvar) {
-                                    for(dim_t ch=0; ch<channels; ++ch)
-                                        means[ch] += tmpclrs[ch];
-                                    shift_x += wi;
-                                    ++hit_count;
-                                }
+                                    // proceed
+                                    float norm = 0.0f;
+                                    for(dim_t ch=0; ch<channels; ++ch) {
+                                        tmpclrs[ch] = inData[ tj*istrides[1] + ti*istrides[0] + ch*istrides[2]];
+                                        norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
+                                    }
 
-                            }
-                            count+= hit_count;
-                            shift_y += wj*hit_count;
-                        }
+                                    if (norm<= cvar) {
+                                        for(dim_t ch=0; ch<channels; ++ch)
+                                            means[ch] += tmpclrs[ch];
+                                        shift_x += wi;
+                                        ++hit_count;
+                                    }
 
-                        if (count==0) { break; }
+                                }
+                                count+= hit_count;
+                                shift_y += wj*hit_count;
+                            }
 
-                        const float fcount = 1.f/count;
-                        const int mean_x = (int)(shift_x*fcount+0.5f);
-                        const int mean_y = (int)(shift_y*fcount+0.5f);
-                        for(dim_t ch=0; ch<channels; ++ch)
-                            means[ch] *= fcount;
+                            if (count==0) { break; }
+
+                            const float fcount = 1.f/count;
+                            const int mean_x = (int)(shift_x*fcount+0.5f);
+                            const int mean_y = (int)(shift_y*fcount+0.5f);
+                            for(dim_t ch=0; ch<channels; ++ch)
+                                means[ch] *= fcount;
+
+                            float norm = 0.f;
+                            for(dim_t ch=0; ch<channels; ++ch)
+                                norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
+                            bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
+                            shift_x = mean_x;
+                            shift_y = mean_y;
+                            for(dim_t ch=0; ch<channels; ++ch)
+                                centers[ch] = means[ch];
+                            if (stop) { break; }
+                        } // scope of meanshift iterations end
 
-                        float norm = 0.f;
-                        for(dim_t ch=0; ch<channels; ++ch)
-                            norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
-                        bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
-                        shift_x = mean_x;
-                        shift_y = mean_y;
                         for(dim_t ch=0; ch<channels; ++ch)
-                            centers[ch] = means[ch];
-                        if (stop) { break; }
-                    } // scope of meanshift iterations end
-
-                    for(dim_t ch=0; ch<channels; ++ch)
-                        outData[j_out_off + i_out_off + ch*ostrides[2]] = centers[ch];
+                            outData[j_out_off + i_out_off + ch*ostrides[2]] = centers[ch];
 
+                    }
                 }
+                outData += ostrides[2];
+                inData  += istrides[2];
             }
-            outData += ostrides[2];
-            inData  += istrides[2];
         }
-    }
+    };
+    getQueue().enqueue(func, out, in, s_sigma, c_sigma, iter);
+
     return out;
 }
 
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 3ded3c045a..ce921fc3b5 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -14,6 +14,8 @@
 #include <medfilt.hpp>
 #include <err_cpu.hpp>
 #include <algorithm>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -23,114 +25,119 @@ namespace cpu
 template<typename T, af_border_type pad>
 Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
-    const dim4 dims     = in.dims();
-    const dim4 istrides = in.strides();
-    Array<T> out        = createEmptyArray<T>(dims);
-    const dim4 ostrides = out.strides();
+    Array<T> out        = createEmptyArray<T>(in.dims());
 
-    std::vector<T> wind_vals;
-    wind_vals.reserve(w_len*w_wid);
+    auto func = [=] (Array<T> out, const Array<T> in,
+                     dim_t w_len, dim_t w_wid) {
+        const dim4 dims     = in.dims();
+        const dim4 istrides = in.strides();
+        const dim4 ostrides = out.strides();
 
-    T const * in_ptr = in.get();
-    T * out_ptr = out.get();
+        std::vector<T> wind_vals;
+        wind_vals.reserve(w_len*w_wid);
 
-    for(int b3=0; b3<(int)dims[3]; b3++) {
+        T const * in_ptr = in.get();
+        T * out_ptr = out.get();
 
-        for(int b2=0; b2<(int)dims[2]; b2++) {
+        for(int b3=0; b3<(int)dims[3]; b3++) {
 
-            for(int col=0; col<(int)dims[1]; col++) {
+            for(int b2=0; b2<(int)dims[2]; b2++) {
 
-                int ocol_off = col*ostrides[1];
+                for(int col=0; col<(int)dims[1]; col++) {
 
-                for(int row=0; row<(int)dims[0]; row++) {
+                    int ocol_off = col*ostrides[1];
 
-                    wind_vals.clear();
+                    for(int row=0; row<(int)dims[0]; row++) {
 
-                    for(int wj=0; wj<(int)w_wid; ++wj) {
+                        wind_vals.clear();
 
-                        bool isColOff = false;
+                        for(int wj=0; wj<(int)w_wid; ++wj) {
 
-                        int im_col = col + wj-w_wid/2;
-                        int im_coff;
-                        switch(pad) {
-                            case AF_PAD_ZERO:
-                                im_coff = im_col * istrides[1];
-                                if (im_col < 0 || im_col>=(int)dims[1])
-                                    isColOff = true;
-                                break;
-                            case AF_PAD_SYM:
-                                {
-                                    if (im_col < 0) {
-                                        im_col *= -1;
-                                        isColOff = true;
-                                    }
+                            bool isColOff = false;
 
-                                    if (im_col>=(int)dims[1]) {
-                                        im_col = 2*((int)dims[1]-1) - im_col;
-                                        isColOff = true;
-                                    }
-
-                                    im_coff = im_col * istrides[1];
-                                }
-                                break;
-                        }
-
-                        for(int wi=0; wi<(int)w_len; ++wi) {
-
-                            bool isRowOff = false;
-
-                            int im_row = row + wi-w_len/2;
-                            int im_roff;
+                            int im_col = col + wj-w_wid/2;
+                            int im_coff;
                             switch(pad) {
                                 case AF_PAD_ZERO:
-                                    im_roff = im_row * istrides[0];
-                                    if (im_row < 0 || im_row>=(int)dims[0])
-                                        isRowOff = true;
+                                    im_coff = im_col * istrides[1];
+                                    if (im_col < 0 || im_col>=(int)dims[1])
+                                        isColOff = true;
                                     break;
                                 case AF_PAD_SYM:
                                     {
-                                        if (im_row < 0) {
-                                            im_row *= -1;
-                                            isRowOff = true;
+                                        if (im_col < 0) {
+                                            im_col *= -1;
+                                            isColOff = true;
                                         }
 
-                                        if (im_row>=(int)dims[0]) {
-                                            im_row = 2*((int)dims[0]-1) - im_row;
-                                            isRowOff = true;
+                                        if (im_col>=(int)dims[1]) {
+                                            im_col = 2*((int)dims[1]-1) - im_col;
+                                            isColOff = true;
                                         }
 
-                                        im_roff = im_row * istrides[0];
+                                        im_coff = im_col * istrides[1];
                                     }
                                     break;
                             }
 
-                            if(isRowOff || isColOff) {
+                            for(int wi=0; wi<(int)w_len; ++wi) {
+
+                                bool isRowOff = false;
+
+                                int im_row = row + wi-w_len/2;
+                                int im_roff;
                                 switch(pad) {
                                     case AF_PAD_ZERO:
-                                        wind_vals.push_back(0);
+                                        im_roff = im_row * istrides[0];
+                                        if (im_row < 0 || im_row>=(int)dims[0])
+                                            isRowOff = true;
                                         break;
                                     case AF_PAD_SYM:
-                                        wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                                        {
+                                            if (im_row < 0) {
+                                                im_row *= -1;
+                                                isRowOff = true;
+                                            }
+
+                                            if (im_row>=(int)dims[0]) {
+                                                im_row = 2*((int)dims[0]-1) - im_row;
+                                                isRowOff = true;
+                                            }
+
+                                            im_roff = im_row * istrides[0];
+                                        }
                                         break;
                                 }
-                            } else
-                                wind_vals.push_back(in_ptr[im_coff+im_roff]);
+
+                                if(isRowOff || isColOff) {
+                                    switch(pad) {
+                                        case AF_PAD_ZERO:
+                                            wind_vals.push_back(0);
+                                            break;
+                                        case AF_PAD_SYM:
+                                            wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                                            break;
+                                    }
+                                } else
+                                    wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                            }
                         }
-                    }
 
-                    std::stable_sort(wind_vals.begin(),wind_vals.end());
-                    int off = wind_vals.size()/2;
-                    if (wind_vals.size()%2==0)
-                        out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
-                    else {
-                        out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
+                        std::stable_sort(wind_vals.begin(),wind_vals.end());
+                        int off = wind_vals.size()/2;
+                        if (wind_vals.size()%2==0)
+                            out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
+                        else {
+                            out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
+                        }
                     }
                 }
+                in_ptr  += istrides[2];
+                out_ptr += ostrides[2];
             }
-            in_ptr  += istrides[2];
-            out_ptr += ostrides[2];
         }
-    }
+    };
+    getQueue().enqueue(func, out, in, w_len, w_wid);
 
     return out;
 }

From b813fd4bf2f49bc5f9af4cae62321b2945b5f129 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 13:51:42 -0500
Subject: [PATCH 0072/2677] nearest neighbour cpu func is asyn call now

---
 src/backend/cpu/nearest_neighbour.cpp | 46 ++++++++++++++++++---------
 1 file changed, 31 insertions(+), 15 deletions(-)

diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 79d41516e3..97f0e0a8f0 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -13,6 +13,8 @@
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <handle.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -90,27 +92,18 @@ struct dist_op<ushort, To, AF_SHD>
 };
 
 template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
-                        const Array<T>& query, const Array<T>& train,
+void nearest_neighbour_(Array<uint> idx, Array<To> dist,
+                        const Array<T> query, const Array<T> train,
                         const uint dist_dim, const uint n_dist)
 {
     uint sample_dim = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
 
-    if (n_dist > 1) {
-        CPU_NOT_SUPPORTED();
-    }
-
     const unsigned distLength = qDims[dist_dim];
     const unsigned nQuery = qDims[sample_dim];
     const unsigned nTrain = tDims[sample_dim];
 
-    const dim4 outDims(n_dist, nQuery);
-
-    idx  = createEmptyArray<uint>(outDims);
-    dist = createEmptyArray<To  >(outDims);
-
     const T* qPtr = query.get();
     const T* tPtr = train.get();
     uint* iPtr = idx.get();
@@ -157,11 +150,34 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
                        const uint dist_dim, const uint n_dist,
                        const af_match_type dist_type)
 {
+    if (n_dist > 1) {
+        CPU_NOT_SUPPORTED();
+    }
+
+    query.eval();
+    train.eval();
+
+    uint sample_dim  = (dist_dim == 0) ? 1 : 0;
+    const dim4 qDims = query.dims();
+    const dim4 outDims(n_dist, qDims[sample_dim]);
+
+    idx  = createEmptyArray<uint>(outDims);
+    dist = createEmptyArray<To  >(outDims);
+    idx.eval();
+    dist.eval();
+
     switch(dist_type) {
-        case AF_SAD: nearest_neighbour_<T, To, AF_SAD>(idx, dist, query, train, dist_dim, n_dist); break;
-        case AF_SSD: nearest_neighbour_<T, To, AF_SSD>(idx, dist, query, train, dist_dim, n_dist); break;
-        case AF_SHD: nearest_neighbour_<T, To, AF_SHD>(idx, dist, query, train, dist_dim, n_dist); break;
-        default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
+        case AF_SAD:
+            getQueue().enqueue(nearest_neighbour_<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
+            break;
+        case AF_SSD:
+            getQueue().enqueue(nearest_neighbour_<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
+            break;
+        case AF_SHD:
+            getQueue().enqueue(nearest_neighbour_<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
+            break;
+        default:
+            AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
 }
 

From 99fe1acf83c17b906a129e0926ae89700cafd664 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Dec 2015 14:15:14 -0500
Subject: [PATCH 0073/2677] Fix examples installation directory

---
 CMakeModules/AFInstallDirs.cmake | 2 +-
 examples/CMakeLists.txt          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/AFInstallDirs.cmake b/CMakeModules/AFInstallDirs.cmake
index 8fc4c3a05c..1060f80952 100644
--- a/CMakeModules/AFInstallDirs.cmake
+++ b/CMakeModules/AFInstallDirs.cmake
@@ -30,7 +30,7 @@ if(NOT DEFINED AF_INSTALL_DOC_DIR)
 endif()
 
 if(NOT DEFINED AF_INSTALL_EXAMPLE_DIR)
-  set(AF_INSTALL_EXAMPLE_DIR "${AF_INSTALL_DATA_DIR}" CACHE PATH "Installation path for examples")
+  set(AF_INSTALL_EXAMPLE_DIR "${AF_INSTALL_DATA_DIR}/examples" CACHE PATH "Installation path for examples")
 endif()
 
 # Man pages
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index d42f273c6b..1ffb82bad0 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -132,5 +132,5 @@ INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/"
     COMPONENT examples)
 
 INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../assets/examples"
-    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/assets/"
+    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/assets"
 )

From 35a462c08b86dcb107f6df84428adb6dea749636 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 14:38:49 -0500
Subject: [PATCH 0074/2677] conversion of listed functions to async calls

* gradient
* histogram
* hsv2rgb
* rgb2hsv
* identity
* inverse
* iota
* lookup
---
 src/backend/cpu/gradient.cpp  |  26 +++--
 src/backend/cpu/histogram.cpp |  44 +++++---
 src/backend/cpu/hsv_rgb.cpp   | 192 ++++++++++++++++++----------------
 src/backend/cpu/identity.cpp  |  44 ++++----
 src/backend/cpu/inverse.cpp   |  13 ++-
 src/backend/cpu/iota.cpp      |  85 ++++++++-------
 src/backend/cpu/lookup.cpp    |  50 +++++----
 7 files changed, 258 insertions(+), 196 deletions(-)

diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index 8ab2fe46fc..504c02a29c 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -12,12 +12,20 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    template<typename T>
-    void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
-    {
+
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
+{
+    grad0.eval();
+    grad1.eval();
+    in.eval();
+
+    auto func = [=] (Array<T> grad0, Array<T> grad1, const Array<T> in) {
         const af::dim4 dims = in.dims();
 
         T *d_grad0    = grad0.get();
@@ -82,13 +90,15 @@ namespace cpu
                 }
             }
         }
-    }
+    };
+    getQueue().enqueue(func, grad0, grad1, in);
+}
 
 #define INSTANTIATE(T)                                                                  \
     template void gradient<T>(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);    \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
 }
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index e382a0ee87..8fb3e43544 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -12,6 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -21,31 +23,39 @@ namespace cpu
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval)
 {
-    float step = (maxval - minval)/(float)nbins;
+    in.eval();
 
     const dim4 inDims  = in.dims();
-    dim4 iStrides      = in.strides();
     dim4 outDims       = dim4(nbins,1,inDims[2],inDims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
-    dim4 oStrides      = out.strides();
-    dim_t nElems    = inDims[0]*inDims[1];
+    out.eval();
 
-    outType *outData    = out.get();
-    const inType* inData= in.get();
+    auto func = [=](Array<outType> out, const Array<inType> in,
+                    const unsigned nbins, const double minval, const double maxval) {
+        const float step     = (maxval - minval)/(float)nbins;
+        const dim4 inDims    = in.dims();
+        const dim4 iStrides  = in.strides();
+        const dim4 oStrides  = out.strides();
+        const dim_t nElems   = inDims[0]*inDims[1];
 
-    for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
-        for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
-            for(dim_t i=0; i<nElems; i++) {
-                int idx = isLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
-                int bin = (int)((inData[idx] - minval) / step);
-                bin = std::max(bin, 0);
-                bin = std::min(bin, (int)(nbins - 1));
-                outData[bin]++;
+        outType *outData    = out.get();
+        const inType* inData= in.get();
+
+        for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
+            for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
+                for(dim_t i=0; i<nElems; i++) {
+                    int idx = isLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
+                    int bin = (int)((inData[idx] - minval) / step);
+                    bin = std::max(bin, 0);
+                    bin = std::min(bin, (int)(nbins - 1));
+                    outData[bin]++;
+                }
+                inData  += iStrides[2];
+                outData += oStrides[2];
             }
-            inData  += iStrides[2];
-            outData += oStrides[2];
         }
-    }
+    };
+    getQueue().enqueue(func, out, in, nbins, minval, maxval);
 
     return out;
 }
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index 82f404fa95..d20416f3c9 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -13,6 +13,8 @@
 #include <hsv_rgb.hpp>
 #include <err_cpu.hpp>
 #include <cmath>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -22,54 +24,60 @@ namespace cpu
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in)
 {
-    const dim4 dims    = in.dims();
-    const dim4 strides = in.strides();
-    Array<T> out       = createEmptyArray<T>(dims);
-    dim_t obStride  = out.strides()[3];
-    dim_t coff      = strides[2];
-    dim_t bCount    = dims[3];
-
-    for(dim_t b=0; b<bCount; ++b) {
-        const T* src = in.get() + b * strides[3];
-        T* dst       = out.get() + b * obStride;
-
-        for(dim_t j=0; j<dims[1]; ++j) {
-            dim_t jOff = j*strides[1];
-            // j steps along 2nd dimension
-            for(dim_t i=0; i<dims[0]; ++i) {
-                // i steps along 1st dimension
-                dim_t hIdx = i*strides[0] + jOff;
-                dim_t sIdx = hIdx + coff;
-                dim_t vIdx = sIdx + coff;
-
-                T H = src[hIdx];
-                T S = src[sIdx];
-                T V = src[vIdx];
-
-                T R, G, B;
-                R = G = B = 0;
-
-                int   m = (int)(H * 6);
-                T f = H * 6 - m;
-                T p = V * (1 - S);
-                T q = V * (1 - f * S);
-                T t = V * (1 - (1 - f) * S);
-
-                switch (m % 6) {
-                    case 0: R = V, G = t, B = p; break;
-                    case 1: R = q, G = V, B = p; break;
-                    case 2: R = p, G = V, B = t; break;
-                    case 3: R = p, G = q, B = V; break;
-                    case 4: R = t, G = p, B = V; break;
-                    case 5: R = V, G = p, B = q; break;
+    in.eval();
+
+    Array<T> out = createEmptyArray<T>(in.dims());
+
+    auto func = [=](Array<T> out, const Array<T> in) {
+        const dim4 dims    = in.dims();
+        const dim4 strides = in.strides();
+        dim_t obStride  = out.strides()[3];
+        dim_t coff      = strides[2];
+        dim_t bCount    = dims[3];
+
+        for(dim_t b=0; b<bCount; ++b) {
+            const T* src = in.get() + b * strides[3];
+            T* dst       = out.get() + b * obStride;
+
+            for(dim_t j=0; j<dims[1]; ++j) {
+                dim_t jOff = j*strides[1];
+                // j steps along 2nd dimension
+                for(dim_t i=0; i<dims[0]; ++i) {
+                    // i steps along 1st dimension
+                    dim_t hIdx = i*strides[0] + jOff;
+                    dim_t sIdx = hIdx + coff;
+                    dim_t vIdx = sIdx + coff;
+
+                    T H = src[hIdx];
+                    T S = src[sIdx];
+                    T V = src[vIdx];
+
+                    T R, G, B;
+                    R = G = B = 0;
+
+                    int   m = (int)(H * 6);
+                    T f = H * 6 - m;
+                    T p = V * (1 - S);
+                    T q = V * (1 - f * S);
+                    T t = V * (1 - (1 - f) * S);
+
+                    switch (m % 6) {
+                        case 0: R = V, G = t, B = p; break;
+                        case 1: R = q, G = V, B = p; break;
+                        case 2: R = p, G = V, B = t; break;
+                        case 3: R = p, G = q, B = V; break;
+                        case 4: R = t, G = p, B = V; break;
+                        case 5: R = V, G = p, B = q; break;
+                    }
+
+                    dst[hIdx] = R;
+                    dst[sIdx] = G;
+                    dst[vIdx] = B;
                 }
-
-                dst[hIdx] = R;
-                dst[sIdx] = G;
-                dst[vIdx] = B;
             }
         }
-    }
+    };
+    getQueue().enqueue(func, out, in);
 
     return out;
 }
@@ -77,53 +85,59 @@ Array<T> hsv2rgb(const Array<T>& in)
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in)
 {
-    const dim4 dims    = in.dims();
-    const dim4 strides = in.strides();
-    Array<T> out       = createEmptyArray<T>(dims);
-    dim4 oStrides      = out.strides();
-    dim_t bCount    = dims[3];
-
-    for(dim_t b=0; b<bCount; ++b) {
-        const T* src = in.get() + b * strides[3];
-        T* dst       = out.get() + b * oStrides[3];
-
-        for(dim_t j=0; j<dims[1]; ++j) {
-            // j steps along 2nd dimension
-            dim_t oj = j * oStrides[1];
-            dim_t ij = j * strides[1];
-
-            for(dim_t i=0; i<dims[0]; ++i) {
-                // i steps along 1st dimension
-                dim_t oIdx0 = i * oStrides[0] + oj;
-                dim_t oIdx1 = oIdx0 + oStrides[2];
-                dim_t oIdx2 = oIdx1 + oStrides[2];
-
-                dim_t iIdx0 = i * strides[0]  + ij;
-                dim_t iIdx1 = iIdx0 + strides[2];
-                dim_t iIdx2 = iIdx1 + strides[2];
-
-                T R = src[iIdx0];
-                T G = src[iIdx1];
-                T B = src[iIdx2];
-                T Cmax = std::max(std::max(R, G), B);
-                T Cmin = std::min(std::min(R, G), B);
-                T delta= Cmax-Cmin;
-
-                T H = 0;
-
-                if (Cmax!=Cmin) {
-                    if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
-                    if (Cmax==G) H = (B-R)/delta + 2;
-                    if (Cmax==B) H = (R-G)/delta + 4;
-                    H = H / 6.0f;
+    in.eval();
+
+    Array<T> out = createEmptyArray<T>(in.dims());
+
+    auto func = [=](Array<T> out, const Array<T> in) {
+        const dim4 dims    = in.dims();
+        const dim4 strides = in.strides();
+        dim4 oStrides      = out.strides();
+        dim_t bCount    = dims[3];
+
+        for(dim_t b=0; b<bCount; ++b) {
+            const T* src = in.get() + b * strides[3];
+            T* dst       = out.get() + b * oStrides[3];
+
+            for(dim_t j=0; j<dims[1]; ++j) {
+                // j steps along 2nd dimension
+                dim_t oj = j * oStrides[1];
+                dim_t ij = j * strides[1];
+
+                for(dim_t i=0; i<dims[0]; ++i) {
+                    // i steps along 1st dimension
+                    dim_t oIdx0 = i * oStrides[0] + oj;
+                    dim_t oIdx1 = oIdx0 + oStrides[2];
+                    dim_t oIdx2 = oIdx1 + oStrides[2];
+
+                    dim_t iIdx0 = i * strides[0]  + ij;
+                    dim_t iIdx1 = iIdx0 + strides[2];
+                    dim_t iIdx2 = iIdx1 + strides[2];
+
+                    T R = src[iIdx0];
+                    T G = src[iIdx1];
+                    T B = src[iIdx2];
+                    T Cmax = std::max(std::max(R, G), B);
+                    T Cmin = std::min(std::min(R, G), B);
+                    T delta= Cmax-Cmin;
+
+                    T H = 0;
+
+                    if (Cmax!=Cmin) {
+                        if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
+                        if (Cmax==G) H = (B-R)/delta + 2;
+                        if (Cmax==B) H = (R-G)/delta + 4;
+                        H = H / 6.0f;
+                    }
+
+                    dst[oIdx0] = H;
+                    dst[oIdx1] = (Cmax==0.0f ? 0 : delta/Cmax);
+                    dst[oIdx2] = Cmax;
                 }
-
-                dst[oIdx0] = H;
-                dst[oIdx1] = (Cmax==0.0f ? 0 : delta/Cmax);
-                dst[oIdx2] = Cmax;
             }
         }
-    }
+    };
+    getQueue().enqueue(func, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index 2973ae4409..f7236bd68d 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -13,13 +13,17 @@
 #include <Array.hpp>
 #include <identity.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    template<typename T>
-    Array<T> identity(const dim4& dims)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
+template<typename T>
+Array<T> identity(const dim4& dims)
+{
+    Array<T> out = createEmptyArray<T>(dims);
+
+    auto func = [=] (Array<T> out) {
         T *ptr = out.get();
         const dim_t *out_dims  = out.dims().get();
 
@@ -31,23 +35,25 @@ namespace cpu
             }
             ptr += out_dims[0] * out_dims[1];
         }
-        return out;
-    }
+    };
+    getQueue().enqueue(func, out);
+
+    return out;
+}
 
 #define INSTANTIATE_IDENTITY(T)                              \
     template Array<T>  identity<T>    (const af::dim4 &dims);
 
-    INSTANTIATE_IDENTITY(float)
-    INSTANTIATE_IDENTITY(double)
-    INSTANTIATE_IDENTITY(cfloat)
-    INSTANTIATE_IDENTITY(cdouble)
-    INSTANTIATE_IDENTITY(int)
-    INSTANTIATE_IDENTITY(uint)
-    INSTANTIATE_IDENTITY(intl)
-    INSTANTIATE_IDENTITY(uintl)
-    INSTANTIATE_IDENTITY(char)
-    INSTANTIATE_IDENTITY(uchar)
-    INSTANTIATE_IDENTITY(short)
-    INSTANTIATE_IDENTITY(ushort)
-
+INSTANTIATE_IDENTITY(float)
+INSTANTIATE_IDENTITY(double)
+INSTANTIATE_IDENTITY(cfloat)
+INSTANTIATE_IDENTITY(cdouble)
+INSTANTIATE_IDENTITY(int)
+INSTANTIATE_IDENTITY(uint)
+INSTANTIATE_IDENTITY(intl)
+INSTANTIATE_IDENTITY(uintl)
+INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(uchar)
+INSTANTIATE_IDENTITY(short)
+INSTANTIATE_IDENTITY(ushort)
 }
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index 129823b963..987ba01c53 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -23,6 +23,8 @@
 #include <lu.hpp>
 #include <identity.hpp>
 #include <solve.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -48,6 +50,7 @@ INV_FUNC(getri , cdouble, z)
 template<typename T>
 Array<T> inverse(const Array<T> &in)
 {
+    in.eval();
 
     int M = in.dims()[0];
     int N = in.dims()[1];
@@ -58,12 +61,14 @@ Array<T> inverse(const Array<T> &in)
     }
 
     Array<T> A = copyArray<T>(in);
-
     Array<int> pivot = lu_inplace<T>(A, false);
 
-    getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
-                    A.get(), A.strides()[1],
-                    pivot.get());
+    auto func = [=] (Array<T> A, Array<int> pivot, int M) {
+        getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
+                A.get(), A.strides()[1],
+                pivot.get());
+    };
+    getQueue().enqueue(func, A, pivot, M);
 
     return A;
 }
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 47bcb924e4..170b6a1570 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -14,59 +14,66 @@
 #include <err_cpu.hpp>
 #include <algorithm>
 #include <numeric>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using namespace std;
 
 namespace cpu
 {
-    ///////////////////////////////////////////////////////////////////////////
-    // Kernel Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T>
-    void iota(T *out, const dim4 &dims, const dim4 &strides, const dim4 &sdims, const dim4 &tdims)
-    {
-        for(dim_t w = 0; w < dims[3]; w++) {
-            dim_t offW = w * strides[3];
-            T valW = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
-            for(dim_t z = 0; z < dims[2]; z++) {
-                dim_t offWZ = offW + z * strides[2];
-                T valZ = valW + (z % sdims[2]) * sdims[0] * sdims[1];
-                for(dim_t y = 0; y < dims[1]; y++) {
-                    dim_t offWZY = offWZ + y * strides[1];
-                    T valY = valZ + (y % sdims[1]) * sdims[0];
-                    for(dim_t x = 0; x < dims[0]; x++) {
-                        dim_t id = offWZY + x;
-                        out[id] = valY + (x % sdims[0]);
-                    }
+///////////////////////////////////////////////////////////////////////////
+// Kernel Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void iota_(Array<T>  output, const dim4 &sdims, const dim4 &tdims)
+{
+    const dim4 dims    = output.dims();
+    T* out             = output.get();
+    const dim4 strides = output.strides();
+
+    for(dim_t w = 0; w < dims[3]; w++) {
+        dim_t offW = w * strides[3];
+        T valW = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
+        for(dim_t z = 0; z < dims[2]; z++) {
+            dim_t offWZ = offW + z * strides[2];
+            T valZ = valW + (z % sdims[2]) * sdims[0] * sdims[1];
+            for(dim_t y = 0; y < dims[1]; y++) {
+                dim_t offWZY = offWZ + y * strides[1];
+                T valY = valZ + (y % sdims[1]) * sdims[0];
+                for(dim_t x = 0; x < dims[0]; x++) {
+                    dim_t id = offWZY + x;
+                    out[id] = valY + (x % sdims[0]);
                 }
             }
         }
     }
+}
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Wrapper Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T>
-    Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
-    {
-        dim4 outdims = dims * tile_dims;
+///////////////////////////////////////////////////////////////////////////
+// Wrapper Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
+{
+    dim4 outdims = dims * tile_dims;
 
-        Array<T> out = createEmptyArray<T>(outdims);
-        iota<T>(out.get(), out.dims(), out.strides(), dims, tile_dims);
+    Array<T> out = createEmptyArray<T>(outdims);
 
-        return out;
-    }
+    getQueue().enqueue(iota_<T>, out, dims, tile_dims);
+
+    return out;
+}
 
 #define INSTANTIATE(T)                                                          \
     template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims); \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 }
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 128cc02823..0aeee4dc81 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -10,6 +10,8 @@
 #include <lookup.hpp>
 #include <err_cpu.hpp>
 #include <cstdlib>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -30,11 +32,10 @@ dim_t trimIndex(int idx, const dim_t &len)
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim)
 {
-    const dim4 iDims = input.dims();
-    const dim4 iStrides = input.strides();
+    input.eval();
+    indices.eval();
 
-    const in_t *inPtr = input.get();
-    const idx_t *idxPtr = indices.get();
+    const dim4 iDims = input.dims();
 
     dim4 oDims(1);
     for (int d=0; d<4; ++d)
@@ -42,35 +43,44 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    dim4 oStrides = out.strides();
+    auto func = [=] (Array<in_t> out, const Array<in_t> input,
+                     const Array<idx_t> indices, const unsigned dim) {
+        const dim4 iDims    = input.dims();
+        const dim4 oDims    = out.dims();
+        const dim4 iStrides = input.strides();
+        const dim4 oStrides = out.strides();
+        const in_t *inPtr   = input.get();
+        const idx_t *idxPtr = indices.get();
 
-    in_t *outPtr = out.get();
+        in_t *outPtr = out.get();
 
-    for (dim_t l=0; l<oDims[3]; ++l) {
+        for (dim_t l=0; l<oDims[3]; ++l) {
 
-        dim_t iLOff = iStrides[3]*(dim==3 ? trimIndex((dim_t)idxPtr[l], iDims[3]): l);
-        dim_t oLOff = l*oStrides[3];
+            dim_t iLOff = iStrides[3]*(dim==3 ? trimIndex((dim_t)idxPtr[l], iDims[3]): l);
+            dim_t oLOff = l*oStrides[3];
 
-        for (dim_t k=0; k<oDims[2]; ++k) {
+            for (dim_t k=0; k<oDims[2]; ++k) {
 
-            dim_t iKOff = iStrides[2]*(dim==2 ? trimIndex((dim_t)idxPtr[k], iDims[2]): k);
-            dim_t oKOff = k*oStrides[2];
+                dim_t iKOff = iStrides[2]*(dim==2 ? trimIndex((dim_t)idxPtr[k], iDims[2]): k);
+                dim_t oKOff = k*oStrides[2];
 
-            for (dim_t j=0; j<oDims[1]; ++j) {
+                for (dim_t j=0; j<oDims[1]; ++j) {
 
-                dim_t iJOff = iStrides[1]*(dim==1 ? trimIndex((dim_t)idxPtr[j], iDims[1]): j);
-                dim_t oJOff = j*oStrides[1];
+                    dim_t iJOff = iStrides[1]*(dim==1 ? trimIndex((dim_t)idxPtr[j], iDims[1]): j);
+                    dim_t oJOff = j*oStrides[1];
 
-                for (dim_t i=0; i<oDims[0]; ++i) {
+                    for (dim_t i=0; i<oDims[0]; ++i) {
 
-                    dim_t iIOff = iStrides[0]*(dim==0 ? trimIndex((dim_t)idxPtr[i], iDims[0]): i);
-                    dim_t oIOff = i*oStrides[0];
+                        dim_t iIOff = iStrides[0]*(dim==0 ? trimIndex((dim_t)idxPtr[i], iDims[0]): i);
+                        dim_t oIOff = i*oStrides[0];
 
-                    outPtr[oLOff+oKOff+oJOff+oIOff] = inPtr[iLOff+iKOff+iJOff+iIOff];
+                        outPtr[oLOff+oKOff+oJOff+oIOff] = inPtr[iLOff+iKOff+iJOff+iIOff];
+                    }
                 }
             }
         }
-    }
+    };
+    getQueue().enqueue(func, out, input, indices, dim);
 
     return out;
 }

From 72fe0aee41d23353fb53f879a0bc7bc67e2a26e2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 2 Dec 2015 14:46:59 -0500
Subject: [PATCH 0075/2677] interop formatting tweaks

---
 docs/pages/interop_cuda.md   | 6 +++---
 docs/pages/interop_opencl.md | 3 ++-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index f2e4e3958c..5ce92d2b3b 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -96,13 +96,13 @@ the appropriate header.
 The first thing these headers allow us to do are to get and set the active device
 using native CUDA device ids. This is achieved through the following functions:
 > **static int getNativeId (int id)** 
-> -- Get the native device id of the CUDA device with id in the ArrayFire context.
+> -- Get the native device id of the CUDA device with **id** in the ArrayFire context.
 
 > **static void setNativeId (int nativeId)**  
-> -- Set the CUDA device with given native id as the active device for ArrayFire.
+> -- Set the CUDA device with given native **id** as the active device for ArrayFire.
 The headers also allow us to retrieve the CUDA stream used internally inside Arrayfire.
 > **static cudaStream_t afcu::getStream(int id)**  
-> -- Get the stream for the CUDA device with id in ArrayFire context.
+> -- Get the stream for the CUDA device with **id** in ArrayFire context.
 These functions are available within the afcu:: namespace and equal C variants 
 can be fund in the full [cuda interop documentation.](\ref cuda_mat.htm)
 
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 6e270a924b..93361d039d 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -172,7 +172,8 @@ void simple_kernel(__global float *d_y,
 
 Unfortunately, Arrayfire's interoperability functions don't yet allow us to work with
 external OpenCL contexts. This is currently an open issue and can be tracked here:
-https://github.com/arrayfire/arrayfire/issues/1002
+https://github.com/arrayfire/arrayfire/issues/1002.
+
 Once the issue is addressed, it will be possible to take the reverse route and start with
 completely custom OpenCL code, then transfer our results into af::array objects.
 

From ffd59413af8d4bbb26d63e4047d8f71d80ae112d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 2 Dec 2015 14:47:27 -0500
Subject: [PATCH 0076/2677] additional vectorization content

---
 docs/layout.xml             |  3 +-
 docs/pages/vectorization.md | 65 ++++++++++++++++++++++++++++++-------
 2 files changed, 56 insertions(+), 12 deletions(-)

diff --git a/docs/layout.xml b/docs/layout.xml
index 76b6bcc6e7..0b272f65f8 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -10,8 +10,9 @@
       <tab type="user" url="\ref gettingstarted" visible="yes" title="Getting Started"/>
       <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
       <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Matrix Manipulation"/>
-      <tab type="user" url="\ref vectorization" visible="yes" title="Vectorization"/>
+      <tab type="user" url="\ref vectorization" visible="yes" title="Introduction to Vectorization"/>
       <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
+      <tab type="user" url="\ref interop_cuda" visible="yes" title="CUDA Interoperability"/>
       <tab type="user" url="\ref interop_opencl" visible="yes" title="OpenCL Interoperability"/>
       <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
       <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index ad51e64bbe..93cdeb5b69 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -1,4 +1,4 @@
-Vectorization {#vectorization}
+Introduction to Vectorization {#vectorization}
 ===================
 
 Programmers and Data Scientists want to take advantage of fast and parallel
@@ -29,7 +29,7 @@ af::array a = af::range(10);  // [0,  9]
 a = a + 1;                    // [1, 10]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Some of the vectorized mathematical functions of Arrayfire include:
+Most Arrayfire functions are vectorized. A small subset of these include:
 
 Operator Category                                           | Functions
 ------------------------------------------------------------|--------------------------
@@ -41,6 +41,8 @@ Operator Category                                           | Functions
 [Numeric functions](\ref numeric_mat)                       | abs(), floor(), round(), min(), max(), etc.
 [Trigonometric functions](\ref trig_mat)                    | sin(), cos(), tan(), etc.
 
+Using the built in vectorized operations should be the first and preferred method
+of vectorizing any code written with Arrayfire.
 
 # GFOR: Parallel for-loops
 Another novel method of vectorization present in Arrayfire is the GFOR loop replacement construct.
@@ -58,17 +60,37 @@ af::array a = af::range(10);
 gfor(seq i, n)
     a(i) = a(i) + 1;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-It is best to vectorize computation as much as possible to avoid the overhead in
-both for-loops and gfor-loops.
 
-To see another example, you could run an FFT on every 2D slice of a volume in a
+To see another example, you could run an accum() on every slice of a matrix in a
 for-loop, or you could "vectorize" and simply do it all in one gfor-loop operation:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 for (int i = 0; i < N; ++i)
-   A(span,span,i) = fft2(A(span,span,i)); // runs each FFT in sequence
+   B(span,i) = accum(A(span,i)); // runs each accum() in sequence
 gfor (seq i, N)
-   A(span,span,i) = fft2(A(span,span,i)); // runs N FFTs in parallel
+   B(span,i) = accum(A(span,i)); // runs N accums in parallel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+However, returning to our previous vectorization technique, accum() is already
+vectorized and the operation could be completely replaced with merely:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    B = accum(A);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+It is best to vectorize computation as much as possible to avoid the overhead in
+both for-loops and gfor-loops. However, the gfor-loop construct is most effective
+in the narrow case of broadcast-style operations. Consider the case when we have
+a vector of constants that we wish to apply to a collection of variables, such as
+expressing the values of a linear combination for multiple vectors. The broadcast
+of one set of constants to many vectors works well with gfor-loops:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+const static int p=4, n=1000;
+af::array consts = af::randu(p);
+af::array var_terms = randn(p, n);
+
+gfor(seq i, n)
+    combination(span, i) = consts * var_terms(span, i);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
 ## GFOR: Usage
 There are three formats for instantiating gfor-loops:
 
@@ -102,12 +124,12 @@ The naive solution would be using a loop as we've seen before:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = constant(0, 5, 5);
 for(int i=0; i<weights.dims(1); ++i){
-    filtered_weights.col(i) = filter * weights(i);
+    filtered_weights.col(i) = filter * weights.col(i);
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 However we would like a vectorized solution. The following syntax begs to be used:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array filtered_weights = filter * weights; //fails due to dimension mismatch
+af::array filtered_weights = filter * weights; // fails due to dimension mismatch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 but it fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if
 Arrayfire could figure out along which dimension we intend to apply the batch
@@ -121,10 +143,18 @@ where __batchFunc_t__ is a function pointer of the form:
 
 
 So, to use batchFunc(), we need to provide the function we will be applying as a
-batch operation. Our final batch call is not much more difficult than the ideal
+batch operation. For illustration's sake, let's "implement" a multiplication
+function following the format.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array my_mult (const af::array &lhs, const af::array &rhs){
+    return lhs * rhs;
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Our final batch call is not much more difficult than the ideal
 syntax we imagined.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array filtered_weights = batchFunc(filter, weights, operator* );
+af::array filtered_weights = batchFunc( filter, weights, my_mult );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The batch function will work with many previously mentioned vectorized Arrayfire
@@ -132,3 +162,16 @@ functions. It can even work with a combination of those functions if they are
 wrapped inside a helper function matching the __batchFunc_t__ signature. Unfortunately,
 the batch function cannot be used within a gfor() construct at this moment.
 
+# Advanced Vectorization
+We have seen the different methods Arrayfire provides to vectorize our code. Tying
+them all together is a slightly more involved process that needs to consider data
+dimensionality and layout, memory usage, nesting order, etc. An excellent example
+and discussion of these factors can be found on our blog:
+http://arrayfire.com/how-to-write-vectorized-code/
+
+It's worth noting that the content discussed in the blog has since been transformed
+into a convenient af::nearestNeighbour() function. Before writing something from
+scratch, check that Arrayfire doesn't already have an implementation. The default
+vectorized nature of Arrayfire and an extensive collection of functions will
+speed things up in addition to replacing dozens of lines of code!
+

From e5ab6713a4260e3b89109f048f133db71f571174 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 16:18:11 -0500
Subject: [PATCH 0077/2677] converted join cpu func to async call

---
 src/backend/cpu/join.cpp | 373 ++++++++++++++++++++-------------------
 1 file changed, 193 insertions(+), 180 deletions(-)

diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 78d2a51ab4..8af9c24f8d 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -11,241 +11,254 @@
 #include <join.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
-    template<typename To, typename Tx, int dim>
-    void join_append(To *out, const Tx *X, const af::dim4 &offset,
-               const af::dim4 &odims, const af::dim4 &xdims,
-               const af::dim4 &ost, const af::dim4 &xst)
-    {
-        for(dim_t ow = 0; ow < xdims[3]; ow++) {
-            const dim_t xW = ow * xst[3];
-            const dim_t oW = (ow + offset[3]) * ost[3];
-
-            for(dim_t oz = 0; oz < xdims[2]; oz++) {
-                const dim_t xZW = xW + oz * xst[2];
-                const dim_t oZW = oW + (oz + offset[2]) * ost[2];
-
-                for(dim_t oy = 0; oy < xdims[1]; oy++) {
-                    const dim_t xYZW = xZW + oy * xst[1];
-                    const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
-
-                    for(dim_t ox = 0; ox < xdims[0]; ox++) {
-                        const dim_t iMem = xYZW + ox;
-                        const dim_t oMem = oYZW + (ox + offset[0]);
-                        out[oMem] = X[iMem];
-                    }
+template<typename To, typename Tx, int dim>
+void join_append(To *out, const Tx *X, const af::dim4 &offset,
+           const af::dim4 &odims, const af::dim4 &xdims,
+           const af::dim4 &ost, const af::dim4 &xst)
+{
+    for(dim_t ow = 0; ow < xdims[3]; ow++) {
+        const dim_t xW = ow * xst[3];
+        const dim_t oW = (ow + offset[3]) * ost[3];
+
+        for(dim_t oz = 0; oz < xdims[2]; oz++) {
+            const dim_t xZW = xW + oz * xst[2];
+            const dim_t oZW = oW + (oz + offset[2]) * ost[2];
+
+            for(dim_t oy = 0; oy < xdims[1]; oy++) {
+                const dim_t xYZW = xZW + oy * xst[1];
+                const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
+
+                for(dim_t ox = 0; ox < xdims[0]; ox++) {
+                    const dim_t iMem = xYZW + ox;
+                    const dim_t oMem = oYZW + (ox + offset[0]);
+                    out[oMem] = X[iMem];
                 }
             }
         }
     }
+}
 
-    template<int dim>
-    af::dim4 calcOffset(const af::dim4 dims)
-    {
-        af::dim4 offset;
-        offset[0] = (dim == 0) ? dims[0] : 0;
-        offset[1] = (dim == 1) ? dims[1] : 0;
-        offset[2] = (dim == 2) ? dims[2] : 0;
-        offset[3] = (dim == 3) ? dims[3] : 0;
-        return offset;
-    }
+template<int dim>
+af::dim4 calcOffset(const af::dim4 dims)
+{
+    af::dim4 offset;
+    offset[0] = (dim == 0) ? dims[0] : 0;
+    offset[1] = (dim == 1) ? dims[1] : 0;
+    offset[2] = (dim == 2) ? dims[2] : 0;
+    offset[3] = (dim == 3) ? dims[3] : 0;
+    return offset;
+}
 
-    template<typename Tx, typename Ty>
-    Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
-    {
-        // All dimensions except join dimension must be equal
-        // Compute output dims
-        af::dim4 odims;
-        af::dim4 fdims = first.dims();
-        af::dim4 sdims = second.dims();
-
-        for(int i = 0; i < 4; i++) {
-            if(i == dim) {
-                odims[i] = fdims[i] + sdims[i];
-            } else {
-                odims[i] = fdims[i];
-            }
+template<typename Tx, typename Ty>
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
+{
+    first.eval();
+    second.eval();
+
+    // All dimensions except join dimension must be equal
+    // Compute output dims
+    af::dim4 odims;
+    af::dim4 fdims = first.dims();
+    af::dim4 sdims = second.dims();
+
+    for(int i = 0; i < 4; i++) {
+        if(i == dim) {
+            odims[i] = fdims[i] + sdims[i];
+        } else {
+            odims[i] = fdims[i];
         }
+    }
 
-        Array<Tx> out = createEmptyArray<Tx>(odims);
+    Array<Tx> out = createEmptyArray<Tx>(odims);
 
+    auto func = [=] (Array<Tx> out, const Array<Tx> first, const Array<Ty> second) {
         Tx* outPtr = out.get();
         const Tx* fptr = first.get();
         const Ty* sptr = second.get();
 
         af::dim4 zero(0,0,0,0);
+        const af::dim4 odims = out.dims();
+        const af::dim4 fdims = first.dims();
+        const af::dim4 sdims = second.dims();
 
         switch(dim) {
             case 0:
                 join_append<Tx, Tx, 0>(outPtr, fptr, zero,
-                                       odims, fdims, out.strides(), first.strides());
+                        odims, fdims, out.strides(), first.strides());
                 join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims),
-                                       odims, sdims, out.strides(), second.strides());
+                        odims, sdims, out.strides(), second.strides());
                 break;
             case 1:
                 join_append<Tx, Tx, 1>(outPtr, fptr, zero,
-                                       odims, fdims, out.strides(), first.strides());
+                        odims, fdims, out.strides(), first.strides());
                 join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims),
-                                       odims, sdims, out.strides(), second.strides());
+                        odims, sdims, out.strides(), second.strides());
                 break;
             case 2:
                 join_append<Tx, Tx, 2>(outPtr, fptr, zero,
-                                       odims, fdims, out.strides(), first.strides());
+                        odims, fdims, out.strides(), first.strides());
                 join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims),
-                                       odims, sdims, out.strides(), second.strides());
+                        odims, sdims, out.strides(), second.strides());
                 break;
             case 3:
                 join_append<Tx, Tx, 3>(outPtr, fptr, zero,
-                                       odims, fdims, out.strides(), first.strides());
+                        odims, fdims, out.strides(), first.strides());
                 join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims),
-                                       odims, sdims, out.strides(), second.strides());
+                        odims, sdims, out.strides(), second.strides());
                 break;
         }
+    };
+    getQueue().enqueue(func, out, first, second);
 
-        return out;
-    }
+    return out;
+}
 
-    template<typename T, int n_arrays>
-    void join_wrapper(const int dim, Array<T> &out, const std::vector<Array<T>> &inputs)
-    {
-        af::dim4 zero(0,0,0,0);
-        af::dim4 d = zero;
-        switch(dim) {
-            case 0:
-                join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
-                            out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    join_append<T, T, 0>(out.get(), inputs[i].get(), calcOffset<0>(d),
-                            out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-                }
-                break;
-            case 1:
-                join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
-                            out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    join_append<T, T, 1>(out.get(), inputs[i].get(), calcOffset<1>(d),
-                            out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-                }
-                break;
-            case 2:
-                join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
-                            out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    join_append<T, T, 2>(out.get(), inputs[i].get(), calcOffset<2>(d),
-                            out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-                }
-                break;
-            case 3:
-                join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
-                            out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    join_append<T, T, 3>(out.get(), inputs[i].get(), calcOffset<3>(d),
-                            out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-                }
-                break;
-        }
+template<typename T, int n_arrays>
+void join_wrapper(const int dim, Array<T> out, const std::vector<Array<T>> inputs)
+{
+    af::dim4 zero(0,0,0,0);
+    af::dim4 d = zero;
+    switch(dim) {
+        case 0:
+            join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 0>(out.get(), inputs[i].get(), calcOffset<0>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+        case 1:
+            join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 1>(out.get(), inputs[i].get(), calcOffset<1>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+        case 2:
+            join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 2>(out.get(), inputs[i].get(), calcOffset<2>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+        case 3:
+            join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 3>(out.get(), inputs[i].get(), calcOffset<3>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
     }
+}
 
-    template<typename T>
-    Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
-    {
-        // All dimensions except join dimension must be equal
-        // Compute output dims
-        af::dim4 odims;
-        const dim_t n_arrays = inputs.size();
-        std::vector<af::dim4> idims(n_arrays);
-
-        dim_t dim_size = 0;
-        for(int i = 0; i < (int)idims.size(); i++) {
-            idims[i] = inputs[i].dims();
-            dim_size += idims[i][dim];
-        }
-
-        for(int i = 0; i < 4; i++) {
-            if(i == dim) {
-                odims[i] = dim_size;
-            } else {
-                odims[i] = idims[0][i];
-            }
-        }
+template<typename T>
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
+{
+    for (int i=0; i<inputs.size(); ++i)
+        inputs[i].eval();
+    // All dimensions except join dimension must be equal
+    // Compute output dims
+    af::dim4 odims;
+    const dim_t n_arrays = inputs.size();
+    std::vector<af::dim4> idims(n_arrays);
 
-        Array<T> out = createEmptyArray<T>(odims);
+    dim_t dim_size = 0;
+    for(int i = 0; i < (int)idims.size(); i++) {
+        idims[i] = inputs[i].dims();
+        dim_size += idims[i][dim];
+    }
 
-        switch(n_arrays) {
-            case 1:
-                join_wrapper<T, 1>(dim, out, inputs);
-                break;
-            case 2:
-                join_wrapper<T, 2>(dim, out, inputs);
-                break;
-            case 3:
-                join_wrapper<T, 3>(dim, out, inputs);
-                break;
-            case 4:
-                join_wrapper<T, 4>(dim, out, inputs);
-                break;
-            case 5:
-                join_wrapper<T, 5>(dim, out, inputs);
-                break;
-            case 6:
-                join_wrapper<T, 6>(dim, out, inputs);
-                break;
-            case 7:
-                join_wrapper<T, 7>(dim, out, inputs);
-                break;
-            case 8:
-                join_wrapper<T, 8>(dim, out, inputs);
-                break;
-            case 9:
-                join_wrapper<T, 9>(dim, out, inputs);
-                break;
-            case 10:
-                join_wrapper<T,10>(dim, out, inputs);
-                break;
+    for(int i = 0; i < 4; i++) {
+        if(i == dim) {
+            odims[i] = dim_size;
+        } else {
+            odims[i] = idims[0][i];
         }
+    }
 
-        return out;
+    Array<T> out = createEmptyArray<T>(odims);
+
+    switch(n_arrays) {
+        case 1:
+            getQueue().enqueue(join_wrapper<T, 1>, dim, out, inputs);
+            break;
+        case 2:
+            getQueue().enqueue(join_wrapper<T, 2>, dim, out, inputs);
+            break;
+        case 3:
+            getQueue().enqueue(join_wrapper<T, 3>, dim, out, inputs);
+            break;
+        case 4:
+            getQueue().enqueue(join_wrapper<T, 4>, dim, out, inputs);
+            break;
+        case 5:
+            getQueue().enqueue(join_wrapper<T, 5>, dim, out, inputs);
+            break;
+        case 6:
+            getQueue().enqueue(join_wrapper<T, 6>, dim, out, inputs);
+            break;
+        case 7:
+            getQueue().enqueue(join_wrapper<T, 7>, dim, out, inputs);
+            break;
+        case 8:
+            getQueue().enqueue(join_wrapper<T, 8>, dim, out, inputs);
+            break;
+        case 9:
+            getQueue().enqueue(join_wrapper<T, 9>, dim, out, inputs);
+            break;
+        case 10:
+            getQueue().enqueue(join_wrapper<T,10>, dim, out, inputs);
+            break;
     }
 
+    return out;
+}
+
 #define INSTANTIATE(Tx, Ty) \
     template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, const Array<Ty> &second);
 
-    INSTANTIATE(float,   float)
-    INSTANTIATE(double,  double)
-    INSTANTIATE(cfloat,  cfloat)
-    INSTANTIATE(cdouble, cdouble)
-    INSTANTIATE(int,     int)
-    INSTANTIATE(uint,    uint)
-    INSTANTIATE(intl,    intl)
-    INSTANTIATE(uintl,   uintl)
-    INSTANTIATE(uchar,   uchar)
-    INSTANTIATE(char,    char)
-    INSTANTIATE(ushort,  ushort)
-    INSTANTIATE(short,   short)
+INSTANTIATE(float,   float)
+INSTANTIATE(double,  double)
+INSTANTIATE(cfloat,  cfloat)
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(int,     int)
+INSTANTIATE(uint,    uint)
+INSTANTIATE(intl,    intl)
+INSTANTIATE(uintl,   uintl)
+INSTANTIATE(uchar,   uchar)
+INSTANTIATE(char,    char)
+INSTANTIATE(ushort,  ushort)
+INSTANTIATE(short,   short)
 
 #undef INSTANTIATE
 
 #define INSTANTIATE(T)      \
     template Array<T> join<T>(const int dim, const std::vector<Array<T>> &inputs);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(ushort)
-    INSTANTIATE(short)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
 #undef INSTANTIATE
 }

From 0c72451eb1ac629940a76f6f57890e4cba0d6df0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2015 16:22:42 -0500
Subject: [PATCH 0078/2677] converted cpu regions function to asynchronous call

---
 src/backend/cpu/regions.cpp | 157 +++++++++++++++++++-----------------
 1 file changed, 83 insertions(+), 74 deletions(-)

diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index b753fb5547..f7309c8dbe 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -17,6 +17,8 @@
 #include <map>
 #include <set>
 #include <algorithm>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -106,97 +108,104 @@ static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity)
 {
-    const dim4 in_dims = in.dims();
+    in.eval();
 
     // Create output placeholder
-    Array<T> out = createValueArray(in_dims, (T)0);
-
-    const char *in_ptr  = in.get();
-          T    *out_ptr = out.get();
-
-    // Map labels
-    typedef typename std::map<T, LabelNode<T>* > label_map_t;
-    typedef typename label_map_t::iterator label_map_iterator_t;
-
-    label_map_t lmap;
-
-    // Initial label
-    T label = (T)1;
-
-    for (int j = 0; j < (int)in_dims[1]; j++) {
-        for (int i = 0; i < (int)in_dims[0]; i++) {
-            int idx = j * in_dims[0] + i;
-            if (in_ptr[idx] != 0) {
-                std::vector<T> l;
-
-                // Test neighbors
-                if (i > 0 && out_ptr[j * (int)in_dims[0] + i-1] > 0)
-                    l.push_back(out_ptr[j * in_dims[0] + i-1]);
-                if (j > 0 && out_ptr[(j-1) * (int)in_dims[0] + i] > 0)
-                    l.push_back(out_ptr[(j-1) * in_dims[0] + i]);
-                if (connectivity == AF_CONNECTIVITY_8 && i > 0 && j > 0 && out_ptr[(j-1) * in_dims[0] + i-1] > 0)
-                    l.push_back(out_ptr[(j-1) * in_dims[0] + i-1]);
-                if (connectivity == AF_CONNECTIVITY_8 && i < (int)in_dims[0] - 1 && j > 0 && out_ptr[(j-1) * in_dims[0] + i+1] != 0)
-                    l.push_back(out_ptr[(j-1) * in_dims[0] + i+1]);
-
-                if (!l.empty()) {
-                    T minl = l[0];
-                    for (size_t k = 0; k < l.size(); k++) {
-                        minl = min(l[k], minl);
-                        label_map_iterator_t cur_map = lmap.find(l[k]);
-                        LabelNode<T> *node = cur_map->second;
-                        // Group labels of the same region under a disjoint set
-                        for (size_t m = k+1; m < l.size(); m++)
-                            setUnion(node, lmap.find(l[m])->second);
+    Array<T> out = createValueArray(in.dims(), (T)0);
+    out.eval();
+
+    auto func = [=] (Array<T> out, const Array<char> in, af_connectivity connectivity) {
+        const dim4 in_dims = in.dims();
+        const char *in_ptr  = in.get();
+        T    *out_ptr = out.get();
+
+        // Map labels
+        typedef typename std::map<T, LabelNode<T>* > label_map_t;
+        typedef typename label_map_t::iterator label_map_iterator_t;
+
+        label_map_t lmap;
+
+        // Initial label
+        T label = (T)1;
+
+        for (int j = 0; j < (int)in_dims[1]; j++) {
+            for (int i = 0; i < (int)in_dims[0]; i++) {
+                int idx = j * in_dims[0] + i;
+                if (in_ptr[idx] != 0) {
+                    std::vector<T> l;
+
+                    // Test neighbors
+                    if (i > 0 && out_ptr[j * (int)in_dims[0] + i-1] > 0)
+                        l.push_back(out_ptr[j * in_dims[0] + i-1]);
+                    if (j > 0 && out_ptr[(j-1) * (int)in_dims[0] + i] > 0)
+                        l.push_back(out_ptr[(j-1) * in_dims[0] + i]);
+                    if (connectivity == AF_CONNECTIVITY_8 && i > 0 &&
+                            j > 0 && out_ptr[(j-1) * in_dims[0] + i-1] > 0)
+                        l.push_back(out_ptr[(j-1) * in_dims[0] + i-1]);
+                    if (connectivity == AF_CONNECTIVITY_8 &&
+                            i < (int)in_dims[0] - 1 && j > 0 && out_ptr[(j-1) * in_dims[0] + i+1] != 0)
+                        l.push_back(out_ptr[(j-1) * in_dims[0] + i+1]);
+
+                    if (!l.empty()) {
+                        T minl = l[0];
+                        for (size_t k = 0; k < l.size(); k++) {
+                            minl = min(l[k], minl);
+                            label_map_iterator_t cur_map = lmap.find(l[k]);
+                            LabelNode<T> *node = cur_map->second;
+                            // Group labels of the same region under a disjoint set
+                            for (size_t m = k+1; m < l.size(); m++)
+                                setUnion(node, lmap.find(l[m])->second);
+                        }
+                        // Set label to smallest neighbor label
+                        out_ptr[idx] = minl;
+                    }
+                    else {
+                        // Insert new label in map
+                        LabelNode<T> *node = new LabelNode<T>(label);
+                        lmap.insert(std::pair<T, LabelNode<T>* >(label, node));
+                        out_ptr[idx] = label++;
                     }
-                    // Set label to smallest neighbor label
-                    out_ptr[idx] = minl;
-                }
-                else {
-                    // Insert new label in map
-                    LabelNode<T> *node = new LabelNode<T>(label);
-                    lmap.insert(std::pair<T, LabelNode<T>* >(label, node));
-                    out_ptr[idx] = label++;
                 }
             }
         }
-    }
 
-    std::set<T> removed;
+        std::set<T> removed;
 
-    for (int j = 0; j < (int)in_dims[1]; j++) {
-        for (int i = 0; i < (int)in_dims[0]; i++) {
-            int idx = j * (int)in_dims[0] + i;
-            if (in_ptr[idx] != 0) {
-                T l = out_ptr[idx];
-                label_map_iterator_t cur_map = lmap.find(l);
+        for (int j = 0; j < (int)in_dims[1]; j++) {
+            for (int i = 0; i < (int)in_dims[0]; i++) {
+                int idx = j * (int)in_dims[0] + i;
+                if (in_ptr[idx] != 0) {
+                    T l = out_ptr[idx];
+                    label_map_iterator_t cur_map = lmap.find(l);
 
-                if (cur_map != lmap.end()) {
-                    LabelNode<T>* node = cur_map->second;
+                    if (cur_map != lmap.end()) {
+                        LabelNode<T>* node = cur_map->second;
 
-                    LabelNode<T>* node_root = find(node);
-                    out_ptr[idx] = node_root->getMinLabel();
+                        LabelNode<T>* node_root = find(node);
+                        out_ptr[idx] = node_root->getMinLabel();
 
-                    // Mark removed labels (those that are part of a region
-                    // that contains a smaller label)
-                    if (node->getMinLabel() < l || node_root->getMinLabel() < l)
-                        removed.insert(l);
-                    if (node->getLabel() > node->getMinLabel())
-                        removed.insert(node->getLabel());
+                        // Mark removed labels (those that are part of a region
+                        // that contains a smaller label)
+                        if (node->getMinLabel() < l || node_root->getMinLabel() < l)
+                            removed.insert(l);
+                        if (node->getLabel() > node->getMinLabel())
+                            removed.insert(node->getLabel());
+                    }
                 }
             }
         }
-    }
 
-    // Calculate final neighbors (ensure final labels are sequential)
-    for (int j = 0; j < (int)in_dims[1]; j++) {
-        for (int i = 0; i < (int)in_dims[0]; i++) {
-            int idx = j * (int)in_dims[0] + i;
-            if (out_ptr[idx] > 0) {
-                out_ptr[idx] -= distance(removed.begin(), removed.lower_bound(out_ptr[idx]));
+        // Calculate final neighbors (ensure final labels are sequential)
+        for (int j = 0; j < (int)in_dims[1]; j++) {
+            for (int i = 0; i < (int)in_dims[0]; i++) {
+                int idx = j * (int)in_dims[0] + i;
+                if (out_ptr[idx] > 0) {
+                    out_ptr[idx] -= distance(removed.begin(), removed.lower_bound(out_ptr[idx]));
+                }
             }
         }
-    }
+    };
+    getQueue().enqueue(func, out, in, connectivity);
 
     return out;
 }

From 384ce6d6e17d7d94509d709e2ed426c3ffe3b2c6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 2 Dec 2015 17:19:59 -0500
Subject: [PATCH 0079/2677] remove extra information from vectorization

---
 docs/pages/vectorization.md | 67 +++++++++++++++++++++++--------------
 1 file changed, 41 insertions(+), 26 deletions(-)

diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index 93cdeb5b69..cb4f529388 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -41,19 +41,48 @@ Operator Category                                           | Functions
 [Numeric functions](\ref numeric_mat)                       | abs(), floor(), round(), min(), max(), etc.
 [Trigonometric functions](\ref trig_mat)                    | sin(), cos(), tan(), etc.
 
-Using the built in vectorized operations should be the first and preferred method
-of vectorizing any code written with Arrayfire.
+Not only elementwise arithmetic operations are vectorized in Arrayfire.
+
+Vector operations such as min() support vectorization:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array arr = randn(100);
+std::cout << min<float>(arr) << std::endl;
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Signal processing functions like convolve() support vectorization:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+float g_coef[] = { 1, 2, 1,
+                   2, 4, 2,
+                   1, 2, 1 };
+
+af::array filter = 1.f/16 * af::array(3, 3, f_coef);
+
+af::array signal = randu(WIDTH, HEIGHT, NUM);
+af::array conv = convolve2(signal, filter);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Image processing functions such as rotate() support vectorization:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array imgs = randu(WIDTH, HEIGHT, 100); // 100 (WIDTH x HEIGHT) images
+af::array rot_imgs = rotate(imgs, 45); // 100 rotated images
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+One class of functions that does not support vectorization is the set of linear
+algebra functions. Using the built in vectorized operations should be the first
+and preferred method of vectorizing any code written with Arrayfire.
 
 # GFOR: Parallel for-loops
-Another novel method of vectorization present in Arrayfire is the GFOR loop replacement construct.
-GFOR allows launching all iterations of a loop in parallel on the GPU or device,
-as long as the iterations are independent. While the standard for-loop performs
-each iteration sequentially, ArrayFire's gfor-loop performs each iteration at
-the same time (in parallel). ArrayFire does this by tiling out the values of all
-loop iterations and then performing computation on those tiles in one pass.
-You can think of gfor as performing auto-vectorization of your code, e.g. you
-write a gfor-loop that increments every element of a vector but behind the scenes
-ArrayFire rewrites it to operate on the entire vector in parallel.
+Another novel method of vectorization present in Arrayfire is the GFOR loop 
+replacement construct. GFOR allows launching all iterations of a loop in parallel
+on the GPU or device, as long as the iterations are independent. While the 
+standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop
+performs each iteration at the same time (in parallel). ArrayFire does this by
+tiling out the values of all loop iterations and then performing computation on 
+those tiles in one pass. You can think of gfor as performing auto-vectorization
+of your code, e.g. you write a gfor-loop that increments every element of a vector
+but behind the scenes ArrayFire rewrites it to operate on the entire vector in
+parallel.
+
 We can remedy our first example with GFOR:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10);
@@ -90,21 +119,6 @@ gfor(seq i, n)
     combination(span, i) = consts * var_terms(span, i);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-
-## GFOR: Usage
-There are three formats for instantiating gfor-loops:
-
- 1. gfor(var,n)-- Creates a sequence <B>{0, 1, ..., n-1}</B>
- 2. gfor(var,first,last)-- Creates a sequence <B>{first, first+1, ..., last}</B>
- 3. gfor(var,first,incr,last)-- Creates a sequence <B>{first, first+inc, first+2 * inc, ..., last}</B>
-
-
-All of the following represent the equivalent sequence: 0,1,2,3,4
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-gfor (seq i, 5)
-gfor (seq i, 0, 4)
-gfor (seq i, 0, 1, 4)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Using GFOR requires following several rules and multiple guidelines for optimal performance.
 The details of this vectorization method can be found in the [GFOR documentation](\ref gfor).
 
@@ -167,6 +181,7 @@ We have seen the different methods Arrayfire provides to vectorize our code. Tyi
 them all together is a slightly more involved process that needs to consider data
 dimensionality and layout, memory usage, nesting order, etc. An excellent example
 and discussion of these factors can be found on our blog:
+
 http://arrayfire.com/how-to-write-vectorized-code/
 
 It's worth noting that the content discussed in the blog has since been transformed

From 53de79030d346f54d7293e159441f3267747489a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 3 Dec 2015 12:07:02 -0500
Subject: [PATCH 0080/2677] Removed dead code from opencl::DeviceManager class

---
 src/backend/opencl/platform.cpp | 5 -----
 src/backend/opencl/platform.hpp | 2 --
 2 files changed, 7 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 85364c4297..6f9ae99116 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -98,7 +98,6 @@ DeviceManager::~DeviceManager()
     for (auto q: mQueues) delete q;
     for (auto d : mDevices) delete d;
     for (auto c : mContexts) delete c;
-    for (auto p : mPlatforms) delete p;
 #endif
 }
 
@@ -123,9 +122,6 @@ DeviceManager::DeviceManager()
 #endif
         };
 
-        for (auto &platform : platforms)
-            mPlatforms.push_back(new Platform(platform));
-
         unsigned nDevices = 0;
         for (auto devType : DEVC_TYPES) {
             for (auto &platform : platforms) {
@@ -150,7 +146,6 @@ DeviceManager::DeviceManager()
                     mDevices.push_back(new Device(dev));
                     mContexts.push_back(ctx);
                     mQueues.push_back(cq);
-                    mCtxOffsets.push_back(nDevices);
                     mIsGLSharingOn.push_back(false);
                 }
             }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 90f57aed39..7f0dab6f94 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -70,8 +70,6 @@ class DeviceManager
         std::vector<cl::CommandQueue*>  mQueues;
         std::vector<cl::Device*>       mDevices;
         std::vector<cl::Context*>     mContexts;
-        std::vector<cl::Platform*>   mPlatforms;
-        std::vector<unsigned>       mCtxOffsets;
         std::vector<bool>        mIsGLSharingOn;
 
         unsigned mActiveCtxId;

From d3f30800dee1717d9fe189c71a7d435d9bfcc9f9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 12:41:50 -0500
Subject: [PATCH 0081/2677] Use folders (VS sln) for examples/tests when built
 out of source

---
 examples/CMakeLists.txt | 1 +
 test/CMakeLists.txt     | 1 +
 2 files changed, 2 insertions(+)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 1ffb82bad0..9acc046ff3 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -23,6 +23,7 @@ IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
         )
     ENDIF()
 ELSE()
+    SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
     FIND_PACKAGE(ArrayFire REQUIRED)
     INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 69850ef4cd..e312e62cb2 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -14,6 +14,7 @@ IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
     SET(ArrayFire_OpenCL_FOUND False)
     SET(ArrayFire_Unified_FOUND False)
 ELSE()
+    SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
     FIND_PACKAGE(ArrayFire REQUIRED)
     INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
     OPTION(BUILD_NONFREE "Build Tests for nonfree algorithms" OFF)

From 92599d7da5c533785bd7b3d8619bbdd26215f338 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 13:39:53 -0500
Subject: [PATCH 0082/2677] Install examples source irrespective of value of
 BUILD_EXAMPLES

* Only the examples source is installed, which does not depend on the value of
BUILD_EXAMPLES.
* So when BUILD_EXAMPLES is OFF, the examples source is installed without
building the examples.
---
 CMakeLists.txt          | 13 +++++++++++++
 examples/CMakeLists.txt |  8 --------
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c76ef4b430..28c983fff8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -235,6 +235,19 @@ INSTALL(FILES ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
     DESTINATION ${AF_INSTALL_CMAKE_DIR}
     COMPONENT cmake)
 
+# install the examples irrespective of the BUILD_EXAMPLES value
+# only the examples source files are installed, so the installation of these
+# source files does not depend on BUILD_EXAMPLES
+# when BUILD_EXAMPLES is OFF, the examples source is installed without
+# building the example executables
+INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/examples/"
+    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}"
+    COMPONENT examples)
+
+INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/assets/examples"
+    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/assets"
+    COMPONENT examples)
+
 IF(APPLE)
     INCLUDE("${CMAKE_MODULE_PATH}/osx_install/OSXInstaller.cmake")
 ENDIF(APPLE)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 9acc046ff3..5144bf6cd4 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -127,11 +127,3 @@ IF (${OpenCL_FOUND})
 ELSE()
     MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. OpenCL was not found")
 ENDIF()
-
-INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/"
-    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}"
-    COMPONENT examples)
-
-INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/../assets/examples"
-    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/assets"
-)

From 8136f2116d9101b36abf52b99d7570085e3cbb0c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 16:11:35 -0500
Subject: [PATCH 0083/2677] Updated forge tag

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 21b8aac8ad..fb885843de 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -22,7 +22,7 @@ ENDIF()
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG af3.2.0
+    GIT_TAG af3.2.1
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 0dce77c9238b15790b6cbffe7a8410603fce71d3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 16:44:24 -0500
Subject: [PATCH 0084/2677] CMake generates the list of examples

* Before we had to manually add each example to arrayfire.h
* Now, CMake generates a list and creates docs/detail/examples.dox
---
 .gitignore                   |  1 +
 CMakeModules/examples.dox.in |  3 +++
 docs/CMakeLists.txt          | 25 ++++++++++++++++++
 include/arrayfire.h          | 51 ------------------------------------
 4 files changed, 29 insertions(+), 51 deletions(-)
 create mode 100644 CMakeModules/examples.dox.in

diff --git a/.gitignore b/.gitignore
index 95a58ae34d..75fa897faf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,4 @@ GRTAGS
 GPATH
 .dir-locals.el
 include/af/version.h
+docs/details/examples.dox
diff --git a/CMakeModules/examples.dox.in b/CMakeModules/examples.dox.in
new file mode 100644
index 0000000000..dfad2dbb50
--- /dev/null
+++ b/CMakeModules/examples.dox.in
@@ -0,0 +1,3 @@
+/**
+@EXAMPLES_LIST@
+*/
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index fbc02e15cf..b42565779f 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -18,6 +18,31 @@ SET(SNIPPETS_DIR "${CMAKE_SOURCE_DIR}/test")
 CONFIGURE_FILE(${AF_DOCS_CONFIG} ${AF_DOCS_CONFIG_OUT})
 CONFIGURE_FILE(${AF_DOCS_LAYOUT} ${AF_DOCS_LAYOUT_OUT})
 
+###########################################################
+## This generates a list of the examples cpp files and
+## creates a dox file under docs/details/examples.dox
+## This is used to generate documentation for examples
+###########################################################
+FILE(GLOB EXAMPLES_CPP
+    "${EXAMPLES_DIR}/*/*.cpp")
+
+# Sort alphabetically
+# Note: example directories will be major sort order
+LIST(SORT EXAMPLES_CPP)
+
+# Get filenames and write to a string
+FOREACH(SRC ${EXAMPLES_CPP})
+    GET_FILENAME_COMPONENT(SRC_NAME ${SRC} NAME)
+    SET(EXAMPLES_LIST "${EXAMPLES_LIST}\\example ${SRC_NAME}\n")
+ENDFOREACH(SRC ${EXAMPLES_CPP})
+
+# Write string containing file names to examples.dox
+CONFIGURE_FILE(
+    ${CMAKE_MODULE_PATH}/examples.dox.in
+    ${DOCS_DIR}/details/examples.dox
+)
+###########################################################
+
 ADD_CUSTOM_TARGET(docs
     ALL
     COMMAND ${DOXYGEN_EXECUTABLE} ${AF_DOCS_CONFIG_OUT}
diff --git a/include/arrayfire.h b/include/arrayfire.h
index e4ac1bbb71..7d9e75a7b4 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -240,57 +240,6 @@
 
 */
 
-/**
-\example matching.cpp
-\example fast.cpp
-\example harris.cpp
-\example susan.cpp
-\example logistic_regression.cpp
-\example rbm.cpp
-\example perceptron.cpp
-\example neural_network.cpp
-\example bagging.cpp
-\example naive_bayes.cpp
-\example deep_belief_net.cpp
-\example kmeans.cpp
-\example softmax_regression.cpp
-\example knn.cpp
-\example monte_carlo_options.cpp
-\example heston_model.cpp
-\example black_scholes_options.cpp
-\example blas.cpp
-\example fft.cpp
-\example pi.cpp
-\example svd.cpp
-\example cholesky.cpp
-\example qr.cpp
-\example lu.cpp
-\example conway.cpp
-\example histogram.cpp
-\example fractal.cpp
-\example plot2d.cpp
-\example plot3.cpp
-\example surface.cpp
-\example conway_pretty.cpp
-\example basic.cpp
-\example helloworld.cpp
-\example vectorize.cpp
-\example integer.cpp
-\example convolve.cpp
-\example rainfall.cpp
-\example swe.cpp
-\example morphing.cpp
-\example image_demo.cpp
-\example brain_segmentation.cpp
-\example pyramids.cpp
-\example binary_thresholding.cpp
-\example optical_flow.cpp
-\example adaptive_thresholding.cpp
-\example image_editing.cpp
-\example edge.cpp
-\example filters.cpp
-*/
-
 #include "af/compatible.h"
 #include "af/algorithm.h"
 #include "af/arith.h"

From d0732f191de00e1acbb208d81ea33c692d0b564a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 16:46:28 -0500
Subject: [PATCH 0085/2677] Generate examples as dir/filename.cpp

---
 docs/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index b42565779f..aa9a259275 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -32,8 +32,10 @@ LIST(SORT EXAMPLES_CPP)
 
 # Get filenames and write to a string
 FOREACH(SRC ${EXAMPLES_CPP})
+    GET_FILENAME_COMPONENT(DIR_PATH ${SRC} DIRECTORY)
+    GET_FILENAME_COMPONENT(DIR_NAME ${DIR_PATH} NAME)
     GET_FILENAME_COMPONENT(SRC_NAME ${SRC} NAME)
-    SET(EXAMPLES_LIST "${EXAMPLES_LIST}\\example ${SRC_NAME}\n")
+    SET(EXAMPLES_LIST "${EXAMPLES_LIST}\\example ${DIR_NAME}/${SRC_NAME}\n")
 ENDFOREACH(SRC ${EXAMPLES_CPP})
 
 # Write string containing file names to examples.dox

From c0aba7f4d52e564194c5dd427f9b5865cd682328 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 16:46:53 -0500
Subject: [PATCH 0086/2677] Update examples refs to match updated example style

---
 docs/pages/release_notes.md   | 12 ++++++------
 docs/pages/timing.md          |  2 +-
 docs/pages/unified_backend.md |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index f1b195b184..a8bb9aed18 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -94,10 +94,10 @@ Documentation Updates
 New Examples
 ------------
 * Graphics
-    * [Plot3](\ref plot3.cpp)
-    * [Surface](\ref surface.cpp)
-* [Shallow Water Equation](\ref swe.cpp)
-* [Basic](\ref basic.cpp) as a Unified backend example
+    * [Plot3](\ref graphics/plot3.cpp)
+    * [Surface](\ref graphics/surface.cpp)
+* [Shallow Water Equation](\ref pde/swe.cpp)
+* [Basic](\ref unified/basic.cpp) as a Unified backend example
 
 Installers
 -----------
@@ -171,12 +171,12 @@ Build
 ------
 
 * `cmake` now includes `PKG_CONFIG` in the search path for CBLAS and LAPACKE libraries
-* [heston_model.cpp](\ref heston_model.cpp) example now builds with the default ArrayFire cmake files after installation
+* [heston_model.cpp](\ref financial/heston_model.cpp) example now builds with the default ArrayFire cmake files after installation
 
 Other
 ------
 
-* Fixed bug in [image_editing.cpp](\ref image_editing.cpp)
+* Fixed bug in [image_editing.cpp](\ref image_processing/image_editing.cpp)
 
 v3.1.0
 ==============
diff --git a/docs/pages/timing.md b/docs/pages/timing.md
index 98042a79e8..675043ff9a 100644
--- a/docs/pages/timing.md
+++ b/docs/pages/timing.md
@@ -37,7 +37,7 @@ To take care of much of this boilerplate, [timeit](\ref af::timeit) provides
 accurate and reliable estimates of both CPU or GPU code.
 
 Here`s a stripped down example of
-[Monte-Carlo estimation of PI](\ref pi.cpp) making use
+[Monte-Carlo estimation of PI](\ref benchmarks/pi.cpp) making use
 of [timeit](\ref af::timeit).  Notice how it expects a `void` function pointer.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
diff --git a/docs/pages/unified_backend.md b/docs/pages/unified_backend.md
index 96bf94d0a3..e11f75dec0 100644
--- a/docs/pages/unified_backend.md
+++ b/docs/pages/unified_backend.md
@@ -79,7 +79,7 @@ backend libraries loaded successfully), call the af::getBackendCount function.
 
 # Example
 
-This example is shortened form of [basic.cpp](\ref basic.cpp).
+This example is shortened form of [basic.cpp](\ref unified/basic.cpp).
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.c}
 #include <arrayfire.h>

From 6978c906c710784ec31a3e4875fc50d5fe42b9d8 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 17:18:11 -0500
Subject: [PATCH 0087/2677] Updated release notes for 3.2.1

---
 docs/pages/release_notes.md | 39 +++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index a8bb9aed18..1a897628e9 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,45 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.2.1
+==============
+
+Bug Fixes
+--------------
+
+* Fixed [bug](https://github.com/arrayfire/arrayfire/pull/1136) in homography()
+* Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1135) in behavior
+  of af::array::device()
+* Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1129) when
+  indexing with span along trailing dimension
+* Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1127) when
+  indexing in [GFor](\ref gfor)
+* Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1122) in CPU
+  information fetching
+* Fixed compilation [bug](https://github.com/arrayfire/arrayfire/issues/1117)
+  in unified backend caused by missing link library
+* Add [missing symbol](https://github.com/arrayfire/arrayfire/pull/1114) for
+  af_draw_surface()
+
+Build
+------
+* Tests can now be used as a [standlone project](https://github.com/arrayfire/arrayfire/pull/1120)
+    * Tests can now be built using pre-compiled libraries
+    * Similar to how the examples are built
+* The install target now installs the examples source irrespective of the
+  BUILD_EXAMPLES value
+    * Examples are not built if BUILD_EXAMPLES is off
+
+Documentation
+------
+* HTML documentation is now [built and installed](https://github.com/arrayfire/arrayfire/pull/1109)
+  in docs/html
+* Added documentation for \ref af::seq class
+* Updated [Matrix Manipulation](\ref matrixmanipulation) tutorial
+* Examples list is now generated by CMake
+    * <a href="examples.htm">Examples</a> are now listed as dir/example.cpp
+* Removed dummy groups used for indexing documentation (affcted doxygen < 1.8.9)
+
 v3.2.0
 =================
 

From 121caefebdf36357fb112b1bcc3b56d1117562d1 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Dec 2015 17:49:30 -0500
Subject: [PATCH 0088/2677] Fix typo

---
 docs/pages/release_notes.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 1a897628e9..a64bf38ff6 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -23,7 +23,7 @@ Bug Fixes
 
 Build
 ------
-* Tests can now be used as a [standlone project](https://github.com/arrayfire/arrayfire/pull/1120)
+* Tests can now be used as a [standalone project](https://github.com/arrayfire/arrayfire/pull/1120)
     * Tests can now be built using pre-compiled libraries
     * Similar to how the examples are built
 * The install target now installs the examples source irrespective of the

From 083646341bc0706294186bbe4e1c62ff288b935c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 4 Dec 2015 11:19:03 -0500
Subject: [PATCH 0089/2677] DOC Add background and bold to inline code tags

---
 docs/arrayfire.css | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/docs/arrayfire.css b/docs/arrayfire.css
index 44808a7538..b1b688ffd0 100644
--- a/docs/arrayfire.css
+++ b/docs/arrayfire.css
@@ -10,6 +10,12 @@ p
     padding-left    :   10px;
 }
 
+p code
+{
+    font-weight     :   bold;
+    background-color:   #F7F7F7;
+}
+
 /* @group Heading Levels */
 /* Increase the size of the page title */
 .title

From 11830298366b856f143200e80c0406a0bab0e584 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 4 Dec 2015 11:21:00 -0500
Subject: [PATCH 0090/2677] DOC corrections, proper linking and syntaxes

---
 docs/pages/interop_cuda.md    | 70 ++++++++++++++++++++---------------
 docs/pages/interop_opencl.md  | 66 +++++++++++++++++++--------------
 docs/pages/timing.md          |  4 +-
 docs/pages/unified_backend.md |  2 +
 docs/pages/vectorization.md   | 34 +++++++++++++----
 5 files changed, 110 insertions(+), 66 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index 5ce92d2b3b..a131bbcb0b 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -10,7 +10,6 @@ native CUDA commands. In this tutorial we are going to talk about how to use nat
 CUDA memory operations and integrate custom CUDA kernels into ArrayFire in a seamless fashion.
 
 # In and Out of Arrayfire
-
 First, let's consider the following code and then break it down bit by bit.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
@@ -37,9 +36,10 @@ int main() {
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 ## Breakdown
-Most kernels require an input. In this case, we created a random uniform array **x**.
-We also go ahead and prepare the output array. 
-The necessary memory required is allocated in array **y** before the kernel launch.
+Most kernels require an input. In this case, we created a random uniform array `x`.
+We also go ahead and prepare the output array. The necessary memory required is
+allocated in array `y` before the kernel launch.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     af::array x = randu(num);
     af::array y = randu(num);
@@ -47,14 +47,16 @@ The necessary memory required is allocated in array **y** before the kernel laun
 
 In this example, the output is the same size as in the input. Note that the actual
 output data type is not specified. For such cases, ArrayFire assumes the data type
-is single precision floating point ( af::f32 ). If necessary, the data type can
-be specified at the end of the array(..) constructor. Once you have the input and
-output arrays, you will need to extract the device pointers / objects using 
-array::device() method in the following manner.
+is single precision floating point (\ref af::f32). If necessary, the data type can be
+specified at the end of the array(..) constructor. Once you have the input and
+output arrays, you will need to extract the device pointers / objects using
+af::array::device() method in the following manner.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 Accesing the device pointer in this manner internally sets a flag prohibiting the
 arrayfire object from further managing the memory. Ownership will need to be
 returned to the af::array object once we are finished using it.
@@ -64,18 +66,23 @@ returned to the af::array object once we are finished using it.
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The function **launch_simple_kernel** handles the launching of your custom kernel.
+
+The function `launch_simple_kernel` handles the launching of your custom kernel.
 We will have a look at how to do this in CUDA later in the post.
 
-Once you have finished your computations, you have to tell ArrayFire to take 
+Once you have finished your computations, you have to tell ArrayFire to take
 control of the memory objects.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     x.unlock();
     y.unlock();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-This is a very crucial step as ArrayFire believes the user is still in control 
+
+This is a very crucial step as ArrayFire believes the user is still in control
 of the pointer. This means that ArrayFire will not perform garbage collection on
-these objects resulting in memory leaks. You can now proceed with the rest of the program.
+these objects resulting in memory leaks. You can now proceed with the rest of the
+program.
+
 In our particular example, we are just performing an error check and exiting.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
@@ -88,38 +95,43 @@ In our particular example, we are just performing an error check and exiting.
 # Launching a CUDA kernel
 Arrayfire provides a collection of CUDA interoperability functions for additional
 capabilities when working with custom CUDA code. To use them, we need to include
-the appropriate header.
+the cuda.h header.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 #include <af/cuda.h>
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The first thing these headers allow us to do are to get and set the active device
 using native CUDA device ids. This is achieved through the following functions:
-> **static int getNativeId (int id)** 
-> -- Get the native device id of the CUDA device with **id** in the ArrayFire context.
 
-> **static void setNativeId (int nativeId)**  
-> -- Set the CUDA device with given native **id** as the active device for ArrayFire.
+> `static int afcu::getNativeId (int id)`
+> -- Get the native device id of the CUDA device with `id` in the ArrayFire context.
+
+> `static void afcu::setNativeId (int nativeId)`
+> -- Set the CUDA device with given native `id` as the active device for ArrayFire.
+
 The headers also allow us to retrieve the CUDA stream used internally inside Arrayfire.
-> **static cudaStream_t afcu::getStream(int id)**  
-> -- Get the stream for the CUDA device with **id** in ArrayFire context.
-These functions are available within the afcu:: namespace and equal C variants 
-can be fund in the full [cuda interop documentation.](\ref cuda_mat.htm)
+
+> `static cudaStream_t afcu::getStream(int id)`
+> -- Get the stream for the CUDA device with `id` in ArrayFire context.
+
+These functions are available within the \ref afcu namespace and equal C variants
+can be found in the full [af/cuda.h documentation](\ref cuda_mat).
 
 To integrate a CUDA kernel into an ArrayFire code base, we first need to get the
 CUDA stream associated with arrayfire. Once we have this stream, we need to make
 sure Arrayfire is done with all computation before we can call our custom kernel
-to avoid out of order execution. We can do this with some variant of 
-**cudaStreamQuery(af_stream)** or **cudaStreamSynchronize(af_stream)** or instead,
+to avoid out of order execution. We can do this with some variant of
+`cudaStreamQuery(af_stream)` or `cudaStreamSynchronize(af_stream)` or instead,
 we could add our kernel launch to Arrayfire's stream as shown below. Once we get
 the associated stream, all that is left is setting up the usual launch configuration
 parameters, launching the kernel and wait for the computations to finish:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
- 
- __global__
- static void simple_kernel(float *d_y,
-                           const float *d_x,
-                           const int num)
+__global__
+static void simple_kernel(float *d_y,
+                          const float *d_x,
+                          const int num)
 {
     const int id = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -143,7 +155,7 @@ void inline launch_simple_kernel(float *d_y,
     const int threads = 256;
     const int blocks = (num / threads) + ((num % threads) ? 1 : 0);
 
-    // execute kernel on Arrayfire's stream, 
+    // execute kernel on Arrayfire's stream,
     // ensuring all previous arrayfire operations complete
     simple_kernel<<<blocks, threads, 0, af_stream>>>(d_y, d_x, num);
 }
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 93361d039d..74c7167b67 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -3,10 +3,10 @@ Interoperability with OpenCL {#interop_opencl}
 
 As extensive as ArrayFire is, there are a few cases where you are still working
 with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels.
-For example, you may want to integrate ArrayFire into an existing code base for 
+For example, you may want to integrate ArrayFire into an existing code base for
 productivity or you may want to keep it around the old implementation for testing
-purposes. Arrayfire provides a number of functions that allow it to work alongside 
-native OpenCL commands. In this tutorial we are going to talk about how to use 
+purposes. Arrayfire provides a number of functions that allow it to work alongside
+native OpenCL commands. In this tutorial we are going to talk about how to use
 native OpenCL memory operations and custom OpenCL kernels alongside ArrayFire
 in a seamless fashion.
 
@@ -38,9 +38,10 @@ int main() {
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 ## Breakdown
-Most kernels require an input. In this case, we created a random uniform array **x**.
+Most kernels require an input. In this case, we created a random uniform array `x`
 We also go ahead and prepare the output array. The necessary memory required is
-allocated in array **y** before the kernel launch.
+allocated in array `y` before the kernel launch.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     af::array x = randu(num);
     af::array y = randu(num);
@@ -48,15 +49,17 @@ allocated in array **y** before the kernel launch.
 
 In this example, the output is the same size as in the input. Note that the actual
 output data type is not specified. For such cases, ArrayFire assumes the data type
-is single precision floating point ( af::f32 ). If necessary, the data type can
+is single precision floating point (\ref af::f32). If necessary, the data type can
 be specified at the end of the array(..) constructor. Once you have the input and
-output arrays, you will need to extract the device pointers / objects using 
-array::device() method in the following manner.
+output arrays, you will need to extract the device pointers / objects using
+af::array::device() method in the following manner.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Accesing the device pointer in this manner internally sets a flag prohibiting 
+
+Accesing the device pointer in this manner internally sets a flag prohibiting
 the arrayfire object from further managing the memory. Ownership will need to be
 returned to the af::array object once we are finished using it.
 
@@ -65,18 +68,21 @@ returned to the af::array object once we are finished using it.
     // y = sin(x)^2 + cos(x)^2
     launch_simple_kernel(d_x, d_y, num);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The function **launch_simple_kernel** handles the launching of your custom kernel.
+
+The function `launch_simple_kernel` handles the launching of your custom kernel.
 We will have a look at the specific functions Arrayfire provides to interface with
-OpenCL later in the post. 
+OpenCL later in the post.
 
 Once you have finished your computations, you have to tell ArrayFire to take control
 of the memory objects.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     x.unlock();
     y.unlock();
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 This is a very crucial step as ArrayFire believes the user is still in control
-of the pointer. This means that ArrayFire will not perform garbage collection 
+of the pointer. This means that ArrayFire will not perform garbage collection
 on these objects resulting in memory leaks. You can now proceed with the rest of
 the program. In our particular example, we are just performing an error check and exiting.
 
@@ -89,9 +95,10 @@ the program. In our particular example, we are just performing an error check an
 
 ## Launching an OpenCL kernel
 If you are integrating an OpenCL kernel into your ArrayFire code base you will
-need several additional steps to access Arrayfire's internal OpenCL context. 
-Once you have access to the same context ArrayFire is using, the rest of the 
+need several additional steps to access Arrayfire's internal OpenCL context.
+Once you have access to the same context ArrayFire is using, the rest of the
 process is exactly the same as launching a stand alone OpenCL context.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 void inline launch_simple_kernel(float *d_y,
                                  const float *d_x,
@@ -122,6 +129,7 @@ void inline launch_simple_kernel(float *d_y,
     return;
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 First of all, to access to OpenCL and the interoperability functions we need to
 include the appropriate headers.
 
@@ -129,25 +137,28 @@ include the appropriate headers.
 #include <af/opencl.h>
 #include <CL/cl.hpp>
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The **opencl.h** header includes a number of functions for getting and setting
-the context, queue, and device ids used internally in Arrayfire. There are also
-a number of methods to construct an af::array from an OpenCL cl_mem buffer object.
-There are both C and C++ versions of these functions, and the C++ versions are
-wrapped inside the afcl:: namespace. See full datails of these functions in the
-[opencl interop documentation.] (\ref opencl_mat)
+
+The opencl.h header includes a number of functions for getting and setting the
+context, queue, and device ids used internally in Arrayfire. There are also a
+number of methods to construct an af::array from an OpenCL `cl_mem` buffer
+object.  There are both C and C++ versions of these functions, and the C++
+versions are wrapped inside the \ref afcl namespace. See full datails of these
+functions in the [af/opencl.h documentation] (\ref opencl_mat).
 
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 cl::Context context(afcl::getContext(true));
 cl::CommandQueue queue(afcl::getQueue(true));
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-We start to use these functions by getting Arrayfire's context and queue. For the
-C++ api, a **true** flag must be passed for the retain parameter which calls the
-clRetainQueue() and clRetainContext() functions before returning. This allows us
-to use Arrayfire's internal OpenCL structures inside of the cl::Context and
-cl::CommandQueue objects from the C++ api. Once we have them, we can proceed to 
-set up and enqueue the kernel like we would in any other OpenCL program. 
-The kernel we are using is actually simple and can be seen below.
+
+We start to use these functions by getting Arrayfire's context and queue. For
+the C++ api, a `true` flag must be passed for the retain parameter which calls
+the `clRetainQueue()` and `clRetainContext()` functions before returning. This
+allows us to use Arrayfire's internal OpenCL structures inside of the
+cl::Context and cl::CommandQueue objects from the C++ api. Once we have them,
+we can proceed to set up and enqueue the kernel like we would in any other
+OpenCL program.  The kernel we are using is actually simple and can be seen
+below.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 std::string CONST_KERNEL_STRING = R"(
@@ -169,7 +180,6 @@ void simple_kernel(__global float *d_y,
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 # Reversing the workflow: Arrayfire arrays from OpenCL Memory
-
 Unfortunately, Arrayfire's interoperability functions don't yet allow us to work with
 external OpenCL contexts. This is currently an open issue and can be tracked here:
 https://github.com/arrayfire/arrayfire/issues/1002.
diff --git a/docs/pages/timing.md b/docs/pages/timing.md
index 675043ff9a..4949c4e97f 100644
--- a/docs/pages/timing.md
+++ b/docs/pages/timing.md
@@ -60,5 +60,5 @@ int main() {
 
 This produces:
 
-	pi_function took 0.007252 seconds
-	(test machine: Core i7 920 @ 2.67GHz with a Tesla C2070)
+    pi_function took 0.007252 seconds
+    (test machine: Core i7 920 @ 2.67GHz with a Tesla C2070)
diff --git a/docs/pages/unified_backend.md b/docs/pages/unified_backend.md
index e11f75dec0..67e340f2ab 100644
--- a/docs/pages/unified_backend.md
+++ b/docs/pages/unified_backend.md
@@ -51,6 +51,7 @@ DYLD_LIBRARY_PATH.
 
 On Windows, you can set up a post build event that copys the NVVM dlls to
 the executable directory by using the following commands:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.c}
 echo copy "$(CUDA_PATH)\nvvm\bin\nvvm64*.dll" "$(OutDir)"
 copy "$(CUDA_PATH)\nvvm\bin\nvvm64*.dll" "$(OutDir)"
@@ -59,6 +60,7 @@ if errorlevel 1 (
     exit /B 0
 )
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 This ensures that the NVVM DLLs are copied if present, but does not fail the
 build if the copy fails. This is how ArrayFire ships it's examples.
 
diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index cb4f529388..8805ecf369 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -15,6 +15,7 @@ arrays as a whole -- on all elements in parallel. Wherever possible, existing
 vectorized functions should be used opposed to manually indexing into arrays.
 For example, consider this valid, yet mislead code that attempts to increment
 each element of an array:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10); // [0,  9]
 for(int i = 0; i < a.dims(0); ++i)
@@ -24,6 +25,7 @@ for(int i = 0; i < a.dims(0); ++i)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Instead, the existing vectorized Arrayfire overload of the + operator should have been used:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10);  // [0,  9]
 a = a + 1;                    // [1, 10]
@@ -37,19 +39,22 @@ Operator Category                                           | Functions
 [Complex operations](\ref complex_mat)                      | real(), imag(), conj(), etc.
 [Exponential and logarithmic functions](\ref explog_mat)    | exp(), log(), expm1(), log1p(), etc.
 [Hyperbolic functions](\ref hyper_mat)                      | sinh(), cosh(), tanh(), etc.
-[Logical operations](\ref logic_mat)                        | [&&](\ref arith_func_and), [\|\|](\ref arith_func_or), [<](\ref arith_func_lt), [>](\ref arith_func_gt), [==](\ref arith_func_eq), [!=](\ref arith_func_neq) etc.
+[Logical operations](\ref logic_mat)                        | [&&](\ref arith_func_and), \|\|[(or)](\ref arith_func_or), [<](\ref arith_func_lt), [>](\ref arith_func_gt), [==](\ref arith_func_eq), [!=](\ref arith_func_neq) etc.
 [Numeric functions](\ref numeric_mat)                       | abs(), floor(), round(), min(), max(), etc.
 [Trigonometric functions](\ref trig_mat)                    | sin(), cos(), tan(), etc.
 
-Not only elementwise arithmetic operations are vectorized in Arrayfire.
+In addition to element-wise operations, many other functions are also
+vectorized in Arrayfire.
 
 Vector operations such as min() support vectorization:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array arr = randn(100);
 std::cout << min<float>(arr) << std::endl;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Signal processing functions like convolve() support vectorization:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 float g_coef[] = { 1, 2, 1,
                    2, 4, 2,
@@ -62,6 +67,7 @@ af::array conv = convolve2(signal, filter);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Image processing functions such as rotate() support vectorization:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array imgs = randu(WIDTH, HEIGHT, 100); // 100 (WIDTH x HEIGHT) images
 af::array rot_imgs = rotate(imgs, 45); // 100 rotated images
@@ -72,18 +78,19 @@ algebra functions. Using the built in vectorized operations should be the first
 and preferred method of vectorizing any code written with Arrayfire.
 
 # GFOR: Parallel for-loops
-Another novel method of vectorization present in Arrayfire is the GFOR loop 
+Another novel method of vectorization present in Arrayfire is the GFOR loop
 replacement construct. GFOR allows launching all iterations of a loop in parallel
-on the GPU or device, as long as the iterations are independent. While the 
+on the GPU or device, as long as the iterations are independent. While the
 standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop
 performs each iteration at the same time (in parallel). ArrayFire does this by
-tiling out the values of all loop iterations and then performing computation on 
+tiling out the values of all loop iterations and then performing computation on
 those tiles in one pass. You can think of gfor as performing auto-vectorization
 of your code, e.g. you write a gfor-loop that increments every element of a vector
 but behind the scenes ArrayFire rewrites it to operate on the entire vector in
 parallel.
 
 We can remedy our first example with GFOR:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10);
 gfor(seq i, n)
@@ -92,14 +99,17 @@ gfor(seq i, n)
 
 To see another example, you could run an accum() on every slice of a matrix in a
 for-loop, or you could "vectorize" and simply do it all in one gfor-loop operation:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 for (int i = 0; i < N; ++i)
    B(span,i) = accum(A(span,i)); // runs each accum() in sequence
 gfor (seq i, N)
    B(span,i) = accum(A(span,i)); // runs N accums in parallel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 However, returning to our previous vectorization technique, accum() is already
 vectorized and the operation could be completely replaced with merely:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
     B = accum(A);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -110,6 +120,7 @@ in the narrow case of broadcast-style operations. Consider the case when we have
 a vector of constants that we wish to apply to a collection of variables, such as
 expressing the values of a linear combination for multiple vectors. The broadcast
 of one set of constants to many vectors works well with gfor-loops:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 const static int p=4, n=1000;
 af::array consts = af::randu(p);
@@ -128,29 +139,36 @@ functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire
 functions to execute in "batch processing" mode. In this mode, functions will
 find a dimension which contains "batches" of data to be processed and will
 parallelize the procedure.
+
 Consider the following example:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filter = randn(1, 5);
 af::array weights = randu(5, 5);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 We have a filter that we would like to apply to each of several weights vectors.
 The naive solution would be using a loop as we've seen before:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = constant(0, 5, 5);
 for(int i=0; i<weights.dims(1); ++i){
     filtered_weights.col(i) = filter * weights.col(i);
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 However we would like a vectorized solution. The following syntax begs to be used:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = filter * weights; // fails due to dimension mismatch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-but it fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if
+
+This fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if
 Arrayfire could figure out along which dimension we intend to apply the batch
 operation? That is exactly what batchFunc() does!
 The signature of the function is:
 
-> array batchFunc(const array &lhs, const array &rhs, batchFunc_t func);
+`array batchFunc(const array &lhs, const array &rhs, batchFunc_t func);`
 
 where __batchFunc_t__ is a function pointer of the form:
 `typedef array (*batchFunc_t) (const array &lhs, const array &rhs);`
@@ -159,6 +177,7 @@ where __batchFunc_t__ is a function pointer of the form:
 So, to use batchFunc(), we need to provide the function we will be applying as a
 batch operation. For illustration's sake, let's "implement" a multiplication
 function following the format.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array my_mult (const af::array &lhs, const af::array &rhs){
     return lhs * rhs;
@@ -167,6 +186,7 @@ af::array my_mult (const af::array &lhs, const af::array &rhs){
 
 Our final batch call is not much more difficult than the ideal
 syntax we imagined.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array filtered_weights = batchFunc( filter, weights, my_mult );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From ce2d6a6c5d2fa20478f701aedd31ea291f3356f6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 3 Dec 2015 16:03:09 -0500
Subject: [PATCH 0091/2677] Enables use of ArrayFire with external context &
 command queue

---
 include/af/opencl.h             |  47 +++++++++++++++
 src/backend/opencl/platform.cpp | 103 +++++++++++++++++++++++++++++++-
 src/backend/opencl/platform.hpp |  17 +++++-
 3 files changed, 164 insertions(+), 3 deletions(-)

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 271879fdc9..99080a518e 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -63,6 +63,53 @@ AFAPI af_err afcl_get_device_id(cl_device_id *id);
 AFAPI af_err afcl_set_device_id(cl_device_id id);
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   Push user provided device control constructs into the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to use an
+   user generated OpenCL context and related objects for ArrayFire operations.
+
+   \param[in] dev is the OpenCL device for which user provided context will be used by ArrayFire
+   \param[in] ctx is the user provided OpenCL cl_context to be used by ArrayFire
+   \param[in] que is the user provided OpenCL cl_command_queue to be used by ArrayFire. If this
+                  parameter is NULL, then we create a command queue for the user using the OpenCL
+                  context they provided us.
+
+   \note The cl_* objects are passed onto c++ objects (cl::Device, cl::Context & cl::CommandQueue)
+   that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
+   be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
+*/
+AFAPI af_err afcl_push_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que);
+#endif
+
+#if AF_API_VERSION >= 33
+/**
+   Set active device using cl_context and cl_device_id
+
+   \param[in] dev is the OpenCL device id that is to be set as Active device inside ArrayFire
+   \param[in] ctx is the OpenCL cl_context being used by ArrayFire
+*/
+AFAPI af_err afcl_set_device_context(cl_device_id dev, cl_context ctx);
+#endif
+
+#if AF_API_VERSION >= 33
+/**
+   Remove the user provided device control constructs from the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to remove an already
+   pushed user generated OpenCL context and related objects.
+
+   \param[in] dev is the OpenCL device id that has to be popped
+   \param[in] ctx is the cl_context object to be removed from ArrayFire pool
+
+   \note Any reference counts incremented for cl_* objects by ArrayFire internally are decremented
+   by this func call and you won't be able to call `afcl_set_device_context` on these objects after
+   this function has been called.
+*/
+AFAPI af_err afcl_pop_device_context(cl_device_id dev, cl_context ctx);
+#endif
+
 /**
   @}
 */
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 6f9ae99116..32ba72d27b 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -108,7 +108,7 @@ void DeviceManager::setContext(int device)
 }
 
 DeviceManager::DeviceManager()
-    : mActiveCtxId(0), mActiveQId(0)
+    : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
 {
     try {
         std::vector<cl::Platform>   platforms;
@@ -181,6 +181,7 @@ DeviceManager::DeviceManager()
         }
     }
 #endif
+    mUserDeviceOffset = mDevices.size();
 }
 
 
@@ -472,6 +473,88 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
 }
 #endif
 
+void pushDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
+{
+    try {
+        DeviceManager& devMngr   = DeviceManager::getInstance();
+        cl::Device* tDevice      = new cl::Device(dev);
+        cl::Context* tContext    = new cl::Context(ctx);
+        cl::CommandQueue* tQueue = (que==NULL ?
+                new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
+        devMngr.mDevices.push_back(tDevice);
+        devMngr.mContexts.push_back(tContext);
+        devMngr.mQueues.push_back(tQueue);
+        // FIXME: add OpenGL Interop for user provided contexts later
+        devMngr.mIsGLSharingOn.push_back(false);
+    } catch (const cl::Error &ex) {
+        CL_TO_AF_ERROR(ex);
+    }
+}
+
+void setDeviceContext(cl_device_id dev, cl_context ctx)
+{
+    // FIXME: add OpenGL Interop for user provided contexts later
+    try {
+        DeviceManager& devMngr = DeviceManager::getInstance();
+        const int dCount = devMngr.mDevices.size();
+        for (int i=0; i<dCount; ++i) {
+            if(devMngr.mDevices[i]->operator()()==dev &&
+                    devMngr.mContexts[i]->operator()()==ctx) {
+                setDevice(i);
+                return;
+            }
+        }
+    } catch (const cl::Error &ex) {
+        CL_TO_AF_ERROR(ex);
+    }
+    AF_ERROR("No matching device found", AF_ERR_ARG);
+}
+
+void popDeviceContext(cl_device_id dev, cl_context ctx)
+{
+    try {
+        if (getDevice()() == dev && getContext()()==ctx) {
+            AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
+        }
+
+        DeviceManager& devMngr = DeviceManager::getInstance();
+        const int dCount = devMngr.mDevices.size();
+        int deleteIdx = -1;
+        for (int i = 0; i<dCount; ++i) {
+            if(devMngr.mDevices[i]->operator()()==dev &&
+                    devMngr.mContexts[i]->operator()()==ctx) {
+                deleteIdx = i;
+                break;
+            }
+        }
+        if (deleteIdx < (int)devMngr.mUserDeviceOffset) {
+            AF_ERROR("Cannot pop ArrayFire internal devices", AF_ERR_ARG);
+        } else if (deleteIdx == -1) {
+            AF_ERROR("No matching device found", AF_ERR_ARG);
+        } else {
+            // FIXME: this case can potentially cause issues due to the
+            // modification of the device pool stl containers.
+
+            // IF the current active device is enumerated at a position
+            // that lies ahead of the device that has been requested
+            // to be removed. We just pop the entries from pool since it
+            // has no side effects.
+            devMngr.mDevices.erase(devMngr.mDevices.begin()+deleteIdx);
+            devMngr.mContexts.erase(devMngr.mContexts.begin()+deleteIdx);
+            devMngr.mQueues.erase(devMngr.mQueues.begin()+deleteIdx);
+            // FIXME: add OpenGL Interop for user provided contexts later
+            devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin()+deleteIdx);
+            // OTHERWISE, update(decrement) the `mActive*Id` variables
+            if (deleteIdx < (int)devMngr.mActiveCtxId) {
+                --devMngr.mActiveCtxId;
+                --devMngr.mActiveQId;
+            }
+        }
+    } catch (const cl::Error &ex) {
+        CL_TO_AF_ERROR(ex);
+    }
+}
+
 }
 
 using namespace opencl;
@@ -502,3 +585,21 @@ af_err afcl_set_device_id(cl_device_id id)
     setDevice(getDeviceIdFromNativeId(id));
     return AF_SUCCESS;
 }
+
+af_err afcl_push_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que)
+{
+    pushDeviceContext(dev, ctx, que);
+    return AF_SUCCESS;
+}
+
+af_err afcl_set_device_context(cl_device_id dev, cl_context ctx)
+{
+    setDeviceContext(dev, ctx);
+    return AF_SUCCESS;
+}
+
+af_err afcl_pop_device_context(cl_device_id dev, cl_context ctx)
+{
+    popDeviceContext(dev, ctx);
+    return AF_SUCCESS;
+}
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 7f0dab6f94..022cd7e52d 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -43,8 +43,14 @@ class DeviceManager
 
     friend int setDevice(int device);
 
+    friend void pushDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+
+    friend void setDeviceContext(cl_device_id dev, cl_context cxt);
+
+    friend void popDeviceContext(cl_device_id dev, cl_context ctx);
+
     public:
-        static const unsigned MAX_DEVICES = 16;
+        static const unsigned MAX_DEVICES = 32;
 
         static DeviceManager& getInstance();
 
@@ -67,10 +73,11 @@ class DeviceManager
 
     private:
         // Attributes
-        std::vector<cl::CommandQueue*>  mQueues;
         std::vector<cl::Device*>       mDevices;
         std::vector<cl::Context*>     mContexts;
+        std::vector<cl::CommandQueue*>  mQueues;
         std::vector<bool>        mIsGLSharingOn;
+        unsigned mUserDeviceOffset;
 
         unsigned mActiveCtxId;
         unsigned mActiveQId;
@@ -100,6 +107,12 @@ std::string getPlatformName(const cl::Device &device);
 
 int setDevice(int device);
 
+void pushDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+
+void setDeviceContext(cl_device_id dev, cl_context cxt);
+
+void popDeviceContext(cl_device_id dev, cl_context ctx);
+
 void sync(int device);
 
 }

From f65ee89baf9270bdd1b85c8317c87820f283d57f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Dec 2015 14:19:54 -0500
Subject: [PATCH 0092/2677] cpp wrappers for opencl external context related
 fns

---
 include/af/opencl.h | 55 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 99080a518e..0aa8981eeb 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -194,6 +194,61 @@ namespace afcl
  }
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   Push user provided device control constructs into the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to use an
+   user generated OpenCL context and related objects for ArrayFire operations.
+
+   \param[in] dev is the OpenCL device for which user provided context will be used by ArrayFire
+   \param[in] ctx is the user provided OpenCL cl_context to be used by ArrayFire
+   \param[in] que is the user provided OpenCL cl_command_queue to be used by ArrayFire. If this
+                  parameter is NULL, then we create a command queue for the user using the OpenCL
+                  context they provided us.
+
+   \note The cl_* objects are passed onto c++ objects (cl::Device, cl::Context & cl::CommandQueue)
+   that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
+   be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
+*/
+static inline void pushDevice(cl_device_id dev, cl_context ctx, cl_command_queue que)
+{
+    af_err err = afcl_push_device_context(dev, ctx, que);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to push user provided device/context to ArrayFire pool");
+}
+
+/**
+   Set active device using cl_context and cl_device_id
+
+   \param[in] dev is the OpenCL device id that is to be set as Active device inside ArrayFire
+   \param[in] ctx is the OpenCL cl_context being used by ArrayFire
+*/
+static inline void setDevice(cl_device_id dev, cl_context ctx)
+{
+    af_err err = afcl_set_device_context(dev, ctx);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to set device based on cl_device_id & cl_context");
+}
+
+/**
+   Remove the user provided device control constructs from the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to remove an already
+   pushed user generated OpenCL context and related objects.
+
+   \param[in] dev is the OpenCL device id that has to be popped
+   \param[in] ctx is the cl_context object to be removed from ArrayFire pool
+
+   \note Any reference counts incremented for cl_* objects by ArrayFire internally are decremented
+   by this func call and you won't be able to call `afcl_set_device_context` on these objects after
+   this function has been called.
+*/
+static inline void popDevice(cl_device_id dev, cl_context ctx)
+{
+    af_err err = afcl_pop_device_context(dev, ctx);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to remove the requested device from ArrayFire device pool");
+}
+#endif
+
  /**
  Create an af::array object from an OpenCL cl_mem buffer
 

From 2bcc6de2932d9070863ee26b6a762020caef90eb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Dec 2015 14:20:27 -0500
Subject: [PATCH 0093/2677] unit tests for afcl::{pushDevice, setDevice,
 popDevice} fns

---
 test/ocl_ext_context.cpp | 112 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100644 test/ocl_ext_context.cpp

diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
new file mode 100644
index 0000000000..6b9b48086e
--- /dev/null
+++ b/test/ocl_ext_context.cpp
@@ -0,0 +1,112 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#if defined(AF_OPENCL)
+#include <af/opencl.h>
+#include <iostream>
+
+using namespace std;
+
+inline void checkErr(cl_int err, const char * name) {
+    if (err != CL_SUCCESS) {
+        std::cerr << "ERROR: " << name  << " (" << err << ")" << std::endl;
+        exit(EXIT_FAILURE);
+    }
+}
+
+void getExternals(cl_device_id &deviceId, cl_context &context, cl_command_queue &queue)
+{
+    static cl_device_id dId = NULL;
+    static cl_context cId = NULL;
+    static cl_command_queue qId = NULL;
+    static bool call_once = true;
+
+    if (call_once) {
+        cl_platform_id platformId = NULL;
+        cl_uint numPlatforms;
+        cl_uint numDevices;
+        cl_int errorCode = 0;
+
+        checkErr(clGetPlatformIDs(1, &platformId, &numPlatforms),
+                "Get Platforms failed");
+
+        checkErr(clGetDeviceIDs(platformId, CL_DEVICE_TYPE_DEFAULT, 1, &dId, &numDevices),
+                "Get cl_device_id failed");
+
+        cId = clCreateContext(NULL, 1, &dId, NULL, NULL, &errorCode);
+        checkErr(errorCode, "Context creation failed");
+
+        qId = clCreateCommandQueue(cId, dId, 0, &errorCode);
+        checkErr(errorCode, "Command queue creation failed");
+        call_once = false;
+    }
+    deviceId = dId;
+    context  = cId;
+    queue    = qId;
+}
+
+TEST(OCLExtContext, push)
+{
+    cl_device_id deviceId = NULL;
+    cl_context context = NULL;
+    cl_command_queue queue = NULL;
+
+    getExternals(deviceId, context, queue);
+    int dCount = af::getDeviceCount();
+    printf("%d devices before afcl::pushDevice\n", dCount);
+    af::info();
+    afcl::pushDevice(deviceId, context, queue);
+    ASSERT_EQ(true, dCount+1==af::getDeviceCount());
+    printf("%d devices after afcl::pushDevice\n", af::getDeviceCount());
+    af::info();
+}
+
+TEST(OCLExtContext, set)
+{
+    cl_device_id deviceId = NULL;
+    cl_context context = NULL;
+    cl_command_queue queue = NULL;
+
+    getExternals(deviceId, context, queue);
+    afcl::setDevice(deviceId, context);
+    af::info();
+
+    const int x = 5;
+    const int y = 5;
+    const int s = x * y;
+    af::array a = af::constant(1, x, y);
+    vector<float> host(s);
+    a.host((void*)host.data());
+    for (int i=0; i<s; ++i)
+        ASSERT_EQ(host[i], 1.0f);
+}
+
+TEST(OCLExtContext, pop)
+{
+    cl_device_id deviceId = NULL;
+    cl_context context = NULL;
+    cl_command_queue queue = NULL;
+
+    getExternals(deviceId, context, queue);
+    int dCount = af::getDeviceCount();
+    printf("%d devices before afcl::popDevice\n", dCount);
+    af::setDevice(0);
+    af::info();
+    afcl::popDevice(deviceId, context);
+    ASSERT_EQ(true, dCount-1==af::getDeviceCount());
+    printf("%d devices after afcl::popDevice\n", af::getDeviceCount());
+    af::info();
+}
+#else
+TEST(OCLExtContext, NoopCPU)
+{
+}
+#endif

From 964b1ead31ed4353fa114dde3a0237de0fec9313 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Dec 2015 14:34:32 -0500
Subject: [PATCH 0094/2677] Style changes in opencl header

---
 include/af/opencl.h | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 0aa8981eeb..6a811422db 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -216,7 +216,9 @@ static inline void pushDevice(cl_device_id dev, cl_context ctx, cl_command_queue
     af_err err = afcl_push_device_context(dev, ctx, que);
     if (err!=AF_SUCCESS) throw af::exception("Failed to push user provided device/context to ArrayFire pool");
 }
+#endif
 
+#if AF_API_VERSION >= 33
 /**
    Set active device using cl_context and cl_device_id
 
@@ -228,7 +230,9 @@ static inline void setDevice(cl_device_id dev, cl_context ctx)
     af_err err = afcl_set_device_context(dev, ctx);
     if (err!=AF_SUCCESS) throw af::exception("Failed to set device based on cl_device_id & cl_context");
 }
+#endif
 
+#if AF_API_VERSION >= 33
 /**
    Remove the user provided device control constructs from the ArrayFire device manager pool
 

From d41839f93a6177eff294192b12b37f52232fe6ff Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Dec 2015 15:39:57 -0500
Subject: [PATCH 0095/2677] api name change for afcl external context
 functionality

---
 include/af/opencl.h             | 12 ++++++------
 src/backend/opencl/platform.cpp | 12 ++++++------
 src/backend/opencl/platform.hpp |  8 ++++----
 test/ocl_ext_context.cpp        | 12 ++++++------
 4 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 6a811422db..88e47d2b16 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -80,7 +80,7 @@ AFAPI af_err afcl_set_device_id(cl_device_id id);
    that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
    be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
 */
-AFAPI af_err afcl_push_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que);
+AFAPI af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que);
 #endif
 
 #if AF_API_VERSION >= 33
@@ -107,7 +107,7 @@ AFAPI af_err afcl_set_device_context(cl_device_id dev, cl_context ctx);
    by this func call and you won't be able to call `afcl_set_device_context` on these objects after
    this function has been called.
 */
-AFAPI af_err afcl_pop_device_context(cl_device_id dev, cl_context ctx);
+AFAPI af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx);
 #endif
 
 /**
@@ -211,9 +211,9 @@ namespace afcl
    that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
    be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
 */
-static inline void pushDevice(cl_device_id dev, cl_context ctx, cl_command_queue que)
+static inline void addDevice(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
-    af_err err = afcl_push_device_context(dev, ctx, que);
+    af_err err = afcl_add_device_context(dev, ctx, que);
     if (err!=AF_SUCCESS) throw af::exception("Failed to push user provided device/context to ArrayFire pool");
 }
 #endif
@@ -246,9 +246,9 @@ static inline void setDevice(cl_device_id dev, cl_context ctx)
    by this func call and you won't be able to call `afcl_set_device_context` on these objects after
    this function has been called.
 */
-static inline void popDevice(cl_device_id dev, cl_context ctx)
+static inline void deleteDevice(cl_device_id dev, cl_context ctx)
 {
-    af_err err = afcl_pop_device_context(dev, ctx);
+    af_err err = afcl_delete_device_context(dev, ctx);
     if (err!=AF_SUCCESS) throw af::exception("Failed to remove the requested device from ArrayFire device pool");
 }
 #endif
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 32ba72d27b..510cb50e48 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -473,7 +473,7 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
 }
 #endif
 
-void pushDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
+void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
     try {
         DeviceManager& devMngr   = DeviceManager::getInstance();
@@ -510,7 +510,7 @@ void setDeviceContext(cl_device_id dev, cl_context ctx)
     AF_ERROR("No matching device found", AF_ERR_ARG);
 }
 
-void popDeviceContext(cl_device_id dev, cl_context ctx)
+void removeDeviceContext(cl_device_id dev, cl_context ctx)
 {
     try {
         if (getDevice()() == dev && getContext()()==ctx) {
@@ -586,9 +586,9 @@ af_err afcl_set_device_id(cl_device_id id)
     return AF_SUCCESS;
 }
 
-af_err afcl_push_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que)
+af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
-    pushDeviceContext(dev, ctx, que);
+    addDeviceContext(dev, ctx, que);
     return AF_SUCCESS;
 }
 
@@ -598,8 +598,8 @@ af_err afcl_set_device_context(cl_device_id dev, cl_context ctx)
     return AF_SUCCESS;
 }
 
-af_err afcl_pop_device_context(cl_device_id dev, cl_context ctx)
+af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx)
 {
-    popDeviceContext(dev, ctx);
+    removeDeviceContext(dev, ctx);
     return AF_SUCCESS;
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 022cd7e52d..154d84bc8e 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -43,11 +43,11 @@ class DeviceManager
 
     friend int setDevice(int device);
 
-    friend void pushDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+    friend void addDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
 
     friend void setDeviceContext(cl_device_id dev, cl_context cxt);
 
-    friend void popDeviceContext(cl_device_id dev, cl_context ctx);
+    friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
     public:
         static const unsigned MAX_DEVICES = 32;
@@ -107,11 +107,11 @@ std::string getPlatformName(const cl::Device &device);
 
 int setDevice(int device);
 
-void pushDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+void addDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
 
 void setDeviceContext(cl_device_id dev, cl_context cxt);
 
-void popDeviceContext(cl_device_id dev, cl_context ctx);
+void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
 void sync(int device);
 
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 6b9b48086e..0d4f89b3fc 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -61,11 +61,11 @@ TEST(OCLExtContext, push)
 
     getExternals(deviceId, context, queue);
     int dCount = af::getDeviceCount();
-    printf("%d devices before afcl::pushDevice\n", dCount);
+    printf("%d devices before afcl::addDevice\n", dCount);
     af::info();
-    afcl::pushDevice(deviceId, context, queue);
+    afcl::addDevice(deviceId, context, queue);
     ASSERT_EQ(true, dCount+1==af::getDeviceCount());
-    printf("%d devices after afcl::pushDevice\n", af::getDeviceCount());
+    printf("%d devices after afcl::addDevice\n", af::getDeviceCount());
     af::info();
 }
 
@@ -97,12 +97,12 @@ TEST(OCLExtContext, pop)
 
     getExternals(deviceId, context, queue);
     int dCount = af::getDeviceCount();
-    printf("%d devices before afcl::popDevice\n", dCount);
+    printf("%d devices before afcl::deleteDevice\n", dCount);
     af::setDevice(0);
     af::info();
-    afcl::popDevice(deviceId, context);
+    afcl::deleteDevice(deviceId, context);
     ASSERT_EQ(true, dCount-1==af::getDeviceCount());
-    printf("%d devices after afcl::popDevice\n", af::getDeviceCount());
+    printf("%d devices after afcl::deleteDevice\n", af::getDeviceCount());
     af::info();
 }
 #else

From 227377d5d891b742ff181fa108b75ad5c5b1fce7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Dec 2015 16:12:51 -0500
Subject: [PATCH 0096/2677] Added OpenCL include dir for unit tests

This is required by the ocl_ext_context unit tests source file
---
 test/CMakeLists.txt | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 30907d3390..b1eb5521b3 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -81,7 +81,10 @@ ELSE(USE_SYSTEM_GTEST)
     INCLUDE("${CMAKE_MODULE_PATH}/build_gtest.cmake")
 ENDIF(USE_SYSTEM_GTEST)
 
-INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
+INCLUDE_DIRECTORIES(
+    ${GTEST_INCLUDE_DIRS}
+    ${OpenCL_INCLUDE_DIRS}
+    )
 
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")

From 67ef0517d77bf5a367f22e2819f9091f7f2f0b94 Mon Sep 17 00:00:00 2001
From: Pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Dec 2015 16:47:28 -0500
Subject: [PATCH 0097/2677] additional style changes

---
 test/CMakeLists.txt | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b1eb5521b3..3ae6ec07bd 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -81,10 +81,7 @@ ELSE(USE_SYSTEM_GTEST)
     INCLUDE("${CMAKE_MODULE_PATH}/build_gtest.cmake")
 ENDIF(USE_SYSTEM_GTEST)
 
-INCLUDE_DIRECTORIES(
-    ${GTEST_INCLUDE_DIRS}
-    ${OpenCL_INCLUDE_DIRS}
-    )
+INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
 
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")
@@ -115,6 +112,7 @@ ENDIF()
 
 IF(${BUILD_OPENCL} AND ${OpenCL_FOUND})
     MESSAGE(STATUS "TESTS: OPENCL backend is ON")
+    INCLUDE_DIRECTORIES(${OpenCL_INCLUDE_DIRS})
     CREATE_TESTS(opencl opencl "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
 ELSE()
     MESSAGE(STATUS "TESTS: OPENCL backend is OFF")

From f263db079443f7818a73ee94e237dd53ef017d01 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 4 Dec 2015 23:54:09 -0500
Subject: [PATCH 0098/2677] Increment version to 3.2.1

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 3a474d1755..2afb82029d 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -3,7 +3,7 @@
 #
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "2")
-SET(AF_VERSION_PATCH "0")
+SET(AF_VERSION_PATCH "1")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From 365dc949deee9818a152f8c26ee8eab4baf17a15 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 5 Dec 2015 15:32:44 -0500
Subject: [PATCH 0099/2677] DOC resolve markerType enum in graphics

---
 include/af/graphics.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index eeb3f09371..600d48a0ea 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -408,7 +408,7 @@ AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array
    \param[in]   wind is the window handle
    \param[in]   X is an \ref af_array with the x-axis data points
    \param[in]   Y is an \ref af_array with the y-axis data points
-   \param[in]   marker is an \ref markerType enum specifying which marker to use in the scatter plot
+   \param[in]   marker is an \ref af_marker_type enum specifying which marker to use in the scatter plot
    \param[in]   props is structure \ref af_cell that has the properties that are used
    for the current rendering.
 
@@ -429,7 +429,7 @@ AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_ar
 
    \param[in]   wind is the window handle
    \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
-   \param[in]   marker is an \ref markerType enum specifying which marker to use in the scatter plot
+   \param[in]   marker is an \ref af_marker_type enum specifying which marker to use in the scatter plot
    \param[in]   props is structure \ref af_cell that has the properties that are used
    for the current rendering.
 

From b878711223123de44a620019b43926e96a04b479 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 5 Dec 2015 16:28:38 -0500
Subject: [PATCH 0100/2677] Remove unused variable warning in homography cuda
 kernel

---
 src/backend/cuda/kernel/homography.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 65d880e59b..90cc3ce46d 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -64,7 +64,7 @@ __device__ void JacobiSVD(int m, int n)
     int tid_x = threadIdx.x;
     int bsz_x = blockDim.x;
     int tid_y = threadIdx.y;
-    int gid_y = blockIdx.y * blockDim.y + tid_y;
+    //int gid_y = blockIdx.y * blockDim.y + tid_y;
 
     __shared__ T acc1[256];
     __shared__ T acc2[256];

From 7e2ecb45eb397a6bbe240de44ebbdc04d7650a1c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 7 Dec 2015 13:01:09 -0500
Subject: [PATCH 0101/2677] Increment version to 3.2.2

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 2afb82029d..a9c78fc807 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -3,7 +3,7 @@
 #
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "2")
-SET(AF_VERSION_PATCH "1")
+SET(AF_VERSION_PATCH "2")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From edee05b863347b0065c6369b372df5b37558afad Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 5 Dec 2015 16:28:38 -0500
Subject: [PATCH 0102/2677] Remove unused variable warning in homography cuda
 kernel

---
 src/backend/cuda/kernel/homography.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 65d880e59b..90cc3ce46d 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -64,7 +64,7 @@ __device__ void JacobiSVD(int m, int n)
     int tid_x = threadIdx.x;
     int bsz_x = blockDim.x;
     int tid_y = threadIdx.y;
-    int gid_y = blockIdx.y * blockDim.y + tid_y;
+    //int gid_y = blockIdx.y * blockDim.y + tid_y;
 
     __shared__ T acc1[256];
     __shared__ T acc2[256];

From cac82df08ca7ffed155ffa91c96c9c9eb61abc44 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 7 Dec 2015 15:34:36 -0500
Subject: [PATCH 0103/2677] Minor cleanup to FindCBLAS.cmake

- Remove searching for MKL
- Fix bug when looking up symbols
- Now check for libblas on ubuntu based systems
---
 CMakeModules/FindCBLAS.cmake | 52 +++++++++++++++---------------------
 1 file changed, 22 insertions(+), 30 deletions(-)

diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index d08b3c25aa..b0cd3bdca0 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -36,7 +36,7 @@ IF(PC_CBLAS_FOUND)
     IF (NOT ${PC_LIB}_LIBRARY)
       message(FATAL_ERROR "Something is wrong in your pkg-config file - lib ${PC_LIB} not found in ${PC_CBLAS_LIBRARY_DIRS}")
     ENDIF (NOT ${PC_LIB}_LIBRARY)
-    LIST(APPEND CBLAS_LIBRARIES ${${PC_LIB}_LIBRARY}) 
+    LIST(APPEND CBLAS_LIBRARIES ${${PC_LIB}_LIBRARY})
   ENDFOREACH(PC_LIB)
 
   FIND_PACKAGE_HANDLE_STANDARD_ARGS(CBLAS DEFAULT_MSG CBLAS_LIBRARIES)
@@ -146,12 +146,12 @@ MACRO(CHECK_ALL_LIBRARIES
 
       SET(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
       SET(_libraries_work ${${_prefix}_${_library}_LIBRARY})
-
     ENDIF(_libraries_work)
   ENDFOREACH(_library)
 
   # Test include
   SET(_bug_search_include ${_search_include}) #CMAKE BUG!!! SHOULD NOT BE THAT
+  SET(_bug_libraries_work_check ${_libraries_work_check}) #CMAKE BUG!!! SHOULD NOT BE THAT
 
   IF(_bug_search_include)
     FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include} ${_paths})
@@ -170,8 +170,7 @@ MACRO(CHECK_ALL_LIBRARIES
     SET(${_prefix}_INCLUDE_FILE ${_include})
   ENDIF(_bug_search_include)
 
-
-  IF (_libraries_work_check)
+  IF (_bug_libraries_work_check)
     # Test this combination of libraries.
     IF(_libraries_work)
       SET(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}})
@@ -179,9 +178,13 @@ MACRO(CHECK_ALL_LIBRARIES
       SET(CMAKE_REQUIRED_LIBRARIES)
       MARK_AS_ADVANCED(${_prefix}${_combined_name}_WORKS)
       SET(_libraries_work ${${_prefix}${_combined_name}_WORKS})
+
       IF(_verbose AND _libraries_work)
-        MESSAGE(STATUS "Libraries found")
+        MESSAGE(STATUS "CBLAS Symbols FOUND")
+      ELSE()
+        MESSAGE(STATUS "CBLAS Symbols NOTFOUND")
       ENDIF(_verbose AND _libraries_work)
+
     ENDIF(_libraries_work)
   ENDIF()
 
@@ -216,31 +219,6 @@ IF( NOT CBLAS_LIBRARIES )
     TRUE)
 ENDIF( NOT CBLAS_LIBRARIES )
 
-# MKL
-IF (INTEL_MKL_ROOT_DIR)
-  IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-    SET(MKL_CBLAS_EXT mkl_gf_lp64)
-  ELSE()
-    SET(MKL_CBLAS_EXT mkl_gf)
-  ENDIF()
-
-  IF(NOT CBLAS_LIBRARIES)
-    CHECK_ALL_LIBRARIES(
-      CBLAS_LIBRARIES
-      CBLAS
-      cblas_dgemm
-      ""
-      "${MKL_CBLAS_EXT};mkl_intel_thread"
-      "mkl_cblas.h"
-      TRUE,
-      FALSE)
-  ENDIF(NOT CBLAS_LIBRARIES)
-
-  IF (CBLAS_LIBRARIES)
-    SET(MKL_CBLAS_FOUND TRUE)
-  ENDIF()
-ENDIF()
-
 # CBLAS in ATLAS library? (http://math-atlas.sourceforge.net/)
 IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
@@ -280,6 +258,20 @@ IF(NOT CBLAS_LIBRARIES)
     TRUE)
 ENDIF(NOT CBLAS_LIBRARIES)
 
+# Generic BLAS+CBLAS library
+# Debian based systems have them as single library
+IF(NOT CBLAS_LIBRARIES)
+  CHECK_ALL_LIBRARIES(
+    CBLAS_LIBRARIES
+    CBLAS
+    cblas_dgemm
+    ""
+    "blas"
+    "cblas.h"
+    TRUE,
+    TRUE)
+ENDIF(NOT CBLAS_LIBRARIES)
+
 IF(CBLAS_LIBRARIES)
   IF (NOT MKL_CBLAS_FOUND)
     SET(CBLAS_FOUND TRUE)

From ba19743bb641d90326326846603a866a9edaa144 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 7 Dec 2015 18:02:19 -0500
Subject: [PATCH 0104/2677] Fix memory leak in cuda random. Additionally allow
 seeds per device

---
 src/backend/cuda/kernel/random.hpp | 106 +++++++++++++++++++++--------
 src/backend/cuda/random.cu         |  10 +--
 2 files changed, 84 insertions(+), 32 deletions(-)

diff --git a/src/backend/cuda/kernel/random.hpp b/src/backend/cuda/kernel/random.hpp
index a79a781e85..01c4d61a2d 100644
--- a/src/backend/cuda/kernel/random.hpp
+++ b/src/backend/cuda/kernel/random.hpp
@@ -20,9 +20,77 @@ namespace kernel
 
     static const int THREADS = 256;
     static const int BLOCKS  = 64;
-    static unsigned long long seed = 0;
-    static curandState_t *states[DeviceManager::MAX_DEVICES];
-    static bool is_init[DeviceManager::MAX_DEVICES] = {0};
+    static unsigned long long seeds[DeviceManager::MAX_DEVICES] = {0};
+
+    __global__ static void
+    setup_kernel(curandState_t *states, unsigned long long seed)
+    {
+        unsigned tid = blockDim.x * blockIdx.x + threadIdx.x;
+        curand_init(seed, tid, 0, &states[tid]);
+    }
+
+    class curandStateManager
+    {
+        curandState_t *_state;
+        unsigned long long _seed;
+
+        void resetSeed()
+        {
+            CUDA_LAUNCH(setup_kernel, BLOCKS, THREADS, _state, _seed);
+
+            POST_LAUNCH_CHECK();
+        }
+
+        public:
+        curandStateManager()
+            : _state(NULL), _seed(0)
+        {
+        }
+
+        ~curandStateManager()
+        {
+            if(_state != NULL) memFree((char*)_state);
+        }
+
+        unsigned long long getSeed() const
+        {
+            return _seed;
+        }
+
+        void setSeed(const unsigned long long in_seed)
+        {
+            _seed = in_seed;
+            this->resetSeed();
+        }
+
+        curandState_t* getState()
+        {
+            if(_state)
+                return _state;
+
+            _state = (curandState_t*)memAlloc<char>(BLOCKS * THREADS * sizeof(curandState_t));
+            this->resetSeed();
+            return _state;
+        }
+    };
+
+    curandState_t* getcurandState()
+    {
+        static curandStateManager states[cuda::DeviceManager::MAX_DEVICES];
+
+        int id = cuda::getActiveDeviceId();
+
+        if(!(states[id].getState())) {
+            // states[id] was not initialized. Very bad.
+            // Throw an error here
+        }
+
+        if(states[id].getSeed() != seeds[id]) {
+            states[id].setSeed(seeds[id]);
+        }
+
+        return states[id].getState();
+    }
 
     template<typename T>
     __device__
@@ -93,13 +161,6 @@ namespace kernel
         cval->y = curand_normal_double(state);
     }
 
-    __global__ static void
-    setup_kernel(curandState_t *states, unsigned long long seed)
-    {
-        unsigned tid = blockDim.x * blockIdx.x + threadIdx.x;
-        curand_init(seed, tid, 0, &states[tid]);
-    }
-
     template<typename T>
     __global__ static void
     uniform_kernel(T *out, curandState_t *states, size_t elements)
@@ -130,15 +191,7 @@ namespace kernel
 
     void setup_states()
     {
-        int device = getActiveDeviceId();
-
-        if (!is_init[device]) {
-            CUDA_CHECK(cudaMalloc(&states[device], BLOCKS * THREADS * sizeof(curandState_t)));
-        }
-
-        CUDA_LAUNCH((setup_kernel), BLOCKS, THREADS, states[device], seed);
-        POST_LAUNCH_CHECK();
-        is_init[device] = true;
+        curandState_t *state = getcurandState();
     }
 
     template<typename T>
@@ -149,7 +202,10 @@ namespace kernel
         int threads = THREADS;
         int blocks  = divup(elements, THREADS);
         if (blocks > BLOCKS) blocks = BLOCKS;
-        CUDA_LAUNCH(uniform_kernel, blocks, threads, out, states[device], elements);
+
+        curandState_t *state = getcurandState();
+
+        CUDA_LAUNCH(uniform_kernel, blocks, threads, out, state, elements);
         POST_LAUNCH_CHECK();
     }
 
@@ -162,15 +218,9 @@ namespace kernel
         int blocks  = divup(elements, THREADS);
         if (blocks > BLOCKS) blocks = BLOCKS;
 
-        if (!states[device]) {
-            CUDA_CHECK(cudaMalloc(&states[device], BLOCKS * THREADS * sizeof(curandState_t)));
-
-            CUDA_LAUNCH(setup_kernel, BLOCKS, THREADS, states[device], seed);
-
-            POST_LAUNCH_CHECK();
-        }
+        curandState_t *state = getcurandState();
 
-        CUDA_LAUNCH(normal_kernel, blocks, threads, out, states[device], elements);
+        CUDA_LAUNCH(normal_kernel, blocks, threads, out, state, elements);
 
         POST_LAUNCH_CHECK();
     }
diff --git a/src/backend/cuda/random.cu b/src/backend/cuda/random.cu
index 07cbdc4d9d..e19a48cdae 100644
--- a/src/backend/cuda/random.cu
+++ b/src/backend/cuda/random.cu
@@ -19,7 +19,7 @@ namespace cuda
     template<typename T>
     Array<T> randu(const af::dim4 &dims)
     {
-        if (!kernel::is_init[getActiveDeviceId()]) kernel::setup_states();
+        kernel::setup_states();
         Array<T> out = createEmptyArray<T>(dims);
         kernel::randu(out.get(), out.elements());
         return out;
@@ -28,7 +28,7 @@ namespace cuda
     template<typename T>
     Array<T> randn(const af::dim4 &dims)
     {
-        if (!kernel::is_init[getActiveDeviceId()]) kernel::setup_states();
+        kernel::setup_states();
         Array<T> out  = createEmptyArray<T>(dims);
         kernel::randn(out.get(), out.elements());
         return out;
@@ -55,13 +55,15 @@ namespace cuda
 
     void setSeed(const uintl seed)
     {
-        kernel::seed = seed;
+        int id = getActiveDeviceId();
+        kernel::seeds[id] = seed;
         kernel::setup_states();
     }
 
     uintl getSeed()
     {
-        return kernel::seed;
+        int id = getActiveDeviceId();
+        return kernel::seeds[id];
     }
 
 

From 6a1806498e200c6bf6d7c394f1670e0a3fc9e827 Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Sat, 21 Nov 2015 15:57:18 +0000
Subject: [PATCH 0105/2677] Use custom cflags in examples.

---
 examples/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 5144bf6cd4..29b40d8f8d 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -36,8 +36,8 @@ IF(WIN32)
     SET(CMAKE_CXX_FLAGS "/we4996")
     SET(CMAKE_C_FLAGS   "/we4996")
 ELSE(WIN32)
-    SET(CMAKE_CXX_FLAGS "-Werror=deprecated-declarations")
-    SET(CMAKE_C_FLAGS   "-Werror=deprecated-declarations")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=deprecated-declarations")
+    SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -Werror=deprecated-declarations")
 ENDIF(WIN32)
 
 # A macro to build an ArrayFire example

From 20ba4b90e936d8244966746d90040b3bc76af974 Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Tue, 8 Dec 2015 08:47:07 +0000
Subject: [PATCH 0106/2677] Fix missing includes in testsuite.

---
 test/testHelpers.hpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index e982e9005d..cd725fe2ab 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -13,6 +13,8 @@
 #include <vector>
 #include <algorithm>
 #include <limits>
+#include <stdexcept>
+#include <cfloat>
 #include <arrayfire.h>
 #include <af/dim4.hpp>
 #include <af/array.h>

From c13227e67ba930021055f6add672cf45b61a737f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 8 Dec 2015 10:45:07 -0500
Subject: [PATCH 0107/2677] Use cudaMalloc/Free for memory ops in curand

---
 src/backend/cuda/kernel/random.hpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/random.hpp b/src/backend/cuda/kernel/random.hpp
index 01c4d61a2d..4d960ae46b 100644
--- a/src/backend/cuda/kernel/random.hpp
+++ b/src/backend/cuda/kernel/random.hpp
@@ -49,7 +49,8 @@ namespace kernel
 
         ~curandStateManager()
         {
-            if(_state != NULL) memFree((char*)_state);
+            //if(_state != NULL) memFree((char*)_state);
+            if(_state != NULL) CUDA_CHECK(cudaFree(_state));
         }
 
         unsigned long long getSeed() const
@@ -68,7 +69,8 @@ namespace kernel
             if(_state)
                 return _state;
 
-            _state = (curandState_t*)memAlloc<char>(BLOCKS * THREADS * sizeof(curandState_t));
+            //_state = (curandState_t*)memAlloc<char>(BLOCKS * THREADS * sizeof(curandState_t));
+            CUDA_CHECK(cudaMalloc((void **)&_state, BLOCKS * THREADS * sizeof(curandState_t)));
             this->resetSeed();
             return _state;
         }

From 2287c5ca6a3a5c99bb32d1378f0573126f711a78 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 8 Dec 2015 14:20:39 -0500
Subject: [PATCH 0108/2677] Move AF_REVISION and AF_COMPILER_STR to
 backend/version.hpp

* AF_REVISION was in version.h which is included in defines.h
    * Which meant with each new commit, the entire source would have to be
      rebuilt
* AF_REVISION is only required by src/backend/*/platform.cpp
* So AF_REVISION has been moved into backend/version.hpp and is compiled into
  the library
* Renamed AF_CMPLR_STR to AF_COMPILER_STR
* Moved AF_COMPILER_STR into version.hpp as it is also only required by
  backend/platform.cpp
* src/backend/version.hpp is generated by CMake using Version.cmake
  and version.hpp.in
---
 .gitignore                      |  1 +
 CMakeModules/Version.cmake      |  5 +++++
 CMakeModules/version.h.in       |  2 --
 CMakeModules/version.hpp.in     | 13 +++++++++++++
 src/backend/cpu/platform.cpp    |  5 +++--
 src/backend/cuda/platform.cpp   |  1 +
 src/backend/opencl/platform.cpp |  1 +
 7 files changed, 24 insertions(+), 4 deletions(-)
 create mode 100644 CMakeModules/version.hpp.in

diff --git a/.gitignore b/.gitignore
index 75fa897faf..948b5962eb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,4 +9,5 @@ GRTAGS
 GPATH
 .dir-locals.el
 include/af/version.h
+src/backend/version.hpp
 docs/details/examples.dox
diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index a9c78fc807..cd5149bd25 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -36,3 +36,8 @@ CONFIGURE_FILE(
     ${CMAKE_MODULE_PATH}/version.h.in
     ${CMAKE_SOURCE_DIR}/include/af/version.h
 )
+
+CONFIGURE_FILE(
+    ${CMAKE_MODULE_PATH}/version.hpp.in
+    ${CMAKE_SOURCE_DIR}/src/backend/version.hpp
+)
diff --git a/CMakeModules/version.h.in b/CMakeModules/version.h.in
index 99ee03fc0b..6af8d45d7b 100644
--- a/CMakeModules/version.h.in
+++ b/CMakeModules/version.h.in
@@ -14,5 +14,3 @@
 #define AF_VERSION_MINOR @AF_VERSION_MINOR@
 #define AF_VERSION_PATCH @AF_VERSION_PATCH@
 #define AF_API_VERSION_CURRENT   @AF_API_VERSION_CURRENT@
-#define AF_REVISION "@GIT_COMMIT_HASH@"
-#define AF_CMPLR_STR "@AF_COMPILER_STRING@"
diff --git a/CMakeModules/version.hpp.in b/CMakeModules/version.hpp.in
new file mode 100644
index 0000000000..f4c9ec6150
--- /dev/null
+++ b/CMakeModules/version.hpp.in
@@ -0,0 +1,13 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#define AF_REVISION "@GIT_COMMIT_HASH@"
+#define AF_COMPILER_STR "@AF_COMPILER_STRING@"
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 2b99037496..4d96a37fbb 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -15,6 +15,7 @@
 #include <iostream>
 #include <string>
 #include <defines.hpp>
+#include <version.hpp>
 
 #ifdef _WIN32
 #include <limits.h>
@@ -203,7 +204,7 @@ std::string getInfo()
     info << string("[0] ") << cinfo.vendor() <<": " << cinfo.model() << " ";
     info << "Max threads("<< cinfo.threads()<<") ";
 #ifndef NDEBUG
-    info << AF_CMPLR_STR;
+    info << AF_COMPILER_STR;
 #endif
     info << std::endl;
     return info.str();
@@ -220,7 +221,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     snprintf(d_name, 64, "%s", cinfo.vendor().c_str());
     snprintf(d_platform, 10, "CPU");
     // report the compiler for toolkit
-    snprintf(d_toolkit, 64, "%s", AF_CMPLR_STR);
+    snprintf(d_toolkit, 64, "%s", AF_COMPILER_STR);
     snprintf(d_compute, 10, "%s", "0.0");
 }
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index c154a7eda1..76b336c5ad 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -11,6 +11,7 @@
 #include <af/cuda.h>
 #include <platform.hpp>
 #include <defines.hpp>
+#include <version.hpp>
 #include <driver.h>
 #include <vector>
 #include <string>
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 85364c4297..16fb3e0d34 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -27,6 +27,7 @@
 #include <af/version.h>
 #include <af/opencl.h>
 #include <defines.hpp>
+#include <version.hpp>
 #include <platform.hpp>
 #include <functional>
 #include <algorithm>

From cad4c2c67c2d34777155a1007f5309cde904a983 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Dec 2015 02:06:35 -0500
Subject: [PATCH 0109/2677] initial gravity example

---
 examples/graphics/gravity_sim.cpp | 126 ++++++++++++++++++++++++++++++
 1 file changed, 126 insertions(+)
 create mode 100644 examples/graphics/gravity_sim.cpp

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
new file mode 100644
index 0000000000..77f662f4db
--- /dev/null
+++ b/examples/graphics/gravity_sim.cpp
@@ -0,0 +1,126 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <iostream>
+#include <cstdio>
+
+using namespace af;
+using namespace std;
+
+static const int width = 512, height = 512;
+
+
+void simulate(af::array &parts, af::array &vels, af::array &forces){
+    parts += vels;
+
+    //calculate distance to center
+    float center_coors[2]   = { width / 2, height / 2 };
+    af::array col  = tile(af::array(1, 2, center_coors), parts.dims(0));
+    af::array diff = parts - col;
+    af::array dist = sqrt( diff.col(0)*diff.col(0) + diff.col(1)*diff.col(1) );
+
+    forces = -1 * diff;
+    forces.col(0) /= dist; //normalize force vectors
+    forces.col(1) /= dist; //normalize force vectors
+
+    //update velocities from forces
+    vels += forces;
+
+}
+
+void collisions(af::array &parts, af::array &vels){
+    //clamp particles inside screen border
+    parts.col(0) = min(width, max(0, parts.col(0)));
+    parts.col(1) = min(height - 1, max(0, parts.col(1)));
+
+    //calculate distance to center
+    float center_coors[2]   = { width / 2, height / 2 };
+    af::array col  = tile(af::array(1, 2, center_coors), parts.dims(0));
+    af::array diff = parts - col;
+    af::array dist = sqrt( diff.col(0)*diff.col(0) + diff.col(1)*diff.col(1) );
+
+    /*
+    //collide with center sphere
+    int radius = 50;
+    af::array col_ids = dist(dist<radius);
+    if(col_ids.dims(0) > 0) {
+        //vels(col_ids, span) += -1 * parts(col_ids, span);
+        vels(col_ids, span) = 0;
+    }
+    */
+
+}
+
+int main(int argc, char *argv[])
+{
+    try {
+        const static int total_particles=200;
+        static const int reset = 500;
+
+        af::info();
+
+        af::Window myWindow(width, height, "Gravity Simulation using ArrayFire");
+
+        int frame_count = 0;
+
+        // Initialize the kernel array just once
+        const af::array draw_kernel = gaussianKernel(3, 3);
+
+        // Generate a random starting state
+        af::array particles = af::randu(total_particles,2);
+        particles.col(0) *= width;
+        particles.col(1) *= height;
+
+        af::array velocities = af::randn(total_particles, 2);
+        af::array forces = af::randn(total_particles, 2);
+
+        af::array image = af::constant(0, width, height);
+        af::array ids(total_particles, u32);
+
+        while(!myWindow.close()) {
+
+            ids = (particles.col(0).as(u32) * height) + particles.col(1).as(u32);
+            image(ids) += 255;
+            image = convolve2(image, draw_kernel);
+            myWindow.image(image);
+            image(span, span) = 0;
+            frame_count++;
+
+            // Generate a random starting state
+            if(frame_count % reset == 0) {
+                particles = af::randu(total_particles,2);
+                particles.col(0) *= width;
+                particles.col(1) *= height;
+
+                velocities = af::randn(total_particles, 2);
+            }
+
+            //run force simulation and update particles
+            simulate(particles, velocities, forces);
+
+            //check for collisions and adjust velocities accordingly
+            collisions(particles, velocities);
+
+        }
+    } catch (af::exception& e) {
+        fprintf(stderr, "%s\n", e.what());
+        throw;
+    }
+
+    #ifdef WIN32 // pause in Windows
+    if (!(argc == 2 && argv[1][0] == '-')) {
+        printf("hit [enter]...");
+        fflush(stdout);
+        getchar();
+    }
+    #endif
+    return 0;
+}
+

From dc1e53f709ed9e1d645261f2c51b6a187789d213 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 9 Dec 2015 14:16:16 -0500
Subject: [PATCH 0110/2677] Adding default parameter for surface

---
 include/af/graphics.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 30cb287351..17cb622383 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -205,7 +205,7 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-        void surface(const array& S, const char* const title);
+        void surface(const array& S, const char* const title = NULL);
 #endif
 
 #if AF_API_VERSION >= 32
@@ -221,7 +221,7 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-        void surface(const array& xVals, const array& yVals, const array& S, const char* const title);
+        void surface(const array& xVals, const array& yVals, const array& S, const char* const title = NULL);
 #endif
 
         /**

From df2c09186386058aef56dc79ee06bd7103bdbae5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 8 Dec 2015 15:03:38 -0500
Subject: [PATCH 0111/2677] Converted iir, fir, fftconvolve to async calls

Added eval, sync statements to orb, fast to make them work with
their asynchronous counter parts. Currently, one test of ORB is failing.
Will fix it later.
---
 src/backend/cpu/convolve.cpp    |   7 +
 src/backend/cpu/fast.cpp        |   7 +-
 src/backend/cpu/fftconvolve.cpp | 280 ++++++++++++++++++--------------
 src/backend/cpu/iir.cpp         |  54 +++---
 src/backend/cpu/orb.cpp         |  18 +-
 5 files changed, 217 insertions(+), 149 deletions(-)

diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index e7533764c1..239b4f0924 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -183,6 +183,9 @@ void convolve_nd(T *optr, T const *iptr, accT const *fptr,
 template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind)
 {
+    signal.eval();
+    filter.eval();
+
     auto sDims    = signal.dims();
     auto fDims    = filter.dims();
     auto sStrides = signal.strides();
@@ -255,6 +258,10 @@ void convolve2_separable(T *optr, T const *iptr, accT const *fptr,
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter)
 {
+    signal.eval();
+    c_filter.eval();
+    r_filter.eval();
+
     auto sDims    = signal.dims();
     auto cfDims   = c_filter.dims();
     auto rfDims   = r_filter.dims();
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 1c8069c24d..c8b0514610 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -14,6 +14,8 @@
 #include <err_cpu.hpp>
 #include <handle.hpp>
 #include <fast.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -248,6 +250,9 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const bool nonmax, const float feature_ratio,
               const unsigned edge)
 {
+    in.eval();
+    getQueue().sync();
+
     dim4 in_dims = in.dims();
     const unsigned max_feat = ceil(in.elements() * feature_ratio);
 
@@ -257,6 +262,7 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     if (nonmax == 1) {
         dim4 V_dims(in_dims[0], in_dims[1]);
         V = createValueArray<float>(V_dims, (float)0);
+        V.eval();
     }
 
     // Arrays containing all features detected before non-maximal suppression.
@@ -282,7 +288,6 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     Array<float> score_total = createEmptyArray<float>(af::dim4());
 
     if (nonmax == 1) {
-
         x_total     = createEmptyArray<float>(feat_found_dims);
         y_total     = createEmptyArray<float>(feat_found_dims);
         score_total = createEmptyArray<float>(feat_found_dims);
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index f76f3a0d3f..6172af86a6 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -17,14 +17,17 @@
 #include <fftw3.h>
 #include <copy.hpp>
 #include <convolve_common.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
 
 template<typename To, typename Ti>
-void packData(To* out_ptr, const af::dim4& od, const af::dim4& os,
-              Array<Ti> const& in)
+void packData(Array<To> out, const af::dim4 od, const af::dim4 os, Array<Ti> const in)
 {
+    To* out_ptr = out.get();
+
     const af::dim4 id = in.dims();
     const af::dim4 is = in.strides();
     const Ti* in_ptr = in.get();
@@ -58,9 +61,10 @@ void packData(To* out_ptr, const af::dim4& od, const af::dim4& os,
 }
 
 template<typename To, typename Ti>
-void padArray(To* out_ptr, const af::dim4& od, const af::dim4& os,
-              Array<Ti> const& in)
+void padArray_(Array<To> out, const af::dim4 od, const af::dim4 os,
+              Array<Ti> const in, const dim_t offset)
 {
+    To* out_ptr = out.get() + offset;
     const af::dim4 id = in.dims();
     const af::dim4 is = in.strides();
     const Ti* in_ptr = in.get();
@@ -89,11 +93,21 @@ void padArray(To* out_ptr, const af::dim4& od, const af::dim4& os,
 }
 
 template<typename T>
-void complexMultiply(T* out_ptr, const af::dim4& od, const af::dim4& os,
-                     T* in1_ptr, const af::dim4& i1d, const af::dim4& i1s,
-                     T* in2_ptr, const af::dim4& i2d, const af::dim4& i2s,
-                     ConvolveBatchKind kind)
+void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
+                     const af::dim4 fit_dims, const af::dim4 fit_strides,
+                     ConvolveBatchKind kind, const dim_t offset)
 {
+    T* out_ptr = packed.get() + (kind==CONVOLVE_BATCH_KERNEL? offset : 0);
+    T* in1_ptr = packed.get();
+    T* in2_ptr = packed.get() + offset;
+
+    const dim4& od = (kind==CONVOLVE_BATCH_KERNEL ? fit_dims : sig_dims);
+    const dim4& os = (kind==CONVOLVE_BATCH_KERNEL ? fit_strides : sig_strides);
+    const dim4& i1d = sig_dims;
+    const dim4& i2d = fit_dims;
+    const dim4& i1s = sig_strides;
+    const dim4& i2s = fit_strides;
+
     for (int d3 = 0; d3 < (int)od[3]; d3++) {
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
             for (int d1 = 0; d1 < (int)od[1]; d1++) {
@@ -219,6 +233,9 @@ template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, ConvolveBatchKind kind)
 {
+    signal.eval();
+    filter.eval();
+
     const af::dim4 sd = signal.dims();
     const af::dim4 fd = filter.dims();
 
@@ -249,9 +266,6 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     packed_dims[baseDim] = (sbatch + fbatch);
 
     Array<convT> packed = createEmptyArray<convT>(packed_dims);
-    convT *packed_ptr = packed.get();
-
-    const af::dim4 packed_strides = packed.strides();
 
     sig_tmp_dims[0]    = filter_tmp_dims[0] = packed_dims[0];
     sig_tmp_strides[0] = filter_tmp_strides[0] = 1;
@@ -270,107 +284,117 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         filter_tmp_strides[k] = filter_tmp_strides[k - 1] * filter_tmp_dims[k - 1];
     }
 
-    // Calculate memory offsets for packed signal and filter
-    convT *sig_tmp_ptr    = packed_ptr;
-    convT *filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
-
     // Number of packed complex elements in dimension 0
     dim_t sig_half_d0 = divup(sd[0], 2);
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    packData<convT, T>(sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides, signal);
+    getQueue().enqueue(packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
 
     // Pad filter array with 0s
-    padArray<convT, T>(filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides, filter);
-
-    // Compute forward FFT
-    if (isDouble) {
-        fftw_plan plan = fftw_plan_many_dft(baseDim,
-                                            fft_dims,
-                                            packed_dims[baseDim],
-                                            (fftw_complex*)packed.get(),
-                                            NULL,
-                                            packed_strides[0],
-                                            packed_strides[baseDim] / 2,
-                                            (fftw_complex*)packed.get(),
-                                            NULL,
-                                            packed_strides[0],
-                                            packed_strides[baseDim] / 2,
-                                            FFTW_FORWARD,
-                                            FFTW_ESTIMATE);
-
-        fftw_execute(plan);
-        fftw_destroy_plan(plan);
-    }
-    else {
-        fftwf_plan plan = fftwf_plan_many_dft(baseDim,
-                                              fft_dims,
-                                              packed_dims[baseDim],
-                                              (fftwf_complex*)packed.get(),
-                                              NULL,
-                                              packed_strides[0],
-                                              packed_strides[baseDim] / 2,
-                                              (fftwf_complex*)packed.get(),
-                                              NULL,
-                                              packed_strides[0],
-                                              packed_strides[baseDim] / 2,
-                                              FFTW_FORWARD,
-                                              FFTW_ESTIMATE);
-
-        fftwf_execute(plan);
-        fftwf_destroy_plan(plan);
-    }
+    const dim_t offset = sig_tmp_strides[3]*sig_tmp_dims[3];
+    getQueue().enqueue(padArray_<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
+                       filter, offset);
+
+    dim4 fftDims(1, 1, 1, 1);
+    for (int i=0; i<baseDim; ++i)
+        fftDims[i] = fft_dims[i];
+
+    auto upstream_dft = [=] (Array<convT> packed, const dim4 fftDims) {
+        int fft_dims[baseDim];
+        for (int i=0; i<baseDim; ++i)
+            fft_dims[i] = fftDims[i];
+        const dim4 packed_dims = packed.dims();
+        const af::dim4 packed_strides = packed.strides();
+        // Compute forward FFT
+        if (isDouble) {
+            fftw_plan plan = fftw_plan_many_dft(baseDim,
+                                                fft_dims,
+                                                packed_dims[baseDim],
+                                                (fftw_complex*)packed.get(),
+                                                NULL,
+                                                packed_strides[0],
+                                                packed_strides[baseDim] / 2,
+                                                (fftw_complex*)packed.get(),
+                                                NULL,
+                                                packed_strides[0],
+                                                packed_strides[baseDim] / 2,
+                                                FFTW_FORWARD,
+                                                FFTW_ESTIMATE);
+
+            fftw_execute(plan);
+            fftw_destroy_plan(plan);
+        } else {
+            fftwf_plan plan = fftwf_plan_many_dft(baseDim,
+                                                  fft_dims,
+                                                  packed_dims[baseDim],
+                                                  (fftwf_complex*)packed.get(),
+                                                  NULL,
+                                                  packed_strides[0],
+                                                  packed_strides[baseDim] / 2,
+                                                  (fftwf_complex*)packed.get(),
+                                                  NULL,
+                                                  packed_strides[0],
+                                                  packed_strides[baseDim] / 2,
+                                                  FFTW_FORWARD,
+                                                  FFTW_ESTIMATE);
+
+            fftwf_execute(plan);
+            fftwf_destroy_plan(plan);
+        }
+    };
+    getQueue().enqueue(upstream_dft, packed, fftDims);
 
     // Multiply filter and signal FFT arrays
-    if (kind == CONVOLVE_BATCH_KERNEL)
-        complexMultiply<convT>(filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-                               sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-                               filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-                               kind);
-    else
-        complexMultiply<convT>(sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-                               sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-                               filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-                               kind);
-
-    // Compute inverse FFT
-    if (isDouble) {
-        fftw_plan plan = fftw_plan_many_dft(baseDim,
-                                            fft_dims,
-                                            packed_dims[baseDim],
-                                            (fftw_complex*)packed.get(),
-                                            NULL,
-                                            packed_strides[0],
-                                            packed_strides[baseDim] / 2,
-                                            (fftw_complex*)packed.get(),
-                                            NULL,
-                                            packed_strides[0],
-                                            packed_strides[baseDim] / 2,
-                                            FFTW_BACKWARD,
-                                            FFTW_ESTIMATE);
-
-        fftw_execute(plan);
-        fftw_destroy_plan(plan);
-    }
-    else {
-        fftwf_plan plan = fftwf_plan_many_dft(baseDim,
-                                              fft_dims,
-                                              packed_dims[baseDim],
-                                              (fftwf_complex*)packed.get(),
-                                              NULL,
-                                              packed_strides[0],
-                                              packed_strides[baseDim] / 2,
-                                              (fftwf_complex*)packed.get(),
-                                              NULL,
-                                              packed_strides[0],
-                                              packed_strides[baseDim] / 2,
-                                              FFTW_BACKWARD,
-                                              FFTW_ESTIMATE);
-
-        fftwf_execute(plan);
-        fftwf_destroy_plan(plan);
-    }
+    getQueue().enqueue(complexMultiply<convT>, packed,
+                       sig_tmp_dims, sig_tmp_strides,
+                       filter_tmp_dims, filter_tmp_strides,
+                       kind, offset);
+
+    auto upstream_idft = [=] (Array<convT> packed, const dim4 fftDims) {
+        int fft_dims[baseDim];
+        for (int i=0; i<baseDim; ++i)
+            fft_dims[i] = fftDims[i];
+        const dim4 packed_dims = packed.dims();
+        const af::dim4 packed_strides = packed.strides();
+        // Compute inverse FFT
+        if (isDouble) {
+            fftw_plan plan = fftw_plan_many_dft(baseDim,
+                                                fft_dims,
+                                                packed_dims[baseDim],
+                                                (fftw_complex*)packed.get(),
+                                                NULL,
+                                                packed_strides[0],
+                                                packed_strides[baseDim] / 2,
+                                                (fftw_complex*)packed.get(),
+                                                NULL,
+                                                packed_strides[0],
+                                                packed_strides[baseDim] / 2,
+                                                FFTW_BACKWARD,
+                                                FFTW_ESTIMATE);
+
+            fftw_execute(plan);
+            fftw_destroy_plan(plan);
+        } else {
+            fftwf_plan plan = fftwf_plan_many_dft(baseDim,
+                                                  fft_dims,
+                                                  packed_dims[baseDim],
+                                                  (fftwf_complex*)packed.get(),
+                                                  NULL,
+                                                  packed_strides[0],
+                                                  packed_strides[baseDim] / 2,
+                                                  (fftwf_complex*)packed.get(),
+                                                  NULL,
+                                                  packed_strides[0],
+                                                  packed_strides[baseDim] / 2,
+                                                  FFTW_BACKWARD,
+                                                  FFTW_ESTIMATE);
+
+            fftwf_execute(plan);
+            fftwf_destroy_plan(plan);
+        }
+    };
+    getQueue().enqueue(upstream_idft, packed, fftDims);
 
     // Compute output dimensions
     dim4 oDims(1);
@@ -391,25 +415,37 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
-    T* out_ptr = out.get();
-    const af::dim4 out_dims = out.dims();
-    const af::dim4 out_strides = out.strides();
-
-    const af::dim4 filter_dims = filter.dims();
-
-    // Reorder the output
-    if (kind == CONVOLVE_BATCH_KERNEL) {
-        reorderOutput<T, convT, roundOut>
-            (out_ptr, out_dims, out_strides,
-             filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-             filter_dims, sig_half_d0, baseDim, fftScale, expand);
-    }
-    else {
-        reorderOutput<T, convT, roundOut>
-            (out_ptr, out_dims, out_strides,
-             sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-             filter_dims, sig_half_d0, baseDim, fftScale, expand);
-    }
+
+    auto reorderFunc = [=] (Array<T> out, Array<convT> packed,
+                            const Array<T> filter, const dim_t sig_hald_d0, const dim_t fftScale,
+                            const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
+                            const dim4 filter_tmp_dims, const dim4 filter_tmp_strides) {
+        T* out_ptr = out.get();
+        const af::dim4 out_dims = out.dims();
+        const af::dim4 out_strides = out.strides();
+
+        const af::dim4 filter_dims = filter.dims();
+
+        convT* packed_ptr = packed.get();
+        convT* sig_tmp_ptr    = packed_ptr;
+        convT* filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
+
+        // Reorder the output
+        if (kind == CONVOLVE_BATCH_KERNEL) {
+            reorderOutput<T, convT, roundOut>
+                (out_ptr, out_dims, out_strides,
+                 filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
+                 filter_dims, sig_half_d0, baseDim, fftScale, expand);
+        } else {
+            reorderOutput<T, convT, roundOut>
+                (out_ptr, out_dims, out_strides,
+                 sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
+                 filter_dims, sig_half_d0, baseDim, fftScale, expand);
+        }
+    };
+    getQueue().enqueue(reorderFunc, out, packed, filter, sig_half_d0, fftScale,
+                       sig_tmp_dims, sig_tmp_strides,
+                       filter_tmp_dims, filter_tmp_strides);
 
     return out;
 }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 615da2238d..3c06275f5a 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -16,32 +16,37 @@
 #include <math.hpp>
 #include <arith.hpp>
 #include <convolve.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
-    {
-        T h_a0 = a.get()[0];
-        Array<T> a0 = createValueArray<T>(b.dims(), h_a0);
-
-        ConvolveBatchKind type = x.ndims() == 1 ? CONVOLVE_BATCH_NONE : CONVOLVE_BATCH_SAME;
-        if (x.ndims() != b.ndims()) {
-            type = (x.ndims() < b.ndims()) ? CONVOLVE_BATCH_KERNEL : CONVOLVE_BATCH_SIGNAL;
-        }
 
-        // Extract the first N elements
-        Array<T> c = convolve<T, T, 1, true>(x, b, type);
-        dim4 cdims = c.dims();
-        cdims[0] = x.dims()[0];
-        c.resetDims(cdims);
+template<typename T>
+Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
+{
+    b.eval();
+    a.eval();
+    x.eval();
 
-        int num_a = a.dims()[0];
+    ConvolveBatchKind type = x.ndims() == 1 ? CONVOLVE_BATCH_NONE : CONVOLVE_BATCH_SAME;
+    if (x.ndims() != b.ndims()) {
+        type = (x.ndims() < b.ndims()) ? CONVOLVE_BATCH_KERNEL : CONVOLVE_BATCH_SIGNAL;
+    }
+
+    // Extract the first N elements
+    Array<T> c = convolve<T, T, 1, true>(x, b, type);
+    dim4 cdims = c.dims();
+    cdims[0] = x.dims()[0];
+    c.resetDims(cdims);
 
+    Array<T> y = createEmptyArray<T>(c.dims());
+
+    auto func = [=] (Array<T> y, Array<T> c, const Array<T> a) {
         dim4 ydims = c.dims();
-        Array<T> y = createEmptyArray<T>(ydims);
+        int num_a = a.dims()[0];
 
         for (int l = 0; l < (int)ydims[3]; l++) {
             dim_t yidx3 = l * y.strides()[3];
@@ -76,17 +81,20 @@ namespace cpu
                 }
             }
         }
+    };
+    getQueue().enqueue(func, y, c, a);
 
-        return y;
-    }
+    return y;
+}
 
 #define INSTANTIATE(T)                          \
     template Array<T> iir(const Array<T> &b,    \
                           const Array<T> &a,    \
                           const Array<T> &x);   \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+
 }
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index d279ba514f..4b6629cb3f 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -19,6 +19,8 @@
 #include <convolve.hpp>
 #include <memory.hpp>
 #include <cstring>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -542,6 +544,8 @@ unsigned orb(Array<float> &x, Array<float> &y,
              const float scl_fctr, const unsigned levels,
              const bool blur_img)
 {
+    image.eval();
+    getQueue().sync();
 
     unsigned patch_size = REF_PAT_SIZE;
 
@@ -607,6 +611,8 @@ unsigned orb(Array<float> &x, Array<float> &y,
             ldims[1] = round(idims[1] / lvl_scl);
 
             lvl_img = resize<T>(prev_img, ldims[0], ldims[1], AF_INTERP_BILINEAR);
+            lvl_img.eval();
+            getQueue().sync();
 
             prev_img = lvl_img;
             prev_ldims = lvl_img.dims();
@@ -627,7 +633,10 @@ unsigned orb(Array<float> &x, Array<float> &y,
 
         unsigned lvl_feat = fast(x_feat, y_feat, score_feat,
                                  lvl_img, fast_thr, 9, 1, 0.15f, edge);
-
+        x_feat.eval();
+        y_feat.eval();
+        score_feat.eval();
+        getQueue().sync();
 
         if (lvl_feat == 0) {
             continue;
@@ -653,7 +662,6 @@ unsigned orb(Array<float> &x, Array<float> &y,
             memFree(h_x_harris);
             memFree(h_y_harris);
             memFree(h_score_harris);
-
             continue;
         }
 
@@ -664,13 +672,15 @@ unsigned orb(Array<float> &x, Array<float> &y,
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(af::dim4());
 
         sort_index<float, false>(harris_sorted, harris_idx, score_harris, 0);
+        harris_sorted.eval();
+        harris_idx.eval();
+        getQueue().sync();
 
         usable_feat = std::min(usable_feat, lvl_best[i]);
 
         if (usable_feat == 0) {
             memFree(h_x_harris);
             memFree(h_y_harris);
-
             continue;
         }
 
@@ -706,6 +716,8 @@ unsigned orb(Array<float> &x, Array<float> &y,
             // Filter level image with Gaussian kernel to reduce noise sensitivity
             lvl_filt = convolve2<T, convAccT, false>(lvl_img, gauss_filter, gauss_filter);
         }
+        lvl_filt.eval();
+        getQueue().sync();
 
         // Compute ORB descriptors
         unsigned* h_desc_lvl = memAlloc<unsigned>(usable_feat * 8);

From 687167b97202542bc1ab7bb0ab58c119d3c04edb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 9 Dec 2015 16:40:02 -0500
Subject: [PATCH 0112/2677] Cleaning up graphics examples

---
 examples/graphics/conway_pretty.cpp |  3 +--
 examples/graphics/fractal.cpp       |  8 ++------
 examples/graphics/plot3.cpp         |  6 ++----
 examples/graphics/surface.cpp       | 21 +++++++++------------
 4 files changed, 14 insertions(+), 24 deletions(-)

diff --git a/examples/graphics/conway_pretty.cpp b/examples/graphics/conway_pretty.cpp
index 5b40990745..6a2218956f 100644
--- a/examples/graphics/conway_pretty.cpp
+++ b/examples/graphics/conway_pretty.cpp
@@ -84,7 +84,7 @@ int main(int argc, char *argv[])
             // Update state
             state = state * C0 + C1;
 
-            double fps = 5;
+            double fps = 30;
             while(timer::stop(delay) < (1 / fps)) { }
         }
     } catch (af::exception& e) {
@@ -101,4 +101,3 @@ int main(int argc, char *argv[])
     #endif
     return 0;
 }
-
diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index a06084e040..9ac5a86ea9 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -22,12 +22,8 @@ array complex_grid(int width, int height, float zoom, float center[2])
 {
 
     // Generate sequences of length width, height
-    array x = (seq(double(height)) - double(height) / 2.0);
-    array y = (seq(double(width )) - double(width)  / 2.0);
-
-    // Tile the sequences to generate grid of image size
-    array X = tile(x.T(), y.elements(), 1) / zoom + center[0];
-    array Y = tile(y    , 1, x.elements()) / zoom + center[1];
+    array X = (iota(dim4(1, height), dim4(width , 1)) -  (float)height / 2.0) / zoom + center[0];
+    array Y = (iota(dim4(width , 1), dim4(1, height)) -  (float)width  / 2.0) / zoom + center[1];
 
     // Return the locations as a complex grid
     return complex(X, Y);
diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index ea2ca8d53d..93b8b8d34a 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -25,13 +25,12 @@ int main(int argc, char *argv[])
 
         static float t=0.1;
         array Z = seq( 0.1f, 10.f, PRECISION);
-        array bounds = constant(1, Z.dims());
 
         do{
             array Y = sin((Z*t) + t) / Z;
             array X = cos((Z*t) + t) / Z;
-            X = max(min(X, bounds),-bounds);
-            Y = max(min(Y, bounds),-bounds);
+            X = max(min(X, 1), -1);
+            Y = max(min(Y, 1), -1);
 
             array Pts = join(1, X, Y, Z);
             //Pts can be passed in as a matrix in the form n x 3, 3 x n
@@ -55,4 +54,3 @@ int main(int argc, char *argv[])
     #endif
     return 0;
 }
-
diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index 92d5185d16..d59c66ca39 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -13,8 +13,8 @@
 
 using namespace af;
 
-static const int ITERATIONS = 30;
-static const float PRECISION = 1.0f/ITERATIONS;
+static const int POINTS = 30;
+static const int N = 2 * POINTS;
 
 int main(int argc, char *argv[])
 {
@@ -23,19 +23,17 @@ int main(int argc, char *argv[])
         af::info();
         af::Window myWindow(800, 800, "3D Surface example: ArrayFire");
 
-        array X = seq(-1, 1, PRECISION);
-        array Y = seq(-1, 1, PRECISION);
-        array Z = randn(X.dims(0), Y.dims(0));
+        // Creates grid of between [-1 1] with precision of 1 / POINTS
+        const array x = iota(dim4(N, 1), dim4(1, N)) / POINTS - 1;
+        const array y = iota(dim4(1, N), dim4(N, 1)) / POINTS - 1;
+
+        std::cout << x.dims()  << y.dims() << std::endl;
 
         static float t=0;
         while(!myWindow.close()) {
             t+=0.07;
-            //Z = sin(tile(X,1, Y.dims(0))*t + t) + cos(transpose(tile(Y, 1, X.dims(0)))*t + t);
-            array x = tile(X,1, Y.dims(0));
-            array y = transpose(tile(Y, 1, X.dims(0)));
-            Z = 10*x*-abs(y) * cos(x*x*(y+t))+sin(y*(x+t))-1.5;
-
-            myWindow.surface(X, Y, Z, NULL);
+            array z = 10*x*-abs(y) * cos(x*x*(y+t))+sin(y*(x+t))-1.5;
+            myWindow.surface(x, y, z);
         }
 
     } catch (af::exception& e) {
@@ -52,4 +50,3 @@ int main(int argc, char *argv[])
     #endif
     return 0;
 }
-

From c06f24d585f67989629a0f7eff7c845334981531 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 9 Dec 2015 17:02:07 -0500
Subject: [PATCH 0113/2677] Change to gfx to handle Arrays created by async
 calls

---
 src/backend/cpu/hist_graphics.cpp |  4 +++
 src/backend/cpu/image.cpp         | 58 +++++++++++++++++--------------
 src/backend/cpu/plot.cpp          | 53 +++++++++++++++-------------
 src/backend/cpu/plot3.cpp         | 53 +++++++++++++++-------------
 src/backend/cpu/surface.cpp       | 53 +++++++++++++++-------------
 5 files changed, 119 insertions(+), 102 deletions(-)

diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 21d3fdf941..56f7646b61 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -11,6 +11,8 @@
 
 #include <hist_graphics.hpp>
 #include <err_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -18,6 +20,8 @@ namespace cpu
 template<typename T>
 void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
 {
+    data.eval();
+    getQueue().sync();
     CheckGL("Begin copy_histogram");
 
     glBindBuffer(GL_ARRAY_BUFFER, hist->vbo());
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 947afa2351..767f9d42f1 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -15,39 +15,43 @@
 #include <Array.hpp>
 #include <image.hpp>
 #include <err_cpu.hpp>
-#include <cstdio>
-#include <stdexcept>
 #include <graphics_common.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    void copy_image(const Array<T> &in, const fg::Image* image)
-    {
-        CheckGL("Before CopyArrayToPBO");
-        const T *d_X = in.get();
-        size_t data_size = image->size();
-
-        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pbo());
-        glBufferSubData(GL_PIXEL_UNPACK_BUFFER, 0, data_size, d_X);
-        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
-
-        CheckGL("In CopyArrayToPBO");
-    }
-
-    #define INSTANTIATE(T)  \
-        template void copy_image<T>(const Array<T> &in, const fg::Image* image);
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(ushort)
-    INSTANTIATE(short)
+
+template<typename T>
+void copy_image(const Array<T> &in, const fg::Image* image)
+{
+    in.eval();
+    getQueue().sync();
+    CheckGL("Before CopyArrayToPBO");
+    const T *d_X = in.get();
+    size_t data_size = image->size();
+
+    glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pbo());
+    glBufferSubData(GL_PIXEL_UNPACK_BUFFER, 0, data_size, d_X);
+    glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
+
+    CheckGL("In CopyArrayToPBO");
+}
+
+#define INSTANTIATE(T)  \
+    template void copy_image<T>(const Array<T> &in, const fg::Image* image);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
 }
 
 #endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 9de1993f2d..9cc7d9d2b9 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -12,37 +12,40 @@
 #include <Array.hpp>
 #include <plot.hpp>
 #include <err_cpu.hpp>
-#include <stdexcept>
 #include <graphics_common.hpp>
-#include <reduce.hpp>
-#include <memory.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    void copy_plot(const Array<T> &P, fg::Plot* plot)
-    {
-        CheckGL("Before CopyArrayToVBO");
-
-        glBindBuffer(GL_ARRAY_BUFFER, plot->vbo());
-        glBufferSubData(GL_ARRAY_BUFFER, 0, plot->size(), P.get());
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
-
-        CheckGL("In CopyArrayToVBO");
-    }
-
-    #define INSTANTIATE(T)  \
-        template void copy_plot<T>(const Array<T> &P, fg::Plot* plot);
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+
+template<typename T>
+void copy_plot(const Array<T> &P, fg::Plot* plot)
+{
+    P.eval();
+    getQueue().sync();
+    CheckGL("Before CopyArrayToVBO");
+
+    glBindBuffer(GL_ARRAY_BUFFER, plot->vbo());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, plot->size(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+    CheckGL("In CopyArrayToVBO");
+}
+
+#define INSTANTIATE(T)  \
+    template void copy_plot<T>(const Array<T> &P, fg::Plot* plot);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
 }
 
 #endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/plot3.cpp b/src/backend/cpu/plot3.cpp
index c0e26aaa34..35a7b2500d 100644
--- a/src/backend/cpu/plot3.cpp
+++ b/src/backend/cpu/plot3.cpp
@@ -12,37 +12,40 @@
 #include <Array.hpp>
 #include <plot3.hpp>
 #include <err_cpu.hpp>
-#include <stdexcept>
 #include <graphics_common.hpp>
-#include <reduce.hpp>
-#include <memory.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
-    {
-        CheckGL("Before CopyArrayToVBO");
-
-        glBindBuffer(GL_ARRAY_BUFFER, plot3->vbo());
-        glBufferSubData(GL_ARRAY_BUFFER, 0, plot3->size(), P.get());
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
-
-        CheckGL("In CopyArrayToVBO");
-    }
-
-    #define INSTANTIATE(T)  \
-        template void copy_plot3<T>(const Array<T> &P, fg::Plot3* plot3);
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+
+template<typename T>
+void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
+{
+    P.eval();
+    getQueue().sync();
+    CheckGL("Before CopyArrayToVBO");
+
+    glBindBuffer(GL_ARRAY_BUFFER, plot3->vbo());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, plot3->size(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+    CheckGL("In CopyArrayToVBO");
+}
+
+#define INSTANTIATE(T)  \
+    template void copy_plot3<T>(const Array<T> &P, fg::Plot3* plot3);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
 }
 
 #endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 39f375a6fe..116c784d89 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -12,37 +12,40 @@
 #include <Array.hpp>
 #include <surface.hpp>
 #include <err_cpu.hpp>
-#include <stdexcept>
 #include <graphics_common.hpp>
-#include <reduce.hpp>
-#include <memory.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    void copy_surface(const Array<T> &P, fg::Surface* surface)
-    {
-        CheckGL("Before CopyArrayToVBO");
-
-        glBindBuffer(GL_ARRAY_BUFFER, surface->vbo());
-        glBufferSubData(GL_ARRAY_BUFFER, 0, surface->size(), P.get());
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
-
-        CheckGL("In CopyArrayToVBO");
-    }
-
-    #define INSTANTIATE(T)  \
-        template void copy_surface<T>(const Array<T> &P, fg::Surface* surface);
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+
+template<typename T>
+void copy_surface(const Array<T> &P, fg::Surface* surface)
+{
+    P.eval();
+    getQueue().sync();
+    CheckGL("Before CopyArrayToVBO");
+
+    glBindBuffer(GL_ARRAY_BUFFER, surface->vbo());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, surface->size(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+    CheckGL("In CopyArrayToVBO");
+}
+
+#define INSTANTIATE(T)  \
+    template void copy_surface<T>(const Array<T> &P, fg::Surface* surface);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
 }
 
 #endif  // WITH_GRAPHICS

From b21a838d7d276d2427a2f117a4c39c8b69740330 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 9 Dec 2015 20:40:52 -0500
Subject: [PATCH 0114/2677] Update number of iterations in black scholes
 example

---
 examples/financial/black_scholes_options.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/financial/black_scholes_options.cpp b/examples/financial/black_scholes_options.cpp
index 03cceb885c..6ef98d332b 100644
--- a/examples/financial/black_scholes_options.cpp
+++ b/examples/financial/black_scholes_options.cpp
@@ -82,7 +82,7 @@ int main(int argc, char **argv)
         af::sync();
 
 
-        int iter = 5;
+        int iter = 100;
         for (int n = 50; n <= 500; n += 50) {
 
             // Create GPU copies of the data

From 21f74eb706752c10901b3988cb709c865904cb72 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 10 Dec 2015 13:47:01 -0500
Subject: [PATCH 0115/2677] Fixed harris & homography cpu fns to work with
 async fns

---
 src/backend/cpu/harris.cpp     | 143 +++++++++++++++++----------------
 src/backend/cpu/homography.cpp |  32 +++++---
 2 files changed, 91 insertions(+), 84 deletions(-)

diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index d16c56a8b2..b57b94025d 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -19,6 +19,8 @@
 #include <gradient.hpp>
 #include <sort_index.hpp>
 #include <cstring>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -44,14 +46,14 @@ void gaussian1D(T* out, const int dim, double sigma=0.0)
 }
 
 template<typename T>
-void second_order_deriv(
-    T* ixx_out,
-    T* ixy_out,
-    T* iyy_out,
-    const unsigned in_len,
-    const T* ix_in,
-    const T* iy_in)
+void second_order_deriv(Array<T> ixx, Array<T> ixy, Array<T> iyy,
+                        const unsigned in_len, const Array<T> ix, const Array<T> iy)
 {
+    T* ixx_out     = ixx.get();
+    T* ixy_out     = ixy.get();
+    T* iyy_out     = iyy.get();
+    const T* ix_in = ix.get();
+    const T* iy_in = iy.get();
     for (unsigned x = 0; x < in_len; x++) {
         ixx_out[x] = ix_in[x] * ix_in[x];
         ixy_out[x] = ix_in[x] * iy_in[x];
@@ -60,16 +62,14 @@ void second_order_deriv(
 }
 
 template<typename T>
-void harris_responses(
-    T* resp_out,
-    const unsigned idim0,
-    const unsigned idim1,
-    const T* ixx_in,
-    const T* ixy_in,
-    const T* iyy_in,
-    const float k_thr,
-    const unsigned border_len)
+void harris_responses(Array<T> resp, const unsigned idim0, const unsigned idim1,
+                      const Array<T> ixx, const Array<T> ixy, const Array<T> iyy,
+                      const float k_thr, const unsigned border_len)
 {
+    T* resp_out      = resp.get();
+    const T* ixx_in  = ixx.get();
+    const T* ixy_in  = ixy.get();
+    const T* iyy_in  = iyy.get();
     const unsigned r = border_len;
 
     for (unsigned x = r; x < idim1 - r; x++) {
@@ -87,18 +87,14 @@ void harris_responses(
 }
 
 template<typename T>
-void non_maximal(
-    float* x_out,
-    float* y_out,
-    float* resp_out,
-    unsigned* count,
-    const unsigned idim0,
-    const unsigned idim1,
-    const T* resp_in,
-    const float min_resp,
-    const unsigned border_len,
-    const unsigned max_corners)
+void non_maximal(Array<float> xOut, Array<float> yOut, Array<float> respOut, unsigned* count,
+                 const unsigned idim0, const unsigned idim1, const Array<T> respIn,
+                 const float min_resp, const unsigned border_len, const unsigned max_corners)
 {
+    float* x_out = xOut.get();
+    float* y_out = yOut.get();
+    float* resp_out = respOut.get();
+    const T* resp_in = respIn.get();
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
 
@@ -131,10 +127,19 @@ void non_maximal(
     }
 }
 
-static void keep_corners(float* x_out, float* y_out, float* resp_out,
-                         const float* x_in, const float* y_in, const float* resp_in,
-                         const unsigned* resp_idx, const unsigned n_corners)
+static void keep_corners(Array<float> xOut, Array<float> yOut, Array<float> respOut,
+                         const Array<float> xIn, const Array<float> yIn,
+                         const Array<float> respIn, const Array<unsigned> respIdx,
+                         const unsigned n_corners)
 {
+    float* x_out = xOut.get();
+    float* y_out = yOut.get();
+    float* resp_out = respOut.get();
+    const float* x_in = xIn.get();
+    const float* y_in = yIn.get();
+    const float* resp_in = respIn.get();
+    const uint* resp_idx = respIdx.get();
+
     // Keep only the first n_feat features
     for (unsigned f = 0; f < n_corners; f++) {
         x_out[f] = x_in[resp_idx[f]];
@@ -148,6 +153,8 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
                 const Array<T> &in, const unsigned max_corners, const float min_response,
                 const float sigma, const unsigned filter_len, const float k_thr)
 {
+    in.eval();
+
     dim4 idims = in.dims();
 
     // Window filter
@@ -156,8 +163,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     if (sigma < 0.5f) {
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
-    }
-    else {
+    } else {
         gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
     }
     Array<convAccT> filter = createDeviceDataArray<convAccT>(dim4(filter_len), (const void*)h_filter);
@@ -168,15 +174,14 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     Array<T> iy = createEmptyArray<T>(idims);
 
     // Compute first order derivatives
-    gradient<T>(iy, ix, in);
+    getQueue().enqueue(gradient<T>, iy, ix, in);
 
     Array<T> ixx = createEmptyArray<T>(idims);
     Array<T> ixy = createEmptyArray<T>(idims);
     Array<T> iyy = createEmptyArray<T>(idims);
 
     // Compute second-order derivatives
-    second_order_deriv<T>(ixx.get(), ixy.get(), iyy.get(),
-                          in.elements(), ix.get(), iy.get());
+    getQueue().enqueue(second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
 
     // Convolve second-order derivatives with proper window filter
     ixx = convolve2<T, convAccT, false>(ixx, filter, filter);
@@ -185,26 +190,22 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
 
     const unsigned corner_lim = in.elements() * 0.2f;
 
-    float* x_corners = memAlloc<float>(corner_lim);
-    float* y_corners = memAlloc<float>(corner_lim);
-    float* resp_corners = memAlloc<float>(corner_lim);
+    Array<T> responses = createEmptyArray<T>(dim4(in.elements()));
 
-    T* resp = memAlloc<T>(in.elements());
+    getQueue().enqueue(harris_responses<T>, responses, idims[0], idims[1],
+                       ixx, ixy, iyy, k_thr, border_len);
 
-    // Calculate Harris responses for all pixels
-    harris_responses<T>(resp,
-                        idims[0], idims[1],
-                        ixx.get(), ixy.get(), iyy.get(),
-                        k_thr, border_len);
+    Array<float> xCorners    = createEmptyArray<float>(dim4(corner_lim));
+    Array<float> yCorners    = createEmptyArray<float>(dim4(corner_lim));
+    Array<float> respCorners = createEmptyArray<float>(dim4(corner_lim));
 
     const unsigned min_r = (max_corners > 0) ? 0.f : min_response;
-    unsigned corners_found = 0;
 
     // Performs non-maximal suppression
-    non_maximal<T>(x_corners, y_corners, resp_corners, &corners_found,
-                   idims[0], idims[1], resp, min_r, border_len, corner_lim);
-
-    memFree(resp);
+    getQueue().sync();
+    unsigned corners_found = 0;
+    non_maximal<T>(xCorners, yCorners, respCorners, &corners_found,
+                   idims[0], idims[1], responses, min_r, border_len, corner_lim);
 
     const unsigned corners_out = (max_corners > 0) ?
                                  min(corners_found, max_corners) :
@@ -213,42 +214,42 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         return 0;
 
     if (max_corners > 0 && corners_found > corners_out) {
-        Array<float> harris_responses = createDeviceDataArray<float>(dim4(corners_found), (void*)resp_corners);
+        respCorners.resetDims(dim4(corners_found));
         Array<float> harris_sorted = createEmptyArray<float>(dim4(corners_found));
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(dim4(corners_found));
 
         // Sort Harris responses
-        sort_index<float, false>(harris_sorted, harris_idx, harris_responses, 0);
+        sort_index<float, false>(harris_sorted, harris_idx, respCorners, 0);
 
         x_out = createEmptyArray<float>(dim4(corners_out));
         y_out = createEmptyArray<float>(dim4(corners_out));
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
         // Keep only the corners with higher Harris responses
-        keep_corners(x_out.get(), y_out.get(), resp_out.get(),
-                     x_corners, y_corners, harris_sorted.get(), harris_idx.get(),
-                     corners_out);
-
-        memFree(x_corners);
-        memFree(y_corners);
-    }
-    else if (max_corners == 0 && corners_found < corner_lim) {
+        getQueue().enqueue(keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
+                           harris_sorted, harris_idx, corners_out);
+    } else if (max_corners == 0 && corners_found < corner_lim) {
         x_out = createEmptyArray<float>(dim4(corners_out));
         y_out = createEmptyArray<float>(dim4(corners_out));
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
-        memcpy(x_out.get(), x_corners, corners_out * sizeof(float));
-        memcpy(y_out.get(), y_corners, corners_out * sizeof(float));
-        memcpy(resp_out.get(), resp_corners, corners_out * sizeof(float));
-
-        memFree(x_corners);
-        memFree(y_corners);
-        memFree(resp_corners);
-    }
-    else {
-        x_out = createDeviceDataArray<float>(dim4(corners_out), (void*)x_corners);
-        y_out = createDeviceDataArray<float>(dim4(corners_out), (void*)y_corners);
-        resp_out = createDeviceDataArray<float>(dim4(corners_out), (void*)resp_corners);
+        auto copyFunc = [=](Array<float> x_out, Array<float> y_out,
+                            Array<float> outResponses, const Array<float> x_crnrs,
+                            const Array<float> y_crnrs, const Array<float> inResponses,
+                            const unsigned corners_out) {
+            memcpy(x_out.get(), x_crnrs.get(), corners_out * sizeof(float));
+            memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
+            memcpy(outResponses.get(), inResponses.get(), corners_out * sizeof(float));
+        };
+        getQueue().enqueue(copyFunc, x_out, y_out, resp_out,
+                           xCorners, yCorners, respCorners, corners_out);
+    } else {
+        x_out = xCorners;
+        y_out = yCorners;
+        resp_out = respCorners;
+        x_out.resetDims(dim4(corners_out));
+        y_out.resetDims(dim4(corners_out));
+        resp_out.resetDims(dim4(corners_out));
     }
 
     return corners_out;
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index d20f0ca00c..d936e21b4c 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -15,13 +15,11 @@
 #include <handle.hpp>
 #include <homography.hpp>
 #include <arith.hpp>
-#include <ireduce.hpp>
 #include <random.hpp>
-#include <svd.hpp>
-#include <memory.hpp>
 #include <cstring>
-
 #include <cfloat>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
@@ -154,12 +152,9 @@ unsigned updateIterations(float inlier_ratio, unsigned iter)
 }
 
 template<typename T>
-int computeHomography(T* H_ptr,
-                      const float* rnd_ptr,
-                      const float* x_src_ptr,
-                      const float* y_src_ptr,
-                      const float* x_dst_ptr,
-                      const float* y_dst_ptr)
+int computeHomography(T* H_ptr, const float* rnd_ptr,
+                      const float* x_src_ptr, const float* y_src_ptr,
+                      const float* x_dst_ptr, const float* y_dst_ptr)
 {
     if ((unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[1] || (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[2] ||
         (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[3] || (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[2] ||
@@ -192,6 +187,8 @@ int computeHomography(T* H_ptr,
     float dst_scale = sqrt(2.0f) / sqrt(dst_var);
 
     Array<T> A = createValueArray<T>(af::dim4(9, 9), (T)0);
+    A.eval();
+    getQueue().sync();
     af::dim4 Adims = A.dims();
     T* A_ptr = A.get();
 
@@ -217,6 +214,8 @@ int computeHomography(T* H_ptr,
     }
 
     Array<T> V = createValueArray<T>(af::dim4(Adims[1], Adims[1]), (T)0);
+    V.eval();
+    getQueue().sync();
     JacobiSVD<T>(A.get(), V.get(), 9, 9);
 
     af::dim4 Vdims = V.dims();
@@ -262,6 +261,8 @@ int findBestHomography(Array<T> &bestH,
     const float* y_dst_ptr = y_dst.get();
 
     Array<T> H = createValueArray<T>(af::dim4(9, iterations), (T)0);
+    H.eval();
+    getQueue().sync();
 
     const af::dim4 rdims = rnd.dims();
     const af::dim4 Hdims = H.dims();
@@ -278,8 +279,7 @@ int findBestHomography(Array<T> &bestH,
         const unsigned ridx = rdims[0] * i;
         const float* rnd_ptr = rnd.get() + ridx;
 
-        if (computeHomography<T>(H_ptr, rnd_ptr, x_src_ptr, y_src_ptr,
-                                 x_dst_ptr, y_dst_ptr))
+        if (computeHomography<T>(H_ptr, rnd_ptr, x_src_ptr, y_src_ptr, x_dst_ptr, y_dst_ptr))
             continue;
 
         if (htype == AF_HOMOGRAPHY_RANSAC) {
@@ -320,7 +320,6 @@ int findBestHomography(Array<T> &bestH,
                 minMedian = median;
                 bestIdx = i;
             }
-
         }
     }
 
@@ -355,6 +354,11 @@ int homography(Array<T> &bestH,
                const float inlier_thr,
                const unsigned iterations)
 {
+    x_src.eval();
+    y_src.eval();
+    x_dst.eval();
+    y_dst.eval();
+
     const af::dim4 idims = x_src.dims();
     const unsigned nsamples = idims[0];
 
@@ -366,6 +370,8 @@ int homography(Array<T> &bestH,
     Array<float> frnd = randu<float>(rdims);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
     Array<float> rnd = arithOp<float, af_mul_t>(frnd, fctr, rdims);
+    rnd.eval();
+    getQueue().sync();
 
     return findBestHomography<T>(bestH, x_src, y_src, x_dst, y_dst, rnd, iter, nsamples, inlier_thr, htype);
 }

From 09f03674f6932f6e847d2a9f955cca10408a443b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 10 Dec 2015 14:30:35 -0500
Subject: [PATCH 0116/2677] Replaced cudaMemcpy with async version calls in
 homography

---
 src/backend/cuda/kernel/homography.hpp | 43 +++++++++++++++++---------
 1 file changed, 28 insertions(+), 15 deletions(-)

diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 90cc3ce46d..68b2f71ce5 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -639,21 +639,28 @@ int computeH(
                         finalMedian, finalIdx, median, idx);
             POST_LAUNCH_CHECK();
 
-            CUDA_CHECK(cudaMemcpy(&minMedian, finalMedian, sizeof(float), cudaMemcpyDeviceToHost));
-            CUDA_CHECK(cudaMemcpy(&minIdx, finalIdx, sizeof(unsigned), cudaMemcpyDeviceToHost));
+            CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian, sizeof(float),
+                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx, sizeof(unsigned),
+                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
 
             memFree(finalMedian);
             memFree(finalIdx);
-        }
-        else {
-            CUDA_CHECK(cudaMemcpy(&minMedian, median.ptr, sizeof(float), cudaMemcpyDeviceToHost));
-            CUDA_CHECK(cudaMemcpy(&minIdx, idx.ptr, sizeof(unsigned), cudaMemcpyDeviceToHost));
+        } else {
+            CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.ptr, sizeof(float),
+                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaMemcpyAsync(&minIdx, idx.ptr, sizeof(unsigned),
+                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
         }
 
         // Copy best homography to output
-        CUDA_CHECK(cudaMemcpy(bestH.ptr, H.ptr + minIdx * 9, 9*sizeof(T), cudaMemcpyDeviceToDevice));
+        CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + minIdx * 9, 9*sizeof(T),
+                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
 
         blocks = dim3(divup(nsamples, threads.x));
+        // sync stream for the device to host copies to be visible for
+        // the subsequent kernel launch
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
         CUDA_LAUNCH((computeLMedSInliers<T>), blocks, threads,
                     inliers, bestH, x_src, y_src, x_dst, y_dst,
@@ -668,12 +675,12 @@ int computeH(
 
         kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
 
-        CUDA_CHECK(cudaMemcpy(&inliersH, totalInliers.ptr, sizeof(unsigned), cudaMemcpyDeviceToHost));
+        CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.ptr, sizeof(unsigned),
+                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
 
         memFree(totalInliers.ptr);
         memFree(median.ptr);
-    }
-    else if (htype == AF_HOMOGRAPHY_RANSAC) {
+    } else if (htype == AF_HOMOGRAPHY_RANSAC) {
         Param<unsigned> bestInliers, bestIdx;
         for (int k = 0; k < 4; k++) {
             bestInliers.dims[k] = bestIdx.dims[k] = 1;
@@ -685,13 +692,16 @@ int computeH(
         kernel::ireduce<unsigned, af_max_t>(bestInliers, bestIdx.ptr, inliers, 0);
 
         unsigned blockIdx;
-        CUDA_CHECK(cudaMemcpy(&blockIdx, bestIdx.ptr, sizeof(unsigned), cudaMemcpyDeviceToHost));
+        CUDA_CHECK(cudaMemcpyAsync(&blockIdx, bestIdx.ptr, sizeof(unsigned),
+                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
 
         // Copies back index and number of inliers of best homography estimation
-        CUDA_CHECK(cudaMemcpy(&idxH, idx.ptr+blockIdx, sizeof(unsigned), cudaMemcpyDeviceToHost));
-        CUDA_CHECK(cudaMemcpy(&inliersH, bestInliers.ptr, sizeof(unsigned), cudaMemcpyDeviceToHost));
-
-        CUDA_CHECK(cudaMemcpy(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T), cudaMemcpyDeviceToDevice));
+        CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.ptr+blockIdx, sizeof(unsigned),
+                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(&inliersH, bestInliers.ptr, sizeof(unsigned),
+                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T),
+                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
 
         memFree(bestInliers.ptr);
         memFree(bestIdx.ptr);
@@ -699,6 +709,9 @@ int computeH(
 
     memFree(inliers.ptr);
     memFree(idx.ptr);
+    // sync stream for the device to host copies to be visible for
+    // the subsequent kernel launch
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
     return (int)inliersH;
 }

From 9459c62b577f5a102e3f53ec38a85d8c712f634c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Dec 2015 16:12:28 -0500
Subject: [PATCH 0117/2677] Fix bug in identity cuda plaguing compute 5.2

* This is similar to the bug in triangle fixed in 144a2db
---
 src/backend/cuda/kernel/identity.hpp | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index ac976709fa..056838a9be 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -23,14 +23,14 @@ namespace kernel
     __global__
     static void identity_kernel(Param<T> out, int blocks_x, int blocks_y)
     {
-        unsigned idz = blockIdx.x / blocks_x;
-        unsigned idw = blockIdx.y / blocks_y;
+        const dim_t idz = blockIdx.x / blocks_x;
+        const dim_t idw = blockIdx.y / blocks_y;
 
-        unsigned blockIdx_x = blockIdx.x - idz * blocks_x;
-        unsigned blockIdx_y = blockIdx.y - idw * blocks_y;
+        const dim_t blockIdx_x = blockIdx.x - idz * blocks_x;
+        const dim_t blockIdx_y = blockIdx.y - idw * blocks_y;
 
-        unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
-        unsigned idy = threadIdx.y + blockIdx_y * blockDim.y;
+        const dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
+        const dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
 
         if(idx >= out.dims[0] ||
            idy >= out.dims[1] ||
@@ -38,8 +38,11 @@ namespace kernel
            idw >= out.dims[3])
             return;
 
+        const T one  = scalar<T>(1);
+        const T zero = scalar<T>(0);
+
         T *ptr = out.ptr + idz * out.strides[2] + idw * out.strides[3];
-        T val = (idx == idy) ? scalar<T>(1) : scalar<T>(0);
+        T val = (idx == idy) ? one : zero;
         ptr[idx + idy * out.strides[1]] = val;
     }
 

From 8405d5db6fdd720fb4fa7869ba8aeaa56f76acbd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Dec 2015 22:30:14 -0500
Subject: [PATCH 0118/2677] Add multiprocess compilation flags for Visual
 Studio

* MP adds multiprocess compilation
* Gm- disables minimal rebuild (this options was being used by default before)
---
 CMakeLists.txt          | 7 +++++++
 examples/CMakeLists.txt | 4 ++--
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 28c983fff8..ea92cbec5d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -145,6 +145,13 @@ IF(UNIX)
     ENDIF()
 ELSE(${UNIX}) #Windows
     ADD_DEFINITIONS(-DOS_WIN -DNOMINMAX)
+    IF(MSVC)
+        # MP is multiprocess compilation. Gm- disables minimal rebuilds
+        # http://stackoverflow.com/questions/6172205/how-can-i-do-a-parallel-build-in-visual-studio-2010vvvvvvvv
+        # http://www.kitware.com/blog/home/post/434
+        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP /Gm-")
+        SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /MP /Gm-")
+    ENDIF(MSVC)
 ENDIF()
 
 # Architechture Definitions
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 29b40d8f8d..5377252019 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -33,8 +33,8 @@ ENDIF()
 IF(WIN32)
     # Deprecated Errors are Warning 4996 on VS2013.
     # https://msdn.microsoft.com/en-us/library/ttcz0bys.aspx
-    SET(CMAKE_CXX_FLAGS "/we4996")
-    SET(CMAKE_C_FLAGS   "/we4996")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /we4996")
+    SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /we4996")
 ELSE(WIN32)
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=deprecated-declarations")
     SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -Werror=deprecated-declarations")

From cda0923579c9700829ce6cc780893b34f0e3fed5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Dec 2015 22:37:27 -0500
Subject: [PATCH 0119/2677] Add MSVC flag around example build flags

---
 examples/CMakeLists.txt | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 5377252019..4710d1b739 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -33,8 +33,10 @@ ENDIF()
 IF(WIN32)
     # Deprecated Errors are Warning 4996 on VS2013.
     # https://msdn.microsoft.com/en-us/library/ttcz0bys.aspx
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /we4996")
-    SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /we4996")
+    IF(MSVC)
+        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /we4996")
+        SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /we4996")
+    ENDIF(MSVC)
 ELSE(WIN32)
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=deprecated-declarations")
     SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -Werror=deprecated-declarations")

From 2217014ba231b8e7ceed9ed3072d2104e3ffb243 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 11 Dec 2015 14:59:57 -0500
Subject: [PATCH 0120/2677] Fix in Array::device method

When lambda functions are enqueued in cpu backend, the pointer that
is shared by Array objects has > 1 reference count making it seem
like it is referenced Array inside ::device member function. This is
now fixed by syncing the operations before fetching the device pointer.
---
 src/api/c/assign.cpp         | 2 +-
 src/backend/cpu/Array.hpp    | 3 +++
 src/backend/cpu/platform.hpp | 2 ++
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 13fa179da8..b8fcb12234 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -39,7 +39,7 @@ void assign(Array<Tout> &out, const unsigned &ndims, const af_seq *index, const
     DIM_ASSERT(0, (outDs.ndims()>=iDims.ndims()));
     DIM_ASSERT(0, (outDs.ndims()>=(dim_t)ndims));
 
-    evalArray(out);
+    out.eval();
 
     vector<af_seq> index_(index, index+ndims);
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 471a6741ea..2b9cbb4fed 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -20,6 +20,8 @@
 #include <memory>
 #include <algorithm>
 #include <vector>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -162,6 +164,7 @@ namespace cpu
 
         T* device()
         {
+            getQueue().sync();
             if (!isOwner() || data.use_count() > 1) {
                 *this = Array<T>(dims(), get(), true, true);
             }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 9abf0755d0..10575520b5 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <string>
 
 class async_queue;

From 919333eb379776267d1728b0747172112a484190 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 11 Dec 2015 17:36:18 -0500
Subject: [PATCH 0121/2677] Fix for getDeviceMemInfo function in cpu

This changed is needed after converting the functions asynchronous
---
 src/backend/cpu/memory.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index ac10643c9b..73120b9171 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -14,6 +14,8 @@
 #include <dispatch.hpp>
 #include <cstdlib>
 #include <mutex>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 namespace cpu
 {
@@ -205,6 +207,7 @@ namespace cpu
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers)
     {
+        getQueue().sync();
         if (alloc_bytes   ) *alloc_bytes   = total_bytes;
         if (alloc_buffers ) *alloc_buffers = memory_map.size();
         if (lock_bytes    ) *lock_bytes    = used_bytes;

From cbe4af5b9897af38cb64507e94c4a27353b4d28f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 11 Dec 2015 17:06:01 -0500
Subject: [PATCH 0122/2677] Cleaning up examples

- Replaced getchar within ifdefs with a new macro
- Cleaned up unneeded "console" options from a few examples
- Removed getchar option for windows.
  - This was already available via cmake
---
 examples/benchmarks/blas.cpp                  | 12 +++------
 examples/benchmarks/fft.cpp                   |  8 +-----
 examples/benchmarks/pi.cpp                    |  8 +-----
 examples/financial/black_scholes_options.cpp  |  8 +-----
 examples/financial/heston_model.cpp           |  2 ++
 examples/financial/monte_carlo_options.cpp    |  1 +
 examples/getting_started/convolve.cpp         |  8 +-----
 examples/getting_started/integer.cpp          |  8 +-----
 examples/getting_started/rainfall.cpp         |  8 +-----
 examples/getting_started/vectorize.cpp        |  9 ++-----
 examples/graphics/conway.cpp                  |  9 -------
 examples/graphics/conway_pretty.cpp           |  8 ------
 examples/graphics/fractal.cpp                 |  1 +
 examples/graphics/histogram.cpp               |  9 -------
 examples/graphics/plot2d.cpp                  |  9 -------
 examples/graphics/plot3.cpp                   |  8 ------
 examples/graphics/surface.cpp                 |  8 ------
 examples/helloworld/helloworld.cpp            |  9 ++-----
 .../adaptive_thresholding.cpp                 | 10 +------
 .../image_processing/binary_thresholding.cpp  |  8 ------
 .../image_processing/brain_segmentation.cpp   |  2 ++
 examples/image_processing/edge.cpp            |  5 ++--
 examples/image_processing/filters.cpp         | 26 +++++++------------
 examples/image_processing/image_demo.cpp      |  5 ++--
 examples/image_processing/image_editing.cpp   |  8 ------
 examples/image_processing/morphing.cpp        |  5 ++--
 examples/image_processing/optical_flow.cpp    |  8 +-----
 examples/image_processing/pyramids.cpp        |  5 ++--
 examples/lin_algebra/cholesky.cpp             |  8 +-----
 examples/lin_algebra/lu.cpp                   |  8 +-----
 examples/lin_algebra/qr.cpp                   |  8 +-----
 examples/lin_algebra/svd.cpp                  |  8 +-----
 examples/machine_learning/bagging.cpp         |  2 ++
 examples/machine_learning/deep_belief_net.cpp |  2 ++
 examples/machine_learning/kmeans.cpp          |  3 +++
 examples/machine_learning/knn.cpp             |  2 ++
 .../machine_learning/logistic_regression.cpp  |  2 ++
 examples/machine_learning/naive_bayes.cpp     |  2 ++
 examples/machine_learning/neural_network.cpp  |  2 ++
 examples/machine_learning/perceptron.cpp      |  2 ++
 examples/machine_learning/rbm.cpp             |  2 ++
 .../machine_learning/softmax_regression.cpp   |  2 ++
 examples/pde/swe.cpp                          |  2 ++
 examples/unified/basic.cpp                    |  9 +------
 include/af/macros.h                           |  1 -
 45 files changed, 66 insertions(+), 214 deletions(-)

diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index 2a6c93d11e..109aa1f24d 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <math.h>
 #include <cstdlib>
@@ -48,15 +49,8 @@ int main(int argc, char ** argv)
         throw;
     }
 
-    if (argc == 2 && argv[1][0] == '-')
-        printf(" ### peak %g GFLOPS\n", peak);
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
+    printf(" ### peak %g GFLOPS\n", peak);
+
     return 0;
 }
diff --git a/examples/benchmarks/fft.cpp b/examples/benchmarks/fft.cpp
index 8063422372..2042411b33 100644
--- a/examples/benchmarks/fft.cpp
+++ b/examples/benchmarks/fft.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <math.h>
 #include <cstdlib>
@@ -45,12 +46,5 @@ int main(int argc, char ** argv)
         fprintf(stderr, "%s\n", e.what());
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/benchmarks/pi.cpp b/examples/benchmarks/pi.cpp
index 0a48f533c0..69384f7130 100644
--- a/examples/benchmarks/pi.cpp
+++ b/examples/benchmarks/pi.cpp
@@ -19,6 +19,7 @@
 #include <math.h>
 #include <cstdlib>
 #include <arrayfire.h>
+#include <af/macros.h>
 using namespace af;
 
 // generate millions of random samples
@@ -66,12 +67,5 @@ int main(int argc, char ** argv)
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/financial/black_scholes_options.cpp b/examples/financial/black_scholes_options.cpp
index 6ef98d332b..a1f1b26f19 100644
--- a/examples/financial/black_scholes_options.cpp
+++ b/examples/financial/black_scholes_options.cpp
@@ -11,6 +11,7 @@
 #include <stdio.h>
 #include <math.h>
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdlib>
 
 #include "input.h"
@@ -112,12 +113,5 @@ int main(int argc, char **argv)
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] =='-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/financial/heston_model.cpp b/examples/financial/heston_model.cpp
index 581f89994e..afa07b20ea 100644
--- a/examples/financial/heston_model.cpp
+++ b/examples/financial/heston_model.cpp
@@ -32,6 +32,7 @@
 #include <stdio.h>
 #include <iostream>
 #include <arrayfire.h>
+#include <af/macros.h>
 
 using namespace std;
 using namespace af;
@@ -109,6 +110,7 @@ int main()
         af_print(C_CPU);
         return 0;
     } catch (af::exception& e) {
+
         fprintf(stderr, "%s\n", e.what());
         return 1;
     }
diff --git a/examples/financial/monte_carlo_options.cpp b/examples/financial/monte_carlo_options.cpp
index 8f733ceb7e..e8c1f23aba 100644
--- a/examples/financial/monte_carlo_options.cpp
+++ b/examples/financial/monte_carlo_options.cpp
@@ -12,6 +12,7 @@
 #include <math.h>
 #include <arrayfire.h>
 #include <af/util.h>
+#include <af/macros.h>
 
 using namespace af;
 template<class ty> dtype get_dtype();
diff --git a/examples/getting_started/convolve.cpp b/examples/getting_started/convolve.cpp
index 50002f2100..0385c7abda 100644
--- a/examples/getting_started/convolve.cpp
+++ b/examples/getting_started/convolve.cpp
@@ -10,6 +10,7 @@
 #include <stdio.h>
 #include <cstdlib>
 #include <arrayfire.h>
+#include <af/macros.h>
 using namespace af;
 
 // use static variables at file scope so timeit() wrapper functions
@@ -55,12 +56,5 @@ int main(int argc, char **argv)
         fprintf(stderr, "%s\n", e.what());
     }
 
-#ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
diff --git a/examples/getting_started/integer.cpp b/examples/getting_started/integer.cpp
index 07d1466412..ab13591a1d 100644
--- a/examples/getting_started/integer.cpp
+++ b/examples/getting_started/integer.cpp
@@ -10,6 +10,7 @@
 #include <stdio.h>
 #include <arrayfire.h>
 #include <af/util.h>
+#include <af/macros.h>
 #include <cstdlib>
 
 using namespace af;
@@ -88,12 +89,5 @@ int main(int argc, char ** argv)
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/getting_started/rainfall.cpp b/examples/getting_started/rainfall.cpp
index c334b272f7..3c4166ed29 100644
--- a/examples/getting_started/rainfall.cpp
+++ b/examples/getting_started/rainfall.cpp
@@ -23,6 +23,7 @@
 
 #include <arrayfire.h>
 #include <af/util.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <cstdlib>
 using namespace af;
@@ -70,12 +71,5 @@ int main(int argc, char **argv)
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        std::cout << "hit [enter]...";
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/getting_started/vectorize.cpp b/examples/getting_started/vectorize.cpp
index 0d482234c7..c640d1fb46 100644
--- a/examples/getting_started/vectorize.cpp
+++ b/examples/getting_started/vectorize.cpp
@@ -10,6 +10,7 @@
 #include <stdio.h>
 #include <arrayfire.h>
 #include <af/util.h>
+#include <af/macros.h>
 
 using namespace af;
 
@@ -217,11 +218,5 @@ int main(int argc, char **argv)
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
+    return 0;
 }
diff --git a/examples/graphics/conway.cpp b/examples/graphics/conway.cpp
index f481156efc..c3e4696ee8 100644
--- a/examples/graphics/conway.cpp
+++ b/examples/graphics/conway.cpp
@@ -67,14 +67,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
-
diff --git a/examples/graphics/conway_pretty.cpp b/examples/graphics/conway_pretty.cpp
index 6a2218956f..58e73a6b42 100644
--- a/examples/graphics/conway_pretty.cpp
+++ b/examples/graphics/conway_pretty.cpp
@@ -91,13 +91,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index 9ac5a86ea9..f78d0313b7 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -10,6 +10,7 @@
 #include <stdio.h>
 #include <iostream>
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <math.h>
 #include <cstdlib>
 
diff --git a/examples/graphics/histogram.cpp b/examples/graphics/histogram.cpp
index 9c7b88fd3d..ddf91b2f8b 100644
--- a/examples/graphics/histogram.cpp
+++ b/examples/graphics/histogram.cpp
@@ -34,14 +34,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-#ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
-
diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index 7e28d34ebd..4f68b92b30 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -47,14 +47,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
-
diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index 93b8b8d34a..3893f01803 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -44,13 +44,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index d59c66ca39..95b87bc141 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -40,13 +40,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/helloworld/helloworld.cpp b/examples/helloworld/helloworld.cpp
index 46a1fc44dd..cad2e82907 100644
--- a/examples/helloworld/helloworld.cpp
+++ b/examples/helloworld/helloworld.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
@@ -60,16 +61,10 @@ int main(int argc, char *argv[])
         af_print(inds);
 
     } catch (af::exception& e) {
+
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/image_processing/adaptive_thresholding.cpp b/examples/image_processing/adaptive_thresholding.cpp
index 026097428b..5ce34e76be 100644
--- a/examples/image_processing/adaptive_thresholding.cpp
+++ b/examples/image_processing/adaptive_thresholding.cpp
@@ -105,13 +105,5 @@ int main(int argc, char **argv)
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-#ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
-}
\ No newline at end of file
+}
diff --git a/examples/image_processing/binary_thresholding.cpp b/examples/image_processing/binary_thresholding.cpp
index 3e8c9ee4f1..319e6eaa60 100644
--- a/examples/image_processing/binary_thresholding.cpp
+++ b/examples/image_processing/binary_thresholding.cpp
@@ -96,13 +96,5 @@ int main(int argc, char **argv)
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-#ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
diff --git a/examples/image_processing/brain_segmentation.cpp b/examples/image_processing/brain_segmentation.cpp
index 92c29bdd90..7f18a7cee8 100644
--- a/examples/image_processing/brain_segmentation.cpp
+++ b/examples/image_processing/brain_segmentation.cpp
@@ -11,6 +11,7 @@
 #include <stdio.h>
 #include <math.h>
 #include <arrayfire.h>
+#include <af/macros.h>
 #include "../common/progress.h"
 
 using namespace af;
@@ -163,5 +164,6 @@ int main(int argc, char* argv[])
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
     }
+
     return 0;
 }
diff --git a/examples/image_processing/edge.cpp b/examples/image_processing/edge.cpp
index 1c3a35bd06..bef7bc10e3 100644
--- a/examples/image_processing/edge.cpp
+++ b/examples/image_processing/edge.cpp
@@ -69,7 +69,7 @@ array edge(const array &in, int method = 0)
     return normalize(mag);
 }
 
-void edge(bool console)
+void edge()
 {
     af::Window myWindow("Edge Dectectors");
     af::Window myWindow2(512, 512, "Histogram");
@@ -99,14 +99,13 @@ void edge(bool console)
 int main(int argc, char* argv[])
 {
     int device = argc > 1 ? atoi(argv[1]) : 0;
-    bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
         af::setDevice(device);
         af::info();
 
         printf("** ArrayFire Edge Detection Demo **\n");
-        edge(console);
+        edge();
 
     } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/image_processing/filters.cpp b/examples/image_processing/filters.cpp
index 2971ee91f2..8b75acf063 100644
--- a/examples/image_processing/filters.cpp
+++ b/examples/image_processing/filters.cpp
@@ -214,21 +214,21 @@ int main(int argc, char **argv)
         af::setDevice(device);
         af::info();
 
-        array lena = loadImage(ASSETS_DIR "/examples/images/vegetable-woman.jpg", true);
+        array img = loadImage(ASSETS_DIR "/examples/images/vegetable-woman.jpg", true);
 
         array prew_mag, prew_dir;
         array sob_mag, sob_dir;
-        array lena1ch = colorSpace(lena, AF_GRAY, AF_RGB);
-        prewitt(prew_mag, prew_dir, lena1ch);
-        sobelFilter(sob_mag, sob_dir, lena1ch);
-        array sprd = spread(lena, 3, 3);
-        array hrl = hurl(lena, 10, 1);
-        array pckng = pick(lena, 40, 2);
-        array difog = DifferenceOfGaussian(lena, 1, 2);
+        array img1ch = colorSpace(img, AF_GRAY, AF_RGB);
+        prewitt(prew_mag, prew_dir, img1ch);
+        sobelFilter(sob_mag, sob_dir, img1ch);
+        array sprd = spread(img, 3, 3);
+        array hrl = hurl(img, 10, 1);
+        array pckng = pick(img, 40, 2);
+        array difog = DifferenceOfGaussian(img, 1, 2);
         array bil = bilateral(hrl, 3.0f, 40.0f);
         array mf = medianfilter(hrl, 5, 5);
         array gb = gaussianblur(hrl, 3, 3, 0.8);
-        array emb = emboss(lena, 45, 20, 10);
+        array emb = emboss(img, 45, 20, 10);
 
         af::Window wnd("Image Filters Demo");
         std::cout << "Press ESC while the window is in focus to exit" << std::endl;
@@ -252,13 +252,5 @@ int main(int argc, char **argv)
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-#ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
diff --git a/examples/image_processing/image_demo.cpp b/examples/image_processing/image_demo.cpp
index 01b13a91bf..477594efe3 100644
--- a/examples/image_processing/image_demo.cpp
+++ b/examples/image_processing/image_demo.cpp
@@ -38,7 +38,7 @@ static const float h_sobel[] = {
 };
 
 // Demonstrates various image manipulations.
-static void img_test_demo(bool console)
+static void img_test_demo()
 {
     af::Window wnd("Image Demo");
 
@@ -95,13 +95,12 @@ static void img_test_demo(bool console)
 int main(int argc, char** argv)
 {
     int device = argc > 1 ? atoi(argv[1]) : 0;
-    bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
         af::setDevice(device);
         af::info();
         printf("** ArrayFire Image Demo **\n\n");
-        img_test_demo(console);
+        img_test_demo();
 
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/image_processing/image_editing.cpp b/examples/image_processing/image_editing.cpp
index d765734559..800f2739cb 100644
--- a/examples/image_processing/image_editing.cpp
+++ b/examples/image_processing/image_editing.cpp
@@ -135,13 +135,5 @@ int main(int argc, char **argv)
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
-
-#ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
diff --git a/examples/image_processing/morphing.cpp b/examples/image_processing/morphing.cpp
index 31f1cf0768..73eb45c4ca 100644
--- a/examples/image_processing/morphing.cpp
+++ b/examples/image_processing/morphing.cpp
@@ -76,7 +76,7 @@ array blur(const array& img, const array mask = gaussianKernel(3,3))
 }
 
 // Demonstrates various image morphing manipulations.
-static void morphing_demo(bool console)
+static void morphing_demo()
 {
     af::Window wnd(1280, 720, "Morphological Operations");
     // load images
@@ -120,13 +120,12 @@ static void morphing_demo(bool console)
 int main(int argc, char** argv)
 {
     int device = argc > 1 ? atoi(argv[1]) : 0;
-    bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
         af::info();
         af::setDevice(device);
         printf("** ArrayFire Image Morphing Demo **\n\n");
-        morphing_demo(console);
+        morphing_demo();
 
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/image_processing/optical_flow.cpp b/examples/image_processing/optical_flow.cpp
index 5c4b60b33e..754b09f816 100644
--- a/examples/image_processing/optical_flow.cpp
+++ b/examples/image_processing/optical_flow.cpp
@@ -12,6 +12,7 @@
 #include <math.h>
 #include <algorithm>
 #include <arrayfire.h>
+#include <af/macros.h>
 
 using namespace af;
 
@@ -121,12 +122,5 @@ int main(int argc, char* argv[])
         throw;
     }
 
-#ifdef WIN32 // pause in Windows
-    if (!console) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
diff --git a/examples/image_processing/pyramids.cpp b/examples/image_processing/pyramids.cpp
index 0f96732ada..18726d7d9f 100644
--- a/examples/image_processing/pyramids.cpp
+++ b/examples/image_processing/pyramids.cpp
@@ -44,7 +44,7 @@ array pyramid(const array& img, const int level, const bool sampling)
     return pyr;
 }
 
-void pyramids_demo(bool console)
+void pyramids_demo()
 {
     af::Window wnd_rgb("Image Pyramids - RGB Images");
     af::Window wnd_gray("Image Pyramids - Grayscale Images");
@@ -88,13 +88,12 @@ void pyramids_demo(bool console)
 int main(int argc, char** argv)
 {
     int device = argc > 1 ? atoi(argv[1]) : 0;
-    bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
         af::setDevice(device);
         af::info();
         printf("** ArrayFire Image Pyramids Demo **\n\n");
-        pyramids_demo(console);
+        pyramids_demo();
 
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/lin_algebra/cholesky.cpp b/examples/lin_algebra/cholesky.cpp
index 999597c913..f3dd6969a1 100644
--- a/examples/lin_algebra/cholesky.cpp
+++ b/examples/lin_algebra/cholesky.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
@@ -46,12 +47,5 @@ int main(int argc, char *argv[])
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/lin_algebra/lu.cpp b/examples/lin_algebra/lu.cpp
index 06cbeb1e9e..1b0e3a5b29 100644
--- a/examples/lin_algebra/lu.cpp
+++ b/examples/lin_algebra/lu.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
@@ -44,12 +45,5 @@ int main(int argc, char *argv[])
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/lin_algebra/qr.cpp b/examples/lin_algebra/qr.cpp
index 6f0fe8eac3..8f9bc40b32 100644
--- a/examples/lin_algebra/qr.cpp
+++ b/examples/lin_algebra/qr.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
@@ -47,12 +48,5 @@ int main(int argc, char *argv[])
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
diff --git a/examples/lin_algebra/svd.cpp b/examples/lin_algebra/svd.cpp
index 964bee7eb6..bab8dad771 100644
--- a/examples/lin_algebra/svd.cpp
+++ b/examples/lin_algebra/svd.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
@@ -44,12 +45,5 @@ int main(int argc, char* argv[])
         throw;
     }
 
-#ifdef WIN32  // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-#endif
     return 0;
 }
diff --git a/examples/machine_learning/bagging.cpp b/examples/machine_learning/bagging.cpp
index 862ffa25e3..3b2cfba938 100644
--- a/examples/machine_learning/bagging.cpp
+++ b/examples/machine_learning/bagging.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
@@ -147,4 +148,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/deep_belief_net.cpp b/examples/machine_learning/deep_belief_net.cpp
index 4dfac9e4ba..e8e0b5b6fb 100644
--- a/examples/machine_learning/deep_belief_net.cpp
+++ b/examples/machine_learning/deep_belief_net.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
@@ -368,4 +369,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index c13b5e63ea..0e251b36ed 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -10,6 +10,7 @@
 #include <iostream>
 #include <stdio.h>
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <af/util.h>
 #include <cstdlib>
 
@@ -175,4 +176,6 @@ int main(int argc, char** argv)
     } catch (af::exception &ae) {
         std::cerr << ae.what() << std::endl;
     }
+
+    return 0;
 }
diff --git a/examples/machine_learning/knn.cpp b/examples/machine_learning/knn.cpp
index bd86ae0e13..8d8d4de2c6 100644
--- a/examples/machine_learning/knn.cpp
+++ b/examples/machine_learning/knn.cpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
+#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
@@ -113,4 +114,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/logistic_regression.cpp b/examples/machine_learning/logistic_regression.cpp
index 58281f0367..c02fcf871e 100644
--- a/examples/machine_learning/logistic_regression.cpp
+++ b/examples/machine_learning/logistic_regression.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
@@ -197,4 +198,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/naive_bayes.cpp b/examples/machine_learning/naive_bayes.cpp
index 1703205b90..daf76d78aa 100644
--- a/examples/machine_learning/naive_bayes.cpp
+++ b/examples/machine_learning/naive_bayes.cpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
+#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
@@ -163,4 +164,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index b249fd01c7..9188fca54d 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
+#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
@@ -278,4 +279,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/perceptron.cpp b/examples/machine_learning/perceptron.cpp
index 7b5a579ea9..c12c8a9a4a 100644
--- a/examples/machine_learning/perceptron.cpp
+++ b/examples/machine_learning/perceptron.cpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
+#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
@@ -143,4 +144,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/rbm.cpp b/examples/machine_learning/rbm.cpp
index d6b68f3fc0..14e9fec71b 100644
--- a/examples/machine_learning/rbm.cpp
+++ b/examples/machine_learning/rbm.cpp
@@ -11,6 +11,7 @@
 #include <stdio.h>
 #include <vector>
 #include <string>
+#include <af/macros.h>
 #include <af/util.h>
 #include <math.h>
 #include "mnist_common.h"
@@ -228,4 +229,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/machine_learning/softmax_regression.cpp b/examples/machine_learning/softmax_regression.cpp
index 45d253c8b9..5b523d9e4f 100644
--- a/examples/machine_learning/softmax_regression.cpp
+++ b/examples/machine_learning/softmax_regression.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
@@ -204,4 +205,5 @@ int main(int argc, char** argv)
         std::cerr << ae.what() << std::endl;
     }
 
+    return 0;
 }
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 84ce1ff4de..a7b9d28efc 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -3,6 +3,7 @@
 #include <string.h>
 #include <math.h>
 #include <arrayfire.h>
+#include <af/macros.h>
 #include "../common/progress.h"
 
 using namespace af;
@@ -82,5 +83,6 @@ int main(int argc, char* argv[])
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
+
     return 0;
 }
diff --git a/examples/unified/basic.cpp b/examples/unified/basic.cpp
index 791466a140..f48c0764a1 100644
--- a/examples/unified/basic.cpp
+++ b/examples/unified/basic.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <af/macros.h>
 #include <cstdio>
 #include <vector>
 #include <algorithm>
@@ -67,13 +68,5 @@ int main(int argc, char *argv[])
         fprintf(stderr, "%s\n", e.what());
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
-
     return 0;
 }
diff --git a/include/af/macros.h b/include/af/macros.h
index 42a4219ac8..62ff3e96ad 100644
--- a/include/af/macros.h
+++ b/include/af/macros.h
@@ -21,4 +21,3 @@
                  __FILE__, __LINE__, ##__VA_ARGS__);      \
         } while (0);
 #endif
-

From 52a8409cb510083e242cf5280bdc63a173ff7902 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 14 Dec 2015 11:18:17 -0500
Subject: [PATCH 0123/2677] Fixing select and replace tests

---
 test/replace.cpp | 22 +++++++++++++++++-----
 test/select.cpp  | 20 +++++++++++++++-----
 2 files changed, 32 insertions(+), 10 deletions(-)

diff --git a/test/replace.cpp b/test/replace.cpp
index c6d3b5d042..9e99eaee8f 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -35,10 +35,17 @@ void replaceTest(const dim4 &dims)
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
-    array c = a.copy();
-    array cond = randu(dims, ty) > constant(0.3, dims, ty);
     array b = randu(dims, ty);
 
+    if (a.isinteger()) {
+        a = (a % (1 << 30)).as(ty);
+        b = (b % (1 << 30)).as(ty);
+    }
+
+    array c = a.copy();
+
+    array cond = randu(dims, ty) > a;
+
     replace(c, cond, b);
 
     int num = (int)a.elements();
@@ -65,8 +72,13 @@ void replaceScalarTest(const dim4 &dims)
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
+
+    if (a.isinteger()) {
+        a = (a % (1 << 30)).as(ty);
+    }
+
     array c = a.copy();
-    array cond = randu(dims, ty) > constant(0.3, dims, ty);
+    array cond = randu(dims, ty) > a;
     double b = 3;
 
     replace(c, cond, b);
@@ -81,7 +93,7 @@ void replaceScalarTest(const dim4 &dims)
     cond.host(&hcond[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hcond[i] ? b : ha[i]);
+        ASSERT_EQ(hc[i], hcond[i] ? T(b) : ha[i]);
     }
 }
 
@@ -103,7 +115,7 @@ TEST(Replace, NaN)
     array a = randu(dims, ty);
     a(seq(a.dims(0) / 2), span, span, span) = af::NaN;
     array c = a.copy();
-    double b = 0;
+    float b = 0;
     replace(c, isNaN(c), b);
 
     int num = (int)a.elements();
diff --git a/test/select.cpp b/test/select.cpp
index 91c8110bc6..1c39282b15 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -34,9 +34,15 @@ void selectTest(const dim4 &dims)
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
-    array cond = randu(dims, ty) > constant(0.3, dims, ty);
     array b = randu(dims, ty);
 
+    if (a.isinteger()) {
+        a = (a % (1 << 30)).as(ty);
+        b = (b % (1 << 30)).as(ty);
+    }
+
+    array cond = randu(dims, ty) > a;
+
     array c = select(cond, a, b);
 
     int num = (int)a.elements();
@@ -63,9 +69,13 @@ void selectScalarTest(const dim4 &dims)
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
-    array cond = randu(dims, ty) > constant(0.3, dims, ty);
+    array cond = randu(dims, ty) > a;
     double b = 3;
 
+    if (a.isinteger()) {
+        a = (a % (1 << 30)).as(ty);
+    }
+
     array c = is_right ? select(cond, a, b) : select(cond, b, a);
 
     int num = (int)a.elements();
@@ -80,11 +90,11 @@ void selectScalarTest(const dim4 &dims)
 
     if (is_right) {
         for (int i = 0; i < num; i++) {
-            ASSERT_EQ(hc[i], hcond[i] ? ha[i] : b);
+            ASSERT_EQ(hc[i], hcond[i] ? ha[i] : T(b));
         }
     } else {
         for (int i = 0; i < num; i++) {
-            ASSERT_EQ(hc[i], hcond[i] ? b : ha[i]);
+            ASSERT_EQ(hc[i], hcond[i] ? T(b) : ha[i]);
         }
     }
 }
@@ -111,7 +121,7 @@ TEST(Select, NaN)
 
     array a = randu(dims, ty);
     a(seq(a.dims(0) / 2), span, span, span) = af::NaN;
-    double b = 0;
+    float b = 0;
     array c = select(isNaN(a), b, a);
 
     int num = (int)a.elements();

From 6b9c157bc3e8124b45146270789d48d13e0bba20 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 14 Dec 2015 13:27:23 -0500
Subject: [PATCH 0124/2677] FEAT added allocHost and freeHost functions

* Added corresponding C/unified API
* Documentation
---
 docs/details/device.dox    | 43 ++++++++++++++++++++++--
 include/af/device.h        | 68 ++++++++++++++++++++++++++++++++------
 src/api/c/device.cpp       | 18 ++++++++++
 src/api/cpp/device.cpp     | 37 +++++++++++++++------
 src/api/unified/device.cpp | 10 ++++++
 5 files changed, 152 insertions(+), 24 deletions(-)

diff --git a/docs/details/device.dox b/docs/details/device.dox
index 230199d583..c89d2a17f0 100644
--- a/docs/details/device.dox
+++ b/docs/details/device.dox
@@ -62,6 +62,16 @@ allocation
 
 ===============================================================================
 
+\defgroup device_func_free free
+\ingroup device_mat
+
+\brief Free device memory allocated by ArrayFire's memory manager
+
+These calls free the device memory. These functions need to be called on
+pointers allocated using alloc function.
+
+===============================================================================
+
 \defgroup device_func_pinned pinned
 \ingroup device_mat
 
@@ -73,12 +83,39 @@ a limited resource.
 
 ===============================================================================
 
-\defgroup device_func_free free
+\defgroup device_func_free_pinned freePinned
 \ingroup device_mat
 
-\brief Free device memory allocated by ArrayFire's memory manager
+\brief Free pinned memory allocated by ArrayFire's memory manager
+
+These calls free the pinned memory on host. These functions need to be called on
+pointers allocated using pinned function.
+
+===============================================================================
+
+\defgroup device_func_alloc_host allocHost
+\ingroup device_mat
+
+\brief Allocate memory on host
+
+This function is used for allocating regular memory on host. This is useful
+where the compiler version of ArrayFire library is different from the
+executable's compiler version.
+
+It does not use ArrayFire's memory manager.
+
+===============================================================================
+
+\defgroup device_func_free_host freeHost
+\ingroup device_mat
+
+\brief Free memory allocated on host internally by ArrayFire
+
+This function is used for freeing memory on host that was allocated within
+ArrayFire. This is useful where the compiler version of ArrayFire library is
+different from the executable's compiler version.
 
-These calls free the device or pinned memory. These functions need to be called
+It does not use ArrayFire's memory manager.
 
 ===============================================================================
 
diff --git a/include/af/device.h b/include/af/device.h
index 826863e6d8..03800c3ffd 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -101,6 +101,12 @@ namespace af
     T* alloc(const size_t elements);
     /// @}
 
+    /// \ingroup device_func_free
+    ///
+    /// \copydoc device_func_free
+    /// \param[in] ptr the memory to free
+    AFAPI void free(const void *ptr);
+
     /// \ingroup device_func_pinned
     /// @{
     ///
@@ -119,15 +125,45 @@ namespace af
     T* pinned(const size_t elements);
     /// @}
 
-    /// \ingroup device_func_free
-    /// @{
-    /// \copydoc device_func_free
+    /// \ingroup device_func_free_pinned
+    ///
+    /// \copydoc device_func_free_pinned
     /// \param[in] ptr the memory to free
-    AFAPI void free(const void *ptr);
-
-    /// \copydoc free()
     AFAPI void freePinned(const void *ptr);
-    ///@}
+
+    /// \brief Allocate memory on host
+    ///
+    /// \copydoc device_func_alloc_host
+    ///
+    /// \param[in] elements the number of elements to allocate
+    /// \param[in] type is the type of the elements to allocate
+    /// \returns the pointer to the memory
+    ///
+    /// \ingroup device_func_alloc_host
+    AFAPI void *allocHost(const size_t elements, const dtype type);
+
+    /// \brief Allocate memory on host
+    ///
+    /// \copydoc device_func_alloc_host
+    ///
+    /// \param[in] elements the number of elements to allocate
+    /// \returns the pointer to the memory
+    ///
+    /// \note the size of the memory allocated is the number of \p elements *
+    ///         sizeof(type)
+    ///
+    /// \ingroup device_func_alloc_host
+    template<typename T>
+    AFAPI T* allocHost(const size_t elements);
+
+    /// \brief Free memory allocated internally by ArrayFire
+    //
+    /// \copydoc device_func_free_host
+    ///
+    /// \param[in] ptr the memory to free
+    ///
+    /// \ingroup device_func_free_host
+    AFAPI void freeHost(const void *ptr);
 
     /// \ingroup device_func_mem
     /// @{
@@ -207,20 +243,30 @@ extern "C" {
     AFAPI af_err af_alloc_device(void **ptr, const dim_t bytes);
 
     /**
-       \ingroup device_func_pinned
+       \ingroup device_func_free
     */
-    AFAPI af_err af_alloc_pinned(void **ptr, const dim_t bytes);
+    AFAPI af_err af_free_device(void *ptr);
 
     /**
-       \ingroup device_func_free
+       \ingroup device_func_pinned
     */
-    AFAPI af_err af_free_device(void *ptr);
+    AFAPI af_err af_alloc_pinned(void **ptr, const dim_t bytes);
 
     /**
        \ingroup device_func_free_pinned
     */
     AFAPI af_err af_free_pinned(void *ptr);
 
+    /**
+       \ingroup device_func_alloc_host
+    */
+    AFAPI af_err af_alloc_host(void **ptr, const dim_t bytes);
+
+    /**
+       \ingroup device_func_free_host
+    */
+    AFAPI af_err af_free_host(void *ptr);
+
     /**
        Create array from device memory
        \ingroup construct_mat
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 28b4cc2c49..39ad217939 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -297,6 +297,24 @@ af_err af_free_pinned(void *ptr)
     return AF_SUCCESS;
 }
 
+af_err af_alloc_host(void **ptr, const dim_t bytes)
+{
+    try {
+        AF_CHECK(af_init());
+        *ptr = malloc(bytes);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_free_host(void *ptr)
+{
+    try {
+        AF_CHECK(af_init());
+        free(ptr);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_device_gc()
 {
     try {
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index bec0a60d59..622809ed06 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -140,6 +140,18 @@ namespace af
         AF_THROW(af_free_pinned((void *)ptr));
     }
 
+    void *allocHost(const size_t elements, const af::dtype type)
+    {
+        void *ptr;
+        AF_THROW(af_alloc_host(&ptr, elements * size_of(type)));
+        return ptr;
+    }
+
+    void freeHost(const void *ptr)
+    {
+        AF_THROW(af_free_host((void *)ptr));
+    }
+
     void deviceGC()
     {
         AF_THROW(af_device_gc());
@@ -164,16 +176,21 @@ namespace af
         return size_bytes;
     }
 
-#define INSTANTIATE(T)                                                  \
-    template<> AFAPI                                                    \
-    T* alloc(const size_t elements)                                     \
-    {                                                                   \
-        return (T*)alloc(elements, (af::dtype)dtype_traits<T>::af_type); \
-    }                                                                   \
-    template<> AFAPI                                                    \
-    T* pinned(const size_t elements)                                    \
-    {                                                                   \
-        return (T*)pinned(elements, (af::dtype)dtype_traits<T>::af_type); \
+#define INSTANTIATE(T)                                                      \
+    template<> AFAPI                                                        \
+    T* alloc(const size_t elements)                                         \
+    {                                                                       \
+        return (T*)alloc(elements, (af::dtype)dtype_traits<T>::af_type);    \
+    }                                                                       \
+    template<> AFAPI                                                        \
+    T* pinned(const size_t elements)                                        \
+    {                                                                       \
+        return (T*)pinned(elements, (af::dtype)dtype_traits<T>::af_type);   \
+    }                                                                       \
+    template<> AFAPI                                                        \
+    T* allocHost(const size_t elements)                                     \
+    {                                                                       \
+        return (T*)allocHost(elements, (af::dtype)dtype_traits<T>::af_type);\
     }
 
     INSTANTIATE(float)
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 43559a077a..4f07788ada 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -95,6 +95,16 @@ af_err af_free_pinned(void *ptr)
     return CALL(ptr);
 }
 
+af_err af_alloc_host(void **ptr, const dim_t bytes)
+{
+    return CALL(ptr, bytes);
+}
+
+af_err af_free_host(void *ptr)
+{
+    return CALL(ptr);
+}
+
 af_err af_device_array(af_array *arr, const void *data, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     return CALL(arr, data, ndims, dims, type);

From 57779c280e564ef3db10b2058eba0763a57a6437 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 14 Dec 2015 15:27:48 -0500
Subject: [PATCH 0125/2677] Fixing surface.cpp example to work on windows

---
 examples/graphics/surface.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index 95b87bc141..0d2648a850 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -13,8 +13,8 @@
 
 using namespace af;
 
-static const int POINTS = 30;
-static const int N = 2 * POINTS;
+static const int M = 30;
+static const int N = 2 * M;
 
 int main(int argc, char *argv[])
 {
@@ -23,9 +23,9 @@ int main(int argc, char *argv[])
         af::info();
         af::Window myWindow(800, 800, "3D Surface example: ArrayFire");
 
-        // Creates grid of between [-1 1] with precision of 1 / POINTS
-        const array x = iota(dim4(N, 1), dim4(1, N)) / POINTS - 1;
-        const array y = iota(dim4(1, N), dim4(N, 1)) / POINTS - 1;
+        // Creates grid of between [-1 1] with precision of 1 / M
+        const array x = iota(dim4(N, 1), dim4(1, N)) / M - 1;
+        const array y = iota(dim4(1, N), dim4(N, 1)) / M - 1;
 
         std::cout << x.dims()  << y.dims() << std::endl;
 

From 59da65f7f60301ecaa09103610c181652b59ffde Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 14 Dec 2015 15:57:07 -0500
Subject: [PATCH 0126/2677] Removing unnecessary af/macros.h from all examples

---
 examples/benchmarks/blas.cpp                      | 1 -
 examples/benchmarks/fft.cpp                       | 1 -
 examples/benchmarks/pi.cpp                        | 1 -
 examples/financial/black_scholes_options.cpp      | 1 -
 examples/financial/heston_model.cpp               | 1 -
 examples/financial/monte_carlo_options.cpp        | 1 -
 examples/getting_started/convolve.cpp             | 1 -
 examples/getting_started/integer.cpp              | 1 -
 examples/getting_started/rainfall.cpp             | 1 -
 examples/getting_started/vectorize.cpp            | 1 -
 examples/graphics/fractal.cpp                     | 1 -
 examples/helloworld/helloworld.cpp                | 1 -
 examples/image_processing/brain_segmentation.cpp  | 1 -
 examples/image_processing/optical_flow.cpp        | 1 -
 examples/lin_algebra/cholesky.cpp                 | 1 -
 examples/lin_algebra/lu.cpp                       | 1 -
 examples/lin_algebra/qr.cpp                       | 1 -
 examples/lin_algebra/svd.cpp                      | 1 -
 examples/machine_learning/bagging.cpp             | 1 -
 examples/machine_learning/deep_belief_net.cpp     | 1 -
 examples/machine_learning/kmeans.cpp              | 1 -
 examples/machine_learning/knn.cpp                 | 1 -
 examples/machine_learning/logistic_regression.cpp | 1 -
 examples/machine_learning/naive_bayes.cpp         | 1 -
 examples/machine_learning/neural_network.cpp      | 1 -
 examples/machine_learning/perceptron.cpp          | 1 -
 examples/machine_learning/rbm.cpp                 | 1 -
 examples/machine_learning/softmax_regression.cpp  | 1 -
 examples/pde/swe.cpp                              | 1 -
 examples/unified/basic.cpp                        | 1 -
 30 files changed, 30 deletions(-)

diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index 109aa1f24d..e1e3f0db60 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <math.h>
 #include <cstdlib>
diff --git a/examples/benchmarks/fft.cpp b/examples/benchmarks/fft.cpp
index 2042411b33..5b196c8877 100644
--- a/examples/benchmarks/fft.cpp
+++ b/examples/benchmarks/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <math.h>
 #include <cstdlib>
diff --git a/examples/benchmarks/pi.cpp b/examples/benchmarks/pi.cpp
index 69384f7130..9ad5ad41b7 100644
--- a/examples/benchmarks/pi.cpp
+++ b/examples/benchmarks/pi.cpp
@@ -19,7 +19,6 @@
 #include <math.h>
 #include <cstdlib>
 #include <arrayfire.h>
-#include <af/macros.h>
 using namespace af;
 
 // generate millions of random samples
diff --git a/examples/financial/black_scholes_options.cpp b/examples/financial/black_scholes_options.cpp
index a1f1b26f19..a8cdc07724 100644
--- a/examples/financial/black_scholes_options.cpp
+++ b/examples/financial/black_scholes_options.cpp
@@ -11,7 +11,6 @@
 #include <stdio.h>
 #include <math.h>
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdlib>
 
 #include "input.h"
diff --git a/examples/financial/heston_model.cpp b/examples/financial/heston_model.cpp
index afa07b20ea..0be51fcfec 100644
--- a/examples/financial/heston_model.cpp
+++ b/examples/financial/heston_model.cpp
@@ -32,7 +32,6 @@
 #include <stdio.h>
 #include <iostream>
 #include <arrayfire.h>
-#include <af/macros.h>
 
 using namespace std;
 using namespace af;
diff --git a/examples/financial/monte_carlo_options.cpp b/examples/financial/monte_carlo_options.cpp
index e8c1f23aba..8f733ceb7e 100644
--- a/examples/financial/monte_carlo_options.cpp
+++ b/examples/financial/monte_carlo_options.cpp
@@ -12,7 +12,6 @@
 #include <math.h>
 #include <arrayfire.h>
 #include <af/util.h>
-#include <af/macros.h>
 
 using namespace af;
 template<class ty> dtype get_dtype();
diff --git a/examples/getting_started/convolve.cpp b/examples/getting_started/convolve.cpp
index 0385c7abda..8a9cfd38db 100644
--- a/examples/getting_started/convolve.cpp
+++ b/examples/getting_started/convolve.cpp
@@ -10,7 +10,6 @@
 #include <stdio.h>
 #include <cstdlib>
 #include <arrayfire.h>
-#include <af/macros.h>
 using namespace af;
 
 // use static variables at file scope so timeit() wrapper functions
diff --git a/examples/getting_started/integer.cpp b/examples/getting_started/integer.cpp
index ab13591a1d..1d1c2cab4c 100644
--- a/examples/getting_started/integer.cpp
+++ b/examples/getting_started/integer.cpp
@@ -10,7 +10,6 @@
 #include <stdio.h>
 #include <arrayfire.h>
 #include <af/util.h>
-#include <af/macros.h>
 #include <cstdlib>
 
 using namespace af;
diff --git a/examples/getting_started/rainfall.cpp b/examples/getting_started/rainfall.cpp
index 3c4166ed29..98c97d30ea 100644
--- a/examples/getting_started/rainfall.cpp
+++ b/examples/getting_started/rainfall.cpp
@@ -23,7 +23,6 @@
 
 #include <arrayfire.h>
 #include <af/util.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <cstdlib>
 using namespace af;
diff --git a/examples/getting_started/vectorize.cpp b/examples/getting_started/vectorize.cpp
index c640d1fb46..55f5e05ebc 100644
--- a/examples/getting_started/vectorize.cpp
+++ b/examples/getting_started/vectorize.cpp
@@ -10,7 +10,6 @@
 #include <stdio.h>
 #include <arrayfire.h>
 #include <af/util.h>
-#include <af/macros.h>
 
 using namespace af;
 
diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index f78d0313b7..9ac5a86ea9 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -10,7 +10,6 @@
 #include <stdio.h>
 #include <iostream>
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <math.h>
 #include <cstdlib>
 
diff --git a/examples/helloworld/helloworld.cpp b/examples/helloworld/helloworld.cpp
index cad2e82907..c3f891a0d1 100644
--- a/examples/helloworld/helloworld.cpp
+++ b/examples/helloworld/helloworld.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
diff --git a/examples/image_processing/brain_segmentation.cpp b/examples/image_processing/brain_segmentation.cpp
index 7f18a7cee8..7349bf258b 100644
--- a/examples/image_processing/brain_segmentation.cpp
+++ b/examples/image_processing/brain_segmentation.cpp
@@ -11,7 +11,6 @@
 #include <stdio.h>
 #include <math.h>
 #include <arrayfire.h>
-#include <af/macros.h>
 #include "../common/progress.h"
 
 using namespace af;
diff --git a/examples/image_processing/optical_flow.cpp b/examples/image_processing/optical_flow.cpp
index 754b09f816..ae77d4e478 100644
--- a/examples/image_processing/optical_flow.cpp
+++ b/examples/image_processing/optical_flow.cpp
@@ -12,7 +12,6 @@
 #include <math.h>
 #include <algorithm>
 #include <arrayfire.h>
-#include <af/macros.h>
 
 using namespace af;
 
diff --git a/examples/lin_algebra/cholesky.cpp b/examples/lin_algebra/cholesky.cpp
index f3dd6969a1..3154c65a61 100644
--- a/examples/lin_algebra/cholesky.cpp
+++ b/examples/lin_algebra/cholesky.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
diff --git a/examples/lin_algebra/lu.cpp b/examples/lin_algebra/lu.cpp
index 1b0e3a5b29..afdd2e8952 100644
--- a/examples/lin_algebra/lu.cpp
+++ b/examples/lin_algebra/lu.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
diff --git a/examples/lin_algebra/qr.cpp b/examples/lin_algebra/qr.cpp
index 8f9bc40b32..e4c954e378 100644
--- a/examples/lin_algebra/qr.cpp
+++ b/examples/lin_algebra/qr.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
diff --git a/examples/lin_algebra/svd.cpp b/examples/lin_algebra/svd.cpp
index bab8dad771..ab0fe9e4fd 100644
--- a/examples/lin_algebra/svd.cpp
+++ b/examples/lin_algebra/svd.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdio>
 #include <cstdlib>
 
diff --git a/examples/machine_learning/bagging.cpp b/examples/machine_learning/bagging.cpp
index 3b2cfba938..7c9895053d 100644
--- a/examples/machine_learning/bagging.cpp
+++ b/examples/machine_learning/bagging.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
diff --git a/examples/machine_learning/deep_belief_net.cpp b/examples/machine_learning/deep_belief_net.cpp
index e8e0b5b6fb..75e982115e 100644
--- a/examples/machine_learning/deep_belief_net.cpp
+++ b/examples/machine_learning/deep_belief_net.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index 0e251b36ed..51351aaff9 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -10,7 +10,6 @@
 #include <iostream>
 #include <stdio.h>
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <af/util.h>
 #include <cstdlib>
 
diff --git a/examples/machine_learning/knn.cpp b/examples/machine_learning/knn.cpp
index 8d8d4de2c6..e07c8536b1 100644
--- a/examples/machine_learning/knn.cpp
+++ b/examples/machine_learning/knn.cpp
@@ -12,7 +12,6 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
-#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
diff --git a/examples/machine_learning/logistic_regression.cpp b/examples/machine_learning/logistic_regression.cpp
index c02fcf871e..00b9eaad40 100644
--- a/examples/machine_learning/logistic_regression.cpp
+++ b/examples/machine_learning/logistic_regression.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
diff --git a/examples/machine_learning/naive_bayes.cpp b/examples/machine_learning/naive_bayes.cpp
index daf76d78aa..788570bcd3 100644
--- a/examples/machine_learning/naive_bayes.cpp
+++ b/examples/machine_learning/naive_bayes.cpp
@@ -12,7 +12,6 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
-#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index 9188fca54d..f6effeb759 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -12,7 +12,6 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
-#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
diff --git a/examples/machine_learning/perceptron.cpp b/examples/machine_learning/perceptron.cpp
index c12c8a9a4a..f04e050e1b 100644
--- a/examples/machine_learning/perceptron.cpp
+++ b/examples/machine_learning/perceptron.cpp
@@ -12,7 +12,6 @@
 #include <vector>
 #include <string>
 #include <af/util.h>
-#include <af/macros.h>
 #include <math.h>
 #include "mnist_common.h"
 
diff --git a/examples/machine_learning/rbm.cpp b/examples/machine_learning/rbm.cpp
index 14e9fec71b..8c832c11ec 100644
--- a/examples/machine_learning/rbm.cpp
+++ b/examples/machine_learning/rbm.cpp
@@ -11,7 +11,6 @@
 #include <stdio.h>
 #include <vector>
 #include <string>
-#include <af/macros.h>
 #include <af/util.h>
 #include <math.h>
 #include "mnist_common.h"
diff --git a/examples/machine_learning/softmax_regression.cpp b/examples/machine_learning/softmax_regression.cpp
index 5b523d9e4f..9d8e36d859 100644
--- a/examples/machine_learning/softmax_regression.cpp
+++ b/examples/machine_learning/softmax_regression.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <stdio.h>
 #include <vector>
 #include <string>
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index a7b9d28efc..0d3b39fda9 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -3,7 +3,6 @@
 #include <string.h>
 #include <math.h>
 #include <arrayfire.h>
-#include <af/macros.h>
 #include "../common/progress.h"
 
 using namespace af;
diff --git a/examples/unified/basic.cpp b/examples/unified/basic.cpp
index f48c0764a1..89364777df 100644
--- a/examples/unified/basic.cpp
+++ b/examples/unified/basic.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <af/macros.h>
 #include <cstdio>
 #include <vector>
 #include <algorithm>

From 3c9d69d209c815c441978dfe0a9a404972d069a5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 14 Dec 2015 17:12:54 -0500
Subject: [PATCH 0127/2677] FEAT added infoString function to return info as
 string

---
 include/af/device.h        | 36 +++++++++++++++++++++++++++++++++++-
 src/api/c/device.cpp       | 13 +++++++++++++
 src/api/cpp/device.cpp     |  7 +++++++
 src/api/unified/device.cpp |  5 +++++
 4 files changed, 60 insertions(+), 1 deletion(-)

diff --git a/include/af/device.h b/include/af/device.h
index 03800c3ffd..d3585c619c 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -28,6 +28,27 @@ namespace af
        @}
     */
 
+    /**
+       \defgroup device_func_info_string infoString
+
+       Get af::info() as a string
+
+       @{
+
+       \brief Returns the output of af::info() as a string
+
+       \param[in] verbose flag to return verbose info
+
+       \returns string containing output of af::info()
+
+       \ingroup arrayfire_func
+       \ingroup device_mat
+    */
+    AFAPI const char* infoString(const bool verbose = false);
+    /**
+       @}
+    */
+
     /**
        \defgroup device_func_prop deviceInfo
 
@@ -205,10 +226,23 @@ extern "C" {
     */
     AFAPI af_err af_info();
 
+    /**
+       \ingroup device_func_info
+    */
     AFAPI af_err af_init();
 
     /**
-       \ingroup device_func_info
+       \brief Gets the output of af_info() as a string
+
+       \param[out] str contains the string
+       \param[in] verbose flag to return verbose info
+
+       \ingroup device_func_info_string
+    */
+    AFAPI af_err af_info_string(char** str, const bool verbose);
+
+    /**
+       \ingroup device_func_prop
     */
     AFAPI af_err af_device_info(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 39ad217939..365ccbe580 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -70,6 +70,19 @@ af_err af_info()
     return AF_SUCCESS;
 }
 
+af_err af_info_string(char **str, const bool verbose)
+{
+    std::string infoStr = getInfo();
+    *str = (char*)malloc(sizeof(char) * (infoStr.size() + 1));
+
+    // Need to do a deep copy
+    // str.c_str wont cut it
+    infoStr.copy(*str, infoStr.size());
+    (*str)[infoStr.size()] = '\0';
+
+    return AF_SUCCESS;
+}
+
 af_err af_get_version(int *major, int *minor, int *patch)
 {
     *major = AF_VERSION_MAJOR;
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 622809ed06..3f2441732d 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -47,6 +47,13 @@ namespace af
         AF_THROW(af_info());
     }
 
+    const char* infoString(const bool verbose)
+    {
+        char *str = NULL;
+        AF_THROW(af_info_string(&str, verbose));
+        return (const char *)str;
+    }
+
     void deviceprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     {
         deviceInfo(d_name, d_platform, d_toolkit, d_compute);
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 4f07788ada..8f04bf6ea1 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -45,6 +45,11 @@ af_err af_init()
     return CALL_NO_PARAMS();
 }
 
+af_err af_info_string(char **str, const bool verbose)
+{
+    return CALL(str, verbose);
+}
+
 af_err af_device_info(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
 {
     return CALL(d_name, d_platform, d_toolkit, d_compute);

From 1de97de812499e6b969e9e23e0e35306246b96f0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 14 Dec 2015 17:33:06 -0500
Subject: [PATCH 0128/2677] Using af_alloc_host when allocating user-return
 string

---
 src/api/c/device.cpp     | 14 ++++++++------
 src/api/c/err_common.cpp |  5 +++--
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 365ccbe580..84cd246a60 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -72,13 +72,15 @@ af_err af_info()
 
 af_err af_info_string(char **str, const bool verbose)
 {
-    std::string infoStr = getInfo();
-    *str = (char*)malloc(sizeof(char) * (infoStr.size() + 1));
+    try {
+        std::string infoStr = getInfo();
+        af_alloc_host((void**)str, sizeof(char) * (infoStr.size() + 1));
 
-    // Need to do a deep copy
-    // str.c_str wont cut it
-    infoStr.copy(*str, infoStr.size());
-    (*str)[infoStr.size()] = '\0';
+        // Need to do a deep copy
+        // str.c_str wont cut it
+        infoStr.copy(*str, infoStr.size());
+        (*str)[infoStr.size()] = '\0';
+    } CATCHALL;
 
     return AF_SUCCESS;
 }
diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 371bbd95fa..3271423289 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/exception.h>
+#include <af/device.h>
 #include <err_common.hpp>
 #include <type_util.hpp>
 #include <string>
@@ -156,8 +157,8 @@ void af_get_last_error(char **str, dim_t *len)
         *str = NULL;
     }
 
-    *str = new char[*len + 1];
-    memcpy(*str, global_err_string.c_str(), *len * sizeof(char));
+    af_alloc_host((void**)str, sizeof(char) * (*len + 1));
+    global_err_string.copy(*str, *len);
 
     (*str)[*len] = '\0';
     global_err_string = std::string("");

From f628fbe535163508328d18836b77c0db72275197 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 14 Dec 2015 17:49:00 -0500
Subject: [PATCH 0129/2677] toString now uses af_alloc_host to allocate memory

---
 src/api/c/print.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index a5c178cf0c..ea84cd61a9 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -172,8 +172,8 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
         default:    TYPE_ERROR(1, type);
         }
         std::string str = ss.str();
-        *output = new char[str.size() + 1];
-        std::copy(str.begin(), str.end(), *output);
+        af_alloc_host((void**)output, sizeof(char) * (str.size() + 1));
+        str.copy(*output, str.size());
         (*output)[str.size()] = '\0'; // don't forget the terminating 0
     }
     CATCHALL;

From 72060282fc4b94133ddb7afd2a12abdb956c7eea Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 14 Dec 2015 18:07:12 -0500
Subject: [PATCH 0130/2677] Add overload of toString that returns a string

---
 include/af/util.h    | 21 ++++++++++++++++++++-
 src/api/cpp/util.cpp |  6 ++++++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/include/af/util.h b/include/af/util.h
index c1fd96ab24..a56337653d 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -95,7 +95,8 @@ namespace af
 #if AF_API_VERSION >= 31
     /**
         \param[out] output is the pointer to the c-string that will hold the data. The memory for
-        output is allocated by the function. The user is responsible for deleting the memory.
+        output is allocated by the function. The user is responsible for deleting the memory using
+        af::freeHost() or af_free_host().
         \param[in] exp is an expression, generally the name of the array
         \param[in] arr is the input array
         \param[in] precision is the precision length for display
@@ -108,6 +109,24 @@ namespace af
                         const int precision = 4, const bool transpose = true);
 #endif
 
+#if AF_API_VERSION >= 33
+    /**
+        \param[in] exp is an expression, generally the name of the array
+        \param[in] arr is the input array
+        \param[in] precision is the precision length for display
+        \param[in] transpose determines whether or not to transpose the array before storing it in
+        the string
+
+        \return output is the pointer to the c-string that will hold the data. The memory for
+        output is allocated by the function. The user is responsible for deleting the memory using
+        af::freeHost() or af_free_host().
+
+        \ingroup print_func_tostring
+    */
+    AFAPI const char* toString(const char *exp, const array &arr,
+                               const int precision = 4, const bool transpose = true);
+#endif
+
     // Purpose of Addition: "How to add Function" documentation
     AFAPI array exampleFunction(const array& in, const af_someenum_t param);
 }
diff --git a/src/api/cpp/util.cpp b/src/api/cpp/util.cpp
index a99b8567e0..895d347d92 100644
--- a/src/api/cpp/util.cpp
+++ b/src/api/cpp/util.cpp
@@ -62,4 +62,10 @@ namespace af
         return;
     }
 
+    const char* toString(const char *exp, const array &arr, const int precision, const bool transpose)
+    {
+        char *output = NULL;
+        AF_THROW(af_array_to_string(&output, exp, arr.get(), precision, transpose));
+        return output;
+    }
 }

From 06d4befbd5282b47b5731b3664ee951e3fbcbace Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 14 Dec 2015 18:44:37 -0500
Subject: [PATCH 0131/2677] FEAT add af_get_revision to get commit instead of
 AF_REVISION

---
 include/af/util.h        | 12 +++++++++++-
 src/api/c/version.cpp    | 16 ++++++++++++++++
 src/api/unified/util.cpp |  5 +++++
 3 files changed, 32 insertions(+), 1 deletion(-)
 create mode 100644 src/api/c/version.cpp

diff --git a/include/af/util.h b/include/af/util.h
index a56337653d..eef46f47c9 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -248,10 +248,20 @@ extern "C" {
     AFAPI af_err af_example_function(af_array* out, const af_array in, const af_someenum_t param);
 
     ///
-    ///Get the version information of the library
+    /// Get the version information of the library
     ///
     AFAPI af_err af_get_version(int *major, int *minor, int *patch);
 
+
+#if AF_API_VERSION >= 33
+    ///
+    /// Get the revision (commit) information of the library.
+    /// This returns a constant string from compile time and should not be
+    /// freed by the user.
+    ///
+    AFAPI const char *af_get_revision();
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/version.cpp b/src/api/c/version.cpp
new file mode 100644
index 0000000000..4eb7883a41
--- /dev/null
+++ b/src/api/c/version.cpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/util.h>
+#include <version.hpp>
+
+const char *af_get_revision()
+{
+    return AF_REVISION;
+}
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 155c4f81b9..1a4dcf54a1 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -61,3 +61,8 @@ af_err af_get_version(int *major, int *minor, int *patch)
 {
     return CALL(major, minor, patch);
 }
+
+const char *af_get_revision()
+{
+    return CALL_NO_PARAMS();
+}

From b3c28b6560147f93d84d1b2102074c47f8f4bbf9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 15 Dec 2015 13:27:25 -0500
Subject: [PATCH 0132/2677] Using c/version.cpp in unified

---
 src/api/unified/CMakeLists.txt | 1 +
 src/api/unified/util.cpp       | 5 -----
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 917c6dce42..a4843bb49c 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -18,6 +18,7 @@ FILE(GLOB common_sources
     "../c/util.cpp"
     "../c/err_common.cpp"
     "../c/type_util.cpp"
+    "../c/version.cpp"
     "../../backend/dim4.cpp"
     )
 
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 1a4dcf54a1..155c4f81b9 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -61,8 +61,3 @@ af_err af_get_version(int *major, int *minor, int *patch)
 {
     return CALL(major, minor, patch);
 }
-
-const char *af_get_revision()
-{
-    return CALL_NO_PARAMS();
-}

From 5507717ce82024f42d1a8c9bba1514215afced5c Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 15 Dec 2015 17:07:43 -0500
Subject: [PATCH 0133/2677] add collisions, split vectors into components for
 performance

---
 examples/graphics/gravity_sim.cpp | 106 ++++++++++++++++++------------
 1 file changed, 65 insertions(+), 41 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 77f662f4db..94d321ba7c 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -15,53 +15,72 @@ using namespace af;
 using namespace std;
 
 static const int width = 512, height = 512;
+static const int pixels_per_unit = 20;
 
+af::array p_x;
+af::array p_y;
+af::array vels_x;
+af::array vels_y;
+af::array forces_x;
+af::array forces_y;
 
-void simulate(af::array &parts, af::array &vels, af::array &forces){
-    parts += vels;
+void simulate(float dt){
+    p_x += vels_x * pixels_per_unit * dt;
+    p_y += vels_y * pixels_per_unit * dt;
 
     //calculate distance to center
-    float center_coors[2]   = { width / 2, height / 2 };
-    af::array col  = tile(af::array(1, 2, center_coors), parts.dims(0));
-    af::array diff = parts - col;
-    af::array dist = sqrt( diff.col(0)*diff.col(0) + diff.col(1)*diff.col(1) );
+    af::array diff_x = p_x - width/2;
+    af::array diff_y = p_y - height/2;
+    af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
 
-    forces = -1 * diff;
-    forces.col(0) /= dist; //normalize force vectors
-    forces.col(1) /= dist; //normalize force vectors
+    //calculate normalised force vectors
+    forces_x = -1 * diff_x / dist;
+    forces_y = -1 * diff_y / dist;
+    //update force scaled to time and magnitude constant
+    forces_x *= pixels_per_unit * dt;
+    forces_y *= pixels_per_unit * dt;
+
+    //dampening
+    vels_x *= 1 - (0.005*dt);
+    vels_y *= 1 - (0.005*dt);
 
     //update velocities from forces
-    vels += forces;
+    vels_x += forces_x;
+    vels_y += forces_y;
 
 }
 
-void collisions(af::array &parts, af::array &vels){
+void collisions(){
     //clamp particles inside screen border
-    parts.col(0) = min(width, max(0, parts.col(0)));
-    parts.col(1) = min(height - 1, max(0, parts.col(1)));
+    af::array projected_px = min(width, max(0, p_x));
+    af::array projected_py = min(height - 1, max(0, p_y));
 
     //calculate distance to center
-    float center_coors[2]   = { width / 2, height / 2 };
-    af::array col  = tile(af::array(1, 2, center_coors), parts.dims(0));
-    af::array diff = parts - col;
-    af::array dist = sqrt( diff.col(0)*diff.col(0) + diff.col(1)*diff.col(1) );
+    af::array diff_x = projected_px - width/2;
+    af::array diff_y = projected_py - height/2;
+    af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
 
-    /*
     //collide with center sphere
-    int radius = 50;
-    af::array col_ids = dist(dist<radius);
-    if(col_ids.dims(0) > 0) {
-        //vels(col_ids, span) += -1 * parts(col_ids, span);
-        vels(col_ids, span) = 0;
+    const int radius = 50;
+    const float elastic_constant = 0.91f;
+    if(sum<int>(dist<radius) > 0) {
+        vels_x(dist<radius) = -elastic_constant * vels_x(dist<radius);
+        vels_y(dist<radius) = -elastic_constant * vels_y(dist<radius);
+
+        //normalize diff vector
+        diff_x /= dist;
+        diff_y /= dist;
+        //place all particle colliding with sphere on surface
+        p_x(dist<radius) = width/2 + diff_x(dist<radius) * radius;
+        p_y(dist<radius) = height/2 +  diff_y(dist<radius) * radius;
     }
-    */
-
 }
 
+
 int main(int argc, char *argv[])
 {
     try {
-        const static int total_particles=200;
+        const static int total_particles = 1000;
         static const int reset = 500;
 
         af::info();
@@ -74,39 +93,44 @@ int main(int argc, char *argv[])
         const af::array draw_kernel = gaussianKernel(3, 3);
 
         // Generate a random starting state
-        af::array particles = af::randu(total_particles,2);
-        particles.col(0) *= width;
-        particles.col(1) *= height;
+        p_x = af::randu(total_particles) * width;
+        p_y = af::randu(total_particles) * height;
+
+        vels_x = af::randn(total_particles);
+        vels_y = af::randn(total_particles);
 
-        af::array velocities = af::randn(total_particles, 2);
-        af::array forces = af::randn(total_particles, 2);
+        forces_x = af::randn(total_particles);
+        forces_y = af::randn(total_particles);
 
         af::array image = af::constant(0, width, height);
         af::array ids(total_particles, u32);
 
+        af::timer timer = af::timer::start();
         while(!myWindow.close()) {
+            float dt = af::timer::stop(timer);
+            timer = af::timer::start();
 
-            ids = (particles.col(0).as(u32) * height) + particles.col(1).as(u32);
+            ids = (p_x.as(u32) * height) + p_y.as(u32);
             image(ids) += 255;
             image = convolve2(image, draw_kernel);
             myWindow.image(image);
-            image(span, span) = 0;
+            image = af::constant(0, image.dims());
             frame_count++;
 
             // Generate a random starting state
             if(frame_count % reset == 0) {
-                particles = af::randu(total_particles,2);
-                particles.col(0) *= width;
-                particles.col(1) *= height;
+                p_x = af::randu(total_particles) * width;
+                p_y = af::randu(total_particles) * height;
 
-                velocities = af::randn(total_particles, 2);
+                vels_x = af::randn(total_particles);
+                vels_y = af::randn(total_particles);
             }
 
-            //run force simulation and update particles
-            simulate(particles, velocities, forces);
-
             //check for collisions and adjust velocities accordingly
-            collisions(particles, velocities);
+            collisions();
+
+            //run force simulation and update particles
+            simulate(dt);
 
         }
     } catch (af::exception& e) {

From 9d218a5a36e365d06ae1cd7f54b763b4368b8629 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 11:27:59 -0500
Subject: [PATCH 0134/2677] Hide scrollbars appearing for quoted/pre text

---
 docs/arrayfire.css | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/docs/arrayfire.css b/docs/arrayfire.css
index b1b688ffd0..75dba64e3a 100644
--- a/docs/arrayfire.css
+++ b/docs/arrayfire.css
@@ -190,4 +190,9 @@ div.fragment
     border              :   1px solid #DFDFDF;
 }
 
+pre
+{
+    overflow            : hidden;
+}
+
 /* @end */

From 59c98201b29a40352a8cf0c85a9e7eabdb4e84aa Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 13:42:11 -0500
Subject: [PATCH 0135/2677] Add function to check Image IO availability

---
 test/testHelpers.hpp | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index cd725fe2ab..a4eff2d94c 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -388,6 +388,19 @@ bool noDoubleTests()
     return ((isTypeDouble && !isDoubleSupported) ? true : false);
 }
 
+bool noImageIOTests()
+{
+    af_array arr = 0;
+    const af_err err = af_load_image(&arr, TEST_DIR"/imageio/color_small.png", true);
+
+    if(arr != 0) af_release_array(arr);
+
+    if(err == AF_ERR_NOT_CONFIGURED)
+        return true;    // Yes, disable test
+    else
+        return false;   // No, let test continue
+}
+
 // TODO: perform conversion on device for CUDA and OpenCL
 template<typename T>
 af_err conv_image(af_array *out, af_array in)

From 5bd550f6dcf424ffeff3ac9877334677a8d7dd77 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 13:43:19 -0500
Subject: [PATCH 0136/2677] Added noImageIOTests() to relevant tests

---
 test/bilateral.cpp    |  1 +
 test/fast.cpp         |  2 ++
 test/gloh_nonfree.cpp |  2 ++
 test/harris.cpp       |  2 ++
 test/homography.cpp   |  3 +++
 test/imageio.cpp      | 12 ++++++++++++
 test/meanshift.cpp    |  2 ++
 test/medfilt.cpp      |  1 +
 test/morph.cpp        |  2 ++
 test/orb.cpp          |  2 ++
 test/sift_nonfree.cpp |  2 ++
 test/susan.cpp        |  1 +
 12 files changed, 32 insertions(+)

diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 08b7a4c2b4..f0825e4893 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -24,6 +24,7 @@ template<typename T, bool isColor>
 void bilateralTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>       inDims;
     vector<string>    inFiles;
diff --git a/test/fast.cpp b/test/fast.cpp
index c13d6da008..a114a8fdc6 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -72,6 +72,7 @@ template<typename T>
 void fastTest(string pTestFile, bool nonmax)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>        inDims;
     vector<string>     inFiles;
@@ -175,6 +176,7 @@ void fastTest(string pTestFile, bool nonmax)
 TEST(FloatFAST, CPP)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>        inDims;
     vector<string>     inFiles;
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 2346269734..a65d52ad43 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -159,6 +159,7 @@ void glohTest(string pTestFile)
 {
 #ifdef AF_BUILD_SIFT
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>           inDims;
     vector<string>         inFiles;
@@ -270,6 +271,7 @@ TEST(GLOH, CPP)
 {
 #ifdef AF_BUILD_SIFT
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>           inDims;
     vector<string>         inFiles;
diff --git a/test/harris.cpp b/test/harris.cpp
index b964e89205..276a3e357f 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -63,6 +63,7 @@ template<typename T>
 void harrisTest(string pTestFile, float sigma, unsigned block_size)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>        inDims;
     vector<string>     inFiles;
@@ -164,6 +165,7 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
 TEST(FloatHarris, CPP)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>        inDims;
     vector<string>     inFiles;
diff --git a/test/homography.cpp b/test/homography.cpp
index 7be9e07473..662b7a2a56 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -59,6 +59,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
                     const bool rotate, const float size_ratio)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>           inDims;
     vector<string>         inFiles;
@@ -216,6 +217,8 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 //
 TEST(Homography, CPP)
 {
+    if (noImageIOTests()) return;
+
     vector<dim4>           inDims;
     vector<string>         inFiles;
     vector<vector<float> > gold;
diff --git a/test/imageio.cpp b/test/imageio.cpp
index a826bb8cf8..d19aac346c 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -41,6 +41,7 @@ TYPED_TEST_CASE(ImageIO, TestTypes);
 void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<af::dim4> numDims;
 
@@ -98,6 +99,8 @@ TYPED_TEST(ImageIO, ColorSeq)
 
 void loadimageArgsTest(string pImageFile, const bool isColor, af_err err)
 {
+    if (noImageIOTests()) return;
+
     af_array imgArray = 0;
 
     ASSERT_EQ(err, af_load_image(&imgArray, pImageFile.c_str(), isColor));
@@ -119,6 +122,7 @@ TYPED_TEST(ImageIO,InvalidArgsWrongExt)
 TEST(ImageIO, CPP)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<af::dim4> numDims;
 
@@ -145,6 +149,8 @@ TEST(ImageIO, CPP)
 
 TEST(ImageIO, SavePNGCPP) {
 
+    if (noImageIOTests()) return;
+
     af::array input(10, 10, 3, f32);
 
     input(af::span, af::span, af::span) = 0;
@@ -161,6 +167,8 @@ TEST(ImageIO, SavePNGCPP) {
 
 TEST(ImageIO, SaveBMPCPP) {
 
+    if (noImageIOTests()) return;
+
     af::array input(10, 10, 3, f32);
 
     input(af::span, af::span, af::span) = 0;
@@ -178,6 +186,7 @@ TEST(ImageIO, SaveBMPCPP) {
 TEST(ImageMem, SaveMemPNG)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
 
@@ -193,6 +202,7 @@ TEST(ImageMem, SaveMemPNG)
 TEST(ImageMem, SaveMemJPG1)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), false);
     af::saveImage("color_seq1.jpg", img);
@@ -210,6 +220,7 @@ TEST(ImageMem, SaveMemJPG1)
 TEST(ImageMem, SaveMemJPG3)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
     af::saveImage("color_seq3.jpg", img);
@@ -227,6 +238,7 @@ TEST(ImageMem, SaveMemJPG3)
 TEST(ImageMem, SaveMemBMP)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     af::array img = af::loadImage(string(TEST_DIR"/imageio/color_rand.png").c_str(), true);
 
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 7363350e80..0116a5e3da 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -51,6 +51,7 @@ template<typename T, bool isColor>
 void meanshiftTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>       inDims;
     vector<string>    inFiles;
@@ -120,6 +121,7 @@ IMAGE_TESTS(double)
 TEST(Meanshift, Color_CPP)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>       inDims;
     vector<string>    inFiles;
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 99dd0b6757..9b4590885b 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -91,6 +91,7 @@ template<typename T,bool isColor>
 void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     using af::dim4;
 
diff --git a/test/morph.cpp b/test/morph.cpp
index d73ca9b50d..d9c5282146 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -121,6 +121,7 @@ template<typename T, bool isDilation, bool isColor>
 void morphImageTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     using af::dim4;
 
@@ -341,6 +342,7 @@ template<typename T, bool isDilation, bool isColor>
 void cppMorphImageTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     using af::dim4;
 
diff --git a/test/orb.cpp b/test/orb.cpp
index 4b3c15864d..5259366901 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -144,6 +144,7 @@ template<typename T>
 void orbTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>             inDims;
     vector<string>           inFiles;
@@ -253,6 +254,7 @@ void orbTest(string pTestFile)
 TEST(ORB, CPP)
 {
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>             inDims;
     vector<string>           inFiles;
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index 28c597ca38..cf1683f775 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -159,6 +159,7 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 {
 #ifdef AF_BUILD_SIFT
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>           inDims;
     vector<string>         inFiles;
@@ -276,6 +277,7 @@ TEST(SIFT, CPP)
 {
 #ifdef AF_BUILD_SIFT
     if (noDoubleTests<float>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>           inDims;
     vector<string>         inFiles;
diff --git a/test/susan.cpp b/test/susan.cpp
index 01ed2288f2..df806c06be 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -63,6 +63,7 @@ template<typename T>
 void susanTest(string pTestFile, float t, float g)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<dim4>        inDims;
     vector<string>     inFiles;

From 4145a040be0c79fb9283c750f31213ab1b56bd0a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 14:12:34 -0500
Subject: [PATCH 0137/2677] Add function to check LAPACK availability

---
 test/testHelpers.hpp | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index a4eff2d94c..758bf98e14 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -401,6 +401,26 @@ bool noImageIOTests()
         return false;   // No, let test continue
 }
 
+bool noLAPACKTests()
+{
+    // Run LU
+    af::dim4 dims(5, 5);
+    af_array in = 0, l = 0, u = 0, p= 0;
+    af_randu(&in, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type);
+
+    af_err err = af_lu(&l, &u, &p, in);
+
+    if(in != 0) af_release_array(in);
+    if(l  != 0) af_release_array(l);
+    if(u  != 0) af_release_array(u);
+    if(p  != 0) af_release_array(p);
+
+    if(err == AF_ERR_NOT_CONFIGURED)
+        return true;    // Yes, disable test
+    else
+        return false;   // No, let test continue
+}
+
 // TODO: perform conversion on device for CUDA and OpenCL
 template<typename T>
 af_err conv_image(af_array *out, af_array in)

From 12378200d3bcb8aaf30fa7c0c5c5a01afe8f8efa Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 14:12:59 -0500
Subject: [PATCH 0138/2677] Added noLAPACKTests() to relevant tests

---
 test/cholesky_dense.cpp | 1 +
 test/inverse_dense.cpp  | 1 +
 test/lu_dense.cpp       | 3 +++
 test/qr_dense.cpp       | 2 ++
 test/rank_dense.cpp     | 7 +++++++
 test/solve_dense.cpp    | 6 ++++++
 test/svd_dense.cpp      | 1 +
 7 files changed, 21 insertions(+)

diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index 93d13316ef..70548d898c 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -29,6 +29,7 @@ template<typename T>
 void choleskyTester(const int n, double eps, bool is_upper)
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
 
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index ea6c22256b..b0568ebbdb 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -32,6 +32,7 @@ template<typename T>
 void inverseTester(const int m, const int n, const int k, double eps)
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(m, n));
 #else
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 7399759931..cdb23ef962 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -30,6 +30,7 @@ using af::cdouble;
 TEST(LU, InPlaceSmall)
 {
     if (noDoubleTests<float>()) return;
+    if (noLAPACKTests()) return;
 
     int resultIdx = 0;
 
@@ -67,6 +68,7 @@ TEST(LU, InPlaceSmall)
 TEST(LU, SplitSmall)
 {
     if (noDoubleTests<float>()) return;
+    if (noLAPACKTests()) return;
 
     int resultIdx = 0;
 
@@ -117,6 +119,7 @@ template<typename T>
 void luTester(const int m, const int n, double eps)
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
 
 #if 1
     af::array a_orig = cpu_randu<T>(af::dim4(m, n));
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index a0a954cae3..708eb5d0cd 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -29,6 +29,7 @@ using af::cdouble;
 TEST(QRFactorized, CPP)
 {
     if (noDoubleTests<float>()) return;
+    if (noLAPACKTests()) return;
 
     int resultIdx = 0;
 
@@ -82,6 +83,7 @@ void qrTester(const int m, const int n, double eps)
 {
     try {
         if (noDoubleTests<T>()) return;
+        if (noLAPACKTests()) return;
 
 #if 1
         af::array in = cpu_randu<T>(af::dim4(m, n));
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index 96b44497f1..d0a19af3b9 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -43,6 +43,7 @@ template<typename T>
 void rankSmall()
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
 
     T ha[] = {1, 4, 7, 2, 5, 8, 3, 6, 20};
     af::array a(3, 3, ha);
@@ -54,6 +55,8 @@ template<typename T>
 void rankBig(const int num)
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
     af::dtype dt = (af::dtype)af::dtype_traits<T>::af_type;
     af::array a = af::randu(num, num, dt);
     ASSERT_EQ(num, (int)af::rank(a));
@@ -67,6 +70,8 @@ template<typename T>
 void rankLow(const int num)
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
     af::dtype dt = (af::dtype)af::dtype_traits<T>::af_type;
 
     af::array a = af::randu(3 * num, num, dt);
@@ -97,6 +102,8 @@ template<typename T>
 void detTest()
 {
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
     af::dtype dt = (af::dtype)af::dtype_traits<T>::af_type;
 
     vector<af::dim4> numDims;
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index d78ceb9d33..bbb67409dc 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -34,6 +34,8 @@ void solveTester(const int m, const int n, const int k, double eps)
     af::deviceGC();
 
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(m, n));
     af::array X0 = cpu_randu<T>(af::dim4(n, k));
@@ -61,6 +63,8 @@ void solveLUTester(const int n, const int k, double eps)
     af::deviceGC();
 
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(n, n));
     af::array X0 = cpu_randu<T>(af::dim4(n, k));
@@ -88,6 +92,8 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
     af::deviceGC();
 
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(n, n));
     af::array X0 = cpu_randu<T>(af::dim4(n, k));
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index a5bf2dda80..f7ef2950e0 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -54,6 +54,7 @@ void svdTest(const int M, const int N)
 {
 
     if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
 
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 

From e94f03799decb1f3b4522797753c6f2e25989091 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 14:24:07 -0500
Subject: [PATCH 0139/2677] Handle printing empty Arrays

---
 src/api/c/print.cpp | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index a5c178cf0c..d2d9921654 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -64,6 +64,22 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
     }
 
     const ArrayInfo info = getInfo(arr);
+
+    std::ios_base::fmtflags backup = os.flags();
+
+    os << "[" << info.dims() << "]\n";
+#ifndef NDEBUG
+    os <<"   Offsets: [" << info.offsets() << "]" << std::endl;
+    os <<"   Strides: [" << info.strides() << "]" << std::endl;
+#endif
+
+    // Handle empty array
+    if(info.elements() == 0) {
+        os << "<empty>" << std::endl;
+        os.flags(backup);
+        return;
+    }
+
     vector<T> data(info.elements());
 
     af_array arrT;
@@ -81,14 +97,6 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
         AF_CHECK(af_release_array(arrT));
     }
 
-    std::ios_base::fmtflags backup = os.flags();
-
-    os << "[" << info.dims() << "]\n";
-#ifndef NDEBUG
-    os <<"   Offsets: [" << info.offsets() << "]" << std::endl;
-    os <<"   Strides: [" << info.strides() << "]" << std::endl;
-#endif
-
     printer(os, &data.front(), infoT, infoT.ndims() - 1, precision);
 
     os.flags(backup);

From fea38e49bcb52c6b5941bc7059ba638f38a30679 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 14:46:26 -0500
Subject: [PATCH 0140/2677] Remove macros.h and MSG from defines.hpp

---
 src/backend/defines.hpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index 4308ca952c..26898370b3 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -9,10 +9,6 @@
 
 #pragma once
 
-#include <af/macros.h>
-
-#define MSG AF_MSG
-
 #if defined(_WIN32) || defined(_MSC_VER)
     #define __PRETTY_FUNCTION__ __FUNCSIG__
     #if _MSC_VER < 1900

From 8c945f38832ed3227faaf72d41f215b736c0f824 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 15:08:29 -0500
Subject: [PATCH 0141/2677] Added MEMINFO macro to print memory stats

---
 include/af/macros.h | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/include/af/macros.h b/include/af/macros.h
index 62ff3e96ad..65077dc613 100644
--- a/include/af/macros.h
+++ b/include/af/macros.h
@@ -21,3 +21,16 @@
                  __FILE__, __LINE__, ##__VA_ARGS__);      \
         } while (0);
 #endif
+
+#define MEMINFO(msg) do {                                                               \
+    size_t abytes = 0, abuffs = 0, lbytes = 0, lbuffs = 0;                              \
+    af_err err = af_device_mem_info(&abytes, &abuffs, &lbytes, &lbuffs);                \
+    if(err == AF_SUCCESS) {                                                             \
+        printf("MemInfo at %s:%d: " msg "\n", __FILE__, __LINE__);                      \
+        printf("Allocated [ Bytes | Buffers ] = [ %ld | %ld ]\n", abytes, abuffs);      \
+        printf("In Use    [ Bytes | Buffers ] = [ %ld | %ld ]\n", lbytes, lbuffs);      \
+    } else {                                                                            \
+        fprintf(stderr, "MemInfo at %s:%d: " msg "\nAF Error %d\n",                     \
+                __FILE__, __LINE__, err);                                               \
+    }                                                                                   \
+} while(0);

From 6bf306e769e11c923f3286e98ffc4c24b11c8ab9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 16:28:05 -0500
Subject: [PATCH 0142/2677] CPU: Using mt19973 as generator instead of
 default_random_engine

---
 src/backend/cpu/random.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index ab4230e682..e93fdf9b8f 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -68,7 +68,7 @@ nrand(GenType &generator)
     return [func] () { return T(func(), func());};
 }
 
-static default_random_engine generator;
+static mt19937 generator;
 static unsigned long long gen_seed = 0;
 static bool is_first = true;
 #define GLOBAL 1

From 0b11c6efc7b012197a2288db6463e7a355b5d76b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Dec 2015 17:25:29 -0500
Subject: [PATCH 0143/2677] MEMINFO -> AF_MEM_INFO. Add documentation for the
 macro

---
 include/af/macros.h | 56 +++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 52 insertions(+), 4 deletions(-)

diff --git a/include/af/macros.h b/include/af/macros.h
index 65077dc613..3c52321c5c 100644
--- a/include/af/macros.h
+++ b/include/af/macros.h
@@ -22,15 +22,63 @@
         } while (0);
 #endif
 
-#define MEMINFO(msg) do {                                                               \
+/**
+ * AF_MEM_INFO macro can be used to print the current stats of ArrayFire's memory
+ * manager.
+ *
+ * AF_MEM_INFO print 4 values:
+ *
+ * ---------------------------------------------------
+ *  Name                    | Description
+ * -------------------------|-------------------------
+ *  Allocated Bytes         | Total number of bytes allocated by the memory manager
+ *  Allocated Buffers       | Total number of buffers allocated
+ *  Locked (In Use) Bytes   | Number of bytes that are in use by active arrays
+ *  Locked (In Use) Buffers | Number of buffers that are in use by active arrays
+ * ---------------------------------------------------
+ *
+ *  The `Allocated Bytes` is always a multiple of the memory step size. The
+ *  default step size is 1024 bytes. This means when a buffer is to be
+ *  allocated, the size is always rounded up to a multiple of the step size.
+ *  You can use af::getMemStepSize() to check the current step size and
+ *  af::setMemStepSize() to set a custom resolution size.
+ *
+ *  The `Allocated Buffers` is the number of buffers that use up the allocated
+ *  bytes. This includes buffers currently in scope, as well as buffers marked
+ *  as free, ie, from arrays gone out of scope. The free buffers are available
+ *  for use by new arrays that might be created.
+ *
+ *  The `Locked Bytes` is the number of bytes in use that cannot be
+ *  reallocated at the moment. The difference of Allocated Bytes and Locked
+ *  Bytes is the total bytes available for reallocation.
+ *
+ *  The `Locked Buffers` is the number of buffer in use that cannot be
+ *  reallocated at the moment. The difference of Allocated Buffers and Locked
+ *  Buffers is the number of buffers available for reallocation.
+ *
+ * The AF_MEM_INFO macro can accept a string an argument that is printed to screen
+ *
+ * \param[in] msg (Optional) A message that is printed to screen
+ *
+ * \code
+ *     AF_MEM_INFO("At start");
+ * \endcode
+ *
+ * Output:
+ *
+ *     AF Memory at /workspace/myfile.cpp:41: At Start
+ *     Allocated [ Bytes | Buffers ] = [ 4096 | 4 ]
+ *     In Use    [ Bytes | Buffers ] = [ 2048 | 2 ]
+ */
+#define AF_MEM_INFO(msg) do {                                                           \
     size_t abytes = 0, abuffs = 0, lbytes = 0, lbuffs = 0;                              \
     af_err err = af_device_mem_info(&abytes, &abuffs, &lbytes, &lbuffs);                \
     if(err == AF_SUCCESS) {                                                             \
-        printf("MemInfo at %s:%d: " msg "\n", __FILE__, __LINE__);                      \
+        printf("AF Memory at %s:%d: " msg "\n", __FILE__, __LINE__);                    \
         printf("Allocated [ Bytes | Buffers ] = [ %ld | %ld ]\n", abytes, abuffs);      \
         printf("In Use    [ Bytes | Buffers ] = [ %ld | %ld ]\n", lbytes, lbuffs);      \
     } else {                                                                            \
-        fprintf(stderr, "MemInfo at %s:%d: " msg "\nAF Error %d\n",                     \
+        fprintf(stderr, "AF Memory at %s:%d: " msg "\nAF Error %d\n",                   \
                 __FILE__, __LINE__, err);                                               \
     }                                                                                   \
-} while(0);
+} while(0)

From 7dac34a56f32bf6ee5431cffe5266cc671144f70 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 16 Dec 2015 19:29:14 -0500
Subject: [PATCH 0144/2677] Fixes for asynchronous cpu copy && set functions

Also, added a check in Array::eval to throw exception if Array::eval
is being called from a queue thread. This change also includes all
the regression fixes for other functions regarding this eval change.
---
 src/backend/cpu/Array.cpp       |   2 +
 src/backend/cpu/approx.cpp      |   7 +
 src/backend/cpu/assign.cpp      |   4 +
 src/backend/cpu/blas.cpp        |   6 +
 src/backend/cpu/copy.cpp        |  12 +-
 src/backend/cpu/diagonal.cpp    |   9 +-
 src/backend/cpu/index.cpp       |   3 +
 src/backend/cpu/ireduce.cpp     |  54 ++---
 src/backend/cpu/morph.cpp       |   6 +
 src/backend/cpu/reduce.cpp      | 381 ++++++++++++++++----------------
 src/backend/cpu/reorder.cpp     |   2 +
 src/backend/cpu/set.cpp         |   5 +-
 src/backend/cpu/sort_by_key.cpp | 173 ++++++++-------
 src/backend/cpu/svd.cpp         |   5 +
 src/backend/cpu/tile.cpp        |   2 +
 src/backend/cpu/transpose.cpp   |  47 ++--
 16 files changed, 392 insertions(+), 326 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 8577374d6e..456f4c8b1f 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
+#include <err_common.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <TNJ/BufferNode.hpp>
@@ -69,6 +70,7 @@ namespace cpu
     void Array<T>::eval()
     {
         if (isReady()) return;
+        if (getQueue().is_worker()) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
 
         this->setId(getActiveDeviceId());
 
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 87ae56f44d..4d3c8803ff 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -136,6 +136,9 @@ namespace cpu
     Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
                        const af_interp_type method, const float offGrid)
     {
+        in.eval();
+        pos.eval();
+
         af::dim4 odims = in.dims();
         odims[0] = pos.dims()[0];
 
@@ -305,6 +308,10 @@ namespace cpu
     Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
                        const af_interp_type method, const float offGrid)
     {
+        in.eval();
+        pos0.eval();
+        pos1.eval();
+
         af::dim4 odims = in.dims();
         odims[0] = pos0.dims()[0];
         odims[1] = pos0.dims()[1];
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index b1578d49f6..c5d733bb17 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -41,6 +41,9 @@ dim_t trimIndex(int idx, const dim_t &len)
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 {
+    out.eval();
+    rhs.eval();
+
     vector<bool> isSeq(4);
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output dimensions, offsets & offsets
@@ -56,6 +59,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
     for (dim_t x=0; x<4; ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
+            idxArrs[x].eval();
         }
     }
 
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 3326241f10..26ec8b488b 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -147,6 +147,9 @@ template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    lhs.eval();
+    rhs.eval();
+
     CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
     CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
 
@@ -225,6 +228,9 @@ template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    lhs.eval();
+    rhs.eval();
+
     Array<T> out = createEmptyArray<T>(af::dim4(1));
     if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
         getQueue().enqueue(dot_<T, false, true>, out, lhs, rhs, optLhs, optRhs);
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 80f28dae13..52403605ca 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -48,7 +48,7 @@ namespace cpu
     template<typename T>
     void copyData(T *to, const Array<T> &from)
     {
-        evalArray(from);
+        from.eval();
         getQueue().sync();
         if(from.isOwner()) {
             // FIXME: Check for errors / exceptions
@@ -118,16 +118,18 @@ namespace cpu
     template<typename T>
     void multiply_inplace(Array<T> &in, double val)
     {
+        in.eval();
         getQueue().enqueue(copy<T, T>, in, in, 0, val);
     }
 
     template<typename inType, typename outType>
-    Array<outType>
-    padArray(Array<inType> const &in, dim4 const &dims,
-             outType default_value, double factor)
+    Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                            outType default_value, double factor)
     {
         Array<outType> ret = createValueArray<outType>(dims, default_value);
         ret.eval();
+        in.eval();
+        // FIXME:
         getQueue().sync();
         getQueue().enqueue(copy<inType, outType>, ret, in, outType(default_value), factor);
         return ret;
@@ -136,6 +138,8 @@ namespace cpu
     template<typename inType, typename outType>
     void copyArray(Array<outType> &out, Array<inType> const &in)
     {
+        out.eval();
+        in.eval();
         getQueue().enqueue(copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
     }
 
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 182027d8e7..856ed6ed44 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -10,6 +10,7 @@
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
+#include <handle.hpp>
 #include <Array.hpp>
 #include <diagonal.hpp>
 #include <math.hpp>
@@ -22,6 +23,8 @@ namespace cpu
     template<typename T>
     Array<T> diagCreate(const Array<T> &in, const int num)
     {
+        in.eval();
+
         int size = in.dims()[0] + std::abs(num);
         int batch = in.dims()[1];
         Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
@@ -52,12 +55,14 @@ namespace cpu
     template<typename T>
     Array<T> diagExtract(const Array<T> &in, const int num)
     {
-        const dim_t *idims = in.dims().get();
+        in.eval();
+
+        const dim4 idims = in.dims();
         dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
         Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
         auto func = [=] (Array<T> out, const Array<T> in) {
-            const dim_t *odims = out.dims().get();
+            const dim4 odims = out.dims();
 
             const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
 
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index c1beeea9c0..68c2f16a23 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -41,6 +41,8 @@ dim_t trimIndex(dim_t idx, const dim_t &len)
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 {
+    in.eval();
+
     vector<bool> isSeq(4);
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
@@ -60,6 +62,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     for (dim_t x=0; x<isSeq.size(); ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
+            idxArrs[x].eval();
             // set output array ith dimension value
             oDims[x] = idxArrs[x].elements();
         }
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 7f4b03c2c5..e562bae068 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -71,19 +71,16 @@ namespace cpu
     template<af_op_t op, typename T, int D>
     struct ireduce_dim
     {
-        void operator()(T *out, const dim4 ostrides, const dim4 odims,
-                        uint *loc,
-                        const T *in , const dim4 istrides, const dim4 idims,
-                        const int dim)
+        void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
+                        const Array<T> input, const dim_t inOffset, const int dim)
         {
+            const dim4 odims    = output.dims();
+            const dim4 ostrides = output.strides();
+            const dim4 istrides = input.strides();
             const int D1 = D - 1;
             for (dim_t i = 0; i < odims[D1]; i++) {
-                ireduce_dim<op, T, D1>()(out + i * ostrides[D1],
-                                         ostrides, odims,
-                                         loc + i * ostrides[D1],
-                                         in  + i * istrides[D1],
-                                         istrides, idims,
-                                         dim);
+                ireduce_dim<op, T, D1>()(output, locArray, outOffset + i * ostrides[D1],
+                                         input, inOffset + i * istrides[D1], dim);
             }
         }
     };
@@ -91,33 +88,38 @@ namespace cpu
     template<af_op_t op, typename T>
     struct ireduce_dim<op, T, 0>
     {
-        void operator()(T *out, const dim4 ostrides, const dim4 odims,
-                        uint *loc,
-                        const T *in , const dim4 istrides, const dim4 idims,
-                        const int dim)
+        void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
+                        const Array<T> input, const dim_t inOffset, const int dim)
         {
+            const dim4 idims = input.dims();
+            const dim4 istrides = input.strides();
+
+            T const * const in = input.get();
+            T * out = output.get();
+            uint * loc = locArray.get();
 
             dim_t stride = istrides[dim];
             MinMaxOp<op, T> Op(in[0], 0);
             for (dim_t i = 0; i < idims[dim]; i++) {
-                Op(in[i * stride], i);
+                Op(in[inOffset + i * stride], i);
             }
 
-            *out = Op.m_val;
-            *loc = Op.m_idx;
+            *(out+outOffset) = Op.m_val;
+            *(loc+outOffset) = Op.m_idx;
         }
     };
 
     template<af_op_t op, typename T>
-    using ireduce_dim_func = std::function<void(T *out, const dim4 ostrides, const dim4 odims,
-                                                uint *loc,
-                                                const T *in , const dim4 istrides, const dim4 idims,
-                                                const int dim)>;
+    using ireduce_dim_func = std::function<void(Array<T>, Array<uint>, const dim_t,
+                                                const Array<T>, const dim_t, const int)>;
 
     template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc,
-                 const Array<T> &in, const int dim)
+    void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
     {
+        out.eval();
+        loc.eval();
+        in.eval();
+
         dim4 odims = in.dims();
         odims[dim] = 1;
         static const ireduce_dim_func<op, T> ireduce_funcs[] = { ireduce_dim<op, T, 1>()
@@ -125,15 +127,15 @@ namespace cpu
                                                                , ireduce_dim<op, T, 3>()
                                                                , ireduce_dim<op, T, 4>()};
 
-        getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out.get(), out.strides(), out.dims(),
-                           loc.get(), in.get(), in.strides(), in.dims(), dim);
+        getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
     }
 
     template<af_op_t op, typename T>
     T ireduce_all(unsigned *loc, const Array<T> &in)
     {
-        evalArray(in);
+        in.eval();
         getQueue().sync();
+
         af::dim4 dims = in.dims();
         af::dim4 strides = in.strides();
         const T *inPtr = in.get();
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index c64d09be30..945c32b310 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -33,6 +33,9 @@ static inline unsigned getIdx(const dim4 &strides,
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask)
 {
+    in.eval();
+    mask.eval();
+
     Array<T> out = createEmptyArray<T>(in.dims());
 
     auto func = [=] (Array<T> out, const Array<T> in, const Array<T> mask) {
@@ -96,6 +99,9 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
 {
+    in.eval();
+    mask.eval();
+
     Array<T> out = createEmptyArray<T>(in.dims());
 
     auto func = [=] (Array<T> out, const Array<T> in, const Array<T> mask) {
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index e01f0c51f1..cce12268e8 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -37,220 +37,229 @@ struct Binary<cdouble, af_add_t>
 
 namespace cpu
 {
-    template<af_op_t op, typename Ti, typename To, int D>
-    struct reduce_dim
-    {
-        void operator()(To *out, const dim4 &ostrides, const dim4 &odims,
-                        const Ti *in , const dim4 &istrides, const dim4 &idims,
-                        const int dim, bool change_nan, double nanval)
-        {
-            static const int D1 = D - 1;
-            static reduce_dim<op, Ti, To, D1> reduce_dim_next;
-            for (dim_t i = 0; i < odims[D1]; i++) {
-                reduce_dim_next(out + i * ostrides[D1],
-                                ostrides, odims,
-                                in  + i * istrides[D1],
-                                istrides, idims,
-                                dim, change_nan, nanval);
-            }
-        }
-    };
 
-    template<af_op_t op, typename Ti, typename To>
-    struct reduce_dim<op, Ti, To, 0>
+template<af_op_t op, typename Ti, typename To, int D>
+struct reduce_dim
+{
+    void operator()(Array<To> out, const dim_t outOffset,
+                    const Array<Ti> in, const dim_t inOffset,
+                    const int dim, bool change_nan, double nanval)
     {
+        static const int D1 = D - 1;
+        static reduce_dim<op, Ti, To, D1> reduce_dim_next;
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
-        void operator()(To *out, const dim4 &ostrides, const dim4 &odims,
-                        const Ti *in , const dim4 &istrides, const dim4 &idims,
-                        const int dim, bool change_nan, double nanval)
-        {
-            dim_t stride = istrides[dim];
-
-            To out_val = reduce.init();
-            for (dim_t i = 0; i < idims[dim]; i++) {
-                To in_val = transform(in[i * stride]);
-                if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
-                out_val = reduce(in_val, out_val);
-            }
+        const dim4 ostrides = out.strides();
+        const dim4 istrides = in.strides();
+        const dim4 odims    = out.dims();
 
-            *out = out_val;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            reduce_dim_next(out, outOffset + i * ostrides[D1],
+                            in, inOffset + i * istrides[D1],
+                            dim, change_nan, nanval);
         }
-    };
+    }
+};
 
-    template<af_op_t op, typename Ti, typename To>
-    using reduce_dim_func = std::function<void(To*,const dim4&, const dim4&,
-                                                const Ti*, const dim4&, const dim4&,
-                                                const int, bool, double)>;
+template<af_op_t op, typename Ti, typename To>
+struct reduce_dim<op, Ti, To, 0>
+{
 
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nanval)
+    Transform<Ti, To, op> transform;
+    Binary<To, op> reduce;
+    void operator()(Array<To> out, const dim_t outOffset,
+                    const Array<Ti> in, const dim_t inOffset,
+                    const int dim, bool change_nan, double nanval)
     {
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        in.eval();
+        const dim4 istrides = in.strides();
+        const dim4 idims    = in.dims();
+
+        To * const outPtr = out.get() + outOffset;
+        Ti const * const inPtr = in.get() + inOffset;
+        dim_t stride = istrides[dim];
+
+        To out_val = reduce.init();
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            To in_val = transform(inPtr[i * stride]);
+            if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
+            out_val = reduce(in_val, out_val);
+        }
 
-        Array<To> out = createEmptyArray<To>(odims);
-        static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { reduce_dim<op, Ti, To, 1>()
-                                                                    , reduce_dim<op, Ti, To, 2>()
-                                                                    , reduce_dim<op, Ti, To, 3>()
-                                                                    , reduce_dim<op, Ti, To, 4>()};
+        *outPtr = out_val;
+    }
+};
 
-        getQueue().enqueue(reduce_funcs[in.ndims() - 1],out.get(), out.strides(), out.dims(),
-                                     in.get(), in.strides(), in.dims(), dim,
-                                     change_nan, nanval);
+template<af_op_t op, typename Ti, typename To>
+using reduce_dim_func = std::function<void(Array<To>, const dim_t,
+                                           const Array<Ti>, const dim_t,
+                                           const int, bool, double)>;
 
-        return out;
-    }
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nanval)
+{
+    dim4 odims = in.dims();
+    odims[dim] = 1;
+    in.eval();
 
-    template<af_op_t op, typename Ti, typename To>
-    To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
-    {
-        evalArray(in);
-        getQueue().sync();
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
+    Array<To> out = createEmptyArray<To>(odims);
+    static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { reduce_dim<op, Ti, To, 1>()
+                                                                , reduce_dim<op, Ti, To, 2>()
+                                                                , reduce_dim<op, Ti, To, 3>()
+                                                                , reduce_dim<op, Ti, To, 4>()};
+
+    getQueue().enqueue(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
+
+    return out;
+}
+
+template<af_op_t op, typename Ti, typename To>
+To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
+{
+    in.eval();
+    getQueue().sync();
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> reduce;
 
-        To out = reduce.init();
+    To out = reduce.init();
 
-        // Decrement dimension of select dimension
-        af::dim4 dims = in.dims();
-        af::dim4 strides = in.strides();
-        const Ti *inPtr = in.get();
+    // Decrement dimension of select dimension
+    af::dim4 dims = in.dims();
+    af::dim4 strides = in.strides();
+    const Ti *inPtr = in.get();
 
-        for(dim_t l = 0; l < dims[3]; l++) {
-            dim_t off3 = l * strides[3];
+    for(dim_t l = 0; l < dims[3]; l++) {
+        dim_t off3 = l * strides[3];
 
-            for(dim_t k = 0; k < dims[2]; k++) {
-                dim_t off2 = k * strides[2];
+        for(dim_t k = 0; k < dims[2]; k++) {
+            dim_t off2 = k * strides[2];
 
-                for(dim_t j = 0; j < dims[1]; j++) {
-                    dim_t off1 = j * strides[1];
+            for(dim_t j = 0; j < dims[1]; j++) {
+                dim_t off1 = j * strides[1];
 
-                    for(dim_t i = 0; i < dims[0]; i++) {
-                        dim_t idx = i + off1 + off2 + off3;
+                for(dim_t i = 0; i < dims[0]; i++) {
+                    dim_t idx = i + off1 + off2 + off3;
 
-                        To in_val = transform(inPtr[idx]);
-                        if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
-                        out = reduce(in_val, out);
-                    }
+                    To in_val = transform(inPtr[idx]);
+                    if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
+                    out = reduce(in_val, out);
                 }
             }
         }
-
-        return out;
     }
 
+    return out;
+}
+
 #define INSTANTIATE(ROp, Ti, To)                                        \
     template Array<To> reduce<ROp, Ti, To>(const Array<Ti> &in, const int dim, \
                                            bool change_nan, double nanval); \
     template To reduce_all<ROp, Ti, To>(const Array<Ti> &in,            \
                                         bool change_nan, double nanval);
 
-    //min
-    INSTANTIATE(af_min_t, float  , float  )
-    INSTANTIATE(af_min_t, double , double )
-    INSTANTIATE(af_min_t, cfloat , cfloat )
-    INSTANTIATE(af_min_t, cdouble, cdouble)
-    INSTANTIATE(af_min_t, int    , int    )
-    INSTANTIATE(af_min_t, uint   , uint   )
-    INSTANTIATE(af_min_t, intl   , intl   )
-    INSTANTIATE(af_min_t, uintl  , uintl  )
-    INSTANTIATE(af_min_t, char   , char   )
-    INSTANTIATE(af_min_t, uchar  , uchar  )
-    INSTANTIATE(af_min_t, short  , short  )
-    INSTANTIATE(af_min_t, ushort , ushort )
-
-    //max
-    INSTANTIATE(af_max_t, float  , float  )
-    INSTANTIATE(af_max_t, double , double )
-    INSTANTIATE(af_max_t, cfloat , cfloat )
-    INSTANTIATE(af_max_t, cdouble, cdouble)
-    INSTANTIATE(af_max_t, int    , int    )
-    INSTANTIATE(af_max_t, uint   , uint   )
-    INSTANTIATE(af_max_t, intl   , intl   )
-    INSTANTIATE(af_max_t, uintl  , uintl  )
-    INSTANTIATE(af_max_t, char   , char   )
-    INSTANTIATE(af_max_t, uchar  , uchar  )
-    INSTANTIATE(af_max_t, short  , short  )
-    INSTANTIATE(af_max_t, ushort , ushort )
-
-    //sum
-    INSTANTIATE(af_add_t, float  , float  )
-    INSTANTIATE(af_add_t, double , double )
-    INSTANTIATE(af_add_t, cfloat , cfloat )
-    INSTANTIATE(af_add_t, cdouble, cdouble)
-    INSTANTIATE(af_add_t, int    , int    )
-    INSTANTIATE(af_add_t, int    , float  )
-    INSTANTIATE(af_add_t, uint   , uint   )
-    INSTANTIATE(af_add_t, uint   , float  )
-    INSTANTIATE(af_add_t, intl   , intl   )
-    INSTANTIATE(af_add_t, intl   , double )
-    INSTANTIATE(af_add_t, uintl  , uintl  )
-    INSTANTIATE(af_add_t, uintl  , double )
-    INSTANTIATE(af_add_t, char   , int    )
-    INSTANTIATE(af_add_t, char   , float  )
-    INSTANTIATE(af_add_t, uchar  , uint   )
-    INSTANTIATE(af_add_t, uchar  , float  )
-    INSTANTIATE(af_add_t, short  , int    )
-    INSTANTIATE(af_add_t, short  , float  )
-    INSTANTIATE(af_add_t, ushort , uint   )
-    INSTANTIATE(af_add_t, ushort , float  )
-
-    //mul
-    INSTANTIATE(af_mul_t, float  , float  )
-    INSTANTIATE(af_mul_t, double , double )
-    INSTANTIATE(af_mul_t, cfloat , cfloat )
-    INSTANTIATE(af_mul_t, cdouble, cdouble)
-    INSTANTIATE(af_mul_t, int    , int    )
-    INSTANTIATE(af_mul_t, uint   , uint   )
-    INSTANTIATE(af_mul_t, intl   , intl   )
-    INSTANTIATE(af_mul_t, uintl  , uintl  )
-    INSTANTIATE(af_mul_t, char   , int    )
-    INSTANTIATE(af_mul_t, uchar  , uint   )
-    INSTANTIATE(af_mul_t, short  , int    )
-    INSTANTIATE(af_mul_t, ushort , uint   )
-
-    // count
-    INSTANTIATE(af_notzero_t, float  , uint)
-    INSTANTIATE(af_notzero_t, double , uint)
-    INSTANTIATE(af_notzero_t, cfloat , uint)
-    INSTANTIATE(af_notzero_t, cdouble, uint)
-    INSTANTIATE(af_notzero_t, int    , uint)
-    INSTANTIATE(af_notzero_t, uint   , uint)
-    INSTANTIATE(af_notzero_t, intl   , uint)
-    INSTANTIATE(af_notzero_t, uintl  , uint)
-    INSTANTIATE(af_notzero_t, char   , uint)
-    INSTANTIATE(af_notzero_t, uchar  , uint)
-    INSTANTIATE(af_notzero_t, short  , uint)
-    INSTANTIATE(af_notzero_t, ushort , uint)
-
-    //anytrue
-    INSTANTIATE(af_or_t, float  , char)
-    INSTANTIATE(af_or_t, double , char)
-    INSTANTIATE(af_or_t, cfloat , char)
-    INSTANTIATE(af_or_t, cdouble, char)
-    INSTANTIATE(af_or_t, int    , char)
-    INSTANTIATE(af_or_t, uint   , char)
-    INSTANTIATE(af_or_t, intl   , char)
-    INSTANTIATE(af_or_t, uintl  , char)
-    INSTANTIATE(af_or_t, char   , char)
-    INSTANTIATE(af_or_t, uchar  , char)
-    INSTANTIATE(af_or_t, short  , char)
-    INSTANTIATE(af_or_t, ushort , char)
-
-    //alltrue
-    INSTANTIATE(af_and_t, float  , char)
-    INSTANTIATE(af_and_t, double , char)
-    INSTANTIATE(af_and_t, cfloat , char)
-    INSTANTIATE(af_and_t, cdouble, char)
-    INSTANTIATE(af_and_t, int    , char)
-    INSTANTIATE(af_and_t, uint   , char)
-    INSTANTIATE(af_and_t, intl   , char)
-    INSTANTIATE(af_and_t, uintl  , char)
-    INSTANTIATE(af_and_t, char   , char)
-    INSTANTIATE(af_and_t, uchar  , char)
-    INSTANTIATE(af_and_t, short  , char)
-    INSTANTIATE(af_and_t, ushort , char)
+//min
+INSTANTIATE(af_min_t, float  , float  )
+INSTANTIATE(af_min_t, double , double )
+INSTANTIATE(af_min_t, cfloat , cfloat )
+INSTANTIATE(af_min_t, cdouble, cdouble)
+INSTANTIATE(af_min_t, int    , int    )
+INSTANTIATE(af_min_t, uint   , uint   )
+INSTANTIATE(af_min_t, intl   , intl   )
+INSTANTIATE(af_min_t, uintl  , uintl  )
+INSTANTIATE(af_min_t, char   , char   )
+INSTANTIATE(af_min_t, uchar  , uchar  )
+INSTANTIATE(af_min_t, short  , short  )
+INSTANTIATE(af_min_t, ushort , ushort )
+
+//max
+INSTANTIATE(af_max_t, float  , float  )
+INSTANTIATE(af_max_t, double , double )
+INSTANTIATE(af_max_t, cfloat , cfloat )
+INSTANTIATE(af_max_t, cdouble, cdouble)
+INSTANTIATE(af_max_t, int    , int    )
+INSTANTIATE(af_max_t, uint   , uint   )
+INSTANTIATE(af_max_t, intl   , intl   )
+INSTANTIATE(af_max_t, uintl  , uintl  )
+INSTANTIATE(af_max_t, char   , char   )
+INSTANTIATE(af_max_t, uchar  , uchar  )
+INSTANTIATE(af_max_t, short  , short  )
+INSTANTIATE(af_max_t, ushort , ushort )
+
+//sum
+INSTANTIATE(af_add_t, float  , float  )
+INSTANTIATE(af_add_t, double , double )
+INSTANTIATE(af_add_t, cfloat , cfloat )
+INSTANTIATE(af_add_t, cdouble, cdouble)
+INSTANTIATE(af_add_t, int    , int    )
+INSTANTIATE(af_add_t, int    , float  )
+INSTANTIATE(af_add_t, uint   , uint   )
+INSTANTIATE(af_add_t, uint   , float  )
+INSTANTIATE(af_add_t, intl   , intl   )
+INSTANTIATE(af_add_t, intl   , double )
+INSTANTIATE(af_add_t, uintl  , uintl  )
+INSTANTIATE(af_add_t, uintl  , double )
+INSTANTIATE(af_add_t, char   , int    )
+INSTANTIATE(af_add_t, char   , float  )
+INSTANTIATE(af_add_t, uchar  , uint   )
+INSTANTIATE(af_add_t, uchar  , float  )
+INSTANTIATE(af_add_t, short  , int    )
+INSTANTIATE(af_add_t, short  , float  )
+INSTANTIATE(af_add_t, ushort , uint   )
+INSTANTIATE(af_add_t, ushort , float  )
+
+//mul
+INSTANTIATE(af_mul_t, float  , float  )
+INSTANTIATE(af_mul_t, double , double )
+INSTANTIATE(af_mul_t, cfloat , cfloat )
+INSTANTIATE(af_mul_t, cdouble, cdouble)
+INSTANTIATE(af_mul_t, int    , int    )
+INSTANTIATE(af_mul_t, uint   , uint   )
+INSTANTIATE(af_mul_t, intl   , intl   )
+INSTANTIATE(af_mul_t, uintl  , uintl  )
+INSTANTIATE(af_mul_t, char   , int    )
+INSTANTIATE(af_mul_t, uchar  , uint   )
+INSTANTIATE(af_mul_t, short  , int    )
+INSTANTIATE(af_mul_t, ushort , uint   )
+
+// count
+INSTANTIATE(af_notzero_t, float  , uint)
+INSTANTIATE(af_notzero_t, double , uint)
+INSTANTIATE(af_notzero_t, cfloat , uint)
+INSTANTIATE(af_notzero_t, cdouble, uint)
+INSTANTIATE(af_notzero_t, int    , uint)
+INSTANTIATE(af_notzero_t, uint   , uint)
+INSTANTIATE(af_notzero_t, intl   , uint)
+INSTANTIATE(af_notzero_t, uintl  , uint)
+INSTANTIATE(af_notzero_t, char   , uint)
+INSTANTIATE(af_notzero_t, uchar  , uint)
+INSTANTIATE(af_notzero_t, short  , uint)
+INSTANTIATE(af_notzero_t, ushort , uint)
+
+//anytrue
+INSTANTIATE(af_or_t, float  , char)
+INSTANTIATE(af_or_t, double , char)
+INSTANTIATE(af_or_t, cfloat , char)
+INSTANTIATE(af_or_t, cdouble, char)
+INSTANTIATE(af_or_t, int    , char)
+INSTANTIATE(af_or_t, uint   , char)
+INSTANTIATE(af_or_t, intl   , char)
+INSTANTIATE(af_or_t, uintl  , char)
+INSTANTIATE(af_or_t, char   , char)
+INSTANTIATE(af_or_t, uchar  , char)
+INSTANTIATE(af_or_t, short  , char)
+INSTANTIATE(af_or_t, ushort , char)
+
+//alltrue
+INSTANTIATE(af_and_t, float  , char)
+INSTANTIATE(af_and_t, double , char)
+INSTANTIATE(af_and_t, cfloat , char)
+INSTANTIATE(af_and_t, cdouble, char)
+INSTANTIATE(af_and_t, int    , char)
+INSTANTIATE(af_and_t, uint   , char)
+INSTANTIATE(af_and_t, intl   , char)
+INSTANTIATE(af_and_t, uintl  , char)
+INSTANTIATE(af_and_t, char   , char)
+INSTANTIATE(af_and_t, uchar  , char)
+INSTANTIATE(af_and_t, short  , char)
+INSTANTIATE(af_and_t, ushort , char)
+
 }
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 7d7558265c..afe562001d 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -53,6 +53,8 @@ namespace cpu
     template<typename T>
     Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
     {
+        in.eval();
+
         const af::dim4 iDims = in.dims();
         af::dim4 oDims(0);
         for(int i = 0; i < 4; i++)
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index d9ca0849c0..67aa5863ea 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -31,12 +31,15 @@ namespace cpu
                         const bool is_sorted)
     {
         in.eval();
-        getQueue().sync();
 
         Array<T> out = createEmptyArray<T>(af::dim4());
         if (is_sorted) out = copyArray<T>(in);
         else           out = sort<T, 1>(in, 0);
 
+        // Need to sync old jobs since we need to
+        // operator on pointers directly in std::unique
+        getQueue().sync();
+
         T *ptr = out.get();
         T *last = std::unique(ptr, ptr + in.elements());
         dim_t dist = (dim_t)std::distance(ptr, last);
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 684b9bac58..d2ebd4296d 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -27,84 +27,92 @@ using std::async;
 
 namespace cpu
 {
-    ///////////////////////////////////////////////////////////////////////////
-    // Kernel Functions
-    ///////////////////////////////////////////////////////////////////////////
-
-    template<typename Tk, typename Tv, bool isAscending>
-    void sort0_by_key(Array<Tk> okey, Array<Tv> oval, const Array<Tk> ikey, const Array<Tv> ival)
-    {
-        function<bool(Tk, Tk)> op = greater<Tk>();
-        if(isAscending) { op = less<Tk>(); }
-
-        // Get pointers and initialize original index locations
-        Array<uint> oidx = createValueArray(ikey.dims(), 0u);
-            uint *oidx_ptr = oidx.get();
-              Tk *okey_ptr = okey.get();
-              Tv *oval_ptr = oval.get();
-        const Tk *ikey_ptr = ikey.get();
-        const Tv *ival_ptr = ival.get();
-
-        std::vector<uint> seq_vec(oidx.dims()[0]);
-        std::iota(seq_vec.begin(), seq_vec.end(), 0);
-
-        const Tk *comp_ptr = nullptr;
-        auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-        for(dim_t w = 0; w < ikey.dims()[3]; w++) {
-            dim_t okeyW = w * okey.strides()[3];
-            dim_t ovalW = w * oval.strides()[3];
-            dim_t oidxW = w * oidx.strides()[3];
-            dim_t ikeyW = w * ikey.strides()[3];
-            dim_t ivalW = w * ival.strides()[3];
-
-            for(dim_t z = 0; z < ikey.dims()[2]; z++) {
-                dim_t okeyWZ = okeyW + z * okey.strides()[2];
-                dim_t ovalWZ = ovalW + z * oval.strides()[2];
-                dim_t oidxWZ = oidxW + z * oidx.strides()[2];
-                dim_t ikeyWZ = ikeyW + z * ikey.strides()[2];
-                dim_t ivalWZ = ivalW + z * ival.strides()[2];
-
-                for(dim_t y = 0; y < ikey.dims()[1]; y++) {
-
-                    dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
-                    dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
-                    dim_t oidxOffset = oidxWZ + y * oidx.strides()[1];
-                    dim_t ikeyOffset = ikeyWZ + y * ikey.strides()[1];
-                    dim_t ivalOffset = ivalWZ + y * ival.strides()[1];
-
-                    uint *ptr = oidx_ptr + oidxOffset;
-                    std::copy(seq_vec.begin(), seq_vec.end(), ptr);
-
-                    comp_ptr = ikey_ptr + ikeyOffset;
-                    std::stable_sort(ptr, ptr + ikey.dims()[0], comparator);
-
-                    for (dim_t i = 0; i < oval.dims()[0]; ++i){
-                        uint sortIdx = oidx_ptr[oidxOffset + i];
-                        okey_ptr[okeyOffset + i] = ikey_ptr[ikeyOffset + sortIdx];
-                        oval_ptr[ovalOffset + i] = ival_ptr[ivalOffset + sortIdx];
-                    }
+
+///////////////////////////////////////////////////////////////////////////
+// Kernel Functions
+///////////////////////////////////////////////////////////////////////////
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0_by_key(Array<Tk> okey, Array<Tv> oval, Array<uint> oidx,
+                  const Array<Tk> ikey, const Array<Tv> ival)
+{
+    function<bool(Tk, Tk)> op = greater<Tk>();
+    if(isAscending) { op = less<Tk>(); }
+
+    // Get pointers and initialize original index locations
+        uint *oidx_ptr = oidx.get();
+          Tk *okey_ptr = okey.get();
+          Tv *oval_ptr = oval.get();
+    const Tk *ikey_ptr = ikey.get();
+    const Tv *ival_ptr = ival.get();
+
+    std::vector<uint> seq_vec(oidx.dims()[0]);
+    std::iota(seq_vec.begin(), seq_vec.end(), 0);
+
+    const Tk *comp_ptr = nullptr;
+    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
+
+    for(dim_t w = 0; w < ikey.dims()[3]; w++) {
+        dim_t okeyW = w * okey.strides()[3];
+        dim_t ovalW = w * oval.strides()[3];
+        dim_t oidxW = w * oidx.strides()[3];
+        dim_t ikeyW = w * ikey.strides()[3];
+        dim_t ivalW = w * ival.strides()[3];
+
+        for(dim_t z = 0; z < ikey.dims()[2]; z++) {
+            dim_t okeyWZ = okeyW + z * okey.strides()[2];
+            dim_t ovalWZ = ovalW + z * oval.strides()[2];
+            dim_t oidxWZ = oidxW + z * oidx.strides()[2];
+            dim_t ikeyWZ = ikeyW + z * ikey.strides()[2];
+            dim_t ivalWZ = ivalW + z * ival.strides()[2];
+
+            for(dim_t y = 0; y < ikey.dims()[1]; y++) {
+
+                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
+                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
+                dim_t oidxOffset = oidxWZ + y * oidx.strides()[1];
+                dim_t ikeyOffset = ikeyWZ + y * ikey.strides()[1];
+                dim_t ivalOffset = ivalWZ + y * ival.strides()[1];
+
+                uint *ptr = oidx_ptr + oidxOffset;
+                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
+
+                comp_ptr = ikey_ptr + ikeyOffset;
+                std::stable_sort(ptr, ptr + ikey.dims()[0], comparator);
+
+                for (dim_t i = 0; i < oval.dims()[0]; ++i){
+                    uint sortIdx = oidx_ptr[oidxOffset + i];
+                    okey_ptr[okeyOffset + i] = ikey_ptr[ikeyOffset + sortIdx];
+                    oval_ptr[ovalOffset + i] = ival_ptr[ivalOffset + sortIdx];
                 }
             }
         }
-
-        return;
     }
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Wrapper Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename Tk, typename Tv, bool isAscending>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
-    {
-        okey = createEmptyArray<Tk>(ikey.dims());
-        oval = createEmptyArray<Tv>(ival.dims());
-        switch(dim) {
-            case 0: getQueue().enqueue(sort0_by_key<Tk, Tv, isAscending>, okey, oval, ikey, ival); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
+    return;
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename Tk, typename Tv, bool isAscending>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
+           const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
+{
+    ikey.eval();
+    ival.eval();
+
+    okey = createEmptyArray<Tk>(ikey.dims());
+    oval = createEmptyArray<Tv>(ival.dims());
+    Array<uint> oidx = createValueArray(ikey.dims(), 0u);
+    oidx.eval();
+
+    switch(dim) {
+        case 0: getQueue().enqueue(sort0_by_key<Tk, Tv, isAscending>,
+                                   okey, oval, oidx, ikey, ival); break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+}
 
 #define INSTANTIATE(Tk, Tv)                                             \
     template void                                                       \
@@ -127,14 +135,15 @@ namespace cpu
     INSTANTIATE(Tk, uintl)     \
 
 
-    INSTANTIATE1(float)
-    INSTANTIATE1(double)
-    INSTANTIATE1(int)
-    INSTANTIATE1(uint)
-    INSTANTIATE1(char)
-    INSTANTIATE1(uchar)
-    INSTANTIATE1(short)
-    INSTANTIATE1(ushort)
-    INSTANTIATE1(intl)
-    INSTANTIATE1(uintl)
+INSTANTIATE1(float)
+INSTANTIATE1(double)
+INSTANTIATE1(int)
+INSTANTIATE1(uint)
+INSTANTIATE1(char)
+INSTANTIATE1(uchar)
+INSTANTIATE1(short)
+INSTANTIATE1(ushort)
+INSTANTIATE1(intl)
+INSTANTIATE1(uintl)
+
 }
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 33bfab75aa..39cbb66343 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -68,6 +68,11 @@ namespace cpu
     template <typename T, typename Tr>
     void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
     {
+        s.eval();
+        u.eval();
+        vt.eval();
+        in.eval();
+
         auto func = [=] (Array<Tr> s, Array<T> u, Array<T> vt, Array<T> in) {
             dim4 iDims = in.dims();
             int M = iDims[0];
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index f7560121f4..4f035450ae 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -20,6 +20,8 @@ namespace cpu
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
 {
+    in.eval();
+
     const af::dim4 iDims = in.dims();
     af::dim4 oDims = iDims;
     oDims *= tileDims;
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index c89243bffd..c3a8a37a72 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -52,9 +52,15 @@ cdouble getConjugate(const cdouble &in)
 }
 
 template<typename T, bool conjugate>
-void transpose_(T *out, const T *in, const af::dim4 &odims, const af::dim4 &idims,
-                const af::dim4 &ostrides, const af::dim4 &istrides)
+void transpose_(Array<T> output, const Array<T> input)
 {
+    const dim4 odims    = output.dims();
+    const dim4 ostrides = output.strides();
+    const dim4 istrides = input.strides();
+
+    T * out = output.get();
+    T const * const in = input.get();
+
     for (dim_t l = 0; l < odims[3]; ++l) {
         for (dim_t k = 0; k < odims[2]; ++k) {
             // Outermost loop handles batch mode
@@ -82,35 +88,32 @@ void transpose_(T *out, const T *in, const af::dim4 &odims, const af::dim4 &idim
 template<typename T>
 void transpose_(Array<T> out, const Array<T> in, const bool conjugate)
 {
-    // get data pointers for input and output Arrays
-    T* outData          = out.get();
-    const T*   inData   = in.get();
-
-    if(conjugate) {
-        transpose_<T, true>(outData, inData,
-                            out.dims(), in.dims(), out.strides(), in.strides());
-    } else {
-        transpose_<T, false>(outData, inData,
-                             out.dims(), in.dims(), out.strides(), in.strides());
-    }
+    return (conjugate ? transpose_<T, true>(out, in) : transpose_<T, false>(out, in));
 }
 
 template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate)
 {
-    const dim4 inDims = in.dims();
-
-    dim4 outDims   = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
+    in.eval();
 
+    const dim4 inDims  = in.dims();
+    const dim4 outDims = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
     // create an array with first two dimensions swapped
     Array<T> out  = createEmptyArray<T>(outDims);
+
     getQueue().enqueue(transpose_<T>, out, in, conjugate);
+
     return out;
 }
 
 template<typename T, bool conjugate>
-void transpose_inplace(T *in, const af::dim4 &idims, const af::dim4 &istrides)
+void transpose_inplace(Array<T> input)
 {
+    const dim4 idims    = input.dims();
+    const dim4 istrides = input.strides();
+
+    T * in = input.get();
+
     for (dim_t l = 0; l < idims[3]; ++l) {
         for (dim_t k = 0; k < idims[2]; ++k) {
             // Outermost loop handles batch mode
@@ -141,19 +144,13 @@ void transpose_inplace(T *in, const af::dim4 &idims, const af::dim4 &istrides)
 template<typename T>
 void transpose_inplace_(Array<T> in, const bool conjugate)
 {
-    // get data pointers for input and output Arrays
-    T* inData = in.get();
-
-    if(conjugate) {
-        transpose_inplace<T, true >(inData, in.dims(), in.strides());
-    } else {
-        transpose_inplace<T, false>(inData, in.dims(), in.strides());
-    }
+    return (conjugate ? transpose_inplace<T, true >(in) : transpose_inplace<T, false>(in));
 }
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate)
 {
+    in.eval();
     getQueue().enqueue(transpose_inplace_<T>, in, conjugate);
 }
 

From 3ba9633e8d1dba645ba29695ef99e11a616ef2f2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2015 14:22:46 -0500
Subject: [PATCH 0145/2677] Added missing eval for input Array's in cpu backend
 fns

This change has some style fixes related to cpu namespace
---
 src/backend/cpu/Array.cpp             |  402 +++---
 src/backend/cpu/approx.cpp            |  584 ++++----
 src/backend/cpu/bilateral.cpp         |    1 +
 src/backend/cpu/cholesky.cpp          |    4 +
 src/backend/cpu/copy.cpp              |  278 ++--
 src/backend/cpu/diagonal.cpp          |  125 +-
 src/backend/cpu/diff.cpp              |  205 +--
 src/backend/cpu/exampleFunction.cpp   |    7 +
 src/backend/cpu/fft.cpp               |    6 +
 src/backend/cpu/gradient.cpp          |    1 +
 src/backend/cpu/identity.cpp          |    2 +
 src/backend/cpu/iota.cpp              |    2 +
 src/backend/cpu/ireduce.cpp           |  282 ++--
 src/backend/cpu/lu.cpp                |    6 +
 src/backend/cpu/match_template.cpp    |    3 +
 src/backend/cpu/math.cpp              |   72 +-
 src/backend/cpu/meanshift.cpp         |    2 +
 src/backend/cpu/medfilt.cpp           |    2 +
 src/backend/cpu/memory.cpp            |  333 ++---
 src/backend/cpu/nearest_neighbour.cpp |    2 -
 src/backend/cpu/qr.cpp                |    6 +
 src/backend/cpu/range.cpp             |    2 +
 src/backend/cpu/reorder.cpp           |   98 +-
 src/backend/cpu/resize.cpp            |    2 +
 src/backend/cpu/rotate.cpp            |  178 +--
 src/backend/cpu/set.cpp               |  178 +--
 src/backend/cpu/shift.cpp             |    6 +-
 src/backend/cpu/sift_nonfree.hpp      | 1811 +++++++++++++------------
 src/backend/cpu/sobel.cpp             |    1 +
 src/backend/cpu/solve.cpp             |   11 +-
 src/backend/cpu/sort.cpp              |   96 +-
 src/backend/cpu/sort_index.cpp        |  140 +-
 src/backend/cpu/susan.cpp             |    2 +
 src/backend/cpu/svd.cpp               |  147 +-
 src/backend/cpu/transform.cpp         |    4 +-
 src/backend/cpu/transpose.cpp         |    1 -
 src/backend/cpu/triangle.cpp          |   25 +-
 src/backend/cpu/unwrap.cpp            |    3 +-
 src/backend/cpu/where.cpp             |   81 +-
 39 files changed, 2606 insertions(+), 2505 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 456f4c8b1f..9c15bc46c6 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -21,250 +21,251 @@
 
 namespace cpu
 {
-    const int MAX_TNJ_LEN = 20;
-    using TNJ::BufferNode;
-    using TNJ::Node;
-    using TNJ::Node_ptr;
-
-    using af::dim4;
-
-    template<typename T>
-    Array<T>::Array(dim4 dims):
-        info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
-    { }
-
-    template<typename T>
-    Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
-        info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
-    {
-        static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
-        static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
-        if (!is_device || copy_device) {
-            std::copy(in_data, in_data + dims.elements(), data.get());
-        }
-    }
 
-    template<typename T>
-    Array<T>::Array(af::dim4 dims, TNJ::Node_ptr n) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(), data_dims(dims),
-        node(n), offset(0), ready(false), owner(true)
-    {
+const int MAX_TNJ_LEN = 20;
+using TNJ::BufferNode;
+using TNJ::Node;
+using TNJ::Node_ptr;
+
+using af::dim4;
+
+template<typename T>
+Array<T>::Array(dim4 dims):
+    info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+    data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
+    node(), offset(0), ready(true), owner(true)
+{ }
+
+template<typename T>
+Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
+    info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+    data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
+    node(), offset(0), ready(true), owner(true)
+{
+    static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
+    static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
+    if (!is_device || copy_device) {
+        std::copy(in_data, in_data + dims.elements(), data.get());
     }
+}
 
-    template<typename T>
-    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim4 &offsets, const dim4 &strides) :
-        info(parent.getDevId(), dims, offsets, strides, (af_dtype)dtype_traits<T>::af_type),
-        data(parent.getData()), data_dims(parent.getDataDims()),
-        node(),
-        offset(parent.getOffset() + calcOffset(parent.strides(), offsets)),
-        ready(true), owner(false)
-    { }
+template<typename T>
+Array<T>::Array(af::dim4 dims, TNJ::Node_ptr n) :
+    info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+    data(), data_dims(dims),
+    node(n), offset(0), ready(false), owner(true)
+{
+}
 
+template<typename T>
+Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim4 &offsets, const dim4 &strides) :
+    info(parent.getDevId(), dims, offsets, strides, (af_dtype)dtype_traits<T>::af_type),
+    data(parent.getData()), data_dims(parent.getDataDims()),
+    node(),
+    offset(parent.getOffset() + calcOffset(parent.strides(), offsets)),
+    ready(true), owner(false)
+{ }
 
-    template<typename T>
-    void Array<T>::eval()
-    {
-        if (isReady()) return;
-        if (getQueue().is_worker()) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
 
-        this->setId(getActiveDeviceId());
+template<typename T>
+void Array<T>::eval()
+{
+    if (isReady()) return;
+    if (getQueue().is_worker()) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
 
-        data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
+    this->setId(getActiveDeviceId());
 
-        auto func = [] (Array<T> in) {
-            in.setId(getActiveDeviceId());
-            T *ptr = in.data.get();
+    data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
-            dim4 odims = in.dims();
-            dim4 ostrs = in.strides();
+    auto func = [] (Array<T> in) {
+        in.setId(getActiveDeviceId());
+        T *ptr = in.data.get();
 
-            bool is_linear = in.node->isLinear(odims.get());
+        dim4 odims = in.dims();
+        dim4 ostrs = in.strides();
 
-            if (is_linear) {
-                int num = in.elements();
-                for (int i = 0; i < num; i++) {
-                    ptr[i] = *(T *)in.node->calc(i);
-                }
-            } else {
-                for (int w = 0; w < (int)odims[3]; w++) {
-                    dim_t offw = w * ostrs[3];
+        bool is_linear = in.node->isLinear(odims.get());
+
+        if (is_linear) {
+            int num = in.elements();
+            for (int i = 0; i < num; i++) {
+                ptr[i] = *(T *)in.node->calc(i);
+            }
+        } else {
+            for (int w = 0; w < (int)odims[3]; w++) {
+                dim_t offw = w * ostrs[3];
 
-                    for (int z = 0; z < (int)odims[2]; z++) {
-                        dim_t offz = z * ostrs[2] + offw;
+                for (int z = 0; z < (int)odims[2]; z++) {
+                    dim_t offz = z * ostrs[2] + offw;
 
-                        for (int y = 0; y < (int)odims[1]; y++) {
-                            dim_t offy = y * ostrs[1] + offz;
+                    for (int y = 0; y < (int)odims[1]; y++) {
+                        dim_t offy = y * ostrs[1] + offz;
 
-                            for (int x = 0; x < (int)odims[0]; x++) {
-                                dim_t id = x + offy;
+                        for (int x = 0; x < (int)odims[0]; x++) {
+                            dim_t id = x + offy;
 
-                                ptr[id] = *(T *)in.node->calc(x, y, z, w);
-                            }
+                            ptr[id] = *(T *)in.node->calc(x, y, z, w);
                         }
                     }
                 }
             }
-        };
-
-        getQueue().enqueue(func, *this);
+        }
+    };
 
-        ready = true;
-        Node_ptr prev = node;
-        prev->reset();
-        // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-        node.reset();
-    }
+    getQueue().enqueue(func, *this);
 
-    template<typename T>
-    void Array<T>::eval() const
-    {
-        if (isReady()) return;
-        const_cast<Array<T> *>(this)->eval();
-    }
+    ready = true;
+    Node_ptr prev = node;
+    prev->reset();
+    // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
+    node.reset();
+}
 
-    template<typename T>
-    Node_ptr Array<T>::getNode() const
-    {
-        if (!node) {
+template<typename T>
+void Array<T>::eval() const
+{
+    if (isReady()) return;
+    const_cast<Array<T> *>(this)->eval();
+}
 
-            unsigned bytes = this->getDataDims().elements() * sizeof(T);
+template<typename T>
+Node_ptr Array<T>::getNode() const
+{
+    if (!node) {
 
-            BufferNode<T> *buf_node = new BufferNode<T>(data,
-                                                        bytes,
-                                                        offset,
-                                                        dims().get(),
-                                                        strides().get(),
-                                                        isLinear());
+        unsigned bytes = this->getDataDims().elements() * sizeof(T);
 
-            const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
-        }
+        BufferNode<T> *buf_node = new BufferNode<T>(data,
+                                                    bytes,
+                                                    offset,
+                                                    dims().get(),
+                                                    strides().get(),
+                                                    isLinear());
 
-        return node;
+        const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
     }
 
-    template<typename T>
-    Array<T>
-    createHostDataArray(const dim4 &size, const T * const data)
-    {
-        return Array<T>(size, data, false);
-    }
+    return node;
+}
 
-    template<typename T>
-    Array<T>
-    createDeviceDataArray(const dim4 &size, const void *data)
-    {
-        return Array<T>(size, (const T * const) data, true);
-    }
+template<typename T>
+Array<T>
+createHostDataArray(const dim4 &size, const T * const data)
+{
+    return Array<T>(size, data, false);
+}
 
-    template<typename T>
-    Array<T>
-    createValueArray(const dim4 &size, const T& value)
-    {
-        TNJ::ScalarNode<T> *node = new TNJ::ScalarNode<T>(value);
-        return createNodeArray<T>(size, TNJ::Node_ptr(
-                                      reinterpret_cast<TNJ::Node *>(node)));
-    }
+template<typename T>
+Array<T>
+createDeviceDataArray(const dim4 &size, const void *data)
+{
+    return Array<T>(size, (const T * const) data, true);
+}
 
-    template<typename T>
-    Array<T>
-    createEmptyArray(const dim4 &size)
-    {
-        return Array<T>(size);
-    }
+template<typename T>
+Array<T>
+createValueArray(const dim4 &size, const T& value)
+{
+    TNJ::ScalarNode<T> *node = new TNJ::ScalarNode<T>(value);
+    return createNodeArray<T>(size, TNJ::Node_ptr(
+                                  reinterpret_cast<TNJ::Node *>(node)));
+}
 
-    template<typename T>
-    Array<T> *initArray() { return new Array<T>(dim4(0, 0, 0, 0)); }
+template<typename T>
+Array<T>
+createEmptyArray(const dim4 &size)
+{
+    return Array<T>(size);
+}
 
+template<typename T>
+Array<T> *initArray() { return new Array<T>(dim4(0, 0, 0, 0)); }
 
-    template<typename T>
-    Array<T>
-    createNodeArray(const dim4 &dims, Node_ptr node)
-    {
-        Array<T> out =  Array<T>(dims, node);
 
-        unsigned length =0, buf_count = 0, bytes = 0;
+template<typename T>
+Array<T>
+createNodeArray(const dim4 &dims, Node_ptr node)
+{
+    Array<T> out =  Array<T>(dims, node);
 
-        Node *n = node.get();
-        n->getInfo(length, buf_count, bytes);
-        n->reset();
+    unsigned length =0, buf_count = 0, bytes = 0;
 
-        if (length > MAX_TNJ_LEN ||
-            buf_count >= MAX_BUFFERS ||
-            bytes >= MAX_BYTES) {
-            out.eval();
-        }
+    Node *n = node.get();
+    n->getInfo(length, buf_count, bytes);
+    n->reset();
 
-        return out;
+    if (length > MAX_TNJ_LEN ||
+        buf_count >= MAX_BUFFERS ||
+        bytes >= MAX_BYTES) {
+        out.eval();
     }
 
+    return out;
+}
 
-    template<typename T>
-    Array<T> createSubArray(const Array<T>& parent,
-                            const std::vector<af_seq> &index,
-                            bool copy)
-    {
-        parent.eval();
 
-        dim4 dDims = parent.getDataDims();
-        dim4 pDims = parent.dims();
+template<typename T>
+Array<T> createSubArray(const Array<T>& parent,
+                        const std::vector<af_seq> &index,
+                        bool copy)
+{
+    parent.eval();
 
-        dim4 dims   = toDims  (index, pDims);
-        dim4 offset = toOffset(index, dDims);
-        dim4 stride = toStride (index, dDims);
+    dim4 dDims = parent.getDataDims();
+    dim4 pDims = parent.dims();
 
-        Array<T> out = Array<T>(parent, dims, offset, stride);
+    dim4 dims   = toDims  (index, pDims);
+    dim4 offset = toOffset(index, dDims);
+    dim4 stride = toStride (index, dDims);
 
-        if (!copy) return out;
+    Array<T> out = Array<T>(parent, dims, offset, stride);
 
-        if (stride[0] != 1 ||
-            stride[1] <  0 ||
-            stride[2] <  0 ||
-            stride[3] <  0) {
+    if (!copy) return out;
 
-            out = copyArray(out);
-        }
+    if (stride[0] != 1 ||
+        stride[1] <  0 ||
+        stride[2] <  0 ||
+        stride[3] <  0) {
 
-        return out;
+        out = copyArray(out);
     }
 
-    template<typename T>
-    void
-    destroyArray(Array<T> *A)
-    {
-        delete A;
-    }
+    return out;
+}
 
+template<typename T>
+void
+destroyArray(Array<T> *A)
+{
+    delete A;
+}
 
-    template<typename T>
-    void evalArray(const Array<T> &A)
-    {
-        A.eval();
-    }
 
-    template<typename T>
-    void
-    writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
-    {
-        if(!arr.isOwner()) {
-            arr = createEmptyArray<T>(arr.dims());
-        }
-        memcpy(arr.get() + arr.getOffset(), data, bytes);
+template<typename T>
+void evalArray(const Array<T> &A)
+{
+    A.eval();
+}
+
+template<typename T>
+void
+writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
+{
+    if(!arr.isOwner()) {
+        arr = createEmptyArray<T>(arr.dims());
     }
+    memcpy(arr.get() + arr.getOffset(), data, bytes);
+}
 
-    template<typename T>
-    void
-    writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
-    {
-        if(!arr.isOwner()) {
-            arr = createEmptyArray<T>(arr.dims());
-        }
-        memcpy(arr.get() + arr.getOffset(), (const T * const)data, bytes);
+template<typename T>
+void
+writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
+{
+    if(!arr.isOwner()) {
+        arr = createEmptyArray<T>(arr.dims());
     }
+    memcpy(arr.get() + arr.getOffset(), (const T * const)data, bytes);
+}
 
 #define INSTANTIATE(T)                                                  \
     template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
@@ -286,16 +287,17 @@ namespace cpu
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
 }
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 4d3c8803ff..7988863d4d 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -17,330 +17,339 @@
 
 namespace cpu
 {
-    ///////////////////////////////////////////////////////////////////////////
-    // Approx1
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename Ty, typename Tp, af_interp_type method>
-    struct approx1_op
+
+///////////////////////////////////////////////////////////////////////////
+// Approx1
+///////////////////////////////////////////////////////////////////////////
+template<typename Ty, typename Tp, af_interp_type method>
+struct approx1_op
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
     {
-        void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-                  const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-                  const Tp *pos, const af::dim4 &pdims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-                  const float offGrid, const bool pBatch,
-                  const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-        {
-            return;
-        }
-    };
+        return;
+    }
+};
 
-    template<typename Ty, typename Tp>
-    struct approx1_op<Ty, Tp, AF_INTERP_NEAREST>
+template<typename Ty, typename Tp>
+struct approx1_op<Ty, Tp, AF_INTERP_NEAREST>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
     {
-        void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-                  const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-                  const Tp *pos, const af::dim4 &pdims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-                  const float offGrid, const bool pBatch,
-                  const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-        {
-            dim_t pmId = idx;
-            if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-            const Tp x = pos[pmId];
-            bool gFlag = false;
-            if (x < 0 || idims[0] < x+1) {  // No need to check y
-                gFlag = true;
-            }
+        dim_t pmId = idx;
+        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
 
-            const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                             + idy * ostrides[1] + idx;
-            if(gFlag) {
-                out[omId] = scalar<Ty>(offGrid);
-            } else {
-                dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                           + idy * istrides[1];
-                const dim_t iMem = round(x) + ioff;
+        const Tp x = pos[pmId];
+        bool gFlag = false;
+        if (x < 0 || idims[0] < x+1) {  // No need to check y
+            gFlag = true;
+        }
 
-                out[omId] = in[iMem];
-            }
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2]
+                       + idy * istrides[1];
+            const dim_t iMem = round(x) + ioff;
+
+            out[omId] = in[iMem];
         }
-    };
+    }
+};
 
-    template<typename Ty, typename Tp>
-    struct approx1_op<Ty, Tp, AF_INTERP_LINEAR>
+template<typename Ty, typename Tp>
+struct approx1_op<Ty, Tp, AF_INTERP_LINEAR>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
     {
-        void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-                  const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-                  const Tp *pos, const af::dim4 &pdims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-                  const float offGrid, const bool pBatch,
-                  const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-        {
-            dim_t pmId = idx;
-            if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-            const Tp x = pos[pmId];
-            bool gFlag = false;
-            if (x < 0 || idims[0] < x+1) {
-                gFlag = true;
-            }
+        dim_t pmId = idx;
+        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
 
-            const dim_t grid_x = floor(x);  // nearest grid
-            const Tp off_x = x - grid_x; // fractional offset
-
-            const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                             + idy * ostrides[1] + idx;
-            if(gFlag) {
-                out[omId] = scalar<Ty>(offGrid);
-            } else {
-                dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
-
-                // Check if x and x + 1 are both valid indices
-                bool cond = (x < idims[0] - 1);
-                // Compute Left and Right Weighted Values
-                Ty yl = ((Tp)1.0 - off_x) * in[ioff];
-                Ty yr = cond ? (off_x) * in[ioff + 1] : scalar<Ty>(0);
-                Ty yo = yl + yr;
-                // Compute Weight used
-                Tp wt = cond ? (Tp)1.0 : (Tp)(1.0 - off_x);
-                // Write final value
-                out[omId] = (yo / wt);
-            }
+        const Tp x = pos[pmId];
+        bool gFlag = false;
+        if (x < 0 || idims[0] < x+1) {
+            gFlag = true;
         }
-    };
-
-    template<typename Ty, typename Tp, af_interp_type method>
-    void approx1_(Ty *out, const af::dim4 &odims, const dim_t oElems,
-            const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-            const Tp *pos, const af::dim4 &pdims,
-            const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-            const float offGrid)
-    {
-        approx1_op<Ty, Tp, method> op;
-        bool pBatch = !(pdims[1] == 1 && pdims[2] == 1 && pdims[3] == 1);
-
-        for(dim_t w = 0; w < odims[3]; w++) {
-            for(dim_t z = 0; z < odims[2]; z++) {
-                for(dim_t y = 0; y < odims[1]; y++) {
-                    for(dim_t x = 0; x < odims[0]; x++) {
-                        op(out, odims, oElems, in, idims, iElems, pos, pdims,
-                           ostrides, istrides, pstrides, offGrid, pBatch, x, y, z, w);
-                    }
+
+        const dim_t grid_x = floor(x);  // nearest grid
+        const Tp off_x = x - grid_x; // fractional offset
+
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
+
+            // Check if x and x + 1 are both valid indices
+            bool cond = (x < idims[0] - 1);
+            // Compute Left and Right Weighted Values
+            Ty yl = ((Tp)1.0 - off_x) * in[ioff];
+            Ty yr = cond ? (off_x) * in[ioff + 1] : scalar<Ty>(0);
+            Ty yo = yl + yr;
+            // Compute Weight used
+            Tp wt = cond ? (Tp)1.0 : (Tp)(1.0 - off_x);
+            // Write final value
+            out[omId] = (yo / wt);
+        }
+    }
+};
+
+template<typename Ty, typename Tp, af_interp_type method>
+void approx1_(Array<Ty> output, Array<Ty> const input,
+              Array<Tp> const position, float const offGrid)
+{
+    Ty * out = output.get();
+    Ty const * const in  = input.get();
+    Tp const * const pos = position.get();
+    dim4 const odims     = output.dims();
+    dim4 const idims     = input.dims();
+    dim4 const pdims     = position.dims();
+    dim4 const ostrides  = output.strides();
+    dim4 const istrides  = input.strides();
+    dim4 const pstrides  = position.strides();
+    dim_t const oElems   = output.elements();
+    dim_t const iElems   = input.elements();
+
+    approx1_op<Ty, Tp, method> op;
+    bool pBatch = !(pdims[1] == 1 && pdims[2] == 1 && pdims[3] == 1);
+
+    for(dim_t w = 0; w < odims[3]; w++) {
+        for(dim_t z = 0; z < odims[2]; z++) {
+            for(dim_t y = 0; y < odims[1]; y++) {
+                for(dim_t x = 0; x < odims[0]; x++) {
+                    op(out, odims, oElems, in, idims, iElems, pos, pdims,
+                       ostrides, istrides, pstrides, offGrid, pBatch, x, y, z, w);
                 }
             }
         }
     }
+}
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
-                       const af_interp_type method, const float offGrid)
-    {
-        in.eval();
-        pos.eval();
-
-        af::dim4 odims = in.dims();
-        odims[0] = pos.dims()[0];
-
-        // Create output placeholder
-        Array<Ty> out = createEmptyArray<Ty>(odims);
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_NEAREST>,
-                        out.get(), out.dims(), out.elements(),
-                         in.get(), in.dims(), in.elements(), pos.get(), pos.dims(),
-                         out.strides(), in.strides(), pos.strides(), offGrid);
-                break;
-            case AF_INTERP_LINEAR:
-                getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_LINEAR>,
-                        out.get(), out.dims(), out.elements(),
-                         in.get(), in.dims(), in.elements(), pos.get(), pos.dims(),
-                         out.strides(), in.strides(), pos.strides(), offGrid);
-                break;
-            default:
-                break;
-        }
-        return out;
+template<typename Ty, typename Tp>
+Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+                   const af_interp_type method, const float offGrid)
+{
+    in.eval();
+    pos.eval();
+
+    af::dim4 odims = in.dims();
+    odims[0] = pos.dims()[0];
+
+    // Create output placeholder
+    Array<Ty> out = createEmptyArray<Ty>(odims);
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_NEAREST>,
+                               out, in, pos, offGrid);
+            break;
+        case AF_INTERP_LINEAR:
+            getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_LINEAR>,
+                               out, in, pos, offGrid);
+            break;
+        default:
+            break;
     }
+    return out;
+}
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Approx2
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename Ty, typename Tp, af_interp_type method>
-    struct approx2_op
+///////////////////////////////////////////////////////////////////////////
+// Approx2
+///////////////////////////////////////////////////////////////////////////
+template<typename Ty, typename Tp, af_interp_type method>
+struct approx2_op
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const af::dim4 &pstrides, const af::dim4 &qstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
     {
-        void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-                  const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-                  const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides,
-                  const af::dim4 &pstrides, const af::dim4 &qstrides,
-                  const float offGrid, const bool pBatch,
-                  const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-        {
-            return;
-        }
-    };
+        return;
+    }
+};
 
-    template<typename Ty, typename Tp>
-    struct approx2_op<Ty, Tp, AF_INTERP_NEAREST>
+template<typename Ty, typename Tp>
+struct approx2_op<Ty, Tp, AF_INTERP_NEAREST>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const af::dim4 &pstrides, const af::dim4 &qstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
     {
-        void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-                  const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-                  const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides,
-                  const af::dim4 &pstrides, const af::dim4 &qstrides,
-                  const float offGrid, const bool pBatch,
-                  const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-        {
-            dim_t pmId = idy * pstrides[1] + idx;
-            dim_t qmId = idy * qstrides[1] + idx;
-            if(pBatch) {
-                pmId += idw * pstrides[3] + idz * pstrides[2];
-                qmId += idw * qstrides[3] + idz * qstrides[2];
-            }
+        dim_t pmId = idy * pstrides[1] + idx;
+        dim_t qmId = idy * qstrides[1] + idx;
+        if(pBatch) {
+            pmId += idw * pstrides[3] + idz * pstrides[2];
+            qmId += idw * qstrides[3] + idz * qstrides[2];
+        }
 
-            bool gFlag = false;
-            const Tp x = pos[pmId], y = qos[qmId];
-            if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
-                gFlag = true;
-            }
+        bool gFlag = false;
+        const Tp x = pos[pmId], y = qos[qmId];
+        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
+            gFlag = true;
+        }
 
-            const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                             + idy * ostrides[1] + idx;
-            if(gFlag) {
-                out[omId] = scalar<Ty>(offGrid);
-            } else {
-                const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
-                const dim_t imId = idw * istrides[3] + idz * istrides[2] +
-                                grid_y * istrides[1] + grid_x;
-                out[omId] = in[imId];
-            }
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
+            const dim_t imId = idw * istrides[3] + idz * istrides[2] +
+                            grid_y * istrides[1] + grid_x;
+            out[omId] = in[imId];
         }
-    };
+    }
+};
 
-    template<typename Ty, typename Tp>
-    struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
+template<typename Ty, typename Tp>
+struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const af::dim4 &pstrides, const af::dim4 &qstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
     {
-        void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-                  const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-                  const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-                  const af::dim4 &ostrides, const af::dim4 &istrides,
-                  const af::dim4 &pstrides, const af::dim4 &qstrides,
-                  const float offGrid, const bool pBatch,
-                  const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-        {
-            dim_t pmId = idy * pstrides[1] + idx;
-            dim_t qmId = idy * qstrides[1] + idx;
-            if(pBatch) {
-                pmId += idw * pstrides[3] + idz * pstrides[2];
-                qmId += idw * qstrides[3] + idz * qstrides[2];
-            }
+        dim_t pmId = idy * pstrides[1] + idx;
+        dim_t qmId = idy * qstrides[1] + idx;
+        if(pBatch) {
+            pmId += idw * pstrides[3] + idz * pstrides[2];
+            qmId += idw * qstrides[3] + idz * qstrides[2];
+        }
 
-            bool gFlag = false;
-            const Tp x = pos[pmId], y = qos[qmId];
-            if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
-                gFlag = true;
-            }
+        bool gFlag = false;
+        const Tp x = pos[pmId], y = qos[qmId];
+        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
+            gFlag = true;
+        }
 
-            const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-            const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
+        const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+        const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
 
-            // Check if pVal and pVal + 1 are both valid indices
-            bool condY = (y < idims[1] - 1);
-            bool condX = (x < idims[0] - 1);
+        // Check if pVal and pVal + 1 are both valid indices
+        bool condY = (y < idims[1] - 1);
+        bool condX = (x < idims[0] - 1);
 
-            // Compute wieghts used
-            Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
-            Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
-            Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
-            Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
+        // Compute wieghts used
+        Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
+        Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
+        Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
+        Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
 
-            Tp wt = wt00 + wt10 + wt01 + wt11;
-            Ty zero = scalar<Ty>(0);
+        Tp wt = wt00 + wt10 + wt01 + wt11;
+        Ty zero = scalar<Ty>(0);
 
-            const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                             + idy * ostrides[1] + idx;
-            if(gFlag) {
-                out[omId] = scalar<Ty>(offGrid);
-            } else {
-                dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                        + grid_y * istrides[1] + grid_x;
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2]
+                    + grid_y * istrides[1] + grid_x;
 
-                // Compute Weighted Values
-                Ty y00 =                    wt00 * in[ioff];
-                Ty y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
-                Ty y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
-                Ty y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
+            // Compute Weighted Values
+            Ty y00 =                    wt00 * in[ioff];
+            Ty y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
+            Ty y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
+            Ty y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
 
-                Ty yo = y00 + y10 + y01 + y11;
+            Ty yo = y00 + y10 + y01 + y11;
 
-                // Write Final Value
-                out[omId] = (yo / wt);
-            }
+            // Write Final Value
+            out[omId] = (yo / wt);
         }
-    };
-
-    template<typename Ty, typename Tp, af_interp_type method>
-    void approx2_(Ty *out, const af::dim4 &odims, const dim_t oElems,
-            const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-            const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const af::dim4 &pstrides, const af::dim4 &qstrides,
-            const float offGrid)
-    {
-        approx2_op<Ty, Tp, method> op;
-        bool pBatch = !(pdims[2] == 1 && pdims[3] == 1);
-
-        for(dim_t w = 0; w < odims[3]; w++) {
-            for(dim_t z = 0; z < odims[2]; z++) {
-                for(dim_t y = 0; y < odims[1]; y++) {
-                    for(dim_t x = 0; x < odims[0]; x++) {
-                        op(out, odims, oElems, in, idims, iElems, pos, pdims, qos, qdims,
-                           ostrides, istrides, pstrides, qstrides, offGrid, pBatch, x, y, z, w);
-                    }
+    }
+};
+
+template<typename Ty, typename Tp, af_interp_type method>
+void approx2_(Array<Ty> output, Array<Ty> const input,
+              Array<Tp> const position, Array<Tp> const qosition,
+              float const offGrid)
+{
+    Ty * out = output.get();
+    Ty const * const in  = input.get();
+    Tp const * const pos = position.get();
+    Tp const * const qos = qosition.get();
+    dim4 const odims     = output.dims();
+    dim4 const idims     = input.dims();
+    dim4 const pdims     = position.dims();
+    dim4 const qdims     = qosition.dims();
+    dim4 const ostrides  = output.strides();
+    dim4 const istrides  = input.strides();
+    dim4 const pstrides  = position.strides();
+    dim4 const qstrides  = qosition.strides();
+    dim_t const oElems   = output.elements();
+    dim_t const iElems   = input.elements();
+
+    approx2_op<Ty, Tp, method> op;
+    bool pBatch = !(pdims[2] == 1 && pdims[3] == 1);
+
+    for(dim_t w = 0; w < odims[3]; w++) {
+        for(dim_t z = 0; z < odims[2]; z++) {
+            for(dim_t y = 0; y < odims[1]; y++) {
+                for(dim_t x = 0; x < odims[0]; x++) {
+                    op(out, odims, oElems, in, idims, iElems, pos, pdims, qos, qdims,
+                       ostrides, istrides, pstrides, qstrides, offGrid, pBatch, x, y, z, w);
                 }
             }
         }
     }
+}
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
-                       const af_interp_type method, const float offGrid)
-    {
-        in.eval();
-        pos0.eval();
-        pos1.eval();
-
-        af::dim4 odims = in.dims();
-        odims[0] = pos0.dims()[0];
-        odims[1] = pos0.dims()[1];
-
-        // Create output placeholder
-        Array<Ty> out = createEmptyArray<Ty>(odims);
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_NEAREST>,
-                        out.get(), out.dims(), out.elements(),
-                         in.get(), in.dims(), in.elements(),
-                         pos0.get(), pos0.dims(), pos1.get(), pos1.dims(),
-                         out.strides(), in.strides(), pos0.strides(), pos1.strides(),
-                         offGrid);
-                break;
-            case AF_INTERP_LINEAR:
-                getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_LINEAR>,
-                        out.get(), out.dims(), out.elements(),
-                         in.get(), in.dims(), in.elements(),
-                         pos0.get(), pos0.dims(), pos1.get(), pos1.dims(),
-                         out.strides(), in.strides(), pos0.strides(), pos1.strides(),
-                         offGrid);
-                break;
-            default:
-                break;
-        }
-        return out;
+template<typename Ty, typename Tp>
+Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+                   const af_interp_type method, const float offGrid)
+{
+    in.eval();
+    pos0.eval();
+    pos1.eval();
+
+    af::dim4 odims = in.dims();
+    odims[0] = pos0.dims()[0];
+    odims[1] = pos0.dims()[1];
+
+    Array<Ty> out = createEmptyArray<Ty>(odims);
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_NEAREST>,
+                               out, in, pos0, pos1, offGrid);
+            break;
+        case AF_INTERP_LINEAR:
+            getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_LINEAR>,
+                               out, in, pos0, pos1, offGrid);
+            break;
+        default:
+            break;
     }
+    return out;
+}
 
 #define INSTANTIATE(Ty, Tp)                                                                    \
     template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos,              \
@@ -349,8 +358,9 @@ namespace cpu
                                        const Array<Tp> &pos1, const af_interp_type method,     \
                                        const float offGrid);                                   \
 
-    INSTANTIATE(float  , float )
-    INSTANTIATE(double , double)
-    INSTANTIATE(cfloat , float )
-    INSTANTIATE(cdouble, double)
+INSTANTIATE(float  , float )
+INSTANTIATE(double , double)
+INSTANTIATE(cfloat , float )
+INSTANTIATE(cdouble, double)
+
 }
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 10856f7166..ea38ea7dd7 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -99,6 +99,7 @@ void bilateral_(Array<outType> out, const Array<inType> in, float s_sigma, float
 template<typename inType, typename outType, bool isColor>
 Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
 {
+    in.eval();
     const dim4 dims     = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
     getQueue().enqueue(bilateral_<inType, outType, isColor>, out, in, s_sigma, c_sigma);
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index d0bd3c8787..ce11867186 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -47,6 +47,8 @@ CH_FUNC(potrf , cdouble, z)
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
 {
+    in.eval();
+
     Array<T> out = copyArray<T>(in);
     *info = cholesky_inplace(out, is_upper);
 
@@ -59,6 +61,8 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper)
 {
+    in.eval();
+
     dim4 iDims = in.dims();
     int N = iDims[0];
 
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 52403605ca..eef5e0e302 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -23,144 +23,144 @@
 
 namespace cpu
 {
-    template<typename T>
-    static void stridedCopy(T* dst, const dim4& ostrides, const T* src, const dim4 &dims, const dim4 &strides, unsigned dim)
-    {
-        if(dim == 0) {
-            if(strides[dim] == 1) {
-                //FIXME: Check for errors / exceptions
-                memcpy(dst, src, dims[dim] * sizeof(T));
-            } else {
-                for(dim_t i = 0; i < dims[dim]; i++) {
-                    dst[i] = src[strides[dim]*i];
-                }
-            }
+
+template<typename T>
+static void stridedCopy(T* dst, const dim4& ostrides, const T* src, const dim4 &dims, const dim4 &strides, unsigned dim)
+{
+    if(dim == 0) {
+        if(strides[dim] == 1) {
+            //FIXME: Check for errors / exceptions
+            memcpy(dst, src, dims[dim] * sizeof(T));
         } else {
-            for(dim_t i = dims[dim]; i > 0; i--) {
-                stridedCopy<T>(dst, ostrides, src, dims, strides, dim - 1);
-                src += strides[dim];
-                dst += ostrides[dim];
+            for(dim_t i = 0; i < dims[dim]; i++) {
+                dst[i] = src[strides[dim]*i];
             }
         }
-    }
-
-    // Assigns to single elements
-    template<typename T>
-    void copyData(T *to, const Array<T> &from)
-    {
-        from.eval();
-        getQueue().sync();
-        if(from.isOwner()) {
-            // FIXME: Check for errors / exceptions
-            memcpy(to, from.get(), from.elements()*sizeof(T));
-        } else {
-            dim4 ostrides = calcStrides(from.dims());
-            stridedCopy<T>(to, ostrides, from.get(), from.dims(), from.strides(), from.ndims() - 1);
+    } else {
+        for(dim_t i = dims[dim]; i > 0; i--) {
+            stridedCopy<T>(dst, ostrides, src, dims, strides, dim - 1);
+            src += strides[dim];
+            dst += ostrides[dim];
         }
     }
+}
 
-    template<typename T>
-    Array<T> copyArray(const Array<T> &A)
-    {
-        Array<T> out = createEmptyArray<T>(A.dims());
-        copyData(out.get(), A);
-        return out;
+// Assigns to single elements
+template<typename T>
+void copyData(T *to, const Array<T> &from)
+{
+    from.eval();
+    getQueue().sync();
+    if(from.isOwner()) {
+        // FIXME: Check for errors / exceptions
+        memcpy(to, from.get(), from.elements()*sizeof(T));
+    } else {
+        dim4 ostrides = calcStrides(from.dims());
+        stridedCopy<T>(to, ostrides, from.get(), from.dims(), from.strides(), from.ndims() - 1);
     }
+}
 
-    template<typename inType, typename outType>
-    static void copy(Array<outType> dst, const Array<inType> src, outType default_value, double factor)
-    {
-        dim4 src_dims       = src.dims();
-        dim4 dst_dims       = dst.dims();
-        dim4 src_strides    = src.strides();
-        dim4 dst_strides    = dst.strides();
+template<typename T>
+Array<T> copyArray(const Array<T> &A)
+{
+    Array<T> out = createEmptyArray<T>(A.dims());
+    copyData(out.get(), A);
+    return out;
+}
 
-        const inType * src_ptr = src.get();
-        outType * dst_ptr      = dst.get();
+template<typename inType, typename outType>
+static void copy(Array<outType> dst, const Array<inType> src, outType default_value, double factor)
+{
+    dim4 src_dims       = src.dims();
+    dim4 dst_dims       = dst.dims();
+    dim4 src_strides    = src.strides();
+    dim4 dst_strides    = dst.strides();
 
-        dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
-        dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
-        dim_t trgt_j = std::min(dst_dims[1], src_dims[1]);
-        dim_t trgt_i = std::min(dst_dims[0], src_dims[0]);
+    const inType * src_ptr = src.get();
+    outType * dst_ptr      = dst.get();
 
-        for(dim_t l=0; l<dst_dims[3]; ++l) {
+    dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
+    dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
+    dim_t trgt_j = std::min(dst_dims[1], src_dims[1]);
+    dim_t trgt_i = std::min(dst_dims[0], src_dims[0]);
 
-            dim_t src_loff = l*src_strides[3];
-            dim_t dst_loff = l*dst_strides[3];
-            bool isLvalid = l<trgt_l;
+    for(dim_t l=0; l<dst_dims[3]; ++l) {
 
-            for(dim_t k=0; k<dst_dims[2]; ++k) {
+        dim_t src_loff = l*src_strides[3];
+        dim_t dst_loff = l*dst_strides[3];
+        bool isLvalid = l<trgt_l;
 
-                dim_t src_koff = k*src_strides[2];
-                dim_t dst_koff = k*dst_strides[2];
-                bool isKvalid = k<trgt_k;
+        for(dim_t k=0; k<dst_dims[2]; ++k) {
 
-                for(dim_t j=0; j<dst_dims[1]; ++j) {
+            dim_t src_koff = k*src_strides[2];
+            dim_t dst_koff = k*dst_strides[2];
+            bool isKvalid = k<trgt_k;
 
-                    dim_t src_joff = j*src_strides[1];
-                    dim_t dst_joff = j*dst_strides[1];
-                    bool isJvalid = j<trgt_j;
+            for(dim_t j=0; j<dst_dims[1]; ++j) {
 
-                    for(dim_t i=0; i<dst_dims[0]; ++i) {
-                        outType temp = default_value;
-                        if (isLvalid && isKvalid && isJvalid && i<trgt_i) {
-                            dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
-                            temp = outType(src_ptr[src_idx])*outType(factor);
-                        }
-                        dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
-                        dst_ptr[dst_idx] = temp;
+                dim_t src_joff = j*src_strides[1];
+                dim_t dst_joff = j*dst_strides[1];
+                bool isJvalid = j<trgt_j;
+
+                for(dim_t i=0; i<dst_dims[0]; ++i) {
+                    outType temp = default_value;
+                    if (isLvalid && isKvalid && isJvalid && i<trgt_i) {
+                        dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
+                        temp = outType(src_ptr[src_idx])*outType(factor);
                     }
+                    dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
+                    dst_ptr[dst_idx] = temp;
                 }
             }
         }
     }
+}
 
-    template<typename T>
-    void multiply_inplace(Array<T> &in, double val)
-    {
-        in.eval();
-        getQueue().enqueue(copy<T, T>, in, in, 0, val);
-    }
-
-    template<typename inType, typename outType>
-    Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                            outType default_value, double factor)
-    {
-        Array<outType> ret = createValueArray<outType>(dims, default_value);
-        ret.eval();
-        in.eval();
-        // FIXME:
-        getQueue().sync();
-        getQueue().enqueue(copy<inType, outType>, ret, in, outType(default_value), factor);
-        return ret;
-    }
+template<typename T>
+void multiply_inplace(Array<T> &in, double val)
+{
+    in.eval();
+    getQueue().enqueue(copy<T, T>, in, in, 0, val);
+}
 
-    template<typename inType, typename outType>
-    void copyArray(Array<outType> &out, Array<inType> const &in)
-    {
-        out.eval();
-        in.eval();
-        getQueue().enqueue(copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
-    }
+template<typename inType, typename outType>
+Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                        outType default_value, double factor)
+{
+    Array<outType> ret = createValueArray<outType>(dims, default_value);
+    ret.eval();
+    in.eval();
+    // FIXME:
+    getQueue().sync();
+    getQueue().enqueue(copy<inType, outType>, ret, in, outType(default_value), factor);
+    return ret;
+}
 
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, Array<inType> const &in)
+{
+    out.eval();
+    in.eval();
+    getQueue().enqueue(copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
+}
 
 #define INSTANTIATE(T)                                                  \
     template void      copyData<T> (T *data, const Array<T> &from);     \
     template Array<T>  copyArray<T>(const Array<T> &A);                 \
     template void      multiply_inplace<T> (Array<T> &in, double norm); \
 
-    INSTANTIATE(float  )
-    INSTANTIATE(double )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(char   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+INSTANTIATE(float  )
+INSTANTIATE(double )
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
+INSTANTIATE(int    )
+INSTANTIATE(uint   )
+INSTANTIATE(uchar  )
+INSTANTIATE(char   )
+INSTANTIATE(intl   )
+INSTANTIATE(uintl  )
+INSTANTIATE(short  )
+INSTANTIATE(ushort )
 
 
 #define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
@@ -189,16 +189,16 @@ namespace cpu
     template void copyArray<SRC_T, uchar  >(Array<uchar  > &dst, Array<SRC_T> const &src);  \
     template void copyArray<SRC_T, char   >(Array<char   > &dst, Array<SRC_T> const &src);
 
-    INSTANTIATE_PAD_ARRAY(float )
-    INSTANTIATE_PAD_ARRAY(double)
-    INSTANTIATE_PAD_ARRAY(int   )
-    INSTANTIATE_PAD_ARRAY(uint  )
-    INSTANTIATE_PAD_ARRAY(intl  )
-    INSTANTIATE_PAD_ARRAY(uintl )
-    INSTANTIATE_PAD_ARRAY(uchar )
-    INSTANTIATE_PAD_ARRAY(char  )
-    INSTANTIATE_PAD_ARRAY(ushort)
-    INSTANTIATE_PAD_ARRAY(short )
+INSTANTIATE_PAD_ARRAY(float )
+INSTANTIATE_PAD_ARRAY(double)
+INSTANTIATE_PAD_ARRAY(int   )
+INSTANTIATE_PAD_ARRAY(uint  )
+INSTANTIATE_PAD_ARRAY(intl  )
+INSTANTIATE_PAD_ARRAY(uintl )
+INSTANTIATE_PAD_ARRAY(uchar )
+INSTANTIATE_PAD_ARRAY(char  )
+INSTANTIATE_PAD_ARRAY(ushort)
+INSTANTIATE_PAD_ARRAY(short )
 
 #define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
     template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
@@ -206,8 +206,8 @@ namespace cpu
     template void copyArray<SRC_T, cfloat  >(Array<cfloat  > &dst, Array<SRC_T> const &src);    \
     template void copyArray<SRC_T, cdouble   >(Array<cdouble > &dst, Array<SRC_T> const &src);
 
-    INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
-    INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
+INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
 
 #define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
     template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
@@ -215,25 +215,25 @@ namespace cpu
         CPU_NOT_SUPPORTED();\
     }
 
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , double)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , float)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , uchar)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , char)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , uint)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , int)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , intl)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , uintl)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , short)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat , ushort)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, double)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, float)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uchar)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, char)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uint)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, int)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, intl)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , double)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , float)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , uchar)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , char)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , uint)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , int)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , intl)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , uintl)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , short)
+SPECILIAZE_UNUSED_COPYARRAY(cfloat , ushort)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, double)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, float)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, uchar)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, char)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, uint)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, int)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, intl)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
+SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
 
 }
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 856ed6ed44..9af78459c1 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -20,87 +20,88 @@
 
 namespace cpu
 {
-    template<typename T>
-    Array<T> diagCreate(const Array<T> &in, const int num)
-    {
-        in.eval();
-
-        int size = in.dims()[0] + std::abs(num);
-        int batch = in.dims()[1];
-        Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
-
-        auto func = [=] (Array<T> out, const Array<T> in) {
-            const T *iptr = in.get();
-                  T *optr = out.get();
-
-            for (int k = 0; k < batch; k++) {
-                for (int j = 0; j < size; j++) {
-                    for (int i = 0; i < size; i++) {
-                        T val = scalar<T>(0);
-                        if (i == j - num) {
-                            val = (num > 0) ? iptr[i] : iptr[j];
-                        }
-                        optr[i + j * out.strides()[1]] = val;
+
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num)
+{
+    in.eval();
+
+    int size = in.dims()[0] + std::abs(num);
+    int batch = in.dims()[1];
+    Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
+
+    auto func = [=] (Array<T> out, const Array<T> in) {
+        const T *iptr = in.get();
+              T *optr = out.get();
+
+        for (int k = 0; k < batch; k++) {
+            for (int j = 0; j < size; j++) {
+                for (int i = 0; i < size; i++) {
+                    T val = scalar<T>(0);
+                    if (i == j - num) {
+                        val = (num > 0) ? iptr[i] : iptr[j];
                     }
+                    optr[i + j * out.strides()[1]] = val;
                 }
-                optr += out.strides()[2];
-                iptr += in.strides()[1];
             }
-        };
-        getQueue().enqueue(func, out, in);
+            optr += out.strides()[2];
+            iptr += in.strides()[1];
+        }
+    };
+    getQueue().enqueue(func, out, in);
 
-        return out;
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T> diagExtract(const Array<T> &in, const int num)
-    {
-        in.eval();
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num)
+{
+    in.eval();
 
-        const dim4 idims = in.dims();
-        dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
-        Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
+    const dim4 idims = in.dims();
+    dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+    Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-        auto func = [=] (Array<T> out, const Array<T> in) {
-            const dim4 odims = out.dims();
+    auto func = [=] (Array<T> out, const Array<T> in) {
+        const dim4 odims = out.dims();
 
-            const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
+        const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
 
-            for (int l = 0; l < (int)odims[3]; l++) {
+        for (int l = 0; l < (int)odims[3]; l++) {
 
-                for (int k = 0; k < (int)odims[2]; k++) {
-                    const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
-                    T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
+            for (int k = 0; k < (int)odims[2]; k++) {
+                const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
+                T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
 
-                    for (int i = 0; i < (int)odims[0]; i++) {
-                        T val = scalar<T>(0);
-                        if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
-                        optr[i] = val;
-                    }
+                for (int i = 0; i < (int)odims[0]; i++) {
+                    T val = scalar<T>(0);
+                    if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
+                    optr[i] = val;
                 }
             }
-        };
+        }
+    };
 
-        getQueue().enqueue(func, out, in);
+    getQueue().enqueue(func, out, in);
 
-        return out;
-    }
+    return out;
+}
 
 #define INSTANTIATE_DIAGONAL(T)                                          \
     template Array<T>  diagExtract<T>    (const Array<T> &in, const int num); \
     template Array<T>  diagCreate <T>    (const Array<T> &in, const int num);
 
-    INSTANTIATE_DIAGONAL(float)
-    INSTANTIATE_DIAGONAL(double)
-    INSTANTIATE_DIAGONAL(cfloat)
-    INSTANTIATE_DIAGONAL(cdouble)
-    INSTANTIATE_DIAGONAL(int)
-    INSTANTIATE_DIAGONAL(uint)
-    INSTANTIATE_DIAGONAL(intl)
-    INSTANTIATE_DIAGONAL(uintl)
-    INSTANTIATE_DIAGONAL(char)
-    INSTANTIATE_DIAGONAL(uchar)
-    INSTANTIATE_DIAGONAL(short)
-    INSTANTIATE_DIAGONAL(ushort)
+INSTANTIATE_DIAGONAL(float)
+INSTANTIATE_DIAGONAL(double)
+INSTANTIATE_DIAGONAL(cfloat)
+INSTANTIATE_DIAGONAL(cdouble)
+INSTANTIATE_DIAGONAL(int)
+INSTANTIATE_DIAGONAL(uint)
+INSTANTIATE_DIAGONAL(intl)
+INSTANTIATE_DIAGONAL(uintl)
+INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(uchar)
+INSTANTIATE_DIAGONAL(short)
+INSTANTIATE_DIAGONAL(ushort)
 
 }
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 321dae7b85..8f9c0f13be 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -16,119 +16,122 @@
 
 namespace cpu
 {
-    unsigned getIdx(af::dim4 strides, af::dim4 offs, int i, int j = 0, int k = 0, int l = 0)
-    {
-        return (l * strides[3] +
-                k * strides[2] +
-                j * strides[1] +
-                i);
-    }
-
-    template<typename T>
-    Array<T>  diff1(const Array<T> &in, const int dim)
-    {
-        // Bool for dimension
-        bool is_dim0 = dim == 0;
-        bool is_dim1 = dim == 1;
-        bool is_dim2 = dim == 2;
-        bool is_dim3 = dim == 3;
-
-        // Decrement dimension of select dimension
-        af::dim4 dims = in.dims();
-        dims[dim]--;
-
-        // Create output placeholder
-        Array<T> outArray = createEmptyArray<T>(dims);
-
-        auto func = [=] (Array<T> outArray, Array<T> in) {
-            // Get pointers to raw data
-            const T *inPtr = in.get();
-                  T *outPtr = outArray.get();
-
-            // TODO: Improve this
-            for(dim_t l = 0; l < dims[3]; l++) {
-                for(dim_t k = 0; k < dims[2]; k++) {
-                    for(dim_t j = 0; j < dims[1]; j++) {
-                        for(dim_t i = 0; i < dims[0]; i++) {
-                            // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                            int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                            int jdx = getIdx(in.strides(), in.offsets(),
-                                            i + is_dim0, j + is_dim1,
-                                            k + is_dim2, l + is_dim3);
-                            int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
-                            outPtr[odx] = inPtr[jdx] - inPtr[idx];
-                        }
+
+unsigned getIdx(af::dim4 strides, af::dim4 offs, int i, int j = 0, int k = 0, int l = 0)
+{
+    return (l * strides[3] +
+            k * strides[2] +
+            j * strides[1] +
+            i);
+}
+
+template<typename T>
+Array<T>  diff1(const Array<T> &in, const int dim)
+{
+    in.eval();
+    // Bool for dimension
+    bool is_dim0 = dim == 0;
+    bool is_dim1 = dim == 1;
+    bool is_dim2 = dim == 2;
+    bool is_dim3 = dim == 3;
+
+    // Decrement dimension of select dimension
+    af::dim4 dims = in.dims();
+    dims[dim]--;
+
+    // Create output placeholder
+    Array<T> outArray = createEmptyArray<T>(dims);
+
+    auto func = [=] (Array<T> outArray, Array<T> in) {
+        // Get pointers to raw data
+        const T *inPtr = in.get();
+              T *outPtr = outArray.get();
+
+        // TODO: Improve this
+        for(dim_t l = 0; l < dims[3]; l++) {
+            for(dim_t k = 0; k < dims[2]; k++) {
+                for(dim_t j = 0; j < dims[1]; j++) {
+                    for(dim_t i = 0; i < dims[0]; i++) {
+                        // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+                        int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
+                        int jdx = getIdx(in.strides(), in.offsets(),
+                                        i + is_dim0, j + is_dim1,
+                                        k + is_dim2, l + is_dim3);
+                        int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
+                        outPtr[odx] = inPtr[jdx] - inPtr[idx];
                     }
                 }
             }
-        };
-        getQueue().enqueue(func, outArray, in);
-
-        return outArray;
-    }
-
-    template<typename T>
-    Array<T>  diff2(const Array<T> &in, const int dim)
-    {
-        // Bool for dimension
-        bool is_dim0 = dim == 0;
-        bool is_dim1 = dim == 1;
-        bool is_dim2 = dim == 2;
-        bool is_dim3 = dim == 3;
-
-        // Decrement dimension of select dimension
-        af::dim4 dims = in.dims();
-        dims[dim] -= 2;
-
-        // Create output placeholder
-        Array<T> outArray = createEmptyArray<T>(dims);
-
-        auto func = [=] (Array<T> outArray, Array<T> in) {
-            // Get pointers to raw data
-            const T *inPtr = in.get();
-                T *outPtr = outArray.get();
-
-            // TODO: Improve this
-            for(dim_t l = 0; l < dims[3]; l++) {
-                for(dim_t k = 0; k < dims[2]; k++) {
-                    for(dim_t j = 0; j < dims[1]; j++) {
-                        for(dim_t i = 0; i < dims[0]; i++) {
-                            // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                            int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                            int jdx = getIdx(in.strides(), in.offsets(),
-                                            i + is_dim0, j + is_dim1,
-                                            k + is_dim2, l + is_dim3);
-                            int kdx = getIdx(in.strides(), in.offsets(),
-                                            i + 2 * is_dim0, j + 2 * is_dim1,
-                                            k + 2 * is_dim2, l + 2 * is_dim3);
-                            int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
-                            outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
-                        }
+        }
+    };
+    getQueue().enqueue(func, outArray, in);
+
+    return outArray;
+}
+
+template<typename T>
+Array<T>  diff2(const Array<T> &in, const int dim)
+{
+    in.eval();
+    // Bool for dimension
+    bool is_dim0 = dim == 0;
+    bool is_dim1 = dim == 1;
+    bool is_dim2 = dim == 2;
+    bool is_dim3 = dim == 3;
+
+    // Decrement dimension of select dimension
+    af::dim4 dims = in.dims();
+    dims[dim] -= 2;
+
+    // Create output placeholder
+    Array<T> outArray = createEmptyArray<T>(dims);
+
+    auto func = [=] (Array<T> outArray, Array<T> in) {
+        // Get pointers to raw data
+        const T *inPtr = in.get();
+            T *outPtr = outArray.get();
+
+        // TODO: Improve this
+        for(dim_t l = 0; l < dims[3]; l++) {
+            for(dim_t k = 0; k < dims[2]; k++) {
+                for(dim_t j = 0; j < dims[1]; j++) {
+                    for(dim_t i = 0; i < dims[0]; i++) {
+                        // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+                        int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
+                        int jdx = getIdx(in.strides(), in.offsets(),
+                                        i + is_dim0, j + is_dim1,
+                                        k + is_dim2, l + is_dim3);
+                        int kdx = getIdx(in.strides(), in.offsets(),
+                                        i + 2 * is_dim0, j + 2 * is_dim1,
+                                        k + 2 * is_dim2, l + 2 * is_dim3);
+                        int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
+                        outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
                     }
                 }
             }
-        };
+        }
+    };
 
-        getQueue().enqueue(func, outArray, in);
+    getQueue().enqueue(func, outArray, in);
 
-        return outArray;
-    }
+    return outArray;
+}
 
 #define INSTANTIATE(T)                                                  \
     template Array<T>  diff1<T>  (const Array<T> &in, const int dim);   \
     template Array<T>  diff2<T>  (const Array<T> &in, const int dim);   \
 
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(ushort)
-    INSTANTIATE(short)
 }
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index a9e7bca9eb..d45b8a28ec 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -24,6 +24,13 @@ namespace cpu
 template<typename T>
 Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
 {
+    in.eval();                          // All input Arrays should call eval mandatorily
+                                        // in CPU backend function implementations. Since
+                                        // the cpu fns are asynchronous launches, any Arrays
+                                        // that are either views/JIT nodes needs to evaluated
+                                        // before they are passed onto functions that are
+                                        // enqueued onto the queues.
+
     dim4 outputDims;                    // this should be '= in.dims();' in most cases
                                         // but would definitely depend on the type of
                                         // algorithm you are implementing.
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 7262e6dd78..e522954cfe 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -95,6 +95,7 @@ void fft_inplace_(Array<T> in)
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in)
 {
+    in.eval();
     getQueue().enqueue(fft_inplace_<T, rank, direction>, in);
 }
 
@@ -165,6 +166,8 @@ void fft_r2c_(Array<Tc> out, const Array<Tr> in)
 template<typename Tc, typename Tr, int rank>
 Array<Tc> fft_r2c(const Array<Tr> &in)
 {
+    in.eval();
+
     dim4 odims = in.dims();
     odims[0] = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
@@ -216,6 +219,8 @@ void fft_c2r_(Array<Tr> out, const Array<Tc> in, const dim4 odims)
 template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
 {
+    in.eval();
+
     Array<Tr> out = createEmptyArray<Tr>(odims);
     getQueue().enqueue(fft_c2r_<Tr, Tc, rank>, out, in, odims);
 
@@ -243,4 +248,5 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
 
     INSTANTIATE_REAL(float , cfloat )
     INSTANTIATE_REAL(double, cdouble)
+
 }
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index 504c02a29c..06c15cff4e 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -101,4 +101,5 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
+
 }
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index f7236bd68d..55c441755c 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -18,6 +18,7 @@
 
 namespace cpu
 {
+
 template<typename T>
 Array<T> identity(const dim4& dims)
 {
@@ -56,4 +57,5 @@ INSTANTIATE_IDENTITY(char)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
+
 }
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 170b6a1570..dcb85fa787 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -21,6 +21,7 @@ using namespace std;
 
 namespace cpu
 {
+
 ///////////////////////////////////////////////////////////////////////////
 // Kernel Functions
 ///////////////////////////////////////////////////////////////////////////
@@ -76,4 +77,5 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+
 }
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index e562bae068..9858cba665 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -21,178 +21,180 @@ using af::dim4;
 
 namespace cpu
 {
-    template<typename T> double cabs(const T in) { return (double)in; }
-    static double cabs(const char in) { return (double)(in > 0); }
-    static double cabs(const cfloat &in) { return (double)abs(in); }
-    static double cabs(const cdouble &in) { return (double)abs(in); }
 
-    template<af_op_t op, typename T>
-    struct MinMaxOp
+template<typename T> double cabs(const T in) { return (double)in; }
+static double cabs(const char in) { return (double)(in > 0); }
+static double cabs(const cfloat &in) { return (double)abs(in); }
+static double cabs(const cdouble &in) { return (double)abs(in); }
+
+template<af_op_t op, typename T>
+struct MinMaxOp
+{
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) :
+        m_val(val), m_idx(idx)
     {
-        T m_val;
-        uint m_idx;
-        MinMaxOp(T val, uint idx) :
-            m_val(val), m_idx(idx)
-        {
-        }
+    }
 
-        void operator()(T val, uint idx)
-        {
-            if (cabs(val) < cabs(m_val) ||
-                (cabs(val) == cabs(m_val) &&
-                 idx > m_idx)) {
-                m_val = val;
-                m_idx = idx;
-            }
+    void operator()(T val, uint idx)
+    {
+        if (cabs(val) < cabs(m_val) ||
+            (cabs(val) == cabs(m_val) &&
+             idx > m_idx)) {
+            m_val = val;
+            m_idx = idx;
         }
-    };
+    }
+};
 
-    template<typename T>
-    struct MinMaxOp<af_max_t, T>
+template<typename T>
+struct MinMaxOp<af_max_t, T>
+{
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) :
+        m_val(val), m_idx(idx)
     {
-        T m_val;
-        uint m_idx;
-        MinMaxOp(T val, uint idx) :
-            m_val(val), m_idx(idx)
-        {
-        }
+    }
 
-        void operator()(T val, uint idx)
-        {
-            if (cabs(val) > cabs(m_val) ||
-                (cabs(val) == cabs(m_val) &&
-                 idx <= m_idx)) {
-                m_val = val;
-                m_idx = idx;
-            }
+    void operator()(T val, uint idx)
+    {
+        if (cabs(val) > cabs(m_val) ||
+            (cabs(val) == cabs(m_val) &&
+             idx <= m_idx)) {
+            m_val = val;
+            m_idx = idx;
         }
-    };
+    }
+};
 
-    template<af_op_t op, typename T, int D>
-    struct ireduce_dim
+template<af_op_t op, typename T, int D>
+struct ireduce_dim
+{
+    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
+                    const Array<T> input, const dim_t inOffset, const int dim)
     {
-        void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
-                        const Array<T> input, const dim_t inOffset, const int dim)
-        {
-            const dim4 odims    = output.dims();
-            const dim4 ostrides = output.strides();
-            const dim4 istrides = input.strides();
-            const int D1 = D - 1;
-            for (dim_t i = 0; i < odims[D1]; i++) {
-                ireduce_dim<op, T, D1>()(output, locArray, outOffset + i * ostrides[D1],
-                                         input, inOffset + i * istrides[D1], dim);
-            }
+        const dim4 odims    = output.dims();
+        const dim4 ostrides = output.strides();
+        const dim4 istrides = input.strides();
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            ireduce_dim<op, T, D1>()(output, locArray, outOffset + i * ostrides[D1],
+                                     input, inOffset + i * istrides[D1], dim);
         }
-    };
+    }
+};
 
-    template<af_op_t op, typename T>
-    struct ireduce_dim<op, T, 0>
+template<af_op_t op, typename T>
+struct ireduce_dim<op, T, 0>
+{
+    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
+                    const Array<T> input, const dim_t inOffset, const int dim)
     {
-        void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
-                        const Array<T> input, const dim_t inOffset, const int dim)
-        {
-            const dim4 idims = input.dims();
-            const dim4 istrides = input.strides();
-
-            T const * const in = input.get();
-            T * out = output.get();
-            uint * loc = locArray.get();
-
-            dim_t stride = istrides[dim];
-            MinMaxOp<op, T> Op(in[0], 0);
-            for (dim_t i = 0; i < idims[dim]; i++) {
-                Op(in[inOffset + i * stride], i);
-            }
+        const dim4 idims = input.dims();
+        const dim4 istrides = input.strides();
 
-            *(out+outOffset) = Op.m_val;
-            *(loc+outOffset) = Op.m_idx;
-        }
-    };
+        T const * const in = input.get();
+        T * out = output.get();
+        uint * loc = locArray.get();
 
-    template<af_op_t op, typename T>
-    using ireduce_dim_func = std::function<void(Array<T>, Array<uint>, const dim_t,
-                                                const Array<T>, const dim_t, const int)>;
+        dim_t stride = istrides[dim];
+        MinMaxOp<op, T> Op(in[0], 0);
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            Op(in[inOffset + i * stride], i);
+        }
 
-    template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
-    {
-        out.eval();
-        loc.eval();
-        in.eval();
-
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        static const ireduce_dim_func<op, T> ireduce_funcs[] = { ireduce_dim<op, T, 1>()
-                                                               , ireduce_dim<op, T, 2>()
-                                                               , ireduce_dim<op, T, 3>()
-                                                               , ireduce_dim<op, T, 4>()};
-
-        getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
+        *(out+outOffset) = Op.m_val;
+        *(loc+outOffset) = Op.m_idx;
     }
+};
 
-    template<af_op_t op, typename T>
-    T ireduce_all(unsigned *loc, const Array<T> &in)
-    {
-        in.eval();
-        getQueue().sync();
+template<af_op_t op, typename T>
+using ireduce_dim_func = std::function<void(Array<T>, Array<uint>, const dim_t,
+                                            const Array<T>, const dim_t, const int)>;
 
-        af::dim4 dims = in.dims();
-        af::dim4 strides = in.strides();
-        const T *inPtr = in.get();
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
+{
+    out.eval();
+    loc.eval();
+    in.eval();
+
+    dim4 odims = in.dims();
+    odims[dim] = 1;
+    static const ireduce_dim_func<op, T> ireduce_funcs[] = { ireduce_dim<op, T, 1>()
+                                                           , ireduce_dim<op, T, 2>()
+                                                           , ireduce_dim<op, T, 3>()
+                                                           , ireduce_dim<op, T, 4>()};
+
+    getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
+}
 
-        MinMaxOp<op, T> Op(inPtr[0], 0);
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in)
+{
+    in.eval();
+    getQueue().sync();
+
+    af::dim4 dims = in.dims();
+    af::dim4 strides = in.strides();
+    const T *inPtr = in.get();
 
-        for(dim_t l = 0; l < dims[3]; l++) {
-            dim_t off3 = l * strides[3];
+    MinMaxOp<op, T> Op(inPtr[0], 0);
 
-            for(dim_t k = 0; k < dims[2]; k++) {
-                dim_t off2 = k * strides[2];
+    for(dim_t l = 0; l < dims[3]; l++) {
+        dim_t off3 = l * strides[3];
 
-                for(dim_t j = 0; j < dims[1]; j++) {
-                    dim_t off1 = j * strides[1];
+        for(dim_t k = 0; k < dims[2]; k++) {
+            dim_t off2 = k * strides[2];
 
-                    for(dim_t i = 0; i < dims[0]; i++) {
-                        dim_t idx = i + off1 + off2 + off3;
-                        Op(inPtr[idx], idx);
-                    }
+            for(dim_t j = 0; j < dims[1]; j++) {
+                dim_t off1 = j * strides[1];
+
+                for(dim_t i = 0; i < dims[0]; i++) {
+                    dim_t idx = i + off1 + off2 + off3;
+                    Op(inPtr[idx], idx);
                 }
             }
         }
-
-        *loc = Op.m_idx;
-        return Op.m_val;
     }
 
+    *loc = Op.m_idx;
+    return Op.m_val;
+}
+
 #define INSTANTIATE(ROp, T)                                             \
     template void ireduce<ROp, T>(Array<T> &out, Array<uint> &loc,      \
                                   const Array<T> &in, const int dim);   \
     template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);  \
 
-    //min
-    INSTANTIATE(af_min_t, float  )
-    INSTANTIATE(af_min_t, double )
-    INSTANTIATE(af_min_t, cfloat )
-    INSTANTIATE(af_min_t, cdouble)
-    INSTANTIATE(af_min_t, int    )
-    INSTANTIATE(af_min_t, uint   )
-    INSTANTIATE(af_min_t, intl   )
-    INSTANTIATE(af_min_t, uintl  )
-    INSTANTIATE(af_min_t, char   )
-    INSTANTIATE(af_min_t, uchar  )
-    INSTANTIATE(af_min_t, short  )
-    INSTANTIATE(af_min_t, ushort )
-
-    //max
-    INSTANTIATE(af_max_t, float  )
-    INSTANTIATE(af_max_t, double )
-    INSTANTIATE(af_max_t, cfloat )
-    INSTANTIATE(af_max_t, cdouble)
-    INSTANTIATE(af_max_t, int    )
-    INSTANTIATE(af_max_t, uint   )
-    INSTANTIATE(af_max_t, intl   )
-    INSTANTIATE(af_max_t, uintl  )
-    INSTANTIATE(af_max_t, char   )
-    INSTANTIATE(af_max_t, uchar  )
-    INSTANTIATE(af_max_t, short  )
-    INSTANTIATE(af_max_t, ushort )
+//min
+INSTANTIATE(af_min_t, float  )
+INSTANTIATE(af_min_t, double )
+INSTANTIATE(af_min_t, cfloat )
+INSTANTIATE(af_min_t, cdouble)
+INSTANTIATE(af_min_t, int    )
+INSTANTIATE(af_min_t, uint   )
+INSTANTIATE(af_min_t, intl   )
+INSTANTIATE(af_min_t, uintl  )
+INSTANTIATE(af_min_t, char   )
+INSTANTIATE(af_min_t, uchar  )
+INSTANTIATE(af_min_t, short  )
+INSTANTIATE(af_min_t, ushort )
+
+//max
+INSTANTIATE(af_max_t, float  )
+INSTANTIATE(af_max_t, double )
+INSTANTIATE(af_max_t, cfloat )
+INSTANTIATE(af_max_t, cdouble)
+INSTANTIATE(af_max_t, int    )
+INSTANTIATE(af_max_t, uint   )
+INSTANTIATE(af_max_t, intl   )
+INSTANTIATE(af_max_t, uintl  )
+INSTANTIATE(af_max_t, char   )
+INSTANTIATE(af_max_t, uchar  )
+INSTANTIATE(af_max_t, short  )
+INSTANTIATE(af_max_t, ushort )
+
 }
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index ff0be438ee..9a046139d4 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -104,6 +104,8 @@ void convertPivot(Array<int> p, Array<int> pivot)
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 {
+    in.eval();
+
     dim4 iDims = in.dims();
     int M = iDims[0];
     int N = iDims[1];
@@ -123,6 +125,8 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 {
+    in.eval();
+
     dim4 iDims = in.dims();
     Array<int> pivot = createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
 
@@ -166,6 +170,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 
 namespace cpu
 {
+
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
@@ -174,4 +179,5 @@ INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
+
 }
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 02a4888864..d4ce95a691 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -24,6 +24,9 @@ namespace cpu
 template<typename inType, typename outType, af_match_type mType>
 Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg)
 {
+    sImg.eval();
+    tImg.eval();
+
     Array<outType> out = createEmptyArray<outType>(sImg.dims());
 
     auto func = [=](Array<outType> out, const Array<inType> sImg, const Array<inType> tImg) {
diff --git a/src/backend/cpu/math.cpp b/src/backend/cpu/math.cpp
index 5a6bcbc67e..e00fd78fcd 100644
--- a/src/backend/cpu/math.cpp
+++ b/src/backend/cpu/math.cpp
@@ -11,39 +11,41 @@
 
 namespace cpu
 {
-    uint abs(uint val) { return val; }
-    uchar abs(uchar val) { return val; }
-    uintl abs(uintl val) { return val; }
-
-    cfloat  scalar(float val)
-    {
-        cfloat  cval = {(float)val, 0};
-        return cval;
-    }
-
-    cdouble scalar(double val)
-    {
-        cdouble  cval = {val, 0};
-        return cval;
-    }
-
-    cfloat min(cfloat lhs, cfloat rhs)
-    {
-        return abs(lhs) < abs(rhs) ? lhs : rhs;
-    }
-
-    cdouble min(cdouble lhs, cdouble rhs)
-    {
-        return abs(lhs) < abs(rhs) ? lhs : rhs;
-    }
-
-    cfloat max(cfloat lhs, cfloat rhs)
-    {
-        return abs(lhs) > abs(rhs) ? lhs : rhs;
-    }
-
-    cdouble max(cdouble lhs, cdouble rhs)
-    {
-        return abs(lhs) > abs(rhs) ? lhs : rhs;
-    }
+
+uint abs(uint val) { return val; }
+uchar abs(uchar val) { return val; }
+uintl abs(uintl val) { return val; }
+
+cfloat  scalar(float val)
+{
+    cfloat  cval = {(float)val, 0};
+    return cval;
+}
+
+cdouble scalar(double val)
+{
+    cdouble  cval = {val, 0};
+    return cval;
+}
+
+cfloat min(cfloat lhs, cfloat rhs)
+{
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+cdouble min(cdouble lhs, cdouble rhs)
+{
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+cfloat max(cfloat lhs, cfloat rhs)
+{
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+cdouble max(cdouble lhs, cdouble rhs)
+{
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
 }
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index 3f99d15b0c..62b80e010e 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -33,6 +33,8 @@ inline dim_t clamp(dim_t a, dim_t mn, dim_t mx)
 template<typename T, bool is_color>
 Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
 {
+    in.eval();
+
     Array<T> out = createEmptyArray<T>(in.dims());
 
     auto func = [=] (Array<T> out, const Array<T> in, const float s_sigma,
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index ce921fc3b5..4e74a55fd2 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -25,6 +25,8 @@ namespace cpu
 template<typename T, af_border_type pad>
 Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
+    in.eval();
+
     Array<T> out        = createEmptyArray<T>(in.dims());
 
     auto func = [=] (Array<T> out, const Array<T> in,
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 73120b9171..e11f994eef 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -20,211 +20,211 @@
 namespace cpu
 {
 
-    static size_t memory_resolution = 1024; //1KB
+static size_t memory_resolution = 1024; //1KB
 
-    void setMemStepSize(size_t step_bytes)
-    {
-        memory_resolution = step_bytes;
-    }
+void setMemStepSize(size_t step_bytes)
+{
+    memory_resolution = step_bytes;
+}
+
+size_t getMemStepSize(void)
+{
+    return memory_resolution;
+}
 
-    size_t getMemStepSize(void)
+class Manager
+{
+    public:
+    static bool initialized;
+    Manager()
     {
-        return memory_resolution;
+        initialized = true;
     }
 
-    class Manager
+    ~Manager()
     {
-        public:
-        static bool initialized;
-        Manager()
-        {
-            initialized = true;
-        }
-
-        ~Manager()
-        {
-            garbageCollect();
-        }
-    };
+        garbageCollect();
+    }
+};
 
-    bool Manager::initialized = false;
+bool Manager::initialized = false;
 
-    static void managerInit()
-    {
-        if(Manager::initialized == false)
-            static Manager pm = Manager();
-    }
+static void managerInit()
+{
+    if(Manager::initialized == false)
+        static Manager pm = Manager();
+}
 
-    typedef struct
-    {
-        bool is_free;
-        bool is_unlinked;
-        size_t bytes;
-    } mem_info;
-
-    static size_t used_bytes = 0;
-    static size_t used_buffers = 0;
-    static size_t total_bytes = 0;
-    typedef std::map<void *, mem_info> mem_t;
-    typedef mem_t::iterator mem_iter;
-
-    mem_t memory_map;
-    std::mutex memory_map_mutex;
-
-    template<typename T>
-    void freeWrapper(T *ptr)
-    {
-        free((void *)ptr);
-    }
+typedef struct
+{
+    bool is_free;
+    bool is_unlinked;
+    size_t bytes;
+} mem_info;
+
+static size_t used_bytes = 0;
+static size_t used_buffers = 0;
+static size_t total_bytes = 0;
+typedef std::map<void *, mem_info> mem_t;
+typedef mem_t::iterator mem_iter;
+
+mem_t memory_map;
+std::mutex memory_map_mutex;
+
+template<typename T>
+void freeWrapper(T *ptr)
+{
+    free((void *)ptr);
+}
 
-    void garbageCollect()
-    {
-        for(mem_iter iter = memory_map.begin();
-            iter != memory_map.end(); ++iter) {
+void garbageCollect()
+{
+    for(mem_iter iter = memory_map.begin();
+        iter != memory_map.end(); ++iter) {
 
-            if ((iter->second).is_free) {
+        if ((iter->second).is_free) {
 
-                if (!(iter->second).is_unlinked) {
-                    freeWrapper(iter->first);
-                    total_bytes -= iter->second.bytes;
-                }
+            if (!(iter->second).is_unlinked) {
+                freeWrapper(iter->first);
+                total_bytes -= iter->second.bytes;
             }
         }
+    }
 
-        mem_iter memory_curr = memory_map.begin();
-        mem_iter memory_end  = memory_map.end();
+    mem_iter memory_curr = memory_map.begin();
+    mem_iter memory_end  = memory_map.end();
 
-        while(memory_curr != memory_end) {
-            if (memory_curr->second.is_free && !memory_curr->second.is_unlinked) {
-                memory_map.erase(memory_curr++);
-            } else {
-                ++memory_curr;
-            }
+    while(memory_curr != memory_end) {
+        if (memory_curr->second.is_free && !memory_curr->second.is_unlinked) {
+            memory_map.erase(memory_curr++);
+        } else {
+            ++memory_curr;
         }
     }
+}
 
-    template<typename T>
-    T* memAlloc(const size_t &elements)
-    {
-        managerInit();
+template<typename T>
+T* memAlloc(const size_t &elements)
+{
+    managerInit();
 
-        T* ptr = NULL;
-        size_t alloc_bytes = divup(sizeof(T) * elements, memory_resolution) * memory_resolution;
+    T* ptr = NULL;
+    size_t alloc_bytes = divup(sizeof(T) * elements, memory_resolution) * memory_resolution;
 
-        if (elements > 0) {
-            std::lock_guard<std::mutex> lock(memory_map_mutex);
+    if (elements > 0) {
+        std::lock_guard<std::mutex> lock(memory_map_mutex);
 
-            // FIXME: Add better checks for garbage collection
-            // Perhaps look at total memory available as a metric
-            if (memory_map.size() > MAX_BUFFERS ||
-                used_bytes >= MAX_BYTES) {
+        // FIXME: Add better checks for garbage collection
+        // Perhaps look at total memory available as a metric
+        if (memory_map.size() > MAX_BUFFERS ||
+            used_bytes >= MAX_BYTES) {
 
-                garbageCollect();
-            }
+            garbageCollect();
+        }
 
-            for(mem_iter iter = memory_map.begin();
-                iter != memory_map.end(); ++iter) {
+        for(mem_iter iter = memory_map.begin();
+            iter != memory_map.end(); ++iter) {
 
-                mem_info info = iter->second;
+            mem_info info = iter->second;
 
-                if ( info.is_free &&
-                    !info.is_unlinked &&
-                     info.bytes == alloc_bytes) {
+            if ( info.is_free &&
+                !info.is_unlinked &&
+                 info.bytes == alloc_bytes) {
 
-                    iter->second.is_free = false;
-                    used_bytes += alloc_bytes;
-                    used_buffers++;
-                    return (T *)iter->first;
-                }
+                iter->second.is_free = false;
+                used_bytes += alloc_bytes;
+                used_buffers++;
+                return (T *)iter->first;
             }
+        }
 
-            // Perform garbage collection if memory can not be allocated
-            ptr = (T *)malloc(alloc_bytes);
+        // Perform garbage collection if memory can not be allocated
+        ptr = (T *)malloc(alloc_bytes);
 
-            if (ptr == NULL) {
-                AF_ERROR("Can not allocate memory", AF_ERR_NO_MEM);
-            }
+        if (ptr == NULL) {
+            AF_ERROR("Can not allocate memory", AF_ERR_NO_MEM);
+        }
 
-            mem_info info = {false, false, alloc_bytes};
-            memory_map[ptr] = info;
+        mem_info info = {false, false, alloc_bytes};
+        memory_map[ptr] = info;
 
-            used_bytes += alloc_bytes;
-            used_buffers++;
-            total_bytes += alloc_bytes;
-        }
-        return ptr;
+        used_bytes += alloc_bytes;
+        used_buffers++;
+        total_bytes += alloc_bytes;
     }
+    return ptr;
+}
 
-    template<typename T>
-    void memFree(T *ptr)
-    {
-        std::lock_guard<std::mutex> lock(memory_map_mutex);
+template<typename T>
+void memFree(T *ptr)
+{
+    std::lock_guard<std::mutex> lock(memory_map_mutex);
 
-        mem_iter iter = memory_map.find((void *)ptr);
+    mem_iter iter = memory_map.find((void *)ptr);
 
-        if (iter != memory_map.end()) {
+    if (iter != memory_map.end()) {
 
-            iter->second.is_free = true;
-            if ((iter->second).is_unlinked) return;
+        iter->second.is_free = true;
+        if ((iter->second).is_unlinked) return;
 
-            used_bytes -= iter->second.bytes;
-            used_buffers--;
+        used_bytes -= iter->second.bytes;
+        used_buffers--;
 
-        } else {
-            freeWrapper(ptr); // Free it because we are not sure what the size is
-        }
+    } else {
+        freeWrapper(ptr); // Free it because we are not sure what the size is
     }
+}
 
-    template<typename T>
-    void memPop(const T *ptr)
-    {
-        std::lock_guard<std::mutex> lock(memory_map_mutex);
+template<typename T>
+void memPop(const T *ptr)
+{
+    std::lock_guard<std::mutex> lock(memory_map_mutex);
 
-        mem_iter iter = memory_map.find((void *)ptr);
+    mem_iter iter = memory_map.find((void *)ptr);
 
-        if (iter != memory_map.end()) {
-            iter->second.is_unlinked = true;
-        } else {
-            mem_info info = { false,
-                              true,
-                              100 }; //This number is not relevant
+    if (iter != memory_map.end()) {
+        iter->second.is_unlinked = true;
+    } else {
+        mem_info info = { false,
+                          true,
+                          100 }; //This number is not relevant
 
-            memory_map[(void *)ptr] = info;
-        }
+        memory_map[(void *)ptr] = info;
     }
+}
 
-    template<typename T>
-    void memPush(const T *ptr)
-    {
-        std::lock_guard<std::mutex> lock(memory_map_mutex);
-        mem_iter iter = memory_map.find((void *)ptr);
-        if (iter != memory_map.end()) {
-            iter->second.is_unlinked = false;
-        }
+template<typename T>
+void memPush(const T *ptr)
+{
+    std::lock_guard<std::mutex> lock(memory_map_mutex);
+    mem_iter iter = memory_map.find((void *)ptr);
+    if (iter != memory_map.end()) {
+        iter->second.is_unlinked = false;
     }
+}
 
 
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers)
-    {
-        getQueue().sync();
-        if (alloc_bytes   ) *alloc_bytes   = total_bytes;
-        if (alloc_buffers ) *alloc_buffers = memory_map.size();
-        if (lock_bytes    ) *lock_bytes    = used_bytes;
-        if (lock_buffers  ) *lock_buffers  = used_buffers;
-    }
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes,  size_t *lock_buffers)
+{
+    getQueue().sync();
+    if (alloc_bytes   ) *alloc_bytes   = total_bytes;
+    if (alloc_buffers ) *alloc_buffers = memory_map.size();
+    if (lock_bytes    ) *lock_bytes    = used_bytes;
+    if (lock_buffers  ) *lock_buffers  = used_buffers;
+}
 
-    template<typename T>
-    T* pinnedAlloc(const size_t &elements)
-    {
-        return memAlloc<T>(elements);
-    }
+template<typename T>
+T* pinnedAlloc(const size_t &elements)
+{
+    return memAlloc<T>(elements);
+}
 
-    template<typename T>
-    void pinnedFree(T* ptr)
-    {
-        memFree<T>(ptr);
-    }
+template<typename T>
+void pinnedFree(T* ptr)
+{
+    memFree<T>(ptr);
+}
 
 #define INSTANTIATE(T)                                  \
     template T* memAlloc(const size_t &elements);       \
@@ -234,16 +234,17 @@ namespace cpu
     template T* pinnedAlloc(const size_t &elements);    \
     template void pinnedFree(T* ptr);                   \
 
-    INSTANTIATE(float)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(double)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(ushort)
-    INSTANTIATE(short )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(ushort)
+INSTANTIATE(short )
+
 }
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 97f0e0a8f0..b6f50c2e32 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -163,8 +163,6 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
 
     idx  = createEmptyArray<uint>(outDims);
     dist = createEmptyArray<To  >(outDims);
-    idx.eval();
-    dist.eval();
 
     switch(dist_type) {
         case AF_SAD:
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index b5f18064f5..78631fccfa 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -59,6 +59,8 @@ GQR_FUNC(gqr , cdouble, zungqr)
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 {
+    in.eval();
+
     dim4 iDims = in.dims();
     int M      = iDims[0];
     int N      = iDims[1];
@@ -83,6 +85,8 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 template<typename T>
 Array<T> qr_inplace(Array<T> &in)
 {
+    in.eval();
+
     dim4 iDims = in.dims();
     int M      = iDims[0];
     int N      = iDims[1];
@@ -121,6 +125,7 @@ Array<T> qr_inplace(Array<T> &in)
 
 namespace cpu
 {
+
 #define INSTANTIATE_QR(T)                                                                           \
     template Array<T> qr_inplace<T>(Array<T> &in);                                                \
     template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
@@ -129,4 +134,5 @@ INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
+
 }
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index 1fa46b2e89..7837db51ff 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -19,6 +19,7 @@
 
 namespace cpu
 {
+
 ///////////////////////////////////////////////////////////////////////////
 // Kernel Functions
 ///////////////////////////////////////////////////////////////////////////
@@ -90,4 +91,5 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+
 }
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index afe562001d..1ad7dad6dc 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -16,70 +16,70 @@
 
 namespace cpu
 {
-    template<typename T>
-    void reorder_(Array<T> out, const Array<T> in, const af::dim4 oDims, const af::dim4 rdims)
-    {
-        T* outPtr = out.get();
-        const T* inPtr = in.get();
 
-        const af::dim4 ist = in.strides();
-        const af::dim4 ost = out.strides();
+template<typename T>
+void reorder_(Array<T> out, const Array<T> in, const af::dim4 oDims, const af::dim4 rdims)
+{
+    T* outPtr = out.get();
+    const T* inPtr = in.get();
+
+    const af::dim4 ist = in.strides();
+    const af::dim4 ost = out.strides();
 
 
-        dim_t ids[4]  = {0};
-        for(dim_t ow = 0; ow < oDims[3]; ow++) {
-            const dim_t oW = ow * ost[3];
-            ids[rdims[3]] = ow;
-            for(dim_t oz = 0; oz < oDims[2]; oz++) {
-                const dim_t oZW = oW + oz * ost[2];
-                ids[rdims[2]] = oz;
-                for(dim_t oy = 0; oy < oDims[1]; oy++) {
-                    const dim_t oYZW = oZW + oy * ost[1];
-                    ids[rdims[1]] = oy;
-                    for(dim_t ox = 0; ox < oDims[0]; ox++) {
-                        const dim_t oIdx = oYZW + ox;
+    dim_t ids[4]  = {0};
+    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+        const dim_t oW = ow * ost[3];
+        ids[rdims[3]] = ow;
+        for(dim_t oz = 0; oz < oDims[2]; oz++) {
+            const dim_t oZW = oW + oz * ost[2];
+            ids[rdims[2]] = oz;
+            for(dim_t oy = 0; oy < oDims[1]; oy++) {
+                const dim_t oYZW = oZW + oy * ost[1];
+                ids[rdims[1]] = oy;
+                for(dim_t ox = 0; ox < oDims[0]; ox++) {
+                    const dim_t oIdx = oYZW + ox;
 
-                        ids[rdims[0]] = ox;
-                        const dim_t iIdx = ids[3] * ist[3] + ids[2] * ist[2] +
-                                              ids[1] * ist[1] + ids[0];
+                    ids[rdims[0]] = ox;
+                    const dim_t iIdx = ids[3] * ist[3] + ids[2] * ist[2] +
+                                          ids[1] * ist[1] + ids[0];
 
-                        outPtr[oIdx] = inPtr[iIdx];
-                    }
+                    outPtr[oIdx] = inPtr[iIdx];
                 }
             }
         }
     }
+}
 
-    template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
-    {
-        in.eval();
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
+{
+    in.eval();
 
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims(0);
-        for(int i = 0; i < 4; i++)
-            oDims[i] = iDims[rdims[i]];
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims(0);
+    for(int i = 0; i < 4; i++)
+        oDims[i] = iDims[rdims[i]];
 
-        Array<T> out = createEmptyArray<T>(oDims);
-        getQueue().enqueue(reorder_<T>, out, in, oDims, rdims);
-        return out;
-    }
+    Array<T> out = createEmptyArray<T>(oDims);
+    getQueue().enqueue(reorder_<T>, out, in, oDims, rdims);
+    return out;
+}
 
 #define INSTANTIATE(T)                                                         \
     template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);  \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
 }
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 160ed46c0d..8fb2edcda6 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -19,6 +19,7 @@
 
 namespace cpu
 {
+
 /**
  * noop function for round to avoid compilation
  * issues due to lack of this function in C90 based
@@ -215,4 +216,5 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+
 }
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 01ec96228c..5687d69c08 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -18,106 +18,110 @@
 
 namespace cpu
 {
-    template<typename T, af_interp_type method>
-    void rotate_(Array<T> output, const Array<T> input, const float theta)
+
+template<typename T, af_interp_type method>
+void rotate_(Array<T> output, const Array<T> input, const float theta)
+{
+    const af::dim4 odims    = output.dims();
+    const af::dim4 idims    = input.dims();
+    const af::dim4 ostrides = output.strides();
+    const af::dim4 istrides = input.strides();
+
+    const T* in   = input.get();
+          T* out  = output.get();
+    dim_t nimages = idims[2];
+
+    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
+                 const af::dim4 &, const af::dim4 &,
+                 const dim_t, const dim_t, const dim_t, const dim_t);
+
+    const float c = cos(-theta), s = sin(-theta);
+    float tx, ty;
     {
-        const af::dim4 odims    = output.dims();
-        const af::dim4 idims    = input.dims();
-        const af::dim4 ostrides = output.strides();
-        const af::dim4 istrides = input.strides();
-
-        const T* in   = input.get();
-              T* out  = output.get();
-        dim_t nimages = idims[2];
-
-        void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
-                     const af::dim4 &, const af::dim4 &,
-                     const dim_t, const dim_t, const dim_t, const dim_t);
-
-        const float c = cos(-theta), s = sin(-theta);
-        float tx, ty;
-        {
-            const float nx = 0.5 * (idims[0] - 1);
-            const float ny = 0.5 * (idims[1] - 1);
-            const float mx = 0.5 * (odims[0] - 1);
-            const float my = 0.5 * (odims[1] - 1);
-            const float sx = (mx * c + my *-s);
-            const float sy = (mx * s + my * c);
-            tx = -(sx - nx);
-            ty = -(sy - ny);
-        }
+        const float nx = 0.5 * (idims[0] - 1);
+        const float ny = 0.5 * (idims[1] - 1);
+        const float mx = 0.5 * (odims[0] - 1);
+        const float my = 0.5 * (odims[1] - 1);
+        const float sx = (mx * c + my *-s);
+        const float sy = (mx * s + my * c);
+        tx = -(sx - nx);
+        ty = -(sy - ny);
+    }
 
-        const float tmat[6] = {std::round( c * 1000) / 1000.0f,
-                               std::round(-s * 1000) / 1000.0f,
-                               std::round(tx * 1000) / 1000.0f,
-                               std::round( s * 1000) / 1000.0f,
-                               std::round( c * 1000) / 1000.0f,
-                               std::round(ty * 1000) / 1000.0f,
-                              };
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                t_fn = &transform_n;
-                break;
-            case AF_INTERP_BILINEAR:
-                t_fn = &transform_b;
-                break;
-            case AF_INTERP_LOWER:
-                t_fn = &transform_l;
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                break;
-        }
+    const float tmat[6] = {std::round( c * 1000) / 1000.0f,
+                           std::round(-s * 1000) / 1000.0f,
+                           std::round(tx * 1000) / 1000.0f,
+                           std::round( s * 1000) / 1000.0f,
+                           std::round( c * 1000) / 1000.0f,
+                           std::round(ty * 1000) / 1000.0f,
+                          };
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            t_fn = &transform_n;
+            break;
+        case AF_INTERP_BILINEAR:
+            t_fn = &transform_b;
+            break;
+        case AF_INTERP_LOWER:
+            t_fn = &transform_l;
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+            break;
+    }
 
 
-        // Do transform for image
-        for(int yy = 0; yy < (int)odims[1]; yy++) {
-            for(int xx = 0; xx < (int)odims[0]; xx++) {
-                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy);
-            }
+    // Do transform for image
+    for(int yy = 0; yy < (int)odims[1]; yy++) {
+        for(int xx = 0; xx < (int)odims[0]; xx++) {
+            t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy);
         }
     }
+}
 
-    template<typename T>
-    Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                     const af_interp_type method)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                getQueue().enqueue(rotate_<T, AF_INTERP_NEAREST>, out, in, theta);
-                break;
-            case AF_INTERP_BILINEAR:
-                getQueue().enqueue(rotate_<T, AF_INTERP_BILINEAR>, out, in, theta);
-                break;
-            case AF_INTERP_LOWER:
-                getQueue().enqueue(rotate_<T, AF_INTERP_LOWER>, out, in, theta);
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                break;
-        }
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                 const af_interp_type method)
+{
+    in.eval();
 
-        return out;
+    Array<T> out = createEmptyArray<T>(odims);
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            getQueue().enqueue(rotate_<T, AF_INTERP_NEAREST>, out, in, theta);
+            break;
+        case AF_INTERP_BILINEAR:
+            getQueue().enqueue(rotate_<T, AF_INTERP_BILINEAR>, out, in, theta);
+            break;
+        case AF_INTERP_LOWER:
+            getQueue().enqueue(rotate_<T, AF_INTERP_LOWER>, out, in, theta);
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+            break;
     }
 
+    return out;
+}
+
 
 #define INSTANTIATE(T)                                                              \
     template Array<T> rotate(const Array<T> &in, const float theta,                 \
                              const af::dim4 &odims, const af_interp_type method);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
 }
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 67aa5863ea..d6321bba55 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -23,116 +23,118 @@
 
 namespace cpu
 {
-    using namespace std;
-    using af::dim4;
-
-    template<typename T>
-    Array<T> setUnique(const Array<T> &in,
-                        const bool is_sorted)
-    {
-        in.eval();
-
-        Array<T> out = createEmptyArray<T>(af::dim4());
-        if (is_sorted) out = copyArray<T>(in);
-        else           out = sort<T, 1>(in, 0);
-
-        // Need to sync old jobs since we need to
-        // operator on pointers directly in std::unique
-        getQueue().sync();
-
-        T *ptr = out.get();
-        T *last = std::unique(ptr, ptr + in.elements());
-        dim_t dist = (dim_t)std::distance(ptr, last);
-
-        dim4 dims(dist, 1, 1, 1);
-        out.resetDims(dims);
-        return out;
-    }
 
-    template<typename T>
-    Array<T> setUnion(const Array<T> &first,
-                       const Array<T> &second,
-                       const bool is_unique)
-    {
-        first.eval();
-        second.eval();
-        getQueue().sync();
+using namespace std;
+using af::dim4;
 
-        Array<T> uFirst = first;
-        Array<T> uSecond = second;
+template<typename T>
+Array<T> setUnique(const Array<T> &in,
+                    const bool is_sorted)
+{
+    in.eval();
 
-        if (!is_unique) {
-            // FIXME: Perhaps copy + unique would do ?
-            uFirst  = setUnique(first, false);
-            uSecond = setUnique(second, false);
-        }
+    Array<T> out = createEmptyArray<T>(af::dim4());
+    if (is_sorted) out = copyArray<T>(in);
+    else           out = sort<T, 1>(in, 0);
 
-        dim_t first_elements  = uFirst.elements();
-        dim_t second_elements = uSecond.elements();
-        dim_t elements = first_elements + second_elements;
+    // Need to sync old jobs since we need to
+    // operator on pointers directly in std::unique
+    getQueue().sync();
 
-        Array<T> out = createEmptyArray<T>(af::dim4(elements));
+    T *ptr = out.get();
+    T *last = std::unique(ptr, ptr + in.elements());
+    dim_t dist = (dim_t)std::distance(ptr, last);
 
-        T *ptr = out.get();
-        T *last = std::set_union(uFirst.get() , uFirst.get()  + first_elements,
-                                 uSecond.get(), uSecond.get() + second_elements,
-                                 ptr);
+    dim4 dims(dist, 1, 1, 1);
+    out.resetDims(dims);
+    return out;
+}
 
-        dim_t dist = (dim_t)std::distance(ptr, last);
-        dim4 dims(dist, 1, 1, 1);
-        out.resetDims(dims);
+template<typename T>
+Array<T> setUnion(const Array<T> &first,
+                   const Array<T> &second,
+                   const bool is_unique)
+{
+    first.eval();
+    second.eval();
+    getQueue().sync();
 
-        return out;
+    Array<T> uFirst = first;
+    Array<T> uSecond = second;
+
+    if (!is_unique) {
+        // FIXME: Perhaps copy + unique would do ?
+        uFirst  = setUnique(first, false);
+        uSecond = setUnique(second, false);
     }
 
-    template<typename T>
-    Array<T> setIntersect(const Array<T> &first,
-                          const Array<T> &second,
-                          const bool is_unique)
-    {
-        first.eval();
-        second.eval();
-        getQueue().sync();
+    dim_t first_elements  = uFirst.elements();
+    dim_t second_elements = uSecond.elements();
+    dim_t elements = first_elements + second_elements;
 
-        Array<T> uFirst = first;
-        Array<T> uSecond = second;
+    Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
-        if (!is_unique) {
-            uFirst  = setUnique(first, false);
-            uSecond = setUnique(second, false);
-        }
+    T *ptr = out.get();
+    T *last = std::set_union(uFirst.get() , uFirst.get()  + first_elements,
+                             uSecond.get(), uSecond.get() + second_elements,
+                             ptr);
 
-        dim_t first_elements  = uFirst.elements();
-        dim_t second_elements = uSecond.elements();
-        dim_t elements = std::max(first_elements, second_elements);
+    dim_t dist = (dim_t)std::distance(ptr, last);
+    dim4 dims(dist, 1, 1, 1);
+    out.resetDims(dims);
 
-        Array<T> out = createEmptyArray<T>(af::dim4(elements));
+    return out;
+}
 
-        T *ptr = out.get();
-        T *last = std::set_intersection(uFirst.get() , uFirst.get()  + first_elements,
-                                        uSecond.get(), uSecond.get() + second_elements,
-                                        ptr);
+template<typename T>
+Array<T> setIntersect(const Array<T> &first,
+                      const Array<T> &second,
+                      const bool is_unique)
+{
+    first.eval();
+    second.eval();
+    getQueue().sync();
 
-        dim_t dist = (dim_t)std::distance(ptr, last);
-        dim4 dims(dist, 1, 1, 1);
-        out.resetDims(dims);
+    Array<T> uFirst = first;
+    Array<T> uSecond = second;
 
-        return out;
+    if (!is_unique) {
+        uFirst  = setUnique(first, false);
+        uSecond = setUnique(second, false);
     }
 
+    dim_t first_elements  = uFirst.elements();
+    dim_t second_elements = uSecond.elements();
+    dim_t elements = std::max(first_elements, second_elements);
+
+    Array<T> out = createEmptyArray<T>(af::dim4(elements));
+
+    T *ptr = out.get();
+    T *last = std::set_intersection(uFirst.get() , uFirst.get()  + first_elements,
+                                    uSecond.get(), uSecond.get() + second_elements,
+                                    ptr);
+
+    dim_t dist = (dim_t)std::distance(ptr, last);
+    dim4 dims(dist, 1, 1, 1);
+    out.resetDims(dims);
+
+    return out;
+}
+
 #define INSTANTIATE(T)                                                  \
     template Array<T> setUnique<T>(const Array<T> &in, const bool is_sorted); \
     template Array<T> setUnion<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
     template Array<T> setIntersect<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+
 }
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index 6a2b939cca..766427bff5 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -17,6 +17,7 @@
 
 namespace cpu
 {
+
 static inline dim_t simple_mod(const dim_t i, const dim_t dim)
 {
     return (i < dim) ? i : (i - dim);
@@ -25,9 +26,9 @@ static inline dim_t simple_mod(const dim_t i, const dim_t dim)
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4])
 {
-    Array<T> out = createEmptyArray<T>(in.dims());
-    out.eval();
     in.eval();
+
+    Array<T> out = createEmptyArray<T>(in.dims());
     const af::dim4 temp(sdims[0], sdims[1], sdims[2], sdims[3]);
 
     auto func = [=] (Array<T> out, const Array<T> in, const af::dim4 sdims) {
@@ -91,4 +92,5 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+
 }
diff --git a/src/backend/cpu/sift_nonfree.hpp b/src/backend/cpu/sift_nonfree.hpp
index 853f407f7f..c1c92a97e6 100644
--- a/src/backend/cpu/sift_nonfree.hpp
+++ b/src/backend/cpu/sift_nonfree.hpp
@@ -76,161 +76,161 @@ using af::dim4;
 namespace cpu
 {
 
-    static const float PI_VAL = 3.14159265358979323846f;
+static const float PI_VAL = 3.14159265358979323846f;
 
 // default width of descriptor histogram array
-    static const int DescrWidth = 4;
+static const int DescrWidth = 4;
 
 // default number of bins per histogram in descriptor array
-    static const int DescrHistBins = 8;
+static const int DescrHistBins = 8;
 
 // assumed gaussian blur for input image
-    static const float InitSigma = 0.5f;
+static const float InitSigma = 0.5f;
 
 // width of border in which to ignore keypoints
-    static const int ImgBorder = 5;
+static const int ImgBorder = 5;
 
 // maximum steps of keypoint interpolation before failure
-    static const int MaxInterpSteps = 5;
+static const int MaxInterpSteps = 5;
 
 // default number of bins in histogram for orientation assignment
-    static const int OriHistBins = 36;
+static const int OriHistBins = 36;
 
 // determines gaussian sigma for orientation assignment
-    static const float OriSigFctr = 1.5f;
+static const float OriSigFctr = 1.5f;
 
 // determines the radius of the region used in orientation assignment */
-    static const float OriRadius = 3.0f * OriSigFctr;
+static const float OriRadius = 3.0f * OriSigFctr;
 
 // number of passes of orientation histogram smoothing
-    static const int SmoothOriPasses = 2;
+static const int SmoothOriPasses = 2;
 
 // orientation magnitude relative to max that results in new feature
-    static const float OriPeakRatio = 0.8f;
+static const float OriPeakRatio = 0.8f;
 
 // determines the size of a single descriptor orientation histogram
-    static const float DescrSclFctr = 3.f;
+static const float DescrSclFctr = 3.f;
 
 // threshold on magnitude of elements of descriptor vector
-    static const float DescrMagThr = 0.2f;
+static const float DescrMagThr = 0.2f;
 
 // factor used to convert floating-point descriptor to unsigned char
-    static const float IntDescrFctr = 512.f;
+static const float IntDescrFctr = 512.f;
 
 // Number of GLOH bins in radial direction
-    static const unsigned GLOHRadialBins = 3;
+static const unsigned GLOHRadialBins = 3;
 
 // Radiuses of GLOH descriptors
-    static const float GLOHRadii[GLOHRadialBins] = {6.f, 11.f, 15.f};
+static const float GLOHRadii[GLOHRadialBins] = {6.f, 11.f, 15.f};
 
 // Number of GLOH angular bins (excluding the inner-most radial section)
-    static const unsigned GLOHAngularBins = 8;
+static const unsigned GLOHAngularBins = 8;
 
 // Number of GLOH bins per histogram in descriptor
-    static const unsigned GLOHHistBins = 16;
+static const unsigned GLOHHistBins = 16;
 
-    typedef struct
-    {
-        float    f[4];
-        unsigned l;
-    } feat_t;
+typedef struct
+{
+    float    f[4];
+    unsigned l;
+} feat_t;
 
-    bool feat_cmp(feat_t i, feat_t j)
-    {
-        for (int k = 0; k < 4; k++)
-            if (i.f[k] != j.f[k])
-                return (i.f[k] < j.f[k]);
-        if (i.l != j.l)
-            return (i.l < j.l);
+bool feat_cmp(feat_t i, feat_t j)
+{
+    for (int k = 0; k < 4; k++)
+        if (i.f[k] != j.f[k])
+            return (i.f[k] < j.f[k]);
+    if (i.l != j.l)
+        return (i.l < j.l);
 
-        return true;
-    }
+    return true;
+}
 
-    void array_to_feat(std::vector<feat_t>& feat, float *x, float *y, unsigned *layer, float *resp, float *size, unsigned nfeat)
-    {
-        feat.resize(nfeat);
-        for (unsigned i = 0; i < feat.size(); i++) {
-            feat[i].f[0] = x[i];
-            feat[i].f[1] = y[i];
-            feat[i].f[2] = resp[i];
-            feat[i].f[3] = size[i];
-            feat[i].l    = layer[i];
-        }
+void array_to_feat(std::vector<feat_t>& feat, float *x, float *y, unsigned *layer, float *resp, float *size, unsigned nfeat)
+{
+    feat.resize(nfeat);
+    for (unsigned i = 0; i < feat.size(); i++) {
+        feat[i].f[0] = x[i];
+        feat[i].f[1] = y[i];
+        feat[i].f[2] = resp[i];
+        feat[i].f[3] = size[i];
+        feat[i].l    = layer[i];
     }
+}
 
-    template<typename T>
-    void gaussian1D(T* out, const int dim, double sigma=0.0)
-    {
-        if(!(sigma>0)) sigma = 0.25*dim;
-
-        T sum = (T)0;
-        for(int i=0;i<dim;i++)
-        {
-            int x = i-(dim-1)/2;
-            T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
-            out[i] = el;
-            sum   += el;
-        }
+template<typename T>
+void gaussian1D(T* out, const int dim, double sigma=0.0)
+{
+    if(!(sigma>0)) sigma = 0.25*dim;
 
-        for(int k=0;k<dim;k++)
-            out[k] /= sum;
+    T sum = (T)0;
+    for(int i=0;i<dim;i++)
+    {
+        int x = i-(dim-1)/2;
+        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+        out[i] = el;
+        sum   += el;
     }
 
-    template<typename T>
-    Array<T> gauss_filter(float sigma)
-    {
-        // Using 6-sigma rule
-        unsigned gauss_len = std::min((unsigned)round(sigma * 6 + 1) | 1, 31u);
+    for(int k=0;k<dim;k++)
+        out[k] /= sum;
+}
 
-        Array<T> filter = createEmptyArray<T>(gauss_len);
-        gaussian1D((T*)getDevicePtr(filter), gauss_len, sigma);
+template<typename T>
+Array<T> gauss_filter(float sigma)
+{
+    // Using 6-sigma rule
+    unsigned gauss_len = std::min((unsigned)round(sigma * 6 + 1) | 1, 31u);
 
-        return filter;
-    }
+    Array<T> filter = createEmptyArray<T>(gauss_len);
+    gaussian1D((T*)getDevicePtr(filter), gauss_len, sigma);
 
-    template<int N>
-    void gaussianElimination(float* A, float* b, float* x)
-    {
-        // forward elimination
-        for (int i = 0; i < N-1; i++) {
-            for (int j = i+1; j < N; j++) {
-                float s = A[j*N+i] / A[i*N+i];
+    return filter;
+}
 
-                for (int k = i; k < N; k++)
-                    A[j*N+k] -= s * A[i*N+k];
+template<int N>
+void gaussianElimination(float* A, float* b, float* x)
+{
+    // forward elimination
+    for (int i = 0; i < N-1; i++) {
+        for (int j = i+1; j < N; j++) {
+            float s = A[j*N+i] / A[i*N+i];
 
-                b[j] -= s * b[i];
-            }
+            for (int k = i; k < N; k++)
+                A[j*N+k] -= s * A[i*N+k];
+
+            b[j] -= s * b[i];
         }
+    }
 
-        for (int i = 0; i < N; i++)
-            x[i] = 0;
+    for (int i = 0; i < N; i++)
+        x[i] = 0;
 
-        // backward substitution
-        float sum = 0;
-        for (int i = 0; i <= N-2; i++) {
-            sum = b[i];
-            for (int j = i+1; j < N; j++)
-                sum -= A[i*N+j] * x[j];
-            x[i] = sum / A[i*N+i];
-        }
+    // backward substitution
+    float sum = 0;
+    for (int i = 0; i <= N-2; i++) {
+        sum = b[i];
+        for (int j = i+1; j < N; j++)
+            sum -= A[i*N+j] * x[j];
+        x[i] = sum / A[i*N+i];
     }
+}
 
-    template<typename T>
-    void sub(
-        Array<T>& out,
-        const Array<T>& in1,
-        const Array<T>& in2)
-    {
-        size_t nel = in1.elements();
-        T* out_ptr = out.get();
-        const T* in1_ptr = in1.get();
-        const T* in2_ptr = in2.get();
+template<typename T>
+void sub(
+    Array<T>& out,
+    const Array<T>& in1,
+    const Array<T>& in2)
+{
+    size_t nel = in1.elements();
+    T* out_ptr = out.get();
+    const T* in1_ptr = in1.get();
+    const T* in2_ptr = in2.get();
 
-        for (size_t i = 0; i < nel; i++) {
-            out_ptr[i] = in1_ptr[i] - in2_ptr[i];
-        }
+    for (size_t i = 0; i < nel; i++) {
+        out_ptr[i] = in1_ptr[i] - in2_ptr[i];
     }
+}
 
 #define CPTR(Y, X) (center_ptr[(Y) * idims[0] + (X)])
 #define PPTR(Y, X) (prev_ptr[(Y) * idims[0] + (X)])
@@ -238,957 +238,958 @@ namespace cpu
 
 // Determines whether a pixel is a scale-space extremum by comparing it to its
 // 3x3x3 pixel neighborhood.
-    template<typename T>
-    void detectExtrema(
-        float* x_out,
-        float* y_out,
-        unsigned* layer_out,
-        unsigned* counter,
-        const Array<T>& prev,
-        const Array<T>& center,
-        const Array<T>& next,
-        const unsigned layer,
-        const unsigned max_feat,
-        const float threshold)
-    {
-        const af::dim4 idims = center.dims();
-        const T* prev_ptr    = prev.get();
-        const T* center_ptr  = center.get();
-        const T* next_ptr    = next.get();
-
-        for (int y = ImgBorder; y < idims[1]-ImgBorder; y++) {
-            for (int x = ImgBorder; x < idims[0]-ImgBorder; x++) {
-                float p = center_ptr[y*idims[0] + x];
-
-                // Find extrema
-                if (abs((float)p) > threshold &&
-                    ((p > 0 && p > CPTR(y-1, x-1) && p > CPTR(y-1, x) &&
-                      p > CPTR(y-1, x+1) && p > CPTR(y, x-1) && p > CPTR(y,   x+1)  &&
-                      p > CPTR(y+1, x-1) && p > CPTR(y+1, x) && p > CPTR(y+1, x+1)  &&
-                      p > PPTR(y-1, x-1) && p > PPTR(y-1, x) && p > PPTR(y-1, x+1)  &&
-                      p > PPTR(y,   x-1) && p > PPTR(y  , x) && p > PPTR(y,   x+1)  &&
-                      p > PPTR(y+1, x-1) && p > PPTR(y+1, x) && p > PPTR(y+1, x+1)  &&
-                      p > NPTR(y-1, x-1) && p > NPTR(y-1, x) && p > NPTR(y-1, x+1)  &&
-                      p > NPTR(y,   x-1) && p > NPTR(y  , x) && p > NPTR(y,   x+1)  &&
-                      p > NPTR(y+1, x-1) && p > NPTR(y+1, x) && p > NPTR(y+1, x+1)) ||
-                     (p < 0 && p < CPTR(y-1, x-1) && p < CPTR(y-1, x) &&
-                      p < CPTR(y-1, x+1) && p < CPTR(y, x-1) && p < CPTR(y,   x+1)  &&
-                      p < CPTR(y+1, x-1) && p < CPTR(y+1, x) && p < CPTR(y+1, x+1)  &&
-                      p < PPTR(y-1, x-1) && p < PPTR(y-1, x) && p < PPTR(y-1, x+1)  &&
-                      p < PPTR(y,   x-1) && p < PPTR(y  , x) && p < PPTR(y,   x+1)  &&
-                      p < PPTR(y+1, x-1) && p < PPTR(y+1, x) && p < PPTR(y+1, x+1)  &&
-                      p < NPTR(y-1, x-1) && p < NPTR(y-1, x) && p < NPTR(y-1, x+1)  &&
-                      p < NPTR(y,   x-1) && p < NPTR(y  , x) && p < NPTR(y,   x+1)  &&
-                      p < NPTR(y+1, x-1) && p < NPTR(y+1, x) && p < NPTR(y+1, x+1)))) {
-
-                    if (*counter < max_feat)
-                    {
-                        x_out[*counter] = (float)y;
-                        y_out[*counter] = (float)x;
-                        layer_out[*counter] = layer;
-                        (*counter)++;
-                    }
+template<typename T>
+void detectExtrema(
+    float* x_out,
+    float* y_out,
+    unsigned* layer_out,
+    unsigned* counter,
+    const Array<T>& prev,
+    const Array<T>& center,
+    const Array<T>& next,
+    const unsigned layer,
+    const unsigned max_feat,
+    const float threshold)
+{
+    const af::dim4 idims = center.dims();
+    const T* prev_ptr    = prev.get();
+    const T* center_ptr  = center.get();
+    const T* next_ptr    = next.get();
+
+    for (int y = ImgBorder; y < idims[1]-ImgBorder; y++) {
+        for (int x = ImgBorder; x < idims[0]-ImgBorder; x++) {
+            float p = center_ptr[y*idims[0] + x];
+
+            // Find extrema
+            if (abs((float)p) > threshold &&
+                ((p > 0 && p > CPTR(y-1, x-1) && p > CPTR(y-1, x) &&
+                  p > CPTR(y-1, x+1) && p > CPTR(y, x-1) && p > CPTR(y,   x+1)  &&
+                  p > CPTR(y+1, x-1) && p > CPTR(y+1, x) && p > CPTR(y+1, x+1)  &&
+                  p > PPTR(y-1, x-1) && p > PPTR(y-1, x) && p > PPTR(y-1, x+1)  &&
+                  p > PPTR(y,   x-1) && p > PPTR(y  , x) && p > PPTR(y,   x+1)  &&
+                  p > PPTR(y+1, x-1) && p > PPTR(y+1, x) && p > PPTR(y+1, x+1)  &&
+                  p > NPTR(y-1, x-1) && p > NPTR(y-1, x) && p > NPTR(y-1, x+1)  &&
+                  p > NPTR(y,   x-1) && p > NPTR(y  , x) && p > NPTR(y,   x+1)  &&
+                  p > NPTR(y+1, x-1) && p > NPTR(y+1, x) && p > NPTR(y+1, x+1)) ||
+                 (p < 0 && p < CPTR(y-1, x-1) && p < CPTR(y-1, x) &&
+                  p < CPTR(y-1, x+1) && p < CPTR(y, x-1) && p < CPTR(y,   x+1)  &&
+                  p < CPTR(y+1, x-1) && p < CPTR(y+1, x) && p < CPTR(y+1, x+1)  &&
+                  p < PPTR(y-1, x-1) && p < PPTR(y-1, x) && p < PPTR(y-1, x+1)  &&
+                  p < PPTR(y,   x-1) && p < PPTR(y  , x) && p < PPTR(y,   x+1)  &&
+                  p < PPTR(y+1, x-1) && p < PPTR(y+1, x) && p < PPTR(y+1, x+1)  &&
+                  p < NPTR(y-1, x-1) && p < NPTR(y-1, x) && p < NPTR(y-1, x+1)  &&
+                  p < NPTR(y,   x-1) && p < NPTR(y  , x) && p < NPTR(y,   x+1)  &&
+                  p < NPTR(y+1, x-1) && p < NPTR(y+1, x) && p < NPTR(y+1, x+1)))) {
+
+                if (*counter < max_feat)
+                {
+                    x_out[*counter] = (float)y;
+                    y_out[*counter] = (float)x;
+                    layer_out[*counter] = layer;
+                    (*counter)++;
                 }
             }
         }
     }
+}
 
 // Interpolates a scale-space extremum's location and scale to subpixel
 // accuracy to form an image feature. Rejects features with low contrast.
 // Based on Section 4 of Lowe's paper.
-    template<typename T>
-    void interpolateExtrema(
-        float* x_out,
-        float* y_out,
-        unsigned* layer_out,
-        float* response_out,
-        float* size_out,
-        unsigned* counter,
-        const float* x_in,
-        const float* y_in,
-        const unsigned* layer_in,
-        const unsigned extrema_feat,
-        std::vector< Array<T> >& dog_pyr,
-        const unsigned max_feat,
-        const unsigned octave,
-        const unsigned n_layers,
-        const float contrast_thr,
-        const float edge_thr,
-        const float sigma,
-        const float img_scale)
-    {
-        for (int f = 0; f < (int)extrema_feat; f++) {
-            const float first_deriv_scale = img_scale*0.5f;
-            const float second_deriv_scale = img_scale;
-            const float cross_deriv_scale = img_scale*0.25f;
-
-            float xl = 0, xy = 0, xx = 0, contr = 0;
-            int i = 0;
-
-            unsigned x = x_in[f];
-            unsigned y = y_in[f];
-            unsigned layer = layer_in[f];
-
-            const T* prev_ptr   = dog_pyr[octave*(n_layers+2) + layer-1].get();
-            const T* center_ptr = dog_pyr[octave*(n_layers+2) + layer].get();
-            const T* next_ptr   = dog_pyr[octave*(n_layers+2) + layer+1].get();
-
-            af::dim4 idims = dog_pyr[octave*(n_layers+2)].dims();
-
-            bool converges = true;
-
-            for (i = 0; i < MaxInterpSteps; i++) {
-                float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                               (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                               (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
-
-                float d2  = CPTR(x, y) * 2.f;
-                float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-                float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-                float dss = (NPTR(x, y  ) + PPTR(x, y  ) - d2) * second_deriv_scale;
-                float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                             CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
-                float dxs = (NPTR(x+1, y) - NPTR(x-1, y) -
-                             PPTR(x+1, y) + PPTR(x-1, y)) * cross_deriv_scale;
-                float dys = (NPTR(x, y+1) - NPTR(x-1, y-1) -
-                             PPTR(x, y-1) + PPTR(x-1, y-1)) * cross_deriv_scale;
-
-                float H[9] = {dxx, dxy, dxs,
-                              dxy, dyy, dys,
-                              dxs, dys, dss};
-
-                float X[3];
-                gaussianElimination<3>(H, dD, X);
-
-                xl = -X[2];
-                xy = -X[1];
-                xx = -X[0];
-
-                if (fabs(xl) < 0.5f && fabs(xy) < 0.5f && fabs(xx) < 0.5f)
-                    break;
-
-                x += round(xx);
-                y += round(xy);
-                layer += round(xl);
-
-                if (layer < 1 || layer > n_layers ||
-                    x < ImgBorder || x >= idims[1] - ImgBorder ||
-                    y < ImgBorder || y >= idims[0] - ImgBorder) {
-                    converges = false;
-                    break;
-                }
-            }
+template<typename T>
+void interpolateExtrema(
+    float* x_out,
+    float* y_out,
+    unsigned* layer_out,
+    float* response_out,
+    float* size_out,
+    unsigned* counter,
+    const float* x_in,
+    const float* y_in,
+    const unsigned* layer_in,
+    const unsigned extrema_feat,
+    std::vector< Array<T> >& dog_pyr,
+    const unsigned max_feat,
+    const unsigned octave,
+    const unsigned n_layers,
+    const float contrast_thr,
+    const float edge_thr,
+    const float sigma,
+    const float img_scale)
+{
+    for (int f = 0; f < (int)extrema_feat; f++) {
+        const float first_deriv_scale = img_scale*0.5f;
+        const float second_deriv_scale = img_scale;
+        const float cross_deriv_scale = img_scale*0.25f;
 
-            // ensure convergence of interpolation
-            if (i >= MaxInterpSteps || !converges)
-                continue;
+        float xl = 0, xy = 0, xx = 0, contr = 0;
+        int i = 0;
+
+        unsigned x = x_in[f];
+        unsigned y = y_in[f];
+        unsigned layer = layer_in[f];
 
+        const T* prev_ptr   = dog_pyr[octave*(n_layers+2) + layer-1].get();
+        const T* center_ptr = dog_pyr[octave*(n_layers+2) + layer].get();
+        const T* next_ptr   = dog_pyr[octave*(n_layers+2) + layer+1].get();
+
+        af::dim4 idims = dog_pyr[octave*(n_layers+2)].dims();
+
+        bool converges = true;
+
+        for (i = 0; i < MaxInterpSteps; i++) {
             float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
                            (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
                            (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
-            float X[3] = {xx, xy, xl};
 
-            float P = dD[0]*X[0] + dD[1]*X[1] + dD[2]*X[2];
-
-            contr = center_ptr[x*idims[0]+y]*img_scale + P * 0.5f;
-            if(abs(contr) < (contrast_thr / n_layers))
-                continue;
-
-            // principal curvatures are computed using the trace and det of Hessian
             float d2  = CPTR(x, y) * 2.f;
             float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
             float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
+            float dss = (NPTR(x, y  ) + PPTR(x, y  ) - d2) * second_deriv_scale;
             float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
                          CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
+            float dxs = (NPTR(x+1, y) - NPTR(x-1, y) -
+                         PPTR(x+1, y) + PPTR(x-1, y)) * cross_deriv_scale;
+            float dys = (NPTR(x, y+1) - NPTR(x-1, y-1) -
+                         PPTR(x, y-1) + PPTR(x-1, y-1)) * cross_deriv_scale;
+
+            float H[9] = {dxx, dxy, dxs,
+                          dxy, dyy, dys,
+                          dxs, dys, dss};
+
+            float X[3];
+            gaussianElimination<3>(H, dD, X);
+
+            xl = -X[2];
+            xy = -X[1];
+            xx = -X[0];
+
+            if (fabs(xl) < 0.5f && fabs(xy) < 0.5f && fabs(xx) < 0.5f)
+                break;
+
+            x += round(xx);
+            y += round(xy);
+            layer += round(xl);
+
+            if (layer < 1 || layer > n_layers ||
+                x < ImgBorder || x >= idims[1] - ImgBorder ||
+                y < ImgBorder || y >= idims[0] - ImgBorder) {
+                converges = false;
+                break;
+            }
+        }
 
-            float tr = dxx + dyy;
-            float det = dxx * dyy - dxy * dxy;
+        // ensure convergence of interpolation
+        if (i >= MaxInterpSteps || !converges)
+            continue;
 
-            // add FLT_EPSILON for double-precision compatibility
-            if (det <= 0 || tr*tr*edge_thr >= (edge_thr + 1)*(edge_thr + 1)*det+FLT_EPSILON)
-                continue;
+        float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
+                       (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
+                       (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
+        float X[3] = {xx, xy, xl};
 
-            if (*counter < max_feat)
-            {
-                x_out[*counter] = (x + xx) * (1 << octave);
-                y_out[*counter] = (y + xy) * (1 << octave);
-                layer_out[*counter] = layer;
-                response_out[*counter] = abs(contr);
-                size_out[*counter] = sigma*pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
-                (*counter)++;
-            }
+        float P = dD[0]*X[0] + dD[1]*X[1] + dD[2]*X[2];
+
+        contr = center_ptr[x*idims[0]+y]*img_scale + P * 0.5f;
+        if(abs(contr) < (contrast_thr / n_layers))
+            continue;
+
+        // principal curvatures are computed using the trace and det of Hessian
+        float d2  = CPTR(x, y) * 2.f;
+        float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
+        float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
+        float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
+                     CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
+
+        float tr = dxx + dyy;
+        float det = dxx * dyy - dxy * dxy;
+
+        // add FLT_EPSILON for double-precision compatibility
+        if (det <= 0 || tr*tr*edge_thr >= (edge_thr + 1)*(edge_thr + 1)*det+FLT_EPSILON)
+            continue;
+
+        if (*counter < max_feat)
+        {
+            x_out[*counter] = (x + xx) * (1 << octave);
+            y_out[*counter] = (y + xy) * (1 << octave);
+            layer_out[*counter] = layer;
+            response_out[*counter] = abs(contr);
+            size_out[*counter] = sigma*pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
+            (*counter)++;
         }
     }
+}
 
 #undef CPTR
 #undef PPTR
 #undef NPTR
 
 // Remove duplicate keypoints
-    void removeDuplicates(
-        float* x_out,
-        float* y_out,
-        unsigned* layer_out,
-        float* response_out,
-        float* size_out,
-        unsigned* counter,
-        const std::vector<feat_t>& sorted_feat)
-    {
-        size_t nfeat = sorted_feat.size();
-
-        for (size_t f = 0; f < nfeat; f++) {
-            float prec_fctr = 1e4f;
-
-            if (f < nfeat-1) {
-                if (round(sorted_feat[f].f[0]*prec_fctr) == round(sorted_feat[f+1].f[0]*prec_fctr) &&
-                    round(sorted_feat[f].f[1]*prec_fctr) == round(sorted_feat[f+1].f[1]*prec_fctr) &&
-                    round(sorted_feat[f].f[2]*prec_fctr) == round(sorted_feat[f+1].f[2]*prec_fctr) &&
-                    round(sorted_feat[f].f[3]*prec_fctr) == round(sorted_feat[f+1].f[3]*prec_fctr) &&
-                    sorted_feat[f].l == sorted_feat[f+1].l)
-                    continue;
-            }
+void removeDuplicates(
+    float* x_out,
+    float* y_out,
+    unsigned* layer_out,
+    float* response_out,
+    float* size_out,
+    unsigned* counter,
+    const std::vector<feat_t>& sorted_feat)
+{
+    size_t nfeat = sorted_feat.size();
 
-            x_out[*counter] = sorted_feat[f].f[0];
-            y_out[*counter] = sorted_feat[f].f[1];
-            response_out[*counter] = sorted_feat[f].f[2];
-            size_out[*counter] = sorted_feat[f].f[3];
-            layer_out[*counter] = sorted_feat[f].l;
-            (*counter)++;
+    for (size_t f = 0; f < nfeat; f++) {
+        float prec_fctr = 1e4f;
+
+        if (f < nfeat-1) {
+            if (round(sorted_feat[f].f[0]*prec_fctr) == round(sorted_feat[f+1].f[0]*prec_fctr) &&
+                round(sorted_feat[f].f[1]*prec_fctr) == round(sorted_feat[f+1].f[1]*prec_fctr) &&
+                round(sorted_feat[f].f[2]*prec_fctr) == round(sorted_feat[f+1].f[2]*prec_fctr) &&
+                round(sorted_feat[f].f[3]*prec_fctr) == round(sorted_feat[f+1].f[3]*prec_fctr) &&
+                sorted_feat[f].l == sorted_feat[f+1].l)
+                continue;
         }
+
+        x_out[*counter] = sorted_feat[f].f[0];
+        y_out[*counter] = sorted_feat[f].f[1];
+        response_out[*counter] = sorted_feat[f].f[2];
+        size_out[*counter] = sorted_feat[f].f[3];
+        layer_out[*counter] = sorted_feat[f].l;
+        (*counter)++;
     }
+}
 
 #define IPTR(Y, X) (img_ptr[(Y) * idims[0] + (X)])
 
 // Computes a canonical orientation for each image feature in an array.  Based
 // on Section 5 of Lowe's paper.  This function adds features to the array when
 // there is more than one dominant orientation at a given feature location.
-    template<typename T>
-    void calcOrientation(
-        float* x_out,
-        float* y_out,
-        unsigned* layer_out,
-        float* response_out,
-        float* size_out,
-        float* ori_out,
-        unsigned* counter,
-        const float* x_in,
-        const float* y_in,
-        const unsigned* layer_in,
-        const float* response_in,
-        const float* size_in,
-        const unsigned total_feat,
-        const std::vector< Array<T> >& gauss_pyr,
-        const unsigned max_feat,
-        const unsigned octave,
-        const unsigned n_layers,
-        const bool double_input)
-    {
-        const int n = OriHistBins;
+template<typename T>
+void calcOrientation(
+    float* x_out,
+    float* y_out,
+    unsigned* layer_out,
+    float* response_out,
+    float* size_out,
+    float* ori_out,
+    unsigned* counter,
+    const float* x_in,
+    const float* y_in,
+    const unsigned* layer_in,
+    const float* response_in,
+    const float* size_in,
+    const unsigned total_feat,
+    const std::vector< Array<T> >& gauss_pyr,
+    const unsigned max_feat,
+    const unsigned octave,
+    const unsigned n_layers,
+    const bool double_input)
+{
+    const int n = OriHistBins;
 
-        float hist[OriHistBins];
-        float temphist[OriHistBins];
+    float hist[OriHistBins];
+    float temphist[OriHistBins];
 
-        for (unsigned f = 0; f < total_feat; f++) {
-            // Load keypoint information
-            const float real_x = x_in[f];
-            const float real_y = y_in[f];
-            const unsigned layer = layer_in[f];
-            const float response = response_in[f];
-            const float size = size_in[f];
+    for (unsigned f = 0; f < total_feat; f++) {
+        // Load keypoint information
+        const float real_x = x_in[f];
+        const float real_y = y_in[f];
+        const unsigned layer = layer_in[f];
+        const float response = response_in[f];
+        const float size = size_in[f];
 
-            const int pt_x = (int)round(real_x / (1 << octave));
-            const int pt_y = (int)round(real_y / (1 << octave));
+        const int pt_x = (int)round(real_x / (1 << octave));
+        const int pt_y = (int)round(real_y / (1 << octave));
 
-            // Calculate auxiliary parameters
-            const float scl_octv = size*0.5f / (1 << octave);
-            const int radius = (int)round(OriRadius * scl_octv);
-            const float sigma = OriSigFctr * scl_octv;
-            const int len = (radius*2+1);
-            const float exp_denom = 2.f * sigma * sigma;
+        // Calculate auxiliary parameters
+        const float scl_octv = size*0.5f / (1 << octave);
+        const int radius = (int)round(OriRadius * scl_octv);
+        const float sigma = OriSigFctr * scl_octv;
+        const int len = (radius*2+1);
+        const float exp_denom = 2.f * sigma * sigma;
 
-            // Points img to correct Gaussian pyramid layer
-            const Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
-            const T* img_ptr = img.get();
+        // Points img to correct Gaussian pyramid layer
+        const Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
+        const T* img_ptr = img.get();
 
-            for (int i = 0; i < OriHistBins; i++)
-                hist[i] = 0.f;
+        for (int i = 0; i < OriHistBins; i++)
+            hist[i] = 0.f;
 
-            af::dim4 idims = img.dims();
+        af::dim4 idims = img.dims();
 
-            // Calculate orientation histogram
-            for (int l = 0; l < len*len; l++) {
-                int i = l / len - radius;
-                int j = l % len - radius;
+        // Calculate orientation histogram
+        for (int l = 0; l < len*len; l++) {
+            int i = l / len - radius;
+            int j = l % len - radius;
 
-                int y = pt_y + i;
-                int x = pt_x + j;
-                if (y < 1 || y >= idims[0] - 1 ||
-                    x < 1 || x >= idims[1] - 1)
-                    continue;
+            int y = pt_y + i;
+            int x = pt_x + j;
+            if (y < 1 || y >= idims[0] - 1 ||
+                x < 1 || x >= idims[1] - 1)
+                continue;
 
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
+            float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
 
-                float mag = sqrt(dx*dx+dy*dy);
-                float ori = atan2(dy,dx);
-                float w = exp(-(i*i + j*j)/exp_denom);
+            float mag = sqrt(dx*dx+dy*dy);
+            float ori = atan2(dy,dx);
+            float w = exp(-(i*i + j*j)/exp_denom);
 
-                int bin = round(n*(ori+PI_VAL)/(2.f*PI_VAL));
-                bin = bin < n ? bin : 0;
+            int bin = round(n*(ori+PI_VAL)/(2.f*PI_VAL));
+            bin = bin < n ? bin : 0;
 
-                hist[bin] += w*mag;
-            }
+            hist[bin] += w*mag;
+        }
 
-            for (int i = 0; i < SmoothOriPasses; i++) {
-                for (int j = 0; j < n; j++) {
-                    temphist[j] = hist[j];
-                }
-                for (int j = 0; j < n; j++) {
-                    float prev = (j == 0) ? temphist[n-1] : temphist[j-1];
-                    float next = (j+1 == n) ? temphist[0] : temphist[j+1];
-                    hist[j] = 0.25f * prev + 0.5f * temphist[j] + 0.25f * next;
-                }
+        for (int i = 0; i < SmoothOriPasses; i++) {
+            for (int j = 0; j < n; j++) {
+                temphist[j] = hist[j];
             }
-
-            float omax = hist[0];
-            for (int i = 1; i < n; i++)
-                omax = max(omax, hist[i]);
-
-            float mag_thr = (float)(omax * OriPeakRatio);
-            int l, r;
             for (int j = 0; j < n; j++) {
-                l = (j == 0) ? n - 1 : j - 1;
-                r = (j + 1) % n;
-                if (hist[j] > hist[l] &&
-                    hist[j] > hist[r] &&
-                    hist[j] >= mag_thr) {
-                    if (*counter < max_feat) {
-                        float bin = j + 0.5f * (hist[l] - hist[r]) /
-                            (hist[l] - 2.0f*hist[j] + hist[r]);
-                        bin = (bin < 0.0f) ? bin + n : (bin >= n) ? bin - n : bin;
-                        float ori = 360.f - ((360.f/n) * bin);
-
-                        float new_real_x = real_x;
-                        float new_real_y = real_y;
-                        float new_size = size;
-
-                        if (double_input) {
-                            float scale = 0.5f;
-                            new_real_x *= scale;
-                            new_real_y *= scale;
-                            new_size *= scale;
-                        }
+                float prev = (j == 0) ? temphist[n-1] : temphist[j-1];
+                float next = (j+1 == n) ? temphist[0] : temphist[j+1];
+                hist[j] = 0.25f * prev + 0.5f * temphist[j] + 0.25f * next;
+            }
+        }
 
-                        x_out[*counter] = new_real_x;
-                        y_out[*counter] = new_real_y;
-                        layer_out[*counter] = layer;
-                        response_out[*counter] = response;
-                        size_out[*counter] = new_size;
-                        ori_out[*counter] = ori;
-                        (*counter)++;
+        float omax = hist[0];
+        for (int i = 1; i < n; i++)
+            omax = max(omax, hist[i]);
+
+        float mag_thr = (float)(omax * OriPeakRatio);
+        int l, r;
+        for (int j = 0; j < n; j++) {
+            l = (j == 0) ? n - 1 : j - 1;
+            r = (j + 1) % n;
+            if (hist[j] > hist[l] &&
+                hist[j] > hist[r] &&
+                hist[j] >= mag_thr) {
+                if (*counter < max_feat) {
+                    float bin = j + 0.5f * (hist[l] - hist[r]) /
+                        (hist[l] - 2.0f*hist[j] + hist[r]);
+                    bin = (bin < 0.0f) ? bin + n : (bin >= n) ? bin - n : bin;
+                    float ori = 360.f - ((360.f/n) * bin);
+
+                    float new_real_x = real_x;
+                    float new_real_y = real_y;
+                    float new_size = size;
+
+                    if (double_input) {
+                        float scale = 0.5f;
+                        new_real_x *= scale;
+                        new_real_y *= scale;
+                        new_size *= scale;
                     }
+
+                    x_out[*counter] = new_real_x;
+                    y_out[*counter] = new_real_y;
+                    layer_out[*counter] = layer;
+                    response_out[*counter] = response;
+                    size_out[*counter] = new_size;
+                    ori_out[*counter] = ori;
+                    (*counter)++;
                 }
             }
         }
     }
+}
 
-    void normalizeDesc(
-        float* desc,
-        const int histlen)
-    {
-        float len_sq = 0.0f;
+void normalizeDesc(
+    float* desc,
+    const int histlen)
+{
+    float len_sq = 0.0f;
 
-        for (int i = 0; i < histlen; i++)
-            len_sq += desc[i] * desc[i];
+    for (int i = 0; i < histlen; i++)
+        len_sq += desc[i] * desc[i];
 
-        float len_inv = 1.0f / sqrt(len_sq);
+    float len_inv = 1.0f / sqrt(len_sq);
 
-        for (int i = 0; i < histlen; i++) {
-            desc[i] *= len_inv;
-        }
+    for (int i = 0; i < histlen; i++) {
+        desc[i] *= len_inv;
     }
+}
 
 // Computes feature descriptors for features in an array.  Based on Section 6
 // of Lowe's paper.
-    template<typename T>
-    void computeDescriptor(
-        float* desc_out,
-        const unsigned desc_len,
-        const float* x_in,
-        const float* y_in,
-        const unsigned* layer_in,
-        const float* response_in,
-        const float* size_in,
-        const float* ori_in,
-        const unsigned total_feat,
-        const std::vector< Array<T> >& gauss_pyr,
-        const int d,
-        const int n,
-        const float scale,
-        const unsigned octave,
-        const unsigned n_layers)
-    {
-        float desc[128];
-
-        for (unsigned f = 0; f < total_feat; f++) {
-            const unsigned layer = layer_in[f];
-            float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-            ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-            const float size = size_in[f];
-            const int fx = round(x_in[f] * scale);
-            const int fy = round(y_in[f] * scale);
-
-            // Points img to correct Gaussian pyramid layer
-            Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
-            const T* img_ptr = img.get();
-            af::dim4 idims = img.dims();
-
-            float cos_t = cos(ori);
-            float sin_t = sin(ori);
-            float bins_per_rad = n / (PI_VAL * 2.f);
-            float exp_denom = d * d * 0.5f;
-            float hist_width = DescrSclFctr * size * scale * 0.5f;
-            int radius = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
-
-            int len = radius*2+1;
-
-            for (int i = 0; i < (int)desc_len; i++)
-                desc[i] = 0.f;
-
-            // Calculate orientation histogram
-            for (int l = 0; l < len*len; l++) {
-                int i = l / len - radius;
-                int j = l % len - radius;
-
-                int y = fy + i;
-                int x = fx + j;
-
-                float x_rot = (j * cos_t - i * sin_t) / hist_width;
-                float y_rot = (j * sin_t + i * cos_t) / hist_width;
-                float xbin = x_rot + d/2 - 0.5f;
-                float ybin = y_rot + d/2 - 0.5f;
-
-                if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d &&
-                    y > 0 && y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
-                    float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                    float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
-
-                    float grad_mag = sqrt(dx*dx + dy*dy);
-                    float grad_ori = atan2(dy, dx) - ori;
-                    while (grad_ori < 0.0f)
-                        grad_ori += PI_VAL*2;
-                    while (grad_ori >= PI_VAL*2)
-                        grad_ori -= PI_VAL*2;
-
-                    float w = exp(-(x_rot*x_rot + y_rot*y_rot) / exp_denom);
-                    float obin = grad_ori * bins_per_rad;
-                    float mag = grad_mag*w;
-
-                    int x0 = floor(xbin);
-                    int y0 = floor(ybin);
-                    int o0 = floor(obin);
-                    xbin -= x0;
-                    ybin -= y0;
-                    obin -= o0;
-
-                    for (int yl = 0; yl <= 1; yl++) {
-                        int yb = y0 + yl;
-                        if (yb >= 0 && yb < d) {
-                            float v_y = mag * ((yl == 0) ? 1.0f - ybin : ybin);
-                            for (int xl = 0; xl <= 1; xl++) {
-                                int xb = x0 + xl;
-                                if (xb >= 0 && xb < d) {
-                                    float v_x = v_y * ((xl == 0) ? 1.0f - xbin : xbin);
-                                    for (int ol = 0; ol <= 1; ol++) {
-                                        int ob = (o0 + ol) % n;
-                                        float v_o = v_x * ((ol == 0) ? 1.0f - obin : obin);
-                                        desc[(yb*d + xb)*n + ob] += v_o;
-                                    }
-                                }
-                            }
-                        }
-                    }
-                }
-            }
+template<typename T>
+void computeDescriptor(
+    float* desc_out,
+    const unsigned desc_len,
+    const float* x_in,
+    const float* y_in,
+    const unsigned* layer_in,
+    const float* response_in,
+    const float* size_in,
+    const float* ori_in,
+    const unsigned total_feat,
+    const std::vector< Array<T> >& gauss_pyr,
+    const int d,
+    const int n,
+    const float scale,
+    const unsigned octave,
+    const unsigned n_layers)
+{
+    float desc[128];
+
+    for (unsigned f = 0; f < total_feat; f++) {
+        const unsigned layer = layer_in[f];
+        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
+        const float size = size_in[f];
+        const int fx = round(x_in[f] * scale);
+        const int fy = round(y_in[f] * scale);
+
+        // Points img to correct Gaussian pyramid layer
+        Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
+        const T* img_ptr = img.get();
+        af::dim4 idims = img.dims();
 
-            normalizeDesc(desc, desc_len);
+        float cos_t = cos(ori);
+        float sin_t = sin(ori);
+        float bins_per_rad = n / (PI_VAL * 2.f);
+        float exp_denom = d * d * 0.5f;
+        float hist_width = DescrSclFctr * size * scale * 0.5f;
+        int radius = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
 
-            for (int i = 0; i < (int)desc_len; i++)
-                desc[i] = min(desc[i], DescrMagThr);
+        int len = radius*2+1;
 
-            normalizeDesc(desc, desc_len);
+        for (int i = 0; i < (int)desc_len; i++)
+            desc[i] = 0.f;
 
-            // Calculate final descriptor values
-            for (int k = 0; k < (int)desc_len; k++) {
-                desc_out[f*desc_len+k] = round(min(255.f, desc[k] * IntDescrFctr));
-            }
-        }
-    }
+        // Calculate orientation histogram
+        for (int l = 0; l < len*len; l++) {
+            int i = l / len - radius;
+            int j = l % len - radius;
 
-// Computes GLOH feature descriptors for features in an array. Based on Section III-B
-// of Mikolajczyk and Schmid paper.
-    template<typename T>
-    void computeGLOHDescriptor(
-        float* desc_out,
-        const unsigned desc_len,
-        const float* x_in,
-        const float* y_in,
-        const unsigned* layer_in,
-        const float* response_in,
-        const float* size_in,
-        const float* ori_in,
-        const unsigned total_feat,
-        const std::vector< Array<T> >& gauss_pyr,
-        const int d,
-        const unsigned rb,
-        const unsigned ab,
-        const unsigned hb,
-        const float scale,
-        const unsigned octave,
-        const unsigned n_layers)
-    {
-        float desc[272];
-
-        for (unsigned f = 0; f < total_feat; f++) {
-            const unsigned layer = layer_in[f];
-            float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-            ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-            const float size = size_in[f];
-            const int fx = round(x_in[f] * scale);
-            const int fy = round(y_in[f] * scale);
-
-            // Points img to correct Gaussian pyramid layer
-            Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
-            const T* img_ptr = img.get();
-            af::dim4 idims = img.dims();
-
-            float cos_t = cos(ori);
-            float sin_t = sin(ori);
-            float hist_bins_per_rad = hb / (PI_VAL * 2.f);
-            float polar_bins_per_rad = ab / (PI_VAL * 2.f);
-            float exp_denom = GLOHRadii[rb-1] * 0.5f;
-
-            float hist_width = DescrSclFctr * size * scale * 0.5f;
-
-            // Keep same descriptor radius used for SIFT
-            int radius = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
-
-            // Alternative radius size calculation, changing the radius weight
-            // (rw) in the range of 0.25f-0.75f gives different results,
-            // increasing it tends to show a better recall rate but with a
-            // smaller amount of correct matches
-            //float rw = 0.5f;
-            //int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
-
-            int len = radius*2+1;
-
-            for (int i = 0; i < (int)desc_len; i++)
-                desc[i] = 0.f;
-
-            // Calculate orientation histogram
-            for (int l = 0; l < len*len; l++) {
-                int i = l / len - radius;
-                int j = l % len - radius;
-
-                int y = fy + i;
-                int x = fx + j;
-
-                float x_rot = (j * cos_t - i * sin_t);
-                float y_rot = (j * sin_t + i * cos_t);
-
-                float r = sqrt(x_rot*x_rot + y_rot*y_rot) / radius * GLOHRadii[rb-1];
-                float theta = atan2(y_rot, x_rot);
-                while (theta < 0.0f)
-                    theta += PI_VAL*2;
-                while (theta >= PI_VAL*2)
-                    theta -= PI_VAL*2;
-
-                float tbin = theta * polar_bins_per_rad;
-                float rbin = (r < GLOHRadii[0]) ? r / GLOHRadii[0] :
-                             ((r < GLOHRadii[1]) ? 1 + (r - GLOHRadii[0]) / (float)(GLOHRadii[1] - GLOHRadii[0]) :
-                             min(2 + (r - GLOHRadii[1]) / (float)(GLOHRadii[2] - GLOHRadii[1]), 3.f-FLT_EPSILON));
-
-                if (r <= GLOHRadii[rb-1] &&
-                    y > 0 && y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
-                    float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                    float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
-
-                    float grad_mag = sqrt(dx*dx + dy*dy);
-                    float grad_ori = atan2(dy, dx) - ori;
-                    while (grad_ori < 0.0f)
-                        grad_ori += PI_VAL*2;
-                    while (grad_ori >= PI_VAL*2)
-                        grad_ori -= PI_VAL*2;
-
-                    float w = exp(-r / exp_denom);
-                    float obin = grad_ori * hist_bins_per_rad;
-                    float mag = grad_mag*w;
-
-                    int t0 = floor(tbin);
-                    int r0 = floor(rbin);
-                    int o0 = floor(obin);
-                    tbin -= t0;
-                    rbin -= r0;
-                    obin -= o0;
-
-                    for (int rl = 0; rl <= 1; rl++) {
-                        int rb = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
-                        float v_r = mag * ((rl == 0) ? 1.0f - rbin : rbin);
-                        if (rb >= 0 && rb <= 2) {
-                            for (int tl = 0; tl <= 1; tl++) {
-                                int tb = (t0 + tl) % ab;
-                                float v_t = v_r * ((tl == 0) ? 1.0f - tbin : tbin);
+            int y = fy + i;
+            int x = fx + j;
+
+            float x_rot = (j * cos_t - i * sin_t) / hist_width;
+            float y_rot = (j * sin_t + i * cos_t) / hist_width;
+            float xbin = x_rot + d/2 - 0.5f;
+            float ybin = y_rot + d/2 - 0.5f;
+
+            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d &&
+                y > 0 && y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
+                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
+                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+
+                float grad_mag = sqrt(dx*dx + dy*dy);
+                float grad_ori = atan2(dy, dx) - ori;
+                while (grad_ori < 0.0f)
+                    grad_ori += PI_VAL*2;
+                while (grad_ori >= PI_VAL*2)
+                    grad_ori -= PI_VAL*2;
+
+                float w = exp(-(x_rot*x_rot + y_rot*y_rot) / exp_denom);
+                float obin = grad_ori * bins_per_rad;
+                float mag = grad_mag*w;
+
+                int x0 = floor(xbin);
+                int y0 = floor(ybin);
+                int o0 = floor(obin);
+                xbin -= x0;
+                ybin -= y0;
+                obin -= o0;
+
+                for (int yl = 0; yl <= 1; yl++) {
+                    int yb = y0 + yl;
+                    if (yb >= 0 && yb < d) {
+                        float v_y = mag * ((yl == 0) ? 1.0f - ybin : ybin);
+                        for (int xl = 0; xl <= 1; xl++) {
+                            int xb = x0 + xl;
+                            if (xb >= 0 && xb < d) {
+                                float v_x = v_y * ((xl == 0) ? 1.0f - xbin : xbin);
                                 for (int ol = 0; ol <= 1; ol++) {
-                                    int ob = (o0 + ol) % hb;
-                                    float v_o = v_t * ((ol == 0) ? 1.0f - obin : obin);
-                                    unsigned idx = (rb > 0) * (hb + ((rb-1) * ab + tb)*hb) + ob;
-                                    desc[idx] += v_o;
+                                    int ob = (o0 + ol) % n;
+                                    float v_o = v_x * ((ol == 0) ? 1.0f - obin : obin);
+                                    desc[(yb*d + xb)*n + ob] += v_o;
                                 }
                             }
                         }
                     }
                 }
             }
+        }
 
-            normalizeDesc(desc, desc_len);
+        normalizeDesc(desc, desc_len);
 
-            for (int i = 0; i < (int)desc_len; i++)
-                desc[i] = min(desc[i], DescrMagThr);
+        for (int i = 0; i < (int)desc_len; i++)
+            desc[i] = min(desc[i], DescrMagThr);
 
-            normalizeDesc(desc, desc_len);
+        normalizeDesc(desc, desc_len);
 
-            // Calculate final descriptor values
-            for (int k = 0; k < (int)desc_len; k++) {
-                desc_out[f*desc_len+k] = round(min(255.f, desc[k] * IntDescrFctr));
-            }
+        // Calculate final descriptor values
+        for (int k = 0; k < (int)desc_len; k++) {
+            desc_out[f*desc_len+k] = round(min(255.f, desc[k] * IntDescrFctr));
         }
     }
+}
 
-#undef IPTR
-
-    template<typename T, typename convAccT>
-    Array<T> createInitialImage(
-        const Array<T>& img,
-        const float init_sigma,
-        const bool double_input)
-    {
+// Computes GLOH feature descriptors for features in an array. Based on Section III-B
+// of Mikolajczyk and Schmid paper.
+template<typename T>
+void computeGLOHDescriptor(
+    float* desc_out,
+    const unsigned desc_len,
+    const float* x_in,
+    const float* y_in,
+    const unsigned* layer_in,
+    const float* response_in,
+    const float* size_in,
+    const float* ori_in,
+    const unsigned total_feat,
+    const std::vector< Array<T> >& gauss_pyr,
+    const int d,
+    const unsigned rb,
+    const unsigned ab,
+    const unsigned hb,
+    const float scale,
+    const unsigned octave,
+    const unsigned n_layers)
+{
+    float desc[272];
+
+    for (unsigned f = 0; f < total_feat; f++) {
+        const unsigned layer = layer_in[f];
+        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
+        const float size = size_in[f];
+        const int fx = round(x_in[f] * scale);
+        const int fy = round(y_in[f] * scale);
+
+        // Points img to correct Gaussian pyramid layer
+        Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
+        const T* img_ptr = img.get();
         af::dim4 idims = img.dims();
 
-        Array<T> init_img = createEmptyArray<T>(af::dim4());
+        float cos_t = cos(ori);
+        float sin_t = sin(ori);
+        float hist_bins_per_rad = hb / (PI_VAL * 2.f);
+        float polar_bins_per_rad = ab / (PI_VAL * 2.f);
+        float exp_denom = GLOHRadii[rb-1] * 0.5f;
 
-        float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma * 4), 0.1f)
-                                 : std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma), 0.1f);
+        float hist_width = DescrSclFctr * size * scale * 0.5f;
 
-        Array<T> filter = gauss_filter<T>(s);
+        // Keep same descriptor radius used for SIFT
+        int radius = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
 
-        if (double_input) {
-            Array<T> double_img = resize<T>(img, idims[0] * 2, idims[1] * 2, AF_INTERP_BILINEAR);
-            init_img = convolve2<T, convAccT, false>(double_img, filter, filter);
-        }
-        else {
-            init_img = convolve2<T, convAccT, false>(img, filter, filter);
-        }
+        // Alternative radius size calculation, changing the radius weight
+        // (rw) in the range of 0.25f-0.75f gives different results,
+        // increasing it tends to show a better recall rate but with a
+        // smaller amount of correct matches
+        //float rw = 0.5f;
+        //int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
 
-        return init_img;
-    }
+        int len = radius*2+1;
 
-    template<typename T, typename convAccT>
-    std::vector< Array<T> > buildGaussPyr(
-        const Array<T>& init_img,
-        const unsigned n_octaves,
-        const unsigned n_layers,
-        const float init_sigma)
-    {
-        // Precompute Gaussian sigmas using the following formula:
-        // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
-        std::vector<float> sig_layers(n_layers + 3);
-        sig_layers[0] = init_sigma;
-        float k = std::pow(2.0f, 1.0f / n_layers);
-        for (unsigned i = 1; i < n_layers + 3; i++) {
-            float sig_prev = std::pow(k, i-1) * init_sigma;
-            float sig_total = sig_prev * k;
-            sig_layers[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);
-        }
+        for (int i = 0; i < (int)desc_len; i++)
+            desc[i] = 0.f;
 
-        // Gaussian Pyramid
-        std::vector< Array<T> > gauss_pyr(n_octaves * (n_layers+3), createEmptyArray<T>(af::dim4()));
-        for (unsigned o = 0; o < n_octaves; o++) {
-            for (unsigned l = 0; l < n_layers+3; l++) {
-                unsigned src_idx = (l == 0) ? (o-1)*(n_layers+3) + n_layers : o*(n_layers+3) + l-1;
-                unsigned idx = o*(n_layers+3) + l;
+        // Calculate orientation histogram
+        for (int l = 0; l < len*len; l++) {
+            int i = l / len - radius;
+            int j = l % len - radius;
 
-                if (o == 0 && l == 0) {
-                    gauss_pyr[idx] = init_img;
-                }
-                else if (l == 0) {
-                    af::dim4 sdims = gauss_pyr[src_idx].dims();
-                    gauss_pyr[idx] = resize<T>(gauss_pyr[src_idx], sdims[0] / 2, sdims[1] / 2, AF_INTERP_BILINEAR);
-                }
-                else {
-                    Array<T> filter = gauss_filter<T>(sig_layers[l]);
+            int y = fy + i;
+            int x = fx + j;
+
+            float x_rot = (j * cos_t - i * sin_t);
+            float y_rot = (j * sin_t + i * cos_t);
+
+            float r = sqrt(x_rot*x_rot + y_rot*y_rot) / radius * GLOHRadii[rb-1];
+            float theta = atan2(y_rot, x_rot);
+            while (theta < 0.0f)
+                theta += PI_VAL*2;
+            while (theta >= PI_VAL*2)
+                theta -= PI_VAL*2;
+
+            float tbin = theta * polar_bins_per_rad;
+            float rbin = (r < GLOHRadii[0]) ? r / GLOHRadii[0] :
+                         ((r < GLOHRadii[1]) ? 1 + (r - GLOHRadii[0]) / (float)(GLOHRadii[1] - GLOHRadii[0]) :
+                         min(2 + (r - GLOHRadii[1]) / (float)(GLOHRadii[2] - GLOHRadii[1]), 3.f-FLT_EPSILON));
 
-                    gauss_pyr[idx] = convolve2<T, convAccT, false>(gauss_pyr[src_idx], filter, filter);
+            if (r <= GLOHRadii[rb-1] &&
+                y > 0 && y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
+                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
+                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+
+                float grad_mag = sqrt(dx*dx + dy*dy);
+                float grad_ori = atan2(dy, dx) - ori;
+                while (grad_ori < 0.0f)
+                    grad_ori += PI_VAL*2;
+                while (grad_ori >= PI_VAL*2)
+                    grad_ori -= PI_VAL*2;
+
+                float w = exp(-r / exp_denom);
+                float obin = grad_ori * hist_bins_per_rad;
+                float mag = grad_mag*w;
+
+                int t0 = floor(tbin);
+                int r0 = floor(rbin);
+                int o0 = floor(obin);
+                tbin -= t0;
+                rbin -= r0;
+                obin -= o0;
+
+                for (int rl = 0; rl <= 1; rl++) {
+                    int rb = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
+                    float v_r = mag * ((rl == 0) ? 1.0f - rbin : rbin);
+                    if (rb >= 0 && rb <= 2) {
+                        for (int tl = 0; tl <= 1; tl++) {
+                            int tb = (t0 + tl) % ab;
+                            float v_t = v_r * ((tl == 0) ? 1.0f - tbin : tbin);
+                            for (int ol = 0; ol <= 1; ol++) {
+                                int ob = (o0 + ol) % hb;
+                                float v_o = v_t * ((ol == 0) ? 1.0f - obin : obin);
+                                unsigned idx = (rb > 0) * (hb + ((rb-1) * ab + tb)*hb) + ob;
+                                desc[idx] += v_o;
+                            }
+                        }
+                    }
                 }
             }
         }
 
-        return gauss_pyr;
-    }
+        normalizeDesc(desc, desc_len);
 
-    template<typename T>
-    std::vector< Array<T> > buildDoGPyr(
-        std::vector< Array<T> >& gauss_pyr,
-        const unsigned n_octaves,
-        const unsigned n_layers)
-    {
-        // DoG Pyramid
-        std::vector< Array<T> > dog_pyr(n_octaves * (n_layers+2), createEmptyArray<T>(af::dim4()));
-        for (unsigned o = 0; o < n_octaves; o++) {
-            for (unsigned l = 0; l < n_layers+2; l++) {
-                unsigned idx    = o*(n_layers+2) + l;
-                unsigned bottom = o*(n_layers+3) + l;
-                unsigned top    = o*(n_layers+3) + l+1;
+        for (int i = 0; i < (int)desc_len; i++)
+            desc[i] = min(desc[i], DescrMagThr);
 
-                dog_pyr[idx] = createEmptyArray<T>(gauss_pyr[bottom].dims());
+        normalizeDesc(desc, desc_len);
 
-                sub<T>(dog_pyr[idx], gauss_pyr[top], gauss_pyr[bottom]);
-            }
+        // Calculate final descriptor values
+        for (int k = 0; k < (int)desc_len; k++) {
+            desc_out[f*desc_len+k] = round(min(255.f, desc[k] * IntDescrFctr));
         }
-
-        return dog_pyr;
     }
+}
 
+#undef IPTR
 
-    template<typename T, typename convAccT>
-    unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
-                       Array<float>& ori, Array<float>& size, Array<float>& desc,
-                       const Array<T>& in, const unsigned n_layers,
-                       const float contrast_thr, const float edge_thr,
-                       const float init_sigma, const bool double_input,
-                       const float img_scale, const float feature_ratio,
-                       const bool compute_GLOH)
-    {
-        in.eval();
-        af::dim4 idims = in.dims();
+template<typename T, typename convAccT>
+Array<T> createInitialImage(
+    const Array<T>& img,
+    const float init_sigma,
+    const bool double_input)
+{
+    af::dim4 idims = img.dims();
 
-        const unsigned min_dim = (double_input) ? min(idims[0]*2, idims[1]*2)
-            : min(idims[0], idims[1]);
-        const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
+    Array<T> init_img = createEmptyArray<T>(af::dim4());
 
-        Array<T> init_img = createInitialImage<T, convAccT>(in, init_sigma, double_input);
+    float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma * 4), 0.1f)
+                             : std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma), 0.1f);
 
-        std::vector< Array<T> > gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+    Array<T> filter = gauss_filter<T>(s);
 
-        std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
+    if (double_input) {
+        Array<T> double_img = resize<T>(img, idims[0] * 2, idims[1] * 2, AF_INTERP_BILINEAR);
+        init_img = convolve2<T, convAccT, false>(double_img, filter, filter);
+    }
+    else {
+        init_img = convolve2<T, convAccT, false>(img, filter, filter);
+    }
 
-        std::vector<float*> x_pyr(n_octaves, NULL);
-        std::vector<float*> y_pyr(n_octaves, NULL);
-        std::vector<float*> response_pyr(n_octaves, NULL);
-        std::vector<float*> size_pyr(n_octaves, NULL);
-        std::vector<float*> ori_pyr(n_octaves, NULL);
-        std::vector<float*> desc_pyr(n_octaves, NULL);
-        std::vector<unsigned> feat_pyr(n_octaves, 0);
-        unsigned total_feat = 0;
+    return init_img;
+}
 
-        const unsigned d = DescrWidth;
-        const unsigned n = DescrHistBins;
-        const unsigned rb = GLOHRadialBins;
-        const unsigned ab = GLOHAngularBins;
-        const unsigned hb = GLOHHistBins;
-        const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
+template<typename T, typename convAccT>
+std::vector< Array<T> > buildGaussPyr(
+    const Array<T>& init_img,
+    const unsigned n_octaves,
+    const unsigned n_layers,
+    const float init_sigma)
+{
+    // Precompute Gaussian sigmas using the following formula:
+    // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
+    std::vector<float> sig_layers(n_layers + 3);
+    sig_layers[0] = init_sigma;
+    float k = std::pow(2.0f, 1.0f / n_layers);
+    for (unsigned i = 1; i < n_layers + 3; i++) {
+        float sig_prev = std::pow(k, i-1) * init_sigma;
+        float sig_total = sig_prev * k;
+        sig_layers[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);
+    }
 
-        for (unsigned i = 0; i < n_octaves; i++) {
-            af::dim4 ddims = dog_pyr[i*(n_layers+2)].dims();
-            if (ddims[0]-2*ImgBorder < 1 ||
-                ddims[1]-2*ImgBorder < 1)
-                continue;
+    // Gaussian Pyramid
+    std::vector< Array<T> > gauss_pyr(n_octaves * (n_layers+3), createEmptyArray<T>(af::dim4()));
+    for (unsigned o = 0; o < n_octaves; o++) {
+        for (unsigned l = 0; l < n_layers+3; l++) {
+            unsigned src_idx = (l == 0) ? (o-1)*(n_layers+3) + n_layers : o*(n_layers+3) + l-1;
+            unsigned idx = o*(n_layers+3) + l;
 
-            const unsigned imel = ddims[0] * ddims[1];
-            const unsigned max_feat = ceil(imel * feature_ratio);
+            if (o == 0 && l == 0) {
+                gauss_pyr[idx] = init_img;
+            }
+            else if (l == 0) {
+                af::dim4 sdims = gauss_pyr[src_idx].dims();
+                gauss_pyr[idx] = resize<T>(gauss_pyr[src_idx], sdims[0] / 2, sdims[1] / 2, AF_INTERP_BILINEAR);
+            }
+            else {
+                Array<T> filter = gauss_filter<T>(sig_layers[l]);
 
-            float* extrema_x = memAlloc<float>(max_feat);
-            float* extrema_y = memAlloc<float>(max_feat);
-            unsigned* extrema_layer = memAlloc<unsigned>(max_feat);
-            unsigned extrema_feat = 0;
+                gauss_pyr[idx] = convolve2<T, convAccT, false>(gauss_pyr[src_idx], filter, filter);
+            }
+        }
+    }
 
-            for (unsigned j = 1; j <= n_layers; j++) {
-                unsigned prev   = i*(n_layers+2) + j-1;
-                unsigned center = i*(n_layers+2) + j;
-                unsigned next   = i*(n_layers+2) + j+1;
+    return gauss_pyr;
+}
 
-                unsigned layer = j;
+template<typename T>
+std::vector< Array<T> > buildDoGPyr(
+    std::vector< Array<T> >& gauss_pyr,
+    const unsigned n_octaves,
+    const unsigned n_layers)
+{
+    // DoG Pyramid
+    std::vector< Array<T> > dog_pyr(n_octaves * (n_layers+2), createEmptyArray<T>(af::dim4()));
+    for (unsigned o = 0; o < n_octaves; o++) {
+        for (unsigned l = 0; l < n_layers+2; l++) {
+            unsigned idx    = o*(n_layers+2) + l;
+            unsigned bottom = o*(n_layers+3) + l;
+            unsigned top    = o*(n_layers+3) + l+1;
 
-                float extrema_thr = 0.5f * contrast_thr / n_layers;
-                detectExtrema<T>(extrema_x, extrema_y, extrema_layer, &extrema_feat,
-                                 dog_pyr[prev], dog_pyr[center], dog_pyr[next],
-                                 layer, max_feat, extrema_thr);
-            }
+            dog_pyr[idx] = createEmptyArray<T>(gauss_pyr[bottom].dims());
 
-            extrema_feat = min(extrema_feat, max_feat);
+            sub<T>(dog_pyr[idx], gauss_pyr[top], gauss_pyr[bottom]);
+        }
+    }
 
-            if (extrema_feat == 0) {
-                memFree(extrema_x);
-                memFree(extrema_y);
-                memFree(extrema_layer);
+    return dog_pyr;
+}
 
-                continue;
-            }
 
-            unsigned interp_feat = 0;
+template<typename T, typename convAccT>
+unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
+                   Array<float>& ori, Array<float>& size, Array<float>& desc,
+                   const Array<T>& in, const unsigned n_layers,
+                   const float contrast_thr, const float edge_thr,
+                   const float init_sigma, const bool double_input,
+                   const float img_scale, const float feature_ratio,
+                   const bool compute_GLOH)
+{
+    in.eval();
+    af::dim4 idims = in.dims();
+
+    const unsigned min_dim = (double_input) ? min(idims[0]*2, idims[1]*2)
+        : min(idims[0], idims[1]);
+    const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
+
+    Array<T> init_img = createInitialImage<T, convAccT>(in, init_sigma, double_input);
+
+    std::vector< Array<T> > gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+
+    std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
+
+    std::vector<float*> x_pyr(n_octaves, NULL);
+    std::vector<float*> y_pyr(n_octaves, NULL);
+    std::vector<float*> response_pyr(n_octaves, NULL);
+    std::vector<float*> size_pyr(n_octaves, NULL);
+    std::vector<float*> ori_pyr(n_octaves, NULL);
+    std::vector<float*> desc_pyr(n_octaves, NULL);
+    std::vector<unsigned> feat_pyr(n_octaves, 0);
+    unsigned total_feat = 0;
+
+    const unsigned d = DescrWidth;
+    const unsigned n = DescrHistBins;
+    const unsigned rb = GLOHRadialBins;
+    const unsigned ab = GLOHAngularBins;
+    const unsigned hb = GLOHHistBins;
+    const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
+
+    for (unsigned i = 0; i < n_octaves; i++) {
+        af::dim4 ddims = dog_pyr[i*(n_layers+2)].dims();
+        if (ddims[0]-2*ImgBorder < 1 ||
+            ddims[1]-2*ImgBorder < 1)
+            continue;
+
+        const unsigned imel = ddims[0] * ddims[1];
+        const unsigned max_feat = ceil(imel * feature_ratio);
+
+        float* extrema_x = memAlloc<float>(max_feat);
+        float* extrema_y = memAlloc<float>(max_feat);
+        unsigned* extrema_layer = memAlloc<unsigned>(max_feat);
+        unsigned extrema_feat = 0;
+
+        for (unsigned j = 1; j <= n_layers; j++) {
+            unsigned prev   = i*(n_layers+2) + j-1;
+            unsigned center = i*(n_layers+2) + j;
+            unsigned next   = i*(n_layers+2) + j+1;
+
+            unsigned layer = j;
+
+            float extrema_thr = 0.5f * contrast_thr / n_layers;
+            detectExtrema<T>(extrema_x, extrema_y, extrema_layer, &extrema_feat,
+                             dog_pyr[prev], dog_pyr[center], dog_pyr[next],
+                             layer, max_feat, extrema_thr);
+        }
+
+        extrema_feat = min(extrema_feat, max_feat);
 
-            float* interp_x = memAlloc<float>(extrema_feat);
-            float* interp_y = memAlloc<float>(extrema_feat);
-            unsigned* interp_layer = memAlloc<unsigned>(extrema_feat);
-            float* interp_response = memAlloc<float>(extrema_feat);
-            float* interp_size = memAlloc<float>(extrema_feat);
+        if (extrema_feat == 0) {
+            memFree(extrema_x);
+            memFree(extrema_y);
+            memFree(extrema_layer);
 
-            interpolateExtrema<T>(interp_x, interp_y, interp_layer,
-                                  interp_response, interp_size, &interp_feat,
-                                  extrema_x, extrema_y, extrema_layer, extrema_feat,
-                                  dog_pyr, max_feat, i, n_layers,
-                                  contrast_thr, edge_thr, init_sigma, img_scale);
+            continue;
+        }
 
-            interp_feat = min(interp_feat, max_feat);
+        unsigned interp_feat = 0;
 
-            if (interp_feat == 0) {
-                memFree(interp_x);
-                memFree(interp_y);
-                memFree(interp_layer);
-                memFree(interp_response);
-                memFree(interp_size);
+        float* interp_x = memAlloc<float>(extrema_feat);
+        float* interp_y = memAlloc<float>(extrema_feat);
+        unsigned* interp_layer = memAlloc<unsigned>(extrema_feat);
+        float* interp_response = memAlloc<float>(extrema_feat);
+        float* interp_size = memAlloc<float>(extrema_feat);
 
-                continue;
-            }
+        interpolateExtrema<T>(interp_x, interp_y, interp_layer,
+                              interp_response, interp_size, &interp_feat,
+                              extrema_x, extrema_y, extrema_layer, extrema_feat,
+                              dog_pyr, max_feat, i, n_layers,
+                              contrast_thr, edge_thr, init_sigma, img_scale);
 
-            std::vector<feat_t> sorted_feat;
-            array_to_feat(sorted_feat, interp_x, interp_y, interp_layer, interp_response, interp_size, interp_feat);
-            std::stable_sort(sorted_feat.begin(), sorted_feat.end(), feat_cmp);
+        interp_feat = min(interp_feat, max_feat);
 
+        if (interp_feat == 0) {
             memFree(interp_x);
             memFree(interp_y);
             memFree(interp_layer);
             memFree(interp_response);
             memFree(interp_size);
 
-            unsigned nodup_feat = 0;
-
-            float* nodup_x = memAlloc<float>(interp_feat);
-            float* nodup_y = memAlloc<float>(interp_feat);
-            unsigned* nodup_layer = memAlloc<unsigned>(interp_feat);
-            float* nodup_response = memAlloc<float>(interp_feat);
-            float* nodup_size = memAlloc<float>(interp_feat);
-
-            removeDuplicates(nodup_x, nodup_y, nodup_layer,
-                             nodup_response, nodup_size, &nodup_feat,
-                             sorted_feat);
-
-            const unsigned max_oriented_feat = nodup_feat * 3;
-
-            float* oriented_x = memAlloc<float>(max_oriented_feat);
-            float* oriented_y = memAlloc<float>(max_oriented_feat);
-            unsigned* oriented_layer = memAlloc<unsigned>(max_oriented_feat);
-            float* oriented_response = memAlloc<float>(max_oriented_feat);
-            float* oriented_size = memAlloc<float>(max_oriented_feat);
-            float* oriented_ori = memAlloc<float>(max_oriented_feat);
-
-            unsigned oriented_feat = 0;
-
-            calcOrientation<T>(oriented_x, oriented_y, oriented_layer,
-                               oriented_response, oriented_size, oriented_ori, &oriented_feat,
-                               nodup_x, nodup_y, nodup_layer,
-                               nodup_response, nodup_size, nodup_feat,
-                               gauss_pyr, max_oriented_feat, i, n_layers, double_input);
-
-            memFree(nodup_x);
-            memFree(nodup_y);
-            memFree(nodup_layer);
-            memFree(nodup_response);
-            memFree(nodup_size);
-
-            if (oriented_feat == 0) {
-                memFree(oriented_x);
-                memFree(oriented_y);
-                memFree(oriented_layer);
-                memFree(oriented_response);
-                memFree(oriented_size);
-                memFree(oriented_ori);
+            continue;
+        }
 
-                continue;
-            }
+        std::vector<feat_t> sorted_feat;
+        array_to_feat(sorted_feat, interp_x, interp_y, interp_layer, interp_response, interp_size, interp_feat);
+        std::stable_sort(sorted_feat.begin(), sorted_feat.end(), feat_cmp);
+
+        memFree(interp_x);
+        memFree(interp_y);
+        memFree(interp_layer);
+        memFree(interp_response);
+        memFree(interp_size);
+
+        unsigned nodup_feat = 0;
+
+        float* nodup_x = memAlloc<float>(interp_feat);
+        float* nodup_y = memAlloc<float>(interp_feat);
+        unsigned* nodup_layer = memAlloc<unsigned>(interp_feat);
+        float* nodup_response = memAlloc<float>(interp_feat);
+        float* nodup_size = memAlloc<float>(interp_feat);
+
+        removeDuplicates(nodup_x, nodup_y, nodup_layer,
+                         nodup_response, nodup_size, &nodup_feat,
+                         sorted_feat);
+
+        const unsigned max_oriented_feat = nodup_feat * 3;
+
+        float* oriented_x = memAlloc<float>(max_oriented_feat);
+        float* oriented_y = memAlloc<float>(max_oriented_feat);
+        unsigned* oriented_layer = memAlloc<unsigned>(max_oriented_feat);
+        float* oriented_response = memAlloc<float>(max_oriented_feat);
+        float* oriented_size = memAlloc<float>(max_oriented_feat);
+        float* oriented_ori = memAlloc<float>(max_oriented_feat);
+
+        unsigned oriented_feat = 0;
+
+        calcOrientation<T>(oriented_x, oriented_y, oriented_layer,
+                           oriented_response, oriented_size, oriented_ori, &oriented_feat,
+                           nodup_x, nodup_y, nodup_layer,
+                           nodup_response, nodup_size, nodup_feat,
+                           gauss_pyr, max_oriented_feat, i, n_layers, double_input);
+
+        memFree(nodup_x);
+        memFree(nodup_y);
+        memFree(nodup_layer);
+        memFree(nodup_response);
+        memFree(nodup_size);
+
+        if (oriented_feat == 0) {
+            memFree(oriented_x);
+            memFree(oriented_y);
+            memFree(oriented_layer);
+            memFree(oriented_response);
+            memFree(oriented_size);
+            memFree(oriented_ori);
+
+            continue;
+        }
 
-            float* desc = memAlloc<float>(oriented_feat * desc_len);
+        float* desc = memAlloc<float>(oriented_feat * desc_len);
 
-            float scale = 1.f/(1 << i);
-            if (double_input) scale *= 2.f;
+        float scale = 1.f/(1 << i);
+        if (double_input) scale *= 2.f;
 
-            if (compute_GLOH)
-                computeGLOHDescriptor<T>(desc, desc_len,
-                                         oriented_x, oriented_y, oriented_layer,
-                                         oriented_response, oriented_size, oriented_ori,
-                                         oriented_feat, gauss_pyr, d, rb, ab, hb,
-                                         scale, i, n_layers);
-            else
-                computeDescriptor<T>(desc, desc_len,
+        if (compute_GLOH)
+            computeGLOHDescriptor<T>(desc, desc_len,
                                      oriented_x, oriented_y, oriented_layer,
                                      oriented_response, oriented_size, oriented_ori,
-                                     oriented_feat, gauss_pyr, d, n, scale, i, n_layers);
-
-            total_feat += oriented_feat;
-            feat_pyr[i] = oriented_feat;
-
-            if (oriented_feat > 0) {
-                x_pyr[i] = oriented_x;
-                y_pyr[i] = oriented_y;
-                response_pyr[i] = oriented_response;
-                ori_pyr[i] = oriented_ori;
-                size_pyr[i] = oriented_size;
-                desc_pyr[i] = desc;
-            }
+                                     oriented_feat, gauss_pyr, d, rb, ab, hb,
+                                     scale, i, n_layers);
+        else
+            computeDescriptor<T>(desc, desc_len,
+                                 oriented_x, oriented_y, oriented_layer,
+                                 oriented_response, oriented_size, oriented_ori,
+                                 oriented_feat, gauss_pyr, d, n, scale, i, n_layers);
+
+        total_feat += oriented_feat;
+        feat_pyr[i] = oriented_feat;
+
+        if (oriented_feat > 0) {
+            x_pyr[i] = oriented_x;
+            y_pyr[i] = oriented_y;
+            response_pyr[i] = oriented_response;
+            ori_pyr[i] = oriented_ori;
+            size_pyr[i] = oriented_size;
+            desc_pyr[i] = desc;
         }
+    }
 
-        if (total_feat > 0) {
-            const af::dim4 total_feat_dims(total_feat);
-            const af::dim4 desc_dims(desc_len, total_feat);
-
-            // Allocate output memory
-            x     = createEmptyArray<float>(total_feat_dims);
-            y     = createEmptyArray<float>(total_feat_dims);
-            score = createEmptyArray<float>(total_feat_dims);
-            ori   = createEmptyArray<float>(total_feat_dims);
-            size  = createEmptyArray<float>(total_feat_dims);
-            desc  = createEmptyArray<float>(desc_dims);
-
-            float* x_ptr = x.get();
-            float* y_ptr = y.get();
-            float* score_ptr = score.get();
-            float* ori_ptr = ori.get();
-            float* size_ptr = size.get();
-            float* desc_ptr = desc.get();
-
-            unsigned offset = 0;
-            for (unsigned i = 0; i < n_octaves; i++) {
-                if (feat_pyr[i] == 0)
-                    continue;
-
-                memcpy(x_ptr+offset,     x_pyr[i],        feat_pyr[i] * sizeof(float));
-                memcpy(y_ptr+offset,     y_pyr[i],        feat_pyr[i] * sizeof(float));
-                memcpy(score_ptr+offset, response_pyr[i], feat_pyr[i] * sizeof(float));
-                memcpy(ori_ptr+offset,   ori_pyr[i],      feat_pyr[i] * sizeof(float));
-                memcpy(size_ptr+offset,  size_pyr[i],     feat_pyr[i] * sizeof(float));
-
-                memcpy(desc_ptr+(offset*desc_len), desc_pyr[i], feat_pyr[i] * desc_len * sizeof(float));
-
-                memFree(x_pyr[i]);
-                memFree(y_pyr[i]);
-                memFree(response_pyr[i]);
-                memFree(ori_pyr[i]);
-                memFree(size_pyr[i]);
-                memFree(desc_pyr[i]);
-
-                offset += feat_pyr[i];
-            }
-        }
+    if (total_feat > 0) {
+        const af::dim4 total_feat_dims(total_feat);
+        const af::dim4 desc_dims(desc_len, total_feat);
+
+        // Allocate output memory
+        x     = createEmptyArray<float>(total_feat_dims);
+        y     = createEmptyArray<float>(total_feat_dims);
+        score = createEmptyArray<float>(total_feat_dims);
+        ori   = createEmptyArray<float>(total_feat_dims);
+        size  = createEmptyArray<float>(total_feat_dims);
+        desc  = createEmptyArray<float>(desc_dims);
+
+        float* x_ptr = x.get();
+        float* y_ptr = y.get();
+        float* score_ptr = score.get();
+        float* ori_ptr = ori.get();
+        float* size_ptr = size.get();
+        float* desc_ptr = desc.get();
+
+        unsigned offset = 0;
+        for (unsigned i = 0; i < n_octaves; i++) {
+            if (feat_pyr[i] == 0)
+                continue;
+
+            memcpy(x_ptr+offset,     x_pyr[i],        feat_pyr[i] * sizeof(float));
+            memcpy(y_ptr+offset,     y_pyr[i],        feat_pyr[i] * sizeof(float));
+            memcpy(score_ptr+offset, response_pyr[i], feat_pyr[i] * sizeof(float));
+            memcpy(ori_ptr+offset,   ori_pyr[i],      feat_pyr[i] * sizeof(float));
+            memcpy(size_ptr+offset,  size_pyr[i],     feat_pyr[i] * sizeof(float));
+
+            memcpy(desc_ptr+(offset*desc_len), desc_pyr[i], feat_pyr[i] * desc_len * sizeof(float));
 
-        return total_feat;
+            memFree(x_pyr[i]);
+            memFree(y_pyr[i]);
+            memFree(response_pyr[i]);
+            memFree(ori_pyr[i]);
+            memFree(size_pyr[i]);
+            memFree(desc_pyr[i]);
+
+            offset += feat_pyr[i];
+        }
     }
+
+    return total_feat;
+}
+
 }
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 9f683fc450..ba47ba9fd6 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -91,6 +91,7 @@ template<typename Ti, typename To>
 std::pair< Array<To>, Array<To> >
 sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
 {
+    img.eval();
     // ket_size is for future proofing, this argument is not used
     // currently
     Array<To> dx = createEmptyArray<To>(img.dims());
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index b279971c7b..0243088fb3 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -75,6 +75,10 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
+    A.eval();
+    pivot.eval();
+    b.eval();
+
     int N        = A.dims()[0];
     int NRHS     = b.dims()[1];
     Array< T > B = copyArray<T>(b);
@@ -114,9 +118,10 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 {
+    a.eval();
+    b.eval();
 
-    if (options & AF_MAT_UPPER ||
-        options & AF_MAT_LOWER) {
+    if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
     }
 
@@ -178,6 +183,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 
 namespace cpu
 {
+
 #define INSTANTIATE_SOLVE(T)                                            \
     template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
                                const af_mat_prop options);              \
@@ -188,4 +194,5 @@ INSTANTIATE_SOLVE(float)
 INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
+
 }
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 94d70a8e49..cbdb50e987 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -25,65 +25,69 @@ using std::function;
 
 namespace cpu
 {
-    ///////////////////////////////////////////////////////////////////////////
-    // Kernel Functions
-    ///////////////////////////////////////////////////////////////////////////
 
-    // Based off of http://stackoverflow.com/a/12399290
-    template<typename T, bool isAscending>
-    void sort0(Array<T> val)
-    {
-        // initialize original index locations
-        T *val_ptr = val.get();
+///////////////////////////////////////////////////////////////////////////
+// Kernel Functions
+///////////////////////////////////////////////////////////////////////////
 
-        function<bool(T, T)> op = greater<T>();
-        if(isAscending) { op = less<T>(); }
+// Based off of http://stackoverflow.com/a/12399290
+template<typename T, bool isAscending>
+void sort0(Array<T> val)
+{
+    // initialize original index locations
+    T *val_ptr = val.get();
+
+    function<bool(T, T)> op = greater<T>();
+    if(isAscending) { op = less<T>(); }
 
-        T *comp_ptr = nullptr;
-        for(dim_t w = 0; w < val.dims()[3]; w++) {
-            dim_t valW = w * val.strides()[3];
-            for(dim_t z = 0; z < val.dims()[2]; z++) {
-                dim_t valWZ = valW + z * val.strides()[2];
-                for(dim_t y = 0; y < val.dims()[1]; y++) {
+    T *comp_ptr = nullptr;
+    for(dim_t w = 0; w < val.dims()[3]; w++) {
+        dim_t valW = w * val.strides()[3];
+        for(dim_t z = 0; z < val.dims()[2]; z++) {
+            dim_t valWZ = valW + z * val.strides()[2];
+            for(dim_t y = 0; y < val.dims()[1]; y++) {
 
-                    dim_t valOffset = valWZ + y * val.strides()[1];
+                dim_t valOffset = valWZ + y * val.strides()[1];
 
-                    comp_ptr = val_ptr + valOffset;
-                    std::sort(comp_ptr, comp_ptr + val.dims()[0], op);
-                }
+                comp_ptr = val_ptr + valOffset;
+                std::sort(comp_ptr, comp_ptr + val.dims()[0], op);
             }
         }
-        return;
     }
+    return;
+}
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Wrapper Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T, bool isAscending>
-    Array<T> sort(const Array<T> &in, const unsigned dim)
-    {
-        Array<T> out = copyArray<T>(in);
-        switch(dim) {
-            case 0: getQueue().enqueue(sort0<T, isAscending>, out); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
-        return out;
+///////////////////////////////////////////////////////////////////////////
+// Wrapper Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool isAscending>
+Array<T> sort(const Array<T> &in, const unsigned dim)
+{
+    in.eval();
+
+    Array<T> out = copyArray<T>(in);
+    switch(dim) {
+        case 0: getQueue().enqueue(sort0<T, isAscending>, out); break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+    return out;
+}
 
 #define INSTANTIATE(T)                                                  \
     template Array<T> sort<T, true>(const Array<T> &in, const unsigned dim); \
     template Array<T> sort<T,false>(const Array<T> &in, const unsigned dim); \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    //INSTANTIATE(cfloat)
-    //INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+INSTANTIATE(float)
+INSTANTIATE(double)
+//INSTANTIATE(cfloat)
+//INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+
 }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index f07d585b41..f9415345ae 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -23,68 +23,71 @@ using std::sort;
 
 namespace cpu
 {
-    ///////////////////////////////////////////////////////////////////////////
-    // Kernel Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T, bool isAscending>
-    void sort0_index(Array<T> &val, Array<uint> &idx, const Array<T> &in)
-    {
-        // initialize original index locations
-           uint *idx_ptr = idx.get();
-              T *val_ptr = val.get();
-        const T *in_ptr  = in.get();
-        function<bool(T, T)> op = greater<T>();
-        if(isAscending) { op = less<T>(); }
-
-        std::vector<uint> seq_vec(idx.dims()[0]);
-        std::iota(seq_vec.begin(), seq_vec.end(), 0);
-
-        const T *comp_ptr = nullptr;
-        auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-        for(dim_t w = 0; w < in.dims()[3]; w++) {
-            dim_t valW = w * val.strides()[3];
-            dim_t idxW = w * idx.strides()[3];
-            dim_t  inW = w *  in.strides()[3];
-            for(dim_t z = 0; z < in.dims()[2]; z++) {
-                dim_t valWZ = valW + z * val.strides()[2];
-                dim_t idxWZ = idxW + z * idx.strides()[2];
-                dim_t  inWZ =  inW + z *  in.strides()[2];
-                for(dim_t y = 0; y < in.dims()[1]; y++) {
-
-                    dim_t valOffset = valWZ + y * val.strides()[1];
-                    dim_t idxOffset = idxWZ + y * idx.strides()[1];
-                    dim_t inOffset  =  inWZ + y *  in.strides()[1];
-
-                    uint *ptr = idx_ptr + idxOffset;
-                    std::copy(seq_vec.begin(), seq_vec.end(), ptr);
-
-                    comp_ptr = in_ptr + inOffset;
-                    std::stable_sort(ptr, ptr + in.dims()[0], comparator);
-
-                    for (dim_t i = 0; i < val.dims()[0]; ++i){
-                        val_ptr[valOffset + i] = in_ptr[inOffset + idx_ptr[idxOffset + i]];
-                    }
+
+///////////////////////////////////////////////////////////////////////////
+// Kernel Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool isAscending>
+void sort0_index(Array<T> &val, Array<uint> &idx, const Array<T> &in)
+{
+    // initialize original index locations
+       uint *idx_ptr = idx.get();
+          T *val_ptr = val.get();
+    const T *in_ptr  = in.get();
+    function<bool(T, T)> op = greater<T>();
+    if(isAscending) { op = less<T>(); }
+
+    std::vector<uint> seq_vec(idx.dims()[0]);
+    std::iota(seq_vec.begin(), seq_vec.end(), 0);
+
+    const T *comp_ptr = nullptr;
+    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
+
+    for(dim_t w = 0; w < in.dims()[3]; w++) {
+        dim_t valW = w * val.strides()[3];
+        dim_t idxW = w * idx.strides()[3];
+        dim_t  inW = w *  in.strides()[3];
+        for(dim_t z = 0; z < in.dims()[2]; z++) {
+            dim_t valWZ = valW + z * val.strides()[2];
+            dim_t idxWZ = idxW + z * idx.strides()[2];
+            dim_t  inWZ =  inW + z *  in.strides()[2];
+            for(dim_t y = 0; y < in.dims()[1]; y++) {
+
+                dim_t valOffset = valWZ + y * val.strides()[1];
+                dim_t idxOffset = idxWZ + y * idx.strides()[1];
+                dim_t inOffset  =  inWZ + y *  in.strides()[1];
+
+                uint *ptr = idx_ptr + idxOffset;
+                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
+
+                comp_ptr = in_ptr + inOffset;
+                std::stable_sort(ptr, ptr + in.dims()[0], comparator);
+
+                for (dim_t i = 0; i < val.dims()[0]; ++i){
+                    val_ptr[valOffset + i] = in_ptr[inOffset + idx_ptr[idxOffset + i]];
                 }
             }
         }
-
-        return;
     }
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Wrapper Functions
-    ///////////////////////////////////////////////////////////////////////////
-    template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
-    {
-        val = createEmptyArray<T>(in.dims());
-        idx = createEmptyArray<uint>(in.dims());
-        switch(dim) {
-            case 0: getQueue().enqueue(sort0_index<T, isAscending>, val, idx, in); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
+    return;
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper Functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool isAscending>
+void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+{
+    in.eval();
+
+    val = createEmptyArray<T>(in.dims());
+    idx = createEmptyArray<uint>(in.dims());
+    switch(dim) {
+        case 0: getQueue().enqueue(sort0_index<T, isAscending>, val, idx, in); break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+}
 
 #define INSTANTIATE(T)                                                  \
     template void sort_index<T, true>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
@@ -92,16 +95,17 @@ namespace cpu
     template void sort_index<T,false>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
                                       const uint dim);                  \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    //INSTANTIATE(cfloat)
-    //INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+INSTANTIATE(float)
+INSTANTIATE(double)
+//INSTANTIATE(cfloat)
+//INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+
 }
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index e2c908c378..c278908e40 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -106,6 +106,8 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                const unsigned radius, const float diff_thr, const float geom_thr,
                const float feature_ratio, const unsigned edge)
 {
+    in.eval();
+
     dim4 idims = in.dims();
     const unsigned corner_lim = in.elements() * feature_ratio;
 
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 39cbb66343..92912ca616 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -30,101 +30,106 @@ namespace cpu
 
 #if defined(USE_MKL) || defined(__APPLE__)
 
-    template<typename T, typename Tr>
-    using svd_func_def = int (*)(ORDER_TYPE,
-                                 char jobz,
-                                 int m, int n,
-                                 T* in, int ldin,
-                                 Tr* s,
-                                 T* u, int ldu,
-                                 T* vt, int ldvt);
-
-    SVD_FUNC_DEF( gesdd )
-    SVD_FUNC(gesdd, float  , float , s)
-    SVD_FUNC(gesdd, double , double, d)
-    SVD_FUNC(gesdd, cfloat , float , c)
-    SVD_FUNC(gesdd, cdouble, double, z)
+template<typename T, typename Tr>
+using svd_func_def = int (*)(ORDER_TYPE,
+                             char jobz,
+                             int m, int n,
+                             T* in, int ldin,
+                             Tr* s,
+                             T* u, int ldu,
+                             T* vt, int ldvt);
+
+SVD_FUNC_DEF( gesdd )
+SVD_FUNC(gesdd, float  , float , s)
+SVD_FUNC(gesdd, double , double, d)
+SVD_FUNC(gesdd, cfloat , float , c)
+SVD_FUNC(gesdd, cdouble, double, z)
 
 #else   // Atlas causes memory freeing issues with using gesdd
 
-    template<typename T, typename Tr>
-    using svd_func_def = int (*)(ORDER_TYPE,
-                                 char jobu, char jobvt,
-                                 int m, int n,
-                                 T* in, int ldin,
-                                 Tr* s,
-                                 T* u, int ldu,
-                                 T* vt, int ldvt,
-                                 Tr *superb);
-
-    SVD_FUNC_DEF( gesvd )
-    SVD_FUNC(gesvd, float  , float , s)
-    SVD_FUNC(gesvd, double , double, d)
-    SVD_FUNC(gesvd, cfloat , float , c)
-    SVD_FUNC(gesvd, cdouble, double, z)
+template<typename T, typename Tr>
+using svd_func_def = int (*)(ORDER_TYPE,
+                             char jobu, char jobvt,
+                             int m, int n,
+                             T* in, int ldin,
+                             Tr* s,
+                             T* u, int ldu,
+                             T* vt, int ldvt,
+                             Tr *superb);
+
+SVD_FUNC_DEF( gesvd )
+SVD_FUNC(gesvd, float  , float , s)
+SVD_FUNC(gesvd, double , double, d)
+SVD_FUNC(gesvd, cfloat , float , c)
+SVD_FUNC(gesvd, cdouble, double, z)
 
 #endif
 
-    template <typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-    {
-        s.eval();
-        u.eval();
-        vt.eval();
-        in.eval();
+template <typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
+{
+    s.eval();
+    u.eval();
+    vt.eval();
+    in.eval();
 
-        auto func = [=] (Array<Tr> s, Array<T> u, Array<T> vt, Array<T> in) {
-            dim4 iDims = in.dims();
-            int M = iDims[0];
-            int N = iDims[1];
+    auto func = [=] (Array<Tr> s, Array<T> u, Array<T> vt, Array<T> in) {
+        dim4 iDims = in.dims();
+        int M = iDims[0];
+        int N = iDims[1];
 
 #if defined(USE_MKL) || defined(__APPLE__)
-            svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides()[1],
-                    s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1]);
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides()[1],
+                s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1]);
 #else
-            std::vector<Tr> superb(std::min(M, N));
-            svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides()[1],
-                    s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
+        std::vector<Tr> superb(std::min(M, N));
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides()[1],
+                s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
 #endif
-        };
-        getQueue().enqueue(func, s, u, vt, in);
-    }
-
-    template <typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-    {
-        Array<T> in_copy = copyArray<T>(in);
-        svdInPlace(s, u, vt, in_copy);
-    }
+    };
+    getQueue().enqueue(func, s, u, vt, in);
+}
+
+template <typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
+{
+    Array<T> in_copy = copyArray<T>(in);
+    svdInPlace(s, u, vt, in_copy);
+}
+
 }
 
 #else
 
 namespace cpu
 {
-    template <typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-    {
-        AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
-    }
-
-    template <typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-    {
-        AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
-    }
+
+template <typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
+{
+    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
+}
+
+template <typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
+{
+    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
+}
+
 }
 
 #endif
 
-namespace cpu {
+namespace cpu
+{
 
 #define INSTANTIATE_SVD(T, Tr)                                          \
     template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, const Array<T> &in); \
     template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, Array<T> &in);
 
-    INSTANTIATE_SVD(float  , float )
-    INSTANTIATE_SVD(double , double)
-    INSTANTIATE_SVD(cfloat , float )
-    INSTANTIATE_SVD(cdouble, double)
+INSTANTIATE_SVD(float  , float )
+INSTANTIATE_SVD(double , double)
+INSTANTIATE_SVD(cfloat , float )
+INSTANTIATE_SVD(cdouble, double)
+
 }
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index f4a05148c5..a7287ceea0 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -107,8 +107,10 @@ template<typename T>
 Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
                     const af_interp_type method, const bool inverse)
 {
-    Array<T> out = createEmptyArray<T>(odims);
     in.eval();
+    transform.eval();
+
+    Array<T> out = createEmptyArray<T>(odims);
 
     switch(method) {
         case AF_INTERP_NEAREST :
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index c3a8a37a72..7e7eec1747 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -171,5 +171,4 @@ INSTANTIATE(uintl  )
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-
 }
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index ed7f348bad..13bee164eb 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -66,6 +66,7 @@ void triangle(Array<T> &out, const Array<T> &in)
 template<typename T, bool is_upper, bool is_unit_diag>
 Array<T> triangle(const Array<T> &in)
 {
+    in.eval();
     Array<T> out = createEmptyArray<T>(in.dims());
     triangle<T, is_upper, is_unit_diag>(out, in);
     return out;
@@ -81,17 +82,17 @@ Array<T> triangle(const Array<T> &in)
     template Array<T> triangle<T, true , false>(const Array<T> &in);    \
     template Array<T> triangle<T, false, false>(const Array<T> &in);    \
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
 }
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index efb46be7f4..41423c746c 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -83,8 +83,9 @@ template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
 {
-    af::dim4 idims = in.dims();
+    in.eval();
 
+    af::dim4 idims = in.dims();
     dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
     dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
 
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index e6a4817f31..441c7ff239 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -23,61 +23,62 @@ using af::dim4;
 
 namespace cpu
 {
-    template<typename T>
-    Array<uint> where(const Array<T> &in)
-    {
-        evalArray(in);
-        getQueue().sync();
 
-        const dim_t *dims    = in.dims().get();
-        const dim_t *strides = in.strides().get();
-        static const T zero = scalar<T>(0);
+template<typename T>
+Array<uint> where(const Array<T> &in)
+{
+    evalArray(in);
+    getQueue().sync();
+
+    const dim_t *dims    = in.dims().get();
+    const dim_t *strides = in.strides().get();
+    static const T zero = scalar<T>(0);
 
-        const T *iptr = in.get();
-        uint *out_vec  = memAlloc<uint>(in.elements());
+    const T *iptr = in.get();
+    uint *out_vec  = memAlloc<uint>(in.elements());
 
-        dim_t count = 0;
-        dim_t idx = 0;
-        for (dim_t w = 0; w < dims[3]; w++) {
-            uint offw = w * strides[3];
+    dim_t count = 0;
+    dim_t idx = 0;
+    for (dim_t w = 0; w < dims[3]; w++) {
+        uint offw = w * strides[3];
 
-            for (dim_t z = 0; z < dims[2]; z++) {
-                uint offz = offw + z * strides[2];
+        for (dim_t z = 0; z < dims[2]; z++) {
+            uint offz = offw + z * strides[2];
 
-                for (dim_t y = 0; y < dims[1]; y++) {
-                    uint offy = y * strides[1] + offz;
+            for (dim_t y = 0; y < dims[1]; y++) {
+                uint offy = y * strides[1] + offz;
 
-                    for (dim_t x = 0; x < dims[0]; x++) {
+                for (dim_t x = 0; x < dims[0]; x++) {
 
-                        T val = iptr[offy + x];
-                        if (val != zero) {
-                            out_vec[count] = idx;
-                            count++;
-                        }
-                        idx++;
+                    T val = iptr[offy + x];
+                    if (val != zero) {
+                        out_vec[count] = idx;
+                        count++;
                     }
+                    idx++;
                 }
             }
         }
-
-        Array<uint> out = createDeviceDataArray<uint>(dim4(count), out_vec);
-        return out;
     }
 
+    Array<uint> out = createDeviceDataArray<uint>(dim4(count), out_vec);
+    return out;
+}
+
 #define INSTANTIATE(T)                                  \
     template Array<uint> where<T>(const Array<T> &in);    \
 
-    INSTANTIATE(float  )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(double )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(char   )
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+INSTANTIATE(float  )
+INSTANTIATE(cfloat )
+INSTANTIATE(double )
+INSTANTIATE(cdouble)
+INSTANTIATE(char   )
+INSTANTIATE(int    )
+INSTANTIATE(uint   )
+INSTANTIATE(intl   )
+INSTANTIATE(uintl  )
+INSTANTIATE(uchar  )
+INSTANTIATE(short  )
+INSTANTIATE(ushort )
 
 }

From 36ed9a49fb6bde980f86fd6ff61b78b631b1a06d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2015 15:33:56 -0500
Subject: [PATCH 0146/2677] bug fix in plot3 graphics example

---
 examples/graphics/plot3.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index 3893f01803..d9e58563da 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -29,8 +29,8 @@ int main(int argc, char *argv[])
         do{
             array Y = sin((Z*t) + t) / Z;
             array X = cos((Z*t) + t) / Z;
-            X = max(min(X, 1), -1);
-            Y = max(min(Y, 1), -1);
+            X = max(min(X, 1.0), -1.0);
+            Y = max(min(Y, 1.0), -1.0);
 
             array Pts = join(1, X, Y, Z);
             //Pts can be passed in as a matrix in the form n x 3, 3 x n

From 8cc9c9cd4f4b23a0ddb58e3feee55c3eccd0b6be Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2015 18:04:12 -0500
Subject: [PATCH 0147/2677] threads library is now a submodule in cpu backend

---
 .gitmodules                    |  3 +++
 src/backend/cpu/CMakeLists.txt | 15 +--------------
 src/backend/cpu/threads        |  1 +
 3 files changed, 5 insertions(+), 14 deletions(-)
 create mode 160000 src/backend/cpu/threads

diff --git a/.gitmodules b/.gitmodules
index 395881a861..1d89315347 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -7,3 +7,6 @@
 [submodule "test/gtest"]
 	path = test/gtest
 	url = https://chromium.googlesource.com/external/googletest
+[submodule "src/backend/cpu/threads"]
+	path = src/backend/cpu/threads
+	url = git@github.com:alltheflops/threads.git
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 57cf3dfe61..62f0b3a55e 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -47,24 +47,12 @@ IF(NOT UNIX)
     ADD_DEFINITIONS(-DAFDLL)
 ENDIF()
 
-INCLUDE(ExternalProject)
-ExternalProject_Add(
-    threads
-    PREFIX ${CMAKE_BINARY_DIR}/third_party/threads
-    GIT_REPOSITORY https://github.com/alltheflops/threads.git
-    CONFIGURE_COMMAND ""
-    BUILD_COMMAND ""
-    INSTALL_COMMAND ${CMAKE_COMMAND} -E copy_directory
-                    <SOURCE_DIR>/threads ${CMAKE_BINARY_DIR}/third_party/threads
-    LOG_DOWNLOAD ON
-    LOG_INSTALL ON
-    )
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
     "${CMAKE_SOURCE_DIR}/src/backend/cpu"
+    "${CMAKE_SOURCE_DIR}/src/backend/cpu/threads"
     ${FFTW_INCLUDES}
     ${CBLAS_INCLUDE_DIR}
-    ${CMAKE_BINARY_DIR}/third_party/threads/src/threads
     )
 
 IF(LAPACK_FOUND)
@@ -164,7 +152,6 @@ TARGET_LINK_LIBRARIES(afcpu
                             PRIVATE ${CBLAS_LIBRARIES}
                             PRIVATE ${FFTW_LIBRARIES})
 
-ADD_DEPENDENCIES(afcpu threads)
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
   ADD_DEPENDENCIES(afcpu forge)
 ENDIF()
diff --git a/src/backend/cpu/threads b/src/backend/cpu/threads
new file mode 160000
index 0000000000..5e778ce0a7
--- /dev/null
+++ b/src/backend/cpu/threads
@@ -0,0 +1 @@
+Subproject commit 5e778ce0a7f0f80af9d32ea3569df3dbec834f59

From abce1e8bf6e3088d41ed87c07048a337a82242cc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2015 18:26:15 -0500
Subject: [PATCH 0148/2677] Moved fns enqueued onto async queue to separate
 folder

---
 src/backend/cpu/approx.cpp         | 293 +----------------------------
 src/backend/cpu/kernel/approx1.hpp | 141 ++++++++++++++
 src/backend/cpu/kernel/approx2.hpp | 169 +++++++++++++++++
 3 files changed, 318 insertions(+), 285 deletions(-)
 create mode 100644 src/backend/cpu/kernel/approx1.hpp
 create mode 100644 src/backend/cpu/kernel/approx2.hpp

diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 7988863d4d..7e65486a66 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -9,142 +9,17 @@
 
 #include <Array.hpp>
 #include <approx.hpp>
-#include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
+#include <kernel/approx1.hpp>
+#include <kernel/approx2.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
 
 namespace cpu
 {
 
-///////////////////////////////////////////////////////////////////////////
-// Approx1
-///////////////////////////////////////////////////////////////////////////
-template<typename Ty, typename Tp, af_interp_type method>
-struct approx1_op
-{
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-    {
-        return;
-    }
-};
-
-template<typename Ty, typename Tp>
-struct approx1_op<Ty, Tp, AF_INTERP_NEAREST>
-{
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-    {
-        dim_t pmId = idx;
-        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-        const Tp x = pos[pmId];
-        bool gFlag = false;
-        if (x < 0 || idims[0] < x+1) {  // No need to check y
-            gFlag = true;
-        }
-
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                       + idy * istrides[1];
-            const dim_t iMem = round(x) + ioff;
-
-            out[omId] = in[iMem];
-        }
-    }
-};
-
-template<typename Ty, typename Tp>
-struct approx1_op<Ty, Tp, AF_INTERP_LINEAR>
-{
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-    {
-        dim_t pmId = idx;
-        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-        const Tp x = pos[pmId];
-        bool gFlag = false;
-        if (x < 0 || idims[0] < x+1) {
-            gFlag = true;
-        }
-
-        const dim_t grid_x = floor(x);  // nearest grid
-        const Tp off_x = x - grid_x; // fractional offset
-
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
-
-            // Check if x and x + 1 are both valid indices
-            bool cond = (x < idims[0] - 1);
-            // Compute Left and Right Weighted Values
-            Ty yl = ((Tp)1.0 - off_x) * in[ioff];
-            Ty yr = cond ? (off_x) * in[ioff + 1] : scalar<Ty>(0);
-            Ty yo = yl + yr;
-            // Compute Weight used
-            Tp wt = cond ? (Tp)1.0 : (Tp)(1.0 - off_x);
-            // Write final value
-            out[omId] = (yo / wt);
-        }
-    }
-};
-
-template<typename Ty, typename Tp, af_interp_type method>
-void approx1_(Array<Ty> output, Array<Ty> const input,
-              Array<Tp> const position, float const offGrid)
-{
-    Ty * out = output.get();
-    Ty const * const in  = input.get();
-    Tp const * const pos = position.get();
-    dim4 const odims     = output.dims();
-    dim4 const idims     = input.dims();
-    dim4 const pdims     = position.dims();
-    dim4 const ostrides  = output.strides();
-    dim4 const istrides  = input.strides();
-    dim4 const pstrides  = position.strides();
-    dim_t const oElems   = output.elements();
-    dim_t const iElems   = input.elements();
-
-    approx1_op<Ty, Tp, method> op;
-    bool pBatch = !(pdims[1] == 1 && pdims[2] == 1 && pdims[3] == 1);
-
-    for(dim_t w = 0; w < odims[3]; w++) {
-        for(dim_t z = 0; z < odims[2]; z++) {
-            for(dim_t y = 0; y < odims[1]; y++) {
-                for(dim_t x = 0; x < odims[0]; x++) {
-                    op(out, odims, oElems, in, idims, iElems, pos, pdims,
-                       ostrides, istrides, pstrides, offGrid, pBatch, x, y, z, w);
-                }
-            }
-        }
-    }
-}
-
 template<typename Ty, typename Tp>
 Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
-                   const af_interp_type method, const float offGrid)
+                  const af_interp_type method, const float offGrid)
 {
     in.eval();
     pos.eval();
@@ -152,16 +27,15 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
     af::dim4 odims = in.dims();
     odims[0] = pos.dims()[0];
 
-    // Create output placeholder
     Array<Ty> out = createEmptyArray<Ty>(odims);
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_NEAREST>,
+            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
                                out, in, pos, offGrid);
             break;
         case AF_INTERP_LINEAR:
-            getQueue().enqueue(approx1_<Ty, Tp, AF_INTERP_LINEAR>,
+            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
                                out, in, pos, offGrid);
             break;
         default:
@@ -170,161 +44,10 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
     return out;
 }
 
-///////////////////////////////////////////////////////////////////////////
-// Approx2
-///////////////////////////////////////////////////////////////////////////
-template<typename Ty, typename Tp, af_interp_type method>
-struct approx2_op
-{
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const af::dim4 &pstrides, const af::dim4 &qstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-    {
-        return;
-    }
-};
-
-template<typename Ty, typename Tp>
-struct approx2_op<Ty, Tp, AF_INTERP_NEAREST>
-{
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const af::dim4 &pstrides, const af::dim4 &qstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-    {
-        dim_t pmId = idy * pstrides[1] + idx;
-        dim_t qmId = idy * qstrides[1] + idx;
-        if(pBatch) {
-            pmId += idw * pstrides[3] + idz * pstrides[2];
-            qmId += idw * qstrides[3] + idz * qstrides[2];
-        }
-
-        bool gFlag = false;
-        const Tp x = pos[pmId], y = qos[qmId];
-        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
-            gFlag = true;
-        }
-
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
-        } else {
-            const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
-            const dim_t imId = idw * istrides[3] + idz * istrides[2] +
-                            grid_y * istrides[1] + grid_x;
-            out[omId] = in[imId];
-        }
-    }
-};
-
-template<typename Ty, typename Tp>
-struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
-{
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const af::dim4 &pstrides, const af::dim4 &qstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
-    {
-        dim_t pmId = idy * pstrides[1] + idx;
-        dim_t qmId = idy * qstrides[1] + idx;
-        if(pBatch) {
-            pmId += idw * pstrides[3] + idz * pstrides[2];
-            qmId += idw * qstrides[3] + idz * qstrides[2];
-        }
-
-        bool gFlag = false;
-        const Tp x = pos[pmId], y = qos[qmId];
-        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
-            gFlag = true;
-        }
-
-        const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-        const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
-
-        // Check if pVal and pVal + 1 are both valid indices
-        bool condY = (y < idims[1] - 1);
-        bool condX = (x < idims[0] - 1);
-
-        // Compute wieghts used
-        Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
-        Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
-        Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
-        Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-        Tp wt = wt00 + wt10 + wt01 + wt11;
-        Ty zero = scalar<Ty>(0);
-
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                    + grid_y * istrides[1] + grid_x;
-
-            // Compute Weighted Values
-            Ty y00 =                    wt00 * in[ioff];
-            Ty y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
-            Ty y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
-            Ty y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
-
-            Ty yo = y00 + y10 + y01 + y11;
-
-            // Write Final Value
-            out[omId] = (yo / wt);
-        }
-    }
-};
-
-template<typename Ty, typename Tp, af_interp_type method>
-void approx2_(Array<Ty> output, Array<Ty> const input,
-              Array<Tp> const position, Array<Tp> const qosition,
-              float const offGrid)
-{
-    Ty * out = output.get();
-    Ty const * const in  = input.get();
-    Tp const * const pos = position.get();
-    Tp const * const qos = qosition.get();
-    dim4 const odims     = output.dims();
-    dim4 const idims     = input.dims();
-    dim4 const pdims     = position.dims();
-    dim4 const qdims     = qosition.dims();
-    dim4 const ostrides  = output.strides();
-    dim4 const istrides  = input.strides();
-    dim4 const pstrides  = position.strides();
-    dim4 const qstrides  = qosition.strides();
-    dim_t const oElems   = output.elements();
-    dim_t const iElems   = input.elements();
-
-    approx2_op<Ty, Tp, method> op;
-    bool pBatch = !(pdims[2] == 1 && pdims[3] == 1);
-
-    for(dim_t w = 0; w < odims[3]; w++) {
-        for(dim_t z = 0; z < odims[2]; z++) {
-            for(dim_t y = 0; y < odims[1]; y++) {
-                for(dim_t x = 0; x < odims[0]; x++) {
-                    op(out, odims, oElems, in, idims, iElems, pos, pdims, qos, qdims,
-                       ostrides, istrides, pstrides, qstrides, offGrid, pBatch, x, y, z, w);
-                }
-            }
-        }
-    }
-}
 
 template<typename Ty, typename Tp>
 Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
-                   const af_interp_type method, const float offGrid)
+                  const af_interp_type method, const float offGrid)
 {
     in.eval();
     pos0.eval();
@@ -338,11 +61,11 @@ Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &p
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_NEAREST>,
+            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
                                out, in, pos0, pos1, offGrid);
             break;
         case AF_INTERP_LINEAR:
-            getQueue().enqueue(approx2_<Ty, Tp, AF_INTERP_LINEAR>,
+            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
                                out, in, pos0, pos1, offGrid);
             break;
         default:
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
new file mode 100644
index 0000000000..9dc681c8fa
--- /dev/null
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -0,0 +1,141 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <math.hpp>
+
+namespace kernel
+{
+
+using af::dim4;
+using cpu::scalar;
+using cpu::Array;
+
+template<typename Ty, typename Tp, af_interp_type method>
+struct approx1_op
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    {
+        return;
+    }
+};
+
+template<typename Ty, typename Tp>
+struct approx1_op<Ty, Tp, AF_INTERP_NEAREST>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    {
+        dim_t pmId = idx;
+        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
+
+        const Tp x = pos[pmId];
+        bool gFlag = false;
+        if (x < 0 || idims[0] < x+1) {  // No need to check y
+            gFlag = true;
+        }
+
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2]
+                       + idy * istrides[1];
+            const dim_t iMem = round(x) + ioff;
+
+            out[omId] = in[iMem];
+        }
+    }
+};
+
+template<typename Ty, typename Tp>
+struct approx1_op<Ty, Tp, AF_INTERP_LINEAR>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    {
+        dim_t pmId = idx;
+        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
+
+        const Tp x = pos[pmId];
+        bool gFlag = false;
+        if (x < 0 || idims[0] < x+1) {
+            gFlag = true;
+        }
+
+        const dim_t grid_x = floor(x);  // nearest grid
+        const Tp off_x = x - grid_x; // fractional offset
+
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
+
+            // Check if x and x + 1 are both valid indices
+            bool cond = (x < idims[0] - 1);
+            // Compute Left and Right Weighted Values
+            Ty yl = ((Tp)1.0 - off_x) * in[ioff];
+            Ty yr = cond ? (off_x) * in[ioff + 1] : scalar<Ty>(0);
+            Ty yo = yl + yr;
+            // Compute Weight used
+            Tp wt = cond ? (Tp)1.0 : (Tp)(1.0 - off_x);
+            // Write final value
+            out[omId] = (yo / wt);
+        }
+    }
+};
+
+template<typename Ty, typename Tp, af_interp_type method>
+void approx1(Array<Ty> output, Array<Ty> const input,
+             Array<Tp> const position, float const offGrid)
+{
+    Ty * out = output.get();
+    Ty const * const in  = input.get();
+    Tp const * const pos = position.get();
+    dim4 const odims     = output.dims();
+    dim4 const idims     = input.dims();
+    dim4 const pdims     = position.dims();
+    dim4 const ostrides  = output.strides();
+    dim4 const istrides  = input.strides();
+    dim4 const pstrides  = position.strides();
+    dim_t const oElems   = output.elements();
+    dim_t const iElems   = input.elements();
+
+    approx1_op<Ty, Tp, method> op;
+    bool pBatch = !(pdims[1] == 1 && pdims[2] == 1 && pdims[3] == 1);
+
+    for(dim_t w = 0; w < odims[3]; w++) {
+        for(dim_t z = 0; z < odims[2]; z++) {
+            for(dim_t y = 0; y < odims[1]; y++) {
+                for(dim_t x = 0; x < odims[0]; x++) {
+                    op(out, odims, oElems, in, idims, iElems, pos, pdims,
+                       ostrides, istrides, pstrides, offGrid, pBatch, x, y, z, w);
+                }
+            }
+        }
+    }
+}
+
+}
diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
new file mode 100644
index 0000000000..8f57b5cd64
--- /dev/null
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -0,0 +1,169 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <math.hpp>
+
+namespace kernel
+{
+
+using af::dim4;
+using cpu::scalar;
+using cpu::Array;
+
+template<typename Ty, typename Tp, af_interp_type method>
+struct approx2_op
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const af::dim4 &pstrides, const af::dim4 &qstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    {
+        return;
+    }
+};
+
+template<typename Ty, typename Tp>
+struct approx2_op<Ty, Tp, AF_INTERP_NEAREST>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const af::dim4 &pstrides, const af::dim4 &qstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    {
+        dim_t pmId = idy * pstrides[1] + idx;
+        dim_t qmId = idy * qstrides[1] + idx;
+        if(pBatch) {
+            pmId += idw * pstrides[3] + idz * pstrides[2];
+            qmId += idw * qstrides[3] + idz * qstrides[2];
+        }
+
+        bool gFlag = false;
+        const Tp x = pos[pmId], y = qos[qmId];
+        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
+            gFlag = true;
+        }
+
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
+            const dim_t imId = idw * istrides[3] + idz * istrides[2] +
+                            grid_y * istrides[1] + grid_x;
+            out[omId] = in[imId];
+        }
+    }
+};
+
+template<typename Ty, typename Tp>
+struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
+{
+    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
+              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
+              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const af::dim4 &pstrides, const af::dim4 &qstrides,
+              const float offGrid, const bool pBatch,
+              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    {
+        dim_t pmId = idy * pstrides[1] + idx;
+        dim_t qmId = idy * qstrides[1] + idx;
+        if(pBatch) {
+            pmId += idw * pstrides[3] + idz * pstrides[2];
+            qmId += idw * qstrides[3] + idz * qstrides[2];
+        }
+
+        bool gFlag = false;
+        const Tp x = pos[pmId], y = qos[qmId];
+        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
+            gFlag = true;
+        }
+
+        const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+        const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
+
+        // Check if pVal and pVal + 1 are both valid indices
+        bool condY = (y < idims[1] - 1);
+        bool condX = (x < idims[0] - 1);
+
+        // Compute wieghts used
+        Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
+        Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
+        Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
+        Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
+
+        Tp wt = wt00 + wt10 + wt01 + wt11;
+        Ty zero = scalar<Ty>(0);
+
+        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        if(gFlag) {
+            out[omId] = scalar<Ty>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2]
+                    + grid_y * istrides[1] + grid_x;
+
+            // Compute Weighted Values
+            Ty y00 =                    wt00 * in[ioff];
+            Ty y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
+            Ty y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
+            Ty y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
+
+            Ty yo = y00 + y10 + y01 + y11;
+
+            // Write Final Value
+            out[omId] = (yo / wt);
+        }
+    }
+};
+
+template<typename Ty, typename Tp, af_interp_type method>
+void approx2(Array<Ty> output, Array<Ty> const input,
+             Array<Tp> const position, Array<Tp> const qosition,
+             float const offGrid)
+{
+    Ty * out = output.get();
+    Ty const * const in  = input.get();
+    Tp const * const pos = position.get();
+    Tp const * const qos = qosition.get();
+    dim4 const odims     = output.dims();
+    dim4 const idims     = input.dims();
+    dim4 const pdims     = position.dims();
+    dim4 const qdims     = qosition.dims();
+    dim4 const ostrides  = output.strides();
+    dim4 const istrides  = input.strides();
+    dim4 const pstrides  = position.strides();
+    dim4 const qstrides  = qosition.strides();
+    dim_t const oElems   = output.elements();
+    dim_t const iElems   = input.elements();
+
+    approx2_op<Ty, Tp, method> op;
+    bool pBatch = !(pdims[2] == 1 && pdims[3] == 1);
+
+    for(dim_t w = 0; w < odims[3]; w++) {
+        for(dim_t z = 0; z < odims[2]; z++) {
+            for(dim_t y = 0; y < odims[1]; y++) {
+                for(dim_t x = 0; x < odims[0]; x++) {
+                    op(out, odims, oElems, in, idims, iElems, pos, pdims, qos, qdims,
+                       ostrides, istrides, pstrides, qstrides, offGrid, pBatch, x, y, z, w);
+                }
+            }
+        }
+    }
+}
+
+}

From 5f2f155f01a714017e190ed9e3564c036889360d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2015 19:07:37 -0500
Subject: [PATCH 0149/2677] Removed obselete fn of evalArray from all backends

Array::eval is already available, thus making this function
redundant
---
 src/api/c/data.cpp           |  2 +-
 src/api/c/moddims.cpp        |  2 +-
 src/backend/cpu/Array.cpp    | 10 ----------
 src/backend/cpu/Array.hpp    |  4 ----
 src/backend/cpu/where.cpp    |  2 +-
 src/backend/cuda/Array.cpp   |  7 -------
 src/backend/cuda/Array.hpp   |  4 ----
 src/backend/cuda/copy.cu     |  2 +-
 src/backend/opencl/Array.cpp |  7 -------
 src/backend/opencl/Array.hpp |  4 ----
 10 files changed, 4 insertions(+), 40 deletions(-)

diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 4d77fb279e..2de2f139e3 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -594,7 +594,7 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
 template<typename T>
 static inline void eval(af_array arr)
 {
-    evalArray(getArray<T>(arr));
+    getArray<T>(arr).eval();
     return;
 }
 
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index bb156ffc2c..4b7a179a95 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -23,7 +23,7 @@ template<typename T>
 Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
 {
     //FIXME: Figure out a better way
-    evalArray<T>(in);
+    in.eval();
 
     Array<T> Out = in;
 
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 9c15bc46c6..64fca1aa6b 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -181,7 +181,6 @@ createEmptyArray(const dim4 &size)
 template<typename T>
 Array<T> *initArray() { return new Array<T>(dim4(0, 0, 0, 0)); }
 
-
 template<typename T>
 Array<T>
 createNodeArray(const dim4 &dims, Node_ptr node)
@@ -203,7 +202,6 @@ createNodeArray(const dim4 &dims, Node_ptr node)
     return out;
 }
 
-
 template<typename T>
 Array<T> createSubArray(const Array<T>& parent,
                         const std::vector<af_seq> &index,
@@ -240,13 +238,6 @@ destroyArray(Array<T> *A)
     delete A;
 }
 
-
-template<typename T>
-void evalArray(const Array<T> &A)
-{
-    A.eval();
-}
-
 template<typename T>
 void
 writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
@@ -277,7 +268,6 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       void      evalArray<T>             (const Array<T> &A); \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, TNJ::Node_ptr node); \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2b9cbb4fed..ece989e2d8 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -65,9 +65,6 @@ namespace cpu
                             const std::vector<af_seq> &index,
                             bool copy=true);
 
-    template<typename T>
-    void evalArray(const Array<T> &A);
-
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     void destroyArray(Array<T> *A);
@@ -208,7 +205,6 @@ namespace cpu
                                           bool copy);
 
         friend void destroyArray<T>(Array<T> *arr);
-        friend void evalArray<T>(const Array<T> &arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
     };
 
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 441c7ff239..018cbdfc36 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -27,7 +27,7 @@ namespace cpu
 template<typename T>
 Array<uint> where(const Array<T> &in)
 {
-    evalArray(in);
+    in.eval();
     getQueue().sync();
 
     const dim_t *dims    = in.dims().get();
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 275ea13a99..39cd06c43b 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -228,12 +228,6 @@ namespace cuda
         delete A;
     }
 
-    template<typename T>
-    void evalArray(const Array<T> &A)
-    {
-        A.eval();
-    }
-
     template<typename T>
     void
     writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
@@ -279,7 +273,6 @@ namespace cuda
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       void      evalArray<T>             (const Array<T> &A); \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
     template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
                                    bool is_device, bool copy_device);   \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 598fdfd35e..638b745d09 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -78,9 +78,6 @@ namespace cuda
                             const std::vector<af_seq> &index,
                             bool copy=true);
 
-    template<typename T>
-    void evalArray(const Array<T> &A);
-
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     void destroyArray(Array<T> *A);
@@ -234,7 +231,6 @@ namespace cuda
                                           bool copy);
 
         friend void destroyArray<T>(Array<T> *arr);
-        friend void evalArray<T>(const Array<T> &arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
     };
 
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 90f9970239..71893b8c16 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -23,7 +23,7 @@ namespace cuda
     void copyData(T *data, const Array<T> &A)
     {
         // FIXME: Merge this with copyArray
-        evalArray(A);
+        A.eval();
 
         Array<T> out = A;
         const T *ptr = NULL;
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 0860098c9f..207a4b0de7 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -258,12 +258,6 @@ namespace opencl
         delete A;
     }
 
-    template<typename T>
-    void evalArray(const Array<T> &A)
-    {
-        A.eval();
-    }
-
     template<typename T>
     void
     writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
@@ -312,7 +306,6 @@ namespace opencl
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       void      evalArray<T>             (const Array<T> &A); \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
     template       Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
     template       Array<T>::~Array        ();                          \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 1db0ab6347..5f86d6d0b6 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -69,9 +69,6 @@ namespace opencl
                             const std::vector<af_seq> &index,
                             bool copy=true);
 
-    template<typename T>
-    void evalArray(const Array<T> &A);
-
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     void destroyArray(Array<T> *A);
@@ -226,7 +223,6 @@ namespace opencl
                                           bool copy);
 
         friend void destroyArray<T>(Array<T> *arr);
-        friend void evalArray<T>(const Array<T> &arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
     };
 

From e651cad87b6a703119587c27626dbd6fc2c404b5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2015 19:32:22 -0500
Subject: [PATCH 0150/2677] cpu::Array::eval queue work moved to kerenel
 namespace

---
 src/backend/cpu/Array.cpp        | 38 ++--------------------
 src/backend/cpu/Array.hpp        | 14 ++++++++
 src/backend/cpu/kernel/Array.hpp | 56 ++++++++++++++++++++++++++++++++
 3 files changed, 72 insertions(+), 36 deletions(-)
 create mode 100644 src/backend/cpu/kernel/Array.hpp

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 64fca1aa6b..40d25aca6f 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -11,6 +11,7 @@
 #include <err_common.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
+#include <kernel/Array.hpp>
 #include <TNJ/BufferNode.hpp>
 #include <TNJ/ScalarNode.hpp>
 #include <memory.hpp>
@@ -77,42 +78,7 @@ void Array<T>::eval()
 
     data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
-    auto func = [] (Array<T> in) {
-        in.setId(getActiveDeviceId());
-        T *ptr = in.data.get();
-
-        dim4 odims = in.dims();
-        dim4 ostrs = in.strides();
-
-        bool is_linear = in.node->isLinear(odims.get());
-
-        if (is_linear) {
-            int num = in.elements();
-            for (int i = 0; i < num; i++) {
-                ptr[i] = *(T *)in.node->calc(i);
-            }
-        } else {
-            for (int w = 0; w < (int)odims[3]; w++) {
-                dim_t offw = w * ostrs[3];
-
-                for (int z = 0; z < (int)odims[2]; z++) {
-                    dim_t offz = z * ostrs[2] + offw;
-
-                    for (int y = 0; y < (int)odims[1]; y++) {
-                        dim_t offy = y * ostrs[1] + offz;
-
-                        for (int x = 0; x < (int)odims[0]; x++) {
-                            dim_t id = x + offy;
-
-                            ptr[id] = *(T *)in.node->calc(x, y, z, w);
-                        }
-                    }
-                }
-            }
-        }
-    };
-
-    getQueue().enqueue(func, *this);
+    getQueue().enqueue(kernel::evalArray<T>, *this);
 
     ready = true;
     Node_ptr prev = node;
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index ece989e2d8..437c47f786 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -23,6 +23,18 @@
 #include <platform.hpp>
 #include <async_queue.hpp>
 
+// cpu::Array class forward declaration
+namespace cpu
+{
+template<typename T> class Array;
+}
+
+// kernel::evalArray fn forward declaration
+namespace kernel
+{
+template<typename T> void evalArray(cpu::Array<T> in);
+}
+
 namespace cpu
 {
 
@@ -204,6 +216,8 @@ namespace cpu
                                           const std::vector<af_seq> &index,
                                           bool copy);
 
+        friend void kernel::evalArray<T>(Array<T> in);
+
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
     };
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
new file mode 100644
index 0000000000..0666d43602
--- /dev/null
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <platform.hpp>
+
+namespace kernel
+{
+
+using af::dim4;
+using cpu::Array;
+
+template<typename T>
+void evalArray(Array<T> in)
+{
+    in.setId(cpu::getActiveDeviceId());
+    T *ptr = in.data.get();
+
+    dim4 odims = in.dims();
+    dim4 ostrs = in.strides();
+
+    bool is_linear = in.node->isLinear(odims.get());
+
+    if (is_linear) {
+        int num = in.elements();
+        for (int i = 0; i < num; i++) {
+            ptr[i] = *(T *)in.node->calc(i);
+        }
+    } else {
+        for (int w = 0; w < (int)odims[3]; w++) {
+            dim_t offw = w * ostrs[3];
+
+            for (int z = 0; z < (int)odims[2]; z++) {
+                dim_t offz = z * ostrs[2] + offw;
+
+                for (int y = 0; y < (int)odims[1]; y++) {
+                    dim_t offy = y * ostrs[1] + offz;
+
+                    for (int x = 0; x < (int)odims[0]; x++) {
+                        dim_t id = x + offy;
+
+                        ptr[id] = *(T *)in.node->calc(x, y, z, w);
+                    }
+                }
+            }
+        }
+    }
+}
+
+}

From 3cddae24f55870d565361de75c1fee55ae2ce19a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 18 Dec 2015 15:45:36 -0500
Subject: [PATCH 0151/2677] moved assign cpu async fn to kernel space

---
 src/backend/cpu/assign.cpp        | 83 ++--------------------------
 src/backend/cpu/kernel/Array.hpp  |  2 +-
 src/backend/cpu/kernel/assign.hpp | 91 +++++++++++++++++++++++++++++++
 3 files changed, 97 insertions(+), 79 deletions(-)
 create mode 100644 src/backend/cpu/kernel/assign.hpp

diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index c5d733bb17..95bb7e5dd4 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -12,31 +12,16 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
+#include <kernel/assign.hpp>
 #include <assign.hpp>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
 
-using af::dim4;
-using std::ref;
-using std::copy;
-using std::vector;
-
 namespace cpu
 {
 
-static inline
-dim_t trimIndex(int idx, const dim_t &len)
-{
-    int ret_val = idx;
-    int offset  = abs(ret_val)%len;
-    if (ret_val<0) {
-        ret_val = offset-1;
-    } else if (ret_val>=(int)len) {
-        ret_val = len-offset-1;
-    }
-    return ret_val;
-}
+using af::dim4;
+using std::vector;
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
@@ -63,66 +48,8 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
         }
     }
 
-    auto func = [=] (Array<T> out, const Array<T> rhs,
-                     const vector<bool> isSeq,
-                     const vector<af_seq> seqs,
-                     const vector< Array<uint> > idxArrs) {
-
-        dim4 dDims = out.getDataDims();
-        dim4 pDims = out.dims();
-        // retrieve dimensions & strides for array to which rhs is being copied to
-        dim4 dst_offsets = toOffset(seqs, dDims);
-        dim4 dst_strides = toStride(seqs, dDims);
-        // retrieve rhs array dimenesions & strides
-        dim4 src_dims    = rhs.dims();
-        dim4 src_strides = rhs.strides();
-        // declare pointers to af_array index data
-        const uint* ptr0 = idxArrs[0].get();
-        const uint* ptr1 = idxArrs[1].get();
-        const uint* ptr2 = idxArrs[2].get();
-        const uint* ptr3 = idxArrs[3].get();
-
-        const T * src= rhs.get();
-        T * dst      = out.get();
-
-        for(dim_t l=0; l<src_dims[3]; ++l) {
-
-            dim_t src_loff = l*src_strides[3];
-
-            dim_t dst_lIdx = trimIndex(isSeq[3] ? l+dst_offsets[3] : ptr3[l], pDims[3]);
-            dim_t dst_loff = dst_lIdx * dst_strides[3];
-
-            for(dim_t k=0; k<src_dims[2]; ++k) {
-
-                dim_t src_koff = k*src_strides[2];
-
-                dim_t dst_kIdx = trimIndex(isSeq[2] ? k+dst_offsets[2] : ptr2[k], pDims[2]);
-                dim_t dst_koff = dst_kIdx * dst_strides[2];
-
-                for(dim_t j=0; j<src_dims[1]; ++j) {
-
-                    dim_t src_joff = j*src_strides[1];
-
-                    dim_t dst_jIdx = trimIndex(isSeq[1] ? j+dst_offsets[1] : ptr1[j], pDims[1]);
-                    dim_t dst_joff = dst_jIdx * dst_strides[1];
-
-                    for(dim_t i=0; i<src_dims[0]; ++i) {
-
-                        dim_t src_ioff = i*src_strides[0];
-                        dim_t src_idx  = src_ioff + src_joff + src_koff + src_loff;
-
-                        dim_t dst_iIdx = trimIndex(isSeq[0] ? i+dst_offsets[0] : ptr0[i], pDims[0]);
-                        dim_t dst_ioff = dst_iIdx * dst_strides[0];
-                        dim_t dst_idx  = dst_ioff + dst_joff + dst_koff + dst_loff;
-
-                        dst[dst_idx] = src[src_idx];
-                    }
-                }
-            }
-        }
-    };
-
-    getQueue().enqueue(func, out, rhs, std::move(isSeq), std::move(seqs), std::move(idxArrs));
+    getQueue().enqueue(kernel::assign<T>, out, rhs, std::move(isSeq),
+            std::move(seqs), std::move(idxArrs));
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 0666d43602..b3a02004d1 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2015, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
new file mode 100644
index 0000000000..16a623f704
--- /dev/null
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -0,0 +1,91 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <vector>
+
+namespace kernel
+{
+
+using af::dim4;
+using cpu::Array;
+using std::vector;
+
+inline
+dim_t trimIndex(int idx, const dim_t &len)
+{
+    int ret_val = idx;
+    int offset  = abs(ret_val)%len;
+    if (ret_val<0) {
+        ret_val = offset-1;
+    } else if (ret_val>=(int)len) {
+        ret_val = len-offset-1;
+    }
+    return ret_val;
+}
+
+template<typename T>
+void assign(Array<T> out, const Array<T> rhs, const vector<bool> isSeq,
+            const vector<af_seq> seqs, const vector< Array<uint> > idxArrs)
+{
+    dim4 dDims = out.getDataDims();
+    dim4 pDims = out.dims();
+    // retrieve dimensions & strides for array to which rhs is being copied to
+    dim4 dst_offsets = toOffset(seqs, dDims);
+    dim4 dst_strides = toStride(seqs, dDims);
+    // retrieve rhs array dimenesions & strides
+    dim4 src_dims    = rhs.dims();
+    dim4 src_strides = rhs.strides();
+    // declare pointers to af_array index data
+    const uint* ptr0 = idxArrs[0].get();
+    const uint* ptr1 = idxArrs[1].get();
+    const uint* ptr2 = idxArrs[2].get();
+    const uint* ptr3 = idxArrs[3].get();
+
+    const T * src= rhs.get();
+    T * dst      = out.get();
+
+    for(dim_t l=0; l<src_dims[3]; ++l) {
+
+        dim_t src_loff = l*src_strides[3];
+
+        dim_t dst_lIdx = trimIndex(isSeq[3] ? l+dst_offsets[3] : ptr3[l], pDims[3]);
+        dim_t dst_loff = dst_lIdx * dst_strides[3];
+
+        for(dim_t k=0; k<src_dims[2]; ++k) {
+
+            dim_t src_koff = k*src_strides[2];
+
+            dim_t dst_kIdx = trimIndex(isSeq[2] ? k+dst_offsets[2] : ptr2[k], pDims[2]);
+            dim_t dst_koff = dst_kIdx * dst_strides[2];
+
+            for(dim_t j=0; j<src_dims[1]; ++j) {
+
+                dim_t src_joff = j*src_strides[1];
+
+                dim_t dst_jIdx = trimIndex(isSeq[1] ? j+dst_offsets[1] : ptr1[j], pDims[1]);
+                dim_t dst_joff = dst_jIdx * dst_strides[1];
+
+                for(dim_t i=0; i<src_dims[0]; ++i) {
+
+                    dim_t src_ioff = i*src_strides[0];
+                    dim_t src_idx  = src_ioff + src_joff + src_koff + src_loff;
+
+                    dim_t dst_iIdx = trimIndex(isSeq[0] ? i+dst_offsets[0] : ptr0[i], pDims[0]);
+                    dim_t dst_ioff = dst_iIdx * dst_strides[0];
+                    dim_t dst_idx  = dst_ioff + dst_joff + dst_koff + dst_loff;
+
+                    dst[dst_idx] = src[src_idx];
+                }
+            }
+        }
+    }
+}
+
+}

From 0e563bac8d11c961450288a78ce9f4119cb68466 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 18 Dec 2015 15:54:35 -0500
Subject: [PATCH 0152/2677] moved kernel namespace in cpu backend inside cpu
 namespace

---
 src/backend/cpu/Array.hpp          | 3 +--
 src/backend/cpu/kernel/Array.hpp   | 4 +++-
 src/backend/cpu/kernel/approx1.hpp | 6 +++---
 src/backend/cpu/kernel/approx2.hpp | 5 +++--
 src/backend/cpu/kernel/assign.hpp  | 4 +++-
 5 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 437c47f786..adb72dc6c5 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -27,13 +27,12 @@
 namespace cpu
 {
 template<typename T> class Array;
-}
-
 // kernel::evalArray fn forward declaration
 namespace kernel
 {
 template<typename T> void evalArray(cpu::Array<T> in);
 }
+}
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index b3a02004d1..e492b92ff0 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -10,11 +10,12 @@
 #include <Array.hpp>
 #include <platform.hpp>
 
+namespace cpu
+{
 namespace kernel
 {
 
 using af::dim4;
-using cpu::Array;
 
 template<typename T>
 void evalArray(Array<T> in)
@@ -54,3 +55,4 @@ void evalArray(Array<T> in)
 }
 
 }
+}
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
index 9dc681c8fa..63bae2d237 100644
--- a/src/backend/cpu/kernel/approx1.hpp
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -9,13 +9,12 @@
 
 #include <Array.hpp>
 #include <math.hpp>
-
+namespace cpu
+{
 namespace kernel
 {
 
 using af::dim4;
-using cpu::scalar;
-using cpu::Array;
 
 template<typename Ty, typename Tp, af_interp_type method>
 struct approx1_op
@@ -139,3 +138,4 @@ void approx1(Array<Ty> output, Array<Ty> const input,
 }
 
 }
+}
diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
index 8f57b5cd64..f80dae17bb 100644
--- a/src/backend/cpu/kernel/approx2.hpp
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -10,12 +10,12 @@
 #include <Array.hpp>
 #include <math.hpp>
 
+namespace cpu
+{
 namespace kernel
 {
 
 using af::dim4;
-using cpu::scalar;
-using cpu::Array;
 
 template<typename Ty, typename Tp, af_interp_type method>
 struct approx2_op
@@ -167,3 +167,4 @@ void approx2(Array<Ty> output, Array<Ty> const input,
 }
 
 }
+}
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 16a623f704..2621ba741f 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -10,11 +10,12 @@
 #include <Array.hpp>
 #include <vector>
 
+namespace cpu
+{
 namespace kernel
 {
 
 using af::dim4;
-using cpu::Array;
 using std::vector;
 
 inline
@@ -89,3 +90,4 @@ void assign(Array<T> out, const Array<T> rhs, const vector<bool> isSeq,
 }
 
 }
+}

From d03bb75f24481953484443cbed46df62264a5008 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 18 Dec 2015 17:09:11 -0500
Subject: [PATCH 0153/2677] moved bilateral, convolve, fftconvolve to cpu
 kernel namespace

---
 src/backend/cpu/bilateral.cpp          |  77 +------
 src/backend/cpu/convolve.cpp           | 247 +----------------------
 src/backend/cpu/fftconvolve.cpp        | 236 ++--------------------
 src/backend/cpu/kernel/approx1.hpp     |   1 +
 src/backend/cpu/kernel/bilateral.hpp   |  90 +++++++++
 src/backend/cpu/kernel/convolve.hpp    | 267 +++++++++++++++++++++++++
 src/backend/cpu/kernel/fftconvolve.hpp | 227 +++++++++++++++++++++
 7 files changed, 611 insertions(+), 534 deletions(-)
 create mode 100644 src/backend/cpu/kernel/bilateral.hpp
 create mode 100644 src/backend/cpu/kernel/convolve.hpp
 create mode 100644 src/backend/cpu/kernel/fftconvolve.hpp

diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index ea38ea7dd7..c751f992d9 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -11,6 +11,7 @@
 #include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
+#include <kernel/bilateral.hpp>
 #include <bilateral.hpp>
 #include <cmath>
 #include <algorithm>
@@ -22,87 +23,13 @@ using af::dim4;
 namespace cpu
 {
 
-static inline dim_t clamp(int a, dim_t mn, dim_t mx)
-{
-    return (a < (int)mn ? mn : (a > (int)mx ? mx : a));
-}
-
-static inline unsigned getIdx(const dim4 &strides,
-        int i, int j = 0, int k = 0, int l = 0)
-{
-    return (l * strides[3] +
-            k * strides[2] +
-            j * strides[1] +
-            i * strides[0]);
-}
-
-template<typename inType, typename outType, bool isColor>
-void bilateral_(Array<outType> out, const Array<inType> in, float s_sigma, float c_sigma)
-{
-    const dim4 dims     = in.dims();
-    const dim4 istrides = in.strides();
-
-    const dim4 ostrides = out.strides();
-
-          outType *outData = out.get();
-    const inType  *inData  = in.get();
-
-    // clamp spatical and chromatic sigma's
-    float space_          = std::min(11.5f, std::max(s_sigma, 0.f));
-    float color_          = std::max(c_sigma, 0.f);
-    const dim_t radius = std::max((dim_t)(space_ * 1.5f), (dim_t)1);
-    const float svar      = space_*space_;
-    const float cvar      = color_*color_;
-
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        // b3 for loop handles following batch configurations
-        //  - gfor
-        //  - input based batch
-        //      - when input is 4d array for color images
-        for(dim_t b2=0; b2<dims[2]; ++b2) {
-            // b2 for loop handles following batch configurations
-            //  - channels
-            //  - input based batch
-            //      - when input is 3d array for grayscale images
-            for(dim_t j=0; j<dims[1]; ++j) {
-                // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
-                    // i steps along 1st dimension
-                    outType norm = 0.0;
-                    outType res  = 0.0;
-                    const outType center = (outType)inData[getIdx(istrides, i, j)];
-                    for(dim_t wj=-radius; wj<=radius; ++wj) {
-                        // clamps offsets
-                        dim_t tj = clamp(j+wj, 0, dims[1]-1);
-                        for(dim_t wi=-radius; wi<=radius; ++wi) {
-                            // clamps offsets
-                            dim_t ti = clamp(i+wi, 0, dims[0]-1);
-                            // proceed
-                            const outType val= (outType)inData[getIdx(istrides, ti, tj)];
-                            const outType gauss_space = (wi*wi+wj*wj)/(-2.0*svar);
-                            const outType gauss_range = ((center-val)*(center-val))/(-2.0*cvar);
-                            const outType weight = std::exp(gauss_space+gauss_range);
-                            norm += weight;
-                            res += val*weight;
-                        }
-                    } // filter loop ends here
-
-                    outData[getIdx(ostrides, i, j)] = res/norm;
-                } //1st dimension loop ends here
-            } //2nd dimension loop ends here
-            outData += ostrides[2];
-            inData  += istrides[2];
-        }
-    }
-}
-
 template<typename inType, typename outType, bool isColor>
 Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
 {
     in.eval();
     const dim4 dims     = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
-    getQueue().enqueue(bilateral_<inType, outType, isColor>, out, in, s_sigma, c_sigma);
+    getQueue().enqueue(kernel::bilateral<inType, outType, isColor>, out, in, s_sigma, c_sigma);
     return out;
 }
 
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 239b4f0924..218ba8e3c0 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -16,170 +16,13 @@
 #include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/convolve.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename T, typename accT, bool expand>
-void one2one_1d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
-                dim4 const &sDims, dim4 const &fDims, dim4 const &sStrides)
-{
-    dim_t start = (expand ? 0 : fDims[0]/2);
-    dim_t end   = (expand ? oDims[0] : start + sDims[0]);
-    for(dim_t i=start; i<end; ++i) {
-        accT accum = 0.0;
-        for(dim_t f=0; f<fDims[0]; ++f) {
-            dim_t iIdx = i-f;
-            T s_val = ((iIdx>=0 &&iIdx<sDims[0])? iptr[iIdx*sStrides[0]] : T(0));
-            accum += accT(s_val * fptr[f]);
-        }
-        optr[i-start] = T(accum);
-    }
-}
-
-template<typename T, typename accT, bool expand>
-void one2one_2d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
-                dim4 const &sDims, dim4 const &fDims, dim4 const &oStrides,
-                dim4 const &sStrides, dim4 const &fStrides)
-{
-    dim_t jStart = (expand ? 0 : fDims[1]/2);
-    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (expand ? 0 : fDims[0]/2);
-    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
-
-    for(dim_t j=jStart; j<jEnd; ++j) {
-        dim_t joff = (j-jStart)*oStrides[1];
-
-        for(dim_t i=iStart; i<iEnd; ++i) {
-
-            accT accum = accT(0);
-            for(dim_t wj=0; wj<fDims[1]; ++wj) {
-                dim_t jIdx  = j-wj;
-                dim_t w_joff = wj*fStrides[1];
-                dim_t s_joff = jIdx * sStrides[1];
-                bool isJValid = (jIdx>=0 && jIdx<sDims[1]);
-
-                for(dim_t wi=0; wi<fDims[0]; ++wi) {
-                    dim_t iIdx = i-wi;
-
-                    T s_val = T(0);
-                    if ( isJValid && (iIdx>=0 && iIdx<sDims[0])) {
-                        s_val = iptr[s_joff+iIdx*sStrides[0]];
-                    }
-
-                    accum += accT(s_val * fptr[w_joff+wi*fStrides[0]]);
-                }
-            }
-            optr[joff+i-iStart] = T(accum);
-        }
-    }
-}
-
-template<typename T, typename accT, bool expand>
-void one2one_3d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
-                dim4 const &sDims, dim4 const &fDims, dim4 const &oStrides,
-                dim4 const &sStrides, dim4 const &fStrides)
-{
-    dim_t kStart = (expand ? 0 : fDims[2]/2);
-    dim_t kEnd   = (expand ? oDims[2] : kStart + sDims[2]);
-    dim_t jStart = (expand ? 0 : fDims[1]/2);
-    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (expand ? 0 : fDims[0]/2);
-    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
-
-    for(dim_t k=kStart; k<kEnd; ++k) {
-        dim_t koff = (k-kStart)*oStrides[2];
-
-        for(dim_t j=jStart; j<jEnd; ++j) {
-            dim_t joff = (j-jStart)*oStrides[1];
-
-            for(dim_t i=iStart; i<iEnd; ++i) {
-
-                accT accum = accT(0);
-                for(dim_t wk=0; wk<fDims[2]; ++wk) {
-                    dim_t kIdx  = k-wk;
-                    dim_t w_koff = wk*fStrides[2];
-                    dim_t s_koff = kIdx * sStrides[2];
-                    bool isKValid = (kIdx>=0 && kIdx<sDims[2]);
-
-                    for(dim_t wj=0; wj<fDims[1]; ++wj) {
-                        dim_t jIdx  = j-wj;
-                        dim_t w_joff = wj*fStrides[1];
-                        dim_t s_joff = jIdx * sStrides[1];
-                        bool isJValid = (jIdx>=0 && jIdx<sDims[1]);
-
-                        for(dim_t wi=0; wi<fDims[0]; ++wi) {
-                            dim_t iIdx = i-wi;
-
-                            T s_val = T(0);
-                            if ( isKValid && isJValid && (iIdx>=0 && iIdx<sDims[0])) {
-                                s_val = iptr[s_koff+s_joff+iIdx*sStrides[0]];
-                            }
-
-                            accum += accT(s_val * fptr[w_koff+w_joff+wi*fStrides[0]]);
-                        }
-                    }
-                }
-                optr[koff+joff+i-iStart] = T(accum);
-            } //i loop ends here
-        } // j loop ends here
-    } // k loop ends here
-}
-
-template<typename T, typename accT, dim_t baseDim, bool expand>
-void convolve_nd(T *optr, T const *iptr, accT const *fptr,
-                dim4 const &oDims, dim4 const &sDims, dim4 const &fDims,
-                dim4 const &oStrides, dim4 const &sStrides, dim4 const &fStrides,
-                ConvolveBatchKind kind)
-{
-    dim_t out_step[4]  = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
-    dim_t in_step[4]   = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
-    dim_t filt_step[4] = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
-    dim_t batch[4]     = {0, 1, 1, 1}; /* first value is never used, and declared for code simplicity */
-
-    for (dim_t i=1; i<4; ++i) {
-        switch(kind) {
-            case CONVOLVE_BATCH_SIGNAL:
-                out_step[i] = oStrides[i];
-                in_step[i]  = sStrides[i];
-                if (i>=baseDim) batch[i] = sDims[i];
-                break;
-            case CONVOLVE_BATCH_SAME:
-                out_step[i]  = oStrides[i];
-                in_step[i]   = sStrides[i];
-                filt_step[i] = fStrides[i];
-                if (i>=baseDim) batch[i] = sDims[i];
-                break;
-            case CONVOLVE_BATCH_KERNEL:
-                out_step[i]  = oStrides[i];
-                filt_step[i] = fStrides[i];
-                if (i>=baseDim) batch[i] = fDims[i];
-                break;
-            default:
-                break;
-        }
-    }
-
-    for (dim_t b3=0; b3<batch[3]; ++b3) {
-        for (dim_t b2=0; b2<batch[2]; ++b2) {
-            for (dim_t b1=0; b1<batch[1]; ++b1) {
-
-                T * out          = optr + b1 * out_step[1] + b2 * out_step[2] + b3 * out_step[3];
-                T const *in      = iptr + b1 *  in_step[1] + b2 *  in_step[2] + b3 *  in_step[3];
-                accT const *filt = fptr + b1 *filt_step[1] + b2 *filt_step[2] + b3 *filt_step[3];
-
-                switch(baseDim) {
-                    case 1: one2one_1d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, sStrides);                     break;
-                    case 2: one2one_2d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
-                    case 3: one2one_3d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
-                }
-            }
-        }
-    }
-}
-
 template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind)
 {
@@ -188,7 +31,6 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
 
     auto sDims    = signal.dims();
     auto fDims    = filter.dims();
-    auto sStrides = signal.strides();
 
     dim4 oDims(1);
     if (expand) {
@@ -209,52 +51,11 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(convolve_nd<T, accT, baseDim, expand>,out.get(), signal.get(), filter.get(),
-                                              oDims, sDims, fDims, out.strides(), sStrides, filter.strides(), kind);
+    getQueue().enqueue(kernel::convolve_nd<T, accT, baseDim, expand>,out, signal, filter, kind);
 
     return out;
 }
 
-template<typename T, typename accT, dim_t conv_dim, bool expand>
-void convolve2_separable(T *optr, T const *iptr, accT const *fptr,
-                        dim4 const &oDims, dim4 const &sDims, dim4 const &orgDims, dim_t fDim,
-                        dim4 const &oStrides, dim4 const &sStrides, dim_t fStride)
-{
-    for(dim_t j=0; j<oDims[1]; ++j) {
-
-        dim_t jOff = j*oStrides[1];
-        dim_t cj = j + (conv_dim==1)*(expand ? 0: fDim>>1);
-
-        for(dim_t i=0; i<oDims[0]; ++i) {
-
-            dim_t iOff = i*oStrides[0];
-            dim_t ci = i + (conv_dim==0)*(expand ? 0 : fDim>>1);
-
-            accT accum = scalar<accT>(0);
-
-            for(dim_t f=0; f<fDim; ++f) {
-                T f_val = fptr[f];
-                T s_val;
-
-                if (conv_dim==0) {
-                    dim_t offi = ci - f;
-                    bool isCIValid = offi>=0 && offi<sDims[0];
-                    bool isCJValid = cj>=0 && cj<sDims[1];
-                    s_val = (isCJValid && isCIValid ? iptr[cj*sDims[0]+offi] : scalar<T>(0));
-                } else {
-                    dim_t offj = cj - f;
-                    bool isCIValid = ci>=0 && ci<sDims[0];
-                    bool isCJValid = offj>=0 && offj<sDims[1];
-                    s_val = (isCJValid && isCIValid ? iptr[offj*sDims[0]+ci] : scalar<T>(0));
-                }
-
-                accum += accT(s_val * f_val);
-            }
-            optr[iOff+jOff] = T(accum);
-        }
-    }
-}
-
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter)
 {
@@ -262,18 +63,16 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
     c_filter.eval();
     r_filter.eval();
 
-    auto sDims    = signal.dims();
-    auto cfDims   = c_filter.dims();
-    auto rfDims   = r_filter.dims();
-    auto sStrides = signal.strides();
-
-    dim_t cflen = (dim_t)cfDims.elements();
-    dim_t rflen = (dim_t)rfDims.elements();
-
+    auto sDims = signal.dims();
     dim4 tDims = sDims;
     dim4 oDims = sDims;
 
     if (expand) {
+        auto cfDims   = c_filter.dims();
+        auto rfDims   = r_filter.dims();
+
+        dim_t cflen = (dim_t)cfDims.elements();
+        dim_t rflen = (dim_t)rfDims.elements();
         // separable convolve only does CONVOLVE_BATCH_NONE and standard batch(CONVOLVE_BATCH_SIGNAL)
         tDims[0] += cflen - 1;
         oDims[0] += cflen - 1;
@@ -282,35 +81,7 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 
     Array<T> out  = createEmptyArray<T>(oDims);
 
-    auto func = [=] (Array<T> out) {
-        Array<T> temp = createEmptyArray<T>(tDims);
-        auto tStrides = temp.strides();
-        auto oStrides = out.strides();
-
-        for (dim_t b3=0; b3<oDims[3]; ++b3) {
-
-            dim_t i_b3Off = b3*sStrides[3];
-            dim_t t_b3Off = b3*tStrides[3];
-            dim_t o_b3Off = b3*oStrides[3];
-
-            for (dim_t b2=0; b2<oDims[2]; ++b2) {
-
-                T const *iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
-                T *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
-                T *optr = out.get()  + b2*oStrides[2] + o_b3Off;
-
-                convolve2_separable<T, accT, 0, expand>(tptr, iptr, c_filter.get(),
-                        tDims, sDims, sDims, cflen,
-                        tStrides, sStrides, c_filter.strides()[0]);
-
-                convolve2_separable<T, accT, 1, expand>(optr, tptr, r_filter.get(),
-                        oDims, tDims, sDims, rflen,
-                        oStrides, tStrides, r_filter.strides()[0]);
-            }
-        }
-    };
-
-    getQueue().enqueue(func, out);
+    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, tDims);
 
     return out;
 }
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 6172af86a6..2678c7b6f0 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -19,216 +19,11 @@
 #include <convolve_common.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/fftconvolve.hpp>
 
 namespace cpu
 {
 
-template<typename To, typename Ti>
-void packData(Array<To> out, const af::dim4 od, const af::dim4 os, Array<Ti> const in)
-{
-    To* out_ptr = out.get();
-
-    const af::dim4 id = in.dims();
-    const af::dim4 is = in.strides();
-    const Ti* in_ptr = in.get();
-
-    int id0_half = divup(id[0], 2);
-    bool odd_id0 = (id[0] % 2 == 1);
-
-    for (int d3 = 0; d3 < (int)od[3]; d3++) {
-        for (int d2 = 0; d2 < (int)od[2]; d2++) {
-            for (int d1 = 0; d1 < (int)od[1]; d1++) {
-                for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
-                    const dim_t oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-
-                    if (d0 < (int)id0_half && d1 < (int)id[1] && d2 < (int)id[2] && d3 < (int)id[3]) {
-                        const dim_t iidx = d3*is[3] + d2*is[2] + d1*is[1] + d0;
-                        out_ptr[oidx]   = (To)in_ptr[iidx];
-                        if (d0 == id0_half-1 && odd_id0)
-                            out_ptr[oidx+1] = (To)0;
-                        else
-                            out_ptr[oidx+1] = (To)in_ptr[iidx+id0_half];
-                    }
-                    else {
-                        // Pad remaining elements with 0s
-                        out_ptr[oidx]   = (To)0;
-                        out_ptr[oidx+1] = (To)0;
-                    }
-                }
-            }
-        }
-    }
-}
-
-template<typename To, typename Ti>
-void padArray_(Array<To> out, const af::dim4 od, const af::dim4 os,
-              Array<Ti> const in, const dim_t offset)
-{
-    To* out_ptr = out.get() + offset;
-    const af::dim4 id = in.dims();
-    const af::dim4 is = in.strides();
-    const Ti* in_ptr = in.get();
-
-    for (int d3 = 0; d3 < (int)od[3]; d3++) {
-        for (int d2 = 0; d2 < (int)od[2]; d2++) {
-            for (int d1 = 0; d1 < (int)od[1]; d1++) {
-                for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
-                    const dim_t oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-
-                    if (d0 < (int)id[0] && d1 < (int)id[1] && d2 < (int)id[2] && d3 < (int)id[3]) {
-                        // Copy input elements to real elements, set imaginary elements to 0
-                        const dim_t iidx = d3*is[3] + d2*is[2] + d1*is[1] + d0;
-                        out_ptr[oidx]   = (To)in_ptr[iidx];
-                        out_ptr[oidx+1] = (To)0;
-                    }
-                    else {
-                        // Pad remaining of the matrix to 0s
-                        out_ptr[oidx]   = (To)0;
-                        out_ptr[oidx+1] = (To)0;
-                    }
-                }
-            }
-        }
-    }
-}
-
-template<typename T>
-void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
-                     const af::dim4 fit_dims, const af::dim4 fit_strides,
-                     ConvolveBatchKind kind, const dim_t offset)
-{
-    T* out_ptr = packed.get() + (kind==CONVOLVE_BATCH_KERNEL? offset : 0);
-    T* in1_ptr = packed.get();
-    T* in2_ptr = packed.get() + offset;
-
-    const dim4& od = (kind==CONVOLVE_BATCH_KERNEL ? fit_dims : sig_dims);
-    const dim4& os = (kind==CONVOLVE_BATCH_KERNEL ? fit_strides : sig_strides);
-    const dim4& i1d = sig_dims;
-    const dim4& i2d = fit_dims;
-    const dim4& i1s = sig_strides;
-    const dim4& i2s = fit_strides;
-
-    for (int d3 = 0; d3 < (int)od[3]; d3++) {
-        for (int d2 = 0; d2 < (int)od[2]; d2++) {
-            for (int d1 = 0; d1 < (int)od[1]; d1++) {
-                for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
-                    if (kind == CONVOLVE_BATCH_NONE || kind == CONVOLVE_BATCH_SAME) {
-                        // Complex multiply each signal to equivalent filter
-                        const int ridx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-                        const int iidx = ridx + 1;
-
-                        T a = in1_ptr[ridx];
-                        T b = in1_ptr[iidx];
-                        T c = in2_ptr[ridx];
-                        T d = in2_ptr[iidx];
-
-                        T ac = a*c;
-                        T bd = b*d;
-
-                        out_ptr[ridx] = ac - bd;
-                        out_ptr[iidx] = (a+b) * (c+d) - ac - bd;
-                    }
-                    else if (kind == CONVOLVE_BATCH_SIGNAL) {
-                        // Complex multiply all signals to filter
-                        const int ridx1 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-                        const int iidx1 = ridx1 + 1;
-                        const int ridx2 = ridx1 % (i2s[3] * i2d[3]);
-                        const int iidx2 = iidx1 % (i2s[3] * i2d[3]);
-
-                        T a = in1_ptr[ridx1];
-                        T b = in1_ptr[iidx1];
-                        T c = in2_ptr[ridx2];
-                        T d = in2_ptr[iidx2];
-
-                        T ac = a*c;
-                        T bd = b*d;
-
-                        out_ptr[ridx1] = ac - bd;
-                        out_ptr[iidx1] = (a+b) * (c+d) - ac - bd;
-                    }
-                    else if (kind == CONVOLVE_BATCH_KERNEL) {
-                        // Complex multiply signal to all filters
-                        const int ridx2 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-                        const int iidx2 = ridx2 + 1;
-                        const int ridx1 = ridx2 % (i1s[3] * i1d[3]);
-                        const int iidx1 = iidx2 % (i1s[3] * i1d[3]);
-
-                        T a = in1_ptr[ridx1];
-                        T b = in1_ptr[iidx1];
-                        T c = in2_ptr[ridx2];
-                        T d = in2_ptr[iidx2];
-
-                        T ac = a*c;
-                        T bd = b*d;
-
-                        out_ptr[ridx2] = ac - bd;
-                        out_ptr[iidx2] = (a+b) * (c+d) - ac - bd;
-                    }
-                }
-            }
-        }
-    }
-}
-
-template<typename To, typename Ti, bool roundOut>
-void reorderOutput(To* out_ptr, const af::dim4& od, const af::dim4& os,
-                   const Ti* in_ptr, const af::dim4& id, const af::dim4& is,
-                   const af::dim4& fd, const int half_di0, const int baseDim,
-                   const int fftScale, const bool expand)
-{
-    for (int d3 = 0; d3 < (int)od[3]; d3++) {
-        for (int d2 = 0; d2 < (int)od[2]; d2++) {
-            for (int d1 = 0; d1 < (int)od[1]; d1++) {
-                for (int d0 = 0; d0 < (int)od[0]; d0++) {
-                    int id0, id1, id2, id3;
-                    if (expand) {
-                        id0 = d0;
-                        id1 = d1 * is[1];
-                        id2 = d2 * is[2];
-                        id3 = d3 * is[3];
-                    }
-                    else {
-                        id0 = d0 + fd[0]/2;
-                        id1 = (d1 + (baseDim > 1)*(fd[1]/2)) * is[1];
-                        id2 = (d2 + (baseDim > 2)*(fd[2]/2)) * is[2];
-                        id3 = d3 * is[3];
-                    }
-
-                    int oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0;
-
-                    // Divide output elements to cuFFT resulting scale, round result if output
-                    // type is single or double precision floating-point
-                    if (id0 < half_di0) {
-                        // Copy top elements
-                        int iidx = id3 + id2 + id1 + id0 * 2;
-                        if (roundOut)
-                            out_ptr[oidx] = (To)roundf((float)(in_ptr[iidx] / fftScale));
-                        else
-                            out_ptr[oidx] = (To)(in_ptr[iidx] / fftScale);
-                    }
-                    else if (id0 < half_di0 + (int)fd[0] - 1) {
-                        // Add signal and filter elements to central part
-                        int iidx1 = id3 + id2 + id1 + id0 * 2;
-                        int iidx2 = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
-                        if (roundOut)
-                            out_ptr[oidx] = (To)roundf((float)((in_ptr[iidx1] + in_ptr[iidx2]) / fftScale));
-                        else
-                            out_ptr[oidx] = (To)((in_ptr[iidx1] + in_ptr[iidx2]) / fftScale);
-                    }
-                    else {
-                        // Copy bottom elements
-                        const int iidx = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
-                        if (roundOut)
-                            out_ptr[oidx] = (To)roundf((float)(in_ptr[iidx] / fftScale));
-                        else
-                            out_ptr[oidx] = (To)(in_ptr[iidx] / fftScale);
-                    }
-                }
-            }
-        }
-    }
-}
-
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, ConvolveBatchKind kind)
@@ -289,11 +84,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    getQueue().enqueue(packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
+    getQueue().enqueue(kernel::packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
 
     // Pad filter array with 0s
     const dim_t offset = sig_tmp_strides[3]*sig_tmp_dims[3];
-    getQueue().enqueue(padArray_<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
+    getQueue().enqueue(kernel::padArray<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
                        filter, offset);
 
     dim4 fftDims(1, 1, 1, 1);
@@ -346,7 +141,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     getQueue().enqueue(upstream_dft, packed, fftDims);
 
     // Multiply filter and signal FFT arrays
-    getQueue().enqueue(complexMultiply<convT>, packed,
+    getQueue().enqueue(kernel::complexMultiply<convT>, packed,
                        sig_tmp_dims, sig_tmp_strides,
                        filter_tmp_dims, filter_tmp_strides,
                        kind, offset);
@@ -416,10 +211,10 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    auto reorderFunc = [=] (Array<T> out, Array<convT> packed,
-                            const Array<T> filter, const dim_t sig_hald_d0, const dim_t fftScale,
-                            const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
-                            const dim4 filter_tmp_dims, const dim4 filter_tmp_strides) {
+    auto reorderFunc = [=](Array<T> out, Array<convT> packed,
+                           const Array<T> filter, const dim_t sig_hald_d0, const dim_t fftScale,
+                           const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
+                           const dim4 filter_tmp_dims, const dim4 filter_tmp_strides) {
         T* out_ptr = out.get();
         const af::dim4 out_dims = out.dims();
         const af::dim4 out_strides = out.strides();
@@ -432,17 +227,16 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
         // Reorder the output
         if (kind == CONVOLVE_BATCH_KERNEL) {
-            reorderOutput<T, convT, roundOut>
-                (out_ptr, out_dims, out_strides,
-                 filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-                 filter_dims, sig_half_d0, baseDim, fftScale, expand);
+            kernel::reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
+                                              filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
+                                              filter_dims, sig_half_d0, baseDim, fftScale, expand);
         } else {
-            reorderOutput<T, convT, roundOut>
-                (out_ptr, out_dims, out_strides,
-                 sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-                 filter_dims, sig_half_d0, baseDim, fftScale, expand);
+            kernel::reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
+                                              sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
+                                              filter_dims, sig_half_d0, baseDim, fftScale, expand);
         }
     };
+
     getQueue().enqueue(reorderFunc, out, packed, filter, sig_half_d0, fftScale,
                        sig_tmp_dims, sig_tmp_strides,
                        filter_tmp_dims, filter_tmp_strides);
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
index 63bae2d237..51c48048c1 100644
--- a/src/backend/cpu/kernel/approx1.hpp
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <math.hpp>
+
 namespace cpu
 {
 namespace kernel
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
new file mode 100644
index 0000000000..2b5764fd37
--- /dev/null
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -0,0 +1,90 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+inline
+dim_t clamp(int a, dim_t mn, dim_t mx)
+{
+    return (a < (int)mn ? mn : (a > (int)mx ? mx : a));
+}
+
+inline
+unsigned getIdx(const dim4 &strides, int i, int j = 0, int k = 0, int l = 0)
+{
+    return (l * strides[3] + k * strides[2] + j * strides[1] + i * strides[0]);
+}
+
+template<typename inType, typename outType, bool isColor>
+void bilateral(Array<outType> out, const Array<inType> in, float s_sigma, float c_sigma)
+{
+    const dim4 dims     = in.dims();
+    const dim4 istrides = in.strides();
+
+    const dim4 ostrides = out.strides();
+
+          outType *outData = out.get();
+    const inType  *inData  = in.get();
+
+    // clamp spatical and chromatic sigma's
+    float space_          = std::min(11.5f, std::max(s_sigma, 0.f));
+    float color_          = std::max(c_sigma, 0.f);
+    const dim_t radius = std::max((dim_t)(space_ * 1.5f), (dim_t)1);
+    const float svar      = space_*space_;
+    const float cvar      = color_*color_;
+
+    for(dim_t b3=0; b3<dims[3]; ++b3) {
+        // b3 for loop handles following batch configurations
+        //  - gfor
+        //  - input based batch
+        //      - when input is 4d array for color images
+        for(dim_t b2=0; b2<dims[2]; ++b2) {
+            // b2 for loop handles following batch configurations
+            //  - channels
+            //  - input based batch
+            //      - when input is 3d array for grayscale images
+            for(dim_t j=0; j<dims[1]; ++j) {
+                // j steps along 2nd dimension
+                for(dim_t i=0; i<dims[0]; ++i) {
+                    // i steps along 1st dimension
+                    outType norm = 0.0;
+                    outType res  = 0.0;
+                    const outType center = (outType)inData[getIdx(istrides, i, j)];
+                    for(dim_t wj=-radius; wj<=radius; ++wj) {
+                        // clamps offsets
+                        dim_t tj = clamp(j+wj, 0, dims[1]-1);
+                        for(dim_t wi=-radius; wi<=radius; ++wi) {
+                            // clamps offsets
+                            dim_t ti = clamp(i+wi, 0, dims[0]-1);
+                            // proceed
+                            const outType val= (outType)inData[getIdx(istrides, ti, tj)];
+                            const outType gauss_space = (wi*wi+wj*wj)/(-2.0*svar);
+                            const outType gauss_range = ((center-val)*(center-val))/(-2.0*cvar);
+                            const outType weight = std::exp(gauss_space+gauss_range);
+                            norm += weight;
+                            res += val*weight;
+                        }
+                    } // filter loop ends here
+
+                    outData[getIdx(ostrides, i, j)] = res/norm;
+                } //1st dimension loop ends here
+            } //2nd dimension loop ends here
+            outData += ostrides[2];
+            inData  += istrides[2];
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
new file mode 100644
index 0000000000..d39acb65c9
--- /dev/null
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -0,0 +1,267 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T, typename accT, bool expand>
+void one2one_1d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
+                dim4 const &sDims, dim4 const &fDims, dim4 const &sStrides)
+{
+    dim_t start = (expand ? 0 : fDims[0]/2);
+    dim_t end   = (expand ? oDims[0] : start + sDims[0]);
+    for(dim_t i=start; i<end; ++i) {
+        accT accum = 0.0;
+        for(dim_t f=0; f<fDims[0]; ++f) {
+            dim_t iIdx = i-f;
+            T s_val = ((iIdx>=0 &&iIdx<sDims[0])? iptr[iIdx*sStrides[0]] : T(0));
+            accum += accT(s_val * fptr[f]);
+        }
+        optr[i-start] = T(accum);
+    }
+}
+
+template<typename T, typename accT, bool expand>
+void one2one_2d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
+                dim4 const &sDims, dim4 const &fDims, dim4 const &oStrides,
+                dim4 const &sStrides, dim4 const &fStrides)
+{
+    dim_t jStart = (expand ? 0 : fDims[1]/2);
+    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
+    dim_t iStart = (expand ? 0 : fDims[0]/2);
+    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
+
+    for(dim_t j=jStart; j<jEnd; ++j) {
+        dim_t joff = (j-jStart)*oStrides[1];
+
+        for(dim_t i=iStart; i<iEnd; ++i) {
+
+            accT accum = accT(0);
+            for(dim_t wj=0; wj<fDims[1]; ++wj) {
+                dim_t jIdx  = j-wj;
+                dim_t w_joff = wj*fStrides[1];
+                dim_t s_joff = jIdx * sStrides[1];
+                bool isJValid = (jIdx>=0 && jIdx<sDims[1]);
+
+                for(dim_t wi=0; wi<fDims[0]; ++wi) {
+                    dim_t iIdx = i-wi;
+
+                    T s_val = T(0);
+                    if ( isJValid && (iIdx>=0 && iIdx<sDims[0])) {
+                        s_val = iptr[s_joff+iIdx*sStrides[0]];
+                    }
+
+                    accum += accT(s_val * fptr[w_joff+wi*fStrides[0]]);
+                }
+            }
+            optr[joff+i-iStart] = T(accum);
+        }
+    }
+}
+
+template<typename T, typename accT, bool expand>
+void one2one_3d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
+                dim4 const &sDims, dim4 const &fDims, dim4 const &oStrides,
+                dim4 const &sStrides, dim4 const &fStrides)
+{
+    dim_t kStart = (expand ? 0 : fDims[2]/2);
+    dim_t kEnd   = (expand ? oDims[2] : kStart + sDims[2]);
+    dim_t jStart = (expand ? 0 : fDims[1]/2);
+    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
+    dim_t iStart = (expand ? 0 : fDims[0]/2);
+    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
+
+    for(dim_t k=kStart; k<kEnd; ++k) {
+        dim_t koff = (k-kStart)*oStrides[2];
+
+        for(dim_t j=jStart; j<jEnd; ++j) {
+            dim_t joff = (j-jStart)*oStrides[1];
+
+            for(dim_t i=iStart; i<iEnd; ++i) {
+
+                accT accum = accT(0);
+                for(dim_t wk=0; wk<fDims[2]; ++wk) {
+                    dim_t kIdx  = k-wk;
+                    dim_t w_koff = wk*fStrides[2];
+                    dim_t s_koff = kIdx * sStrides[2];
+                    bool isKValid = (kIdx>=0 && kIdx<sDims[2]);
+
+                    for(dim_t wj=0; wj<fDims[1]; ++wj) {
+                        dim_t jIdx  = j-wj;
+                        dim_t w_joff = wj*fStrides[1];
+                        dim_t s_joff = jIdx * sStrides[1];
+                        bool isJValid = (jIdx>=0 && jIdx<sDims[1]);
+
+                        for(dim_t wi=0; wi<fDims[0]; ++wi) {
+                            dim_t iIdx = i-wi;
+
+                            T s_val = T(0);
+                            if ( isKValid && isJValid && (iIdx>=0 && iIdx<sDims[0])) {
+                                s_val = iptr[s_koff+s_joff+iIdx*sStrides[0]];
+                            }
+
+                            accum += accT(s_val * fptr[w_koff+w_joff+wi*fStrides[0]]);
+                        }
+                    }
+                }
+                optr[koff+joff+i-iStart] = T(accum);
+            } //i loop ends here
+        } // j loop ends here
+    } // k loop ends here
+}
+
+template<typename T, typename accT, dim_t baseDim, bool expand>
+void convolve_nd(Array<T> out, Array<T> const signal, Array<accT> const filter, ConvolveBatchKind kind)
+{
+    T * optr = out.get();
+    T const * const iptr = signal.get();
+    accT const * const fptr = filter.get();
+
+    dim4 const oDims = out.dims();
+    dim4 const sDims = signal.dims();
+    dim4 const fDims = filter.dims();
+
+    dim4 const oStrides = out.strides();
+    dim4 const sStrides = signal.strides();
+    dim4 const fStrides = filter.strides();
+
+    dim_t out_step[4]  = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
+    dim_t in_step[4]   = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
+    dim_t filt_step[4] = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
+    dim_t batch[4]     = {0, 1, 1, 1}; /* first value is never used, and declared for code simplicity */
+
+    for (dim_t i=1; i<4; ++i) {
+        switch(kind) {
+            case CONVOLVE_BATCH_SIGNAL:
+                out_step[i] = oStrides[i];
+                in_step[i]  = sStrides[i];
+                if (i>=baseDim) batch[i] = sDims[i];
+                break;
+            case CONVOLVE_BATCH_SAME:
+                out_step[i]  = oStrides[i];
+                in_step[i]   = sStrides[i];
+                filt_step[i] = fStrides[i];
+                if (i>=baseDim) batch[i] = sDims[i];
+                break;
+            case CONVOLVE_BATCH_KERNEL:
+                out_step[i]  = oStrides[i];
+                filt_step[i] = fStrides[i];
+                if (i>=baseDim) batch[i] = fDims[i];
+                break;
+            default:
+                break;
+        }
+    }
+
+    for (dim_t b3=0; b3<batch[3]; ++b3) {
+        for (dim_t b2=0; b2<batch[2]; ++b2) {
+            for (dim_t b1=0; b1<batch[1]; ++b1) {
+
+                T * out          = optr + b1 * out_step[1] + b2 * out_step[2] + b3 * out_step[3];
+                T const *in      = iptr + b1 *  in_step[1] + b2 *  in_step[2] + b3 *  in_step[3];
+                accT const *filt = fptr + b1 *filt_step[1] + b2 *filt_step[2] + b3 *filt_step[3];
+
+                switch(baseDim) {
+                    case 1: one2one_1d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, sStrides);                     break;
+                    case 2: one2one_2d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
+                    case 3: one2one_3d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
+                }
+            }
+        }
+    }
+}
+
+template<typename T, typename accT, dim_t conv_dim, bool expand>
+void convolve2_separable(T *optr, T const *iptr, accT const *fptr,
+                        dim4 const &oDims, dim4 const &sDims, dim4 const &orgDims, dim_t fDim,
+                        dim4 const &oStrides, dim4 const &sStrides, dim_t fStride)
+{
+    for(dim_t j=0; j<oDims[1]; ++j) {
+
+        dim_t jOff = j*oStrides[1];
+        dim_t cj = j + (conv_dim==1)*(expand ? 0: fDim>>1);
+
+        for(dim_t i=0; i<oDims[0]; ++i) {
+
+            dim_t iOff = i*oStrides[0];
+            dim_t ci = i + (conv_dim==0)*(expand ? 0 : fDim>>1);
+
+            accT accum = scalar<accT>(0);
+
+            for(dim_t f=0; f<fDim; ++f) {
+                T f_val = fptr[f];
+                T s_val;
+
+                if (conv_dim==0) {
+                    dim_t offi = ci - f;
+                    bool isCIValid = offi>=0 && offi<sDims[0];
+                    bool isCJValid = cj>=0 && cj<sDims[1];
+                    s_val = (isCJValid && isCIValid ? iptr[cj*sDims[0]+offi] : scalar<T>(0));
+                } else {
+                    dim_t offj = cj - f;
+                    bool isCIValid = ci>=0 && ci<sDims[0];
+                    bool isCJValid = offj>=0 && offj<sDims[1];
+                    s_val = (isCJValid && isCIValid ? iptr[offj*sDims[0]+ci] : scalar<T>(0));
+                }
+
+                accum += accT(s_val * f_val);
+            }
+            optr[iOff+jOff] = T(accum);
+        }
+    }
+}
+
+template<typename T, typename accT, bool expand>
+void convolve2(Array<T> out, Array<T> const signal,
+               Array<accT> const c_filter, Array<accT> const r_filter,
+               dim4 const tDims)
+{
+    Array<T> temp = createEmptyArray<T>(tDims);
+
+    dim_t cflen = (dim_t)c_filter.elements();
+    dim_t rflen = (dim_t)r_filter.elements();
+
+    auto oDims = out.dims();
+    auto sDims = signal.dims();
+
+    auto oStrides = out.strides();
+    auto sStrides = signal.strides();
+    auto tStrides = temp.strides();
+
+    for (dim_t b3=0; b3<oDims[3]; ++b3) {
+
+        dim_t i_b3Off = b3*sStrides[3];
+        dim_t t_b3Off = b3*tStrides[3];
+        dim_t o_b3Off = b3*oStrides[3];
+
+        for (dim_t b2=0; b2<oDims[2]; ++b2) {
+
+            T const *iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
+            T *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
+            T *optr = out.get()  + b2*oStrides[2] + o_b3Off;
+
+            convolve2_separable<T, accT, 0, expand>(tptr, iptr, c_filter.get(),
+                    tDims, sDims, sDims, cflen,
+                    tStrides, sStrides, c_filter.strides()[0]);
+
+            convolve2_separable<T, accT, 1, expand>(optr, tptr, r_filter.get(),
+                    oDims, tDims, sDims, rflen,
+                    oStrides, tStrides, r_filter.strides()[0]);
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
new file mode 100644
index 0000000000..30bac668f1
--- /dev/null
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -0,0 +1,227 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <convolve_common.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename To, typename Ti>
+void packData(Array<To> out, const af::dim4 od, const af::dim4 os, Array<Ti> const in)
+{
+    To* out_ptr = out.get();
+
+    const af::dim4 id = in.dims();
+    const af::dim4 is = in.strides();
+    const Ti* in_ptr = in.get();
+
+    int id0_half = divup(id[0], 2);
+    bool odd_id0 = (id[0] % 2 == 1);
+
+    for (int d3 = 0; d3 < (int)od[3]; d3++) {
+        for (int d2 = 0; d2 < (int)od[2]; d2++) {
+            for (int d1 = 0; d1 < (int)od[1]; d1++) {
+                for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
+                    const dim_t oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+
+                    if (d0 < (int)id0_half && d1 < (int)id[1] && d2 < (int)id[2] && d3 < (int)id[3]) {
+                        const dim_t iidx = d3*is[3] + d2*is[2] + d1*is[1] + d0;
+                        out_ptr[oidx]   = (To)in_ptr[iidx];
+                        if (d0 == id0_half-1 && odd_id0)
+                            out_ptr[oidx+1] = (To)0;
+                        else
+                            out_ptr[oidx+1] = (To)in_ptr[iidx+id0_half];
+                    }
+                    else {
+                        // Pad remaining elements with 0s
+                        out_ptr[oidx]   = (To)0;
+                        out_ptr[oidx+1] = (To)0;
+                    }
+                }
+            }
+        }
+    }
+}
+
+template<typename To, typename Ti>
+void padArray(Array<To> out, const af::dim4 od, const af::dim4 os,
+              Array<Ti> const in, const dim_t offset)
+{
+    To* out_ptr = out.get() + offset;
+    const af::dim4 id = in.dims();
+    const af::dim4 is = in.strides();
+    const Ti* in_ptr = in.get();
+
+    for (int d3 = 0; d3 < (int)od[3]; d3++) {
+        for (int d2 = 0; d2 < (int)od[2]; d2++) {
+            for (int d1 = 0; d1 < (int)od[1]; d1++) {
+                for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
+                    const dim_t oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+
+                    if (d0 < (int)id[0] && d1 < (int)id[1] && d2 < (int)id[2] && d3 < (int)id[3]) {
+                        // Copy input elements to real elements, set imaginary elements to 0
+                        const dim_t iidx = d3*is[3] + d2*is[2] + d1*is[1] + d0;
+                        out_ptr[oidx]   = (To)in_ptr[iidx];
+                        out_ptr[oidx+1] = (To)0;
+                    }
+                    else {
+                        // Pad remaining of the matrix to 0s
+                        out_ptr[oidx]   = (To)0;
+                        out_ptr[oidx+1] = (To)0;
+                    }
+                }
+            }
+        }
+    }
+}
+
+template<typename T>
+void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
+                     const af::dim4 fit_dims, const af::dim4 fit_strides,
+                     ConvolveBatchKind kind, const dim_t offset)
+{
+    T* out_ptr = packed.get() + (kind==CONVOLVE_BATCH_KERNEL? offset : 0);
+    T* in1_ptr = packed.get();
+    T* in2_ptr = packed.get() + offset;
+
+    const dim4& od = (kind==CONVOLVE_BATCH_KERNEL ? fit_dims : sig_dims);
+    const dim4& os = (kind==CONVOLVE_BATCH_KERNEL ? fit_strides : sig_strides);
+    const dim4& i1d = sig_dims;
+    const dim4& i2d = fit_dims;
+    const dim4& i1s = sig_strides;
+    const dim4& i2s = fit_strides;
+
+    for (int d3 = 0; d3 < (int)od[3]; d3++) {
+        for (int d2 = 0; d2 < (int)od[2]; d2++) {
+            for (int d1 = 0; d1 < (int)od[1]; d1++) {
+                for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
+                    if (kind == CONVOLVE_BATCH_NONE || kind == CONVOLVE_BATCH_SAME) {
+                        // Complex multiply each signal to equivalent filter
+                        const int ridx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+                        const int iidx = ridx + 1;
+
+                        T a = in1_ptr[ridx];
+                        T b = in1_ptr[iidx];
+                        T c = in2_ptr[ridx];
+                        T d = in2_ptr[iidx];
+
+                        T ac = a*c;
+                        T bd = b*d;
+
+                        out_ptr[ridx] = ac - bd;
+                        out_ptr[iidx] = (a+b) * (c+d) - ac - bd;
+                    }
+                    else if (kind == CONVOLVE_BATCH_SIGNAL) {
+                        // Complex multiply all signals to filter
+                        const int ridx1 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+                        const int iidx1 = ridx1 + 1;
+                        const int ridx2 = ridx1 % (i2s[3] * i2d[3]);
+                        const int iidx2 = iidx1 % (i2s[3] * i2d[3]);
+
+                        T a = in1_ptr[ridx1];
+                        T b = in1_ptr[iidx1];
+                        T c = in2_ptr[ridx2];
+                        T d = in2_ptr[iidx2];
+
+                        T ac = a*c;
+                        T bd = b*d;
+
+                        out_ptr[ridx1] = ac - bd;
+                        out_ptr[iidx1] = (a+b) * (c+d) - ac - bd;
+                    }
+                    else if (kind == CONVOLVE_BATCH_KERNEL) {
+                        // Complex multiply signal to all filters
+                        const int ridx2 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+                        const int iidx2 = ridx2 + 1;
+                        const int ridx1 = ridx2 % (i1s[3] * i1d[3]);
+                        const int iidx1 = iidx2 % (i1s[3] * i1d[3]);
+
+                        T a = in1_ptr[ridx1];
+                        T b = in1_ptr[iidx1];
+                        T c = in2_ptr[ridx2];
+                        T d = in2_ptr[iidx2];
+
+                        T ac = a*c;
+                        T bd = b*d;
+
+                        out_ptr[ridx2] = ac - bd;
+                        out_ptr[iidx2] = (a+b) * (c+d) - ac - bd;
+                    }
+                }
+            }
+        }
+    }
+}
+
+template<typename To, typename Ti, bool roundOut>
+void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
+                   const Ti* in_ptr, const af::dim4& id, const af::dim4& is,
+                   const af::dim4& fd, const int half_di0, const int baseDim,
+                   const int fftScale, const bool expand)
+{
+    for (int d3 = 0; d3 < (int)od[3]; d3++) {
+        for (int d2 = 0; d2 < (int)od[2]; d2++) {
+            for (int d1 = 0; d1 < (int)od[1]; d1++) {
+                for (int d0 = 0; d0 < (int)od[0]; d0++) {
+                    int id0, id1, id2, id3;
+                    if (expand) {
+                        id0 = d0;
+                        id1 = d1 * is[1];
+                        id2 = d2 * is[2];
+                        id3 = d3 * is[3];
+                    }
+                    else {
+                        id0 = d0 + fd[0]/2;
+                        id1 = (d1 + (baseDim > 1)*(fd[1]/2)) * is[1];
+                        id2 = (d2 + (baseDim > 2)*(fd[2]/2)) * is[2];
+                        id3 = d3 * is[3];
+                    }
+
+                    int oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0;
+
+                    // Divide output elements to cuFFT resulting scale, round result if output
+                    // type is single or double precision floating-point
+                    if (id0 < half_di0) {
+                        // Copy top elements
+                        int iidx = id3 + id2 + id1 + id0 * 2;
+                        if (roundOut)
+                            out_ptr[oidx] = (To)roundf((float)(in_ptr[iidx] / fftScale));
+                        else
+                            out_ptr[oidx] = (To)(in_ptr[iidx] / fftScale);
+                    }
+                    else if (id0 < half_di0 + (int)fd[0] - 1) {
+                        // Add signal and filter elements to central part
+                        int iidx1 = id3 + id2 + id1 + id0 * 2;
+                        int iidx2 = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
+                        if (roundOut)
+                            out_ptr[oidx] = (To)roundf((float)((in_ptr[iidx1] + in_ptr[iidx2]) / fftScale));
+                        else
+                            out_ptr[oidx] = (To)((in_ptr[iidx1] + in_ptr[iidx2]) / fftScale);
+                    }
+                    else {
+                        // Copy bottom elements
+                        const int iidx = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
+                        if (roundOut)
+                            out_ptr[oidx] = (To)roundf((float)(in_ptr[iidx] / fftScale));
+                        else
+                            out_ptr[oidx] = (To)(in_ptr[iidx] / fftScale);
+                    }
+                }
+            }
+        }
+    }
+}
+
+}
+}

From e8f0242168e24f24d432606fc5974900e6ea206a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 18 Dec 2015 17:33:19 -0500
Subject: [PATCH 0154/2677] moved copy queue fns from cpu backend to kernel
 namespace

---
 src/backend/cpu/copy.cpp        | 79 ++---------------------------
 src/backend/cpu/kernel/copy.hpp | 90 +++++++++++++++++++++++++++++++++
 2 files changed, 95 insertions(+), 74 deletions(-)
 create mode 100644 src/backend/cpu/kernel/copy.hpp

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index eef5e0e302..84cb0d1a54 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -20,32 +20,11 @@
 #include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/copy.hpp>
 
 namespace cpu
 {
 
-template<typename T>
-static void stridedCopy(T* dst, const dim4& ostrides, const T* src, const dim4 &dims, const dim4 &strides, unsigned dim)
-{
-    if(dim == 0) {
-        if(strides[dim] == 1) {
-            //FIXME: Check for errors / exceptions
-            memcpy(dst, src, dims[dim] * sizeof(T));
-        } else {
-            for(dim_t i = 0; i < dims[dim]; i++) {
-                dst[i] = src[strides[dim]*i];
-            }
-        }
-    } else {
-        for(dim_t i = dims[dim]; i > 0; i--) {
-            stridedCopy<T>(dst, ostrides, src, dims, strides, dim - 1);
-            src += strides[dim];
-            dst += ostrides[dim];
-        }
-    }
-}
-
-// Assigns to single elements
 template<typename T>
 void copyData(T *to, const Array<T> &from)
 {
@@ -56,7 +35,7 @@ void copyData(T *to, const Array<T> &from)
         memcpy(to, from.get(), from.elements()*sizeof(T));
     } else {
         dim4 ostrides = calcStrides(from.dims());
-        stridedCopy<T>(to, ostrides, from.get(), from.dims(), from.strides(), from.ndims() - 1);
+        kernel::stridedCopy<T>(to, ostrides, from.get(), from.dims(), from.strides(), from.ndims() - 1);
     }
 }
 
@@ -68,59 +47,11 @@ Array<T> copyArray(const Array<T> &A)
     return out;
 }
 
-template<typename inType, typename outType>
-static void copy(Array<outType> dst, const Array<inType> src, outType default_value, double factor)
-{
-    dim4 src_dims       = src.dims();
-    dim4 dst_dims       = dst.dims();
-    dim4 src_strides    = src.strides();
-    dim4 dst_strides    = dst.strides();
-
-    const inType * src_ptr = src.get();
-    outType * dst_ptr      = dst.get();
-
-    dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
-    dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
-    dim_t trgt_j = std::min(dst_dims[1], src_dims[1]);
-    dim_t trgt_i = std::min(dst_dims[0], src_dims[0]);
-
-    for(dim_t l=0; l<dst_dims[3]; ++l) {
-
-        dim_t src_loff = l*src_strides[3];
-        dim_t dst_loff = l*dst_strides[3];
-        bool isLvalid = l<trgt_l;
-
-        for(dim_t k=0; k<dst_dims[2]; ++k) {
-
-            dim_t src_koff = k*src_strides[2];
-            dim_t dst_koff = k*dst_strides[2];
-            bool isKvalid = k<trgt_k;
-
-            for(dim_t j=0; j<dst_dims[1]; ++j) {
-
-                dim_t src_joff = j*src_strides[1];
-                dim_t dst_joff = j*dst_strides[1];
-                bool isJvalid = j<trgt_j;
-
-                for(dim_t i=0; i<dst_dims[0]; ++i) {
-                    outType temp = default_value;
-                    if (isLvalid && isKvalid && isJvalid && i<trgt_i) {
-                        dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
-                        temp = outType(src_ptr[src_idx])*outType(factor);
-                    }
-                    dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
-                    dst_ptr[dst_idx] = temp;
-                }
-            }
-        }
-    }
-}
-
 template<typename T>
 void multiply_inplace(Array<T> &in, double val)
 {
     in.eval();
-    getQueue().enqueue(copy<T, T>, in, in, 0, val);
+    getQueue().enqueue(kernel::copy<T, T>, in, in, 0, val);
 }
 
 template<typename inType, typename outType>
@@ -132,7 +63,7 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
     in.eval();
     // FIXME:
     getQueue().sync();
-    getQueue().enqueue(copy<inType, outType>, ret, in, outType(default_value), factor);
+    getQueue().enqueue(kernel::copy<inType, outType>, ret, in, outType(default_value), factor);
     return ret;
 }
 
@@ -141,7 +72,7 @@ void copyArray(Array<outType> &out, Array<inType> const &in)
 {
     out.eval();
     in.eval();
-    getQueue().enqueue(copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
+    getQueue().enqueue(kernel::copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
new file mode 100644
index 0000000000..063fb29f0c
--- /dev/null
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -0,0 +1,90 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T>
+void stridedCopy(T* dst, const dim4& ostrides, const T* src,
+                 const dim4 &dims, const dim4 &strides, unsigned dim)
+{
+    if(dim == 0) {
+        if(strides[dim] == 1) {
+            //FIXME: Check for errors / exceptions
+            memcpy(dst, src, dims[dim] * sizeof(T));
+        } else {
+            for(dim_t i = 0; i < dims[dim]; i++) {
+                dst[i] = src[strides[dim]*i];
+            }
+        }
+    } else {
+        for(dim_t i = dims[dim]; i > 0; i--) {
+            stridedCopy<T>(dst, ostrides, src, dims, strides, dim - 1);
+            src += strides[dim];
+            dst += ostrides[dim];
+        }
+    }
+}
+
+template<typename inType, typename outType>
+void copy(Array<outType> dst, const Array<inType> src, outType default_value, double factor)
+{
+    dim4 src_dims       = src.dims();
+    dim4 dst_dims       = dst.dims();
+    dim4 src_strides    = src.strides();
+    dim4 dst_strides    = dst.strides();
+
+    const inType * src_ptr = src.get();
+    outType * dst_ptr      = dst.get();
+
+    dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
+    dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
+    dim_t trgt_j = std::min(dst_dims[1], src_dims[1]);
+    dim_t trgt_i = std::min(dst_dims[0], src_dims[0]);
+
+    for(dim_t l=0; l<dst_dims[3]; ++l) {
+
+        dim_t src_loff = l*src_strides[3];
+        dim_t dst_loff = l*dst_strides[3];
+        bool isLvalid = l<trgt_l;
+
+        for(dim_t k=0; k<dst_dims[2]; ++k) {
+
+            dim_t src_koff = k*src_strides[2];
+            dim_t dst_koff = k*dst_strides[2];
+            bool isKvalid = k<trgt_k;
+
+            for(dim_t j=0; j<dst_dims[1]; ++j) {
+
+                dim_t src_joff = j*src_strides[1];
+                dim_t dst_joff = j*dst_strides[1];
+                bool isJvalid = j<trgt_j;
+
+                for(dim_t i=0; i<dst_dims[0]; ++i) {
+                    outType temp = default_value;
+                    if (isLvalid && isKvalid && isJvalid && i<trgt_i) {
+                        dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
+                        temp = outType(src_ptr[src_idx])*outType(factor);
+                    }
+                    dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
+                    dst_ptr[dst_idx] = temp;
+                }
+            }
+        }
+    }
+}
+
+}
+}

From 913850d9761463dbe5930e5ed6af0d3156e70a49 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 18 Dec 2015 17:46:19 -0500
Subject: [PATCH 0155/2677] Moved diagonal cpu implementation to kernel
 namespace

---
 src/backend/cpu/diagonal.cpp        | 43 ++----------------
 src/backend/cpu/kernel/diagonal.hpp | 67 +++++++++++++++++++++++++++++
 2 files changed, 70 insertions(+), 40 deletions(-)
 create mode 100644 src/backend/cpu/kernel/diagonal.hpp

diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 9af78459c1..6c20f2e7f2 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -17,6 +17,7 @@
 #include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/diagonal.hpp>
 
 namespace cpu
 {
@@ -30,25 +31,7 @@ Array<T> diagCreate(const Array<T> &in, const int num)
     int batch = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
-    auto func = [=] (Array<T> out, const Array<T> in) {
-        const T *iptr = in.get();
-              T *optr = out.get();
-
-        for (int k = 0; k < batch; k++) {
-            for (int j = 0; j < size; j++) {
-                for (int i = 0; i < size; i++) {
-                    T val = scalar<T>(0);
-                    if (i == j - num) {
-                        val = (num > 0) ? iptr[i] : iptr[j];
-                    }
-                    optr[i + j * out.strides()[1]] = val;
-                }
-            }
-            optr += out.strides()[2];
-            iptr += in.strides()[1];
-        }
-    };
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(kernel::diagCreate<T>, out, in, num);
 
     return out;
 }
@@ -62,27 +45,7 @@ Array<T> diagExtract(const Array<T> &in, const int num)
     dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
     Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-    auto func = [=] (Array<T> out, const Array<T> in) {
-        const dim4 odims = out.dims();
-
-        const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
-
-        for (int l = 0; l < (int)odims[3]; l++) {
-
-            for (int k = 0; k < (int)odims[2]; k++) {
-                const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
-                T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
-
-                for (int i = 0; i < (int)odims[0]; i++) {
-                    T val = scalar<T>(0);
-                    if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
-                    optr[i] = val;
-                }
-            }
-        }
-    };
-
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
 
     return out;
 }
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
new file mode 100644
index 0000000000..596080b108
--- /dev/null
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T>
+void diagCreate(Array<T> out, Array<T> const in, int const num)
+{
+    int batch = in.dims()[1];
+    int size  = out.dims()[0];
+
+    const T *iptr = in.get();
+    T *optr = out.get();
+
+    for (int k = 0; k < batch; k++) {
+        for (int j = 0; j < size; j++) {
+            for (int i = 0; i < size; i++) {
+                T val = scalar<T>(0);
+                if (i == j - num) {
+                    val = (num > 0) ? iptr[i] : iptr[j];
+                }
+                optr[i + j * out.strides()[1]] = val;
+            }
+        }
+        optr += out.strides()[2];
+        iptr += in.strides()[1];
+    }
+}
+
+template<typename T>
+void diagExtract(Array<T> out, Array<T> const in, int const num)
+{
+    const dim4 odims = out.dims();
+    const dim4 idims = in.dims();
+
+    const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
+
+    for (int l = 0; l < (int)odims[3]; l++) {
+
+        for (int k = 0; k < (int)odims[2]; k++) {
+            const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
+            T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
+
+            for (int i = 0; i < (int)odims[0]; i++) {
+                T val = scalar<T>(0);
+                if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
+                optr[i] = val;
+            }
+        }
+    }
+}
+
+}
+}

From 71298c69887f23b3f18354098b6cadd62968158b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 19 Dec 2015 00:43:53 -0500
Subject: [PATCH 0156/2677] moved diff, fast, gradient, harris, histogram to
 kernel namespace

---
 src/backend/cpu/diff.cpp             |  75 +--------
 src/backend/cpu/fast.cpp             | 227 +-------------------------
 src/backend/cpu/gradient.cpp         |  69 +-------
 src/backend/cpu/harris.cpp           | 133 +---------------
 src/backend/cpu/histogram.cpp        |  32 +---
 src/backend/cpu/kernel/diff.hpp      |  91 +++++++++++
 src/backend/cpu/kernel/fast.hpp      | 228 +++++++++++++++++++++++++++
 src/backend/cpu/kernel/gradient.hpp  |  87 ++++++++++
 src/backend/cpu/kernel/harris.hpp    | 139 ++++++++++++++++
 src/backend/cpu/kernel/histogram.hpp |  47 ++++++
 10 files changed, 611 insertions(+), 517 deletions(-)
 create mode 100644 src/backend/cpu/kernel/diff.hpp
 create mode 100644 src/backend/cpu/kernel/fast.hpp
 create mode 100644 src/backend/cpu/kernel/gradient.hpp
 create mode 100644 src/backend/cpu/kernel/harris.hpp
 create mode 100644 src/backend/cpu/kernel/histogram.hpp

diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 8f9c0f13be..3f639ca46f 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -9,62 +9,25 @@
 
 #include <Array.hpp>
 #include <diff.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/diff.hpp>
 
 namespace cpu
 {
 
-unsigned getIdx(af::dim4 strides, af::dim4 offs, int i, int j = 0, int k = 0, int l = 0)
-{
-    return (l * strides[3] +
-            k * strides[2] +
-            j * strides[1] +
-            i);
-}
-
 template<typename T>
 Array<T>  diff1(const Array<T> &in, const int dim)
 {
     in.eval();
-    // Bool for dimension
-    bool is_dim0 = dim == 0;
-    bool is_dim1 = dim == 1;
-    bool is_dim2 = dim == 2;
-    bool is_dim3 = dim == 3;
 
     // Decrement dimension of select dimension
     af::dim4 dims = in.dims();
     dims[dim]--;
 
-    // Create output placeholder
     Array<T> outArray = createEmptyArray<T>(dims);
 
-    auto func = [=] (Array<T> outArray, Array<T> in) {
-        // Get pointers to raw data
-        const T *inPtr = in.get();
-              T *outPtr = outArray.get();
-
-        // TODO: Improve this
-        for(dim_t l = 0; l < dims[3]; l++) {
-            for(dim_t k = 0; k < dims[2]; k++) {
-                for(dim_t j = 0; j < dims[1]; j++) {
-                    for(dim_t i = 0; i < dims[0]; i++) {
-                        // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                        int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                        int jdx = getIdx(in.strides(), in.offsets(),
-                                        i + is_dim0, j + is_dim1,
-                                        k + is_dim2, l + is_dim3);
-                        int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
-                        outPtr[odx] = inPtr[jdx] - inPtr[idx];
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, outArray, in);
+    getQueue().enqueue(kernel::diff1<T>, outArray, in, dim);
 
     return outArray;
 }
@@ -73,46 +36,14 @@ template<typename T>
 Array<T>  diff2(const Array<T> &in, const int dim)
 {
     in.eval();
-    // Bool for dimension
-    bool is_dim0 = dim == 0;
-    bool is_dim1 = dim == 1;
-    bool is_dim2 = dim == 2;
-    bool is_dim3 = dim == 3;
 
     // Decrement dimension of select dimension
     af::dim4 dims = in.dims();
     dims[dim] -= 2;
 
-    // Create output placeholder
     Array<T> outArray = createEmptyArray<T>(dims);
 
-    auto func = [=] (Array<T> outArray, Array<T> in) {
-        // Get pointers to raw data
-        const T *inPtr = in.get();
-            T *outPtr = outArray.get();
-
-        // TODO: Improve this
-        for(dim_t l = 0; l < dims[3]; l++) {
-            for(dim_t k = 0; k < dims[2]; k++) {
-                for(dim_t j = 0; j < dims[1]; j++) {
-                    for(dim_t i = 0; i < dims[0]; i++) {
-                        // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                        int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                        int jdx = getIdx(in.strides(), in.offsets(),
-                                        i + is_dim0, j + is_dim1,
-                                        k + is_dim2, l + is_dim3);
-                        int kdx = getIdx(in.strides(), in.offsets(),
-                                        i + 2 * is_dim0, j + 2 * is_dim1,
-                                        k + 2 * is_dim2, l + 2 * is_dim3);
-                        int odx = getIdx(outArray.strides(), outArray.offsets(), i, j, k, l);
-                        outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
-                    }
-                }
-            }
-        }
-    };
-
-    getQueue().enqueue(func, outArray, in);
+    getQueue().enqueue(kernel::diff2<T>, outArray, in, dim);
 
     return outArray;
 }
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index c8b0514610..fe02387102 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -16,234 +16,13 @@
 #include <fast.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/fast.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-inline int clamp(int f, int a, int b)
-{
-    return std::max(a, std::min(f, b));
-}
-
-inline int idx_y(int i)
-{
-    if (i >= 8)
-        return clamp(-(i-8-4), -3, 3);
-
-    return clamp(i-4, -3, 3);
-}
-
-inline int idx_x(int i)
-{
-    if (i < 12)
-        return idx_y(i+4);
-
-    return idx_y(i-12);
-}
-
-inline int idx(int y, int x, unsigned idim0)
-{
-    return x * idim0 + y;
-}
-
-// test_greater()
-// Tests if a pixel x > p + thr
-inline int test_greater(float x, float p, float thr)
-{
-    return (x >= p + thr);
-}
-
-// test_smaller()
-// Tests if a pixel x < p - thr
-inline int test_smaller(float x, float p, float thr)
-{
-    return (x <= p - thr);
-}
-
-// test_pixel()
-// Returns -1 when x < p - thr
-// Returns  0 when x >= p - thr && x <= p + thr
-// Returns  1 when x > p + thr
-template<typename T>
-inline int test_pixel(const T* image, const float p, float thr, int y, int x, unsigned idim0)
-{
-    return -test_smaller((float)image[idx(y,x,idim0)], p, thr) | test_greater((float)image[idx(y,x,idim0)], p, thr);
-}
-
-// abs_diff()
-// Returns absolute difference of x and y
-inline int abs_diff(int x, int y)
-{
-    return abs(x - y);
-}
-inline unsigned abs_diff(unsigned x, unsigned y)
-{
-    return (unsigned)abs((int)x - (int)y);
-}
-inline float abs_diff(float x, float y)
-{
-    return fabs(x - y);
-}
-inline double abs_diff(double x, double y)
-{
-    return fabs(x - y);
-}
-
-template<typename T>
-void locate_features(
-    const Array<T> &in,
-    Array<float> &score,
-    Array<float> &x_out,
-    Array<float> &y_out,
-    Array<float> &score_out,
-    unsigned* count,
-    const float thr,
-    const unsigned arc_length,
-    const unsigned nonmax,
-    const unsigned max_feat,
-    const unsigned edge)
-{
-    dim4 in_dims = in.dims();
-    const T* in_ptr = in.get();
-
-    for (int y = edge; y < (int)(in_dims[0] - edge); y++) {
-        for (int x = edge; x < (int)(in_dims[1] - edge); x++) {
-            float p = in_ptr[idx(y, x, in_dims[0])];
-
-            // Start by testing opposite pixels of the circle that will result in
-            // a non-kepoint
-            int d;
-            d  = test_pixel<T>(in_ptr, p, thr, y-3,   x, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+3,   x, in_dims[0]);
-            if (d == 0)
-                continue;
-
-            d &= test_pixel<T>(in_ptr, p, thr, y-2, x+2, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+2, x-2, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y  , x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y  , x-3, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y+2, x+2, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-2, x-2, in_dims[0]);
-            if (d == 0)
-                continue;
-
-            d &= test_pixel<T>(in_ptr, p, thr, y-3, x+1, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+3, x-1, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y-1, x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+1, x-3, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y+1, x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-1, x-3, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y+3, x+1, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-3, x-1, in_dims[0]);
-            if (d == 0)
-                continue;
-
-            int sum = 0;
-
-            // Sum responses [-1, 0 or 1] of first arc_length pixels
-            for (int i = 0; i < static_cast<int>(arc_length); i++)
-                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
-
-            // Test maximum and mininmum responses of first segment of arc_length
-            // pixels
-            int max_sum = 0, min_sum = 0;
-            max_sum = std::max(max_sum, sum);
-            min_sum = std::min(min_sum, sum);
-
-            // Sum responses and test the remaining 16-arc_length pixels of the circle
-            for (int i = arc_length; i < 16; i++) {
-                sum -= test_pixel<T>(in_ptr, p, thr, y+idx_y(i-arc_length), x+idx_x(i-arc_length), in_dims[0]);
-                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
-                max_sum = std::max(max_sum, sum);
-                min_sum = std::min(min_sum, sum);
-            }
-
-            // To completely test all possible segments, it's necessary to test
-            // segments that include the top junction of the circle
-            for (int i = 0; i < static_cast<int>(arc_length-1); i++) {
-                sum -= test_pixel<T>(in_ptr, p, thr, y+idx_y(16-arc_length+i), x+idx_x(16-arc_length+i), in_dims[0]);
-                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
-                max_sum = std::max(max_sum, sum);
-                min_sum = std::min(min_sum, sum);
-            }
-
-            float s_bright = 0, s_dark = 0;
-            for (int i = 0; i < 16; i++) {
-                float p_x = (float)in_ptr[idx(y+idx_y(i), x+idx_x(i), in_dims[0])];
-
-                s_bright += test_greater(p_x, p, thr) * (abs_diff(p_x, p) - thr);
-                s_dark   += test_smaller(p_x, p, thr) * (abs_diff(p, p_x) - thr);
-            }
-
-            // If sum at some point was equal to (+-)arc_length, there is a segment
-            // that for which all pixels are much brighter or much brighter than
-            // central pixel p.
-            if (max_sum == static_cast<int>(arc_length) || min_sum == -static_cast<int>(arc_length)) {
-                unsigned j = *count;
-                ++*count;
-                if (j < max_feat) {
-                    float *x_out_ptr = x_out.get();
-                    float *y_out_ptr = y_out.get();
-                    float *score_out_ptr = score_out.get();
-                    x_out_ptr[j]     = static_cast<float>(x);
-                    y_out_ptr[j]     = static_cast<float>(y);
-                    score_out_ptr[j] = static_cast<float>(std::max(s_bright, s_dark));
-                    if (nonmax == 1) {
-                        float* score_ptr = score.get();
-                        score_ptr[idx(y, x, in_dims[0])] = std::max(s_bright, s_dark);
-                    }
-                }
-            }
-        }
-    }
-}
-
-void non_maximal(
-    const Array<float> &score,
-    const Array<float> &x_in,
-    const Array<float> &y_in,
-    Array<float> &x_out,
-    Array<float> &y_out,
-    Array<float> &score_out,
-    unsigned* count,
-    const unsigned total_feat,
-    const unsigned edge)
-{
-    const float *score_ptr = score.get();
-    const float *x_in_ptr = x_in.get();
-    const float *y_in_ptr = y_in.get();
-
-    dim4 score_dims = score.dims();
-
-    for (unsigned k = 0; k < total_feat; k++) {
-        unsigned x = static_cast<unsigned>(round(x_in_ptr[k]));
-        unsigned y = static_cast<unsigned>(round(y_in_ptr[k]));
-
-        float v = score_ptr[y + score_dims[0] * x];
-        float max_v;
-        max_v = std::max(score_ptr[y-1 + score_dims[0] * (x-1)], score_ptr[y-1 + score_dims[0] * x]);
-        max_v = std::max(max_v, score_ptr[y-1 + score_dims[0] * (x+1)]);
-        max_v = std::max(max_v, score_ptr[y   + score_dims[0] * (x-1)]);
-        max_v = std::max(max_v, score_ptr[y   + score_dims[0] * (x+1)]);
-        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x-1)]);
-        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x)  ]);
-        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x+1)]);
-
-        if (y >= score_dims[1] - edge - 1 || y <= edge + 1 ||
-            x >= score_dims[0] - edge - 1 || x <= edge + 1)
-            continue;
-
-        // Stores keypoint to feat_out if it's response is maximum compared to
-        // its 8-neighborhood
-        if (v > max_v) {
-            unsigned j = *count;
-            ++*count;
-
-            float *x_out_ptr = x_out.get();
-            float *y_out_ptr = y_out.get();
-            float *score_out_ptr = score_out.get();
-
-            x_out_ptr[j]     = static_cast<float>(x);
-            y_out_ptr[j]     = static_cast<float>(y);
-            score_out_ptr[j] = static_cast<float>(v);
-        }
-    }
-}
-
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
@@ -274,7 +53,7 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     // Feature counter
     unsigned count = 0;
 
-    locate_features<T>(in, V, x, y, score, &count, thr, arc_length,
+    kernel::locate_features<T>(in, V, x, y, score, &count, thr, arc_length,
                        nonmax, max_feat, edge);
 
     // If more features than max_feat were detected, feat wasn't populated
@@ -293,7 +72,7 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
         score_total = createEmptyArray<float>(feat_found_dims);
 
         count = 0;
-        non_maximal(V, x, y,
+        kernel::non_maximal(V, x, y,
                     x_total, y_total, score_total,
                     &count, feat_found, edge);
 
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index 06c15cff4e..d1a8b0d2c9 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -14,6 +14,7 @@
 #include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/gradient.hpp>
 
 namespace cpu
 {
@@ -25,73 +26,7 @@ void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
     grad1.eval();
     in.eval();
 
-    auto func = [=] (Array<T> grad0, Array<T> grad1, const Array<T> in) {
-        const af::dim4 dims = in.dims();
-
-        T *d_grad0    = grad0.get();
-        T *d_grad1    = grad1.get();
-        const T *d_in = in.get();
-
-        const af::dim4 inst = in.strides();
-        const af::dim4 g0st = grad0.strides();
-        const af::dim4 g1st = grad1.strides();
-
-        T v5 = scalar<T>(0.5);
-        T v1 = scalar<T>(1.0);
-
-        for(dim_t idw = 0; idw < dims[3]; idw++) {
-            const dim_t inW = idw * inst[3];
-            const dim_t g0W = idw * g0st[3];
-            const dim_t g1W = idw * g1st[3];
-            for(dim_t idz = 0; idz < dims[2]; idz++) {
-                const dim_t inZW = inW + idz * inst[2];
-                const dim_t g0ZW = g0W + idz * g0st[2];
-                const dim_t g1ZW = g1W + idz * g1st[2];
-                dim_t xl, xr, yl,yr;
-                T f0, f1;
-                for(dim_t idy = 0; idy < dims[1]; idy++) {
-                    const dim_t inYZW = inZW + idy * inst[1];
-                    const dim_t g0YZW = g0ZW + idy * g0st[1];
-                    const dim_t g1YZW = g1ZW + idy * g1st[1];
-                    if(idy == 0) {
-                        yl = inYZW + inst[1];
-                        yr = inYZW;
-                        f1 = v1;
-                    } else if(idy == dims[1] - 1) {
-                        yl = inYZW;
-                        yr = inYZW - inst[1];
-                        f1 = v1;
-                    } else {
-                        yl = inYZW + inst[1];
-                        yr = inYZW - inst[1];
-                        f1 = v5;
-                    }
-                    for(dim_t idx = 0; idx < dims[0]; idx++) {
-                        const dim_t inMem = inYZW + idx;
-                        const dim_t g0Mem = g0YZW + idx;
-                        const dim_t g1Mem = g1YZW + idx;
-                        if(idx == 0) {
-                            xl = inMem + 1;
-                            xr = inMem;
-                            f0 = v1;
-                        } else if(idx == dims[0] - 1) {
-                            xl = inMem;
-                            xr = inMem - 1;
-                            f0 = v1;
-                        } else {
-                            xl = inMem + 1;
-                            xr = inMem - 1;
-                            f0 = v5;
-                        }
-
-                        d_grad0[g0Mem] = f0 * (d_in[xl] - d_in[xr]);
-                        d_grad1[g1Mem] = f1 * (d_in[yl + idx] - d_in[yr + idx]);
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, grad0, grad1, in);
+    getQueue().enqueue(kernel::gradient<T>, grad0, grad1, in);
 }
 
 #define INSTANTIATE(T)                                                                  \
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index b57b94025d..e5ff906dd6 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -12,7 +12,6 @@
 #include <af/constants.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
-#include <err_cpu.hpp>
 #include <handle.hpp>
 #include <harris.hpp>
 #include <convolve.hpp>
@@ -21,133 +20,13 @@
 #include <cstring>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/harris.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
-
-    T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * af::Pi * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
-        out[i] = el;
-        sum   += el;
-    }
-
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
-}
-
-template<typename T>
-void second_order_deriv(Array<T> ixx, Array<T> ixy, Array<T> iyy,
-                        const unsigned in_len, const Array<T> ix, const Array<T> iy)
-{
-    T* ixx_out     = ixx.get();
-    T* ixy_out     = ixy.get();
-    T* iyy_out     = iyy.get();
-    const T* ix_in = ix.get();
-    const T* iy_in = iy.get();
-    for (unsigned x = 0; x < in_len; x++) {
-        ixx_out[x] = ix_in[x] * ix_in[x];
-        ixy_out[x] = ix_in[x] * iy_in[x];
-        iyy_out[x] = iy_in[x] * iy_in[x];
-    }
-}
-
-template<typename T>
-void harris_responses(Array<T> resp, const unsigned idim0, const unsigned idim1,
-                      const Array<T> ixx, const Array<T> ixy, const Array<T> iyy,
-                      const float k_thr, const unsigned border_len)
-{
-    T* resp_out      = resp.get();
-    const T* ixx_in  = ixx.get();
-    const T* ixy_in  = ixy.get();
-    const T* iyy_in  = iyy.get();
-    const unsigned r = border_len;
-
-    for (unsigned x = r; x < idim1 - r; x++) {
-        for (unsigned y = r; y < idim0 - r; y++) {
-            const unsigned idx = x * idim0 + y;
-
-            // Calculates matrix trace and determinant
-            T tr = ixx_in[idx] + iyy_in[idx];
-            T det = ixx_in[idx] * iyy_in[idx] - ixy_in[idx] * ixy_in[idx];
-
-            // Calculates local Harris response
-            resp_out[idx] = det - k_thr * (tr*tr);
-        }
-    }
-}
-
-template<typename T>
-void non_maximal(Array<float> xOut, Array<float> yOut, Array<float> respOut, unsigned* count,
-                 const unsigned idim0, const unsigned idim1, const Array<T> respIn,
-                 const float min_resp, const unsigned border_len, const unsigned max_corners)
-{
-    float* x_out = xOut.get();
-    float* y_out = yOut.get();
-    float* resp_out = respOut.get();
-    const T* resp_in = respIn.get();
-    // Responses on the border don't have 8-neighbors to compare, discard them
-    const unsigned r = border_len + 1;
-
-    for (unsigned x = r; x < idim1 - r; x++) {
-        for (unsigned y = r; y < idim0 - r; y++) {
-            const T v = resp_in[x * idim0 + y];
-
-            // Find maximum neighborhood response
-            T max_v;
-            max_v = max(resp_in[(x-1) * idim0 + y-1], resp_in[x * idim0 + y-1]);
-            max_v = max(max_v, resp_in[(x+1) * idim0 + y-1]);
-            max_v = max(max_v, resp_in[(x-1) * idim0 + y  ]);
-            max_v = max(max_v, resp_in[(x+1) * idim0 + y  ]);
-            max_v = max(max_v, resp_in[(x-1) * idim0 + y+1]);
-            max_v = max(max_v, resp_in[(x)   * idim0 + y+1]);
-            max_v = max(max_v, resp_in[(x+1) * idim0 + y+1]);
-
-            // Stores corner to {x,y,resp}_out if it's response is maximum compared
-            // to its 8-neighborhood and greater or equal minimum response
-            if (v > max_v && v >= (T)min_resp) {
-                const unsigned idx = *count;
-                *count += 1;
-                if (idx < max_corners) {
-                    x_out[idx]    = (float)x;
-                    y_out[idx]    = (float)y;
-                    resp_out[idx] = (float)v;
-                }
-            }
-        }
-    }
-}
-
-static void keep_corners(Array<float> xOut, Array<float> yOut, Array<float> respOut,
-                         const Array<float> xIn, const Array<float> yIn,
-                         const Array<float> respIn, const Array<unsigned> respIdx,
-                         const unsigned n_corners)
-{
-    float* x_out = xOut.get();
-    float* y_out = yOut.get();
-    float* resp_out = respOut.get();
-    const float* x_in = xIn.get();
-    const float* y_in = yIn.get();
-    const float* resp_in = respIn.get();
-    const uint* resp_idx = respIdx.get();
-
-    // Keep only the first n_feat features
-    for (unsigned f = 0; f < n_corners; f++) {
-        x_out[f] = x_in[resp_idx[f]];
-        y_out[f] = y_in[resp_idx[f]];
-        resp_out[f] = resp_in[f];
-    }
-}
-
 template<typename T, typename convAccT>
 unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                 const Array<T> &in, const unsigned max_corners, const float min_response,
@@ -164,7 +43,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
     } else {
-        gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
+        kernel::gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
     }
     Array<convAccT> filter = createDeviceDataArray<convAccT>(dim4(filter_len), (const void*)h_filter);
 
@@ -181,7 +60,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     Array<T> iyy = createEmptyArray<T>(idims);
 
     // Compute second-order derivatives
-    getQueue().enqueue(second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
+    getQueue().enqueue(kernel::second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
 
     // Convolve second-order derivatives with proper window filter
     ixx = convolve2<T, convAccT, false>(ixx, filter, filter);
@@ -192,7 +71,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
 
     Array<T> responses = createEmptyArray<T>(dim4(in.elements()));
 
-    getQueue().enqueue(harris_responses<T>, responses, idims[0], idims[1],
+    getQueue().enqueue(kernel::harris_responses<T>, responses, idims[0], idims[1],
                        ixx, ixy, iyy, k_thr, border_len);
 
     Array<float> xCorners    = createEmptyArray<float>(dim4(corner_lim));
@@ -204,7 +83,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     // Performs non-maximal suppression
     getQueue().sync();
     unsigned corners_found = 0;
-    non_maximal<T>(xCorners, yCorners, respCorners, &corners_found,
+    kernel::non_maximal<T>(xCorners, yCorners, respCorners, &corners_found,
                    idims[0], idims[1], responses, min_r, border_len, corner_lim);
 
     const unsigned corners_out = (max_corners > 0) ?
@@ -226,7 +105,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
         // Keep only the corners with higher Harris responses
-        getQueue().enqueue(keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
+        getQueue().enqueue(kernel::keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
                            harris_sorted, harris_idx, corners_out);
     } else if (max_corners == 0 && corners_found < corner_lim) {
         x_out = createEmptyArray<float>(dim4(corners_out));
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 8fb3e43544..7e20247231 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -14,6 +14,7 @@
 #include <histogram.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/histogram.hpp>
 
 using af::dim4;
 
@@ -21,7 +22,8 @@ namespace cpu
 {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval)
+Array<outType> histogram(const Array<inType> &in,
+        const unsigned &nbins, const double &minval, const double &maxval)
 {
     in.eval();
 
@@ -30,32 +32,8 @@ Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const d
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
     out.eval();
 
-    auto func = [=](Array<outType> out, const Array<inType> in,
-                    const unsigned nbins, const double minval, const double maxval) {
-        const float step     = (maxval - minval)/(float)nbins;
-        const dim4 inDims    = in.dims();
-        const dim4 iStrides  = in.strides();
-        const dim4 oStrides  = out.strides();
-        const dim_t nElems   = inDims[0]*inDims[1];
-
-        outType *outData    = out.get();
-        const inType* inData= in.get();
-
-        for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
-            for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
-                for(dim_t i=0; i<nElems; i++) {
-                    int idx = isLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
-                    int bin = (int)((inData[idx] - minval) / step);
-                    bin = std::max(bin, 0);
-                    bin = std::min(bin, (int)(nbins - 1));
-                    outData[bin]++;
-                }
-                inData  += iStrides[2];
-                outData += oStrides[2];
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in, nbins, minval, maxval);
+    getQueue().enqueue(kernel::histogram<inType, outType, isLinear>,
+            out, in, nbins, minval, maxval);
 
     return out;
 }
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
new file mode 100644
index 0000000000..e0693b1349
--- /dev/null
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -0,0 +1,91 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+unsigned getIdx(af::dim4 strides, af::dim4 offs, int i, int j = 0, int k = 0, int l = 0)
+{
+    return (l * strides[3] + k * strides[2] + j * strides[1] + i);
+}
+
+
+template<typename T>
+void diff1(Array<T> out, Array<T> const in, int const dim)
+{
+    af::dim4 dims = out.dims();
+    // Bool for dimension
+    bool is_dim0 = dim == 0;
+    bool is_dim1 = dim == 1;
+    bool is_dim2 = dim == 2;
+    bool is_dim3 = dim == 3;
+
+    // Get pointers to raw data
+    const T *inPtr = in.get();
+    T *outPtr = out.get();
+
+    // TODO: Improve this
+    for(dim_t l = 0; l < dims[3]; l++) {
+        for(dim_t k = 0; k < dims[2]; k++) {
+            for(dim_t j = 0; j < dims[1]; j++) {
+                for(dim_t i = 0; i < dims[0]; i++) {
+                    // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+                    int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
+                    int jdx = getIdx(in.strides(), in.offsets(),
+                            i + is_dim0, j + is_dim1,
+                            k + is_dim2, l + is_dim3);
+                    int odx = getIdx(out.strides(), out.offsets(), i, j, k, l);
+                    outPtr[odx] = inPtr[jdx] - inPtr[idx];
+                }
+            }
+        }
+    }
+}
+
+template<typename T>
+void diff2(Array<T> out, Array<T> const in, int const dim)
+{
+    af::dim4 dims = out.dims();
+    // Bool for dimension
+    bool is_dim0 = dim == 0;
+    bool is_dim1 = dim == 1;
+    bool is_dim2 = dim == 2;
+    bool is_dim3 = dim == 3;
+
+    // Get pointers to raw data
+    const T *inPtr = in.get();
+    T *outPtr = out.get();
+
+    // TODO: Improve this
+    for(dim_t l = 0; l < dims[3]; l++) {
+        for(dim_t k = 0; k < dims[2]; k++) {
+            for(dim_t j = 0; j < dims[1]; j++) {
+                for(dim_t i = 0; i < dims[0]; i++) {
+                    // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+                    int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
+                    int jdx = getIdx(in.strides(), in.offsets(),
+                            i + is_dim0, j + is_dim1,
+                            k + is_dim2, l + is_dim3);
+                    int kdx = getIdx(in.strides(), in.offsets(),
+                            i + 2 * is_dim0, j + 2 * is_dim1,
+                            k + 2 * is_dim2, l + 2 * is_dim3);
+                    int odx = getIdx(out.strides(), out.offsets(), i, j, k, l);
+                    outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
new file mode 100644
index 0000000000..a3971dd136
--- /dev/null
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -0,0 +1,228 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+inline int clamp(int f, int a, int b)
+{
+    return std::max(a, std::min(f, b));
+}
+
+inline int idx_y(int i)
+{
+    if (i >= 8)
+        return clamp(-(i-8-4), -3, 3);
+
+    return clamp(i-4, -3, 3);
+}
+
+inline int idx_x(int i)
+{
+    if (i < 12)
+        return idx_y(i+4);
+
+    return idx_y(i-12);
+}
+
+inline int idx(int y, int x, unsigned idim0)
+{
+    return x * idim0 + y;
+}
+
+// test_greater()
+// Tests if a pixel x > p + thr
+inline int test_greater(float x, float p, float thr)
+{
+    return (x >= p + thr);
+}
+
+// test_smaller()
+// Tests if a pixel x < p - thr
+inline int test_smaller(float x, float p, float thr)
+{
+    return (x <= p - thr);
+}
+
+// test_pixel()
+// Returns -1 when x < p - thr
+// Returns  0 when x >= p - thr && x <= p + thr
+// Returns  1 when x > p + thr
+template<typename T>
+inline int test_pixel(const T* image, const float p, float thr, int y, int x, unsigned idim0)
+{
+    return -test_smaller((float)image[idx(y,x,idim0)], p, thr) | test_greater((float)image[idx(y,x,idim0)], p, thr);
+}
+
+// abs_diff()
+// Returns absolute difference of x and y
+inline int abs_diff(int x, int y)
+{
+    return abs(x - y);
+}
+inline unsigned abs_diff(unsigned x, unsigned y)
+{
+    return (unsigned)abs((int)x - (int)y);
+}
+inline float abs_diff(float x, float y)
+{
+    return fabs(x - y);
+}
+inline double abs_diff(double x, double y)
+{
+    return fabs(x - y);
+}
+
+template<typename T>
+void locate_features(const Array<T> &in, Array<float> &score,
+                     Array<float> &x_out, Array<float> &y_out,
+                     Array<float> &score_out, unsigned* count, const float thr,
+                     const unsigned arc_length, const unsigned nonmax,
+                     const unsigned max_feat, const unsigned edge)
+{
+    dim4 in_dims = in.dims();
+    const T* in_ptr = in.get();
+
+    for (int y = edge; y < (int)(in_dims[0] - edge); y++) {
+        for (int x = edge; x < (int)(in_dims[1] - edge); x++) {
+            float p = in_ptr[idx(y, x, in_dims[0])];
+
+            // Start by testing opposite pixels of the circle that will result in
+            // a non-kepoint
+            int d;
+            d  = test_pixel<T>(in_ptr, p, thr, y-3,   x, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+3,   x, in_dims[0]);
+            if (d == 0)
+                continue;
+
+            d &= test_pixel<T>(in_ptr, p, thr, y-2, x+2, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+2, x-2, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y  , x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y  , x-3, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y+2, x+2, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-2, x-2, in_dims[0]);
+            if (d == 0)
+                continue;
+
+            d &= test_pixel<T>(in_ptr, p, thr, y-3, x+1, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+3, x-1, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y-1, x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+1, x-3, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y+1, x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-1, x-3, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y+3, x+1, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-3, x-1, in_dims[0]);
+            if (d == 0)
+                continue;
+
+            int sum = 0;
+
+            // Sum responses [-1, 0 or 1] of first arc_length pixels
+            for (int i = 0; i < static_cast<int>(arc_length); i++)
+                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
+
+            // Test maximum and mininmum responses of first segment of arc_length
+            // pixels
+            int max_sum = 0, min_sum = 0;
+            max_sum = std::max(max_sum, sum);
+            min_sum = std::min(min_sum, sum);
+
+            // Sum responses and test the remaining 16-arc_length pixels of the circle
+            for (int i = arc_length; i < 16; i++) {
+                sum -= test_pixel<T>(in_ptr, p, thr, y+idx_y(i-arc_length), x+idx_x(i-arc_length), in_dims[0]);
+                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
+                max_sum = std::max(max_sum, sum);
+                min_sum = std::min(min_sum, sum);
+            }
+
+            // To completely test all possible segments, it's necessary to test
+            // segments that include the top junction of the circle
+            for (int i = 0; i < static_cast<int>(arc_length-1); i++) {
+                sum -= test_pixel<T>(in_ptr, p, thr, y+idx_y(16-arc_length+i), x+idx_x(16-arc_length+i), in_dims[0]);
+                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
+                max_sum = std::max(max_sum, sum);
+                min_sum = std::min(min_sum, sum);
+            }
+
+            float s_bright = 0, s_dark = 0;
+            for (int i = 0; i < 16; i++) {
+                float p_x = (float)in_ptr[idx(y+idx_y(i), x+idx_x(i), in_dims[0])];
+
+                s_bright += test_greater(p_x, p, thr) * (abs_diff(p_x, p) - thr);
+                s_dark   += test_smaller(p_x, p, thr) * (abs_diff(p, p_x) - thr);
+            }
+
+            // If sum at some point was equal to (+-)arc_length, there is a segment
+            // that for which all pixels are much brighter or much brighter than
+            // central pixel p.
+            if (max_sum == static_cast<int>(arc_length) || min_sum == -static_cast<int>(arc_length)) {
+                unsigned j = *count;
+                ++*count;
+                if (j < max_feat) {
+                    float *x_out_ptr = x_out.get();
+                    float *y_out_ptr = y_out.get();
+                    float *score_out_ptr = score_out.get();
+                    x_out_ptr[j]     = static_cast<float>(x);
+                    y_out_ptr[j]     = static_cast<float>(y);
+                    score_out_ptr[j] = static_cast<float>(std::max(s_bright, s_dark));
+                    if (nonmax == 1) {
+                        float* score_ptr = score.get();
+                        score_ptr[idx(y, x, in_dims[0])] = std::max(s_bright, s_dark);
+                    }
+                }
+            }
+        }
+    }
+}
+
+void non_maximal(const Array<float> &score, const Array<float> &x_in, const Array<float> &y_in,
+                 Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
+                 unsigned* count, const unsigned total_feat, const unsigned edge)
+{
+    const float *score_ptr = score.get();
+    const float *x_in_ptr = x_in.get();
+    const float *y_in_ptr = y_in.get();
+
+    dim4 score_dims = score.dims();
+
+    for (unsigned k = 0; k < total_feat; k++) {
+        unsigned x = static_cast<unsigned>(round(x_in_ptr[k]));
+        unsigned y = static_cast<unsigned>(round(y_in_ptr[k]));
+
+        float v = score_ptr[y + score_dims[0] * x];
+        float max_v;
+        max_v = std::max(score_ptr[y-1 + score_dims[0] * (x-1)], score_ptr[y-1 + score_dims[0] * x]);
+        max_v = std::max(max_v, score_ptr[y-1 + score_dims[0] * (x+1)]);
+        max_v = std::max(max_v, score_ptr[y   + score_dims[0] * (x-1)]);
+        max_v = std::max(max_v, score_ptr[y   + score_dims[0] * (x+1)]);
+        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x-1)]);
+        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x)  ]);
+        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x+1)]);
+
+        if (y >= score_dims[1] - edge - 1 || y <= edge + 1 ||
+            x >= score_dims[0] - edge - 1 || x <= edge + 1)
+            continue;
+
+        // Stores keypoint to feat_out if it's response is maximum compared to
+        // its 8-neighborhood
+        if (v > max_v) {
+            unsigned j = *count;
+            ++*count;
+
+            float *x_out_ptr = x_out.get();
+            float *y_out_ptr = y_out.get();
+            float *score_out_ptr = score_out.get();
+
+            x_out_ptr[j]     = static_cast<float>(x);
+            y_out_ptr[j]     = static_cast<float>(y);
+            score_out_ptr[j] = static_cast<float>(v);
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/gradient.hpp b/src/backend/cpu/kernel/gradient.hpp
new file mode 100644
index 0000000000..c152fb343a
--- /dev/null
+++ b/src/backend/cpu/kernel/gradient.hpp
@@ -0,0 +1,87 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void gradient(Array<T> grad0, Array<T> grad1, Array<T> const in)
+{
+    const af::dim4 dims = in.dims();
+
+    T *d_grad0    = grad0.get();
+    T *d_grad1    = grad1.get();
+    const T *d_in = in.get();
+
+    const af::dim4 inst = in.strides();
+    const af::dim4 g0st = grad0.strides();
+    const af::dim4 g1st = grad1.strides();
+
+    T v5 = scalar<T>(0.5);
+    T v1 = scalar<T>(1.0);
+
+    for(dim_t idw = 0; idw < dims[3]; idw++) {
+        const dim_t inW = idw * inst[3];
+        const dim_t g0W = idw * g0st[3];
+        const dim_t g1W = idw * g1st[3];
+        for(dim_t idz = 0; idz < dims[2]; idz++) {
+            const dim_t inZW = inW + idz * inst[2];
+            const dim_t g0ZW = g0W + idz * g0st[2];
+            const dim_t g1ZW = g1W + idz * g1st[2];
+            dim_t xl, xr, yl,yr;
+            T f0, f1;
+            for(dim_t idy = 0; idy < dims[1]; idy++) {
+                const dim_t inYZW = inZW + idy * inst[1];
+                const dim_t g0YZW = g0ZW + idy * g0st[1];
+                const dim_t g1YZW = g1ZW + idy * g1st[1];
+                if(idy == 0) {
+                    yl = inYZW + inst[1];
+                    yr = inYZW;
+                    f1 = v1;
+                } else if(idy == dims[1] - 1) {
+                    yl = inYZW;
+                    yr = inYZW - inst[1];
+                    f1 = v1;
+                } else {
+                    yl = inYZW + inst[1];
+                    yr = inYZW - inst[1];
+                    f1 = v5;
+                }
+                for(dim_t idx = 0; idx < dims[0]; idx++) {
+                    const dim_t inMem = inYZW + idx;
+                    const dim_t g0Mem = g0YZW + idx;
+                    const dim_t g1Mem = g1YZW + idx;
+                    if(idx == 0) {
+                        xl = inMem + 1;
+                        xr = inMem;
+                        f0 = v1;
+                    } else if(idx == dims[0] - 1) {
+                        xl = inMem;
+                        xr = inMem - 1;
+                        f0 = v1;
+                    } else {
+                        xl = inMem + 1;
+                        xr = inMem - 1;
+                        f0 = v5;
+                    }
+
+                    d_grad0[g0Mem] = f0 * (d_in[xl] - d_in[xr]);
+                    d_grad1[g1Mem] = f1 * (d_in[yl + idx] - d_in[yr + idx]);
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/harris.hpp b/src/backend/cpu/kernel/harris.hpp
new file mode 100644
index 0000000000..db6551bbde
--- /dev/null
+++ b/src/backend/cpu/kernel/harris.hpp
@@ -0,0 +1,139 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void gaussian1D(T* out, const int dim, double sigma=0.0)
+{
+    if(!(sigma>0)) sigma = 0.25*dim;
+
+    T sum = (T)0;
+    for(int i=0;i<dim;i++)
+    {
+        int x = i-(dim-1)/2;
+        T el = 1. / sqrt(2 * af::Pi * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+        out[i] = el;
+        sum   += el;
+    }
+
+    for(int k=0;k<dim;k++)
+        out[k] /= sum;
+}
+
+template<typename T>
+void second_order_deriv(Array<T> ixx, Array<T> ixy, Array<T> iyy,
+                        const unsigned in_len, const Array<T> ix, const Array<T> iy)
+{
+    T* ixx_out     = ixx.get();
+    T* ixy_out     = ixy.get();
+    T* iyy_out     = iyy.get();
+    const T* ix_in = ix.get();
+    const T* iy_in = iy.get();
+    for (unsigned x = 0; x < in_len; x++) {
+        ixx_out[x] = ix_in[x] * ix_in[x];
+        ixy_out[x] = ix_in[x] * iy_in[x];
+        iyy_out[x] = iy_in[x] * iy_in[x];
+    }
+}
+
+template<typename T>
+void harris_responses(Array<T> resp, const unsigned idim0, const unsigned idim1,
+                      const Array<T> ixx, const Array<T> ixy, const Array<T> iyy,
+                      const float k_thr, const unsigned border_len)
+{
+    T* resp_out      = resp.get();
+    const T* ixx_in  = ixx.get();
+    const T* ixy_in  = ixy.get();
+    const T* iyy_in  = iyy.get();
+    const unsigned r = border_len;
+
+    for (unsigned x = r; x < idim1 - r; x++) {
+        for (unsigned y = r; y < idim0 - r; y++) {
+            const unsigned idx = x * idim0 + y;
+
+            // Calculates matrix trace and determinant
+            T tr = ixx_in[idx] + iyy_in[idx];
+            T det = ixx_in[idx] * iyy_in[idx] - ixy_in[idx] * ixy_in[idx];
+
+            // Calculates local Harris response
+            resp_out[idx] = det - k_thr * (tr*tr);
+        }
+    }
+}
+
+template<typename T>
+void non_maximal(Array<float> xOut, Array<float> yOut, Array<float> respOut, unsigned* count,
+                 const unsigned idim0, const unsigned idim1, const Array<T> respIn,
+                 const float min_resp, const unsigned border_len, const unsigned max_corners)
+{
+    float* x_out = xOut.get();
+    float* y_out = yOut.get();
+    float* resp_out = respOut.get();
+    const T* resp_in = respIn.get();
+    // Responses on the border don't have 8-neighbors to compare, discard them
+    const unsigned r = border_len + 1;
+
+    for (unsigned x = r; x < idim1 - r; x++) {
+        for (unsigned y = r; y < idim0 - r; y++) {
+            const T v = resp_in[x * idim0 + y];
+
+            // Find maximum neighborhood response
+            T max_v;
+            max_v = max(resp_in[(x-1) * idim0 + y-1], resp_in[x * idim0 + y-1]);
+            max_v = max(max_v, resp_in[(x+1) * idim0 + y-1]);
+            max_v = max(max_v, resp_in[(x-1) * idim0 + y  ]);
+            max_v = max(max_v, resp_in[(x+1) * idim0 + y  ]);
+            max_v = max(max_v, resp_in[(x-1) * idim0 + y+1]);
+            max_v = max(max_v, resp_in[(x)   * idim0 + y+1]);
+            max_v = max(max_v, resp_in[(x+1) * idim0 + y+1]);
+
+            // Stores corner to {x,y,resp}_out if it's response is maximum compared
+            // to its 8-neighborhood and greater or equal minimum response
+            if (v > max_v && v >= (T)min_resp) {
+                const unsigned idx = *count;
+                *count += 1;
+                if (idx < max_corners) {
+                    x_out[idx]    = (float)x;
+                    y_out[idx]    = (float)y;
+                    resp_out[idx] = (float)v;
+                }
+            }
+        }
+    }
+}
+
+static void keep_corners(Array<float> xOut, Array<float> yOut, Array<float> respOut,
+                         const Array<float> xIn, const Array<float> yIn,
+                         const Array<float> respIn, const Array<unsigned> respIdx,
+                         const unsigned n_corners)
+{
+    float* x_out = xOut.get();
+    float* y_out = yOut.get();
+    float* resp_out = respOut.get();
+    const float* x_in = xIn.get();
+    const float* y_in = yIn.get();
+    const float* resp_in = respIn.get();
+    const uint* resp_idx = respIdx.get();
+
+    // Keep only the first n_feat features
+    for (unsigned f = 0; f < n_corners; f++) {
+        x_out[f] = x_in[resp_idx[f]];
+        y_out[f] = y_in[resp_idx[f]];
+        resp_out[f] = resp_in[f];
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
new file mode 100644
index 0000000000..e26965aa04
--- /dev/null
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -0,0 +1,47 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename inType, typename outType, bool isLinear>
+void histogram(Array<outType> out, Array<inType> const in,
+               unsigned const nbins, double const minval, double const maxval)
+{
+    dim4 const outDims   = out.dims();
+    float const step     = (maxval - minval)/(float)nbins;
+    dim4 const inDims    = in.dims();
+    dim4 const iStrides  = in.strides();
+    dim4 const oStrides  = out.strides();
+    dim_t const nElems   = inDims[0]*inDims[1];
+
+    outType *outData    = out.get();
+    const inType* inData= in.get();
+
+    for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
+        for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
+            for(dim_t i=0; i<nElems; i++) {
+                int idx = isLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
+                int bin = (int)((inData[idx] - minval) / step);
+                bin = std::max(bin, 0);
+                bin = std::min(bin, (int)(nbins - 1));
+                outData[bin]++;
+            }
+            inData  += iStrides[2];
+            outData += oStrides[2];
+        }
+    }
+}
+
+}
+}

From cc7d665c6b526a6285e9b75be90bb41f493fac35 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 19 Dec 2015 11:21:36 -0500
Subject: [PATCH 0157/2677] moved rgb_hsv & identity fns to kernel namespace

---
 src/backend/cpu/hsv_rgb.cpp         | 104 +----------------------
 src/backend/cpu/identity.cpp        |  19 +----
 src/backend/cpu/kernel/hsv_rgb.hpp  | 124 ++++++++++++++++++++++++++++
 src/backend/cpu/kernel/identity.hpp |  37 +++++++++
 4 files changed, 166 insertions(+), 118 deletions(-)
 create mode 100644 src/backend/cpu/kernel/hsv_rgb.hpp
 create mode 100644 src/backend/cpu/kernel/identity.hpp

diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index d20416f3c9..c0f19db773 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -11,10 +11,9 @@
 #include <Array.hpp>
 #include <ArrayInfo.hpp>
 #include <hsv_rgb.hpp>
-#include <err_cpu.hpp>
-#include <cmath>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/hsv_rgb.hpp>
 
 using af::dim4;
 
@@ -28,56 +27,7 @@ Array<T> hsv2rgb(const Array<T>& in)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    auto func = [=](Array<T> out, const Array<T> in) {
-        const dim4 dims    = in.dims();
-        const dim4 strides = in.strides();
-        dim_t obStride  = out.strides()[3];
-        dim_t coff      = strides[2];
-        dim_t bCount    = dims[3];
-
-        for(dim_t b=0; b<bCount; ++b) {
-            const T* src = in.get() + b * strides[3];
-            T* dst       = out.get() + b * obStride;
-
-            for(dim_t j=0; j<dims[1]; ++j) {
-                dim_t jOff = j*strides[1];
-                // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
-                    // i steps along 1st dimension
-                    dim_t hIdx = i*strides[0] + jOff;
-                    dim_t sIdx = hIdx + coff;
-                    dim_t vIdx = sIdx + coff;
-
-                    T H = src[hIdx];
-                    T S = src[sIdx];
-                    T V = src[vIdx];
-
-                    T R, G, B;
-                    R = G = B = 0;
-
-                    int   m = (int)(H * 6);
-                    T f = H * 6 - m;
-                    T p = V * (1 - S);
-                    T q = V * (1 - f * S);
-                    T t = V * (1 - (1 - f) * S);
-
-                    switch (m % 6) {
-                        case 0: R = V, G = t, B = p; break;
-                        case 1: R = q, G = V, B = p; break;
-                        case 2: R = p, G = V, B = t; break;
-                        case 3: R = p, G = q, B = V; break;
-                        case 4: R = t, G = p, B = V; break;
-                        case 5: R = V, G = p, B = q; break;
-                    }
-
-                    dst[hIdx] = R;
-                    dst[sIdx] = G;
-                    dst[vIdx] = B;
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(kernel::hsv2rgb<T>, out, in);
 
     return out;
 }
@@ -89,55 +39,7 @@ Array<T> rgb2hsv(const Array<T>& in)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    auto func = [=](Array<T> out, const Array<T> in) {
-        const dim4 dims    = in.dims();
-        const dim4 strides = in.strides();
-        dim4 oStrides      = out.strides();
-        dim_t bCount    = dims[3];
-
-        for(dim_t b=0; b<bCount; ++b) {
-            const T* src = in.get() + b * strides[3];
-            T* dst       = out.get() + b * oStrides[3];
-
-            for(dim_t j=0; j<dims[1]; ++j) {
-                // j steps along 2nd dimension
-                dim_t oj = j * oStrides[1];
-                dim_t ij = j * strides[1];
-
-                for(dim_t i=0; i<dims[0]; ++i) {
-                    // i steps along 1st dimension
-                    dim_t oIdx0 = i * oStrides[0] + oj;
-                    dim_t oIdx1 = oIdx0 + oStrides[2];
-                    dim_t oIdx2 = oIdx1 + oStrides[2];
-
-                    dim_t iIdx0 = i * strides[0]  + ij;
-                    dim_t iIdx1 = iIdx0 + strides[2];
-                    dim_t iIdx2 = iIdx1 + strides[2];
-
-                    T R = src[iIdx0];
-                    T G = src[iIdx1];
-                    T B = src[iIdx2];
-                    T Cmax = std::max(std::max(R, G), B);
-                    T Cmin = std::min(std::min(R, G), B);
-                    T delta= Cmax-Cmin;
-
-                    T H = 0;
-
-                    if (Cmax!=Cmin) {
-                        if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
-                        if (Cmax==G) H = (B-R)/delta + 2;
-                        if (Cmax==B) H = (R-G)/delta + 4;
-                        H = H / 6.0f;
-                    }
-
-                    dst[oIdx0] = H;
-                    dst[oIdx1] = (Cmax==0.0f ? 0 : delta/Cmax);
-                    dst[oIdx2] = Cmax;
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(kernel::rgb2hsv<T>, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index 55c441755c..949fceda81 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -7,14 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <identity.hpp>
-#include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/identity.hpp>
 
 namespace cpu
 {
@@ -24,20 +22,7 @@ Array<T> identity(const dim4& dims)
 {
     Array<T> out = createEmptyArray<T>(dims);
 
-    auto func = [=] (Array<T> out) {
-        T *ptr = out.get();
-        const dim_t *out_dims  = out.dims().get();
-
-        for (dim_t k = 0; k < out_dims[2] * out_dims[3]; k++) {
-            for (dim_t j = 0; j < out_dims[1]; j++) {
-                for (dim_t i = 0; i < out_dims[0]; i++) {
-                    ptr[j * out_dims[0] + i]  = (i == j) ? scalar<T>(1) : scalar<T>(0);
-                }
-            }
-            ptr += out_dims[0] * out_dims[1];
-        }
-    };
-    getQueue().enqueue(func, out);
+    getQueue().enqueue(kernel::identity<T>, out);
 
     return out;
 }
diff --git a/src/backend/cpu/kernel/hsv_rgb.hpp b/src/backend/cpu/kernel/hsv_rgb.hpp
new file mode 100644
index 0000000000..d8aa954df7
--- /dev/null
+++ b/src/backend/cpu/kernel/hsv_rgb.hpp
@@ -0,0 +1,124 @@
+/*******************************************************
+* Copyright (c) 2015, ArrayFire
+* All rights reserved.
+*
+* This file is distributed under 3-clause BSD license.
+* The complete license agreement can be obtained at:
+* http://arrayfire.com/licenses/BSD-3-Clause
+********************************************************/
+
+#include <Array.hpp>
+#include <cmath>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T>
+void hsv2rgb(Array<T> out, Array<T> const in)
+{
+    const dim4 dims    = in.dims();
+    const dim4 strides = in.strides();
+    dim_t obStride  = out.strides()[3];
+    dim_t coff      = strides[2];
+    dim_t bCount    = dims[3];
+
+    for(dim_t b=0; b<bCount; ++b) {
+        const T* src = in.get() + b * strides[3];
+        T* dst       = out.get() + b * obStride;
+
+        for(dim_t j=0; j<dims[1]; ++j) {
+            dim_t jOff = j*strides[1];
+            // j steps along 2nd dimension
+            for(dim_t i=0; i<dims[0]; ++i) {
+                // i steps along 1st dimension
+                dim_t hIdx = i*strides[0] + jOff;
+                dim_t sIdx = hIdx + coff;
+                dim_t vIdx = sIdx + coff;
+
+                T H = src[hIdx];
+                T S = src[sIdx];
+                T V = src[vIdx];
+
+                T R, G, B;
+                R = G = B = 0;
+
+                int   m = (int)(H * 6);
+                T f = H * 6 - m;
+                T p = V * (1 - S);
+                T q = V * (1 - f * S);
+                T t = V * (1 - (1 - f) * S);
+
+                switch (m % 6) {
+                    case 0: R = V, G = t, B = p; break;
+                    case 1: R = q, G = V, B = p; break;
+                    case 2: R = p, G = V, B = t; break;
+                    case 3: R = p, G = q, B = V; break;
+                    case 4: R = t, G = p, B = V; break;
+                    case 5: R = V, G = p, B = q; break;
+                }
+
+                dst[hIdx] = R;
+                dst[sIdx] = G;
+                dst[vIdx] = B;
+            }
+        }
+    }
+}
+
+template<typename T>
+void rgb2hsv(Array<T> out, Array<T> const in)
+{
+    const dim4 dims    = in.dims();
+    const dim4 strides = in.strides();
+    dim4 oStrides      = out.strides();
+    dim_t bCount    = dims[3];
+
+    for(dim_t b=0; b<bCount; ++b) {
+        const T* src = in.get() + b * strides[3];
+        T* dst       = out.get() + b * oStrides[3];
+
+        for(dim_t j=0; j<dims[1]; ++j) {
+            // j steps along 2nd dimension
+            dim_t oj = j * oStrides[1];
+            dim_t ij = j * strides[1];
+
+            for(dim_t i=0; i<dims[0]; ++i) {
+                // i steps along 1st dimension
+                dim_t oIdx0 = i * oStrides[0] + oj;
+                dim_t oIdx1 = oIdx0 + oStrides[2];
+                dim_t oIdx2 = oIdx1 + oStrides[2];
+
+                dim_t iIdx0 = i * strides[0]  + ij;
+                dim_t iIdx1 = iIdx0 + strides[2];
+                dim_t iIdx2 = iIdx1 + strides[2];
+
+                T R = src[iIdx0];
+                T G = src[iIdx1];
+                T B = src[iIdx2];
+                T Cmax = std::max(std::max(R, G), B);
+                T Cmin = std::min(std::min(R, G), B);
+                T delta= Cmax-Cmin;
+
+                T H = 0;
+
+                if (Cmax!=Cmin) {
+                    if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
+                    if (Cmax==G) H = (B-R)/delta + 2;
+                    if (Cmax==B) H = (R-G)/delta + 4;
+                    H = H / 6.0f;
+                }
+
+                dst[oIdx0] = H;
+                dst[oIdx1] = (Cmax==0.0f ? 0 : delta/Cmax);
+                dst[oIdx2] = Cmax;
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/identity.hpp b/src/backend/cpu/kernel/identity.hpp
new file mode 100644
index 0000000000..9eab13b070
--- /dev/null
+++ b/src/backend/cpu/kernel/identity.hpp
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <math.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T>
+void identity(Array<T> out)
+{
+    T *ptr = out.get();
+    const dim4 out_dims  = out.dims();
+
+    for (dim_t k = 0; k < out_dims[2] * out_dims[3]; k++) {
+        for (dim_t j = 0; j < out_dims[1]; j++) {
+            for (dim_t i = 0; i < out_dims[0]; i++) {
+                ptr[j * out_dims[0] + i]  = (i == j) ? scalar<T>(1) : scalar<T>(0);
+            }
+        }
+        ptr += out_dims[0] * out_dims[1];
+    }
+}
+
+}
+}

From 696657cb3cde7d660b0558269c85fa24cdda2f6d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 19 Dec 2015 11:47:51 -0500
Subject: [PATCH 0158/2677] moved indexing & assignment fns to kernel namespace

Also moved the common utility function trimIndex to a common
location.
---
 src/backend/cpu/iir.cpp           | 44 +------------------
 src/backend/cpu/index.cpp         | 69 ++----------------------------
 src/backend/cpu/kernel/assign.hpp | 21 ++-------
 src/backend/cpu/kernel/iir.hpp    | 61 ++++++++++++++++++++++++++
 src/backend/cpu/kernel/index.hpp  | 71 +++++++++++++++++++++++++++++++
 src/backend/cpu/kernel/lookup.hpp | 62 +++++++++++++++++++++++++++
 src/backend/cpu/lookup.cpp        | 54 +----------------------
 src/backend/cpu/utility.hpp       | 30 +++++++++++++
 8 files changed, 236 insertions(+), 176 deletions(-)
 create mode 100644 src/backend/cpu/kernel/iir.hpp
 create mode 100644 src/backend/cpu/kernel/index.hpp
 create mode 100644 src/backend/cpu/kernel/lookup.hpp
 create mode 100644 src/backend/cpu/utility.hpp

diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 3c06275f5a..225f39b859 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -12,12 +12,10 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
-#include <err_cpu.hpp>
-#include <math.hpp>
-#include <arith.hpp>
 #include <convolve.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/iir.hpp>
 
 using af::dim4;
 
@@ -44,45 +42,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
 
     Array<T> y = createEmptyArray<T>(c.dims());
 
-    auto func = [=] (Array<T> y, Array<T> c, const Array<T> a) {
-        dim4 ydims = c.dims();
-        int num_a = a.dims()[0];
-
-        for (int l = 0; l < (int)ydims[3]; l++) {
-            dim_t yidx3 = l * y.strides()[3];
-            dim_t cidx3 = l * c.strides()[3];
-            dim_t aidx3 = l * a.strides()[3];
-
-            for (int k = 0; k < (int)ydims[2]; k++) {
-
-                dim_t yidx2 = k * y.strides()[2] + yidx3;
-                dim_t cidx2 = k * c.strides()[2] + cidx3;
-                dim_t aidx2 = k * a.strides()[2] + aidx3;
-
-                for (int j = 0; j < (int)ydims[1]; j++) {
-
-                    dim_t yidx1 = j * y.strides()[1] + yidx2;
-                    dim_t cidx1 = j * c.strides()[1] + cidx2;
-                    dim_t aidx1 = j * a.strides()[1] + aidx2;
-
-                    std::vector<T> h_z(num_a);
-
-                    const T *h_a = a.get() + (a.ndims() > 1 ? aidx1 : 0);
-                    T *h_c = c.get() + cidx1;
-                    T *h_y = y.get() + yidx1;
-
-                    for (int i = 0; i < (int)ydims[0]; i++) {
-
-                        T y = h_y[i] = (h_c[i] + h_z[0]) /  h_a[0];
-                        for (int ii = 1; ii < num_a; ii++) {
-                            h_z[ii - 1] = h_z[ii] - h_a[ii] * y;
-                        }
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, y, c, a);
+    getQueue().enqueue(kernel::iir<T>, y, c, a);
 
     return y;
 }
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 68c2f16a23..bd569de44a 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -13,11 +13,11 @@
 #include <Array.hpp>
 #include <index.hpp>
 #include <handle.hpp>
-#include <err_cpu.hpp>
 #include <vector>
 #include <platform.hpp>
 #include <async_queue.hpp>
 #include <utility>
+#include <kernel/index.hpp>
 
 using std::vector;
 using af::dim4;
@@ -25,19 +25,6 @@ using af::dim4;
 namespace cpu
 {
 
-static inline
-dim_t trimIndex(dim_t idx, const dim_t &len)
-{
-    dim_t ret_val = idx;
-    dim_t offset  = abs(ret_val)%len;
-    if (ret_val<0) {
-        ret_val = offset-1;
-    } else if (ret_val>=len) {
-        ret_val = len-offset-1;
-    }
-    return ret_val;
-}
-
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 {
@@ -47,7 +34,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (dim_t x=0; x<isSeq.size(); ++x) {
+    for (unsigned x=0; x<isSeq.size(); ++x) {
         if (idxrs[x].isSeq) {
             seqs[x] = idxrs[x].idx.seq;
         }
@@ -59,7 +46,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 
     vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<isSeq.size(); ++x) {
+    for (unsigned x=0; x<isSeq.size(); ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
             idxArrs[x].eval();
@@ -70,56 +57,8 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    auto func = [=] (Array<T> out, const Array<T> in,
-                     const vector<bool> isSeq,
-                     const vector<af_seq> seqs,
-                     const vector< Array<uint> > idxArrs) {
-
-        const dim4 iDims    = in.dims();
-        const dim4 dDims    = in.getDataDims();
-        const dim4 iOffs    = toOffset(seqs, dDims);
-        const dim4 iStrds   = toStride(seqs, dDims);
-        const dim4 oDims    = out.dims();
-        const dim4 oStrides = out.strides();
-        const T *src        = in.get();
-              T *dst        = out.get();
-        const uint* ptr0    = idxArrs[0].get();
-        const uint* ptr1    = idxArrs[1].get();
-        const uint* ptr2    = idxArrs[2].get();
-        const uint* ptr3    = idxArrs[3].get();
-
-        for (dim_t l=0; l<oDims[3]; ++l) {
-
-            dim_t lOff   = l*oStrides[3];
-            dim_t inIdx3 = trimIndex(isSeq[3] ? l+iOffs[3] : ptr3[l], iDims[3]);
-            dim_t inOff3 = inIdx3*iStrds[3];
-
-            for (dim_t k=0; k<oDims[2]; ++k) {
-
-                dim_t kOff   = k*oStrides[2];
-                dim_t inIdx2 = trimIndex(isSeq[2] ? k+iOffs[2] : ptr2[k], iDims[2]);
-                dim_t inOff2 = inIdx2*iStrds[2];
-
-                for (dim_t j=0; j<oDims[1]; ++j) {
-
-                    dim_t jOff   = j*oStrides[1];
-                    dim_t inIdx1 = trimIndex(isSeq[1] ? j+iOffs[1] : ptr1[j], iDims[1]);
-                    dim_t inOff1 = inIdx1*iStrds[1];
-
-                    for (dim_t i=0; i<oDims[0]; ++i) {
-
-                        dim_t iOff   = i*oStrides[0];
-                        dim_t inIdx0 = trimIndex(isSeq[0] ? i+iOffs[0] : ptr0[i], iDims[0]);
-                        dim_t inOff0 = inIdx0*iStrds[0];
-
-                        dst[lOff+kOff+jOff+iOff] = src[inOff3+inOff2+inOff1+inOff0];
-                    }
-                }
-            }
-        }
-    };
 
-    getQueue().enqueue(func, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
+    getQueue().enqueue(kernel::index<T>, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
 
     return out;
 }
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 2621ba741f..83f48e9f75 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <vector>
+#include <Array.hpp>
+#include <utility.hpp>
 
 namespace cpu
 {
@@ -16,24 +17,10 @@ namespace kernel
 {
 
 using af::dim4;
-using std::vector;
-
-inline
-dim_t trimIndex(int idx, const dim_t &len)
-{
-    int ret_val = idx;
-    int offset  = abs(ret_val)%len;
-    if (ret_val<0) {
-        ret_val = offset-1;
-    } else if (ret_val>=(int)len) {
-        ret_val = len-offset-1;
-    }
-    return ret_val;
-}
 
 template<typename T>
-void assign(Array<T> out, const Array<T> rhs, const vector<bool> isSeq,
-            const vector<af_seq> seqs, const vector< Array<uint> > idxArrs)
+void assign(Array<T> out, const Array<T> rhs, const std::vector<bool> isSeq,
+            const std::vector<af_seq> seqs, const std::vector< Array<uint> > idxArrs)
 {
     dim4 dDims = out.getDataDims();
     dim4 pDims = out.dims();
diff --git a/src/backend/cpu/kernel/iir.hpp b/src/backend/cpu/kernel/iir.hpp
new file mode 100644
index 0000000000..d1ca464365
--- /dev/null
+++ b/src/backend/cpu/kernel/iir.hpp
@@ -0,0 +1,61 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T>
+void iir(Array<T> y, Array<T> c, Array<T> const a)
+{
+    dim4 ydims = c.dims();
+    int num_a = a.dims()[0];
+
+    for (int l = 0; l < (int)ydims[3]; l++) {
+        dim_t yidx3 = l * y.strides()[3];
+        dim_t cidx3 = l * c.strides()[3];
+        dim_t aidx3 = l * a.strides()[3];
+
+        for (int k = 0; k < (int)ydims[2]; k++) {
+
+            dim_t yidx2 = k * y.strides()[2] + yidx3;
+            dim_t cidx2 = k * c.strides()[2] + cidx3;
+            dim_t aidx2 = k * a.strides()[2] + aidx3;
+
+            for (int j = 0; j < (int)ydims[1]; j++) {
+
+                dim_t yidx1 = j * y.strides()[1] + yidx2;
+                dim_t cidx1 = j * c.strides()[1] + cidx2;
+                dim_t aidx1 = j * a.strides()[1] + aidx2;
+
+                std::vector<T> h_z(num_a);
+
+                const T *h_a = a.get() + (a.ndims() > 1 ? aidx1 : 0);
+                T *h_c = c.get() + cidx1;
+                T *h_y = y.get() + yidx1;
+
+                for (int i = 0; i < (int)ydims[0]; i++) {
+
+                    T y = h_y[i] = (h_c[i] + h_z[0]) /  h_a[0];
+                    for (int ii = 1; ii < num_a; ii++) {
+                        h_z[ii - 1] = h_z[ii] - h_a[ii] * y;
+                    }
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
new file mode 100644
index 0000000000..ee20c24d44
--- /dev/null
+++ b/src/backend/cpu/kernel/index.hpp
@@ -0,0 +1,71 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <vector>
+#include <Array.hpp>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename T>
+void index(Array<T> out, Array<T> const in,
+           std::vector<bool> const isSeq, std::vector<af_seq> const seqs,
+           std::vector< Array<uint> > const idxArrs)
+{
+    const dim4 iDims    = in.dims();
+    const dim4 dDims    = in.getDataDims();
+    const dim4 iOffs    = toOffset(seqs, dDims);
+    const dim4 iStrds   = toStride(seqs, dDims);
+    const dim4 oDims    = out.dims();
+    const dim4 oStrides = out.strides();
+    const T *src        = in.get();
+    T *dst        = out.get();
+    const uint* ptr0    = idxArrs[0].get();
+    const uint* ptr1    = idxArrs[1].get();
+    const uint* ptr2    = idxArrs[2].get();
+    const uint* ptr3    = idxArrs[3].get();
+
+    for (dim_t l=0; l<oDims[3]; ++l) {
+
+        dim_t lOff   = l*oStrides[3];
+        dim_t inIdx3 = trimIndex(isSeq[3] ? l+iOffs[3] : ptr3[l], iDims[3]);
+        dim_t inOff3 = inIdx3*iStrds[3];
+
+        for (dim_t k=0; k<oDims[2]; ++k) {
+
+            dim_t kOff   = k*oStrides[2];
+            dim_t inIdx2 = trimIndex(isSeq[2] ? k+iOffs[2] : ptr2[k], iDims[2]);
+            dim_t inOff2 = inIdx2*iStrds[2];
+
+            for (dim_t j=0; j<oDims[1]; ++j) {
+
+                dim_t jOff   = j*oStrides[1];
+                dim_t inIdx1 = trimIndex(isSeq[1] ? j+iOffs[1] : ptr1[j], iDims[1]);
+                dim_t inOff1 = inIdx1*iStrds[1];
+
+                for (dim_t i=0; i<oDims[0]; ++i) {
+
+                    dim_t iOff   = i*oStrides[0];
+                    dim_t inIdx0 = trimIndex(isSeq[0] ? i+iOffs[0] : ptr0[i], iDims[0]);
+                    dim_t inOff0 = inIdx0*iStrds[0];
+
+                    dst[lOff+kOff+jOff+iOff] = src[inOff3+inOff2+inOff1+inOff0];
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/lookup.hpp b/src/backend/cpu/kernel/lookup.hpp
new file mode 100644
index 0000000000..551cd2fd03
--- /dev/null
+++ b/src/backend/cpu/kernel/lookup.hpp
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <vector>
+#include <Array.hpp>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using af::dim4;
+
+template<typename in_t, typename idx_t>
+void lookup(Array<in_t> out, Array<in_t> const input,
+            Array<idx_t> const indices, unsigned const dim)
+{
+    const dim4 iDims    = input.dims();
+    const dim4 oDims    = out.dims();
+    const dim4 iStrides = input.strides();
+    const dim4 oStrides = out.strides();
+    const in_t *inPtr   = input.get();
+    const idx_t *idxPtr = indices.get();
+
+    in_t *outPtr = out.get();
+
+    for (dim_t l=0; l<oDims[3]; ++l) {
+
+        dim_t iLOff = iStrides[3]*(dim==3 ? trimIndex((dim_t)idxPtr[l], iDims[3]): l);
+        dim_t oLOff = l*oStrides[3];
+
+        for (dim_t k=0; k<oDims[2]; ++k) {
+
+            dim_t iKOff = iStrides[2]*(dim==2 ? trimIndex((dim_t)idxPtr[k], iDims[2]): k);
+            dim_t oKOff = k*oStrides[2];
+
+            for (dim_t j=0; j<oDims[1]; ++j) {
+
+                dim_t iJOff = iStrides[1]*(dim==1 ? trimIndex((dim_t)idxPtr[j], iDims[1]): j);
+                dim_t oJOff = j*oStrides[1];
+
+                for (dim_t i=0; i<oDims[0]; ++i) {
+
+                    dim_t iIOff = iStrides[0]*(dim==0 ? trimIndex((dim_t)idxPtr[i], iDims[0]): i);
+                    dim_t oIOff = i*oStrides[0];
+
+                    outPtr[oLOff+oKOff+oJOff+oIOff] = inPtr[iLOff+iKOff+iJOff+iIOff];
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 0aeee4dc81..457cdaea5a 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -8,27 +8,14 @@
  ********************************************************/
 
 #include <lookup.hpp>
-#include <err_cpu.hpp>
 #include <cstdlib>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/lookup.hpp>
 
 namespace cpu
 {
 
-static inline
-dim_t trimIndex(int idx, const dim_t &len)
-{
-    int ret_val = idx;
-    int offset  = abs(ret_val)%len;
-    if (ret_val<0) {
-        ret_val = offset-1;
-    } else if (ret_val>=len) {
-        ret_val = len-offset-1;
-    }
-    return ret_val;
-}
-
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim)
 {
@@ -43,44 +30,7 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    auto func = [=] (Array<in_t> out, const Array<in_t> input,
-                     const Array<idx_t> indices, const unsigned dim) {
-        const dim4 iDims    = input.dims();
-        const dim4 oDims    = out.dims();
-        const dim4 iStrides = input.strides();
-        const dim4 oStrides = out.strides();
-        const in_t *inPtr   = input.get();
-        const idx_t *idxPtr = indices.get();
-
-        in_t *outPtr = out.get();
-
-        for (dim_t l=0; l<oDims[3]; ++l) {
-
-            dim_t iLOff = iStrides[3]*(dim==3 ? trimIndex((dim_t)idxPtr[l], iDims[3]): l);
-            dim_t oLOff = l*oStrides[3];
-
-            for (dim_t k=0; k<oDims[2]; ++k) {
-
-                dim_t iKOff = iStrides[2]*(dim==2 ? trimIndex((dim_t)idxPtr[k], iDims[2]): k);
-                dim_t oKOff = k*oStrides[2];
-
-                for (dim_t j=0; j<oDims[1]; ++j) {
-
-                    dim_t iJOff = iStrides[1]*(dim==1 ? trimIndex((dim_t)idxPtr[j], iDims[1]): j);
-                    dim_t oJOff = j*oStrides[1];
-
-                    for (dim_t i=0; i<oDims[0]; ++i) {
-
-                        dim_t iIOff = iStrides[0]*(dim==0 ? trimIndex((dim_t)idxPtr[i], iDims[0]): i);
-                        dim_t oIOff = i*oStrides[0];
-
-                        outPtr[oLOff+oKOff+oJOff+oIOff] = inPtr[iLOff+iKOff+iJOff+iIOff];
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, input, indices, dim);
+    getQueue().enqueue(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
 
     return out;
 }
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
new file mode 100644
index 0000000000..18a38f3149
--- /dev/null
+++ b/src/backend/cpu/utility.hpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include "backend.hpp"
+
+namespace cpu
+{
+
+static inline
+dim_t trimIndex(const int &idx, const dim_t &len)
+{
+    int ret_val = idx;
+    int offset  = abs(ret_val)%len;
+    if (ret_val<0) {
+        ret_val = offset-1;
+    } else if (ret_val>=(int)len) {
+        ret_val = len-offset-1;
+    }
+    return ret_val;
+}
+
+}

From edda52acad309e5ff672f2eb62289a1d311e367f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 19 Dec 2015 13:07:50 -0500
Subject: [PATCH 0159/2677] Update README.md with updated status badges

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 695adbed03..f43b9fd098 100644
--- a/README.md
+++ b/README.md
@@ -22,8 +22,8 @@ ArrayFire binary installers can be downloaded at the [ArrayFire Downloads](http:
 ### Build Status
 |         | Linux x86 | Linux armv7l | Linux aarch64 | Windows | OSX |
 |:-------:|:---------:|:------------:|:-------------:|:-------:|:---:|
-| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/devel)](http://ci.arrayfire.org/job/arrayfire-linux/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/devel)](http://ci.arrayfire.org/job/arrayfire-windows/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/devel)](http://ci.arrayfire.org/job/arrayfire-osx/branch/devel/) |
-| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux-test/devel)](http://ci.arrayfire.org/job/arrayfire-linux-test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1-test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1-test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1-test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1-test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows-test/devel)](http://ci.arrayfire.org/job/arrayfire-windows-test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx-test/devel)](http://ci.arrayfire.org/job/arrayfire-osx-test/branch/devel/) |
+| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
+| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
 
 Test coverage: [![Coverage Status](https://coveralls.io/repos/arrayfire/arrayfire/badge.svg?branch=HEAD)](https://coveralls.io/r/arrayfire/arrayfire?branch=HEAD)
 

From f2b84dd3ac65aea385bd0ac1a69aa0255e9b7169 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 19 Dec 2015 13:19:32 -0500
Subject: [PATCH 0160/2677] template parameters style fixes in cpu kernel
 namespace fns

---
 src/backend/cpu/bilateral.cpp          |   2 +-
 src/backend/cpu/copy.cpp               |   4 +-
 src/backend/cpu/harris.cpp             |   2 +-
 src/backend/cpu/histogram.cpp          |   2 +-
 src/backend/cpu/kernel/Array.hpp       |   8 +-
 src/backend/cpu/kernel/approx1.hpp     | 110 +++++++++---------
 src/backend/cpu/kernel/approx2.hpp     | 130 ++++++++++-----------
 src/backend/cpu/kernel/assign.hpp      |  28 ++---
 src/backend/cpu/kernel/bilateral.hpp   |  55 ++++-----
 src/backend/cpu/kernel/convolve.hpp    | 154 ++++++++++++-------------
 src/backend/cpu/kernel/copy.hpp        |  28 ++---
 src/backend/cpu/kernel/diagonal.hpp    |  14 +--
 src/backend/cpu/kernel/diff.hpp        |  33 +++---
 src/backend/cpu/kernel/fast.hpp        |  38 +++---
 src/backend/cpu/kernel/fftconvolve.hpp |  16 +--
 src/backend/cpu/kernel/gradient.hpp    |   2 +
 src/backend/cpu/kernel/harris.hpp      |  21 +---
 src/backend/cpu/kernel/histogram.hpp   |  12 +-
 src/backend/cpu/kernel/hsv_rgb.hpp     |  14 +--
 src/backend/cpu/kernel/identity.hpp    |   6 +-
 src/backend/cpu/kernel/iir.hpp         |   4 +-
 src/backend/cpu/kernel/index.hpp       |  16 +--
 src/backend/cpu/kernel/lookup.hpp      |  26 ++---
 src/backend/cpu/utility.hpp            |  35 +++++-
 24 files changed, 383 insertions(+), 377 deletions(-)

diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index c751f992d9..bc3ad6e14b 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -29,7 +29,7 @@ Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const fl
     in.eval();
     const dim4 dims     = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
-    getQueue().enqueue(kernel::bilateral<inType, outType, isColor>, out, in, s_sigma, c_sigma);
+    getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in, s_sigma, c_sigma);
     return out;
 }
 
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 84cb0d1a54..9f6068dd65 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -63,7 +63,7 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
     in.eval();
     // FIXME:
     getQueue().sync();
-    getQueue().enqueue(kernel::copy<inType, outType>, ret, in, outType(default_value), factor);
+    getQueue().enqueue(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
     return ret;
 }
 
@@ -72,7 +72,7 @@ void copyArray(Array<outType> &out, Array<inType> const &in)
 {
     out.eval();
     in.eval();
-    getQueue().enqueue(kernel::copy<inType, outType>, out, in, scalar<outType>(0), 1.0);
+    getQueue().enqueue(kernel::copy<outType, inType>, out, in, scalar<outType>(0), 1.0);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index e5ff906dd6..905b0467c7 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -43,7 +43,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
     } else {
-        kernel::gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
+        gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
     }
     Array<convAccT> filter = createDeviceDataArray<convAccT>(dim4(filter_len), (const void*)h_filter);
 
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 7e20247231..19314e052a 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -32,7 +32,7 @@ Array<outType> histogram(const Array<inType> &in,
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
     out.eval();
 
-    getQueue().enqueue(kernel::histogram<inType, outType, isLinear>,
+    getQueue().enqueue(kernel::histogram<outType, inType, isLinear>,
             out, in, nbins, minval, maxval);
 
     return out;
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index e492b92ff0..08ade502e5 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 #include <platform.hpp>
 
@@ -15,16 +17,14 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
 void evalArray(Array<T> in)
 {
     in.setId(cpu::getActiveDeviceId());
     T *ptr = in.data.get();
 
-    dim4 odims = in.dims();
-    dim4 ostrs = in.strides();
+    af::dim4 odims = in.dims();
+    af::dim4 ostrs = in.strides();
 
     bool is_linear = in.node->isLinear(odims.get());
 
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
index 51c48048c1..ab12ebc813 100644
--- a/src/backend/cpu/kernel/approx1.hpp
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 
@@ -15,115 +17,115 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
-template<typename Ty, typename Tp, af_interp_type method>
+template<typename InT, typename LocT, af_interp_type Method>
 struct approx1_op
 {
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
     {
         return;
     }
 };
 
-template<typename Ty, typename Tp>
-struct approx1_op<Ty, Tp, AF_INTERP_NEAREST>
+template<typename InT, typename LocT>
+struct approx1_op<InT, LocT, AF_INTERP_NEAREST>
 {
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
     {
         dim_t pmId = idx;
         if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
 
-        const Tp x = pos[pmId];
+        LocT const x = pos[pmId];
         bool gFlag = false;
         if (x < 0 || idims[0] < x+1) {  // No need to check y
             gFlag = true;
         }
 
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
                          + idy * ostrides[1] + idx;
         if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
+            out[omId] = scalar<InT>(offGrid);
         } else {
             dim_t ioff = idw * istrides[3] + idz * istrides[2]
                        + idy * istrides[1];
-            const dim_t iMem = round(x) + ioff;
+            dim_t const iMem = round(x) + ioff;
 
             out[omId] = in[iMem];
         }
     }
 };
 
-template<typename Ty, typename Tp>
-struct approx1_op<Ty, Tp, AF_INTERP_LINEAR>
+template<typename InT, typename LocT>
+struct approx1_op<InT, LocT, AF_INTERP_LINEAR>
 {
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides, const af::dim4 &pstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
     {
         dim_t pmId = idx;
         if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
 
-        const Tp x = pos[pmId];
+        LocT const x = pos[pmId];
         bool gFlag = false;
         if (x < 0 || idims[0] < x+1) {
             gFlag = true;
         }
 
-        const dim_t grid_x = floor(x);  // nearest grid
-        const Tp off_x = x - grid_x; // fractional offset
+        dim_t const grid_x = floor(x);  // nearest grid
+        LocT const off_x = x - grid_x; // fractional offset
 
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
                          + idy * ostrides[1] + idx;
         if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
+            out[omId] = scalar<InT>(offGrid);
         } else {
             dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
 
             // Check if x and x + 1 are both valid indices
             bool cond = (x < idims[0] - 1);
             // Compute Left and Right Weighted Values
-            Ty yl = ((Tp)1.0 - off_x) * in[ioff];
-            Ty yr = cond ? (off_x) * in[ioff + 1] : scalar<Ty>(0);
-            Ty yo = yl + yr;
+            InT yl = ((LocT)1.0 - off_x) * in[ioff];
+            InT yr = cond ? (off_x) * in[ioff + 1] : scalar<InT>(0);
+            InT yo = yl + yr;
             // Compute Weight used
-            Tp wt = cond ? (Tp)1.0 : (Tp)(1.0 - off_x);
+            LocT wt = cond ? (LocT)1.0 : (LocT)(1.0 - off_x);
             // Write final value
             out[omId] = (yo / wt);
         }
     }
 };
 
-template<typename Ty, typename Tp, af_interp_type method>
-void approx1(Array<Ty> output, Array<Ty> const input,
-             Array<Tp> const position, float const offGrid)
+template<typename InT, typename LocT, af_interp_type Method>
+void approx1(Array<InT> output, Array<InT> const input,
+             Array<LocT> const position, float const offGrid)
 {
-    Ty * out = output.get();
-    Ty const * const in  = input.get();
-    Tp const * const pos = position.get();
-    dim4 const odims     = output.dims();
-    dim4 const idims     = input.dims();
-    dim4 const pdims     = position.dims();
-    dim4 const ostrides  = output.strides();
-    dim4 const istrides  = input.strides();
-    dim4 const pstrides  = position.strides();
-    dim_t const oElems   = output.elements();
-    dim_t const iElems   = input.elements();
-
-    approx1_op<Ty, Tp, method> op;
+    InT * out = output.get();
+    InT const * const in  = input.get();
+    LocT const * const pos = position.get();
+
+    af::dim4 const odims     = output.dims();
+    af::dim4 const idims     = input.dims();
+    af::dim4 const pdims     = position.dims();
+    af::dim4 const ostrides  = output.strides();
+    af::dim4 const istrides  = input.strides();
+    af::dim4 const pstrides  = position.strides();
+
+    dim_t const oElems = output.elements();
+    dim_t const iElems = input.elements();
+
+    approx1_op<InT, LocT, Method> op;
     bool pBatch = !(pdims[1] == 1 && pdims[2] == 1 && pdims[3] == 1);
 
     for(dim_t w = 0; w < odims[3]; w++) {
diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
index f80dae17bb..b5115e2e49 100644
--- a/src/backend/cpu/kernel/approx2.hpp
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 
@@ -15,33 +17,31 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
-template<typename Ty, typename Tp, af_interp_type method>
+template<typename InT, typename LocT, af_interp_type Method>
 struct approx2_op
 {
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const af::dim4 &pstrides, const af::dim4 &qstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides,
+              af::dim4 const & pstrides, af::dim4 const & qstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
     {
         return;
     }
 };
 
-template<typename Ty, typename Tp>
-struct approx2_op<Ty, Tp, AF_INTERP_NEAREST>
+template<typename InT, typename LocT>
+struct approx2_op<InT, LocT, AF_INTERP_NEAREST>
 {
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const af::dim4 &pstrides, const af::dim4 &qstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides,
+              af::dim4 const & pstrides, af::dim4 const & qstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
     {
         dim_t pmId = idy * pstrides[1] + idx;
         dim_t qmId = idy * qstrides[1] + idx;
@@ -51,34 +51,34 @@ struct approx2_op<Ty, Tp, AF_INTERP_NEAREST>
         }
 
         bool gFlag = false;
-        const Tp x = pos[pmId], y = qos[qmId];
+        LocT const x = pos[pmId], y = qos[qmId];
         if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
             gFlag = true;
         }
 
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
                          + idy * ostrides[1] + idx;
         if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
+            out[omId] = scalar<InT>(offGrid);
         } else {
-            const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
-            const dim_t imId = idw * istrides[3] + idz * istrides[2] +
+            dim_t const grid_x = round(x), grid_y = round(y); // nearest grid
+            dim_t const imId = idw * istrides[3] + idz * istrides[2] +
                             grid_y * istrides[1] + grid_x;
             out[omId] = in[imId];
         }
     }
 };
 
-template<typename Ty, typename Tp>
-struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
+template<typename InT, typename LocT>
+struct approx2_op<InT, LocT, AF_INTERP_LINEAR>
 {
-    void operator()(Ty *out, const af::dim4 &odims, const dim_t oElems,
-              const Ty *in,  const af::dim4 &idims, const dim_t iElems,
-              const Tp *pos, const af::dim4 &pdims, const Tp *qos, const af::dim4 &qdims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const af::dim4 &pstrides, const af::dim4 &qstrides,
-              const float offGrid, const bool pBatch,
-              const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw)
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides,
+              af::dim4 const & pstrides, af::dim4 const & qstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
     {
         dim_t pmId = idy * pstrides[1] + idx;
         dim_t qmId = idy * qstrides[1] + idx;
@@ -88,42 +88,42 @@ struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
         }
 
         bool gFlag = false;
-        const Tp x = pos[pmId], y = qos[qmId];
+        LocT const x = pos[pmId], y = qos[qmId];
         if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
             gFlag = true;
         }
 
-        const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-        const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
+        dim_t const grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+        LocT const off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
 
         // Check if pVal and pVal + 1 are both valid indices
         bool condY = (y < idims[1] - 1);
         bool condX = (x < idims[0] - 1);
 
         // Compute wieghts used
-        Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
-        Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
-        Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
-        Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
+        LocT wt00 = ((LocT)1.0 - off_x) * ((LocT)1.0 - off_y);
+        LocT wt10 = (condY) ? ((LocT)1.0 - off_x) * (off_y) : 0;
+        LocT wt01 = (condX) ? (off_x) * ((LocT)1.0 - off_y) : 0;
+        LocT wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
 
-        Tp wt = wt00 + wt10 + wt01 + wt11;
-        Ty zero = scalar<Ty>(0);
+        LocT wt = wt00 + wt10 + wt01 + wt11;
+        InT zero = scalar<InT>(0);
 
-        const dim_t omId = idw * ostrides[3] + idz * ostrides[2]
+        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
                          + idy * ostrides[1] + idx;
         if(gFlag) {
-            out[omId] = scalar<Ty>(offGrid);
+            out[omId] = scalar<InT>(offGrid);
         } else {
             dim_t ioff = idw * istrides[3] + idz * istrides[2]
                     + grid_y * istrides[1] + grid_x;
 
             // Compute Weighted Values
-            Ty y00 =                    wt00 * in[ioff];
-            Ty y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
-            Ty y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
-            Ty y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
+            InT y00 =                    wt00 * in[ioff];
+            InT y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
+            InT y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
+            InT y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
 
-            Ty yo = y00 + y10 + y01 + y11;
+            InT yo = y00 + y10 + y01 + y11;
 
             // Write Final Value
             out[omId] = (yo / wt);
@@ -131,27 +131,27 @@ struct approx2_op<Ty, Tp, AF_INTERP_LINEAR>
     }
 };
 
-template<typename Ty, typename Tp, af_interp_type method>
-void approx2(Array<Ty> output, Array<Ty> const input,
-             Array<Tp> const position, Array<Tp> const qosition,
+template<typename InT, typename LocT, af_interp_type Method>
+void approx2(Array<InT> output, Array<InT> const input,
+             Array<LocT> const position, Array<LocT> const qosition,
              float const offGrid)
 {
-    Ty * out = output.get();
-    Ty const * const in  = input.get();
-    Tp const * const pos = position.get();
-    Tp const * const qos = qosition.get();
-    dim4 const odims     = output.dims();
-    dim4 const idims     = input.dims();
-    dim4 const pdims     = position.dims();
-    dim4 const qdims     = qosition.dims();
-    dim4 const ostrides  = output.strides();
-    dim4 const istrides  = input.strides();
-    dim4 const pstrides  = position.strides();
-    dim4 const qstrides  = qosition.strides();
+    InT * out = output.get();
+    InT const * const in  = input.get();
+    LocT const * const pos = position.get();
+    LocT const * const qos = qosition.get();
+    af::dim4 const odims     = output.dims();
+    af::dim4 const idims     = input.dims();
+    af::dim4 const pdims     = position.dims();
+    af::dim4 const qdims     = qosition.dims();
+    af::dim4 const ostrides  = output.strides();
+    af::dim4 const istrides  = input.strides();
+    af::dim4 const pstrides  = position.strides();
+    af::dim4 const qstrides  = qosition.strides();
     dim_t const oElems   = output.elements();
     dim_t const iElems   = input.elements();
 
-    approx2_op<Ty, Tp, method> op;
+    approx2_op<InT, LocT, Method> op;
     bool pBatch = !(pdims[2] == 1 && pdims[3] == 1);
 
     for(dim_t w = 0; w < odims[3]; w++) {
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 83f48e9f75..86befaf74e 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <vector>
 #include <Array.hpp>
 #include <utility.hpp>
@@ -16,25 +18,23 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
-void assign(Array<T> out, const Array<T> rhs, const std::vector<bool> isSeq,
-            const std::vector<af_seq> seqs, const std::vector< Array<uint> > idxArrs)
+void assign(Array<T> out, Array<T> const rhs, std::vector<bool> const isSeq,
+            std::vector<af_seq> const seqs, std::vector< Array<uint> > const idxArrs)
 {
-    dim4 dDims = out.getDataDims();
-    dim4 pDims = out.dims();
+    af::dim4 dDims = out.getDataDims();
+    af::dim4 pDims = out.dims();
     // retrieve dimensions & strides for array to which rhs is being copied to
-    dim4 dst_offsets = toOffset(seqs, dDims);
-    dim4 dst_strides = toStride(seqs, dDims);
+    af::dim4 dst_offsets = toOffset(seqs, dDims);
+    af::dim4 dst_strides = toStride(seqs, dDims);
     // retrieve rhs array dimenesions & strides
-    dim4 src_dims    = rhs.dims();
-    dim4 src_strides = rhs.strides();
+    af::dim4 src_dims    = rhs.dims();
+    af::dim4 src_strides = rhs.strides();
     // declare pointers to af_array index data
-    const uint* ptr0 = idxArrs[0].get();
-    const uint* ptr1 = idxArrs[1].get();
-    const uint* ptr2 = idxArrs[2].get();
-    const uint* ptr3 = idxArrs[3].get();
+    uint const * const ptr0 = idxArrs[0].get();
+    uint const * const ptr1 = idxArrs[1].get();
+    uint const * const ptr2 = idxArrs[2].get();
+    uint const * const ptr3 = idxArrs[3].get();
 
     const T * src= rhs.get();
     T * dst      = out.get();
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index 2b5764fd37..c950bbd084 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -7,42 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
+#include <utility.hpp>
+#include <cmath>
 
 namespace cpu
 {
 namespace kernel
 {
 
-inline
-dim_t clamp(int a, dim_t mn, dim_t mx)
+template<typename OutT, typename InT, bool IsColor>
+void bilateral(Array<OutT> out, Array<InT> const in, float const s_sigma, float const c_sigma)
 {
-    return (a < (int)mn ? mn : (a > (int)mx ? mx : a));
-}
-
-inline
-unsigned getIdx(const dim4 &strides, int i, int j = 0, int k = 0, int l = 0)
-{
-    return (l * strides[3] + k * strides[2] + j * strides[1] + i * strides[0]);
-}
-
-template<typename inType, typename outType, bool isColor>
-void bilateral(Array<outType> out, const Array<inType> in, float s_sigma, float c_sigma)
-{
-    const dim4 dims     = in.dims();
-    const dim4 istrides = in.strides();
-
-    const dim4 ostrides = out.strides();
+    af::dim4 const dims     = in.dims();
+    af::dim4 const istrides = in.strides();
+    af::dim4 const ostrides = out.strides();
 
-          outType *outData = out.get();
-    const inType  *inData  = in.get();
+          OutT *outData = out.get();
+    InT const * inData  = in.get();
 
     // clamp spatical and chromatic sigma's
-    float space_          = std::min(11.5f, std::max(s_sigma, 0.f));
-    float color_          = std::max(c_sigma, 0.f);
-    const dim_t radius = std::max((dim_t)(space_ * 1.5f), (dim_t)1);
-    const float svar      = space_*space_;
-    const float cvar      = color_*color_;
+    float space_       = std::min(11.5f, std::max(s_sigma, 0.f));
+    float color_       = std::max(c_sigma, 0.f);
+    dim_t const radius = std::max((dim_t)(space_ * 1.5f), (dim_t)1);
+    float const svar   = space_*space_;
+    float const cvar   = color_*color_;
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
         // b3 for loop handles following batch configurations
@@ -58,9 +49,9 @@ void bilateral(Array<outType> out, const Array<inType> in, float s_sigma, float
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    outType norm = 0.0;
-                    outType res  = 0.0;
-                    const outType center = (outType)inData[getIdx(istrides, i, j)];
+                    OutT norm = 0.0;
+                    OutT res  = 0.0;
+                    OutT const center = (OutT)inData[getIdx(istrides, i, j)];
                     for(dim_t wj=-radius; wj<=radius; ++wj) {
                         // clamps offsets
                         dim_t tj = clamp(j+wj, 0, dims[1]-1);
@@ -68,10 +59,10 @@ void bilateral(Array<outType> out, const Array<inType> in, float s_sigma, float
                             // clamps offsets
                             dim_t ti = clamp(i+wi, 0, dims[0]-1);
                             // proceed
-                            const outType val= (outType)inData[getIdx(istrides, ti, tj)];
-                            const outType gauss_space = (wi*wi+wj*wj)/(-2.0*svar);
-                            const outType gauss_range = ((center-val)*(center-val))/(-2.0*cvar);
-                            const outType weight = std::exp(gauss_space+gauss_range);
+                            OutT const val= (OutT)inData[getIdx(istrides, ti, tj)];
+                            OutT const gauss_space = (wi*wi+wj*wj)/(-2.0*svar);
+                            OutT const gauss_range = ((center-val)*(center-val))/(-2.0*cvar);
+                            OutT const weight = std::exp(gauss_space+gauss_range);
                             norm += weight;
                             res += val*weight;
                         }
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index d39acb65c9..79d684dd64 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
@@ -14,41 +16,39 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
-template<typename T, typename accT, bool expand>
-void one2one_1d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
-                dim4 const &sDims, dim4 const &fDims, dim4 const &sStrides)
+template<typename InT, typename AccT, bool Expand>
+void one2one_1d(InT *optr, InT const * const iptr, AccT const * const fptr, af::dim4 const & oDims,
+                af::dim4 const & sDims, af::dim4 const & fDims, af::dim4 const & sStrides)
 {
-    dim_t start = (expand ? 0 : fDims[0]/2);
-    dim_t end   = (expand ? oDims[0] : start + sDims[0]);
+    dim_t start = (Expand ? 0 : fDims[0]/2);
+    dim_t end   = (Expand ? oDims[0] : start + sDims[0]);
     for(dim_t i=start; i<end; ++i) {
-        accT accum = 0.0;
+        AccT accum = 0.0;
         for(dim_t f=0; f<fDims[0]; ++f) {
             dim_t iIdx = i-f;
-            T s_val = ((iIdx>=0 &&iIdx<sDims[0])? iptr[iIdx*sStrides[0]] : T(0));
-            accum += accT(s_val * fptr[f]);
+            InT s_val = ((iIdx>=0 &&iIdx<sDims[0])? iptr[iIdx*sStrides[0]] : InT(0));
+            accum += AccT(s_val * fptr[f]);
         }
-        optr[i-start] = T(accum);
+        optr[i-start] = InT(accum);
     }
 }
 
-template<typename T, typename accT, bool expand>
-void one2one_2d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
-                dim4 const &sDims, dim4 const &fDims, dim4 const &oStrides,
-                dim4 const &sStrides, dim4 const &fStrides)
+template<typename InT, typename AccT, bool Expand>
+void one2one_2d(InT *optr, InT const * const iptr, AccT const * const fptr, af::dim4 const & oDims,
+                af::dim4 const & sDims, af::dim4 const & fDims, af::dim4 const & oStrides,
+                af::dim4 const & sStrides, af::dim4 const & fStrides)
 {
-    dim_t jStart = (expand ? 0 : fDims[1]/2);
-    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (expand ? 0 : fDims[0]/2);
-    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
+    dim_t jStart = (Expand ? 0 : fDims[1]/2);
+    dim_t jEnd   = (Expand ? oDims[1] : jStart + sDims[1]);
+    dim_t iStart = (Expand ? 0 : fDims[0]/2);
+    dim_t iEnd   = (Expand ? oDims[0] : iStart + sDims[0]);
 
     for(dim_t j=jStart; j<jEnd; ++j) {
         dim_t joff = (j-jStart)*oStrides[1];
 
         for(dim_t i=iStart; i<iEnd; ++i) {
 
-            accT accum = accT(0);
+            AccT accum = AccT(0);
             for(dim_t wj=0; wj<fDims[1]; ++wj) {
                 dim_t jIdx  = j-wj;
                 dim_t w_joff = wj*fStrides[1];
@@ -58,30 +58,30 @@ void one2one_2d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
                 for(dim_t wi=0; wi<fDims[0]; ++wi) {
                     dim_t iIdx = i-wi;
 
-                    T s_val = T(0);
+                    InT s_val = InT(0);
                     if ( isJValid && (iIdx>=0 && iIdx<sDims[0])) {
                         s_val = iptr[s_joff+iIdx*sStrides[0]];
                     }
 
-                    accum += accT(s_val * fptr[w_joff+wi*fStrides[0]]);
+                    accum += AccT(s_val * fptr[w_joff+wi*fStrides[0]]);
                 }
             }
-            optr[joff+i-iStart] = T(accum);
+            optr[joff+i-iStart] = InT(accum);
         }
     }
 }
 
-template<typename T, typename accT, bool expand>
-void one2one_3d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
-                dim4 const &sDims, dim4 const &fDims, dim4 const &oStrides,
-                dim4 const &sStrides, dim4 const &fStrides)
+template<typename InT, typename AccT, bool Expand>
+void one2one_3d(InT *optr, InT const * const iptr, AccT const * const fptr, af::dim4 const & oDims,
+                af::dim4 const & sDims, af::dim4 const & fDims, af::dim4 const & oStrides,
+                af::dim4 const & sStrides, af::dim4 const & fStrides)
 {
-    dim_t kStart = (expand ? 0 : fDims[2]/2);
-    dim_t kEnd   = (expand ? oDims[2] : kStart + sDims[2]);
-    dim_t jStart = (expand ? 0 : fDims[1]/2);
-    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (expand ? 0 : fDims[0]/2);
-    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
+    dim_t kStart = (Expand ? 0 : fDims[2]/2);
+    dim_t kEnd   = (Expand ? oDims[2] : kStart + sDims[2]);
+    dim_t jStart = (Expand ? 0 : fDims[1]/2);
+    dim_t jEnd   = (Expand ? oDims[1] : jStart + sDims[1]);
+    dim_t iStart = (Expand ? 0 : fDims[0]/2);
+    dim_t iEnd   = (Expand ? oDims[0] : iStart + sDims[0]);
 
     for(dim_t k=kStart; k<kEnd; ++k) {
         dim_t koff = (k-kStart)*oStrides[2];
@@ -91,7 +91,7 @@ void one2one_3d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
 
             for(dim_t i=iStart; i<iEnd; ++i) {
 
-                accT accum = accT(0);
+                AccT accum = AccT(0);
                 for(dim_t wk=0; wk<fDims[2]; ++wk) {
                     dim_t kIdx  = k-wk;
                     dim_t w_koff = wk*fStrides[2];
@@ -107,35 +107,35 @@ void one2one_3d(T *optr, T const *iptr, accT const *fptr, dim4 const &oDims,
                         for(dim_t wi=0; wi<fDims[0]; ++wi) {
                             dim_t iIdx = i-wi;
 
-                            T s_val = T(0);
+                            InT s_val = InT(0);
                             if ( isKValid && isJValid && (iIdx>=0 && iIdx<sDims[0])) {
                                 s_val = iptr[s_koff+s_joff+iIdx*sStrides[0]];
                             }
 
-                            accum += accT(s_val * fptr[w_koff+w_joff+wi*fStrides[0]]);
+                            accum += AccT(s_val * fptr[w_koff+w_joff+wi*fStrides[0]]);
                         }
                     }
                 }
-                optr[koff+joff+i-iStart] = T(accum);
+                optr[koff+joff+i-iStart] = InT(accum);
             } //i loop ends here
         } // j loop ends here
     } // k loop ends here
 }
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
-void convolve_nd(Array<T> out, Array<T> const signal, Array<accT> const filter, ConvolveBatchKind kind)
+template<typename InT, typename AccT, dim_t baseDim, bool Expand>
+void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filter, ConvolveBatchKind kind)
 {
-    T * optr = out.get();
-    T const * const iptr = signal.get();
-    accT const * const fptr = filter.get();
+    InT * optr = out.get();
+    InT const * const iptr = signal.get();
+    AccT const * const fptr = filter.get();
 
-    dim4 const oDims = out.dims();
-    dim4 const sDims = signal.dims();
-    dim4 const fDims = filter.dims();
+    af::dim4 const oDims = out.dims();
+    af::dim4 const sDims = signal.dims();
+    af::dim4 const fDims = filter.dims();
 
-    dim4 const oStrides = out.strides();
-    dim4 const sStrides = signal.strides();
-    dim4 const fStrides = filter.strides();
+    af::dim4 const oStrides = out.strides();
+    af::dim4 const sStrides = signal.strides();
+    af::dim4 const fStrides = filter.strides();
 
     dim_t out_step[4]  = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
     dim_t in_step[4]   = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
@@ -169,66 +169,66 @@ void convolve_nd(Array<T> out, Array<T> const signal, Array<accT> const filter,
         for (dim_t b2=0; b2<batch[2]; ++b2) {
             for (dim_t b1=0; b1<batch[1]; ++b1) {
 
-                T * out          = optr + b1 * out_step[1] + b2 * out_step[2] + b3 * out_step[3];
-                T const *in      = iptr + b1 *  in_step[1] + b2 *  in_step[2] + b3 *  in_step[3];
-                accT const *filt = fptr + b1 *filt_step[1] + b2 *filt_step[2] + b3 *filt_step[3];
+                InT * out          = optr + b1 * out_step[1] + b2 * out_step[2] + b3 * out_step[3];
+                InT const *in      = iptr + b1 *  in_step[1] + b2 *  in_step[2] + b3 *  in_step[3];
+                AccT const *filt = fptr + b1 *filt_step[1] + b2 *filt_step[2] + b3 *filt_step[3];
 
                 switch(baseDim) {
-                    case 1: one2one_1d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, sStrides);                     break;
-                    case 2: one2one_2d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
-                    case 3: one2one_3d<T, accT, expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
+                    case 1: one2one_1d<InT, AccT, Expand>(out, in, filt, oDims, sDims, fDims, sStrides);                     break;
+                    case 2: one2one_2d<InT, AccT, Expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
+                    case 3: one2one_3d<InT, AccT, Expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
                 }
             }
         }
     }
 }
 
-template<typename T, typename accT, dim_t conv_dim, bool expand>
-void convolve2_separable(T *optr, T const *iptr, accT const *fptr,
-                        dim4 const &oDims, dim4 const &sDims, dim4 const &orgDims, dim_t fDim,
-                        dim4 const &oStrides, dim4 const &sStrides, dim_t fStride)
+template<typename InT, typename AccT, dim_t conv_dim, bool Expand>
+void convolve2_separable(InT *optr, InT const * const iptr, AccT const * const fptr,
+                        af::dim4 const & oDims, af::dim4 const & sDims, af::dim4 const & orgDims, dim_t fDim,
+                        af::dim4 const & oStrides, af::dim4 const & sStrides, dim_t fStride)
 {
     for(dim_t j=0; j<oDims[1]; ++j) {
 
         dim_t jOff = j*oStrides[1];
-        dim_t cj = j + (conv_dim==1)*(expand ? 0: fDim>>1);
+        dim_t cj = j + (conv_dim==1)*(Expand ? 0: fDim>>1);
 
         for(dim_t i=0; i<oDims[0]; ++i) {
 
             dim_t iOff = i*oStrides[0];
-            dim_t ci = i + (conv_dim==0)*(expand ? 0 : fDim>>1);
+            dim_t ci = i + (conv_dim==0)*(Expand ? 0 : fDim>>1);
 
-            accT accum = scalar<accT>(0);
+            AccT accum = scalar<AccT>(0);
 
             for(dim_t f=0; f<fDim; ++f) {
-                T f_val = fptr[f];
-                T s_val;
+                InT f_val = fptr[f];
+                InT s_val;
 
                 if (conv_dim==0) {
                     dim_t offi = ci - f;
                     bool isCIValid = offi>=0 && offi<sDims[0];
                     bool isCJValid = cj>=0 && cj<sDims[1];
-                    s_val = (isCJValid && isCIValid ? iptr[cj*sDims[0]+offi] : scalar<T>(0));
+                    s_val = (isCJValid && isCIValid ? iptr[cj*sDims[0]+offi] : scalar<InT>(0));
                 } else {
                     dim_t offj = cj - f;
                     bool isCIValid = ci>=0 && ci<sDims[0];
                     bool isCJValid = offj>=0 && offj<sDims[1];
-                    s_val = (isCJValid && isCIValid ? iptr[offj*sDims[0]+ci] : scalar<T>(0));
+                    s_val = (isCJValid && isCIValid ? iptr[offj*sDims[0]+ci] : scalar<InT>(0));
                 }
 
-                accum += accT(s_val * f_val);
+                accum += AccT(s_val * f_val);
             }
-            optr[iOff+jOff] = T(accum);
+            optr[iOff+jOff] = InT(accum);
         }
     }
 }
 
-template<typename T, typename accT, bool expand>
-void convolve2(Array<T> out, Array<T> const signal,
-               Array<accT> const c_filter, Array<accT> const r_filter,
-               dim4 const tDims)
+template<typename InT, typename AccT, bool Expand>
+void convolve2(Array<InT> out, Array<InT> const signal,
+               Array<AccT> const c_filter, Array<AccT> const r_filter,
+               af::dim4 const tDims)
 {
-    Array<T> temp = createEmptyArray<T>(tDims);
+    Array<InT> temp = createEmptyArray<InT>(tDims);
 
     dim_t cflen = (dim_t)c_filter.elements();
     dim_t rflen = (dim_t)r_filter.elements();
@@ -248,15 +248,15 @@ void convolve2(Array<T> out, Array<T> const signal,
 
         for (dim_t b2=0; b2<oDims[2]; ++b2) {
 
-            T const *iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
-            T *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
-            T *optr = out.get()  + b2*oStrides[2] + o_b3Off;
+            InT const * const iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
+            InT *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
+            InT *optr = out.get()  + b2*oStrides[2] + o_b3Off;
 
-            convolve2_separable<T, accT, 0, expand>(tptr, iptr, c_filter.get(),
+            convolve2_separable<InT, AccT, 0, Expand>(tptr, iptr, c_filter.get(),
                     tDims, sDims, sDims, cflen,
                     tStrides, sStrides, c_filter.strides()[0]);
 
-            convolve2_separable<T, accT, 1, expand>(optr, tptr, r_filter.get(),
+            convolve2_separable<InT, AccT, 1, Expand>(optr, tptr, r_filter.get(),
                     oDims, tDims, sDims, rflen,
                     oStrides, tStrides, r_filter.strides()[0]);
         }
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index 063fb29f0c..70d6705ec2 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
@@ -14,11 +16,9 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
-void stridedCopy(T* dst, const dim4& ostrides, const T* src,
-                 const dim4 &dims, const dim4 &strides, unsigned dim)
+void stridedCopy(T* dst, af::dim4 const & ostrides, T const * src,
+                 af::dim4 const & dims, af::dim4 const & strides, unsigned dim)
 {
     if(dim == 0) {
         if(strides[dim] == 1) {
@@ -38,16 +38,16 @@ void stridedCopy(T* dst, const dim4& ostrides, const T* src,
     }
 }
 
-template<typename inType, typename outType>
-void copy(Array<outType> dst, const Array<inType> src, outType default_value, double factor)
+template<typename OutT, typename InT>
+void copy(Array<OutT> dst, Array<InT> const src, OutT default_value, double factor)
 {
-    dim4 src_dims       = src.dims();
-    dim4 dst_dims       = dst.dims();
-    dim4 src_strides    = src.strides();
-    dim4 dst_strides    = dst.strides();
+    af::dim4 src_dims       = src.dims();
+    af::dim4 dst_dims       = dst.dims();
+    af::dim4 src_strides    = src.strides();
+    af::dim4 dst_strides    = dst.strides();
 
-    const inType * src_ptr = src.get();
-    outType * dst_ptr      = dst.get();
+    InT const * const src_ptr = src.get();
+    OutT * dst_ptr      = dst.get();
 
     dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
     dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
@@ -73,10 +73,10 @@ void copy(Array<outType> dst, const Array<inType> src, outType default_value, do
                 bool isJvalid = j<trgt_j;
 
                 for(dim_t i=0; i<dst_dims[0]; ++i) {
-                    outType temp = default_value;
+                    OutT temp = default_value;
                     if (isLvalid && isKvalid && isJvalid && i<trgt_i) {
                         dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
-                        temp = outType(src_ptr[src_idx])*outType(factor);
+                        temp = OutT(src_ptr[src_idx])*OutT(factor);
                     }
                     dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
                     dst_ptr[dst_idx] = temp;
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
index 596080b108..0c81fc90f2 100644
--- a/src/backend/cpu/kernel/diagonal.hpp
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
@@ -14,16 +16,14 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
 void diagCreate(Array<T> out, Array<T> const in, int const num)
 {
     int batch = in.dims()[1];
     int size  = out.dims()[0];
 
-    const T *iptr = in.get();
-    T *optr = out.get();
+    T const * iptr = in.get();
+    T * optr = out.get();
 
     for (int k = 0; k < batch; k++) {
         for (int j = 0; j < size; j++) {
@@ -43,10 +43,10 @@ void diagCreate(Array<T> out, Array<T> const in, int const num)
 template<typename T>
 void diagExtract(Array<T> out, Array<T> const in, int const num)
 {
-    const dim4 odims = out.dims();
-    const dim4 idims = in.dims();
+    dim4 const odims = out.dims();
+    dim4 const idims = in.dims();
 
-    const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
+    int const i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
 
     for (int l = 0; l < (int)odims[3]; l++) {
 
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
index e0693b1349..1a3d7ba110 100644
--- a/src/backend/cpu/kernel/diff.hpp
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -7,19 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
+#include <utility.hpp>
 
 namespace cpu
 {
 namespace kernel
 {
 
-unsigned getIdx(af::dim4 strides, af::dim4 offs, int i, int j = 0, int k = 0, int l = 0)
-{
-    return (l * strides[3] + k * strides[2] + j * strides[1] + i);
-}
-
-
 template<typename T>
 void diff1(Array<T> out, Array<T> const in, int const dim)
 {
@@ -30,9 +27,8 @@ void diff1(Array<T> out, Array<T> const in, int const dim)
     bool is_dim2 = dim == 2;
     bool is_dim3 = dim == 3;
 
-    // Get pointers to raw data
-    const T *inPtr = in.get();
-    T *outPtr = out.get();
+    T const * const inPtr = in.get();
+    T * outPtr = out.get();
 
     // TODO: Improve this
     for(dim_t l = 0; l < dims[3]; l++) {
@@ -40,11 +36,11 @@ void diff1(Array<T> out, Array<T> const in, int const dim)
             for(dim_t j = 0; j < dims[1]; j++) {
                 for(dim_t i = 0; i < dims[0]; i++) {
                     // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                    int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                    int jdx = getIdx(in.strides(), in.offsets(),
+                    int idx = getIdx(in.strides(), i, j, k, l);
+                    int jdx = getIdx(in.strides(),
                             i + is_dim0, j + is_dim1,
                             k + is_dim2, l + is_dim3);
-                    int odx = getIdx(out.strides(), out.offsets(), i, j, k, l);
+                    int odx = getIdx(out.strides(), i, j, k, l);
                     outPtr[odx] = inPtr[jdx] - inPtr[idx];
                 }
             }
@@ -62,9 +58,8 @@ void diff2(Array<T> out, Array<T> const in, int const dim)
     bool is_dim2 = dim == 2;
     bool is_dim3 = dim == 3;
 
-    // Get pointers to raw data
-    const T *inPtr = in.get();
-    T *outPtr = out.get();
+    T const * const inPtr = in.get();
+    T * outPtr = out.get();
 
     // TODO: Improve this
     for(dim_t l = 0; l < dims[3]; l++) {
@@ -72,14 +67,14 @@ void diff2(Array<T> out, Array<T> const in, int const dim)
             for(dim_t j = 0; j < dims[1]; j++) {
                 for(dim_t i = 0; i < dims[0]; i++) {
                     // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                    int idx = getIdx(in.strides(), in.offsets(), i, j, k, l);
-                    int jdx = getIdx(in.strides(), in.offsets(),
+                    int idx = getIdx(in.strides(), i, j, k, l);
+                    int jdx = getIdx(in.strides(),
                             i + is_dim0, j + is_dim1,
                             k + is_dim2, l + is_dim3);
-                    int kdx = getIdx(in.strides(), in.offsets(),
+                    int kdx = getIdx(in.strides(),
                             i + 2 * is_dim0, j + 2 * is_dim1,
                             k + 2 * is_dim2, l + 2 * is_dim3);
-                    int odx = getIdx(out.strides(), out.offsets(), i, j, k, l);
+                    int odx = getIdx(out.strides(), i, j, k, l);
                     outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
                 }
             }
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index a3971dd136..02da3e4d33 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -7,20 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
+#include <utility.hpp>
 
 namespace cpu
 {
 namespace kernel
 {
 
-using af::dim4;
-
-inline int clamp(int f, int a, int b)
-{
-    return std::max(a, std::min(f, b));
-}
-
 inline int idx_y(int i)
 {
     if (i >= 8)
@@ -86,14 +82,14 @@ inline double abs_diff(double x, double y)
 }
 
 template<typename T>
-void locate_features(const Array<T> &in, Array<float> &score,
-                     Array<float> &x_out, Array<float> &y_out,
-                     Array<float> &score_out, unsigned* count, const float thr,
-                     const unsigned arc_length, const unsigned nonmax,
-                     const unsigned max_feat, const unsigned edge)
+void locate_features(Array<T> const & in, Array<float> & score,
+                     Array<float> & x_out, Array<float> & y_out,
+                     Array<float> & score_out, unsigned* count, float const thr,
+                     unsigned const arc_length, unsigned const nonmax,
+                     unsigned const max_feat, unsigned const edge)
 {
-    dim4 in_dims = in.dims();
-    const T* in_ptr = in.get();
+    af::dim4 in_dims = in.dims();
+    T const * in_ptr = in.get();
 
     for (int y = edge; y < (int)(in_dims[0] - edge); y++) {
         for (int x = edge; x < (int)(in_dims[1] - edge); x++) {
@@ -179,15 +175,15 @@ void locate_features(const Array<T> &in, Array<float> &score,
     }
 }
 
-void non_maximal(const Array<float> &score, const Array<float> &x_in, const Array<float> &y_in,
-                 Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-                 unsigned* count, const unsigned total_feat, const unsigned edge)
+void non_maximal(Array<float> const & score, const Array<float> & x_in, const Array<float> & y_in,
+                 Array<float> & x_out, Array<float> & y_out, Array<float> & score_out,
+                 unsigned* count, unsigned const total_feat, unsigned const edge)
 {
-    const float *score_ptr = score.get();
-    const float *x_in_ptr = x_in.get();
-    const float *y_in_ptr = y_in.get();
+    float const * score_ptr = score.get();
+    float const * x_in_ptr = x_in.get();
+    float const * y_in_ptr = y_in.get();
 
-    dim4 score_dims = score.dims();
+    af::dim4 score_dims = score.dims();
 
     for (unsigned k = 0; k < total_feat; k++) {
         unsigned x = static_cast<unsigned>(round(x_in_ptr[k]));
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 30bac668f1..6213cb2730 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 #include <convolve_common.hpp>
 
@@ -15,8 +17,6 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename To, typename Ti>
 void packData(Array<To> out, const af::dim4 od, const af::dim4 os, Array<Ti> const in)
 {
@@ -95,12 +95,12 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
     T* in1_ptr = packed.get();
     T* in2_ptr = packed.get() + offset;
 
-    const dim4& od = (kind==CONVOLVE_BATCH_KERNEL ? fit_dims : sig_dims);
-    const dim4& os = (kind==CONVOLVE_BATCH_KERNEL ? fit_strides : sig_strides);
-    const dim4& i1d = sig_dims;
-    const dim4& i2d = fit_dims;
-    const dim4& i1s = sig_strides;
-    const dim4& i2s = fit_strides;
+    const af::dim4& od = (kind==CONVOLVE_BATCH_KERNEL ? fit_dims : sig_dims);
+    const af::dim4& os = (kind==CONVOLVE_BATCH_KERNEL ? fit_strides : sig_strides);
+    const af::dim4& i1d = sig_dims;
+    const af::dim4& i2d = fit_dims;
+    const af::dim4& i1s = sig_strides;
+    const af::dim4& i2s = fit_strides;
 
     for (int d3 = 0; d3 < (int)od[3]; d3++) {
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
diff --git a/src/backend/cpu/kernel/gradient.hpp b/src/backend/cpu/kernel/gradient.hpp
index c152fb343a..1ab01abb0f 100644
--- a/src/backend/cpu/kernel/gradient.hpp
+++ b/src/backend/cpu/kernel/gradient.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/harris.hpp b/src/backend/cpu/kernel/harris.hpp
index db6551bbde..183cf37e77 100644
--- a/src/backend/cpu/kernel/harris.hpp
+++ b/src/backend/cpu/kernel/harris.hpp
@@ -7,31 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
+#include <utility.hpp>
 
 namespace cpu
 {
 namespace kernel
 {
 
-template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
-
-    T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * af::Pi * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
-        out[i] = el;
-        sum   += el;
-    }
-
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
-}
-
 template<typename T>
 void second_order_deriv(Array<T> ixx, Array<T> ixy, Array<T> iyy,
                         const unsigned in_len, const Array<T> ix, const Array<T> iy)
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index e26965aa04..9b9b897c02 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
@@ -14,8 +16,8 @@ namespace cpu
 namespace kernel
 {
 
-template<typename inType, typename outType, bool isLinear>
-void histogram(Array<outType> out, Array<inType> const in,
+template<typename OutT, typename InT, bool IsLinear>
+void histogram(Array<OutT> out, Array<InT> const in,
                unsigned const nbins, double const minval, double const maxval)
 {
     dim4 const outDims   = out.dims();
@@ -25,13 +27,13 @@ void histogram(Array<outType> out, Array<inType> const in,
     dim4 const oStrides  = out.strides();
     dim_t const nElems   = inDims[0]*inDims[1];
 
-    outType *outData    = out.get();
-    const inType* inData= in.get();
+    OutT *outData    = out.get();
+    const InT* inData= in.get();
 
     for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
         for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
             for(dim_t i=0; i<nElems; i++) {
-                int idx = isLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
+                int idx = IsLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
                 int bin = (int)((inData[idx] - minval) / step);
                 bin = std::max(bin, 0);
                 bin = std::min(bin, (int)(nbins - 1));
diff --git a/src/backend/cpu/kernel/hsv_rgb.hpp b/src/backend/cpu/kernel/hsv_rgb.hpp
index d8aa954df7..c1f59a1737 100644
--- a/src/backend/cpu/kernel/hsv_rgb.hpp
+++ b/src/backend/cpu/kernel/hsv_rgb.hpp
@@ -7,6 +7,8 @@
 * http://arrayfire.com/licenses/BSD-3-Clause
 ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 #include <cmath>
 
@@ -15,13 +17,11 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
 void hsv2rgb(Array<T> out, Array<T> const in)
 {
-    const dim4 dims    = in.dims();
-    const dim4 strides = in.strides();
+    const af::dim4 dims    = in.dims();
+    const af::dim4 strides = in.strides();
     dim_t obStride  = out.strides()[3];
     dim_t coff      = strides[2];
     dim_t bCount    = dims[3];
@@ -72,9 +72,9 @@ void hsv2rgb(Array<T> out, Array<T> const in)
 template<typename T>
 void rgb2hsv(Array<T> out, Array<T> const in)
 {
-    const dim4 dims    = in.dims();
-    const dim4 strides = in.strides();
-    dim4 oStrides      = out.strides();
+    const af::dim4 dims    = in.dims();
+    const af::dim4 strides = in.strides();
+    af::dim4 oStrides      = out.strides();
     dim_t bCount    = dims[3];
 
     for(dim_t b=0; b<bCount; ++b) {
diff --git a/src/backend/cpu/kernel/identity.hpp b/src/backend/cpu/kernel/identity.hpp
index 9eab13b070..242ba9dae3 100644
--- a/src/backend/cpu/kernel/identity.hpp
+++ b/src/backend/cpu/kernel/identity.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 
@@ -15,13 +17,11 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
 void identity(Array<T> out)
 {
     T *ptr = out.get();
-    const dim4 out_dims  = out.dims();
+    const af::dim4 out_dims  = out.dims();
 
     for (dim_t k = 0; k < out_dims[2] * out_dims[3]; k++) {
         for (dim_t j = 0; j < out_dims[1]; j++) {
diff --git a/src/backend/cpu/kernel/iir.hpp b/src/backend/cpu/kernel/iir.hpp
index d1ca464365..5182094fc2 100644
--- a/src/backend/cpu/kernel/iir.hpp
+++ b/src/backend/cpu/kernel/iir.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
@@ -14,8 +16,6 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
 void iir(Array<T> y, Array<T> c, Array<T> const a)
 {
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index ee20c24d44..343d7ae4e7 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <vector>
 #include <Array.hpp>
 #include <utility.hpp>
@@ -16,19 +18,17 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
 template<typename T>
 void index(Array<T> out, Array<T> const in,
            std::vector<bool> const isSeq, std::vector<af_seq> const seqs,
            std::vector< Array<uint> > const idxArrs)
 {
-    const dim4 iDims    = in.dims();
-    const dim4 dDims    = in.getDataDims();
-    const dim4 iOffs    = toOffset(seqs, dDims);
-    const dim4 iStrds   = toStride(seqs, dDims);
-    const dim4 oDims    = out.dims();
-    const dim4 oStrides = out.strides();
+    const af::dim4 iDims    = in.dims();
+    const af::dim4 dDims    = in.getDataDims();
+    const af::dim4 iOffs    = toOffset(seqs, dDims);
+    const af::dim4 iStrds   = toStride(seqs, dDims);
+    const af::dim4 oDims    = out.dims();
+    const af::dim4 oStrides = out.strides();
     const T *src        = in.get();
     T *dst        = out.get();
     const uint* ptr0    = idxArrs[0].get();
diff --git a/src/backend/cpu/kernel/lookup.hpp b/src/backend/cpu/kernel/lookup.hpp
index 551cd2fd03..a290ef2fca 100644
--- a/src/backend/cpu/kernel/lookup.hpp
+++ b/src/backend/cpu/kernel/lookup.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <af/defines.h>
 #include <vector>
 #include <Array.hpp>
 #include <utility.hpp>
@@ -16,20 +18,18 @@ namespace cpu
 namespace kernel
 {
 
-using af::dim4;
-
-template<typename in_t, typename idx_t>
-void lookup(Array<in_t> out, Array<in_t> const input,
-            Array<idx_t> const indices, unsigned const dim)
+template<typename InT, typename IndexT>
+void lookup(Array<InT> out, Array<InT> const input,
+            Array<IndexT> const indices, unsigned const dim)
 {
-    const dim4 iDims    = input.dims();
-    const dim4 oDims    = out.dims();
-    const dim4 iStrides = input.strides();
-    const dim4 oStrides = out.strides();
-    const in_t *inPtr   = input.get();
-    const idx_t *idxPtr = indices.get();
-
-    in_t *outPtr = out.get();
+    const af::dim4 iDims    = input.dims();
+    const af::dim4 oDims    = out.dims();
+    const af::dim4 iStrides = input.strides();
+    const af::dim4 oStrides = out.strides();
+    const InT *inPtr   = input.get();
+    const IndexT *idxPtr = indices.get();
+
+    InT *outPtr = out.get();
 
     for (dim_t l=0; l<oDims[3]; ++l) {
 
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index 18a38f3149..ed8bbd79f7 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -9,13 +9,16 @@
 
 #pragma once
 #include <af/defines.h>
+#include <af/constants.h>
+#include <cmath>
+#include <algorithm>
 #include "backend.hpp"
 
 namespace cpu
 {
 
 static inline
-dim_t trimIndex(const int &idx, const dim_t &len)
+dim_t trimIndex(int const & idx, dim_t const & len)
 {
     int ret_val = idx;
     int offset  = abs(ret_val)%len;
@@ -27,4 +30,34 @@ dim_t trimIndex(const int &idx, const dim_t &len)
     return ret_val;
 }
 
+static inline
+dim_t clamp(int a, dim_t mn, dim_t mx)
+{
+    return (a < (int)mn ? mn : (a > (int)mx ? mx : a));
+}
+
+static inline
+unsigned getIdx(af::dim4 const & strides, int i, int j = 0, int k = 0, int l = 0)
+{
+    return (l * strides[3] + k * strides[2] + j * strides[1] + i * strides[0]);
+}
+
+template<typename T>
+void gaussian1D(T* out, int const dim, double sigma=0.0)
+{
+    if(!(sigma>0)) sigma = 0.25*dim;
+
+    T sum = (T)0;
+    for(int i=0;i<dim;i++)
+    {
+        int x = i-(dim-1)/2;
+        T el = 1. / std::sqrt(2 * af::Pi * sigma*sigma) * std::exp(-((x*x)/(2*(sigma*sigma))));
+        out[i] = el;
+        sum   += el;
+    }
+
+    for(int k=0;k<dim;k++)
+        out[k] /= sum;
+}
+
 }

From bb0a22c6f6ddc1d3b2f07f1cf322722fa595c17c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 19 Dec 2015 14:06:37 -0500
Subject: [PATCH 0161/2677] Moved more cpu fns implementations to kernel
 namespace

Below given is the list of functions that have undergone this change:
* iota
* ireduce
* join
* lu decomposition
* template matching
* mean shift
* median filter
* morphological operations
---
 src/backend/cpu/iota.cpp                  |  38 +-----
 src/backend/cpu/ireduce.cpp               | 100 +--------------
 src/backend/cpu/join.cpp                  | 148 ++--------------------
 src/backend/cpu/kernel/iota.hpp           |  45 +++++++
 src/backend/cpu/kernel/ireduce.hpp        | 108 ++++++++++++++++
 src/backend/cpu/kernel/join.hpp           | 144 +++++++++++++++++++++
 src/backend/cpu/kernel/lu.hpp             |  80 ++++++++++++
 src/backend/cpu/kernel/match_template.hpp | 141 +++++++++++++++++++++
 src/backend/cpu/kernel/meanshift.hpp      | 138 ++++++++++++++++++++
 src/backend/cpu/kernel/medfilt.hpp        | 135 ++++++++++++++++++++
 src/backend/cpu/kernel/morph.hpp          | 140 ++++++++++++++++++++
 src/backend/cpu/lu.cpp                    |  66 +---------
 src/backend/cpu/match_template.cpp        | 127 +------------------
 src/backend/cpu/meanshift.cpp             | 121 +-----------------
 src/backend/cpu/medfilt.cpp               | 117 +----------------
 src/backend/cpu/morph.cpp                 | 126 +-----------------
 src/backend/cpu/utility.hpp               |   4 +-
 17 files changed, 971 insertions(+), 807 deletions(-)
 create mode 100644 src/backend/cpu/kernel/iota.hpp
 create mode 100644 src/backend/cpu/kernel/ireduce.hpp
 create mode 100644 src/backend/cpu/kernel/join.hpp
 create mode 100644 src/backend/cpu/kernel/lu.hpp
 create mode 100644 src/backend/cpu/kernel/match_template.hpp
 create mode 100644 src/backend/cpu/kernel/meanshift.hpp
 create mode 100644 src/backend/cpu/kernel/medfilt.hpp
 create mode 100644 src/backend/cpu/kernel/morph.hpp

diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index dcb85fa787..41f0c9c518 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -10,49 +10,15 @@
 #include <Array.hpp>
 #include <iota.hpp>
 #include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
-#include <algorithm>
-#include <numeric>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/iota.hpp>
 
 using namespace std;
 
 namespace cpu
 {
 
-///////////////////////////////////////////////////////////////////////////
-// Kernel Functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void iota_(Array<T>  output, const dim4 &sdims, const dim4 &tdims)
-{
-    const dim4 dims    = output.dims();
-    T* out             = output.get();
-    const dim4 strides = output.strides();
-
-    for(dim_t w = 0; w < dims[3]; w++) {
-        dim_t offW = w * strides[3];
-        T valW = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
-        for(dim_t z = 0; z < dims[2]; z++) {
-            dim_t offWZ = offW + z * strides[2];
-            T valZ = valW + (z % sdims[2]) * sdims[0] * sdims[1];
-            for(dim_t y = 0; y < dims[1]; y++) {
-                dim_t offWZY = offWZ + y * strides[1];
-                T valY = valZ + (y % sdims[1]) * sdims[0];
-                for(dim_t x = 0; x < dims[0]; x++) {
-                    dim_t id = offWZY + x;
-                    out[id] = valY + (x % sdims[0]);
-                }
-            }
-        }
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper Functions
-///////////////////////////////////////////////////////////////////////////
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
 {
@@ -60,7 +26,7 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
 
     Array<T> out = createEmptyArray<T>(outdims);
 
-    getQueue().enqueue(iota_<T>, out, dims, tile_dims);
+    getQueue().enqueue(kernel::iota<T>, out, dims, tile_dims);
 
     return out;
 }
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 9858cba665..f1efcf646a 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -13,103 +13,15 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
-
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/ireduce.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename T> double cabs(const T in) { return (double)in; }
-static double cabs(const char in) { return (double)(in > 0); }
-static double cabs(const cfloat &in) { return (double)abs(in); }
-static double cabs(const cdouble &in) { return (double)abs(in); }
-
-template<af_op_t op, typename T>
-struct MinMaxOp
-{
-    T m_val;
-    uint m_idx;
-    MinMaxOp(T val, uint idx) :
-        m_val(val), m_idx(idx)
-    {
-    }
-
-    void operator()(T val, uint idx)
-    {
-        if (cabs(val) < cabs(m_val) ||
-            (cabs(val) == cabs(m_val) &&
-             idx > m_idx)) {
-            m_val = val;
-            m_idx = idx;
-        }
-    }
-};
-
-template<typename T>
-struct MinMaxOp<af_max_t, T>
-{
-    T m_val;
-    uint m_idx;
-    MinMaxOp(T val, uint idx) :
-        m_val(val), m_idx(idx)
-    {
-    }
-
-    void operator()(T val, uint idx)
-    {
-        if (cabs(val) > cabs(m_val) ||
-            (cabs(val) == cabs(m_val) &&
-             idx <= m_idx)) {
-            m_val = val;
-            m_idx = idx;
-        }
-    }
-};
-
-template<af_op_t op, typename T, int D>
-struct ireduce_dim
-{
-    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
-                    const Array<T> input, const dim_t inOffset, const int dim)
-    {
-        const dim4 odims    = output.dims();
-        const dim4 ostrides = output.strides();
-        const dim4 istrides = input.strides();
-        const int D1 = D - 1;
-        for (dim_t i = 0; i < odims[D1]; i++) {
-            ireduce_dim<op, T, D1>()(output, locArray, outOffset + i * ostrides[D1],
-                                     input, inOffset + i * istrides[D1], dim);
-        }
-    }
-};
-
-template<af_op_t op, typename T>
-struct ireduce_dim<op, T, 0>
-{
-    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
-                    const Array<T> input, const dim_t inOffset, const int dim)
-    {
-        const dim4 idims = input.dims();
-        const dim4 istrides = input.strides();
-
-        T const * const in = input.get();
-        T * out = output.get();
-        uint * loc = locArray.get();
-
-        dim_t stride = istrides[dim];
-        MinMaxOp<op, T> Op(in[0], 0);
-        for (dim_t i = 0; i < idims[dim]; i++) {
-            Op(in[inOffset + i * stride], i);
-        }
-
-        *(out+outOffset) = Op.m_val;
-        *(loc+outOffset) = Op.m_idx;
-    }
-};
-
 template<af_op_t op, typename T>
 using ireduce_dim_func = std::function<void(Array<T>, Array<uint>, const dim_t,
                                             const Array<T>, const dim_t, const int)>;
@@ -123,10 +35,10 @@ void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
 
     dim4 odims = in.dims();
     odims[dim] = 1;
-    static const ireduce_dim_func<op, T> ireduce_funcs[] = { ireduce_dim<op, T, 1>()
-                                                           , ireduce_dim<op, T, 2>()
-                                                           , ireduce_dim<op, T, 3>()
-                                                           , ireduce_dim<op, T, 4>()};
+    static const ireduce_dim_func<op, T> ireduce_funcs[] = { kernel::ireduce_dim<op, T, 1>()
+                                                           , kernel::ireduce_dim<op, T, 2>()
+                                                           , kernel::ireduce_dim<op, T, 3>()
+                                                           , kernel::ireduce_dim<op, T, 4>()};
 
     getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
 }
@@ -141,7 +53,7 @@ T ireduce_all(unsigned *loc, const Array<T> &in)
     af::dim4 strides = in.strides();
     const T *inPtr = in.get();
 
-    MinMaxOp<op, T> Op(inPtr[0], 0);
+    kernel::MinMaxOp<op, T> Op(inPtr[0], 0);
 
     for(dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 8af9c24f8d..e39280c943 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -9,50 +9,12 @@
 
 #include <Array.hpp>
 #include <join.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/join.hpp>
 
 namespace cpu
 {
-template<typename To, typename Tx, int dim>
-void join_append(To *out, const Tx *X, const af::dim4 &offset,
-           const af::dim4 &odims, const af::dim4 &xdims,
-           const af::dim4 &ost, const af::dim4 &xst)
-{
-    for(dim_t ow = 0; ow < xdims[3]; ow++) {
-        const dim_t xW = ow * xst[3];
-        const dim_t oW = (ow + offset[3]) * ost[3];
-
-        for(dim_t oz = 0; oz < xdims[2]; oz++) {
-            const dim_t xZW = xW + oz * xst[2];
-            const dim_t oZW = oW + (oz + offset[2]) * ost[2];
-
-            for(dim_t oy = 0; oy < xdims[1]; oy++) {
-                const dim_t xYZW = xZW + oy * xst[1];
-                const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
-
-                for(dim_t ox = 0; ox < xdims[0]; ox++) {
-                    const dim_t iMem = xYZW + ox;
-                    const dim_t oMem = oYZW + (ox + offset[0]);
-                    out[oMem] = X[iMem];
-                }
-            }
-        }
-    }
-}
-
-template<int dim>
-af::dim4 calcOffset(const af::dim4 dims)
-{
-    af::dim4 offset;
-    offset[0] = (dim == 0) ? dims[0] : 0;
-    offset[1] = (dim == 1) ? dims[1] : 0;
-    offset[2] = (dim == 2) ? dims[2] : 0;
-    offset[3] = (dim == 3) ? dims[3] : 0;
-    return offset;
-}
 
 template<typename Tx, typename Ty>
 Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
@@ -76,97 +38,15 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
 
     Array<Tx> out = createEmptyArray<Tx>(odims);
 
-    auto func = [=] (Array<Tx> out, const Array<Tx> first, const Array<Ty> second) {
-        Tx* outPtr = out.get();
-        const Tx* fptr = first.get();
-        const Ty* sptr = second.get();
-
-        af::dim4 zero(0,0,0,0);
-        const af::dim4 odims = out.dims();
-        const af::dim4 fdims = first.dims();
-        const af::dim4 sdims = second.dims();
-
-        switch(dim) {
-            case 0:
-                join_append<Tx, Tx, 0>(outPtr, fptr, zero,
-                        odims, fdims, out.strides(), first.strides());
-                join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims),
-                        odims, sdims, out.strides(), second.strides());
-                break;
-            case 1:
-                join_append<Tx, Tx, 1>(outPtr, fptr, zero,
-                        odims, fdims, out.strides(), first.strides());
-                join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims),
-                        odims, sdims, out.strides(), second.strides());
-                break;
-            case 2:
-                join_append<Tx, Tx, 2>(outPtr, fptr, zero,
-                        odims, fdims, out.strides(), first.strides());
-                join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims),
-                        odims, sdims, out.strides(), second.strides());
-                break;
-            case 3:
-                join_append<Tx, Tx, 3>(outPtr, fptr, zero,
-                        odims, fdims, out.strides(), first.strides());
-                join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims),
-                        odims, sdims, out.strides(), second.strides());
-                break;
-        }
-    };
-    getQueue().enqueue(func, out, first, second);
+    getQueue().enqueue(kernel::join<Tx, Ty>, out, dim, first, second);
 
     return out;
 }
 
-template<typename T, int n_arrays>
-void join_wrapper(const int dim, Array<T> out, const std::vector<Array<T>> inputs)
-{
-    af::dim4 zero(0,0,0,0);
-    af::dim4 d = zero;
-    switch(dim) {
-        case 0:
-            join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 0>(out.get(), inputs[i].get(), calcOffset<0>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-            }
-            break;
-        case 1:
-            join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 1>(out.get(), inputs[i].get(), calcOffset<1>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-            }
-            break;
-        case 2:
-            join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 2>(out.get(), inputs[i].get(), calcOffset<2>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-            }
-            break;
-        case 3:
-            join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 3>(out.get(), inputs[i].get(), calcOffset<3>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
-            }
-            break;
-    }
-}
-
 template<typename T>
 Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
 {
-    for (int i=0; i<inputs.size(); ++i)
+    for (unsigned i=0; i<inputs.size(); ++i)
         inputs[i].eval();
     // All dimensions except join dimension must be equal
     // Compute output dims
@@ -175,7 +55,7 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
     std::vector<af::dim4> idims(n_arrays);
 
     dim_t dim_size = 0;
-    for(int i = 0; i < (int)idims.size(); i++) {
+    for(unsigned i = 0; i < idims.size(); i++) {
         idims[i] = inputs[i].dims();
         dim_size += idims[i][dim];
     }
@@ -192,34 +72,34 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
 
     switch(n_arrays) {
         case 1:
-            getQueue().enqueue(join_wrapper<T, 1>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 1>, dim, out, inputs);
             break;
         case 2:
-            getQueue().enqueue(join_wrapper<T, 2>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 2>, dim, out, inputs);
             break;
         case 3:
-            getQueue().enqueue(join_wrapper<T, 3>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 3>, dim, out, inputs);
             break;
         case 4:
-            getQueue().enqueue(join_wrapper<T, 4>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 4>, dim, out, inputs);
             break;
         case 5:
-            getQueue().enqueue(join_wrapper<T, 5>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 5>, dim, out, inputs);
             break;
         case 6:
-            getQueue().enqueue(join_wrapper<T, 6>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 6>, dim, out, inputs);
             break;
         case 7:
-            getQueue().enqueue(join_wrapper<T, 7>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 7>, dim, out, inputs);
             break;
         case 8:
-            getQueue().enqueue(join_wrapper<T, 8>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 8>, dim, out, inputs);
             break;
         case 9:
-            getQueue().enqueue(join_wrapper<T, 9>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 9>, dim, out, inputs);
             break;
         case 10:
-            getQueue().enqueue(join_wrapper<T,10>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T,10>, dim, out, inputs);
             break;
     }
 
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
new file mode 100644
index 0000000000..0f824295a4
--- /dev/null
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -0,0 +1,45 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void iota(Array<T> output, const af::dim4 &sdims, const af::dim4 &tdims)
+{
+    const af::dim4 dims    = output.dims();
+    T* out             = output.get();
+    const af::dim4 strides = output.strides();
+
+    for(dim_t w = 0; w < dims[3]; w++) {
+        dim_t offW = w * strides[3];
+        T valW = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
+        for(dim_t z = 0; z < dims[2]; z++) {
+            dim_t offWZ = offW + z * strides[2];
+            T valZ = valW + (z % sdims[2]) * sdims[0] * sdims[1];
+            for(dim_t y = 0; y < dims[1]; y++) {
+                dim_t offWZY = offWZ + y * strides[1];
+                T valY = valZ + (y % sdims[1]) * sdims[0];
+                for(dim_t x = 0; x < dims[0]; x++) {
+                    dim_t id = offWZY + x;
+                    out[id] = valY + (x % sdims[0]);
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
new file mode 100644
index 0000000000..1f5a51da62
--- /dev/null
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -0,0 +1,108 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T> double cabs(const T in) { return (double)in; }
+static double cabs(const char in) { return (double)(in > 0); }
+static double cabs(const cfloat &in) { return (double)abs(in); }
+static double cabs(const cdouble &in) { return (double)abs(in); }
+
+template<af_op_t op, typename T>
+struct MinMaxOp
+{
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) :
+        m_val(val), m_idx(idx)
+    {
+    }
+
+    void operator()(T val, uint idx)
+    {
+        if (cabs(val) < cabs(m_val) ||
+            (cabs(val) == cabs(m_val) &&
+             idx > m_idx)) {
+            m_val = val;
+            m_idx = idx;
+        }
+    }
+};
+
+template<typename T>
+struct MinMaxOp<af_max_t, T>
+{
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) :
+        m_val(val), m_idx(idx)
+    {
+    }
+
+    void operator()(T val, uint idx)
+    {
+        if (cabs(val) > cabs(m_val) ||
+            (cabs(val) == cabs(m_val) &&
+             idx <= m_idx)) {
+            m_val = val;
+            m_idx = idx;
+        }
+    }
+};
+
+template<af_op_t op, typename T, int D>
+struct ireduce_dim
+{
+    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
+                    const Array<T> input, const dim_t inOffset, const int dim)
+    {
+        const af::dim4 odims    = output.dims();
+        const af::dim4 ostrides = output.strides();
+        const af::dim4 istrides = input.strides();
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            ireduce_dim<op, T, D1>()(output, locArray, outOffset + i * ostrides[D1],
+                                     input, inOffset + i * istrides[D1], dim);
+        }
+    }
+};
+
+template<af_op_t op, typename T>
+struct ireduce_dim<op, T, 0>
+{
+    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
+                    const Array<T> input, const dim_t inOffset, const int dim)
+    {
+        const af::dim4 idims = input.dims();
+        const af::dim4 istrides = input.strides();
+
+        T const * const in = input.get();
+        T * out = output.get();
+        uint * loc = locArray.get();
+
+        dim_t stride = istrides[dim];
+        MinMaxOp<op, T> Op(in[0], 0);
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            Op(in[inOffset + i * stride], i);
+        }
+
+        *(out+outOffset) = Op.m_val;
+        *(loc+outOffset) = Op.m_idx;
+    }
+};
+
+}
+}
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
new file mode 100644
index 0000000000..b0d92c9978
--- /dev/null
+++ b/src/backend/cpu/kernel/join.hpp
@@ -0,0 +1,144 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<int dim>
+af::dim4 calcOffset(const af::dim4 dims)
+{
+    af::dim4 offset;
+    offset[0] = (dim == 0) ? dims[0] : 0;
+    offset[1] = (dim == 1) ? dims[1] : 0;
+    offset[2] = (dim == 2) ? dims[2] : 0;
+    offset[3] = (dim == 3) ? dims[3] : 0;
+    return offset;
+}
+
+template<typename To, typename Tx, int dim>
+void join_append(To *out, const Tx *X, const af::dim4 &offset,
+           const af::dim4 &odims, const af::dim4 &xdims,
+           const af::dim4 &ost, const af::dim4 &xst)
+{
+    for(dim_t ow = 0; ow < xdims[3]; ow++) {
+        const dim_t xW = ow * xst[3];
+        const dim_t oW = (ow + offset[3]) * ost[3];
+
+        for(dim_t oz = 0; oz < xdims[2]; oz++) {
+            const dim_t xZW = xW + oz * xst[2];
+            const dim_t oZW = oW + (oz + offset[2]) * ost[2];
+
+            for(dim_t oy = 0; oy < xdims[1]; oy++) {
+                const dim_t xYZW = xZW + oy * xst[1];
+                const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
+
+                for(dim_t ox = 0; ox < xdims[0]; ox++) {
+                    const dim_t iMem = xYZW + ox;
+                    const dim_t oMem = oYZW + (ox + offset[0]);
+                    out[oMem] = X[iMem];
+                }
+            }
+        }
+    }
+}
+
+template<typename Tx, typename Ty>
+void join(Array<Tx> out, const int dim, const Array<Tx> first, const Array<Ty> second)
+{
+    Tx* outPtr = out.get();
+    const Tx* fptr = first.get();
+    const Ty* sptr = second.get();
+
+    af::dim4 zero(0,0,0,0);
+    const af::dim4 odims = out.dims();
+    const af::dim4 fdims = first.dims();
+    const af::dim4 sdims = second.dims();
+
+    switch(dim) {
+        case 0:
+            join_append<Tx, Tx, 0>(outPtr, fptr, zero,
+                    odims, fdims, out.strides(), first.strides());
+            join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims),
+                    odims, sdims, out.strides(), second.strides());
+            break;
+        case 1:
+            join_append<Tx, Tx, 1>(outPtr, fptr, zero,
+                    odims, fdims, out.strides(), first.strides());
+            join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims),
+                    odims, sdims, out.strides(), second.strides());
+            break;
+        case 2:
+            join_append<Tx, Tx, 2>(outPtr, fptr, zero,
+                    odims, fdims, out.strides(), first.strides());
+            join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims),
+                    odims, sdims, out.strides(), second.strides());
+            break;
+        case 3:
+            join_append<Tx, Tx, 3>(outPtr, fptr, zero,
+                    odims, fdims, out.strides(), first.strides());
+            join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims),
+                    odims, sdims, out.strides(), second.strides());
+            break;
+    }
+}
+
+template<typename T, int n_arrays>
+void join(const int dim, Array<T> out, const std::vector<Array<T>> inputs)
+{
+    af::dim4 zero(0,0,0,0);
+    af::dim4 d = zero;
+    switch(dim) {
+        case 0:
+            join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 0>(out.get(), inputs[i].get(), calcOffset<0>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+        case 1:
+            join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 1>(out.get(), inputs[i].get(), calcOffset<1>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+        case 2:
+            join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 2>(out.get(), inputs[i].get(), calcOffset<2>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+        case 3:
+            join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
+                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+            for(int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                join_append<T, T, 3>(out.get(), inputs[i].get(), calcOffset<3>(d),
+                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+            }
+            break;
+    }
+}
+
+}
+}
+
diff --git a/src/backend/cpu/kernel/lu.hpp b/src/backend/cpu/kernel/lu.hpp
new file mode 100644
index 0000000000..35b0c19b84
--- /dev/null
+++ b/src/backend/cpu/kernel/lu.hpp
@@ -0,0 +1,80 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void lu_split(Array<T> lower, Array<T> upper, const Array<T> in)
+{
+    T *l = lower.get();
+    T *u = upper.get();
+    const T *i = in.get();
+
+    af::dim4 ldm = lower.dims();
+    af::dim4 udm = upper.dims();
+    af::dim4 idm = in.dims();
+    af::dim4 lst = lower.strides();
+    af::dim4 ust = upper.strides();
+    af::dim4 ist = in.strides();
+
+    for(dim_t ow = 0; ow < idm[3]; ow++) {
+        const dim_t lW = ow * lst[3];
+        const dim_t uW = ow * ust[3];
+        const dim_t iW = ow * ist[3];
+
+        for(dim_t oz = 0; oz < idm[2]; oz++) {
+            const dim_t lZW = lW + oz * lst[2];
+            const dim_t uZW = uW + oz * ust[2];
+            const dim_t iZW = iW + oz * ist[2];
+
+            for(dim_t oy = 0; oy < idm[1]; oy++) {
+                const dim_t lYZW = lZW + oy * lst[1];
+                const dim_t uYZW = uZW + oy * ust[1];
+                const dim_t iYZW = iZW + oy * ist[1];
+
+                for(dim_t ox = 0; ox < idm[0]; ox++) {
+                    const dim_t lMem = lYZW + ox;
+                    const dim_t uMem = uYZW + ox;
+                    const dim_t iMem = iYZW + ox;
+                    if(ox > oy) {
+                        if(oy < ldm[1]) l[lMem] = i[iMem];
+                        if(ox < udm[0]) u[uMem] = scalar<T>(0);
+                    } else if (oy > ox) {
+                        if(oy < ldm[1]) l[lMem] = scalar<T>(0);
+                        if(ox < udm[0]) u[uMem] = i[iMem];
+                    } else if(ox == oy) {
+                        if(oy < ldm[1]) l[lMem] = scalar<T>(1.0);
+                        if(ox < udm[0]) u[uMem] = i[iMem];
+                    }
+                }
+            }
+        }
+    }
+}
+
+void convertPivot(Array<int> p, Array<int> pivot)
+{
+    int *d_pi = pivot.get();
+    int *d_po = p.get();
+    dim_t d0  = pivot.dims()[0];
+    for(int j = 0; j < (int)d0; j++) {
+        // 1 indexed in pivot
+        std::swap(d_po[j], d_po[d_pi[j] - 1]);
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
new file mode 100644
index 0000000000..ae41364018
--- /dev/null
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -0,0 +1,141 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename OutT, typename InT, af_match_type MatchT>
+void matchTemplate(Array<OutT> out, const Array<InT> sImg, const Array<InT> tImg)
+{
+    const af::dim4 sDims = sImg.dims();
+    const af::dim4 tDims = tImg.dims();
+    const af::dim4 sStrides = sImg.strides();
+    const af::dim4 tStrides = tImg.strides();
+
+    const dim_t tDim0  = tDims[0];
+    const dim_t tDim1  = tDims[1];
+    const dim_t sDim0  = sDims[0];
+    const dim_t sDim1  = sDims[1];
+
+    const af::dim4 oStrides = out.strides();
+
+    OutT tImgMean = OutT(0);
+    dim_t winNumElements = tImg.elements();
+    bool needMean = MatchT==AF_ZSAD || MatchT==AF_LSAD ||
+        MatchT==AF_ZSSD || MatchT==AF_LSSD ||
+        MatchT==AF_ZNCC;
+    const InT * tpl = tImg.get();
+
+    if (needMean) {
+        for(dim_t tj=0; tj<tDim1; tj++) {
+            dim_t tjStride = tj*tStrides[1];
+
+            for(dim_t ti=0; ti<tDim0; ti++) {
+                tImgMean += (OutT)tpl[tjStride+ti*tStrides[0]];
+            }
+        }
+        tImgMean /= winNumElements;
+    }
+
+    OutT * dst      = out.get();
+    const InT * src = sImg.get();
+
+    for(dim_t b3=0; b3<sDims[3]; ++b3) {
+        for(dim_t b2=0; b2<sDims[2]; ++b2) {
+
+            // slide through image window after window
+            for(dim_t sj=0; sj<sDim1; sj++) {
+
+                dim_t ojStride = sj*oStrides[1];
+
+                for(dim_t si=0; si<sDim0; si++) {
+                    OutT disparity = OutT(0);
+
+                    // mean for window
+                    // this variable will be used based on MatchT value
+                    OutT wImgMean = OutT(0);
+                    if (needMean) {
+                        for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
+                            dim_t jStride = j*sStrides[1];
+
+                            for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
+                                InT sVal = ((j<sDim1 && i<sDim0) ?
+                                        src[jStride + i*sStrides[0]] : InT(0));
+                                wImgMean += (OutT)sVal;
+                            }
+                        }
+                        wImgMean /= winNumElements;
+                    }
+
+                    // run the window match metric
+                    for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
+                        dim_t jStride = j*sStrides[1];
+                        dim_t tjStride = tj*tStrides[1];
+
+                        for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
+                            InT sVal = ((j<sDim1 && i<sDim0) ?
+                                    src[jStride + i*sStrides[0]] : InT(0));
+                            InT tVal = tpl[tjStride+ti*tStrides[0]];
+                            OutT temp;
+                            switch(MatchT) {
+                                case AF_SAD:
+                                    disparity += fabs((OutT)sVal-(OutT)tVal);
+                                    break;
+                                case AF_ZSAD:
+                                    disparity += fabs((OutT)sVal - wImgMean -
+                                            (OutT)tVal + tImgMean);
+                                    break;
+                                case AF_LSAD:
+                                    disparity += fabs((OutT)sVal-(wImgMean/tImgMean)*tVal);
+                                    break;
+                                case AF_SSD:
+                                    disparity += ((OutT)sVal-(OutT)tVal)*((OutT)sVal-(OutT)tVal);
+                                    break;
+                                case AF_ZSSD:
+                                    temp = ((OutT)sVal - wImgMean - (OutT)tVal + tImgMean);
+                                    disparity += temp*temp;
+                                    break;
+                                case AF_LSSD:
+                                    temp = ((OutT)sVal-(wImgMean/tImgMean)*tVal);
+                                    disparity += temp*temp;
+                                    break;
+                                case AF_NCC:
+                                    //TODO: furture implementation
+                                    break;
+                                case AF_ZNCC:
+                                    //TODO: furture implementation
+                                    break;
+                                case AF_SHD:
+                                    //TODO: furture implementation
+                                    break;
+                            }
+                        }
+                    }
+                    // output is just created, hence not doing the
+                    // extra multiplication for 0th dim stride
+                    dst[ojStride + si] = disparity;
+                }
+            }
+            src += sStrides[2];
+            dst += oStrides[2];
+        }
+        src += sStrides[3];
+        dst += oStrides[3];
+    }
+};
+
+
+}
+}
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
new file mode 100644
index 0000000000..f173b2fd8b
--- /dev/null
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -0,0 +1,138 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <vector>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, bool IsColor>
+void meanShift(Array<T> out, const Array<T> in, const float s_sigma,
+               const float c_sigma, const unsigned iter)
+{
+    const af::dim4 dims     = in.dims();
+    const af::dim4 istrides = in.strides();
+    const af::dim4 ostrides = out.strides();
+
+    const dim_t bCount   = (IsColor ? 1 : dims[2]);
+    const dim_t channels = (IsColor ? dims[2] : 1);
+
+    // clamp spatical and chromatic sigma's
+    float space_          = std::min(11.5f, s_sigma);
+    const dim_t radius = std::max((int)(space_ * 1.5f), 1);
+    const float cvar      = c_sigma*c_sigma;
+
+    std::vector<float> means;
+    std::vector<float> centers;
+    std::vector<float> tmpclrs;
+    means.reserve(channels);
+    centers.reserve(channels);
+    tmpclrs.reserve(channels);
+
+    T *outData       = out.get();
+    const T * inData = in.get();
+
+    for(dim_t b3=0; b3<dims[3]; ++b3) {
+        for(dim_t b2=0; b2<bCount; ++b2) {
+
+            for(dim_t j=0; j<dims[1]; ++j) {
+
+                dim_t j_in_off  = j*istrides[1];
+                dim_t j_out_off = j*ostrides[1];
+
+                for(dim_t i=0; i<dims[0]; ++i) {
+
+                    dim_t i_in_off  = i*istrides[0];
+                    dim_t i_out_off = i*ostrides[0];
+
+                    // clear means and centers for this pixel
+                    for(dim_t ch=0; ch<channels; ++ch) {
+                        means[ch] = 0.0f;
+                        // the expression ch*istrides[2] will only effect when ch>1
+                        // i.e for color images where batch is along fourth dimension
+                        centers[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
+                    }
+
+                    // scope of meanshift iterationd begin
+                    for(unsigned it=0; it<iter; ++it) {
+
+                        int count   = 0;
+                        int shift_x = 0;
+                        int shift_y = 0;
+
+                        for(dim_t wj=-radius; wj<=radius; ++wj) {
+
+                            int hit_count = 0;
+
+                            for(dim_t wi=-radius; wi<=radius; ++wi) {
+
+                                dim_t tj = j + wj;
+                                dim_t ti = i + wi;
+
+                                // clamps offsets
+                                tj = clamp(tj, 0ll, dims[1]-1);
+                                ti = clamp(ti, 0ll, dims[0]-1);
+
+                                // proceed
+                                float norm = 0.0f;
+                                for(dim_t ch=0; ch<channels; ++ch) {
+                                    tmpclrs[ch] = inData[ tj*istrides[1] + ti*istrides[0] + ch*istrides[2]];
+                                    norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
+                                }
+
+                                if (norm<= cvar) {
+                                    for(dim_t ch=0; ch<channels; ++ch)
+                                        means[ch] += tmpclrs[ch];
+                                    shift_x += wi;
+                                    ++hit_count;
+                                }
+
+                            }
+                            count+= hit_count;
+                            shift_y += wj*hit_count;
+                        }
+
+                        if (count==0) { break; }
+
+                        const float fcount = 1.f/count;
+                        const int mean_x = (int)(shift_x*fcount+0.5f);
+                        const int mean_y = (int)(shift_y*fcount+0.5f);
+                        for(dim_t ch=0; ch<channels; ++ch)
+                            means[ch] *= fcount;
+
+                        float norm = 0.f;
+                        for(dim_t ch=0; ch<channels; ++ch)
+                            norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
+                        bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
+                        shift_x = mean_x;
+                        shift_y = mean_y;
+                        for(dim_t ch=0; ch<channels; ++ch)
+                            centers[ch] = means[ch];
+                        if (stop) { break; }
+                    } // scope of meanshift iterations end
+
+                    for(dim_t ch=0; ch<channels; ++ch)
+                        outData[j_out_off + i_out_off + ch*ostrides[2]] = centers[ch];
+
+                }
+            }
+            outData += ostrides[2];
+            inData  += istrides[2];
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
new file mode 100644
index 0000000000..bc639a89b5
--- /dev/null
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -0,0 +1,135 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <vector>
+#include <algorithm>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, af_border_type Pad>
+void medfilt(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
+{
+    const af::dim4 dims     = in.dims();
+    const af::dim4 istrides = in.strides();
+    const af::dim4 ostrides = out.strides();
+
+    std::vector<T> wind_vals;
+    wind_vals.reserve(w_len*w_wid);
+
+    T const * in_ptr = in.get();
+    T * out_ptr = out.get();
+
+    for(int b3=0; b3<(int)dims[3]; b3++) {
+
+        for(int b2=0; b2<(int)dims[2]; b2++) {
+
+            for(int col=0; col<(int)dims[1]; col++) {
+
+                int ocol_off = col*ostrides[1];
+
+                for(int row=0; row<(int)dims[0]; row++) {
+
+                    wind_vals.clear();
+
+                    for(int wj=0; wj<(int)w_wid; ++wj) {
+
+                        bool isColOff = false;
+
+                        int im_col = col + wj-w_wid/2;
+                        int im_coff;
+                        switch(Pad) {
+                            case AF_PAD_ZERO:
+                                im_coff = im_col * istrides[1];
+                                if (im_col < 0 || im_col>=(int)dims[1])
+                                    isColOff = true;
+                                break;
+                            case AF_PAD_SYM:
+                                {
+                                    if (im_col < 0) {
+                                        im_col *= -1;
+                                        isColOff = true;
+                                    }
+
+                                    if (im_col>=(int)dims[1]) {
+                                        im_col = 2*((int)dims[1]-1) - im_col;
+                                        isColOff = true;
+                                    }
+
+                                    im_coff = im_col * istrides[1];
+                                }
+                                break;
+                        }
+
+                        for(int wi=0; wi<(int)w_len; ++wi) {
+
+                            bool isRowOff = false;
+
+                            int im_row = row + wi-w_len/2;
+                            int im_roff;
+                            switch(Pad) {
+                                case AF_PAD_ZERO:
+                                    im_roff = im_row * istrides[0];
+                                    if (im_row < 0 || im_row>=(int)dims[0])
+                                        isRowOff = true;
+                                    break;
+                                case AF_PAD_SYM:
+                                    {
+                                        if (im_row < 0) {
+                                            im_row *= -1;
+                                            isRowOff = true;
+                                        }
+
+                                        if (im_row>=(int)dims[0]) {
+                                            im_row = 2*((int)dims[0]-1) - im_row;
+                                            isRowOff = true;
+                                        }
+
+                                        im_roff = im_row * istrides[0];
+                                    }
+                                    break;
+                            }
+
+                            if(isRowOff || isColOff) {
+                                switch(Pad) {
+                                    case AF_PAD_ZERO:
+                                        wind_vals.push_back(0);
+                                        break;
+                                    case AF_PAD_SYM:
+                                        wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                                        break;
+                                }
+                            } else
+                                wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                        }
+                    }
+
+                    std::stable_sort(wind_vals.begin(),wind_vals.end());
+                    int off = wind_vals.size()/2;
+                    if (wind_vals.size()%2==0)
+                        out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
+                    else {
+                        out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
+                    }
+                }
+            }
+            in_ptr  += istrides[2];
+            out_ptr += ostrides[2];
+        }
+    }
+}
+
+
+}
+}
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
new file mode 100644
index 0000000000..af9b7e9373
--- /dev/null
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -0,0 +1,140 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, bool IsDilation>
+void morph(Array<T> out, Array<T> const in, Array<T> const mask)
+{
+    const af::dim4 ostrides = out.strides();
+    const af::dim4 istrides = in.strides();
+    const af::dim4 fstrides = mask.strides();
+    const af::dim4 dims     = in.dims();
+    const af::dim4 window   = mask.dims();
+    T* outData          = out.get();
+    const T*   inData   = in.get();
+    const T*   filter   = mask.get();
+    const dim_t R0      = window[0]/2;
+    const dim_t R1      = window[1]/2;
+
+    for(dim_t b3=0; b3<dims[3]; ++b3) {
+        for(dim_t b2=0; b2<dims[2]; ++b2) {
+            // either channels or batch is handled by outer most loop
+            for(dim_t j=0; j<dims[1]; ++j) {
+                // j steps along 2nd dimension
+                for(dim_t i=0; i<dims[0]; ++i) {
+                    // i steps along 1st dimension
+                    T filterResult = inData[ getIdx(istrides, i, j) ];
+
+                    // wj,wi steps along 2nd & 1st dimensions of filter window respectively
+                    for(dim_t wj=0; wj<window[1]; wj++) {
+                        for(dim_t wi=0; wi<window[0]; wi++) {
+
+                            dim_t offj = j+wj-R1;
+                            dim_t offi = i+wi-R0;
+
+                            T maskValue = filter[ getIdx(fstrides, wi, wj) ];
+
+                            if ((maskValue > (T)0) && offi>=0 && offj>=0 && offi<dims[0] && offj<dims[1]) {
+
+                                T inValue   = inData[ getIdx(istrides, offi, offj) ];
+
+                                if (IsDilation)
+                                    filterResult = std::max(filterResult, inValue);
+                                else
+                                    filterResult = std::min(filterResult, inValue);
+                            }
+
+                        } // window 1st dimension loop ends here
+                    } // filter window loop ends here
+
+                    outData[ getIdx(ostrides, i, j) ] = filterResult;
+                } //1st dimension loop ends here
+            } // 2nd dimension loop ends here
+
+            // next iteration will be next batch if any
+            outData += ostrides[2];
+            inData  += istrides[2];
+        }
+    }
+}
+
+template<typename T, bool IsDilation>
+void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
+{
+    const af::dim4 dims     = in.dims();
+    const af::dim4 window   = mask.dims();
+    const dim_t R0      = window[0]/2;
+    const dim_t R1      = window[1]/2;
+    const dim_t R2      = window[2]/2;
+    const af::dim4 istrides = in.strides();
+    const af::dim4 fstrides = mask.strides();
+    const dim_t bCount  = dims[3];
+    const af::dim4 ostrides = out.strides();
+    T* outData          = out.get();
+    const T*   inData   = in.get();
+    const T*   filter   = mask.get();
+
+    for(dim_t batchId=0; batchId<bCount; ++batchId) {
+        // either channels or batch is handled by outer most loop
+        for(dim_t k=0; k<dims[2]; ++k) {
+            // k steps along 3rd dimension
+            for(dim_t j=0; j<dims[1]; ++j) {
+                // j steps along 2nd dimension
+                for(dim_t i=0; i<dims[0]; ++i) {
+                    // i steps along 1st dimension
+                    T filterResult = inData[ getIdx(istrides, i, j, k) ];
+
+                    // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
+                    for(dim_t wk=0; wk<window[2]; wk++) {
+                        for(dim_t wj=0; wj<window[1]; wj++) {
+                            for(dim_t wi=0; wi<window[0]; wi++) {
+
+                                dim_t offk = k+wk-R2;
+                                dim_t offj = j+wj-R1;
+                                dim_t offi = i+wi-R0;
+
+                                T maskValue = filter[ getIdx(fstrides, wi, wj, wk) ];
+
+                                if ((maskValue > (T)0) && offi>=0 && offj>=0 && offk>=0 &&
+                                        offi<dims[0] && offj<dims[1] && offk<dims[2]) {
+
+                                    T inValue   = inData[ getIdx(istrides, offi, offj, offk) ];
+
+                                    if (IsDilation)
+                                        filterResult = std::max(filterResult, inValue);
+                                    else
+                                        filterResult = std::min(filterResult, inValue);
+                                }
+
+                            } // window 1st dimension loop ends here
+                        }  // window 1st dimension loop ends here
+                    }// filter window loop ends here
+
+                    outData[ getIdx(ostrides, i, j, k) ] = filterResult;
+                } //1st dimension loop ends here
+            } // 2nd dimension loop ends here
+        } // 3rd dimension loop ends here
+        // next iteration will be next batch if any
+        outData += ostrides[3];
+        inData  += istrides[3];
+    }
+}
+
+
+}
+}
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 9a046139d4..f0e1593f1a 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -15,11 +15,11 @@
 #include <handle.hpp>
 #include <iostream>
 #include <cassert>
-#include <err_cpu.hpp>
 #include <range.hpp>
 #include <lapack_helper.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/lu.hpp>
 
 namespace cpu
 {
@@ -41,66 +41,6 @@ LU_FUNC(getrf , double , d)
 LU_FUNC(getrf , cfloat , c)
 LU_FUNC(getrf , cdouble, z)
 
-template<typename T>
-void lu_split(Array<T> lower, Array<T> upper, const Array<T> in)
-{
-    T *l = lower.get();
-    T *u = upper.get();
-    const T *i = in.get();
-
-    dim4 ldm = lower.dims();
-    dim4 udm = upper.dims();
-    dim4 idm = in.dims();
-    dim4 lst = lower.strides();
-    dim4 ust = upper.strides();
-    dim4 ist = in.strides();
-
-    for(dim_t ow = 0; ow < idm[3]; ow++) {
-        const dim_t lW = ow * lst[3];
-        const dim_t uW = ow * ust[3];
-        const dim_t iW = ow * ist[3];
-
-        for(dim_t oz = 0; oz < idm[2]; oz++) {
-            const dim_t lZW = lW + oz * lst[2];
-            const dim_t uZW = uW + oz * ust[2];
-            const dim_t iZW = iW + oz * ist[2];
-
-            for(dim_t oy = 0; oy < idm[1]; oy++) {
-                const dim_t lYZW = lZW + oy * lst[1];
-                const dim_t uYZW = uZW + oy * ust[1];
-                const dim_t iYZW = iZW + oy * ist[1];
-
-                for(dim_t ox = 0; ox < idm[0]; ox++) {
-                    const dim_t lMem = lYZW + ox;
-                    const dim_t uMem = uYZW + ox;
-                    const dim_t iMem = iYZW + ox;
-                    if(ox > oy) {
-                        if(oy < ldm[1]) l[lMem] = i[iMem];
-                        if(ox < udm[0]) u[uMem] = scalar<T>(0);
-                    } else if (oy > ox) {
-                        if(oy < ldm[1]) l[lMem] = scalar<T>(0);
-                        if(ox < udm[0]) u[uMem] = i[iMem];
-                    } else if(ox == oy) {
-                        if(oy < ldm[1]) l[lMem] = scalar<T>(1.0);
-                        if(ox < udm[0]) u[uMem] = i[iMem];
-                    }
-                }
-            }
-        }
-    }
-}
-
-void convertPivot(Array<int> p, Array<int> pivot)
-{
-    int *d_pi = pivot.get();
-    int *d_po = p.get();
-    dim_t d0  = pivot.dims()[0];
-    for(int j = 0; j < (int)d0; j++) {
-        // 1 indexed in pivot
-        std::swap(d_po[j], d_po[d_pi[j] - 1]);
-    }
-}
-
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 {
@@ -119,7 +59,7 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
 
-    getQueue().enqueue(lu_split<T>, lower, upper, in_copy);
+    getQueue().enqueue(kernel::lu_split<T>, lower, upper, in_copy);
 }
 
 template<typename T>
@@ -138,7 +78,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 
     if(convert_pivot) {
         Array<int> p = range<int>(dim4(iDims[0]), 0);
-        getQueue().enqueue(convertPivot, p, pivot);
+        getQueue().enqueue(kernel::convertPivot, p, pivot);
         return p;
     } else {
         return pivot;
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index d4ce95a691..e5b030be64 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -12,141 +12,24 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <match_template.hpp>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/match_template.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename inType, typename outType, af_match_type mType>
-Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg)
+template<typename InT, typename OutT, af_match_type MatchT>
+Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg)
 {
     sImg.eval();
     tImg.eval();
 
-    Array<outType> out = createEmptyArray<outType>(sImg.dims());
+    Array<OutT> out = createEmptyArray<OutT>(sImg.dims());
 
-    auto func = [=](Array<outType> out, const Array<inType> sImg, const Array<inType> tImg) {
-        const dim4 sDims = sImg.dims();
-        const dim4 tDims = tImg.dims();
-        const dim4 sStrides = sImg.strides();
-        const dim4 tStrides = tImg.strides();
-
-        const dim_t tDim0  = tDims[0];
-        const dim_t tDim1  = tDims[1];
-        const dim_t sDim0  = sDims[0];
-        const dim_t sDim1  = sDims[1];
-
-        const dim4 oStrides = out.strides();
-
-        outType tImgMean = outType(0);
-        dim_t winNumElements = tImg.elements();
-        bool needMean = mType==AF_ZSAD || mType==AF_LSAD ||
-            mType==AF_ZSSD || mType==AF_LSSD ||
-            mType==AF_ZNCC;
-        const inType * tpl = tImg.get();
-
-        if (needMean) {
-            for(dim_t tj=0; tj<tDim1; tj++) {
-                dim_t tjStride = tj*tStrides[1];
-
-                for(dim_t ti=0; ti<tDim0; ti++) {
-                    tImgMean += (outType)tpl[tjStride+ti*tStrides[0]];
-                }
-            }
-            tImgMean /= winNumElements;
-        }
-
-        outType * dst      = out.get();
-        const inType * src = sImg.get();
-
-        for(dim_t b3=0; b3<sDims[3]; ++b3) {
-            for(dim_t b2=0; b2<sDims[2]; ++b2) {
-
-                // slide through image window after window
-                for(dim_t sj=0; sj<sDim1; sj++) {
-
-                    dim_t ojStride = sj*oStrides[1];
-
-                    for(dim_t si=0; si<sDim0; si++) {
-                        outType disparity = outType(0);
-
-                        // mean for window
-                        // this variable will be used based on mType value
-                        outType wImgMean = outType(0);
-                        if (needMean) {
-                            for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
-                                dim_t jStride = j*sStrides[1];
-
-                                for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
-                                    inType sVal = ((j<sDim1 && i<sDim0) ?
-                                            src[jStride + i*sStrides[0]] : inType(0));
-                                    wImgMean += (outType)sVal;
-                                }
-                            }
-                            wImgMean /= winNumElements;
-                        }
-
-                        // run the window match metric
-                        for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
-                            dim_t jStride = j*sStrides[1];
-                            dim_t tjStride = tj*tStrides[1];
-
-                            for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
-                                inType sVal = ((j<sDim1 && i<sDim0) ?
-                                        src[jStride + i*sStrides[0]] : inType(0));
-                                inType tVal = tpl[tjStride+ti*tStrides[0]];
-                                outType temp;
-                                switch(mType) {
-                                    case AF_SAD:
-                                        disparity += fabs((outType)sVal-(outType)tVal);
-                                        break;
-                                    case AF_ZSAD:
-                                        disparity += fabs((outType)sVal - wImgMean -
-                                                (outType)tVal + tImgMean);
-                                        break;
-                                    case AF_LSAD:
-                                        disparity += fabs((outType)sVal-(wImgMean/tImgMean)*tVal);
-                                        break;
-                                    case AF_SSD:
-                                        disparity += ((outType)sVal-(outType)tVal)*((outType)sVal-(outType)tVal);
-                                        break;
-                                    case AF_ZSSD:
-                                        temp = ((outType)sVal - wImgMean - (outType)tVal + tImgMean);
-                                        disparity += temp*temp;
-                                        break;
-                                    case AF_LSSD:
-                                        temp = ((outType)sVal-(wImgMean/tImgMean)*tVal);
-                                        disparity += temp*temp;
-                                        break;
-                                    case AF_NCC:
-                                        //TODO: furture implementation
-                                        break;
-                                    case AF_ZNCC:
-                                        //TODO: furture implementation
-                                        break;
-                                    case AF_SHD:
-                                        //TODO: furture implementation
-                                        break;
-                                }
-                            }
-                        }
-                        // output is just created, hence not doing the
-                        // extra multiplication for 0th dim stride
-                        dst[ojStride + si] = disparity;
-                    }
-                }
-                src += sStrides[2];
-                dst += oStrides[2];
-            }
-            src += sStrides[3];
-            dst += oStrides[3];
-        }
-    };
-    getQueue().enqueue(func, out, sImg, tImg);
+    getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg, tImg);
 
     return out;
 }
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index 62b80e010e..6c3417a62e 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -18,6 +18,7 @@
 #include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/meanshift.hpp>
 
 using af::dim4;
 using std::vector;
@@ -25,11 +26,6 @@ using std::vector;
 namespace cpu
 {
 
-inline dim_t clamp(dim_t a, dim_t mn, dim_t mx)
-{
-    return (a<mn ? mn : (a>mx ? mx : a));
-}
-
 template<typename T, bool is_color>
 Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
 {
@@ -37,120 +33,7 @@ Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sig
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    auto func = [=] (Array<T> out, const Array<T> in, const float s_sigma,
-                     const float c_sigma, const unsigned iter) {
-        const dim4 dims     = in.dims();
-        const dim4 istrides = in.strides();
-        const dim4 ostrides = out.strides();
-
-        const dim_t bCount   = (is_color ? 1 : dims[2]);
-        const dim_t channels = (is_color ? dims[2] : 1);
-
-        // clamp spatical and chromatic sigma's
-        float space_          = std::min(11.5f, s_sigma);
-        const dim_t radius = std::max((int)(space_ * 1.5f), 1);
-        const float cvar      = c_sigma*c_sigma;
-
-        vector<float> means;
-        vector<float> centers;
-        vector<float> tmpclrs;
-        means.reserve(channels);
-        centers.reserve(channels);
-        tmpclrs.reserve(channels);
-
-        T *outData       = out.get();
-        const T * inData = in.get();
-
-        for(dim_t b3=0; b3<dims[3]; ++b3) {
-            for(dim_t b2=0; b2<bCount; ++b2) {
-
-                for(dim_t j=0; j<dims[1]; ++j) {
-
-                    dim_t j_in_off  = j*istrides[1];
-                    dim_t j_out_off = j*ostrides[1];
-
-                    for(dim_t i=0; i<dims[0]; ++i) {
-
-                        dim_t i_in_off  = i*istrides[0];
-                        dim_t i_out_off = i*ostrides[0];
-
-                        // clear means and centers for this pixel
-                        for(dim_t ch=0; ch<channels; ++ch) {
-                            means[ch] = 0.0f;
-                            // the expression ch*istrides[2] will only effect when ch>1
-                            // i.e for color images where batch is along fourth dimension
-                            centers[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
-                        }
-
-                        // scope of meanshift iterationd begin
-                        for(unsigned it=0; it<iter; ++it) {
-
-                            int count   = 0;
-                            int shift_x = 0;
-                            int shift_y = 0;
-
-                            for(dim_t wj=-radius; wj<=radius; ++wj) {
-
-                                int hit_count = 0;
-
-                                for(dim_t wi=-radius; wi<=radius; ++wi) {
-
-                                    dim_t tj = j + wj;
-                                    dim_t ti = i + wi;
-
-                                    // clamps offsets
-                                    tj = clamp(tj, 0ll, dims[1]-1);
-                                    ti = clamp(ti, 0ll, dims[0]-1);
-
-                                    // proceed
-                                    float norm = 0.0f;
-                                    for(dim_t ch=0; ch<channels; ++ch) {
-                                        tmpclrs[ch] = inData[ tj*istrides[1] + ti*istrides[0] + ch*istrides[2]];
-                                        norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
-                                    }
-
-                                    if (norm<= cvar) {
-                                        for(dim_t ch=0; ch<channels; ++ch)
-                                            means[ch] += tmpclrs[ch];
-                                        shift_x += wi;
-                                        ++hit_count;
-                                    }
-
-                                }
-                                count+= hit_count;
-                                shift_y += wj*hit_count;
-                            }
-
-                            if (count==0) { break; }
-
-                            const float fcount = 1.f/count;
-                            const int mean_x = (int)(shift_x*fcount+0.5f);
-                            const int mean_y = (int)(shift_y*fcount+0.5f);
-                            for(dim_t ch=0; ch<channels; ++ch)
-                                means[ch] *= fcount;
-
-                            float norm = 0.f;
-                            for(dim_t ch=0; ch<channels; ++ch)
-                                norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
-                            bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
-                            shift_x = mean_x;
-                            shift_y = mean_y;
-                            for(dim_t ch=0; ch<channels; ++ch)
-                                centers[ch] = means[ch];
-                            if (stop) { break; }
-                        } // scope of meanshift iterations end
-
-                        for(dim_t ch=0; ch<channels; ++ch)
-                            outData[j_out_off + i_out_off + ch*ostrides[2]] = centers[ch];
-
-                    }
-                }
-                outData += ostrides[2];
-                inData  += istrides[2];
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in, s_sigma, c_sigma, iter);
+    getQueue().enqueue(kernel::meanShift<T, is_color>, out, in, s_sigma, c_sigma, iter);
 
     return out;
 }
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 4e74a55fd2..06cc0dff44 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -12,10 +12,9 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
-#include <err_cpu.hpp>
-#include <algorithm>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/medfilt.hpp>
 
 using af::dim4;
 
@@ -27,119 +26,9 @@ Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
     in.eval();
 
-    Array<T> out        = createEmptyArray<T>(in.dims());
+    Array<T> out = createEmptyArray<T>(in.dims());
 
-    auto func = [=] (Array<T> out, const Array<T> in,
-                     dim_t w_len, dim_t w_wid) {
-        const dim4 dims     = in.dims();
-        const dim4 istrides = in.strides();
-        const dim4 ostrides = out.strides();
-
-        std::vector<T> wind_vals;
-        wind_vals.reserve(w_len*w_wid);
-
-        T const * in_ptr = in.get();
-        T * out_ptr = out.get();
-
-        for(int b3=0; b3<(int)dims[3]; b3++) {
-
-            for(int b2=0; b2<(int)dims[2]; b2++) {
-
-                for(int col=0; col<(int)dims[1]; col++) {
-
-                    int ocol_off = col*ostrides[1];
-
-                    for(int row=0; row<(int)dims[0]; row++) {
-
-                        wind_vals.clear();
-
-                        for(int wj=0; wj<(int)w_wid; ++wj) {
-
-                            bool isColOff = false;
-
-                            int im_col = col + wj-w_wid/2;
-                            int im_coff;
-                            switch(pad) {
-                                case AF_PAD_ZERO:
-                                    im_coff = im_col * istrides[1];
-                                    if (im_col < 0 || im_col>=(int)dims[1])
-                                        isColOff = true;
-                                    break;
-                                case AF_PAD_SYM:
-                                    {
-                                        if (im_col < 0) {
-                                            im_col *= -1;
-                                            isColOff = true;
-                                        }
-
-                                        if (im_col>=(int)dims[1]) {
-                                            im_col = 2*((int)dims[1]-1) - im_col;
-                                            isColOff = true;
-                                        }
-
-                                        im_coff = im_col * istrides[1];
-                                    }
-                                    break;
-                            }
-
-                            for(int wi=0; wi<(int)w_len; ++wi) {
-
-                                bool isRowOff = false;
-
-                                int im_row = row + wi-w_len/2;
-                                int im_roff;
-                                switch(pad) {
-                                    case AF_PAD_ZERO:
-                                        im_roff = im_row * istrides[0];
-                                        if (im_row < 0 || im_row>=(int)dims[0])
-                                            isRowOff = true;
-                                        break;
-                                    case AF_PAD_SYM:
-                                        {
-                                            if (im_row < 0) {
-                                                im_row *= -1;
-                                                isRowOff = true;
-                                            }
-
-                                            if (im_row>=(int)dims[0]) {
-                                                im_row = 2*((int)dims[0]-1) - im_row;
-                                                isRowOff = true;
-                                            }
-
-                                            im_roff = im_row * istrides[0];
-                                        }
-                                        break;
-                                }
-
-                                if(isRowOff || isColOff) {
-                                    switch(pad) {
-                                        case AF_PAD_ZERO:
-                                            wind_vals.push_back(0);
-                                            break;
-                                        case AF_PAD_SYM:
-                                            wind_vals.push_back(in_ptr[im_coff+im_roff]);
-                                            break;
-                                    }
-                                } else
-                                    wind_vals.push_back(in_ptr[im_coff+im_roff]);
-                            }
-                        }
-
-                        std::stable_sort(wind_vals.begin(),wind_vals.end());
-                        int off = wind_vals.size()/2;
-                        if (wind_vals.size()%2==0)
-                            out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
-                        else {
-                            out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
-                        }
-                    }
-                }
-                in_ptr  += istrides[2];
-                out_ptr += ostrides[2];
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in, w_len, w_wid);
+    getQueue().enqueue(kernel::medfilt<T, pad>, out, in, w_len, w_wid);
 
     return out;
 }
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 945c32b310..462319d0af 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -15,21 +15,13 @@
 #include <algorithm>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/morph.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-static inline unsigned getIdx(const dim4 &strides,
-        int i, int j = 0, int k = 0, int l = 0)
-{
-    return (l * strides[3] +
-            k * strides[2] +
-            j * strides[1] +
-            i * strides[0]);
-}
-
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask)
 {
@@ -38,60 +30,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    auto func = [=] (Array<T> out, const Array<T> in, const Array<T> mask) {
-        const dim4 ostrides = out.strides();
-        const dim4 istrides = in.strides();
-        const dim4 fstrides = mask.strides();
-        const dim4 dims     = in.dims();
-        const dim4 window   = mask.dims();
-        T* outData          = out.get();
-        const T*   inData   = in.get();
-        const T*   filter   = mask.get();
-        const dim_t R0      = window[0]/2;
-        const dim_t R1      = window[1]/2;
-
-        for(dim_t b3=0; b3<dims[3]; ++b3) {
-            for(dim_t b2=0; b2<dims[2]; ++b2) {
-                // either channels or batch is handled by outer most loop
-                for(dim_t j=0; j<dims[1]; ++j) {
-                    // j steps along 2nd dimension
-                    for(dim_t i=0; i<dims[0]; ++i) {
-                        // i steps along 1st dimension
-                        T filterResult = inData[ getIdx(istrides, i, j) ];
-
-                        // wj,wi steps along 2nd & 1st dimensions of filter window respectively
-                        for(dim_t wj=0; wj<window[1]; wj++) {
-                            for(dim_t wi=0; wi<window[0]; wi++) {
-
-                                dim_t offj = j+wj-R1;
-                                dim_t offi = i+wi-R0;
-
-                                T maskValue = filter[ getIdx(fstrides, wi, wj) ];
-
-                                if ((maskValue > (T)0) && offi>=0 && offj>=0 && offi<dims[0] && offj<dims[1]) {
-
-                                    T inValue   = inData[ getIdx(istrides, offi, offj) ];
-
-                                    if (isDilation)
-                                        filterResult = std::max(filterResult, inValue);
-                                    else
-                                        filterResult = std::min(filterResult, inValue);
-                                }
-
-                            } // window 1st dimension loop ends here
-                        } // filter window loop ends here
-
-                        outData[ getIdx(ostrides, i, j) ] = filterResult;
-                    } //1st dimension loop ends here
-                } // 2nd dimension loop ends here
-
-                // next iteration will be next batch if any
-                outData += ostrides[2];
-                inData  += istrides[2];
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in, mask);
+    getQueue().enqueue(kernel::morph<T, isDilation>, out, in, mask);
 
     return out;
 }
@@ -104,66 +43,7 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    auto func = [=] (Array<T> out, const Array<T> in, const Array<T> mask) {
-        const dim4 dims     = in.dims();
-        const dim4 window   = mask.dims();
-        const dim_t R0      = window[0]/2;
-        const dim_t R1      = window[1]/2;
-        const dim_t R2      = window[2]/2;
-        const dim4 istrides = in.strides();
-        const dim4 fstrides = mask.strides();
-        const dim_t bCount  = dims[3];
-        const dim4 ostrides = out.strides();
-        T* outData          = out.get();
-        const T*   inData   = in.get();
-        const T*   filter   = mask.get();
-
-        for(dim_t batchId=0; batchId<bCount; ++batchId) {
-            // either channels or batch is handled by outer most loop
-            for(dim_t k=0; k<dims[2]; ++k) {
-                // k steps along 3rd dimension
-                for(dim_t j=0; j<dims[1]; ++j) {
-                    // j steps along 2nd dimension
-                    for(dim_t i=0; i<dims[0]; ++i) {
-                        // i steps along 1st dimension
-                        T filterResult = inData[ getIdx(istrides, i, j, k) ];
-
-                        // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
-                        for(dim_t wk=0; wk<window[2]; wk++) {
-                            for(dim_t wj=0; wj<window[1]; wj++) {
-                                for(dim_t wi=0; wi<window[0]; wi++) {
-
-                                    dim_t offk = k+wk-R2;
-                                    dim_t offj = j+wj-R1;
-                                    dim_t offi = i+wi-R0;
-
-                                    T maskValue = filter[ getIdx(fstrides, wi, wj, wk) ];
-
-                                    if ((maskValue > (T)0) && offi>=0 && offj>=0 && offk>=0 &&
-                                            offi<dims[0] && offj<dims[1] && offk<dims[2]) {
-
-                                        T inValue   = inData[ getIdx(istrides, offi, offj, offk) ];
-
-                                        if (isDilation)
-                                            filterResult = std::max(filterResult, inValue);
-                                        else
-                                            filterResult = std::min(filterResult, inValue);
-                                    }
-
-                                } // window 1st dimension loop ends here
-                            }  // window 1st dimension loop ends here
-                        }// filter window loop ends here
-
-                        outData[ getIdx(ostrides, i, j, k) ] = filterResult;
-                    } //1st dimension loop ends here
-                } // 2nd dimension loop ends here
-            } // 3rd dimension loop ends here
-            // next iteration will be next batch if any
-            outData += ostrides[3];
-            inData  += istrides[3];
-        }
-    };
-    getQueue().enqueue(func, out, in, mask);
+    getQueue().enqueue(kernel::morph3d<T, isDilation>, out, in, mask);
 
     return out;
 }
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index ed8bbd79f7..68cef5a440 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -31,9 +31,9 @@ dim_t trimIndex(int const & idx, dim_t const & len)
 }
 
 static inline
-dim_t clamp(int a, dim_t mn, dim_t mx)
+dim_t clamp(dim_t a, dim_t mn, dim_t mx)
 {
-    return (a < (int)mn ? mn : (a > (int)mx ? mx : a));
+    return (a<mn ? mn : (a>mx ? mx : a));
 }
 
 static inline

From 7d7f32ffd165f952e85cfe8d711ba147afbbe65d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 19 Dec 2015 16:04:37 -0500
Subject: [PATCH 0162/2677] moved the left over fns to cpu kernel namespace

---
 src/backend/cpu/kernel/nearest_neighbour.hpp  | 143 +++++
 src/backend/cpu/kernel/orb.hpp                | 509 +++++++++++++++++
 src/backend/cpu/kernel/random.hpp             | 143 +++++
 src/backend/cpu/kernel/range.hpp              |  52 ++
 src/backend/cpu/kernel/reduce.hpp             |  71 +++
 src/backend/cpu/kernel/regions.hpp            | 194 +++++++
 src/backend/cpu/kernel/reorder.hpp            |  55 ++
 src/backend/cpu/kernel/resize.hpp             | 177 ++++++
 src/backend/cpu/kernel/rotate.hpp             |  83 +++
 src/backend/cpu/kernel/scan.hpp               |  72 +++
 src/backend/cpu/kernel/select.hpp             | 124 +++++
 src/backend/cpu/kernel/shift.hpp              |  69 +++
 src/backend/cpu/{ => kernel}/sift_nonfree.hpp |   0
 src/backend/cpu/kernel/sobel.hpp              |  86 +++
 src/backend/cpu/kernel/sort.hpp               |  51 ++
 src/backend/cpu/kernel/sort_by_key.hpp        |  85 +++
 src/backend/cpu/kernel/sort_index.hpp         |  70 +++
 src/backend/cpu/kernel/susan.hpp              |  99 ++++
 src/backend/cpu/kernel/tile.hpp               |  55 ++
 src/backend/cpu/kernel/transform.hpp          | 105 ++++
 src/backend/cpu/kernel/transpose.hpp          | 122 ++++
 src/backend/cpu/kernel/triangle.hpp           |  61 ++
 src/backend/cpu/kernel/unwrap.hpp             |  81 +++
 src/backend/cpu/kernel/wrap.hpp               |  80 +++
 src/backend/cpu/nearest_neighbour.cpp         | 131 +----
 src/backend/cpu/orb.cpp                       | 520 +-----------------
 src/backend/cpu/random.cpp                    | 176 +-----
 src/backend/cpu/range.cpp                     |  46 +-
 src/backend/cpu/reduce.cpp                    |  60 +-
 src/backend/cpu/regions.cpp                   | 175 +-----
 src/backend/cpu/reorder.cpp                   |  39 +-
 src/backend/cpu/resize.cpp                    | 166 +-----
 src/backend/cpu/rotate.cpp                    |  71 +--
 src/backend/cpu/scan.cpp                      |  61 +-
 src/backend/cpu/select.cpp                    | 103 +---
 src/backend/cpu/shift.cpp                     |  52 +-
 src/backend/cpu/sift.cpp                      |   2 +-
 src/backend/cpu/sobel.cpp                     |  71 +--
 src/backend/cpu/sort.cpp                      |  44 +-
 src/backend/cpu/sort_by_key.cpp               |  83 +--
 src/backend/cpu/sort_index.cpp                |  61 +-
 src/backend/cpu/susan.cpp                     |  84 +--
 src/backend/cpu/tile.cpp                      |  38 +-
 src/backend/cpu/transform.cpp                 |  93 +---
 src/backend/cpu/transform_interp.hpp          |   2 +
 src/backend/cpu/transpose.cpp                 | 115 +---
 src/backend/cpu/triangle.cpp                  |  42 +-
 src/backend/cpu/unwrap.cpp                    |  67 +--
 src/backend/cpu/wrap.cpp                      |  66 +--
 49 files changed, 2691 insertions(+), 2264 deletions(-)
 create mode 100644 src/backend/cpu/kernel/nearest_neighbour.hpp
 create mode 100644 src/backend/cpu/kernel/orb.hpp
 create mode 100644 src/backend/cpu/kernel/random.hpp
 create mode 100644 src/backend/cpu/kernel/range.hpp
 create mode 100644 src/backend/cpu/kernel/reduce.hpp
 create mode 100644 src/backend/cpu/kernel/regions.hpp
 create mode 100644 src/backend/cpu/kernel/reorder.hpp
 create mode 100644 src/backend/cpu/kernel/resize.hpp
 create mode 100644 src/backend/cpu/kernel/rotate.hpp
 create mode 100644 src/backend/cpu/kernel/scan.hpp
 create mode 100644 src/backend/cpu/kernel/select.hpp
 create mode 100644 src/backend/cpu/kernel/shift.hpp
 rename src/backend/cpu/{ => kernel}/sift_nonfree.hpp (100%)
 create mode 100644 src/backend/cpu/kernel/sobel.hpp
 create mode 100644 src/backend/cpu/kernel/sort.hpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key.hpp
 create mode 100644 src/backend/cpu/kernel/sort_index.hpp
 create mode 100644 src/backend/cpu/kernel/susan.hpp
 create mode 100644 src/backend/cpu/kernel/tile.hpp
 create mode 100644 src/backend/cpu/kernel/transform.hpp
 create mode 100644 src/backend/cpu/kernel/transpose.hpp
 create mode 100644 src/backend/cpu/kernel/triangle.hpp
 create mode 100644 src/backend/cpu/kernel/unwrap.hpp
 create mode 100644 src/backend/cpu/kernel/wrap.hpp

diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
new file mode 100644
index 0000000000..4916463aed
--- /dev/null
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -0,0 +1,143 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+#if defined(_WIN32) || defined(_MSC_VER)
+
+#include <intrin.h>
+#define __builtin_popcount __popcnt
+
+#endif
+
+template<typename T, typename To, af_match_type dist_type>
+struct dist_op
+{
+    To operator()(T v1, T v2)
+    {
+        return v1 - v2;     // Garbage distance
+    }
+};
+
+template<typename T, typename To>
+struct dist_op<T, To, AF_SAD>
+{
+    To operator()(T v1, T v2)
+    {
+        return std::abs((double)v1 - (double)v2);
+    }
+};
+
+template<typename T, typename To>
+struct dist_op<T, To, AF_SSD>
+{
+    To operator()(T v1, T v2)
+    {
+        return (v1 - v2) * (v1 - v2);
+    }
+};
+
+template<typename To>
+struct dist_op<uint, To, AF_SHD>
+{
+    To operator()(uint v1, uint v2)
+    {
+        return __builtin_popcount(v1 ^ v2);
+    }
+};
+
+template<typename To>
+struct dist_op<uintl, To, AF_SHD>
+{
+    To operator()(uintl v1, uintl v2)
+    {
+        return __builtin_popcount(v1 ^ v2);
+    }
+};
+
+template<typename To>
+struct dist_op<uchar, To, AF_SHD>
+{
+    To operator()(uchar v1, uchar v2)
+    {
+        return __builtin_popcount(v1 ^ v2);
+    }
+};
+
+template<typename To>
+struct dist_op<ushort, To, AF_SHD>
+{
+    To operator()(ushort v1, ushort v2)
+    {
+        return __builtin_popcount(v1 ^ v2);
+    }
+};
+
+template<typename T, typename To, af_match_type dist_type>
+void nearest_neighbour(Array<uint> idx, Array<To> dist,
+                       const Array<T> query, const Array<T> train,
+                       const uint dist_dim, const uint n_dist)
+{
+    uint sample_dim = (dist_dim == 0) ? 1 : 0;
+    const dim4 qDims = query.dims();
+    const dim4 tDims = train.dims();
+
+    const unsigned distLength = qDims[dist_dim];
+    const unsigned nQuery = qDims[sample_dim];
+    const unsigned nTrain = tDims[sample_dim];
+
+    const T* qPtr = query.get();
+    const T* tPtr = train.get();
+    uint* iPtr = idx.get();
+    To* dPtr = dist.get();
+
+    dist_op<T, To, dist_type> op;
+
+    for (unsigned i = 0; i < nQuery; i++) {
+        To best_dist = limit_max<To>();
+        unsigned best_idx  = 0;
+
+        for (unsigned j = 0; j < nTrain; j++) {
+            To local_dist = 0;
+            for (unsigned k = 0; k < distLength; k++) {
+                size_t qIdx, tIdx;
+                if (sample_dim == 0) {
+                    qIdx = k * qDims[0] + i;
+                    tIdx = k * tDims[0] + j;
+                }
+                else {
+                    qIdx = i * qDims[0] + k;
+                    tIdx = j * tDims[0] + k;
+                }
+
+                local_dist += op(qPtr[qIdx], tPtr[tIdx]);
+            }
+
+            if (local_dist < best_dist) {
+                best_dist = local_dist;
+                best_idx  = j;
+            }
+        }
+
+        size_t oIdx;
+        oIdx = i;
+        iPtr[oIdx] = best_idx;
+        dPtr[oIdx] = best_dist;
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/orb.hpp b/src/backend/cpu/kernel/orb.hpp
new file mode 100644
index 0000000000..acd508cb70
--- /dev/null
+++ b/src/backend/cpu/kernel/orb.hpp
@@ -0,0 +1,509 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+// Reference pattern, generated for a patch size of 31x31, as suggested by
+// original ORB paper
+#define REF_PAT_SIZE 31
+#define REF_PAT_SAMPLES 256
+#define REF_PAT_COORDS 4
+#define REF_PAT_LENGTH (REF_PAT_SAMPLES*REF_PAT_COORDS)
+
+// Current reference pattern was borrowed from OpenCV, to build a pattern with
+// similar quality, a training process must be applied, as described in
+// sections 4.2 and 4.3 of the original ORB paper.
+const int ref_pat[REF_PAT_LENGTH] = {
+    8,-3, 9,5,
+    4,2, 7,-12,
+    -11,9, -8,2,
+    7,-12, 12,-13,
+    2,-13, 2,12,
+    1,-7, 1,6,
+    -2,-10, -2,-4,
+    -13,-13, -11,-8,
+    -13,-3, -12,-9,
+    10,4, 11,9,
+    -13,-8, -8,-9,
+    -11,7, -9,12,
+    7,7, 12,6,
+    -4,-5, -3,0,
+    -13,2, -12,-3,
+    -9,0, -7,5,
+    12,-6, 12,-1,
+    -3,6, -2,12,
+    -6,-13, -4,-8,
+    11,-13, 12,-8,
+    4,7, 5,1,
+    5,-3, 10,-3,
+    3,-7, 6,12,
+    -8,-7, -6,-2,
+    -2,11, -1,-10,
+    -13,12, -8,10,
+    -7,3, -5,-3,
+    -4,2, -3,7,
+    -10,-12, -6,11,
+    5,-12, 6,-7,
+    5,-6, 7,-1,
+    1,0, 4,-5,
+    9,11, 11,-13,
+    4,7, 4,12,
+    2,-1, 4,4,
+    -4,-12, -2,7,
+    -8,-5, -7,-10,
+    4,11, 9,12,
+    0,-8, 1,-13,
+    -13,-2, -8,2,
+    -3,-2, -2,3,
+    -6,9, -4,-9,
+    8,12, 10,7,
+    0,9, 1,3,
+    7,-5, 11,-10,
+    -13,-6, -11,0,
+    10,7, 12,1,
+    -6,-3, -6,12,
+    10,-9, 12,-4,
+    -13,8, -8,-12,
+    -13,0, -8,-4,
+    3,3, 7,8,
+    5,7, 10,-7,
+    -1,7, 1,-12,
+    3,-10, 5,6,
+    2,-4, 3,-10,
+    -13,0, -13,5,
+    -13,-7, -12,12,
+    -13,3, -11,8,
+    -7,12, -4,7,
+    6,-10, 12,8,
+    -9,-1, -7,-6,
+    -2,-5, 0,12,
+    -12,5, -7,5,
+    3,-10, 8,-13,
+    -7,-7, -4,5,
+    -3,-2, -1,-7,
+    2,9, 5,-11,
+    -11,-13, -5,-13,
+    -1,6, 0,-1,
+    5,-3, 5,2,
+    -4,-13, -4,12,
+    -9,-6, -9,6,
+    -12,-10, -8,-4,
+    10,2, 12,-3,
+    7,12, 12,12,
+    -7,-13, -6,5,
+    -4,9, -3,4,
+    7,-1, 12,2,
+    -7,6, -5,1,
+    -13,11, -12,5,
+    -3,7, -2,-6,
+    7,-8, 12,-7,
+    -13,-7, -11,-12,
+    1,-3, 12,12,
+    2,-6, 3,0,
+    -4,3, -2,-13,
+    -1,-13, 1,9,
+    7,1, 8,-6,
+    1,-1, 3,12,
+    9,1, 12,6,
+    -1,-9, -1,3,
+    -13,-13, -10,5,
+    7,7, 10,12,
+    12,-5, 12,9,
+    6,3, 7,11,
+    5,-13, 6,10,
+    2,-12, 2,3,
+    3,8, 4,-6,
+    2,6, 12,-13,
+    9,-12, 10,3,
+    -8,4, -7,9,
+    -11,12, -4,-6,
+    1,12, 2,-8,
+    6,-9, 7,-4,
+    2,3, 3,-2,
+    6,3, 11,0,
+    3,-3, 8,-8,
+    7,8, 9,3,
+    -11,-5, -6,-4,
+    -10,11, -5,10,
+    -5,-8, -3,12,
+    -10,5, -9,0,
+    8,-1, 12,-6,
+    4,-6, 6,-11,
+    -10,12, -8,7,
+    4,-2, 6,7,
+    -2,0, -2,12,
+    -5,-8, -5,2,
+    7,-6, 10,12,
+    -9,-13, -8,-8,
+    -5,-13, -5,-2,
+    8,-8, 9,-13,
+    -9,-11, -9,0,
+    1,-8, 1,-2,
+    7,-4, 9,1,
+    -2,1, -1,-4,
+    11,-6, 12,-11,
+    -12,-9, -6,4,
+    3,7, 7,12,
+    5,5, 10,8,
+    0,-4, 2,8,
+    -9,12, -5,-13,
+    0,7, 2,12,
+    -1,2, 1,7,
+    5,11, 7,-9,
+    3,5, 6,-8,
+    -13,-4, -8,9,
+    -5,9, -3,-3,
+    -4,-7, -3,-12,
+    6,5, 8,0,
+    -7,6, -6,12,
+    -13,6, -5,-2,
+    1,-10, 3,10,
+    4,1, 8,-4,
+    -2,-2, 2,-13,
+    2,-12, 12,12,
+    -2,-13, 0,-6,
+    4,1, 9,3,
+    -6,-10, -3,-5,
+    -3,-13, -1,1,
+    7,5, 12,-11,
+    4,-2, 5,-7,
+    -13,9, -9,-5,
+    7,1, 8,6,
+    7,-8, 7,6,
+    -7,-4, -7,1,
+    -8,11, -7,-8,
+    -13,6, -12,-8,
+    2,4, 3,9,
+    10,-5, 12,3,
+    -6,-5, -6,7,
+    8,-3, 9,-8,
+    2,-12, 2,8,
+    -11,-2, -10,3,
+    -12,-13, -7,-9,
+    -11,0, -10,-5,
+    5,-3, 11,8,
+    -2,-13, -1,12,
+    -1,-8, 0,9,
+    -13,-11, -12,-5,
+    -10,-2, -10,11,
+    -3,9, -2,-13,
+    2,-3, 3,2,
+    -9,-13, -4,0,
+    -4,6, -3,-10,
+    -4,12, -2,-7,
+    -6,-11, -4,9,
+    6,-3, 6,11,
+    -13,11, -5,5,
+    11,11, 12,6,
+    7,-5, 12,-2,
+    -1,12, 0,7,
+    -4,-8, -3,-2,
+    -7,1, -6,7,
+    -13,-12, -8,-13,
+    -7,-2, -6,-8,
+    -8,5, -6,-9,
+    -5,-1, -4,5,
+    -13,7, -8,10,
+    1,5, 5,-13,
+    1,0, 10,-13,
+    9,12, 10,-1,
+    5,-8, 10,-9,
+    -1,11, 1,-13,
+    -9,-3, -6,2,
+    -1,-10, 1,12,
+    -13,1, -8,-10,
+    8,-11, 10,-6,
+    2,-13, 3,-6,
+    7,-13, 12,-9,
+    -10,-10, -5,-7,
+    -10,-8, -8,-13,
+    4,-6, 8,5,
+    3,12, 8,-13,
+    -4,2, -3,-3,
+    5,-13, 10,-12,
+    4,-13, 5,-1,
+    -9,9, -4,3,
+    0,3, 3,-9,
+    -12,1, -6,1,
+    3,2, 4,-8,
+    -10,-10, -10,9,
+    8,-13, 12,12,
+    -8,-12, -6,-5,
+    2,2, 3,7,
+    10,6, 11,-8,
+    6,8, 8,-12,
+    -7,10, -6,5,
+    -3,-9, -3,9,
+    -1,-13, -1,5,
+    -3,-7, -3,4,
+    -8,-2, -8,3,
+    4,2, 12,12,
+    2,-5, 3,11,
+    6,-9, 11,-13,
+    3,-1, 7,12,
+    11,-1, 12,4,
+    -3,0, -3,6,
+    4,-11, 4,12,
+    2,-4, 2,1,
+    -10,-6, -8,1,
+    -13,7, -11,1,
+    -13,12, -11,-13,
+    6,0, 11,-13,
+    0,-1, 1,4,
+    -13,3, -9,-2,
+    -9,8, -6,-3,
+    -13,-6, -8,-2,
+    5,-9, 8,10,
+    2,7, 3,-9,
+    -1,-6, -1,-1,
+    9,5, 11,-2,
+    11,-3, 12,-8,
+    3,0, 3,5,
+    -1,4, 0,10,
+    3,-6, 4,5,
+    -13,0, -10,5,
+    5,8, 12,11,
+    8,9, 9,-6,
+    7,-4, 8,-12,
+    -10,4, -10,9,
+    7,3, 12,4,
+    9,-7, 10,-2,
+    7,0, 12,-2,
+    -1,-6, 0,-11,
+};
+
+template<typename T>
+void keep_features(
+    float* x_out,
+    float* y_out,
+    float* score_out,
+    float* size_out,
+    const float* x_in,
+    const float* y_in,
+    const float* score_in,
+    const unsigned* score_idx,
+    const float* size_in,
+    const unsigned n_feat)
+{
+    // Keep only the first n_feat features
+    for (unsigned f = 0; f < n_feat; f++) {
+        x_out[f] = x_in[score_idx[f]];
+        y_out[f] = y_in[score_idx[f]];
+        score_out[f] = score_in[f];
+        if (size_in != nullptr && size_out != nullptr)
+            size_out[f] = size_in[score_idx[f]];
+    }
+}
+
+template<typename T, bool use_scl>
+void harris_response(
+    float* x_out,
+    float* y_out,
+    float* score_out,
+    float* size_out,
+    const float* x_in,
+    const float* y_in,
+    const float* scl_in,
+    const unsigned total_feat,
+    unsigned* usable_feat,
+    const Array<T>& image,
+    const unsigned block_size,
+    const float k_thr,
+    const unsigned patch_size)
+{
+    const af::dim4 idims = image.dims();
+    const T* image_ptr = image.get();
+    for (unsigned f = 0; f < total_feat; f++) {
+        unsigned x, y;
+        float scl = 1.f;
+        if (use_scl) {
+            // Update x and y coordinates according to scale
+            scl = scl_in[f];
+            x = (unsigned)round(x_in[f] * scl);
+            y = (unsigned)round(y_in[f] * scl);
+        }
+        else {
+            x = (unsigned)round(x_in[f]);
+            y = (unsigned)round(y_in[f]);
+        }
+
+        // Round feature size to nearest odd integer
+        float size = 2.f * floor((patch_size * scl) / 2.f) + 1.f;
+
+        // Avoid keeping features that might be too wide and might not fit on
+        // the image, sqrt(2.f) is the radius when angle is 45 degrees and
+        // represents widest case possible
+        unsigned patch_r = ceil(size * sqrt(2.f) / 2.f);
+        if (x < patch_r || y < patch_r || x >= idims[1] - patch_r || y >= idims[0] - patch_r)
+            continue;
+
+        unsigned r = block_size / 2;
+
+        float ixx = 0.f, iyy = 0.f, ixy = 0.f;
+        unsigned block_size_sq = block_size * block_size;
+        for (unsigned k = 0; k < block_size_sq; k++) {
+            int i = k / block_size - r;
+            int j = k % block_size - r;
+
+            // Calculate local x and y derivatives
+            float ix = image_ptr[(x+i+1) * idims[0] + y+j] - image_ptr[(x+i-1) * idims[0] + y+j];
+            float iy = image_ptr[(x+i) * idims[0] + y+j+1] - image_ptr[(x+i) * idims[0] + y+j-1];
+
+            // Accumulate second order derivatives
+            ixx += ix*ix;
+            iyy += iy*iy;
+            ixy += ix*iy;
+        }
+
+        unsigned idx = *usable_feat;
+        *usable_feat += 1;
+        float tr = ixx + iyy;
+        float det = ixx*iyy - ixy*ixy;
+
+        // Calculate Harris responses
+        float resp = det - k_thr * (tr*tr);
+
+        // Scale factor
+        // TODO: improve response scaling
+        float rscale = 0.001f;
+        rscale = rscale * rscale * rscale * rscale;
+
+        x_out[idx] = x;
+        y_out[idx] = y;
+        score_out[idx] = resp * rscale;
+        if (use_scl)
+            size_out[idx] = size;
+    }
+}
+
+template<typename T>
+void centroid_angle(
+    const float* x_in,
+    const float* y_in,
+    float* orientation_out,
+    const unsigned total_feat,
+    const Array<T>& image,
+    const unsigned patch_size)
+{
+    const af::dim4 idims = image.dims();
+    const T* image_ptr = image.get();
+    for (unsigned f = 0; f < total_feat; f++) {
+        unsigned x = (unsigned)round(x_in[f]);
+        unsigned y = (unsigned)round(y_in[f]);
+
+        unsigned r = patch_size / 2;
+        if (x < r || y < r || x > idims[1] - r || y > idims[0] - r)
+            continue;
+
+        T m01 = (T)0, m10 = (T)0;
+        unsigned patch_size_sq = patch_size * patch_size;
+        for (unsigned k = 0; k < patch_size_sq; k++) {
+            int i = k / patch_size - r;
+            int j = k % patch_size - r;
+
+            // Calculate first order moments
+            T p = image_ptr[(x+i) * idims[0] + y+j];
+            m01 += j * p;
+            m10 += i * p;
+        }
+
+        float angle = atan2(m01, m10);
+        orientation_out[f] = angle;
+    }
+}
+
+template<typename T>
+inline T get_pixel(
+    unsigned x,
+    unsigned y,
+    const float ori,
+    const unsigned size,
+    const int dist_x,
+    const int dist_y,
+    const Array<T>& image,
+    const unsigned patch_size)
+{
+    const af::dim4 idims = image.dims();
+    const T* image_ptr = image.get();
+    float ori_sin = sin(ori);
+    float ori_cos = cos(ori);
+    float patch_scl = (float)size / (float)patch_size;
+
+    // Calculate point coordinates based on orientation and size
+    x += round(dist_x * patch_scl * ori_cos - dist_y * patch_scl * ori_sin);
+    y += round(dist_x * patch_scl * ori_sin + dist_y * patch_scl * ori_cos);
+
+    return image_ptr[x * idims[0] + y];
+}
+
+template<typename T>
+void extract_orb(
+    unsigned* desc_out,
+    const unsigned n_feat,
+    float* x_in_out,
+    float* y_in_out,
+    const float* ori_in,
+    float* size_out,
+    const Array<T>& image,
+    const float scl,
+    const unsigned patch_size)
+{
+    const af::dim4 idims = image.dims();
+    for (unsigned f = 0; f < n_feat; f++) {
+        unsigned x = (unsigned)round(x_in_out[f]);
+        unsigned y = (unsigned)round(y_in_out[f]);
+        float ori = ori_in[f];
+        unsigned size = patch_size;
+
+        unsigned r = ceil(patch_size * sqrt(2.f) / 2.f);
+        if (x < r || y < r || x >= idims[1] - r || y >= idims[0] - r)
+            continue;
+
+        // Descriptor fixed at 256 bits for now
+        // Storing descriptor as a vector of 8 x 32-bit unsigned numbers
+        for (unsigned i = 0; i < 8; i++) {
+            unsigned v = 0;
+
+            // j < 32 for 256 bits descriptor
+            for (unsigned j = 0; j < 32; j++) {
+                // Get position from distribution pattern and values of points p1 and p2
+                int dist_x = ref_pat[i*32*4 + j*4];
+                int dist_y = ref_pat[i*32*4 + j*4+1];
+                T p1 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
+
+                dist_x = ref_pat[i*32*4 + j*4+2];
+                dist_y = ref_pat[i*32*4 + j*4+3];
+                T p2 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
+
+                // Calculate bit based on p1 and p2 and shifts it to correct position
+                v |= (p1 < p2) << j;
+            }
+
+            // Store 32 bits of descriptor
+            desc_out[f * 8 + i] += v;
+        }
+
+        x_in_out[f] = round(x * scl);
+        y_in_out[f] = round(y * scl);
+        size_out[f] = patch_size * scl;
+    }
+}
+
+
+
+}
+}
diff --git a/src/backend/cpu/kernel/random.hpp b/src/backend/cpu/kernel/random.hpp
new file mode 100644
index 0000000000..357cbd210d
--- /dev/null
+++ b/src/backend/cpu/kernel/random.hpp
@@ -0,0 +1,143 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <type_traits>
+#include <random>
+#include <algorithm>
+#include <functional>
+#include <limits>
+#include <type_traits>
+
+namespace cpu
+{
+namespace kernel
+{
+
+using namespace std;
+
+template<typename T>
+using is_arithmetic_t       = typename enable_if< is_arithmetic<T>::value,      function<T()>>::type;
+template<typename T>
+using is_complex_t          = typename enable_if< is_complex<T>::value,         function<T()>>::type;
+template<typename T>
+using is_floating_point_t   = typename enable_if< is_floating_point<T>::value,  function<T()>>::type;
+
+template<typename T, typename GenType>
+is_arithmetic_t<T>
+urand(GenType &generator)
+{
+    typedef typename conditional<   is_floating_point<T>::value,
+                                    uniform_real_distribution<T>,
+#if OS_WIN
+                                    uniform_int_distribution<unsigned>>::type dist;
+#else
+                                    uniform_int_distribution<T >> ::type dist;
+#endif
+    return bind(dist(), generator);
+}
+
+template<typename T, typename GenType>
+is_complex_t<T>
+urand(GenType &generator)
+{
+    auto func = urand<typename T::value_type>(generator);
+    return [func] () { return T(func(), func());};
+}
+
+template<typename T, typename GenType>
+is_floating_point_t<T>
+nrand(GenType &generator)
+{
+    return bind(normal_distribution<T>(), generator);
+}
+
+template<typename T, typename GenType>
+is_complex_t<T>
+nrand(GenType &generator)
+{
+    auto func = nrand<typename T::value_type>(generator);
+    return [func] () { return T(func(), func());};
+}
+
+static default_random_engine generator;
+static unsigned long long gen_seed = 0;
+static bool is_first = true;
+#define GLOBAL 1
+
+template<typename T>
+void randn(Array<T> out)
+{
+    static unsigned long long my_seed = 0;
+    if (is_first) {
+        setSeed(gen_seed);
+        my_seed = gen_seed;
+    }
+
+    static auto gen = nrand<T>(generator);
+
+    if (my_seed != gen_seed) {
+        gen = nrand<T>(generator);
+        my_seed = gen_seed;
+    }
+
+    T *outPtr = out.get();
+    for (int i = 0; i < (int)out.elements(); i++) {
+        outPtr[i] = gen();
+    }
+}
+
+template<typename T>
+void randu(Array<T> out)
+{
+    static unsigned long long my_seed = 0;
+    if (is_first) {
+        setSeed(gen_seed);
+        my_seed = gen_seed;
+    }
+
+    static auto gen = urand<T>(generator);
+
+    if (my_seed != gen_seed) {
+        gen = urand<T>(generator);
+        my_seed = gen_seed;
+    }
+
+    T *outPtr = out.get();
+    for (int i = 0; i < (int)out.elements(); i++) {
+        outPtr[i] = gen();
+    }
+}
+
+template<>
+void randu(Array<char> out)
+{
+    static unsigned long long my_seed = 0;
+    if (is_first) {
+        setSeed(gen_seed);
+        my_seed = gen_seed;
+    }
+
+    static auto gen = urand<float>(generator);
+
+    if (my_seed != gen_seed) {
+        gen = urand<float>(generator);
+        my_seed = gen_seed;
+    }
+
+    char *outPtr = out.get();
+    for (int i = 0; i < (int)out.elements(); i++) {
+        outPtr[i] = gen() > 0.5;
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/range.hpp b/src/backend/cpu/kernel/range.hpp
new file mode 100644
index 0000000000..b244a19c85
--- /dev/null
+++ b/src/backend/cpu/kernel/range.hpp
@@ -0,0 +1,52 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, int dim>
+void range(Array<T> output)
+{
+    T* out = output.get();
+
+    const dim4 dims = output.dims();
+    const dim4 strides = output.strides();
+
+    for(dim_t w = 0; w < dims[3]; w++) {
+        dim_t offW = w * strides[3];
+        for(dim_t z = 0; z < dims[2]; z++) {
+            dim_t offWZ = offW + z * strides[2];
+            for(dim_t y = 0; y < dims[1]; y++) {
+                dim_t offWZY = offWZ + y * strides[1];
+                for(dim_t x = 0; x < dims[0]; x++) {
+                    dim_t id = offWZY + x;
+                    if(dim == 0) {
+                        out[id] = x;
+                    } else if(dim == 1) {
+                        out[id] = y;
+                    } else if(dim == 2) {
+                        out[id] = z;
+                    } else if(dim == 3) {
+                        out[id] = w;
+                    }
+                }
+            }
+        }
+    }
+}
+
+}
+}
+
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
new file mode 100644
index 0000000000..85119dcee7
--- /dev/null
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -0,0 +1,71 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<af_op_t op, typename Ti, typename To, int D>
+struct reduce_dim
+{
+    void operator()(Array<To> out, const dim_t outOffset,
+                    const Array<Ti> in, const dim_t inOffset,
+                    const int dim, bool change_nan, double nanval)
+    {
+        static const int D1 = D - 1;
+        static reduce_dim<op, Ti, To, D1> reduce_dim_next;
+
+        const af::dim4 ostrides = out.strides();
+        const af::dim4 istrides = in.strides();
+        const af::dim4 odims    = out.dims();
+
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            reduce_dim_next(out, outOffset + i * ostrides[D1],
+                            in, inOffset + i * istrides[D1],
+                            dim, change_nan, nanval);
+        }
+    }
+};
+
+template<af_op_t op, typename Ti, typename To>
+struct reduce_dim<op, Ti, To, 0>
+{
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> reduce;
+    void operator()(Array<To> out, const dim_t outOffset,
+                    const Array<Ti> in, const dim_t inOffset,
+                    const int dim, bool change_nan, double nanval)
+    {
+        const af::dim4 istrides = in.strides();
+        const af::dim4 idims    = in.dims();
+
+        To * const outPtr = out.get() + outOffset;
+        Ti const * const inPtr = in.get() + inOffset;
+        dim_t stride = istrides[dim];
+
+        To out_val = reduce.init();
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            To in_val = transform(inPtr[i * stride]);
+            if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
+            out_val = reduce(in_val, out_val);
+        }
+
+        *outPtr = out_val;
+    }
+};
+
+
+}
+}
diff --git a/src/backend/cpu/kernel/regions.hpp b/src/backend/cpu/kernel/regions.hpp
new file mode 100644
index 0000000000..863ebc5f48
--- /dev/null
+++ b/src/backend/cpu/kernel/regions.hpp
@@ -0,0 +1,194 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+class LabelNode
+{
+private:
+    T label;
+    T minLabel;
+    unsigned rank;
+    LabelNode* parent;
+
+public:
+    LabelNode() : label(0), minLabel(0), rank(0), parent(this) { }
+    LabelNode(T label) : label(label), minLabel(label), rank(0), parent(this) { }
+
+    T getLabel()
+    {
+        return label;
+    }
+
+    T getMinLabel()
+    {
+        return minLabel;
+    }
+
+    LabelNode* getParent()
+    {
+        return parent;
+    }
+
+    unsigned getRank()
+    {
+        return rank;
+    }
+
+    void setMinLabel(T l)
+    {
+        minLabel = l;
+    }
+
+    void setParent(LabelNode* p)
+    {
+        parent = p;
+    }
+
+    void setRank(unsigned r)
+    {
+        rank = r;
+    }
+};
+
+template<typename T>
+static LabelNode<T>* find(LabelNode<T>* x)
+{
+    if (x->getParent() != x)
+        x->setParent(find(x->getParent()));
+    return x->getParent();
+}
+
+template<typename T>
+static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
+{
+    LabelNode<T>* xRoot = find(x);
+    LabelNode<T>* yRoot = find(y);
+    if (xRoot == yRoot)
+        return;
+
+    T xMinLabel = xRoot->getMinLabel();
+    T yMinLabel = yRoot->getMinLabel();
+    xRoot->setMinLabel(min(xMinLabel, yMinLabel));
+    yRoot->setMinLabel(min(xMinLabel, yMinLabel));
+
+    if (xRoot->getRank() < yRoot->getRank())
+        xRoot->setParent(yRoot);
+    else if (xRoot->getRank() > yRoot->getRank())
+        yRoot->setParent(xRoot);
+    else {
+        yRoot->setParent(xRoot);
+        xRoot->setRank(xRoot->getRank() + 1);
+    }
+}
+
+template<typename T>
+void regions(Array<T> out, const Array<char> in, af_connectivity connectivity)
+{
+    const af::dim4 in_dims = in.dims();
+    const char *in_ptr  = in.get();
+    T    *out_ptr = out.get();
+
+    // Map labels
+    typedef typename std::map<T, LabelNode<T>* > label_map_t;
+    typedef typename label_map_t::iterator label_map_iterator_t;
+
+    label_map_t lmap;
+
+    // Initial label
+    T label = (T)1;
+
+    for (int j = 0; j < (int)in_dims[1]; j++) {
+        for (int i = 0; i < (int)in_dims[0]; i++) {
+            int idx = j * in_dims[0] + i;
+            if (in_ptr[idx] != 0) {
+                std::vector<T> l;
+
+                // Test neighbors
+                if (i > 0 && out_ptr[j * (int)in_dims[0] + i-1] > 0)
+                    l.push_back(out_ptr[j * in_dims[0] + i-1]);
+                if (j > 0 && out_ptr[(j-1) * (int)in_dims[0] + i] > 0)
+                    l.push_back(out_ptr[(j-1) * in_dims[0] + i]);
+                if (connectivity == AF_CONNECTIVITY_8 && i > 0 &&
+                        j > 0 && out_ptr[(j-1) * in_dims[0] + i-1] > 0)
+                    l.push_back(out_ptr[(j-1) * in_dims[0] + i-1]);
+                if (connectivity == AF_CONNECTIVITY_8 &&
+                        i < (int)in_dims[0] - 1 && j > 0 && out_ptr[(j-1) * in_dims[0] + i+1] != 0)
+                    l.push_back(out_ptr[(j-1) * in_dims[0] + i+1]);
+
+                if (!l.empty()) {
+                    T minl = l[0];
+                    for (size_t k = 0; k < l.size(); k++) {
+                        minl = min(l[k], minl);
+                        label_map_iterator_t cur_map = lmap.find(l[k]);
+                        LabelNode<T> *node = cur_map->second;
+                        // Group labels of the same region under a disjoint set
+                        for (size_t m = k+1; m < l.size(); m++)
+                            setUnion(node, lmap.find(l[m])->second);
+                    }
+                    // Set label to smallest neighbor label
+                    out_ptr[idx] = minl;
+                }
+                else {
+                    // Insert new label in map
+                    LabelNode<T> *node = new LabelNode<T>(label);
+                    lmap.insert(std::pair<T, LabelNode<T>* >(label, node));
+                    out_ptr[idx] = label++;
+                }
+            }
+        }
+    }
+
+    std::set<T> removed;
+
+    for (int j = 0; j < (int)in_dims[1]; j++) {
+        for (int i = 0; i < (int)in_dims[0]; i++) {
+            int idx = j * (int)in_dims[0] + i;
+            if (in_ptr[idx] != 0) {
+                T l = out_ptr[idx];
+                label_map_iterator_t cur_map = lmap.find(l);
+
+                if (cur_map != lmap.end()) {
+                    LabelNode<T>* node = cur_map->second;
+
+                    LabelNode<T>* node_root = find(node);
+                    out_ptr[idx] = node_root->getMinLabel();
+
+                    // Mark removed labels (those that are part of a region
+                    // that contains a smaller label)
+                    if (node->getMinLabel() < l || node_root->getMinLabel() < l)
+                        removed.insert(l);
+                    if (node->getLabel() > node->getMinLabel())
+                        removed.insert(node->getLabel());
+                }
+            }
+        }
+    }
+
+    // Calculate final neighbors (ensure final labels are sequential)
+    for (int j = 0; j < (int)in_dims[1]; j++) {
+        for (int i = 0; i < (int)in_dims[0]; i++) {
+            int idx = j * (int)in_dims[0] + i;
+            if (out_ptr[idx] > 0) {
+                out_ptr[idx] -= distance(removed.begin(), removed.lower_bound(out_ptr[idx]));
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/reorder.hpp b/src/backend/cpu/kernel/reorder.hpp
new file mode 100644
index 0000000000..c10c96ef36
--- /dev/null
+++ b/src/backend/cpu/kernel/reorder.hpp
@@ -0,0 +1,55 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void reorder(Array<T> out, const Array<T> in, const af::dim4 oDims, const af::dim4 rdims)
+{
+    T* outPtr = out.get();
+    const T* inPtr = in.get();
+
+    const af::dim4 ist = in.strides();
+    const af::dim4 ost = out.strides();
+
+
+    dim_t ids[4]  = {0};
+    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+        const dim_t oW = ow * ost[3];
+        ids[rdims[3]] = ow;
+        for(dim_t oz = 0; oz < oDims[2]; oz++) {
+            const dim_t oZW = oW + oz * ost[2];
+            ids[rdims[2]] = oz;
+            for(dim_t oy = 0; oy < oDims[1]; oy++) {
+                const dim_t oYZW = oZW + oy * ost[1];
+                ids[rdims[1]] = oy;
+                for(dim_t ox = 0; ox < oDims[0]; ox++) {
+                    const dim_t oIdx = oYZW + ox;
+
+                    ids[rdims[0]] = ox;
+                    const dim_t iIdx = ids[3] * ist[3] + ids[2] * ist[2] +
+                                          ids[1] * ist[1] + ids[0];
+
+                    outPtr[oIdx] = inPtr[iIdx];
+                }
+            }
+        }
+    }
+}
+
+}
+}
+
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
new file mode 100644
index 0000000000..19d7ec7cf1
--- /dev/null
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -0,0 +1,177 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+/**
+ * noop function for round to avoid compilation
+ * issues due to lack of this function in C90 based
+ * compilers, it is only present in C99 and C++11
+ *
+ * This is not a full fledged implementation, this function
+ * is to be used only for positive numbers, i m using it here
+ * for calculating dimensions of arrays
+ */
+dim_t round2int(float value)
+{
+    return (dim_t)(value+0.5f);
+}
+
+using std::conditional;
+using std::is_same;
+
+template<typename T>
+using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+
+template<typename T>
+using vtype_t = typename conditional<is_complex<T>::value,
+                                     T, wtype_t<T>
+                                    >::type;
+
+template<typename T, af_interp_type method>
+struct resize_op
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+              const af::dim4 &ostrides, const af::dim4 &istrides,
+              const dim_t x, const dim_t y)
+    {
+        return;
+    }
+};
+
+template<typename T>
+struct resize_op<T, AF_INTERP_NEAREST>
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+            const af::dim4 &ostrides, const af::dim4 &istrides,
+            const dim_t x, const dim_t y)
+    {
+        // Compute Indices
+        dim_t i_x = round2int((float)x / (odims[0] / (float)idims[0]));
+        dim_t i_y = round2int((float)y / (odims[1] / (float)idims[1]));
+
+        if (i_x >= idims[0]) i_x = idims[0] - 1;
+        if (i_y >= idims[1]) i_y = idims[1] - 1;
+
+        dim_t i_off = i_y * istrides[1] + i_x;
+        dim_t o_off =   y * ostrides[1] + x;
+        // Copy values from all channels
+        for(dim_t w = 0; w < odims[3]; w++) {
+            dim_t wost = w * ostrides[3];
+            dim_t wist = w * istrides[3];
+            for(dim_t z = 0; z < odims[2]; z++) {
+                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
+            }
+        }
+    }
+};
+
+template<typename T>
+struct resize_op<T, AF_INTERP_BILINEAR>
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+            const af::dim4 &ostrides, const af::dim4 &istrides,
+            const dim_t x, const dim_t y)
+    {
+        // Compute Indices
+        float f_x = (float)x / (odims[0] / (float)idims[0]);
+        float f_y = (float)y / (odims[1] / (float)idims[1]);
+
+        dim_t i1_x  = floor(f_x);
+        dim_t i1_y  = floor(f_y);
+
+        if (i1_x >= idims[0]) i1_x = idims[0] - 1;
+        if (i1_y >= idims[1]) i1_y = idims[1] - 1;
+
+        float b   = f_x - i1_x;
+        float a   = f_y - i1_y;
+
+        dim_t i2_x  = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
+        dim_t i2_y  = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
+
+        typedef typename dtype_traits<T>::base_type BT;
+        typedef wtype_t<BT> WT;
+        typedef vtype_t<T> VT;
+
+        dim_t o_off = y * ostrides[1] + x;
+        // Copy values from all channels
+        for(dim_t w = 0; w < odims[3]; w++) {
+            dim_t wst = w * istrides[3];
+            for(dim_t z = 0; z < odims[2]; z++) {
+                dim_t zst = z * istrides[2];
+                dim_t channel_off = zst + wst;
+                VT p1 = inPtr[i1_y * istrides[1] + i1_x + channel_off];
+                VT p2 = inPtr[i2_y * istrides[1] + i1_x + channel_off];
+                VT p3 = inPtr[i1_y * istrides[1] + i2_x + channel_off];
+                VT p4 = inPtr[i2_y * istrides[1] + i2_x + channel_off];
+
+                outPtr[o_off + z * ostrides[2] + w * ostrides[3]] =
+                                scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
+                                scalar<WT>((    a   ) * (1.0f - b)) * p2 +
+                                scalar<WT>((1.0f - a) * (    b   )) * p3 +
+                                scalar<WT>((    a   ) * (    b   )) * p4;
+            }
+        }
+    }
+};
+
+template<typename T>
+struct resize_op<T, AF_INTERP_LOWER>
+{
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
+            const af::dim4 &ostrides, const af::dim4 &istrides,
+            const dim_t x, const dim_t y)
+    {
+        // Compute Indices
+        dim_t i_x = floor((float)x / (odims[0] / (float)idims[0]));
+        dim_t i_y = floor((float)y / (odims[1] / (float)idims[1]));
+
+        if (i_x >= idims[0]) i_x = idims[0] - 1;
+        if (i_y >= idims[1]) i_y = idims[1] - 1;
+
+        dim_t i_off = i_y * istrides[1] + i_x;
+        dim_t o_off =   y * ostrides[1] + x;
+        // Copy values from all channels
+        for(dim_t w = 0; w < odims[3]; w++) {
+            dim_t wost = w * ostrides[3];
+            dim_t wist = w * istrides[3];
+            for(dim_t z = 0; z < odims[2]; z++) {
+                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
+            }
+        }
+    }
+};
+
+template<typename T, af_interp_type method>
+void resize(Array<T> out, const Array<T> in)
+{
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    const T *inPtr    = in.get();
+          T *outPtr   = out.get();
+    af::dim4 ostrides = out.strides();
+    af::dim4 istrides = in.strides();
+
+    resize_op<T, method> op;
+    for(dim_t y = 0; y < odims[1]; y++) {
+        for(dim_t x = 0; x < odims[0]; x++) {
+            op(outPtr, inPtr, odims, idims, ostrides, istrides, x, y);
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
new file mode 100644
index 0000000000..6e4f75863f
--- /dev/null
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -0,0 +1,83 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <math.hpp>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, af_interp_type method>
+void rotate(Array<T> output, const Array<T> input, const float theta)
+{
+    const af::dim4 odims    = output.dims();
+    const af::dim4 idims    = input.dims();
+    const af::dim4 ostrides = output.strides();
+    const af::dim4 istrides = input.strides();
+
+    const T* in   = input.get();
+          T* out  = output.get();
+    dim_t nimages = idims[2];
+
+    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
+                 const af::dim4 &, const af::dim4 &,
+                 const dim_t, const dim_t, const dim_t, const dim_t);
+
+    const float c = cos(-theta), s = sin(-theta);
+    float tx, ty;
+    {
+        const float nx = 0.5 * (idims[0] - 1);
+        const float ny = 0.5 * (idims[1] - 1);
+        const float mx = 0.5 * (odims[0] - 1);
+        const float my = 0.5 * (odims[1] - 1);
+        const float sx = (mx * c + my *-s);
+        const float sy = (mx * s + my * c);
+        tx = -(sx - nx);
+        ty = -(sy - ny);
+    }
+
+    const float tmat[6] = {std::round( c * 1000) / 1000.0f,
+                           std::round(-s * 1000) / 1000.0f,
+                           std::round(tx * 1000) / 1000.0f,
+                           std::round( s * 1000) / 1000.0f,
+                           std::round( c * 1000) / 1000.0f,
+                           std::round(ty * 1000) / 1000.0f,
+                          };
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            t_fn = &transform_n;
+            break;
+        case AF_INTERP_BILINEAR:
+            t_fn = &transform_b;
+            break;
+        case AF_INTERP_LOWER:
+            t_fn = &transform_l;
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+            break;
+    }
+
+
+    // Do transform for image
+    for(int yy = 0; yy < (int)odims[1]; yy++) {
+        for(int xx = 0; xx < (int)odims[0]; xx++) {
+            t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy);
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
new file mode 100644
index 0000000000..0bcfe7df17
--- /dev/null
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -0,0 +1,72 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<af_op_t op, typename Ti, typename To, int D>
+struct scan_dim
+{
+    void operator()(Array<To> out, dim_t outOffset,
+                    const Array<Ti> in, dim_t inOffset,
+                    const int dim) const
+    {
+        const dim4 odims    = out.dims();
+        const dim4 ostrides = out.strides();
+        const dim4 istrides = in.strides();
+
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            scan_dim<op, Ti, To, D1> func;
+            getQueue().enqueue(func,
+                    out, outOffset + i * ostrides[D1],
+                    in, inOffset + i * istrides[D1], dim);
+            if (D1 == dim) break;
+        }
+    }
+};
+
+template<af_op_t op, typename Ti, typename To>
+struct scan_dim<op, Ti, To, 0>
+{
+    void operator()(Array<To> output, dim_t outOffset,
+                    const Array<Ti> input,  dim_t inOffset,
+                    const int dim) const
+    {
+        const Ti* in = input.get() + inOffset;
+              To* out= output.get()+ outOffset;
+
+        const dim4 ostrides = output.strides();
+        const dim4 istrides = input.strides();
+        const dim4 idims    = input.dims();
+
+        dim_t istride = istrides[dim];
+        dim_t ostride = ostrides[dim];
+
+        Transform<Ti, To, op> transform;
+        // FIXME: Change the name to something better
+        Binary<To, op> scan;
+
+        To out_val = scan.init();
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            To in_val = transform(in[i * istride]);
+            out_val = scan(in_val, out_val);
+            out[i * ostride] = out_val;
+        }
+    }
+};
+
+}
+}
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
new file mode 100644
index 0000000000..1099c7e437
--- /dev/null
+++ b/src/backend/cpu/kernel/select.hpp
@@ -0,0 +1,124 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void select(Array<T> out, const Array<char> cond, const Array<T> a, const Array<T> b)
+{
+    af::dim4 adims = a.dims();
+    af::dim4 astrides = a.strides();
+    af::dim4 bdims = b.dims();
+    af::dim4 bstrides = b.strides();
+
+    af::dim4 cdims = cond.dims();
+    af::dim4 cstrides = cond.strides();
+
+    af::dim4 odims = out.dims();
+    af::dim4 ostrides = out.strides();
+
+    bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
+        adims[2] == odims[2], adims[3] == odims[3]};
+
+    bool is_b_same[] = {bdims[0] == odims[0], bdims[1] == odims[1],
+        bdims[2] == odims[2], bdims[3] == odims[3]};
+
+    bool is_c_same[] = {cdims[0] == odims[0], cdims[1] == odims[1],
+        cdims[2] == odims[2], cdims[3] == odims[3]};
+
+    const T *aptr = a.get();
+    const T *bptr = b.get();
+    T *optr = out.get();
+    const char *cptr = cond.get();
+
+    for (int l = 0; l < odims[3]; l++) {
+
+        int o_off3   = ostrides[3] * l;
+        int a_off3   = astrides[3] * is_a_same[3] * l;
+        int b_off3   = bstrides[3] * is_b_same[3] * l;
+        int c_off3   = cstrides[3] * is_c_same[3] * l;
+
+        for (int k = 0; k < odims[2]; k++) {
+
+            int o_off2   = ostrides[2] * k + o_off3;
+            int a_off2   = astrides[2] * is_a_same[2] * k + a_off3;
+            int b_off2   = bstrides[2] * is_b_same[2] * k + b_off3;
+            int c_off2   = cstrides[2] * is_c_same[2] * k + c_off3;
+
+            for (int j = 0; j < odims[1]; j++) {
+
+                int o_off1   = ostrides[1] * j + o_off2;
+                int a_off1   = astrides[1] * is_a_same[1] * j + a_off2;
+                int b_off1   = bstrides[1] * is_b_same[1] * j + b_off2;
+                int c_off1   = cstrides[1] * is_c_same[1] * j + c_off2;
+
+                for (int i = 0; i < odims[0]; i++) {
+
+                    bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
+                    T    aval = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
+                    T    bval = is_b_same[0] ? bptr[b_off1 + i] : bptr[b_off1];
+                    T    oval = cval ? aval : bval;
+                    optr[o_off1 + i] = oval;
+                }
+            }
+        }
+    }
+}
+
+template<typename T, bool flip>
+void select_scalar(Array<T> out, const Array<char> cond, const Array<T> a, const double b)
+{
+    af::dim4 astrides = a.strides();
+    af::dim4 cstrides = cond.strides();
+
+    af::dim4 odims = out.dims();
+    af::dim4 ostrides = out.strides();
+
+    const T *aptr = a.get();
+    T *optr = out.get();
+    const char *cptr = cond.get();
+
+    for (int l = 0; l < odims[3]; l++) {
+
+        int o_off3 = ostrides[3] * l;
+        int a_off3 = astrides[3] * l;
+        int c_off3 = cstrides[3] * l;
+
+        for (int k = 0; k < odims[2]; k++) {
+
+            int o_off2 = ostrides[2] * k + o_off3;
+            int a_off2 = astrides[2] * k + a_off3;
+            int c_off2 = cstrides[2] * k + c_off3;
+
+            for (int j = 0; j < odims[1]; j++) {
+
+                int o_off1 = ostrides[1] * j + o_off2;
+                int a_off1 = astrides[1] * j + a_off2;
+                int c_off1 = cstrides[1] * j + c_off2;
+
+                for (int i = 0; i < odims[0]; i++) {
+
+                    optr[o_off1 + i] = (flip ^ cptr[c_off1 + i]) ? aptr[a_off1 + i] : b;
+                }
+            }
+        }
+    }
+}
+
+
+
+}
+}
diff --git a/src/backend/cpu/kernel/shift.hpp b/src/backend/cpu/kernel/shift.hpp
new file mode 100644
index 0000000000..8beb975486
--- /dev/null
+++ b/src/backend/cpu/kernel/shift.hpp
@@ -0,0 +1,69 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <cassert>
+
+namespace cpu
+{
+namespace kernel
+{
+
+static inline dim_t simple_mod(const dim_t i, const dim_t dim)
+{
+    return (i < dim) ? i : (i - dim);
+}
+
+template<typename T>
+void shift(Array<T> out, const Array<T> in, const af::dim4 sdims)
+{
+    T* outPtr = out.get();
+    const T* inPtr = in.get();
+
+    const af::dim4 oDims = out.dims();
+    const af::dim4 ist   = in.strides();
+    const af::dim4 ost   = out.strides();
+
+    int sdims_[4];
+    // Need to do this because we are mapping output to input in the kernel
+    for(int i = 0; i < 4; i++) {
+        // sdims_[i] will always be positive and always [0, oDims[i]].
+        // Negative shifts are converted to position by going the other way round
+        sdims_[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+        assert(sdims_[i] >= 0 && sdims_[i] <= oDims[i]);
+    }
+
+    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+        const int oW = ow * ost[3];
+        const int iw = simple_mod((ow + sdims_[3]), oDims[3]);
+        const int iW = iw * ist[3];
+        for(dim_t oz = 0; oz < oDims[2]; oz++) {
+            const int oZW = oW + oz * ost[2];
+            const int iz = simple_mod((oz + sdims_[2]), oDims[2]);
+            const int iZW = iW + iz * ist[2];
+            for(dim_t oy = 0; oy < oDims[1]; oy++) {
+                const int oYZW = oZW + oy * ost[1];
+                const int iy = simple_mod((oy + sdims_[1]), oDims[1]);
+                const int iYZW = iZW + iy * ist[1];
+                for(dim_t ox = 0; ox < oDims[0]; ox++) {
+                    const int oIdx = oYZW + ox;
+                    const int ix = simple_mod((ox + sdims_[0]), oDims[0]);
+                    const int iIdx = iYZW + ix;
+
+                    outPtr[oIdx] = inPtr[iIdx];
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
similarity index 100%
rename from src/backend/cpu/sift_nonfree.hpp
rename to src/backend/cpu/kernel/sift_nonfree.hpp
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
new file mode 100644
index 0000000000..49d33cdbb4
--- /dev/null
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <cassert>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename Ti, typename To, bool isDX>
+void derivative(Array<To> output, const Array<Ti> input)
+{
+    const af::dim4 dims    = input.dims();
+    const af::dim4 strides = input.strides();
+          To* optr     = output.get();
+    const Ti* iptr     = input.get();
+
+    for(dim_t b3=0; b3<dims[3]; ++b3) {
+    for(dim_t b2=0; b2<dims[2]; ++b2) {
+
+        for(dim_t j=0; j<dims[1]; ++j) {
+
+            int joff  = j;
+            int _joff = j-1;
+            int joff_ = j+1;
+            int joffset = j*strides[1];
+
+            for(dim_t i=0; i<dims[0]; ++i) {
+
+                To accum = To(0);
+
+                int  ioff = i;
+                int _ioff = i-1;
+                int ioff_ = i+1;
+
+                To NW = (_ioff>=0 && _joff>=0) ?
+                        iptr[_joff*strides[1]+_ioff*strides[0]] : 0;
+                To SW = (ioff_<(int)dims[0] && _joff>=0) ?
+                        iptr[_joff*strides[1]+ioff_*strides[0]] : 0;
+                To NE = (_ioff>=0 && joff_<(int)dims[1]) ?
+                        iptr[joff_*strides[1]+_ioff*strides[0]] : 0;
+                To SE = (ioff_<(int)dims[0] && joff_<(int)dims[1]) ?
+                        iptr[joff_*strides[1]+ioff_*strides[0]] : 0;
+
+                if (isDX) {
+                    To W  = _joff>=0 ?
+                            iptr[_joff*strides[1]+ioff*strides[0]] : 0;
+
+                    To E  = joff_<(int)dims[1] ?
+                            iptr[joff_*strides[1]+ioff*strides[0]] : 0;
+
+                    accum = NW+SW - (NE+SE) + 2*(W-E);
+                } else {
+                    To N  = _ioff>=0 ?
+                            iptr[joff*strides[1]+_ioff*strides[0]] : 0;
+
+                    To S  = ioff_<(int)dims[0] ?
+                            iptr[joff*strides[1]+ioff_*strides[0]] : 0;
+
+                    accum = NW+NE - (SW+SE) + 2*(N-S);
+                }
+
+                optr[joffset+i*strides[0]] = accum;
+            }
+        }
+
+        optr += strides[2];
+        iptr += strides[2];
+    }
+    optr += strides[3];
+    iptr += strides[3];
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
new file mode 100644
index 0000000000..cba07fabdf
--- /dev/null
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -0,0 +1,51 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <math.hpp>
+#include <algorithm>
+#include <numeric>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+// Based off of http://stackoverflow.com/a/12399290
+template<typename T, bool isAscending>
+void sort0(Array<T> val)
+{
+    // initialize original index locations
+    T *val_ptr = val.get();
+
+    function<bool(T, T)> op = std::greater<T>();
+    if(isAscending) { op = std::less<T>(); }
+
+    T *comp_ptr = nullptr;
+    for(dim_t w = 0; w < val.dims()[3]; w++) {
+        dim_t valW = w * val.strides()[3];
+        for(dim_t z = 0; z < val.dims()[2]; z++) {
+            dim_t valWZ = valW + z * val.strides()[2];
+            for(dim_t y = 0; y < val.dims()[1]; y++) {
+
+                dim_t valOffset = valWZ + y * val.strides()[1];
+
+                comp_ptr = val_ptr + valOffset;
+                std::sort(comp_ptr, comp_ptr + val.dims()[0], op);
+            }
+        }
+    }
+    return;
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
new file mode 100644
index 0000000000..77713a7240
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -0,0 +1,85 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <math.hpp>
+#include <algorithm>
+#include <numeric>
+#include <queue>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0_by_key(Array<Tk> okey, Array<Tv> oval, Array<uint> oidx,
+                  const Array<Tk> ikey, const Array<Tv> ival)
+{
+    function<bool(Tk, Tk)> op = std::greater<Tk>();
+    if(isAscending) { op = std::less<Tk>(); }
+
+    // Get pointers and initialize original index locations
+        uint *oidx_ptr = oidx.get();
+          Tk *okey_ptr = okey.get();
+          Tv *oval_ptr = oval.get();
+    const Tk *ikey_ptr = ikey.get();
+    const Tv *ival_ptr = ival.get();
+
+    std::vector<uint> seq_vec(oidx.dims()[0]);
+    std::iota(seq_vec.begin(), seq_vec.end(), 0);
+
+    const Tk *comp_ptr = nullptr;
+    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
+
+    for(dim_t w = 0; w < ikey.dims()[3]; w++) {
+        dim_t okeyW = w * okey.strides()[3];
+        dim_t ovalW = w * oval.strides()[3];
+        dim_t oidxW = w * oidx.strides()[3];
+        dim_t ikeyW = w * ikey.strides()[3];
+        dim_t ivalW = w * ival.strides()[3];
+
+        for(dim_t z = 0; z < ikey.dims()[2]; z++) {
+            dim_t okeyWZ = okeyW + z * okey.strides()[2];
+            dim_t ovalWZ = ovalW + z * oval.strides()[2];
+            dim_t oidxWZ = oidxW + z * oidx.strides()[2];
+            dim_t ikeyWZ = ikeyW + z * ikey.strides()[2];
+            dim_t ivalWZ = ivalW + z * ival.strides()[2];
+
+            for(dim_t y = 0; y < ikey.dims()[1]; y++) {
+
+                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
+                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
+                dim_t oidxOffset = oidxWZ + y * oidx.strides()[1];
+                dim_t ikeyOffset = ikeyWZ + y * ikey.strides()[1];
+                dim_t ivalOffset = ivalWZ + y * ival.strides()[1];
+
+                uint *ptr = oidx_ptr + oidxOffset;
+                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
+
+                comp_ptr = ikey_ptr + ikeyOffset;
+                std::stable_sort(ptr, ptr + ikey.dims()[0], comparator);
+
+                for (dim_t i = 0; i < oval.dims()[0]; ++i){
+                    uint sortIdx = oidx_ptr[oidxOffset + i];
+                    okey_ptr[okeyOffset + i] = ikey_ptr[ikeyOffset + sortIdx];
+                    oval_ptr[ovalOffset + i] = ival_ptr[ivalOffset + sortIdx];
+                }
+            }
+        }
+    }
+
+    return;
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/sort_index.hpp b/src/backend/cpu/kernel/sort_index.hpp
new file mode 100644
index 0000000000..d2de05a559
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_index.hpp
@@ -0,0 +1,70 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <math.hpp>
+#include <algorithm>
+#include <numeric>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, bool isAscending>
+void sort0_index(Array<T> val, Array<uint> idx, const Array<T> in)
+{
+    // initialize original index locations
+       uint *idx_ptr = idx.get();
+          T *val_ptr = val.get();
+    const T *in_ptr  = in.get();
+    function<bool(T, T)> op = std::greater<T>();
+    if(isAscending) { op = std::less<T>(); }
+
+    std::vector<uint> seq_vec(idx.dims()[0]);
+    std::iota(seq_vec.begin(), seq_vec.end(), 0);
+
+    const T *comp_ptr = nullptr;
+    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
+
+    for(dim_t w = 0; w < in.dims()[3]; w++) {
+        dim_t valW = w * val.strides()[3];
+        dim_t idxW = w * idx.strides()[3];
+        dim_t  inW = w *  in.strides()[3];
+        for(dim_t z = 0; z < in.dims()[2]; z++) {
+            dim_t valWZ = valW + z * val.strides()[2];
+            dim_t idxWZ = idxW + z * idx.strides()[2];
+            dim_t  inWZ =  inW + z *  in.strides()[2];
+            for(dim_t y = 0; y < in.dims()[1]; y++) {
+
+                dim_t valOffset = valWZ + y * val.strides()[1];
+                dim_t idxOffset = idxWZ + y * idx.strides()[1];
+                dim_t inOffset  =  inWZ + y *  in.strides()[1];
+
+                uint *ptr = idx_ptr + idxOffset;
+                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
+
+                comp_ptr = in_ptr + inOffset;
+                std::stable_sort(ptr, ptr + in.dims()[0], comparator);
+
+                for (dim_t i = 0; i < val.dims()[0]; ++i){
+                    val_ptr[valOffset + i] = in_ptr[inOffset + idx_ptr[idxOffset + i]];
+                }
+            }
+        }
+    }
+
+    return;
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/susan.hpp b/src/backend/cpu/kernel/susan.hpp
new file mode 100644
index 0000000000..f543967799
--- /dev/null
+++ b/src/backend/cpu/kernel/susan.hpp
@@ -0,0 +1,99 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void susan_responses(Array<T> output, const Array<T> input,
+                     const unsigned idim0, const unsigned idim1,
+                     const int radius, const float t, const float g,
+                     const unsigned border_len)
+{
+    T* resp_out = output.get();
+    const T* in = input.get();
+
+    const unsigned r = border_len;
+    const int rSqrd = radius*radius;
+
+    for (unsigned y = r; y < idim1 - r; ++y) {
+        for (unsigned x = r; x < idim0 - r; ++x) {
+            const unsigned idx = y * idim0 + x;
+            T m_0 = in[idx];
+            float nM = 0.0f;
+
+            for (int i=-radius; i<=radius; ++i) {
+                for (int j=-radius; j<=radius; ++j) {
+                    if (i*i + j*j < rSqrd) {
+                        int p = x + i;
+                        int q = y + j;
+                        T m = in[p + idim0 * q];
+                        float exp_pow = std::pow((m - m_0)/t, 6.0);
+                        float cM = std::exp(-exp_pow);
+                        nM += cM;
+                    }
+                }
+            }
+
+            resp_out[idx] = nM < g ? g - nM : T(0);
+        }
+    }
+}
+
+template<typename T>
+void non_maximal(Array<float> xcoords, Array<float> ycoords, Array<float> response,
+                 shared_ptr<unsigned> counter, const unsigned idim0, const unsigned idim1,
+                 const Array<T> input, const unsigned border_len, const unsigned max_corners)
+{
+    float* x_out    = xcoords.get();
+    float* y_out    = ycoords.get();
+    float* resp_out = response.get();
+    unsigned* count = counter.get();
+    const T* resp_in= input.get();
+
+    // Responses on the border don't have 8-neighbors to compare, discard them
+    const unsigned r = border_len + 1;
+
+    for (unsigned y = r; y < idim1 - r; y++) {
+        for (unsigned x = r; x < idim0 - r; x++) {
+            const T v = resp_in[y * idim0 + x];
+
+            // Find maximum neighborhood response
+            T max_v;
+            max_v = max(resp_in[(y-1) * idim0 + x-1], resp_in[y * idim0 + x-1]);
+            max_v = max(max_v, resp_in[(y+1) * idim0 + x-1]);
+            max_v = max(max_v, resp_in[(y-1) * idim0 + x  ]);
+            max_v = max(max_v, resp_in[(y+1) * idim0 + x  ]);
+            max_v = max(max_v, resp_in[(y-1) * idim0 + x+1]);
+            max_v = max(max_v, resp_in[(y)   * idim0 + x+1]);
+            max_v = max(max_v, resp_in[(y+1) * idim0 + x+1]);
+
+            // Stores corner to {x,y,resp}_out if it's response is maximum compared
+            // to its 8-neighborhood and greater or equal minimum response
+            if (v > max_v) {
+                const unsigned idx = *count;
+                *count += 1;
+                if (idx < max_corners) {
+                    x_out[idx]    = (float)x;
+                    y_out[idx]    = (float)y;
+                    resp_out[idx] = (float)v;
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/tile.hpp b/src/backend/cpu/kernel/tile.hpp
new file mode 100644
index 0000000000..3ad3009041
--- /dev/null
+++ b/src/backend/cpu/kernel/tile.hpp
@@ -0,0 +1,55 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void tile(Array<T> out, const Array<T> in)
+{
+
+    T* outPtr = out.get();
+    const T* inPtr = in.get();
+
+    const af::dim4 iDims = in.dims();
+    const af::dim4 oDims = out.dims();
+    const af::dim4 ist = in.strides();
+    const af::dim4 ost = out.strides();
+
+    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+        const dim_t iw = ow % iDims[3];
+        const dim_t iW = iw * ist[3];
+        const dim_t oW = ow * ost[3];
+        for(dim_t oz = 0; oz < oDims[2]; oz++) {
+            const dim_t iz = oz % iDims[2];
+            const dim_t iZW = iW + iz * ist[2];
+            const dim_t oZW = oW + oz * ost[2];
+            for(dim_t oy = 0; oy < oDims[1]; oy++) {
+                const dim_t iy = oy % iDims[1];
+                const dim_t iYZW = iZW + iy * ist[1];
+                const dim_t oYZW = oZW + oy * ost[1];
+                for(dim_t ox = 0; ox < oDims[0]; ox++) {
+                    const dim_t ix = ox % iDims[0];
+                    const dim_t iMem = iYZW + ix;
+                    const dim_t oMem = oYZW + ox;
+                    outPtr[oMem] = inPtr[iMem];
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
new file mode 100644
index 0000000000..d97613a78c
--- /dev/null
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -0,0 +1,105 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template <typename T>
+void calc_affine_inverse(T *txo, const T *txi)
+{
+    T det = txi[0]*txi[4] - txi[1]*txi[3];
+
+    txo[0] = txi[4] / det;
+    txo[1] = txi[3] / det;
+    txo[3] = txi[1] / det;
+    txo[4] = txi[0] / det;
+
+    txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+    txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+}
+
+template <typename T>
+void calc_affine_inverse(T *tmat, const T *tmat_ptr, const bool inverse)
+{
+    // The way kernel is structured, it expects an inverse
+    // transform matrix by default.
+    // If it is an forward transform, then we need its inverse
+    if(inverse) {
+        for(int i = 0; i < 6; i++)
+            tmat[i] = tmat_ptr[i];
+    } else {
+        calc_affine_inverse(tmat, tmat_ptr);
+    }
+}
+
+template<typename T, af_interp_type method>
+void transform(Array<T> output, const Array<T> input,
+               const Array<float> transform, const bool inverse)
+{
+    const af::dim4 idims    = input.dims();
+    const af::dim4 odims    = output.dims();
+    const af::dim4 istrides = input.strides();
+    const af::dim4 ostrides = output.strides();
+
+    T * out = output.get();
+    const T * in = input.get();
+    const float* tf = transform.get();
+
+    dim_t nimages     = idims[2];
+    // Multiplied in src/backend/transform.cpp
+    dim_t ntransforms = odims[2] / idims[2];
+
+    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
+                 const af::dim4 &, const af::dim4 &,
+                 const dim_t, const dim_t, const dim_t, const dim_t);
+
+    switch(method) {
+        case AF_INTERP_NEAREST:
+            t_fn = &transform_n;
+            break;
+        case AF_INTERP_BILINEAR:
+            t_fn = &transform_b;
+            break;
+        case AF_INTERP_LOWER:
+            t_fn = &transform_l;
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+            break;
+    }
+
+
+    // For each transform channel
+    for(int t_idx = 0; t_idx < (int)ntransforms; t_idx++) {
+        // Compute inverse if required
+        const float *tmat_ptr = tf + t_idx * 6;
+        float tmat[6];
+        calc_affine_inverse(tmat, tmat_ptr, inverse);
+
+        // Offset for output pointer
+        dim_t o_offset = t_idx * nimages * ostrides[2];
+
+        // Do transform for image
+        for(int yy = 0; yy < (int)odims[1]; yy++) {
+            for(int xx = 0; xx < (int)odims[0]; xx++) {
+                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy);
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
new file mode 100644
index 0000000000..576de873ed
--- /dev/null
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -0,0 +1,122 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <utility.hpp>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+T getConjugate(const T &in)
+{
+    // For non-complex types return same
+    return in;
+}
+
+template<>
+cfloat getConjugate(const cfloat &in)
+{
+    return std::conj(in);
+}
+
+template<>
+cdouble getConjugate(const cdouble &in)
+{
+    return std::conj(in);
+}
+
+template<typename T, bool conjugate>
+void transpose(Array<T> output, const Array<T> input)
+{
+    const dim4 odims    = output.dims();
+    const dim4 ostrides = output.strides();
+    const dim4 istrides = input.strides();
+
+    T * out = output.get();
+    T const * const in = input.get();
+
+    for (dim_t l = 0; l < odims[3]; ++l) {
+        for (dim_t k = 0; k < odims[2]; ++k) {
+            // Outermost loop handles batch mode
+            // if input has no data along third dimension
+            // this loop runs only once
+            for (dim_t j = 0; j < odims[1]; ++j) {
+                for (dim_t i = 0; i < odims[0]; ++i) {
+                    // calculate array indices based on offsets and strides
+                    // the helper getIdx takes care of indices
+                    const dim_t inIdx  = getIdx(istrides,j,i,k,l);
+                    const dim_t outIdx = getIdx(ostrides,i,j,k,l);
+                    if(conjugate)
+                        out[outIdx] = getConjugate(in[inIdx]);
+                    else
+                        out[outIdx] = in[inIdx];
+                }
+            }
+            // outData and inData pointers doesn't need to be
+            // offset as the getIdx function is taking care
+            // of the batch parameter
+        }
+    }
+}
+
+template<typename T>
+void transpose(Array<T> out, const Array<T> in, const bool conjugate)
+{
+    return (conjugate ? transpose<T, true>(out, in) : transpose<T, false>(out, in));
+}
+
+template<typename T, bool conjugate>
+void transpose_inplace(Array<T> input)
+{
+    const dim4 idims    = input.dims();
+    const dim4 istrides = input.strides();
+
+    T * in = input.get();
+
+    for (dim_t l = 0; l < idims[3]; ++l) {
+        for (dim_t k = 0; k < idims[2]; ++k) {
+            // Outermost loop handles batch mode
+            // if input has no data along third dimension
+            // this loop runs only once
+            //
+            // Run only bottom triangle. std::swap swaps with upper triangle
+            for (dim_t j = 0; j < idims[1]; ++j) {
+                for (dim_t i = j + 1; i < idims[0]; ++i) {
+                    // calculate array indices based on offsets and strides
+                    // the helper getIdx takes care of indices
+                    const dim_t iIdx  = getIdx(istrides,j,i,k,l);
+                    const dim_t oIdx = getIdx(istrides,i,j,k,l);
+                    if(conjugate) {
+                        in[iIdx] = getConjugate(in[iIdx]);
+                        in[oIdx] = getConjugate(in[oIdx]);
+                        std::swap(in[iIdx], in[oIdx]);
+                    }
+                    else {
+                        std::swap(in[iIdx], in[oIdx]);
+                    }
+                }
+            }
+        }
+    }
+}
+
+template<typename T>
+void transpose_inplace(Array<T> in, const bool conjugate)
+{
+    return (conjugate ? transpose_inplace<T, true >(in) : transpose_inplace<T, false>(in));
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
new file mode 100644
index 0000000000..7059de5981
--- /dev/null
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -0,0 +1,61 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, bool is_upper, bool is_unit_diag>
+void triangle(Array<T> out, const Array<T> in)
+{
+    T *o = out.get();
+    const T *i = in.get();
+
+    af::dim4 odm = out.dims();
+
+    af::dim4 ost = out.strides();
+    af::dim4 ist = in.strides();
+
+    for(dim_t ow = 0; ow < odm[3]; ow++) {
+        const dim_t oW = ow * ost[3];
+        const dim_t iW = ow * ist[3];
+
+        for(dim_t oz = 0; oz < odm[2]; oz++) {
+            const dim_t oZW = oW + oz * ost[2];
+            const dim_t iZW = iW + oz * ist[2];
+
+            for(dim_t oy = 0; oy < odm[1]; oy++) {
+                const dim_t oYZW = oZW + oy * ost[1];
+                const dim_t iYZW = iZW + oy * ist[1];
+
+                for(dim_t ox = 0; ox < odm[0]; ox++) {
+                    const dim_t oMem = oYZW + ox;
+                    const dim_t iMem = iYZW + ox;
+
+                    bool cond = is_upper ? (oy >= ox) : (oy <= ox);
+                    bool do_unit_diag = (is_unit_diag && ox == oy);
+                    if(cond) {
+                        o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
+                    } else {
+                        o[oMem] = scalar<T>(0);
+                    }
+
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
new file mode 100644
index 0000000000..1d996ff1f3
--- /dev/null
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -0,0 +1,81 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, int d>
+void unwrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
+{
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
+
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    af::dim4 istrides = in.strides();
+    af::dim4 ostrides = out.strides();
+
+    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
+
+    for(dim_t w = 0; w < odims[3]; w++) {
+        for(dim_t z = 0; z < odims[2]; z++) {
+
+            dim_t cOut = w * ostrides[3] + z * ostrides[2];
+            dim_t cIn  = w * istrides[3] + z * istrides[2];
+            const T* iptr = inPtr  + cIn;
+            T* optr_= outPtr + cOut;
+
+            for(dim_t col = 0; col < odims[d]; col++) {
+                // Offset output ptr
+                T* optr = optr_ + col * ostrides[d];
+
+                // Calculate input window index
+                dim_t winy = (col / nx);
+                dim_t winx = (col % nx);
+
+                dim_t startx = winx * sx;
+                dim_t starty = winy * sy;
+
+                dim_t spx = startx - px;
+                dim_t spy = starty - py;
+
+                // Short cut condition ensuring all values within input dimensions
+                bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 && spy + wy < idims[1]);
+
+                for(dim_t y = 0; y < wy; y++) {
+                    for(dim_t x = 0; x < wx; x++) {
+                        dim_t xpad = spx + x;
+                        dim_t ypad = spy + y;
+
+                        dim_t oloc = (y * wx + x);
+                        if (d == 0) oloc *= ostrides[1];
+
+                        if(cond || (xpad >= 0 && xpad < idims[0] && ypad >= 0 && ypad < idims[1])) {
+                            dim_t iloc = (ypad * istrides[1] + xpad * istrides[0]);
+                            optr[oloc] = iptr[iloc];
+                        } else {
+                            optr[oloc] = scalar<T>(0.0);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
new file mode 100644
index 0000000000..70be3ad652
--- /dev/null
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -0,0 +1,80 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, int d>
+void wrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
+              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
+{
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
+
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    af::dim4 istrides = in.strides();
+    af::dim4 ostrides = out.strides();
+
+    dim_t nx = (odims[0] + 2 * px - wx) / sx + 1;
+
+    for(dim_t w = 0; w < idims[3]; w++) {
+        for(dim_t z = 0; z < idims[2]; z++) {
+
+            dim_t cIn  = w * istrides[3] + z * istrides[2];
+            dim_t cOut = w * ostrides[3] + z * ostrides[2];
+            const T* iptr_ = inPtr  + cIn;
+            T* optr= outPtr + cOut;
+
+            for(dim_t col = 0; col < idims[d]; col++) {
+                // Offset output ptr
+                const T* iptr = iptr_ + col * istrides[d];
+
+                // Calculate input window index
+                dim_t winy = (col / nx);
+                dim_t winx = (col % nx);
+
+                dim_t startx = winx * sx;
+                dim_t starty = winy * sy;
+
+                dim_t spx = startx - px;
+                dim_t spy = starty - py;
+
+                // Short cut condition ensuring all values within input dimensions
+                bool cond = (spx >= 0 && spx + wx < odims[0] && spy >= 0 && spy + wy < odims[1]);
+
+                for(dim_t y = 0; y < wy; y++) {
+                    for(dim_t x = 0; x < wx; x++) {
+                        dim_t xpad = spx + x;
+                        dim_t ypad = spy + y;
+
+                        dim_t iloc = (y * wx + x);
+                        if (d == 0) iloc *= istrides[1];
+
+                        if(cond || (xpad >= 0 && xpad < odims[0] && ypad >= 0 && ypad < odims[1])) {
+                            dim_t oloc = (ypad * ostrides[1] + xpad * ostrides[0]);
+                            // FIXME: When using threads, atomize this
+                            optr[oloc] += iptr[iloc];
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index b6f50c2e32..82925622ae 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -11,139 +11,16 @@
 #include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
-#include <err_cpu.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/nearest_neighbour.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-#if defined(_WIN32) || defined(_MSC_VER)
-
-#include <intrin.h>
-#define __builtin_popcount __popcnt
-
-#endif
-
-template<typename T, typename To, af_match_type dist_type>
-struct dist_op
-{
-    To operator()(T v1, T v2)
-    {
-        return v1 - v2;     // Garbage distance
-    }
-};
-
-template<typename T, typename To>
-struct dist_op<T, To, AF_SAD>
-{
-    To operator()(T v1, T v2)
-    {
-        return std::abs((double)v1 - (double)v2);
-    }
-};
-
-template<typename T, typename To>
-struct dist_op<T, To, AF_SSD>
-{
-    To operator()(T v1, T v2)
-    {
-        return (v1 - v2) * (v1 - v2);
-    }
-};
-
-template<typename To>
-struct dist_op<uint, To, AF_SHD>
-{
-    To operator()(uint v1, uint v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
-};
-
-template<typename To>
-struct dist_op<uintl, To, AF_SHD>
-{
-    To operator()(uintl v1, uintl v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
-};
-
-template<typename To>
-struct dist_op<uchar, To, AF_SHD>
-{
-    To operator()(uchar v1, uchar v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
-};
-
-template<typename To>
-struct dist_op<ushort, To, AF_SHD>
-{
-    To operator()(ushort v1, ushort v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
-};
-
-template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour_(Array<uint> idx, Array<To> dist,
-                        const Array<T> query, const Array<T> train,
-                        const uint dist_dim, const uint n_dist)
-{
-    uint sample_dim = (dist_dim == 0) ? 1 : 0;
-    const dim4 qDims = query.dims();
-    const dim4 tDims = train.dims();
-
-    const unsigned distLength = qDims[dist_dim];
-    const unsigned nQuery = qDims[sample_dim];
-    const unsigned nTrain = tDims[sample_dim];
-
-    const T* qPtr = query.get();
-    const T* tPtr = train.get();
-    uint* iPtr = idx.get();
-    To* dPtr = dist.get();
-
-    dist_op<T, To, dist_type> op;
-
-    for (unsigned i = 0; i < nQuery; i++) {
-        To best_dist = limit_max<To>();
-        unsigned best_idx  = 0;
-
-        for (unsigned j = 0; j < nTrain; j++) {
-            To local_dist = 0;
-            for (unsigned k = 0; k < distLength; k++) {
-                size_t qIdx, tIdx;
-                if (sample_dim == 0) {
-                    qIdx = k * qDims[0] + i;
-                    tIdx = k * tDims[0] + j;
-                }
-                else {
-                    qIdx = i * qDims[0] + k;
-                    tIdx = j * tDims[0] + k;
-                }
-
-                local_dist += op(qPtr[qIdx], tPtr[tIdx]);
-            }
-
-            if (local_dist < best_dist) {
-                best_dist = local_dist;
-                best_idx  = j;
-            }
-        }
-
-        size_t oIdx;
-        oIdx = i;
-        iPtr[oIdx] = best_idx;
-        dPtr[oIdx] = best_dist;
-    }
-}
-
 template<typename T, typename To>
 void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
                        const Array<T>& query, const Array<T>& train,
@@ -166,13 +43,13 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
 
     switch(dist_type) {
         case AF_SAD:
-            getQueue().enqueue(nearest_neighbour_<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         case AF_SSD:
-            getQueue().enqueue(nearest_neighbour_<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         case AF_SHD:
-            getQueue().enqueue(nearest_neighbour_<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         default:
             AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 4b6629cb3f..00fe8203d4 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -11,7 +11,6 @@
 #include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
-#include <err_cpu.hpp>
 #include <handle.hpp>
 #include <resize.hpp>
 #include <fast.hpp>
@@ -21,520 +20,13 @@
 #include <cstring>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/orb.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-static const float PI_VAL = 3.14159265358979323846f;
-
-// Reference pattern, generated for a patch size of 31x31, as suggested by
-// original ORB paper
-#define REF_PAT_SIZE 31
-#define REF_PAT_SAMPLES 256
-#define REF_PAT_COORDS 4
-#define REF_PAT_LENGTH (REF_PAT_SAMPLES*REF_PAT_COORDS)
-
-// Current reference pattern was borrowed from OpenCV, to build a pattern with
-// similar quality, a training process must be applied, as described in
-// sections 4.2 and 4.3 of the original ORB paper.
-const int ref_pat[REF_PAT_LENGTH] = {
-    8,-3, 9,5,
-    4,2, 7,-12,
-    -11,9, -8,2,
-    7,-12, 12,-13,
-    2,-13, 2,12,
-    1,-7, 1,6,
-    -2,-10, -2,-4,
-    -13,-13, -11,-8,
-    -13,-3, -12,-9,
-    10,4, 11,9,
-    -13,-8, -8,-9,
-    -11,7, -9,12,
-    7,7, 12,6,
-    -4,-5, -3,0,
-    -13,2, -12,-3,
-    -9,0, -7,5,
-    12,-6, 12,-1,
-    -3,6, -2,12,
-    -6,-13, -4,-8,
-    11,-13, 12,-8,
-    4,7, 5,1,
-    5,-3, 10,-3,
-    3,-7, 6,12,
-    -8,-7, -6,-2,
-    -2,11, -1,-10,
-    -13,12, -8,10,
-    -7,3, -5,-3,
-    -4,2, -3,7,
-    -10,-12, -6,11,
-    5,-12, 6,-7,
-    5,-6, 7,-1,
-    1,0, 4,-5,
-    9,11, 11,-13,
-    4,7, 4,12,
-    2,-1, 4,4,
-    -4,-12, -2,7,
-    -8,-5, -7,-10,
-    4,11, 9,12,
-    0,-8, 1,-13,
-    -13,-2, -8,2,
-    -3,-2, -2,3,
-    -6,9, -4,-9,
-    8,12, 10,7,
-    0,9, 1,3,
-    7,-5, 11,-10,
-    -13,-6, -11,0,
-    10,7, 12,1,
-    -6,-3, -6,12,
-    10,-9, 12,-4,
-    -13,8, -8,-12,
-    -13,0, -8,-4,
-    3,3, 7,8,
-    5,7, 10,-7,
-    -1,7, 1,-12,
-    3,-10, 5,6,
-    2,-4, 3,-10,
-    -13,0, -13,5,
-    -13,-7, -12,12,
-    -13,3, -11,8,
-    -7,12, -4,7,
-    6,-10, 12,8,
-    -9,-1, -7,-6,
-    -2,-5, 0,12,
-    -12,5, -7,5,
-    3,-10, 8,-13,
-    -7,-7, -4,5,
-    -3,-2, -1,-7,
-    2,9, 5,-11,
-    -11,-13, -5,-13,
-    -1,6, 0,-1,
-    5,-3, 5,2,
-    -4,-13, -4,12,
-    -9,-6, -9,6,
-    -12,-10, -8,-4,
-    10,2, 12,-3,
-    7,12, 12,12,
-    -7,-13, -6,5,
-    -4,9, -3,4,
-    7,-1, 12,2,
-    -7,6, -5,1,
-    -13,11, -12,5,
-    -3,7, -2,-6,
-    7,-8, 12,-7,
-    -13,-7, -11,-12,
-    1,-3, 12,12,
-    2,-6, 3,0,
-    -4,3, -2,-13,
-    -1,-13, 1,9,
-    7,1, 8,-6,
-    1,-1, 3,12,
-    9,1, 12,6,
-    -1,-9, -1,3,
-    -13,-13, -10,5,
-    7,7, 10,12,
-    12,-5, 12,9,
-    6,3, 7,11,
-    5,-13, 6,10,
-    2,-12, 2,3,
-    3,8, 4,-6,
-    2,6, 12,-13,
-    9,-12, 10,3,
-    -8,4, -7,9,
-    -11,12, -4,-6,
-    1,12, 2,-8,
-    6,-9, 7,-4,
-    2,3, 3,-2,
-    6,3, 11,0,
-    3,-3, 8,-8,
-    7,8, 9,3,
-    -11,-5, -6,-4,
-    -10,11, -5,10,
-    -5,-8, -3,12,
-    -10,5, -9,0,
-    8,-1, 12,-6,
-    4,-6, 6,-11,
-    -10,12, -8,7,
-    4,-2, 6,7,
-    -2,0, -2,12,
-    -5,-8, -5,2,
-    7,-6, 10,12,
-    -9,-13, -8,-8,
-    -5,-13, -5,-2,
-    8,-8, 9,-13,
-    -9,-11, -9,0,
-    1,-8, 1,-2,
-    7,-4, 9,1,
-    -2,1, -1,-4,
-    11,-6, 12,-11,
-    -12,-9, -6,4,
-    3,7, 7,12,
-    5,5, 10,8,
-    0,-4, 2,8,
-    -9,12, -5,-13,
-    0,7, 2,12,
-    -1,2, 1,7,
-    5,11, 7,-9,
-    3,5, 6,-8,
-    -13,-4, -8,9,
-    -5,9, -3,-3,
-    -4,-7, -3,-12,
-    6,5, 8,0,
-    -7,6, -6,12,
-    -13,6, -5,-2,
-    1,-10, 3,10,
-    4,1, 8,-4,
-    -2,-2, 2,-13,
-    2,-12, 12,12,
-    -2,-13, 0,-6,
-    4,1, 9,3,
-    -6,-10, -3,-5,
-    -3,-13, -1,1,
-    7,5, 12,-11,
-    4,-2, 5,-7,
-    -13,9, -9,-5,
-    7,1, 8,6,
-    7,-8, 7,6,
-    -7,-4, -7,1,
-    -8,11, -7,-8,
-    -13,6, -12,-8,
-    2,4, 3,9,
-    10,-5, 12,3,
-    -6,-5, -6,7,
-    8,-3, 9,-8,
-    2,-12, 2,8,
-    -11,-2, -10,3,
-    -12,-13, -7,-9,
-    -11,0, -10,-5,
-    5,-3, 11,8,
-    -2,-13, -1,12,
-    -1,-8, 0,9,
-    -13,-11, -12,-5,
-    -10,-2, -10,11,
-    -3,9, -2,-13,
-    2,-3, 3,2,
-    -9,-13, -4,0,
-    -4,6, -3,-10,
-    -4,12, -2,-7,
-    -6,-11, -4,9,
-    6,-3, 6,11,
-    -13,11, -5,5,
-    11,11, 12,6,
-    7,-5, 12,-2,
-    -1,12, 0,7,
-    -4,-8, -3,-2,
-    -7,1, -6,7,
-    -13,-12, -8,-13,
-    -7,-2, -6,-8,
-    -8,5, -6,-9,
-    -5,-1, -4,5,
-    -13,7, -8,10,
-    1,5, 5,-13,
-    1,0, 10,-13,
-    9,12, 10,-1,
-    5,-8, 10,-9,
-    -1,11, 1,-13,
-    -9,-3, -6,2,
-    -1,-10, 1,12,
-    -13,1, -8,-10,
-    8,-11, 10,-6,
-    2,-13, 3,-6,
-    7,-13, 12,-9,
-    -10,-10, -5,-7,
-    -10,-8, -8,-13,
-    4,-6, 8,5,
-    3,12, 8,-13,
-    -4,2, -3,-3,
-    5,-13, 10,-12,
-    4,-13, 5,-1,
-    -9,9, -4,3,
-    0,3, 3,-9,
-    -12,1, -6,1,
-    3,2, 4,-8,
-    -10,-10, -10,9,
-    8,-13, 12,12,
-    -8,-12, -6,-5,
-    2,2, 3,7,
-    10,6, 11,-8,
-    6,8, 8,-12,
-    -7,10, -6,5,
-    -3,-9, -3,9,
-    -1,-13, -1,5,
-    -3,-7, -3,4,
-    -8,-2, -8,3,
-    4,2, 12,12,
-    2,-5, 3,11,
-    6,-9, 11,-13,
-    3,-1, 7,12,
-    11,-1, 12,4,
-    -3,0, -3,6,
-    4,-11, 4,12,
-    2,-4, 2,1,
-    -10,-6, -8,1,
-    -13,7, -11,1,
-    -13,12, -11,-13,
-    6,0, 11,-13,
-    0,-1, 1,4,
-    -13,3, -9,-2,
-    -9,8, -6,-3,
-    -13,-6, -8,-2,
-    5,-9, 8,10,
-    2,7, 3,-9,
-    -1,-6, -1,-1,
-    9,5, 11,-2,
-    11,-3, 12,-8,
-    3,0, 3,5,
-    -1,4, 0,10,
-    3,-6, 4,5,
-    -13,0, -10,5,
-    5,8, 12,11,
-    8,9, 9,-6,
-    7,-4, 8,-12,
-    -10,4, -10,9,
-    7,3, 12,4,
-    9,-7, 10,-2,
-    7,0, 12,-2,
-    -1,-6, 0,-11,
-};
-
-template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
-
-    T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
-        out[i] = el;
-        sum   += el;
-    }
-
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
-}
-
-template<typename T>
-void keep_features(
-    float* x_out,
-    float* y_out,
-    float* score_out,
-    float* size_out,
-    const float* x_in,
-    const float* y_in,
-    const float* score_in,
-    const unsigned* score_idx,
-    const float* size_in,
-    const unsigned n_feat)
-{
-    // Keep only the first n_feat features
-    for (unsigned f = 0; f < n_feat; f++) {
-        x_out[f] = x_in[score_idx[f]];
-        y_out[f] = y_in[score_idx[f]];
-        score_out[f] = score_in[f];
-        if (size_in != nullptr && size_out != nullptr)
-            size_out[f] = size_in[score_idx[f]];
-    }
-}
-
-template<typename T, bool use_scl>
-void harris_response(
-    float* x_out,
-    float* y_out,
-    float* score_out,
-    float* size_out,
-    const float* x_in,
-    const float* y_in,
-    const float* scl_in,
-    const unsigned total_feat,
-    unsigned* usable_feat,
-    const Array<T>& image,
-    const unsigned block_size,
-    const float k_thr,
-    const unsigned patch_size)
-{
-    const af::dim4 idims = image.dims();
-    const T* image_ptr = image.get();
-    for (unsigned f = 0; f < total_feat; f++) {
-        unsigned x, y;
-        float scl = 1.f;
-        if (use_scl) {
-            // Update x and y coordinates according to scale
-            scl = scl_in[f];
-            x = (unsigned)round(x_in[f] * scl);
-            y = (unsigned)round(y_in[f] * scl);
-        }
-        else {
-            x = (unsigned)round(x_in[f]);
-            y = (unsigned)round(y_in[f]);
-        }
-
-        // Round feature size to nearest odd integer
-        float size = 2.f * floor((patch_size * scl) / 2.f) + 1.f;
-
-        // Avoid keeping features that might be too wide and might not fit on
-        // the image, sqrt(2.f) is the radius when angle is 45 degrees and
-        // represents widest case possible
-        unsigned patch_r = ceil(size * sqrt(2.f) / 2.f);
-        if (x < patch_r || y < patch_r || x >= idims[1] - patch_r || y >= idims[0] - patch_r)
-            continue;
-
-        unsigned r = block_size / 2;
-
-        float ixx = 0.f, iyy = 0.f, ixy = 0.f;
-        unsigned block_size_sq = block_size * block_size;
-        for (unsigned k = 0; k < block_size_sq; k++) {
-            int i = k / block_size - r;
-            int j = k % block_size - r;
-
-            // Calculate local x and y derivatives
-            float ix = image_ptr[(x+i+1) * idims[0] + y+j] - image_ptr[(x+i-1) * idims[0] + y+j];
-            float iy = image_ptr[(x+i) * idims[0] + y+j+1] - image_ptr[(x+i) * idims[0] + y+j-1];
-
-            // Accumulate second order derivatives
-            ixx += ix*ix;
-            iyy += iy*iy;
-            ixy += ix*iy;
-        }
-
-        unsigned idx = *usable_feat;
-        *usable_feat += 1;
-        float tr = ixx + iyy;
-        float det = ixx*iyy - ixy*ixy;
-
-        // Calculate Harris responses
-        float resp = det - k_thr * (tr*tr);
-
-        // Scale factor
-        // TODO: improve response scaling
-        float rscale = 0.001f;
-        rscale = rscale * rscale * rscale * rscale;
-
-        x_out[idx] = x;
-        y_out[idx] = y;
-        score_out[idx] = resp * rscale;
-        if (use_scl)
-            size_out[idx] = size;
-    }
-}
-
-template<typename T>
-void centroid_angle(
-    const float* x_in,
-    const float* y_in,
-    float* orientation_out,
-    const unsigned total_feat,
-    const Array<T>& image,
-    const unsigned patch_size)
-{
-    const af::dim4 idims = image.dims();
-    const T* image_ptr = image.get();
-    for (unsigned f = 0; f < total_feat; f++) {
-        unsigned x = (unsigned)round(x_in[f]);
-        unsigned y = (unsigned)round(y_in[f]);
-
-        unsigned r = patch_size / 2;
-        if (x < r || y < r || x > idims[1] - r || y > idims[0] - r)
-            continue;
-
-        T m01 = (T)0, m10 = (T)0;
-        unsigned patch_size_sq = patch_size * patch_size;
-        for (unsigned k = 0; k < patch_size_sq; k++) {
-            int i = k / patch_size - r;
-            int j = k % patch_size - r;
-
-            // Calculate first order moments
-            T p = image_ptr[(x+i) * idims[0] + y+j];
-            m01 += j * p;
-            m10 += i * p;
-        }
-
-        float angle = atan2(m01, m10);
-        orientation_out[f] = angle;
-    }
-}
-
-template<typename T>
-inline T get_pixel(
-    unsigned x,
-    unsigned y,
-    const float ori,
-    const unsigned size,
-    const int dist_x,
-    const int dist_y,
-    const Array<T>& image,
-    const unsigned patch_size)
-{
-    const af::dim4 idims = image.dims();
-    const T* image_ptr = image.get();
-    float ori_sin = sin(ori);
-    float ori_cos = cos(ori);
-    float patch_scl = (float)size / (float)patch_size;
-
-    // Calculate point coordinates based on orientation and size
-    x += round(dist_x * patch_scl * ori_cos - dist_y * patch_scl * ori_sin);
-    y += round(dist_x * patch_scl * ori_sin + dist_y * patch_scl * ori_cos);
-
-    return image_ptr[x * idims[0] + y];
-}
-
-template<typename T>
-void extract_orb(
-    unsigned* desc_out,
-    const unsigned n_feat,
-    float* x_in_out,
-    float* y_in_out,
-    const float* ori_in,
-    float* size_out,
-    const Array<T>& image,
-    const float scl,
-    const unsigned patch_size)
-{
-    const af::dim4 idims = image.dims();
-    for (unsigned f = 0; f < n_feat; f++) {
-        unsigned x = (unsigned)round(x_in_out[f]);
-        unsigned y = (unsigned)round(y_in_out[f]);
-        float ori = ori_in[f];
-        unsigned size = patch_size;
-
-        unsigned r = ceil(patch_size * sqrt(2.f) / 2.f);
-        if (x < r || y < r || x >= idims[1] - r || y >= idims[0] - r)
-            continue;
-
-        // Descriptor fixed at 256 bits for now
-        // Storing descriptor as a vector of 8 x 32-bit unsigned numbers
-        for (unsigned i = 0; i < 8; i++) {
-            unsigned v = 0;
-
-            // j < 32 for 256 bits descriptor
-            for (unsigned j = 0; j < 32; j++) {
-                // Get position from distribution pattern and values of points p1 and p2
-                int dist_x = ref_pat[i*32*4 + j*4];
-                int dist_y = ref_pat[i*32*4 + j*4+1];
-                T p1 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
-
-                dist_x = ref_pat[i*32*4 + j*4+2];
-                dist_y = ref_pat[i*32*4 + j*4+3];
-                T p2 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
-
-                // Calculate bit based on p1 and p2 and shifts it to correct position
-                v |= (p1 < p2) << j;
-            }
-
-            // Store 32 bits of descriptor
-            desc_out[f * 8 + i] += v;
-        }
-
-        x_in_out[f] = round(x * scl);
-        y_in_out[f] = round(y * scl);
-        size_out[f] = patch_size * scl;
-    }
-}
-
-
-
 template<typename T, typename convAccT>
 unsigned orb(Array<float> &x, Array<float> &y,
              Array<float> &score, Array<float> &ori,
@@ -652,7 +144,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
         unsigned usable_feat = 0;
-        harris_response<T, false>(h_x_harris, h_y_harris, h_score_harris, nullptr,
+        kernel::harris_response<T, false>(h_x_harris, h_y_harris, h_score_harris, nullptr,
                                   h_x_feat, h_y_feat, nullptr,
                                   lvl_feat, &usable_feat,
                                   lvl_img,
@@ -689,7 +181,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
         float* h_score_lvl = memAlloc<float>(usable_feat);
 
         // Keep only features with higher Harris responses
-        keep_features<T>(h_x_lvl, h_y_lvl, h_score_lvl, nullptr,
+        kernel::keep_features<T>(h_x_lvl, h_y_lvl, h_score_lvl, nullptr,
                          h_x_harris, h_y_harris, harris_sorted.get(), harris_idx.get(),
                          nullptr, usable_feat);
 
@@ -700,7 +192,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
         float* h_size_lvl = memAlloc<float>(usable_feat);
 
         // Compute orientation of features
-        centroid_angle<T>(h_x_lvl, h_y_lvl, h_ori_lvl, usable_feat,
+        kernel::centroid_angle<T>(h_x_lvl, h_y_lvl, h_ori_lvl, usable_feat,
                           lvl_img, patch_size);
 
         Array<T> lvl_filt = createEmptyArray<T>(dim4());
@@ -723,11 +215,11 @@ unsigned orb(Array<float> &x, Array<float> &y,
         unsigned* h_desc_lvl = memAlloc<unsigned>(usable_feat * 8);
         memset(h_desc_lvl, 0, usable_feat * 8 * sizeof(unsigned));
         if (blur_img)
-            extract_orb<T>(h_desc_lvl, usable_feat,
+            kernel::extract_orb<T>(h_desc_lvl, usable_feat,
                            h_x_lvl, h_y_lvl, h_ori_lvl, h_size_lvl,
                            lvl_filt, lvl_scl, patch_size);
         else
-            extract_orb<T>(h_desc_lvl, usable_feat,
+            kernel::extract_orb<T>(h_desc_lvl, usable_feat,
                            h_x_lvl, h_y_lvl, h_ori_lvl, h_size_lvl,
                            lvl_img, lvl_scl, patch_size);
 
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index 8c83ad68ae..55cf2956a8 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -7,12 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <type_traits>
-#include <random>
-#include <algorithm>
-#include <functional>
-#include <limits>
-#include <type_traits>
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
@@ -20,140 +14,16 @@
 #include <random.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/random.hpp>
 
 namespace cpu
 {
 
-using namespace std;
-
-template<typename T>
-using is_arithmetic_t       = typename enable_if< is_arithmetic<T>::value,      function<T()>>::type;
-template<typename T>
-using is_complex_t          = typename enable_if< is_complex<T>::value,         function<T()>>::type;
-template<typename T>
-using is_floating_point_t   = typename enable_if< is_floating_point<T>::value,  function<T()>>::type;
-
-template<typename T, typename GenType>
-is_arithmetic_t<T>
-urand(GenType &generator)
-{
-    typedef typename conditional<   is_floating_point<T>::value,
-                                    uniform_real_distribution<T>,
-#if OS_WIN
-                                    uniform_int_distribution<unsigned>>::type dist;
-#else
-                                    uniform_int_distribution<T >> ::type dist;
-#endif
-    return bind(dist(), generator);
-}
-
-template<typename T, typename GenType>
-is_complex_t<T>
-urand(GenType &generator)
-{
-    auto func = urand<typename T::value_type>(generator);
-    return [func] () { return T(func(), func());};
-}
-
-template<typename T, typename GenType>
-is_floating_point_t<T>
-nrand(GenType &generator)
-{
-    return bind(normal_distribution<T>(), generator);
-}
-
-template<typename T, typename GenType>
-is_complex_t<T>
-nrand(GenType &generator)
-{
-    auto func = nrand<typename T::value_type>(generator);
-    return [func] () { return T(func(), func());};
-}
-
-static default_random_engine generator;
-static unsigned long long gen_seed = 0;
-static bool is_first = true;
-#define GLOBAL 1
-
-template<typename T>
-void randn_(Array<T> out)
-{
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
-    }
-
-    static auto gen = nrand<T>(generator);
-
-    if (my_seed != gen_seed) {
-        gen = nrand<T>(generator);
-        my_seed = gen_seed;
-    }
-
-    T *outPtr = out.get();
-    for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = gen();
-    }
-}
-
-template<typename T>
-Array<T> randn(const af::dim4 &dims)
-{
-    Array<T> outArray = createEmptyArray<T>(dims);
-    getQueue().enqueue(randn_<T>, outArray);
-    return outArray;
-}
-
-template<typename T>
-void randu_(Array<T> out)
-{
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
-    }
-
-    static auto gen = urand<T>(generator);
-
-    if (my_seed != gen_seed) {
-        gen = urand<T>(generator);
-        my_seed = gen_seed;
-    }
-
-    T *outPtr = out.get();
-    for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = gen();
-    }
-}
-
-template<>
-void randu_(Array<char> out)
-{
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
-    }
-
-    static auto gen = urand<float>(generator);
-
-    if (my_seed != gen_seed) {
-        gen = urand<float>(generator);
-        my_seed = gen_seed;
-    }
-
-    char *outPtr = out.get();
-    for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = gen() > 0.5;
-    }
-}
-
 template<typename T>
 Array<T> randu(const af::dim4 &dims)
 {
     Array<T> outArray = createEmptyArray<T>(dims);
-    getQueue().enqueue(randu_<T>, outArray);
+    getQueue().enqueue(kernel::randu<T>, outArray);
     return outArray;
 }
 
@@ -172,6 +42,14 @@ INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
 
+template<typename T>
+Array<T> randn(const af::dim4 &dims)
+{
+    Array<T> outArray = createEmptyArray<T>(dims);
+    getQueue().enqueue(kernel::randn<T>, outArray);
+    return outArray;
+}
+
 #define INSTANTIATE_NORMAL(T)                              \
     template Array<T>  randn<T>(const af::dim4 &dims);
 
@@ -184,32 +62,36 @@ template<>
 Array<char> randu(const af::dim4 &dims)
 {
     static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
+    if (kernel::is_first) {
+        setSeed(kernel::gen_seed);
+        my_seed = kernel::gen_seed;
     }
 
-    static auto gen = urand<float>(generator);
+    static auto gen = kernel::urand<float>(kernel::generator);
 
-    if (my_seed != gen_seed) {
-        gen = urand<float>(generator);
-        my_seed = gen_seed;
+    if (my_seed != kernel::gen_seed) {
+        gen = kernel::urand<float>(kernel::generator);
+        my_seed = kernel::gen_seed;
     }
 
     Array<char> outArray = createEmptyArray<char>(dims);
-    char *outPtr = outArray.get();
-    for (int i = 0; i < (int)outArray.elements(); i++) {
-        outPtr[i] = gen() > 0.5;
-    }
+    auto func = [=](Array<char> outArray) {
+        char *outPtr = outArray.get();
+        for (int i = 0; i < (int)outArray.elements(); i++) {
+            outPtr[i] = gen() > 0.5;
+        }
+    };
+    getQueue().enqueue(func, outArray);
+
     return outArray;
 }
 
 void setSeed(const uintl seed)
 {
     auto f = [=](const uintl seed){
-        generator.seed(seed);
-        is_first = false;
-        gen_seed = seed;
+        kernel::generator.seed(seed);
+        kernel::is_first = false;
+        kernel::gen_seed = seed;
     };
     getQueue().enqueue(f, seed);
 }
@@ -217,7 +99,7 @@ void setSeed(const uintl seed)
 uintl getSeed()
 {
     getQueue().sync();
-    return gen_seed;
+    return kernel::gen_seed;
 }
 
 }
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index 7837db51ff..b5ba5f89c4 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -16,47 +16,11 @@
 #include <numeric>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/range.hpp>
 
 namespace cpu
 {
 
-///////////////////////////////////////////////////////////////////////////
-// Kernel Functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, int dim>
-void range(Array<T> output)
-{
-    T* out = output.get();
-
-    const dim4 dims = output.dims();
-    const dim4 strides = output.strides();
-
-    for(dim_t w = 0; w < dims[3]; w++) {
-        dim_t offW = w * strides[3];
-        for(dim_t z = 0; z < dims[2]; z++) {
-            dim_t offWZ = offW + z * strides[2];
-            for(dim_t y = 0; y < dims[1]; y++) {
-                dim_t offWZY = offWZ + y * strides[1];
-                for(dim_t x = 0; x < dims[0]; x++) {
-                    dim_t id = offWZY + x;
-                    if(dim == 0) {
-                        out[id] = x;
-                    } else if(dim == 1) {
-                        out[id] = y;
-                    } else if(dim == 2) {
-                        out[id] = z;
-                    } else if(dim == 3) {
-                        out[id] = w;
-                    }
-                }
-            }
-        }
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper Functions
-///////////////////////////////////////////////////////////////////////////
 template<typename T>
 Array<T> range(const dim4& dims, const int seq_dim)
 {
@@ -69,10 +33,10 @@ Array<T> range(const dim4& dims, const int seq_dim)
 
     Array<T> out = createEmptyArray<T>(dims);
     switch(_seq_dim) {
-        case 0: getQueue().enqueue(range<T, 0>, out); break;
-        case 1: getQueue().enqueue(range<T, 1>, out); break;
-        case 2: getQueue().enqueue(range<T, 2>, out); break;
-        case 3: getQueue().enqueue(range<T, 3>, out); break;
+        case 0: getQueue().enqueue(kernel::range<T, 0>, out); break;
+        case 1: getQueue().enqueue(kernel::range<T, 1>, out); break;
+        case 2: getQueue().enqueue(kernel::range<T, 2>, out); break;
+        case 3: getQueue().enqueue(kernel::range<T, 3>, out); break;
         default : AF_ERROR("Invalid rep selection", AF_ERR_ARG);
     }
 
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index cce12268e8..cd44b5e2d0 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -15,9 +15,9 @@
 #include <ops.hpp>
 #include <functional>
 #include <complex>
-
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/reduce.hpp>
 
 using af::dim4;
 
@@ -38,56 +38,6 @@ struct Binary<cdouble, af_add_t>
 namespace cpu
 {
 
-template<af_op_t op, typename Ti, typename To, int D>
-struct reduce_dim
-{
-    void operator()(Array<To> out, const dim_t outOffset,
-                    const Array<Ti> in, const dim_t inOffset,
-                    const int dim, bool change_nan, double nanval)
-    {
-        static const int D1 = D - 1;
-        static reduce_dim<op, Ti, To, D1> reduce_dim_next;
-
-        const dim4 ostrides = out.strides();
-        const dim4 istrides = in.strides();
-        const dim4 odims    = out.dims();
-
-        for (dim_t i = 0; i < odims[D1]; i++) {
-            reduce_dim_next(out, outOffset + i * ostrides[D1],
-                            in, inOffset + i * istrides[D1],
-                            dim, change_nan, nanval);
-        }
-    }
-};
-
-template<af_op_t op, typename Ti, typename To>
-struct reduce_dim<op, Ti, To, 0>
-{
-
-    Transform<Ti, To, op> transform;
-    Binary<To, op> reduce;
-    void operator()(Array<To> out, const dim_t outOffset,
-                    const Array<Ti> in, const dim_t inOffset,
-                    const int dim, bool change_nan, double nanval)
-    {
-        const dim4 istrides = in.strides();
-        const dim4 idims    = in.dims();
-
-        To * const outPtr = out.get() + outOffset;
-        Ti const * const inPtr = in.get() + inOffset;
-        dim_t stride = istrides[dim];
-
-        To out_val = reduce.init();
-        for (dim_t i = 0; i < idims[dim]; i++) {
-            To in_val = transform(inPtr[i * stride]);
-            if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
-            out_val = reduce(in_val, out_val);
-        }
-
-        *outPtr = out_val;
-    }
-};
-
 template<af_op_t op, typename Ti, typename To>
 using reduce_dim_func = std::function<void(Array<To>, const dim_t,
                                            const Array<Ti>, const dim_t,
@@ -101,10 +51,10 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nan
     in.eval();
 
     Array<To> out = createEmptyArray<To>(odims);
-    static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { reduce_dim<op, Ti, To, 1>()
-                                                                , reduce_dim<op, Ti, To, 2>()
-                                                                , reduce_dim<op, Ti, To, 3>()
-                                                                , reduce_dim<op, Ti, To, 4>()};
+    static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { kernel::reduce_dim<op, Ti, To, 1>()
+                                                                , kernel::reduce_dim<op, Ti, To, 2>()
+                                                                , kernel::reduce_dim<op, Ti, To, 3>()
+                                                                , kernel::reduce_dim<op, Ti, To, 4>()};
 
     getQueue().enqueue(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
 
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index f7309c8dbe..ffac11c01d 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -19,193 +19,22 @@
 #include <algorithm>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/regions.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename T>
-class LabelNode
-{
-private:
-    T label;
-    T minLabel;
-    unsigned rank;
-    LabelNode* parent;
-
-public:
-    LabelNode() : label(0), minLabel(0), rank(0), parent(this) { }
-    LabelNode(T label) : label(label), minLabel(label), rank(0), parent(this) { }
-
-    T getLabel()
-    {
-        return label;
-    }
-
-    T getMinLabel()
-    {
-        return minLabel;
-    }
-
-    LabelNode* getParent()
-    {
-        return parent;
-    }
-
-    unsigned getRank()
-    {
-        return rank;
-    }
-
-    void setMinLabel(T l)
-    {
-        minLabel = l;
-    }
-
-    void setParent(LabelNode* p)
-    {
-        parent = p;
-    }
-
-    void setRank(unsigned r)
-    {
-        rank = r;
-    }
-};
-
-template<typename T>
-static LabelNode<T>* find(LabelNode<T>* x)
-{
-    if (x->getParent() != x)
-        x->setParent(find(x->getParent()));
-    return x->getParent();
-}
-
-template<typename T>
-static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
-{
-    LabelNode<T>* xRoot = find(x);
-    LabelNode<T>* yRoot = find(y);
-    if (xRoot == yRoot)
-        return;
-
-    T xMinLabel = xRoot->getMinLabel();
-    T yMinLabel = yRoot->getMinLabel();
-    xRoot->setMinLabel(min(xMinLabel, yMinLabel));
-    yRoot->setMinLabel(min(xMinLabel, yMinLabel));
-
-    if (xRoot->getRank() < yRoot->getRank())
-        xRoot->setParent(yRoot);
-    else if (xRoot->getRank() > yRoot->getRank())
-        yRoot->setParent(xRoot);
-    else {
-        yRoot->setParent(xRoot);
-        xRoot->setRank(xRoot->getRank() + 1);
-    }
-}
-
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity)
 {
     in.eval();
 
-    // Create output placeholder
     Array<T> out = createValueArray(in.dims(), (T)0);
     out.eval();
 
-    auto func = [=] (Array<T> out, const Array<char> in, af_connectivity connectivity) {
-        const dim4 in_dims = in.dims();
-        const char *in_ptr  = in.get();
-        T    *out_ptr = out.get();
-
-        // Map labels
-        typedef typename std::map<T, LabelNode<T>* > label_map_t;
-        typedef typename label_map_t::iterator label_map_iterator_t;
-
-        label_map_t lmap;
-
-        // Initial label
-        T label = (T)1;
-
-        for (int j = 0; j < (int)in_dims[1]; j++) {
-            for (int i = 0; i < (int)in_dims[0]; i++) {
-                int idx = j * in_dims[0] + i;
-                if (in_ptr[idx] != 0) {
-                    std::vector<T> l;
-
-                    // Test neighbors
-                    if (i > 0 && out_ptr[j * (int)in_dims[0] + i-1] > 0)
-                        l.push_back(out_ptr[j * in_dims[0] + i-1]);
-                    if (j > 0 && out_ptr[(j-1) * (int)in_dims[0] + i] > 0)
-                        l.push_back(out_ptr[(j-1) * in_dims[0] + i]);
-                    if (connectivity == AF_CONNECTIVITY_8 && i > 0 &&
-                            j > 0 && out_ptr[(j-1) * in_dims[0] + i-1] > 0)
-                        l.push_back(out_ptr[(j-1) * in_dims[0] + i-1]);
-                    if (connectivity == AF_CONNECTIVITY_8 &&
-                            i < (int)in_dims[0] - 1 && j > 0 && out_ptr[(j-1) * in_dims[0] + i+1] != 0)
-                        l.push_back(out_ptr[(j-1) * in_dims[0] + i+1]);
-
-                    if (!l.empty()) {
-                        T minl = l[0];
-                        for (size_t k = 0; k < l.size(); k++) {
-                            minl = min(l[k], minl);
-                            label_map_iterator_t cur_map = lmap.find(l[k]);
-                            LabelNode<T> *node = cur_map->second;
-                            // Group labels of the same region under a disjoint set
-                            for (size_t m = k+1; m < l.size(); m++)
-                                setUnion(node, lmap.find(l[m])->second);
-                        }
-                        // Set label to smallest neighbor label
-                        out_ptr[idx] = minl;
-                    }
-                    else {
-                        // Insert new label in map
-                        LabelNode<T> *node = new LabelNode<T>(label);
-                        lmap.insert(std::pair<T, LabelNode<T>* >(label, node));
-                        out_ptr[idx] = label++;
-                    }
-                }
-            }
-        }
-
-        std::set<T> removed;
-
-        for (int j = 0; j < (int)in_dims[1]; j++) {
-            for (int i = 0; i < (int)in_dims[0]; i++) {
-                int idx = j * (int)in_dims[0] + i;
-                if (in_ptr[idx] != 0) {
-                    T l = out_ptr[idx];
-                    label_map_iterator_t cur_map = lmap.find(l);
-
-                    if (cur_map != lmap.end()) {
-                        LabelNode<T>* node = cur_map->second;
-
-                        LabelNode<T>* node_root = find(node);
-                        out_ptr[idx] = node_root->getMinLabel();
-
-                        // Mark removed labels (those that are part of a region
-                        // that contains a smaller label)
-                        if (node->getMinLabel() < l || node_root->getMinLabel() < l)
-                            removed.insert(l);
-                        if (node->getLabel() > node->getMinLabel())
-                            removed.insert(node->getLabel());
-                    }
-                }
-            }
-        }
-
-        // Calculate final neighbors (ensure final labels are sequential)
-        for (int j = 0; j < (int)in_dims[1]; j++) {
-            for (int i = 0; i < (int)in_dims[0]; i++) {
-                int idx = j * (int)in_dims[0] + i;
-                if (out_ptr[idx] > 0) {
-                    out_ptr[idx] -= distance(removed.begin(), removed.lower_bound(out_ptr[idx]));
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in, connectivity);
+    getQueue().enqueue(kernel::regions<T>, out, in, connectivity);
 
     return out;
 }
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 1ad7dad6dc..162039b36c 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -9,48 +9,13 @@
 
 #include <Array.hpp>
 #include <reorder.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/reorder.hpp>
 
 namespace cpu
 {
 
-template<typename T>
-void reorder_(Array<T> out, const Array<T> in, const af::dim4 oDims, const af::dim4 rdims)
-{
-    T* outPtr = out.get();
-    const T* inPtr = in.get();
-
-    const af::dim4 ist = in.strides();
-    const af::dim4 ost = out.strides();
-
-
-    dim_t ids[4]  = {0};
-    for(dim_t ow = 0; ow < oDims[3]; ow++) {
-        const dim_t oW = ow * ost[3];
-        ids[rdims[3]] = ow;
-        for(dim_t oz = 0; oz < oDims[2]; oz++) {
-            const dim_t oZW = oW + oz * ost[2];
-            ids[rdims[2]] = oz;
-            for(dim_t oy = 0; oy < oDims[1]; oy++) {
-                const dim_t oYZW = oZW + oy * ost[1];
-                ids[rdims[1]] = oy;
-                for(dim_t ox = 0; ox < oDims[0]; ox++) {
-                    const dim_t oIdx = oYZW + ox;
-
-                    ids[rdims[0]] = ox;
-                    const dim_t iIdx = ids[3] * ist[3] + ids[2] * ist[2] +
-                                          ids[1] * ist[1] + ids[0];
-
-                    outPtr[oIdx] = inPtr[iIdx];
-                }
-            }
-        }
-    }
-}
-
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
 {
@@ -62,7 +27,7 @@ Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
         oDims[i] = iDims[rdims[i]];
 
     Array<T> out = createEmptyArray<T>(oDims);
-    getQueue().enqueue(reorder_<T>, out, in, oDims, rdims);
+    getQueue().enqueue(kernel::reorder<T>, out, in, oDims, rdims);
     return out;
 }
 
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 8fb2edcda6..9a5c85bf1e 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -9,174 +9,16 @@
 
 #include <Array.hpp>
 #include <resize.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <math.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/resize.hpp>
 
 namespace cpu
 {
 
-/**
- * noop function for round to avoid compilation
- * issues due to lack of this function in C90 based
- * compilers, it is only present in C99 and C++11
- *
- * This is not a full fledged implementation, this function
- * is to be used only for positive numbers, i m using it here
- * for calculating dimensions of arrays
- */
-dim_t round2int(float value)
-{
-    return (dim_t)(value+0.5f);
-}
-
-using std::conditional;
-using std::is_same;
-
-template<typename T>
-using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
-
-template<typename T>
-using vtype_t = typename conditional<is_complex<T>::value,
-                                     T, wtype_t<T>
-                                    >::type;
-
-template<typename T, af_interp_type method>
-struct resize_op
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const dim_t x, const dim_t y)
-    {
-        return;
-    }
-};
-
-template<typename T>
-struct resize_op<T, AF_INTERP_NEAREST>
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const dim_t x, const dim_t y)
-    {
-        // Compute Indices
-        dim_t i_x = round2int((float)x / (odims[0] / (float)idims[0]));
-        dim_t i_y = round2int((float)y / (odims[1] / (float)idims[1]));
-
-        if (i_x >= idims[0]) i_x = idims[0] - 1;
-        if (i_y >= idims[1]) i_y = idims[1] - 1;
-
-        dim_t i_off = i_y * istrides[1] + i_x;
-        dim_t o_off =   y * ostrides[1] + x;
-        // Copy values from all channels
-        for(dim_t w = 0; w < odims[3]; w++) {
-            dim_t wost = w * ostrides[3];
-            dim_t wist = w * istrides[3];
-            for(dim_t z = 0; z < odims[2]; z++) {
-                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
-            }
-        }
-    }
-};
-
-template<typename T>
-struct resize_op<T, AF_INTERP_BILINEAR>
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const dim_t x, const dim_t y)
-    {
-        // Compute Indices
-        float f_x = (float)x / (odims[0] / (float)idims[0]);
-        float f_y = (float)y / (odims[1] / (float)idims[1]);
-
-        dim_t i1_x  = floor(f_x);
-        dim_t i1_y  = floor(f_y);
-
-        if (i1_x >= idims[0]) i1_x = idims[0] - 1;
-        if (i1_y >= idims[1]) i1_y = idims[1] - 1;
-
-        float b   = f_x - i1_x;
-        float a   = f_y - i1_y;
-
-        dim_t i2_x  = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
-        dim_t i2_y  = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
-
-        typedef typename dtype_traits<T>::base_type BT;
-        typedef wtype_t<BT> WT;
-        typedef vtype_t<T> VT;
-
-        dim_t o_off = y * ostrides[1] + x;
-        // Copy values from all channels
-        for(dim_t w = 0; w < odims[3]; w++) {
-            dim_t wst = w * istrides[3];
-            for(dim_t z = 0; z < odims[2]; z++) {
-                dim_t zst = z * istrides[2];
-                dim_t channel_off = zst + wst;
-                VT p1 = inPtr[i1_y * istrides[1] + i1_x + channel_off];
-                VT p2 = inPtr[i2_y * istrides[1] + i1_x + channel_off];
-                VT p3 = inPtr[i1_y * istrides[1] + i2_x + channel_off];
-                VT p4 = inPtr[i2_y * istrides[1] + i2_x + channel_off];
-
-                outPtr[o_off + z * ostrides[2] + w * ostrides[3]] =
-                                scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
-                                scalar<WT>((    a   ) * (1.0f - b)) * p2 +
-                                scalar<WT>((1.0f - a) * (    b   )) * p3 +
-                                scalar<WT>((    a   ) * (    b   )) * p4;
-            }
-        }
-    }
-};
-
-template<typename T>
-struct resize_op<T, AF_INTERP_LOWER>
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const dim_t x, const dim_t y)
-    {
-        // Compute Indices
-        dim_t i_x = floor((float)x / (odims[0] / (float)idims[0]));
-        dim_t i_y = floor((float)y / (odims[1] / (float)idims[1]));
-
-        if (i_x >= idims[0]) i_x = idims[0] - 1;
-        if (i_y >= idims[1]) i_y = idims[1] - 1;
-
-        dim_t i_off = i_y * istrides[1] + i_x;
-        dim_t o_off =   y * ostrides[1] + x;
-        // Copy values from all channels
-        for(dim_t w = 0; w < odims[3]; w++) {
-            dim_t wost = w * ostrides[3];
-            dim_t wist = w * istrides[3];
-            for(dim_t z = 0; z < odims[2]; z++) {
-                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
-            }
-        }
-    }
-};
-
-template<typename T, af_interp_type method>
-void resize_(Array<T> out, const Array<T> in)
-{
-    af::dim4 idims    = in.dims();
-    af::dim4 odims    = out.dims();
-    const T *inPtr    = in.get();
-          T *outPtr   = out.get();
-    af::dim4 ostrides = out.strides();
-    af::dim4 istrides = in.strides();
-
-    resize_op<T, method> op;
-    for(dim_t y = 0; y < odims[1]; y++) {
-        for(dim_t x = 0; x < odims[0]; x++) {
-            op(outPtr, inPtr, odims, idims, ostrides, istrides, x, y);
-        }
-    }
-}
-
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method)
@@ -190,11 +32,11 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(resize_<T, AF_INTERP_NEAREST>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(resize_<T, AF_INTERP_BILINEAR>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
         case AF_INTERP_LOWER:
-            getQueue().enqueue(resize_<T, AF_INTERP_LOWER>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
         default: break;
     }
     return out;
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 5687d69c08..e81ee04c80 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -9,77 +9,14 @@
 
 #include <Array.hpp>
 #include <rotate.hpp>
-#include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
 #include "transform_interp.hpp"
+#include <kernel/rotate.hpp>
 
 namespace cpu
 {
 
-template<typename T, af_interp_type method>
-void rotate_(Array<T> output, const Array<T> input, const float theta)
-{
-    const af::dim4 odims    = output.dims();
-    const af::dim4 idims    = input.dims();
-    const af::dim4 ostrides = output.strides();
-    const af::dim4 istrides = input.strides();
-
-    const T* in   = input.get();
-          T* out  = output.get();
-    dim_t nimages = idims[2];
-
-    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
-                 const af::dim4 &, const af::dim4 &,
-                 const dim_t, const dim_t, const dim_t, const dim_t);
-
-    const float c = cos(-theta), s = sin(-theta);
-    float tx, ty;
-    {
-        const float nx = 0.5 * (idims[0] - 1);
-        const float ny = 0.5 * (idims[1] - 1);
-        const float mx = 0.5 * (odims[0] - 1);
-        const float my = 0.5 * (odims[1] - 1);
-        const float sx = (mx * c + my *-s);
-        const float sy = (mx * s + my * c);
-        tx = -(sx - nx);
-        ty = -(sy - ny);
-    }
-
-    const float tmat[6] = {std::round( c * 1000) / 1000.0f,
-                           std::round(-s * 1000) / 1000.0f,
-                           std::round(tx * 1000) / 1000.0f,
-                           std::round( s * 1000) / 1000.0f,
-                           std::round( c * 1000) / 1000.0f,
-                           std::round(ty * 1000) / 1000.0f,
-                          };
-
-    switch(method) {
-        case AF_INTERP_NEAREST:
-            t_fn = &transform_n;
-            break;
-        case AF_INTERP_BILINEAR:
-            t_fn = &transform_b;
-            break;
-        case AF_INTERP_LOWER:
-            t_fn = &transform_l;
-            break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-            break;
-    }
-
-
-    // Do transform for image
-    for(int yy = 0; yy < (int)odims[1]; yy++) {
-        for(int xx = 0; xx < (int)odims[0]; xx++) {
-            t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy);
-        }
-    }
-}
-
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                  const af_interp_type method)
@@ -90,13 +27,13 @@ Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(rotate_<T, AF_INTERP_NEAREST>, out, in, theta);
+            getQueue().enqueue(kernel::rotate<T, AF_INTERP_NEAREST>, out, in, theta);
             break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(rotate_<T, AF_INTERP_BILINEAR>, out, in, theta);
+            getQueue().enqueue(kernel::rotate<T, AF_INTERP_BILINEAR>, out, in, theta);
             break;
         case AF_INTERP_LOWER:
-            getQueue().enqueue(rotate_<T, AF_INTERP_LOWER>, out, in, theta);
+            getQueue().enqueue(kernel::rotate<T, AF_INTERP_LOWER>, out, in, theta);
             break;
         default:
             AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 39157ca9a1..615744fd67 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -16,64 +16,13 @@
 #include <ops.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/scan.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<af_op_t op, typename Ti, typename To, int D>
-struct scan_dim
-{
-    void operator()(Array<To> out, dim_t outOffset,
-                    const Array<Ti> in, dim_t inOffset,
-                    const int dim) const
-    {
-        const dim4 odims    = out.dims();
-        const dim4 ostrides = out.strides();
-        const dim4 istrides = in.strides();
-
-        const int D1 = D - 1;
-        for (dim_t i = 0; i < odims[D1]; i++) {
-            scan_dim<op, Ti, To, D1> func;
-            getQueue().enqueue(func,
-                    out, outOffset + i * ostrides[D1],
-                    in, inOffset + i * istrides[D1], dim);
-            if (D1 == dim) break;
-        }
-    }
-};
-
-template<af_op_t op, typename Ti, typename To>
-struct scan_dim<op, Ti, To, 0>
-{
-    void operator()(Array<To> output, dim_t outOffset,
-                    const Array<Ti> input,  dim_t inOffset,
-                    const int dim) const
-    {
-        const Ti* in = input.get() + inOffset;
-              To* out= output.get()+ outOffset;
-
-        const dim4 ostrides = output.strides();
-        const dim4 istrides = input.strides();
-        const dim4 idims    = input.dims();
-
-        dim_t istride = istrides[dim];
-        dim_t ostride = ostrides[dim];
-
-        Transform<Ti, To, op> transform;
-        // FIXME: Change the name to something better
-        Binary<To, op> scan;
-
-        To out_val = scan.init();
-        for (dim_t i = 0; i < idims[dim]; i++) {
-            To in_val = transform(in[i * istride]);
-            out_val = scan(in_val, out_val);
-            out[i * ostride] = out_val;
-        }
-    }
-};
-
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim)
 {
@@ -84,19 +33,19 @@ Array<To> scan(const Array<Ti>& in, const int dim)
 
     switch (in.ndims()) {
         case 1:
-            scan_dim<op, Ti, To, 1> func1;
+            kernel::scan_dim<op, Ti, To, 1> func1;
             getQueue().enqueue(func1, out, 0, in, 0, dim);
             break;
         case 2:
-            scan_dim<op, Ti, To, 2> func2;
+            kernel::scan_dim<op, Ti, To, 2> func2;
             getQueue().enqueue(func2, out, 0, in, 0, dim);
             break;
         case 3:
-            scan_dim<op, Ti, To, 3> func3;
+            kernel::scan_dim<op, Ti, To, 3> func3;
             getQueue().enqueue(func3, out, 0, in, 0, dim);
             break;
         case 4:
-            scan_dim<op, Ti, To, 4> func4;
+            kernel::scan_dim<op, Ti, To, 4> func4;
             getQueue().enqueue(func4, out, 0, in, 0, dim);
             break;
     }
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 4a219eda04..d9a6795a41 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -6,12 +6,13 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <select.hpp>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/select.hpp>
 
 using af::dim4;
 
@@ -25,66 +26,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Arr
     cond.eval();
     a.eval();
     b.eval();
-    auto func = [=] (Array<T> out, const Array<char> cond, const Array<T> a, const Array<T> b) {
-        dim4 adims = a.dims();
-        dim4 astrides = a.strides();
-        dim4 bdims = b.dims();
-        dim4 bstrides = b.strides();
-
-        dim4 cdims = cond.dims();
-        dim4 cstrides = cond.strides();
-
-        dim4 odims = out.dims();
-        dim4 ostrides = out.strides();
-
-        bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
-            adims[2] == odims[2], adims[3] == odims[3]};
-
-        bool is_b_same[] = {bdims[0] == odims[0], bdims[1] == odims[1],
-            bdims[2] == odims[2], bdims[3] == odims[3]};
-
-        bool is_c_same[] = {cdims[0] == odims[0], cdims[1] == odims[1],
-            cdims[2] == odims[2], cdims[3] == odims[3]};
-
-        const T *aptr = a.get();
-        const T *bptr = b.get();
-        T *optr = out.get();
-        const char *cptr = cond.get();
-
-        for (int l = 0; l < odims[3]; l++) {
-
-            int o_off3   = ostrides[3] * l;
-            int a_off3   = astrides[3] * is_a_same[3] * l;
-            int b_off3   = bstrides[3] * is_b_same[3] * l;
-            int c_off3   = cstrides[3] * is_c_same[3] * l;
-
-            for (int k = 0; k < odims[2]; k++) {
-
-                int o_off2   = ostrides[2] * k + o_off3;
-                int a_off2   = astrides[2] * is_a_same[2] * k + a_off3;
-                int b_off2   = bstrides[2] * is_b_same[2] * k + b_off3;
-                int c_off2   = cstrides[2] * is_c_same[2] * k + c_off3;
-
-                for (int j = 0; j < odims[1]; j++) {
-
-                    int o_off1   = ostrides[1] * j + o_off2;
-                    int a_off1   = astrides[1] * is_a_same[1] * j + a_off2;
-                    int b_off1   = bstrides[1] * is_b_same[1] * j + b_off2;
-                    int c_off1   = cstrides[1] * is_c_same[1] * j + c_off2;
-
-                    for (int i = 0; i < odims[0]; i++) {
-
-                        bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
-                        T    aval = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
-                        T    bval = is_b_same[0] ? bptr[b_off1 + i] : bptr[b_off1];
-                        T    oval = cval ? aval : bval;
-                        optr[o_off1 + i] = oval;
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, cond, a, b);
+    getQueue().enqueue(kernel::select<T>, out, cond, a, b);
 }
 
 template<typename T, bool flip>
@@ -93,44 +35,7 @@ void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, co
     out.eval();
     cond.eval();
     a.eval();
-    auto func = [=] (Array<T> out, const Array<char> cond, const Array<T> a, const double b) {
-        dim4 astrides = a.strides();
-        dim4 cstrides = cond.strides();
-
-        dim4 odims = out.dims();
-        dim4 ostrides = out.strides();
-
-        const T *aptr = a.get();
-        T *optr = out.get();
-        const char *cptr = cond.get();
-
-        for (int l = 0; l < odims[3]; l++) {
-
-            int o_off3 = ostrides[3] * l;
-            int a_off3 = astrides[3] * l;
-            int c_off3 = cstrides[3] * l;
-
-            for (int k = 0; k < odims[2]; k++) {
-
-                int o_off2 = ostrides[2] * k + o_off3;
-                int a_off2 = astrides[2] * k + a_off3;
-                int c_off2 = cstrides[2] * k + c_off3;
-
-                for (int j = 0; j < odims[1]; j++) {
-
-                    int o_off1 = ostrides[1] * j + o_off2;
-                    int a_off1 = astrides[1] * j + a_off2;
-                    int c_off1 = cstrides[1] * j + c_off2;
-
-                    for (int i = 0; i < odims[0]; i++) {
-
-                        optr[o_off1 + i] = (flip ^ cptr[c_off1 + i]) ? aptr[a_off1 + i] : b;
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, cond, a, b);
+    getQueue().enqueue(kernel::select_scalar<T, flip>, out, cond, a, b);
 }
 
 #define INSTANTIATE(T)                                              \
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index 766427bff5..eca1e5063f 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -9,20 +9,13 @@
 
 #include <Array.hpp>
 #include <shift.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
-#include <cassert>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/shift.hpp>
 
 namespace cpu
 {
 
-static inline dim_t simple_mod(const dim_t i, const dim_t dim)
-{
-    return (i < dim) ? i : (i - dim);
-}
-
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4])
 {
@@ -31,48 +24,7 @@ Array<T> shift(const Array<T> &in, const int sdims[4])
     Array<T> out = createEmptyArray<T>(in.dims());
     const af::dim4 temp(sdims[0], sdims[1], sdims[2], sdims[3]);
 
-    auto func = [=] (Array<T> out, const Array<T> in, const af::dim4 sdims) {
-
-        T* outPtr = out.get();
-        const T* inPtr = in.get();
-
-        const af::dim4 oDims = out.dims();
-        const af::dim4 ist   = in.strides();
-        const af::dim4 ost   = out.strides();
-
-        int sdims_[4];
-        // Need to do this because we are mapping output to input in the kernel
-        for(int i = 0; i < 4; i++) {
-            // sdims_[i] will always be positive and always [0, oDims[i]].
-            // Negative shifts are converted to position by going the other way round
-            sdims_[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
-            assert(sdims_[i] >= 0 && sdims_[i] <= oDims[i]);
-        }
-
-        for(dim_t ow = 0; ow < oDims[3]; ow++) {
-            const int oW = ow * ost[3];
-            const int iw = simple_mod((ow + sdims_[3]), oDims[3]);
-            const int iW = iw * ist[3];
-            for(dim_t oz = 0; oz < oDims[2]; oz++) {
-                const int oZW = oW + oz * ost[2];
-                const int iz = simple_mod((oz + sdims_[2]), oDims[2]);
-                const int iZW = iW + iz * ist[2];
-                for(dim_t oy = 0; oy < oDims[1]; oy++) {
-                    const int oYZW = oZW + oy * ost[1];
-                    const int iy = simple_mod((oy + sdims_[1]), oDims[1]);
-                    const int iYZW = iZW + iy * ist[1];
-                    for(dim_t ox = 0; ox < oDims[0]; ox++) {
-                        const int oIdx = oYZW + ox;
-                        const int ix = simple_mod((ox + sdims_[0]), oDims[0]);
-                        const int iIdx = iYZW + ix;
-
-                        outPtr[oIdx] = inPtr[iIdx];
-                    }
-                }
-            }
-        }
-    };
-    getQueue().enqueue(func, out, in, temp);
+    getQueue().enqueue(kernel::shift<T>, out, in, temp);
 
     return out;
 }
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 70bb11d1ae..4b20f8ab49 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -22,7 +22,7 @@
 #include <vector>
 
 #ifdef AF_BUILD_SIFT
-#include <sift_nonfree.hpp>
+#include <kernel/sift_nonfree.hpp>
 #endif
 
 using af::dim4;
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index ba47ba9fd6..161266d7cf 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -13,80 +13,15 @@
 #include <Array.hpp>
 #include <sobel.hpp>
 #include <convolve.hpp>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/sobel.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename Ti, typename To, bool isDX>
-void derivative(Array<To> output, const Array<Ti> input)
-{
-    const dim4 dims    = input.dims();
-    const dim4 strides = input.strides();
-          To* optr     = output.get();
-    const Ti* iptr     = input.get();
-
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-    for(dim_t b2=0; b2<dims[2]; ++b2) {
-
-        for(dim_t j=0; j<dims[1]; ++j) {
-
-            int joff  = j;
-            int _joff = j-1;
-            int joff_ = j+1;
-            int joffset = j*strides[1];
-
-            for(dim_t i=0; i<dims[0]; ++i) {
-
-                To accum = To(0);
-
-                int  ioff = i;
-                int _ioff = i-1;
-                int ioff_ = i+1;
-
-                To NW = (_ioff>=0 && _joff>=0) ?
-                        iptr[_joff*strides[1]+_ioff*strides[0]] : 0;
-                To SW = (ioff_<(int)dims[0] && _joff>=0) ?
-                        iptr[_joff*strides[1]+ioff_*strides[0]] : 0;
-                To NE = (_ioff>=0 && joff_<(int)dims[1]) ?
-                        iptr[joff_*strides[1]+_ioff*strides[0]] : 0;
-                To SE = (ioff_<(int)dims[0] && joff_<(int)dims[1]) ?
-                        iptr[joff_*strides[1]+ioff_*strides[0]] : 0;
-
-                if (isDX) {
-                    To W  = _joff>=0 ?
-                            iptr[_joff*strides[1]+ioff*strides[0]] : 0;
-
-                    To E  = joff_<(int)dims[1] ?
-                            iptr[joff_*strides[1]+ioff*strides[0]] : 0;
-
-                    accum = NW+SW - (NE+SE) + 2*(W-E);
-                } else {
-                    To N  = _ioff>=0 ?
-                            iptr[joff*strides[1]+_ioff*strides[0]] : 0;
-
-                    To S  = ioff_<(int)dims[0] ?
-                            iptr[joff*strides[1]+ioff_*strides[0]] : 0;
-
-                    accum = NW+NE - (SW+SE) + 2*(N-S);
-                }
-
-                optr[joffset+i*strides[0]] = accum;
-            }
-        }
-
-        optr += strides[2];
-        iptr += strides[2];
-    }
-    optr += strides[3];
-    iptr += strides[3];
-    }
-}
-
 template<typename Ti, typename To>
 std::pair< Array<To>, Array<To> >
 sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
@@ -97,8 +32,8 @@ sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
-    getQueue().enqueue(derivative<Ti, To, true >, dx, img);
-    getQueue().enqueue(derivative<Ti, To, false>, dy, img);
+    getQueue().enqueue(kernel::derivative<Ti, To, true >, dx, img);
+    getQueue().enqueue(kernel::derivative<Ti, To, false>, dy, img);
 
     return std::make_pair(dx, dy);
 }
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index cbdb50e987..6a0465cf37 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -11,55 +11,15 @@
 #include <sort.hpp>
 #include <math.hpp>
 #include <copy.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <algorithm>
 #include <functional>
 #include <platform.hpp>
 #include <async_queue.hpp>
-
-using std::greater;
-using std::less;
-using std::sort;
-using std::function;
+#include <kernel/sort.hpp>
 
 namespace cpu
 {
 
-///////////////////////////////////////////////////////////////////////////
-// Kernel Functions
-///////////////////////////////////////////////////////////////////////////
-
-// Based off of http://stackoverflow.com/a/12399290
-template<typename T, bool isAscending>
-void sort0(Array<T> val)
-{
-    // initialize original index locations
-    T *val_ptr = val.get();
-
-    function<bool(T, T)> op = greater<T>();
-    if(isAscending) { op = less<T>(); }
-
-    T *comp_ptr = nullptr;
-    for(dim_t w = 0; w < val.dims()[3]; w++) {
-        dim_t valW = w * val.strides()[3];
-        for(dim_t z = 0; z < val.dims()[2]; z++) {
-            dim_t valWZ = valW + z * val.strides()[2];
-            for(dim_t y = 0; y < val.dims()[1]; y++) {
-
-                dim_t valOffset = valWZ + y * val.strides()[1];
-
-                comp_ptr = val_ptr + valOffset;
-                std::sort(comp_ptr, comp_ptr + val.dims()[0], op);
-            }
-        }
-    }
-    return;
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper Functions
-///////////////////////////////////////////////////////////////////////////
 template<typename T, bool isAscending>
 Array<T> sort(const Array<T> &in, const unsigned dim)
 {
@@ -67,7 +27,7 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
 
     Array<T> out = copyArray<T>(in);
     switch(dim) {
-        case 0: getQueue().enqueue(sort0<T, isAscending>, out); break;
+        case 0: getQueue().enqueue(kernel::sort0<T, isAscending>, out); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
     return out;
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index d2ebd4296d..409b82538e 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -9,92 +9,13 @@
 
 #include <Array.hpp>
 #include <sort_by_key.hpp>
-#include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
-#include <algorithm>
-#include <numeric>
-#include <queue>
 #include <platform.hpp>
 #include <async_queue.hpp>
-
-using std::greater;
-using std::less;
-using std::sort;
-using std::function;
-using std::queue;
-using std::async;
+#include <kernel/sort_by_key.hpp>
 
 namespace cpu
 {
 
-///////////////////////////////////////////////////////////////////////////
-// Kernel Functions
-///////////////////////////////////////////////////////////////////////////
-
-template<typename Tk, typename Tv, bool isAscending>
-void sort0_by_key(Array<Tk> okey, Array<Tv> oval, Array<uint> oidx,
-                  const Array<Tk> ikey, const Array<Tv> ival)
-{
-    function<bool(Tk, Tk)> op = greater<Tk>();
-    if(isAscending) { op = less<Tk>(); }
-
-    // Get pointers and initialize original index locations
-        uint *oidx_ptr = oidx.get();
-          Tk *okey_ptr = okey.get();
-          Tv *oval_ptr = oval.get();
-    const Tk *ikey_ptr = ikey.get();
-    const Tv *ival_ptr = ival.get();
-
-    std::vector<uint> seq_vec(oidx.dims()[0]);
-    std::iota(seq_vec.begin(), seq_vec.end(), 0);
-
-    const Tk *comp_ptr = nullptr;
-    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-    for(dim_t w = 0; w < ikey.dims()[3]; w++) {
-        dim_t okeyW = w * okey.strides()[3];
-        dim_t ovalW = w * oval.strides()[3];
-        dim_t oidxW = w * oidx.strides()[3];
-        dim_t ikeyW = w * ikey.strides()[3];
-        dim_t ivalW = w * ival.strides()[3];
-
-        for(dim_t z = 0; z < ikey.dims()[2]; z++) {
-            dim_t okeyWZ = okeyW + z * okey.strides()[2];
-            dim_t ovalWZ = ovalW + z * oval.strides()[2];
-            dim_t oidxWZ = oidxW + z * oidx.strides()[2];
-            dim_t ikeyWZ = ikeyW + z * ikey.strides()[2];
-            dim_t ivalWZ = ivalW + z * ival.strides()[2];
-
-            for(dim_t y = 0; y < ikey.dims()[1]; y++) {
-
-                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
-                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
-                dim_t oidxOffset = oidxWZ + y * oidx.strides()[1];
-                dim_t ikeyOffset = ikeyWZ + y * ikey.strides()[1];
-                dim_t ivalOffset = ivalWZ + y * ival.strides()[1];
-
-                uint *ptr = oidx_ptr + oidxOffset;
-                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
-
-                comp_ptr = ikey_ptr + ikeyOffset;
-                std::stable_sort(ptr, ptr + ikey.dims()[0], comparator);
-
-                for (dim_t i = 0; i < oval.dims()[0]; ++i){
-                    uint sortIdx = oidx_ptr[oidxOffset + i];
-                    okey_ptr[okeyOffset + i] = ikey_ptr[ikeyOffset + sortIdx];
-                    oval_ptr[ovalOffset + i] = ival_ptr[ivalOffset + sortIdx];
-                }
-            }
-        }
-    }
-
-    return;
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper Functions
-///////////////////////////////////////////////////////////////////////////
 template<typename Tk, typename Tv, bool isAscending>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
            const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
@@ -108,7 +29,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     oidx.eval();
 
     switch(dim) {
-        case 0: getQueue().enqueue(sort0_by_key<Tk, Tv, isAscending>,
+        case 0: getQueue().enqueue(kernel::sort0_by_key<Tk, Tv, isAscending>,
                                    okey, oval, oidx, ikey, ival); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index f9415345ae..ed6afea814 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -10,72 +10,15 @@
 #include <Array.hpp>
 #include <sort_index.hpp>
 #include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <algorithm>
 #include <numeric>
 #include <platform.hpp>
 #include <async_queue.hpp>
-
-using std::greater;
-using std::less;
-using std::sort;
+#include <kernel/sort_index.hpp>
 
 namespace cpu
 {
 
-///////////////////////////////////////////////////////////////////////////
-// Kernel Functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, bool isAscending>
-void sort0_index(Array<T> &val, Array<uint> &idx, const Array<T> &in)
-{
-    // initialize original index locations
-       uint *idx_ptr = idx.get();
-          T *val_ptr = val.get();
-    const T *in_ptr  = in.get();
-    function<bool(T, T)> op = greater<T>();
-    if(isAscending) { op = less<T>(); }
-
-    std::vector<uint> seq_vec(idx.dims()[0]);
-    std::iota(seq_vec.begin(), seq_vec.end(), 0);
-
-    const T *comp_ptr = nullptr;
-    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-    for(dim_t w = 0; w < in.dims()[3]; w++) {
-        dim_t valW = w * val.strides()[3];
-        dim_t idxW = w * idx.strides()[3];
-        dim_t  inW = w *  in.strides()[3];
-        for(dim_t z = 0; z < in.dims()[2]; z++) {
-            dim_t valWZ = valW + z * val.strides()[2];
-            dim_t idxWZ = idxW + z * idx.strides()[2];
-            dim_t  inWZ =  inW + z *  in.strides()[2];
-            for(dim_t y = 0; y < in.dims()[1]; y++) {
-
-                dim_t valOffset = valWZ + y * val.strides()[1];
-                dim_t idxOffset = idxWZ + y * idx.strides()[1];
-                dim_t inOffset  =  inWZ + y *  in.strides()[1];
-
-                uint *ptr = idx_ptr + idxOffset;
-                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
-
-                comp_ptr = in_ptr + inOffset;
-                std::stable_sort(ptr, ptr + in.dims()[0], comparator);
-
-                for (dim_t i = 0; i < val.dims()[0]; ++i){
-                    val_ptr[valOffset + i] = in_ptr[inOffset + idx_ptr[idxOffset + i]];
-                }
-            }
-        }
-    }
-
-    return;
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper Functions
-///////////////////////////////////////////////////////////////////////////
 template<typename T, bool isAscending>
 void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
 {
@@ -84,7 +27,7 @@ void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint
     val = createEmptyArray<T>(in.dims());
     idx = createEmptyArray<uint>(in.dims());
     switch(dim) {
-        case 0: getQueue().enqueue(sort0_index<T, isAscending>, val, idx, in); break;
+        case 0: getQueue().enqueue(kernel::sort0_index<T, isAscending>, val, idx, in); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 }
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index c278908e40..6e8d0fe5b0 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -14,6 +14,7 @@
 #include <memory>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/susan.hpp>
 
 using af::features;
 using std::shared_ptr;
@@ -21,85 +22,6 @@ using std::shared_ptr;
 namespace cpu
 {
 
-template<typename T>
-void susan_responses(Array<T> output, const Array<T> input,
-                     const unsigned idim0, const unsigned idim1,
-                     const int radius, const float t, const float g,
-                     const unsigned border_len)
-{
-    T* resp_out = output.get();
-    const T* in = input.get();
-
-    const unsigned r = border_len;
-    const int rSqrd = radius*radius;
-
-    for (unsigned y = r; y < idim1 - r; ++y) {
-        for (unsigned x = r; x < idim0 - r; ++x) {
-            const unsigned idx = y * idim0 + x;
-            T m_0 = in[idx];
-            float nM = 0.0f;
-
-            for (int i=-radius; i<=radius; ++i) {
-                for (int j=-radius; j<=radius; ++j) {
-                    if (i*i + j*j < rSqrd) {
-                        int p = x + i;
-                        int q = y + j;
-                        T m = in[p + idim0 * q];
-                        float exp_pow = std::pow((m - m_0)/t, 6.0);
-                        float cM = std::exp(-exp_pow);
-                        nM += cM;
-                    }
-                }
-            }
-
-            resp_out[idx] = nM < g ? g - nM : T(0);
-        }
-    }
-}
-
-template<typename T>
-void non_maximal(Array<float> xcoords, Array<float> ycoords, Array<float> response,
-                 shared_ptr<unsigned> counter, const unsigned idim0, const unsigned idim1,
-                 const Array<T> input, const unsigned border_len, const unsigned max_corners)
-{
-    float* x_out    = xcoords.get();
-    float* y_out    = ycoords.get();
-    float* resp_out = response.get();
-    unsigned* count = counter.get();
-    const T* resp_in= input.get();
-
-    // Responses on the border don't have 8-neighbors to compare, discard them
-    const unsigned r = border_len + 1;
-
-    for (unsigned y = r; y < idim1 - r; y++) {
-        for (unsigned x = r; x < idim0 - r; x++) {
-            const T v = resp_in[y * idim0 + x];
-
-            // Find maximum neighborhood response
-            T max_v;
-            max_v = max(resp_in[(y-1) * idim0 + x-1], resp_in[y * idim0 + x-1]);
-            max_v = max(max_v, resp_in[(y+1) * idim0 + x-1]);
-            max_v = max(max_v, resp_in[(y-1) * idim0 + x  ]);
-            max_v = max(max_v, resp_in[(y+1) * idim0 + x  ]);
-            max_v = max(max_v, resp_in[(y-1) * idim0 + x+1]);
-            max_v = max(max_v, resp_in[(y)   * idim0 + x+1]);
-            max_v = max(max_v, resp_in[(y+1) * idim0 + x+1]);
-
-            // Stores corner to {x,y,resp}_out if it's response is maximum compared
-            // to its 8-neighborhood and greater or equal minimum response
-            if (v > max_v) {
-                const unsigned idx = *count;
-                *count += 1;
-                if (idx < max_corners) {
-                    x_out[idx]    = (float)x;
-                    y_out[idx]    = (float)y;
-                    resp_out[idx] = (float)v;
-                }
-            }
-        }
-    }
-}
-
 template<typename T>
 unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                const Array<T> &in,
@@ -118,9 +40,9 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1), memFree<unsigned>);
     corners_found.get()[0] = 0;
 
-    getQueue().enqueue(susan_responses<T>, response, in, idims[0], idims[1],
+    getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0], idims[1],
                        radius, diff_thr, geom_thr, edge);
-    getQueue().enqueue(non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
+    getQueue().enqueue(kernel::non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
                        idims[0], idims[1], response, edge, corner_lim);
     getQueue().sync();
 
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 4f035450ae..6526917d3a 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 #include <tile.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/tile.hpp>
 
 namespace cpu
 {
@@ -32,40 +31,7 @@ Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    auto func = [=] (Array<T> out, const Array<T> in) {
-
-        T* outPtr = out.get();
-        const T* inPtr = in.get();
-
-        const af::dim4 iDims = in.dims();
-        const af::dim4 oDims = out.dims();
-        const af::dim4 ist = in.strides();
-        const af::dim4 ost = out.strides();
-
-        for(dim_t ow = 0; ow < oDims[3]; ow++) {
-            const dim_t iw = ow % iDims[3];
-            const dim_t iW = iw * ist[3];
-            const dim_t oW = ow * ost[3];
-            for(dim_t oz = 0; oz < oDims[2]; oz++) {
-                const dim_t iz = oz % iDims[2];
-                const dim_t iZW = iW + iz * ist[2];
-                const dim_t oZW = oW + oz * ost[2];
-                for(dim_t oy = 0; oy < oDims[1]; oy++) {
-                    const dim_t iy = oy % iDims[1];
-                    const dim_t iYZW = iZW + iy * ist[1];
-                    const dim_t oYZW = oZW + oy * ost[1];
-                    for(dim_t ox = 0; ox < oDims[0]; ox++) {
-                        const dim_t ix = ox % iDims[0];
-                        const dim_t iMem = iYZW + ix;
-                        const dim_t oMem = oYZW + ox;
-                        outPtr[oMem] = inPtr[iMem];
-                    }
-                }
-            }
-        }
-    };
-
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(kernel::tile<T>, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index a7287ceea0..fc7145854b 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -10,99 +10,14 @@
 #include <Array.hpp>
 #include <transform.hpp>
 #include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
 #include "transform_interp.hpp"
+#include <kernel/transform.hpp>
 
 namespace cpu
 {
 
-template <typename T>
-void calc_affine_inverse(T *txo, const T *txi)
-{
-    T det = txi[0]*txi[4] - txi[1]*txi[3];
-
-    txo[0] = txi[4] / det;
-    txo[1] = txi[3] / det;
-    txo[3] = txi[1] / det;
-    txo[4] = txi[0] / det;
-
-    txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-    txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
-}
-
-template <typename T>
-void calc_affine_inverse(T *tmat, const T *tmat_ptr, const bool inverse)
-{
-    // The way kernel is structured, it expects an inverse
-    // transform matrix by default.
-    // If it is an forward transform, then we need its inverse
-    if(inverse) {
-        for(int i = 0; i < 6; i++)
-            tmat[i] = tmat_ptr[i];
-    } else {
-        calc_affine_inverse(tmat, tmat_ptr);
-    }
-}
-
-template<typename T, af_interp_type method>
-void transform_(Array<T> output, const Array<T> input,
-                const Array<float> transform, const bool inverse)
-{
-    const af::dim4 idims    = input.dims();
-    const af::dim4 odims    = output.dims();
-    const af::dim4 istrides = input.strides();
-    const af::dim4 ostrides = output.strides();
-
-    T * out = output.get();
-    const T * in = input.get();
-    const float* tf = transform.get();
-
-    dim_t nimages     = idims[2];
-    // Multiplied in src/backend/transform.cpp
-    dim_t ntransforms = odims[2] / idims[2];
-
-    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
-                 const af::dim4 &, const af::dim4 &,
-                 const dim_t, const dim_t, const dim_t, const dim_t);
-
-    switch(method) {
-        case AF_INTERP_NEAREST:
-            t_fn = &transform_n;
-            break;
-        case AF_INTERP_BILINEAR:
-            t_fn = &transform_b;
-            break;
-        case AF_INTERP_LOWER:
-            t_fn = &transform_l;
-            break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-            break;
-    }
-
-
-    // For each transform channel
-    for(int t_idx = 0; t_idx < (int)ntransforms; t_idx++) {
-        // Compute inverse if required
-        const float *tmat_ptr = tf + t_idx * 6;
-        float tmat[6];
-        calc_affine_inverse(tmat, tmat_ptr, inverse);
-
-        // Offset for output pointer
-        dim_t o_offset = t_idx * nimages * ostrides[2];
-
-        // Do transform for image
-        for(int yy = 0; yy < (int)odims[1]; yy++) {
-            for(int xx = 0; xx < (int)odims[0]; xx++) {
-                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy);
-            }
-        }
-    }
-}
-
 template<typename T>
 Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
                     const af_interp_type method, const bool inverse)
@@ -114,13 +29,13 @@ Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::
 
     switch(method) {
         case AF_INTERP_NEAREST :
-            getQueue().enqueue(transform_<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
             break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(transform_<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
             break;
         case AF_INTERP_LOWER   :
-            getQueue().enqueue(transform_<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
diff --git a/src/backend/cpu/transform_interp.hpp b/src/backend/cpu/transform_interp.hpp
index 5ad47507b2..d90ae38f71 100644
--- a/src/backend/cpu/transform_interp.hpp
+++ b/src/backend/cpu/transform_interp.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <math.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
 
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 7e7eec1747..32663e1f94 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -14,7 +14,7 @@
 #include <transpose.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
-
+#include <kernel/transpose.hpp>
 #include <utility>
 #include <cassert>
 
@@ -23,74 +23,6 @@ using af::dim4;
 namespace cpu
 {
 
-static inline unsigned getIdx(const dim4 &strides,
-        int i, int j = 0, int k = 0, int l = 0)
-{
-    return (l * strides[3] +
-            k * strides[2] +
-            j * strides[1] +
-            i );
-}
-
-template<typename T>
-T getConjugate(const T &in)
-{
-    // For non-complex types return same
-    return in;
-}
-
-template<>
-cfloat getConjugate(const cfloat &in)
-{
-    return std::conj(in);
-}
-
-template<>
-cdouble getConjugate(const cdouble &in)
-{
-    return std::conj(in);
-}
-
-template<typename T, bool conjugate>
-void transpose_(Array<T> output, const Array<T> input)
-{
-    const dim4 odims    = output.dims();
-    const dim4 ostrides = output.strides();
-    const dim4 istrides = input.strides();
-
-    T * out = output.get();
-    T const * const in = input.get();
-
-    for (dim_t l = 0; l < odims[3]; ++l) {
-        for (dim_t k = 0; k < odims[2]; ++k) {
-            // Outermost loop handles batch mode
-            // if input has no data along third dimension
-            // this loop runs only once
-            for (dim_t j = 0; j < odims[1]; ++j) {
-                for (dim_t i = 0; i < odims[0]; ++i) {
-                    // calculate array indices based on offsets and strides
-                    // the helper getIdx takes care of indices
-                    const dim_t inIdx  = getIdx(istrides,j,i,k,l);
-                    const dim_t outIdx = getIdx(ostrides,i,j,k,l);
-                    if(conjugate)
-                        out[outIdx] = getConjugate(in[inIdx]);
-                    else
-                        out[outIdx] = in[inIdx];
-                }
-            }
-            // outData and inData pointers doesn't need to be
-            // offset as the getIdx function is taking care
-            // of the batch parameter
-        }
-    }
-}
-
-template<typename T>
-void transpose_(Array<T> out, const Array<T> in, const bool conjugate)
-{
-    return (conjugate ? transpose_<T, true>(out, in) : transpose_<T, false>(out, in));
-}
-
 template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate)
 {
@@ -101,57 +33,16 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
     // create an array with first two dimensions swapped
     Array<T> out  = createEmptyArray<T>(outDims);
 
-    getQueue().enqueue(transpose_<T>, out, in, conjugate);
+    getQueue().enqueue(kernel::transpose<T>, out, in, conjugate);
 
     return out;
 }
 
-template<typename T, bool conjugate>
-void transpose_inplace(Array<T> input)
-{
-    const dim4 idims    = input.dims();
-    const dim4 istrides = input.strides();
-
-    T * in = input.get();
-
-    for (dim_t l = 0; l < idims[3]; ++l) {
-        for (dim_t k = 0; k < idims[2]; ++k) {
-            // Outermost loop handles batch mode
-            // if input has no data along third dimension
-            // this loop runs only once
-            //
-            // Run only bottom triangle. std::swap swaps with upper triangle
-            for (dim_t j = 0; j < idims[1]; ++j) {
-                for (dim_t i = j + 1; i < idims[0]; ++i) {
-                    // calculate array indices based on offsets and strides
-                    // the helper getIdx takes care of indices
-                    const dim_t iIdx  = getIdx(istrides,j,i,k,l);
-                    const dim_t oIdx = getIdx(istrides,i,j,k,l);
-                    if(conjugate) {
-                        in[iIdx] = getConjugate(in[iIdx]);
-                        in[oIdx] = getConjugate(in[oIdx]);
-                        std::swap(in[iIdx], in[oIdx]);
-                    }
-                    else {
-                        std::swap(in[iIdx], in[oIdx]);
-                    }
-                }
-            }
-        }
-    }
-}
-
-template<typename T>
-void transpose_inplace_(Array<T> in, const bool conjugate)
-{
-    return (conjugate ? transpose_inplace<T, true >(in) : transpose_inplace<T, false>(in));
-}
-
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate)
 {
     in.eval();
-    getQueue().enqueue(transpose_inplace_<T>, in, conjugate);
+    getQueue().enqueue(kernel::transpose_inplace<T>, in, conjugate);
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 13bee164eb..2a9553c83a 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -14,6 +14,7 @@
 #include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/triangle.hpp>
 
 namespace cpu
 {
@@ -21,46 +22,7 @@ namespace cpu
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in)
 {
-    auto func = [=] (Array<T> out, const Array<T> in) {
-        T *o = out.get();
-        const T *i = in.get();
-
-        dim4 odm = out.dims();
-
-        dim4 ost = out.strides();
-        dim4 ist = in.strides();
-
-        for(dim_t ow = 0; ow < odm[3]; ow++) {
-            const dim_t oW = ow * ost[3];
-            const dim_t iW = ow * ist[3];
-
-            for(dim_t oz = 0; oz < odm[2]; oz++) {
-                const dim_t oZW = oW + oz * ost[2];
-                const dim_t iZW = iW + oz * ist[2];
-
-                for(dim_t oy = 0; oy < odm[1]; oy++) {
-                    const dim_t oYZW = oZW + oy * ost[1];
-                    const dim_t iYZW = iZW + oy * ist[1];
-
-                    for(dim_t ox = 0; ox < odm[0]; ox++) {
-                        const dim_t oMem = oYZW + ox;
-                        const dim_t iMem = iYZW + ox;
-
-                        bool cond = is_upper ? (oy >= ox) : (oy <= ox);
-                        bool do_unit_diag = (is_unit_diag && ox == oy);
-                        if(cond) {
-                            o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
-                        } else {
-                            o[oMem] = scalar<T>(0);
-                        }
-
-                    }
-                }
-            }
-        }
-    };
-
-    getQueue().enqueue(func, out, in);
+    getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index 41423c746c..1aa37a4762 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -9,76 +9,15 @@
 
 #include <Array.hpp>
 #include <unwrap.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/unwrap.hpp>
 
 namespace cpu
 {
 
-template<typename T, int d>
-void unwrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
-                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
-{
-    const T *inPtr = in.get();
-    T *outPtr      = out.get();
-
-    af::dim4 idims    = in.dims();
-    af::dim4 odims    = out.dims();
-    af::dim4 istrides = in.strides();
-    af::dim4 ostrides = out.strides();
-
-    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-
-    for(dim_t w = 0; w < odims[3]; w++) {
-        for(dim_t z = 0; z < odims[2]; z++) {
-
-            dim_t cOut = w * ostrides[3] + z * ostrides[2];
-            dim_t cIn  = w * istrides[3] + z * istrides[2];
-            const T* iptr = inPtr  + cIn;
-            T* optr_= outPtr + cOut;
-
-            for(dim_t col = 0; col < odims[d]; col++) {
-                // Offset output ptr
-                T* optr = optr_ + col * ostrides[d];
-
-                // Calculate input window index
-                dim_t winy = (col / nx);
-                dim_t winx = (col % nx);
-
-                dim_t startx = winx * sx;
-                dim_t starty = winy * sy;
-
-                dim_t spx = startx - px;
-                dim_t spy = starty - py;
-
-                // Short cut condition ensuring all values within input dimensions
-                bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 && spy + wy < idims[1]);
-
-                for(dim_t y = 0; y < wy; y++) {
-                    for(dim_t x = 0; x < wx; x++) {
-                        dim_t xpad = spx + x;
-                        dim_t ypad = spy + y;
-
-                        dim_t oloc = (y * wx + x);
-                        if (d == 0) oloc *= ostrides[1];
-
-                        if(cond || (xpad >= 0 && xpad < idims[0] && ypad >= 0 && ypad < idims[1])) {
-                            dim_t iloc = (ypad * istrides[1] + xpad * istrides[0]);
-                            optr[oloc] = iptr[iloc];
-                        } else {
-                            optr[oloc] = scalar<T>(0.0);
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
-
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
@@ -98,9 +37,9 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
     Array<T> outArray = createEmptyArray<T>(odims);
 
     if (is_column) {
-        getQueue().enqueue(unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
     } else {
-        getQueue().enqueue(unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
     }
 
     return outArray;
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 3ff54de640..07487e0d68 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -9,75 +9,15 @@
 
 #include <Array.hpp>
 #include <wrap.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
+#include <kernel/wrap.hpp>
 
 namespace cpu
 {
 
-template<typename T, int d>
-void wrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
-              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
-{
-    const T *inPtr = in.get();
-    T *outPtr      = out.get();
-
-    af::dim4 idims    = in.dims();
-    af::dim4 odims    = out.dims();
-    af::dim4 istrides = in.strides();
-    af::dim4 ostrides = out.strides();
-
-    dim_t nx = (odims[0] + 2 * px - wx) / sx + 1;
-
-    for(dim_t w = 0; w < idims[3]; w++) {
-        for(dim_t z = 0; z < idims[2]; z++) {
-
-            dim_t cIn  = w * istrides[3] + z * istrides[2];
-            dim_t cOut = w * ostrides[3] + z * ostrides[2];
-            const T* iptr_ = inPtr  + cIn;
-            T* optr= outPtr + cOut;
-
-            for(dim_t col = 0; col < idims[d]; col++) {
-                // Offset output ptr
-                const T* iptr = iptr_ + col * istrides[d];
-
-                // Calculate input window index
-                dim_t winy = (col / nx);
-                dim_t winx = (col % nx);
-
-                dim_t startx = winx * sx;
-                dim_t starty = winy * sy;
-
-                dim_t spx = startx - px;
-                dim_t spy = starty - py;
-
-                // Short cut condition ensuring all values within input dimensions
-                bool cond = (spx >= 0 && spx + wx < odims[0] && spy >= 0 && spy + wy < odims[1]);
-
-                for(dim_t y = 0; y < wy; y++) {
-                    for(dim_t x = 0; x < wx; x++) {
-                        dim_t xpad = spx + x;
-                        dim_t ypad = spy + y;
-
-                        dim_t iloc = (y * wx + x);
-                        if (d == 0) iloc *= istrides[1];
-
-                        if(cond || (xpad >= 0 && xpad < odims[0] && ypad >= 0 && ypad < odims[1])) {
-                            dim_t oloc = (ypad * ostrides[1] + xpad * ostrides[0]);
-                            // FIXME: When using threads, atomize this
-                            optr[oloc] += iptr[iloc];
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
-
 template<typename T>
 Array<T> wrap(const Array<T> &in,
               const dim_t ox, const dim_t oy,
@@ -94,9 +34,9 @@ Array<T> wrap(const Array<T> &in,
     in.eval();
 
     if (is_column) {
-        getQueue().enqueue(wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
     } else {
-        getQueue().enqueue(wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
     }
 
     return out;

From 1313f984734b6cee4d254de34d40abb5682fa42c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 20 Dec 2015 11:17:08 -0500
Subject: [PATCH 0163/2677] Fixed the bug in cpu ireduce kernel function

---
 src/backend/cpu/kernel/ireduce.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index 1f5a51da62..848885515b 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -94,13 +94,13 @@ struct ireduce_dim<op, T, 0>
         uint * loc = locArray.get();
 
         dim_t stride = istrides[dim];
-        MinMaxOp<op, T> Op(in[0], 0);
+        MinMaxOp<op, T> Op(in[inOffset], 0);
         for (dim_t i = 0; i < idims[dim]; i++) {
             Op(in[inOffset + i * stride], i);
         }
 
-        *(out+outOffset) = Op.m_val;
-        *(loc+outOffset) = Op.m_idx;
+        out[outOffset] = Op.m_val;
+        loc[outOffset] = Op.m_idx;
     }
 };
 

From 35f0fc29e3172a2ebf36e446e1fe6258aa506e75 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 21 Dec 2015 12:44:30 -0500
Subject: [PATCH 0164/2677] Change clBLAS/FFT external projects to
 clBLAS/FFT-ext

---
 CMakeModules/build_clBLAS.cmake | 6 +++---
 CMakeModules/build_clFFT.cmake  | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index d0a9e135bf..6cb1ae8aaf 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -12,7 +12,7 @@ ELSE()
 ENDIF()
 
 ExternalProject_Add(
-    clBLAS-external
+    clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
     GIT_TAG 102c832825e8e4d60ad73ca97e95668463294068
     PREFIX "${prefix}"
@@ -33,10 +33,10 @@ ExternalProject_Add(
     ${byproducts}
     )
 
-ExternalProject_Get_Property(clBLAS-external install_dir)
+ExternalProject_Get_Property(clBLAS-ext install_dir)
 ADD_LIBRARY(clBLAS IMPORTED STATIC)
 SET_TARGET_PROPERTIES(clBLAS PROPERTIES IMPORTED_LOCATION ${clBLAS_location})
-ADD_DEPENDENCIES(clBLAS clBLAS-external)
+ADD_DEPENDENCIES(clBLAS clBLAS-ext)
 SET(CLBLAS_INCLUDE_DIRS ${install_dir}/include)
 SET(CLBLAS_LIBRARIES clBLAS)
 SET(CLBLAS_FOUND ON)
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 0886679e58..e1dbb3fe1c 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -12,7 +12,7 @@ ELSE()
 ENDIF()
 
 ExternalProject_Add(
-    clFFT-external
+    clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
     GIT_TAG 1597f0f35a644789c7ad77efe79014236cca2fab
     PREFIX "${prefix}"
@@ -34,10 +34,10 @@ ExternalProject_Add(
     ${byproducts}
     )
 
-ExternalProject_Get_Property(clFFT-external install_dir)
+ExternalProject_Get_Property(clFFT-ext install_dir)
 ADD_LIBRARY(clFFT IMPORTED STATIC)
 SET_TARGET_PROPERTIES(clFFT PROPERTIES IMPORTED_LOCATION ${clFFT_location})
-ADD_DEPENDENCIES(clFFT clFFT-external)
+ADD_DEPENDENCIES(clFFT clFFT-ext)
 SET(CLFFT_INCLUDE_DIRS ${install_dir}/include)
 SET(CLFFT_LIBRARIES clFFT)
 SET(CLFFT_FOUND ON)

From 5c0160863c9dd64c1733497fdce24dfdef823bc7 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 21 Dec 2015 13:24:01 -0500
Subject: [PATCH 0165/2677] remove state globals

---
 examples/graphics/gravity_sim.cpp | 79 +++++++++++++++----------------
 1 file changed, 38 insertions(+), 41 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 94d321ba7c..25ca0da4d7 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -17,43 +17,36 @@ using namespace std;
 static const int width = 512, height = 512;
 static const int pixels_per_unit = 20;
 
-af::array p_x;
-af::array p_y;
-af::array vels_x;
-af::array vels_y;
-af::array forces_x;
-af::array forces_y;
-
-void simulate(float dt){
-    p_x += vels_x * pixels_per_unit * dt;
-    p_y += vels_y * pixels_per_unit * dt;
+void simulate(af::array *pos, af::array *vels, af::array *forces, float dt){
+    pos[0] += vels[0] * pixels_per_unit * dt;
+    pos[1] += vels[1] * pixels_per_unit * dt;
 
     //calculate distance to center
-    af::array diff_x = p_x - width/2;
-    af::array diff_y = p_y - height/2;
+    af::array diff_x = pos[0] - width/2;
+    af::array diff_y = pos[1] - height/2;
     af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
 
     //calculate normalised force vectors
-    forces_x = -1 * diff_x / dist;
-    forces_y = -1 * diff_y / dist;
+    forces[0] = -1 * diff_x / dist;
+    forces[1] = -1 * diff_y / dist;
     //update force scaled to time and magnitude constant
-    forces_x *= pixels_per_unit * dt;
-    forces_y *= pixels_per_unit * dt;
+    forces[0] *= pixels_per_unit * dt;
+    forces[1] *= pixels_per_unit * dt;
 
     //dampening
-    vels_x *= 1 - (0.005*dt);
-    vels_y *= 1 - (0.005*dt);
+    vels[0] *= 1 - (0.005*dt);
+    vels[1] *= 1 - (0.005*dt);
 
     //update velocities from forces
-    vels_x += forces_x;
-    vels_y += forces_y;
+    vels[0] += forces[0];
+    vels[1] += forces[1];
 
 }
 
-void collisions(){
+void collisions(af::array *pos, af::array *vels){
     //clamp particles inside screen border
-    af::array projected_px = min(width, max(0, p_x));
-    af::array projected_py = min(height - 1, max(0, p_y));
+    af::array projected_px = min(width, max(0, pos[0]));
+    af::array projected_py = min(height - 1, max(0, pos[1]));
 
     //calculate distance to center
     af::array diff_x = projected_px - width/2;
@@ -64,15 +57,15 @@ void collisions(){
     const int radius = 50;
     const float elastic_constant = 0.91f;
     if(sum<int>(dist<radius) > 0) {
-        vels_x(dist<radius) = -elastic_constant * vels_x(dist<radius);
-        vels_y(dist<radius) = -elastic_constant * vels_y(dist<radius);
+        vels[0](dist<radius) = -elastic_constant * vels[0](dist<radius);
+        vels[1](dist<radius) = -elastic_constant * vels[1](dist<radius);
 
         //normalize diff vector
         diff_x /= dist;
         diff_y /= dist;
         //place all particle colliding with sphere on surface
-        p_x(dist<radius) = width/2 + diff_x(dist<radius) * radius;
-        p_y(dist<radius) = height/2 +  diff_y(dist<radius) * radius;
+        pos[0](dist<radius) = width/2 + diff_x(dist<radius) * radius;
+        pos[1](dist<radius) = height/2 +  diff_y(dist<radius) * radius;
     }
 }
 
@@ -92,15 +85,19 @@ int main(int argc, char *argv[])
         // Initialize the kernel array just once
         const af::array draw_kernel = gaussianKernel(3, 3);
 
+        af::array pos[2];
+        af::array vels[2];
+        af::array forces[2];
+
         // Generate a random starting state
-        p_x = af::randu(total_particles) * width;
-        p_y = af::randu(total_particles) * height;
+        pos[0] = af::randu(total_particles) * width;
+        pos[1] = af::randu(total_particles) * height;
 
-        vels_x = af::randn(total_particles);
-        vels_y = af::randn(total_particles);
+        vels[0] = af::randn(total_particles);
+        vels[1] = af::randn(total_particles);
 
-        forces_x = af::randn(total_particles);
-        forces_y = af::randn(total_particles);
+        forces[0] = af::randn(total_particles);
+        forces[1] = af::randn(total_particles);
 
         af::array image = af::constant(0, width, height);
         af::array ids(total_particles, u32);
@@ -110,7 +107,7 @@ int main(int argc, char *argv[])
             float dt = af::timer::stop(timer);
             timer = af::timer::start();
 
-            ids = (p_x.as(u32) * height) + p_y.as(u32);
+            ids = (pos[0].as(u32) * height) + pos[1].as(u32);
             image(ids) += 255;
             image = convolve2(image, draw_kernel);
             myWindow.image(image);
@@ -119,18 +116,18 @@ int main(int argc, char *argv[])
 
             // Generate a random starting state
             if(frame_count % reset == 0) {
-                p_x = af::randu(total_particles) * width;
-                p_y = af::randu(total_particles) * height;
+                pos[0] = af::randu(total_particles) * width;
+                pos[1] = af::randu(total_particles) * height;
 
-                vels_x = af::randn(total_particles);
-                vels_y = af::randn(total_particles);
+                vels[0] = af::randn(total_particles);
+                vels[1] = af::randn(total_particles);
             }
 
-            //check for collisions and adjust velocities accordingly
-            collisions();
+            //check for collisions and adjust positions/velocities accordingly
+            collisions(pos, vels);
 
             //run force simulation and update particles
-            simulate(dt);
+            simulate(pos, vels, forces, dt);
 
         }
     } catch (af::exception& e) {

From b1aa87590336b011040279151d3790eb8519441d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 21 Dec 2015 13:43:09 -0500
Subject: [PATCH 0166/2677] remove windows pause ifdef

---
 examples/graphics/gravity_sim.cpp | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 25ca0da4d7..3fc19d8c65 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -135,13 +135,6 @@ int main(int argc, char *argv[])
         throw;
     }
 
-    #ifdef WIN32 // pause in Windows
-    if (!(argc == 2 && argv[1][0] == '-')) {
-        printf("hit [enter]...");
-        fflush(stdout);
-        getchar();
-    }
-    #endif
     return 0;
 }
 

From ed0373fa2d24113fc52d6d4f222a7d3826161372 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 21 Dec 2015 14:35:34 -0500
Subject: [PATCH 0167/2677] Creating streams for devices only when device is
 active

---
 src/backend/cuda/platform.cpp | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 76b336c5ad..0dcad3892a 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -332,10 +332,10 @@ DeviceManager::DeviceManager()
 
     sortDevices();
 
-    for(int i = 0; i < nDevices; i++) {
-        setActiveDevice(i, cuDevices[i].nativeId);
-        CUDA_CHECK(cudaStreamCreate(&streams[i]));
-    }
+    // Initialize all streams to 0.
+    // Streams will be created in setActiveDevice()
+    for(int i = 0; i < (int)MAX_DEVICES; i++)
+        streams[i] = (cudaStream_t)0;
 
     const char* deviceENV = getenv("AF_CUDA_DEFAULT_DEVICE");
     if(!deviceENV) {
@@ -381,6 +381,11 @@ int DeviceManager::setActiveDevice(int device, int nId)
         if(nId == -1) nId = getDeviceNativeId(device);
         CUDA_CHECK(cudaSetDevice(nId));
         activeDev = device;
+
+        if(!streams[device]) {
+            CUDA_CHECK(cudaStreamCreate(&streams[device]));
+        }
+
         return old;
     }
 }

From bf9d70ae153dc01a4d1031d3355b194ee645839c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 21 Dec 2015 15:55:08 -0500
Subject: [PATCH 0168/2677] Check the stream before returning in getStream

---
 src/backend/cuda/platform.cpp | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 0dcad3892a..38e8a04d16 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -289,7 +289,17 @@ int getDeviceIdFromNativeId(int nativeId)
 
 cudaStream_t getStream(int device)
 {
-    return DeviceManager::getInstance().streams[device];
+    cudaStream_t str = DeviceManager::getInstance().streams[device];
+    // if the stream has not yet been initialized, ie. the device has not been
+    // set to active at least once (cuz that's where the stream is created)
+    // then set the device, get the stream, reset the device to current
+    if(!str) {
+        int active_dev = DeviceManager::getInstance().activeDev;
+        setDevice(device);
+        str = DeviceManager::getInstance().streams[device];
+        setDevice(active_dev);
+    }
+    return str;
 }
 
 int setDevice(int device)

From b684b06418efe9d2c0f0f3d4be8389c4b2d7789a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 21 Dec 2015 18:19:39 -0500
Subject: [PATCH 0169/2677] Fixed orb async cpu fn

It was a bug in upstream function cpu::fast
---
 src/backend/cpu/fast.cpp |  2 +-
 src/backend/cpu/orb.cpp  | 12 ++++--------
 2 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index fe02387102..42607d888f 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -30,7 +30,6 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const unsigned edge)
 {
     in.eval();
-    getQueue().sync();
 
     dim4 in_dims = in.dims();
     const unsigned max_feat = ceil(in.elements() * feature_ratio);
@@ -43,6 +42,7 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
         V = createValueArray<float>(V_dims, (float)0);
         V.eval();
     }
+    getQueue().sync();
 
     // Arrays containing all features detected before non-maximal suppression.
     dim4 max_feat_dims(max_feat);
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 00fe8203d4..5dd9326134 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -103,12 +103,13 @@ unsigned orb(Array<float> &x, Array<float> &y,
             ldims[1] = round(idims[1] / lvl_scl);
 
             lvl_img = resize<T>(prev_img, ldims[0], ldims[1], AF_INTERP_BILINEAR);
-            lvl_img.eval();
-            getQueue().sync();
 
             prev_img = lvl_img;
             prev_ldims = lvl_img.dims();
         }
+        prev_img.eval();
+        lvl_img.eval();
+        getQueue().sync();
 
 
         Array<float> x_feat = createEmptyArray<float>(dim4());
@@ -125,10 +126,6 @@ unsigned orb(Array<float> &x, Array<float> &y,
 
         unsigned lvl_feat = fast(x_feat, y_feat, score_feat,
                                  lvl_img, fast_thr, 9, 1, 0.15f, edge);
-        x_feat.eval();
-        y_feat.eval();
-        score_feat.eval();
-        getQueue().sync();
 
         if (lvl_feat == 0) {
             continue;
@@ -164,8 +161,6 @@ unsigned orb(Array<float> &x, Array<float> &y,
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(af::dim4());
 
         sort_index<float, false>(harris_sorted, harris_idx, score_harris, 0);
-        harris_sorted.eval();
-        harris_idx.eval();
         getQueue().sync();
 
         usable_feat = std::min(usable_feat, lvl_best[i]);
@@ -203,6 +198,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
                 h_gauss = memAlloc<T>(gauss_dims[0]);
                 gaussian1D(h_gauss, gauss_dims[0], 2.f);
                 gauss_filter = createDeviceDataArray<T>(gauss_dims, h_gauss);
+                gauss_filter.eval();
             }
 
             // Filter level image with Gaussian kernel to reduce noise sensitivity

From ac09f917981295097d30b1fc5a9224d4e8141056 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Dec 2015 14:47:12 -0500
Subject: [PATCH 0170/2677] Fixing documentation for replace function

---
 include/af/data.h | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/include/af/data.h b/include/af/data.h
index bab14905d8..5808833644 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -517,9 +517,9 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is true
+       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is false
        \param[in]  cond is the conditional array
-       \param[in]  b is the array containing elements which replace elements in \p a when \p cond is true
+       \param[in]  b is the array containing elements which replace elements in \p a when \p cond is false
 
        \ingroup data_func_replace
     */
@@ -528,9 +528,9 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is true
+       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is false
        \param[in]  cond is the conditional array
-       \param[in]  b is value that replaces elements in \p a when \p cond is true
+       \param[in]  b is value that replaces elements in \p a when \p cond is false
 
        \ingroup data_func_replace
     */
@@ -836,7 +836,7 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout] a is the array whose values are replaced by \p b when \p cond is true
+       \param[inout] a is the array whose values are replaced by \p b when \p cond is false
        \param[in]  cond is the conditional array
        \param[in]  b is the array containing elements that replaces elements of a where \p cond is false
 
@@ -847,7 +847,7 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout] a is the array whose values are replaced by \p b when \p cond is true
+       \param[inout] a is the array whose values are replaced by \p b when \p cond is false
        \param[in]  cond is the conditional array
        \param[in]  b is the scalar that replaces the false parts of \p a
 

From eed0651712f6428c0544269e8729a7e34f774152 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Dec 2015 14:47:27 -0500
Subject: [PATCH 0171/2677] Adding documentation for `device` that it locks the
 memory.

---
 include/af/array.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/af/array.h b/include/af/array.h
index 193cf8d14d..03f3eeb23a 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -669,7 +669,7 @@ namespace af
         /**
            \defgroup device_func_device array::device<T>
 
-           Get the device pointer from the array
+           Get the device pointer from the array and lock the buffer in memory manager.
            @{
 
            \ingroup arrayfire_func

From cd26c4e4d83145e2e3c28875ca5c34af9a81338e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Dec 2015 15:34:35 -0500
Subject: [PATCH 0172/2677] Updating forge tag to reduce the path lengths

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index fb885843de..9d804943fd 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -22,7 +22,7 @@ ENDIF()
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG af3.2.1
+    GIT_TAG 84f82e8f54746d75ddf66eeef0e4368881da1f6e
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 1c584de84106936a1776ead3bb3ec96093fedf68 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 13:36:42 -0500
Subject: [PATCH 0173/2677] Add extra paths to check for unified on Linux/OSX

---
 src/api/unified/symbol_manager.cpp | 46 ++++++++++++++++++++++++++----
 1 file changed, 41 insertions(+), 5 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 1139f99b3e..0746eb9990 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -71,18 +71,18 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
      * * DYLD_LIBRARY_PATH (Apple)
      * * PATH (Windows)
     */
-    string bkndName = getBkndLibName(bknd_idx);
+    string bkndLibName = getBkndLibName(bknd_idx);
     string show_flag = getEnvVar("AF_SHOW_LOAD_PATH");
     bool show_load_path = show_flag=="1";
 
 #if defined(OS_WIN)
-    HMODULE retVal = LoadLibrary(bkndName.c_str());
+    HMODULE retVal = LoadLibrary(bkndLibName.c_str());
 #else
-    LibHandle retVal = dlopen(bkndName.c_str(), flag);
+    LibHandle retVal = dlopen(bkndLibName.c_str(), flag);
 #endif
     if(retVal != NULL) { // Success
         if (show_load_path)
-            printf("Using %s from system path\n", bkndName.c_str());
+            printf("Using %s from system path\n", bkndLibName.c_str());
     } else {
         /*
          * In the event that dlopen returns NULL, search for the lib
@@ -94,11 +94,12 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
          * Note: This does not guarantee successful loading as the dependent
          * libraries may still not load
         */
+
         for (int i=0; i<NUM_ENV_VARS; ++i) {
             string abs_path = getEnvVar(LIB_AF_ENVARS[i])
                                  + LIB_AF_RPATHS[i]
                                  + (LIB_AF_RPATH_SUFFIX[i] ? LIB_AF_BKND_NAME[bknd_idx]+"/" : "")
-                                 + bkndName;
+                                 + bkndLibName;
 #if defined(OS_WIN)
             replace(abs_path.begin(), abs_path.end(), '/', '\\');
             retVal = LoadLibrary(abs_path.c_str());
@@ -114,7 +115,42 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
                 break;
             }
         }
+
+#if !defined(OS_WIN)
+        /*
+         * If Linux/OSX, then the following are also checked
+         * (only if lib is not found)
+         * /opt/arrayfire/lib
+         * /opt/arrayfire-3/lib
+         * /usr/local/lib
+         * /usr/local/arrayfire/lib
+         * /usr/local/arrayfire-3/lib
+        */
+        if (retVal == NULL) {
+            static const char* extraLibPaths[] = {"/opt/arrayfire-3/lib/",
+                                                  "/opt/arrayfire/lib/",
+                                                  "/usr/local/lib/",
+                                                  "/usr/local/arrayfire-3/lib/",
+                                                  "/usr/local/arrayfire/lib/",
+                                                 };
+            const int nPaths = sizeof(extraLibPaths) / sizeof(extraLibPaths[0]);
+
+            for (int i = 0; i < nPaths; ++i) {
+                string abs_path = extraLibPaths[i] + bkndLibName;
+                retVal = dlopen(abs_path.c_str(), flag);
+                if (retVal != NULL) {
+                    if (show_load_path)
+                        printf("Using %s\n", abs_path.c_str());
+                    // if the current absolute path based dlopen
+                    // search is a success, then abandon search
+                    // and proceed for compute
+                    break;
+                }
+            }
+        }
+#endif
     }
+
     return retVal;
 }
 

From dc4c47926a4870fa722c40dd088116f46ca2b1e5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 14:17:52 -0500
Subject: [PATCH 0174/2677] Added documentation link when library fails to load
 in unified

---
 src/api/c/err_common.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 371bbd95fa..e7faeaead8 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -179,7 +179,7 @@ const char *af_err_to_string(const af_err err)
     case AF_ERR_NOT_CONFIGURED: return "Function not configured to build";
     case AF_ERR_TYPE:           return "Function does not support this data type";
     case AF_ERR_NO_DBL:         return "Double precision not supported for this device";
-    case AF_ERR_LOAD_LIB:       return "Failed to load dynamic library";
+    case AF_ERR_LOAD_LIB:       return "Failed to load dynamic library. See http://www.arrayfire.com/docs/unifiedbackend.htm for instructions to set up environment for Unified backend";
     case AF_ERR_LOAD_SYM:       return "Failed to load symbol";
     case AF_ERR_UNKNOWN:
     default:

From 1ed27ff87fb9227e988eee60168bea1700167bdb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 14:18:14 -0500
Subject: [PATCH 0175/2677] Add detail for AF_ERR_ARR_BKND_MISMATCH

---
 src/api/c/err_common.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index e7faeaead8..7f8d89ec02 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -181,6 +181,8 @@ const char *af_err_to_string(const af_err err)
     case AF_ERR_NO_DBL:         return "Double precision not supported for this device";
     case AF_ERR_LOAD_LIB:       return "Failed to load dynamic library. See http://www.arrayfire.com/docs/unifiedbackend.htm for instructions to set up environment for Unified backend";
     case AF_ERR_LOAD_SYM:       return "Failed to load symbol";
+    case AF_ERR_ARR_BKND_MISMATCH   :
+                                return "There was a mismatch between an array and the current backend";
     case AF_ERR_UNKNOWN:
     default:
         return "Unknown error";

From 7a93d9a2a7f3b96427be352fb0f6892131326eb0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 15:06:39 -0500
Subject: [PATCH 0176/2677] Using vector<string> in unified for extra paths

---
 src/api/unified/symbol_manager.cpp | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 0746eb9990..f79dc6d79f 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -127,15 +127,15 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
          * /usr/local/arrayfire-3/lib
         */
         if (retVal == NULL) {
-            static const char* extraLibPaths[] = {"/opt/arrayfire-3/lib/",
-                                                  "/opt/arrayfire/lib/",
-                                                  "/usr/local/lib/",
-                                                  "/usr/local/arrayfire-3/lib/",
-                                                  "/usr/local/arrayfire/lib/",
-                                                 };
-            const int nPaths = sizeof(extraLibPaths) / sizeof(extraLibPaths[0]);
+            static
+            std::vector<std::string> extraLibPaths {"/opt/arrayfire-3/lib/",
+                                                    "/opt/arrayfire/lib/",
+                                                    "/usr/local/lib/",
+                                                    "/usr/local/arrayfire-3/lib/",
+                                                    "/usr/local/arrayfire/lib/",
+                                                   };
 
-            for (int i = 0; i < nPaths; ++i) {
+            for (int i = 0; i < (int)extraLibPaths.size(); ++i) {
                 string abs_path = extraLibPaths[i] + bkndLibName;
                 retVal = dlopen(abs_path.c_str(), flag);
                 if (retVal != NULL) {

From a0f17b6ba7adedf0f7ee1093bf8217661fe77679 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 16:29:49 -0500
Subject: [PATCH 0177/2677] cmake fix to check for threads submodule

---
 src/backend/cpu/CMakeLists.txt | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 62f0b3a55e..c2b4e97cd2 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -47,6 +47,18 @@ IF(NOT UNIX)
     ADD_DEFINITIONS(-DAFDLL)
 ENDIF()
 
+SET(THREADS_SRC_DIR "${CMAKE_CURRENT_SOURCE_DIR}/threads")
+IF(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}")
+    # threads submodule has been initialized
+    # Nothing to do
+ELSE(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}")
+    MESSAGE(STATUS "threads submodule unavailable. Updating submodules.")
+    EXECUTE_PROCESS(
+        COMMAND git submodule update --init --recursive
+        WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+        )
+ENDIF(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}")
+
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
     "${CMAKE_SOURCE_DIR}/src/backend/cpu"

From c539f1d5ab0fbb5b609f30df858fbb8270e1df99 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 16:40:25 -0500
Subject: [PATCH 0178/2677] moved fft cpu fns implementations to kernel
 namespace

---
 src/backend/cpu/fft.cpp        | 188 ++------------------------------
 src/backend/cpu/kernel/fft.hpp | 192 +++++++++++++++++++++++++++++++++
 2 files changed, 200 insertions(+), 180 deletions(-)
 create mode 100644 src/backend/cpu/kernel/fft.hpp

diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index e522954cfe..2edced2219 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -12,8 +12,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fft.hpp>
-#include <err_cpu.hpp>
-#include <fftw3.h>
+#include <kernel/fft.hpp>
 #include <copy.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -24,143 +23,11 @@ using af::dim4;
 namespace cpu
 {
 
-template<int rank>
-void computeDims(int rdims[rank], const dim4 &idims)
-{
-    for (int i = 0; i < rank; i++) {
-        rdims[i] = idims[(rank -1) - i];
-    }
-}
-
-template<typename T>
-struct fftw_transform;
-
-#define TRANSFORM(PRE, TY)                                              \
-    template<>                                                          \
-    struct fftw_transform<TY>                                           \
-    {                                                                   \
-        typedef PRE##_plan plan_t;                                      \
-        typedef PRE##_complex ctype_t;                                  \
-                                                                        \
-        template<typename... Args>                                      \
-            plan_t create(Args... args)                                 \
-        { return PRE##_plan_many_dft(args...); }                        \
-        void execute(plan_t plan) { return PRE##_execute(plan); }       \
-        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); }  \
-    };                                                                  \
-
-
-TRANSFORM(fftwf, cfloat)
-TRANSFORM(fftw, cdouble)
-
-template<typename T, int rank, bool direction>
-void fft_inplace_(Array<T> in)
-{
-    int t_dims[rank];
-    int in_embed[rank];
-
-    const dim4 idims = in.dims();
-
-    computeDims<rank>(t_dims  , idims);
-    computeDims<rank>(in_embed , in.getDataDims());
-
-    const dim4 istrides = in.strides();
-
-    typedef typename fftw_transform<T>::ctype_t ctype_t;
-    typename fftw_transform<T>::plan_t plan;
-
-    fftw_transform<T> transform;
-
-    int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= idims[i];
-    }
-
-    plan = transform.create(rank,
-                            t_dims,
-                            (int)batch,
-                            (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            direction ? FFTW_FORWARD : FFTW_BACKWARD,
-                            FFTW_ESTIMATE);
-
-    transform.execute(plan);
-    transform.destroy(plan);
-}
-
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in)
 {
     in.eval();
-    getQueue().enqueue(fft_inplace_<T, rank, direction>, in);
-}
-
-template<typename To, typename Ti>
-struct fftw_real_transform;
-
-#define TRANSFORM_REAL(PRE, To, Ti, POST)                               \
-    template<>                                                          \
-    struct fftw_real_transform<To, Ti>                                  \
-    {                                                                   \
-        typedef PRE##_plan plan_t;                                      \
-        typedef PRE##_complex ctype_t;                                  \
-                                                                        \
-        template<typename... Args>                                      \
-            plan_t create(Args... args)                                 \
-        { return PRE##_plan_many_dft_##POST(args...); }                 \
-        void execute(plan_t plan) { return PRE##_execute(plan); }       \
-        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); }  \
-    };                                                                  \
-
-
-TRANSFORM_REAL(fftwf, cfloat , float , r2c)
-TRANSFORM_REAL(fftw , cdouble, double, r2c)
-TRANSFORM_REAL(fftwf, float , cfloat , c2r)
-TRANSFORM_REAL(fftw , double, cdouble, c2r)
-
-template<typename Tc, typename Tr, int rank>
-void fft_r2c_(Array<Tc> out, const Array<Tr> in)
-{
-    dim4 idims = in.dims();
-
-    int t_dims[rank];
-    int in_embed[rank];
-    int out_embed[rank];
-
-    computeDims<rank>(t_dims  , idims);
-    computeDims<rank>(in_embed , in.getDataDims());
-    computeDims<rank>(out_embed , out.getDataDims());
-
-    const dim4 istrides = in.strides();
-    const dim4 ostrides = out.strides();
-
-    typedef typename fftw_real_transform<Tc, Tr>::ctype_t ctype_t;
-    typename fftw_real_transform<Tc, Tr>::plan_t plan;
-
-    fftw_real_transform<Tc, Tr> transform;
-
-    int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= idims[i];
-    }
-
-    plan = transform.create(rank,
-                            t_dims,
-                            (int)batch,
-                            (Tr *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            (ctype_t *)out.get(),
-                            out_embed, (int)ostrides[0],
-                            (int)ostrides[rank],
-                            FFTW_ESTIMATE);
-
-    transform.execute(plan);
-    transform.destroy(plan);
+    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in);
 }
 
 template<typename Tc, typename Tr, int rank>
@@ -172,57 +39,18 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     odims[0] = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    getQueue().enqueue(fft_r2c_<Tc, Tr, rank>, out, in);
+    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, in);
 
     return out;
 }
 
-template<typename Tr, typename Tc, int rank>
-void fft_c2r_(Array<Tr> out, const Array<Tc> in, const dim4 odims)
-{
-    int t_dims[rank];
-    int in_embed[rank];
-    int out_embed[rank];
-
-    computeDims<rank>(t_dims  , odims);
-    computeDims<rank>(in_embed , in.getDataDims());
-    computeDims<rank>(out_embed , out.getDataDims());
-
-    const dim4 istrides = in.strides();
-    const dim4 ostrides = out.strides();
-
-    typedef typename fftw_real_transform<Tr, Tc>::ctype_t ctype_t;
-    typename fftw_real_transform<Tr, Tc>::plan_t plan;
-
-    fftw_real_transform<Tr, Tc> transform;
-
-    int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= odims[i];
-    }
-
-    plan = transform.create(rank,
-                            t_dims,
-                            (int)batch,
-                            (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            (Tr *)out.get(),
-                            out_embed, (int)ostrides[0],
-                            (int)ostrides[rank],
-                            FFTW_ESTIMATE);
-
-    transform.execute(plan);
-    transform.destroy(plan);
-}
-
 template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
 {
     in.eval();
 
     Array<Tr> out = createEmptyArray<Tr>(odims);
-    getQueue().enqueue(fft_c2r_<Tr, Tc, rank>, out, in, odims);
+    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, in, odims);
 
     return out;
 }
@@ -235,8 +63,8 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     template void fft_inplace<T, 2, false>(Array<T> &in);   \
     template void fft_inplace<T, 3, false>(Array<T> &in);
 
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
 
 #define INSTANTIATE_REAL(Tr, Tc)                                        \
     template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in);         \
@@ -246,7 +74,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in, const dim4 &odims); \
     template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in, const dim4 &odims); \
 
-    INSTANTIATE_REAL(float , cfloat )
-    INSTANTIATE_REAL(double, cdouble)
+INSTANTIATE_REAL(float , cfloat )
+INSTANTIATE_REAL(double, cdouble)
 
 }
diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
new file mode 100644
index 0000000000..906c8ef5f5
--- /dev/null
+++ b/src/backend/cpu/kernel/fft.hpp
@@ -0,0 +1,192 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <fftw3.h>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<int rank>
+void computeDims(int rdims[rank], const af::dim4 &idims)
+{
+    for (int i = 0; i < rank; i++) {
+        rdims[i] = idims[(rank -1) - i];
+    }
+}
+
+template<typename T>
+struct fftw_transform;
+
+#define TRANSFORM(PRE, TY)                                              \
+    template<>                                                          \
+    struct fftw_transform<TY>                                           \
+    {                                                                   \
+        typedef PRE##_plan plan_t;                                      \
+        typedef PRE##_complex ctype_t;                                  \
+                                                                        \
+        template<typename... Args>                                      \
+            plan_t create(Args... args)                                 \
+        { return PRE##_plan_many_dft(args...); }                        \
+        void execute(plan_t plan) { return PRE##_execute(plan); }       \
+        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); }  \
+    };                                                                  \
+
+
+TRANSFORM(fftwf, cfloat)
+TRANSFORM(fftw, cdouble)
+
+template<typename To, typename Ti>
+struct fftw_real_transform;
+
+#define TRANSFORM_REAL(PRE, To, Ti, POST)                               \
+    template<>                                                          \
+    struct fftw_real_transform<To, Ti>                                  \
+    {                                                                   \
+        typedef PRE##_plan plan_t;                                      \
+        typedef PRE##_complex ctype_t;                                  \
+                                                                        \
+        template<typename... Args>                                      \
+            plan_t create(Args... args)                                 \
+        { return PRE##_plan_many_dft_##POST(args...); }                 \
+        void execute(plan_t plan) { return PRE##_execute(plan); }       \
+        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); }  \
+    };                                                                  \
+
+
+TRANSFORM_REAL(fftwf, cfloat , float , r2c)
+TRANSFORM_REAL(fftw , cdouble, double, r2c)
+TRANSFORM_REAL(fftwf, float , cfloat , c2r)
+TRANSFORM_REAL(fftw , double, cdouble, c2r)
+
+
+template<typename T, int rank, bool direction>
+void fft_inplace(Array<T> in)
+{
+    int t_dims[rank];
+    int in_embed[rank];
+
+    const af::dim4 idims = in.dims();
+
+    computeDims<rank>(t_dims  , idims);
+    computeDims<rank>(in_embed , in.getDataDims());
+
+    const af::dim4 istrides = in.strides();
+
+    typedef typename fftw_transform<T>::ctype_t ctype_t;
+    typename fftw_transform<T>::plan_t plan;
+
+    fftw_transform<T> transform;
+
+    int batch = 1;
+    for (int i = rank; i < 4; i++) {
+        batch *= idims[i];
+    }
+
+    plan = transform.create(rank,
+                            t_dims,
+                            (int)batch,
+                            (ctype_t *)in.get(),
+                            in_embed, (int)istrides[0],
+                            (int)istrides[rank],
+                            (ctype_t *)in.get(),
+                            in_embed, (int)istrides[0],
+                            (int)istrides[rank],
+                            direction ? FFTW_FORWARD : FFTW_BACKWARD,
+                            FFTW_ESTIMATE);
+
+    transform.execute(plan);
+    transform.destroy(plan);
+}
+
+template<typename Tc, typename Tr, int rank>
+void fft_r2c(Array<Tc> out, const Array<Tr> in)
+{
+    af::dim4 idims = in.dims();
+
+    int t_dims[rank];
+    int in_embed[rank];
+    int out_embed[rank];
+
+    computeDims<rank>(t_dims  , idims);
+    computeDims<rank>(in_embed , in.getDataDims());
+    computeDims<rank>(out_embed , out.getDataDims());
+
+    const af::dim4 istrides = in.strides();
+    const af::dim4 ostrides = out.strides();
+
+    typedef typename fftw_real_transform<Tc, Tr>::ctype_t ctype_t;
+    typename fftw_real_transform<Tc, Tr>::plan_t plan;
+
+    fftw_real_transform<Tc, Tr> transform;
+
+    int batch = 1;
+    for (int i = rank; i < 4; i++) {
+        batch *= idims[i];
+    }
+
+    plan = transform.create(rank,
+                            t_dims,
+                            (int)batch,
+                            (Tr *)in.get(),
+                            in_embed, (int)istrides[0],
+                            (int)istrides[rank],
+                            (ctype_t *)out.get(),
+                            out_embed, (int)ostrides[0],
+                            (int)ostrides[rank],
+                            FFTW_ESTIMATE);
+
+    transform.execute(plan);
+    transform.destroy(plan);
+}
+
+template<typename Tr, typename Tc, int rank>
+void fft_c2r(Array<Tr> out, const Array<Tc> in, const af::dim4 odims)
+{
+    int t_dims[rank];
+    int in_embed[rank];
+    int out_embed[rank];
+
+    computeDims<rank>(t_dims  , odims);
+    computeDims<rank>(in_embed , in.getDataDims());
+    computeDims<rank>(out_embed , out.getDataDims());
+
+    const af::dim4 istrides = in.strides();
+    const af::dim4 ostrides = out.strides();
+
+    typedef typename fftw_real_transform<Tr, Tc>::ctype_t ctype_t;
+    typename fftw_real_transform<Tr, Tc>::plan_t plan;
+
+    fftw_real_transform<Tr, Tc> transform;
+
+    int batch = 1;
+    for (int i = rank; i < 4; i++) {
+        batch *= odims[i];
+    }
+
+    plan = transform.create(rank,
+                            t_dims,
+                            (int)batch,
+                            (ctype_t *)in.get(),
+                            in_embed, (int)istrides[0],
+                            (int)istrides[rank],
+                            (Tr *)out.get(),
+                            out_embed, (int)ostrides[0],
+                            (int)ostrides[rank],
+                            FFTW_ESTIMATE);
+
+    transform.execute(plan);
+    transform.destroy(plan);
+}
+
+}
+}

From 483121596dd76d4d9c7227d2057b49863d073275 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 16:47:42 -0500
Subject: [PATCH 0179/2677] moved dot cpu implementation to kernel namespace

---
 src/backend/cpu/blas.cpp       | 47 ++++++++--------------------------
 src/backend/cpu/kernel/dot.hpp | 46 +++++++++++++++++++++++++++++++++
 2 files changed, 57 insertions(+), 36 deletions(-)
 create mode 100644 src/backend/cpu/kernel/dot.hpp

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 26ec8b488b..d6f5dee203 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -11,20 +11,20 @@
 #include <af/dim4.hpp>
 #include <handle.hpp>
 #include <cassert>
-#include <err_cpu.hpp>
 #include <err_common.hpp>
+#include <kernel/dot.hpp>
 #include <platform.hpp>
 #include <async_queue.hpp>
 
 namespace cpu
 {
 
-    using std::add_const;
-    using std::add_pointer;
-    using std::enable_if;
-    using std::is_floating_point;
-    using std::remove_const;
-    using std::conditional;
+using std::add_const;
+using std::add_pointer;
+using std::enable_if;
+using std::is_floating_point;
+using std::remove_const;
+using std::conditional;
 
 // Some implementations of BLAS require void* for complex pointers while others use float*/double*
 //
@@ -199,31 +199,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     return out;
 }
 
-template<typename T> T
-conj(T  x) { return x; }
-
-template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
-template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
-
-template<typename T, bool conjugate, bool both_conjugate>
-void dot_(Array<T> output, const Array<T> &lhs, const Array<T> &rhs,
-              af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    int N = lhs.dims()[0];
-
-    T out = 0;
-    const T *pL = lhs.get();
-    const T *pR = rhs.get();
-
-    for(int i = 0; i < N; i++)
-        out += (conjugate ? cpu::conj(pL[i]) : pL[i]) * pR[i];
-
-    if(both_conjugate) out = cpu::conj(out);
-
-    *output.get() = out;
-
-}
-
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
@@ -233,13 +208,13 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
 
     Array<T> out = createEmptyArray<T>(af::dim4(1));
     if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        getQueue().enqueue(dot_<T, false, true>, out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, false, true>, out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        getQueue().enqueue(dot_<T, true, false>,out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, true, false>,out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        getQueue().enqueue(dot_<T, true, false>,out, rhs, lhs, optRhs, optLhs);
+        getQueue().enqueue(kernel::dot<T, true, false>,out, rhs, lhs, optRhs, optLhs);
     } else {
-        getQueue().enqueue(dot_<T, false, false>,out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, false, false>,out, lhs, rhs, optLhs, optRhs);
     }
     return out;
 }
diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
new file mode 100644
index 0000000000..ef518413c7
--- /dev/null
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -0,0 +1,46 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <complex>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T> T
+conj(T  x) { return x; }
+
+template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
+template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
+
+template<typename T, bool conjugate, bool both_conjugate>
+void dot(Array<T> output, const Array<T> &lhs, const Array<T> &rhs,
+         af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    int N = lhs.dims()[0];
+
+    T out = 0;
+    const T *pL = lhs.get();
+    const T *pR = rhs.get();
+
+    for(int i = 0; i < N; i++)
+        out += (conjugate ? kernel::conj(pL[i]) : pL[i]) * pR[i];
+
+    if(both_conjugate) out = kernel::conj(out);
+
+    *output.get() = out;
+
+}
+
+}
+}

From d1089f858eb15d2e74b2f63a5194ca8ab8db59a6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 16:54:30 -0500
Subject: [PATCH 0180/2677] moved fftconvolve reorder helper fn to kernel
 namespace

---
 src/backend/cpu/fftconvolve.cpp        | 32 +++-----------------------
 src/backend/cpu/kernel/fftconvolve.hpp | 29 +++++++++++++++++++++++
 2 files changed, 32 insertions(+), 29 deletions(-)

diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 2678c7b6f0..c0a9a41240 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -211,35 +211,9 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    auto reorderFunc = [=](Array<T> out, Array<convT> packed,
-                           const Array<T> filter, const dim_t sig_hald_d0, const dim_t fftScale,
-                           const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
-                           const dim4 filter_tmp_dims, const dim4 filter_tmp_strides) {
-        T* out_ptr = out.get();
-        const af::dim4 out_dims = out.dims();
-        const af::dim4 out_strides = out.strides();
-
-        const af::dim4 filter_dims = filter.dims();
-
-        convT* packed_ptr = packed.get();
-        convT* sig_tmp_ptr    = packed_ptr;
-        convT* filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
-
-        // Reorder the output
-        if (kind == CONVOLVE_BATCH_KERNEL) {
-            kernel::reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
-                                              filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-                                              filter_dims, sig_half_d0, baseDim, fftScale, expand);
-        } else {
-            kernel::reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
-                                              sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-                                              filter_dims, sig_half_d0, baseDim, fftScale, expand);
-        }
-    };
-
-    getQueue().enqueue(reorderFunc, out, packed, filter, sig_half_d0, fftScale,
-                       sig_tmp_dims, sig_tmp_strides,
-                       filter_tmp_dims, filter_tmp_strides);
+    getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out, packed, filter,
+                       sig_half_d0, fftScale, sig_tmp_dims, sig_tmp_strides, filter_tmp_dims,
+                       filter_tmp_strides, expand, kind);
 
     return out;
 }
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 6213cb2730..ad586f7d28 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -223,5 +223,34 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
     }
 }
 
+template<typename T, typename convT, bool roundOut, int baseDim>
+void reorder(Array<T> out, Array<convT> packed,
+             const Array<T> filter, const dim_t sig_half_d0, const dim_t fftScale,
+             const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
+             const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
+             bool expand, ConvolveBatchKind kind)
+{
+    T* out_ptr = out.get();
+    const af::dim4 out_dims = out.dims();
+    const af::dim4 out_strides = out.strides();
+
+    const af::dim4 filter_dims = filter.dims();
+
+    convT* packed_ptr = packed.get();
+    convT* sig_tmp_ptr    = packed_ptr;
+    convT* filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
+
+    // Reorder the output
+    if (kind == CONVOLVE_BATCH_KERNEL) {
+        reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
+                filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
+                filter_dims, sig_half_d0, baseDim, fftScale, expand);
+    } else {
+        reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
+                sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
+                filter_dims, sig_half_d0, baseDim, fftScale, expand);
+    }
+}
+
 }
 }

From 4f8b3fad7de425ceca4ce80e40163c9d8f9c6160 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 16:55:54 -0500
Subject: [PATCH 0181/2677] fixed cpu::kernel::dot fn signature

---
 src/backend/cpu/kernel/dot.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
index ef518413c7..71f2c6f959 100644
--- a/src/backend/cpu/kernel/dot.hpp
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -24,7 +24,7 @@ template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
 template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
 
 template<typename T, bool conjugate, bool both_conjugate>
-void dot(Array<T> output, const Array<T> &lhs, const Array<T> &rhs,
+void dot(Array<T> output, const Array<T> lhs, const Array<T> rhs,
          af_mat_prop optLhs, af_mat_prop optRhs)
 {
     int N = lhs.dims()[0];

From e60fa941e49bc7c9d7f4042746c757b96d2f9d27 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 17:26:12 -0500
Subject: [PATCH 0182/2677] Using auto to iterated extra paths in unified

---
 src/api/unified/symbol_manager.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index f79dc6d79f..51d669381e 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -135,8 +135,8 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
                                                     "/usr/local/arrayfire/lib/",
                                                    };
 
-            for (int i = 0; i < (int)extraLibPaths.size(); ++i) {
-                string abs_path = extraLibPaths[i] + bkndLibName;
+            for (auto libPath: extraLibPaths) {
+                string abs_path = libPath + bkndLibName;
                 retVal = dlopen(abs_path.c_str(), flag);
                 if (retVal != NULL) {
                     if (show_load_path)

From 1e0ab509188b8bd90cd5e76923722e1a4f6a73bc Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 17:33:21 -0500
Subject: [PATCH 0183/2677] Fix colorspace c functions to use exceptions
 properly

---
 src/api/c/colorspace.cpp | 63 +++++++++++++++++++++++-----------------
 1 file changed, 36 insertions(+), 27 deletions(-)

diff --git a/src/api/c/colorspace.cpp b/src/api/c/colorspace.cpp
index c73424fdf4..eb5b722638 100644
--- a/src/api/c/colorspace.cpp
+++ b/src/api/c/colorspace.cpp
@@ -13,23 +13,24 @@
 #include <err_common.hpp>
 
 template<af_cspace_t FROM, af_cspace_t TO>
-af_err convert(af_array *out, const af_array image)
+void color_space(af_array *out, const af_array image)
 {
-    return AF_ERR_NOT_SUPPORTED;
+    AF_ERROR("Color Space: Conversion from source type to output type not supported",
+             AF_ERR_NOT_SUPPORTED);
 }
 
-#define INSTANTIATE_CSPACE_DEFS1(F, T, FUNC)                \
-template<>                                                  \
-af_err convert<F, T>(af_array *out, const af_array image)   \
-{                                                           \
-    return FUNC(out, image);                                \
+#define INSTANTIATE_CSPACE_DEFS1(F, T, FUNC)                    \
+template<>                                                      \
+void color_space<F, T>(af_array *out, const af_array image)     \
+{                                                               \
+    AF_CHECK(FUNC(out, image));                                 \
 }
 
-#define INSTANTIATE_CSPACE_DEFS2(F, T, FUNC, ...)           \
-template<>                                                  \
-af_err convert<F, T>(af_array *out, const af_array image)   \
-{                                                           \
-    return FUNC(out, image, __VA_ARGS__);                   \
+#define INSTANTIATE_CSPACE_DEFS2(F, T, FUNC, ...)               \
+template<>                                                      \
+void color_space<F, T>(af_array *out, const af_array image)     \
+{                                                               \
+    AF_CHECK(FUNC(out, image, __VA_ARGS__));                    \
 }
 
 INSTANTIATE_CSPACE_DEFS1(AF_HSV  , AF_RGB  , af_hsv2rgb  );
@@ -40,28 +41,36 @@ INSTANTIATE_CSPACE_DEFS2(AF_YCbCr, AF_RGB  , af_ycbcr2rgb, AF_YCC_601);
 INSTANTIATE_CSPACE_DEFS2(AF_RGB  , AF_YCbCr, af_rgb2ycbcr, AF_YCC_601);
 
 template<af_cspace_t FROM>
-static af_err convert(af_array *out, const af_array image, const af_cspace_t to)
+static void color_space(af_array *out, const af_array image, const af_cspace_t to)
 {
     switch(to) {
-        case AF_GRAY : return convert<FROM, AF_GRAY >(out, image);
-        case AF_RGB  : return convert<FROM, AF_RGB  >(out, image);
-        case AF_HSV  : return convert<FROM, AF_HSV  >(out, image);
-        case AF_YCbCr: return convert<FROM, AF_YCbCr>(out, image);
-        default: return AF_ERR_ARG;
+        case AF_GRAY : color_space<FROM, AF_GRAY >(out, image);     break;
+        case AF_RGB  : color_space<FROM, AF_RGB  >(out, image);     break;
+        case AF_HSV  : color_space<FROM, AF_HSV  >(out, image);     break;
+        case AF_YCbCr: color_space<FROM, AF_YCbCr>(out, image);     break;
+        default: AF_ERROR("Incorrect enum value for output color type", AF_ERR_ARG);
     }
 }
 
 af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to, const af_cspace_t from)
 {
-    if (from==to) {
-        return af_retain_array(out, image);
-    }
+    try {
+        if (from == to) {
+            return af_retain_array(out, image);
+        }
+
+        ARG_ASSERT(2, (to == AF_GRAY || to == AF_RGB || to == AF_HSV || to == AF_YCbCr));
+        ARG_ASSERT(2, (from == AF_GRAY || from == AF_RGB || from == AF_HSV || from == AF_YCbCr));
 
-    switch(from) {
-        case AF_GRAY : return convert<AF_GRAY >(out, image, to);
-        case AF_RGB  : return convert<AF_RGB  >(out, image, to);
-        case AF_HSV  : return convert<AF_HSV  >(out, image, to);
-        case AF_YCbCr: return convert<AF_YCbCr>(out, image, to);
-        default: return AF_ERR_ARG;
+        switch(from) {
+            case AF_GRAY : color_space<AF_GRAY >(out, image, to);   break;
+            case AF_RGB  : color_space<AF_RGB  >(out, image, to);   break;
+            case AF_HSV  : color_space<AF_HSV  >(out, image, to);   break;
+            case AF_YCbCr: color_space<AF_YCbCr>(out, image, to);   break;
+            default: AF_ERROR("Incorrect enum value for input color type", AF_ERR_ARG);
+        }
     }
+    CATCHALL;
+
+    return AF_SUCCESS;
 }

From 95d934613425559fa9048433bfe77bb8f151c18f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 17:46:06 -0500
Subject: [PATCH 0184/2677] Added ENQUEUE macro in cpu backend

this macro takes care of asynchronous kernel launch and calls
sync on the queue when in debug mode.
---
 src/backend/cpu/Array.cpp             |  5 ++---
 src/backend/cpu/approx.cpp            | 19 ++++++++--------
 src/backend/cpu/assign.cpp            |  5 ++---
 src/backend/cpu/bilateral.cpp         |  5 ++---
 src/backend/cpu/blas.cpp              | 13 ++++++-----
 src/backend/cpu/cholesky.cpp          |  5 ++---
 src/backend/cpu/convolve.cpp          |  7 +++---
 src/backend/cpu/copy.cpp              |  9 ++++----
 src/backend/cpu/debug_cpu.hpp         | 31 +++++++++++++++++++++++++++
 src/backend/cpu/diagonal.cpp          |  7 +++---
 src/backend/cpu/diff.cpp              |  7 +++---
 src/backend/cpu/fast.cpp              |  3 +--
 src/backend/cpu/fft.cpp               |  9 ++++----
 src/backend/cpu/fftconvolve.cpp       | 15 ++++++-------
 src/backend/cpu/gradient.cpp          |  5 ++---
 src/backend/cpu/harris.cpp            | 13 ++++++-----
 src/backend/cpu/hist_graphics.cpp     |  3 +--
 src/backend/cpu/histogram.cpp         |  5 ++---
 src/backend/cpu/homography.cpp        |  3 +--
 src/backend/cpu/hsv_rgb.cpp           |  7 +++---
 src/backend/cpu/identity.cpp          |  5 ++---
 src/backend/cpu/iir.cpp               |  5 ++---
 src/backend/cpu/image.cpp             |  3 +--
 src/backend/cpu/index.cpp             |  5 ++---
 src/backend/cpu/inverse.cpp           |  5 ++---
 src/backend/cpu/iota.cpp              |  5 ++---
 src/backend/cpu/ireduce.cpp           |  5 ++---
 src/backend/cpu/join.cpp              | 25 +++++++++++----------
 src/backend/cpu/lookup.cpp            |  5 ++---
 src/backend/cpu/lu.cpp                |  9 ++++----
 src/backend/cpu/match_template.cpp    |  5 ++---
 src/backend/cpu/meanshift.cpp         |  5 ++---
 src/backend/cpu/medfilt.cpp           |  5 ++---
 src/backend/cpu/memory.cpp            |  3 +--
 src/backend/cpu/morph.cpp             |  7 +++---
 src/backend/cpu/nearest_neighbour.cpp |  9 ++++----
 src/backend/cpu/orb.cpp               |  3 +--
 src/backend/cpu/platform.cpp          |  3 +--
 src/backend/cpu/plot.cpp              |  3 +--
 src/backend/cpu/plot3.cpp             |  3 +--
 src/backend/cpu/qr.cpp                |  7 +++---
 src/backend/cpu/random.cpp            | 11 +++++-----
 src/backend/cpu/range.cpp             | 11 +++++-----
 src/backend/cpu/reduce.cpp            |  5 ++---
 src/backend/cpu/regions.cpp           |  5 ++---
 src/backend/cpu/reorder.cpp           |  5 ++---
 src/backend/cpu/resize.cpp            |  9 ++++----
 src/backend/cpu/rotate.cpp            |  9 ++++----
 src/backend/cpu/scan.cpp              | 11 +++++-----
 src/backend/cpu/select.cpp            |  7 +++---
 src/backend/cpu/set.cpp               |  3 +--
 src/backend/cpu/shift.cpp             |  5 ++---
 src/backend/cpu/sobel.cpp             |  7 +++---
 src/backend/cpu/solve.cpp             | 11 +++++-----
 src/backend/cpu/sort.cpp              |  5 ++---
 src/backend/cpu/sort_by_key.cpp       |  5 ++---
 src/backend/cpu/sort_index.cpp        |  5 ++---
 src/backend/cpu/surface.cpp           |  3 +--
 src/backend/cpu/susan.cpp             |  7 +++---
 src/backend/cpu/svd.cpp               |  5 ++---
 src/backend/cpu/tile.cpp              |  5 ++---
 src/backend/cpu/transform.cpp         |  9 ++++----
 src/backend/cpu/transpose.cpp         |  7 +++---
 src/backend/cpu/triangle.cpp          |  5 ++---
 src/backend/cpu/unwrap.cpp            |  7 +++---
 src/backend/cpu/where.cpp             |  3 +--
 src/backend/cpu/wrap.cpp              |  7 +++---
 67 files changed, 219 insertions(+), 254 deletions(-)
 create mode 100644 src/backend/cpu/debug_cpu.hpp

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 40d25aca6f..34c99e4566 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -15,8 +15,7 @@
 #include <TNJ/BufferNode.hpp>
 #include <TNJ/ScalarNode.hpp>
 #include <memory.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <cstring>
 #include <cstddef>
 
@@ -78,7 +77,7 @@ void Array<T>::eval()
 
     data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
-    getQueue().enqueue(kernel::evalArray<T>, *this);
+    ENQUEUE(kernel::evalArray<T>, *this);
 
     ready = true;
     Node_ptr prev = node;
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 7e65486a66..57d3cc4c45 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -11,8 +11,7 @@
 #include <approx.hpp>
 #include <kernel/approx1.hpp>
 #include <kernel/approx2.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -31,12 +30,12 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
-                               out, in, pos, offGrid);
+            ENQUEUE(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
+                    out, in, pos, offGrid);
             break;
         case AF_INTERP_LINEAR:
-            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
-                               out, in, pos, offGrid);
+            ENQUEUE(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
+                    out, in, pos, offGrid);
             break;
         default:
             break;
@@ -61,12 +60,12 @@ Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &p
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
-                               out, in, pos0, pos1, offGrid);
+            ENQUEUE(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
+                    out, in, pos0, pos1, offGrid);
             break;
         case AF_INTERP_LINEAR:
-            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
-                               out, in, pos0, pos1, offGrid);
+            ENQUEUE(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
+                    out, in, pos0, pos1, offGrid);
             break;
         default:
             break;
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index 95bb7e5dd4..df903449a0 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -14,8 +14,7 @@
 #include <handle.hpp>
 #include <kernel/assign.hpp>
 #include <assign.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -48,7 +47,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
         }
     }
 
-    getQueue().enqueue(kernel::assign<T>, out, rhs, std::move(isSeq),
+    ENQUEUE(kernel::assign<T>, out, rhs, std::move(isSeq),
             std::move(seqs), std::move(idxArrs));
 }
 
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index bc3ad6e14b..ceb8be95d9 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -15,8 +15,7 @@
 #include <bilateral.hpp>
 #include <cmath>
 #include <algorithm>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
@@ -29,7 +28,7 @@ Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const fl
     in.eval();
     const dim4 dims     = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
-    getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in, s_sigma, c_sigma);
+    ENQUEUE(kernel::bilateral<outType, inType, isColor>, out, in, s_sigma, c_sigma);
     return out;
 }
 
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index d6f5dee203..70c8d9ca77 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -13,8 +13,7 @@
 #include <cassert>
 #include <err_common.hpp>
 #include <kernel/dot.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -194,7 +193,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 reinterpret_cast<BT*>(output.get()), output.dims()[0]);
         }
     };
-    getQueue().enqueue(func, out, lhs, rhs);
+    ENQUEUE(func, out, lhs, rhs);
 
     return out;
 }
@@ -208,13 +207,13 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
 
     Array<T> out = createEmptyArray<T>(af::dim4(1));
     if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        getQueue().enqueue(kernel::dot<T, false, true>, out, lhs, rhs, optLhs, optRhs);
+        ENQUEUE(kernel::dot<T, false, true>, out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        getQueue().enqueue(kernel::dot<T, true, false>,out, lhs, rhs, optLhs, optRhs);
+        ENQUEUE(kernel::dot<T, true, false>,out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        getQueue().enqueue(kernel::dot<T, true, false>,out, rhs, lhs, optRhs, optLhs);
+        ENQUEUE(kernel::dot<T, true, false>,out, rhs, lhs, optRhs, optLhs);
     } else {
-        getQueue().enqueue(kernel::dot<T, false, false>,out, lhs, rhs, optLhs, optRhs);
+        ENQUEUE(kernel::dot<T, false, false>,out, lhs, rhs, optLhs, optRhs);
     }
     return out;
 }
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index ce11867186..b21d9c8fd0 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -19,8 +19,7 @@
 #include <err_cpu.hpp>
 #include <triangle.hpp>
 #include <lapack_helper.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -75,7 +74,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
         info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(), in.strides()[1]);
     };
 
-    getQueue().enqueue(func, info, in);
+    ENQUEUE(func, info, in);
     getQueue().sync();
 
     return info;
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 218ba8e3c0..cf241c3eaa 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -14,8 +14,7 @@
 #include <convolve.hpp>
 #include <err_cpu.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/convolve.hpp>
 
 using af::dim4;
@@ -51,7 +50,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::convolve_nd<T, accT, baseDim, expand>,out, signal, filter, kind);
+    ENQUEUE(kernel::convolve_nd<T, accT, baseDim, expand>,out, signal, filter, kind);
 
     return out;
 }
@@ -81,7 +80,7 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 
     Array<T> out  = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, tDims);
+    ENQUEUE(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, tDims);
 
     return out;
 }
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 9f6068dd65..8085a0fdb5 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -18,8 +18,7 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/copy.hpp>
 
 namespace cpu
@@ -51,7 +50,7 @@ template<typename T>
 void multiply_inplace(Array<T> &in, double val)
 {
     in.eval();
-    getQueue().enqueue(kernel::copy<T, T>, in, in, 0, val);
+    ENQUEUE(kernel::copy<T, T>, in, in, 0, val);
 }
 
 template<typename inType, typename outType>
@@ -63,7 +62,7 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
     in.eval();
     // FIXME:
     getQueue().sync();
-    getQueue().enqueue(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
+    ENQUEUE(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
     return ret;
 }
 
@@ -72,7 +71,7 @@ void copyArray(Array<outType> &out, Array<inType> const &in)
 {
     out.eval();
     in.eval();
-    getQueue().enqueue(kernel::copy<outType, inType>, out, in, scalar<outType>(0), 1.0);
+    ENQUEUE(kernel::copy<outType, inType>, out, in, scalar<outType>(0), 1.0);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cpu/debug_cpu.hpp b/src/backend/cpu/debug_cpu.hpp
new file mode 100644
index 0000000000..b1d8e17484
--- /dev/null
+++ b/src/backend/cpu/debug_cpu.hpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <platform.hpp>
+#include <async_queue.hpp>
+#include <err_cpu.hpp>
+
+#ifndef NDEBUG
+
+#define POST_LAUNCH_CHECK() do {                        \
+        getQueue().sync();                              \
+    } while(0)                                          \
+
+#else
+
+#define POST_LAUNCH_CHECK() //no-op
+
+#endif
+
+#define ENQUEUE(...)                        \
+    do {                                    \
+        getQueue().enqueue(__VA_ARGS__);    \
+        POST_LAUNCH_CHECK();                \
+    } while(0)
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 6c20f2e7f2..6fd918d66d 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -15,8 +15,7 @@
 #include <diagonal.hpp>
 #include <math.hpp>
 #include <err_cpu.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/diagonal.hpp>
 
 namespace cpu
@@ -31,7 +30,7 @@ Array<T> diagCreate(const Array<T> &in, const int num)
     int batch = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
-    getQueue().enqueue(kernel::diagCreate<T>, out, in, num);
+    ENQUEUE(kernel::diagCreate<T>, out, in, num);
 
     return out;
 }
@@ -45,7 +44,7 @@ Array<T> diagExtract(const Array<T> &in, const int num)
     dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
     Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-    getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
+    ENQUEUE(kernel::diagExtract<T>, out, in, num);
 
     return out;
 }
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 3f639ca46f..efab130cc6 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <diff.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/diff.hpp>
 
 namespace cpu
@@ -27,7 +26,7 @@ Array<T>  diff1(const Array<T> &in, const int dim)
 
     Array<T> outArray = createEmptyArray<T>(dims);
 
-    getQueue().enqueue(kernel::diff1<T>, outArray, in, dim);
+    ENQUEUE(kernel::diff1<T>, outArray, in, dim);
 
     return outArray;
 }
@@ -43,7 +42,7 @@ Array<T>  diff2(const Array<T> &in, const int dim)
 
     Array<T> outArray = createEmptyArray<T>(dims);
 
-    getQueue().enqueue(kernel::diff2<T>, outArray, in, dim);
+    ENQUEUE(kernel::diff2<T>, outArray, in, dim);
 
     return outArray;
 }
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 42607d888f..1b3a7aa973 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -14,8 +14,7 @@
 #include <err_cpu.hpp>
 #include <handle.hpp>
 #include <fast.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/fast.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 2edced2219..1282963003 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -15,8 +15,7 @@
 #include <kernel/fft.hpp>
 #include <copy.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
@@ -27,7 +26,7 @@ template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in)
 {
     in.eval();
-    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in);
+    ENQUEUE(kernel::fft_inplace<T, rank, direction>, in);
 }
 
 template<typename Tc, typename Tr, int rank>
@@ -39,7 +38,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     odims[0] = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, in);
+    ENQUEUE(kernel::fft_r2c<Tc, Tr, rank>, out, in);
 
     return out;
 }
@@ -50,7 +49,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     in.eval();
 
     Array<Tr> out = createEmptyArray<Tr>(odims);
-    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, in, odims);
+    ENQUEUE(kernel::fft_c2r<Tr, Tc, rank>, out, in, odims);
 
     return out;
 }
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index c0a9a41240..aac66cdbe4 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -17,8 +17,7 @@
 #include <fftw3.h>
 #include <copy.hpp>
 #include <convolve_common.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/fftconvolve.hpp>
 
 namespace cpu
@@ -84,11 +83,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    getQueue().enqueue(kernel::packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
+    ENQUEUE(kernel::packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
 
     // Pad filter array with 0s
     const dim_t offset = sig_tmp_strides[3]*sig_tmp_dims[3];
-    getQueue().enqueue(kernel::padArray<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
+    ENQUEUE(kernel::padArray<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
                        filter, offset);
 
     dim4 fftDims(1, 1, 1, 1);
@@ -138,10 +137,10 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
             fftwf_destroy_plan(plan);
         }
     };
-    getQueue().enqueue(upstream_dft, packed, fftDims);
+    ENQUEUE(upstream_dft, packed, fftDims);
 
     // Multiply filter and signal FFT arrays
-    getQueue().enqueue(kernel::complexMultiply<convT>, packed,
+    ENQUEUE(kernel::complexMultiply<convT>, packed,
                        sig_tmp_dims, sig_tmp_strides,
                        filter_tmp_dims, filter_tmp_strides,
                        kind, offset);
@@ -189,7 +188,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
             fftwf_destroy_plan(plan);
         }
     };
-    getQueue().enqueue(upstream_idft, packed, fftDims);
+    ENQUEUE(upstream_idft, packed, fftDims);
 
     // Compute output dimensions
     dim4 oDims(1);
@@ -211,7 +210,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out, packed, filter,
+    ENQUEUE(kernel::reorder<T, convT, roundOut, baseDim>, out, packed, filter,
                        sig_half_d0, fftScale, sig_tmp_dims, sig_tmp_strides, filter_tmp_dims,
                        filter_tmp_strides, expand, kind);
 
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index d1a8b0d2c9..57776e5750 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -12,8 +12,7 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/gradient.hpp>
 
 namespace cpu
@@ -26,7 +25,7 @@ void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
     grad1.eval();
     in.eval();
 
-    getQueue().enqueue(kernel::gradient<T>, grad0, grad1, in);
+    ENQUEUE(kernel::gradient<T>, grad0, grad1, in);
 }
 
 #define INSTANTIATE(T)                                                                  \
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 905b0467c7..07b9bed516 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -18,8 +18,7 @@
 #include <gradient.hpp>
 #include <sort_index.hpp>
 #include <cstring>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/harris.hpp>
 
 using af::dim4;
@@ -53,14 +52,14 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     Array<T> iy = createEmptyArray<T>(idims);
 
     // Compute first order derivatives
-    getQueue().enqueue(gradient<T>, iy, ix, in);
+    ENQUEUE(gradient<T>, iy, ix, in);
 
     Array<T> ixx = createEmptyArray<T>(idims);
     Array<T> ixy = createEmptyArray<T>(idims);
     Array<T> iyy = createEmptyArray<T>(idims);
 
     // Compute second-order derivatives
-    getQueue().enqueue(kernel::second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
+    ENQUEUE(kernel::second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
 
     // Convolve second-order derivatives with proper window filter
     ixx = convolve2<T, convAccT, false>(ixx, filter, filter);
@@ -71,7 +70,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
 
     Array<T> responses = createEmptyArray<T>(dim4(in.elements()));
 
-    getQueue().enqueue(kernel::harris_responses<T>, responses, idims[0], idims[1],
+    ENQUEUE(kernel::harris_responses<T>, responses, idims[0], idims[1],
                        ixx, ixy, iyy, k_thr, border_len);
 
     Array<float> xCorners    = createEmptyArray<float>(dim4(corner_lim));
@@ -105,7 +104,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
         // Keep only the corners with higher Harris responses
-        getQueue().enqueue(kernel::keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
+        ENQUEUE(kernel::keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
                            harris_sorted, harris_idx, corners_out);
     } else if (max_corners == 0 && corners_found < corner_lim) {
         x_out = createEmptyArray<float>(dim4(corners_out));
@@ -120,7 +119,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
             memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
             memcpy(outResponses.get(), inResponses.get(), corners_out * sizeof(float));
         };
-        getQueue().enqueue(copyFunc, x_out, y_out, resp_out,
+        ENQUEUE(copyFunc, x_out, y_out, resp_out,
                            xCorners, yCorners, respCorners, corners_out);
     } else {
         x_out = xCorners;
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 56f7646b61..c58f5c687e 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -11,8 +11,7 @@
 
 #include <hist_graphics.hpp>
 #include <err_cpu.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 19314e052a..2571f3e4d0 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -12,8 +12,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/histogram.hpp>
 
 using af::dim4;
@@ -32,7 +31,7 @@ Array<outType> histogram(const Array<inType> &in,
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
     out.eval();
 
-    getQueue().enqueue(kernel::histogram<outType, inType, isLinear>,
+    ENQUEUE(kernel::histogram<outType, inType, isLinear>,
             out, in, nbins, minval, maxval);
 
     return out;
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index d936e21b4c..147f5e8751 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -18,8 +18,7 @@
 #include <random.hpp>
 #include <cstring>
 #include <cfloat>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index c0f19db773..da5dbe0594 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -11,8 +11,7 @@
 #include <Array.hpp>
 #include <ArrayInfo.hpp>
 #include <hsv_rgb.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/hsv_rgb.hpp>
 
 using af::dim4;
@@ -27,7 +26,7 @@ Array<T> hsv2rgb(const Array<T>& in)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::hsv2rgb<T>, out, in);
+    ENQUEUE(kernel::hsv2rgb<T>, out, in);
 
     return out;
 }
@@ -39,7 +38,7 @@ Array<T> rgb2hsv(const Array<T>& in)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::rgb2hsv<T>, out, in);
+    ENQUEUE(kernel::rgb2hsv<T>, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index 949fceda81..071bb04642 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -10,8 +10,7 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <identity.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/identity.hpp>
 
 namespace cpu
@@ -22,7 +21,7 @@ Array<T> identity(const dim4& dims)
 {
     Array<T> out = createEmptyArray<T>(dims);
 
-    getQueue().enqueue(kernel::identity<T>, out);
+    ENQUEUE(kernel::identity<T>, out);
 
     return out;
 }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 225f39b859..cb390b3018 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -13,8 +13,7 @@
 #include <Array.hpp>
 #include <iir.hpp>
 #include <convolve.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/iir.hpp>
 
 using af::dim4;
@@ -42,7 +41,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
 
     Array<T> y = createEmptyArray<T>(c.dims());
 
-    getQueue().enqueue(kernel::iir<T>, y, c, a);
+    ENQUEUE(kernel::iir<T>, y, c, a);
 
     return y;
 }
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 767f9d42f1..d23ba80ba8 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -16,8 +16,7 @@
 #include <image.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index bd569de44a..9c951ff0d3 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -14,8 +14,7 @@
 #include <index.hpp>
 #include <handle.hpp>
 #include <vector>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <utility>
 #include <kernel/index.hpp>
 
@@ -58,7 +57,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     Array<T> out = createEmptyArray<T>(oDims);
 
 
-    getQueue().enqueue(kernel::index<T>, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
+    ENQUEUE(kernel::index<T>, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
 
     return out;
 }
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index 987ba01c53..71cc9fefca 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -23,8 +23,7 @@
 #include <lu.hpp>
 #include <identity.hpp>
 #include <solve.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -68,7 +67,7 @@ Array<T> inverse(const Array<T> &in)
                 A.get(), A.strides()[1],
                 pivot.get());
     };
-    getQueue().enqueue(func, A, pivot, M);
+    ENQUEUE(func, A, pivot, M);
 
     return A;
 }
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 41f0c9c518..124ec5c48a 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <iota.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/iota.hpp>
 
 using namespace std;
@@ -26,7 +25,7 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
 
     Array<T> out = createEmptyArray<T>(outdims);
 
-    getQueue().enqueue(kernel::iota<T>, out, dims, tile_dims);
+    ENQUEUE(kernel::iota<T>, out, dims, tile_dims);
 
     return out;
 }
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index f1efcf646a..9de4a781b3 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -13,8 +13,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/ireduce.hpp>
 
 using af::dim4;
@@ -40,7 +39,7 @@ void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
                                                            , kernel::ireduce_dim<op, T, 3>()
                                                            , kernel::ireduce_dim<op, T, 4>()};
 
-    getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
+    ENQUEUE(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
 }
 
 template<af_op_t op, typename T>
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index e39280c943..6c9ba8ff9b 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <join.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/join.hpp>
 
 namespace cpu
@@ -38,7 +37,7 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
 
     Array<Tx> out = createEmptyArray<Tx>(odims);
 
-    getQueue().enqueue(kernel::join<Tx, Ty>, out, dim, first, second);
+    ENQUEUE(kernel::join<Tx, Ty>, out, dim, first, second);
 
     return out;
 }
@@ -72,34 +71,34 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
 
     switch(n_arrays) {
         case 1:
-            getQueue().enqueue(kernel::join<T, 1>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 1>, dim, out, inputs);
             break;
         case 2:
-            getQueue().enqueue(kernel::join<T, 2>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 2>, dim, out, inputs);
             break;
         case 3:
-            getQueue().enqueue(kernel::join<T, 3>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 3>, dim, out, inputs);
             break;
         case 4:
-            getQueue().enqueue(kernel::join<T, 4>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 4>, dim, out, inputs);
             break;
         case 5:
-            getQueue().enqueue(kernel::join<T, 5>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 5>, dim, out, inputs);
             break;
         case 6:
-            getQueue().enqueue(kernel::join<T, 6>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 6>, dim, out, inputs);
             break;
         case 7:
-            getQueue().enqueue(kernel::join<T, 7>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 7>, dim, out, inputs);
             break;
         case 8:
-            getQueue().enqueue(kernel::join<T, 8>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 8>, dim, out, inputs);
             break;
         case 9:
-            getQueue().enqueue(kernel::join<T, 9>, dim, out, inputs);
+            ENQUEUE(kernel::join<T, 9>, dim, out, inputs);
             break;
         case 10:
-            getQueue().enqueue(kernel::join<T,10>, dim, out, inputs);
+            ENQUEUE(kernel::join<T,10>, dim, out, inputs);
             break;
     }
 
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 457cdaea5a..4cc5359002 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -9,8 +9,7 @@
 
 #include <lookup.hpp>
 #include <cstdlib>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/lookup.hpp>
 
 namespace cpu
@@ -30,7 +29,7 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    getQueue().enqueue(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
+    ENQUEUE(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
 
     return out;
 }
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index f0e1593f1a..551c9c98e2 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -17,8 +17,7 @@
 #include <cassert>
 #include <range.hpp>
 #include <lapack_helper.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/lu.hpp>
 
 namespace cpu
@@ -59,7 +58,7 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
 
-    getQueue().enqueue(kernel::lu_split<T>, lower, upper, in_copy);
+    ENQUEUE(kernel::lu_split<T>, lower, upper, in_copy);
 }
 
 template<typename T>
@@ -74,11 +73,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
         dim4 iDims = in.dims();
         getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides()[1], pivot.get());
     };
-    getQueue().enqueue(func, in, pivot);
+    ENQUEUE(func, in, pivot);
 
     if(convert_pivot) {
         Array<int> p = range<int>(dim4(iDims[0]), 0);
-        getQueue().enqueue(kernel::convertPivot, p, pivot);
+        ENQUEUE(kernel::convertPivot, p, pivot);
         return p;
     } else {
         return pivot;
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index e5b030be64..724b773638 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -12,8 +12,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <match_template.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/match_template.hpp>
 
 using af::dim4;
@@ -29,7 +28,7 @@ Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg)
 
     Array<OutT> out = createEmptyArray<OutT>(sImg.dims());
 
-    getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg, tImg);
+    ENQUEUE(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg, tImg);
 
     return out;
 }
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index 6c3417a62e..f4a0b29e86 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -16,8 +16,7 @@
 #include <algorithm>
 #include <err_cpu.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/meanshift.hpp>
 
 using af::dim4;
@@ -33,7 +32,7 @@ Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sig
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::meanShift<T, is_color>, out, in, s_sigma, c_sigma, iter);
+    ENQUEUE(kernel::meanShift<T, is_color>, out, in, s_sigma, c_sigma, iter);
 
     return out;
 }
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 06cc0dff44..9e761c6cc0 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -12,8 +12,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/medfilt.hpp>
 
 using af::dim4;
@@ -28,7 +27,7 @@ Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::medfilt<T, pad>, out, in, w_len, w_wid);
+    ENQUEUE(kernel::medfilt<T, pad>, out, in, w_len, w_wid);
 
     return out;
 }
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index e11f994eef..79f2e57a0c 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -14,8 +14,7 @@
 #include <dispatch.hpp>
 #include <cstdlib>
 #include <mutex>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 462319d0af..337e8a9574 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -13,8 +13,7 @@
 #include <Array.hpp>
 #include <morph.hpp>
 #include <algorithm>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/morph.hpp>
 
 using af::dim4;
@@ -30,7 +29,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::morph<T, isDilation>, out, in, mask);
+    ENQUEUE(kernel::morph<T, isDilation>, out, in, mask);
 
     return out;
 }
@@ -43,7 +42,7 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::morph3d<T, isDilation>, out, in, mask);
+    ENQUEUE(kernel::morph3d<T, isDilation>, out, in, mask);
 
     return out;
 }
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 82925622ae..a3c2bb1ea9 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -12,8 +12,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/nearest_neighbour.hpp>
 
 using af::dim4;
@@ -43,13 +42,13 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
 
     switch(dist_type) {
         case AF_SAD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
+            ENQUEUE(kernel::nearest_neighbour<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         case AF_SSD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
+            ENQUEUE(kernel::nearest_neighbour<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         case AF_SHD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
+            ENQUEUE(kernel::nearest_neighbour<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         default:
             AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 5dd9326134..649619e143 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -18,8 +18,7 @@
 #include <convolve.hpp>
 #include <memory.hpp>
 #include <cstring>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/orb.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index c4ac0af3ab..98cfad4b53 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -9,9 +9,8 @@
 
 #include <af/version.h>
 #include <af/defines.h>
-#include <platform.hpp>
+#include <debug_cpu.hpp>
 #include <sstream>
-#include <async_queue.hpp>
 #include <array>
 #include <algorithm>
 #include <iostream>
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 9cc7d9d2b9..8afdea288f 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -13,8 +13,7 @@
 #include <plot.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/plot3.cpp b/src/backend/cpu/plot3.cpp
index 35a7b2500d..c7beed69d6 100644
--- a/src/backend/cpu/plot3.cpp
+++ b/src/backend/cpu/plot3.cpp
@@ -13,8 +13,7 @@
 #include <plot3.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index 78631fccfa..ca04ec9c20 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -17,8 +17,7 @@
 #include <err_cpu.hpp>
 #include <triangle.hpp>
 #include <lapack_helper.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -79,7 +78,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
         gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(), q.strides()[1], t.get());
     };
     q.resetDims(dim4(M, M));
-    getQueue().enqueue(func, q, t, M, N);
+    ENQUEUE(func, q, t, M, N);
 }
 
 template<typename T>
@@ -95,7 +94,7 @@ Array<T> qr_inplace(Array<T> &in)
     auto func = [=] (Array<T> in, Array<T> t, int M, int N) {
         geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides()[1], t.get());
     };
-    getQueue().enqueue(func, in, t, M, N);
+    ENQUEUE(func, in, t, M, N);
 
     return t;
 }
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index 55cf2956a8..f49420f13d 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -12,8 +12,7 @@
 #include <af/defines.h>
 #include <Array.hpp>
 #include <random.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/random.hpp>
 
 namespace cpu
@@ -23,7 +22,7 @@ template<typename T>
 Array<T> randu(const af::dim4 &dims)
 {
     Array<T> outArray = createEmptyArray<T>(dims);
-    getQueue().enqueue(kernel::randu<T>, outArray);
+    ENQUEUE(kernel::randu<T>, outArray);
     return outArray;
 }
 
@@ -46,7 +45,7 @@ template<typename T>
 Array<T> randn(const af::dim4 &dims)
 {
     Array<T> outArray = createEmptyArray<T>(dims);
-    getQueue().enqueue(kernel::randn<T>, outArray);
+    ENQUEUE(kernel::randn<T>, outArray);
     return outArray;
 }
 
@@ -81,7 +80,7 @@ Array<char> randu(const af::dim4 &dims)
             outPtr[i] = gen() > 0.5;
         }
     };
-    getQueue().enqueue(func, outArray);
+    ENQUEUE(func, outArray);
 
     return outArray;
 }
@@ -93,7 +92,7 @@ void setSeed(const uintl seed)
         kernel::is_first = false;
         kernel::gen_seed = seed;
     };
-    getQueue().enqueue(f, seed);
+    ENQUEUE(f, seed);
 }
 
 uintl getSeed()
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index b5ba5f89c4..6be78d5d0e 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -14,8 +14,7 @@
 #include <err_cpu.hpp>
 #include <algorithm>
 #include <numeric>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/range.hpp>
 
 namespace cpu
@@ -33,10 +32,10 @@ Array<T> range(const dim4& dims, const int seq_dim)
 
     Array<T> out = createEmptyArray<T>(dims);
     switch(_seq_dim) {
-        case 0: getQueue().enqueue(kernel::range<T, 0>, out); break;
-        case 1: getQueue().enqueue(kernel::range<T, 1>, out); break;
-        case 2: getQueue().enqueue(kernel::range<T, 2>, out); break;
-        case 3: getQueue().enqueue(kernel::range<T, 3>, out); break;
+        case 0: ENQUEUE(kernel::range<T, 0>, out); break;
+        case 1: ENQUEUE(kernel::range<T, 1>, out); break;
+        case 2: ENQUEUE(kernel::range<T, 2>, out); break;
+        case 3: ENQUEUE(kernel::range<T, 3>, out); break;
         default : AF_ERROR("Invalid rep selection", AF_ERR_ARG);
     }
 
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index cd44b5e2d0..90ad1f9023 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -15,8 +15,7 @@
 #include <ops.hpp>
 #include <functional>
 #include <complex>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/reduce.hpp>
 
 using af::dim4;
@@ -56,7 +55,7 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nan
                                                                 , kernel::reduce_dim<op, Ti, To, 3>()
                                                                 , kernel::reduce_dim<op, Ti, To, 4>()};
 
-    getQueue().enqueue(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
+    ENQUEUE(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
 
     return out;
 }
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index ffac11c01d..eafc161ff5 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -17,8 +17,7 @@
 #include <map>
 #include <set>
 #include <algorithm>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/regions.hpp>
 
 using af::dim4;
@@ -34,7 +33,7 @@ Array<T> regions(const Array<char> &in, af_connectivity connectivity)
     Array<T> out = createValueArray(in.dims(), (T)0);
     out.eval();
 
-    getQueue().enqueue(kernel::regions<T>, out, in, connectivity);
+    ENQUEUE(kernel::regions<T>, out, in, connectivity);
 
     return out;
 }
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 162039b36c..237e5d687a 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <reorder.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/reorder.hpp>
 
 namespace cpu
@@ -27,7 +26,7 @@ Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
         oDims[i] = iDims[rdims[i]];
 
     Array<T> out = createEmptyArray<T>(oDims);
-    getQueue().enqueue(kernel::reorder<T>, out, in, oDims, rdims);
+    ENQUEUE(kernel::reorder<T>, out, in, oDims, rdims);
     return out;
 }
 
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 9a5c85bf1e..d6349a9c0b 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -12,8 +12,7 @@
 #include <math.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/resize.hpp>
 
 namespace cpu
@@ -32,11 +31,11 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
+            ENQUEUE(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
+            ENQUEUE(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
         case AF_INTERP_LOWER:
-            getQueue().enqueue(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
+            ENQUEUE(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
         default: break;
     }
     return out;
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index e81ee04c80..289f3697a0 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <rotate.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include "transform_interp.hpp"
 #include <kernel/rotate.hpp>
 
@@ -27,13 +26,13 @@ Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::rotate<T, AF_INTERP_NEAREST>, out, in, theta);
+            ENQUEUE(kernel::rotate<T, AF_INTERP_NEAREST>, out, in, theta);
             break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::rotate<T, AF_INTERP_BILINEAR>, out, in, theta);
+            ENQUEUE(kernel::rotate<T, AF_INTERP_BILINEAR>, out, in, theta);
             break;
         case AF_INTERP_LOWER:
-            getQueue().enqueue(kernel::rotate<T, AF_INTERP_LOWER>, out, in, theta);
+            ENQUEUE(kernel::rotate<T, AF_INTERP_LOWER>, out, in, theta);
             break;
         default:
             AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 615744fd67..adeb3d23b7 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -14,8 +14,7 @@
 #include <Array.hpp>
 #include <scan.hpp>
 #include <ops.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/scan.hpp>
 
 using af::dim4;
@@ -34,19 +33,19 @@ Array<To> scan(const Array<Ti>& in, const int dim)
     switch (in.ndims()) {
         case 1:
             kernel::scan_dim<op, Ti, To, 1> func1;
-            getQueue().enqueue(func1, out, 0, in, 0, dim);
+            ENQUEUE(func1, out, 0, in, 0, dim);
             break;
         case 2:
             kernel::scan_dim<op, Ti, To, 2> func2;
-            getQueue().enqueue(func2, out, 0, in, 0, dim);
+            ENQUEUE(func2, out, 0, in, 0, dim);
             break;
         case 3:
             kernel::scan_dim<op, Ti, To, 3> func3;
-            getQueue().enqueue(func3, out, 0, in, 0, dim);
+            ENQUEUE(func3, out, 0, in, 0, dim);
             break;
         case 4:
             kernel::scan_dim<op, Ti, To, 4> func4;
-            getQueue().enqueue(func4, out, 0, in, 0, dim);
+            ENQUEUE(func4, out, 0, in, 0, dim);
             break;
     }
 
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index d9a6795a41..4f845bc084 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -10,8 +10,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <select.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/select.hpp>
 
 using af::dim4;
@@ -26,7 +25,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Arr
     cond.eval();
     a.eval();
     b.eval();
-    getQueue().enqueue(kernel::select<T>, out, cond, a, b);
+    ENQUEUE(kernel::select<T>, out, cond, a, b);
 }
 
 template<typename T, bool flip>
@@ -35,7 +34,7 @@ void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, co
     out.eval();
     cond.eval();
     a.eval();
-    getQueue().enqueue(kernel::select_scalar<T, flip>, out, cond, a, b);
+    ENQUEUE(kernel::select_scalar<T, flip>, out, cond, a, b);
 }
 
 #define INSTANTIATE(T)                                              \
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index d6321bba55..49ce186412 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -18,8 +18,7 @@
 #include <sort.hpp>
 #include <err_cpu.hpp>
 #include <vector>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index eca1e5063f..fd56e4ce2e 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <shift.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/shift.hpp>
 
 namespace cpu
@@ -24,7 +23,7 @@ Array<T> shift(const Array<T> &in, const int sdims[4])
     Array<T> out = createEmptyArray<T>(in.dims());
     const af::dim4 temp(sdims[0], sdims[1], sdims[2], sdims[3]);
 
-    getQueue().enqueue(kernel::shift<T>, out, in, temp);
+    ENQUEUE(kernel::shift<T>, out, in, temp);
 
     return out;
 }
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 161266d7cf..86c7363c6d 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -13,8 +13,7 @@
 #include <Array.hpp>
 #include <sobel.hpp>
 #include <convolve.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/sobel.hpp>
 
 using af::dim4;
@@ -32,8 +31,8 @@ sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
-    getQueue().enqueue(kernel::derivative<Ti, To, true >, dx, img);
-    getQueue().enqueue(kernel::derivative<Ti, To, false>, dy, img);
+    ENQUEUE(kernel::derivative<Ti, To, true >, dx, img);
+    ENQUEUE(kernel::derivative<Ti, To, false>, dy, img);
 
     return std::make_pair(dx, dy);
 }
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 0243088fb3..5d1ec3bba3 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -16,8 +16,7 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <lapack_helper.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -88,7 +87,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                         N, NRHS, A.get(), A.strides()[1],
                         pivot.get(), B.get(), B.strides()[1]);
     };
-    getQueue().enqueue(func, A, B, pivot, N, NRHS);
+    ENQUEUE(func, A, B, pivot, N, NRHS);
 
     return B;
 }
@@ -109,7 +108,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
                         A.get(), A.strides()[1],
                         B.get(), B.strides()[1]);
     };
-    getQueue().enqueue(func, A, B, N, NRHS, options);
+    ENQUEUE(func, A, B, N, NRHS, options);
 
     return B;
 }
@@ -139,7 +138,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
             gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides()[1],
                            pivot.get(), B.get(), B.strides()[1]);
         };
-        getQueue().enqueue(func, A, B, pivot, N, K);
+        ENQUEUE(func, A, B, pivot, N, K);
     } else {
         auto func = [=] (Array<T> A, Array<T> B, int M, int N, int K) {
             int sM = A.strides()[1];
@@ -151,7 +150,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
                     B.get(), max(sM, sN));
         };
         B.resetDims(dim4(N, K));
-        getQueue().enqueue(func, A, B, M, N, K);
+        ENQUEUE(func, A, B, M, N, K);
     }
 
     return B;
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 6a0465cf37..104a3df2eb 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -13,8 +13,7 @@
 #include <copy.hpp>
 #include <algorithm>
 #include <functional>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/sort.hpp>
 
 namespace cpu
@@ -27,7 +26,7 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
 
     Array<T> out = copyArray<T>(in);
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0<T, isAscending>, out); break;
+        case 0: ENQUEUE(kernel::sort0<T, isAscending>, out); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
     return out;
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 409b82538e..c6832881d8 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <sort_by_key.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/sort_by_key.hpp>
 
 namespace cpu
@@ -29,7 +28,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     oidx.eval();
 
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0_by_key<Tk, Tv, isAscending>,
+        case 0: ENQUEUE(kernel::sort0_by_key<Tk, Tv, isAscending>,
                                    okey, oval, oidx, ikey, ival); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index ed6afea814..c8c6d6e08f 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -12,8 +12,7 @@
 #include <math.hpp>
 #include <algorithm>
 #include <numeric>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/sort_index.hpp>
 
 namespace cpu
@@ -27,7 +26,7 @@ void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint
     val = createEmptyArray<T>(in.dims());
     idx = createEmptyArray<uint>(in.dims());
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0_index<T, isAscending>, val, idx, in); break;
+        case 0: ENQUEUE(kernel::sort0_index<T, isAscending>, val, idx, in); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 }
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 116c784d89..00d2b00c0f 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -13,8 +13,7 @@
 #include <surface.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 6e8d0fe5b0..4f1c327dd3 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -12,8 +12,7 @@
 #include <cmath>
 #include <math.hpp>
 #include <memory>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/susan.hpp>
 
 using af::features;
@@ -40,9 +39,9 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1), memFree<unsigned>);
     corners_found.get()[0] = 0;
 
-    getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0], idims[1],
+    ENQUEUE(kernel::susan_responses<T>, response, in, idims[0], idims[1],
                        radius, diff_thr, geom_thr, edge);
-    getQueue().enqueue(kernel::non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
+    ENQUEUE(kernel::non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
                        idims[0], idims[1], response, edge, corner_lim);
     getQueue().sync();
 
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 92912ca616..3ce627c5f9 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -15,8 +15,7 @@
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 #include <lapack_helper.hpp>
 #include <copy.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 namespace cpu
 {
@@ -87,7 +86,7 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
                 s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
 #endif
     };
-    getQueue().enqueue(func, s, u, vt, in);
+    ENQUEUE(func, s, u, vt, in);
 }
 
 template <typename T, typename Tr>
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 6526917d3a..9237a79eb9 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <tile.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/tile.hpp>
 
 namespace cpu
@@ -31,7 +30,7 @@ Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::tile<T>, out, in);
+    ENQUEUE(kernel::tile<T>, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index fc7145854b..5874e7abd0 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <transform.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include "transform_interp.hpp"
 #include <kernel/transform.hpp>
 
@@ -29,13 +28,13 @@ Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::
 
     switch(method) {
         case AF_INTERP_NEAREST :
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
+            ENQUEUE(kernel::transform<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
             break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
+            ENQUEUE(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
             break;
         case AF_INTERP_LOWER   :
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
+            ENQUEUE(kernel::transform<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 32663e1f94..c1d5d1d236 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -12,8 +12,7 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <transpose.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/transpose.hpp>
 #include <utility>
 #include <cassert>
@@ -33,7 +32,7 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
     // create an array with first two dimensions swapped
     Array<T> out  = createEmptyArray<T>(outDims);
 
-    getQueue().enqueue(kernel::transpose<T>, out, in, conjugate);
+    ENQUEUE(kernel::transpose<T>, out, in, conjugate);
 
     return out;
 }
@@ -42,7 +41,7 @@ template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate)
 {
     in.eval();
-    getQueue().enqueue(kernel::transpose_inplace<T>, in, conjugate);
+    ENQUEUE(kernel::transpose_inplace<T>, in, conjugate);
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 2a9553c83a..fbc7f658d0 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -12,8 +12,7 @@
 #include <Array.hpp>
 #include <triangle.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/triangle.hpp>
 
 namespace cpu
@@ -22,7 +21,7 @@ namespace cpu
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in)
 {
-    getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
+    ENQUEUE(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index 1aa37a4762..d40acde555 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -11,8 +11,7 @@
 #include <unwrap.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/unwrap.hpp>
 
 namespace cpu
@@ -37,9 +36,9 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
     Array<T> outArray = createEmptyArray<T>(odims);
 
     if (is_column) {
-        getQueue().enqueue(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
+        ENQUEUE(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
     } else {
-        getQueue().enqueue(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
+        ENQUEUE(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
     }
 
     return outArray;
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 018cbdfc36..734b768385 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -16,8 +16,7 @@
 #include <where.hpp>
 #include <ops.hpp>
 #include <vector>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 07487e0d68..87de234d36 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -11,8 +11,7 @@
 #include <wrap.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <async_queue.hpp>
+#include <debug_cpu.hpp>
 #include <kernel/wrap.hpp>
 
 namespace cpu
@@ -34,9 +33,9 @@ Array<T> wrap(const Array<T> &in,
     in.eval();
 
     if (is_column) {
-        getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
+        ENQUEUE(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
     } else {
-        getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
+        ENQUEUE(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
     }
 
     return out;

From 7dad2efd3940d12eee71e3092b9cc7f93e3e1212 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 18:07:08 -0500
Subject: [PATCH 0185/2677] Removed obsolete queue sync in cpu::padArray fn

---
 src/backend/cpu/copy.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 8085a0fdb5..91a1513fd9 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -60,8 +60,6 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
     Array<outType> ret = createValueArray<outType>(dims, default_value);
     ret.eval();
     in.eval();
-    // FIXME:
-    getQueue().sync();
     ENQUEUE(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
     return ret;
 }

From 90611a24093aaa95a7aadb6a4b60cd5d98857c8f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 28 Dec 2015 18:20:39 -0500
Subject: [PATCH 0186/2677] Fixed cmake condition for threads submodule check

---
 src/backend/cpu/CMakeLists.txt | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index c2b4e97cd2..b0ab17a616 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -48,16 +48,17 @@ IF(NOT UNIX)
 ENDIF()
 
 SET(THREADS_SRC_DIR "${CMAKE_CURRENT_SOURCE_DIR}/threads")
-IF(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}")
+IF(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}"
+        AND EXISTS "${THREADS_SRC_DIR}/LICENSE")
     # threads submodule has been initialized
     # Nothing to do
-ELSE(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}")
+ELSE()
     MESSAGE(STATUS "threads submodule unavailable. Updating submodules.")
     EXECUTE_PROCESS(
         COMMAND git submodule update --init --recursive
         WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
         )
-ENDIF(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}")
+ENDIF()
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}

From affc59b7a9da6435a14030ea87c38afa7ae49615 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 20:25:13 -0500
Subject: [PATCH 0187/2677] Adding __AF_FILENAME__ to give the just filename
 without the path

---
 src/api/c/err_common.hpp          | 11 ++++++-----
 src/api/c/graphics_common.hpp     |  6 +++---
 src/api/cpp/error.hpp             |  7 ++++---
 src/backend/cpu/err_cpu.hpp       |  2 +-
 src/backend/cuda/err_cuda.hpp     |  2 +-
 src/backend/defines.hpp           |  8 +++++++-
 src/backend/opencl/err_opencl.hpp |  2 +-
 7 files changed, 23 insertions(+), 15 deletions(-)

diff --git a/src/api/c/err_common.hpp b/src/api/c/err_common.hpp
index 7c4a6f23cd..0c40a7d11b 100644
--- a/src/api/c/err_common.hpp
+++ b/src/api/c/err_common.hpp
@@ -13,6 +13,7 @@
 #include <string>
 #include <cassert>
 #include <af/defines.h>
+#include <defines.hpp>
 #include <vector>
 
 class AfError   : public std::logic_error
@@ -121,26 +122,26 @@ af_err processException();
 
 #define DIM_ASSERT(INDEX, COND) do {                    \
         if((COND) == false) {                           \
-            throw DimensionError(__FILE__, __LINE__,    \
+            throw DimensionError(__AF_FILENAME__, __LINE__,    \
                                  INDEX, #COND);         \
         }                                               \
     } while(0)
 
 #define ARG_ASSERT(INDEX, COND) do {                \
         if((COND) == false) {                       \
-            throw ArgumentError(__FILE__, __LINE__, \
+            throw ArgumentError(__AF_FILENAME__, __LINE__, \
                                 INDEX, #COND);      \
         }                                           \
     } while(0)
 
 #define TYPE_ERROR(INDEX, type) do {            \
-        throw TypeError(__FILE__, __LINE__,     \
+        throw TypeError(__AF_FILENAME__, __LINE__,     \
                         INDEX, type);           \
     } while(0)                                  \
 
 
 #define AF_ERROR(MSG, ERR_TYPE) do {            \
-        throw AfError(__FILE__, __LINE__,       \
+        throw AfError(__AF_FILENAME__, __LINE__,       \
                       MSG, ERR_TYPE);           \
     } while(0)
 
@@ -162,6 +163,6 @@ af_err processException();
 #define AF_CHECK(fn) do {                       \
         af_err __err = fn;                      \
         if (__err == AF_SUCCESS) break;         \
-        throw AfError(__FILE__, __LINE__,       \
+        throw AfError(__AF_FILENAME__, __LINE__,       \
                       "\n", __err);             \
     } while(0)
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 39225e6a0c..082c0c7ba8 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -26,9 +26,9 @@ GLenum glErrorSkip(const char *msg, const char* file, int line);
 GLenum glErrorCheck(const char *msg, const char* file, int line);
 GLenum glForceErrorCheck(const char *msg, const char* file, int line);
 
-#define CheckGL(msg)      glErrorCheck     (msg, __FILE__, __LINE__)
-#define ForceCheckGL(msg) glForceErrorCheck(msg, __FILE__, __LINE__)
-#define CheckGLSkip(msg)  glErrorSkip      (msg, __FILE__, __LINE__)
+#define CheckGL(msg)      glErrorCheck     (msg, __AF_FILENAME__, __LINE__)
+#define ForceCheckGL(msg) glForceErrorCheck(msg, __AF_FILENAME__, __LINE__)
+#define CheckGLSkip(msg)  glErrorSkip      (msg, __AF_FILENAME__, __LINE__)
 
 namespace graphics
 {
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index 7e4854cc0a..cb5a573e32 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -8,16 +8,17 @@
  ********************************************************/
 
 #include <af/exception.h>
+#include <defines.hpp>
 
 #define AF_THROW(fn) do {                               \
         af_err __err = fn;                              \
         if (__err == AF_SUCCESS) break;                 \
-        throw af::exception(__FILE__, __LINE__, __err); \
+        throw af::exception(__AF_FILENAME__, __LINE__, __err); \
     } while(0)
 
 #define AF_THROW_MSG(__msg, __err) do {                         \
         if (__err == AF_SUCCESS) break;                         \
-        throw af::exception(__msg, __FILE__, __LINE__, __err);  \
+        throw af::exception(__msg, __AF_FILENAME__, __LINE__, __err);  \
     } while(0);
 
-#define THROW(__err) throw af::exception(__FILE__, __LINE__, __err)
+#define THROW(__err) throw af::exception(__AF_FILENAME__, __LINE__, __err)
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index e0359a84b4..86239ed761 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -10,5 +10,5 @@
 #include <err_common.hpp>
 
 #define CPU_NOT_SUPPORTED() do {                       \
-        throw SupportError(__FILE__, __LINE__, "CPU"); \
+        throw SupportError(__AF_FILENAME__, __LINE__, "CPU"); \
     } while(0)
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 0692f1eb21..74599f8714 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -13,7 +13,7 @@
 #include <err_common.hpp>
 
 #define CUDA_NOT_SUPPORTED() do {                       \
-        throw SupportError(__FILE__, __LINE__, "CUDA"); \
+        throw SupportError(__AF_FILENAME__, __LINE__, "CUDA"); \
     } while(0)
 
 #define CUDA_CHECK(fn) do {                                 \
diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index 26898370b3..c65fe51f5c 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -9,13 +9,19 @@
 
 #pragma once
 
+#include <string.h>
 #if defined(_WIN32) || defined(_MSC_VER)
     #define __PRETTY_FUNCTION__ __FUNCSIG__
     #if _MSC_VER < 1900
         #define snprintf sprintf_s
     #endif
     #define STATIC_ static
+    #define __AF_FILENAME__ (strrchr(__FILE__, '\\') ? strrchr(__FILE__, '\\') + 1 : __FILE__)
 #else
-    #define __PRETTY_FUNCTION__ __func__
+    #ifndef __PRETTY_FUNCTION__
+        #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
+    #endif
     #define STATIC_ inline
+    #define __AF_FILENAME__ (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__)
 #endif
+
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 0379402ac4..3b56ae7ddd 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -15,7 +15,7 @@
 #include <types.hpp>
 
 #define OPENCL_NOT_SUPPORTED() do {                         \
-        throw SupportError(__FILE__, __LINE__, "OPENCL");   \
+        throw SupportError(__AF_FILENAME__, __LINE__, "OPENCL");   \
     } while(0)
 
 #define CL_TO_AF_ERROR(ERR) do {                                \

From ca6ed2b5a8b10855a60a77689953756aa65592ff Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 21:27:32 -0500
Subject: [PATCH 0188/2677] Add AF_RETURN_ERROR macro to return errors and
 print msg

---
 src/api/c/err_common.cpp | 20 ++++++++++----------
 src/api/c/err_common.hpp | 14 ++++++++++++++
 2 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 7f8d89ec02..945857e25b 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -138,14 +138,14 @@ static const int MAX_ERR_SIZE = 1024;
 static std::string global_err_string;
 
 void
-print_error(const stringstream &msg)
+print_error(const string &msg)
 {
     const char* perr = getenv("AF_PRINT_ERRORS");
     if(perr != nullptr) {
         if(std::strncmp(perr, "0", 1) != 0)
-            fprintf(stderr, "%s\n", msg.str().c_str());
+            fprintf(stderr, "%s\n", msg.c_str());
     }
-    global_err_string = msg.str();
+    global_err_string = msg;
 }
 
 void af_get_last_error(char **str, dim_t *len)
@@ -202,7 +202,7 @@ af_err processException()
            << "Invalid dimension for argument " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
-        print_error(ss);
+        print_error(ss.str());
         err = AF_ERR_SIZE;
     } catch (const ArgumentError &ex) {
         ss << "In function " << ex.getFunctionName()
@@ -210,37 +210,37 @@ af_err processException()
            << "Invalid argument at index " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
-        print_error(ss);
+        print_error(ss.str());
         err = AF_ERR_ARG;
     } catch (const SupportError &ex) {
         ss << ex.getFunctionName()
            << " not supported for " << ex.getBackendName()
            << " backend\n";
 
-        print_error(ss);
+        print_error(ss.str());
         err = AF_ERR_NOT_SUPPORTED;
     } catch (const TypeError &ex) {
         ss << "In function " << ex.getFunctionName()
            << "(" << ex.getLine() << "):\n"
            << "Invalid type for argument " << ex.getArgIndex() << "\n";
 
-        print_error(ss);
+        print_error(ss.str());
         err = AF_ERR_TYPE;
     } catch (const AfError &ex) {
         ss << "Error in " << ex.getFunctionName()
            << "(" << ex.getLine() << "):\n"
            << ex.what() << "\n";
 
-        print_error(ss);
+        print_error(ss.str());
         err = ex.getError();
 #if defined(WITH_GRAPHICS) && !defined(AF_UNIFIED)
     } catch (const fg::Error &ex) {
         ss << ex << "\n";
-        print_error(ss);
+        print_error(ss.str());
         err = AF_ERR_INTERNAL;
 #endif
     } catch (...) {
-        print_error(ss);
+        print_error(ss.str());
         err = AF_ERR_UNKNOWN;
     }
 
diff --git a/src/api/c/err_common.hpp b/src/api/c/err_common.hpp
index 0c40a7d11b..66d2f4a642 100644
--- a/src/api/c/err_common.hpp
+++ b/src/api/c/err_common.hpp
@@ -120,6 +120,8 @@ class DimensionError : public AfError
 
 af_err processException();
 
+void print_error(const std::string &msg);
+
 #define DIM_ASSERT(INDEX, COND) do {                    \
         if((COND) == false) {                           \
             throw DimensionError(__AF_FILENAME__, __LINE__,    \
@@ -145,6 +147,18 @@ af_err processException();
                       MSG, ERR_TYPE);           \
     } while(0)
 
+#define AF_RETURN_ERROR(MSG, ERR_TYPE) do {     \
+        AfError err(__AF_FILENAME__, __LINE__,  \
+                      MSG, ERR_TYPE);           \
+        std::string str = "Error in "           \
+                        + err,getFunctionName() \
+                        + "(" + ex.getLine()    \
+                        + "):\n"                \
+                        + ex.what() + "\n";     \
+        print_error(str);                       \
+        return ERR_TYPE;                        \
+    } while(0)
+
 #define TYPE_ASSERT(COND) do {                  \
         if ((COND) == false) {                  \
             AF_ERROR("Type mismatch inputs",    \

From 7cb790e76fcd5231f013de5040814360b7a5b8d4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 21:28:25 -0500
Subject: [PATCH 0189/2677] Use AF_RETURN_ERROR when graphics is not configured

---
 src/api/c/hist.cpp    |  2 +-
 src/api/c/image.cpp   | 18 +++++++++---------
 src/api/c/plot.cpp    |  2 +-
 src/api/c/plot3.cpp   |  2 +-
 src/api/c/surface.cpp |  2 +-
 5 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 4ddf43bbb4..76731f360a 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -84,6 +84,6 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index ee2520cfc1..1d3e0970ba 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -116,7 +116,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
 
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -146,7 +146,7 @@ af_err af_create_window(af_window *out, const int width, const int height, const
     *out = reinterpret_cast<af_window>(wnd);
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -165,7 +165,7 @@ af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -184,7 +184,7 @@ af_err af_set_title(const af_window wind, const char* const title)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -203,7 +203,7 @@ af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -222,7 +222,7 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -241,7 +241,7 @@ af_err af_show(const af_window wind)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -260,7 +260,7 @@ af_err af_is_window_closed(bool *out, const af_window wind)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
 
@@ -279,6 +279,6 @@ af_err af_destroy_window(const af_window wind)
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index b22e92850b..26b58a8b08 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -102,6 +102,6 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 473bce0b96..1ef30e657e 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -108,6 +108,6 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 835849d15a..7db8441163 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -130,6 +130,6 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
     CATCHALL;
     return AF_SUCCESS;
 #else
-    return AF_ERR_NO_GFX;
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }

From e26c34156655daff878e60ab1d18bafed973082f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 21:28:47 -0500
Subject: [PATCH 0190/2677] Use AF_RETURN_ERROR when Image IO is not configured

---
 src/api/c/imageio.cpp  | 15 +++++----------
 src/api/c/imageio2.cpp |  6 ++----
 2 files changed, 7 insertions(+), 14 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 3442a2adff..d4855ad778 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -715,31 +715,26 @@ af_err af_delete_image_memory(void *ptr)
 #include <stdio.h>
 af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 
 af_err af_save_image(const char* filename, const af_array in_)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 
 af_err af_load_image_memory(af_array *out, const void* ptr)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 
 af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_format format)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 
 af_err af_delete_image_memory(void *ptr)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 #endif  // WITH_FREEIMAGE
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index de12fc7d8a..a51bacb20d 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -377,13 +377,11 @@ af_err af_save_image_native(const char* filename, const af_array in)
 #include <stdio.h>
 af_err af_load_image_native(af_array *out, const char* filename)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 
 af_err af_save_image_native(const char* filename, const af_array in)
 {
-    printf("Error: Image IO requires FreeImage. See https://github.com/arrayfire/arrayfire\n");
-    return AF_ERR_NOT_CONFIGURED;
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
 #endif  // WITH_FREEIMAGE

From 2717d42dba6f41486648d63844906609beb49cdb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 22:38:07 -0500
Subject: [PATCH 0191/2677] Add function name to exceptions in internal error
 classes

* Errors now generated as function_sign()(file:line): Message
* TODO Add function name to af::exception class in devel branch for v3.3.0
---
 src/api/c/err_common.cpp          |  52 +++++++-----
 src/api/c/err_common.hpp          | 126 ++++++++++++++++++------------
 src/backend/cpu/err_cpu.hpp       |   5 +-
 src/backend/cuda/err_cuda.hpp     |   3 +-
 src/backend/defines.hpp           |   6 +-
 src/backend/opencl/err_opencl.hpp |   5 +-
 6 files changed, 118 insertions(+), 79 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 945857e25b..926639ec45 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -23,20 +23,24 @@
 using std::string;
 using std::stringstream;
 
-AfError::AfError(const char * const funcName,
+AfError::AfError(const char * const func,
+                 const char * const file,
                  const int line,
                  const char * const message, af_err err)
     : logic_error   (message),
-      functionName  (funcName),
+      functionName  (func),
+      fileName      (file),
       lineNumber(line),
       error(err)
 {}
 
-AfError::AfError(string funcName,
+AfError::AfError(string func,
+                 string file,
                  const int line,
                  string message, af_err err)
     : logic_error   (message),
-      functionName  (funcName),
+      functionName  (func),
+      fileName      (file),
       lineNumber(line),
       error(err)
 {}
@@ -47,6 +51,12 @@ AfError::getFunctionName() const
     return functionName;
 }
 
+const string&
+AfError::getFileName() const
+{
+    return fileName;
+}
+
 int
 AfError::getLine() const
 {
@@ -61,10 +71,11 @@ AfError::getError() const
 
 AfError::~AfError() throw() {}
 
-TypeError::TypeError(const char * const  funcName,
+TypeError::TypeError(const char * const  func,
+                     const char * const file,
                      const int line,
                      const int index, const af_dtype type)
-    : AfError (funcName, line, "Invalid data type", AF_ERR_TYPE),
+    : AfError (func, file, line, "Invalid data type", AF_ERR_TYPE),
       argIndex(index),
       errTypeName(getName(type))
 {}
@@ -79,11 +90,12 @@ int TypeError::getArgIndex() const
     return argIndex;
 }
 
-ArgumentError::ArgumentError(const char * const  funcName,
+ArgumentError::ArgumentError(const char * const  func,
+                             const char * const file,
                              const int line,
                              const int index,
                              const char * const  expectString)
-    : AfError(funcName, line, "Invalid argument", AF_ERR_ARG),
+    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG),
       argIndex(index),
       expected(expectString)
 {
@@ -101,10 +113,11 @@ int ArgumentError::getArgIndex() const
 }
 
 
-SupportError::SupportError(const char * const funcName,
+SupportError::SupportError(const char * const func,
+                           const char * const file,
                            const int line,
                            const char * const back)
-    : AfError(funcName, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED),
+    : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED),
       backend(back)
 {}
 
@@ -113,11 +126,12 @@ const string& SupportError::getBackendName() const
     return backend;
 }
 
-DimensionError::DimensionError(const char * const  funcName,
-                             const int line,
-                             const int index,
-                             const char * const  expectString)
-    : AfError(funcName, line, "Invalid size", AF_ERR_SIZE),
+DimensionError::DimensionError(const char * const  func,
+                               const char * const file,
+                               const int line,
+                               const int index,
+                               const char * const  expectString)
+    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE),
       argIndex(index),
       expected(expectString)
 {
@@ -198,7 +212,7 @@ af_err processException()
         throw;
     } catch (const DimensionError &ex) {
         ss << "In function " << ex.getFunctionName()
-           << "(" << ex.getLine() << "):\n"
+           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
            << "Invalid dimension for argument " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
@@ -206,7 +220,7 @@ af_err processException()
         err = AF_ERR_SIZE;
     } catch (const ArgumentError &ex) {
         ss << "In function " << ex.getFunctionName()
-           << "(" << ex.getLine() << "):\n"
+           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
            << "Invalid argument at index " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
@@ -221,14 +235,14 @@ af_err processException()
         err = AF_ERR_NOT_SUPPORTED;
     } catch (const TypeError &ex) {
         ss << "In function " << ex.getFunctionName()
-           << "(" << ex.getLine() << "):\n"
+           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
            << "Invalid type for argument " << ex.getArgIndex() << "\n";
 
         print_error(ss.str());
         err = AF_ERR_TYPE;
     } catch (const AfError &ex) {
         ss << "Error in " << ex.getFunctionName()
-           << "(" << ex.getLine() << "):\n"
+           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
            << ex.what() << "\n";
 
         print_error(ss.str());
diff --git a/src/api/c/err_common.hpp b/src/api/c/err_common.hpp
index 66d2f4a642..a4ca57f62b 100644
--- a/src/api/c/err_common.hpp
+++ b/src/api/c/err_common.hpp
@@ -19,23 +19,29 @@
 class AfError   : public std::logic_error
 {
     std::string functionName;
+    std::string fileName;
     int lineNumber;
     af_err error;
     AfError();
 
 public:
 
-    AfError(const char * const funcName,
+    AfError(const char * const func,
+            const char * const file,
             const int line,
             const char * const message, af_err err);
 
-    AfError(std::string funcName,
+    AfError(std::string func,
+            std::string file,
             const int line,
             std::string message, af_err err);
 
     const std::string&
     getFunctionName() const;
 
+    const std::string&
+    getFileName() const;
+
     int getLine() const;
 
     af_err getError() const;
@@ -52,7 +58,8 @@ class TypeError : public AfError
 
 public:
 
-    TypeError(const char * const  funcName,
+    TypeError(const char * const func,
+              const char * const file,
               const int line,
               const int index,
               const af_dtype type);
@@ -68,14 +75,16 @@ class TypeError : public AfError
 class ArgumentError : public AfError
 {
     int argIndex;
-    std::string    expected;
+    std::string expected;
     ArgumentError();
 
 public:
-    ArgumentError(const char * const funcName,
-                   const int line,
-                   const int index,
-                   const char * const expectString);
+
+    ArgumentError(const char * const func,
+                  const char * const file,
+                  const int line,
+                  const int index,
+                  const char * const expectString);
 
     const std::string&
     getExpectedCondition() const;
@@ -89,11 +98,16 @@ class SupportError  :   public AfError
 {
     std::string backend;
     SupportError();
+
 public:
-    SupportError(const char * const funcName,
+
+    SupportError(const char * const func,
+                 const char * const file,
                  const int line,
                  const char * const back);
+
     ~SupportError()throw() {}
+
     const std::string&
     getBackendName() const;
 };
@@ -101,11 +115,13 @@ class SupportError  :   public AfError
 class DimensionError : public AfError
 {
     int argIndex;
-    std::string    expected;
+    std::string expected;
     DimensionError();
 
 public:
-    DimensionError(const char * const funcName,
+
+    DimensionError(const char * const func,
+                   const char * const file,
                    const int line,
                    const int index,
                    const char * const expectString);
@@ -122,61 +138,67 @@ af_err processException();
 
 void print_error(const std::string &msg);
 
-#define DIM_ASSERT(INDEX, COND) do {                    \
-        if((COND) == false) {                           \
-            throw DimensionError(__AF_FILENAME__, __LINE__,    \
-                                 INDEX, #COND);         \
-        }                                               \
+#define DIM_ASSERT(INDEX, COND) do {                        \
+        if((COND) == false) {                               \
+            throw DimensionError(__PRETTY_FUNCTION__,       \
+                                 __AF_FILENAME__, __LINE__, \
+                                 INDEX, #COND);             \
+        }                                                   \
     } while(0)
 
-#define ARG_ASSERT(INDEX, COND) do {                \
-        if((COND) == false) {                       \
-            throw ArgumentError(__AF_FILENAME__, __LINE__, \
-                                INDEX, #COND);      \
-        }                                           \
+#define ARG_ASSERT(INDEX, COND) do {                        \
+        if((COND) == false) {                               \
+            throw ArgumentError(__PRETTY_FUNCTION__,        \
+                                __AF_FILENAME__, __LINE__,  \
+                                INDEX, #COND);              \
+        }                                                   \
     } while(0)
 
-#define TYPE_ERROR(INDEX, type) do {            \
-        throw TypeError(__AF_FILENAME__, __LINE__,     \
-                        INDEX, type);           \
-    } while(0)                                  \
+#define TYPE_ERROR(INDEX, type) do {                        \
+        throw TypeError(__PRETTY_FUNCTION__,                \
+                        __AF_FILENAME__, __LINE__,          \
+                        INDEX, type);                       \
+    } while(0)                                              \
 
 
-#define AF_ERROR(MSG, ERR_TYPE) do {            \
-        throw AfError(__AF_FILENAME__, __LINE__,       \
-                      MSG, ERR_TYPE);           \
+#define AF_ERROR(MSG, ERR_TYPE) do {                        \
+        throw AfError(__PRETTY_FUNCTION__,                  \
+                      __AF_FILENAME__, __LINE__,            \
+                      MSG, ERR_TYPE);                       \
     } while(0)
 
-#define AF_RETURN_ERROR(MSG, ERR_TYPE) do {     \
-        AfError err(__AF_FILENAME__, __LINE__,  \
-                      MSG, ERR_TYPE);           \
-        std::string str = "Error in "           \
-                        + err,getFunctionName() \
-                        + "(" + ex.getLine()    \
-                        + "):\n"                \
-                        + ex.what() + "\n";     \
-        print_error(str);                       \
-        return ERR_TYPE;                        \
+#define AF_RETURN_ERROR(MSG, ERR_TYPE) do {                 \
+        AfError err(__PRETTY_FUNCTION__,                    \
+                    __AF_FILENAME__, __LINE__,              \
+                    MSG, ERR_TYPE);                         \
+        std::string s = "Error in " + err.getFunctionName() \
+                        + "(" + err.getFileName()           \
+                        + ":" + err.getLine() + "):\n"      \
+                        + err.getError().what() + "\n"      \
+                        ;                                   \
+        print_error(str);                                   \
+        return ERR_TYPE;                                    \
     } while(0)
 
-#define TYPE_ASSERT(COND) do {                  \
-        if ((COND) == false) {                  \
-            AF_ERROR("Type mismatch inputs",    \
-                     AF_ERR_DIFF_TYPE);         \
-        }                                       \
+#define TYPE_ASSERT(COND) do {                              \
+        if ((COND) == false) {                              \
+            AF_ERROR("Type mismatch inputs",                \
+                     AF_ERR_DIFF_TYPE);                     \
+        }                                                   \
     } while(0)
 
-#define AF_ASSERT(COND, MESSAGE)                \
+#define AF_ASSERT(COND, MESSAGE)                            \
     assert(MESSAGE && COND)
 
-#define CATCHALL                                \
-    catch(...) {                                \
-        return processException();              \
+#define CATCHALL                                            \
+    catch(...) {                                            \
+        return processException();                          \
     }
 
-#define AF_CHECK(fn) do {                       \
-        af_err __err = fn;                      \
-        if (__err == AF_SUCCESS) break;         \
-        throw AfError(__AF_FILENAME__, __LINE__,       \
-                      "\n", __err);             \
+#define AF_CHECK(fn) do {                                   \
+        af_err __err = fn;                                  \
+        if (__err == AF_SUCCESS) break;                     \
+        throw AfError(__PRETTY_FUNCTION__,                  \
+                      __AF_FILENAME__, __LINE__,            \
+                      "\n", __err);                         \
     } while(0)
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index 86239ed761..9e995f779e 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -9,6 +9,7 @@
 
 #include <err_common.hpp>
 
-#define CPU_NOT_SUPPORTED() do {                       \
-        throw SupportError(__AF_FILENAME__, __LINE__, "CPU"); \
+#define CPU_NOT_SUPPORTED() do {                        \
+        throw SupportError(__PRETTY_FUNCTION__,         \
+                __AF_FILENAME__, __LINE__, "CPU");      \
     } while(0)
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 74599f8714..a975fb5336 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -13,7 +13,8 @@
 #include <err_common.hpp>
 
 #define CUDA_NOT_SUPPORTED() do {                       \
-        throw SupportError(__AF_FILENAME__, __LINE__, "CUDA"); \
+        throw SupportError(__PRETTY_FUNCTION__,         \
+                __AF_FILENAME__, __LINE__, "CUDA");     \
     } while(0)
 
 #define CUDA_CHECK(fn) do {                                 \
diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index c65fe51f5c..74457524e6 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -18,9 +18,9 @@
     #define STATIC_ static
     #define __AF_FILENAME__ (strrchr(__FILE__, '\\') ? strrchr(__FILE__, '\\') + 1 : __FILE__)
 #else
-    #ifndef __PRETTY_FUNCTION__
-        #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
-    #endif
+    //#ifndef __PRETTY_FUNCTION__
+    //    #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
+    //#endif
     #define STATIC_ inline
     #define __AF_FILENAME__ (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__)
 #endif
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 3b56ae7ddd..15855f3b08 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -14,8 +14,9 @@
 #include <platform.hpp>
 #include <types.hpp>
 
-#define OPENCL_NOT_SUPPORTED() do {                         \
-        throw SupportError(__AF_FILENAME__, __LINE__, "OPENCL");   \
+#define OPENCL_NOT_SUPPORTED() do {                     \
+        throw SupportError(__PRETTY_FUNCTION__,         \
+                __AF_FILENAME__, __LINE__, "OpenCL");   \
     } while(0)
 
 #define CL_TO_AF_ERROR(ERR) do {                                \

From c074de73da4823aad84887696d7a4a45cc08770c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Dec 2015 23:32:22 -0500
Subject: [PATCH 0192/2677] Add missing vector include

---
 src/api/unified/symbol_manager.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 51d669381e..f721e30c5f 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -9,6 +9,7 @@
 
 #include "symbol_manager.hpp"
 #include <algorithm>
+#include <vector>
 #include <string>
 #include <cmath>
 

From 301b21d2c70d39093ed9c144c429ceae8c8d0e6c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 10:44:01 -0500
Subject: [PATCH 0193/2677] Using AF_THROW_MSG instead of THROW in
 cpp/array.cpp

---
 src/api/cpp/array.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 76b6e2e569..ef9a06a184 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -32,7 +32,7 @@ namespace af
     static af_array gforReorder(const af_array in, unsigned dim)
     {
         // This is here to stop gcc from complaining
-        if (dim > 3) THROW(AF_ERR_SIZE);
+        if (dim > 3) AF_THROW_MSG("GFor: Dimension is invalid", AF_ERR_SIZE);
         unsigned order[AF_MAX_DIMS] = {0, 1, 2, dim};
         order[dim] = 3;
         af_array out;
@@ -347,7 +347,7 @@ namespace af
                 case 2: return gen_indexing(*this, z, s0, z, z);
                 case 3: return gen_indexing(*this, z, z, s0, z);
                 case 4: return gen_indexing(*this, z, z, z, s0);
-                default: THROW(AF_ERR_SIZE);
+                default: AF_THROW_MSG("ndims for Array is invalid", AF_ERR_SIZE);
             }
         }
         else {

From 5ecdc54b53b21b831241b2ee442a8e36e8680254 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 11:26:24 -0500
Subject: [PATCH 0194/2677] Added API support for perspective transform

---
 src/api/c/transform.cpp | 32 +++++++++++++++++---------------
 1 file changed, 17 insertions(+), 15 deletions(-)

diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index bacb008c78..ffd86dcd58 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -20,9 +20,9 @@ using namespace detail;
 
 template<typename T>
 static inline af_array transform(const af_array in, const af_array tf, const af::dim4 &odims,
-                                 const af_interp_type method, const bool inverse)
+                                 const af_interp_type method, const bool inverse, const bool perspective)
 {
-    return getHandle(transform<T>(getArray<T>(in), getArray<float>(tf), odims, method, inverse));
+    return getHandle(transform<T>(getArray<T>(in), getArray<float>(tf), odims, method, inverse, perspective));
 }
 
 af_err af_transform(af_array *out, const af_array in, const af_array tf,
@@ -41,10 +41,12 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
         ARG_ASSERT(5, method == AF_INTERP_NEAREST  ||
                       method == AF_INTERP_BILINEAR ||
                       method == AF_INTERP_LOWER);
-        DIM_ASSERT(2, (tdims[0] == 3 && tdims[1] == 2));
+        DIM_ASSERT(2, (tdims[0] == 3 && (tdims[1] == 2 || tdims[1] == 3)));
         DIM_ASSERT(1, idims.elements() > 0);
         DIM_ASSERT(1, (idims.ndims() == 2 || idims.ndims() == 3));
 
+        const bool perspective = (tdims[1] == 3) ? true : false;
+
         dim_t o0 = odim0, o1 = odim1;
         dim_t o2 = idims[2] * tdims[2];
         if (odim0 * odim1 == 0) {
@@ -55,18 +57,18 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
 
         af_array output = 0;
         switch(itype) {
-            case f32: output = transform<float  >(in, tf, odims, method, inverse);  break;
-            case f64: output = transform<double >(in, tf, odims, method, inverse);  break;
-            case c32: output = transform<cfloat >(in, tf, odims, method, inverse);  break;
-            case c64: output = transform<cdouble>(in, tf, odims, method, inverse);  break;
-            case s32: output = transform<int    >(in, tf, odims, method, inverse);  break;
-            case u32: output = transform<uint   >(in, tf, odims, method, inverse);  break;
-            case s64: output = transform<intl   >(in, tf, odims, method, inverse);  break;
-            case u64: output = transform<uintl  >(in, tf, odims, method, inverse);  break;
-            case s16: output = transform<short  >(in, tf, odims, method, inverse);  break;
-            case u16: output = transform<ushort >(in, tf, odims, method, inverse);  break;
-            case u8:  output = transform<uchar  >(in, tf, odims, method, inverse);  break;
-            case b8:  output = transform<char   >(in, tf, odims, method, inverse);  break;
+            case f32: output = transform<float  >(in, tf, odims, method, inverse, perspective);  break;
+            case f64: output = transform<double >(in, tf, odims, method, inverse, perspective);  break;
+            case c32: output = transform<cfloat >(in, tf, odims, method, inverse, perspective);  break;
+            case c64: output = transform<cdouble>(in, tf, odims, method, inverse, perspective);  break;
+            case s32: output = transform<int    >(in, tf, odims, method, inverse, perspective);  break;
+            case u32: output = transform<uint   >(in, tf, odims, method, inverse, perspective);  break;
+            case s64: output = transform<intl   >(in, tf, odims, method, inverse, perspective);  break;
+            case u64: output = transform<uintl  >(in, tf, odims, method, inverse, perspective);  break;
+            case s16: output = transform<short  >(in, tf, odims, method, inverse, perspective);  break;
+            case u16: output = transform<ushort >(in, tf, odims, method, inverse, perspective);  break;
+            case u8:  output = transform<uchar  >(in, tf, odims, method, inverse, perspective);  break;
+            case b8:  output = transform<char   >(in, tf, odims, method, inverse, perspective);  break;
             default:  TYPE_ERROR(1, itype);
         }
         std::swap(*out,output);

From 2a438713f9c6a42737a98722553147f1ed0b55bd Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 11:27:22 -0500
Subject: [PATCH 0195/2677] Added perspective transform to CPU backend

---
 src/backend/cpu/rotate.cpp           |  4 +-
 src/backend/cpu/transform.cpp        | 72 +++++++++++++++++--------
 src/backend/cpu/transform.hpp        |  2 +-
 src/backend/cpu/transform_interp.hpp | 78 ++++++++++++++++++++--------
 4 files changed, 110 insertions(+), 46 deletions(-)

diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index a4af64b669..9756323676 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -25,7 +25,7 @@ namespace cpu
 
         void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
                      const af::dim4 &, const af::dim4 &,
-                     const dim_t, const dim_t, const dim_t, const dim_t);
+                     const dim_t, const dim_t, const dim_t, const dim_t, const bool);
 
         const float c = cos(-theta), s = sin(-theta);
         float tx, ty;
@@ -67,7 +67,7 @@ namespace cpu
         // Do transform for image
         for(int yy = 0; yy < (int)odims[1]; yy++) {
             for(int xx = 0; xx < (int)odims[0]; xx++) {
-                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy);
+                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy, false);
             }
         }
     }
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 68e8d96eba..bf072c3aaf 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -17,30 +17,52 @@
 namespace cpu
 {
     template <typename T>
-    void calc_affine_inverse(T *txo, const T *txi)
+    void calc_transform_inverse(T *txo, const T *txi, const bool perspective)
     {
-        T det = txi[0]*txi[4] - txi[1]*txi[3];
+        if (perspective) {
+            txo[0] =   txi[4]*txi[8] - txi[5]*txi[7];
+            txo[1] = -(txi[1]*txi[8] - txi[2]*txi[7]);
+            txo[2] =   txi[1]*txi[5] - txi[2]*txi[4];
 
-        txo[0] = txi[4] / det;
-        txo[1] = txi[3] / det;
-        txo[3] = txi[1] / det;
-        txo[4] = txi[0] / det;
+            txo[3] = -(txi[3]*txi[8] - txi[5]*txi[6]);
+            txo[4] =   txi[0]*txi[8] - txi[2]*txi[6];
+            txo[5] = -(txi[0]*txi[5] - txi[2]*txi[3]);
 
-        txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-        txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+            txo[6] =   txi[3]*txi[7] - txi[4]*txi[6];
+            txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
+            txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
+
+            T det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
+
+            txo[0] /= det; txo[1] /= det; txo[2] /= det;
+            txo[3] /= det; txo[4] /= det; txo[5] /= det;
+            txo[6] /= det; txo[7] /= det; txo[8] /= det;
+        }
+        else {
+            T det = txi[0]*txi[4] - txi[1]*txi[3];
+
+            txo[0] = txi[4] / det;
+            txo[1] = txi[3] / det;
+            txo[3] = txi[1] / det;
+            txo[4] = txi[0] / det;
+
+            txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+            txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+        }
     }
 
     template <typename T>
-    void calc_affine_inverse(T *tmat, const T *tmat_ptr, const bool inverse)
+    void calc_transform_inverse(T *tmat, const T *tmat_ptr, const bool inverse,
+                                const bool perspective, const unsigned transf_len)
     {
         // The way kernel is structured, it expects an inverse
         // transform matrix by default.
         // If it is an forward transform, then we need its inverse
         if(inverse) {
-            for(int i = 0; i < 6; i++)
+            for(int i = 0; i < (int)transf_len; i++)
                 tmat[i] = tmat_ptr[i];
         } else {
-            calc_affine_inverse(tmat, tmat_ptr);
+            calc_transform_inverse(tmat, tmat_ptr, perspective);
         }
     }
 
@@ -48,7 +70,8 @@ namespace cpu
     void transform_(T *out, const T *in, const float *tf,
                     const af::dim4 &odims, const af::dim4 &idims,
                     const af::dim4 &ostrides, const af::dim4 &istrides,
-                    const af::dim4 &tstrides, const bool inverse)
+                    const af::dim4 &tstrides, const bool inverse,
+                    const bool perspective)
     {
         dim_t nimages     = idims[2];
         // Multiplied in src/backend/transform.cpp
@@ -56,7 +79,7 @@ namespace cpu
 
         void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
                      const af::dim4 &, const af::dim4 &,
-                     const dim_t, const dim_t, const dim_t, const dim_t);
+                     const dim_t, const dim_t, const dim_t, const dim_t, const bool);
 
         switch(method) {
             case AF_INTERP_NEAREST:
@@ -73,13 +96,14 @@ namespace cpu
                 break;
         }
 
+        const int transf_len = (perspective) ? 9 : 6;
 
         // For each transform channel
         for(int t_idx = 0; t_idx < (int)ntransforms; t_idx++) {
             // Compute inverse if required
-            const float *tmat_ptr = tf + t_idx * 6;
-            float tmat[6];
-            calc_affine_inverse(tmat, tmat_ptr, inverse);
+            const float *tmat_ptr = tf + t_idx * transf_len;
+            float* tmat = new float[transf_len];
+            calc_transform_inverse(tmat, tmat_ptr, inverse, perspective, transf_len);
 
             // Offset for output pointer
             dim_t o_offset = t_idx * nimages * ostrides[2];
@@ -87,15 +111,16 @@ namespace cpu
             // Do transform for image
             for(int yy = 0; yy < (int)odims[1]; yy++) {
                 for(int xx = 0; xx < (int)odims[0]; xx++) {
-                    t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy);
+                    t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy, perspective);
                 }
             }
+            delete[] tmat;
         }
     }
 
     template<typename T>
     Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse)
+                        const af_interp_type method, const bool inverse, const bool perspective)
     {
         const af::dim4 idims = in.dims();
 
@@ -105,17 +130,20 @@ namespace cpu
             case AF_INTERP_NEAREST:
                 transform_<T, AF_INTERP_NEAREST>
                           (out.get(), in.get(), transform.get(), odims, idims,
-                           out.strides(), in.strides(), transform.strides(), inverse);
+                           out.strides(), in.strides(), transform.strides(), inverse,
+                           perspective);
                 break;
             case AF_INTERP_BILINEAR:
                 transform_<T, AF_INTERP_BILINEAR>
                           (out.get(), in.get(), transform.get(), odims, idims,
-                           out.strides(), in.strides(), transform.strides(), inverse);
+                           out.strides(), in.strides(), transform.strides(), inverse,
+                           perspective);
                 break;
             case AF_INTERP_LOWER:
                 transform_<T, AF_INTERP_LOWER>
                           (out.get(), in.get(), transform.get(), odims, idims,
-                           out.strides(), in.strides(), transform.strides(), inverse);
+                           out.strides(), in.strides(), transform.strides(), inverse,
+                           perspective);
                 break;
             default:
                 AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -129,7 +157,7 @@ namespace cpu
 #define INSTANTIATE(T)                                                                  \
     template Array<T> transform(const Array<T> &in, const Array<float> &transform,      \
                                 const af::dim4 &odims, const af_interp_type method,     \
-                                const bool inverse);
+                                const bool inverse, const bool perspective);
 
 
     INSTANTIATE(float)
diff --git a/src/backend/cpu/transform.hpp b/src/backend/cpu/transform.hpp
index f9e730b1d4..ad4ebba5c3 100644
--- a/src/backend/cpu/transform.hpp
+++ b/src/backend/cpu/transform.hpp
@@ -14,5 +14,5 @@ namespace cpu
 {
     template<typename T>
     Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse);
+                        const af_interp_type method, const bool inverse, const bool perspective);
 }
diff --git a/src/backend/cpu/transform_interp.hpp b/src/backend/cpu/transform_interp.hpp
index 5ad47507b2..dacd2e9a93 100644
--- a/src/backend/cpu/transform_interp.hpp
+++ b/src/backend/cpu/transform_interp.hpp
@@ -27,15 +27,27 @@ namespace cpu
     void transform_n(T *out, const T *in, const float *tmat, const af::dim4 &idims,
                       const af::dim4 &ostrides, const af::dim4 &istrides,
                       const dim_t nimages, const dim_t o_offset,
-                      const dim_t xx, const dim_t yy)
+                      const dim_t xx, const dim_t yy, const bool perspective)
     {
+        dim_t yi = 0, xi = 0;
         // Compute output index
-        const dim_t xi = round(xx * tmat[0]
-                             + yy * tmat[1]
-                                  + tmat[2]);
-        const dim_t yi = round(xx * tmat[3]
-                             + yy * tmat[4]
-                                  + tmat[5]);
+        if (perspective) {
+            const float W = xx * tmat[6] + yy * tmat[7] + tmat[8];
+            xi = round((xx * tmat[0]
+                      + yy * tmat[1]
+                           + tmat[2]) / W);
+            yi = round((xx * tmat[3]
+                      + yy * tmat[4]
+                           + tmat[5]) / W);
+        }
+        else {
+            xi = round(xx * tmat[0]
+                     + yy * tmat[1]
+                          + tmat[2]);
+            yi = round(xx * tmat[3]
+                     + yy * tmat[4]
+                          + tmat[5]);
+        }
 
         // Compute memory location of indices
         dim_t loci = (yi * istrides[1] + xi);
@@ -62,16 +74,28 @@ namespace cpu
     void transform_b(T *out, const T *in, const float *tmat, const af::dim4 &idims,
                       const af::dim4 &ostrides, const af::dim4 &istrides,
                       const dim_t nimages, const dim_t o_offset,
-                      const dim_t xx, const dim_t yy)
+                      const dim_t xx, const dim_t yy, const bool perspective)
     {
         dim_t loco = (yy * ostrides[1] + xx);
         // Compute input index
-        const float xi = xx * tmat[0]
-                       + yy * tmat[1]
-                            + tmat[2];
-        const float yi = xx * tmat[3]
-                       + yy * tmat[4]
-                            + tmat[5];
+        float xi = 0.0f, yi = 0.0f;
+        if (perspective) {
+            const float W = xx * tmat[6] + yy * tmat[7] + tmat[8];
+            xi = (xx * tmat[0]
+                + yy * tmat[1]
+                     + tmat[2]) / W;
+            yi = (xx * tmat[3]
+                + yy * tmat[4]
+                     + tmat[5]) / W;
+        }
+        else {
+            xi = xx * tmat[0]
+               + yy * tmat[1]
+                    + tmat[2];
+            yi = xx * tmat[3]
+               + yy * tmat[4]
+                    + tmat[5];
+        }
 
         if (xi < -0.0001 || yi < -0.0001 || idims[0] < xi || idims[1] < yi) {
             for(int i_idx = 0; i_idx < (int)nimages; i_idx++) {
@@ -126,15 +150,27 @@ namespace cpu
     void transform_l(T *out, const T *in, const float *tmat, const af::dim4 &idims,
                       const af::dim4 &ostrides, const af::dim4 &istrides,
                       const dim_t nimages, const dim_t o_offset,
-                      const dim_t xx, const dim_t yy)
+                      const dim_t xx, const dim_t yy, const bool perspective)
     {
         // Compute output index
-        const dim_t xi = floor(xx * tmat[0]
-                             + yy * tmat[1]
-                                  + tmat[2]);
-        const dim_t yi = floor(xx * tmat[3]
-                             + yy * tmat[4]
-                                  + tmat[5]);
+        dim_t xi = 0, yi = 0;
+        if (perspective) {
+            const float W = xx * tmat[6] + yy * tmat[7] + tmat[8];
+            xi = floor((xx * tmat[0]
+                      + yy * tmat[1]
+                           + tmat[2]) / W);
+            yi = floor((xx * tmat[3]
+                      + yy * tmat[4]
+                           + tmat[5]) / W);
+        }
+        else {
+            xi = floor(xx * tmat[0]
+                     + yy * tmat[1]
+                          + tmat[2]);
+            yi = floor(xx * tmat[3]
+                     + yy * tmat[4]
+                          + tmat[5]);
+        }
 
         // Compute memory location of indices
         dim_t loci = (yi * istrides[1] + xi);

From 7fdfe3e6437b507d11290a76d02bc2801f6a9663 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 11:28:48 -0500
Subject: [PATCH 0196/2677] Added perspective transform to CUDA backend

---
 src/backend/cuda/kernel/rotate.hpp           |  6 +-
 src/backend/cuda/kernel/transform.hpp        | 79 +++++++++++++-------
 src/backend/cuda/kernel/transform_interp.hpp | 65 ++++++++++++----
 src/backend/cuda/transform.cu                | 10 +--
 src/backend/cuda/transform.hpp               |  3 +-
 5 files changed, 116 insertions(+), 47 deletions(-)

diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index d63f010c3b..3cea7f2698 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -60,11 +60,11 @@ namespace cuda
 
             switch(method) {
                 case AF_INTERP_NEAREST:
-                    transform_n(optr, out, iptr, in, t.tmat, xx, yy, limages); break;
+                    transform_n(optr, out, iptr, in, t.tmat, xx, yy, limages, false); break;
                 case AF_INTERP_BILINEAR:
-                    transform_b(optr, out, iptr, in, t.tmat, xx, yy, limages); break;
+                    transform_b(optr, out, iptr, in, t.tmat, xx, yy, limages, false); break;
                 case AF_INTERP_LOWER:
-                    transform_l(optr, out, iptr, in, t.tmat, xx, yy, limages); break;
+                    transform_l(optr, out, iptr, in, t.tmat, xx, yy, limages, false); break;
                 default: break;
             }
         }
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 07be0a35b3..599e62cf9d 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -24,21 +24,42 @@ namespace cuda
         // Used for batching images
         static const unsigned TI = 4;
 
-        __constant__ float c_tmat[6 * 256];
+        __constant__ float c_tmat[9 * 256];
 
         template <typename T>
         __host__ __device__
-        void calc_affine_inverse(T *txo, const T *txi)
+        void calc_transf_inverse(T *txo, const T *txi, const bool perspective)
         {
-            T det = txi[0]*txi[4] - txi[1]*txi[3];
-
-            txo[0] = txi[4] / det;
-            txo[1] = txi[3] / det;
-            txo[3] = txi[1] / det;
-            txo[4] = txi[0] / det;
-
-            txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-            txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+            if (perspective) {
+                txo[0] =   txi[4]*txi[8] - txi[5]*txi[7];
+                txo[1] = -(txi[1]*txi[8] - txi[2]*txi[7]);
+                txo[2] =   txi[1]*txi[5] - txi[2]*txi[4];
+
+                txo[3] = -(txi[3]*txi[8] - txi[5]*txi[6]);
+                txo[4] =   txi[0]*txi[8] - txi[2]*txi[6];
+                txo[5] = -(txi[0]*txi[5] - txi[2]*txi[3]);
+
+                txo[6] =   txi[3]*txi[7] - txi[4]*txi[6];
+                txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
+                txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
+
+                T det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
+
+                txo[0] /= det; txo[1] /= det; txo[2] /= det;
+                txo[3] /= det; txo[4] /= det; txo[5] /= det;
+                txo[6] /= det; txo[7] /= det; txo[8] /= det;
+                }
+            else {
+                T det = txi[0]*txi[4] - txi[1]*txi[3];
+
+                txo[0] = txi[4] / det;
+                txo[1] = txi[3] / det;
+                txo[3] = txi[1] / det;
+                txo[4] = txi[0] / det;
+
+                txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+                txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+            }
         }
 
         ///////////////////////////////////////////////////////////////////////////
@@ -47,7 +68,8 @@ namespace cuda
         template<typename T, bool inverse, af_interp_type method>
         __global__ static void
         transform_kernel(Param<T> out, CParam<T> in, const int nimages,
-                         const int ntransforms, const int blocksXPerImage)
+                         const int ntransforms, const int blocksXPerImage,
+                         const int transf_len, const bool perspective)
         {
             // Compute which image set
             const int setId = blockIdx.x / blocksXPerImage;
@@ -77,30 +99,32 @@ namespace cuda
             const T *iptr = in.ptr  + setId * nimages * in.strides[2];
 
             // Transform is in constant memory.
-            const float *tmat_ptr = c_tmat + t_idx * 6;
-            float tmat[6];
+            const float *tmat_ptr = c_tmat + t_idx * transf_len;
+            float* tmat = new float[transf_len];
 
             // We expect a inverse transform matrix by default
             // If it is an forward transform, then we need its inverse
             if(inverse) {
-                #pragma unroll
-                for(int i = 0; i < 6; i++)
+                #pragma unroll 3
+                for(int i = 0; i < transf_len; i++)
                     tmat[i] = tmat_ptr[i];
             } else {
-                calc_affine_inverse(tmat, tmat_ptr);
+                calc_transf_inverse(tmat, tmat_ptr, perspective);
             }
 
             if (xido >= out.dims[0] && yido >= out.dims[1]) return;
 
             switch(method) {
                 case AF_INTERP_NEAREST:
-                    transform_n(optr, out, iptr, in, tmat, xido, yido, limages); break;
+                    transform_n(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
                 case AF_INTERP_BILINEAR:
-                    transform_b(optr, out, iptr, in, tmat, xido, yido, limages); break;
+                    transform_b(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
                 case AF_INTERP_LOWER:
-                    transform_l(optr, out, iptr, in, tmat, xido, yido, limages); break;
+                    transform_l(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
                 default: break;
             }
+
+            delete[] tmat;
         }
 
         ///////////////////////////////////////////////////////////////////////////
@@ -108,15 +132,18 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         template <typename T, af_interp_type method>
         void transform(Param<T> out, CParam<T> in, CParam<float> tf,
-                       const bool inverse)
+                       const bool inverse, const bool perspective)
         {
             int nimages = in.dims[2];
             // Multiplied in src/backend/transform.cpp
             const int ntransforms = out.dims[2] / in.dims[2];
 
+
+            const int transf_len = (perspective) ? 9 : 6;
+
             // Copy transform to constant memory.
-            CUDA_CHECK(cudaMemcpyToSymbolAsync(c_tmat, tf.ptr, ntransforms * 6 * sizeof(float), 0,
-                                          cudaMemcpyDeviceToDevice,
+            CUDA_CHECK(cudaMemcpyToSymbolAsync(c_tmat, tf.ptr, ntransforms * transf_len * sizeof(float),
+                                          0, cudaMemcpyDeviceToDevice,
                                           cuda::getStream(cuda::getActiveDeviceId())));
 
             dim3 threads(TX, TY, 1);
@@ -133,10 +160,12 @@ namespace cuda
 
             if(inverse) {
                 CUDA_LAUNCH((transform_kernel<T, true, method>), blocks, threads,
-                                out, in, nimages, ntransforms, blocksXPerImage);
+                                out, in, nimages, ntransforms, blocksXPerImage,
+                                transf_len, perspective);
             } else {
                 CUDA_LAUNCH((transform_kernel<T, false, method>), blocks, threads,
-                                out, in, nimages, ntransforms, blocksXPerImage);
+                                out, in, nimages, ntransforms, blocksXPerImage,
+                                transf_len, perspective);
             }
             POST_LAUNCH_CHECK();
         }
diff --git a/src/backend/cuda/kernel/transform_interp.hpp b/src/backend/cuda/kernel/transform_interp.hpp
index 5a88fc4d76..1554b8ec62 100644
--- a/src/backend/cuda/kernel/transform_interp.hpp
+++ b/src/backend/cuda/kernel/transform_interp.hpp
@@ -42,15 +42,28 @@ namespace cuda
         template<typename T>
         __device__
         void transform_n(T *optr, Param<T> out, const T *iptr, CParam<T> in, const float *tmat,
-                         const int xido, const int yido, const int nimages)
+                         const int xido, const int yido, const int nimages,
+                         const bool perspective)
         {
             // Compute input index
-            int xidi = round(xido * tmat[0]
+            int xidi = 0, yidi = 0;
+            if (perspective) {
+                const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+                xidi = round((xido * tmat[0]
+                            + yido * tmat[1]
+                                   + tmat[2]) / W);
+                yidi = round((xido * tmat[3]
+                            + yido * tmat[4]
+                                   + tmat[5]) / W);
+            }
+            else {
+                xidi = round(xido * tmat[0]
                            + yido * tmat[1]
                                   + tmat[2]);
-            int yidi = round(xido * tmat[3]
+                yidi = round(xido * tmat[3]
                            + yido * tmat[4]
                                   + tmat[5]);
+            }
 
             // Makes scale give same output as resize
             // But fails rotate tests
@@ -76,17 +89,30 @@ namespace cuda
         template<typename T>
         __device__
         void transform_b(T *optr, Param<T> out, const T *iptr, CParam<T> in, const float *tmat,
-                         const int xido, const int yido, const int nimages)
+                         const int xido, const int yido, const int nimages,
+                         const bool perspective)
         {
             const int loco = (yido * out.strides[1] + xido);
 
             // Compute input index
-            const float xidi = xido * tmat[0]
-                             + yido * tmat[1]
-                                    + tmat[2];
-            const float yidi = xido * tmat[3]
-                             + yido * tmat[4]
-                                    + tmat[5];
+            float xidi = 0.0f, yidi = 0.0f;
+            if (perspective) {
+                const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+                xidi = (xido * tmat[0]
+                      + yido * tmat[1]
+                             + tmat[2]) / W;
+                yidi = (xido * tmat[3]
+                      + yido * tmat[4]
+                             + tmat[5]) / W;
+            }
+            else {
+                xidi = xido * tmat[0]
+                     + yido * tmat[1]
+                            + tmat[2];
+                yidi = xido * tmat[3]
+                     + yido * tmat[4]
+                            + tmat[5];
+            }
 
             if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi || in.dims[1] < yidi) {
                 for(int i = 0; i < nimages; i++) {
@@ -133,15 +159,28 @@ namespace cuda
         template<typename T>
         __device__
         void transform_l(T *optr, Param<T> out, const T *iptr, CParam<T> in, const float *tmat,
-                         const int xido, const int yido, const int nimages)
+                         const int xido, const int yido, const int nimages,
+                         const bool perspective)
         {
             // Compute input index
-            int xidi = floor(xido * tmat[0]
+            int xidi = 0, yidi = 0;
+            if (perspective) {
+                const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+                xidi = floor((xido * tmat[0]
+                            + yido * tmat[1]
+                                   + tmat[2]) / W);
+                yidi = floor((xido * tmat[3]
+                            + yido * tmat[4]
+                                   + tmat[5]) / W);
+            }
+            else {
+                xidi = floor(xido * tmat[0]
                            + yido * tmat[1]
                                   + tmat[2]);
-            int yidi = floor(xido * tmat[3]
+                yidi = floor(xido * tmat[3]
                            + yido * tmat[4]
                                   + tmat[5]);
+            }
 
             // Makes scale give same output as resize
             // But fails rotate tests
diff --git a/src/backend/cuda/transform.cu b/src/backend/cuda/transform.cu
index 853617c0a4..07c312353c 100644
--- a/src/backend/cuda/transform.cu
+++ b/src/backend/cuda/transform.cu
@@ -16,7 +16,7 @@ namespace cuda
 {
     template<typename T>
     Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse)
+                        const af_interp_type method, const bool inverse, const bool perspective)
     {
         const af::dim4 idims = in.dims();
 
@@ -24,13 +24,13 @@ namespace cuda
 
         switch(method) {
             case AF_INTERP_NEAREST:
-                kernel::transform<T, AF_INTERP_NEAREST> (out, in, transform, inverse);
+                kernel::transform<T, AF_INTERP_NEAREST> (out, in, transform, inverse, perspective);
                 break;
             case AF_INTERP_BILINEAR:
-                kernel::transform<T, AF_INTERP_BILINEAR>(out, in, transform, inverse);
+                kernel::transform<T, AF_INTERP_BILINEAR>(out, in, transform, inverse, perspective);
                 break;
             case AF_INTERP_LOWER:
-                kernel::transform<T, AF_INTERP_LOWER>   (out, in, transform, inverse);
+                kernel::transform<T, AF_INTERP_LOWER>   (out, in, transform, inverse, perspective);
                 break;
             default:
                 AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -43,7 +43,7 @@ namespace cuda
 #define INSTANTIATE(T)                                                                      \
     template Array<T> transform(const Array<T> &in, const Array<float> &transform,          \
                                 const af::dim4 &odims, const af_interp_type method,         \
-                                const bool inverse);
+                                const bool inverse, const bool perspective);
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/cuda/transform.hpp b/src/backend/cuda/transform.hpp
index eb3d71d097..316953d614 100644
--- a/src/backend/cuda/transform.hpp
+++ b/src/backend/cuda/transform.hpp
@@ -14,5 +14,6 @@ namespace cuda
 {
     template<typename T>
     Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse);
+                       const af_interp_type method, const bool inverse,
+                       const bool perspective);
 }

From 15b9ad6ae46e76bb086a6bf136ffce0bf147a8b0 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 11:30:59 -0500
Subject: [PATCH 0197/2677] Added perspective transform to OpenCL backend

---
 src/backend/opencl/kernel/transform.cl        |  32 ++++-
 src/backend/opencl/kernel/transform.hpp       |  14 ++-
 src/backend/opencl/kernel/transform_interp.cl |  69 ++++++++---
 src/backend/opencl/transform.cpp              | 110 ++++++++++++------
 src/backend/opencl/transform.hpp              |   2 +-
 5 files changed, 161 insertions(+), 66 deletions(-)

diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 824f50cc5d..d746525ed6 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -11,9 +11,28 @@
 #define BILINEAR transform_b
 #define LOWER transform_l
 
-void calc_affine_inverse(float* txo, __global const float* txi)
+void calc_transf_inverse(float* txo, __global const float* txi)
 {
-    float det = txi[0]*txi[4] - txi[1]*txi[3];
+#if PERSPECTIVE
+    txo[0] =   txi[4]*txi[8] - txi[5]*txi[7];
+    txo[1] = -(txi[1]*txi[8] - txi[2]*txi[7]);
+    txo[2] =   txi[1]*txi[5] - txi[2]*txi[4];
+
+    txo[3] = -(txi[3]*txi[8] - txi[5]*txi[6]);
+    txo[4] =   txi[0]*txi[8] - txi[2]*txi[6];
+    txo[5] = -(txi[0]*txi[5] - txi[2]*txi[3]);
+
+    txo[6] =   txi[3]*txi[7] - txi[4]*txi[6];
+    txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
+    txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
+
+    T det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
+
+    txo[0] /= det; txo[1] /= det; txo[2] /= det;
+    txo[3] /= det; txo[4] /= det; txo[5] /= det;
+    txo[6] /= det; txo[7] /= det; txo[8] /= det;
+#else
+    T det = txi[0]*txi[4] - txi[1]*txi[3];
 
     txo[0] = txi[4] / det;
     txo[1] = txi[3] / det;
@@ -22,6 +41,7 @@ void calc_affine_inverse(float* txo, __global const float* txi)
 
     txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
     txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+#endif
 }
 
 __kernel
@@ -59,17 +79,17 @@ void transform_kernel(__global T *d_out, const KParam out,
 
     // Transform is in global memory.
     // Needs offset to correct transform being processed.
-    __global const float *tmat_ptr = c_tmat + t_idx * 6;
-    float tmat[6];
+    __global const float *tmat_ptr = c_tmat + t_idx * TRANSF_LEN;
+    float tmat[TRANSF_LEN];
 
     // We expect a inverse transform matrix by default
     // If it is an forward transform, then we need its inverse
     if(INVERSE == 1) {
         #pragma unroll
-        for(int i = 0; i < 6; i++)
+        for(int i = 0; i < TRANSF_LEN; i++)
             tmat[i] = tmat_ptr[i];
     } else {
-        calc_affine_inverse(tmat, tmat_ptr);
+        calc_transf_inverse(tmat, tmat_ptr);
     }
 
     if (xido >= out.dims[0] && yido >= out.dims[1]) return;
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 677acc31fe..f78c7b0ebe 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -50,7 +50,7 @@ namespace opencl
                                             >::type;
 
 
-        template<typename T, bool isInverse, af_interp_type method>
+        template<typename T, bool isInverse, bool isPerspective, af_interp_type method>
         void transform(Param out, const Param in, const Param tf)
         {
             try {
@@ -64,11 +64,13 @@ namespace opencl
                 std::call_once( compileFlags[device], [device] () {
                     ToNum<T> toNum;
                     std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName()
-                            << " -D INVERSE="  << (isInverse ? 1 : 0)
-                            << " -D ZERO="     << toNum(scalar<T>(0));
-                    options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
-                    options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
+                    options << " -D T="           << dtype_traits<T>::getName()
+                            << " -D INVERSE="     << (isInverse ? 1 : 0)
+                            << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
+                            << " -D TRANSF_LEN="  << (isPerspective ? 9 : 6)
+                            << " -D ZERO="        << toNum(scalar<T>(0));
+                    options << " -D VT="          << dtype_traits<vtype_t<T>>::getName();
+                    options << " -D WT="          << dtype_traits<wtype_t<BT>>::getName();
 
                     if((af_dtype) dtype_traits<T>::af_type == c32 ||
                        (af_dtype) dtype_traits<T>::af_type == c64) {
diff --git a/src/backend/opencl/kernel/transform_interp.cl b/src/backend/opencl/kernel/transform_interp.cl
index 1d82951b9d..a083df0ff6 100644
--- a/src/backend/opencl/kernel/transform_interp.cl
+++ b/src/backend/opencl/kernel/transform_interp.cl
@@ -25,12 +25,23 @@ void transform_n(__global T *d_out, const KParam out, __global const T *d_in, co
                  const float *tmat, const int xido, const int yido, const int nimages)
 {
     // Compute input index
-    const int xidi = round(xido * tmat[0]
-                         + yido * tmat[1]
-                                + tmat[2]);
-    const int yidi = round(xido * tmat[3]
-                         + yido * tmat[4]
-                                + tmat[5]);
+    int xidi = 0, yidi = 0;
+#if PERSPECTIVE
+    const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+    xidi = round((xido * tmat[0]
+                + yido * tmat[1]
+                       + tmat[2]) / W);
+    yidi = round((xido * tmat[3]
+                + yido * tmat[4]
+                       + tmat[5]) / W);
+#else
+    xidi = round(xido * tmat[0]
+               + yido * tmat[1]
+                      + tmat[2]);
+    yidi = round(xido * tmat[3]
+               + yido * tmat[4]
+                      + tmat[5]);
+#endif
 
     // Compute memory location of indices
     const int loci = yidi * in.strides[1]  + xidi;
@@ -54,12 +65,23 @@ void transform_b(__global T *d_out, const KParam out, __global const T *d_in, co
     const int loco = (yido * out.strides[1] + xido);
 
     // Compute input index
-    const float xid = xido * tmat[0]
-                    + yido * tmat[1]
-                           + tmat[2];
-    const float yid = xido * tmat[3]
-                    + yido * tmat[4]
-                           + tmat[5];
+    float xid = 0.0f, yid = 0.0f;
+#if PERSPECTIVE
+    const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+    xid = (xido * tmat[0]
+         + yido * tmat[1]
+                + tmat[2]) / W;
+    yid = (xido * tmat[3]
+         + yido * tmat[4]
+                + tmat[5]) / W;
+#else
+    xid = xido * tmat[0]
+        + yido * tmat[1]
+               + tmat[2];
+    yid = xido * tmat[3]
+        + yido * tmat[4]
+               + tmat[5];
+#endif
 
     T zero = ZERO;
     if (xid < -0.001 || yid < -0.001 || in.dims[0] < xid || in.dims[1] < yid) {
@@ -104,12 +126,23 @@ void transform_l(__global T *d_out, const KParam out, __global const T *d_in, co
                  const float *tmat, const int xido, const int yido, const int nimages)
 {
     // Compute input index
-    const int xidi = floor(xido * tmat[0]
-                         + yido * tmat[1]
-                                + tmat[2]);
-    const int yidi = floor(xido * tmat[3]
-                         + yido * tmat[4]
-                                + tmat[5]);
+    int xidi = 0, yidi = 0;
+#if PERSPECTIVE
+    const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+    xidi = floor((xido * tmat[0]
+                + yido * tmat[1]
+                       + tmat[2]) / W);
+    yidi = floor((xido * tmat[3]
+                + yido * tmat[4]
+                       + tmat[5]) / W);
+#else
+    xidi = floor(xido * tmat[0]
+               + yido * tmat[1]
+                      + tmat[2]);
+    yidi = floor(xido * tmat[3]
+               + yido * tmat[4]
+                      + tmat[5]);
+#endif
 
     // Compute memory location of indices
     const int loci = yidi * in.strides[1]  + xidi;
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index c8e2b69a8b..379fd2a5b7 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -18,46 +18,86 @@ namespace opencl
 {
     template<typename T>
     Array<T> transform(const Array<T> &in, const Array<float> &transform,
-                       const af::dim4 &odims,
-                       const af_interp_type method, const bool inverse)
+                       const af::dim4 &odims, const af_interp_type method,
+                       const bool inverse, const bool perspective)
     {
         Array<T> out = createEmptyArray<T>(odims);
 
         if(inverse) {
-            switch(method) {
-                case AF_INTERP_NEAREST:
-                    kernel::transform<T, true, AF_INTERP_NEAREST>
-                                     (out, in, transform);
-                    break;
-                case AF_INTERP_BILINEAR:
-                    kernel::transform<T, true, AF_INTERP_BILINEAR>
-                                     (out, in, transform);
-                    break;
-                case AF_INTERP_LOWER:
-                    kernel::transform<T, true, AF_INTERP_LOWER>
-                                     (out, in, transform);
-                    break;
-                default:
-                    AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                    break;
+            if (perspective) {
+                switch(method) {
+                    case AF_INTERP_NEAREST:
+                        kernel::transform<T, true, true, AF_INTERP_NEAREST>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_BILINEAR:
+                        kernel::transform<T, true, true, AF_INTERP_BILINEAR>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_LOWER:
+                        kernel::transform<T, true, true, AF_INTERP_LOWER>
+                                         (out, in, transform);
+                        break;
+                    default:
+                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+                        break;
+                }
+            } else {
+                switch(method) {
+                    case AF_INTERP_NEAREST:
+                        kernel::transform<T, true, false, AF_INTERP_NEAREST>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_BILINEAR:
+                        kernel::transform<T, true, false, AF_INTERP_BILINEAR>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_LOWER:
+                        kernel::transform<T, true, false, AF_INTERP_LOWER>
+                                         (out, in, transform);
+                        break;
+                    default:
+                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+                        break;
+                }
             }
         } else {
-            switch(method) {
-                case AF_INTERP_NEAREST:
-                    kernel::transform<T, false, AF_INTERP_NEAREST>
-                                     (out, in, transform);
-                    break;
-                case AF_INTERP_BILINEAR:
-                    kernel::transform<T, false, AF_INTERP_BILINEAR>
-                                     (out, in, transform);
-                    break;
-                case AF_INTERP_LOWER:
-                    kernel::transform<T, false, AF_INTERP_LOWER>
-                                     (out, in, transform);
-                    break;
-                default:
-                    AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                    break;
+            if (perspective) {
+                switch(method) {
+                    case AF_INTERP_NEAREST:
+                        kernel::transform<T, false, true, AF_INTERP_NEAREST>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_BILINEAR:
+                        kernel::transform<T, false, true, AF_INTERP_BILINEAR>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_LOWER:
+                        kernel::transform<T, false, true, AF_INTERP_LOWER>
+                                         (out, in, transform);
+                        break;
+                    default:
+                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+                        break;
+                }
+            } else {
+                switch(method) {
+                    case AF_INTERP_NEAREST:
+                        kernel::transform<T, false, false, AF_INTERP_NEAREST>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_BILINEAR:
+                        kernel::transform<T, false, false, AF_INTERP_BILINEAR>
+                                         (out, in, transform);
+                        break;
+                    case AF_INTERP_LOWER:
+                        kernel::transform<T, false, false, AF_INTERP_LOWER>
+                                         (out, in, transform);
+                        break;
+                    default:
+                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+                        break;
+                }
             }
         }
 
@@ -68,7 +108,7 @@ namespace opencl
 #define INSTANTIATE(T)                                                                  \
     template Array<T> transform(const Array<T> &in, const Array<float> &transform,      \
                                 const af::dim4 &odims, const af_interp_type method,     \
-                                const bool inverse);
+                                const bool inverse, const bool perspective);
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/opencl/transform.hpp b/src/backend/opencl/transform.hpp
index f0b4d4c955..064817a537 100644
--- a/src/backend/opencl/transform.hpp
+++ b/src/backend/opencl/transform.hpp
@@ -14,5 +14,5 @@ namespace opencl
 {
     template<typename T>
     Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse);
+                        const af_interp_type method, const bool inverse, const bool perspective);
 }

From 81dca062d9691905c8f13291f3ccd6b66186859d Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 11:36:31 -0500
Subject: [PATCH 0198/2677] Updated transform documentation

---
 docs/details/image.dox | 47 ++++++++++++++++++++++++++++--------------
 1 file changed, 31 insertions(+), 16 deletions(-)

diff --git a/docs/details/image.dox b/docs/details/image.dox
index 234f4f72e9..53ac7616fc 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -501,10 +501,12 @@ grad(dx, dy, in);
 
 Resize an input image
 
-Resizing an input image can be done using either \ref AF_INTERP_NEAREST or
-\ref AF_INTERP_BILINEAR interpolations. Nearest interpolation will pick the
-nearest value to the location, whereas bilinear interpolation will do a
-weighted interpolation for calculate the new size.
+Resizing an input image can be done using either \ref AF_INTERP_NEAREST,
+\ref AF_INTERP_BILINEAR or \ref AF_INTERP_LOWER, interpolations. Nearest
+interpolation will pick the nearest value to the location, bilinear
+interpolation will do a weighted interpolation for calculate the new size
+and lower interpolation is similar to the nearest, except it will use the
+floor function to get the lower neighbor.
 
 This function does not differentiate between images and data. As long as
 the array is defined and the output dimensions are not 0, it will resize any
@@ -556,10 +558,10 @@ Rotate an input image
 
 The angle theta is in radians.
 
-Rotating an input image can be done using either \ref AF_INTERP_NEAREST or
-\ref AF_INTERP_BILINEAR interpolations. Nearest interpolation will pick the
-nearest value to the location, whereas bilinear interpolation will do a
-weighted interpolation for calculate the new size.
+Rotating an input image can be done using \ref AF_INTERP_NEAREST,
+\ref AF_INTERP_BILINEAR or \ref AF_INTERP_LOWER interpolations. Nearest
+interpolation will pick the nearest value to the location, whereas bilinear
+interpolation will do a weighted interpolation for calculate the new size.
 
 This function does not differentiate between images and data. As long as
 the array is defined, it will rotate any type or size of array.
@@ -659,22 +661,35 @@ Skew is a special case of the \ref af::transform function.
 
 Transform an input image
 
-The transform function uses an affine transform matrix to tranform an input
+The transform function uses an affine or perspective transform matrix to tranform an input
 image into a new one.
 
-The transform matrix \p tf is a 3x2 matrix of type float. The matrix operation
-is applied to each location (x, y) that is then transformed to (x', y') of the
+If matrix \p tf is is a 3x2 matrix, an affine transformation will be performed. The matrix
+operation is applied to each location (x, y) that is then transformed to (x', y') of the
 new array. Hence the transformation is an element-wise operation.
 
-The operation is as below:
-tf = [r00 r10
-      r01 r11
+The operation is as below:\n
+tf = [r00 r10\n
+      r01 r11\n
       t0  t1]
 
-x' = x * r00 + y * r01 + t0;
+x' = x * r00 + y * r01 + t0;\n
 y' = x * r10 + y * r11 + t1;
 
-Interpolation types of \ref AF_INTERP_NEAREST and \ref AF_INTERP_BILINEAR are allowed.
+If matrix \p tf is is a 3x3 matrix, a perspective transformation will be performed.
+
+The operation is as below:\n
+tf = [r00 r10 r20\n
+      r01 r11 r21\n
+      t0  t1  t2]
+
+x' = (x * r00 + y * r01 + t0) / (x * r20 + y * r21 + t2);\n
+y' = (x * r10 + y * r11 + t1) / (x * r20 + y * r21 + t2);
+
+The transformation matrix \p tf should always be of type f32.
+
+Interpolation types of \ref AF_INTERP_NEAREST, \ref AF_INTERP_BILINEAR and
+AF_INTERP_LOWER are allowed.
 
 Affine transforms can be used for various purposes. \ref af::translate, \ref af::scale and \ref af::skew
 are specializations of the transform function.

From 8e4e766b717e85cd7c7b477bf94e9dd249d1e037 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 12:33:46 -0500
Subject: [PATCH 0199/2677] Added perspective transform unit tests

---
 test/transform.cpp | 267 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 267 insertions(+)
 create mode 100644 test/transform.cpp

diff --git a/test/transform.cpp b/test/transform.cpp
new file mode 100644
index 0000000000..fa0006cbf2
--- /dev/null
+++ b/test/transform.cpp
@@ -0,0 +1,267 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+
+template<typename T>
+class Transform : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {}
+};
+
+template<typename T>
+class TransformInt : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {
+        }
+};
+
+typedef ::testing::Types<float, double> TestTypes;
+typedef ::testing::Types<int, intl, uint, uintl, short, ushort, uchar> TestTypesInt;
+
+TYPED_TEST_CASE(Transform, TestTypes);
+TYPED_TEST_CASE(TransformInt, TestTypesInt);
+
+template<typename T>
+void transformTest(string pTestFile, string pHomographyFile, const af_interp_type method, const bool invert)
+{
+    if (noDoubleTests<T>()) return;
+
+    vector<af::dim4> inNumDims;
+    vector<string>   inFiles;
+    vector<dim_t>    goldNumDims;
+    vector<string>   goldFiles;
+
+    readImageTests(pTestFile, inNumDims, inFiles, goldNumDims, goldFiles);
+
+    inFiles[0].insert(0,string(TEST_DIR"/transform/"));
+    inFiles[1].insert(0,string(TEST_DIR"/transform/"));
+    goldFiles[0].insert(0,string(TEST_DIR"/transform/"));
+
+    af::dim4 objDims = inNumDims[0];
+
+    vector<af::dim4>       HNumDims;
+    vector<vector<float> > HIn;
+    vector<vector<float> > HTests;
+    readTests<float, float, float>(pHomographyFile, HNumDims, HIn, HTests);
+
+    af::dim4 HDims = HNumDims[0];
+
+    af_array sceneArray_f32 = 0;
+    af_array goldArray_f32 = 0;
+    af_array outArray_f32 = 0;
+    af_array sceneArray = 0;
+    af_array goldArray = 0;
+    af_array outArray = 0;
+    af_array HArray = 0;
+
+    ASSERT_EQ(AF_SUCCESS, af_load_image(&sceneArray_f32, inFiles[1].c_str(), false));
+    ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray_f32, goldFiles[0].c_str(), false));
+
+    ASSERT_EQ(AF_SUCCESS, conv_image<T>(&sceneArray, sceneArray_f32));
+    ASSERT_EQ(AF_SUCCESS, conv_image<T>(&goldArray, goldArray_f32));
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&HArray, &(HIn[0].front()), HDims.ndims(), HDims.get(), f32));
+
+    ASSERT_EQ(AF_SUCCESS, af_transform(&outArray, sceneArray, HArray, objDims[0], objDims[1], method, invert));
+
+    // Get gold data
+    dim_t goldEl = 0;
+    ASSERT_EQ(AF_SUCCESS, af_get_elements(&goldEl, goldArray));
+    T* goldData = new T[goldEl];
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+
+    // Get result
+    dim_t outEl = 0;
+    ASSERT_EQ(AF_SUCCESS, af_get_elements(&outEl, outArray));
+    T* outData = new T[outEl];
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+    const float thr = 1.1f;
+
+    // Maximum number of wrong pixels must be <= 0.01% of number of elements,
+    // this metric is necessary due to rounding errors between different
+    // backends for AF_INTERP_NEAREST and AF_INTERP_LOWER
+    const size_t maxErr = goldEl * 0.0001f;
+    size_t err = 0;
+
+    for (dim_t elIter = 0; elIter < goldEl; elIter++) {
+        err += fabs((float)floor(outData[elIter]) - (float)floor(goldData[elIter])) > thr;
+        if (err > maxErr)
+            ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
+    }
+
+    delete[] goldData;
+    delete[] outData;
+
+    if(sceneArray_f32 != 0) af_release_array(sceneArray_f32);
+    if(goldArray_f32  != 0) af_release_array(goldArray_f32);
+    if(outArray_f32   != 0) af_release_array(outArray_f32);
+    if(sceneArray     != 0) af_release_array(sceneArray);
+    if(goldArray      != 0) af_release_array(goldArray);
+    if(outArray       != 0) af_release_array(outArray);
+    if(HArray         != 0) af_release_array(HArray);
+}
+
+TYPED_TEST(Transform, PerspectiveNearest)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
+                             string(TEST_DIR"/transform/tux_tmat.test"),
+                             AF_INTERP_NEAREST, false);
+}
+
+TYPED_TEST(Transform, PerspectiveBilinear)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
+                             string(TEST_DIR"/transform/tux_tmat.test"),
+                             AF_INTERP_BILINEAR, false);
+}
+
+TYPED_TEST(Transform, PerspectiveLower)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
+                             string(TEST_DIR"/transform/tux_tmat.test"),
+                             AF_INTERP_LOWER, false);
+}
+
+TYPED_TEST(Transform, PerspectiveNearestInvert)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
+                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
+                             AF_INTERP_NEAREST, true);
+}
+
+TYPED_TEST(Transform, PerspectiveBilinearInvert)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
+                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
+                             AF_INTERP_BILINEAR, true);
+}
+
+TYPED_TEST(Transform, PerspectiveLowerInvert)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
+                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
+                             AF_INTERP_LOWER, true);
+}
+
+TYPED_TEST(TransformInt, PerspectiveNearest)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
+                             string(TEST_DIR"/transform/tux_tmat.test"),
+                             AF_INTERP_NEAREST, false);
+}
+
+TYPED_TEST(TransformInt, PerspectiveBilinear)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
+                             string(TEST_DIR"/transform/tux_tmat.test"),
+                             AF_INTERP_BILINEAR, false);
+}
+
+TYPED_TEST(TransformInt, PerspectiveLower)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
+                             string(TEST_DIR"/transform/tux_tmat.test"),
+                             AF_INTERP_LOWER, false);
+}
+
+TYPED_TEST(TransformInt, PerspectiveNearestInvert)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
+                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
+                             AF_INTERP_NEAREST, true);
+}
+
+TYPED_TEST(TransformInt, PerspectiveBilinearInvert)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
+                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
+                             AF_INTERP_BILINEAR, true);
+}
+
+TYPED_TEST(TransformInt, PerspectiveLowerInvert)
+{
+    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
+                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
+                             AF_INTERP_LOWER, true);
+}
+
+
+///////////////////////////////////// CPP ////////////////////////////////
+//
+TEST(Transform, CPP)
+{
+    vector<af::dim4>   inDims;
+    vector<string> inFiles;
+    vector<dim_t>  goldDim;
+    vector<string> goldFiles;
+
+    vector<af::dim4> HDims;
+    vector<vector<float> >   HIn;
+    vector<vector<float> >   HTests;
+    readTests<float, float, float>(TEST_DIR"/transform/tux_tmat.test",HDims,HIn,HTests);
+
+    readImageTests(string(TEST_DIR"/transform/tux_nearest.test"), inDims, inFiles, goldDim, goldFiles);
+
+    inFiles[0].insert(0,string(TEST_DIR"/transform/"));
+    inFiles[1].insert(0,string(TEST_DIR"/transform/"));
+
+    goldFiles[0].insert(0,string(TEST_DIR"/transform/"));
+
+    af::array H = af::array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
+    af::array IH = af::array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
+
+    af::array scene_img = af::loadImage(inFiles[1].c_str(), false);
+
+    af::array gold_img = af::loadImage(goldFiles[0].c_str(), false);
+
+    af::array out_img = af::transform(scene_img, IH, inDims[0][0], inDims[0][1], AF_INTERP_NEAREST, false);
+
+    af::dim4 outDims = out_img.dims();
+    af::dim4 goldDims = gold_img.dims();
+
+    float* h_out_img = new float[outDims[0] * outDims[1]];
+    out_img.host(h_out_img);
+    float* h_gold_img = new float[goldDims[0] * goldDims[1]];
+    gold_img.host(h_gold_img);
+
+    const dim_t n = gold_img.elements();
+
+    const float thr = 1.0f;
+
+    // Maximum number of wrong pixels must be <= 0.01% of number of elements,
+    // this metric is necessary due to rounding errors between different
+    // backends for AF_INTERP_NEAREST and AF_INTERP_LOWER
+    const size_t maxErr = n * 0.0001f;
+    size_t err = 0;
+
+    for (dim_t elIter = 0; elIter < n; elIter++) {
+        err += fabs((int)h_out_img[elIter] - h_gold_img[elIter]) > thr;
+        if (err > maxErr)
+            ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
+    }
+
+    delete[] h_gold_img;
+    delete[] h_out_img;
+}

From 7327fb24b176f4ca3a3d3fe4835c5af0e56f5c95 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 12:34:28 -0500
Subject: [PATCH 0200/2677] Updated test data

---
 test/data | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/data b/test/data
index db4f6e8062..4a735db351 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit db4f6e80629fb41580ab93208db6b8be958871df
+Subproject commit 4a735db3515db3f8f914e0b69fa2e11add9cd50f

From ff8326722228fd6663f32e5f8a6b19ce834677fc Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 13:29:30 -0500
Subject: [PATCH 0201/2677] __AF_FILENAME__ returns path from src

---
 src/backend/defines.hpp | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index 74457524e6..a878dff489 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -9,19 +9,31 @@
 
 #pragma once
 
-#include <string.h>
+#include <string>
+
+inline std::string
+clipFilePath(std::string path, std::string str)
+{
+    std::string::size_type pos = path.rfind(str);
+    if(pos == std::string::npos) {
+        return path;
+    } else {
+        return path.substr(pos);
+    }
+}
+
 #if defined(_WIN32) || defined(_MSC_VER)
     #define __PRETTY_FUNCTION__ __FUNCSIG__
     #if _MSC_VER < 1900
         #define snprintf sprintf_s
     #endif
     #define STATIC_ static
-    #define __AF_FILENAME__ (strrchr(__FILE__, '\\') ? strrchr(__FILE__, '\\') + 1 : __FILE__)
+    #define __AF_FILENAME__ (clipFilePath(__FILE__, "src\\").c_str())
 #else
     //#ifndef __PRETTY_FUNCTION__
     //    #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
     //#endif
     #define STATIC_ inline
-    #define __AF_FILENAME__ (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__)
+    #define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
 #endif
 

From 194de52d1ca71adf526c6adccb689df8fea69aa3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 13:29:44 -0500
Subject: [PATCH 0202/2677] Formatting the exception string

---
 src/api/c/err_common.cpp | 20 ++++++++++----------
 src/backend/defines.hpp  | 12 ++++++++----
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 926639ec45..2b8a441bd3 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -71,7 +71,7 @@ AfError::getError() const
 
 AfError::~AfError() throw() {}
 
-TypeError::TypeError(const char * const  func,
+TypeError::TypeError(const char * const func,
                      const char * const file,
                      const int line,
                      const int index, const af_dtype type)
@@ -90,7 +90,7 @@ int TypeError::getArgIndex() const
     return argIndex;
 }
 
-ArgumentError::ArgumentError(const char * const  func,
+ArgumentError::ArgumentError(const char * const func,
                              const char * const file,
                              const int line,
                              const int index,
@@ -211,16 +211,16 @@ af_err processException()
     try {
         throw;
     } catch (const DimensionError &ex) {
-        ss << "In function " << ex.getFunctionName()
-           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
+        ss << "In function " << ex.getFunctionName() << "\n"
+           << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid dimension for argument " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
         print_error(ss.str());
         err = AF_ERR_SIZE;
     } catch (const ArgumentError &ex) {
-        ss << "In function " << ex.getFunctionName()
-           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
+        ss << "In function " << ex.getFunctionName() << "\n"
+           << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid argument at index " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
@@ -234,15 +234,15 @@ af_err processException()
         print_error(ss.str());
         err = AF_ERR_NOT_SUPPORTED;
     } catch (const TypeError &ex) {
-        ss << "In function " << ex.getFunctionName()
-           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
+        ss << "In function " << ex.getFunctionName() << "\n"
+           << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid type for argument " << ex.getArgIndex() << "\n";
 
         print_error(ss.str());
         err = AF_ERR_TYPE;
     } catch (const AfError &ex) {
-        ss << "Error in " << ex.getFunctionName()
-           << "(" << ex.getFileName() << ":" << ex.getLine() << "):\n"
+        ss << "In function " << ex.getFunctionName() << "\n"
+           << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << ex.what() << "\n";
 
         print_error(ss.str());
diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index a878dff489..2ad71f3afd 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -14,11 +14,15 @@
 inline std::string
 clipFilePath(std::string path, std::string str)
 {
-    std::string::size_type pos = path.rfind(str);
-    if(pos == std::string::npos) {
+    try {
+        std::string::size_type pos = path.rfind(str);
+        if(pos == std::string::npos) {
+            return path;
+        } else {
+            return path.substr(pos);
+        }
+    } catch(...) {
         return path;
-    } else {
-        return path.substr(pos);
     }
 }
 

From 4e06483a488bcc37496b1dbfa672c36e266146cc Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 14:33:52 -0500
Subject: [PATCH 0203/2677] DOC Add code sample to convert available backends
 to bool

---
 docs/details/backend.dox | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/docs/details/backend.dox b/docs/details/backend.dox
index fafa453e6f..4d9cdf6f53 100644
--- a/docs/details/backend.dox
+++ b/docs/details/backend.dox
@@ -45,6 +45,15 @@ Return Value | Backends Available
 6            | CUDA and OpenCL
 7            | CPU, CUDA and OpenCL
 
+To convert the integer back into bools for each device, use the following code
+\code
+int backends = af::getAvailableBackends();
+
+bool cpu    = backends & AF_BACKEND_CPU;
+bool cuda   = backends & AF_BACKEND_CUDA;
+bool opencl = backends & AF_BACKEND_OPENCL;
+\endcode
+
 \ingroup unified_func
 \ingroup arrayfire_func
 

From a75590bff63daa72872c9f0956f819264b661c22 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 14:34:24 -0500
Subject: [PATCH 0204/2677] TESTS Removed typed_test from info.cpp

---
 test/info.cpp | 25 ++++---------------------
 1 file changed, 4 insertions(+), 21 deletions(-)

diff --git a/test/info.cpp b/test/info.cpp
index ff2cea6e89..e0e45ed4e7 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -21,20 +21,6 @@
 using std::string;
 using std::vector;
 
-template<typename T>
-class Info : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
-};
-
-// create a list of types to be tested
-typedef ::testing::Types<float> TestTypes;
-
-// register the type list
-TYPED_TEST_CASE(Info, TestTypes);
-
 template<typename T>
 void testFunction()
 {
@@ -47,14 +33,11 @@ void testFunction()
     if(outArray != 0) ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
 
-template<typename T>
 void infoTest()
 {
-    if (noDoubleTests<T>()) return;
-
     const char* ENV = getenv("AF_MULTI_GPU_TESTS");
     if(ENV && ENV[0] == '0') {
-        testFunction<T>();
+        testFunction<float>();
     } else {
         int nDevices = 0;
         ASSERT_EQ(AF_SUCCESS, af_get_device_count(&nDevices));
@@ -62,13 +45,13 @@ void infoTest()
         int oldDevice = af::getDevice();
         for(int d = 0; d < nDevices; d++) {
             af::setDevice(d);
-            testFunction<T>();
+            testFunction<float>();
         }
         af::setDevice(oldDevice);
     }
 }
 
-TYPED_TEST(Info, All)
+TEST(Info, All)
 {
-    infoTest<TypeParam>();
+    infoTest();
 }

From 27aeed060aa87e03b69f31e14558c468e2d9dcc9 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 15:04:43 -0500
Subject: [PATCH 0205/2677] Fixed wrong data type in OpenCL transform

---
 src/backend/opencl/kernel/transform.cl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index d746525ed6..c44c18457a 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -26,13 +26,13 @@ void calc_transf_inverse(float* txo, __global const float* txi)
     txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
     txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
 
-    T det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
+    float det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
 
     txo[0] /= det; txo[1] /= det; txo[2] /= det;
     txo[3] /= det; txo[4] /= det; txo[5] /= det;
     txo[6] /= det; txo[7] /= det; txo[8] /= det;
 #else
-    T det = txi[0]*txi[4] - txi[1]*txi[3];
+    float det = txi[0]*txi[4] - txi[1]*txi[3];
 
     txo[0] = txi[4] / det;
     txo[1] = txi[3] / det;
@@ -85,7 +85,7 @@ void transform_kernel(__global T *d_out, const KParam out,
     // We expect a inverse transform matrix by default
     // If it is an forward transform, then we need its inverse
     if(INVERSE == 1) {
-        #pragma unroll
+        #pragma unroll 3
         for(int i = 0; i < TRANSF_LEN; i++)
             tmat[i] = tmat_ptr[i];
     } else {

From 05471d19543af8f9f4500be5e56c3cb18c341901 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 14:34:43 -0500
Subject: [PATCH 0206/2677] TEST add a test for unified api

---
 test/unified.cpp | 68 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 test/unified.cpp

diff --git a/test/unified.cpp b/test/unified.cpp
new file mode 100644
index 0000000000..fc9ec02a59
--- /dev/null
+++ b/test/unified.cpp
@@ -0,0 +1,68 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+#include <af/device.h>
+
+using std::string;
+using std::vector;
+
+template<typename T>
+void testFunction()
+{
+    af_info();
+
+    af_array outArray = 0;
+    dim_t dims[] = {32, 32};
+    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) af::dtype_traits<T>::af_type));
+    // cleanup
+    if(outArray != 0) ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+}
+
+void unifiedTest()
+{
+    int backends = af::getAvailableBackends();
+
+    bool cpu    = backends & AF_BACKEND_CPU;
+    bool cuda   = backends & AF_BACKEND_CUDA;
+    bool opencl = backends & AF_BACKEND_OPENCL;
+
+    if(cpu) {
+        printf("Running CPU Backend...\n");
+        af::setBackend(AF_BACKEND_CPU);
+        testFunction<float>();
+    }
+
+    if(cuda) {
+        printf("Running CUDA Backend...\n");
+        af::setBackend(AF_BACKEND_CUDA);
+        testFunction<float>();
+    }
+
+    if(opencl) {
+        printf("Running OpenCL Backend...\n");
+        af::setBackend(AF_BACKEND_OPENCL);
+        testFunction<float>();
+    }
+
+    af::setBackend(AF_BACKEND_DEFAULT);
+}
+
+TEST(UNIFIED_TEST, Basic)
+{
+    unifiedTest();
+}

From bdc31d04b810958cf714ae6fefd21ac76edaa861 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Tue, 29 Dec 2015 15:08:44 -0500
Subject: [PATCH 0207/2677] Simplified test for perspective transform in API

---
 src/api/c/transform.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index ffd86dcd58..785a05438e 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -45,7 +45,7 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
         DIM_ASSERT(1, idims.elements() > 0);
         DIM_ASSERT(1, (idims.ndims() == 2 || idims.ndims() == 3));
 
-        const bool perspective = (tdims[1] == 3) ? true : false;
+        const bool perspective = (tdims[1] == 3);
 
         dim_t o0 = odim0, o1 = odim1;
         dim_t o2 = idims[2] * tdims[2];

From 9604fccba36b24822d4fdb2ace67cd267a7f9cb3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 15:48:10 -0500
Subject: [PATCH 0208/2677] Change AF_THROW_MSG to AF_THROW_ERR - Does not
 check for AF_SUCCESS

* Also removed the THROW macro
---
 src/api/cpp/array.cpp | 10 +++++-----
 src/api/cpp/error.hpp |  7 ++-----
 src/api/cpp/gfor.cpp  |  2 +-
 src/api/cpp/seq.cpp   |  4 ++--
 src/api/cpp/where.cpp |  2 +-
 5 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index ef9a06a184..f7931cfa9f 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -32,7 +32,7 @@ namespace af
     static af_array gforReorder(const af_array in, unsigned dim)
     {
         // This is here to stop gcc from complaining
-        if (dim > 3) AF_THROW_MSG("GFor: Dimension is invalid", AF_ERR_SIZE);
+        if (dim > 3) AF_THROW_ERR("GFor: Dimension is invalid", AF_ERR_SIZE);
         unsigned order[AF_MAX_DIMS] = {0, 1, 2, dim};
         order[dim] = 3;
         af_array out;
@@ -67,7 +67,7 @@ namespace af
             }
             return odims;
         } catch(std::logic_error &err) {
-            AF_THROW_MSG(err.what(), AF_ERR_SIZE);
+            AF_THROW_ERR(err.what(), AF_ERR_SIZE);
         }
     }
 
@@ -138,7 +138,7 @@ namespace af
         switch (src) {
         case afHost:   AF_THROW(af_create_array(arr, (const void * const)ptr, AF_MAX_DIMS, my_dims, ty)); break;
         case afDevice: AF_THROW(af_device_array(arr, (const void *      )ptr, AF_MAX_DIMS, my_dims, ty)); break;
-        default: AF_THROW_MSG("Can not create array from the requested source pointer",
+        default: AF_THROW_ERR("Can not create array from the requested source pointer",
                               AF_ERR_ARG);
         }
     }
@@ -347,7 +347,7 @@ namespace af
                 case 2: return gen_indexing(*this, z, s0, z, z);
                 case 3: return gen_indexing(*this, z, z, s0, z);
                 case 4: return gen_indexing(*this, z, z, z, s0);
-                default: AF_THROW_MSG("ndims for Array is invalid", AF_ERR_SIZE);
+                default: AF_THROW_ERR("ndims for Array is invalid", AF_ERR_SIZE);
             }
         }
         else {
@@ -968,7 +968,7 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     template<> AFAPI T *array::host() const                         \
     {                                                               \
         if (type() != (af::dtype)dtype_traits<T>::af_type) {        \
-            AF_THROW_MSG("Requested type doesn't match with array", \
+            AF_THROW_ERR("Requested type doesn't match with array", \
                          AF_ERR_TYPE);                              \
         }                                                           \
                                                                     \
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index cb5a573e32..157f8193ab 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -16,9 +16,6 @@
         throw af::exception(__AF_FILENAME__, __LINE__, __err); \
     } while(0)
 
-#define AF_THROW_MSG(__msg, __err) do {                         \
-        if (__err == AF_SUCCESS) break;                         \
+#define AF_THROW_ERR(__msg, __err) do {                         \
         throw af::exception(__msg, __AF_FILENAME__, __LINE__, __err);  \
-    } while(0);
-
-#define THROW(__err) throw af::exception(__AF_FILENAME__, __LINE__, __err)
+    } while(0)
diff --git a/src/api/cpp/gfor.cpp b/src/api/cpp/gfor.cpp
index 3918b39b33..b442164e24 100644
--- a/src/api/cpp/gfor.cpp
+++ b/src/api/cpp/gfor.cpp
@@ -32,7 +32,7 @@ namespace af
 
     array batchFunc(const array &lhs, const array &rhs, batchFunc_t func)
     {
-        if (gforGet()) AF_THROW_MSG("batchFunc can not be used inside GFOR",
+        if (gforGet()) AF_THROW_ERR("batchFunc can not be used inside GFOR",
                                     AF_ERR_ARG);
         gforSet(true);
         array res = func(lhs, rhs);
diff --git a/src/api/cpp/seq.cpp b/src/api/cpp/seq.cpp
index 0ef9326640..dff2e39c8b 100644
--- a/src/api/cpp/seq.cpp
+++ b/src/api/cpp/seq.cpp
@@ -67,10 +67,10 @@ seq::seq(double begin, double end, double step): m_gfor(false)
 {
     if (step == 0) {
         if (begin != end)   // Span
-            AF_THROW_MSG("Invalid step size", AF_ERR_ARG);
+            AF_THROW_ERR("Invalid step size", AF_ERR_ARG);
     }
     if (end >= 0 && begin >= 0 && signbit(end-begin) != signbit(step))
-        AF_THROW_MSG("Sequence is invalid", AF_ERR_ARG);
+        AF_THROW_ERR("Sequence is invalid", AF_ERR_ARG);
         //AF_THROW("step must match direction of sequence");
     init(begin, end, step);
 }
diff --git a/src/api/cpp/where.cpp b/src/api/cpp/where.cpp
index aa1681842b..fd9705998a 100644
--- a/src/api/cpp/where.cpp
+++ b/src/api/cpp/where.cpp
@@ -17,7 +17,7 @@ namespace af
     array where(const array& in)
     {
         if (gforGet()) {
-            AF_THROW_MSG("WHERE can not be used inside GFOR", AF_ERR_RUNTIME);
+            AF_THROW_ERR("WHERE can not be used inside GFOR", AF_ERR_RUNTIME);
         }
 
         af_array out = 0;

From 50bccdd939e6e2f0aa3e6063bf1877ebe1b5864c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 15:52:16 -0500
Subject: [PATCH 0209/2677] Change CHECK_ARRAYS to be used like a function

---
 src/api/unified/data.cpp           | 6 +++---
 src/api/unified/symbol_manager.hpp | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index 236b11f7e2..9579e094e4 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -76,19 +76,19 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
 
 af_err af_diag_create(af_array *out, const af_array in, const int num)
 {
-    CHECK_ARRAYS(in)
+    CHECK_ARRAYS(in);
     return CALL(out, in, num);
 }
 
 af_err af_diag_extract(af_array *out, const af_array in, const int num)
 {
-    CHECK_ARRAYS(in)
+    CHECK_ARRAYS(in);
     return CALL(out, in, num);
 }
 
 af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)
 {
-    CHECK_ARRAYS(first, second)
+    CHECK_ARRAYS(first, second);
     return CALL(out, dim, first, second);
 }
 
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index f4cf913ac6..f26e708728 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -97,7 +97,7 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg)
     af_backend backendId = unified::AFSymbolManager::getInstance().getActiveBackend();      \
     if(!unified::checkArrays(backendId, __VA_ARGS__))                                       \
         return AF_ERR_ARR_BKND_MISMATCH;                                                    \
-} while(0);
+} while(0)
 
 #if defined(OS_WIN)
 #define CALL(...) unified::AFSymbolManager::getInstance().call(__FUNCTION__, __VA_ARGS__)

From 5df0b29126c28cbe9fd7b3a226643efdc45349d4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 16:22:33 -0500
Subject: [PATCH 0210/2677] TEST Rename test/unified.cpp to test/backend.cpp

---
 test/{unified.cpp => backend.cpp} | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)
 rename test/{unified.cpp => backend.cpp} (84%)

diff --git a/test/unified.cpp b/test/backend.cpp
similarity index 84%
rename from test/unified.cpp
rename to test/backend.cpp
index fc9ec02a59..59b8fd5129 100644
--- a/test/unified.cpp
+++ b/test/backend.cpp
@@ -33,7 +33,7 @@ void testFunction()
     if(outArray != 0) ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
 
-void unifiedTest()
+void backendTest()
 {
     int backends = af::getAvailableBackends();
 
@@ -42,27 +42,25 @@ void unifiedTest()
     bool opencl = backends & AF_BACKEND_OPENCL;
 
     if(cpu) {
-        printf("Running CPU Backend...\n");
+        printf("\nRunning CPU Backend...\n");
         af::setBackend(AF_BACKEND_CPU);
         testFunction<float>();
     }
 
     if(cuda) {
-        printf("Running CUDA Backend...\n");
+        printf("\nRunning CUDA Backend...\n");
         af::setBackend(AF_BACKEND_CUDA);
         testFunction<float>();
     }
 
     if(opencl) {
-        printf("Running OpenCL Backend...\n");
+        printf("\nRunning OpenCL Backend...\n");
         af::setBackend(AF_BACKEND_OPENCL);
         testFunction<float>();
     }
-
-    af::setBackend(AF_BACKEND_DEFAULT);
 }
 
-TEST(UNIFIED_TEST, Basic)
+TEST(BACKEND_TEST, Basic)
 {
-    unifiedTest();
+    backendTest();
 }

From db3626f7dddb345e5c94e4bb8490a6972fbe9d95 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 17:31:08 -0500
Subject: [PATCH 0211/2677] Build only info and backend test for unified

---
 test/CMakeLists.txt | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index e312e62cb2..c18e3fdadf 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -33,7 +33,14 @@ REMOVE_DEFINITIONS(-std=c++11)
 MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
     STRING(TOUPPER ${BACKEND} DEF_NAME)
 
-    FOREACH(FILE ${FILES})
+    # For some reason passing FILES/UNIFIED_FILES to macro doesn't work
+    IF(${BACKEND} STREQUAL "unified")
+      SET(TEST_FILES ${UNIFIED_FILES})
+    ELSE(${BACKEND} STREQUAL "unified")
+      SET(TEST_FILES ${FILES})
+    ENDIF(${BACKEND} STREQUAL "unified")
+
+    FOREACH(FILE ${TEST_FILES})
         GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
         SET(TEST_NAME ${FNAME}_${BACKEND})
 
@@ -112,6 +119,9 @@ INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")
 
+# We only build info.cpp and backend.cpp for Unified backend
+SET(UNIFIED_FILES "info.cpp;backend.cpp")
+
 # Next we build each example using every backend.
 IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
     MESSAGE(STATUS "TESTS: CPU backend is ON.")
@@ -123,7 +133,7 @@ ELSE()
     MESSAGE(STATUS "TESTS: CPU backend is OFF. afcpu was not found.")
 ENDIF()
 
-# Next we build each example using every backend.
+# Unified Backend
 IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
     MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
     CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
@@ -134,6 +144,7 @@ ELSE()
     MESSAGE(STATUS "TESTS: UNIFIED backend is OFF. af was not found.")
 ENDIF()
 
+# CUDA Backend
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
@@ -185,6 +196,7 @@ ELSE()
     MESSAGE(STATUS "TESTS: CUDA backend is OFF. CUDA was not found")
 ENDIF()
 
+# OpenCL Backend
 IF (${OpenCL_FOUND})
     IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
         MESSAGE(STATUS "TESTS: OpenCL backend is ON.")

From 091cdf9143a944784c5e35671927509d4f8b3d70 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 19:19:31 -0500
Subject: [PATCH 0212/2677] Adding a Fast configuration

---
 CMakeLists.txt                | 19 ++++++---
 CMakeModules/FastConfig.cmake | 78 +++++++++++++++++++++++++++++++++++
 2 files changed, 92 insertions(+), 5 deletions(-)
 create mode 100644 CMakeModules/FastConfig.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea92cbec5d..5478143342 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -37,13 +37,22 @@ MARK_AS_ADVANCED(BUILD_SIFT)
 
 OPTION(BUILD_UNIFIED "Build Backend-Independent ArrayFire API" ON)
 
+# Add a Fast compiling config
+INCLUDE("${CMAKE_MODULE_PATH}/FastConfig.cmake")
+
 # Set a default build type if none was specified
-if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
-    set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE)
+IF(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    SET(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE)
     # Set the possible values of build type for cmake-gui
-    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release"
-      "MinSizeRel" "RelWithDebInfo")
-endif()
+
+    IF(${FAST_CONFIG_ENABLED})
+        SET_PROPERTY(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS
+            "Debug" "Fast" "Release" "MinSizeRel" "RelWithDebInfo")
+    ELSE()
+        SET_PROPERTY(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS
+            "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+    ENDIF()
+ENDIF()
 
 FIND_PACKAGE(FreeImage)
 IF(FREEIMAGE_FOUND)
diff --git a/CMakeModules/FastConfig.cmake b/CMakeModules/FastConfig.cmake
new file mode 100644
index 0000000000..4a9a2cdc90
--- /dev/null
+++ b/CMakeModules/FastConfig.cmake
@@ -0,0 +1,78 @@
+MESSAGE(STATUS "Adding Fast Build Type")
+
+IF(MSVC)
+
+    SET(CMAKE_CXX_FLAGS_FAST
+        "/MD /Od /Ob1 /D NDEBUG"
+        CACHE STRING "Flags used by the C++ compiler during Fast builds."
+        FORCE )
+    SET(CMAKE_C_FLAGS_FAST
+        "/MD /Od /Ob1 /D NDEBUG"
+        CACHE STRING "Flags used by the C compiler during Fast builds."
+        FORCE )
+    SET(CMAKE_EXE_LINKER_FLAGS_FAST
+        "/INCREMENTAL:NO"
+        CACHE STRING "Flags used for linking binaries during Fast builds."
+        FORCE )
+    SET(CMAKE_MODULE_LINKER_FLAGS_FAST
+        "/INCREMENTAL:NO"
+        CACHE STRING "Flags used by the modules linker during Fast builds."
+        FORCE )
+    SET(CMAKE_STATIC_LINKER_FLAGS_FAST
+        ""
+        CACHE STRING "Flags used by the static libraries linker during Fast builds."
+        FORCE )
+    SET(CMAKE_SHARED_LINKER_FLAGS_FAST
+        "/INCREMENTAL:NO"
+        CACHE STRING "Flags used by the shared libraries linker during Fast builds."
+        FORCE )
+
+    LIST(APPEND CMAKE_CONFIGURATION_TYPES Fast)
+    LIST(REMOVE_DUPLICATES CMAKE_CONFIGURATION_TYPES)
+    SET(CMAKE_CONFIGURATION_TYPES "${CMAKE_CONFIGURATION_TYPES}" CACHE STRING
+        "Semicolon separated list of supported configuration types [Debug|Release|MinSizeRel|RelWithDebInfo|Fast]"
+        FORCE)
+
+    # Needed for config to show up in VS
+    # http://cmake.3232098.n2.nabble.com/Custom-configuration-types-in-Visual-Studio-td7181786.html
+    ENABLE_LANGUAGE(CXX)
+
+ELSE(MSVC)
+
+    SET(CMAKE_CXX_FLAGS_FAST
+        "-O0 -DNDEBUG"
+        CACHE STRING "Flags used by the C++ compiler during Fast builds."
+        FORCE )
+    SET(CMAKE_C_FLAGS_FAST
+        "-O0 -DNDEBUG"
+        CACHE STRING "Flags used by the C compiler during Fast builds."
+        FORCE )
+    SET(CMAKE_EXE_LINKER_FLAGS_FAST
+        ""
+        CACHE STRING "Flags used for linking binaries during Fast builds."
+        FORCE )
+    SET(CMAKE_MODULE_LINKER_FLAGS_FAST
+        ""
+        CACHE STRING "Flags used by the modules linker during Fast builds."
+        FORCE )
+    SET(CMAKE_STATIC_LINKER_FLAGS_FAST
+        ""
+        CACHE STRING "Flags used by the static libraries linker during Fast builds."
+        FORCE )
+    SET(CMAKE_SHARED_LINKER_FLAGS_FAST
+        ""
+        CACHE STRING "Flags used by the shared libraries linker during Fast builds."
+        FORCE )
+
+ENDIF(MSVC)
+
+SET(FAST_CONFIG_ENABLED ON CACHE INTERNAL "" FORCE)
+
+MARK_AS_ADVANCED(
+    CMAKE_CXX_FLAGS_FAST
+    CMAKE_C_FLAGS_FAST
+    CMAKE_EXE_LINKER_FLAGS_FAST
+    CMAKE_MODULE_LINKER_FLAGS_FAST
+    CMAKE_STATIC_LINKER_FLAGS_FAST
+    CMAKE_SHARED_LINKER_FLAGS_FAST
+    )

From 862b5232e74a79dc090b66fdaed1d1957700fc1e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 20:22:06 -0500
Subject: [PATCH 0213/2677] Fixes for exceptions in minimal builds

---
 src/api/c/err_common.hpp | 13 +++++++------
 src/api/c/imageio.cpp    |  2 ++
 src/api/c/imageio2.cpp   |  2 ++
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/src/api/c/err_common.hpp b/src/api/c/err_common.hpp
index a4ca57f62b..c8eb90a7f6 100644
--- a/src/api/c/err_common.hpp
+++ b/src/api/c/err_common.hpp
@@ -11,6 +11,7 @@
 
 #include <stdexcept>
 #include <string>
+#include <sstream>
 #include <cassert>
 #include <af/defines.h>
 #include <defines.hpp>
@@ -171,12 +172,12 @@ void print_error(const std::string &msg);
         AfError err(__PRETTY_FUNCTION__,                    \
                     __AF_FILENAME__, __LINE__,              \
                     MSG, ERR_TYPE);                         \
-        std::string s = "Error in " + err.getFunctionName() \
-                        + "(" + err.getFileName()           \
-                        + ":" + err.getLine() + "):\n"      \
-                        + err.getError().what() + "\n"      \
-                        ;                                   \
-        print_error(str);                                   \
+        std::stringstream s;                                \
+        s << "Error in " << err.getFunctionName() << "\n"   \
+          << "In file " << err.getFileName()                \
+          << ":" << err.getLine()  << "\n"                  \
+          << err.what() << "\n";                            \
+        print_error(s.str());                               \
         return ERR_TYPE;                                    \
     } while(0)
 
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index d4855ad778..746ee69142 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -23,6 +23,7 @@
 #include <ArrayInfo.hpp>
 #include <traits.hpp>
 #include <memory.hpp>
+#include <err_common.hpp>
 
 #include <string>
 #include <cstring>
@@ -713,6 +714,7 @@ af_err af_delete_image_memory(void *ptr)
 #else   // WITH_FREEIMAGE
 #include <af/image.h>
 #include <stdio.h>
+#include <err_common.hpp>
 af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index a51bacb20d..d50afefb92 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -23,6 +23,7 @@
 #include <ArrayInfo.hpp>
 #include <traits.hpp>
 #include <memory.hpp>
+#include <err_common.hpp>
 
 #include <string>
 #include <cstring>
@@ -375,6 +376,7 @@ af_err af_save_image_native(const char* filename, const af_array in)
 #else   // WITH_FREEIMAGE
 #include <af/image.h>
 #include <stdio.h>
+#include <err_common.hpp>
 af_err af_load_image_native(af_array *out, const char* filename)
 {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);

From 613557c090af8d4a0f84b06ad4b63ff77d2843a5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 29 Dec 2015 20:37:32 -0500
Subject: [PATCH 0214/2677] Changes to opencl backend when building with
 openblas

---
 src/backend/opencl/magma/labrd.cpp        | 119 ++++++++++++----------
 src/backend/opencl/magma/magma_cpu_blas.h |  31 ++++--
 2 files changed, 85 insertions(+), 65 deletions(-)

diff --git a/src/backend/opencl/magma/labrd.cpp b/src/backend/opencl/magma/labrd.cpp
index 0284ee835d..115b48d2cd 100644
--- a/src/backend/opencl/magma/labrd.cpp
+++ b/src/backend/opencl/magma/labrd.cpp
@@ -64,6 +64,12 @@
 
 #include <algorithm>
 
+#define cpu_blas_gemv_macro(_trans, _m, _n, _alpha, _aptr, _lda, _xptr, _incx, _beta, _yptr, _incy) \
+    cpu_blas_gemv(_trans, _m, _n,                                       \
+                  cblas_scalar(_alpha), cblas_ptr(_aptr), _lda,         \
+                  cblas_ptr(_xptr), _incx,                              \
+                  cblas_scalar(_beta), cblas_ptr(_yptr), _incy)
+
 template<typename Ty>  magma_int_t
 magma_labrd_gpu(
     magma_int_t m, magma_int_t n, magma_int_t nb,
@@ -264,15 +270,15 @@ magma_labrd_gpu(
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__+y_dim1], ldy));
             }
 
-            cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one), &a[i__ + a_dim1], lda,
-                          &y[i__+y_dim1], ldy, cblas_scalar(&c_one), &a[i__ + i__ * a_dim1], c__1);
+            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &a[i__ + a_dim1], lda,
+                          &y[i__+y_dim1], ldy, (&c_one), &a[i__ + i__ * a_dim1], c__1);
 
             if (is_cplx) {
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__+y_dim1], ldy));
             }
 
-            cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one), &x[i__ + x_dim1], ldx,
-                          &a[i__*a_dim1+1], c__1, cblas_scalar(&c_one), &a[i__+i__*a_dim1], c__1);
+            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &x[i__ + x_dim1], ldx,
+                          &a[i__*a_dim1+1], c__1, (&c_one), &a[i__+i__*a_dim1], c__1);
 
             /* Generate reflection Q(i) to annihilate A(i+1:m,i) */
             alpha = a[i__ + i__ * a_dim1];
@@ -310,19 +316,19 @@ magma_labrd_gpu(
                                           queue, &event);
                 i__2 = m - i__ + 1;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_one), &a[i__ + a_dim1],
-                              lda, &a[i__ + i__ * a_dim1], c__1, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &a[i__ + a_dim1],
+                              lda, &a[i__ + i__ * a_dim1], c__1, (&c_zero),
                               &y[i__ * y_dim1 + 1], c__1);
 
                 i__2 = n - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one), &y[i__ + 1 +y_dim1], ldy,
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &y[i__ + 1 +y_dim1], ldy,
                               &y[i__ * y_dim1 + 1], c__1,
-                              cblas_scalar(&c_zero), f, c__1);
+                              (&c_zero), f, c__1);
                 i__2 = m - i__ + 1;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_one), &x[i__ + x_dim1],
-                              ldx, &a[i__ + i__ * a_dim1], c__1, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &x[i__ + x_dim1],
+                              ldx, &a[i__ + i__ * a_dim1], c__1, (&c_zero),
                               &y[i__ * y_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
@@ -330,16 +336,17 @@ magma_labrd_gpu(
 
                 if (i__3 != 0){
                     i__2 = n - i__;
-                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), f,c__1, &y[i__+1+i__*y_dim1],c__1);
+                    cpu_blas_axpy(i__2, cblas_scalar(&c_one),
+                                  cblas_ptr(f),c__1, cblas_ptr(&y[i__+1+i__*y_dim1]), c__1);
                 }
 
                 i__2 = i__ - 1;
                 i__3 = n - i__;
-                cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_neg_one),
-                              &a[(i__ + 1) * a_dim1 + 1], lda, &y[i__ * y_dim1 + 1], c__1, cblas_scalar(&c_one),
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one),
+                              &a[(i__ + 1) * a_dim1 + 1], lda, &y[i__ * y_dim1 + 1], c__1, (&c_one),
                               &y[i__ + 1 + i__ * y_dim1], c__1);
                 i__2 = n - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]), &y[i__ + 1 + i__ * y_dim1], c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]), cblas_ptr(&y[i__ + 1 + i__ * y_dim1]), c__1);
 
                 /* Update A(i,i+1:n) */
                 i__2 = n - i__;
@@ -348,9 +355,9 @@ magma_labrd_gpu(
                     LAPACKE_CHECK(cpu_lapack_lacgv(i__,  &a[i__+a_dim1], lda));
                 }
 
-                cpu_blas_gemv(CblasNoTrans, i__2, i__, cblas_scalar(&c_neg_one),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__, (&c_neg_one),
                               &y[i__ + 1 + y_dim1], ldy, &a[i__ + a_dim1], lda,
-                              cblas_scalar(&c_one), &a[i__ + (i__ + 1) * a_dim1], lda);
+                              (&c_one), &a[i__ + (i__ + 1) * a_dim1], lda);
                 i__2 = i__ - 1;
                 i__3 = n - i__;
 
@@ -359,8 +366,8 @@ magma_labrd_gpu(
                     LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &x[i__+x_dim1], ldx));
                 }
 
-                cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_neg_one), &a[(i__ + 1) *
-                                                                              a_dim1 + 1], lda, &x[i__ + x_dim1], ldx, cblas_scalar(&c_one), &a[
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one), &a[(i__ + 1) *
+                                                                              a_dim1 + 1], lda, &x[i__ + x_dim1], ldx, (&c_one), &a[
                                                                                   i__ + (i__ + 1) * a_dim1], lda);
                 if (is_cplx) {
                     LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &x[i__+x_dim1], ldx));
@@ -402,35 +409,35 @@ magma_labrd_gpu(
                                           queue, &event);
 
                 i__2 = n - i__;
-                cpu_blas_gemv(CblasTransParam, i__2, i__, cblas_scalar(&c_one), &y[i__ + 1 + y_dim1],
-                              ldy, &a[i__ + (i__ + 1) * a_dim1], lda, cblas_scalar(&c_zero), &x[
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__, (&c_one), &y[i__ + 1 + y_dim1],
+                              ldy, &a[i__ + (i__ + 1) * a_dim1], lda, (&c_zero), &x[
                                   i__ * x_dim1 + 1], c__1);
 
                 i__2 = m - i__;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__, cblas_scalar(&c_neg_one), &a[i__ + 1 + a_dim1], lda,
-                              &x[i__ * x_dim1 + 1], c__1, cblas_scalar(&c_zero), f, c__1);
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__, (&c_neg_one), &a[i__ + 1 + a_dim1], lda,
+                              &x[i__ * x_dim1 + 1], c__1, (&c_zero), f, c__1);
                 i__2 = i__ - 1;
                 i__3 = n - i__;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_one), &a[(i__ + 1) * a_dim1 + 1],
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_one), &a[(i__ + 1) * a_dim1 + 1],
                               lda, &a[i__ + (i__ + 1) * a_dim1], lda,
-                              cblas_scalar(&c_zero), &x[i__ * x_dim1 + 1], c__1);
+                              (&c_zero), &x[i__ * x_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
 
                 if (i__!=0){
                     i__2 = m - i__;
-                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), f,c__1, &x[i__+1+i__*x_dim1],c__1);
+                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),c__1, cblas_ptr(&x[i__+1+i__*x_dim1]),c__1);
                 }
 
 
                 i__2 = m - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one), &x[i__ + 1 +
-                                                                           x_dim1], ldx, &x[i__ * x_dim1 + 1], c__1, cblas_scalar(&c_one), &x[
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &x[i__ + 1 +
+                                                                           x_dim1], ldx, &x[i__ * x_dim1 + 1], c__1, (&c_one), &x[
                                                                                i__ + 1 + i__ * x_dim1], c__1);
                 i__2 = m - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]), &x[i__ + 1 + i__ * x_dim1], c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]), cblas_ptr(&x[i__ + 1 + i__ * x_dim1]), c__1);
 
                 if (is_cplx) {
                     i__2 = n - i__;
@@ -455,16 +462,16 @@ magma_labrd_gpu(
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &a[i__ + a_dim1], lda));
             }
-            cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one), &y[i__ + y_dim1], ldy,
-                          &a[i__ + a_dim1], lda, cblas_scalar(&c_one), &a[i__ + i__ * a_dim1], lda);
+            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &y[i__ + y_dim1], ldy,
+                          &a[i__ + a_dim1], lda, (&c_one), &a[i__ + i__ * a_dim1], lda);
             i__2 = i__ - 1;
             if (is_cplx) {
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &a[i__ + a_dim1], lda));
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &x[i__ + x_dim1], ldx));
             }
             i__3 = n - i__ + 1;
-            cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_neg_one), &a[i__ * a_dim1 + 1],
-                          lda, &x[i__ + x_dim1], ldx, cblas_scalar(&c_one), &a[i__ + i__ * a_dim1], lda);
+            cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one), &a[i__ * a_dim1 + 1],
+                          lda, &x[i__ + x_dim1], ldx, (&c_one), &a[i__ + i__ * a_dim1], lda);
             if (is_cplx) {
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &x[i__ + x_dim1], ldx));
             }
@@ -510,35 +517,35 @@ magma_labrd_gpu(
 
                 i__2 = n - i__ + 1;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_one), &y[i__ + y_dim1],
-                              ldy, &a[i__ + i__ * a_dim1], lda, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &y[i__ + y_dim1],
+                              ldy, &a[i__ + i__ * a_dim1], lda, (&c_zero),
                               &x[i__ *  x_dim1 + 1], c__1);
                 i__2 = m - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one),
-                              &a[i__ + 1 + a_dim1], lda, &x[i__ * x_dim1 + 1], c__1, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                              &a[i__ + 1 + a_dim1], lda, &x[i__ * x_dim1 + 1], c__1, (&c_zero),
                               f, c__1);
 
                 i__2 = i__ - 1;
                 i__3 = n - i__ + 1;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_one),
-                              &a[i__ * a_dim1 + 1], lda, &a[i__ + i__ * a_dim1], lda, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_one),
+                              &a[i__ * a_dim1 + 1], lda, &a[i__ + i__ * a_dim1], lda, (&c_zero),
                               &x[i__ * x_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
                 if (i__2 != 0){
                     i__3 = m - i__;
-                    cpu_blas_axpy(i__3, cblas_scalar(&c_one), f,c__1, &x[i__+1+i__*x_dim1],c__1);
+                    cpu_blas_axpy(i__3, cblas_scalar(&c_one), cblas_ptr(f),c__1, cblas_ptr(&x[i__+1+i__*x_dim1]),c__1);
                 }
 
                 i__2 = m - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one),
-                              &x[i__ + 1 + x_dim1], ldx, &x[i__ * x_dim1 + 1], c__1, cblas_scalar(&c_one),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                              &x[i__ + 1 + x_dim1], ldx, &x[i__ * x_dim1 + 1], c__1, (&c_one),
                               &x[i__ + 1 + i__ * x_dim1], c__1);
                 i__2 = m - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]), &x[i__ + 1 + i__ * x_dim1], c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]), cblas_ptr(&x[i__ + 1 + i__ * x_dim1]), c__1);
                 i__2 = n - i__ + 1;
 
                 if (is_cplx) {
@@ -557,15 +564,15 @@ magma_labrd_gpu(
                     LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
                 }
 
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one),
-                              &a[i__ + 1 + a_dim1], lda, &y[i__ + y_dim1], ldy, cblas_scalar(&c_one),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                              &a[i__ + 1 + a_dim1], lda, &y[i__ + y_dim1], ldy, (&c_one),
                               &a[i__ + 1 + i__ * a_dim1], c__1);
                 i__2 = m - i__;
                 if (is_cplx) {
                     LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
                 }
-                cpu_blas_gemv(CblasNoTrans, i__2, i__, cblas_scalar(&c_neg_one),
-                              &x[i__ + 1 + x_dim1], ldx, &a[i__ * a_dim1 + 1], c__1, cblas_scalar(&c_one),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__, (&c_neg_one),
+                              &x[i__ + 1 + x_dim1], ldx, &a[i__ * a_dim1 + 1], c__1, (&c_one),
                               &a[i__ + 1 + i__ * a_dim1], c__1);
 
                 /* Generate reflection Q(i) to annihilate A(i+2:m,i) */
@@ -602,33 +609,33 @@ magma_labrd_gpu(
 
                 i__2 = m - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasTransParam, i__2, i__3, cblas_scalar(&c_one), &a[i__ + 1 + a_dim1],
-                              lda, &a[i__ + 1 + i__ * a_dim1], c__1, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &a[i__ + 1 + a_dim1],
+                              lda, &a[i__ + 1 + i__ * a_dim1], c__1, (&c_zero),
                               &y[ i__ * y_dim1 + 1], c__1);
                 i__2 = n - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv(CblasNoTrans, i__2, i__3, cblas_scalar(&c_neg_one),
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
                               &y[i__ + 1 + y_dim1], ldy, &y[i__ * y_dim1 + 1], c__1,
-                              cblas_scalar(&c_zero), f, c__1);
+                              (&c_zero), f, c__1);
 
                 i__2 = m - i__;
-                cpu_blas_gemv(CblasTransParam, i__2, i__, cblas_scalar(&c_one), &x[i__ + 1 + x_dim1],
-                              ldx, &a[i__ + 1 + i__ * a_dim1], c__1, cblas_scalar(&c_zero),
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__, (&c_one), &x[i__ + 1 + x_dim1],
+                              ldx, &a[i__ + 1 + i__ * a_dim1], c__1, (&c_zero),
                               &y[i__ * y_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
                 if (i__3 != 0){
                     i__2 = n - i__;
-                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), f,c__1, &y[i__+1+i__*y_dim1],c__1);
+                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),c__1, cblas_ptr(&y[i__+1+i__*y_dim1]),c__1);
                 }
 
                 i__2 = n - i__;
-                cpu_blas_gemv(CblasTransParam, i__, i__2, cblas_scalar(&c_neg_one),
+                cpu_blas_gemv_macro(CblasTransParam, i__, i__2, (&c_neg_one),
                               &a[(i__ + 1) * a_dim1 + 1], lda, &y[i__ * y_dim1 + 1],
-                              c__1, cblas_scalar(&c_one), &y[i__ + 1 + i__ * y_dim1], c__1);
+                              c__1, (&c_one), &y[i__ + 1 + i__ * y_dim1], c__1);
                 i__2 = n - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]), &y[i__ + 1 + i__ * y_dim1], c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]), cblas_ptr(&y[i__ + 1 + i__ * y_dim1]), c__1);
             }
             else {
                 if (is_cplx) {
diff --git a/src/backend/opencl/magma/magma_cpu_blas.h b/src/backend/opencl/magma/magma_cpu_blas.h
index 6ae4f8f39f..b3cba096b5 100644
--- a/src/backend/opencl/magma/magma_cpu_blas.h
+++ b/src/backend/opencl/magma/magma_cpu_blas.h
@@ -41,14 +41,14 @@ typedef int blasint;
     template<typename T>                        \
     struct cpu_blas_##NAME##_func;
 
-#define CPU_BLAS_FUNC1(NAME, TYPE, X)                       \
-    template<>                                              \
-    struct cpu_blas_##NAME##_func<TYPE>                     \
-    {                                                       \
-        template<typename... Args>                          \
-            void                                            \
-            operator() (Args... args)                       \
-        { return cblas_##X##NAME(CblasColMajor, args...); } \
+#define CPU_BLAS_FUNC1(NAME, TYPE, X)                   \
+    template<>                                          \
+    struct cpu_blas_##NAME##_func<TYPE>                 \
+    {                                                   \
+        template<typename... Args>                      \
+            void                                        \
+            operator() (Args... args)                   \
+        { cblas_##X##NAME(CblasColMajor, args...); }    \
     };
 
 #define CPU_BLAS_FUNC2(NAME, TYPE, X)           \
@@ -58,7 +58,7 @@ typedef int blasint;
         template<typename... Args>              \
             void                                \
             operator() (Args... args)           \
-        { return cblas_##X##NAME(args...); }    \
+        {  cblas_##X##NAME(args...); }          \
     };
 
 #define CPU_BLAS_DECL1(NAME)                        \
@@ -81,11 +81,24 @@ CPU_BLAS_DECL2(axpy)
 
 inline float * cblas_ptr(float *in) { return in; }
 inline double * cblas_ptr(double *in) { return in; }
+
+#if defined(IS_OPENBLAS)
+inline float * cblas_ptr(magmaFloatComplex *in) { return (float *)in; }
+inline double * cblas_ptr(magmaDoubleComplex *in) { return (double *)in; }
+#else
 inline void * cblas_ptr(magmaFloatComplex *in) { return (void *)in; }
 inline void * cblas_ptr(magmaDoubleComplex *in) { return (void *)in; }
+#endif
 
 inline float cblas_scalar(float *in) { return *in; }
 inline double cblas_scalar(double *in) { return *in; }
+
+#if defined(IS_OPENBLAS)
+inline float *cblas_scalar(magmaFloatComplex *in) { return (float *)in; }
+inline double *cblas_scalar(magmaDoubleComplex *in) { return (double *)in; }
+#else
 inline void *cblas_scalar(magmaFloatComplex *in) { return (void *)in; }
 inline void *cblas_scalar(magmaDoubleComplex *in) { return (void *)in; }
 #endif
+
+#endif

From 0c7789314b747f356d471c53a56c37f8d1b44d2c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 29 Dec 2015 20:38:05 -0500
Subject: [PATCH 0215/2677] Changes to find OpenBLAS as lapack and lapacke
 alternative

- Needed when liblapack or liblapacke are not symlinked to libopenblas
---
 CMakeModules/FindLAPACKE.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 945ba0cb58..3bf8a1f362 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -27,7 +27,7 @@ IF(PC_LAPACKE_FOUND)
       IF (NOT ${PC_LIB}_LIBRARY)
         MESSAGE(FATAL_ERROR "Something is wrong in your pkg-config file - lib ${PC_LIB} not found in ${PC_LAPACKE_LIBRARY_DIRS}")
       ENDIF (NOT ${PC_LIB}_LIBRARY)
-      LIST(APPEND LAPACKE_LIB ${${PC_LIB}_LIBRARY}) 
+      LIST(APPEND LAPACKE_LIB ${${PC_LIB}_LIBRARY})
     ENDFOREACH(PC_LIB)
 
     FIND_PATH(
@@ -78,7 +78,7 @@ ELSE(PC_LAPACKE_FOUND)
     ELSE()
         FIND_LIBRARY(
             LAPACKE_LIB
-            NAMES "lapacke" "liblapacke"
+            NAMES "lapacke" "liblapacke" "openblas"
             PATHS
             ${PC_LAPACKE_LIBRARY_DIRS}
             ${LIB_INSTALL_DIR}
@@ -92,7 +92,7 @@ ELSE(PC_LAPACKE_FOUND)
             )
         FIND_LIBRARY(
            LAPACK_LIB
-            NAMES "lapack" "liblapack"
+            NAMES "lapack" "liblapack" "openblas"
             PATHS
             ${PC_LAPACKE_LIBRARY_DIRS}
             ${LIB_INSTALL_DIR}

From 40f2cbef76c5e885cc8735b729d917f556dad0b9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Dec 2015 22:04:50 -0500
Subject: [PATCH 0216/2677] Test reordering: Sort by backend, then
 alphabetically

* Mainly affects the order in which tests are executed
* Cleaner, more organized
* Tests are
  * 1 - N     -> CPU
  * N+1 - 2N  -> CUDA
  * 2N+1 - 3N -> OpenCL
  * 3N+1 - E  -> Unified
---
 test/CMakeLists.txt | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index c18e3fdadf..44192eda3a 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -118,9 +118,11 @@ INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
 
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")
+LIST(SORT FILES)  # Tests execute in alphabetical order
 
 # We only build info.cpp and backend.cpp for Unified backend
-SET(UNIFIED_FILES "info.cpp;backend.cpp")
+SET(UNIFIED_FILES "backend.cpp;info.cpp")
+LIST(SORT UNIFIED_FILES)  # Tests execute in alphabetical order
 
 # Next we build each example using every backend.
 IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
@@ -133,17 +135,6 @@ ELSE()
     MESSAGE(STATUS "TESTS: CPU backend is OFF. afcpu was not found.")
 ENDIF()
 
-# Unified Backend
-IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
-    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
-    CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
-ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
-    CREATE_TESTS(unified af "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
-ELSE()
-    MESSAGE(STATUS "TESTS: UNIFIED backend is OFF. af was not found.")
-ENDIF()
-
 # CUDA Backend
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
@@ -210,3 +201,14 @@ IF (${OpenCL_FOUND})
 ELSE()
     MESSAGE(STATUS "TESTS: OpenCL backend is OFF. OpenCL was not found")
 ENDIF()
+
+# Unified Backend
+IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
+    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
+    CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
+ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
+    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
+    CREATE_TESTS(unified af "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
+ELSE()
+    MESSAGE(STATUS "TESTS: UNIFIED backend is OFF. af was not found.")
+ENDIF()

From 1cbffbbbca2aa94b6c01daed167b36534831273a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 Dec 2015 10:09:15 -0500
Subject: [PATCH 0217/2677] Synchronize when AF_SYNCHRONOUS_CALLS is set to 1

---
 .gitignore                             |  1 +
 CMakeLists.txt                         |  1 +
 src/api/unified/CMakeLists.txt         |  1 +
 src/api/unified/symbol_manager.cpp     | 19 ------------
 src/api/unified/symbol_manager.hpp     |  2 ++
 src/backend/cpu/Array.hpp              |  2 +-
 src/backend/cpu/CMakeLists.txt         |  1 +
 src/backend/cpu/debug_cpu.hpp          |  2 +-
 src/backend/cpu/platform.cpp           |  6 ++--
 src/backend/cpu/platform.hpp           |  6 ++--
 src/backend/cpu/queue.hpp              | 42 ++++++++++++++++++++++++++
 src/backend/cuda/CMakeLists.txt        |  1 +
 src/backend/cuda/debug_cuda.hpp        | 10 ++++--
 src/backend/cuda/platform.cpp          |  6 ++++
 src/backend/cuda/platform.hpp          |  3 ++
 src/backend/opencl/CMakeLists.txt      |  1 +
 src/backend/opencl/debug_opencl.hpp    |  7 ++++-
 src/backend/opencl/kernel/convolve.hpp |  1 +
 src/backend/opencl/platform.cpp        |  6 ++++
 src/backend/opencl/platform.hpp        |  2 ++
 src/util.cpp                           | 40 ++++++++++++++++++++++++
 src/util.hpp                           | 16 ++++++++++
 22 files changed, 146 insertions(+), 30 deletions(-)
 create mode 100644 src/backend/cpu/queue.hpp
 create mode 100644 src/util.cpp
 create mode 100644 src/util.hpp

diff --git a/.gitignore b/.gitignore
index 948b5962eb..d032d3d5dd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,4 @@ GPATH
 include/af/version.h
 src/backend/version.hpp
 docs/details/examples.dox
+/TAGS
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea92cbec5d..fda27036fd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -110,6 +110,7 @@ IF(BUILD_SIFT)
 ENDIF(BUILD_SIFT)
 
 INCLUDE_DIRECTORIES(
+    "${CMAKE_CURRENT_SOURCE_DIR}/src"
     "${CMAKE_CURRENT_SOURCE_DIR}/include"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/backend"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/api/c"
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index a4843bb49c..b6980d6bb3 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -15,6 +15,7 @@ FILE(GLOB cpp_sources
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB common_sources
+    "../../util.cpp"
     "../c/util.cpp"
     "../c/err_common.cpp"
     "../c/type_util.cpp"
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 1139f99b3e..bc1f14b459 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -42,25 +42,6 @@ inline string getBkndLibName(const int backend_index)
     return LIB_AF_BKND_PREFIX + LIB_AF_BKND_NAME[i] + LIB_AF_BKND_SUFFIX;
 }
 
-inline std::string getEnvVar(const std::string &key)
-{
-#if defined(OS_WIN)
-    DWORD bufSize = 32767; // limit according to GetEnvironment Variable documentation
-    string retVal;
-    retVal.resize(bufSize);
-    bufSize = GetEnvironmentVariable(key.c_str(), &retVal[0], bufSize);
-    if (!bufSize) {
-        return string("");
-    } else {
-        retVal.resize(bufSize);
-        return retVal;
-    }
-#else
-    char * str = getenv(key.c_str());
-    return str==NULL ? string("") : string(str);
-#endif
-}
-
 /*flag parameter is not used on windows platform */
 LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
 {
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index f4cf913ac6..eb33c20995 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -11,6 +11,8 @@
 #include <af/defines.h>
 #include <string>
 #include <stdlib.h>
+#include <util.hpp>
+
 #if defined(OS_WIN)
 #include <Windows.h>
 typedef HMODULE LibHandle;
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index adb72dc6c5..e0709d36d3 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -21,7 +21,7 @@
 #include <algorithm>
 #include <vector>
 #include <platform.hpp>
-#include <async_queue.hpp>
+#include <queue.hpp>
 
 // cpu::Array class forward declaration
 namespace cpu
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index b0ab17a616..bf72a8a6fa 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -107,6 +107,7 @@ source_group(api\\c\\Headers FILES ${c_headers})
 source_group(api\\c\\Sources FILES ${c_sources})
 
 FILE(GLOB cpp_sources
+    "../../util.cpp"
     "../../api/cpp/*.cpp"
     )
 
diff --git a/src/backend/cpu/debug_cpu.hpp b/src/backend/cpu/debug_cpu.hpp
index b1d8e17484..cbcdc2230a 100644
--- a/src/backend/cpu/debug_cpu.hpp
+++ b/src/backend/cpu/debug_cpu.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <platform.hpp>
-#include <async_queue.hpp>
+#include <queue.hpp>
 #include <err_cpu.hpp>
 
 #ifndef NDEBUG
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 98cfad4b53..6ae63a919e 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -17,6 +17,7 @@
 #include <string>
 #include <defines.hpp>
 #include <version.hpp>
+#include <queue.hpp>
 
 #ifdef _WIN32
 #include <limits.h>
@@ -249,8 +250,9 @@ int getActiveDeviceId()
 
 static const int MAX_QUEUES = 1;
 
-async_queue& getQueue(int idx) {
-    static std::array<async_queue, MAX_QUEUES> queues;
+
+queue& getQueue(int idx) {
+    static std::array<queue, MAX_QUEUES> queues;
     return queues[idx];
 }
 
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 10575520b5..0cd42ae068 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -11,9 +11,9 @@
 
 #include <string>
 
-class async_queue;
-
 namespace cpu {
+    class queue;
+
     int getBackend();
 
     std::string getInfo();
@@ -30,5 +30,5 @@ namespace cpu {
 
     void sync(int device);
 
-    async_queue& getQueue(int idx = 0);
+    queue& getQueue(int idx = 0);
 }
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
new file mode 100644
index 0000000000..6e5cd71f33
--- /dev/null
+++ b/src/backend/cpu/queue.hpp
@@ -0,0 +1,42 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <util.hpp>
+#include <async_queue.hpp>
+
+#pragma once
+
+namespace cpu {
+
+/// Wraps the async_queue class
+class queue {
+public:
+  queue()
+    : sync_calls( getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
+
+  template <typename F, typename... Args>
+  void enqueue(const F func, Args... args) {
+
+    if(sync_calls) { func( args... ); }
+    else           { aQueue.enqueue( func, args... ); }
+  }
+  void sync() {
+    if(!sync_calls) aQueue.sync();
+  }
+
+  bool is_worker() const {
+    return (!sync_calls) ? aQueue.is_worker() : false;
+  }
+
+private:
+  const bool sync_calls;
+  async_queue aQueue;
+};
+
+}
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index bb8fca013c..ee7b86ff2c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -178,6 +178,7 @@ FILE(GLOB c_headers
     )
 
 FILE(GLOB c_sources
+    "../../util.cpp"
     "../../api/c/*.cpp"
     )
 
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index 084d12f804..f5424950dc 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -51,8 +51,12 @@
 
 #else
 
-#define POST_LAUNCH_CHECK() do {                \
-        CUDA_CHECK(cudaPeekAtLastError());      \
-    } while(0)                                  \
+#define POST_LAUNCH_CHECK() do {                                        \
+    if(cuda::synchronize_calls()) {                                     \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId()))); \
+    } else {                                                            \
+        CUDA_CHECK(cudaPeekAtLastError());                              \
+    }                                                                   \
+  } while(0)                                                            \
 
 #endif
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 76b336c5ad..a263bea2ca 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -21,6 +21,7 @@
 #include <cstdio>
 #include <cstring>
 #include <err_cuda.hpp>
+#include <util.hpp>
 
 using namespace std;
 
@@ -393,6 +394,11 @@ void sync(int device)
     setDevice(currDevice);
 }
 
+bool synchronize_calls() {
+    static bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
+    return sync;
+}
+
 }
 
 af_err afcu_get_stream(cudaStream_t* stream, int id)
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 7b649686dc..20862fb886 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -50,6 +50,9 @@ int setDevice(int device);
 
 void sync(int device);
 
+// Returns true if the AF_SYNCHRONIZE_CALLS environment variable is set to 1
+bool synchronize_calls();
+
 cudaDeviceProp getDeviceProp(int device);
 
 struct cudaDevice_t {
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 86ba1b2aad..223752cc28 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -152,6 +152,7 @@ FILE(GLOB backend_headers
     )
 
 FILE(GLOB backend_sources
+    "../../util.cpp"
     "../*.cpp"
     )
 source_group(backend\\Headers FILES ${backend_headers})
diff --git a/src/backend/opencl/debug_opencl.hpp b/src/backend/opencl/debug_opencl.hpp
index 74b3f7cf59..b4126f9abe 100644
--- a/src/backend/opencl/debug_opencl.hpp
+++ b/src/backend/opencl/debug_opencl.hpp
@@ -16,5 +16,10 @@
 #include <iostream>
 #define CL_DEBUG_FINISH(Q) Q.finish()
 #else
-#define CL_DEBUG_FINISH(Q)
+#define CL_DEBUG_FINISH(Q)                      \
+    do {                                        \
+        if(synchronize_calls()) {               \
+            Q.finish();                         \
+        }                                       \
+    } while (false); 
 #endif
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index 035f4c23aa..6d1d7de7ee 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -52,6 +52,7 @@ void convolve_nd(Param out, const Param signal, const Param filter, ConvolveBatc
         case 3: conv3<T, accType, expand>(param, out, signal, filter); break;
     }
 
+    CL_DEBUG_FINISH(getQueue());
     bufferFree(param.impulse);
 }
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 1301af9459..57726d2e87 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -41,6 +41,7 @@
 #include <map>
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
+#include <util.hpp>
 
 using std::string;
 using std::vector;
@@ -556,6 +557,11 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
     }
 }
 
+bool synchronize_calls() {
+    static bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
+    return sync;
+}
+
 }
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 154d84bc8e..84cb7b854c 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -115,4 +115,6 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
 void sync(int device);
 
+bool synchronize_calls();
+
 }
diff --git a/src/util.cpp b/src/util.cpp
new file mode 100644
index 0000000000..5607292c0d
--- /dev/null
+++ b/src/util.cpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/// This file contains platform independent utility functions
+#include <string>
+#include <cstdlib>
+
+#if defined(OS_WIN)
+#include <Windows.h>
+typedef HMODULE LibHandle;
+#else
+#include <dlfcn.h>
+#endif
+
+using std::string;
+
+string getEnvVar(const std::string &key)
+{
+#if defined(OS_WIN)
+  DWORD bufSize = 32767; // limit according to GetEnvironment Variable documentation
+  string retVal;
+  retVal.resize(bufSize);
+  bufSize = GetEnvironmentVariable(key.c_str(), &retVal[0], bufSize);
+  if (!bufSize) {
+    return string("");
+  } else {
+    retVal.resize(bufSize);
+    return retVal;
+  }
+#else
+  char * str = getenv(key.c_str());
+  return str==NULL ? string("") : string(str);
+#endif
+}
diff --git a/src/util.hpp b/src/util.hpp
new file mode 100644
index 0000000000..e1cd85a69c
--- /dev/null
+++ b/src/util.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/// This file contains platform independent utility functions
+
+#include <string>
+
+#pragma once
+
+std::string getEnvVar(const std::string &key);

From 235728aeb5fcf126308db18034581e8b0ad588e5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Dec 2015 10:36:18 -0500
Subject: [PATCH 0218/2677] Revert "Adding a Fast configuration"

This reverts commit 091cdf9143a944784c5e35671927509d4f8b3d70.
---
 CMakeLists.txt                | 19 +++------
 CMakeModules/FastConfig.cmake | 78 -----------------------------------
 2 files changed, 5 insertions(+), 92 deletions(-)
 delete mode 100644 CMakeModules/FastConfig.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5478143342..ea92cbec5d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -37,22 +37,13 @@ MARK_AS_ADVANCED(BUILD_SIFT)
 
 OPTION(BUILD_UNIFIED "Build Backend-Independent ArrayFire API" ON)
 
-# Add a Fast compiling config
-INCLUDE("${CMAKE_MODULE_PATH}/FastConfig.cmake")
-
 # Set a default build type if none was specified
-IF(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
-    SET(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE)
+if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE)
     # Set the possible values of build type for cmake-gui
-
-    IF(${FAST_CONFIG_ENABLED})
-        SET_PROPERTY(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS
-            "Debug" "Fast" "Release" "MinSizeRel" "RelWithDebInfo")
-    ELSE()
-        SET_PROPERTY(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS
-            "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
-    ENDIF()
-ENDIF()
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release"
+      "MinSizeRel" "RelWithDebInfo")
+endif()
 
 FIND_PACKAGE(FreeImage)
 IF(FREEIMAGE_FOUND)
diff --git a/CMakeModules/FastConfig.cmake b/CMakeModules/FastConfig.cmake
deleted file mode 100644
index 4a9a2cdc90..0000000000
--- a/CMakeModules/FastConfig.cmake
+++ /dev/null
@@ -1,78 +0,0 @@
-MESSAGE(STATUS "Adding Fast Build Type")
-
-IF(MSVC)
-
-    SET(CMAKE_CXX_FLAGS_FAST
-        "/MD /Od /Ob1 /D NDEBUG"
-        CACHE STRING "Flags used by the C++ compiler during Fast builds."
-        FORCE )
-    SET(CMAKE_C_FLAGS_FAST
-        "/MD /Od /Ob1 /D NDEBUG"
-        CACHE STRING "Flags used by the C compiler during Fast builds."
-        FORCE )
-    SET(CMAKE_EXE_LINKER_FLAGS_FAST
-        "/INCREMENTAL:NO"
-        CACHE STRING "Flags used for linking binaries during Fast builds."
-        FORCE )
-    SET(CMAKE_MODULE_LINKER_FLAGS_FAST
-        "/INCREMENTAL:NO"
-        CACHE STRING "Flags used by the modules linker during Fast builds."
-        FORCE )
-    SET(CMAKE_STATIC_LINKER_FLAGS_FAST
-        ""
-        CACHE STRING "Flags used by the static libraries linker during Fast builds."
-        FORCE )
-    SET(CMAKE_SHARED_LINKER_FLAGS_FAST
-        "/INCREMENTAL:NO"
-        CACHE STRING "Flags used by the shared libraries linker during Fast builds."
-        FORCE )
-
-    LIST(APPEND CMAKE_CONFIGURATION_TYPES Fast)
-    LIST(REMOVE_DUPLICATES CMAKE_CONFIGURATION_TYPES)
-    SET(CMAKE_CONFIGURATION_TYPES "${CMAKE_CONFIGURATION_TYPES}" CACHE STRING
-        "Semicolon separated list of supported configuration types [Debug|Release|MinSizeRel|RelWithDebInfo|Fast]"
-        FORCE)
-
-    # Needed for config to show up in VS
-    # http://cmake.3232098.n2.nabble.com/Custom-configuration-types-in-Visual-Studio-td7181786.html
-    ENABLE_LANGUAGE(CXX)
-
-ELSE(MSVC)
-
-    SET(CMAKE_CXX_FLAGS_FAST
-        "-O0 -DNDEBUG"
-        CACHE STRING "Flags used by the C++ compiler during Fast builds."
-        FORCE )
-    SET(CMAKE_C_FLAGS_FAST
-        "-O0 -DNDEBUG"
-        CACHE STRING "Flags used by the C compiler during Fast builds."
-        FORCE )
-    SET(CMAKE_EXE_LINKER_FLAGS_FAST
-        ""
-        CACHE STRING "Flags used for linking binaries during Fast builds."
-        FORCE )
-    SET(CMAKE_MODULE_LINKER_FLAGS_FAST
-        ""
-        CACHE STRING "Flags used by the modules linker during Fast builds."
-        FORCE )
-    SET(CMAKE_STATIC_LINKER_FLAGS_FAST
-        ""
-        CACHE STRING "Flags used by the static libraries linker during Fast builds."
-        FORCE )
-    SET(CMAKE_SHARED_LINKER_FLAGS_FAST
-        ""
-        CACHE STRING "Flags used by the shared libraries linker during Fast builds."
-        FORCE )
-
-ENDIF(MSVC)
-
-SET(FAST_CONFIG_ENABLED ON CACHE INTERNAL "" FORCE)
-
-MARK_AS_ADVANCED(
-    CMAKE_CXX_FLAGS_FAST
-    CMAKE_C_FLAGS_FAST
-    CMAKE_EXE_LINKER_FLAGS_FAST
-    CMAKE_MODULE_LINKER_FLAGS_FAST
-    CMAKE_STATIC_LINKER_FLAGS_FAST
-    CMAKE_SHARED_LINKER_FLAGS_FAST
-    )

From 6058dd283ea132cef41d834ef068afad3a719200 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 Dec 2015 10:32:00 -0500
Subject: [PATCH 0219/2677] Revert "Added ENQUEUE macro in cpu backend"

This reverts commit 95d934613425559fa9048433bfe77bb8f151c18f.

* Not necessary with the new queue class (see queue.hpp)
* Macros bad
---
 src/backend/cpu/Array.cpp             |  5 +++--
 src/backend/cpu/approx.cpp            | 19 ++++++++--------
 src/backend/cpu/assign.cpp            |  5 +++--
 src/backend/cpu/bilateral.cpp         |  5 +++--
 src/backend/cpu/blas.cpp              | 13 +++++------
 src/backend/cpu/cholesky.cpp          |  5 +++--
 src/backend/cpu/convolve.cpp          |  7 +++---
 src/backend/cpu/copy.cpp              |  9 ++++----
 src/backend/cpu/debug_cpu.hpp         | 31 ---------------------------
 src/backend/cpu/diagonal.cpp          |  7 +++---
 src/backend/cpu/diff.cpp              |  7 +++---
 src/backend/cpu/fast.cpp              |  3 ++-
 src/backend/cpu/fft.cpp               |  9 ++++----
 src/backend/cpu/fftconvolve.cpp       | 15 +++++++------
 src/backend/cpu/gradient.cpp          |  5 +++--
 src/backend/cpu/harris.cpp            | 13 +++++------
 src/backend/cpu/hist_graphics.cpp     |  3 ++-
 src/backend/cpu/histogram.cpp         |  5 +++--
 src/backend/cpu/homography.cpp        |  3 ++-
 src/backend/cpu/hsv_rgb.cpp           |  7 +++---
 src/backend/cpu/identity.cpp          |  5 +++--
 src/backend/cpu/iir.cpp               |  5 +++--
 src/backend/cpu/image.cpp             |  3 ++-
 src/backend/cpu/index.cpp             |  5 +++--
 src/backend/cpu/inverse.cpp           |  5 +++--
 src/backend/cpu/iota.cpp              |  5 +++--
 src/backend/cpu/ireduce.cpp           |  5 +++--
 src/backend/cpu/join.cpp              | 25 ++++++++++-----------
 src/backend/cpu/lookup.cpp            |  5 +++--
 src/backend/cpu/lu.cpp                |  9 ++++----
 src/backend/cpu/match_template.cpp    |  5 +++--
 src/backend/cpu/meanshift.cpp         |  5 +++--
 src/backend/cpu/medfilt.cpp           |  5 +++--
 src/backend/cpu/memory.cpp            |  3 ++-
 src/backend/cpu/morph.cpp             |  7 +++---
 src/backend/cpu/nearest_neighbour.cpp |  9 ++++----
 src/backend/cpu/orb.cpp               |  3 ++-
 src/backend/cpu/platform.cpp          |  3 ++-
 src/backend/cpu/plot.cpp              |  3 ++-
 src/backend/cpu/plot3.cpp             |  3 ++-
 src/backend/cpu/qr.cpp                |  7 +++---
 src/backend/cpu/queue.hpp             |  4 ++++
 src/backend/cpu/random.cpp            | 11 +++++-----
 src/backend/cpu/range.cpp             | 11 +++++-----
 src/backend/cpu/reduce.cpp            |  5 +++--
 src/backend/cpu/regions.cpp           |  5 +++--
 src/backend/cpu/reorder.cpp           |  5 +++--
 src/backend/cpu/resize.cpp            |  9 ++++----
 src/backend/cpu/rotate.cpp            |  9 ++++----
 src/backend/cpu/scan.cpp              | 11 +++++-----
 src/backend/cpu/select.cpp            |  7 +++---
 src/backend/cpu/set.cpp               |  3 ++-
 src/backend/cpu/shift.cpp             |  5 +++--
 src/backend/cpu/sobel.cpp             |  7 +++---
 src/backend/cpu/solve.cpp             | 11 +++++-----
 src/backend/cpu/sort.cpp              |  5 +++--
 src/backend/cpu/sort_by_key.cpp       |  5 +++--
 src/backend/cpu/sort_index.cpp        |  5 +++--
 src/backend/cpu/surface.cpp           |  3 ++-
 src/backend/cpu/susan.cpp             |  7 +++---
 src/backend/cpu/svd.cpp               |  5 +++--
 src/backend/cpu/tile.cpp              |  5 +++--
 src/backend/cpu/transform.cpp         |  9 ++++----
 src/backend/cpu/transpose.cpp         |  7 +++---
 src/backend/cpu/triangle.cpp          |  5 +++--
 src/backend/cpu/unwrap.cpp            |  7 +++---
 src/backend/cpu/where.cpp             |  3 ++-
 src/backend/cpu/wrap.cpp              |  7 +++---
 68 files changed, 258 insertions(+), 219 deletions(-)
 delete mode 100644 src/backend/cpu/debug_cpu.hpp

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 34c99e4566..862c576afe 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -15,7 +15,8 @@
 #include <TNJ/BufferNode.hpp>
 #include <TNJ/ScalarNode.hpp>
 #include <memory.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <cstring>
 #include <cstddef>
 
@@ -77,7 +78,7 @@ void Array<T>::eval()
 
     data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
-    ENQUEUE(kernel::evalArray<T>, *this);
+    getQueue().enqueue(kernel::evalArray<T>, *this);
 
     ready = true;
     Node_ptr prev = node;
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 57d3cc4c45..b817b840b4 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -11,7 +11,8 @@
 #include <approx.hpp>
 #include <kernel/approx1.hpp>
 #include <kernel/approx2.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -30,12 +31,12 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            ENQUEUE(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
-                    out, in, pos, offGrid);
+            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
+                               out, in, pos, offGrid);
             break;
         case AF_INTERP_LINEAR:
-            ENQUEUE(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
-                    out, in, pos, offGrid);
+            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
+                               out, in, pos, offGrid);
             break;
         default:
             break;
@@ -60,12 +61,12 @@ Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &p
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            ENQUEUE(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
-                    out, in, pos0, pos1, offGrid);
+            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
+                               out, in, pos0, pos1, offGrid);
             break;
         case AF_INTERP_LINEAR:
-            ENQUEUE(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
-                    out, in, pos0, pos1, offGrid);
+            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
+                               out, in, pos0, pos1, offGrid);
             break;
         default:
             break;
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index df903449a0..463b30c733 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -14,7 +14,8 @@
 #include <handle.hpp>
 #include <kernel/assign.hpp>
 #include <assign.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -47,7 +48,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
         }
     }
 
-    ENQUEUE(kernel::assign<T>, out, rhs, std::move(isSeq),
+    getQueue().enqueue(kernel::assign<T>, out, rhs, std::move(isSeq),
             std::move(seqs), std::move(idxArrs));
 }
 
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index ceb8be95d9..abd985768d 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -15,7 +15,8 @@
 #include <bilateral.hpp>
 #include <cmath>
 #include <algorithm>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
@@ -28,7 +29,7 @@ Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const fl
     in.eval();
     const dim4 dims     = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
-    ENQUEUE(kernel::bilateral<outType, inType, isColor>, out, in, s_sigma, c_sigma);
+    getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in, s_sigma, c_sigma);
     return out;
 }
 
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 70c8d9ca77..3ecb502ffa 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -13,7 +13,8 @@
 #include <cassert>
 #include <err_common.hpp>
 #include <kernel/dot.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -193,7 +194,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 reinterpret_cast<BT*>(output.get()), output.dims()[0]);
         }
     };
-    ENQUEUE(func, out, lhs, rhs);
+    getQueue().enqueue(func, out, lhs, rhs);
 
     return out;
 }
@@ -207,13 +208,13 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
 
     Array<T> out = createEmptyArray<T>(af::dim4(1));
     if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        ENQUEUE(kernel::dot<T, false, true>, out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, false, true>, out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        ENQUEUE(kernel::dot<T, true, false>,out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, true, false>,out, lhs, rhs, optLhs, optRhs);
     } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        ENQUEUE(kernel::dot<T, true, false>,out, rhs, lhs, optRhs, optLhs);
+        getQueue().enqueue(kernel::dot<T, true, false>,out, rhs, lhs, optRhs, optLhs);
     } else {
-        ENQUEUE(kernel::dot<T, false, false>,out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, false, false>,out, lhs, rhs, optLhs, optRhs);
     }
     return out;
 }
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index b21d9c8fd0..5e393f0082 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -19,7 +19,8 @@
 #include <err_cpu.hpp>
 #include <triangle.hpp>
 #include <lapack_helper.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -74,7 +75,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
         info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(), in.strides()[1]);
     };
 
-    ENQUEUE(func, info, in);
+    getQueue().enqueue(func, info, in);
     getQueue().sync();
 
     return info;
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index cf241c3eaa..8218a3f9a3 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -14,7 +14,8 @@
 #include <convolve.hpp>
 #include <err_cpu.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/convolve.hpp>
 
 using af::dim4;
@@ -50,7 +51,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    ENQUEUE(kernel::convolve_nd<T, accT, baseDim, expand>,out, signal, filter, kind);
+    getQueue().enqueue(kernel::convolve_nd<T, accT, baseDim, expand>,out, signal, filter, kind);
 
     return out;
 }
@@ -80,7 +81,7 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 
     Array<T> out  = createEmptyArray<T>(oDims);
 
-    ENQUEUE(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, tDims);
+    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, tDims);
 
     return out;
 }
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 91a1513fd9..f844d959a2 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -18,7 +18,8 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/copy.hpp>
 
 namespace cpu
@@ -50,7 +51,7 @@ template<typename T>
 void multiply_inplace(Array<T> &in, double val)
 {
     in.eval();
-    ENQUEUE(kernel::copy<T, T>, in, in, 0, val);
+    getQueue().enqueue(kernel::copy<T, T>, in, in, 0, val);
 }
 
 template<typename inType, typename outType>
@@ -60,7 +61,7 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
     Array<outType> ret = createValueArray<outType>(dims, default_value);
     ret.eval();
     in.eval();
-    ENQUEUE(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
+    getQueue().enqueue(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
     return ret;
 }
 
@@ -69,7 +70,7 @@ void copyArray(Array<outType> &out, Array<inType> const &in)
 {
     out.eval();
     in.eval();
-    ENQUEUE(kernel::copy<outType, inType>, out, in, scalar<outType>(0), 1.0);
+    getQueue().enqueue(kernel::copy<outType, inType>, out, in, scalar<outType>(0), 1.0);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cpu/debug_cpu.hpp b/src/backend/cpu/debug_cpu.hpp
deleted file mode 100644
index cbcdc2230a..0000000000
--- a/src/backend/cpu/debug_cpu.hpp
+++ /dev/null
@@ -1,31 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <platform.hpp>
-#include <queue.hpp>
-#include <err_cpu.hpp>
-
-#ifndef NDEBUG
-
-#define POST_LAUNCH_CHECK() do {                        \
-        getQueue().sync();                              \
-    } while(0)                                          \
-
-#else
-
-#define POST_LAUNCH_CHECK() //no-op
-
-#endif
-
-#define ENQUEUE(...)                        \
-    do {                                    \
-        getQueue().enqueue(__VA_ARGS__);    \
-        POST_LAUNCH_CHECK();                \
-    } while(0)
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 6fd918d66d..c818f82795 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -15,7 +15,8 @@
 #include <diagonal.hpp>
 #include <math.hpp>
 #include <err_cpu.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/diagonal.hpp>
 
 namespace cpu
@@ -30,7 +31,7 @@ Array<T> diagCreate(const Array<T> &in, const int num)
     int batch = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
-    ENQUEUE(kernel::diagCreate<T>, out, in, num);
+    getQueue().enqueue(kernel::diagCreate<T>, out, in, num);
 
     return out;
 }
@@ -44,7 +45,7 @@ Array<T> diagExtract(const Array<T> &in, const int num)
     dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
     Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-    ENQUEUE(kernel::diagExtract<T>, out, in, num);
+    getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
 
     return out;
 }
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index efab130cc6..1e374e95da 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <diff.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/diff.hpp>
 
 namespace cpu
@@ -26,7 +27,7 @@ Array<T>  diff1(const Array<T> &in, const int dim)
 
     Array<T> outArray = createEmptyArray<T>(dims);
 
-    ENQUEUE(kernel::diff1<T>, outArray, in, dim);
+    getQueue().enqueue(kernel::diff1<T>, outArray, in, dim);
 
     return outArray;
 }
@@ -42,7 +43,7 @@ Array<T>  diff2(const Array<T> &in, const int dim)
 
     Array<T> outArray = createEmptyArray<T>(dims);
 
-    ENQUEUE(kernel::diff2<T>, outArray, in, dim);
+    getQueue().enqueue(kernel::diff2<T>, outArray, in, dim);
 
     return outArray;
 }
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 1b3a7aa973..954f457cf4 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -14,7 +14,8 @@
 #include <err_cpu.hpp>
 #include <handle.hpp>
 #include <fast.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/fast.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 1282963003..3c1d10a4f3 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -15,7 +15,8 @@
 #include <kernel/fft.hpp>
 #include <copy.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
@@ -26,7 +27,7 @@ template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in)
 {
     in.eval();
-    ENQUEUE(kernel::fft_inplace<T, rank, direction>, in);
+    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in);
 }
 
 template<typename Tc, typename Tr, int rank>
@@ -38,7 +39,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     odims[0] = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    ENQUEUE(kernel::fft_r2c<Tc, Tr, rank>, out, in);
+    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, in);
 
     return out;
 }
@@ -49,7 +50,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     in.eval();
 
     Array<Tr> out = createEmptyArray<Tr>(odims);
-    ENQUEUE(kernel::fft_c2r<Tr, Tc, rank>, out, in, odims);
+    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, in, odims);
 
     return out;
 }
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index aac66cdbe4..3b4b864452 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -17,7 +17,8 @@
 #include <fftw3.h>
 #include <copy.hpp>
 #include <convolve_common.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/fftconvolve.hpp>
 
 namespace cpu
@@ -83,11 +84,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    ENQUEUE(kernel::packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
+    getQueue().enqueue(kernel::packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
 
     // Pad filter array with 0s
     const dim_t offset = sig_tmp_strides[3]*sig_tmp_dims[3];
-    ENQUEUE(kernel::padArray<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
+    getQueue().enqueue(kernel::padArray<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
                        filter, offset);
 
     dim4 fftDims(1, 1, 1, 1);
@@ -137,10 +138,10 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
             fftwf_destroy_plan(plan);
         }
     };
-    ENQUEUE(upstream_dft, packed, fftDims);
+    getQueue().enqueue(upstream_dft, packed, fftDims);
 
     // Multiply filter and signal FFT arrays
-    ENQUEUE(kernel::complexMultiply<convT>, packed,
+    getQueue().enqueue(kernel::complexMultiply<convT>, packed,
                        sig_tmp_dims, sig_tmp_strides,
                        filter_tmp_dims, filter_tmp_strides,
                        kind, offset);
@@ -188,7 +189,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
             fftwf_destroy_plan(plan);
         }
     };
-    ENQUEUE(upstream_idft, packed, fftDims);
+    getQueue().enqueue(upstream_idft, packed, fftDims);
 
     // Compute output dimensions
     dim4 oDims(1);
@@ -210,7 +211,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    ENQUEUE(kernel::reorder<T, convT, roundOut, baseDim>, out, packed, filter,
+    getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out, packed, filter,
                        sig_half_d0, fftScale, sig_tmp_dims, sig_tmp_strides, filter_tmp_dims,
                        filter_tmp_strides, expand, kind);
 
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index 57776e5750..aa417f49e1 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -12,7 +12,8 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cpu.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/gradient.hpp>
 
 namespace cpu
@@ -25,7 +26,7 @@ void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
     grad1.eval();
     in.eval();
 
-    ENQUEUE(kernel::gradient<T>, grad0, grad1, in);
+    getQueue().enqueue(kernel::gradient<T>, grad0, grad1, in);
 }
 
 #define INSTANTIATE(T)                                                                  \
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 07b9bed516..b5ea0ca20e 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -18,7 +18,8 @@
 #include <gradient.hpp>
 #include <sort_index.hpp>
 #include <cstring>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/harris.hpp>
 
 using af::dim4;
@@ -52,14 +53,14 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     Array<T> iy = createEmptyArray<T>(idims);
 
     // Compute first order derivatives
-    ENQUEUE(gradient<T>, iy, ix, in);
+    getQueue().enqueue(gradient<T>, iy, ix, in);
 
     Array<T> ixx = createEmptyArray<T>(idims);
     Array<T> ixy = createEmptyArray<T>(idims);
     Array<T> iyy = createEmptyArray<T>(idims);
 
     // Compute second-order derivatives
-    ENQUEUE(kernel::second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
+    getQueue().enqueue(kernel::second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
 
     // Convolve second-order derivatives with proper window filter
     ixx = convolve2<T, convAccT, false>(ixx, filter, filter);
@@ -70,7 +71,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
 
     Array<T> responses = createEmptyArray<T>(dim4(in.elements()));
 
-    ENQUEUE(kernel::harris_responses<T>, responses, idims[0], idims[1],
+    getQueue().enqueue(kernel::harris_responses<T>, responses, idims[0], idims[1],
                        ixx, ixy, iyy, k_thr, border_len);
 
     Array<float> xCorners    = createEmptyArray<float>(dim4(corner_lim));
@@ -104,7 +105,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
         // Keep only the corners with higher Harris responses
-        ENQUEUE(kernel::keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
+        getQueue().enqueue(kernel::keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
                            harris_sorted, harris_idx, corners_out);
     } else if (max_corners == 0 && corners_found < corner_lim) {
         x_out = createEmptyArray<float>(dim4(corners_out));
@@ -119,7 +120,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
             memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
             memcpy(outResponses.get(), inResponses.get(), corners_out * sizeof(float));
         };
-        ENQUEUE(copyFunc, x_out, y_out, resp_out,
+        getQueue().enqueue(copyFunc, x_out, y_out, resp_out,
                            xCorners, yCorners, respCorners, corners_out);
     } else {
         x_out = xCorners;
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index c58f5c687e..ad7d69067d 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -11,7 +11,8 @@
 
 #include <hist_graphics.hpp>
 #include <err_cpu.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 2571f3e4d0..6aa60e59e4 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -12,7 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/histogram.hpp>
 
 using af::dim4;
@@ -31,7 +32,7 @@ Array<outType> histogram(const Array<inType> &in,
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
     out.eval();
 
-    ENQUEUE(kernel::histogram<outType, inType, isLinear>,
+    getQueue().enqueue(kernel::histogram<outType, inType, isLinear>,
             out, in, nbins, minval, maxval);
 
     return out;
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 147f5e8751..4d131cf695 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -18,7 +18,8 @@
 #include <random.hpp>
 #include <cstring>
 #include <cfloat>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index da5dbe0594..404491766c 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -11,7 +11,8 @@
 #include <Array.hpp>
 #include <ArrayInfo.hpp>
 #include <hsv_rgb.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/hsv_rgb.hpp>
 
 using af::dim4;
@@ -26,7 +27,7 @@ Array<T> hsv2rgb(const Array<T>& in)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    ENQUEUE(kernel::hsv2rgb<T>, out, in);
+    getQueue().enqueue(kernel::hsv2rgb<T>, out, in);
 
     return out;
 }
@@ -38,7 +39,7 @@ Array<T> rgb2hsv(const Array<T>& in)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    ENQUEUE(kernel::rgb2hsv<T>, out, in);
+    getQueue().enqueue(kernel::rgb2hsv<T>, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index 071bb04642..c5e11029fc 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -10,7 +10,8 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <identity.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/identity.hpp>
 
 namespace cpu
@@ -21,7 +22,7 @@ Array<T> identity(const dim4& dims)
 {
     Array<T> out = createEmptyArray<T>(dims);
 
-    ENQUEUE(kernel::identity<T>, out);
+    getQueue().enqueue(kernel::identity<T>, out);
 
     return out;
 }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index cb390b3018..049212ad69 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -13,7 +13,8 @@
 #include <Array.hpp>
 #include <iir.hpp>
 #include <convolve.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/iir.hpp>
 
 using af::dim4;
@@ -41,7 +42,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
 
     Array<T> y = createEmptyArray<T>(c.dims());
 
-    ENQUEUE(kernel::iir<T>, y, c, a);
+    getQueue().enqueue(kernel::iir<T>, y, c, a);
 
     return y;
 }
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index d23ba80ba8..b71ba23c12 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -16,7 +16,8 @@
 #include <image.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 9c951ff0d3..a2cdac888f 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -14,7 +14,8 @@
 #include <index.hpp>
 #include <handle.hpp>
 #include <vector>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <utility>
 #include <kernel/index.hpp>
 
@@ -57,7 +58,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     Array<T> out = createEmptyArray<T>(oDims);
 
 
-    ENQUEUE(kernel::index<T>, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
+    getQueue().enqueue(kernel::index<T>, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
 
     return out;
 }
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index 71cc9fefca..ea7d7ee828 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -23,7 +23,8 @@
 #include <lu.hpp>
 #include <identity.hpp>
 #include <solve.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -67,7 +68,7 @@ Array<T> inverse(const Array<T> &in)
                 A.get(), A.strides()[1],
                 pivot.get());
     };
-    ENQUEUE(func, A, pivot, M);
+    getQueue().enqueue(func, A, pivot, M);
 
     return A;
 }
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 124ec5c48a..db19708b46 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -10,7 +10,8 @@
 #include <Array.hpp>
 #include <iota.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/iota.hpp>
 
 using namespace std;
@@ -25,7 +26,7 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
 
     Array<T> out = createEmptyArray<T>(outdims);
 
-    ENQUEUE(kernel::iota<T>, out, dims, tile_dims);
+    getQueue().enqueue(kernel::iota<T>, out, dims, tile_dims);
 
     return out;
 }
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 9de4a781b3..a40fbdf958 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -13,7 +13,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/ireduce.hpp>
 
 using af::dim4;
@@ -39,7 +40,7 @@ void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
                                                            , kernel::ireduce_dim<op, T, 3>()
                                                            , kernel::ireduce_dim<op, T, 4>()};
 
-    ENQUEUE(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
+    getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
 }
 
 template<af_op_t op, typename T>
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 6c9ba8ff9b..0a5b99cd13 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <join.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/join.hpp>
 
 namespace cpu
@@ -37,7 +38,7 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
 
     Array<Tx> out = createEmptyArray<Tx>(odims);
 
-    ENQUEUE(kernel::join<Tx, Ty>, out, dim, first, second);
+    getQueue().enqueue(kernel::join<Tx, Ty>, out, dim, first, second);
 
     return out;
 }
@@ -71,34 +72,34 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
 
     switch(n_arrays) {
         case 1:
-            ENQUEUE(kernel::join<T, 1>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 1>, dim, out, inputs);
             break;
         case 2:
-            ENQUEUE(kernel::join<T, 2>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 2>, dim, out, inputs);
             break;
         case 3:
-            ENQUEUE(kernel::join<T, 3>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 3>, dim, out, inputs);
             break;
         case 4:
-            ENQUEUE(kernel::join<T, 4>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 4>, dim, out, inputs);
             break;
         case 5:
-            ENQUEUE(kernel::join<T, 5>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 5>, dim, out, inputs);
             break;
         case 6:
-            ENQUEUE(kernel::join<T, 6>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 6>, dim, out, inputs);
             break;
         case 7:
-            ENQUEUE(kernel::join<T, 7>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 7>, dim, out, inputs);
             break;
         case 8:
-            ENQUEUE(kernel::join<T, 8>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 8>, dim, out, inputs);
             break;
         case 9:
-            ENQUEUE(kernel::join<T, 9>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 9>, dim, out, inputs);
             break;
         case 10:
-            ENQUEUE(kernel::join<T,10>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T,10>, dim, out, inputs);
             break;
     }
 
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 4cc5359002..1e09f4dd48 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -9,7 +9,8 @@
 
 #include <lookup.hpp>
 #include <cstdlib>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/lookup.hpp>
 
 namespace cpu
@@ -29,7 +30,7 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    ENQUEUE(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
+    getQueue().enqueue(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
 
     return out;
 }
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 551c9c98e2..93862f24c0 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -17,7 +17,8 @@
 #include <cassert>
 #include <range.hpp>
 #include <lapack_helper.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/lu.hpp>
 
 namespace cpu
@@ -58,7 +59,7 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
 
-    ENQUEUE(kernel::lu_split<T>, lower, upper, in_copy);
+    getQueue().enqueue(kernel::lu_split<T>, lower, upper, in_copy);
 }
 
 template<typename T>
@@ -73,11 +74,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
         dim4 iDims = in.dims();
         getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides()[1], pivot.get());
     };
-    ENQUEUE(func, in, pivot);
+    getQueue().enqueue(func, in, pivot);
 
     if(convert_pivot) {
         Array<int> p = range<int>(dim4(iDims[0]), 0);
-        ENQUEUE(kernel::convertPivot, p, pivot);
+        getQueue().enqueue(kernel::convertPivot, p, pivot);
         return p;
     } else {
         return pivot;
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 724b773638..58091a1f49 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -12,7 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <match_template.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/match_template.hpp>
 
 using af::dim4;
@@ -28,7 +29,7 @@ Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg)
 
     Array<OutT> out = createEmptyArray<OutT>(sImg.dims());
 
-    ENQUEUE(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg, tImg);
+    getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg, tImg);
 
     return out;
 }
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index f4a0b29e86..b5bbf758a1 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -16,7 +16,8 @@
 #include <algorithm>
 #include <err_cpu.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/meanshift.hpp>
 
 using af::dim4;
@@ -32,7 +33,7 @@ Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sig
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    ENQUEUE(kernel::meanShift<T, is_color>, out, in, s_sigma, c_sigma, iter);
+    getQueue().enqueue(kernel::meanShift<T, is_color>, out, in, s_sigma, c_sigma, iter);
 
     return out;
 }
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 9e761c6cc0..8ae4e33921 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -12,7 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/medfilt.hpp>
 
 using af::dim4;
@@ -27,7 +28,7 @@ Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    ENQUEUE(kernel::medfilt<T, pad>, out, in, w_len, w_wid);
+    getQueue().enqueue(kernel::medfilt<T, pad>, out, in, w_len, w_wid);
 
     return out;
 }
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 79f2e57a0c..85ba4f27fb 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -14,7 +14,8 @@
 #include <dispatch.hpp>
 #include <cstdlib>
 #include <mutex>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 337e8a9574..1ae4680b9d 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -13,7 +13,8 @@
 #include <Array.hpp>
 #include <morph.hpp>
 #include <algorithm>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/morph.hpp>
 
 using af::dim4;
@@ -29,7 +30,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    ENQUEUE(kernel::morph<T, isDilation>, out, in, mask);
+    getQueue().enqueue(kernel::morph<T, isDilation>, out, in, mask);
 
     return out;
 }
@@ -42,7 +43,7 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    ENQUEUE(kernel::morph3d<T, isDilation>, out, in, mask);
+    getQueue().enqueue(kernel::morph3d<T, isDilation>, out, in, mask);
 
     return out;
 }
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index a3c2bb1ea9..f1daba7526 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -12,7 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/nearest_neighbour.hpp>
 
 using af::dim4;
@@ -42,13 +43,13 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
 
     switch(dist_type) {
         case AF_SAD:
-            ENQUEUE(kernel::nearest_neighbour<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         case AF_SSD:
-            ENQUEUE(kernel::nearest_neighbour<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         case AF_SHD:
-            ENQUEUE(kernel::nearest_neighbour<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
             break;
         default:
             AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 649619e143..8bbfd41932 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -18,7 +18,8 @@
 #include <convolve.hpp>
 #include <memory.hpp>
 #include <cstring>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/orb.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 6ae63a919e..19942f0312 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -9,8 +9,9 @@
 
 #include <af/version.h>
 #include <af/defines.h>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
 #include <sstream>
+#include <queue.hpp>
 #include <array>
 #include <algorithm>
 #include <iostream>
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 8afdea288f..2ab69643c8 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -13,7 +13,8 @@
 #include <plot.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/plot3.cpp b/src/backend/cpu/plot3.cpp
index c7beed69d6..515fe0336c 100644
--- a/src/backend/cpu/plot3.cpp
+++ b/src/backend/cpu/plot3.cpp
@@ -13,7 +13,8 @@
 #include <plot3.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index ca04ec9c20..34a39f64b8 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -17,7 +17,8 @@
 #include <err_cpu.hpp>
 #include <triangle.hpp>
 #include <lapack_helper.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -78,7 +79,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
         gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(), q.strides()[1], t.get());
     };
     q.resetDims(dim4(M, M));
-    ENQUEUE(func, q, t, M, N);
+    getQueue().enqueue(func, q, t, M, N);
 }
 
 template<typename T>
@@ -94,7 +95,7 @@ Array<T> qr_inplace(Array<T> &in)
     auto func = [=] (Array<T> in, Array<T> t, int M, int N) {
         geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides()[1], t.get());
     };
-    ENQUEUE(func, in, t, M, N);
+    getQueue().enqueue(func, in, t, M, N);
 
     return t;
 }
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 6e5cd71f33..942ae259b1 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -25,6 +25,10 @@ class queue {
 
     if(sync_calls) { func( args... ); }
     else           { aQueue.enqueue( func, args... ); }
+#ifndef NDEBUG
+    sync();
+#endif
+
   }
   void sync() {
     if(!sync_calls) aQueue.sync();
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index f49420f13d..89d86c3848 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -12,7 +12,8 @@
 #include <af/defines.h>
 #include <Array.hpp>
 #include <random.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/random.hpp>
 
 namespace cpu
@@ -22,7 +23,7 @@ template<typename T>
 Array<T> randu(const af::dim4 &dims)
 {
     Array<T> outArray = createEmptyArray<T>(dims);
-    ENQUEUE(kernel::randu<T>, outArray);
+    getQueue().enqueue(kernel::randu<T>, outArray);
     return outArray;
 }
 
@@ -45,7 +46,7 @@ template<typename T>
 Array<T> randn(const af::dim4 &dims)
 {
     Array<T> outArray = createEmptyArray<T>(dims);
-    ENQUEUE(kernel::randn<T>, outArray);
+    getQueue().enqueue(kernel::randn<T>, outArray);
     return outArray;
 }
 
@@ -80,7 +81,7 @@ Array<char> randu(const af::dim4 &dims)
             outPtr[i] = gen() > 0.5;
         }
     };
-    ENQUEUE(func, outArray);
+    getQueue().enqueue(func, outArray);
 
     return outArray;
 }
@@ -92,7 +93,7 @@ void setSeed(const uintl seed)
         kernel::is_first = false;
         kernel::gen_seed = seed;
     };
-    ENQUEUE(f, seed);
+    getQueue().enqueue(f, seed);
 }
 
 uintl getSeed()
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index 6be78d5d0e..e91ba1e241 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -14,7 +14,8 @@
 #include <err_cpu.hpp>
 #include <algorithm>
 #include <numeric>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/range.hpp>
 
 namespace cpu
@@ -32,10 +33,10 @@ Array<T> range(const dim4& dims, const int seq_dim)
 
     Array<T> out = createEmptyArray<T>(dims);
     switch(_seq_dim) {
-        case 0: ENQUEUE(kernel::range<T, 0>, out); break;
-        case 1: ENQUEUE(kernel::range<T, 1>, out); break;
-        case 2: ENQUEUE(kernel::range<T, 2>, out); break;
-        case 3: ENQUEUE(kernel::range<T, 3>, out); break;
+        case 0: getQueue().enqueue(kernel::range<T, 0>, out); break;
+        case 1: getQueue().enqueue(kernel::range<T, 1>, out); break;
+        case 2: getQueue().enqueue(kernel::range<T, 2>, out); break;
+        case 3: getQueue().enqueue(kernel::range<T, 3>, out); break;
         default : AF_ERROR("Invalid rep selection", AF_ERR_ARG);
     }
 
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 90ad1f9023..2d4d18e682 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -15,7 +15,8 @@
 #include <ops.hpp>
 #include <functional>
 #include <complex>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/reduce.hpp>
 
 using af::dim4;
@@ -55,7 +56,7 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nan
                                                                 , kernel::reduce_dim<op, Ti, To, 3>()
                                                                 , kernel::reduce_dim<op, Ti, To, 4>()};
 
-    ENQUEUE(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
+    getQueue().enqueue(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
 
     return out;
 }
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index eafc161ff5..2384dd3341 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -17,7 +17,8 @@
 #include <map>
 #include <set>
 #include <algorithm>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/regions.hpp>
 
 using af::dim4;
@@ -33,7 +34,7 @@ Array<T> regions(const Array<char> &in, af_connectivity connectivity)
     Array<T> out = createValueArray(in.dims(), (T)0);
     out.eval();
 
-    ENQUEUE(kernel::regions<T>, out, in, connectivity);
+    getQueue().enqueue(kernel::regions<T>, out, in, connectivity);
 
     return out;
 }
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 237e5d687a..bd156585ee 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <reorder.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/reorder.hpp>
 
 namespace cpu
@@ -26,7 +27,7 @@ Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
         oDims[i] = iDims[rdims[i]];
 
     Array<T> out = createEmptyArray<T>(oDims);
-    ENQUEUE(kernel::reorder<T>, out, in, oDims, rdims);
+    getQueue().enqueue(kernel::reorder<T>, out, in, oDims, rdims);
     return out;
 }
 
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index d6349a9c0b..eaeb5d4e3d 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -12,7 +12,8 @@
 #include <math.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/resize.hpp>
 
 namespace cpu
@@ -31,11 +32,11 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            ENQUEUE(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
         case AF_INTERP_BILINEAR:
-            ENQUEUE(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
         case AF_INTERP_LOWER:
-            ENQUEUE(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
         default: break;
     }
     return out;
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 289f3697a0..0fb9b17674 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <rotate.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include "transform_interp.hpp"
 #include <kernel/rotate.hpp>
 
@@ -26,13 +27,13 @@ Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
 
     switch(method) {
         case AF_INTERP_NEAREST:
-            ENQUEUE(kernel::rotate<T, AF_INTERP_NEAREST>, out, in, theta);
+            getQueue().enqueue(kernel::rotate<T, AF_INTERP_NEAREST>, out, in, theta);
             break;
         case AF_INTERP_BILINEAR:
-            ENQUEUE(kernel::rotate<T, AF_INTERP_BILINEAR>, out, in, theta);
+            getQueue().enqueue(kernel::rotate<T, AF_INTERP_BILINEAR>, out, in, theta);
             break;
         case AF_INTERP_LOWER:
-            ENQUEUE(kernel::rotate<T, AF_INTERP_LOWER>, out, in, theta);
+            getQueue().enqueue(kernel::rotate<T, AF_INTERP_LOWER>, out, in, theta);
             break;
         default:
             AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index adeb3d23b7..08431f8baa 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -14,7 +14,8 @@
 #include <Array.hpp>
 #include <scan.hpp>
 #include <ops.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/scan.hpp>
 
 using af::dim4;
@@ -33,19 +34,19 @@ Array<To> scan(const Array<Ti>& in, const int dim)
     switch (in.ndims()) {
         case 1:
             kernel::scan_dim<op, Ti, To, 1> func1;
-            ENQUEUE(func1, out, 0, in, 0, dim);
+            getQueue().enqueue(func1, out, 0, in, 0, dim);
             break;
         case 2:
             kernel::scan_dim<op, Ti, To, 2> func2;
-            ENQUEUE(func2, out, 0, in, 0, dim);
+            getQueue().enqueue(func2, out, 0, in, 0, dim);
             break;
         case 3:
             kernel::scan_dim<op, Ti, To, 3> func3;
-            ENQUEUE(func3, out, 0, in, 0, dim);
+            getQueue().enqueue(func3, out, 0, in, 0, dim);
             break;
         case 4:
             kernel::scan_dim<op, Ti, To, 4> func4;
-            ENQUEUE(func4, out, 0, in, 0, dim);
+            getQueue().enqueue(func4, out, 0, in, 0, dim);
             break;
     }
 
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 4f845bc084..1545a81f46 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -10,7 +10,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <select.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/select.hpp>
 
 using af::dim4;
@@ -25,7 +26,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Arr
     cond.eval();
     a.eval();
     b.eval();
-    ENQUEUE(kernel::select<T>, out, cond, a, b);
+    getQueue().enqueue(kernel::select<T>, out, cond, a, b);
 }
 
 template<typename T, bool flip>
@@ -34,7 +35,7 @@ void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, co
     out.eval();
     cond.eval();
     a.eval();
-    ENQUEUE(kernel::select_scalar<T, flip>, out, cond, a, b);
+    getQueue().enqueue(kernel::select_scalar<T, flip>, out, cond, a, b);
 }
 
 #define INSTANTIATE(T)                                              \
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 49ce186412..d6c2a611e0 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -18,7 +18,8 @@
 #include <sort.hpp>
 #include <err_cpu.hpp>
 #include <vector>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index fd56e4ce2e..041f1ab8ba 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <shift.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/shift.hpp>
 
 namespace cpu
@@ -23,7 +24,7 @@ Array<T> shift(const Array<T> &in, const int sdims[4])
     Array<T> out = createEmptyArray<T>(in.dims());
     const af::dim4 temp(sdims[0], sdims[1], sdims[2], sdims[3]);
 
-    ENQUEUE(kernel::shift<T>, out, in, temp);
+    getQueue().enqueue(kernel::shift<T>, out, in, temp);
 
     return out;
 }
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 86c7363c6d..5ece9bf65e 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -13,7 +13,8 @@
 #include <Array.hpp>
 #include <sobel.hpp>
 #include <convolve.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/sobel.hpp>
 
 using af::dim4;
@@ -31,8 +32,8 @@ sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
-    ENQUEUE(kernel::derivative<Ti, To, true >, dx, img);
-    ENQUEUE(kernel::derivative<Ti, To, false>, dy, img);
+    getQueue().enqueue(kernel::derivative<Ti, To, true >, dx, img);
+    getQueue().enqueue(kernel::derivative<Ti, To, false>, dy, img);
 
     return std::make_pair(dx, dy);
 }
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 5d1ec3bba3..48ea4de3c5 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -16,7 +16,8 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <lapack_helper.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -87,7 +88,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                         N, NRHS, A.get(), A.strides()[1],
                         pivot.get(), B.get(), B.strides()[1]);
     };
-    ENQUEUE(func, A, B, pivot, N, NRHS);
+    getQueue().enqueue(func, A, B, pivot, N, NRHS);
 
     return B;
 }
@@ -108,7 +109,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
                         A.get(), A.strides()[1],
                         B.get(), B.strides()[1]);
     };
-    ENQUEUE(func, A, B, N, NRHS, options);
+    getQueue().enqueue(func, A, B, N, NRHS, options);
 
     return B;
 }
@@ -138,7 +139,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
             gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides()[1],
                            pivot.get(), B.get(), B.strides()[1]);
         };
-        ENQUEUE(func, A, B, pivot, N, K);
+        getQueue().enqueue(func, A, B, pivot, N, K);
     } else {
         auto func = [=] (Array<T> A, Array<T> B, int M, int N, int K) {
             int sM = A.strides()[1];
@@ -150,7 +151,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
                     B.get(), max(sM, sN));
         };
         B.resetDims(dim4(N, K));
-        ENQUEUE(func, A, B, M, N, K);
+        getQueue().enqueue(func, A, B, M, N, K);
     }
 
     return B;
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 104a3df2eb..bc6396b258 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -13,7 +13,8 @@
 #include <copy.hpp>
 #include <algorithm>
 #include <functional>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/sort.hpp>
 
 namespace cpu
@@ -26,7 +27,7 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
 
     Array<T> out = copyArray<T>(in);
     switch(dim) {
-        case 0: ENQUEUE(kernel::sort0<T, isAscending>, out); break;
+        case 0: getQueue().enqueue(kernel::sort0<T, isAscending>, out); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
     return out;
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index c6832881d8..5a99257033 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <sort_by_key.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/sort_by_key.hpp>
 
 namespace cpu
@@ -28,7 +29,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     oidx.eval();
 
     switch(dim) {
-        case 0: ENQUEUE(kernel::sort0_by_key<Tk, Tv, isAscending>,
+        case 0: getQueue().enqueue(kernel::sort0_by_key<Tk, Tv, isAscending>,
                                    okey, oval, oidx, ikey, ival); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index c8c6d6e08f..77860ede18 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -12,7 +12,8 @@
 #include <math.hpp>
 #include <algorithm>
 #include <numeric>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/sort_index.hpp>
 
 namespace cpu
@@ -26,7 +27,7 @@ void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint
     val = createEmptyArray<T>(in.dims());
     idx = createEmptyArray<uint>(in.dims());
     switch(dim) {
-        case 0: ENQUEUE(kernel::sort0_index<T, isAscending>, val, idx, in); break;
+        case 0: getQueue().enqueue(kernel::sort0_index<T, isAscending>, val, idx, in); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 }
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 00d2b00c0f..24c945c20b 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -13,7 +13,8 @@
 #include <surface.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 4f1c327dd3..55a2357206 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -12,7 +12,8 @@
 #include <cmath>
 #include <math.hpp>
 #include <memory>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 #include <kernel/susan.hpp>
 
 using af::features;
@@ -39,9 +40,9 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1), memFree<unsigned>);
     corners_found.get()[0] = 0;
 
-    ENQUEUE(kernel::susan_responses<T>, response, in, idims[0], idims[1],
+    getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0], idims[1],
                        radius, diff_thr, geom_thr, edge);
-    ENQUEUE(kernel::non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
+    getQueue().enqueue(kernel::non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
                        idims[0], idims[1], response, edge, corner_lim);
     getQueue().sync();
 
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 3ce627c5f9..2ac58aab3f 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -15,7 +15,8 @@
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 #include <lapack_helper.hpp>
 #include <copy.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
 
 namespace cpu
 {
@@ -86,7 +87,7 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
                 s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
 #endif
     };
-    ENQUEUE(func, s, u, vt, in);
+    getQueue().enqueue(func, s, u, vt, in);
 }
 
 template <typename T, typename Tr>
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 9237a79eb9..6526917d3a 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -9,7 +9,8 @@
 
 #include <Array.hpp>
 #include <tile.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include <kernel/tile.hpp>
 
 namespace cpu
@@ -30,7 +31,7 @@ Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    ENQUEUE(kernel::tile<T>, out, in);
+    getQueue().enqueue(kernel::tile<T>, out, in);
 
     return out;
 }
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 5874e7abd0..fc7145854b 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -10,7 +10,8 @@
 #include <Array.hpp>
 #include <transform.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include "transform_interp.hpp"
 #include <kernel/transform.hpp>
 
@@ -28,13 +29,13 @@ Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::
 
     switch(method) {
         case AF_INTERP_NEAREST :
-            ENQUEUE(kernel::transform<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_NEAREST >, out, in, transform, inverse);
             break;
         case AF_INTERP_BILINEAR:
-            ENQUEUE(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, transform, inverse);
             break;
         case AF_INTERP_LOWER   :
-            ENQUEUE(kernel::transform<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_LOWER   >, out, in, transform, inverse);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index c1d5d1d236..32663e1f94 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -12,7 +12,8 @@
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <transpose.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include <kernel/transpose.hpp>
 #include <utility>
 #include <cassert>
@@ -32,7 +33,7 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
     // create an array with first two dimensions swapped
     Array<T> out  = createEmptyArray<T>(outDims);
 
-    ENQUEUE(kernel::transpose<T>, out, in, conjugate);
+    getQueue().enqueue(kernel::transpose<T>, out, in, conjugate);
 
     return out;
 }
@@ -41,7 +42,7 @@ template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate)
 {
     in.eval();
-    ENQUEUE(kernel::transpose_inplace<T>, in, conjugate);
+    getQueue().enqueue(kernel::transpose_inplace<T>, in, conjugate);
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index fbc7f658d0..2a9553c83a 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -12,7 +12,8 @@
 #include <Array.hpp>
 #include <triangle.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include <kernel/triangle.hpp>
 
 namespace cpu
@@ -21,7 +22,7 @@ namespace cpu
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in)
 {
-    ENQUEUE(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
+    getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index d40acde555..1aa37a4762 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -11,7 +11,8 @@
 #include <unwrap.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include <kernel/unwrap.hpp>
 
 namespace cpu
@@ -36,9 +37,9 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
     Array<T> outArray = createEmptyArray<T>(odims);
 
     if (is_column) {
-        ENQUEUE(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
     } else {
-        ENQUEUE(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
     }
 
     return outArray;
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 734b768385..018cbdfc36 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -16,7 +16,8 @@
 #include <where.hpp>
 #include <ops.hpp>
 #include <vector>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 87de234d36..07487e0d68 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -11,7 +11,8 @@
 #include <wrap.hpp>
 #include <dispatch.hpp>
 #include <math.hpp>
-#include <debug_cpu.hpp>
+#include <platform.hpp>
+#include <async_queue.hpp>
 #include <kernel/wrap.hpp>
 
 namespace cpu
@@ -33,9 +34,9 @@ Array<T> wrap(const Array<T> &in,
     in.eval();
 
     if (is_column) {
-        ENQUEUE(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
     } else {
-        ENQUEUE(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
     }
 
     return out;

From 4243ffcd2a45d74ba33d6edd4873ad2acabb2848 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Dec 2015 11:41:29 -0500
Subject: [PATCH 0220/2677] BUILD Adding option MIN_BUILD_TIME to CMake.
 Options sets O0 for fast compile

* Od on MSVC
* Default is OFF. Flags are set when toggled to ON.
* Resets the flags to default release when toggled back to OFF.
---
 CMakeLists.txt                  |  3 ++
 CMakeModules/MinBuildTime.cmake | 93 +++++++++++++++++++++++++++++++++
 2 files changed, 96 insertions(+)
 create mode 100644 CMakeModules/MinBuildTime.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea92cbec5d..c79fbcaab0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -45,6 +45,9 @@ if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
       "MinSizeRel" "RelWithDebInfo")
 endif()
 
+OPTION(MIN_BUILD_TIME "This flag compiles ArrayFire with O0, which is the fastest way to compile" OFF)
+INCLUDE(${CMAKE_MODULE_PATH}/MinBuildTime.cmake)
+
 FIND_PACKAGE(FreeImage)
 IF(FREEIMAGE_FOUND)
     ADD_DEFINITIONS(-DWITH_FREEIMAGE)
diff --git a/CMakeModules/MinBuildTime.cmake b/CMakeModules/MinBuildTime.cmake
new file mode 100644
index 0000000000..dcd3d359a7
--- /dev/null
+++ b/CMakeModules/MinBuildTime.cmake
@@ -0,0 +1,93 @@
+IF(NOT DEFINED MINBUILDTIME_FLAG)
+    SET(MINBUILDTIME_FLAG OFF CACHE INTERNAL "Flag" FORCE)
+ENDIF()
+
+IF(${MIN_BUILD_TIME})
+    IF(NOT ${CMAKE_BUILD_TYPE} MATCHES "Release")
+        MESSAGE(WARNING "The MIN_BUILD_TIME Flag only works with Release.\
+                        Other CMAKE_BUILD_TYPEs will be ignore this flag")
+    ELSEIF(NOT ${MINBUILDTIME_FLAG})
+    # BUILD_TYPE is Release - Set the flags
+    # The flags should be set only when going from OFF -> ON. This is
+    # determined by MINBUILDTIME_FLAG
+    # IF FLAG is ON, then the flags were already set, no need to set them again
+    # IF FLAG is OFF, then the flags are not set, so set them now, and back up
+    # release flags
+    MESSAGE(STATUS "Setting Release flags to no optimizations")
+
+        # Backup Default Release Flags
+        SET(CMAKE_CXX_FLAGS_RELEASE_DEFAULT ${CMAKE_CXX_FLAGS_RELEASE} CACHE
+            INTERNAL "Default compiler flags during release build" FORCE)
+        SET(CMAKE_C_FLAGS_RELEASE_DEFAULT ${CMAKE_C_FLAGS_RELEASE} CACHE
+            INTERNAL "Default compiler flags during release build" FORCE)
+        SET(CMAKE_EXE_LINKER_FLAGS_RELEASE_DEFAULT ${CMAKE_EXE_LINKER_FLAGS_RELEASE} CACHE
+            INTERNAL "Default linker flags during release build" FORCE)
+        SET(CMAKE_MODULE_LINKER_FLAGS_RELEASE_DEFAULT ${CMAKE_MODULE_LINKER_FLAGS_RELEASE} CACHE
+            INTERNAL "Default linker flags during release build" FORCE)
+        SET(CMAKE_STATIC_LINKER_FLAGS_RELEASE_DEFAULT ${CMAKE_STATIC_LINKER_FLAGS_RELEASE} CACHE
+            INTERNAL "Default linker flags during release build" FORCE)
+        SET(CMAKE_SHARED_LINKER_FLAGS_RELEASE_DEFAULT ${CMAKE_SHARED_LINKER_FLAGS_RELEASE} CACHE
+            INTERNAL "Default linker flags during release build" FORCE)
+
+        IF(MSVC)
+            MESSAGE(STATUS "MSVC Flags")
+            SET(CMAKE_CXX_FLAGS_RELEASE "/MD /Od /Ob1 /D NDEBUG" CACHE
+                STRING "Flags used by the compiler during release builds." FORCE)
+            SET(CMAKE_C_FLAGS_RELEASE "/MD /Od /Ob1 /D NDEBUG" CACHE
+                STRING "Flags used by the compiler during release builds." FORCE)
+            SET(CMAKE_EXE_LINKER_FLAGS_RELEASE "/INCREMENTAL:NO" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+            SET(CMAKE_MODULE_LINKER_FLAGS_RELEASE "/INCREMENTAL:NO" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+            SET(CMAKE_STATIC_LINKER_FLAGS_RELEASE "" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+            SET(CMAKE_SHARED_LINKER_FLAGS_RELEASE "/INCREMENTAL:NO" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+        ELSE(MSVC)
+            MESSAGE(STATUS "Other Flags")
+            SET(CMAKE_CXX_FLAGS_RELEASE "-O0 -DNDEBUG" CACHE
+                STRING "Flags used by the compiler during release builds." FORCE)
+            SET(CMAKE_C_FLAGS_RELEASE "-O0 -DNDEBUG" CACHE
+                STRING "Flags used by the compiler during release builds." FORCE)
+            SET(CMAKE_EXE_LINKER_FLAGS_RELEASE "" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+            SET(CMAKE_MODULE_LINKER_FLAGS_RELEASE "" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+            SET(CMAKE_STATIC_LINKER_FLAGS_RELEASE "" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+            SET(CMAKE_SHARED_LINKER_FLAGS_RELEASE "" CACHE
+                STRING "Flags used by the linker during release builds." FORCE)
+        ENDIF(MSVC)
+
+        SET(MINBUILDTIME_FLAG ON CACHE INTERNAL "Flag" FORCE)
+    ENDIF()
+ELSE()
+  MESSAGE(STATUS "MIN_BUILD_TIME IS OFF")
+
+    # MIN_BUILD_TIME is OFF. Change the flags back only if the flag was set before
+    IF(${MINBUILDTIME_FLAG})
+        MESSAGE(STATUS "MIN_BUILD_FLAG was toggled. Resetting Release FLags")
+        SET(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE_DEFAULT} CACHE
+            STRING "Flags used by the compiler during release builds." FORCE)
+        SET(CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE_DEFAULT} CACHE
+            STRING "Flags used by the compiler during release builds." FORCE)
+        SET(CMAKE_EXE_LINKER_FLAGS_RELEASE ${CMAKE_EXE_LINKER_FLAGS_RELEASE_DEFAULT} CACHE
+            STRING "Flags used by the linker during release builds." FORCE)
+        SET(CMAKE_MODULE_LINKER_FLAGS_RELEASE ${CMAKE_MODULE_LINKER_FLAGS_RELEASE_DEFAULT} CACHE
+            STRING "Flags used by the linker during release builds." FORCE)
+        SET(CMAKE_STATIC_LINKER_FLAGS_RELEASE ${CMAKE_STATIC_LINKER_FLAGS_RELEASE_DEFAULT} CACHE
+            STRING "Flags used by the linker during release builds." FORCE)
+        SET(CMAKE_SHARED_LINKER_FLAGS_RELEASE ${CMAKE_SHARED_LINKER_FLAGS_RELEASE_DEFAULT} CACHE
+            STRING "Flags used by the linker during release builds." FORCE)
+        SET(MINBUILDTIME_FLAG OFF CACHE INTERNAL "Flag" FORCE)
+    ENDIF()
+ENDIF()
+
+MARK_AS_ADVANCED(
+    CMAKE_CXX_FLAGS_RELEASE
+    CMAKE_C_FLAGS_RELEASE
+    CMAKE_EXE_LINKER_FLAGS_RELEASE
+    CMAKE_MODULE_LINKER_FLAGS_RELEASE
+    CMAKE_STATIC_LINKER_FLAGS_RELEASE
+    CMAKE_SHARED_LINKER_FLAGS_RELEASE
+    )

From d308ae1c3a915ba74ffc7e54ecc3923dd1e0863e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Dec 2015 13:28:08 -0500
Subject: [PATCH 0221/2677] DOC Fix typo in af_div

---
 include/af/arith.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/af/arith.h b/include/af/arith.h
index b5f6f17ba9..59f76776d5 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -578,7 +578,7 @@ extern "C" {
     /**
        C Interface for dividing an array by another
 
-       \param[out] out will contain result of \p lhs / \p rhs. out is of type b8
+       \param[out] out will contain result of \p lhs / \p rhs.
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode

From 7c96fd41162b675d412e1c3dfb0437996f2c7149 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Dec 2015 14:59:30 -0500
Subject: [PATCH 0222/2677] Update release notes for v3.2.2

---
 docs/pages/release_notes.md | 75 +++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index a64bf38ff6..4f13cc7434 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,81 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.2.2
+==============
+
+Bug Fixes
+--------------
+
+* Fixed [memory leak](https://github.com/arrayfire/arrayfire/pull/1145) in
+  CUDA Random number generators
+* Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1157) in
+  af::select() and af::replace() tests
+* Fixed [exception](https://github.com/arrayfire/arrayfire/issues/1164)
+  thrown when printing empty arrays with af::print()
+* Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1170) in CPU
+  random number generation. Changed the generator to
+  [mt19937](http://en.cppreference.com/w/cpp/numeric/random)
+* Fixed exception handling (internal)
+    * [Exceptions](https://github.com/arrayfire/arrayfire/issues/1188)
+      now show function, short file name and line number
+    * Added [AF_RETURN_ERROR](https://github.com/arrayfire/arrayfire/issues/1186)
+      macro to handle returning errors.
+    * Removed THROW macro, and renamed AF_THROW_MSG to AF_THROW_ERR.
+* Fixed [bug](https://github.com/arrayfire/arrayfire/commit/9459c6)
+  in \ref af::identity() that may have affected CUDA Compute 5.2 cards
+
+
+Build
+------
+* Added a [MIN_BUILD_TIME](https://github.com/arrayfire/arrayfire/issues/1193)
+  option to build with minimum optimization compiler flags resulting in faster
+  compile times
+* Fixed [issue](https://github.com/arrayfire/arrayfire/issues/1143) in CBLAS
+  detection by CMake
+* Fixed tests failing for builds without optional components
+  [FreeImage](https://github.com/arrayfire/arrayfire/issues/1143) and
+  [LAPACK](https://github.com/arrayfire/arrayfire/issues/1167)
+* Added a [test](https://github.com/arrayfire/arrayfire/issues/1192)
+  for unified backend
+* Only [info and backend tests](https://github.com/arrayfire/arrayfire/issues/1192)
+  are now built for unified backend
+* [Sort tests](https://github.com/arrayfire/arrayfire/issues/1199)
+  execution alphabetically
+* Fixed compilation flags and errors in tests and examples
+* [Moved AF_REVISION and AF_COMPILER_STR](https://github.com/arrayfire/arrayfire/commit/2287c5)
+  into src/backend. This is because as revision is updated with every commit,
+  entire ArrayFire would have to be rebuilt in the old code.
+    * v3.3 will add a af_get_revision() function to get the revision string.
+* [Clean up examples](https://github.com/arrayfire/arrayfire/pull/1158)
+    * Remove getchar for Windows (this will be handled by the installer)
+    * Other miscellaneous code cleanup
+    * Fixed bug in [plot3.cpp](\ref graphics/plot3.cpp) example
+* [Rename](https://github.com/arrayfire/arrayfire/commit/35f0fc2) clBLAS/clFFT
+  external project suffix from external -> ext
+* [Add OpenBLAS](https://github.com/arrayfire/arrayfire/pull/1197) as a
+  lapack/lapacke alternative
+
+Improvements
+------------
+* Added \ref AF_MEM_INFO macro to print memory info from ArrayFire's memory
+  manager ([cross issue](https://github.com/arrayfire/arrayfire/issues/1172))
+* Added [additional paths](https://github.com/arrayfire/arrayfire/issues/1184)
+  for searching for `libaf*` for Unified backend on unix-style OS.
+    * Note: This still requires dependencies such as forge, CUDA, NVVM etc to be
+      in `LD_LIBRARY_PATH` as described in [Unified Backend](\ref unifiedbackend)
+* [Create streams](https://github.com/arrayfire/arrayfire/commit/ed0373f)
+  for devices only when required in CUDA Backend
+
+Documentation
+------
+* [Hide scrollbars](https://github.com/arrayfire/arrayfire/commit/9d218a5)
+  appearing for pre and code styles
+* Fix [documentation](https://github.com/arrayfire/arrayfire/commit/ac09f91) for af::replace
+* Add [code sample](https://github.com/arrayfire/arrayfire/commit/4e06483)
+  for converting the output of af::getAvailableBackends() into bools
+* Minor fixes in documentation
+
 v3.2.1
 ==============
 

From 59dcacd3d8c3c14ccc53d7b379dd65afc28eefff Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Dec 2015 15:10:52 -0500
Subject: [PATCH 0223/2677] Update forge tag for release v3.2.2

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 9d804943fd..a134b642be 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -22,7 +22,7 @@ ENDIF()
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG 84f82e8f54746d75ddf66eeef0e4368881da1f6e
+    GIT_TAG af3.2.2
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 1dd21957148047897d387cb154c3856ad90a8d32 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 10:25:26 -0500
Subject: [PATCH 0224/2677] Cleanup util.cpp

---
 CMakeLists.txt                    |  1 -
 src/api/unified/CMakeLists.txt    |  2 +-
 src/backend/cpu/CMakeLists.txt    |  1 -
 src/backend/cuda/CMakeLists.txt   |  1 -
 src/backend/opencl/CMakeLists.txt |  1 -
 src/{ => backend}/util.cpp        | 25 +++++++++++--------------
 src/{ => backend}/util.hpp        |  0
 7 files changed, 12 insertions(+), 19 deletions(-)
 rename src/{ => backend}/util.cpp (59%)
 rename src/{ => backend}/util.hpp (100%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bc00e1542a..c79fbcaab0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -113,7 +113,6 @@ IF(BUILD_SIFT)
 ENDIF(BUILD_SIFT)
 
 INCLUDE_DIRECTORIES(
-    "${CMAKE_CURRENT_SOURCE_DIR}/src"
     "${CMAKE_CURRENT_SOURCE_DIR}/include"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/backend"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/api/c"
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index b6980d6bb3..21c9aebf97 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -15,12 +15,12 @@ FILE(GLOB cpp_sources
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB common_sources
-    "../../util.cpp"
     "../c/util.cpp"
     "../c/err_common.cpp"
     "../c/type_util.cpp"
     "../c/version.cpp"
     "../../backend/dim4.cpp"
+    "../../backend/util.cpp"
     )
 
 SOURCE_GROUP(common FILES ${common_sources})
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index bf72a8a6fa..b0ab17a616 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -107,7 +107,6 @@ source_group(api\\c\\Headers FILES ${c_headers})
 source_group(api\\c\\Sources FILES ${c_sources})
 
 FILE(GLOB cpp_sources
-    "../../util.cpp"
     "../../api/cpp/*.cpp"
     )
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ee7b86ff2c..bb8fca013c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -178,7 +178,6 @@ FILE(GLOB c_headers
     )
 
 FILE(GLOB c_sources
-    "../../util.cpp"
     "../../api/c/*.cpp"
     )
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 223752cc28..86ba1b2aad 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -152,7 +152,6 @@ FILE(GLOB backend_headers
     )
 
 FILE(GLOB backend_sources
-    "../../util.cpp"
     "../*.cpp"
     )
 source_group(backend\\Headers FILES ${backend_headers})
diff --git a/src/util.cpp b/src/backend/util.cpp
similarity index 59%
rename from src/util.cpp
rename to src/backend/util.cpp
index 5607292c0d..7c4cd2e614 100644
--- a/src/util.cpp
+++ b/src/backend/util.cpp
@@ -13,9 +13,6 @@
 
 #if defined(OS_WIN)
 #include <Windows.h>
-typedef HMODULE LibHandle;
-#else
-#include <dlfcn.h>
 #endif
 
 using std::string;
@@ -23,18 +20,18 @@ using std::string;
 string getEnvVar(const std::string &key)
 {
 #if defined(OS_WIN)
-  DWORD bufSize = 32767; // limit according to GetEnvironment Variable documentation
-  string retVal;
-  retVal.resize(bufSize);
-  bufSize = GetEnvironmentVariable(key.c_str(), &retVal[0], bufSize);
-  if (!bufSize) {
-    return string("");
-  } else {
+    DWORD bufSize = 32767; // limit according to GetEnvironment Variable documentation
+    string retVal;
     retVal.resize(bufSize);
-    return retVal;
-  }
+    bufSize = GetEnvironmentVariable(key.c_str(), &retVal[0], bufSize);
+    if (!bufSize) {
+        return string("");
+    } else {
+        retVal.resize(bufSize);
+        return retVal;
+    }
 #else
-  char * str = getenv(key.c_str());
-  return str==NULL ? string("") : string(str);
+    char * str = getenv(key.c_str());
+    return str==NULL ? string("") : string(str);
 #endif
 }
diff --git a/src/util.hpp b/src/backend/util.hpp
similarity index 100%
rename from src/util.hpp
rename to src/backend/util.hpp

From e19a6bef84ae8c2740385167ea0f8aef2dda7c86 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 11:02:15 -0500
Subject: [PATCH 0225/2677] Using getEnvVar instead of getenv

---
 src/api/c/err_common.cpp           | 7 ++++---
 src/api/c/graphics_common.cpp      | 5 +++--
 src/backend/cuda/interopManager.cu | 9 +++++----
 src/backend/cuda/memory.cpp        | 8 +++++---
 src/backend/cuda/platform.cpp      | 5 +++--
 src/backend/opencl/platform.cpp    | 9 +++++----
 src/backend/opencl/program.hpp     | 5 +++--
 7 files changed, 28 insertions(+), 20 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 9ff731f79d..b9fa49221c 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -11,6 +11,7 @@
 #include <af/device.h>
 #include <err_common.hpp>
 #include <type_util.hpp>
+#include <util.hpp>
 #include <string>
 #include <sstream>
 #include <cstring>
@@ -155,9 +156,9 @@ static std::string global_err_string;
 void
 print_error(const string &msg)
 {
-    const char* perr = getenv("AF_PRINT_ERRORS");
-    if(perr != nullptr) {
-        if(std::strncmp(perr, "0", 1) != 0)
+    std::string perr = getEnvVar("AF_PRINT_ERRORS");
+    if(!perr.empty()) {
+        if(perr != "0")
             fprintf(stderr, "%s\n", msg.c_str());
     }
     global_err_string = msg;
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index a4132b55dd..291bf84275 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -13,6 +13,7 @@
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <platform.hpp>
+#include <util.hpp>
 
 using namespace std;
 
@@ -145,8 +146,8 @@ fg::Window* ForgeManager::getMainWindow(const bool dontCreate)
     static fg::Window* wnd = NULL;
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
-    const char* noGraphicsENV = getenv("AF_DISABLE_GRAPHICS");
-    if(!noGraphicsENV) { // If AF_DISABLE_GRAPHICS is not defined
+    std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
+    if(!noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         if (flag && !dontCreate) {
             wnd = new fg::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
             CheckGL("End ForgeManager::getMainWindow");
diff --git a/src/backend/cuda/interopManager.cu b/src/backend/cuda/interopManager.cu
index b492a5ee1d..a6e2fcf9bd 100644
--- a/src/backend/cuda/interopManager.cu
+++ b/src/backend/cuda/interopManager.cu
@@ -14,6 +14,7 @@
 
 #include <interopManager.hpp>
 #include <err_cuda.hpp>
+#include <util.hpp>
 #include <cstdio>
 
 namespace cuda
@@ -36,10 +37,10 @@ InteropManager::~InteropManager()
         }
     } catch (AfError &ex) {
 
-        const char* perr = getenv("AF_PRINT_ERRORS");
-
-        if(perr && perr[0] != '0') {
-            fprintf(stderr, "%s\n", ex.what());
+        std::string perr = getEnvVar("AF_PRINT_ERRORS");
+        if(!perr.empty()) {
+            if(perr != "0")
+                fprintf(stderr, "%s\n", ex.what());
         }
     }
 }
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 9b3d731b4b..2632a0a3b4 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -12,6 +12,7 @@
 #include <cuda_runtime_api.h>
 #include <cuda_runtime.h>
 #include <err_cuda.hpp>
+#include <util.hpp>
 #include <types.hpp>
 #include <map>
 #include <dispatch.hpp>
@@ -123,9 +124,10 @@ namespace cuda
 
             } catch (AfError &ex) {
 
-                const char* perr = getenv("AF_PRINT_ERRORS");
-                if(perr && perr[0] != '0') {
-                    fprintf(stderr, "%s\n", ex.what());
+                std::string perr = getEnvVar("AF_PRINT_ERRORS");
+                if(!perr.empty()) {
+                    if(perr != "0")
+                        fprintf(stderr, "%s\n", ex.what());
                 }
             }
         }
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 6854535deb..f5f6599419 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -11,6 +11,7 @@
 #include <af/cuda.h>
 #include <platform.hpp>
 #include <defines.hpp>
+#include <util.hpp>
 #include <version.hpp>
 #include <driver.h>
 #include <vector>
@@ -348,8 +349,8 @@ DeviceManager::DeviceManager()
     for(int i = 0; i < (int)MAX_DEVICES; i++)
         streams[i] = (cudaStream_t)0;
 
-    const char* deviceENV = getenv("AF_CUDA_DEFAULT_DEVICE");
-    if(!deviceENV) {
+    std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
+    if(deviceENV.empty()) {
         setActiveDevice(0, cuDevices[0].nativeId);
     } else {
         stringstream s(deviceENV);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 57726d2e87..8d77e24cbd 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -29,6 +29,7 @@
 #include <defines.hpp>
 #include <version.hpp>
 #include <platform.hpp>
+#include <util.hpp>
 #include <functional>
 #include <algorithm>
 #include <cctype>
@@ -153,8 +154,8 @@ DeviceManager::DeviceManager()
             }
         }
 
-        const char* deviceENV = getenv("AF_OPENCL_DEFAULT_DEVICE");
-        if(deviceENV) {
+        std::string deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
+        if(!deviceENV.empty()) {
             std::stringstream s(deviceENV);
             int def_device = -1;
             s >> def_device;
@@ -172,8 +173,8 @@ DeviceManager::DeviceManager()
      * OpenGL shared contexts whereever applicable */
 #if defined(WITH_GRAPHICS)
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
-    const char* noGraphicsENV = getenv("AF_DISABLE_GRAPHICS");
-    if(!noGraphicsENV) { // If AF_DISABLE_GRAPHICS is not defined
+    std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
+    if(!noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         try {
             int devCount = mDevices.size();
             fg::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 1b76a75ce8..6a2af45131 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <platform.hpp>
+#include <util.hpp>
 #include <string>
 #include <mutex>
 
@@ -35,8 +36,8 @@ using std::string;
 #if defined(NDEBUG)
 
 #define SHOW_BUILD_INFO(PROG) do {                                  \
-        const char *info = getenv("AF_OPENCL_SHOW_BUILD_INFO");     \
-        if (info != nullptr && std::strncmp(info,"0", 1) != 0) {    \
+        std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");  \
+        if (!info.empty() && info != "0") {                         \
             SHOW_DEBUG_BUILD_INFO(prog);                            \
         }                                                           \
     } while(0)

From b260abf1703e3adea569f477335ad4020e72e7da Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 11:12:24 -0500
Subject: [PATCH 0226/2677] Cleanup/improve backend test

---
 test/backend.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/test/backend.cpp b/test/backend.cpp
index 59b8fd5129..7b8dbddade 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -37,10 +37,15 @@ void backendTest()
 {
     int backends = af::getAvailableBackends();
 
+    ASSERT_NE(backends, 0);
+
     bool cpu    = backends & AF_BACKEND_CPU;
     bool cuda   = backends & AF_BACKEND_CUDA;
     bool opencl = backends & AF_BACKEND_OPENCL;
 
+    printf("\nRunning Default Backend...\n");
+    testFunction<float>();
+
     if(cpu) {
         printf("\nRunning CPU Backend...\n");
         af::setBackend(AF_BACKEND_CPU);

From de4851d06784984ece5c476ad84e4856da4b70c2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 11:12:47 -0500
Subject: [PATCH 0227/2677] Not building info for unified. backend does the
 same as info

---
 test/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index d341164e82..3b7b42c87e 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -120,8 +120,8 @@ INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")
 LIST(SORT FILES)  # Tests execute in alphabetical order
 
-# We only build info.cpp and backend.cpp for Unified backend
-SET(UNIFIED_FILES "backend.cpp;info.cpp")
+# We only build backend.cpp for Unified backend
+SET(UNIFIED_FILES "backend.cpp")
 LIST(SORT UNIFIED_FILES)  # Tests execute in alphabetical order
 
 # Next we build each example using every backend.

From 4d06c748f98cde6840b4cbfdc52dcb50ac53f8d3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 12:11:51 -0500
Subject: [PATCH 0228/2677] FEAT Added isImageIOAvailable function to check
 support

---
 docs/details/image.dox    |  6 ++++++
 include/af/image.h        | 24 ++++++++++++++++++++++++
 src/api/c/imageio2.cpp    | 12 ++++++++++++
 src/api/cpp/imageio.cpp   |  7 +++++++
 src/api/unified/image.cpp |  5 +++++
 5 files changed, 54 insertions(+)

diff --git a/docs/details/image.dox b/docs/details/image.dox
index 53ac7616fc..288e4f6b0f 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -430,6 +430,12 @@ Save an array to disk as an image
 Supported formats include JPG, PNG, PPM and other formats supported by freeimage
 
 
+\defgroup imageio_func_available isImageIoAvailable
+\ingroup imageio_mat
+
+Returns true if ArrayFire was compiled with ImageIO (FreeImage) support
+
+
 \defgroup imagemem_func_load loadImageMem
 \ingroup imageio_mat
 
diff --git a/include/af/image.h b/include/af/image.h
index f38bb41694..ad56cfc081 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -147,6 +147,16 @@ AFAPI array loadImageNative(const char* filename);
 AFAPI void saveImageNative(const char* filename, const array& in);
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+    Function to check if Image IO is available
+
+    \returns true if ArrayFire was commpiled with ImageIO support, false otherwise.
+    \ingroup imageio_func_available
+*/
+AFAPI bool isImageIOAvailable();
+#endif
+
 /**
     C++ Interface for resizing an image to specified dimensions
 
@@ -794,6 +804,20 @@ extern "C" {
     AFAPI af_err af_save_image_native(const char* filename, const af_array in);
 #endif
 
+#if AF_API_VERSION >= 33
+    /**
+        Function to check if Image IO is available
+
+        \param[out] out is true if ArrayFire was commpiled with ImageIO support,
+        false otherwise.
+
+        \return     \ref AF_SUCCESS if successful
+
+        \ingroup imageio_func_available
+    */
+    AFAPI af_err af_is_image_io_available(bool *out);
+#endif
+
     /**
        C Interface for resizing an image to specified dimensions
 
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index d50afefb92..adc4244953 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -373,6 +373,12 @@ af_err af_save_image_native(const char* filename, const af_array in)
     return AF_SUCCESS;
 }
 
+af_err af_is_image_io_available(bool *out)
+{
+    *out = true;
+    return AF_SUCCESS;
+}
+
 #else   // WITH_FREEIMAGE
 #include <af/image.h>
 #include <stdio.h>
@@ -386,4 +392,10 @@ af_err af_save_image_native(const char* filename, const af_array in)
 {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
 }
+
+af_err af_is_image_io_available(bool *out)
+{
+    *out = false;
+    return AF_SUCCESS;
+}
 #endif  // WITH_FREEIMAGE
diff --git a/src/api/cpp/imageio.cpp b/src/api/cpp/imageio.cpp
index e70b26d1d2..75ef5fe9c4 100644
--- a/src/api/cpp/imageio.cpp
+++ b/src/api/cpp/imageio.cpp
@@ -68,4 +68,11 @@ void saveImageNative(const char* filename, const array& in)
     AF_THROW(af_save_image_native(filename, in.get()));
 }
 
+bool isImageIOAvailable()
+{
+    bool out = false;
+    AF_THROW(af_is_image_io_available(&out));
+    return out;
+}
+
 }
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index d0f9aa6200..7b1159516c 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -55,6 +55,11 @@ af_err af_save_image_native(const char* filename, const af_array in)
     return CALL(filename, in);
 }
 
+af_err af_is_image_io_available(bool *out)
+{
+    return CALL(out);
+}
+
 af_err af_resize(af_array *out, const af_array in, const dim_t odim0, const dim_t odim1, const af_interp_type method)
 {
     CHECK_ARRAYS(in);

From 1b85d6d1acf795193080593e970888a01f6d0e85 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 12:12:09 -0500
Subject: [PATCH 0229/2677] FEAT Added isLAPACKAvailable function to check
 support

---
 docs/details/lapack.dox    |  8 ++++++++
 include/af/lapack.h        | 25 +++++++++++++++++++++++++
 include/arrayfire.h        |  2 ++
 src/api/c/lu.cpp           | 10 ++++++++++
 src/api/cpp/lapack.cpp     |  7 +++++++
 src/api/unified/lapack.cpp |  5 +++++
 src/backend/cpu/lu.cpp     |  5 +++++
 src/backend/cpu/lu.hpp     |  2 ++
 src/backend/cuda/lu.cu     | 15 +++++++++++++++
 src/backend/cuda/lu.hpp    |  2 ++
 src/backend/opencl/lu.cpp  | 10 ++++++++++
 src/backend/opencl/lu.hpp  |  2 ++
 12 files changed, 93 insertions(+)

diff --git a/docs/details/lapack.dox b/docs/details/lapack.dox
index c0d8aae5b9..522dbe544f 100644
--- a/docs/details/lapack.dox
+++ b/docs/details/lapack.dox
@@ -287,5 +287,13 @@ This function can return the norm using various metrics based on the type paramt
 
 ===============================================================================
 
+\defgroup lapack_helper_func_available isLAPACKAvailable
+
+\ingroup lapack_helper
+
+\brief Returns true is ArrayFire is compiled with LAPACK support
+
+===============================================================================
+
 @}
 */
diff --git a/include/af/lapack.h b/include/af/lapack.h
index f1cf87ad82..bb54069550 100644
--- a/include/af/lapack.h
+++ b/include/af/lapack.h
@@ -237,6 +237,18 @@ namespace af
     */
     AFAPI double norm(const array &in, const normType type=AF_NORM_EUCLID,
                       const double p=1, const double q=1);
+
+#if AF_API_VERSION >= 33
+    /**
+       Returns true is ArrayFire is compiled with LAPACK support
+
+       \returns true is LAPACK support is available, false otherwise
+
+       \ingroup lapack_ops_func_norm
+    */
+    AFAPI bool isLAPACKAvailable();
+#endif
+
 }
 #endif
 
@@ -425,6 +437,19 @@ extern "C" {
     */
     AFAPI af_err af_norm(double *out, const af_array in, const af_norm_type type, const double p, const double q);
 
+#if AF_API_VERSION >= 33
+    /**
+       Returns true is ArrayFire is compiled with LAPACK support
+
+       \param[out] out is true if LAPACK support is available, false otherwise
+
+       \returns AF_SUCCESS if successful (does not depend on the value of out)
+
+       \ingroup lapack_ops_func_norm
+    */
+    AFAPI af_err af_is_lapack_available(bool *out);
+#endif
+
 
 #ifdef __cplusplus
 }
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 7d9e75a7b4..73b417b3ad 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -113,6 +113,8 @@
 
      @defgroup lapack_ops_mat Matrix operations
      inverse, det, rank, norm etc.
+
+     @defgroup lapack_helper LAPACK Helper functions
    @}
 
    @defgroup image_mat Image Processing
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index c6004bc6cf..1d98e02490 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -95,3 +95,13 @@ af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv)
 
     return AF_SUCCESS;
 }
+
+af_err af_is_lapack_available(bool *out)
+{
+    try {
+        *out = isLAPACKAvailable();
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/lapack.cpp b/src/api/cpp/lapack.cpp
index cf9b3ecfd2..091c807612 100644
--- a/src/api/cpp/lapack.cpp
+++ b/src/api/cpp/lapack.cpp
@@ -153,4 +153,11 @@ namespace af
         AF_THROW(af_norm(&out, in.get(), type, p, q));
         return out;
     }
+
+    bool isLAPACKAvailable()
+    {
+        bool out = false;
+        AF_THROW(af_is_lapack_available(&out));
+        return out;
+    }
 }
diff --git a/src/api/unified/lapack.cpp b/src/api/unified/lapack.cpp
index b2364ac858..8a367017cf 100644
--- a/src/api/unified/lapack.cpp
+++ b/src/api/unified/lapack.cpp
@@ -96,3 +96,8 @@ af_err af_norm(double *out, const af_array in, const af_norm_type type, const do
     CHECK_ARRAYS(in);
     return CALL(out, in, type, p, q);
 }
+
+af_err af_is_lapack_available(bool *out)
+{
+    return CALL(out);
+}
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 93862f24c0..f8fc92de8d 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -85,6 +85,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     }
 }
 
+bool isLAPACKAvailable()
+{
+    return true;
+}
+
 }
 
 #else
diff --git a/src/backend/cpu/lu.hpp b/src/backend/cpu/lu.hpp
index c25dcaaa16..3fef461067 100644
--- a/src/backend/cpu/lu.hpp
+++ b/src/backend/cpu/lu.hpp
@@ -17,4 +17,6 @@ namespace cpu
 
     template<typename T>
     Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+
+    bool isLAPACKAvailable();
 }
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index 2a45d4b9f5..ce0b545a84 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -156,6 +156,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     return pivot;
 }
 
+bool isLAPACKAvailable()
+{
+    return true;
+}
+
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
@@ -186,6 +191,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     return cpu::lu_inplace(in, convert_pivot);
 }
 
+bool isLAPACKAvailable()
+{
+    return true;
+}
+
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
@@ -213,6 +223,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
              AF_ERR_NOT_CONFIGURED);
 }
 
+bool isLAPACKAvailable()
+{
+    return false;
+}
+
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
diff --git a/src/backend/cuda/lu.hpp b/src/backend/cuda/lu.hpp
index 0753129d6b..acf9dbaad7 100644
--- a/src/backend/cuda/lu.hpp
+++ b/src/backend/cuda/lu.hpp
@@ -17,4 +17,6 @@ namespace cuda
 
     template<typename T>
     Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+
+    bool isLAPACKAvailable();
 }
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index ee76f47201..2d94d4d326 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -88,6 +88,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     }
 }
 
+bool isLAPACKAvailable()
+{
+    return true;
+}
+
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
@@ -116,6 +121,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
+bool isLAPACKAvailable()
+{
+    return false;
+}
+
 #define INSTANTIATE_LU(T)                                                                           \
     template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
     template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
diff --git a/src/backend/opencl/lu.hpp b/src/backend/opencl/lu.hpp
index af43f24614..b44eca8c60 100644
--- a/src/backend/opencl/lu.hpp
+++ b/src/backend/opencl/lu.hpp
@@ -17,4 +17,6 @@ namespace opencl
 
     template<typename T>
     Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+
+    bool isLAPACKAvailable();
 }

From 7747ee6bf0ba1719a21faf86e142201d92b75b5f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 12:31:33 -0500
Subject: [PATCH 0230/2677] Use isImageIOAvailable in testHelper

---
 test/testHelpers.hpp | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 758bf98e14..0b22cef283 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -390,15 +390,9 @@ bool noDoubleTests()
 
 bool noImageIOTests()
 {
-    af_array arr = 0;
-    const af_err err = af_load_image(&arr, TEST_DIR"/imageio/color_small.png", true);
-
-    if(arr != 0) af_release_array(arr);
-
-    if(err == AF_ERR_NOT_CONFIGURED)
-        return true;    // Yes, disable test
-    else
-        return false;   // No, let test continue
+    bool ret = !af::isImageIOAvailable();
+    if(ret) printf("Image IO Not Configured. Test will exit\n");
+    return ret;
 }
 
 bool noLAPACKTests()

From fe3fa66c5cdd70cca6c53c17454402d03d057a1d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 12:48:45 -0500
Subject: [PATCH 0231/2677] Use isLAPACKAvailable in testHelper

---
 test/testHelpers.hpp | 19 +++----------------
 1 file changed, 3 insertions(+), 16 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 0b22cef283..2744a8d67e 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -397,22 +397,9 @@ bool noImageIOTests()
 
 bool noLAPACKTests()
 {
-    // Run LU
-    af::dim4 dims(5, 5);
-    af_array in = 0, l = 0, u = 0, p= 0;
-    af_randu(&in, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type);
-
-    af_err err = af_lu(&l, &u, &p, in);
-
-    if(in != 0) af_release_array(in);
-    if(l  != 0) af_release_array(l);
-    if(u  != 0) af_release_array(u);
-    if(p  != 0) af_release_array(p);
-
-    if(err == AF_ERR_NOT_CONFIGURED)
-        return true;    // Yes, disable test
-    else
-        return false;   // No, let test continue
+    bool ret = !af::isLAPACKAvailable();
+    if(ret) printf("LAPACK Not Configured. Test will exit\n");
+    return ret;
 }
 
 // TODO: perform conversion on device for CUDA and OpenCL

From b89ab5dba7487e0b30191371d7111845fa75cdde Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 13:54:04 -0500
Subject: [PATCH 0232/2677] Add missing af_err to string

---
 src/api/c/err_common.cpp | 43 ++++++++++++++++++++++------------------
 1 file changed, 24 insertions(+), 19 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index b9fa49221c..886e43ba6f 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -182,26 +182,31 @@ void af_get_last_error(char **str, dim_t *len)
 const char *af_err_to_string(const af_err err)
 {
     switch (err) {
-    case AF_SUCCESS:            return "Success";
-    case AF_ERR_INTERNAL:       return "Internal error";
-    case AF_ERR_NO_MEM:         return "Device out of memory";
-    case AF_ERR_DRIVER:         return "Driver not available or incompatible";
-    case AF_ERR_RUNTIME:        return "Runtime error ";
-    case AF_ERR_INVALID_ARRAY:  return "Invalid array";
-    case AF_ERR_ARG:            return "Invalid input argument";
-    case AF_ERR_SIZE:           return "Invalid input size";
-    case AF_ERR_DIFF_TYPE:      return "Input types are not the same";
-    case AF_ERR_NOT_SUPPORTED:  return "Function not supported";
-    case AF_ERR_NOT_CONFIGURED: return "Function not configured to build";
-    case AF_ERR_TYPE:           return "Function does not support this data type";
-    case AF_ERR_NO_DBL:         return "Double precision not supported for this device";
-    case AF_ERR_LOAD_LIB:       return "Failed to load dynamic library. See http://www.arrayfire.com/docs/unifiedbackend.htm for instructions to set up environment for Unified backend";
-    case AF_ERR_LOAD_SYM:       return "Failed to load symbol";
-    case AF_ERR_ARR_BKND_MISMATCH   :
-                                return "There was a mismatch between an array and the current backend";
+    case AF_SUCCESS:                return "Success";
+    case AF_ERR_NO_MEM:             return "Device out of memory";
+    case AF_ERR_DRIVER:             return "Driver not available or incompatible";
+    case AF_ERR_RUNTIME:            return "Runtime error ";
+    case AF_ERR_INVALID_ARRAY:      return "Invalid array";
+    case AF_ERR_ARG:                return "Invalid input argument";
+    case AF_ERR_SIZE:               return "Invalid input size";
+    case AF_ERR_TYPE:               return "Function does not support this data type";
+    case AF_ERR_DIFF_TYPE:          return "Input types are not the same";
+    case AF_ERR_BATCH:              return "Invalid batch configuration";
+    case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
+    case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
+    case AF_ERR_NONFREE:            return "Function unavailable."
+                                           "ArrayFire compiled without Non-Free algorithms support";
+    case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
+    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable."
+                                           "ArrayFire compiled without Graphics support";
+    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library."
+                                           "See http://www.arrayfire.com/docs/unifiedbackend.htm"
+                                           "for instructions to set up environment for Unified backend";
+    case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
+    case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
+    case AF_ERR_INTERNAL:           return "Internal error";
     case AF_ERR_UNKNOWN:
-    default:
-        return "Unknown error";
+    default:                        return "Unknown error";
     }
 }
 

From 8813a2eff1f75f056e8dc6865595100f1fd9d16a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 16:05:12 -0500
Subject: [PATCH 0233/2677] af_get_last_error supports NULL as valid argument
 for len

---
 src/api/c/err_common.cpp | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 886e43ba6f..382dac1af1 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -166,17 +166,21 @@ print_error(const string &msg)
 
 void af_get_last_error(char **str, dim_t *len)
 {
-    *len = std::min(MAX_ERR_SIZE, (int)global_err_string.size());
+    dim_t slen = std::min(MAX_ERR_SIZE, (int)global_err_string.size());
 
-    if (*len == 0) {
+    if (len && slen == 0) {
+        *len = 0;
         *str = NULL;
+        return;
     }
 
-    af_alloc_host((void**)str, sizeof(char) * (*len + 1));
-    global_err_string.copy(*str, *len);
+    af_alloc_host((void**)str, sizeof(char) * (slen + 1));
+    global_err_string.copy(*str, slen);
 
-    (*str)[*len] = '\0';
+    (*str)[slen] = '\0';
     global_err_string = std::string("");
+
+    if(len) *len = slen;
 }
 
 const char *af_err_to_string(const af_err err)

From b7af25a1b7a5b61b9ba7a3aacaac084a24867d93 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 16:06:15 -0500
Subject: [PATCH 0234/2677] Improvements to af::exception messages

* Now prints enum string
* Prints functions
* Prints last error
---
 include/af/exception.h    |  3 +++
 src/api/cpp/error.hpp     | 12 +++++++++---
 src/api/cpp/exception.cpp | 16 ++++++++++++----
 3 files changed, 24 insertions(+), 7 deletions(-)

diff --git a/include/af/exception.h b/include/af/exception.h
index ee10c5db7b..a43d26dbaa 100644
--- a/include/af/exception.h
+++ b/include/af/exception.h
@@ -27,6 +27,9 @@ class AFAPI exception : public std::exception
     exception(const char *msg);
     exception(const char *file, unsigned line, af_err err);
     exception(const char *msg, const char *file, unsigned line, af_err err);
+#if AF_API_VERSION >= 33
+    exception(const char *msg, const char *func, const char *file, unsigned line, af_err err);
+#endif
     virtual ~exception() throw() {}
     virtual const char *what() const throw() { return m_msg; }
     friend inline std::ostream& operator<<(std::ostream &s, const exception &e)
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index 157f8193ab..c888db8646 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -8,14 +8,20 @@
  ********************************************************/
 
 #include <af/exception.h>
+#include <af/device.h>
 #include <defines.hpp>
 
 #define AF_THROW(fn) do {                               \
         af_err __err = fn;                              \
         if (__err == AF_SUCCESS) break;                 \
-        throw af::exception(__AF_FILENAME__, __LINE__, __err); \
+        char *msg = NULL; af_get_last_error(&msg, NULL);\
+        af::exception ex(msg, __PRETTY_FUNCTION__,      \
+                __AF_FILENAME__, __LINE__, __err);      \
+        af_free_host(msg);                              \
+        throw ex;                                       \
     } while(0)
 
-#define AF_THROW_ERR(__msg, __err) do {                         \
-        throw af::exception(__msg, __AF_FILENAME__, __LINE__, __err);  \
+#define AF_THROW_ERR(__msg, __err) do {                 \
+        throw af::exception(__msg, __PRETTY_FUNCTION__, \
+                __AF_FILENAME__, __LINE__, __err);      \
     } while(0)
diff --git a/src/api/cpp/exception.cpp b/src/api/cpp/exception.cpp
index 373ae29c55..f88f98b0f2 100644
--- a/src/api/cpp/exception.cpp
+++ b/src/api/cpp/exception.cpp
@@ -32,8 +32,8 @@ exception::exception(const char *msg): m_err(AF_ERR_UNKNOWN)
 exception::exception(const char *file, unsigned line, af_err err): m_err(err)
 {
     snprintf(m_msg, sizeof(m_msg) - 1,
-             "ArrayFire Exception(%d): %s\nIn %s:%u",
-             (int)err, af_err_to_string(err), file, line);
+             "ArrayFire Exception (%s:%d):\nIn %s:%u",
+             af_err_to_string(err), (int)err, file, line);
 
     m_msg[sizeof(m_msg)-1] = '\0';
 }
@@ -41,11 +41,19 @@ exception::exception(const char *file, unsigned line, af_err err): m_err(err)
 exception::exception(const char *msg, const char *file, unsigned line, af_err err): m_err(err)
 {
     snprintf(m_msg, sizeof(m_msg) - 1,
-             "ArrayFire Exception(%d): %s\nIn %s:%u",
-             (int)(err), msg, file, line);
+             "ArrayFire Exception (%s:%d):\n%s\nIn %s:%u",
+             af_err_to_string(err), (int)(err), msg, file, line);
 
     m_msg[sizeof(m_msg)-1] = '\0';
 }
 
+exception::exception(const char *msg, const char *func, const char *file, unsigned line, af_err err): m_err(err)
+{
+    snprintf(m_msg, sizeof(m_msg) - 1,
+             "ArrayFire Exception (%s:%d):\n%s\nIn function %s\nIn file %s:%u",
+             af_err_to_string(err), (int)(err), msg, func, file, line);
+
+    m_msg[sizeof(m_msg)-1] = '\0';
+}
 
 }

From 7b6eee1385bc9b275e7eb3518ff454ac6faf825c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 18:17:14 -0500
Subject: [PATCH 0235/2677] Add version guards around allocHost and freeHost

---
 include/af/device.h | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/include/af/device.h b/include/af/device.h
index d3585c619c..ff33b3327f 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -152,6 +152,7 @@ namespace af
     /// \param[in] ptr the memory to free
     AFAPI void freePinned(const void *ptr);
 
+#if AF_API_VERSION >= 33
     /// \brief Allocate memory on host
     ///
     /// \copydoc device_func_alloc_host
@@ -162,7 +163,9 @@ namespace af
     ///
     /// \ingroup device_func_alloc_host
     AFAPI void *allocHost(const size_t elements, const dtype type);
+#endif
 
+#if AF_API_VERSION >= 33
     /// \brief Allocate memory on host
     ///
     /// \copydoc device_func_alloc_host
@@ -176,7 +179,9 @@ namespace af
     /// \ingroup device_func_alloc_host
     template<typename T>
     AFAPI T* allocHost(const size_t elements);
+#endif
 
+#if AF_API_VERSION >= 33
     /// \brief Free memory allocated internally by ArrayFire
     //
     /// \copydoc device_func_free_host
@@ -185,6 +190,7 @@ namespace af
     ///
     /// \ingroup device_func_free_host
     AFAPI void freeHost(const void *ptr);
+#endif
 
     /// \ingroup device_func_mem
     /// @{
@@ -291,15 +297,19 @@ extern "C" {
     */
     AFAPI af_err af_free_pinned(void *ptr);
 
+#if AF_API_VERSION >= 33
     /**
        \ingroup device_func_alloc_host
     */
     AFAPI af_err af_alloc_host(void **ptr, const dim_t bytes);
+#endif
 
+#if AF_API_VERSION >= 33
     /**
        \ingroup device_func_free_host
     */
     AFAPI af_err af_free_host(void *ptr);
+#endif
 
     /**
        Create array from device memory

From 960574050303c363ef26be8c8a7c05c15974904f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 18:19:08 -0500
Subject: [PATCH 0236/2677] Deprecate af_(lock/unlock)_device_ptr. Use
 af_(lock/unlock)_array

---
 include/af/device.h        | 28 ++++++++++++++++-
 src/api/c/device.cpp       | 62 ++++++++++++++++++++++----------------
 src/api/cpp/array.cpp      |  4 +--
 src/api/unified/device.cpp | 12 ++++++++
 4 files changed, 77 insertions(+), 29 deletions(-)

diff --git a/include/af/device.h b/include/af/device.h
index ff33b3327f..394170c624 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -346,9 +346,12 @@ extern "C" {
     /**
        Lock the device buffer in the memory manager.
 
-       Locked buffers are not freed by memory manager until \ref af_unlock_device_ptr is called.
+       Locked buffers are not freed by memory manager until \ref af_unlock_array is called.
        \ingroup device_func_mem
     */
+#if AF_API_VERSION >= 33
+    DEPRECATED("Use af_lock_array instead")
+#endif
     AFAPI af_err af_lock_device_ptr(const af_array arr);
 #endif
 
@@ -359,9 +362,32 @@ extern "C" {
        This function will give back the control over the device pointer to the memory manager.
        \ingroup device_func_mem
     */
+#if AF_API_VERSION >= 33
+    DEPRECATED("Use af_unlock_array instead")
+#endif
     AFAPI af_err af_unlock_device_ptr(const af_array arr);
 #endif
 
+#if AF_API_VERSION >= 33
+    /**
+       Lock the device buffer in the memory manager.
+
+       Locked buffers are not freed by memory manager until \ref af_unlock_array is called.
+       \ingroup device_func_mem
+    */
+    AFAPI af_err af_lock_array(const af_array arr);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       Unlock device buffer in the memory manager.
+
+       This function will give back the control over the device pointer to the memory manager.
+       \ingroup device_func_mem
+    */
+    AFAPI af_err af_unlock_array(const af_array arr);
+#endif
+
     /**
        Get the device pointer and lock the buffer in memory manager.
 
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 84cd246a60..51eb613bfa 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -214,29 +214,34 @@ af_err af_get_device_ptr(void **data, const af_array arr)
 }
 
 template <typename T>
-inline void lockDevicePtr(const af_array arr)
+inline void lockArray(const af_array arr)
 {
     memPop<T>((const T *)getArray<T>(arr).get());
 }
 
 af_err af_lock_device_ptr(const af_array arr)
+{
+    return af_lock_array(arr);
+}
+
+af_err af_lock_array(const af_array arr)
 {
     try {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-        case f32: lockDevicePtr<float  >(arr); break;
-        case f64: lockDevicePtr<double >(arr); break;
-        case c32: lockDevicePtr<cfloat >(arr); break;
-        case c64: lockDevicePtr<cdouble>(arr); break;
-        case s32: lockDevicePtr<int    >(arr); break;
-        case u32: lockDevicePtr<uint   >(arr); break;
-        case s64: lockDevicePtr<intl   >(arr); break;
-        case u64: lockDevicePtr<uintl  >(arr); break;
-        case s16: lockDevicePtr<short  >(arr); break;
-        case u16: lockDevicePtr<ushort >(arr); break;
-        case u8 : lockDevicePtr<uchar  >(arr); break;
-        case b8 : lockDevicePtr<char   >(arr); break;
+        case f32: lockArray<float  >(arr); break;
+        case f64: lockArray<double >(arr); break;
+        case c32: lockArray<cfloat >(arr); break;
+        case c64: lockArray<cdouble>(arr); break;
+        case s32: lockArray<int    >(arr); break;
+        case u32: lockArray<uint   >(arr); break;
+        case s64: lockArray<intl   >(arr); break;
+        case u64: lockArray<uintl  >(arr); break;
+        case s16: lockArray<short  >(arr); break;
+        case u16: lockArray<ushort >(arr); break;
+        case u8 : lockArray<uchar  >(arr); break;
+        case b8 : lockArray<char   >(arr); break;
         default: TYPE_ERROR(4, type);
         }
 
@@ -246,29 +251,34 @@ af_err af_lock_device_ptr(const af_array arr)
 }
 
 template <typename T>
-inline void unlockDevicePtr(const af_array arr)
+inline void unlockArray(const af_array arr)
 {
     memPush<T>((const T *)getArray<T>(arr).get());
 }
 
 af_err af_unlock_device_ptr(const af_array arr)
+{
+    return af_unlock_array(arr);
+}
+
+af_err af_unlock_array(const af_array arr)
 {
     try {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-        case f32: unlockDevicePtr<float  >(arr); break;
-        case f64: unlockDevicePtr<double >(arr); break;
-        case c32: unlockDevicePtr<cfloat >(arr); break;
-        case c64: unlockDevicePtr<cdouble>(arr); break;
-        case s32: unlockDevicePtr<int    >(arr); break;
-        case u32: unlockDevicePtr<uint   >(arr); break;
-        case s64: unlockDevicePtr<intl   >(arr); break;
-        case u64: unlockDevicePtr<uintl  >(arr); break;
-        case s16: unlockDevicePtr<short  >(arr); break;
-        case u16: unlockDevicePtr<ushort >(arr); break;
-        case u8 : unlockDevicePtr<uchar  >(arr); break;
-        case b8 : unlockDevicePtr<char   >(arr); break;
+        case f32: unlockArray<float  >(arr); break;
+        case f64: unlockArray<double >(arr); break;
+        case c32: unlockArray<cfloat >(arr); break;
+        case c64: unlockArray<cdouble>(arr); break;
+        case s32: unlockArray<int    >(arr); break;
+        case u32: unlockArray<uint   >(arr); break;
+        case s64: unlockArray<intl   >(arr); break;
+        case u64: unlockArray<uintl  >(arr); break;
+        case s16: unlockArray<short  >(arr); break;
+        case u16: unlockArray<ushort >(arr); break;
+        case u8 : unlockArray<uchar  >(arr); break;
+        case b8 : unlockArray<char   >(arr); break;
         default: TYPE_ERROR(4, type);
         }
 
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index f7931cfa9f..b993e2f7e8 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1057,11 +1057,11 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
 
     void array::lock() const
     {
-        AF_THROW(af_lock_device_ptr(get()));
+        AF_THROW(af_lock_array(get()));
     }
 
     void array::unlock() const
     {
-        AF_THROW(af_unlock_device_ptr(get()));
+        AF_THROW(af_unlock_array(get()));
     }
 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 8f04bf6ea1..1d5979ad13 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -148,6 +148,18 @@ af_err af_unlock_device_ptr(const af_array arr)
     return CALL(arr);
 }
 
+af_err af_lock_array(const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(arr);
+}
+
+af_err af_unlock_array(const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(arr);
+}
+
 af_err af_get_device_ptr(void **ptr, const af_array arr)
 {
     CHECK_ARRAYS(arr);

From d02636a4570b17b75a78b7adca6cb83199f57e9f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 18:21:36 -0500
Subject: [PATCH 0237/2677] Add memFreeUnlinked to free locked device ptrs

---
 src/api/c/device.cpp          |  2 +-
 src/backend/cpu/memory.cpp    | 26 +++++++++++++++++---------
 src/backend/cpu/memory.hpp    |  1 +
 src/backend/cuda/memory.cpp   | 33 ++++++++++++++++++++++++---------
 src/backend/cuda/memory.hpp   |  1 +
 src/backend/opencl/memory.cpp | 32 +++++++++++++++++++++++---------
 src/backend/opencl/memory.hpp |  4 +++-
 7 files changed, 70 insertions(+), 29 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 51eb613bfa..c2f21a273b 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -309,7 +309,7 @@ af_err af_alloc_pinned(void **ptr, const dim_t bytes)
 af_err af_free_device(void *ptr)
 {
     try {
-        memFree<char>((char *)ptr);
+        memFreeUnlinked<char>((char *)ptr, true);
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 85ba4f27fb..0e14450fad 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -156,7 +156,7 @@ T* memAlloc(const size_t &elements)
 }
 
 template<typename T>
-void memFree(T *ptr)
+void memFreeUnlinked(T *ptr, bool free_unlinked)
 {
     std::lock_guard<std::mutex> lock(memory_map_mutex);
 
@@ -165,8 +165,9 @@ void memFree(T *ptr)
     if (iter != memory_map.end()) {
 
         iter->second.is_free = true;
-        if ((iter->second).is_unlinked) return;
+        if ((iter->second).is_unlinked && !free_unlinked) return;
 
+        iter->second.is_unlinked = false;
         used_bytes -= iter->second.bytes;
         used_buffers--;
 
@@ -175,6 +176,12 @@ void memFree(T *ptr)
     }
 }
 
+template<typename T>
+void memFree(T *ptr)
+{
+    memFreeUnlinked(ptr, false);
+}
+
 template<typename T>
 void memPop(const T *ptr)
 {
@@ -226,13 +233,14 @@ void pinnedFree(T* ptr)
     memFree<T>(ptr);
 }
 
-#define INSTANTIATE(T)                                  \
-    template T* memAlloc(const size_t &elements);       \
-    template void memFree(T* ptr);                      \
-    template void memPop(const T* ptr);                 \
-    template void memPush(const T* ptr);                \
-    template T* pinnedAlloc(const size_t &elements);    \
-    template void pinnedFree(T* ptr);                   \
+#define INSTANTIATE(T)                                          \
+    template T* memAlloc(const size_t &elements);               \
+    template void memFree(T* ptr);                              \
+    template void memFreeUnlinked(T* ptr, bool free_unlinked);  \
+    template void memPop(const T* ptr);                         \
+    template void memPush(const T* ptr);                        \
+    template T* pinnedAlloc(const size_t &elements);            \
+    template void pinnedFree(T* ptr);                           \
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 0b1c960ed4..1fb8c64bbc 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -13,6 +13,7 @@ namespace cpu
 {
     template<typename T> T* memAlloc(const size_t &elements);
     template<typename T> void memFree(T* ptr);
+    template<typename T> void memFreeUnlinked(T* ptr, bool free_unlinked);
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 2632a0a3b4..e7ed8ac90e 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -64,6 +64,12 @@ namespace cuda
         cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
     }
 
+    template<typename T>
+    void memFreeUnlinked(T *ptr, bool free_unlinked)
+    {
+        cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
+    }
+
     template<typename T>
     void memPop(const T *ptr)
     {
@@ -232,7 +238,7 @@ namespace cuda
     }
 
     template<typename T>
-    void memFree(T *ptr)
+    void memFreeUnlinked(T *ptr, bool free_unlinked)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find((void *)ptr);
@@ -240,7 +246,9 @@ namespace cuda
         if (iter != memory_maps[n].end()) {
 
             iter->second.is_free = true;
-            if ((iter->second).is_unlinked) return;
+            if ((iter->second).is_unlinked && !free_unlinked) return;
+
+            iter->second.is_unlinked = false;
 
             used_bytes[n] -= iter->second.bytes;
             used_buffers[n]--;
@@ -250,6 +258,12 @@ namespace cuda
         }
     }
 
+    template<typename T>
+    void memFree(T *ptr)
+    {
+        memFreeUnlinked(ptr, false);
+    }
+
     template<typename T>
     void memPop(const T *ptr)
     {
@@ -368,13 +382,14 @@ namespace cuda
 
 #endif
 
-#define INSTANTIATE(T)                                  \
-    template T* memAlloc(const size_t &elements);       \
-    template void memFree(T* ptr);                      \
-    template void memPop(const T* ptr);                 \
-    template void memPush(const T* ptr);                \
-    template T* pinnedAlloc(const size_t &elements);    \
-    template void pinnedFree(T* ptr);                   \
+#define INSTANTIATE(T)                                          \
+    template T* memAlloc(const size_t &elements);               \
+    template void memFree(T* ptr);                              \
+    template void memFreeUnlinked(T* ptr, bool free_unlinked);  \
+    template void memPop(const T* ptr);                         \
+    template void memPush(const T* ptr);                        \
+    template T* pinnedAlloc(const size_t &elements);            \
+    template void pinnedFree(T* ptr);                           \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 2e5fef2593..a4450f3ccf 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -13,6 +13,7 @@ namespace cuda
 {
     template<typename T> T* memAlloc(const size_t &elements);
     template<typename T> void memFree(T* ptr);
+    template<typename T> void memFreeUnlinked(T* ptr, bool free_unlinked);
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index f4c740482e..7475710176 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -149,6 +149,11 @@ namespace opencl
     }
 
     void bufferFree(cl::Buffer *ptr)
+    {
+        bufferFreeUnlinked(ptr, false);
+    }
+
+    void bufferFreeUnlinked(cl::Buffer *ptr, bool free_unlinked)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find(ptr);
@@ -156,7 +161,9 @@ namespace opencl
         if (iter != memory_maps[n].end()) {
 
             iter->second.is_free = true;
-            if ((iter->second).is_unlinked) return;
+            if ((iter->second).is_unlinked && !free_unlinked) return;
+
+            iter->second.is_unlinked = false;
 
             used_bytes[n] -= iter->second.bytes;
             used_buffers[n]--;
@@ -212,7 +219,13 @@ namespace opencl
     template<typename T>
     void memFree(T *ptr)
     {
-        return bufferFree((cl::Buffer *)ptr);
+        return bufferFreeUnlinked((cl::Buffer *)ptr, false);
+    }
+
+    template<typename T>
+    void memFreeUnlinked(T *ptr, bool free_unlinked)
+    {
+        return bufferFreeUnlinked((cl::Buffer *)ptr, free_unlinked);
     }
 
     template<typename T>
@@ -341,13 +354,14 @@ namespace opencl
         return pinnedBufferFree((void *) ptr);
     }
 
-#define INSTANTIATE(T)                                  \
-    template T* memAlloc(const size_t &elements);       \
-    template void memFree(T* ptr);                      \
-    template void memPop(const T* ptr);                 \
-    template void memPush(const T* ptr);                \
-    template T* pinnedAlloc(const size_t &elements);    \
-    template void pinnedFree(T* ptr);                   \
+#define INSTANTIATE(T)                                          \
+    template T* memAlloc(const size_t &elements);               \
+    template void memFree(T* ptr);                              \
+    template void memFreeUnlinked(T* ptr, bool free_unlinked);  \
+    template void memPop(const T* ptr);                         \
+    template void memPush(const T* ptr);                        \
+    template T* pinnedAlloc(const size_t &elements);            \
+    template void pinnedFree(T* ptr);                           \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index c315a9a2f6..40e30ebce7 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -16,9 +16,11 @@ namespace opencl
 
     cl::Buffer *bufferAlloc(const size_t &bytes);
     void bufferFree(cl::Buffer *buf);
+    void bufferFreeUnlinked(cl::Buffer *buf, bool free_unlinked);
 
     template<typename T> T *memAlloc(const size_t &elements);
-    template<typename T> void memFree(T *ptr);
+    template<typename T> void memFree(T* ptr);
+    template<typename T> void memFreeUnlinked(T* ptr, bool free_unlinked);
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 

From 330f4f87df56263375f904660ad7d800d94161f5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Dec 2015 18:22:44 -0500
Subject: [PATCH 0238/2677] FEAT Add printMemInfo to print memory information

---
 include/af/device.h           | 28 ++++++++++++++++++++++
 src/api/c/device.cpp          | 17 +++++++++++++
 src/api/cpp/device.cpp        |  5 ++++
 src/api/unified/device.cpp    |  5 ++++
 src/backend/cpu/memory.cpp    | 40 +++++++++++++++++++++++++++++++
 src/backend/cpu/memory.hpp    |  2 ++
 src/backend/cuda/memory.cpp   | 45 +++++++++++++++++++++++++++++++++++
 src/backend/cuda/memory.hpp   |  2 ++
 src/backend/opencl/memory.cpp | 41 +++++++++++++++++++++++++++++++
 src/backend/opencl/memory.hpp |  2 ++
 10 files changed, 187 insertions(+)

diff --git a/include/af/device.h b/include/af/device.h
index 394170c624..4a3006ffc7 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -205,6 +205,19 @@ namespace af
     AFAPI void deviceMemInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                              size_t *lock_bytes, size_t *lock_buffers);
 
+#if AF_API_VERSION >= 33
+    ///
+    /// Prints buffer details from the ArrayFire Device Manager
+    //
+    /// \param [in] msg A message to print before the table
+    /// \param [in] device_id print the memory info of the specified device.
+    ///  -1 signifies active device.
+    //
+    /// \ingroup device_func_mem
+    ///
+    AFAPI void printMemInfo(const char *msg = NULL, const int device_id = -1);
+#endif
+
     /// \brief Call the garbage collection function in the memory manager
     ///
     /// \ingroup device_func_mem
@@ -324,6 +337,21 @@ extern "C" {
     AFAPI af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
                                     size_t *lock_bytes, size_t *lock_buffers);
 
+#if AF_API_VERSION >= 33
+    ///
+    /// Prints buffer details from the ArrayFire Device Manager
+    //
+    /// \param [in] msg A message to print before the table
+    /// \param [in] device_id print the memory info of the specified device.
+    ///  -1 signifies active device.
+    ///
+    /// return AF_SUCCESS if successful
+    ///
+    /// \ingroup device_func_mem
+    ///
+    AFAPI af_err af_print_mem_info(const char *msg, const int device_id);
+#endif
+
     /**
        Call the garbage collection routine
        \ingroup device_func_mem
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index c2f21a273b..007e0ab7f2 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -17,6 +17,7 @@
 #include <handle.hpp>
 #include <memory.hpp>
 #include "err_common.hpp"
+#include <cstring>
 
 using namespace detail;
 
@@ -340,6 +341,22 @@ af_err af_free_host(void *ptr)
     return AF_SUCCESS;
 }
 
+af_err af_print_mem_info(const char *msg, const int device_id)
+{
+    try {
+        int device = device_id;
+        if(device == -1) {
+            device = getActiveDeviceId();
+        }
+
+        if(msg != NULL) ARG_ASSERT(0, strlen(msg) < 256); // 256 character limit on msg
+        ARG_ASSERT(1, device >= 0 && device < getDeviceCount());
+
+        printMemInfo(msg ? msg : "", device);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_device_gc()
 {
     try {
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 3f2441732d..3b1609b9d4 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -159,6 +159,11 @@ namespace af
         AF_THROW(af_free_host((void *)ptr));
     }
 
+    void printMemInfo(const char *msg, const int device_id)
+    {
+        AF_THROW(af_print_mem_info(msg, device_id));
+    }
+
     void deviceGC()
     {
         AF_THROW(af_device_gc());
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 1d5979ad13..f7e95569c9 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -121,6 +121,11 @@ af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
     return CALL(alloc_bytes, alloc_buffers, lock_bytes, lock_buffers);
 }
 
+af_err af_print_mem_info(const char *msg, const int device_id)
+{
+    return CALL(msg, device_id);
+}
+
 af_err af_device_gc()
 {
     return CALL_NO_PARAMS();
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 0e14450fad..8718c1e30e 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -14,6 +14,9 @@
 #include <dispatch.hpp>
 #include <cstdlib>
 #include <mutex>
+#include <iostream>
+#include <iomanip>
+#include <string>
 #include <platform.hpp>
 #include <queue.hpp>
 
@@ -103,6 +106,43 @@ void garbageCollect()
     }
 }
 
+void printMemInfo(const char *msg, const int device)
+{
+    std::cout << msg << std::endl;
+
+    static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
+    static const std::string line(head.size(), '-');
+    std::cout << line << std::endl << head << std::endl << line << std::endl;
+
+    for(mem_iter iter = memory_map.begin();
+        iter != memory_map.end(); ++iter) {
+
+        std::string status_af("Unknown");
+        std::string status_us("Unknown");
+
+        if(!(iter->second.is_free))    status_af = "Yes";
+        else                           status_af = " No";
+
+        if((iter->second.is_unlinked)) status_us = "Yes";
+        else                           status_us = " No";
+
+        std::string unit = "KB";
+        double size = (double)(iter->second.bytes) / 1024;
+        if(size >= 1024) {
+            size = size / 1024;
+            unit = "MB";
+        }
+
+        std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
+                  << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                  << " | " << std::setw(9) << status_af
+                  << " | " << std::setw(9) << status_us
+                  << " |"  << std::endl;
+    }
+
+    std::cout << line << std::endl;
+}
+
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 1fb8c64bbc..41a156f174 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -28,6 +28,8 @@ namespace cpu
     void garbageCollect();
     void pinnedGarbageCollect();
 
+    void printMemInfo(const char *msg, const int device);
+
     void setMemStepSize(size_t step_bytes);
     size_t getMemStepSize(void);
 }
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index e7ed8ac90e..50b13019c6 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -14,6 +14,9 @@
 #include <err_cuda.hpp>
 #include <util.hpp>
 #include <types.hpp>
+#include <iostream>
+#include <iomanip>
+#include <string>
 #include <map>
 #include <dispatch.hpp>
 #include <platform.hpp>
@@ -105,6 +108,10 @@ namespace cuda
     {
     }
 
+    void printMemInfo(const char *msg, const int device)
+    {
+        std::cout << "printMemInfo() disabled in AF_CUDA_MEM_DEBUG Mode" << std::endl;
+    }
 #else
 
     // Manager Class
@@ -190,6 +197,44 @@ namespace cuda
         }
     }
 
+    void printMemInfo(const char *msg, const int device)
+    {
+        std::cout << msg << std::endl;
+        std::cout << "Memory Map for Device: " << device << std::endl;
+
+        static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
+        static const std::string line(head.size(), '-');
+        std::cout << line << std::endl << head << std::endl << line << std::endl;
+
+        for(mem_iter iter = memory_maps[device].begin();
+            iter != memory_maps[device].end(); ++iter) {
+
+            std::string status_af("Unknown");
+            std::string status_us("Unknown");
+
+            if(!(iter->second.is_free))    status_af = "Yes";
+            else                           status_af = " No";
+
+            if((iter->second.is_unlinked)) status_us = "Yes";
+            else                           status_us = " No";
+
+            std::string unit = "KB";
+            double size = (double)(iter->second.bytes) / 1024;
+            if(size >= 1024) {
+                size = size / 1024;
+                unit = "MB";
+            }
+
+            std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
+                      << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                      << " | " << std::setw(9) << status_af
+                      << " | " << std::setw(9) << status_us
+                      << " |"  << std::endl;
+        }
+
+        std::cout << line << std::endl;
+    }
+
     template<typename T>
     T* memAlloc(const size_t &elements)
     {
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index a4450f3ccf..2d419f2a2c 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -28,6 +28,8 @@ namespace cuda
     void garbageCollect();
     void pinnedGarbageCollect();
 
+    void printMemInfo(const char *msg, const int device);
+
     void setMemStepSize(size_t step_bytes);
     size_t getMemStepSize(void);
 }
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 7475710176..2c9a613754 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -10,6 +10,9 @@
 #include <memory.hpp>
 #include <dispatch.hpp>
 #include <map>
+#include <iostream>
+#include <iomanip>
+#include <string>
 #include <types.hpp>
 
 namespace opencl
@@ -102,6 +105,44 @@ namespace opencl
         }
     }
 
+    void printMemInfo(const char *msg, const int device)
+    {
+        std::cout << msg << std::endl;
+        std::cout << "Memory Map for Device: " << device << std::endl;
+
+        static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
+        static const std::string line(head.size(), '-');
+        std::cout << line << std::endl << head << std::endl << line << std::endl;
+
+        for(mem_iter iter = memory_maps[device].begin();
+            iter != memory_maps[device].end(); ++iter) {
+
+            std::string status_af("Unknown");
+            std::string status_us("Unknown");
+
+            if(!(iter->second.is_free))    status_af = "Yes";
+            else                           status_af = " No";
+
+            if((iter->second.is_unlinked)) status_us = "Yes";
+            else                           status_us = " No";
+
+            std::string unit = "KB";
+            double size = (double)(iter->second.bytes) / 1024;
+            if(size >= 1024) {
+                size = size / 1024;
+                unit = "MB";
+            }
+
+            std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
+                      << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                      << " | " << std::setw(9) << status_af
+                      << " | " << std::setw(9) << status_us
+                      << " |"  << std::endl;
+        }
+
+        std::cout << line << std::endl;
+    }
+
     cl::Buffer *bufferAlloc(const size_t &bytes)
     {
         int n = getActiveDeviceId();
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 40e30ebce7..625bd10343 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -35,6 +35,8 @@ namespace opencl
     void garbageCollect();
     void pinnedGarbageCollect();
 
+    void printMemInfo(const char *msg, const int device);
+
     void setMemStepSize(size_t step_bytes);
     size_t getMemStepSize(void);
 }

From ed5556c8d8d480ef42190e439e45ee2fb0165751 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 11:59:10 -0500
Subject: [PATCH 0239/2677] Renamed is_free -> mngr_lock and is_unlinked ->
 user_lock in cpu memory mngr

---
 src/backend/cpu/memory.cpp | 46 +++++++++++++++++++-------------------
 1 file changed, 23 insertions(+), 23 deletions(-)

diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 8718c1e30e..046e897455 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -60,8 +60,8 @@ static void managerInit()
 
 typedef struct
 {
-    bool is_free;
-    bool is_unlinked;
+    bool mngr_lock; // True if locked by memory manager, false if free
+    bool user_lock; // True if locked by user, false if free
     size_t bytes;
 } mem_info;
 
@@ -85,9 +85,9 @@ void garbageCollect()
     for(mem_iter iter = memory_map.begin();
         iter != memory_map.end(); ++iter) {
 
-        if ((iter->second).is_free) {
+        if (!(iter->second).mngr_lock) {
 
-            if (!(iter->second).is_unlinked) {
+            if (!(iter->second).user_lock) {
                 freeWrapper(iter->first);
                 total_bytes -= iter->second.bytes;
             }
@@ -98,7 +98,7 @@ void garbageCollect()
     mem_iter memory_end  = memory_map.end();
 
     while(memory_curr != memory_end) {
-        if (memory_curr->second.is_free && !memory_curr->second.is_unlinked) {
+        if (!(memory_curr->second.mngr_lock) && !memory_curr->second.user_lock) {
             memory_map.erase(memory_curr++);
         } else {
             ++memory_curr;
@@ -117,14 +117,14 @@ void printMemInfo(const char *msg, const int device)
     for(mem_iter iter = memory_map.begin();
         iter != memory_map.end(); ++iter) {
 
-        std::string status_af("Unknown");
-        std::string status_us("Unknown");
+        std::string status_mngr("Unknown");
+        std::string status_user("Unknown");
 
-        if(!(iter->second.is_free))    status_af = "Yes";
-        else                           status_af = " No";
+        if(iter->second.mngr_lock)  status_mngr = "Yes";
+        else                        status_mngr = " No";
 
-        if((iter->second.is_unlinked)) status_us = "Yes";
-        else                           status_us = " No";
+        if(iter->second.user_lock)  status_user = "Yes";
+        else                        status_user = " No";
 
         std::string unit = "KB";
         double size = (double)(iter->second.bytes) / 1024;
@@ -135,8 +135,8 @@ void printMemInfo(const char *msg, const int device)
 
         std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
                   << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                  << " | " << std::setw(9) << status_af
-                  << " | " << std::setw(9) << status_us
+                  << " | " << std::setw(9) << status_mngr
+                  << " | " << std::setw(9) << status_user
                   << " |"  << std::endl;
     }
 
@@ -167,11 +167,11 @@ T* memAlloc(const size_t &elements)
 
             mem_info info = iter->second;
 
-            if ( info.is_free &&
-                !info.is_unlinked &&
+            if (!info.mngr_lock &&
+                !info.user_lock &&
                  info.bytes == alloc_bytes) {
 
-                iter->second.is_free = false;
+                iter->second.mngr_lock = true;
                 used_bytes += alloc_bytes;
                 used_buffers++;
                 return (T *)iter->first;
@@ -185,7 +185,7 @@ T* memAlloc(const size_t &elements)
             AF_ERROR("Can not allocate memory", AF_ERR_NO_MEM);
         }
 
-        mem_info info = {false, false, alloc_bytes};
+        mem_info info = {true, false, alloc_bytes};
         memory_map[ptr] = info;
 
         used_bytes += alloc_bytes;
@@ -204,10 +204,10 @@ void memFreeUnlinked(T *ptr, bool free_unlinked)
 
     if (iter != memory_map.end()) {
 
-        iter->second.is_free = true;
-        if ((iter->second).is_unlinked && !free_unlinked) return;
+        iter->second.mngr_lock = false;
+        if ((iter->second).user_lock && !free_unlinked) return;
 
-        iter->second.is_unlinked = false;
+        iter->second.user_lock = false;
         used_bytes -= iter->second.bytes;
         used_buffers--;
 
@@ -230,9 +230,9 @@ void memPop(const T *ptr)
     mem_iter iter = memory_map.find((void *)ptr);
 
     if (iter != memory_map.end()) {
-        iter->second.is_unlinked = true;
+        iter->second.user_lock = true;
     } else {
-        mem_info info = { false,
+        mem_info info = { true,
                           true,
                           100 }; //This number is not relevant
 
@@ -246,7 +246,7 @@ void memPush(const T *ptr)
     std::lock_guard<std::mutex> lock(memory_map_mutex);
     mem_iter iter = memory_map.find((void *)ptr);
     if (iter != memory_map.end()) {
-        iter->second.is_unlinked = false;
+        iter->second.user_lock = false;
     }
 }
 

From aa25b17796b046b3c85e45e367fc57d1ddad25b9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 12:39:17 -0500
Subject: [PATCH 0240/2677] Renamed is_free -> mngr_lock and is_unlinked ->
 user_lock in cuda memory mngr

---
 src/backend/cuda/memory.cpp | 60 ++++++++++++++++++-------------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 50b13019c6..4937ddd196 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -156,8 +156,8 @@ namespace cuda
 
     typedef struct
     {
-        bool is_free;
-        bool is_unlinked;
+        bool mngr_lock;
+        bool user_lock;
         size_t bytes;
     } mem_info;
 
@@ -176,9 +176,9 @@ namespace cuda
         for(mem_iter iter = memory_maps[n].begin();
             iter != memory_maps[n].end(); ++iter) {
 
-            if ((iter->second).is_free) {
+            if (!(iter->second.mngr_lock)) {
 
-                if (!(iter->second).is_unlinked) {
+                if (!(iter->second.user_lock)) {
                     cudaFreeWrapper(iter->first);
                     total_bytes[n] -= iter->second.bytes;
                 }
@@ -189,7 +189,7 @@ namespace cuda
         mem_iter memory_end  = memory_maps[n].end();
 
         while(memory_curr != memory_end) {
-            if (memory_curr->second.is_free  && !memory_curr->second.is_unlinked) {
+            if (!(memory_curr->second.mngr_lock) && !(memory_curr->second.user_lock)) {
                 memory_maps[n].erase(memory_curr++);
             } else {
                 ++memory_curr;
@@ -209,14 +209,14 @@ namespace cuda
         for(mem_iter iter = memory_maps[device].begin();
             iter != memory_maps[device].end(); ++iter) {
 
-            std::string status_af("Unknown");
-            std::string status_us("Unknown");
+            std::string status_mngr("Unknown");
+            std::string status_user("Unknown");
 
-            if(!(iter->second.is_free))    status_af = "Yes";
-            else                           status_af = " No";
+            if(iter->second.mngr_lock)  status_mngr = "Yes";
+            else                        status_mngr = " No";
 
-            if((iter->second.is_unlinked)) status_us = "Yes";
-            else                           status_us = " No";
+            if(iter->second.user_lock)  status_user = "Yes";
+            else                        status_user = " No";
 
             std::string unit = "KB";
             double size = (double)(iter->second.bytes) / 1024;
@@ -227,8 +227,8 @@ namespace cuda
 
             std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
                       << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                      << " | " << std::setw(9) << status_af
-                      << " | " << std::setw(9) << status_us
+                      << " | " << std::setw(9) << status_mngr
+                      << " | " << std::setw(9) << status_user
                       << " |"  << std::endl;
         }
 
@@ -256,11 +256,11 @@ namespace cuda
 
                 mem_info info = iter->second;
 
-                if (  info.is_free &&
-                     !info.is_unlinked &&
-                      info.bytes == alloc_bytes) {
+                if (!info.mngr_lock &&
+                    !info.user_lock &&
+                     info.bytes == alloc_bytes) {
 
-                    iter->second.is_free = false;
+                    iter->second.mngr_lock = true;
                     used_bytes[n] += alloc_bytes;
                     used_buffers[n]++;
                     return (T *)iter->first;
@@ -273,7 +273,7 @@ namespace cuda
                 CUDA_CHECK(cudaMalloc((void **)(&ptr), alloc_bytes));
             }
 
-            mem_info info = {false, false, alloc_bytes};
+            mem_info info = {true, false, alloc_bytes};
             memory_maps[n][ptr] = info;
             used_bytes[n] += alloc_bytes;
             used_buffers[n]++;
@@ -290,10 +290,10 @@ namespace cuda
 
         if (iter != memory_maps[n].end()) {
 
-            iter->second.is_free = true;
-            if ((iter->second).is_unlinked && !free_unlinked) return;
+            iter->second.mngr_lock = false;
+            if ((iter->second.user_lock) && !free_unlinked) return;
 
-            iter->second.is_unlinked = false;
+            iter->second.user_lock = false;
 
             used_bytes[n] -= iter->second.bytes;
             used_buffers[n]--;
@@ -316,10 +316,10 @@ namespace cuda
         mem_iter iter = memory_maps[n].find((void *)ptr);
 
         if (iter != memory_maps[n].end()) {
-            iter->second.is_unlinked = true;
+            iter->second.user_lock = true;
         } else {
 
-            mem_info info = { false,
+            mem_info info = { true,
                               true,
                               100 }; //This number is not relevant
 
@@ -333,7 +333,7 @@ namespace cuda
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find((void *)ptr);
         if (iter != memory_maps[n].end()) {
-            iter->second.is_unlinked = false;
+            iter->second.user_lock = false;
         }
     }
 
@@ -354,7 +354,7 @@ namespace cuda
     void pinnedGarbageCollect()
     {
         for(mem_iter iter = pinned_maps.begin(); iter != pinned_maps.end(); ++iter) {
-            if ((iter->second).is_free) {
+            if (!(iter->second.mngr_lock)) {
                 pinnedFreeWrapper(iter->first);
             }
         }
@@ -363,7 +363,7 @@ namespace cuda
         mem_iter memory_end  = pinned_maps.end();
 
         while(memory_curr != memory_end) {
-            if (memory_curr->second.is_free) {
+            if (!(memory_curr->second.mngr_lock)) {
                 pinned_maps.erase(memory_curr++);
             } else {
                 ++memory_curr;
@@ -392,8 +392,8 @@ namespace cuda
                 iter != pinned_maps.end(); ++iter) {
 
                 mem_info info = iter->second;
-                if (info.is_free && info.bytes == alloc_bytes) {
-                    iter->second.is_free = false;
+                if (!info.mngr_lock && info.bytes == alloc_bytes) {
+                    iter->second.mngr_lock = true;
                     pinned_used_bytes += alloc_bytes;
                     return (T *)iter->first;
                 }
@@ -405,7 +405,7 @@ namespace cuda
                 CUDA_CHECK(cudaMallocHost((void **)(&ptr), alloc_bytes));
             }
 
-            mem_info info = {false, false, alloc_bytes};
+            mem_info info = {true, false, alloc_bytes};
             pinned_maps[ptr] = info;
             pinned_used_bytes += alloc_bytes;
         }
@@ -418,7 +418,7 @@ namespace cuda
         mem_iter iter = pinned_maps.find((void *)ptr);
 
         if (iter != pinned_maps.end()) {
-            iter->second.is_free = true;
+            iter->second.mngr_lock = false;
             pinned_used_bytes -= iter->second.bytes;
         } else {
             pinnedFreeWrapper(ptr); // Free it because we are not sure what the size is

From cef8559e520276e08b38b38861a697fbcfde2a37 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 12:40:21 -0500
Subject: [PATCH 0241/2677] Renamed is_free -> mngr_lock and is_unlinked ->
 user_lock in opencl memory mngr

---
 src/backend/opencl/memory.cpp | 60 +++++++++++++++++------------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 2c9a613754..1ba4ce1cbb 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -60,8 +60,8 @@ namespace opencl
 
     typedef struct
     {
-        bool is_free;
-        bool is_unlinked;
+        bool mngr_lock;
+        bool user_lock;
         size_t bytes;
     } mem_info;
 
@@ -84,9 +84,9 @@ namespace opencl
         for(mem_iter iter = memory_maps[n].begin();
             iter != memory_maps[n].end(); ++iter) {
 
-            if ((iter->second).is_free) {
+            if (!(iter->second).mngr_lock) {
 
-                if (!(iter->second).is_unlinked) {
+                if (!(iter->second).user_lock) {
                     destroy(iter->first);
                     total_bytes[n] -= iter->second.bytes;
                 }
@@ -97,7 +97,7 @@ namespace opencl
         mem_iter memory_end  = memory_maps[n].end();
 
         while(memory_curr != memory_end) {
-            if (memory_curr->second.is_free  && !memory_curr->second.is_unlinked) {
+            if (!memory_curr->second.mngr_lock && !memory_curr->second.user_lock) {
                 memory_curr = memory_maps[n].erase(memory_curr);
             } else {
                 ++memory_curr;
@@ -117,14 +117,14 @@ namespace opencl
         for(mem_iter iter = memory_maps[device].begin();
             iter != memory_maps[device].end(); ++iter) {
 
-            std::string status_af("Unknown");
-            std::string status_us("Unknown");
+            std::string status_mngr("Unknown");
+            std::string status_user("Unknown");
 
-            if(!(iter->second.is_free))    status_af = "Yes";
-            else                           status_af = " No";
+            if(iter->second.mngr_lock)  status_mngr = "Yes";
+            else                        status_mngr = " No";
 
-            if((iter->second.is_unlinked)) status_us = "Yes";
-            else                           status_us = " No";
+            if(iter->second.user_lock)  status_user = "Yes";
+            else                        status_user = " No";
 
             std::string unit = "KB";
             double size = (double)(iter->second.bytes) / 1024;
@@ -135,8 +135,8 @@ namespace opencl
 
             std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
                       << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                      << " | " << std::setw(9) << status_af
-                      << " | " << std::setw(9) << status_us
+                      << " | " << std::setw(9) << status_mngr
+                      << " | " << std::setw(9) << status_user
                       << " |"  << std::endl;
         }
 
@@ -162,11 +162,11 @@ namespace opencl
 
                 mem_info info = iter->second;
 
-                if ( info.is_free &&
-                    !info.is_unlinked &&
+                if (!info.mngr_lock &&
+                    !info.user_lock &&
                      info.bytes == alloc_bytes) {
 
-                    iter->second.is_free = false;
+                    iter->second.mngr_lock = true;
                     used_bytes[n] += alloc_bytes;
                     used_buffers[n]++;
                     return iter->first;
@@ -180,7 +180,7 @@ namespace opencl
                 ptr = new cl::Buffer(getContext(), CL_MEM_READ_WRITE, alloc_bytes);
             }
 
-            mem_info info = {false, false, alloc_bytes};
+            mem_info info = {true, false, alloc_bytes};
             memory_maps[n][ptr] = info;
             used_bytes[n] += alloc_bytes;
             used_buffers[n]++;
@@ -201,10 +201,10 @@ namespace opencl
 
         if (iter != memory_maps[n].end()) {
 
-            iter->second.is_free = true;
-            if ((iter->second).is_unlinked && !free_unlinked) return;
+            iter->second.mngr_lock = false;
+            if ((iter->second).user_lock && !free_unlinked) return;
 
-            iter->second.is_unlinked = false;
+            iter->second.user_lock = false;
 
             used_bytes[n] -= iter->second.bytes;
             used_buffers[n]--;
@@ -219,11 +219,11 @@ namespace opencl
         mem_iter iter = memory_maps[n].find(ptr);
 
         if (iter != memory_maps[n].end()) {
-            iter->second.is_unlinked = true;
+            iter->second.user_lock = true;
         } else {
 
-            mem_info info = { false,
-                              false,
+            mem_info info = { true,
+                              true,
                               100 }; //This number is not relevant
 
             memory_maps[n][ptr] = info;
@@ -236,7 +236,7 @@ namespace opencl
         mem_iter iter = memory_maps[n].find(ptr);
 
         if (iter != memory_maps[n].end()) {
-            iter->second.is_unlinked = false;
+            iter->second.user_lock = false;
         }
     }
 
@@ -302,7 +302,7 @@ namespace opencl
     {
         int n = getActiveDeviceId();
         for(auto &iter : pinned_maps[n]) {
-            if ((iter.second).info.is_free) {
+            if (!(iter.second).info.mngr_lock) {
                 pinnedDestroy(iter.second.buf, iter.first);
             }
         }
@@ -311,7 +311,7 @@ namespace opencl
         pinned_iter memory_end  = pinned_maps[n].end();
 
         while(memory_curr != memory_end) {
-            if (memory_curr->second.info.is_free) {
+            if (!memory_curr->second.info.mngr_lock) {
                 memory_curr = pinned_maps[n].erase(memory_curr);
             } else {
                 ++memory_curr;
@@ -341,8 +341,8 @@ namespace opencl
                 iter != pinned_maps[n].end(); ++iter) {
 
                 mem_info info = iter->second.info;
-                if (info.is_free && info.bytes == alloc_bytes) {
-                    iter->second.info.is_free = false;
+                if (!info.mngr_lock && info.bytes == alloc_bytes) {
+                    iter->second.info.mngr_lock = true;
                     pinned_used_bytes += alloc_bytes;
                     return iter->first;
                 }
@@ -360,7 +360,7 @@ namespace opencl
                 ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ|CL_MAP_WRITE,
                                                   0, alloc_bytes);
             }
-            mem_info info = {false, false, alloc_bytes};
+            mem_info info = {true, false, alloc_bytes};
             pinned_info pt = {buf, info};
             pinned_maps[n][ptr] = pt;
             pinned_used_bytes += alloc_bytes;
@@ -374,7 +374,7 @@ namespace opencl
         pinned_iter iter = pinned_maps[n].find(ptr);
 
         if (iter != pinned_maps[n].end()) {
-            iter->second.info.is_free = true;
+            iter->second.info.mngr_lock = false;
             pinned_used_bytes -= iter->second.info.bytes;
         } else {
             pinnedDestroy(iter->second.buf, ptr); // Free it because we are not sure what the size is

From dbe861ebea0ad871349d5659e8bbc890efacd151 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 12:45:54 -0500
Subject: [PATCH 0242/2677] Reverse conditions for freeing in memory managers

---
 src/backend/cpu/memory.cpp    |  6 +++---
 src/backend/cuda/memory.cpp   | 12 ++++++------
 src/backend/opencl/memory.cpp | 12 ++++++------
 3 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 046e897455..e2204eccd5 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -98,10 +98,10 @@ void garbageCollect()
     mem_iter memory_end  = memory_map.end();
 
     while(memory_curr != memory_end) {
-        if (!(memory_curr->second.mngr_lock) && !memory_curr->second.user_lock) {
-            memory_map.erase(memory_curr++);
-        } else {
+        if (memory_curr->second.mngr_lock || memory_curr->second.user_lock) {
             ++memory_curr;
+        } else {
+            memory_map.erase(memory_curr++);
         }
     }
 }
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 4937ddd196..52609506c0 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -189,10 +189,10 @@ namespace cuda
         mem_iter memory_end  = memory_maps[n].end();
 
         while(memory_curr != memory_end) {
-            if (!(memory_curr->second.mngr_lock) && !(memory_curr->second.user_lock)) {
-                memory_maps[n].erase(memory_curr++);
-            } else {
+            if (memory_curr->second.mngr_lock || memory_curr->second.user_lock) {
                 ++memory_curr;
+            } else {
+                memory_maps[n].erase(memory_curr++);
             }
         }
     }
@@ -363,10 +363,10 @@ namespace cuda
         mem_iter memory_end  = pinned_maps.end();
 
         while(memory_curr != memory_end) {
-            if (!(memory_curr->second.mngr_lock)) {
-                pinned_maps.erase(memory_curr++);
-            } else {
+            if (memory_curr->second.mngr_lock) {
                 ++memory_curr;
+            } else {
+                pinned_maps.erase(memory_curr++);
             }
         }
     }
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 1ba4ce1cbb..c37ae2a4e1 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -97,10 +97,10 @@ namespace opencl
         mem_iter memory_end  = memory_maps[n].end();
 
         while(memory_curr != memory_end) {
-            if (!memory_curr->second.mngr_lock && !memory_curr->second.user_lock) {
-                memory_curr = memory_maps[n].erase(memory_curr);
-            } else {
+            if (memory_curr->second.mngr_lock || memory_curr->second.user_lock) {
                 ++memory_curr;
+            } else {
+                memory_maps[n].erase(memory_curr++);
             }
         }
     }
@@ -311,10 +311,10 @@ namespace opencl
         pinned_iter memory_end  = pinned_maps[n].end();
 
         while(memory_curr != memory_end) {
-            if (!memory_curr->second.info.mngr_lock) {
-                memory_curr = pinned_maps[n].erase(memory_curr);
-            } else {
+            if (memory_curr->second.info.mngr_lock) {
                 ++memory_curr;
+            } else {
+                memory_curr = pinned_maps[n].erase(memory_curr);
             }
         }
 

From 33fbf33e4383b610f01efe0294a246574f260b56 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 16:01:26 -0500
Subject: [PATCH 0243/2677] Renamed internal memFree functions

* FreeUnlinked -> FreeLocked
---
 src/api/c/device.cpp          |  2 +-
 src/backend/cpu/memory.cpp    |  8 ++++----
 src/backend/cpu/memory.hpp    |  7 ++++++-
 src/backend/cuda/memory.cpp   | 10 +++++-----
 src/backend/cuda/memory.hpp   |  6 +++++-
 src/backend/opencl/memory.cpp | 14 +++++++-------
 src/backend/opencl/memory.hpp | 12 ++++++++++--
 7 files changed, 38 insertions(+), 21 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 007e0ab7f2..8f332994e7 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -310,7 +310,7 @@ af_err af_alloc_pinned(void **ptr, const dim_t bytes)
 af_err af_free_device(void *ptr)
 {
     try {
-        memFreeUnlinked<char>((char *)ptr, true);
+        memFreeLocked<char>((char *)ptr, true);
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index e2204eccd5..625f9b2416 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -196,7 +196,7 @@ T* memAlloc(const size_t &elements)
 }
 
 template<typename T>
-void memFreeUnlinked(T *ptr, bool free_unlinked)
+void memFreeLocked(T *ptr, bool freeLocked)
 {
     std::lock_guard<std::mutex> lock(memory_map_mutex);
 
@@ -205,7 +205,7 @@ void memFreeUnlinked(T *ptr, bool free_unlinked)
     if (iter != memory_map.end()) {
 
         iter->second.mngr_lock = false;
-        if ((iter->second).user_lock && !free_unlinked) return;
+        if ((iter->second).user_lock && !freeLocked) return;
 
         iter->second.user_lock = false;
         used_bytes -= iter->second.bytes;
@@ -219,7 +219,7 @@ void memFreeUnlinked(T *ptr, bool free_unlinked)
 template<typename T>
 void memFree(T *ptr)
 {
-    memFreeUnlinked(ptr, false);
+    memFreeLocked(ptr, false);
 }
 
 template<typename T>
@@ -276,7 +276,7 @@ void pinnedFree(T* ptr)
 #define INSTANTIATE(T)                                          \
     template T* memAlloc(const size_t &elements);               \
     template void memFree(T* ptr);                              \
-    template void memFreeUnlinked(T* ptr, bool free_unlinked);  \
+    template void memFreeLocked(T* ptr, bool freeLocked);       \
     template void memPop(const T* ptr);                         \
     template void memPush(const T* ptr);                        \
     template T* pinnedAlloc(const size_t &elements);            \
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 41a156f174..19846c46bf 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -12,8 +12,13 @@
 namespace cpu
 {
     template<typename T> T* memAlloc(const size_t &elements);
+
+    // Need these as 2 separate function and not a default argument
+    // This is because it is used as the deleter in shared pointer
+    // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeUnlinked(T* ptr, bool free_unlinked);
+    template<typename T> void memFreeLocked(T* ptr, bool freeLocked);
+
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 52609506c0..8152c8a25d 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -68,7 +68,7 @@ namespace cuda
     }
 
     template<typename T>
-    void memFreeUnlinked(T *ptr, bool free_unlinked)
+    void memFreeLocked(T *ptr, bool freeLocked)
     {
         cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
     }
@@ -283,7 +283,7 @@ namespace cuda
     }
 
     template<typename T>
-    void memFreeUnlinked(T *ptr, bool free_unlinked)
+    void memFreeLocked(T *ptr, bool freeLocked)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find((void *)ptr);
@@ -291,7 +291,7 @@ namespace cuda
         if (iter != memory_maps[n].end()) {
 
             iter->second.mngr_lock = false;
-            if ((iter->second.user_lock) && !free_unlinked) return;
+            if ((iter->second.user_lock) && !freeLocked) return;
 
             iter->second.user_lock = false;
 
@@ -306,7 +306,7 @@ namespace cuda
     template<typename T>
     void memFree(T *ptr)
     {
-        memFreeUnlinked(ptr, false);
+        memFreeLocked(ptr, false);
     }
 
     template<typename T>
@@ -430,7 +430,7 @@ namespace cuda
 #define INSTANTIATE(T)                                          \
     template T* memAlloc(const size_t &elements);               \
     template void memFree(T* ptr);                              \
-    template void memFreeUnlinked(T* ptr, bool free_unlinked);  \
+    template void memFreeLocked(T* ptr, bool freeLocked);       \
     template void memPop(const T* ptr);                         \
     template void memPush(const T* ptr);                        \
     template T* pinnedAlloc(const size_t &elements);            \
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 2d419f2a2c..5644a52371 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -12,8 +12,12 @@
 namespace cuda
 {
     template<typename T> T* memAlloc(const size_t &elements);
+
+    // Need these as 2 separate function and not a default argument
+    // This is because it is used as the deleter in shared pointer
+    // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeUnlinked(T* ptr, bool free_unlinked);
+    template<typename T> void memFreeLocked(T* ptr, bool freeLocked);
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index c37ae2a4e1..141610d71f 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -191,10 +191,10 @@ namespace opencl
 
     void bufferFree(cl::Buffer *ptr)
     {
-        bufferFreeUnlinked(ptr, false);
+        bufferFreeLocked(ptr, false);
     }
 
-    void bufferFreeUnlinked(cl::Buffer *ptr, bool free_unlinked)
+    void bufferFreeLocked(cl::Buffer *ptr, bool freeLocked)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find(ptr);
@@ -202,7 +202,7 @@ namespace opencl
         if (iter != memory_maps[n].end()) {
 
             iter->second.mngr_lock = false;
-            if ((iter->second).user_lock && !free_unlinked) return;
+            if ((iter->second).user_lock && !freeLocked) return;
 
             iter->second.user_lock = false;
 
@@ -260,13 +260,13 @@ namespace opencl
     template<typename T>
     void memFree(T *ptr)
     {
-        return bufferFreeUnlinked((cl::Buffer *)ptr, false);
+        return bufferFreeLocked((cl::Buffer *)ptr, false);
     }
 
     template<typename T>
-    void memFreeUnlinked(T *ptr, bool free_unlinked)
+    void memFreeLocked(T *ptr, bool freeLocked)
     {
-        return bufferFreeUnlinked((cl::Buffer *)ptr, free_unlinked);
+        return bufferFreeLocked((cl::Buffer *)ptr, freeLocked);
     }
 
     template<typename T>
@@ -398,7 +398,7 @@ namespace opencl
 #define INSTANTIATE(T)                                          \
     template T* memAlloc(const size_t &elements);               \
     template void memFree(T* ptr);                              \
-    template void memFreeUnlinked(T* ptr, bool free_unlinked);  \
+    template void memFreeLocked(T* ptr, bool freeLocked);       \
     template void memPop(const T* ptr);                         \
     template void memPush(const T* ptr);                        \
     template T* pinnedAlloc(const size_t &elements);            \
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 625bd10343..96292cdfac 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -15,12 +15,20 @@ namespace opencl
 {
 
     cl::Buffer *bufferAlloc(const size_t &bytes);
+
+    // Need these as 2 separate function and not a default argument
+    // This is because it is used as the deleter in shared pointer
+    // which cannot support default arguments
     void bufferFree(cl::Buffer *buf);
-    void bufferFreeUnlinked(cl::Buffer *buf, bool free_unlinked);
+    void bufferFreeLocked(cl::Buffer *buf, bool freeLocked);
 
     template<typename T> T *memAlloc(const size_t &elements);
+
+    // Need these as 2 separate function and not a default argument
+    // This is because it is used as the deleter in shared pointer
+    // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeUnlinked(T* ptr, bool free_unlinked);
+    template<typename T> void memFreeLocked(T* ptr, bool freeLocked);
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 

From 8cb21a432c4957af96125fe844f48fe84dc5f345 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 21:24:54 -0500
Subject: [PATCH 0244/2677] Fix AF_DISABLE_GRAPHICS condition (Fixes e19a6be)

---
 src/api/c/graphics_common.cpp   | 2 +-
 src/backend/opencl/platform.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 291bf84275..dc5a46b5e1 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -147,7 +147,7 @@ fg::Window* ForgeManager::getMainWindow(const bool dontCreate)
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
-    if(!noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
+    if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         if (flag && !dontCreate) {
             wnd = new fg::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
             CheckGL("End ForgeManager::getMainWindow");
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 8d77e24cbd..0cd46d25f6 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -174,7 +174,7 @@ DeviceManager::DeviceManager()
 #if defined(WITH_GRAPHICS)
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
-    if(!noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
+    if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         try {
             int devCount = mDevices.size();
             fg::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();

From c2d7e42cc21cba574b3afa65b6ffc9c3e048c342 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Jan 2016 19:19:44 -0500
Subject: [PATCH 0245/2677] Fix clang warnings (std::abs, pragma ignores)

Fix clang warnings for abs in tests

Fix clang warnings for abs in examples

Fix orb maybe-initialized pragma for clang and msvc

Ignore unused function warning in opencl math and ireduce

Ignore missing braces warning from clang in opencl magma_helper
---
 examples/graphics/fractal.cpp                 |  3 +-
 .../adaptive_thresholding.cpp                 |  1 +
 .../image_processing/brain_segmentation.cpp   | 10 +++--
 examples/image_processing/filters.cpp         |  2 +-
 src/api/c/assign.cpp                          |  2 +-
 src/api/c/index.cpp                           |  2 +-
 src/backend/opencl/kernel/ireduce.hpp         | 14 +++++++
 src/backend/opencl/kernel/orb.hpp             | 37 +++++++++++++++++--
 src/backend/opencl/magma/magma_helper.cpp     | 15 ++++++++
 src/backend/opencl/math.hpp                   | 15 ++++++++
 test/approx1.cpp                              |  1 +
 test/approx2.cpp                              |  1 +
 test/bilateral.cpp                            |  1 +
 test/binary.cpp                               |  1 +
 test/cholesky_dense.cpp                       |  1 +
 test/convolve.cpp                             |  1 +
 test/diagonal.cpp                             |  1 +
 test/dot.cpp                                  |  1 +
 test/fast.cpp                                 |  1 +
 test/fft.cpp                                  |  1 +
 test/fft_real.cpp                             |  1 +
 test/fftconvolve.cpp                          |  1 +
 test/getting_started.cpp                      |  1 +
 test/gloh_nonfree.cpp                         |  1 +
 test/harris.cpp                               |  1 +
 test/histogram.cpp                            |  1 +
 test/homography.cpp                           |  1 +
 test/inverse_dense.cpp                        |  1 +
 test/lu_dense.cpp                             |  1 +
 test/math.cpp                                 |  1 +
 test/meanshift.cpp                            |  1 +
 test/medfilt.cpp                              |  1 +
 test/morph.cpp                                |  1 +
 test/orb.cpp                                  |  1 +
 test/qr_dense.cpp                             |  1 +
 test/rank_dense.cpp                           |  1 +
 test/resize.cpp                               |  1 +
 test/rotate.cpp                               |  1 +
 test/rotate_linear.cpp                        |  1 +
 test/sift_nonfree.cpp                         |  1 +
 test/solve_dense.cpp                          |  1 +
 test/susan.cpp                                |  1 +
 test/svd_dense.cpp                            |  1 +
 test/transform.cpp                            |  1 +
 test/translate.cpp                            |  1 +
 test/transpose.cpp                            |  1 +
 test/triangle.cpp                             |  1 +
 test/wrap.cpp                                 |  1 +
 48 files changed, 128 insertions(+), 11 deletions(-)

diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index 9ac5a86ea9..9781b61c90 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -10,13 +10,14 @@
 #include <stdio.h>
 #include <iostream>
 #include <arrayfire.h>
-#include <math.h>
+#include <cmath>
 #include <cstdlib>
 
 #define WIDTH 400 // Width of image
 #define HEIGHT 400 // Width of image
 
 using namespace af;
+using std::abs;
 
 array complex_grid(int width, int height, float zoom, float center[2])
 {
diff --git a/examples/image_processing/adaptive_thresholding.cpp b/examples/image_processing/adaptive_thresholding.cpp
index 5ce34e76be..1004285148 100644
--- a/examples/image_processing/adaptive_thresholding.cpp
+++ b/examples/image_processing/adaptive_thresholding.cpp
@@ -13,6 +13,7 @@
 #include <arrayfire.h>
 
 using namespace af;
+using std::abs;
 
 typedef enum {
     MEAN = 0,
diff --git a/examples/image_processing/brain_segmentation.cpp b/examples/image_processing/brain_segmentation.cpp
index 7349bf258b..253d37e5f1 100644
--- a/examples/image_processing/brain_segmentation.cpp
+++ b/examples/image_processing/brain_segmentation.cpp
@@ -23,10 +23,12 @@ const float h_sy_kernel[] = { -1, 0, 1,
     -2, 0, 2,
     -1, 0, 1
 };
-const float h_lp_kernel[] = { -0.5f, -1.0f, -0.5f,
-    -1.0f,  6.0f, -1.0f,
-    -0.5f, -1.0f, -0.5f
-};
+
+// Unused
+//const float h_lp_kernel[] = { -0.5f, -1.0f, -0.5f,
+//    -1.0f,  6.0f, -1.0f,
+//    -0.5f, -1.0f, -0.5f
+//};
 
 array edges_slice(array x)
 {
diff --git a/examples/image_processing/filters.cpp b/examples/image_processing/filters.cpp
index 8b75acf063..ae1d7c155c 100644
--- a/examples/image_processing/filters.cpp
+++ b/examples/image_processing/filters.cpp
@@ -151,7 +151,7 @@ array medianfilter(const array &in, int window_width, int window_height)
     return ret_val;
 }
 
-array gaussianblur(const array &in, int window_width, int window_height, int sigma)
+array gaussianblur(const array &in, int window_width, int window_height, double sigma)
 {
     array g = gaussianKernel(window_width, window_height, sigma, sigma);
     return convolve(in, g);
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index b8fcb12234..50224d32a6 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -125,7 +125,7 @@ af_err af_assign_seq(af_array *out,
 
         ArrayInfo lInfo = getInfo(lhs);
 
-        if (ndims == 1 && ndims != (dim_t)lInfo.ndims()) {
+        if (ndims == 1 && ndims != lInfo.ndims()) {
             af_array tmp_in, tmp_out;
             AF_CHECK(af_flat(&tmp_in, lhs));
             AF_CHECK(af_assign_seq(&tmp_out, tmp_in, ndims, index, rhs));
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index b6eb8ab4cd..2f5b06aa07 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -42,7 +42,7 @@ af_err af_index(af_array *result, const af_array in, const unsigned ndims, const
     try {
 
         ArrayInfo iInfo = getInfo(in);
-        if (ndims == 1 && ndims != (dim_t)iInfo.ndims()) {
+        if (ndims == 1 && ndims != iInfo.ndims()) {
             af_array tmp_in;
             AF_CHECK(af_flat(&tmp_in, in));
             AF_CHECK(af_index(result, tmp_in, ndims, index));
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 0adc0c8e47..17fc460970 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -281,6 +281,14 @@ namespace kernel
         }
     }
 
+#if defined(__GNUC__) || defined(__GNUG__)
+    /* GCC/G++, Clang/LLVM, Intel ICC */
+    #pragma GCC diagnostic push
+    #pragma GCC diagnostic ignored "-Wunused-function"
+#else
+    /* Other */
+#endif
+
     template<typename T> double cabs(const T in) { return (double)in; }
     static double cabs(const cfloat in) { return (double)abs(in); }
     static double cabs(const cdouble in) { return (double)abs(in); }
@@ -327,6 +335,12 @@ namespace kernel
         }
     };
 
+#if defined(__GNUC__) || defined(__GNUG__)
+    /* GCC/G++, Clang/LLVM, Intel ICC */
+    #pragma GCC diagnostic pop
+#else
+    /* Other */
+#endif
 
     template<typename T, af_op_t op>
     T ireduce_all(uint *loc, Param in)
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 871370d63b..69c1176210 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -29,8 +29,24 @@ using cl::LocalSpaceArg;
 using cl::NDRange;
 using std::vector;
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#if defined(__clang__)
+    /* Clang/LLVM */
+    #pragma clang diagnostic push
+    #pragma clang diagnostic ignored "-Wsometimes-uninitialized"
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+    /* Intel ICC/ICPC */
+    // Fix the warning code here, if any
+#elif defined(__GNUC__) || defined(__GNUG__)
+    /* GNU GCC/G++ */
+    #pragma GCC diagnostic push
+    #pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#elif defined(_MSC_VER)
+    /* Microsoft Visual Studio */
+    #pragma warning( push )
+    #pragma warning( disable : 4700 )
+#else
+    /* Other */
+#endif
 
 namespace opencl
 {
@@ -505,4 +521,19 @@ void orb(unsigned* out_feat,
 } //namespace kernel
 
 } //namespace opencl
-#pragma GCC diagnostic pop
+
+#if defined(__clang__)
+    /* Clang/LLVM */
+    #pragma clang diagnostic pop
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+    /* Intel ICC/ICPC */
+    // Fix the warning code here, if any
+#elif defined(__GNUC__) || defined(__GNUG__)
+    /* GNU GCC/G++ */
+    #pragma GCC diagnostic pop
+#elif defined(_MSC_VER)
+    /* Microsoft Visual Studio */
+    #pragma warning( pop )
+#else
+    /* Other */
+#endif
diff --git a/src/backend/opencl/magma/magma_helper.cpp b/src/backend/opencl/magma/magma_helper.cpp
index 584a412191..481f08c346 100644
--- a/src/backend/opencl/magma/magma_helper.cpp
+++ b/src/backend/opencl/magma/magma_helper.cpp
@@ -159,6 +159,14 @@ magma_int_t magma_get_geqrf_nb<magmaDoubleComplex>( magma_int_t m )
     else                return 128;
 }
 
+#if defined(__GNUC__) || defined(__GNUG__)
+    /* GCC/G++, Clang/LLVM, Intel ICC */
+    #pragma GCC diagnostic push
+    #pragma GCC diagnostic ignored "-Wmissing-braces"
+#else
+    /* Other */
+#endif
+
 template<typename T> T magma_make(double r, double i) { return (T) r; }
 template float magma_make<float>(double r, double i);
 template double magma_make<double>(double r, double i);
@@ -172,3 +180,10 @@ template<> magmaDoubleComplex magma_make<magmaDoubleComplex>(double r, double i)
     magmaDoubleComplex tmp = {r, i};
     return tmp;
 }
+
+#if defined(__GNUC__) || defined(__GNUG__)
+    /* GCC/G++, Clang/LLVM, Intel ICC */
+    #pragma GCC diagnostic pop
+#else
+    /* Other */
+#endif
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 9292d398a0..f090062b03 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -17,6 +17,14 @@
 #include "backend.hpp"
 #include "types.hpp"
 
+#if defined(__GNUC__) || defined(__GNUG__)
+    /* GCC/G++, Clang/LLVM, Intel ICC */
+    #pragma GCC diagnostic push
+    #pragma GCC diagnostic ignored "-Wunused-function"
+#else
+    /* Other */
+#endif
+
 namespace opencl
 {
 
@@ -123,3 +131,10 @@ namespace opencl
     cfloat operator *(cfloat a, cfloat b);
     cdouble operator *(cdouble a, cdouble b);
 }
+
+#if defined(__GNUC__) || defined(__GNUG__)
+    /* GCC/G++, Clang/LLVM, Intel ICC */
+    #pragma GCC diagnostic pop
+#else
+    /* Other */
+#endif
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 7a6b66fce8..e7ea94e51e 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -23,6 +23,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/approx2.cpp b/test/approx2.cpp
index f1a1accc51..75a650631b 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index f0825e4893..cde330dca4 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -18,6 +18,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 template<typename T, bool isColor>
diff --git a/test/binary.cpp b/test/binary.cpp
index 477748792f..91ebcbc8b2 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -14,6 +14,7 @@
 #include <testHelpers.hpp>
 
 using namespace std;
+using std::abs;
 using namespace af;
 
 const int num = 10000;
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index 70548d898c..7fd238d215 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/convolve.cpp b/test/convolve.cpp
index f3ff9fd6ef..fff5ebffea 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -17,6 +17,7 @@
 
 using std::vector;
 using std::string;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index c88f0fbeb1..c4becab2dc 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -14,6 +14,7 @@
 
 using namespace af;
 using std::vector;
+using std::abs;
 
 template<typename T>
 class Diagonal : public ::testing::Test
diff --git a/test/dot.cpp b/test/dot.cpp
index a25f59f27e..58cfbb2ed6 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -18,6 +18,7 @@
 
 using std::vector;
 using std::string;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/fast.cpp b/test/fast.cpp
index a114a8fdc6..e7df638b80 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 typedef struct
diff --git a/test/fft.cpp b/test/fft.cpp
index 84f0e2382e..48ff865d2a 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -18,6 +18,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/fft_real.cpp b/test/fft_real.cpp
index c8d9a55ff0..8cd6612712 100644
--- a/test/fft_real.cpp
+++ b/test/fft_real.cpp
@@ -18,6 +18,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index cd82ab20d9..ec6a3f3279 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -17,6 +17,7 @@
 
 using std::vector;
 using std::string;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index 12d0b6b1de..9d77af2b30 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -15,6 +15,7 @@
 
 using namespace af;
 using std::vector;
+using std::abs;
 
 TEST(GettingStarted, SNIPPET_getting_started_gen)
 {
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index a65d52ad43..052351a6fb 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 typedef struct
diff --git a/test/harris.cpp b/test/harris.cpp
index 276a3e357f..604e73d41c 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 typedef struct
diff --git a/test/histogram.cpp b/test/histogram.cpp
index f1d7af51b9..c83ba0464f 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -18,6 +18,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 
 template<typename T>
 class Histogram : public ::testing::Test
diff --git a/test/homography.cpp b/test/homography.cpp
index 662b7a2a56..1bd24425be 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 template<typename T>
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index b0568ebbdb..1b990b6900 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index cdb23ef962..0783fb3425 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/math.cpp b/test/math.cpp
index 035ca257d2..e286e2a202 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -14,6 +14,7 @@
 
 using namespace std;
 using namespace af;
+using std::abs;
 
 const int num = 10000;
 const float flt_err = 1e-3;
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 0116a5e3da..34b622be1a 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -18,6 +18,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 template<typename T>
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 9b4590885b..2e3a1fcb6b 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -17,6 +17,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 
 template<typename T>
 class MedianFilter : public ::testing::Test
diff --git a/test/morph.cpp b/test/morph.cpp
index d9c5282146..c42ddf0cba 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -18,6 +18,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 
 template<typename T>
 class Morph : public ::testing::Test
diff --git a/test/orb.cpp b/test/orb.cpp
index 5259366901..b499fb3824 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 typedef struct
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 708eb5d0cd..e3809546b1 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index d0a19af3b9..7f2e76db0d 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/resize.cpp b/test/resize.cpp
index 6ec4e553c6..e0f1ea0810 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -20,6 +20,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/rotate.cpp b/test/rotate.cpp
index f97cd3ab96..0d4b460033 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -20,6 +20,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 29a9107e4c..ce7a921260 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -20,6 +20,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index cf1683f775..45c9462b36 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 typedef struct
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index bbb67409dc..09addc7c48 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/susan.cpp b/test/susan.cpp
index df806c06be..591c2f01e5 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -20,6 +20,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::dim4;
 
 typedef struct
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index f7ef2950e0..9d4060bd7f 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -22,6 +22,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/transform.cpp b/test/transform.cpp
index fa0006cbf2..1950284c2d 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -18,6 +18,7 @@
 
 using std::vector;
 using std::string;
+using std::abs;
 using std::cout;
 using std::endl;
 
diff --git a/test/translate.cpp b/test/translate.cpp
index 5b00c04ec8..355d30a553 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -20,6 +20,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 6be1ba49ab..8437a12615 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -17,6 +17,7 @@
 
 using std::string;
 using std::vector;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 
diff --git a/test/triangle.cpp b/test/triangle.cpp
index e0b609b9ab..6322070226 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -23,6 +23,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 using af::dim4;
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 0cc6fab909..7552400db9 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -23,6 +23,7 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using std::abs;
 using af::cfloat;
 using af::cdouble;
 

From 6988950605c2d075f49873c7db0827a9b8a9b323 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 3 Jan 2016 05:06:36 -0500
Subject: [PATCH 0246/2677] Build fix for CUDA backend when using boost 1.60

---
 src/backend/cuda/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ee7b86ff2c..c2c87b83af 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -57,6 +57,8 @@ FOREACH(VER 20 30 32 35 50 52 53)
 ENDFOREACH()
 
 IF(UNIX)
+    # Forcing STRICT ANSI should resolve a bunch of issues that NVIDIA seems to face with GCC compilers.
+    ADD_DEFINITIONS(-D__STRICT_ANSI__)
     SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler -fvisibility=hidden)
     REMOVE_DEFINITIONS(-std=c++0x)
     IF(${WITH_COVERAGE})

From e5bb33442a95ca7e4d8e2d82e29246f3e74f4a7c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 4 Jan 2016 10:17:17 -0500
Subject: [PATCH 0247/2677] Add missing isLAPACKAvailable implementation in CPU
 backend

---
 src/backend/cpu/lu.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index f8fc92de8d..265fdfaec5 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -109,6 +109,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
+bool isLAPACKAvailable()
+{
+    return false;
+}
+
 }
 
 #endif

From 775747e383a3bd91204b785cc4c75daa62031520 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 4 Jan 2016 10:19:35 -0500
Subject: [PATCH 0248/2677] Set revision to "default" when git is not available

This can happen when compiling releases which are downloaded without git files
---
 CMakeModules/Version.cmake | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 4e0ddb5a61..8d5b575399 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -32,6 +32,11 @@ EXECUTE_PROCESS(
     OUTPUT_STRIP_TRAILING_WHITESPACE
 )
 
+IF(NOT GIT_COMMIT_HASH)
+    MESSAGE(STATUS "No git. Setting hash to default")
+    SET(GIT_COMMIT_HASH "default")
+ENDIF()
+
 CONFIGURE_FILE(
     ${CMAKE_MODULE_PATH}/version.h.in
     ${CMAKE_SOURCE_DIR}/include/af/version.h

From 84dccc841b64ee1f23d69ca30849bbdba2f9fe68 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 4 Jan 2016 11:30:13 -0500
Subject: [PATCH 0249/2677] Documentation fixes

---
 docs/pages/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/pages/README.md b/docs/pages/README.md
index 302690242e..8a395a70af 100644
--- a/docs/pages/README.md
+++ b/docs/pages/README.md
@@ -76,7 +76,7 @@ Each ArrayFire installation comes with:
 ArrayFire supports batched operations on N-dimensional arrays.
 Batch operations in ArrayFire are run in parallel ensuring an optimal usage of your CUDA or OpenCL device.
 
-You can get the best performance out of ArrayFire using [vectorization techniques]().
+You can get the best performance out of ArrayFire using [vectorization techniques](\ref vectorization).
 
 ArrayFire can also execute loop iterations in parallel with
 [the gfor function](\ref gfor).
@@ -92,8 +92,8 @@ Read more about how [ArrayFire JIT](http://arrayfire.com/performance-of-arrayfir
 
 ## Simple Example
 
-Here's a live example to let you see ArrayFire code. You create [arrays](\ref
-construct_mat) which reside on CUDA or OpenCL devices. Then you can use
+Here's a live example to let you see ArrayFire code. You create [arrays](\ref construct_mat)
+which reside on CUDA or OpenCL devices. Then you can use
 [ArrayFire functions](modules.htm) on those [arrays](\ref construct_mat).
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}

From 3c919354a7a66f6db03552cf5b44cede9015de77 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 4 Jan 2016 14:31:42 -0500
Subject: [PATCH 0250/2677] Replaced ssh based url with http url for threads
 submodule

---
 .gitmodules | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitmodules b/.gitmodules
index 1d89315347..c91b7f1585 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -9,4 +9,4 @@
 	url = https://chromium.googlesource.com/external/googletest
 [submodule "src/backend/cpu/threads"]
 	path = src/backend/cpu/threads
-	url = git@github.com:alltheflops/threads.git
+	url = https://github.com/alltheflops/threads.git

From d9e5288006a1cafdb1e0a26ba3b063e52f65a554 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 6 Jan 2016 13:05:39 -0500
Subject: [PATCH 0251/2677] Fix NONFREE Build CMake Options

* NONFREE becomes the parent flag. If off, then child flags are unset
* When NONFREE is on, each child flag can be set to on or off
* Changed child (SIFT) flag to be BUILD_NONFREE_SIFT
* Changed child (SIFT) define to be AF_BUILD_NONFREE_SIFT
* Made the changes in test as well
---
 CMakeLists.txt              | 24 +++++++++++-------------
 src/api/c/sift.cpp          |  4 ++--
 src/backend/cpu/sift.cpp    |  4 ++--
 src/backend/cuda/sift.cu    |  4 ++--
 src/backend/opencl/sift.cpp |  4 ++--
 test/CMakeLists.txt         | 22 ++++++++++++++++++++--
 test/gloh_nonfree.cpp       |  4 ++--
 test/sift_nonfree.cpp       |  4 ++--
 8 files changed, 43 insertions(+), 27 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c79fbcaab0..61a78a635f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -31,9 +31,6 @@ OPTION(BUILD_DOCS "Create ArrayFire Documentation" OFF)
 OPTION(WITH_COVERAGE "Added code coverage flags" OFF)
 
 OPTION(BUILD_NONFREE "Build ArrayFire nonfree algorithms" OFF)
-OPTION(BUILD_SIFT "Build ArrayFire nonfree algorithms" OFF)
-
-MARK_AS_ADVANCED(BUILD_SIFT)
 
 OPTION(BUILD_UNIFIED "Build Backend-Independent ArrayFire API" ON)
 
@@ -91,17 +88,18 @@ IF(BUILD_GRAPHICS)
 
 ENDIF(BUILD_GRAPHICS)
 
-IF(BUILD_NONFREE)
-  MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
-  SET(BUILD_SIFT ON)
-ENDIF(BUILD_NONFREE)
+IF(${BUILD_NONFREE})
+    MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
+    SET(BUILD_NONFREE_SIFT ON CACHE BOOL "Build ArrayFire with SIFT")
+    MARK_AS_ADVANCED(BUILD_NONFREE_SIFT)
+ELSE(${BUILD_NONFREE})
+    UNSET(BUILD_NONFREE_SIFT CACHE) # BUILD_NONFREE_SIFT cannot be built without BUILD_NONFREE
+ENDIF(${BUILD_NONFREE})
 
-IF(BUILD_SIFT)
-  ADD_DEFINITIONS(-DAF_BUILD_SIFT)
+IF(${BUILD_NONFREE_SIFT})
+  ADD_DEFINITIONS(-DAF_BUILD_NONFREE_SIFT)
 
-  IF (NOT BUILD_NONFREE)
-    MESSAGE(WARNING "Building with SIFT requires the following patents")
-  ENDIF()
+  MESSAGE(WARNING "Building with SIFT requires the following patents")
 
   MESSAGE("Method and apparatus for identifying scale invariant features"
     "in an image and use of same for locating an object in an image,\" David"
@@ -110,7 +108,7 @@ IF(BUILD_SIFT)
     "further details, contact David Lowe (lowe@cs.ubc.ca) or the"
     "University-Industry Liaison Office of the University of British"
     "Columbia.")
-ENDIF(BUILD_SIFT)
+ENDIF(${BUILD_NONFREE_SIFT})
 
 INCLUDE_DIRECTORIES(
     "${CMAKE_CURRENT_SOURCE_DIR}/include"
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index c7a38582aa..a14badc88d 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -54,7 +54,7 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
                const bool double_input, const float img_scale, const float feature_ratio)
 {
     try {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
         ArrayInfo info = getInfo(in);
         af::dim4 dims  = info.dims();
 
@@ -95,7 +95,7 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsig
                const bool double_input, const float img_scale, const float feature_ratio)
 {
     try {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
         ArrayInfo info = getInfo(in);
         af::dim4 dims  = info.dims();
 
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 4b20f8ab49..0345e37485 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -21,7 +21,7 @@
 #include <cfloat>
 #include <vector>
 
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
 #endif
 
@@ -39,7 +39,7 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     return sift_impl<T, convAccT>(x, y, score, ori, size, desc, in, n_layers,
                                   contrast_thr, edge_thr, init_sigma, double_input,
                                   img_scale, feature_ratio, compute_GLOH);
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index f3d36d7dfb..ad668af924 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -15,7 +15,7 @@
 #include <err_cuda.hpp>
 #include <handle.hpp>
 
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
 #endif
 
@@ -34,7 +34,7 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     const dim4 dims = in.dims();
 
     unsigned nfeat_out;
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index 5bd940d127..632647ca19 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -15,7 +15,7 @@
 #include <err_opencl.hpp>
 #include <handle.hpp>
 
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
 #endif
 
@@ -34,7 +34,7 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     unsigned nfeat_out;
     unsigned desc_len;
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 3b7b42c87e..1bcdde95af 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -18,10 +18,28 @@ ELSE()
     FIND_PACKAGE(ArrayFire REQUIRED)
     INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
     OPTION(BUILD_NONFREE "Build Tests for nonfree algorithms" OFF)
-    IF(${BUILD_NONFREE}) # Add definition. Not required when building with AF
-        ADD_DEFINITIONS(-DAF_BUILD_SIFT)
+
+    IF(${BUILD_NONFREE})
+        MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
+        SET(BUILD_NONFREE_SIFT ON CACHE BOOL "Build ArrayFire with SIFT")
+    ELSE(${BUILD_NONFREE})
+        UNSET(BUILD_NONFREE_SIFT CACHE) # BUILD_NONFREE_SIFT cannot be built without BUILD_NONFREE
     ENDIF(${BUILD_NONFREE})
 
+    IF(${BUILD_NONFREE_SIFT})
+      ADD_DEFINITIONS(-DAF_BUILD_NONFREE_SIFT)
+
+      MESSAGE(WARNING "Building with SIFT requires the following patents")
+
+      MESSAGE("Method and apparatus for identifying scale invariant features"
+        "in an image and use of same for locating an object in an image,\" David"
+        "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application"
+        "filed March 8, 1999. Asignee: The University of British Columbia. For"
+        "further details, contact David Lowe (lowe@cs.ubc.ca) or the"
+        "University-Industry Liaison Office of the University of British"
+        "Columbia.")
+    ENDIF(${BUILD_NONFREE_SIFT})
+
     # ENABLE_TESTING is required when building only tests
     # When building from source, enable_testing is picked from from the main
     # CMakeLists.txt
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 052351a6fb..f50e4031aa 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -158,7 +158,7 @@ TYPED_TEST_CASE(GLOH, TestTypes);
 template<typename T>
 void glohTest(string pTestFile)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
@@ -270,7 +270,7 @@ void glohTest(string pTestFile)
 //
 TEST(GLOH, CPP)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index 45c9462b36..2e069fd3d3 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -158,7 +158,7 @@ TYPED_TEST_CASE(SIFT, TestTypes);
 template<typename T>
 void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeThr, float initSigma, bool doubleInput)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
@@ -276,7 +276,7 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 //
 TEST(SIFT, CPP)
 {
-#ifdef AF_BUILD_SIFT
+#ifdef AF_BUILD_NONFREE_SIFT
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 

From 5be5511fc22ceb6b38a2d19fa7ad158d2aeede4d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 6 Jan 2016 14:52:16 -0500
Subject: [PATCH 0252/2677] Handle compute_53 (tegra x1) for cuda lapack

---
 src/backend/cuda/CMakeLists.txt | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index fc9a75cb12..7bcc133407 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -70,9 +70,9 @@ ENDIF()
 
 ADD_DEFINITIONS(-DAF_CUDA)
 
-IF(${CUDA_VERSION_MAJOR} LESS 7)
+IF(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
     # Use CPU Lapack as fallback?
-    OPTION(CUDA_LAPACK_CPU_FALLBACK "Use CPU LAPACK as fallback for CUDA LAPACK when CUDA is 6.5 or older" OFF)
+    OPTION(CUDA_LAPACK_CPU_FALLBACK "Use CPU LAPACK as fallback for CUDA LAPACK when cusolver is not available" OFF)
     MARK_AS_ADVANCED(CUDA_LAPACK_CPU_FALLBACK)
 
     IF(${CUDA_LAPACK_CPU_FALLBACK})
@@ -84,9 +84,9 @@ IF(${CUDA_VERSION_MAJOR} LESS 7)
         ENDIF(APPLE)
 
         IF(NOT LAPACK_FOUND)
-            MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cuSolve library. Linear Algebra will not be available.")
+            MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. Linear Algebra will not be available.")
         ELSE(NOT LAPACK_FOUND)
-            MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cuSolve library. But CPU LAPACK libraries are available. Will fallback to using host side code.")
+            MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. But CPU LAPACK libraries are available. Will fallback to using host side code.")
             ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
             IF(USE_CUDA_MKL)
                 MESSAGE("Using MKL")
@@ -94,13 +94,13 @@ IF(${CUDA_VERSION_MAJOR} LESS 7)
             ENDIF()
         ENDIF()
     ELSE()
-        MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cuSolve library. Linear Algebra will not be available.")
+        MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. Linear Algebra will not be available.")
     ENDIF()
     IF(CMAKE_VERSION VERSION_LESS 3.2)
         SET(CUDA_cusolver_LIBRARY)
         MARK_AS_ADVANCED(CUDA_cusolver_LIBRARY)
     ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
-ELSE(${CUDA_VERSION_MAJOR} LESS 7)
+ELSE(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
     MESSAGE(STATUS "CUDA cusolver library available in CUDA Version ${CUDA_VERSION_STRING}")
     ADD_DEFINITIONS(-DWITH_CUDA_LINEAR_ALGEBRA)
     IF(CMAKE_VERSION VERSION_LESS 3.2)
@@ -113,7 +113,7 @@ ELSE(${CUDA_VERSION_MAJOR} LESS 7)
             NO_DEFAULT_PATH
             )
     ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
-ENDIF(${CUDA_VERSION_MAJOR} LESS 7)
+ENDIF(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}

From cc00f35930e44484553c00ec679f49e3d5595db4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 6 Jan 2016 15:28:36 -0500
Subject: [PATCH 0253/2677] Add definition for each compute type

---
 src/backend/cuda/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 7bcc133407..fee9c78dad 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -44,6 +44,7 @@ MACRO(SET_COMPUTE VERSION)
     SET(CUDA_GENERATE_CODE_${VERSION} "-gencode arch=compute_${VERSION},code=sm_${VERSION}")
     SET(CUDA_GENERATE_CODE ${CUDA_GENERATE_CODE} ${CUDA_GENERATE_CODE_${VERSION}})
     LIST(APPEND COMPUTE_VERSIONS "${VERSION}")
+    ADD_DEFINITIONS(-DCUDA_COMPUTE_${VERSION})
     MESSAGE(STATUS "Setting Compute ${VERSION} to ON")
 ENDMACRO(SET_COMPUTE)
 

From a1823b3efd981f008bc1884dcd22935f4467cf9c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 14:44:09 -0500
Subject: [PATCH 0254/2677] Added helper functions for device type and unified
 mem in OpenCL

---
 src/backend/opencl/platform.cpp | 24 ++++++++++++++++++++++++
 src/backend/opencl/platform.hpp |  6 ++++++
 2 files changed, 30 insertions(+)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 0cd46d25f6..005f2c1189 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -256,6 +256,10 @@ std::string getInfo()
                  << (device.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False")
                  << ")";
 #endif
+            // TODO Move this inside debug
+            info << "Unified Memory("
+                 << (isHostUnifiedMemory(device) ? "True" : "False")
+                 << ")";
             info << std::endl;
 
             nDevices++;
@@ -311,6 +315,26 @@ const cl::Device& getDevice()
     return *(devMngr.mDevices[devMngr.mActiveQId]);
 }
 
+cl_device_type getDeviceType()
+{
+    cl::Device device = getDevice();
+    cl_device_type type = device.getInfo<CL_DEVICE_TYPE>();
+    return type;
+}
+
+bool isHostUnifiedMemory(const cl::Device &device)
+{
+    return device.getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+}
+
+bool OpenCLCPUOffload()
+{
+    static const bool sync = getEnvVar("AF_OPENCL_CPU_OFFLOAD") == "1";
+    bool offload = false;
+    if(sync) offload = isHostUnifiedMemory(getDevice());
+    return offload;
+}
+
 bool isGLSharingSupported()
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 84cb7b854c..85c533fa84 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -97,6 +97,12 @@ cl::CommandQueue& getQueue();
 
 const cl::Device& getDevice();
 
+cl_device_type getDeviceType();
+
+bool isHostUnifiedMemory(const cl::Device &device);
+
+bool OpenCLCPUOffload();
+
 bool isGLSharingSupported();
 
 bool isDoubleSupported(int device);

From 4275f5f2dda6a089fcbae77418204a5c1c76a2f3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 15:08:51 -0500
Subject: [PATCH 0255/2677] Added getMappedPtr and unmapPtr functions in opencl
 memory

---
 src/backend/opencl/memory.cpp | 26 ++++++++++++++++++++++++++
 src/backend/opencl/memory.hpp |  3 +++
 2 files changed, 29 insertions(+)

diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 141610d71f..924e370a64 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -281,6 +281,29 @@ namespace opencl
         return bufferPush((cl::Buffer *)ptr);
     }
 
+    template<typename T>
+    T *getMappedPtr(const cl::Buffer *buf)
+    {
+        int n = getActiveDeviceId();
+        mem_iter iter = memory_maps[n].find(const_cast<cl::Buffer*>(buf));
+
+        if (iter == memory_maps[n].end()) {
+            // Buffer not found in memory manager
+            // Very Very Bad
+            return NULL;
+        }
+        size_t alloc_bytes = iter->second.bytes;
+
+        T *ptr = (T*)getQueue().enqueueMapBuffer(
+                     *buf, true, CL_MAP_READ, 0, alloc_bytes);
+        return ptr;
+    }
+
+    void unmapPtr(const cl::Buffer *buf, void *ptr)
+    {
+        getQueue().enqueueUnmapMemObject(*buf, ptr);
+    }
+
     // pinned memory manager
     typedef struct {
         cl::Buffer *buf;
@@ -403,6 +426,7 @@ namespace opencl
     template void memPush(const T* ptr);                        \
     template T* pinnedAlloc(const size_t &elements);            \
     template void pinnedFree(T* ptr);                           \
+    template T* getMappedPtr(const cl::Buffer *buf);            \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
@@ -416,4 +440,6 @@ namespace opencl
     INSTANTIATE(uintl)
     INSTANTIATE(short)
     INSTANTIATE(ushort)
+
+    template void* getMappedPtr(const cl::Buffer *buf);
 }
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 96292cdfac..f337a7a1bd 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -32,6 +32,9 @@ namespace opencl
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 
+    template<typename T> T *getMappedPtr(const cl::Buffer *buf);
+    void unmapPtr(const cl::Buffer *buf, void *ptr);
+
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 

From 3c1ab9f0902a37bd7b3c31bc533790d950407b52 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 16:10:43 -0500
Subject: [PATCH 0256/2677] Added matmul offloading to CPU

---
 src/backend/cpu/blas.hpp              |   6 +
 src/backend/opencl/CMakeLists.txt     |  12 ++
 src/backend/opencl/blas.cpp           |   6 +
 src/backend/opencl/cpu/cpu_blas.cpp   | 268 ++++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_blas.hpp   |  23 +++
 src/backend/opencl/cpu/cpu_helper.hpp |  46 +++++
 test/blas.cpp                         |   1 +
 7 files changed, 362 insertions(+)
 create mode 100644 src/backend/opencl/cpu/cpu_blas.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_blas.hpp
 create mode 100644 src/backend/opencl/cpu/cpu_helper.hpp

diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 117d3a2145..934a2c6ec7 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -45,4 +45,10 @@ template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs);
 
+typedef std::complex<float>     cfloat;
+typedef std::complex<double>    cdouble;
+
+template<typename T> struct is_complex          { static const bool value = false;  };
+template<> struct           is_complex<cfloat>  { static const bool value = true;   };
+template<> struct           is_complex<cdouble> { static const bool value = true;   };
 }
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 86ba1b2aad..c9c47d0198 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -123,6 +123,12 @@ FILE(GLOB conv_ker_headers
 FILE(GLOB conv_ker_sources
      "kernel/convolve/*.cpp")
 
+FILE(GLOB cpu_headers
+     "cpu/*.hpp")
+
+FILE(GLOB cpu_sources
+     "cpu/*.cpp")
+
 source_group(backend\\opencl\\Headers FILES ${opencl_headers})
 source_group(backend\\opencl\\Sources FILES ${opencl_sources})
 source_group(backend\\opencl\\JIT FILES ${jit_sources})
@@ -131,6 +137,8 @@ source_group(backend\\opencl\\kernel\\cl FILES ${opencl_kernels})
 source_group(backend\\opencl\\kernel\\Sources FILES ${kernel_sources})
 source_group(backend\\opencl\\kernel\\convolve\\Headers FILES ${conv_ker_headers})
 source_group(backend\\opencl\\kernel\\convolve\\Sources FILES ${conv_ker_sources})
+source_group(backend\\opencl\\cpu\\Headers FILES ${cpu_headers})
+source_group(backend\\opencl\\cpu\\Sources FILES ${cpu_sources})
 
 IF(LAPACK_FOUND)
     FILE(GLOB magma_sources
@@ -206,6 +214,8 @@ IF(DEFINED BLAS_SYM_FILE)
                 ${kernel_sources}
                 ${conv_ker_headers}
                 ${conv_ker_sources}
+                ${cpu_headers}
+                ${cpu_sources}
                 ${backend_headers}
                 ${backend_sources}
                 ${magma_sources}
@@ -244,6 +254,8 @@ ELSE(DEFINED BLAS_SYM_FILE)
                 ${kernel_sources}
                 ${conv_ker_headers}
                 ${conv_ker_sources}
+                ${cpu_sources}
+                ${cpu_sources}
                 ${backend_headers}
                 ${backend_sources}
                 ${c_headers}
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 6173a684ea..f9f8af1253 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -20,6 +20,8 @@
 #include <math.hpp>
 #include <transpose.hpp>
 
+#include <cpu/cpu_blas.hpp>
+
 namespace opencl
 {
 
@@ -113,6 +115,10 @@ template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    if(OpenCLCPUOffload()) {
+        return cpu::matmul(lhs, rhs, optLhs, optRhs);
+    }
+
     initBlas();
     clblasTranspose lOpts = toClblasTranspose(optLhs);
     clblasTranspose rOpts = toClblasTranspose(optRhs);
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
new file mode 100644
index 0000000000..524777a6c6
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -0,0 +1,268 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_blas.hpp>
+#include <af/dim4.hpp>
+#include <handle.hpp>
+#include <cassert>
+#include <err_common.hpp>
+#include <platform.hpp>
+#include <af/macros.h>
+
+namespace opencl
+{
+namespace cpu
+{
+
+using std::add_const;
+using std::add_pointer;
+using std::enable_if;
+using std::is_floating_point;
+using std::remove_const;
+using std::conditional;
+
+// Some implementations of BLAS require void* for complex pointers while others use float*/double*
+//
+// Sample cgemm API
+// OpenBLAS
+// void cblas_cgemm(OPENBLAS_CONST enum CBLAS_ORDER Order, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransA, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransB,
+//                  OPENBLAS_CONST blasint M, OPENBLAS_CONST blasint N, OPENBLAS_CONST blasint K,
+//                  OPENBLAS_CONST float *alpha, OPENBLAS_CONST float *A, OPENBLAS_CONST blasint lda,
+//                  OPENBLAS_CONST float *B, OPENBLAS_CONST blasint ldb, OPENBLAS_CONST float *beta,
+//                  float *C, OPENBLAS_CONST blasint ldc);
+//
+// MKL
+// void cblas_cgemm(const  CBLAS_LAYOUT Layout, const CBLAS_TRANSPOSE TransA, const  CBLAS_TRANSPOSE TransB,
+//                  const MKL_INT M, const MKL_INT N, const MKL_INT K,
+//                  const void *alpha, const void *A, const MKL_INT lda,
+//                  const void *B, const MKL_INT ldb, const void *beta,
+//                  void *C, const MKL_INT ldc);
+// atlas cblas
+// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE TransA,
+//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+//                  const void *alpha, const void *A, const int lda,
+//                  const void *B, const int ldb, const void *beta,
+//                  void *C, const int ldc);
+//
+// LAPACKE
+// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE TransA,
+//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+//                  const void *alpha, const void *A, const int lda,
+//                  const void *B, const int ldb, const void *beta,
+//                  void *C, const int ldc);
+#if defined(IS_OPENBLAS)
+    static const bool cplx_void_ptr = false;
+#else
+    static const bool cplx_void_ptr = true;
+#endif
+
+template<typename T, class Enable = void>
+struct blas_base {
+    using type = typename dtype_traits<T>::base_type;
+};
+
+template<typename T>
+struct blas_base <T, typename enable_if<is_complex<T>::value && cplx_void_ptr>::type> {
+    using type = void;
+};
+
+
+template<typename T>
+using cptr_type     =   typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T*>::type;
+template<typename T>
+using ptr_type     =    typename conditional<   is_complex<T>::value,
+                                                typename blas_base<T>::type *,
+                                                T*>::type;
+template<typename T>
+using scale_type   =    typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T>::type;
+
+template<typename T>
+using gemm_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE, const CBLAS_TRANSPOSE,
+                                const blasint, const blasint, const blasint,
+                                scale_type<T>, cptr_type<T>, const blasint,
+                                cptr_type<T>, const blasint,
+                                scale_type<T>, ptr_type<T>, const blasint);
+
+template<typename T>
+using gemv_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE,
+                                const blasint, const blasint,
+                                scale_type<T>, cptr_type<T>, const blasint,
+                                cptr_type<T>, const blasint,
+                                scale_type<T>, ptr_type<T>, const blasint);
+
+#define BLAS_FUNC_DEF( FUNC )                           \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+#define BLAS_FUNC( FUNC, TYPE, PREFIX )                 \
+  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
+{ return &cblas_##PREFIX##FUNC; }
+
+BLAS_FUNC_DEF( gemm )
+BLAS_FUNC(gemm , float   , s)
+BLAS_FUNC(gemm , double  , d)
+BLAS_FUNC(gemm , cfloat  , c)
+BLAS_FUNC(gemm , cdouble , z)
+
+BLAS_FUNC_DEF(gemv)
+BLAS_FUNC(gemv , float   , s)
+BLAS_FUNC(gemv , double  , d)
+BLAS_FUNC(gemv , cfloat  , c)
+BLAS_FUNC(gemv , cdouble , z)
+
+template<typename T, int value>
+typename enable_if<is_floating_point<T>::value, scale_type<T>>::type
+getScale() { return T(value); }
+
+template<typename T, int value>
+typename enable_if<is_complex<T>::value, scale_type<T>>::type
+getScale()
+{
+    static T val = scalar<T>(value);
+    return (const typename blas_base<T>::type *)&val;
+}
+
+CBLAS_TRANSPOSE
+toCblasTranspose(af_mat_prop opt)
+{
+    CBLAS_TRANSPOSE out = CblasNoTrans;
+    switch(opt) {
+        case AF_MAT_NONE        : out = CblasNoTrans;   break;
+        case AF_MAT_TRANS       : out = CblasTrans;     break;
+        case AF_MAT_CTRANS      : out = CblasConjTrans; break;
+        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+    return out;
+}
+
+template<typename T>
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
+    CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
+
+    int aRowDim = (lOpts == CblasNoTrans) ? 0 : 1;
+    int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
+    int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhs.dims();
+    int M = lDims[aRowDim];
+    int N = rDims[bColDim];
+    int K = lDims[aColDim];
+
+    //FIXME: Leaks on errors.
+    Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
+    auto alpha = getScale<T, 1>();
+    auto beta  = getScale<T, 0>();
+
+    dim4 lStrides = lhs.strides();
+    dim4 rStrides = rhs.strides();
+    using BT  =       typename blas_base<T>::type;
+    using CBT = const typename blas_base<T>::type;
+
+    // get host pointers from mapped memory
+    BT *lPtr = getMappedPtr<BT>(lhs.get());
+    BT *rPtr = getMappedPtr<BT>(rhs.get());
+    BT *oPtr = getMappedPtr<BT>(out.get());
+
+    if(rDims[bColDim] == 1) {
+        N = lDims[aColDim];
+        gemv_func<T>()(
+            CblasColMajor, lOpts,
+            lDims[0], lDims[1],
+            alpha,
+            lPtr, lStrides[1],
+            rPtr, rStrides[0],
+            beta,
+            oPtr, 1);
+    } else {
+        gemm_func<T>()(
+            CblasColMajor, lOpts, rOpts,
+            M, N, K,
+            alpha,
+            lPtr, lStrides[1],
+            rPtr, rStrides[1],
+            beta,
+            oPtr, out.dims()[0]);
+    }
+
+    unmapPtr(lhs.get(), lPtr);
+    unmapPtr(rhs.get(), rPtr);
+    unmapPtr(out.get(), oPtr);
+
+    return out;
+}
+
+//template<typename T> T
+//conj(T  x) { return x; }
+//
+//template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
+//template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
+//
+//template<typename T, bool conjugate, bool both_conjugate>
+//Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
+//        af_mat_prop optLhs, af_mat_prop optRhs)
+//{
+//    int N = lhs.dims()[0];
+//
+//    T out = 0;
+//    const T *pL = lhs.get();
+//    const T *pR = rhs.get();
+//
+//    for(int i = 0; i < N; i++)
+//        out += (conjugate ? cpu::conj(pL[i]) : pL[i]) * pR[i];
+//
+//    if(both_conjugate) out = cpu::conj(out);
+//
+//    return createValueArray(af::dim4(1), out);
+//}
+//
+//template<typename T>
+//Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
+//        af_mat_prop optLhs, af_mat_prop optRhs)
+//{
+//    if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
+//        return dot_<T, false, true>(lhs, rhs, optLhs, optRhs);
+//    } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
+//        return dot_<T, true, false>(lhs, rhs, optLhs, optRhs);
+//    } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
+//        return dot_<T, true, false>(rhs, lhs, optRhs, optLhs);
+//    } else {
+//        return dot_<T, false, false>(lhs, rhs, optLhs, optRhs);
+//    }
+//}
+
+#undef BT
+#undef REINTEPRET_CAST
+
+#define INSTANTIATE_BLAS(TYPE)                                                          \
+    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
+                                      af_mat_prop optLhs, af_mat_prop optRhs);
+
+INSTANTIATE_BLAS(float)
+INSTANTIATE_BLAS(cfloat)
+INSTANTIATE_BLAS(double)
+INSTANTIATE_BLAS(cdouble)
+
+//#define INSTANTIATE_DOT(TYPE)                                                               \
+//    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,          \
+//                                   af_mat_prop optLhs, af_mat_prop optRhs);
+//
+//INSTANTIATE_DOT(float)
+//INSTANTIATE_DOT(double)
+//INSTANTIATE_DOT(cfloat)
+//INSTANTIATE_DOT(cdouble)
+
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
new file mode 100644
index 0000000000..303b60ced8
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_helper.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T>
+    Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
+                    af_mat_prop optLhs, af_mat_prop optRhs);
+//    template<typename T>
+//    Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
+//                 af_mat_prop optLhs, af_mat_prop optRhs);
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
new file mode 100644
index 0000000000..afc60d3b9f
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -0,0 +1,46 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/defines.h>
+#include <af/blas.h>
+#include <af/lapack.h>
+#include <Array.hpp>
+#include <memory.hpp>
+
+#ifdef __APPLE__
+#include <Accelerate/Accelerate.h>
+#else
+#ifdef USE_MKL
+#include <mkl_cblas.h>
+#else
+extern "C" {
+#include <cblas.h>
+}
+#endif
+#endif
+
+// TODO: Ask upstream for a more official way to detect it
+#ifdef OPENBLAS_CONST
+#define IS_OPENBLAS
+#endif
+
+// Make sure we get the correct type signature for OpenBLAS
+// OpenBLAS defines blasint as it's index type. Emulate this
+// if we're not dealing with openblas and use it where applicable
+#ifndef IS_OPENBLAS
+typedef int blasint;
+#endif
+
+namespace opencl
+{
+namespace cpu
+{
+}
+}
+
diff --git a/test/blas.cpp b/test/blas.cpp
index 507cc6dc7b..b5d92f1073 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -36,6 +36,7 @@ template<typename T, bool isBVector>
 void MatMulCheck(string TestFile)
 {
     if (noDoubleTests<T>()) return;
+    af::info();
 
     using std::vector;
     vector<af::dim4> numDims;

From f9819f78c191aea10331e42ec361836f2a1e3c57 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 16:53:55 -0500
Subject: [PATCH 0257/2677] Fix blas header types in cpu

---
 src/backend/cpu/blas.hpp | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 934a2c6ec7..05484338cd 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -10,6 +10,7 @@
 #include <af/defines.h>
 #include <af/blas.h>
 #include <Array.hpp>
+#include <types.hpp>
 
 #ifdef __APPLE__
 #include <Accelerate/Accelerate.h>
@@ -45,10 +46,4 @@ template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs);
 
-typedef std::complex<float>     cfloat;
-typedef std::complex<double>    cdouble;
-
-template<typename T> struct is_complex          { static const bool value = false;  };
-template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-template<> struct           is_complex<cdouble> { static const bool value = true;   };
 }

From d5077ecfdf8d04077ed356d793f84faacab1d929 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 18:14:14 -0500
Subject: [PATCH 0258/2677] Fix bug in OpenCL JIT when calling functions that
 return same value

* Such as calling conj on float
---
 src/backend/opencl/binary.hpp    | 2 +-
 src/backend/opencl/kernel/jit.cl | 1 +
 src/backend/opencl/unary.hpp     | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 4f58cb49e6..11493a5966 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -22,7 +22,7 @@ namespace opencl
     {
         const char *name()
         {
-            return "noop";
+            return "__invalid";
         }
     };
 
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index b34bbcddd8..3092449418 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #define sign(in) signbit((in))
+#define __noop(a) (a)
 #define __add(lhs, rhs) (lhs) + (rhs)
 #define __sub(lhs, rhs) (lhs) - (rhs)
 #define __mul(lhs, rhs) (lhs) * (rhs)
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 5a2cc9e33f..1e363d7dcb 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -16,7 +16,7 @@ namespace opencl
 {
 
 template<af_op_t op>
-static const char *unaryName() { return "noop"; }
+static const char *unaryName() { return "__noop"; }
 
 #define UNARY_DECL(OP, FNAME)                   \
     template<> STATIC_                          \

From ac25f5bb19c0f0db90d47576f29bfa81f6e060d6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 18:15:41 -0500
Subject: [PATCH 0259/2677] Fix bug in CUDA JIT when calling functions that
 return same value

* Such as calling conj on float
---
 src/backend/cuda/JIT/numeric.cu | 13 +++++++++++++
 src/backend/cuda/complex.hpp    | 22 +++++++++++-----------
 2 files changed, 24 insertions(+), 11 deletions(-)

diff --git a/src/backend/cuda/JIT/numeric.cu b/src/backend/cuda/JIT/numeric.cu
index 8253db6d22..2bcb15a112 100644
--- a/src/backend/cuda/JIT/numeric.cu
+++ b/src/backend/cuda/JIT/numeric.cu
@@ -119,6 +119,19 @@ MATH_CAST(lgamma, intl  , float)
 MATH_CAST(lgamma, ushort, float)
 MATH_CAST(lgamma, short , float)
 
+MATH_NOOP(noop, float)
+MATH_NOOP(noop, double)
+MATH_NOOP(noop, cfloat)
+MATH_NOOP(noop, cdouble)
+MATH_NOOP(noop, int)
+MATH_NOOP(noop, uint)
+MATH_NOOP(noop, char)
+MATH_NOOP(noop, uchar)
+MATH_NOOP(noop, uintl)
+MATH_NOOP(noop, intl)
+MATH_NOOP(noop, ushort)
+MATH_NOOP(noop, short)
+
 __device__ float ___abs(cfloat a) { return cuCabsf(a); }
 __device__ double ___abs(cdouble a) { return cuCabs(a); }
 
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 82304b9a22..b7de74a7de 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -17,25 +17,25 @@
 
 namespace cuda
 {
-    template<typename T> static const std::string cplx_name() { return "@___noop"; }
-    template<> STATIC_ const std::string cplx_name<cfloat >() { return cuMangledName<float , true>("___cplx"); }
-    template<> STATIC_ const std::string cplx_name<cdouble>() { return cuMangledName<double, true>("___cplx"); }
+    template<typename T> static const std::string cplx_name() { return cuMangledName<T, false>("___noop");       }
+    template<> STATIC_ const std::string cplx_name<cfloat >() { return cuMangledName<float , true>("___cplx");   }
+    template<> STATIC_ const std::string cplx_name<cdouble>() { return cuMangledName<double, true>("___cplx");   }
 
-    template<typename T> static const std::string real_name() { return "@___noop"; }
+    template<typename T> static const std::string real_name() { return cuMangledName<T, false>("___noop");       }
     template<> STATIC_ const std::string real_name<cfloat >() { return cuMangledName<cfloat , false>("___real"); }
     template<> STATIC_ const std::string real_name<cdouble>() { return cuMangledName<cdouble, false>("___real"); }
 
-    template<typename T> static const std::string imag_name() { return "@___noop"; }
+    template<typename T> static const std::string imag_name() { return cuMangledName<T, false>("___noop");       }
     template<> STATIC_ const std::string imag_name<cfloat >() { return cuMangledName<cfloat , false>("___imag"); }
     template<> STATIC_ const std::string imag_name<cdouble>() { return cuMangledName<cdouble, false>("___imag"); }
 
-    template<typename T> static const std::string abs_name() { return "@___noop"; }
-    template<> STATIC_ const std::string abs_name<float  >() { return cuMangledName<float  , false>("___abs"); }
-    template<> STATIC_ const std::string abs_name<double >() { return cuMangledName<double , false>("___abs"); }
-    template<> STATIC_ const std::string abs_name<cfloat >() { return cuMangledName<cfloat , false>("___abs"); }
-    template<> STATIC_ const std::string abs_name<cdouble>() { return cuMangledName<cdouble, false>("___abs"); }
+    template<typename T> static const std::string abs_name()  { return cuMangledName<T, false>("___noop");       }
+    template<> STATIC_ const std::string abs_name<float  >()  { return cuMangledName<float  , false>("___abs");  }
+    template<> STATIC_ const std::string abs_name<double >()  { return cuMangledName<double , false>("___abs");  }
+    template<> STATIC_ const std::string abs_name<cfloat >()  { return cuMangledName<cfloat , false>("___abs");  }
+    template<> STATIC_ const std::string abs_name<cdouble>()  { return cuMangledName<cdouble, false>("___abs");  }
 
-    template<typename T> static const std::string conj_name() { return "@___noop"; }
+    template<typename T> static const std::string conj_name() { return cuMangledName<T, false>("___noop");       }
     template<> STATIC_ const std::string conj_name<cfloat >() { return cuMangledName<cfloat , false>("___conj"); }
     template<> STATIC_ const std::string conj_name<cdouble>() { return cuMangledName<cdouble, false>("___conj"); }
 

From 507ec929888bf137db79648778701db7b1ca5532 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Jan 2016 18:17:06 -0500
Subject: [PATCH 0260/2677] dot in CUDA/OpenCL now uses mul followed by
 reduction

---
 src/backend/cuda/blas.cpp   | 58 +++++++++++++++----------------
 src/backend/opencl/blas.cpp | 68 ++++++++++++++++++-------------------
 2 files changed, 63 insertions(+), 63 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 85f48da750..1e5dd5de39 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -18,6 +18,9 @@
 #include <math.hpp>
 #include <err_common.hpp>
 #include <cublasManager.hpp>
+#include <arith.hpp>
+#include <reduce.hpp>
+#include <complex.hpp>
 
 namespace cuda
 {
@@ -197,40 +200,37 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
 }
 
-template<typename T, bool conjugate, bool both_conjugate>
-Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
-              af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    int N = lhs.dims()[0];
-
-    T out;
-
-    CUBLAS_CHECK((dot_func<T, conjugate>()(
-                 getHandle(),
-                 N,
-                 lhs.get(), lhs.strides()[0],
-                 rhs.get(), rhs.strides()[0],
-                 &out)));
-
-    if(both_conjugate)
-        return createValueArray(af::dim4(1), conj(out));
-    else
-        return createValueArray(af::dim4(1), out);
-}
+// Keeping this around for future reference
+//template<typename T, bool conjugate, bool both_conjugate>
+//Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
+//              af_mat_prop optLhs, af_mat_prop optRhs)
+//{
+//    int N = lhs.dims()[0];
+//
+//    T out;
+//
+//    CUBLAS_CHECK((dot_func<T, conjugate>()(
+//                 getHandle(),
+//                 N,
+//                 lhs.get(), lhs.strides()[0],
+//                 rhs.get(), rhs.strides()[0],
+//                 &out)));
+//
+//    if(both_conjugate)
+//        return createValueArray(af::dim4(1), conj(out));
+//    else
+//        return createValueArray(af::dim4(1), out);
+//}
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
 {
-    if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        return dot_<T, false, true>(lhs, rhs, optLhs, optRhs);
-    } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        return dot_<T, true, false>(lhs, rhs, optLhs, optRhs);
-    } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        return dot_<T, true, false>(rhs, lhs, optRhs, optLhs);
-    } else {
-        return dot_<T, false, false>(lhs, rhs, optLhs, optRhs);
-    }
+    const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
+    const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
+
+    const Array<T> temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
+    return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
 template<typename T>
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index f9f8af1253..15e2373783 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -19,6 +19,9 @@
 #include <err_clblas.hpp>
 #include <math.hpp>
 #include <transpose.hpp>
+#include <arith.hpp>
+#include <reduce.hpp>
+#include <complex.hpp>
 
 #include <cpu/cpu_blas.hpp>
 
@@ -174,45 +177,42 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     return out;
 }
 
-template<typename T, bool conjugate, bool both_conjugate>
-Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
-              af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    initBlas();
-
-    int N = lhs.dims()[0];
-    dot_func<T, conjugate> dot;
-    cl::Event event;
-    Array<T> out = createEmptyArray<T>(af::dim4(1));
-    cl::Buffer scratch(getContext(), CL_MEM_READ_WRITE, sizeof(T) * N);
-    CLBLAS_CHECK(
-        dot(N,
-            (*out.get())(), out.getOffset(),
-            (*lhs.get())(),  lhs.getOffset(), lhs.strides()[0],
-            (*rhs.get())(),  rhs.getOffset(), rhs.strides()[0],
-            scratch(),
-            1, &getQueue()(), 0, nullptr, &event())
-        );
-
-    if(both_conjugate)
-        transpose_inplace<T>(out, true);
-
-    return out;
-}
+// Keeping this around for future reference
+//template<typename T, bool conjugate, bool both_conjugate>
+//Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
+//              af_mat_prop optLhs, af_mat_prop optRhs)
+//{
+//    initBlas();
+//
+//    int N = lhs.dims()[0];
+//    dot_func<T, conjugate> dot;
+//    cl::Event event;
+//    Array<T> out = createEmptyArray<T>(af::dim4(1));
+//    cl::Buffer scratch(getContext(), CL_MEM_READ_WRITE, sizeof(T) * N);
+//    CLBLAS_CHECK(
+//        dot(N,
+//            (*out.get())(), out.getOffset(),
+//            (*lhs.get())(),  lhs.getOffset(), lhs.strides()[0],
+//            (*rhs.get())(),  rhs.getOffset(), rhs.strides()[0],
+//            scratch(),
+//            1, &getQueue()(), 0, nullptr, &event())
+//        );
+//
+//    if(both_conjugate)
+//        transpose_inplace<T>(out, true);
+//
+//    return out;
+//}
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
 {
-    if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        return dot_<T, false, true>(lhs, rhs, optLhs, optRhs);
-    } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        return dot_<T, true, false>(lhs, rhs, optLhs, optRhs);
-    } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        return dot_<T, true, false>(rhs, lhs, optRhs, optLhs);
-    } else {
-        return dot_<T, false, false>(lhs, rhs, optLhs, optRhs);
-    }
+    const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
+    const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
+
+    const Array<T> temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
+    return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
 #define INSTANTIATE_BLAS(TYPE)                                                          \

From 5940d4bc93a5f644ffb51d699d6effa2564418c1 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 8 Jan 2016 08:43:51 -0500
Subject: [PATCH 0261/2677] Always use freeimage flags instead of hardcoded
 offsets

---
 src/api/c/imageio.cpp | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 746ee69142..c6a20a85a2 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -63,9 +63,9 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (float) *(src + (x * step + 0));
-                    pDst1[indx] = (float) *(src + (x * step + 1));
-                    pDst2[indx] = (float) *(src + (x * step + 2));
+                    pDst0[indx] = (float) *(src + (x * step + FI_RGBA_RED));
+                    pDst1[indx] = (float) *(src + (x * step + FI_RGBA_GREEN));
+                    pDst2[indx] = (float) *(src + (x * step + FI_RGBA_BLUE));
                 }
                 if (fo_color == 4) pDst3[indx] = (float) *(src + (x * step + FI_RGBA_ALPHA));
             }
@@ -104,9 +104,9 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    r = (T) *(src + (x * step + 0));
-                    g = (T) *(src + (x * step + 1));
-                    b = (T) *(src + (x * step + 2));
+                    r = (T) *(src + (x * step + FI_RGBA_RED));
+                    g = (T) *(src + (x * step + FI_RGBA_GREEN));
+                    b = (T) *(src + (x * step + FI_RGBA_BLUE));
                 }
                 pDst[indx] = r * 0.2989f + g * 0.5870f + b * 0.1140f;
             }
@@ -333,10 +333,10 @@ af_err af_save_image(const char* filename, const af_array in_)
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + 0) = (uchar) pSrc2[indx]; // r
-                    *(pDstLine + x * step + 1) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + 2) = (uchar) pSrc0[indx]; // b
-                    *(pDstLine + x * step + 3) = (uchar) pSrc3[indx]; // a
+                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
+                    *(pDstLine + x * step + FI_RGBA_ALPHA) = (uchar) pSrc3[indx]; // a
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -362,9 +362,9 @@ af_err af_save_image(const char* filename, const af_array in_)
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + 0) = (uchar) pSrc2[indx]; // r
-                    *(pDstLine + x * step + 1) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + 2) = (uchar) pSrc0[indx]; // b
+                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -602,10 +602,10 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + 2) = (uchar) pSrc0[indx]; // b
-                    *(pDstLine + x * step + 1) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + 0) = (uchar) pSrc2[indx]; // r
-                    *(pDstLine + x * step + 3) = (uchar) pSrc3[indx]; // a
+                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
+                    *(pDstLine + x * step + FI_RGBA_ALPHA) = (uchar) pSrc3[indx]; // a
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -631,9 +631,9 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + 2) = (uchar) pSrc0[indx]; // b
-                    *(pDstLine + x * step + 1) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + 0) = (uchar) pSrc2[indx]; // r
+                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
                     ++indx;
                 }
                 pDstLine -= nDstPitch;

From 7eafd44ef20ac89b3f0744aad6e58d155bd48b13 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 10:21:28 -0500
Subject: [PATCH 0262/2677] Remove cpu dot fallback. Slower than opencl dot
 always

---
 src/backend/opencl/cpu/cpu_blas.cpp | 53 -----------------------------
 src/backend/opencl/cpu/cpu_blas.hpp |  3 --
 2 files changed, 56 deletions(-)

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 524777a6c6..ff7170d331 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -13,7 +13,6 @@
 #include <cassert>
 #include <err_common.hpp>
 #include <platform.hpp>
-#include <af/macros.h>
 
 namespace opencl
 {
@@ -169,7 +168,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
     using BT  =       typename blas_base<T>::type;
-    using CBT = const typename blas_base<T>::type;
 
     // get host pointers from mapped memory
     BT *lPtr = getMappedPtr<BT>(lhs.get());
@@ -204,48 +202,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     return out;
 }
 
-//template<typename T> T
-//conj(T  x) { return x; }
-//
-//template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
-//template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
-//
-//template<typename T, bool conjugate, bool both_conjugate>
-//Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
-//        af_mat_prop optLhs, af_mat_prop optRhs)
-//{
-//    int N = lhs.dims()[0];
-//
-//    T out = 0;
-//    const T *pL = lhs.get();
-//    const T *pR = rhs.get();
-//
-//    for(int i = 0; i < N; i++)
-//        out += (conjugate ? cpu::conj(pL[i]) : pL[i]) * pR[i];
-//
-//    if(both_conjugate) out = cpu::conj(out);
-//
-//    return createValueArray(af::dim4(1), out);
-//}
-//
-//template<typename T>
-//Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-//        af_mat_prop optLhs, af_mat_prop optRhs)
-//{
-//    if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-//        return dot_<T, false, true>(lhs, rhs, optLhs, optRhs);
-//    } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-//        return dot_<T, true, false>(lhs, rhs, optLhs, optRhs);
-//    } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-//        return dot_<T, true, false>(rhs, lhs, optRhs, optLhs);
-//    } else {
-//        return dot_<T, false, false>(lhs, rhs, optLhs, optRhs);
-//    }
-//}
-
-#undef BT
-#undef REINTEPRET_CAST
-
 #define INSTANTIATE_BLAS(TYPE)                                                          \
     template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
                                       af_mat_prop optLhs, af_mat_prop optRhs);
@@ -255,14 +211,5 @@ INSTANTIATE_BLAS(cfloat)
 INSTANTIATE_BLAS(double)
 INSTANTIATE_BLAS(cdouble)
 
-//#define INSTANTIATE_DOT(TYPE)                                                               \
-//    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,          \
-//                                   af_mat_prop optLhs, af_mat_prop optRhs);
-//
-//INSTANTIATE_DOT(float)
-//INSTANTIATE_DOT(double)
-//INSTANTIATE_DOT(cfloat)
-//INSTANTIATE_DOT(cdouble)
-
 }
 }
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
index 303b60ced8..836d6e02de 100644
--- a/src/backend/opencl/cpu/cpu_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -16,8 +16,5 @@ namespace cpu
     template<typename T>
     Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                     af_mat_prop optLhs, af_mat_prop optRhs);
-//    template<typename T>
-//    Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-//                 af_mat_prop optLhs, af_mat_prop optRhs);
 }
 }

From 45abbc35741f5e04a6c9655b30bd5dc3f7b47b46 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 11:02:11 -0500
Subject: [PATCH 0263/2677] Add OpenCL-CPU fallback for LU

---
 src/backend/opencl/cpu/cpu_lapack_helper.hpp |  35 ++++
 src/backend/opencl/cpu/cpu_lu.cpp            | 178 +++++++++++++++++++
 src/backend/opencl/cpu/cpu_lu.hpp            |  22 +++
 src/backend/opencl/lu.cpp                    |  11 +-
 4 files changed, 245 insertions(+), 1 deletion(-)
 create mode 100644 src/backend/opencl/cpu/cpu_lapack_helper.hpp
 create mode 100644 src/backend/opencl/cpu/cpu_lu.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_lu.hpp

diff --git a/src/backend/opencl/cpu/cpu_lapack_helper.hpp b/src/backend/opencl/cpu/cpu_lapack_helper.hpp
new file mode 100644
index 0000000000..174022e772
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_lapack_helper.hpp
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#ifndef AFCPU_LAPACK
+#define AFCPU_LAPACK
+
+#include <types.hpp>
+
+#define lapack_complex_float opencl::cfloat
+#define lapack_complex_double opencl::cdouble
+#define LAPACK_PREFIX LAPACKE_
+#define ORDER_TYPE int
+#define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
+#define LAPACK_NAME(fn) LAPACKE_##fn
+
+#ifdef __APPLE__
+#include <Accelerate/Accelerate.h>
+#include <lapacke.hpp>
+#undef AF_LAPACK_COL_MAJOR
+#define AF_LAPACK_COL_MAJOR 0
+#else
+#ifdef USE_MKL
+#include<mkl_lapacke.h>
+#else // NETLIB LAPACKE
+#include<lapacke.h>
+#endif
+#endif
+
+#endif
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
new file mode 100644
index 0000000000..f415cb3983
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -0,0 +1,178 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_lu.hpp>
+#include <err_common.hpp>
+
+#include <af/dim4.hpp>
+#include <handle.hpp>
+#include <iostream>
+#include <cassert>
+
+#include <range.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T>
+using getrf_func_def = int (*)(ORDER_TYPE, int, int,
+                               T*, int,
+                               int*);
+
+#define LU_FUNC_DEF( FUNC )                                     \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+
+#define LU_FUNC( FUNC, TYPE, PREFIX )                           \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
+{ return & LAPACK_NAME(PREFIX##FUNC); }
+
+LU_FUNC_DEF( getrf )
+LU_FUNC(getrf , float  , s)
+LU_FUNC(getrf , double , d)
+LU_FUNC(getrf , cfloat , c)
+LU_FUNC(getrf , cdouble, z)
+
+template<typename T>
+void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
+{
+    T *l = getMappedPtr<T>(lower.get());
+    T *u = getMappedPtr<T>(upper.get());
+    T *i = getMappedPtr<T>(in.get());
+
+    dim4 ldm = lower.dims();
+    dim4 udm = upper.dims();
+    dim4 idm = in.dims();
+
+    dim4 lst = lower.strides();
+    dim4 ust = upper.strides();
+    dim4 ist = in.strides();
+
+    for(dim_t ow = 0; ow < idm[3]; ow++) {
+        const dim_t lW = ow * lst[3];
+        const dim_t uW = ow * ust[3];
+        const dim_t iW = ow * ist[3];
+
+        for(dim_t oz = 0; oz < idm[2]; oz++) {
+            const dim_t lZW = lW + oz * lst[2];
+            const dim_t uZW = uW + oz * ust[2];
+            const dim_t iZW = iW + oz * ist[2];
+
+            for(dim_t oy = 0; oy < idm[1]; oy++) {
+                const dim_t lYZW = lZW + oy * lst[1];
+                const dim_t uYZW = uZW + oy * ust[1];
+                const dim_t iYZW = iZW + oy * ist[1];
+
+                for(dim_t ox = 0; ox < idm[0]; ox++) {
+                    const dim_t lMem = lYZW + ox;
+                    const dim_t uMem = uYZW + ox;
+                    const dim_t iMem = iYZW + ox;
+                    if(ox > oy) {
+                        if(oy < ldm[1])
+                            l[lMem] = i[iMem];
+                        if(ox < udm[0])
+                            u[uMem] = scalar<T>(0);
+                    } else if (oy > ox) {
+                        if(oy < ldm[1])
+                            l[lMem] = scalar<T>(0);
+                        if(ox < udm[0])
+                            u[uMem] = i[iMem];
+                    } else if(ox == oy) {
+                        if(oy < ldm[1])
+                            l[lMem] = scalar<T>(1.0);
+                        if(ox < udm[0])
+                            u[uMem] = i[iMem];
+                    }
+                }
+            }
+        }
+    }
+
+    unmapPtr(lower.get(), l);
+    unmapPtr(upper.get(), u);
+    unmapPtr(in.get(), i);
+}
+
+void convertPivot(Array<int> &pivot, int out_sz)
+{
+    Array<int> p = range<int>(dim4(out_sz), 0); // Runs opencl
+
+    int *d_pi = getMappedPtr<int>(pivot.get());
+    int *d_po = getMappedPtr<int>(p.get());
+
+    dim_t d0 = pivot.dims()[0];
+
+    for(int j = 0; j < (int)d0; j++) {
+        // 1 indexed in pivot
+        std::swap(d_po[j], d_po[d_pi[j] - 1]);
+    }
+
+    unmapPtr(pivot.get(), d_pi);
+    unmapPtr(p.get(), d_po);
+
+    pivot = p;
+}
+
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
+{
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+
+    Array<T> in_copy = copyArray<T>(in);
+    pivot = lu_inplace(in_copy);
+
+    // SPLIT into lower and upper
+    dim4 ldims(M, min(M, N));
+    dim4 udims(min(M, N), N);
+    lower = createEmptyArray<T>(ldims);
+    upper = createEmptyArray<T>(udims);
+
+    lu_split<T>(lower, upper, in_copy);
+}
+
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
+{
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+
+    Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
+
+    T *inPtr = getMappedPtr<T>(in.get());
+    int *pivotPtr = getMappedPtr<int>(pivot.get());
+
+    getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
+                    inPtr, in.strides()[1],
+                    pivotPtr);
+
+    unmapPtr(in.get(), inPtr);
+    unmapPtr(pivot.get(), pivotPtr);
+
+    if(convert_pivot) convertPivot(pivot, M);
+
+    return pivot;
+}
+
+#define INSTANTIATE_LU(T)                                                                           \
+    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
+    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+
+INSTANTIATE_LU(float)
+INSTANTIATE_LU(cfloat)
+INSTANTIATE_LU(double)
+INSTANTIATE_LU(cdouble)
+
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_lu.hpp b/src/backend/opencl/cpu/cpu_lu.hpp
new file mode 100644
index 0000000000..6c038f20c7
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_lu.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T>
+    void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+
+    template<typename T>
+    Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+}
+}
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index 2d94d4d326..0bc6bd5283 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -14,7 +14,9 @@
 #include <kernel/lu_split.hpp>
 #include <copy.hpp>
 #include <blas.hpp>
+#include <platform.hpp>
 #include <magma/magma.h>
+#include <cpu/cpu_lu.hpp>
 
 namespace opencl
 {
@@ -41,8 +43,11 @@ Array<int> convertPivot(int *ipiv, int in_sz, int out_sz)
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 {
-
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::lu(lower, upper, pivot, in);
+        }
+
         dim4 iDims = in.dims();
         int M = iDims[0];
         int N = iDims[1];
@@ -67,6 +72,10 @@ template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 {
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::lu_inplace(in, convert_pivot);
+        }
+
         initBlas();
         dim4 iDims = in.dims();
         int M = iDims[0];

From 88e910d9a9e91dc38b888fabd70a3a307e79307c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 11:21:01 -0500
Subject: [PATCH 0264/2677] Add OpenCL-CPU fallback for Cholesky

---
 src/backend/opencl/cholesky.cpp         | 13 +++-
 src/backend/opencl/cpu/cpu_cholesky.cpp | 88 +++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_cholesky.hpp | 22 +++++++
 src/backend/opencl/cpu/cpu_triangle.hpp | 52 +++++++++++++++
 4 files changed, 173 insertions(+), 2 deletions(-)
 create mode 100644 src/backend/opencl/cpu/cpu_cholesky.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_cholesky.hpp
 create mode 100644 src/backend/opencl/cpu/cpu_triangle.hpp

diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index 78fe999645..a2034a331a 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -8,14 +8,16 @@
  ********************************************************/
 
 #include <cholesky.hpp>
-#include <copy.hpp>
 #include <err_common.hpp>
-#include <blas.hpp>
 #include <err_opencl.hpp>
+#include <blas.hpp>
+#include <copy.hpp>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <magma/magma.h>
 #include <triangle.hpp>
+#include <platform.hpp>
+#include <cpu/cpu_cholesky.hpp>
 
 namespace opencl
 {
@@ -24,6 +26,10 @@ template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper)
 {
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::cholesky_inplace(in, is_upper);
+        }
+
         initBlas();
 
         dim4 iDims = in.dims();
@@ -46,6 +52,9 @@ template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
 {
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::cholesky(info, in, is_upper);
+        }
 
         Array<T> out = copyArray<T>(in);
         *info = cholesky_inplace(out, is_upper);
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
new file mode 100644
index 0000000000..234df2b242
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -0,0 +1,88 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_cholesky.hpp>
+#include <err_common.hpp>
+
+#include <af/dim4.hpp>
+#include <handle.hpp>
+#include <iostream>
+#include <cassert>
+
+#include <cpu/cpu_triangle.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T>
+using potrf_func_def = int (*)(ORDER_TYPE, char,
+                               int,
+                               T*, int);
+
+#define CH_FUNC_DEF( FUNC )                                     \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+
+#define CH_FUNC( FUNC, TYPE, PREFIX )                           \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
+{ return & LAPACK_NAME(PREFIX##FUNC); }
+
+CH_FUNC_DEF( potrf )
+CH_FUNC(potrf , float  , s)
+CH_FUNC(potrf , double , d)
+CH_FUNC(potrf , cfloat , c)
+CH_FUNC(potrf , cdouble, z)
+
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
+{
+    Array<T> out = copyArray<T>(in);
+    *info = cholesky_inplace(out, is_upper);
+
+    T* oPtr = getMappedPtr<T>(out.get());
+    if (is_upper) triangle<T, true , false>(oPtr, oPtr, out.dims(), out.strides(), out.strides());
+    else          triangle<T, false, false>(oPtr, oPtr, out.dims(), out.strides(), out.strides());
+    unmapPtr(out.get(), oPtr);
+
+    return out;
+}
+
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper)
+{
+    dim4 iDims = in.dims();
+    int N = iDims[0];
+
+    char uplo = 'L';
+    if(is_upper)
+        uplo = 'U';
+
+    T* inPtr = getMappedPtr<T>(in.get());
+    int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo,
+                               N, inPtr, in.strides()[1]);
+    unmapPtr(in.get(), inPtr);
+
+    return info;
+}
+
+#define INSTANTIATE_CH(T)                                                                   \
+    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
+    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
+
+
+INSTANTIATE_CH(float)
+INSTANTIATE_CH(cfloat)
+INSTANTIATE_CH(double)
+INSTANTIATE_CH(cdouble)
+
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_cholesky.hpp b/src/backend/opencl/cpu/cpu_cholesky.hpp
new file mode 100644
index 0000000000..041e93980e
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_cholesky.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T>
+    Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
+
+    template<typename T>
+    int cholesky_inplace(Array<T> &in, const bool is_upper);
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_triangle.hpp b/src/backend/opencl/cpu/cpu_triangle.hpp
new file mode 100644
index 0000000000..5e40f929b9
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_triangle.hpp
@@ -0,0 +1,52 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#ifndef CPU_LAPACK_TRIANGLE
+#define CPU_LAPACK_TRIANGLE
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T, bool is_upper, bool is_unit_diag>
+void triangle(T *o, const T *i, const dim4 odm, const dim4 ost, const dim4 ist)
+{
+    for(dim_t ow = 0; ow < odm[3]; ow++) {
+        const dim_t oW = ow * ost[3];
+        const dim_t iW = ow * ist[3];
+
+        for(dim_t oz = 0; oz < odm[2]; oz++) {
+            const dim_t oZW = oW + oz * ost[2];
+            const dim_t iZW = iW + oz * ist[2];
+
+            for(dim_t oy = 0; oy < odm[1]; oy++) {
+                const dim_t oYZW = oZW + oy * ost[1];
+                const dim_t iYZW = iZW + oy * ist[1];
+
+                for(dim_t ox = 0; ox < odm[0]; ox++) {
+                    const dim_t oMem = oYZW + ox;
+                    const dim_t iMem = iYZW + ox;
+
+                    bool cond = is_upper ? (oy >= ox) : (oy <= ox);
+                    bool do_unit_diag = (is_unit_diag && ox == oy);
+                    if(cond) {
+                        o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
+                    } else {
+                        o[oMem] = scalar<T>(0);
+                    }
+                }
+            }
+        }
+    }
+}
+
+}
+}
+
+#endif

From 872acfb2c15b7e44014c42f38df3d5843abee860 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 12:27:47 -0500
Subject: [PATCH 0265/2677] Add OpenCL-CPU fallback for QR

---
 src/backend/opencl/cpu/cpu_qr.cpp | 130 ++++++++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_qr.hpp |  22 +++++
 src/backend/opencl/qr.cpp         |  19 ++++-
 3 files changed, 167 insertions(+), 4 deletions(-)
 create mode 100644 src/backend/opencl/cpu/cpu_qr.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_qr.hpp

diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
new file mode 100644
index 0000000000..080ebb6b69
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -0,0 +1,130 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_qr.hpp>
+#include <err_common.hpp>
+#include <copy.hpp>
+
+#include <af/dim4.hpp>
+#include <handle.hpp>
+#include <iostream>
+#include <cassert>
+
+#include <cpu/cpu_triangle.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T>
+using geqrf_func_def = int (*)(ORDER_TYPE, int, int,
+                               T*, int,
+                               T*);
+
+template<typename T>
+using gqr_func_def = int (*)(ORDER_TYPE, int, int, int,
+                             T*, int,
+                             const T*);
+
+#define QR_FUNC_DEF( FUNC )                                         \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+
+#define QR_FUNC( FUNC, TYPE, PREFIX )                               \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
+{ return & LAPACK_NAME(PREFIX##FUNC); }
+
+QR_FUNC_DEF( geqrf )
+QR_FUNC(geqrf , float  , s)
+QR_FUNC(geqrf , double , d)
+QR_FUNC(geqrf , cfloat , c)
+QR_FUNC(geqrf , cdouble, z)
+
+#define GQR_FUNC_DEF( FUNC )                                         \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+#define GQR_FUNC( FUNC, TYPE, PREFIX )                               \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()             \
+{ return & LAPACK_NAME(PREFIX); }
+
+GQR_FUNC_DEF( gqr )
+GQR_FUNC(gqr , float  , sorgqr)
+GQR_FUNC(gqr , double , dorgqr)
+GQR_FUNC(gqr , cfloat , cungqr)
+GQR_FUNC(gqr , cdouble, zungqr)
+
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
+{
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+
+    dim4 padDims(M, max(M, N));
+    q = padArray<T, T>(in, padDims, scalar<T>(0));
+    q.resetDims(iDims);
+    t = qr_inplace(q);
+
+    // SPLIT into q and r
+    dim4 rdims(M, N);
+    r = createEmptyArray<T>(rdims);
+
+    T *qPtr = getMappedPtr<T>(q.get());
+    T *rPtr = getMappedPtr<T>(r.get());
+    T *tPtr = getMappedPtr<T>(t.get());
+
+    triangle<T, true, false>(rPtr, qPtr, rdims, r.strides(), q.strides());
+
+    gqr_func<T>()(AF_LAPACK_COL_MAJOR,
+                  M, M, min(M, N),
+                  qPtr, q.strides()[1],
+                  tPtr);
+
+    unmapPtr(q.get(), qPtr);
+    unmapPtr(r.get(), rPtr);
+    unmapPtr(t.get(), tPtr);
+
+    q.resetDims(dim4(M, M));
+}
+
+template<typename T>
+Array<T> qr_inplace(Array<T> &in)
+{
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+
+    Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
+
+    T *iPtr = getMappedPtr<T>(in.get());
+    T *tPtr = getMappedPtr<T>(t.get());
+
+    geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
+                    iPtr, in.strides()[1],
+                    tPtr);
+
+    unmapPtr(in.get(), iPtr);
+    unmapPtr(t.get(), tPtr);
+
+    return t;
+}
+
+#define INSTANTIATE_QR(T)                                                                           \
+    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
+    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+
+INSTANTIATE_QR(float)
+INSTANTIATE_QR(cfloat)
+INSTANTIATE_QR(double)
+INSTANTIATE_QR(cdouble)
+
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_qr.hpp b/src/backend/opencl/cpu/cpu_qr.hpp
new file mode 100644
index 0000000000..c499b9d03b
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_qr.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T>
+    void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+
+    template<typename T>
+    Array<T> qr_inplace(Array<T> &in);
+}
+}
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 9e30b43435..56101a8b97 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -9,16 +9,19 @@
 
 #include <qr.hpp>
 #include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <blas.hpp>
 #include <copy.hpp>
-#include <identity.hpp>
-#include <err_opencl.hpp>
+
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+
 #include <magma/magma.h>
 #include <magma/magma_helper.h>
 #include <magma/magma_data.h>
 #include <kernel/triangle.hpp>
-
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#include <platform.hpp>
+#include <identity.hpp>
+#include <cpu/cpu_qr.hpp>
 
 namespace opencl
 {
@@ -27,6 +30,10 @@ template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig)
 {
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::qr(q, r, t, orig);
+        }
+
         initBlas();
         dim4 iDims = orig.dims();
         int M = iDims[0];
@@ -81,6 +88,10 @@ template<typename T>
 Array<T> qr_inplace(Array<T> &in)
 {
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::qr_inplace(in);
+        }
+
         initBlas();
         dim4 iDims = in.dims();
         int M = iDims[0];

From 59a9df0957537e4933c145482034704d68915c1b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 12:48:30 -0500
Subject: [PATCH 0266/2677] Add OpenCL-CPU fallback for SVD

---
 src/backend/opencl/cpu/cpu_svd.cpp | 103 +++++++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_svd.hpp |  22 ++++++
 src/backend/opencl/svd.cpp         |  10 +++
 3 files changed, 135 insertions(+)
 create mode 100644 src/backend/opencl/cpu/cpu_svd.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_svd.hpp

diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
new file mode 100644
index 0000000000..85b9ee8280
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -0,0 +1,103 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_svd.hpp>
+#include <err_common.hpp>
+
+#include <copy.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+#define SVD_FUNC_DEF( FUNC )                                            \
+    template<typename T,typename Tr> svd_func_def<T, Tr> svd_func();
+
+#define SVD_FUNC( FUNC, T, Tr, PREFIX )                     \
+    template<> svd_func_def<T, Tr>     svd_func<T, Tr>()    \
+    { return & LAPACK_NAME(PREFIX##FUNC); }
+
+#if defined(USE_MKL) || defined(__APPLE__)
+
+    template<typename T, typename Tr>
+    using svd_func_def = int (*)(ORDER_TYPE,
+                                 char jobz,
+                                 int m, int n,
+                                 T* in, int ldin,
+                                 Tr* s,
+                                 T* u, int ldu,
+                                 T* vt, int ldvt);
+
+    SVD_FUNC_DEF( gesdd )
+    SVD_FUNC(gesdd, float  , float , s)
+    SVD_FUNC(gesdd, double , double, d)
+    SVD_FUNC(gesdd, cfloat , float , c)
+    SVD_FUNC(gesdd, cdouble, double, z)
+
+#else   // Atlas causes memory freeing issues with using gesdd
+
+    template<typename T, typename Tr>
+    using svd_func_def = int (*)(ORDER_TYPE,
+                                 char jobu, char jobvt,
+                                 int m, int n,
+                                 T* in, int ldin,
+                                 Tr* s,
+                                 T* u, int ldu,
+                                 T* vt, int ldvt,
+                                 Tr *superb);
+
+    SVD_FUNC_DEF( gesvd )
+    SVD_FUNC(gesvd, float  , float , s)
+    SVD_FUNC(gesvd, double , double, d)
+    SVD_FUNC(gesvd, cfloat , float , c)
+    SVD_FUNC(gesvd, cdouble, double, z)
+
+#endif
+
+    template <typename T, typename Tr>
+    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
+    {
+        dim4 iDims = in.dims();
+        int M = iDims[0];
+        int N = iDims[1];
+
+        Tr *sPtr = getMappedPtr<Tr>(s.get());
+        T  *uPtr = getMappedPtr<T >(u.get());
+        T  *vPtr = getMappedPtr<T >(vt.get());
+        T  *iPtr = getMappedPtr<T >(in.get());
+
+#if defined(USE_MKL) || defined(__APPLE__)
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, iPtr, in.strides()[1],
+                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1]);
+#else
+        std::vector<Tr> superb(std::min(M, N));
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, iPtr, in.strides()[1],
+                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1], &superb[0]);
+#endif
+    }
+
+    template <typename T, typename Tr>
+    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
+    {
+        Array<T> in_copy = copyArray<T>(in);
+        svdInPlace(s, u, vt, in_copy);
+    }
+
+#define INSTANTIATE_SVD(T, Tr)                                          \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, const Array<T> &in); \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, Array<T> &in);
+
+    INSTANTIATE_SVD(float  , float )
+    INSTANTIATE_SVD(double , double)
+    INSTANTIATE_SVD(cfloat , float )
+    INSTANTIATE_SVD(cdouble, double)
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_svd.hpp b/src/backend/opencl/cpu/cpu_svd.hpp
new file mode 100644
index 0000000000..4f271af8b9
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_svd.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T, typename Tr>
+    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
+
+    template<typename T, typename Tr>
+    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+}
+}
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 77f7c8aa37..61da27bdcd 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -20,6 +20,8 @@
 #include <magma/magma.h>
 #include <magma/magma_cpu_lapack.h>
 #include <magma/magma_helper.h>
+#include <platform.hpp>
+#include <cpu/cpu_svd.hpp>
 
 namespace opencl
 {
@@ -196,6 +198,10 @@ void svd(Array<T > &arrU,
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
 {
+    if(OpenCLCPUOffload()) {
+        return cpu::svdInPlace(s, u, vt, in);
+    }
+
     initBlas();
     svd<T, Tr>(u, s, vt, in, true);
 }
@@ -203,6 +209,10 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
 template<typename T, typename Tr>
 void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
 {
+    if(OpenCLCPUOffload()) {
+        return cpu::svd(s, u, vt, in);
+    }
+
     dim4 iDims = in.dims();
     int M = iDims[0];
     int N = iDims[1];

From ffb191cbce56297e391f2f12ec45351dd8ebf1d8 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 13:08:01 -0500
Subject: [PATCH 0267/2677] Add OpenCL-CPU fallback for solve

---
 src/backend/opencl/cpu/cpu_solve.cpp | 187 +++++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_solve.hpp |  23 ++++
 src/backend/opencl/solve.cpp         |  11 ++
 3 files changed, 221 insertions(+)
 create mode 100644 src/backend/opencl/cpu/cpu_solve.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_solve.hpp

diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
new file mode 100644
index 0000000000..824bce2173
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -0,0 +1,187 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_solve.hpp>
+#include <err_common.hpp>
+#include <copy.hpp>
+#include <math.hpp>
+
+#include <af/dim4.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T>
+using gesv_func_def = int (*)(ORDER_TYPE, int, int,
+                              T *, int,
+                              int *,
+                              T *, int);
+
+template<typename T>
+using gels_func_def = int (*)(ORDER_TYPE, char,
+                              int, int, int,
+                              T *, int,
+                              T *, int);
+
+template<typename T>
+using getrs_func_def = int (*)(ORDER_TYPE, char,
+                               int, int,
+                               const T *, int,
+                               const int *,
+                               T *, int);
+
+template<typename T>
+using trtrs_func_def = int (*)(ORDER_TYPE,
+                               char, char, char,
+                               int, int,
+                               const T *, int,
+                               T *, int);
+
+
+#define SOLVE_FUNC_DEF( FUNC )                                      \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+
+#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                            \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
+{ return & LAPACK_NAME(PREFIX##FUNC); }
+
+SOLVE_FUNC_DEF( gesv )
+SOLVE_FUNC(gesv , float  , s)
+SOLVE_FUNC(gesv , double , d)
+SOLVE_FUNC(gesv , cfloat , c)
+SOLVE_FUNC(gesv , cdouble, z)
+
+SOLVE_FUNC_DEF( gels )
+SOLVE_FUNC(gels , float  , s)
+SOLVE_FUNC(gels , double , d)
+SOLVE_FUNC(gels , cfloat , c)
+SOLVE_FUNC(gels , cdouble, z)
+
+SOLVE_FUNC_DEF( getrs )
+SOLVE_FUNC(getrs , float  , s)
+SOLVE_FUNC(getrs , double , d)
+SOLVE_FUNC(getrs , cfloat , c)
+SOLVE_FUNC(getrs , cdouble, z)
+
+SOLVE_FUNC_DEF( trtrs )
+SOLVE_FUNC(trtrs , float  , s)
+SOLVE_FUNC(trtrs , double , d)
+SOLVE_FUNC(trtrs , cfloat , c)
+SOLVE_FUNC(trtrs , cdouble, z)
+
+template<typename T>
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
+                 const Array<T> &b, const af_mat_prop options)
+{
+    int N = A.dims()[0];
+    int NRHS = b.dims()[1];
+
+    Array<T> B = copyArray<T>(b);
+
+    T *aPtr = getMappedPtr<T>(A.get());
+    T *bPtr = getMappedPtr<T>(B.get());
+    int *pPtr = getMappedPtr<int>(pivot.get());
+
+    getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
+                    N, NRHS,
+                    aPtr, A.strides()[1],
+                    pPtr,
+                    bPtr, B.strides()[1]);
+
+    unmapPtr(A.get(), aPtr);
+    unmapPtr(B.get(), bPtr);
+    unmapPtr(pivot.get(), pPtr);
+
+    return B;
+}
+
+template<typename T>
+Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
+{
+    Array<T> B = copyArray<T>(b);
+    int N = B.dims()[0];
+    int NRHS = B.dims()[1];
+
+    T *aPtr = getMappedPtr<T>(A.get());
+    T *bPtr = getMappedPtr<T>(B.get());
+
+    trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
+                    options & AF_MAT_UPPER ? 'U' : 'L',
+                    'N', // transpose flag
+                    options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
+                    N, NRHS,
+                    aPtr, A.strides()[1],
+                    bPtr, B.strides()[1]);
+
+    unmapPtr(A.get(), aPtr);
+    unmapPtr(B.get(), bPtr);
+
+    return B;
+}
+
+
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
+{
+
+    if (options & AF_MAT_UPPER ||
+        options & AF_MAT_LOWER) {
+        return triangleSolve<T>(a, b, options);
+    }
+
+    int M = a.dims()[0];
+    int N = a.dims()[1];
+    int K = b.dims()[1];
+
+    Array<T> A = copyArray<T>(a);
+    Array<T> B = padArray<T, T>(b, dim4(max(M, N), K), scalar<T>(0));
+
+    T *aPtr = getMappedPtr<T>(A.get());
+    T *bPtr = getMappedPtr<T>(B.get());
+
+    if(M == N) {
+        std::vector<int> pivot(N);
+        gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K,
+                       aPtr, A.strides()[1],
+                       &pivot.front(),
+                       bPtr, B.strides()[1]);
+    } else {
+        int sM = a.strides()[1];
+        int sN = a.strides()[2] / sM;
+
+        gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
+                       M, N, K,
+                       aPtr, A.strides()[1],
+                       bPtr, max(sM, sN));
+        B.resetDims(dim4(N, K));
+    }
+
+    unmapPtr(A.get(), aPtr);
+    unmapPtr(B.get(), bPtr);
+
+    return B;
+}
+
+#define INSTANTIATE_SOLVE(T)                                            \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
+                               const af_mat_prop options);              \
+    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
+                                 const Array<T> &b, const af_mat_prop options); \
+
+INSTANTIATE_SOLVE(float)
+INSTANTIATE_SOLVE(cfloat)
+INSTANTIATE_SOLVE(double)
+INSTANTIATE_SOLVE(cdouble)
+
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_solve.hpp b/src/backend/opencl/cpu/cpu_solve.hpp
new file mode 100644
index 0000000000..6c3de642ad
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_solve.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T>
+    Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
+
+    template<typename T>
+    Array<T> solveLU(const Array<T> &a, const Array<int> &pivot,
+                     const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
+}
+}
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 6d2bea4b4e..4fede07e56 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -25,6 +25,9 @@
 #include <algorithm>
 #include <string>
 
+#include <platform.hpp>
+#include <cpu/cpu_solve.hpp>
+
 namespace opencl
 {
 
@@ -32,6 +35,10 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
+    if(OpenCLCPUOffload()) {
+        return cpu::solveLU(A, pivot, b, options);
+    }
+
     int N = A.dims()[0];
     int NRHS = b.dims()[1];
 
@@ -296,6 +303,10 @@ template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 {
     try {
+        if(OpenCLCPUOffload()) {
+            return cpu::solve(a, b, options);
+        }
+
         initBlas();
 
         if (options & AF_MAT_UPPER ||

From 4e2d46cec61b0226f3e1cbe558b58aa21c4f4cc5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 15:26:08 -0500
Subject: [PATCH 0268/2677] Add OpenCL-CPU fallback for inverse

---
 src/backend/opencl/cpu/cpu_inverse.cpp | 77 ++++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_inverse.hpp | 19 +++++++
 src/backend/opencl/inverse.cpp         |  6 ++
 3 files changed, 102 insertions(+)
 create mode 100644 src/backend/opencl/cpu/cpu_inverse.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_inverse.hpp

diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
new file mode 100644
index 0000000000..f1418b23bd
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -0,0 +1,77 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_inverse.hpp>
+#include <err_common.hpp>
+#include <copy.hpp>
+#include <cpu/cpu_lu.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T>
+using getri_func_def = int (*)(ORDER_TYPE, int,
+                               T *, int,
+                               const int *);
+
+#define INV_FUNC_DEF( FUNC )                                        \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+#define INV_FUNC( FUNC, TYPE, PREFIX )                              \
+template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
+{ return & LAPACK_NAME(PREFIX##FUNC); }
+
+INV_FUNC_DEF( getri )
+INV_FUNC(getri , float  , s)
+INV_FUNC(getri , double , d)
+INV_FUNC(getri , cfloat , c)
+INV_FUNC(getri , cdouble, z)
+
+template<typename T>
+Array<T> inverse(const Array<T> &in)
+{
+    int M = in.dims()[0];
+    //int N = in.dims()[1];
+
+    // This condition is already handled in opencl/inverse.cpp
+    //if (M != N) {
+        //Array<T> I = identity<T>(in.dims());
+        //return solve(in, I);
+    //}
+
+    Array<T> A = copyArray<T>(in);
+
+    Array<int> pivot = cpu::lu_inplace<T>(A, false);
+
+    T *aPtr = getMappedPtr<T>(A.get());
+    int *pPtr = getMappedPtr<int>(pivot.get());
+
+    getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
+                    aPtr, A.strides()[1],
+                    pPtr);
+
+    unmapPtr(A.get(), aPtr);
+    unmapPtr(pivot.get(), pPtr);
+
+    return A;
+}
+
+#define INSTANTIATE(T)                                                                   \
+    template Array<T> inverse<T> (const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+
+}
+}
diff --git a/src/backend/opencl/cpu/cpu_inverse.hpp b/src/backend/opencl/cpu/cpu_inverse.hpp
new file mode 100644
index 0000000000..38581a1906
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_inverse.hpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+    template<typename T>
+    Array<T> inverse(const Array<T> &in);
+}
+}
diff --git a/src/backend/opencl/inverse.cpp b/src/backend/opencl/inverse.cpp
index eb8348edd4..df955547ba 100644
--- a/src/backend/opencl/inverse.cpp
+++ b/src/backend/opencl/inverse.cpp
@@ -12,6 +12,8 @@
 #include <identity.hpp>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#include <platform.hpp>
+#include <cpu/cpu_inverse.hpp>
 
 namespace opencl
 {
@@ -19,6 +21,10 @@ namespace opencl
 template<typename T>
 Array<T> inverse(const Array<T> &in)
 {
+    if(OpenCLCPUOffload()) {
+        if (in.dims()[0] == in.dims()[1])
+            return cpu::inverse(in);
+    }
     Array<T> I = identity<T>(in.dims());
     return solve<T>(in, I);
 }

From 210a64cbb6c824d483f1ed201e78d151423c7a46 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 16:56:12 -0500
Subject: [PATCH 0269/2677] Clean up header files in opencl/cpu/

---
 src/backend/opencl/cpu/cpu_blas.cpp          |  7 ++--
 src/backend/opencl/cpu/cpu_blas.hpp          |  2 +-
 src/backend/opencl/cpu/cpu_cholesky.cpp      | 10 ++----
 src/backend/opencl/cpu/cpu_helper.hpp        | 37 ++++++++++++++------
 src/backend/opencl/cpu/cpu_inverse.cpp       |  5 ++-
 src/backend/opencl/cpu/cpu_lapack_helper.hpp | 35 ------------------
 src/backend/opencl/cpu/cpu_lu.cpp            | 11 ++----
 src/backend/opencl/cpu/cpu_qr.cpp            | 11 ++----
 src/backend/opencl/cpu/cpu_solve.cpp         |  5 +--
 src/backend/opencl/cpu/cpu_svd.cpp           |  8 +++--
 src/backend/opencl/cpu/cpu_triangle.hpp      |  3 ++
 11 files changed, 48 insertions(+), 86 deletions(-)
 delete mode 100644 src/backend/opencl/cpu/cpu_lapack_helper.hpp

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index ff7170d331..8c77fff8fd 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -7,12 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_blas.hpp>
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <cassert>
-#include <err_common.hpp>
-#include <platform.hpp>
+#include <math.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
index 836d6e02de..908742471d 100644
--- a/src/backend/opencl/cpu/cpu_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_helper.hpp>
+#include <Array.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index 234df2b242..74bbf594ae 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -7,16 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_cholesky.hpp>
-#include <err_common.hpp>
-
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <iostream>
-#include <cassert>
-
 #include <cpu/cpu_triangle.hpp>
+#include <copy.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index afc60d3b9f..d407bb83cc 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -7,22 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#ifndef AF_OPENCL_CPU
+#define AF_OPENCL_CPU
+
 #include <af/defines.h>
-#include <af/blas.h>
-#include <af/lapack.h>
 #include <Array.hpp>
 #include <memory.hpp>
+#include <types.hpp>
+#include <err_common.hpp>
+#include <platform.hpp>
+
+#define lapack_complex_float opencl::cfloat
+#define lapack_complex_double opencl::cdouble
+#define LAPACK_PREFIX LAPACKE_
+#define ORDER_TYPE int
+#define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
+#define LAPACK_NAME(fn) LAPACKE_##fn
 
 #ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#else
-#ifdef USE_MKL
-#include <mkl_cblas.h>
+    #include <Accelerate/Accelerate.h>
+    #include <lapacke.hpp>
+    #undef AF_LAPACK_COL_MAJOR
+    #define AF_LAPACK_COL_MAJOR 0
 #else
-extern "C" {
-#include <cblas.h>
-}
-#endif
+    #ifdef USE_MKL
+        #include <mkl_cblas.h>
+        #include<mkl_lapacke.h>
+    #else
+        extern "C" {
+        #include <cblas.h>
+        }
+        #include<lapacke.h>
+    #endif
 #endif
 
 // TODO: Ask upstream for a more official way to detect it
@@ -44,3 +60,4 @@ namespace cpu
 }
 }
 
+#endif
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index f1418b23bd..24b4a670fd 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_inverse.hpp>
-#include <err_common.hpp>
-#include <copy.hpp>
 #include <cpu/cpu_lu.hpp>
+#include <copy.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_lapack_helper.hpp b/src/backend/opencl/cpu/cpu_lapack_helper.hpp
deleted file mode 100644
index 174022e772..0000000000
--- a/src/backend/opencl/cpu/cpu_lapack_helper.hpp
+++ /dev/null
@@ -1,35 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#ifndef AFCPU_LAPACK
-#define AFCPU_LAPACK
-
-#include <types.hpp>
-
-#define lapack_complex_float opencl::cfloat
-#define lapack_complex_double opencl::cdouble
-#define LAPACK_PREFIX LAPACKE_
-#define ORDER_TYPE int
-#define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
-#define LAPACK_NAME(fn) LAPACKE_##fn
-
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#include <lapacke.hpp>
-#undef AF_LAPACK_COL_MAJOR
-#define AF_LAPACK_COL_MAJOR 0
-#else
-#ifdef USE_MKL
-#include<mkl_lapacke.h>
-#else // NETLIB LAPACKE
-#include<lapacke.h>
-#endif
-#endif
-
-#endif
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index f415cb3983..293cb8af86 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -7,15 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_lu.hpp>
-#include <err_common.hpp>
-
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <iostream>
-#include <cassert>
-
+#include <math.hpp>
+#include <copy.hpp>
 #include <range.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index 080ebb6b69..24a915a5d1 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -7,17 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_qr.hpp>
-#include <err_common.hpp>
-#include <copy.hpp>
-
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <iostream>
-#include <cassert>
-
 #include <cpu/cpu_triangle.hpp>
+#include <copy.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 824bce2173..522454aa81 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -7,14 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
-#include <err_common.hpp>
 #include <copy.hpp>
 #include <math.hpp>
 
-#include <af/dim4.hpp>
-
 namespace opencl
 {
 namespace cpu
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index 85b9ee8280..66e4c0a7c5 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -7,10 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cpu/cpu_lapack_helper.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_svd.hpp>
-#include <err_common.hpp>
-
 #include <copy.hpp>
 
 namespace opencl
@@ -82,6 +80,10 @@ namespace cpu
         svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, iPtr, in.strides()[1],
                           sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1], &superb[0]);
 #endif
+        unmapPtr(s.get() , sPtr);
+        unmapPtr(u.get() , uPtr);
+        unmapPtr(vt.get(), vPtr);
+        unmapPtr(in.get(), iPtr);
     }
 
     template <typename T, typename Tr>
diff --git a/src/backend/opencl/cpu/cpu_triangle.hpp b/src/backend/opencl/cpu/cpu_triangle.hpp
index 5e40f929b9..f953d58507 100644
--- a/src/backend/opencl/cpu/cpu_triangle.hpp
+++ b/src/backend/opencl/cpu/cpu_triangle.hpp
@@ -9,6 +9,9 @@
 
 #ifndef CPU_LAPACK_TRIANGLE
 #define CPU_LAPACK_TRIANGLE
+
+#include <math.hpp>
+
 namespace opencl
 {
 namespace cpu

From e08d41bcced48eadca8c7f83e01eee616b0dc62a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 17:10:34 -0500
Subject: [PATCH 0270/2677] Update environment variables doc

---
 .../configuring_arrayfire_environment.md      | 53 +++++++++++++++----
 1 file changed, 43 insertions(+), 10 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 054068e224..3de8fbe295 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -18,6 +18,16 @@ This is the path with ArrayFire gets installed, ie. the includes and libs are
 present in this directory. You can use this variable to add include paths and
 libraries to your projects.
 
+AF_PRINT_ERRORS {#af_print_errors}
+-------------------------------------------------------------------------------
+
+When AF_PRINT_ERRORS is set to 1, the exceptions thrown are more verbose and
+detailed. This helps in locating the exact failure.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AF_PRINT_ERRORS=1 ./myprogram_opencl
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 AF_CUDA_DEFAULT_DEVICE {#af_cuda_default_device}
 -------------------------------------------------------------------------------
 
@@ -44,25 +54,48 @@ AF_OPENCL_DEFAULT_DEVICE=1 ./myprogram_opencl
 Note: af::setDevice call in the source code will take precedence over this
 variable.
 
+AF_OPENCL_CPU_OFFLOAD {#af_opencl_cpu_offload}
+-------------------------------------------------------------------------------
+
+When this variable is set to 1, and the selected OpenCL device has unified
+memory with the host (ie. `CL_DEVICE_HOST_UNIFIED_MEMORY` is true for device),
+then certain functions are offloaded to run on the CPU using mapped buffers.
+
+This takes advantage of fast libraries such as MKL while spending no time
+copying memory from device to host. The device memory is mapped to a host
+pointer which can be used in the offloaded functions.
+
+AF_OPENCL_SHOW_BUILD_INFO {#af_opencl_show_build_info}
+-------------------------------------------------------------------------------
+
+This variable is useful when debuggin OpenCL kernel compilation failures. When
+this variable is set to 1, and an error occurs during a OpenCL kernel
+compilation, then the log and kernel are printed to screen.
+
 AF_DISABLE_GRAPHICS {#af_disable_graphics}
 -------------------------------------------------------------------------------
 
-Setting this variable will disable window creation when graphics functions are
-being called. Simply setting this variable will disable functionality, any
-value will suffice. Disabling window creation will disable all other graphics
-calls at runtime as well.
+Setting this variable to 1 will disable window creation when graphics
+functions are being called. Disabling window creation will disable all other
+graphics calls at runtime as well.
 
 This is a useful enviornment variable when running code on servers and systems
 without displays. When graphics calls are run on such machines, they will
 print warning about window creation failing. To suppress those calls, set this
 variable.
 
-AF_PRINT_ERRORS {#af_print_errors}
+AF_SYNCHRONOUS_CALLS {#af_synchronous_calls}
 -------------------------------------------------------------------------------
 
-When AF_PRINT_ERRORS is set to 1, the exceptions thrown are more verbose and
-detailed. This helps in locating the exact failure.
+When this environment variable is set to 1, ArrayFire will execute all
+functions synchronously.
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-AF_PRINT_ERRORS=1 ./myprogram_opencl
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AF_SHOW_LOAD_PATH {#af_show_load_path}
+-------------------------------------------------------------------------------
+
+When using the Unified backend, if this variable is set to 1, it will show the
+path where the ArrayFire backend libraries are loaded from.
+
+If the libraries are loaded from system paths, such as PATH or LD_LIBRARY_PATH
+etc, then it will print "system path". If the libraries are loaded from other
+paths, then those paths are shown in full.

From 685dccd363e8da0e95b00b8ac73f70254f68d072 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 18:31:55 -0500
Subject: [PATCH 0271/2677] Update boost compute release tag

---
 CMakeModules/build_boost_compute.cmake | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
index c0de1cb291..03c20435a8 100644
--- a/CMakeModules/build_boost_compute.cmake
+++ b/CMakeModules/build_boost_compute.cmake
@@ -1,6 +1,9 @@
-SET(VER 79aa8f9086fdf6ef6db78e889de0273b0eb7bd19)
-SET(URL https://github.com/boostorg/compute/archive/${VER}.tar.gz)
-SET(MD5 dba3318cbdac912dddce71f2a38ffa43)
+# If using a commit, remove the v prefix to VER in URL.
+# If using a tag, don't use v in VER
+# This is because of how github handles it's release tar balls
+SET(VER 0.5)
+SET(URL https://github.com/boostorg/compute/archive/v${VER}.tar.gz)
+SET(MD5 69a52598ac539d3b7f6005a3dd2b6f58)
 
 SET(thirdPartyDir "${CMAKE_BINARY_DIR}/third_party")
 SET(srcDir "${thirdPartyDir}/compute-${VER}")

From 6b7b1ce4ac32ea4dc9442a343822f90ada95cd37 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 18:32:11 -0500
Subject: [PATCH 0272/2677] Update clFFT release tag

---
 CMakeModules/build_clFFT.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index e1dbb3fe1c..961347f913 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG 1597f0f35a644789c7ad77efe79014236cca2fab
+    GIT_TAG arrayfire-release-test
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From b36d003e56222a2888184e985442339e8e5af567 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 22:11:41 -0500
Subject: [PATCH 0273/2677] Move MappedPtr into the Array class

---
 src/backend/opencl/Array.hpp            | 43 +++++++++++++++++++++++++
 src/backend/opencl/cpu/cpu_blas.cpp     | 22 ++++++-------
 src/backend/opencl/cpu/cpu_cholesky.cpp | 14 ++++----
 src/backend/opencl/cpu/cpu_inverse.cpp  | 12 +++----
 src/backend/opencl/cpu/cpu_lu.cpp       | 37 +++++++++++----------
 src/backend/opencl/cpu/cpu_qr.cpp       | 27 ++++++----------
 src/backend/opencl/cpu/cpu_solve.cpp    | 42 +++++++++---------------
 src/backend/opencl/cpu/cpu_svd.cpp      | 29 ++++++++++-------
 src/backend/opencl/memory.cpp           | 27 +---------------
 src/backend/opencl/memory.hpp           |  3 --
 10 files changed, 128 insertions(+), 128 deletions(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 5f86d6d0b6..abce5b9166 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -21,6 +21,7 @@
 #include <memory.hpp>
 #include <memory>
 #include <err_common.hpp>
+#include <err_opencl.hpp>
 
 namespace opencl
 {
@@ -209,6 +210,48 @@ namespace opencl
 
         JIT::Node_ptr getNode() const;
 
+    private:
+        bool is_const() const
+        {
+            return true;
+        }
+
+        bool is_const()
+        {
+            return false;
+        }
+
+    public:
+        std::shared_ptr<T> getMappedPtr() const
+        {
+            auto func = [=] (void* ptr) {
+                try {
+                    if(ptr != nullptr)
+                        getQueue().enqueueUnmapMemObject(*data, ptr);
+                        ptr = nullptr;
+                } catch(cl::Error err) {
+                    CL_TO_AF_ERROR(err);
+                }
+            };
+
+            T *ptr = nullptr;
+            try {
+                if(ptr == nullptr) {
+                    if(is_const()) {
+                        ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()), true, CL_MAP_READ,
+                                getOffset(), getDataDims().elements() * sizeof(T));
+                    } else {
+                        ptr = (T*)getQueue().enqueueMapBuffer(*(get()), true, CL_MAP_READ|CL_MAP_WRITE,
+                                getOffset(), getDataDims().elements() * sizeof(T));
+                    }
+                }
+            } catch(cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+
+            return std::shared_ptr<T>(ptr, func);
+        }
+
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 8c77fff8fd..1ff7e145d6 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -167,9 +167,9 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     using BT  =       typename blas_base<T>::type;
 
     // get host pointers from mapped memory
-    BT *lPtr = getMappedPtr<BT>(lhs.get());
-    BT *rPtr = getMappedPtr<BT>(rhs.get());
-    BT *oPtr = getMappedPtr<BT>(out.get());
+    std::shared_ptr<BT> lPtr = lhs.getMappedPtr();
+    std::shared_ptr<BT> rPtr = rhs.getMappedPtr();
+    std::shared_ptr<BT> oPtr = out.getMappedPtr();
 
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
@@ -177,25 +177,21 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
             CblasColMajor, lOpts,
             lDims[0], lDims[1],
             alpha,
-            lPtr, lStrides[1],
-            rPtr, rStrides[0],
+            lPtr.get(), lStrides[1],
+            rPtr.get(), rStrides[0],
             beta,
-            oPtr, 1);
+            oPtr.get(), 1);
     } else {
         gemm_func<T>()(
             CblasColMajor, lOpts, rOpts,
             M, N, K,
             alpha,
-            lPtr, lStrides[1],
-            rPtr, rStrides[1],
+            lPtr.get(), lStrides[1],
+            rPtr.get(), rStrides[1],
             beta,
-            oPtr, out.dims()[0]);
+            oPtr.get(), out.dims()[0]);
     }
 
-    unmapPtr(lhs.get(), lPtr);
-    unmapPtr(rhs.get(), rPtr);
-    unmapPtr(out.get(), oPtr);
-
     return out;
 }
 
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index 74bbf594ae..bd871d7518 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -42,10 +42,10 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
     Array<T> out = copyArray<T>(in);
     *info = cholesky_inplace(out, is_upper);
 
-    T* oPtr = getMappedPtr<T>(out.get());
-    if (is_upper) triangle<T, true , false>(oPtr, oPtr, out.dims(), out.strides(), out.strides());
-    else          triangle<T, false, false>(oPtr, oPtr, out.dims(), out.strides(), out.strides());
-    unmapPtr(out.get(), oPtr);
+    std::shared_ptr<T> oPtr = out.getMappedPtr();
+
+    if (is_upper) triangle<T, true , false>(oPtr.get(), oPtr.get(), out.dims(), out.strides(), out.strides());
+    else          triangle<T, false, false>(oPtr.get(), oPtr.get(), out.dims(), out.strides(), out.strides());
 
     return out;
 }
@@ -60,10 +60,10 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     if(is_upper)
         uplo = 'U';
 
-    T* inPtr = getMappedPtr<T>(in.get());
+    std::shared_ptr<T> inPtr = in.getMappedPtr();
+
     int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo,
-                               N, inPtr, in.strides()[1]);
-    unmapPtr(in.get(), inPtr);
+                               N, inPtr.get(), in.strides()[1]);
 
     return info;
 }
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index 24b4a670fd..fee171929a 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -51,15 +51,13 @@ Array<T> inverse(const Array<T> &in)
 
     Array<int> pivot = cpu::lu_inplace<T>(A, false);
 
-    T *aPtr = getMappedPtr<T>(A.get());
-    int *pPtr = getMappedPtr<int>(pivot.get());
 
-    getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
-                    aPtr, A.strides()[1],
-                    pPtr);
+    std::shared_ptr<T>   aPtr = A.getMappedPtr();
+    std::shared_ptr<int> pPtr = pivot.getMappedPtr();
 
-    unmapPtr(A.get(), aPtr);
-    unmapPtr(pivot.get(), pPtr);
+    getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
+                    aPtr.get(), A.strides()[1],
+                    pPtr.get());
 
     return A;
 }
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index 293cb8af86..3eb574e743 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -40,9 +40,13 @@ LU_FUNC(getrf , cdouble, z)
 template<typename T>
 void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
 {
-    T *l = getMappedPtr<T>(lower.get());
-    T *u = getMappedPtr<T>(upper.get());
-    T *i = getMappedPtr<T>(in.get());
+    std::shared_ptr<T> ls = lower.getMappedPtr();
+    std::shared_ptr<T> us = upper.getMappedPtr();
+    std::shared_ptr<T> is = in.getMappedPtr();
+
+    T *l = ls.get();
+    T *u = us.get();
+    T *i = is.get();
 
     dim4 ldm = lower.dims();
     dim4 udm = upper.dims();
@@ -91,18 +95,17 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
             }
         }
     }
-
-    unmapPtr(lower.get(), l);
-    unmapPtr(upper.get(), u);
-    unmapPtr(in.get(), i);
 }
 
 void convertPivot(Array<int> &pivot, int out_sz)
 {
     Array<int> p = range<int>(dim4(out_sz), 0); // Runs opencl
 
-    int *d_pi = getMappedPtr<int>(pivot.get());
-    int *d_po = getMappedPtr<int>(p.get());
+    std::shared_ptr<int> pi = pivot.getMappedPtr();
+    std::shared_ptr<int> po = p.getMappedPtr();
+
+    int *d_pi = pi.get();
+    int *d_po = po.get();
 
     dim_t d0 = pivot.dims()[0];
 
@@ -111,8 +114,8 @@ void convertPivot(Array<int> &pivot, int out_sz)
         std::swap(d_po[j], d_po[d_pi[j] - 1]);
     }
 
-    unmapPtr(pivot.get(), d_pi);
-    unmapPtr(p.get(), d_po);
+    pi.reset();
+    po.reset();
 
     pivot = p;
 }
@@ -145,15 +148,15 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 
     Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
 
-    T *inPtr = getMappedPtr<T>(in.get());
-    int *pivotPtr = getMappedPtr<int>(pivot.get());
+    std::shared_ptr<T>   inPtr = in.getMappedPtr();
+    std::shared_ptr<int> piPtr = pivot.getMappedPtr();
 
     getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    inPtr, in.strides()[1],
-                    pivotPtr);
+                    inPtr.get(), in.strides()[1],
+                    piPtr.get());
 
-    unmapPtr(in.get(), inPtr);
-    unmapPtr(pivot.get(), pivotPtr);
+    inPtr.reset();
+    piPtr.reset();
 
     if(convert_pivot) convertPivot(pivot, M);
 
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index 24a915a5d1..32eca92963 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -70,20 +70,16 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     dim4 rdims(M, N);
     r = createEmptyArray<T>(rdims);
 
-    T *qPtr = getMappedPtr<T>(q.get());
-    T *rPtr = getMappedPtr<T>(r.get());
-    T *tPtr = getMappedPtr<T>(t.get());
+    std::shared_ptr<T> qPtr = q.getMappedPtr();
+    std::shared_ptr<T> rPtr = r.getMappedPtr();
+    std::shared_ptr<T> tPtr = t.getMappedPtr();
 
-    triangle<T, true, false>(rPtr, qPtr, rdims, r.strides(), q.strides());
+    triangle<T, true, false>(rPtr.get(), qPtr.get(), rdims, r.strides(), q.strides());
 
     gqr_func<T>()(AF_LAPACK_COL_MAJOR,
                   M, M, min(M, N),
-                  qPtr, q.strides()[1],
-                  tPtr);
-
-    unmapPtr(q.get(), qPtr);
-    unmapPtr(r.get(), rPtr);
-    unmapPtr(t.get(), tPtr);
+                  qPtr.get(), q.strides()[1],
+                  tPtr.get());
 
     q.resetDims(dim4(M, M));
 }
@@ -97,15 +93,12 @@ Array<T> qr_inplace(Array<T> &in)
 
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
-    T *iPtr = getMappedPtr<T>(in.get());
-    T *tPtr = getMappedPtr<T>(t.get());
+    std::shared_ptr<T> iPtr = in.getMappedPtr();
+    std::shared_ptr<T> tPtr = t.getMappedPtr();
 
     geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    iPtr, in.strides()[1],
-                    tPtr);
-
-    unmapPtr(in.get(), iPtr);
-    unmapPtr(t.get(), tPtr);
+                    iPtr.get(), in.strides()[1],
+                    tPtr.get());
 
     return t;
 }
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 522454aa81..9e4f0932ac 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -85,19 +85,15 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 
     Array<T> B = copyArray<T>(b);
 
-    T *aPtr = getMappedPtr<T>(A.get());
-    T *bPtr = getMappedPtr<T>(B.get());
-    int *pPtr = getMappedPtr<int>(pivot.get());
+    std::shared_ptr<T  > aPtr = A.getMappedPtr();
+    std::shared_ptr<T  > bPtr = B.getMappedPtr();
+    std::shared_ptr<int> pPtr = pivot.getMappedPtr();
 
     getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
                     N, NRHS,
-                    aPtr, A.strides()[1],
-                    pPtr,
-                    bPtr, B.strides()[1]);
-
-    unmapPtr(A.get(), aPtr);
-    unmapPtr(B.get(), bPtr);
-    unmapPtr(pivot.get(), pPtr);
+                    aPtr.get(), A.strides()[1],
+                    pPtr.get(),
+                    bPtr.get(), B.strides()[1]);
 
     return B;
 }
@@ -109,19 +105,16 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
     int N = B.dims()[0];
     int NRHS = B.dims()[1];
 
-    T *aPtr = getMappedPtr<T>(A.get());
-    T *bPtr = getMappedPtr<T>(B.get());
+    std::shared_ptr<T> aPtr = A.getMappedPtr();
+    std::shared_ptr<T> bPtr = B.getMappedPtr();
 
     trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
                     options & AF_MAT_UPPER ? 'U' : 'L',
                     'N', // transpose flag
                     options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
                     N, NRHS,
-                    aPtr, A.strides()[1],
-                    bPtr, B.strides()[1]);
-
-    unmapPtr(A.get(), aPtr);
-    unmapPtr(B.get(), bPtr);
+                    aPtr.get(), A.strides()[1],
+                    bPtr.get(), B.strides()[1]);
 
     return B;
 }
@@ -143,29 +136,26 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
     Array<T> A = copyArray<T>(a);
     Array<T> B = padArray<T, T>(b, dim4(max(M, N), K), scalar<T>(0));
 
-    T *aPtr = getMappedPtr<T>(A.get());
-    T *bPtr = getMappedPtr<T>(B.get());
+    std::shared_ptr<T> aPtr = A.getMappedPtr();
+    std::shared_ptr<T> bPtr = B.getMappedPtr();
 
     if(M == N) {
         std::vector<int> pivot(N);
         gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K,
-                       aPtr, A.strides()[1],
+                       aPtr.get(), A.strides()[1],
                        &pivot.front(),
-                       bPtr, B.strides()[1]);
+                       bPtr.get(), B.strides()[1]);
     } else {
         int sM = a.strides()[1];
         int sN = a.strides()[2] / sM;
 
         gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
                        M, N, K,
-                       aPtr, A.strides()[1],
-                       bPtr, max(sM, sN));
+                       aPtr.get(), A.strides()[1],
+                       bPtr.get(), max(sM, sN));
         B.resetDims(dim4(N, K));
     }
 
-    unmapPtr(A.get(), aPtr);
-    unmapPtr(B.get(), bPtr);
-
     return B;
 }
 
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index 66e4c0a7c5..c53df8ae78 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -67,23 +67,28 @@ namespace cpu
         int M = iDims[0];
         int N = iDims[1];
 
-        Tr *sPtr = getMappedPtr<Tr>(s.get());
-        T  *uPtr = getMappedPtr<T >(u.get());
-        T  *vPtr = getMappedPtr<T >(vt.get());
-        T  *iPtr = getMappedPtr<T >(in.get());
+        std::shared_ptr<Tr> sPtr = s.getMappedPtr();
+        std::shared_ptr<T > uPtr = u.getMappedPtr();
+        std::shared_ptr<T > vPtr = vt.getMappedPtr();
+        std::shared_ptr<T > iPtr = in.getMappedPtr();
 
 #if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, iPtr, in.strides()[1],
-                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1]);
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A',
+                          M, N,
+                          iPtr.get(), in.strides()[1],
+                          sPtr.get(),
+                          uPtr.get(), u.strides()[1],
+                          vPtr.get(), vt.strides()[1]);
 #else
         std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, iPtr, in.strides()[1],
-                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1], &superb[0]);
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A',
+                          M, N,
+                          iPtr.get(), in.strides()[1],
+                          sPtr.get(),
+                          uPtr.get(), u.strides()[1],
+                          vPtr.get(), vt.strides()[1],
+                          &superb[0]);
 #endif
-        unmapPtr(s.get() , sPtr);
-        unmapPtr(u.get() , uPtr);
-        unmapPtr(vt.get(), vPtr);
-        unmapPtr(in.get(), iPtr);
     }
 
     template <typename T, typename Tr>
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 924e370a64..cf3f4ccc4e 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -14,6 +14,7 @@
 #include <iomanip>
 #include <string>
 #include <types.hpp>
+#include <err_opencl.hpp>
 
 namespace opencl
 {
@@ -281,29 +282,6 @@ namespace opencl
         return bufferPush((cl::Buffer *)ptr);
     }
 
-    template<typename T>
-    T *getMappedPtr(const cl::Buffer *buf)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find(const_cast<cl::Buffer*>(buf));
-
-        if (iter == memory_maps[n].end()) {
-            // Buffer not found in memory manager
-            // Very Very Bad
-            return NULL;
-        }
-        size_t alloc_bytes = iter->second.bytes;
-
-        T *ptr = (T*)getQueue().enqueueMapBuffer(
-                     *buf, true, CL_MAP_READ, 0, alloc_bytes);
-        return ptr;
-    }
-
-    void unmapPtr(const cl::Buffer *buf, void *ptr)
-    {
-        getQueue().enqueueUnmapMemObject(*buf, ptr);
-    }
-
     // pinned memory manager
     typedef struct {
         cl::Buffer *buf;
@@ -426,7 +404,6 @@ namespace opencl
     template void memPush(const T* ptr);                        \
     template T* pinnedAlloc(const size_t &elements);            \
     template void pinnedFree(T* ptr);                           \
-    template T* getMappedPtr(const cl::Buffer *buf);            \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
@@ -440,6 +417,4 @@ namespace opencl
     INSTANTIATE(uintl)
     INSTANTIATE(short)
     INSTANTIATE(ushort)
-
-    template void* getMappedPtr(const cl::Buffer *buf);
 }
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index f337a7a1bd..96292cdfac 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -32,9 +32,6 @@ namespace opencl
     template<typename T> void memPop(const T *ptr);
     template<typename T> void memPush(const T *ptr);
 
-    template<typename T> T *getMappedPtr(const cl::Buffer *buf);
-    void unmapPtr(const cl::Buffer *buf, void *ptr);
-
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 

From 56f9140d880b5816817aa8e3bd78c114492de56b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 23:21:01 -0500
Subject: [PATCH 0274/2677] FEAT Add getActiveBackend function

---
 docs/details/backend.dox   |  9 +++++++++
 include/af/backend.h       | 20 ++++++++++++++++++++
 src/api/c/device.cpp       |  6 ++++++
 src/api/cpp/device.cpp     |  7 +++++++
 src/api/unified/device.cpp |  6 ++++++
 test/backend.cpp           | 15 +++++++++++++++
 6 files changed, 63 insertions(+)

diff --git a/docs/details/backend.dox b/docs/details/backend.dox
index 4d9cdf6f53..146cc14313 100644
--- a/docs/details/backend.dox
+++ b/docs/details/backend.dox
@@ -71,5 +71,14 @@ The return value specifies which backend the array was created on.
 
 =======================================================================
 
+\defgroup unified_func_getactivebackend getActiveBackend
+
+\brief Get's the backend enum for the active backend
+
+\ingroup unified_func
+\ingroup arrayfire_func
+
+=======================================================================
+
 @}
 */
diff --git a/include/af/backend.h b/include/af/backend.h
index 93d8d8de58..0342ef0ade 100644
--- a/include/af/backend.h
+++ b/include/af/backend.h
@@ -55,6 +55,17 @@ AFAPI af_err af_get_available_backends(int* backends);
 AFAPI af_err af_get_backend_id(af_backend *backend, const af_array in);
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   \param[out] backend takes one of the values of enum \ref af_backend
+   from the backend that is currently set to active
+   \returns \ref af_err error code
+
+   \ingroup unified_func_getactivebackend
+ */
+AFAPI af_err af_get_active_backend(af_backend *backend);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
@@ -101,5 +112,14 @@ AFAPI int getAvailableBackends();
 AFAPI af::Backend getBackendId(const array &in);
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   \returns \ref af_backend which is the backend is currently active
+
+   \ingroup unified_func_getctivebackend
+ */
+AFAPI af::Backend getActiveBackend();
+#endif
+
 }
 #endif
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 8f332994e7..d782211367 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -53,6 +53,12 @@ af_err af_get_backend_id(af_backend *result, const af_array in)
     return AF_SUCCESS;
 }
 
+af_err af_get_active_backend(af_backend *result)
+{
+    *result = (af_backend)getBackend();
+    return AF_SUCCESS;
+}
+
 af_err af_init()
 {
     try {
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 3b1609b9d4..5e4b0f7bf0 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -42,6 +42,13 @@ namespace af
         return result;
     }
 
+    af::Backend getActiveBackend()
+    {
+        af::Backend result = (af::Backend)0;
+        AF_THROW(af_get_active_backend(&result));
+        return result;
+    }
+
     void info()
     {
         AF_THROW(af_info());
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index f7e95569c9..fbd8e32f90 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -35,6 +35,12 @@ af_err af_get_backend_id(af_backend *result, const af_array in)
     return CALL(result, in);
 }
 
+af_err af_get_active_backend(af_backend *result)
+{
+    *result = unified::AFSymbolManager::getInstance().getActiveBackend();
+    return AF_SUCCESS;
+}
+
 af_err af_info()
 {
     return CALL_NO_PARAMS();
diff --git a/test/backend.cpp b/test/backend.cpp
index 7b8dbddade..4bb5cdf7fe 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -21,11 +21,26 @@
 using std::string;
 using std::vector;
 
+const char *getActiveBackendString()
+{
+    af_backend active = (af_backend)0;
+    af_get_active_backend(&active);
+
+    switch(active) {
+        case AF_BACKEND_CPU   : return "AF_BACKEND_CPU";
+        case AF_BACKEND_CUDA  : return "AF_BACKEND_CUDA";
+        case AF_BACKEND_OPENCL: return "AF_BACKEND_OPENCL";
+        default               : return "AF_BACKEND_DEFAULT";
+    }
+}
+
 template<typename T>
 void testFunction()
 {
     af_info();
 
+    printf("Active Backend Enum = %s\n", getActiveBackendString());
+
     af_array outArray = 0;
     dim_t dims[] = {32, 32};
     ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) af::dtype_traits<T>::af_type));

From 3047acd599e86136578e80f5f35ca706456ffe7a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 9 Jan 2016 04:59:39 -0500
Subject: [PATCH 0275/2677] Add the ability to sort OpenCL devices Using the
 following criterion

1. GPUs > Accelerators > CPUs.

2. IN GPUs:
   a. Discreet preferred to integrated
   b. AMD > NVIDIA > APPLE > Intel / BEIGNET

3. IN CPUs Intel > AMD > POCL > other

4. While everything above is the same:
   a. Higher OpenCL compute version preferred
   b. Higher amount of memory preferred
---
 src/backend/opencl/platform.cpp | 215 +++++++++++++++++++++++---------
 1 file changed, 155 insertions(+), 60 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 0cd46d25f6..3bf13c0690 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -110,6 +110,94 @@ void DeviceManager::setContext(int device)
     mActiveCtxId = device;
 }
 
+static inline bool verify_present(std::string pname, const char *ref)
+{
+    return pname.find(ref) != std::string::npos;
+}
+
+static inline bool compare_default(const Device *ldev, const Device *rdev)
+{
+    const cl_device_type device_types[] = {CL_DEVICE_TYPE_GPU,
+                                           CL_DEVICE_TYPE_ACCELERATOR};
+
+    auto l_dev_type = ldev->getInfo<CL_DEVICE_TYPE>();
+    auto r_dev_type = rdev->getInfo<CL_DEVICE_TYPE>();
+
+    // This ensures GPU > ACCELERATOR > CPU
+    for (auto current_type : device_types) {
+        auto is_l_curr_type = l_dev_type == current_type;
+        auto is_r_curr_type = r_dev_type == current_type;
+
+        if ( is_l_curr_type && !is_r_curr_type) return true;
+        if (!is_l_curr_type &&  is_r_curr_type) return false;
+    }
+
+    // For GPUs, this ensures discreet > integrated
+    auto is_l_integrared = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+    auto is_r_integrared = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+
+    if (!is_l_integrared &&  is_r_integrared) return true;
+    if ( is_l_integrared && !is_r_integrared) return false;
+
+    // At this point, the devices are of same type.
+    // Sort based on emperical evidence of preferred platforms
+
+    // Prefer AMD first
+    std::string lPlatName = getPlatformName(*ldev);
+    std::string rPlatName = getPlatformName(*rdev);
+
+    if (l_dev_type == CL_DEVICE_TYPE_GPU &&
+        r_dev_type == CL_DEVICE_TYPE_GPU ) {
+        // If GPU, prefer AMD > NVIDIA > Beignet / Intel > APPLE
+        const char *platforms[] = {"AMD", "NVIDIA", "APPLE", "INTEL", "BEIGNET"};
+
+        for (auto ref_name : platforms) {
+            if ( verify_present(lPlatName, ref_name) &&
+                !verify_present(rPlatName, ref_name)) return true;
+
+            if (!verify_present(lPlatName, ref_name) &&
+                 verify_present(rPlatName, ref_name)) return false;
+        }
+
+        // Intel falls back to compare based on memory
+    } else {
+        // If CPU, prefer Intel > AMD > POCL > APPLE
+        const char *platforms[] = {"INTEL", "AMD", "POCL", "APPLE"};
+
+        for (auto ref_name : platforms) {
+            if ( verify_present(lPlatName, ref_name) &&
+                !verify_present(rPlatName, ref_name)) return true;
+
+            if (!verify_present(lPlatName, ref_name) &&
+                 verify_present(rPlatName, ref_name)) return false;
+        }
+    }
+
+
+    // Compare device compute versions
+
+    {
+        // Check Device OpenCL Version
+        auto lversion =  ldev->getInfo<CL_DEVICE_VERSION>();
+        auto rversion =  rdev->getInfo<CL_DEVICE_VERSION>();
+
+        auto lres = (lversion[7] > rversion[7]) ||
+            ((lversion[7] == rversion[7]) && (lversion[9] > rversion[9]));
+
+        auto rres = (lversion[7] < rversion[7]) ||
+            ((lversion[7] == rversion[7]) && (lversion[9] < rversion[9]));
+
+        if (lres > 0) return true;
+        if (rres < 0) return false;
+    }
+
+    // Default crietria, sort based on memory
+    // Sort based on memory
+    auto l_mem = ldev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+    auto r_mem = rdev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+    return l_mem >= r_mem;
+}
+
 DeviceManager::DeviceManager()
     : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
 {
@@ -117,41 +205,46 @@ DeviceManager::DeviceManager()
         std::vector<cl::Platform>   platforms;
         Platform::get(&platforms);
 
-        cl_device_type DEVC_TYPES[] = {
-            CL_DEVICE_TYPE_GPU,
-#ifndef OS_MAC
-            CL_DEVICE_TYPE_ACCELERATOR,
-            CL_DEVICE_TYPE_CPU
+        // This is all we need because the sort takes care of the order of devices
+#ifdef OS_MAC
+        cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+#else
+        cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
 #endif
-        };
-
-        unsigned nDevices = 0;
-        for (auto devType : DEVC_TYPES) {
-            for (auto &platform : platforms) {
-
-                cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
-                    (cl_context_properties)(platform()),
-                    0};
-
-                std::vector<Device> devs;
-                try {
-                    platform.getDevices(devType, &devs);
-                } catch(const cl::Error &err) {
-                    if (err.err() != CL_DEVICE_NOT_FOUND) {
-                        throw;
-                    }
-                }
 
-                for (auto dev : devs) {
-                    nDevices++;
-                    Context *ctx = new Context(dev, cps);
-                    CommandQueue *cq = new CommandQueue(*ctx, dev);
-                    mDevices.push_back(new Device(dev));
-                    mContexts.push_back(ctx);
-                    mQueues.push_back(cq);
-                    mIsGLSharingOn.push_back(false);
+        // Iterate through platforms, get all available devices and store them
+        for (auto &platform : platforms) {
+            std::vector<Device> current_devices;
+
+            try {
+                platform.getDevices(DEVICE_TYPES, &current_devices);
+            } catch(const cl::Error &err) {
+                if (err.err() != CL_DEVICE_NOT_FOUND) {
+                    throw;
                 }
             }
+
+            for (auto dev : current_devices) {
+                mDevices.push_back(new Device(dev));
+            }
+        }
+
+        // Sort OpenCL devices based on default criteria
+        std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
+
+        // Create contexts and queues once the sort is done
+        int nDevices = mDevices.size();
+        for (int i = 0; i < nDevices; i++) {
+            cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
+            cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
+                                            (cl_context_properties)(device_platform),
+                                            0};
+
+            Context *ctx = new Context(*mDevices[i], cps);
+            CommandQueue *cq = new CommandQueue(*ctx, *mDevices[i]);
+            mContexts.push_back(ctx);
+            mQueues.push_back(cq);
+            mIsGLSharingOn.push_back(false);
         }
 
         std::string deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
@@ -204,11 +297,12 @@ static std::string platformMap(std::string &platStr)
     typedef std::map<std::string, std::string> strmap_t;
     static strmap_t platMap;
     if (isFirst) {
-        platMap["NVIDIA CUDA"] = "NVIDIA  ";
-        platMap["Intel(R) OpenCL"] = "INTEL   ";
+        platMap["NVIDIA CUDA"]                         = "NVIDIA  ";
+        platMap["Intel(R) OpenCL"]                     = "INTEL   ";
         platMap["AMD Accelerated Parallel Processing"] = "AMD     ";
-        platMap["Intel Gen OCL Driver"] = "BEIGNET ";
-        platMap["Apple"] = "APPLE   ";
+        platMap["Intel Gen OCL Driver"]                = "BEIGNET ";
+        platMap["Apple"]                               = "APPLE   ";
+        platMap["Portable Computing Language"]         = "POCL    ";
         isFirst = false;
     }
 
@@ -228,38 +322,37 @@ std::string getInfo()
          << " (OpenCL, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
 
     unsigned nDevices = 0;
-    for (auto context : DeviceManager::getInstance().mContexts) {
-        vector<Device> devices = context->getInfo<CL_CONTEXT_DEVICES>();
+    for(auto &device: DeviceManager::getInstance().mDevices) {
+        const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
-        for(auto &device:devices) {
-            const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
+        string dstr = device->getInfo<CL_DEVICE_NAME>();
 
-            string platStr = platform.getInfo<CL_PLATFORM_NAME>();
-            string dstr = device.getInfo<CL_DEVICE_NAME>();
+        // Remove null termination character from the strings
+        dstr.pop_back();
+
+        bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
 
-            // Remove null termination character from the strings
-            platStr.pop_back();
-            dstr.pop_back();
+        string id =
+            (show_braces ? string("[") : "-") +
+            std::to_string(nDevices) +
+            (show_braces ? string("]") : "-");
 
-            bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
-            string id = (show_braces ? string("[") : "-") + std::to_string(nDevices) +
-                        (show_braces ? string("]") : "-");
-            info << id << " " << platformMap(platStr) << ": " << ltrim(dstr) << " ";
+        info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr);
 #ifndef NDEBUG
-            string devVersion = device.getInfo<CL_DEVICE_VERSION>();
-            string driVersion = device.getInfo<CL_DRIVER_VERSION>();
-            devVersion.pop_back();
-            driVersion.pop_back();
-            info << devVersion;
-            info << " Device driver " << driVersion;
-            info << " FP64 Support("
-                 << (device.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False")
-                 << ")";
+        info << " -- ";
+        string devVersion = device->getInfo<CL_DEVICE_VERSION>();
+        string driVersion = device->getInfo<CL_DRIVER_VERSION>();
+        devVersion.pop_back();
+        driVersion.pop_back();
+        info << devVersion;
+        info << " -- Device driver " << driVersion;
+        info << " -- FP64 Support: "
+             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False")
+             << "";
 #endif
-            info << std::endl;
+        info << std::endl;
 
-            nDevices++;
-        }
+        nDevices++;
     }
     return info.str();
 }
@@ -268,6 +361,8 @@ std::string getPlatformName(const cl::Device &device)
 {
     const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
     std::string platStr = platform.getInfo<CL_PLATFORM_NAME>();
+    // Remove null termination character from the strings
+    platStr.pop_back();
     return platformMap(platStr);
 }
 

From 8873ed244abaabb3bedf8781a44f096d790fa4b5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 10 Jan 2016 02:45:28 -0500
Subject: [PATCH 0276/2677] Using proper offsets for loadImageNative and
 saveImageNative

---
 src/api/c/imageio2.cpp | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index adc4244953..aed793e64a 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -61,9 +61,9 @@ static af_err readImage_t(af_array *rImage, const uchar* pSrcLine, const int nSr
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (T) *(src + (x * step + 0));
-                    pDst1[indx] = (T) *(src + (x * step + 1));
-                    pDst2[indx] = (T) *(src + (x * step + 2));
+                    pDst0[indx] = (T) *(src + (x * step + FI_RGBA_RED));
+                    pDst1[indx] = (T) *(src + (x * step + FI_RGBA_GREEN));
+                    pDst2[indx] = (T) *(src + (x * step + FI_RGBA_BLUE));
                 }
                 if (fi_color == 4) pDst3[indx] = (T) *(src + (x * step + FI_RGBA_ALPHA));
             }
@@ -239,15 +239,15 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims, uint nDstPit
                 *(pDstLine + x * step + FI_RGBA_RED) = (T) pSrc0[indx]; // r -> 0
             } else if(channels >=3) {
                 if((af_dtype) af::dtype_traits<T>::af_type == u8) {
-                    *(pDstLine + x * step + FI_RGBA_BLUE)  = (T) pSrc2[indx]; // b -> 0
+                    *(pDstLine + x * step + FI_RGBA_RED  ) = (T) pSrc0[indx]; // r -> 0
                     *(pDstLine + x * step + FI_RGBA_GREEN) = (T) pSrc1[indx]; // g -> 1
-                    *(pDstLine + x * step + FI_RGBA_RED)   = (T) pSrc0[indx]; // r -> 2
+                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (T) pSrc2[indx]; // b -> 2
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    *(pDstLine + x * step + 0) = (T) pSrc0[indx]; // r -> 0
-                    *(pDstLine + x * step + 1) = (T) pSrc1[indx]; // g -> 1
-                    *(pDstLine + x * step + 2) = (T) pSrc2[indx]; // b -> 2
+                    *(pDstLine + x * step + FI_RGBA_RED  ) = (T) pSrc0[indx]; // r -> 0
+                    *(pDstLine + x * step + FI_RGBA_GREEN) = (T) pSrc1[indx]; // g -> 1
+                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (T) pSrc2[indx]; // b -> 2
                 }
             }
             if(channels >= 4) *(pDstLine + x * step + FI_RGBA_ALPHA) = (T) pSrc3[indx]; // a

From 14230d21b36ddbc40f59e33011dc8153861ed92a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 10 Jan 2016 02:46:12 -0500
Subject: [PATCH 0277/2677] Adding environment variables to choose OpenCL
 device

1. AF_OPENCL_DEFAULT_DEVICE_TYPE
   - Can be one of CPU, GPU and ACC
   - When not set, defaults to first available device
   - Chooses what the default device should be
   - Does not disable other devices

2. AF_OPENCL_DEVICE_TYPE
   - Can be one of CPU, GPU, ACC, ALL
   - When not set defaults to ALL
   - Only chooses devices of given type
---
 src/backend/opencl/platform.cpp | 54 ++++++++++++++++++++++++++++++---
 1 file changed, 50 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 3bf13c0690..822fdfceb7 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -212,6 +212,18 @@ DeviceManager::DeviceManager()
         cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
 #endif
 
+        std::string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
+
+        if (deviceENV.compare("GPU") == 0) {
+            DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+        } else if (deviceENV.compare("CPU") == 0) {
+            DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
+        } else if (deviceENV.compare("ACC") >= 0) {
+            DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
+        }
+
+
+
         // Iterate through platforms, get all available devices and store them
         for (auto &platform : platforms) {
             std::vector<Device> current_devices;
@@ -229,11 +241,14 @@ DeviceManager::DeviceManager()
             }
         }
 
+        int nDevices = mDevices.size();
+
+        if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
+
         // Sort OpenCL devices based on default criteria
         std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
 
         // Create contexts and queues once the sort is done
-        int nDevices = mDevices.size();
         for (int i = 0; i < nDevices; i++) {
             cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
             cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
@@ -247,7 +262,8 @@ DeviceManager::DeviceManager()
             mIsGLSharingOn.push_back(false);
         }
 
-        std::string deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
+        bool default_device_set = false;
+        deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
         if(!deviceENV.empty()) {
             std::stringstream s(deviceENV);
             int def_device = -1;
@@ -257,18 +273,48 @@ DeviceManager::DeviceManager()
                 printf("Setting default device as 0\n");
             } else {
                 setContext(def_device);
+                default_device_set = true;
             }
         }
+
+        deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
+        if (!default_device_set && !deviceENV.empty())
+        {
+            cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
+            if (deviceENV.compare("CPU") == 0) {
+                default_device_type = CL_DEVICE_TYPE_CPU;
+            } else if (deviceENV.compare("ACC") >= 0) {
+                default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
+            }
+
+            bool default_device_set = false;
+            for (int i = 0; i < nDevices; i++) {
+                if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
+                    default_device_set = true;
+                    setContext(i);
+                    break;
+                }
+            }
+
+            if (!default_device_set) {
+                printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
+                       deviceENV.c_str());
+                printf("Using default device as 0\n");
+            }
+        }
+
     } catch (const cl::Error &error) {
             CL_TO_AF_ERROR(error);
     }
-    /* loop over devices and replace contexts with
-     * OpenGL shared contexts whereever applicable */
+
+
 #if defined(WITH_GRAPHICS)
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
     if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         try {
+            /* loop over devices and replace contexts with
+             * OpenGL shared contexts whereever applicable */
             int devCount = mDevices.size();
             fg::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
             for(int i=0; i<devCount; ++i)

From 2f16824e77568bc83bb698280692e657ce565a1b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 10 Jan 2016 03:22:38 -0500
Subject: [PATCH 0278/2677] Cleaning up exception handling in src/api/c

---
 src/api/c/assign.cpp |  4 ++--
 src/api/c/device.cpp | 18 ++++++++++++------
 src/api/c/flip.cpp   |  2 +-
 src/api/c/image.cpp  |  2 +-
 src/api/c/index.cpp  |  6 ++----
 5 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 50224d32a6..bf2c185a10 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -350,10 +350,10 @@ af_err af_assign_gen(af_array *out,
             throw;
         }
         if (is_vector) { AF_CHECK(af_release_array(rhs)); }
+
+        std::swap(*out, output);
     }
     CATCHALL;
 
-    std::swap(*out, output);
-
     return AF_SUCCESS;
 }
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 8f332994e7..731e98efec 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -39,7 +39,9 @@ af_err af_get_backend_count(unsigned* num_backends)
 
 af_err af_get_available_backends(int* result)
 {
-    *result = getBackend();
+    try {
+        *result = getBackend();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
@@ -67,7 +69,9 @@ af_err af_init()
 
 af_err af_info()
 {
-    printf("%s", getInfo().c_str());
+    try {
+        printf("%s", getInfo().c_str());
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
@@ -326,7 +330,6 @@ af_err af_free_pinned(void *ptr)
 af_err af_alloc_host(void **ptr, const dim_t bytes)
 {
     try {
-        AF_CHECK(af_init());
         *ptr = malloc(bytes);
     } CATCHALL;
     return AF_SUCCESS;
@@ -335,7 +338,6 @@ af_err af_alloc_host(void **ptr, const dim_t bytes)
 af_err af_free_host(void *ptr)
 {
     try {
-        AF_CHECK(af_init());
         free(ptr);
     } CATCHALL;
     return AF_SUCCESS;
@@ -376,12 +378,16 @@ af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
 
 af_err af_set_mem_step_size(const size_t step_bytes)
 {
-    detail::setMemStepSize(step_bytes);
+    try{
+        detail::setMemStepSize(step_bytes);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_get_mem_step_size(size_t *step_bytes)
 {
-    *step_bytes =  detail::getMemStepSize();
+    try {
+        *step_bytes =  detail::getMemStepSize();
+    } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 3d5bf53da8..09cbaf75e4 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -74,9 +74,9 @@ af_err af_flip(af_array *result, const af_array in, const unsigned dim)
         case u8:     out = flipArray<uchar>   (in, dim);  break;
         default:    TYPE_ERROR(1, in_type);
         }
+        swap(*result, out);
     }
     CATCHALL
 
-    swap(*result, out);
     return AF_SUCCESS;
 }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 1d3e0970ba..db40934e50 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -141,9 +141,9 @@ af_err af_create_window(af_window *out, const int width, const int height, const
 
         wnd = new fg::Window(width, height, title, mainWnd);
         wnd->setFont(fgMngr.getFont());
+        *out = reinterpret_cast<af_window>(wnd);
     }
     CATCHALL;
-    *out = reinterpret_cast<af_window>(wnd);
     return AF_SUCCESS;
 #else
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 2f5b06aa07..f5a214f8e5 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -67,10 +67,10 @@ af_err af_index(af_array *result, const af_array in, const unsigned ndims, const
         case u8:     indexArray<uchar>   (out, in, ndims, index);  break;
         default:    TYPE_ERROR(1, in_type);
         }
+        swap(*result, out);
     }
     CATCHALL
 
-    swap(*result, out);
     return AF_SUCCESS;
 }
 
@@ -127,11 +127,9 @@ af_err af_lookup(af_array *out, const af_array in, const af_array indices, const
             case  u8: output = lookup<uchar   >(in, indices, dim); break;
             default : TYPE_ERROR(1, idxType);
         }
+        std::swap(*out, output);
     }
     CATCHALL;
-
-    std::swap(*out, output);
-
     return AF_SUCCESS;
 }
 

From b42cbebd971bdb5b51e7710d870ff4e644225291 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 10 Jan 2016 03:47:36 -0500
Subject: [PATCH 0279/2677] Updating docs for new AF_OPENCL_*_TYPE environment
 variables

---
 .../configuring_arrayfire_environment.md      | 31 +++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 054068e224..7e197e4954 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -44,6 +44,37 @@ AF_OPENCL_DEFAULT_DEVICE=1 ./myprogram_opencl
 Note: af::setDevice call in the source code will take precedence over this
 variable.
 
+AF_OPENCL_DEFAULT_DEVICE_TYPE {#af_opencl_default_device_type}
+-------------------------------------------------------------------------------
+
+Use this variable to set the default OpenCL device type. Valid values for this
+variable are: CPU, GPU, ACC (Accelerators).
+
+When set, the first device of the specified type is chosen as default device.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AF_OPENCL_DEFAULT_DEVICE_TYPE=CPU ./myprogram_opencl
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Note: `AF_OPENCL_DEFAULT_DEVICE`  and af::setDevice takes precedence over this variable.
+
+AF_OPENCL_DEVICE_TYPE {#af_opencl_device_type}
+-------------------------------------------------------------------------------
+
+Use this variable to only choose OpenCL devices of specified type. Valid values for this
+variable are:
+
+- ALL: All OpenCL devices. (Default behavior).
+- CPU: CPU devices only.
+- GPU: GPU devices only.
+- ACC: Accelerator devices only.
+
+When set, the remaining OpenCL device types are ignored by the OpenCL backend.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AF_OPENCL_DEVICE_TYPE=CPU ./myprogram_opencl
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 AF_DISABLE_GRAPHICS {#af_disable_graphics}
 -------------------------------------------------------------------------------
 

From 17b2600f9ba4e0d5b258213655c65053201c5ad7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 10 Jan 2016 13:11:21 -0500
Subject: [PATCH 0280/2677] Freeimage only requires the flags for 24 / 32 bit
 images

---
 src/api/c/imageio.cpp  | 15 ++++++++-------
 src/api/c/imageio2.cpp | 18 ++++++++++--------
 2 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index c6a20a85a2..e372cd7e64 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -60,14 +60,15 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
                     pDst0[indx] = (float) *(src + (x * step + FI_RGBA_RED));
                     pDst1[indx] = (float) *(src + (x * step + FI_RGBA_GREEN));
                     pDst2[indx] = (float) *(src + (x * step + FI_RGBA_BLUE));
+                    if (fo_color == 4) pDst3[indx] = (float) *(src + (x * step + FI_RGBA_ALPHA));
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (float) *(src + (x * step + FI_RGBA_RED));
-                    pDst1[indx] = (float) *(src + (x * step + FI_RGBA_GREEN));
-                    pDst2[indx] = (float) *(src + (x * step + FI_RGBA_BLUE));
+                    pDst0[indx] = (float) *(src + (x * step + 0));
+                    pDst1[indx] = (float) *(src + (x * step + 1));
+                    pDst2[indx] = (float) *(src + (x * step + 2));
+                    if (fo_color == 4) pDst3[indx] = (float) *(src + (x * step + 3));
                 }
-                if (fo_color == 4) pDst3[indx] = (float) *(src + (x * step + FI_RGBA_ALPHA));
             }
             indx++;
         }
@@ -104,9 +105,9 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    r = (T) *(src + (x * step + FI_RGBA_RED));
-                    g = (T) *(src + (x * step + FI_RGBA_GREEN));
-                    b = (T) *(src + (x * step + FI_RGBA_BLUE));
+                    r = (T) *(src + (x * step + 0));
+                    g = (T) *(src + (x * step + 1));
+                    b = (T) *(src + (x * step + 2));
                 }
                 pDst[indx] = r * 0.2989f + g * 0.5870f + b * 0.1140f;
             }
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index aed793e64a..a1374a2944 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -58,14 +58,15 @@ static af_err readImage_t(af_array *rImage, const uchar* pSrcLine, const int nSr
                     pDst0[indx] = (T) *(src + (x * step + FI_RGBA_RED));
                     pDst1[indx] = (T) *(src + (x * step + FI_RGBA_GREEN));
                     pDst2[indx] = (T) *(src + (x * step + FI_RGBA_BLUE));
+                    if (fi_color == 4) pDst3[indx] = (T) *(src + (x * step + FI_RGBA_ALPHA));
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (T) *(src + (x * step + FI_RGBA_RED));
-                    pDst1[indx] = (T) *(src + (x * step + FI_RGBA_GREEN));
-                    pDst2[indx] = (T) *(src + (x * step + FI_RGBA_BLUE));
+                    pDst0[indx] = (T) *(src + (x * step + 0));
+                    pDst1[indx] = (T) *(src + (x * step + 1));
+                    pDst2[indx] = (T) *(src + (x * step + 2));
+                    if (fi_color == 4) pDst3[indx] = (T) *(src + (x * step + 3));
                 }
-                if (fi_color == 4) pDst3[indx] = (T) *(src + (x * step + FI_RGBA_ALPHA));
             }
             indx++;
         }
@@ -242,15 +243,16 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims, uint nDstPit
                     *(pDstLine + x * step + FI_RGBA_RED  ) = (T) pSrc0[indx]; // r -> 0
                     *(pDstLine + x * step + FI_RGBA_GREEN) = (T) pSrc1[indx]; // g -> 1
                     *(pDstLine + x * step + FI_RGBA_BLUE ) = (T) pSrc2[indx]; // b -> 2
+                    if(channels >= 4) *(pDstLine + x * step + FI_RGBA_ALPHA) = (T) pSrc3[indx]; // a
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    *(pDstLine + x * step + FI_RGBA_RED  ) = (T) pSrc0[indx]; // r -> 0
-                    *(pDstLine + x * step + FI_RGBA_GREEN) = (T) pSrc1[indx]; // g -> 1
-                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (T) pSrc2[indx]; // b -> 2
+                    *(pDstLine + x * step + 0) = (T) pSrc0[indx]; // r -> 0
+                    *(pDstLine + x * step + 1) = (T) pSrc1[indx]; // g -> 1
+                    *(pDstLine + x * step + 2) = (T) pSrc2[indx]; // b -> 2
+                    if(channels >= 4) *(pDstLine + x * step + 3) = (T) pSrc3[indx]; // a
                 }
             }
-            if(channels >= 4) *(pDstLine + x * step + FI_RGBA_ALPHA) = (T) pSrc3[indx]; // a
             ++indx;
         }
         pDstLine = (T*)(((uchar*)pDstLine) - nDstPitch);

From 777abcb786cce8be378ed2207f83525350a28cff Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 10:27:09 -0500
Subject: [PATCH 0281/2677] Moving dispatch.hpp / dispatch.cpp to src/backend/

---
 src/{api/c => backend}/dispatch.cpp | 0
 src/{api/c => backend}/dispatch.hpp | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename src/{api/c => backend}/dispatch.cpp (100%)
 rename src/{api/c => backend}/dispatch.hpp (100%)

diff --git a/src/api/c/dispatch.cpp b/src/backend/dispatch.cpp
similarity index 100%
rename from src/api/c/dispatch.cpp
rename to src/backend/dispatch.cpp
diff --git a/src/api/c/dispatch.hpp b/src/backend/dispatch.hpp
similarity index 100%
rename from src/api/c/dispatch.hpp
rename to src/backend/dispatch.hpp

From 828138c60b1a3a05536650fb59a7b53d62fbc43c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 10:39:12 -0500
Subject: [PATCH 0282/2677] Renaming a few internal functions

- memPush --> memLock
- memPop --> memUnlock
---
 src/api/c/device.cpp          |  4 ++--
 src/backend/cpu/Array.hpp     |  2 +-
 src/backend/cpu/memory.cpp    | 24 ++++++++++++------------
 src/backend/cpu/memory.hpp    |  6 +++---
 src/backend/cuda/Array.hpp    |  2 +-
 src/backend/cuda/memory.cpp   | 30 +++++++++++++++---------------
 src/backend/cuda/memory.hpp   |  6 +++---
 src/backend/opencl/Array.hpp  |  2 +-
 src/backend/opencl/memory.cpp | 28 ++++++++++++++--------------
 src/backend/opencl/memory.hpp |  8 ++++----
 10 files changed, 56 insertions(+), 56 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 731e98efec..c37e2934ae 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -221,7 +221,7 @@ af_err af_get_device_ptr(void **data, const af_array arr)
 template <typename T>
 inline void lockArray(const af_array arr)
 {
-    memPop<T>((const T *)getArray<T>(arr).get());
+    memLock<T>((const T *)getArray<T>(arr).get());
 }
 
 af_err af_lock_device_ptr(const af_array arr)
@@ -258,7 +258,7 @@ af_err af_lock_array(const af_array arr)
 template <typename T>
 inline void unlockArray(const af_array arr)
 {
-    memPush<T>((const T *)getArray<T>(arr).get());
+    memUnlock<T>((const T *)getArray<T>(arr).get());
 }
 
 af_err af_unlock_device_ptr(const af_array arr)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index e0709d36d3..9cd154ec50 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -84,7 +84,7 @@ namespace cpu
     void *getDevicePtr(const Array<T>& arr)
     {
         T *ptr = arr.device();
-        memPop(ptr);
+        memLock(ptr);
         return (void *)ptr;
     }
 
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 625f9b2416..5eebf18a43 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -196,7 +196,7 @@ T* memAlloc(const size_t &elements)
 }
 
 template<typename T>
-void memFreeLocked(T *ptr, bool freeLocked)
+void memFreeLocked(T *ptr, bool user_unlock)
 {
     std::lock_guard<std::mutex> lock(memory_map_mutex);
 
@@ -205,7 +205,7 @@ void memFreeLocked(T *ptr, bool freeLocked)
     if (iter != memory_map.end()) {
 
         iter->second.mngr_lock = false;
-        if ((iter->second).user_lock && !freeLocked) return;
+        if ((iter->second).user_lock && !user_unlock) return;
 
         iter->second.user_lock = false;
         used_bytes -= iter->second.bytes;
@@ -223,7 +223,7 @@ void memFree(T *ptr)
 }
 
 template<typename T>
-void memPop(const T *ptr)
+void memLock(const T *ptr)
 {
     std::lock_guard<std::mutex> lock(memory_map_mutex);
 
@@ -241,7 +241,7 @@ void memPop(const T *ptr)
 }
 
 template<typename T>
-void memPush(const T *ptr)
+void memUnlock(const T *ptr)
 {
     std::lock_guard<std::mutex> lock(memory_map_mutex);
     mem_iter iter = memory_map.find((void *)ptr);
@@ -273,14 +273,14 @@ void pinnedFree(T* ptr)
     memFree<T>(ptr);
 }
 
-#define INSTANTIATE(T)                                          \
-    template T* memAlloc(const size_t &elements);               \
-    template void memFree(T* ptr);                              \
-    template void memFreeLocked(T* ptr, bool freeLocked);       \
-    template void memPop(const T* ptr);                         \
-    template void memPush(const T* ptr);                        \
-    template T* pinnedAlloc(const size_t &elements);            \
-    template void pinnedFree(T* ptr);                           \
+#define INSTANTIATE(T)                                      \
+    template T* memAlloc(const size_t &elements);           \
+    template void memFree(T* ptr);                          \
+    template void memFreeLocked(T* ptr, bool user_unlock);  \
+    template void memLock(const T* ptr);                    \
+    template void memUnlock(const T* ptr);                  \
+    template T* pinnedAlloc(const size_t &elements);        \
+    template void pinnedFree(T* ptr);                       \
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 19846c46bf..6524fe6f94 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -17,10 +17,10 @@ namespace cpu
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeLocked(T* ptr, bool freeLocked);
+    template<typename T> void memFreeLocked(T* ptr, bool user_unlock);
 
-    template<typename T> void memPop(const T *ptr);
-    template<typename T> void memPush(const T *ptr);
+    template<typename T> void memLock(const T *ptr);
+    template<typename T> void memUnlock(const T *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 638b745d09..ad4396b48c 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -86,7 +86,7 @@ namespace cuda
     void *getDevicePtr(const Array<T>& arr)
     {
         T *ptr = arr.device();
-        memPop(ptr);
+        memLock(ptr);
         return (void *)ptr;
     }
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 8152c8a25d..f37a0fe19a 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -68,19 +68,19 @@ namespace cuda
     }
 
     template<typename T>
-    void memFreeLocked(T *ptr, bool freeLocked)
+    void memFreeLocked(T *ptr, bool user_unlock)
     {
         cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
     }
 
     template<typename T>
-    void memPop(const T *ptr)
+    void memLock(const T *ptr)
     {
         return;
     }
 
     template<typename T>
-    void memPush(const T *ptr)
+    void memUnlock(const T *ptr)
     {
         return;
     }
@@ -283,7 +283,7 @@ namespace cuda
     }
 
     template<typename T>
-    void memFreeLocked(T *ptr, bool freeLocked)
+    void memFreeLocked(T *ptr, bool user_unlock)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find((void *)ptr);
@@ -291,7 +291,7 @@ namespace cuda
         if (iter != memory_maps[n].end()) {
 
             iter->second.mngr_lock = false;
-            if ((iter->second.user_lock) && !freeLocked) return;
+            if ((iter->second.user_lock) && !user_unlock) return;
 
             iter->second.user_lock = false;
 
@@ -310,7 +310,7 @@ namespace cuda
     }
 
     template<typename T>
-    void memPop(const T *ptr)
+    void memLock(const T *ptr)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find((void *)ptr);
@@ -328,7 +328,7 @@ namespace cuda
     }
 
     template<typename T>
-    void memPush(const T *ptr)
+    void memUnlock(const T *ptr)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find((void *)ptr);
@@ -427,14 +427,14 @@ namespace cuda
 
 #endif
 
-#define INSTANTIATE(T)                                          \
-    template T* memAlloc(const size_t &elements);               \
-    template void memFree(T* ptr);                              \
-    template void memFreeLocked(T* ptr, bool freeLocked);       \
-    template void memPop(const T* ptr);                         \
-    template void memPush(const T* ptr);                        \
-    template T* pinnedAlloc(const size_t &elements);            \
-    template void pinnedFree(T* ptr);                           \
+#define INSTANTIATE(T)                                      \
+    template T* memAlloc(const size_t &elements);           \
+    template void memFree(T* ptr);                          \
+    template void memFreeLocked(T* ptr, bool user_unlock);  \
+    template void memLock(const T* ptr);                    \
+    template void memUnlock(const T* ptr);                  \
+    template T* pinnedAlloc(const size_t &elements);        \
+    template void pinnedFree(T* ptr);                       \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 5644a52371..29e4e76597 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -17,9 +17,9 @@ namespace cuda
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeLocked(T* ptr, bool freeLocked);
-    template<typename T> void memPop(const T *ptr);
-    template<typename T> void memPush(const T *ptr);
+    template<typename T> void memFreeLocked(T* ptr, bool user_unlock);
+    template<typename T> void memLock(const T *ptr);
+    template<typename T> void memUnlock(const T *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 5f86d6d0b6..a6d3f4f869 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -77,7 +77,7 @@ namespace opencl
     void *getDevicePtr(const Array<T>& arr)
     {
         cl::Buffer *buf = arr.device();
-        memPop((T *)buf);
+        memLock((T *)buf);
         return (void *)((*buf)());
     }
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 141610d71f..b75955efd9 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -194,7 +194,7 @@ namespace opencl
         bufferFreeLocked(ptr, false);
     }
 
-    void bufferFreeLocked(cl::Buffer *ptr, bool freeLocked)
+    void bufferFreeLocked(cl::Buffer *ptr, bool user_unlock)
     {
         int n = getActiveDeviceId();
         mem_iter iter = memory_maps[n].find(ptr);
@@ -202,7 +202,7 @@ namespace opencl
         if (iter != memory_maps[n].end()) {
 
             iter->second.mngr_lock = false;
-            if ((iter->second).user_lock && !freeLocked) return;
+            if ((iter->second).user_lock && !user_unlock) return;
 
             iter->second.user_lock = false;
 
@@ -264,19 +264,19 @@ namespace opencl
     }
 
     template<typename T>
-    void memFreeLocked(T *ptr, bool freeLocked)
+    void memFreeLocked(T *ptr, bool user_unlock)
     {
-        return bufferFreeLocked((cl::Buffer *)ptr, freeLocked);
+        return bufferFreeLocked((cl::Buffer *)ptr, user_unlock);
     }
 
     template<typename T>
-    void memPop(const T *ptr)
+    void memLock(const T *ptr)
     {
         return bufferPop((cl::Buffer *)ptr);
     }
 
     template<typename T>
-    void memPush(const T *ptr)
+    void memUnlock(const T *ptr)
     {
         return bufferPush((cl::Buffer *)ptr);
     }
@@ -395,14 +395,14 @@ namespace opencl
         return pinnedBufferFree((void *) ptr);
     }
 
-#define INSTANTIATE(T)                                          \
-    template T* memAlloc(const size_t &elements);               \
-    template void memFree(T* ptr);                              \
-    template void memFreeLocked(T* ptr, bool freeLocked);       \
-    template void memPop(const T* ptr);                         \
-    template void memPush(const T* ptr);                        \
-    template T* pinnedAlloc(const size_t &elements);            \
-    template void pinnedFree(T* ptr);                           \
+#define INSTANTIATE(T)                                      \
+    template T* memAlloc(const size_t &elements);           \
+    template void memFree(T* ptr);                          \
+    template void memFreeLocked(T* ptr, bool user_unlock);  \
+    template void memLock(const T* ptr);                    \
+    template void memUnlock(const T* ptr);                  \
+    template T* pinnedAlloc(const size_t &elements);        \
+    template void pinnedFree(T* ptr);                       \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 96292cdfac..dce142805a 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -20,7 +20,7 @@ namespace opencl
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     void bufferFree(cl::Buffer *buf);
-    void bufferFreeLocked(cl::Buffer *buf, bool freeLocked);
+    void bufferFreeLocked(cl::Buffer *buf, bool user_unlock);
 
     template<typename T> T *memAlloc(const size_t &elements);
 
@@ -28,9 +28,9 @@ namespace opencl
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeLocked(T* ptr, bool freeLocked);
-    template<typename T> void memPop(const T *ptr);
-    template<typename T> void memPush(const T *ptr);
+    template<typename T> void memFreeLocked(T* ptr, bool user_unlock);
+    template<typename T> void memLock(const T *ptr);
+    template<typename T> void memUnlock(const T *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);

From c8cd29b1267580a851da82ac3fcb2c2762119f3a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 12:47:11 -0500
Subject: [PATCH 0283/2677] Adding a unified memory manager for all backends

---
 src/backend/MemoryManager.cpp | 250 +++++++++++++++++
 src/backend/MemoryManager.hpp |  99 +++++++
 src/backend/cpu/memory.cpp    | 247 ++++-------------
 src/backend/cpu/memory.hpp    |   1 +
 src/backend/cuda/memory.cpp   | 506 +++++++++-------------------------
 src/backend/cuda/memory.hpp   |   1 +
 src/backend/opencl/memory.cpp | 499 +++++++++++----------------------
 src/backend/opencl/memory.hpp |   5 -
 8 files changed, 710 insertions(+), 898 deletions(-)
 create mode 100644 src/backend/MemoryManager.cpp
 create mode 100644 src/backend/MemoryManager.hpp

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
new file mode 100644
index 0000000000..621ce624e7
--- /dev/null
+++ b/src/backend/MemoryManager.cpp
@@ -0,0 +1,250 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <iostream>
+#include <iomanip>
+#include <string>
+#include "MemoryManager.hpp"
+#include "dispatch.hpp"
+#include "err_common.hpp"
+#include "util.hpp"
+
+namespace common
+{
+
+MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX_BYTES, bool debug):
+    mem_step_size(1024),
+    max_buffers(MAX_BUFFERS),
+    max_bytes(MAX_BYTES),
+    memory(num_devices),
+    debug_mode(debug)
+{
+    std::string env_var = getEnvVar("AF_MEM_DEBUG");
+    if (!env_var.empty()) {
+        this->debug_mode = env_var[0] != '0';
+    }
+    if (this->debug_mode) mem_step_size = 1;
+}
+
+void MemoryManager::garbageCollect()
+{
+    if (this->debug_mode) return;
+
+    memory_info& current = this->getCurrentMemoryInfo();
+
+    for(buffer_iter iter = current.map.begin();
+        iter != current.map.end(); ++iter) {
+
+        if (!(iter->second).manager_lock) {
+
+            if (!(iter->second).user_lock) {
+                if ((iter->second).bytes > 0) {
+                    this->nativeFree(iter->first);
+                }
+                current.total_bytes -= iter->second.bytes;
+            }
+        }
+    }
+
+    buffer_iter memory_curr = current.map.begin();
+    buffer_iter memory_end  = current.map.end();
+
+    while(memory_curr != memory_end) {
+        if (memory_curr->second.manager_lock || memory_curr->second.user_lock) {
+            ++memory_curr;
+        } else {
+            current.map.erase(memory_curr++);
+        }
+    }
+}
+
+void MemoryManager::unlock(void *ptr, bool user_unlock)
+{
+    memory_info& current = this->getCurrentMemoryInfo();
+    lock_guard_t lock(this->memory_mutex);
+
+    buffer_iter iter = current.map.find((void *)ptr);
+
+    if (iter != current.map.end()) {
+
+        iter->second.manager_lock = false;
+        if ((iter->second).user_lock && !user_unlock) return;
+
+        iter->second.user_lock = false;
+        current.lock_bytes -= iter->second.bytes;
+        current.lock_buffers--;
+
+        if (this->debug_mode) {
+            if ((iter->second).bytes > 0) {
+                this->nativeFree(iter->first);
+            }
+        }
+
+    } else {
+        this->nativeFree(ptr); // Free it because we are not sure what the size is
+    }
+}
+
+void *MemoryManager::alloc(const size_t bytes)
+{
+    memory_info& current = this->getCurrentMemoryInfo();
+
+    void *ptr = NULL;
+    size_t alloc_bytes = this->debug_mode ? bytes : (divup(bytes, mem_step_size) * mem_step_size);
+
+    if (bytes > 0) {
+
+        lock_guard_t lock(this->memory_mutex);
+
+        // There is no memory cache in debug mode
+        if (!this->debug_mode) {
+
+            // FIXME: Add better checks for garbage collection
+            // Perhaps look at total memory available as a metric
+            if (current.map.size() > this->max_buffers ||
+                current.lock_bytes >= this->max_bytes) {
+
+                this->garbageCollect();
+            }
+
+            for(buffer_iter iter = current.map.begin();
+                iter != current.map.end(); ++iter) {
+
+                buffer_info info = iter->second;
+
+                if (!info.manager_lock &&
+                    !info.user_lock &&
+                    info.bytes == alloc_bytes) {
+
+                    iter->second.manager_lock = true;
+                    current.lock_bytes += alloc_bytes;
+                    current.lock_buffers++;
+                    return iter->first;
+                }
+            }
+        }
+
+        // Perform garbage collection if memory can not be allocated
+        ptr = this->nativeAlloc(alloc_bytes);
+
+        if (!ptr) {
+            this->garbageCollect();
+            ptr = this->nativeAlloc(alloc_bytes);
+            if (!ptr) AF_ERROR("Can not allocate memory", AF_ERR_NO_MEM);
+        }
+
+        buffer_info info = {true, false, alloc_bytes};
+        current.map[ptr] = info;
+
+        current.lock_bytes += alloc_bytes;
+        current.lock_buffers++;
+        current.total_bytes += alloc_bytes;
+    }
+    return ptr;
+}
+
+void MemoryManager::userLock(const void *ptr)
+{
+    memory_info& current = this->getCurrentMemoryInfo();
+
+    lock_guard_t lock(this->memory_mutex);
+
+    buffer_iter iter = current.map.find(const_cast<void *>(ptr));
+
+    if (iter != current.map.end()) {
+        iter->second.user_lock = true;
+    } else {
+        buffer_info info = { true,
+                          true,
+                          100 }; //This number is not relevant
+
+        current.map[(void *)ptr] = info;
+    }
+}
+
+void MemoryManager::userUnlock(const void *ptr)
+{
+    memory_info& current = this->getCurrentMemoryInfo();
+
+    lock_guard_t lock(this->memory_mutex);
+
+    buffer_iter iter = current.map.find((void *)ptr);
+    if (iter != current.map.end()) {
+        iter->second.user_lock = false;
+        if (this->debug_mode) {
+            if ((iter->second).bytes > 0) {
+                this->nativeFree(iter->first);
+            }
+        }
+    }
+}
+
+size_t MemoryManager::getMemStepSize()
+{
+    lock_guard_t lock(this->memory_mutex);
+    return this->mem_step_size;
+}
+
+void MemoryManager::setMemStepSize(size_t new_step_size)
+{
+    lock_guard_t lock(this->memory_mutex);
+    this->mem_step_size = new_step_size;
+}
+
+void MemoryManager::printInfo(const char *msg, const int device)
+{
+    lock_guard_t lock(this->memory_mutex);
+    memory_info& current = this->getCurrentMemoryInfo();
+
+    std::cout << msg << std::endl;
+
+    static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
+    static const std::string line(head.size(), '-');
+    std::cout << line << std::endl << head << std::endl << line << std::endl;
+
+    for(buffer_iter iter = current.map.begin();
+        iter != current.map.end(); ++iter) {
+
+        std::string status_mngr("Unknown");
+        std::string status_user("Unknown");
+
+        if(iter->second.manager_lock)  status_mngr = "Yes";
+        else                           status_mngr = " No";
+
+        if(iter->second.user_lock)     status_user = "Yes";
+        else                           status_user = " No";
+
+        std::string unit = "KB";
+        double size = (double)(iter->second.bytes) / 1024;
+        if(size >= 1024) {
+            size = size / 1024;
+            unit = "MB";
+        }
+
+        std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
+                  << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                  << " | " << std::setw(9) << status_mngr
+                  << " | " << std::setw(9) << status_user
+                  << " |"  << std::endl;
+    }
+
+    std::cout << line << std::endl;
+}
+
+void MemoryManager::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                               size_t *lock_bytes,  size_t *lock_buffers)
+{
+    memory_info current = this->getCurrentMemoryInfo();
+    lock_guard_t lock(this->memory_mutex);
+    if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
+    if (alloc_buffers ) *alloc_buffers = current.map.size();
+    if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
+    if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
+}
+}
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
new file mode 100644
index 0000000000..1f87ea2dfe
--- /dev/null
+++ b/src/backend/MemoryManager.hpp
@@ -0,0 +1,99 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <vector>
+#include <map>
+#include <mutex>
+
+namespace common
+{
+
+typedef std::mutex mutex_t;
+typedef std::lock_guard<std::mutex> lock_guard_t;
+
+class MemoryManager
+{
+    typedef struct
+    {
+        bool manager_lock;
+        bool user_lock;
+        size_t bytes;
+    } buffer_info;
+
+    typedef std::map<void *, buffer_info> buffer_t;
+    typedef buffer_t::iterator buffer_iter;
+
+    typedef struct
+    {
+        buffer_t map;
+        size_t lock_bytes;
+        size_t lock_buffers;
+        size_t total_bytes;
+    } memory_info;
+
+    size_t mem_step_size;
+    unsigned max_buffers;
+    unsigned max_bytes;
+    std::vector<memory_info> memory;
+    bool debug_mode;
+
+    memory_info& getCurrentMemoryInfo()
+    {
+        return memory[this->getActiveDeviceId()];
+    }
+
+    virtual int getActiveDeviceId()
+    {
+        return 0;
+    }
+
+public:
+    MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX_BYTES, bool debug);
+
+    void *alloc(const size_t bytes);
+
+    void unlock(void *ptr, bool user_unlock);
+
+    void garbageCollect();
+
+    void printInfo(const char *msg, const int device);
+
+    void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                    size_t *lock_bytes,  size_t *lock_buffers);
+
+    void userLock(const void *ptr);
+
+    void userUnlock(const void *ptr);
+
+    size_t getMemStepSize();
+
+    void setMemStepSize(size_t new_step_size);
+
+    virtual void *nativeAlloc(const size_t bytes)
+    {
+        return malloc(bytes);
+    }
+
+    virtual void nativeFree(void *ptr)
+    {
+        return free((void *)ptr);
+    }
+
+    virtual ~MemoryManager()
+    {
+    }
+
+protected:
+    mutex_t memory_mutex;
+
+};
+
+}
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 5eebf18a43..2687b3018b 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -10,244 +10,111 @@
 #include <memory.hpp>
 #include <err_cpu.hpp>
 #include <types.hpp>
-#include <map>
-#include <dispatch.hpp>
-#include <cstdlib>
-#include <mutex>
-#include <iostream>
-#include <iomanip>
-#include <string>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <memory>
+#include <MemoryManager.hpp>
 
-namespace cpu
-{
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
 
-static size_t memory_resolution = 1024; //1KB
+#ifndef AF_CPU_MEM_DEBUG
+#define AF_CPU_MEM_DEBUG 0
+#endif
 
-void setMemStepSize(size_t step_bytes)
-{
-    memory_resolution = step_bytes;
-}
-
-size_t getMemStepSize(void)
+namespace cpu
 {
-    return memory_resolution;
-}
 
-class Manager
+class MemoryManager  : public common::MemoryManager
 {
-    public:
-    static bool initialized;
-    Manager()
+    int getActiveDeviceId();
+public:
+    MemoryManager();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManager()
     {
-        initialized = true;
-    }
-
-    ~Manager()
-    {
-        garbageCollect();
+        common::lock_guard_t lock(this->memory_mutex);
+        this->garbageCollect();
     }
 };
 
-bool Manager::initialized = false;
-
-static void managerInit()
+int MemoryManager::getActiveDeviceId()
 {
-    if(Manager::initialized == false)
-        static Manager pm = Manager();
+    return cpu::getActiveDeviceId();
 }
 
-typedef struct
-{
-    bool mngr_lock; // True if locked by memory manager, false if free
-    bool user_lock; // True if locked by user, false if free
-    size_t bytes;
-} mem_info;
+MemoryManager::MemoryManager() :
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
+{}
 
-static size_t used_bytes = 0;
-static size_t used_buffers = 0;
-static size_t total_bytes = 0;
-typedef std::map<void *, mem_info> mem_t;
-typedef mem_t::iterator mem_iter;
 
-mem_t memory_map;
-std::mutex memory_map_mutex;
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    return malloc(bytes);
+}
 
-template<typename T>
-void freeWrapper(T *ptr)
+void MemoryManager::nativeFree(void *ptr)
 {
-    free((void *)ptr);
+    return free((void *)ptr);
 }
 
-void garbageCollect()
+static MemoryManager &getMemoryManager()
 {
-    for(mem_iter iter = memory_map.begin();
-        iter != memory_map.end(); ++iter) {
+    static MemoryManager instance;
+    return instance;
+}
 
-        if (!(iter->second).mngr_lock) {
+void setMemStepSize(size_t step_bytes)
+{
+    getMemoryManager().setMemStepSize(step_bytes);
+}
 
-            if (!(iter->second).user_lock) {
-                freeWrapper(iter->first);
-                total_bytes -= iter->second.bytes;
-            }
-        }
-    }
+size_t getMemStepSize(void)
+{
+    return getMemoryManager().getMemStepSize();
+}
 
-    mem_iter memory_curr = memory_map.begin();
-    mem_iter memory_end  = memory_map.end();
 
-    while(memory_curr != memory_end) {
-        if (memory_curr->second.mngr_lock || memory_curr->second.user_lock) {
-            ++memory_curr;
-        } else {
-            memory_map.erase(memory_curr++);
-        }
-    }
+void garbageCollect()
+{
+    getMemoryManager().garbageCollect();
 }
 
 void printMemInfo(const char *msg, const int device)
 {
-    std::cout << msg << std::endl;
-
-    static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
-    static const std::string line(head.size(), '-');
-    std::cout << line << std::endl << head << std::endl << line << std::endl;
-
-    for(mem_iter iter = memory_map.begin();
-        iter != memory_map.end(); ++iter) {
-
-        std::string status_mngr("Unknown");
-        std::string status_user("Unknown");
-
-        if(iter->second.mngr_lock)  status_mngr = "Yes";
-        else                        status_mngr = " No";
-
-        if(iter->second.user_lock)  status_user = "Yes";
-        else                        status_user = " No";
-
-        std::string unit = "KB";
-        double size = (double)(iter->second.bytes) / 1024;
-        if(size >= 1024) {
-            size = size / 1024;
-            unit = "MB";
-        }
-
-        std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
-                  << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                  << " | " << std::setw(9) << status_mngr
-                  << " | " << std::setw(9) << status_user
-                  << " |"  << std::endl;
-    }
-
-    std::cout << line << std::endl;
+    getMemoryManager().printInfo(msg, device);
 }
 
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    managerInit();
-
-    T* ptr = NULL;
-    size_t alloc_bytes = divup(sizeof(T) * elements, memory_resolution) * memory_resolution;
-
-    if (elements > 0) {
-        std::lock_guard<std::mutex> lock(memory_map_mutex);
-
-        // FIXME: Add better checks for garbage collection
-        // Perhaps look at total memory available as a metric
-        if (memory_map.size() > MAX_BUFFERS ||
-            used_bytes >= MAX_BYTES) {
-
-            garbageCollect();
-        }
-
-        for(mem_iter iter = memory_map.begin();
-            iter != memory_map.end(); ++iter) {
-
-            mem_info info = iter->second;
-
-            if (!info.mngr_lock &&
-                !info.user_lock &&
-                 info.bytes == alloc_bytes) {
-
-                iter->second.mngr_lock = true;
-                used_bytes += alloc_bytes;
-                used_buffers++;
-                return (T *)iter->first;
-            }
-        }
-
-        // Perform garbage collection if memory can not be allocated
-        ptr = (T *)malloc(alloc_bytes);
-
-        if (ptr == NULL) {
-            AF_ERROR("Can not allocate memory", AF_ERR_NO_MEM);
-        }
-
-        mem_info info = {true, false, alloc_bytes};
-        memory_map[ptr] = info;
-
-        used_bytes += alloc_bytes;
-        used_buffers++;
-        total_bytes += alloc_bytes;
-    }
-    return ptr;
+    return (T *)getMemoryManager().alloc(elements * sizeof(T));
 }
 
 template<typename T>
-void memFreeLocked(T *ptr, bool user_unlock)
+void memFree(T *ptr)
 {
-    std::lock_guard<std::mutex> lock(memory_map_mutex);
-
-    mem_iter iter = memory_map.find((void *)ptr);
-
-    if (iter != memory_map.end()) {
-
-        iter->second.mngr_lock = false;
-        if ((iter->second).user_lock && !user_unlock) return;
-
-        iter->second.user_lock = false;
-        used_bytes -= iter->second.bytes;
-        used_buffers--;
-
-    } else {
-        freeWrapper(ptr); // Free it because we are not sure what the size is
-    }
+    return getMemoryManager().unlock((void *)ptr, false);
 }
 
 template<typename T>
-void memFree(T *ptr)
+void memFreeLocked(T *ptr, bool user_unlock)
 {
-    memFreeLocked(ptr, false);
+    return getMemoryManager().unlock((void *)ptr, user_unlock);
 }
 
 template<typename T>
 void memLock(const T *ptr)
 {
-    std::lock_guard<std::mutex> lock(memory_map_mutex);
-
-    mem_iter iter = memory_map.find((void *)ptr);
-
-    if (iter != memory_map.end()) {
-        iter->second.user_lock = true;
-    } else {
-        mem_info info = { true,
-                          true,
-                          100 }; //This number is not relevant
-
-        memory_map[(void *)ptr] = info;
-    }
+    getMemoryManager().userLock((void *)ptr);
 }
 
 template<typename T>
 void memUnlock(const T *ptr)
 {
-    std::lock_guard<std::mutex> lock(memory_map_mutex);
-    mem_iter iter = memory_map.find((void *)ptr);
-    if (iter != memory_map.end()) {
-        iter->second.user_lock = false;
-    }
+    getMemoryManager().userUnlock((void *)ptr);
 }
 
 
@@ -255,22 +122,20 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
     getQueue().sync();
-    if (alloc_bytes   ) *alloc_bytes   = total_bytes;
-    if (alloc_buffers ) *alloc_buffers = memory_map.size();
-    if (lock_bytes    ) *lock_bytes    = used_bytes;
-    if (lock_buffers  ) *lock_buffers  = used_buffers;
+    getMemoryManager().bufferInfo(alloc_bytes, alloc_buffers,
+                                  lock_bytes,  lock_buffers);
 }
 
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return memAlloc<T>(elements);
+    return (T *)getMemoryManager().alloc(elements * sizeof(T));
 }
 
 template<typename T>
 void pinnedFree(T* ptr)
 {
-    memFree<T>(ptr);
+    return getMemoryManager().unlock((void *)ptr, false);
 }
 
 #define INSTANTIATE(T)                                      \
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 6524fe6f94..279b3dbd28 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -9,6 +9,7 @@
 #pragma once
 
 #include <af/defines.h>
+
 namespace cpu
 {
     template<typename T> T* memAlloc(const size_t &elements);
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index f37a0fe19a..43c37e016f 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -20,412 +20,178 @@
 #include <map>
 #include <dispatch.hpp>
 #include <platform.hpp>
+#include <MemoryManager.hpp>
 
-namespace cuda
-{
-    static size_t memory_resolution = 1024; //1KB
-
-    void setMemStepSize(size_t step_bytes)
-    {
-        memory_resolution = step_bytes;
-    }
-
-    size_t getMemStepSize(void)
-    {
-        return memory_resolution;
-    }
-
-    template<typename T>
-    static void cudaFreeWrapper(T *ptr)
-    {
-        cudaError_t err = cudaFree(ptr);
-        if (err != cudaErrorCudartUnloading) // see issue #167
-            CUDA_CHECK(err);
-    }
-
-    template<typename T>
-    static void pinnedFreeWrapper(T *ptr)
-    {
-        cudaError_t err = cudaFreeHost(ptr);
-        if (err != cudaErrorCudartUnloading) // see issue #167
-            CUDA_CHECK(err);
-    }
-
-#ifdef AF_CUDA_MEM_DEBUG
-
-    template<typename T>
-    T* memAlloc(const size_t &elements)
-    {
-        T* ptr = NULL;
-        CUDA_CHECK(cudaMalloc(&ptr, elements * sizeof(T)));
-        return ptr;
-    }
-
-    template<typename T>
-    void memFree(T *ptr)
-    {
-        cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
-    }
-
-    template<typename T>
-    void memFreeLocked(T *ptr, bool user_unlock)
-    {
-        cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
-    }
-
-    template<typename T>
-    void memLock(const T *ptr)
-    {
-        return;
-    }
-
-    template<typename T>
-    void memUnlock(const T *ptr)
-    {
-        return;
-    }
-
-    template<typename T>
-    T* pinnedAlloc(const size_t &elements)
-    {
-        T* ptr = NULL;
-        CUDA_CHECK(cudaMallocHost((void **)(&ptr), elements * sizeof(T)));
-        return (T*)ptr;
-    }
 
-    template<typename T>
-    void pinnedFree(T *ptr)
-    {
-        pinnedFreeWrapper(ptr); // Free it because we are not sure what the size is
-    }
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
 
-    void garbageCollect()
-    {
-    }
+#ifndef AF_CUDA_MEM_DEBUG
+#define AF_CUDA_MEM_DEBUG 0
+#endif
 
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers)
-    {
-    }
+namespace cuda
+{
 
-    void printMemInfo(const char *msg, const int device)
+class MemoryManager  : public common::MemoryManager
+{
+    int getActiveDeviceId();
+public:
+    MemoryManager();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManager()
     {
-        std::cout << "printMemInfo() disabled in AF_CUDA_MEM_DEBUG Mode" << std::endl;
+        common::lock_guard_t lock(this->memory_mutex);
+        this->garbageCollect();
     }
-#else
+};
 
-    // Manager Class
-    // Dummy used to call garbage collection at the end of the program
-    class Manager
-    {
-        public:
-        static bool initialized;
-        Manager()
-        {
-            initialized = true;
-        }
-
-        ~Manager()
-        {
-            // Destructors should not through exceptions
-            try {
-                for(int i = 0; i < getDeviceCount(); i++) {
-                    setDevice(i);
-                    garbageCollect();
-                }
-                pinnedGarbageCollect();
-
-            } catch (AfError &ex) {
-
-                std::string perr = getEnvVar("AF_PRINT_ERRORS");
-                if(!perr.empty()) {
-                    if(perr != "0")
-                        fprintf(stderr, "%s\n", ex.what());
-                }
-            }
-        }
-    };
-
-    bool Manager::initialized = false;
-
-    static void managerInit()
+class MemoryManagerPinned  : public common::MemoryManager
+{
+    int getActiveDeviceId();
+public:
+    MemoryManagerPinned();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManagerPinned()
     {
-        if(Manager::initialized == false)
-            static Manager pm = Manager();
+        common::lock_guard_t lock(this->memory_mutex);
+        this->garbageCollect();
     }
+};
 
-    typedef struct
-    {
-        bool mngr_lock;
-        bool user_lock;
-        size_t bytes;
-    } mem_info;
+int MemoryManager::getActiveDeviceId()
+{
+    return cuda::getActiveDeviceId();
+}
 
-    static size_t used_bytes[DeviceManager::MAX_DEVICES] = {0};
-    static size_t used_buffers[DeviceManager::MAX_DEVICES] = {0};
-    static size_t total_bytes[DeviceManager::MAX_DEVICES] = {0};
-    typedef std::map<void *, mem_info> mem_t;
-    typedef mem_t::iterator mem_iter;
+MemoryManager::MemoryManager() :
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+{}
 
-    mem_t memory_maps[DeviceManager::MAX_DEVICES];
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    void *ptr = NULL;
+    CUDA_CHECK(cudaMalloc(&ptr, bytes));
+    return ptr;
+}
 
-    void garbageCollect()
-    {
-        int n = getActiveDeviceId();
-
-        for(mem_iter iter = memory_maps[n].begin();
-            iter != memory_maps[n].end(); ++iter) {
-
-            if (!(iter->second.mngr_lock)) {
-
-                if (!(iter->second.user_lock)) {
-                    cudaFreeWrapper(iter->first);
-                    total_bytes[n] -= iter->second.bytes;
-                }
-            }
-        }
-
-        mem_iter memory_curr = memory_maps[n].begin();
-        mem_iter memory_end  = memory_maps[n].end();
-
-        while(memory_curr != memory_end) {
-            if (memory_curr->second.mngr_lock || memory_curr->second.user_lock) {
-                ++memory_curr;
-            } else {
-                memory_maps[n].erase(memory_curr++);
-            }
-        }
+void MemoryManager::nativeFree(void *ptr)
+{
+    cudaError_t err = cudaFree(ptr);
+    if (err != cudaErrorCudartUnloading) {
+        CUDA_CHECK(err);
     }
+}
 
-    void printMemInfo(const char *msg, const int device)
-    {
-        std::cout << msg << std::endl;
-        std::cout << "Memory Map for Device: " << device << std::endl;
-
-        static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
-        static const std::string line(head.size(), '-');
-        std::cout << line << std::endl << head << std::endl << line << std::endl;
-
-        for(mem_iter iter = memory_maps[device].begin();
-            iter != memory_maps[device].end(); ++iter) {
-
-            std::string status_mngr("Unknown");
-            std::string status_user("Unknown");
-
-            if(iter->second.mngr_lock)  status_mngr = "Yes";
-            else                        status_mngr = " No";
-
-            if(iter->second.user_lock)  status_user = "Yes";
-            else                        status_user = " No";
+static MemoryManager &getMemoryManager()
+{
+    static MemoryManager instance;
+    return instance;
+}
 
-            std::string unit = "KB";
-            double size = (double)(iter->second.bytes) / 1024;
-            if(size >= 1024) {
-                size = size / 1024;
-                unit = "MB";
-            }
+int MemoryManagerPinned::getActiveDeviceId()
+{
+    return cuda::getActiveDeviceId();
+}
 
-            std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
-                      << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                      << " | " << std::setw(9) << status_mngr
-                      << " | " << std::setw(9) << status_user
-                      << " |"  << std::endl;
-        }
+MemoryManagerPinned::MemoryManagerPinned() :
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+{}
 
-        std::cout << line << std::endl;
-    }
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
+{
+    void *ptr;
+    CUDA_CHECK(cudaMallocHost(&ptr, bytes));
+    return ptr;
+}
 
-    template<typename T>
-    T* memAlloc(const size_t &elements)
-    {
-        managerInit();
-        int n = getActiveDeviceId();
-        T* ptr = NULL;
-        size_t alloc_bytes = divup(sizeof(T) * elements, memory_resolution) * memory_resolution;
-
-        if (elements > 0) {
-
-            // FIXME: Add better checks for garbage collection
-            // Perhaps look at total memory available as a metric
-            if (memory_maps[n].size() >= MAX_BUFFERS || used_bytes[n] >= MAX_BYTES) {
-                garbageCollect();
-            }
-
-            for(mem_iter iter = memory_maps[n].begin();
-                iter != memory_maps[n].end(); ++iter) {
-
-                mem_info info = iter->second;
-
-                if (!info.mngr_lock &&
-                    !info.user_lock &&
-                     info.bytes == alloc_bytes) {
-
-                    iter->second.mngr_lock = true;
-                    used_bytes[n] += alloc_bytes;
-                    used_buffers[n]++;
-                    return (T *)iter->first;
-                }
-            }
-
-            // Perform garbage collection if memory can not be allocated
-            if (cudaMalloc((void **)&ptr, alloc_bytes) != cudaSuccess) {
-                garbageCollect();
-                CUDA_CHECK(cudaMalloc((void **)(&ptr), alloc_bytes));
-            }
-
-            mem_info info = {true, false, alloc_bytes};
-            memory_maps[n][ptr] = info;
-            used_bytes[n] += alloc_bytes;
-            used_buffers[n]++;
-            total_bytes[n] += alloc_bytes;
-        }
-        return ptr;
+void MemoryManagerPinned::nativeFree(void *ptr)
+{
+    cudaError_t err = cudaFreeHost(ptr);
+    if (err != cudaErrorCudartUnloading) {
+        CUDA_CHECK(err);
     }
+}
 
-    template<typename T>
-    void memFreeLocked(T *ptr, bool user_unlock)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find((void *)ptr);
-
-        if (iter != memory_maps[n].end()) {
-
-            iter->second.mngr_lock = false;
-            if ((iter->second.user_lock) && !user_unlock) return;
-
-            iter->second.user_lock = false;
+static MemoryManagerPinned &getMemoryManagerPinned()
+{
+    static MemoryManagerPinned instance;
+    return instance;
+}
 
-            used_bytes[n] -= iter->second.bytes;
-            used_buffers[n]--;
+void setMemStepSize(size_t step_bytes)
+{
+    getMemoryManager().setMemStepSize(step_bytes);
+}
 
-        } else {
-            cudaFreeWrapper(ptr); // Free it because we are not sure what the size is
-        }
-    }
+size_t getMemStepSize(void)
+{
+    return getMemoryManager().getMemStepSize();
+}
 
-    template<typename T>
-    void memFree(T *ptr)
-    {
-        memFreeLocked(ptr, false);
-    }
 
-    template<typename T>
-    void memLock(const T *ptr)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find((void *)ptr);
+void garbageCollect()
+{
+    getMemoryManager().garbageCollect();
+}
 
-        if (iter != memory_maps[n].end()) {
-            iter->second.user_lock = true;
-        } else {
+void printMemInfo(const char *msg, const int device)
+{
+    getMemoryManager().printInfo(msg, device);
+}
 
-            mem_info info = { true,
-                              true,
-                              100 }; //This number is not relevant
+template<typename T>
+T* memAlloc(const size_t &elements)
+{
+    return (T *)getMemoryManager().alloc(elements * sizeof(T));
+}
 
-            memory_maps[n][(void *)ptr] = info;
-        }
-    }
+template<typename T>
+void memFree(T *ptr)
+{
+    return getMemoryManager().unlock((void *)ptr, false);
+}
 
-    template<typename T>
-    void memUnlock(const T *ptr)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find((void *)ptr);
-        if (iter != memory_maps[n].end()) {
-            iter->second.user_lock = false;
-        }
-    }
+template<typename T>
+void memFreeLocked(T *ptr, bool user_unlock)
+{
+    return getMemoryManager().unlock((void *)ptr, user_unlock);
+}
 
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers)
-    {
-        int n = getActiveDeviceId();
-        if (alloc_bytes   ) *alloc_bytes   = total_bytes[n];
-        if (alloc_buffers ) *alloc_buffers = memory_maps[n].size();
-        if (lock_bytes    ) *lock_bytes    = used_bytes[n];
-        if (lock_buffers  ) *lock_buffers  = used_buffers[n];
-    }
+template<typename T>
+void memLock(const T *ptr)
+{
+    getMemoryManager().userLock((void *)ptr);
+}
 
-    //////////////////////////////////////////////////////////////////////////////
-    mem_t pinned_maps;
-    static size_t pinned_used_bytes = 0;
+template<typename T>
+void memUnlock(const T *ptr)
+{
+    getMemoryManager().userUnlock((void *)ptr);
+}
 
-    void pinnedGarbageCollect()
-    {
-        for(mem_iter iter = pinned_maps.begin(); iter != pinned_maps.end(); ++iter) {
-            if (!(iter->second.mngr_lock)) {
-                pinnedFreeWrapper(iter->first);
-            }
-        }
-
-        mem_iter memory_curr = pinned_maps.begin();
-        mem_iter memory_end  = pinned_maps.end();
-
-        while(memory_curr != memory_end) {
-            if (memory_curr->second.mngr_lock) {
-                ++memory_curr;
-            } else {
-                pinned_maps.erase(memory_curr++);
-            }
-        }
-    }
 
-    template<typename T>
-    T* pinnedAlloc(const size_t &elements)
-    {
-        managerInit();
-        T* ptr = NULL;
-        // Allocate the higher megabyte. Overhead of creating pinned memory is
-        // more so we want more resuable memory.
-        size_t alloc_bytes = divup(sizeof(T) * elements, 1048576) * 1048576;
-
-        if (elements > 0) {
-
-            // FIXME: Add better checks for garbage collection
-            // Perhaps look at total memory available as a metric
-            if (pinned_maps.size() >= MAX_BUFFERS || pinned_used_bytes >= MAX_BYTES) {
-                pinnedGarbageCollect();
-            }
-
-            for(mem_iter iter = pinned_maps.begin();
-                iter != pinned_maps.end(); ++iter) {
-
-                mem_info info = iter->second;
-                if (!info.mngr_lock && info.bytes == alloc_bytes) {
-                    iter->second.mngr_lock = true;
-                    pinned_used_bytes += alloc_bytes;
-                    return (T *)iter->first;
-                }
-            }
-
-            // Perform garbage collection if memory can not be allocated
-            if (cudaMallocHost((void **)&ptr, alloc_bytes) != cudaSuccess) {
-                pinnedGarbageCollect();
-                CUDA_CHECK(cudaMallocHost((void **)(&ptr), alloc_bytes));
-            }
-
-            mem_info info = {true, false, alloc_bytes};
-            pinned_maps[ptr] = info;
-            pinned_used_bytes += alloc_bytes;
-        }
-        return (T*)ptr;
-    }
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes,  size_t *lock_buffers)
+{
+    getMemoryManager().bufferInfo(alloc_bytes, alloc_buffers,
+                                  lock_bytes,  lock_buffers);
+}
 
-    template<typename T>
-    void pinnedFree(T *ptr)
-    {
-        mem_iter iter = pinned_maps.find((void *)ptr);
-
-        if (iter != pinned_maps.end()) {
-            iter->second.mngr_lock = false;
-            pinned_used_bytes -= iter->second.bytes;
-        } else {
-            pinnedFreeWrapper(ptr); // Free it because we are not sure what the size is
-        }
-    }
+template<typename T>
+T* pinnedAlloc(const size_t &elements)
+{
+    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T));
+}
 
-#endif
+template<typename T>
+void pinnedFree(T* ptr)
+{
+    return getMemoryManagerPinned().unlock((void *)ptr, false);
+}
 
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 29e4e76597..5b362cd587 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -9,6 +9,7 @@
 #pragma once
 
 #include <af/defines.h>
+
 namespace cuda
 {
     template<typename T> T* memAlloc(const size_t &elements);
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index b75955efd9..45b8e96ba4 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -14,386 +14,221 @@
 #include <iomanip>
 #include <string>
 #include <types.hpp>
+#include "err_opencl.hpp"
 
-namespace opencl
-{
-    static size_t memory_resolution = 1024; //1KB
-
-    void setMemStepSize(size_t step_bytes)
-    {
-        memory_resolution = step_bytes;
-    }
+#include <MemoryManager.hpp>
 
-    size_t getMemStepSize(void)
-    {
-        return memory_resolution;
-    }
-
-    // Manager Class
-    // Dummy used to call garbage collection at the end of the program
-    class Manager
-    {
-        public:
-        static bool initialized;
-        Manager()
-        {
-            initialized = true;
-        }
-
-        ~Manager()
-        {
-            for(int i = 0; i < (int)getDeviceCount(); i++) {
-                setDevice(i);
-                garbageCollect();
-                pinnedGarbageCollect();
-            }
-        }
-    };
-
-    bool Manager::initialized = false;
-
-    static void managerInit()
-    {
-        if(Manager::initialized == false)
-            static Manager pm = Manager();
-    }
-
-    typedef struct
-    {
-        bool mngr_lock;
-        bool user_lock;
-        size_t bytes;
-    } mem_info;
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
 
-    static size_t used_bytes[DeviceManager::MAX_DEVICES] = {0};
-    static size_t used_buffers[DeviceManager::MAX_DEVICES] = {0};
-    static size_t total_bytes[DeviceManager::MAX_DEVICES] = {0};
+#ifndef AF_OPENCL_MEM_DEBUG
+#define AF_OPENCL_MEM_DEBUG 0
+#endif
 
-    typedef std::map<cl::Buffer *, mem_info> mem_t;
-    typedef mem_t::iterator mem_iter;
-    mem_t memory_maps[DeviceManager::MAX_DEVICES];
+namespace opencl
+{
 
-    static void destroy(cl::Buffer *ptr)
+class MemoryManager  : public common::MemoryManager
+{
+    int getActiveDeviceId();
+public:
+    MemoryManager();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManager()
     {
-        delete ptr;
+        common::lock_guard_t lock(this->memory_mutex);
+        this->garbageCollect();
     }
+};
 
-    void garbageCollect()
-    {
-        int n = getActiveDeviceId();
-        for(mem_iter iter = memory_maps[n].begin();
-            iter != memory_maps[n].end(); ++iter) {
-
-            if (!(iter->second).mngr_lock) {
+class MemoryManagerPinned  : public common::MemoryManager
+{
+    std::vector<
+        std::map<void *, cl::Buffer>
+        > pinned_maps;
+    int getActiveDeviceId();
 
-                if (!(iter->second).user_lock) {
-                    destroy(iter->first);
-                    total_bytes[n] -= iter->second.bytes;
-                }
-            }
-        }
+public:
 
-        mem_iter memory_curr = memory_maps[n].begin();
-        mem_iter memory_end  = memory_maps[n].end();
+    MemoryManagerPinned();
 
-        while(memory_curr != memory_end) {
-            if (memory_curr->second.mngr_lock || memory_curr->second.user_lock) {
-                ++memory_curr;
-            } else {
-                memory_maps[n].erase(memory_curr++);
-            }
-        }
-    }
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
 
-    void printMemInfo(const char *msg, const int device)
+    ~MemoryManagerPinned()
     {
-        std::cout << msg << std::endl;
-        std::cout << "Memory Map for Device: " << device << std::endl;
-
-        static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
-        static const std::string line(head.size(), '-');
-        std::cout << line << std::endl << head << std::endl << line << std::endl;
-
-        for(mem_iter iter = memory_maps[device].begin();
-            iter != memory_maps[device].end(); ++iter) {
-
-            std::string status_mngr("Unknown");
-            std::string status_user("Unknown");
-
-            if(iter->second.mngr_lock)  status_mngr = "Yes";
-            else                        status_mngr = " No";
-
-            if(iter->second.user_lock)  status_user = "Yes";
-            else                        status_user = " No";
-
-            std::string unit = "KB";
-            double size = (double)(iter->second.bytes) / 1024;
-            if(size >= 1024) {
-                size = size / 1024;
-                unit = "MB";
+        common::lock_guard_t lock(this->memory_mutex);
+        this->garbageCollect();
+        for (int n = 0; n < (int)pinned_maps.size(); n++) {
+            auto pinned_curr_iter = pinned_maps[n].begin();
+            auto pinned_end_iter  = pinned_maps[n].end();
+            while (pinned_curr_iter != pinned_end_iter) {
+                pinned_maps[n].erase(pinned_curr_iter++);
             }
-
-            std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
-                      << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                      << " | " << std::setw(9) << status_mngr
-                      << " | " << std::setw(9) << status_user
-                      << " |"  << std::endl;
         }
-
-        std::cout << line << std::endl;
     }
+};
 
-    cl::Buffer *bufferAlloc(const size_t &bytes)
-    {
-        int n = getActiveDeviceId();
-        cl::Buffer *ptr = NULL;
-        size_t alloc_bytes = divup(bytes, memory_resolution) * memory_resolution;
-
-        if (bytes > 0) {
-
-            // FIXME: Add better checks for garbage collection
-            // Perhaps look at total memory available as a metric
-            if (memory_maps[n].size() >= MAX_BUFFERS || used_bytes[n] >= MAX_BYTES) {
-                garbageCollect();
-            }
-
-            for(mem_iter iter = memory_maps[n].begin();
-                iter != memory_maps[n].end(); ++iter) {
-
-                mem_info info = iter->second;
-
-                if (!info.mngr_lock &&
-                    !info.user_lock &&
-                     info.bytes == alloc_bytes) {
-
-                    iter->second.mngr_lock = true;
-                    used_bytes[n] += alloc_bytes;
-                    used_buffers[n]++;
-                    return iter->first;
-                }
-            }
+int MemoryManager::getActiveDeviceId()
+{
+    return opencl::getActiveDeviceId();
+}
 
-            try {
-                ptr = new cl::Buffer(getContext(), CL_MEM_READ_WRITE, alloc_bytes);
-            } catch(...) {
-                garbageCollect();
-                ptr = new cl::Buffer(getContext(), CL_MEM_READ_WRITE, alloc_bytes);
-            }
+MemoryManager::MemoryManager() :
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
+{}
 
-            mem_info info = {true, false, alloc_bytes};
-            memory_maps[n][ptr] = info;
-            used_bytes[n] += alloc_bytes;
-            used_buffers[n]++;
-            total_bytes[n] += alloc_bytes;
-        }
-        return ptr;
-    }
-
-    void bufferFree(cl::Buffer *ptr)
-    {
-        bufferFreeLocked(ptr, false);
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    try {
+        return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
+    } catch(cl::Error err) {
+        CL_TO_AF_ERROR(err);
     }
+}
 
-    void bufferFreeLocked(cl::Buffer *ptr, bool user_unlock)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find(ptr);
-
-        if (iter != memory_maps[n].end()) {
-
-            iter->second.mngr_lock = false;
-            if ((iter->second).user_lock && !user_unlock) return;
-
-            iter->second.user_lock = false;
-
-            used_bytes[n] -= iter->second.bytes;
-            used_buffers[n]--;
-        } else {
-            destroy(ptr); // Free it because we are not sure what the size is
-        }
+void MemoryManager::nativeFree(void *ptr)
+{
+    try {
+        delete (cl::Buffer *)ptr;
+    } catch(cl::Error err) {
+        CL_TO_AF_ERROR(err);
     }
+}
 
-    void bufferPop(cl::Buffer *ptr)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find(ptr);
+static MemoryManager &getMemoryManager()
+{
+    static MemoryManager instance;
+    return instance;
+}
 
-        if (iter != memory_maps[n].end()) {
-            iter->second.user_lock = true;
-        } else {
+int MemoryManagerPinned::getActiveDeviceId()
+{
+    return opencl::getActiveDeviceId();
+}
 
-            mem_info info = { true,
-                              true,
-                              100 }; //This number is not relevant
+MemoryManagerPinned::MemoryManagerPinned() :
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
+    pinned_maps(getDeviceCount())
+{}
 
-            memory_maps[n][ptr] = info;
-        }
-    }
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
+{
+    void *ptr = NULL;
+    try {
+        cl::Buffer buf= cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
+        ptr = getQueue().enqueueMapBuffer(buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
+        pinned_maps[opencl::getActiveDeviceId()][ptr] = buf;
+    } catch(cl::Error err) {
+        CL_TO_AF_ERROR(err);
+    }
+    return ptr;
+}
 
-    void bufferPush(cl::Buffer *ptr)
-    {
-        int n = getActiveDeviceId();
-        mem_iter iter = memory_maps[n].find(ptr);
+void MemoryManagerPinned::nativeFree(void *ptr)
+{
+    try {
+        int n = opencl::getActiveDeviceId();
+        auto iter = pinned_maps[n].find(ptr);
 
-        if (iter != memory_maps[n].end()) {
-            iter->second.user_lock = false;
+        if (iter != pinned_maps[n].end()) {
+            getQueue().enqueueUnmapMemObject(pinned_maps[n][ptr], ptr);
+            pinned_maps[n].erase(iter);
         }
-    }
-
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers)
-    {
-        int n = getActiveDeviceId();
-        if (alloc_bytes   ) *alloc_bytes   = total_bytes[n];
-        if (alloc_buffers ) *alloc_buffers = memory_maps[n].size();
-        if (lock_bytes    ) *lock_bytes    = used_bytes[n];
-        if (lock_buffers  ) *lock_buffers  = used_buffers[n];
-    }
 
-    template<typename T>
-    T *memAlloc(const size_t &elements)
-    {
-        managerInit();
-        return (T *)bufferAlloc(elements * sizeof(T));
-    }
-
-    template<typename T>
-    void memFree(T *ptr)
-    {
-        return bufferFreeLocked((cl::Buffer *)ptr, false);
-    }
-
-    template<typename T>
-    void memFreeLocked(T *ptr, bool user_unlock)
-    {
-        return bufferFreeLocked((cl::Buffer *)ptr, user_unlock);
-    }
-
-    template<typename T>
-    void memLock(const T *ptr)
-    {
-        return bufferPop((cl::Buffer *)ptr);
-    }
-
-    template<typename T>
-    void memUnlock(const T *ptr)
-    {
-        return bufferPush((cl::Buffer *)ptr);
+    } catch(cl::Error err) {
+        CL_TO_AF_ERROR(err);
     }
+}
 
-    // pinned memory manager
-    typedef struct {
-        cl::Buffer *buf;
-        mem_info info;
-    } pinned_info;
+static MemoryManagerPinned &getMemoryManagerPinned()
+{
+    static MemoryManagerPinned instance;
+    return instance;
+}
 
-    typedef std::map<void*, pinned_info> pinned_t;
-    typedef pinned_t::iterator pinned_iter;
-    pinned_t pinned_maps[DeviceManager::MAX_DEVICES];
-    static size_t pinned_used_bytes = 0;
+void setMemStepSize(size_t step_bytes)
+{
+    getMemoryManager().setMemStepSize(step_bytes);
+}
 
-    static void pinnedDestroy(cl::Buffer *buf, void *ptr)
-    {
-        getQueue().enqueueUnmapMemObject(*buf, (void *)ptr);
-        destroy(buf);
-    }
+size_t getMemStepSize(void)
+{
+    return getMemoryManager().getMemStepSize();
+}
 
-    void pinnedGarbageCollect()
-    {
-        int n = getActiveDeviceId();
-        for(auto &iter : pinned_maps[n]) {
-            if (!(iter.second).info.mngr_lock) {
-                pinnedDestroy(iter.second.buf, iter.first);
-            }
-        }
 
-        pinned_iter memory_curr = pinned_maps[n].begin();
-        pinned_iter memory_end  = pinned_maps[n].end();
+void garbageCollect()
+{
+    getMemoryManager().garbageCollect();
+}
 
-        while(memory_curr != memory_end) {
-            if (memory_curr->second.info.mngr_lock) {
-                ++memory_curr;
-            } else {
-                memory_curr = pinned_maps[n].erase(memory_curr);
-            }
-        }
+void printMemInfo(const char *msg, const int device)
+{
+    getMemoryManager().printInfo(msg, device);
+}
 
-    }
+template<typename T>
+T* memAlloc(const size_t &elements)
+{
+    return (T *)getMemoryManager().alloc(elements * sizeof(T));
+}
 
-    void *pinnedBufferAlloc(const size_t &bytes)
-    {
-        void *ptr = NULL;
-        int n = getActiveDeviceId();
-        // Allocate the higher megabyte. Overhead of creating pinned memory is
-        // more so we want more resuable memory.
-        size_t alloc_bytes = divup(bytes, 1048576) * 1048576;
-
-        if (bytes > 0) {
-            cl::Buffer *buf = NULL;
-
-            // FIXME: Add better checks for garbage collection
-            // Perhaps look at total memory available as a metric
-            if (pinned_maps[n].size() >= MAX_BUFFERS || pinned_used_bytes >= MAX_BYTES) {
-                pinnedGarbageCollect();
-            }
+cl::Buffer *bufferAlloc(const size_t &bytes)
+{
+    return (cl::Buffer *)getMemoryManager().alloc(bytes);
+}
 
-            for(pinned_iter iter = pinned_maps[n].begin();
-                iter != pinned_maps[n].end(); ++iter) {
+template<typename T>
+void memFree(T *ptr)
+{
+    return getMemoryManager().unlock((void *)ptr, false);
+}
 
-                mem_info info = iter->second.info;
-                if (!info.mngr_lock && info.bytes == alloc_bytes) {
-                    iter->second.info.mngr_lock = true;
-                    pinned_used_bytes += alloc_bytes;
-                    return iter->first;
-                }
-            }
+void bufferFree(cl::Buffer *buf)
+{
+    return getMemoryManager().unlock((void *)buf, false);
+}
 
-            try {
-                buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, alloc_bytes);
+template<typename T>
+void memFreeLocked(T *ptr, bool user_unlock)
+{
+    return getMemoryManager().unlock((void *)ptr, user_unlock);
+}
 
-                ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ|CL_MAP_WRITE,
-                                                  0, alloc_bytes);
-            } catch(...) {
-                pinnedGarbageCollect();
-                buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, alloc_bytes);
+template<typename T>
+void memLock(const T *ptr)
+{
+    getMemoryManager().userLock((void *)ptr);
+}
 
-                ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ|CL_MAP_WRITE,
-                                                  0, alloc_bytes);
-            }
-            mem_info info = {true, false, alloc_bytes};
-            pinned_info pt = {buf, info};
-            pinned_maps[n][ptr] = pt;
-            pinned_used_bytes += alloc_bytes;
-        }
-        return ptr;
-    }
+template<typename T>
+void memUnlock(const T *ptr)
+{
+    getMemoryManager().userUnlock((void *)ptr);
+}
 
-    void pinnedBufferFree(void *ptr)
-    {
-        int n = getActiveDeviceId();
-        pinned_iter iter = pinned_maps[n].find(ptr);
 
-        if (iter != pinned_maps[n].end()) {
-            iter->second.info.mngr_lock = false;
-            pinned_used_bytes -= iter->second.info.bytes;
-        } else {
-            pinnedDestroy(iter->second.buf, ptr); // Free it because we are not sure what the size is
-            pinned_maps[n].erase(iter);
-        }
-    }
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes,  size_t *lock_buffers)
+{
+    getMemoryManager().bufferInfo(alloc_bytes, alloc_buffers,
+                                  lock_bytes,  lock_buffers);
+}
 
-    template<typename T>
-    T* pinnedAlloc(const size_t &elements)
-    {
-        managerInit();
-        return (T *)pinnedBufferAlloc(elements * sizeof(T));
-    }
+template<typename T>
+T* pinnedAlloc(const size_t &elements)
+{
+    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T));
+}
 
-    template<typename T>
-    void pinnedFree(T* ptr)
-    {
-        return pinnedBufferFree((void *) ptr);
-    }
+template<typename T>
+void pinnedFree(T* ptr)
+{
+    return getMemoryManagerPinned().unlock((void *)ptr, false);
+}
 
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index dce142805a..da27e0d8d5 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -15,12 +15,7 @@ namespace opencl
 {
 
     cl::Buffer *bufferAlloc(const size_t &bytes);
-
-    // Need these as 2 separate function and not a default argument
-    // This is because it is used as the deleter in shared pointer
-    // which cannot support default arguments
     void bufferFree(cl::Buffer *buf);
-    void bufferFreeLocked(cl::Buffer *buf, bool user_unlock);
 
     template<typename T> T *memAlloc(const size_t &elements);
 

From a1754327e4223ebad411699e2833aaf890861846 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 13:58:55 -0500
Subject: [PATCH 0284/2677] Remove unnecessary line from CMakeLists

---
 src/backend/cuda/CMakeLists.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index fc9a75cb12..e13f8274f7 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -60,7 +60,6 @@ IF(UNIX)
     # Forcing STRICT ANSI should resolve a bunch of issues that NVIDIA seems to face with GCC compilers.
     ADD_DEFINITIONS(-D__STRICT_ANSI__)
     SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler -fvisibility=hidden)
-    REMOVE_DEFINITIONS(-std=c++0x)
     IF(${WITH_COVERAGE})
         SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -Xcompiler -fprofile-arcs -Xcompiler -ftest-coverage -Xlinker -fprofile-arcs -Xlinker -ftest-coverage")
     ENDIF(${WITH_COVERAGE})

From 43d030dfd3e69082555af743b0b8b395c6ce00d1 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 13:59:31 -0500
Subject: [PATCH 0285/2677] Cleaning up error messages in loading and saving
 files

---
 src/api/c/stream.cpp | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index a7b5771ee0..17cc945520 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -249,12 +249,17 @@ static af_array checkVersionAndRead(const char *filename, const unsigned index)
 {
     char version = 0;
 
-    std::fstream fs(filename, std::fstream::in | std::fstream::binary);
+    std::string filenameStr = std::string(filename);
+    std::fstream fs(filenameStr, std::fstream::in | std::fstream::binary);
     // Throw exception if file is not open
-    if(!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+    if(!fs.is_open()) {
+        std::string errStr = "Failed to open: " + filenameStr;
+        AF_ERROR(errStr.c_str(), AF_ERR_ARG);
+    }
 
     if(fs.peek() == std::fstream::traits_type::eof()) {
-        AF_ERROR("File is empty", AF_ERR_ARG);
+        std::string errStr = filenameStr + " is empty";
+        AF_ERROR(errStr.c_str(), AF_ERR_ARG);
     } else {
         fs.read(&version, sizeof(char));
     }
@@ -270,13 +275,18 @@ int checkVersionAndFindIndex(const char *filename, const char *k)
 {
     char version = 0;
     std::string key(k);
+    std::string filenameStr(filename);
+    std::ifstream fs(filenameStr, std::ifstream::in | std::ifstream::binary);
 
-    std::ifstream fs(filename, std::ifstream::in | std::ifstream::binary);
     // Throw exception if file is not open
-    if(!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+    if(!fs.is_open()) {
+        std::string errStr = "Failed to open: " + filenameStr;
+        AF_ERROR(errStr.c_str(), AF_ERR_ARG);
+    }
 
     if(fs.peek() == std::ifstream::traits_type::eof()) {
-        AF_ERROR("File is empty", AF_ERR_ARG);
+        std::string errStr = filenameStr + " is empty";
+        AF_ERROR(errStr.c_str(), AF_ERR_ARG);
     } else {
         fs.read(&version, sizeof(char));
     }

From 73b7cacb0c8126ea7224062ca923e40cb2c1a9e7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 14:05:14 -0500
Subject: [PATCH 0286/2677] Fixing CUDA platform manager to sort devices in a
 more saner manner.

---
 src/backend/cuda/platform.cpp | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index f5f6599419..72fc0bc75d 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -62,13 +62,13 @@ static inline int compute2cores(int major, int minor)
     return 0;
 }
 
-// compare two cards based on (in order):
-//   1. flops (theoretical)
-//   2. total memory
-
+// Return true if greater, false if lesser.
+// if equal, it continues to next comparison
 #define COMPARE(a,b,f) do {                     \
-        return ((a)->f >= (b)->f);              \
-    } while (0);
+        if ((a)->f > (b)->f) return true;       \
+        if ((a)->f < (b)->f) return false;      \
+        break;                                  \
+    } while (0)
 
 
 static inline bool card_compare_compute(const cudaDevice_t &l, const cudaDevice_t &r)
@@ -81,7 +81,7 @@ static inline bool card_compare_compute(const cudaDevice_t &l, const cudaDevice_
     COMPARE(lc, rc, flops);
     COMPARE(lc, rc, prop.totalGlobalMem);
     COMPARE(lc, rc, nativeId);
-    return 0;
+    return false;
 }
 
 static inline bool card_compare_flops(const cudaDevice_t &l, const cudaDevice_t &r)
@@ -94,7 +94,7 @@ static inline bool card_compare_flops(const cudaDevice_t &l, const cudaDevice_t
     COMPARE(lc, rc, prop.major);
     COMPARE(lc, rc, prop.minor);
     COMPARE(lc, rc, nativeId);
-    return 0;
+    return false;
 }
 
 static inline bool card_compare_mem(const cudaDevice_t &l, const cudaDevice_t &r)
@@ -107,7 +107,7 @@ static inline bool card_compare_mem(const cudaDevice_t &l, const cudaDevice_t &r
     COMPARE(lc, rc, prop.major);
     COMPARE(lc, rc, prop.minor);
     COMPARE(lc, rc, nativeId);
-    return 0;
+    return false;
 }
 
 static inline bool card_compare_num(const cudaDevice_t &l, const cudaDevice_t &r)
@@ -116,7 +116,7 @@ static inline bool card_compare_num(const cudaDevice_t &l, const cudaDevice_t &r
     const cudaDevice_t *rc = &r;
 
     COMPARE(lc, rc, nativeId);
-    return 0;
+    return false;
 }
 
 static const std::string get_system(void)
@@ -370,16 +370,16 @@ void DeviceManager::sortDevices(sort_mode mode)
 {
     switch(mode) {
         case memory :
-            sort(cuDevices.begin(), cuDevices.end(), card_compare_mem);
+            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_mem);
             break;
         case flops :
-            sort(cuDevices.begin(), cuDevices.end(), card_compare_flops);
+            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_flops);
             break;
         case compute :
-            sort(cuDevices.begin(), cuDevices.end(), card_compare_compute);
+            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_compute);
             break;
         case none : default :
-            sort(cuDevices.begin(), cuDevices.end(), card_compare_num);
+            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_num);
             break;
     }
 }

From d75b899d2e4dc88607fe69bbcdfdebf20a764765 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 14:16:55 -0500
Subject: [PATCH 0287/2677] Adding lock to memory allocated using
 af_alloc_device / af::alloc

---
 include/af/array.h   |  4 +++-
 include/af/device.h  | 10 ++++++++++
 src/api/c/device.cpp |  1 +
 3 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/include/af/array.h b/include/af/array.h
index 03f3eeb23a..de746d9384 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -672,6 +672,8 @@ namespace af
            Get the device pointer from the array and lock the buffer in memory manager.
            @{
 
+           The device memory returned by this function is not freed until unlock() is called.
+
            \ingroup arrayfire_func
            \ingroup device_mat
         */
@@ -961,7 +963,7 @@ namespace af
         /// \brief Locks the device buffer in the memory manager.
         ///
         /// This method can be called to take control of the device pointer from the memory manager.
-        /// While a buffer is locked, the memory manager does not free the memory.
+        /// While a buffer is locked, the memory manager doesn't free the memory until unlock() is invoked.
         void lock() const;
 
         ///
diff --git a/include/af/device.h b/include/af/device.h
index 4a3006ffc7..28830675f8 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -108,6 +108,8 @@ namespace af
     /// \param[in] type is the type of the elements to allocate
     /// \returns the pointer to the memory
     ///
+    /// \note The device memory returned by this function is only freed if af::free() is called explicitly
+
     AFAPI void *alloc(const size_t elements, const dtype type);
 
     /// \brief Allocates memory using ArrayFire's memory manager
@@ -118,6 +120,8 @@ namespace af
     ///
     /// \note the size of the memory allocated is the number of \p elements *
     ///         sizeof(type)
+    ///
+    /// \note The device memory returned by this function is only freed if af::free() is called explicitly
     template<typename T>
     T* alloc(const size_t elements);
     /// @}
@@ -126,6 +130,8 @@ namespace af
     ///
     /// \copydoc device_func_free
     /// \param[in] ptr the memory to free
+    ///
+    /// This function will free a device pointer even if it has been previously locked.
     AFAPI void free(const void *ptr);
 
     /// \ingroup device_func_pinned
@@ -292,11 +298,15 @@ extern "C" {
 
     /**
        \ingroup device_func_alloc
+
+       This device memory returned by this function can only be freed using af_free_device
     */
     AFAPI af_err af_alloc_device(void **ptr, const dim_t bytes);
 
     /**
        \ingroup device_func_free
+
+       This function will free a device pointer even if it has been previously locked.
     */
     AFAPI af_err af_free_device(void *ptr);
 
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index c37e2934ae..24a8ad5d00 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -298,6 +298,7 @@ af_err af_alloc_device(void **ptr, const dim_t bytes)
     try {
         AF_CHECK(af_init());
         *ptr = (void *)memAlloc<char>(bytes);
+        memLock<char>((const char *)*ptr);
     } CATCHALL;
     return AF_SUCCESS;
 }

From d5f3bf13f7c687519ec4309392051b4d88f44a4b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 15:00:25 -0500
Subject: [PATCH 0288/2677] Adding documentation for AF_MEM_DEBUG

---
 docs/pages/configuring_arrayfire_environment.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 7e197e4954..37327ac93c 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -97,3 +97,15 @@ detailed. This helps in locating the exact failure.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 AF_PRINT_ERRORS=1 ./myprogram_opencl
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+AF_MEM_DEBUG (#af_mem_debug)
+-------------------------------------------------------------------------------
+
+When AF_MEM_DEBUG is set to 1 (or anything not equal to 0), the caching mechanism in the memory manager.
+The device buffers are allocated using native functions as needed and freed when going out of scope.
+
+When the environment variable is not set, it is treated to be non zero.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AF_MEM_DEBUG=1 ./myprogram
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From f9a83360e2443476357a50763010f476ac11fb48 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 15:01:09 -0500
Subject: [PATCH 0289/2677] Additional sanitizing for mutex locks

Use std::recursive_mutex instead of std::mutex for the cases
when a mutex lock is called from within another call.

Make lock_guard the first call to all the functions
---
 src/backend/MemoryManager.cpp | 11 ++++++-----
 src/backend/MemoryManager.hpp |  4 ++--
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 621ce624e7..696c9af621 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -25,6 +25,7 @@ MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX
     memory(num_devices),
     debug_mode(debug)
 {
+    lock_guard_t lock(this->memory_mutex);
     std::string env_var = getEnvVar("AF_MEM_DEBUG");
     if (!env_var.empty()) {
         this->debug_mode = env_var[0] != '0';
@@ -36,6 +37,7 @@ void MemoryManager::garbageCollect()
 {
     if (this->debug_mode) return;
 
+    lock_guard_t lock(this->memory_mutex);
     memory_info& current = this->getCurrentMemoryInfo();
 
     for(buffer_iter iter = current.map.begin();
@@ -66,8 +68,8 @@ void MemoryManager::garbageCollect()
 
 void MemoryManager::unlock(void *ptr, bool user_unlock)
 {
-    memory_info& current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
+    memory_info& current = this->getCurrentMemoryInfo();
 
     buffer_iter iter = current.map.find((void *)ptr);
 
@@ -93,14 +95,13 @@ void MemoryManager::unlock(void *ptr, bool user_unlock)
 
 void *MemoryManager::alloc(const size_t bytes)
 {
-    memory_info& current = this->getCurrentMemoryInfo();
+    lock_guard_t lock(this->memory_mutex);
 
     void *ptr = NULL;
     size_t alloc_bytes = this->debug_mode ? bytes : (divup(bytes, mem_step_size) * mem_step_size);
 
     if (bytes > 0) {
-
-        lock_guard_t lock(this->memory_mutex);
+        memory_info& current = this->getCurrentMemoryInfo();
 
         // There is no memory cache in debug mode
         if (!this->debug_mode) {
@@ -240,8 +241,8 @@ void MemoryManager::printInfo(const char *msg, const int device)
 void MemoryManager::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                                size_t *lock_bytes,  size_t *lock_buffers)
 {
-    memory_info current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
+    memory_info current = this->getCurrentMemoryInfo();
     if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
     if (alloc_buffers ) *alloc_buffers = current.map.size();
     if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index 1f87ea2dfe..cfcc60f2bb 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -16,8 +16,8 @@
 namespace common
 {
 
-typedef std::mutex mutex_t;
-typedef std::lock_guard<std::mutex> lock_guard_t;
+typedef std::recursive_mutex mutex_t;
+typedef std::lock_guard<mutex_t> lock_guard_t;
 
 class MemoryManager
 {

From 0638f3f0d99ac0297031ffb00006e2e72fc9d297 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 15:28:02 -0500
Subject: [PATCH 0290/2677] Removing unnecessary returns from void functions

---
 src/backend/MemoryManager.hpp | 2 +-
 src/backend/cpu/memory.cpp    | 2 +-
 src/backend/cuda/memory.cpp   | 2 +-
 src/backend/opencl/memory.cpp | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index cfcc60f2bb..5de8e4d823 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -84,7 +84,7 @@ class MemoryManager
 
     virtual void nativeFree(void *ptr)
     {
-        return free((void *)ptr);
+        free((void *)ptr);
     }
 
     virtual ~MemoryManager()
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 2687b3018b..4af348692f 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -102,7 +102,7 @@ void memFree(T *ptr)
 template<typename T>
 void memFreeLocked(T *ptr, bool user_unlock)
 {
-    return getMemoryManager().unlock((void *)ptr, user_unlock);
+    getMemoryManager().unlock((void *)ptr, user_unlock);
 }
 
 template<typename T>
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 43c37e016f..a3e995f2e7 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -158,7 +158,7 @@ void memFree(T *ptr)
 template<typename T>
 void memFreeLocked(T *ptr, bool user_unlock)
 {
-    return getMemoryManager().unlock((void *)ptr, user_unlock);
+    getMemoryManager().unlock((void *)ptr, user_unlock);
 }
 
 template<typename T>
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 45b8e96ba4..9e1344d7ba 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -195,7 +195,7 @@ void bufferFree(cl::Buffer *buf)
 template<typename T>
 void memFreeLocked(T *ptr, bool user_unlock)
 {
-    return getMemoryManager().unlock((void *)ptr, user_unlock);
+    getMemoryManager().unlock((void *)ptr, user_unlock);
 }
 
 template<typename T>

From 1520dc3bea5185d5ac000b18d93f8a51cd1d6e39 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 16:36:18 -0500
Subject: [PATCH 0291/2677] Fixing issue where garbageCollect was only called
 on current device

---
 src/backend/cpu/memory.cpp    |  5 ++++-
 src/backend/cuda/memory.cpp   | 10 ++++++++--
 src/backend/opencl/memory.cpp | 10 +++++++---
 3 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 4af348692f..c387b68b71 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -36,7 +36,10 @@ class MemoryManager  : public common::MemoryManager
     ~MemoryManager()
     {
         common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
+        for (int n = 0; n < getDeviceCount(); n++) {
+            cpu::setDevice(n);
+            this->garbageCollect();
+        }
     }
 };
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index a3e995f2e7..0e3fb5afde 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -44,7 +44,10 @@ class MemoryManager  : public common::MemoryManager
     ~MemoryManager()
     {
         common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
+        for (int n = 0; n < getDeviceCount(); n++) {
+            cuda::setDevice(n);
+            this->garbageCollect();
+        }
     }
 };
 
@@ -58,7 +61,10 @@ class MemoryManagerPinned  : public common::MemoryManager
     ~MemoryManagerPinned()
     {
         common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
+        for (int n = 0; n < getDeviceCount(); n++) {
+            cuda::setDevice(n);
+            this->garbageCollect();
+        }
     }
 };
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 9e1344d7ba..8a48a48c02 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -39,7 +39,10 @@ class MemoryManager  : public common::MemoryManager
     ~MemoryManager()
     {
         common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
+        for (int n = 0; n < getDeviceCount(); n++) {
+            opencl::setDevice(n);
+            this->garbageCollect();
+        }
     }
 };
 
@@ -60,8 +63,9 @@ class MemoryManagerPinned  : public common::MemoryManager
     ~MemoryManagerPinned()
     {
         common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
-        for (int n = 0; n < (int)pinned_maps.size(); n++) {
+        for (int n = 0; n < getDeviceCount(); n++) {
+            opencl::setDevice(n);
+            this->garbageCollect();
             auto pinned_curr_iter = pinned_maps[n].begin();
             auto pinned_end_iter  = pinned_maps[n].end();
             while (pinned_curr_iter != pinned_end_iter) {

From aaf554e7162df65bb90412cf08a95a30ebbc43d3 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 17:04:20 -0500
Subject: [PATCH 0292/2677] BUGFIX: Initialize buffer counts to 0

---
 src/backend/MemoryManager.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 696c9af621..cea4ae6b76 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -31,6 +31,12 @@ MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX
         this->debug_mode = env_var[0] != '0';
     }
     if (this->debug_mode) mem_step_size = 1;
+
+    for (int n = 0; n < num_devices; n++) {
+        memory[n].total_bytes = 0;
+        memory[n].lock_bytes = 0;
+        memory[n].lock_buffers = 0;
+    }
 }
 
 void MemoryManager::garbageCollect()

From 9d0c159d249e512f86c9c8a79f483213772abaa5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jan 2016 17:04:38 -0500
Subject: [PATCH 0293/2677] af_set_device now only warns when device > 0 on CPU

---
 src/backend/cpu/platform.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 19942f0312..0039b208d9 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -237,11 +237,11 @@ int getDeviceCount()
 int setDevice(int device)
 {
     static bool flag;
-    if(!flag) {
-        printf("WARNING: af_set_device not supported for CPU\n");
+    if(!flag && device != 0) {
+        printf("WARNING af_set_device(device): device can only be 0 for CPU\n");
         flag = 1;
     }
-    return 1;
+    return 0;
 }
 
 int getActiveDeviceId()

From db14451e7e2ac0784fc7ac475b65feb8697bf48a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sun, 10 Jan 2016 17:26:46 -0500
Subject: [PATCH 0294/2677] Re-enable disabled sort tests from issue #995

---
 test/sort_by_key.cpp | 5 ++---
 test/sort_index.cpp  | 5 ++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index 3d82b9fd90..289e407ad9 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -116,9 +116,8 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(Sort10x10False, sort_by_key_2D,    false, 2, 3);
     SORT_INIT(Sort1000True,   sort_by_key_1000,  true,  0, 1);
     SORT_INIT(SortMedTrue,    sort_by_key_med,   true,  0, 1);
-    // FIXME: below two tests are disabled temporarily until issue#995 is fixed
-    //SORT_INIT(Sort1000False,  sort_by_key_1000,  false, 2, 3);
-    //SORT_INIT(SortMedFalse,   sort_by_key_med,   false, 2, 3);
+    SORT_INIT(Sort1000False,  sort_by_key_1000,  false, 2, 3);
+    SORT_INIT(SortMedFalse,   sort_by_key_med,   false, 2, 3);
     // Takes too much time in current implementation. Enable when everything is parallel
     //SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
     //SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 0711e8b494..abe7910a58 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -117,9 +117,8 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(Sort10x10False, sort_10x10, false, 2, 3);
     SORT_INIT(Sort1000True,   sort_1000,  true,  0, 1);
     SORT_INIT(SortMedTrue,    sort_med1,  true,  0, 1);
-    // FIXME: below two tests are disabled temporarily until issue#995 is fixed
-    //SORT_INIT(Sort1000False,  sort_1000,  false, 2, 3);
-    //SORT_INIT(SortMedFalse,   sort_med1,  false, 2, 3);
+    SORT_INIT(Sort1000False,  sort_1000,  false, 2, 3);
+    SORT_INIT(SortMedFalse,   sort_med1,  false, 2, 3);
     // Takes too much time in current implementation. Enable when everything is parallel
     //SORT_INIT(SortMed5True,   sort_med,   true,  0, 1);
     //SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);

From 6da71e59db4d2839585c63187ec7dc7a7d4dec2d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 11 Jan 2016 14:22:28 -0500
Subject: [PATCH 0295/2677] BUGFIX Handle 16-bit data in saveImage

---
 src/api/c/imageio.cpp | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index e372cd7e64..5471305cee 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -299,9 +299,16 @@ af_err af_save_image(const char* filename, const af_array in_)
             AF_CHECK(af_mul(&in, in_, c255, false));
             AF_CHECK(af_release_array(c255));
             free_in = true;
-        } else {
+        } else if(max_real < 256) {
             in = in_;
         }
+        else if (max_real < 65536) {
+            af_array c255 = 0;
+            AF_CHECK(af_constant(&c255, 257.0, info.ndims(), info.dims().get(), f32));
+            AF_CHECK(af_div(&in, in_, c255, false));
+            AF_CHECK(af_release_array(c255));
+            free_in = true;
+        }
 
         // FI = row major | AF = column major
         uint nDstPitch = FreeImage_GetPitch(pResultBitmap);

From b14ae20f39c1bb41aa6298da6f6cc4a628b10c5f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 11 Jan 2016 14:26:27 -0500
Subject: [PATCH 0296/2677] Fix saveImageNative for 1-channel images

---
 src/api/c/imageio2.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index a1374a2944..ff7a4a8d34 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -237,7 +237,7 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims, uint nDstPit
     for (uint y = 0; y < fi_h; ++y) {
         for (uint x = 0; x < fi_w; ++x) {
             if(channels == 1) {
-                *(pDstLine + x * step + FI_RGBA_RED) = (T) pSrc0[indx]; // r -> 0
+                *(pDstLine + x * step) = (T) pSrc0[indx]; // r -> 0
             } else if(channels >=3) {
                 if((af_dtype) af::dtype_traits<T>::af_type == u8) {
                     *(pDstLine + x * step + FI_RGBA_RED  ) = (T) pSrc0[indx]; // r -> 0

From a6a4cdbc1c0688e9eb72fb09332962a2ac2beaf5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 11 Jan 2016 14:31:18 -0500
Subject: [PATCH 0297/2677] Update test data submodule commit

---
 test/data | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/data b/test/data
index 4a735db351..d134732012 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 4a735db3515db3f8f914e0b69fa2e11add9cd50f
+Subproject commit d1347320125a0315a4ef03e63630b5b3249d189d

From 88cf4713e3040450e0200faa1d65ac9079296243 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 11 Jan 2016 14:31:57 -0500
Subject: [PATCH 0298/2677] Add tests for 16-bit images for ImageIO+Native

---
 test/imageio.cpp | 140 ++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 137 insertions(+), 3 deletions(-)

diff --git a/test/imageio.cpp b/test/imageio.cpp
index d19aac346c..4029de5a1b 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -36,8 +36,6 @@ typedef ::testing::Types<float> TestTypes;
 // register the type list
 TYPED_TEST_CASE(ImageIO, TestTypes);
 
-// Disable tests if FreeImage is not found
-#if defined(WITH_FREEIMAGE)
 void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
 {
     if (noDoubleTests<float>()) return;
@@ -251,4 +249,140 @@ TEST(ImageMem, SaveMemBMP)
     af::deleteImageMem(savedMem);
 }
 
-#endif // WITH_FREEIMAGE
+TEST(ImageIO, LoadImage16CPP)
+{
+    if (noImageIOTests()) return;
+
+    vector<af::dim4> numDims;
+
+    vector<vector<float> >   in;
+    vector<vector<float> >   tests;
+    readTests<float, float, float>(string(TEST_DIR"/imageio/color_seq_16.test"),numDims,in,tests);
+
+    af::dim4 dims = numDims[0];
+
+    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq_16.png").c_str(), true);
+    ASSERT_EQ(img.type(), f32); // loadImage should always return float
+
+    // Get result
+    float *imgData = new float[dims.elements()];
+    img.host((void*)imgData);
+
+    // Compare result
+    size_t nElems = in[0].size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] imgData;
+}
+
+TEST(ImageIO, SaveImage16CPP)
+{
+    if (noImageIOTests()) return;
+
+    af::dim4 dims(16, 24, 3);
+
+    af::array input = af::randu(dims, u16);
+    af::array input_255 = (input / 257).as(u16);
+
+    af::saveImage("saveImage16CPP.png", input);
+
+    af::array img = af::loadImage("saveImage16CPP.png", true);
+    ASSERT_EQ(img.type(), f32); // loadImage should always return float
+
+    ASSERT_FALSE(af::anyTrue<bool>(abs(img - input_255)));
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Image IO Native Tests
+////////////////////////////////////////////////////////////////////////////////
+
+template<typename T>
+void loadImageNativeCPPTest(string pTestFile, string pImageFile)
+{
+    if (noImageIOTests()) return;
+
+    vector<af::dim4> numDims;
+
+    vector<vector<float> >   in;
+    vector<vector<float> >   tests;
+    readTests<float, float, float>(pTestFile,numDims,in,tests);
+
+    af::dim4 dims = numDims[0];
+    af::array img = af::loadImageNative(pImageFile.c_str());
+    ASSERT_EQ(img.type(), (af_dtype)af::dtype_traits<T>::af_type);
+
+    // Get result
+    T *imgData = new T[dims.elements()];
+    img.host((void*)imgData);
+
+    // Compare result
+    size_t nElems = in[0].size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] imgData;
+}
+
+TEST(ImageIONative, LoadImageNative8CPP)
+{
+    loadImageNativeCPPTest<uchar>(string(TEST_DIR"/imageio/color_small.test"),
+                                  string(TEST_DIR"/imageio/color_small.png"));
+}
+
+TEST(ImageIONative, LoadImageNative16SmallCPP)
+{
+    loadImageNativeCPPTest<ushort>(string(TEST_DIR"/imageio/color_small_16.test"),
+                                   string(TEST_DIR"/imageio/color_small_16.png"));
+}
+
+TEST(ImageIONative, LoadImageNative16ColorCPP)
+{
+    loadImageNativeCPPTest<ushort>(string(TEST_DIR"/imageio/color_seq_16.test"),
+                                   string(TEST_DIR"/imageio/color_seq_16.png"));
+}
+
+TEST(ImageIONative, LoadImageNative16GrayCPP)
+{
+    loadImageNativeCPPTest<ushort>(string(TEST_DIR"/imageio/gray_seq_16.test"),
+                                   string(TEST_DIR"/imageio/gray_seq_16.png"));
+}
+
+template<typename T>
+void saveLoadImageNativeCPPTest(af::dim4 dims)
+{
+    if (noImageIOTests()) return;
+
+    af::array input = af::randu(dims, (af_dtype)af::dtype_traits<T>::af_type);
+
+    af::saveImageNative("saveImageNative.png", input);
+
+    af::array loaded = af::loadImageNative("saveImageNative.png");
+    ASSERT_EQ(loaded.type(), input.type());
+
+    ASSERT_FALSE(af::anyTrue<bool>(input - loaded));
+}
+
+TEST(ImageIONative, SaveLoadImageNative8CPP)
+{
+    saveLoadImageNativeCPPTest<uchar>(af::dim4(480, 720, 3, 1));
+}
+
+TEST(ImageIONative, SaveLoadImageNative16SmallCPP)
+{
+    saveLoadImageNativeCPPTest<ushort>(af::dim4(8, 12, 3, 1));
+}
+
+TEST(ImageIONative, SaveLoadImageNative16ColorCPP)
+{
+    saveLoadImageNativeCPPTest<ushort>(af::dim4(480, 720, 3, 1));
+}
+
+TEST(ImageIONative, SaveLoadImageNative16GrayCPP)
+{
+    saveLoadImageNativeCPPTest<ushort>(af::dim4(24, 32, 1, 1));
+}

From 968ae4e80ce8e6263fdc3f4381ae8b895df44bc4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 11 Jan 2016 17:11:44 -0500
Subject: [PATCH 0299/2677] Handle CUDA devices locked in exclusive mode

* When the default device 0 is exclusively locked, ArrayFire will try to pick
  a different device
* When the user uses setDevice to set a device that is locked, then ArrayFire
  will error out
* Handle such a case when freeing memory in memory manager destructor

Signed-off-by: Shehzan Mohammed <shehzan@arrayfire.com>
---
 src/backend/cuda/err_cuda.hpp | 37 +++++++++++-----------
 src/backend/cuda/memory.cpp   | 12 ++++++--
 src/backend/cuda/platform.cpp | 58 +++++++++++++++++++++++++++++------
 3 files changed, 77 insertions(+), 30 deletions(-)

diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index a975fb5336..dd87bdfc2b 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -17,22 +17,23 @@
                 __AF_FILENAME__, __LINE__, "CUDA");     \
     } while(0)
 
-#define CUDA_CHECK(fn) do {                                 \
-        cudaError_t _cuda_error = fn;                       \
-        if (_cuda_error != cudaSuccess) {                   \
-            char cuda_err_msg[1024];                        \
-            snprintf(cuda_err_msg,                          \
-                     sizeof(cuda_err_msg),                  \
-                     "CUDA Error (%d): %s\n",               \
-                     (int)(_cuda_error),                    \
-                     cudaGetErrorString(                    \
-                         cudaGetLastError()));              \
-                                                            \
-            if (_cuda_error == cudaErrorMemoryAllocation) { \
-                AF_ERROR(cuda_err_msg, AF_ERR_NO_MEM);      \
-            } else {                                        \
-                AF_ERROR(cuda_err_msg,                      \
-                         AF_ERR_INTERNAL);                  \
-            }                                               \
-        }                                                   \
+#define CUDA_CHECK(fn) do {                                         \
+        cudaError_t _cuda_error = fn;                               \
+        if (_cuda_error != cudaSuccess) {                           \
+            char cuda_err_msg[1024];                                \
+            snprintf(cuda_err_msg,                                  \
+                     sizeof(cuda_err_msg),                          \
+                     "CUDA Error (%d): %s\n",                       \
+                     (int)(_cuda_error),                            \
+                     cudaGetErrorString(                            \
+                         cudaGetLastError()));                      \
+                                                                    \
+            if (_cuda_error == cudaErrorMemoryAllocation) {         \
+                AF_ERROR(cuda_err_msg, AF_ERR_NO_MEM);              \
+            } else if (_cuda_error == cudaErrorDevicesUnavailable) {\
+                AF_ERROR(cuda_err_msg, AF_ERR_DRIVER);              \
+            } else {                                                \
+                AF_ERROR(cuda_err_msg, AF_ERR_INTERNAL);            \
+            }                                                       \
+        }                                                           \
     } while(0)
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 0e3fb5afde..20e25475cf 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -45,8 +45,16 @@ class MemoryManager  : public common::MemoryManager
     {
         common::lock_guard_t lock(this->memory_mutex);
         for (int n = 0; n < getDeviceCount(); n++) {
-            cuda::setDevice(n);
-            this->garbageCollect();
+            try {
+                cuda::setDevice(n);
+                this->garbageCollect();
+            } catch(AfError err) {
+                if(err.getError() == AF_ERR_DRIVER) { // Can happen from cudaErrorDevicesUnavailable
+                    continue;
+                } else {
+                    throw err;
+                }
+            }
         }
     }
 };
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 72fc0bc75d..6919a04158 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -386,20 +386,58 @@ void DeviceManager::sortDevices(sort_mode mode)
 
 int DeviceManager::setActiveDevice(int device, int nId)
 {
-    if(device > (int)cuDevices.size()) {
-        return -1;
-    } else {
-        int old = activeDev;
-        if(nId == -1) nId = getDeviceNativeId(device);
-        CUDA_CHECK(cudaSetDevice(nId));
-        activeDev = device;
+    static bool first = true;
 
-        if(!streams[device]) {
-            CUDA_CHECK(cudaStreamCreate(&streams[device]));
-        }
+    int numDevices = cuDevices.size();
+
+    if(device > numDevices) return -1;
 
+    int old = activeDev;
+    if(nId == -1) nId = getDeviceNativeId(device);
+    CUDA_CHECK(cudaSetDevice(nId));
+    cudaError_t err = cudaStreamCreate(&streams[device]);
+    activeDev = device;
+
+    if (err == cudaSuccess) return old;
+
+    // Comes when user sets device
+    // If success, return. Else throw error
+    if (!first) {
+        CUDA_CHECK(err);
         return old;
     }
+
+    // Comes only when first is true. Set it to false
+    first = false;
+
+    while(device < numDevices) {
+        // Check for errors other than DevicesUnavailable
+        // If success, return. Else throw error
+        // If DevicesUnavailable, try other devices (while loop below)
+        if (err != cudaErrorDevicesUnavailable) {
+            CUDA_CHECK(err);
+            activeDev = device;
+            return old;
+        }
+        cudaGetLastError(); // Reset error stack
+        printf("Warning: Device %d is unavailable. Incrementing to next device \n", device);
+
+        // Comes here is the device is in exclusive mode or
+        // otherwise fails streamCreate with this error.
+        // All other errors will error out
+        device++;
+
+        // Can't call getNativeId here as it will cause an infinite loop with the constructor
+        nId = cuDevices[device].nativeId;
+
+        CUDA_CHECK(cudaSetDevice(nId));
+        err = cudaStreamCreate(&streams[device]);
+    }
+
+    // If all devices fail with DevicesUnavailable, then throw this error
+    CUDA_CHECK(err);
+
+    return old;
 }
 
 void sync(int device)

From cc9018e402e2370438c1558c19e7ff21447f36d7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 12 Jan 2016 10:53:37 -0500
Subject: [PATCH 0300/2677] Add try/catch around cuda::setDevice in Pinned
 Memory Manager

---
 src/backend/cuda/memory.cpp | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 20e25475cf..69eb8f8895 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -70,8 +70,16 @@ class MemoryManagerPinned  : public common::MemoryManager
     {
         common::lock_guard_t lock(this->memory_mutex);
         for (int n = 0; n < getDeviceCount(); n++) {
-            cuda::setDevice(n);
-            this->garbageCollect();
+            try {
+                cuda::setDevice(n);
+                this->garbageCollect();
+            } catch(AfError err) {
+                if(err.getError() == AF_ERR_DRIVER) { // Can happen from cudaErrorDevicesUnavailable
+                    continue;
+                } else {
+                    throw err;
+                }
+            }
         }
     }
 };

From 904d3e0b8d8d85f07d650010662ba9cebf1c0c6b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 12 Jan 2016 11:53:14 -0500
Subject: [PATCH 0301/2677] Using device independent vector for cuda Pinned
 Memory Manager

---
 src/api/c/imageio.cpp       |  5 +++--
 src/backend/cuda/memory.cpp | 21 +++++++--------------
 2 files changed, 10 insertions(+), 16 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 5471305cee..748ddbc58e 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -301,13 +301,14 @@ af_err af_save_image(const char* filename, const af_array in_)
             free_in = true;
         } else if(max_real < 256) {
             in = in_;
-        }
-        else if (max_real < 65536) {
+        } else if (max_real < 65536) {
             af_array c255 = 0;
             AF_CHECK(af_constant(&c255, 257.0, info.ndims(), info.dims().get(), f32));
             AF_CHECK(af_div(&in, in_, c255, false));
             AF_CHECK(af_release_array(c255));
             free_in = true;
+        } else {
+            in = in_;
         }
 
         // FI = row major | AF = column major
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 69eb8f8895..15786d9498 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -59,6 +59,10 @@ class MemoryManager  : public common::MemoryManager
     }
 };
 
+// CUDA Pinned Memory does not depend on device
+// So we pass 1 as numDevices to the constructor so that it creates 1 vector
+// of memory_info
+// When allocating and freeing, it doesn't really matter which device is active
 class MemoryManagerPinned  : public common::MemoryManager
 {
     int getActiveDeviceId();
@@ -69,18 +73,7 @@ class MemoryManagerPinned  : public common::MemoryManager
     ~MemoryManagerPinned()
     {
         common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            try {
-                cuda::setDevice(n);
-                this->garbageCollect();
-            } catch(AfError err) {
-                if(err.getError() == AF_ERR_DRIVER) { // Can happen from cudaErrorDevicesUnavailable
-                    continue;
-                } else {
-                    throw err;
-                }
-            }
-        }
+        this->garbageCollect();
     }
 };
 
@@ -116,11 +109,11 @@ static MemoryManager &getMemoryManager()
 
 int MemoryManagerPinned::getActiveDeviceId()
 {
-    return cuda::getActiveDeviceId();
+    return 0; // pinned uses a single vector
 }
 
 MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+    common::MemoryManager(1, MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
 {}
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)

From a8b831b5022c6c8a840fccbaa0d5e12107aecd81 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 13 Jan 2016 11:15:15 -0500
Subject: [PATCH 0302/2677] Cleanup for opencl-cpu offload

---
 src/backend/cuda/blas.cpp    | 22 ----------------------
 src/backend/opencl/Array.hpp | 21 +++------------------
 src/backend/opencl/blas.cpp  | 27 ---------------------------
 test/backend.cpp             | 16 +++++++++++-----
 test/blas.cpp                |  1 -
 5 files changed, 14 insertions(+), 73 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 1e5dd5de39..9d3b9ca7b7 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -200,28 +200,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
 }
 
-// Keeping this around for future reference
-//template<typename T, bool conjugate, bool both_conjugate>
-//Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
-//              af_mat_prop optLhs, af_mat_prop optRhs)
-//{
-//    int N = lhs.dims()[0];
-//
-//    T out;
-//
-//    CUBLAS_CHECK((dot_func<T, conjugate>()(
-//                 getHandle(),
-//                 N,
-//                 lhs.get(), lhs.strides()[0],
-//                 rhs.get(), rhs.strides()[0],
-//                 &out)));
-//
-//    if(both_conjugate)
-//        return createValueArray(af::dim4(1), conj(out));
-//    else
-//        return createValueArray(af::dim4(1), out);
-//}
-
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index ce9c3c7fbb..2793d5e099 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -210,17 +210,6 @@ namespace opencl
 
         JIT::Node_ptr getNode() const;
 
-    private:
-        bool is_const() const
-        {
-            return true;
-        }
-
-        bool is_const()
-        {
-            return false;
-        }
-
     public:
         std::shared_ptr<T> getMappedPtr() const
         {
@@ -237,13 +226,9 @@ namespace opencl
             T *ptr = nullptr;
             try {
                 if(ptr == nullptr) {
-                    if(is_const()) {
-                        ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()), true, CL_MAP_READ,
-                                getOffset(), getDataDims().elements() * sizeof(T));
-                    } else {
-                        ptr = (T*)getQueue().enqueueMapBuffer(*(get()), true, CL_MAP_READ|CL_MAP_WRITE,
-                                getOffset(), getDataDims().elements() * sizeof(T));
-                    }
+                    ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()),
+                            true, CL_MAP_READ|CL_MAP_WRITE,
+                            getOffset(), getDataDims().elements() * sizeof(T));
                 }
             } catch(cl::Error err) {
                 CL_TO_AF_ERROR(err);
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 15e2373783..97a5c1ab70 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -177,33 +177,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     return out;
 }
 
-// Keeping this around for future reference
-//template<typename T, bool conjugate, bool both_conjugate>
-//Array<T> dot_(const Array<T> &lhs, const Array<T> &rhs,
-//              af_mat_prop optLhs, af_mat_prop optRhs)
-//{
-//    initBlas();
-//
-//    int N = lhs.dims()[0];
-//    dot_func<T, conjugate> dot;
-//    cl::Event event;
-//    Array<T> out = createEmptyArray<T>(af::dim4(1));
-//    cl::Buffer scratch(getContext(), CL_MEM_READ_WRITE, sizeof(T) * N);
-//    CLBLAS_CHECK(
-//        dot(N,
-//            (*out.get())(), out.getOffset(),
-//            (*lhs.get())(),  lhs.getOffset(), lhs.strides()[0],
-//            (*rhs.get())(),  rhs.getOffset(), rhs.strides()[0],
-//            scratch(),
-//            1, &getQueue()(), 0, nullptr, &event())
-//        );
-//
-//    if(both_conjugate)
-//        transpose_inplace<T>(out, true);
-//
-//    return out;
-//}
-
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs)
diff --git a/test/backend.cpp b/test/backend.cpp
index 4bb5cdf7fe..78b64309db 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -21,11 +21,8 @@
 using std::string;
 using std::vector;
 
-const char *getActiveBackendString()
+const char *getActiveBackendString(af_backend active)
 {
-    af_backend active = (af_backend)0;
-    af_get_active_backend(&active);
-
     switch(active) {
         case AF_BACKEND_CPU   : return "AF_BACKEND_CPU";
         case AF_BACKEND_CUDA  : return "AF_BACKEND_CUDA";
@@ -39,11 +36,20 @@ void testFunction()
 {
     af_info();
 
-    printf("Active Backend Enum = %s\n", getActiveBackendString());
+    af_backend activeBackend = (af_backend)0;
+    af_get_active_backend(&activeBackend);
+
+    printf("Active Backend Enum = %s\n", getActiveBackendString(activeBackend));
 
     af_array outArray = 0;
     dim_t dims[] = {32, 32};
     ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) af::dtype_traits<T>::af_type));
+
+    // Verify backends returned by array and by function are the same
+    af_backend arrayBackend = (af_backend)0;
+    af_get_backend_id(&arrayBackend, outArray);
+    ASSERT_EQ(arrayBackend, activeBackend);
+
     // cleanup
     if(outArray != 0) ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
diff --git a/test/blas.cpp b/test/blas.cpp
index b5d92f1073..507cc6dc7b 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -36,7 +36,6 @@ template<typename T, bool isBVector>
 void MatMulCheck(string TestFile)
 {
     if (noDoubleTests<T>()) return;
-    af::info();
 
     using std::vector;
     vector<af::dim4> numDims;

From f6e309bfbc9a0571ae805576acca0a4e3e0c1d9b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 12 Jan 2016 17:43:20 -0500
Subject: [PATCH 0303/2677] Clean up cusolver finding in cmake

---
 src/backend/cuda/CMakeLists.txt | 52 +++++++++++++++++++--------------
 1 file changed, 30 insertions(+), 22 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 601e6c9022..4c74070492 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -70,7 +70,30 @@ ENDIF()
 
 ADD_DEFINITIONS(-DAF_CUDA)
 
-IF(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
+# CMake 3.2 Adds CUDA_cusolver_LIBRARY variable to FindCUDA
+# Older version, use FIND_LIBRARY
+IF(CMAKE_VERSION VERSION_LESS 3.2)
+    IF(${CUDA_cusolver_LIBRARY} MATCHES " ")
+        UNSET(CUDA_cusolver_LIBRARY CACHE)  # When going from higher version to lower version
+    ENDIF()
+    FIND_LIBRARY (
+        CUDA_cusolver_LIBRARY
+        NAMES "cusolver"
+        PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+        PATH_SUFFIXES "lib64" "lib/x64" "lib"
+        DOC "CUDA cusolver Library"
+        NO_DEFAULT_PATH
+        )
+ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
+
+IF(${CUDA_VERSION_MAJOR} LESS 7 AND CUDA_cusolver_LIBRARY)
+  UNSET(CUDA_cusolver_LIBRARY CACHE)  # Failsafe when going from higher version to lower version
+ENDIF()
+
+IF(CUDA_cusolver_LIBRARY)
+    MESSAGE(STATUS "CUDA cusolver library available in CUDA Version ${CUDA_VERSION_STRING}")
+    ADD_DEFINITIONS(-DWITH_CUDA_LINEAR_ALGEBRA)
+ELSE(CUDA_cusolver_LIBRARY)
     # Use CPU Lapack as fallback?
     OPTION(CUDA_LAPACK_CPU_FALLBACK "Use CPU LAPACK as fallback for CUDA LAPACK when cusolver is not available" OFF)
     MARK_AS_ADVANCED(CUDA_LAPACK_CPU_FALLBACK)
@@ -96,24 +119,8 @@ IF(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
     ELSE()
         MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. Linear Algebra will not be available.")
     ENDIF()
-    IF(CMAKE_VERSION VERSION_LESS 3.2)
-        SET(CUDA_cusolver_LIBRARY)
-        MARK_AS_ADVANCED(CUDA_cusolver_LIBRARY)
-    ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
-ELSE(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
-    MESSAGE(STATUS "CUDA cusolver library available in CUDA Version ${CUDA_VERSION_STRING}")
-    ADD_DEFINITIONS(-DWITH_CUDA_LINEAR_ALGEBRA)
-    IF(CMAKE_VERSION VERSION_LESS 3.2)
-        FIND_LIBRARY(
-            CUDA_cusolver_LIBRARY
-            NAMES "cusolver"
-            PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-            PATH_SUFFIXES "lib64" "lib/x64" "lib"
-            DOC "CUDA cusolver Library"
-            NO_DEFAULT_PATH
-            )
-    ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
-ENDIF(${CUDA_VERSION_MAJOR} LESS 7 OR ${CUDA_COMPUTE_53})
+    UNSET(CUDA_cusolver_LIBRARY CACHE)  # Failsafe when going from higher version to lower version
+ENDIF(CUDA_cusolver_LIBRARY)
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
@@ -310,7 +317,6 @@ ADD_DEPENDENCIES(afcuda ${ptx_targets})
 
 TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
-                                PRIVATE ${CUDA_cusolver_LIBRARY}
                                 PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
                                 PRIVATE ${CUDA_NVVM_LIBRARIES}
@@ -320,8 +326,10 @@ IF(FORGE_FOUND)
     TARGET_LINK_LIBRARIES(afcuda PRIVATE ${FORGE_LIBRARIES})
 ENDIF()
 
-IF(CUDA_LAPACK_CPU_FALLBACK)
-  TARGET_LINK_LIBRARIES(afcuda PRIVATE ${LAPACK_LIBRARIES})
+IF(CUDA_cusolver_LIBRARY)
+    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${CUDA_cusolver_LIBRARY})
+ELSEIF(CUDA_LAPACK_CPU_FALLBACK)
+    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${LAPACK_LIBRARIES})
 ENDIF()
 
 SET_TARGET_PROPERTIES(afcuda PROPERTIES

From 3941550c448246601d21681202614067401ddad5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 14 Jan 2016 13:06:41 -0500
Subject: [PATCH 0304/2677] Move asserts inside try/catch in indexer functions
 in util

---
 src/api/c/util.cpp | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/api/c/util.cpp b/src/api/c/util.cpp
index cc9a07ac4f..9b16fe98df 100644
--- a/src/api/c/util.cpp
+++ b/src/api/c/util.cpp
@@ -30,45 +30,45 @@ af_err af_create_indexers(af_index_t** indexers)
 
 af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim)
 {
-    ARG_ASSERT(0, (indexer!=NULL));
-    ARG_ASSERT(1, (idx!=NULL));
-    ARG_ASSERT(2, (dim>=0 && dim<=3));
     try {
+        ARG_ASSERT(0, (indexer!=NULL));
+        ARG_ASSERT(1, (idx!=NULL));
+        ARG_ASSERT(2, (dim>=0 && dim<=3));
         indexer[dim].idx.arr = idx;
         indexer[dim].isBatch = false;
         indexer[dim].isSeq   = false;
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
 af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx, const dim_t dim, const bool is_batch)
 {
-    ARG_ASSERT(0, (indexer!=NULL));
-    ARG_ASSERT(1, (idx!=NULL));
-    ARG_ASSERT(2, (dim>=0 && dim<=3));
     try {
+        ARG_ASSERT(0, (indexer!=NULL));
+        ARG_ASSERT(1, (idx!=NULL));
+        ARG_ASSERT(2, (dim>=0 && dim<=3));
         indexer[dim].idx.seq = *idx;
         indexer[dim].isBatch = is_batch;
         indexer[dim].isSeq   = true;
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
 af_err af_set_seq_param_indexer(af_index_t* indexer,
                               const double begin, const double end, const double step,
                               const dim_t dim, const bool is_batch)
 {
-    ARG_ASSERT(0, (indexer!=NULL));
-    ARG_ASSERT(4, (dim>=0 && dim<=3));
     try {
+        ARG_ASSERT(0, (indexer!=NULL));
+        ARG_ASSERT(4, (dim>=0 && dim<=3));
         indexer[dim].idx.seq = af_make_seq(begin, end, step);
         indexer[dim].isBatch = is_batch;
         indexer[dim].isSeq   = true;
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
 af_err af_release_indexers(af_index_t* indexers)

From 735b66b5916a1b4ae79e179de2f06924a397c5bf Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 14 Jan 2016 17:20:26 -0500
Subject: [PATCH 0305/2677] Fix OpenCL-CPU offload when OpenCL is built without
 lapack

---
 src/backend/opencl/blas.cpp             |  4 ++++
 src/backend/opencl/cpu/cpu_blas.cpp     |  2 ++
 src/backend/opencl/cpu/cpu_cholesky.cpp |  2 ++
 src/backend/opencl/cpu/cpu_helper.hpp   | 29 +++++++++++++++++--------
 src/backend/opencl/cpu/cpu_inverse.cpp  |  2 ++
 src/backend/opencl/cpu/cpu_lu.cpp       |  2 ++
 src/backend/opencl/cpu/cpu_qr.cpp       |  2 ++
 src/backend/opencl/cpu/cpu_solve.cpp    |  2 ++
 src/backend/opencl/cpu/cpu_svd.cpp      |  2 ++
 src/backend/opencl/cpu/cpu_triangle.hpp |  2 ++
 10 files changed, 40 insertions(+), 9 deletions(-)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 97a5c1ab70..365e6e5680 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -23,7 +23,9 @@
 #include <reduce.hpp>
 #include <complex.hpp>
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_blas.hpp>
+#endif
 
 namespace opencl
 {
@@ -118,9 +120,11 @@ template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
     if(OpenCLCPUOffload()) {
         return cpu::matmul(lhs, rhs, optLhs, optRhs);
     }
+#endif
 
     initBlas();
     clblasTranspose lOpts = toClblasTranspose(optLhs);
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 1ff7e145d6..fe6fe9959a 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_blas.hpp>
 #include <math.hpp>
@@ -206,3 +207,4 @@ INSTANTIATE_BLAS(cdouble)
 
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index bd871d7518..9acbcc4fad 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_cholesky.hpp>
 #include <cpu/cpu_triangle.hpp>
@@ -80,3 +81,4 @@ INSTANTIATE_CH(cdouble)
 
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index d407bb83cc..cbdc470e19 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -17,6 +17,11 @@
 #include <err_common.hpp>
 #include <platform.hpp>
 
+//********************************************************/
+// LAPACK
+//********************************************************/
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+
 #define lapack_complex_float opencl::cfloat
 #define lapack_complex_double opencl::cdouble
 #define LAPACK_PREFIX LAPACKE_
@@ -31,13 +36,26 @@
     #define AF_LAPACK_COL_MAJOR 0
 #else
     #ifdef USE_MKL
-        #include <mkl_cblas.h>
         #include<mkl_lapacke.h>
+    #else
+        #include<lapacke.h>
+    #endif
+#endif //OS
+
+#endif // WITH_OPENCL_LINEAR_ALGEBRA
+
+//********************************************************/
+// BLAS
+//********************************************************/
+#ifdef __APPLE__
+    #include <Accelerate/Accelerate.h>
+#else
+    #ifdef USE_MKL
+        #include <mkl_cblas.h>
     #else
         extern "C" {
         #include <cblas.h>
         }
-        #include<lapacke.h>
     #endif
 #endif
 
@@ -53,11 +71,4 @@
 typedef int blasint;
 #endif
 
-namespace opencl
-{
-namespace cpu
-{
-}
-}
-
 #endif
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index fee171929a..4f73a80707 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_inverse.hpp>
 #include <cpu/cpu_lu.hpp>
@@ -72,3 +73,4 @@ INSTANTIATE(cdouble)
 
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index 3eb574e743..e0234fb7de 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_lu.hpp>
 #include <math.hpp>
@@ -174,3 +175,4 @@ INSTANTIATE_LU(cdouble)
 
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index 32eca92963..737a7aec2f 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_qr.hpp>
 #include <cpu/cpu_triangle.hpp>
@@ -114,3 +115,4 @@ INSTANTIATE_QR(cdouble)
 
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 9e4f0932ac..1bb72f8768 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
 #include <copy.hpp>
@@ -172,3 +173,4 @@ INSTANTIATE_SOLVE(cdouble)
 
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index c53df8ae78..3608bf69ce 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_svd.hpp>
 #include <copy.hpp>
@@ -108,3 +109,4 @@ namespace cpu
     INSTANTIATE_SVD(cdouble, double)
 }
 }
+#endif
diff --git a/src/backend/opencl/cpu/cpu_triangle.hpp b/src/backend/opencl/cpu/cpu_triangle.hpp
index f953d58507..e705420582 100644
--- a/src/backend/opencl/cpu/cpu_triangle.hpp
+++ b/src/backend/opencl/cpu/cpu_triangle.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #ifndef CPU_LAPACK_TRIANGLE
 #define CPU_LAPACK_TRIANGLE
 
@@ -53,3 +54,4 @@ void triangle(T *o, const T *i, const dim4 odm, const dim4 ost, const dim4 ist)
 }
 
 #endif
+#endif

From 323bf75b44731c11eeee3c7ba866e6803bfab9b4 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Fri, 15 Jan 2016 23:41:34 -0500
Subject: [PATCH 0306/2677] Added tranform coordinates functionality

---
 include/af/image.h                    | 25 +++++++
 src/api/c/transform_coordinates.cpp   | 96 +++++++++++++++++++++++++++
 src/api/cpp/transform_coordinates.cpp | 24 +++++++
 3 files changed, 145 insertions(+)
 create mode 100644 src/api/c/transform_coordinates.cpp
 create mode 100644 src/api/cpp/transform_coordinates.cpp

diff --git a/include/af/image.h b/include/af/image.h
index ad56cfc081..d25f64f058 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -223,6 +223,18 @@ AFAPI array rotate(const array& in, const float theta, const bool crop=true, con
 */
 AFAPI array transform(const array& in, const array& transform, const dim_t odim0 = 0, const dim_t odim1 = 0, const interpType method=AF_INTERP_NEAREST, const bool inverse=true);
 
+/**
+    C++ Interface for transforming coordinates
+
+    \param[in] tf is transformation matrix
+    \param[in] d0 is the first input dimension
+    \param[in] d1 is the second input dimension
+    \return the transformed coordinates
+
+    \ingroup transform_func_coordinates
+*/
+AFAPI array transformCoordinates(const array& tf, const float d0, const float d1);
+
 /**
     C++ Interface for translating an image
 
@@ -853,6 +865,19 @@ extern "C" {
                               const dim_t odim0, const dim_t odim1,
                               const af_interp_type method, const bool inverse);
 
+    /**
+       C Interface for transforming an image
+       C++ Interface for transforming coordinates
+
+       \param[out] out the transformed coordinates
+       \param[in] tf is transformation matrix
+       \param[in] d0 is the first input dimension
+       \param[in] d1 is the second input dimension
+
+       \ingroup transform_func_coordinates
+    */
+    AFAPI af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0, const float d1);
+
     /**
        C Interface for rotating an image
 
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
new file mode 100644
index 0000000000..79b448db5d
--- /dev/null
+++ b/src/api/c/transform_coordinates.cpp
@@ -0,0 +1,96 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/vision.h>
+#include <af/image.h>
+#include <af/arith.h>
+#include <af/blas.h>
+#include <af/data.h>
+#include <err_common.hpp>
+#include <backend.hpp>
+#include <handle.hpp>
+#include <convolve.hpp>
+#include <arith.hpp>
+
+using af::dim4;
+using namespace detail;
+
+template<typename T>
+static af_array transform_coordinates(const af_array& tf, const float d0, const float d1)
+{
+    dim_t in_dims[2] = { 4, 3 };
+    T h_in[4*3] = { (T)0, (T)0,  (T)d1, (T)d1,
+                    (T)0, (T)d0, (T)d0, (T)0,
+                    (T)1, (T)1,  (T)1,  (T)1 };
+
+    af_array in  = 0;
+    af_array w   = 0;
+    af_array tmp = 0;
+    af_array xt  = 0;
+    af_array yt  = 0;
+    af_array t   = 0;
+
+    AF_CHECK(af_create_array(&in, h_in, 2, in_dims, (af_dtype) af::dtype_traits<T>::af_type));
+
+    af_array tfIdx = 0;
+    af_index_t tfIndexs[2];
+    tfIndexs[0].isSeq = true;
+    tfIndexs[1].isSeq = true;
+    tfIndexs[0].idx.seq = af_make_seq(0, 2, 1);
+    tfIndexs[1].idx.seq = af_make_seq(2, 2, 1);
+    AF_CHECK(af_index_gen(&tfIdx, tf, 2, tfIndexs));
+
+    AF_CHECK(af_matmul(&tmp, in, tfIdx, AF_MAT_NONE, AF_MAT_NONE));
+    T h_w[4] = { 1, 1, 1, 1 };
+    dim_t w_dims = 4;
+    AF_CHECK(af_create_array(&w, h_w, 1, &w_dims, (af_dtype) af::dtype_traits<T>::af_type));
+    AF_CHECK(af_div(&w, w, tmp, false));
+
+    tfIndexs[1].idx.seq = af_make_seq(0, 0, 1);
+    AF_CHECK(af_index_gen(&tfIdx, tf, 2, tfIndexs));
+    AF_CHECK(af_matmul(&tmp, in, tfIdx, AF_MAT_NONE, AF_MAT_NONE));
+    AF_CHECK(af_mul(&xt, tmp, w, false));
+
+    tfIndexs[1].idx.seq = af_make_seq(1, 1, 1);
+    AF_CHECK(af_index_gen(&tfIdx, tf, 2, tfIndexs));
+    AF_CHECK(af_matmul(&tmp, in, tfIdx, AF_MAT_NONE, AF_MAT_NONE));
+    AF_CHECK(af_mul(&yt, tmp, w, false));
+
+    AF_CHECK(af_join(&t, 1, xt, yt));
+
+    AF_CHECK(af_release_array(w));
+    AF_CHECK(af_release_array(tmp));
+    AF_CHECK(af_release_array(xt));
+    AF_CHECK(af_release_array(yt));
+
+    return t;
+}
+
+af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0, const float d1)
+{
+    try {
+        ArrayInfo tfInfo = getInfo(tf);
+        dim4 tfDims = tfInfo.dims();
+        ARG_ASSERT(1, (tfDims[0]==3 && tfDims[1]==3 && tfDims.ndims()==2));
+
+        af_array output;
+        af_dtype type  = tfInfo.getType();
+        switch(type) {
+            case f32: output = transform_coordinates<float >(tf, d0, d1); break;
+            case f64: output = transform_coordinates<double>(tf, d0, d1); break;
+            default : TYPE_ERROR(1, type);
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/transform_coordinates.cpp b/src/api/cpp/transform_coordinates.cpp
new file mode 100644
index 0000000000..4d896e7194
--- /dev/null
+++ b/src/api/cpp/transform_coordinates.cpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/image.h>
+#include <af/array.h>
+#include "error.hpp"
+
+namespace af
+{
+
+array transformCoordinates(const array& tf, const float d0, const float d1)
+{
+    af_array out = 0;
+    AF_THROW(af_transform_coordinates(&out, tf.get(), d0, d1));
+    return array(out);
+}
+
+}

From ba483f19ee5500c1ce0a3f820989cdb49322084c Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Fri, 15 Jan 2016 23:42:00 -0500
Subject: [PATCH 0307/2677] Added transform coordinates to unified backend

---
 src/api/unified/image.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 7b1159516c..0ee211d585 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -74,6 +74,13 @@ af_err af_transform(af_array *out, const af_array in, const af_array transform,
     return CALL(out, in, transform, odim0, odim1, method, inverse);
 }
 
+af_err af_transform_coordinates(af_array *out, const af_array tf,
+        const float d0, const float d1)
+{
+    CHECK_ARRAYS(tf);
+    return CALL(out, tf, d0, d1);
+}
+
 af_err af_rotate(af_array *out, const af_array in, const float theta,
         const bool crop, const af_interp_type method)
 {

From 3522f80c5d5d861788d298c75faa70366d65b89f Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Fri, 15 Jan 2016 23:42:32 -0500
Subject: [PATCH 0308/2677] Added transform coordinates documentation

---
 docs/details/image.dox | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/docs/details/image.dox b/docs/details/image.dox
index 288e4f6b0f..ef6d12a4f0 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -700,6 +700,18 @@ AF_INTERP_LOWER are allowed.
 Affine transforms can be used for various purposes. \ref af::translate, \ref af::scale and \ref af::skew
 are specializations of the transform function.
 
+
+\defgroup transform_func_coordinates transformcoordinates
+\ingroup transform_mat
+
+Transform input coordinates
+
+The transform function uses a perspective transform matrix to transform input
+coordinates (given as two dimensions) into a coordinates matrix.
+
+The output is a 4x2 matrix, indicating the coordinates of the 4 bidimensional
+transformed points.
+
 =======================================================================
 
 \defgroup image_func_sat SAT

From 7f3e2159537da233adcd3f66492e7e0cf11814fe Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Fri, 15 Jan 2016 23:42:52 -0500
Subject: [PATCH 0309/2677] Added transform coordinates unit tests

---
 test/transform_coordinates.cpp | 118 +++++++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)
 create mode 100644 test/transform_coordinates.cpp

diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
new file mode 100644
index 0000000000..7f1ac4e893
--- /dev/null
+++ b/test/transform_coordinates.cpp
@@ -0,0 +1,118 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+
+template<typename T>
+class TransformCoordinates : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {}
+};
+
+typedef ::testing::Types<float, double> TestTypes;
+
+TYPED_TEST_CASE(TransformCoordinates, TestTypes);
+
+template<typename T>
+void transformCoordinatesTest(string pTestFile)
+{
+    if (noDoubleTests<T>()) return;
+
+    vector<af::dim4>       inDims;
+    vector<vector<T> >     in;
+    vector<vector<float> > gold;
+
+    readTests<T, float, float>(pTestFile, inDims, in, gold);
+
+    af_array tfArray = 0;
+    af_array outArray = 0;
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)af::dtype_traits<T>::af_type));
+
+    size_t nTests = in.size();
+
+    for (int test = 1; test < nTests; test++) {
+        dim_t d0 = (dim_t)in[test][0];
+        dim_t d1 = (dim_t)in[test][1];
+
+        ASSERT_EQ(AF_SUCCESS, af_transform_coordinates(&outArray, tfArray, d0, d1));
+
+        // Get result
+        dim_t outEl = 0;
+        ASSERT_EQ(AF_SUCCESS, af_get_elements(&outEl, outArray));
+        T* outData = new T[outEl];
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+        const float thr = 1.f;
+
+        for (size_t elIter = 0; elIter < outEl; elIter++) {
+            ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << std::endl;
+        }
+
+        delete[] outData;
+    }
+
+    if(tfArray  != 0) af_release_array(tfArray);
+    if(outArray != 0) af_release_array(outArray);
+}
+
+TYPED_TEST(TransformCoordinates, RotateMatrix)
+{
+    transformCoordinatesTest<TypeParam>(string(TEST_DIR"/transformCoordinates/rotate_matrix.test"));
+}
+
+TYPED_TEST(TransformCoordinates, 3DMatrix)
+{
+    transformCoordinatesTest<TypeParam>(string(TEST_DIR"/transformCoordinates/3d_matrix.test"));
+}
+
+///////////////////////////////////// CPP ////////////////////////////////
+//
+TEST(TransformCoordinates, CPP)
+{
+    vector<af::dim4>       inDims;
+    vector<vector<float> > in;
+    vector<vector<float> > gold;
+
+    readTests<float, float, float>(TEST_DIR"/transformCoordinates/3d_matrix.test",inDims,in,gold);
+
+    af::array tf = af::array(inDims[0][0], inDims[0][1], &(in[0].front()));
+
+    float d0 = in[1][0];
+    float d1 = in[1][1];
+
+    af::array out = af::transformCoordinates(tf, d0, d1);
+
+    af::dim4 outDims = out.dims();
+
+    float* h_out = new float[outDims[0] * outDims[1]];
+    out.host(h_out);
+
+    const size_t n = gold[0].size();
+
+    const float thr = 1.f;
+
+    for (size_t elIter = 0; elIter < n; elIter++) {
+        ASSERT_LE(fabs(h_out[elIter] - gold[0][elIter]), thr) << "at: " << elIter << std::endl;
+    }
+
+    delete[] h_out;
+}

From 230c603e1a988bda7665abaabc911ec5d4241e67 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@arrayfire.com>
Date: Fri, 15 Jan 2016 23:45:21 -0500
Subject: [PATCH 0310/2677] Updated test data

---
 test/data | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/data b/test/data
index d134732012..414f02d905 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit d1347320125a0315a4ef03e63630b5b3249d189d
+Subproject commit 414f02d90588ec2cde177202bd340c57be6e7d9a

From 3389940d894d7425bdc30561901d06042dbf2606 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 18 Jan 2016 13:59:30 -0500
Subject: [PATCH 0311/2677] Fix resize unit test.

---
 src/backend/cpu/kernel/meanshift.hpp | 9 +++------
 test/resize.cpp                      | 2 +-
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index f173b2fd8b..54fb1a89bf 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -34,12 +34,9 @@ void meanShift(Array<T> out, const Array<T> in, const float s_sigma,
     const dim_t radius = std::max((int)(space_ * 1.5f), 1);
     const float cvar      = c_sigma*c_sigma;
 
-    std::vector<float> means;
-    std::vector<float> centers;
-    std::vector<float> tmpclrs;
-    means.reserve(channels);
-    centers.reserve(channels);
-    tmpclrs.reserve(channels);
+    std::vector<float> means(channels);
+    std::vector<float> centers(channels);
+    std::vector<float> tmpclrs(channels);
 
     T *outData       = out.get();
     const T * inData = in.get();
diff --git a/test/resize.cpp b/test/resize.cpp
index e0f1ea0810..6c29e61cc6 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -65,7 +65,7 @@ TYPED_TEST(Resize, InvalidDims)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    vector<TypeParam> in(8,8);
+    vector<TypeParam> in(8*8);
 
     af_array inArray  = 0;
     af_array outArray = 0;

From 6a34bee575de572010abe80ddf999c82da6b91b8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Jan 2016 14:20:59 -0500
Subject: [PATCH 0312/2677] Compile fixes for gcc 5.3

---
 src/backend/opencl/cpu/cpu_blas.cpp | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 1ff7e145d6..029421b374 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -167,9 +167,9 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     using BT  =       typename blas_base<T>::type;
 
     // get host pointers from mapped memory
-    std::shared_ptr<BT> lPtr = lhs.getMappedPtr();
-    std::shared_ptr<BT> rPtr = rhs.getMappedPtr();
-    std::shared_ptr<BT> oPtr = out.getMappedPtr();
+    auto lPtr = lhs.getMappedPtr();
+    auto rPtr = rhs.getMappedPtr();
+    auto oPtr = out.getMappedPtr();
 
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
@@ -177,19 +177,19 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
             CblasColMajor, lOpts,
             lDims[0], lDims[1],
             alpha,
-            lPtr.get(), lStrides[1],
-            rPtr.get(), rStrides[0],
+            (BT*)lPtr.get(), lStrides[1],
+            (BT*)rPtr.get(), rStrides[0],
             beta,
-            oPtr.get(), 1);
+            (BT*)oPtr.get(), 1);
     } else {
         gemm_func<T>()(
             CblasColMajor, lOpts, rOpts,
             M, N, K,
             alpha,
-            lPtr.get(), lStrides[1],
-            rPtr.get(), rStrides[1],
+            (BT*)lPtr.get(), lStrides[1],
+            (BT*)rPtr.get(), rStrides[1],
             beta,
-            oPtr.get(), out.dims()[0]);
+            (BT*)oPtr.get(), out.dims()[0]);
     }
 
     return out;

From 46042feed38a8871ddf7bd7fb0cf3747d70b0c35 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Jan 2016 15:39:33 -0500
Subject: [PATCH 0313/2677] Fixing compiler warnings

---
 src/backend/opencl/platform.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index ef9f8f63be..d7c3e1cfc0 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -181,14 +181,14 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
         auto lversion =  ldev->getInfo<CL_DEVICE_VERSION>();
         auto rversion =  rdev->getInfo<CL_DEVICE_VERSION>();
 
-        auto lres = (lversion[7] > rversion[7]) ||
+        bool lres = (lversion[7] > rversion[7]) ||
             ((lversion[7] == rversion[7]) && (lversion[9] > rversion[9]));
 
-        auto rres = (lversion[7] < rversion[7]) ||
+        bool rres = (lversion[7] < rversion[7]) ||
             ((lversion[7] == rversion[7]) && (lversion[9] < rversion[9]));
 
-        if (lres > 0) return true;
-        if (rres < 0) return false;
+        if (lres) return true;
+        if (rres) return false;
     }
 
     // Default crietria, sort based on memory

From 5fba37c972d1a1bf92bc8bf5870a6442a1a6db32 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Jan 2016 17:36:14 -0500
Subject: [PATCH 0314/2677] Functions to get opencl device type and platforms

- Also use this mechanism for checking for particular device type / platform
---
 include/af/opencl.h                | 77 ++++++++++++++++++++++++++++--
 src/backend/opencl/magma/getrs.cpp |  4 +-
 src/backend/opencl/platform.cpp    | 44 +++++++++++++++++
 src/backend/opencl/platform.hpp    |  8 ++++
 src/backend/opencl/solve.cpp       |  8 ++--
 test/ocl_ext_context.cpp           | 19 ++++++++
 6 files changed, 150 insertions(+), 10 deletions(-)

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 88e47d2b16..16b85d763f 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #if defined(__APPLE__) || defined(__MACOSX)
 #include <OpenCL/cl.h>
 #else
@@ -19,6 +20,29 @@
 extern "C" {
 #endif
 
+#if AF_API_VERSION >= 33
+typedef enum
+{
+    AFCL_DEVICE_TYPE_CPU     = CL_DEVICE_TYPE_CPU,
+    AFCL_DEVICE_TYPE_GPU     = CL_DEVICE_TYPE_GPU,
+    AFCL_DEVICE_TYPE_ACC     = CL_DEVICE_TYPE_ACCELERATOR,
+    AFCL_DEVICE_TYPE_UNKNOWN = -1
+} afcl_device_type;
+#endif
+
+#if AF_API_VERSION >= 33
+typedef enum
+{
+    AFCL_PLATFORM_AMD     = 0,
+    AFCL_PLATFORM_APPLE   = 1,
+    AFCL_PLATFORM_INTEL   = 2,
+    AFCL_PLATFORM_NVIDIA  = 3,
+    AFCL_PLATFORM_BEIGNET = 4,
+    AFCL_PLATFORM_POCL    = 5,
+    AFCL_PLATFORM_UNKNOWN = -1
+} afcl_platform;
+#endif
+
 /**
     \ingroup opencl_mat
     @{
@@ -110,6 +134,20 @@ AFAPI af_err afcl_set_device_context(cl_device_id dev, cl_context ctx);
 AFAPI af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx);
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   Get the type of the current device
+*/
+AFAPI af_err afcl_get_device_type(afcl_device_type *res);
+#endif
+
+#if AF_API_VERSION >= 33
+/**
+   Get the platform of the current device
+*/
+AFAPI af_err afcl_get_platform(afcl_platform *res);
+#endif
+
 /**
   @}
 */
@@ -253,6 +291,38 @@ static inline void deleteDevice(cl_device_id dev, cl_context ctx)
 }
 #endif
 
+
+#if AF_API_VERSION >= 33
+ typedef afcl_device_type deviceType;
+ typedef afcl_platform platform;
+#endif
+
+#if AF_API_VERSION >= 33
+/**
+   Get the type of the current device
+*/
+static inline deviceType getDeviceType()
+{
+    afcl_device_type res = AFCL_DEVICE_TYPE_UNKNOWN;
+    af_err err = afcl_get_device_type(&res);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to get OpenCL device type");
+    return res;
+}
+#endif
+
+#if AF_API_VERSION >= 33
+/**
+   Get the type of the current device
+*/
+static inline platform getPlatform()
+{
+    afcl_platform res = AFCL_PLATFORM_UNKNOWN;
+    af_err err = afcl_get_platform(&res);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to get OpenCL platform");
+    return res;
+}
+#endif
+
  /**
  Create an af::array object from an OpenCL cl_mem buffer
 
@@ -369,15 +439,15 @@ static inline void deleteDevice(cl_device_id dev, cl_context ctx)
      return afcl::array(af::dim4(dim0, dim1, dim2, dim3), buf, type, retain);
  }
 
- /**
+/**
    @}
- */
-
+*/
 }
 
 namespace af
 {
 
+#if !defined(AF_OPENCL)
 template<> AFAPI cl_mem *array::device() const
 {
     cl_mem *mem = new cl_mem;
@@ -385,6 +455,7 @@ template<> AFAPI cl_mem *array::device() const
     if (err != AF_SUCCESS) throw af::exception("Failed to get cl_mem from array object");
     return mem;
 }
+#endif
 
 }
 
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index 1dc106c0c5..eb28a5175a 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -61,6 +61,7 @@
 #include <platform.hpp>
 #include <algorithm>
 #include <string>
+#include <af/opencl.h>
 
 template<typename Ty>  magma_int_t
 magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
@@ -168,8 +169,7 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
     clblasTranspose cltrans =(trans == MagmaNoTrans) ? clblasNoTrans :
         (trans == MagmaTrans ? clblasTrans : clblasConjTrans);
 
-    std::string pName = opencl::getPlatformName(opencl::getDevice());
-    bool cond = pName.find("NVIDIA") != std::string::npos;
+    bool cond = opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
     cl_mem dAT = 0;
     if (nrhs > 1 && cond) {
         magma_malloc<Ty>(&dAT, n * n);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d7c3e1cfc0..884dca14d1 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -198,6 +198,25 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
     return l_mem >= r_mem;
 }
 
+static afcl::deviceType getDeviceTypeEnum(cl::Device dev)
+{
+    return (afcl::deviceType)dev.getInfo<CL_DEVICE_TYPE>();
+}
+
+
+static afcl::platform getPlatformEnum(cl::Device dev)
+{
+    std::string pname = getPlatformName(dev);
+    if (verify_present(pname, "AMD")) return AFCL_PLATFORM_AMD;
+    if (verify_present(pname, "NVIDIA")) return AFCL_PLATFORM_NVIDIA;
+    if (verify_present(pname, "INTEL")) return AFCL_PLATFORM_INTEL;
+    if (verify_present(pname, "APPLE")) return AFCL_PLATFORM_APPLE;
+    if (verify_present(pname, "BEIGNET")) return AFCL_PLATFORM_BEIGNET;
+    if (verify_present(pname, "POCL")) return AFCL_PLATFORM_POCL;
+    return AFCL_PLATFORM_UNKNOWN;
+}
+
+
 DeviceManager::DeviceManager()
     : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
 {
@@ -260,6 +279,8 @@ DeviceManager::DeviceManager()
             mContexts.push_back(ctx);
             mQueues.push_back(cq);
             mIsGLSharingOn.push_back(false);
+            mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
+            mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
         }
 
         bool default_device_set = false;
@@ -437,6 +458,17 @@ int getDeviceIdFromNativeId(cl_device_id id)
     return devId;
 }
 
+int getActiveDeviceType()
+{
+    DeviceManager &instance = DeviceManager::getInstance();
+    return instance.mDeviceTypes[instance.mActiveQId];
+}
+
+int getActivePlatform()
+{
+    DeviceManager &instance = DeviceManager::getInstance();
+    return instance.mPlatforms[instance.mActiveQId];
+}
 const Context& getContext()
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -731,6 +763,18 @@ bool synchronize_calls() {
 
 using namespace opencl;
 
+af_err afcl_get_device_type(afcl_device_type *res)
+{
+    *res = (afcl_device_type)getActiveDeviceType();
+    return AF_SUCCESS;
+}
+
+af_err afcl_get_platform(afcl_platform *res)
+{
+    *res = (afcl_platform)getActivePlatform();
+    return AF_SUCCESS;
+}
+
 af_err afcl_get_context(cl_context *ctx, const bool retain)
 {
     *ctx = getContext()();
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 85c533fa84..d4f9f0e5ef 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -49,6 +49,9 @@ class DeviceManager
 
     friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
+    friend int getActiveDeviceType();
+    friend int getActivePlatform();
+
     public:
         static const unsigned MAX_DEVICES = 32;
 
@@ -77,6 +80,8 @@ class DeviceManager
         std::vector<cl::Context*>     mContexts;
         std::vector<cl::CommandQueue*>  mQueues;
         std::vector<bool>        mIsGLSharingOn;
+        std::vector<int>         mDeviceTypes;
+        std::vector<int>         mPlatforms;
         unsigned mUserDeviceOffset;
 
         unsigned mActiveCtxId;
@@ -123,4 +128,7 @@ void sync(int device);
 
 bool synchronize_calls();
 
+int getActiveDeviceType();
+int getActivePlatform();
+
 }
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 4fede07e56..93176752b5 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -21,6 +21,7 @@
 #include <blas.hpp>
 #include <transpose.hpp>
 #include <math.hpp>
+#include <af/opencl.h>
 
 #include <algorithm>
 #include <string>
@@ -226,9 +227,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                               (*dT)(), tmp.getOffset() + NB * MN,
                               NB, 0, queue);
 
-
-        std::string pName = getPlatformName(getDevice());
-        if(pName.find("NVIDIA") != std::string::npos)
+        if(getActivePlatform() == AFCL_PLATFORM_NVIDIA)
         {
             Array<T> AT = transpose<T>(A, true);
             cl::Buffer* AT_buf = AT.get();
@@ -268,8 +267,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
     cl_event event = 0;
     cl_command_queue queue = getQueue()();
 
-    std::string pName = getPlatformName(getDevice());
-    if(pName.find("NVIDIA") != std::string::npos && (options & AF_MAT_UPPER))
+    if(getActivePlatform() == AFCL_PLATFORM_NVIDIA && (options & AF_MAT_UPPER))
     {
         Array<T> AT = transpose<T>(A, true);
 
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 0d4f89b3fc..e711c631e4 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -105,6 +105,25 @@ TEST(OCLExtContext, pop)
     printf("%d devices after afcl::deleteDevice\n", af::getDeviceCount());
     af::info();
 }
+
+TEST(OCLCheck, DeviceType)
+{
+    afcl::deviceType devType = afcl::getDeviceType();
+    cl_device_type type = -100;
+    clGetDeviceInfo(afcl::getDeviceId(),
+                    CL_DEVICE_TYPE,
+                    sizeof(cl_device_type),
+                    &type,
+                    NULL);
+    ASSERT_EQ(type, (cl_device_type)devType);
+}
+
+TEST(OCLCheck, DevicePlatform)
+{
+    afcl::platform platform = afcl::getPlatform();
+    ASSERT_NE(platform, AFCL_PLATFORM_UNKNOWN);
+}
+
 #else
 TEST(OCLExtContext, NoopCPU)
 {

From 34c8c97c8f2a6c8424433ddd89bc5b23c646985e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Jan 2016 19:15:33 -0500
Subject: [PATCH 0315/2677] Work around for a bug in AMD's clBuildProgram

 - Get stuck when the kernel is too large
---
 src/backend/opencl/Array.cpp | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 207a4b0de7..044a6322ab 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -16,6 +16,8 @@
 #include <memory.hpp>
 #include <platform.hpp>
 #include <cstddef>
+#include <af/opencl.h>
+#include <util.hpp>
 
 using af::dim4;
 
@@ -23,6 +25,7 @@ namespace opencl
 {
 
     const int MAX_JIT_LEN = 20;
+    const int MAX_JIT_LEN_AMD = 16; //FIXME: Change this when bug is fixed
     using JIT::BufferNode;
     using JIT::Node;
     using JIT::Node_ptr;
@@ -153,6 +156,14 @@ namespace opencl
 
     using af::dim4;
 
+    inline bool is_max_jit_len(const unsigned &len)
+    {
+        if (getActivePlatform() == AFCL_PLATFORM_AMD) {
+            return len >= MAX_JIT_LEN_AMD;
+        }
+        return len >= MAX_JIT_LEN;
+    }
+
     template<typename T>
     Array<T> createNodeArray(const dim4 &dims, Node_ptr node)
     {
@@ -166,7 +177,7 @@ namespace opencl
         n->getInfo(length, buf_count, bytes);
         n->resetFlags();
 
-        if (length > MAX_JIT_LEN ||
+        if (is_max_jit_len(length) ||
             buf_count >= MAX_BUFFERS ||
             bytes >= MAX_BYTES) {
             out.eval();

From ffc6e7f251cb37889b5fecfc06a385db5d24b4b0 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Jan 2016 22:41:21 -0500
Subject: [PATCH 0316/2677] Putting transform coordinates within version guards

---
 include/af/image.h | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/include/af/image.h b/include/af/image.h
index d25f64f058..0e0c0ba901 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -223,6 +223,7 @@ AFAPI array rotate(const array& in, const float theta, const bool crop=true, con
 */
 AFAPI array transform(const array& in, const array& transform, const dim_t odim0 = 0, const dim_t odim1 = 0, const interpType method=AF_INTERP_NEAREST, const bool inverse=true);
 
+#if AF_API_VERSION >= 33
 /**
     C++ Interface for transforming coordinates
 
@@ -234,6 +235,7 @@ AFAPI array transform(const array& in, const array& transform, const dim_t odim0
     \ingroup transform_func_coordinates
 */
 AFAPI array transformCoordinates(const array& tf, const float d0, const float d1);
+#endif
 
 /**
     C++ Interface for translating an image
@@ -865,6 +867,7 @@ extern "C" {
                               const dim_t odim0, const dim_t odim1,
                               const af_interp_type method, const bool inverse);
 
+#if AF_API_VERSION >= 33
     /**
        C Interface for transforming an image
        C++ Interface for transforming coordinates
@@ -877,6 +880,7 @@ extern "C" {
        \ingroup transform_func_coordinates
     */
     AFAPI af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0, const float d1);
+#endif
 
     /**
        C Interface for rotating an image

From e7e608023b4a4bc431b585caf33393f85015129b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 8 Jan 2016 17:17:11 -0500
Subject: [PATCH 0317/2677] Update clBLAS release tag

---
 CMakeModules/build_clBLAS.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 6cb1ae8aaf..d486b31801 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG 102c832825e8e4d60ad73ca97e95668463294068
+    GIT_TAG arrayfire-release-test
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 845d3b3d31d2173255d4c1e8df66f04bf501ab42 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 18 Jan 2016 14:59:02 -0500
Subject: [PATCH 0318/2677] Fixes in magma potrf (opencl cholesky)

---
 src/backend/opencl/magma/potrf.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/magma/potrf.cpp b/src/backend/opencl/magma/potrf.cpp
index d048ed4dac..4f9984f325 100644
--- a/src/backend/opencl/magma/potrf.cpp
+++ b/src/backend/opencl/magma/potrf.cpp
@@ -199,7 +199,7 @@ magma_int_t magma_potrf_gpu(
                 magma_getmatrix_async<Ty>(jb, jb, dA(j,j), ldda, work, jb, queue, &event);
 
                 // apply all previous updates to block row right of diagonal block
-                if (j+jb < n) {
+                if (j+jb < n && j > 0) {
                     CLBLAS_CHECK(gpu_blas_gemm(
                                      transType, clblasNoTrans,
                                      jb, n-j-jb, j,
@@ -259,7 +259,7 @@ magma_int_t magma_potrf_gpu(
                 magma_getmatrix_async<Ty>(jb, jb, dA(j,j), ldda, work, jb, queue, &event);
 
                 // apply all previous updates to block column below diagonal block
-                if (j+jb < n) {
+                if (j+jb < n && j > 0) {
                     CLBLAS_CHECK(gpu_blas_gemm(
                                      clblasNoTrans, transType,
                                      n-j-jb, jb, j,

From 3ce49a5dd347892d9a9226dc9d31360e7aa2851c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 20 Jan 2016 14:40:28 -0500
Subject: [PATCH 0319/2677] BUGFIX Fix how streams are created in
 setActiveDevice (CUDA)

Ref 968ae4e80ce8e6263fdc3f4381ae8b895df44bc4
---
 src/backend/cuda/platform.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 6919a04158..d172903084 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -395,7 +395,11 @@ int DeviceManager::setActiveDevice(int device, int nId)
     int old = activeDev;
     if(nId == -1) nId = getDeviceNativeId(device);
     CUDA_CHECK(cudaSetDevice(nId));
-    cudaError_t err = cudaStreamCreate(&streams[device]);
+
+    cudaError_t err = cudaSuccess;
+    if(!streams[device])
+        err = cudaStreamCreate(&streams[device]);
+
     activeDev = device;
 
     if (err == cudaSuccess) return old;

From 58fc4c8ea4869b62ad75627f24a8a274d51ce26e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 20 Jan 2016 16:58:08 -0500
Subject: [PATCH 0320/2677] Fixes to getMappedPtr in OpenCL backend

- Also changed CL_TO_AF_ERROR to display OpenCL error number
---
 src/backend/opencl/Array.hpp      | 6 ++++--
 src/backend/opencl/err_opencl.hpp | 4 ++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 2793d5e099..4c8c05a231 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -227,8 +227,10 @@ namespace opencl
             try {
                 if(ptr == nullptr) {
                     ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()),
-                            true, CL_MAP_READ|CL_MAP_WRITE,
-                            getOffset(), getDataDims().elements() * sizeof(T));
+                                                          true, CL_MAP_READ|CL_MAP_WRITE,
+                                                          getOffset(),
+                                                          (getDataDims().elements() - getOffset())
+                                                          * sizeof(T));
                 }
             } catch(cl::Error err) {
                 CL_TO_AF_ERROR(err);
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 15855f3b08..955275203a 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -23,8 +23,8 @@
         char opencl_err_msg[1024];                              \
         snprintf(opencl_err_msg,                                \
                  sizeof(opencl_err_msg),                        \
-                 "OpenCL Error: %s when calling %s",            \
-                 getErrorMessage(ERR.err()).c_str(),            \
+                 "OpenCL Error (%d): %s when calling %s",       \
+                 ERR.err(), getErrorMessage(ERR.err()).c_str(), \
                  ERR.what());                                   \
         if (ERR.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {    \
             AF_ERROR(opencl_err_msg, AF_ERR_NO_MEM);            \

From d3d2996374de5cf4b59e60dc024ae40b72139d93 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 20 Jan 2016 17:51:37 -0500
Subject: [PATCH 0321/2677] Add getHostMemorySize and getDeviceMemorySize
 functions

* Print memory size in CPU and OpenCL info
* Change opencl::getDevice to accept id
* Fix multi-line error strings
---
 src/api/c/err_common.cpp        |   8 +--
 src/backend/cpu/platform.cpp    |  24 ++++++-
 src/backend/cpu/platform.hpp    |   4 ++
 src/backend/cuda/platform.cpp   |  11 ++++
 src/backend/cuda/platform.hpp   |   4 ++
 src/backend/host_memory.cpp     | 113 ++++++++++++++++++++++++++++++++
 src/backend/host_memory.hpp     |  18 +++++
 src/backend/opencl/platform.cpp |  22 ++++++-
 src/backend/opencl/platform.hpp |  10 ++-
 9 files changed, 204 insertions(+), 10 deletions(-)
 create mode 100644 src/backend/host_memory.cpp
 create mode 100644 src/backend/host_memory.hpp

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 382dac1af1..495967a891 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -198,13 +198,13 @@ const char *af_err_to_string(const af_err err)
     case AF_ERR_BATCH:              return "Invalid batch configuration";
     case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
     case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
-    case AF_ERR_NONFREE:            return "Function unavailable."
+    case AF_ERR_NONFREE:            return "Function unavailable. "
                                            "ArrayFire compiled without Non-Free algorithms support";
     case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
-    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable."
+    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
                                            "ArrayFire compiled without Graphics support";
-    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library."
-                                           "See http://www.arrayfire.com/docs/unifiedbackend.htm"
+    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. "
+                                           "See http://www.arrayfire.com/docs/unifiedbackend.htm "
                                            "for instructions to set up environment for Unified backend";
     case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
     case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 0039b208d9..49abda3c8d 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -19,6 +19,7 @@
 #include <defines.hpp>
 #include <version.hpp>
 #include <queue.hpp>
+#include <host_memory.hpp>
 
 #ifdef _WIN32
 #include <limits.h>
@@ -197,6 +198,15 @@ static const std::string get_system(void)
 #endif
 }
 
+// http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
+// trim from start
+static inline std::string &ltrim(std::string &s)
+{
+    s.erase(s.begin(), std::find_if(s.begin(), s.end(),
+                                    std::not1(std::ptr_fun<int, int>(std::isspace))));
+    return s;
+}
+
 std::string getInfo()
 {
     std::ostringstream info;
@@ -204,7 +214,9 @@ std::string getInfo()
 
     info << "ArrayFire v" << AF_VERSION
          << " (CPU, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
-    info << string("[0] ") << cinfo.vendor() <<": " << cinfo.model() << " ";
+    std::string model = cinfo.model();
+    info << string("[0] ") << cinfo.vendor() <<": " << ltrim(model)
+         << ", " << (int)(getDeviceMemorySize(getActiveDeviceId()) / 1048576.0) << " MB, ";
     info << "Max threads("<< cinfo.threads()<<") ";
 #ifndef NDEBUG
     info << AF_COMPILER_STR;
@@ -249,6 +261,16 @@ int getActiveDeviceId()
     return 0;
 }
 
+size_t getDeviceMemorySize(int device)
+{
+    return common::getHostMemorySize();
+}
+
+size_t getHostMemorySize()
+{
+    return common::getHostMemorySize();
+}
+
 static const int MAX_QUEUES = 1;
 
 
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 0cd42ae068..9118ade8bd 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -28,6 +28,10 @@ namespace cpu {
 
     int getActiveDeviceId();
 
+    size_t getDeviceMemorySize(int device);
+
+    size_t getHostMemorySize();
+
     void sync(int device);
 
     queue& getQueue(int idx = 0);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index d172903084..46b730314f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -23,6 +23,7 @@
 #include <cstring>
 #include <err_cuda.hpp>
 #include <util.hpp>
+#include <host_memory.hpp>
 
 using namespace std;
 
@@ -304,6 +305,16 @@ cudaStream_t getStream(int device)
     return str;
 }
 
+size_t getDeviceMemorySize(int device)
+{
+    return getDeviceProp(device).totalGlobalMem;
+}
+
+size_t getHostMemorySize()
+{
+    return common::getHostMemorySize();
+}
+
 int setDevice(int device)
 {
     return DeviceManager::getInstance().setActiveDevice(device);
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 20862fb886..9302f4160e 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -46,6 +46,10 @@ int getDeviceNativeId(int device);
 
 cudaStream_t getStream(int device);
 
+size_t getDeviceMemorySize(int device);
+
+size_t getHostMemorySize();
+
 int setDevice(int device);
 
 void sync(int device);
diff --git a/src/backend/host_memory.cpp b/src/backend/host_memory.cpp
new file mode 100644
index 0000000000..9b4f1e5f54
--- /dev/null
+++ b/src/backend/host_memory.cpp
@@ -0,0 +1,113 @@
+/*
+ * Author:  David Robert Nadeau
+ * Site:    http://NadeauSoftware.com/
+ * License: Creative Commons Attribution 3.0 Unported License
+ *          http://creativecommons.org/licenses/by/3.0/deed.en_US
+ * Source:  http://nadeausoftware.com/sites/NadeauSoftware.com/files/getMemorySize.c
+ */
+
+#include "host_memory.hpp"
+
+#if defined(_WIN32)
+#include <Windows.h>
+
+#elif defined(__unix__) || defined(__unix) || defined(unix) || (defined(__APPLE__) && defined(__MACH__))
+#include <unistd.h>
+#include <sys/types.h>
+#include <sys/param.h>
+
+#if defined(BSD)
+#include <sys/sysctl.h>
+#endif
+
+#else
+#define NOMEMORYSIZE
+#endif
+
+namespace common
+{
+
+#ifdef NOMEMORYSIZE
+size_t getHostMemorySize()
+{
+    return 0L; // Can't detect
+}
+
+#else
+
+/**
+ * Returns the size of physical memory (RAM) in bytes.
+ */
+size_t getHostMemorySize()
+{
+#if defined(_WIN32) && (defined(__CYGWIN__) || defined(__CYGWIN32__))
+    /* Cygwin under Windows. ------------------------------------ */
+    /* New 64-bit MEMORYSTATUSEX isn't available.  Use old 32.bit */
+    MEMORYSTATUS status;
+    status.dwLength = sizeof(status);
+    GlobalMemoryStatus( &status );
+    return (size_t)status.dwTotalPhys;
+
+#elif defined(_WIN32)
+    /* Windows. ------------------------------------------------- */
+    /* Use new 64-bit MEMORYSTATUSEX, not old 32-bit MEMORYSTATUS */
+    MEMORYSTATUSEX status;
+    status.dwLength = sizeof(status);
+    GlobalMemoryStatusEx( &status );
+    return (size_t)status.ullTotalPhys;
+
+#elif defined(__unix__) || defined(__unix) || defined(unix) || (defined(__APPLE__) && defined(__MACH__))
+    /* UNIX variants. ------------------------------------------- */
+    /* Prefer sysctl() over sysconf() except sysctl() HW_REALMEM and HW_PHYSMEM */
+
+#if defined(CTL_HW) && (defined(HW_MEMSIZE) || defined(HW_PHYSMEM64))
+    int mib[2];
+    mib[0] = CTL_HW;
+#if defined(HW_MEMSIZE)
+    mib[1] = HW_MEMSIZE;        /* OSX. --------------------- */
+#elif defined(HW_PHYSMEM64)
+    mib[1] = HW_PHYSMEM64;      /* NetBSD, OpenBSD. --------- */
+#endif
+    int64_t size = 0;       /* 64-bit */
+    size_t len = sizeof( size );
+    if ( sysctl( mib, 2, &size, &len, NULL, 0 ) == 0 )
+        return (size_t)size;
+    return 0L;          /* Failed? */
+
+#elif defined(_SC_AIX_REALMEM)
+    /* AIX. ----------------------------------------------------- */
+    return (size_t)sysconf( _SC_AIX_REALMEM ) * (size_t)1024L;
+
+#elif defined(_SC_PHYS_PAGES) && defined(_SC_PAGESIZE)
+    /* FreeBSD, Linux, OpenBSD, and Solaris. -------------------- */
+    return (size_t)sysconf( _SC_PHYS_PAGES ) *
+        (size_t)sysconf( _SC_PAGESIZE );
+
+#elif defined(_SC_PHYS_PAGES) && defined(_SC_PAGE_SIZE)
+    /* Legacy. -------------------------------------------------- */
+    return (size_t)sysconf( _SC_PHYS_PAGES ) *
+        (size_t)sysconf( _SC_PAGE_SIZE );
+
+#elif defined(CTL_HW) && (defined(HW_PHYSMEM) || defined(HW_REALMEM))
+    /* DragonFly BSD, FreeBSD, NetBSD, OpenBSD, and OSX. -------- */
+    int mib[2];
+    mib[0] = CTL_HW;
+#if defined(HW_REALMEM)
+    mib[1] = HW_REALMEM;        /* FreeBSD. ----------------- */
+#elif defined(HW_PYSMEM)
+    mib[1] = HW_PHYSMEM;        /* Others. ------------------ */
+#endif
+    unsigned int size = 0;      /* 32-bit */
+    size_t len = sizeof( size );
+    if ( sysctl( mib, 2, &size, &len, NULL, 0 ) == 0 )
+        return (size_t)size;
+    return 0L;          /* Failed? */
+#endif /* sysctl and sysconf variants */
+
+#else
+    return 0L;          /* Unknown OS. */
+#endif
+}
+
+#endif // NOMEMORYSIZE
+} // namespace common
diff --git a/src/backend/host_memory.hpp b/src/backend/host_memory.hpp
new file mode 100644
index 0000000000..5955cbfbd9
--- /dev/null
+++ b/src/backend/host_memory.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <cstddef>
+
+namespace common
+{
+
+size_t getHostMemorySize();
+
+}
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 884dca14d1..94efd7a876 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -43,6 +43,7 @@
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
 #include <util.hpp>
+#include <host_memory.hpp>
 
 using std::string;
 using std::vector;
@@ -404,7 +405,9 @@ std::string getInfo()
             std::to_string(nDevices) +
             (show_braces ? string("]") : "-");
 
-        info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr);
+        size_t msize = device->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+        info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
+             << ", " << msize / 1048576 << " MB";
 #ifndef NDEBUG
         info << " -- ";
         string devVersion = device->getInfo<CL_DEVICE_VERSION>();
@@ -481,10 +484,23 @@ CommandQueue& getQueue()
     return *(devMngr.mQueues[devMngr.mActiveQId]);
 }
 
-const cl::Device& getDevice()
+const cl::Device& getDevice(int id)
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
-    return *(devMngr.mDevices[devMngr.mActiveQId]);
+    if(id == -1) id = devMngr.mActiveQId;
+    return *(devMngr.mDevices[id]);
+}
+
+size_t getDeviceMemorySize(int device)
+{
+    const cl::Device& dev = getDevice(device);
+    size_t msize = dev.getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+    return msize;
+}
+
+size_t getHostMemorySize()
+{
+    return common::getHostMemorySize();
 }
 
 cl_device_type getDeviceType()
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index d4f9f0e5ef..9b5377dc3c 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -33,7 +33,9 @@ class DeviceManager
 
     friend cl::CommandQueue& getQueue();
 
-    friend const cl::Device& getDevice();
+    friend const cl::Device& getDevice(int id);
+
+    friend size_t getDeviceMemorySize(int device);
 
     friend bool isGLSharingSupported();
 
@@ -100,7 +102,11 @@ const cl::Context& getContext();
 
 cl::CommandQueue& getQueue();
 
-const cl::Device& getDevice();
+const cl::Device& getDevice(int id = -1);
+
+size_t getDeviceMemorySize(int device);
+
+size_t getHostMemorySize();
 
 cl_device_type getDeviceType();
 

From 6c306528854bafd05599ffbb928aaa171c6d0c9c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 20 Jan 2016 18:30:03 -0500
Subject: [PATCH 0322/2677] Updates to Memory Manager and Garbage Collection
 Heuristics

* Add getMaxMemorySize virtual function to MemoryManager
    * Used to compute the max_bytes for each memory manager instance
* max_bytes
    * This is now a part of memory_info
    * Gets it's size from the device memory rather than a generic size
    * For CPU and CUDA Pinned Memory, use getHostDeviceMemory
* Add getMaxBytes function to fetch the max_bytes of active device
* MAX_BUFFERS is now 1000

Fix missing include
---
 src/backend/MemoryManager.cpp | 23 ++++++++++++++++++-----
 src/backend/MemoryManager.hpp | 11 +++++++++--
 src/backend/cpu/Array.cpp     |  2 +-
 src/backend/cpu/memory.cpp    | 12 +++++++++++-
 src/backend/cpu/memory.hpp    |  5 +++--
 src/backend/cpu/platform.cpp  |  9 +++++++--
 src/backend/cuda/Array.cpp    |  2 +-
 src/backend/cuda/memory.cpp   | 20 ++++++++++++++++++--
 src/backend/cuda/memory.hpp   |  5 +++--
 src/backend/opencl/Array.cpp  |  2 +-
 src/backend/opencl/memory.cpp | 20 ++++++++++++++++++--
 src/backend/opencl/memory.hpp |  5 +++--
 12 files changed, 93 insertions(+), 23 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index cea4ae6b76..03cefe710d 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -10,6 +10,7 @@
 #include <iostream>
 #include <iomanip>
 #include <string>
+#include <algorithm>
 #include "MemoryManager.hpp"
 #include "dispatch.hpp"
 #include "err_common.hpp"
@@ -18,10 +19,9 @@
 namespace common
 {
 
-MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX_BYTES, bool debug):
+MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug):
     mem_step_size(1024),
     max_buffers(MAX_BUFFERS),
-    max_bytes(MAX_BYTES),
     memory(num_devices),
     debug_mode(debug)
 {
@@ -32,9 +32,16 @@ MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX
     }
     if (this->debug_mode) mem_step_size = 1;
 
+    static const size_t oneGB = 1 << 30;
     for (int n = 0; n < num_devices; n++) {
-        memory[n].total_bytes = 0;
-        memory[n].lock_bytes = 0;
+        size_t memsize = getMaxMemorySize(n);
+        // Calls garbage collection when:
+        // total_bytes > memsize * 0.75 when memsize <  4GB
+        // total_bytes > memsize - 1 GB when memsize >= 4GB
+        // If memsize returned 0, then use 1GB
+        memory[n].max_bytes    = memsize == 0 ? oneGB : std::max(memsize * 0.75, (double)(memsize - oneGB));
+        memory[n].total_bytes  = 0;
+        memory[n].lock_bytes   = 0;
         memory[n].lock_buffers = 0;
     }
 }
@@ -115,7 +122,7 @@ void *MemoryManager::alloc(const size_t bytes)
             // FIXME: Add better checks for garbage collection
             // Perhaps look at total memory available as a metric
             if (current.map.size() > this->max_buffers ||
-                current.lock_bytes >= this->max_bytes) {
+                current.lock_bytes >= current.max_bytes) {
 
                 this->garbageCollect();
             }
@@ -204,6 +211,12 @@ void MemoryManager::setMemStepSize(size_t new_step_size)
     this->mem_step_size = new_step_size;
 }
 
+size_t MemoryManager::getMaxBytes()
+{
+    lock_guard_t lock(this->memory_mutex);
+    return this->getCurrentMemoryInfo().max_bytes;
+}
+
 void MemoryManager::printInfo(const char *msg, const int device)
 {
     lock_guard_t lock(this->memory_mutex);
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index 5de8e4d823..8bb9941b87 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -37,11 +37,11 @@ class MemoryManager
         size_t lock_bytes;
         size_t lock_buffers;
         size_t total_bytes;
+        size_t max_bytes;
     } memory_info;
 
     size_t mem_step_size;
     unsigned max_buffers;
-    unsigned max_bytes;
     std::vector<memory_info> memory;
     bool debug_mode;
 
@@ -55,8 +55,13 @@ class MemoryManager
         return 0;
     }
 
+    virtual size_t getMaxMemorySize(int id)
+    {
+        return 0;
+    }
+
 public:
-    MemoryManager(int num_devices, unsigned MAX_BUFFERS, unsigned MAX_BYTES, bool debug);
+    MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug);
 
     void *alloc(const size_t bytes);
 
@@ -75,6 +80,8 @@ class MemoryManager
 
     size_t getMemStepSize();
 
+    size_t getMaxBytes();
+
     void setMemStepSize(size_t new_step_size);
 
     virtual void *nativeAlloc(const size_t bytes)
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 862c576afe..891604cd27 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -161,7 +161,7 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 
     if (length > MAX_TNJ_LEN ||
         buf_count >= MAX_BUFFERS ||
-        bytes >= MAX_BYTES) {
+        bytes >= getMaxBytes()) {
         out.eval();
     }
 
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index c387b68b71..09a0e83c80 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -29,6 +29,7 @@ namespace cpu
 class MemoryManager  : public common::MemoryManager
 {
     int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
 public:
     MemoryManager();
     void *nativeAlloc(const size_t bytes);
@@ -48,8 +49,13 @@ int MemoryManager::getActiveDeviceId()
     return cpu::getActiveDeviceId();
 }
 
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return cpu::getDeviceMemorySize(id);
+}
+
 MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
 {}
 
 
@@ -79,6 +85,10 @@ size_t getMemStepSize(void)
     return getMemoryManager().getMemStepSize();
 }
 
+size_t getMaxBytes()
+{
+    return getMemoryManager().getMaxBytes();
+}
 
 void garbageCollect()
 {
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 279b3dbd28..8f61f11f7b 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -26,8 +26,9 @@ namespace cpu
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 
-    static const unsigned MAX_BUFFERS = 100;
-    static const unsigned MAX_BYTES = 100 * (1 << 20);
+    static const unsigned MAX_BUFFERS = 1000;
+
+    size_t getMaxBytes();
 
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers);
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 49abda3c8d..65a5ab1faf 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -20,6 +20,7 @@
 #include <version.hpp>
 #include <queue.hpp>
 #include <host_memory.hpp>
+#include <cctype>
 
 #ifdef _WIN32
 #include <limits.h>
@@ -215,8 +216,12 @@ std::string getInfo()
     info << "ArrayFire v" << AF_VERSION
          << " (CPU, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
     std::string model = cinfo.model();
-    info << string("[0] ") << cinfo.vendor() <<": " << ltrim(model)
-         << ", " << (int)(getDeviceMemorySize(getActiveDeviceId()) / 1048576.0) << " MB, ";
+    size_t memMB = getDeviceMemorySize(getActiveDeviceId()) / 1048576;
+    info << string("[0] ") << cinfo.vendor() <<": " << ltrim(model);
+
+    if(memMB) info << ", " << memMB << " MB, ";
+    else      info << ", Unknown MB, ";
+
     info << "Max threads("<< cinfo.threads()<<") ";
 #ifndef NDEBUG
     info << AF_COMPILER_STR;
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 39cd06c43b..1ca6012211 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -150,7 +150,7 @@ namespace cuda
 
         if (length > MAX_JIT_LEN ||
             buf_count >= MAX_BUFFERS ||
-            bytes >= MAX_BYTES) {
+            bytes >= getMaxBytes()) {
             out.eval();
         }
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 15786d9498..f5dc6ca048 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -37,6 +37,7 @@ namespace cuda
 class MemoryManager  : public common::MemoryManager
 {
     int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
 public:
     MemoryManager();
     void *nativeAlloc(const size_t bytes);
@@ -66,6 +67,7 @@ class MemoryManager  : public common::MemoryManager
 class MemoryManagerPinned  : public common::MemoryManager
 {
     int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
 public:
     MemoryManagerPinned();
     void *nativeAlloc(const size_t bytes);
@@ -82,8 +84,13 @@ int MemoryManager::getActiveDeviceId()
     return cuda::getActiveDeviceId();
 }
 
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return cuda::getDeviceMemorySize(id);
+}
+
 MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
 {}
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
@@ -112,8 +119,13 @@ int MemoryManagerPinned::getActiveDeviceId()
     return 0; // pinned uses a single vector
 }
 
+size_t MemoryManagerPinned::getMaxMemorySize(int id)
+{
+    return cuda::getHostMemorySize();
+}
+
 MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(1, MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+    common::MemoryManager(1, MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
 {}
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
@@ -147,6 +159,10 @@ size_t getMemStepSize(void)
     return getMemoryManager().getMemStepSize();
 }
 
+size_t getMaxBytes()
+{
+    return getMemoryManager().getMaxBytes();
+}
 
 void garbageCollect()
 {
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 5b362cd587..590ba3b880 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -25,8 +25,9 @@ namespace cuda
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 
-    static const unsigned MAX_BUFFERS   = 100;
-    static const unsigned MAX_BYTES     = (1 << 30);
+    static const unsigned MAX_BUFFERS   = 1000;
+
+    size_t getMaxBytes();
 
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers);
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 044a6322ab..7b6a26eb4d 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -179,7 +179,7 @@ namespace opencl
 
         if (is_max_jit_len(length) ||
             buf_count >= MAX_BUFFERS ||
-            bytes >= MAX_BYTES) {
+            bytes >= getMaxBytes()) {
             out.eval();
         }
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 2427581f93..7054e96479 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -32,6 +32,7 @@ namespace opencl
 class MemoryManager  : public common::MemoryManager
 {
     int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
 public:
     MemoryManager();
     void *nativeAlloc(const size_t bytes);
@@ -52,6 +53,7 @@ class MemoryManagerPinned  : public common::MemoryManager
         std::map<void *, cl::Buffer>
         > pinned_maps;
     int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
 
 public:
 
@@ -80,8 +82,13 @@ int MemoryManager::getActiveDeviceId()
     return opencl::getActiveDeviceId();
 }
 
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return opencl::getDeviceMemorySize(id);
+}
+
 MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
 {}
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
@@ -113,8 +120,13 @@ int MemoryManagerPinned::getActiveDeviceId()
     return opencl::getActiveDeviceId();
 }
 
+size_t MemoryManagerPinned::getMaxMemorySize(int id)
+{
+    return opencl::getDeviceMemorySize(id);
+}
+
 MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, MAX_BYTES, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
+    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
     pinned_maps(getDeviceCount())
 {}
 
@@ -163,6 +175,10 @@ size_t getMemStepSize(void)
     return getMemoryManager().getMemStepSize();
 }
 
+size_t getMaxBytes()
+{
+    return getMemoryManager().getMaxBytes();
+}
 
 void garbageCollect()
 {
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index da27e0d8d5..ea40b4b96f 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -30,8 +30,9 @@ namespace opencl
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 
-    static const unsigned MAX_BUFFERS   = 100;
-    static const unsigned MAX_BYTES     = (1 << 30);
+    static const unsigned MAX_BUFFERS   = 1000;
+
+    size_t getMaxBytes();
 
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers);

From 043739fd255b8a7a90bd4e7722462888b999a885 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Jan 2016 14:38:45 -0500
Subject: [PATCH 0323/2677] Move ArrayFireConfig, CPack (as CPackConfig) into
 CMakeModules

---
 CMakeLists.txt                                            | 8 ++++----
 .../ArrayFireConfig.cmake.in                              | 0
 .../ArrayFireConfigVersion.cmake.in                       | 0
 CPack.cmake => CMakeModules/CPackConfig.cmake             | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)
 rename ArrayFireConfig.cmake.in => CMakeModules/ArrayFireConfig.cmake.in (100%)
 rename ArrayFireConfigVersion.cmake.in => CMakeModules/ArrayFireConfigVersion.cmake.in (100%)
 rename CPack.cmake => CMakeModules/CPackConfig.cmake (98%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 61a78a635f..f54a9be748 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -221,7 +221,7 @@ ENDIF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
 SET(INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 SET(BACKEND_DIR "src/backend/\${lowerbackend}")
 CONFIGURE_FILE(
-    ${CMAKE_CURRENT_SOURCE_DIR}/ArrayFireConfig.cmake.in
+    ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfig.cmake
     @ONLY)
 
@@ -231,11 +231,11 @@ STRING(REGEX REPLACE "[^/]+" ".." reldir "${AF_INSTALL_CMAKE_DIR}")
 SET(INCLUDE_DIR "\${CMAKE_CURRENT_LIST_DIR}/${reldir}/include")
 set(BACKEND_DIR)
 CONFIGURE_FILE(
-    ${CMAKE_CURRENT_SOURCE_DIR}/ArrayFireConfig.cmake.in
+    ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
     @ONLY)
 CONFIGURE_FILE(
-    ${CMAKE_CURRENT_SOURCE_DIR}/ArrayFireConfigVersion.cmake.in
+    ${CMAKE_MODULE_PATH}/ArrayFireConfigVersion.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfigVersion.cmake
     @ONLY)
 INSTALL(FILES ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
@@ -263,4 +263,4 @@ ENDIF(APPLE)
 ##
 # Packaging
 ##
-include(${CMAKE_CURRENT_SOURCE_DIR}/CPack.cmake)
+include(${CMAKE_MODULE_PATH}/CPackConfig.cmake)
diff --git a/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
similarity index 100%
rename from ArrayFireConfig.cmake.in
rename to CMakeModules/ArrayFireConfig.cmake.in
diff --git a/ArrayFireConfigVersion.cmake.in b/CMakeModules/ArrayFireConfigVersion.cmake.in
similarity index 100%
rename from ArrayFireConfigVersion.cmake.in
rename to CMakeModules/ArrayFireConfigVersion.cmake.in
diff --git a/CPack.cmake b/CMakeModules/CPackConfig.cmake
similarity index 98%
rename from CPack.cmake
rename to CMakeModules/CPackConfig.cmake
index 2e7f1d5a03..de242a99b7 100644
--- a/CPack.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -1,6 +1,6 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
-include("${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules/Version.cmake")
+INCLUDE("${CMAKE_MODULE_PATH}/Version.cmake")
 
 # CPack package generation
 #SET(CPACK_GENERATOR "TGZ;STGZ")

From cfd60f1fa85606293126f09f72f0bdc0a9ac0824 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Jan 2016 17:30:49 -0500
Subject: [PATCH 0324/2677] Move /bigobj definitions into main CMakeList
 (windows)

/bigobj is now required for Debug builds for CPU. Since is it being used for 3
backends, it makes sense to move it into the central CMakeList
---
 CMakeLists.txt                    | 4 ++++
 src/api/unified/CMakeLists.txt    | 4 ----
 src/backend/opencl/CMakeLists.txt | 4 ----
 3 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f54a9be748..2cfeb18fed 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -152,6 +152,10 @@ ELSE(${UNIX}) #Windows
         # http://www.kitware.com/blog/home/post/434
         SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP /Gm-")
         SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /MP /Gm-")
+
+        # Builds that contain debug info require /bigobj
+        SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj")
+        SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /bigobj")
     ENDIF(MSVC)
 ENDIF()
 
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 21c9aebf97..6ed95d088c 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -32,10 +32,6 @@ ENDIF()
 # OS Definitions
 IF(UNIX)
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
-ELSE(${UNIX}) #Windows
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-    SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj")
-    SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /bigobj")
 ENDIF()
 
 ADD_LIBRARY(af SHARED
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index c9c47d0198..232b652bba 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -197,10 +197,6 @@ CL_KERNEL_TO_H(
 # OS Definitions
 IF(UNIX)
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
-ELSE(${UNIX}) #Windows
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-    SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj")
-    SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /bigobj")
 ENDIF()
 
 IF(DEFINED BLAS_SYM_FILE)

From cc2dda092b6b12684a42837a4784b9b9bc75cc8f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 21 Jan 2016 18:38:03 -0500
Subject: [PATCH 0325/2677] Fixes to build with MKL when INTEL_MKL_ROOT is
 exported

---
 CMakeModules/FindCBLAS.cmake      | 64 ++++++++++++++++++++++++-------
 CMakeModules/FindLAPACKE.cmake    | 56 +++++++++++++++++++--------
 src/backend/cpu/CMakeLists.txt    |  1 +
 src/backend/opencl/CMakeLists.txt |  4 ++
 4 files changed, 95 insertions(+), 30 deletions(-)

diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index b0cd3bdca0..efef36b093 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -53,19 +53,40 @@ SET(CBLAS_ROOT_DIR CACHE STRING
 INCLUDE(CheckTypeSize)
 CHECK_TYPE_SIZE("void*" SIZE_OF_VOIDP)
 
-SET(CBLAS_LIB_DIR)
+IF (NOT INTEL_MKL_ROOT_DIR)
+  SET(INTEL_MKL_ROOT_DIR $ENV{INTEL_MKL_ROOT})
+ENDIF()
 
-SET(CBLAS_ROOT_DIR "${INTEL_MKL_ROOT_DIR}")
+IF(NOT CBLAS_ROOT_DIR)
 
-IF(CBLAS_ROOT_DIR)
-    IF(INTEL_MKL_ROOT_DIR)
-      IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-        SET(CBLAS_LIB_DIR "${INTEL_MKL_ROOT_DIR}/lib/intel64")
-      ELSE()
-        SET(CBLAS_LIB_DIR "${INTEL_MKL_ROOT_DIR}/lib/ia32")
-      ENDIF()
+  IF (ENV{CBLASDIR})
+    SET(CBLAS_ROOT_DIR $ENV{CBLASDIR})
+    IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib64")
+    ELSE()
+      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib")
+    ENDIF()
+  ENDIF()
+
+  IF (ENV{CBLAS_ROOT_DIR})
+    SET(CBLAS_ROOT_DIR $ENV{CBLAS_ROOT_DIR})
+    IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib64")
+    ELSE()
+      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib")
+    ENDIF()
+  ENDIF()
+
+  IF (INTEL_MKL_ROOT_DIR)
+    SET(CBLAS_ROOT_DIR ${INTEL_MKL_ROOT_DIR})
+    IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib/intel64")
+    ELSE()
+      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib/ia32")
     ENDIF()
-    SET(CBLAS_INCLUDE_DIR "${INTEL_MKL_ROOT_DIR}/include")
+  ENDIF()
+
+  SET(CBLAS_INCLUDE_DIR "${CBLAS_ROOT_DIR}/include")
 ENDIF()
 
 # Old CBLAS search
@@ -116,14 +137,14 @@ MACRO(CHECK_ALL_LIBRARIES
           NAMES ${_library}
           PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64
           ENV DYLD_LIBRARY_PATH
-          "{CBLAS_LIB_DIR}"
+          "${CBLAS_LIB_DIR}" "${CBLAS_LIB32_DIR}" "${CBLAS_LIB64_DIR}"
           )
       ELSE(APPLE)
         FIND_LIBRARY(${_prefix}_${_library}_LIBRARY
           NAMES ${_library}
           PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64
           ENV LD_LIBRARY_PATH
-          "${CBLAS_LIB_DIR}"
+          "${CBLAS_LIB_DIR}" "${CBLAS_LIB32_DIR}" "${CBLAS_LIB64_DIR}"
           PATH_SUFFIXES atlas
           )
         IF(NOT ${_prefix}_${library}_LIBRARY)
@@ -132,7 +153,7 @@ MACRO(CHECK_ALL_LIBRARIES
               NAMES ${_library}
               PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64
               ENV LD_LIBRARY_PATH
-              "${CBLAS_LIB_DIR}"
+              "${CBLAS_LIB_DIR}" "${CBLAS_LIB32_DIR}" "${CBLAS_LIB64_DIR}"
               PATH_SUFFIXES atlas
               )
           ENDIF(NOT ${_prefix}_${library}_LIBRARY)
@@ -194,6 +215,23 @@ MACRO(CHECK_ALL_LIBRARIES
   ENDIF(NOT _libraries_work)
 ENDMACRO(CHECK_ALL_LIBRARIES)
 
+# MKL CBLAS library?
+IF(NOT CBLAS_LIBRARIES)
+  CHECK_ALL_LIBRARIES(
+    CBLAS_LIBRARIES
+    CBLAS
+    cblas_dgemm
+    ""
+    "mkl_rt"
+    "mkl_cblas.h"
+    FALSE,
+    TRUE)
+ENDIF(NOT CBLAS_LIBRARIES)
+
+IF(CBLAS_LIBRARIES)
+  SET(MKL_FOUND ON)
+ENDIF()
+
 # Apple CBLAS library?
 IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 3bf8a1f362..dc4a045370 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -9,15 +9,33 @@
 #   LAPACK_INCLUDES            ... LAPACKE include directory
 #
 
-IF(NOT LAPACKE_ROOT AND ENV{LAPACKEDIR})
-  SET(LAPACKE_ROOT $ENV{LAPACKEDIR})
+SET(LAPACKE_ROOT_DIR CACHE STRING
+  "Root directory for custom LAPACK implementation")
+
+IF (NOT INTEL_MKL_ROOT_DIR)
+  SET(INTEL_MKL_ROOT_DIR $ENV{INTEL_MKL_ROOT})
+ENDIF()
+
+IF(NOT LAPACKE_ROOT_DIR)
+
+  IF (ENV{LAPACKEDIR})
+    SET(LAPACKE_ROOT_DIR $ENV{LAPACKEDIR})
+  ENDIF()
+
+  IF (ENV{LAPACKE_ROOT_DIR_DIR})
+    SET(LAPACKE_ROOT_DIR $ENV{LAPACKE_ROOT_DIR})
+  ENDIF()
+
+  IF (INTEL_MKL_ROOT_DIR)
+    SET(LAPACKE_ROOT_DIR ${INTEL_MKL_ROOT_DIR})
+  ENDIF()
 ENDIF()
 
 # Check if we can use PkgConfig
 FIND_PACKAGE(PkgConfig)
 
 #Determine from PKG
-IF(PKG_CONFIG_FOUND AND NOT LAPACKE_ROOT)
+IF(PKG_CONFIG_FOUND AND NOT LAPACKE_ROOT_DIR)
   PKG_CHECK_MODULES( PC_LAPACKE QUIET "lapacke")
 ENDIF()
 
@@ -48,40 +66,41 @@ IF(PC_LAPACKE_FOUND)
 
 ELSE(PC_LAPACKE_FOUND)
 
-    IF(LAPACKE_ROOT)
+    IF(LAPACKE_ROOT_DIR)
         #find libs
         FIND_LIBRARY(
             LAPACKE_LIB
-            NAMES "lapacke" "LAPACKE" "liblapacke"
-            PATHS ${LAPACKE_ROOT}
-            PATH_SUFFIXES "lib" "lib64"
+            NAMES "lapacke" "LAPACKE" "liblapacke" "mkl_rt"
+            PATHS ${LAPACKE_ROOT_DIR}
+            PATH_SUFFIXES "lib" "lib64" "lib/ia32" "lib/intel64"
             DOC "LAPACKE Library"
             NO_DEFAULT_PATH
             )
         FIND_LIBRARY(
             LAPACK_LIB
-            NAMES "lapack" "LAPACK" "liblapack"
-            PATHS ${LAPACKE_ROOT}
-            PATH_SUFFIXES "lib" "lib64"
+            NAMES "lapack" "LAPACK" "liblapack" "mkl_rt"
+            PATHS ${LAPACKE_ROOT_DIR}
+            PATH_SUFFIXES "lib" "lib64" "lib/ia32" "lib/intel64"
             DOC "LAPACK Library"
             NO_DEFAULT_PATH
             )
         FIND_PATH(
             LAPACKE_INCLUDES
-            NAMES "lapacke.h"
-            PATHS ${LAPACKE_ROOT}
+            NAMES "lapacke.h" "mkl_lapacke.h"
+            PATHS ${LAPACKE_ROOT_DIR}
             PATH_SUFFIXES "include"
             DOC "LAPACKE Include Directory"
             NO_DEFAULT_PATH
             )
-
     ELSE()
         FIND_LIBRARY(
             LAPACKE_LIB
-            NAMES "lapacke" "liblapacke" "openblas"
+            NAMES "lapacke" "liblapacke" "openblas" "mkl_rt"
             PATHS
             ${PC_LAPACKE_LIBRARY_DIRS}
             ${LIB_INSTALL_DIR}
+            /opt/intel/mkl/lib/ia32
+            /opt/intel/mkl/lib/intel64
             /usr/lib64
             /usr/lib
             /usr/local/lib64
@@ -92,10 +111,12 @@ ELSE(PC_LAPACKE_FOUND)
             )
         FIND_LIBRARY(
            LAPACK_LIB
-            NAMES "lapack" "liblapack" "openblas"
+            NAMES "lapack" "liblapack" "openblas" "mkl_rt"
             PATHS
             ${PC_LAPACKE_LIBRARY_DIRS}
             ${LIB_INSTALL_DIR}
+            /opt/intel/mkl/lib/ia32
+            /opt/intel/mkl/lib/intel64
             /usr/lib64
             /usr/lib
             /usr/local/lib64
@@ -106,17 +127,18 @@ ELSE(PC_LAPACKE_FOUND)
             )
         FIND_PATH(
             LAPACKE_INCLUDES
-            NAMES "lapacke.h"
+            NAMES "lapacke.h" "mkl_lapacke.h"
             PATHS
             ${PC_LAPACKE_INCLUDE_DIRS}
             ${INCLUDE_INSTALL_DIR}
+            /opt/intel/mkl/include
             /usr/include
             /usr/local/include
             /sw/include
             /opt/local/include
             DOC "LAPACKE Include Directory"
             )
-    ENDIF(LAPACKE_ROOT)
+    ENDIF(LAPACKE_ROOT_DIR)
 ENDIF(PC_LAPACKE_FOUND)
 
 SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index b0ab17a616..5dee6de3f5 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -41,6 +41,7 @@ IF(NOT LAPACK_FOUND)
     MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
 ELSE(NOT LAPACK_FOUND)
     ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
+    MESSAGE(STATUS "LAPACK libraries found: ${LAPACK_LIBRARIES}")
 ENDIF()
 
 IF(NOT UNIX)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index c9c47d0198..6731c67605 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -42,6 +42,10 @@ ELSE(NOT LAPACK_FOUND)
   ENDIF()
 ENDIF()
 
+IF(${MKL_FOUND})
+  ADD_DEFINITIONS(-DUSE_MKL)
+ENDIF()
+
 IF(NOT UNIX)
     ADD_DEFINITIONS(-DAFDLL)
 ENDIF()

From aba1851efcad92cd9c95cfd795de97ef2a9e6dcb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Jan 2016 22:53:02 -0500
Subject: [PATCH 0326/2677] BUGFIX Add/remove entries for platform when adding
 external device/context

---
 src/backend/opencl/platform.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 94efd7a876..1abb03279f 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -699,6 +699,7 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
         devMngr.mDevices.push_back(tDevice);
         devMngr.mContexts.push_back(tContext);
         devMngr.mQueues.push_back(tQueue);
+        devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
     } catch (const cl::Error &ex) {
@@ -757,6 +758,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
             devMngr.mDevices.erase(devMngr.mDevices.begin()+deleteIdx);
             devMngr.mContexts.erase(devMngr.mContexts.begin()+deleteIdx);
             devMngr.mQueues.erase(devMngr.mQueues.begin()+deleteIdx);
+            devMngr.mPlatforms.erase(devMngr.mPlatforms.begin()+deleteIdx);
             // FIXME: add OpenGL Interop for user provided contexts later
             devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin()+deleteIdx);
             // OTHERWISE, update(decrement) the `mActive*Id` variables

From 163b5fbf8d51e7c3f7666b3a68cd21740b8284e6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Jan 2016 22:55:19 -0500
Subject: [PATCH 0327/2677] BUGFIX Fix CUDA device management and free at
 destructor

---
 src/backend/cuda/kernel/random.hpp | 15 ++++++++++++---
 src/backend/cuda/platform.cpp      |  5 ++++-
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/random.hpp b/src/backend/cuda/kernel/random.hpp
index 4d960ae46b..96cf098c03 100644
--- a/src/backend/cuda/kernel/random.hpp
+++ b/src/backend/cuda/kernel/random.hpp
@@ -49,8 +49,18 @@ namespace kernel
 
         ~curandStateManager()
         {
-            //if(_state != NULL) memFree((char*)_state);
-            if(_state != NULL) CUDA_CHECK(cudaFree(_state));
+            try {
+                if (_state != NULL) {
+                    cudaError_t err = cudaFree(_state);
+                    if (err != cudaErrorCudartUnloading) {
+                        CUDA_CHECK(err);
+                    }
+                }
+            } catch (AfError err) {
+                if (err.getError() != AF_ERR_DRIVER) { // Can happen from cudaErrorDevicesUnavailable
+                    throw err;
+                }
+            }
         }
 
         unsigned long long getSeed() const
@@ -69,7 +79,6 @@ namespace kernel
             if(_state)
                 return _state;
 
-            //_state = (curandState_t*)memAlloc<char>(BLOCKS * THREADS * sizeof(curandState_t));
             CUDA_CHECK(cudaMalloc((void **)&_state, BLOCKS * THREADS * sizeof(curandState_t)));
             this->resetSeed();
             return _state;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 46b730314f..744bf7eb2c 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -425,7 +425,7 @@ int DeviceManager::setActiveDevice(int device, int nId)
     // Comes only when first is true. Set it to false
     first = false;
 
-    while(device < numDevices) {
+    while(true) {
         // Check for errors other than DevicesUnavailable
         // If success, return. Else throw error
         // If DevicesUnavailable, try other devices (while loop below)
@@ -435,12 +435,15 @@ int DeviceManager::setActiveDevice(int device, int nId)
             return old;
         }
         cudaGetLastError(); // Reset error stack
+#ifndef NDEBUG
         printf("Warning: Device %d is unavailable. Incrementing to next device \n", device);
+#endif
 
         // Comes here is the device is in exclusive mode or
         // otherwise fails streamCreate with this error.
         // All other errors will error out
         device++;
+        if (device >= numDevices) break;
 
         // Can't call getNativeId here as it will cause an infinite loop with the constructor
         nId = cuDevices[device].nativeId;

From 805dc5b60937cc6d3f9ddd912b540bf967f5dfdc Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Jan 2016 22:56:59 -0500
Subject: [PATCH 0328/2677] Fix Tests: ORB, Meanshift, basic_c, solve

* Fix vector in fast_pyramid - use resize instead of reserve
* Fix meanshift test. Use proper types and arrays
* Fix memory leak in basic_c
* Enable solve tests that were disabled for windows opencl
---
 src/backend/cuda/kernel/fast_pyramid.hpp |  6 +++++-
 test/basic_c.c                           |  1 +
 test/meanshift.cpp                       | 15 +++++++++------
 test/solve_dense.cpp                     |  5 +----
 4 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index 61a9c7ac32..d2e5903788 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -65,7 +65,11 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
     lvl_best[max_levels-1] = max_feat - feat_sum;
 
     // Hold multi-scale image pyramids
-    img_pyr.reserve(max_levels);
+    static const dim4 dims0;
+    static const CParam<T> emptyCParam(NULL, dims0.get(), dims0.get());
+    // Need to do this as CParam does not have a default constructor
+    // And resize needs a default constructor or default value prior to C++11
+    img_pyr.resize(max_levels, emptyCParam);
 
     // Create multi-scale image pyramid
     for (unsigned i = 0; i < max_levels; i++) {
diff --git a/test/basic_c.c b/test/basic_c.c
index f6c731092a..0caca290ec 100644
--- a/test/basic_c.c
+++ b/test/basic_c.c
@@ -13,5 +13,6 @@ int main() {
     af_array out = 0;
     dim_t s[] = {10, 10, 1, 1};
     af_err e = af_randu(&out, 4, s, f32);
+    if(out != 0) af_release_array(out);
     return (AF_SUCCESS != e);
 }
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 34b622be1a..a35ca288d9 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -65,11 +65,12 @@ void meanshiftTest(string pTestFile)
 
     for (size_t testId=0; testId<testCount; ++testId) {
 
-        af_array inArray     = 0;
-        af_array inArray_f32 = 0;
-        af_array outArray    = 0;
-        af_array goldArray   = 0;
-        dim_t nElems      = 0;
+        af_array inArray        = 0;
+        af_array inArray_f32    = 0;
+        af_array outArray       = 0;
+        af_array goldArray      = 0;
+        af_array goldArray_f32  = 0;
+        dim_t nElems            = 0;
 
         inFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
         outFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
@@ -77,7 +78,8 @@ void meanshiftTest(string pTestFile)
         ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), isColor));
         ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray_f32, outFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&goldArray, goldArray_f32)); // af_load_image always returns float array
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
 
         ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, 2.25f, 25.56f, 5, isColor));
@@ -94,6 +96,7 @@ void meanshiftTest(string pTestFile)
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray_f32));
     }
 }
 
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 09addc7c48..183afdbcc8 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -186,15 +186,12 @@ SOLVE_TESTS(cdouble, 1E-5)
 #define SOLVE_TESTS(T, eps)                     \
     TEST(SOLVE, T##RectOver)                    \
     {                                           \
-        solveTester<T>(800, 600, 50, eps);      \
+        solveTester<T>(800, 600, 64, eps);      \
     }
 
 SOLVE_TESTS(float, 0.01)
 SOLVE_TESTS(double, 1E-5)
-// Fails on Windows on some devices
-#if !(defined(OS_WIN) && defined(AF_OPENCL))
 SOLVE_TESTS(cfloat, 0.01)
 SOLVE_TESTS(cdouble, 1E-5)
-#endif
 
 #undef SOLVE_TESTS

From 7eb905f1f05bd9e3906551dcc801c05254aa202d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 25 Jan 2016 17:47:21 -0500
Subject: [PATCH 0329/2677] Add documentation for deviceInfo

---
 docs/details/device.dox         | 16 ++++++++++++++++
 include/af/device.h             | 16 +++++-----------
 src/backend/cuda/platform.cpp   |  4 ++--
 src/backend/opencl/platform.cpp |  3 +--
 4 files changed, 24 insertions(+), 15 deletions(-)

diff --git a/docs/details/device.dox b/docs/details/device.dox
index c89d2a17f0..1aa43e7465 100644
--- a/docs/details/device.dox
+++ b/docs/details/device.dox
@@ -2,6 +2,22 @@
 \addtogroup arrayfire_func
 @{
 
+\defgroup device_func_prop deviceInfo
+\ingroup device_mat
+
+\brief Gets the information about device and platform as strings
+
+\param d_name pointer to a user-allocated char array. Recommended minimum size is 64.
+The name of the device is stored in this array.
+\param d_platform pointer to a user-allocated char array. Recommended minimum size is 10.
+The platform information is stored in this array.
+\param d_toolkit pointer to a user-allocated char array. Recommended minimum size is 64.
+The toolkit information is stored in this array.
+\param d_compute pointer to a user-allocated char array. Recommended minimum size is 10.
+The compute version of the device is stored in this array.
+
+===============================================================================
+
 \defgroup device_func_count getDeviceCount
 \ingroup device_mat
 
diff --git a/include/af/device.h b/include/af/device.h
index 28830675f8..c0d787ea80 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -50,19 +50,11 @@ namespace af
     */
 
     /**
-       \defgroup device_func_prop deviceInfo
+        \copydoc device_func_prop
 
-       Get device information
-
-       @{
-
-       \ingroup arrayfire_func
-       \ingroup device_mat
+        \ingroup device_func_prop
     */
     AFAPI void deviceInfo(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
-    /**
-       @}
-    */
 
     /// \brief Gets the number of devices
     ///
@@ -267,7 +259,9 @@ extern "C" {
     AFAPI af_err af_info_string(char** str, const bool verbose);
 
     /**
-       \ingroup device_func_prop
+        \copydoc device_func_prop
+
+        \ingroup device_func_prop
     */
     AFAPI af_err af_device_info(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 744bf7eb2c..5e53fc0034 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -214,7 +214,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     cudaDeviceProp dev = getDeviceProp(getActiveDeviceId());
 
     // Name
-    snprintf(d_name, 32, "%s", dev.name);
+    snprintf(d_name, 64, "%s", dev.name);
 
     //Platform
     std::string cudaRuntime = getCUDARuntimeVersion();
@@ -225,7 +225,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     snprintf(d_compute, 10, "%d.%d", dev.major, dev.minor);
 
     // Sanitize input
-    for (int i = 0; i < 31; i++) {
+    for (int i = 0; i < 63; i++) {
         if (d_name[i] == ' ') {
             if (d_name[i + 1] == 0 || d_name[i + 1] == ' ') d_name[i] = 0;
             else d_name[i] = '_';
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 1abb03279f..12bb71db51 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -418,8 +418,7 @@ std::string getInfo()
         info << " -- Device driver " << driVersion;
         info << " -- FP64 Support: "
              << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False")
-             << "";
-        info << "Unified Memory("
+        info << " -- Unified Memory ("
              << (isHostUnifiedMemory(*device) ? "True" : "False")
              << ")";
 #endif

From 0039cdba798a675b3004217a37c004aaefe7a85f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jan 2016 15:55:04 -0500
Subject: [PATCH 0330/2677] Proper exception handling for memory manager

---
 src/backend/MemoryManager.cpp | 9 +++++----
 src/backend/cpu/memory.cpp    | 4 +++-
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 03cefe710d..814262829e 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -145,12 +145,13 @@ void *MemoryManager::alloc(const size_t bytes)
         }
 
         // Perform garbage collection if memory can not be allocated
-        ptr = this->nativeAlloc(alloc_bytes);
-
-        if (!ptr) {
+        try {
+            ptr = this->nativeAlloc(alloc_bytes);
+        } catch (AfError &ex) {
+            // If out of memory, run garbage collect and try again
+            if (ex.getError() != AF_ERR_NO_MEM) throw;
             this->garbageCollect();
             ptr = this->nativeAlloc(alloc_bytes);
-            if (!ptr) AF_ERROR("Can not allocate memory", AF_ERR_NO_MEM);
         }
 
         buffer_info info = {true, false, alloc_bytes};
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 09a0e83c80..cf7e1ba48b 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -61,7 +61,9 @@ MemoryManager::MemoryManager() :
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
-    return malloc(bytes);
+    void *ptr = malloc(bytes);
+    if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
+    return ptr;
 }
 
 void MemoryManager::nativeFree(void *ptr)

From 91bed334073ea413ce2633d976f0801d97a73677 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jan 2016 15:58:13 -0500
Subject: [PATCH 0331/2677] Removing unneeded cudaDeviceSynchronize()

---
 src/backend/cuda/copy.cu | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 71893b8c16..df435d245c 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -71,7 +71,6 @@ namespace cuda
         ARG_ASSERT(1, (in.ndims() == dims.ndims()));
         Array<outType> ret = createEmptyArray<outType>(dims);
         kernel::copy<inType, outType>(ret, in, in.ndims(), default_value, factor);
-        CUDA_CHECK(cudaDeviceSynchronize());
         return ret;
     }
 

From 519d3bb3f5a7e243223fea2da6709a6550b32816 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 28 Jan 2016 15:37:57 -0500
Subject: [PATCH 0332/2677] Adding compute 37 to list of accepted CUDA computes

---
 src/backend/cuda/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4c74070492..81d6ba243c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -18,6 +18,7 @@ IF(    CUDA_COMPUTE_20
     OR CUDA_COMPUTE_30
     OR CUDA_COMPUTE_32
     OR CUDA_COMPUTE_35
+    OR CUDA_COMPUTE_37
     OR CUDA_COMPUTE_50
     OR CUDA_COMPUTE_52
     OR CUDA_COMPUTE_53
@@ -49,7 +50,7 @@ MACRO(SET_COMPUTE VERSION)
 ENDMACRO(SET_COMPUTE)
 
 # Iterate over compute versions. Create variables and enable computes if needed
-FOREACH(VER 20 30 32 35 50 52 53)
+FOREACH(VER 20 30 32 35 37 50 52 53)
     OPTION(CUDA_COMPUTE_${VER} "CUDA Compute Capability ${VER}" OFF)
     MARK_AS_ADVANCED(CUDA_COMPUTE_${VER})
     IF(${CUDA_COMPUTE_${VER}})

From 96041b5f2103e0025cf378d11a184bba63cf1681 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 28 Jan 2016 17:09:20 -0500
Subject: [PATCH 0333/2677] BUGFIX: incorrect index for 3rd dimension in select
 / replace

Affects both CUDA and OpenCL abckends
---
 src/backend/cuda/kernel/select.hpp  | 4 ++--
 src/backend/opencl/kernel/select.cl | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index ab5bf2da7b..ea242e45dd 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -41,7 +41,7 @@ namespace cuda
             const int idw = blockIdx.y / blk_y;
 
             const int blockIdx_x = blockIdx.x - idz * blk_x;
-            const int blockIdx_y = blockIdx.y - idz * blk_y;
+            const int blockIdx_y = blockIdx.y - idw * blk_y;
 
             const int idx = blockIdx_x * blockDim.x + threadIdx.x;
             const int idy = blockIdx_y * blockDim.y + threadIdx.y;
@@ -110,7 +110,7 @@ namespace cuda
             const int idw = blockIdx.y / blk_y;
 
             const int blockIdx_x = blockIdx.x - idz * blk_x;
-            const int blockIdx_y = blockIdx.y - idz * blk_y;
+            const int blockIdx_y = blockIdx.y - idw * blk_y;
 
             const int idx = blockIdx_x * blockDim.x + threadIdx.x;
             const int idy = blockIdx_y * blockDim.y + threadIdx.y;
diff --git a/src/backend/opencl/kernel/select.cl b/src/backend/opencl/kernel/select.cl
index 94a36031c3..03248be1b9 100644
--- a/src/backend/opencl/kernel/select.cl
+++ b/src/backend/opencl/kernel/select.cl
@@ -41,7 +41,7 @@ void select_kernel(__global T *optr, KParam oinfo,
     const int idw = get_group_id(1) / groups_1;
 
     const int group_id_0 = get_group_id(0) - idz * groups_0;
-    const int group_id_1 = get_group_id(1) - idz * groups_1;
+    const int group_id_1 = get_group_id(1) - idw * groups_1;
 
     const int idx = group_id_0 * get_local_size(0) + get_local_id(0);
     const int idy = group_id_1 * get_local_size(1) + get_local_id(1);
@@ -80,7 +80,7 @@ void select_scalar_kernel(__global T *optr, KParam oinfo,
     const int idw = get_group_id(1) / groups_1;
 
     const int group_id_0 = get_group_id(0) - idz * groups_0;
-    const int group_id_1 = get_group_id(1) - idz * groups_1;
+    const int group_id_1 = get_group_id(1) - idw * groups_1;
 
     const int idx = group_id_0 * get_local_size(0) + get_local_id(0);
     const int idy = group_id_1 * get_local_size(1) + get_local_id(1);

From 32426184a3f3b9ab5ab960a367edc89a5de34356 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 29 Jan 2016 10:32:48 +0530
Subject: [PATCH 0334/2677] Documentation fix in matchTemplate function

---
 docs/details/vision.dox | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/details/vision.dox b/docs/details/vision.dox
index 1d9d6b99ac..99582c3729 100644
--- a/docs/details/vision.dox
+++ b/docs/details/vision.dox
@@ -166,9 +166,12 @@ from the other and returns the result.
 
 \brief Template Matching
 
-Template matching is an image processing technique to find small patches of an image which
-match a given template image. A more in depth discussion on the topic can be found
-[here](http://en.wikipedia.org/wiki/Template_matching).
+Template matching is an image processing technique to find small patches of an image which match a given template image. Currently, this function doesn't support the following three metrics yet.
+- \ref AF_NCC
+- \ref AF_ZNCC
+- \ref AF_SHD
+
+A more in depth discussion about template matching can be found [here](http://en.wikipedia.org/wiki/Template_matching).
 
 =======================================================================
 

From 209643ba71796031f9d1dc5fbe273fe5a9ba3227 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 29 Jan 2016 18:28:24 +0530
Subject: [PATCH 0335/2677] syntax+typo fix in opencl backend

---
 src/backend/opencl/platform.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 12bb71db51..9dbb3ca38c 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -417,7 +417,7 @@ std::string getInfo()
         info << devVersion;
         info << " -- Device driver " << driVersion;
         info << " -- FP64 Support: "
-             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False")
+             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False");
         info << " -- Unified Memory ("
              << (isHostUnifiedMemory(*device) ? "True" : "False")
              << ")";

From 9bf14556a34895d3171793db5d6f54c0d7a0b555 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 29 Jan 2016 19:55:26 +0530
Subject: [PATCH 0336/2677] Updated test data for meanshift, bilateral & morph

Replaced lena image from test data
---
 test/data | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/data b/test/data
index 414f02d905..cec85080f1 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 414f02d90588ec2cde177202bd340c57be6e7d9a
+Subproject commit cec85080f12c25486d025d1fb1cf69e1beb03e58

From f228de3243492817f4909991ec8c96457d42c6aa Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 28 Jan 2016 17:18:16 -0500
Subject: [PATCH 0337/2677] TEST: Adding tests for 3D and 4D select and replace

---
 test/replace.cpp | 43 +++++++++++++++++++++++++++++++++++++++++++
 test/select.cpp  | 40 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 83 insertions(+)

diff --git a/test/replace.cpp b/test/replace.cpp
index 9e99eaee8f..faa5636eb8 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -130,3 +130,46 @@ TEST(Replace, NaN)
         ASSERT_EQ(hc[i], std::isnan(ha[i]) ? b : ha[i]);
     }
 }
+
+TEST(Replace, ISSUE_1249)
+{
+    dim4 dims(2, 3, 4);
+    array cond = af::randu(dims) > 0.5;
+    array a = af::randu(dims);
+    array b = a.copy();
+    replace(b, !cond, a - a * 0.9);
+    array c = a - a * cond * 0.9;
+
+    int num = (int)dims.elements();
+    std::vector<float> hb(num);
+    std::vector<float> hc(num);
+
+    b.host(&hb[0]);
+    c.host(&hc[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
+    }
+}
+
+
+TEST(Replace, 4D)
+{
+    dim4 dims(2, 3, 4, 2);
+    array cond = af::randu(dims) > 0.5;
+    array a = af::randu(dims);
+    array b = a.copy();
+    replace(b, !cond, a - a * 0.9);
+    array c = a - a * cond * 0.9;
+
+    int num = (int)dims.elements();
+    std::vector<float> hb(num);
+    std::vector<float> hc(num);
+
+    b.host(&hb[0]);
+    c.host(&hc[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
+    }
+}
diff --git a/test/select.cpp b/test/select.cpp
index 1c39282b15..6e772ac7c4 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -136,3 +136,43 @@ TEST(Select, NaN)
         ASSERT_EQ(hc[i], std::isnan(ha[i]) ? b : ha[i]);
     }
 }
+
+TEST(Select, ISSUE_1249)
+{
+    dim4 dims(2, 3, 4);
+    array cond = af::randu(dims) > 0.5;
+    array a = af::randu(dims);
+    array b = select(cond, a - a * 0.9, a);
+    array c = a - a * cond * 0.9;
+
+    int num = (int)dims.elements();
+    std::vector<float> hb(num);
+    std::vector<float> hc(num);
+
+    b.host(&hb[0]);
+    c.host(&hc[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
+    }
+}
+
+TEST(Select, 4D)
+{
+    dim4 dims(2, 3, 4, 2);
+    array cond = af::randu(dims) > 0.5;
+    array a = af::randu(dims);
+    array b = select(cond, a - a * 0.9, a);
+    array c = a - a * cond * 0.9;
+
+    int num = (int)dims.elements();
+    std::vector<float> hb(num);
+    std::vector<float> hc(num);
+
+    b.host(&hb[0]);
+    c.host(&hc[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
+    }
+}

From 41bad15c0cea1bbbea2ba540c212a63102500137 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 28 Jan 2016 15:38:17 -0500
Subject: [PATCH 0338/2677] Changes required to build tests in a single file

---
 test/CMakeLists.txt    | 48 ++++++++++++++++++++++++++++++++----------
 test/basic_c.c         |  3 ++-
 test/fast.cpp          |  4 ++--
 test/gloh_nonfree.cpp  | 16 ++++++++------
 test/harris.cpp        |  4 ++--
 test/main.cpp          |  6 ++++++
 test/orb.cpp           | 12 +++++------
 test/reduce.cpp        | 10 ---------
 test/rotate_linear.cpp | 14 ++++++------
 test/scan.cpp          | 10 ---------
 test/sift_nonfree.cpp  | 17 ++++++++-------
 test/sort_by_key.cpp   | 13 ++++++------
 test/sort_index.cpp    | 12 +++++------
 test/susan.cpp         |  4 ++--
 test/svd_dense.cpp     |  4 ++--
 test/testHelpers.hpp   | 30 ++++++++++++++------------
 test/where.cpp         | 11 ----------
 test/wrap.cpp          | 10 ++++-----
 18 files changed, 118 insertions(+), 110 deletions(-)
 create mode 100644 test/main.cpp

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 1bcdde95af..bea93d554e 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -6,6 +6,8 @@ SET(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 FIND_PACKAGE(CUDA QUIET)
 FIND_PACKAGE(OpenCL QUIET)
 
+OPTION(BUILD_SINGLE_TEST_FILE "Build tests in a single file" OFF)
+
 # If the tests are not being built at the same time as ArrayFire,
 # we need to first find the ArrayFire library
 IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
@@ -58,14 +60,36 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
       SET(TEST_FILES ${FILES})
     ENDIF(${BACKEND} STREQUAL "unified")
 
-    FOREACH(FILE ${TEST_FILES})
+    IF (${BUILD_SINGLE_TEST_FILE})
+      SET(TEST_NAME test_${BACKEND})
+      SET(TEST_NAME_BASIC test_basic_${BACKEND})
+      ADD_EXECUTABLE(${TEST_NAME} ${CPP_FILES})
+      ADD_EXECUTABLE(${TEST_NAME_BASIC} basic_c.c)
+
+      TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE ${AFLIBNAME}
+        PRIVATE ${THREAD_LIB_FLAG}
+        PRIVATE ${GTEST_LIBS}
+        PRIVATE ${OTHER_LIBS})
+
+      TARGET_LINK_LIBRARIES(${TEST_NAME_BASIC}  PRIVATE ${AFLIBNAME}
+        PRIVATE ${THREAD_LIB_FLAG}
+        PRIVATE ${GTEST_LIBS}
+        PRIVATE ${OTHER_LIBS})
+
+      SET_TARGET_PROPERTIES(${TEST_NAME_BASIC}
+        PROPERTIES
+        COMPILE_FLAGS -DAF_${DEF_NAME}
+        FOLDER "Tests/${BACKEND}")
+
+    ELSE()
+      FOREACH(FILE ${TEST_FILES})
         GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
         SET(TEST_NAME ${FNAME}_${BACKEND})
 
         IF(NOT ${BUILD_NONFREE} AND "${FILE}" MATCHES ".nonfree.")
-            MESSAGE(STATUS "Removing ${FILE} from ctest")
+          MESSAGE(STATUS "Removing ${FILE} from ctest")
         ELSEIF("${FILE}" MATCHES ".manual.")
-            MESSAGE(STATUS "Removing ${FILE} from ctest")
+          MESSAGE(STATUS "Removing ${FILE} from ctest")
         ELSE()
           ADD_TEST(Test_${TEST_NAME} ${TEST_NAME})
         ENDIF()
@@ -73,15 +97,16 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
         FILE(GLOB TEST_FILE "${FNAME}.cpp" "${FNAME}.c")
         ADD_EXECUTABLE(${TEST_NAME} ${TEST_FILE})
         TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE ${AFLIBNAME}
-                                            PRIVATE ${THREAD_LIB_FLAG}
-                                            PRIVATE ${GTEST_LIBS}
-                                            PRIVATE ${OTHER_LIBS})
+          PRIVATE ${THREAD_LIB_FLAG}
+          PRIVATE ${GTEST_LIBS}
+          PRIVATE ${OTHER_LIBS})
 
         SET_TARGET_PROPERTIES(${TEST_NAME}
-                      PROPERTIES
-                      COMPILE_FLAGS -DAF_${DEF_NAME}
-                      FOLDER "Tests/${BACKEND}")
-    ENDFOREACH()
+          PROPERTIES
+          COMPILE_FLAGS -DAF_${DEF_NAME}
+          FOLDER "Tests/${BACKEND}")
+      ENDFOREACH()
+    ENDIF()
 
 ENDMACRO(CREATE_TESTS)
 
@@ -136,10 +161,11 @@ INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
 
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 FILE(GLOB FILES "*.cpp" "*.c")
+FILE(GLOB CPP_FILES "*.cpp")
 LIST(SORT FILES)  # Tests execute in alphabetical order
 
 # We only build backend.cpp for Unified backend
-SET(UNIFIED_FILES "backend.cpp")
+SET(UNIFIED_FILES "backend.cpp;main.cpp")
 LIST(SORT UNIFIED_FILES)  # Tests execute in alphabetical order
 
 # Next we build each example using every backend.
diff --git a/test/basic_c.c b/test/basic_c.c
index 0caca290ec..aac34e142d 100644
--- a/test/basic_c.c
+++ b/test/basic_c.c
@@ -9,7 +9,8 @@
 
 #include <arrayfire.h>
 
-int main() {
+int main()
+{
     af_array out = 0;
     dim_t s[] = {10, 10, 1, 1};
     af_err e = af_randu(&out, 4, s, f32);
diff --git a/test/fast.cpp b/test/fast.cpp
index e7df638b80..8cb90574a6 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -28,7 +28,7 @@ typedef struct
     float f[5];
 } feat_t;
 
-bool feat_cmp(feat_t i, feat_t j)
+static bool feat_cmp(feat_t i, feat_t j)
 {
     for (int k = 0; k < 5; k++)
         if (i.f[k] != j.f[k])
@@ -37,7 +37,7 @@ bool feat_cmp(feat_t i, feat_t j)
     return false;
 }
 
-void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
+static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index f50e4031aa..5794051152 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -39,7 +39,8 @@ typedef struct
     float d[272];
 } desc_t;
 
-bool feat_cmp(feat_desc_t i, feat_desc_t j)
+#ifdef  AF_BUILD_NONFREE_SIFT
+static bool feat_cmp(feat_desc_t i, feat_desc_t j)
 {
     for (int k = 0; k < 5; k++)
         if (round(i.f[k]*1e1f) != round(j.f[k]*1e1f))
@@ -48,7 +49,7 @@ bool feat_cmp(feat_desc_t i, feat_desc_t j)
     return true;
 }
 
-void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, float* desc, unsigned nfeat)
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, float* desc, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
@@ -62,7 +63,7 @@ void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* sc
     }
 }
 
-void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<float> >& desc, unsigned nfeat)
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<float> >& desc, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
@@ -76,7 +77,7 @@ void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* sc
     }
 }
 
-void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
+static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
@@ -88,7 +89,7 @@ void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float
     }
 }
 
-void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
+static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
     d.resize(fd.size());
@@ -103,7 +104,7 @@ void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>&
     }
 }
 
-unsigned popcount(unsigned x)
+static unsigned popcount(unsigned x)
 {
     x = x - ((x >> 1) & 0x55555555);
     x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
@@ -113,7 +114,7 @@ unsigned popcount(unsigned x)
     return x & 0x0000003F;
 }
 
-bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
+static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
 {
     bool ret = true;
     float sum = 0.0f;
@@ -143,6 +144,7 @@ bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float
 
     return ret;
 }
+#endif
 
 template<typename T>
 class GLOH : public ::testing::Test
diff --git a/test/harris.cpp b/test/harris.cpp
index 604e73d41c..0adde6f95d 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -28,7 +28,7 @@ typedef struct
     float f[5];
 } feat_t;
 
-bool feat_cmp(feat_t i, feat_t j)
+static bool feat_cmp(feat_t i, feat_t j)
 {
     for (int k = 0; k < 5; k++)
         if (i.f[k] != j.f[k])
@@ -37,7 +37,7 @@ bool feat_cmp(feat_t i, feat_t j)
     return false;
 }
 
-void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
+static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
diff --git a/test/main.cpp b/test/main.cpp
new file mode 100644
index 0000000000..76f841f1b1
--- /dev/null
+++ b/test/main.cpp
@@ -0,0 +1,6 @@
+#include <gtest/gtest.h>
+
+int main(int argc, char **argv) {
+    ::testing::InitGoogleTest(&argc, argv);
+    return RUN_ALL_TESTS();
+}
diff --git a/test/orb.cpp b/test/orb.cpp
index b499fb3824..1266f20eb6 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -39,7 +39,7 @@ typedef struct
     unsigned d[8];
 } desc_t;
 
-bool feat_cmp(feat_desc_t i, feat_desc_t j)
+static bool feat_cmp(feat_desc_t i, feat_desc_t j)
 {
     for (int k = 0; k < 5; k++)
         if (i.f[k] != j.f[k])
@@ -48,7 +48,7 @@ bool feat_cmp(feat_desc_t i, feat_desc_t j)
     return true;
 }
 
-void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, unsigned* desc, unsigned nfeat)
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, unsigned* desc, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
@@ -62,7 +62,7 @@ void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* sc
     }
 }
 
-void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<unsigned> >& desc, unsigned nfeat)
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<unsigned> >& desc, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
@@ -76,7 +76,7 @@ void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* sc
     }
 }
 
-void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
+static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
@@ -88,7 +88,7 @@ void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float
     }
 }
 
-void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
+static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
     d.resize(fd.size());
@@ -103,7 +103,7 @@ void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>&
     }
 }
 
-unsigned popcount(unsigned x)
+static unsigned popcount(unsigned x)
 {
     x = x - ((x >> 1) & 0x55555555);
     x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index f71dc76b80..675ed8fc4a 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -109,16 +109,6 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
 
-vector<af_seq> init_subs()
-{
-    vector<af_seq> subs;
-    subs.push_back(af_make_seq(2, 6, 1));
-    subs.push_back(af_make_seq(1, 5, 1));
-    subs.push_back(af_make_seq(1, 3, 1));
-    subs.push_back(af_make_seq(1, 2, 1));
-    return subs;
-}
-
 template<typename T,reduceFunc OP>
 struct promote_type {
     typedef T type;
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index ce7a921260..15734a3cc2 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -25,7 +25,7 @@ using af::cfloat;
 using af::cdouble;
 
 template<typename T>
-class Rotate : public ::testing::Test
+class RotateLinear : public ::testing::Test
 {
     public:
         virtual void SetUp() {
@@ -40,7 +40,7 @@ class Rotate : public ::testing::Test
 typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Rotate, TestTypes);
+TYPED_TEST_CASE(RotateLinear, TestTypes);
 
 #define PI 3.1415926535897931f
 
@@ -108,10 +108,10 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define ROTATE_INIT(desc, file, resultIdx, angle, crop, recenter)                               \
-    TYPED_TEST(Rotate, desc)                                                                    \
-    {                                                                                           \
-        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop, recenter);\
+#define ROTATE_INIT(desc, file, resultIdx, angle, crop, recenter)       \
+    TYPED_TEST(RotateLinear, desc)                                      \
+    {                                                                   \
+        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop, recenter); \
     }
 
     ROTATE_INIT(Square180NoCropRecenter     , rotatelinear1,  0, 180, false, true);
@@ -166,7 +166,7 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 
 ////////////////////////////////// CPP //////////////////////////////////////
 
-TEST(Rotate, CPP)
+TEST(RotateLinear, CPP)
 {
     if (noDoubleTests<float>()) return;
 
diff --git a/test/scan.cpp b/test/scan.cpp
index 386568d402..34a077f122 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -82,16 +82,6 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
 
-vector<af_seq> init_subs()
-{
-    vector<af_seq> subs;
-    subs.push_back(af_make_seq(2, 6, 1));
-    subs.push_back(af_make_seq(1, 5, 1));
-    subs.push_back(af_make_seq(1, 3, 1));
-    subs.push_back(af_make_seq(1, 2, 1));
-    return subs;
-}
-
 #define SCAN_TESTS(FN, TAG, Ti, To)             \
     TEST(Scan,Test_##FN##_##TAG)                \
     {                                           \
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index 2e069fd3d3..6776c18a86 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -38,8 +38,8 @@ typedef struct
 {
     float d[128];
 } desc_t;
-
-bool feat_cmp(feat_desc_t i, feat_desc_t j)
+#ifdef AF_BUILD_NONFREE_SIFT
+static bool feat_cmp(feat_desc_t i, feat_desc_t j)
 {
     for (int k = 0; k < 5; k++)
         if (round(i.f[k]*1e1f) != round(j.f[k]*1e1f))
@@ -48,7 +48,7 @@ bool feat_cmp(feat_desc_t i, feat_desc_t j)
     return true;
 }
 
-void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, float* desc, unsigned nfeat)
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, float* desc, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
@@ -62,7 +62,7 @@ void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* sc
     }
 }
 
-void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<float> >& desc, unsigned nfeat)
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<float> >& desc, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
@@ -76,7 +76,7 @@ void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* sc
     }
 }
 
-void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
+static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
@@ -88,7 +88,7 @@ void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float
     }
 }
 
-void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
+static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
     d.resize(fd.size());
@@ -103,7 +103,7 @@ void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>&
     }
 }
 
-unsigned popcount(unsigned x)
+static unsigned popcount(unsigned x)
 {
     x = x - ((x >> 1) & 0x55555555);
     x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
@@ -113,7 +113,7 @@ unsigned popcount(unsigned x)
     return x & 0x0000003F;
 }
 
-bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
+static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
 {
     bool ret = true;
     float sum = 0.0f;
@@ -143,6 +143,7 @@ bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float
 
     return ret;
 }
+#endif
 
 template<typename T>
 class SIFT : public ::testing::Test
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index 289e407ad9..ed827c9da5 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -26,7 +26,7 @@ using af::cfloat;
 using af::cdouble;
 
 template<typename T>
-class Sort : public ::testing::Test
+class SortByKey : public ::testing::Test
 {
     public:
         virtual void SetUp() {
@@ -41,7 +41,7 @@ class Sort : public ::testing::Test
 typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl, uintl> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Sort, TestTypes);
+TYPED_TEST_CASE(SortByKey, TestTypes);
 
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const unsigned resultIdx1, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
@@ -104,10 +104,10 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)                                       \
-    TYPED_TEST(Sort, desc)                                                                       \
-    {                                                                                            \
-        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0, resultIdx1);  \
+#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)              \
+    TYPED_TEST(SortByKey, desc)                                         \
+    {                                                                   \
+        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0, resultIdx1); \
     }
 
     SORT_INIT(Sort0True,      sort_by_key_tiny,  true,  0, 1);
@@ -168,4 +168,3 @@ TEST(SortByKey, CPP)
     delete[] keyData;
     delete[] valData;
 }
-
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index abe7910a58..6aa240d5a5 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -26,7 +26,7 @@ using af::cfloat;
 using af::cdouble;
 
 template<typename T>
-class Sort : public ::testing::Test
+class SortIndex : public ::testing::Test
 {
     public:
         virtual void SetUp() {
@@ -41,7 +41,7 @@ class Sort : public ::testing::Test
 typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl, uintl> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Sort, TestTypes);
+TYPED_TEST_CASE(SortIndex, TestTypes);
 
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const unsigned resultIdx1, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
@@ -102,10 +102,10 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)                                       \
-    TYPED_TEST(Sort, desc)                                                                       \
-    {                                                                                            \
-        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0, resultIdx1);  \
+#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)              \
+    TYPED_TEST(SortIndex, desc)                                         \
+    {                                                                   \
+        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0, resultIdx1); \
     }
 
     SORT_INIT(Sort0True,  sort, true, 0, 1);
diff --git a/test/susan.cpp b/test/susan.cpp
index 591c2f01e5..259a319ce7 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -28,7 +28,7 @@ typedef struct
     float f[5];
 } feat_t;
 
-bool feat_cmp(feat_t i, feat_t j)
+static bool feat_cmp(feat_t i, feat_t j)
 {
     for (int k = 0; k < 5; k++)
         if (i.f[k] != j.f[k])
@@ -37,7 +37,7 @@ bool feat_cmp(feat_t i, feat_t j)
     return false;
 }
 
-void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
+static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
 {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index 9d4060bd7f..7ce31e2ee5 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -35,12 +35,12 @@ typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(svd, TestTypes);
 
 template<typename T>
-double get_val(T val)
+inline double get_val(T val)
 {
     return val;
 }
 
-template<> double get_val<cfloat>(cfloat val)
+template<> inline double get_val<cfloat>(cfloat val)
 {
     return abs(val);
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 2744a8d67e..83f2552e08 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -6,6 +6,8 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
 
 #include <string>
 #include <fstream>
@@ -127,11 +129,11 @@ void readTestsFromFile(const std::string &FileName, std::vector<af::dim4> &input
     }
 }
 
-void readImageTests(const std::string        &pFileName,
-                    std::vector<af::dim4>    &pInputDims,
-                    std::vector<std::string> &pTestInputs,
-                    std::vector<dim_t>    &pTestOutSizes,
-                    std::vector<std::string> &pTestOutputs)
+inline void readImageTests(const std::string        &pFileName,
+                           std::vector<af::dim4>    &pInputDims,
+                           std::vector<std::string> &pTestInputs,
+                           std::vector<dim_t>    &pTestOutSizes,
+                           std::vector<std::string> &pTestOutputs)
 {
     using std::vector;
 
@@ -364,18 +366,18 @@ struct cond_type<false, T, Other> {
 };
 
 template<typename T>
-double real(T val) { return (double)val; }
+inline double real(T val) { return (double)val; }
 template<>
-double real<af::cdouble>(af::cdouble val) { return real(val); }
+inline double real<af::cdouble>(af::cdouble val) { return real(val); }
 template<>
-double real<af::cfloat> (af::cfloat val) { return real(val); }
+inline double real<af::cfloat> (af::cfloat val) { return real(val); }
 
 template<typename T>
-double imag(T val) { return (double)val; }
+inline double imag(T val) { return (double)val; }
 template<>
-double imag<af::cdouble>(af::cdouble val) { return imag(val); }
+inline double imag<af::cdouble>(af::cdouble val) { return imag(val); }
 template<>
-double imag<af::cfloat> (af::cfloat val) { return imag(val); }
+inline double imag<af::cfloat> (af::cfloat val) { return imag(val); }
 
 template<typename T>
 bool noDoubleTests()
@@ -388,14 +390,14 @@ bool noDoubleTests()
     return ((isTypeDouble && !isDoubleSupported) ? true : false);
 }
 
-bool noImageIOTests()
+inline bool noImageIOTests()
 {
     bool ret = !af::isImageIOAvailable();
     if(ret) printf("Image IO Not Configured. Test will exit\n");
     return ret;
 }
 
-bool noLAPACKTests()
+inline bool noLAPACKTests()
 {
     bool ret = !af::isLAPACKAvailable();
     if(ret) printf("LAPACK Not Configured. Test will exit\n");
@@ -450,3 +452,5 @@ af::array cpu_randu(const af::dim4 dims)
 
     return af::array(dims, (T *)&out[0]);
 }
+
+#pragma GCC diagnostic pop
diff --git a/test/where.cpp b/test/where.cpp
index eb21e0d6dc..37208f2ee2 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -78,17 +78,6 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-vector<af_seq> init_subs()
-{
-    vector<af_seq> subs;
-    subs.push_back(af_make_seq(2, 6, 1));
-    subs.push_back(af_make_seq(1, 5, 1));
-    subs.push_back(af_make_seq(1, 3, 1));
-    subs.push_back(af_make_seq(1, 2, 1));
-    return subs;
-}
-
-
 #define WHERE_TESTS(T)                          \
     TEST(Where,Test_##T)                        \
     {                                           \
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 7552400db9..091c5341c1 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -42,27 +42,27 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, intl
 TYPED_TEST_CASE(Wrap, TestTypes);
 
 template<typename T>
-double get_val(T val)
+inline double get_val(T val)
 {
     return val;
 }
 
-template<> double get_val<cfloat>(cfloat val)
+template<> inline double get_val<cfloat>(cfloat val)
 {
     return abs(val);
 }
 
-template<> double get_val<cdouble>(cdouble val)
+template<> inline double get_val<cdouble>(cdouble val)
 {
     return abs(val);
 }
 
-template<> double get_val<unsigned char>(unsigned char val)
+template<> inline double get_val<unsigned char>(unsigned char val)
 {
     return ((int)(val)) % 256;
 }
 
-template<> double get_val<char>(char val)
+template<> inline double get_val<char>(char val)
 {
     return (val != 0);
 }

From 3fc6939afe3c77c1a656367b14494cd41de8abad Mon Sep 17 00:00:00 2001
From: Mani Chandra <manic@illinois.edu>
Date: Sat, 30 Jan 2016 00:12:47 -0800
Subject: [PATCH 0339/2677] Fixes issues when compiling with icc

---
 src/api/c/assign.cpp         |  1 +
 src/api/c/moddims.cpp        | 13 +++++++++++++
 src/backend/cuda/Array.cpp   |  1 +
 src/backend/opencl/Array.cpp |  1 +
 4 files changed, 16 insertions(+)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index bf2c185a10..8ff37630e8 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -29,6 +29,7 @@ using std::swap;
 template<typename T>
 Array<T> modDims(const Array<T>& in, const af::dim4 &newDims);
 
+
 template<typename Tout, typename Tin>
 static
 void assign(Array<Tout> &out, const unsigned &ndims, const af_seq *index, const Array<Tin> &in_)
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 4b7a179a95..132086a6ef 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -36,6 +36,19 @@ Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
     return Out;
 }
 
+template Array<float> modDims(const Array<float> &in, const af::dim4 &newDims);
+template Array<double> modDims(const Array<double> &in, const af::dim4 &newDims);
+template Array<cfloat> modDims(const Array<cfloat> &in, const af::dim4 &newDims);
+template Array<cdouble> modDims(const Array<cdouble> &in, const af::dim4 &newDims);
+template Array<int> modDims(const Array<int> &in, const af::dim4 &newDims);
+template Array<uint> modDims(const Array<uint> &in, const af::dim4 &newDims);
+template Array<intl> modDims(const Array<intl> &in, const af::dim4 &newDims);
+template Array<uintl> modDims(const Array<uintl> &in, const af::dim4 &newDims);
+template Array<short> modDims(const Array<short> &in, const af::dim4 &newDims);
+template Array<ushort> modDims(const Array<ushort> &in, const af::dim4 &newDims);
+template Array<uchar> modDims(const Array<uchar> &in, const af::dim4 &newDims);
+template Array<char> modDims(const Array<char> &in, const af::dim4 &newDims);
+
 af_err af_moddims(af_array *out, const af_array in,
                   const unsigned ndims, const dim_t * const dims)
 {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 1ca6012211..6e95dd1102 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -277,6 +277,7 @@ namespace cuda
     template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
                                    bool is_device, bool copy_device);   \
     template       Array<T>::~Array        ();                          \
+    template       Node_ptr Array<T>::getNode() const;             \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 7b6a26eb4d..c470a351f3 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -320,6 +320,7 @@ namespace opencl
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
     template       Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
     template       Array<T>::~Array        ();                          \
+    template       Node_ptr Array<T>::getNode() const;             \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \

From ee7fa33d84b5f25bf53ce65644380f6d5b01b2c4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 1 Feb 2016 16:44:41 -0500
Subject: [PATCH 0340/2677] Removing unnecessary option "BUILD_GTEST"

---
 CMakeLists.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2cfeb18fed..8bdf93cd52 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -9,7 +9,6 @@ INCLUDE(AFInstallDirs)
 
 OPTION(BUILD_TEST "Build Tests" ON)
 OPTION(BUILD_EXAMPLES "Build Examples" ON)
-OPTION(BUILD_GTEST "Download gtest and check for updates. Necessary if you change compilers" ON)
 
 OPTION(BUILD_CPU "Build ArrayFire with a CPU backend" ON)
 

From 653416db6c8d6406a72a0f5e698232382cee4b3b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 1 Feb 2016 18:31:23 -0500
Subject: [PATCH 0341/2677] Updating release notes for 3.3 pre-release

---
 docs/pages/release_notes.md | 70 +++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 4f13cc7434..1063b054e3 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,76 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.3.0
+==============
+
+Major Updates
+-------------
+
+* CPU backend supports aysnchronous execution.
+* Performance improvements to OpenCL BLAS and FFT functions.
+* Improved performance of memory manager.
+* Improvements to visualization functions.
+* Improved sorted order for OpenCL devices.
+* Integration with external OpenCL projects.
+
+Features
+----------
+
+* \ref af::getActiveBackend(): Returns the current backend being used.
+* [Scatter plot](https://github.com/arrayfire/arrayfire/pull/1116) added to graphics.
+* \ref af::transform() now supports perspective transformation matrices.
+* \ref af::infoString(): Returns `af::info()` as a string.
+* \ref af::allocHost(): Allocates memory on host.
+* \ref af::freeHost(): Frees host side memory allocated by arrayfire.
+* Functions specific to OpenCl backend.
+    * \ref afcl::addDevice(): Adds an external device and context to ArrayFire's device manager.
+    * \ref afcl::deleteDevice(): Removes an external device and context from ArrayFire's device manager.
+    * \ref afcl::setDevice(): Sets an external device and context from ArrayFire's device manager.
+    * \ref afcl::getDeviceType(): Gets the device type of the current device.
+    * \ref afcl::getPlatform(): Gets the platform of the current device.
+
+Bug Fixes
+--------------
+
+* Fixed [errors when using 3D / 4D arrays](https://github.com/arrayfire/arrayfire/pull/1251) in select and replace
+* Fixed [JIT errors on AMD devices](https://github.com/arrayfire/arrayfire/pull/1238) for OpenCL backend.
+* Fixed [imageio bugs](https://github.com/arrayfire/arrayfire/pull/1229) for 16 bit images.
+* Fixed [bugs when loading and storing images](https://github.com/arrayfire/arrayfire/pull/1228) natively.
+* Fixed [bug in FFT for NVIDIA GPUs](https://github.com/arrayfire/arrayfire/issues/615) when using OpenCL backend.
+
+Improvements
+--------------
+
+* Optionally [offload BLAS and LAPACK](https://github.com/arrayfire/arrayfire/pull/1221) functions to CPU implementations to improve performance.
+* Performance improvements to the memory manager.
+* Error messages are now more detailed.
+* Improved sorted order for OpenCL devices.
+
+Examples
+----------
+
+* New visualization [example simulating gravity](\ref graphics/gravity_sim.cpp).
+
+Build
+----------
+
+* Support for Intel `icc` compiler
+* Support to compile with Intel MKL as a BLAS and LAPACK provider
+
+Deprecations
+-----------
+
+* `af_lock_device_arr` is now deprecated to be removed in v4.0.0. Use \ref af_lock_array() instead.
+* `af_unlock_device_arr` is now deprecated to be removed in v4.0.0. use \ref af_unlock_array() instead.
+
+Documentation
+--------------
+
+* Fixes to documentation for matchTemplate.
+* Improved documentation for deviceInfo.
+
+
 v3.2.2
 ==============
 

From fc7553df8377ee6c4b7f25878aaf6bf0727bcf6b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 2 Feb 2016 14:31:34 -0500
Subject: [PATCH 0342/2677] BUGFIX: max_bytes were being set incorrectly in
 MemoryManager

---
 src/backend/MemoryManager.cpp | 21 +++++++++++++++------
 src/backend/MemoryManager.hpp |  2 ++
 src/backend/cpu/memory.cpp    |  4 +++-
 src/backend/cuda/memory.cpp   |  8 ++++++--
 src/backend/opencl/memory.cpp |  8 ++++++--
 5 files changed, 32 insertions(+), 11 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 814262829e..d82436e177 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -19,6 +19,7 @@
 namespace common
 {
 
+const size_t ONE_GB = 1 << 30;
 MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug):
     mem_step_size(1024),
     max_buffers(MAX_BUFFERS),
@@ -32,17 +33,25 @@ MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug):
     }
     if (this->debug_mode) mem_step_size = 1;
 
-    static const size_t oneGB = 1 << 30;
     for (int n = 0; n < num_devices; n++) {
-        size_t memsize = getMaxMemorySize(n);
+        // Calling getMaxMemorySize() here calls the virtual function that returns 0
+        // Call it from outside the constructor.
+        memory[n].max_bytes    = ONE_GB;
+        memory[n].total_bytes  = 0;
+        memory[n].lock_bytes   = 0;
+        memory[n].lock_buffers = 0;
+    }
+}
+
+void MemoryManager::setMaxMemorySize()
+{
+    for (unsigned n = 0; n < memory.size(); n++) {
         // Calls garbage collection when:
         // total_bytes > memsize * 0.75 when memsize <  4GB
         // total_bytes > memsize - 1 GB when memsize >= 4GB
         // If memsize returned 0, then use 1GB
-        memory[n].max_bytes    = memsize == 0 ? oneGB : std::max(memsize * 0.75, (double)(memsize - oneGB));
-        memory[n].total_bytes  = 0;
-        memory[n].lock_bytes   = 0;
-        memory[n].lock_buffers = 0;
+        size_t memsize = this->getMaxMemorySize(n);
+        memory[n].max_bytes = memsize == 0 ? ONE_GB : std::max(memsize * 0.75, (double)(memsize - ONE_GB));
     }
 }
 
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index 8bb9941b87..faae7fa609 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -63,6 +63,8 @@ class MemoryManager
 public:
     MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug);
 
+    void setMaxMemorySize();
+
     void *alloc(const size_t bytes);
 
     void unlock(void *ptr, bool user_unlock);
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index cf7e1ba48b..8a89cb19dc 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -56,7 +56,9 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 MemoryManager::MemoryManager() :
     common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
-{}
+{
+    this->setMaxMemorySize();
+}
 
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index f5dc6ca048..6a947c634c 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -91,7 +91,9 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 MemoryManager::MemoryManager() :
     common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{}
+{
+    this->setMaxMemorySize();
+}
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
@@ -126,7 +128,9 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 
 MemoryManagerPinned::MemoryManagerPinned() :
     common::MemoryManager(1, MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{}
+{
+    this->setMaxMemorySize();
+}
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 7054e96479..01c93bb318 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -89,7 +89,9 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 MemoryManager::MemoryManager() :
     common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
-{}
+{
+    this->setMaxMemorySize();
+}
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
@@ -128,7 +130,9 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 MemoryManagerPinned::MemoryManagerPinned() :
     common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
     pinned_maps(getDeviceCount())
-{}
+{
+    this->setMaxMemorySize();
+}
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {

From 5183a357535d10fef63862f74608bc5a5f82eadb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 2 Feb 2016 19:11:02 -0500
Subject: [PATCH 0343/2677] Cleaning up internal API for memory functions

- Also Split up device.cpp and memory.cpp
---
 include/af/device.h           |   3 +
 src/api/c/device.cpp          | 243 -------------------------------
 src/api/c/memory.cpp          | 263 ++++++++++++++++++++++++++++++++++
 src/backend/MemoryManager.cpp |   9 +-
 src/backend/MemoryManager.hpp |   7 +-
 src/backend/cpu/memory.cpp    |  30 ++--
 src/backend/cpu/memory.hpp    |  10 +-
 src/backend/cuda/memory.cpp   |  38 +++--
 src/backend/cuda/memory.hpp   |  11 +-
 src/backend/opencl/memory.cpp |  42 +++---
 src/backend/opencl/memory.hpp |  13 +-
 11 files changed, 353 insertions(+), 316 deletions(-)
 create mode 100644 src/api/c/memory.cpp

diff --git a/include/af/device.h b/include/af/device.h
index c0d787ea80..b08bd519b3 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -200,6 +200,8 @@ namespace af
     //                              manager
     /// \param[out] lock_bytes The number of bytes in use
     /// \param[out] lock_buffers The number of buffers in use
+    ///
+    /// \note This function performs a synchronization operation
     AFAPI void deviceMemInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                              size_t *lock_bytes, size_t *lock_buffers);
 
@@ -213,6 +215,7 @@ namespace af
     //
     /// \ingroup device_func_mem
     ///
+    /// \note This function performs a synchronization operation
     AFAPI void printMemInfo(const char *msg = NULL, const int device_id = -1);
 #endif
 
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index e3ec476b93..304d0c753b 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -15,7 +15,6 @@
 #include <platform.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
-#include <memory.hpp>
 #include "err_common.hpp"
 #include <cstring>
 
@@ -156,245 +155,3 @@ af_err af_sync(const int device)
     } CATCHALL;
     return AF_SUCCESS;
 }
-
-af_err af_device_array(af_array *arr, const void *data,
-                       const unsigned ndims,
-                       const dim_t * const dims,
-                       const af_dtype type)
-{
-    try {
-        AF_CHECK(af_init());
-
-        af_array res;
-
-        DIM_ASSERT(1, ndims >= 1);
-        dim4 d(1, 1, 1, 1);
-        for(unsigned i = 0; i < ndims; i++) {
-            d[i] = dims[i];
-            DIM_ASSERT(3, dims[i] >= 1);
-        }
-
-        switch (type) {
-        case f32: res = getHandle(createDeviceDataArray<float  >(d, data)); break;
-        case f64: res = getHandle(createDeviceDataArray<double >(d, data)); break;
-        case c32: res = getHandle(createDeviceDataArray<cfloat >(d, data)); break;
-        case c64: res = getHandle(createDeviceDataArray<cdouble>(d, data)); break;
-        case s32: res = getHandle(createDeviceDataArray<int    >(d, data)); break;
-        case u32: res = getHandle(createDeviceDataArray<uint   >(d, data)); break;
-        case s64: res = getHandle(createDeviceDataArray<intl   >(d, data)); break;
-        case u64: res = getHandle(createDeviceDataArray<uintl  >(d, data)); break;
-        case s16: res = getHandle(createDeviceDataArray<short  >(d, data)); break;
-        case u16: res = getHandle(createDeviceDataArray<ushort >(d, data)); break;
-        case u8 : res = getHandle(createDeviceDataArray<uchar  >(d, data)); break;
-        case b8 : res = getHandle(createDeviceDataArray<char   >(d, data)); break;
-        default: TYPE_ERROR(4, type);
-        }
-
-        std::swap(*arr, res);
-    } CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_get_device_ptr(void **data, const af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-
-        switch (type) {
-            //FIXME: Perform copy if memory not continuous
-        case f32: *data = getDevicePtr(getArray<float  >(arr)); break;
-        case f64: *data = getDevicePtr(getArray<double >(arr)); break;
-        case c32: *data = getDevicePtr(getArray<cfloat >(arr)); break;
-        case c64: *data = getDevicePtr(getArray<cdouble>(arr)); break;
-        case s32: *data = getDevicePtr(getArray<int    >(arr)); break;
-        case u32: *data = getDevicePtr(getArray<uint   >(arr)); break;
-        case s64: *data = getDevicePtr(getArray<intl   >(arr)); break;
-        case u64: *data = getDevicePtr(getArray<uintl  >(arr)); break;
-        case s16: *data = getDevicePtr(getArray<short  >(arr)); break;
-        case u16: *data = getDevicePtr(getArray<ushort >(arr)); break;
-        case u8 : *data = getDevicePtr(getArray<uchar  >(arr)); break;
-        case b8 : *data = getDevicePtr(getArray<char   >(arr)); break;
-
-        default: TYPE_ERROR(4, type);
-        }
-
-    } CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-template <typename T>
-inline void lockArray(const af_array arr)
-{
-    memLock<T>((const T *)getArray<T>(arr).get());
-}
-
-af_err af_lock_device_ptr(const af_array arr)
-{
-    return af_lock_array(arr);
-}
-
-af_err af_lock_array(const af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-
-        switch (type) {
-        case f32: lockArray<float  >(arr); break;
-        case f64: lockArray<double >(arr); break;
-        case c32: lockArray<cfloat >(arr); break;
-        case c64: lockArray<cdouble>(arr); break;
-        case s32: lockArray<int    >(arr); break;
-        case u32: lockArray<uint   >(arr); break;
-        case s64: lockArray<intl   >(arr); break;
-        case u64: lockArray<uintl  >(arr); break;
-        case s16: lockArray<short  >(arr); break;
-        case u16: lockArray<ushort >(arr); break;
-        case u8 : lockArray<uchar  >(arr); break;
-        case b8 : lockArray<char   >(arr); break;
-        default: TYPE_ERROR(4, type);
-        }
-
-    } CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-template <typename T>
-inline void unlockArray(const af_array arr)
-{
-    memUnlock<T>((const T *)getArray<T>(arr).get());
-}
-
-af_err af_unlock_device_ptr(const af_array arr)
-{
-    return af_unlock_array(arr);
-}
-
-af_err af_unlock_array(const af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-
-        switch (type) {
-        case f32: unlockArray<float  >(arr); break;
-        case f64: unlockArray<double >(arr); break;
-        case c32: unlockArray<cfloat >(arr); break;
-        case c64: unlockArray<cdouble>(arr); break;
-        case s32: unlockArray<int    >(arr); break;
-        case u32: unlockArray<uint   >(arr); break;
-        case s64: unlockArray<intl   >(arr); break;
-        case u64: unlockArray<uintl  >(arr); break;
-        case s16: unlockArray<short  >(arr); break;
-        case u16: unlockArray<ushort >(arr); break;
-        case u8 : unlockArray<uchar  >(arr); break;
-        case b8 : unlockArray<char   >(arr); break;
-        default: TYPE_ERROR(4, type);
-        }
-
-    } CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-
-af_err af_alloc_device(void **ptr, const dim_t bytes)
-{
-    try {
-        AF_CHECK(af_init());
-        *ptr = (void *)memAlloc<char>(bytes);
-        memLock<char>((const char *)*ptr);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_alloc_pinned(void **ptr, const dim_t bytes)
-{
-    try {
-        AF_CHECK(af_init());
-        *ptr = (void *)pinnedAlloc<char>(bytes);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_free_device(void *ptr)
-{
-    try {
-        memFreeLocked<char>((char *)ptr, true);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_free_pinned(void *ptr)
-{
-    try {
-        pinnedFree<char>((char *)ptr);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_alloc_host(void **ptr, const dim_t bytes)
-{
-    try {
-        *ptr = malloc(bytes);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_free_host(void *ptr)
-{
-    try {
-        free(ptr);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_print_mem_info(const char *msg, const int device_id)
-{
-    try {
-        int device = device_id;
-        if(device == -1) {
-            device = getActiveDeviceId();
-        }
-
-        if(msg != NULL) ARG_ASSERT(0, strlen(msg) < 256); // 256 character limit on msg
-        ARG_ASSERT(1, device >= 0 && device < getDeviceCount());
-
-        printMemInfo(msg ? msg : "", device);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_device_gc()
-{
-    try {
-        garbageCollect();
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers)
-{
-    try {
-        deviceMemoryInfo(alloc_bytes, alloc_buffers, lock_bytes, lock_buffers);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_set_mem_step_size(const size_t step_bytes)
-{
-    try{
-        detail::setMemStepSize(step_bytes);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_get_mem_step_size(size_t *step_bytes)
-{
-    try {
-        *step_bytes =  detail::getMemStepSize();
-    } CATCHALL;
-    return AF_SUCCESS;
-}
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
new file mode 100644
index 0000000000..098665ba03
--- /dev/null
+++ b/src/api/c/memory.cpp
@@ -0,0 +1,263 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <af/device.h>
+#include <af/version.h>
+#include <af/backend.h>
+#include <backend.hpp>
+#include <platform.hpp>
+#include <Array.hpp>
+#include <handle.hpp>
+#include <memory.hpp>
+#include "err_common.hpp"
+#include <cstring>
+
+using namespace detail;
+
+af_err af_device_array(af_array *arr, const void *data,
+                       const unsigned ndims,
+                       const dim_t * const dims,
+                       const af_dtype type)
+{
+    try {
+        AF_CHECK(af_init());
+
+        af_array res;
+
+        DIM_ASSERT(1, ndims >= 1);
+        dim4 d(1, 1, 1, 1);
+        for(unsigned i = 0; i < ndims; i++) {
+            d[i] = dims[i];
+            DIM_ASSERT(3, dims[i] >= 1);
+        }
+
+        switch (type) {
+        case f32: res = getHandle(createDeviceDataArray<float  >(d, data)); break;
+        case f64: res = getHandle(createDeviceDataArray<double >(d, data)); break;
+        case c32: res = getHandle(createDeviceDataArray<cfloat >(d, data)); break;
+        case c64: res = getHandle(createDeviceDataArray<cdouble>(d, data)); break;
+        case s32: res = getHandle(createDeviceDataArray<int    >(d, data)); break;
+        case u32: res = getHandle(createDeviceDataArray<uint   >(d, data)); break;
+        case s64: res = getHandle(createDeviceDataArray<intl   >(d, data)); break;
+        case u64: res = getHandle(createDeviceDataArray<uintl  >(d, data)); break;
+        case s16: res = getHandle(createDeviceDataArray<short  >(d, data)); break;
+        case u16: res = getHandle(createDeviceDataArray<ushort >(d, data)); break;
+        case u8 : res = getHandle(createDeviceDataArray<uchar  >(d, data)); break;
+        case b8 : res = getHandle(createDeviceDataArray<char   >(d, data)); break;
+        default: TYPE_ERROR(4, type);
+        }
+
+        std::swap(*arr, res);
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_get_device_ptr(void **data, const af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+
+        switch (type) {
+            //FIXME: Perform copy if memory not continuous
+        case f32: *data = getDevicePtr(getArray<float  >(arr)); break;
+        case f64: *data = getDevicePtr(getArray<double >(arr)); break;
+        case c32: *data = getDevicePtr(getArray<cfloat >(arr)); break;
+        case c64: *data = getDevicePtr(getArray<cdouble>(arr)); break;
+        case s32: *data = getDevicePtr(getArray<int    >(arr)); break;
+        case u32: *data = getDevicePtr(getArray<uint   >(arr)); break;
+        case s64: *data = getDevicePtr(getArray<intl   >(arr)); break;
+        case u64: *data = getDevicePtr(getArray<uintl  >(arr)); break;
+        case s16: *data = getDevicePtr(getArray<short  >(arr)); break;
+        case u16: *data = getDevicePtr(getArray<ushort >(arr)); break;
+        case u8 : *data = getDevicePtr(getArray<uchar  >(arr)); break;
+        case b8 : *data = getDevicePtr(getArray<char   >(arr)); break;
+
+        default: TYPE_ERROR(4, type);
+        }
+
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+template <typename T>
+inline void lockArray(const af_array arr)
+{
+    memLock((void *)getArray<T>(arr).get());
+}
+
+af_err af_lock_device_ptr(const af_array arr)
+{
+    return af_lock_array(arr);
+}
+
+af_err af_lock_array(const af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+
+        switch (type) {
+        case f32: lockArray<float  >(arr); break;
+        case f64: lockArray<double >(arr); break;
+        case c32: lockArray<cfloat >(arr); break;
+        case c64: lockArray<cdouble>(arr); break;
+        case s32: lockArray<int    >(arr); break;
+        case u32: lockArray<uint   >(arr); break;
+        case s64: lockArray<intl   >(arr); break;
+        case u64: lockArray<uintl  >(arr); break;
+        case s16: lockArray<short  >(arr); break;
+        case u16: lockArray<ushort >(arr); break;
+        case u8 : lockArray<uchar  >(arr); break;
+        case b8 : lockArray<char   >(arr); break;
+        default: TYPE_ERROR(4, type);
+        }
+
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+template <typename T>
+inline void unlockArray(const af_array arr)
+{
+    memUnlock((void *)getArray<T>(arr).get());
+}
+
+af_err af_unlock_device_ptr(const af_array arr)
+{
+    return af_unlock_array(arr);
+}
+
+af_err af_unlock_array(const af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+
+        switch (type) {
+        case f32: unlockArray<float  >(arr); break;
+        case f64: unlockArray<double >(arr); break;
+        case c32: unlockArray<cfloat >(arr); break;
+        case c64: unlockArray<cdouble>(arr); break;
+        case s32: unlockArray<int    >(arr); break;
+        case u32: unlockArray<uint   >(arr); break;
+        case s64: unlockArray<intl   >(arr); break;
+        case u64: unlockArray<uintl  >(arr); break;
+        case s16: unlockArray<short  >(arr); break;
+        case u16: unlockArray<ushort >(arr); break;
+        case u8 : unlockArray<uchar  >(arr); break;
+        case b8 : unlockArray<char   >(arr); break;
+        default: TYPE_ERROR(4, type);
+        }
+
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+
+af_err af_alloc_device(void **ptr, const dim_t bytes)
+{
+    try {
+        AF_CHECK(af_init());
+        *ptr = memAllocUser(bytes);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_alloc_pinned(void **ptr, const dim_t bytes)
+{
+    try {
+        AF_CHECK(af_init());
+        *ptr = (void *)pinnedAlloc<char>(bytes);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_free_device(void *ptr)
+{
+    try {
+        memFreeUser(ptr);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_free_pinned(void *ptr)
+{
+    try {
+        pinnedFree<char>((char *)ptr);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_alloc_host(void **ptr, const dim_t bytes)
+{
+    try {
+        *ptr = malloc(bytes);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_free_host(void *ptr)
+{
+    try {
+        free(ptr);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_print_mem_info(const char *msg, const int device_id)
+{
+    try {
+        int device = device_id;
+        if(device == -1) {
+            device = getActiveDeviceId();
+        }
+
+        if(msg != NULL) ARG_ASSERT(0, strlen(msg) < 256); // 256 character limit on msg
+        ARG_ASSERT(1, device >= 0 && device < getDeviceCount());
+
+        printMemInfo(msg ? msg : "", device);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_device_gc()
+{
+    try {
+        garbageCollect();
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
+                          size_t *lock_bytes,  size_t *lock_buffers)
+{
+    try {
+        deviceMemoryInfo(alloc_bytes, alloc_buffers, lock_bytes, lock_buffers);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_set_mem_step_size(const size_t step_bytes)
+{
+    try{
+        detail::setMemStepSize(step_bytes);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_get_mem_step_size(size_t *step_bytes)
+{
+    try {
+        *step_bytes =  detail::getMemStepSize();
+    } CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index d82436e177..5773c19de7 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -19,7 +19,6 @@
 namespace common
 {
 
-const size_t ONE_GB = 1 << 30;
 MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug):
     mem_step_size(1024),
     max_buffers(MAX_BUFFERS),
@@ -115,7 +114,7 @@ void MemoryManager::unlock(void *ptr, bool user_unlock)
     }
 }
 
-void *MemoryManager::alloc(const size_t bytes)
+void *MemoryManager::alloc(const size_t bytes, bool user_lock)
 {
     lock_guard_t lock(this->memory_mutex);
 
@@ -277,4 +276,10 @@ void MemoryManager::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
     if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
     if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
 }
+
+unsigned MemoryManager::getMaxBuffers()
+{
+    return this->max_buffers;
+}
+
 }
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index faae7fa609..a010f30064 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -19,6 +19,9 @@ namespace common
 typedef std::recursive_mutex mutex_t;
 typedef std::lock_guard<mutex_t> lock_guard_t;
 
+const unsigned MAX_BUFFERS   = 1000;
+const size_t ONE_GB = 1 << 30;
+
 class MemoryManager
 {
     typedef struct
@@ -65,7 +68,7 @@ class MemoryManager
 
     void setMaxMemorySize();
 
-    void *alloc(const size_t bytes);
+    void *alloc(const size_t bytes, bool user_lock);
 
     void unlock(void *ptr, bool user_unlock);
 
@@ -84,6 +87,8 @@ class MemoryManager
 
     size_t getMaxBytes();
 
+    unsigned getMaxBuffers();
+
     void setMemStepSize(size_t new_step_size);
 
     virtual void *nativeAlloc(const size_t bytes)
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 8a89cb19dc..016428a6d9 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -55,7 +55,7 @@ size_t MemoryManager::getMaxMemorySize(int id)
 }
 
 MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
 {
     this->setMaxMemorySize();
 }
@@ -94,6 +94,11 @@ size_t getMaxBytes()
     return getMemoryManager().getMaxBytes();
 }
 
+unsigned getMaxBuffers()
+{
+    return getMemoryManager().getMaxBuffers();
+}
+
 void garbageCollect()
 {
     getMemoryManager().garbageCollect();
@@ -107,34 +112,34 @@ void printMemInfo(const char *msg, const int device)
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T));
+    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
 }
 
+void* memAllocUser(const size_t &bytes)
+{
+    return getMemoryManager().alloc(bytes, true);
+}
 template<typename T>
 void memFree(T *ptr)
 {
     return getMemoryManager().unlock((void *)ptr, false);
 }
 
-template<typename T>
-void memFreeLocked(T *ptr, bool user_unlock)
+void memFreeUser(void *ptr)
 {
-    getMemoryManager().unlock((void *)ptr, user_unlock);
+    getMemoryManager().unlock((void *)ptr, true);
 }
 
-template<typename T>
-void memLock(const T *ptr)
+void memLock(const void *ptr)
 {
     getMemoryManager().userLock((void *)ptr);
 }
 
-template<typename T>
-void memUnlock(const T *ptr)
+void memUnlock(const void *ptr)
 {
     getMemoryManager().userUnlock((void *)ptr);
 }
 
-
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
@@ -146,7 +151,7 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T));
+    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
@@ -158,9 +163,6 @@ void pinnedFree(T* ptr)
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
     template void memFree(T* ptr);                          \
-    template void memFreeLocked(T* ptr, bool user_unlock);  \
-    template void memLock(const T* ptr);                    \
-    template void memUnlock(const T* ptr);                  \
     template T* pinnedAlloc(const size_t &elements);        \
     template void pinnedFree(T* ptr);                       \
 
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 8f61f11f7b..80ee86ddc8 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -13,22 +13,22 @@
 namespace cpu
 {
     template<typename T> T* memAlloc(const size_t &elements);
+    void *memAllocUser(const size_t &bytes);
 
     // Need these as 2 separate function and not a default argument
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeLocked(T* ptr, bool user_unlock);
+    void memFreeUser(void* ptr);
 
-    template<typename T> void memLock(const T *ptr);
-    template<typename T> void memUnlock(const T *ptr);
+    void memLock(const void *ptr);
+    void memUnlock(const void *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 
-    static const unsigned MAX_BUFFERS = 1000;
-
     size_t getMaxBytes();
+    unsigned getMaxBuffers();
 
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers);
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 6a947c634c..ff62661601 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -50,11 +50,7 @@ class MemoryManager  : public common::MemoryManager
                 cuda::setDevice(n);
                 this->garbageCollect();
             } catch(AfError err) {
-                if(err.getError() == AF_ERR_DRIVER) { // Can happen from cudaErrorDevicesUnavailable
-                    continue;
-                } else {
-                    throw err;
-                }
+                continue; // Do not throw any errors while shutting down
             }
         }
     }
@@ -90,7 +86,7 @@ size_t MemoryManager::getMaxMemorySize(int id)
 }
 
 MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
 {
     this->setMaxMemorySize();
 }
@@ -127,7 +123,7 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 }
 
 MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(1, MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+    common::MemoryManager(1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
 {
     this->setMaxMemorySize();
 }
@@ -168,6 +164,11 @@ size_t getMaxBytes()
     return getMemoryManager().getMaxBytes();
 }
 
+unsigned getMaxBuffers()
+{
+    return getMemoryManager().getMaxBuffers();
+}
+
 void garbageCollect()
 {
     getMemoryManager().garbageCollect();
@@ -181,34 +182,34 @@ void printMemInfo(const char *msg, const int device)
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T));
+    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
 }
 
+void* memAllocUser(const size_t &bytes)
+{
+    return getMemoryManager().alloc(bytes, true);
+}
 template<typename T>
 void memFree(T *ptr)
 {
     return getMemoryManager().unlock((void *)ptr, false);
 }
 
-template<typename T>
-void memFreeLocked(T *ptr, bool user_unlock)
+void memFreeUser(void *ptr)
 {
-    getMemoryManager().unlock((void *)ptr, user_unlock);
+    getMemoryManager().unlock((void *)ptr, true);
 }
 
-template<typename T>
-void memLock(const T *ptr)
+void memLock(const void *ptr)
 {
     getMemoryManager().userLock((void *)ptr);
 }
 
-template<typename T>
-void memUnlock(const T *ptr)
+void memUnlock(const void *ptr)
 {
     getMemoryManager().userUnlock((void *)ptr);
 }
 
-
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
@@ -219,7 +220,7 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T));
+    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
@@ -231,9 +232,6 @@ void pinnedFree(T* ptr)
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
     template void memFree(T* ptr);                          \
-    template void memFreeLocked(T* ptr, bool user_unlock);  \
-    template void memLock(const T* ptr);                    \
-    template void memUnlock(const T* ptr);                  \
     template T* pinnedAlloc(const size_t &elements);        \
     template void pinnedFree(T* ptr);                       \
 
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 590ba3b880..9bf69df9d4 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -13,21 +13,22 @@
 namespace cuda
 {
     template<typename T> T* memAlloc(const size_t &elements);
+    void *memAllocUser(const size_t &bytes);
 
     // Need these as 2 separate function and not a default argument
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeLocked(T* ptr, bool user_unlock);
-    template<typename T> void memLock(const T *ptr);
-    template<typename T> void memUnlock(const T *ptr);
+    void memFreeUser(void* ptr);
+
+    void memLock(const void *ptr);
+    void memUnlock(const void *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 
-    static const unsigned MAX_BUFFERS   = 1000;
-
     size_t getMaxBytes();
+    unsigned getMaxBuffers();
 
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers);
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 01c93bb318..756d18749e 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -88,7 +88,7 @@ size_t MemoryManager::getMaxMemorySize(int id)
 }
 
 MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
 {
     this->setMaxMemorySize();
 }
@@ -128,7 +128,7 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 }
 
 MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(getDeviceCount(), MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
     pinned_maps(getDeviceCount())
 {
     this->setMaxMemorySize();
@@ -184,6 +184,11 @@ size_t getMaxBytes()
     return getMemoryManager().getMaxBytes();
 }
 
+unsigned getMaxBuffers()
+{
+    return getMemoryManager().getMaxBuffers();
+}
+
 void garbageCollect()
 {
     getMemoryManager().garbageCollect();
@@ -197,44 +202,44 @@ void printMemInfo(const char *msg, const int device)
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T));
+    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
 }
 
-cl::Buffer *bufferAlloc(const size_t &bytes)
+void* memAllocUser(const size_t &bytes)
 {
-    return (cl::Buffer *)getMemoryManager().alloc(bytes);
+    return getMemoryManager().alloc(bytes, true);
 }
-
 template<typename T>
 void memFree(T *ptr)
 {
     return getMemoryManager().unlock((void *)ptr, false);
 }
 
-void bufferFree(cl::Buffer *buf)
+void memFreeUser(void *ptr)
 {
-    return getMemoryManager().unlock((void *)buf, false);
+    getMemoryManager().unlock((void *)ptr, true);
 }
 
-template<typename T>
-void memFreeLocked(T *ptr, bool user_unlock)
+cl::Buffer *bufferAlloc(const size_t &bytes)
 {
-    getMemoryManager().unlock((void *)ptr, user_unlock);
+    return (cl::Buffer *)getMemoryManager().alloc(bytes, false);
 }
 
-template<typename T>
-void memLock(const T *ptr)
+void bufferFree(cl::Buffer *buf)
+{
+    return getMemoryManager().unlock((void *)buf, false);
+}
+
+void memLock(const void *ptr)
 {
     getMemoryManager().userLock((void *)ptr);
 }
 
-template<typename T>
-void memUnlock(const T *ptr)
+void memUnlock(const void *ptr)
 {
     getMemoryManager().userUnlock((void *)ptr);
 }
 
-
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
@@ -245,7 +250,7 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T));
+    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
@@ -257,9 +262,6 @@ void pinnedFree(T* ptr)
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
     template void memFree(T* ptr);                          \
-    template void memFreeLocked(T* ptr, bool user_unlock);  \
-    template void memLock(const T* ptr);                    \
-    template void memUnlock(const T* ptr);                  \
     template T* pinnedAlloc(const size_t &elements);        \
     template void pinnedFree(T* ptr);                       \
 
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index ea40b4b96f..f4d06a3324 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -17,22 +17,23 @@ namespace opencl
     cl::Buffer *bufferAlloc(const size_t &bytes);
     void bufferFree(cl::Buffer *buf);
 
-    template<typename T> T *memAlloc(const size_t &elements);
+    template<typename T> T* memAlloc(const size_t &elements);
+    void *memAllocUser(const size_t &bytes);
 
     // Need these as 2 separate function and not a default argument
     // This is because it is used as the deleter in shared pointer
     // which cannot support default arguments
     template<typename T> void memFree(T* ptr);
-    template<typename T> void memFreeLocked(T* ptr, bool user_unlock);
-    template<typename T> void memLock(const T *ptr);
-    template<typename T> void memUnlock(const T *ptr);
+    void memFreeUser(void* ptr);
+
+    void memLock(const void *ptr);
+    void memUnlock(const void *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
 
-    static const unsigned MAX_BUFFERS   = 1000;
-
     size_t getMaxBytes();
+    unsigned getMaxBuffers();
 
     void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes,  size_t *lock_buffers);

From 82e655825f74b9faf371550a20c839fee1701564 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 2 Feb 2016 19:12:29 -0500
Subject: [PATCH 0344/2677] JIT evaluation can now be tweaked by environment
 variables

---
 src/backend/cpu/Array.cpp       |  5 +++--
 src/backend/cpu/platform.cpp    | 16 ++++++++++++++++
 src/backend/cpu/platform.hpp    |  2 ++
 src/backend/cuda/Array.cpp      |  5 +++--
 src/backend/cuda/platform.cpp   | 17 +++++++++++++++++
 src/backend/cuda/platform.hpp   |  2 ++
 src/backend/opencl/Array.cpp    | 16 +++-------------
 src/backend/opencl/platform.cpp | 22 ++++++++++++++++++++++
 src/backend/opencl/platform.hpp |  2 ++
 9 files changed, 70 insertions(+), 17 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 891604cd27..6d51f63ba0 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -19,6 +19,7 @@
 #include <queue.hpp>
 #include <cstring>
 #include <cstddef>
+#include <MemoryManager.hpp>
 
 namespace cpu
 {
@@ -159,8 +160,8 @@ createNodeArray(const dim4 &dims, Node_ptr node)
     n->getInfo(length, buf_count, bytes);
     n->reset();
 
-    if (length > MAX_TNJ_LEN ||
-        buf_count >= MAX_BUFFERS ||
+    if (length > getMaxJitSize() ||
+        buf_count >= getMaxBuffers() ||
         bytes >= getMaxBytes()) {
         out.eval();
     }
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 65a5ab1faf..7e6bc81e43 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -180,6 +180,22 @@ CPUInfo::CPUInfo()
 namespace cpu
 {
 
+unsigned getMaxJitSize()
+{
+    const int MAX_JIT_LEN = 20;
+
+    static int length = 0;
+    if (length == 0) {
+        std::string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
+        if (!env_var.empty()) {
+            length = std::stoi(env_var);
+        } else {
+            length = MAX_JIT_LEN;
+        }
+    }
+    return length;
+}
+
 int getBackend()
 {
     return AF_BACKEND_CPU;
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 9118ade8bd..82ed42c8f9 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -35,4 +35,6 @@ namespace cpu {
     void sync(int device);
 
     queue& getQueue(int idx = 0);
+
+    unsigned getMaxJitSize();
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 6e95dd1102..48bee655d1 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -16,6 +16,7 @@
 #include <memory.hpp>
 #include <platform.hpp>
 #include <cstddef>
+#include <MemoryManager.hpp>
 
 using af::dim4;
 
@@ -148,8 +149,8 @@ namespace cuda
         n->getInfo(length, buf_count, bytes);
         n->resetFlags();
 
-        if (length > MAX_JIT_LEN ||
-            buf_count >= MAX_BUFFERS ||
+        if (length > getMaxJitSize() ||
+            buf_count >= getMaxBuffers() ||
             bytes >= getMaxBytes()) {
             out.eval();
         }
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 5e53fc0034..67f3f08428 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -261,6 +261,23 @@ string getCUDARuntimeVersion()
 
 }
 
+unsigned getMaxJitSize()
+{
+    const int MAX_JIT_LEN = 20;
+
+    static int length = 0;
+    if (length == 0) {
+        std::string env_var = getEnvVar("AF_CUDA_MAX_JIT_LEN");
+        if (!env_var.empty()) {
+            length = std::stoi(env_var);
+        } else {
+            length = MAX_JIT_LEN;
+        }
+    }
+
+    return length;
+}
+
 int getDeviceCount()
 {
     return DeviceManager::getInstance().nDevices;
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 9302f4160e..6b4186b2c2 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -38,6 +38,8 @@ bool isDoubleSupported(int device);
 
 void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
+unsigned getMaxJitSize();
+
 int getDeviceCount();
 
 int getActiveDeviceId();
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index c470a351f3..178be5be32 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -18,14 +18,12 @@
 #include <cstddef>
 #include <af/opencl.h>
 #include <util.hpp>
+#include <MemoryManager.hpp>
 
 using af::dim4;
 
 namespace opencl
 {
-
-    const int MAX_JIT_LEN = 20;
-    const int MAX_JIT_LEN_AMD = 16; //FIXME: Change this when bug is fixed
     using JIT::BufferNode;
     using JIT::Node;
     using JIT::Node_ptr;
@@ -156,14 +154,6 @@ namespace opencl
 
     using af::dim4;
 
-    inline bool is_max_jit_len(const unsigned &len)
-    {
-        if (getActivePlatform() == AFCL_PLATFORM_AMD) {
-            return len >= MAX_JIT_LEN_AMD;
-        }
-        return len >= MAX_JIT_LEN;
-    }
-
     template<typename T>
     Array<T> createNodeArray(const dim4 &dims, Node_ptr node)
     {
@@ -177,8 +167,8 @@ namespace opencl
         n->getInfo(length, buf_count, bytes);
         n->resetFlags();
 
-        if (is_max_jit_len(length) ||
-            buf_count >= MAX_BUFFERS ||
+        if (length > getMaxJitSize() ||
+            buf_count >= getMaxBuffers() ||
             bytes >= getMaxBytes()) {
             out.eval();
         }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 9dbb3ca38c..6855e79f66 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -776,6 +776,28 @@ bool synchronize_calls() {
     return sync;
 }
 
+
+unsigned getMaxJitSize()
+{
+    const int MAX_JIT_LEN = 20;
+    const int MAX_JIT_LEN_AMD = 16; //FIXME: Change this when bug is fixed
+
+    static int length = 0;
+    if (length == 0) {
+        std::string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
+        if (!env_var.empty()) {
+            length = std::stoi(env_var);
+        } else {
+            length = MAX_JIT_LEN;
+        }
+    }
+
+    if (getActivePlatform() == AFCL_PLATFORM_AMD) {
+        return std::min(length, MAX_JIT_LEN_AMD);
+    }
+    return length;
+}
+
 }
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 9b5377dc3c..4c745e0c91 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -98,6 +98,8 @@ int getDeviceCount();
 
 int getActiveDeviceId();
 
+unsigned getMaxJitSize();
+
 const cl::Context& getContext();
 
 cl::CommandQueue& getQueue();

From f674cdacf2bc1bc27f3f4649da2f80f35ec632d4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 2 Feb 2016 18:42:55 -0500
Subject: [PATCH 0345/2677] BUGFIX: Fixing error in where for OpenCL backend

- Was erroring out when no elemnts were found
- Adding necessary test
---
 src/backend/opencl/kernel/where.hpp | 4 +++-
 test/where.cpp                      | 7 +++++++
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 2cbf8c1019..2b1308fcec 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -159,7 +159,9 @@ namespace kernel
                 out.info.strides[k] = total;
             }
 
-            get_out_idx<T>(out.data, otmp, rtmp, in, threads_x, groups_x, groups_y);
+            if (total > 0) {
+                get_out_idx<T>(out.data, otmp, rtmp, in, threads_x, groups_x, groups_y);
+            }
 
             bufferFree(rtmp.data);
             bufferFree(otmp.data);
diff --git a/test/where.cpp b/test/where.cpp
index 37208f2ee2..08ed878aea 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -121,3 +121,10 @@ TYPED_TEST(Where, CPP)
                                                         << std::endl;
     }
 }
+
+TEST(Where, ISSUE_1259)
+{
+    af::array a = af::randu(10, 10, 10);
+    af::array indices = af::where(a > 2);
+    ASSERT_EQ(indices.elements(), 0);
+}

From 01d819af995f6de6d18036b18aaa75f7e734384a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 4 Feb 2016 10:28:41 +0530
Subject: [PATCH 0346/2677] Prevent copy assignment & construction of
 af::Window object

Fixes #1244
---
 include/af/graphics.h | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index defdbc165d..7485686479 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -30,6 +30,8 @@ namespace af
 
    \brief Window object to render af::arrays
 
+   Windows are not CopyConstructible or CopyAssignable.
+
    \ingroup graphics_func
  */
 class AFAPI Window {
@@ -43,6 +45,9 @@ class AFAPI Window {
 
         void initWindow(const int width, const int height, const char* const title);
 
+        Window(const Window&);                 // Prevent copy-construction
+        Window& operator=(const Window&);      // Prevent assignment
+
     public:
         /**
            Creates a window object with default width
@@ -84,6 +89,7 @@ class AFAPI Window {
            \ingroup gfx_func_window
          */
         Window(const af_window wnd);
+
         /**
            Destroys the window handle
 

From a1b7f8c55032d12350f9cef07a25d1b94a9e3042 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 2 Feb 2016 19:15:20 -0500
Subject: [PATCH 0347/2677] Changes to internal memory manager

- Manager now contains list of locked and free buffers separately
- Should improve performance when allocationg new buffers
- Added proper documentation
---
 .../configuring_arrayfire_environment.md      |  25 ++
 src/backend/MemoryManager.cpp                 | 227 ++++++++++--------
 src/backend/MemoryManager.hpp                 |  14 +-
 3 files changed, 160 insertions(+), 106 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index a9ec486d10..d554046f1e 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -142,3 +142,28 @@ When the environment variable is not set, it is treated to be non zero.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 AF_MEM_DEBUG=1 ./myprogram
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+AF_MAX_BUFFERS {#af_max_buffers}
+-------------------------------------------------------------------------
+
+When AF_MAX_BUFFERS is set, this environment variable specifies the maximum number of buffers allocated before garbage collection kicks in.
+
+Please note that the total number of buffers that can exist simultaneously can be higher than this number. This variable tells the garbage collector that it should free any available buffers immediately if the treshold is reached.
+
+When not set, the default value is 1000.
+
+AF_OPENCL_MAX_JIT_LEN {#af_opencl_max_jit_len}
+-------------------------------------------------------------------------------
+
+When set, this environment variable specifies the maximum length of the OpenCL JIT tree after which evaluation is forced. The default value for this is 16 for AMD devices and 20 otherwise.
+
+AF_CUDA_MAX_JIT_LEN {#af_cuda_max_jit_len}
+-------------------------------------------------------------------------------
+
+When set, this environment variable specifies the maximum length of the CUDA JIT tree after which evaluation is forced. The default value for this is 20.
+
+AF_CPU_MAX_JIT_LEN {#af_cpu_max_jit_len}
+-------------------------------------------------------------------------------
+
+When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced. The default value for this is 20.
diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 5773c19de7..b66dfc33e7 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -26,19 +26,32 @@ MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug):
     debug_mode(debug)
 {
     lock_guard_t lock(this->memory_mutex);
-    std::string env_var = getEnvVar("AF_MEM_DEBUG");
+
+    for (int n = 0; n < num_devices; n++) {
+        // Calling getMaxMemorySize() here calls the virtual function that returns 0
+        // Call it from outside the constructor.
+        memory[n].max_bytes     = ONE_GB;
+        memory[n].total_bytes   = 0;
+        memory[n].total_buffers = 0;
+        memory[n].lock_bytes    = 0;
+        memory[n].lock_buffers  = 0;
+    }
+
+    // Check for environment variables
+
+    std::string env_var;
+
+    // Debug mode
+    env_var = getEnvVar("AF_MEM_DEBUG");
     if (!env_var.empty()) {
         this->debug_mode = env_var[0] != '0';
     }
     if (this->debug_mode) mem_step_size = 1;
 
-    for (int n = 0; n < num_devices; n++) {
-        // Calling getMaxMemorySize() here calls the virtual function that returns 0
-        // Call it from outside the constructor.
-        memory[n].max_bytes    = ONE_GB;
-        memory[n].total_bytes  = 0;
-        memory[n].lock_bytes   = 0;
-        memory[n].lock_buffers = 0;
+    // Max Buffer count
+    env_var = getEnvVar("AF_MAX_BUFFERS");
+    if (!env_var.empty()) {
+        this->max_buffers = std::max(1, std::stoi(env_var));
     }
 }
 
@@ -61,28 +74,17 @@ void MemoryManager::garbageCollect()
     lock_guard_t lock(this->memory_mutex);
     memory_info& current = this->getCurrentMemoryInfo();
 
-    for(buffer_iter iter = current.map.begin();
-        iter != current.map.end(); ++iter) {
-
-        if (!(iter->second).manager_lock) {
-
-            if (!(iter->second).user_lock) {
-                if ((iter->second).bytes > 0) {
-                    this->nativeFree(iter->first);
-                }
-                current.total_bytes -= iter->second.bytes;
-            }
-        }
-    }
-
-    buffer_iter memory_curr = current.map.begin();
-    buffer_iter memory_end  = current.map.end();
-
-    while(memory_curr != memory_end) {
-        if (memory_curr->second.manager_lock || memory_curr->second.user_lock) {
-            ++memory_curr;
-        } else {
-            current.map.erase(memory_curr++);
+    // Return if all buffers are locked
+    if (current.total_buffers == current.lock_buffers) return;
+
+    for (auto &kv : current.free_map) {
+        size_t num_ptrs = kv.second.size();
+        //Free memory by popping the last element
+        for (int n = num_ptrs-1; n >= 0; n--) {
+            this->nativeFree(kv.second[n]);
+            current.total_bytes -= kv.first;
+            current.total_buffers--;
+            kv.second.pop_back();
         }
     }
 }
@@ -92,25 +94,47 @@ void MemoryManager::unlock(void *ptr, bool user_unlock)
     lock_guard_t lock(this->memory_mutex);
     memory_info& current = this->getCurrentMemoryInfo();
 
-    buffer_iter iter = current.map.find((void *)ptr);
+    locked_iter iter = current.locked_map.find((void *)ptr);
+
+    // Pointer not found in locked map
+    if (iter == current.locked_map.end()) {
+        // Probably came from user, just free it
+        this->nativeFree(ptr);
+        return;
+    }
 
-    if (iter != current.map.end()) {
+    if (user_unlock) {
+        (iter->second).user_lock = false;
+    } else {
+        (iter->second).manager_lock = false;
+    }
 
-        iter->second.manager_lock = false;
-        if ((iter->second).user_lock && !user_unlock) return;
+    // Return early if either one is locked
+    if ((iter->second).user_lock || (iter->second).manager_lock) return;
 
-        iter->second.user_lock = false;
-        current.lock_bytes -= iter->second.bytes;
-        current.lock_buffers--;
+    size_t bytes = iter->second.bytes;
+    current.lock_bytes -= iter->second.bytes;
+    current.lock_buffers--;
 
-        if (this->debug_mode) {
-            if ((iter->second).bytes > 0) {
-                this->nativeFree(iter->first);
-            }
-        }
+    current.locked_map.erase(iter);
 
+    if (this->debug_mode) {
+        // Just free memory in debug mode
+        if ((iter->second).bytes > 0) {
+            this->nativeFree(iter->first);
+        }
     } else {
-        this->nativeFree(ptr); // Free it because we are not sure what the size is
+        // In regular mode, move buffer to free map
+        free_iter fiter = current.free_map.find(bytes);
+        if (fiter != current.free_map.end()) {
+            // If found, push back
+            fiter->second.push_back(ptr);
+        } else {
+            // If not found, create new vector for this size
+            std::vector<void *> ptrs;
+            ptrs.push_back(ptr);
+            current.free_map[bytes] = ptrs;
+        }
     }
 }
 
@@ -129,45 +153,41 @@ void *MemoryManager::alloc(const size_t bytes, bool user_lock)
 
             // FIXME: Add better checks for garbage collection
             // Perhaps look at total memory available as a metric
-            if (current.map.size() > this->max_buffers ||
-                current.lock_bytes >= current.max_bytes) {
-
+            if (current.lock_bytes >= current.max_bytes ||
+                current.total_buffers >= this->max_buffers) {
                 this->garbageCollect();
             }
 
-            for(buffer_iter iter = current.map.begin();
-                iter != current.map.end(); ++iter) {
-
-                buffer_info info = iter->second;
-
-                if (!info.manager_lock &&
-                    !info.user_lock &&
-                    info.bytes == alloc_bytes) {
+            free_iter iter = current.free_map.find(alloc_bytes);
 
-                    iter->second.manager_lock = true;
-                    current.lock_bytes += alloc_bytes;
-                    current.lock_buffers++;
-                    return iter->first;
-                }
+            if (iter != current.free_map.end() && !iter->second.empty()) {
+                ptr = iter->second.back();
+                iter->second.pop_back();
             }
+
         }
 
-        // Perform garbage collection if memory can not be allocated
-        try {
-            ptr = this->nativeAlloc(alloc_bytes);
-        } catch (AfError &ex) {
-            // If out of memory, run garbage collect and try again
-            if (ex.getError() != AF_ERR_NO_MEM) throw;
-            this->garbageCollect();
-            ptr = this->nativeAlloc(alloc_bytes);
+        // Only comes here if buffer size not found or in debug mode
+        if (ptr == NULL) {
+            // Perform garbage collection if memory can not be allocated
+            try {
+                ptr = this->nativeAlloc(alloc_bytes);
+            } catch (AfError &ex) {
+                // If out of memory, run garbage collect and try again
+                if (ex.getError() != AF_ERR_NO_MEM) throw;
+                this->garbageCollect();
+                ptr = this->nativeAlloc(alloc_bytes);
+            }
+            // Increment these two only when it succeeds to come here.
+            current.total_bytes += alloc_bytes;
+            current.total_buffers += 1;
         }
 
-        buffer_info info = {true, false, alloc_bytes};
-        current.map[ptr] = info;
 
+        locked_info info = {true, user_lock, alloc_bytes};
+        current.locked_map[ptr] = info;
         current.lock_bytes += alloc_bytes;
         current.lock_buffers++;
-        current.total_bytes += alloc_bytes;
     }
     return ptr;
 }
@@ -178,34 +198,22 @@ void MemoryManager::userLock(const void *ptr)
 
     lock_guard_t lock(this->memory_mutex);
 
-    buffer_iter iter = current.map.find(const_cast<void *>(ptr));
+    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
 
-    if (iter != current.map.end()) {
+    if (iter != current.locked_map.end()) {
         iter->second.user_lock = true;
     } else {
-        buffer_info info = { true,
-                          true,
-                          100 }; //This number is not relevant
+        locked_info info = {false,
+                            true,
+                            100}; //This number is not relevant
 
-        current.map[(void *)ptr] = info;
+        current.locked_map[(void *)ptr] = info;
     }
 }
 
 void MemoryManager::userUnlock(const void *ptr)
 {
-    memory_info& current = this->getCurrentMemoryInfo();
-
-    lock_guard_t lock(this->memory_mutex);
-
-    buffer_iter iter = current.map.find((void *)ptr);
-    if (iter != current.map.end()) {
-        iter->second.user_lock = false;
-        if (this->debug_mode) {
-            if ((iter->second).bytes > 0) {
-                this->nativeFree(iter->first);
-            }
-        }
-    }
+    this->unlock(const_cast<void *>(ptr), true);
 }
 
 size_t MemoryManager::getMemStepSize()
@@ -237,32 +245,47 @@ void MemoryManager::printInfo(const char *msg, const int device)
     static const std::string line(head.size(), '-');
     std::cout << line << std::endl << head << std::endl << line << std::endl;
 
-    for(buffer_iter iter = current.map.begin();
-        iter != current.map.end(); ++iter) {
-
-        std::string status_mngr("Unknown");
+    for(auto& kv : current.locked_map) {
+        std::string status_mngr("Yes");
         std::string status_user("Unknown");
-
-        if(iter->second.manager_lock)  status_mngr = "Yes";
-        else                           status_mngr = " No";
-
-        if(iter->second.user_lock)     status_user = "Yes";
-        else                           status_user = " No";
+        if(kv.second.user_lock)     status_user = "Yes";
+        else                        status_user = " No";
 
         std::string unit = "KB";
-        double size = (double)(iter->second.bytes) / 1024;
+        double size = (double)(kv.second.bytes) / 1024;
         if(size >= 1024) {
             size = size / 1024;
             unit = "MB";
         }
 
-        std::cout << "|  " << std::right << std::setw(14) << iter->first << " "
+        std::cout << " |  " << std::right << std::setw(14) << kv.first << " "
                   << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
                   << " | " << std::setw(9) << status_mngr
                   << " | " << std::setw(9) << status_user
                   << " |"  << std::endl;
     }
 
+    for(auto &kv : current.free_map) {
+
+        std::string status_mngr("No");
+        std::string status_user("No");
+
+        std::string unit = "KB";
+        double size = (double)(kv.first) / 1024;
+        if(size >= 1024) {
+            size = size / 1024;
+            unit = "MB";
+        }
+
+        for (auto &ptr : kv.second) {
+            std::cout << " |  " << std::right << std::setw(14) << ptr << " "
+                      << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                      << " | " << std::setw(9) << status_mngr
+                      << " | " << std::setw(9) << status_user
+                      << " |"  << std::endl;
+        }
+    }
+
     std::cout << line << std::endl;
 }
 
@@ -272,7 +295,7 @@ void MemoryManager::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
     lock_guard_t lock(this->memory_mutex);
     memory_info current = this->getCurrentMemoryInfo();
     if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
-    if (alloc_buffers ) *alloc_buffers = current.map.size();
+    if (alloc_buffers ) *alloc_buffers = current.total_buffers;
     if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
     if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
 }
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index a010f30064..015fa6db3d 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -29,17 +29,23 @@ class MemoryManager
         bool manager_lock;
         bool user_lock;
         size_t bytes;
-    } buffer_info;
+    } locked_info;
 
-    typedef std::map<void *, buffer_info> buffer_t;
-    typedef buffer_t::iterator buffer_iter;
+    typedef std::map<void *, locked_info> locked_t;
+    typedef locked_t::iterator locked_iter;
+
+    typedef std::map<size_t, std::vector<void *> >free_t;
+    typedef free_t::iterator free_iter;
 
     typedef struct
     {
-        buffer_t map;
+        locked_t locked_map;
+        free_t   free_map;
+
         size_t lock_bytes;
         size_t lock_buffers;
         size_t total_bytes;
+        size_t total_buffers;
         size_t max_bytes;
     } memory_info;
 

From a9385003330a999b125eaf2f8d193bf78954b424 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 4 Feb 2016 01:53:08 -0500
Subject: [PATCH 0348/2677] Fixes to random.hpp to work in multi-threaded
 environment

---
 src/backend/cpu/kernel/random.hpp | 119 ++++++++++++++++++++++--------
 src/backend/cpu/random.cpp        |  40 ++--------
 test/random.cpp                   |   4 +
 3 files changed, 97 insertions(+), 66 deletions(-)

diff --git a/src/backend/cpu/kernel/random.hpp b/src/backend/cpu/kernel/random.hpp
index f9cb3906f7..9c59a64db9 100644
--- a/src/backend/cpu/kernel/random.hpp
+++ b/src/backend/cpu/kernel/random.hpp
@@ -24,6 +24,12 @@ namespace kernel
 
 using namespace std;
 
+#if defined(_WIN32)
+    #define __THREAD_LOCAL static __declspec(thread)
+#else
+    #define __THREAD_LOCAL static __thread
+#endif
+
 template<typename T>
 using is_arithmetic_t       = typename enable_if< is_arithmetic<T>::value,      function<T()>>::type;
 template<typename T>
@@ -68,74 +74,125 @@ nrand(GenType &generator)
     return [func] () { return T(func(), func());};
 }
 
-static mt19937 generator;
-static unsigned long long gen_seed = 0;
-static bool is_first = true;
-#define GLOBAL 1
+mt19937& getGenerator()
+{
+    // FIXME: This abomination of a work around is brought to you
+    // by incomplete standards from Xcode and Visual Studio
+    // Should ideally be using thread_local on object instead of pointer
+    __THREAD_LOCAL mt19937 *generator = NULL;
+    if (generator == NULL) generator = new mt19937();
+    return *generator;
+}
+
+unsigned long long& getSeed()
+{
+    __THREAD_LOCAL unsigned long long gen_seed = 0;
+    return gen_seed;
+}
+
+void getSeedPtr(unsigned long long *seed)
+{
+    *seed = getSeed();
+}
+
+bool& isFirst()
+{
+    __THREAD_LOCAL bool is_first = true;
+    return is_first;
+}
+
+void setSeed(const uintl seed)
+{
+    getGenerator().seed(seed);
+    getSeed() = seed;
+    isFirst() = false;
+}
+
+//FIXME: See if we can use functors instead of function pointer directly
+template<typename T>
+struct RandomDistribution
+{
+    std::function<T()> func;
+    RandomDistribution(std::function<T()> dist_func) : func(dist_func)
+    {
+    }
+};
 
 template<typename T>
 void randn(Array<T> out)
 {
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
+    __THREAD_LOCAL unsigned long long my_seed = 0;
+    if (isFirst()) {
+        my_seed = getSeed();
+        setSeed(my_seed);
     }
 
-    static auto gen = nrand<T>(generator);
+    // FIXME: This abomination of a work around is brought to you
+    // by incomplete standards from Xcode and Visual Studio
+    // Should ideally be using thread_local on object instead of pointer
+    __THREAD_LOCAL RandomDistribution<T> *distPtr = NULL;
 
-    if (my_seed != gen_seed) {
-        gen = nrand<T>(generator);
-        my_seed = gen_seed;
+    if (!distPtr || my_seed != getSeed()) {
+        if (distPtr) delete distPtr;
+        distPtr = new RandomDistribution<T>(nrand<T>(getGenerator()));
+        my_seed = getSeed();
     }
 
     T *outPtr = out.get();
     for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = gen();
+        outPtr[i] = distPtr->func();
     }
 }
 
 template<typename T>
 void randu(Array<T> out)
 {
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
+    __THREAD_LOCAL unsigned long long my_seed = 0;
+    if (isFirst()) {
+        my_seed = getSeed();
+        setSeed(my_seed);
     }
 
-    static auto gen = urand<T>(generator);
+    // FIXME: This abomination of a work around is brought to you
+    // by incomplete standards from Xcode and Visual Studio
+    // Should ideally be using thread_local on object instead of pointer
+    __THREAD_LOCAL RandomDistribution<T> *distPtr = NULL;
 
-    if (my_seed != gen_seed) {
-        gen = urand<T>(generator);
-        my_seed = gen_seed;
+    if (!distPtr || my_seed != getSeed()) {
+        if (distPtr) delete distPtr;
+        distPtr = new RandomDistribution<T>(urand<T>(getGenerator()));
+        my_seed = getSeed();
     }
 
     T *outPtr = out.get();
     for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = gen();
+        outPtr[i] = distPtr->func();
     }
 }
 
 template<>
 void randu(Array<char> out)
 {
-    static unsigned long long my_seed = 0;
-    if (is_first) {
-        setSeed(gen_seed);
-        my_seed = gen_seed;
+    __THREAD_LOCAL unsigned long long my_seed = 0;
+    if (isFirst()) {
+        my_seed = getSeed();
+        setSeed(my_seed);
     }
 
-    static auto gen = urand<float>(generator);
+    // FIXME: This abomination of a work around is brought to you
+    // by incomplete standards from Xcode and Visual Studio
+    // Should ideally be using thread_local on object instead of pointer
+    __THREAD_LOCAL RandomDistribution<float> *distPtr = NULL;
 
-    if (my_seed != gen_seed) {
-        gen = urand<float>(generator);
-        my_seed = gen_seed;
+    if (!distPtr || my_seed != getSeed()) {
+        if (distPtr) delete distPtr;
+        distPtr = new RandomDistribution<float>(nrand<float>(getGenerator()));
+        my_seed = getSeed();
     }
 
     char *outPtr = out.get();
     for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = gen() > 0.5;
+        outPtr[i] = distPtr->func() > 0.5;
     }
 }
 
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index 89d86c3848..06cbca34d7 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -39,6 +39,7 @@ INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
 INSTANTIATE_UNIFORM(uintl)
 INSTANTIATE_UNIFORM(uchar)
+INSTANTIATE_UNIFORM(char)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
 
@@ -58,48 +59,17 @@ INSTANTIATE_NORMAL(double)
 INSTANTIATE_NORMAL(cfloat)
 INSTANTIATE_NORMAL(cdouble)
 
-template<>
-Array<char> randu(const af::dim4 &dims)
-{
-    static unsigned long long my_seed = 0;
-    if (kernel::is_first) {
-        setSeed(kernel::gen_seed);
-        my_seed = kernel::gen_seed;
-    }
-
-    static auto gen = kernel::urand<float>(kernel::generator);
-
-    if (my_seed != kernel::gen_seed) {
-        gen = kernel::urand<float>(kernel::generator);
-        my_seed = kernel::gen_seed;
-    }
-
-    Array<char> outArray = createEmptyArray<char>(dims);
-    auto func = [=](Array<char> outArray) {
-        char *outPtr = outArray.get();
-        for (int i = 0; i < (int)outArray.elements(); i++) {
-            outPtr[i] = gen() > 0.5;
-        }
-    };
-    getQueue().enqueue(func, outArray);
-
-    return outArray;
-}
-
 void setSeed(const uintl seed)
 {
-    auto f = [=](const uintl seed){
-        kernel::generator.seed(seed);
-        kernel::is_first = false;
-        kernel::gen_seed = seed;
-    };
-    getQueue().enqueue(f, seed);
+    getQueue().enqueue(kernel::setSeed, seed);
 }
 
 uintl getSeed()
 {
+    uintl seed = 0;
+    getQueue().enqueue(kernel::getSeedPtr, &seed);
     getQueue().sync();
-    return kernel::gen_seed;
+    return seed;
 }
 
 }
diff --git a/test/random.cpp b/test/random.cpp
index 29f157a776..74f7e6541b 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -59,6 +59,7 @@ void randuTest(af::dim4 & dims)
 
     af_array outArray = 0;
     ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
 
@@ -69,6 +70,7 @@ void randnTest(af::dim4 &dims)
 
     af_array outArray = 0;
     ASSERT_EQ(AF_SUCCESS, af_randn(&outArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
 
@@ -124,6 +126,7 @@ void randuArgsTest()
     dim_t dims[] = {1, 2, 3, 0};
     af_array outArray = 0;
     ASSERT_EQ(AF_ERR_SIZE, af_randu(&outArray, ndims, dims, (af_dtype) af::dtype_traits<char>::af_type));
+    ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
 
@@ -143,6 +146,7 @@ TEST(Random, CPP)
     af::dim4 dims(1, 2, 3, 1);
     af::array out1 = af::randu(dims);
     af::array out2 = af::randn(dims);
+    af::sync();
 }
 
 template<typename T>

From 95aaf729dfc362b08646870fa5f01d91bdebb600 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 4 Feb 2016 01:53:32 -0500
Subject: [PATCH 0349/2677] OpenCL JIT now launches more threads per work group
 for CPU devices

---
 src/backend/opencl/jit.cpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 66c7c1e9f7..d6ab240fd6 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -19,6 +19,7 @@
 #include <dispatch.hpp>
 #include <err_opencl.hpp>
 #include <functional>
+#include <af/opencl.h>
 
 namespace opencl
 {
@@ -180,13 +181,16 @@ void evalNodes(Param &out, Node *node)
         uint groups_1 = 1;
         uint num_odims = 4;
 
+        // CPUs seem to perform better with work group size 1024
+        const int work_group_size = (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
+
         while (num_odims >= 1) {
             if (out.info.dims[num_odims - 1] == 1) num_odims--;
             else break;
         }
 
         if (is_linear) {
-            local_0 = 256;
+            local_0 = work_group_size;
             uint out_elements = out.info.dims[3] * out.info.strides[3];
             uint groups = divup(out_elements, local_0);
 
@@ -194,8 +198,8 @@ void evalNodes(Param &out, Node *node)
             global_0 = divup(groups, global_1) * local_0;
 
         } else {
-            local_0 = 64;
             local_1 =  4;
+            local_0 = work_group_size / local_1;
 
             groups_0 = divup(out.info.dims[0], local_0);
             groups_1 = divup(out.info.dims[1], local_1);

From e0879cb37f9223d68004f65b984183eb83e8ddc7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 5 Feb 2016 01:44:16 -0500
Subject: [PATCH 0350/2677] FEAT: Adding functions exposing Array internals

- af_create_array_with_strides
- af_get_strides
- af_get_offset
- af_get_raw_ptr
- af_is_linear
- af_is_owner
---
 include/af/internal.h        |  62 ++++++++++++
 src/api/c/internal.cpp       | 181 +++++++++++++++++++++++++++++++++++
 src/api/cpp/internal.cpp     |  63 ++++++++++++
 src/api/unified/internal.cpp |  54 +++++++++++
 src/backend/cpu/Array.cpp    |  18 ++++
 src/backend/cpu/Array.hpp    |   5 +
 src/backend/cuda/Array.cpp   |  21 ++++
 src/backend/cuda/Array.hpp   |   4 +
 src/backend/opencl/Array.cpp |  21 ++++
 src/backend/opencl/Array.hpp |   4 +
 10 files changed, 433 insertions(+)
 create mode 100644 include/af/internal.h
 create mode 100644 src/api/c/internal.cpp
 create mode 100644 src/api/cpp/internal.cpp
 create mode 100644 src/api/unified/internal.cpp

diff --git a/include/af/internal.h b/include/af/internal.h
new file mode 100644
index 0000000000..fdd0158e2e
--- /dev/null
+++ b/include/af/internal.h
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <af/dim4.hpp>
+
+#ifdef __cplusplus
+namespace af
+{
+    class array;
+
+    AFAPI array createArray(const void *data, const dim_t offset,
+                            const dim4 dims, const dim4 strides,
+                            const af::dtype ty,
+                            const af::source location);
+
+    AFAPI dim4 getStrides(const array &in);
+
+    AFAPI dim_t getOffset(const array &in);
+
+    AFAPI void *getRawPtr(const array &in);
+
+    AFAPI bool isLinear(const array &in);
+
+    AFAPI bool isOwner(const array &in);
+}
+#endif
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+    AFAPI af_err af_create_array_with_strides(af_array *arr,
+                                              const void *data,
+                                              const dim_t offset,
+                                              const unsigned ndims,
+                                              const dim_t *const dims,
+                                              const dim_t *const strides,
+                                              const af_dtype ty,
+                                              const af_source location);
+
+    AFAPI af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array arr);
+
+    AFAPI af_err af_get_offset(dim_t *offset, const af_array arr);
+
+    AFAPI af_err af_get_raw_ptr(void **ptr, const af_array arr);
+
+    AFAPI af_err af_is_linear(bool *result, const af_array arr);
+
+    AFAPI af_err af_is_owner(bool *result, const af_array arr);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
new file mode 100644
index 0000000000..d086d431f1
--- /dev/null
+++ b/src/api/c/internal.cpp
@@ -0,0 +1,181 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <af/device.h>
+#include <af/version.h>
+#include <af/internal.h>
+#include <backend.hpp>
+#include <platform.hpp>
+#include <Array.hpp>
+#include <handle.hpp>
+#include "err_common.hpp"
+#include <cstring>
+
+using namespace detail;
+
+af_err af_create_array_with_strides(af_array *arr,
+                                    const void *data,
+                                    const dim_t offset,
+                                    const unsigned ndims,
+                                    const dim_t *const dims_,
+                                    const dim_t *const strides_,
+                                    const af_dtype ty,
+                                    const af_source location)
+{
+    try {
+
+        ARG_ASSERT(2, offset >= 0);
+        ARG_ASSERT(3, ndims >=1 && ndims <= 4);
+        ARG_ASSERT(4, dims_ != NULL);
+        ARG_ASSERT(5, strides_ != NULL);
+        ARG_ASSERT(5, strides_[0] == 1);
+
+        dim4 dims(ndims, dims_);
+        dim4 strides(ndims, strides_);
+
+        bool isdev = location == afDevice;
+
+        af_array res;
+        AF_CHECK(af_init());
+
+        switch (ty) {
+        case f32: res = getHandle(Array<float  >(dims, strides, offset, (float   *)data, isdev)); break;
+        case f64: res = getHandle(Array<double >(dims, strides, offset, (double  *)data, isdev)); break;
+        case c32: res = getHandle(Array<cfloat >(dims, strides, offset, (cfloat  *)data, isdev)); break;
+        case c64: res = getHandle(Array<cdouble>(dims, strides, offset, (cdouble *)data, isdev)); break;
+        case u32: res = getHandle(Array<uint   >(dims, strides, offset, (uint    *)data, isdev)); break;
+        case s32: res = getHandle(Array<int    >(dims, strides, offset, (int     *)data, isdev)); break;
+        case u64: res = getHandle(Array<uintl  >(dims, strides, offset, (uintl   *)data, isdev)); break;
+        case s64: res = getHandle(Array<intl   >(dims, strides, offset, (intl    *)data, isdev)); break;
+        case u16: res = getHandle(Array<ushort >(dims, strides, offset, (ushort  *)data, isdev)); break;
+        case s16: res = getHandle(Array<short  >(dims, strides, offset, (short   *)data, isdev)); break;
+        case b8 : res = getHandle(Array<char   >(dims, strides, offset, (char    *)data, isdev)); break;
+        case u8 : res = getHandle(Array<uchar  >(dims, strides, offset, (uchar   *)data, isdev)); break;
+        default: TYPE_ERROR(6, ty);
+        }
+
+        std::swap(*arr, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array in)
+{
+    try {
+        ArrayInfo info = getInfo(in);
+        *s0 = info.strides()[0];
+        *s1 = info.strides()[1];
+        *s2 = info.strides()[2];
+        *s3 = info.strides()[3];
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_get_offset(dim_t *offset, const af_array arr)
+{
+    try {
+
+        dim_t res = 0;
+
+        af_dtype ty = getInfo(arr).getType();
+
+        switch (ty) {
+        case f32: res = getArray<float  >(arr).getOffset(); break;
+        case f64: res = getArray<double >(arr).getOffset(); break;
+        case c32: res = getArray<cfloat >(arr).getOffset(); break;
+        case c64: res = getArray<cdouble>(arr).getOffset(); break;
+        case u32: res = getArray<uint   >(arr).getOffset(); break;
+        case s32: res = getArray<int    >(arr).getOffset(); break;
+        case u64: res = getArray<uintl  >(arr).getOffset(); break;
+        case s64: res = getArray<intl   >(arr).getOffset(); break;
+        case u16: res = getArray<ushort >(arr).getOffset(); break;
+        case s16: res = getArray<short  >(arr).getOffset(); break;
+        case b8 : res = getArray<char   >(arr).getOffset(); break;
+        case u8 : res = getArray<uchar  >(arr).getOffset(); break;
+        default: TYPE_ERROR(6, ty);
+        }
+
+        std::swap(*offset, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+
+}
+
+af_err af_get_raw_ptr(void **ptr, const af_array arr)
+{
+    try {
+
+        void *res = NULL;
+
+        af_dtype ty = getInfo(arr).getType();
+
+        switch (ty) {
+        case f32: res = (void *)getArray<float  >(arr).get(); break;
+        case f64: res = (void *)getArray<double >(arr).get(); break;
+        case c32: res = (void *)getArray<cfloat >(arr).get(); break;
+        case c64: res = (void *)getArray<cdouble>(arr).get(); break;
+        case u32: res = (void *)getArray<uint   >(arr).get(); break;
+        case s32: res = (void *)getArray<int    >(arr).get(); break;
+        case u64: res = (void *)getArray<uintl  >(arr).get(); break;
+        case s64: res = (void *)getArray<intl   >(arr).get(); break;
+        case u16: res = (void *)getArray<ushort >(arr).get(); break;
+        case s16: res = (void *)getArray<short  >(arr).get(); break;
+        case b8 : res = (void *)getArray<char   >(arr).get(); break;
+        case u8 : res = (void *)getArray<uchar  >(arr).get(); break;
+        default: TYPE_ERROR(6, ty);
+        }
+
+        std::swap(*ptr, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_is_linear(bool *result, const af_array arr)
+{
+    try {
+        *result = getInfo(arr).isLinear();
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_is_owner(bool *result, const af_array arr)
+{
+    try {
+
+        bool res = false;
+
+        af_dtype ty = getInfo(arr).getType();
+
+        switch (ty) {
+        case f32: res = (void *)getArray<float  >(arr).isOwner(); break;
+        case f64: res = (void *)getArray<double >(arr).isOwner(); break;
+        case c32: res = (void *)getArray<cfloat >(arr).isOwner(); break;
+        case c64: res = (void *)getArray<cdouble>(arr).isOwner(); break;
+        case u32: res = (void *)getArray<uint   >(arr).isOwner(); break;
+        case s32: res = (void *)getArray<int    >(arr).isOwner(); break;
+        case u64: res = (void *)getArray<uintl  >(arr).isOwner(); break;
+        case s64: res = (void *)getArray<intl   >(arr).isOwner(); break;
+        case u16: res = (void *)getArray<ushort >(arr).isOwner(); break;
+        case s16: res = (void *)getArray<short  >(arr).isOwner(); break;
+        case b8 : res = (void *)getArray<char   >(arr).isOwner(); break;
+        case u8 : res = (void *)getArray<uchar  >(arr).isOwner(); break;
+        default: TYPE_ERROR(6, ty);
+        }
+
+        std::swap(*result, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/internal.cpp b/src/api/cpp/internal.cpp
new file mode 100644
index 0000000000..f26f9f8bb4
--- /dev/null
+++ b/src/api/cpp/internal.cpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/internal.h>
+#include <af/array.h>
+#include "error.hpp"
+
+namespace af
+{
+    array createArray(const void *data, const dim_t offset,
+                      const dim4 dims, const dim4 strides,
+                      const af::dtype ty,
+                      const af::source location)
+    {
+        af_array res;
+        AF_THROW(af_create_array_with_strides(&res, data, offset,
+                                              dims.ndims(), dims.get(), strides.get(),
+                                              ty, location));
+        return array(res);
+    }
+
+    dim4 getStrides(const array &in)
+    {
+        dim_t s0, s1, s2, s3;
+        AF_THROW(af_get_strides(&s0, &s1, &s2, &s3, in.get()));
+        return dim4(s0, s1, s2, s3);
+    }
+
+    dim_t getOffset(const array &in)
+    {
+        dim_t offset;
+        AF_THROW(af_get_offset(&offset, in.get()));
+        return offset;
+    }
+
+    void *getRawPtr(const array &in)
+    {
+        void *ptr = NULL;
+        AF_THROW(af_get_raw_ptr(&ptr, in.get()));
+        return ptr;
+    }
+
+    bool isLinear(const array &in)
+    {
+        bool is_linear = false;
+        AF_THROW(af_is_linear(&is_linear, in.get()));
+        return is_linear;
+    }
+
+    bool isOwner(const array &in)
+    {
+        bool is_owner = false;
+        AF_THROW(af_is_owner(&is_owner, in.get()));
+        return is_owner;
+    }
+
+}
diff --git a/src/api/unified/internal.cpp b/src/api/unified/internal.cpp
new file mode 100644
index 0000000000..7c223e741c
--- /dev/null
+++ b/src/api/unified/internal.cpp
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/internal.h>
+#include "symbol_manager.hpp"
+
+
+af_err af_create_array_with_strides(af_array *arr,
+                                    const void *data,
+                                    const dim_t offset,
+                                    const unsigned ndims,
+                                    const dim_t *const dims_,
+                                    const dim_t *const strides_,
+                                    const af_dtype ty,
+                                    const af_source location)
+{
+    return CALL(arr, data, offset, ndims, dims_, strides_, ty, location);
+}
+
+af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(s0, s1, s2, s3, in);
+}
+
+af_err af_get_offset(dim_t *offset, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(offset, arr);
+}
+
+af_err af_get_raw_ptr(void **ptr, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(ptr, arr);
+}
+
+af_err af_is_linear(bool *result, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(result, arr);
+}
+
+af_err af_is_owner(bool *result, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(result, arr);
+}
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 6d51f63ba0..fbb8e10b34 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -68,6 +68,21 @@ Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim4 &offsets, c
     ready(true), owner(false)
 { }
 
+template<typename T>
+Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
+                const T * const in_data, bool is_device) :
+    info(getActiveDeviceId(), dims, af::dim4(offset_), strides, (af_dtype)dtype_traits<T>::af_type),
+    data(is_device ? (T*)in_data : memAlloc<T>(info.elements()), memFree<T>),
+    data_dims(dims),
+    node(),
+    offset(offset_),
+    ready(true),
+    owner(true)
+{
+    if (!is_device) {
+        std::copy(in_data, in_data + dims.elements(), data.get());
+    }
+}
 
 template<typename T>
 void Array<T>::eval()
@@ -240,6 +255,9 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     template       void Array<T>::eval() const;                         \
     template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
                                    bool is_device, bool copy_device);   \
+    template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
+                                   const T * const in_data,             \
+                                   bool is_device);                     \
     template       TNJ::Node_ptr Array<T>::getNode() const;             \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 9cd154ec50..891d867d7d 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -106,12 +106,17 @@ namespace cpu
 
         Array() = default;
         Array(dim4 dims);
+
         explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
         explicit Array(af::dim4 dims, TNJ::Node_ptr n);
 
     public:
 
+
+        Array(af::dim4 dims, af::dim4 strides, dim_t offset,
+              const T * const in_data, bool is_device = false);
+
         void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
         void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
         void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 48bee655d1..366d8e2b52 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -86,6 +86,24 @@ namespace cuda
     {
     }
 
+    template<typename T>
+    Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
+                    const T * const in_data, bool is_device) :
+        info(getActiveDeviceId(), dims, af::dim4(offset_), strides, (af_dtype)dtype_traits<T>::af_type),
+        data(is_device ? (T*)in_data : memAlloc<T>(info.elements()), memFree<T>),
+        data_dims(dims),
+        node(),
+        offset(offset_),
+        ready(true),
+        owner(true)
+    {
+        if (!is_device) {
+            cudaStream_t stream = getStream(getActiveDeviceId());
+            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, info.elements() * sizeof(T),
+                                       cudaMemcpyHostToDevice, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
+        }
+    }
 
     template<typename T>
     void Array<T>::eval()
@@ -275,6 +293,9 @@ namespace cuda
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
+    template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
+                                   const T * const in_data,             \
+                                   bool is_device);                     \
     template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
                                    bool is_device, bool copy_device);   \
     template       Array<T>::~Array        ();                          \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index ad4396b48c..b8832db1c6 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -103,12 +103,16 @@ namespace cuda
         bool owner;
 
         Array(af::dim4 dims);
+
         explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false, bool copy_device = false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
         Array(Param<T> &tmp);
         Array(af::dim4 dims, JIT::Node_ptr n);
     public:
 
+        Array(af::dim4 dims, af::dim4 strides, dim_t offset,
+              const T * const in_data, bool is_device = false);
+
         void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
         void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
         void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 178be5be32..f41b2c795d 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -99,6 +99,24 @@ namespace opencl
     {
     }
 
+    template<typename T>
+    Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
+                    const T * const in_data, bool is_device) :
+        info(getActiveDeviceId(), dims, af::dim4(offset_), strides, (af_dtype)dtype_traits<T>::af_type),
+        data(is_device ?
+             (new cl::Buffer((cl_mem)in_data)) :
+             (bufferAlloc(info.elements() * sizeof(T))), bufferFree),
+        data_dims(dims),
+        node(),
+        offset(offset_),
+        ready(true),
+        owner(true)
+    {
+        if (!is_device) {
+            getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T) * info.elements(), in_data);
+        }
+    }
+
 
     template<typename T>
     void Array<T>::eval()
@@ -308,6 +326,9 @@ namespace opencl
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
+    template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
+                                   const T * const in_data,             \
+                                   bool is_device);                     \
     template       Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
     template       Array<T>::~Array        ();                          \
     template       Node_ptr Array<T>::getNode() const;             \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 4c8c05a231..d1a4d973c2 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -95,6 +95,7 @@ namespace opencl
         bool owner;
 
         Array(af::dim4 dims);
+
         Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
         Array(Param &tmp);
         explicit Array(af::dim4 dims, JIT::Node_ptr n);
@@ -103,6 +104,9 @@ namespace opencl
 
     public:
 
+        Array(af::dim4 dims, af::dim4 strides, dim_t offset,
+              const T * const in_data, bool is_device = false);
+
         void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
         void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
         void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }

From b6ccdefa24fb5aaebc1c49b4f2b54f07c6d36640 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 9 Feb 2016 12:17:46 +0530
Subject: [PATCH 0351/2677] Memory leak fix in af_median_all

---
 src/api/c/median.cpp | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 716df78028..50bcad25ee 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -37,12 +37,18 @@ static double median(const af_array& in)
 
     Array<T> sortedArr = sort<T, true>(input, 0);
 
+    af_array sarrHandle = getHandle<T>(sortedArr);
+
     double result;
     T resPtr[2];
     af_array res = 0;
-    AF_CHECK(af_index(&res, getHandle<T>(sortedArr), 1, mdSpan));
+    AF_CHECK(af_index(&res, sarrHandle, 1, mdSpan));
     AF_CHECK(af_get_data_ptr((void*)&resPtr, res));
 
+    AF_CHECK(af_release_array(res));
+    AF_CHECK(af_release_array(sarrHandle));
+    AF_CHECK(af_release_array(temp));
+
     if (nElems % 2 == 1) {
         result = resPtr[0];
     } else {
@@ -53,9 +59,6 @@ static double median(const af_array& in)
         }
     }
 
-    AF_CHECK(af_release_array(res));
-    AF_CHECK(af_release_array(temp));
-
     return result;
 }
 

From d0f401e6a21028e70230753c08c47217d1880d37 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 15:16:59 -0500
Subject: [PATCH 0352/2677] Compile fix for armel architecture

---
 src/backend/cpu/kernel/sort.hpp        |  1 +
 src/backend/cpu/kernel/sort_by_key.hpp |  1 +
 src/backend/cpu/kernel/sort_index.hpp  |  1 +
 src/backend/cpu/queue.hpp              | 37 ++++++++++++++++++++++++--
 src/backend/cpu/tile.cpp               |  1 -
 src/backend/cpu/transform.cpp          |  1 -
 src/backend/cpu/transpose.cpp          |  1 -
 src/backend/cpu/triangle.cpp           |  1 -
 src/backend/cpu/unwrap.cpp             |  1 -
 src/backend/cpu/where.cpp              |  1 -
 src/backend/cpu/wrap.cpp               |  1 -
 11 files changed, 38 insertions(+), 9 deletions(-)

diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index cba07fabdf..292c6383dc 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -14,6 +14,7 @@
 #include <algorithm>
 #include <numeric>
 #include <err_cpu.hpp>
+#include <functional>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 77713a7240..f9d391dc46 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -15,6 +15,7 @@
 #include <numeric>
 #include <queue>
 #include <err_cpu.hpp>
+#include <functional>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/sort_index.hpp b/src/backend/cpu/kernel/sort_index.hpp
index d2de05a559..b71cc47071 100644
--- a/src/backend/cpu/kernel/sort_index.hpp
+++ b/src/backend/cpu/kernel/sort_index.hpp
@@ -14,6 +14,7 @@
 #include <algorithm>
 #include <numeric>
 #include <err_cpu.hpp>
+#include <functional>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 942ae259b1..c321644444 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -8,7 +8,40 @@
  ********************************************************/
 
 #include <util.hpp>
+
+//FIXME: Is there a better way to check for std::future not being supported ?
+#if defined(__GNUC__) && (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2)
+
+#include <functional>
+using std::function;
+#include <err_cpu.hpp>
+#define __SYNCHRONOUS_ARCH 1
+class queue_impl
+{
+public:
+    template <typename F, typename... Args>
+    void enqueue(const F func, Args... args) const {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+    }
+
+    void sync() const {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+    }
+
+    bool is_worker() const {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+        return false;
+    }
+
+};
+
+#else
+
 #include <async_queue.hpp>
+#define __SYNCHRONOUS_ARCH 0
+typedef async_queue queue_impl;
+
+#endif
 
 #pragma once
 
@@ -18,7 +51,7 @@ namespace cpu {
 class queue {
 public:
   queue()
-    : sync_calls( getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
+    : sync_calls( __SYNCHRONOUS_ARCH == 1 || getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
 
   template <typename F, typename... Args>
   void enqueue(const F func, Args... args) {
@@ -40,7 +73,7 @@ class queue {
 
 private:
   const bool sync_calls;
-  async_queue aQueue;
+  queue_impl aQueue;
 };
 
 }
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 6526917d3a..0fe52c6398 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -10,7 +10,6 @@
 #include <Array.hpp>
 #include <tile.hpp>
 #include <platform.hpp>
-#include <async_queue.hpp>
 #include <kernel/tile.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index b2ab8dba79..3a76fb2f24 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -11,7 +11,6 @@
 #include <transform.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <async_queue.hpp>
 #include "transform_interp.hpp"
 #include <kernel/transform.hpp>
 
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 32663e1f94..a6d410757b 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -13,7 +13,6 @@
 #include <Array.hpp>
 #include <transpose.hpp>
 #include <platform.hpp>
-#include <async_queue.hpp>
 #include <kernel/transpose.hpp>
 #include <utility>
 #include <cassert>
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 2a9553c83a..57f61b1331 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -13,7 +13,6 @@
 #include <triangle.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <async_queue.hpp>
 #include <kernel/triangle.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index 1aa37a4762..d19286f496 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -12,7 +12,6 @@
 #include <dispatch.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <async_queue.hpp>
 #include <kernel/unwrap.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 018cbdfc36..249327163d 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -17,7 +17,6 @@
 #include <ops.hpp>
 #include <vector>
 #include <platform.hpp>
-#include <async_queue.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 07487e0d68..8e0f6fe2f7 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -12,7 +12,6 @@
 #include <dispatch.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <async_queue.hpp>
 #include <kernel/wrap.hpp>
 
 namespace cpu

From 4d7b37a57a01369fdf1ac1b3efc71e746c42d140 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 15:33:00 -0500
Subject: [PATCH 0353/2677] Adding cmake option to disable async queues

---
 src/backend/cpu/CMakeLists.txt | 6 ++++++
 src/backend/cpu/queue.hpp      | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 5dee6de3f5..8ada1d6935 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -3,6 +3,12 @@ ADD_DEFINITIONS(-DAF_CPU)
 
 FIND_PACKAGE(CBLAS REQUIRED)
 
+OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
+
+IF (NOT ${BUILD_CPU_ASYNC})
+    ADD_DEFINITIONS(-DAF_DISABLE_CPU_ASYNC)
+ENDIF()
+
 IF(USE_CPU_F77_BLAS)
     MESSAGE("Using F77 BLAS")
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index c321644444..6d32b85a65 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -10,7 +10,7 @@
 #include <util.hpp>
 
 //FIXME: Is there a better way to check for std::future not being supported ?
-#if defined(__GNUC__) && (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2)
+#if defined(AF_DISABLE_CPU_ASYNC) || (defined(__GNUC__) && (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2))
 
 #include <functional>
 using std::function;

From 16fd976597dffa12ae6793f87a192965c8c113db Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 15:42:51 -0500
Subject: [PATCH 0354/2677] Changes to remove unneeded font

---
 assets             | 2 +-
 docs/arrayfire.css | 9 ---------
 2 files changed, 1 insertion(+), 10 deletions(-)

diff --git a/assets b/assets
index 8030a5c626..f16f8bf74f 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit 8030a5c626777a5b3f46b319dd4d1723eca4b0f9
+Subproject commit f16f8bf74fe4a255db05884cfff8f5cb0e6e8e09
diff --git a/docs/arrayfire.css b/docs/arrayfire.css
index 75dba64e3a..e4fe2860be 100644
--- a/docs/arrayfire.css
+++ b/docs/arrayfire.css
@@ -52,12 +52,6 @@ a.codeRef, a.codeRef:visited, a.lineRef, a.lineRef:visited
     color       :   #4665A2;
 }
 
-@font-face
-{
-    font-family :   prototype;
-    src         :   url('Prototype.ttf');
-}
-
 /*image and image groups*/
 div.image_group
 {
@@ -96,7 +90,6 @@ div.support *
 
 #under_logo
 {
-    font-family :   prototype;
     font-size   :   2em;
     max-width   :   25px;
     color       :   #000000;
@@ -104,7 +97,6 @@ div.support *
 
 #projectbrief
 {
-    font-family :   prototype;
     color       :   #555555
 }
 
@@ -121,7 +113,6 @@ div.support *
 
 #projectname
 {
-    font-family     :   prototype;
     font-size       :   3em;
     max-width       :   25px;
     color           :   #555555

From 2325ca2134563896221656c25273026e9486b245 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 16:05:48 -0500
Subject: [PATCH 0355/2677] Fixing memory leak in plot3

---
 src/api/c/plot3.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 4e8742458a..63920bb3d6 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -48,13 +48,13 @@ fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtyp
 
     T max[3], min[3];
     if(P_dims[0] == 3) {
-        af_get_data_ptr(max, getHandle(reduce<af_max_t, T, T>(pIn, 1)));
-        af_get_data_ptr(min, getHandle(reduce<af_min_t, T, T>(pIn, 1)));
+        copyData(max, reduce<af_max_t, T, T>(pIn, 1));
+        copyData(min, reduce<af_min_t, T, T>(pIn, 1));
     }
 
     if(P_dims[1] == 3) {
-        af_get_data_ptr(max, getHandle(reduce<af_max_t, T, T>(pIn, 0)));
-        af_get_data_ptr(min, getHandle(reduce<af_min_t, T, T>(pIn, 0)));
+        copyData(max, reduce<af_max_t, T, T>(pIn, 0));
+        copyData(min, reduce<af_min_t, T, T>(pIn, 0));
     }
 
     ForgeManager& fgMngr = ForgeManager::getInstance();

From 05296fba34458aaa9506e6e4d1339328c42094b3 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 16:19:20 -0500
Subject: [PATCH 0356/2677] Cleaning up code in plot3

---
 src/api/c/plot3.cpp | 18 +++++-------------
 1 file changed, 5 insertions(+), 13 deletions(-)

diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 63920bb3d6..2e18251b45 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -46,16 +46,13 @@ fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtyp
         P_dims = pIn.dims();
     }
 
-    T max[3], min[3];
-    if(P_dims[0] == 3) {
-        copyData(max, reduce<af_max_t, T, T>(pIn, 1));
-        copyData(min, reduce<af_min_t, T, T>(pIn, 1));
+    if(P_dims[1] == 3){
+        pIn = transpose(pIn, false);
     }
 
-    if(P_dims[1] == 3) {
-        copyData(max, reduce<af_max_t, T, T>(pIn, 0));
-        copyData(min, reduce<af_min_t, T, T>(pIn, 0));
-    }
+    T max[3], min[3];
+    copyData(max, reduce<af_max_t, T, T>(pIn, 1));
+    copyData(min, reduce<af_min_t, T, T>(pIn, 1));
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
     fg::Plot3* plot3 = fgMngr.getPlot3(P_dims.elements()/3, getGLType<T>(), ptype, mtype);
@@ -64,12 +61,7 @@ fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtyp
                          max[1], min[1],
                          max[2], min[2]);
     plot3->setAxesTitles("X Axis", "Y Axis", "Z Axis");
-
-    if(P_dims[1] == 3){
-        pIn = transpose(pIn, false);
-    }
     copy_plot3<T>(pIn, plot3);
-
     return plot3;
 }
 

From ff275362b8c80b9352b5766c547bde0416a51ecb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 16:19:38 -0500
Subject: [PATCH 0357/2677] Bugfixes, code clean up of plot

- Fixes issues where X and Y indices are non column vectors
- Avoids reorder by using row vectors
---
 src/api/c/plot.cpp | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 273d922f12..a812947228 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -39,14 +39,19 @@ fg::Plot* setup_plot(const af_array X, const af_array Y, fg::PlotType type, fg::
 
     dim4 rdims(1, 0, 2, 3);
 
-    Array<T> Z = join(1, xIn, yIn);
-    Array<T> P = reorder(Z, rdims);
+    dim_t elements = xIn.elements();
+    dim4 rowDims = dim4(1, elements, 1, 1);
 
-    ArrayInfo Xinfo = getInfo(X);
-    af::dim4 X_dims = Xinfo.dims();
+    // Force the vectors to be row vectors
+    // This ensures we can use join(0,..) and skip reorder
+    xIn.modDims(rowDims);
+    yIn.modDims(rowDims);
+
+    // join along first dimension, skip reorder
+    Array<T> P = join(0, xIn, yIn);
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Plot* plot = fgMngr.getPlot(X_dims.elements(), getGLType<T>(), type, marker);
+    fg::Plot* plot = fgMngr.getPlot(elements, getGLType<T>(), type, marker);
     plot->setColor(1.0, 0.0, 0.0);
     plot->setAxesLimits(xmax, xmin, ymax, ymin);
     plot->setAxesTitles("X Axis", "Y Axis");

From 213c8e6c3711ed55420fcbb6267f7334cddaf43a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 9 Feb 2016 16:39:20 -0500
Subject: [PATCH 0358/2677] Clean up of surface()

- Avoids unnecessary reorders by transposing vectors (more efficient)
---
 src/api/c/surface.cpp | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 7db8441163..2394f5f96c 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -49,21 +49,29 @@ fg::Surface* setup_surface(const af_array xVals, const af_array yVals, const af_
     af::dim4 Y_dims = Yinfo.dims();
     af::dim4 Z_dims = Zinfo.dims();
 
-    dim4   rdims(1, 0, 2, 3);
-    dim4 x_tdims(1, Y_dims[0], 1, 1);
-    dim4 y_tdims(1, X_dims[0], 1, 1);
     if(Xinfo.isVector()){
+        // Convert xIn is a column vector
+        xIn.modDims(xIn.elements());
+        // Now tile along second dimension
+        dim4 x_tdims(1, Y_dims[0], 1, 1);
         xIn = tile(xIn, x_tdims);
+
+        // Convert yIn to a row vector
+        yIn.modDims(af::dim4(1, yIn.elements()));
+        // Now tile along first dimension
+        dim4 y_tdims(X_dims[0], 1, 1, 1);
         yIn = tile(yIn, y_tdims);
-        yIn = reorder(yIn, rdims);
     }
 
-    xIn.modDims(xIn.elements());
-    yIn.modDims(yIn.elements());
-    zIn.modDims(zIn.elements());
-    Array<T> Z = join(1, join(1, xIn, yIn), zIn);
-    Z = reorder(Z, rdims);
-    Z.modDims(Z.elements());
+    // Flatten xIn, yIn and zIn into row vectors
+    dim4 rowDims = dim4(1, zIn.elements());
+    xIn.modDims(rowDims);
+    yIn.modDims(rowDims);
+    zIn.modDims(rowDims);
+
+    // Now join along first dimension, skip reorder
+    std::vector<Array<T> > inputs{xIn, yIn, zIn};
+    Array<T> Z = join(0, inputs);
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
     fg::Surface* surface = fgMngr.getSurface(Z_dims[0], Z_dims[1], getGLType<T>());

From d7d79af742ba999f44c5c8b4275cae0dd5ae8c26 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 10 Feb 2016 23:30:55 -0500
Subject: [PATCH 0359/2677] BUGFIX: Fixing offsets when writing to Arrays for
 CPU and CUDA backends

---
 src/backend/cpu/Array.cpp  | 4 ++--
 src/backend/cuda/Array.cpp | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index fbb8e10b34..0db8a8203b 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -227,7 +227,7 @@ writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
     if(!arr.isOwner()) {
         arr = createEmptyArray<T>(arr.dims());
     }
-    memcpy(arr.get() + arr.getOffset(), data, bytes);
+    memcpy(arr.get(), data, bytes);
 }
 
 template<typename T>
@@ -237,7 +237,7 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     if(!arr.isOwner()) {
         arr = createEmptyArray<T>(arr.dims());
     }
-    memcpy(arr.get() + arr.getOffset(), (const T * const)data, bytes);
+    memcpy(arr.get(), (const T * const)data, bytes);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 366d8e2b52..c44db357e7 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -257,7 +257,7 @@ namespace cuda
 
         T *ptr = arr.get();
 
-        CUDA_CHECK(cudaMemcpyAsync(ptr + arr.getOffset(), data, bytes, cudaMemcpyHostToDevice,
+        CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice,
                     cuda::getStream(cuda::getActiveDeviceId())));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
@@ -274,7 +274,7 @@ namespace cuda
 
         T *ptr = arr.get();
 
-        CUDA_CHECK(cudaMemcpyAsync(ptr + arr.getOffset(), data,
+        CUDA_CHECK(cudaMemcpyAsync(ptr, data,
                                    bytes, cudaMemcpyDeviceToDevice,
                                    cuda::getStream(cuda::getActiveDeviceId())));
 

From 1b623a01721ba9ee4857fa7a3e88f6b0925fafe1 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 00:09:47 -0500
Subject: [PATCH 0360/2677] Reorganizing offset to be inside ArrayInfo

- Removed unnecessary dim_offset
---
 src/api/c/internal.cpp              | 21 +----------
 src/api/c/print.cpp                 |  3 +-
 src/backend/ArrayInfo.cpp           |  9 -----
 src/backend/ArrayInfo.hpp           | 14 +++----
 src/backend/cpu/Array.cpp           | 43 +++++++++++----------
 src/backend/cpu/Array.hpp           |  8 ++--
 src/backend/cpu/exampleFunction.cpp |  3 +-
 src/backend/cuda/Array.cpp          | 52 ++++++++++++++------------
 src/backend/cuda/Array.hpp          |  8 ++--
 src/backend/opencl/Array.cpp        | 58 +++++++++++++++--------------
 src/backend/opencl/Array.hpp        |  6 +--
 11 files changed, 100 insertions(+), 125 deletions(-)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index d086d431f1..d5f449e7ac 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -84,26 +84,7 @@ af_err af_get_offset(dim_t *offset, const af_array arr)
 {
     try {
 
-        dim_t res = 0;
-
-        af_dtype ty = getInfo(arr).getType();
-
-        switch (ty) {
-        case f32: res = getArray<float  >(arr).getOffset(); break;
-        case f64: res = getArray<double >(arr).getOffset(); break;
-        case c32: res = getArray<cfloat >(arr).getOffset(); break;
-        case c64: res = getArray<cdouble>(arr).getOffset(); break;
-        case u32: res = getArray<uint   >(arr).getOffset(); break;
-        case s32: res = getArray<int    >(arr).getOffset(); break;
-        case u64: res = getArray<uintl  >(arr).getOffset(); break;
-        case s64: res = getArray<intl   >(arr).getOffset(); break;
-        case u16: res = getArray<ushort >(arr).getOffset(); break;
-        case s16: res = getArray<short  >(arr).getOffset(); break;
-        case b8 : res = getArray<char   >(arr).getOffset(); break;
-        case u8 : res = getArray<uchar  >(arr).getOffset(); break;
-        default: TYPE_ERROR(6, ty);
-        }
-
+        dim_t res = getInfo(arr).getOffset();
         std::swap(*offset, res);
     }
     CATCHALL;
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 181dd3505f..b243491832 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -14,6 +14,7 @@
 #include <sstream>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/internal.h>
 #include <copy.hpp>
 #include <print.hpp>
 #include <ArrayInfo.hpp>
@@ -69,7 +70,7 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
 
     os << "[" << info.dims() << "]\n";
 #ifndef NDEBUG
-    os <<"   Offsets: [" << info.offsets() << "]" << std::endl;
+    os <<"   Offset: " << info.getOffset() << std::endl;
     os <<"   Strides: [" << info.strides() << "]" << std::endl;
 #endif
 
diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index 219bc1991c..43d2627a84 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -18,15 +18,6 @@
 
 using af::dim4;
 
-dim_t
-calcOffset(const af::dim4 &strides, const af::dim4 &offsets)
-{
-    dim_t offset = 0;
-    for (int i = 0; i < 4; i++) offset += offsets[i] * strides[i];
-    return offset;
-}
-
-
 const ArrayInfo&
 getInfo(af_array arr)
 {
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index ca6fcd394c..38e5ea61ab 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -16,9 +16,6 @@
 #include <vector>
 #include <cstddef>
 
-dim_t
-calcOffset(const af::dim4 &strides, const af::dim4 &offsets);
-
 af::dim4
 calcStrides(const af::dim4 &parentDim);
 
@@ -48,14 +45,15 @@ class ArrayInfo
     int             devId;
     af_dtype        type;
     af::dim4        dim_size;
-    af::dim4        dim_offsets, dim_strides;
+    dim_t           offset;
+    af::dim4        dim_strides;
 
 public:
-    ArrayInfo(int id, af::dim4 size, af::dim4 offset, af::dim4 stride, af_dtype af_type):
+    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride, af_dtype af_type):
         devId(id),
         type(af_type),
         dim_size(size),
-        dim_offsets(offset),
+        offset(offset_),
         dim_strides(stride)
     {
         af_init();
@@ -77,7 +75,7 @@ class ArrayInfo
 
     const af_dtype& getType() const     { return type;                  }
 
-    const af::dim4& offsets() const     { return dim_offsets;           }
+    dim_t getOffset() const             { return offset;                }
 
     const af::dim4& strides() const     { return dim_strides;           }
 
@@ -97,7 +95,7 @@ class ArrayInfo
     {
         dim_size = dims;
         dim_strides = calcStrides(dims);
-        dim_offsets = af::dim4(0,0,0,0);
+        offset = 0;
     }
 
     void resetDims(const af::dim4& dims)
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 0db8a8203b..1b6098df41 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -33,16 +33,16 @@ using af::dim4;
 
 template<typename T>
 Array<T>::Array(dim4 dims):
-    info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+    info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-    node(), offset(0), ready(true), owner(true)
+    node(), ready(true), owner(true)
 { }
 
 template<typename T>
 Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
-    info(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+    info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-    node(), offset(0), ready(true), owner(true)
+    node(), ready(true), owner(true)
 {
     static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
     static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
@@ -53,29 +53,27 @@ Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_de
 
 template<typename T>
 Array<T>::Array(af::dim4 dims, TNJ::Node_ptr n) :
-    info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+    info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data(), data_dims(dims),
-    node(n), offset(0), ready(false), owner(true)
+    node(n), ready(false), owner(true)
 {
 }
 
 template<typename T>
-Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim4 &offsets, const dim4 &strides) :
-    info(parent.getDevId(), dims, offsets, strides, (af_dtype)dtype_traits<T>::af_type),
+Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &strides) :
+    info(parent.getDevId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
     data(parent.getData()), data_dims(parent.getDataDims()),
     node(),
-    offset(parent.getOffset() + calcOffset(parent.strides(), offsets)),
     ready(true), owner(false)
 { }
 
 template<typename T>
 Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                 const T * const in_data, bool is_device) :
-    info(getActiveDeviceId(), dims, af::dim4(offset_), strides, (af_dtype)dtype_traits<T>::af_type),
+    info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
     data(is_device ? (T*)in_data : memAlloc<T>(info.elements()), memFree<T>),
     data_dims(dims),
     node(),
-    offset(offset_),
     ready(true),
     owner(true)
 {
@@ -119,7 +117,7 @@ Node_ptr Array<T>::getNode() const
 
         BufferNode<T> *buf_node = new BufferNode<T>(data,
                                                     bytes,
-                                                    offset,
+                                                    getOffset(),
                                                     dims().get(),
                                                     strides().get(),
                                                     isLinear());
@@ -194,18 +192,23 @@ Array<T> createSubArray(const Array<T>& parent,
     dim4 dDims = parent.getDataDims();
     dim4 pDims = parent.dims();
 
-    dim4 dims   = toDims  (index, pDims);
-    dim4 offset = toOffset(index, dDims);
-    dim4 stride = toStride (index, dDims);
+    dim4 dims    = toDims  (index, pDims);
+    dim4 strides = toStride (index, dDims);
 
-    Array<T> out = Array<T>(parent, dims, offset, stride);
+    // Find total offsets after indexing
+    dim4 offsets = toOffset(index, pDims);
+    dim4 parent_strides = parent.strides();
+    dim_t offset = parent.getOffset();
+    for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+
+    Array<T> out = Array<T>(parent, dims, offset, strides);
 
     if (!copy) return out;
 
-    if (stride[0] != 1 ||
-        stride[1] <  0 ||
-        stride[2] <  0 ||
-        stride[3] <  0) {
+    if (strides[0] != 1 ||
+        strides[1] <  0 ||
+        strides[2] <  0 ||
+        strides[3] <  0) {
 
         out = copyArray(out);
     }
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 891d867d7d..eb17852c27 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -100,7 +100,6 @@ namespace cpu
         af::dim4 data_dims;
         TNJ::Node_ptr node;
 
-        dim_t offset;
         bool ready;
         bool owner;
 
@@ -108,7 +107,7 @@ namespace cpu
         Array(dim4 dims);
 
         explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
-        Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
+        Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
         explicit Array(af::dim4 dims, TNJ::Node_ptr n);
 
     public:
@@ -127,7 +126,6 @@ namespace cpu
     RET_TYPE NAME() const { return info.NAME(); }
 
         INFO_FUNC(const af_dtype& ,getType)
-        INFO_FUNC(const af::dim4& ,offsets)
         INFO_FUNC(const af::dim4& ,strides)
         INFO_FUNC(size_t          ,elements)
         INFO_FUNC(size_t          ,ndims)
@@ -165,7 +163,7 @@ namespace cpu
         void eval();
         void eval() const;
 
-        dim_t getOffset() const { return offset; }
+        dim_t getOffset() const { return info.getOffset(); }
         shared_ptr<T> getData() const {return data; }
 
         dim4 getDataDims() const
@@ -197,7 +195,7 @@ namespace cpu
         const T* get(bool withOffset = true) const
         {
             if (!isReady()) eval();
-            return data.get() + (withOffset ? offset : 0);
+            return data.get() + (withOffset ? getOffset() : 0);
         }
 
         int useCount() const
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index d45b8a28ec..0eb86462e1 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -44,7 +44,7 @@ Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
 
     //dim4 in_dims    = in.dims();        // you can retrieve dimensions
 
-    //dim4 in_offsets = in.offsets();     // you can retrieve offsets - used when given array
+    //dim_t in_offset = in.getOffset(); // you can retrieve the offset - used when given array
                                         // is an sub-array pointing to some other array and
                                         // doesn't have memory of its own
 
@@ -77,4 +77,3 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }
-
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index c44db357e7..370e8eca31 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -30,17 +30,17 @@ namespace cuda
 
     template<typename T>
     Array<T>::Array(af::dim4 dims) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {}
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, const T * const in_data, bool is_device, bool copy_device) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(((is_device & !copy_device) ? (T *)in_data : memAlloc<T>(dims.elements())), memFree<T>),
         data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {
 #if __cplusplus > 199711L
         static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
@@ -58,42 +58,41 @@ namespace cuda
     }
 
     template<typename T>
-    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim4 &offsets, const dim4 &strides) :
-        info(parent.getDevId(), dims, offsets, strides, (af_dtype)dtype_traits<T>::af_type),
+    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &strides) :
+        info(parent.getDevId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(parent.getData()), data_dims(parent.getDataDims()),
         node(),
-        offset(parent.getOffset() + calcOffset(parent.strides(), offsets)),
         ready(true), owner(false)
     { }
 
     template<typename T>
     Array<T>::Array(Param<T> &tmp) :
-        info(getActiveDeviceId(), af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]),
-                  af::dim4(0, 0, 0, 0),
-                  af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2], tmp.strides[3]),
-                  (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(),
+             af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]),
+             0,
+             af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2], tmp.strides[3]),
+             (af_dtype)dtype_traits<T>::af_type),
         data(tmp.ptr, memFree<T>),
         data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3])),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {
     }
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, JIT::Node_ptr n) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(), data_dims(dims),
-        node(n), offset(0), ready(false), owner(true)
+        node(n), ready(false), owner(true)
     {
     }
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                     const T * const in_data, bool is_device) :
-        info(getActiveDeviceId(), dims, af::dim4(offset_), strides, (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(is_device ? (T*)in_data : memAlloc<T>(info.elements()), memFree<T>),
         data_dims(dims),
         node(),
-        offset(offset_),
         ready(true),
         owner(true)
     {
@@ -216,18 +215,23 @@ namespace cuda
         dim4 dDims = parent.getDataDims();
         dim4 pDims = parent.dims();
 
-        dim4 dims   = toDims  (index, pDims);
-        dim4 offset = toOffset(index, dDims);
-        dim4 stride = toStride (index, dDims);
+        dim4 dims    = toDims  (index, pDims);
+        dim4 strides = toStride (index, dDims);
 
-        Array<T> out = Array<T>(parent, dims, offset, stride);
+        // Find total offsets after indexing
+        dim4 offsets = toOffset(index, pDims);
+        dim4 parent_strides = parent.strides();
+        dim_t offset = parent.getOffset();
+        for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+
+        Array<T> out = Array<T>(parent, dims, offset, strides);
 
         if (!copy) return out;
 
-        if (stride[0] != 1 ||
-            stride[1] <  0 ||
-            stride[2] <  0 ||
-            stride[3] <  0) {
+        if (strides[0] != 1 ||
+            strides[1] <  0 ||
+            strides[2] <  0 ||
+            strides[3] <  0) {
 
             out = copyArray(out);
         }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index b8832db1c6..c6cdd2121d 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -98,14 +98,13 @@ namespace cuda
         af::dim4 data_dims;
 
         JIT::Node_ptr node;
-        dim_t offset;
         bool ready;
         bool owner;
 
         Array(af::dim4 dims);
 
         explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false, bool copy_device = false);
-        Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
+        Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
         Array(Param<T> &tmp);
         Array(af::dim4 dims, JIT::Node_ptr n);
     public:
@@ -123,7 +122,6 @@ namespace cuda
     RET_TYPE NAME() const { return info.NAME(); }
 
         INFO_FUNC(const af_dtype& ,getType)
-        INFO_FUNC(const af::dim4& ,offsets)
         INFO_FUNC(const af::dim4& ,strides)
         INFO_FUNC(size_t          ,elements)
         INFO_FUNC(size_t          ,ndims)
@@ -160,7 +158,7 @@ namespace cuda
         void eval();
         void eval() const;
 
-        dim_t getOffset() const { return offset; }
+        dim_t getOffset() const { return info.getOffset(); }
         shared_ptr<T> getData() const { return data; }
 
         dim4 getDataDims() const
@@ -193,7 +191,7 @@ namespace cuda
         const   T* get(bool withOffset = true) const
         {
             if (!isReady()) eval();
-            return data.get() + (withOffset ? offset : 0);
+            return data.get() + (withOffset ? getOffset() : 0);
         }
 
         int useCount() const
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index f41b2c795d..fb3e63beaf 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -30,28 +30,28 @@ namespace opencl
 
     template<typename T>
     Array<T>::Array(af::dim4 dims) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(bufferAlloc(info.elements() * sizeof(T)), bufferFree),
         data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {
     }
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, JIT::Node_ptr n) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(),
         data_dims(dims),
-        node(n), offset(0), ready(false), owner(true)
+        node(n), ready(false), owner(true)
     {
     }
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, const T * const in_data) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(bufferAlloc(info.elements()*sizeof(T)), bufferFree),
         data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {
         static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
         static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
@@ -60,10 +60,10 @@ namespace opencl
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy) :
-        info(getActiveDeviceId(), dims, af::dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new cl::Buffer(mem), bufferFree),
         data_dims(dims),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {
         if (copy) {
             clRetainMemObject(mem);
@@ -75,12 +75,11 @@ namespace opencl
     }
 
     template<typename T>
-    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim4 &offsets, const dim4 &stride) :
-        info(parent.getDevId(), dims, offsets, stride, (af_dtype)dtype_traits<T>::af_type),
+    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &stride) :
+        info(parent.getDevId(), dims, offset_, stride, (af_dtype)dtype_traits<T>::af_type),
         data(parent.getData()),
         data_dims(parent.getDataDims()),
         node(),
-        offset(parent.getOffset() + calcOffset(parent.strides(), offsets)),
         ready(true),
         owner(false)
     { }
@@ -88,27 +87,27 @@ namespace opencl
 
     template<typename T>
     Array<T>::Array(Param &tmp) :
-        info(getActiveDeviceId(), af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3]),
-                  af::dim4(0, 0, 0, 0),
-                  af::dim4(tmp.info.strides[0], tmp.info.strides[1],
-                           tmp.info.strides[2], tmp.info.strides[3]),
-                  (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(),
+             af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3]),
+             0,
+             af::dim4(tmp.info.strides[0], tmp.info.strides[1],
+                      tmp.info.strides[2], tmp.info.strides[3]),
+             (af_dtype)dtype_traits<T>::af_type),
         data(tmp.data, bufferFree),
         data_dims(af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
-        node(), offset(0), ready(true), owner(true)
+        node(), ready(true), owner(true)
     {
     }
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                     const T * const in_data, bool is_device) :
-        info(getActiveDeviceId(), dims, af::dim4(offset_), strides, (af_dtype)dtype_traits<T>::af_type),
+        info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(is_device ?
              (new cl::Buffer((cl_mem)in_data)) :
              (bufferAlloc(info.elements() * sizeof(T))), bufferFree),
         data_dims(dims),
         node(),
-        offset(offset_),
         ready(true),
         owner(true)
     {
@@ -204,18 +203,23 @@ namespace opencl
         dim4 dDims = parent.getDataDims();
         dim4 pDims = parent.dims();
 
-        dim4 dims   = toDims  (index, pDims);
-        dim4 offset = toOffset(index, dDims);
-        dim4 stride = toStride (index, dDims);
+        dim4 dims    = toDims  (index, pDims);
+        dim4 strides = toStride (index, dDims);
 
-        Array<T> out = Array<T>(parent, dims, offset, stride);
+        // Find total offsets after indexing
+        dim4 offsets = toOffset(index, pDims);
+        dim4 parent_strides = parent.strides();
+        dim_t offset = parent.getOffset();
+        for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+
+        Array<T> out = Array<T>(parent, dims, offset, strides);
 
         if (!copy) return out;
 
-        if (stride[0] != 1 ||
-            stride[1] <  0 ||
-            stride[2] <  0 ||
-            stride[3] <  0) {
+        if (strides[0] != 1 ||
+            strides[1] <  0 ||
+            strides[2] <  0 ||
+            strides[3] <  0) {
 
             out = copyArray(out);
         }
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index d1a4d973c2..207e303e52 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -90,13 +90,12 @@ namespace opencl
         af::dim4 data_dims;
 
         JIT::Node_ptr node;
-        dim_t offset;
         bool ready;
         bool owner;
 
         Array(af::dim4 dims);
 
-        Array(const Array<T>& parnt, const dim4 &dims, const dim4 &offset, const dim4 &stride);
+        Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
         Array(Param &tmp);
         explicit Array(af::dim4 dims, JIT::Node_ptr n);
         explicit Array(af::dim4 dims, const T * const in_data);
@@ -117,7 +116,6 @@ namespace opencl
     RET_TYPE NAME() const { return info.NAME(); }
 
         INFO_FUNC(const af_dtype& ,getType)
-        INFO_FUNC(const af::dim4& ,offsets)
         INFO_FUNC(const af::dim4& ,strides)
         INFO_FUNC(size_t          ,elements)
         INFO_FUNC(size_t          ,ndims)
@@ -187,7 +185,7 @@ namespace opencl
 
         const dim_t getOffset() const
         {
-            return offset;
+            return info.getOffset();
         }
 
         Buffer_ptr getData() const

From 2d595034d8eff1d69df86912fa5f72356cbed834 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 13:41:44 -0500
Subject: [PATCH 0361/2677] BUGFIX: Fixed issues with offsets in moddims after
 using indexing

---
 src/api/c/moddims.cpp        | 1 +
 src/backend/cpu/Array.hpp    | 5 +++++
 src/backend/cuda/Array.hpp   | 5 +++++
 src/backend/opencl/Array.hpp | 5 +++++
 4 files changed, 16 insertions(+)

diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 132086a6ef..b8f1fafa6c 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -32,6 +32,7 @@ Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
     }
 
     Out.modDims(newDims);
+    Out.setDataDims(newDims);
 
     return Out;
 }
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index eb17852c27..0c6e701981 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -173,6 +173,11 @@ namespace cpu
             return isOwner() ? info.dims() : data_dims;
         }
 
+        void setDataDims(const dim4 &new_dims)
+        {
+            data_dims = new_dims;
+        }
+
         T* device()
         {
             getQueue().sync();
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index c6cdd2121d..03bd8b3a29 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -168,6 +168,11 @@ namespace cuda
             return isOwner() ? dims() : data_dims;
         }
 
+        void setDataDims(const dim4 &new_dims)
+        {
+            data_dims = new_dims;
+        }
+
         T* device()
         {
             if (!isOwner() || data.use_count() > 1) {
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 207e303e52..f2a217e001 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -200,6 +200,11 @@ namespace opencl
             return isOwner() ? dims() : data_dims;
         }
 
+        void setDataDims(const dim4 &new_dims)
+        {
+            data_dims = new_dims;
+        }
+
         operator Param() const
         {
             KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},

From b260cc8ffb2261b5a00cb36d6531fa4b43b747ea Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 13:42:19 -0500
Subject: [PATCH 0362/2677] Fixes to internal functions

- Was using incorrect number of elements for the total
- Fixed copy because right now isOwner() does not mean isLinear()
  - Potentially improves performance when isLinear() is not isOwner()
---
 src/api/c/internal.cpp       | 4 ++++
 src/backend/ArrayInfo.hpp    | 1 +
 src/backend/cpu/Array.cpp    | 4 ++--
 src/backend/cpu/copy.cpp     | 2 +-
 src/backend/cuda/Array.cpp   | 4 ++--
 src/backend/cuda/copy.cu     | 2 +-
 src/backend/opencl/Array.cpp | 4 ++--
 src/backend/opencl/copy.cpp  | 2 +-
 8 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index d5f449e7ac..cad4a466ce 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -40,6 +40,10 @@ af_err af_create_array_with_strides(af_array *arr,
         dim4 dims(ndims, dims_);
         dim4 strides(ndims, strides_);
 
+        for (int i = ndims; i < 4; i++) {
+            strides[i] = strides[i - 1] * dims[i - 1];
+        }
+
         bool isdev = location == afDevice;
 
         af_array res;
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index 38e5ea61ab..0983f06f28 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -82,6 +82,7 @@ class ArrayInfo
     size_t elements() const             { return dim_size.elements();   }
     size_t ndims() const                { return dim_size.ndims();      }
     const af::dim4& dims() const        { return dim_size;              }
+    size_t total() const                { return offset + dim_strides[3] * dim_size[3]; }
 
     int getDevId() const;
 
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 1b6098df41..3edca877cd 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -71,14 +71,14 @@ template<typename T>
 Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                 const T * const in_data, bool is_device) :
     info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-    data(is_device ? (T*)in_data : memAlloc<T>(info.elements()), memFree<T>),
+    data(is_device ? (T*)in_data : memAlloc<T>(info.total()), memFree<T>),
     data_dims(dims),
     node(),
     ready(true),
     owner(true)
 {
     if (!is_device) {
-        std::copy(in_data, in_data + dims.elements(), data.get());
+        std::copy(in_data, in_data + info.total(), data.get());
     }
 }
 
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index f844d959a2..0da304b3ca 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -30,7 +30,7 @@ void copyData(T *to, const Array<T> &from)
 {
     from.eval();
     getQueue().sync();
-    if(from.isOwner()) {
+    if(from.isLinear()) {
         // FIXME: Check for errors / exceptions
         memcpy(to, from.get(), from.elements()*sizeof(T));
     } else {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 370e8eca31..c1cf8102eb 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -90,7 +90,7 @@ namespace cuda
     Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                     const T * const in_data, bool is_device) :
         info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-        data(is_device ? (T*)in_data : memAlloc<T>(info.elements()), memFree<T>),
+        data(is_device ? (T*)in_data : memAlloc<T>(info.total()), memFree<T>),
         data_dims(dims),
         node(),
         ready(true),
@@ -98,7 +98,7 @@ namespace cuda
     {
         if (!is_device) {
             cudaStream_t stream = getStream(getActiveDeviceId());
-            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, info.elements() * sizeof(T),
+            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, info.total() * sizeof(T),
                                        cudaMemcpyHostToDevice, stream));
             CUDA_CHECK(cudaStreamSynchronize(stream));
         }
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index df435d245c..35e5c83178 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -28,7 +28,7 @@ namespace cuda
         Array<T> out = A;
         const T *ptr = NULL;
 
-        if (A.isOwner() || // No offsets, No strides
+        if (A.isLinear() || // No offsets, No strides
             A.ndims() == 1 // Simple offset, no strides.
             ) {
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index fb3e63beaf..bd576ca88a 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -105,14 +105,14 @@ namespace opencl
         info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(is_device ?
              (new cl::Buffer((cl_mem)in_data)) :
-             (bufferAlloc(info.elements() * sizeof(T))), bufferFree),
+             (bufferAlloc(info.total() * sizeof(T))), bufferFree),
         data_dims(dims),
         node(),
         ready(true),
         owner(true)
     {
         if (!is_device) {
-            getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T) * info.elements(), in_data);
+            getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T) * info.total(), in_data);
         }
     }
 
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 39cbf4b59d..e1716f1632 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -29,7 +29,7 @@ namespace opencl
         cl::Buffer buf;
         Array<T> out = A;
 
-        if (A.isOwner() || // No offsets, No strides
+        if (A.isLinear() || // No offsets, No strides
             A.ndims() == 1 // Simple offset, no strides.
             ) {
             buf = *A.get();

From 6e9eacb152b119d2fd1e037bed4514b50abccb0a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 15:57:28 -0500
Subject: [PATCH 0363/2677] DOCS: Adding documentation for internal functions

---
 docs/details/internal.dox |  29 ++++++++++
 include/af/internal.h     | 119 ++++++++++++++++++++++++++++++++++++++
 include/arrayfire.h       |   9 +++
 3 files changed, 157 insertions(+)
 create mode 100644 docs/details/internal.dox

diff --git a/docs/details/internal.dox b/docs/details/internal.dox
new file mode 100644
index 0000000000..879f8196df
--- /dev/null
+++ b/docs/details/internal.dox
@@ -0,0 +1,29 @@
+/**
+\addtogroup internal_func
+@{
+
+\defgroup internal_func_create createArray
+
+Create an array with specified strides and offset.
+
+
+\defgroup internal_func_strides getStrides
+
+Get strides of underlying data.
+
+
+\defgroup internal_func_offset getOffset
+
+Get Offset of the underlying data.
+
+
+\defgroup internal_func_linear isLinear
+
+Check if all elements in array are contiguous.
+
+\defgroup internal_func_owner isOwner
+
+Check if underlying data is owned by the current array.
+
+@}
+*/
diff --git a/include/af/internal.h b/include/af/internal.h
index fdd0158e2e..6ba42b3028 100644
--- a/include/af/internal.h
+++ b/include/af/internal.h
@@ -16,20 +16,78 @@ namespace af
 {
     class array;
 
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] data is the raw data pointer.
+       \param[in] offset specifies the number of elements to skip.
+       \param[in] dims specifies the dimensions for the region of interest.
+       \param[in] strides specifies the distance between each element of a given dimension.
+       \param[in] ty specifies the data type of \p data.
+       \param[in] location specifies if the data is on host or the device.
+
+       \note: If \p location is `afHost`, a memory copy is performed.
+
+       \returns an af::array() with specified offset, dimensions and strides.
+
+       \ingroup internal_func_create
+    */
     AFAPI array createArray(const void *data, const dim_t offset,
                             const dim4 dims, const dim4 strides,
                             const af::dtype ty,
                             const af::source location);
+#endif
 
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] in An multi dimensional array.
+       \returns af::dim4() containing distance between consecutive elements in each dimension.
+
+       \ingroup internal_func_strides
+    */
     AFAPI dim4 getStrides(const array &in);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] in An multi dimensional array.
+       \returns offset from the starting location of data pointer specified in number of elements.
 
+       \ingroup internal_func_offset
+    */
     AFAPI dim_t getOffset(const array &in);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] in An multi dimensional array.
+       \returns Returns the raw pointer location to the array.
 
+       \note This pointer may be shared with other arrays. Use this function with caution.
+
+       \ingroup internal_func_rawptr
+    */
     AFAPI void *getRawPtr(const array &in);
+#endif
 
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] in An multi dimensional array.
+       \returns a boolean specifying if all elements in the array are contiguous.
+
+       \ingroup internal_func_linear
+    */
     AFAPI bool isLinear(const array &in);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] in An multi dimensional array.
+       \returns a boolean specifying if the array owns the raw pointer. It is false if it is a sub array.
 
+       \ingroup internal_func_owner
+    */
     AFAPI bool isOwner(const array &in);
+#endif
 }
 #endif
 
@@ -38,6 +96,21 @@ extern "C"
 {
 #endif
 
+#if AF_API_VERSION >= 33
+    /**
+       \param[out] arr an af_array with specified offset, dimensions and strides.
+       \param[in] data is the raw data pointer.
+       \param[in] offset specifies the number of elements to skip.
+       \param[in] ndims specifies the number of array dimensions.
+       \param[in] dims specifies the dimensions for the region of interest.
+       \param[in] strides specifies the distance between each element of a given dimension.
+       \param[in] ty specifies the data type of \p data.
+       \param[in] location specifies if the data is on host or the device.
+
+       \note If \p location is `afHost`, a memory copy is performed.
+
+       \ingroup internal_func_create
+    */
     AFAPI af_err af_create_array_with_strides(af_array *arr,
                                               const void *data,
                                               const dim_t offset,
@@ -46,16 +119,62 @@ extern "C"
                                               const dim_t *const strides,
                                               const af_dtype ty,
                                               const af_source location);
+#endif
 
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] arr An multi dimensional array.
+       \param[out] s0 distance between each consecutive element along first  dimension.
+       \param[out] s1 distance between each consecutive element along second dimension.
+       \param[out] s2 distance between each consecutive element along third  dimension.
+       \param[out] s3 distance between each consecutive element along fourth dimension.
+
+       \ingroup internal_func_strides
+    */
     AFAPI af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array arr);
+#endif
 
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] arr An multi dimensional array.
+       \param[out] offset: Offset from the starting location of data pointer specified in number of elements. distance between each consecutive element along first  dimension.
+
+       \ingroup internal_func_offset
+    */
     AFAPI af_err af_get_offset(dim_t *offset, const af_array arr);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] arr An multi dimensional array.
+       \param[out] ptr the raw pointer location to the array.
 
+       \note This pointer may be shared with other arrays. Use this function with caution.
+
+       \ingroup internal_func_rawptr
+    */
     AFAPI af_err af_get_raw_ptr(void **ptr, const af_array arr);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] arr An multi dimensional array.
+       \param[out] result: a boolean specifying if all elements in the array are contiguous.
 
+       \ingroup internal_func_linear
+    */
     AFAPI af_err af_is_linear(bool *result, const af_array arr);
+#endif
+
+#if AF_API_VERSION >= 33
+    /**
+       \param[in] arr An multi dimensional array.
+       \param[out] result: a boolean specifying if the array owns the raw pointer. It is false if it is a sub array.
 
+       \ingroup internal_func_owner
+    */
     AFAPI af_err af_is_owner(bool *result, const af_array arr);
+#endif
 
 #ifdef __cplusplus
 }
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 73b417b3ad..60df3176d1 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -209,6 +209,15 @@
 
    @}
 
+   @defgroup internal_func Functions to work with internal array layout
+   @{
+
+     Functions to work with arrayfire's internal data structure.
+
+     Note: The behavior of these functions is not promised to be consistent across versions.
+
+   @}
+
    @defgroup external Interface Functions
    @{
 

From f6d02366edf0709b3af3ebbbaca6de43407975a0 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 16:00:07 -0500
Subject: [PATCH 0364/2677] Renaming createArray to be createStridedArray

---
 docs/details/internal.dox    |  2 +-
 include/af/internal.h        | 24 ++++++++++++------------
 src/api/c/internal.cpp       | 16 ++++++++--------
 src/api/cpp/internal.cpp     | 14 +++++++-------
 src/api/unified/internal.cpp | 16 ++++++++--------
 5 files changed, 36 insertions(+), 36 deletions(-)

diff --git a/docs/details/internal.dox b/docs/details/internal.dox
index 879f8196df..5ac06422ca 100644
--- a/docs/details/internal.dox
+++ b/docs/details/internal.dox
@@ -2,7 +2,7 @@
 \addtogroup internal_func
 @{
 
-\defgroup internal_func_create createArray
+\defgroup internal_func_create createStridedArray
 
 Create an array with specified strides and offset.
 
diff --git a/include/af/internal.h b/include/af/internal.h
index 6ba42b3028..53002929c3 100644
--- a/include/af/internal.h
+++ b/include/af/internal.h
@@ -31,10 +31,10 @@ namespace af
 
        \ingroup internal_func_create
     */
-    AFAPI array createArray(const void *data, const dim_t offset,
-                            const dim4 dims, const dim4 strides,
-                            const af::dtype ty,
-                            const af::source location);
+    AFAPI array createStridedArray(const void *data, const dim_t offset,
+                                   const dim4 dims, const dim4 strides,
+                                   const af::dtype ty,
+                                   const af::source location);
 #endif
 
 #if AF_API_VERSION >= 33
@@ -111,14 +111,14 @@ extern "C"
 
        \ingroup internal_func_create
     */
-    AFAPI af_err af_create_array_with_strides(af_array *arr,
-                                              const void *data,
-                                              const dim_t offset,
-                                              const unsigned ndims,
-                                              const dim_t *const dims,
-                                              const dim_t *const strides,
-                                              const af_dtype ty,
-                                              const af_source location);
+    AFAPI af_err af_create_strided_array(af_array *arr,
+                                         const void *data,
+                                         const dim_t offset,
+                                         const unsigned ndims,
+                                         const dim_t *const dims,
+                                         const dim_t *const strides,
+                                         const af_dtype ty,
+                                         const af_source location);
 #endif
 
 #if AF_API_VERSION >= 33
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index cad4a466ce..ac7a374f95 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -20,14 +20,14 @@
 
 using namespace detail;
 
-af_err af_create_array_with_strides(af_array *arr,
-                                    const void *data,
-                                    const dim_t offset,
-                                    const unsigned ndims,
-                                    const dim_t *const dims_,
-                                    const dim_t *const strides_,
-                                    const af_dtype ty,
-                                    const af_source location)
+af_err af_create_strided_array(af_array *arr,
+                               const void *data,
+                               const dim_t offset,
+                               const unsigned ndims,
+                               const dim_t *const dims_,
+                               const dim_t *const strides_,
+                               const af_dtype ty,
+                               const af_source location)
 {
     try {
 
diff --git a/src/api/cpp/internal.cpp b/src/api/cpp/internal.cpp
index f26f9f8bb4..bdce6e155c 100644
--- a/src/api/cpp/internal.cpp
+++ b/src/api/cpp/internal.cpp
@@ -13,15 +13,15 @@
 
 namespace af
 {
-    array createArray(const void *data, const dim_t offset,
-                      const dim4 dims, const dim4 strides,
-                      const af::dtype ty,
-                      const af::source location)
+    array createStridedArray(const void *data, const dim_t offset,
+                             const dim4 dims, const dim4 strides,
+                             const af::dtype ty,
+                             const af::source location)
     {
         af_array res;
-        AF_THROW(af_create_array_with_strides(&res, data, offset,
-                                              dims.ndims(), dims.get(), strides.get(),
-                                              ty, location));
+        AF_THROW(af_create_strided_array(&res, data, offset,
+                                         dims.ndims(), dims.get(), strides.get(),
+                                         ty, location));
         return array(res);
     }
 
diff --git a/src/api/unified/internal.cpp b/src/api/unified/internal.cpp
index 7c223e741c..b9ac0ac277 100644
--- a/src/api/unified/internal.cpp
+++ b/src/api/unified/internal.cpp
@@ -11,14 +11,14 @@
 #include "symbol_manager.hpp"
 
 
-af_err af_create_array_with_strides(af_array *arr,
-                                    const void *data,
-                                    const dim_t offset,
-                                    const unsigned ndims,
-                                    const dim_t *const dims_,
-                                    const dim_t *const strides_,
-                                    const af_dtype ty,
-                                    const af_source location)
+af_err af_create_strided_array(af_array *arr,
+                               const void *data,
+                               const dim_t offset,
+                               const unsigned ndims,
+                               const dim_t *const dims_,
+                               const dim_t *const strides_,
+                               const af_dtype ty,
+                               const af_source location)
 {
     return CALL(arr, data, offset, ndims, dims_, strides_, ty, location);
 }

From f728c03f5fec4d446b35a3bee05bae544cfe50cd Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 16:05:57 -0500
Subject: [PATCH 0365/2677] Adding additional constraints when creating strided
 array

---
 src/api/c/internal.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index ac7a374f95..8afdefa30f 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -37,6 +37,10 @@ af_err af_create_strided_array(af_array *arr,
         ARG_ASSERT(5, strides_ != NULL);
         ARG_ASSERT(5, strides_[0] == 1);
 
+        for (int i = 1; i < (int)ndims; i++) {
+            ARG_ASSERT(5, strides_[i] > 0);
+        }
+
         dim4 dims(ndims, dims_);
         dim4 strides(ndims, strides_);
 

From 7bc56a78cb31bff14bac6b5aa9d08c181c2be65c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 16:45:20 -0500
Subject: [PATCH 0366/2677] Adding functions to get raw pointer out of Array<T>

---
 src/api/c/internal.cpp       | 24 ++++++++++++------------
 src/backend/cpu/Array.hpp    |  7 +++++++
 src/backend/cuda/Array.hpp   |  7 +++++++
 src/backend/opencl/Array.hpp |  7 +++++++
 4 files changed, 33 insertions(+), 12 deletions(-)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 8afdefa30f..47c62c6478 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -109,18 +109,18 @@ af_err af_get_raw_ptr(void **ptr, const af_array arr)
         af_dtype ty = getInfo(arr).getType();
 
         switch (ty) {
-        case f32: res = (void *)getArray<float  >(arr).get(); break;
-        case f64: res = (void *)getArray<double >(arr).get(); break;
-        case c32: res = (void *)getArray<cfloat >(arr).get(); break;
-        case c64: res = (void *)getArray<cdouble>(arr).get(); break;
-        case u32: res = (void *)getArray<uint   >(arr).get(); break;
-        case s32: res = (void *)getArray<int    >(arr).get(); break;
-        case u64: res = (void *)getArray<uintl  >(arr).get(); break;
-        case s64: res = (void *)getArray<intl   >(arr).get(); break;
-        case u16: res = (void *)getArray<ushort >(arr).get(); break;
-        case s16: res = (void *)getArray<short  >(arr).get(); break;
-        case b8 : res = (void *)getArray<char   >(arr).get(); break;
-        case u8 : res = (void *)getArray<uchar  >(arr).get(); break;
+        case f32: res = (void *)getRawPtr(getArray<float  >(arr)); break;
+        case f64: res = (void *)getRawPtr(getArray<double >(arr)); break;
+        case c32: res = (void *)getRawPtr(getArray<cfloat >(arr)); break;
+        case c64: res = (void *)getRawPtr(getArray<cdouble>(arr)); break;
+        case u32: res = (void *)getRawPtr(getArray<uint   >(arr)); break;
+        case s32: res = (void *)getRawPtr(getArray<int    >(arr)); break;
+        case u64: res = (void *)getRawPtr(getArray<uintl  >(arr)); break;
+        case s64: res = (void *)getRawPtr(getArray<intl   >(arr)); break;
+        case u16: res = (void *)getRawPtr(getArray<ushort >(arr)); break;
+        case s16: res = (void *)getRawPtr(getArray<short  >(arr)); break;
+        case b8 : res = (void *)getRawPtr(getArray<char   >(arr)); break;
+        case u8 : res = (void *)getRawPtr(getArray<uchar  >(arr)); break;
         default: TYPE_ERROR(6, ty);
         }
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 0c6e701981..2a3afcf617 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -88,6 +88,12 @@ namespace cpu
         return (void *)ptr;
     }
 
+    template<typename T>
+    void *getRawPtr(const Array<T>& arr)
+    {
+        return (void *)(arr.get(false));
+    }
+
     // Array Array Implementation
     template<typename T>
     class Array
@@ -227,6 +233,7 @@ namespace cpu
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
+        friend void *getRawPtr<T>(const Array<T>& arr);
     };
 
 }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 03bd8b3a29..7678754bc3 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -90,6 +90,12 @@ namespace cuda
         return (void *)ptr;
     }
 
+    template<typename T>
+    void *getRawPtr(const Array<T>& arr)
+    {
+        return (void *)(arr.get(false));
+    }
+
     template<typename T>
     class Array
     {
@@ -239,6 +245,7 @@ namespace cuda
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
+        friend void *getRawPtr<T>(const Array<T>& arr);
     };
 
 }
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index f2a217e001..8c5bda90de 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -82,6 +82,12 @@ namespace opencl
         return (void *)((*buf)());
     }
 
+    template<typename T>
+    void *getRawPtr(const Array<T>& arr)
+    {
+        return (void *)(arr.get());
+    }
+
     template<typename T>
     class Array
     {
@@ -261,6 +267,7 @@ namespace opencl
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
+        friend void *getRawPtr<T>(const Array<T>& arr);
     };
 
 }

From 86ff134cac58bd104f10aebb51cd5bf4f897a160 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 11 Feb 2016 16:45:42 -0500
Subject: [PATCH 0367/2677] TEST: Adding tests for internal functions

---
 test/internal.cpp | 124 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 test/internal.cpp

diff --git a/test/internal.cpp b/test/internal.cpp
new file mode 100644
index 0000000000..75fa54fdb9
--- /dev/null
+++ b/test/internal.cpp
@@ -0,0 +1,124 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <af/internal.h>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+TEST(Internal, CreateStrided)
+{
+    float ha[] = {1,
+                  101, 102, 103, 104, 105,
+                  201, 202, 203, 204, 205,
+                  301, 302, 303, 304, 305,
+                  401, 402, 403, 404, 405,
+
+                  1010, 1020, 1030, 1040, 1050,
+                  2010, 2020, 2030, 2040, 2050,
+                  3010, 3020, 3030, 3040, 3050,
+                  4010, 4020, 4030, 4040, 4050};
+
+    dim_t offset = 1;
+    unsigned ndims = 3;
+    dim_t dims[] = {3, 3, 2};
+    dim_t strides[] = {1, 5, 20};
+    af::array a = createStridedArray((void *)ha,
+                                     offset,
+                                     af::dim4(ndims, dims),
+                                     af::dim4(ndims, strides),
+                                     f32,
+                                     afHost);
+
+    af::dim4 astrides = getStrides(a);
+    af::dim4 adims = a.dims();
+
+    ASSERT_EQ(offset, getOffset(a));
+    for (int i = 0; i < (int)ndims; i++) {
+        ASSERT_EQ(strides[i], astrides[i]);
+        ASSERT_EQ(dims[i], adims[i]);
+    }
+
+    std::vector<float> va(a.elements());
+    a.host(&va[0]);
+
+    int o = offset;
+    for (int k = 0; k < dims[2]; k++) {
+        for (int j = 0; j < dims[1]; j++) {
+            for (int i = 0; i < dims[0]; i++) {
+                ASSERT_EQ(va[i + j * dims[0] + k * dims[0] * dims[1]],
+                          ha[i * strides[0] + j * strides[1] + k * strides[2] + o])
+                    << "at ("
+                    << i << ","
+                    << j << ","
+                    << k << ")";
+            }
+        }
+    }
+}
+
+TEST(Internal, CheckInfo)
+{
+    int xdim = 10;
+    int ydim = 8;
+
+    int xoff = 1;
+    int yoff = 2;
+
+    int xnum = 5;
+    int ynum = 3;
+
+    af::array a = af::randu(10, 8);
+
+    af::array b = a(af::seq(xoff, xoff + xnum - 1),
+                    af::seq(yoff, yoff + ynum - 1));
+
+    af::dim4 strides = getStrides(b);
+    af::dim4 dims = b.dims();
+
+    dim_t offset = xoff + yoff * xdim;
+
+    ASSERT_EQ(dims[0], xnum);
+    ASSERT_EQ(dims[1], ynum);
+    ASSERT_EQ(isOwner(a), true);
+    ASSERT_EQ(isOwner(b), false);
+
+    ASSERT_EQ(getOffset(b), offset);
+    ASSERT_EQ(strides[0], 1);
+    ASSERT_EQ(strides[1], xdim);
+    ASSERT_EQ(strides[2], xdim * ydim);
+    ASSERT_EQ(getRawPtr(a), getRawPtr(b));
+}
+
+TEST(Internal, Linear)
+{
+    af::array c;
+    {
+        af::array a = af::randu(10, 8);
+
+        // b is just pointing to same underlying data
+        // b is an owner;
+        af::array b = a;
+        ASSERT_EQ(isOwner(b), true);
+
+        // C is considered sub array
+        // C will not be an owner
+        c = a(af::span);
+        ASSERT_EQ(isOwner(c), false);
+    }
+
+    // Even though a and b are out of scope, c is still not an owner
+    {
+        ASSERT_EQ(isOwner(c), false);
+    }
+}

From 5be6cd78a869ad8fb3a147fb3ec793ca019391f8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 12 Feb 2016 17:29:26 -0500
Subject: [PATCH 0368/2677] BUGFIX: Fixed bug in CUDA and OpenCL when re-using
 same JIT nodes.

---
 src/backend/cuda/JIT/BinaryNode.hpp   |  6 +-----
 src/backend/cuda/JIT/BufferNode.hpp   |  7 +------
 src/backend/cuda/JIT/Node.hpp         | 19 ++++++++++++++++++-
 src/backend/cuda/JIT/ScalarNode.hpp   |  7 +------
 src/backend/cuda/JIT/UnaryNode.hpp    |  6 +-----
 src/backend/opencl/JIT/BinaryNode.hpp |  8 ++++----
 src/backend/opencl/JIT/BufferNode.hpp |  9 +--------
 src/backend/opencl/JIT/Node.hpp       | 18 +++++++++++++++++-
 src/backend/opencl/JIT/ScalarNode.hpp | 11 ++---------
 src/backend/opencl/JIT/UnaryNode.hpp  |  7 +++----
 10 files changed, 49 insertions(+), 49 deletions(-)

diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index 2a2abb0610..f916d85576 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -126,11 +126,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_set_arg = false;
+            resetCommonFlags();
             m_lhs->resetFlags();
             m_rhs->resetFlags();
         }
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index efe32f8b72..342e1ed0b7 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -178,12 +178,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_gen_name = false;
-            m_set_arg = false;
+            resetCommonFlags();
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index e30a1cf63b..00fed9fda7 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -37,6 +37,19 @@ namespace JIT
         bool m_set_arg;
         bool m_gen_name;
 
+    protected:
+
+        void resetCommonFlags()
+        {
+            m_set_id = false;
+            m_gen_func = false;
+            m_gen_param = false;
+            m_gen_offset = false;
+            m_set_arg = false;
+            m_gen_name = false;
+        }
+
+
     public:
 
         Node(const char *type_str, const char *name_str)
@@ -62,7 +75,11 @@ namespace JIT
         virtual void setArgs(std::vector<void *> &args, bool is_linear) { m_set_arg = true; }
         virtual bool isLinear(dim_t dims[4]) { return true; }
 
-        virtual void resetFlags() {}
+        virtual void resetFlags()
+        {
+            resetCommonFlags();
+        }
+
         virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
             len = 0;
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index 288af4dcdb..34f316d34b 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -87,12 +87,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_gen_name = false;
-            m_set_arg = false;
+            resetCommonFlags();
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index caa573104b..94ee96ece7 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -118,11 +118,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_set_arg = false;
+            resetCommonFlags();
             m_child->resetFlags();
         }
 
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index f087760b87..b1f6d112b7 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -51,6 +51,9 @@ namespace JIT
 
         int setArgs(cl::Kernel &ker, int id)
         {
+            if (m_set_arg) return id;
+            m_set_arg = true;
+
             id = m_lhs->setArgs(ker, id);
             id = m_rhs->setArgs(ker, id);
             return id;
@@ -120,10 +123,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
+            resetCommonFlags();
             m_lhs->resetFlags();
             m_rhs->resetFlags();
         }
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 71723b99df..9306d59ef5 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -24,7 +24,6 @@ namespace JIT
         const std::shared_ptr<cl::Buffer> m_data;
         const Param m_param;
         const unsigned m_bytes;
-        bool m_set_arg;
         bool m_linear;
 
     public:
@@ -39,7 +38,6 @@ namespace JIT
               m_data(data),
               m_param(param),
               m_bytes(bytes),
-              m_set_arg(false),
               m_linear(is_linear)
         {}
 
@@ -140,12 +138,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_gen_name = false;
-            m_set_arg = false;
+            resetCommonFlags();
         }
     };
 
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index fedf7fb9bd..fc34c09c19 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -32,8 +32,20 @@ namespace JIT
         bool m_gen_func;
         bool m_gen_param;
         bool m_gen_offset;
+        bool m_set_arg;
         bool m_gen_name;
 
+    protected:
+        void resetCommonFlags()
+        {
+            m_set_id = false;
+            m_gen_func = false;
+            m_gen_param = false;
+            m_gen_offset = false;
+            m_set_arg = false;
+            m_gen_name = false;
+        }
+
     public:
 
         Node(const char *type_str, const char *name_str)
@@ -44,6 +56,7 @@ namespace JIT
               m_gen_func(false),
               m_gen_param(false),
               m_gen_offset(false),
+              m_set_arg(false),
               m_gen_name(false)
         {}
 
@@ -64,7 +77,10 @@ namespace JIT
         }
 
 
-        virtual void resetFlags() {}
+        virtual void resetFlags()
+        {
+            resetCommonFlags();
+        }
 
         virtual bool isLinear(dim_t dims[4]) { return true; }
 
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index 9eaa544134..0bba7a2fc9 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -24,14 +24,12 @@ namespace JIT
     {
     private:
         const T m_val;
-        bool m_set_arg;
 
     public:
 
         ScalarNode(T val)
             : Node(dtype_traits<T>::getName(), shortname<T>(false)),
-              m_val(val),
-              m_set_arg(false)
+              m_val(val)
         {
         }
 
@@ -101,12 +99,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_gen_name = false;
-            m_set_arg = false;
+            resetCommonFlags();
         }
     };
 
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index 78fda23e92..e1f32ded8f 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -49,6 +49,8 @@ namespace JIT
 
         int setArgs(cl::Kernel &ker, int id)
         {
+            if (m_set_arg) return id;
+            m_set_arg = true;
             return m_child->setArgs(ker, id);
         }
 
@@ -108,10 +110,7 @@ namespace JIT
 
         void resetFlags()
         {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
+            resetCommonFlags();
             m_child->resetFlags();
         }
     };

From 3faa83dc14c657e5ebd6de4d60d5d8a67a8fa138 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 12 Feb 2016 18:42:06 -0500
Subject: [PATCH 0369/2677] Adding tests for to check for resetting in JIT

---
 test/jit.cpp | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)

diff --git a/test/jit.cpp b/test/jit.cpp
index 3c2308d5eb..a20b0f4b19 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -65,3 +65,53 @@ TEST(JIT, CPP_JIT_HASH)
         delete[] hF2;
     }
 }
+
+TEST(JIT, CPP_JIT_Reset_Binary)
+{
+    using af::array;
+
+    af::array a = af::constant(2, 5,5);
+    af::array b = af::constant(1, 5,5);
+    af::array c = a + b;
+    af::array d = a - b;
+    af::array e = c * d;
+    e.eval();
+    af::array f = c - d;
+    f.eval();
+    af::array g = d - c;
+    g.eval();
+
+    std::vector<float> hf(f.elements());
+    std::vector<float> hg(g.elements());
+    f.host(&hf[0]);
+    g.host(&hg[0]);
+
+    for (int i = 0; i < (int)f.elements(); i++) {
+        ASSERT_EQ(hf[i], -hg[i]);
+    }
+}
+
+TEST(JIT, CPP_JIT_Reset_Unary)
+{
+    using af::array;
+
+    af::array a = af::constant(2, 5,5);
+    af::array b = af::constant(1, 5,5);
+    af::array c = af::sin(a);
+    af::array d = af::cos(b);
+    af::array e = c * d;
+    e.eval();
+    af::array f = c - d;
+    f.eval();
+    af::array g = d - c;
+    g.eval();
+
+    std::vector<float> hf(f.elements());
+    std::vector<float> hg(g.elements());
+    f.host(&hf[0]);
+    g.host(&hg[0]);
+
+    for (int i = 0; i < (int)f.elements(); i++) {
+        ASSERT_EQ(hf[i], -hg[i]);
+    }
+}

From 199ea82b15314a3be3e28675185395b972a13ff0 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 14 Feb 2016 21:46:48 -0500
Subject: [PATCH 0370/2677] Moving af_get_version to version.cpp

---
 src/api/c/device.cpp  | 9 ---------
 src/api/c/version.cpp | 9 +++++++++
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 304d0c753b..937b0a66c5 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -95,15 +95,6 @@ af_err af_info_string(char **str, const bool verbose)
     return AF_SUCCESS;
 }
 
-af_err af_get_version(int *major, int *minor, int *patch)
-{
-    *major = AF_VERSION_MAJOR;
-    *minor = AF_VERSION_MINOR;
-    *patch = AF_VERSION_PATCH;
-
-    return AF_SUCCESS;
-}
-
 af_err af_device_info(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
 {
     try {
diff --git a/src/api/c/version.cpp b/src/api/c/version.cpp
index 4eb7883a41..91d24cb823 100644
--- a/src/api/c/version.cpp
+++ b/src/api/c/version.cpp
@@ -10,6 +10,15 @@
 #include <af/util.h>
 #include <version.hpp>
 
+af_err af_get_version(int *major, int *minor, int *patch)
+{
+    *major = AF_VERSION_MAJOR;
+    *minor = AF_VERSION_MINOR;
+    *patch = AF_VERSION_PATCH;
+
+    return AF_SUCCESS;
+}
+
 const char *af_get_revision()
 {
     return AF_REVISION;

From 11aa9339fc60a8888b76e140ce933e773386fab1 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 14 Feb 2016 21:47:48 -0500
Subject: [PATCH 0371/2677] Fixing af_get_last_error for unified backend

The way it was implemented, it was getting the last error
generated from  unified backend. This change gets the last error
generated from a particular backend instead.
---
 src/api/c/err_common.cpp           | 61 +++-------------------
 src/api/c/err_common.hpp           |  4 ++
 src/api/c/error.cpp                | 65 ++++++++++++++++++++++++
 src/api/c/index.cpp                | 68 +++++++++++++++++++++++++
 src/api/c/util.cpp                 | 81 ------------------------------
 src/api/unified/CMakeLists.txt     | 13 +++--
 src/api/unified/error.cpp          | 26 ++++++++++
 src/api/unified/index.cpp          | 34 +++++++++++++
 src/api/unified/symbol_manager.hpp |  8 +++
 src/api/unified/util.cpp           |  5 --
 10 files changed, 218 insertions(+), 147 deletions(-)
 create mode 100644 src/api/c/error.cpp
 delete mode 100644 src/api/c/util.cpp
 create mode 100644 src/api/unified/error.cpp

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 495967a891..e95ece6d4b 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -150,9 +150,6 @@ int DimensionError::getArgIndex() const
     return argIndex;
 }
 
-static const int MAX_ERR_SIZE = 1024;
-static std::string global_err_string;
-
 void
 print_error(const string &msg)
 {
@@ -161,57 +158,7 @@ print_error(const string &msg)
         if(perr != "0")
             fprintf(stderr, "%s\n", msg.c_str());
     }
-    global_err_string = msg;
-}
-
-void af_get_last_error(char **str, dim_t *len)
-{
-    dim_t slen = std::min(MAX_ERR_SIZE, (int)global_err_string.size());
-
-    if (len && slen == 0) {
-        *len = 0;
-        *str = NULL;
-        return;
-    }
-
-    af_alloc_host((void**)str, sizeof(char) * (slen + 1));
-    global_err_string.copy(*str, slen);
-
-    (*str)[slen] = '\0';
-    global_err_string = std::string("");
-
-    if(len) *len = slen;
-}
-
-const char *af_err_to_string(const af_err err)
-{
-    switch (err) {
-    case AF_SUCCESS:                return "Success";
-    case AF_ERR_NO_MEM:             return "Device out of memory";
-    case AF_ERR_DRIVER:             return "Driver not available or incompatible";
-    case AF_ERR_RUNTIME:            return "Runtime error ";
-    case AF_ERR_INVALID_ARRAY:      return "Invalid array";
-    case AF_ERR_ARG:                return "Invalid input argument";
-    case AF_ERR_SIZE:               return "Invalid input size";
-    case AF_ERR_TYPE:               return "Function does not support this data type";
-    case AF_ERR_DIFF_TYPE:          return "Input types are not the same";
-    case AF_ERR_BATCH:              return "Invalid batch configuration";
-    case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
-    case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
-    case AF_ERR_NONFREE:            return "Function unavailable. "
-                                           "ArrayFire compiled without Non-Free algorithms support";
-    case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
-    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
-                                           "ArrayFire compiled without Graphics support";
-    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. "
-                                           "See http://www.arrayfire.com/docs/unifiedbackend.htm "
-                                           "for instructions to set up environment for Unified backend";
-    case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
-    case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
-    case AF_ERR_INTERNAL:           return "Internal error";
-    case AF_ERR_UNKNOWN:
-    default:                        return "Unknown error";
-    }
+    get_global_error_string() = msg;
 }
 
 af_err processException()
@@ -271,3 +218,9 @@ af_err processException()
 
     return err;
 }
+
+std::string& get_global_error_string()
+{
+    static std::string global_error_string = std::string("");
+    return global_error_string;
+}
diff --git a/src/api/c/err_common.hpp b/src/api/c/err_common.hpp
index c8eb90a7f6..60ef64276b 100644
--- a/src/api/c/err_common.hpp
+++ b/src/api/c/err_common.hpp
@@ -203,3 +203,7 @@ void print_error(const std::string &msg);
                       __AF_FILENAME__, __LINE__,            \
                       "\n", __err);                         \
     } while(0)
+
+
+static const int MAX_ERR_SIZE = 1024;
+std::string& get_global_error_string();
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
new file mode 100644
index 0000000000..4a7d4b29b9
--- /dev/null
+++ b/src/api/c/error.cpp
@@ -0,0 +1,65 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/exception.h>
+#include <af/device.h>
+#include <err_common.hpp>
+#include <string>
+#include <algorithm>
+
+void af_get_last_error(char **str, dim_t *len)
+{
+    std::string &global_error_string = get_global_error_string();
+    dim_t slen = std::min(MAX_ERR_SIZE, (int)global_error_string.size());
+
+    if (len && slen == 0) {
+        *len = 0;
+        *str = NULL;
+        return;
+    }
+
+    af_alloc_host((void**)str, sizeof(char) * (slen + 1));
+    global_error_string.copy(*str, slen);
+
+    (*str)[slen] = '\0';
+    global_error_string = std::string("");
+
+    if(len) *len = slen;
+}
+
+const char *af_err_to_string(const af_err err)
+{
+    switch (err) {
+    case AF_SUCCESS:                return "Success";
+    case AF_ERR_NO_MEM:             return "Device out of memory";
+    case AF_ERR_DRIVER:             return "Driver not available or incompatible";
+    case AF_ERR_RUNTIME:            return "Runtime error ";
+    case AF_ERR_INVALID_ARRAY:      return "Invalid array";
+    case AF_ERR_ARG:                return "Invalid input argument";
+    case AF_ERR_SIZE:               return "Invalid input size";
+    case AF_ERR_TYPE:               return "Function does not support this data type";
+    case AF_ERR_DIFF_TYPE:          return "Input types are not the same";
+    case AF_ERR_BATCH:              return "Invalid batch configuration";
+    case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
+    case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
+    case AF_ERR_NONFREE:            return "Function unavailable. "
+                                           "ArrayFire compiled without Non-Free algorithms support";
+    case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
+    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
+                                           "ArrayFire compiled without Graphics support";
+    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. "
+                                           "See http://www.arrayfire.com/docs/unifiedbackend.htm "
+                                           "for instructions to set up environment for Unified backend";
+    case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
+    case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
+    case AF_ERR_INTERNAL:           return "Internal error";
+    case AF_ERR_UNKNOWN:
+    default:                        return "Unknown error";
+    }
+}
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index f5a214f8e5..4a20ca2b34 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -230,3 +230,71 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
 
     return AF_SUCCESS;
 }
+
+af_seq af_make_seq(double begin, double end, double step)
+{
+    af_seq seq = {begin, end, step};
+    return seq;
+}
+
+af_err af_create_indexers(af_index_t** indexers)
+{
+    try {
+        af_index_t* out = new af_index_t[4];
+        std::swap(*indexers, out);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim)
+{
+    try {
+        ARG_ASSERT(0, (indexer!=NULL));
+        ARG_ASSERT(1, (idx!=NULL));
+        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        indexer[dim].idx.arr = idx;
+        indexer[dim].isBatch = false;
+        indexer[dim].isSeq   = false;
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx, const dim_t dim, const bool is_batch)
+{
+    try {
+        ARG_ASSERT(0, (indexer!=NULL));
+        ARG_ASSERT(1, (idx!=NULL));
+        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        indexer[dim].idx.seq = *idx;
+        indexer[dim].isBatch = is_batch;
+        indexer[dim].isSeq   = true;
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_set_seq_param_indexer(af_index_t* indexer,
+                              const double begin, const double end, const double step,
+                              const dim_t dim, const bool is_batch)
+{
+    try {
+        ARG_ASSERT(0, (indexer!=NULL));
+        ARG_ASSERT(4, (dim>=0 && dim<=3));
+        indexer[dim].idx.seq = af_make_seq(begin, end, step);
+        indexer[dim].isBatch = is_batch;
+        indexer[dim].isSeq   = true;
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_release_indexers(af_index_t* indexers)
+{
+    try {
+        delete[] indexers;
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/util.cpp b/src/api/c/util.cpp
deleted file mode 100644
index 9b16fe98df..0000000000
--- a/src/api/c/util.cpp
+++ /dev/null
@@ -1,81 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/index.h>
-// The following should be included using double quotes
-// to enable it's use in unified wrapper
-#include "err_common.hpp"
-
-af_seq af_make_seq(double begin, double end, double step)
-{
-    af_seq seq = {begin, end, step};
-    return seq;
-}
-
-af_err af_create_indexers(af_index_t** indexers)
-{
-    try {
-        af_index_t* out = new af_index_t[4];
-        std::swap(*indexers, out);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim)
-{
-    try {
-        ARG_ASSERT(0, (indexer!=NULL));
-        ARG_ASSERT(1, (idx!=NULL));
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
-        indexer[dim].idx.arr = idx;
-        indexer[dim].isBatch = false;
-        indexer[dim].isSeq   = false;
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx, const dim_t dim, const bool is_batch)
-{
-    try {
-        ARG_ASSERT(0, (indexer!=NULL));
-        ARG_ASSERT(1, (idx!=NULL));
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
-        indexer[dim].idx.seq = *idx;
-        indexer[dim].isBatch = is_batch;
-        indexer[dim].isSeq   = true;
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-af_err af_set_seq_param_indexer(af_index_t* indexer,
-                              const double begin, const double end, const double step,
-                              const dim_t dim, const bool is_batch)
-{
-    try {
-        ARG_ASSERT(0, (indexer!=NULL));
-        ARG_ASSERT(4, (dim>=0 && dim<=3));
-        indexer[dim].idx.seq = af_make_seq(begin, end, step);
-        indexer[dim].isBatch = is_batch;
-        indexer[dim].isSeq   = true;
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-af_err af_release_indexers(af_index_t* indexers)
-{
-    try {
-        delete[] indexers;
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-}
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 6ed95d088c..c44e43b5fc 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -15,13 +15,12 @@ FILE(GLOB cpp_sources
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB common_sources
-    "../c/util.cpp"
-    "../c/err_common.cpp"
-    "../c/type_util.cpp"
-    "../c/version.cpp"
-    "../../backend/dim4.cpp"
-    "../../backend/util.cpp"
-    )
+  "../c/version.cpp"
+  "../c/err_common.cpp"
+  "../c/type_util.cpp"
+  "../../backend/dim4.cpp"
+  "../../backend/util.cpp"
+  )
 
 SOURCE_GROUP(common FILES ${common_sources})
 
diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
new file mode 100644
index 0000000000..00b07396a1
--- /dev/null
+++ b/src/api/unified/error.cpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/exception.h>
+#include "symbol_manager.hpp"
+
+void af_get_last_error(char **str, dim_t *len)
+{
+    typedef void(*af_func)(char **, dim_t *);
+    af_func func = (af_func)LOAD_SYMBOL();
+    return func(str, len);
+}
+
+const char *af_err_to_string(const af_err err)
+{
+    typedef char *(*af_func)(af_err);
+    af_func func = (af_func)LOAD_SYMBOL();
+    return func(err);
+}
diff --git a/src/api/unified/index.cpp b/src/api/unified/index.cpp
index 0927dd8b71..4df5926d62 100644
--- a/src/api/unified/index.cpp
+++ b/src/api/unified/index.cpp
@@ -52,3 +52,37 @@ af_err af_assign_gen( af_array *out,
     CHECK_ARRAYS(lhs, rhs);
     return CALL(out, lhs, ndims, indices, rhs);
 }
+
+af_seq af_make_seq(double begin, double end, double step)
+{
+    af_seq seq = {begin, end, step};
+    return seq;
+}
+
+af_err af_create_indexers(af_index_t** indexers)
+{
+    return CALL(indexers);
+}
+
+af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim)
+{
+    CHECK_ARRAYS(idx);
+    return CALL(indexer, idx, dim);
+}
+
+af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx, const dim_t dim, const bool is_batch)
+{
+    return CALL(indexer, idx, dim, is_batch);
+}
+
+af_err af_set_seq_param_indexer(af_index_t* indexer,
+                              const double begin, const double end, const double step,
+                              const dim_t dim, const bool is_batch)
+{
+    return CALL(indexer, begin, end, step, dim, is_batch);
+}
+
+af_err af_release_indexers(af_index_t* indexers)
+{
+    return CALL(indexers);
+}
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 048d1843c7..1530102022 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -59,6 +59,8 @@ class AFSymbolManager {
             return funcHandle(args...);
         }
 
+        LibHandle getHandle() { return activeHandle; }
+
     protected:
         AFSymbolManager();
 
@@ -108,3 +110,9 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg)
 #define CALL(...) unified::AFSymbolManager::getInstance().call(__func__, __VA_ARGS__)
 #define CALL_NO_PARAMS() unified::AFSymbolManager::getInstance().call(__func__)
 #endif
+
+#if defined(OS_WIN)
+#define LOAD_SYMBOL() GetProcAddress(unified::AFSymbolManager::getInstance().getHandle(), __FUNCTION__)
+#else
+#define LOAD_SYMBOL() dlsym(unified::AFSymbolManager::getInstance().getHandle(), __func__)
+#endif
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 155c4f81b9..178ac87ad8 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -56,8 +56,3 @@ af_err af_example_function(af_array* out, const af_array in, const af_someenum_t
     CHECK_ARRAYS(in);
     return CALL(out, in, param);
 }
-
-af_err af_get_version(int *major, int *minor, int *patch)
-{
-    return CALL(major, minor, patch);
-}

From fd87af4a91e302efdebc71e43655c1bb16ccbea7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 15 Feb 2016 19:32:09 -0500
Subject: [PATCH 0372/2677] Add better error messages coming out of unified api

---
 src/api/c/error.cpp                |  4 +---
 src/api/unified/array.cpp          |  1 +
 src/api/unified/error.cpp          | 31 +++++++++++++++++++++++++++---
 src/api/unified/symbol_manager.cpp |  7 ++++---
 src/api/unified/symbol_manager.hpp | 29 ++++++++++++++++++++--------
 5 files changed, 55 insertions(+), 17 deletions(-)

diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 4a7d4b29b9..521ca9bef5 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -53,9 +53,7 @@ const char *af_err_to_string(const af_err err)
     case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
     case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
                                            "ArrayFire compiled without Graphics support";
-    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. "
-                                           "See http://www.arrayfire.com/docs/unifiedbackend.htm "
-                                           "for instructions to set up environment for Unified backend";
+    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. ";
     case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
     case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
     case AF_ERR_INTERNAL:           return "Internal error";
diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index 59158ca195..7d4f9486f0 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/array.h>
+#include <af/backend.h>
 #include "symbol_manager.hpp"
 
 af_err af_create_array(af_array *arr, const void * const data, const unsigned ndims, const dim_t * const dims, const af_dtype type)
diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index 00b07396a1..0224876ec3 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -9,13 +9,38 @@
 
 #include <af/array.h>
 #include <af/exception.h>
+#include <af/device.h>
+#include <algorithm>
 #include "symbol_manager.hpp"
 
 void af_get_last_error(char **str, dim_t *len)
 {
-    typedef void(*af_func)(char **, dim_t *);
-    af_func func = (af_func)LOAD_SYMBOL();
-    return func(str, len);
+    // Set error message from unified backend
+    std::string &global_error_string = get_global_error_string();
+    dim_t slen = std::min(MAX_ERR_SIZE, (int)global_error_string.size());
+
+    // If this is true, the error is coming from the unified backend.
+    if (slen != 0) {
+
+        if (len && slen == 0) {
+            *len = 0;
+            *str = NULL;
+            return;
+        }
+
+        af_alloc_host((void**)str, sizeof(char) * (slen + 1));
+        global_error_string.copy(*str, slen);
+
+        (*str)[slen] = '\0';
+        global_error_string = std::string("");
+
+        if (len) *len = slen;
+    } else {
+        // If false, the error is coming from active backend.
+        typedef void(*af_func)(char **, dim_t *);
+        af_func func = (af_func)LOAD_SYMBOL();
+        func(str, len);
+    }
 }
 
 const char *af_err_to_string(const af_err err)
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 94fef2d541..96cec0b6ac 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -203,8 +203,9 @@ af_err AFSymbolManager::setBackend(af::Backend bknd)
             activeHandle = defaultHandle;
             activeBackend = defaultBackend;
             return AF_SUCCESS;
-        } else
-            return AF_ERR_LOAD_LIB;
+        } else {
+            UNIFIED_ERROR_LOAD_LIB();
+        }
     }
     int idx = bknd >> 1;    // Convert 1, 2, 4 -> 0, 1, 2
     if(bkndHandles[idx]) {
@@ -212,7 +213,7 @@ af_err AFSymbolManager::setBackend(af::Backend bknd)
         activeBackend = bknd;
         return AF_SUCCESS;
     } else {
-        return AF_ERR_LOAD_LIB;
+        UNIFIED_ERROR_LOAD_LIB();
     }
 }
 
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 1530102022..658ac74b64 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -12,6 +12,7 @@
 #include <string>
 #include <stdlib.h>
 #include <util.hpp>
+#include <err_common.hpp>
 
 #if defined(OS_WIN)
 #include <Windows.h>
@@ -27,6 +28,13 @@ namespace unified
 const int NUM_BACKENDS = 3;
 const int NUM_ENV_VARS = 2;
 
+#define UNIFIED_ERROR_LOAD_LIB()                                        \
+    AF_RETURN_ERROR("Failed to load dynamic library. "                  \
+                    "See http://www.arrayfire.com/docs/unifiedbackend.htm " \
+                    "for instructions to set up environment for Unified backend.", \
+                    AF_ERR_LOAD_LIB)
+
+
 class AFSymbolManager {
     public:
         static AFSymbolManager& getInstance();
@@ -43,8 +51,9 @@ class AFSymbolManager {
 
         template<typename... CalleeArgs>
         af_err call(const char* symbolName, CalleeArgs... args) {
-            if (!activeHandle)
-                return AF_ERR_LOAD_LIB;
+            if (!activeHandle) {
+                UNIFIED_ERROR_LOAD_LIB();
+            }
             typedef af_err(*af_func)(CalleeArgs...);
             af_func funcHandle;
 #if defined(OS_WIN)
@@ -53,7 +62,10 @@ class AFSymbolManager {
             funcHandle = (af_func)dlsym(activeHandle, symbolName);
 #endif
             if (!funcHandle) {
-                return AF_ERR_LOAD_SYM;
+                std::string str = "Failed to load symbol: ";
+                str += symbolName;
+                AF_RETURN_ERROR(str.c_str(),
+                                AF_ERR_LOAD_SYM);
             }
 
             return funcHandle(args...);
@@ -97,11 +109,12 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg)
 
 // Macro to check af_array as inputs. The arguments to this macro should be
 // only input af_arrays. Not outputs or other types.
-#define CHECK_ARRAYS(...) do {                                                              \
-    af_backend backendId = unified::AFSymbolManager::getInstance().getActiveBackend();      \
-    if(!unified::checkArrays(backendId, __VA_ARGS__))                                       \
-        return AF_ERR_ARR_BKND_MISMATCH;                                                    \
-} while(0)
+#define CHECK_ARRAYS(...) do {                                          \
+        af_backend backendId = unified::AFSymbolManager::getInstance().getActiveBackend(); \
+        if(!unified::checkArrays(backendId, __VA_ARGS__))               \
+            AF_RETURN_ERROR("Input array does not belong to current backend", \
+                            AF_ERR_ARR_BKND_MISMATCH);                  \
+    } while(0)
 
 #if defined(OS_WIN)
 #define CALL(...) unified::AFSymbolManager::getInstance().call(__FUNCTION__, __VA_ARGS__)

From 52158efc3a6ff3c14343f033e2fec7d9c0e24e55 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 16 Feb 2016 13:26:16 -0500
Subject: [PATCH 0373/2677] Properly handle af_release_array when using a
 different backend

---
 src/api/unified/array.cpp | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index 7d4f9486f0..809c9d4e6b 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -41,8 +41,16 @@ af_err af_get_data_ptr(void *data, const af_array arr)
 
 af_err af_release_array(af_array arr)
 {
-    CHECK_ARRAYS(arr);
-    return CALL(arr);
+    af_backend curr = unified::AFSymbolManager::getInstance().getActiveBackend();
+    af_backend other = curr;
+
+    af_err err = af_get_backend_id(&other, arr);
+    if (err != AF_SUCCESS) return err;
+
+    unified::AFSymbolManager::getInstance().setBackend(other);
+    err = CALL(arr);
+    unified::AFSymbolManager::getInstance().setBackend(curr);
+    return err;
 }
 
 af_err af_retain_array(af_array *out, const af_array in)

From 4c045b1188cb5ee952a88a8618cb0766c10e9224 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 16 Feb 2016 14:41:16 -0500
Subject: [PATCH 0374/2677] Set minimum CMake version to 2.8.12 (previously
 2.8)

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8bdf93cd52..0def888f6c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
+CMAKE_MINIMUM_REQUIRED(VERSION 2.8.12)
 PROJECT(ARRAYFIRE)
 
 SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)

From 3c06fa081f781d5d590fc6f9f00a76ea09b1d80a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 16 Feb 2016 14:59:18 -0500
Subject: [PATCH 0375/2677] Force offload OSX LAPACK on unified memory devices

---
 src/backend/opencl/blas.cpp     |  2 +-
 src/backend/opencl/platform.cpp | 17 ++++++++++++++---
 src/backend/opencl/platform.hpp |  2 +-
 3 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 365e6e5680..77531154e5 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -121,7 +121,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
-    if(OpenCLCPUOffload()) {
+    if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
         return cpu::matmul(lhs, rhs, optLhs, optRhs);
     }
 #endif
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 6855e79f66..c2c13c7ae3 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -514,11 +514,22 @@ bool isHostUnifiedMemory(const cl::Device &device)
     return device.getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
 }
 
-bool OpenCLCPUOffload()
+bool OpenCLCPUOffload(bool forceOffloadOSX)
 {
-    static const bool sync = getEnvVar("AF_OPENCL_CPU_OFFLOAD") == "1";
+    static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") == "1";
     bool offload = false;
-    if(sync) offload = isHostUnifiedMemory(getDevice());
+    if(offloadEnv) offload = isHostUnifiedMemory(getDevice());
+#if OS_MAC
+    // FORCED OFFLOAD FOR LAPACK FUNCTIONS ON OSX UNIFIED MEMORY DEVICES
+    //
+    // On OSX Unified Memory devices (Intel), always offload LAPACK but not GEMM
+    // irrespective of the AF_OPENCL_CPU_OFFLOAD value
+    // From GEMM, OpenCLCPUOffload(false) is called which will render the
+    // variable inconsequential to the returned result.
+    //
+    // Issue https://github.com/arrayfire/arrayfire/issues/662
+    offload = offload || forceOffloadOSX;
+#endif
     return offload;
 }
 
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 4c745e0c91..095fdf9ae7 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -114,7 +114,7 @@ cl_device_type getDeviceType();
 
 bool isHostUnifiedMemory(const cl::Device &device);
 
-bool OpenCLCPUOffload();
+bool OpenCLCPUOffload(bool forceOffloadOSX = true);
 
 bool isGLSharingSupported();
 

From 3c385b3909d7588eea30e16984f2c3c9069105bc Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 16 Feb 2016 16:05:29 -0500
Subject: [PATCH 0376/2677] Add BUILD_* Options for examples and tests when
 building standalone

---
 examples/CMakeLists.txt | 29 +++++++++++++--------------
 test/CMakeLists.txt     | 43 ++++++++++++++++++++++++-----------------
 2 files changed, 40 insertions(+), 32 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 4710d1b739..be0f6407be 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -61,13 +61,17 @@ ENDMACRO()
 # and TARGET_LINK_LIBRARIES(... ${ARRAYFIRE_LIBRARIES}) are needed
 MACRO(BUILD_ALL FILES BACKEND_NAME BACKEND_LIBRARIES OTHER_LIBRARIES)
 
-    FOREACH(FILE ${FILES})
-        GET_FILENAME_COMPONENT(EXAMPLE ${FILE} NAME_WE)
-        GET_FILENAME_COMPONENT(FULL_DIR_NAME ${FILE} PATH)
-        GET_FILENAME_COMPONENT(DIR_NAME ${FULL_DIR_NAME} NAME)
+    STRING(TOUPPER ${BACKEND_NAME} BACKEND_NAME_UPPER)
+    MESSAGE(STATUS "EXAMPLES: ${BACKEND_NAME_UPPER} backend is ${BUILD_${BACKEND_NAME_UPPER}}.")
+    IF(${BUILD_${BACKEND_NAME_UPPER}})
+        FOREACH(FILE ${FILES})
+            GET_FILENAME_COMPONENT(EXAMPLE ${FILE} NAME_WE)
+            GET_FILENAME_COMPONENT(FULL_DIR_NAME ${FILE} PATH)
+            GET_FILENAME_COMPONENT(DIR_NAME ${FULL_DIR_NAME} NAME)
 
-        BUILD_EXAMPLE(${EXAMPLE} ${FILE} ${BACKEND_NAME} "${BACKEND_LIBRARIES}" "${OTHER_LIBRARIES}" ${DIR_NAME})
-    ENDFOREACH()
+            BUILD_EXAMPLE(${EXAMPLE} ${FILE} ${BACKEND_NAME} "${BACKEND_LIBRARIES}" "${OTHER_LIBRARIES}" ${DIR_NAME})
+        ENDFOREACH()
+    ENDIF()
 ENDMACRO()
 
 # Collect the source
@@ -76,10 +80,9 @@ ADD_DEFINITIONS("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
 # Next we build each example using every backend.
 IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
-    MESSAGE(STATUS "EXAMPLES: CPU backend is ON.")
+    OPTION(BUILD_CPU "Build ArrayFire Examples for CPU backend" ON)
     BUILD_ALL("${FILES}" cpu ${ArrayFire_CPU_LIBRARIES} "")
 ELSEIF(TARGET afcpu)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "EXAMPLES: CPU backend is ON.")
     BUILD_ALL("${FILES}" cpu afcpu "")
 ELSE()
     MESSAGE(STATUS "EXAMPLES: CPU backend is OFF. afcpu was not found.")
@@ -87,10 +90,9 @@ ENDIF()
 
 # Next we build each example using every backend.
 IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
-    MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
+    OPTION(BUILD_UNIFIED "Build ArrayFire Examples for Unified backend" ON)
     BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "${CMAKE_DL_LIBS}")
 ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "EXAMPLES: UNIFIED backend is ON.")
     BUILD_ALL("${FILES}" unified af "${CMAKE_DL_LIBS}")
 ELSE()
     MESSAGE(STATUS "EXAMPLES: UNIFIED backend is OFF. af was not found.")
@@ -104,10 +106,10 @@ IF (${CUDA_FOUND})
           PATHS ${CUDA_TOOLKIT_ROOT_DIR}
           DOC "CUDA NVVM Library"
           )
-        MESSAGE(STATUS "EXAMPLES: CUDA backend is ON.")
+        MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+        OPTION(BUILD_CUDA "Build ArrayFire Examples for CUDA backend" ON)
         BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
-        MESSAGE(STATUS "EXAMPLES: CUDA backend is ON.")
         BUILD_ALL("${FILES}" cuda afcuda "")
     ELSE()
         MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. afcuda was not found")
@@ -118,10 +120,9 @@ ENDIF()
 
 IF (${OpenCL_FOUND})
     IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
-        MESSAGE(STATUS "EXAMPLES: OpenCL backend is ON.")
+        OPTION(BUILD_OPENCL "Build ArrayFire Examples for OpenCL backend" ON)
         BUILD_ALL("${FILES}" opencl ${ArrayFire_OpenCL_LIBRARIES} "${OpenCL_LIBRARIES}")
     ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
-        MESSAGE(STATUS "EXAMPLES: OpenCL backend is ON.")
         BUILD_ALL("${FILES}" opencl afopencl "${OpenCL_LIBRARIES}")
     ELSE()
         MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. afopencl was not found")
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index bea93d554e..5db23714d3 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -110,6 +110,14 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
 
 ENDMACRO(CREATE_TESTS)
 
+MACRO(CHECK_AND_CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
+    STRING(TOUPPER ${BACKEND} BACKEND_NAME_UPPER)
+    MESSAGE(STATUS "TESTS: ${BACKEND_NAME_UPPER} backend is ${BUILD_${BACKEND_NAME_UPPER}}.")
+    IF(${BUILD_${BACKEND_NAME_UPPER}})
+        CREATE_TESTS(${BACKEND} ${AFLIBNAME} "${GTEST_LIBS}" "${OTHER_LIBS}")
+    ENDIF()
+ENDMACRO(CHECK_AND_CREATE_TESTS)
+
 FIND_PACKAGE(Threads REQUIRED)
 IF(CMAKE_USE_PTHREADS_INIT AND NOT "${APPLE}")
     SET(THREAD_LIB_FLAG "-pthread")
@@ -170,11 +178,10 @@ LIST(SORT UNIFIED_FILES)  # Tests execute in alphabetical order
 
 # Next we build each example using every backend.
 IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
-    MESSAGE(STATUS "TESTS: CPU backend is ON.")
-    CREATE_TESTS(cpu ${ArrayFire_CPU_LIBRARIES} "${GTEST_LIBRARIES}" "")
+    OPTION(BUILD_CPU "Build ArrayFire Tests for CPU backend" ON)
+    CHECK_AND_CREATE_TESTS(cpu ${ArrayFire_CPU_LIBRARIES} "${GTEST_LIBRARIES}" "")
 ELSEIF(TARGET afcpu)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "TESTS: CPU backend is ON.")
-    CREATE_TESTS(cpu afcpu "${GTEST_LIBRARIES}" "")
+    CHECK_AND_CREATE_TESTS(cpu afcpu "${GTEST_LIBRARIES}" "")
 ELSE()
     MESSAGE(STATUS "TESTS: CPU backend is OFF. afcpu was not found.")
 ENDIF()
@@ -188,10 +195,11 @@ IF (${CUDA_FOUND})
           PATHS ${CUDA_TOOLKIT_ROOT_DIR}
           DOC "CUDA NVVM Library"
           )
-        MESSAGE(STATUS "TESTS: CUDA backend is ON.")
+        MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
+            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
             FOREACH(FILE ${FILES})
                 GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -202,15 +210,15 @@ IF (${CUDA_FOUND})
 
         # ELSE OSX && CLANG && CUDA < 7
         ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
+            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
         ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
 
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
-        MESSAGE(STATUS "TESTS: CUDA backend is ON.")
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
             FOREACH(FILE ${FILES})
                 GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -221,7 +229,7 @@ IF (${CUDA_FOUND})
 
         # ELSE OSX && CLANG && CUDA < 7
         ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
         ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
     ELSE()
@@ -235,11 +243,11 @@ ENDIF()
 IF (${OpenCL_FOUND})
     INCLUDE_DIRECTORIES(${OpenCL_INCLUDE_DIRS})
     IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
-        MESSAGE(STATUS "TESTS: OpenCL backend is ON.")
-        CREATE_TESTS(opencl ${ArrayFire_OpenCL_LIBRARIES} "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
+        OPTION(BUILD_OPENCL "Build ArrayFire Tests for OpenCL backend" ON)
+        MESSAGE(${OpenCL_LIBRARIES})
+        CHECK_AND_CREATE_TESTS(opencl ${ArrayFire_OpenCL_LIBRARIES} "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
     ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
-        MESSAGE(STATUS "TESTS: OpenCL backend is ON.")
-        CREATE_TESTS(opencl afopencl "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
+        CHECK_AND_CREATE_TESTS(opencl afopencl "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
     ELSE()
         MESSAGE(STATUS "TESTS: OpenCL backend is OFF. afopencl was not found")
     ENDIF()
@@ -249,11 +257,10 @@ ENDIF()
 
 # Unified Backend
 IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
-    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
-    CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
+  OPTION(BUILD_UNIFIED "Build ArrayFire Tests for Unified backend" ON)
+    CHECK_AND_CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
 ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
-    MESSAGE(STATUS "TESTS: UNIFIED backend is ON.")
-    CREATE_TESTS(unified af "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
+    CHECK_AND_CREATE_TESTS(unified af "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
 ELSE()
     MESSAGE(STATUS "TESTS: UNIFIED backend is OFF. af was not found.")
 ENDIF()

From 9b10c0e7c8ff267582e65700e9efcdfdb986414a Mon Sep 17 00:00:00 2001
From: Youssef Nashed <ynashed@anl.gov>
Date: Tue, 16 Feb 2016 14:40:45 -0600
Subject: [PATCH 0377/2677] Added support for loading 32 bit integer images

---
 src/api/c/imageio.cpp  | 40 ++++++++++++++++++++++++++++++++++------
 src/api/c/imageio2.cpp | 26 ++++++++++++++++++++++----
 2 files changed, 56 insertions(+), 10 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 748ddbc58e..d990b10904 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -186,7 +186,10 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
         if(fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
             AF_ERROR("FreeImage Error: Bits per channel not supported", AF_ERR_NOT_SUPPORTED);
         }
-
+        
+        // data type
+        FREE_IMAGE_TYPE image_type = FreeImage_GetImageType(pBitmap);
+        
         // sizes
         uint fi_w = FreeImage_GetWidth(pBitmap);
         uint fi_h = FreeImage_GetHeight(pBitmap);
@@ -204,21 +207,36 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
                 else if(fi_bpc == 16)
                     AF_CHECK((readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 32)
-                    AF_CHECK((readImage<float,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                    switch(image_type) {
+						case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_INT32: AF_CHECK((readImage<int,   AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                    }
             } else if (fi_color == 1) {
                 if(fi_bpc == 8)
                     AF_CHECK((readImage<uchar,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 16)
                     AF_CHECK((readImage<ushort, AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 32)
-                    AF_CHECK((readImage<float,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                    switch(image_type) {
+						case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_INT32: AF_CHECK((readImage<int,   AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                    }
             } else {             //3 channel image
                 if(fi_bpc == 8)
                     AF_CHECK((readImage<uchar,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 16)
                     AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 32)
-                    AF_CHECK((readImage<float,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                    switch(image_type) {
+						case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_INT32: AF_CHECK((readImage<int,   AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                    }
             }
         } else {                    //output gray irrespective
             if(fi_color == 1) {     //4 channel image
@@ -227,14 +245,24 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
                 else if(fi_bpc == 16)
                     AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 32)
-                    AF_CHECK((readImage<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                    switch(image_type) {
+						case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_INT32: AF_CHECK((readImage<int,   AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                    }
             } else if (fi_color == 3 || fi_color == 4) {
                 if(fi_bpc == 8)
                     AF_CHECK((readImage<uchar,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 16)
                     AF_CHECK((readImage<ushort, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
                 else if(fi_bpc == 32)
-                    AF_CHECK((readImage<float,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                    switch(image_type) {
+						case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_INT32: AF_CHECK((readImage<int,   AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+						default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                    }
             }
         }
 
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index ff7a4a8d34..44886aac50 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -162,7 +162,10 @@ af_err af_load_image_native(af_array *out, const char* filename)
         if(fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
             AF_ERROR("FreeImage Error: Bits per channel not supported", AF_ERR_NOT_SUPPORTED);
         }
-
+        
+        // data type
+        FREE_IMAGE_TYPE image_type = FreeImage_GetImageType(pBitmap);
+        
         // sizes
         uint fi_w = FreeImage_GetWidth(pBitmap);
         uint fi_h = FreeImage_GetHeight(pBitmap);
@@ -179,21 +182,36 @@ af_err af_load_image_native(af_array *out, const char* filename)
             else if(fi_bpc == 16)
                 AF_CHECK((readImage_t<ushort, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
             else if(fi_bpc == 32)
-                AF_CHECK((readImage_t<float,  AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                switch(image_type) {
+                    case FIT_UINT32: AF_CHECK((readImage_t<uint, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    case FIT_INT32: AF_CHECK((readImage_t<int, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    case FIT_FLOAT: AF_CHECK((readImage_t<float, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                }
         } else if (fi_color == 1) {
             if(fi_bpc == 8)
                 AF_CHECK((readImage_t<uchar,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
             else if(fi_bpc == 16)
                 AF_CHECK((readImage_t<ushort, AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
             else if(fi_bpc == 32)
-                AF_CHECK((readImage_t<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                switch(image_type) {
+                    case FIT_UINT32: AF_CHECK((readImage_t<uint,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    case FIT_INT32: AF_CHECK((readImage_t<int,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    case FIT_FLOAT: AF_CHECK((readImage_t<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                }
         } else {             //3 channel imag
             if(fi_bpc == 8)
                 AF_CHECK((readImage_t<uchar,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
             else if(fi_bpc == 16)
                 AF_CHECK((readImage_t<ushort, AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
             else if(fi_bpc == 32)
-                AF_CHECK((readImage_t<float,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+                switch(image_type) {
+                    case FIT_UINT32: AF_CHECK((readImage_t<uint,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    case FIT_INT32: AF_CHECK((readImage_t<int,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    case FIT_FLOAT: AF_CHECK((readImage_t<float,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
+                    default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                }
         }
 
         std::swap(*out,rImage);

From 0a0f6e3680a21948c677796c89e0dc94d83ccef8 Mon Sep 17 00:00:00 2001
From: Johan Pauwels <johan.pauwels@gmail.com>
Date: Sat, 20 Feb 2016 22:29:43 +0100
Subject: [PATCH 0378/2677] Search for GLEWmx in default paths too

Is there a reason the search needs to be limited to those specific locations? CMake now can't find the GLEW I installed under $HOME (even though I added it to CMAKE_SYSTEM_PREFIX_PATH).
---
 CMakeModules/FindGLEWmx.cmake | 2 --
 1 file changed, 2 deletions(-)

diff --git a/CMakeModules/FindGLEWmx.cmake b/CMakeModules/FindGLEWmx.cmake
index b90919eb98..a6da72bbf2 100644
--- a/CMakeModules/FindGLEWmx.cmake
+++ b/CMakeModules/FindGLEWmx.cmake
@@ -55,7 +55,6 @@ ELSE (WIN32)
         /sw/lib
         /opt/local/lib
         ${GLEW_ROOT_DIR}/lib
-        NO_DEFAULT_PATH
         DOC "The GLEWmx library")
 
     SET(PX ${CMAKE_STATIC_LIBRARY_PREFIX})
@@ -72,7 +71,6 @@ ELSE (WIN32)
         /sw/lib
         /opt/local/lib
         ${GLEW_ROOT_DIR}/lib
-        NO_DEFAULT_PATH
         DOC "The GLEWmx library")
     UNSET(PX)
     UNSET(SX)

From e4facbb73c5c06095514d177e75adff467c7111e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 21 Feb 2016 22:21:27 -0500
Subject: [PATCH 0379/2677] Adding function to check if memory usage is
 approaching the limits

---
 src/backend/MemoryManager.cpp | 9 +++++++--
 src/backend/MemoryManager.hpp | 2 ++
 src/backend/cpu/memory.cpp    | 5 +++++
 src/backend/cpu/memory.hpp    | 1 +
 src/backend/cuda/memory.cpp   | 5 +++++
 src/backend/cuda/memory.hpp   | 2 ++
 src/backend/opencl/memory.cpp | 5 +++++
 src/backend/opencl/memory.hpp | 1 +
 8 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index b66dfc33e7..0879e98cea 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -153,8 +153,7 @@ void *MemoryManager::alloc(const size_t bytes, bool user_lock)
 
             // FIXME: Add better checks for garbage collection
             // Perhaps look at total memory available as a metric
-            if (current.lock_bytes >= current.max_bytes ||
-                current.total_buffers >= this->max_buffers) {
+            if (this->checkMemoryLimit()) {
                 this->garbageCollect();
             }
 
@@ -305,4 +304,10 @@ unsigned MemoryManager::getMaxBuffers()
     return this->max_buffers;
 }
 
+bool MemoryManager::checkMemoryLimit()
+{
+    memory_info& current = this->getCurrentMemoryInfo();
+    return current.lock_bytes >= current.max_bytes || current.total_buffers >= this->max_buffers;
+}
+
 }
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index 015fa6db3d..0db70b572d 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -111,6 +111,8 @@ class MemoryManager
     {
     }
 
+    bool checkMemoryLimit();
+
 protected:
     mutex_t memory_mutex;
 
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 016428a6d9..8837e27da1 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -160,6 +160,11 @@ void pinnedFree(T* ptr)
     return getMemoryManager().unlock((void *)ptr, false);
 }
 
+bool checkMemoryLimit()
+{
+    return getMemoryManager().checkMemoryLimit();
+}
+
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
     template void memFree(T* ptr);                          \
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 80ee86ddc8..91116fbcfc 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -39,4 +39,5 @@ namespace cpu
 
     void setMemStepSize(size_t step_bytes);
     size_t getMemStepSize(void);
+    bool checkMemoryLimit();
 }
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index ff62661601..51eb507320 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -229,6 +229,11 @@ void pinnedFree(T* ptr)
     return getMemoryManagerPinned().unlock((void *)ptr, false);
 }
 
+bool checkMemoryLimit()
+{
+    return getMemoryManager().checkMemoryLimit();
+}
+
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
     template void memFree(T* ptr);                          \
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 9bf69df9d4..80478c13dc 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -39,4 +39,6 @@ namespace cuda
 
     void setMemStepSize(size_t step_bytes);
     size_t getMemStepSize(void);
+
+    bool checkMemoryLimit();
 }
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 756d18749e..5df64d6d86 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -259,6 +259,11 @@ void pinnedFree(T* ptr)
     return getMemoryManagerPinned().unlock((void *)ptr, false);
 }
 
+bool checkMemoryLimit()
+{
+    return getMemoryManager().checkMemoryLimit();
+}
+
 #define INSTANTIATE(T)                                      \
     template T* memAlloc(const size_t &elements);           \
     template void memFree(T* ptr);                          \
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index f4d06a3324..a02d387591 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -44,4 +44,5 @@ namespace opencl
 
     void setMemStepSize(size_t step_bytes);
     size_t getMemStepSize(void);
+    bool checkMemoryLimit();
 }

From e1abe128ae19b769c1b48855df3306d87afcc847 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 12:27:44 -0500
Subject: [PATCH 0380/2677] Changes to make sure cpu backend does not enqueue
 too many functions.

This fix synchronizes when the queue hits 25 functions or when
the memory used is approaching the device limit.
---
 src/backend/MemoryManager.cpp |  4 +--
 src/backend/cpu/memory.cpp    | 21 ++++++++++++--
 src/backend/cpu/queue.hpp     | 52 ++++++++++++++++++++++-------------
 3 files changed, 54 insertions(+), 23 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 0879e98cea..379c2e2af2 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -257,7 +257,7 @@ void MemoryManager::printInfo(const char *msg, const int device)
             unit = "MB";
         }
 
-        std::cout << " |  " << std::right << std::setw(14) << kv.first << " "
+        std::cout << "|  " << std::right << std::setw(14) << kv.first << " "
                   << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
                   << " | " << std::setw(9) << status_mngr
                   << " | " << std::setw(9) << status_user
@@ -277,7 +277,7 @@ void MemoryManager::printInfo(const char *msg, const int device)
         }
 
         for (auto &ptr : kv.second) {
-            std::cout << " |  " << std::right << std::setw(14) << ptr << " "
+            std::cout << "|  " << std::right << std::setw(14) << ptr << " "
                       << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
                       << " | " << std::setw(9) << status_mngr
                       << " | " << std::setw(9) << status_user
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 8837e27da1..b4b1b450d9 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -112,13 +112,30 @@ void printMemInfo(const char *msg, const int device)
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+    T *ptr = nullptr;
+
+    try {
+        ptr = (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+    } catch(...) {
+        getQueue().sync();
+        ptr = (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+    }
+    return ptr;
 }
 
 void* memAllocUser(const size_t &bytes)
 {
-    return getMemoryManager().alloc(bytes, true);
+    void *ptr = nullptr;
+
+    try {
+        ptr = getMemoryManager().alloc(bytes, true);
+    } catch(...) {
+        getQueue().sync();
+        ptr = getMemoryManager().alloc(bytes, true);
+    }
+    return ptr;
 }
+
 template<typename T>
 void memFree(T *ptr)
 {
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 6d32b85a65..2f32b4d852 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <util.hpp>
+#include <memory.hpp>
 
 //FIXME: Is there a better way to check for std::future not being supported ?
 #if defined(AF_DISABLE_CPU_ASYNC) || (defined(__GNUC__) && (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2))
@@ -48,32 +49,45 @@ typedef async_queue queue_impl;
 namespace cpu {
 
 /// Wraps the async_queue class
-class queue {
+class queue
+{
 public:
-  queue()
-    : sync_calls( __SYNCHRONOUS_ARCH == 1 || getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
-
-  template <typename F, typename... Args>
-  void enqueue(const F func, Args... args) {
+    queue()
+        :
+        count(0),
+        sync_calls( __SYNCHRONOUS_ARCH == 1 || getEnvVar("AF_SYNCHRONOUS_CALLS") == "1")
+    {}
 
-    if(sync_calls) { func( args... ); }
-    else           { aQueue.enqueue( func, args... ); }
+    template <typename F, typename... Args>
+    void enqueue(const F func, Args... args)
+    {
+        count++;
+        if(sync_calls) { func( args... ); }
+        else           { aQueue.enqueue( func, args... ); }
 #ifndef NDEBUG
-    sync();
+        sync();
+#else
+        if (checkMemoryLimit() || count >= 25) {
+            sync();
+        }
 #endif
+    }
 
-  }
-  void sync() {
-    if(!sync_calls) aQueue.sync();
-  }
+    void sync()
+    {
+        count = 0;
+        if(!sync_calls) aQueue.sync();
+    }
 
-  bool is_worker() const {
-    return (!sync_calls) ? aQueue.is_worker() : false;
-  }
+    bool is_worker() const
+    {
+        return (!sync_calls) ? aQueue.is_worker() : false;
+    }
 
-private:
-  const bool sync_calls;
-  queue_impl aQueue;
+    private:
+        int count;
+        const bool sync_calls;
+        queue_impl aQueue;
 };
 
 }

From e59df758ad1116ff56d369edb9fa889be5528a55 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 12:28:09 -0500
Subject: [PATCH 0381/2677] Making copyArray from cpu backend asynchronous

---
 src/backend/cpu/copy.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 0da304b3ca..27e80f8afb 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -42,8 +42,9 @@ void copyData(T *to, const Array<T> &from)
 template<typename T>
 Array<T> copyArray(const Array<T> &A)
 {
+    A.eval();
     Array<T> out = createEmptyArray<T>(A.dims());
-    copyData(out.get(), A);
+    getQueue().enqueue(kernel::copy<T, T>, out, A, scalar<T>(0), 1.0);
     return out;
 }
 

From cfe76f376f4f594a3ba340e09bd5177c44e4e72c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 12:28:48 -0500
Subject: [PATCH 0382/2677] Adding missing evals in cpu backend

---
 src/backend/cpu/histogram.cpp           | 3 ++-
 src/backend/cpu/kernel/sift_nonfree.hpp | 1 +
 src/backend/cpu/lu.cpp                  | 3 +++
 src/backend/cpu/nearest_neighbour.cpp   | 2 ++
 src/backend/cpu/qr.cpp                  | 3 +++
 src/backend/cpu/scan.cpp                | 3 +--
 src/backend/cpu/solve.cpp               | 3 +++
 src/backend/cpu/triangle.cpp            | 1 +
 8 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 6aa60e59e4..3c30402b47 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -23,7 +23,8 @@ namespace cpu
 
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in,
-        const unsigned &nbins, const double &minval, const double &maxval)
+                         const unsigned &nbins,
+                         const double &minval, const double &maxval)
 {
     in.eval();
 
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index c1c92a97e6..e7ca19175c 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -969,6 +969,7 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
                    const bool compute_GLOH)
 {
     in.eval();
+    getQueue().sync();
     af::dim4 idims = in.dims();
 
     const unsigned min_dim = (double_input) ? min(idims[0]*2, idims[1]*2)
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 265fdfaec5..24ca4acd78 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -44,6 +44,9 @@ LU_FUNC(getrf , cdouble, z)
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 {
+    lower.eval();
+    upper.eval();
+    pivot.eval();
     in.eval();
 
     dim4 iDims = in.dims();
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index f1daba7526..17e892f492 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -31,6 +31,8 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
         CPU_NOT_SUPPORTED();
     }
 
+    idx.eval();
+    dist.eval();
     query.eval();
     train.eval();
 
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index 34a39f64b8..f8dbfa2013 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -59,6 +59,9 @@ GQR_FUNC(gqr , cdouble, zungqr)
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 {
+    q.eval();
+    r.eval();
+    t.eval();
     in.eval();
 
     dim4 iDims = in.dims();
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 08431f8baa..78de4142c8 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -27,8 +27,7 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim)
 {
     dim4 dims     = in.dims();
-    Array<To> out = createValueArray<To>(dims, 0);
-    out.eval();
+    Array<To> out = createEmptyArray<To>(dims);
     in.eval();
 
     switch (in.ndims()) {
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 48ea4de3c5..367afa3884 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -96,6 +96,9 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 template<typename T>
 Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
 {
+    A.eval();
+    b.eval();
+
     Array<T> B = copyArray<T>(b);
     int N      = B.dims()[0];
     int NRHS   = B.dims()[1];
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 57f61b1331..eaad1b9f86 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -21,6 +21,7 @@ namespace cpu
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in)
 {
+    in.eval();
     getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
 }
 

From c66da4028452e5adcba2d7e6c3adff37e95b8b33 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 12:45:10 -0500
Subject: [PATCH 0383/2677] BUGFIX: Fixing array.write for all backends

---
 src/backend/cpu/Array.cpp    | 5 +++--
 src/backend/cuda/Array.cpp   | 4 ++--
 src/backend/opencl/Array.cpp | 4 ++--
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 3edca877cd..2c296d02d3 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -228,8 +228,9 @@ void
 writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
 {
     if(!arr.isOwner()) {
-        arr = createEmptyArray<T>(arr.dims());
+        arr = copyArray<T>(arr);
     }
+    arr.eval();
     memcpy(arr.get(), data, bytes);
 }
 
@@ -238,7 +239,7 @@ void
 writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
 {
     if(!arr.isOwner()) {
-        arr = createEmptyArray<T>(arr.dims());
+        arr = copyArray<T>(arr);
     }
     memcpy(arr.get(), (const T * const)data, bytes);
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index c1cf8102eb..786574129b 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -256,7 +256,7 @@ namespace cuda
     writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
     {
         if (!arr.isOwner()) {
-            arr = createEmptyArray<T>(arr.dims());
+            arr = copyArray<T>(arr);
         }
 
         T *ptr = arr.get();
@@ -273,7 +273,7 @@ namespace cuda
     writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     {
         if (!arr.isOwner()) {
-            arr = createEmptyArray<T>(arr.dims());
+            arr = copyArray<T>(arr);
         }
 
         T *ptr = arr.get();
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index bd576ca88a..002c1d5b82 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -286,7 +286,7 @@ namespace opencl
     writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
     {
         if (!arr.isOwner()) {
-            arr = createEmptyArray<T>(arr.dims());
+            arr = copyArray<T>(arr);
         }
 
         getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE,
@@ -302,7 +302,7 @@ namespace opencl
     writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     {
         if (!arr.isOwner()) {
-            arr = createEmptyArray<T>(arr.dims());
+            arr = copyArray<T>(arr);
         }
 
         cl::Buffer& buf = *arr.get();

From 49a18e04f17de4e3f2d8a08fa2a6b9ff7c120a18 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 22 Feb 2016 14:12:45 -0500
Subject: [PATCH 0384/2677] Change clBLAS commit tag to af3.3.0

---
 CMakeModules/build_clBLAS.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index d486b31801..2289c26393 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG arrayfire-release-test
+    GIT_TAG af3.3.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 31b864314ca4a772e7620d53363769a7fbfa06aa Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 22 Feb 2016 14:12:53 -0500
Subject: [PATCH 0385/2677] Change clFFT commit tag to af3.3.0

---
 CMakeModules/build_clFFT.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 961347f913..2ab9ccc1ea 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG arrayfire-release-test
+    GIT_TAG af3.3.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 513e7115b2699f15aff17fafbbd5f9fde38e383c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 22 Feb 2016 16:34:38 -0500
Subject: [PATCH 0386/2677] Fixes for using MKL on OSX

---
 src/backend/cblas.cpp                         |  8 +++---
 src/backend/cpu/CMakeLists.txt                | 13 +++++----
 src/backend/cpu/blas.hpp                      | 16 +++++------
 src/backend/cpu/lapack_helper.hpp             | 20 ++++++-------
 src/backend/cuda/CMakeLists.txt               |  9 ++++--
 src/backend/cuda/cpu_lapack/lapack_helper.hpp | 20 ++++++-------
 src/backend/opencl/CMakeLists.txt             | 13 +++++----
 src/backend/opencl/cpu/cpu_helper.hpp         | 28 +++++++++----------
 src/backend/opencl/magma/magma_cpu_blas.h     | 16 +++++------
 src/backend/opencl/magma/magma_cpu_lapack.h   | 18 +++++++-----
 10 files changed, 86 insertions(+), 75 deletions(-)

diff --git a/src/backend/cblas.cpp b/src/backend/cblas.cpp
index 4d99d457c2..1be15e47c9 100644
--- a/src/backend/cblas.cpp
+++ b/src/backend/cblas.cpp
@@ -12,11 +12,11 @@
 #ifdef AF_CPU
     #include <blas.hpp>
 #else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
+    #ifdef USE_MKL
+        #include <mkl_cblas.h>
     #else
-        #ifdef USE_MKL
-            #include <mkl_cblas.h>
+        #ifdef __APPLE__
+            #include <Accelerate/Accelerate.h>
         #else
             extern "C" {
                 #include <cblas.h>
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 8ada1d6935..2032f0b7e9 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -14,9 +14,14 @@ IF(USE_CPU_F77_BLAS)
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
 ENDIF()
 
-IF(USE_CPU_MKL)
-    MESSAGE("Using MKL")
+IF(USE_CPU_MKL) # Manual MKL Setup
+    MESSAGE("CPU Backend Using MKL")
     ADD_DEFINITIONS(-DUSE_MKL)
+ELSE(USE_CPU_MKL)
+    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+        MESSAGE("CPU Backend Using MKL RT")
+        ADD_DEFINITIONS(-DUSE_MKL)
+    ENDIF()
 ENDIF()
 
 IF (NOT CBLAS_LIBRARIES)
@@ -29,10 +34,6 @@ IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU" AND "${APPLE}")
     ADD_DEFINITIONS(-flax-vector-conversions)
 ENDIF()
 
-IF(${MKL_FOUND})
-    ADD_DEFINITIONS(-DUSE_MKL)
-ENDIF()
-
 FIND_PACKAGE(FFTW REQUIRED)
 MESSAGE(STATUS "FFTW Found ? ${FFTW_FOUND}")
 MESSAGE(STATUS "FFTW Library: ${FFTW_LIBRARIES}")
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 05484338cd..3f5b7451ad 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -12,16 +12,16 @@
 #include <Array.hpp>
 #include <types.hpp>
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#else
 #ifdef USE_MKL
-#include <mkl_cblas.h>
+    #include <mkl_cblas.h>
 #else
-extern "C" {
-#include <cblas.h>
-}
-#endif
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+    #else
+        extern "C" {
+            #include <cblas.h>
+        }
+    #endif
 #endif
 
 // TODO: Ask upstream for a more official way to detect it
diff --git a/src/backend/cpu/lapack_helper.hpp b/src/backend/cpu/lapack_helper.hpp
index f978ecb92b..c5ed4fa83f 100644
--- a/src/backend/cpu/lapack_helper.hpp
+++ b/src/backend/cpu/lapack_helper.hpp
@@ -17,17 +17,17 @@
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#include <lapacke.hpp>
-#undef AF_LAPACK_COL_MAJOR
-#define AF_LAPACK_COL_MAJOR 0
-#else
 #ifdef USE_MKL
-#include<mkl_lapacke.h>
-#else // NETLIB LAPACKE
-#include<lapacke.h>
-#endif
+    #include<mkl_lapacke.h>
+#else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
+    #else // NETLIB LAPACKE
+        #include<lapacke.h>
+    #endif
 #endif
 
 #endif
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 81d6ba243c..8cecd812f2 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -112,9 +112,14 @@ ELSE(CUDA_cusolver_LIBRARY)
         ELSE(NOT LAPACK_FOUND)
             MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. But CPU LAPACK libraries are available. Will fallback to using host side code.")
             ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
-            IF(USE_CUDA_MKL)
-                MESSAGE("Using MKL")
+            IF(USE_CUDA_MKL) # Manual MKL Setup
+                MESSAGE("CUDA LAPACK CPU Fallback Using MKL")
                 ADD_DEFINITIONS(-DUSE_MKL)
+            ELSE(USE_CUDA_MKL)
+                IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+                    MESSAGE("CUDA LAPACK CPU Fallback Using MKL RT")
+                    ADD_DEFINITIONS(-DUSE_MKL)
+                ENDIF()
             ENDIF()
         ENDIF()
     ELSE()
diff --git a/src/backend/cuda/cpu_lapack/lapack_helper.hpp b/src/backend/cuda/cpu_lapack/lapack_helper.hpp
index 58265871c2..b85a80b10c 100644
--- a/src/backend/cuda/cpu_lapack/lapack_helper.hpp
+++ b/src/backend/cuda/cpu_lapack/lapack_helper.hpp
@@ -19,17 +19,17 @@
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#include <lapacke.hpp>
-#undef AF_LAPACK_COL_MAJOR
-#define AF_LAPACK_COL_MAJOR 0
-#else
 #ifdef USE_MKL
-#include<mkl_lapacke.h>
-#else // NETLIB LAPACKE
-#include<lapacke.h>
-#endif
+    #include<mkl_lapacke.h>
+#else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
+    #else // NETLIB LAPACKE
+        #include<lapacke.h>
+    #endif
 #endif
 
 #endif
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index e598a973df..ce45c4bdaa 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -10,9 +10,14 @@ IF(USE_OPENCL_F77_BLAS)
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
 ENDIF()
 
-IF(USE_OPENCL_MKL)
-    MESSAGE("Using MKL")
+IF(USE_OPENCL_MKL) # Manual MKL Setup
+    MESSAGE("OpenCL Backend Using MKL")
     ADD_DEFINITIONS(-DUSE_MKL)
+ELSE(USE_OPENCL_MKL)
+    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+        MESSAGE("OpenCL Backend Using MKL RT")
+        ADD_DEFINITIONS(-DUSE_MKL)
+    ENDIF()
 ENDIF()
 
 IF(APPLE)
@@ -42,10 +47,6 @@ ELSE(NOT LAPACK_FOUND)
   ENDIF()
 ENDIF()
 
-IF(${MKL_FOUND})
-  ADD_DEFINITIONS(-DUSE_MKL)
-ENDIF()
-
 IF(NOT UNIX)
     ADD_DEFINITIONS(-DAFDLL)
 ENDIF()
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index cbdc470e19..f7f690322c 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -29,32 +29,32 @@
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#ifdef __APPLE__
-    #include <Accelerate/Accelerate.h>
-    #include <lapacke.hpp>
-    #undef AF_LAPACK_COL_MAJOR
-    #define AF_LAPACK_COL_MAJOR 0
+#ifdef USE_MKL
+    #include<mkl_lapacke.h>
 #else
-    #ifdef USE_MKL
-        #include<mkl_lapacke.h>
-    #else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
+    #else // NETLIB LAPACKE
         #include<lapacke.h>
     #endif
-#endif //OS
+#endif
 
 #endif // WITH_OPENCL_LINEAR_ALGEBRA
 
 //********************************************************/
 // BLAS
 //********************************************************/
-#ifdef __APPLE__
-    #include <Accelerate/Accelerate.h>
+#ifdef USE_MKL
+    #include <mkl_cblas.h>
 #else
-    #ifdef USE_MKL
-        #include <mkl_cblas.h>
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
     #else
         extern "C" {
-        #include <cblas.h>
+            #include <cblas.h>
         }
     #endif
 #endif
diff --git a/src/backend/opencl/magma/magma_cpu_blas.h b/src/backend/opencl/magma/magma_cpu_blas.h
index b3cba096b5..6661aad657 100644
--- a/src/backend/opencl/magma/magma_cpu_blas.h
+++ b/src/backend/opencl/magma/magma_cpu_blas.h
@@ -13,16 +13,16 @@
 #include <defines.hpp>
 #include "magma_types.h"
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#else
 #ifdef USE_MKL
-#include <mkl_cblas.h>
+    #include <mkl_cblas.h>
 #else
-extern "C" {
-#include <cblas.h>
-}
-#endif
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+    #else
+        extern "C" {
+            #include <cblas.h>
+        }
+    #endif
 #endif
 
 // Todo: Ask upstream for a more official way to detect it
diff --git a/src/backend/opencl/magma/magma_cpu_lapack.h b/src/backend/opencl/magma/magma_cpu_lapack.h
index 5974dab8a9..54c26ae0e9 100644
--- a/src/backend/opencl/magma/magma_cpu_lapack.h
+++ b/src/backend/opencl/magma/magma_cpu_lapack.h
@@ -39,16 +39,20 @@ int LAPACKE_dlacgv_work(Args... args) { return 0; }
 #define ORDER_TYPE int
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#if defined(__APPLE__)
-    #define LAPACK_COL_MAJOR 102
-    #include "../../lapacke.hpp"
+#ifdef USE_MKL
+    #include<mkl_lapacke.h>
 #else
-    #ifdef USE_MKL
-        #include<mkl_lapacke.h>
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef LAPACK_COL_MAJOR
+        #define LAPACK_COL_MAJOR 102
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
     #else // NETLIB LAPACKE
         #include<lapacke.h>
-    #endif  // MKL/NETLIB
-#endif  //APPLE
+    #endif
+#endif
 
 #define LAPACKE_CHECK(fn) do {                  \
         int __info = fn;                        \

From cc59efa6f88dbf03682a7e3e3af09587adc98dc8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 16:40:32 -0500
Subject: [PATCH 0387/2677] Fix to MemoryManager in debug mode

---
 src/backend/MemoryManager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index b66dfc33e7..a761162cdc 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -122,6 +122,8 @@ void MemoryManager::unlock(void *ptr, bool user_unlock)
         // Just free memory in debug mode
         if ((iter->second).bytes > 0) {
             this->nativeFree(iter->first);
+            current.total_buffers--;
+            current.total_bytes -= iter->second.bytes;
         }
     } else {
         // In regular mode, move buffer to free map

From d6d08f96384a1b1b441f61caafe3a41001eb0482 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 16:40:54 -0500
Subject: [PATCH 0388/2677] Clear the free_map after calling garbageCollect in
 MemoryManager

---
 src/backend/MemoryManager.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index a761162cdc..a2bbb7d628 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -87,6 +87,7 @@ void MemoryManager::garbageCollect()
             kv.second.pop_back();
         }
     }
+    current.free_map.clear();
 }
 
 void MemoryManager::unlock(void *ptr, bool user_unlock)

From 6325406afa60d5d98a2c4e349fb92e994b33fbab Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 22 Feb 2016 16:42:11 -0500
Subject: [PATCH 0389/2677] Exit early when destructor is called on empty
 arrays.

This should speed things up when a lot of buffers are
present in the MemoryManager.
---
 src/backend/MemoryManager.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index a2bbb7d628..5910eabeea 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -92,6 +92,9 @@ void MemoryManager::garbageCollect()
 
 void MemoryManager::unlock(void *ptr, bool user_unlock)
 {
+    // Shortcut for empty arrays
+    if (!ptr) return;
+
     lock_guard_t lock(this->memory_mutex);
     memory_info& current = this->getCurrentMemoryInfo();
 

From d4fb656e09dd89994ae65bf643cfefe2f58f3f47 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 22 Feb 2016 17:59:53 -0500
Subject: [PATCH 0390/2677] Added support for finding MKL RT on OSX for BLAS,
 LAPACKE, FFTW

* Uses INTEL_MKL_ROOT as enviornment variable. Commonly /opt/intel/mkl
* If using RT, then add /opt/intel/mkl/lib and
  /opt/intel/compilers_and_libraries/mac/lib/ to DYLD_LIBRARY_PATH
---
 CMakeModules/FindCBLAS.cmake      | 26 +++++++++++++++++---------
 CMakeModules/FindFFTW.cmake       | 29 ++++++++++++++++++++++++-----
 CMakeModules/FindLAPACKE.cmake    |  8 ++++++--
 src/backend/cpu/CMakeLists.txt    | 10 +++++++++-
 src/backend/cuda/CMakeLists.txt   | 10 +++++++++-
 src/backend/opencl/CMakeLists.txt | 10 +++++++++-
 6 files changed, 74 insertions(+), 19 deletions(-)

diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index efef36b093..db1d783e9e 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -62,28 +62,36 @@ IF(NOT CBLAS_ROOT_DIR)
   IF (ENV{CBLASDIR})
     SET(CBLAS_ROOT_DIR $ENV{CBLASDIR})
     IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib64")
+        SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib64")
     ELSE()
-      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib")
+        SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib")
     ENDIF()
   ENDIF()
 
   IF (ENV{CBLAS_ROOT_DIR})
     SET(CBLAS_ROOT_DIR $ENV{CBLAS_ROOT_DIR})
     IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib64")
+        SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib64")
     ELSE()
-      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib")
+        SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib")
     ENDIF()
   ENDIF()
 
   IF (INTEL_MKL_ROOT_DIR)
     SET(CBLAS_ROOT_DIR ${INTEL_MKL_ROOT_DIR})
-    IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib/intel64")
-    ELSE()
-      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib/ia32")
-    ENDIF()
+    IF(APPLE)
+        IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+            SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib")
+        ELSE()
+            SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib")
+        ENDIF()
+    ELSE(APPLE) # Windows and Linux
+        IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+            SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib/intel64")
+        ELSE()
+            SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib/ia32")
+        ENDIF()
+    ENDIF(APPLE)
   ENDIF()
 
   SET(CBLAS_INCLUDE_DIR "${CBLAS_ROOT_DIR}/include")
diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index a725f64ecd..3156cec89b 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -24,6 +24,25 @@ IF(NOT FFTW_ROOT AND ENV{FFTWDIR})
     SET(FFTW_ROOT $ENV{FFTWDIR})
 ENDIF()
 
+IF (NOT INTEL_MKL_ROOT_DIR)
+  SET(INTEL_MKL_ROOT_DIR $ENV{INTEL_MKL_ROOT})
+ENDIF()
+
+IF(NOT FFTW_ROOT)
+
+  IF (ENV{FFTWDIR})
+    SET(FFTW_ROOT $ENV{FFTWDIR})
+  ENDIF()
+
+  IF (ENV{FFTW_ROOT_DIR})
+    SET(FFTW_ROOT $ENV{FFTW_ROOT_DIR})
+  ENDIF()
+
+  IF (INTEL_MKL_ROOT_DIR)
+    SET(FFTW_ROOT ${INTEL_MKL_ROOT_DIR})
+  ENDIF()
+ENDIF()
+
 # Check if we can use PkgConfig
 FIND_PACKAGE(PkgConfig)
 
@@ -44,14 +63,14 @@ IF(FFTW_ROOT)
     #find libs
     FIND_LIBRARY(
         FFTW_LIB
-        NAMES "fftw3" "libfftw3-3" "fftw3-3"
+        NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
         PATH_SUFFIXES "lib" "lib64"
         NO_DEFAULT_PATH
         )
     FIND_LIBRARY(
         FFTWF_LIB
-        NAMES "fftw3f" "libfftw3f-3" "fftw3f-3"
+        NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
         PATH_SUFFIXES "lib" "lib64"
         NO_DEFAULT_PATH
@@ -62,18 +81,18 @@ IF(FFTW_ROOT)
         FFTW_INCLUDES
         NAMES "fftw3.h"
         PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "include"
+        PATH_SUFFIXES "include" "include/fftw"
         NO_DEFAULT_PATH
         )
 ELSE()
     FIND_LIBRARY(
         FFTW_LIB
-        NAMES "fftw3"
+        NAMES "fftw3" "mkl_rt"
         PATHS ${PKG_FFTW_LIBRARY_DIRS} ${LIB_INSTALL_DIR}
         )
     FIND_LIBRARY(
         FFTWF_LIB
-        NAMES "fftw3f"
+        NAMES "fftw3f" "mkl_rt"
         PATHS ${PKG_FFTW_LIBRARY_DIRS} ${LIB_INSTALL_DIR}
         )
     FIND_PATH(
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index dc4a045370..0732cfaa83 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -141,8 +141,12 @@ ELSE(PC_LAPACKE_FOUND)
     ENDIF(LAPACKE_ROOT_DIR)
 ENDIF(PC_LAPACKE_FOUND)
 
-SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
-SET(LAPACK_INCLUDE_DIR ${LAPACKE_INCLUDES})
+IF(LAPACKE_LIB AND LAPACK_LIB)
+    SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
+ENDIF()
+IF(LAPACKE_INCLUDES)
+    SET(LAPACK_INCLUDE_DIR ${LAPACKE_INCLUDES})
+ENDIF()
 
 INCLUDE(FindPackageHandleStandardArgs)
 FIND_PACKAGE_HANDLE_STANDARD_ARGS(LAPACK DEFAULT_MSG
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 2032f0b7e9..9387323592 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -39,7 +39,15 @@ MESSAGE(STATUS "FFTW Found ? ${FFTW_FOUND}")
 MESSAGE(STATUS "FFTW Library: ${FFTW_LIBRARIES}")
 
 IF(APPLE)
-    FIND_PACKAGE(LAPACK)
+    FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
+    IF(NOT LAPACK_FOUND)
+        # UNSET THE VARIABLES FROM LAPACKE
+        UNSET(LAPACKE_LIB CACHE)
+        UNSET(LAPACK_LIB CACHE)
+        UNSET(LAPACKE_INCLUDES CACHE)
+        UNSET(LAPACKE_ROOT_DIR CACHE)
+        FIND_PACKAGE(LAPACK)
+    ENDIF()
 ELSE(APPLE) # Linux and Windows
     FIND_PACKAGE(LAPACKE)
 ENDIF(APPLE)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8cecd812f2..ae0690dba2 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -102,7 +102,15 @@ ELSE(CUDA_cusolver_LIBRARY)
     IF(${CUDA_LAPACK_CPU_FALLBACK})
         ## Try to use CPU side lapack
         IF(APPLE)
-            FIND_PACKAGE(LAPACK)
+            FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
+            IF(NOT LAPACK_FOUND)
+                # UNSET THE VARIABLES FROM LAPACKE
+                UNSET(LAPACKE_LIB CACHE)
+                UNSET(LAPACK_LIB CACHE)
+                UNSET(LAPACKE_INCLUDES CACHE)
+                UNSET(LAPACKE_ROOT_DIR CACHE)
+                FIND_PACKAGE(LAPACK)
+            ENDIF()
         ELSE(APPLE) # Linux and Windows
             FIND_PACKAGE(LAPACKE)
         ENDIF(APPLE)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index ce45c4bdaa..bbe430df15 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -21,7 +21,15 @@ ELSE(USE_OPENCL_MKL)
 ENDIF()
 
 IF(APPLE)
-    FIND_PACKAGE(LAPACK)
+    FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
+    IF(NOT LAPACK_FOUND)
+        # UNSET THE VARIABLES FROM LAPACKE
+        UNSET(LAPACKE_LIB CACHE)
+        UNSET(LAPACK_LIB CACHE)
+        UNSET(LAPACKE_INCLUDES CACHE)
+        UNSET(LAPACKE_ROOT_DIR CACHE)
+        FIND_PACKAGE(LAPACK)
+    ENDIF()
 ELSE(APPLE) # Linux and Windows
     FIND_PACKAGE(LAPACKE)
 ENDIF(APPLE)

From 96baaf9b33bc33a661cde292e3aed9074b85251e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 23 Feb 2016 14:31:33 -0500
Subject: [PATCH 0391/2677] Fixes to and reorganization of OSX Installer

* ArrayFire<Backend>.config and forge are now installed with the library component
* ArrayFireConfig.cmake files are installed by all repos
* Examples and Documentation and now independent components
* No changes to install paths
* When make osx_installer is called, it creates a new directory called
  osx_install_files which are then used to generate the installers
* osx_installer target depends on make install being called first
---
 CMakeModules/osx_install/OSXInstaller.cmake | 109 ++++++++++++++++----
 CMakeModules/osx_install/distribution.dist  |  21 ++--
 CMakeModules/osx_install/readme.html        |  13 +--
 3 files changed, 102 insertions(+), 41 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index dc3a8b2491..d79d68f2b6 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -8,8 +8,62 @@ SET(BIN2CPP_PROGRAM "bin2cpp")
 
 SET(OSX_INSTALL_DIR ${CMAKE_MODULE_PATH}/osx_install)
 
+################################################################################
+## Create Directory Structure
+################################################################################
+SET(OSX_TEMP "${CMAKE_BINARY_DIR}/osx_install_files")
+
+FILE(GLOB COMMONLIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
+FILE(GLOB COMMONCMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFireConfig*.cmake")
+
+MACRO(OSX_INSTALL_SETUP BACKEND LIB)
+    FILE(GLOB ${BACKEND}LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/lib${LIB}*.dylib")
+    FILE(GLOB ${BACKEND}CMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFire${BACKEND}*.cmake")
+
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_${BACKEND})
+    FOREACH(SRC ${${BACKEND}LIB} ${COMMONLIB} ${${BACKEND}CMAKE} ${COMMONCMAKE})
+        FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
+        ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
+                           COMMAND ${CMAKE_COMMAND} -E copy
+                           ${SRC} "${OSX_TEMP}/${BACKEND}/${SRC_REL}"
+                           WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                           COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir"
+                           )
+    ENDFOREACH()
+ENDMACRO(OSX_INSTALL_SETUP)
+
+OSX_INSTALL_SETUP(CPU afcpu)
+OSX_INSTALL_SETUP(CUDA afcuda)
+OSX_INSTALL_SETUP(OpenCL afopencl)
+OSX_INSTALL_SETUP(Unified af)
+
+# Headers
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_INCLUDE
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  ${CMAKE_INSTALL_PREFIX}/include "${OSX_TEMP}/include"
+                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  COMMENT "Copying header files to temporary OSX Install Dir"
+                  )
+
+# Examples
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_EXAMPLES
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/examples" "${OSX_TEMP}/examples"
+                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  COMMENT "Copying examples files to temporary OSX Install Dir"
+                  )
+
+# Documentation
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/doc" "${OSX_TEMP}/doc"
+                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  COMMENT "Copying documentation files to temporary OSX Install Dir"
+                  )
+################################################################################
+
 FUNCTION(PKG_BUILD)
-    CMAKE_PARSE_ARGUMENTS(ARGS "" "INSTALL_LOCATION;IDENTIFIER;PATH_TO_FILES;PKG_NAME;TARGETS;SCRIPT_DIR" "FILTERS" ${ARGN})
+    CMAKE_PARSE_ARGUMENTS(ARGS "" "DEPENDS;INSTALL_LOCATION;IDENTIFIER;PATH_TO_FILES;PKG_NAME;TARGETS;SCRIPT_DIR" "FILTERS" ${ARGN})
 
     FOREACH(filter ${ARGS_FILTERS})
         LIST(APPEND  FILTER_LIST --filter ${filter})
@@ -70,50 +124,61 @@ ENDFUNCTION(PRODUCT_BUILD)
 
 
 PKG_BUILD(  PKG_NAME        ArrayFireCPU
-            DEPENDS         afcpu
+            DEPENDS         OSX_INSTALL_SETUP_CPU
             TARGETS         cpu_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             SCRIPT_DIR      ${OSX_INSTALL_DIR}/cpu_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cpu.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/CPU
             FILTERS         opencl cuda unified)
 
 PKG_BUILD(  PKG_NAME        ArrayFireCUDA
-            DEPENDS         afcuda
+            DEPENDS         OSX_INSTALL_SETUP_CUDA
             TARGETS         cuda_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             SCRIPT_DIR      ${OSX_INSTALL_DIR}/cuda_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cuda.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/CUDA
             FILTERS         cpu opencl unified)
 
 PKG_BUILD(  PKG_NAME        ArrayFireOPENCL
-            DEPENDS         afopencl
+            DEPENDS         OSX_INSTALL_SETUP_OpenCL
             TARGETS         opencl_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             IDENTIFIER      com.arrayfire.pkg.arrayfire.opencl.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/OpenCL
             FILTERS         cpu cuda unified)
 
 PKG_BUILD(  PKG_NAME        ArrayFireUNIFIED
-            DEPENDS         af
+            DEPENDS         OSX_INSTALL_SETUP_Unified
             TARGETS         unified_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             IDENTIFIER      com.arrayfire.pkg.arrayfire.unified.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/Unified
             FILTERS         cpu cuda opencl)
 
 PKG_BUILD(  PKG_NAME        ArrayFireHeaders
+            DEPENDS         OSX_INSTALL_SETUP_INCLUDE
             TARGETS         header_package
             INSTALL_LOCATION /usr/local/include
             IDENTIFIER      com.arrayfire.pkg.arrayfire.inc
-            PATH_TO_FILES   package/include)
-
-PKG_BUILD(  PKG_NAME        ArrayFireExtra
-            TARGETS         extra_package
-            INSTALL_LOCATION /usr/local/share
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.extra
-            PATH_TO_FILES   package/share)
-
-PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${header_package} ${extra_package})
+            PATH_TO_FILES   ${OSX_TEMP}/include)
+
+PKG_BUILD(  PKG_NAME        ArrayFireExamples
+            DEPENDS         OSX_INSTALL_SETUP_EXAMPLES
+            TARGETS         examples_package
+            INSTALL_LOCATION /usr/local/share/ArrayFire/examples
+            IDENTIFIER      com.arrayfire.pkg.arrayfire.examples
+            PATH_TO_FILES   ${OSX_TEMP}/examples
+            FILTERS         cmake)
+
+PKG_BUILD(  PKG_NAME        ArrayFireDoc
+            DEPENDS         OSX_INSTALL_SETUP_DOC
+            TARGETS         doc_package
+            INSTALL_LOCATION /usr/local/share/ArrayFire/doc
+            IDENTIFIER      com.arrayfire.pkg.arrayfire.doc
+            PATH_TO_FILES   ${OSX_TEMP}/doc
+            FILTERS         cmake)
+
+PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${header_package} ${examples_package} ${doc_package})
 
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
index 3dc82379c9..6c460a6a26 100644
--- a/CMakeModules/osx_install/distribution.dist
+++ b/CMakeModules/osx_install/distribution.dist
@@ -17,7 +17,8 @@
     <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"    version="${AF_VERSION}" onConclusion="none">ArrayFireOPENCL.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.unified.lib"   version="${AF_VERSION}" onConclusion="none">ArrayFireUNIFIED.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.inc"           version="${AF_VERSION}" onConclusion="none">ArrayFireHeaders.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.extra"         version="${AF_VERSION}" onConclusion="none">ArrayFireExtra.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
     <options customize="always" require-scripts="false"/>
     <choices-outline>
         <line choice="libs">
@@ -27,26 +28,27 @@
             <line choice="com.arrayfire.arrayfire.unified.lib"/>
         </line>
         <line choice="com.arrayfire.arrayfire.inc"/>
-        <line choice="com.arrayfire.arrayfire.extra"/>
+        <line choice="com.arrayfire.arrayfire.examples"/>
+        <line choice="com.arrayfire.arrayfire.doc"/>
     </choices-outline>
     <choice id="libs" title="ArrayFire Libraries" visible="true" />
-    <choice title="CPU Libraries"
+    <choice title="ArrayFire CPU Libraries. Also installs the corresponding CMake config files."
             description="ArrayFire targeting CPUs."
             id="cpu_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"/>
     </choice>
     <choice title="CUDA Libraries"
-            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code."
+            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the corresponding CMake config files."
             id="cuda_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"/>
     </choice>
     <choice title="OpenCL Libraries"
-            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
+            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. Also installs the corresponding CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
             id="opencl_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"/>
     </choice>
     <choice title="Unified Library"
-            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. NOTE: This option requires the other platforms to work properly"
+            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. Also installs the corresponding CMake config files. NOTE: This option requires the other platforms to work properly"
             id="com.arrayfire.arrayfire.unified.lib"
             selected="CheckBackendSelected()"
             visible="true"
@@ -56,7 +58,10 @@
     <choice title="ArrayFire Headers" description="ArrayFire Headers" id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.inc"/>
     </choice>
-    <choice title="Extras" description="Extra files include documentation, examples, and cmake scripts to find and use ArrayFire with cmake," id="com.arrayfire.arrayfire.extra" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.extra"/>
+    <choice title="Examples" description="ArrayFire Examples" id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.examples"/>
+    </choice>
+    <choice title="Documentation" description="ArrayFire Documentation" id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.doc"/>
     </choice>
 </installer-gui-script>
diff --git a/CMakeModules/osx_install/readme.html b/CMakeModules/osx_install/readme.html
index 41d4ab8cf0..482b7add7e 100644
--- a/CMakeModules/osx_install/readme.html
+++ b/CMakeModules/osx_install/readme.html
@@ -5,18 +5,9 @@ <h2>Install Directories</h2>
     <ul>
         <li> Libraries will be installed in <code>/usr/local/lib</code> </li>
         <li> Headers will be installed in <code>/usr/local/include</code> </li>
-        <li> Docs and other files will be installed in <code>/usr/local/share</code> </li>
-    </ul>
-
-    <h2> Major Updates </h2>
-    <ul>
-        <li> ArrayFire is now open source</li>
-        <li> Major changes to the visualization library</li>
-        <li> Introducing handle based C API</li>
-        <li> New backend: CPU fallback available for systems without GPUs</li>
-        <li> Dense linear algebra functions available for all backends</li>
-        <li> Support for 64 bit integers</li>
+        <li> Examples, documentation and CMake config files will be installed in <code>/usr/local/share</code> </li>
     </ul>
+    <p> For complete list of updates, visit <a href="http://www.arrayfire.com/docs/releasenotes.htm">ArrayFire Release Notes</a></p>
 
 </body>
 </html>

From 529b638ad1d0f683c093ca9945aa89ec3d1cc59f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 23 Feb 2016 15:20:47 -0500
Subject: [PATCH 0392/2677] OSX Installer: Move libforge and ArrayFireConfig
 into common sub package

* libforge and ArrayFireConfig.cmake files are now in a common package
* This package is no visible at install time
* The package is enabled if any of the backends are enabled (like unified)
* This is done so that the common files are installed only once rather than by
  each backend package
---
 CMakeModules/osx_install/OSXInstaller.cmake | 25 +++++++++++++++++++--
 CMakeModules/osx_install/distribution.dist  | 14 ++++++++++--
 2 files changed, 35 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index d79d68f2b6..b2514f8e2a 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -13,15 +13,28 @@ SET(OSX_INSTALL_DIR ${CMAKE_MODULE_PATH}/osx_install)
 ################################################################################
 SET(OSX_TEMP "${CMAKE_BINARY_DIR}/osx_install_files")
 
+# Common files - libforge, ArrayFireConfig*.cmake
 FILE(GLOB COMMONLIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
 FILE(GLOB COMMONCMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFireConfig*.cmake")
 
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_COMMON)
+FOREACH(SRC ${COMMONLIB} ${COMMONCMAKE})
+    FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
+    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_COMMON PRE_BUILD
+                       COMMAND ${CMAKE_COMMAND} -E copy
+                       ${SRC} "${OSX_TEMP}/common/${SRC_REL}"
+                       WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                       COMMENT "Copying Common files to temporary OSX Install Dir"
+                       )
+ENDFOREACH()
+
+# Backends - CPU, CUDA, OpenCL, Unified
 MACRO(OSX_INSTALL_SETUP BACKEND LIB)
     FILE(GLOB ${BACKEND}LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/lib${LIB}*.dylib")
     FILE(GLOB ${BACKEND}CMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFire${BACKEND}*.cmake")
 
     ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_${BACKEND})
-    FOREACH(SRC ${${BACKEND}LIB} ${COMMONLIB} ${${BACKEND}CMAKE} ${COMMONCMAKE})
+    FOREACH(SRC ${${BACKEND}LIB} ${${BACKEND}CMAKE})
         FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
         ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
                            COMMAND ${CMAKE_COMMAND} -E copy
@@ -157,6 +170,14 @@ PKG_BUILD(  PKG_NAME        ArrayFireUNIFIED
             PATH_TO_FILES   ${OSX_TEMP}/Unified
             FILTERS         cpu cuda opencl)
 
+PKG_BUILD(  PKG_NAME        ArrayFireCommon
+            DEPENDS         OSX_INSTALL_SETUP_COMMON
+            TARGETS         common_package
+            INSTALL_LOCATION /usr/local
+            IDENTIFIER      com.arrayfire.pkg.arrayfire.libcommon
+            PATH_TO_FILES   ${OSX_TEMP}/common
+            FILTERS         cpu cuda opencl unified)
+
 PKG_BUILD(  PKG_NAME        ArrayFireHeaders
             DEPENDS         OSX_INSTALL_SETUP_INCLUDE
             TARGETS         header_package
@@ -180,5 +201,5 @@ PKG_BUILD(  PKG_NAME        ArrayFireDoc
             PATH_TO_FILES   ${OSX_TEMP}/doc
             FILTERS         cmake)
 
-PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${header_package} ${examples_package} ${doc_package})
+PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${common_package} ${header_package} ${examples_package} ${doc_package})
 
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
index 6c460a6a26..b476bf013f 100644
--- a/CMakeModules/osx_install/distribution.dist
+++ b/CMakeModules/osx_install/distribution.dist
@@ -19,6 +19,7 @@
     <pkg-ref id="com.arrayfire.arrayfire.inc"           version="${AF_VERSION}" onConclusion="none">ArrayFireHeaders.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
     <options customize="always" require-scripts="false"/>
     <choices-outline>
         <line choice="libs">
@@ -26,14 +27,15 @@
             <line choice="cuda_lib"/>
             <line choice="opencl_lib"/>
             <line choice="com.arrayfire.arrayfire.unified.lib"/>
+            <line choice="com.arrayfire.arrayfire.libcommon"/>
         </line>
         <line choice="com.arrayfire.arrayfire.inc"/>
         <line choice="com.arrayfire.arrayfire.examples"/>
         <line choice="com.arrayfire.arrayfire.doc"/>
     </choices-outline>
     <choice id="libs" title="ArrayFire Libraries" visible="true" />
-    <choice title="ArrayFire CPU Libraries. Also installs the corresponding CMake config files."
-            description="ArrayFire targeting CPUs."
+    <choice title="CPU Libraries"
+            description="ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
             id="cpu_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"/>
     </choice>
@@ -55,6 +57,14 @@
             enabled="CheckBackendSelected()">
         <pkg-ref id="com.arrayfire.arrayfire.unified.lib"/>
     </choice>
+    <choice title="Library Common"
+            description="Installs Forge and ArrayFireConfig.cmake files"
+            id="com.arrayfire.arrayfire.libcommon"
+            selected="CheckBackendSelected()"
+            visible="false"
+            enabled="CheckBackendSelected()">
+        <pkg-ref id="com.arrayfire.arrayfire.libcommon"/>
+    </choice>
     <choice title="ArrayFire Headers" description="ArrayFire Headers" id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.inc"/>
     </choice>

From 571f0caed833a5fc1e2ff2629f47bd15b273c86c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 23 Feb 2016 17:00:45 -0500
Subject: [PATCH 0393/2677] Renaming ambiguous getInfo to getDeviceInfo

---
 src/api/c/device.cpp            |  6 +++---
 src/backend/cpu/platform.cpp    |  2 +-
 src/backend/cpu/platform.hpp    |  2 +-
 src/backend/cuda/platform.cpp   | 24 ++++++++++++------------
 src/backend/cuda/platform.hpp   |  7 ++-----
 src/backend/opencl/platform.cpp |  2 +-
 src/backend/opencl/platform.hpp |  4 ++--
 7 files changed, 22 insertions(+), 25 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 937b0a66c5..6c089f57c0 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -65,7 +65,7 @@ af_err af_init()
     try {
         static bool first = true;
         if(first) {
-            getInfo();
+            getDeviceInfo();
             first = false;
         }
     } CATCHALL;
@@ -75,7 +75,7 @@ af_err af_init()
 af_err af_info()
 {
     try {
-        printf("%s", getInfo().c_str());
+        printf("%s", getDeviceInfo().c_str());
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -83,7 +83,7 @@ af_err af_info()
 af_err af_info_string(char **str, const bool verbose)
 {
     try {
-        std::string infoStr = getInfo();
+        std::string infoStr = getDeviceInfo();
         af_alloc_host((void**)str, sizeof(char) * (infoStr.size() + 1));
 
         // Need to do a deep copy
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 7e6bc81e43..9474c792f3 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -224,7 +224,7 @@ static inline std::string &ltrim(std::string &s)
     return s;
 }
 
-std::string getInfo()
+std::string getDeviceInfo()
 {
     std::ostringstream info;
     static CPUInfo cinfo;
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 82ed42c8f9..7caddccc72 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -16,7 +16,7 @@ namespace cpu {
 
     int getBackend();
 
-    std::string getInfo();
+    std::string getDeviceInfo();
 
     bool isDoubleSupported(int device);
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 67f3f08428..10cfdc886c 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -150,18 +150,6 @@ int getBackend()
     return AF_BACKEND_CUDA;
 }
 
-string getInfo()
-{
-    ostringstream info;
-    info << "ArrayFire v" << AF_VERSION
-         << " (CUDA, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
-    info << getPlatformInfo();
-    for (int i = 0; i < getDeviceCount(); ++i) {
-        info << getDeviceInfo(i);
-    }
-    return info.str();
-}
-
 string getDeviceInfo(int device)
 {
     cudaDeviceProp dev = getDeviceProp(device);
@@ -186,6 +174,18 @@ string getDeviceInfo(int device)
     return info;
 }
 
+string getDeviceInfo()
+{
+    ostringstream info;
+    info << "ArrayFire v" << AF_VERSION
+         << " (CUDA, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
+    info << getPlatformInfo();
+    for (int i = 0; i < getDeviceCount(); ++i) {
+        info << getDeviceInfo(i);
+    }
+    return info.str();
+}
+
 string getPlatformInfo()
 {
     string driverVersion = getDriverVersion();
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 6b4186b2c2..3fcc67ea5b 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -22,8 +22,7 @@ namespace cuda
 
 int getBackend();
 
-std::string getInfo();
-
+std::string getDeviceInfo();
 std::string getDeviceInfo(int device);
 
 std::string getPlatformInfo();
@@ -32,8 +31,6 @@ std::string getDriverVersion();
 
 std::string getCUDARuntimeVersion();
 
-std::string getInfo();
-
 bool isDoubleSupported(int device);
 
 void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
@@ -82,7 +79,7 @@ class DeviceManager
 
         friend std::string getCUDARuntimeVersion();
 
-        friend std::string getInfo();
+        friend std::string getDeviceInfo();
 
         friend int getDeviceCount();
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index c2c13c7ae3..dc8ab4ea65 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -383,7 +383,7 @@ static std::string platformMap(std::string &platStr)
     }
 }
 
-std::string getInfo()
+std::string getDeviceInfo()
 {
     ostringstream info;
     info << "ArrayFire v" << AF_VERSION
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 095fdf9ae7..42579f89d1 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -21,7 +21,7 @@ namespace opencl
 
 class DeviceManager
 {
-    friend std::string getInfo();
+    friend std::string getDeviceInfo();
 
     friend int getDeviceCount();
 
@@ -92,7 +92,7 @@ class DeviceManager
 
 int getBackend();
 
-std::string getInfo();
+std::string getDeviceInfo();
 
 int getDeviceCount();
 

From f0d11b30427e2199a4121a4de4819783ebde15ee Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 23 Feb 2016 17:09:54 -0500
Subject: [PATCH 0394/2677] Make getInfo check if af_array belongs to current
 device.

- This behavior can be turned off optionally
---
 include/af/defines.h      |  7 +++
 src/api/c/array.cpp       | 96 +++++++++++++++++++++++++++++++++++++++
 src/api/c/data.cpp        | 52 ---------------------
 src/api/c/handle.hpp      |  2 +
 src/api/c/imageio.cpp     |  1 +
 src/api/c/imageio2.cpp    |  1 +
 src/api/c/print.cpp       |  1 +
 src/backend/ArrayInfo.cpp | 20 --------
 src/backend/ArrayInfo.hpp |  6 ---
 9 files changed, 108 insertions(+), 78 deletions(-)
 create mode 100644 src/api/c/array.cpp

diff --git a/include/af/defines.h b/include/af/defines.h
index 2b53baabed..77508f2870 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -120,6 +120,13 @@ typedef enum {
     AF_ERR_BATCH          = 207,
 
 
+#if AF_API_VERSION >= 33
+    ///
+    /// Input does not belong to the current device.
+    ///
+    AF_ERR_DEVICE         = 208,
+#endif
+
     // 300-399 Errors for missing software features
 
     ///
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
new file mode 100644
index 0000000000..cefdde1d75
--- /dev/null
+++ b/src/api/c/array.cpp
@@ -0,0 +1,96 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <handle.hpp>
+#include <ArrayInfo.hpp>
+#include <platform.hpp>
+
+const ArrayInfo&
+getInfo(const af_array arr, bool check)
+{
+    const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
+
+    if (check && info->getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    }
+
+    return *info;
+}
+
+af_err af_get_elements(dim_t *elems, const af_array arr)
+{
+    try {
+        // Do not check for device mismatch
+        *elems =  getInfo(arr, false).elements();
+    } CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_get_type(af_dtype *type, const af_array arr)
+{
+    try {
+        // Do not check for device mismatch
+        *type = getInfo(arr, false).getType();
+    } CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
+                   const af_array in)
+{
+    try {
+        // Do not check for device mismatch
+        ArrayInfo info = getInfo(in, false);
+        *d0 = info.dims()[0];
+        *d1 = info.dims()[1];
+        *d2 = info.dims()[2];
+        *d3 = info.dims()[3];
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_get_numdims(unsigned *nd, const af_array in)
+{
+    try {
+        // Do not check for device mismatch
+        ArrayInfo info = getInfo(in, false);
+        *nd = info.ndims();
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+
+#undef INSTANTIATE
+#define INSTANTIATE(fn1, fn2)                           \
+    af_err fn1(bool *result, const af_array in)         \
+    {                                                   \
+        try {                                           \
+            ArrayInfo info = getInfo(in, false);   \
+            *result = info.fn2();                       \
+        }                                               \
+        CATCHALL                                        \
+            return AF_SUCCESS;                          \
+    }
+
+INSTANTIATE(af_is_empty       , isEmpty       )
+INSTANTIATE(af_is_scalar      , isScalar      )
+INSTANTIATE(af_is_row         , isRow         )
+INSTANTIATE(af_is_column      , isColumn      )
+INSTANTIATE(af_is_vector      , isVector      )
+INSTANTIATE(af_is_complex     , isComplex     )
+INSTANTIATE(af_is_real        , isReal        )
+INSTANTIATE(af_is_double      , isDouble      )
+INSTANTIATE(af_is_single      , isSingle      )
+INSTANTIATE(af_is_realfloating, isRealFloating)
+INSTANTIATE(af_is_floating    , isFloating    )
+INSTANTIATE(af_is_integer     , isInteger     )
+INSTANTIATE(af_is_bool        , isBool        )
+
+#undef INSTANTIATE
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 2de2f139e3..522eb7dfcb 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -539,58 +539,6 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
     return AF_SUCCESS;
 }
 
-#undef INSTANTIATE
-#define INSTANTIATE(fn1, fn2)                   \
-    af_err fn1(bool *result, const af_array in) \
-    {                                           \
-        try {                                   \
-            ArrayInfo info = getInfo(in);       \
-            *result = info.fn2();               \
-        }                                       \
-        CATCHALL                                \
-            return AF_SUCCESS;                  \
-    }
-
-INSTANTIATE(af_is_empty       , isEmpty       )
-INSTANTIATE(af_is_scalar      , isScalar      )
-INSTANTIATE(af_is_row         , isRow         )
-INSTANTIATE(af_is_column      , isColumn      )
-INSTANTIATE(af_is_vector      , isVector      )
-INSTANTIATE(af_is_complex     , isComplex     )
-INSTANTIATE(af_is_real        , isReal        )
-INSTANTIATE(af_is_double      , isDouble      )
-INSTANTIATE(af_is_single      , isSingle      )
-INSTANTIATE(af_is_realfloating, isRealFloating)
-INSTANTIATE(af_is_floating    , isFloating    )
-INSTANTIATE(af_is_integer     , isInteger     )
-INSTANTIATE(af_is_bool        , isBool        )
-
-#undef INSTANTIATE
-
-af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
-                   const af_array in)
-{
-    try {
-        ArrayInfo info = getInfo(in);
-        *d0 = info.dims()[0];
-        *d1 = info.dims()[1];
-        *d2 = info.dims()[2];
-        *d3 = info.dims()[3];
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-af_err af_get_numdims(unsigned *nd, const af_array in)
-{
-    try {
-        ArrayInfo info = getInfo(in);
-        *nd = info.ndims();
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
 template<typename T>
 static inline void eval(af_array arr)
 {
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 70f17eb18e..ac7b74a193 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -16,6 +16,8 @@
 #include <copy.hpp>
 #include <cast.hpp>
 
+const ArrayInfo& getInfo(const af_array arr, bool check = true);
+
 template<typename T>
 static const detail::Array<T> &
 getArray(const af_array &arr)
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 9f996eb64e..5e3f7a59cb 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -24,6 +24,7 @@
 #include <traits.hpp>
 #include <memory.hpp>
 #include <err_common.hpp>
+#include <handle.hpp>
 
 #include <string>
 #include <cstring>
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index cfad2faa7b..76c53f4ab4 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -24,6 +24,7 @@
 #include <traits.hpp>
 #include <memory.hpp>
 #include <err_common.hpp>
+#include <handle.hpp>
 
 #include <string>
 #include <cstring>
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index b243491832..66133503ef 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -21,6 +21,7 @@
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <type_util.hpp>
+#include <handle.hpp>
 
 #include <af/index.h>
 
diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index 43d2627a84..a835353453 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -18,26 +18,6 @@
 
 using af::dim4;
 
-const ArrayInfo&
-getInfo(af_array arr)
-{
-    const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
-    return *info;
-}
-
-af_err
-af_get_elements(dim_t *elems, const af_array arr)
-{
-    *elems =  getInfo(arr).elements();
-    return AF_SUCCESS; //FIXME: Catch exceptions correctly
-}
-
-af_err af_get_type(af_dtype *type, const af_array arr)
-{
-    *type = getInfo(arr).getType();
-    return AF_SUCCESS; //FIXME: Catch exceptions correctly
-}
-
 dim4 calcStrides(const dim4 &parentDim)
 {
     dim4 out(1, 1, 1, 1);
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index 0983f06f28..88ba26b6aa 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -140,12 +140,6 @@ class ArrayInfo
     static_assert(std::is_standard_layout<ArrayInfo>::value, "ArrayInfo must be a standard layout type");
 #endif
 
-// Returns size and time info for an array object.
-// Note this doesn't require template parameters.
-const  ArrayInfo&
-getInfo(const af_array arr);
-
-
 af::dim4 toDims(const std::vector<af_seq>& seqs, const af::dim4 &parentDims);
 
 af::dim4 toOffset(const std::vector<af_seq>& seqs, const af::dim4 &parentDims);

From 0258883fe2a219c90456f97d86fd340c9f56940a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 23 Feb 2016 18:19:36 -0500
Subject: [PATCH 0395/2677] BUGFIX: Fixing getId() from ArrayInfo

- device id now occupies the last 8 bits.
---
 src/backend/ArrayInfo.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index a835353453..0937641afc 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -35,33 +35,33 @@ int ArrayInfo::getDevId() const
 {
     // The actual device ID is only stored in the first 4 bits of devId
     // See ArrayInfo.hpp for more
-    return devId & 0xf;
+    return devId & 0xff;
 }
 
 void ArrayInfo::setId(int id) const
 {
-    // 1 << (backendId + 3) sets the 4th, 5th or 6th bit of devId to 1
+    // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
     int backendId = detail::getBackend() >> 1; // Convert enums 1, 2, 4 to ints 0, 1, 2
-    const_cast<ArrayInfo *>(this)->setId(id | 1 << (backendId + 3));
+    const_cast<ArrayInfo *>(this)->setId(id | 1 << (backendId + 8));
 }
 
 void ArrayInfo::setId(int id)
 {
-    // 1 << (backendId + 3) sets the 4th, 5th or 6th bit of devId to 1
+    // 1 << (backendId + 3) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
     int backendId = detail::getBackend() >> 1; // Convert enums 1, 2, 4 to ints 0, 1, 2
-    devId = id | 1 << (backendId + 3);
+    devId = id | 1 << (backendId + 8);
 }
 
 af_backend ArrayInfo::getBackendId() const
 {
-    // devId >> 3 converts the backend info to 1, 2, 4 which are enums
+    // devId >> 8 converts the backend info to 1, 2, 4 which are enums
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
-    int backendId = devId >> 3;
+    int backendId = devId >> 8;
     return (af_backend)backendId;
 }
 

From c38cc2d989fae401daa6cd6a7621f4e6527844b0 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 24 Feb 2016 15:16:47 -0500
Subject: [PATCH 0396/2677] BUGFIX: Ensure set operations work on vectors only

---
 src/api/c/set.cpp          | 24 +++++++++++++++++++-----
 src/backend/cuda/set.cu    | 14 +++++++-------
 src/backend/opencl/set.cpp | 14 +++++++-------
 3 files changed, 33 insertions(+), 19 deletions(-)

diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index 1643fad95b..db9b5782e5 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -28,7 +28,9 @@ af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
 {
     try {
 
-        af_dtype type = getInfo(in).getType();
+        ArrayInfo in_info = getInfo(in);
+        ARG_ASSERT(1, in_info.isVector());
+        af_dtype type = in_info.getType();
 
         af_array res;
         switch(type) {
@@ -62,8 +64,14 @@ af_err af_set_union(af_array *out, const af_array first, const af_array second,
 {
     try {
 
-        af_dtype first_type = getInfo(first).getType();
-        af_dtype second_type = getInfo(second).getType();
+        ArrayInfo first_info = getInfo(first);
+        ArrayInfo second_info = getInfo(second);
+
+        ARG_ASSERT(1, first_info.isVector());
+        ARG_ASSERT(1, second_info.isVector());
+
+        af_dtype first_type = first_info.getType();
+        af_dtype second_type = second_info.getType();
 
         ARG_ASSERT(1, first_type == second_type);
 
@@ -98,8 +106,14 @@ af_err af_set_intersect(af_array *out, const af_array first, const af_array seco
 {
     try {
 
-        af_dtype first_type = getInfo(first).getType();
-        af_dtype second_type = getInfo(second).getType();
+        ArrayInfo first_info = getInfo(first);
+        ArrayInfo second_info = getInfo(second);
+
+        ARG_ASSERT(1, first_info.isVector());
+        ARG_ASSERT(1, second_info.isVector());
+
+        af_dtype first_type = first_info.getType();
+        af_dtype second_type = second_info.getType();
 
         ARG_ASSERT(1, first_type == second_type);
 
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index 63501d3f2a..4629b8b3dc 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -32,7 +32,7 @@ namespace cuda
         Array<T> out = copyArray<T>(in);
 
         thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
-        thrust::device_ptr<T> out_ptr_end = out_ptr + out.dims()[0];
+        thrust::device_ptr<T> out_ptr_end = out_ptr + out.elements();
 
         if(!is_sorted) THRUST_SELECT(thrust::sort, out_ptr, out_ptr_end);
         thrust::device_ptr<T> out_ptr_last;
@@ -55,14 +55,14 @@ namespace cuda
             unique_second = setUnique(second, false);
         }
 
-        dim_t out_size = unique_first.dims()[0] + unique_second.dims()[0];
+        dim_t out_size = unique_first.elements() + unique_second.elements();
         Array<T> out = createEmptyArray<T>(dim4(out_size));
 
         thrust::device_ptr<T> first_ptr = thrust::device_pointer_cast<T>(unique_first.get());
-        thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.dims()[0];
+        thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.elements();
 
         thrust::device_ptr<T> second_ptr = thrust::device_pointer_cast<T>(unique_second.get());
-        thrust::device_ptr<T> second_ptr_end = second_ptr + unique_second.dims()[0];
+        thrust::device_ptr<T> second_ptr_end = second_ptr + unique_second.elements();
 
         thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
 
@@ -87,14 +87,14 @@ namespace cuda
             unique_second = setUnique(second, false);
         }
 
-        dim_t out_size = std::max(unique_first.dims()[0], unique_second.dims()[0]);
+        dim_t out_size = std::max(unique_first.elements(), unique_second.elements());
         Array<T> out = createEmptyArray<T>(dim4(out_size));
 
         thrust::device_ptr<T> first_ptr = thrust::device_pointer_cast<T>(unique_first.get());
-        thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.dims()[0];
+        thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.elements();
 
         thrust::device_ptr<T> second_ptr = thrust::device_pointer_cast<T>(unique_second.get());
-        thrust::device_ptr<T> second_ptr_end = second_ptr + unique_second.dims()[0];
+        thrust::device_ptr<T> second_ptr_end = second_ptr + unique_second.elements();
 
         thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
 
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index 5604ff4ad9..c37b7c4c4e 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -53,7 +53,7 @@ namespace opencl
             compute::buffer out_data((*out.get())());
 
             compute::buffer_iterator< type_t<T> > begin(out_data, 0);
-            compute::buffer_iterator< type_t<T> > end(out_data, out.dims()[0]);
+            compute::buffer_iterator< type_t<T> > end(out_data, out.elements());
 
             if (!is_sorted) {
                 compute::sort(begin, end, queue);
@@ -83,7 +83,7 @@ namespace opencl
                 unique_second = setUnique(second, false);
             }
 
-            size_t out_size = unique_first.dims()[0] + unique_second.dims()[0];
+            size_t out_size = unique_first.elements() + unique_second.elements();
             Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
 
             compute::command_queue queue(getQueue()());
@@ -93,9 +93,9 @@ namespace opencl
             compute::buffer out_data((*out.get())());
 
             compute::buffer_iterator< type_t<T> > first_begin(first_data, 0);
-            compute::buffer_iterator< type_t<T> > first_end(first_data, unique_first.dims()[0]);
+            compute::buffer_iterator< type_t<T> > first_end(first_data, unique_first.elements());
             compute::buffer_iterator< type_t<T> > second_begin(second_data, 0);
-            compute::buffer_iterator< type_t<T> > second_end(second_data, unique_second.dims()[0]);
+            compute::buffer_iterator< type_t<T> > second_end(second_data, unique_second.elements());
             compute::buffer_iterator< type_t<T> > out_begin(out_data, 0);
 
             compute::buffer_iterator< type_t<T> > out_end = compute::set_union(
@@ -124,7 +124,7 @@ namespace opencl
                 unique_second = setUnique(second, false);
             }
 
-            size_t out_size = std::max(unique_first.dims()[0], unique_second.dims()[0]);
+            size_t out_size = std::max(unique_first.elements(), unique_second.elements());
             Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
 
             compute::command_queue queue(getQueue()());
@@ -134,9 +134,9 @@ namespace opencl
             compute::buffer out_data((*out.get())());
 
             compute::buffer_iterator< type_t<T> > first_begin(first_data, 0);
-            compute::buffer_iterator< type_t<T> > first_end(first_data, unique_first.dims()[0]);
+            compute::buffer_iterator< type_t<T> > first_end(first_data, unique_first.elements());
             compute::buffer_iterator< type_t<T> > second_begin(second_data, 0);
-            compute::buffer_iterator< type_t<T> > second_end(second_data, unique_second.dims()[0]);
+            compute::buffer_iterator< type_t<T> > second_end(second_data, unique_second.elements());
             compute::buffer_iterator< type_t<T> > out_begin(out_data, 0);
 
             compute::buffer_iterator< type_t<T> > out_end = compute::set_intersection(

From 483163123ddbb0c60e6456148fbb8ef05f91753a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 24 Feb 2016 15:19:49 -0500
Subject: [PATCH 0397/2677] DOCS: Fixing documentation for exp

---
 docs/details/arith.dox | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 50f82aafed..a75c3a2cc4 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -448,8 +448,6 @@ Raise an array to a power
 
 Exponential of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_expm1 expm1
 

From 9b793f00927d3253fbfe28840171e34b749ebdf7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 23 Feb 2016 18:20:35 -0500
Subject: [PATCH 0398/2677] FEAT,TEST,DOC: Adding function to query which
 device an array was created.

- Adding relevant tests and docs
---
 docs/details/backend.dox   |  9 +++++++++
 include/af/backend.h       | 24 ++++++++++++++++++++++++
 src/api/c/device.cpp       | 12 +++++++++++-
 src/api/cpp/device.cpp     |  7 +++++++
 src/api/unified/device.cpp |  6 ++++++
 test/array.cpp             | 31 +++++++++++++++++++++++++++++++
 6 files changed, 88 insertions(+), 1 deletion(-)

diff --git a/docs/details/backend.dox b/docs/details/backend.dox
index 146cc14313..893567b696 100644
--- a/docs/details/backend.dox
+++ b/docs/details/backend.dox
@@ -80,5 +80,14 @@ The return value specifies which backend the array was created on.
 
 =======================================================================
 
+\defgroup unified_func_getdeviceid getDeviceId
+
+\brief Get's the id of the device an array was created on.
+
+\ingroup unified_func
+\ingroup arrayfire_func
+
+=======================================================================
+
 @}
 */
diff --git a/include/af/backend.h b/include/af/backend.h
index 0342ef0ade..0770feb5b1 100644
--- a/include/af/backend.h
+++ b/include/af/backend.h
@@ -66,6 +66,18 @@ AFAPI af_err af_get_backend_id(af_backend *backend, const af_array in);
 AFAPI af_err af_get_active_backend(af_backend *backend);
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   \param[out] dev contains the device on which \p in was created.
+   \param[in] in is the array who's device is to be queried.
+   \returns \ref af_err error code
+
+   \ingroup unified_func_getdeviceid
+ */
+AFAPI af_err af_get_device_id(int *device, const af_array in);
+#endif
+
+
 #ifdef __cplusplus
 }
 #endif
@@ -121,5 +133,17 @@ AFAPI af::Backend getBackendId(const array &in);
 AFAPI af::Backend getActiveBackend();
 #endif
 
+#if AF_API_VERSION >= 33
+/**
+   \param[in] in is the array who's device is to be queried.
+   \returns The id of the device on which this array was created.
+
+   \note Device ID can be the same for arrays belonging to different backends.
+
+   \ingroup unified_func_getdeviceid
+ */
+AFAPI int getDeviceId(const array &in);
+#endif
+
 }
 #endif
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 6c089f57c0..abe0b01e32 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -48,12 +48,22 @@ af_err af_get_backend_id(af_backend *result, const af_array in)
 {
     try {
         ARG_ASSERT(1, in != 0);
-        ArrayInfo info = getInfo(in);
+        ArrayInfo info = getInfo(in, false);
         *result = info.getBackendId();
     } CATCHALL;
     return AF_SUCCESS;
 }
 
+af_err af_get_device_id(int *device, const af_array in)
+{
+    try {
+        ARG_ASSERT(1, in != 0);
+        ArrayInfo info = getInfo(in, false);
+        *device = info.getDevId();
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_get_active_backend(af_backend *result)
 {
     *result = (af_backend)getBackend();
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 5e4b0f7bf0..faf0b0e7dd 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -42,6 +42,13 @@ namespace af
         return result;
     }
 
+    int getDeviceId(const array &in)
+    {
+        int device = getDevice();;
+        AF_THROW(af_get_device_id(&device, in.get()));
+        return device;
+    }
+
     af::Backend getActiveBackend()
     {
         af::Backend result = (af::Backend)0;
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index fbd8e32f90..ed8e6a37f6 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -35,6 +35,12 @@ af_err af_get_backend_id(af_backend *result, const af_array in)
     return CALL(result, in);
 }
 
+af_err af_get_device_id(int *device, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(device, in);
+}
+
 af_err af_get_active_backend(af_backend *result)
 {
     *result = unified::AFSymbolManager::getInstance().getActiveBackend();
diff --git a/test/array.cpp b/test/array.cpp
index 6c1f511410..293b888a8f 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -454,3 +454,34 @@ TEST(Device, unequal)
         ASSERT_EQ(ptr, b.device<float>());
     }
 }
+
+TEST(DeviceId, Same)
+{
+    array a = randu(5,5);
+    ASSERT_EQ(getDevice(), getDeviceId(a));
+}
+
+TEST(DeviceId, Different)
+{
+    int ndevices = getDeviceCount();
+    if (ndevices < 2) return;
+
+    int id0 = getDevice();
+    int id1 = (id0 + 1) % ndevices;
+
+    array a = randu(5,5);
+    ASSERT_EQ(getDeviceId(a), id0);
+    setDevice(id1);
+
+    array b = randu(5,5);
+
+    ASSERT_EQ(getDeviceId(a), id0);
+    ASSERT_EQ(getDeviceId(b), id1);
+    ASSERT_NE(getDevice(), getDeviceId(a));
+    ASSERT_EQ(getDevice(), getDeviceId(b));
+
+    af_array c;
+    af_err err = af_matmul(&c, a.get(), b.get(), AF_MAT_NONE, AF_MAT_NONE);
+    ASSERT_EQ(err, AF_ERR_DEVICE);
+    setDevice(id0);
+}

From e83fcafacc03b40594bee736b736a0b76f5fa24a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 25 Feb 2016 11:19:40 -0500
Subject: [PATCH 0399/2677] Added release notes

---
 docs/pages/release_notes.md | 56 +++++++++++++++++++++++++++++++++++--
 include/af/backend.h        |  2 +-
 2 files changed, 55 insertions(+), 3 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 1063b054e3..738d2b0a4f 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -21,14 +21,34 @@ Features
 * [Scatter plot](https://github.com/arrayfire/arrayfire/pull/1116) added to graphics.
 * \ref af::transform() now supports perspective transformation matrices.
 * \ref af::infoString(): Returns `af::info()` as a string.
+* \ref af::printMemInfo(): Print a table showing information about buffer from the memory manager
+    * The \ref AF_MEM_INFO macro prints numbers and total sizes of all buffers (requires including af/macros.h)
 * \ref af::allocHost(): Allocates memory on host.
 * \ref af::freeHost(): Frees host side memory allocated by arrayfire.
-* Functions specific to OpenCl backend.
+* OpenCL functions can now use CPU implementation.
+    * Currently limited to Unified Memory devices (CPU and On-board Graphics).
+    * Functions: af::matmul() and all [LAPACK](\ref linalg_mat) functions.
+    * Takes advantage of optimized libraries such as MKL without doing memory copies.
+    * Use the environment variable `AF_OPENCL_CPU_OFFLOAD=1` to take advantage of this feature.
+* Functions specific to OpenCL backend.
     * \ref afcl::addDevice(): Adds an external device and context to ArrayFire's device manager.
     * \ref afcl::deleteDevice(): Removes an external device and context from ArrayFire's device manager.
     * \ref afcl::setDevice(): Sets an external device and context from ArrayFire's device manager.
     * \ref afcl::getDeviceType(): Gets the device type of the current device.
     * \ref afcl::getPlatform(): Gets the platform of the current device.
+* \ref af::createStridedArray() allows [array creation user-defined strides](https://github.com/arrayfire/arrayfire/issues/1177) and device pointer.
+* [Expose functions](https://github.com/arrayfire/arrayfire/issues/1131) that provide information
+  about memory layout of Arrays.
+    * \ref af::getStrides(): Gets the strides for each dimension of the array.
+    * \ref af::getOffset(): Gets the offsets for each dimension of the array.
+    * \ref af::getRawPtr(): Gets raw pointer to the location of the array on device.
+    * \ref af::isLinear(): Returns true if all elements in the array are contiguous.
+    * \ref af::isOwner(): Returns true if the array owns the raw pointer, false if it is a sub-array.
+    * \ref af::getStrides(): Gets the strides of the array.
+    * \ref af::getStrides(): Gets the strides of the array.
+* \ref af::getDeviceId(): Gets the device id on which the array resides.
+* \ref af::isImageIOAvailable(): Returns true if ArrayFire was compiled with Freeimage enabled
+* \ref af::isLAPACKAvailable(): Returns true if ArrayFire was compiled with LAPACK functions enabled
 
 Bug Fixes
 --------------
@@ -38,6 +58,16 @@ Bug Fixes
 * Fixed [imageio bugs](https://github.com/arrayfire/arrayfire/pull/1229) for 16 bit images.
 * Fixed [bugs when loading and storing images](https://github.com/arrayfire/arrayfire/pull/1228) natively.
 * Fixed [bug in FFT for NVIDIA GPUs](https://github.com/arrayfire/arrayfire/issues/615) when using OpenCL backend.
+* Fixed [bug when using external context](https://github.com/arrayfire/arrayfire/pull/1241) with OpenCL backend.
+* Fixed [memory leak](https://github.com/arrayfire/arrayfire/issues/1269) in \ref af_median_all().
+* Fixed [memory leaks and performance](https://github.com/arrayfire/arrayfire/pull/1274) in graphics functions.
+* Fixed [bugs when indexing followed by moddims](https://github.com/arrayfire/arrayfire/issues/1275).
+* \ref af_get_revision() now returns actual commit rather than AF_REVISION.
+* Fixed [releasing arrays](https://github.com/arrayfire/arrayfire/issues/1282) when using different backends.
+* OS X OpenCL: [LAPACK functions](\ref linalg_mat) on CPU devices use OpenCL offload (previously threw errors).
+* [Add support for 32-bit integer image types](https://github.com/arrayfire/arrayfire/pull/1287) in Image IO.
+* Fixed [set operations for row vectors](https://github.com/arrayfire/arrayfire/issues/1300)
+* Fixed [bugs](https://github.com/arrayfire/arrayfire/issues/1243) in \ref af::meanShift() and af::orb().
 
 Improvements
 --------------
@@ -46,6 +76,10 @@ Improvements
 * Performance improvements to the memory manager.
 * Error messages are now more detailed.
 * Improved sorted order for OpenCL devices.
+* JIT heuristics can now be tweaked using environment variables. See
+  [Environment Variables](\ref configuring_environment) tutorial.
+* Add `BUILD_<BACKEND>` [options to examples and tests](https://github.com/arrayfire/arrayfire/issues/1286)
+  to toggle backends when compiling independently.
 
 Examples
 ----------
@@ -57,6 +91,17 @@ Build
 
 * Support for Intel `icc` compiler
 * Support to compile with Intel MKL as a BLAS and LAPACK provider
+* Tests are now available for building as standalone (like examples)
+* Tests can now be built as a single file for each backend
+* Better handling of NONFREE build options
+* [Searching for GLEW in CMake default paths](https://github.com/arrayfire/arrayfire/pull/1292)
+* Fixes for compiling with MKL on OSX.
+
+Installers
+----------
+* Improvements to OSX Installer
+    * CMake config files are now installed with libraries
+    * Independent options for installing examples and documentation components
 
 Deprecations
 -----------
@@ -67,8 +112,15 @@ Deprecations
 Documentation
 --------------
 
-* Fixes to documentation for matchTemplate.
+* Fixes to documentation for \ref matchTemplate().
 * Improved documentation for deviceInfo.
+* Fixes to documentation for \ref exp().
+
+Known Issues
+------------
+
+* [Solve OpenCL fails on NVIDIA Maxwell devices](https://github.com/arrayfire/arrayfire/issues/1246)
+  for f32 and c32 when M > N and K % 4 is 1 or 2.
 
 
 v3.2.2
diff --git a/include/af/backend.h b/include/af/backend.h
index 0770feb5b1..94c4951d45 100644
--- a/include/af/backend.h
+++ b/include/af/backend.h
@@ -68,7 +68,7 @@ AFAPI af_err af_get_active_backend(af_backend *backend);
 
 #if AF_API_VERSION >= 33
 /**
-   \param[out] dev contains the device on which \p in was created.
+   \param[out] device contains the device on which \p in was created.
    \param[in] in is the array who's device is to be queried.
    \returns \ref af_err error code
 

From 58809cb68eed2b2896ffd57b969ac87a2433bd30 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 Feb 2016 23:36:05 +0530
Subject: [PATCH 0400/2677] Support to set visibility of windows
 programmatically

---
 include/af/graphics.h        | 23 +++++++++++++++++++++++
 src/api/c/image.cpp          | 22 ++++++++++++++++++++++
 src/api/cpp/graphics.cpp     |  5 +++++
 src/api/unified/graphics.cpp |  5 +++++
 4 files changed, 55 insertions(+)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 7485686479..b69a83854a 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -289,6 +289,17 @@ class AFAPI Window {
         */
         bool close();
 
+#if AF_API_VERSION >= 33
+        /**
+           Hide/Show the window
+
+           \param[in] isVisible indicates if the window is to be hidden or brought into focus
+
+           \ingroup gfx_func_window
+         */
+        void setVisibility(const bool isVisible);
+#endif
+
         /**
            This function is used to keep track of which cell in the grid mode is
            being currently rendered. When a user does Window(0,0), we internally
@@ -547,6 +558,18 @@ AFAPI af_err af_show(const af_window wind);
 */
 AFAPI af_err af_is_window_closed(bool *out, const af_window wind);
 
+#if AF_API_VERSION >= 33
+/**
+   Hide/Show a window
+
+   \param[in] wind is the window whose visibility is to be changed
+   \param[in] is_visible indicates if the window is to be hidden or brought into focus
+
+   \ingroup gfx_func_window
+ */
+AFAPI af_err af_set_visibility(const af_window wind, const bool is_visible);
+#endif
+
 /**
    C Interface wrapper for destroying a window handle
 
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index db40934e50..2c523d0947 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -264,6 +264,28 @@ af_err af_is_window_closed(bool *out, const af_window wind)
 #endif
 }
 
+af_err af_set_visibility(const af_window wind, const bool is_visible)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        if (is_visible)
+            wnd->show();
+        else
+            wnd->hide();
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
 af_err af_destroy_window(const af_window wind)
 {
 #if defined(WITH_GRAPHICS)
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 162bacb4ab..8b53825c25 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -136,4 +136,9 @@ bool Window::close()
     return temp;
 }
 
+void Window::setVisibility(const bool isVisible)
+{
+    AF_THROW(af_set_visibility(get(), isVisible));
+}
+
 }
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 2895cc7afc..9e3f1c8b38 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -89,6 +89,11 @@ af_err af_is_window_closed(bool *out, const af_window wind)
     return CALL(out, wind);
 }
 
+af_err af_set_visibility(const af_window wind, const bool is_visible)
+{
+    return CALL(wind, is_visible);
+}
+
 af_err af_destroy_window(const af_window wind)
 {
     return CALL(wind);

From 5a2267461cff78c9383d3e10d93e71cdbaa0d1d8 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 25 Feb 2016 15:55:08 -0500
Subject: [PATCH 0401/2677] DOC Typo corrections in Installation page

---
 docs/pages/INSTALL.md | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/docs/pages/INSTALL.md b/docs/pages/INSTALL.md
index 3565889571..d31affaefe 100644
--- a/docs/pages/INSTALL.md
+++ b/docs/pages/INSTALL.md
@@ -108,13 +108,14 @@ First install the prerequisite packages:
     # Prerequisite packages:
     sudo apt-get install libfreeimage-dev libatlas3gf-base libfftw3-dev cmake
 
-Ubuntu 14.04 will not have the libglfw3-dev package in its repositories. You can either build the library from source (following the instructions listed) or install the library from a PPA as follows:
-
-```
-sudo apt-add repository ppa:keithw/glfw3
-sudo apt-get update
-sudo apt-get install glfw3
-```
+Ubuntu 14.04 will not have the libglfw3-dev package in its repositories. You can either build the
+library from source (following the
+[instructions listed here](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire)) or
+install the library from a PPA as follows:
+
+    sudo apt-add-repository ppa:keithw/glfw3
+    sudo apt-get update
+    sudo apt-get install glfw3
 
 After this point, the installation should proceed identically to Ubuntu 14.10 or newer.
 

From caa08ec76675d3e830c4e53dfa34750b35de0079 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 29 Feb 2016 10:17:17 -0500
Subject: [PATCH 0402/2677] Increment version to 3.3.1

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 8d5b575399..5ec89a6fb5 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -3,7 +3,7 @@
 #
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "3")
-SET(AF_VERSION_PATCH "0")
+SET(AF_VERSION_PATCH "1")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From 3dbb7a94f510e8efb7ac86c4fa5e682c67599f53 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 29 Feb 2016 12:59:32 -0500
Subject: [PATCH 0403/2677] Increment version to 3.4.0

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 8d5b575399..8171a57818 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -2,7 +2,7 @@
 # Make a version file that includes the ArrayFire version and git revision
 #
 SET(AF_VERSION_MAJOR "3")
-SET(AF_VERSION_MINOR "3")
+SET(AF_VERSION_MINOR "4")
 SET(AF_VERSION_PATCH "0")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")

From dd2d89f904793a973d94d57e3c631813531e7857 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Mar 2016 13:26:03 -0500
Subject: [PATCH 0404/2677] Sort files for compilation. Compiles files in
 alphabetical order

---
 examples/CMakeLists.txt           |  1 +
 src/api/unified/CMakeLists.txt    |  7 +++++++
 src/backend/cpu/CMakeLists.txt    | 11 +++++++++++
 src/backend/cuda/CMakeLists.txt   | 16 ++++++++++++++++
 src/backend/opencl/CMakeLists.txt | 28 +++++++++++++++++++++++++++-
 5 files changed, 62 insertions(+), 1 deletion(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index be0f6407be..ca7853832f 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -76,6 +76,7 @@ ENDMACRO()
 
 # Collect the source
 FILE(GLOB FILES "*/*.cpp")
+LIST(SORT FILES)
 ADD_DEFINITIONS("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
 # Next we build each example using every backend.
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index c44e43b5fc..18d15c474c 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -6,12 +6,17 @@ FILE(GLOB unified_headers
 FILE(GLOB unified_sources
     "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp")
 
+LIST(SORT unified_headers)
+LIST(SORT unified_sources)
+
 SOURCE_GROUP(api\\unified\\Headers FILES ${unified_headers})
 SOURCE_GROUP(api\\unified\\Sources FILES ${unified_sources})
 
 FILE(GLOB cpp_sources
     "../cpp/*.cpp")
 
+LIST(SORT cpp_sources)
+
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB common_sources
@@ -22,6 +27,8 @@ FILE(GLOB common_sources
   "../../backend/util.cpp"
   )
 
+LIST(SORT common_sources)
+
 SOURCE_GROUP(common FILES ${common_sources})
 
 IF(NOT UNIX)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 9387323592..f7857ec6d6 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -95,6 +95,9 @@ FILE(GLOB cpu_headers
 FILE(GLOB cpu_sources
     "*.cpp")
 
+LIST(SORT cpu_headers)
+LIST(SORT cpu_sources)
+
 source_group(backend\\cpu\\Headers FILES ${cpu_headers})
 source_group(backend\\cpu\\Sources FILES ${cpu_sources})
 
@@ -107,6 +110,9 @@ FILE(GLOB backend_sources
     "../*.cpp"
     )
 
+LIST(SORT backend_headers)
+LIST(SORT backend_sources)
+
 source_group(backend\\Headers FILES ${backend_headers})
 source_group(backend\\Sources FILES ${backend_sources})
 
@@ -119,6 +125,9 @@ FILE(GLOB c_sources
     "../../api/c/*.cpp"
     )
 
+LIST(SORT c_headers)
+LIST(SORT c_sources)
+
 source_group(api\\c\\Headers FILES ${c_headers})
 source_group(api\\c\\Sources FILES ${c_sources})
 
@@ -126,6 +135,8 @@ FILE(GLOB cpp_sources
     "../../api/cpp/*.cpp"
     )
 
+LIST(SORT cpp_sources)
+
 source_group(api\\cpp\\Sources FILES ${cpp_sources})
 
 # OS Definitions
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ae0690dba2..ab29899772 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -168,6 +168,12 @@ FILE(GLOB kernel_headers
 FILE(GLOB ptx_sources
     "JIT/*.cu")
 
+LIST(SORT cuda_headers)
+LIST(SORT cuda_sources)
+LIST(SORT jit_sources)
+LIST(SORT kernel_headers)
+LIST(SORT ptx_sources)
+
 SOURCE_GROUP(backend\\cuda\\Headers FILES ${cuda_headers})
 SOURCE_GROUP(backend\\cuda\\Sources FILES ${cuda_sources})
 SOURCE_GROUP(backend\\cuda\\JIT FILES ${jit_sources})
@@ -181,6 +187,8 @@ IF(CUDA_LAPACK_CPU_FALLBACK)
 
     SOURCE_GROUP(backend\\cuda\\cpu_lapack\\Headers FILES ${cpu_lapack_headers})
     SOURCE_GROUP(backend\\cuda\\cpu_lapack\\Sources FILES ${cpu_lapack_sources})
+    LIST(SORT cpu_lapack_headers)
+    LIST(SORT cpu_lapack_sources)
 ENDIF()
 
 FILE(GLOB backend_headers
@@ -192,6 +200,9 @@ FILE(GLOB backend_sources
     "../*.cpp"
     )
 
+LIST(SORT backend_headers)
+LIST(SORT backend_sources)
+
 SOURCE_GROUP(backend\\Headers FILES ${backend_headers})
 SOURCE_GROUP(backend\\Sources FILES ${backend_sources})
 
@@ -204,6 +215,9 @@ FILE(GLOB c_sources
     "../../api/c/*.cpp"
     )
 
+LIST(SORT c_headers)
+LIST(SORT c_sources)
+
 SOURCE_GROUP(api\\c\\Headers FILES ${c_headers})
 SOURCE_GROUP(api\\c\\Sources FILES ${c_sources})
 
@@ -211,6 +225,8 @@ FILE(GLOB cpp_sources
     "../../api/cpp/*.cpp"
     )
 
+LIST(SORT cpp_sources)
+
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index bbe430df15..2cb8ddfdf9 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -142,6 +142,17 @@ FILE(GLOB cpu_headers
 FILE(GLOB cpu_sources
      "cpu/*.cpp")
 
+LIST(SORT opencl_headers)
+LIST(SORT opencl_sources)
+LIST(SORT jit_sources)
+LIST(SORT kernel_headers)
+LIST(SORT opencl_kernels)
+LIST(SORT kernel_sources)
+LIST(SORT conv_ker_headers)
+LIST(SORT conv_ker_sources)
+LIST(SORT cpu_headers)
+LIST(SORT cpu_sources)
+
 source_group(backend\\opencl\\Headers FILES ${opencl_headers})
 source_group(backend\\opencl\\Sources FILES ${opencl_sources})
 source_group(backend\\opencl\\JIT FILES ${jit_sources})
@@ -160,6 +171,9 @@ IF(LAPACK_FOUND)
     FILE(GLOB magma_headers
         "magma/*.h")
 
+    LIST(SORT magma_headers)
+    LIST(SORT magma_sources)
+
     source_group(backend\\opencl\\magma\\Sources FILES ${magma_sources})
     source_group(backend\\opencl\\magma\\Headers FILES ${magma_headers})
 ELSE()
@@ -175,6 +189,10 @@ FILE(GLOB backend_headers
 FILE(GLOB backend_sources
     "../*.cpp"
     )
+
+LIST(SORT backend_headers)
+LIST(SORT backend_sources)
+
 source_group(backend\\Headers FILES ${backend_headers})
 source_group(backend\\Sources FILES ${backend_sources})
 
@@ -186,17 +204,25 @@ FILE(GLOB c_headers
 FILE(GLOB c_sources
     "../../api/c/*.cpp"
     )
+
+LIST(SORT c_headers)
+LIST(SORT c_sources)
+
 source_group(api\\c\\Headers FILES ${c_headers})
 source_group(api\\c\\Sources FILES ${c_sources})
 
-
 FILE(GLOB cpp_sources
     "../../api/cpp/*.cpp"
     )
+
+LIST(SORT cpp_sources)
+
 source_group(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB kernel_src ${opencl_kernels} "kernel/KParam.hpp")
 
+LIST(SORT kernel_src)
+
 CL_KERNEL_TO_H(
     SOURCES ${kernel_src}
     VARNAME kernel_files

From b2110ac97026ffe089d480785fe2aaf0b0c97031 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 3 Mar 2016 14:43:49 -0500
Subject: [PATCH 0405/2677] BUGFIX: Return cl_mem properly from OpenCL backend.

---
 include/af/device.h          | 2 ++
 include/af/opencl.h          | 6 +++---
 src/backend/opencl/Array.hpp | 9 ++++++---
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/include/af/device.h b/include/af/device.h
index b08bd519b3..62971025da 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -428,6 +428,8 @@ extern "C" {
 
        The device pointer \p ptr is notfreed by memory manager until \ref af_unlock_device_ptr is called.
        \ingroup device_func_mem
+
+       \note For OpenCL backend *ptr should be cast to cl_mem.
     */
     AFAPI af_err af_get_device_ptr(void **ptr, const af_array arr);
 
diff --git a/include/af/opencl.h b/include/af/opencl.h
index 16b85d763f..8cb6e8ecff 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -450,10 +450,10 @@ namespace af
 #if !defined(AF_OPENCL)
 template<> AFAPI cl_mem *array::device() const
 {
-    cl_mem *mem = new cl_mem;
-    af_err err = af_get_device_ptr((void **)mem, get());
+    cl_mem *mem_ptr = new cl_mem;
+    af_err err = af_get_device_ptr((void **)mem_ptr, get());
     if (err != AF_SUCCESS) throw af::exception("Failed to get cl_mem from array object");
-    return mem;
+    return mem_ptr;
 }
 #endif
 
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 8c5bda90de..0b1f019c03 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -77,15 +77,18 @@ namespace opencl
     template<typename T>
     void *getDevicePtr(const Array<T>& arr)
     {
-        cl::Buffer *buf = arr.device();
+        const cl::Buffer *buf = arr.device();
         memLock((T *)buf);
-        return (void *)((*buf)());
+        cl_mem mem = (*buf)();
+        return (void *)mem;
     }
 
     template<typename T>
     void *getRawPtr(const Array<T>& arr)
     {
-        return (void *)(arr.get());
+        const cl::Buffer *buf = arr.get();
+        cl_mem mem = (*buf)();
+        return (void *)mem;
     }
 
     template<typename T>

From 3f3e5c5df39d2612757f00bedf01601c5ca64eb3 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 3 Mar 2016 14:44:44 -0500
Subject: [PATCH 0406/2677] BUGFIX: Append so names when loading the libraries
 in unified backend.

---
 src/api/unified/symbol_manager.cpp | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 96cec0b6ac..ef92cd3902 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <string>
 #include <cmath>
+#include <af/version.h>
 
 using std::string;
 using std::replace;
@@ -25,12 +26,15 @@ static const string LIB_AF_BKND_PREFIX = "af";
 static const string LIB_AF_BKND_SUFFIX = ".dll";
 #define RTLD_LAZY 0
 #else
-static const string LIB_AF_BKND_PREFIX = "libaf";
 #if defined(__APPLE__)
-static const string LIB_AF_BKND_SUFFIX = ".dylib";
+#define SO_SUFFIX_HELPER(VER) "." #VER ".dylib"
 #else
-static const string LIB_AF_BKND_SUFFIX = ".so";
+#define SO_SUFFIX_HELPER(VER) ".so." #VER
 #endif // APPLE
+static const string LIB_AF_BKND_PREFIX = "libaf";
+
+#define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
+static const string LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
 #endif
 
 static const string LIB_AF_ENVARS[NUM_ENV_VARS] = {"AF_PATH", "AF_BUILD_PATH"};

From d932a6fa3946decb5d1dfa6784c114c27dd4aff2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Mar 2016 15:39:10 -0500
Subject: [PATCH 0407/2677] BUGFIX % operator uses mod instead of rem

---
 src/api/cpp/array.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index b993e2f7e8..4766e7561d 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -930,7 +930,7 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     BINARY_OP(>=, af_ge)
     BINARY_OP(&&, af_and)
     BINARY_OP(||, af_or)
-    BINARY_OP(%, af_rem)
+    BINARY_OP(%, af_mod)
     BINARY_OP(&, af_bitand)
     BINARY_OP(|, af_bitor)
     BINARY_OP(^, af_bitxor)

From 980b8fcc0cf03513c0d0b715011954e31741f4e7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 4 Mar 2016 10:14:17 -0500
Subject: [PATCH 0408/2677] TYPO Correction to comments after fixes from commit
 0258883

---
 src/backend/ArrayInfo.cpp |  4 ++--
 src/backend/ArrayInfo.hpp | 12 ++++++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index 0937641afc..98a2264b5c 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -33,7 +33,7 @@ dim4 calcStrides(const dim4 &parentDim)
 
 int ArrayInfo::getDevId() const
 {
-    // The actual device ID is only stored in the first 4 bits of devId
+    // The actual device ID is only stored in the first 8 bits of devId
     // See ArrayInfo.hpp for more
     return devId & 0xff;
 }
@@ -49,7 +49,7 @@ void ArrayInfo::setId(int id) const
 
 void ArrayInfo::setId(int id)
 {
-    // 1 << (backendId + 3) sets the 9th, 10th or 11th bit of devId to 1
+    // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
     int backendId = detail::getBackend() >> 1; // Convert enums 1, 2, 4 to ints 0, 1, 2
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index 88ba26b6aa..7d3606e129 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -29,14 +29,14 @@ class ArrayInfo
 {
 private:
     // The devId variable stores information about the deviceId as well as the backend.
-    // The 4 LSBs (0-3) are used to store the device ID.
-    // The 4th LSB is set to 1 if backend is CPU
-    // The 5th LSB is set to 1 if backend is CUDA
-    // The 6th LSB is set to 1 if backend is OpenCL
+    // The 8 LSBs (0-7) are used to store the device ID.
+    // The 09th LSB is set to 1 if backend is CPU
+    // The 10th LSB is set to 1 if backend is CUDA
+    // The 11th LSB is set to 1 if backend is OpenCL
     // This information can be retrieved directly from an af_array by doing
     //     int* devId = reinterpret_cast<int*>(a); // a is an af_array
-    //     af_backend backendID = *devId >> 3;  // Returns 1, 2, 4 for CPU, CUDA or OpenCL respectively
-    //     int        deviceID  = *devId & 0xf; // Returns devices ID between 0-15
+    //     af_backend backendID = *devId >> 8;   // Returns 1, 2, 4 for CPU, CUDA or OpenCL respectively
+    //     int        deviceID  = *devId & 0xff; // Returns devices ID between 0-255
     // This is possible by doing a static_assert on devId
     //
     // This can be changed in the future if the need arises for more devices as this

From 6dbf441efe713eadc665211028ad2ffcfb99b0a3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 9 Mar 2016 19:05:44 +0530
Subject: [PATCH 0409/2677] Replace lena image in kmeans example

---
 examples/machine_learning/kmeans.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index 51351aaff9..f75da9e333 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -112,7 +112,7 @@ int kmeans_demo(int k, bool console)
 {
     printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);
 
-    array img = loadImage(ASSETS_DIR"/examples/images/lena.ppm", true) / 255; // [0-255]
+    array img = loadImage(ASSETS_DIR"/examples/images/vegetable-woman.jpg", true) / 255; // [0-255]
 
     int w = img.dims(0), h = img.dims(1), c = img.dims(2);
     array vec = moddims(img, w * h, 1, c);

From 28305c8b54459c849ad629fe0426ab8a3643f2f2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Mar 2016 11:13:39 -0500
Subject: [PATCH 0410/2677] BUGFIX cuFFT plans when using multiple devices

---
 src/backend/cuda/fft.cpp |  4 ++--
 test/fft.cpp             | 36 ++++++++++++++++++++++++++++++++++++
 2 files changed, 38 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 13d108a2af..29f85f96c2 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -41,8 +41,8 @@ class cuFFTPlanner
 
     public:
         static cuFFTPlanner& getInstance() {
-            static cuFFTPlanner single_instance;
-            return single_instance;
+            static cuFFTPlanner instances[cuda::DeviceManager::MAX_DEVICES];
+            return instances[cuda::getActiveDeviceId()];
         }
 
     private:
diff --git a/test/fft.cpp b/test/fft.cpp
index 48ff865d2a..19b0ae0950 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -683,3 +683,39 @@ TEST(ifft3, InPlace)
         ASSERT_EQ(ha[i], hb[i]);
     }
 }
+
+void fft2InPlaceFunc()
+{
+    af::array a = af::randu(1024, 1024, c32);
+    af::array b = af::fft2(a);
+    af::fft2InPlace(a);
+
+    std::vector<af::cfloat> ha(a.elements());
+    std::vector<af::cfloat> hb(b.elements());
+
+    a.host(&ha[0]);
+    b.host(&hb[0]);
+
+    for (int i = 0; i < (int)a.elements(); i++) {
+        ASSERT_EQ(ha[i], hb[i]);
+    }
+}
+
+#define DEVICE_ITERATE(func) do {                                           \
+    const char* ENV = getenv("AF_MULTI_GPU_TESTS");                         \
+    if(ENV && ENV[0] == '0') {                                              \
+        func;                                                               \
+    } else {                                                                \
+        int oldDevice = af::getDevice();                                    \
+        for(int i = 0; i < af::getDeviceCount(); i++) {                     \
+            af::setDevice(i);                                               \
+            func;                                                           \
+        }                                                                   \
+        af::setDevice(oldDevice);                                           \
+    }                                                                       \
+} while(0);
+
+TEST(FFT2, MultiGPUInPlaceSquare_CPP)
+{
+    DEVICE_ITERATE((fft2InPlaceFunc()));
+}

From 82144954a13273ef23253cf52bf5d0bf607ae1e9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Mar 2016 15:12:15 -0500
Subject: [PATCH 0411/2677] BUGFIX clFFT plans when using multiple devices

---
 src/backend/opencl/fft.cpp | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index d5922f0c76..777788727c 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -42,12 +42,16 @@ class clFFTPlanner
 
     public:
         static clFFTPlanner& getInstance() {
-            static clFFTPlanner single_instance;
-            return single_instance;
+            static clFFTPlanner instances[opencl::DeviceManager::MAX_DEVICES];
+            return instances[opencl::getActiveDeviceId()];
         }
 
         ~clFFTPlanner() {
-            CLFFT_CHECK(clfftTeardown());
+            static bool flag = true;
+            if(flag) {
+                CLFFT_CHECK(clfftTeardown());
+                flag = false;
+            }
         }
 
     private:

From 5f3bbb9978d4f7a302ec918a4359dcc02fdfbc31 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 6 Mar 2016 01:04:28 -0500
Subject: [PATCH 0412/2677] BUGFIX: Ensuring the data is destroyed using the
 proper memory manage.

---
 src/api/c/data.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 522eb7dfcb..4a88899e4e 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -24,6 +24,7 @@
 #include <identity.hpp>
 #include <diagonal.hpp>
 #include <triangle.hpp>
+#include <platform.hpp>
 
 using af::dim4;
 using namespace detail;
@@ -404,7 +405,13 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
 af_err af_release_array(af_array arr)
 {
     try {
-        af_dtype type = getInfo(arr).getType();
+        int dev = getActiveDeviceId();
+
+        ArrayInfo info = getInfo(arr, false);
+
+        setDevice(info.getDevId());
+
+        af_dtype type = info.getType();
 
         switch(type) {
         case f32:   releaseHandle<float   >(arr); break;
@@ -421,6 +428,8 @@ af_err af_release_array(af_array arr)
         case u16:   releaseHandle<ushort  >(arr); break;
         default:    TYPE_ERROR(0, type);
         }
+
+        setDevice(dev);
     }
     CATCHALL
 

From 00131de43a8f04b7097337eab70a40398671e359 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 6 Mar 2016 02:54:35 -0500
Subject: [PATCH 0413/2677] Moving all functions related to af_array handling
 to api/c/array.cpp

---
 src/api/c/array.cpp  | 263 ++++++++++++++++++++++++++++++++++++++
 src/api/c/data.cpp   | 294 +------------------------------------------
 src/api/c/device.cpp |  33 +++++
 src/api/c/handle.hpp |   3 +
 4 files changed, 300 insertions(+), 293 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index cefdde1d75..80b0d85e60 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -9,6 +9,10 @@
 #include <handle.hpp>
 #include <ArrayInfo.hpp>
 #include <platform.hpp>
+#include <handle.hpp>
+#include <backend.hpp>
+
+using namespace detail;
 
 const ArrayInfo&
 getInfo(const af_array arr, bool check)
@@ -22,6 +26,265 @@ getInfo(const af_array arr, bool check)
     return *info;
 }
 
+af_err af_get_data_ptr(void *data, const af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+        switch(type) {
+        case f32:   copyData(static_cast<float    *>(data), arr);  break;
+        case c32:   copyData(static_cast<cfloat   *>(data), arr);  break;
+        case f64:   copyData(static_cast<double   *>(data), arr);  break;
+        case c64:   copyData(static_cast<cdouble  *>(data), arr);  break;
+        case b8:    copyData(static_cast<char     *>(data), arr);  break;
+        case s32:   copyData(static_cast<int      *>(data), arr);  break;
+        case u32:   copyData(static_cast<unsigned *>(data), arr);  break;
+        case u8:    copyData(static_cast<uchar    *>(data), arr);  break;
+        case s64:   copyData(static_cast<intl     *>(data), arr);  break;
+        case u64:   copyData(static_cast<uintl    *>(data), arr);  break;
+        case s16:   copyData(static_cast<short    *>(data), arr);  break;
+        case u16:   copyData(static_cast<ushort   *>(data), arr);  break;
+        default:    TYPE_ERROR(1, type);
+        }
+    }
+    CATCHALL
+        return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_create_array(af_array *result, const void * const data,
+                       const unsigned ndims, const dim_t * const dims,
+                       const af_dtype type)
+{
+    try {
+        af_array out;
+        AF_CHECK(af_init());
+
+        dim4 d = verifyDims(ndims, dims);
+
+        switch(type) {
+        case f32:   out = createHandleFromData(d, static_cast<const float   *>(data)); break;
+        case c32:   out = createHandleFromData(d, static_cast<const cfloat  *>(data)); break;
+        case f64:   out = createHandleFromData(d, static_cast<const double  *>(data)); break;
+        case c64:   out = createHandleFromData(d, static_cast<const cdouble *>(data)); break;
+        case b8:    out = createHandleFromData(d, static_cast<const char    *>(data)); break;
+        case s32:   out = createHandleFromData(d, static_cast<const int     *>(data)); break;
+        case u32:   out = createHandleFromData(d, static_cast<const uint    *>(data)); break;
+        case u8:    out = createHandleFromData(d, static_cast<const uchar   *>(data)); break;
+        case s64:   out = createHandleFromData(d, static_cast<const intl    *>(data)); break;
+        case u64:   out = createHandleFromData(d, static_cast<const uintl   *>(data)); break;
+        case s16:   out = createHandleFromData(d, static_cast<const short   *>(data)); break;
+        case u16:   out = createHandleFromData(d, static_cast<const ushort  *>(data)); break;
+        default:    TYPE_ERROR(4, type);
+        }
+        std::swap(*result, out);
+    }
+    CATCHALL
+        return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * const dims,
+                        const af_dtype type)
+{
+    try {
+        af_array out;
+        AF_CHECK(af_init());
+
+        dim4 d((size_t)dims[0]);
+        for(unsigned i = 1; i < ndims; i++) {
+            d[i] = dims[i];
+        }
+
+        switch(type) {
+        case f32:   out = createHandle<float  >(d); break;
+        case c32:   out = createHandle<cfloat >(d); break;
+        case f64:   out = createHandle<double >(d); break;
+        case c64:   out = createHandle<cdouble>(d); break;
+        case b8:    out = createHandle<char   >(d); break;
+        case s32:   out = createHandle<int    >(d); break;
+        case u32:   out = createHandle<uint   >(d); break;
+        case u8:    out = createHandle<uchar  >(d); break;
+        case s64:   out = createHandle<intl   >(d); break;
+        case u64:   out = createHandle<uintl  >(d); break;
+        case s16:   out = createHandle<short  >(d); break;
+        case u16:   out = createHandle<ushort >(d); break;
+        default:    TYPE_ERROR(3, type);
+        }
+        std::swap(*result, out);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_copy_array(af_array *out, const af_array in)
+{
+    try {
+        ArrayInfo info = getInfo(in);
+        const af_dtype type = info.getType();
+
+        af_array res;
+        switch(type) {
+        case f32:   res = copyArray<float   >(in); break;
+        case c32:   res = copyArray<cfloat  >(in); break;
+        case f64:   res = copyArray<double  >(in); break;
+        case c64:   res = copyArray<cdouble >(in); break;
+        case b8:    res = copyArray<char    >(in); break;
+        case s32:   res = copyArray<int     >(in); break;
+        case u32:   res = copyArray<uint    >(in); break;
+        case u8:    res = copyArray<uchar   >(in); break;
+        case s64:   res = copyArray<intl    >(in); break;
+        case u64:   res = copyArray<uintl   >(in); break;
+        case s16:   res = copyArray<short   >(in); break;
+        case u16:   res = copyArray<ushort  >(in); break;
+        default:    TYPE_ERROR(1, type);
+        }
+        std::swap(*out, res);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_get_data_ref_count(int *use_count, const af_array in)
+{
+    try {
+        ArrayInfo info = getInfo(in);
+        const af_dtype type = info.getType();
+
+        int res;
+        switch(type) {
+        case f32:   res = getArray<float   >(in).useCount(); break;
+        case c32:   res = getArray<cfloat  >(in).useCount(); break;
+        case f64:   res = getArray<double  >(in).useCount(); break;
+        case c64:   res = getArray<cdouble >(in).useCount(); break;
+        case b8:    res = getArray<char    >(in).useCount(); break;
+        case s32:   res = getArray<int     >(in).useCount(); break;
+        case u32:   res = getArray<uint    >(in).useCount(); break;
+        case u8:    res = getArray<uchar   >(in).useCount(); break;
+        case s64:   res = getArray<intl    >(in).useCount(); break;
+        case u64:   res = getArray<uintl   >(in).useCount(); break;
+        case s16:   res = getArray<short   >(in).useCount(); break;
+        case u16:   res = getArray<ushort  >(in).useCount(); break;
+        default:    TYPE_ERROR(1, type);
+        }
+        std::swap(*use_count, res);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_release_array(af_array arr)
+{
+    try {
+        int dev = getActiveDeviceId();
+
+        ArrayInfo info = getInfo(arr, false);
+
+        setDevice(info.getDevId());
+
+        af_dtype type = info.getType();
+
+        switch(type) {
+        case f32:   releaseHandle<float   >(arr); break;
+        case c32:   releaseHandle<cfloat  >(arr); break;
+        case f64:   releaseHandle<double  >(arr); break;
+        case c64:   releaseHandle<cdouble >(arr); break;
+        case b8:    releaseHandle<char    >(arr); break;
+        case s32:   releaseHandle<int     >(arr); break;
+        case u32:   releaseHandle<uint    >(arr); break;
+        case u8:    releaseHandle<uchar   >(arr); break;
+        case s64:   releaseHandle<intl    >(arr); break;
+        case u64:   releaseHandle<uintl   >(arr); break;
+        case s16:   releaseHandle<short   >(arr); break;
+        case u16:   releaseHandle<ushort  >(arr); break;
+        default:    TYPE_ERROR(0, type);
+        }
+
+        setDevice(dev);
+    }
+    CATCHALL
+
+    return AF_SUCCESS;
+}
+
+
+template<typename T>
+static af_array retainHandle(const af_array in)
+{
+    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
+    detail::Array<T> *out = detail::initArray<T>();
+    *out= *A;
+    return reinterpret_cast<af_array>(out);
+}
+
+af_array retain(const af_array in)
+{
+    af_dtype ty = getInfo(in).getType();
+    switch(ty) {
+    case f32: return retainHandle<float           >(in);
+    case f64: return retainHandle<double          >(in);
+    case s32: return retainHandle<int             >(in);
+    case u32: return retainHandle<uint            >(in);
+    case u8:  return retainHandle<uchar           >(in);
+    case c32: return retainHandle<detail::cfloat  >(in);
+    case c64: return retainHandle<detail::cdouble >(in);
+    case b8:  return retainHandle<char            >(in);
+    case s64: return retainHandle<intl            >(in);
+    case u64: return retainHandle<uintl           >(in);
+    case s16: return retainHandle<short           >(in);
+    case u16: return retainHandle<ushort          >(in);
+    default:
+        TYPE_ERROR(1, ty);
+    }
+}
+
+af_err af_retain_array(af_array *out, const af_array in)
+{
+    try {
+        *out = retain(in);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+template<typename T>
+void write_array(af_array arr, const T * const data, const size_t bytes, af_source src)
+{
+    if(src == afHost) {
+        writeHostDataArray(getWritableArray<T>(arr), data, bytes);
+    } else {
+        writeDeviceDataArray(getWritableArray<T>(arr), data, bytes);
+    }
+    return;
+}
+
+af_err af_write_array(af_array arr, const void *data, const size_t bytes, af_source src)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+        //DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
+
+        switch(type) {
+        case f32:   write_array(arr, static_cast<const float   *>(data), bytes, src); break;
+        case c32:   write_array(arr, static_cast<const cfloat  *>(data), bytes, src); break;
+        case f64:   write_array(arr, static_cast<const double  *>(data), bytes, src); break;
+        case c64:   write_array(arr, static_cast<const cdouble *>(data), bytes, src); break;
+        case b8:    write_array(arr, static_cast<const char    *>(data), bytes, src); break;
+        case s32:   write_array(arr, static_cast<const int     *>(data), bytes, src); break;
+        case u32:   write_array(arr, static_cast<const uint    *>(data), bytes, src); break;
+        case u8:    write_array(arr, static_cast<const uchar   *>(data), bytes, src); break;
+        case s64:   write_array(arr, static_cast<const intl    *>(data), bytes, src); break;
+        case u64:   write_array(arr, static_cast<const uintl   *>(data), bytes, src); break;
+        case s16:   write_array(arr, static_cast<const short   *>(data), bytes, src); break;
+        case u16:   write_array(arr, static_cast<const ushort  *>(data), bytes, src); break;
+        default:    TYPE_ERROR(4, type);
+        }
+    }
+    CATCHALL
+        return AF_SUCCESS;
+}
+
 af_err af_get_elements(dim_t *elems, const af_array arr)
 {
     try {
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 4a88899e4e..295fa83cc7 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -30,7 +30,7 @@ using af::dim4;
 using namespace detail;
 using namespace std;
 
-static inline dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
+dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
 {
 
     DIM_ASSERT(1, ndims >= 1);
@@ -45,62 +45,6 @@ static inline dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
     return d;
 }
 
-af_err af_get_data_ptr(void *data, const af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-        switch(type) {
-        case f32:   copyData(static_cast<float    *>(data), arr);  break;
-        case c32:   copyData(static_cast<cfloat   *>(data), arr);  break;
-        case f64:   copyData(static_cast<double   *>(data), arr);  break;
-        case c64:   copyData(static_cast<cdouble  *>(data), arr);  break;
-        case b8:    copyData(static_cast<char     *>(data), arr);  break;
-        case s32:   copyData(static_cast<int      *>(data), arr);  break;
-        case u32:   copyData(static_cast<unsigned *>(data), arr);  break;
-        case u8:    copyData(static_cast<uchar    *>(data), arr);  break;
-        case s64:   copyData(static_cast<intl     *>(data), arr);  break;
-        case u64:   copyData(static_cast<uintl    *>(data), arr);  break;
-        case s16:   copyData(static_cast<short    *>(data), arr);  break;
-        case u16:   copyData(static_cast<ushort   *>(data), arr);  break;
-        default:    TYPE_ERROR(1, type);
-        }
-    }
-    CATCHALL
-        return AF_SUCCESS;
-}
-
-//Strong Exception Guarantee
-af_err af_create_array(af_array *result, const void * const data,
-                       const unsigned ndims, const dim_t * const dims,
-                       const af_dtype type)
-{
-    try {
-        af_array out;
-        AF_CHECK(af_init());
-
-        dim4 d = verifyDims(ndims, dims);
-
-        switch(type) {
-        case f32:   out = createHandleFromData(d, static_cast<const float   *>(data)); break;
-        case c32:   out = createHandleFromData(d, static_cast<const cfloat  *>(data)); break;
-        case f64:   out = createHandleFromData(d, static_cast<const double  *>(data)); break;
-        case c64:   out = createHandleFromData(d, static_cast<const cdouble *>(data)); break;
-        case b8:    out = createHandleFromData(d, static_cast<const char    *>(data)); break;
-        case s32:   out = createHandleFromData(d, static_cast<const int     *>(data)); break;
-        case u32:   out = createHandleFromData(d, static_cast<const uint    *>(data)); break;
-        case u8:    out = createHandleFromData(d, static_cast<const uchar   *>(data)); break;
-        case s64:   out = createHandleFromData(d, static_cast<const intl    *>(data)); break;
-        case u64:   out = createHandleFromData(d, static_cast<const uintl   *>(data)); break;
-        case s16:   out = createHandleFromData(d, static_cast<const short   *>(data)); break;
-        case u16:   out = createHandleFromData(d, static_cast<const ushort  *>(data)); break;
-        default:    TYPE_ERROR(4, type);
-        }
-        std::swap(*result, out);
-    }
-    CATCHALL
-        return AF_SUCCESS;
-}
-
 //Strong Exception Guarantee
 af_err af_constant(af_array *result, const double value,
                    const unsigned ndims, const dim_t * const dims,
@@ -195,99 +139,6 @@ af_err af_constant_ulong(af_array *result, const uintl val,
     return AF_SUCCESS;
 }
 
-//Strong Exception Guarantee
-af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * const dims,
-                        const af_dtype type)
-{
-    try {
-        af_array out;
-        AF_CHECK(af_init());
-
-        dim4 d((size_t)dims[0]);
-        for(unsigned i = 1; i < ndims; i++) {
-            d[i] = dims[i];
-        }
-
-        switch(type) {
-        case f32:   out = createHandle<float  >(d); break;
-        case c32:   out = createHandle<cfloat >(d); break;
-        case f64:   out = createHandle<double >(d); break;
-        case c64:   out = createHandle<cdouble>(d); break;
-        case b8:    out = createHandle<char   >(d); break;
-        case s32:   out = createHandle<int    >(d); break;
-        case u32:   out = createHandle<uint   >(d); break;
-        case u8:    out = createHandle<uchar  >(d); break;
-        case s64:   out = createHandle<intl   >(d); break;
-        case u64:   out = createHandle<uintl  >(d); break;
-        case s16:   out = createHandle<short  >(d); break;
-        case u16:   out = createHandle<ushort >(d); break;
-        default:    TYPE_ERROR(3, type);
-        }
-        std::swap(*result, out);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-//Strong Exception Guarantee
-af_err af_copy_array(af_array *out, const af_array in)
-{
-    try {
-        ArrayInfo info = getInfo(in);
-        const af_dtype type = info.getType();
-
-        af_array res;
-        switch(type) {
-        case f32:   res = copyArray<float   >(in); break;
-        case c32:   res = copyArray<cfloat  >(in); break;
-        case f64:   res = copyArray<double  >(in); break;
-        case c64:   res = copyArray<cdouble >(in); break;
-        case b8:    res = copyArray<char    >(in); break;
-        case s32:   res = copyArray<int     >(in); break;
-        case u32:   res = copyArray<uint    >(in); break;
-        case u8:    res = copyArray<uchar   >(in); break;
-        case s64:   res = copyArray<intl    >(in); break;
-        case u64:   res = copyArray<uintl   >(in); break;
-        case s16:   res = copyArray<short   >(in); break;
-        case u16:   res = copyArray<ushort  >(in); break;
-        default:    TYPE_ERROR(1, type);
-        }
-        std::swap(*out, res);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-//Strong Exception Guarantee
-af_err af_get_data_ref_count(int *use_count, const af_array in)
-{
-    try {
-        ArrayInfo info = getInfo(in);
-        const af_dtype type = info.getType();
-
-        int res;
-        switch(type) {
-        case f32:   res = getArray<float   >(in).useCount(); break;
-        case c32:   res = getArray<cfloat  >(in).useCount(); break;
-        case f64:   res = getArray<double  >(in).useCount(); break;
-        case c64:   res = getArray<cdouble >(in).useCount(); break;
-        case b8:    res = getArray<char    >(in).useCount(); break;
-        case s32:   res = getArray<int     >(in).useCount(); break;
-        case u32:   res = getArray<uint    >(in).useCount(); break;
-        case u8:    res = getArray<uchar   >(in).useCount(); break;
-        case s64:   res = getArray<intl    >(in).useCount(); break;
-        case u64:   res = getArray<uintl   >(in).useCount(); break;
-        case s16:   res = getArray<short   >(in).useCount(); break;
-        case u16:   res = getArray<ushort  >(in).useCount(); break;
-        default:    TYPE_ERROR(1, type);
-        }
-        std::swap(*use_count, res);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-
 template<typename T>
 static inline af_array randn_(const af::dim4 &dims)
 {
@@ -402,80 +253,6 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
     return AF_SUCCESS;
 }
 
-af_err af_release_array(af_array arr)
-{
-    try {
-        int dev = getActiveDeviceId();
-
-        ArrayInfo info = getInfo(arr, false);
-
-        setDevice(info.getDevId());
-
-        af_dtype type = info.getType();
-
-        switch(type) {
-        case f32:   releaseHandle<float   >(arr); break;
-        case c32:   releaseHandle<cfloat  >(arr); break;
-        case f64:   releaseHandle<double  >(arr); break;
-        case c64:   releaseHandle<cdouble >(arr); break;
-        case b8:    releaseHandle<char    >(arr); break;
-        case s32:   releaseHandle<int     >(arr); break;
-        case u32:   releaseHandle<uint    >(arr); break;
-        case u8:    releaseHandle<uchar   >(arr); break;
-        case s64:   releaseHandle<intl    >(arr); break;
-        case u64:   releaseHandle<uintl   >(arr); break;
-        case s16:   releaseHandle<short   >(arr); break;
-        case u16:   releaseHandle<ushort  >(arr); break;
-        default:    TYPE_ERROR(0, type);
-        }
-
-        setDevice(dev);
-    }
-    CATCHALL
-
-    return AF_SUCCESS;
-}
-
-
-template<typename T>
-static af_array retainHandle(const af_array in)
-{
-    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
-    detail::Array<T> *out = detail::initArray<T>();
-    *out= *A;
-    return reinterpret_cast<af_array>(out);
-}
-
-af_array retain(const af_array in)
-{
-    af_dtype ty = getInfo(in).getType();
-    switch(ty) {
-    case f32: return retainHandle<float           >(in);
-    case f64: return retainHandle<double          >(in);
-    case s32: return retainHandle<int             >(in);
-    case u32: return retainHandle<uint            >(in);
-    case u8:  return retainHandle<uchar           >(in);
-    case c32: return retainHandle<detail::cfloat  >(in);
-    case c64: return retainHandle<detail::cdouble >(in);
-    case b8:  return retainHandle<char            >(in);
-    case s64: return retainHandle<intl            >(in);
-    case u64: return retainHandle<uintl           >(in);
-    case s16: return retainHandle<short           >(in);
-    case u16: return retainHandle<ushort          >(in);
-    default:
-        TYPE_ERROR(1, ty);
-    }
-}
-
-af_err af_retain_array(af_array *out, const af_array in)
-{
-    try {
-        *out = retain(in);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-}
-
 template<typename T>
 static inline af_array range_(const dim4& d, const int seq_dim)
 {
@@ -548,38 +325,6 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
     return AF_SUCCESS;
 }
 
-template<typename T>
-static inline void eval(af_array arr)
-{
-    getArray<T>(arr).eval();
-    return;
-}
-
-af_err af_eval(af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-        switch (type) {
-        case f32: eval<float  >(arr); break;
-        case f64: eval<double >(arr); break;
-        case c32: eval<cfloat >(arr); break;
-        case c64: eval<cdouble>(arr); break;
-        case s32: eval<int    >(arr); break;
-        case u32: eval<uint   >(arr); break;
-        case u8 : eval<uchar  >(arr); break;
-        case b8 : eval<char   >(arr); break;
-        case s64: eval<intl   >(arr); break;
-        case u64: eval<uintl  >(arr); break;
-        case s16: eval<short  >(arr); break;
-        case u16: eval<ushort >(arr); break;
-        default:
-            TYPE_ERROR(0, type);
-        }
-    } CATCHALL;
-
-    return AF_SUCCESS;
-}
-
 template<typename T>
 static inline af_array diagCreate(const af_array in, const int num)
 {
@@ -654,43 +399,6 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num)
     return AF_SUCCESS;
 }
 
-template<typename T>
-void write_array(af_array arr, const T * const data, const size_t bytes, af_source src)
-{
-    if(src == afHost) {
-        writeHostDataArray(getWritableArray<T>(arr), data, bytes);
-    } else {
-        writeDeviceDataArray(getWritableArray<T>(arr), data, bytes);
-    }
-    return;
-}
-
-af_err af_write_array(af_array arr, const void *data, const size_t bytes, af_source src)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-        //DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
-
-        switch(type) {
-        case f32:   write_array(arr, static_cast<const float   *>(data), bytes, src); break;
-        case c32:   write_array(arr, static_cast<const cfloat  *>(data), bytes, src); break;
-        case f64:   write_array(arr, static_cast<const double  *>(data), bytes, src); break;
-        case c64:   write_array(arr, static_cast<const cdouble *>(data), bytes, src); break;
-        case b8:    write_array(arr, static_cast<const char    *>(data), bytes, src); break;
-        case s32:   write_array(arr, static_cast<const int     *>(data), bytes, src); break;
-        case u32:   write_array(arr, static_cast<const uint    *>(data), bytes, src); break;
-        case u8:    write_array(arr, static_cast<const uchar   *>(data), bytes, src); break;
-        case s64:   write_array(arr, static_cast<const intl    *>(data), bytes, src); break;
-        case u64:   write_array(arr, static_cast<const uintl   *>(data), bytes, src); break;
-        case s16:   write_array(arr, static_cast<const short   *>(data), bytes, src); break;
-        case u16:   write_array(arr, static_cast<const ushort  *>(data), bytes, src); break;
-        default:    TYPE_ERROR(4, type);
-        }
-    }
-    CATCHALL
-        return AF_SUCCESS;
-}
-
 template<typename T, bool is_upper>
 af_array triangle(const af_array in, bool is_unit_diag)
 {
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index abe0b01e32..b93907d55e 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -156,3 +156,36 @@ af_err af_sync(const int device)
     } CATCHALL;
     return AF_SUCCESS;
 }
+
+
+template<typename T>
+static inline void eval(af_array arr)
+{
+    getArray<T>(arr).eval();
+    return;
+}
+
+af_err af_eval(af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+        switch (type) {
+        case f32: eval<float  >(arr); break;
+        case f64: eval<double >(arr); break;
+        case c32: eval<cfloat >(arr); break;
+        case c64: eval<cdouble>(arr); break;
+        case s32: eval<int    >(arr); break;
+        case u32: eval<uint   >(arr); break;
+        case u8 : eval<uchar  >(arr); break;
+        case b8 : eval<char   >(arr); break;
+        case s64: eval<intl   >(arr); break;
+        case u64: eval<uintl  >(arr); break;
+        case s16: eval<short  >(arr); break;
+        case u16: eval<ushort >(arr); break;
+        default:
+            TYPE_ERROR(0, type);
+        }
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index ac7b74a193..e5dc3f43fe 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -15,6 +15,7 @@
 #include <math.hpp>
 #include <copy.hpp>
 #include <cast.hpp>
+#include <af/dim4.hpp>
 
 const ArrayInfo& getInfo(const af_array arr, bool check = true);
 
@@ -109,3 +110,5 @@ static void releaseHandle(const af_array arr)
 }
 
 af_array retain(const af_array in);
+
+af::dim4 verifyDims(const unsigned ndims, const dim_t * const dims);

From c1c86b6192aab09da2f97a762d1a75ca8dc26a6f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 9 Mar 2016 23:09:41 -0500
Subject: [PATCH 0414/2677] BUGFIX: Getting device pointer on empty arrays no
 longer segfaults.

Added relevant test.
---
 src/backend/opencl/Array.hpp | 2 ++
 test/array.cpp               | 7 +++++++
 2 files changed, 9 insertions(+)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 0b1f019c03..f87c7724b4 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -78,6 +78,7 @@ namespace opencl
     void *getDevicePtr(const Array<T>& arr)
     {
         const cl::Buffer *buf = arr.device();
+        if (!buf) return NULL;
         memLock((T *)buf);
         cl_mem mem = (*buf)();
         return (void *)mem;
@@ -87,6 +88,7 @@ namespace opencl
     void *getRawPtr(const Array<T>& arr)
     {
         const cl::Buffer *buf = arr.get();
+        if (!buf) return NULL;
         cl_mem mem = (*buf)();
         return (void *)mem;
     }
diff --git a/test/array.cpp b/test/array.cpp
index 293b888a8f..47e0c3a22a 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <cstddef>
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <testHelpers.hpp>
@@ -485,3 +486,9 @@ TEST(DeviceId, Different)
     ASSERT_EQ(err, AF_ERR_DEVICE);
     setDevice(id0);
 }
+
+TEST(Device, empty)
+{
+    array a = array();
+    ASSERT_EQ(a.device<float>() == NULL, 1);
+}

From 332b6c478d301ac5e66b637bad8f2daabcbd5148 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 9 Mar 2016 23:59:23 -0500
Subject: [PATCH 0415/2677] BUGFIX: Getting device ptr now forces JIT
 evaluation.

- Added relevant tests
---
 src/backend/cpu/Array.hpp    | 6 ++++--
 src/backend/cuda/Array.hpp   | 4 ++--
 src/backend/opencl/Array.hpp | 4 ++--
 test/array.cpp               | 6 ++++++
 4 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2a3afcf617..da7b80450d 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -85,12 +85,14 @@ namespace cpu
     {
         T *ptr = arr.device();
         memLock(ptr);
+
         return (void *)ptr;
     }
 
     template<typename T>
     void *getRawPtr(const Array<T>& arr)
     {
+        getQueue().sync();
         return (void *)(arr.get(false));
     }
 
@@ -187,10 +189,10 @@ namespace cpu
         T* device()
         {
             getQueue().sync();
-            if (!isOwner() || data.use_count() > 1) {
+            if (!isOwner() || getOffset() || data.use_count() > 1) {
                 *this = Array<T>(dims(), get(), true, true);
             }
-            return this->data.get();
+            return this->get();
         }
 
         T* device() const
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 7678754bc3..d6adfd0955 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -181,10 +181,10 @@ namespace cuda
 
         T* device()
         {
-            if (!isOwner() || data.use_count() > 1) {
+            if (!isOwner() || getOffset() || data.use_count() > 1) {
                 *this = Array<T>(dims(), get(), true, true);
             }
-            return this->data.get();
+            return this->get();
         }
 
         T* device() const
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index f87c7724b4..d2fa942d1d 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -164,10 +164,10 @@ namespace opencl
 
         cl::Buffer* device()
         {
-            if (!isOwner() || data.use_count() > 1) {
+            if (!isOwner() || getOffset() || data.use_count() > 1) {
                 *this = Array<T>(dims(), (*get())(), (size_t)getOffset(), true);
             }
-            return this->data.get();
+            return this->get();
         }
 
         cl::Buffer* device() const
diff --git a/test/array.cpp b/test/array.cpp
index 47e0c3a22a..e08e6046db 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -492,3 +492,9 @@ TEST(Device, empty)
     array a = array();
     ASSERT_EQ(a.device<float>() == NULL, 1);
 }
+
+TEST(Device, JIT)
+{
+    array a = constant(1, 5, 5);
+    ASSERT_EQ(a.device<float>() != NULL, 1);
+}

From 24bc76ea2247f05acf975c7b41ed07b47760877e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 10 Mar 2016 01:53:44 -0500
Subject: [PATCH 0416/2677] BUGFIX: fixed issue in indexing after calling
 resetDims

---
 src/backend/cpu/Array.hpp    | 4 +---
 src/backend/cuda/Array.hpp   | 4 +---
 src/backend/opencl/Array.hpp | 4 +---
 3 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2a3afcf617..64ef2bf08f 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -174,9 +174,7 @@ namespace cpu
 
         dim4 getDataDims() const
         {
-            // This is for moddims
-            // dims and data_dims are different when moddims is used
-            return isOwner() ? info.dims() : data_dims;
+            return data_dims;
         }
 
         void setDataDims(const dim4 &new_dims)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 7678754bc3..634960e6ac 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -169,9 +169,7 @@ namespace cuda
 
         dim4 getDataDims() const
         {
-            // This is for moddims
-            // dims and data_dims are different when moddims is used
-            return isOwner() ? dims() : data_dims;
+            return data_dims;
         }
 
         void setDataDims(const dim4 &new_dims)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 0b1f019c03..3de54baa5a 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -204,9 +204,7 @@ namespace opencl
 
         dim4 getDataDims() const
         {
-            // This is for moddims
-            // dims and data_dims are different when moddims is used
-            return isOwner() ? dims() : data_dims;
+            return data_dims;
         }
 
         void setDataDims(const dim4 &new_dims)

From 01f1588be80ea47ce46245dc5d0c43a9c8fe5e7d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 10 Mar 2016 18:31:10 +0530
Subject: [PATCH 0417/2677] Fix in kmeans example

---
 examples/machine_learning/kmeans.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index f75da9e333..8a5e3da917 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -72,7 +72,7 @@ void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
     array maximum = max(in);
 
     gfor(seq ii, d) {
-        data(span, span, ii) = (in(span, span, ii) - minimum(ii)) / maximum(ii);
+        data(span, span, ii) = (in(span, span, ii) - minimum(ii).scalar<float>()) / maximum(ii).scalar<float>();
     }
 
     // Initial guess of means

From 1fc86c44774ccd4b04b44450cf170b1b753d0d07 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Mar 2016 15:12:43 -0500
Subject: [PATCH 0418/2677] BUGFIX diagonal extract length is min instead of
 max of dims

---
 src/backend/cpu/diagonal.cpp    |  2 +-
 src/backend/cuda/diagonal.cu    |  2 +-
 src/backend/opencl/diagonal.cpp |  2 +-
 test/diagonal.cpp               | 31 +++++++++++++++++++++++++++++++
 4 files changed, 34 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index c818f82795..80375eaa71 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -42,7 +42,7 @@ Array<T> diagExtract(const Array<T> &in, const int num)
     in.eval();
 
     const dim4 idims = in.dims();
-    dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+    dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
     Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
     getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cu
index fd023c9f16..db0d1b4617 100644
--- a/src/backend/cuda/diagonal.cu
+++ b/src/backend/cuda/diagonal.cu
@@ -34,7 +34,7 @@ namespace cuda
     Array<T> diagExtract(const Array<T> &in, const int num)
     {
         const dim_t *idims = in.dims().get();
-        dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+        dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
         Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
         kernel::diagExtract<T>(out, in, num);
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 79cd758bd5..8693b11be3 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -34,7 +34,7 @@ namespace opencl
     Array<T> diagExtract(const Array<T> &in, const int num)
     {
         const dim_t *idims = in.dims().get();
-        dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+        dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
         Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
         kernel::diagExtract<T>(out, in, num);
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index c4becab2dc..3f5e441c33 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -80,6 +80,37 @@ TYPED_TEST(Diagonal, Extract)
     }
 }
 
+TYPED_TEST(Diagonal, ExtractRect)
+{
+    if (noDoubleTests<TypeParam>()) return;
+
+    try {
+        static const int size0 = 1000, size1 = 900;
+        vector<TypeParam> input (size0 * size1);
+        for(int i = 0; i < size0 * size1; i++) {
+            input[i] = i;
+        }
+
+        for(int jj = 10; jj < size0; jj += 100) {
+            for(int kk = 10; kk < size1; kk += 90) {
+                array data(jj, kk, &input.front(), afHost);
+                array out = diag(data, 0);
+
+                vector<TypeParam> h_out(out.elements());
+                out.host(&h_out.front());
+
+                ASSERT_EQ(out.dims(0), std::min(jj, kk));
+
+                for(int i =0; i < (int)out.dims(0); i++) {
+                    ASSERT_EQ(input[i * data.dims(0) + i], h_out[i]);
+                }
+            }
+        }
+    } catch (const af::exception& ex) {
+        FAIL() << ex.what() << std::endl;
+    }
+}
+
 TEST(Diagonal, ExtractGFOR)
 {
     dim4 dims = dim4(100, 100, 3);

From b771ffe836d3c0fff7c70424f73218bd3267ebf9 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 14 Mar 2016 17:07:15 -0400
Subject: [PATCH 0419/2677] Fixes to find lapacke.h on Fedora23

---
 CMakeModules/FindLAPACKE.cmake | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 0732cfaa83..9251ee93c0 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -137,6 +137,8 @@ ELSE(PC_LAPACKE_FOUND)
             /sw/include
             /opt/local/include
             DOC "LAPACKE Include Directory"
+            PATH_SUFFIXES
+            lapacke
             )
     ENDIF(LAPACKE_ROOT_DIR)
 ENDIF(PC_LAPACKE_FOUND)

From 08590100380f68f7e64872af309ca126245dee73 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 14 Mar 2016 17:07:40 -0400
Subject: [PATCH 0420/2677] Fixing the logic to find cblas.h in FindCBLAS.cmake

---
 CMakeModules/FindCBLAS.cmake | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index db1d783e9e..f64fd6f1b4 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -109,7 +109,7 @@ MACRO(CHECK_ALL_LIBRARIES
     _flags
     _list
     _include
-    _search_include,
+    _search_include
     _libraries_work_check)
   # This macro checks for the existence of the combination of fortran libraries
   # given by _list.  If the combination is found, this macro checks (using the
@@ -168,11 +168,6 @@ MACRO(CHECK_ALL_LIBRARIES
       ENDIF(APPLE)
       MARK_AS_ADVANCED(${_prefix}_${_library}_LIBRARY)
 
-      IF(${_prefix}_${_library}_LIBRARY)
-        GET_FILENAME_COMPONENT(_path ${${_prefix}_${_library}_LIBRARY} PATH)
-        LIST(APPEND _paths ${_path}/../include ${_path}/../../include ${CBLAS_ROOT_DIR}/include)
-      ENDIF(${_prefix}_${_library}_LIBRARY)
-
       SET(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
       SET(_libraries_work ${${_prefix}_${_library}_LIBRARY})
     ENDIF(_libraries_work)
@@ -183,8 +178,17 @@ MACRO(CHECK_ALL_LIBRARIES
   SET(_bug_libraries_work_check ${_libraries_work_check}) #CMAKE BUG!!! SHOULD NOT BE THAT
 
   IF(_bug_search_include)
-    FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include} ${_paths})
+    FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include}
+      /opt/intel/mkl/include
+      /usr/include
+      /usr/local/include
+      /sw/include
+      /opt/local/include
+      PATH_SUFFIXES
+      openblas
+      )
     MARK_AS_ADVANCED(${_prefix}${_combined_name}_INCLUDE)
+
     IF(${_prefix}${_combined_name}_INCLUDE)
       IF (_verbose)
         MESSAGE(STATUS "Includes found")
@@ -194,6 +198,7 @@ MACRO(CHECK_ALL_LIBRARIES
     ELSE(${_prefix}${_combined_name}_INCLUDE)
       SET(_libraries_work FALSE)
     ENDIF(${_prefix}${_combined_name}_INCLUDE)
+
   ELSE(_bug_search_include)
     SET(${_prefix}_INCLUDE_DIR)
     SET(${_prefix}_INCLUDE_FILE ${_include})

From c39e4b77322071cc4b5d6decf23304034e414e34 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 14 Mar 2016 17:08:36 -0400
Subject: [PATCH 0421/2677] array test cleanup required for windows

---
 test/array.cpp | 29 +++++++++++++++++------------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/test/array.cpp b/test/array.cpp
index e08e6046db..b712df302e 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -466,25 +466,30 @@ TEST(DeviceId, Different)
 {
     int ndevices = getDeviceCount();
     if (ndevices < 2) return;
-
     int id0 = getDevice();
     int id1 = (id0 + 1) % ndevices;
 
-    array a = randu(5,5);
-    ASSERT_EQ(getDeviceId(a), id0);
-    setDevice(id1);
+    {
+        array a = randu(5,5);
+        ASSERT_EQ(getDeviceId(a), id0);
+        setDevice(id1);
+
+        array b = randu(5,5);
 
-    array b = randu(5,5);
+        ASSERT_EQ(getDeviceId(a), id0);
+        ASSERT_EQ(getDeviceId(b), id1);
+        ASSERT_NE(getDevice(), getDeviceId(a));
+        ASSERT_EQ(getDevice(), getDeviceId(b));
 
-    ASSERT_EQ(getDeviceId(a), id0);
-    ASSERT_EQ(getDeviceId(b), id1);
-    ASSERT_NE(getDevice(), getDeviceId(a));
-    ASSERT_EQ(getDevice(), getDeviceId(b));
+        af_array c;
+        af_err err = af_matmul(&c, a.get(), b.get(), AF_MAT_NONE, AF_MAT_NONE);
+        ASSERT_EQ(err, AF_ERR_DEVICE);
+    }
 
-    af_array c;
-    af_err err = af_matmul(&c, a.get(), b.get(), AF_MAT_NONE, AF_MAT_NONE);
-    ASSERT_EQ(err, AF_ERR_DEVICE);
+    setDevice(id1);
+    af::deviceGC();
     setDevice(id0);
+    af::deviceGC();
 }
 
 TEST(Device, empty)

From f2d60b666efd7ee6cd24d2783531650461bc36e2 Mon Sep 17 00:00:00 2001
From: mlloreda <miguel@arrayfire.com>
Date: Mon, 14 Mar 2016 15:24:19 -0400
Subject: [PATCH 0422/2677] README.md restructuring

* Reorganized structure of the document. Most notably, the example code
has been pulled near the top and the contact section has been pushed to the
bottom of the document.
* `Hello, world` example has been stripped down
* Added a new example, featuring a stripped version of `conway.cpp`
* Conway GIF
* Citation information has been moved to new location, `.github/CITATION.md`
* Link to `forge` in introductory paragraph
* New 'Language Wrapper' section
* Removed `techical@arrayfire.com` email address. Users should use the
issue tracker or the Google Groups mailing list, instead.
* Example code now using two-space indentation.
---
 .github/CITATION.md |  24 ++++++
 README.md           | 185 +++++++++++++++++++++-----------------------
 assets              |   2 +-
 3 files changed, 112 insertions(+), 99 deletions(-)
 create mode 100644 .github/CITATION.md

diff --git a/.github/CITATION.md b/.github/CITATION.md
new file mode 100644
index 0000000000..4e78352060
--- /dev/null
+++ b/.github/CITATION.md
@@ -0,0 +1,24 @@
+If you redistribute ArrayFire, please follow the terms established in
+[the license](../LICENSE). If you wish to cite ArrayFire in an academic
+publication, please use the following reference:
+
+Formatted:
+```
+Yalamanchili, P., Arshad, U., Mohammed, Z., Garigipati, P., Entschev, P.,
+Kloppenborg, B., Malcolm, J. and Melonakos, J. (2015).
+ArrayFire - A high performance software library for parallel computing with an
+easy-to-use API. Atlanta: AccelerEyes. Retrieved from https://github.com/arrayfire/arrayfire
+```
+
+BibTeX:
+```bibtex
+@misc{Yalamanchili2015,
+abstract = {ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming simple. ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable. A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.},
+address = {Atlanta},
+author = {Yalamanchili, Pavan and Arshad, Umar and Mohammed, Zakiuddin and Garigipati, Pradeep and Entschev, Peter and Kloppenborg, Brian and Malcolm, James and Melonakos, John},
+publisher = {AccelerEyes},
+title = {{ArrayFire - A high performance software library for parallel computing with an easy-to-use API}},
+url = {https://github.com/arrayfire/arrayfire},
+year = {2015}
+}
+```
diff --git a/README.md b/README.md
index f43b9fd098..5b4a0548fa 100644
--- a/README.md
+++ b/README.md
@@ -1,139 +1,128 @@
 <a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="300"></a>
 
-ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its **array** based function set makes parallel programming simple.
+ArrayFire is a high performance software library for parallel computing with an
+easy-to-use API. Its **array** based function set makes parallel programming
+simple.
 
-ArrayFire's multiple backends (**CUDA**, **OpenCL** and native **CPU**) make it platform independent and highly portable.
+ArrayFire's multiple backends (**CUDA**, **OpenCL** and native **CPU**) make it
+platform independent and highly portable. ArrayFire provides visualization
+capabilities using our OpenGL-based,
+[high performance visualization library](https://github.com/arrayfire/forge).
 
-A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.
+A few lines of code in ArrayFire can replace dozens of lines of parallel
+computing code, saving you valuable time and lowering development costs.
 
-### Build ArrayFire from source
-To build ArrayFire from source, please follow the instructions on our [wiki](https://github.com/arrayfire/arrayfire/wiki).
-
-### Download ArrayFire Installers
-ArrayFire binary installers can be downloaded at the [ArrayFire Downloads](http://go.arrayfire.com/l/37882/2015-03-31/mmhqy) page.
-
-### Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-
-* Google Groups: https://groups.google.com/forum/#!forum/arrayfire-users
-* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
-* ArrayFire Blogs: http://arrayfire.com/blog/
-* Email: <mailto:technical@arrayfire.com>
-
-### Build Status
-|         | Linux x86 | Linux armv7l | Linux aarch64 | Windows | OSX |
-|:-------:|:---------:|:------------:|:-------------:|:-------:|:---:|
+|         | Linux x86_64 | Linux armv7l | Linux aarch64 | Windows | OSX |
+|:-------:|:------------:|:------------:|:-------------:|:-------:|:---:|
 | Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
 | Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
 
-Test coverage: [![Coverage Status](https://coveralls.io/repos/arrayfire/arrayfire/badge.svg?branch=HEAD)](https://coveralls.io/r/arrayfire/arrayfire?branch=HEAD)
+### Installation
 
-### Example
+You can install the ArrayFire library from one of the following ways:
 
-``` C++
+#### Official installers
 
-#include <arrayfire.h>
-#include <cstdio>
+Execute one of our [official binary installers](https://arrayfire.com/download)
+for Linux, OSX, and Windows platforms.
 
-using namespace af;
+#### Build from source
 
-int main(int argc, char *argv[])
-{
-    try {
+Build from source by following instructions on our
+[wiki](https://github.com/arrayfire/arrayfire/wiki).
 
-        // Select a device and display arrayfire info
-        int device = argc > 1 ? atoi(argv[1]) : 0;
-        af::setDevice(device);
-        af::info();
+### Examples
 
-        printf("Create a 5-by-3 matrix of random floats on the GPU\n");
-        array A = randu(5,3, f32);
-        af_print(A);
+The following examples are simplified versions of
+[`helloworld.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/helloworld/helloworld.cpp)
+and
+[`conway_pretty.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/graphics/conway_pretty.cpp),
+respectively. For more code examples, visit the
+[`examples/`](https://github.com/arrayfire/arrayfire/tree/devel/examples)
+directory.
 
-        printf("Element-wise arithmetic\n");
-        array B = sin(A) + 1.5;
-        af_print(B);
+#### Hello, world!
 
-        printf("Negate the first three elements of second column\n");
-        B(seq(0, 2), 1) = B(seq(0, 2), 1) * -1;
-        af_print(B);
+```cpp
+array A = randu(5, 3, f32); // Create 5x3 matrix of random floats on the GPU
+array B = sin(A) + 1.5;     // Element-wise arithmetic
+array C = fft(B);           // Fourier transform the result
 
-        printf("Fourier transform the result\n");
-        array C = fft(B);
-        af_print(C);
+float d[] = { 1, 2, 3, 4, 5, 6 };
+array D(2, 3, d, afHost);   // Create 2x3 matrix from host data
+D.col(0) = D.col(end);      // Copy last column onto first
 
-        printf("Grab last row\n");
-        array c = C.row(end);
-        af_print(c);
+array vals, inds;
+sort(vals, inds, A);        // Sort A and print sorted array and corresponding indices
+af_print(vals);
+af_print(inds);
+```
 
-        printf("Create 2-by-3 matrix from host data\n");
-        float d[] = { 1, 2, 3, 4, 5, 6 };
-        array D(2, 3, d, af::afHost);
-        af_print(D);
+#### Conway's Game of Life
 
-        printf("Copy last column onto first\n");
-        D.col(0) = D.col(end);
-        af_print(D);
+Visit the
+[Wikipedia page](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) for a
+description of Conway's Game of Life.
 
-        // Sort A
-        printf("Sort A and print sorted array and corresponding indices\n");
-        array vals, inds;
-        sort(vals, inds, A);
-        af_print(vals);
-        af_print(inds);
+```cpp
+static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
+static const array kernel(3, 3, h_kernel, afHost);
 
-    } catch (af::exception& e) {
-        fprintf(stderr, "%s\n", e.what());
-        throw;
-    }
+array state = (randu(128, 128, f32) > 0.5).as(f32); // Generate starting state
+Window myWindow(256, 256);
+while(!myWindow.close()) {
+  array nHood = convolve(state, kernel); // Obtain neighbors
+  array C0 = (nHood == 2);               // Generate conditions for life
+  array C1 = (nHood == 3);
+  state = state * C0 + C1;               // Update state
+  myWindow.image(state);                 // Display
 }
 
 ```
 
+<p align="center">
+<img src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif" alt="Conway's Game of Life" height="256" width="256">
+</p>
+
 ### Documentation
 
-You can find our complete documentation over [here](http://www.arrayfire.com/docs/index.htm).
+You can find our complete documentation [here](http://www.arrayfire.com/docs/index.htm).
 
 Quick links:
 
-- [Download Binaries](http://www.arrayfire.com/download/)
-- [List of functions](http://www.arrayfire.com/docs/group__arrayfire__func.htm)
-- [Tutorials](http://www.arrayfire.com/docs/gettingstarted.htm)
-- [Examples](http://www.arrayfire.com/docs/examples.htm)
+* [List of functions](http://www.arrayfire.org/docs/group__arrayfire__func.htm)
+* [Tutorials](http://www.arrayfire.org/docs/usergroup0.htm)
+* [Examples](http://www.arrayfire.org/docs/examples.htm)
+* [Blog](http://arrayfire.com/blog/)
 
-### Contribute
+### Language wrappers
 
-Contributions of any kind are welcome! Please refer to
-[this document](https://github.com/arrayfire/arrayfire/blob/master/CONTRIBUTING.md)
- to learn more about how you can get involved with ArrayFire.
+We currently support the following language wrappers for ArrayFire:
 
-## Citations and Acknowledgements
+* [`arrayfire-python`](https://github.com/arrayfire/arrayfire-python)
+* [`arrayfire-rust`](https://github.com/arrayfire/arrayfire-rust)
 
-If you redistribute ArrayFire, please follow the terms established in
-[the license](LICENSE).
-If you wish to cite ArrayFire in an academic publication, please use the
-following reference:
+Wrappers for other languages are a work in progress:
 
-Formatted:
-```
-Yalamanchili, P., Arshad, U., Mohammed, Z., Garigipati, P., Entschev, P.,
-Kloppenborg, B., Malcolm, J. and Melonakos, J. (2015).
-ArrayFire - A high performance software library for parallel computing with an
-easy-to-use API. Atlanta: AccelerEyes. Retrieved from https://github.com/arrayfire/arrayfire
-```
+[`arrayfire-dotnet`](https://github.com/arrayfire/arrayfire-dotnet), [`arrayfire-fortran`](https://github.com/arrayfire/arrayfire-fortran), [`arrayfire-go`](https://github.com/arrayfire/arrayfire-go), [`arrayfire-java`](https://github.com/arrayfire/arrayfire-java), [`arrayfire-lua`](https://github.com/arrayfire/arrayfire-lua), [`arrayfire-nodejs`](https://github.com/arrayfire/arrayfire-js), [`arrayfire-r`](https://github.com/arrayfire/arrayfire-r)
 
-BibTeX:
-```bibtex
-@misc{Yalamanchili2015,
-abstract = {ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming simple. ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable. A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.},
-address = {Atlanta},
-author = {Yalamanchili, Pavan and Arshad, Umar and Mohammed, Zakiuddin and Garigipati, Pradeep and Entschev, Peter and Kloppenborg, Brian and Malcolm, James and Melonakos, John},
-publisher = {AccelerEyes},
-title = {{ArrayFire - A high performance software library for parallel computing with an easy-to-use API}},
-url = {https://github.com/arrayfire/arrayfire},
-year = {2015}
-}
-```
+### Contributing
+
+Contributions of any kind are welcome! Please refer to
+[CONTRIBUTING.md](https://github.com/arrayfire/arrayfire/blob/master/CONTRIBUTING.md)
+to learn more about how you can get involved with ArrayFire.
+
+### Citations and Acknowledgements
+
+If you redistribute ArrayFire, please follow the terms established in
+[the license](LICENSE). If you wish to cite ArrayFire in an academic
+publication, please use the following [citation document](.github/CITATION.md).
 
 ArrayFire development is funded by ArrayFire LLC and several third parties,
-please see the list of [acknowledgements](https://github.com/arrayfire/arrayfire/blob/master/ACKNOWLEDGEMENTS.md) for further details.
+please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for further
+details.
 
+### Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+* [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
+* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
diff --git a/assets b/assets
index f16f8bf74f..8aaab831f7 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit f16f8bf74fe4a255db05884cfff8f5cb0e6e8e09
+Subproject commit 8aaab831f79d7b0894b7e2eb7cdffc3df7510d9d

From 32965efa669d654772b6bc5c749dfef162b17fbe Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 15 Mar 2016 11:20:57 -0400
Subject: [PATCH 0423/2677] BUGFIX unresolved external for array_proxy::scalar
 etc

---
 src/api/cpp/array.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 4766e7561d..f1d401f000 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1022,7 +1022,7 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
 
 // array_proxy instanciations
 #define TEMPLATE_MEM_FUNC(TYPE, RETURN_TYPE, FUNC)      \
-    template <>                                         \
+    template <> AFAPI                                   \
     RETURN_TYPE array::array_proxy::FUNC() const        \
     {                                                   \
         array out = *this;                              \

From 3755f55bc055dcd59bdcce48f608fe158060897c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 15 Mar 2016 15:53:43 -0400
Subject: [PATCH 0424/2677] Update clFFT build tag

---
 CMakeModules/build_clFFT.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 2ab9ccc1ea..d9441bb271 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG af3.3.0
+    GIT_TAG af3.3.1
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 8b234b4f1c27952e2d2eac694c82a7681297692b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 15 Mar 2016 15:53:52 -0400
Subject: [PATCH 0425/2677] Update clBLAS build tag

---
 CMakeModules/build_clBLAS.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 2289c26393..ac8949ac7e 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG af3.3.0
+    GIT_TAG af3.3.1
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 27c8bfbffdefd808b6d9867e1a91297f6fc0737d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Mar 2016 14:08:44 -0400
Subject: [PATCH 0426/2677] Disable clfftTeardown call for Windows

---
 src/backend/opencl/fft.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index 777788727c..b3cdfb5517 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -47,11 +47,17 @@ class clFFTPlanner
         }
 
         ~clFFTPlanner() {
+            //TODO: FIXME:
+            // clfftTeardown() cause a "Pure Virtual Function Called" crash on
+            // Window only when Intel devices are called. This causes tests to
+            // fail.
+            #ifndef OS_WIN
             static bool flag = true;
             if(flag) {
                 CLFFT_CHECK(clfftTeardown());
                 flag = false;
             }
+            #endif
         }
 
     private:

From c72b25c5415cc1d2bf4d4dcbdbe1082d477c08e2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 16 Mar 2016 17:34:27 -0400
Subject: [PATCH 0427/2677] Updated Release Notes

---
 docs/pages/release_notes.md | 39 +++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 738d2b0a4f..7aa387847c 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,45 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.3.1
+==============
+
+Bug Fixes
+--------------
+
+* Fixes to \ref af::array::device()
+    * CPU Backend: [evaluate arrays](https://github.com/arrayfire/arrayfire/issues/1316)
+      before returning pointer with asynchronous calls in CPU backend.
+    * OpenCL Backend: [fix segfaults](https://github.com/arrayfire/arrayfire/issues/1324)
+      when requested for device pointers on empty arrays.
+* Fixed \ref af::array::operator%() from using [rem to mod](https://github.com/arrayfire/arrayfire/issues/1318).
+* Fixed [array destruction](https://github.com/arrayfire/arrayfire/issues/1321)
+  when backends are switched in Unified API.
+* Fixed [indexing](https://github.com/arrayfire/arrayfire/issues/1331) after
+  \ref af::moddims() is called.
+* Fixes FFT calls for CUDA and OpenCL backends when used on
+  [multiple devices](https://github.com/arrayfire/arrayfire/issues/1332).
+* Fixed [unresolved external](https://github.com/arrayfire/arrayfire/commit/32965ef)
+  for some functions from \ref af::array::array_proxy class.
+
+Build
+------
+* CMake compiles files in alphabetical order.
+* CMake fixes for BLAS and LAPACK on some Linux distributions.
+
+Improvements
+------------
+* Fixed [OpenCL FFT performance](https://github.com/arrayfire/arrayfire/issues/1323) regression.
+* \ref af::array::device() on OpenCL backend [returns](https://github.com/arrayfire/arrayfire/issues/1311)
+  `cl_mem` instead of `(void*)cl::Buffer*`.
+* In Unified backend, [load versioned libraries](https://github.com/arrayfire/arrayfire/issues/1312)
+  at runtime.
+
+Documentation
+------
+* Reorganized, cleaner README file.
+* Replaced non-free lena image in assets with free-to-distribute lena image.
+
 v3.3.0
 ==============
 

From 300970a4f7d516182f4d979da5da5feef91c2c5c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Mar 2016 14:21:05 -0400
Subject: [PATCH 0428/2677] BUGFIX: Handle errors being returned by the C
 binary functions

---
 src/api/c/binary.cpp   |  5 ++---
 src/api/cpp/binary.cpp | 12 ++++++------
 2 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 2997c13692..95a133557f 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -36,13 +36,12 @@ template<af_op_t op>
 static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        const af_dtype otype = implicit(lhs, rhs);
-
         ArrayInfo linfo = getInfo(lhs);
         ArrayInfo rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
         case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
@@ -70,13 +69,13 @@ template<af_op_t op>
 static af_err af_arith_real(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        const af_dtype otype = implicit(lhs, rhs);
 
         ArrayInfo linfo = getInfo(lhs);
         ArrayInfo rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
         case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
diff --git a/src/api/cpp/binary.cpp b/src/api/cpp/binary.cpp
index 2a9161615b..966397e8f8 100644
--- a/src/api/cpp/binary.cpp
+++ b/src/api/cpp/binary.cpp
@@ -16,12 +16,12 @@
 namespace af
 {
 
-#define INSTANTIATE(cppfunc, cfunc)                     \
-    array cppfunc(const array &lhs, const array &rhs)   \
-    {                                                   \
-        af_array out = 0;                               \
-        cfunc(&out, lhs.get(), rhs.get(), gforGet());   \
-        return array(out);                              \
+#define INSTANTIATE(cppfunc, cfunc)                             \
+    array cppfunc(const array &lhs, const array &rhs)           \
+    {                                                           \
+        af_array out = 0;                                       \
+        AF_THROW(cfunc(&out, lhs.get(), rhs.get(), gforGet())); \
+        return array(out);                                      \
     }
 
     INSTANTIATE(min , af_minof)

From 0b4c0727fd4eafbc2de04568b7e2ec17d8720958 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Mar 2016 14:29:44 -0400
Subject: [PATCH 0429/2677] Handle errors for CUDA and OpenCL specific
 functions

---
 src/backend/cuda/platform.cpp   | 12 +++++++---
 src/backend/opencl/platform.cpp | 40 ++++++++++++++++++++++++---------
 2 files changed, 38 insertions(+), 14 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 10cfdc886c..23735389e5 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -492,18 +492,24 @@ bool synchronize_calls() {
 
 af_err afcu_get_stream(cudaStream_t* stream, int id)
 {
-    *stream = cuda::getStream(id);
+    try{
+        *stream = cuda::getStream(id);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcu_get_native_id(int* nativeid, int id)
 {
-    *nativeid = cuda::getDeviceNativeId(id);
+    try {
+        *nativeid = cuda::getDeviceNativeId(id);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcu_set_native_id(int nativeid)
 {
-    cuda::setDevice(cuda::getDeviceIdFromNativeId(nativeid));
+    try {
+        cuda::setDevice(cuda::getDeviceIdFromNativeId(nativeid));
+    } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index dc8ab4ea65..70d3a09e9e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -815,57 +815,75 @@ using namespace opencl;
 
 af_err afcl_get_device_type(afcl_device_type *res)
 {
-    *res = (afcl_device_type)getActiveDeviceType();
+    try {
+        *res = (afcl_device_type)getActiveDeviceType();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_get_platform(afcl_platform *res)
 {
-    *res = (afcl_platform)getActivePlatform();
+    try {
+        *res = (afcl_platform)getActivePlatform();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_get_context(cl_context *ctx, const bool retain)
 {
-    *ctx = getContext()();
-    if (retain) clRetainContext(*ctx);
+    try {
+        *ctx = getContext()();
+        if (retain) clRetainContext(*ctx);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 
 af_err afcl_get_queue(cl_command_queue *queue, const bool retain)
 {
-    *queue = getQueue()();
-    if (retain) clRetainCommandQueue(*queue);
+    try {
+        *queue = getQueue()();
+        if (retain) clRetainCommandQueue(*queue);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_get_device_id(cl_device_id *id)
 {
-    *id = getDevice()();
+    try {
+        *id = getDevice()();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_set_device_id(cl_device_id id)
 {
-    setDevice(getDeviceIdFromNativeId(id));
+    try {
+        setDevice(getDeviceIdFromNativeId(id));
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
-    addDeviceContext(dev, ctx, que);
+    try {
+        addDeviceContext(dev, ctx, que);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_set_device_context(cl_device_id dev, cl_context ctx)
 {
-    setDeviceContext(dev, ctx);
+    try {
+        setDeviceContext(dev, ctx);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx)
 {
-    removeDeviceContext(dev, ctx);
+    try {
+        removeDeviceContext(dev, ctx);
+    } CATCHALL;
     return AF_SUCCESS;
 }

From 8fb4709856c80e458f610e924fb37e304b6de3c5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Mar 2016 14:38:51 -0400
Subject: [PATCH 0430/2677] BUGFIX: Do not take control of external OpenCL
 device and contexts

---
 include/af/opencl.h             | 16 ++++------------
 src/backend/opencl/platform.cpp | 10 ++++++++++
 2 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 8cb6e8ecff..34206325eb 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -100,9 +100,7 @@ AFAPI af_err afcl_set_device_id(cl_device_id id);
                   parameter is NULL, then we create a command queue for the user using the OpenCL
                   context they provided us.
 
-   \note The cl_* objects are passed onto c++ objects (cl::Device, cl::Context & cl::CommandQueue)
-   that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
-   be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 AFAPI af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que);
 #endif
@@ -127,9 +125,7 @@ AFAPI af_err afcl_set_device_context(cl_device_id dev, cl_context ctx);
    \param[in] dev is the OpenCL device id that has to be popped
    \param[in] ctx is the cl_context object to be removed from ArrayFire pool
 
-   \note Any reference counts incremented for cl_* objects by ArrayFire internally are decremented
-   by this func call and you won't be able to call `afcl_set_device_context` on these objects after
-   this function has been called.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 AFAPI af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx);
 #endif
@@ -245,9 +241,7 @@ namespace afcl
                   parameter is NULL, then we create a command queue for the user using the OpenCL
                   context they provided us.
 
-   \note The cl_* objects are passed onto c++ objects (cl::Device, cl::Context & cl::CommandQueue)
-   that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
-   be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 static inline void addDevice(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
@@ -280,9 +274,7 @@ static inline void setDevice(cl_device_id dev, cl_context ctx)
    \param[in] dev is the OpenCL device id that has to be popped
    \param[in] ctx is the cl_context object to be removed from ArrayFire pool
 
-   \note Any reference counts incremented for cl_* objects by ArrayFire internally are decremented
-   by this func call and you won't be able to call `afcl_set_device_context` on these objects after
-   this function has been called.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 static inline void deleteDevice(cl_device_id dev, cl_context ctx)
 {
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 70d3a09e9e..d5d70af8fa 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -701,6 +701,11 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
 void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
     try {
+
+        clRetainDevice(dev);
+        clRetainContext(ctx);
+        clRetainCommandQueue(que);
+
         DeviceManager& devMngr   = DeviceManager::getInstance();
         cl::Device* tDevice      = new cl::Device(dev);
         cl::Context* tContext    = new cl::Context(ctx);
@@ -758,6 +763,11 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
         } else if (deleteIdx == -1) {
             AF_ERROR("No matching device found", AF_ERR_ARG);
         } else {
+
+            clReleaseDevice((*devMngr.mDevices[deleteIdx])());
+            clReleaseContext((*devMngr.mContexts[deleteIdx])());
+            clReleaseCommandQueue((*devMngr.mQueues[deleteIdx])());
+
             // FIXME: this case can potentially cause issues due to the
             // modification of the device pool stl containers.
 

From 8e733d76a9416cf6a25bd91a33ac2d7b3a17d257 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 22 Mar 2016 18:33:50 -0400
Subject: [PATCH 0431/2677] Incrementing the version to 3.3.2

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 5ec89a6fb5..6ef9912f5f 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -3,7 +3,7 @@
 #
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "3")
-SET(AF_VERSION_PATCH "1")
+SET(AF_VERSION_PATCH "2")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From fe037d93286f11152c9dd4aae515f57b5cca08d1 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 24 Mar 2016 14:22:36 -0400
Subject: [PATCH 0432/2677] More detailed message about CUDA computes in CMake

---
 src/backend/cuda/CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ab29899772..df6e4e6491 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -31,6 +31,9 @@ ENDIF()
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
 IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
     MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50")
+    MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon
+    separated list of CUDA Compute versions to enable the specified computes")
+    MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
     LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50")
 ENDIF()
 

From 7129359b785fddc2e9d5f0c38f9636d13b5a358b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 24 Mar 2016 14:24:46 -0400
Subject: [PATCH 0433/2677] Disable JPEG_GREYSCALE flag in imageio if not
 defined in freeimage header file

---
 src/api/c/imageio.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 5e3f7a59cb..cef40ee99f 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -149,7 +149,9 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 
         int flags = 0;
         if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+#ifdef JPEG_GREYSCALE
         if(fif == FIF_JPEG && !isColor) flags = flags | JPEG_GREYSCALE;
+#endif
 
         // check that the plugin has reading capabilities ...
         FIBITMAP* pBitmap = NULL;

From 3e462b4c9eed4cbed518f324789a122157d385a7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 28 Mar 2016 14:27:06 -0400
Subject: [PATCH 0434/2677] If CUDA_CUDA_LIBRARY is not found, check for stub
 and throw error

---
 examples/CMakeLists.txt         | 21 +++++++++++++++++++++
 src/backend/cuda/CMakeLists.txt | 21 +++++++++++++++++++--
 test/CMakeLists.txt             | 21 +++++++++++++++++++++
 3 files changed, 61 insertions(+), 2 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index ca7853832f..9418bf056b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -101,6 +101,7 @@ ENDIF()
 
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
+        # Find NVVM
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
           NAMES "nvvm"
           PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
@@ -108,6 +109,26 @@ IF (${CUDA_FOUND})
           DOC "CUDA NVVM Library"
           )
         MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+
+        # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
+        IF(NOT CUDA_CUDA_LIBRARY)
+            MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
+            MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
+            FIND_FILE(CUDA_CUDA_LIBRARY_STUB
+                      NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
+                      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+                      PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
+                      DOC "CUDA Library STUB"
+                     )
+            IF(CUDA_CUDA_LIBRARY_STUB)
+                MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
+                MESSAGE("Run the following commands (Linux) to set it up:")
+                MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
+                MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
+            ENDIF()
+            MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
+        ENDIF(NOT CUDA_CUDA_LIBRARY)
+
         OPTION(BUILD_CUDA "Build ArrayFire Examples for CUDA backend" ON)
         BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index df6e4e6491..b887a98d67 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -31,8 +31,7 @@ ENDIF()
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
 IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
     MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50")
-    MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon
-    separated list of CUDA Compute versions to enable the specified computes")
+    MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon separated list of CUDA Compute versions to enable the specified computes")
     MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
     LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50")
 ENDIF()
@@ -332,6 +331,24 @@ macro(MY_CUDA_ADD_LIBRARY cuda_target)
 
 endmacro()
 
+IF(NOT CUDA_CUDA_LIBRARY)
+    MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
+    MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
+    FIND_FILE(CUDA_CUDA_LIBRARY_STUB
+              NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
+              PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+              PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
+              DOC "CUDA Library STUB"
+             )
+    IF(CUDA_CUDA_LIBRARY_STUB)
+        MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
+        MESSAGE("Run the following commands (Linux) to set it up:")
+        MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
+        MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
+    ENDIF()
+    MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
+ENDIF(NOT CUDA_CUDA_LIBRARY)
+
 MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cuda_headers}
                 ${cuda_sources}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5db23714d3..8254881983 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -189,6 +189,7 @@ ENDIF()
 # CUDA Backend
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
+        # Find NVVM
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
           NAMES "nvvm"
           PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
@@ -196,6 +197,26 @@ IF (${CUDA_FOUND})
           DOC "CUDA NVVM Library"
           )
         MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+
+        # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
+        IF(NOT CUDA_CUDA_LIBRARY)
+            MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
+            MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
+            FIND_FILE(CUDA_CUDA_LIBRARY_STUB
+                      NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
+                      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+                      PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
+                      DOC "CUDA Library STUB"
+                     )
+            IF(CUDA_CUDA_LIBRARY_STUB)
+                MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
+                MESSAGE("Run the following commands (Linux) to set it up:")
+                MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
+                MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
+            ENDIF()
+            MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
+        ENDIF(NOT CUDA_CUDA_LIBRARY)
+
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
             OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)

From 8ce7eb6297404ab8312711c8dbb93fe42f736d59 Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Mon, 28 Mar 2016 20:23:56 +0100
Subject: [PATCH 0435/2677] Prevent inclusion of <sys/sysctl.h> on gnu hurd.

---
 src/backend/host_memory.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/host_memory.cpp b/src/backend/host_memory.cpp
index 9b4f1e5f54..b81d4fcf5c 100644
--- a/src/backend/host_memory.cpp
+++ b/src/backend/host_memory.cpp
@@ -16,7 +16,7 @@
 #include <sys/types.h>
 #include <sys/param.h>
 
-#if defined(BSD)
+#if defined(BSD) && !defined(__gnu_hurd__)
 #include <sys/sysctl.h>
 #endif
 

From 986b6c306dd77e977d14c0c19905436cf01be640 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 29 Mar 2016 15:37:27 -0400
Subject: [PATCH 0436/2677] BUILD: fix to disable using CPUID on demand

The current macro tests don't correctly predict if CPUID is available
---
 include/af/defines.h           |  4 ----
 src/backend/cpu/CMakeLists.txt |  8 ++++++++
 src/backend/cpu/platform.cpp   | 11 +++++++++--
 3 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 77508f2870..d3ba5fdfa7 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -50,10 +50,6 @@
     typedef long long   dim_t;
 #endif
 
-#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
-#define USE_CPUID
-#endif
-
 #include <stdlib.h>
 
 typedef long long intl;
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index f7857ec6d6..3718c3d284 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -4,6 +4,14 @@ ADD_DEFINITIONS(-DAF_CPU)
 FIND_PACKAGE(CBLAS REQUIRED)
 
 OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
+OPTION(USE_CPUID "Build CPU backend with CPUID support" ON)
+MARK_AS_ADVANCED(USE_CPUID)
+
+if (USE_CPUID)
+  ADD_DEFINITIONS(-DUSE_CPUID=1)
+ELSE(USE_CPUID)
+  ADD_DEFINITIONS(-DUSE_CPUID=0)
+ENDIF(USE_CPUID)
 
 IF (NOT ${BUILD_CPU_ASYNC})
     ADD_DEFINITIONS(-DAF_DISABLE_CPU_ASYNC)
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 9474c792f3..3b31226b38 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -22,6 +22,13 @@
 #include <host_memory.hpp>
 #include <cctype>
 
+
+#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
+#define CPUID_CAPABLE USE_CPUID
+#else
+#define CPUID_CAPABLE 0
+#endif
+
 #ifdef _WIN32
 #include <limits.h>
 #include <intrin.h>
@@ -32,7 +39,7 @@ typedef unsigned __int32  uint32_t;
 
 using namespace std;
 
-#ifdef USE_CPUID
+#if CPUID_CAPABLE
 
 #define MAX_INTEL_TOP_LVL 4
 
@@ -82,7 +89,7 @@ class CPUInfo {
         bool   mIsHTT;
 };
 
-#ifndef USE_CPUID
+#if !CPUID_CAPABLE
 
 CPUInfo::CPUInfo()
     : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)

From 98e023d7a10e301f793c04246c80bcc1c1bcfd1b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Mar 2016 15:22:00 -0400
Subject: [PATCH 0437/2677] Updated boost.compute to latest develop commit

---
 CMakeModules/build_boost_compute.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
index 03c20435a8..eca4e32771 100644
--- a/CMakeModules/build_boost_compute.cmake
+++ b/CMakeModules/build_boost_compute.cmake
@@ -1,9 +1,9 @@
 # If using a commit, remove the v prefix to VER in URL.
 # If using a tag, don't use v in VER
 # This is because of how github handles it's release tar balls
-SET(VER 0.5)
-SET(URL https://github.com/boostorg/compute/archive/v${VER}.tar.gz)
-SET(MD5 69a52598ac539d3b7f6005a3dd2b6f58)
+SET(VER 523d8e974559977fab006190e9d40eb2e4f87bd0)
+SET(URL https://github.com/boostorg/compute/archive/${VER}.tar.gz)
+SET(MD5 bbce9e2730e449db5c8f88eae160ea12)
 
 SET(thirdPartyDir "${CMAKE_BINARY_DIR}/third_party")
 SET(srcDir "${thirdPartyDir}/compute-${VER}")

From 8026cdb82984d610037667a25f4460f191115ef7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Mar 2016 15:22:39 -0400
Subject: [PATCH 0438/2677] Use stable sort for thrust and boost compute

---
 src/backend/cuda/kernel/sort_by_key.hpp   | 4 ++--
 src/backend/cuda/kernel/sort_index.hpp    | 4 ++--
 src/backend/opencl/kernel/sort_by_key.hpp | 8 ++++----
 src/backend/opencl/kernel/sort_index.hpp  | 6 +++---
 4 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 42a3256a1c..e06d9202b6 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -44,9 +44,9 @@ namespace cuda
                         int ovalOffset = ovalWZ + y * oval.strides[1];
 
                         if(isAscending) {
-                            THRUST_SELECT(thrust::sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset);
+                            THRUST_SELECT(thrust::stable_sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset);
                         } else {
-                            THRUST_SELECT(thrust::sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset, thrust::greater<Tk>());
+                            THRUST_SELECT(thrust::stable_sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset, thrust::greater<Tk>());
                         }
                     }
                 }
diff --git a/src/backend/cuda/kernel/sort_index.hpp b/src/backend/cuda/kernel/sort_index.hpp
index 9d29914f23..8762f28a4a 100644
--- a/src/backend/cuda/kernel/sort_index.hpp
+++ b/src/backend/cuda/kernel/sort_index.hpp
@@ -42,11 +42,11 @@ namespace cuda
 
                         THRUST_SELECT(thrust::sequence, idx_ptr + idxOffset, idx_ptr + idxOffset + idx.dims[0]);
                         if(isAscending) {
-                            THRUST_SELECT(thrust::sort_by_key,
+                            THRUST_SELECT(thrust::stable_sort_by_key,
                                     val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
                                     idx_ptr + idxOffset);
                         } else {
-                            THRUST_SELECT(thrust::sort_by_key,
+                            THRUST_SELECT(thrust::stable_sort_by_key,
                                         val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
                                         idx_ptr + idxOffset, thrust::greater<T>());
                         }
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 0cb9cb042d..555e39cdf1 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -20,7 +20,7 @@
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
 #include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
+#include <boost/compute/algorithm/stable_sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -72,10 +72,10 @@ namespace opencl
                             compute::buffer_iterator< type_t<Tk> > end = compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset + okey.info.dims[0]);
                             compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(oval_buf, ovalOffset);
                             if(isAscending) {
-                                compute::sort_by_key(start, end, vals, c_queue);
+                                compute::stable_sort_by_key(start, end, vals, c_queue);
                             } else {
-                                compute::sort_by_key(start, end, vals,
-                                                     compute::greater< type_t<Tk> >(), c_queue);
+                                compute::stable_sort_by_key(start, end, vals,
+                                                            compute::greater< type_t<Tk> >(), c_queue);
                             }
                         }
                     }
diff --git a/src/backend/opencl/kernel/sort_index.hpp b/src/backend/opencl/kernel/sort_index.hpp
index 3a8ab1401e..4926dc34c3 100644
--- a/src/backend/opencl/kernel/sort_index.hpp
+++ b/src/backend/opencl/kernel/sort_index.hpp
@@ -21,7 +21,7 @@
 
 #include <boost/compute/core.hpp>
 #include <boost/compute/algorithm/iota.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
+#include <boost/compute/algorithm/stable_sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -73,12 +73,12 @@ namespace opencl
                             compute::iota(idx_begin, idx_begin + val.info.dims[0], 0, c_queue);
 
                             if(isAscending) {
-                                compute::sort_by_key(
+                                compute::stable_sort_by_key(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         idx_begin, compute::less< type_t<T> >(), c_queue);
                             } else {
-                                compute::sort_by_key(
+                                compute::stable_sort_by_key(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         idx_begin, compute::greater< type_t<T> >(), c_queue);

From 09129b00a072c5ada07cb3321b16cf582f0f0d32 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 30 Mar 2016 16:49:21 -0400
Subject: [PATCH 0439/2677] Allow complex types as value type in sort_by_key

This allows complex sorting based on a key, which can be the abs, real, imag
etc of the value array. So the user can choose which metric they want to use.
---
 src/api/c/sort.cpp                        | 16 +++++++++------
 src/backend/cpu/sort_by_key.cpp           |  2 ++
 src/backend/cuda/sort_by_key_impl.hpp     | 24 ++++++++++++-----------
 src/backend/opencl/kernel/sort_by_key.hpp | 19 +++++++++++++++++-
 src/backend/opencl/sort_by_key/impl.hpp   | 22 +++++++++++----------
 5 files changed, 55 insertions(+), 28 deletions(-)

diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 1de63c5052..66ffce9eb1 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -150,6 +150,8 @@ void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey, cons
     switch(vtype) {
     case f32: sort_by_key<Tk, float  >(okey, oval, ikey, ival, dim, isAscending);  break;
     case f64: sort_by_key<Tk, double >(okey, oval, ikey, ival, dim, isAscending);  break;
+    case c32: sort_by_key<Tk, cfloat >(okey, oval, ikey, ival, dim, isAscending);  break;
+    case c64: sort_by_key<Tk, cdouble>(okey, oval, ikey, ival, dim, isAscending);  break;
     case s32: sort_by_key<Tk, int    >(okey, oval, ikey, ival, dim, isAscending);  break;
     case u32: sort_by_key<Tk, uint   >(okey, oval, ikey, ival, dim, isAscending);  break;
     case s16: sort_by_key<Tk, short  >(okey, oval, ikey, ival, dim, isAscending);  break;
@@ -169,20 +171,22 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                       const unsigned dim, const bool isAscending)
 {
     try {
-        ArrayInfo info = getInfo(keys);
-        af_dtype type = info.getType();
+        ArrayInfo kinfo = getInfo(keys);
+        af_dtype ktype = kinfo.getType();
 
         ArrayInfo vinfo = getInfo(values);
 
-        DIM_ASSERT(3, info.elements() > 0);
-        DIM_ASSERT(4, info.dims() == vinfo.dims());
+        DIM_ASSERT(3, kinfo.elements() > 0);
+        DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
         // Only Dim 0 supported
         ARG_ASSERT(5, dim == 0);
 
+        TYPE_ASSERT(kinfo.isReal());
+
         af_array oKey;
         af_array oVal;
 
-        switch(type) {
+        switch(ktype) {
             case f32: sort_by_key_tmplt<float  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case f64: sort_by_key_tmplt<double >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case s32: sort_by_key_tmplt<int    >(&oKey, &oVal, keys, values, dim, isAscending);  break;
@@ -193,7 +197,7 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
             case u64: sort_by_key_tmplt<uintl  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case u8:  sort_by_key_tmplt<uchar  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case b8:  sort_by_key_tmplt<char   >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            default:  TYPE_ERROR(1, type);
+            default:  TYPE_ERROR(1, ktype);
         }
         std::swap(*out_keys , oKey);
         std::swap(*out_values , oVal);
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 5a99257033..46ced4b9ef 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -46,6 +46,8 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
 #define INSTANTIATE1(Tk)       \
     INSTANTIATE(Tk, float)     \
     INSTANTIATE(Tk, double)    \
+    INSTANTIATE(Tk, cfloat)    \
+    INSTANTIATE(Tk, cdouble)   \
     INSTANTIATE(Tk, int)       \
     INSTANTIATE(Tk, uint)      \
     INSTANTIATE(Tk, char)      \
diff --git a/src/backend/cuda/sort_by_key_impl.hpp b/src/backend/cuda/sort_by_key_impl.hpp
index d01ace404e..217b17dc8a 100644
--- a/src/backend/cuda/sort_by_key_impl.hpp
+++ b/src/backend/cuda/sort_by_key_impl.hpp
@@ -35,15 +35,17 @@ namespace cuda
     sort_by_key<Tk, Tv, dr>(Array<Tk> &okey, Array<Tv> &oval,           \
                             const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim); \
 
-#define INSTANTIATE1(Tk,    dr) \
-    INSTANTIATE(Tk, float,  dr) \
-    INSTANTIATE(Tk, double, dr) \
-    INSTANTIATE(Tk, int,    dr) \
-    INSTANTIATE(Tk, uint,   dr) \
-    INSTANTIATE(Tk, short,  dr) \
-    INSTANTIATE(Tk, ushort, dr) \
-    INSTANTIATE(Tk, char,   dr) \
-    INSTANTIATE(Tk, uchar,  dr) \
-    INSTANTIATE(Tk, intl,   dr) \
-    INSTANTIATE(Tk, uintl,  dr)
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
 }
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 555e39cdf1..e3306e6e68 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -40,9 +40,26 @@ namespace opencl
     {
         using std::conditional;
         using std::is_same;
+
+        // If type is cdouble, return std::complex<double>, else return T
+        template<typename T>
+        using ztype_t = typename conditional<is_same<T, cdouble>::value,
+                                             std::complex<double>, T
+                                            >::type;
+
+        // If type is cfloat, return std::complex<float>, else return ztype_t
+        template<typename T>
+        using ctype_t = typename conditional<is_same<T, cfloat>::value,
+                                             std::complex<float>, ztype_t<T>
+                                            >::type;
+
+        // If type is intl, return cl_long, else return ctype_t
         template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
+        using ltype_t = typename conditional<is_same<T, intl>::value,
+                                             cl_long, ctype_t<T>
+                                            >::type;
 
+        // If type is uintl, return cl_ulong, else return ltype_t
         template<typename T>
         using type_t = typename conditional<is_same<T, uintl>::value,
                                             cl_ulong, ltype_t<T>
diff --git a/src/backend/opencl/sort_by_key/impl.hpp b/src/backend/opencl/sort_by_key/impl.hpp
index 49d184113f..68c5ce70ae 100644
--- a/src/backend/opencl/sort_by_key/impl.hpp
+++ b/src/backend/opencl/sort_by_key/impl.hpp
@@ -43,15 +43,17 @@ namespace opencl
 
 
 #define INSTANTIATE1(Tk, isAscending)           \
-    INSTANTIATE(Tk, float , isAscending)        \
-    INSTANTIATE(Tk, double, isAscending)        \
-    INSTANTIATE(Tk, int   , isAscending)        \
-    INSTANTIATE(Tk, uint  , isAscending)        \
-    INSTANTIATE(Tk, char  , isAscending)        \
-    INSTANTIATE(Tk, uchar , isAscending)        \
-    INSTANTIATE(Tk, short , isAscending)        \
-    INSTANTIATE(Tk, ushort, isAscending)        \
-    INSTANTIATE(Tk, intl  , isAscending)        \
-    INSTANTIATE(Tk, uintl , isAscending)        \
+    INSTANTIATE(Tk, float  , isAscending)       \
+    INSTANTIATE(Tk, double , isAscending)       \
+    INSTANTIATE(Tk, cfloat , isAscending)       \
+    INSTANTIATE(Tk, cdouble, isAscending)       \
+    INSTANTIATE(Tk, int    , isAscending)       \
+    INSTANTIATE(Tk, uint   , isAscending)       \
+    INSTANTIATE(Tk, char   , isAscending)       \
+    INSTANTIATE(Tk, uchar  , isAscending)       \
+    INSTANTIATE(Tk, short  , isAscending)       \
+    INSTANTIATE(Tk, ushort , isAscending)       \
+    INSTANTIATE(Tk, intl   , isAscending)       \
+    INSTANTIATE(Tk, uintl  , isAscending)       \
 
 }

From cda059fcb22a22446210e0c9d120353821102382 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Mar 2016 17:28:02 -0400
Subject: [PATCH 0440/2677] Improvements to sort

* Sort now allows all dimensions
* Sort if much faster by using batched mode. This takes up more memory though.
* Enabled large tests for sort
* Added tests for sorting on dim1 and dim2
---
 src/api/c/sort.cpp                        |  2 -
 src/backend/cpu/kernel/sort.hpp           |  2 +-
 src/backend/cpu/sort.cpp                  | 48 +++++++++++-
 src/backend/cuda/kernel/sort.hpp          | 77 ++++++++++++++++++--
 src/backend/cuda/kernel/sort_by_key.hpp   | 14 ++--
 src/backend/cuda/sort.cu                  |  9 ++-
 src/backend/opencl/kernel/sort.hpp        | 89 ++++++++++++++++++++---
 src/backend/opencl/kernel/sort_by_key.hpp | 28 +------
 src/backend/opencl/kernel/sort_helper.hpp | 46 ++++++++++++
 src/backend/opencl/kernel/sort_index.hpp  | 11 +--
 src/backend/opencl/sort.cpp               |  8 +-
 test/sort.cpp                             | 77 ++++++++++++++++++--
 12 files changed, 336 insertions(+), 75 deletions(-)
 create mode 100644 src/backend/opencl/kernel/sort_helper.hpp

diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 66ffce9eb1..e3f3ae35da 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -42,8 +42,6 @@ af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool
         af_dtype type = info.getType();
 
         DIM_ASSERT(1, info.elements() > 0);
-        // Only Dim 0 supported
-        ARG_ASSERT(2, dim == 0);
 
         af_array val;
 
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index 292c6383dc..e0ae62c932 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -23,7 +23,7 @@ namespace kernel
 
 // Based off of http://stackoverflow.com/a/12399290
 template<typename T, bool isAscending>
-void sort0(Array<T> val)
+void sort0Iterative(Array<T> val)
 {
     // initialize original index locations
     T *val_ptr = val.get();
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index bc6396b258..c3c5286fbf 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -15,11 +15,54 @@
 #include <functional>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <range.hpp>
+#include <iota.hpp>
+#include <handle.hpp>
+#include <sort_by_key.hpp>
 #include <kernel/sort.hpp>
 
 namespace cpu
 {
 
+template<typename T, bool isAscending, int dim>
+void sortBatched(Array<T>& val)
+{
+    af::dim4 inDims = val.dims();
+
+    // Sort dimension
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim] = inDims[dim];
+    seqDims[dim] = 1;
+
+    Array<uint> key = iota<uint>(seqDims, tileDims);
+
+    Array<uint> *resKey = initArray<uint>();
+    Array<T   > *resVal = initArray<T>();
+
+    val.modDims(inDims.elements());
+    key.modDims(inDims.elements());
+
+    sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+
+    // Needs to be ascending (true) in order to maintain the indices properly
+    sort_by_key<uint, T, true>(key, val, *resKey, *resVal, 0);
+    val.eval();
+
+    val.modDims(inDims);
+}
+
+template<typename T, bool isAscending>
+void sort0(Array<T>& val)
+{
+    int higherDims = val.elements() / val.dims()[0];
+    // TODO Make a better heurisitic
+    if(higherDims > 10)
+        sortBatched<T, isAscending, 0>(val);
+    else
+        getQueue().enqueue(kernel::sort0Iterative<T, isAscending>, val);
+}
+
 template<typename T, bool isAscending>
 Array<T> sort(const Array<T> &in, const unsigned dim)
 {
@@ -27,7 +70,10 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
 
     Array<T> out = copyArray<T>(in);
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0<T, isAscending>, out); break;
+        case 0: sort0<T, isAscending>(out); break;
+        case 1: sortBatched<T, isAscending, 1>(out); break;
+        case 2: sortBatched<T, isAscending, 2>(out); break;
+        case 3: sortBatched<T, isAscending, 3>(out); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
     return out;
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index b23e308633..21d9122d64 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -10,6 +10,8 @@
 #include <math.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
+#include <kernel/iota.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <thrust/device_ptr.h>
@@ -19,15 +21,11 @@ namespace cuda
 {
     namespace kernel
     {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename T, bool isAscending>
-        void sort0(Param<T> val)
+        void sort0Iterative(Param<T> val)
         {
             thrust::device_ptr<T> val_ptr = thrust::device_pointer_cast(val.ptr);
 
@@ -49,5 +47,74 @@ namespace cuda
             }
             POST_LAUNCH_CHECK();
         }
+
+        template<typename T, bool isAscending, int dim>
+        void sortBatched(Param<T> pVal)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pVal.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            dim4 keydims = inDims;
+            uint* key = memAlloc<uint>(keydims.elements());
+            Param<uint> pKey;
+            pKey.ptr = key;
+            pKey.strides[0] = 1;
+            pKey.dims[0] = keydims[0];
+            for(int i = 1; i < 4; i++) {
+                pKey.dims[i] = keydims[i];
+                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
+            }
+            kernel::iota<uint>(pKey, seqDims, tileDims);
+
+            // Flat
+            //val.modDims(inDims.elements());
+            //key.modDims(inDims.elements());
+            pKey.dims[0] = inDims.elements();
+            pKey.strides[0] = 1;
+            pVal.dims[0] = inDims.elements();
+            pVal.strides[0] = 1;
+            for(int i = 1; i < 4; i++) {
+                pKey.dims[i] = 1;
+                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
+                pVal.dims[i] = 1;
+                pVal.strides[i] = pVal.strides[i - 1] * pVal.dims[i - 1];
+            }
+
+            // Sort indices
+            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+            kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+
+            // Needs to be ascending (true) in order to maintain the indices properly
+            kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            // Not really necessary
+            // CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+            memFree(key);
+        }
+
+        template<typename T, bool isAscending>
+        void sort0(Param<T> val)
+        {
+            int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 10)
+                sortBatched<T, isAscending, 0>(val);
+            else
+                kernel::sort0Iterative<T, isAscending>(val);
+        }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index e06d9202b6..bfaa79a311 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -19,10 +19,6 @@ namespace cuda
 {
     namespace kernel
     {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
@@ -44,9 +40,15 @@ namespace cuda
                         int ovalOffset = ovalWZ + y * oval.strides[1];
 
                         if(isAscending) {
-                            THRUST_SELECT(thrust::stable_sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset);
+                            THRUST_SELECT(thrust::stable_sort_by_key,
+                                          okey_ptr + okeyOffset,
+                                          okey_ptr + okeyOffset + okey.dims[0],
+                                          oval_ptr + ovalOffset);
                         } else {
-                            THRUST_SELECT(thrust::stable_sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset, thrust::greater<Tk>());
+                            THRUST_SELECT(thrust::stable_sort_by_key,
+                                          okey_ptr + okeyOffset,
+                                          okey_ptr + okeyOffset + okey.dims[0],
+                                          oval_ptr + ovalOffset, thrust::greater<Tk>());
                         }
                     }
                 }
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 6d14c0309f..4ae3b759fb 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -22,10 +22,11 @@ namespace cuda
     {
         Array<T> out = copyArray<T>(in);
         switch(dim) {
-
-        case 0: kernel::sort0<T, isAscending>(out);
-            break;
-        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+            case 0: kernel::sort0<T, isAscending>(out); break;
+            case 1: kernel::sortBatched<T, isAscending, 1>(out); break;
+            case 2: kernel::sortBatched<T, isAscending, 2>(out); break;
+            case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         return out;
     }
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 013d8c53a9..7b7799ca89 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -15,6 +15,9 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/sort_helper.hpp>
+#include <kernel/iota.hpp>
+#include <kernel/sort_by_key.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -38,18 +41,8 @@ namespace opencl
 {
     namespace kernel
     {
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
-
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-
         template<typename T, bool isAscending>
-        void sort0(Param val)
+        void sort0Iterative(Param val)
         {
             try {
                 compute::command_queue c_queue(getQueue()());
@@ -85,6 +78,80 @@ namespace opencl
                 throw;
             }
         }
+
+        template<typename T, bool isAscending, int dim>
+        void sortBatched(Param pVal)
+        {
+            try{
+                af::dim4 inDims;
+                for(int i = 0; i < 4; i++)
+                    inDims[i] = pVal.info.dims[i];
+
+                // Sort dimension
+                // tileDims * seqDims = inDims
+                af::dim4 tileDims(1);
+                af::dim4 seqDims = inDims;
+                tileDims[dim] = inDims[dim];
+                seqDims[dim] = 1;
+
+                // Create/call iota
+                // Array<uint> key = iota<uint>(seqDims, tileDims);
+                dim4 keydims = inDims;
+                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(uint));
+                Param pKey;
+                pKey.data = key;
+                pKey.info.offset = 0;
+                pKey.info.dims[0] = keydims[0];
+                pKey.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pKey.info.dims[i] = keydims[i];
+                    pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
+                }
+                kernel::iota<uint>(pKey, seqDims, tileDims);
+
+                // Flat
+                //val.modDims(inDims.elements());
+                //key.modDims(inDims.elements());
+                pKey.info.dims[0] = inDims.elements();
+                pKey.info.strides[0] = 1;
+                pVal.info.dims[0] = inDims.elements();
+                pVal.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pKey.info.dims[i] = 1;
+                    pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
+                    pVal.info.dims[i] = 1;
+                    pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
+                }
+
+                // Sort indices
+                // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+                kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+
+                // Needs to be ascending (true) in order to maintain the indices properly
+                kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+
+                // No need of doing moddims here because the original Array<T>
+                // dimensions have not been changed
+                //val.modDims(inDims);
+
+                CL_DEBUG_FINISH(getQueue());
+                bufferFree(key);
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename T, bool isAscending>
+        void sort0(Param val)
+        {
+            int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 10)
+                sortBatched<T, isAscending, 0>(val);
+            else
+                kernel::sort0Iterative<T, isAscending>(val);
+        }
     }
 }
 
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index e3306e6e68..c3807f7a31 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -15,6 +15,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/sort_helper.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -38,33 +39,6 @@ namespace opencl
 {
     namespace kernel
     {
-        using std::conditional;
-        using std::is_same;
-
-        // If type is cdouble, return std::complex<double>, else return T
-        template<typename T>
-        using ztype_t = typename conditional<is_same<T, cdouble>::value,
-                                             std::complex<double>, T
-                                            >::type;
-
-        // If type is cfloat, return std::complex<float>, else return ztype_t
-        template<typename T>
-        using ctype_t = typename conditional<is_same<T, cfloat>::value,
-                                             std::complex<float>, ztype_t<T>
-                                            >::type;
-
-        // If type is intl, return cl_long, else return ctype_t
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value,
-                                             cl_long, ctype_t<T>
-                                            >::type;
-
-        // If type is uintl, return cl_ulong, else return ltype_t
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-
         template<typename Tk, typename Tv, bool isAscending>
         void sort0_by_key(Param okey, Param oval)
         {
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
new file mode 100644
index 0000000000..07ab0eeb69
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -0,0 +1,46 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <program.hpp>
+#include <traits.hpp>
+
+namespace opencl
+{
+    namespace kernel
+    {
+        using std::conditional;
+        using std::is_same;
+
+        // If type is cdouble, return std::complex<double>, else return T
+        template<typename T>
+        using ztype_t = typename conditional<is_same<T, cdouble>::value,
+                                             std::complex<double>, T
+                                            >::type;
+
+        // If type is cfloat, return std::complex<float>, else return ztype_t
+        template<typename T>
+        using ctype_t = typename conditional<is_same<T, cfloat>::value,
+                                             std::complex<float>, ztype_t<T>
+                                            >::type;
+
+        // If type is intl, return cl_long, else return ctype_t
+        template<typename T>
+        using ltype_t = typename conditional<is_same<T, intl>::value,
+                                             cl_long, ctype_t<T>
+                                            >::type;
+
+        // If type is uintl, return cl_ulong, else return ltype_t
+        template<typename T>
+        using type_t = typename conditional<is_same<T, uintl>::value,
+                                            cl_ulong, ltype_t<T>
+                                           >::type;
+
+    }
+}
diff --git a/src/backend/opencl/kernel/sort_index.hpp b/src/backend/opencl/kernel/sort_index.hpp
index 4926dc34c3..0fa4847fc1 100644
--- a/src/backend/opencl/kernel/sort_index.hpp
+++ b/src/backend/opencl/kernel/sort_index.hpp
@@ -15,6 +15,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/sort_helper.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -39,16 +40,6 @@ namespace opencl
 {
     namespace kernel
     {
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
-
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-
         template<typename T, bool isAscending>
         void sort0_index(Param val, Param idx)
         {
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 762d815095..0bf2dc04cd 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -23,9 +23,11 @@ namespace opencl
         try {
             Array<T> out = copyArray<T>(in);
             switch(dim) {
-            case 0: kernel::sort0<T, isAscending>(out);
-                break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+                case 0: kernel::sort0<T, isAscending>(out); break;
+                case 1: kernel::sortBatched<T, isAscending, 1>(out); break;
+                case 2: kernel::sortBatched<T, isAscending, 2>(out); break;
+                case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
+                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
             return out;
         } catch (std::exception &ex) {
diff --git a/test/sort.cpp b/test/sort.cpp
index 7ec6f5565e..116b136abe 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -107,15 +107,15 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
     SORT_INIT(SortMedTrue,    sort_med1,  true,  0);
     SORT_INIT(SortMedFalse,   sort_med1,  false, 2);
     // Takes too much time in current implementation. Enable when everything is parallel
-    //SORT_INIT(SortMed5True,   sort_med,   true,  0);
-    //SORT_INIT(SortMed5False,  sort_med,   false, 2);
-    //SORT_INIT(SortLargeTrue,  sort_large, true,  0);
-    //SORT_INIT(SortLargeFalse, sort_large, false, 2);
+    SORT_INIT(SortMed5True,   sort_med,   true,  0);
+    SORT_INIT(SortMed5False,  sort_med,   false, 2);
+    SORT_INIT(SortLargeTrue,  sort_large, true,  0);
+    SORT_INIT(SortLargeFalse, sort_large, false, 2);
 
 
 ////////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(Sort, CPP)
+TEST(Sort, CPPDim0)
 {
     if (noDoubleTests<float>()) return;
 
@@ -147,3 +147,70 @@ TEST(Sort, CPP)
     delete[] sxData;
 }
 
+TEST(Sort, CPPDim1)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = true;
+    const unsigned resultIdx0 = 0;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input(idims, &(in[0].front()));
+
+    af::array input_ = reorder(input, 1, 0, 2, 3);
+
+    af::array output = af::sort(input_, 1, dir);
+
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    output.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+}
+
+TEST(Sort, CPPDim2)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = false;
+    const unsigned resultIdx0 = 2;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input(idims, &(in[0].front()));
+
+    af::array input_ = reorder(input, 1, 2, 0, 3);
+
+    af::array output = af::sort(input_, 2, dir);
+
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    output.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+}

From e6c9e934a33f5e97a1ff97061cde490d8d5b898e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Mar 2016 17:29:44 -0400
Subject: [PATCH 0441/2677] Revert "Updated boost.compute to latest develop
 commit"

This reverts commit 98e023d7a10e301f793c04246c80bcc1c1bcfd1b.
---
 CMakeModules/build_boost_compute.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
index eca4e32771..03c20435a8 100644
--- a/CMakeModules/build_boost_compute.cmake
+++ b/CMakeModules/build_boost_compute.cmake
@@ -1,9 +1,9 @@
 # If using a commit, remove the v prefix to VER in URL.
 # If using a tag, don't use v in VER
 # This is because of how github handles it's release tar balls
-SET(VER 523d8e974559977fab006190e9d40eb2e4f87bd0)
-SET(URL https://github.com/boostorg/compute/archive/${VER}.tar.gz)
-SET(MD5 bbce9e2730e449db5c8f88eae160ea12)
+SET(VER 0.5)
+SET(URL https://github.com/boostorg/compute/archive/v${VER}.tar.gz)
+SET(MD5 69a52598ac539d3b7f6005a3dd2b6f58)
 
 SET(thirdPartyDir "${CMAKE_BINARY_DIR}/third_party")
 SET(srcDir "${thirdPartyDir}/compute-${VER}")

From aaa0a056744d46c4d9027ff9ef25ceed3fe8fe3a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 31 Mar 2016 17:40:13 -0400
Subject: [PATCH 0442/2677] Boost.Compute sort/sort_by_key are stable in v0.5.
 So revert to that

---
 src/backend/opencl/kernel/sort.hpp        | 6 +++---
 src/backend/opencl/kernel/sort_by_key.hpp | 8 ++++----
 src/backend/opencl/kernel/sort_index.hpp  | 6 +++---
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 7b7799ca89..63f1658208 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -23,7 +23,7 @@
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
 #include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/stable_sort.hpp>
+#include <boost/compute/algorithm/sort.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -58,12 +58,12 @@ namespace opencl
                             int valOffset = valWZ + y * val.info.strides[1];
 
                             if(isAscending) {
-                                compute::stable_sort(
+                                compute::sort(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         compute::less< type_t<T> >(), c_queue);
                             } else {
-                                compute::stable_sort(
+                                compute::sort(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         compute::greater< type_t<T> >(), c_queue);
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index c3807f7a31..513ddbfb6d 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -21,7 +21,7 @@
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
 #include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/stable_sort_by_key.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -60,12 +60,12 @@ namespace opencl
                             int ovalOffset = ovalWZ + y * oval.info.strides[1];
 
                             compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset);
-                            compute::buffer_iterator< type_t<Tk> > end = compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset + okey.info.dims[0]);
+                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset + okey.info.dims[0]);
                             compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(oval_buf, ovalOffset);
                             if(isAscending) {
-                                compute::stable_sort_by_key(start, end, vals, c_queue);
+                                compute::sort_by_key(start, end, vals, c_queue);
                             } else {
-                                compute::stable_sort_by_key(start, end, vals,
+                                compute::sort_by_key(start, end, vals,
                                                             compute::greater< type_t<Tk> >(), c_queue);
                             }
                         }
diff --git a/src/backend/opencl/kernel/sort_index.hpp b/src/backend/opencl/kernel/sort_index.hpp
index 0fa4847fc1..aae0a94ea6 100644
--- a/src/backend/opencl/kernel/sort_index.hpp
+++ b/src/backend/opencl/kernel/sort_index.hpp
@@ -22,7 +22,7 @@
 
 #include <boost/compute/core.hpp>
 #include <boost/compute/algorithm/iota.hpp>
-#include <boost/compute/algorithm/stable_sort_by_key.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -64,12 +64,12 @@ namespace opencl
                             compute::iota(idx_begin, idx_begin + val.info.dims[0], 0, c_queue);
 
                             if(isAscending) {
-                                compute::stable_sort_by_key(
+                                compute::sort_by_key(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         idx_begin, compute::less< type_t<T> >(), c_queue);
                             } else {
-                                compute::stable_sort_by_key(
+                                compute::sort_by_key(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         idx_begin, compute::greater< type_t<T> >(), c_queue);

From 3422d012df209db8a1c563d5937b12b8b8b90235 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Apr 2016 11:45:09 -0400
Subject: [PATCH 0443/2677] Call modDims when setDataDims is called

---
 src/api/c/moddims.cpp        | 1 -
 src/backend/cpu/Array.hpp    | 1 +
 src/backend/cuda/Array.hpp   | 1 +
 src/backend/opencl/Array.hpp | 1 +
 4 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index b8f1fafa6c..1d326c0846 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -31,7 +31,6 @@ Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
         Out = copyArray<T>(in);
     }
 
-    Out.modDims(newDims);
     Out.setDataDims(newDims);
 
     return Out;
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2809a2b80d..cf970d18c7 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -181,6 +181,7 @@ namespace cpu
 
         void setDataDims(const dim4 &new_dims)
         {
+            modDims(new_dims);
             data_dims = new_dims;
         }
 
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index c2292087aa..1f9512fb8d 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -174,6 +174,7 @@ namespace cuda
 
         void setDataDims(const dim4 &new_dims)
         {
+            modDims(new_dims);
             data_dims = new_dims;
         }
 
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index ada3b41dc3..f83d5c0120 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -211,6 +211,7 @@ namespace opencl
 
         void setDataDims(const dim4 &new_dims)
         {
+            modDims(new_dims);
             data_dims = new_dims;
         }
 

From 3fea8a2649a0a077dadb2de5fba7174c67c8c70b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 1 Apr 2016 11:46:49 -0400
Subject: [PATCH 0444/2677] Fix temp Array T and moddims in CPU batched sort

---
 src/backend/cpu/sort.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index c3c5286fbf..fbf613f962 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -37,19 +37,19 @@ void sortBatched(Array<T>& val)
 
     Array<uint> key = iota<uint>(seqDims, tileDims);
 
-    Array<uint> *resKey = initArray<uint>();
-    Array<T   > *resVal = initArray<T>();
+    Array<uint> resKey = createEmptyArray<uint>(dim4());
+    Array<T   > resVal = createEmptyArray<T>(dim4());
 
-    val.modDims(inDims.elements());
-    key.modDims(inDims.elements());
+    val.setDataDims(inDims.elements());
+    key.setDataDims(inDims.elements());
 
-    sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+    sort_by_key<T, uint, isAscending>(resVal, resKey, val, key, 0);
 
     // Needs to be ascending (true) in order to maintain the indices properly
-    sort_by_key<uint, T, true>(key, val, *resKey, *resVal, 0);
+    sort_by_key<uint, T, true>(key, val, resKey, resVal, 0);
     val.eval();
 
-    val.modDims(inDims);
+    val.setDataDims(inDims);
 }
 
 template<typename T, bool isAscending>

From cb2d5327b8c3dbf3ea1d56b4e38632c0b4388bbf Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 22 Mar 2016 17:36:48 -0400
Subject: [PATCH 0445/2677] initial nbody sim

---
 examples/graphics/gravity_sim.cpp | 86 +++++++++++++++++++++----------
 1 file changed, 58 insertions(+), 28 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 3fc19d8c65..c0405888aa 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -14,47 +14,75 @@
 using namespace af;
 using namespace std;
 
-static const int width = 512, height = 512;
-static const int pixels_per_unit = 20;
+static const int width = 768, height = 768;
+static const float eps = 10.f;
+static const int gravity_constant = 5000;
 
 void simulate(af::array *pos, af::array *vels, af::array *forces, float dt){
-    pos[0] += vels[0] * pixels_per_unit * dt;
-    pos[1] += vels[1] * pixels_per_unit * dt;
-
-    //calculate distance to center
-    af::array diff_x = pos[0] - width/2;
-    af::array diff_y = pos[1] - height/2;
-    af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
-
-    //calculate normalised force vectors
-    forces[0] = -1 * diff_x / dist;
-    forces[1] = -1 * diff_y / dist;
-    //update force scaled to time and magnitude constant
-    forces[0] *= pixels_per_unit * dt;
-    forces[1] *= pixels_per_unit * dt;
+    pos[0] += vels[0] * dt;
+    pos[1] += vels[1] * dt;
+
+    //calculate forces to each particle
+    af::array diff_x = tile(pos[0], 1, pos[0].dims(0))-transpose(tile(pos[0], 1, pos[0].dims(0)));
+    af::array diff_y = tile(pos[1], 1, pos[1].dims(0))-transpose(tile(pos[1], 1, pos[1].dims(0)));
+    af::array dist = af::sqrt( diff_x*diff_x + diff_y*diff_y );
+    //dist = af::max(eps, dist);
+    dist *= dist * dist;
+
+    //calculate force vectors
+    forces[0] = -diff_x / dist;
+    forces[1] = -diff_y / dist;
+    forces[0](af::isNaN(forces[0]))  = 0;
+    forces[1](af::isNaN(forces[1]))  = 0;
+    forces[0] = sum(forces[0], 1);
+    forces[1] = sum(forces[1], 1);
+
+    //update force scaled to time, magnitude constant
+    forces[0] *= (gravity_constant);
+    forces[1] *= (gravity_constant);
+
+    //noise
+    /*
+    forces[0] += 0.1 * af::randn(forces[0].dims(0));
+    forces[0] += 0.1 * af::randn(forces[0].dims(0));
+    */
 
     //dampening
+    /*
     vels[0] *= 1 - (0.005*dt);
     vels[1] *= 1 - (0.005*dt);
+    */
 
     //update velocities from forces
-    vels[0] += forces[0];
-    vels[1] += forces[1];
+    vels[0] += forces[0] * dt;
+    vels[1] += forces[1] * dt;
+
+    //temporary
+    vels[0] = min(100, vels[0]);
+    vels[1] = min(100, vels[1]);
 
 }
 
 void collisions(af::array *pos, af::array *vels){
     //clamp particles inside screen border
-    af::array projected_px = min(width, max(0, pos[0]));
+    af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
+    af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
+    vels[0]= invalid_x * vels[0] ;
+    vels[1]= invalid_y * vels[1] ;
+
+    af::array projected_px = min(width-1, max(0, pos[0]));
     af::array projected_py = min(height - 1, max(0, pos[1]));
+    pos[0] = projected_px;
+    pos[1] = projected_py;
 
+    /*
     //calculate distance to center
     af::array diff_x = projected_px - width/2;
     af::array diff_y = projected_py - height/2;
     af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
 
     //collide with center sphere
-    const int radius = 50;
+    const int radius = 20;
     const float elastic_constant = 0.91f;
     if(sum<int>(dist<radius) > 0) {
         vels[0](dist<radius) = -elastic_constant * vels[0](dist<radius);
@@ -67,14 +95,15 @@ void collisions(af::array *pos, af::array *vels){
         pos[0](dist<radius) = width/2 + diff_x(dist<radius) * radius;
         pos[1](dist<radius) = height/2 +  diff_y(dist<radius) * radius;
     }
+    */
 }
 
 
 int main(int argc, char *argv[])
 {
     try {
-        const static int total_particles = 1000;
-        static const int reset = 500;
+        const static int total_particles = 300;
+        static const int reset = 5000;
 
         af::info();
 
@@ -93,8 +122,8 @@ int main(int argc, char *argv[])
         pos[0] = af::randu(total_particles) * width;
         pos[1] = af::randu(total_particles) * height;
 
-        vels[0] = af::randn(total_particles);
-        vels[1] = af::randn(total_particles);
+        vels[0] = 1 * af::randn(total_particles);
+        vels[1] = 10 * af::randn(total_particles);
 
         forces[0] = af::randn(total_particles);
         forces[1] = af::randn(total_particles);
@@ -108,8 +137,8 @@ int main(int argc, char *argv[])
             timer = af::timer::start();
 
             ids = (pos[0].as(u32) * height) + pos[1].as(u32);
-            image(ids) += 255;
-            image = convolve2(image, draw_kernel);
+            image(ids) += 5.f;
+            image = convolve(image, draw_kernel);
             myWindow.image(image);
             image = af::constant(0, image.dims());
             frame_count++;
@@ -123,12 +152,13 @@ int main(int argc, char *argv[])
                 vels[1] = af::randn(total_particles);
             }
 
-            //check for collisions and adjust positions/velocities accordingly
-            collisions(pos, vels);
 
             //run force simulation and update particles
             simulate(pos, vels, forces, dt);
 
+            //check for collisions and adjust positions/velocities accordingly
+            collisions(pos, vels);
+
         }
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());

From b1b5f878fe64032063f1f646a3e5bb48a0161e92 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 24 Mar 2016 17:26:39 -0400
Subject: [PATCH 0446/2677] adds 3d nbody simulation to gravity_sim

update swe example as gtc demo
---
 examples/graphics/gravity_sim.cpp | 176 +++++++++++++++---------------
 examples/pde/swe.cpp              |  40 +++++--
 2 files changed, 117 insertions(+), 99 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index c0405888aa..3f91c40b2a 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -10,63 +10,76 @@
 #include <arrayfire.h>
 #include <iostream>
 #include <cstdio>
+#include <vector>
 
 using namespace af;
 using namespace std;
 
-static const int width = 768, height = 768;
+static const bool is3D = false; const static int total_particles = 2500;
+static const int reset = 5000;
+static const int width = 768, height = 768, depth = 768;
 static const float eps = 10.f;
-static const int gravity_constant = 5000;
+static const int gravity_constant = 9000;
 
-void simulate(af::array *pos, af::array *vels, af::array *forces, float dt){
-    pos[0] += vels[0] * dt;
-    pos[1] += vels[1] * dt;
+void initial_conditions_rand(vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
+    for(int i=0; i<pos.size(); ++i) {
+        pos[i]    = af::randn(total_particles) * width + width;
+        vels[i]   = 0 * af::randu(total_particles) - 0.5;
+        forces[i] = af::constant(0, total_particles);
+    }
+}
+
+void simulate(vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces, float dt) {
+    for(int i=0; i<pos.size(); ++i) {
+        pos[i] += vels[i] * dt;
+        pos[i].eval();
+    }
 
     //calculate forces to each particle
-    af::array diff_x = tile(pos[0], 1, pos[0].dims(0))-transpose(tile(pos[0], 1, pos[0].dims(0)));
-    af::array diff_y = tile(pos[1], 1, pos[1].dims(0))-transpose(tile(pos[1], 1, pos[1].dims(0)));
-    af::array dist = af::sqrt( diff_x*diff_x + diff_y*diff_y );
-    //dist = af::max(eps, dist);
+    vector<af::array> diff(pos.size());
+    af::array dist = af::constant(0, pos[0].dims(0),pos[0].dims(0));
+
+    for(int i=0; i<pos.size(); ++i) {
+        diff[i] = tile(pos[i], 1, pos[i].dims(0)) - transpose(tile(pos[i], 1, pos[i].dims(0)));
+        dist += (diff[i]*diff[i]);
+    }
+
+    dist = sqrt(dist);
+    dist = af::max(20, dist);
     dist *= dist * dist;
 
-    //calculate force vectors
-    forces[0] = -diff_x / dist;
-    forces[1] = -diff_y / dist;
-    forces[0](af::isNaN(forces[0]))  = 0;
-    forces[1](af::isNaN(forces[1]))  = 0;
-    forces[0] = sum(forces[0], 1);
-    forces[1] = sum(forces[1], 1);
-
-    //update force scaled to time, magnitude constant
-    forces[0] *= (gravity_constant);
-    forces[1] *= (gravity_constant);
-
-    //noise
-    /*
-    forces[0] += 0.1 * af::randn(forces[0].dims(0));
-    forces[0] += 0.1 * af::randn(forces[0].dims(0));
-    */
-
-    //dampening
-    /*
-    vels[0] *= 1 - (0.005*dt);
-    vels[1] *= 1 - (0.005*dt);
-    */
-
-    //update velocities from forces
-    vels[0] += forces[0] * dt;
-    vels[1] += forces[1] * dt;
-
-    //temporary
-    vels[0] = min(100, vels[0]);
-    vels[1] = min(100, vels[1]);
+    for(int i=0; i<pos.size(); ++i) {
+        //calculate force vectors
+        forces[i] = diff[i] / dist;
+        forces[i].eval();
+
+        //af::array idx = af::where(af::isNaN(forces[i]));
+        //if(idx.elements() > 0)
+        //    forces[i](idx) = 0;
+        forces[i] = sum(forces[i]).T();
+
+        //update force scaled to time, magnitude constant
+        forces[i] *= (gravity_constant);
+        forces[i].eval();
 
+        //update velocities from forces
+        vels[i] += forces[i] * dt;
+        vels[i].eval();
+
+        //noise
+        //forces[i] += 0.1 * af::randn(forces[i].dims(0));
+
+        //dampening
+        //vels[i] *= 1 - (0.005*dt);
+    }
 }
 
-void collisions(af::array *pos, af::array *vels){
+void collisions(vector<af::array> &pos, vector<af::array> &vels, bool is3D) {
     //clamp particles inside screen border
-    af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
-    af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
+    //af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
+    //af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
+    af::array invalid_x = (pos[0] < width-1 || pos[0] > 0);
+    af::array invalid_y = (pos[1] < height-1 || pos[1] > 0);
     vels[0]= invalid_x * vels[0] ;
     vels[1]= invalid_y * vels[1] ;
 
@@ -75,58 +88,37 @@ void collisions(af::array *pos, af::array *vels){
     pos[0] = projected_px;
     pos[1] = projected_py;
 
-    /*
-    //calculate distance to center
-    af::array diff_x = projected_px - width/2;
-    af::array diff_y = projected_py - height/2;
-    af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
-
-    //collide with center sphere
-    const int radius = 20;
-    const float elastic_constant = 0.91f;
-    if(sum<int>(dist<radius) > 0) {
-        vels[0](dist<radius) = -elastic_constant * vels[0](dist<radius);
-        vels[1](dist<radius) = -elastic_constant * vels[1](dist<radius);
-
-        //normalize diff vector
-        diff_x /= dist;
-        diff_y /= dist;
-        //place all particle colliding with sphere on surface
-        pos[0](dist<radius) = width/2 + diff_x(dist<radius) * radius;
-        pos[1](dist<radius) = height/2 +  diff_y(dist<radius) * radius;
+    if(is3D){
+        af::array invalid_z = -2 * (pos[2] > depth-1 || pos[2] < 0) + 1;
+        vels[2]= invalid_z * vels[2] ;
+        af::array projected_pz = min(depth - 1, max(0, pos[2]));
+        pos[2] = projected_pz;
     }
-    */
 }
 
 
 int main(int argc, char *argv[])
 {
     try {
-        const static int total_particles = 300;
-        static const int reset = 5000;
 
         af::info();
 
         af::Window myWindow(width, height, "Gravity Simulation using ArrayFire");
+        myWindow.setColorMap(AF_COLORMAP_HEAT);
 
         int frame_count = 0;
 
         // Initialize the kernel array just once
-        const af::array draw_kernel = gaussianKernel(3, 3);
+        const af::array draw_kernel = gaussianKernel(5, 5);
 
-        af::array pos[2];
-        af::array vels[2];
-        af::array forces[2];
-
-        // Generate a random starting state
-        pos[0] = af::randu(total_particles) * width;
-        pos[1] = af::randu(total_particles) * height;
+        const int dims = (is3D)? 3 : 2;
 
-        vels[0] = 1 * af::randn(total_particles);
-        vels[1] = 10 * af::randn(total_particles);
+        vector<af::array> pos(dims);
+        vector<af::array> vels(dims);
+        vector<af::array> forces(dims);
 
-        forces[0] = af::randn(total_particles);
-        forces[1] = af::randn(total_particles);
+        // Generate a random starting state
+        initial_conditions_rand(pos, vels, forces);
 
         af::array image = af::constant(0, width, height);
         af::array ids(total_particles, u32);
@@ -136,28 +128,32 @@ int main(int argc, char *argv[])
             float dt = af::timer::stop(timer);
             timer = af::timer::start();
 
-            ids = (pos[0].as(u32) * height) + pos[1].as(u32);
-            image(ids) += 5.f;
-            image = convolve(image, draw_kernel);
-            myWindow.image(image);
-            image = af::constant(0, image.dims());
+            //if(is3D) {
+                //array Pts = join(1, pos[0], pos[1], pos[2]);
+                //myWindow.scatter3(Pts);
+            //} else {
+                ids = (pos[0].as(u32) * height) + pos[1].as(u32);
+                image(ids) += 15.f;
+                image = convolve(image, draw_kernel);
+                myWindow.image(image);
+                image = af::constant(0, image.dims());
+                /*
+                myWindow.scatter(pos[0], pos[1]);
+                */
+            //}
+
             frame_count++;
 
             // Generate a random starting state
             if(frame_count % reset == 0) {
-                pos[0] = af::randu(total_particles) * width;
-                pos[1] = af::randu(total_particles) * height;
-
-                vels[0] = af::randn(total_particles);
-                vels[1] = af::randn(total_particles);
+                initial_conditions_rand(pos, vels, forces);
             }
 
-
-            //run force simulation and update particles
+            //simulate
             simulate(pos, vels, forces, dt);
 
             //check for collisions and adjust positions/velocities accordingly
-            collisions(pos, vels);
+            collisions(pos, vels, is3D);
 
         }
     } catch (af::exception& e) {
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 0d3b39fda9..3e1aab3d55 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -1,6 +1,7 @@
 #include <math.h>
 #include <stdio.h>
 #include <string.h>
+#include <stdlib.h>
 #include <math.h>
 #include <arrayfire.h>
 #include "../common/progress.h"
@@ -18,10 +19,10 @@ array normalize(array a, float max)
 
 static void swe(bool console)
 {
-    double time_total = 20; // run for N seconds
+    double time_total = 40; // run for N seconds
     // Grid length, number and spacing
-    const unsigned Lx = 512, nx = Lx + 1;
-    const unsigned Ly = 512, ny = Ly + 1;
+    const unsigned Lx = 1600, nx = Lx + 1;
+    const unsigned Ly = 1600, ny = Ly + 1;
     const float dx = Lx / (nx - 1);
     const float dy = Ly / (ny - 1);
 
@@ -29,7 +30,7 @@ static void swe(bool console)
     array um = ZERO, vm = ZERO;
     unsigned io = (unsigned)floor(Lx  / 5.0f),
              jo = (unsigned)floor(Ly / 5.0f),
-             k = 20;
+             k = 15;
     array x = tile(moddims(seq(nx),nx,1), 1,ny);
     array y = tile(moddims(seq(ny),1,ny), nx,1);
 
@@ -41,19 +42,32 @@ static void swe(bool console)
 
     // conv kernels
     float h_diff_kernel[] = {9.81f * (dt / dx), 0, -9.81f * (dt / dx)};
-    float h_lap_kernel[] = {0, 1, 0, 1, -4, 1, 0, 1, 0};
+    float h_lap_kernel[] = {0, 1, 0,
+                            1, -4, 1, 
+                            0, 1, 0};
 
     array h_diff_kernel_arr(3, h_diff_kernel);
     array h_lap_kernel_arr(3, 3, h_lap_kernel);
 
     if(!console) {
-        win = new Window(512, 512,"Shallow Water Equations");
-        win->setColorMap(AF_COLORMAP_MOOD);
+        win = new Window(1536, 768,"Shallow Water Equations");
+        win->setColorMap(AF_COLORMAP_BLUE);
+        win->grid(2, 2);
     }
 
     timer t = timer::start();
     unsigned iter = 0;
-    while (progress(iter, t, time_total)) {
+    unsigned random_interval = 30;
+    //while (progress(iter, t, time_total)) {
+    while (!win->close()) {
+        //raindrops
+        if(iter % 100 == 0 || iter % 130 == 0 || iter % random_interval == 0) {
+            unsigned io = (unsigned)floor(rand() % Lx),
+                     jo = (unsigned)floor(rand() % Ly);
+            random_interval = rand() % 200;
+            eta += 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+        }
+
         // compute
         array up = um + convolve(eta, h_diff_kernel_arr);
         array vp = um + convolve(eta, h_diff_kernel_arr.T());
@@ -62,9 +76,17 @@ static void swe(bool console)
 
         etam = eta;
         eta = etap;
+
+        m_eta = max<float>(etam);
+
         if (!console) {
-            win->image(normalize(eta, m_eta));
+            (*win)(0,0).image(normalize(eta, m_eta));
+            array hist_out = histogram(normalize(eta, m_eta), 15);
+            (*win)(0,1).hist(hist_out, 0, 1);
+            (*win)(1,0).plot(seq(up.dims(1)), vp.col(0), "Pressure at left boundary");
+            (*win)(1,1).plot3(join(1, flat(eta), flat(up), flat(vp)), "Gradients versus Magnitude");
             // viz
+            win->show();
         } else eval(eta, up, vp);
         iter++;
     }

From 03ce51f87c7fd3ff69808ff41cb75e33b64edb13 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 4 Apr 2016 15:40:25 -0400
Subject: [PATCH 0447/2677] Call thrust/compute::sort_by_key instead of
 kernel::sort_by_key wrapper

---
 src/backend/cuda/kernel/sort.hpp   | 24 +++++++++++++++++++++---
 src/backend/opencl/kernel/sort.hpp | 21 ++++++++++++++++++---
 2 files changed, 39 insertions(+), 6 deletions(-)

diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index 21d9122d64..f0095b144d 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -11,7 +11,6 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <kernel/iota.hpp>
-#include <kernel/sort_by_key.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <thrust/device_ptr.h>
@@ -92,10 +91,29 @@ namespace cuda
 
             // Sort indices
             // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-            kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+            //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+            thrust::device_ptr<T>    pVal_ptr = thrust::device_pointer_cast(pVal.ptr);
+            thrust::device_ptr<uint> pKey_ptr = thrust::device_pointer_cast(pKey.ptr);
+            if(isAscending) {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              pVal_ptr,
+                              pVal_ptr + pVal.dims[0],
+                              pKey_ptr);
+            } else {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              pVal_ptr,
+                              pVal_ptr + pVal.dims[0],
+                              pKey_ptr, thrust::greater<T>());
+            }
+            POST_LAUNCH_CHECK();
 
             // Needs to be ascending (true) in order to maintain the indices properly
-            kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          pKey_ptr,
+                          pKey_ptr + pVal.dims[0],
+                          pVal_ptr);
+            POST_LAUNCH_CHECK();
 
             // No need of doing moddims here because the original Array<T>
             // dimensions have not been changed
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 63f1658208..b9a7d39b28 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -17,13 +17,13 @@
 #include <debug_opencl.hpp>
 #include <kernel/sort_helper.hpp>
 #include <kernel/iota.hpp>
-#include <kernel/sort_by_key.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
 #include <boost/compute/core.hpp>
 #include <boost/compute/algorithm/sort.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -125,10 +125,25 @@ namespace opencl
 
                 // Sort indices
                 // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-                kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+                //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+                compute::command_queue c_queue(getQueue()());
+
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+
+                compute::buffer_iterator< type_t<T>    > val0 = compute::make_buffer_iterator< type_t<T>    >(pVal_buf, 0);
+                compute::buffer_iterator< type_t<T>    > valN = compute::make_buffer_iterator< type_t<T>    >(pVal_buf,+ pVal.info.dims[0]);
+                compute::buffer_iterator< type_t<uint> > key0 = compute::make_buffer_iterator< type_t<uint> >(pKey_buf, 0);
+                compute::buffer_iterator< type_t<uint> > keyN = compute::make_buffer_iterator< type_t<uint> >(pKey_buf, pKey.info.dims[0]);
+                if(isAscending) {
+                    compute::sort_by_key(val0, valN, key0, c_queue);
+                } else {
+                    compute::sort_by_key(val0, valN, key0, compute::greater< type_t<T> >(), c_queue);
+                }
 
                 // Needs to be ascending (true) in order to maintain the indices properly
-                kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+                //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+                compute::sort_by_key(key0, keyN, val0, c_queue);
 
                 // No need of doing moddims here because the original Array<T>
                 // dimensions have not been changed

From 97eb252b1e68c9bd57deae7c47e4458173400bdd Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 5 Apr 2016 11:37:19 -0400
Subject: [PATCH 0448/2677] adds 3d rotation, stable galaxy constants,
 mass-based forces and color intensity

---
 examples/graphics/gravity_sim.cpp    |  133 +-
 examples/graphics/gravity_sim_init.h | 4006 ++++++++++++++++++++++++++
 2 files changed, 4111 insertions(+), 28 deletions(-)
 create mode 100644 examples/graphics/gravity_sim_init.h

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 3f91c40b2a..6acccf8813 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -11,25 +11,96 @@
 #include <iostream>
 #include <cstdio>
 #include <vector>
+#include "gravity_sim_init.h"
 
 using namespace af;
 using namespace std;
 
-static const bool is3D = false; const static int total_particles = 2500;
-static const int reset = 5000;
+static const bool is3D = true; const static int total_particles = 4000;
+static const int reset = 3000;
+static const float min_dist = 3;
 static const int width = 768, height = 768, depth = 768;
 static const float eps = 10.f;
-static const int gravity_constant = 9000;
+static const int gravity_constant = 20000;
 
-void initial_conditions_rand(vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
+float mass_range = 0;
+float min_mass   = 0;
+
+void initial_conditions_rand(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
     for(int i=0; i<pos.size(); ++i) {
         pos[i]    = af::randn(total_particles) * width + width;
         vels[i]   = 0 * af::randu(total_particles) - 0.5;
         forces[i] = af::constant(0, total_particles);
     }
+    mass = af::constant(gravity_constant, total_particles);
 }
 
-void simulate(vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces, float dt) {
+void initial_conditions_galaxy(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
+    af::array initial_cond_consts(af::dim4(7, total_particles), hbd);
+    initial_cond_consts = initial_cond_consts.T();
+
+    for(int i=0; i<pos.size(); ++i) {
+        pos[i]    = af::randn(total_particles) * width + width;
+        vels[i]   = 0 * (af::randu(total_particles) - 0.5);
+        forces[i] = af::constant(0, total_particles);
+    }
+
+    mass    =  initial_cond_consts(span, 0);
+    pos[0]  = (initial_cond_consts(span, 1)/32 + 0.6) * width;
+    pos[1]  = (initial_cond_consts(span, 2)/32 + 0.3) * height;
+    pos[2]  = (initial_cond_consts(span, 3)/32 + 0.5) * depth;
+    vels[0] = (initial_cond_consts(span, 4)/32) * width;
+    vels[1] = (initial_cond_consts(span, 5)/32) * height;
+    vels[2] = (initial_cond_consts(span, 6)/32) * depth;
+
+    pos[0](seq(0, pos[0].dims(0)-1, 2)) -=  0.4 * width;
+    pos[1](seq(0, pos[0].dims(0)-1, 2)) +=  0.4 * height;
+    vels[0](seq(0, pos[0].dims(0)-1, 2)) +=  4;
+
+    min_mass = min<float>(mass);
+    mass_range = max<float>(mass) - min<float>(mass);
+}
+
+af::array ids_from_pos(vector<af::array> &pos) {
+    return (pos[0].as(u32) * height) + pos[1].as(u32);
+}
+
+af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz) {
+    af::array x0  = (pos[0] - width/2);
+    af::array y0  = (pos[1] - height/2) * cos(Rx) + (pos[2] - depth/2) * sin(Rx);
+    af::array z0  = (pos[2] - depth/2)  * cos(Rx) - (pos[2] - depth/2) * sin(Rx);
+
+    af::array x1  =  x0*cos(Ry) - z0*sin(Ry);
+    af::array y1  =  y0;
+
+    af::array x2  =  x1*cos(Rz) + y1*sin(Rz);
+    af::array y2  =  y1*cos(Rz) - x1*sin(Rz);
+
+    x2 += width/2;
+    y2 += height/2;
+
+    return (x2.as(u32) * height) + y2.as(u32);
+}
+
+af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz, af::array filter) {
+    af::array x0  = (pos[0](filter) - width/2);
+    af::array y0  = (pos[1](filter) - height/2) * cos(Rx) + (pos[2](filter) - depth/2) * sin(Rx);
+    af::array z0  = (pos[2](filter) - depth/2)  * cos(Rx) - (pos[2](filter) - depth/2) * sin(Rx);
+
+    af::array x1  =  x0*cos(Ry) - z0*sin(Ry);
+    af::array y1  =  y0;
+
+    af::array x2  =  x1*cos(Rz) + y1*sin(Rz);
+    af::array y2  =  y1*cos(Rz) - x1*sin(Rz);
+
+    x2 += width/2;
+    y2 += height/2;
+
+    return (x2.as(u32) * height) + y2.as(u32);
+}
+
+
+void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces, float dt) {
     for(int i=0; i<pos.size(); ++i) {
         pos[i] += vels[i] * dt;
         pos[i].eval();
@@ -45,7 +116,7 @@ void simulate(vector<af::array> &pos, vector<af::array> &vels, vector<af::array>
     }
 
     dist = sqrt(dist);
-    dist = af::max(20, dist);
+    dist = af::max(min_dist, dist);
     dist *= dist * dist;
 
     for(int i=0; i<pos.size(); ++i) {
@@ -56,7 +127,8 @@ void simulate(vector<af::array> &pos, vector<af::array> &vels, vector<af::array>
         //af::array idx = af::where(af::isNaN(forces[i]));
         //if(idx.elements() > 0)
         //    forces[i](idx) = 0;
-        forces[i] = sum(forces[i]).T();
+        //forces[i] = sum(forces[i]).T();
+        forces[i] = matmul(forces[i].T(), mass);
 
         //update force scaled to time, magnitude constant
         forces[i] *= (gravity_constant);
@@ -76,10 +148,10 @@ void simulate(vector<af::array> &pos, vector<af::array> &vels, vector<af::array>
 
 void collisions(vector<af::array> &pos, vector<af::array> &vels, bool is3D) {
     //clamp particles inside screen border
-    //af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
-    //af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
-    af::array invalid_x = (pos[0] < width-1 || pos[0] > 0);
-    af::array invalid_y = (pos[1] < height-1 || pos[1] > 0);
+    af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
+    af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
+    //af::array invalid_x = (pos[0] < width-1 || pos[0] > 0);
+    //af::array invalid_y = (pos[1] < height-1 || pos[1] > 0);
     vels[0]= invalid_x * vels[0] ;
     vels[1]= invalid_y * vels[1] ;
 
@@ -109,16 +181,17 @@ int main(int argc, char *argv[])
         int frame_count = 0;
 
         // Initialize the kernel array just once
-        const af::array draw_kernel = gaussianKernel(5, 5);
+        const af::array draw_kernel = gaussianKernel(7, 7);
 
         const int dims = (is3D)? 3 : 2;
 
         vector<af::array> pos(dims);
         vector<af::array> vels(dims);
         vector<af::array> forces(dims);
+        af::array         mass;
 
         // Generate a random starting state
-        initial_conditions_rand(pos, vels, forces);
+        initial_conditions_galaxy(mass, pos, vels, forces);
 
         af::array image = af::constant(0, width, height);
         af::array ids(total_particles, u32);
@@ -128,29 +201,33 @@ int main(int argc, char *argv[])
             float dt = af::timer::stop(timer);
             timer = af::timer::start();
 
-            //if(is3D) {
-                //array Pts = join(1, pos[0], pos[1], pos[2]);
-                //myWindow.scatter3(Pts);
-            //} else {
-                ids = (pos[0].as(u32) * height) + pos[1].as(u32);
-                image(ids) += 15.f;
-                image = convolve(image, draw_kernel);
-                myWindow.image(image);
-                image = af::constant(0, image.dims());
-                /*
-                myWindow.scatter(pos[0], pos[1]);
-                */
-            //}
+            af::array mid = mass(span) > (min_mass + mass_range/3);
+            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0, mid) :  ids_from_pos(pos);
+            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            image(ids) += 4.f;
+
+            mid = mass(span) > (min_mass + 2*mass_range/3);
+            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0, mid) :  ids_from_pos(pos);
+            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            image(ids) += 4.f;
+
+            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0) :  ids_from_pos(pos);
+            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            image(ids) += 4.f;
+
+            image = convolve(image, draw_kernel);
+            myWindow.image(image);
+            image = af::constant(0, image.dims());
 
             frame_count++;
 
             // Generate a random starting state
             if(frame_count % reset == 0) {
-                initial_conditions_rand(pos, vels, forces);
+                initial_conditions_galaxy(mass, pos, vels, forces);
             }
 
             //simulate
-            simulate(pos, vels, forces, dt);
+            simulate(mass, pos, vels, forces, dt);
 
             //check for collisions and adjust positions/velocities accordingly
             collisions(pos, vels, is3D);
diff --git a/examples/graphics/gravity_sim_init.h b/examples/graphics/gravity_sim_init.h
new file mode 100644
index 0000000000..6596a2615a
--- /dev/null
+++ b/examples/graphics/gravity_sim_init.h
@@ -0,0 +1,4006 @@
+const int HBD_NUM_ELEMENTS = 4000 * 7;
+//halo, bulge, and disk particles
+float hbd[] = {
+ 4.9161855e-03, -1.5334119e+00, -8.3381424e+00,  4.4288845e+00, -2.3778248e-01,  4.2592272e-02, -4.4895774e-01,
+ 4.9161855e-03,  1.9886702e-02,  6.0085773e+00,  3.1188631e-01,  8.1422836e-01, -1.4591325e-02,  7.5382882e-01,
+ 4.9161855e-03,  1.1676190e+00, -4.6193779e-01, -5.0477743e-01, -1.4803666e+00,  5.6056118e-01, -2.9858449e-02,
+ 4.9161855e-03, -1.4250363e+00,  1.0891747e+01,  2.5225203e+00, -6.5798134e-02, -3.5946497e-01,  1.7471495e-01,
+ 4.9161855e-03, -3.7135857e-01,  4.8796633e-01, -3.7898597e-01,  8.5347527e-01,  2.2493289e-01, -2.7678892e-01,
+ 4.9161855e-03,  2.2072470e+00, -2.5046587e+00,  2.6029270e+00,  3.0826443e-01,  5.8606583e-01,  2.0105042e-01,
+ 4.9161855e-03,  1.0779227e+00, -4.0834007e+00, -3.3965745e+00, -4.8430148e-01, -7.1573091e-01,  1.2384786e-01,
+ 4.9161855e-03, -3.8722844e+00, -4.2357988e+00, -1.9723746e+00,  3.5759529e-01,  4.8990592e-01, -4.3040028e-01,
+ 4.9161855e-03, -1.3005282e-01, -2.3483203e-01,  1.3832784e-01,  1.3746375e+00, -1.2947829e+00,  6.1215276e-01,
+ 4.9161855e-03,  3.6822948e-01,  4.2760900e-01,  1.1544695e+00, -2.3177411e-02, -6.9136995e-01, -6.6200425e-03,
+ 4.9161855e-03, -1.2485707e+00,  2.0474775e-01, -2.1652168e-01,  2.7034196e-01,  1.6398503e+00, -7.8224945e-01,
+ 4.9161855e-03, -3.3862705e+00,  1.2049110e+00,  1.0672448e+00, -1.6531572e-01, -2.4370559e-01,  8.7125647e-01,
+ 4.9161855e-03,  3.4262960e+00,  3.9102471e+00,  6.6162848e-01,  7.8005123e-01, -1.0415094e-01,  5.0161743e-01,
+ 4.9161855e-03,  1.5740298e-01,  1.3008093e+00,  7.8130345e+00, -1.6444305e-01,  3.3037327e-03,  1.9713788e-01,
+ 4.9161855e-03,  5.6700945e-01,  1.8889900e-01,  2.7523971e+00, -3.4313673e-01, -6.4287108e-01, -1.8927544e-01,
+ 4.9161855e-03,  1.8354661e+00,  1.3209668e+00,  1.6966065e+00,  5.3318393e-01,  3.4129089e-01, -8.0587679e-01,
+ 4.9161855e-03, -7.8488460e+00,  3.2376931e+00,  2.6638079e+00,  3.4405673e-01, -2.1986680e-01,  1.6776933e-01,
+ 4.9161855e-03,  3.2422847e-01, -1.2311785e+00,  9.0597588e-01,  3.6714745e-01, -1.3913552e-01,  9.0002306e-02,
+ 4.9161855e-03, -1.9477528e-01, -2.3987198e+00, -4.2354431e+00, -2.1188869e-01, -6.4195746e-01,  1.5219630e-01,
+ 4.9161855e-03,  3.2330542e+00,  1.1787817e+00, -1.3654234e+00,  1.9920348e-01, -1.0560199e+00, -4.0022919e-01,
+ 4.9161855e-03, -2.2656450e+00,  2.3343153e+00,  3.0343585e+00,  1.3909769e-01, -5.8018422e-01,  7.7305830e-01,
+ 4.9161855e-03,  1.0106117e+01,  8.4062157e+00, -5.3659506e+00, -3.3819172e-01, -5.7871189e-02, -5.2655820e-02,
+ 4.9161855e-03, -8.4759682e-02, -2.4386784e-01,  2.2389056e-01, -8.3496273e-01,  1.1504352e+00,  3.2196254e-03,
+ 4.9161855e-03, -4.8354459e+00, -1.1709679e+01, -4.4684467e+00, -3.7076837e-01,  2.6136923e-01, -1.4268482e-01,
+ 4.9161855e-03, -1.3268198e+00, -2.3238692e+00,  6.7897618e-01,  3.0518329e-01,  6.8463421e-01, -7.1791840e-01,
+ 4.9161855e-03, -5.2054877e+00,  2.0948052e+00,  1.9656231e+00,  7.4416548e-01,  4.4825464e-01, -3.2727838e-01,
+ 4.9161855e-03, -8.2616639e-01,  1.0700088e+00,  3.5586545e+00,  4.8024514e-01,  1.1944018e-01,  3.0837712e-01,
+ 4.9161855e-03, -2.9101398e+00, -3.6366568e+00,  8.7982547e-01,  3.6643305e-01, -3.8197124e-01, -1.1440479e-01,
+ 4.9161855e-03,  3.5198438e-01,  4.9096385e-01, -6.6494130e-02, -1.0383745e-01,  3.9406076e-01,  7.3723292e-01,
+ 4.9161855e-03, -6.9214082e+00, -5.5405111e+00, -2.3041859e+00,  3.3985880e-01,  1.0167535e-02,  1.0593475e-01,
+ 4.9161855e-03,  1.0908546e+00, -5.3155913e+00, -4.5045247e+00,  1.8077201e-01, -4.4904891e-01,  4.7391072e-01,
+ 4.9161855e-03, -1.0766581e-01,  6.7338924e+00,  6.1174130e+00, -2.3362583e-01,  7.6430768e-02, -2.4832390e-01,
+ 4.9161855e-03, -4.9775305e-01,  1.6378751e+00, -2.6263945e+00, -3.0084690e-01, -5.1551086e-01, -6.6373748e-01,
+ 4.9161855e-03, -3.8946674e+00, -1.4725525e+00,  2.4148097e+00, -1.7075756e-01,  5.3592271e-01,  7.2393781e-01,
+ 4.9161855e-03,  6.8583161e-02, -1.5991354e+00, -3.0150402e-01,  1.5219669e-01, -5.6440836e-01,  1.5284424e+00,
+ 4.9161855e-03, -4.2822695e+00,  4.0367408e+00, -2.2387395e+00,  1.0239060e-01,  3.2810995e-01, -1.4511149e-01,
+ 4.9161855e-03,  5.3348875e-01, -3.6950427e-01,  1.0364149e+00,  7.8612208e-02, -2.7073494e-01,  1.9663854e-01,
+ 4.9161855e-03, -3.3353384e+00,  4.3220544e+00, -1.5343003e+00,  6.7457032e-01, -1.8098858e-01,  7.6241505e-01,
+ 4.9161855e-03, -8.8430309e+00,  6.6101489e+00,  2.2365890e+00, -2.9622875e-03, -5.7892501e-01,  2.3848678e-01,
+ 4.9161855e-03, -2.7121809e+00, -3.7584829e+00,  2.4702384e+00,  3.9350358e-01, -6.7748266e-01, -5.7142133e-01,
+ 4.9161855e-03,  1.7517463e+00, -5.2237463e-01,  1.2052536e+00,  2.6133826e-01, -4.3084338e-01, -2.8758329e-01,
+ 4.9161855e-03, -4.4221100e-01,  2.4987850e-01, -9.0834004e-01, -1.6435069e+00, -3.5537782e-01, -5.6679737e-02,
+ 4.9161855e-03,  9.5630264e+00,  7.2472978e-01, -2.7188256e+00,  4.1388586e-01, -2.7986884e-01,  9.9171564e-02,
+ 4.9161855e-03, -2.5304942e+00, -1.9891304e-01, -1.3565568e+00,  1.6445565e-01,  6.5720814e-01,  8.8133616e-04,
+ 4.9161855e-03, -6.8739529e+00,  6.0871582e+00,  4.0246663e+00, -1.1313155e-01,  2.6078510e-01,  1.1052500e-02,
+ 4.9161855e-03,  1.8411478e-01,  6.3666153e-01, -1.7665352e+00,  7.3893017e-01,  8.2843482e-02,  1.3584135e-01,
+ 4.9161855e-03,  1.2281631e-01, -4.8358020e-01, -4.2862403e-01, -1.4062686e+00,  2.6675841e-01, -5.2812093e-01,
+ 4.9161855e-03, -1.8010849e+00,  2.5018549e+00, -1.1007906e+00, -3.0198583e-01, -2.5083411e-01, -9.4572407e-01,
+ 4.9161855e-03,  2.9228494e-02,  2.8824418e+00, -7.7373713e-01, -8.9457905e-01, -3.9830649e-01, -8.2690775e-01,
+ 4.9161855e-03, -4.8449464e+00, -3.5136631e+00,  2.6319263e+00,  2.3270021e-01,  6.2155128e-01, -6.9675374e-01,
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+ 4.2524221e-04, -1.0044957e-01, -9.4999395e-02, -1.8605889e-01,  1.8979494e-01, -8.5543871e-01,  5.3148580e-01,
+ 4.2524221e-04, -2.4865381e-01,  2.2518732e-01, -1.0148249e-01, -2.2050242e-01,  5.3008753e-01, -3.9897123e-01,
+ 4.2524221e-04,  7.3146023e-02, -1.3554707e-01, -2.5761548e-01,  3.1436664e-01, -8.2433552e-01,  2.7389117e-02,
+ 4.2524221e-04,  5.5880195e-01, -1.7010997e-01,  3.7886339e-01,  3.4537455e-01,  1.6899250e-01, -4.0871644e-01,
+ 4.2524221e-04,  3.3027393e-01,  5.2694689e-02, -3.2332891e-01,  2.3347795e-01,  3.2150295e-01,  2.1555850e-01,
+ 4.2524221e-04,  1.4437835e-02, -1.4030455e-01, -2.8837410e-01,  3.0297443e-01, -5.1224962e-02, -5.0067031e-01,
+ 4.2524221e-04,  2.8251413e-01,  2.2796902e-01, -3.2044646e-01, -2.3228103e-01, -1.6037621e-01, -2.6131482e-03,
+ 4.2524221e-04,  5.2314814e-02, -2.0229014e-02, -6.8570655e-03,  2.0827544e-01, -2.2427905e-02, -3.7649903e-02,
+ 4.2524221e-04, -9.2880584e-02,  9.8891854e-03, -3.9208323e-02, -6.0296351e-01,  6.1879003e-01, -3.7303507e-01,
+ 4.2524221e-04, -1.9322397e-01,  2.0262747e-01,  8.0153726e-02, -2.3856657e-02,  4.0623334e-01,  6.2071621e-01,
+ 4.2524221e-04, -4.4426578e-01,  2.0553674e-01, -2.6441025e-02, -1.6482647e-01, -8.7054305e-02, -8.2128918e-01,
+ 4.2524221e-04, -2.8677690e-01, -1.0196485e-01,  1.3304503e-01, -7.6817560e-01,  1.9562703e-01, -4.6528971e-01,
+ 4.2524221e-04, -2.0077555e-01, -1.5366915e-01,  1.1841840e-01, -1.7148955e-01,  9.5784628e-01,  7.9418994e-02,
+ 4.2524221e-04, -1.2745425e-01,  3.1222694e-02, -1.9043627e-01,  4.9706772e-02, -1.8966989e-01, -1.1206242e-01,
+ 4.2524221e-04, -7.4478179e-02,  1.3656577e-02, -1.2854090e-01,  3.0771527e-01,  7.3823595e-01,  6.9908720e-01,
+ 4.2524221e-04, -1.7966473e-01, -2.9162148e-01, -2.1245839e-02, -2.6599333e-01,  1.9704431e-01,  5.4458129e-01,
+ 4.2524221e-04,  1.1969655e-01, -3.1876512e-02,  1.9230773e-01,  9.9345565e-01, -2.2614142e-01, -7.7471659e-02,
+ 4.2524221e-04,  7.2612032e-02,  7.9093436e-03,  9.1707774e-02,  3.9948497e-02, -7.6741409e-01, -2.7649629e-01,
+ 4.2524221e-04, -3.1801498e-01,  9.1305524e-02,  1.1569420e-01, -1.2343646e-01,  6.5492535e-01, -1.5559088e-01,
+ 4.2524221e-04,  8.8576578e-02, -1.1602592e-01,  3.0858183e-02,  4.6493343e-01,  4.3753752e-01,  1.5579678e-01,
+ 4.2524221e-04, -2.3568103e-01, -3.1387237e-01,  1.7740901e-01, -2.2428825e-01, -7.9772305e-01,  2.2299300e-01,
+ 4.2524221e-04,  1.0266142e-01, -3.9200943e-02, -1.6250725e-01, -2.1084811e-01,  4.7313869e-01,  7.5736183e-01,
+ 4.2524221e-04, -5.2503270e-01, -2.5550249e-01,  2.4210323e-01,  4.2290211e-01, -1.1937749e-03, -2.8803447e-01,
+ 4.2524221e-04,  6.8656705e-02,  2.3230983e-01, -1.0208790e-02, -1.9244626e-01,  8.1877112e-01, -2.5449389e-01,
+ 4.2524221e-04, -5.4129776e-02,  2.9140076e-01, -4.6895444e-01, -2.3883762e-02, -1.9746602e-01, -1.4508346e-02,
+ 4.2524221e-04, -3.0830520e-01, -2.6217067e-01, -2.6785174e-01,  6.7281228e-01,  3.7336886e-01, -1.4304060e-01,
+ 4.2524221e-04,  1.5217099e-01,  2.0078890e-01,  7.7753231e-02, -3.3346283e-01, -1.2821050e-01, -4.3130264e-01,
+ 4.2524221e-04,  3.8476987e-04, -7.6562621e-02, -4.8909627e-02, -1.1036193e-01,  2.4940021e-01,  2.4720046e-01,
+ 4.2524221e-04,  1.9815315e-01,  1.9162391e-01,  6.0125452e-02, -7.7126014e-01,  4.2003978e-02,  6.3951693e-02,
+ 4.2524221e-04,  9.2402853e-02, -1.9484653e-01, -1.4663309e-01,  1.7251915e-01, -1.6592954e-01, -3.1574631e-01,
+ 4.2524221e-04,  1.4493692e-01, -3.1712703e-02, -1.5764284e-01, -1.6178896e-01,  3.3917201e-01, -4.9173659e-01,
+ 4.2524221e-04,  2.1914667e-01, -7.4241884e-02, -9.9493600e-02, -1.7168714e-01,  1.7520438e-01,  1.1748855e+00,
+ 4.2524221e-04, -1.6493322e-01,  2.1094975e-01,  2.6855225e-02,  8.0839500e-02,  6.4471591e-01,  2.5444278e-01,
+ 4.2524221e-04, -1.0818439e-01,  5.0222378e-02,  1.0443858e-01,  7.3543733e-01, -5.2923161e-01,  2.3857592e-02,
+ 4.2524221e-04, -1.3066588e-01,  3.3706114e-01, -6.5367684e-02, -1.9584729e-01, -9.6636809e-02,  5.7062846e-01,
+ 4.2524221e-04,  8.9271449e-02, -1.5417366e-02, -8.2307503e-02, -5.0039625e-01,  2.5350851e-01, -2.4847549e-01,
+ 4.2524221e-04, -2.8799692e-01, -1.0268785e-01, -6.9768213e-02,  1.9839688e-01, -9.6014850e-02,  1.1959620e-02,
+ 4.2524221e-04, -7.6331727e-02,  1.0289106e-01,  2.5628258e-02, -9.5651820e-02, -3.1599486e-01,  3.4648609e-01,
+ 4.2524221e-04, -4.9910601e-02,  8.5599929e-02, -3.1449606e-03, -1.6781870e-01,  1.0333546e+00, -6.6645592e-01,
+ 4.2524221e-04,  8.2493991e-02, -9.5790043e-02,  4.3036491e-02,  1.8140252e-01,  5.4385066e-01,  3.2726720e-02,
+ 4.2524221e-04,  2.2156011e-01,  3.1133004e-02, -1.4379646e-01, -5.9910184e-01,  1.0038698e+00, -3.0557862e-01,
+ 4.2524221e-04,  3.7525645e-01,  7.0815518e-02,  2.8620017e-01,  6.9975668e-01,  1.0616329e-01,  1.8318458e-01,
+ 4.2524221e-04,  9.5496923e-02, -3.8357295e-02,  7.5472467e-02,  1.4580189e-02,  1.3419588e-01, -2.0312097e-02,
+ 4.2524221e-04,  4.9029529e-02,  1.7314212e-01, -4.9041037e-02, -2.6927444e-01, -2.4882385e-01, -2.5494534e-01,
+ 4.2524221e-04, -6.4100541e-02,  2.6978979e-01,  2.4858065e-02, -8.1361562e-01, -3.7216064e-01,  4.3392561e-02,
+ 4.2524221e-04,  6.9799364e-02, -1.3860419e-01,  1.0984455e-01,  4.8301801e-01,  5.5070144e-01, -3.3188796e-01,
+ 4.2524221e-04, -8.2801402e-02, -6.8652697e-02, -1.9647431e-02,  1.8623030e-01, -1.3855183e-01,  3.1506360e-01,
+ 4.2524221e-04,  3.6300448e-01, -8.0298670e-02, -3.1002939e-01, -3.3787906e-01, -3.0862695e-01,  2.7613443e-01,
+ 4.2524221e-04,  3.7739474e-01,  1.1907437e-01, -3.9434172e-02,  5.8045042e-01,  4.5934165e-01,  2.9962903e-01,
+ 4.2524221e-04,  2.9385680e-02,  1.1072745e-01,  5.8579307e-02, -2.8264758e-01, -1.0784884e-01,  1.2321078e+00,
+ 4.2524221e-04,  7.9958871e-02,  1.2411897e-01,  9.8061837e-02,  3.3262360e-01, -8.3796644e-01,  4.0548918e-01,
+ 4.2524221e-04,  7.8290664e-02,  4.5500584e-02,  9.9731199e-02, -4.6239632e-01,  3.0574635e-01, -4.3212789e-01,
+ 4.2524221e-04,  3.6696273e-01,  5.7200775e-03,  5.3992327e-02, -1.6632666e-01, -3.1065517e-03, -1.1606836e-01,
+ 4.2524221e-04,  2.3191632e-01,  3.3108935e-01,  2.0009531e-02,  4.3141481e-01,  7.1523404e-01, -4.0791895e-02,
+ 4.2524221e-04, -2.0644982e-01,  3.2929885e-01, -2.1481182e-01,  3.4483513e-01,  8.7951744e-01,  2.2883956e-01,
+ 4.2524221e-04, -2.4269024e-02,  8.0496661e-02, -2.2875665e-02, -4.7301382e-02, -1.2039685e-01, -4.8519605e-01,
+ 4.2524221e-04, -3.5178763e-01, -1.1468551e-01, -7.2022155e-02,  7.1914357e-01, -1.8774068e-01,  2.9152307e-01,
+ 4.2524221e-04,  1.5231021e-01,  2.1161540e-01, -1.1754553e-01, -7.1294534e-01, -6.2154621e-01, -1.9393834e-01,
+ 4.2524221e-04, -7.8070223e-02,  1.7216440e-01,  1.7939833e-01,  4.8407644e-01, -1.7517121e-01,  4.1451525e-02,
+ 4.2524221e-04,  1.9436933e-02,  4.3368284e-02, -3.5639319e-03,  6.7544144e-01,  5.4782498e-01,  3.4879735e-01,
+ 4.2524221e-04, -1.3366042e-01, -8.3979061e-03, -8.7891303e-02, -9.8265654e-01, -4.2677250e-02, -1.1890029e-01,
+ 4.2524221e-04,  1.2091810e-01, -1.8473221e-01,  3.7591079e-01,  1.7912203e-01,  7.1378611e-03,  5.6433028e-01,
+ 4.2524221e-04, -3.0588778e-02, -8.0224700e-02,  2.0911565e-01,  1.7871276e-01, -4.5090526e-01,  1.7313591e-01,
+ 4.2524221e-04,  2.1592773e-01, -1.0682704e-01, -1.4687291e-01, -2.1309285e-01,  3.2003528e-01,  9.6824163e-01,
+ 4.2524221e-04, -7.1326107e-02, -1.8375346e-01,  1.6073698e-01,  6.6706583e-02, -2.2058874e-01, -1.6864805e-01,
+ 4.2524221e-04, -4.4198960e-02, -1.1312663e-01,  1.0822348e-01,  1.3487945e-01, -7.0401341e-01, -1.2007080e+00,
+ 4.2524221e-04, -2.9746767e-02, -1.3425194e-01, -2.5086749e-01, -1.1511848e-01, -8.7276441e-01,  1.6036594e-01,
+ 4.2524221e-04,  1.7037044e-01,  1.7299759e-01,  4.6205060e-03,  5.1056665e-01,  1.0041865e+00,  2.3419438e-01,
+ 4.2524221e-04,  1.6252996e-01,  1.1271755e-01,  4.6216175e-02,  5.6226152e-01,  6.6637951e-01,  5.3371119e-01,
+ 4.2524221e-04, -1.9546813e-01,  1.3906172e-01, -5.5975009e-02, -1.0969467e-01, -1.2633232e+00, -4.3421894e-02,
+ 4.2524221e-04, -1.4044075e-01, -2.6630515e-01,  6.1962787e-02,  4.6771467e-01, -6.9051319e-01,  2.6465434e-01,
+ 4.2524221e-04,  1.7195286e-01, -5.2851868e-01, -1.6422449e-01,  1.1703679e-01,  7.2824037e-01, -3.6378372e-01,
+ 4.2524221e-04,  1.0194746e-01, -9.7751893e-02,  1.6529745e-01,  2.4984296e-01,  3.8181201e-02,  2.7078211e-01,
+ 4.2524221e-04,  2.0533490e-01,  1.9480339e-01, -6.6993818e-02,  3.9745870e-01, -7.9133675e-02, -1.1942380e-01,
+ 4.2524221e-04, -3.9208923e-02,  9.8150961e-02,  1.0030308e-01, -5.7831265e-02, -6.4350224e-01,  8.4775603e-01,
+ 4.2524221e-04,  1.3816082e-01, -1.4092979e-02, -1.0894109e-01,  2.8519067e-01,  5.8030725e-01,  6.5652287e-01,
+ 4.2524221e-04,  3.1362314e-02, -6.5740333e-03,  6.7480214e-02,  4.2265895e-01, -5.1995921e-01, -2.8980300e-02,
+ 4.2524221e-04, -1.1953717e-01,  1.5453845e-01,  1.3720915e-01, -1.5399654e-01, -1.2724885e-01,  6.4902240e-01,
+ 4.2524221e-04, -2.4549389e-01, -7.9987049e-02,  8.9279823e-02, -9.2930816e-02, -6.1336237e-01,  4.7973198e-01,
+ 4.2524221e-04,  2.5360553e-02, -2.6513871e-02,  5.4526389e-02, -9.8100655e-02,  6.5327984e-01, -5.2721924e-01,
+ 4.2524221e-04, -1.0606319e-01, -6.9447577e-02,  4.3061398e-02, -1.0653659e+00,  6.2340677e-01,  4.6419606e-02
+};

From 00a904c4a100893a530b82e89092845879943f60 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 5 Apr 2016 14:10:38 -0400
Subject: [PATCH 0449/2677] fix glitches and labels in swe

---
 examples/pde/swe.cpp | 35 +++++++++++++++++++++--------------
 1 file changed, 21 insertions(+), 14 deletions(-)

diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 3e1aab3d55..db89ff99bd 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -28,23 +28,23 @@ static void swe(bool console)
 
     array ZERO = constant(0, nx, ny);
     array um = ZERO, vm = ZERO;
-    unsigned io = (unsigned)floor(Lx  / 5.0f),
-             jo = (unsigned)floor(Ly / 5.0f),
+    unsigned io = (unsigned)floor(Lx  / 6.0f),
+             jo = (unsigned)floor(Ly / 6.0f),
              k = 15;
     array x = tile(moddims(seq(nx),nx,1), 1,ny);
     array y = tile(moddims(seq(ny),1,ny), nx,1);
 
-    // Initial condition
-    array etam = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+    //initial condition
+    array etam  = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
     float m_eta = max<float>(etam);
-    array eta = etam;
+    array eta   = etam;
     float dt = 0.5;
 
     // conv kernels
     float h_diff_kernel[] = {9.81f * (dt / dx), 0, -9.81f * (dt / dx)};
-    float h_lap_kernel[] = {0, 1, 0,
-                            1, -4, 1, 
-                            0, 1, 0};
+    float h_lap_kernel[] = { 0,  1,  0,
+                             1, -4,  1,
+                             0,  1,  0 };
 
     array h_diff_kernel_arr(3, h_diff_kernel);
     array h_lap_kernel_arr(3, 3, h_lap_kernel);
@@ -58,13 +58,21 @@ static void swe(bool console)
     timer t = timer::start();
     unsigned iter = 0;
     unsigned random_interval = 30;
-    //while (progress(iter, t, time_total)) {
+
     while (!win->close()) {
+        if( iter>2000 ) {
+            // Initial condition
+            etam = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+            m_eta = max<float>(etam);
+            eta = etam;
+            iter = 0;
+        }
+
         //raindrops
         if(iter % 100 == 0 || iter % 130 == 0 || iter % random_interval == 0) {
             unsigned io = (unsigned)floor(rand() % Lx),
                      jo = (unsigned)floor(rand() % Ly);
-            random_interval = rand() % 200;
+            random_interval = rand() % 200 + 1;
             eta += 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
         }
 
@@ -78,19 +86,18 @@ static void swe(bool console)
         eta = etap;
 
         m_eta = max<float>(etam);
-
         if (!console) {
             (*win)(0,0).image(normalize(eta, m_eta));
             array hist_out = histogram(normalize(eta, m_eta), 15);
-            (*win)(0,1).hist(hist_out, 0, 1);
+            (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
             (*win)(1,0).plot(seq(up.dims(1)), vp.col(0), "Pressure at left boundary");
-            (*win)(1,1).plot3(join(1, flat(eta), flat(up), flat(vp)), "Gradients versus Magnitude");
-            // viz
+            (*win)(1,1).plot3(join(1, flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0))), "Gradients versus Magnitude at left boundary"); // viz
             win->show();
         } else eval(eta, up, vp);
         iter++;
     }
 }
+
 int main(int argc, char* argv[])
 {
     int device = argc > 1 ? atoi(argv[1]) : 0;

From 6c47b15657019ad815db966512a096b212ca8d8e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 4 Apr 2016 19:28:22 -0400
Subject: [PATCH 0450/2677] PERF Add batched sort to sort_index for CPU and
 CUDA

---
 src/api/c/sort.cpp                      |   2 -
 src/backend/cpu/kernel/sort_helper.hpp  |  56 ++++++++++++
 src/backend/cpu/kernel/sort_index.hpp   | 117 +++++++++++++++++++-----
 src/backend/cpu/sort_index.cpp          |  13 ++-
 src/backend/cuda/kernel/harris.hpp      |   2 +-
 src/backend/cuda/kernel/iota.hpp        |   6 +-
 src/backend/cuda/kernel/orb.hpp         |   2 +-
 src/backend/cuda/kernel/sort_helper.hpp |  66 +++++++++++++
 src/backend/cuda/kernel/sort_index.hpp  |  87 +++++++++++++++++-
 src/backend/cuda/sort_index.cu          |  14 ++-
 test/sort_index.cpp                     |   3 +-
 11 files changed, 325 insertions(+), 43 deletions(-)
 create mode 100644 src/backend/cpu/kernel/sort_helper.hpp
 create mode 100644 src/backend/cuda/kernel/sort_helper.hpp

diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index e3f3ae35da..7f81fbc540 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -91,8 +91,6 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const
         af_dtype type = info.getType();
 
         DIM_ASSERT(2, info.elements() > 0);
-        // Only Dim 0 supported
-        ARG_ASSERT(3, dim == 0);
 
         af_array val;
         af_array idx;
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
new file mode 100644
index 0000000000..4b8f2f95b0
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+    namespace kernel
+    {
+        static const int copyPairIter = 4;
+
+        template <typename T>
+        using IndexPair = std::pair<T, uint>;
+
+        template <typename T, bool isAscending>
+        struct IPCompare
+        {
+            bool operator()(const IndexPair<T> &lhs, const IndexPair<T> &rhs)
+            {
+                // Check stable sort condition
+                if(isAscending) return (lhs.first < rhs.first);
+                else return (lhs.first > rhs.first);
+            }
+        };
+
+        template <typename T>
+        using KeyIndexPair = std::pair<IndexPair<T>, uint>;
+
+        template <typename T, bool isAscending>
+        struct KIPCompareV
+        {
+            bool operator()(const KeyIndexPair<T> &lhs, const KeyIndexPair<T> &rhs)
+            {
+                // Check stable sort condition
+                if(isAscending) return (lhs.first.first < rhs.first.first);
+                else return (lhs.first.first > rhs.first.first);
+            }
+        };
+
+        template <typename T, bool isAscending>
+        struct KIPCompareK
+        {
+            bool operator()(const KeyIndexPair<T> &lhs, const KeyIndexPair<T> &rhs)
+            {
+                if(isAscending) return (lhs.second < rhs.second);
+                else return (lhs.second > rhs.second);
+            }
+        };
+    }
+}
diff --git a/src/backend/cpu/kernel/sort_index.hpp b/src/backend/cpu/kernel/sort_index.hpp
index b71cc47071..1b86507aae 100644
--- a/src/backend/cpu/kernel/sort_index.hpp
+++ b/src/backend/cpu/kernel/sort_index.hpp
@@ -15,6 +15,7 @@
 #include <numeric>
 #include <err_cpu.hpp>
 #include <functional>
+#include <kernel/sort_helper.hpp>
 
 namespace cpu
 {
@@ -22,50 +23,118 @@ namespace kernel
 {
 
 template<typename T, bool isAscending>
-void sort0_index(Array<T> val, Array<uint> idx, const Array<T> in)
+void sort0IndexIterative(Array<T> val, Array<uint> idx)
 {
     // initialize original index locations
-       uint *idx_ptr = idx.get();
-          T *val_ptr = val.get();
-    const T *in_ptr  = in.get();
-    function<bool(T, T)> op = std::greater<T>();
-    if(isAscending) { op = std::less<T>(); }
+    uint *idx_ptr = idx.get();
+       T *val_ptr = val.get();
 
-    std::vector<uint> seq_vec(idx.dims()[0]);
-    std::iota(seq_vec.begin(), seq_vec.end(), 0);
+    std::vector<IndexPair<T> > X;
+    X.reserve(val.dims()[0]);
 
-    const T *comp_ptr = nullptr;
-    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-    for(dim_t w = 0; w < in.dims()[3]; w++) {
+    for(dim_t w = 0; w < val.dims()[3]; w++) {
         dim_t valW = w * val.strides()[3];
         dim_t idxW = w * idx.strides()[3];
-        dim_t  inW = w *  in.strides()[3];
-        for(dim_t z = 0; z < in.dims()[2]; z++) {
+        for(dim_t z = 0; z < val.dims()[2]; z++) {
             dim_t valWZ = valW + z * val.strides()[2];
             dim_t idxWZ = idxW + z * idx.strides()[2];
-            dim_t  inWZ =  inW + z *  in.strides()[2];
-            for(dim_t y = 0; y < in.dims()[1]; y++) {
-
+            for(dim_t y = 0; y < val.dims()[1]; y++) {
                 dim_t valOffset = valWZ + y * val.strides()[1];
                 dim_t idxOffset = idxWZ + y * idx.strides()[1];
-                dim_t inOffset  =  inWZ + y *  in.strides()[1];
 
-                uint *ptr = idx_ptr + idxOffset;
-                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
+                X.clear();
+                std::transform(val_ptr + valOffset, val_ptr + valOffset + val.dims()[0],
+                               idx_ptr + idxOffset,
+                               std::back_inserter(X),
+                               [](T v_, uint i_) { return std::make_pair(v_, i_); }
+                               );
+
+                //comp_ptr = &X.front();
+                std::stable_sort(X.begin(), X.end(), IPCompare<T, isAscending>());
+
+                for(unsigned it = 0; it < X.size(); it++) {
+                    val_ptr[valOffset + it] = X[it].first;
+                    idx_ptr[idxOffset + it] = X[it].second;
+                }
+            }
+        }
+    }
+
+    return;
+}
+
+template<typename T, bool isAscending, int dim>
+void sortIndexBatched(Array<T> val, Array<uint> idx)
+{
+    af::dim4 inDims = val.dims();
+
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim] = inDims[dim];
+    seqDims[dim] = 1;
 
-                comp_ptr = in_ptr + inOffset;
-                std::stable_sort(ptr, ptr + in.dims()[0], comparator);
+    uint* key = memAlloc<uint>(inDims.elements());
+    // IOTA
+    {
+        af::dim4 dims    = inDims;
+        uint* out        = key;
+        af::dim4 strides(1);
+        for(int i = 1; i < 4; i++)
+            strides[i] = strides[i-1] * dims[i-1];
 
-                for (dim_t i = 0; i < val.dims()[0]; ++i){
-                    val_ptr[valOffset + i] = in_ptr[inOffset + idx_ptr[idxOffset + i]];
+        for(dim_t w = 0; w < dims[3]; w++) {
+            dim_t offW = w * strides[3];
+            T valW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            for(dim_t z = 0; z < dims[2]; z++) {
+                dim_t offWZ = offW + z * strides[2];
+                T valZ = valW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                for(dim_t y = 0; y < dims[1]; y++) {
+                    dim_t offWZY = offWZ + y * strides[1];
+                    T valY = valZ + (y % seqDims[1]) * seqDims[0];
+                    for(dim_t x = 0; x < dims[0]; x++) {
+                        dim_t id = offWZY + x;
+                        out[id] = valY + (x % seqDims[0]);
+                    }
                 }
             }
         }
     }
 
+    // initialize original index locations
+    uint *idx_ptr = idx.get();
+       T *val_ptr = val.get();
+
+    std::vector<KeyIndexPair<T> > X;
+    X.reserve(val.elements());
+
+    for(unsigned i = 0; i < val.elements(); i++) {
+        X.push_back(std::make_pair(std::make_pair(val_ptr[i], idx_ptr[i]), key[i]));
+    }
+
+    memFree(key); // key is no longer required
+
+    std::stable_sort(X.begin(), X.end(), KIPCompareV<T, isAscending>());
+
+    std::stable_sort(X.begin(), X.end(), KIPCompareK<T, true>());
+
+    for(unsigned it = 0; it < val.elements(); it++) {
+        val_ptr[it] = X[it].first.first;
+        idx_ptr[it] = X[it].first.second;
+    }
+
     return;
 }
 
+template<typename T, bool isAscending>
+void sort0Index(Array<T> val, Array<unsigned> idx)
+{
+    int higherDims =  val.dims()[1] * val.dims()[2] * val.dims()[3];
+    // TODO Make a better heurisitic
+    if(higherDims > 0)
+        kernel::sortIndexBatched<T, isAscending, 0>(val, idx);
+    else
+        kernel::sort0IndexIterative<T, isAscending>(val, idx);
+}
+
 }
 }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index 77860ede18..883cb24bb3 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -14,6 +14,8 @@
 #include <numeric>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <range.hpp>
+#include <copy.hpp>
 #include <kernel/sort_index.hpp>
 
 namespace cpu
@@ -24,10 +26,15 @@ void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint
 {
     in.eval();
 
-    val = createEmptyArray<T>(in.dims());
-    idx = createEmptyArray<uint>(in.dims());
+    val = copyArray<T>(in);
+    idx = range<uint>(in.dims(), dim);
+    idx.eval();
+
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0_index<T, isAscending>, val, idx, in); break;
+        case 0: getQueue().enqueue(kernel::sort0Index<T, isAscending>, val, idx); break;
+        case 1: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 1>, val, idx); break;
+        case 2: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 2>, val, idx); break;
+        case 3: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 3>, val, idx); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 }
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 44f98d92c1..3cb28b2b2f 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -339,7 +339,7 @@ void harris(unsigned* corners_out,
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
 
         // Sort Harris responses
-        sort0_index<float, false>(harris_responses, harris_idx);
+        sort0Index<float, false>(harris_responses, harris_idx);
 
         *x_out = memAlloc<float>(*corners_out);
         *y_out = memAlloc<float>(*corners_out);
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 2632266c92..fc28c82882 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -18,8 +18,8 @@ namespace cuda
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
+        static const unsigned IOTA_TX = 32;
+        static const unsigned IOTA_TY = 8;
         static const unsigned TILEX = 512;
         static const unsigned TILEY = 32;
 
@@ -71,7 +71,7 @@ namespace cuda
         template<typename T>
         void iota(Param<T> out, const dim4 &sdims, const dim4 &tdims)
         {
-            dim3 threads(TX, TY, 1);
+            dim3 threads(IOTA_TX, IOTA_TY, 1);
 
             int blocksPerMatX = divup(out.dims[0], TILEX);
             int blocksPerMatY = divup(out.dims[1], TILEY);
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 89de56065d..8448418f8b 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -397,7 +397,7 @@ void orb(unsigned* out_feat,
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
 
         // Sort features according to Harris responses
-        sort0_index<float, false>(harris_sorted, harris_idx);
+        sort0Index<float, false>(harris_sorted, harris_idx);
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
diff --git a/src/backend/cuda/kernel/sort_helper.hpp b/src/backend/cuda/kernel/sort_helper.hpp
new file mode 100644
index 0000000000..445e9a28f5
--- /dev/null
+++ b/src/backend/cuda/kernel/sort_helper.hpp
@@ -0,0 +1,66 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <dispatch.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+
+// This needs to be in global namespace as it is used by thrust
+template<typename T>
+struct IndexPair
+{
+    T val;
+    uint idx;
+};
+
+template <typename T, bool isAscending>
+struct IPCompare
+{
+    __host__ __device__
+    bool operator()(const IndexPair<T> &lhs, const IndexPair<T> &rhs) const
+    {
+        // Check stable sort condition
+        if(isAscending) return (lhs.val < rhs.val);
+        else return (lhs.val > rhs.val);
+    }
+};
+
+namespace cuda
+{
+    namespace kernel
+    {
+        static const int copyPairIter = 4;
+
+        template <typename Tk, typename Tv>
+        __global__
+        void makeIndexPair(IndexPair<Tv> *out, const Tk *key, const Tv *val, const int N)
+        {
+            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
+
+            for(int i = tIdx; i < N; i += blockDim.x)
+            {
+                out[i].val = val[i];
+                out[i].idx = key[i];
+            }
+        }
+
+        template <typename Tk, typename Tv>
+        __global__
+        void splitIndexPair(Tk *key, Tv *val, const IndexPair<Tv> *out, const int N)
+        {
+            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
+
+            for(int i = tIdx; i < N; i += blockDim.x)
+            {
+                val[i] = out[i].val;
+                key[i] = out[i].idx;
+            }
+        }
+    }
+}
diff --git a/src/backend/cuda/kernel/sort_index.hpp b/src/backend/cuda/kernel/sort_index.hpp
index 8762f28a4a..d23c503005 100644
--- a/src/backend/cuda/kernel/sort_index.hpp
+++ b/src/backend/cuda/kernel/sort_index.hpp
@@ -12,8 +12,11 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <kernel/sort_helper.hpp>
+#include <kernel/iota.hpp>
+
 #include <thrust/device_ptr.h>
-#include <thrust/sequence.h>
+#include <thrust/device_vector.h>
 #include <thrust/sort.h>
 
 namespace cuda
@@ -24,7 +27,7 @@ namespace cuda
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename T, bool isAscending>
-        void sort0_index(Param<T> val, Param<unsigned> idx)
+        void sort0IndexIterative(Param<T> val, Param<unsigned> idx)
         {
             thrust::device_ptr<T>        val_ptr = thrust::device_pointer_cast(val.ptr);
             thrust::device_ptr<unsigned> idx_ptr = thrust::device_pointer_cast(idx.ptr);
@@ -40,7 +43,6 @@ namespace cuda
                         int valOffset = valWZ + y * val.strides[1];
                         int idxOffset = idxWZ + y * idx.strides[1];
 
-                        THRUST_SELECT(thrust::sequence, idx_ptr + idxOffset, idx_ptr + idxOffset + idx.dims[0]);
                         if(isAscending) {
                             THRUST_SELECT(thrust::stable_sort_by_key,
                                     val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
@@ -55,5 +57,84 @@ namespace cuda
             }
             POST_LAUNCH_CHECK();
         }
+
+        template<typename T, bool isAscending, int dim>
+        void sortIndexBatched(Param<T> pVal, Param<unsigned> pIdx)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pVal.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            dim4 keydims = inDims;
+            uint* key = memAlloc<uint>(keydims.elements());
+            Param<uint> pKey;
+            pKey.ptr = key;
+            pKey.strides[0] = 1;
+            pKey.dims[0] = keydims[0];
+            for(int i = 1; i < 4; i++) {
+                pKey.dims[i] = keydims[i];
+                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
+            }
+            cuda::kernel::iota<uint>(pKey, seqDims, tileDims);
+
+            // Flat - Not required since inplace and both are continuous
+            //val.modDims(inDims.elements());
+            //key.modDims(inDims.elements());
+
+            // Make val, idx into a pair
+            thrust::device_vector<IndexPair<T> > X(inDims.elements());
+            IndexPair<T> *Xptr = thrust::raw_pointer_cast(X.data());
+
+            const int threads = 256;
+            int blocks = divup(inDims.elements(), threads * copyPairIter);
+            CUDA_LAUNCH((makeIndexPair<unsigned, T>), blocks, threads,
+                        Xptr, pIdx.ptr, pVal.ptr, inDims.elements());
+
+            // Sort indices
+            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          X.begin(), X.end(),
+                          pKey.ptr,
+                          IPCompare<T, isAscending>());
+            POST_LAUNCH_CHECK();
+
+            // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          pKey.ptr,
+                          pKey.ptr + inDims.elements(),
+                          X.begin());
+            POST_LAUNCH_CHECK();
+
+            CUDA_LAUNCH((splitIndexPair<unsigned, T>), blocks, threads,
+                        pIdx.ptr, pVal.ptr, Xptr, inDims.elements());
+            POST_LAUNCH_CHECK();
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            memFree(key);
+        }
+
+        template<typename T, bool isAscending>
+        void sort0Index(Param<T> val, Param<unsigned> idx)
+        {
+            int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 5)
+                sortIndexBatched<T, isAscending, 0>(val, idx);
+            else
+                kernel::sort0IndexIterative<T, isAscending>(val, idx);
+        }
     }
 }
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 606aab4eb1..270df30128 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -9,11 +9,13 @@
 
 #include <Array.hpp>
 #include <sort_index.hpp>
-#include <copy.hpp>
 #include <kernel/sort_index.hpp>
+#include <copy.hpp>
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
+#include <range.hpp>
+#include <reorder.hpp>
 
 namespace cuda
 {
@@ -21,10 +23,14 @@ namespace cuda
     void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
     {
         val = copyArray<T>(in);
-        idx = createEmptyArray<uint>(in.dims());
+        idx = range<uint>(in.dims(), dim);
+        idx.eval();
+
         switch(dim) {
-            case 0: kernel::sort0_index<T, isAscending>(val, idx);
-                    break;
+            case 0: kernel::sort0Index<T, isAscending>(val, idx); break;
+            case 1: kernel::sortIndexBatched<T, isAscending, 1>(val, idx); break;
+            case 2: kernel::sortIndexBatched<T, isAscending, 2>(val, idx); break;
+            case 3: kernel::sortIndexBatched<T, isAscending, 3>(val, idx); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 6aa240d5a5..2326ed706f 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -124,12 +124,11 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     //SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);
     //SORT_INIT(SortLargeTrue,  sort_large, true,  0, 1);
     //SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
-;
 
 
 //////////////////////////////////// CPP /////////////////////////////////
 //
-TEST(SortIndex, CPP)
+TEST(SortIndex, CPPDim0)
 {
     if (noDoubleTests<float>()) return;
 

From 92103cb52c317b4ac44f0899f9c8e1bbad581402 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 7 Apr 2016 11:29:25 -0400
Subject: [PATCH 0451/2677] Disable CPU Async if GCC Version is less than 4.8.4

User can still set it on in CMake
---
 src/backend/cpu/CMakeLists.txt | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index f7857ec6d6..9b1a98dbc3 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -3,7 +3,15 @@ ADD_DEFINITIONS(-DAF_CPU)
 
 FIND_PACKAGE(CBLAS REQUIRED)
 
-OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
+IF(NOT DEFINED BUILD_CPU_ASYNC)
+    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.4")
+        MESSAGE("Disabling CPU Async as GCC Version ${COMPILER_VERSION} has known issues.")
+        MESSAGE("CPU Backend will use Synchronous Calls")
+        OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" OFF)
+    ELSE()
+        OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
+    ENDIF()
+ENDIF(NOT DEFINED BUILD_CPU_ASYNC)
 
 IF (NOT ${BUILD_CPU_ASYNC})
     ADD_DEFINITIONS(-DAF_DISABLE_CPU_ASYNC)

From 962b34d4f7d8f674886158798b4dcb7a0196ee61 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 14 Apr 2016 17:44:16 -0400
Subject: [PATCH 0452/2677] Fixes for compiling CPU/OpenCL on Windows with
 static MKL and freeimage

If static freeimage lib comes prior to static MKL libs, then there is a
duplication of math symbols like sqrtf, ceilf etc which will cause the builds
to fail at link time
---
 src/backend/cpu/CMakeLists.txt    | 5 +++--
 src/backend/opencl/CMakeLists.txt | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 22978e477e..25f738edc6 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -203,9 +203,10 @@ ELSE(DEFINED BLAS_SYM_FILE)
 ENDIF(DEFINED BLAS_SYM_FILE)
 
 TARGET_LINK_LIBRARIES(afcpu
-                            PRIVATE ${FreeImage_LIBS}
                             PRIVATE ${CBLAS_LIBRARIES}
-                            PRIVATE ${FFTW_LIBRARIES})
+                            PRIVATE ${FFTW_LIBRARIES}
+                            PRIVATE ${FreeImage_LIBS}
+                     )
 
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
   ADD_DEPENDENCIES(afcpu forge)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 2cb8ddfdf9..f3735d5569 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -305,11 +305,10 @@ ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
 
 TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${OpenCL_LIBRARIES}
-                                PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CLBLAS_LIBRARIES}
                                 PRIVATE ${CLFFT_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
-                                PRIVATE ${Boost_LIBRARIES})
+                     )
 
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
     ADD_DEPENDENCIES(afopencl forge)
@@ -321,6 +320,8 @@ IF(LAPACK_FOUND)
     PRIVATE ${CBLAS_LIBRARIES})
 ENDIF()
 
+TARGET_LINK_LIBRARIES(afopencl PRIVATE ${FreeImage_LIBS})
+
 SET_TARGET_PROPERTIES(afopencl PROPERTIES
     VERSION "${AF_VERSION}"
     SOVERSION "${AF_VERSION_MAJOR}")

From 1a928fd7386e9fe63ee590db19cda7cd1f11fdcb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 15 Apr 2016 15:56:15 -0400
Subject: [PATCH 0453/2677] Add NOMINMAX for windows for tests when build out
 of source

---
 test/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 8254881983..e07e8138cf 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -21,6 +21,10 @@ ELSE()
     INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
     OPTION(BUILD_NONFREE "Build Tests for nonfree algorithms" OFF)
 
+    IF(WIN32)
+        ADD_DEFINITIONS(-DOS_WIN -DNOMINMAX)
+    ENDIF(WIN32)
+
     IF(${BUILD_NONFREE})
         MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
         SET(BUILD_NONFREE_SIFT ON CACHE BOOL "Build ArrayFire with SIFT")

From b2a55a48593de41310bf233fa7fbcb9d207d0080 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 15 Apr 2016 15:56:32 -0400
Subject: [PATCH 0454/2677] Check for MKL options after find blas

---
 src/backend/opencl/CMakeLists.txt | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f3735d5569..dd28bedc2b 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -10,16 +10,6 @@ IF(USE_OPENCL_F77_BLAS)
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
 ENDIF()
 
-IF(USE_OPENCL_MKL) # Manual MKL Setup
-    MESSAGE("OpenCL Backend Using MKL")
-    ADD_DEFINITIONS(-DUSE_MKL)
-ELSE(USE_OPENCL_MKL)
-    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
-        MESSAGE("OpenCL Backend Using MKL RT")
-        ADD_DEFINITIONS(-DUSE_MKL)
-    ENDIF()
-ENDIF()
-
 IF(APPLE)
     FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
     IF(NOT LAPACK_FOUND)
@@ -55,6 +45,16 @@ ELSE(NOT LAPACK_FOUND)
   ENDIF()
 ENDIF()
 
+IF(USE_OPENCL_MKL) # Manual MKL Setup
+    MESSAGE("OpenCL Backend Using MKL")
+    ADD_DEFINITIONS(-DUSE_MKL)
+ELSE(USE_OPENCL_MKL)
+    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+        MESSAGE("OpenCL Backend Using MKL RT")
+        ADD_DEFINITIONS(-DUSE_MKL)
+    ENDIF()
+ENDIF()
+
 IF(NOT UNIX)
     ADD_DEFINITIONS(-DAFDLL)
 ENDIF()

From 46547a176d8c1aa81024cfc688aa0f34cbfc035d Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Apr 2016 10:20:18 -0400
Subject: [PATCH 0455/2677] TEST: Adding rgb <--> gray conversion tests

---
 test/gray_rgb.cpp | 105 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 test/gray_rgb.cpp

diff --git a/test/gray_rgb.cpp b/test/gray_rgb.cpp
new file mode 100644
index 0000000000..0ee7078cef
--- /dev/null
+++ b/test/gray_rgb.cpp
@@ -0,0 +1,105 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+TEST(rgb_gray, 32bit)
+{
+    af::array rgb = af::randu(10, 10, 3);
+    af::array gray = af::rgb2gray(rgb);
+
+    std::vector<float> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    rgb.host(&h_rgb[0]);
+    gray.host(&h_gray[0]);
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    const float rPercent=0.2126f;
+    const float gPercent=0.7152f;
+    const float bPercent=0.0722f;
+
+    for (int i = 0; i < num; i++) {
+        float res =
+            rPercent * h_rgb[i + roff] +
+            gPercent * h_rgb[i + goff] +
+            bPercent * h_rgb[i + boff];
+
+        ASSERT_FLOAT_EQ(res, h_gray[i]);
+    }
+}
+
+TEST(rgb_gray, 8bit)
+{
+    af::array rgb = af::randu(10, 10, 3, u8);
+    af::array gray = af::rgb2gray(rgb);
+
+    std::vector<uchar> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    rgb.host(&h_rgb[0]);
+    gray.host(&h_gray[0]);
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    const float rPercent=0.2126f;
+    const float gPercent=0.7152f;
+    const float bPercent=0.0722f;
+
+    for (int i = 0; i < num; i++) {
+        float res =
+            rPercent * h_rgb[i + roff] +
+            gPercent * h_rgb[i + goff] +
+            bPercent * h_rgb[i + boff];
+
+        ASSERT_FLOAT_EQ(res, h_gray[i]);
+    }
+}
+
+TEST(gray_rgb, 32bit)
+{
+    af::array gray = af::randu(10, 10);
+
+    const float rPercent=0.33f;
+    const float gPercent=0.34f;
+    const float bPercent=0.33f;
+
+    af::array rgb = af::gray2rgb(gray, rPercent, gPercent, bPercent);
+    std::vector<float> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    for (int i = 0; i < num; i++) {
+        float gray = h_gray[i];
+
+        float r = rPercent * gray;
+        float g = gPercent * gray;
+        float b = bPercent * gray;
+
+        ASSERT_FLOAT_EQ(r, h_rgb[i + roff]);
+        ASSERT_FLOAT_EQ(g, h_rgb[i + goff]);
+        ASSERT_FLOAT_EQ(b, h_rgb[i + boff]);
+    }
+}

From 8d7eaadd2a85c006033ffb3711ea16f43a60784a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 18 Apr 2016 10:20:51 -0400
Subject: [PATCH 0456/2677] BUGFIX: Fixing rgb to gray conversion

---
 src/api/c/rgb_gray.cpp | 24 ++++++++++--------------
 1 file changed, 10 insertions(+), 14 deletions(-)

diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 1e52ae0899..da7ebb2bf3 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -36,31 +36,27 @@ static af_array rgb2gray(const af_array& in, const float r, const float g, const
     Array<cType> gCnst = createValueArray<cType>(matDims, scalar<cType>(g));
     Array<cType> bCnst = createValueArray<cType>(matDims, scalar<cType>(b));
 
+    std::vector<af_seq> slice1(4, af_span), slice2(4, af_span), slice3(4, af_span);
     // extract three channels as three slices
-    af_seq slice1[4] = { af_span, af_span, {0, 0, 1}, af_span };
-    af_seq slice2[4] = { af_span, af_span, {1, 1, 1}, af_span };
-    af_seq slice3[4] = { af_span, af_span, {2, 2, 1}, af_span };
+    slice1[2] = {0, 0, 1};
+    slice2[2] = {1, 1, 1};
+    slice3[2] = {2, 2, 1};
 
-    af_array ch1Temp=0, ch2Temp=0, ch3Temp=0;
-    AF_CHECK(af_index(&ch1Temp, in, 4, slice1));
-    AF_CHECK(af_index(&ch2Temp, in, 4, slice2));
-    AF_CHECK(af_index(&ch3Temp, in, 4, slice3));
+    Array<cType> ch1Temp = createSubArray(input, slice1);
+    Array<cType> ch2Temp = createSubArray(input, slice2);
+    Array<cType> ch3Temp = createSubArray(input, slice3);
 
     // r*Slice0
-    Array<cType> expr1 = arithOp<cType, af_mul_t>(getArray<cType>(ch1Temp), rCnst, matDims);
+    Array<cType> expr1 = arithOp<cType, af_mul_t>(ch1Temp, rCnst, matDims);
     //g*Slice1
-    Array<cType> expr2 = arithOp<cType, af_mul_t>(getArray<cType>(ch2Temp), gCnst, matDims);
+    Array<cType> expr2 = arithOp<cType, af_mul_t>(ch2Temp, gCnst, matDims);
     //b*Slice2
-    Array<cType> expr3 = arithOp<cType, af_mul_t>(getArray<cType>(ch3Temp), bCnst, matDims);
+    Array<cType> expr3 = arithOp<cType, af_mul_t>(ch3Temp, bCnst, matDims);
     //r*Slice0 + g*Slice1
     Array<cType> expr4 = arithOp<cType, af_add_t>(expr1, expr2, matDims);
     //r*Slice0 + g*Slice1 + b*Slice2
     Array<cType> result= arithOp<cType, af_add_t>(expr3, expr4, matDims);
 
-    AF_CHECK(af_release_array(ch1Temp));
-    AF_CHECK(af_release_array(ch2Temp));
-    AF_CHECK(af_release_array(ch3Temp));
-
     return getHandle<cType>(result);
 }
 

From 48dcfb0cb5c7d6c94d028f13eafcba7852a67741 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 18 Apr 2016 18:08:44 -0400
Subject: [PATCH 0457/2677] Fix ordering of data from sort

---
 src/backend/cpu/sort.cpp    | 16 +++++++++++++++-
 src/backend/cuda/sort.cu    | 14 ++++++++++++++
 src/backend/opencl/sort.cpp | 14 ++++++++++++++
 test/sort.cpp               |  4 ++++
 4 files changed, 47 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index fbf613f962..7fa8769f11 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -19,6 +19,7 @@
 #include <iota.hpp>
 #include <handle.hpp>
 #include <sort_by_key.hpp>
+#include <reorder.hpp>
 #include <kernel/sort.hpp>
 
 namespace cpu
@@ -49,7 +50,7 @@ void sortBatched(Array<T>& val)
     sort_by_key<uint, T, true>(key, val, resKey, resVal, 0);
     val.eval();
 
-    val.setDataDims(inDims);
+    val.setDataDims(inDims); // This is correct only for dim0
 }
 
 template<typename T, bool isAscending>
@@ -76,6 +77,19 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
         case 3: sortBatched<T, isAscending, 3>(out); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+
+    if(dim != 0) {
+        af::dim4 preorderDims = out.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0] = out.dims()[dim];
+        for(int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i] = out.dims()[i - 1];
+        }
+
+        out = reorder<T>(out, reorderDims);
+    }
     return out;
 }
 
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 4ae3b759fb..99b42d4196 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -12,6 +12,7 @@
 #include <sort.hpp>
 #include <kernel/sort.hpp>
 #include <math.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
 
@@ -28,6 +29,19 @@ namespace cuda
             case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
+
+        if(dim != 0) {
+            af::dim4 preorderDims = out.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0] = out.dims()[dim];
+            for(int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i] = out.dims()[i - 1];
+            }
+
+            out = reorder<T>(out, reorderDims);
+        }
         return out;
     }
 
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 0bf2dc04cd..c7bd774ecd 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -12,6 +12,7 @@
 #include <copy.hpp>
 #include <kernel/sort.hpp>
 #include <math.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
 
@@ -29,6 +30,19 @@ namespace opencl
                 case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
+
+            if(dim != 0) {
+                af::dim4 preorderDims = out.dims();
+                af::dim4 reorderDims(0, 1, 2, 3);
+                reorderDims[dim] = 0;
+                preorderDims[0] = out.dims()[dim];
+                for(int i = 1; i <= (int)dim; i++) {
+                    reorderDims[i - 1] = i;
+                    preorderDims[i] = out.dims()[i - 1];
+                }
+
+                out = reorder<T>(out, reorderDims);
+            }
             return out;
         } catch (std::exception &ex) {
             AF_ERROR(ex.what(), AF_ERR_INTERNAL);
diff --git a/test/sort.cpp b/test/sort.cpp
index 116b136abe..977b54b5c7 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -166,6 +166,8 @@ TEST(Sort, CPPDim1)
 
     af::array output = af::sort(input_, 1, dir);
 
+    output = reorder(output, 1, 0, 2, 3); // Required for checking with test data
+
     size_t nElems = tests[resultIdx0].size();
 
     // Get result
@@ -200,6 +202,8 @@ TEST(Sort, CPPDim2)
 
     af::array output = af::sort(input_, 2, dir);
 
+    output = reorder(output, 2, 0, 1, 3); // Required for checking with test data
+
     size_t nElems = tests[resultIdx0].size();
 
     // Get result

From c79b26fa58dfd3d8493a1cea5460fa573cced894 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 19 Apr 2016 10:46:23 -0400
Subject: [PATCH 0458/2677] Fix reordering of data for sort_index in cpu and
 cuda

---
 src/backend/cpu/sort.cpp       |  1 +
 src/backend/cpu/sort_index.cpp | 18 ++++++++++++++++++
 src/backend/cuda/sort.cu       |  1 +
 src/backend/cuda/sort_index.cu | 17 +++++++++++++++++
 src/backend/opencl/sort.cpp    |  1 +
 5 files changed, 38 insertions(+)

diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 7fa8769f11..4a649e0b23 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -88,6 +88,7 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
             preorderDims[i] = out.dims()[i - 1];
         }
 
+        out.setDataDims(preorderDims);
         out = reorder<T>(out, reorderDims);
     }
     return out;
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index 883cb24bb3..36ca57b3e8 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -16,6 +16,7 @@
 #include <queue.hpp>
 #include <range.hpp>
 #include <copy.hpp>
+#include <reorder.hpp>
 #include <kernel/sort_index.hpp>
 
 namespace cpu
@@ -37,6 +38,23 @@ void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint
         case 3: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 3>, val, idx); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+
+    if(dim != 0) {
+        af::dim4 preorderDims = val.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0] = val.dims()[dim];
+        for(int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i] = val.dims()[i - 1];
+        }
+
+        val.setDataDims(preorderDims);
+        idx.setDataDims(preorderDims);
+
+        val = reorder<T>(val, reorderDims);
+        idx = reorder<uint>(idx, reorderDims);
+    }
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 99b42d4196..9b0f4c53af 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -40,6 +40,7 @@ namespace cuda
                 preorderDims[i] = out.dims()[i - 1];
             }
 
+            out.setDataDims(preorderDims);
             out = reorder<T>(out, reorderDims);
         }
         return out;
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 270df30128..ab54c24a9c 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -33,6 +33,23 @@ namespace cuda
             case 3: kernel::sortIndexBatched<T, isAscending, 3>(val, idx); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
+
+        if(dim != 0) {
+            af::dim4 preorderDims = val.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0] = val.dims()[dim];
+            for(int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i] = val.dims()[i - 1];
+            }
+
+            val.setDataDims(preorderDims);
+            idx.setDataDims(preorderDims);
+
+            val = reorder<T>(val, reorderDims);
+            idx = reorder<uint>(idx, reorderDims);
+        }
     }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index c7bd774ecd..1548f27472 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -41,6 +41,7 @@ namespace opencl
                     preorderDims[i] = out.dims()[i - 1];
                 }
 
+                out.setDataDims(preorderDims);
                 out = reorder<T>(out, reorderDims);
             }
             return out;

From 08613a817564f89d18397f53de83b2633ed9d97b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 20 Apr 2016 13:22:00 -0400
Subject: [PATCH 0459/2677] Added sort_by_key batching to CPU and CUDA

---
 src/api/c/sort.cpp                      |   2 -
 src/backend/cpu/kernel/sort_by_key.hpp  | 127 +++++++++++++++++-------
 src/backend/cpu/kernel/sort_helper.hpp  |  22 ++--
 src/backend/cpu/kernel/sort_index.hpp   |  16 +--
 src/backend/cpu/sort_by_key.cpp         |  32 ++++--
 src/backend/cuda/kernel/sort_by_key.hpp |  83 +++++++++++++++-
 src/backend/cuda/kernel/sort_helper.hpp |  26 ++---
 src/backend/cuda/kernel/sort_index.hpp  |  37 +++----
 src/backend/cuda/sort_by_key_impl.hpp   |  27 ++++-
 9 files changed, 274 insertions(+), 98 deletions(-)

diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 7f81fbc540..dd58175936 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -174,8 +174,6 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
 
         DIM_ASSERT(3, kinfo.elements() > 0);
         DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
-        // Only Dim 0 supported
-        ARG_ASSERT(5, dim == 0);
 
         TYPE_ASSERT(kinfo.isReal());
 
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index f9d391dc46..1be4a94d3a 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -16,6 +16,7 @@
 #include <queue>
 #include <err_cpu.hpp>
 #include <functional>
+#include <kernel/sort_helper.hpp>
 
 namespace cpu
 {
@@ -23,57 +24,40 @@ namespace kernel
 {
 
 template<typename Tk, typename Tv, bool isAscending>
-void sort0_by_key(Array<Tk> okey, Array<Tv> oval, Array<uint> oidx,
-                  const Array<Tk> ikey, const Array<Tv> ival)
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
 {
-    function<bool(Tk, Tk)> op = std::greater<Tk>();
-    if(isAscending) { op = std::less<Tk>(); }
-
     // Get pointers and initialize original index locations
-        uint *oidx_ptr = oidx.get();
-          Tk *okey_ptr = okey.get();
-          Tv *oval_ptr = oval.get();
-    const Tk *ikey_ptr = ikey.get();
-    const Tv *ival_ptr = ival.get();
-
-    std::vector<uint> seq_vec(oidx.dims()[0]);
-    std::iota(seq_vec.begin(), seq_vec.end(), 0);
+    Tk *okey_ptr = okey.get();
+    Tv *oval_ptr = oval.get();
 
-    const Tk *comp_ptr = nullptr;
-    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
+    std::vector<IndexPair<Tk, Tv> > X;
+    X.reserve(okey.dims()[0]);
 
-    for(dim_t w = 0; w < ikey.dims()[3]; w++) {
+    for(dim_t w = 0; w < okey.dims()[3]; w++) {
         dim_t okeyW = w * okey.strides()[3];
         dim_t ovalW = w * oval.strides()[3];
-        dim_t oidxW = w * oidx.strides()[3];
-        dim_t ikeyW = w * ikey.strides()[3];
-        dim_t ivalW = w * ival.strides()[3];
 
-        for(dim_t z = 0; z < ikey.dims()[2]; z++) {
+        for(dim_t z = 0; z < okey.dims()[2]; z++) {
             dim_t okeyWZ = okeyW + z * okey.strides()[2];
             dim_t ovalWZ = ovalW + z * oval.strides()[2];
-            dim_t oidxWZ = oidxW + z * oidx.strides()[2];
-            dim_t ikeyWZ = ikeyW + z * ikey.strides()[2];
-            dim_t ivalWZ = ivalW + z * ival.strides()[2];
 
-            for(dim_t y = 0; y < ikey.dims()[1]; y++) {
+            for(dim_t y = 0; y < okey.dims()[1]; y++) {
 
                 dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
                 dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
-                dim_t oidxOffset = oidxWZ + y * oidx.strides()[1];
-                dim_t ikeyOffset = ikeyWZ + y * ikey.strides()[1];
-                dim_t ivalOffset = ivalWZ + y * ival.strides()[1];
 
-                uint *ptr = oidx_ptr + oidxOffset;
-                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
+                X.clear();
+                std::transform(okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims()[0],
+                               oval_ptr + ovalOffset,
+                               std::back_inserter(X),
+                               [](Tk v_, Tv i_) { return std::make_pair(v_, i_); }
+                               );
 
-                comp_ptr = ikey_ptr + ikeyOffset;
-                std::stable_sort(ptr, ptr + ikey.dims()[0], comparator);
+                std::stable_sort(X.begin(), X.end(), IPCompare<Tk, Tv, isAscending>());
 
-                for (dim_t i = 0; i < oval.dims()[0]; ++i){
-                    uint sortIdx = oidx_ptr[oidxOffset + i];
-                    okey_ptr[okeyOffset + i] = ikey_ptr[ikeyOffset + sortIdx];
-                    oval_ptr[ovalOffset + i] = ival_ptr[ivalOffset + sortIdx];
+                for(unsigned it = 0; it < X.size(); it++) {
+                    okey_ptr[okeyOffset + it] = X[it].first;
+                    oval_ptr[ovalOffset + it] = X[it].second;
                 }
             }
         }
@@ -82,5 +66,78 @@ void sort0_by_key(Array<Tk> okey, Array<Tv> oval, Array<uint> oidx,
     return;
 }
 
+template<typename Tk, typename Tv, bool isAscending, int dim>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
+{
+    af::dim4 inDims = okey.dims();
+
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim] = inDims[dim];
+    seqDims[dim] = 1;
+
+    uint* key = memAlloc<uint>(inDims.elements());
+    // IOTA
+    {
+        af::dim4 dims    = inDims;
+        uint* out        = key;
+        af::dim4 strides(1);
+        for(int i = 1; i < 4; i++)
+            strides[i] = strides[i-1] * dims[i-1];
+
+        for(dim_t w = 0; w < dims[3]; w++) {
+            dim_t offW = w * strides[3];
+            uint okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            for(dim_t z = 0; z < dims[2]; z++) {
+                dim_t offWZ = offW + z * strides[2];
+                uint okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                for(dim_t y = 0; y < dims[1]; y++) {
+                    dim_t offWZY = offWZ + y * strides[1];
+                    uint okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
+                    for(dim_t x = 0; x < dims[0]; x++) {
+                        dim_t id = offWZY + x;
+                        out[id] = okeyY + (x % seqDims[0]);
+                    }
+                }
+            }
+        }
+    }
+
+    // initialize original index locations
+    Tk *okey_ptr = okey.get();
+    Tv *oval_ptr = oval.get();
+
+    std::vector<KeyIndexPair<Tk, Tv> > X;
+    X.reserve(okey.elements());
+
+    for(unsigned i = 0; i < okey.elements(); i++) {
+        X.push_back(std::make_pair(std::make_pair(okey_ptr[i], oval_ptr[i]), key[i]));
+    }
+
+    memFree(key); // key is no longer required
+
+    std::stable_sort(X.begin(), X.end(), KIPCompareV<Tk, Tv, isAscending>());
+
+    std::stable_sort(X.begin(), X.end(), KIPCompareK<Tk, Tv, true>());
+
+    for(unsigned it = 0; it < okey.elements(); it++) {
+        okey_ptr[it] = X[it].first.first;
+        oval_ptr[it] = X[it].first.second;
+    }
+
+    return;
+}
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
+{
+    int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
+    // TODO Make a better heurisitic
+    if(higherDims > 0)
+        kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+    else
+        kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+}
+
 }
 }
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
index 4b8f2f95b0..ff7da3560b 100644
--- a/src/backend/cpu/kernel/sort_helper.hpp
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -13,15 +13,13 @@ namespace cpu
 {
     namespace kernel
     {
-        static const int copyPairIter = 4;
+        template <typename Tk, typename Tv>
+        using IndexPair = std::pair<Tk, Tv>;
 
-        template <typename T>
-        using IndexPair = std::pair<T, uint>;
-
-        template <typename T, bool isAscending>
+        template <typename Tk, typename Tv, bool isAscending>
         struct IPCompare
         {
-            bool operator()(const IndexPair<T> &lhs, const IndexPair<T> &rhs)
+            bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs)
             {
                 // Check stable sort condition
                 if(isAscending) return (lhs.first < rhs.first);
@@ -29,13 +27,13 @@ namespace cpu
             }
         };
 
-        template <typename T>
-        using KeyIndexPair = std::pair<IndexPair<T>, uint>;
+        template <typename Tk, typename Tv>
+        using KeyIndexPair = std::pair<IndexPair<Tk, Tv>, uint>;
 
-        template <typename T, bool isAscending>
+        template <typename Tk, typename Tv, bool isAscending>
         struct KIPCompareV
         {
-            bool operator()(const KeyIndexPair<T> &lhs, const KeyIndexPair<T> &rhs)
+            bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
             {
                 // Check stable sort condition
                 if(isAscending) return (lhs.first.first < rhs.first.first);
@@ -43,10 +41,10 @@ namespace cpu
             }
         };
 
-        template <typename T, bool isAscending>
+        template <typename Tk, typename Tv, bool isAscending>
         struct KIPCompareK
         {
-            bool operator()(const KeyIndexPair<T> &lhs, const KeyIndexPair<T> &rhs)
+            bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
             {
                 if(isAscending) return (lhs.second < rhs.second);
                 else return (lhs.second > rhs.second);
diff --git a/src/backend/cpu/kernel/sort_index.hpp b/src/backend/cpu/kernel/sort_index.hpp
index 1b86507aae..7a23a7df49 100644
--- a/src/backend/cpu/kernel/sort_index.hpp
+++ b/src/backend/cpu/kernel/sort_index.hpp
@@ -29,7 +29,7 @@ void sort0IndexIterative(Array<T> val, Array<uint> idx)
     uint *idx_ptr = idx.get();
        T *val_ptr = val.get();
 
-    std::vector<IndexPair<T> > X;
+    std::vector<IndexPair<T, uint> > X;
     X.reserve(val.dims()[0]);
 
     for(dim_t w = 0; w < val.dims()[3]; w++) {
@@ -50,7 +50,7 @@ void sort0IndexIterative(Array<T> val, Array<uint> idx)
                                );
 
                 //comp_ptr = &X.front();
-                std::stable_sort(X.begin(), X.end(), IPCompare<T, isAscending>());
+                std::stable_sort(X.begin(), X.end(), IPCompare<T, uint, isAscending>());
 
                 for(unsigned it = 0; it < X.size(); it++) {
                     val_ptr[valOffset + it] = X[it].first;
@@ -84,13 +84,13 @@ void sortIndexBatched(Array<T> val, Array<uint> idx)
 
         for(dim_t w = 0; w < dims[3]; w++) {
             dim_t offW = w * strides[3];
-            T valW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            uint valW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
             for(dim_t z = 0; z < dims[2]; z++) {
                 dim_t offWZ = offW + z * strides[2];
-                T valZ = valW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                uint valZ = valW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
                 for(dim_t y = 0; y < dims[1]; y++) {
                     dim_t offWZY = offWZ + y * strides[1];
-                    T valY = valZ + (y % seqDims[1]) * seqDims[0];
+                    uint valY = valZ + (y % seqDims[1]) * seqDims[0];
                     for(dim_t x = 0; x < dims[0]; x++) {
                         dim_t id = offWZY + x;
                         out[id] = valY + (x % seqDims[0]);
@@ -104,7 +104,7 @@ void sortIndexBatched(Array<T> val, Array<uint> idx)
     uint *idx_ptr = idx.get();
        T *val_ptr = val.get();
 
-    std::vector<KeyIndexPair<T> > X;
+    std::vector<KeyIndexPair<T, uint> > X;
     X.reserve(val.elements());
 
     for(unsigned i = 0; i < val.elements(); i++) {
@@ -113,9 +113,9 @@ void sortIndexBatched(Array<T> val, Array<uint> idx)
 
     memFree(key); // key is no longer required
 
-    std::stable_sort(X.begin(), X.end(), KIPCompareV<T, isAscending>());
+    std::stable_sort(X.begin(), X.end(), KIPCompareV<T, uint, isAscending>());
 
-    std::stable_sort(X.begin(), X.end(), KIPCompareK<T, true>());
+    std::stable_sort(X.begin(), X.end(), KIPCompareK<T, uint, true>());
 
     for(unsigned it = 0; it < val.elements(); it++) {
         val_ptr[it] = X[it].first.first;
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 46ced4b9ef..46b06602b4 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -11,6 +11,9 @@
 #include <sort_by_key.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <copy.hpp>
+#include <range.hpp>
+#include <reorder.hpp>
 #include <kernel/sort_by_key.hpp>
 
 namespace cpu
@@ -23,16 +26,33 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     ikey.eval();
     ival.eval();
 
-    okey = createEmptyArray<Tk>(ikey.dims());
-    oval = createEmptyArray<Tv>(ival.dims());
-    Array<uint> oidx = createValueArray(ikey.dims(), 0u);
-    oidx.eval();
+    okey = copyArray<Tk>(ikey);
+    oval = copyArray<Tv>(ival);
 
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0_by_key<Tk, Tv, isAscending>,
-                                   okey, oval, oidx, ikey, ival); break;
+        case 0: getQueue().enqueue(kernel::sort0ByKey<Tk, Tv, isAscending>, okey, oval); break;
+        case 1: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>, okey, oval); break;
+        case 2: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>, okey, oval); break;
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>, okey, oval); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+
+    if(dim != 0) {
+        af::dim4 preorderDims = okey.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0] = okey.dims()[dim];
+        for(int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i] = okey.dims()[i - 1];
+        }
+
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
+
+        okey = reorder<Tk>(okey, reorderDims);
+        oval = reorder<Tv>(oval, reorderDims);
+    }
 }
 
 #define INSTANTIATE(Tk, Tv)                                             \
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index bfaa79a311..beffa5476e 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -12,7 +12,11 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <kernel/sort_helper.hpp>
+#include <kernel/iota.hpp>
+
 #include <thrust/device_ptr.h>
+#include <thrust/device_vector.h>
 #include <thrust/sort.h>
 
 namespace cuda
@@ -23,7 +27,7 @@ namespace cuda
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0_by_key(Param<Tk> okey, Param<Tv> oval)
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval)
         {
             thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
             thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
@@ -55,5 +59,82 @@ namespace cuda
             }
             POST_LAUNCH_CHECK();
         }
+
+        template<typename Tk, typename Tv, bool isAscending, int dim>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pKey.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            dim4 keydims = inDims;
+            uint* key = memAlloc<uint>(keydims.elements());
+            Param<uint> pSeq;
+            pSeq.ptr = key;
+            pSeq.strides[0] = 1;
+            pSeq.dims[0] = keydims[0];
+            for(int i = 1; i < 4; i++) {
+                pSeq.dims[i] = keydims[i];
+                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
+            }
+            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
+
+            // Make pkey, pVal into a pair
+            thrust::device_vector<IndexPair<Tk, Tv> > X(inDims.elements());
+            IndexPair<Tk, Tv> *Xptr = thrust::raw_pointer_cast(X.data());
+
+            const int threads = 256;
+            int blocks = divup(inDims.elements(), threads * copyPairIter);
+            CUDA_LAUNCH((makeIndexPair<Tk, Tv>), blocks, threads,
+                        Xptr, pKey.ptr, pVal.ptr, inDims.elements());
+            POST_LAUNCH_CHECK();
+
+            // Sort indices
+            // Need to convert pSeq to thrust::device_ptr, otherwise thrust
+            // throws weird errors for all *64 data types (double, intl, uintl etc)
+            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          X.begin(), X.end(),
+                          dSeq,
+                          IPCompare<Tk, Tv, isAscending>());
+            POST_LAUNCH_CHECK();
+
+            // Needs to be ascending (true) in order to maintain the indices properly
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          dSeq,
+                          dSeq + inDims.elements(),
+                          X.begin());
+            POST_LAUNCH_CHECK();
+
+            CUDA_LAUNCH((splitIndexPair<Tk, Tv>), blocks, threads,
+                        pKey.ptr, pVal.ptr, Xptr, inDims.elements());
+            POST_LAUNCH_CHECK();
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            memFree(key);
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval)
+        {
+            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 5)
+                sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+        }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_helper.hpp b/src/backend/cuda/kernel/sort_helper.hpp
index 445e9a28f5..93fb33ac8f 100644
--- a/src/backend/cuda/kernel/sort_helper.hpp
+++ b/src/backend/cuda/kernel/sort_helper.hpp
@@ -12,22 +12,22 @@
 #include <debug_cuda.hpp>
 
 // This needs to be in global namespace as it is used by thrust
-template<typename T>
+template<typename Tk, typename Tv>
 struct IndexPair
 {
-    T val;
-    uint idx;
+    Tk first;
+    Tv second;
 };
 
-template <typename T, bool isAscending>
+template <typename Tk, typename Tv, bool isAscending>
 struct IPCompare
 {
     __host__ __device__
-    bool operator()(const IndexPair<T> &lhs, const IndexPair<T> &rhs) const
+    bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs) const
     {
         // Check stable sort condition
-        if(isAscending) return (lhs.val < rhs.val);
-        else return (lhs.val > rhs.val);
+        if(isAscending) return (lhs.first < rhs.first);
+        else return (lhs.first > rhs.first);
     }
 };
 
@@ -39,27 +39,27 @@ namespace cuda
 
         template <typename Tk, typename Tv>
         __global__
-        void makeIndexPair(IndexPair<Tv> *out, const Tk *key, const Tv *val, const int N)
+        void makeIndexPair(IndexPair<Tk, Tv> *out, const Tk *first, const Tv *second, const int N)
         {
             int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
 
             for(int i = tIdx; i < N; i += blockDim.x)
             {
-                out[i].val = val[i];
-                out[i].idx = key[i];
+                out[i].first = first[i];
+                out[i].second = second[i];
             }
         }
 
         template <typename Tk, typename Tv>
         __global__
-        void splitIndexPair(Tk *key, Tv *val, const IndexPair<Tv> *out, const int N)
+        void splitIndexPair(Tk *first, Tv *second, const IndexPair<Tk, Tv> *out, const int N)
         {
             int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
 
             for(int i = tIdx; i < N; i += blockDim.x)
             {
-                val[i] = out[i].val;
-                key[i] = out[i].idx;
+                first[i]  = out[i].first;
+                second[i] = out[i].second;
             }
         }
     }
diff --git a/src/backend/cuda/kernel/sort_index.hpp b/src/backend/cuda/kernel/sort_index.hpp
index d23c503005..40a5d59311 100644
--- a/src/backend/cuda/kernel/sort_index.hpp
+++ b/src/backend/cuda/kernel/sort_index.hpp
@@ -73,50 +73,51 @@ namespace cuda
             seqDims[dim] = 1;
 
             // Create/call iota
-            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            // Array<uint> seq = iota<uint>(seqDims, tileDims);
             dim4 keydims = inDims;
             uint* key = memAlloc<uint>(keydims.elements());
-            Param<uint> pKey;
-            pKey.ptr = key;
-            pKey.strides[0] = 1;
-            pKey.dims[0] = keydims[0];
+            Param<uint> pSeq;
+            pSeq.ptr = key;
+            pSeq.strides[0] = 1;
+            pSeq.dims[0] = keydims[0];
             for(int i = 1; i < 4; i++) {
-                pKey.dims[i] = keydims[i];
-                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
+                pSeq.dims[i] = keydims[i];
+                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
             }
-            cuda::kernel::iota<uint>(pKey, seqDims, tileDims);
+            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
 
             // Flat - Not required since inplace and both are continuous
             //val.modDims(inDims.elements());
             //key.modDims(inDims.elements());
 
             // Make val, idx into a pair
-            thrust::device_vector<IndexPair<T> > X(inDims.elements());
-            IndexPair<T> *Xptr = thrust::raw_pointer_cast(X.data());
+            thrust::device_vector<IndexPair<T, uint> > X(inDims.elements());
+            IndexPair<T, uint> *Xptr = thrust::raw_pointer_cast(X.data());
 
             const int threads = 256;
             int blocks = divup(inDims.elements(), threads * copyPairIter);
-            CUDA_LAUNCH((makeIndexPair<unsigned, T>), blocks, threads,
-                        Xptr, pIdx.ptr, pVal.ptr, inDims.elements());
+            CUDA_LAUNCH((makeIndexPair<T, unsigned>), blocks, threads,
+                        Xptr, pVal.ptr, pIdx.ptr, inDims.elements());
 
             // Sort indices
             // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
             THRUST_SELECT(thrust::stable_sort_by_key,
                           X.begin(), X.end(),
-                          pKey.ptr,
-                          IPCompare<T, isAscending>());
+                          dSeq,
+                          IPCompare<T, uint, isAscending>());
             POST_LAUNCH_CHECK();
 
             // Needs to be ascending (true) in order to maintain the indices properly
             //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
             THRUST_SELECT(thrust::stable_sort_by_key,
-                          pKey.ptr,
-                          pKey.ptr + inDims.elements(),
+                          dSeq,
+                          dSeq + inDims.elements(),
                           X.begin());
             POST_LAUNCH_CHECK();
 
-            CUDA_LAUNCH((splitIndexPair<unsigned, T>), blocks, threads,
-                        pIdx.ptr, pVal.ptr, Xptr, inDims.elements());
+            CUDA_LAUNCH((splitIndexPair<T, unsigned>), blocks, threads,
+                        pVal.ptr, pIdx.ptr, Xptr, inDims.elements());
             POST_LAUNCH_CHECK();
 
             // No need of doing moddims here because the original Array<T>
diff --git a/src/backend/cuda/sort_by_key_impl.hpp b/src/backend/cuda/sort_by_key_impl.hpp
index 217b17dc8a..8cc86b55db 100644
--- a/src/backend/cuda/sort_by_key_impl.hpp
+++ b/src/backend/cuda/sort_by_key_impl.hpp
@@ -12,6 +12,7 @@
 #include <sort_by_key.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <math.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
 
@@ -23,10 +24,30 @@ namespace cuda
     {
         okey = copyArray<Tk>(ikey);
         oval = copyArray<Tv>(ival);
+
         switch(dim) {
-        case 0: kernel::sort0_by_key<Tk, Tv, isAscending>(okey, oval);
-            break;
-        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+            case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
+            case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>(okey, oval); break;
+            case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>(okey, oval); break;
+            case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>(okey, oval); break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
+
+        if(dim != 0) {
+            af::dim4 preorderDims = okey.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0] = okey.dims()[dim];
+            for(int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i] = okey.dims()[i - 1];
+            }
+
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
+
+            okey = reorder<Tk>(okey, reorderDims);
+            oval = reorder<Tv>(oval, reorderDims);
         }
     }
 

From aaa13f6cb514395dd723a1b6e09388f3825ee258 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 20 Apr 2016 13:50:59 -0400
Subject: [PATCH 0460/2677] Added tests for sort_index and sort_by_key for
 higher dimensions

---
 test/sort_by_key.cpp | 104 +++++++++++++++++++++++++++++++++++++++++--
 test/sort_index.cpp  |  95 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 196 insertions(+), 3 deletions(-)

diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index ed827c9da5..cbb13b8785 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -119,15 +119,15 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(Sort1000False,  sort_by_key_1000,  false, 2, 3);
     SORT_INIT(SortMedFalse,   sort_by_key_med,   false, 2, 3);
     // Takes too much time in current implementation. Enable when everything is parallel
-    //SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
-    //SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
+    SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
+    SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
 
 
 
 
 ////////////////////////////////////// CPP ///////////////////////////////
 //
-TEST(SortByKey, CPP)
+TEST(SortByKey, CPPDim0)
 {
     if (noDoubleTests<float>()) return;
 
@@ -168,3 +168,101 @@ TEST(SortByKey, CPP)
     delete[] keyData;
     delete[] valData;
 }
+
+TEST(SortByKey, CPPDim1)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = true;
+    const unsigned resultIdx0 = 0;
+    const unsigned resultIdx1 = 1;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array keys(idims, &(in[0].front()));
+    af::array vals(idims, &(in[1].front()));
+
+    af::array keys_ = reorder(keys, 1, 0, 2, 3);
+    af::array vals_ = reorder(vals, 1, 0, 2, 3);
+
+    af::array out_keys, out_vals;
+    af::sort(out_keys, out_vals, keys_, vals_, 1, dir);
+
+    out_keys = reorder(out_keys, 1, 0, 2, 3);
+    out_vals = reorder(out_vals, 1, 0, 2, 3);
+
+    size_t nElems = tests[resultIdx0].size();
+    // Get result
+    float* keyData = new float[tests[resultIdx0].size()];
+    out_keys.host((void*)keyData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    float* valData = new float[tests[resultIdx1].size()];
+    out_vals.host((void*)valData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] keyData;
+    delete[] valData;
+}
+
+TEST(SortByKey, CPPDim2)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = false;
+    const unsigned resultIdx0 = 2;
+    const unsigned resultIdx1 = 3;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array keys(idims, &(in[0].front()));
+    af::array vals(idims, &(in[1].front()));
+
+    af::array keys_ = reorder(keys, 1, 2, 0, 3);
+    af::array vals_ = reorder(vals, 1, 2, 0, 3);
+
+    af::array out_keys, out_vals;
+    af::sort(out_keys, out_vals, keys_, vals_, 2, dir);
+
+    out_keys = reorder(out_keys, 2, 0, 1, 3);
+    out_vals = reorder(out_vals, 2, 0, 1, 3);
+
+    size_t nElems = tests[resultIdx0].size();
+    // Get result
+    float* keyData = new float[tests[resultIdx0].size()];
+    out_keys.host((void*)keyData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    float* valData = new float[tests[resultIdx1].size()];
+    out_vals.host((void*)valData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] keyData;
+    delete[] valData;
+}
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 2326ed706f..eed85047bf 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -170,3 +170,98 @@ TEST(SortIndex, CPPDim0)
     delete[] sxData;
     delete[] ixData;
 }
+
+TEST(SortIndex, CPPDim1)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = true;
+    const unsigned resultIdx0 = 0;
+    const unsigned resultIdx1 = 1;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input_(idims, &(in[0].front()));
+    af::array input = reorder(input_, 1, 0, 2, 3);
+
+    af::array outValues, outIndices;
+    af::sort(outValues, outIndices, input, 1, dir);
+
+    outValues  = reorder(outValues,  1, 0, 2, 3);
+    outIndices = reorder(outIndices, 1, 0, 2, 3);
+
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    outValues.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Get result
+    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
+    outIndices.host((void*)ixData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+    delete[] ixData;
+}
+
+TEST(SortIndex, CPPDim2)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = false;
+    const unsigned resultIdx0 = 2;
+    const unsigned resultIdx1 = 3;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input_(idims, &(in[0].front()));
+    af::array input = reorder(input_, 1, 2, 0, 3);
+
+    af::array outValues, outIndices;
+    af::sort(outValues, outIndices, input, 2, dir);
+
+    outValues  = reorder(outValues,  2, 0, 1, 3);
+    outIndices = reorder(outIndices, 2, 0, 1, 3);
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    outValues.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Get result
+    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
+    outIndices.host((void*)ixData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        EXPECT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+    delete[] ixData;
+}

From 9066af5900bfb4010c51a25c48749dfa38180b8b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 20 Apr 2016 18:05:32 -0400
Subject: [PATCH 0461/2677] BUILD: Add options to build deb and rpm packages on
 demand

---
 CMakeModules/CPackConfig.cmake | 35 +++++++++++++++++++++-------------
 1 file changed, 22 insertions(+), 13 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index de242a99b7..deb154c6c0 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -2,16 +2,27 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
 INCLUDE("${CMAKE_MODULE_PATH}/Version.cmake")
 
+OPTION(CREATE_STGZ "Create .sh install file" ON)
+MARK_AS_ADVANCED(CREATE_STGZ)
+
 # CPack package generation
-#SET(CPACK_GENERATOR "TGZ;STGZ")
-SET(CPACK_GENERATOR "STGZ")
-# Create the following installers are as follows:
-#  Windows: Use external packaging, do nothing here
-#  OSX: Deploy as TGZ and STGZ
-#IF("${CMAKE_SYSTEM}" MATCHES "Linux")
-#    #  Linux: TGZ, STGZ, DEB
-#    SET(CPACK_GENERATOR "TGZ;STGZ;DEB;RPM")
-#ENDIF()
+IF(${CREATE_STGZ})
+  LIST(APPEND CPACK_GENERATOR "STGZ")
+ENDIF()
+
+OPTION(CREATE_DEB "Create .deb install file" OFF)
+MARK_AS_ADVANCED(CREATE_DEB)
+
+IF(${CREATE_DEB})
+  LIST(APPEND CPACK_GENERATOR "DEB")
+ENDIF()
+
+OPTION(CREATE_RPM "Create .rpm install file" OFF)
+MARK_AS_ADVANCED(CREATE_RPM)
+
+IF(${CREATE_RPM})
+  LIST(APPEND CPACK_GENERATOR "RPM")
+ENDIF()
 
 # Common settings to all packaging tools
 SET(CPACK_PREFIX_DIR ${CMAKE_INSTALL_PREFIX})
@@ -59,16 +70,14 @@ SET(CPACK_COMPONENTS_ALL libraries headers documentation cmake)
 # Debian package
 ##
 SET(CPACK_DEBIAN_PACKAGE_ARCHITECTURE ${PROCESSOR_ARCHITECTURE})
-SET(CPACK_DEBIAN_PACKAGE_DEPENDS "libfreeimage-dev, libatlas3gf-base, libfftw3-dev, liblapacke-dev")
-SET(CPACK_DEBIAN_PACKAGE_SUGGESTS "ocl-icd-libopencl1 (>= 2.0), nvidia-cuda-dev (>= 6.0)")
 
 ##
 # RPM package
 ##
 SET(CPACK_RPM_PACKAGE_LICENSE "BSD")
-SET(CPACK_PACKAGE_GROUP "Development/Libraries")
-SET(CPACK_RPM_PACKAGE_REQUIRES "freeimage atlas fftw lapack")
+set(CPACK_RPM_PACKAGE_AUTOREQPROV " no")
 
+SET(CPACK_PACKAGE_GROUP "Development/Libraries")
 ##
 # Source package
 ##

From 8dad427391087265bc9df8b3ec6f528b3fd5cc11 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 12:00:24 -0400
Subject: [PATCH 0462/2677] Add sort index, sort by key batching to OpenCL

---
 src/backend/opencl/kernel/harris.hpp        |   2 +-
 src/backend/opencl/kernel/iota.hpp          |   6 +-
 src/backend/opencl/kernel/orb.hpp           |   2 +-
 src/backend/opencl/kernel/sort.hpp          |   8 +-
 src/backend/opencl/kernel/sort_by_key.hpp   | 143 +++++++++++++++++---
 src/backend/opencl/kernel/sort_helper.hpp   | 118 ++++++++++++++++
 src/backend/opencl/kernel/sort_index.hpp    | 123 ++++++++++++++++-
 src/backend/opencl/kernel/sort_make_pair.cl |  43 ++++++
 src/backend/opencl/sort_by_key/impl.hpp     |  29 +++-
 src/backend/opencl/sort_index.cpp           |  33 ++++-
 10 files changed, 466 insertions(+), 41 deletions(-)
 create mode 100644 src/backend/opencl/kernel/sort_make_pair.cl

diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 7fffdee423..3c0e531d6d 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -287,7 +287,7 @@ void harris(unsigned* corners_out,
             harris_idx.data = bufferAlloc(sort_elem * sizeof(unsigned));
 
             // Sort Harris responses
-            sort0_index<float, false>(harris_resp, harris_idx);
+            sort0Index<float, false>(harris_resp, harris_idx);
 
             x_out.data = bufferAlloc(*corners_out * sizeof(float));
             y_out.data = bufferAlloc(*corners_out * sizeof(float));
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index bad486abd2..210b6b202e 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -31,8 +31,8 @@ namespace opencl
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
+        static const int IOTA_TX = 32;
+        static const int IOTA_TY = 8;
         static const int TILEX = 512;
         static const int TILEY = 32;
 
@@ -64,7 +64,7 @@ namespace opencl
                                           const int, const int, const int, const int,
                                           const int, const int> (*iotaKernels[device]);
 
-                NDRange local(TX, TY, 1);
+                NDRange local(IOTA_TX, IOTA_TY, 1);
 
                 int blocksPerMatX = divup(out.info.dims[0], TILEX);
                 int blocksPerMatY = divup(out.info.dims[1], TILEY);
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 69c1176210..317bb4e3d8 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -305,7 +305,7 @@ void orb(unsigned* out_feat,
             d_harris_sorted.data = d_score_harris;
             d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
 
-            sort0_index<float, false>(d_harris_sorted, d_harris_idx);
+            sort0Index<float, false>(d_harris_sorted, d_harris_idx);
 
             cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
             cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index b9a7d39b28..98ba75977a 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -131,10 +131,10 @@ namespace opencl
                 compute::buffer pKey_buf((*pKey.data)());
                 compute::buffer pVal_buf((*pVal.data)());
 
-                compute::buffer_iterator< type_t<T>    > val0 = compute::make_buffer_iterator< type_t<T>    >(pVal_buf, 0);
-                compute::buffer_iterator< type_t<T>    > valN = compute::make_buffer_iterator< type_t<T>    >(pVal_buf,+ pVal.info.dims[0]);
-                compute::buffer_iterator< type_t<uint> > key0 = compute::make_buffer_iterator< type_t<uint> >(pKey_buf, 0);
-                compute::buffer_iterator< type_t<uint> > keyN = compute::make_buffer_iterator< type_t<uint> >(pKey_buf, pKey.info.dims[0]);
+                compute::buffer_iterator<type_t<T> > val0 = compute::make_buffer_iterator<type_t<T> >(pVal_buf, 0);
+                compute::buffer_iterator<type_t<T> > valN = compute::make_buffer_iterator<type_t<T> >(pVal_buf,+ pVal.info.dims[0]);
+                compute::buffer_iterator<uint      > key0 = compute::make_buffer_iterator<uint>(pKey_buf, 0);
+                compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.info.dims[0]);
                 if(isAscending) {
                     compute::sort_by_key(val0, valN, key0, c_queue);
                 } else {
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 513ddbfb6d..33a020712e 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -16,6 +16,7 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/sort_helper.hpp>
+#include <kernel/iota.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -24,6 +25,12 @@
 #include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
+#include <boost/compute/algorithm/copy.hpp>
+#include <boost/compute/algorithm/transform.hpp>
+#include <boost/compute/container/vector.hpp>
+#include <boost/compute/functional/get.hpp>
+#include <boost/compute/functional/field.hpp>
+#include <boost/compute/types/pair.hpp>
 
 namespace compute = boost::compute;
 
@@ -40,33 +47,33 @@ namespace opencl
     namespace kernel
     {
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0_by_key(Param okey, Param oval)
+        void sort0ByKeyIterative(Param pKey, Param pVal)
         {
             try {
                 compute::command_queue c_queue(getQueue()());
 
-                compute::buffer okey_buf((*okey.data)());
-                compute::buffer oval_buf((*oval.data)());
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
 
-                for(int w = 0; w < okey.info.dims[3]; w++) {
-                    int okeyW = w * okey.info.strides[3];
-                    int ovalW = w * oval.info.strides[3];
-                    for(int z = 0; z < okey.info.dims[2]; z++) {
-                        int okeyWZ = okeyW + z * okey.info.strides[2];
-                        int ovalWZ = ovalW + z * oval.info.strides[2];
-                        for(int y = 0; y < okey.info.dims[1]; y++) {
+                for(int w = 0; w < pKey.info.dims[3]; w++) {
+                    int pKeyW = w * pKey.info.strides[3];
+                    int pValW = w * pVal.info.strides[3];
+                    for(int z = 0; z < pKey.info.dims[2]; z++) {
+                        int pKeyWZ = pKeyW + z * pKey.info.strides[2];
+                        int pValWZ = pValW + z * pVal.info.strides[2];
+                        for(int y = 0; y < pKey.info.dims[1]; y++) {
 
-                            int okeyOffset = okeyWZ + y * okey.info.strides[1];
-                            int ovalOffset = ovalWZ + y * oval.info.strides[1];
+                            int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
+                            int pValOffset = pValWZ + y * pVal.info.strides[1];
 
-                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset);
-                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset + okey.info.dims[0]);
-                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(oval_buf, ovalOffset);
+                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
+                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
+                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
                             if(isAscending) {
                                 compute::sort_by_key(start, end, vals, c_queue);
                             } else {
                                 compute::sort_by_key(start, end, vals,
-                                                            compute::greater< type_t<Tk> >(), c_queue);
+                                                     compute::greater< type_t<Tk> >(), c_queue);
                             }
                         }
                     }
@@ -78,6 +85,110 @@ namespace opencl
                 throw;
             }
         }
+
+        template<typename Tk_, typename Tv_, bool isAscending, int dim>
+        void sortByKeyBatched(Param pKey, Param pVal)
+        {
+            typedef type_t<Tk_> Tk;
+            typedef type_t<Tv_> Tv;
+            typedef std::pair<Tk, Tv> IndexPair;
+
+            try {
+                af::dim4 inDims;
+                for(int i = 0; i < 4; i++)
+                    inDims[i] = pKey.info.dims[i];
+
+                // Sort dimension
+                // tileDims * seqDims = inDims
+                af::dim4 tileDims(1);
+                af::dim4 seqDims = inDims;
+                tileDims[dim] = inDims[dim];
+                seqDims[dim] = 1;
+
+                // Create/call iota
+                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
+                dim4 keydims = inDims;
+                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(unsigned));
+                Param pSeq;
+                pSeq.data = key;
+                pSeq.info.offset = 0;
+                pSeq.info.dims[0] = keydims[0];
+                pSeq.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pSeq.info.dims[i] = keydims[i];
+                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
+                }
+                kernel::iota<unsigned>(pSeq, seqDims, tileDims);
+
+                int elements = inDims.elements();
+
+                // Flat - Not required since inplace and both are continuous
+                //val.modDims(inDims.elements());
+                //key.modDims(inDims.elements());
+
+                // Sort indices
+                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
+                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
+                compute::command_queue c_queue(getQueue()());
+                compute::context c_context(getContext()());
+
+                // Create buffer iterators for seq
+                compute::buffer pSeq_buf((*pSeq.data)());
+                compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
+                compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
+
+                // Copy key, val into X pair
+                cl::Buffer* X = bufferAlloc(elements * sizeof(IndexPair));
+                // Use Tk_ and Tv_ here, not Tk and Tv
+                kernel::makePair<Tk_, Tv_>(X, pKey.data, pVal.data, elements);
+                compute::buffer X_buf((*X)());
+                compute::buffer_iterator<IndexPair> X0 = compute::make_buffer_iterator<IndexPair>(X_buf, 0);
+                compute::buffer_iterator<IndexPair> XN = compute::make_buffer_iterator<IndexPair>(X_buf, elements);
+
+                // FIRST SORT CALL
+                compute::function<bool(IndexPair, IndexPair)> IPCompare =
+                    makeCompareFunction<Tk, Tv, isAscending>();
+
+                compute::sort_by_key(X0, XN, seq0, IPCompare, c_queue);
+                getQueue().finish();
+
+                // Needs to be ascending (true) in order to maintain the indices properly
+                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
+                //
+                // Because we use a pair as values, we need to use a custom comparator
+                BOOST_COMPUTE_FUNCTION(bool, Compare_Seq, (const unsigned lhs, const unsigned rhs),
+                    {
+                        return lhs < rhs;
+                    }
+                );
+                compute::sort_by_key(seq0, seqN, X0, Compare_Seq, c_queue);
+                getQueue().finish();
+
+                kernel::splitPair<Tk_, Tv_>(pKey.data, pVal.data, X, elements);
+
+                //// No need of doing moddims here because the original Array<T>
+                //// dimensions have not been changed
+                ////val.modDims(inDims);
+
+                CL_DEBUG_FINISH(getQueue());
+                bufferFree(key);
+                bufferFree(X);
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param pKey, Param pVal)
+        {
+            int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 5)
+                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
+        }
     }
 }
 
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 07ab0eeb69..6ba9eff0ae 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -8,8 +8,41 @@
  ********************************************************/
 
 #pragma once
+#include <kernel_headers/sort_make_pair.hpp>
 #include <program.hpp>
 #include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <utility>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+
+#include <boost/compute/function.hpp>
+
+template<typename Tk, typename Tv, bool isAscending>
+inline
+boost::compute::function<bool(const std::pair<Tk, Tv>, const std::pair<Tk, Tv>)>
+makeCompareFunction()
+{
+    // Cannot use isAscending in BOOST_COMPUTE_FUNCTION
+    if(isAscending) {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+            {
+                return lhs.first < rhs.first;
+            }
+        );
+        return IPCompare;
+    } else {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+            {
+                return lhs.first > rhs.first;
+            }
+        );
+        return IPCompare;
+    }
+}
 
 namespace opencl
 {
@@ -42,5 +75,90 @@ namespace opencl
                                             cl_ulong, ltype_t<T>
                                            >::type;
 
+        static const int copyPairIter = 4;
+
+        template<typename Tk, typename Tv>
+        void makePair(cl::Buffer *out, const cl::Buffer *first, const cl::Buffer *second, const unsigned N)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   makePairProgs;
+                static std::map<int, Kernel*>  makePairKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D Tk="      << dtype_traits<Tk>::getName()
+                            << " -D Tv="      << dtype_traits<Tv>::getName()
+                            << " -D copyPairIter=" << copyPairIter;
+                    if (std::is_same<Tk, double >::value ||
+                        std::is_same<Tk, cdouble>::value ||
+                        std::is_same<Tv, double >::value ||
+                        std::is_same<Tv, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, sort_make_pair_cl, sort_make_pair_cl_len, options.str());
+                    makePairProgs[device]   = new Program(prog);
+                    makePairKernels[device] = new Kernel(*makePairProgs[device], "make_pair_kernel");
+                });
+
+                auto makePairOp = make_kernel<Buffer, const Buffer, const Buffer, const unsigned>
+                                          (*makePairKernels[device]);
+
+                NDRange local(256, 1, 1);
+                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
+
+                makePairOp(EnqueueArgs(getQueue(), global, local), *out, *first, *second, N);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv>
+        void splitPair(cl::Buffer *first, cl::Buffer *second, const cl::Buffer *in, const unsigned N)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   splitPairProgs;
+                static std::map<int, Kernel*>  splitPairKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D Tk="      << dtype_traits<Tk>::getName()
+                            << " -D Tv="      << dtype_traits<Tv>::getName()
+                            << " -D copyPairIter=" << copyPairIter;
+                    if (std::is_same<Tk, double >::value ||
+                        std::is_same<Tk, cdouble>::value ||
+                        std::is_same<Tv, double >::value ||
+                        std::is_same<Tv, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, sort_make_pair_cl, sort_make_pair_cl_len, options.str());
+                    splitPairProgs[device]   = new Program(prog);
+                    splitPairKernels[device] = new Kernel(*splitPairProgs[device], "split_pair_kernel");
+                });
+
+                auto splitPairOp = make_kernel<Buffer, Buffer, const Buffer, const unsigned>
+                                          (*splitPairKernels[device]);
+
+                NDRange local(256, 1, 1);
+                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
+
+                splitPairOp(EnqueueArgs(getQueue(), global, local), *first, *second, *in, N);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/sort_index.hpp b/src/backend/opencl/kernel/sort_index.hpp
index aae0a94ea6..ef5faa612f 100644
--- a/src/backend/opencl/kernel/sort_index.hpp
+++ b/src/backend/opencl/kernel/sort_index.hpp
@@ -16,15 +16,21 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/sort_helper.hpp>
+#include <kernel/iota.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
 #include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/iota.hpp>
 #include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
+#include <boost/compute/algorithm/copy.hpp>
+#include <boost/compute/algorithm/transform.hpp>
+#include <boost/compute/container/vector.hpp>
+#include <boost/compute/functional/get.hpp>
+#include <boost/compute/functional/field.hpp>
+#include <boost/compute/types/pair.hpp>
 
 namespace compute = boost::compute;
 
@@ -41,7 +47,7 @@ namespace opencl
     namespace kernel
     {
         template<typename T, bool isAscending>
-        void sort0_index(Param val, Param idx)
+        void sort0IndexIterative(Param val, Param idx)
         {
             try {
                 compute::command_queue c_queue(getQueue()());
@@ -60,19 +66,18 @@ namespace opencl
                             int valOffset = valWZ + y * val.info.strides[1];
                             int idxOffset = idxWZ + y * idx.info.strides[1];
 
-                            compute::buffer_iterator<unsigned> idx_begin(idx_buf, idxOffset);
-                            compute::iota(idx_begin, idx_begin + val.info.dims[0], 0, c_queue);
-
                             if(isAscending) {
                                 compute::sort_by_key(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        idx_begin, compute::less< type_t<T> >(), c_queue);
+                                        compute::make_buffer_iterator< type_t<uint> >(idx_buf, idxOffset),
+                                        compute::less< type_t<T> >(), c_queue);
                             } else {
                                 compute::sort_by_key(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        idx_begin, compute::greater< type_t<T> >(), c_queue);
+                                        compute::make_buffer_iterator< type_t<uint> >(idx_buf, idxOffset),
+                                        compute::greater< type_t<T> >(), c_queue);
                             }
                         }
                     }
@@ -84,6 +89,110 @@ namespace opencl
                 throw;
             }
         }
+
+        template<typename T, bool isAscending, int dim>
+        void sortIndexBatched(Param pVal, Param pIdx)
+        {
+            typedef type_t<T> Tk;
+            typedef uint Tv;
+            typedef std::pair<Tk, Tv> IndexPair;
+
+            try {
+                af::dim4 inDims;
+                for(int i = 0; i < 4; i++)
+                    inDims[i] = pVal.info.dims[i];
+
+                // Sort dimension
+                // tileDims * seqDims = inDims
+                af::dim4 tileDims(1);
+                af::dim4 seqDims = inDims;
+                tileDims[dim] = inDims[dim];
+                seqDims[dim] = 1;
+
+                // Create/call iota
+                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
+                dim4 keydims = inDims;
+                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(Tv));
+                Param pSeq;
+                pSeq.data = key;
+                pSeq.info.offset = 0;
+                pSeq.info.dims[0] = keydims[0];
+                pSeq.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pSeq.info.dims[i] = keydims[i];
+                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
+                }
+                kernel::iota<Tv>(pSeq, seqDims, tileDims);
+
+                int elements = inDims.elements();
+
+                // Flat - Not required since inplace and both are continuous
+                //val.modDims(inDims.elements());
+                //key.modDims(inDims.elements());
+
+                // Sort indices
+                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
+                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
+                compute::command_queue c_queue(getQueue()());
+                compute::context c_context(getContext()());
+
+                // Create buffer iterators for seq
+                compute::buffer pSeq_buf((*pSeq.data)());
+                compute::buffer_iterator<Tv> seq0 = compute::make_buffer_iterator<Tv>(pSeq_buf, 0);
+                compute::buffer_iterator<Tv> seqN = compute::make_buffer_iterator<Tv>(pSeq_buf, elements);
+
+                // Copy val, idx into X pair
+                cl::Buffer* X = bufferAlloc(elements * sizeof(IndexPair));
+                // Use T here, not Tk
+                kernel::makePair<T, Tv>(X, pVal.data, pIdx.data, elements);
+                compute::buffer X_buf((*X)());
+                compute::buffer_iterator<IndexPair> X0 = compute::make_buffer_iterator<IndexPair>(X_buf, 0);
+                compute::buffer_iterator<IndexPair> XN = compute::make_buffer_iterator<IndexPair>(X_buf, elements);
+
+                // FIRST SORT CALL
+                compute::function<bool(IndexPair, IndexPair)> IPCompare =
+                    makeCompareFunction<Tk, Tv, isAscending>();
+
+                compute::sort_by_key(X0, XN, seq0, IPCompare, c_queue);
+                getQueue().finish();
+
+                // Needs to be ascending (true) in order to maintain the indices properly
+                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
+                //
+                // Because we use a pair as values, we need to use a custom comparator
+                BOOST_COMPUTE_FUNCTION(bool, Compare_Tv, (const Tv lhs, const Tv rhs),
+                    {
+                        return lhs < rhs;
+                    }
+                );
+                compute::sort_by_key(seq0, seqN, X0, Compare_Tv, c_queue);
+                getQueue().finish();
+
+                kernel::splitPair<T, Tv>(pVal.data, pIdx.data, X, elements);
+
+                //// No need of doing moddims here because the original Array<T>
+                //// dimensions have not been changed
+                ////val.modDims(inDims);
+
+                CL_DEBUG_FINISH(getQueue());
+                bufferFree(key);
+                bufferFree(X);
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename T, bool isAscending>
+        void sort0Index(Param val, Param idx)
+        {
+            int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 5)
+              sortIndexBatched<T, isAscending, 0>(val, idx);
+            else
+                kernel::sort0IndexIterative<T, isAscending>(val, idx);
+        }
     }
 }
 
diff --git a/src/backend/opencl/kernel/sort_make_pair.cl b/src/backend/opencl/kernel/sort_make_pair.cl
new file mode 100644
index 0000000000..f5e5413d73
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_make_pair.cl
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+struct IndexPair
+{
+    Tk first;
+    Tv second;
+};
+
+typedef struct IndexPair IndexPair_t;
+
+__kernel
+void make_pair_kernel(__global IndexPair_t *out,
+                      __global const Tk *first, __global const Tv *second,
+                      const unsigned N)
+{
+    int tIdx = get_group_id(0) * get_local_size(0) * copyPairIter + get_local_id(0);
+    const int blockDimX = get_local_size(0);
+
+    for(int i = tIdx; i < N; i += blockDimX) {
+        out[i].first  = first[i];
+        out[i].second = second[i];
+    }
+}
+
+__kernel
+void split_pair_kernel( __global Tk *first, __global Tv *second,
+                       __global const IndexPair_t *out, const unsigned N)
+{
+    int tIdx = get_group_id(0) * get_local_size(0) * copyPairIter + get_local_id(0);
+    const int blockDimX = get_local_size(0);
+
+    for(int i = tIdx; i < N; i += blockDimX) {
+        first[i]  = out[i].first;
+        second[i] = out[i].second;
+    }
+}
diff --git a/src/backend/opencl/sort_by_key/impl.hpp b/src/backend/opencl/sort_by_key/impl.hpp
index 68c5ce70ae..f68fe91b3c 100644
--- a/src/backend/opencl/sort_by_key/impl.hpp
+++ b/src/backend/opencl/sort_by_key/impl.hpp
@@ -11,6 +11,7 @@
 #include <copy.hpp>
 #include <sort_by_key.hpp>
 #include <kernel/sort_by_key.hpp>
+#include <reorder.hpp>
 #include <math.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
@@ -24,12 +25,32 @@ namespace opencl
         try {
             okey = copyArray<Tk>(ikey);
             oval = copyArray<Tv>(ival);
+
             switch(dim) {
-            case 0: kernel::sort0_by_key<Tk, Tv, isAscending>(okey, oval);
-                break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+                case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
+                case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>(okey, oval); break;
+                case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>(okey, oval); break;
+                case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>(okey, oval); break;
+                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+            }
+
+            if(dim != 0) {
+                af::dim4 preorderDims = okey.dims();
+                af::dim4 reorderDims(0, 1, 2, 3);
+                reorderDims[dim] = 0;
+                preorderDims[0] = okey.dims()[dim];
+                for(int i = 1; i <= (int)dim; i++) {
+                    reorderDims[i - 1] = i;
+                    preorderDims[i] = okey.dims()[i - 1];
+                }
+
+                okey.setDataDims(preorderDims);
+                oval.setDataDims(preorderDims);
+
+                okey = reorder<Tk>(okey, reorderDims);
+                oval = reorder<Tv>(oval, reorderDims);
             }
-        }catch(std::exception &ex) {
+        } catch(std::exception &ex) {
             AF_ERROR(ex.what(), AF_ERR_INTERNAL);
         }
     }
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index c7aaa70feb..49795c2eb5 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -14,6 +14,8 @@
 #include <math.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
+#include <reorder.hpp>
+#include <range.hpp>
 
 namespace opencl
 {
@@ -22,17 +24,38 @@ namespace opencl
     {
         try {
             val = copyArray<T>(in);
-            idx = createEmptyArray<uint>(in.dims());
+            idx = range<uint>(in.dims(), dim);
+            idx.eval();
 
             switch(dim) {
-            case 0: kernel::sort0_index<T, isAscending>(val, idx);
-                break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+                case 0: kernel::sort0Index<T, isAscending>(val, idx); break;
+                case 1: kernel::sortIndexBatched<T, isAscending, 1>(val, idx); break;
+                case 2: kernel::sortIndexBatched<T, isAscending, 2>(val, idx); break;
+                case 3: kernel::sortIndexBatched<T, isAscending, 3>(val, idx); break;
+                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
-        }         catch (std::exception &ex) {
+
+            if(dim != 0) {
+                af::dim4 preorderDims = val.dims();
+                af::dim4 reorderDims(0, 1, 2, 3);
+                reorderDims[dim] = 0;
+                preorderDims[0] = val.dims()[dim];
+                for(int i = 1; i <= (int)dim; i++) {
+                    reorderDims[i - 1] = i;
+                    preorderDims[i] = val.dims()[i - 1];
+                }
+
+                val.setDataDims(preorderDims);
+                idx.setDataDims(preorderDims);
+
+                val = reorder<T>(val, reorderDims);
+                idx = reorder<uint>(idx, reorderDims);
+            }
+        } catch (std::exception &ex) {
             AF_ERROR(ex.what(), AF_ERR_INTERNAL);
         }
     }
+
 #define INSTANTIATE(T)                                                  \
     template void sort_index<T, true>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
                                       const uint dim);                  \

From efdc54264dde88b3635e0c6c0a1017293cd25353 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 13:18:18 -0400
Subject: [PATCH 0463/2677] Combine sort_index and sort_by_key kernels in CPU

---
 src/backend/cpu/kernel/sort_index.hpp | 140 --------------------------
 src/backend/cpu/sort_index.cpp        |  33 +++---
 2 files changed, 17 insertions(+), 156 deletions(-)
 delete mode 100644 src/backend/cpu/kernel/sort_index.hpp

diff --git a/src/backend/cpu/kernel/sort_index.hpp b/src/backend/cpu/kernel/sort_index.hpp
deleted file mode 100644
index 7a23a7df49..0000000000
--- a/src/backend/cpu/kernel/sort_index.hpp
+++ /dev/null
@@ -1,140 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <af/defines.h>
-#include <Array.hpp>
-#include <math.hpp>
-#include <algorithm>
-#include <numeric>
-#include <err_cpu.hpp>
-#include <functional>
-#include <kernel/sort_helper.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-
-template<typename T, bool isAscending>
-void sort0IndexIterative(Array<T> val, Array<uint> idx)
-{
-    // initialize original index locations
-    uint *idx_ptr = idx.get();
-       T *val_ptr = val.get();
-
-    std::vector<IndexPair<T, uint> > X;
-    X.reserve(val.dims()[0]);
-
-    for(dim_t w = 0; w < val.dims()[3]; w++) {
-        dim_t valW = w * val.strides()[3];
-        dim_t idxW = w * idx.strides()[3];
-        for(dim_t z = 0; z < val.dims()[2]; z++) {
-            dim_t valWZ = valW + z * val.strides()[2];
-            dim_t idxWZ = idxW + z * idx.strides()[2];
-            for(dim_t y = 0; y < val.dims()[1]; y++) {
-                dim_t valOffset = valWZ + y * val.strides()[1];
-                dim_t idxOffset = idxWZ + y * idx.strides()[1];
-
-                X.clear();
-                std::transform(val_ptr + valOffset, val_ptr + valOffset + val.dims()[0],
-                               idx_ptr + idxOffset,
-                               std::back_inserter(X),
-                               [](T v_, uint i_) { return std::make_pair(v_, i_); }
-                               );
-
-                //comp_ptr = &X.front();
-                std::stable_sort(X.begin(), X.end(), IPCompare<T, uint, isAscending>());
-
-                for(unsigned it = 0; it < X.size(); it++) {
-                    val_ptr[valOffset + it] = X[it].first;
-                    idx_ptr[idxOffset + it] = X[it].second;
-                }
-            }
-        }
-    }
-
-    return;
-}
-
-template<typename T, bool isAscending, int dim>
-void sortIndexBatched(Array<T> val, Array<uint> idx)
-{
-    af::dim4 inDims = val.dims();
-
-    af::dim4 tileDims(1);
-    af::dim4 seqDims = inDims;
-    tileDims[dim] = inDims[dim];
-    seqDims[dim] = 1;
-
-    uint* key = memAlloc<uint>(inDims.elements());
-    // IOTA
-    {
-        af::dim4 dims    = inDims;
-        uint* out        = key;
-        af::dim4 strides(1);
-        for(int i = 1; i < 4; i++)
-            strides[i] = strides[i-1] * dims[i-1];
-
-        for(dim_t w = 0; w < dims[3]; w++) {
-            dim_t offW = w * strides[3];
-            uint valW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
-            for(dim_t z = 0; z < dims[2]; z++) {
-                dim_t offWZ = offW + z * strides[2];
-                uint valZ = valW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
-                for(dim_t y = 0; y < dims[1]; y++) {
-                    dim_t offWZY = offWZ + y * strides[1];
-                    uint valY = valZ + (y % seqDims[1]) * seqDims[0];
-                    for(dim_t x = 0; x < dims[0]; x++) {
-                        dim_t id = offWZY + x;
-                        out[id] = valY + (x % seqDims[0]);
-                    }
-                }
-            }
-        }
-    }
-
-    // initialize original index locations
-    uint *idx_ptr = idx.get();
-       T *val_ptr = val.get();
-
-    std::vector<KeyIndexPair<T, uint> > X;
-    X.reserve(val.elements());
-
-    for(unsigned i = 0; i < val.elements(); i++) {
-        X.push_back(std::make_pair(std::make_pair(val_ptr[i], idx_ptr[i]), key[i]));
-    }
-
-    memFree(key); // key is no longer required
-
-    std::stable_sort(X.begin(), X.end(), KIPCompareV<T, uint, isAscending>());
-
-    std::stable_sort(X.begin(), X.end(), KIPCompareK<T, uint, true>());
-
-    for(unsigned it = 0; it < val.elements(); it++) {
-        val_ptr[it] = X[it].first.first;
-        idx_ptr[it] = X[it].first.second;
-    }
-
-    return;
-}
-
-template<typename T, bool isAscending>
-void sort0Index(Array<T> val, Array<unsigned> idx)
-{
-    int higherDims =  val.dims()[1] * val.dims()[2] * val.dims()[3];
-    // TODO Make a better heurisitic
-    if(higherDims > 0)
-        kernel::sortIndexBatched<T, isAscending, 0>(val, idx);
-    else
-        kernel::sort0IndexIterative<T, isAscending>(val, idx);
-}
-
-}
-}
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index 36ca57b3e8..b865db9c1c 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -17,43 +17,44 @@
 #include <range.hpp>
 #include <copy.hpp>
 #include <reorder.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 
 namespace cpu
 {
 
 template<typename T, bool isAscending>
-void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
 {
     in.eval();
 
-    val = copyArray<T>(in);
-    idx = range<uint>(in.dims(), dim);
-    idx.eval();
+    // okey is values, oval is indices
+    okey = copyArray<T>(in);
+    oval = range<uint>(in.dims(), dim);
+    oval.eval();
 
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0Index<T, isAscending>, val, idx); break;
-        case 1: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 1>, val, idx); break;
-        case 2: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 2>, val, idx); break;
-        case 3: getQueue().enqueue(kernel::sortIndexBatched<T, isAscending, 3>, val, idx); break;
+        case 0: getQueue().enqueue(kernel::sort0ByKey<T, uint, isAscending>, okey, oval); break;
+        case 1: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 1>, okey, oval); break;
+        case 2: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 2>, okey, oval); break;
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 3>, okey, oval); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
     if(dim != 0) {
-        af::dim4 preorderDims = val.dims();
+        af::dim4 preorderDims = okey.dims();
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
-        preorderDims[0] = val.dims()[dim];
+        preorderDims[0] = okey.dims()[dim];
         for(int i = 1; i <= (int)dim; i++) {
             reorderDims[i - 1] = i;
-            preorderDims[i] = val.dims()[i - 1];
+            preorderDims[i] = okey.dims()[i - 1];
         }
 
-        val.setDataDims(preorderDims);
-        idx.setDataDims(preorderDims);
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
 
-        val = reorder<T>(val, reorderDims);
-        idx = reorder<uint>(idx, reorderDims);
+        okey = reorder<T>(okey, reorderDims);
+        oval = reorder<uint>(oval, reorderDims);
     }
 }
 

From 460bf6c8baaf1b7fce4a758dbe3f9cd48c291499 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 13:29:17 -0400
Subject: [PATCH 0464/2677] Combine sort_index and sort_by_key kernels in CUDA

---
 src/backend/cuda/kernel/harris.hpp      |   4 +-
 src/backend/cuda/kernel/orb.hpp         |   4 +-
 src/backend/cuda/kernel/sort_by_key.hpp |   3 +-
 src/backend/cuda/kernel/sort_index.hpp  | 141 ------------------------
 src/backend/cuda/sort_index.cu          |  32 +++---
 5 files changed, 22 insertions(+), 162 deletions(-)
 delete mode 100644 src/backend/cuda/kernel/sort_index.hpp

diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 3cb28b2b2f..c773ae45c5 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -18,7 +18,7 @@
 #include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "gradient.hpp"
-#include "sort_index.hpp"
+#include "sort_by_key.hpp"
 
 namespace cuda
 {
@@ -339,7 +339,7 @@ void harris(unsigned* corners_out,
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
 
         // Sort Harris responses
-        sort0Index<float, false>(harris_responses, harris_idx);
+        sort0ByKey<float, uint, false>(harris_responses, harris_idx);
 
         *x_out = memAlloc<float>(*corners_out);
         *y_out = memAlloc<float>(*corners_out);
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 8448418f8b..b5ed10340e 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -16,7 +16,7 @@
 #include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "orb_patch.hpp"
-#include "sort_index.hpp"
+#include "sort_by_key.hpp"
 
 #include <boost/scoped_ptr.hpp>
 
@@ -397,7 +397,7 @@ void orb(unsigned* out_feat,
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
 
         // Sort features according to Harris responses
-        sort0Index<float, false>(harris_sorted, harris_idx);
+        sort0ByKey<float, uint, false>(harris_sorted, harris_idx);
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index beffa5476e..1536d1bf53 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -109,6 +109,7 @@ namespace cuda
             POST_LAUNCH_CHECK();
 
             // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
             THRUST_SELECT(thrust::stable_sort_by_key,
                           dSeq,
                           dSeq + inDims.elements(),
@@ -132,7 +133,7 @@ namespace cuda
             int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 5)
-                sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
         }
diff --git a/src/backend/cuda/kernel/sort_index.hpp b/src/backend/cuda/kernel/sort_index.hpp
deleted file mode 100644
index 40a5d59311..0000000000
--- a/src/backend/cuda/kernel/sort_index.hpp
+++ /dev/null
@@ -1,141 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <math.hpp>
-#include <dispatch.hpp>
-#include <Param.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
-#include <kernel/sort_helper.hpp>
-#include <kernel/iota.hpp>
-
-#include <thrust/device_ptr.h>
-#include <thrust/device_vector.h>
-#include <thrust/sort.h>
-
-namespace cuda
-{
-    namespace kernel
-    {
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool isAscending>
-        void sort0IndexIterative(Param<T> val, Param<unsigned> idx)
-        {
-            thrust::device_ptr<T>        val_ptr = thrust::device_pointer_cast(val.ptr);
-            thrust::device_ptr<unsigned> idx_ptr = thrust::device_pointer_cast(idx.ptr);
-
-            for(int w = 0; w < val.dims[3]; w++) {
-                int valW = w * val.strides[3];
-                int idxW = w * idx.strides[3];
-                for(int z = 0; z < val.dims[2]; z++) {
-                    int valWZ = valW + z * val.strides[2];
-                    int idxWZ = idxW + z * idx.strides[2];
-                    for(int y = 0; y < val.dims[1]; y++) {
-
-                        int valOffset = valWZ + y * val.strides[1];
-                        int idxOffset = idxWZ + y * idx.strides[1];
-
-                        if(isAscending) {
-                            THRUST_SELECT(thrust::stable_sort_by_key,
-                                    val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
-                                    idx_ptr + idxOffset);
-                        } else {
-                            THRUST_SELECT(thrust::stable_sort_by_key,
-                                        val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
-                                        idx_ptr + idxOffset, thrust::greater<T>());
-                        }
-                    }
-                }
-            }
-            POST_LAUNCH_CHECK();
-        }
-
-        template<typename T, bool isAscending, int dim>
-        void sortIndexBatched(Param<T> pVal, Param<unsigned> pIdx)
-        {
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pVal.dims[i];
-
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
-
-            // Create/call iota
-            // Array<uint> seq = iota<uint>(seqDims, tileDims);
-            dim4 keydims = inDims;
-            uint* key = memAlloc<uint>(keydims.elements());
-            Param<uint> pSeq;
-            pSeq.ptr = key;
-            pSeq.strides[0] = 1;
-            pSeq.dims[0] = keydims[0];
-            for(int i = 1; i < 4; i++) {
-                pSeq.dims[i] = keydims[i];
-                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
-            }
-            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
-
-            // Flat - Not required since inplace and both are continuous
-            //val.modDims(inDims.elements());
-            //key.modDims(inDims.elements());
-
-            // Make val, idx into a pair
-            thrust::device_vector<IndexPair<T, uint> > X(inDims.elements());
-            IndexPair<T, uint> *Xptr = thrust::raw_pointer_cast(X.data());
-
-            const int threads = 256;
-            int blocks = divup(inDims.elements(), threads * copyPairIter);
-            CUDA_LAUNCH((makeIndexPair<T, unsigned>), blocks, threads,
-                        Xptr, pVal.ptr, pIdx.ptr, inDims.elements());
-
-            // Sort indices
-            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          X.begin(), X.end(),
-                          dSeq,
-                          IPCompare<T, uint, isAscending>());
-            POST_LAUNCH_CHECK();
-
-            // Needs to be ascending (true) in order to maintain the indices properly
-            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          dSeq,
-                          dSeq + inDims.elements(),
-                          X.begin());
-            POST_LAUNCH_CHECK();
-
-            CUDA_LAUNCH((splitIndexPair<T, unsigned>), blocks, threads,
-                        pVal.ptr, pIdx.ptr, Xptr, inDims.elements());
-            POST_LAUNCH_CHECK();
-
-            // No need of doing moddims here because the original Array<T>
-            // dimensions have not been changed
-            //val.modDims(inDims);
-
-            memFree(key);
-        }
-
-        template<typename T, bool isAscending>
-        void sort0Index(Param<T> val, Param<unsigned> idx)
-        {
-            int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 5)
-                sortIndexBatched<T, isAscending, 0>(val, idx);
-            else
-                kernel::sort0IndexIterative<T, isAscending>(val, idx);
-        }
-    }
-}
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index ab54c24a9c..03c69ad4f3 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <sort_index.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <copy.hpp>
 #include <math.hpp>
 #include <stdexcept>
@@ -20,35 +20,35 @@
 namespace cuda
 {
     template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
     {
-        val = copyArray<T>(in);
-        idx = range<uint>(in.dims(), dim);
-        idx.eval();
+        okey = copyArray<T>(in);
+        oval = range<uint>(in.dims(), dim);
+        oval.eval();
 
         switch(dim) {
-            case 0: kernel::sort0Index<T, isAscending>(val, idx); break;
-            case 1: kernel::sortIndexBatched<T, isAscending, 1>(val, idx); break;
-            case 2: kernel::sortIndexBatched<T, isAscending, 2>(val, idx); break;
-            case 3: kernel::sortIndexBatched<T, isAscending, 3>(val, idx); break;
+            case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
+            case 1: kernel::sortByKeyBatched<T, uint, isAscending, 1>(okey, oval); break;
+            case 2: kernel::sortByKeyBatched<T, uint, isAscending, 2>(okey, oval); break;
+            case 3: kernel::sortByKeyBatched<T, uint, isAscending, 3>(okey, oval); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
         if(dim != 0) {
-            af::dim4 preorderDims = val.dims();
+            af::dim4 preorderDims = okey.dims();
             af::dim4 reorderDims(0, 1, 2, 3);
             reorderDims[dim] = 0;
-            preorderDims[0] = val.dims()[dim];
+            preorderDims[0] = okey.dims()[dim];
             for(int i = 1; i <= (int)dim; i++) {
                 reorderDims[i - 1] = i;
-                preorderDims[i] = val.dims()[i - 1];
+                preorderDims[i] = okey.dims()[i - 1];
             }
 
-            val.setDataDims(preorderDims);
-            idx.setDataDims(preorderDims);
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
 
-            val = reorder<T>(val, reorderDims);
-            idx = reorder<uint>(idx, reorderDims);
+            okey = reorder<T>(okey, reorderDims);
+            oval = reorder<uint>(oval, reorderDims);
         }
     }
 

From 363e86e5031f64689db50efb08b3323dea443dba Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 13:29:46 -0400
Subject: [PATCH 0465/2677] Combine sort_index and sort_by_key kernels in
 OpenCL

---
 src/backend/opencl/kernel/harris.hpp          |   4 +-
 src/backend/opencl/kernel/orb.hpp             |   4 +-
 src/backend/opencl/kernel/sift_nonfree.hpp    |   3 +-
 src/backend/opencl/kernel/sort_helper.hpp     |  26 +--
 src/backend/opencl/kernel/sort_index.hpp      | 199 ------------------
 .../{sort_make_pair.cl => sort_pair.cl}       |   0
 src/backend/opencl/sort_index.cpp             |  33 +--
 7 files changed, 36 insertions(+), 233 deletions(-)
 delete mode 100644 src/backend/opencl/kernel/sort_index.hpp
 rename src/backend/opencl/kernel/{sort_make_pair.cl => sort_pair.cl} (100%)

diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 3c0e531d6d..4c203c9377 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -16,7 +16,7 @@
 #include <convolve_common.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/gradient.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
 #include <map>
@@ -287,7 +287,7 @@ void harris(unsigned* corners_out,
             harris_idx.data = bufferAlloc(sort_elem * sizeof(unsigned));
 
             // Sort Harris responses
-            sort0Index<float, false>(harris_resp, harris_idx);
+            sort0ByKey<float, uint, false>(harris_resp, harris_idx);
 
             x_out.data = bufferAlloc(*corners_out * sizeof(float));
             y_out.data = bufferAlloc(*corners_out * sizeof(float));
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 317bb4e3d8..612edacfe0 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -16,7 +16,7 @@
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <kernel_headers/orb.hpp>
 #include <memory.hpp>
 #include <vector>
@@ -305,7 +305,7 @@ void orb(unsigned* out_feat,
             d_harris_sorted.data = d_score_harris;
             d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
 
-            sort0Index<float, false>(d_harris_sorted, d_harris_idx);
+            sort0ByKey<float, uint, false>(d_harris_sorted, d_harris_idx);
 
             cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
             cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index c28f432fce..0b78ef02f3 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -92,11 +92,12 @@
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
-#include <kernel/sort_index.hpp>
 #include <kernel_headers/sift_nonfree.hpp>
 #include <memory.hpp>
 #include <vector>
 
+namespace compute = boost::compute;
+
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 6ba9eff0ae..7f500c6bb0 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/sort_make_pair.hpp>
+#include <kernel_headers/sort_pair.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -82,8 +82,8 @@ namespace opencl
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   makePairProgs;
-                static std::map<int, Kernel*>  makePairKernels;
+                static std::map<int, Program*>   sortPairProgs;
+                static std::map<int, Kernel*>  sortPairKernels;
 
                 int device = getActiveDeviceId();
 
@@ -99,13 +99,13 @@ namespace opencl
                         options << " -D USE_DOUBLE";
                     }
                     Program prog;
-                    buildProgram(prog, sort_make_pair_cl, sort_make_pair_cl_len, options.str());
-                    makePairProgs[device]   = new Program(prog);
-                    makePairKernels[device] = new Kernel(*makePairProgs[device], "make_pair_kernel");
+                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
+                    sortPairProgs[device]   = new Program(prog);
+                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "make_pair_kernel");
                 });
 
                 auto makePairOp = make_kernel<Buffer, const Buffer, const Buffer, const unsigned>
-                                          (*makePairKernels[device]);
+                                          (*sortPairKernels[device]);
 
                 NDRange local(256, 1, 1);
                 NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
@@ -124,8 +124,8 @@ namespace opencl
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   splitPairProgs;
-                static std::map<int, Kernel*>  splitPairKernels;
+                static std::map<int, Program*>   sortPairProgs;
+                static std::map<int, Kernel*>  sortPairKernels;
 
                 int device = getActiveDeviceId();
 
@@ -141,13 +141,13 @@ namespace opencl
                         options << " -D USE_DOUBLE";
                     }
                     Program prog;
-                    buildProgram(prog, sort_make_pair_cl, sort_make_pair_cl_len, options.str());
-                    splitPairProgs[device]   = new Program(prog);
-                    splitPairKernels[device] = new Kernel(*splitPairProgs[device], "split_pair_kernel");
+                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
+                    sortPairProgs[device]   = new Program(prog);
+                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "split_pair_kernel");
                 });
 
                 auto splitPairOp = make_kernel<Buffer, Buffer, const Buffer, const unsigned>
-                                          (*splitPairKernels[device]);
+                                          (*sortPairKernels[device]);
 
                 NDRange local(256, 1, 1);
                 NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
diff --git a/src/backend/opencl/kernel/sort_index.hpp b/src/backend/opencl/kernel/sort_index.hpp
deleted file mode 100644
index ef5faa612f..0000000000
--- a/src/backend/opencl/kernel/sort_index.hpp
+++ /dev/null
@@ -1,199 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <kernel/sort_helper.hpp>
-#include <kernel/iota.hpp>
-
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
-#include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
-#include <boost/compute/functional/operator.hpp>
-#include <boost/compute/iterator/buffer_iterator.hpp>
-#include <boost/compute/algorithm/copy.hpp>
-#include <boost/compute/algorithm/transform.hpp>
-#include <boost/compute/container/vector.hpp>
-#include <boost/compute/functional/get.hpp>
-#include <boost/compute/functional/field.hpp>
-#include <boost/compute/types/pair.hpp>
-
-namespace compute = boost::compute;
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::make_kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T, bool isAscending>
-        void sort0IndexIterative(Param val, Param idx)
-        {
-            try {
-                compute::command_queue c_queue(getQueue()());
-
-                compute::buffer val_buf((*val.data)());
-                compute::buffer idx_buf((*idx.data)());
-
-                for(int w = 0; w < (int)val.info.dims[3]; w++) {
-                    int valW = w * (int)val.info.strides[3];
-                    int idxW = w * idx.info.strides[3];
-                    for(int z = 0; z < (int)val.info.dims[2]; z++) {
-                        int valWZ = valW + z * (int)val.info.strides[2];
-                        int idxWZ = idxW + z * idx.info.strides[2];
-                        for(int y = 0; y < (int)val.info.dims[1]; y++) {
-
-                            int valOffset = valWZ + y * val.info.strides[1];
-                            int idxOffset = idxWZ + y * idx.info.strides[1];
-
-                            if(isAscending) {
-                                compute::sort_by_key(
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        compute::make_buffer_iterator< type_t<uint> >(idx_buf, idxOffset),
-                                        compute::less< type_t<T> >(), c_queue);
-                            } else {
-                                compute::sort_by_key(
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        compute::make_buffer_iterator< type_t<uint> >(idx_buf, idxOffset),
-                                        compute::greater< type_t<T> >(), c_queue);
-                            }
-                        }
-                    }
-                }
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
-
-        template<typename T, bool isAscending, int dim>
-        void sortIndexBatched(Param pVal, Param pIdx)
-        {
-            typedef type_t<T> Tk;
-            typedef uint Tv;
-            typedef std::pair<Tk, Tv> IndexPair;
-
-            try {
-                af::dim4 inDims;
-                for(int i = 0; i < 4; i++)
-                    inDims[i] = pVal.info.dims[i];
-
-                // Sort dimension
-                // tileDims * seqDims = inDims
-                af::dim4 tileDims(1);
-                af::dim4 seqDims = inDims;
-                tileDims[dim] = inDims[dim];
-                seqDims[dim] = 1;
-
-                // Create/call iota
-                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
-                dim4 keydims = inDims;
-                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(Tv));
-                Param pSeq;
-                pSeq.data = key;
-                pSeq.info.offset = 0;
-                pSeq.info.dims[0] = keydims[0];
-                pSeq.info.strides[0] = 1;
-                for(int i = 1; i < 4; i++) {
-                    pSeq.info.dims[i] = keydims[i];
-                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
-                }
-                kernel::iota<Tv>(pSeq, seqDims, tileDims);
-
-                int elements = inDims.elements();
-
-                // Flat - Not required since inplace and both are continuous
-                //val.modDims(inDims.elements());
-                //key.modDims(inDims.elements());
-
-                // Sort indices
-                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
-                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
-                compute::command_queue c_queue(getQueue()());
-                compute::context c_context(getContext()());
-
-                // Create buffer iterators for seq
-                compute::buffer pSeq_buf((*pSeq.data)());
-                compute::buffer_iterator<Tv> seq0 = compute::make_buffer_iterator<Tv>(pSeq_buf, 0);
-                compute::buffer_iterator<Tv> seqN = compute::make_buffer_iterator<Tv>(pSeq_buf, elements);
-
-                // Copy val, idx into X pair
-                cl::Buffer* X = bufferAlloc(elements * sizeof(IndexPair));
-                // Use T here, not Tk
-                kernel::makePair<T, Tv>(X, pVal.data, pIdx.data, elements);
-                compute::buffer X_buf((*X)());
-                compute::buffer_iterator<IndexPair> X0 = compute::make_buffer_iterator<IndexPair>(X_buf, 0);
-                compute::buffer_iterator<IndexPair> XN = compute::make_buffer_iterator<IndexPair>(X_buf, elements);
-
-                // FIRST SORT CALL
-                compute::function<bool(IndexPair, IndexPair)> IPCompare =
-                    makeCompareFunction<Tk, Tv, isAscending>();
-
-                compute::sort_by_key(X0, XN, seq0, IPCompare, c_queue);
-                getQueue().finish();
-
-                // Needs to be ascending (true) in order to maintain the indices properly
-                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
-                //
-                // Because we use a pair as values, we need to use a custom comparator
-                BOOST_COMPUTE_FUNCTION(bool, Compare_Tv, (const Tv lhs, const Tv rhs),
-                    {
-                        return lhs < rhs;
-                    }
-                );
-                compute::sort_by_key(seq0, seqN, X0, Compare_Tv, c_queue);
-                getQueue().finish();
-
-                kernel::splitPair<T, Tv>(pVal.data, pIdx.data, X, elements);
-
-                //// No need of doing moddims here because the original Array<T>
-                //// dimensions have not been changed
-                ////val.modDims(inDims);
-
-                CL_DEBUG_FINISH(getQueue());
-                bufferFree(key);
-                bufferFree(X);
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
-
-        template<typename T, bool isAscending>
-        void sort0Index(Param val, Param idx)
-        {
-            int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 5)
-              sortIndexBatched<T, isAscending, 0>(val, idx);
-            else
-                kernel::sort0IndexIterative<T, isAscending>(val, idx);
-        }
-    }
-}
-
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_make_pair.cl b/src/backend/opencl/kernel/sort_pair.cl
similarity index 100%
rename from src/backend/opencl/kernel/sort_make_pair.cl
rename to src/backend/opencl/kernel/sort_pair.cl
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index 49795c2eb5..bb5474909d 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <sort_index.hpp>
 #include <copy.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
@@ -20,36 +20,37 @@
 namespace opencl
 {
     template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
     {
         try {
-            val = copyArray<T>(in);
-            idx = range<uint>(in.dims(), dim);
-            idx.eval();
+            // okey contains values, oval contains indices
+            okey = copyArray<T>(in);
+            oval = range<uint>(in.dims(), dim);
+            oval.eval();
 
             switch(dim) {
-                case 0: kernel::sort0Index<T, isAscending>(val, idx); break;
-                case 1: kernel::sortIndexBatched<T, isAscending, 1>(val, idx); break;
-                case 2: kernel::sortIndexBatched<T, isAscending, 2>(val, idx); break;
-                case 3: kernel::sortIndexBatched<T, isAscending, 3>(val, idx); break;
+                case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
+                case 1: kernel::sortByKeyBatched<T, uint, isAscending, 1>(okey, oval); break;
+                case 2: kernel::sortByKeyBatched<T, uint, isAscending, 2>(okey, oval); break;
+                case 3: kernel::sortByKeyBatched<T, uint, isAscending, 3>(okey, oval); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
             if(dim != 0) {
-                af::dim4 preorderDims = val.dims();
+                af::dim4 preorderDims = okey.dims();
                 af::dim4 reorderDims(0, 1, 2, 3);
                 reorderDims[dim] = 0;
-                preorderDims[0] = val.dims()[dim];
+                preorderDims[0] = okey.dims()[dim];
                 for(int i = 1; i <= (int)dim; i++) {
                     reorderDims[i - 1] = i;
-                    preorderDims[i] = val.dims()[i - 1];
+                    preorderDims[i] = okey.dims()[i - 1];
                 }
 
-                val.setDataDims(preorderDims);
-                idx.setDataDims(preorderDims);
+                okey.setDataDims(preorderDims);
+                oval.setDataDims(preorderDims);
 
-                val = reorder<T>(val, reorderDims);
-                idx = reorder<uint>(idx, reorderDims);
+                okey = reorder<T>(okey, reorderDims);
+                oval = reorder<uint>(oval, reorderDims);
             }
         } catch (std::exception &ex) {
             AF_ERROR(ex.what(), AF_ERR_INTERNAL);

From 87513e07d16958cceed4b896ad135c55e4316028 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 13:42:54 -0400
Subject: [PATCH 0466/2677] Fix sort calls from harris and orb in CUDA

---
 src/backend/cuda/kernel/harris.hpp |  3 +++
 src/backend/cuda/kernel/orb.hpp    |  5 ++++-
 src/backend/cuda/kernel/range.hpp  | 14 +++++++-------
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index c773ae45c5..9361b72e23 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -19,6 +19,7 @@
 #include "convolve.hpp"
 #include "gradient.hpp"
 #include "sort_by_key.hpp"
+#include "range.hpp"
 
 namespace cuda
 {
@@ -336,7 +337,9 @@ void harris(unsigned* corners_out,
 
         int sort_elem = harris_responses.strides[3] * harris_responses.dims[3];
         harris_responses.ptr = d_resp_corners;
+        // Create indices using range
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
+        kernel::range<uint>(harris_idx, 0);
 
         // Sort Harris responses
         sort0ByKey<float, uint, false>(harris_responses, harris_idx);
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index b5ed10340e..8a2b535cee 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -17,6 +17,7 @@
 #include "convolve.hpp"
 #include "orb_patch.hpp"
 #include "sort_by_key.hpp"
+#include "range.hpp"
 
 #include <boost/scoped_ptr.hpp>
 
@@ -394,10 +395,12 @@ void orb(unsigned* out_feat,
 
         int sort_elem = harris_sorted.strides[3] * harris_sorted.dims[3];
         harris_sorted.ptr = d_score_harris;
+        // Create indices using range
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
+        kernel::range<uint>(harris_idx, 0);
 
         // Sort features according to Harris responses
-        sort0ByKey<float, uint, false>(harris_sorted, harris_idx);
+        kernel::sort0ByKey<float, uint, false>(harris_sorted, harris_idx);
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 9670b07bd6..6880ed566a 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -18,10 +18,10 @@ namespace cuda
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 512;
-        static const unsigned TILEY = 32;
+        static const unsigned RANGE_TX = 32;
+        static const unsigned RANGE_TY = 8;
+        static const unsigned RANGE_TILEX = 512;
+        static const unsigned RANGE_TILEY = 32;
 
         template<typename T>
         __global__
@@ -74,10 +74,10 @@ namespace cuda
         template<typename T>
         void range(Param<T> out, const int dim)
         {
-            dim3 threads(TX, TY, 1);
+            dim3 threads(RANGE_TX, RANGE_TY, 1);
 
-            int blocksPerMatX = divup(out.dims[0], TILEX);
-            int blocksPerMatY = divup(out.dims[1], TILEY);
+            int blocksPerMatX = divup(out.dims[0], RANGE_TILEX);
+            int blocksPerMatY = divup(out.dims[1], RANGE_TILEY);
             dim3 blocks(blocksPerMatX * out.dims[2],
                         blocksPerMatY * out.dims[3],
                         1);

From c6e08d5f14ca6a3d8f51f19c98c2223e2a158251 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 13:46:51 -0400
Subject: [PATCH 0467/2677] Fix sort calls from harris and orb in OpenCL

---
 src/backend/opencl/kernel/harris.hpp |  5 ++++-
 src/backend/opencl/kernel/orb.hpp    |  5 ++++-
 src/backend/opencl/kernel/range.hpp  | 14 +++++++-------
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 4c203c9377..442275d326 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -17,6 +17,7 @@
 #include <kernel/convolve_separable.hpp>
 #include <kernel/gradient.hpp>
 #include <kernel/sort_by_key.hpp>
+#include <kernel/range.hpp>
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
 #include <map>
@@ -284,10 +285,12 @@ void harris(unsigned* corners_out,
 
             int sort_elem = harris_resp.info.strides[3] * harris_resp.info.dims[3];
             harris_resp.data = d_resp_corners;
+            // Create indices using range
             harris_idx.data = bufferAlloc(sort_elem * sizeof(unsigned));
+            kernel::range<uint>(harris_idx, 0);
 
             // Sort Harris responses
-            sort0ByKey<float, uint, false>(harris_resp, harris_idx);
+            kernel::sort0ByKey<float, uint, false>(harris_resp, harris_idx);
 
             x_out.data = bufferAlloc(*corners_out * sizeof(float));
             y_out.data = bufferAlloc(*corners_out * sizeof(float));
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 612edacfe0..0c752d2c21 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -17,6 +17,7 @@
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
 #include <kernel/sort_by_key.hpp>
+#include <kernel/range.hpp>
 #include <kernel_headers/orb.hpp>
 #include <memory.hpp>
 #include <vector>
@@ -303,9 +304,11 @@ void orb(unsigned* out_feat,
             d_harris_sorted.info.offset = 0;
             d_harris_idx.info.offset = 0;
             d_harris_sorted.data = d_score_harris;
+            // Create indices using range
             d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
+            kernel::range<uint>(d_harris_idx, 0);
 
-            sort0ByKey<float, uint, false>(d_harris_sorted, d_harris_idx);
+            kernel::sort0ByKey<float, uint, false>(d_harris_sorted, d_harris_idx);
 
             cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
             cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 2f8be8cd4a..0299c030d4 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -31,10 +31,10 @@ namespace opencl
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
-        static const int TILEX = 512;
-        static const int TILEY = 32;
+        static const int RANGE_TX = 32;
+        static const int RANGE_TY = 8;
+        static const int RANGE_TILEX = 512;
+        static const int RANGE_TILEY = 32;
 
         template<typename T>
         void range(Param out, const int dim)
@@ -62,10 +62,10 @@ namespace opencl
                 auto rangeOp = make_kernel<Buffer, const KParam, const int,
                                            const int, const int> (*rangeKernels[device]);
 
-                NDRange local(TX, TY, 1);
+                NDRange local(RANGE_TX, RANGE_TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], TILEY);
+                int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
+                int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
                 NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                                local[1] * blocksPerMatY * out.info.dims[3],
                                1);

From 8f48cdcd6a3cdc48ed050f0372a75c9a255b521d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 15:02:26 -0400
Subject: [PATCH 0468/2677] Clean up sort tests

---
 test/sort.cpp        |  2 +-
 test/sort_by_key.cpp |  2 +-
 test/sort_index.cpp  | 10 +++++-----
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/test/sort.cpp b/test/sort.cpp
index 977b54b5c7..9a496f3236 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -106,7 +106,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
     SORT_INIT(Sort1000False,  sort_1000,  false, 2);
     SORT_INIT(SortMedTrue,    sort_med1,  true,  0);
     SORT_INIT(SortMedFalse,   sort_med1,  false, 2);
-    // Takes too much time in current implementation. Enable when everything is parallel
+
     SORT_INIT(SortMed5True,   sort_med,   true,  0);
     SORT_INIT(SortMed5False,  sort_med,   false, 2);
     SORT_INIT(SortLargeTrue,  sort_large, true,  0);
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index cbb13b8785..dae46bef54 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -118,7 +118,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(SortMedTrue,    sort_by_key_med,   true,  0, 1);
     SORT_INIT(Sort1000False,  sort_by_key_1000,  false, 2, 3);
     SORT_INIT(SortMedFalse,   sort_by_key_med,   false, 2, 3);
-    // Takes too much time in current implementation. Enable when everything is parallel
+
     SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
     SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
 
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index eed85047bf..fe11462310 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -119,11 +119,11 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(SortMedTrue,    sort_med1,  true,  0, 1);
     SORT_INIT(Sort1000False,  sort_1000,  false, 2, 3);
     SORT_INIT(SortMedFalse,   sort_med1,  false, 2, 3);
-    // Takes too much time in current implementation. Enable when everything is parallel
-    //SORT_INIT(SortMed5True,   sort_med,   true,  0, 1);
-    //SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);
-    //SORT_INIT(SortLargeTrue,  sort_large, true,  0, 1);
-    //SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
+
+    SORT_INIT(SortMed5True,   sort_med,   true,  0, 1);
+    SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);
+    SORT_INIT(SortLargeTrue,  sort_large, true,  0, 1);
+    SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
 
 
 //////////////////////////////////// CPP /////////////////////////////////

From 7f1a3fd7d9bb01fefef987ef8210dad0729ef7fa Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 21 Apr 2016 16:10:15 -0400
Subject: [PATCH 0469/2677] DOCS: Updating documentation for replace.

---
 docs/details/data.dox |  4 ++--
 include/af/data.h     | 32 ++++++++++++++++++++------------
 2 files changed, 22 insertions(+), 14 deletions(-)

diff --git a/docs/details/data.dox b/docs/details/data.dox
index d1dcfa4044..d3f470d113 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -338,8 +338,8 @@ array/value is selected.
 
 \brief Replace elements of an array based on an conditional array
 
-If the condition array has an element as true, then the element is
-replaced by the array/value, otherwise no change.
+- Input values are retained when corresponding elements from condition array are true.
+- Input values are replaced when corresponding elements from condition array are false.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
diff --git a/include/af/data.h b/include/af/data.h
index 5808833644..f402b9e085 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -517,9 +517,11 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is the array containing elements which replace elements in \p a when \p cond is false
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -528,9 +530,11 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is value that replaces elements in \p a when \p cond is false
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement value.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -836,9 +840,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout] a is the array whose values are replaced by \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is the array containing elements that replaces elements of a where \p cond is false
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -847,9 +853,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout] a is the array whose values are replaced by \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is the scalar that replaces the false parts of \p a
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */

From b6a6a8732bb43e257c0744a6c183f7d602f8d56d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 18:56:16 -0400
Subject: [PATCH 0470/2677] Fixed sort_by_key kernel for OpenCL

---
 src/backend/opencl/kernel/sort_by_key.hpp | 70 +++++++++++++----------
 src/backend/opencl/kernel/sort_helper.hpp | 33 +++++++++++
 test/sort_index.cpp                       |  2 +-
 3 files changed, 75 insertions(+), 30 deletions(-)

diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 33a020712e..6984603882 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -15,6 +15,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <math.hpp>
 #include <kernel/sort_helper.hpp>
 #include <kernel/iota.hpp>
 
@@ -91,7 +92,6 @@ namespace opencl
         {
             typedef type_t<Tk_> Tk;
             typedef type_t<Tv_> Tv;
-            typedef std::pair<Tk, Tv> IndexPair;
 
             try {
                 af::dim4 inDims;
@@ -136,35 +136,46 @@ namespace opencl
                 compute::buffer pSeq_buf((*pSeq.data)());
                 compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
                 compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
-
-                // Copy key, val into X pair
-                cl::Buffer* X = bufferAlloc(elements * sizeof(IndexPair));
-                // Use Tk_ and Tv_ here, not Tk and Tv
-                kernel::makePair<Tk_, Tv_>(X, pKey.data, pVal.data, elements);
-                compute::buffer X_buf((*X)());
-                compute::buffer_iterator<IndexPair> X0 = compute::make_buffer_iterator<IndexPair>(X_buf, 0);
-                compute::buffer_iterator<IndexPair> XN = compute::make_buffer_iterator<IndexPair>(X_buf, elements);
-
-                // FIRST SORT CALL
-                compute::function<bool(IndexPair, IndexPair)> IPCompare =
-                    makeCompareFunction<Tk, Tv, isAscending>();
-
-                compute::sort_by_key(X0, XN, seq0, IPCompare, c_queue);
-                getQueue().finish();
-
+                // Create buffer iterators for key and val
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+                compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
+                compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
+                compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
+                compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
+
+                // Sort By Key for descending is stable in the reverse
+                // (greater) order. Sorting in ascending with negated values
+                // will give the right result
+                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+                // Create a copy of the pKey buffer
+                cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
+                compute::buffer cKey_buf((*cKey)());
+                compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
+                compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
+                compute::copy(key0, keyN, cKey0, c_queue);
+
+                // FIRST SORT
+                compute::sort_by_key(key0, keyN, seq0, c_queue);
+                compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
+
+                // Create a copy of the seq buffer after first sort
+                cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
+                compute::buffer cSeq_buf((*cSeq)());
+                compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
+                compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
+                compute::copy(seq0, seqN, cSeq0, c_queue);
+
+                // SECOND SORT
+                // First call will sort key, second sort will sort val
                 // Needs to be ascending (true) in order to maintain the indices properly
                 //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
-                //
-                // Because we use a pair as values, we need to use a custom comparator
-                BOOST_COMPUTE_FUNCTION(bool, Compare_Seq, (const unsigned lhs, const unsigned rhs),
-                    {
-                        return lhs < rhs;
-                    }
-                );
-                compute::sort_by_key(seq0, seqN, X0, Compare_Seq, c_queue);
-                getQueue().finish();
+                compute::sort_by_key(seq0, seqN, key0, c_queue);
+                compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
 
-                kernel::splitPair<Tk_, Tv_>(pKey.data, pVal.data, X, elements);
+                // If descending, flip it back
+                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
 
                 //// No need of doing moddims here because the original Array<T>
                 //// dimensions have not been changed
@@ -172,7 +183,8 @@ namespace opencl
 
                 CL_DEBUG_FINISH(getQueue());
                 bufferFree(key);
-                bufferFree(X);
+                bufferFree(cSeq);
+                bufferFree(cKey);
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
                 throw;
@@ -184,7 +196,7 @@ namespace opencl
         {
             int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
             // TODO Make a better heurisitic
-            if(higherDims > 5)
+            if(higherDims > 0)
                 kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 7f500c6bb0..899e0873fa 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -44,6 +44,39 @@ makeCompareFunction()
     }
 }
 
+template<typename Tk>
+inline boost::compute::function<Tk(Tk)>
+flipFunction()
+{
+    BOOST_COMPUTE_FUNCTION(Tk, negateFn, (const Tk x),
+        {
+            return -x;
+        }
+    );
+
+    return negateFn;
+}
+
+#define INSTANTIATE_FLIP(TY, XMAX)                              \
+template<> inline boost::compute::function<TY(TY)>              \
+flipFunction<TY>()                                              \
+{                                                               \
+    BOOST_COMPUTE_FUNCTION(TY, negateFn, (const TY x),          \
+       {                                                        \
+            return XMAX - x;                                    \
+        }                                                       \
+    );                                                          \
+                                                                \
+    return negateFn;                                            \
+}
+
+INSTANTIATE_FLIP(unsigned, UINT_MAX)
+INSTANTIATE_FLIP(unsigned short, USHRT_MAX)
+INSTANTIATE_FLIP(unsigned char, UCHAR_MAX)
+INSTANTIATE_FLIP(cl_ulong, ULONG_MAX)
+
+#undef INSTANTIATE_FLIP
+
 namespace opencl
 {
     namespace kernel
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index fe11462310..0df4744c02 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -258,7 +258,7 @@ TEST(SortIndex, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        EXPECT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
     }
 
     // Delete

From 75b1a6bc0f428fd43f3189239aa355cce9f50e1a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 20:10:20 -0400
Subject: [PATCH 0471/2677] Instantiate sort_by_key kernels in separately

---
 src/backend/cuda/CMakeLists.txt               |   2 +-
 src/backend/cuda/kernel/iota.hpp              |   3 +-
 src/backend/cuda/kernel/sort_by_key.hpp       | 119 +---------
 .../cuda/{ => kernel}/sort_by_key/ascd_f32.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_f64.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_s16.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_s32.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_s64.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_s8.cu  |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_u16.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_u32.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_u64.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/ascd_u8.cu  |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_f32.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_f64.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_s16.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_s32.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_s64.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_s8.cu  |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_u16.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_u32.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_u64.cu |   5 +-
 .../cuda/{ => kernel}/sort_by_key/desc_u8.cu  |   5 +-
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 213 ++++++++++++++++++
 src/backend/cuda/kernel/sort_helper.hpp       |  66 ------
 .../{sort_by_key_impl.hpp => sort_by_key.cu}  |  48 ++--
 26 files changed, 331 insertions(+), 220 deletions(-)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_f32.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_f64.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_s16.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_s32.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_s64.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_s8.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_u16.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_u32.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_u64.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/ascd_u8.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_f32.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_f64.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_s16.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_s32.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_s64.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_s8.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_u16.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_u32.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_u64.cu (86%)
 rename src/backend/cuda/{ => kernel}/sort_by_key/desc_u8.cu (86%)
 create mode 100644 src/backend/cuda/kernel/sort_by_key_impl.hpp
 delete mode 100644 src/backend/cuda/kernel/sort_helper.hpp
 rename src/backend/cuda/{sort_by_key_impl.hpp => sort_by_key.cu} (63%)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b887a98d67..4efb42764a 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -158,7 +158,7 @@ FILE(GLOB cuda_headers
 FILE(GLOB cuda_sources
     "*.cu"
     "*.cpp"
-    "sort_by_key/*.cu"
+    "kernel/sort_by_key/*.cu"
     "kernel/*.cu")
 
 FILE(GLOB jit_sources
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index fc28c82882..e2f7e591fb 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/dim4.hpp>
 #include <math.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
@@ -69,7 +70,7 @@ namespace cuda
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename T>
-        void iota(Param<T> out, const dim4 &sdims, const dim4 &tdims)
+        void iota(Param<T> out, const af::dim4 &sdims, const af::dim4 &tdims)
         {
             dim3 threads(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 1536d1bf53..35250a8ad1 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -12,130 +12,19 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <kernel/sort_helper.hpp>
-#include <kernel/iota.hpp>
-
-#include <thrust/device_ptr.h>
-#include <thrust/device_vector.h>
-#include <thrust/sort.h>
 
 namespace cuda
 {
     namespace kernel
     {
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval)
-        {
-            thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
-            thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
-
-            for(int w = 0; w < okey.dims[3]; w++) {
-                int okeyW = w * okey.strides[3];
-                int ovalW = w * oval.strides[3];
-                for(int z = 0; z < okey.dims[2]; z++) {
-                    int okeyWZ = okeyW + z * okey.strides[2];
-                    int ovalWZ = ovalW + z * oval.strides[2];
-                    for(int y = 0; y < okey.dims[1]; y++) {
-
-                        int okeyOffset = okeyWZ + y * okey.strides[1];
-                        int ovalOffset = ovalWZ + y * oval.strides[1];
-
-                        if(isAscending) {
-                            THRUST_SELECT(thrust::stable_sort_by_key,
-                                          okey_ptr + okeyOffset,
-                                          okey_ptr + okeyOffset + okey.dims[0],
-                                          oval_ptr + ovalOffset);
-                        } else {
-                            THRUST_SELECT(thrust::stable_sort_by_key,
-                                          okey_ptr + okeyOffset,
-                                          okey_ptr + okeyOffset + okey.dims[0],
-                                          oval_ptr + ovalOffset, thrust::greater<Tk>());
-                        }
-                    }
-                }
-            }
-            POST_LAUNCH_CHECK();
-        }
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval);
 
         template<typename Tk, typename Tv, bool isAscending, int dim>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal)
-        {
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pKey.dims[i];
-
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
-
-            // Create/call iota
-            // Array<uint> key = iota<uint>(seqDims, tileDims);
-            dim4 keydims = inDims;
-            uint* key = memAlloc<uint>(keydims.elements());
-            Param<uint> pSeq;
-            pSeq.ptr = key;
-            pSeq.strides[0] = 1;
-            pSeq.dims[0] = keydims[0];
-            for(int i = 1; i < 4; i++) {
-                pSeq.dims[i] = keydims[i];
-                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
-            }
-            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
-
-            // Make pkey, pVal into a pair
-            thrust::device_vector<IndexPair<Tk, Tv> > X(inDims.elements());
-            IndexPair<Tk, Tv> *Xptr = thrust::raw_pointer_cast(X.data());
-
-            const int threads = 256;
-            int blocks = divup(inDims.elements(), threads * copyPairIter);
-            CUDA_LAUNCH((makeIndexPair<Tk, Tv>), blocks, threads,
-                        Xptr, pKey.ptr, pVal.ptr, inDims.elements());
-            POST_LAUNCH_CHECK();
-
-            // Sort indices
-            // Need to convert pSeq to thrust::device_ptr, otherwise thrust
-            // throws weird errors for all *64 data types (double, intl, uintl etc)
-            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          X.begin(), X.end(),
-                          dSeq,
-                          IPCompare<Tk, Tv, isAscending>());
-            POST_LAUNCH_CHECK();
-
-            // Needs to be ascending (true) in order to maintain the indices properly
-            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          dSeq,
-                          dSeq + inDims.elements(),
-                          X.begin());
-            POST_LAUNCH_CHECK();
-
-            CUDA_LAUNCH((splitIndexPair<Tk, Tv>), blocks, threads,
-                        pKey.ptr, pVal.ptr, Xptr, inDims.elements());
-            POST_LAUNCH_CHECK();
-
-            // No need of doing moddims here because the original Array<T>
-            // dimensions have not been changed
-            //val.modDims(inDims);
-
-            memFree(key);
-        }
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal);
 
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKey(Param<Tk> okey, Param<Tv> oval)
-        {
-            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 5)
-                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
-            else
-                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
-        }
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval);
+
     }
 }
diff --git a/src/backend/cuda/sort_by_key/ascd_f32.cu b/src/backend/cuda/kernel/sort_by_key/ascd_f32.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_f32.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_f32.cu
index 44b770402c..284e8b4938 100644
--- a/src/backend/cuda/sort_by_key/ascd_f32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_f32.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(float, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_f64.cu b/src/backend/cuda/kernel/sort_by_key/ascd_f64.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_f64.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_f64.cu
index 17b54a3903..ba19ec447c 100644
--- a/src/backend/cuda/sort_by_key/ascd_f64.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_f64.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(double, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_s16.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s16.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_s16.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_s16.cu
index d51e9ae671..1be6e540ca 100644
--- a/src/backend/cuda/sort_by_key/ascd_s16.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_s16.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(short, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_s32.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s32.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_s32.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_s32.cu
index 75adbddc0b..8cee7c9b49 100644
--- a/src/backend/cuda/sort_by_key/ascd_s32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_s32.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(int, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_s64.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s64.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_s64.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_s64.cu
index 25a1e589f8..0e5a7c81a2 100644
--- a/src/backend/cuda/sort_by_key/ascd_s64.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_s64.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(intl, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_s8.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s8.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_s8.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_s8.cu
index f47a397727..81ed32952f 100644
--- a/src/backend/cuda/sort_by_key/ascd_s8.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_s8.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(char, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_u16.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u16.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_u16.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_u16.cu
index e06036abc7..e232c08376 100644
--- a/src/backend/cuda/sort_by_key/ascd_u16.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_u16.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(ushort, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_u32.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u32.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_u32.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_u32.cu
index 6f7939aa12..34a4580936 100644
--- a/src/backend/cuda/sort_by_key/ascd_u32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_u32.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(uint, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_u64.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u64.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_u64.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_u64.cu
index 63eec5fdd4..fc576e7f99 100644
--- a/src/backend/cuda/sort_by_key/ascd_u64.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_u64.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(uintl, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_u8.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u8.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/ascd_u8.cu
rename to src/backend/cuda/kernel/sort_by_key/ascd_u8.cu
index a2e1dec887..ed8454d53e 100644
--- a/src/backend/cuda/sort_by_key/ascd_u8.cu
+++ b/src/backend/cuda/kernel/sort_by_key/ascd_u8.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(uchar, true)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_f32.cu b/src/backend/cuda/kernel/sort_by_key/desc_f32.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_f32.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_f32.cu
index 1bbb10bbba..73459ac033 100644
--- a/src/backend/cuda/sort_by_key/desc_f32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_f32.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(float, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_f64.cu b/src/backend/cuda/kernel/sort_by_key/desc_f64.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_f64.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_f64.cu
index ecbed78878..be0536b1e3 100644
--- a/src/backend/cuda/sort_by_key/desc_f64.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_f64.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(double, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_s16.cu b/src/backend/cuda/kernel/sort_by_key/desc_s16.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_s16.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_s16.cu
index 63967b6117..0fc3b50827 100644
--- a/src/backend/cuda/sort_by_key/desc_s16.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_s16.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(short, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_s32.cu b/src/backend/cuda/kernel/sort_by_key/desc_s32.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_s32.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_s32.cu
index 49904437f4..cfda29c7de 100644
--- a/src/backend/cuda/sort_by_key/desc_s32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_s32.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(int, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_s64.cu b/src/backend/cuda/kernel/sort_by_key/desc_s64.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_s64.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_s64.cu
index a10ee11475..b334a91a99 100644
--- a/src/backend/cuda/sort_by_key/desc_s64.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_s64.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(intl, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_s8.cu b/src/backend/cuda/kernel/sort_by_key/desc_s8.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_s8.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_s8.cu
index cad78dfc84..f02d5ce2fe 100644
--- a/src/backend/cuda/sort_by_key/desc_s8.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_s8.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(char, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_u16.cu b/src/backend/cuda/kernel/sort_by_key/desc_u16.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_u16.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_u16.cu
index 69dc01634b..9b0a77cb25 100644
--- a/src/backend/cuda/sort_by_key/desc_u16.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_u16.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(ushort, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_u32.cu b/src/backend/cuda/kernel/sort_by_key/desc_u32.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_u32.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_u32.cu
index ae2ad4bc84..1d02aec848 100644
--- a/src/backend/cuda/sort_by_key/desc_u32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_u32.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(uint, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_u64.cu b/src/backend/cuda/kernel/sort_by_key/desc_u64.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_u64.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_u64.cu
index 43f60c075b..597bd2c1b4 100644
--- a/src/backend/cuda/sort_by_key/desc_u64.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_u64.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(uintl, false)
 }
+}
diff --git a/src/backend/cuda/sort_by_key/desc_u8.cu b/src/backend/cuda/kernel/sort_by_key/desc_u8.cu
similarity index 86%
rename from src/backend/cuda/sort_by_key/desc_u8.cu
rename to src/backend/cuda/kernel/sort_by_key/desc_u8.cu
index 51d8096620..4f55479604 100644
--- a/src/backend/cuda/sort_by_key/desc_u8.cu
+++ b/src/backend/cuda/kernel/sort_by_key/desc_u8.cu
@@ -7,9 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace cuda
+{
+namespace kernel
 {
     INSTANTIATE1(uchar, false)
 }
+}
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..66a6087401
--- /dev/null
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,213 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key.hpp>
+#include <af/dim4.hpp>
+#include <memory.hpp>
+#include <math.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <kernel/iota.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/device_vector.h>
+#include <thrust/sort.h>
+
+// This needs to be in global namespace as it is used by thrust
+template<typename Tk, typename Tv>
+struct IndexPair
+{
+    Tk first;
+    Tv second;
+};
+
+template <typename Tk, typename Tv, bool isAscending>
+struct IPCompare
+{
+    __host__ __device__
+    bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs) const
+    {
+        // Check stable sort condition
+        if(isAscending) return (lhs.first < rhs.first);
+        else return (lhs.first > rhs.first);
+    }
+};
+
+namespace cuda
+{
+    namespace kernel
+    {
+        static const int copyPairIter = 4;
+
+        template <typename Tk, typename Tv>
+        __global__
+        void makeIndexPair(IndexPair<Tk, Tv> *out, const Tk *first, const Tv *second, const int N)
+        {
+            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
+
+            for(int i = tIdx; i < N; i += blockDim.x)
+            {
+                out[i].first = first[i];
+                out[i].second = second[i];
+            }
+        }
+
+        template <typename Tk, typename Tv>
+        __global__
+        void splitIndexPair(Tk *first, Tv *second, const IndexPair<Tk, Tv> *out, const int N)
+        {
+            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
+
+            for(int i = tIdx; i < N; i += blockDim.x)
+            {
+                first[i]  = out[i].first;
+                second[i] = out[i].second;
+            }
+        }
+
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval)
+        {
+            thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
+            thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
+
+            for(int w = 0; w < okey.dims[3]; w++) {
+                int okeyW = w * okey.strides[3];
+                int ovalW = w * oval.strides[3];
+                for(int z = 0; z < okey.dims[2]; z++) {
+                    int okeyWZ = okeyW + z * okey.strides[2];
+                    int ovalWZ = ovalW + z * oval.strides[2];
+                    for(int y = 0; y < okey.dims[1]; y++) {
+
+                        int okeyOffset = okeyWZ + y * okey.strides[1];
+                        int ovalOffset = ovalWZ + y * oval.strides[1];
+
+                        if(isAscending) {
+                            THRUST_SELECT(thrust::stable_sort_by_key,
+                                          okey_ptr + okeyOffset,
+                                          okey_ptr + okeyOffset + okey.dims[0],
+                                          oval_ptr + ovalOffset);
+                        } else {
+                            THRUST_SELECT(thrust::stable_sort_by_key,
+                                          okey_ptr + okeyOffset,
+                                          okey_ptr + okeyOffset + okey.dims[0],
+                                          oval_ptr + ovalOffset, thrust::greater<Tk>());
+                        }
+                    }
+                }
+            }
+            POST_LAUNCH_CHECK();
+        }
+
+        template<typename Tk, typename Tv, bool isAscending, int dim>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pKey.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            af::dim4 keydims = inDims;
+            uint* key = memAlloc<uint>(keydims.elements());
+            Param<uint> pSeq;
+            pSeq.ptr = key;
+            pSeq.strides[0] = 1;
+            pSeq.dims[0] = keydims[0];
+            for(int i = 1; i < 4; i++) {
+                pSeq.dims[i] = keydims[i];
+                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
+            }
+            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
+
+            // Make pkey, pVal into a pair
+            thrust::device_vector<IndexPair<Tk, Tv> > X(inDims.elements());
+            IndexPair<Tk, Tv> *Xptr = thrust::raw_pointer_cast(X.data());
+
+            const int threads = 256;
+            int blocks = divup(inDims.elements(), threads * copyPairIter);
+            CUDA_LAUNCH((makeIndexPair<Tk, Tv>), blocks, threads,
+                        Xptr, pKey.ptr, pVal.ptr, inDims.elements());
+            POST_LAUNCH_CHECK();
+
+            // Sort indices
+            // Need to convert pSeq to thrust::device_ptr, otherwise thrust
+            // throws weird errors for all *64 data types (double, intl, uintl etc)
+            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          X.begin(), X.end(),
+                          dSeq,
+                          IPCompare<Tk, Tv, isAscending>());
+            POST_LAUNCH_CHECK();
+
+            // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          dSeq,
+                          dSeq + inDims.elements(),
+                          X.begin());
+            POST_LAUNCH_CHECK();
+
+            CUDA_LAUNCH((splitIndexPair<Tk, Tv>), blocks, threads,
+                        pKey.ptr, pVal.ptr, Xptr, inDims.elements());
+            POST_LAUNCH_CHECK();
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            memFree(key);
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval)
+        {
+            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 5)
+                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+        }
+
+#define INSTANTIATE(Tk, Tv, dr)                                                         \
+    template void sort0ByKey<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);               \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 0>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 1>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 2>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 3>(Param<Tk> okey, Param<Tv> oval);      \
+
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
+    }
+}
diff --git a/src/backend/cuda/kernel/sort_helper.hpp b/src/backend/cuda/kernel/sort_helper.hpp
deleted file mode 100644
index 93fb33ac8f..0000000000
--- a/src/backend/cuda/kernel/sort_helper.hpp
+++ /dev/null
@@ -1,66 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <dispatch.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
-
-// This needs to be in global namespace as it is used by thrust
-template<typename Tk, typename Tv>
-struct IndexPair
-{
-    Tk first;
-    Tv second;
-};
-
-template <typename Tk, typename Tv, bool isAscending>
-struct IPCompare
-{
-    __host__ __device__
-    bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs) const
-    {
-        // Check stable sort condition
-        if(isAscending) return (lhs.first < rhs.first);
-        else return (lhs.first > rhs.first);
-    }
-};
-
-namespace cuda
-{
-    namespace kernel
-    {
-        static const int copyPairIter = 4;
-
-        template <typename Tk, typename Tv>
-        __global__
-        void makeIndexPair(IndexPair<Tk, Tv> *out, const Tk *first, const Tv *second, const int N)
-        {
-            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
-
-            for(int i = tIdx; i < N; i += blockDim.x)
-            {
-                out[i].first = first[i];
-                out[i].second = second[i];
-            }
-        }
-
-        template <typename Tk, typename Tv>
-        __global__
-        void splitIndexPair(Tk *first, Tv *second, const IndexPair<Tk, Tv> *out, const int N)
-        {
-            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
-
-            for(int i = tIdx; i < N; i += blockDim.x)
-            {
-                first[i]  = out[i].first;
-                second[i] = out[i].second;
-            }
-        }
-    }
-}
diff --git a/src/backend/cuda/sort_by_key_impl.hpp b/src/backend/cuda/sort_by_key.cu
similarity index 63%
rename from src/backend/cuda/sort_by_key_impl.hpp
rename to src/backend/cuda/sort_by_key.cu
index 8cc86b55db..2d5d68eef0 100644
--- a/src/backend/cuda/sort_by_key_impl.hpp
+++ b/src/backend/cuda/sort_by_key.cu
@@ -51,22 +51,36 @@ namespace cuda
         }
     }
 
-#define INSTANTIATE(Tk, Tv, dr)                                         \
-    template void                                                       \
-    sort_by_key<Tk, Tv, dr>(Array<Tk> &okey, Array<Tv> &oval,           \
-                            const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim); \
+#define INSTANTIATE(Tk, Tv)                                                         \
+    template void sort_by_key<Tk, Tv, true >(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+    template void sort_by_key<Tk, Tv, false>(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
+
+
+INSTANTIATE1(float )
+INSTANTIATE1(double)
+INSTANTIATE1(int   )
+INSTANTIATE1(uint  )
+INSTANTIATE1(short )
+INSTANTIATE1(ushort)
+INSTANTIATE1(char  )
+INSTANTIATE1(uchar )
+INSTANTIATE1(intl  )
+INSTANTIATE1(uintl )
 
-#define INSTANTIATE1(Tk    , dr) \
-    INSTANTIATE(Tk, float  , dr) \
-    INSTANTIATE(Tk, double , dr) \
-    INSTANTIATE(Tk, cfloat , dr) \
-    INSTANTIATE(Tk, cdouble, dr) \
-    INSTANTIATE(Tk, int    , dr) \
-    INSTANTIATE(Tk, uint   , dr) \
-    INSTANTIATE(Tk, short  , dr) \
-    INSTANTIATE(Tk, ushort , dr) \
-    INSTANTIATE(Tk, char   , dr) \
-    INSTANTIATE(Tk, uchar  , dr) \
-    INSTANTIATE(Tk, intl   , dr) \
-    INSTANTIATE(Tk, uintl  , dr)
 }

From bbdae15c0875d633d18fa2b5ed63122a14b6c3a2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 20:37:31 -0400
Subject: [PATCH 0472/2677] Instantiate sort_by_key kernels in separately in
 opencl

---
 src/backend/opencl/CMakeLists.txt             |   9 +-
 src/backend/opencl/kernel/iota.hpp            |   3 +-
 src/backend/opencl/kernel/sort_by_key.hpp     | 184 +--------
 .../opencl/{ => kernel}/sort_by_key/b8.cpp    |   5 +-
 .../opencl/{ => kernel}/sort_by_key/f32.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/f64.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/s16.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/s32.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/s64.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/u16.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/u32.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/u64.cpp   |   5 +-
 .../opencl/{ => kernel}/sort_by_key/u8.cpp    |   5 +-
 .../opencl/kernel/sort_by_key_impl.hpp        | 373 ++++++++++++++++++
 src/backend/opencl/kernel/sort_helper.hpp     | 151 +------
 .../{sort_by_key/impl.hpp => sort_by_key.cpp} |  48 ++-
 src/backend/opencl/traits.hpp                 |   1 +
 17 files changed, 453 insertions(+), 366 deletions(-)
 rename src/backend/opencl/{ => kernel}/sort_by_key/b8.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/f32.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/f64.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/s16.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/s32.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/s64.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/u16.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/u32.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/u64.cpp (87%)
 rename src/backend/opencl/{ => kernel}/sort_by_key/u8.cpp (87%)
 create mode 100644 src/backend/opencl/kernel/sort_by_key_impl.hpp
 rename src/backend/opencl/{sort_by_key/impl.hpp => sort_by_key.cpp} (65%)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 2cb8ddfdf9..b7eb77ded5 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -111,12 +111,10 @@ ENDIF()
 
 FILE(GLOB opencl_headers
   "*.hpp"
-  "*.h"
-  "sort_by_key/*.hpp")
+  "*.h")
 
 FILE(GLOB opencl_sources
-    "*.cpp"
-    "sort_by_key/*.cpp")
+    "*.cpp")
 
 FILE(GLOB jit_sources
     "jit/*.hpp")
@@ -128,7 +126,8 @@ FILE(GLOB opencl_kernels
     "kernel/*.cl")
 
 FILE(GLOB kernel_sources
-     "kernel/*.cpp")
+     "kernel/*.cpp"
+     "kernel/sort_by_key/*.cpp")
 
 FILE(GLOB conv_ker_headers
     "kernel/convolve/*.hpp")
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 210b6b202e..7cd8046d68 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <kernel_headers/iota.hpp>
+#include <af/dim4.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -37,7 +38,7 @@ namespace opencl
         static const int TILEY = 32;
 
         template<typename T>
-        void iota(Param out, const dim4 &sdims, const dim4 &tdims)
+        void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 6984603882..224f6411ff 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -8,200 +8,22 @@
  ********************************************************/
 
 #pragma once
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <math.hpp>
-#include <kernel/sort_helper.hpp>
-#include <kernel/iota.hpp>
-
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
-#include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
-#include <boost/compute/functional/operator.hpp>
-#include <boost/compute/iterator/buffer_iterator.hpp>
-#include <boost/compute/algorithm/copy.hpp>
-#include <boost/compute/algorithm/transform.hpp>
-#include <boost/compute/container/vector.hpp>
-#include <boost/compute/functional/get.hpp>
-#include <boost/compute/functional/field.hpp>
-#include <boost/compute/types/pair.hpp>
-
-namespace compute = boost::compute;
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::make_kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
 
 namespace opencl
 {
     namespace kernel
     {
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKeyIterative(Param pKey, Param pVal)
-        {
-            try {
-                compute::command_queue c_queue(getQueue()());
-
-                compute::buffer pKey_buf((*pKey.data)());
-                compute::buffer pVal_buf((*pVal.data)());
-
-                for(int w = 0; w < pKey.info.dims[3]; w++) {
-                    int pKeyW = w * pKey.info.strides[3];
-                    int pValW = w * pVal.info.strides[3];
-                    for(int z = 0; z < pKey.info.dims[2]; z++) {
-                        int pKeyWZ = pKeyW + z * pKey.info.strides[2];
-                        int pValWZ = pValW + z * pVal.info.strides[2];
-                        for(int y = 0; y < pKey.info.dims[1]; y++) {
-
-                            int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
-                            int pValOffset = pValWZ + y * pVal.info.strides[1];
-
-                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
-                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
-                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
-                            if(isAscending) {
-                                compute::sort_by_key(start, end, vals, c_queue);
-                            } else {
-                                compute::sort_by_key(start, end, vals,
-                                                     compute::greater< type_t<Tk> >(), c_queue);
-                            }
-                        }
-                    }
-                }
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
+        void sort0ByKeyIterative(Param pKey, Param pVal);
 
         template<typename Tk_, typename Tv_, bool isAscending, int dim>
-        void sortByKeyBatched(Param pKey, Param pVal)
-        {
-            typedef type_t<Tk_> Tk;
-            typedef type_t<Tv_> Tv;
-
-            try {
-                af::dim4 inDims;
-                for(int i = 0; i < 4; i++)
-                    inDims[i] = pKey.info.dims[i];
-
-                // Sort dimension
-                // tileDims * seqDims = inDims
-                af::dim4 tileDims(1);
-                af::dim4 seqDims = inDims;
-                tileDims[dim] = inDims[dim];
-                seqDims[dim] = 1;
-
-                // Create/call iota
-                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
-                dim4 keydims = inDims;
-                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(unsigned));
-                Param pSeq;
-                pSeq.data = key;
-                pSeq.info.offset = 0;
-                pSeq.info.dims[0] = keydims[0];
-                pSeq.info.strides[0] = 1;
-                for(int i = 1; i < 4; i++) {
-                    pSeq.info.dims[i] = keydims[i];
-                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
-                }
-                kernel::iota<unsigned>(pSeq, seqDims, tileDims);
-
-                int elements = inDims.elements();
-
-                // Flat - Not required since inplace and both are continuous
-                //val.modDims(inDims.elements());
-                //key.modDims(inDims.elements());
-
-                // Sort indices
-                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
-                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
-                compute::command_queue c_queue(getQueue()());
-                compute::context c_context(getContext()());
-
-                // Create buffer iterators for seq
-                compute::buffer pSeq_buf((*pSeq.data)());
-                compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
-                compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
-                // Create buffer iterators for key and val
-                compute::buffer pKey_buf((*pKey.data)());
-                compute::buffer pVal_buf((*pVal.data)());
-                compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
-                compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
-                compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
-                compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
-
-                // Sort By Key for descending is stable in the reverse
-                // (greater) order. Sorting in ascending with negated values
-                // will give the right result
-                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
-
-                // Create a copy of the pKey buffer
-                cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
-                compute::buffer cKey_buf((*cKey)());
-                compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
-                compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
-                compute::copy(key0, keyN, cKey0, c_queue);
-
-                // FIRST SORT
-                compute::sort_by_key(key0, keyN, seq0, c_queue);
-                compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
-
-                // Create a copy of the seq buffer after first sort
-                cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
-                compute::buffer cSeq_buf((*cSeq)());
-                compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
-                compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
-                compute::copy(seq0, seqN, cSeq0, c_queue);
-
-                // SECOND SORT
-                // First call will sort key, second sort will sort val
-                // Needs to be ascending (true) in order to maintain the indices properly
-                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
-                compute::sort_by_key(seq0, seqN, key0, c_queue);
-                compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
-
-                // If descending, flip it back
-                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
-
-                //// No need of doing moddims here because the original Array<T>
-                //// dimensions have not been changed
-                ////val.modDims(inDims);
-
-                CL_DEBUG_FINISH(getQueue());
-                bufferFree(key);
-                bufferFree(cSeq);
-                bufferFree(cKey);
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
+        void sortByKeyBatched(Param pKey, Param pVal);
 
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKey(Param pKey, Param pVal)
-        {
-            int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 0)
-                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
-            else
-                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
-        }
+        void sort0ByKey(Param pKey, Param pVal);
     }
 }
-
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/sort_by_key/b8.cpp b/src/backend/opencl/kernel/sort_by_key/b8.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/b8.cpp
rename to src/backend/opencl/kernel/sort_by_key/b8.cpp
index 118d20dc92..ad0d7f48ae 100644
--- a/src/backend/opencl/sort_by_key/b8.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/b8.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(char,true)
     INSTANTIATE1(char,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/f32.cpp b/src/backend/opencl/kernel/sort_by_key/f32.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/f32.cpp
rename to src/backend/opencl/kernel/sort_by_key/f32.cpp
index a7baf486f1..a1e9ae5f1f 100644
--- a/src/backend/opencl/sort_by_key/f32.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/f32.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(float,true)
     INSTANTIATE1(float,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/f64.cpp b/src/backend/opencl/kernel/sort_by_key/f64.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/f64.cpp
rename to src/backend/opencl/kernel/sort_by_key/f64.cpp
index 6971c90982..7fb7a79bd8 100644
--- a/src/backend/opencl/sort_by_key/f64.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/f64.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(double,true)
     INSTANTIATE1(double,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/s16.cpp b/src/backend/opencl/kernel/sort_by_key/s16.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/s16.cpp
rename to src/backend/opencl/kernel/sort_by_key/s16.cpp
index 44e17b5030..491ea0e3a2 100644
--- a/src/backend/opencl/sort_by_key/s16.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/s16.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(short,true)
     INSTANTIATE1(short,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/s32.cpp b/src/backend/opencl/kernel/sort_by_key/s32.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/s32.cpp
rename to src/backend/opencl/kernel/sort_by_key/s32.cpp
index 9fed1a53b3..67ba20e7dd 100644
--- a/src/backend/opencl/sort_by_key/s32.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/s32.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(int,true)
     INSTANTIATE1(int,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/s64.cpp b/src/backend/opencl/kernel/sort_by_key/s64.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/s64.cpp
rename to src/backend/opencl/kernel/sort_by_key/s64.cpp
index e2ed8d687b..a48f36ee47 100644
--- a/src/backend/opencl/sort_by_key/s64.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/s64.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(intl,true)
     INSTANTIATE1(intl,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/u16.cpp b/src/backend/opencl/kernel/sort_by_key/u16.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/u16.cpp
rename to src/backend/opencl/kernel/sort_by_key/u16.cpp
index c53b68fb53..36678d0a42 100644
--- a/src/backend/opencl/sort_by_key/u16.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/u16.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(ushort,true)
     INSTANTIATE1(ushort,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/u32.cpp b/src/backend/opencl/kernel/sort_by_key/u32.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/u32.cpp
rename to src/backend/opencl/kernel/sort_by_key/u32.cpp
index c2e3e62163..f1e4b5322f 100644
--- a/src/backend/opencl/sort_by_key/u32.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/u32.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(uint,true)
     INSTANTIATE1(uint,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/u64.cpp b/src/backend/opencl/kernel/sort_by_key/u64.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/u64.cpp
rename to src/backend/opencl/kernel/sort_by_key/u64.cpp
index 89649b1ba5..0a6f5b0c4f 100644
--- a/src/backend/opencl/sort_by_key/u64.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/u64.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(uintl,true)
     INSTANTIATE1(uintl,false)
 }
+}
diff --git a/src/backend/opencl/sort_by_key/u8.cpp b/src/backend/opencl/kernel/sort_by_key/u8.cpp
similarity index 87%
rename from src/backend/opencl/sort_by_key/u8.cpp
rename to src/backend/opencl/kernel/sort_by_key/u8.cpp
index 2dfb4c3a73..45af011a86 100644
--- a/src/backend/opencl/sort_by_key/u8.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/u8.cpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
 
 namespace opencl
+{
+namespace kernel
 {
     INSTANTIATE1(uchar,true)
     INSTANTIATE1(uchar,false)
 }
+}
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..dc1aa2735f
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,373 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/sort_pair.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <kernel/sort_helper.hpp>
+#include <program.hpp>
+#include <memory.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <math.hpp>
+#include <kernel/iota.hpp>
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+
+#include <boost/compute/core.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
+#include <boost/compute/functional/operator.hpp>
+#include <boost/compute/iterator/buffer_iterator.hpp>
+#include <boost/compute/algorithm/copy.hpp>
+#include <boost/compute/algorithm/transform.hpp>
+#include <boost/compute/container/vector.hpp>
+#include <boost/compute/functional/get.hpp>
+#include <boost/compute/functional/field.hpp>
+#include <boost/compute/types/pair.hpp>
+
+namespace compute = boost::compute;
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+template<typename Tk, typename Tv, bool isAscending>
+inline
+boost::compute::function<bool(const std::pair<Tk, Tv>, const std::pair<Tk, Tv>)>
+makeCompareFunction()
+{
+    // Cannot use isAscending in BOOST_COMPUTE_FUNCTION
+    if(isAscending) {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+            {
+                return lhs.first < rhs.first;
+            }
+        );
+        return IPCompare;
+    } else {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+            {
+                return lhs.first > rhs.first;
+            }
+        );
+        return IPCompare;
+    }
+}
+
+template<typename Tk>
+inline boost::compute::function<Tk(Tk)>
+flipFunction()
+{
+    BOOST_COMPUTE_FUNCTION(Tk, negateFn, (const Tk x),
+        {
+            return -x;
+        }
+    );
+
+    return negateFn;
+}
+
+#define INSTANTIATE_FLIP(TY, XMAX)                              \
+template<> inline boost::compute::function<TY(TY)>              \
+flipFunction<TY>()                                              \
+{                                                               \
+    BOOST_COMPUTE_FUNCTION(TY, negateFn, (const TY x),          \
+       {                                                        \
+            return XMAX - x;                                    \
+        }                                                       \
+    );                                                          \
+                                                                \
+    return negateFn;                                            \
+}
+
+INSTANTIATE_FLIP(unsigned, UINT_MAX)
+INSTANTIATE_FLIP(unsigned short, USHRT_MAX)
+INSTANTIATE_FLIP(unsigned char, UCHAR_MAX)
+INSTANTIATE_FLIP(cl_ulong, ULONG_MAX)
+
+#undef INSTANTIATE_FLIP
+
+namespace opencl
+{
+    namespace kernel
+    {
+        static const int copyPairIter = 4;
+
+        template<typename Tk, typename Tv>
+        void makePair(cl::Buffer *out, const cl::Buffer *first, const cl::Buffer *second, const unsigned N)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sortPairProgs;
+                static std::map<int, Kernel*>  sortPairKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D Tk="      << dtype_traits<Tk>::getName()
+                            << " -D Tv="      << dtype_traits<Tv>::getName()
+                            << " -D copyPairIter=" << copyPairIter;
+                    if (std::is_same<Tk, double >::value ||
+                        std::is_same<Tk, cdouble>::value ||
+                        std::is_same<Tv, double >::value ||
+                        std::is_same<Tv, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
+                    sortPairProgs[device]   = new Program(prog);
+                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "make_pair_kernel");
+                });
+
+                auto makePairOp = make_kernel<Buffer, const Buffer, const Buffer, const unsigned>
+                                          (*sortPairKernels[device]);
+
+                NDRange local(256, 1, 1);
+                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
+
+                makePairOp(EnqueueArgs(getQueue(), global, local), *out, *first, *second, N);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv>
+        void splitPair(cl::Buffer *first, cl::Buffer *second, const cl::Buffer *in, const unsigned N)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sortPairProgs;
+                static std::map<int, Kernel*>  sortPairKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D Tk="      << dtype_traits<Tk>::getName()
+                            << " -D Tv="      << dtype_traits<Tv>::getName()
+                            << " -D copyPairIter=" << copyPairIter;
+                    if (std::is_same<Tk, double >::value ||
+                        std::is_same<Tk, cdouble>::value ||
+                        std::is_same<Tv, double >::value ||
+                        std::is_same<Tv, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
+                    sortPairProgs[device]   = new Program(prog);
+                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "split_pair_kernel");
+                });
+
+                auto splitPairOp = make_kernel<Buffer, Buffer, const Buffer, const unsigned>
+                                          (*sortPairKernels[device]);
+
+                NDRange local(256, 1, 1);
+                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
+
+                splitPairOp(EnqueueArgs(getQueue(), global, local), *first, *second, *in, N);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKeyIterative(Param pKey, Param pVal)
+        {
+            try {
+                compute::command_queue c_queue(getQueue()());
+
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+
+                for(int w = 0; w < pKey.info.dims[3]; w++) {
+                    int pKeyW = w * pKey.info.strides[3];
+                    int pValW = w * pVal.info.strides[3];
+                    for(int z = 0; z < pKey.info.dims[2]; z++) {
+                        int pKeyWZ = pKeyW + z * pKey.info.strides[2];
+                        int pValWZ = pValW + z * pVal.info.strides[2];
+                        for(int y = 0; y < pKey.info.dims[1]; y++) {
+
+                            int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
+                            int pValOffset = pValWZ + y * pVal.info.strides[1];
+
+                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
+                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
+                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
+                            if(isAscending) {
+                                compute::sort_by_key(start, end, vals, c_queue);
+                            } else {
+                                compute::sort_by_key(start, end, vals,
+                                                     compute::greater< type_t<Tk> >(), c_queue);
+                            }
+                        }
+                    }
+                }
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk_, typename Tv_, bool isAscending, int dim>
+        void sortByKeyBatched(Param pKey, Param pVal)
+        {
+            typedef type_t<Tk_> Tk;
+            typedef type_t<Tv_> Tv;
+
+            try {
+                af::dim4 inDims;
+                for(int i = 0; i < 4; i++)
+                    inDims[i] = pKey.info.dims[i];
+
+                // Sort dimension
+                // tileDims * seqDims = inDims
+                af::dim4 tileDims(1);
+                af::dim4 seqDims = inDims;
+                tileDims[dim] = inDims[dim];
+                seqDims[dim] = 1;
+
+                // Create/call iota
+                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
+                cl::Buffer* key = bufferAlloc(inDims.elements() * sizeof(unsigned));
+                Param pSeq;
+                pSeq.data = key;
+                pSeq.info.offset = 0;
+                pSeq.info.dims[0] = inDims[0];
+                pSeq.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pSeq.info.dims[i] = inDims[i];
+                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
+                }
+                kernel::iota<unsigned>(pSeq, seqDims, tileDims);
+
+                int elements = inDims.elements();
+
+                // Flat - Not required since inplace and both are continuous
+                //val.modDims(inDims.elements());
+                //key.modDims(inDims.elements());
+
+                // Sort indices
+                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
+                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
+                compute::command_queue c_queue(getQueue()());
+                compute::context c_context(getContext()());
+
+                // Create buffer iterators for seq
+                compute::buffer pSeq_buf((*pSeq.data)());
+                compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
+                compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
+                // Create buffer iterators for key and val
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+                compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
+                compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
+                compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
+                compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
+
+                // Sort By Key for descending is stable in the reverse
+                // (greater) order. Sorting in ascending with negated values
+                // will give the right result
+                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+                // Create a copy of the pKey buffer
+                cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
+                compute::buffer cKey_buf((*cKey)());
+                compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
+                compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
+                compute::copy(key0, keyN, cKey0, c_queue);
+
+                // FIRST SORT
+                compute::sort_by_key(key0, keyN, seq0, c_queue);
+                compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
+
+                // Create a copy of the seq buffer after first sort
+                cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
+                compute::buffer cSeq_buf((*cSeq)());
+                compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
+                compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
+                compute::copy(seq0, seqN, cSeq0, c_queue);
+
+                // SECOND SORT
+                // First call will sort key, second sort will sort val
+                // Needs to be ascending (true) in order to maintain the indices properly
+                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
+                compute::sort_by_key(seq0, seqN, key0, c_queue);
+                compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
+
+                // If descending, flip it back
+                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+                //// No need of doing moddims here because the original Array<T>
+                //// dimensions have not been changed
+                ////val.modDims(inDims);
+
+                CL_DEBUG_FINISH(getQueue());
+                bufferFree(key);
+                bufferFree(cSeq);
+                bufferFree(cKey);
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param pKey, Param pVal)
+        {
+            int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 0)
+                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
+        }
+
+#define INSTANTIATE(Tk, Tv, dr)                                                 \
+    template void sort0ByKey<Tk, Tv, dr>(Param okey, Param oval);               \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 0>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 1>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 2>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 3>(Param okey, Param oval);      \
+
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
+    }
+}
+
+#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 899e0873fa..b8031c2314 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -8,75 +8,11 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/sort_pair.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <utility>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
-#include <boost/compute/function.hpp>
-
-template<typename Tk, typename Tv, bool isAscending>
-inline
-boost::compute::function<bool(const std::pair<Tk, Tv>, const std::pair<Tk, Tv>)>
-makeCompareFunction()
-{
-    // Cannot use isAscending in BOOST_COMPUTE_FUNCTION
-    if(isAscending) {
-        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
-            {
-                return lhs.first < rhs.first;
-            }
-        );
-        return IPCompare;
-    } else {
-        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
-            {
-                return lhs.first > rhs.first;
-            }
-        );
-        return IPCompare;
-    }
-}
-
-template<typename Tk>
-inline boost::compute::function<Tk(Tk)>
-flipFunction()
-{
-    BOOST_COMPUTE_FUNCTION(Tk, negateFn, (const Tk x),
-        {
-            return -x;
-        }
-    );
-
-    return negateFn;
-}
-
-#define INSTANTIATE_FLIP(TY, XMAX)                              \
-template<> inline boost::compute::function<TY(TY)>              \
-flipFunction<TY>()                                              \
-{                                                               \
-    BOOST_COMPUTE_FUNCTION(TY, negateFn, (const TY x),          \
-       {                                                        \
-            return XMAX - x;                                    \
-        }                                                       \
-    );                                                          \
-                                                                \
-    return negateFn;                                            \
-}
-
-INSTANTIATE_FLIP(unsigned, UINT_MAX)
-INSTANTIATE_FLIP(unsigned short, USHRT_MAX)
-INSTANTIATE_FLIP(unsigned char, UCHAR_MAX)
-INSTANTIATE_FLIP(cl_ulong, ULONG_MAX)
-
-#undef INSTANTIATE_FLIP
-
 namespace opencl
 {
     namespace kernel
@@ -107,91 +43,6 @@ namespace opencl
         using type_t = typename conditional<is_same<T, uintl>::value,
                                             cl_ulong, ltype_t<T>
                                            >::type;
-
-        static const int copyPairIter = 4;
-
-        template<typename Tk, typename Tv>
-        void makePair(cl::Buffer *out, const cl::Buffer *first, const cl::Buffer *second, const unsigned N)
-        {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sortPairProgs;
-                static std::map<int, Kernel*>  sortPairKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D Tk="      << dtype_traits<Tk>::getName()
-                            << " -D Tv="      << dtype_traits<Tv>::getName()
-                            << " -D copyPairIter=" << copyPairIter;
-                    if (std::is_same<Tk, double >::value ||
-                        std::is_same<Tk, cdouble>::value ||
-                        std::is_same<Tv, double >::value ||
-                        std::is_same<Tv, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
-                    sortPairProgs[device]   = new Program(prog);
-                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "make_pair_kernel");
-                });
-
-                auto makePairOp = make_kernel<Buffer, const Buffer, const Buffer, const unsigned>
-                                          (*sortPairKernels[device]);
-
-                NDRange local(256, 1, 1);
-                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
-
-                makePairOp(EnqueueArgs(getQueue(), global, local), *out, *first, *second, N);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
-
-        template<typename Tk, typename Tv>
-        void splitPair(cl::Buffer *first, cl::Buffer *second, const cl::Buffer *in, const unsigned N)
-        {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sortPairProgs;
-                static std::map<int, Kernel*>  sortPairKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D Tk="      << dtype_traits<Tk>::getName()
-                            << " -D Tv="      << dtype_traits<Tv>::getName()
-                            << " -D copyPairIter=" << copyPairIter;
-                    if (std::is_same<Tk, double >::value ||
-                        std::is_same<Tk, cdouble>::value ||
-                        std::is_same<Tv, double >::value ||
-                        std::is_same<Tv, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
-                    sortPairProgs[device]   = new Program(prog);
-                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "split_pair_kernel");
-                });
-
-                auto splitPairOp = make_kernel<Buffer, Buffer, const Buffer, const unsigned>
-                                          (*sortPairKernels[device]);
-
-                NDRange local(256, 1, 1);
-                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
-
-                splitPairOp(EnqueueArgs(getQueue(), global, local), *first, *second, *in, N);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
     }
 }
+
diff --git a/src/backend/opencl/sort_by_key/impl.hpp b/src/backend/opencl/sort_by_key.cpp
similarity index 65%
rename from src/backend/opencl/sort_by_key/impl.hpp
rename to src/backend/opencl/sort_by_key.cpp
index f68fe91b3c..27c2dc2462 100644
--- a/src/backend/opencl/sort_by_key/impl.hpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -55,26 +55,36 @@ namespace opencl
         }
     }
 
-#define INSTANTIATE(Tk, Tv, isAscending)                                \
-    template void                                                       \
-    sort_by_key<Tk, Tv, isAscending>(Array<Tk> &okey, Array<Tv> &oval,  \
-                                     const Array<Tk> &ikey,             \
-                                     const Array<Tv> &ival,             \
-                                     const unsigned dim);               \
+#define INSTANTIATE(Tk, Tv)                                                         \
+    template void sort_by_key<Tk, Tv, true >(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+    template void sort_by_key<Tk, Tv, false>(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
 
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
 
-#define INSTANTIATE1(Tk, isAscending)           \
-    INSTANTIATE(Tk, float  , isAscending)       \
-    INSTANTIATE(Tk, double , isAscending)       \
-    INSTANTIATE(Tk, cfloat , isAscending)       \
-    INSTANTIATE(Tk, cdouble, isAscending)       \
-    INSTANTIATE(Tk, int    , isAscending)       \
-    INSTANTIATE(Tk, uint   , isAscending)       \
-    INSTANTIATE(Tk, char   , isAscending)       \
-    INSTANTIATE(Tk, uchar  , isAscending)       \
-    INSTANTIATE(Tk, short  , isAscending)       \
-    INSTANTIATE(Tk, ushort , isAscending)       \
-    INSTANTIATE(Tk, intl   , isAscending)       \
-    INSTANTIATE(Tk, uintl  , isAscending)       \
+
+INSTANTIATE1(float )
+INSTANTIATE1(double)
+INSTANTIATE1(int   )
+INSTANTIATE1(uint  )
+INSTANTIATE1(short )
+INSTANTIATE1(ushort)
+INSTANTIATE1(char  )
+INSTANTIATE1(uchar )
+INSTANTIATE1(intl  )
+INSTANTIATE1(uintl )
 
 }
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 4e63095421..54ba158e8a 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <defines.hpp>
+#include <types.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <sstream>

From cbefc1f2952ed780b4c4f71b98f6bb9bdf54d646 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 21:22:48 -0400
Subject: [PATCH 0473/2677] Instantiate sort_by_key kernels in separately in
 cpu

---
 src/backend/cpu/CMakeLists.txt              |   3 +-
 src/backend/cpu/kernel/sort_by_key.hpp      | 120 +-------------
 src/backend/cpu/kernel/sort_by_key/b8.cpp   |  19 +++
 src/backend/cpu/kernel/sort_by_key/f32.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/f64.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/s16.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/s32.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/s64.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/u16.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/u32.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/u64.cpp  |  19 +++
 src/backend/cpu/kernel/sort_by_key/u8.cpp   |  19 +++
 src/backend/cpu/kernel/sort_by_key_impl.hpp | 166 ++++++++++++++++++++
 13 files changed, 361 insertions(+), 118 deletions(-)
 create mode 100644 src/backend/cpu/kernel/sort_by_key/b8.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/f32.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/f64.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/s16.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/s32.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/s64.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/u16.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/u32.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/u64.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key/u8.cpp
 create mode 100644 src/backend/cpu/kernel/sort_by_key_impl.hpp

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index f7857ec6d6..0ae74e8384 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -93,7 +93,8 @@ FILE(GLOB cpu_headers
     "*.h")
 
 FILE(GLOB cpu_sources
-    "*.cpp")
+    "*.cpp"
+    "kernel/sort_by_key/*.cpp")
 
 LIST(SORT cpu_headers)
 LIST(SORT cpu_sources)
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 1be4a94d3a..55d5a89337 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -8,15 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
-#include <math.hpp>
-#include <algorithm>
-#include <numeric>
-#include <queue>
 #include <err_cpu.hpp>
-#include <functional>
-#include <kernel/sort_helper.hpp>
 
 namespace cpu
 {
@@ -24,120 +17,13 @@ namespace kernel
 {
 
 template<typename Tk, typename Tv, bool isAscending>
-void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
-{
-    // Get pointers and initialize original index locations
-    Tk *okey_ptr = okey.get();
-    Tv *oval_ptr = oval.get();
-
-    std::vector<IndexPair<Tk, Tv> > X;
-    X.reserve(okey.dims()[0]);
-
-    for(dim_t w = 0; w < okey.dims()[3]; w++) {
-        dim_t okeyW = w * okey.strides()[3];
-        dim_t ovalW = w * oval.strides()[3];
-
-        for(dim_t z = 0; z < okey.dims()[2]; z++) {
-            dim_t okeyWZ = okeyW + z * okey.strides()[2];
-            dim_t ovalWZ = ovalW + z * oval.strides()[2];
-
-            for(dim_t y = 0; y < okey.dims()[1]; y++) {
-
-                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
-                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
-
-                X.clear();
-                std::transform(okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims()[0],
-                               oval_ptr + ovalOffset,
-                               std::back_inserter(X),
-                               [](Tk v_, Tv i_) { return std::make_pair(v_, i_); }
-                               );
-
-                std::stable_sort(X.begin(), X.end(), IPCompare<Tk, Tv, isAscending>());
-
-                for(unsigned it = 0; it < X.size(); it++) {
-                    okey_ptr[okeyOffset + it] = X[it].first;
-                    oval_ptr[ovalOffset + it] = X[it].second;
-                }
-            }
-        }
-    }
-
-    return;
-}
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval);
 
 template<typename Tk, typename Tv, bool isAscending, int dim>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
-{
-    af::dim4 inDims = okey.dims();
-
-    af::dim4 tileDims(1);
-    af::dim4 seqDims = inDims;
-    tileDims[dim] = inDims[dim];
-    seqDims[dim] = 1;
-
-    uint* key = memAlloc<uint>(inDims.elements());
-    // IOTA
-    {
-        af::dim4 dims    = inDims;
-        uint* out        = key;
-        af::dim4 strides(1);
-        for(int i = 1; i < 4; i++)
-            strides[i] = strides[i-1] * dims[i-1];
-
-        for(dim_t w = 0; w < dims[3]; w++) {
-            dim_t offW = w * strides[3];
-            uint okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
-            for(dim_t z = 0; z < dims[2]; z++) {
-                dim_t offWZ = offW + z * strides[2];
-                uint okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
-                for(dim_t y = 0; y < dims[1]; y++) {
-                    dim_t offWZY = offWZ + y * strides[1];
-                    uint okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
-                    for(dim_t x = 0; x < dims[0]; x++) {
-                        dim_t id = offWZY + x;
-                        out[id] = okeyY + (x % seqDims[0]);
-                    }
-                }
-            }
-        }
-    }
-
-    // initialize original index locations
-    Tk *okey_ptr = okey.get();
-    Tv *oval_ptr = oval.get();
-
-    std::vector<KeyIndexPair<Tk, Tv> > X;
-    X.reserve(okey.elements());
-
-    for(unsigned i = 0; i < okey.elements(); i++) {
-        X.push_back(std::make_pair(std::make_pair(okey_ptr[i], oval_ptr[i]), key[i]));
-    }
-
-    memFree(key); // key is no longer required
-
-    std::stable_sort(X.begin(), X.end(), KIPCompareV<Tk, Tv, isAscending>());
-
-    std::stable_sort(X.begin(), X.end(), KIPCompareK<Tk, Tv, true>());
-
-    for(unsigned it = 0; it < okey.elements(); it++) {
-        okey_ptr[it] = X[it].first.first;
-        oval_ptr[it] = X[it].first.second;
-    }
-
-    return;
-}
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval);
 
 template<typename Tk, typename Tv, bool isAscending>
-void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
-{
-    int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
-    // TODO Make a better heurisitic
-    if(higherDims > 0)
-        kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
-    else
-        kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
-}
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval);
 
 }
 }
diff --git a/src/backend/cpu/kernel/sort_by_key/b8.cpp b/src/backend/cpu/kernel/sort_by_key/b8.cpp
new file mode 100644
index 0000000000..855e7a93ca
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/b8.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(char,true)
+    INSTANTIATE1(char,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/f32.cpp b/src/backend/cpu/kernel/sort_by_key/f32.cpp
new file mode 100644
index 0000000000..11d8139957
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/f32.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(float,true)
+    INSTANTIATE1(float,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/f64.cpp b/src/backend/cpu/kernel/sort_by_key/f64.cpp
new file mode 100644
index 0000000000..21746d773a
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/f64.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(double,true)
+    INSTANTIATE1(double,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/s16.cpp b/src/backend/cpu/kernel/sort_by_key/s16.cpp
new file mode 100644
index 0000000000..50b718d04c
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/s16.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(short,true)
+    INSTANTIATE1(short,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/s32.cpp b/src/backend/cpu/kernel/sort_by_key/s32.cpp
new file mode 100644
index 0000000000..c50efcdc26
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/s32.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(int,true)
+    INSTANTIATE1(int,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/s64.cpp b/src/backend/cpu/kernel/sort_by_key/s64.cpp
new file mode 100644
index 0000000000..82946f820e
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/s64.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(intl,true)
+    INSTANTIATE1(intl,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/u16.cpp b/src/backend/cpu/kernel/sort_by_key/u16.cpp
new file mode 100644
index 0000000000..feedd1d56e
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/u16.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(ushort,true)
+    INSTANTIATE1(ushort,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/u32.cpp b/src/backend/cpu/kernel/sort_by_key/u32.cpp
new file mode 100644
index 0000000000..cd514af19a
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/u32.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(uint,true)
+    INSTANTIATE1(uint,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/u64.cpp b/src/backend/cpu/kernel/sort_by_key/u64.cpp
new file mode 100644
index 0000000000..ec955b3de7
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/u64.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(uintl,true)
+    INSTANTIATE1(uintl,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key/u8.cpp b/src/backend/cpu/kernel/sort_by_key/u8.cpp
new file mode 100644
index 0000000000..fd58cbfaa1
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/u8.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    INSTANTIATE1(uchar,true)
+    INSTANTIATE1(uchar,false)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..fcd415c29c
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,166 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <kernel/sort_by_key.hpp>
+#include <kernel/sort_helper.hpp>
+#include <Array.hpp>
+#include <math.hpp>
+#include <algorithm>
+#include <numeric>
+#include <queue>
+#include <err_cpu.hpp>
+#include <functional>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
+{
+    // Get pointers and initialize original index locations
+    Tk *okey_ptr = okey.get();
+    Tv *oval_ptr = oval.get();
+
+    std::vector<IndexPair<Tk, Tv> > X;
+    X.reserve(okey.dims()[0]);
+
+    for(dim_t w = 0; w < okey.dims()[3]; w++) {
+        dim_t okeyW = w * okey.strides()[3];
+        dim_t ovalW = w * oval.strides()[3];
+
+        for(dim_t z = 0; z < okey.dims()[2]; z++) {
+            dim_t okeyWZ = okeyW + z * okey.strides()[2];
+            dim_t ovalWZ = ovalW + z * oval.strides()[2];
+
+            for(dim_t y = 0; y < okey.dims()[1]; y++) {
+
+                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
+                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
+
+                X.clear();
+                std::transform(okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims()[0],
+                               oval_ptr + ovalOffset,
+                               std::back_inserter(X),
+                               [](Tk v_, Tv i_) { return std::make_pair(v_, i_); }
+                               );
+
+                std::stable_sort(X.begin(), X.end(), IPCompare<Tk, Tv, isAscending>());
+
+                for(unsigned it = 0; it < X.size(); it++) {
+                    okey_ptr[okeyOffset + it] = X[it].first;
+                    oval_ptr[ovalOffset + it] = X[it].second;
+                }
+            }
+        }
+    }
+
+    return;
+}
+
+template<typename Tk, typename Tv, bool isAscending, int dim>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
+{
+    af::dim4 inDims = okey.dims();
+
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim] = inDims[dim];
+    seqDims[dim] = 1;
+
+    uint* key = memAlloc<uint>(inDims.elements());
+    // IOTA
+    {
+        af::dim4 dims    = inDims;
+        uint* out        = key;
+        af::dim4 strides(1);
+        for(int i = 1; i < 4; i++)
+            strides[i] = strides[i-1] * dims[i-1];
+
+        for(dim_t w = 0; w < dims[3]; w++) {
+            dim_t offW = w * strides[3];
+            uint okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            for(dim_t z = 0; z < dims[2]; z++) {
+                dim_t offWZ = offW + z * strides[2];
+                uint okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                for(dim_t y = 0; y < dims[1]; y++) {
+                    dim_t offWZY = offWZ + y * strides[1];
+                    uint okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
+                    for(dim_t x = 0; x < dims[0]; x++) {
+                        dim_t id = offWZY + x;
+                        out[id] = okeyY + (x % seqDims[0]);
+                    }
+                }
+            }
+        }
+    }
+
+    // initialize original index locations
+    Tk *okey_ptr = okey.get();
+    Tv *oval_ptr = oval.get();
+
+    std::vector<KeyIndexPair<Tk, Tv> > X;
+    X.reserve(okey.elements());
+
+    for(unsigned i = 0; i < okey.elements(); i++) {
+        X.push_back(std::make_pair(std::make_pair(okey_ptr[i], oval_ptr[i]), key[i]));
+    }
+
+    memFree(key); // key is no longer required
+
+    std::stable_sort(X.begin(), X.end(), KIPCompareV<Tk, Tv, isAscending>());
+
+    std::stable_sort(X.begin(), X.end(), KIPCompareK<Tk, Tv, true>());
+
+    for(unsigned it = 0; it < okey.elements(); it++) {
+        okey_ptr[it] = X[it].first.first;
+        oval_ptr[it] = X[it].first.second;
+    }
+
+    return;
+}
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
+{
+    int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
+    // TODO Make a better heurisitic
+    if(higherDims > 4)
+        kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+    else
+        kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+}
+
+#define INSTANTIATE(Tk, Tv, dr)                                                         \
+    template void sort0ByKey<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);               \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 0>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 1>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 2>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 3>(Array<Tk> okey, Array<Tv> oval);      \
+
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
+}
+}
+

From 45574db72c7bdc3470ec9d3f4f0fd212dd76dac1 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 21 Apr 2016 21:24:08 -0400
Subject: [PATCH 0474/2677] Sort by key cuda - create pair memory using
 memalloc, reasonable heuristic

---
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 30 ++++++++++---------
 .../opencl/kernel/sort_by_key_impl.hpp        |  2 +-
 2 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index 66a6087401..bcc2fefeb4 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -117,6 +117,8 @@ namespace cuda
             for(int i = 0; i < 4; i++)
                 inDims[i] = pKey.dims[i];
 
+            const dim_t elements = inDims.elements();
+
             // Sort dimension
             // tileDims * seqDims = inDims
             af::dim4 tileDims(1);
@@ -126,34 +128,34 @@ namespace cuda
 
             // Create/call iota
             // Array<uint> key = iota<uint>(seqDims, tileDims);
-            af::dim4 keydims = inDims;
-            uint* key = memAlloc<uint>(keydims.elements());
+            uint* key = memAlloc<uint>(elements);
             Param<uint> pSeq;
             pSeq.ptr = key;
             pSeq.strides[0] = 1;
-            pSeq.dims[0] = keydims[0];
+            pSeq.dims[0] = inDims[0];
             for(int i = 1; i < 4; i++) {
-                pSeq.dims[i] = keydims[i];
+                pSeq.dims[i] = inDims[i];
                 pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
             }
             cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
 
             // Make pkey, pVal into a pair
-            thrust::device_vector<IndexPair<Tk, Tv> > X(inDims.elements());
-            IndexPair<Tk, Tv> *Xptr = thrust::raw_pointer_cast(X.data());
+            IndexPair<Tk, Tv> *Xptr = (IndexPair<Tk, Tv>*)memAlloc<char>(sizeof(IndexPair<Tk, Tv>) * elements);
 
             const int threads = 256;
-            int blocks = divup(inDims.elements(), threads * copyPairIter);
+            int blocks = divup(elements, threads * copyPairIter);
             CUDA_LAUNCH((makeIndexPair<Tk, Tv>), blocks, threads,
-                        Xptr, pKey.ptr, pVal.ptr, inDims.elements());
+                        Xptr, pKey.ptr, pVal.ptr, elements);
             POST_LAUNCH_CHECK();
 
+            thrust::device_ptr<IndexPair<Tk, Tv> > X = thrust::device_pointer_cast(Xptr);
+
             // Sort indices
             // Need to convert pSeq to thrust::device_ptr, otherwise thrust
             // throws weird errors for all *64 data types (double, intl, uintl etc)
             thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
             THRUST_SELECT(thrust::stable_sort_by_key,
-                          X.begin(), X.end(),
+                          X, X + elements,
                           dSeq,
                           IPCompare<Tk, Tv, isAscending>());
             POST_LAUNCH_CHECK();
@@ -161,13 +163,12 @@ namespace cuda
             // Needs to be ascending (true) in order to maintain the indices properly
             //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
             THRUST_SELECT(thrust::stable_sort_by_key,
-                          dSeq,
-                          dSeq + inDims.elements(),
-                          X.begin());
+                          dSeq, dSeq + elements,
+                          X);
             POST_LAUNCH_CHECK();
 
             CUDA_LAUNCH((splitIndexPair<Tk, Tv>), blocks, threads,
-                        pKey.ptr, pVal.ptr, Xptr, inDims.elements());
+                        pKey.ptr, pVal.ptr, Xptr, elements);
             POST_LAUNCH_CHECK();
 
             // No need of doing moddims here because the original Array<T>
@@ -175,6 +176,7 @@ namespace cuda
             //val.modDims(inDims);
 
             memFree(key);
+            memFree((char*)Xptr);
         }
 
         template<typename Tk, typename Tv, bool isAscending>
@@ -182,7 +184,7 @@ namespace cuda
         {
             int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
             // TODO Make a better heurisitic
-            if(higherDims > 5)
+            if(higherDims > 4)
                 kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index dc1aa2735f..243034541f 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -340,7 +340,7 @@ namespace opencl
         {
             int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
             // TODO Make a better heurisitic
-            if(higherDims > 0)
+            if(higherDims > 5)
                 kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);

From 840ea28d28fa0f63e860989008490f491e5e1fc3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 14:20:14 -0400
Subject: [PATCH 0475/2677] Remove sort 0 dim restriction note from
 documentation

---
 include/af/algorithm.h | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 792d6e2f44..a25120ffe3 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -357,8 +357,6 @@ namespace af
        \return the sorted output
 
        \ingroup sort_func_sort
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI array sort(const array &in, const unsigned dim = 0, const bool isAscending = true);
 
@@ -372,8 +370,6 @@ namespace af
        \param[in] isAscending specifies the sorting order
 
        \ingroup sort_func_sort_index
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI void  sort(array &out, array &indices, const array &in, const unsigned dim = 0,
                      const bool isAscending = true);
@@ -388,8 +384,6 @@ namespace af
        \param[in] isAscending specifies the sorting order
 
        \ingroup sort_func_sort_keys
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI void  sort(array &out_keys, array &out_values, const array &keys, const array &values,
                      const unsigned dim = 0, const bool isAscending = true);
@@ -794,8 +788,6 @@ extern "C" {
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup sort_func_sort
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending);
 
@@ -810,8 +802,6 @@ extern "C" {
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup sort_func_sort_index
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
                                const unsigned dim, const bool isAscending);
@@ -827,8 +817,6 @@ extern "C" {
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup sort_func_sort_keys
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                                 const af_array keys, const af_array values,

From f6eae071675f8fe4f2c5672b104a3c1394de66b7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 15:58:11 -0400
Subject: [PATCH 0476/2677] Add multi dimension support to median, tests

---
 src/api/c/median.cpp |  13 ++--
 test/median.cpp      | 156 ++++++++++++++++++++++++++++---------------
 2 files changed, 108 insertions(+), 61 deletions(-)

diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 50bcad25ee..b5c033f461 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -68,8 +68,8 @@ static af_array median(const af_array& in, const dim_t dim)
     const Array<T> input = getArray<T>(in);
     Array<T> sortedIn   = sort<T, true>(input, dim);
 
-    int nElems    = input.dims()[0];
-    double mid    = (nElems + 1) / 2;
+    int dimLength = input.dims()[dim];
+    double mid    = (dimLength + 1) / 2;
     af_array left = 0;
 
     af_seq slices[4] = {af_span, af_span, af_span, af_span};
@@ -78,7 +78,7 @@ static af_array median(const af_array& in, const dim_t dim)
     af_array sortedIn_handle = getHandle<T>(sortedIn);
     AF_CHECK(af_index(&left, sortedIn_handle, input.ndims(), slices));
 
-    if (nElems % 2 == 1) {
+    if (dimLength % 2 == 1) {
         // mid-1 is our guy
         if (input.isFloating()) return left;
 
@@ -90,7 +90,7 @@ static af_array median(const af_array& in, const dim_t dim)
         return out;
     } else {
         // ((mid-1)+mid)/2 is our guy
-        dim4  dims = input.dims();
+        dim4 dims = input.dims();
         af_array right = 0;
         slices[dim] = af_make_seq(mid, mid, 1.0);
 
@@ -100,7 +100,8 @@ static af_array median(const af_array& in, const dim_t dim)
         af_array carr   = 0;
         af_array result = 0;
 
-        dim4 cdims = dim4(1, dims[1], dims[2], dims[3]);
+        dim4 cdims = dims;
+        cdims[dim] = 1;
         AF_CHECK(af_constant(&carr, 0.5, cdims.ndims(), cdims.get(), input.isDouble() ? f64 : f32));
 
         if (!input.isFloating()) {
@@ -148,7 +149,7 @@ af_err af_median_all(double *realVal, double *imagVal, const af_array in)
 af_err af_median(af_array* out, const af_array in, const dim_t dim)
 {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=0));
+        ARG_ASSERT(2, (dim >= 0 && dim <= 4));
 
         af_array output = 0;
         ArrayInfo info = getInfo(in);
diff --git a/test/median.cpp b/test/median.cpp
index e0b21ba281..5b26a44a97 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -37,96 +37,142 @@ af::array generateArray<unsigned int>(int nx, int ny, int nz, int nw)
     return a;
 }
 
-template<typename To, typename Ti, bool flat>
-void median0(int nx, int ny=1, int nz=1, int nw=1)
+template<typename To, typename Ti>
+void median_flat(int nx, int ny=1, int nz=1, int nw=1)
 {
     if (noDoubleTests<Ti>()) return;
     array a = generateArray<Ti>(nx, ny, nz, nw);
-    array sa = sort(a);
 
-    Ti *h_sa = sa.host<Ti>();
+    // Verification
+    array sa = sort(flat(a));
+    dim_t mid = (sa.dims(0) + 1) / 2;
 
-    To *h_b = NULL;
-    To val = 0;
+    To verify;
 
-    if (flat) {
-        val = median<To>(a);
-        h_b = &val;
+    To *h_sa = sa.as((af_dtype)af::dtype_traits<To>::af_type).host<To>();
+    if(sa.dims(0) % 2 == 1) {
+        verify = h_sa[mid - 1];
     } else {
-        array b = median(a);
-        h_b = b.host<To>();
+        verify = (h_sa[mid - 1] + h_sa[mid]) / (To)2;
     }
 
-    for (int w = 0; w < nw; w++) {
-        for (int z = 0; z < nz; z++) {
-            for (int y = 0; y < ny; y++) {
+    // Test Part
+    To val = median<To>(a);
 
-                int off = (y  + ny * (z + nz * w));
-                int id = nx / 2;
+    ASSERT_EQ(verify, val);
 
-                if (nx & 2) {
-                    ASSERT_EQ(h_sa[id + off * nx], h_b[off]);
-                } else {
-                    To left = h_sa[id + off * nx - 1];
-                    To right = h_sa[id + off * nx];
+    delete[] h_sa;
+}
+
+template<typename To, typename Ti, int dim>
+void median_test(int nx, int ny=1, int nz=1, int nw=1)
+{
+    if (noDoubleTests<Ti>()) return;
+
+    array a = generateArray<Ti>(nx, ny, nz, nw);
+
+    // If selected dim is higher than input ndims, then return
+    if(dim >= a.dims().ndims())
+        return;
+
+    array verify;
+
+    // Verification
+    array sa = sort(a, dim);
+
+    double mid = (a.dims(dim) + 1) / 2;
+    af::seq mSeq[4] = {span, span, span, span};
+    mSeq[dim] = af::seq(mid, mid, 1.0);
 
-                    ASSERT_NEAR((left + right) / 2, h_b[off], 1e-5);
-                }
-            }
-        }
+    if(sa.dims(dim) % 2 == 1) {
+        mSeq[dim] = mSeq[dim] - 1.0;
+        verify = sa(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
+    } else {
+        dim_t sdim[4] = {0};
+        sdim[dim] = 1;
+        sa = sa.as((af_dtype)af::dtype_traits<To>::af_type);
+        array sas = shift(sa, sdim[0], sdim[1], sdim[2], sdim[3]);
+        verify = ((sa + sas) / 2)(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     }
 
-    delete[] h_sa;
-    if (!flat) delete[] h_b;
+    // Test Part
+    array out = median(a, dim);
+
+    ASSERT_EQ(out.dims() == verify.dims(), true);
+    ASSERT_NEAR(0, sum<double>(af::abs(out - verify)), 1e-5);
 }
 
-#define MEDIAN0(To, Ti)                         \
-    TEST(median0, Ti##_1D_even)                 \
+#define MEDIAN_FLAT(To, Ti)                     \
+    TEST(MedianFlat, Ti##_flat_even)            \
+    {                                           \
+        median_flat<To, Ti>(1000);              \
+    }                                           \
+    TEST(MedianFlat, Ti##_flat_odd)             \
     {                                           \
-        median0<To, Ti, false>(1000);           \
+        median_flat<To, Ti>(783);               \
     }                                           \
-    TEST(median0, Ti##_2D_even)                 \
+    TEST(MedianFlat, Ti##_flat_multi_even)      \
     {                                           \
-        median0<To, Ti, false>(1000, 100);      \
+        median_flat<To, Ti>(24, 11, 3);         \
     }                                           \
-    TEST(median0, Ti##_3D_even)                 \
+    TEST(MedianFlat, Ti##_flat_multi_odd)       \
     {                                           \
-        median0<To, Ti, false>(1000, 25, 4);    \
+        median_flat<To, Ti>(15, 21, 7);         \
     }                                           \
-    TEST(median0, Ti##_4D_even)                 \
+
+MEDIAN_FLAT(float, float)
+MEDIAN_FLAT(float, int)
+MEDIAN_FLAT(float, uint)
+MEDIAN_FLAT(float, uchar)
+MEDIAN_FLAT(float, short)
+MEDIAN_FLAT(float, ushort)
+MEDIAN_FLAT(double, double)
+
+#define MEDIAN_TEST(To, Ti, dim)                \
+    TEST(Median, Ti##_1D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, false>(1000, 25, 2, 2); \
+        median_test<To, Ti, dim>(1000);         \
     }                                           \
-    TEST(median0, Ti##_flat_even)               \
+    TEST(Median, Ti##_2D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, true>(1000);            \
+        median_test<To, Ti, dim>(1000, 25);     \
     }                                           \
-    TEST(median0, Ti##_1D_odd)                  \
+    TEST(Median, Ti##_3D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, false>(783);            \
+        median_test<To, Ti, dim>(100, 25, 4);   \
     }                                           \
-    TEST(median0, Ti##_2D_odd)                  \
+    TEST(Median, Ti##_4D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, false>(783, 100);       \
+        median_test<To, Ti, dim>(100, 25, 2, 2);\
     }                                           \
-    TEST(median0, Ti##_3D_odd)                  \
+    TEST(Median, Ti##_1D_##dim##_odd)           \
     {                                           \
-        median0<To, Ti, false>(783, 25, 4);     \
+        median_test<To, Ti, dim>(783);          \
     }                                           \
-    TEST(median0, Ti##_4D_odd)                  \
+    TEST(Median, Ti##_2D_##dim##_odd)           \
     {                                           \
-        median0<To, Ti, false>(783, 25, 2, 2);  \
+        median_test<To, Ti, dim>(783, 25);      \
     }                                           \
-    TEST(median0, Ti##_flat_odd)                \
+    TEST(Median, Ti##_3D_##dim##_odd)           \
     {                                           \
-        median0<To, Ti, true>(783);             \
+        median_test<To, Ti, dim>(123, 25, 3);   \
     }                                           \
+    TEST(Median, Ti##_4D_##dim##_odd)           \
+    {                                           \
+        median_test<To, Ti, dim>(123, 25, 3, 3);\
+    }                                           \
+
 
+#define MEDIAN(To, Ti)      \
+    MEDIAN_TEST(To, Ti, 0)  \
+    MEDIAN_TEST(To, Ti, 1)  \
+    MEDIAN_TEST(To, Ti, 2)  \
+    MEDIAN_TEST(To, Ti, 3)  \
 
-MEDIAN0(float, float)
-MEDIAN0(float, int)
-MEDIAN0(float, uint)
-MEDIAN0(float, uchar)
-MEDIAN0(float, short)
-MEDIAN0(float, ushort)
-MEDIAN0(double, double)
+MEDIAN(float, float)
+MEDIAN(float, int)
+MEDIAN(float, uint)
+MEDIAN(float, uchar)
+MEDIAN(float, short)
+MEDIAN(float, ushort)
+MEDIAN(double, double)

From 4a3a839cb9a607d937b27789f31e997010436b23 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 24 Apr 2016 07:21:44 -0400
Subject: [PATCH 0477/2677] Converting std::pair to std::tuple to get for
 sorting in CPU backend

- Makes it cleaner / consistent
- More importantly, gets rid of annoying messages from -Wpsabi in gcc 5.3
---
 src/backend/cpu/kernel/sort_by_key_impl.hpp | 39 ++++++++++-----------
 src/backend/cpu/kernel/sort_helper.hpp      | 22 +++++++-----
 2 files changed, 32 insertions(+), 29 deletions(-)

diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index fcd415c29c..1c9ce7f5a6 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -31,8 +31,7 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
     Tk *okey_ptr = okey.get();
     Tv *oval_ptr = oval.get();
 
-    std::vector<IndexPair<Tk, Tv> > X;
-    X.reserve(okey.dims()[0]);
+    std::vector<IndexPair<Tk, Tv> > pairKeyVal(okey.dims()[0]);
 
     for(dim_t w = 0; w < okey.dims()[3]; w++) {
         dim_t okeyW = w * okey.strides()[3];
@@ -47,18 +46,18 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
                 dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
                 dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
 
-                X.clear();
-                std::transform(okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims()[0],
-                               oval_ptr + ovalOffset,
-                               std::back_inserter(X),
-                               [](Tk v_, Tv i_) { return std::make_pair(v_, i_); }
-                               );
+                Tk *okey_col_ptr = okey_ptr + okeyOffset;
+                Tv *oval_col_ptr = oval_ptr + ovalOffset;
 
-                std::stable_sort(X.begin(), X.end(), IPCompare<Tk, Tv, isAscending>());
+                for(dim_t x = 0; x < (dim_t)pairKeyVal.size(); x++) {
+                   pairKeyVal[x] = std::make_tuple(okey_col_ptr[x], oval_col_ptr[x]);
+                }
+
+                std::stable_sort(std::begin(pairKeyVal), std::end(pairKeyVal), IPCompare<Tk, Tv, isAscending>());
 
-                for(unsigned it = 0; it < X.size(); it++) {
-                    okey_ptr[okeyOffset + it] = X[it].first;
-                    oval_ptr[ovalOffset + it] = X[it].second;
+                for(unsigned x = 0; x < pairKeyVal.size(); x++) {
+                    okey_ptr[okeyOffset + x] = std::get<0>(pairKeyVal[x]);
+                    oval_ptr[ovalOffset + x] = std::get<1>(pairKeyVal[x]);
                 }
             }
         }
@@ -108,22 +107,21 @@ void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
     Tk *okey_ptr = okey.get();
     Tv *oval_ptr = oval.get();
 
-    std::vector<KeyIndexPair<Tk, Tv> > X;
-    X.reserve(okey.elements());
+    std::vector<KeyIndexPair<Tk, Tv> > pairKeyVal(okey.elements());
 
     for(unsigned i = 0; i < okey.elements(); i++) {
-        X.push_back(std::make_pair(std::make_pair(okey_ptr[i], oval_ptr[i]), key[i]));
+        pairKeyVal[i] = std::make_tuple(okey_ptr[i], oval_ptr[i], key[i]);
     }
 
     memFree(key); // key is no longer required
 
-    std::stable_sort(X.begin(), X.end(), KIPCompareV<Tk, Tv, isAscending>());
+    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), KIPCompareV<Tk, Tv, isAscending>());
 
-    std::stable_sort(X.begin(), X.end(), KIPCompareK<Tk, Tv, true>());
+    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), KIPCompareK<Tk, Tv, true>());
 
-    for(unsigned it = 0; it < okey.elements(); it++) {
-        okey_ptr[it] = X[it].first.first;
-        oval_ptr[it] = X[it].first.second;
+    for(unsigned x = 0; x < okey.elements(); x++) {
+        okey_ptr[x] = std::get<0>(pairKeyVal[x]);
+        oval_ptr[x] = std::get<1>(pairKeyVal[x]);
     }
 
     return;
@@ -163,4 +161,3 @@ void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
     INSTANTIATE(Tk, uintl  , dr)
 }
 }
-
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
index ff7da3560b..99479fddc3 100644
--- a/src/backend/cpu/kernel/sort_helper.hpp
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -14,7 +14,7 @@ namespace cpu
     namespace kernel
     {
         template <typename Tk, typename Tv>
-        using IndexPair = std::pair<Tk, Tv>;
+        using IndexPair = std::tuple<Tk, Tv>;
 
         template <typename Tk, typename Tv, bool isAscending>
         struct IPCompare
@@ -22,13 +22,15 @@ namespace cpu
             bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs)
             {
                 // Check stable sort condition
-                if(isAscending) return (lhs.first < rhs.first);
-                else return (lhs.first > rhs.first);
+                Tk lhsVal = std::get<0>(lhs);
+                Tk rhsVal = std::get<0>(rhs);
+                if(isAscending) return (lhsVal < rhsVal);
+                else return (lhsVal > rhsVal);
             }
         };
 
         template <typename Tk, typename Tv>
-        using KeyIndexPair = std::pair<IndexPair<Tk, Tv>, uint>;
+        using KeyIndexPair = std::tuple<Tk, Tv, uint>;
 
         template <typename Tk, typename Tv, bool isAscending>
         struct KIPCompareV
@@ -36,8 +38,10 @@ namespace cpu
             bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
             {
                 // Check stable sort condition
-                if(isAscending) return (lhs.first.first < rhs.first.first);
-                else return (lhs.first.first > rhs.first.first);
+                Tk lhsVal = std::get<0>(lhs);
+                Tk rhsVal = std::get<0>(rhs);
+                if(isAscending) return (lhsVal < rhsVal);
+                else return (lhsVal > rhsVal);
             }
         };
 
@@ -46,8 +50,10 @@ namespace cpu
         {
             bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
             {
-                if(isAscending) return (lhs.second < rhs.second);
-                else return (lhs.second > rhs.second);
+                uint lhsVal = std::get<2>(lhs);
+                uint rhsVal = std::get<2>(rhs);
+                if(isAscending) return (lhsVal < rhsVal);
+                else return (lhsVal > rhsVal);
             }
         };
     }

From 9443bf29831a489d919a0941ff7259be4f5e06c4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 24 Apr 2016 09:10:42 -0400
Subject: [PATCH 0478/2677] Use memAlloc and memFree instead of std::vector
 internally

---
 src/backend/cpu/kernel/sort_by_key_impl.hpp | 31 +++++++++++++--------
 1 file changed, 20 insertions(+), 11 deletions(-)

diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index 1c9ce7f5a6..12ba793285 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -31,7 +31,11 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
     Tk *okey_ptr = okey.get();
     Tv *oval_ptr = oval.get();
 
-    std::vector<IndexPair<Tk, Tv> > pairKeyVal(okey.dims()[0]);
+    typedef IndexPair<Tk, Tv> CurrentPair;
+
+    dim_t size = okey.dims()[0];
+    size_t bytes = size * sizeof(CurrentPair);
+    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
 
     for(dim_t w = 0; w < okey.dims()[3]; w++) {
         dim_t okeyW = w * okey.strides()[3];
@@ -49,13 +53,13 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
                 Tk *okey_col_ptr = okey_ptr + okeyOffset;
                 Tv *oval_col_ptr = oval_ptr + ovalOffset;
 
-                for(dim_t x = 0; x < (dim_t)pairKeyVal.size(); x++) {
+                for(dim_t x = 0; x < size; x++) {
                    pairKeyVal[x] = std::make_tuple(okey_col_ptr[x], oval_col_ptr[x]);
                 }
 
-                std::stable_sort(std::begin(pairKeyVal), std::end(pairKeyVal), IPCompare<Tk, Tv, isAscending>());
+                std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, isAscending>());
 
-                for(unsigned x = 0; x < pairKeyVal.size(); x++) {
+                for(unsigned x = 0; x < size; x++) {
                     okey_ptr[okeyOffset + x] = std::get<0>(pairKeyVal[x]);
                     oval_ptr[ovalOffset + x] = std::get<1>(pairKeyVal[x]);
                 }
@@ -63,6 +67,7 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
         }
     }
 
+    memFree((char *)pairKeyVal);
     return;
 }
 
@@ -107,23 +112,27 @@ void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
     Tk *okey_ptr = okey.get();
     Tv *oval_ptr = oval.get();
 
-    std::vector<KeyIndexPair<Tk, Tv> > pairKeyVal(okey.elements());
+    typedef KeyIndexPair<Tk, Tv> CurrentTuple;
+    size_t size = okey.elements();
+    size_t bytes = okey.elements() * sizeof(CurrentTuple);
+    CurrentTuple *tupleKeyValIdx = (CurrentTuple *)memAlloc<char>(bytes);
 
-    for(unsigned i = 0; i < okey.elements(); i++) {
-        pairKeyVal[i] = std::make_tuple(okey_ptr[i], oval_ptr[i], key[i]);
+    for(unsigned i = 0; i < size; i++) {
+        tupleKeyValIdx[i] = std::make_tuple(okey_ptr[i], oval_ptr[i], key[i]);
     }
 
     memFree(key); // key is no longer required
 
-    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), KIPCompareV<Tk, Tv, isAscending>());
+    std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, isAscending>());
 
-    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), KIPCompareK<Tk, Tv, true>());
+    std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareK<Tk, Tv, true>());
 
     for(unsigned x = 0; x < okey.elements(); x++) {
-        okey_ptr[x] = std::get<0>(pairKeyVal[x]);
-        oval_ptr[x] = std::get<1>(pairKeyVal[x]);
+        okey_ptr[x] = std::get<0>(tupleKeyValIdx[x]);
+        oval_ptr[x] = std::get<1>(tupleKeyValIdx[x]);
     }
 
+    memFree((char *)tupleKeyValIdx);
     return;
 }
 

From 4cdb9fb573a1c5c5f749d82a1d454d97f175a5b6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 25 Apr 2016 11:41:13 -0400
Subject: [PATCH 0479/2677] Fixes to Using on OSX page

---
 docs/pages/using_on_osx.md | 185 +++++++++++++++++++++++--------------
 1 file changed, 116 insertions(+), 69 deletions(-)

diff --git a/docs/pages/using_on_osx.md b/docs/pages/using_on_osx.md
index ccb0fb523a..ef7e1e4255 100644
--- a/docs/pages/using_on_osx.md
+++ b/docs/pages/using_on_osx.md
@@ -27,75 +27,12 @@ any build system to create and compile projects that use ArrayFire.
 Among the many possible build systems on Linux we suggest using ArrayFire with
 either CMake or Makefiles with CMake being our preferred build system.
 
-## XCode
+## Build Instructions:
+* [CMake](#CMake)
+* [MakeFiles](#MakeFiles)
+* [XCode](#XCode)
 
-Although we recommend using CMake to build ArrayFire projects on OSX, you can
-use XCode if this is your preferred development platform.
-To save some time, we have created an sample XCode project in our 
-[ArrayFire Project Templates repository](https://github.com/arrayfire/arrayfire-project-templates).
-
-To set up a basic C/C++ project in XCode do the following:
-
-1. Start up XCode. Choose OSX -> Application, Command Line Tool for the project:
-<img src="xcode-setup/xcode-startup.png" alt="Create a command line too XCode Project" width="100%" />
-
-2. Fill in the details for your project and choose either C or C++ for the project:
-<img src="xcode-setup/project-options.png" alt="Create a C/C++ project" width="100%" />
-
-3. Next we need to configure the build settings. In the left-hand pane, click
-   on the project. In the center pane, click on "Build Settings" followed by
-   the "All" button:
-<img src="xcode-setup/build-settings.png" alt="Configure build settings" width="100%" />
-
-4. Now search for "Header Search Paths" and add `/usr/local/include` to the list:
-<img src="xcode-setup/header-search-paths.png" alt="Configure build settings" width="100%" />
-
-5. Then search for "Library Search Paths" and add `/usr/local/lib` to the list:
-<img src="xcode-setup/library-search-paths.png" alt="Configure build settings" width="100%" />
-
-6. Next, we need to make sure the executable is linked with an ArrayFire library:
-   To do this, click the "Build Phases" tab and expand the "Link with Binary Library"
-   menu:
-<img src="xcode-setup/build-phases.png" alt="Configure build settings" width="100%" />
-
-7. In the search dialog that pops up, choose the "Add Other" button from the
-   lower right. Specify the `/usr/local/lib` folder:
-<img src="xcode-setup/library-folder-path.png" alt="Configure build settings" width="100%" />
-
-8. Lastly, select the ArrayFire library with which you wish to link your program.
-  Your options will be:
-
-~~~~~
-libafcuda.*.dylib   - CUDA backend
-libafopencl.*.dylib - OpenCL backend
-libafcpu.*.dylib    - CPU backend
-libaf.*.dylib       - Unified backend
-~~~~~
-
-In the picture below, we have elected to link with the OpenCL backend:
-
-<img src="xcode-setup/pick-arrayfire-library.png" alt="Configure build settings" width="100%" />
-
-9. Lastly, lets test ArrayFire's functionality. In the left hand pane open
-   the main.cpp` file and insert the following code:
-
-~~~~~
-// Include the ArrayFire header file
-#include <arrayfire.h>
-
-int main(int argc, const char * argv[]) {
-    // Gather some information about the ArrayFire device
-    af::info();
-    return 0;
-}
-~~~~~
-
-Finally, click the build button and you should see some information about your
-graphics card in the lower-section of your screen:
-
-<img src="xcode-setup/afinfo-result.png" alt="Configure build settings" width="100%" />
-
-## CMake
+## <a name="CMake"></a>CMake
 
 We recommend that the CMake build system be used to create ArrayFire projects.
 If you are writing a new ArrayFire project in C/C++ from scratch, we suggest
@@ -193,7 +130,7 @@ would modify the `cmake` command above to contain the following definition:
 
 You can also specify this information in the ccmake command-line interface.
 
-## MakeFiles
+## <a name="MakeFiles"></a> MakeFiles
 
 Building ArrayFire projects with Makefiles is fairly similar to CMake except
 you must specify all paths and libraries manually.
@@ -217,3 +154,113 @@ Here is a minimial example MakeFile which uses ArrayFire's CPU backend:
 
     all: main.cpp Makefile
         $(CC) main.cpp -o test $(INCLUDES) $(LIBS) $(LIB_PATHS)
+
+## <a name="XCode"></a> XCode
+
+Although we recommend using CMake to build ArrayFire projects on OSX, you can
+use XCode if this is your preferred development platform.
+To save some time, we have created an sample XCode project in our
+[ArrayFire Project Templates repository](https://github.com/arrayfire/arrayfire-project-templates).
+
+To set up a basic C/C++ project in XCode do the following:
+
+1. Start up XCode. Choose OSX -> Application, Command Line Tool for the project:
+\htmlonly
+<br />
+<a href="xcode-setup/xcode-startup.png">
+<img src="xcode-setup/xcode-startup.png" alt="Create a command line too XCode Project" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+2. Fill in the details for your project and choose either C or C++ for the project:
+\htmlonly
+<br />
+<a href="xcode-setup/project-options.png">
+<img src="xcode-setup/project-options.png" alt="Create a C/C++ project" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+3. Next we need to configure the build settings. In the left-hand pane, click
+   on the project. In the center pane, click on "Build Settings" followed by
+   the "All" button:
+\htmlonly
+<br />
+<a href="xcode-setup/build-settings.png">
+<img src="xcode-setup/build-settings.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+4. Now search for "Header Search Paths" and add `/usr/local/include` to the list:
+\htmlonly
+<br />
+<a href="xcode-setup/header-search-paths.png">
+<img src="xcode-setup/header-search-paths.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+5. Then search for "Library Search Paths" and add `/usr/local/lib` to the list:
+\htmlonly
+<br />
+<a href="xcode-setup/library-search-paths.png">
+<img src="xcode-setup/library-search-paths.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+6. Next, we need to make sure the executable is linked with an ArrayFire library:
+   To do this, click the "Build Phases" tab and expand the "Link with Binary Library"
+   menu:
+\htmlonly
+<br />
+<a href="xcode-setup/build-phases.png">
+<img src="xcode-setup/build-phases.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+7. In the search dialog that pops up, choose the "Add Other" button from the
+   lower right. Specify the `/usr/local/lib` folder:
+\htmlonly
+<br />
+<a href="xcode-setup/library-folder-path.png">
+<img src="xcode-setup/library-folder-path.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+8. Lastly, select the ArrayFire library with which you wish to link your program.
+  Your options will be:
+~~~~~
+libafcuda.*.dylib   - CUDA backend
+libafopencl.*.dylib - OpenCL backend
+libafcpu.*.dylib    - CPU backend
+libaf.*.dylib       - Unified backend
+~~~~~
+In the picture below, we have elected to link with the OpenCL backend:
+\htmlonly
+<br />
+<a href="xcode-setup/pick-arrayfire-library.png">
+<img src="xcode-setup/pick-arrayfire-library.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+9. Lastly, lets test ArrayFire's functionality. In the left hand pane open
+   the main.cpp` file and insert the following code:
+
+~~~~~
+// Include the ArrayFire header file
+#include <arrayfire.h>
+
+int main(int argc, const char * argv[]) {
+    // Gather some information about the ArrayFire device
+    af::info();
+    return 0;
+}
+~~~~~
+
+Finally, click the build button and you should see some information about your
+graphics card in the lower-section of your screen:
+
+\htmlonly
+<br />
+<a href="xcode-setup/afinfo-result.png">
+<img src="xcode-setup/afinfo-result.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly

From ad7256dc176bbb79651d3c0470e43d2a2a1dd493 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 25 Apr 2016 16:54:32 -0400
Subject: [PATCH 0480/2677] OSX Installer: better error handling from brew
 failures

* When bre install fails, the installer will give pop up messages.
* It will also open the web page to the wiki entry
* Although all 3 scripts have the same error handling, only the first one will
  be executed.
* Added missing opencl script entry
---
 CMakeModules/osx_install/OSXInstaller.cmake     |  1 +
 .../osx_install/cpu_scripts/postinstall         | 17 ++++++++++++++---
 .../osx_install/cuda_scripts/postinstall        | 17 ++++++++++++++---
 .../osx_install/opencl_scripts/postinstall      | 17 ++++++++++++++---
 CMakeModules/osx_install/readme.html            |  1 +
 5 files changed, 44 insertions(+), 9 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index b2514f8e2a..2b2a52be62 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -158,6 +158,7 @@ PKG_BUILD(  PKG_NAME        ArrayFireOPENCL
             DEPENDS         OSX_INSTALL_SETUP_OpenCL
             TARGETS         opencl_package
             INSTALL_LOCATION /usr/local
+            SCRIPT_DIR      ${OSX_INSTALL_DIR}/opencl_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.opencl.lib
             PATH_TO_FILES   ${OSX_TEMP}/OpenCL
             FILTERS         cpu cuda unified)
diff --git a/CMakeModules/osx_install/cpu_scripts/postinstall b/CMakeModules/osx_install/cpu_scripts/postinstall
index 730065a710..a9bce9de8e 100755
--- a/CMakeModules/osx_install/cpu_scripts/postinstall
+++ b/CMakeModules/osx_install/cpu_scripts/postinstall
@@ -6,8 +6,10 @@ set -o pipefail
 err_file=/tmp/AFInstallerCPU.err
 brew=/usr/local/bin/brew
 
+echo $(date) > $err_file
+
 if [ ! -f $brew ]; then
-    osascript -e 'tell app "Finder" to display dialog "Brew not installed. Please install brew at http://brew.sh"'
+    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at http://brew.sh"'
     open http://brew.sh
     echo "Brew not found" >> $err_file
     exit 1
@@ -20,5 +22,14 @@ if [ -z $user ]; then
     exit 1
 fi
 
-su $user -c "$brew tap homebrew/versions" 2> $err_file
-su $user -c "$brew install fftw glfw3 fontconfig" 2> $err_file
+function deps_err
+{
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
+    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
+    echo "Dependencies failed to install" >> $err_file
+    exit 1
+}
+
+su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+su $user -c "$brew install fftw glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/cuda_scripts/postinstall b/CMakeModules/osx_install/cuda_scripts/postinstall
index 4713f46645..49f0fd2e2f 100755
--- a/CMakeModules/osx_install/cuda_scripts/postinstall
+++ b/CMakeModules/osx_install/cuda_scripts/postinstall
@@ -6,8 +6,10 @@ set -o pipefail
 err_file=/tmp/AFInstallerCUDA.err
 brew=/usr/local/bin/brew
 
+echo $(date) > $err_file
+
 if [ ! -f $brew ]; then
-    osascript -e 'tell app "Finder" to display dialog "Brew not installed. Please install brew at brew.sh"'
+    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at brew.sh"'
     echo "Brew not found" >> $err_file
     exit 1
 fi
@@ -19,5 +21,14 @@ if [ -z $user ]; then
     exit 1
 fi
 
-su $user -c "$brew tap homebrew/versions" 2> $err_file
-su $user -c "$brew install glfw3 fontconfig" 2> $err_file
+function deps_err
+{
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
+    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
+    echo "Dependencies failed to install" >> $err_file
+    exit 1
+}
+
+su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+su $user -c "$brew install glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/opencl_scripts/postinstall b/CMakeModules/osx_install/opencl_scripts/postinstall
index 0dd01a29f9..54ecb4df19 100755
--- a/CMakeModules/osx_install/opencl_scripts/postinstall
+++ b/CMakeModules/osx_install/opencl_scripts/postinstall
@@ -6,8 +6,10 @@ set -o pipefail
 err_file=/tmp/AFInstallerOpenCL.err
 brew=/usr/local/bin/brew
 
+echo $(date) > $err_file
+
 if [ ! -f $brew ]; then
-    osascript -e 'tell app "Finder" to display dialog "Brew not installed. Please install brew at brew.sh"'
+    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at brew.sh"'
     echo "Brew not found" >> $err_file
     exit 1
 fi
@@ -19,5 +21,14 @@ if [ -z $user ]; then
     exit 1
 fi
 
-su $user -c "$brew tap homebrew/versions" 2> $err_file
-su $user -c "$brew install glfw3 fontconfig" 2> $err_file
+function deps_err
+{
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
+    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
+    echo "Dependencies failed to install" >> $err_file
+    exit 1
+}
+
+su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+su $user -c "$brew install fftw glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/readme.html b/CMakeModules/osx_install/readme.html
index 482b7add7e..2443b5fae3 100644
--- a/CMakeModules/osx_install/readme.html
+++ b/CMakeModules/osx_install/readme.html
@@ -9,5 +9,6 @@ <h2>Install Directories</h2>
     </ul>
     <p> For complete list of updates, visit <a href="http://www.arrayfire.com/docs/releasenotes.htm">ArrayFire Release Notes</a></p>
 
+    <p> For questions about ArrayFire or this installer, visit <a href="https://groups.google.com/forum/#!forum/arrayfire-users"> ArrayFire User Forums</a> </p>
 </body>
 </html>

From 98cbd9c6f162a44017354d98240fc1c4c0787112 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 25 Apr 2016 16:57:45 -0400
Subject: [PATCH 0481/2677] Updated release notes for v3.3.2

---
 docs/pages/release_notes.md | 45 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 44 insertions(+), 1 deletion(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 7aa387847c..2f3a447718 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,49 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.3.2
+==============
+
+Improvements
+------------
+* Family of [Sort](\ref sort_mat) functions now support
+  [higher order dimensions](https://github.com/arrayfire/arrayfire/pull/1373).
+* Improved performance of batched sort on dim 0 for all [Sort](\ref sort_mat) functions.
+* [Median](\ref stat_func_median) now also supports higher order dimensions.
+
+Bug Fixes
+--------------
+
+* Fixes to [error handling](https://github.com/arrayfire/arrayfire/issues/1352) in C++ API for binary functions.
+* Fixes to [external OpenCL context management](https://github.com/arrayfire/arrayfire/issues/1350).
+* Fixes to [JPEG_GREYSCALE](https://github.com/arrayfire/arrayfire/issues/1360) for FreeImage versions <= 3.154.
+* Fixed for [non-float inputs](https://github.com/arrayfire/arrayfire/issues/1386) to \ref af::rgb2gray().
+
+Build
+------
+* [Disable CPU Async](https://github.com/arrayfire/arrayfire/issues/1378) when building with GCC < 4.8.4.
+* Add option to [disable CPUID](https://github.com/arrayfire/arrayfire/issues/1369) from CMake.
+* More verbose message when [CUDA Compute Detection fails](https://github.com/arrayfire/arrayfire/issues/1362).
+* Print message to use [CUDA library stub](https://github.com/arrayfire/arrayfire/issues/1363)
+  from CUDA Toolkit if CUDA Library is not found from default paths.
+* [Build Fixes](https://github.com/arrayfire/arrayfire/pull/1385) on Windows.
+  * For compiling tests our of source.
+  * For compiling ArrayFire with static MKL.
+* [Exclude <sys/sysctl.h>](https://github.com/arrayfire/arrayfire/pull/1368) when building on GNU Hurd.
+* Add [manual CMake options](https://github.com/arrayfire/arrayfire/pull/1389) to build DEB and RPM packages.
+
+Documentation
+-------------
+* Fixed documentation for \ref af::replace().
+* Fixed images in [Using on OSX](\ref using_on_osx) page.
+
+Installer
+---------
+* Linux x64 installers will now be compiled with GCC 4.9.2.
+* OSX installer gives better error messages on brew failures and
+  now includes link to [Fixing OS X Installer Failures] (https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures)
+  for brew installation failures.
+
 v3.3.1
 ==============
 
@@ -361,7 +404,7 @@ Bug Fixes
 Documentation Updates
 ---------------------
 * Improved tutorials documentation
-    * More detailed Using on [Linux](\ref using_on_windows), [OSX](\ref using_on_windows),
+    * More detailed Using on [Linux](\ref using_on_linux), [OSX](\ref using_on_osx),
       [Windows](\ref using_on_windows) pages.
 * Added return type information for functions that return different type
   arrays

From 60a9c8b33c6da9e64f7e5ef3178c8275895d519e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 26 Apr 2016 17:46:42 -0400
Subject: [PATCH 0482/2677] Fix compiler warning in tests

---
 test/gloh_nonfree.cpp          | 22 ----------------------
 test/orb.cpp                   | 12 ------------
 test/sift_nonfree.cpp          | 22 ----------------------
 test/transform_coordinates.cpp |  4 ++--
 4 files changed, 2 insertions(+), 58 deletions(-)

diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 5794051152..558acabe25 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -77,18 +77,6 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
     }
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
-{
-    feat.resize(nfeat);
-    for (unsigned i = 0; i < feat.size(); i++) {
-        feat[i].f[0] = x[i];
-        feat[i].f[1] = y[i];
-        feat[i].f[2] = score[i];
-        feat[i].f[3] = ori[i];
-        feat[i].f[4] = size[i];
-    }
-}
-
 static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
@@ -104,16 +92,6 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
     }
 }
 
-static unsigned popcount(unsigned x)
-{
-    x = x - ((x >> 1) & 0x55555555);
-    x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
-    x = (x + (x >> 4)) & 0x0F0F0F0F;
-    x = x + (x >> 8);
-    x = x + (x >> 16);
-    return x & 0x0000003F;
-}
-
 static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
 {
     bool ret = true;
diff --git a/test/orb.cpp b/test/orb.cpp
index 1266f20eb6..28e56a1132 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -76,18 +76,6 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
     }
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
-{
-    feat.resize(nfeat);
-    for (unsigned i = 0; i < feat.size(); i++) {
-        feat[i].f[0] = x[i];
-        feat[i].f[1] = y[i];
-        feat[i].f[2] = score[i];
-        feat[i].f[3] = ori[i];
-        feat[i].f[4] = size[i];
-    }
-}
-
 static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index 6776c18a86..f6dca7ba16 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -76,18 +76,6 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
     }
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
-{
-    feat.resize(nfeat);
-    for (unsigned i = 0; i < feat.size(); i++) {
-        feat[i].f[0] = x[i];
-        feat[i].f[1] = y[i];
-        feat[i].f[2] = score[i];
-        feat[i].f[3] = ori[i];
-        feat[i].f[4] = size[i];
-    }
-}
-
 static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
@@ -103,16 +91,6 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
     }
 }
 
-static unsigned popcount(unsigned x)
-{
-    x = x - ((x >> 1) & 0x55555555);
-    x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
-    x = (x + (x >> 4)) & 0x0F0F0F0F;
-    x = x + (x >> 8);
-    x = x + (x >> 16);
-    return x & 0x0000003F;
-}
-
 static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
 {
     bool ret = true;
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 7f1ac4e893..dc8598121e 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -47,7 +47,7 @@ void transformCoordinatesTest(string pTestFile)
     af_array outArray = 0;
     ASSERT_EQ(AF_SUCCESS, af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)af::dtype_traits<T>::af_type));
 
-    size_t nTests = in.size();
+    int nTests = in.size();
 
     for (int test = 1; test < nTests; test++) {
         dim_t d0 = (dim_t)in[test][0];
@@ -63,7 +63,7 @@ void transformCoordinatesTest(string pTestFile)
 
         const float thr = 1.f;
 
-        for (size_t elIter = 0; elIter < outEl; elIter++) {
+        for (dim_t elIter = 0; elIter < outEl; elIter++) {
             ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << std::endl;
         }
 

From b8c506d14fb504b24abdb8410559b236776a4029 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 26 Apr 2016 18:01:58 -0400
Subject: [PATCH 0483/2677] CPU: Compile sort_by_key instantiations from single
 file into objects

---
 src/backend/cpu/CMakeLists.txt                | 24 ++++++++++---------
 .../cpu/kernel/sort_by_key/CMakeLists.txt     | 15 ++++++++++++
 src/backend/cpu/kernel/sort_by_key/f32.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/f64.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/s16.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/s32.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/s64.cpp    | 19 ---------------
 .../{b8.cpp => sort_by_key_impl.cpp}          |  6 +++--
 src/backend/cpu/kernel/sort_by_key/u16.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/u32.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/u64.cpp    | 19 ---------------
 src/backend/cpu/kernel/sort_by_key/u8.cpp     | 19 ---------------
 12 files changed, 32 insertions(+), 184 deletions(-)
 create mode 100644 src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/f32.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/f64.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/s16.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/s32.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/s64.cpp
 rename src/backend/cpu/kernel/sort_by_key/{b8.cpp => sort_by_key_impl.cpp} (76%)
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/u16.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/u32.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/u64.cpp
 delete mode 100644 src/backend/cpu/kernel/sort_by_key/u8.cpp

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index ea572693e1..0863266c4f 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -108,8 +108,7 @@ FILE(GLOB cpu_headers
     "*.h")
 
 FILE(GLOB cpu_sources
-    "*.cpp"
-    "kernel/sort_by_key/*.cpp")
+    "*.cpp")
 
 LIST(SORT cpu_headers)
 LIST(SORT cpu_sources)
@@ -162,13 +161,15 @@ ELSE(${UNIX}) #Windows
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 ENDIF()
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
 IF(DEFINED BLAS_SYM_FILE)
   ADD_LIBRARY(afcpu_static STATIC
               ${cpu_headers}
               ${cpu_sources}
               ${backend_headers}
-              ${backend_sources})
+              ${backend_sources}
+              ${SORT_BY_KEY_OBJECTS})
 
   ADD_LIBRARY(afcpu SHARED
               ${c_headers}
@@ -192,14 +193,15 @@ IF(DEFINED BLAS_SYM_FILE)
 
 ELSE(DEFINED BLAS_SYM_FILE)
 
-  ADD_LIBRARY(afcpu SHARED
-              ${cpu_headers}
-              ${cpu_sources}
-              ${backend_headers}
-              ${backend_sources}
-              ${c_headers}
-              ${c_sources}
-              ${cpp_sources})
+ADD_LIBRARY(afcpu SHARED
+            ${cpu_headers}
+            ${cpu_sources}
+            ${backend_headers}
+            ${backend_sources}
+            ${c_headers}
+            ${c_sources}
+            ${cpp_sources}
+            ${SORT_BY_KEY_OBJECTS})
 
 ENDIF(DEFINED BLAS_SYM_FILE)
 
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..017bb90bb6
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,15 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    ADD_LIBRARY(cpu_sort_by_key_${SBK_TYPE} OBJECT
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp")
+    SET_TARGET_PROPERTIES(cpu_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
+    LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:cpu_sort_by_key_${SBK_TYPE}>)
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/cpu/kernel/sort_by_key/f32.cpp b/src/backend/cpu/kernel/sort_by_key/f32.cpp
deleted file mode 100644
index 11d8139957..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/f32.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(float,true)
-    INSTANTIATE1(float,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/f64.cpp b/src/backend/cpu/kernel/sort_by_key/f64.cpp
deleted file mode 100644
index 21746d773a..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/f64.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(double,true)
-    INSTANTIATE1(double,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/s16.cpp b/src/backend/cpu/kernel/sort_by_key/s16.cpp
deleted file mode 100644
index 50b718d04c..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/s16.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(short,true)
-    INSTANTIATE1(short,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/s32.cpp b/src/backend/cpu/kernel/sort_by_key/s32.cpp
deleted file mode 100644
index c50efcdc26..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/s32.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(int,true)
-    INSTANTIATE1(int,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/s64.cpp b/src/backend/cpu/kernel/sort_by_key/s64.cpp
deleted file mode 100644
index 82946f820e..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/s64.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(intl,true)
-    INSTANTIATE1(intl,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/b8.cpp b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
similarity index 76%
rename from src/backend/cpu/kernel/sort_by_key/b8.cpp
rename to src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index 855e7a93ca..fbdedfde39 100644
--- a/src/backend/cpu/kernel/sort_by_key/b8.cpp
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -9,11 +9,13 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+
 namespace cpu
 {
 namespace kernel
 {
-    INSTANTIATE1(char,true)
-    INSTANTIATE1(char,false)
+    INSTANTIATE1(TYPE,true)
+    INSTANTIATE1(TYPE,false)
 }
 }
diff --git a/src/backend/cpu/kernel/sort_by_key/u16.cpp b/src/backend/cpu/kernel/sort_by_key/u16.cpp
deleted file mode 100644
index feedd1d56e..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/u16.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(ushort,true)
-    INSTANTIATE1(ushort,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/u32.cpp b/src/backend/cpu/kernel/sort_by_key/u32.cpp
deleted file mode 100644
index cd514af19a..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/u32.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(uint,true)
-    INSTANTIATE1(uint,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/u64.cpp b/src/backend/cpu/kernel/sort_by_key/u64.cpp
deleted file mode 100644
index ec955b3de7..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/u64.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(uintl,true)
-    INSTANTIATE1(uintl,false)
-}
-}
diff --git a/src/backend/cpu/kernel/sort_by_key/u8.cpp b/src/backend/cpu/kernel/sort_by_key/u8.cpp
deleted file mode 100644
index fd58cbfaa1..0000000000
--- a/src/backend/cpu/kernel/sort_by_key/u8.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(uchar,true)
-    INSTANTIATE1(uchar,false)
-}
-}

From c354015282c322ff9418f2d4b1814d5f0263951c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 26 Apr 2016 17:47:30 -0400
Subject: [PATCH 0484/2677] CUDA: Generate sort_by_key instantiations using
 CMake

---
 src/backend/cuda/CMakeLists.txt               |  4 ++-
 .../cuda/kernel/sort_by_key/CMakeLists.txt    | 29 +++++++++++++++++++
 .../cuda/kernel/sort_by_key/ascd_f64.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_s16.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_s32.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_s64.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_s8.cu        | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_u16.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_u32.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_u64.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/ascd_u8.cu        | 18 ------------
 .../cuda/kernel/sort_by_key/desc_f32.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_f64.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_s16.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_s32.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_s64.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_s8.cu        | 18 ------------
 .../cuda/kernel/sort_by_key/desc_u16.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_u32.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_u64.cu       | 18 ------------
 .../cuda/kernel/sort_by_key/desc_u8.cu        | 18 ------------
 .../{ascd_f32.cu => sort_by_key_impl.cu.in}   |  8 ++++-
 src/backend/cuda/kernel/sort_by_key_impl.hpp  |  9 ++++--
 23 files changed, 45 insertions(+), 347 deletions(-)
 create mode 100644 src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_f64.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_s16.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_s32.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_s64.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_s8.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_u16.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_u32.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_u64.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/ascd_u8.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_f32.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_f64.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_s16.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_s32.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_s64.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_s8.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_u16.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_u32.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_u64.cu
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/desc_u8.cu
 rename src/backend/cuda/kernel/sort_by_key/{ascd_f32.cu => sort_by_key_impl.cu.in} (57%)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4efb42764a..d1727ffd40 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -158,7 +158,6 @@ FILE(GLOB cuda_headers
 FILE(GLOB cuda_sources
     "*.cu"
     "*.cpp"
-    "kernel/sort_by_key/*.cu"
     "kernel/*.cu")
 
 FILE(GLOB jit_sources
@@ -231,6 +230,8 @@ LIST(SORT cpp_sources)
 
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+
 LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
 IF(${COMPUTE_COUNT} EQUAL 1)
     SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} ${CUDA_GENERATE_CODE}")
@@ -361,6 +362,7 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${c_headers}
                 ${c_sources}
                 ${cpp_sources}
+                ${sort_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE})
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
diff --git a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..a9143f282d
--- /dev/null
+++ b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,29 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ELSEIF(${STR} MATCHES "// SBK_DIRS:")
+        STRING(REPLACE "// SBK_DIRS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_DIRS ${TEMP})
+    ELSEIF(${STR} MATCHES "// SBK_INSTS:")
+        STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    FOREACH(SBK_DIR ${SBK_DIRS})
+        FOREACH(SBK_INST ${SBK_INSTS})
+            CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in"
+            "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu")
+        ENDFOREACH(SBK_INST ${SBK_INSTS})
+    ENDFOREACH(SBK_DIR ${SBK_DIRS})
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
+
+FILE(GLOB sort_by_key_sources
+  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/*.cu"
+)
+
+LIST(SORT sort_by_key_sources)
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_f64.cu b/src/backend/cuda/kernel/sort_by_key/ascd_f64.cu
deleted file mode 100644
index ba19ec447c..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_f64.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(double, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_s16.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s16.cu
deleted file mode 100644
index 1be6e540ca..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_s16.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(short, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_s32.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s32.cu
deleted file mode 100644
index 8cee7c9b49..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_s32.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(int, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_s64.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s64.cu
deleted file mode 100644
index 0e5a7c81a2..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_s64.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(intl, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_s8.cu b/src/backend/cuda/kernel/sort_by_key/ascd_s8.cu
deleted file mode 100644
index 81ed32952f..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_s8.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(char, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_u16.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u16.cu
deleted file mode 100644
index e232c08376..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_u16.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(ushort, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_u32.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u32.cu
deleted file mode 100644
index 34a4580936..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_u32.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(uint, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_u64.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u64.cu
deleted file mode 100644
index fc576e7f99..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_u64.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(uintl, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_u8.cu b/src/backend/cuda/kernel/sort_by_key/ascd_u8.cu
deleted file mode 100644
index ed8454d53e..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/ascd_u8.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(uchar, true)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_f32.cu b/src/backend/cuda/kernel/sort_by_key/desc_f32.cu
deleted file mode 100644
index 73459ac033..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_f32.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(float, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_f64.cu b/src/backend/cuda/kernel/sort_by_key/desc_f64.cu
deleted file mode 100644
index be0536b1e3..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_f64.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(double, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_s16.cu b/src/backend/cuda/kernel/sort_by_key/desc_s16.cu
deleted file mode 100644
index 0fc3b50827..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_s16.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(short, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_s32.cu b/src/backend/cuda/kernel/sort_by_key/desc_s32.cu
deleted file mode 100644
index cfda29c7de..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_s32.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(int, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_s64.cu b/src/backend/cuda/kernel/sort_by_key/desc_s64.cu
deleted file mode 100644
index b334a91a99..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_s64.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(intl, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_s8.cu b/src/backend/cuda/kernel/sort_by_key/desc_s8.cu
deleted file mode 100644
index f02d5ce2fe..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_s8.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(char, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_u16.cu b/src/backend/cuda/kernel/sort_by_key/desc_u16.cu
deleted file mode 100644
index 9b0a77cb25..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_u16.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(ushort, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_u32.cu b/src/backend/cuda/kernel/sort_by_key/desc_u32.cu
deleted file mode 100644
index 1d02aec848..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_u32.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(uint, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_u64.cu b/src/backend/cuda/kernel/sort_by_key/desc_u64.cu
deleted file mode 100644
index 597bd2c1b4..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_u64.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(uintl, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/desc_u8.cu b/src/backend/cuda/kernel/sort_by_key/desc_u8.cu
deleted file mode 100644
index 4f55479604..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/desc_u8.cu
+++ /dev/null
@@ -1,18 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE1(uchar, false)
-}
-}
diff --git a/src/backend/cuda/kernel/sort_by_key/ascd_f32.cu b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
similarity index 57%
rename from src/backend/cuda/kernel/sort_by_key/ascd_f32.cu
rename to src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
index 284e8b4938..94168df1de 100644
--- a/src/backend/cuda/kernel/sort_by_key/ascd_f32.cu
+++ b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
@@ -9,10 +9,16 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
+// This file instantiates sort_by_key as separate object files from CMake
+// The 3 lines below are read by CMake to determenine the instantiations
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+// SBK_DIRS:true false
+// SBK_INSTS:0 1
+
 namespace cuda
 {
 namespace kernel
 {
-    INSTANTIATE1(float, true)
+    INSTANTIATE@SBK_INST@(@SBK_TYPE@, @SBK_DIR@)
 }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index bcc2fefeb4..c035e3ae07 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -198,18 +198,21 @@ namespace cuda
     template void sortByKeyBatched<Tk, Tv, dr, 2>(Param<Tk> okey, Param<Tv> oval);      \
     template void sortByKeyBatched<Tk, Tv, dr, 3>(Param<Tk> okey, Param<Tv> oval);      \
 
-#define INSTANTIATE1(Tk    , dr) \
+#define INSTANTIATE0(Tk    , dr) \
     INSTANTIATE(Tk, float  , dr) \
     INSTANTIATE(Tk, double , dr) \
     INSTANTIATE(Tk, cfloat , dr) \
     INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+
+#define INSTANTIATE1(Tk    , dr) \
     INSTANTIATE(Tk, int    , dr) \
     INSTANTIATE(Tk, uint   , dr) \
     INSTANTIATE(Tk, short  , dr) \
     INSTANTIATE(Tk, ushort , dr) \
-    INSTANTIATE(Tk, char   , dr) \
-    INSTANTIATE(Tk, uchar  , dr) \
     INSTANTIATE(Tk, intl   , dr) \
     INSTANTIATE(Tk, uintl  , dr)
+
     }
 }

From da69c30a3556cc7909d5193c8e8dcbb68591ab81 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 26 Apr 2016 18:11:16 -0400
Subject: [PATCH 0485/2677] OpenCL: Compile sort_by_key instantiations from
 single file into objects

---
 src/backend/opencl/CMakeLists.txt             | 11 +++++++----
 .../opencl/kernel/sort_by_key/CMakeLists.txt  | 19 +++++++++++++++++++
 src/backend/opencl/kernel/sort_by_key/f32.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/f64.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/s16.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/s32.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/s64.cpp | 19 -------------------
 .../{b8.cpp => sort_by_key_impl.cpp}          |  6 ++++--
 src/backend/opencl/kernel/sort_by_key/u16.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/u32.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/u64.cpp | 19 -------------------
 src/backend/opencl/kernel/sort_by_key/u8.cpp  | 19 -------------------
 12 files changed, 30 insertions(+), 177 deletions(-)
 create mode 100644 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/f32.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/f64.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/s16.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/s32.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/s64.cpp
 rename src/backend/opencl/kernel/sort_by_key/{b8.cpp => sort_by_key_impl.cpp} (76%)
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/u16.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/u32.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/u64.cpp
 delete mode 100644 src/backend/opencl/kernel/sort_by_key/u8.cpp

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 71247ce3da..9e4918a5ff 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -126,8 +126,7 @@ FILE(GLOB opencl_kernels
     "kernel/*.cl")
 
 FILE(GLOB kernel_sources
-     "kernel/*.cpp"
-     "kernel/sort_by_key/*.cpp")
+     "kernel/*.cpp")
 
 FILE(GLOB conv_ker_headers
     "kernel/convolve/*.hpp")
@@ -237,6 +236,8 @@ IF(UNIX)
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
 ENDIF()
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+
 IF(DEFINED BLAS_SYM_FILE)
 
     ADD_LIBRARY(afopencl_static STATIC
@@ -253,7 +254,8 @@ IF(DEFINED BLAS_SYM_FILE)
                 ${backend_headers}
                 ${backend_sources}
                 ${magma_sources}
-                ${magma_headers})
+                ${magma_headers}
+                ${SORT_BY_KEY_OBJECTS})
 
     ADD_LIBRARY(afopencl SHARED
                ${c_headers}
@@ -296,7 +298,8 @@ ELSE(DEFINED BLAS_SYM_FILE)
                 ${c_sources}
                 ${cpp_sources}
                 ${magma_sources}
-                ${magma_headers})
+                ${magma_headers}
+                ${SORT_BY_KEY_OBJECTS})
 
 ENDIF()
 
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..760fe6b634
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,19 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    ADD_LIBRARY(opencl_sort_by_key_${SBK_TYPE} OBJECT
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp")
+    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets})
+    IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
+        ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} forge)
+    ENDIF()
+    SET_TARGET_PROPERTIES(opencl_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
+    LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:opencl_sort_by_key_${SBK_TYPE}>)
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/opencl/kernel/sort_by_key/f32.cpp b/src/backend/opencl/kernel/sort_by_key/f32.cpp
deleted file mode 100644
index a1e9ae5f1f..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/f32.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(float,true)
-    INSTANTIATE1(float,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/f64.cpp b/src/backend/opencl/kernel/sort_by_key/f64.cpp
deleted file mode 100644
index 7fb7a79bd8..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/f64.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(double,true)
-    INSTANTIATE1(double,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/s16.cpp b/src/backend/opencl/kernel/sort_by_key/s16.cpp
deleted file mode 100644
index 491ea0e3a2..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/s16.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(short,true)
-    INSTANTIATE1(short,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/s32.cpp b/src/backend/opencl/kernel/sort_by_key/s32.cpp
deleted file mode 100644
index 67ba20e7dd..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/s32.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(int,true)
-    INSTANTIATE1(int,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/s64.cpp b/src/backend/opencl/kernel/sort_by_key/s64.cpp
deleted file mode 100644
index a48f36ee47..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/s64.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(intl,true)
-    INSTANTIATE1(intl,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/b8.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
similarity index 76%
rename from src/backend/opencl/kernel/sort_by_key/b8.cpp
rename to src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index ad0d7f48ae..bf4c96bbb2 100644
--- a/src/backend/opencl/kernel/sort_by_key/b8.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -9,11 +9,13 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+
 namespace opencl
 {
 namespace kernel
 {
-    INSTANTIATE1(char,true)
-    INSTANTIATE1(char,false)
+    INSTANTIATE1(TYPE,true)
+    INSTANTIATE1(TYPE,false)
 }
 }
diff --git a/src/backend/opencl/kernel/sort_by_key/u16.cpp b/src/backend/opencl/kernel/sort_by_key/u16.cpp
deleted file mode 100644
index 36678d0a42..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/u16.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(ushort,true)
-    INSTANTIATE1(ushort,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/u32.cpp b/src/backend/opencl/kernel/sort_by_key/u32.cpp
deleted file mode 100644
index f1e4b5322f..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/u32.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(uint,true)
-    INSTANTIATE1(uint,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/u64.cpp b/src/backend/opencl/kernel/sort_by_key/u64.cpp
deleted file mode 100644
index 0a6f5b0c4f..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/u64.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(uintl,true)
-    INSTANTIATE1(uintl,false)
-}
-}
diff --git a/src/backend/opencl/kernel/sort_by_key/u8.cpp b/src/backend/opencl/kernel/sort_by_key/u8.cpp
deleted file mode 100644
index 45af011a86..0000000000
--- a/src/backend/opencl/kernel/sort_by_key/u8.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <kernel/sort_by_key_impl.hpp>
-
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(uchar,true)
-    INSTANTIATE1(uchar,false)
-}
-}

From dbf0bcb86aa06fd732efbc19568ea2202d6db21a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 16:36:43 -0400
Subject: [PATCH 0486/2677] CLEANUP unused af/ header files from backend/cpu

---
 src/api/c/resize.cpp                 | 1 +
 src/backend/ArrayInfo.hpp            | 1 -
 src/backend/cpu/Array.hpp            | 1 -
 src/backend/cpu/TNJ/BinaryNode.hpp   | 1 -
 src/backend/cpu/TNJ/BufferNode.hpp   | 1 -
 src/backend/cpu/TNJ/Node.hpp         | 1 -
 src/backend/cpu/TNJ/ScalarNode.hpp   | 1 -
 src/backend/cpu/TNJ/UnaryNode.hpp    | 1 -
 src/backend/cpu/approx.hpp           | 1 -
 src/backend/cpu/arith.hpp            | 1 -
 src/backend/cpu/blas.hpp             | 1 -
 src/backend/cpu/cholesky.hpp         | 1 -
 src/backend/cpu/complex.hpp          | 1 -
 src/backend/cpu/copy.cpp             | 1 -
 src/backend/cpu/copy.hpp             | 1 -
 src/backend/cpu/diagonal.cpp         | 1 -
 src/backend/cpu/diagonal.hpp         | 1 -
 src/backend/cpu/diff.hpp             | 1 -
 src/backend/cpu/exampleFunction.hpp  | 1 -
 src/backend/cpu/gradient.hpp         | 1 -
 src/backend/cpu/harris.cpp           | 1 -
 src/backend/cpu/identity.hpp         | 1 -
 src/backend/cpu/index.hpp            | 1 +
 src/backend/cpu/inverse.hpp          | 1 -
 src/backend/cpu/iota.hpp             | 1 -
 src/backend/cpu/ireduce.hpp          | 1 -
 src/backend/cpu/join.hpp             | 1 -
 src/backend/cpu/logic.hpp            | 1 -
 src/backend/cpu/lu.hpp               | 1 -
 src/backend/cpu/qr.hpp               | 1 -
 src/backend/cpu/random.cpp           | 1 -
 src/backend/cpu/random.hpp           | 1 -
 src/backend/cpu/range.hpp            | 1 -
 src/backend/cpu/reduce.hpp           | 1 -
 src/backend/cpu/reorder.hpp          | 1 -
 src/backend/cpu/resize.cpp           | 2 --
 src/backend/cpu/resize.hpp           | 1 -
 src/backend/cpu/rotate.hpp           | 1 -
 src/backend/cpu/scan.hpp             | 1 -
 src/backend/cpu/set.hpp              | 1 -
 src/backend/cpu/shift.hpp            | 1 -
 src/backend/cpu/solve.hpp            | 1 -
 src/backend/cpu/sort.hpp             | 1 -
 src/backend/cpu/sort_by_key.hpp      | 1 -
 src/backend/cpu/sort_index.hpp       | 1 -
 src/backend/cpu/svd.hpp              | 1 -
 src/backend/cpu/tile.hpp             | 1 -
 src/backend/cpu/transform.hpp        | 1 -
 src/backend/cpu/transform_interp.hpp | 2 +-
 src/backend/cpu/triangle.hpp         | 1 -
 src/backend/cpu/where.hpp            | 1 -
 51 files changed, 3 insertions(+), 50 deletions(-)

diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index d17bd291f5..50992b9da9 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/array.h>
+#include <af/image.h>
 #include <af/defines.h>
 #include <err_common.hpp>
 #include <handle.hpp>
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index 7d3606e129..d4876ed524 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <defines.hpp>
-#include <af/array.h>
 #include <af/util.h>
 #include <af/dim4.hpp>
 #include <af/device.h>
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index cf970d18c7..091ef540a0 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -9,7 +9,6 @@
 
 //This is the array implementation class.
 #pragma once
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <ArrayInfo.hpp>
 #include <backend.hpp>
diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index f86869b291..f183698e1b 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <optypes.hpp>
 #include <vector>
 #include <math.hpp>
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index a215aaca76..ada1aba54c 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <optypes.hpp>
 #include <vector>
 #include "Node.hpp"
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index 21c672d435..c6b48d49f6 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <optypes.hpp>
 #include <vector>
 #include <memory>
diff --git a/src/backend/cpu/TNJ/ScalarNode.hpp b/src/backend/cpu/TNJ/ScalarNode.hpp
index c6527fd85e..a85dfdae02 100644
--- a/src/backend/cpu/TNJ/ScalarNode.hpp
+++ b/src/backend/cpu/TNJ/ScalarNode.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <optypes.hpp>
 #include <vector>
 #include "Node.hpp"
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 4320eb3260..7217164ae0 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <optypes.hpp>
 #include <vector>
 #include <math.hpp>
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index f282f8f43c..4da27fae98 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index fe19551356..8f66631825 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 3f5b7451ad..7998a6e7e4 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/blas.h>
 #include <Array.hpp>
 #include <types.hpp>
 
diff --git a/src/backend/cpu/cholesky.hpp b/src/backend/cpu/cholesky.hpp
index 322a789666..002189a55f 100644
--- a/src/backend/cpu/cholesky.hpp
+++ b/src/backend/cpu/cholesky.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index 367b0a6c5a..a2f0c9d42b 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 27e80f8afb..93088aefd7 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <type_traits>
-#include <af/array.h>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <cstring>
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index e9b91f6c8c..8e02e6cd98 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 80375eaa71..d68a73a812 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <handle.hpp>
diff --git a/src/backend/cpu/diagonal.hpp b/src/backend/cpu/diagonal.hpp
index d2a21e932b..6c354d0690 100644
--- a/src/backend/cpu/diagonal.hpp
+++ b/src/backend/cpu/diagonal.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cpu/diff.hpp b/src/backend/cpu/diff.hpp
index 2556d0d619..b8d7bf495a 100644
--- a/src/backend/cpu/diff.hpp
+++ b/src/backend/cpu/diff.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/exampleFunction.hpp b/src/backend/cpu/exampleFunction.hpp
index 5393c2c495..107bd41a3d 100644
--- a/src/backend/cpu/exampleFunction.hpp
+++ b/src/backend/cpu/exampleFunction.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/util.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/gradient.hpp b/src/backend/cpu/gradient.hpp
index 0fc8690634..b2070585b1 100644
--- a/src/backend/cpu/gradient.hpp
+++ b/src/backend/cpu/gradient.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index b5ea0ca20e..f85e950139 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/defines.h>
-#include <af/constants.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/identity.hpp b/src/backend/cpu/identity.hpp
index 3506dabe61..4fd81b6d43 100644
--- a/src/backend/cpu/identity.hpp
+++ b/src/backend/cpu/identity.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/index.hpp b/src/backend/cpu/index.hpp
index ed116657b4..d14b31b967 100644
--- a/src/backend/cpu/index.hpp
+++ b/src/backend/cpu/index.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/index.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/inverse.hpp b/src/backend/cpu/inverse.hpp
index 7ab44b109a..5823a6a318 100644
--- a/src/backend/cpu/inverse.hpp
+++ b/src/backend/cpu/inverse.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/iota.hpp b/src/backend/cpu/iota.hpp
index c437425cb3..1ab9a9f3e0 100644
--- a/src/backend/cpu/iota.hpp
+++ b/src/backend/cpu/iota.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/ireduce.hpp b/src/backend/cpu/ireduce.hpp
index 22f43e9a50..00206293fe 100644
--- a/src/backend/cpu/ireduce.hpp
+++ b/src/backend/cpu/ireduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cpu/join.hpp b/src/backend/cpu/join.hpp
index 4848edd27d..aa2ae8d76c 100644
--- a/src/backend/cpu/join.hpp
+++ b/src/backend/cpu/join.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <vector>
 
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index 0b3b5f7f27..abd0ff4b17 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cpu/lu.hpp b/src/backend/cpu/lu.hpp
index 3fef461067..1164664534 100644
--- a/src/backend/cpu/lu.hpp
+++ b/src/backend/cpu/lu.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/qr.hpp b/src/backend/cpu/qr.hpp
index 82d7c1b8a9..cb4adc003d 100644
--- a/src/backend/cpu/qr.hpp
+++ b/src/backend/cpu/qr.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index 06cbca34d7..1f14e836d7 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <Array.hpp>
diff --git a/src/backend/cpu/random.hpp b/src/backend/cpu/random.hpp
index 1707e44ccf..1f471627d7 100644
--- a/src/backend/cpu/random.hpp
+++ b/src/backend/cpu/random.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/range.hpp b/src/backend/cpu/range.hpp
index a6d10a5cd8..cb373c1216 100644
--- a/src/backend/cpu/range.hpp
+++ b/src/backend/cpu/range.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/reduce.hpp b/src/backend/cpu/reduce.hpp
index 4e139f0fcb..2af78566c2 100644
--- a/src/backend/cpu/reduce.hpp
+++ b/src/backend/cpu/reduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cpu/reorder.hpp b/src/backend/cpu/reorder.hpp
index 01f8b3c292..d4f81e78ca 100644
--- a/src/backend/cpu/reorder.hpp
+++ b/src/backend/cpu/reorder.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index eaeb5d4e3d..342b269d69 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -10,8 +10,6 @@
 #include <Array.hpp>
 #include <resize.hpp>
 #include <math.hpp>
-#include <types.hpp>
-#include <af/traits.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <kernel/resize.hpp>
diff --git a/src/backend/cpu/resize.hpp b/src/backend/cpu/resize.hpp
index 8a10d9df3e..a96a04f249 100644
--- a/src/backend/cpu/resize.hpp
+++ b/src/backend/cpu/resize.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/rotate.hpp b/src/backend/cpu/rotate.hpp
index c49ad8fe55..93d838737c 100644
--- a/src/backend/cpu/rotate.hpp
+++ b/src/backend/cpu/rotate.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index 2d5deda00c..30c3835948 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cpu/set.hpp b/src/backend/cpu/set.hpp
index f007cdf101..a0e48c7076 100644
--- a/src/backend/cpu/set.hpp
+++ b/src/backend/cpu/set.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/shift.hpp b/src/backend/cpu/shift.hpp
index ce76eee3dc..e55cc564aa 100644
--- a/src/backend/cpu/shift.hpp
+++ b/src/backend/cpu/shift.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/solve.hpp b/src/backend/cpu/solve.hpp
index 84166015e0..8580707b4a 100644
--- a/src/backend/cpu/solve.hpp
+++ b/src/backend/cpu/solve.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/sort.hpp b/src/backend/cpu/sort.hpp
index 79caf3aa2d..645f5a1f10 100644
--- a/src/backend/cpu/sort.hpp
+++ b/src/backend/cpu/sort.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/sort_by_key.hpp b/src/backend/cpu/sort_by_key.hpp
index e11ddd6d21..20908e6014 100644
--- a/src/backend/cpu/sort_by_key.hpp
+++ b/src/backend/cpu/sort_by_key.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/sort_index.hpp b/src/backend/cpu/sort_index.hpp
index b3cccec789..0dd2ca80f3 100644
--- a/src/backend/cpu/sort_index.hpp
+++ b/src/backend/cpu/sort_index.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/svd.hpp b/src/backend/cpu/svd.hpp
index e9934ce096..2d409aec31 100644
--- a/src/backend/cpu/svd.hpp
+++ b/src/backend/cpu/svd.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/util.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/tile.hpp b/src/backend/cpu/tile.hpp
index e03ba7233a..0b4fbd8e9e 100644
--- a/src/backend/cpu/tile.hpp
+++ b/src/backend/cpu/tile.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/transform.hpp b/src/backend/cpu/transform.hpp
index ad4ebba5c3..bfe4ef71a0 100644
--- a/src/backend/cpu/transform.hpp
+++ b/src/backend/cpu/transform.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/transform_interp.hpp b/src/backend/cpu/transform_interp.hpp
index d8b9ee2a06..3728434028 100644
--- a/src/backend/cpu/transform_interp.hpp
+++ b/src/backend/cpu/transform_interp.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <math.hpp>
 #include <types.hpp>
-#include <af/traits.hpp>
+#include <type_traits>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/triangle.hpp b/src/backend/cpu/triangle.hpp
index 6ae0df2e9f..531de0ff79 100644
--- a/src/backend/cpu/triangle.hpp
+++ b/src/backend/cpu/triangle.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/where.hpp b/src/backend/cpu/where.hpp
index c615def543..368d8457d8 100644
--- a/src/backend/cpu/where.hpp
+++ b/src/backend/cpu/where.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cpu

From cff4a6fca49cfb84bbe1d942d320c8b2c67bdc29 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 16:38:42 -0400
Subject: [PATCH 0487/2677] CLEANUP unused af/ header files from backend/opencl

---
 src/backend/opencl/Array.hpp              | 1 -
 src/backend/opencl/JIT/Node.hpp           | 1 -
 src/backend/opencl/approx.cpp             | 1 -
 src/backend/opencl/approx.hpp             | 1 -
 src/backend/opencl/arith.hpp              | 1 -
 src/backend/opencl/blas.cpp               | 1 -
 src/backend/opencl/blas.hpp               | 1 -
 src/backend/opencl/cholesky.hpp           | 1 -
 src/backend/opencl/complex.hpp            | 1 -
 src/backend/opencl/copy.cpp               | 1 -
 src/backend/opencl/copy.hpp               | 1 -
 src/backend/opencl/diagonal.cpp           | 1 -
 src/backend/opencl/diagonal.hpp           | 1 -
 src/backend/opencl/diff.cpp               | 1 -
 src/backend/opencl/diff.hpp               | 1 -
 src/backend/opencl/gradient.hpp           | 1 -
 src/backend/opencl/identity.cpp           | 1 -
 src/backend/opencl/identity.hpp           | 1 -
 src/backend/opencl/inverse.hpp            | 1 -
 src/backend/opencl/iota.hpp               | 1 -
 src/backend/opencl/ireduce.hpp            | 1 -
 src/backend/opencl/join.hpp               | 1 -
 src/backend/opencl/kernel/sort_helper.hpp | 2 +-
 src/backend/opencl/logic.hpp              | 1 -
 src/backend/opencl/lu.hpp                 | 1 -
 src/backend/opencl/qr.hpp                 | 1 -
 src/backend/opencl/random.cpp             | 1 -
 src/backend/opencl/random.hpp             | 1 -
 src/backend/opencl/range.hpp              | 1 -
 src/backend/opencl/reduce.hpp             | 1 -
 src/backend/opencl/reorder.hpp            | 1 -
 src/backend/opencl/resize.cpp             | 1 -
 src/backend/opencl/resize.hpp             | 1 -
 src/backend/opencl/rotate.hpp             | 1 -
 src/backend/opencl/scan.hpp               | 1 -
 src/backend/opencl/set.hpp                | 1 -
 src/backend/opencl/shift.hpp              | 1 -
 src/backend/opencl/solve.hpp              | 1 -
 src/backend/opencl/sort.hpp               | 1 -
 src/backend/opencl/sort_by_key.hpp        | 1 -
 src/backend/opencl/sort_index.hpp         | 1 -
 src/backend/opencl/tile.hpp               | 1 -
 src/backend/opencl/transform.cpp          | 1 -
 src/backend/opencl/transform.hpp          | 1 -
 src/backend/opencl/triangle.hpp           | 1 -
 src/backend/opencl/where.hpp              | 1 -
 46 files changed, 1 insertion(+), 46 deletions(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index f83d5c0120..5683546367 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <platform.hpp>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <ArrayInfo.hpp>
 #include <traits.hpp>
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index fc34c09c19..4437432e39 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <platform.hpp>
-#include <af/array.h>
 #include <optypes.hpp>
 #include <string>
 #include <vector>
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index 867157264d..8933ce0ccf 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <approx.hpp>
diff --git a/src/backend/opencl/approx.hpp b/src/backend/opencl/approx.hpp
index 4e515f6f64..108dcedb94 100644
--- a/src/backend/opencl/approx.hpp
+++ b/src/backend/opencl/approx.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index 244b32d339..c522d69281 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 77531154e5..4045bdee97 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <blas.hpp>
-#include <af/blas.h>
 #include <Array.hpp>
 #include <cassert>
 #include <string>
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index 2d7a89a46e..ecf8721946 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/defines.h>
-#include <af/blas.h>
 #include <Array.hpp>
 #include <clBLAS.h>
 #include <mutex>
diff --git a/src/backend/opencl/cholesky.hpp b/src/backend/opencl/cholesky.hpp
index ff973a2df4..34f774e6bd 100644
--- a/src/backend/opencl/cholesky.hpp
+++ b/src/backend/opencl/cholesky.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index bdd42ba553..25eb5954d7 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index e1716f1632..aa5ed8c1bf 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/defines.h>
 #include <Array.hpp>
 #include <copy.hpp>
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index 818cfa44d2..ea26df45c6 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 8693b11be3..45d8ce9867 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <Array.hpp>
diff --git a/src/backend/opencl/diagonal.hpp b/src/backend/opencl/diagonal.hpp
index cd6e9e0ab0..5244fe098a 100644
--- a/src/backend/opencl/diagonal.hpp
+++ b/src/backend/opencl/diagonal.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/opencl/diff.cpp b/src/backend/opencl/diff.cpp
index b466b8a739..7e95692584 100644
--- a/src/backend/opencl/diff.cpp
+++ b/src/backend/opencl/diff.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <diff.hpp>
diff --git a/src/backend/opencl/diff.hpp b/src/backend/opencl/diff.hpp
index 5298d8bf62..81ef63a855 100644
--- a/src/backend/opencl/diff.hpp
+++ b/src/backend/opencl/diff.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/gradient.hpp b/src/backend/opencl/gradient.hpp
index f8b229cea2..c6bb5a4b24 100644
--- a/src/backend/opencl/gradient.hpp
+++ b/src/backend/opencl/gradient.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/identity.cpp b/src/backend/opencl/identity.cpp
index 4f10a191c5..b8658b00b8 100644
--- a/src/backend/opencl/identity.cpp
+++ b/src/backend/opencl/identity.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <Array.hpp>
diff --git a/src/backend/opencl/identity.hpp b/src/backend/opencl/identity.hpp
index 3a56e182db..542db7a0fb 100644
--- a/src/backend/opencl/identity.hpp
+++ b/src/backend/opencl/identity.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/inverse.hpp b/src/backend/opencl/inverse.hpp
index b28c3a4180..753e3d232c 100644
--- a/src/backend/opencl/inverse.hpp
+++ b/src/backend/opencl/inverse.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/iota.hpp b/src/backend/opencl/iota.hpp
index 192c09d9f3..87e1f4c734 100644
--- a/src/backend/opencl/iota.hpp
+++ b/src/backend/opencl/iota.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/ireduce.hpp b/src/backend/opencl/ireduce.hpp
index 2a1059aaad..75d097cefd 100644
--- a/src/backend/opencl/ireduce.hpp
+++ b/src/backend/opencl/ireduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/opencl/join.hpp b/src/backend/opencl/join.hpp
index 9068d756d0..398a36c98e 100644
--- a/src/backend/opencl/join.hpp
+++ b/src/backend/opencl/join.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index b8031c2314..078ff7c0c6 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #pragma once
-#include <traits.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <type_traits>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/logic.hpp b/src/backend/opencl/logic.hpp
index 949fa4d2a6..90f241c038 100644
--- a/src/backend/opencl/logic.hpp
+++ b/src/backend/opencl/logic.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/opencl/lu.hpp b/src/backend/opencl/lu.hpp
index b44eca8c60..3eab168d9a 100644
--- a/src/backend/opencl/lu.hpp
+++ b/src/backend/opencl/lu.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/qr.hpp b/src/backend/opencl/qr.hpp
index aa70199f3e..72bf669f2f 100644
--- a/src/backend/opencl/qr.hpp
+++ b/src/backend/opencl/qr.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/random.cpp b/src/backend/opencl/random.cpp
index 3d98fc6698..39d8503384 100644
--- a/src/backend/opencl/random.cpp
+++ b/src/backend/opencl/random.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <random.hpp>
diff --git a/src/backend/opencl/random.hpp b/src/backend/opencl/random.hpp
index c07332eb4b..e7c812d040 100644
--- a/src/backend/opencl/random.hpp
+++ b/src/backend/opencl/random.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/range.hpp b/src/backend/opencl/range.hpp
index 81e75b44b8..88ffba2373 100644
--- a/src/backend/opencl/range.hpp
+++ b/src/backend/opencl/range.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 0ddc76586b..88e0193614 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/opencl/reorder.hpp b/src/backend/opencl/reorder.hpp
index ad06dafa8e..057f601c55 100644
--- a/src/backend/opencl/reorder.hpp
+++ b/src/backend/opencl/reorder.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/resize.cpp b/src/backend/opencl/resize.cpp
index 051d9554db..9c246d128d 100644
--- a/src/backend/opencl/resize.cpp
+++ b/src/backend/opencl/resize.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <resize.hpp>
diff --git a/src/backend/opencl/resize.hpp b/src/backend/opencl/resize.hpp
index 04a6b937ee..b42ca024b5 100644
--- a/src/backend/opencl/resize.hpp
+++ b/src/backend/opencl/resize.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/rotate.hpp b/src/backend/opencl/rotate.hpp
index ea75f585ae..3c5d40dfcd 100644
--- a/src/backend/opencl/rotate.hpp
+++ b/src/backend/opencl/rotate.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index df03d8282f..e3958c6f21 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/opencl/set.hpp b/src/backend/opencl/set.hpp
index d27dd3b86d..592489d539 100644
--- a/src/backend/opencl/set.hpp
+++ b/src/backend/opencl/set.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/shift.hpp b/src/backend/opencl/shift.hpp
index 26603362eb..d93a4c9ae6 100644
--- a/src/backend/opencl/shift.hpp
+++ b/src/backend/opencl/shift.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/solve.hpp b/src/backend/opencl/solve.hpp
index f3d234bbf3..d3c7bd29c4 100644
--- a/src/backend/opencl/solve.hpp
+++ b/src/backend/opencl/solve.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/sort.hpp b/src/backend/opencl/sort.hpp
index a63dc38495..5bb74f52a9 100644
--- a/src/backend/opencl/sort.hpp
+++ b/src/backend/opencl/sort.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/sort_by_key.hpp b/src/backend/opencl/sort_by_key.hpp
index a3380daf55..712f0be615 100644
--- a/src/backend/opencl/sort_by_key.hpp
+++ b/src/backend/opencl/sort_by_key.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/sort_index.hpp b/src/backend/opencl/sort_index.hpp
index 48a7acb74c..995d57bc06 100644
--- a/src/backend/opencl/sort_index.hpp
+++ b/src/backend/opencl/sort_index.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/tile.hpp b/src/backend/opencl/tile.hpp
index 4547bf1cb0..b61c8aec32 100644
--- a/src/backend/opencl/tile.hpp
+++ b/src/backend/opencl/tile.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 379fd2a5b7..8046573dee 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <transform.hpp>
diff --git a/src/backend/opencl/transform.hpp b/src/backend/opencl/transform.hpp
index 064817a537..03fc3074c0 100644
--- a/src/backend/opencl/transform.hpp
+++ b/src/backend/opencl/transform.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/triangle.hpp b/src/backend/opencl/triangle.hpp
index f54acfebd8..28fd309226 100644
--- a/src/backend/opencl/triangle.hpp
+++ b/src/backend/opencl/triangle.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/where.hpp b/src/backend/opencl/where.hpp
index 481d34f6f1..ea623e7159 100644
--- a/src/backend/opencl/where.hpp
+++ b/src/backend/opencl/where.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace opencl

From 3ffc498ef7eecbc1f97c3b08646d39becea0bf5f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 16:56:14 -0400
Subject: [PATCH 0488/2677] CLEANUP unused af/ header files from backend/cuda

---
 src/backend/cuda/Array.hpp       | 1 -
 src/backend/cuda/JIT/Node.hpp    | 1 -
 src/backend/cuda/approx.hpp      | 1 -
 src/backend/cuda/arith.hpp       | 1 -
 src/backend/cuda/blas.hpp        | 1 -
 src/backend/cuda/cholesky.hpp    | 1 -
 src/backend/cuda/complex.hpp     | 1 -
 src/backend/cuda/copy.cu         | 1 -
 src/backend/cuda/copy.hpp        | 1 -
 src/backend/cuda/diagonal.cu     | 1 -
 src/backend/cuda/diagonal.hpp    | 1 -
 src/backend/cuda/diff.hpp        | 1 -
 src/backend/cuda/gradient.hpp    | 1 -
 src/backend/cuda/identity.cu     | 1 -
 src/backend/cuda/identity.hpp    | 1 -
 src/backend/cuda/inverse.hpp     | 1 -
 src/backend/cuda/iota.hpp        | 1 -
 src/backend/cuda/ireduce.hpp     | 1 -
 src/backend/cuda/join.hpp        | 1 -
 src/backend/cuda/logic.hpp       | 1 -
 src/backend/cuda/lu.hpp          | 1 -
 src/backend/cuda/qr.hpp          | 1 -
 src/backend/cuda/random.cu       | 1 -
 src/backend/cuda/random.hpp      | 1 -
 src/backend/cuda/range.hpp       | 1 -
 src/backend/cuda/reduce.hpp      | 1 -
 src/backend/cuda/reorder.hpp     | 1 -
 src/backend/cuda/resize.hpp      | 1 -
 src/backend/cuda/rotate.hpp      | 1 -
 src/backend/cuda/scan.hpp        | 1 -
 src/backend/cuda/set.hpp         | 1 -
 src/backend/cuda/shift.hpp       | 1 -
 src/backend/cuda/solve.hpp       | 1 -
 src/backend/cuda/sort.hpp        | 1 -
 src/backend/cuda/sort_by_key.hpp | 1 -
 src/backend/cuda/sort_index.hpp  | 1 -
 src/backend/cuda/svd.hpp         | 1 -
 src/backend/cuda/tile.hpp        | 1 -
 src/backend/cuda/transform.hpp   | 1 -
 src/backend/cuda/triangle.hpp    | 1 -
 src/backend/cuda/where.hpp       | 1 -
 41 files changed, 41 deletions(-)

diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 1f9512fb8d..2adbd35a84 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -15,7 +15,6 @@
 #endif
 #endif
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <ArrayInfo.hpp>
 #include "traits.hpp"
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 00fed9fda7..90f6273be2 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <optypes.hpp>
 #include <string>
 #include <vector>
diff --git a/src/backend/cuda/approx.hpp b/src/backend/cuda/approx.hpp
index 34bc954dce..902e1dbd3b 100644
--- a/src/backend/cuda/approx.hpp
+++ b/src/backend/cuda/approx.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index cc3e6dc5cc..ff88335072 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index c0a6e966e6..816e024cb1 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/blas.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/cholesky.hpp b/src/backend/cuda/cholesky.hpp
index cae0484f1d..f39f8f01e4 100644
--- a/src/backend/cuda/cholesky.hpp
+++ b/src/backend/cuda/cholesky.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index b7de74a7de..92c7867fbd 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 35e5c83178..c5a9b73b7c 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <cuda_runtime_api.h>
-#include <af/array.h>
 #include <af/defines.h>
 #include <Array.hpp>
 #include <copy.hpp>
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index f71504a45c..ff72af7fee 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cu
index db0d1b4617..288f952db9 100644
--- a/src/backend/cuda/diagonal.cu
+++ b/src/backend/cuda/diagonal.cu
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <Array.hpp>
diff --git a/src/backend/cuda/diagonal.hpp b/src/backend/cuda/diagonal.hpp
index db671dece3..c385efe08d 100644
--- a/src/backend/cuda/diagonal.hpp
+++ b/src/backend/cuda/diagonal.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cuda/diff.hpp b/src/backend/cuda/diff.hpp
index b0b66d0b54..eac2ef60b3 100644
--- a/src/backend/cuda/diff.hpp
+++ b/src/backend/cuda/diff.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/gradient.hpp b/src/backend/cuda/gradient.hpp
index ecae97d854..3cc27d92c9 100644
--- a/src/backend/cuda/gradient.hpp
+++ b/src/backend/cuda/gradient.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/identity.cu b/src/backend/cuda/identity.cu
index 6765766237..b6bd3f0f8a 100644
--- a/src/backend/cuda/identity.cu
+++ b/src/backend/cuda/identity.cu
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <Array.hpp>
diff --git a/src/backend/cuda/identity.hpp b/src/backend/cuda/identity.hpp
index 9b92f7d989..2dbf9a5776 100644
--- a/src/backend/cuda/identity.hpp
+++ b/src/backend/cuda/identity.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/inverse.hpp b/src/backend/cuda/inverse.hpp
index a8eb3eaa96..d9d35746fb 100644
--- a/src/backend/cuda/inverse.hpp
+++ b/src/backend/cuda/inverse.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/iota.hpp b/src/backend/cuda/iota.hpp
index a63b3b3259..19922def39 100644
--- a/src/backend/cuda/iota.hpp
+++ b/src/backend/cuda/iota.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/ireduce.hpp b/src/backend/cuda/ireduce.hpp
index 483cb255e5..a446553d5a 100644
--- a/src/backend/cuda/ireduce.hpp
+++ b/src/backend/cuda/ireduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cuda/join.hpp b/src/backend/cuda/join.hpp
index 6f9ee27782..722d46cc05 100644
--- a/src/backend/cuda/join.hpp
+++ b/src/backend/cuda/join.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/logic.hpp b/src/backend/cuda/logic.hpp
index 7b29b19d48..a1447ae731 100644
--- a/src/backend/cuda/logic.hpp
+++ b/src/backend/cuda/logic.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/lu.hpp b/src/backend/cuda/lu.hpp
index acf9dbaad7..507564ff23 100644
--- a/src/backend/cuda/lu.hpp
+++ b/src/backend/cuda/lu.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/qr.hpp b/src/backend/cuda/qr.hpp
index acedfd520c..dc0f56a6dc 100644
--- a/src/backend/cuda/qr.hpp
+++ b/src/backend/cuda/qr.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/random.cu b/src/backend/cuda/random.cu
index e19a48cdae..8c75af1c4e 100644
--- a/src/backend/cuda/random.cu
+++ b/src/backend/cuda/random.cu
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <random.hpp>
diff --git a/src/backend/cuda/random.hpp b/src/backend/cuda/random.hpp
index 250af773b3..d7d996f425 100644
--- a/src/backend/cuda/random.hpp
+++ b/src/backend/cuda/random.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/range.hpp b/src/backend/cuda/range.hpp
index f49f3dff46..b6cf0c1393 100644
--- a/src/backend/cuda/range.hpp
+++ b/src/backend/cuda/range.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 #pragma once
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/reduce.hpp b/src/backend/cuda/reduce.hpp
index 82755bc618..d3189cd9d3 100644
--- a/src/backend/cuda/reduce.hpp
+++ b/src/backend/cuda/reduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cuda/reorder.hpp b/src/backend/cuda/reorder.hpp
index 3adb5e20dc..8d58189971 100644
--- a/src/backend/cuda/reorder.hpp
+++ b/src/backend/cuda/reorder.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/resize.hpp b/src/backend/cuda/resize.hpp
index 025e149115..2b2f97cf6f 100644
--- a/src/backend/cuda/resize.hpp
+++ b/src/backend/cuda/resize.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/rotate.hpp b/src/backend/cuda/rotate.hpp
index 91f761bdb5..4ca7bac527 100644
--- a/src/backend/cuda/rotate.hpp
+++ b/src/backend/cuda/rotate.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/scan.hpp b/src/backend/cuda/scan.hpp
index 536accd1d3..94b63c7473 100644
--- a/src/backend/cuda/scan.hpp
+++ b/src/backend/cuda/scan.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 #include <ops.hpp>
 
diff --git a/src/backend/cuda/set.hpp b/src/backend/cuda/set.hpp
index 01f048bf07..5c77106983 100644
--- a/src/backend/cuda/set.hpp
+++ b/src/backend/cuda/set.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/shift.hpp b/src/backend/cuda/shift.hpp
index b8b4377eed..b08db93f7a 100644
--- a/src/backend/cuda/shift.hpp
+++ b/src/backend/cuda/shift.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/solve.hpp b/src/backend/cuda/solve.hpp
index 34da8f6527..43933ec19d 100644
--- a/src/backend/cuda/solve.hpp
+++ b/src/backend/cuda/solve.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/sort.hpp b/src/backend/cuda/sort.hpp
index 8f4f3a03ef..ad191bb22a 100644
--- a/src/backend/cuda/sort.hpp
+++ b/src/backend/cuda/sort.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/sort_by_key.hpp b/src/backend/cuda/sort_by_key.hpp
index 561df04d80..f752dfaf3a 100644
--- a/src/backend/cuda/sort_by_key.hpp
+++ b/src/backend/cuda/sort_by_key.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/sort_index.hpp b/src/backend/cuda/sort_index.hpp
index d85c076d1c..19736bd435 100644
--- a/src/backend/cuda/sort_index.hpp
+++ b/src/backend/cuda/sort_index.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/svd.hpp b/src/backend/cuda/svd.hpp
index 5a833cecbc..5713adcef6 100644
--- a/src/backend/cuda/svd.hpp
+++ b/src/backend/cuda/svd.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/tile.hpp b/src/backend/cuda/tile.hpp
index 85c895a6fe..0cfc0efd12 100644
--- a/src/backend/cuda/tile.hpp
+++ b/src/backend/cuda/tile.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/transform.hpp b/src/backend/cuda/transform.hpp
index 316953d614..29ae83640c 100644
--- a/src/backend/cuda/transform.hpp
+++ b/src/backend/cuda/transform.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/triangle.hpp b/src/backend/cuda/triangle.hpp
index 2a37f39c62..539e70e081 100644
--- a/src/backend/cuda/triangle.hpp
+++ b/src/backend/cuda/triangle.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/where.hpp b/src/backend/cuda/where.hpp
index 1f181ba389..1e955522b6 100644
--- a/src/backend/cuda/where.hpp
+++ b/src/backend/cuda/where.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <Array.hpp>
 
 namespace cuda

From 74dd0007bd6eae18e5064fb1e40133112f10cb13 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 17:48:35 -0400
Subject: [PATCH 0489/2677] CLEANUP unused af/defines.h from backend

---
 src/backend/cpu/arith.hpp                     | 1 -
 src/backend/cpu/assign.cpp                    | 1 -
 src/backend/cpu/bilateral.cpp                 | 1 -
 src/backend/cpu/blas.hpp                      | 1 -
 src/backend/cpu/cast.hpp                      | 1 -
 src/backend/cpu/complex.hpp                   | 1 -
 src/backend/cpu/convolve.cpp                  | 1 -
 src/backend/cpu/diagonal.cpp                  | 1 -
 src/backend/cpu/fast.cpp                      | 1 -
 src/backend/cpu/fft.cpp                       | 1 -
 src/backend/cpu/fftconvolve.cpp               | 1 -
 src/backend/cpu/harris.cpp                    | 1 -
 src/backend/cpu/histogram.cpp                 | 1 -
 src/backend/cpu/homography.cpp                | 1 -
 src/backend/cpu/iir.cpp                       | 1 -
 src/backend/cpu/index.cpp                     | 1 -
 src/backend/cpu/ireduce.cpp                   | 1 -
 src/backend/cpu/kernel/Array.hpp              | 1 -
 src/backend/cpu/kernel/approx1.hpp            | 1 -
 src/backend/cpu/kernel/approx2.hpp            | 1 -
 src/backend/cpu/kernel/assign.hpp             | 1 -
 src/backend/cpu/kernel/bilateral.hpp          | 1 -
 src/backend/cpu/kernel/convolve.hpp           | 1 -
 src/backend/cpu/kernel/copy.hpp               | 1 -
 src/backend/cpu/kernel/diagonal.hpp           | 1 -
 src/backend/cpu/kernel/diff.hpp               | 1 -
 src/backend/cpu/kernel/dot.hpp                | 1 -
 src/backend/cpu/kernel/fast.hpp               | 1 -
 src/backend/cpu/kernel/fftconvolve.hpp        | 1 -
 src/backend/cpu/kernel/gradient.hpp           | 1 -
 src/backend/cpu/kernel/harris.hpp             | 1 -
 src/backend/cpu/kernel/histogram.hpp          | 1 -
 src/backend/cpu/kernel/hsv_rgb.hpp            | 1 -
 src/backend/cpu/kernel/identity.hpp           | 1 -
 src/backend/cpu/kernel/iir.hpp                | 1 -
 src/backend/cpu/kernel/index.hpp              | 1 -
 src/backend/cpu/kernel/iota.hpp               | 1 -
 src/backend/cpu/kernel/ireduce.hpp            | 1 -
 src/backend/cpu/kernel/join.hpp               | 1 -
 src/backend/cpu/kernel/lookup.hpp             | 1 -
 src/backend/cpu/kernel/lu.hpp                 | 1 -
 src/backend/cpu/kernel/match_template.hpp     | 1 -
 src/backend/cpu/kernel/meanshift.hpp          | 1 -
 src/backend/cpu/kernel/medfilt.hpp            | 1 -
 src/backend/cpu/kernel/morph.hpp              | 1 -
 src/backend/cpu/kernel/nearest_neighbour.hpp  | 1 -
 src/backend/cpu/kernel/orb.hpp                | 1 -
 src/backend/cpu/kernel/random.hpp             | 1 -
 src/backend/cpu/kernel/range.hpp              | 1 -
 src/backend/cpu/kernel/reduce.hpp             | 1 -
 src/backend/cpu/kernel/regions.hpp            | 1 -
 src/backend/cpu/kernel/reorder.hpp            | 1 -
 src/backend/cpu/kernel/resize.hpp             | 1 -
 src/backend/cpu/kernel/rotate.hpp             | 1 -
 src/backend/cpu/kernel/scan.hpp               | 1 -
 src/backend/cpu/kernel/select.hpp             | 1 -
 src/backend/cpu/kernel/shift.hpp              | 1 -
 src/backend/cpu/kernel/sobel.hpp              | 1 -
 src/backend/cpu/kernel/sort.hpp               | 1 -
 src/backend/cpu/kernel/sort_by_key_impl.hpp   | 1 -
 src/backend/cpu/kernel/susan.hpp              | 1 -
 src/backend/cpu/kernel/tile.hpp               | 1 -
 src/backend/cpu/kernel/transform.hpp          | 1 -
 src/backend/cpu/kernel/transpose.hpp          | 1 -
 src/backend/cpu/kernel/triangle.hpp           | 1 -
 src/backend/cpu/kernel/unwrap.hpp             | 1 -
 src/backend/cpu/kernel/wrap.hpp               | 1 -
 src/backend/cpu/logic.hpp                     | 1 -
 src/backend/cpu/match_template.cpp            | 1 -
 src/backend/cpu/meanshift.cpp                 | 1 -
 src/backend/cpu/medfilt.cpp                   | 1 -
 src/backend/cpu/morph.cpp                     | 1 -
 src/backend/cpu/nearest_neighbour.cpp         | 1 -
 src/backend/cpu/orb.cpp                       | 1 -
 src/backend/cpu/platform.cpp                  | 1 -
 src/backend/cpu/random.cpp                    | 1 -
 src/backend/cpu/reduce.cpp                    | 1 -
 src/backend/cpu/regions.cpp                   | 1 -
 src/backend/cpu/scan.cpp                      | 1 -
 src/backend/cpu/set.cpp                       | 1 -
 src/backend/cpu/sift.cpp                      | 1 -
 src/backend/cpu/sobel.cpp                     | 1 -
 src/backend/cpu/transpose.cpp                 | 1 -
 src/backend/cpu/triangle.cpp                  | 1 -
 src/backend/cpu/utility.hpp                   | 1 -
 src/backend/cpu/where.cpp                     | 1 -
 src/backend/cuda/arith.hpp                    | 1 -
 src/backend/cuda/assign.cu                    | 1 -
 src/backend/cuda/bilateral.cu                 | 1 -
 src/backend/cuda/blas.hpp                     | 1 -
 src/backend/cuda/cast.hpp                     | 1 -
 src/backend/cuda/complex.hpp                  | 1 -
 src/backend/cuda/convolve.cpp                 | 1 -
 src/backend/cuda/copy.cu                      | 1 -
 src/backend/cuda/diagonal.cu                  | 1 -
 src/backend/cuda/fast.cu                      | 1 -
 src/backend/cuda/fast_pyramid.cu              | 1 -
 src/backend/cuda/fft.cpp                      | 1 -
 src/backend/cuda/fftconvolve.cu               | 1 -
 src/backend/cuda/harris.cu                    | 1 -
 src/backend/cuda/histogram.cu                 | 1 -
 src/backend/cuda/homography.cu                | 1 -
 src/backend/cuda/hsv_rgb.cu                   | 1 -
 src/backend/cuda/identity.cu                  | 1 -
 src/backend/cuda/iir.cu                       | 1 -
 src/backend/cuda/index.cu                     | 1 -
 src/backend/cuda/ireduce.cu                   | 1 -
 src/backend/cuda/kernel/assign.hpp            | 1 -
 src/backend/cuda/kernel/bilateral.hpp         | 1 -
 src/backend/cuda/kernel/convolve.cu           | 1 -
 src/backend/cuda/kernel/convolve.hpp          | 1 -
 src/backend/cuda/kernel/convolve_separable.cu | 1 -
 src/backend/cuda/kernel/fast.hpp              | 1 -
 src/backend/cuda/kernel/fast_pyramid.hpp      | 1 -
 src/backend/cuda/kernel/fftconvolve.hpp       | 1 -
 src/backend/cuda/kernel/harris.hpp            | 1 -
 src/backend/cuda/kernel/histogram.hpp         | 1 -
 src/backend/cuda/kernel/homography.hpp        | 1 -
 src/backend/cuda/kernel/hsv_rgb.hpp           | 1 -
 src/backend/cuda/kernel/iir.hpp               | 1 -
 src/backend/cuda/kernel/index.hpp             | 1 -
 src/backend/cuda/kernel/ireduce.hpp           | 1 -
 src/backend/cuda/kernel/lookup.hpp            | 1 -
 src/backend/cuda/kernel/match_template.hpp    | 1 -
 src/backend/cuda/kernel/meanshift.hpp         | 1 -
 src/backend/cuda/kernel/medfilt.hpp           | 1 -
 src/backend/cuda/kernel/memcopy.hpp           | 1 -
 src/backend/cuda/kernel/morph.hpp             | 1 -
 src/backend/cuda/kernel/nearest_neighbour.hpp | 1 -
 src/backend/cuda/kernel/orb.hpp               | 1 -
 src/backend/cuda/kernel/reduce.hpp            | 1 -
 src/backend/cuda/kernel/regions.hpp           | 1 -
 src/backend/cuda/kernel/scan_dim.hpp          | 1 -
 src/backend/cuda/kernel/scan_first.hpp        | 1 -
 src/backend/cuda/kernel/sift_nonfree.hpp      | 1 -
 src/backend/cuda/kernel/sobel.hpp             | 1 -
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 2 +-
 src/backend/cuda/kernel/susan.hpp             | 1 -
 src/backend/cuda/kernel/transpose.hpp         | 1 -
 src/backend/cuda/kernel/transpose_inplace.hpp | 1 -
 src/backend/cuda/kernel/where.hpp             | 1 -
 src/backend/cuda/logic.hpp                    | 1 -
 src/backend/cuda/match_template.cu            | 1 -
 src/backend/cuda/math.hpp                     | 1 -
 src/backend/cuda/meanshift.cu                 | 1 -
 src/backend/cuda/medfilt.cu                   | 1 -
 src/backend/cuda/memory.hpp                   | 2 +-
 src/backend/cuda/morph3d_impl.hpp             | 1 -
 src/backend/cuda/morph_impl.hpp               | 1 -
 src/backend/cuda/nearest_neighbour.cu         | 1 -
 src/backend/cuda/orb.cu                       | 1 -
 src/backend/cuda/reduce_impl.hpp              | 1 -
 src/backend/cuda/regions.cu                   | 1 -
 src/backend/cuda/scan.cu                      | 1 -
 src/backend/cuda/set.cu                       | 1 -
 src/backend/cuda/sift.cu                      | 1 -
 src/backend/cuda/sobel.cu                     | 1 -
 src/backend/cuda/where.cu                     | 1 -
 src/backend/lapacke.hpp                       | 1 -
 src/backend/opencl/Param.cpp                  | 1 -
 src/backend/opencl/arith.hpp                  | 1 -
 src/backend/opencl/assign.cpp                 | 1 -
 src/backend/opencl/bilateral.cpp              | 1 -
 src/backend/opencl/blas.hpp                   | 1 -
 src/backend/opencl/cast.hpp                   | 1 -
 src/backend/opencl/complex.hpp                | 1 -
 src/backend/opencl/convolve.cpp               | 1 -
 src/backend/opencl/convolve_separable.cpp     | 1 -
 src/backend/opencl/copy.cpp                   | 1 -
 src/backend/opencl/cpu/cpu_helper.hpp         | 1 -
 src/backend/opencl/diagonal.cpp               | 1 -
 src/backend/opencl/fast.cpp                   | 1 -
 src/backend/opencl/fft.cpp                    | 1 -
 src/backend/opencl/fftconvolve.cpp            | 1 -
 src/backend/opencl/harris.cpp                 | 1 -
 src/backend/opencl/histogram.cpp              | 1 -
 src/backend/opencl/homography.cpp             | 1 -
 src/backend/opencl/identity.cpp               | 1 -
 src/backend/opencl/iir.cpp                    | 1 -
 src/backend/opencl/index.cpp                  | 1 -
 src/backend/opencl/ireduce.cpp                | 1 -
 src/backend/opencl/kernel/KParam.hpp          | 4 ++++
 src/backend/opencl/meanshift.cpp              | 1 -
 src/backend/opencl/medfilt.cpp                | 1 -
 src/backend/opencl/memory.hpp                 | 1 -
 src/backend/opencl/morph3d_impl.hpp           | 1 -
 src/backend/opencl/morph_impl.hpp             | 1 -
 src/backend/opencl/nearest_neighbour.cpp      | 1 -
 src/backend/opencl/orb.cpp                    | 1 -
 src/backend/opencl/reduce_impl.hpp            | 1 -
 src/backend/opencl/regions.cpp                | 1 -
 src/backend/opencl/scan.cpp                   | 1 -
 src/backend/opencl/set.cpp                    | 1 -
 src/backend/opencl/sift.cpp                   | 1 -
 src/backend/opencl/sobel.cpp                  | 1 -
 src/backend/opencl/where.cpp                  | 1 -
 196 files changed, 6 insertions(+), 195 deletions(-)

diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 8f66631825..6e5921b357 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index 463b30c733..375f435502 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index abd985768d..58a671a2dc 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <kernel/bilateral.hpp>
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 7998a6e7e4..6cedac6169 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <Array.hpp>
 #include <types.hpp>
 
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 67a1c057e5..2cb7f4cb50 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <complex>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index a2f0c9d42b..d5b471db0f 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <complex>
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 8218a3f9a3..64a8b3f19b 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index d68a73a812..3b4b2a28c7 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <handle.hpp>
 #include <Array.hpp>
 #include <diagonal.hpp>
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 954f457cf4..6cd1c38e2a 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 3c1d10a4f3..4fd38c7138 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fft.hpp>
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 3b4b864452..727713d9fa 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <dispatch.hpp>
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index f85e950139..d78c008866 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 3c30402b47..d2f1ea68e3 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 4d131cf695..dd7f5e8e04 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 049212ad69..0a2f3d1ae7 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index a2cdac888f..8840b866e6 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <index.hpp>
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index a40fbdf958..77f1749884 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 08ade502e5..1bbb7512f6 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <platform.hpp>
 
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
index ab12ebc813..2ba1fd40b5 100644
--- a/src/backend/cpu/kernel/approx1.hpp
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 
diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
index b5115e2e49..a29a11d9f9 100644
--- a/src/backend/cpu/kernel/approx2.hpp
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 86befaf74e..470979fb2f 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <vector>
 #include <Array.hpp>
 #include <utility.hpp>
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index c950bbd084..0b7b2d56af 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 #include <cmath>
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 79d684dd64..af9b9cd3f6 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index 70d6705ec2..fad122cdce 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
index 0c81fc90f2..f887f7fc9a 100644
--- a/src/backend/cpu/kernel/diagonal.hpp
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
index 1a3d7ba110..937748316d 100644
--- a/src/backend/cpu/kernel/diff.hpp
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 
diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
index 71f2c6f959..6b31d8d07f 100644
--- a/src/backend/cpu/kernel/dot.hpp
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <complex>
 
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index 02da3e4d33..7054ddb8db 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index ad586f7d28..ca192f5626 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <convolve_common.hpp>
 
diff --git a/src/backend/cpu/kernel/gradient.hpp b/src/backend/cpu/kernel/gradient.hpp
index 1ab01abb0f..178d581c65 100644
--- a/src/backend/cpu/kernel/gradient.hpp
+++ b/src/backend/cpu/kernel/gradient.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/harris.hpp b/src/backend/cpu/kernel/harris.hpp
index 183cf37e77..00df1a608e 100644
--- a/src/backend/cpu/kernel/harris.hpp
+++ b/src/backend/cpu/kernel/harris.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 9b9b897c02..64ee72658a 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/hsv_rgb.hpp b/src/backend/cpu/kernel/hsv_rgb.hpp
index c1f59a1737..b2fbf8ac7d 100644
--- a/src/backend/cpu/kernel/hsv_rgb.hpp
+++ b/src/backend/cpu/kernel/hsv_rgb.hpp
@@ -8,7 +8,6 @@
 ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <cmath>
 
diff --git a/src/backend/cpu/kernel/identity.hpp b/src/backend/cpu/kernel/identity.hpp
index 242ba9dae3..4b950b0a9b 100644
--- a/src/backend/cpu/kernel/identity.hpp
+++ b/src/backend/cpu/kernel/identity.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 
diff --git a/src/backend/cpu/kernel/iir.hpp b/src/backend/cpu/kernel/iir.hpp
index 5182094fc2..b7f243b41a 100644
--- a/src/backend/cpu/kernel/iir.hpp
+++ b/src/backend/cpu/kernel/iir.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index 343d7ae4e7..f52e5db3ff 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <vector>
 #include <Array.hpp>
 #include <utility.hpp>
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index 0f824295a4..d867914523 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index 848885515b..d860425112 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index b0d92c9978..de044d66b3 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/lookup.hpp b/src/backend/cpu/kernel/lookup.hpp
index a290ef2fca..3886474d05 100644
--- a/src/backend/cpu/kernel/lookup.hpp
+++ b/src/backend/cpu/kernel/lookup.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <vector>
 #include <Array.hpp>
 #include <utility.hpp>
diff --git a/src/backend/cpu/kernel/lu.hpp b/src/backend/cpu/kernel/lu.hpp
index 35b0c19b84..d69d6ee3a8 100644
--- a/src/backend/cpu/kernel/lu.hpp
+++ b/src/backend/cpu/kernel/lu.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
index ae41364018..afbef67a7e 100644
--- a/src/backend/cpu/kernel/match_template.hpp
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 54fb1a89bf..7008995a32 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <vector>
 #include <utility.hpp>
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index bc639a89b5..e6e1a24499 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <vector>
 #include <algorithm>
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index af9b7e9373..d990bb873b 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 4916463aed..14d841091c 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/orb.hpp b/src/backend/cpu/kernel/orb.hpp
index acd508cb70..12cd5eb4ef 100644
--- a/src/backend/cpu/kernel/orb.hpp
+++ b/src/backend/cpu/kernel/orb.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 
diff --git a/src/backend/cpu/kernel/random.hpp b/src/backend/cpu/kernel/random.hpp
index 9c59a64db9..9b2d311007 100644
--- a/src/backend/cpu/kernel/random.hpp
+++ b/src/backend/cpu/kernel/random.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <type_traits>
 #include <random>
diff --git a/src/backend/cpu/kernel/range.hpp b/src/backend/cpu/kernel/range.hpp
index b244a19c85..0732d30e0a 100644
--- a/src/backend/cpu/kernel/range.hpp
+++ b/src/backend/cpu/kernel/range.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index 85119dcee7..9479fa62f6 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/regions.hpp b/src/backend/cpu/kernel/regions.hpp
index 863ebc5f48..95484d422d 100644
--- a/src/backend/cpu/kernel/regions.hpp
+++ b/src/backend/cpu/kernel/regions.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/reorder.hpp b/src/backend/cpu/kernel/reorder.hpp
index c10c96ef36..dcd894c0f9 100644
--- a/src/backend/cpu/kernel/reorder.hpp
+++ b/src/backend/cpu/kernel/reorder.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index 19d7ec7cf1..df8fc702a5 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index 395ea3f303..088d5aaac8 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index 0bcfe7df17..62cc5ace86 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index 1099c7e437..c3fb47be69 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/shift.hpp b/src/backend/cpu/kernel/shift.hpp
index 8beb975486..bef796ecb7 100644
--- a/src/backend/cpu/kernel/shift.hpp
+++ b/src/backend/cpu/kernel/shift.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <cassert>
 
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index 49d33cdbb4..e59f742c85 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <cassert>
 
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index e0ae62c932..db82d4159c 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <math.hpp>
 #include <algorithm>
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index 12ba793285..220295f2a2 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <kernel/sort_by_key.hpp>
 #include <kernel/sort_helper.hpp>
 #include <Array.hpp>
diff --git a/src/backend/cpu/kernel/susan.hpp b/src/backend/cpu/kernel/susan.hpp
index f543967799..2fb72d4ba4 100644
--- a/src/backend/cpu/kernel/susan.hpp
+++ b/src/backend/cpu/kernel/susan.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/tile.hpp b/src/backend/cpu/kernel/tile.hpp
index 3ad3009041..c51ecafbc7 100644
--- a/src/backend/cpu/kernel/tile.hpp
+++ b/src/backend/cpu/kernel/tile.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index 2311e4efaa..5c7233d28e 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <err_cpu.hpp>
 
diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
index 576de873ed..85d499a2df 100644
--- a/src/backend/cpu/kernel/transpose.hpp
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <utility.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index 7059de5981..ee32f48359 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
index 1d996ff1f3..52b57eb380 100644
--- a/src/backend/cpu/kernel/unwrap.hpp
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <err_cpu.hpp>
 
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 70be3ad652..0bf31da053 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <err_cpu.hpp>
 
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index abd0ff4b17..3967767576 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 58091a1f49..3786bd8c47 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <match_template.hpp>
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index b5bbf758a1..651e93ba6c 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <meanshift.hpp>
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 8ae4e33921..35fe31d70f 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 1ae4680b9d..fc0556b476 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <morph.hpp>
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 17e892f492..56b4b26939 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 8bbfd41932..e3a74bc222 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 3b31226b38..947f0c2c46 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/version.h>
-#include <af/defines.h>
 #include <platform.hpp>
 #include <sstream>
 #include <queue.hpp>
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
index 1f14e836d7..ec484fa0db 100644
--- a/src/backend/cpu/random.cpp
+++ b/src/backend/cpu/random.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <random.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 2d4d18e682..10292f8291 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <reduce.hpp>
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index 2384dd3341..e7c5abd65f 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <regions.hpp>
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 78de4142c8..7d5875ee14 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <scan.hpp>
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index d6c2a611e0..5a6e224b3d 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -10,7 +10,6 @@
 #include <complex>
 #include <algorithm>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <set.hpp>
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 0345e37485..74f2f66e28 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 5ece9bf65e..189874b2dd 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <sobel.hpp>
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index a6d410757b..9d194c403a 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <transpose.hpp>
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index eaad1b9f86..8a392ea5c0 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <triangle.hpp>
 #include <math.hpp>
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index 68cef5a440..53978a1403 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <af/constants.h>
 #include <cmath>
 #include <algorithm>
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 249327163d..e0ac683a92 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index ff88335072..5a39fcdf1c 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/assign.cu b/src/backend/cuda/assign.cu
index 7d00b15c5f..89dd5d2dde 100644
--- a/src/backend/cuda/assign.cu
+++ b/src/backend/cuda/assign.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/bilateral.cu b/src/backend/cuda/bilateral.cu
index bdb19fdef5..2d5a219887 100644
--- a/src/backend/cuda/bilateral.cu
+++ b/src/backend/cuda/bilateral.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <bilateral.hpp>
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index 816e024cb1..ff43715495 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 0b9bd81e25..a03f7f6344 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <complex>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 92c7867fbd..e67806fceb 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -6,7 +6,6 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 5f2e57c07b..45d4dd09c6 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index c5a9b73b7c..7164d63635 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <cuda_runtime_api.h>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <kernel/memcopy.hpp>
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cu
index 288f952db9..6b6736f3bc 100644
--- a/src/backend/cuda/diagonal.cu
+++ b/src/backend/cuda/diagonal.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <diagonal.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index 53741e3bf5..037e7f4467 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/cuda/fast_pyramid.cu b/src/backend/cuda/fast_pyramid.cu
index 1e1b047d2d..269e2bf194 100644
--- a/src/backend/cuda/fast_pyramid.cu
+++ b/src/backend/cuda/fast_pyramid.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 29f85f96c2..435e7c87c1 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cu
index 57fcb1071d..1ff357492d 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fftconvolve.hpp>
diff --git a/src/backend/cuda/harris.cu b/src/backend/cuda/harris.cu
index 2a5f2729e0..a6c36659de 100644
--- a/src/backend/cuda/harris.cu
+++ b/src/backend/cuda/harris.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/cuda/histogram.cu b/src/backend/cuda/histogram.cu
index d17d390cdf..95f137f640 100644
--- a/src/backend/cuda/histogram.cu
+++ b/src/backend/cuda/histogram.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index e522e814f2..62e8fe2d06 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/hsv_rgb.cu b/src/backend/cuda/hsv_rgb.cu
index f2e4f3f84d..e50ed8d5cc 100644
--- a/src/backend/cuda/hsv_rgb.cu
+++ b/src/backend/cuda/hsv_rgb.cu
@@ -8,7 +8,6 @@
 ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <hsv_rgb.hpp>
diff --git a/src/backend/cuda/identity.cu b/src/backend/cuda/identity.cu
index b6bd3f0f8a..a47bdc2c9c 100644
--- a/src/backend/cuda/identity.cu
+++ b/src/backend/cuda/identity.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <identity.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/iir.cu b/src/backend/cuda/iir.cu
index 22b889a33c..072ea8a163 100644
--- a/src/backend/cuda/iir.cu
+++ b/src/backend/cuda/iir.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cu
index b1d528c4da..3750d47ac0 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/ireduce.cu b/src/backend/cuda/ireduce.cu
index dece64c8af..d9735920ad 100644
--- a/src/backend/cuda/ireduce.cu
+++ b/src/backend/cuda/ireduce.cu
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 5e792a5805..b807c5807a 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 566f1cceaa..e8f6fb41da 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 468ae2bf51..c01453c00c 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 47e0267d62..521a45cd1e 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/convolve_separable.cu b/src/backend/cuda/kernel/convolve_separable.cu
index 654ec09fbc..1b34e64043 100644
--- a/src/backend/cuda/kernel/convolve_separable.cu
+++ b/src/backend/cuda/kernel/convolve_separable.cu
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 6d6b0e0992..c50a3a1a3c 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index d2e5903788..06c7767b80 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 186fb117de..8684c1e643 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 9361b72e23..4935c8e92a 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/constants.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 5d0b1e375e..a0a6c5cf91 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 68b2f71ce5..b414825d9d 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 8e8dd04b85..7d7b8a9358 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index 1916ea3225..867f812342 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 66e8d188ed..10a32313e2 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 7aaeb248cd..4531d66440 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 3936c8dd00..a325ba3cc8 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 675ef6c812..5e6cf5cadb 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index fef61287e6..dde8318b7f 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index ac614a334f..9b2e39f0ae 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <platform.hpp>
 #include <backend.hpp>
 #include <dispatch.hpp>
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index dc437b4142..2dda550e58 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <backend.hpp>
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index be99a68c4f..f92ed01c26 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 9b14cb5da6..e9f7e9553c 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 8a2b535cee..f116c5962b 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 118ba4e87c..e95bc4e2b0 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 87fa78c808..f5d75c4934 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index af90de0f9c..2c9fd1ced2 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 4c63942a0f..c627d128df 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index bcc8ac0566..a8ed251f2f 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -70,7 +70,6 @@
 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-#include <af/defines.h>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index f8c9e986f6..6a5bb7c861 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index c035e3ae07..cf1ff52541 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/sort_by_key.hpp>
 #include <af/dim4.hpp>
 #include <memory.hpp>
 #include <math.hpp>
@@ -15,6 +14,7 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <kernel/iota.hpp>
 
 #include <thrust/device_ptr.h>
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 30b40baf89..5c11e35367 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <Param.hpp>
 #include <dispatch.hpp>
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index d8dfb7dfb1..b69fc16ed3 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index f004fc4491..cfff48c271 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 746e2b82ac..d0644627dc 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/logic.hpp b/src/backend/cuda/logic.hpp
index a1447ae731..8261a02d73 100644
--- a/src/backend/cuda/logic.hpp
+++ b/src/backend/cuda/logic.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/match_template.cu b/src/backend/cuda/match_template.cu
index 0ce0ce20e2..bed4a25adb 100644
--- a/src/backend/cuda/match_template.cu
+++ b/src/backend/cuda/match_template.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index ad7563f672..44ae45b98c 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <defines.hpp>
 #include <limits>
 #include <algorithm>
diff --git a/src/backend/cuda/meanshift.cu b/src/backend/cuda/meanshift.cu
index 2e6dcfcc57..90b3cd7fa1 100644
--- a/src/backend/cuda/meanshift.cu
+++ b/src/backend/cuda/meanshift.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <meanshift.hpp>
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cu
index c87aea4dbe..1b8e165d27 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 80478c13dc..25ce2a0203 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 
-#include <af/defines.h>
+#include <cstdlib>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index 5a02fad70d..cb231dc551 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <morph.hpp>
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index ea517d0121..6ea8fcb215 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <morph.hpp>
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index 789c0f5b12..0874a3416b 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index b29c7affe7..af0dd849fc 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index b1899f44a0..c8220f5bbe 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 
diff --git a/src/backend/cuda/regions.cu b/src/backend/cuda/regions.cu
index 6b50b71477..0848fe0147 100644
--- a/src/backend/cuda/regions.cu
+++ b/src/backend/cuda/regions.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <regions.hpp>
diff --git a/src/backend/cuda/scan.cu b/src/backend/cuda/scan.cu
index 15ee6b4c93..5dd84c4358 100644
--- a/src/backend/cuda/scan.cu
+++ b/src/backend/cuda/scan.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index 4629b8b3dc..47617e5cd8 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <set.hpp>
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index ad668af924..40480cd17f 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/cuda/sobel.cu b/src/backend/cuda/sobel.cu
index ab5a69370d..dff886cf10 100644
--- a/src/backend/cuda/sobel.cu
+++ b/src/backend/cuda/sobel.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <sobel.hpp>
diff --git a/src/backend/cuda/where.cu b/src/backend/cuda/where.cu
index a43e339cdd..d9cbec548b 100644
--- a/src/backend/cuda/where.cu
+++ b/src/backend/cuda/where.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/lapacke.hpp b/src/backend/lapacke.hpp
index a9e885c0f7..e13c6c113e 100644
--- a/src/backend/lapacke.hpp
+++ b/src/backend/lapacke.hpp
@@ -8,7 +8,6 @@
 ********************************************************/
 
 #if defined(__APPLE__)
-#include <af/defines.h>
 #include <types.hpp>
 #include <backend.hpp>
 
diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index 552513aaf2..f482d30e8e 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <Param.hpp>
 #include <platform.hpp>
 #include <kernel/KParam.hpp>
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index c522d69281..1d80db80de 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 903b59b804..39d3deba34 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index c1a42ac8fc..a6c107d422 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <bilateral.hpp>
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index ecf8721946..f6676abeff 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
 #include <clBLAS.h>
 #include <mutex>
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index f99a86d38d..9fcf03d826 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <complex>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index 25eb5954d7..0838370c3c 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 18d719eff6..a55e206e11 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 6b52168e7b..8fa15cb976 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index aa5ed8c1bf..7221a1f015 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <kernel/memcopy.hpp>
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index f7f690322c..d4862f983b 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -10,7 +10,6 @@
 #ifndef AF_OPENCL_CPU
 #define AF_OPENCL_CPU
 
-#include <af/defines.h>
 #include <Array.hpp>
 #include <memory.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 45d8ce9867..70e80ec5a2 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <diagonal.hpp>
 #include <math.hpp>
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index 0813595144..46a5c342fc 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index b3cdfb5517..d11f9198f5 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index f824f75cae..7e205e57d2 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fftconvolve.hpp>
diff --git a/src/backend/opencl/harris.cpp b/src/backend/opencl/harris.cpp
index cc9a384cc9..86d2b3faf1 100644
--- a/src/backend/opencl/harris.cpp
+++ b/src/backend/opencl/harris.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index d7de9915fa..8a60cb336b 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 97c5d21c9d..eafd4941c2 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/identity.cpp b/src/backend/opencl/identity.cpp
index b8658b00b8..e94c25cb33 100644
--- a/src/backend/opencl/identity.cpp
+++ b/src/backend/opencl/identity.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <identity.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 300f06fe92..72819a1795 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 6502ee0f43..a8b68e0f97 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index e02c7e55d4..8304f0c33f 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
diff --git a/src/backend/opencl/kernel/KParam.hpp b/src/backend/opencl/kernel/KParam.hpp
index 820db70887..7a286878c1 100644
--- a/src/backend/opencl/kernel/KParam.hpp
+++ b/src/backend/opencl/kernel/KParam.hpp
@@ -9,10 +9,14 @@
 
 #ifndef __KPARAM_H
 #define __KPARAM_H
+
+#include <af/defines.h>
+
 typedef struct
 {
     dim_t dims[4];
     dim_t strides[4];
     dim_t offset;
 } KParam;
+
 #endif
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index ab884d42e4..3334567f0e 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <meanshift.hpp>
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 410dbb30af..ff1c8125af 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index a02d387591..72b259ad0b 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -9,7 +9,6 @@
 #pragma once
 
 #include <platform.hpp>
-#include <af/defines.h>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index 8cb7e2f5a2..e42300494d 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <math.hpp>
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
index b4c57d5d4e..8db16c54ca 100644
--- a/src/backend/opencl/morph_impl.hpp
+++ b/src/backend/opencl/morph_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <math.hpp>
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 58be9678ce..304daddb42 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/orb.cpp b/src/backend/opencl/orb.cpp
index 67aae9ec83..e0d96d765b 100644
--- a/src/backend/opencl/orb.cpp
+++ b/src/backend/opencl/orb.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index a6e8efb3d1..3f9266ea52 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <reduce.hpp>
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index 001a0002cf..b12e649a76 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <regions.hpp>
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 3ac929a537..48e3f67a02 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <scan.hpp>
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index c37b7c4c4e..5a68f1b6a3 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <set.hpp>
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index 632647ca19..3bf80eaf42 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <af/features.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
diff --git a/src/backend/opencl/sobel.cpp b/src/backend/opencl/sobel.cpp
index 7acb007156..52d329701b 100644
--- a/src/backend/opencl/sobel.cpp
+++ b/src/backend/opencl/sobel.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <sobel.hpp>
diff --git a/src/backend/opencl/where.cpp b/src/backend/opencl/where.cpp
index 19bc7cf1bc..69a291120e 100644
--- a/src/backend/opencl/where.cpp
+++ b/src/backend/opencl/where.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <af/defines.h>
 #include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>

From d599bf702bb9cb232eecf5038ecf81ff1b0463e0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 22 Apr 2016 18:09:43 -0400
Subject: [PATCH 0490/2677] CLEANUP unused ArrayInfo.hpp from backend

---
 src/backend/cpu/assign.cpp                | 1 -
 src/backend/cpu/bilateral.cpp             | 1 -
 src/backend/cpu/cast.hpp                  | 1 -
 src/backend/cpu/convolve.cpp              | 1 -
 src/backend/cpu/fast.cpp                  | 1 -
 src/backend/cpu/fft.cpp                   | 1 -
 src/backend/cpu/fftconvolve.cpp           | 1 -
 src/backend/cpu/harris.cpp                | 1 -
 src/backend/cpu/histogram.cpp             | 1 -
 src/backend/cpu/homography.cpp            | 1 -
 src/backend/cpu/hsv_rgb.cpp               | 1 -
 src/backend/cpu/iir.cpp                   | 1 -
 src/backend/cpu/index.cpp                 | 1 -
 src/backend/cpu/ireduce.cpp               | 1 -
 src/backend/cpu/match_template.cpp        | 1 -
 src/backend/cpu/meanshift.cpp             | 1 -
 src/backend/cpu/medfilt.cpp               | 1 -
 src/backend/cpu/morph.cpp                 | 1 -
 src/backend/cpu/nearest_neighbour.cpp     | 1 -
 src/backend/cpu/orb.cpp                   | 1 -
 src/backend/cpu/reduce.cpp                | 1 -
 src/backend/cpu/regions.cpp               | 1 -
 src/backend/cpu/scan.cpp                  | 1 -
 src/backend/cpu/select.cpp                | 1 -
 src/backend/cpu/set.cpp                   | 1 -
 src/backend/cpu/sift.cpp                  | 1 -
 src/backend/cpu/sobel.cpp                 | 1 -
 src/backend/cpu/transpose.cpp             | 1 -
 src/backend/cpu/where.cpp                 | 1 -
 src/backend/cuda/assign.cu                | 1 -
 src/backend/cuda/bilateral.cu             | 1 -
 src/backend/cuda/cast.hpp                 | 1 -
 src/backend/cuda/convolve.cpp             | 1 -
 src/backend/cuda/fast.cu                  | 1 -
 src/backend/cuda/fast_pyramid.cu          | 1 -
 src/backend/cuda/fft.cpp                  | 1 -
 src/backend/cuda/fftconvolve.cu           | 1 -
 src/backend/cuda/harris.cu                | 1 -
 src/backend/cuda/histogram.cu             | 1 -
 src/backend/cuda/homography.cu            | 1 -
 src/backend/cuda/hsv_rgb.cu               | 1 -
 src/backend/cuda/iir.cu                   | 1 -
 src/backend/cuda/index.cu                 | 1 -
 src/backend/cuda/ireduce.cu               | 1 -
 src/backend/cuda/match_template.cu        | 1 -
 src/backend/cuda/meanshift.cu             | 1 -
 src/backend/cuda/medfilt.cu               | 1 -
 src/backend/cuda/morph3d_impl.hpp         | 1 -
 src/backend/cuda/morph_impl.hpp           | 1 -
 src/backend/cuda/nearest_neighbour.cu     | 1 -
 src/backend/cuda/orb.cu                   | 1 -
 src/backend/cuda/reduce_impl.hpp          | 1 -
 src/backend/cuda/regions.cu               | 1 -
 src/backend/cuda/scan.cu                  | 1 -
 src/backend/cuda/select.cu                | 1 -
 src/backend/cuda/set.cu                   | 1 -
 src/backend/cuda/sift.cu                  | 1 -
 src/backend/cuda/sobel.cu                 | 1 -
 src/backend/cuda/where.cu                 | 1 -
 src/backend/opencl/assign.cpp             | 1 -
 src/backend/opencl/bilateral.cpp          | 1 -
 src/backend/opencl/cast.hpp               | 1 -
 src/backend/opencl/convolve.cpp           | 1 -
 src/backend/opencl/convolve_separable.cpp | 1 -
 src/backend/opencl/fast.cpp               | 1 -
 src/backend/opencl/fft.cpp                | 1 -
 src/backend/opencl/fftconvolve.cpp        | 1 -
 src/backend/opencl/harris.cpp             | 1 -
 src/backend/opencl/histogram.cpp          | 1 -
 src/backend/opencl/homography.cpp         | 1 -
 src/backend/opencl/hsv_rgb.cpp            | 1 -
 src/backend/opencl/iir.cpp                | 1 -
 src/backend/opencl/index.cpp              | 1 -
 src/backend/opencl/ireduce.cpp            | 1 -
 src/backend/opencl/match_template.cpp     | 1 -
 src/backend/opencl/meanshift.cpp          | 1 -
 src/backend/opencl/medfilt.cpp            | 1 -
 src/backend/opencl/morph3d_impl.hpp       | 1 -
 src/backend/opencl/morph_impl.hpp         | 1 -
 src/backend/opencl/nearest_neighbour.cpp  | 1 -
 src/backend/opencl/orb.cpp                | 1 -
 src/backend/opencl/reduce_impl.hpp        | 1 -
 src/backend/opencl/regions.cpp            | 1 -
 src/backend/opencl/scan.cpp               | 1 -
 src/backend/opencl/select.cpp             | 1 -
 src/backend/opencl/set.cpp                | 1 -
 src/backend/opencl/sift.cpp               | 1 -
 src/backend/opencl/sobel.cpp              | 1 -
 src/backend/opencl/where.cpp              | 1 -
 89 files changed, 89 deletions(-)

diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index 375f435502..d3a44e19df 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <kernel/assign.hpp>
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 58a671a2dc..35ceb6143a 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <kernel/bilateral.hpp>
 #include <bilateral.hpp>
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 2cb7f4cb50..83a2623801 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <complex>
 #include <err_cpu.hpp>
 #include <math.hpp>
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 64a8b3f19b..e3e2486cd6 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 6cd1c38e2a..2e0c3d1168 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 4fd38c7138..59ccee6246 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fft.hpp>
 #include <kernel/fft.hpp>
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 727713d9fa..ef5830409e 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <dispatch.hpp>
 #include <fft.hpp>
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index d78c008866..cf55395255 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <harris.hpp>
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index d2f1ea68e3..8c255163b1 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index dd7f5e8e04..16abd56ac0 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index 404491766c..5c572cd4a9 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <ArrayInfo.hpp>
 #include <hsv_rgb.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 0a2f3d1ae7..a835e8208d 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 8840b866e6..f70e961299 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <index.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 77f1749884..58259a382f 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 3786bd8c47..7e0457fee9 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <match_template.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index 651e93ba6c..6407616e92 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <meanshift.hpp>
 #include <cmath>
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 35fe31d70f..8c50a98263 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index fc0556b476..56143595fe 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <morph.hpp>
 #include <algorithm>
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 56b4b26939..257a541712 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index e3a74bc222..1279400d21 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <resize.hpp>
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 10292f8291..22c46d7d07 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <reduce.hpp>
 #include <ops.hpp>
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index e7c5abd65f..4886544623 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <regions.hpp>
 #include <err_cpu.hpp>
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 7d5875ee14..61600bc166 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <scan.hpp>
 #include <ops.hpp>
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 1545a81f46..982c1100a6 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <select.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 5a6e224b3d..7c970e787f 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -10,7 +10,6 @@
 #include <complex>
 #include <algorithm>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <set.hpp>
 #include <copy.hpp>
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 74f2f66e28..8c55f82047 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 189874b2dd..f01e670ef8 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <sobel.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 9d194c403a..2cfe936624 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <transpose.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index e0ac683a92..bd7427a921 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <memory.hpp>
 #include <where.hpp>
diff --git a/src/backend/cuda/assign.cu b/src/backend/cuda/assign.cu
index 89dd5d2dde..2806ea69ef 100644
--- a/src/backend/cuda/assign.cu
+++ b/src/backend/cuda/assign.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <assign.hpp>
diff --git a/src/backend/cuda/bilateral.cu b/src/backend/cuda/bilateral.cu
index 2d5a219887..bef64db1a3 100644
--- a/src/backend/cuda/bilateral.cu
+++ b/src/backend/cuda/bilateral.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <bilateral.hpp>
 #include <kernel/bilateral.hpp>
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index a03f7f6344..906620036e 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <complex>
 #include <err_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 45d4dd09c6..a96e358371 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
 #include <kernel/convolve.hpp>
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index 037e7f4467..263445a7f0 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/fast_pyramid.cu b/src/backend/cuda/fast_pyramid.cu
index 269e2bf194..3f29f761c5 100644
--- a/src/backend/cuda/fast_pyramid.cu
+++ b/src/backend/cuda/fast_pyramid.cu
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 435e7c87c1..c8fc020769 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <fft.hpp>
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cu
index 1ff357492d..74c8bb088d 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fftconvolve.hpp>
 #include <kernel/fftconvolve.hpp>
diff --git a/src/backend/cuda/harris.cu b/src/backend/cuda/harris.cu
index a6c36659de..2e05d5b843 100644
--- a/src/backend/cuda/harris.cu
+++ b/src/backend/cuda/harris.cu
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/histogram.cu b/src/backend/cuda/histogram.cu
index 95f137f640..e1630e3cea 100644
--- a/src/backend/cuda/histogram.cu
+++ b/src/backend/cuda/histogram.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index 62e8fe2d06..a32f5cbfa9 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/hsv_rgb.cu b/src/backend/cuda/hsv_rgb.cu
index e50ed8d5cc..9f693c9fa1 100644
--- a/src/backend/cuda/hsv_rgb.cu
+++ b/src/backend/cuda/hsv_rgb.cu
@@ -8,7 +8,6 @@
 ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <hsv_rgb.hpp>
 #include <kernel/hsv_rgb.hpp>
diff --git a/src/backend/cuda/iir.cu b/src/backend/cuda/iir.cu
index 072ea8a163..c03c15f4aa 100644
--- a/src/backend/cuda/iir.cu
+++ b/src/backend/cuda/iir.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cu
index 3750d47ac0..f2148840c7 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <index.hpp>
diff --git a/src/backend/cuda/ireduce.cu b/src/backend/cuda/ireduce.cu
index d9735920ad..945908c2ea 100644
--- a/src/backend/cuda/ireduce.cu
+++ b/src/backend/cuda/ireduce.cu
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
 
diff --git a/src/backend/cuda/match_template.cu b/src/backend/cuda/match_template.cu
index bed4a25adb..7307e1969a 100644
--- a/src/backend/cuda/match_template.cu
+++ b/src/backend/cuda/match_template.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <match_template.hpp>
diff --git a/src/backend/cuda/meanshift.cu b/src/backend/cuda/meanshift.cu
index 90b3cd7fa1..ad8d109839 100644
--- a/src/backend/cuda/meanshift.cu
+++ b/src/backend/cuda/meanshift.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <meanshift.hpp>
 #include <kernel/meanshift.hpp>
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cu
index 1b8e165d27..7f4d386177 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
 #include <kernel/medfilt.hpp>
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index cb231dc551..d5a19f7128 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <morph.hpp>
 #include <kernel/morph.hpp>
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index 6ea8fcb215..16437985bd 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <morph.hpp>
 #include <kernel/morph.hpp>
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index 0874a3416b..8c9b7c2280 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index af0dd849fc..76903ee79e 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index c8220f5bbe..6b400286f3 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 
 #undef _GLIBCXX_USE_INT128
diff --git a/src/backend/cuda/regions.cu b/src/backend/cuda/regions.cu
index 0848fe0147..1909cfdfef 100644
--- a/src/backend/cuda/regions.cu
+++ b/src/backend/cuda/regions.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <regions.hpp>
 #include <kernel/regions.hpp>
diff --git a/src/backend/cuda/scan.cu b/src/backend/cuda/scan.cu
index 5dd84c4358..48780cf00f 100644
--- a/src/backend/cuda/scan.cu
+++ b/src/backend/cuda/scan.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index 9697da4821..41741284e9 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -6,7 +6,6 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <select.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index 47617e5cd8..133537ffa3 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <set.hpp>
 #include <copy.hpp>
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index 40480cd17f..3734f7ba07 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
diff --git a/src/backend/cuda/sobel.cu b/src/backend/cuda/sobel.cu
index dff886cf10..a86d8e497f 100644
--- a/src/backend/cuda/sobel.cu
+++ b/src/backend/cuda/sobel.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <sobel.hpp>
 #include <kernel/sobel.hpp>
diff --git a/src/backend/cuda/where.cu b/src/backend/cuda/where.cu
index d9cbec548b..ed188e5fd7 100644
--- a/src/backend/cuda/where.cu
+++ b/src/backend/cuda/where.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 39d3deba34..998947514a 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <assign.hpp>
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index a6c107d422..37d1808695 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <bilateral.hpp>
 #include <kernel/bilateral.hpp>
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index 9fcf03d826..bddbd5ad34 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <complex>
 #include <err_opencl.hpp>
 #include <math.hpp>
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index a55e206e11..2ed8e27637 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
 #include <kernel/convolve.hpp>
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 8fa15cb976..162e93a289 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
 #include <kernel/convolve_separable.hpp>
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index 46a5c342fc..72b44ccb4f 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index d11f9198f5..bd337a43f0 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <fft.hpp>
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 7e205e57d2..827a9802e6 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <fftconvolve.hpp>
 #include <kernel/fftconvolve.hpp>
diff --git a/src/backend/opencl/harris.cpp b/src/backend/opencl/harris.cpp
index 86d2b3faf1..d13cc26e4e 100644
--- a/src/backend/opencl/harris.cpp
+++ b/src/backend/opencl/harris.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 8a60cb336b..7142228089 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index eafd4941c2..8d7b04dae6 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/hsv_rgb.cpp b/src/backend/opencl/hsv_rgb.cpp
index 1c840a4a64..41fc69c1d8 100644
--- a/src/backend/opencl/hsv_rgb.cpp
+++ b/src/backend/opencl/hsv_rgb.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <ArrayInfo.hpp>
 #include <hsv_rgb.hpp>
 #include <kernel/hsv_rgb.hpp>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 72819a1795..12d088fd2c 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <iir.hpp>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index a8b68e0f97..110f7e26b6 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
 #include <index.hpp>
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index 8304f0c33f..529bbe6b52 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
 #include <ops.hpp>
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index 3d0841025b..98b07c49c3 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/defines.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <match_template.hpp>
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index 3334567f0e..d028ba7f2c 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <meanshift.hpp>
 #include <kernel/meanshift.hpp>
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index ff1c8125af..2e561f44fc 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <medfilt.hpp>
 #include <kernel/medfilt.hpp>
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index e42300494d..77452bbb2f 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <math.hpp>
 #include <morph.hpp>
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
index 8db16c54ca..00e079185d 100644
--- a/src/backend/opencl/morph_impl.hpp
+++ b/src/backend/opencl/morph_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <math.hpp>
 #include <morph.hpp>
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 304daddb42..7a994b03fc 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/orb.cpp b/src/backend/opencl/orb.cpp
index e0d96d765b..241158b608 100644
--- a/src/backend/opencl/orb.cpp
+++ b/src/backend/opencl/orb.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index 3f9266ea52..12148cc240 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -9,7 +9,6 @@
 
 #include <complex>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <reduce.hpp>
 #include <kernel/reduce.hpp>
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index b12e649a76..1583e29449 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <regions.hpp>
 #include <kernel/regions.hpp>
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 48e3f67a02..967a5377d5 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <scan.hpp>
 #include <complex>
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 7e7200167b..333e8fa0e8 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -6,7 +6,6 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <select.hpp>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index 5a68f1b6a3..9d4f2aa999 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <set.hpp>
 #include <copy.hpp>
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index 3bf80eaf42..d2f02375e0 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <af/features.h>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
diff --git a/src/backend/opencl/sobel.cpp b/src/backend/opencl/sobel.cpp
index 52d329701b..b8ac4d710d 100644
--- a/src/backend/opencl/sobel.cpp
+++ b/src/backend/opencl/sobel.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <sobel.hpp>
 #include <kernel/sobel.hpp>
diff --git a/src/backend/opencl/where.cpp b/src/backend/opencl/where.cpp
index 69a291120e..35d067aec6 100644
--- a/src/backend/opencl/where.cpp
+++ b/src/backend/opencl/where.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <where.hpp>

From 92086d25d4d2eae5a7a014f13e6b758cac5390d3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 29 Apr 2016 17:23:45 -0400
Subject: [PATCH 0491/2677] Fix bug in array op for seq when start and end are
 floating

---
 src/api/cpp/seq.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/cpp/seq.cpp b/src/api/cpp/seq.cpp
index dff2e39c8b..56160f9299 100644
--- a/src/api/cpp/seq.cpp
+++ b/src/api/cpp/seq.cpp
@@ -83,7 +83,7 @@ seq::seq(seq other, bool is_gfor)
 
 seq::operator array() const
 {
-    dim_t diff = s.end - s.begin;
+    double diff = s.end - s.begin;
     dim_t len = (int)((diff + fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) / s.step);
 
     array tmp = (m_gfor) ? range(1, 1, 1, len, 3) : range(len);

From 06da6f179faf122fef03303e26595a84c8e03a7e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 30 Apr 2016 12:25:33 -0400
Subject: [PATCH 0492/2677] Add CMake regenerate sort_by_key items when header
 modified

---
 src/backend/cpu/kernel/sort_by_key/CMakeLists.txt    | 3 ++-
 src/backend/cuda/kernel/sort_by_key/CMakeLists.txt   | 4 ++++
 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt | 3 ++-
 3 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
index 017bb90bb6..53287de9c4 100644
--- a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -9,7 +9,8 @@ ENDFOREACH()
 
 FOREACH(SBK_TYPE ${SBK_TYPES})
     ADD_LIBRARY(cpu_sort_by_key_${SBK_TYPE} OBJECT
-        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp")
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
     SET_TARGET_PROPERTIES(cpu_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
     LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:cpu_sort_by_key_${SBK_TYPE}>)
 ENDFOREACH(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
index a9143f282d..61529754c4 100644
--- a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
@@ -18,6 +18,10 @@ FOREACH(SBK_TYPE ${SBK_TYPES})
         FOREACH(SBK_INST ${SBK_INSTS})
             CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in"
             "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu")
+            ADD_CUSTOM_COMMAND(
+              OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu"
+              COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu"
+              DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
         ENDFOREACH(SBK_INST ${SBK_INSTS})
     ENDFOREACH(SBK_DIR ${SBK_DIRS})
 ENDFOREACH(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 760fe6b634..0ea404baf9 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -9,7 +9,8 @@ ENDFOREACH()
 
 FOREACH(SBK_TYPE ${SBK_TYPES})
     ADD_LIBRARY(opencl_sort_by_key_${SBK_TYPE} OBJECT
-        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp")
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
     ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets})
     IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
         ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} forge)

From 7299b5d064738ec00f069765eb82501ba11ce6c0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 30 Apr 2016 13:24:02 -0400
Subject: [PATCH 0493/2677] Fix header file in KParam causing kernel
 compilation failure

---
 src/backend/opencl/Param.cpp         | 1 +
 src/backend/opencl/kernel/KParam.hpp | 2 --
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index f482d30e8e..552513aaf2 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/defines.h>
 #include <Param.hpp>
 #include <platform.hpp>
 #include <kernel/KParam.hpp>
diff --git a/src/backend/opencl/kernel/KParam.hpp b/src/backend/opencl/kernel/KParam.hpp
index 7a286878c1..6ca6aa4c97 100644
--- a/src/backend/opencl/kernel/KParam.hpp
+++ b/src/backend/opencl/kernel/KParam.hpp
@@ -10,8 +10,6 @@
 #ifndef __KPARAM_H
 #define __KPARAM_H
 
-#include <af/defines.h>
-
 typedef struct
 {
     dim_t dims[4];

From 225f6ab2e07fae70427e3f9f58cf36e9608e4b43 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 30 Apr 2016 13:24:30 -0400
Subject: [PATCH 0494/2677] Remove sortByKeyBatched template param for dim

---
 src/backend/cpu/kernel/sort_by_key.hpp        |  4 ++--
 src/backend/cpu/kernel/sort_by_key_impl.hpp   | 17 +++++++----------
 src/backend/cpu/sort_by_key.cpp               |  6 +++---
 src/backend/cpu/sort_index.cpp                |  6 +++---
 src/backend/cuda/kernel/sort_by_key.hpp       |  4 ++--
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 19 ++++++++-----------
 src/backend/cuda/sort_by_key.cu               |  6 +++---
 src/backend/cuda/sort_index.cu                |  6 +++---
 src/backend/opencl/kernel/sort_by_key.hpp     |  4 ++--
 .../opencl/kernel/sort_by_key_impl.hpp        | 17 +++++++----------
 src/backend/opencl/program.cpp                |  2 +-
 src/backend/opencl/sort_by_key.cpp            |  6 +++---
 src/backend/opencl/sort_index.cpp             |  6 +++---
 13 files changed, 47 insertions(+), 56 deletions(-)

diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 55d5a89337..dc8a543430 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -19,8 +19,8 @@ namespace kernel
 template<typename Tk, typename Tv, bool isAscending>
 void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval);
 
-template<typename Tk, typename Tv, bool isAscending, int dim>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval);
+template<typename Tk, typename Tv, bool isAscending>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim);
 
 template<typename Tk, typename Tv, bool isAscending>
 void sort0ByKey(Array<Tk> okey, Array<Tv> oval);
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index 220295f2a2..dde75b50bf 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -70,8 +70,8 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
     return;
 }
 
-template<typename Tk, typename Tv, bool isAscending, int dim>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
+template<typename Tk, typename Tv, bool isAscending>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim)
 {
     af::dim4 inDims = okey.dims();
 
@@ -141,18 +141,15 @@ void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
     int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
     // TODO Make a better heurisitic
     if(higherDims > 4)
-        kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+        kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 0);
     else
         kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
 }
 
-#define INSTANTIATE(Tk, Tv, dr)                                                         \
-    template void sort0ByKey<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);               \
-    template void sort0ByKeyIterative<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 0>(Array<Tk> okey, Array<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 1>(Array<Tk> okey, Array<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 2>(Array<Tk> okey, Array<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 3>(Array<Tk> okey, Array<Tv> oval);      \
+#define INSTANTIATE(Tk, Tv, dr)                                                                 \
+    template void sort0ByKey<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);                       \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);              \
+    template void sortByKeyBatched<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval, const int dim);
 
 #define INSTANTIATE1(Tk    , dr) \
     INSTANTIATE(Tk, float  , dr) \
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 46b06602b4..df0648d47f 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -31,9 +31,9 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
 
     switch(dim) {
         case 0: getQueue().enqueue(kernel::sort0ByKey<Tk, Tv, isAscending>, okey, oval); break;
-        case 1: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>, okey, oval); break;
-        case 2: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>, okey, oval); break;
-        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>, okey, oval); break;
+        case 1: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending>, okey, oval, 1); break;
+        case 2: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending>, okey, oval, 2); break;
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending>, okey, oval, 3); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index b865db9c1c..e8058a7233 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -34,9 +34,9 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uin
 
     switch(dim) {
         case 0: getQueue().enqueue(kernel::sort0ByKey<T, uint, isAscending>, okey, oval); break;
-        case 1: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 1>, okey, oval); break;
-        case 2: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 2>, okey, oval); break;
-        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 3>, okey, oval); break;
+        case 1: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending>, okey, oval, 1); break;
+        case 2: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending>, okey, oval, 2); break;
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending>, okey, oval, 3); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 35250a8ad1..082368ab22 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -20,8 +20,8 @@ namespace cuda
         template<typename Tk, typename Tv, bool isAscending>
         void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval);
 
-        template<typename Tk, typename Tv, bool isAscending, int dim>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal);
+        template<typename Tk, typename Tv, bool isAscending>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim);
 
         template<typename Tk, typename Tv, bool isAscending>
         void sort0ByKey(Param<Tk> okey, Param<Tv> oval);
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index cf1ff52541..20a4b50ffd 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -110,8 +110,8 @@ namespace cuda
             POST_LAUNCH_CHECK();
         }
 
-        template<typename Tk, typename Tv, bool isAscending, int dim>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal)
+        template<typename Tk, typename Tv, bool isAscending>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim)
         {
             af::dim4 inDims;
             for(int i = 0; i < 4; i++)
@@ -185,18 +185,15 @@ namespace cuda
             int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 4)
-                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+                kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 0);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
         }
 
-#define INSTANTIATE(Tk, Tv, dr)                                                         \
-    template void sort0ByKey<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);               \
-    template void sort0ByKeyIterative<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 0>(Param<Tk> okey, Param<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 1>(Param<Tk> okey, Param<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 2>(Param<Tk> okey, Param<Tv> oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 3>(Param<Tk> okey, Param<Tv> oval);      \
+#define INSTANTIATE(Tk, Tv, dr)                                                                 \
+    template void sort0ByKey<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);                       \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);              \
+    template void sortByKeyBatched<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval, const int dim);
 
 #define INSTANTIATE0(Tk    , dr) \
     INSTANTIATE(Tk, float  , dr) \
@@ -204,7 +201,7 @@ namespace cuda
     INSTANTIATE(Tk, cfloat , dr) \
     INSTANTIATE(Tk, cdouble, dr) \
     INSTANTIATE(Tk, char   , dr) \
-    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, uchar  , dr)
 
 #define INSTANTIATE1(Tk    , dr) \
     INSTANTIATE(Tk, int    , dr) \
diff --git a/src/backend/cuda/sort_by_key.cu b/src/backend/cuda/sort_by_key.cu
index 2d5d68eef0..be5557c939 100644
--- a/src/backend/cuda/sort_by_key.cu
+++ b/src/backend/cuda/sort_by_key.cu
@@ -27,9 +27,9 @@ namespace cuda
 
         switch(dim) {
             case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
-            case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>(okey, oval); break;
-            case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>(okey, oval); break;
-            case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>(okey, oval); break;
+            case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 1); break;
+            case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 2); break;
+            case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 3); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 03c69ad4f3..02485b9e1e 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -28,9 +28,9 @@ namespace cuda
 
         switch(dim) {
             case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
-            case 1: kernel::sortByKeyBatched<T, uint, isAscending, 1>(okey, oval); break;
-            case 2: kernel::sortByKeyBatched<T, uint, isAscending, 2>(okey, oval); break;
-            case 3: kernel::sortByKeyBatched<T, uint, isAscending, 3>(okey, oval); break;
+            case 1: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 1); break;
+            case 2: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 2); break;
+            case 3: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 3); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 224f6411ff..3aff6fb4e9 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -20,8 +20,8 @@ namespace opencl
         template<typename Tk, typename Tv, bool isAscending>
         void sort0ByKeyIterative(Param pKey, Param pVal);
 
-        template<typename Tk_, typename Tv_, bool isAscending, int dim>
-        void sortByKeyBatched(Param pKey, Param pVal);
+        template<typename Tk_, typename Tv_, bool isAscending>
+        void sortByKeyBatched(Param pKey, Param pVal, const int dim);
 
         template<typename Tk, typename Tv, bool isAscending>
         void sort0ByKey(Param pKey, Param pVal);
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 243034541f..f9b16ac0c4 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -232,8 +232,8 @@ namespace opencl
             }
         }
 
-        template<typename Tk_, typename Tv_, bool isAscending, int dim>
-        void sortByKeyBatched(Param pKey, Param pVal)
+        template<typename Tk_, typename Tv_, bool isAscending>
+        void sortByKeyBatched(Param pKey, Param pVal, const int dim)
         {
             typedef type_t<Tk_> Tk;
             typedef type_t<Tv_> Tv;
@@ -341,18 +341,15 @@ namespace opencl
             int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 5)
-                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
+                kernel::sortByKeyBatched<Tk, Tv, isAscending>(pKey, pVal, 0);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
         }
 
-#define INSTANTIATE(Tk, Tv, dr)                                                 \
-    template void sort0ByKey<Tk, Tv, dr>(Param okey, Param oval);               \
-    template void sort0ByKeyIterative<Tk, Tv, dr>(Param okey, Param oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 0>(Param okey, Param oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 1>(Param okey, Param oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 2>(Param okey, Param oval);      \
-    template void sortByKeyBatched<Tk, Tv, dr, 3>(Param okey, Param oval);      \
+#define INSTANTIATE(Tk, Tv, dr)                                                         \
+    template void sort0ByKey<Tk, Tv, dr>(Param okey, Param oval);                       \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Param okey, Param oval);              \
+    template void sortByKeyBatched<Tk, Tv, dr>(Param okey, Param oval, const int dim);
 
 #define INSTANTIATE1(Tk    , dr) \
     INSTANTIATE(Tk, float  , dr) \
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 36a8972f80..6b49730708 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -55,7 +55,7 @@ namespace opencl
             prog.build(targetDevices, (defaults + options).c_str());
 
         } catch (...) {
-            SHOW_BUILD_INFO(prog);
+            SHOW_DEBUG_BUILD_INFO(prog);
             throw;
         }
     }
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
index 27c2dc2462..53809c92e7 100644
--- a/src/backend/opencl/sort_by_key.cpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -28,9 +28,9 @@ namespace opencl
 
             switch(dim) {
                 case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
-                case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>(okey, oval); break;
-                case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>(okey, oval); break;
-                case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>(okey, oval); break;
+                case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 1); break;
+                case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 2); break;
+                case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 3); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index bb5474909d..bf4c031027 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -30,9 +30,9 @@ namespace opencl
 
             switch(dim) {
                 case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
-                case 1: kernel::sortByKeyBatched<T, uint, isAscending, 1>(okey, oval); break;
-                case 2: kernel::sortByKeyBatched<T, uint, isAscending, 2>(okey, oval); break;
-                case 3: kernel::sortByKeyBatched<T, uint, isAscending, 3>(okey, oval); break;
+                case 1: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 1); break;
+                case 2: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 2); break;
+                case 3: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 3); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 

From fff212468fe94ce789accdb6a38233b1d1dcc68c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 2 May 2016 11:06:04 -0400
Subject: [PATCH 0495/2677] Add NVVM path suffix for Windows

---
 examples/CMakeLists.txt | 2 +-
 test/CMakeLists.txt     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 9418bf056b..576c96c18b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -104,7 +104,7 @@ IF (${CUDA_FOUND})
         # Find NVVM
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
           NAMES "nvvm"
-          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
+          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib" "nvvm/lib/x64"
           PATHS ${CUDA_TOOLKIT_ROOT_DIR}
           DOC "CUDA NVVM Library"
           )
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index e07e8138cf..0f71176ffb 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -196,7 +196,7 @@ IF (${CUDA_FOUND})
         # Find NVVM
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
           NAMES "nvvm"
-          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
+          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib" "nmmv/lib/x64"
           PATHS ${CUDA_TOOLKIT_ROOT_DIR}
           DOC "CUDA NVVM Library"
           )

From 5ec56123f1fb10c996dff007b4f388cc6192f14e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 2 May 2016 13:14:08 -0400
Subject: [PATCH 0496/2677] Add missing CPP instantiations for alloc, pinned,
 free

---
 src/api/cpp/device.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index faf0b0e7dd..66ad4ef5c9 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -129,6 +129,8 @@ namespace af
         case c64: return sizeof(double) * 2;
         case s16: return sizeof(short);
         case u16: return sizeof(unsigned short);
+        case s64: return sizeof(intl);
+        case u64: return sizeof(uintl);
         default: return sizeof(float);
         }
     }
@@ -229,5 +231,7 @@ namespace af
     INSTANTIATE(char)
     INSTANTIATE(short)
     INSTANTIATE(unsigned short)
+    INSTANTIATE(intl)
+    INSTANTIATE(uintl)
 
 }

From 2679611375282b7109a09c89c8cfd885b61d233d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 2 May 2016 13:14:50 -0400
Subject: [PATCH 0497/2677] Memory Manager memAlloc - set AF_LOCK as not
 USER_LOCK

---
 src/backend/MemoryManager.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 83f2de1d8d..d5be32f9ae 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -189,7 +189,7 @@ void *MemoryManager::alloc(const size_t bytes, bool user_lock)
         }
 
 
-        locked_info info = {true, user_lock, alloc_bytes};
+        locked_info info = {!user_lock, user_lock, alloc_bytes};
         current.locked_map[ptr] = info;
         current.lock_bytes += alloc_bytes;
         current.lock_buffers++;

From 1504dad597494162811f14c3eee43289f8bc749d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 2 May 2016 13:15:12 -0400
Subject: [PATCH 0498/2677] Add memory tests to check allocation of all types

---
 test/memory.cpp | 155 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 155 insertions(+)

diff --git a/test/memory.cpp b/test/memory.cpp
index 57f81241ec..b13948303f 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -20,6 +20,8 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::cfloat;
+using af::cdouble;
 
 const size_t step_bytes = 1024;
 
@@ -74,6 +76,159 @@ TEST(Memory, Scope)
     ASSERT_EQ(lock_bytes, 0u);
 }
 
+template<typename T>
+class MemAlloc: public ::testing::Test
+{
+    public:
+        virtual void SetUp() { }
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, double, cfloat, cdouble,
+                         int, unsigned int, intl, uintl,
+                         char, unsigned char, short, ushort
+                        > TestTypes;
+
+// register the type list
+TYPED_TEST_CASE(MemAlloc, TestTypes);
+
+size_t roundUpToStep(size_t bytes)
+{
+    if (step_bytes == 0)
+        return bytes;
+
+    size_t remainder = bytes % step_bytes;
+    if (remainder == 0)
+        return bytes;
+
+    return bytes + step_bytes - remainder;
+}
+
+template<typename T>
+void memAllocArrayScopeTest(int elements)
+{
+    if (noDoubleTests<T>()) return;
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    cleanSlate(); // Clean up everything done so far
+
+    {
+        af::array a = af::randu(elements, (af_dtype)af::dtype_traits<T>::af_type);
+
+        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                          &lock_bytes, &lock_buffers);
+
+        ASSERT_EQ(alloc_buffers, 1u);
+        ASSERT_EQ(lock_buffers, 1u);
+
+        ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
+        ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
+    }
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(alloc_buffers, 1u);
+    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+
+    ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
+    ASSERT_EQ(lock_bytes, 0u);
+}
+
+template<typename T>
+void memAllocPtrScopeTest(int elements)
+{
+    if (noDoubleTests<T>()) return;
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    cleanSlate(); // Clean up everything done so far
+
+    {
+        T *ptr = af::alloc<T>(elements);
+
+        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                          &lock_bytes, &lock_buffers);
+
+        ASSERT_EQ(alloc_buffers, 1u);
+        ASSERT_EQ(lock_buffers, 1u);
+
+        ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
+        ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
+
+        af::free(ptr);
+    }
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(alloc_buffers, 1u);
+    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+
+    ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
+    ASSERT_EQ(lock_bytes, 0u);
+
+    // Do without using templated alloc
+    cleanSlate(); // Clean up everything done so far
+
+    {
+        void *ptr = af::alloc(elements, (af_dtype)af::dtype_traits<T>::af_type);
+
+        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                          &lock_bytes, &lock_buffers);
+
+        ASSERT_EQ(alloc_buffers, 1u);
+        ASSERT_EQ(lock_buffers, 1u);
+
+        ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
+        ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
+
+        af::free(ptr);
+    }
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(alloc_buffers, 1u);
+    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+
+    ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
+    ASSERT_EQ(lock_bytes, 0u);
+}
+
+TYPED_TEST(MemAlloc, ArrayScope25)
+{
+    memAllocArrayScopeTest<TypeParam>(25);
+}
+
+TYPED_TEST(MemAlloc, ArrayScope2048)
+{
+    memAllocArrayScopeTest<TypeParam>(2048);
+}
+
+TYPED_TEST(MemAlloc, ArrayScope2293)
+{
+    memAllocArrayScopeTest<TypeParam>(2293);
+}
+
+TYPED_TEST(MemAlloc, PtrScope25)
+{
+    memAllocPtrScopeTest<TypeParam>(25);
+}
+
+TYPED_TEST(MemAlloc, PtrScope2048)
+{
+    memAllocPtrScopeTest<TypeParam>(2048);
+}
+
+TYPED_TEST(MemAlloc, PtrScope2293)
+{
+    memAllocPtrScopeTest<TypeParam>(2293);
+}
+
 TEST(Memory, SingleSizeLoop)
 {
     size_t alloc_bytes, alloc_buffers;

From f676334b790aa40adc3ff06568e17613c7582a77 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 2 May 2016 17:19:04 -0400
Subject: [PATCH 0499/2677] PERF Remove new operator from CUDA transform kernel

---
 src/backend/cuda/kernel/transform.hpp   | 11 +++++------
 src/backend/opencl/kernel/transform.cl  | 12 +++++++++---
 src/backend/opencl/kernel/transform.hpp |  1 -
 3 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 599e62cf9d..0dddb1ae1c 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -69,7 +69,7 @@ namespace cuda
         __global__ static void
         transform_kernel(Param<T> out, CParam<T> in, const int nimages,
                          const int ntransforms, const int blocksXPerImage,
-                         const int transf_len, const bool perspective)
+                         const bool perspective)
         {
             // Compute which image set
             const int setId = blockIdx.x / blocksXPerImage;
@@ -99,8 +99,9 @@ namespace cuda
             const T *iptr = in.ptr  + setId * nimages * in.strides[2];
 
             // Transform is in constant memory.
+            const int transf_len = (perspective ? 9 : 6);
             const float *tmat_ptr = c_tmat + t_idx * transf_len;
-            float* tmat = new float[transf_len];
+            float tmat[9];
 
             // We expect a inverse transform matrix by default
             // If it is an forward transform, then we need its inverse
@@ -123,8 +124,6 @@ namespace cuda
                     transform_l(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
                 default: break;
             }
-
-            delete[] tmat;
         }
 
         ///////////////////////////////////////////////////////////////////////////
@@ -161,11 +160,11 @@ namespace cuda
             if(inverse) {
                 CUDA_LAUNCH((transform_kernel<T, true, method>), blocks, threads,
                                 out, in, nimages, ntransforms, blocksXPerImage,
-                                transf_len, perspective);
+                                perspective);
             } else {
                 CUDA_LAUNCH((transform_kernel<T, false, method>), blocks, threads,
                                 out, in, nimages, ntransforms, blocksXPerImage,
-                                transf_len, perspective);
+                                perspective);
             }
             POST_LAUNCH_CHECK();
         }
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index c44c18457a..b5be2977c0 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -79,14 +79,20 @@ void transform_kernel(__global T *d_out, const KParam out,
 
     // Transform is in global memory.
     // Needs offset to correct transform being processed.
-    __global const float *tmat_ptr = c_tmat + t_idx * TRANSF_LEN;
-    float tmat[TRANSF_LEN];
+#if PERSPECTIVE
+    const int transf_len = 9;
+    float tmat[9];
+#else
+    const int transf_len = 6;
+    float tmat[6];
+#endif
+    __global const float *tmat_ptr = c_tmat + t_idx * transf_len;
 
     // We expect a inverse transform matrix by default
     // If it is an forward transform, then we need its inverse
     if(INVERSE == 1) {
         #pragma unroll 3
-        for(int i = 0; i < TRANSF_LEN; i++)
+        for(int i = 0; i < transf_len; i++)
             tmat[i] = tmat_ptr[i];
     } else {
         calc_transf_inverse(tmat, tmat_ptr);
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index f78c7b0ebe..3334d9aa41 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -67,7 +67,6 @@ namespace opencl
                     options << " -D T="           << dtype_traits<T>::getName()
                             << " -D INVERSE="     << (isInverse ? 1 : 0)
                             << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
-                            << " -D TRANSF_LEN="  << (isPerspective ? 9 : 6)
                             << " -D ZERO="        << toNum(scalar<T>(0));
                     options << " -D VT="          << dtype_traits<vtype_t<T>>::getName();
                     options << " -D WT="          << dtype_traits<wtype_t<BT>>::getName();

From b2bdf3268563debfa803f0ed181abb3fcb71a5e8 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 3 May 2016 10:17:20 -0400
Subject: [PATCH 0500/2677] Fix for GCC > 5.3 errors for CUDA backend

---
 src/backend/cuda/CMakeLists.txt | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index d1727ffd40..5a6f588741 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -61,6 +61,12 @@ FOREACH(VER 20 30 32 35 37 50 52 53)
 ENDFOREACH()
 
 IF(UNIX)
+    # GCC 5.3 and above give errors for mempcy from <string.h>
+    # This is a (temporary) fix for that
+    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
+        ADD_DEFINITIONS(-D_FORCE_INLINES)
+    ENDIF()
+
     # Forcing STRICT ANSI should resolve a bunch of issues that NVIDIA seems to face with GCC compilers.
     ADD_DEFINITIONS(-D__STRICT_ANSI__)
     SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler -fvisibility=hidden)

From d29bb3a804d2540f73bfee0000762b8712436494 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 4 May 2016 16:05:52 -0400
Subject: [PATCH 0501/2677] Remove af/util.h header

---
 src/api/c/svd.cpp         | 1 -
 src/backend/ArrayInfo.hpp | 1 -
 2 files changed, 2 deletions(-)

diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index 80b11f730c..244579aefe 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -11,7 +11,6 @@
 #include <af/array.h>
 #include <af/lapack.h>
 
-#include <af/util.h>
 #include <af/defines.h>
 #include <err_common.hpp>
 #include <backend.hpp>
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index d4876ed524..d61dccf0af 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <defines.hpp>
-#include <af/util.h>
 #include <af/dim4.hpp>
 #include <af/device.h>
 #include <vector>

From d45a0c8f1cbc68f0efc39dace49ab59c017a5cbf Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 4 May 2016 16:06:13 -0400
Subject: [PATCH 0502/2677] Add a function to get size of af_dtype

---
 include/af/util.h       | 14 ++++++++++++++
 src/api/c/type_util.cpp | 31 +++++++++++++++++++++++++++++++
 src/api/c/type_util.hpp |  2 ++
 src/api/cpp/array.cpp   | 21 +--------------------
 src/api/cpp/device.cpp  | 20 +-------------------
 src/api/cpp/util.cpp    |  7 +++++++
 6 files changed, 56 insertions(+), 39 deletions(-)

diff --git a/include/af/util.h b/include/af/util.h
index eef46f47c9..75db59c605 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -129,6 +129,13 @@ namespace af
 
     // Purpose of Addition: "How to add Function" documentation
     AFAPI array exampleFunction(const array& in, const af_someenum_t param);
+
+#if AF_API_VERSION >= 34
+    ///
+    /// Get the size of the type represented by an af_dtype enum
+    ///
+    AFAPI size_t getSizeOf(af::dtype type);
+#endif
 }
 
 #if AF_API_VERSION >= 31
@@ -262,6 +269,13 @@ extern "C" {
     AFAPI const char *af_get_revision();
 #endif
 
+#if AF_API_VERSION >= 34
+    ///
+    /// Get the size of the type represented by an af_dtype enum
+    ///
+    AFAPI af_err af_get_size_of(size_t *size, af_dtype type);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index 39a9af60d7..5d2669adf5 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -7,7 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/util.h>
 #include <type_util.hpp>
+#include <err_common.hpp>
 
 const char *getName(af_dtype type)
 {
@@ -27,3 +29,32 @@ const char *getName(af_dtype type)
     default : return "unknown type";
     }
 }
+
+size_t size_of(af_dtype type)
+{
+    try {
+        switch(type) {
+            case f32: return sizeof(float);
+            case f64: return sizeof(double);
+            case s32: return sizeof(int);
+            case u32: return sizeof(unsigned);
+            case u8 : return sizeof(unsigned char);
+            case b8 : return sizeof(unsigned char);
+            case c32: return sizeof(float) * 2;
+            case c64: return sizeof(double) * 2;
+            case s16: return sizeof(short);
+            case u16: return sizeof(unsigned short);
+            case s64: return sizeof(intl);
+            case u64: return sizeof(uintl);
+            default : TYPE_ERROR(1, type);
+        }
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_get_size_of(size_t *size, af_dtype type)
+{
+    *size = size_of(type);
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/type_util.hpp b/src/api/c/type_util.hpp
index e4ecf8aebe..5fd37fd8fe 100644
--- a/src/api/c/type_util.hpp
+++ b/src/api/c/type_util.hpp
@@ -31,3 +31,5 @@ struct ToNum<char>
 {
     inline int operator()(char val) { return static_cast<int>(val); }
 };
+
+size_t size_of(af_dtype type);
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index f1d401f000..c32704b08a 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -71,25 +71,6 @@ namespace af
         }
     }
 
-    static unsigned size_of(af::dtype type)
-    {
-        switch(type) {
-        case f32: return sizeof(float);
-        case f64: return sizeof(double);
-        case s32: return sizeof(int);
-        case u32: return sizeof(unsigned);
-        case s64: return sizeof(intl);
-        case u64: return sizeof(uintl);
-        case u8 : return sizeof(unsigned char);
-        case b8 : return sizeof(unsigned char);
-        case c32: return sizeof(float) * 2;
-        case c64: return sizeof(double) * 2;
-        case s16: return sizeof(short);
-        case u16: return sizeof(unsigned short);
-        default: return sizeof(float);
-        }
-    }
-
     static unsigned numDims(const af_array arr)
     {
         unsigned nd;
@@ -282,7 +263,7 @@ namespace af
     {
         dim_t nElements;
         AF_THROW(af_get_elements(&nElements, get()));
-        return nElements * size_of(type());
+        return nElements * getSizeOf(type());
     }
 
     array array::copy() const
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 66ad4ef5c9..1b68c4b8ce 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -12,6 +12,7 @@
 #include <af/compatible.h>
 #include <af/traits.hpp>
 #include <af/backend.h>
+#include "type_util.hpp"
 #include "error.hpp"
 
 namespace af
@@ -116,25 +117,6 @@ namespace af
 
     ///////////////////////////////////////////////////////////////////////////
     // Alloc and free host, pinned, zero copy
-    static unsigned size_of(af::dtype type)
-    {
-        switch(type) {
-        case f32: return sizeof(float);
-        case f64: return sizeof(double);
-        case s32: return sizeof(int);
-        case u32: return sizeof(unsigned);
-        case u8 : return sizeof(unsigned char);
-        case b8 : return sizeof(unsigned char);
-        case c32: return sizeof(float) * 2;
-        case c64: return sizeof(double) * 2;
-        case s16: return sizeof(short);
-        case u16: return sizeof(unsigned short);
-        case s64: return sizeof(intl);
-        case u64: return sizeof(uintl);
-        default: return sizeof(float);
-        }
-    }
-
     void *alloc(const size_t elements, const af::dtype type)
     {
         void *ptr;
diff --git a/src/api/cpp/util.cpp b/src/api/cpp/util.cpp
index 895d347d92..b3fafad987 100644
--- a/src/api/cpp/util.cpp
+++ b/src/api/cpp/util.cpp
@@ -68,4 +68,11 @@ namespace af
         AF_THROW(af_array_to_string(&output, exp, arr.get(), precision, transpose));
         return output;
     }
+
+    size_t getSizeOf(af::dtype type)
+    {
+        size_t size = 0;
+        AF_THROW(af_get_size_of(&size, type));
+        return size;
+    }
 }

From e1f16e65f88d09f86d62b06ad26427ca8f27dc73 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 5 May 2016 16:26:52 -0400
Subject: [PATCH 0503/2677] Fixes for OpenCL graphics copy buffer on NVIDIA
 GPUs

---
 src/backend/opencl/hist_graphics.cpp | 14 ++++++++++----
 src/backend/opencl/image.cpp         | 14 ++++++++++----
 src/backend/opencl/plot.cpp          | 14 ++++++++++----
 src/backend/opencl/plot3.cpp         | 14 ++++++++++----
 src/backend/opencl/surface.cpp       | 14 ++++++++++----
 5 files changed, 50 insertions(+), 20 deletions(-)

diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 022bcf1aaf..d26fcc4e6e 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -34,10 +34,16 @@ void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
         shared_objects.push_back(*clPBOResource);
 
         glFinish();
-        getQueue().enqueueAcquireGLObjects(&shared_objects);
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, NULL);
-        getQueue().finish();
-        getQueue().enqueueReleaseGLObjects(&shared_objects);
+
+        // Use of events:
+        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+        cl::Event event;
+
+        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+        event.wait();
 
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index c758df4953..7f6b054739 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -35,10 +35,16 @@ void copy_image(const Array<T> &in, const fg::Image* image)
         shared_objects.push_back(*clPBOResource);
 
         glFinish();
-        getQueue().enqueueAcquireGLObjects(&shared_objects);
-        getQueue().enqueueCopyBuffer(*d_X, *clPBOResource, 0, 0, num_bytes, NULL, NULL);
-        getQueue().finish();
-        getQueue().enqueueReleaseGLObjects(&shared_objects);
+
+        // Use of events:
+        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+        cl::Event event;
+
+        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+        getQueue().enqueueCopyBuffer(*d_X, *clPBOResource, 0, 0, num_bytes, NULL, &event);
+        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+        event.wait();
 
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End opencl resource copy");
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 4eb240f3e9..ba7d49b8c0 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -39,10 +39,16 @@ void copy_plot(const Array<T> &P, fg::Plot* plot)
         shared_objects.push_back(*clPBOResource);
 
         glFinish();
-        getQueue().enqueueAcquireGLObjects(&shared_objects);
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, NULL);
-        getQueue().finish();
-        getQueue().enqueueReleaseGLObjects(&shared_objects);
+
+        // Use of events:
+        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+        cl::Event event;
+
+        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+        event.wait();
 
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
diff --git a/src/backend/opencl/plot3.cpp b/src/backend/opencl/plot3.cpp
index ce3355d63c..de6769cabf 100644
--- a/src/backend/opencl/plot3.cpp
+++ b/src/backend/opencl/plot3.cpp
@@ -36,10 +36,16 @@ void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
         shared_objects.push_back(*clPBOResource);
 
         glFinish();
-        getQueue().enqueueAcquireGLObjects(&shared_objects);
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, NULL);
-        getQueue().finish();
-        getQueue().enqueueReleaseGLObjects(&shared_objects);
+
+        // Use of events:
+        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+        cl::Event event;
+
+        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+        event.wait();
 
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 8116941a77..4bca24848b 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -39,10 +39,16 @@ void copy_surface(const Array<T> &P, fg::Surface* surface)
         shared_objects.push_back(*clPBOResource);
 
         glFinish();
-        getQueue().enqueueAcquireGLObjects(&shared_objects);
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, NULL);
-        getQueue().finish();
-        getQueue().enqueueReleaseGLObjects(&shared_objects);
+
+        // Use of events:
+        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+        cl::Event event;
+
+        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+        event.wait();
 
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");

From 61593821449efa84c7e8677d3515a868666a1955 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 4 May 2016 18:44:30 -0400
Subject: [PATCH 0504/2677] Remove verbose cmake messages. Better handle
 package arguments

---
 CMakeLists.txt                    |  1 -
 CMakeModules/FindCBLAS.cmake      |  7 +++++++
 CMakeModules/FindFFTW.cmake       |  2 +-
 CMakeModules/FindFreeImage.cmake  |  4 ++--
 CMakeModules/FindGLEWmx.cmake     | 10 +++++-----
 CMakeModules/FindLAPACKE.cmake    |  8 +++++++-
 CMakeModules/MinBuildTime.cmake   |  6 +-----
 examples/CMakeLists.txt           |  6 +++---
 src/backend/cpu/CMakeLists.txt    |  5 -----
 src/backend/cuda/CMakeLists.txt   |  6 ++++++
 src/backend/opencl/CMakeLists.txt |  4 ----
 test/CMakeLists.txt               | 12 ++++++------
 12 files changed, 38 insertions(+), 33 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0def888f6c..795df3edb4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -48,7 +48,6 @@ FIND_PACKAGE(FreeImage)
 IF(FREEIMAGE_FOUND)
     ADD_DEFINITIONS(-DWITH_FREEIMAGE)
     SET(FreeImage_LIBS ${FREEIMAGE_LIBRARY})
-    MESSAGE(STATUS "Using FreeImage library ${FreeImage_LIBS}")
     INCLUDE_DIRECTORIES(${FREEIMAGE_INCLUDE_PATH})
 ELSE(FREEIMAGE_FOUND)
     MESSAGE(WARNING, "FreeImage not found!")
diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index f64fd6f1b4..52fa44879b 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -50,6 +50,8 @@ SET(INTEL_MKL_ROOT_DIR CACHE STRING
 SET(CBLAS_ROOT_DIR CACHE STRING
   "Root directory for custom CBLAS implementation")
 
+MARK_AS_ADVANCED(INTEL_MKL_ROOT_DIR CBLAS_ROOT_DIR)
+
 INCLUDE(CheckTypeSize)
 CHECK_TYPE_SIZE("void*" SIZE_OF_VOIDP)
 
@@ -345,3 +347,8 @@ IF(NOT CBLAS_FIND_QUIETLY)
 ENDIF(NOT CBLAS_FIND_QUIETLY)
 
 ENDIF(PC_CBLAS_FOUND)
+
+MARK_AS_ADVANCED(
+    CBLAS_INCLUDE_DIR
+    CBLAS_INCLUDE_FILE
+    CBLAS_LIBRARIES)
diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index 3156cec89b..a7ce3e7826 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -110,4 +110,4 @@ INCLUDE(FindPackageHandleStandardArgs)
 FIND_PACKAGE_HANDLE_STANDARD_ARGS(FFTW DEFAULT_MSG
     FFTW_INCLUDES FFTW_LIBRARIES)
 
-MARK_AS_ADVANCED(FFTW_INCLUDES FFTW_LIBRARIES)
+MARK_AS_ADVANCED(FFTW_INCLUDES FFTW_LIBRARIES FFTW_LIB FFTWF_LIB)
diff --git a/CMakeModules/FindFreeImage.cmake b/CMakeModules/FindFreeImage.cmake
index 09fbcbfc3c..0b3651c947 100644
--- a/CMakeModules/FindFreeImage.cmake
+++ b/CMakeModules/FindFreeImage.cmake
@@ -50,11 +50,9 @@ UNSET(PX)
 UNSET(SX)
 
 IF(USE_FREEIMAGE_STATIC)
-  MESSAGE(STATUS "Using Static FreeImage Lib")
   ADD_DEFINITIONS(-DFREEIMAGE_LIB)
   SET(FREEIMAGE_LIBRARY ${FREEIMAGE_STATIC_LIBRARY})
 ELSE(USE_FREEIMAGE_STATIC)
-  MESSAGE(STATUS "Using Dynamic FreeImage Lib")
   REMOVE_DEFINITIONS(-DFREEIMAGE_LIB)
   SET(FREEIMAGE_LIBRARY ${FREEIMAGE_DYNAMIC_LIBRARY})
 ENDIF(USE_FREEIMAGE_STATIC)
@@ -62,6 +60,8 @@ ENDIF(USE_FREEIMAGE_STATIC)
 MARK_AS_ADVANCED(
     FREEIMAGE_DYNAMIC_LIBRARY
     FREEIMAGE_STATIC_LIBRARY
+    FREEIMAGE_LIBRARY
+    FREEIMAGE_INCLUDE_PATH
     )
 INCLUDE(FindPackageHandleStandardArgs)
 FIND_PACKAGE_HANDLE_STANDARD_ARGS(FREEIMAGE DEFAULT_MSG
diff --git a/CMakeModules/FindGLEWmx.cmake b/CMakeModules/FindGLEWmx.cmake
index a6da72bbf2..8231d33e15 100644
--- a/CMakeModules/FindGLEWmx.cmake
+++ b/CMakeModules/FindGLEWmx.cmake
@@ -77,15 +77,15 @@ ELSE (WIN32)
 ENDIF (WIN32)
 
 IF(USE_GLEWmx_STATIC)
-    MESSAGE(STATUS "Using Static GLEWmx Lib")
     ADD_DEFINITIONS(-DGLEW_STATIC)
     SET(GLEWmx_LIBRARY ${GLEWmxs_LIBRARY})
 ELSE(USE_GLEWmx_STATIC)
-    MESSAGE(STATUS "Using Dynamic GLEWmx Lib")
     REMOVE_DEFINITIONS(-DGLEW_STATIC)
     SET(GLEWmx_LIBRARY ${GLEWmxd_LIBRARY})
 ENDIF(USE_GLEWmx_STATIC)
 
-IF (GLEW_INCLUDE_DIR AND GLEWmx_LIBRARY)
-    SET(GLEWmx_FOUND "YES")
-ENDIF (GLEW_INCLUDE_DIR AND GLEWmx_LIBRARY)
+MARK_AS_ADVANCED(GLEWmxs_LIBRARY GLEWmxd_LIBRARY GLEWmx_LIBRARY GLEW_INCLUDE_DIR)
+
+INCLUDE(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(GLEWmx DEFAULT_MSG
+    GLEW_INCLUDE_DIR GLEWmx_LIBRARY)
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 9251ee93c0..5ecf7be55d 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -154,4 +154,10 @@ INCLUDE(FindPackageHandleStandardArgs)
 FIND_PACKAGE_HANDLE_STANDARD_ARGS(LAPACK DEFAULT_MSG
   LAPACK_INCLUDE_DIR LAPACK_LIBRARIES)
 
-MARK_AS_ADVANCED(LAPACK_INCLUDES LAPACK_LIBRARIES)
+MARK_AS_ADVANCED(
+  LAPACKE_ROOT_DIR
+  LAPACK_INCLUDES
+  LAPACK_LIBRARIES
+  LAPACK_LIB
+  LAPACKE_INCLUDES
+  LAPACKE_LIB)
diff --git a/CMakeModules/MinBuildTime.cmake b/CMakeModules/MinBuildTime.cmake
index dcd3d359a7..e48ab0263b 100644
--- a/CMakeModules/MinBuildTime.cmake
+++ b/CMakeModules/MinBuildTime.cmake
@@ -13,7 +13,7 @@ IF(${MIN_BUILD_TIME})
     # IF FLAG is ON, then the flags were already set, no need to set them again
     # IF FLAG is OFF, then the flags are not set, so set them now, and back up
     # release flags
-    MESSAGE(STATUS "Setting Release flags to no optimizations")
+    MESSAGE(STATUS "MIN_BUILD_TIME: Setting Release flags to no optimizations")
 
         # Backup Default Release Flags
         SET(CMAKE_CXX_FLAGS_RELEASE_DEFAULT ${CMAKE_CXX_FLAGS_RELEASE} CACHE
@@ -30,7 +30,6 @@ IF(${MIN_BUILD_TIME})
             INTERNAL "Default linker flags during release build" FORCE)
 
         IF(MSVC)
-            MESSAGE(STATUS "MSVC Flags")
             SET(CMAKE_CXX_FLAGS_RELEASE "/MD /Od /Ob1 /D NDEBUG" CACHE
                 STRING "Flags used by the compiler during release builds." FORCE)
             SET(CMAKE_C_FLAGS_RELEASE "/MD /Od /Ob1 /D NDEBUG" CACHE
@@ -44,7 +43,6 @@ IF(${MIN_BUILD_TIME})
             SET(CMAKE_SHARED_LINKER_FLAGS_RELEASE "/INCREMENTAL:NO" CACHE
                 STRING "Flags used by the linker during release builds." FORCE)
         ELSE(MSVC)
-            MESSAGE(STATUS "Other Flags")
             SET(CMAKE_CXX_FLAGS_RELEASE "-O0 -DNDEBUG" CACHE
                 STRING "Flags used by the compiler during release builds." FORCE)
             SET(CMAKE_C_FLAGS_RELEASE "-O0 -DNDEBUG" CACHE
@@ -62,8 +60,6 @@ IF(${MIN_BUILD_TIME})
         SET(MINBUILDTIME_FLAG ON CACHE INTERNAL "Flag" FORCE)
     ENDIF()
 ELSE()
-  MESSAGE(STATUS "MIN_BUILD_TIME IS OFF")
-
     # MIN_BUILD_TIME is OFF. Change the flags back only if the flag was set before
     IF(${MINBUILDTIME_FLAG})
         MESSAGE(STATUS "MIN_BUILD_FLAG was toggled. Resetting Release FLags")
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 576c96c18b..3aa609d796 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -102,13 +102,13 @@ ENDIF()
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
         # Find NVVM
-        FIND_LIBRARY( CUDA_NVVM_LIBRARY
+        FIND_LIBRARY(CUDA_nvvm_LIBRARY
           NAMES "nvvm"
           PATH_SUFFIXES "nvvm/lib64" "nvvm/lib" "nvvm/lib/x64"
           PATHS ${CUDA_TOOLKIT_ROOT_DIR}
           DOC "CUDA NVVM Library"
           )
-        MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+        MARK_AS_ADVANCED(CUDA_nvvm_LIBRARY)
 
         # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
         IF(NOT CUDA_CUDA_LIBRARY)
@@ -130,7 +130,7 @@ IF (${CUDA_FOUND})
         ENDIF(NOT CUDA_CUDA_LIBRARY)
 
         OPTION(BUILD_CUDA "Build ArrayFire Examples for CUDA backend" ON)
-        BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+        BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
         BUILD_ALL("${FILES}" cuda afcuda "")
     ELSE()
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 0863266c4f..7be5bda858 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -41,8 +41,6 @@ ENDIF()
 
 IF (NOT CBLAS_LIBRARIES)
     MESSAGE(SEND_ERROR "CBLAS Library not set")
-ELSE()
-    MESSAGE(STATUS "Using CBLAS Library: ${CBLAS_LIBRARIES}")
 ENDIF()
 
 IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU" AND "${APPLE}")
@@ -50,8 +48,6 @@ IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU" AND "${APPLE}")
 ENDIF()
 
 FIND_PACKAGE(FFTW REQUIRED)
-MESSAGE(STATUS "FFTW Found ? ${FFTW_FOUND}")
-MESSAGE(STATUS "FFTW Library: ${FFTW_LIBRARIES}")
 
 IF(APPLE)
     FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
@@ -71,7 +67,6 @@ IF(NOT LAPACK_FOUND)
     MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
 ELSE(NOT LAPACK_FOUND)
     ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
-    MESSAGE(STATUS "LAPACK libraries found: ${LAPACK_LIBRARIES}")
 ENDIF()
 
 IF(NOT UNIX)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 5a6f588741..94eb393a9f 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -6,6 +6,12 @@ FIND_PACKAGE(Boost REQUIRED)
 INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
 INCLUDE("${CMAKE_MODULE_PATH}/FindNVVM.cmake")
 
+MARK_AS_ADVANCED(
+    CUDA_BUILD_CUBIN
+    CUDA_BUILD_EMULATION
+    CUDA_SDK_ROOT_DIR
+    CUDA_VERBOSE_BUILD)
+
 # Disables running cuda_compute_check.c when build windows using remote
 OPTION(CUDA_COMPUTE_DETECT "Run autodetection of CUDA Architecture" ON)
 MARK_AS_ADVANCED(CUDA_COMPUTE_DETECT)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 9e4918a5ff..72372f98bc 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -6,7 +6,6 @@ FIND_PACKAGE(OpenCL REQUIRED)
 INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
 
 IF(USE_OPENCL_F77_BLAS)
-    MESSAGE("Using F77 BLAS")
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
 ENDIF()
 
@@ -33,14 +32,11 @@ ELSE(NOT LAPACK_FOUND)
     FIND_PACKAGE(CBLAS REQUIRED)
 
     IF(USE_CPU_F77_BLAS)
-      MESSAGE("Using F77 BLAS")
       ADD_DEFINITIONS(-DUSE_F77_BLAS)
     ENDIF()
 
     IF (NOT CBLAS_LIBRARIES)
       MESSAGE(SEND_ERROR "CBLAS Library not set")
-    ELSE()
-      MESSAGE(STATUS "Using CBLAS Library: ${CBLAS_LIBRARIES}")
     ENDIF()
   ENDIF()
 ENDIF()
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 0f71176ffb..863353dcbb 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -194,13 +194,13 @@ ENDIF()
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
         # Find NVVM
-        FIND_LIBRARY( CUDA_NVVM_LIBRARY
+        FIND_LIBRARY(CUDA_nvvm_LIBRARY
           NAMES "nvvm"
           PATH_SUFFIXES "nvvm/lib64" "nvvm/lib" "nmmv/lib/x64"
           PATHS ${CUDA_TOOLKIT_ROOT_DIR}
           DOC "CUDA NVVM Library"
           )
-        MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+        MARK_AS_ADVANCED(CUDA_nvvm_LIBRARY)
 
         # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
         IF(NOT CUDA_CUDA_LIBRARY)
@@ -224,7 +224,7 @@ IF (${CUDA_FOUND})
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
             OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
-            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
             FOREACH(FILE ${FILES})
                 GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -236,14 +236,14 @@ IF (${CUDA_FOUND})
         # ELSE OSX && CLANG && CUDA < 7
         ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
             OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
-            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
         ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
 
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
             FOREACH(FILE ${FILES})
                 GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -254,7 +254,7 @@ IF (${CUDA_FOUND})
 
         # ELSE OSX && CLANG && CUDA < 7
         ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
         ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
     ELSE()

From 9f5e030038007527f6f40d6a5715c9b83bddc7db Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 4 May 2016 18:45:10 -0400
Subject: [PATCH 0505/2677] Cleanup FindNVVM and cusolver - Updates when
 toolkit is updated

Before, if toolkit was updated, the path for NVVM and cusolver did not change.
This fixes it
---
 CMakeModules/FindNVVM.cmake     | 94 ++++++++++++---------------------
 src/backend/cuda/CMakeLists.txt | 29 ++++++----
 2 files changed, 52 insertions(+), 71 deletions(-)

diff --git a/CMakeModules/FindNVVM.cmake b/CMakeModules/FindNVVM.cmake
index 5ffe2d4f1d..2504eae99d 100644
--- a/CMakeModules/FindNVVM.cmake
+++ b/CMakeModules/FindNVVM.cmake
@@ -1,70 +1,42 @@
 # - Find the NVVM include directory and libraries
 # Modified version of the file found here:
 # https://raw.githubusercontent.com/nvidia-compiler-sdk/nvvmir-samples/master/CMakeLists.txt
+# CUDA_NVVM_FOUND
+# CUDA_NVVM_INCLUDE_DIR
+# CUDA_NVVM_LIBRARY
 
 # libNVVM
-if(NOT DEFINED ENV{LIBNVVM_HOME})
-  set(LIBNVVM_HOME "${CUDA_TOOLKIT_ROOT_DIR}/nvvm")
-else()
-  set(LIBNVVM_HOME "$ENV{LIBNVVM_HOME}")
-endif()
-message(STATUS "Using LIBNVVM_HOME: ${LIBNVVM_HOME}")
+IF(NOT DEFINED ENV{CUDA_NVVM_HOME})
+    # If the toolkit path was changed then refind the library
+    IF(NOT "${CUDA_NVVM_HOME}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR}/nvvm")
+        UNSET(CUDA_NVVM_HOME CACHE)
+        UNSET(CUDA_nvvm_INCLUDE_DIR CACHE)
+        UNSET(CUDA_nvvm_LIBRARY CACHE)
+        SET(CUDA_NVVM_HOME "${CUDA_TOOLKIT_ROOT_DIR}/nvvm" CACHE INTERNAL "CUDA NVVM Directory")
+    ENDIF()
+ELSE()
+  SET(CUDA_NVVM_HOME "$ENV{CUDA_NVVM_HOME}" CACHE INTERNAL "CUDA NVVM Directory")
+  MESSAGE(STATUS "Using CUDA_NVVM_HOME: ${CUDA_NVVM_HOME}")
+ENDIF()
 
-IF(${CUDA_VERSION_MAJOR} LESS 7)
-	SET(NVVM_DLL_VERSION 20_0)
-ELSE(${CUDA_VERSION_MAJOR} LESS 7)
-	SET(NVVM_DLL_VERSION 30_0)
-ENDIF(${CUDA_VERSION_MAJOR} LESS 7)
+FIND_LIBRARY(CUDA_nvvm_LIBRARY
+             NAMES "nvvm"
+             PATHS ${CUDA_NVVM_HOME}
+             PATH_SUFFIXES "lib64" "lib" "lib/x64" "lib/Win32"
+             DOC "CUDA NVVM Library"
+            )
 
-if (CMAKE_SIZEOF_VOID_P STREQUAL "8")
-  if (WIN32)
-    set (CUDA_LIB_SEARCH_PATH "${CUDA_TOOLKIT_ROOT_DIR}/lib/x64")
-    set (NVVM_DLL_NAME nvvm64_${NVVM_DLL_VERSION}.dll)
-  else ()
-    set (CUDA_LIB_SEARCH_PATH "")
-  endif()
-else()
-  if (WIN32)
-    set (CUDA_LIB_SEARCH_PATH "${CUDA_TOOLKIT_ROOT_DIR}/lib/Win32")
-    set (NVVM_DLL_NAME nvvm32_${NVVM_DLL_VERSION}.dll)
-  else()
-    set (CUDA_LIB_SEARCH_PATH "")
-  endif()
-endif()
+FIND_PATH(CUDA_nvvm_INCLUDE_DIR
+          NAMES nvvm.h
+          PATHS ${CUDA_NVVM_HOME}
+          PATH_SUFFIXES "include"
+          DOC "CUDA NVVM Include Directory"
+         )
 
-### Find libNVVM
-# The directory structure for nvvm is a bit complex.
-# On Windows:
-#   32-bit -- nvvm/lib/Win32
-#   64-bit -- nvvm/lib/x64
-# On Linux:
-#   32-bit -- nvvm/lib
-#   64-bit -- nvvm/lib64
-# On Mac:
-#   Universal -- nvvm/lib
-if (CMAKE_SIZEOF_VOID_P STREQUAL "8")
-  if (WIN32)
-    set (LIB_ARCH_SUFFIX "/x64")
-  elseif (APPLE)
-    set (LIB_ARCH_SUFFIX "")
-  else ()
-    set (LIB_ARCH_SUFFIX "64")
-  endif()
-else()
-  if (WIN32)
-    set (LIB_ARCH_SUFFIX "/Win32")
-  else()
-    set (LIB_ARCH_SUFFIX "")
-  endif()
-endif()
+MARK_AS_ADVANCED(
+    CUDA_nvvm_INCLUDE_DIR
+    CUDA_nvvm_LIBRARY)
 
-find_library(NVVM_LIB nvvm PATHS "${LIBNVVM_HOME}/lib${LIB_ARCH_SUFFIX}")
-find_file(NVVM_H nvvm.h PATHS "${LIBNVVM_HOME}/include")
-
-if(NVVM_H)
-  get_filename_component(CUDA_NVVM_INCLUDE_DIR ${NVVM_H} PATH)
-else()
-  message(FATAL_ERROR "Unable to find nvvm.h")
-endif()
-
-set(CUDA_NVVM_LIBRARIES ${NVVM_LIB})
+INCLUDE(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(NVVM DEFAULT_MSG
+    CUDA_nvvm_INCLUDE_DIR CUDA_nvvm_LIBRARY)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 94eb393a9f..beaef73b1a 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -91,14 +91,23 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
     IF(${CUDA_cusolver_LIBRARY} MATCHES " ")
         UNSET(CUDA_cusolver_LIBRARY CACHE)  # When going from higher version to lower version
     ENDIF()
-    FIND_LIBRARY (
-        CUDA_cusolver_LIBRARY
-        NAMES "cusolver"
-        PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-        PATH_SUFFIXES "lib64" "lib/x64" "lib"
-        DOC "CUDA cusolver Library"
-        NO_DEFAULT_PATH
-        )
+
+    # Use CUDA_cusolver_DIR to keep track of CUDA Toolkit for which cusolver was found.
+    # If the toolkit changed, then find cusolver again
+    IF(NOT "${CUDA_cusolver_DIR}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR}")
+        UNSET(CUDA_cusolver_DIR CACHE)
+        UNSET(CUDA_cusolver_LIBRARY CACHE)
+        FIND_LIBRARY (
+            CUDA_cusolver_LIBRARY
+            NAMES "cusolver"
+            PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+            PATH_SUFFIXES "lib64" "lib/x64" "lib"
+            DOC "CUDA cusolver Library"
+            NO_DEFAULT_PATH
+            )
+        SET(CUDA_cusolver_DIR "${CUDA_TOOLKIT_ROOT_DIR}" CACHE INTERNAL "CUDA cusolver Root Directory")
+    ENDIF()
+    MARK_AS_ADVANCED(CUDA_cusolver_LIBRARY)
 ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
 
 IF(${CUDA_VERSION_MAJOR} LESS 7 AND CUDA_cusolver_LIBRARY)
@@ -156,7 +165,7 @@ INCLUDE_DIRECTORIES(
     ${CUDA_INCLUDE_DIRS}
     "${CMAKE_SOURCE_DIR}/src/backend/cuda"
     "${CMAKE_CURRENT_BINARY_DIR}"
-    ${CUDA_NVVM_INCLUDE_DIR}
+    ${CUDA_nvvm_INCLUDE_DIR}
     )
 
 IF(CUDA_LAPACK_CPU_FALLBACK)
@@ -383,7 +392,7 @@ TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
-                                PRIVATE ${CUDA_NVVM_LIBRARIES}
+                                PRIVATE ${CUDA_nvvm_LIBRARY}
                                 PRIVATE ${CUDA_CUDA_LIBRARY})
 
 IF(FORGE_FOUND)

From 59c635a6d754e5a293c3e450d753893c2b5bcc1f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 5 May 2016 12:20:50 -0400
Subject: [PATCH 0506/2677] CMake Fix for CMP0054

---
 CMakeModules/Version.cmake     | 13 +++++++++++++
 src/backend/cpu/CMakeLists.txt |  8 ++++++++
 2 files changed, 21 insertions(+)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 8171a57818..a390593e74 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -1,6 +1,13 @@
 #
 # Make a version file that includes the ArrayFire version and git revision
 #
+CMAKE_POLICY(PUSH)
+
+# https://cmake.org/cmake/help/v3.1/policy/CMP0054.html
+IF("${CMAKE_VERSION}" VERSION_GREATER "3.1" OR "${CMAKE_VERSION}" VERSION_EQUAL "3.1")
+    CMAKE_POLICY(SET CMP0054 OLD)
+ENDIF()
+
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "4")
 SET(AF_VERSION_PATCH "0")
@@ -8,6 +15,10 @@ SET(AF_VERSION_PATCH "0")
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})
 
+IF (${CMAKE_MAJOR_VERSION} GREATER 2 AND ${CMAKE_MINOR_VERSION} GREATER 1)
+    CMAKE_POLICY(SET CMP0054 OLD)
+ENDIF()
+
 # From CMake 3.0.0 CMAKE_<LANG>_COMPILER_ID is AppleClang for OSX machines
 # that use clang for compilations
 IF("${CMAKE_C_COMPILER_ID}" STREQUAL "AppleClang")
@@ -46,3 +57,5 @@ CONFIGURE_FILE(
     ${CMAKE_MODULE_PATH}/version.hpp.in
     ${CMAKE_SOURCE_DIR}/src/backend/version.hpp
 )
+
+CMAKE_POLICY(POP)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 7be5bda858..7901715963 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -4,6 +4,12 @@ ADD_DEFINITIONS(-DAF_CPU)
 FIND_PACKAGE(CBLAS REQUIRED)
 
 IF(NOT DEFINED BUILD_CPU_ASYNC)
+    CMAKE_POLICY(PUSH)
+    # https://cmake.org/cmake/help/v3.1/policy/CMP0054.html
+    IF("${CMAKE_VERSION}" VERSION_GREATER "3.1" OR "${CMAKE_VERSION}" VERSION_EQUAL "3.1")
+        CMAKE_POLICY(SET CMP0054 OLD)
+    ENDIF()
+
     IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.4")
         MESSAGE("Disabling CPU Async as GCC Version ${COMPILER_VERSION} has known issues.")
         MESSAGE("CPU Backend will use Synchronous Calls")
@@ -11,6 +17,8 @@ IF(NOT DEFINED BUILD_CPU_ASYNC)
     ELSE()
         OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
     ENDIF()
+
+    CMAKE_POLICY(POP)
 ENDIF(NOT DEFINED BUILD_CPU_ASYNC)
 MARK_AS_ADVANCED(USE_CPUID)
 

From a616bd9fbf942808dfb521055d37526203f0dbf3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 5 May 2016 13:17:21 -0400
Subject: [PATCH 0507/2677] Update clBLAS tag to staged branch

---
 CMakeModules/build_clBLAS.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index ac8949ac7e..d486b31801 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG af3.3.1
+    GIT_TAG arrayfire-release-test
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 792554219c267777a46b6c8a14c484644b2a5ae2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 5 May 2016 13:17:36 -0400
Subject: [PATCH 0508/2677] Update clFFT tag to staged branch

---
 CMakeModules/build_clFFT.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index d9441bb271..961347f913 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG af3.3.1
+    GIT_TAG arrayfire-release-test
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 5b4e162620c1cd91758d0c1ccdf5fadc71d23371 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 5 May 2016 17:57:24 -0400
Subject: [PATCH 0509/2677] Update forge tag to staged branch

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index a134b642be..17aafcee9d 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -22,7 +22,7 @@ ENDIF()
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG af3.2.2
+    GIT_TAG master
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 38ae37e51f47b976b27f633a9ef94e50e03d88b6 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 6 May 2016 17:25:26 -0400
Subject: [PATCH 0510/2677] Generalized Scan

---
 .github/CITATION.md                           |   24 +
 CMakeModules/CPackConfig.cmake                |   35 +-
 CMakeModules/FindCBLAS.cmake                  |   45 +-
 CMakeModules/FindFFTW.cmake                   |   29 +-
 CMakeModules/FindLAPACKE.cmake                |   10 +-
 CMakeModules/Version.cmake                    |    2 +-
 CMakeModules/build_clBLAS.cmake               |    2 +-
 CMakeModules/build_clFFT.cmake                |    2 +-
 CMakeModules/osx_install/OSXInstaller.cmake   |  131 +-
 .../osx_install/cpu_scripts/postinstall       |   17 +-
 .../osx_install/cuda_scripts/postinstall      |   17 +-
 CMakeModules/osx_install/distribution.dist    |   31 +-
 .../osx_install/opencl_scripts/postinstall    |   17 +-
 CMakeModules/osx_install/readme.html          |   14 +-
 README.md                                     |  185 +-
 docs/details/data.dox                         |    4 +-
 docs/pages/INSTALL.md                         |   15 +-
 docs/pages/release_notes.md                   |  140 +-
 docs/pages/using_on_osx.md                    |  185 +-
 examples/CMakeLists.txt                       |   22 +
 examples/graphics/gravity_sim.cpp             |  239 +-
 examples/graphics/gravity_sim_init.h          | 4006 +++++++++++++++++
 examples/helloworld/helloworld.cpp            |    9 +
 examples/machine_learning/kmeans.cpp          |    4 +-
 examples/pde/swe.cpp                          |   59 +-
 include/af/algorithm.h                        |   43 +-
 include/af/backend.h                          |    2 +-
 include/af/data.h                             |   32 +-
 include/af/defines.h                          |   18 +-
 include/af/device.h                           |    2 +
 include/af/graphics.h                         |   23 +
 include/af/opencl.h                           |   22 +-
 src/api/c/array.cpp                           |  263 ++
 src/api/c/binary.cpp                          |    5 +-
 src/api/c/data.cpp                            |  287 +-
 src/api/c/device.cpp                          |   33 +
 src/api/c/handle.hpp                          |    3 +
 src/api/c/image.cpp                           |   22 +
 src/api/c/imageio.cpp                         |    2 +
 src/api/c/median.cpp                          |   13 +-
 src/api/c/moddims.cpp                         |    1 -
 src/api/c/rgb_gray.cpp                        |   24 +-
 src/api/c/scan.cpp                            |   62 +-
 src/api/c/sort.cpp                            |   22 +-
 src/api/cpp/array.cpp                         |    4 +-
 src/api/cpp/binary.cpp                        |   12 +-
 src/api/cpp/graphics.cpp                      |    5 +
 src/api/cpp/scan.cpp                          |    7 +
 src/api/unified/CMakeLists.txt                |    7 +
 src/api/unified/algorithm.cpp                 |    6 +
 src/api/unified/graphics.cpp                  |    5 +
 src/api/unified/symbol_manager.cpp            |   10 +-
 src/backend/ArrayInfo.cpp                     |    4 +-
 src/backend/ArrayInfo.hpp                     |   12 +-
 src/backend/MemoryManager.cpp                 |    6 +
 src/backend/cblas.cpp                         |    8 +-
 src/backend/cpu/Array.hpp                     |   11 +-
 src/backend/cpu/CMakeLists.txt                |   77 +-
 src/backend/cpu/blas.hpp                      |   16 +-
 src/backend/cpu/diagonal.cpp                  |    2 +-
 src/backend/cpu/kernel/scan.hpp               |   18 +-
 src/backend/cpu/kernel/sort.hpp               |    2 +-
 src/backend/cpu/kernel/sort_by_key.hpp        |   67 +-
 .../cpu/kernel/sort_by_key/CMakeLists.txt     |   15 +
 .../kernel/sort_by_key/sort_by_key_impl.cpp}  |   12 +-
 src/backend/cpu/kernel/sort_by_key_impl.hpp   |  172 +
 src/backend/cpu/kernel/sort_helper.hpp        |   60 +
 src/backend/cpu/kernel/sort_index.hpp         |   71 -
 src/backend/cpu/lapack_helper.hpp             |   20 +-
 src/backend/cpu/platform.cpp                  |   11 +-
 src/backend/cpu/scan.cpp                      |  113 +-
 src/backend/cpu/scan.hpp                      |    2 +-
 src/backend/cpu/sort.cpp                      |   63 +-
 src/backend/cpu/sort_by_key.cpp               |   34 +-
 src/backend/cpu/sort_index.cpp                |   36 +-
 src/backend/cuda/Array.hpp                    |    9 +-
 src/backend/cuda/CMakeLists.txt               |   59 +-
 src/backend/cuda/cpu_lapack/lapack_helper.hpp |   20 +-
 src/backend/cuda/diagonal.cu                  |    2 +-
 src/backend/cuda/fft.cpp                      |    4 +-
 src/backend/cuda/kernel/harris.hpp            |    7 +-
 src/backend/cuda/kernel/iota.hpp              |    9 +-
 src/backend/cuda/kernel/orb.hpp               |    7 +-
 src/backend/cuda/kernel/range.hpp             |   14 +-
 src/backend/cuda/kernel/scan_dim.hpp          |   37 +-
 src/backend/cuda/kernel/scan_first.hpp        |   69 +-
 src/backend/cuda/kernel/sort.hpp              |   95 +-
 src/backend/cuda/kernel/sort_by_key.hpp       |   37 +-
 .../cuda/kernel/sort_by_key/CMakeLists.txt    |   29 +
 .../kernel/sort_by_key/sort_by_key_impl.cu.in |   24 +
 src/backend/cuda/kernel/sort_by_key_impl.hpp  |  218 +
 src/backend/cuda/kernel/sort_index.hpp        |   59 -
 src/backend/cuda/kernel/where.hpp             |    4 +-
 src/backend/cuda/platform.cpp                 |   12 +-
 src/backend/cuda/scan.cu                      |   60 +-
 src/backend/cuda/scan.hpp                     |    2 +-
 src/backend/cuda/sort.cu                      |   22 +-
 src/backend/cuda/sort_by_key.cu               |   86 +
 src/backend/cuda/sort_by_key/ascd_f32.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_f64.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_s32.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_s64.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_s8.cu       |   15 -
 src/backend/cuda/sort_by_key/ascd_u16.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_u32.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_u64.cu      |   15 -
 src/backend/cuda/sort_by_key/ascd_u8.cu       |   15 -
 src/backend/cuda/sort_by_key/desc_f32.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_f64.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_s16.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_s32.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_s64.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_s8.cu       |   15 -
 src/backend/cuda/sort_by_key/desc_u16.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_u32.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_u64.cu      |   15 -
 src/backend/cuda/sort_by_key/desc_u8.cu       |   15 -
 src/backend/cuda/sort_by_key_impl.hpp         |   49 -
 src/backend/cuda/sort_index.cu                |   35 +-
 src/backend/host_memory.cpp                   |    2 +-
 src/backend/opencl/Array.hpp                  |   20 +-
 src/backend/opencl/CMakeLists.txt             |   72 +-
 src/backend/opencl/cpu/cpu_helper.hpp         |   28 +-
 src/backend/opencl/diagonal.cpp               |    2 +-
 src/backend/opencl/fft.cpp                    |   16 +-
 src/backend/opencl/kernel/harris.hpp          |    7 +-
 src/backend/opencl/kernel/iota.hpp            |    9 +-
 src/backend/opencl/kernel/orb.hpp             |    7 +-
 src/backend/opencl/kernel/range.hpp           |   14 +-
 src/backend/opencl/kernel/scan_dim.cl         |   16 +-
 src/backend/opencl/kernel/scan_dim.hpp        |   29 +-
 src/backend/opencl/kernel/scan_first.cl       |   13 +-
 src/backend/opencl/kernel/scan_first.hpp      |   29 +-
 src/backend/opencl/kernel/sift_nonfree.hpp    |    3 +-
 src/backend/opencl/kernel/sort.hpp            |  110 +-
 src/backend/opencl/kernel/sort_by_key.hpp     |   74 +-
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |   19 +
 .../sort_by_key/sort_by_key_impl.cpp}         |   11 +-
 .../opencl/kernel/sort_by_key_impl.hpp        |  373 ++
 src/backend/opencl/kernel/sort_helper.hpp     |   48 +
 src/backend/opencl/kernel/sort_index.hpp      |   99 -
 src/backend/opencl/kernel/sort_pair.cl        |   43 +
 src/backend/opencl/magma/magma_cpu_blas.h     |   16 +-
 src/backend/opencl/magma/magma_cpu_lapack.h   |   18 +-
 src/backend/opencl/platform.cpp               |   50 +-
 src/backend/opencl/program.cpp                |    2 +-
 src/backend/opencl/scan.cpp                   |   54 +-
 src/backend/opencl/scan.hpp                   |    2 +-
 src/backend/opencl/sort.cpp                   |   23 +-
 src/backend/opencl/sort_by_key.cpp            |   90 +
 src/backend/opencl/sort_by_key/f32.cpp        |   16 -
 src/backend/opencl/sort_by_key/f64.cpp        |   16 -
 src/backend/opencl/sort_by_key/impl.hpp       |   57 -
 src/backend/opencl/sort_by_key/s16.cpp        |   16 -
 src/backend/opencl/sort_by_key/s32.cpp        |   16 -
 src/backend/opencl/sort_by_key/s64.cpp        |   16 -
 src/backend/opencl/sort_by_key/u16.cpp        |   16 -
 src/backend/opencl/sort_by_key/u32.cpp        |   16 -
 src/backend/opencl/sort_by_key/u64.cpp        |   16 -
 src/backend/opencl/sort_by_key/u8.cpp         |   16 -
 src/backend/opencl/sort_index.cpp             |   40 +-
 src/backend/opencl/traits.hpp                 |    1 +
 test/CMakeLists.txt                           |   25 +
 test/array.cpp                                |   42 +-
 test/diagonal.cpp                             |   31 +
 test/fft.cpp                                  |   36 +
 test/gloh_nonfree.cpp                         |   22 -
 test/gray_rgb.cpp                             |  105 +
 test/median.cpp                               |  156 +-
 test/orb.cpp                                  |   12 -
 test/sift_nonfree.cpp                         |   22 -
 test/sort.cpp                                 |   83 +-
 test/sort_by_key.cpp                          |  106 +-
 test/sort_index.cpp                           |  108 +-
 test/transform_coordinates.cpp                |    4 +-
 175 files changed, 8368 insertions(+), 2190 deletions(-)
 create mode 100644 .github/CITATION.md
 create mode 100644 examples/graphics/gravity_sim_init.h
 create mode 100644 src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
 rename src/backend/{cuda/sort_by_key/ascd_s16.cu => cpu/kernel/sort_by_key/sort_by_key_impl.cpp} (62%)
 create mode 100644 src/backend/cpu/kernel/sort_by_key_impl.hpp
 create mode 100644 src/backend/cpu/kernel/sort_helper.hpp
 delete mode 100644 src/backend/cpu/kernel/sort_index.hpp
 create mode 100644 src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
 create mode 100644 src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
 create mode 100644 src/backend/cuda/kernel/sort_by_key_impl.hpp
 delete mode 100644 src/backend/cuda/kernel/sort_index.hpp
 create mode 100644 src/backend/cuda/sort_by_key.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_f32.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_f64.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_s32.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_s64.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_s8.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_u16.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_u32.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_u64.cu
 delete mode 100644 src/backend/cuda/sort_by_key/ascd_u8.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_f32.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_f64.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_s16.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_s32.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_s64.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_s8.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_u16.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_u32.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_u64.cu
 delete mode 100644 src/backend/cuda/sort_by_key/desc_u8.cu
 delete mode 100644 src/backend/cuda/sort_by_key_impl.hpp
 create mode 100644 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
 rename src/backend/opencl/{sort_by_key/b8.cpp => kernel/sort_by_key/sort_by_key_impl.cpp} (65%)
 create mode 100644 src/backend/opencl/kernel/sort_by_key_impl.hpp
 create mode 100644 src/backend/opencl/kernel/sort_helper.hpp
 delete mode 100644 src/backend/opencl/kernel/sort_index.hpp
 create mode 100644 src/backend/opencl/kernel/sort_pair.cl
 create mode 100644 src/backend/opencl/sort_by_key.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/f32.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/f64.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/impl.hpp
 delete mode 100644 src/backend/opencl/sort_by_key/s16.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/s32.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/s64.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/u16.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/u32.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/u64.cpp
 delete mode 100644 src/backend/opencl/sort_by_key/u8.cpp
 create mode 100644 test/gray_rgb.cpp

diff --git a/.github/CITATION.md b/.github/CITATION.md
new file mode 100644
index 0000000000..4e78352060
--- /dev/null
+++ b/.github/CITATION.md
@@ -0,0 +1,24 @@
+If you redistribute ArrayFire, please follow the terms established in
+[the license](../LICENSE). If you wish to cite ArrayFire in an academic
+publication, please use the following reference:
+
+Formatted:
+```
+Yalamanchili, P., Arshad, U., Mohammed, Z., Garigipati, P., Entschev, P.,
+Kloppenborg, B., Malcolm, J. and Melonakos, J. (2015).
+ArrayFire - A high performance software library for parallel computing with an
+easy-to-use API. Atlanta: AccelerEyes. Retrieved from https://github.com/arrayfire/arrayfire
+```
+
+BibTeX:
+```bibtex
+@misc{Yalamanchili2015,
+abstract = {ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming simple. ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable. A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.},
+address = {Atlanta},
+author = {Yalamanchili, Pavan and Arshad, Umar and Mohammed, Zakiuddin and Garigipati, Pradeep and Entschev, Peter and Kloppenborg, Brian and Malcolm, James and Melonakos, John},
+publisher = {AccelerEyes},
+title = {{ArrayFire - A high performance software library for parallel computing with an easy-to-use API}},
+url = {https://github.com/arrayfire/arrayfire},
+year = {2015}
+}
+```
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index de242a99b7..deb154c6c0 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -2,16 +2,27 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
 INCLUDE("${CMAKE_MODULE_PATH}/Version.cmake")
 
+OPTION(CREATE_STGZ "Create .sh install file" ON)
+MARK_AS_ADVANCED(CREATE_STGZ)
+
 # CPack package generation
-#SET(CPACK_GENERATOR "TGZ;STGZ")
-SET(CPACK_GENERATOR "STGZ")
-# Create the following installers are as follows:
-#  Windows: Use external packaging, do nothing here
-#  OSX: Deploy as TGZ and STGZ
-#IF("${CMAKE_SYSTEM}" MATCHES "Linux")
-#    #  Linux: TGZ, STGZ, DEB
-#    SET(CPACK_GENERATOR "TGZ;STGZ;DEB;RPM")
-#ENDIF()
+IF(${CREATE_STGZ})
+  LIST(APPEND CPACK_GENERATOR "STGZ")
+ENDIF()
+
+OPTION(CREATE_DEB "Create .deb install file" OFF)
+MARK_AS_ADVANCED(CREATE_DEB)
+
+IF(${CREATE_DEB})
+  LIST(APPEND CPACK_GENERATOR "DEB")
+ENDIF()
+
+OPTION(CREATE_RPM "Create .rpm install file" OFF)
+MARK_AS_ADVANCED(CREATE_RPM)
+
+IF(${CREATE_RPM})
+  LIST(APPEND CPACK_GENERATOR "RPM")
+ENDIF()
 
 # Common settings to all packaging tools
 SET(CPACK_PREFIX_DIR ${CMAKE_INSTALL_PREFIX})
@@ -59,16 +70,14 @@ SET(CPACK_COMPONENTS_ALL libraries headers documentation cmake)
 # Debian package
 ##
 SET(CPACK_DEBIAN_PACKAGE_ARCHITECTURE ${PROCESSOR_ARCHITECTURE})
-SET(CPACK_DEBIAN_PACKAGE_DEPENDS "libfreeimage-dev, libatlas3gf-base, libfftw3-dev, liblapacke-dev")
-SET(CPACK_DEBIAN_PACKAGE_SUGGESTS "ocl-icd-libopencl1 (>= 2.0), nvidia-cuda-dev (>= 6.0)")
 
 ##
 # RPM package
 ##
 SET(CPACK_RPM_PACKAGE_LICENSE "BSD")
-SET(CPACK_PACKAGE_GROUP "Development/Libraries")
-SET(CPACK_RPM_PACKAGE_REQUIRES "freeimage atlas fftw lapack")
+set(CPACK_RPM_PACKAGE_AUTOREQPROV " no")
 
+SET(CPACK_PACKAGE_GROUP "Development/Libraries")
 ##
 # Source package
 ##
diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index efef36b093..f64fd6f1b4 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -62,28 +62,36 @@ IF(NOT CBLAS_ROOT_DIR)
   IF (ENV{CBLASDIR})
     SET(CBLAS_ROOT_DIR $ENV{CBLASDIR})
     IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib64")
+        SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib64")
     ELSE()
-      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib")
+        SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib")
     ENDIF()
   ENDIF()
 
   IF (ENV{CBLAS_ROOT_DIR})
     SET(CBLAS_ROOT_DIR $ENV{CBLAS_ROOT_DIR})
     IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib64")
+        SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib64")
     ELSE()
-      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib")
+        SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib")
     ENDIF()
   ENDIF()
 
   IF (INTEL_MKL_ROOT_DIR)
     SET(CBLAS_ROOT_DIR ${INTEL_MKL_ROOT_DIR})
-    IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-      SET(CBLAS_LIB64_DIR "${INTEL_MKL_ROOT_DIR}/lib/intel64")
-    ELSE()
-      SET(CBLAS_LIB32_DIR "${INTEL_MKL_ROOT_DIR}/lib/ia32")
-    ENDIF()
+    IF(APPLE)
+        IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+            SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib")
+        ELSE()
+            SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib")
+        ENDIF()
+    ELSE(APPLE) # Windows and Linux
+        IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+            SET(CBLAS_LIB64_DIR "${CBLAS_ROOT_DIR}/lib/intel64")
+        ELSE()
+            SET(CBLAS_LIB32_DIR "${CBLAS_ROOT_DIR}/lib/ia32")
+        ENDIF()
+    ENDIF(APPLE)
   ENDIF()
 
   SET(CBLAS_INCLUDE_DIR "${CBLAS_ROOT_DIR}/include")
@@ -101,7 +109,7 @@ MACRO(CHECK_ALL_LIBRARIES
     _flags
     _list
     _include
-    _search_include,
+    _search_include
     _libraries_work_check)
   # This macro checks for the existence of the combination of fortran libraries
   # given by _list.  If the combination is found, this macro checks (using the
@@ -160,11 +168,6 @@ MACRO(CHECK_ALL_LIBRARIES
       ENDIF(APPLE)
       MARK_AS_ADVANCED(${_prefix}_${_library}_LIBRARY)
 
-      IF(${_prefix}_${_library}_LIBRARY)
-        GET_FILENAME_COMPONENT(_path ${${_prefix}_${_library}_LIBRARY} PATH)
-        LIST(APPEND _paths ${_path}/../include ${_path}/../../include ${CBLAS_ROOT_DIR}/include)
-      ENDIF(${_prefix}_${_library}_LIBRARY)
-
       SET(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
       SET(_libraries_work ${${_prefix}_${_library}_LIBRARY})
     ENDIF(_libraries_work)
@@ -175,8 +178,17 @@ MACRO(CHECK_ALL_LIBRARIES
   SET(_bug_libraries_work_check ${_libraries_work_check}) #CMAKE BUG!!! SHOULD NOT BE THAT
 
   IF(_bug_search_include)
-    FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include} ${_paths})
+    FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include}
+      /opt/intel/mkl/include
+      /usr/include
+      /usr/local/include
+      /sw/include
+      /opt/local/include
+      PATH_SUFFIXES
+      openblas
+      )
     MARK_AS_ADVANCED(${_prefix}${_combined_name}_INCLUDE)
+
     IF(${_prefix}${_combined_name}_INCLUDE)
       IF (_verbose)
         MESSAGE(STATUS "Includes found")
@@ -186,6 +198,7 @@ MACRO(CHECK_ALL_LIBRARIES
     ELSE(${_prefix}${_combined_name}_INCLUDE)
       SET(_libraries_work FALSE)
     ENDIF(${_prefix}${_combined_name}_INCLUDE)
+
   ELSE(_bug_search_include)
     SET(${_prefix}_INCLUDE_DIR)
     SET(${_prefix}_INCLUDE_FILE ${_include})
diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index a725f64ecd..3156cec89b 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -24,6 +24,25 @@ IF(NOT FFTW_ROOT AND ENV{FFTWDIR})
     SET(FFTW_ROOT $ENV{FFTWDIR})
 ENDIF()
 
+IF (NOT INTEL_MKL_ROOT_DIR)
+  SET(INTEL_MKL_ROOT_DIR $ENV{INTEL_MKL_ROOT})
+ENDIF()
+
+IF(NOT FFTW_ROOT)
+
+  IF (ENV{FFTWDIR})
+    SET(FFTW_ROOT $ENV{FFTWDIR})
+  ENDIF()
+
+  IF (ENV{FFTW_ROOT_DIR})
+    SET(FFTW_ROOT $ENV{FFTW_ROOT_DIR})
+  ENDIF()
+
+  IF (INTEL_MKL_ROOT_DIR)
+    SET(FFTW_ROOT ${INTEL_MKL_ROOT_DIR})
+  ENDIF()
+ENDIF()
+
 # Check if we can use PkgConfig
 FIND_PACKAGE(PkgConfig)
 
@@ -44,14 +63,14 @@ IF(FFTW_ROOT)
     #find libs
     FIND_LIBRARY(
         FFTW_LIB
-        NAMES "fftw3" "libfftw3-3" "fftw3-3"
+        NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
         PATH_SUFFIXES "lib" "lib64"
         NO_DEFAULT_PATH
         )
     FIND_LIBRARY(
         FFTWF_LIB
-        NAMES "fftw3f" "libfftw3f-3" "fftw3f-3"
+        NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
         PATH_SUFFIXES "lib" "lib64"
         NO_DEFAULT_PATH
@@ -62,18 +81,18 @@ IF(FFTW_ROOT)
         FFTW_INCLUDES
         NAMES "fftw3.h"
         PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "include"
+        PATH_SUFFIXES "include" "include/fftw"
         NO_DEFAULT_PATH
         )
 ELSE()
     FIND_LIBRARY(
         FFTW_LIB
-        NAMES "fftw3"
+        NAMES "fftw3" "mkl_rt"
         PATHS ${PKG_FFTW_LIBRARY_DIRS} ${LIB_INSTALL_DIR}
         )
     FIND_LIBRARY(
         FFTWF_LIB
-        NAMES "fftw3f"
+        NAMES "fftw3f" "mkl_rt"
         PATHS ${PKG_FFTW_LIBRARY_DIRS} ${LIB_INSTALL_DIR}
         )
     FIND_PATH(
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index dc4a045370..9251ee93c0 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -137,12 +137,18 @@ ELSE(PC_LAPACKE_FOUND)
             /sw/include
             /opt/local/include
             DOC "LAPACKE Include Directory"
+            PATH_SUFFIXES
+            lapacke
             )
     ENDIF(LAPACKE_ROOT_DIR)
 ENDIF(PC_LAPACKE_FOUND)
 
-SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
-SET(LAPACK_INCLUDE_DIR ${LAPACKE_INCLUDES})
+IF(LAPACKE_LIB AND LAPACK_LIB)
+    SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
+ENDIF()
+IF(LAPACKE_INCLUDES)
+    SET(LAPACK_INCLUDE_DIR ${LAPACKE_INCLUDES})
+ENDIF()
 
 INCLUDE(FindPackageHandleStandardArgs)
 FIND_PACKAGE_HANDLE_STANDARD_ARGS(LAPACK DEFAULT_MSG
diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 8d5b575399..8171a57818 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -2,7 +2,7 @@
 # Make a version file that includes the ArrayFire version and git revision
 #
 SET(AF_VERSION_MAJOR "3")
-SET(AF_VERSION_MINOR "3")
+SET(AF_VERSION_MINOR "4")
 SET(AF_VERSION_PATCH "0")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 2289c26393..ac8949ac7e 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG af3.3.0
+    GIT_TAG af3.3.1
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 2ab9ccc1ea..d9441bb271 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG af3.3.0
+    GIT_TAG af3.3.1
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index dc3a8b2491..2b2a52be62 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -8,8 +8,75 @@ SET(BIN2CPP_PROGRAM "bin2cpp")
 
 SET(OSX_INSTALL_DIR ${CMAKE_MODULE_PATH}/osx_install)
 
+################################################################################
+## Create Directory Structure
+################################################################################
+SET(OSX_TEMP "${CMAKE_BINARY_DIR}/osx_install_files")
+
+# Common files - libforge, ArrayFireConfig*.cmake
+FILE(GLOB COMMONLIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
+FILE(GLOB COMMONCMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFireConfig*.cmake")
+
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_COMMON)
+FOREACH(SRC ${COMMONLIB} ${COMMONCMAKE})
+    FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
+    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_COMMON PRE_BUILD
+                       COMMAND ${CMAKE_COMMAND} -E copy
+                       ${SRC} "${OSX_TEMP}/common/${SRC_REL}"
+                       WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                       COMMENT "Copying Common files to temporary OSX Install Dir"
+                       )
+ENDFOREACH()
+
+# Backends - CPU, CUDA, OpenCL, Unified
+MACRO(OSX_INSTALL_SETUP BACKEND LIB)
+    FILE(GLOB ${BACKEND}LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/lib${LIB}*.dylib")
+    FILE(GLOB ${BACKEND}CMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFire${BACKEND}*.cmake")
+
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_${BACKEND})
+    FOREACH(SRC ${${BACKEND}LIB} ${${BACKEND}CMAKE})
+        FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
+        ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
+                           COMMAND ${CMAKE_COMMAND} -E copy
+                           ${SRC} "${OSX_TEMP}/${BACKEND}/${SRC_REL}"
+                           WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                           COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir"
+                           )
+    ENDFOREACH()
+ENDMACRO(OSX_INSTALL_SETUP)
+
+OSX_INSTALL_SETUP(CPU afcpu)
+OSX_INSTALL_SETUP(CUDA afcuda)
+OSX_INSTALL_SETUP(OpenCL afopencl)
+OSX_INSTALL_SETUP(Unified af)
+
+# Headers
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_INCLUDE
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  ${CMAKE_INSTALL_PREFIX}/include "${OSX_TEMP}/include"
+                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  COMMENT "Copying header files to temporary OSX Install Dir"
+                  )
+
+# Examples
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_EXAMPLES
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/examples" "${OSX_TEMP}/examples"
+                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  COMMENT "Copying examples files to temporary OSX Install Dir"
+                  )
+
+# Documentation
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/doc" "${OSX_TEMP}/doc"
+                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  COMMENT "Copying documentation files to temporary OSX Install Dir"
+                  )
+################################################################################
+
 FUNCTION(PKG_BUILD)
-    CMAKE_PARSE_ARGUMENTS(ARGS "" "INSTALL_LOCATION;IDENTIFIER;PATH_TO_FILES;PKG_NAME;TARGETS;SCRIPT_DIR" "FILTERS" ${ARGN})
+    CMAKE_PARSE_ARGUMENTS(ARGS "" "DEPENDS;INSTALL_LOCATION;IDENTIFIER;PATH_TO_FILES;PKG_NAME;TARGETS;SCRIPT_DIR" "FILTERS" ${ARGN})
 
     FOREACH(filter ${ARGS_FILTERS})
         LIST(APPEND  FILTER_LIST --filter ${filter})
@@ -70,50 +137,70 @@ ENDFUNCTION(PRODUCT_BUILD)
 
 
 PKG_BUILD(  PKG_NAME        ArrayFireCPU
-            DEPENDS         afcpu
+            DEPENDS         OSX_INSTALL_SETUP_CPU
             TARGETS         cpu_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             SCRIPT_DIR      ${OSX_INSTALL_DIR}/cpu_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cpu.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/CPU
             FILTERS         opencl cuda unified)
 
 PKG_BUILD(  PKG_NAME        ArrayFireCUDA
-            DEPENDS         afcuda
+            DEPENDS         OSX_INSTALL_SETUP_CUDA
             TARGETS         cuda_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             SCRIPT_DIR      ${OSX_INSTALL_DIR}/cuda_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cuda.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/CUDA
             FILTERS         cpu opencl unified)
 
 PKG_BUILD(  PKG_NAME        ArrayFireOPENCL
-            DEPENDS         afopencl
+            DEPENDS         OSX_INSTALL_SETUP_OpenCL
             TARGETS         opencl_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
+            SCRIPT_DIR      ${OSX_INSTALL_DIR}/opencl_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.opencl.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/OpenCL
             FILTERS         cpu cuda unified)
 
 PKG_BUILD(  PKG_NAME        ArrayFireUNIFIED
-            DEPENDS         af
+            DEPENDS         OSX_INSTALL_SETUP_Unified
             TARGETS         unified_package
-            INSTALL_LOCATION /usr/local/lib
+            INSTALL_LOCATION /usr/local
             IDENTIFIER      com.arrayfire.pkg.arrayfire.unified.lib
-            PATH_TO_FILES   package/lib
+            PATH_TO_FILES   ${OSX_TEMP}/Unified
             FILTERS         cpu cuda opencl)
 
+PKG_BUILD(  PKG_NAME        ArrayFireCommon
+            DEPENDS         OSX_INSTALL_SETUP_COMMON
+            TARGETS         common_package
+            INSTALL_LOCATION /usr/local
+            IDENTIFIER      com.arrayfire.pkg.arrayfire.libcommon
+            PATH_TO_FILES   ${OSX_TEMP}/common
+            FILTERS         cpu cuda opencl unified)
+
 PKG_BUILD(  PKG_NAME        ArrayFireHeaders
+            DEPENDS         OSX_INSTALL_SETUP_INCLUDE
             TARGETS         header_package
             INSTALL_LOCATION /usr/local/include
             IDENTIFIER      com.arrayfire.pkg.arrayfire.inc
-            PATH_TO_FILES   package/include)
-
-PKG_BUILD(  PKG_NAME        ArrayFireExtra
-            TARGETS         extra_package
-            INSTALL_LOCATION /usr/local/share
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.extra
-            PATH_TO_FILES   package/share)
-
-PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${header_package} ${extra_package})
+            PATH_TO_FILES   ${OSX_TEMP}/include)
+
+PKG_BUILD(  PKG_NAME        ArrayFireExamples
+            DEPENDS         OSX_INSTALL_SETUP_EXAMPLES
+            TARGETS         examples_package
+            INSTALL_LOCATION /usr/local/share/ArrayFire/examples
+            IDENTIFIER      com.arrayfire.pkg.arrayfire.examples
+            PATH_TO_FILES   ${OSX_TEMP}/examples
+            FILTERS         cmake)
+
+PKG_BUILD(  PKG_NAME        ArrayFireDoc
+            DEPENDS         OSX_INSTALL_SETUP_DOC
+            TARGETS         doc_package
+            INSTALL_LOCATION /usr/local/share/ArrayFire/doc
+            IDENTIFIER      com.arrayfire.pkg.arrayfire.doc
+            PATH_TO_FILES   ${OSX_TEMP}/doc
+            FILTERS         cmake)
+
+PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${common_package} ${header_package} ${examples_package} ${doc_package})
 
diff --git a/CMakeModules/osx_install/cpu_scripts/postinstall b/CMakeModules/osx_install/cpu_scripts/postinstall
index 730065a710..a9bce9de8e 100755
--- a/CMakeModules/osx_install/cpu_scripts/postinstall
+++ b/CMakeModules/osx_install/cpu_scripts/postinstall
@@ -6,8 +6,10 @@ set -o pipefail
 err_file=/tmp/AFInstallerCPU.err
 brew=/usr/local/bin/brew
 
+echo $(date) > $err_file
+
 if [ ! -f $brew ]; then
-    osascript -e 'tell app "Finder" to display dialog "Brew not installed. Please install brew at http://brew.sh"'
+    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at http://brew.sh"'
     open http://brew.sh
     echo "Brew not found" >> $err_file
     exit 1
@@ -20,5 +22,14 @@ if [ -z $user ]; then
     exit 1
 fi
 
-su $user -c "$brew tap homebrew/versions" 2> $err_file
-su $user -c "$brew install fftw glfw3 fontconfig" 2> $err_file
+function deps_err
+{
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
+    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
+    echo "Dependencies failed to install" >> $err_file
+    exit 1
+}
+
+su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+su $user -c "$brew install fftw glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/cuda_scripts/postinstall b/CMakeModules/osx_install/cuda_scripts/postinstall
index 4713f46645..49f0fd2e2f 100755
--- a/CMakeModules/osx_install/cuda_scripts/postinstall
+++ b/CMakeModules/osx_install/cuda_scripts/postinstall
@@ -6,8 +6,10 @@ set -o pipefail
 err_file=/tmp/AFInstallerCUDA.err
 brew=/usr/local/bin/brew
 
+echo $(date) > $err_file
+
 if [ ! -f $brew ]; then
-    osascript -e 'tell app "Finder" to display dialog "Brew not installed. Please install brew at brew.sh"'
+    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at brew.sh"'
     echo "Brew not found" >> $err_file
     exit 1
 fi
@@ -19,5 +21,14 @@ if [ -z $user ]; then
     exit 1
 fi
 
-su $user -c "$brew tap homebrew/versions" 2> $err_file
-su $user -c "$brew install glfw3 fontconfig" 2> $err_file
+function deps_err
+{
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
+    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
+    echo "Dependencies failed to install" >> $err_file
+    exit 1
+}
+
+su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+su $user -c "$brew install glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
index 3dc82379c9..b476bf013f 100644
--- a/CMakeModules/osx_install/distribution.dist
+++ b/CMakeModules/osx_install/distribution.dist
@@ -17,7 +17,9 @@
     <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"    version="${AF_VERSION}" onConclusion="none">ArrayFireOPENCL.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.unified.lib"   version="${AF_VERSION}" onConclusion="none">ArrayFireUNIFIED.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.inc"           version="${AF_VERSION}" onConclusion="none">ArrayFireHeaders.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.extra"         version="${AF_VERSION}" onConclusion="none">ArrayFireExtra.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
     <options customize="always" require-scripts="false"/>
     <choices-outline>
         <line choice="libs">
@@ -25,38 +27,51 @@
             <line choice="cuda_lib"/>
             <line choice="opencl_lib"/>
             <line choice="com.arrayfire.arrayfire.unified.lib"/>
+            <line choice="com.arrayfire.arrayfire.libcommon"/>
         </line>
         <line choice="com.arrayfire.arrayfire.inc"/>
-        <line choice="com.arrayfire.arrayfire.extra"/>
+        <line choice="com.arrayfire.arrayfire.examples"/>
+        <line choice="com.arrayfire.arrayfire.doc"/>
     </choices-outline>
     <choice id="libs" title="ArrayFire Libraries" visible="true" />
     <choice title="CPU Libraries"
-            description="ArrayFire targeting CPUs."
+            description="ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
             id="cpu_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"/>
     </choice>
     <choice title="CUDA Libraries"
-            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code."
+            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the corresponding CMake config files."
             id="cuda_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"/>
     </choice>
     <choice title="OpenCL Libraries"
-            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
+            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. Also installs the corresponding CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
             id="opencl_lib" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"/>
     </choice>
     <choice title="Unified Library"
-            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. NOTE: This option requires the other platforms to work properly"
+            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. Also installs the corresponding CMake config files. NOTE: This option requires the other platforms to work properly"
             id="com.arrayfire.arrayfire.unified.lib"
             selected="CheckBackendSelected()"
             visible="true"
             enabled="CheckBackendSelected()">
         <pkg-ref id="com.arrayfire.arrayfire.unified.lib"/>
     </choice>
+    <choice title="Library Common"
+            description="Installs Forge and ArrayFireConfig.cmake files"
+            id="com.arrayfire.arrayfire.libcommon"
+            selected="CheckBackendSelected()"
+            visible="false"
+            enabled="CheckBackendSelected()">
+        <pkg-ref id="com.arrayfire.arrayfire.libcommon"/>
+    </choice>
     <choice title="ArrayFire Headers" description="ArrayFire Headers" id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.inc"/>
     </choice>
-    <choice title="Extras" description="Extra files include documentation, examples, and cmake scripts to find and use ArrayFire with cmake," id="com.arrayfire.arrayfire.extra" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.extra"/>
+    <choice title="Examples" description="ArrayFire Examples" id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.examples"/>
+    </choice>
+    <choice title="Documentation" description="ArrayFire Documentation" id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.doc"/>
     </choice>
 </installer-gui-script>
diff --git a/CMakeModules/osx_install/opencl_scripts/postinstall b/CMakeModules/osx_install/opencl_scripts/postinstall
index 0dd01a29f9..54ecb4df19 100755
--- a/CMakeModules/osx_install/opencl_scripts/postinstall
+++ b/CMakeModules/osx_install/opencl_scripts/postinstall
@@ -6,8 +6,10 @@ set -o pipefail
 err_file=/tmp/AFInstallerOpenCL.err
 brew=/usr/local/bin/brew
 
+echo $(date) > $err_file
+
 if [ ! -f $brew ]; then
-    osascript -e 'tell app "Finder" to display dialog "Brew not installed. Please install brew at brew.sh"'
+    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at brew.sh"'
     echo "Brew not found" >> $err_file
     exit 1
 fi
@@ -19,5 +21,14 @@ if [ -z $user ]; then
     exit 1
 fi
 
-su $user -c "$brew tap homebrew/versions" 2> $err_file
-su $user -c "$brew install glfw3 fontconfig" 2> $err_file
+function deps_err
+{
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
+    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
+    echo "Dependencies failed to install" >> $err_file
+    exit 1
+}
+
+su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+su $user -c "$brew install fftw glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/readme.html b/CMakeModules/osx_install/readme.html
index 41d4ab8cf0..2443b5fae3 100644
--- a/CMakeModules/osx_install/readme.html
+++ b/CMakeModules/osx_install/readme.html
@@ -5,18 +5,10 @@ <h2>Install Directories</h2>
     <ul>
         <li> Libraries will be installed in <code>/usr/local/lib</code> </li>
         <li> Headers will be installed in <code>/usr/local/include</code> </li>
-        <li> Docs and other files will be installed in <code>/usr/local/share</code> </li>
-    </ul>
-
-    <h2> Major Updates </h2>
-    <ul>
-        <li> ArrayFire is now open source</li>
-        <li> Major changes to the visualization library</li>
-        <li> Introducing handle based C API</li>
-        <li> New backend: CPU fallback available for systems without GPUs</li>
-        <li> Dense linear algebra functions available for all backends</li>
-        <li> Support for 64 bit integers</li>
+        <li> Examples, documentation and CMake config files will be installed in <code>/usr/local/share</code> </li>
     </ul>
+    <p> For complete list of updates, visit <a href="http://www.arrayfire.com/docs/releasenotes.htm">ArrayFire Release Notes</a></p>
 
+    <p> For questions about ArrayFire or this installer, visit <a href="https://groups.google.com/forum/#!forum/arrayfire-users"> ArrayFire User Forums</a> </p>
 </body>
 </html>
diff --git a/README.md b/README.md
index f43b9fd098..5b4a0548fa 100644
--- a/README.md
+++ b/README.md
@@ -1,139 +1,128 @@
 <a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="300"></a>
 
-ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its **array** based function set makes parallel programming simple.
+ArrayFire is a high performance software library for parallel computing with an
+easy-to-use API. Its **array** based function set makes parallel programming
+simple.
 
-ArrayFire's multiple backends (**CUDA**, **OpenCL** and native **CPU**) make it platform independent and highly portable.
+ArrayFire's multiple backends (**CUDA**, **OpenCL** and native **CPU**) make it
+platform independent and highly portable. ArrayFire provides visualization
+capabilities using our OpenGL-based,
+[high performance visualization library](https://github.com/arrayfire/forge).
 
-A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.
+A few lines of code in ArrayFire can replace dozens of lines of parallel
+computing code, saving you valuable time and lowering development costs.
 
-### Build ArrayFire from source
-To build ArrayFire from source, please follow the instructions on our [wiki](https://github.com/arrayfire/arrayfire/wiki).
-
-### Download ArrayFire Installers
-ArrayFire binary installers can be downloaded at the [ArrayFire Downloads](http://go.arrayfire.com/l/37882/2015-03-31/mmhqy) page.
-
-### Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-
-* Google Groups: https://groups.google.com/forum/#!forum/arrayfire-users
-* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
-* ArrayFire Blogs: http://arrayfire.com/blog/
-* Email: <mailto:technical@arrayfire.com>
-
-### Build Status
-|         | Linux x86 | Linux armv7l | Linux aarch64 | Windows | OSX |
-|:-------:|:---------:|:------------:|:-------------:|:-------:|:---:|
+|         | Linux x86_64 | Linux armv7l | Linux aarch64 | Windows | OSX |
+|:-------:|:------------:|:------------:|:-------------:|:-------:|:---:|
 | Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
 | Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
 
-Test coverage: [![Coverage Status](https://coveralls.io/repos/arrayfire/arrayfire/badge.svg?branch=HEAD)](https://coveralls.io/r/arrayfire/arrayfire?branch=HEAD)
+### Installation
 
-### Example
+You can install the ArrayFire library from one of the following ways:
 
-``` C++
+#### Official installers
 
-#include <arrayfire.h>
-#include <cstdio>
+Execute one of our [official binary installers](https://arrayfire.com/download)
+for Linux, OSX, and Windows platforms.
 
-using namespace af;
+#### Build from source
 
-int main(int argc, char *argv[])
-{
-    try {
+Build from source by following instructions on our
+[wiki](https://github.com/arrayfire/arrayfire/wiki).
 
-        // Select a device and display arrayfire info
-        int device = argc > 1 ? atoi(argv[1]) : 0;
-        af::setDevice(device);
-        af::info();
+### Examples
 
-        printf("Create a 5-by-3 matrix of random floats on the GPU\n");
-        array A = randu(5,3, f32);
-        af_print(A);
+The following examples are simplified versions of
+[`helloworld.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/helloworld/helloworld.cpp)
+and
+[`conway_pretty.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/graphics/conway_pretty.cpp),
+respectively. For more code examples, visit the
+[`examples/`](https://github.com/arrayfire/arrayfire/tree/devel/examples)
+directory.
 
-        printf("Element-wise arithmetic\n");
-        array B = sin(A) + 1.5;
-        af_print(B);
+#### Hello, world!
 
-        printf("Negate the first three elements of second column\n");
-        B(seq(0, 2), 1) = B(seq(0, 2), 1) * -1;
-        af_print(B);
+```cpp
+array A = randu(5, 3, f32); // Create 5x3 matrix of random floats on the GPU
+array B = sin(A) + 1.5;     // Element-wise arithmetic
+array C = fft(B);           // Fourier transform the result
 
-        printf("Fourier transform the result\n");
-        array C = fft(B);
-        af_print(C);
+float d[] = { 1, 2, 3, 4, 5, 6 };
+array D(2, 3, d, afHost);   // Create 2x3 matrix from host data
+D.col(0) = D.col(end);      // Copy last column onto first
 
-        printf("Grab last row\n");
-        array c = C.row(end);
-        af_print(c);
+array vals, inds;
+sort(vals, inds, A);        // Sort A and print sorted array and corresponding indices
+af_print(vals);
+af_print(inds);
+```
 
-        printf("Create 2-by-3 matrix from host data\n");
-        float d[] = { 1, 2, 3, 4, 5, 6 };
-        array D(2, 3, d, af::afHost);
-        af_print(D);
+#### Conway's Game of Life
 
-        printf("Copy last column onto first\n");
-        D.col(0) = D.col(end);
-        af_print(D);
+Visit the
+[Wikipedia page](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) for a
+description of Conway's Game of Life.
 
-        // Sort A
-        printf("Sort A and print sorted array and corresponding indices\n");
-        array vals, inds;
-        sort(vals, inds, A);
-        af_print(vals);
-        af_print(inds);
+```cpp
+static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
+static const array kernel(3, 3, h_kernel, afHost);
 
-    } catch (af::exception& e) {
-        fprintf(stderr, "%s\n", e.what());
-        throw;
-    }
+array state = (randu(128, 128, f32) > 0.5).as(f32); // Generate starting state
+Window myWindow(256, 256);
+while(!myWindow.close()) {
+  array nHood = convolve(state, kernel); // Obtain neighbors
+  array C0 = (nHood == 2);               // Generate conditions for life
+  array C1 = (nHood == 3);
+  state = state * C0 + C1;               // Update state
+  myWindow.image(state);                 // Display
 }
 
 ```
 
+<p align="center">
+<img src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif" alt="Conway's Game of Life" height="256" width="256">
+</p>
+
 ### Documentation
 
-You can find our complete documentation over [here](http://www.arrayfire.com/docs/index.htm).
+You can find our complete documentation [here](http://www.arrayfire.com/docs/index.htm).
 
 Quick links:
 
-- [Download Binaries](http://www.arrayfire.com/download/)
-- [List of functions](http://www.arrayfire.com/docs/group__arrayfire__func.htm)
-- [Tutorials](http://www.arrayfire.com/docs/gettingstarted.htm)
-- [Examples](http://www.arrayfire.com/docs/examples.htm)
+* [List of functions](http://www.arrayfire.org/docs/group__arrayfire__func.htm)
+* [Tutorials](http://www.arrayfire.org/docs/usergroup0.htm)
+* [Examples](http://www.arrayfire.org/docs/examples.htm)
+* [Blog](http://arrayfire.com/blog/)
 
-### Contribute
+### Language wrappers
 
-Contributions of any kind are welcome! Please refer to
-[this document](https://github.com/arrayfire/arrayfire/blob/master/CONTRIBUTING.md)
- to learn more about how you can get involved with ArrayFire.
+We currently support the following language wrappers for ArrayFire:
 
-## Citations and Acknowledgements
+* [`arrayfire-python`](https://github.com/arrayfire/arrayfire-python)
+* [`arrayfire-rust`](https://github.com/arrayfire/arrayfire-rust)
 
-If you redistribute ArrayFire, please follow the terms established in
-[the license](LICENSE).
-If you wish to cite ArrayFire in an academic publication, please use the
-following reference:
+Wrappers for other languages are a work in progress:
 
-Formatted:
-```
-Yalamanchili, P., Arshad, U., Mohammed, Z., Garigipati, P., Entschev, P.,
-Kloppenborg, B., Malcolm, J. and Melonakos, J. (2015).
-ArrayFire - A high performance software library for parallel computing with an
-easy-to-use API. Atlanta: AccelerEyes. Retrieved from https://github.com/arrayfire/arrayfire
-```
+[`arrayfire-dotnet`](https://github.com/arrayfire/arrayfire-dotnet), [`arrayfire-fortran`](https://github.com/arrayfire/arrayfire-fortran), [`arrayfire-go`](https://github.com/arrayfire/arrayfire-go), [`arrayfire-java`](https://github.com/arrayfire/arrayfire-java), [`arrayfire-lua`](https://github.com/arrayfire/arrayfire-lua), [`arrayfire-nodejs`](https://github.com/arrayfire/arrayfire-js), [`arrayfire-r`](https://github.com/arrayfire/arrayfire-r)
 
-BibTeX:
-```bibtex
-@misc{Yalamanchili2015,
-abstract = {ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming simple. ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable. A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.},
-address = {Atlanta},
-author = {Yalamanchili, Pavan and Arshad, Umar and Mohammed, Zakiuddin and Garigipati, Pradeep and Entschev, Peter and Kloppenborg, Brian and Malcolm, James and Melonakos, John},
-publisher = {AccelerEyes},
-title = {{ArrayFire - A high performance software library for parallel computing with an easy-to-use API}},
-url = {https://github.com/arrayfire/arrayfire},
-year = {2015}
-}
-```
+### Contributing
+
+Contributions of any kind are welcome! Please refer to
+[CONTRIBUTING.md](https://github.com/arrayfire/arrayfire/blob/master/CONTRIBUTING.md)
+to learn more about how you can get involved with ArrayFire.
+
+### Citations and Acknowledgements
+
+If you redistribute ArrayFire, please follow the terms established in
+[the license](LICENSE). If you wish to cite ArrayFire in an academic
+publication, please use the following [citation document](.github/CITATION.md).
 
 ArrayFire development is funded by ArrayFire LLC and several third parties,
-please see the list of [acknowledgements](https://github.com/arrayfire/arrayfire/blob/master/ACKNOWLEDGEMENTS.md) for further details.
+please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for further
+details.
 
+### Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+* [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
+* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
diff --git a/docs/details/data.dox b/docs/details/data.dox
index d1dcfa4044..d3f470d113 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -338,8 +338,8 @@ array/value is selected.
 
 \brief Replace elements of an array based on an conditional array
 
-If the condition array has an element as true, then the element is
-replaced by the array/value, otherwise no change.
+- Input values are retained when corresponding elements from condition array are true.
+- Input values are replaced when corresponding elements from condition array are false.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
diff --git a/docs/pages/INSTALL.md b/docs/pages/INSTALL.md
index 3565889571..d31affaefe 100644
--- a/docs/pages/INSTALL.md
+++ b/docs/pages/INSTALL.md
@@ -108,13 +108,14 @@ First install the prerequisite packages:
     # Prerequisite packages:
     sudo apt-get install libfreeimage-dev libatlas3gf-base libfftw3-dev cmake
 
-Ubuntu 14.04 will not have the libglfw3-dev package in its repositories. You can either build the library from source (following the instructions listed) or install the library from a PPA as follows:
-
-```
-sudo apt-add repository ppa:keithw/glfw3
-sudo apt-get update
-sudo apt-get install glfw3
-```
+Ubuntu 14.04 will not have the libglfw3-dev package in its repositories. You can either build the
+library from source (following the
+[instructions listed here](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire)) or
+install the library from a PPA as follows:
+
+    sudo apt-add-repository ppa:keithw/glfw3
+    sudo apt-get update
+    sudo apt-get install glfw3
 
 After this point, the installation should proceed identically to Ubuntu 14.10 or newer.
 
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 1063b054e3..2f3a447718 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,88 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.3.2
+==============
+
+Improvements
+------------
+* Family of [Sort](\ref sort_mat) functions now support
+  [higher order dimensions](https://github.com/arrayfire/arrayfire/pull/1373).
+* Improved performance of batched sort on dim 0 for all [Sort](\ref sort_mat) functions.
+* [Median](\ref stat_func_median) now also supports higher order dimensions.
+
+Bug Fixes
+--------------
+
+* Fixes to [error handling](https://github.com/arrayfire/arrayfire/issues/1352) in C++ API for binary functions.
+* Fixes to [external OpenCL context management](https://github.com/arrayfire/arrayfire/issues/1350).
+* Fixes to [JPEG_GREYSCALE](https://github.com/arrayfire/arrayfire/issues/1360) for FreeImage versions <= 3.154.
+* Fixed for [non-float inputs](https://github.com/arrayfire/arrayfire/issues/1386) to \ref af::rgb2gray().
+
+Build
+------
+* [Disable CPU Async](https://github.com/arrayfire/arrayfire/issues/1378) when building with GCC < 4.8.4.
+* Add option to [disable CPUID](https://github.com/arrayfire/arrayfire/issues/1369) from CMake.
+* More verbose message when [CUDA Compute Detection fails](https://github.com/arrayfire/arrayfire/issues/1362).
+* Print message to use [CUDA library stub](https://github.com/arrayfire/arrayfire/issues/1363)
+  from CUDA Toolkit if CUDA Library is not found from default paths.
+* [Build Fixes](https://github.com/arrayfire/arrayfire/pull/1385) on Windows.
+  * For compiling tests our of source.
+  * For compiling ArrayFire with static MKL.
+* [Exclude <sys/sysctl.h>](https://github.com/arrayfire/arrayfire/pull/1368) when building on GNU Hurd.
+* Add [manual CMake options](https://github.com/arrayfire/arrayfire/pull/1389) to build DEB and RPM packages.
+
+Documentation
+-------------
+* Fixed documentation for \ref af::replace().
+* Fixed images in [Using on OSX](\ref using_on_osx) page.
+
+Installer
+---------
+* Linux x64 installers will now be compiled with GCC 4.9.2.
+* OSX installer gives better error messages on brew failures and
+  now includes link to [Fixing OS X Installer Failures] (https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures)
+  for brew installation failures.
+
+v3.3.1
+==============
+
+Bug Fixes
+--------------
+
+* Fixes to \ref af::array::device()
+    * CPU Backend: [evaluate arrays](https://github.com/arrayfire/arrayfire/issues/1316)
+      before returning pointer with asynchronous calls in CPU backend.
+    * OpenCL Backend: [fix segfaults](https://github.com/arrayfire/arrayfire/issues/1324)
+      when requested for device pointers on empty arrays.
+* Fixed \ref af::array::operator%() from using [rem to mod](https://github.com/arrayfire/arrayfire/issues/1318).
+* Fixed [array destruction](https://github.com/arrayfire/arrayfire/issues/1321)
+  when backends are switched in Unified API.
+* Fixed [indexing](https://github.com/arrayfire/arrayfire/issues/1331) after
+  \ref af::moddims() is called.
+* Fixes FFT calls for CUDA and OpenCL backends when used on
+  [multiple devices](https://github.com/arrayfire/arrayfire/issues/1332).
+* Fixed [unresolved external](https://github.com/arrayfire/arrayfire/commit/32965ef)
+  for some functions from \ref af::array::array_proxy class.
+
+Build
+------
+* CMake compiles files in alphabetical order.
+* CMake fixes for BLAS and LAPACK on some Linux distributions.
+
+Improvements
+------------
+* Fixed [OpenCL FFT performance](https://github.com/arrayfire/arrayfire/issues/1323) regression.
+* \ref af::array::device() on OpenCL backend [returns](https://github.com/arrayfire/arrayfire/issues/1311)
+  `cl_mem` instead of `(void*)cl::Buffer*`.
+* In Unified backend, [load versioned libraries](https://github.com/arrayfire/arrayfire/issues/1312)
+  at runtime.
+
+Documentation
+------
+* Reorganized, cleaner README file.
+* Replaced non-free lena image in assets with free-to-distribute lena image.
+
 v3.3.0
 ==============
 
@@ -21,14 +103,34 @@ Features
 * [Scatter plot](https://github.com/arrayfire/arrayfire/pull/1116) added to graphics.
 * \ref af::transform() now supports perspective transformation matrices.
 * \ref af::infoString(): Returns `af::info()` as a string.
+* \ref af::printMemInfo(): Print a table showing information about buffer from the memory manager
+    * The \ref AF_MEM_INFO macro prints numbers and total sizes of all buffers (requires including af/macros.h)
 * \ref af::allocHost(): Allocates memory on host.
 * \ref af::freeHost(): Frees host side memory allocated by arrayfire.
-* Functions specific to OpenCl backend.
+* OpenCL functions can now use CPU implementation.
+    * Currently limited to Unified Memory devices (CPU and On-board Graphics).
+    * Functions: af::matmul() and all [LAPACK](\ref linalg_mat) functions.
+    * Takes advantage of optimized libraries such as MKL without doing memory copies.
+    * Use the environment variable `AF_OPENCL_CPU_OFFLOAD=1` to take advantage of this feature.
+* Functions specific to OpenCL backend.
     * \ref afcl::addDevice(): Adds an external device and context to ArrayFire's device manager.
     * \ref afcl::deleteDevice(): Removes an external device and context from ArrayFire's device manager.
     * \ref afcl::setDevice(): Sets an external device and context from ArrayFire's device manager.
     * \ref afcl::getDeviceType(): Gets the device type of the current device.
     * \ref afcl::getPlatform(): Gets the platform of the current device.
+* \ref af::createStridedArray() allows [array creation user-defined strides](https://github.com/arrayfire/arrayfire/issues/1177) and device pointer.
+* [Expose functions](https://github.com/arrayfire/arrayfire/issues/1131) that provide information
+  about memory layout of Arrays.
+    * \ref af::getStrides(): Gets the strides for each dimension of the array.
+    * \ref af::getOffset(): Gets the offsets for each dimension of the array.
+    * \ref af::getRawPtr(): Gets raw pointer to the location of the array on device.
+    * \ref af::isLinear(): Returns true if all elements in the array are contiguous.
+    * \ref af::isOwner(): Returns true if the array owns the raw pointer, false if it is a sub-array.
+    * \ref af::getStrides(): Gets the strides of the array.
+    * \ref af::getStrides(): Gets the strides of the array.
+* \ref af::getDeviceId(): Gets the device id on which the array resides.
+* \ref af::isImageIOAvailable(): Returns true if ArrayFire was compiled with Freeimage enabled
+* \ref af::isLAPACKAvailable(): Returns true if ArrayFire was compiled with LAPACK functions enabled
 
 Bug Fixes
 --------------
@@ -38,6 +140,16 @@ Bug Fixes
 * Fixed [imageio bugs](https://github.com/arrayfire/arrayfire/pull/1229) for 16 bit images.
 * Fixed [bugs when loading and storing images](https://github.com/arrayfire/arrayfire/pull/1228) natively.
 * Fixed [bug in FFT for NVIDIA GPUs](https://github.com/arrayfire/arrayfire/issues/615) when using OpenCL backend.
+* Fixed [bug when using external context](https://github.com/arrayfire/arrayfire/pull/1241) with OpenCL backend.
+* Fixed [memory leak](https://github.com/arrayfire/arrayfire/issues/1269) in \ref af_median_all().
+* Fixed [memory leaks and performance](https://github.com/arrayfire/arrayfire/pull/1274) in graphics functions.
+* Fixed [bugs when indexing followed by moddims](https://github.com/arrayfire/arrayfire/issues/1275).
+* \ref af_get_revision() now returns actual commit rather than AF_REVISION.
+* Fixed [releasing arrays](https://github.com/arrayfire/arrayfire/issues/1282) when using different backends.
+* OS X OpenCL: [LAPACK functions](\ref linalg_mat) on CPU devices use OpenCL offload (previously threw errors).
+* [Add support for 32-bit integer image types](https://github.com/arrayfire/arrayfire/pull/1287) in Image IO.
+* Fixed [set operations for row vectors](https://github.com/arrayfire/arrayfire/issues/1300)
+* Fixed [bugs](https://github.com/arrayfire/arrayfire/issues/1243) in \ref af::meanShift() and af::orb().
 
 Improvements
 --------------
@@ -46,6 +158,10 @@ Improvements
 * Performance improvements to the memory manager.
 * Error messages are now more detailed.
 * Improved sorted order for OpenCL devices.
+* JIT heuristics can now be tweaked using environment variables. See
+  [Environment Variables](\ref configuring_environment) tutorial.
+* Add `BUILD_<BACKEND>` [options to examples and tests](https://github.com/arrayfire/arrayfire/issues/1286)
+  to toggle backends when compiling independently.
 
 Examples
 ----------
@@ -57,6 +173,17 @@ Build
 
 * Support for Intel `icc` compiler
 * Support to compile with Intel MKL as a BLAS and LAPACK provider
+* Tests are now available for building as standalone (like examples)
+* Tests can now be built as a single file for each backend
+* Better handling of NONFREE build options
+* [Searching for GLEW in CMake default paths](https://github.com/arrayfire/arrayfire/pull/1292)
+* Fixes for compiling with MKL on OSX.
+
+Installers
+----------
+* Improvements to OSX Installer
+    * CMake config files are now installed with libraries
+    * Independent options for installing examples and documentation components
 
 Deprecations
 -----------
@@ -67,8 +194,15 @@ Deprecations
 Documentation
 --------------
 
-* Fixes to documentation for matchTemplate.
+* Fixes to documentation for \ref matchTemplate().
 * Improved documentation for deviceInfo.
+* Fixes to documentation for \ref exp().
+
+Known Issues
+------------
+
+* [Solve OpenCL fails on NVIDIA Maxwell devices](https://github.com/arrayfire/arrayfire/issues/1246)
+  for f32 and c32 when M > N and K % 4 is 1 or 2.
 
 
 v3.2.2
@@ -270,7 +404,7 @@ Bug Fixes
 Documentation Updates
 ---------------------
 * Improved tutorials documentation
-    * More detailed Using on [Linux](\ref using_on_windows), [OSX](\ref using_on_windows),
+    * More detailed Using on [Linux](\ref using_on_linux), [OSX](\ref using_on_osx),
       [Windows](\ref using_on_windows) pages.
 * Added return type information for functions that return different type
   arrays
diff --git a/docs/pages/using_on_osx.md b/docs/pages/using_on_osx.md
index ccb0fb523a..ef7e1e4255 100644
--- a/docs/pages/using_on_osx.md
+++ b/docs/pages/using_on_osx.md
@@ -27,75 +27,12 @@ any build system to create and compile projects that use ArrayFire.
 Among the many possible build systems on Linux we suggest using ArrayFire with
 either CMake or Makefiles with CMake being our preferred build system.
 
-## XCode
+## Build Instructions:
+* [CMake](#CMake)
+* [MakeFiles](#MakeFiles)
+* [XCode](#XCode)
 
-Although we recommend using CMake to build ArrayFire projects on OSX, you can
-use XCode if this is your preferred development platform.
-To save some time, we have created an sample XCode project in our 
-[ArrayFire Project Templates repository](https://github.com/arrayfire/arrayfire-project-templates).
-
-To set up a basic C/C++ project in XCode do the following:
-
-1. Start up XCode. Choose OSX -> Application, Command Line Tool for the project:
-<img src="xcode-setup/xcode-startup.png" alt="Create a command line too XCode Project" width="100%" />
-
-2. Fill in the details for your project and choose either C or C++ for the project:
-<img src="xcode-setup/project-options.png" alt="Create a C/C++ project" width="100%" />
-
-3. Next we need to configure the build settings. In the left-hand pane, click
-   on the project. In the center pane, click on "Build Settings" followed by
-   the "All" button:
-<img src="xcode-setup/build-settings.png" alt="Configure build settings" width="100%" />
-
-4. Now search for "Header Search Paths" and add `/usr/local/include` to the list:
-<img src="xcode-setup/header-search-paths.png" alt="Configure build settings" width="100%" />
-
-5. Then search for "Library Search Paths" and add `/usr/local/lib` to the list:
-<img src="xcode-setup/library-search-paths.png" alt="Configure build settings" width="100%" />
-
-6. Next, we need to make sure the executable is linked with an ArrayFire library:
-   To do this, click the "Build Phases" tab and expand the "Link with Binary Library"
-   menu:
-<img src="xcode-setup/build-phases.png" alt="Configure build settings" width="100%" />
-
-7. In the search dialog that pops up, choose the "Add Other" button from the
-   lower right. Specify the `/usr/local/lib` folder:
-<img src="xcode-setup/library-folder-path.png" alt="Configure build settings" width="100%" />
-
-8. Lastly, select the ArrayFire library with which you wish to link your program.
-  Your options will be:
-
-~~~~~
-libafcuda.*.dylib   - CUDA backend
-libafopencl.*.dylib - OpenCL backend
-libafcpu.*.dylib    - CPU backend
-libaf.*.dylib       - Unified backend
-~~~~~
-
-In the picture below, we have elected to link with the OpenCL backend:
-
-<img src="xcode-setup/pick-arrayfire-library.png" alt="Configure build settings" width="100%" />
-
-9. Lastly, lets test ArrayFire's functionality. In the left hand pane open
-   the main.cpp` file and insert the following code:
-
-~~~~~
-// Include the ArrayFire header file
-#include <arrayfire.h>
-
-int main(int argc, const char * argv[]) {
-    // Gather some information about the ArrayFire device
-    af::info();
-    return 0;
-}
-~~~~~
-
-Finally, click the build button and you should see some information about your
-graphics card in the lower-section of your screen:
-
-<img src="xcode-setup/afinfo-result.png" alt="Configure build settings" width="100%" />
-
-## CMake
+## <a name="CMake"></a>CMake
 
 We recommend that the CMake build system be used to create ArrayFire projects.
 If you are writing a new ArrayFire project in C/C++ from scratch, we suggest
@@ -193,7 +130,7 @@ would modify the `cmake` command above to contain the following definition:
 
 You can also specify this information in the ccmake command-line interface.
 
-## MakeFiles
+## <a name="MakeFiles"></a> MakeFiles
 
 Building ArrayFire projects with Makefiles is fairly similar to CMake except
 you must specify all paths and libraries manually.
@@ -217,3 +154,113 @@ Here is a minimial example MakeFile which uses ArrayFire's CPU backend:
 
     all: main.cpp Makefile
         $(CC) main.cpp -o test $(INCLUDES) $(LIBS) $(LIB_PATHS)
+
+## <a name="XCode"></a> XCode
+
+Although we recommend using CMake to build ArrayFire projects on OSX, you can
+use XCode if this is your preferred development platform.
+To save some time, we have created an sample XCode project in our
+[ArrayFire Project Templates repository](https://github.com/arrayfire/arrayfire-project-templates).
+
+To set up a basic C/C++ project in XCode do the following:
+
+1. Start up XCode. Choose OSX -> Application, Command Line Tool for the project:
+\htmlonly
+<br />
+<a href="xcode-setup/xcode-startup.png">
+<img src="xcode-setup/xcode-startup.png" alt="Create a command line too XCode Project" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+2. Fill in the details for your project and choose either C or C++ for the project:
+\htmlonly
+<br />
+<a href="xcode-setup/project-options.png">
+<img src="xcode-setup/project-options.png" alt="Create a C/C++ project" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+3. Next we need to configure the build settings. In the left-hand pane, click
+   on the project. In the center pane, click on "Build Settings" followed by
+   the "All" button:
+\htmlonly
+<br />
+<a href="xcode-setup/build-settings.png">
+<img src="xcode-setup/build-settings.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+4. Now search for "Header Search Paths" and add `/usr/local/include` to the list:
+\htmlonly
+<br />
+<a href="xcode-setup/header-search-paths.png">
+<img src="xcode-setup/header-search-paths.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+5. Then search for "Library Search Paths" and add `/usr/local/lib` to the list:
+\htmlonly
+<br />
+<a href="xcode-setup/library-search-paths.png">
+<img src="xcode-setup/library-search-paths.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+6. Next, we need to make sure the executable is linked with an ArrayFire library:
+   To do this, click the "Build Phases" tab and expand the "Link with Binary Library"
+   menu:
+\htmlonly
+<br />
+<a href="xcode-setup/build-phases.png">
+<img src="xcode-setup/build-phases.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+7. In the search dialog that pops up, choose the "Add Other" button from the
+   lower right. Specify the `/usr/local/lib` folder:
+\htmlonly
+<br />
+<a href="xcode-setup/library-folder-path.png">
+<img src="xcode-setup/library-folder-path.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+8. Lastly, select the ArrayFire library with which you wish to link your program.
+  Your options will be:
+~~~~~
+libafcuda.*.dylib   - CUDA backend
+libafopencl.*.dylib - OpenCL backend
+libafcpu.*.dylib    - CPU backend
+libaf.*.dylib       - Unified backend
+~~~~~
+In the picture below, we have elected to link with the OpenCL backend:
+\htmlonly
+<br />
+<a href="xcode-setup/pick-arrayfire-library.png">
+<img src="xcode-setup/pick-arrayfire-library.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
+
+9. Lastly, lets test ArrayFire's functionality. In the left hand pane open
+   the main.cpp` file and insert the following code:
+
+~~~~~
+// Include the ArrayFire header file
+#include <arrayfire.h>
+
+int main(int argc, const char * argv[]) {
+    // Gather some information about the ArrayFire device
+    af::info();
+    return 0;
+}
+~~~~~
+
+Finally, click the build button and you should see some information about your
+graphics card in the lower-section of your screen:
+
+\htmlonly
+<br />
+<a href="xcode-setup/afinfo-result.png">
+<img src="xcode-setup/afinfo-result.png" alt="Configure build settings" align="middle" width="50%" />
+</a>
+\endhtmlonly
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index be0f6407be..9418bf056b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -76,6 +76,7 @@ ENDMACRO()
 
 # Collect the source
 FILE(GLOB FILES "*/*.cpp")
+LIST(SORT FILES)
 ADD_DEFINITIONS("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
 # Next we build each example using every backend.
@@ -100,6 +101,7 @@ ENDIF()
 
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
+        # Find NVVM
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
           NAMES "nvvm"
           PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
@@ -107,6 +109,26 @@ IF (${CUDA_FOUND})
           DOC "CUDA NVVM Library"
           )
         MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+
+        # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
+        IF(NOT CUDA_CUDA_LIBRARY)
+            MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
+            MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
+            FIND_FILE(CUDA_CUDA_LIBRARY_STUB
+                      NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
+                      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+                      PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
+                      DOC "CUDA Library STUB"
+                     )
+            IF(CUDA_CUDA_LIBRARY_STUB)
+                MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
+                MESSAGE("Run the following commands (Linux) to set it up:")
+                MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
+                MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
+            ENDIF()
+            MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
+        ENDIF(NOT CUDA_CUDA_LIBRARY)
+
         OPTION(BUILD_CUDA "Build ArrayFire Examples for CUDA backend" ON)
         BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_NVVM_LIBRARY};${CUDA_CUDA_LIBRARY}")
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 3fc19d8c65..6acccf8813 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -10,62 +10,161 @@
 #include <arrayfire.h>
 #include <iostream>
 #include <cstdio>
+#include <vector>
+#include "gravity_sim_init.h"
 
 using namespace af;
 using namespace std;
 
-static const int width = 512, height = 512;
-static const int pixels_per_unit = 20;
+static const bool is3D = true; const static int total_particles = 4000;
+static const int reset = 3000;
+static const float min_dist = 3;
+static const int width = 768, height = 768, depth = 768;
+static const float eps = 10.f;
+static const int gravity_constant = 20000;
+
+float mass_range = 0;
+float min_mass   = 0;
+
+void initial_conditions_rand(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
+    for(int i=0; i<pos.size(); ++i) {
+        pos[i]    = af::randn(total_particles) * width + width;
+        vels[i]   = 0 * af::randu(total_particles) - 0.5;
+        forces[i] = af::constant(0, total_particles);
+    }
+    mass = af::constant(gravity_constant, total_particles);
+}
+
+void initial_conditions_galaxy(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
+    af::array initial_cond_consts(af::dim4(7, total_particles), hbd);
+    initial_cond_consts = initial_cond_consts.T();
+
+    for(int i=0; i<pos.size(); ++i) {
+        pos[i]    = af::randn(total_particles) * width + width;
+        vels[i]   = 0 * (af::randu(total_particles) - 0.5);
+        forces[i] = af::constant(0, total_particles);
+    }
+
+    mass    =  initial_cond_consts(span, 0);
+    pos[0]  = (initial_cond_consts(span, 1)/32 + 0.6) * width;
+    pos[1]  = (initial_cond_consts(span, 2)/32 + 0.3) * height;
+    pos[2]  = (initial_cond_consts(span, 3)/32 + 0.5) * depth;
+    vels[0] = (initial_cond_consts(span, 4)/32) * width;
+    vels[1] = (initial_cond_consts(span, 5)/32) * height;
+    vels[2] = (initial_cond_consts(span, 6)/32) * depth;
+
+    pos[0](seq(0, pos[0].dims(0)-1, 2)) -=  0.4 * width;
+    pos[1](seq(0, pos[0].dims(0)-1, 2)) +=  0.4 * height;
+    vels[0](seq(0, pos[0].dims(0)-1, 2)) +=  4;
+
+    min_mass = min<float>(mass);
+    mass_range = max<float>(mass) - min<float>(mass);
+}
+
+af::array ids_from_pos(vector<af::array> &pos) {
+    return (pos[0].as(u32) * height) + pos[1].as(u32);
+}
+
+af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz) {
+    af::array x0  = (pos[0] - width/2);
+    af::array y0  = (pos[1] - height/2) * cos(Rx) + (pos[2] - depth/2) * sin(Rx);
+    af::array z0  = (pos[2] - depth/2)  * cos(Rx) - (pos[2] - depth/2) * sin(Rx);
+
+    af::array x1  =  x0*cos(Ry) - z0*sin(Ry);
+    af::array y1  =  y0;
+
+    af::array x2  =  x1*cos(Rz) + y1*sin(Rz);
+    af::array y2  =  y1*cos(Rz) - x1*sin(Rz);
+
+    x2 += width/2;
+    y2 += height/2;
+
+    return (x2.as(u32) * height) + y2.as(u32);
+}
+
+af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz, af::array filter) {
+    af::array x0  = (pos[0](filter) - width/2);
+    af::array y0  = (pos[1](filter) - height/2) * cos(Rx) + (pos[2](filter) - depth/2) * sin(Rx);
+    af::array z0  = (pos[2](filter) - depth/2)  * cos(Rx) - (pos[2](filter) - depth/2) * sin(Rx);
+
+    af::array x1  =  x0*cos(Ry) - z0*sin(Ry);
+    af::array y1  =  y0;
+
+    af::array x2  =  x1*cos(Rz) + y1*sin(Rz);
+    af::array y2  =  y1*cos(Rz) - x1*sin(Rz);
+
+    x2 += width/2;
+    y2 += height/2;
 
-void simulate(af::array *pos, af::array *vels, af::array *forces, float dt){
-    pos[0] += vels[0] * pixels_per_unit * dt;
-    pos[1] += vels[1] * pixels_per_unit * dt;
+    return (x2.as(u32) * height) + y2.as(u32);
+}
+
+
+void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces, float dt) {
+    for(int i=0; i<pos.size(); ++i) {
+        pos[i] += vels[i] * dt;
+        pos[i].eval();
+    }
+
+    //calculate forces to each particle
+    vector<af::array> diff(pos.size());
+    af::array dist = af::constant(0, pos[0].dims(0),pos[0].dims(0));
 
-    //calculate distance to center
-    af::array diff_x = pos[0] - width/2;
-    af::array diff_y = pos[1] - height/2;
-    af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
+    for(int i=0; i<pos.size(); ++i) {
+        diff[i] = tile(pos[i], 1, pos[i].dims(0)) - transpose(tile(pos[i], 1, pos[i].dims(0)));
+        dist += (diff[i]*diff[i]);
+    }
 
-    //calculate normalised force vectors
-    forces[0] = -1 * diff_x / dist;
-    forces[1] = -1 * diff_y / dist;
-    //update force scaled to time and magnitude constant
-    forces[0] *= pixels_per_unit * dt;
-    forces[1] *= pixels_per_unit * dt;
+    dist = sqrt(dist);
+    dist = af::max(min_dist, dist);
+    dist *= dist * dist;
 
-    //dampening
-    vels[0] *= 1 - (0.005*dt);
-    vels[1] *= 1 - (0.005*dt);
+    for(int i=0; i<pos.size(); ++i) {
+        //calculate force vectors
+        forces[i] = diff[i] / dist;
+        forces[i].eval();
 
-    //update velocities from forces
-    vels[0] += forces[0];
-    vels[1] += forces[1];
+        //af::array idx = af::where(af::isNaN(forces[i]));
+        //if(idx.elements() > 0)
+        //    forces[i](idx) = 0;
+        //forces[i] = sum(forces[i]).T();
+        forces[i] = matmul(forces[i].T(), mass);
 
+        //update force scaled to time, magnitude constant
+        forces[i] *= (gravity_constant);
+        forces[i].eval();
+
+        //update velocities from forces
+        vels[i] += forces[i] * dt;
+        vels[i].eval();
+
+        //noise
+        //forces[i] += 0.1 * af::randn(forces[i].dims(0));
+
+        //dampening
+        //vels[i] *= 1 - (0.005*dt);
+    }
 }
 
-void collisions(af::array *pos, af::array *vels){
+void collisions(vector<af::array> &pos, vector<af::array> &vels, bool is3D) {
     //clamp particles inside screen border
-    af::array projected_px = min(width, max(0, pos[0]));
+    af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
+    af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
+    //af::array invalid_x = (pos[0] < width-1 || pos[0] > 0);
+    //af::array invalid_y = (pos[1] < height-1 || pos[1] > 0);
+    vels[0]= invalid_x * vels[0] ;
+    vels[1]= invalid_y * vels[1] ;
+
+    af::array projected_px = min(width-1, max(0, pos[0]));
     af::array projected_py = min(height - 1, max(0, pos[1]));
-
-    //calculate distance to center
-    af::array diff_x = projected_px - width/2;
-    af::array diff_y = projected_py - height/2;
-    af::array dist = sqrt( diff_x*diff_x + diff_y*diff_y );
-
-    //collide with center sphere
-    const int radius = 50;
-    const float elastic_constant = 0.91f;
-    if(sum<int>(dist<radius) > 0) {
-        vels[0](dist<radius) = -elastic_constant * vels[0](dist<radius);
-        vels[1](dist<radius) = -elastic_constant * vels[1](dist<radius);
-
-        //normalize diff vector
-        diff_x /= dist;
-        diff_y /= dist;
-        //place all particle colliding with sphere on surface
-        pos[0](dist<radius) = width/2 + diff_x(dist<radius) * radius;
-        pos[1](dist<radius) = height/2 +  diff_y(dist<radius) * radius;
+    pos[0] = projected_px;
+    pos[1] = projected_py;
+
+    if(is3D){
+        af::array invalid_z = -2 * (pos[2] > depth-1 || pos[2] < 0) + 1;
+        vels[2]= invalid_z * vels[2] ;
+        af::array projected_pz = min(depth - 1, max(0, pos[2]));
+        pos[2] = projected_pz;
     }
 }
 
@@ -73,31 +172,26 @@ void collisions(af::array *pos, af::array *vels){
 int main(int argc, char *argv[])
 {
     try {
-        const static int total_particles = 1000;
-        static const int reset = 500;
 
         af::info();
 
         af::Window myWindow(width, height, "Gravity Simulation using ArrayFire");
+        myWindow.setColorMap(AF_COLORMAP_HEAT);
 
         int frame_count = 0;
 
         // Initialize the kernel array just once
-        const af::array draw_kernel = gaussianKernel(3, 3);
+        const af::array draw_kernel = gaussianKernel(7, 7);
 
-        af::array pos[2];
-        af::array vels[2];
-        af::array forces[2];
+        const int dims = (is3D)? 3 : 2;
 
-        // Generate a random starting state
-        pos[0] = af::randu(total_particles) * width;
-        pos[1] = af::randu(total_particles) * height;
-
-        vels[0] = af::randn(total_particles);
-        vels[1] = af::randn(total_particles);
+        vector<af::array> pos(dims);
+        vector<af::array> vels(dims);
+        vector<af::array> forces(dims);
+        af::array         mass;
 
-        forces[0] = af::randn(total_particles);
-        forces[1] = af::randn(total_particles);
+        // Generate a random starting state
+        initial_conditions_galaxy(mass, pos, vels, forces);
 
         af::array image = af::constant(0, width, height);
         af::array ids(total_particles, u32);
@@ -107,27 +201,36 @@ int main(int argc, char *argv[])
             float dt = af::timer::stop(timer);
             timer = af::timer::start();
 
-            ids = (pos[0].as(u32) * height) + pos[1].as(u32);
-            image(ids) += 255;
-            image = convolve2(image, draw_kernel);
+            af::array mid = mass(span) > (min_mass + mass_range/3);
+            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0, mid) :  ids_from_pos(pos);
+            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            image(ids) += 4.f;
+
+            mid = mass(span) > (min_mass + 2*mass_range/3);
+            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0, mid) :  ids_from_pos(pos);
+            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            image(ids) += 4.f;
+
+            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0) :  ids_from_pos(pos);
+            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            image(ids) += 4.f;
+
+            image = convolve(image, draw_kernel);
             myWindow.image(image);
             image = af::constant(0, image.dims());
+
             frame_count++;
 
             // Generate a random starting state
             if(frame_count % reset == 0) {
-                pos[0] = af::randu(total_particles) * width;
-                pos[1] = af::randu(total_particles) * height;
-
-                vels[0] = af::randn(total_particles);
-                vels[1] = af::randn(total_particles);
+                initial_conditions_galaxy(mass, pos, vels, forces);
             }
 
-            //check for collisions and adjust positions/velocities accordingly
-            collisions(pos, vels);
+            //simulate
+            simulate(mass, pos, vels, forces, dt);
 
-            //run force simulation and update particles
-            simulate(pos, vels, forces, dt);
+            //check for collisions and adjust positions/velocities accordingly
+            collisions(pos, vels, is3D);
 
         }
     } catch (af::exception& e) {
diff --git a/examples/graphics/gravity_sim_init.h b/examples/graphics/gravity_sim_init.h
new file mode 100644
index 0000000000..6596a2615a
--- /dev/null
+++ b/examples/graphics/gravity_sim_init.h
@@ -0,0 +1,4006 @@
+const int HBD_NUM_ELEMENTS = 4000 * 7;
+//halo, bulge, and disk particles
+float hbd[] = {
+ 4.9161855e-03, -1.5334119e+00, -8.3381424e+00,  4.4288845e+00, -2.3778248e-01,  4.2592272e-02, -4.4895774e-01,
+ 4.9161855e-03,  1.9886702e-02,  6.0085773e+00,  3.1188631e-01,  8.1422836e-01, -1.4591325e-02,  7.5382882e-01,
+ 4.9161855e-03,  1.1676190e+00, -4.6193779e-01, -5.0477743e-01, -1.4803666e+00,  5.6056118e-01, -2.9858449e-02,
+ 4.9161855e-03, -1.4250363e+00,  1.0891747e+01,  2.5225203e+00, -6.5798134e-02, -3.5946497e-01,  1.7471495e-01,
+ 4.9161855e-03, -3.7135857e-01,  4.8796633e-01, -3.7898597e-01,  8.5347527e-01,  2.2493289e-01, -2.7678892e-01,
+ 4.9161855e-03,  2.2072470e+00, -2.5046587e+00,  2.6029270e+00,  3.0826443e-01,  5.8606583e-01,  2.0105042e-01,
+ 4.9161855e-03,  1.0779227e+00, -4.0834007e+00, -3.3965745e+00, -4.8430148e-01, -7.1573091e-01,  1.2384786e-01,
+ 4.9161855e-03, -3.8722844e+00, -4.2357988e+00, -1.9723746e+00,  3.5759529e-01,  4.8990592e-01, -4.3040028e-01,
+ 4.9161855e-03, -1.3005282e-01, -2.3483203e-01,  1.3832784e-01,  1.3746375e+00, -1.2947829e+00,  6.1215276e-01,
+ 4.9161855e-03,  3.6822948e-01,  4.2760900e-01,  1.1544695e+00, -2.3177411e-02, -6.9136995e-01, -6.6200425e-03,
+ 4.9161855e-03, -1.2485707e+00,  2.0474775e-01, -2.1652168e-01,  2.7034196e-01,  1.6398503e+00, -7.8224945e-01,
+ 4.9161855e-03, -3.3862705e+00,  1.2049110e+00,  1.0672448e+00, -1.6531572e-01, -2.4370559e-01,  8.7125647e-01,
+ 4.9161855e-03,  3.4262960e+00,  3.9102471e+00,  6.6162848e-01,  7.8005123e-01, -1.0415094e-01,  5.0161743e-01,
+ 4.9161855e-03,  1.5740298e-01,  1.3008093e+00,  7.8130345e+00, -1.6444305e-01,  3.3037327e-03,  1.9713788e-01,
+ 4.9161855e-03,  5.6700945e-01,  1.8889900e-01,  2.7523971e+00, -3.4313673e-01, -6.4287108e-01, -1.8927544e-01,
+ 4.9161855e-03,  1.8354661e+00,  1.3209668e+00,  1.6966065e+00,  5.3318393e-01,  3.4129089e-01, -8.0587679e-01,
+ 4.9161855e-03, -7.8488460e+00,  3.2376931e+00,  2.6638079e+00,  3.4405673e-01, -2.1986680e-01,  1.6776933e-01,
+ 4.9161855e-03,  3.2422847e-01, -1.2311785e+00,  9.0597588e-01,  3.6714745e-01, -1.3913552e-01,  9.0002306e-02,
+ 4.9161855e-03, -1.9477528e-01, -2.3987198e+00, -4.2354431e+00, -2.1188869e-01, -6.4195746e-01,  1.5219630e-01,
+ 4.9161855e-03,  3.2330542e+00,  1.1787817e+00, -1.3654234e+00,  1.9920348e-01, -1.0560199e+00, -4.0022919e-01,
+ 4.9161855e-03, -2.2656450e+00,  2.3343153e+00,  3.0343585e+00,  1.3909769e-01, -5.8018422e-01,  7.7305830e-01,
+ 4.9161855e-03,  1.0106117e+01,  8.4062157e+00, -5.3659506e+00, -3.3819172e-01, -5.7871189e-02, -5.2655820e-02,
+ 4.9161855e-03, -8.4759682e-02, -2.4386784e-01,  2.2389056e-01, -8.3496273e-01,  1.1504352e+00,  3.2196254e-03,
+ 4.9161855e-03, -4.8354459e+00, -1.1709679e+01, -4.4684467e+00, -3.7076837e-01,  2.6136923e-01, -1.4268482e-01,
+ 4.9161855e-03, -1.3268198e+00, -2.3238692e+00,  6.7897618e-01,  3.0518329e-01,  6.8463421e-01, -7.1791840e-01,
+ 4.9161855e-03, -5.2054877e+00,  2.0948052e+00,  1.9656231e+00,  7.4416548e-01,  4.4825464e-01, -3.2727838e-01,
+ 4.9161855e-03, -8.2616639e-01,  1.0700088e+00,  3.5586545e+00,  4.8024514e-01,  1.1944018e-01,  3.0837712e-01,
+ 4.9161855e-03, -2.9101398e+00, -3.6366568e+00,  8.7982547e-01,  3.6643305e-01, -3.8197124e-01, -1.1440479e-01,
+ 4.9161855e-03,  3.5198438e-01,  4.9096385e-01, -6.6494130e-02, -1.0383745e-01,  3.9406076e-01,  7.3723292e-01,
+ 4.9161855e-03, -6.9214082e+00, -5.5405111e+00, -2.3041859e+00,  3.3985880e-01,  1.0167535e-02,  1.0593475e-01,
+ 4.9161855e-03,  1.0908546e+00, -5.3155913e+00, -4.5045247e+00,  1.8077201e-01, -4.4904891e-01,  4.7391072e-01,
+ 4.9161855e-03, -1.0766581e-01,  6.7338924e+00,  6.1174130e+00, -2.3362583e-01,  7.6430768e-02, -2.4832390e-01,
+ 4.9161855e-03, -4.9775305e-01,  1.6378751e+00, -2.6263945e+00, -3.0084690e-01, -5.1551086e-01, -6.6373748e-01,
+ 4.9161855e-03, -3.8946674e+00, -1.4725525e+00,  2.4148097e+00, -1.7075756e-01,  5.3592271e-01,  7.2393781e-01,
+ 4.9161855e-03,  6.8583161e-02, -1.5991354e+00, -3.0150402e-01,  1.5219669e-01, -5.6440836e-01,  1.5284424e+00,
+ 4.9161855e-03, -4.2822695e+00,  4.0367408e+00, -2.2387395e+00,  1.0239060e-01,  3.2810995e-01, -1.4511149e-01,
+ 4.9161855e-03,  5.3348875e-01, -3.6950427e-01,  1.0364149e+00,  7.8612208e-02, -2.7073494e-01,  1.9663854e-01,
+ 4.9161855e-03, -3.3353384e+00,  4.3220544e+00, -1.5343003e+00,  6.7457032e-01, -1.8098858e-01,  7.6241505e-01,
+ 4.9161855e-03, -8.8430309e+00,  6.6101489e+00,  2.2365890e+00, -2.9622875e-03, -5.7892501e-01,  2.3848678e-01,
+ 4.9161855e-03, -2.7121809e+00, -3.7584829e+00,  2.4702384e+00,  3.9350358e-01, -6.7748266e-01, -5.7142133e-01,
+ 4.9161855e-03,  1.7517463e+00, -5.2237463e-01,  1.2052536e+00,  2.6133826e-01, -4.3084338e-01, -2.8758329e-01,
+ 4.9161855e-03, -4.4221100e-01,  2.4987850e-01, -9.0834004e-01, -1.6435069e+00, -3.5537782e-01, -5.6679737e-02,
+ 4.9161855e-03,  9.5630264e+00,  7.2472978e-01, -2.7188256e+00,  4.1388586e-01, -2.7986884e-01,  9.9171564e-02,
+ 4.9161855e-03, -2.5304942e+00, -1.9891304e-01, -1.3565568e+00,  1.6445565e-01,  6.5720814e-01,  8.8133616e-04,
+ 4.9161855e-03, -6.8739529e+00,  6.0871582e+00,  4.0246663e+00, -1.1313155e-01,  2.6078510e-01,  1.1052500e-02,
+ 4.9161855e-03,  1.8411478e-01,  6.3666153e-01, -1.7665352e+00,  7.3893017e-01,  8.2843482e-02,  1.3584135e-01,
+ 4.9161855e-03,  1.2281631e-01, -4.8358020e-01, -4.2862403e-01, -1.4062686e+00,  2.6675841e-01, -5.2812093e-01,
+ 4.9161855e-03, -1.8010849e+00,  2.5018549e+00, -1.1007906e+00, -3.0198583e-01, -2.5083411e-01, -9.4572407e-01,
+ 4.9161855e-03,  2.9228494e-02,  2.8824418e+00, -7.7373713e-01, -8.9457905e-01, -3.9830649e-01, -8.2690775e-01,
+ 4.9161855e-03, -4.8449464e+00, -3.5136631e+00,  2.6319263e+00,  2.3270021e-01,  6.2155128e-01, -6.9675374e-01,
+ 4.9161855e-03, -2.4690704e-01, -3.6131024e+00,  5.7440319e+00, -5.6087500e-01, -2.9587632e-01, -7.5861102e-01,
+ 4.9161855e-03,  5.2307582e+00,  2.1941881e+00, -4.2112174e+00,  2.3945954e-01,  2.5676125e-01,  3.2575151e-01,
+ 4.9161855e-03,  4.8397323e-01,  3.7831066e+00,  4.4692445e+00,  2.4802294e-02,  6.5026706e-01, -1.1542060e-02,
+ 4.9161855e-03,  7.9952207e+00,  4.5379916e-01,  1.4309001e-01, -2.2018740e-01, -2.1911193e-01, -4.8267773e-01,
+ 4.9161855e-03, -2.0976503e+00, -2.4728169e-01,  6.3614302e+00, -7.4839890e-02, -4.1690156e-01, -1.7862423e-01,
+ 4.9161855e-03,  3.4107253e-01, -1.2668414e+00,  1.2606201e+00,  3.6496368e-01, -3.5874972e-01, -1.0340087e+00,
+ 4.9161855e-03,  8.9313567e-01,  3.6050075e-01,  3.4469640e-01, -8.6372048e-01, -6.3587260e-01,  7.4591488e-01,
+ 4.9161855e-03,  2.9728930e+00, -5.2957177e+00, -7.3298526e+00, -1.9522749e-01, -2.2528295e-01,  1.9373624e-01,
+ 4.9161855e-03, -1.7334032e+00,  1.9857804e+00, -4.9017177e+00, -6.8124956e-01,  8.3835334e-01, -7.8357399e-02,
+ 4.9161855e-03,  2.0978465e+00,  1.9166039e+00,  1.0677823e+00, -2.6128739e-01, -9.3216664e-01,  8.0752736e-01,
+ 4.9161855e-03, -2.6831132e-01,  1.6412498e-01, -5.8062166e-01, -3.9843372e-01,  1.5403072e+00, -2.5054911e-01,
+ 4.9161855e-03,  1.7003990e+00,  3.3006930e+00, -1.7119979e+00, -1.0552487e-01, -8.4340447e-01,  9.8853576e-01,
+ 4.9161855e-03, -5.5339479e+00,  4.8888919e-01,  9.1028652e+00,  4.6380356e-01, -4.4314775e-01,  3.4938701e-03,
+ 4.9161855e-03, -3.9364102e+00, -3.4606054e+00,  2.2803564e+00,  1.2712850e-01, -3.2586256e-01, -6.5546811e-02,
+ 4.9161855e-03, -6.6842210e-01, -8.6578093e-02, -9.9518037e-01,  3.0050567e-01, -1.3251954e+00, -6.3900441e-01,
+ 4.9161855e-03, -1.7707565e+00, -2.3981299e+00, -2.8610508e+00,  8.0815405e-02,  2.6192275e-01, -4.4141706e-02,
+ 4.9161855e-03,  5.2352209e+00,  4.3753624e+00,  5.2761130e+00, -3.6126247e-01, -3.6049706e-01, -5.0132203e-01,
+ 4.9161855e-03,  4.0741138e+00, -2.7320893e+00, -5.8015996e-01, -3.3409804e-01, -7.4342436e-01, -8.1080115e-01,
+ 4.9161855e-03,  1.0308882e+01,  3.3621982e-01, -1.2449891e+01, -2.8561455e-01, -1.0982110e-01, -1.0319072e-02,
+ 4.9161855e-03,  8.3470430e+00, -9.4488649e+00, -6.6161261e+00, -2.6525149e-01,  5.0971325e-02,  5.4980908e-02,
+ 4.9161855e-03, -4.8979187e-01, -2.1835434e+00,  1.3237199e+00, -2.0376731e-01, -4.8289922e-01, -1.9313942e-01,
+ 4.9161855e-03,  3.8070815e+00, -4.1728072e+00,  6.8302398e+00,  2.1417937e-01, -5.6412149e-02,  9.7045694e-03,
+ 4.9161855e-03, -1.7183731e+00,  1.7611129e+00,  5.8284336e-01,  1.2992284e-01, -1.3527862e+00, -4.3186599e-01,
+ 4.9161855e-03, -1.1291479e+01, -3.0248559e+00, -6.1554856e+00, -6.8934292e-02, -3.0177805e-01, -1.8667488e-01,
+ 4.9161855e-03, -2.3688557e+00,  7.7071247e+00, -2.0670973e-01, -2.1208389e-01,  2.8578773e-01,  2.0644853e-01,
+ 4.9161855e-03,  8.2679868e-01, -2.1197610e+00,  1.0767980e+00,  2.4679126e-01, -4.0421063e-01, -5.7845503e-01,
+ 4.9161855e-03,  4.1475649e+00, -4.3077379e-01,  5.4239964e+00,  7.0667878e-02,  4.9151066e-01, -5.2980289e-02,
+ 4.9161855e-03, -7.7668630e-02, -4.1514721e+00, -8.0719125e-01, -4.2308268e-01, -5.9619360e-03, -5.4758888e-01,
+ 4.9161855e-03,  7.3864212e+00, -7.1388471e-01,  4.2682199e+00,  8.6512074e-02, -3.9517093e-01,  3.4532326e-01,
+ 4.9161855e-03,  3.1821191e+00,  5.0156546e+00, -7.2775478e+00,  3.8633448e-01,  4.1517708e-01, -4.7167987e-01,
+ 4.9161855e-03, -5.5158086e+00, -1.8736273e+00,  1.2083918e+00, -5.2377588e-01, -5.1698190e-01, -1.7996560e-01,
+ 4.9161855e-03, -7.5245118e-01, -5.0066152e+00, -3.6176472e+00, -1.4140940e-01,  4.9951354e-01, -5.1893300e-01,
+ 4.9161855e-03,  1.7928425e+00,  2.7725005e+00, -2.2401933e-02, -8.6086380e-01, -3.3671090e-01,  8.4016019e-01,
+ 4.9161855e-03,  5.5359507e+00, -1.0514329e+01,  3.6608188e+00, -1.5433036e-01, -7.8473240e-03,  2.5746456e-01,
+ 4.9161855e-03,  1.8312926e+00, -6.6526437e-01, -1.4381752e+00, -1.5768304e-01,  4.5808712e-01,  4.9162623e-01,
+ 4.9161855e-03,  5.4815245e+00, -3.7619928e-01,  3.7529993e-01, -3.4403029e-01, -1.9848712e-02,  3.1211856e-01,
+ 4.9161855e-03, -2.8452486e-01,  1.0852966e+00, -7.1417332e-01,  8.5701519e-01, -1.9785182e-01,  7.2242868e-01,
+ 4.9161855e-03,  1.6400850e+00,  6.0924044e+00, -6.7533379e+00, -1.4117804e-01, -2.7584502e-01,  1.8720052e-01,
+ 4.9161855e-03,  5.8992994e-01, -1.4057723e+00,  1.7555045e+00,  3.0828384e-01, -1.7618947e-01,  5.7791591e-01,
+ 4.9161855e-03,  3.2523406e+00,  6.4261597e-01, -3.2577946e+00,  4.3461993e-03,  1.6368487e-01, -2.7604485e-01,
+ 4.9161855e-03, -4.4885483e+00,  2.9889661e-01,  7.7495706e-01,  8.4083831e-01, -6.1657476e-01, -2.8107607e-01,
+ 4.9161855e-03, -8.8879662e+00,  6.2833142e-01, -1.1011785e+01,  4.1822538e-01,  1.0211676e-01, -3.1296456e-01,
+ 4.9161855e-03,  2.7859297e+00, -3.9616172e+00, -9.8269482e+00,  1.1758713e-01, -3.9799199e-01,  3.1546867e-01,
+ 4.9161855e-03,  4.7954245e+00, -3.0205333e-01,  2.0376158e+00, -8.4786171e-01,  3.1084442e-01, -2.9132118e-02,
+ 4.9161855e-03, -2.5424831e+00, -2.2019272e+00,  1.2129050e+00, -7.6038790e-01,  1.3783433e-01, -2.2782549e-02,
+ 4.9161855e-03, -1.7519760e+00,  4.8521647e-01,  6.5459456e+00,  2.1810593e-01, -1.0864632e-01, -2.8022933e-01,
+ 4.9161855e-03,  1.1203793e+01,  3.8465612e+00, -7.5724998e+00, -3.2845536e-01, -5.3839471e-02, -8.3486214e-02,
+ 4.9161855e-03, -3.2320779e-02, -3.1065380e-02,  6.4219080e-02, -2.2246722e-02,  5.6946766e-01,  1.1582422e-01,
+ 4.9161855e-03, -9.3361330e-01,  4.6081281e+00, -3.0114322e+00, -6.3036418e-01, -1.4130452e-01, -7.0592797e-01,
+ 4.9161855e-03,  6.5746963e-01, -2.6720290e+00,  1.4632640e+00, -7.3338515e-01, -9.7944528e-01,  1.1936308e-01,
+ 4.9161855e-03, -1.2494113e+01, -1.0112607e+00, -6.1200657e+00, -4.6759155e-01, -1.0928699e-01,  1.0739395e-02,
+ 4.9161855e-03,  1.4548665e+00, -1.5041708e+00,  4.7451344e+00,  5.3424448e-01, -2.7125362e-01,  1.3840736e-01,
+ 4.9161855e-03,  9.2012796e+00, -4.8018866e+00, -6.6422758e+00, -2.6537961e-01,  2.8879899e-01, -2.9193002e-01,
+ 4.9161855e-03, -3.7384963e+00,  2.0661526e+00,  7.5109011e-01, -4.0893826e-01,  2.1268708e-01, -3.2584268e-01,
+ 4.9161855e-03,  1.2519404e+00,  7.4001670e+00, -4.9840989e+00, -2.6203468e-01, -2.9252869e-01, -1.5676203e-01,
+ 4.9161855e-03,  1.8744209e+00, -2.2234895e+00,  8.1060524e+00, -1.5346730e-01, -6.9368631e-01,  2.6046190e-01,
+ 4.9161855e-03, -1.4101373e+00,  1.0645522e+00, -5.6520933e-01,  1.4722762e-01,  1.4932915e+00, -1.1569133e-01,
+ 4.9161855e-03,  1.4165136e+00,  3.5563886e+00,  1.1791783e-01, -3.3764324e-01, -7.5716054e-01,  3.2871431e-01,
+ 4.9161855e-03,  1.6921350e+00,  4.4273725e+00, -4.7639960e-01, -5.4349893e-01,  3.2590839e-01, -8.8562638e-01,
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+ 4.2524221e-04,  1.2091810e-01, -1.8473221e-01,  3.7591079e-01,  1.7912203e-01,  7.1378611e-03,  5.6433028e-01,
+ 4.2524221e-04, -3.0588778e-02, -8.0224700e-02,  2.0911565e-01,  1.7871276e-01, -4.5090526e-01,  1.7313591e-01,
+ 4.2524221e-04,  2.1592773e-01, -1.0682704e-01, -1.4687291e-01, -2.1309285e-01,  3.2003528e-01,  9.6824163e-01,
+ 4.2524221e-04, -7.1326107e-02, -1.8375346e-01,  1.6073698e-01,  6.6706583e-02, -2.2058874e-01, -1.6864805e-01,
+ 4.2524221e-04, -4.4198960e-02, -1.1312663e-01,  1.0822348e-01,  1.3487945e-01, -7.0401341e-01, -1.2007080e+00,
+ 4.2524221e-04, -2.9746767e-02, -1.3425194e-01, -2.5086749e-01, -1.1511848e-01, -8.7276441e-01,  1.6036594e-01,
+ 4.2524221e-04,  1.7037044e-01,  1.7299759e-01,  4.6205060e-03,  5.1056665e-01,  1.0041865e+00,  2.3419438e-01,
+ 4.2524221e-04,  1.6252996e-01,  1.1271755e-01,  4.6216175e-02,  5.6226152e-01,  6.6637951e-01,  5.3371119e-01,
+ 4.2524221e-04, -1.9546813e-01,  1.3906172e-01, -5.5975009e-02, -1.0969467e-01, -1.2633232e+00, -4.3421894e-02,
+ 4.2524221e-04, -1.4044075e-01, -2.6630515e-01,  6.1962787e-02,  4.6771467e-01, -6.9051319e-01,  2.6465434e-01,
+ 4.2524221e-04,  1.7195286e-01, -5.2851868e-01, -1.6422449e-01,  1.1703679e-01,  7.2824037e-01, -3.6378372e-01,
+ 4.2524221e-04,  1.0194746e-01, -9.7751893e-02,  1.6529745e-01,  2.4984296e-01,  3.8181201e-02,  2.7078211e-01,
+ 4.2524221e-04,  2.0533490e-01,  1.9480339e-01, -6.6993818e-02,  3.9745870e-01, -7.9133675e-02, -1.1942380e-01,
+ 4.2524221e-04, -3.9208923e-02,  9.8150961e-02,  1.0030308e-01, -5.7831265e-02, -6.4350224e-01,  8.4775603e-01,
+ 4.2524221e-04,  1.3816082e-01, -1.4092979e-02, -1.0894109e-01,  2.8519067e-01,  5.8030725e-01,  6.5652287e-01,
+ 4.2524221e-04,  3.1362314e-02, -6.5740333e-03,  6.7480214e-02,  4.2265895e-01, -5.1995921e-01, -2.8980300e-02,
+ 4.2524221e-04, -1.1953717e-01,  1.5453845e-01,  1.3720915e-01, -1.5399654e-01, -1.2724885e-01,  6.4902240e-01,
+ 4.2524221e-04, -2.4549389e-01, -7.9987049e-02,  8.9279823e-02, -9.2930816e-02, -6.1336237e-01,  4.7973198e-01,
+ 4.2524221e-04,  2.5360553e-02, -2.6513871e-02,  5.4526389e-02, -9.8100655e-02,  6.5327984e-01, -5.2721924e-01,
+ 4.2524221e-04, -1.0606319e-01, -6.9447577e-02,  4.3061398e-02, -1.0653659e+00,  6.2340677e-01,  4.6419606e-02
+};
diff --git a/examples/helloworld/helloworld.cpp b/examples/helloworld/helloworld.cpp
index c3f891a0d1..ad34e58fa2 100644
--- a/examples/helloworld/helloworld.cpp
+++ b/examples/helloworld/helloworld.cpp
@@ -43,6 +43,15 @@ int main(int argc, char *argv[])
         array c = C.row(end);
         af_print(c);
 
+        printf("Scan Test\n");
+        dim4 dims(16, 4, 1, 1);
+        array r = constant(2, dims);
+        af_print(r);
+
+        printf("Scan\n");
+        array S = af::scan(r, 0, AF_MUL);
+        af_print(S);
+
         printf("Create 2-by-3 matrix from host data\n");
         float d[] = { 1, 2, 3, 4, 5, 6 };
         array D(2, 3, d, afHost);
diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index 51351aaff9..8a5e3da917 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -72,7 +72,7 @@ void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
     array maximum = max(in);
 
     gfor(seq ii, d) {
-        data(span, span, ii) = (in(span, span, ii) - minimum(ii)) / maximum(ii);
+        data(span, span, ii) = (in(span, span, ii) - minimum(ii).scalar<float>()) / maximum(ii).scalar<float>();
     }
 
     // Initial guess of means
@@ -112,7 +112,7 @@ int kmeans_demo(int k, bool console)
 {
     printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);
 
-    array img = loadImage(ASSETS_DIR"/examples/images/lena.ppm", true) / 255; // [0-255]
+    array img = loadImage(ASSETS_DIR"/examples/images/vegetable-woman.jpg", true) / 255; // [0-255]
 
     int w = img.dims(0), h = img.dims(1), c = img.dims(2);
     array vec = moddims(img, w * h, 1, c);
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 0d3b39fda9..db89ff99bd 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -1,6 +1,7 @@
 #include <math.h>
 #include <stdio.h>
 #include <string.h>
+#include <stdlib.h>
 #include <math.h>
 #include <arrayfire.h>
 #include "../common/progress.h"
@@ -18,42 +19,63 @@ array normalize(array a, float max)
 
 static void swe(bool console)
 {
-    double time_total = 20; // run for N seconds
+    double time_total = 40; // run for N seconds
     // Grid length, number and spacing
-    const unsigned Lx = 512, nx = Lx + 1;
-    const unsigned Ly = 512, ny = Ly + 1;
+    const unsigned Lx = 1600, nx = Lx + 1;
+    const unsigned Ly = 1600, ny = Ly + 1;
     const float dx = Lx / (nx - 1);
     const float dy = Ly / (ny - 1);
 
     array ZERO = constant(0, nx, ny);
     array um = ZERO, vm = ZERO;
-    unsigned io = (unsigned)floor(Lx  / 5.0f),
-             jo = (unsigned)floor(Ly / 5.0f),
-             k = 20;
+    unsigned io = (unsigned)floor(Lx  / 6.0f),
+             jo = (unsigned)floor(Ly / 6.0f),
+             k = 15;
     array x = tile(moddims(seq(nx),nx,1), 1,ny);
     array y = tile(moddims(seq(ny),1,ny), nx,1);
 
-    // Initial condition
-    array etam = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+    //initial condition
+    array etam  = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
     float m_eta = max<float>(etam);
-    array eta = etam;
+    array eta   = etam;
     float dt = 0.5;
 
     // conv kernels
     float h_diff_kernel[] = {9.81f * (dt / dx), 0, -9.81f * (dt / dx)};
-    float h_lap_kernel[] = {0, 1, 0, 1, -4, 1, 0, 1, 0};
+    float h_lap_kernel[] = { 0,  1,  0,
+                             1, -4,  1,
+                             0,  1,  0 };
 
     array h_diff_kernel_arr(3, h_diff_kernel);
     array h_lap_kernel_arr(3, 3, h_lap_kernel);
 
     if(!console) {
-        win = new Window(512, 512,"Shallow Water Equations");
-        win->setColorMap(AF_COLORMAP_MOOD);
+        win = new Window(1536, 768,"Shallow Water Equations");
+        win->setColorMap(AF_COLORMAP_BLUE);
+        win->grid(2, 2);
     }
 
     timer t = timer::start();
     unsigned iter = 0;
-    while (progress(iter, t, time_total)) {
+    unsigned random_interval = 30;
+
+    while (!win->close()) {
+        if( iter>2000 ) {
+            // Initial condition
+            etam = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+            m_eta = max<float>(etam);
+            eta = etam;
+            iter = 0;
+        }
+
+        //raindrops
+        if(iter % 100 == 0 || iter % 130 == 0 || iter % random_interval == 0) {
+            unsigned io = (unsigned)floor(rand() % Lx),
+                     jo = (unsigned)floor(rand() % Ly);
+            random_interval = rand() % 200 + 1;
+            eta += 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+        }
+
         // compute
         array up = um + convolve(eta, h_diff_kernel_arr);
         array vp = um + convolve(eta, h_diff_kernel_arr.T());
@@ -62,13 +84,20 @@ static void swe(bool console)
 
         etam = eta;
         eta = etap;
+
+        m_eta = max<float>(etam);
         if (!console) {
-            win->image(normalize(eta, m_eta));
-            // viz
+            (*win)(0,0).image(normalize(eta, m_eta));
+            array hist_out = histogram(normalize(eta, m_eta), 15);
+            (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
+            (*win)(1,0).plot(seq(up.dims(1)), vp.col(0), "Pressure at left boundary");
+            (*win)(1,1).plot3(join(1, flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0))), "Gradients versus Magnitude at left boundary"); // viz
+            win->show();
         } else eval(eta, up, vp);
         iter++;
     }
 }
+
 int main(int argc, char* argv[])
 {
     int device = argc > 1 ? atoi(argv[1]) : 0;
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 792d6e2f44..1ae9c7903d 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -316,6 +316,21 @@ namespace af
     */
     AFAPI array accum(const array &in, const int dim = 0);
 
+#if AF_API_VERSION >=34
+    /**
+       C++ Interface exclusive sum (cumulative sum) of an array
+
+       \param[in] in is the input array
+       \param[in] dim The dimension along which exclusive sum is performed
+       \param[in] op is the type of binary operations used
+       \param[in] inclusive_scan is flag specifying whether scan is inclusive
+       \return the output containing exclusive sums of the input
+
+       \ingroup scan_func_scan
+    */
+    AFAPI array scan(const array &in, const int dim = 0, af_binary_op op = AF_ADD, bool inclusive_scan = true);
+#endif
+
     /**
        C++ Interface for finding the locations of non-zero values in an array
 
@@ -357,8 +372,6 @@ namespace af
        \return the sorted output
 
        \ingroup sort_func_sort
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI array sort(const array &in, const unsigned dim = 0, const bool isAscending = true);
 
@@ -372,8 +385,6 @@ namespace af
        \param[in] isAscending specifies the sorting order
 
        \ingroup sort_func_sort_index
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI void  sort(array &out, array &indices, const array &in, const unsigned dim = 0,
                      const bool isAscending = true);
@@ -388,8 +399,6 @@ namespace af
        \param[in] isAscending specifies the sorting order
 
        \ingroup sort_func_sort_keys
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI void  sort(array &out_keys, array &out_values, const array &keys, const array &values,
                      const unsigned dim = 0, const bool isAscending = true);
@@ -749,6 +758,22 @@ extern "C" {
     */
     AFAPI af_err af_accum(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >=34
+    /**
+       C Interface generalized scan of an array
+
+       \param[out] out will contain exclusive sums of the input
+       \param[in] in is the input array
+       \param[in] dim The dimension along which exclusive sum is performed
+       \param[in] op is the type of binary operations used
+       \param[in] inclusive_scan is flag specifying whether scan is inclusive
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup scan_func_scan
+    */
+    AFAPI af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan);
+#endif
+
     /**
        C Interface for finding the locations of non-zero values in an array
 
@@ -794,8 +819,6 @@ extern "C" {
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup sort_func_sort
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending);
 
@@ -810,8 +833,6 @@ extern "C" {
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup sort_func_sort_index
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
                                const unsigned dim, const bool isAscending);
@@ -827,8 +848,6 @@ extern "C" {
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup sort_func_sort_keys
-
-       \note \p dim is currently restricted to 0.
     */
     AFAPI af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                                 const af_array keys, const af_array values,
diff --git a/include/af/backend.h b/include/af/backend.h
index 0770feb5b1..94c4951d45 100644
--- a/include/af/backend.h
+++ b/include/af/backend.h
@@ -68,7 +68,7 @@ AFAPI af_err af_get_active_backend(af_backend *backend);
 
 #if AF_API_VERSION >= 33
 /**
-   \param[out] dev contains the device on which \p in was created.
+   \param[out] device contains the device on which \p in was created.
    \param[in] in is the array who's device is to be queried.
    \returns \ref af_err error code
 
diff --git a/include/af/data.h b/include/af/data.h
index 5808833644..f402b9e085 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -517,9 +517,11 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is the array containing elements which replace elements in \p a when \p cond is false
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -528,9 +530,11 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the array whose values are replaced with values from \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is value that replaces elements in \p a when \p cond is false
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement value.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -836,9 +840,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout] a is the array whose values are replaced by \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is the array containing elements that replaces elements of a where \p cond is false
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -847,9 +853,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout] a is the array whose values are replaced by \p b when \p cond is false
-       \param[in]  cond is the conditional array
-       \param[in]  b is the scalar that replaces the false parts of \p a
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
diff --git a/include/af/defines.h b/include/af/defines.h
index 77508f2870..04e06ea38d 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -50,10 +50,6 @@
     typedef long long   dim_t;
 #endif
 
-#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
-#define USE_CPUID
-#endif
-
 #include <stdlib.h>
 
 typedef long long intl;
@@ -385,6 +381,17 @@ typedef enum {
     AF_ID = 0
 } af_someenum_t;
 
+#if AF_API_VERSION >=34
+typedef enum {
+    AF_ADD  = 0,
+    AF_SUB  = 1,
+    AF_MUL  = 2,
+    AF_DIV  = 3,
+    AF_MIN  = 4,
+    AF_MAX  = 5
+} af_binary_op;
+#endif
+
 #if AF_API_VERSION >=32
 typedef enum {
     AF_MARKER_NONE         = 0,
@@ -427,6 +434,9 @@ namespace af
 #if AF_API_VERSION >= 32
     typedef af_marker_type markerType;
 #endif
+#if AF_API_VERSION >= 34
+    typedef af_binary_op binaryOp;
+#endif
 }
 
 #endif
diff --git a/include/af/device.h b/include/af/device.h
index b08bd519b3..62971025da 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -428,6 +428,8 @@ extern "C" {
 
        The device pointer \p ptr is notfreed by memory manager until \ref af_unlock_device_ptr is called.
        \ingroup device_func_mem
+
+       \note For OpenCL backend *ptr should be cast to cl_mem.
     */
     AFAPI af_err af_get_device_ptr(void **ptr, const af_array arr);
 
diff --git a/include/af/graphics.h b/include/af/graphics.h
index 7485686479..b69a83854a 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -289,6 +289,17 @@ class AFAPI Window {
         */
         bool close();
 
+#if AF_API_VERSION >= 33
+        /**
+           Hide/Show the window
+
+           \param[in] isVisible indicates if the window is to be hidden or brought into focus
+
+           \ingroup gfx_func_window
+         */
+        void setVisibility(const bool isVisible);
+#endif
+
         /**
            This function is used to keep track of which cell in the grid mode is
            being currently rendered. When a user does Window(0,0), we internally
@@ -547,6 +558,18 @@ AFAPI af_err af_show(const af_window wind);
 */
 AFAPI af_err af_is_window_closed(bool *out, const af_window wind);
 
+#if AF_API_VERSION >= 33
+/**
+   Hide/Show a window
+
+   \param[in] wind is the window whose visibility is to be changed
+   \param[in] is_visible indicates if the window is to be hidden or brought into focus
+
+   \ingroup gfx_func_window
+ */
+AFAPI af_err af_set_visibility(const af_window wind, const bool is_visible);
+#endif
+
 /**
    C Interface wrapper for destroying a window handle
 
diff --git a/include/af/opencl.h b/include/af/opencl.h
index 16b85d763f..34206325eb 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -100,9 +100,7 @@ AFAPI af_err afcl_set_device_id(cl_device_id id);
                   parameter is NULL, then we create a command queue for the user using the OpenCL
                   context they provided us.
 
-   \note The cl_* objects are passed onto c++ objects (cl::Device, cl::Context & cl::CommandQueue)
-   that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
-   be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 AFAPI af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que);
 #endif
@@ -127,9 +125,7 @@ AFAPI af_err afcl_set_device_context(cl_device_id dev, cl_context ctx);
    \param[in] dev is the OpenCL device id that has to be popped
    \param[in] ctx is the cl_context object to be removed from ArrayFire pool
 
-   \note Any reference counts incremented for cl_* objects by ArrayFire internally are decremented
-   by this func call and you won't be able to call `afcl_set_device_context` on these objects after
-   this function has been called.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 AFAPI af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx);
 #endif
@@ -245,9 +241,7 @@ namespace afcl
                   parameter is NULL, then we create a command queue for the user using the OpenCL
                   context they provided us.
 
-   \note The cl_* objects are passed onto c++ objects (cl::Device, cl::Context & cl::CommandQueue)
-   that are defined in the `cl.hpp` OpenCL c++ header provided by Khronos Group Inc. Therefore, please
-   be aware of the lifetime of the cl_* objects before passing them to ArrayFire.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 static inline void addDevice(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
@@ -280,9 +274,7 @@ static inline void setDevice(cl_device_id dev, cl_context ctx)
    \param[in] dev is the OpenCL device id that has to be popped
    \param[in] ctx is the cl_context object to be removed from ArrayFire pool
 
-   \note Any reference counts incremented for cl_* objects by ArrayFire internally are decremented
-   by this func call and you won't be able to call `afcl_set_device_context` on these objects after
-   this function has been called.
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
 */
 static inline void deleteDevice(cl_device_id dev, cl_context ctx)
 {
@@ -450,10 +442,10 @@ namespace af
 #if !defined(AF_OPENCL)
 template<> AFAPI cl_mem *array::device() const
 {
-    cl_mem *mem = new cl_mem;
-    af_err err = af_get_device_ptr((void **)mem, get());
+    cl_mem *mem_ptr = new cl_mem;
+    af_err err = af_get_device_ptr((void **)mem_ptr, get());
     if (err != AF_SUCCESS) throw af::exception("Failed to get cl_mem from array object");
-    return mem;
+    return mem_ptr;
 }
 #endif
 
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index cefdde1d75..80b0d85e60 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -9,6 +9,10 @@
 #include <handle.hpp>
 #include <ArrayInfo.hpp>
 #include <platform.hpp>
+#include <handle.hpp>
+#include <backend.hpp>
+
+using namespace detail;
 
 const ArrayInfo&
 getInfo(const af_array arr, bool check)
@@ -22,6 +26,265 @@ getInfo(const af_array arr, bool check)
     return *info;
 }
 
+af_err af_get_data_ptr(void *data, const af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+        switch(type) {
+        case f32:   copyData(static_cast<float    *>(data), arr);  break;
+        case c32:   copyData(static_cast<cfloat   *>(data), arr);  break;
+        case f64:   copyData(static_cast<double   *>(data), arr);  break;
+        case c64:   copyData(static_cast<cdouble  *>(data), arr);  break;
+        case b8:    copyData(static_cast<char     *>(data), arr);  break;
+        case s32:   copyData(static_cast<int      *>(data), arr);  break;
+        case u32:   copyData(static_cast<unsigned *>(data), arr);  break;
+        case u8:    copyData(static_cast<uchar    *>(data), arr);  break;
+        case s64:   copyData(static_cast<intl     *>(data), arr);  break;
+        case u64:   copyData(static_cast<uintl    *>(data), arr);  break;
+        case s16:   copyData(static_cast<short    *>(data), arr);  break;
+        case u16:   copyData(static_cast<ushort   *>(data), arr);  break;
+        default:    TYPE_ERROR(1, type);
+        }
+    }
+    CATCHALL
+        return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_create_array(af_array *result, const void * const data,
+                       const unsigned ndims, const dim_t * const dims,
+                       const af_dtype type)
+{
+    try {
+        af_array out;
+        AF_CHECK(af_init());
+
+        dim4 d = verifyDims(ndims, dims);
+
+        switch(type) {
+        case f32:   out = createHandleFromData(d, static_cast<const float   *>(data)); break;
+        case c32:   out = createHandleFromData(d, static_cast<const cfloat  *>(data)); break;
+        case f64:   out = createHandleFromData(d, static_cast<const double  *>(data)); break;
+        case c64:   out = createHandleFromData(d, static_cast<const cdouble *>(data)); break;
+        case b8:    out = createHandleFromData(d, static_cast<const char    *>(data)); break;
+        case s32:   out = createHandleFromData(d, static_cast<const int     *>(data)); break;
+        case u32:   out = createHandleFromData(d, static_cast<const uint    *>(data)); break;
+        case u8:    out = createHandleFromData(d, static_cast<const uchar   *>(data)); break;
+        case s64:   out = createHandleFromData(d, static_cast<const intl    *>(data)); break;
+        case u64:   out = createHandleFromData(d, static_cast<const uintl   *>(data)); break;
+        case s16:   out = createHandleFromData(d, static_cast<const short   *>(data)); break;
+        case u16:   out = createHandleFromData(d, static_cast<const ushort  *>(data)); break;
+        default:    TYPE_ERROR(4, type);
+        }
+        std::swap(*result, out);
+    }
+    CATCHALL
+        return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * const dims,
+                        const af_dtype type)
+{
+    try {
+        af_array out;
+        AF_CHECK(af_init());
+
+        dim4 d((size_t)dims[0]);
+        for(unsigned i = 1; i < ndims; i++) {
+            d[i] = dims[i];
+        }
+
+        switch(type) {
+        case f32:   out = createHandle<float  >(d); break;
+        case c32:   out = createHandle<cfloat >(d); break;
+        case f64:   out = createHandle<double >(d); break;
+        case c64:   out = createHandle<cdouble>(d); break;
+        case b8:    out = createHandle<char   >(d); break;
+        case s32:   out = createHandle<int    >(d); break;
+        case u32:   out = createHandle<uint   >(d); break;
+        case u8:    out = createHandle<uchar  >(d); break;
+        case s64:   out = createHandle<intl   >(d); break;
+        case u64:   out = createHandle<uintl  >(d); break;
+        case s16:   out = createHandle<short  >(d); break;
+        case u16:   out = createHandle<ushort >(d); break;
+        default:    TYPE_ERROR(3, type);
+        }
+        std::swap(*result, out);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_copy_array(af_array *out, const af_array in)
+{
+    try {
+        ArrayInfo info = getInfo(in);
+        const af_dtype type = info.getType();
+
+        af_array res;
+        switch(type) {
+        case f32:   res = copyArray<float   >(in); break;
+        case c32:   res = copyArray<cfloat  >(in); break;
+        case f64:   res = copyArray<double  >(in); break;
+        case c64:   res = copyArray<cdouble >(in); break;
+        case b8:    res = copyArray<char    >(in); break;
+        case s32:   res = copyArray<int     >(in); break;
+        case u32:   res = copyArray<uint    >(in); break;
+        case u8:    res = copyArray<uchar   >(in); break;
+        case s64:   res = copyArray<intl    >(in); break;
+        case u64:   res = copyArray<uintl   >(in); break;
+        case s16:   res = copyArray<short   >(in); break;
+        case u16:   res = copyArray<ushort  >(in); break;
+        default:    TYPE_ERROR(1, type);
+        }
+        std::swap(*out, res);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+//Strong Exception Guarantee
+af_err af_get_data_ref_count(int *use_count, const af_array in)
+{
+    try {
+        ArrayInfo info = getInfo(in);
+        const af_dtype type = info.getType();
+
+        int res;
+        switch(type) {
+        case f32:   res = getArray<float   >(in).useCount(); break;
+        case c32:   res = getArray<cfloat  >(in).useCount(); break;
+        case f64:   res = getArray<double  >(in).useCount(); break;
+        case c64:   res = getArray<cdouble >(in).useCount(); break;
+        case b8:    res = getArray<char    >(in).useCount(); break;
+        case s32:   res = getArray<int     >(in).useCount(); break;
+        case u32:   res = getArray<uint    >(in).useCount(); break;
+        case u8:    res = getArray<uchar   >(in).useCount(); break;
+        case s64:   res = getArray<intl    >(in).useCount(); break;
+        case u64:   res = getArray<uintl   >(in).useCount(); break;
+        case s16:   res = getArray<short   >(in).useCount(); break;
+        case u16:   res = getArray<ushort  >(in).useCount(); break;
+        default:    TYPE_ERROR(1, type);
+        }
+        std::swap(*use_count, res);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_release_array(af_array arr)
+{
+    try {
+        int dev = getActiveDeviceId();
+
+        ArrayInfo info = getInfo(arr, false);
+
+        setDevice(info.getDevId());
+
+        af_dtype type = info.getType();
+
+        switch(type) {
+        case f32:   releaseHandle<float   >(arr); break;
+        case c32:   releaseHandle<cfloat  >(arr); break;
+        case f64:   releaseHandle<double  >(arr); break;
+        case c64:   releaseHandle<cdouble >(arr); break;
+        case b8:    releaseHandle<char    >(arr); break;
+        case s32:   releaseHandle<int     >(arr); break;
+        case u32:   releaseHandle<uint    >(arr); break;
+        case u8:    releaseHandle<uchar   >(arr); break;
+        case s64:   releaseHandle<intl    >(arr); break;
+        case u64:   releaseHandle<uintl   >(arr); break;
+        case s16:   releaseHandle<short   >(arr); break;
+        case u16:   releaseHandle<ushort  >(arr); break;
+        default:    TYPE_ERROR(0, type);
+        }
+
+        setDevice(dev);
+    }
+    CATCHALL
+
+    return AF_SUCCESS;
+}
+
+
+template<typename T>
+static af_array retainHandle(const af_array in)
+{
+    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
+    detail::Array<T> *out = detail::initArray<T>();
+    *out= *A;
+    return reinterpret_cast<af_array>(out);
+}
+
+af_array retain(const af_array in)
+{
+    af_dtype ty = getInfo(in).getType();
+    switch(ty) {
+    case f32: return retainHandle<float           >(in);
+    case f64: return retainHandle<double          >(in);
+    case s32: return retainHandle<int             >(in);
+    case u32: return retainHandle<uint            >(in);
+    case u8:  return retainHandle<uchar           >(in);
+    case c32: return retainHandle<detail::cfloat  >(in);
+    case c64: return retainHandle<detail::cdouble >(in);
+    case b8:  return retainHandle<char            >(in);
+    case s64: return retainHandle<intl            >(in);
+    case u64: return retainHandle<uintl           >(in);
+    case s16: return retainHandle<short           >(in);
+    case u16: return retainHandle<ushort          >(in);
+    default:
+        TYPE_ERROR(1, ty);
+    }
+}
+
+af_err af_retain_array(af_array *out, const af_array in)
+{
+    try {
+        *out = retain(in);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+template<typename T>
+void write_array(af_array arr, const T * const data, const size_t bytes, af_source src)
+{
+    if(src == afHost) {
+        writeHostDataArray(getWritableArray<T>(arr), data, bytes);
+    } else {
+        writeDeviceDataArray(getWritableArray<T>(arr), data, bytes);
+    }
+    return;
+}
+
+af_err af_write_array(af_array arr, const void *data, const size_t bytes, af_source src)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+        //DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
+
+        switch(type) {
+        case f32:   write_array(arr, static_cast<const float   *>(data), bytes, src); break;
+        case c32:   write_array(arr, static_cast<const cfloat  *>(data), bytes, src); break;
+        case f64:   write_array(arr, static_cast<const double  *>(data), bytes, src); break;
+        case c64:   write_array(arr, static_cast<const cdouble *>(data), bytes, src); break;
+        case b8:    write_array(arr, static_cast<const char    *>(data), bytes, src); break;
+        case s32:   write_array(arr, static_cast<const int     *>(data), bytes, src); break;
+        case u32:   write_array(arr, static_cast<const uint    *>(data), bytes, src); break;
+        case u8:    write_array(arr, static_cast<const uchar   *>(data), bytes, src); break;
+        case s64:   write_array(arr, static_cast<const intl    *>(data), bytes, src); break;
+        case u64:   write_array(arr, static_cast<const uintl   *>(data), bytes, src); break;
+        case s16:   write_array(arr, static_cast<const short   *>(data), bytes, src); break;
+        case u16:   write_array(arr, static_cast<const ushort  *>(data), bytes, src); break;
+        default:    TYPE_ERROR(4, type);
+        }
+    }
+    CATCHALL
+        return AF_SUCCESS;
+}
+
 af_err af_get_elements(dim_t *elems, const af_array arr)
 {
     try {
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 2997c13692..95a133557f 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -36,13 +36,12 @@ template<af_op_t op>
 static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        const af_dtype otype = implicit(lhs, rhs);
-
         ArrayInfo linfo = getInfo(lhs);
         ArrayInfo rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
         case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
@@ -70,13 +69,13 @@ template<af_op_t op>
 static af_err af_arith_real(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        const af_dtype otype = implicit(lhs, rhs);
 
         ArrayInfo linfo = getInfo(lhs);
         ArrayInfo rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
         case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 522eb7dfcb..295fa83cc7 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -24,12 +24,13 @@
 #include <identity.hpp>
 #include <diagonal.hpp>
 #include <triangle.hpp>
+#include <platform.hpp>
 
 using af::dim4;
 using namespace detail;
 using namespace std;
 
-static inline dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
+dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
 {
 
     DIM_ASSERT(1, ndims >= 1);
@@ -44,62 +45,6 @@ static inline dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
     return d;
 }
 
-af_err af_get_data_ptr(void *data, const af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-        switch(type) {
-        case f32:   copyData(static_cast<float    *>(data), arr);  break;
-        case c32:   copyData(static_cast<cfloat   *>(data), arr);  break;
-        case f64:   copyData(static_cast<double   *>(data), arr);  break;
-        case c64:   copyData(static_cast<cdouble  *>(data), arr);  break;
-        case b8:    copyData(static_cast<char     *>(data), arr);  break;
-        case s32:   copyData(static_cast<int      *>(data), arr);  break;
-        case u32:   copyData(static_cast<unsigned *>(data), arr);  break;
-        case u8:    copyData(static_cast<uchar    *>(data), arr);  break;
-        case s64:   copyData(static_cast<intl     *>(data), arr);  break;
-        case u64:   copyData(static_cast<uintl    *>(data), arr);  break;
-        case s16:   copyData(static_cast<short    *>(data), arr);  break;
-        case u16:   copyData(static_cast<ushort   *>(data), arr);  break;
-        default:    TYPE_ERROR(1, type);
-        }
-    }
-    CATCHALL
-        return AF_SUCCESS;
-}
-
-//Strong Exception Guarantee
-af_err af_create_array(af_array *result, const void * const data,
-                       const unsigned ndims, const dim_t * const dims,
-                       const af_dtype type)
-{
-    try {
-        af_array out;
-        AF_CHECK(af_init());
-
-        dim4 d = verifyDims(ndims, dims);
-
-        switch(type) {
-        case f32:   out = createHandleFromData(d, static_cast<const float   *>(data)); break;
-        case c32:   out = createHandleFromData(d, static_cast<const cfloat  *>(data)); break;
-        case f64:   out = createHandleFromData(d, static_cast<const double  *>(data)); break;
-        case c64:   out = createHandleFromData(d, static_cast<const cdouble *>(data)); break;
-        case b8:    out = createHandleFromData(d, static_cast<const char    *>(data)); break;
-        case s32:   out = createHandleFromData(d, static_cast<const int     *>(data)); break;
-        case u32:   out = createHandleFromData(d, static_cast<const uint    *>(data)); break;
-        case u8:    out = createHandleFromData(d, static_cast<const uchar   *>(data)); break;
-        case s64:   out = createHandleFromData(d, static_cast<const intl    *>(data)); break;
-        case u64:   out = createHandleFromData(d, static_cast<const uintl   *>(data)); break;
-        case s16:   out = createHandleFromData(d, static_cast<const short   *>(data)); break;
-        case u16:   out = createHandleFromData(d, static_cast<const ushort  *>(data)); break;
-        default:    TYPE_ERROR(4, type);
-        }
-        std::swap(*result, out);
-    }
-    CATCHALL
-        return AF_SUCCESS;
-}
-
 //Strong Exception Guarantee
 af_err af_constant(af_array *result, const double value,
                    const unsigned ndims, const dim_t * const dims,
@@ -194,99 +139,6 @@ af_err af_constant_ulong(af_array *result, const uintl val,
     return AF_SUCCESS;
 }
 
-//Strong Exception Guarantee
-af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * const dims,
-                        const af_dtype type)
-{
-    try {
-        af_array out;
-        AF_CHECK(af_init());
-
-        dim4 d((size_t)dims[0]);
-        for(unsigned i = 1; i < ndims; i++) {
-            d[i] = dims[i];
-        }
-
-        switch(type) {
-        case f32:   out = createHandle<float  >(d); break;
-        case c32:   out = createHandle<cfloat >(d); break;
-        case f64:   out = createHandle<double >(d); break;
-        case c64:   out = createHandle<cdouble>(d); break;
-        case b8:    out = createHandle<char   >(d); break;
-        case s32:   out = createHandle<int    >(d); break;
-        case u32:   out = createHandle<uint   >(d); break;
-        case u8:    out = createHandle<uchar  >(d); break;
-        case s64:   out = createHandle<intl   >(d); break;
-        case u64:   out = createHandle<uintl  >(d); break;
-        case s16:   out = createHandle<short  >(d); break;
-        case u16:   out = createHandle<ushort >(d); break;
-        default:    TYPE_ERROR(3, type);
-        }
-        std::swap(*result, out);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-//Strong Exception Guarantee
-af_err af_copy_array(af_array *out, const af_array in)
-{
-    try {
-        ArrayInfo info = getInfo(in);
-        const af_dtype type = info.getType();
-
-        af_array res;
-        switch(type) {
-        case f32:   res = copyArray<float   >(in); break;
-        case c32:   res = copyArray<cfloat  >(in); break;
-        case f64:   res = copyArray<double  >(in); break;
-        case c64:   res = copyArray<cdouble >(in); break;
-        case b8:    res = copyArray<char    >(in); break;
-        case s32:   res = copyArray<int     >(in); break;
-        case u32:   res = copyArray<uint    >(in); break;
-        case u8:    res = copyArray<uchar   >(in); break;
-        case s64:   res = copyArray<intl    >(in); break;
-        case u64:   res = copyArray<uintl   >(in); break;
-        case s16:   res = copyArray<short   >(in); break;
-        case u16:   res = copyArray<ushort  >(in); break;
-        default:    TYPE_ERROR(1, type);
-        }
-        std::swap(*out, res);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-//Strong Exception Guarantee
-af_err af_get_data_ref_count(int *use_count, const af_array in)
-{
-    try {
-        ArrayInfo info = getInfo(in);
-        const af_dtype type = info.getType();
-
-        int res;
-        switch(type) {
-        case f32:   res = getArray<float   >(in).useCount(); break;
-        case c32:   res = getArray<cfloat  >(in).useCount(); break;
-        case f64:   res = getArray<double  >(in).useCount(); break;
-        case c64:   res = getArray<cdouble >(in).useCount(); break;
-        case b8:    res = getArray<char    >(in).useCount(); break;
-        case s32:   res = getArray<int     >(in).useCount(); break;
-        case u32:   res = getArray<uint    >(in).useCount(); break;
-        case u8:    res = getArray<uchar   >(in).useCount(); break;
-        case s64:   res = getArray<intl    >(in).useCount(); break;
-        case u64:   res = getArray<uintl   >(in).useCount(); break;
-        case s16:   res = getArray<short   >(in).useCount(); break;
-        case u16:   res = getArray<ushort  >(in).useCount(); break;
-        default:    TYPE_ERROR(1, type);
-        }
-        std::swap(*use_count, res);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-
 template<typename T>
 static inline af_array randn_(const af::dim4 &dims)
 {
@@ -401,72 +253,6 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
     return AF_SUCCESS;
 }
 
-af_err af_release_array(af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-
-        switch(type) {
-        case f32:   releaseHandle<float   >(arr); break;
-        case c32:   releaseHandle<cfloat  >(arr); break;
-        case f64:   releaseHandle<double  >(arr); break;
-        case c64:   releaseHandle<cdouble >(arr); break;
-        case b8:    releaseHandle<char    >(arr); break;
-        case s32:   releaseHandle<int     >(arr); break;
-        case u32:   releaseHandle<uint    >(arr); break;
-        case u8:    releaseHandle<uchar   >(arr); break;
-        case s64:   releaseHandle<intl    >(arr); break;
-        case u64:   releaseHandle<uintl   >(arr); break;
-        case s16:   releaseHandle<short   >(arr); break;
-        case u16:   releaseHandle<ushort  >(arr); break;
-        default:    TYPE_ERROR(0, type);
-        }
-    }
-    CATCHALL
-
-    return AF_SUCCESS;
-}
-
-
-template<typename T>
-static af_array retainHandle(const af_array in)
-{
-    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
-    detail::Array<T> *out = detail::initArray<T>();
-    *out= *A;
-    return reinterpret_cast<af_array>(out);
-}
-
-af_array retain(const af_array in)
-{
-    af_dtype ty = getInfo(in).getType();
-    switch(ty) {
-    case f32: return retainHandle<float           >(in);
-    case f64: return retainHandle<double          >(in);
-    case s32: return retainHandle<int             >(in);
-    case u32: return retainHandle<uint            >(in);
-    case u8:  return retainHandle<uchar           >(in);
-    case c32: return retainHandle<detail::cfloat  >(in);
-    case c64: return retainHandle<detail::cdouble >(in);
-    case b8:  return retainHandle<char            >(in);
-    case s64: return retainHandle<intl            >(in);
-    case u64: return retainHandle<uintl           >(in);
-    case s16: return retainHandle<short           >(in);
-    case u16: return retainHandle<ushort          >(in);
-    default:
-        TYPE_ERROR(1, ty);
-    }
-}
-
-af_err af_retain_array(af_array *out, const af_array in)
-{
-    try {
-        *out = retain(in);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-}
-
 template<typename T>
 static inline af_array range_(const dim4& d, const int seq_dim)
 {
@@ -539,38 +325,6 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
     return AF_SUCCESS;
 }
 
-template<typename T>
-static inline void eval(af_array arr)
-{
-    getArray<T>(arr).eval();
-    return;
-}
-
-af_err af_eval(af_array arr)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-        switch (type) {
-        case f32: eval<float  >(arr); break;
-        case f64: eval<double >(arr); break;
-        case c32: eval<cfloat >(arr); break;
-        case c64: eval<cdouble>(arr); break;
-        case s32: eval<int    >(arr); break;
-        case u32: eval<uint   >(arr); break;
-        case u8 : eval<uchar  >(arr); break;
-        case b8 : eval<char   >(arr); break;
-        case s64: eval<intl   >(arr); break;
-        case u64: eval<uintl  >(arr); break;
-        case s16: eval<short  >(arr); break;
-        case u16: eval<ushort >(arr); break;
-        default:
-            TYPE_ERROR(0, type);
-        }
-    } CATCHALL;
-
-    return AF_SUCCESS;
-}
-
 template<typename T>
 static inline af_array diagCreate(const af_array in, const int num)
 {
@@ -645,43 +399,6 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num)
     return AF_SUCCESS;
 }
 
-template<typename T>
-void write_array(af_array arr, const T * const data, const size_t bytes, af_source src)
-{
-    if(src == afHost) {
-        writeHostDataArray(getWritableArray<T>(arr), data, bytes);
-    } else {
-        writeDeviceDataArray(getWritableArray<T>(arr), data, bytes);
-    }
-    return;
-}
-
-af_err af_write_array(af_array arr, const void *data, const size_t bytes, af_source src)
-{
-    try {
-        af_dtype type = getInfo(arr).getType();
-        //DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
-
-        switch(type) {
-        case f32:   write_array(arr, static_cast<const float   *>(data), bytes, src); break;
-        case c32:   write_array(arr, static_cast<const cfloat  *>(data), bytes, src); break;
-        case f64:   write_array(arr, static_cast<const double  *>(data), bytes, src); break;
-        case c64:   write_array(arr, static_cast<const cdouble *>(data), bytes, src); break;
-        case b8:    write_array(arr, static_cast<const char    *>(data), bytes, src); break;
-        case s32:   write_array(arr, static_cast<const int     *>(data), bytes, src); break;
-        case u32:   write_array(arr, static_cast<const uint    *>(data), bytes, src); break;
-        case u8:    write_array(arr, static_cast<const uchar   *>(data), bytes, src); break;
-        case s64:   write_array(arr, static_cast<const intl    *>(data), bytes, src); break;
-        case u64:   write_array(arr, static_cast<const uintl   *>(data), bytes, src); break;
-        case s16:   write_array(arr, static_cast<const short   *>(data), bytes, src); break;
-        case u16:   write_array(arr, static_cast<const ushort  *>(data), bytes, src); break;
-        default:    TYPE_ERROR(4, type);
-        }
-    }
-    CATCHALL
-        return AF_SUCCESS;
-}
-
 template<typename T, bool is_upper>
 af_array triangle(const af_array in, bool is_unit_diag)
 {
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index abe0b01e32..b93907d55e 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -156,3 +156,36 @@ af_err af_sync(const int device)
     } CATCHALL;
     return AF_SUCCESS;
 }
+
+
+template<typename T>
+static inline void eval(af_array arr)
+{
+    getArray<T>(arr).eval();
+    return;
+}
+
+af_err af_eval(af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+        switch (type) {
+        case f32: eval<float  >(arr); break;
+        case f64: eval<double >(arr); break;
+        case c32: eval<cfloat >(arr); break;
+        case c64: eval<cdouble>(arr); break;
+        case s32: eval<int    >(arr); break;
+        case u32: eval<uint   >(arr); break;
+        case u8 : eval<uchar  >(arr); break;
+        case b8 : eval<char   >(arr); break;
+        case s64: eval<intl   >(arr); break;
+        case u64: eval<uintl  >(arr); break;
+        case s16: eval<short  >(arr); break;
+        case u16: eval<ushort >(arr); break;
+        default:
+            TYPE_ERROR(0, type);
+        }
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index ac7b74a193..e5dc3f43fe 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -15,6 +15,7 @@
 #include <math.hpp>
 #include <copy.hpp>
 #include <cast.hpp>
+#include <af/dim4.hpp>
 
 const ArrayInfo& getInfo(const af_array arr, bool check = true);
 
@@ -109,3 +110,5 @@ static void releaseHandle(const af_array arr)
 }
 
 af_array retain(const af_array in);
+
+af::dim4 verifyDims(const unsigned ndims, const dim_t * const dims);
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index db40934e50..2c523d0947 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -264,6 +264,28 @@ af_err af_is_window_closed(bool *out, const af_window wind)
 #endif
 }
 
+af_err af_set_visibility(const af_window wind, const bool is_visible)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        if (is_visible)
+            wnd->show();
+        else
+            wnd->hide();
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
 af_err af_destroy_window(const af_window wind)
 {
 #if defined(WITH_GRAPHICS)
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 5e3f7a59cb..cef40ee99f 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -149,7 +149,9 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 
         int flags = 0;
         if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+#ifdef JPEG_GREYSCALE
         if(fif == FIF_JPEG && !isColor) flags = flags | JPEG_GREYSCALE;
+#endif
 
         // check that the plugin has reading capabilities ...
         FIBITMAP* pBitmap = NULL;
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 50bcad25ee..b5c033f461 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -68,8 +68,8 @@ static af_array median(const af_array& in, const dim_t dim)
     const Array<T> input = getArray<T>(in);
     Array<T> sortedIn   = sort<T, true>(input, dim);
 
-    int nElems    = input.dims()[0];
-    double mid    = (nElems + 1) / 2;
+    int dimLength = input.dims()[dim];
+    double mid    = (dimLength + 1) / 2;
     af_array left = 0;
 
     af_seq slices[4] = {af_span, af_span, af_span, af_span};
@@ -78,7 +78,7 @@ static af_array median(const af_array& in, const dim_t dim)
     af_array sortedIn_handle = getHandle<T>(sortedIn);
     AF_CHECK(af_index(&left, sortedIn_handle, input.ndims(), slices));
 
-    if (nElems % 2 == 1) {
+    if (dimLength % 2 == 1) {
         // mid-1 is our guy
         if (input.isFloating()) return left;
 
@@ -90,7 +90,7 @@ static af_array median(const af_array& in, const dim_t dim)
         return out;
     } else {
         // ((mid-1)+mid)/2 is our guy
-        dim4  dims = input.dims();
+        dim4 dims = input.dims();
         af_array right = 0;
         slices[dim] = af_make_seq(mid, mid, 1.0);
 
@@ -100,7 +100,8 @@ static af_array median(const af_array& in, const dim_t dim)
         af_array carr   = 0;
         af_array result = 0;
 
-        dim4 cdims = dim4(1, dims[1], dims[2], dims[3]);
+        dim4 cdims = dims;
+        cdims[dim] = 1;
         AF_CHECK(af_constant(&carr, 0.5, cdims.ndims(), cdims.get(), input.isDouble() ? f64 : f32));
 
         if (!input.isFloating()) {
@@ -148,7 +149,7 @@ af_err af_median_all(double *realVal, double *imagVal, const af_array in)
 af_err af_median(af_array* out, const af_array in, const dim_t dim)
 {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=0));
+        ARG_ASSERT(2, (dim >= 0 && dim <= 4));
 
         af_array output = 0;
         ArrayInfo info = getInfo(in);
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index b8f1fafa6c..1d326c0846 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -31,7 +31,6 @@ Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
         Out = copyArray<T>(in);
     }
 
-    Out.modDims(newDims);
     Out.setDataDims(newDims);
 
     return Out;
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 1e52ae0899..da7ebb2bf3 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -36,31 +36,27 @@ static af_array rgb2gray(const af_array& in, const float r, const float g, const
     Array<cType> gCnst = createValueArray<cType>(matDims, scalar<cType>(g));
     Array<cType> bCnst = createValueArray<cType>(matDims, scalar<cType>(b));
 
+    std::vector<af_seq> slice1(4, af_span), slice2(4, af_span), slice3(4, af_span);
     // extract three channels as three slices
-    af_seq slice1[4] = { af_span, af_span, {0, 0, 1}, af_span };
-    af_seq slice2[4] = { af_span, af_span, {1, 1, 1}, af_span };
-    af_seq slice3[4] = { af_span, af_span, {2, 2, 1}, af_span };
+    slice1[2] = {0, 0, 1};
+    slice2[2] = {1, 1, 1};
+    slice3[2] = {2, 2, 1};
 
-    af_array ch1Temp=0, ch2Temp=0, ch3Temp=0;
-    AF_CHECK(af_index(&ch1Temp, in, 4, slice1));
-    AF_CHECK(af_index(&ch2Temp, in, 4, slice2));
-    AF_CHECK(af_index(&ch3Temp, in, 4, slice3));
+    Array<cType> ch1Temp = createSubArray(input, slice1);
+    Array<cType> ch2Temp = createSubArray(input, slice2);
+    Array<cType> ch3Temp = createSubArray(input, slice3);
 
     // r*Slice0
-    Array<cType> expr1 = arithOp<cType, af_mul_t>(getArray<cType>(ch1Temp), rCnst, matDims);
+    Array<cType> expr1 = arithOp<cType, af_mul_t>(ch1Temp, rCnst, matDims);
     //g*Slice1
-    Array<cType> expr2 = arithOp<cType, af_mul_t>(getArray<cType>(ch2Temp), gCnst, matDims);
+    Array<cType> expr2 = arithOp<cType, af_mul_t>(ch2Temp, gCnst, matDims);
     //b*Slice2
-    Array<cType> expr3 = arithOp<cType, af_mul_t>(getArray<cType>(ch3Temp), bCnst, matDims);
+    Array<cType> expr3 = arithOp<cType, af_mul_t>(ch3Temp, bCnst, matDims);
     //r*Slice0 + g*Slice1
     Array<cType> expr4 = arithOp<cType, af_add_t>(expr1, expr2, matDims);
     //r*Slice0 + g*Slice1 + b*Slice2
     Array<cType> result= arithOp<cType, af_add_t>(expr3, expr4, matDims);
 
-    AF_CHECK(af_release_array(ch1Temp));
-    AF_CHECK(af_release_array(ch2Temp));
-    AF_CHECK(af_release_array(ch3Temp));
-
     return getHandle<cType>(result);
 }
 
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 321324be83..ebd9d410cc 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -21,11 +21,27 @@ using af::dim4;
 using namespace detail;
 
 template<af_op_t op, typename Ti, typename To>
-static inline af_array scan(const af_array in, const int dim)
+static inline af_array scan(const af_array in, const int dim, bool inclusive_scan = true)
 {
-    return getHandle(scan<op,Ti,To>(getArray<Ti>(in), dim));
+    return getHandle(scan<op,Ti,To>(getArray<Ti>(in), dim, inclusive_scan));
 }
 
+template<typename Ti, typename To>
+static inline af_array scan_op(const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
+{
+    af_array out;
+
+    switch(op) {
+    case AF_ADD:    out = scan<af_add_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_SUB:    out = scan<af_sub_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_MUL:    out = scan<af_mul_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_DIV:    out = scan<af_div_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_MIN:    out = scan<af_min_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_MAX:    out = scan<af_max_t, Ti, To>(in, dim, inclusive_scan); break;
+                    //TODO Error for op in default case
+    }
+    return out;
+}
 
 af_err af_accum(af_array *out, const af_array in, const int dim)
 {
@@ -68,3 +84,45 @@ af_err af_accum(af_array *out, const af_array in, const int dim)
 
     return AF_SUCCESS;
 }
+
+af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
+{
+    ARG_ASSERT(2, dim >= 0);
+    ARG_ASSERT(2, dim <  4);
+
+    try {
+
+        const ArrayInfo& in_info = getInfo(in);
+
+        if (dim >= (int)in_info.ndims()) {
+            *out = retain(in);
+            return AF_SUCCESS;
+        }
+
+        af_dtype type = in_info.getType();
+        af_array res;
+
+        switch(type) {
+        case f32:  res = scan_op<float  , float  >(in, dim, op, inclusive_scan); break;
+        case f64:  res = scan_op<double , double >(in, dim, op, inclusive_scan); break;
+        case c32:  res = scan_op<cfloat , cfloat >(in, dim, op, inclusive_scan); break;
+        case c64:  res = scan_op<cdouble, cdouble>(in, dim, op, inclusive_scan); break;
+        case u32:  res = scan_op<uint   , uint   >(in, dim, op, inclusive_scan); break;
+        case s32:  res = scan_op<int    , int    >(in, dim, op, inclusive_scan); break;
+        case u64:  res = scan_op<uintl  , uintl  >(in, dim, op, inclusive_scan); break;
+        case s64:  res = scan_op<intl   , intl   >(in, dim, op, inclusive_scan); break;
+        case u16:  res = scan_op<ushort , uint   >(in, dim, op, inclusive_scan); break;
+        case s16:  res = scan_op<short  , int    >(in, dim, op, inclusive_scan); break;
+        case u8:   res = scan_op<uchar  , uint   >(in, dim, op, inclusive_scan); break;
+        case b8:   res = scan_op<char   , uint   >(in, dim, op, inclusive_scan); break;
+        // Make sure you are adding only "1" for every non zero value, even if op == af_add_t
+        default:
+            TYPE_ERROR(1, type);
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 1de63c5052..dd58175936 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -42,8 +42,6 @@ af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool
         af_dtype type = info.getType();
 
         DIM_ASSERT(1, info.elements() > 0);
-        // Only Dim 0 supported
-        ARG_ASSERT(2, dim == 0);
 
         af_array val;
 
@@ -93,8 +91,6 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const
         af_dtype type = info.getType();
 
         DIM_ASSERT(2, info.elements() > 0);
-        // Only Dim 0 supported
-        ARG_ASSERT(3, dim == 0);
 
         af_array val;
         af_array idx;
@@ -150,6 +146,8 @@ void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey, cons
     switch(vtype) {
     case f32: sort_by_key<Tk, float  >(okey, oval, ikey, ival, dim, isAscending);  break;
     case f64: sort_by_key<Tk, double >(okey, oval, ikey, ival, dim, isAscending);  break;
+    case c32: sort_by_key<Tk, cfloat >(okey, oval, ikey, ival, dim, isAscending);  break;
+    case c64: sort_by_key<Tk, cdouble>(okey, oval, ikey, ival, dim, isAscending);  break;
     case s32: sort_by_key<Tk, int    >(okey, oval, ikey, ival, dim, isAscending);  break;
     case u32: sort_by_key<Tk, uint   >(okey, oval, ikey, ival, dim, isAscending);  break;
     case s16: sort_by_key<Tk, short  >(okey, oval, ikey, ival, dim, isAscending);  break;
@@ -169,20 +167,20 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                       const unsigned dim, const bool isAscending)
 {
     try {
-        ArrayInfo info = getInfo(keys);
-        af_dtype type = info.getType();
+        ArrayInfo kinfo = getInfo(keys);
+        af_dtype ktype = kinfo.getType();
 
         ArrayInfo vinfo = getInfo(values);
 
-        DIM_ASSERT(3, info.elements() > 0);
-        DIM_ASSERT(4, info.dims() == vinfo.dims());
-        // Only Dim 0 supported
-        ARG_ASSERT(5, dim == 0);
+        DIM_ASSERT(3, kinfo.elements() > 0);
+        DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
+
+        TYPE_ASSERT(kinfo.isReal());
 
         af_array oKey;
         af_array oVal;
 
-        switch(type) {
+        switch(ktype) {
             case f32: sort_by_key_tmplt<float  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case f64: sort_by_key_tmplt<double >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case s32: sort_by_key_tmplt<int    >(&oKey, &oVal, keys, values, dim, isAscending);  break;
@@ -193,7 +191,7 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
             case u64: sort_by_key_tmplt<uintl  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case u8:  sort_by_key_tmplt<uchar  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
             case b8:  sort_by_key_tmplt<char   >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            default:  TYPE_ERROR(1, type);
+            default:  TYPE_ERROR(1, ktype);
         }
         std::swap(*out_keys , oKey);
         std::swap(*out_values , oVal);
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index b993e2f7e8..f1d401f000 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -930,7 +930,7 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     BINARY_OP(>=, af_ge)
     BINARY_OP(&&, af_and)
     BINARY_OP(||, af_or)
-    BINARY_OP(%, af_rem)
+    BINARY_OP(%, af_mod)
     BINARY_OP(&, af_bitand)
     BINARY_OP(|, af_bitor)
     BINARY_OP(^, af_bitxor)
@@ -1022,7 +1022,7 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
 
 // array_proxy instanciations
 #define TEMPLATE_MEM_FUNC(TYPE, RETURN_TYPE, FUNC)      \
-    template <>                                         \
+    template <> AFAPI                                   \
     RETURN_TYPE array::array_proxy::FUNC() const        \
     {                                                   \
         array out = *this;                              \
diff --git a/src/api/cpp/binary.cpp b/src/api/cpp/binary.cpp
index 2a9161615b..966397e8f8 100644
--- a/src/api/cpp/binary.cpp
+++ b/src/api/cpp/binary.cpp
@@ -16,12 +16,12 @@
 namespace af
 {
 
-#define INSTANTIATE(cppfunc, cfunc)                     \
-    array cppfunc(const array &lhs, const array &rhs)   \
-    {                                                   \
-        af_array out = 0;                               \
-        cfunc(&out, lhs.get(), rhs.get(), gforGet());   \
-        return array(out);                              \
+#define INSTANTIATE(cppfunc, cfunc)                             \
+    array cppfunc(const array &lhs, const array &rhs)           \
+    {                                                           \
+        af_array out = 0;                                       \
+        AF_THROW(cfunc(&out, lhs.get(), rhs.get(), gforGet())); \
+        return array(out);                                      \
     }
 
     INSTANTIATE(min , af_minof)
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 162bacb4ab..8b53825c25 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -136,4 +136,9 @@ bool Window::close()
     return temp;
 }
 
+void Window::setVisibility(const bool isVisible)
+{
+    AF_THROW(af_set_visibility(get(), isVisible));
+}
+
 }
diff --git a/src/api/cpp/scan.cpp b/src/api/cpp/scan.cpp
index 9cc313971b..7fbbf39efd 100644
--- a/src/api/cpp/scan.cpp
+++ b/src/api/cpp/scan.cpp
@@ -19,4 +19,11 @@ namespace af
         AF_THROW(af_accum(&out, in.get(), dim));
         return array(out);
     }
+
+    array scan(const array& in, const int dim, af_binary_op op, bool inclusive_scan)
+    {
+        af_array out = 0;
+        AF_THROW(af_scan(&out, in.get(), dim, op, inclusive_scan));
+        return array(out);
+    }
 }
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index c44e43b5fc..18d15c474c 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -6,12 +6,17 @@ FILE(GLOB unified_headers
 FILE(GLOB unified_sources
     "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp")
 
+LIST(SORT unified_headers)
+LIST(SORT unified_sources)
+
 SOURCE_GROUP(api\\unified\\Headers FILES ${unified_headers})
 SOURCE_GROUP(api\\unified\\Sources FILES ${unified_sources})
 
 FILE(GLOB cpp_sources
     "../cpp/*.cpp")
 
+LIST(SORT cpp_sources)
+
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB common_sources
@@ -22,6 +27,8 @@ FILE(GLOB common_sources
   "../../backend/util.cpp"
   )
 
+LIST(SORT common_sources)
+
 SOURCE_GROUP(common FILES ${common_sources})
 
 IF(NOT UNIX)
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index 934b7ae2fc..fd06f53b48 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -104,6 +104,12 @@ af_err af_where(af_array *idx, const af_array in)
     return CALL(idx, in);
 }
 
+af_err af_scan(af_array* out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, dim, op, inclusive_scan);
+}
+
 af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending)
 {
     CHECK_ARRAYS(in);
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 2895cc7afc..9e3f1c8b38 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -89,6 +89,11 @@ af_err af_is_window_closed(bool *out, const af_window wind)
     return CALL(out, wind);
 }
 
+af_err af_set_visibility(const af_window wind, const bool is_visible)
+{
+    return CALL(wind, is_visible);
+}
+
 af_err af_destroy_window(const af_window wind)
 {
     return CALL(wind);
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 96cec0b6ac..ef92cd3902 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <string>
 #include <cmath>
+#include <af/version.h>
 
 using std::string;
 using std::replace;
@@ -25,12 +26,15 @@ static const string LIB_AF_BKND_PREFIX = "af";
 static const string LIB_AF_BKND_SUFFIX = ".dll";
 #define RTLD_LAZY 0
 #else
-static const string LIB_AF_BKND_PREFIX = "libaf";
 #if defined(__APPLE__)
-static const string LIB_AF_BKND_SUFFIX = ".dylib";
+#define SO_SUFFIX_HELPER(VER) "." #VER ".dylib"
 #else
-static const string LIB_AF_BKND_SUFFIX = ".so";
+#define SO_SUFFIX_HELPER(VER) ".so." #VER
 #endif // APPLE
+static const string LIB_AF_BKND_PREFIX = "libaf";
+
+#define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
+static const string LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
 #endif
 
 static const string LIB_AF_ENVARS[NUM_ENV_VARS] = {"AF_PATH", "AF_BUILD_PATH"};
diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index 0937641afc..98a2264b5c 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -33,7 +33,7 @@ dim4 calcStrides(const dim4 &parentDim)
 
 int ArrayInfo::getDevId() const
 {
-    // The actual device ID is only stored in the first 4 bits of devId
+    // The actual device ID is only stored in the first 8 bits of devId
     // See ArrayInfo.hpp for more
     return devId & 0xff;
 }
@@ -49,7 +49,7 @@ void ArrayInfo::setId(int id) const
 
 void ArrayInfo::setId(int id)
 {
-    // 1 << (backendId + 3) sets the 9th, 10th or 11th bit of devId to 1
+    // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
     int backendId = detail::getBackend() >> 1; // Convert enums 1, 2, 4 to ints 0, 1, 2
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index 88ba26b6aa..7d3606e129 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -29,14 +29,14 @@ class ArrayInfo
 {
 private:
     // The devId variable stores information about the deviceId as well as the backend.
-    // The 4 LSBs (0-3) are used to store the device ID.
-    // The 4th LSB is set to 1 if backend is CPU
-    // The 5th LSB is set to 1 if backend is CUDA
-    // The 6th LSB is set to 1 if backend is OpenCL
+    // The 8 LSBs (0-7) are used to store the device ID.
+    // The 09th LSB is set to 1 if backend is CPU
+    // The 10th LSB is set to 1 if backend is CUDA
+    // The 11th LSB is set to 1 if backend is OpenCL
     // This information can be retrieved directly from an af_array by doing
     //     int* devId = reinterpret_cast<int*>(a); // a is an af_array
-    //     af_backend backendID = *devId >> 3;  // Returns 1, 2, 4 for CPU, CUDA or OpenCL respectively
-    //     int        deviceID  = *devId & 0xf; // Returns devices ID between 0-15
+    //     af_backend backendID = *devId >> 8;   // Returns 1, 2, 4 for CPU, CUDA or OpenCL respectively
+    //     int        deviceID  = *devId & 0xff; // Returns devices ID between 0-255
     // This is possible by doing a static_assert on devId
     //
     // This can be changed in the future if the need arises for more devices as this
diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 379c2e2af2..83f2de1d8d 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -87,10 +87,14 @@ void MemoryManager::garbageCollect()
             kv.second.pop_back();
         }
     }
+    current.free_map.clear();
 }
 
 void MemoryManager::unlock(void *ptr, bool user_unlock)
 {
+    // Shortcut for empty arrays
+    if (!ptr) return;
+
     lock_guard_t lock(this->memory_mutex);
     memory_info& current = this->getCurrentMemoryInfo();
 
@@ -122,6 +126,8 @@ void MemoryManager::unlock(void *ptr, bool user_unlock)
         // Just free memory in debug mode
         if ((iter->second).bytes > 0) {
             this->nativeFree(iter->first);
+            current.total_buffers--;
+            current.total_bytes -= iter->second.bytes;
         }
     } else {
         // In regular mode, move buffer to free map
diff --git a/src/backend/cblas.cpp b/src/backend/cblas.cpp
index 4d99d457c2..1be15e47c9 100644
--- a/src/backend/cblas.cpp
+++ b/src/backend/cblas.cpp
@@ -12,11 +12,11 @@
 #ifdef AF_CPU
     #include <blas.hpp>
 #else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
+    #ifdef USE_MKL
+        #include <mkl_cblas.h>
     #else
-        #ifdef USE_MKL
-            #include <mkl_cblas.h>
+        #ifdef __APPLE__
+            #include <Accelerate/Accelerate.h>
         #else
             extern "C" {
                 #include <cblas.h>
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2a3afcf617..cf970d18c7 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -85,12 +85,14 @@ namespace cpu
     {
         T *ptr = arr.device();
         memLock(ptr);
+
         return (void *)ptr;
     }
 
     template<typename T>
     void *getRawPtr(const Array<T>& arr)
     {
+        getQueue().sync();
         return (void *)(arr.get(false));
     }
 
@@ -174,23 +176,22 @@ namespace cpu
 
         dim4 getDataDims() const
         {
-            // This is for moddims
-            // dims and data_dims are different when moddims is used
-            return isOwner() ? info.dims() : data_dims;
+            return data_dims;
         }
 
         void setDataDims(const dim4 &new_dims)
         {
+            modDims(new_dims);
             data_dims = new_dims;
         }
 
         T* device()
         {
             getQueue().sync();
-            if (!isOwner() || data.use_count() > 1) {
+            if (!isOwner() || getOffset() || data.use_count() > 1) {
                 *this = Array<T>(dims(), get(), true, true);
             }
-            return this->data.get();
+            return this->get();
         }
 
         T* device() const
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 8ada1d6935..0863266c4f 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -3,7 +3,22 @@ ADD_DEFINITIONS(-DAF_CPU)
 
 FIND_PACKAGE(CBLAS REQUIRED)
 
-OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
+IF(NOT DEFINED BUILD_CPU_ASYNC)
+    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.4")
+        MESSAGE("Disabling CPU Async as GCC Version ${COMPILER_VERSION} has known issues.")
+        MESSAGE("CPU Backend will use Synchronous Calls")
+        OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" OFF)
+    ELSE()
+        OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
+    ENDIF()
+ENDIF(NOT DEFINED BUILD_CPU_ASYNC)
+MARK_AS_ADVANCED(USE_CPUID)
+
+if (USE_CPUID)
+  ADD_DEFINITIONS(-DUSE_CPUID=1)
+ELSE(USE_CPUID)
+  ADD_DEFINITIONS(-DUSE_CPUID=0)
+ENDIF(USE_CPUID)
 
 IF (NOT ${BUILD_CPU_ASYNC})
     ADD_DEFINITIONS(-DAF_DISABLE_CPU_ASYNC)
@@ -14,9 +29,14 @@ IF(USE_CPU_F77_BLAS)
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
 ENDIF()
 
-IF(USE_CPU_MKL)
-    MESSAGE("Using MKL")
+IF(USE_CPU_MKL) # Manual MKL Setup
+    MESSAGE("CPU Backend Using MKL")
     ADD_DEFINITIONS(-DUSE_MKL)
+ELSE(USE_CPU_MKL)
+    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+        MESSAGE("CPU Backend Using MKL RT")
+        ADD_DEFINITIONS(-DUSE_MKL)
+    ENDIF()
 ENDIF()
 
 IF (NOT CBLAS_LIBRARIES)
@@ -29,16 +49,20 @@ IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU" AND "${APPLE}")
     ADD_DEFINITIONS(-flax-vector-conversions)
 ENDIF()
 
-IF(${MKL_FOUND})
-    ADD_DEFINITIONS(-DUSE_MKL)
-ENDIF()
-
 FIND_PACKAGE(FFTW REQUIRED)
 MESSAGE(STATUS "FFTW Found ? ${FFTW_FOUND}")
 MESSAGE(STATUS "FFTW Library: ${FFTW_LIBRARIES}")
 
 IF(APPLE)
-    FIND_PACKAGE(LAPACK)
+    FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
+    IF(NOT LAPACK_FOUND)
+        # UNSET THE VARIABLES FROM LAPACKE
+        UNSET(LAPACKE_LIB CACHE)
+        UNSET(LAPACK_LIB CACHE)
+        UNSET(LAPACKE_INCLUDES CACHE)
+        UNSET(LAPACKE_ROOT_DIR CACHE)
+        FIND_PACKAGE(LAPACK)
+    ENDIF()
 ELSE(APPLE) # Linux and Windows
     FIND_PACKAGE(LAPACKE)
 ENDIF(APPLE)
@@ -86,6 +110,9 @@ FILE(GLOB cpu_headers
 FILE(GLOB cpu_sources
     "*.cpp")
 
+LIST(SORT cpu_headers)
+LIST(SORT cpu_sources)
+
 source_group(backend\\cpu\\Headers FILES ${cpu_headers})
 source_group(backend\\cpu\\Sources FILES ${cpu_sources})
 
@@ -98,6 +125,9 @@ FILE(GLOB backend_sources
     "../*.cpp"
     )
 
+LIST(SORT backend_headers)
+LIST(SORT backend_sources)
+
 source_group(backend\\Headers FILES ${backend_headers})
 source_group(backend\\Sources FILES ${backend_sources})
 
@@ -110,6 +140,9 @@ FILE(GLOB c_sources
     "../../api/c/*.cpp"
     )
 
+LIST(SORT c_headers)
+LIST(SORT c_sources)
+
 source_group(api\\c\\Headers FILES ${c_headers})
 source_group(api\\c\\Sources FILES ${c_sources})
 
@@ -117,6 +150,8 @@ FILE(GLOB cpp_sources
     "../../api/cpp/*.cpp"
     )
 
+LIST(SORT cpp_sources)
+
 source_group(api\\cpp\\Sources FILES ${cpp_sources})
 
 # OS Definitions
@@ -126,13 +161,15 @@ ELSE(${UNIX}) #Windows
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 ENDIF()
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
 IF(DEFINED BLAS_SYM_FILE)
   ADD_LIBRARY(afcpu_static STATIC
               ${cpu_headers}
               ${cpu_sources}
               ${backend_headers}
-              ${backend_sources})
+              ${backend_sources}
+              ${SORT_BY_KEY_OBJECTS})
 
   ADD_LIBRARY(afcpu SHARED
               ${c_headers}
@@ -156,21 +193,23 @@ IF(DEFINED BLAS_SYM_FILE)
 
 ELSE(DEFINED BLAS_SYM_FILE)
 
-  ADD_LIBRARY(afcpu SHARED
-              ${cpu_headers}
-              ${cpu_sources}
-              ${backend_headers}
-              ${backend_sources}
-              ${c_headers}
-              ${c_sources}
-              ${cpp_sources})
+ADD_LIBRARY(afcpu SHARED
+            ${cpu_headers}
+            ${cpu_sources}
+            ${backend_headers}
+            ${backend_sources}
+            ${c_headers}
+            ${c_sources}
+            ${cpp_sources}
+            ${SORT_BY_KEY_OBJECTS})
 
 ENDIF(DEFINED BLAS_SYM_FILE)
 
 TARGET_LINK_LIBRARIES(afcpu
-                            PRIVATE ${FreeImage_LIBS}
                             PRIVATE ${CBLAS_LIBRARIES}
-                            PRIVATE ${FFTW_LIBRARIES})
+                            PRIVATE ${FFTW_LIBRARIES}
+                            PRIVATE ${FreeImage_LIBS}
+                     )
 
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
   ADD_DEPENDENCIES(afcpu forge)
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 05484338cd..3f5b7451ad 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -12,16 +12,16 @@
 #include <Array.hpp>
 #include <types.hpp>
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#else
 #ifdef USE_MKL
-#include <mkl_cblas.h>
+    #include <mkl_cblas.h>
 #else
-extern "C" {
-#include <cblas.h>
-}
-#endif
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+    #else
+        extern "C" {
+            #include <cblas.h>
+        }
+    #endif
 #endif
 
 // TODO: Ask upstream for a more official way to detect it
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index c818f82795..80375eaa71 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -42,7 +42,7 @@ Array<T> diagExtract(const Array<T> &in, const int num)
     in.eval();
 
     const dim4 idims = in.dims();
-    dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+    dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
     Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
     getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index 0bcfe7df17..f1c2f5351f 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -16,7 +16,7 @@ namespace cpu
 namespace kernel
 {
 
-template<af_op_t op, typename Ti, typename To, int D>
+template<af_op_t op, typename Ti, typename To, int D, bool inclusive_scan>
 struct scan_dim
 {
     void operator()(Array<To> out, dim_t outOffset,
@@ -29,7 +29,7 @@ struct scan_dim
 
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
-            scan_dim<op, Ti, To, D1> func;
+            scan_dim<op, Ti, To, D1, inclusive_scan> func;
             getQueue().enqueue(func,
                     out, outOffset + i * ostrides[D1],
                     in, inOffset + i * istrides[D1], dim);
@@ -38,8 +38,8 @@ struct scan_dim
     }
 };
 
-template<af_op_t op, typename Ti, typename To>
-struct scan_dim<op, Ti, To, 0>
+template<af_op_t op, typename Ti, typename To, bool inclusive_scan>
+struct scan_dim<op, Ti, To, 0, inclusive_scan>
 {
     void operator()(Array<To> output, dim_t outOffset,
                     const Array<Ti> input,  dim_t inOffset,
@@ -63,7 +63,15 @@ struct scan_dim<op, Ti, To, 0>
         for (dim_t i = 0; i < idims[dim]; i++) {
             To in_val = transform(in[i * istride]);
             out_val = scan(in_val, out_val);
-            out[i * ostride] = out_val;
+            if (!inclusive_scan) {
+                if (i == (idims[dim] - 1)) {
+                    out[0] = scan.init();
+                } else {
+                    out[(i + 1) * ostride] = out_val;
+                }
+            } else {
+                out[i * ostride] = out_val;
+            }
         }
     }
 };
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index 292c6383dc..e0ae62c932 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -23,7 +23,7 @@ namespace kernel
 
 // Based off of http://stackoverflow.com/a/12399290
 template<typename T, bool isAscending>
-void sort0(Array<T> val)
+void sort0Iterative(Array<T> val)
 {
     // initialize original index locations
     T *val_ptr = val.get();
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index f9d391dc46..55d5a89337 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -8,14 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Array.hpp>
-#include <math.hpp>
-#include <algorithm>
-#include <numeric>
-#include <queue>
 #include <err_cpu.hpp>
-#include <functional>
 
 namespace cpu
 {
@@ -23,64 +17,13 @@ namespace kernel
 {
 
 template<typename Tk, typename Tv, bool isAscending>
-void sort0_by_key(Array<Tk> okey, Array<Tv> oval, Array<uint> oidx,
-                  const Array<Tk> ikey, const Array<Tv> ival)
-{
-    function<bool(Tk, Tk)> op = std::greater<Tk>();
-    if(isAscending) { op = std::less<Tk>(); }
-
-    // Get pointers and initialize original index locations
-        uint *oidx_ptr = oidx.get();
-          Tk *okey_ptr = okey.get();
-          Tv *oval_ptr = oval.get();
-    const Tk *ikey_ptr = ikey.get();
-    const Tv *ival_ptr = ival.get();
-
-    std::vector<uint> seq_vec(oidx.dims()[0]);
-    std::iota(seq_vec.begin(), seq_vec.end(), 0);
-
-    const Tk *comp_ptr = nullptr;
-    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-    for(dim_t w = 0; w < ikey.dims()[3]; w++) {
-        dim_t okeyW = w * okey.strides()[3];
-        dim_t ovalW = w * oval.strides()[3];
-        dim_t oidxW = w * oidx.strides()[3];
-        dim_t ikeyW = w * ikey.strides()[3];
-        dim_t ivalW = w * ival.strides()[3];
-
-        for(dim_t z = 0; z < ikey.dims()[2]; z++) {
-            dim_t okeyWZ = okeyW + z * okey.strides()[2];
-            dim_t ovalWZ = ovalW + z * oval.strides()[2];
-            dim_t oidxWZ = oidxW + z * oidx.strides()[2];
-            dim_t ikeyWZ = ikeyW + z * ikey.strides()[2];
-            dim_t ivalWZ = ivalW + z * ival.strides()[2];
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval);
 
-            for(dim_t y = 0; y < ikey.dims()[1]; y++) {
+template<typename Tk, typename Tv, bool isAscending, int dim>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval);
 
-                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
-                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
-                dim_t oidxOffset = oidxWZ + y * oidx.strides()[1];
-                dim_t ikeyOffset = ikeyWZ + y * ikey.strides()[1];
-                dim_t ivalOffset = ivalWZ + y * ival.strides()[1];
-
-                uint *ptr = oidx_ptr + oidxOffset;
-                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
-
-                comp_ptr = ikey_ptr + ikeyOffset;
-                std::stable_sort(ptr, ptr + ikey.dims()[0], comparator);
-
-                for (dim_t i = 0; i < oval.dims()[0]; ++i){
-                    uint sortIdx = oidx_ptr[oidxOffset + i];
-                    okey_ptr[okeyOffset + i] = ikey_ptr[ikeyOffset + sortIdx];
-                    oval_ptr[ovalOffset + i] = ival_ptr[ivalOffset + sortIdx];
-                }
-            }
-        }
-    }
-
-    return;
-}
+template<typename Tk, typename Tv, bool isAscending>
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval);
 
 }
 }
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..017bb90bb6
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,15 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    ADD_LIBRARY(cpu_sort_by_key_${SBK_TYPE} OBJECT
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp")
+    SET_TARGET_PROPERTIES(cpu_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
+    LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:cpu_sort_by_key_${SBK_TYPE}>)
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/cuda/sort_by_key/ascd_s16.cu b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
similarity index 62%
rename from src/backend/cuda/sort_by_key/ascd_s16.cu
rename to src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index d51e9ae671..fbdedfde39 100644
--- a/src/backend/cuda/sort_by_key/ascd_s16.cu
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -7,9 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sort_by_key_impl.hpp>
+#include <kernel/sort_by_key_impl.hpp>
 
-namespace cuda
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+
+namespace cpu
+{
+namespace kernel
 {
-    INSTANTIATE1(short, true)
+    INSTANTIATE1(TYPE,true)
+    INSTANTIATE1(TYPE,false)
+}
 }
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..12ba793285
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,172 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <kernel/sort_by_key.hpp>
+#include <kernel/sort_helper.hpp>
+#include <Array.hpp>
+#include <math.hpp>
+#include <algorithm>
+#include <numeric>
+#include <queue>
+#include <err_cpu.hpp>
+#include <functional>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
+{
+    // Get pointers and initialize original index locations
+    Tk *okey_ptr = okey.get();
+    Tv *oval_ptr = oval.get();
+
+    typedef IndexPair<Tk, Tv> CurrentPair;
+
+    dim_t size = okey.dims()[0];
+    size_t bytes = size * sizeof(CurrentPair);
+    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+
+    for(dim_t w = 0; w < okey.dims()[3]; w++) {
+        dim_t okeyW = w * okey.strides()[3];
+        dim_t ovalW = w * oval.strides()[3];
+
+        for(dim_t z = 0; z < okey.dims()[2]; z++) {
+            dim_t okeyWZ = okeyW + z * okey.strides()[2];
+            dim_t ovalWZ = ovalW + z * oval.strides()[2];
+
+            for(dim_t y = 0; y < okey.dims()[1]; y++) {
+
+                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
+                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
+
+                Tk *okey_col_ptr = okey_ptr + okeyOffset;
+                Tv *oval_col_ptr = oval_ptr + ovalOffset;
+
+                for(dim_t x = 0; x < size; x++) {
+                   pairKeyVal[x] = std::make_tuple(okey_col_ptr[x], oval_col_ptr[x]);
+                }
+
+                std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, isAscending>());
+
+                for(unsigned x = 0; x < size; x++) {
+                    okey_ptr[okeyOffset + x] = std::get<0>(pairKeyVal[x]);
+                    oval_ptr[ovalOffset + x] = std::get<1>(pairKeyVal[x]);
+                }
+            }
+        }
+    }
+
+    memFree((char *)pairKeyVal);
+    return;
+}
+
+template<typename Tk, typename Tv, bool isAscending, int dim>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval)
+{
+    af::dim4 inDims = okey.dims();
+
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim] = inDims[dim];
+    seqDims[dim] = 1;
+
+    uint* key = memAlloc<uint>(inDims.elements());
+    // IOTA
+    {
+        af::dim4 dims    = inDims;
+        uint* out        = key;
+        af::dim4 strides(1);
+        for(int i = 1; i < 4; i++)
+            strides[i] = strides[i-1] * dims[i-1];
+
+        for(dim_t w = 0; w < dims[3]; w++) {
+            dim_t offW = w * strides[3];
+            uint okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            for(dim_t z = 0; z < dims[2]; z++) {
+                dim_t offWZ = offW + z * strides[2];
+                uint okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                for(dim_t y = 0; y < dims[1]; y++) {
+                    dim_t offWZY = offWZ + y * strides[1];
+                    uint okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
+                    for(dim_t x = 0; x < dims[0]; x++) {
+                        dim_t id = offWZY + x;
+                        out[id] = okeyY + (x % seqDims[0]);
+                    }
+                }
+            }
+        }
+    }
+
+    // initialize original index locations
+    Tk *okey_ptr = okey.get();
+    Tv *oval_ptr = oval.get();
+
+    typedef KeyIndexPair<Tk, Tv> CurrentTuple;
+    size_t size = okey.elements();
+    size_t bytes = okey.elements() * sizeof(CurrentTuple);
+    CurrentTuple *tupleKeyValIdx = (CurrentTuple *)memAlloc<char>(bytes);
+
+    for(unsigned i = 0; i < size; i++) {
+        tupleKeyValIdx[i] = std::make_tuple(okey_ptr[i], oval_ptr[i], key[i]);
+    }
+
+    memFree(key); // key is no longer required
+
+    std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, isAscending>());
+
+    std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareK<Tk, Tv, true>());
+
+    for(unsigned x = 0; x < okey.elements(); x++) {
+        okey_ptr[x] = std::get<0>(tupleKeyValIdx[x]);
+        oval_ptr[x] = std::get<1>(tupleKeyValIdx[x]);
+    }
+
+    memFree((char *)tupleKeyValIdx);
+    return;
+}
+
+template<typename Tk, typename Tv, bool isAscending>
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
+{
+    int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
+    // TODO Make a better heurisitic
+    if(higherDims > 4)
+        kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+    else
+        kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+}
+
+#define INSTANTIATE(Tk, Tv, dr)                                                         \
+    template void sort0ByKey<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);               \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 0>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 1>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 2>(Array<Tk> okey, Array<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 3>(Array<Tk> okey, Array<Tv> oval);      \
+
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
+}
+}
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
new file mode 100644
index 0000000000..99479fddc3
--- /dev/null
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -0,0 +1,60 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <err_cpu.hpp>
+
+namespace cpu
+{
+    namespace kernel
+    {
+        template <typename Tk, typename Tv>
+        using IndexPair = std::tuple<Tk, Tv>;
+
+        template <typename Tk, typename Tv, bool isAscending>
+        struct IPCompare
+        {
+            bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs)
+            {
+                // Check stable sort condition
+                Tk lhsVal = std::get<0>(lhs);
+                Tk rhsVal = std::get<0>(rhs);
+                if(isAscending) return (lhsVal < rhsVal);
+                else return (lhsVal > rhsVal);
+            }
+        };
+
+        template <typename Tk, typename Tv>
+        using KeyIndexPair = std::tuple<Tk, Tv, uint>;
+
+        template <typename Tk, typename Tv, bool isAscending>
+        struct KIPCompareV
+        {
+            bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
+            {
+                // Check stable sort condition
+                Tk lhsVal = std::get<0>(lhs);
+                Tk rhsVal = std::get<0>(rhs);
+                if(isAscending) return (lhsVal < rhsVal);
+                else return (lhsVal > rhsVal);
+            }
+        };
+
+        template <typename Tk, typename Tv, bool isAscending>
+        struct KIPCompareK
+        {
+            bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
+            {
+                uint lhsVal = std::get<2>(lhs);
+                uint rhsVal = std::get<2>(rhs);
+                if(isAscending) return (lhsVal < rhsVal);
+                else return (lhsVal > rhsVal);
+            }
+        };
+    }
+}
diff --git a/src/backend/cpu/kernel/sort_index.hpp b/src/backend/cpu/kernel/sort_index.hpp
deleted file mode 100644
index b71cc47071..0000000000
--- a/src/backend/cpu/kernel/sort_index.hpp
+++ /dev/null
@@ -1,71 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <af/defines.h>
-#include <Array.hpp>
-#include <math.hpp>
-#include <algorithm>
-#include <numeric>
-#include <err_cpu.hpp>
-#include <functional>
-
-namespace cpu
-{
-namespace kernel
-{
-
-template<typename T, bool isAscending>
-void sort0_index(Array<T> val, Array<uint> idx, const Array<T> in)
-{
-    // initialize original index locations
-       uint *idx_ptr = idx.get();
-          T *val_ptr = val.get();
-    const T *in_ptr  = in.get();
-    function<bool(T, T)> op = std::greater<T>();
-    if(isAscending) { op = std::less<T>(); }
-
-    std::vector<uint> seq_vec(idx.dims()[0]);
-    std::iota(seq_vec.begin(), seq_vec.end(), 0);
-
-    const T *comp_ptr = nullptr;
-    auto comparator = [&comp_ptr, &op](size_t i1, size_t i2) {return op(comp_ptr[i1], comp_ptr[i2]);};
-
-    for(dim_t w = 0; w < in.dims()[3]; w++) {
-        dim_t valW = w * val.strides()[3];
-        dim_t idxW = w * idx.strides()[3];
-        dim_t  inW = w *  in.strides()[3];
-        for(dim_t z = 0; z < in.dims()[2]; z++) {
-            dim_t valWZ = valW + z * val.strides()[2];
-            dim_t idxWZ = idxW + z * idx.strides()[2];
-            dim_t  inWZ =  inW + z *  in.strides()[2];
-            for(dim_t y = 0; y < in.dims()[1]; y++) {
-
-                dim_t valOffset = valWZ + y * val.strides()[1];
-                dim_t idxOffset = idxWZ + y * idx.strides()[1];
-                dim_t inOffset  =  inWZ + y *  in.strides()[1];
-
-                uint *ptr = idx_ptr + idxOffset;
-                std::copy(seq_vec.begin(), seq_vec.end(), ptr);
-
-                comp_ptr = in_ptr + inOffset;
-                std::stable_sort(ptr, ptr + in.dims()[0], comparator);
-
-                for (dim_t i = 0; i < val.dims()[0]; ++i){
-                    val_ptr[valOffset + i] = in_ptr[inOffset + idx_ptr[idxOffset + i]];
-                }
-            }
-        }
-    }
-
-    return;
-}
-
-}
-}
diff --git a/src/backend/cpu/lapack_helper.hpp b/src/backend/cpu/lapack_helper.hpp
index f978ecb92b..c5ed4fa83f 100644
--- a/src/backend/cpu/lapack_helper.hpp
+++ b/src/backend/cpu/lapack_helper.hpp
@@ -17,17 +17,17 @@
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#include <lapacke.hpp>
-#undef AF_LAPACK_COL_MAJOR
-#define AF_LAPACK_COL_MAJOR 0
-#else
 #ifdef USE_MKL
-#include<mkl_lapacke.h>
-#else // NETLIB LAPACKE
-#include<lapacke.h>
-#endif
+    #include<mkl_lapacke.h>
+#else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
+    #else // NETLIB LAPACKE
+        #include<lapacke.h>
+    #endif
 #endif
 
 #endif
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 9474c792f3..3b31226b38 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -22,6 +22,13 @@
 #include <host_memory.hpp>
 #include <cctype>
 
+
+#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
+#define CPUID_CAPABLE USE_CPUID
+#else
+#define CPUID_CAPABLE 0
+#endif
+
 #ifdef _WIN32
 #include <limits.h>
 #include <intrin.h>
@@ -32,7 +39,7 @@ typedef unsigned __int32  uint32_t;
 
 using namespace std;
 
-#ifdef USE_CPUID
+#if CPUID_CAPABLE
 
 #define MAX_INTEL_TOP_LVL 4
 
@@ -82,7 +89,7 @@ class CPUInfo {
         bool   mIsHTT;
 };
 
-#ifndef USE_CPUID
+#if !CPUID_CAPABLE
 
 CPUInfo::CPUInfo()
     : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 78de4142c8..c833692e2f 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -23,51 +23,80 @@ using af::dim4;
 namespace cpu
 {
 
-template<af_op_t op, typename Ti, typename To>
-Array<To> scan(const Array<Ti>& in, const int dim)
-{
-    dim4 dims     = in.dims();
-    Array<To> out = createEmptyArray<To>(dims);
-    in.eval();
+    template<af_op_t op, typename Ti, typename To>
+    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan)
+    {
+        dim4 dims     = in.dims();
+        Array<To> out = createEmptyArray<To>(dims);
+        in.eval();
 
-    switch (in.ndims()) {
-        case 1:
-            kernel::scan_dim<op, Ti, To, 1> func1;
-            getQueue().enqueue(func1, out, 0, in, 0, dim);
-            break;
-        case 2:
-            kernel::scan_dim<op, Ti, To, 2> func2;
-            getQueue().enqueue(func2, out, 0, in, 0, dim);
-            break;
-        case 3:
-            kernel::scan_dim<op, Ti, To, 3> func3;
-            getQueue().enqueue(func3, out, 0, in, 0, dim);
-            break;
-        case 4:
-            kernel::scan_dim<op, Ti, To, 4> func4;
-            getQueue().enqueue(func4, out, 0, in, 0, dim);
-            break;
-    }
+        if (inclusive_scan) {
+            switch (in.ndims()) {
+                case 1:
+                    kernel::scan_dim<op, Ti, To, 1, true> func1;
+                    getQueue().enqueue(func1, out, 0, in, 0, dim);
+                    break;
+                case 2:
+                    kernel::scan_dim<op, Ti, To, 2, true> func2;
+                    getQueue().enqueue(func2, out, 0, in, 0, dim);
+                    break;
+                case 3:
+                    kernel::scan_dim<op, Ti, To, 3, true> func3;
+                    getQueue().enqueue(func3, out, 0, in, 0, dim);
+                    break;
+                case 4:
+                    kernel::scan_dim<op, Ti, To, 4, true> func4;
+                    getQueue().enqueue(func4, out, 0, in, 0, dim);
+                    break;
+            }
+        } else {
+            switch (in.ndims()) {
+                case 1:
+                    kernel::scan_dim<op, Ti, To, 1, false> func1;
+                    getQueue().enqueue(func1, out, 0, in, 0, dim);
+                    break;
+                case 2:
+                    kernel::scan_dim<op, Ti, To, 2, false> func2;
+                    getQueue().enqueue(func2, out, 0, in, 0, dim);
+                    break;
+                case 3:
+                    kernel::scan_dim<op, Ti, To, 3, false> func3;
+                    getQueue().enqueue(func3, out, 0, in, 0, dim);
+                    break;
+                case 4:
+                    kernel::scan_dim<op, Ti, To, 4, false> func4;
+                    getQueue().enqueue(func4, out, 0, in, 0, dim);
+                    break;
+            }
+        }
 
-    return out;
-}
+        return out;
+    }
 
-#define INSTANTIATE(ROp, Ti, To)                                        \
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim); \
+#define INSTANTIATE(ROp, Ti, To)\
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
-//accum
-INSTANTIATE(af_add_t, float  , float  )
-INSTANTIATE(af_add_t, double , double )
-INSTANTIATE(af_add_t, cfloat , cfloat )
-INSTANTIATE(af_add_t, cdouble, cdouble)
-INSTANTIATE(af_add_t, int    , int    )
-INSTANTIATE(af_add_t, uint   , uint   )
-INSTANTIATE(af_add_t, intl   , intl   )
-INSTANTIATE(af_add_t, uintl  , uintl  )
-INSTANTIATE(af_add_t, char   , int    )
-INSTANTIATE(af_add_t, uchar  , uint   )
-INSTANTIATE(af_add_t, short  , int    )
-INSTANTIATE(af_add_t, ushort , uint   )
-INSTANTIATE(af_notzero_t, char  , uint)
+#define INSTANTIATE_SCAN(ROp)           \
+    INSTANTIATE(ROp, float  , float  )  \
+    INSTANTIATE(ROp, double , double )  \
+    INSTANTIATE(ROp, cfloat , cfloat )  \
+    INSTANTIATE(ROp, cdouble, cdouble)  \
+    INSTANTIATE(ROp, int    , int    )  \
+    INSTANTIATE(ROp, uint   , uint   )  \
+    INSTANTIATE(ROp, intl   , intl   )  \
+    INSTANTIATE(ROp, uintl  , uintl  )  \
+    INSTANTIATE(ROp, char   , int    )  \
+    INSTANTIATE(ROp, char   , uint   )  \
+    INSTANTIATE(ROp, uchar  , uint   )  \
+    INSTANTIATE(ROp, short  , int    )  \
+    INSTANTIATE(ROp, ushort , uint   )
 
+    //accum
+    INSTANTIATE(af_notzero_t, char  , uint)
+    INSTANTIATE_SCAN(af_add_t)
+    INSTANTIATE_SCAN(af_sub_t)
+    INSTANTIATE_SCAN(af_mul_t)
+    INSTANTIATE_SCAN(af_div_t)
+    INSTANTIATE_SCAN(af_min_t)
+    INSTANTIATE_SCAN(af_max_t)
 }
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index 2d5deda00c..c0ac30db1d 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -14,5 +14,5 @@
 namespace cpu
 {
     template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim);
+    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index bc6396b258..4a649e0b23 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -15,11 +15,55 @@
 #include <functional>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <range.hpp>
+#include <iota.hpp>
+#include <handle.hpp>
+#include <sort_by_key.hpp>
+#include <reorder.hpp>
 #include <kernel/sort.hpp>
 
 namespace cpu
 {
 
+template<typename T, bool isAscending, int dim>
+void sortBatched(Array<T>& val)
+{
+    af::dim4 inDims = val.dims();
+
+    // Sort dimension
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim] = inDims[dim];
+    seqDims[dim] = 1;
+
+    Array<uint> key = iota<uint>(seqDims, tileDims);
+
+    Array<uint> resKey = createEmptyArray<uint>(dim4());
+    Array<T   > resVal = createEmptyArray<T>(dim4());
+
+    val.setDataDims(inDims.elements());
+    key.setDataDims(inDims.elements());
+
+    sort_by_key<T, uint, isAscending>(resVal, resKey, val, key, 0);
+
+    // Needs to be ascending (true) in order to maintain the indices properly
+    sort_by_key<uint, T, true>(key, val, resKey, resVal, 0);
+    val.eval();
+
+    val.setDataDims(inDims); // This is correct only for dim0
+}
+
+template<typename T, bool isAscending>
+void sort0(Array<T>& val)
+{
+    int higherDims = val.elements() / val.dims()[0];
+    // TODO Make a better heurisitic
+    if(higherDims > 10)
+        sortBatched<T, isAscending, 0>(val);
+    else
+        getQueue().enqueue(kernel::sort0Iterative<T, isAscending>, val);
+}
+
 template<typename T, bool isAscending>
 Array<T> sort(const Array<T> &in, const unsigned dim)
 {
@@ -27,9 +71,26 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
 
     Array<T> out = copyArray<T>(in);
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0<T, isAscending>, out); break;
+        case 0: sort0<T, isAscending>(out); break;
+        case 1: sortBatched<T, isAscending, 1>(out); break;
+        case 2: sortBatched<T, isAscending, 2>(out); break;
+        case 3: sortBatched<T, isAscending, 3>(out); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+
+    if(dim != 0) {
+        af::dim4 preorderDims = out.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0] = out.dims()[dim];
+        for(int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i] = out.dims()[i - 1];
+        }
+
+        out.setDataDims(preorderDims);
+        out = reorder<T>(out, reorderDims);
+    }
     return out;
 }
 
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 5a99257033..46b06602b4 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -11,6 +11,9 @@
 #include <sort_by_key.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <copy.hpp>
+#include <range.hpp>
+#include <reorder.hpp>
 #include <kernel/sort_by_key.hpp>
 
 namespace cpu
@@ -23,16 +26,33 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     ikey.eval();
     ival.eval();
 
-    okey = createEmptyArray<Tk>(ikey.dims());
-    oval = createEmptyArray<Tv>(ival.dims());
-    Array<uint> oidx = createValueArray(ikey.dims(), 0u);
-    oidx.eval();
+    okey = copyArray<Tk>(ikey);
+    oval = copyArray<Tv>(ival);
 
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0_by_key<Tk, Tv, isAscending>,
-                                   okey, oval, oidx, ikey, ival); break;
+        case 0: getQueue().enqueue(kernel::sort0ByKey<Tk, Tv, isAscending>, okey, oval); break;
+        case 1: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>, okey, oval); break;
+        case 2: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>, okey, oval); break;
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>, okey, oval); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+
+    if(dim != 0) {
+        af::dim4 preorderDims = okey.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0] = okey.dims()[dim];
+        for(int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i] = okey.dims()[i - 1];
+        }
+
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
+
+        okey = reorder<Tk>(okey, reorderDims);
+        oval = reorder<Tv>(oval, reorderDims);
+    }
 }
 
 #define INSTANTIATE(Tk, Tv)                                             \
@@ -46,6 +66,8 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
 #define INSTANTIATE1(Tk)       \
     INSTANTIATE(Tk, float)     \
     INSTANTIATE(Tk, double)    \
+    INSTANTIATE(Tk, cfloat)    \
+    INSTANTIATE(Tk, cdouble)   \
     INSTANTIATE(Tk, int)       \
     INSTANTIATE(Tk, uint)      \
     INSTANTIATE(Tk, char)      \
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index 77860ede18..b865db9c1c 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -14,22 +14,48 @@
 #include <numeric>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/sort_index.hpp>
+#include <range.hpp>
+#include <copy.hpp>
+#include <reorder.hpp>
+#include <kernel/sort_by_key.hpp>
 
 namespace cpu
 {
 
 template<typename T, bool isAscending>
-void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
 {
     in.eval();
 
-    val = createEmptyArray<T>(in.dims());
-    idx = createEmptyArray<uint>(in.dims());
+    // okey is values, oval is indices
+    okey = copyArray<T>(in);
+    oval = range<uint>(in.dims(), dim);
+    oval.eval();
+
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0_index<T, isAscending>, val, idx, in); break;
+        case 0: getQueue().enqueue(kernel::sort0ByKey<T, uint, isAscending>, okey, oval); break;
+        case 1: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 1>, okey, oval); break;
+        case 2: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 2>, okey, oval); break;
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending, 3>, okey, oval); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
+
+    if(dim != 0) {
+        af::dim4 preorderDims = okey.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0] = okey.dims()[dim];
+        for(int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i] = okey.dims()[i - 1];
+        }
+
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
+
+        okey = reorder<T>(okey, reorderDims);
+        oval = reorder<uint>(oval, reorderDims);
+    }
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 7678754bc3..1f9512fb8d 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -169,22 +169,21 @@ namespace cuda
 
         dim4 getDataDims() const
         {
-            // This is for moddims
-            // dims and data_dims are different when moddims is used
-            return isOwner() ? dims() : data_dims;
+            return data_dims;
         }
 
         void setDataDims(const dim4 &new_dims)
         {
+            modDims(new_dims);
             data_dims = new_dims;
         }
 
         T* device()
         {
-            if (!isOwner() || data.use_count() > 1) {
+            if (!isOwner() || getOffset() || data.use_count() > 1) {
                 *this = Array<T>(dims(), get(), true, true);
             }
-            return this->data.get();
+            return this->get();
         }
 
         T* device() const
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 81d6ba243c..d1727ffd40 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -31,6 +31,8 @@ ENDIF()
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
 IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
     MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50")
+    MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon separated list of CUDA Compute versions to enable the specified computes")
+    MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
     LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50")
 ENDIF()
 
@@ -102,7 +104,15 @@ ELSE(CUDA_cusolver_LIBRARY)
     IF(${CUDA_LAPACK_CPU_FALLBACK})
         ## Try to use CPU side lapack
         IF(APPLE)
-            FIND_PACKAGE(LAPACK)
+            FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
+            IF(NOT LAPACK_FOUND)
+                # UNSET THE VARIABLES FROM LAPACKE
+                UNSET(LAPACKE_LIB CACHE)
+                UNSET(LAPACK_LIB CACHE)
+                UNSET(LAPACKE_INCLUDES CACHE)
+                UNSET(LAPACKE_ROOT_DIR CACHE)
+                FIND_PACKAGE(LAPACK)
+            ENDIF()
         ELSE(APPLE) # Linux and Windows
             FIND_PACKAGE(LAPACKE)
         ENDIF(APPLE)
@@ -112,9 +122,14 @@ ELSE(CUDA_cusolver_LIBRARY)
         ELSE(NOT LAPACK_FOUND)
             MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. But CPU LAPACK libraries are available. Will fallback to using host side code.")
             ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
-            IF(USE_CUDA_MKL)
-                MESSAGE("Using MKL")
+            IF(USE_CUDA_MKL) # Manual MKL Setup
+                MESSAGE("CUDA LAPACK CPU Fallback Using MKL")
                 ADD_DEFINITIONS(-DUSE_MKL)
+            ELSE(USE_CUDA_MKL)
+                IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+                    MESSAGE("CUDA LAPACK CPU Fallback Using MKL RT")
+                    ADD_DEFINITIONS(-DUSE_MKL)
+                ENDIF()
             ENDIF()
         ENDIF()
     ELSE()
@@ -143,7 +158,6 @@ FILE(GLOB cuda_headers
 FILE(GLOB cuda_sources
     "*.cu"
     "*.cpp"
-    "sort_by_key/*.cu"
     "kernel/*.cu")
 
 FILE(GLOB jit_sources
@@ -155,6 +169,12 @@ FILE(GLOB kernel_headers
 FILE(GLOB ptx_sources
     "JIT/*.cu")
 
+LIST(SORT cuda_headers)
+LIST(SORT cuda_sources)
+LIST(SORT jit_sources)
+LIST(SORT kernel_headers)
+LIST(SORT ptx_sources)
+
 SOURCE_GROUP(backend\\cuda\\Headers FILES ${cuda_headers})
 SOURCE_GROUP(backend\\cuda\\Sources FILES ${cuda_sources})
 SOURCE_GROUP(backend\\cuda\\JIT FILES ${jit_sources})
@@ -168,6 +188,8 @@ IF(CUDA_LAPACK_CPU_FALLBACK)
 
     SOURCE_GROUP(backend\\cuda\\cpu_lapack\\Headers FILES ${cpu_lapack_headers})
     SOURCE_GROUP(backend\\cuda\\cpu_lapack\\Sources FILES ${cpu_lapack_sources})
+    LIST(SORT cpu_lapack_headers)
+    LIST(SORT cpu_lapack_sources)
 ENDIF()
 
 FILE(GLOB backend_headers
@@ -179,6 +201,9 @@ FILE(GLOB backend_sources
     "../*.cpp"
     )
 
+LIST(SORT backend_headers)
+LIST(SORT backend_sources)
+
 SOURCE_GROUP(backend\\Headers FILES ${backend_headers})
 SOURCE_GROUP(backend\\Sources FILES ${backend_sources})
 
@@ -191,6 +216,9 @@ FILE(GLOB c_sources
     "../../api/c/*.cpp"
     )
 
+LIST(SORT c_headers)
+LIST(SORT c_sources)
+
 SOURCE_GROUP(api\\c\\Headers FILES ${c_headers})
 SOURCE_GROUP(api\\c\\Sources FILES ${c_sources})
 
@@ -198,8 +226,12 @@ FILE(GLOB cpp_sources
     "../../api/cpp/*.cpp"
     )
 
+LIST(SORT cpp_sources)
+
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+
 LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
 IF(${COMPUTE_COUNT} EQUAL 1)
     SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} ${CUDA_GENERATE_CODE}")
@@ -300,6 +332,24 @@ macro(MY_CUDA_ADD_LIBRARY cuda_target)
 
 endmacro()
 
+IF(NOT CUDA_CUDA_LIBRARY)
+    MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
+    MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
+    FIND_FILE(CUDA_CUDA_LIBRARY_STUB
+              NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
+              PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+              PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
+              DOC "CUDA Library STUB"
+             )
+    IF(CUDA_CUDA_LIBRARY_STUB)
+        MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
+        MESSAGE("Run the following commands (Linux) to set it up:")
+        MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
+        MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
+    ENDIF()
+    MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
+ENDIF(NOT CUDA_CUDA_LIBRARY)
+
 MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cuda_headers}
                 ${cuda_sources}
@@ -312,6 +362,7 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${c_headers}
                 ${c_sources}
                 ${cpp_sources}
+                ${sort_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE})
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
diff --git a/src/backend/cuda/cpu_lapack/lapack_helper.hpp b/src/backend/cuda/cpu_lapack/lapack_helper.hpp
index 58265871c2..b85a80b10c 100644
--- a/src/backend/cuda/cpu_lapack/lapack_helper.hpp
+++ b/src/backend/cuda/cpu_lapack/lapack_helper.hpp
@@ -19,17 +19,17 @@
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#include <lapacke.hpp>
-#undef AF_LAPACK_COL_MAJOR
-#define AF_LAPACK_COL_MAJOR 0
-#else
 #ifdef USE_MKL
-#include<mkl_lapacke.h>
-#else // NETLIB LAPACKE
-#include<lapacke.h>
-#endif
+    #include<mkl_lapacke.h>
+#else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
+    #else // NETLIB LAPACKE
+        #include<lapacke.h>
+    #endif
 #endif
 
 #endif
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cu
index fd023c9f16..db0d1b4617 100644
--- a/src/backend/cuda/diagonal.cu
+++ b/src/backend/cuda/diagonal.cu
@@ -34,7 +34,7 @@ namespace cuda
     Array<T> diagExtract(const Array<T> &in, const int num)
     {
         const dim_t *idims = in.dims().get();
-        dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+        dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
         Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
         kernel::diagExtract<T>(out, in, num);
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 13d108a2af..29f85f96c2 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -41,8 +41,8 @@ class cuFFTPlanner
 
     public:
         static cuFFTPlanner& getInstance() {
-            static cuFFTPlanner single_instance;
-            return single_instance;
+            static cuFFTPlanner instances[cuda::DeviceManager::MAX_DEVICES];
+            return instances[cuda::getActiveDeviceId()];
         }
 
     private:
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 44f98d92c1..9361b72e23 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -18,7 +18,8 @@
 #include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "gradient.hpp"
-#include "sort_index.hpp"
+#include "sort_by_key.hpp"
+#include "range.hpp"
 
 namespace cuda
 {
@@ -336,10 +337,12 @@ void harris(unsigned* corners_out,
 
         int sort_elem = harris_responses.strides[3] * harris_responses.dims[3];
         harris_responses.ptr = d_resp_corners;
+        // Create indices using range
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
+        kernel::range<uint>(harris_idx, 0);
 
         // Sort Harris responses
-        sort0_index<float, false>(harris_responses, harris_idx);
+        sort0ByKey<float, uint, false>(harris_responses, harris_idx);
 
         *x_out = memAlloc<float>(*corners_out);
         *y_out = memAlloc<float>(*corners_out);
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 2632266c92..e2f7e591fb 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/dim4.hpp>
 #include <math.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
@@ -18,8 +19,8 @@ namespace cuda
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
+        static const unsigned IOTA_TX = 32;
+        static const unsigned IOTA_TY = 8;
         static const unsigned TILEX = 512;
         static const unsigned TILEY = 32;
 
@@ -69,9 +70,9 @@ namespace cuda
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename T>
-        void iota(Param<T> out, const dim4 &sdims, const dim4 &tdims)
+        void iota(Param<T> out, const af::dim4 &sdims, const af::dim4 &tdims)
         {
-            dim3 threads(TX, TY, 1);
+            dim3 threads(IOTA_TX, IOTA_TY, 1);
 
             int blocksPerMatX = divup(out.dims[0], TILEX);
             int blocksPerMatY = divup(out.dims[1], TILEY);
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 89de56065d..8a2b535cee 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -16,7 +16,8 @@
 #include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "orb_patch.hpp"
-#include "sort_index.hpp"
+#include "sort_by_key.hpp"
+#include "range.hpp"
 
 #include <boost/scoped_ptr.hpp>
 
@@ -394,10 +395,12 @@ void orb(unsigned* out_feat,
 
         int sort_elem = harris_sorted.strides[3] * harris_sorted.dims[3];
         harris_sorted.ptr = d_score_harris;
+        // Create indices using range
         harris_idx.ptr = memAlloc<unsigned>(sort_elem);
+        kernel::range<uint>(harris_idx, 0);
 
         // Sort features according to Harris responses
-        sort0_index<float, false>(harris_sorted, harris_idx);
+        kernel::sort0ByKey<float, uint, false>(harris_sorted, harris_idx);
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 9670b07bd6..6880ed566a 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -18,10 +18,10 @@ namespace cuda
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 512;
-        static const unsigned TILEY = 32;
+        static const unsigned RANGE_TX = 32;
+        static const unsigned RANGE_TY = 8;
+        static const unsigned RANGE_TILEX = 512;
+        static const unsigned RANGE_TILEY = 32;
 
         template<typename T>
         __global__
@@ -74,10 +74,10 @@ namespace cuda
         template<typename T>
         void range(Param<T> out, const int dim)
         {
-            dim3 threads(TX, TY, 1);
+            dim3 threads(RANGE_TX, RANGE_TY, 1);
 
-            int blocksPerMatX = divup(out.dims[0], TILEX);
-            int blocksPerMatY = divup(out.dims[1], TILEY);
+            int blocksPerMatX = divup(out.dims[0], RANGE_TILEX);
+            int blocksPerMatY = divup(out.dims[1], RANGE_TILEY);
             dim3 blocks(blocksPerMatX * out.dims[2],
                         blocksPerMatY * out.dims[3],
                         1);
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index af90de0f9c..7b8584e3f6 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -23,7 +23,7 @@ namespace cuda
 namespace kernel
 {
 
-    template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass, uint DIMY>
+    template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass, uint DIMY, bool inclusive_scan>
     __global__
     static void scan_dim_kernel(Param<To> out,
                                 Param<To> tmp,
@@ -104,12 +104,21 @@ namespace kernel
             }
 
             val = binop(val, s_tmp[tidx]);
-            __syncthreads();
-            if (cond) *optr = val;
-
+            if (inclusive_scan) {
+                if (cond) {
+                    *optr = val;
+                }
+            } else if (is_valid) {
+                if (id_dim == (out_dim - 1)) {
+                    *(optr - (id_dim*ostride_dim)) = init;
+                } else if (id_dim < (out_dim - 1)) {
+                    *(optr + ostride_dim) = val;
+                }
+            }
             id_dim += blockDim.y;
             iptr += blockDim.y * istride_dim;
             optr += blockDim.y * ostride_dim;
+            __syncthreads();
         }
 
         if (!isFinalPass &&
@@ -178,7 +187,7 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass>
+    template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass, bool inclusive_scan>
     static void scan_dim_launcher(Param<To> out,
                            Param<To> tmp,
                            CParam<Ti> in,
@@ -194,16 +203,16 @@ namespace kernel
 
         switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 8>), blocks, threads,
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 8, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
         case 4:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 4>), blocks, threads,
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 4, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
         case 2:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 2>), blocks, threads,
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 2, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
         case 1:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 1>), blocks, threads,
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 1, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
         }
 
@@ -232,7 +241,7 @@ namespace kernel
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename To, af_op_t op, int dim>
+    template<typename Ti, typename To, af_op_t op, int dim, bool inclusive_scan>
     static void scan_dim(Param<To> out, CParam<Ti> in)
     {
         uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
@@ -245,7 +254,7 @@ namespace kernel
 
         if (blocks_all[dim] == 1) {
 
-            scan_dim_launcher<Ti, To, op, dim, true>(out, out, in,
+            scan_dim_launcher<Ti, To, op, dim, true, inclusive_scan>(out, out, in,
                                                      threads_y,
                                                      blocks_all);
 
@@ -260,7 +269,7 @@ namespace kernel
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
             tmp.ptr = memAlloc<To>(tmp_elements);
 
-            scan_dim_launcher<Ti, To, op, dim, false>(out, tmp, in,
+            scan_dim_launcher<Ti, To, op, dim, false, inclusive_scan>(out, tmp, in,
                                                       threads_y,
                                                       blocks_all);
 
@@ -269,11 +278,11 @@ namespace kernel
 
             //FIXME: Is there an alternative to the if condition ?
             if (op == af_notzero_t) {
-                scan_dim_launcher<To, To, af_add_t, dim, true>(tmp, tmp, tmp,
+                scan_dim_launcher<To, To, af_add_t, dim, true, true>(tmp, tmp, tmp,
                                                                threads_y,
                                                                blocks_all);
             } else {
-                scan_dim_launcher<To, To,       op, dim, true>(tmp, tmp, tmp,
+                scan_dim_launcher<To, To,       op, dim, true, true>(tmp, tmp, tmp,
                                                                threads_y,
                                                                blocks_all);
             }
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 4c63942a0f..70ddfa1135 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -22,7 +22,7 @@ namespace cuda
 {
 namespace kernel
 {
-    template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX>
+    template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX, bool inclusive_scan>
     __global__
     static void scan_first_kernel(Param<To> out,
                                   Param<To> tmp,
@@ -75,7 +75,7 @@ namespace kernel
 
             if (isLast) s_tmp[tidy] = val;
 
-            bool cond = ((id < out.dims[0]));
+            bool cond = (id < out.dims[0]);
             val = cond ? transform(iptr[id]) : init;
             sptr[tidx] = val;
             __syncthreads();
@@ -93,7 +93,18 @@ namespace kernel
             }
 
             val = binop(val, s_tmp[tidy]);
-            if (cond) optr[id] = val;
+
+            if (inclusive_scan) {
+                if (cond) {
+                    optr[id] = val;
+                }
+            } else {
+                if (id == (out.dims[0] - 1)) {
+                    optr[0] = init;
+                } else if (id < (out.dims[0] - 1)) {
+                    optr[id + 1] = val;
+                }
+            }
             id += blockDim.x;
             __syncthreads();
         }
@@ -144,7 +155,7 @@ namespace kernel
 
     }
 
-    template<typename Ti, typename To, af_op_t op, bool isFinalPass>
+    template<typename Ti, typename To, af_op_t op, bool isFinalPass, bool inclusive_scan>
     static void scan_first_launcher(Param<To> out,
                              Param<To> tmp,
                              CParam<Ti> in,
@@ -161,19 +172,51 @@ namespace kernel
 
         switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32>), blocks, threads,
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_x, blocks_y, lim); break;
         case 64:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64>), blocks, threads,
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_x, blocks_y, lim); break;
         case 128:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128>), blocks, threads,
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_x, blocks_y, lim); break;
         case 256:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256>), blocks, threads,
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256, inclusive_scan>), blocks, threads,
                 out, tmp, in, blocks_x, blocks_y, lim); break;
         }
 
+        //if (inclusive_scan) {
+        //    switch (threads_x) {
+        //    case 32:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32, true>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    case 64:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64, true>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    case 128:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128, true>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    case 256:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256, true>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    }
+        //} else {
+        //    switch (threads_x) {
+        //    case 32:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32, false>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    case 64:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64, false>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    case 128:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128, false>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    case 256:
+        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256, false>), blocks, threads,
+        //            out, tmp, in, blocks_x, blocks_y, lim); break;
+        //    }
+        //}
+
         POST_LAUNCH_CHECK();
     }
 
@@ -198,7 +241,7 @@ namespace kernel
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
     static void scan_first(Param<To> out, CParam<Ti> in)
     {
         uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
@@ -210,7 +253,7 @@ namespace kernel
 
         if (blocks_x == 1) {
 
-            scan_first_launcher<Ti, To, op, true>(out, out, in,
+            scan_first_launcher<Ti, To, op, true, inclusive_scan>(out, out, in,
                                                   blocks_x, blocks_y,
                                                   threads_x);
 
@@ -225,17 +268,17 @@ namespace kernel
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
             tmp.ptr = memAlloc<To>(tmp_elements);
 
-            scan_first_launcher<Ti, To, op, false>(out, tmp, in,
+            scan_first_launcher<Ti, To, op, false, inclusive_scan>(out, tmp, in,
                                                    blocks_x, blocks_y,
                                                    threads_x);
 
             //FIXME: Is there an alternative to the if condition ?
             if (op == af_notzero_t) {
-                scan_first_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
+                scan_first_launcher<To, To, af_add_t, true, true>(tmp, tmp, tmp,
                                                              1, blocks_y,
                                                              threads_x);
             } else {
-                scan_first_launcher<To, To,       op, true>(tmp, tmp, tmp,
+                scan_first_launcher<To, To,       op, true, true>(tmp, tmp, tmp,
                                                             1, blocks_y,
                                                             threads_x);
             }
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index b23e308633..f0095b144d 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -10,6 +10,7 @@
 #include <math.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
+#include <kernel/iota.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <thrust/device_ptr.h>
@@ -19,15 +20,11 @@ namespace cuda
 {
     namespace kernel
     {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename T, bool isAscending>
-        void sort0(Param<T> val)
+        void sort0Iterative(Param<T> val)
         {
             thrust::device_ptr<T> val_ptr = thrust::device_pointer_cast(val.ptr);
 
@@ -49,5 +46,93 @@ namespace cuda
             }
             POST_LAUNCH_CHECK();
         }
+
+        template<typename T, bool isAscending, int dim>
+        void sortBatched(Param<T> pVal)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pVal.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            dim4 keydims = inDims;
+            uint* key = memAlloc<uint>(keydims.elements());
+            Param<uint> pKey;
+            pKey.ptr = key;
+            pKey.strides[0] = 1;
+            pKey.dims[0] = keydims[0];
+            for(int i = 1; i < 4; i++) {
+                pKey.dims[i] = keydims[i];
+                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
+            }
+            kernel::iota<uint>(pKey, seqDims, tileDims);
+
+            // Flat
+            //val.modDims(inDims.elements());
+            //key.modDims(inDims.elements());
+            pKey.dims[0] = inDims.elements();
+            pKey.strides[0] = 1;
+            pVal.dims[0] = inDims.elements();
+            pVal.strides[0] = 1;
+            for(int i = 1; i < 4; i++) {
+                pKey.dims[i] = 1;
+                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
+                pVal.dims[i] = 1;
+                pVal.strides[i] = pVal.strides[i - 1] * pVal.dims[i - 1];
+            }
+
+            // Sort indices
+            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+            //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+            thrust::device_ptr<T>    pVal_ptr = thrust::device_pointer_cast(pVal.ptr);
+            thrust::device_ptr<uint> pKey_ptr = thrust::device_pointer_cast(pKey.ptr);
+            if(isAscending) {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              pVal_ptr,
+                              pVal_ptr + pVal.dims[0],
+                              pKey_ptr);
+            } else {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              pVal_ptr,
+                              pVal_ptr + pVal.dims[0],
+                              pKey_ptr, thrust::greater<T>());
+            }
+            POST_LAUNCH_CHECK();
+
+            // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          pKey_ptr,
+                          pKey_ptr + pVal.dims[0],
+                          pVal_ptr);
+            POST_LAUNCH_CHECK();
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            // Not really necessary
+            // CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+            memFree(key);
+        }
+
+        template<typename T, bool isAscending>
+        void sort0(Param<T> val)
+        {
+            int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 10)
+                sortBatched<T, isAscending, 0>(val);
+            else
+                kernel::sort0Iterative<T, isAscending>(val);
+        }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 42a3256a1c..35250a8ad1 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -12,46 +12,19 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <thrust/device_ptr.h>
-#include <thrust/sort.h>
 
 namespace cuda
 {
     namespace kernel
     {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0_by_key(Param<Tk> okey, Param<Tv> oval)
-        {
-            thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
-            thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval);
 
-            for(int w = 0; w < okey.dims[3]; w++) {
-                int okeyW = w * okey.strides[3];
-                int ovalW = w * oval.strides[3];
-                for(int z = 0; z < okey.dims[2]; z++) {
-                    int okeyWZ = okeyW + z * okey.strides[2];
-                    int ovalWZ = ovalW + z * oval.strides[2];
-                    for(int y = 0; y < okey.dims[1]; y++) {
+        template<typename Tk, typename Tv, bool isAscending, int dim>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal);
 
-                        int okeyOffset = okeyWZ + y * okey.strides[1];
-                        int ovalOffset = ovalWZ + y * oval.strides[1];
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval);
 
-                        if(isAscending) {
-                            THRUST_SELECT(thrust::sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset);
-                        } else {
-                            THRUST_SELECT(thrust::sort_by_key, okey_ptr + okeyOffset, okey_ptr + okeyOffset + okey.dims[0], oval_ptr + ovalOffset, thrust::greater<Tk>());
-                        }
-                    }
-                }
-            }
-            POST_LAUNCH_CHECK();
-        }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..a9143f282d
--- /dev/null
+++ b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,29 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ELSEIF(${STR} MATCHES "// SBK_DIRS:")
+        STRING(REPLACE "// SBK_DIRS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_DIRS ${TEMP})
+    ELSEIF(${STR} MATCHES "// SBK_INSTS:")
+        STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    FOREACH(SBK_DIR ${SBK_DIRS})
+        FOREACH(SBK_INST ${SBK_INSTS})
+            CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in"
+            "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu")
+        ENDFOREACH(SBK_INST ${SBK_INSTS})
+    ENDFOREACH(SBK_DIR ${SBK_DIRS})
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
+
+FILE(GLOB sort_by_key_sources
+  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/*.cu"
+)
+
+LIST(SORT sort_by_key_sources)
diff --git a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
new file mode 100644
index 0000000000..94168df1de
--- /dev/null
+++ b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+// This file instantiates sort_by_key as separate object files from CMake
+// The 3 lines below are read by CMake to determenine the instantiations
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+// SBK_DIRS:true false
+// SBK_INSTS:0 1
+
+namespace cuda
+{
+namespace kernel
+{
+    INSTANTIATE@SBK_INST@(@SBK_TYPE@, @SBK_DIR@)
+}
+}
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..c035e3ae07
--- /dev/null
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,218 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key.hpp>
+#include <af/dim4.hpp>
+#include <memory.hpp>
+#include <math.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <kernel/iota.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/device_vector.h>
+#include <thrust/sort.h>
+
+// This needs to be in global namespace as it is used by thrust
+template<typename Tk, typename Tv>
+struct IndexPair
+{
+    Tk first;
+    Tv second;
+};
+
+template <typename Tk, typename Tv, bool isAscending>
+struct IPCompare
+{
+    __host__ __device__
+    bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs) const
+    {
+        // Check stable sort condition
+        if(isAscending) return (lhs.first < rhs.first);
+        else return (lhs.first > rhs.first);
+    }
+};
+
+namespace cuda
+{
+    namespace kernel
+    {
+        static const int copyPairIter = 4;
+
+        template <typename Tk, typename Tv>
+        __global__
+        void makeIndexPair(IndexPair<Tk, Tv> *out, const Tk *first, const Tv *second, const int N)
+        {
+            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
+
+            for(int i = tIdx; i < N; i += blockDim.x)
+            {
+                out[i].first = first[i];
+                out[i].second = second[i];
+            }
+        }
+
+        template <typename Tk, typename Tv>
+        __global__
+        void splitIndexPair(Tk *first, Tv *second, const IndexPair<Tk, Tv> *out, const int N)
+        {
+            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
+
+            for(int i = tIdx; i < N; i += blockDim.x)
+            {
+                first[i]  = out[i].first;
+                second[i] = out[i].second;
+            }
+        }
+
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval)
+        {
+            thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
+            thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
+
+            for(int w = 0; w < okey.dims[3]; w++) {
+                int okeyW = w * okey.strides[3];
+                int ovalW = w * oval.strides[3];
+                for(int z = 0; z < okey.dims[2]; z++) {
+                    int okeyWZ = okeyW + z * okey.strides[2];
+                    int ovalWZ = ovalW + z * oval.strides[2];
+                    for(int y = 0; y < okey.dims[1]; y++) {
+
+                        int okeyOffset = okeyWZ + y * okey.strides[1];
+                        int ovalOffset = ovalWZ + y * oval.strides[1];
+
+                        if(isAscending) {
+                            THRUST_SELECT(thrust::stable_sort_by_key,
+                                          okey_ptr + okeyOffset,
+                                          okey_ptr + okeyOffset + okey.dims[0],
+                                          oval_ptr + ovalOffset);
+                        } else {
+                            THRUST_SELECT(thrust::stable_sort_by_key,
+                                          okey_ptr + okeyOffset,
+                                          okey_ptr + okeyOffset + okey.dims[0],
+                                          oval_ptr + ovalOffset, thrust::greater<Tk>());
+                        }
+                    }
+                }
+            }
+            POST_LAUNCH_CHECK();
+        }
+
+        template<typename Tk, typename Tv, bool isAscending, int dim>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pKey.dims[i];
+
+            const dim_t elements = inDims.elements();
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            uint* key = memAlloc<uint>(elements);
+            Param<uint> pSeq;
+            pSeq.ptr = key;
+            pSeq.strides[0] = 1;
+            pSeq.dims[0] = inDims[0];
+            for(int i = 1; i < 4; i++) {
+                pSeq.dims[i] = inDims[i];
+                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
+            }
+            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
+
+            // Make pkey, pVal into a pair
+            IndexPair<Tk, Tv> *Xptr = (IndexPair<Tk, Tv>*)memAlloc<char>(sizeof(IndexPair<Tk, Tv>) * elements);
+
+            const int threads = 256;
+            int blocks = divup(elements, threads * copyPairIter);
+            CUDA_LAUNCH((makeIndexPair<Tk, Tv>), blocks, threads,
+                        Xptr, pKey.ptr, pVal.ptr, elements);
+            POST_LAUNCH_CHECK();
+
+            thrust::device_ptr<IndexPair<Tk, Tv> > X = thrust::device_pointer_cast(Xptr);
+
+            // Sort indices
+            // Need to convert pSeq to thrust::device_ptr, otherwise thrust
+            // throws weird errors for all *64 data types (double, intl, uintl etc)
+            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          X, X + elements,
+                          dSeq,
+                          IPCompare<Tk, Tv, isAscending>());
+            POST_LAUNCH_CHECK();
+
+            // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          dSeq, dSeq + elements,
+                          X);
+            POST_LAUNCH_CHECK();
+
+            CUDA_LAUNCH((splitIndexPair<Tk, Tv>), blocks, threads,
+                        pKey.ptr, pVal.ptr, Xptr, elements);
+            POST_LAUNCH_CHECK();
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            memFree(key);
+            memFree((char*)Xptr);
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval)
+        {
+            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 4)
+                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(okey, oval);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+        }
+
+#define INSTANTIATE(Tk, Tv, dr)                                                         \
+    template void sort0ByKey<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);               \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 0>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 1>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 2>(Param<Tk> okey, Param<Tv> oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 3>(Param<Tk> okey, Param<Tv> oval);      \
+
+#define INSTANTIATE0(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
+
+    }
+}
diff --git a/src/backend/cuda/kernel/sort_index.hpp b/src/backend/cuda/kernel/sort_index.hpp
deleted file mode 100644
index 9d29914f23..0000000000
--- a/src/backend/cuda/kernel/sort_index.hpp
+++ /dev/null
@@ -1,59 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <math.hpp>
-#include <dispatch.hpp>
-#include <Param.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
-#include <thrust/device_ptr.h>
-#include <thrust/sequence.h>
-#include <thrust/sort.h>
-
-namespace cuda
-{
-    namespace kernel
-    {
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool isAscending>
-        void sort0_index(Param<T> val, Param<unsigned> idx)
-        {
-            thrust::device_ptr<T>        val_ptr = thrust::device_pointer_cast(val.ptr);
-            thrust::device_ptr<unsigned> idx_ptr = thrust::device_pointer_cast(idx.ptr);
-
-            for(int w = 0; w < val.dims[3]; w++) {
-                int valW = w * val.strides[3];
-                int idxW = w * idx.strides[3];
-                for(int z = 0; z < val.dims[2]; z++) {
-                    int valWZ = valW + z * val.strides[2];
-                    int idxWZ = idxW + z * idx.strides[2];
-                    for(int y = 0; y < val.dims[1]; y++) {
-
-                        int valOffset = valWZ + y * val.strides[1];
-                        int idxOffset = idxWZ + y * idx.strides[1];
-
-                        THRUST_SELECT(thrust::sequence, idx_ptr + idxOffset, idx_ptr + idxOffset + idx.dims[0]);
-                        if(isAscending) {
-                            THRUST_SELECT(thrust::sort_by_key,
-                                    val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
-                                    idx_ptr + idxOffset);
-                        } else {
-                            THRUST_SELECT(thrust::sort_by_key,
-                                        val_ptr + valOffset, val_ptr + valOffset + val.dims[0],
-                                        idx_ptr + idxOffset, thrust::greater<T>());
-                        }
-                    }
-                }
-            }
-            POST_LAUNCH_CHECK();
-        }
-    }
-}
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 746e2b82ac..62767fd2e8 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -101,7 +101,7 @@ namespace kernel
         int otmp_elements = otmp.strides[3] * otmp.dims[3];
         otmp.ptr = memAlloc<uint>(otmp_elements);
 
-        scan_first_launcher<T, uint, af_notzero_t, false>(otmp, rtmp, in,
+        scan_first_launcher<T, uint, af_notzero_t, false, true>(otmp, rtmp, in,
                                                           blocks_x, blocks_y,
                                                           threads_x);
 
@@ -113,7 +113,7 @@ namespace kernel
             ltmp.strides[k] = rtmp_elements;
         }
 
-        scan_first<uint, uint, af_add_t>(ltmp, ltmp);
+        scan_first<uint, uint, af_add_t, true>(ltmp, ltmp);
 
         // Get output size and allocate output
         uint total;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 10cfdc886c..23735389e5 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -492,18 +492,24 @@ bool synchronize_calls() {
 
 af_err afcu_get_stream(cudaStream_t* stream, int id)
 {
-    *stream = cuda::getStream(id);
+    try{
+        *stream = cuda::getStream(id);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcu_get_native_id(int* nativeid, int id)
 {
-    *nativeid = cuda::getDeviceNativeId(id);
+    try {
+        *nativeid = cuda::getDeviceNativeId(id);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcu_set_native_id(int nativeid)
 {
-    cuda::setDevice(cuda::getDeviceIdFromNativeId(nativeid));
+    try {
+        cuda::setDevice(cuda::getDeviceIdFromNativeId(nativeid));
+    } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/cuda/scan.cu b/src/backend/cuda/scan.cu
index 15ee6b4c93..48b4f689eb 100644
--- a/src/backend/cuda/scan.cu
+++ b/src/backend/cuda/scan.cu
@@ -22,36 +22,54 @@
 namespace cuda
 {
     template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti> &in, const int dim)
+    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan)
     {
         Array<To> out = createEmptyArray<To>(in.dims());
 
-        switch (dim) {
-        case 0: kernel::scan_first<Ti, To, op   >(out, in); break;
-        case 1: kernel::scan_dim  <Ti, To, op, 1>(out, in); break;
-        case 2: kernel::scan_dim  <Ti, To, op, 2>(out, in); break;
-        case 3: kernel::scan_dim  <Ti, To, op, 3>(out, in); break;
+        if (inclusive_scan) {
+            switch (dim) {
+            case 0: kernel::scan_first<Ti, To, op   , true>(out, in); break;
+            case 1: kernel::scan_dim  <Ti, To, op, 1, true>(out, in); break;
+            case 2: kernel::scan_dim  <Ti, To, op, 2, true>(out, in); break;
+            case 3: kernel::scan_dim  <Ti, To, op, 3, true>(out, in); break;
+            }
+        } else {
+            switch (dim) {
+            case 0: kernel::scan_first<Ti, To, op   , false>(out, in); break;
+            case 1: kernel::scan_dim  <Ti, To, op, 1, false>(out, in); break;
+            case 2: kernel::scan_dim  <Ti, To, op, 2, false>(out, in); break;
+            case 3: kernel::scan_dim  <Ti, To, op, 3, false>(out, in); break;
+            }
         }
 
         return out;
     }
 
 
-#define INSTANTIATE(ROp, Ti, To)                                        \
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim); \
+#define INSTANTIATE(ROp, Ti, To)\
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN(ROp)           \
+    INSTANTIATE(ROp, float  , float  )  \
+    INSTANTIATE(ROp, double , double )  \
+    INSTANTIATE(ROp, cfloat , cfloat )  \
+    INSTANTIATE(ROp, cdouble, cdouble)  \
+    INSTANTIATE(ROp, int    , int    )  \
+    INSTANTIATE(ROp, uint   , uint   )  \
+    INSTANTIATE(ROp, intl   , intl   )  \
+    INSTANTIATE(ROp, uintl  , uintl  )  \
+    INSTANTIATE(ROp, char   , int    )  \
+    INSTANTIATE(ROp, char   , uint   )  \
+    INSTANTIATE(ROp, uchar  , uint   )  \
+    INSTANTIATE(ROp, short  , int    )  \
+    INSTANTIATE(ROp, ushort , uint   )
 
     //accum
-    INSTANTIATE(af_add_t, float  , float  )
-    INSTANTIATE(af_add_t, double , double )
-    INSTANTIATE(af_add_t, cfloat , cfloat )
-    INSTANTIATE(af_add_t, cdouble, cdouble)
-    INSTANTIATE(af_add_t, int    , int    )
-    INSTANTIATE(af_add_t, uint   , uint   )
-    INSTANTIATE(af_add_t, intl   , intl   )
-    INSTANTIATE(af_add_t, uintl  , uintl  )
-    INSTANTIATE(af_add_t, char   , int    )
-    INSTANTIATE(af_add_t, uchar  , uint   )
-    INSTANTIATE(af_add_t, short  , int    )
-    INSTANTIATE(af_add_t, ushort , uint   )
-    INSTANTIATE(af_notzero_t, char  , uint   )
+    INSTANTIATE(af_notzero_t, char  , uint)
+    INSTANTIATE_SCAN(af_add_t)
+    INSTANTIATE_SCAN(af_sub_t)
+    INSTANTIATE_SCAN(af_mul_t)
+    INSTANTIATE_SCAN(af_div_t)
+    INSTANTIATE_SCAN(af_min_t)
+    INSTANTIATE_SCAN(af_max_t)
 }
diff --git a/src/backend/cuda/scan.hpp b/src/backend/cuda/scan.hpp
index 536accd1d3..05603c1fe4 100644
--- a/src/backend/cuda/scan.hpp
+++ b/src/backend/cuda/scan.hpp
@@ -14,5 +14,5 @@
 namespace cuda
 {
     template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim);
+    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 6d14c0309f..9b0f4c53af 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -12,6 +12,7 @@
 #include <sort.hpp>
 #include <kernel/sort.hpp>
 #include <math.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
 
@@ -22,10 +23,25 @@ namespace cuda
     {
         Array<T> out = copyArray<T>(in);
         switch(dim) {
+            case 0: kernel::sort0<T, isAscending>(out); break;
+            case 1: kernel::sortBatched<T, isAscending, 1>(out); break;
+            case 2: kernel::sortBatched<T, isAscending, 2>(out); break;
+            case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
+
+        if(dim != 0) {
+            af::dim4 preorderDims = out.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0] = out.dims()[dim];
+            for(int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i] = out.dims()[i - 1];
+            }
 
-        case 0: kernel::sort0<T, isAscending>(out);
-            break;
-        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+            out.setDataDims(preorderDims);
+            out = reorder<T>(out, reorderDims);
         }
         return out;
     }
diff --git a/src/backend/cuda/sort_by_key.cu b/src/backend/cuda/sort_by_key.cu
new file mode 100644
index 0000000000..2d5d68eef0
--- /dev/null
+++ b/src/backend/cuda/sort_by_key.cu
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <copy.hpp>
+#include <sort_by_key.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <math.hpp>
+#include <reorder.hpp>
+#include <stdexcept>
+#include <err_cuda.hpp>
+
+namespace cuda
+{
+    template<typename Tk, typename Tv, bool isAscending>
+    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
+               const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
+    {
+        okey = copyArray<Tk>(ikey);
+        oval = copyArray<Tv>(ival);
+
+        switch(dim) {
+            case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
+            case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>(okey, oval); break;
+            case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>(okey, oval); break;
+            case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>(okey, oval); break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
+
+        if(dim != 0) {
+            af::dim4 preorderDims = okey.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0] = okey.dims()[dim];
+            for(int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i] = okey.dims()[i - 1];
+            }
+
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
+
+            okey = reorder<Tk>(okey, reorderDims);
+            oval = reorder<Tv>(oval, reorderDims);
+        }
+    }
+
+#define INSTANTIATE(Tk, Tv)                                                         \
+    template void sort_by_key<Tk, Tv, true >(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+    template void sort_by_key<Tk, Tv, false>(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
+
+
+INSTANTIATE1(float )
+INSTANTIATE1(double)
+INSTANTIATE1(int   )
+INSTANTIATE1(uint  )
+INSTANTIATE1(short )
+INSTANTIATE1(ushort)
+INSTANTIATE1(char  )
+INSTANTIATE1(uchar )
+INSTANTIATE1(intl  )
+INSTANTIATE1(uintl )
+
+}
diff --git a/src/backend/cuda/sort_by_key/ascd_f32.cu b/src/backend/cuda/sort_by_key/ascd_f32.cu
deleted file mode 100644
index 44b770402c..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_f32.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(float, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_f64.cu b/src/backend/cuda/sort_by_key/ascd_f64.cu
deleted file mode 100644
index 17b54a3903..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_f64.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(double, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_s32.cu b/src/backend/cuda/sort_by_key/ascd_s32.cu
deleted file mode 100644
index 75adbddc0b..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_s32.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(int, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_s64.cu b/src/backend/cuda/sort_by_key/ascd_s64.cu
deleted file mode 100644
index 25a1e589f8..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_s64.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(intl, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_s8.cu b/src/backend/cuda/sort_by_key/ascd_s8.cu
deleted file mode 100644
index f47a397727..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_s8.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(char, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_u16.cu b/src/backend/cuda/sort_by_key/ascd_u16.cu
deleted file mode 100644
index e06036abc7..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_u16.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(ushort, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_u32.cu b/src/backend/cuda/sort_by_key/ascd_u32.cu
deleted file mode 100644
index 6f7939aa12..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_u32.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(uint, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_u64.cu b/src/backend/cuda/sort_by_key/ascd_u64.cu
deleted file mode 100644
index 63eec5fdd4..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_u64.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(uintl, true)
-}
diff --git a/src/backend/cuda/sort_by_key/ascd_u8.cu b/src/backend/cuda/sort_by_key/ascd_u8.cu
deleted file mode 100644
index a2e1dec887..0000000000
--- a/src/backend/cuda/sort_by_key/ascd_u8.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(uchar, true)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_f32.cu b/src/backend/cuda/sort_by_key/desc_f32.cu
deleted file mode 100644
index 1bbb10bbba..0000000000
--- a/src/backend/cuda/sort_by_key/desc_f32.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(float, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_f64.cu b/src/backend/cuda/sort_by_key/desc_f64.cu
deleted file mode 100644
index ecbed78878..0000000000
--- a/src/backend/cuda/sort_by_key/desc_f64.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(double, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_s16.cu b/src/backend/cuda/sort_by_key/desc_s16.cu
deleted file mode 100644
index 63967b6117..0000000000
--- a/src/backend/cuda/sort_by_key/desc_s16.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(short, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_s32.cu b/src/backend/cuda/sort_by_key/desc_s32.cu
deleted file mode 100644
index 49904437f4..0000000000
--- a/src/backend/cuda/sort_by_key/desc_s32.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(int, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_s64.cu b/src/backend/cuda/sort_by_key/desc_s64.cu
deleted file mode 100644
index a10ee11475..0000000000
--- a/src/backend/cuda/sort_by_key/desc_s64.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(intl, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_s8.cu b/src/backend/cuda/sort_by_key/desc_s8.cu
deleted file mode 100644
index cad78dfc84..0000000000
--- a/src/backend/cuda/sort_by_key/desc_s8.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(char, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_u16.cu b/src/backend/cuda/sort_by_key/desc_u16.cu
deleted file mode 100644
index 69dc01634b..0000000000
--- a/src/backend/cuda/sort_by_key/desc_u16.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(ushort, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_u32.cu b/src/backend/cuda/sort_by_key/desc_u32.cu
deleted file mode 100644
index ae2ad4bc84..0000000000
--- a/src/backend/cuda/sort_by_key/desc_u32.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(uint, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_u64.cu b/src/backend/cuda/sort_by_key/desc_u64.cu
deleted file mode 100644
index 43f60c075b..0000000000
--- a/src/backend/cuda/sort_by_key/desc_u64.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(uintl, false)
-}
diff --git a/src/backend/cuda/sort_by_key/desc_u8.cu b/src/backend/cuda/sort_by_key/desc_u8.cu
deleted file mode 100644
index 51d8096620..0000000000
--- a/src/backend/cuda/sort_by_key/desc_u8.cu
+++ /dev/null
@@ -1,15 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sort_by_key_impl.hpp>
-
-namespace cuda
-{
-    INSTANTIATE1(uchar, false)
-}
diff --git a/src/backend/cuda/sort_by_key_impl.hpp b/src/backend/cuda/sort_by_key_impl.hpp
deleted file mode 100644
index d01ace404e..0000000000
--- a/src/backend/cuda/sort_by_key_impl.hpp
+++ /dev/null
@@ -1,49 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <copy.hpp>
-#include <sort_by_key.hpp>
-#include <kernel/sort_by_key.hpp>
-#include <math.hpp>
-#include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
-    template<typename Tk, typename Tv, bool isAscending>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
-    {
-        okey = copyArray<Tk>(ikey);
-        oval = copyArray<Tv>(ival);
-        switch(dim) {
-        case 0: kernel::sort0_by_key<Tk, Tv, isAscending>(okey, oval);
-            break;
-        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
-    }
-
-#define INSTANTIATE(Tk, Tv, dr)                                         \
-    template void                                                       \
-    sort_by_key<Tk, Tv, dr>(Array<Tk> &okey, Array<Tv> &oval,           \
-                            const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim); \
-
-#define INSTANTIATE1(Tk,    dr) \
-    INSTANTIATE(Tk, float,  dr) \
-    INSTANTIATE(Tk, double, dr) \
-    INSTANTIATE(Tk, int,    dr) \
-    INSTANTIATE(Tk, uint,   dr) \
-    INSTANTIATE(Tk, short,  dr) \
-    INSTANTIATE(Tk, ushort, dr) \
-    INSTANTIATE(Tk, char,   dr) \
-    INSTANTIATE(Tk, uchar,  dr) \
-    INSTANTIATE(Tk, intl,   dr) \
-    INSTANTIATE(Tk, uintl,  dr)
-}
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 606aab4eb1..03c69ad4f3 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -9,24 +9,47 @@
 
 #include <Array.hpp>
 #include <sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <copy.hpp>
-#include <kernel/sort_index.hpp>
 #include <math.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
+#include <range.hpp>
+#include <reorder.hpp>
 
 namespace cuda
 {
     template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
     {
-        val = copyArray<T>(in);
-        idx = createEmptyArray<uint>(in.dims());
+        okey = copyArray<T>(in);
+        oval = range<uint>(in.dims(), dim);
+        oval.eval();
+
         switch(dim) {
-            case 0: kernel::sort0_index<T, isAscending>(val, idx);
-                    break;
+            case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
+            case 1: kernel::sortByKeyBatched<T, uint, isAscending, 1>(okey, oval); break;
+            case 2: kernel::sortByKeyBatched<T, uint, isAscending, 2>(okey, oval); break;
+            case 3: kernel::sortByKeyBatched<T, uint, isAscending, 3>(okey, oval); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
+
+        if(dim != 0) {
+            af::dim4 preorderDims = okey.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0] = okey.dims()[dim];
+            for(int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i] = okey.dims()[i - 1];
+            }
+
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
+
+            okey = reorder<T>(okey, reorderDims);
+            oval = reorder<uint>(oval, reorderDims);
+        }
     }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/host_memory.cpp b/src/backend/host_memory.cpp
index 9b4f1e5f54..b81d4fcf5c 100644
--- a/src/backend/host_memory.cpp
+++ b/src/backend/host_memory.cpp
@@ -16,7 +16,7 @@
 #include <sys/types.h>
 #include <sys/param.h>
 
-#if defined(BSD)
+#if defined(BSD) && !defined(__gnu_hurd__)
 #include <sys/sysctl.h>
 #endif
 
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 8c5bda90de..f83d5c0120 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -77,15 +77,20 @@ namespace opencl
     template<typename T>
     void *getDevicePtr(const Array<T>& arr)
     {
-        cl::Buffer *buf = arr.device();
+        const cl::Buffer *buf = arr.device();
+        if (!buf) return NULL;
         memLock((T *)buf);
-        return (void *)((*buf)());
+        cl_mem mem = (*buf)();
+        return (void *)mem;
     }
 
     template<typename T>
     void *getRawPtr(const Array<T>& arr)
     {
-        return (void *)(arr.get());
+        const cl::Buffer *buf = arr.get();
+        if (!buf) return NULL;
+        cl_mem mem = (*buf)();
+        return (void *)mem;
     }
 
     template<typename T>
@@ -159,10 +164,10 @@ namespace opencl
 
         cl::Buffer* device()
         {
-            if (!isOwner() || data.use_count() > 1) {
+            if (!isOwner() || getOffset() || data.use_count() > 1) {
                 *this = Array<T>(dims(), (*get())(), (size_t)getOffset(), true);
             }
-            return this->data.get();
+            return this->get();
         }
 
         cl::Buffer* device() const
@@ -201,13 +206,12 @@ namespace opencl
 
         dim4 getDataDims() const
         {
-            // This is for moddims
-            // dims and data_dims are different when moddims is used
-            return isOwner() ? dims() : data_dims;
+            return data_dims;
         }
 
         void setDataDims(const dim4 &new_dims)
         {
+            modDims(new_dims);
             data_dims = new_dims;
         }
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index e598a973df..9e4918a5ff 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -10,13 +10,16 @@ IF(USE_OPENCL_F77_BLAS)
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
 ENDIF()
 
-IF(USE_OPENCL_MKL)
-    MESSAGE("Using MKL")
-    ADD_DEFINITIONS(-DUSE_MKL)
-ENDIF()
-
 IF(APPLE)
-    FIND_PACKAGE(LAPACK)
+    FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
+    IF(NOT LAPACK_FOUND)
+        # UNSET THE VARIABLES FROM LAPACKE
+        UNSET(LAPACKE_LIB CACHE)
+        UNSET(LAPACK_LIB CACHE)
+        UNSET(LAPACKE_INCLUDES CACHE)
+        UNSET(LAPACKE_ROOT_DIR CACHE)
+        FIND_PACKAGE(LAPACK)
+    ENDIF()
 ELSE(APPLE) # Linux and Windows
     FIND_PACKAGE(LAPACKE)
 ENDIF(APPLE)
@@ -42,8 +45,14 @@ ELSE(NOT LAPACK_FOUND)
   ENDIF()
 ENDIF()
 
-IF(${MKL_FOUND})
-  ADD_DEFINITIONS(-DUSE_MKL)
+IF(USE_OPENCL_MKL) # Manual MKL Setup
+    MESSAGE("OpenCL Backend Using MKL")
+    ADD_DEFINITIONS(-DUSE_MKL)
+ELSE(USE_OPENCL_MKL)
+    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
+        MESSAGE("OpenCL Backend Using MKL RT")
+        ADD_DEFINITIONS(-DUSE_MKL)
+    ENDIF()
 ENDIF()
 
 IF(NOT UNIX)
@@ -102,12 +111,10 @@ ENDIF()
 
 FILE(GLOB opencl_headers
   "*.hpp"
-  "*.h"
-  "sort_by_key/*.hpp")
+  "*.h")
 
 FILE(GLOB opencl_sources
-    "*.cpp"
-    "sort_by_key/*.cpp")
+    "*.cpp")
 
 FILE(GLOB jit_sources
     "jit/*.hpp")
@@ -133,6 +140,17 @@ FILE(GLOB cpu_headers
 FILE(GLOB cpu_sources
      "cpu/*.cpp")
 
+LIST(SORT opencl_headers)
+LIST(SORT opencl_sources)
+LIST(SORT jit_sources)
+LIST(SORT kernel_headers)
+LIST(SORT opencl_kernels)
+LIST(SORT kernel_sources)
+LIST(SORT conv_ker_headers)
+LIST(SORT conv_ker_sources)
+LIST(SORT cpu_headers)
+LIST(SORT cpu_sources)
+
 source_group(backend\\opencl\\Headers FILES ${opencl_headers})
 source_group(backend\\opencl\\Sources FILES ${opencl_sources})
 source_group(backend\\opencl\\JIT FILES ${jit_sources})
@@ -151,6 +169,9 @@ IF(LAPACK_FOUND)
     FILE(GLOB magma_headers
         "magma/*.h")
 
+    LIST(SORT magma_headers)
+    LIST(SORT magma_sources)
+
     source_group(backend\\opencl\\magma\\Sources FILES ${magma_sources})
     source_group(backend\\opencl\\magma\\Headers FILES ${magma_headers})
 ELSE()
@@ -166,6 +187,10 @@ FILE(GLOB backend_headers
 FILE(GLOB backend_sources
     "../*.cpp"
     )
+
+LIST(SORT backend_headers)
+LIST(SORT backend_sources)
+
 source_group(backend\\Headers FILES ${backend_headers})
 source_group(backend\\Sources FILES ${backend_sources})
 
@@ -177,17 +202,25 @@ FILE(GLOB c_headers
 FILE(GLOB c_sources
     "../../api/c/*.cpp"
     )
+
+LIST(SORT c_headers)
+LIST(SORT c_sources)
+
 source_group(api\\c\\Headers FILES ${c_headers})
 source_group(api\\c\\Sources FILES ${c_sources})
 
-
 FILE(GLOB cpp_sources
     "../../api/cpp/*.cpp"
     )
+
+LIST(SORT cpp_sources)
+
 source_group(api\\cpp\\Sources FILES ${cpp_sources})
 
 FILE(GLOB kernel_src ${opencl_kernels} "kernel/KParam.hpp")
 
+LIST(SORT kernel_src)
+
 CL_KERNEL_TO_H(
     SOURCES ${kernel_src}
     VARNAME kernel_files
@@ -203,6 +236,8 @@ IF(UNIX)
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
 ENDIF()
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+
 IF(DEFINED BLAS_SYM_FILE)
 
     ADD_LIBRARY(afopencl_static STATIC
@@ -219,7 +254,8 @@ IF(DEFINED BLAS_SYM_FILE)
                 ${backend_headers}
                 ${backend_sources}
                 ${magma_sources}
-                ${magma_headers})
+                ${magma_headers}
+                ${SORT_BY_KEY_OBJECTS})
 
     ADD_LIBRARY(afopencl SHARED
                ${c_headers}
@@ -262,7 +298,8 @@ ELSE(DEFINED BLAS_SYM_FILE)
                 ${c_sources}
                 ${cpp_sources}
                 ${magma_sources}
-                ${magma_headers})
+                ${magma_headers}
+                ${SORT_BY_KEY_OBJECTS})
 
 ENDIF()
 
@@ -270,11 +307,10 @@ ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
 
 TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${OpenCL_LIBRARIES}
-                                PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CLBLAS_LIBRARIES}
                                 PRIVATE ${CLFFT_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
-                                PRIVATE ${Boost_LIBRARIES})
+                     )
 
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
     ADD_DEPENDENCIES(afopencl forge)
@@ -286,6 +322,8 @@ IF(LAPACK_FOUND)
     PRIVATE ${CBLAS_LIBRARIES})
 ENDIF()
 
+TARGET_LINK_LIBRARIES(afopencl PRIVATE ${FreeImage_LIBS})
+
 SET_TARGET_PROPERTIES(afopencl PROPERTIES
     VERSION "${AF_VERSION}"
     SOVERSION "${AF_VERSION_MAJOR}")
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index cbdc470e19..f7f690322c 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -29,32 +29,32 @@
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#ifdef __APPLE__
-    #include <Accelerate/Accelerate.h>
-    #include <lapacke.hpp>
-    #undef AF_LAPACK_COL_MAJOR
-    #define AF_LAPACK_COL_MAJOR 0
+#ifdef USE_MKL
+    #include<mkl_lapacke.h>
 #else
-    #ifdef USE_MKL
-        #include<mkl_lapacke.h>
-    #else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
+    #else // NETLIB LAPACKE
         #include<lapacke.h>
     #endif
-#endif //OS
+#endif
 
 #endif // WITH_OPENCL_LINEAR_ALGEBRA
 
 //********************************************************/
 // BLAS
 //********************************************************/
-#ifdef __APPLE__
-    #include <Accelerate/Accelerate.h>
+#ifdef USE_MKL
+    #include <mkl_cblas.h>
 #else
-    #ifdef USE_MKL
-        #include <mkl_cblas.h>
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
     #else
         extern "C" {
-        #include <cblas.h>
+            #include <cblas.h>
         }
     #endif
 #endif
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 79cd758bd5..8693b11be3 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -34,7 +34,7 @@ namespace opencl
     Array<T> diagExtract(const Array<T> &in, const int num)
     {
         const dim_t *idims = in.dims().get();
-        dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
+        dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
         Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
         kernel::diagExtract<T>(out, in, num);
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index d5922f0c76..b3cdfb5517 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -42,12 +42,22 @@ class clFFTPlanner
 
     public:
         static clFFTPlanner& getInstance() {
-            static clFFTPlanner single_instance;
-            return single_instance;
+            static clFFTPlanner instances[opencl::DeviceManager::MAX_DEVICES];
+            return instances[opencl::getActiveDeviceId()];
         }
 
         ~clFFTPlanner() {
-            CLFFT_CHECK(clfftTeardown());
+            //TODO: FIXME:
+            // clfftTeardown() cause a "Pure Virtual Function Called" crash on
+            // Window only when Intel devices are called. This causes tests to
+            // fail.
+            #ifndef OS_WIN
+            static bool flag = true;
+            if(flag) {
+                CLFFT_CHECK(clfftTeardown());
+                flag = false;
+            }
+            #endif
         }
 
     private:
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 7fffdee423..442275d326 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -16,7 +16,8 @@
 #include <convolve_common.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/gradient.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <kernel/range.hpp>
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
 #include <map>
@@ -284,10 +285,12 @@ void harris(unsigned* corners_out,
 
             int sort_elem = harris_resp.info.strides[3] * harris_resp.info.dims[3];
             harris_resp.data = d_resp_corners;
+            // Create indices using range
             harris_idx.data = bufferAlloc(sort_elem * sizeof(unsigned));
+            kernel::range<uint>(harris_idx, 0);
 
             // Sort Harris responses
-            sort0_index<float, false>(harris_resp, harris_idx);
+            kernel::sort0ByKey<float, uint, false>(harris_resp, harris_idx);
 
             x_out.data = bufferAlloc(*corners_out * sizeof(float));
             y_out.data = bufferAlloc(*corners_out * sizeof(float));
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index bad486abd2..7cd8046d68 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <kernel_headers/iota.hpp>
+#include <af/dim4.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -31,13 +32,13 @@ namespace opencl
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
+        static const int IOTA_TX = 32;
+        static const int IOTA_TY = 8;
         static const int TILEX = 512;
         static const int TILEY = 32;
 
         template<typename T>
-        void iota(Param out, const dim4 &sdims, const dim4 &tdims)
+        void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -64,7 +65,7 @@ namespace opencl
                                           const int, const int, const int, const int,
                                           const int, const int> (*iotaKernels[device]);
 
-                NDRange local(TX, TY, 1);
+                NDRange local(IOTA_TX, IOTA_TY, 1);
 
                 int blocksPerMatX = divup(out.info.dims[0], TILEX);
                 int blocksPerMatY = divup(out.info.dims[1], TILEY);
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 69c1176210..0c752d2c21 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -16,7 +16,8 @@
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <kernel/range.hpp>
 #include <kernel_headers/orb.hpp>
 #include <memory.hpp>
 #include <vector>
@@ -303,9 +304,11 @@ void orb(unsigned* out_feat,
             d_harris_sorted.info.offset = 0;
             d_harris_idx.info.offset = 0;
             d_harris_sorted.data = d_score_harris;
+            // Create indices using range
             d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
+            kernel::range<uint>(d_harris_idx, 0);
 
-            sort0_index<float, false>(d_harris_sorted, d_harris_idx);
+            kernel::sort0ByKey<float, uint, false>(d_harris_sorted, d_harris_idx);
 
             cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
             cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 2f8be8cd4a..0299c030d4 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -31,10 +31,10 @@ namespace opencl
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
-        static const int TILEX = 512;
-        static const int TILEY = 32;
+        static const int RANGE_TX = 32;
+        static const int RANGE_TY = 8;
+        static const int RANGE_TILEX = 512;
+        static const int RANGE_TILEY = 32;
 
         template<typename T>
         void range(Param out, const int dim)
@@ -62,10 +62,10 @@ namespace opencl
                 auto rangeOp = make_kernel<Buffer, const KParam, const int,
                                            const int, const int> (*rangeKernels[device]);
 
-                NDRange local(TX, TY, 1);
+                NDRange local(RANGE_TX, RANGE_TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], TILEY);
+                int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
+                int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
                 NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                                local[1] * blocksPerMatY * out.info.dims[3],
                                1);
diff --git a/src/backend/opencl/kernel/scan_dim.cl b/src/backend/opencl/kernel/scan_dim.cl
index b15d8edd2d..cd3ad6887d 100644
--- a/src/backend/opencl/kernel/scan_dim.cl
+++ b/src/backend/opencl/kernel/scan_dim.cl
@@ -84,12 +84,24 @@ void scan_dim_kernel(__global To *oData, KParam oInfo,
         }
 
         val = binOp(val, l_tmp[lidx]);
-        if (cond) *oData = val;
-        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (inclusive_scan != 0) {
+            if (cond) {
+                *oData = val;
+            }
+        }
+        else if (is_valid) {
+            if (id_dim == (out_dim - 1)) {
+                *(oData - (id_dim*ostride_dim)) = init_val;
+            } else if (id_dim < (out_dim - 1)) {
+                *(oData + ostride_dim) = val;
+            }
+        }
 
         id_dim += DIMY;
         iData += DIMY * istride_dim;
         oData += DIMY * ostride_dim;
+        barrier(CLK_LOCAL_MEM_FENCE);
     }
 
     if (!isFinalPass &&
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 84cc722bbd..577ce00bcf 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -35,7 +35,7 @@ namespace opencl
 {
 namespace kernel
 {
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
     static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool isFinalPass, uint threads_y)
     {
         std::string ref_name =
@@ -50,7 +50,9 @@ namespace kernel
             std::string("_") +
             std::to_string(op) +
             std::string("_") +
-            std::to_string(threads_y);
+            std::to_string(threads_y) +
+            std::string("_") +
+            std::to_string(int(inclusive_scan));
 
         int device = getActiveDeviceId();
         kc_t::iterator idx = kernelCaches[device].find(ref_name);
@@ -71,7 +73,8 @@ namespace kernel
                     << " -D init=" << toNum(scan.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D isFinalPass=" << (int)(isFinalPass);
+                    << " -D isFinalPass=" << (int)(isFinalPass)
+                    << " -D inclusive_scan=" << inclusive_scan;
             if (std::is_same<Ti, double>::value ||
                 std::is_same<Ti, cdouble>::value) {
                 options << " -D USE_DOUBLE";
@@ -97,7 +100,7 @@ namespace kernel
         return entry.ker[kerIdx];
     }
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
     static void scan_dim_launcher(Param &out,
                                   Param &tmp,
                                   const Param &in,
@@ -105,7 +108,7 @@ namespace kernel
                                   const uint groups_all[4])
     {
         try {
-            Kernel ker = get_scan_dim_kernels<Ti, To, op>(0, dim, isFinalPass, threads_y);
+            Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(0, dim, isFinalPass, threads_y);
 
             NDRange local(THREADS_X, threads_y);
             NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -131,14 +134,14 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
     static void bcast_dim_launcher(Param &out,
                                    Param &tmp,
                                    int dim, bool isFinalPass, uint threads_y,
                                    const uint groups_all[4])
     {
         try {
-            Kernel ker = get_scan_dim_kernels<Ti, To, op>(1, dim, isFinalPass, threads_y);
+            Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(1, dim, isFinalPass, threads_y);
 
             NDRange local(THREADS_X, threads_y);
             NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -162,7 +165,7 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
     static void scan_dim(Param &out, const Param &in, int dim)
     {
         try {
@@ -178,7 +181,7 @@ namespace kernel
 
             if (groups_all[dim] == 1) {
 
-                scan_dim_launcher<Ti, To, op>(out, out, in,
+                scan_dim_launcher<Ti, To, op, inclusive_scan>(out, out, in,
                                               dim, true,
                                               threads_y,
                                               groups_all);
@@ -196,7 +199,7 @@ namespace kernel
                 // FIXME: Do I need to free this ?
                 tmp.data = bufferAlloc(tmp_elements * sizeof(To));
 
-                scan_dim_launcher<Ti, To, op>(out, tmp, in,
+                scan_dim_launcher<Ti, To, op, inclusive_scan>(out, tmp, in,
                                               dim, false,
                                               threads_y,
                                               groups_all);
@@ -205,19 +208,19 @@ namespace kernel
                 groups_all[dim] = 1;
 
                 if (op == af_notzero_t) {
-                    scan_dim_launcher<To, To, af_add_t>(tmp, tmp, tmp,
+                    scan_dim_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
                                                         dim, true,
                                                         threads_y,
                                                         groups_all);
                 } else {
-                    scan_dim_launcher<To, To,       op>(tmp, tmp, tmp,
+                    scan_dim_launcher<To, To,       op, true>(tmp, tmp, tmp,
                                                         dim, true,
                                                         threads_y,
                                                         groups_all);
                 }
 
                 groups_all[dim] = gdim;
-                bcast_dim_launcher<To, To, op>(out, tmp,
+                bcast_dim_launcher<To, To, op, inclusive_scan>(out, tmp,
                                                dim, true,
                                                threads_y,
                                                groups_all);
diff --git a/src/backend/opencl/kernel/scan_first.cl b/src/backend/opencl/kernel/scan_first.cl
index d8b08a9ea1..ecda3f90f9 100644
--- a/src/backend/opencl/kernel/scan_first.cl
+++ b/src/backend/opencl/kernel/scan_first.cl
@@ -68,7 +68,18 @@ void scan_first_kernel(__global To *oData, KParam oInfo,
         }
 
         val = binOp(val, l_tmp[lidy]);
-        if (cond) oData[id] = val;
+        if (inclusive_scan != 0) {
+            if (cond) {
+                oData[id] = val;
+            }
+        }
+        else {
+            if (id == (oInfo.dims[0] - 1)) {
+                oData[0] = init_val;
+            } else if (id < (oInfo.dims[0] - 1)) {
+                oData[id + 1] = val;
+            }
+        }
         id += DIMX;
         barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
     }
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index d7a284da9b..c9f5c8b5e2 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -37,7 +37,7 @@ namespace opencl
 namespace kernel
 {
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
     static Kernel get_scan_first_kernels(int kerIdx, bool isFinalPass, uint threads_x)
     {
         std::string ref_name =
@@ -51,7 +51,9 @@ namespace kernel
             std::string("_") +
             std::to_string(op) +
             std::string("_") +
-            std::to_string(threads_x);
+            std::to_string(threads_x) +
+            std::string("_") +
+            std::to_string(int(inclusive_scan));
 
         int device = getActiveDeviceId();
         kc_t::iterator idx = kernelCaches[device].find(ref_name);
@@ -75,7 +77,8 @@ namespace kernel
                     << " -D init=" << toNum(scan.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D isFinalPass=" << (int)(isFinalPass);
+                    << " -D isFinalPass=" << (int)(isFinalPass)
+                    << " -D inclusive_scan=" << inclusive_scan;
             if (std::is_same<Ti, double>::value ||
                 std::is_same<Ti, cdouble>::value) {
                 options << " -D USE_DOUBLE";
@@ -101,7 +104,7 @@ namespace kernel
         return entry.ker[kerIdx];
     }
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
     static void scan_first_launcher(Param &out,
                                     Param &tmp,
                                     const Param &in,
@@ -110,7 +113,7 @@ namespace kernel
                                     const uint groups_y,
                                     const uint threads_x)
     {
-        Kernel ker = get_scan_first_kernels<Ti, To, op>(0, isFinalPass, threads_x);
+        Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(0, isFinalPass, threads_x);
 
         NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
         NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -130,7 +133,7 @@ namespace kernel
         CL_DEBUG_FINISH(getQueue());
     }
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
     static void bcast_first_launcher(Param &out,
                                      Param &tmp,
                                      const bool isFinalPass,
@@ -139,7 +142,7 @@ namespace kernel
                                      const uint threads_x)
     {
 
-        Kernel ker = get_scan_first_kernels<Ti, To, op>(1, isFinalPass, threads_x);
+        Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(1, isFinalPass, threads_x);
 
         NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
         NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -159,7 +162,7 @@ namespace kernel
     }
 
 
-    template<typename Ti, typename To, af_op_t op>
+    template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
     static void scan_first(Param &out, const Param &in)
     {
         uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
@@ -170,7 +173,7 @@ namespace kernel
         uint groups_y = divup(out.info.dims[1], threads_y);
 
         if (groups_x == 1) {
-            scan_first_launcher<Ti, To, op>(out, out, in,
+            scan_first_launcher<Ti, To, op, inclusive_scan>(out, out, in,
                                             true,
                                             groups_x, groups_y,
                                             threads_x);
@@ -188,24 +191,24 @@ namespace kernel
 
             tmp.data = bufferAlloc(tmp_elements * sizeof(To));
 
-            scan_first_launcher<Ti, To, op>(out, tmp, in,
+            scan_first_launcher<Ti, To, op, inclusive_scan>(out, tmp, in,
                                             false,
                                             groups_x, groups_y,
                                             threads_x);
 
             if (op == af_notzero_t) {
-                scan_first_launcher<To, To, af_add_t>(tmp, tmp, tmp,
+                scan_first_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
                                                       true,
                                                       1, groups_y,
                                                       threads_x);
             } else {
-                scan_first_launcher<To, To,       op>(tmp, tmp, tmp,
+                scan_first_launcher<To, To,       op, true>(tmp, tmp, tmp,
                                                       true,
                                                       1, groups_y,
                                                       threads_x);
             }
 
-            bcast_first_launcher<To, To, op>(out, tmp,
+            bcast_first_launcher<To, To, op, inclusive_scan>(out, tmp,
                                              true,
                                              groups_x,
                                              groups_y,
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index c28f432fce..0b78ef02f3 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -92,11 +92,12 @@
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
-#include <kernel/sort_index.hpp>
 #include <kernel_headers/sift_nonfree.hpp>
 #include <memory.hpp>
 #include <vector>
 
+namespace compute = boost::compute;
+
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 013d8c53a9..98ba75977a 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -15,12 +15,15 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/sort_helper.hpp>
+#include <kernel/iota.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
 #include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/stable_sort.hpp>
+#include <boost/compute/algorithm/sort.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
@@ -38,18 +41,8 @@ namespace opencl
 {
     namespace kernel
     {
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
-
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-
         template<typename T, bool isAscending>
-        void sort0(Param val)
+        void sort0Iterative(Param val)
         {
             try {
                 compute::command_queue c_queue(getQueue()());
@@ -65,12 +58,12 @@ namespace opencl
                             int valOffset = valWZ + y * val.info.strides[1];
 
                             if(isAscending) {
-                                compute::stable_sort(
+                                compute::sort(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         compute::less< type_t<T> >(), c_queue);
                             } else {
-                                compute::stable_sort(
+                                compute::sort(
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
                                         compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
                                         compute::greater< type_t<T> >(), c_queue);
@@ -85,6 +78,95 @@ namespace opencl
                 throw;
             }
         }
+
+        template<typename T, bool isAscending, int dim>
+        void sortBatched(Param pVal)
+        {
+            try{
+                af::dim4 inDims;
+                for(int i = 0; i < 4; i++)
+                    inDims[i] = pVal.info.dims[i];
+
+                // Sort dimension
+                // tileDims * seqDims = inDims
+                af::dim4 tileDims(1);
+                af::dim4 seqDims = inDims;
+                tileDims[dim] = inDims[dim];
+                seqDims[dim] = 1;
+
+                // Create/call iota
+                // Array<uint> key = iota<uint>(seqDims, tileDims);
+                dim4 keydims = inDims;
+                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(uint));
+                Param pKey;
+                pKey.data = key;
+                pKey.info.offset = 0;
+                pKey.info.dims[0] = keydims[0];
+                pKey.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pKey.info.dims[i] = keydims[i];
+                    pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
+                }
+                kernel::iota<uint>(pKey, seqDims, tileDims);
+
+                // Flat
+                //val.modDims(inDims.elements());
+                //key.modDims(inDims.elements());
+                pKey.info.dims[0] = inDims.elements();
+                pKey.info.strides[0] = 1;
+                pVal.info.dims[0] = inDims.elements();
+                pVal.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pKey.info.dims[i] = 1;
+                    pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
+                    pVal.info.dims[i] = 1;
+                    pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
+                }
+
+                // Sort indices
+                // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+                //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+                compute::command_queue c_queue(getQueue()());
+
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+
+                compute::buffer_iterator<type_t<T> > val0 = compute::make_buffer_iterator<type_t<T> >(pVal_buf, 0);
+                compute::buffer_iterator<type_t<T> > valN = compute::make_buffer_iterator<type_t<T> >(pVal_buf,+ pVal.info.dims[0]);
+                compute::buffer_iterator<uint      > key0 = compute::make_buffer_iterator<uint>(pKey_buf, 0);
+                compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.info.dims[0]);
+                if(isAscending) {
+                    compute::sort_by_key(val0, valN, key0, c_queue);
+                } else {
+                    compute::sort_by_key(val0, valN, key0, compute::greater< type_t<T> >(), c_queue);
+                }
+
+                // Needs to be ascending (true) in order to maintain the indices properly
+                //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+                compute::sort_by_key(key0, keyN, val0, c_queue);
+
+                // No need of doing moddims here because the original Array<T>
+                // dimensions have not been changed
+                //val.modDims(inDims);
+
+                CL_DEBUG_FINISH(getQueue());
+                bufferFree(key);
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename T, bool isAscending>
+        void sort0(Param val)
+        {
+            int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 10)
+                sortBatched<T, isAscending, 0>(val);
+            else
+                kernel::sort0Iterative<T, isAscending>(val);
+        }
     }
 }
 
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 0cb9cb042d..224f6411ff 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -8,86 +8,22 @@
  ********************************************************/
 
 #pragma once
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
-#include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
-#include <boost/compute/functional/operator.hpp>
-#include <boost/compute/iterator/buffer_iterator.hpp>
-
-namespace compute = boost::compute;
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::make_kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
 namespace opencl
 {
     namespace kernel
     {
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
-
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-
         template<typename Tk, typename Tv, bool isAscending>
-        void sort0_by_key(Param okey, Param oval)
-        {
-            try {
-                compute::command_queue c_queue(getQueue()());
+        void sort0ByKeyIterative(Param pKey, Param pVal);
 
-                compute::buffer okey_buf((*okey.data)());
-                compute::buffer oval_buf((*oval.data)());
+        template<typename Tk_, typename Tv_, bool isAscending, int dim>
+        void sortByKeyBatched(Param pKey, Param pVal);
 
-                for(int w = 0; w < okey.info.dims[3]; w++) {
-                    int okeyW = w * okey.info.strides[3];
-                    int ovalW = w * oval.info.strides[3];
-                    for(int z = 0; z < okey.info.dims[2]; z++) {
-                        int okeyWZ = okeyW + z * okey.info.strides[2];
-                        int ovalWZ = ovalW + z * oval.info.strides[2];
-                        for(int y = 0; y < okey.info.dims[1]; y++) {
-
-                            int okeyOffset = okeyWZ + y * okey.info.strides[1];
-                            int ovalOffset = ovalWZ + y * oval.info.strides[1];
-
-                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset);
-                            compute::buffer_iterator< type_t<Tk> > end = compute::make_buffer_iterator< type_t<Tk> >(okey_buf, okeyOffset + okey.info.dims[0]);
-                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(oval_buf, ovalOffset);
-                            if(isAscending) {
-                                compute::sort_by_key(start, end, vals, c_queue);
-                            } else {
-                                compute::sort_by_key(start, end, vals,
-                                                     compute::greater< type_t<Tk> >(), c_queue);
-                            }
-                        }
-                    }
-                }
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param pKey, Param pVal);
     }
 }
-
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..760fe6b634
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,19 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    ADD_LIBRARY(opencl_sort_by_key_${SBK_TYPE} OBJECT
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp")
+    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets})
+    IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
+        ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} forge)
+    ENDIF()
+    SET_TARGET_PROPERTIES(opencl_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
+    LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:opencl_sort_by_key_${SBK_TYPE}>)
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/opencl/sort_by_key/b8.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
similarity index 65%
rename from src/backend/opencl/sort_by_key/b8.cpp
rename to src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index 118d20dc92..bf4c96bbb2 100644
--- a/src/backend/opencl/sort_by_key/b8.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -7,10 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "impl.hpp"
+#include <kernel/sort_by_key_impl.hpp>
+
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar
 
 namespace opencl
 {
-    INSTANTIATE1(char,true)
-    INSTANTIATE1(char,false)
+namespace kernel
+{
+    INSTANTIATE1(TYPE,true)
+    INSTANTIATE1(TYPE,false)
+}
 }
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..243034541f
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,373 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/sort_pair.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <kernel/sort_helper.hpp>
+#include <program.hpp>
+#include <memory.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <math.hpp>
+#include <kernel/iota.hpp>
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+
+#include <boost/compute/core.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
+#include <boost/compute/functional/operator.hpp>
+#include <boost/compute/iterator/buffer_iterator.hpp>
+#include <boost/compute/algorithm/copy.hpp>
+#include <boost/compute/algorithm/transform.hpp>
+#include <boost/compute/container/vector.hpp>
+#include <boost/compute/functional/get.hpp>
+#include <boost/compute/functional/field.hpp>
+#include <boost/compute/types/pair.hpp>
+
+namespace compute = boost::compute;
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+template<typename Tk, typename Tv, bool isAscending>
+inline
+boost::compute::function<bool(const std::pair<Tk, Tv>, const std::pair<Tk, Tv>)>
+makeCompareFunction()
+{
+    // Cannot use isAscending in BOOST_COMPUTE_FUNCTION
+    if(isAscending) {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+            {
+                return lhs.first < rhs.first;
+            }
+        );
+        return IPCompare;
+    } else {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+            {
+                return lhs.first > rhs.first;
+            }
+        );
+        return IPCompare;
+    }
+}
+
+template<typename Tk>
+inline boost::compute::function<Tk(Tk)>
+flipFunction()
+{
+    BOOST_COMPUTE_FUNCTION(Tk, negateFn, (const Tk x),
+        {
+            return -x;
+        }
+    );
+
+    return negateFn;
+}
+
+#define INSTANTIATE_FLIP(TY, XMAX)                              \
+template<> inline boost::compute::function<TY(TY)>              \
+flipFunction<TY>()                                              \
+{                                                               \
+    BOOST_COMPUTE_FUNCTION(TY, negateFn, (const TY x),          \
+       {                                                        \
+            return XMAX - x;                                    \
+        }                                                       \
+    );                                                          \
+                                                                \
+    return negateFn;                                            \
+}
+
+INSTANTIATE_FLIP(unsigned, UINT_MAX)
+INSTANTIATE_FLIP(unsigned short, USHRT_MAX)
+INSTANTIATE_FLIP(unsigned char, UCHAR_MAX)
+INSTANTIATE_FLIP(cl_ulong, ULONG_MAX)
+
+#undef INSTANTIATE_FLIP
+
+namespace opencl
+{
+    namespace kernel
+    {
+        static const int copyPairIter = 4;
+
+        template<typename Tk, typename Tv>
+        void makePair(cl::Buffer *out, const cl::Buffer *first, const cl::Buffer *second, const unsigned N)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sortPairProgs;
+                static std::map<int, Kernel*>  sortPairKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D Tk="      << dtype_traits<Tk>::getName()
+                            << " -D Tv="      << dtype_traits<Tv>::getName()
+                            << " -D copyPairIter=" << copyPairIter;
+                    if (std::is_same<Tk, double >::value ||
+                        std::is_same<Tk, cdouble>::value ||
+                        std::is_same<Tv, double >::value ||
+                        std::is_same<Tv, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
+                    sortPairProgs[device]   = new Program(prog);
+                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "make_pair_kernel");
+                });
+
+                auto makePairOp = make_kernel<Buffer, const Buffer, const Buffer, const unsigned>
+                                          (*sortPairKernels[device]);
+
+                NDRange local(256, 1, 1);
+                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
+
+                makePairOp(EnqueueArgs(getQueue(), global, local), *out, *first, *second, N);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv>
+        void splitPair(cl::Buffer *first, cl::Buffer *second, const cl::Buffer *in, const unsigned N)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sortPairProgs;
+                static std::map<int, Kernel*>  sortPairKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D Tk="      << dtype_traits<Tk>::getName()
+                            << " -D Tv="      << dtype_traits<Tv>::getName()
+                            << " -D copyPairIter=" << copyPairIter;
+                    if (std::is_same<Tk, double >::value ||
+                        std::is_same<Tk, cdouble>::value ||
+                        std::is_same<Tv, double >::value ||
+                        std::is_same<Tv, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
+                    sortPairProgs[device]   = new Program(prog);
+                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "split_pair_kernel");
+                });
+
+                auto splitPairOp = make_kernel<Buffer, Buffer, const Buffer, const unsigned>
+                                          (*sortPairKernels[device]);
+
+                NDRange local(256, 1, 1);
+                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
+
+                splitPairOp(EnqueueArgs(getQueue(), global, local), *first, *second, *in, N);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKeyIterative(Param pKey, Param pVal)
+        {
+            try {
+                compute::command_queue c_queue(getQueue()());
+
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+
+                for(int w = 0; w < pKey.info.dims[3]; w++) {
+                    int pKeyW = w * pKey.info.strides[3];
+                    int pValW = w * pVal.info.strides[3];
+                    for(int z = 0; z < pKey.info.dims[2]; z++) {
+                        int pKeyWZ = pKeyW + z * pKey.info.strides[2];
+                        int pValWZ = pValW + z * pVal.info.strides[2];
+                        for(int y = 0; y < pKey.info.dims[1]; y++) {
+
+                            int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
+                            int pValOffset = pValWZ + y * pVal.info.strides[1];
+
+                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
+                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
+                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
+                            if(isAscending) {
+                                compute::sort_by_key(start, end, vals, c_queue);
+                            } else {
+                                compute::sort_by_key(start, end, vals,
+                                                     compute::greater< type_t<Tk> >(), c_queue);
+                            }
+                        }
+                    }
+                }
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk_, typename Tv_, bool isAscending, int dim>
+        void sortByKeyBatched(Param pKey, Param pVal)
+        {
+            typedef type_t<Tk_> Tk;
+            typedef type_t<Tv_> Tv;
+
+            try {
+                af::dim4 inDims;
+                for(int i = 0; i < 4; i++)
+                    inDims[i] = pKey.info.dims[i];
+
+                // Sort dimension
+                // tileDims * seqDims = inDims
+                af::dim4 tileDims(1);
+                af::dim4 seqDims = inDims;
+                tileDims[dim] = inDims[dim];
+                seqDims[dim] = 1;
+
+                // Create/call iota
+                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
+                cl::Buffer* key = bufferAlloc(inDims.elements() * sizeof(unsigned));
+                Param pSeq;
+                pSeq.data = key;
+                pSeq.info.offset = 0;
+                pSeq.info.dims[0] = inDims[0];
+                pSeq.info.strides[0] = 1;
+                for(int i = 1; i < 4; i++) {
+                    pSeq.info.dims[i] = inDims[i];
+                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
+                }
+                kernel::iota<unsigned>(pSeq, seqDims, tileDims);
+
+                int elements = inDims.elements();
+
+                // Flat - Not required since inplace and both are continuous
+                //val.modDims(inDims.elements());
+                //key.modDims(inDims.elements());
+
+                // Sort indices
+                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
+                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
+                compute::command_queue c_queue(getQueue()());
+                compute::context c_context(getContext()());
+
+                // Create buffer iterators for seq
+                compute::buffer pSeq_buf((*pSeq.data)());
+                compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
+                compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
+                // Create buffer iterators for key and val
+                compute::buffer pKey_buf((*pKey.data)());
+                compute::buffer pVal_buf((*pVal.data)());
+                compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
+                compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
+                compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
+                compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
+
+                // Sort By Key for descending is stable in the reverse
+                // (greater) order. Sorting in ascending with negated values
+                // will give the right result
+                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+                // Create a copy of the pKey buffer
+                cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
+                compute::buffer cKey_buf((*cKey)());
+                compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
+                compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
+                compute::copy(key0, keyN, cKey0, c_queue);
+
+                // FIRST SORT
+                compute::sort_by_key(key0, keyN, seq0, c_queue);
+                compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
+
+                // Create a copy of the seq buffer after first sort
+                cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
+                compute::buffer cSeq_buf((*cSeq)());
+                compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
+                compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
+                compute::copy(seq0, seqN, cSeq0, c_queue);
+
+                // SECOND SORT
+                // First call will sort key, second sort will sort val
+                // Needs to be ascending (true) in order to maintain the indices properly
+                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
+                compute::sort_by_key(seq0, seqN, key0, c_queue);
+                compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
+
+                // If descending, flip it back
+                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+                //// No need of doing moddims here because the original Array<T>
+                //// dimensions have not been changed
+                ////val.modDims(inDims);
+
+                CL_DEBUG_FINISH(getQueue());
+                bufferFree(key);
+                bufferFree(cSeq);
+                bufferFree(cKey);
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename Tk, typename Tv, bool isAscending>
+        void sort0ByKey(Param pKey, Param pVal)
+        {
+            int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
+            // TODO Make a better heurisitic
+            if(higherDims > 5)
+                kernel::sortByKeyBatched<Tk, Tv, isAscending, 0>(pKey, pVal);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
+        }
+
+#define INSTANTIATE(Tk, Tv, dr)                                                 \
+    template void sort0ByKey<Tk, Tv, dr>(Param okey, Param oval);               \
+    template void sort0ByKeyIterative<Tk, Tv, dr>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 0>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 1>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 2>(Param okey, Param oval);      \
+    template void sortByKeyBatched<Tk, Tv, dr, 3>(Param okey, Param oval);      \
+
+#define INSTANTIATE1(Tk    , dr) \
+    INSTANTIATE(Tk, float  , dr) \
+    INSTANTIATE(Tk, double , dr) \
+    INSTANTIATE(Tk, cfloat , dr) \
+    INSTANTIATE(Tk, cdouble, dr) \
+    INSTANTIATE(Tk, int    , dr) \
+    INSTANTIATE(Tk, uint   , dr) \
+    INSTANTIATE(Tk, short  , dr) \
+    INSTANTIATE(Tk, ushort , dr) \
+    INSTANTIATE(Tk, char   , dr) \
+    INSTANTIATE(Tk, uchar  , dr) \
+    INSTANTIATE(Tk, intl   , dr) \
+    INSTANTIATE(Tk, uintl  , dr)
+    }
+}
+
+#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
new file mode 100644
index 0000000000..b8031c2314
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+
+namespace opencl
+{
+    namespace kernel
+    {
+        using std::conditional;
+        using std::is_same;
+
+        // If type is cdouble, return std::complex<double>, else return T
+        template<typename T>
+        using ztype_t = typename conditional<is_same<T, cdouble>::value,
+                                             std::complex<double>, T
+                                            >::type;
+
+        // If type is cfloat, return std::complex<float>, else return ztype_t
+        template<typename T>
+        using ctype_t = typename conditional<is_same<T, cfloat>::value,
+                                             std::complex<float>, ztype_t<T>
+                                            >::type;
+
+        // If type is intl, return cl_long, else return ctype_t
+        template<typename T>
+        using ltype_t = typename conditional<is_same<T, intl>::value,
+                                             cl_long, ctype_t<T>
+                                            >::type;
+
+        // If type is uintl, return cl_ulong, else return ltype_t
+        template<typename T>
+        using type_t = typename conditional<is_same<T, uintl>::value,
+                                            cl_ulong, ltype_t<T>
+                                           >::type;
+    }
+}
+
diff --git a/src/backend/opencl/kernel/sort_index.hpp b/src/backend/opencl/kernel/sort_index.hpp
deleted file mode 100644
index 3a8ab1401e..0000000000
--- a/src/backend/opencl/kernel/sort_index.hpp
+++ /dev/null
@@ -1,99 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
-#include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/iota.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
-#include <boost/compute/functional/operator.hpp>
-#include <boost/compute/iterator/buffer_iterator.hpp>
-
-namespace compute = boost::compute;
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::make_kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
-namespace opencl
-{
-    namespace kernel
-    {
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
-
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-
-        template<typename T, bool isAscending>
-        void sort0_index(Param val, Param idx)
-        {
-            try {
-                compute::command_queue c_queue(getQueue()());
-
-                compute::buffer val_buf((*val.data)());
-                compute::buffer idx_buf((*idx.data)());
-
-                for(int w = 0; w < (int)val.info.dims[3]; w++) {
-                    int valW = w * (int)val.info.strides[3];
-                    int idxW = w * idx.info.strides[3];
-                    for(int z = 0; z < (int)val.info.dims[2]; z++) {
-                        int valWZ = valW + z * (int)val.info.strides[2];
-                        int idxWZ = idxW + z * idx.info.strides[2];
-                        for(int y = 0; y < (int)val.info.dims[1]; y++) {
-
-                            int valOffset = valWZ + y * val.info.strides[1];
-                            int idxOffset = idxWZ + y * idx.info.strides[1];
-
-                            compute::buffer_iterator<unsigned> idx_begin(idx_buf, idxOffset);
-                            compute::iota(idx_begin, idx_begin + val.info.dims[0], 0, c_queue);
-
-                            if(isAscending) {
-                                compute::sort_by_key(
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        idx_begin, compute::less< type_t<T> >(), c_queue);
-                            } else {
-                                compute::sort_by_key(
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        idx_begin, compute::greater< type_t<T> >(), c_queue);
-                            }
-                        }
-                    }
-                }
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
-    }
-}
-
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_pair.cl b/src/backend/opencl/kernel/sort_pair.cl
new file mode 100644
index 0000000000..f5e5413d73
--- /dev/null
+++ b/src/backend/opencl/kernel/sort_pair.cl
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+struct IndexPair
+{
+    Tk first;
+    Tv second;
+};
+
+typedef struct IndexPair IndexPair_t;
+
+__kernel
+void make_pair_kernel(__global IndexPair_t *out,
+                      __global const Tk *first, __global const Tv *second,
+                      const unsigned N)
+{
+    int tIdx = get_group_id(0) * get_local_size(0) * copyPairIter + get_local_id(0);
+    const int blockDimX = get_local_size(0);
+
+    for(int i = tIdx; i < N; i += blockDimX) {
+        out[i].first  = first[i];
+        out[i].second = second[i];
+    }
+}
+
+__kernel
+void split_pair_kernel( __global Tk *first, __global Tv *second,
+                       __global const IndexPair_t *out, const unsigned N)
+{
+    int tIdx = get_group_id(0) * get_local_size(0) * copyPairIter + get_local_id(0);
+    const int blockDimX = get_local_size(0);
+
+    for(int i = tIdx; i < N; i += blockDimX) {
+        first[i]  = out[i].first;
+        second[i] = out[i].second;
+    }
+}
diff --git a/src/backend/opencl/magma/magma_cpu_blas.h b/src/backend/opencl/magma/magma_cpu_blas.h
index b3cba096b5..6661aad657 100644
--- a/src/backend/opencl/magma/magma_cpu_blas.h
+++ b/src/backend/opencl/magma/magma_cpu_blas.h
@@ -13,16 +13,16 @@
 #include <defines.hpp>
 #include "magma_types.h"
 
-#ifdef __APPLE__
-#include <Accelerate/Accelerate.h>
-#else
 #ifdef USE_MKL
-#include <mkl_cblas.h>
+    #include <mkl_cblas.h>
 #else
-extern "C" {
-#include <cblas.h>
-}
-#endif
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+    #else
+        extern "C" {
+            #include <cblas.h>
+        }
+    #endif
 #endif
 
 // Todo: Ask upstream for a more official way to detect it
diff --git a/src/backend/opencl/magma/magma_cpu_lapack.h b/src/backend/opencl/magma/magma_cpu_lapack.h
index 5974dab8a9..54c26ae0e9 100644
--- a/src/backend/opencl/magma/magma_cpu_lapack.h
+++ b/src/backend/opencl/magma/magma_cpu_lapack.h
@@ -39,16 +39,20 @@ int LAPACKE_dlacgv_work(Args... args) { return 0; }
 #define ORDER_TYPE int
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
-#if defined(__APPLE__)
-    #define LAPACK_COL_MAJOR 102
-    #include "../../lapacke.hpp"
+#ifdef USE_MKL
+    #include<mkl_lapacke.h>
 #else
-    #ifdef USE_MKL
-        #include<mkl_lapacke.h>
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+        #include <lapacke.hpp>
+        #undef LAPACK_COL_MAJOR
+        #define LAPACK_COL_MAJOR 102
+        #undef AF_LAPACK_COL_MAJOR
+        #define AF_LAPACK_COL_MAJOR 0
     #else // NETLIB LAPACKE
         #include<lapacke.h>
-    #endif  // MKL/NETLIB
-#endif  //APPLE
+    #endif
+#endif
 
 #define LAPACKE_CHECK(fn) do {                  \
         int __info = fn;                        \
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index dc8ab4ea65..d5d70af8fa 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -701,6 +701,11 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
 void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
     try {
+
+        clRetainDevice(dev);
+        clRetainContext(ctx);
+        clRetainCommandQueue(que);
+
         DeviceManager& devMngr   = DeviceManager::getInstance();
         cl::Device* tDevice      = new cl::Device(dev);
         cl::Context* tContext    = new cl::Context(ctx);
@@ -758,6 +763,11 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
         } else if (deleteIdx == -1) {
             AF_ERROR("No matching device found", AF_ERR_ARG);
         } else {
+
+            clReleaseDevice((*devMngr.mDevices[deleteIdx])());
+            clReleaseContext((*devMngr.mContexts[deleteIdx])());
+            clReleaseCommandQueue((*devMngr.mQueues[deleteIdx])());
+
             // FIXME: this case can potentially cause issues due to the
             // modification of the device pool stl containers.
 
@@ -815,57 +825,75 @@ using namespace opencl;
 
 af_err afcl_get_device_type(afcl_device_type *res)
 {
-    *res = (afcl_device_type)getActiveDeviceType();
+    try {
+        *res = (afcl_device_type)getActiveDeviceType();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_get_platform(afcl_platform *res)
 {
-    *res = (afcl_platform)getActivePlatform();
+    try {
+        *res = (afcl_platform)getActivePlatform();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_get_context(cl_context *ctx, const bool retain)
 {
-    *ctx = getContext()();
-    if (retain) clRetainContext(*ctx);
+    try {
+        *ctx = getContext()();
+        if (retain) clRetainContext(*ctx);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 
 af_err afcl_get_queue(cl_command_queue *queue, const bool retain)
 {
-    *queue = getQueue()();
-    if (retain) clRetainCommandQueue(*queue);
+    try {
+        *queue = getQueue()();
+        if (retain) clRetainCommandQueue(*queue);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_get_device_id(cl_device_id *id)
 {
-    *id = getDevice()();
+    try {
+        *id = getDevice()();
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_set_device_id(cl_device_id id)
 {
-    setDevice(getDeviceIdFromNativeId(id));
+    try {
+        setDevice(getDeviceIdFromNativeId(id));
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
-    addDeviceContext(dev, ctx, que);
+    try {
+        addDeviceContext(dev, ctx, que);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_set_device_context(cl_device_id dev, cl_context ctx)
 {
-    setDeviceContext(dev, ctx);
+    try {
+        setDeviceContext(dev, ctx);
+    } CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx)
 {
-    removeDeviceContext(dev, ctx);
+    try {
+        removeDeviceContext(dev, ctx);
+    } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 36a8972f80..6b49730708 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -55,7 +55,7 @@ namespace opencl
             prog.build(targetDevices, (defaults + options).c_str());
 
         } catch (...) {
-            SHOW_BUILD_INFO(prog);
+            SHOW_DEBUG_BUILD_INFO(prog);
             throw;
         }
     }
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 3ac929a537..3b6dfa7701 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -21,7 +21,7 @@
 namespace opencl
 {
     template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim)
+    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan)
     {
         Array<To> out = createEmptyArray<To>(in.dims());
 
@@ -29,10 +29,17 @@ namespace opencl
             Param Out = out;
             Param In  =   in;
 
-            if (dim == 0)
-                kernel::scan_first<Ti, To, op>(Out, In);
-            else
-                kernel::scan_dim  <Ti, To, op>(Out, In, dim);
+            if (inclusive_scan) {
+                if (dim == 0)
+                    kernel::scan_first<Ti, To, op, true>(Out, In);
+                else
+                    kernel::scan_dim  <Ti, To, op, true>(Out, In, dim);
+            } else {
+                if (dim == 0)
+                    kernel::scan_first<Ti, To, op, false>(Out, In);
+                else
+                    kernel::scan_dim  <Ti, To, op, false>(Out, In, dim);
+            }
 
         } catch (cl::Error &ex) {
 
@@ -42,21 +49,30 @@ namespace opencl
         return out;
     }
 
-#define INSTANTIATE(ROp, Ti, To)                                        \
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti>& in, const int dim); \
+#define INSTANTIATE(ROp, Ti, To)\
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN(ROp)           \
+    INSTANTIATE(ROp, float  , float  )  \
+    INSTANTIATE(ROp, double , double )  \
+    INSTANTIATE(ROp, cfloat , cfloat )  \
+    INSTANTIATE(ROp, cdouble, cdouble)  \
+    INSTANTIATE(ROp, int    , int    )  \
+    INSTANTIATE(ROp, uint   , uint   )  \
+    INSTANTIATE(ROp, intl   , intl   )  \
+    INSTANTIATE(ROp, uintl  , uintl  )  \
+    INSTANTIATE(ROp, char   , int    )  \
+    INSTANTIATE(ROp, char   , uint   )  \
+    INSTANTIATE(ROp, uchar  , uint   )  \
+    INSTANTIATE(ROp, short  , int    )  \
+    INSTANTIATE(ROp, ushort , uint   )
 
     //accum
-    INSTANTIATE(af_add_t, float  , float  )
-    INSTANTIATE(af_add_t, double , double )
-    INSTANTIATE(af_add_t, cfloat , cfloat )
-    INSTANTIATE(af_add_t, cdouble, cdouble)
-    INSTANTIATE(af_add_t, int    , int    )
-    INSTANTIATE(af_add_t, uint   , uint   )
-    INSTANTIATE(af_add_t, intl   , intl   )
-    INSTANTIATE(af_add_t, uintl  , uintl  )
-    INSTANTIATE(af_add_t, char   , int    )
-    INSTANTIATE(af_add_t, uchar  , uint   )
-    INSTANTIATE(af_add_t, short  , int    )
-    INSTANTIATE(af_add_t, ushort , uint   )
     INSTANTIATE(af_notzero_t, char  , uint)
+    INSTANTIATE_SCAN(af_add_t)
+    INSTANTIATE_SCAN(af_sub_t)
+    INSTANTIATE_SCAN(af_mul_t)
+    INSTANTIATE_SCAN(af_div_t)
+    INSTANTIATE_SCAN(af_min_t)
+    INSTANTIATE_SCAN(af_max_t)
 }
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index df03d8282f..d54005bf07 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -14,5 +14,5 @@
 namespace opencl
 {
     template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim);
+    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 762d815095..1548f27472 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -12,6 +12,7 @@
 #include <copy.hpp>
 #include <kernel/sort.hpp>
 #include <math.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
 
@@ -23,9 +24,25 @@ namespace opencl
         try {
             Array<T> out = copyArray<T>(in);
             switch(dim) {
-            case 0: kernel::sort0<T, isAscending>(out);
-                break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+                case 0: kernel::sort0<T, isAscending>(out); break;
+                case 1: kernel::sortBatched<T, isAscending, 1>(out); break;
+                case 2: kernel::sortBatched<T, isAscending, 2>(out); break;
+                case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
+                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+            }
+
+            if(dim != 0) {
+                af::dim4 preorderDims = out.dims();
+                af::dim4 reorderDims(0, 1, 2, 3);
+                reorderDims[dim] = 0;
+                preorderDims[0] = out.dims()[dim];
+                for(int i = 1; i <= (int)dim; i++) {
+                    reorderDims[i - 1] = i;
+                    preorderDims[i] = out.dims()[i - 1];
+                }
+
+                out.setDataDims(preorderDims);
+                out = reorder<T>(out, reorderDims);
             }
             return out;
         } catch (std::exception &ex) {
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
new file mode 100644
index 0000000000..27c2dc2462
--- /dev/null
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -0,0 +1,90 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <copy.hpp>
+#include <sort_by_key.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <reorder.hpp>
+#include <math.hpp>
+#include <stdexcept>
+#include <err_opencl.hpp>
+
+namespace opencl
+{
+    template<typename Tk, typename Tv, bool isAscending>
+    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
+               const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim)
+    {
+        try {
+            okey = copyArray<Tk>(ikey);
+            oval = copyArray<Tv>(ival);
+
+            switch(dim) {
+                case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
+                case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending, 1>(okey, oval); break;
+                case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending, 2>(okey, oval); break;
+                case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending, 3>(okey, oval); break;
+                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+            }
+
+            if(dim != 0) {
+                af::dim4 preorderDims = okey.dims();
+                af::dim4 reorderDims(0, 1, 2, 3);
+                reorderDims[dim] = 0;
+                preorderDims[0] = okey.dims()[dim];
+                for(int i = 1; i <= (int)dim; i++) {
+                    reorderDims[i - 1] = i;
+                    preorderDims[i] = okey.dims()[i - 1];
+                }
+
+                okey.setDataDims(preorderDims);
+                oval.setDataDims(preorderDims);
+
+                okey = reorder<Tk>(okey, reorderDims);
+                oval = reorder<Tv>(oval, reorderDims);
+            }
+        } catch(std::exception &ex) {
+            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+        }
+    }
+
+#define INSTANTIATE(Tk, Tv)                                                         \
+    template void sort_by_key<Tk, Tv, true >(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+    template void sort_by_key<Tk, Tv, false>(Array<Tk> &okey, Array<Tv> &oval,      \
+                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
+
+
+INSTANTIATE1(float )
+INSTANTIATE1(double)
+INSTANTIATE1(int   )
+INSTANTIATE1(uint  )
+INSTANTIATE1(short )
+INSTANTIATE1(ushort)
+INSTANTIATE1(char  )
+INSTANTIATE1(uchar )
+INSTANTIATE1(intl  )
+INSTANTIATE1(uintl )
+
+}
diff --git a/src/backend/opencl/sort_by_key/f32.cpp b/src/backend/opencl/sort_by_key/f32.cpp
deleted file mode 100644
index a7baf486f1..0000000000
--- a/src/backend/opencl/sort_by_key/f32.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(float,true)
-    INSTANTIATE1(float,false)
-}
diff --git a/src/backend/opencl/sort_by_key/f64.cpp b/src/backend/opencl/sort_by_key/f64.cpp
deleted file mode 100644
index 6971c90982..0000000000
--- a/src/backend/opencl/sort_by_key/f64.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(double,true)
-    INSTANTIATE1(double,false)
-}
diff --git a/src/backend/opencl/sort_by_key/impl.hpp b/src/backend/opencl/sort_by_key/impl.hpp
deleted file mode 100644
index 49d184113f..0000000000
--- a/src/backend/opencl/sort_by_key/impl.hpp
+++ /dev/null
@@ -1,57 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <copy.hpp>
-#include <sort_by_key.hpp>
-#include <kernel/sort_by_key.hpp>
-#include <math.hpp>
-#include <stdexcept>
-#include <err_opencl.hpp>
-
-namespace opencl
-{
-    template<typename Tk, typename Tv, bool isAscending>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim)
-    {
-        try {
-            okey = copyArray<Tk>(ikey);
-            oval = copyArray<Tv>(ival);
-            switch(dim) {
-            case 0: kernel::sort0_by_key<Tk, Tv, isAscending>(okey, oval);
-                break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-            }
-        }catch(std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
-        }
-    }
-
-#define INSTANTIATE(Tk, Tv, isAscending)                                \
-    template void                                                       \
-    sort_by_key<Tk, Tv, isAscending>(Array<Tk> &okey, Array<Tv> &oval,  \
-                                     const Array<Tk> &ikey,             \
-                                     const Array<Tv> &ival,             \
-                                     const unsigned dim);               \
-
-
-#define INSTANTIATE1(Tk, isAscending)           \
-    INSTANTIATE(Tk, float , isAscending)        \
-    INSTANTIATE(Tk, double, isAscending)        \
-    INSTANTIATE(Tk, int   , isAscending)        \
-    INSTANTIATE(Tk, uint  , isAscending)        \
-    INSTANTIATE(Tk, char  , isAscending)        \
-    INSTANTIATE(Tk, uchar , isAscending)        \
-    INSTANTIATE(Tk, short , isAscending)        \
-    INSTANTIATE(Tk, ushort, isAscending)        \
-    INSTANTIATE(Tk, intl  , isAscending)        \
-    INSTANTIATE(Tk, uintl , isAscending)        \
-
-}
diff --git a/src/backend/opencl/sort_by_key/s16.cpp b/src/backend/opencl/sort_by_key/s16.cpp
deleted file mode 100644
index 44e17b5030..0000000000
--- a/src/backend/opencl/sort_by_key/s16.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(short,true)
-    INSTANTIATE1(short,false)
-}
diff --git a/src/backend/opencl/sort_by_key/s32.cpp b/src/backend/opencl/sort_by_key/s32.cpp
deleted file mode 100644
index 9fed1a53b3..0000000000
--- a/src/backend/opencl/sort_by_key/s32.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(int,true)
-    INSTANTIATE1(int,false)
-}
diff --git a/src/backend/opencl/sort_by_key/s64.cpp b/src/backend/opencl/sort_by_key/s64.cpp
deleted file mode 100644
index e2ed8d687b..0000000000
--- a/src/backend/opencl/sort_by_key/s64.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(intl,true)
-    INSTANTIATE1(intl,false)
-}
diff --git a/src/backend/opencl/sort_by_key/u16.cpp b/src/backend/opencl/sort_by_key/u16.cpp
deleted file mode 100644
index c53b68fb53..0000000000
--- a/src/backend/opencl/sort_by_key/u16.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(ushort,true)
-    INSTANTIATE1(ushort,false)
-}
diff --git a/src/backend/opencl/sort_by_key/u32.cpp b/src/backend/opencl/sort_by_key/u32.cpp
deleted file mode 100644
index c2e3e62163..0000000000
--- a/src/backend/opencl/sort_by_key/u32.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(uint,true)
-    INSTANTIATE1(uint,false)
-}
diff --git a/src/backend/opencl/sort_by_key/u64.cpp b/src/backend/opencl/sort_by_key/u64.cpp
deleted file mode 100644
index 89649b1ba5..0000000000
--- a/src/backend/opencl/sort_by_key/u64.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(uintl,true)
-    INSTANTIATE1(uintl,false)
-}
diff --git a/src/backend/opencl/sort_by_key/u8.cpp b/src/backend/opencl/sort_by_key/u8.cpp
deleted file mode 100644
index 2dfb4c3a73..0000000000
--- a/src/backend/opencl/sort_by_key/u8.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "impl.hpp"
-
-namespace opencl
-{
-    INSTANTIATE1(uchar,true)
-    INSTANTIATE1(uchar,false)
-}
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index c7aaa70feb..bb5474909d 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -10,29 +10,53 @@
 #include <Array.hpp>
 #include <sort_index.hpp>
 #include <copy.hpp>
-#include <kernel/sort_index.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
+#include <reorder.hpp>
+#include <range.hpp>
 
 namespace opencl
 {
     template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<uint> &idx, const Array<T> &in, const uint dim)
+    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
     {
         try {
-            val = copyArray<T>(in);
-            idx = createEmptyArray<uint>(in.dims());
+            // okey contains values, oval contains indices
+            okey = copyArray<T>(in);
+            oval = range<uint>(in.dims(), dim);
+            oval.eval();
 
             switch(dim) {
-            case 0: kernel::sort0_index<T, isAscending>(val, idx);
-                break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+                case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
+                case 1: kernel::sortByKeyBatched<T, uint, isAscending, 1>(okey, oval); break;
+                case 2: kernel::sortByKeyBatched<T, uint, isAscending, 2>(okey, oval); break;
+                case 3: kernel::sortByKeyBatched<T, uint, isAscending, 3>(okey, oval); break;
+                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
-        }         catch (std::exception &ex) {
+
+            if(dim != 0) {
+                af::dim4 preorderDims = okey.dims();
+                af::dim4 reorderDims(0, 1, 2, 3);
+                reorderDims[dim] = 0;
+                preorderDims[0] = okey.dims()[dim];
+                for(int i = 1; i <= (int)dim; i++) {
+                    reorderDims[i - 1] = i;
+                    preorderDims[i] = okey.dims()[i - 1];
+                }
+
+                okey.setDataDims(preorderDims);
+                oval.setDataDims(preorderDims);
+
+                okey = reorder<T>(okey, reorderDims);
+                oval = reorder<uint>(oval, reorderDims);
+            }
+        } catch (std::exception &ex) {
             AF_ERROR(ex.what(), AF_ERR_INTERNAL);
         }
     }
+
 #define INSTANTIATE(T)                                                  \
     template void sort_index<T, true>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
                                       const uint dim);                  \
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 4e63095421..54ba158e8a 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <defines.hpp>
+#include <types.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <sstream>
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5db23714d3..e07e8138cf 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -21,6 +21,10 @@ ELSE()
     INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
     OPTION(BUILD_NONFREE "Build Tests for nonfree algorithms" OFF)
 
+    IF(WIN32)
+        ADD_DEFINITIONS(-DOS_WIN -DNOMINMAX)
+    ENDIF(WIN32)
+
     IF(${BUILD_NONFREE})
         MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
         SET(BUILD_NONFREE_SIFT ON CACHE BOOL "Build ArrayFire with SIFT")
@@ -189,6 +193,7 @@ ENDIF()
 # CUDA Backend
 IF (${CUDA_FOUND})
     IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
+        # Find NVVM
         FIND_LIBRARY( CUDA_NVVM_LIBRARY
           NAMES "nvvm"
           PATH_SUFFIXES "nvvm/lib64" "nvvm/lib"
@@ -196,6 +201,26 @@ IF (${CUDA_FOUND})
           DOC "CUDA NVVM Library"
           )
         MARK_AS_ADVANCED(CUDA_NVVM_LIBRARY)
+
+        # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
+        IF(NOT CUDA_CUDA_LIBRARY)
+            MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
+            MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
+            FIND_FILE(CUDA_CUDA_LIBRARY_STUB
+                      NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
+                      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+                      PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
+                      DOC "CUDA Library STUB"
+                     )
+            IF(CUDA_CUDA_LIBRARY_STUB)
+                MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
+                MESSAGE("Run the following commands (Linux) to set it up:")
+                MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
+                MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
+            ENDIF()
+            MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
+        ENDIF(NOT CUDA_CUDA_LIBRARY)
+
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
             OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
diff --git a/test/array.cpp b/test/array.cpp
index 293b888a8f..b712df302e 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <cstddef>
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <testHelpers.hpp>
@@ -465,23 +466,40 @@ TEST(DeviceId, Different)
 {
     int ndevices = getDeviceCount();
     if (ndevices < 2) return;
-
     int id0 = getDevice();
     int id1 = (id0 + 1) % ndevices;
 
-    array a = randu(5,5);
-    ASSERT_EQ(getDeviceId(a), id0);
-    setDevice(id1);
+    {
+        array a = randu(5,5);
+        ASSERT_EQ(getDeviceId(a), id0);
+        setDevice(id1);
+
+        array b = randu(5,5);
 
-    array b = randu(5,5);
+        ASSERT_EQ(getDeviceId(a), id0);
+        ASSERT_EQ(getDeviceId(b), id1);
+        ASSERT_NE(getDevice(), getDeviceId(a));
+        ASSERT_EQ(getDevice(), getDeviceId(b));
 
-    ASSERT_EQ(getDeviceId(a), id0);
-    ASSERT_EQ(getDeviceId(b), id1);
-    ASSERT_NE(getDevice(), getDeviceId(a));
-    ASSERT_EQ(getDevice(), getDeviceId(b));
+        af_array c;
+        af_err err = af_matmul(&c, a.get(), b.get(), AF_MAT_NONE, AF_MAT_NONE);
+        ASSERT_EQ(err, AF_ERR_DEVICE);
+    }
 
-    af_array c;
-    af_err err = af_matmul(&c, a.get(), b.get(), AF_MAT_NONE, AF_MAT_NONE);
-    ASSERT_EQ(err, AF_ERR_DEVICE);
+    setDevice(id1);
+    af::deviceGC();
     setDevice(id0);
+    af::deviceGC();
+}
+
+TEST(Device, empty)
+{
+    array a = array();
+    ASSERT_EQ(a.device<float>() == NULL, 1);
+}
+
+TEST(Device, JIT)
+{
+    array a = constant(1, 5, 5);
+    ASSERT_EQ(a.device<float>() != NULL, 1);
 }
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index c4becab2dc..3f5e441c33 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -80,6 +80,37 @@ TYPED_TEST(Diagonal, Extract)
     }
 }
 
+TYPED_TEST(Diagonal, ExtractRect)
+{
+    if (noDoubleTests<TypeParam>()) return;
+
+    try {
+        static const int size0 = 1000, size1 = 900;
+        vector<TypeParam> input (size0 * size1);
+        for(int i = 0; i < size0 * size1; i++) {
+            input[i] = i;
+        }
+
+        for(int jj = 10; jj < size0; jj += 100) {
+            for(int kk = 10; kk < size1; kk += 90) {
+                array data(jj, kk, &input.front(), afHost);
+                array out = diag(data, 0);
+
+                vector<TypeParam> h_out(out.elements());
+                out.host(&h_out.front());
+
+                ASSERT_EQ(out.dims(0), std::min(jj, kk));
+
+                for(int i =0; i < (int)out.dims(0); i++) {
+                    ASSERT_EQ(input[i * data.dims(0) + i], h_out[i]);
+                }
+            }
+        }
+    } catch (const af::exception& ex) {
+        FAIL() << ex.what() << std::endl;
+    }
+}
+
 TEST(Diagonal, ExtractGFOR)
 {
     dim4 dims = dim4(100, 100, 3);
diff --git a/test/fft.cpp b/test/fft.cpp
index 48ff865d2a..19b0ae0950 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -683,3 +683,39 @@ TEST(ifft3, InPlace)
         ASSERT_EQ(ha[i], hb[i]);
     }
 }
+
+void fft2InPlaceFunc()
+{
+    af::array a = af::randu(1024, 1024, c32);
+    af::array b = af::fft2(a);
+    af::fft2InPlace(a);
+
+    std::vector<af::cfloat> ha(a.elements());
+    std::vector<af::cfloat> hb(b.elements());
+
+    a.host(&ha[0]);
+    b.host(&hb[0]);
+
+    for (int i = 0; i < (int)a.elements(); i++) {
+        ASSERT_EQ(ha[i], hb[i]);
+    }
+}
+
+#define DEVICE_ITERATE(func) do {                                           \
+    const char* ENV = getenv("AF_MULTI_GPU_TESTS");                         \
+    if(ENV && ENV[0] == '0') {                                              \
+        func;                                                               \
+    } else {                                                                \
+        int oldDevice = af::getDevice();                                    \
+        for(int i = 0; i < af::getDeviceCount(); i++) {                     \
+            af::setDevice(i);                                               \
+            func;                                                           \
+        }                                                                   \
+        af::setDevice(oldDevice);                                           \
+    }                                                                       \
+} while(0);
+
+TEST(FFT2, MultiGPUInPlaceSquare_CPP)
+{
+    DEVICE_ITERATE((fft2InPlaceFunc()));
+}
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 5794051152..558acabe25 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -77,18 +77,6 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
     }
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
-{
-    feat.resize(nfeat);
-    for (unsigned i = 0; i < feat.size(); i++) {
-        feat[i].f[0] = x[i];
-        feat[i].f[1] = y[i];
-        feat[i].f[2] = score[i];
-        feat[i].f[3] = ori[i];
-        feat[i].f[4] = size[i];
-    }
-}
-
 static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
@@ -104,16 +92,6 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
     }
 }
 
-static unsigned popcount(unsigned x)
-{
-    x = x - ((x >> 1) & 0x55555555);
-    x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
-    x = (x + (x >> 4)) & 0x0F0F0F0F;
-    x = x + (x >> 8);
-    x = x + (x >> 16);
-    return x & 0x0000003F;
-}
-
 static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
 {
     bool ret = true;
diff --git a/test/gray_rgb.cpp b/test/gray_rgb.cpp
new file mode 100644
index 0000000000..0ee7078cef
--- /dev/null
+++ b/test/gray_rgb.cpp
@@ -0,0 +1,105 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+TEST(rgb_gray, 32bit)
+{
+    af::array rgb = af::randu(10, 10, 3);
+    af::array gray = af::rgb2gray(rgb);
+
+    std::vector<float> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    rgb.host(&h_rgb[0]);
+    gray.host(&h_gray[0]);
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    const float rPercent=0.2126f;
+    const float gPercent=0.7152f;
+    const float bPercent=0.0722f;
+
+    for (int i = 0; i < num; i++) {
+        float res =
+            rPercent * h_rgb[i + roff] +
+            gPercent * h_rgb[i + goff] +
+            bPercent * h_rgb[i + boff];
+
+        ASSERT_FLOAT_EQ(res, h_gray[i]);
+    }
+}
+
+TEST(rgb_gray, 8bit)
+{
+    af::array rgb = af::randu(10, 10, 3, u8);
+    af::array gray = af::rgb2gray(rgb);
+
+    std::vector<uchar> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    rgb.host(&h_rgb[0]);
+    gray.host(&h_gray[0]);
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    const float rPercent=0.2126f;
+    const float gPercent=0.7152f;
+    const float bPercent=0.0722f;
+
+    for (int i = 0; i < num; i++) {
+        float res =
+            rPercent * h_rgb[i + roff] +
+            gPercent * h_rgb[i + goff] +
+            bPercent * h_rgb[i + boff];
+
+        ASSERT_FLOAT_EQ(res, h_gray[i]);
+    }
+}
+
+TEST(gray_rgb, 32bit)
+{
+    af::array gray = af::randu(10, 10);
+
+    const float rPercent=0.33f;
+    const float gPercent=0.34f;
+    const float bPercent=0.33f;
+
+    af::array rgb = af::gray2rgb(gray, rPercent, gPercent, bPercent);
+    std::vector<float> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    for (int i = 0; i < num; i++) {
+        float gray = h_gray[i];
+
+        float r = rPercent * gray;
+        float g = gPercent * gray;
+        float b = bPercent * gray;
+
+        ASSERT_FLOAT_EQ(r, h_rgb[i + roff]);
+        ASSERT_FLOAT_EQ(g, h_rgb[i + goff]);
+        ASSERT_FLOAT_EQ(b, h_rgb[i + boff]);
+    }
+}
diff --git a/test/median.cpp b/test/median.cpp
index e0b21ba281..5b26a44a97 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -37,96 +37,142 @@ af::array generateArray<unsigned int>(int nx, int ny, int nz, int nw)
     return a;
 }
 
-template<typename To, typename Ti, bool flat>
-void median0(int nx, int ny=1, int nz=1, int nw=1)
+template<typename To, typename Ti>
+void median_flat(int nx, int ny=1, int nz=1, int nw=1)
 {
     if (noDoubleTests<Ti>()) return;
     array a = generateArray<Ti>(nx, ny, nz, nw);
-    array sa = sort(a);
 
-    Ti *h_sa = sa.host<Ti>();
+    // Verification
+    array sa = sort(flat(a));
+    dim_t mid = (sa.dims(0) + 1) / 2;
 
-    To *h_b = NULL;
-    To val = 0;
+    To verify;
 
-    if (flat) {
-        val = median<To>(a);
-        h_b = &val;
+    To *h_sa = sa.as((af_dtype)af::dtype_traits<To>::af_type).host<To>();
+    if(sa.dims(0) % 2 == 1) {
+        verify = h_sa[mid - 1];
     } else {
-        array b = median(a);
-        h_b = b.host<To>();
+        verify = (h_sa[mid - 1] + h_sa[mid]) / (To)2;
     }
 
-    for (int w = 0; w < nw; w++) {
-        for (int z = 0; z < nz; z++) {
-            for (int y = 0; y < ny; y++) {
+    // Test Part
+    To val = median<To>(a);
 
-                int off = (y  + ny * (z + nz * w));
-                int id = nx / 2;
+    ASSERT_EQ(verify, val);
 
-                if (nx & 2) {
-                    ASSERT_EQ(h_sa[id + off * nx], h_b[off]);
-                } else {
-                    To left = h_sa[id + off * nx - 1];
-                    To right = h_sa[id + off * nx];
+    delete[] h_sa;
+}
+
+template<typename To, typename Ti, int dim>
+void median_test(int nx, int ny=1, int nz=1, int nw=1)
+{
+    if (noDoubleTests<Ti>()) return;
+
+    array a = generateArray<Ti>(nx, ny, nz, nw);
+
+    // If selected dim is higher than input ndims, then return
+    if(dim >= a.dims().ndims())
+        return;
+
+    array verify;
+
+    // Verification
+    array sa = sort(a, dim);
+
+    double mid = (a.dims(dim) + 1) / 2;
+    af::seq mSeq[4] = {span, span, span, span};
+    mSeq[dim] = af::seq(mid, mid, 1.0);
 
-                    ASSERT_NEAR((left + right) / 2, h_b[off], 1e-5);
-                }
-            }
-        }
+    if(sa.dims(dim) % 2 == 1) {
+        mSeq[dim] = mSeq[dim] - 1.0;
+        verify = sa(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
+    } else {
+        dim_t sdim[4] = {0};
+        sdim[dim] = 1;
+        sa = sa.as((af_dtype)af::dtype_traits<To>::af_type);
+        array sas = shift(sa, sdim[0], sdim[1], sdim[2], sdim[3]);
+        verify = ((sa + sas) / 2)(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     }
 
-    delete[] h_sa;
-    if (!flat) delete[] h_b;
+    // Test Part
+    array out = median(a, dim);
+
+    ASSERT_EQ(out.dims() == verify.dims(), true);
+    ASSERT_NEAR(0, sum<double>(af::abs(out - verify)), 1e-5);
 }
 
-#define MEDIAN0(To, Ti)                         \
-    TEST(median0, Ti##_1D_even)                 \
+#define MEDIAN_FLAT(To, Ti)                     \
+    TEST(MedianFlat, Ti##_flat_even)            \
+    {                                           \
+        median_flat<To, Ti>(1000);              \
+    }                                           \
+    TEST(MedianFlat, Ti##_flat_odd)             \
     {                                           \
-        median0<To, Ti, false>(1000);           \
+        median_flat<To, Ti>(783);               \
     }                                           \
-    TEST(median0, Ti##_2D_even)                 \
+    TEST(MedianFlat, Ti##_flat_multi_even)      \
     {                                           \
-        median0<To, Ti, false>(1000, 100);      \
+        median_flat<To, Ti>(24, 11, 3);         \
     }                                           \
-    TEST(median0, Ti##_3D_even)                 \
+    TEST(MedianFlat, Ti##_flat_multi_odd)       \
     {                                           \
-        median0<To, Ti, false>(1000, 25, 4);    \
+        median_flat<To, Ti>(15, 21, 7);         \
     }                                           \
-    TEST(median0, Ti##_4D_even)                 \
+
+MEDIAN_FLAT(float, float)
+MEDIAN_FLAT(float, int)
+MEDIAN_FLAT(float, uint)
+MEDIAN_FLAT(float, uchar)
+MEDIAN_FLAT(float, short)
+MEDIAN_FLAT(float, ushort)
+MEDIAN_FLAT(double, double)
+
+#define MEDIAN_TEST(To, Ti, dim)                \
+    TEST(Median, Ti##_1D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, false>(1000, 25, 2, 2); \
+        median_test<To, Ti, dim>(1000);         \
     }                                           \
-    TEST(median0, Ti##_flat_even)               \
+    TEST(Median, Ti##_2D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, true>(1000);            \
+        median_test<To, Ti, dim>(1000, 25);     \
     }                                           \
-    TEST(median0, Ti##_1D_odd)                  \
+    TEST(Median, Ti##_3D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, false>(783);            \
+        median_test<To, Ti, dim>(100, 25, 4);   \
     }                                           \
-    TEST(median0, Ti##_2D_odd)                  \
+    TEST(Median, Ti##_4D_##dim##_even)          \
     {                                           \
-        median0<To, Ti, false>(783, 100);       \
+        median_test<To, Ti, dim>(100, 25, 2, 2);\
     }                                           \
-    TEST(median0, Ti##_3D_odd)                  \
+    TEST(Median, Ti##_1D_##dim##_odd)           \
     {                                           \
-        median0<To, Ti, false>(783, 25, 4);     \
+        median_test<To, Ti, dim>(783);          \
     }                                           \
-    TEST(median0, Ti##_4D_odd)                  \
+    TEST(Median, Ti##_2D_##dim##_odd)           \
     {                                           \
-        median0<To, Ti, false>(783, 25, 2, 2);  \
+        median_test<To, Ti, dim>(783, 25);      \
     }                                           \
-    TEST(median0, Ti##_flat_odd)                \
+    TEST(Median, Ti##_3D_##dim##_odd)           \
     {                                           \
-        median0<To, Ti, true>(783);             \
+        median_test<To, Ti, dim>(123, 25, 3);   \
     }                                           \
+    TEST(Median, Ti##_4D_##dim##_odd)           \
+    {                                           \
+        median_test<To, Ti, dim>(123, 25, 3, 3);\
+    }                                           \
+
 
+#define MEDIAN(To, Ti)      \
+    MEDIAN_TEST(To, Ti, 0)  \
+    MEDIAN_TEST(To, Ti, 1)  \
+    MEDIAN_TEST(To, Ti, 2)  \
+    MEDIAN_TEST(To, Ti, 3)  \
 
-MEDIAN0(float, float)
-MEDIAN0(float, int)
-MEDIAN0(float, uint)
-MEDIAN0(float, uchar)
-MEDIAN0(float, short)
-MEDIAN0(float, ushort)
-MEDIAN0(double, double)
+MEDIAN(float, float)
+MEDIAN(float, int)
+MEDIAN(float, uint)
+MEDIAN(float, uchar)
+MEDIAN(float, short)
+MEDIAN(float, ushort)
+MEDIAN(double, double)
diff --git a/test/orb.cpp b/test/orb.cpp
index 1266f20eb6..28e56a1132 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -76,18 +76,6 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
     }
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
-{
-    feat.resize(nfeat);
-    for (unsigned i = 0; i < feat.size(); i++) {
-        feat[i].f[0] = x[i];
-        feat[i].f[1] = y[i];
-        feat[i].f[2] = score[i];
-        feat[i].f[3] = ori[i];
-        feat[i].f[4] = size[i];
-    }
-}
-
 static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index 6776c18a86..f6dca7ba16 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -76,18 +76,6 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
     }
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *ori, float *size, unsigned nfeat)
-{
-    feat.resize(nfeat);
-    for (unsigned i = 0; i < feat.size(); i++) {
-        feat[i].f[0] = x[i];
-        feat[i].f[1] = y[i];
-        feat[i].f[2] = score[i];
-        feat[i].f[3] = ori[i];
-        feat[i].f[4] = size[i];
-    }
-}
-
 static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
 {
     f.resize(fd.size());
@@ -103,16 +91,6 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
     }
 }
 
-static unsigned popcount(unsigned x)
-{
-    x = x - ((x >> 1) & 0x55555555);
-    x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
-    x = (x + (x >> 4)) & 0x0F0F0F0F;
-    x = x + (x >> 8);
-    x = x + (x >> 16);
-    return x & 0x0000003F;
-}
-
 static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
 {
     bool ret = true;
diff --git a/test/sort.cpp b/test/sort.cpp
index 7ec6f5565e..9a496f3236 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -106,16 +106,16 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
     SORT_INIT(Sort1000False,  sort_1000,  false, 2);
     SORT_INIT(SortMedTrue,    sort_med1,  true,  0);
     SORT_INIT(SortMedFalse,   sort_med1,  false, 2);
-    // Takes too much time in current implementation. Enable when everything is parallel
-    //SORT_INIT(SortMed5True,   sort_med,   true,  0);
-    //SORT_INIT(SortMed5False,  sort_med,   false, 2);
-    //SORT_INIT(SortLargeTrue,  sort_large, true,  0);
-    //SORT_INIT(SortLargeFalse, sort_large, false, 2);
+
+    SORT_INIT(SortMed5True,   sort_med,   true,  0);
+    SORT_INIT(SortMed5False,  sort_med,   false, 2);
+    SORT_INIT(SortLargeTrue,  sort_large, true,  0);
+    SORT_INIT(SortLargeFalse, sort_large, false, 2);
 
 
 ////////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(Sort, CPP)
+TEST(Sort, CPPDim0)
 {
     if (noDoubleTests<float>()) return;
 
@@ -147,3 +147,74 @@ TEST(Sort, CPP)
     delete[] sxData;
 }
 
+TEST(Sort, CPPDim1)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = true;
+    const unsigned resultIdx0 = 0;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input(idims, &(in[0].front()));
+
+    af::array input_ = reorder(input, 1, 0, 2, 3);
+
+    af::array output = af::sort(input_, 1, dir);
+
+    output = reorder(output, 1, 0, 2, 3); // Required for checking with test data
+
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    output.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+}
+
+TEST(Sort, CPPDim2)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = false;
+    const unsigned resultIdx0 = 2;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input(idims, &(in[0].front()));
+
+    af::array input_ = reorder(input, 1, 2, 0, 3);
+
+    af::array output = af::sort(input_, 2, dir);
+
+    output = reorder(output, 2, 0, 1, 3); // Required for checking with test data
+
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    output.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+}
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index ed827c9da5..dae46bef54 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -118,16 +118,16 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(SortMedTrue,    sort_by_key_med,   true,  0, 1);
     SORT_INIT(Sort1000False,  sort_by_key_1000,  false, 2, 3);
     SORT_INIT(SortMedFalse,   sort_by_key_med,   false, 2, 3);
-    // Takes too much time in current implementation. Enable when everything is parallel
-    //SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
-    //SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
+
+    SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
+    SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
 
 
 
 
 ////////////////////////////////////// CPP ///////////////////////////////
 //
-TEST(SortByKey, CPP)
+TEST(SortByKey, CPPDim0)
 {
     if (noDoubleTests<float>()) return;
 
@@ -168,3 +168,101 @@ TEST(SortByKey, CPP)
     delete[] keyData;
     delete[] valData;
 }
+
+TEST(SortByKey, CPPDim1)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = true;
+    const unsigned resultIdx0 = 0;
+    const unsigned resultIdx1 = 1;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array keys(idims, &(in[0].front()));
+    af::array vals(idims, &(in[1].front()));
+
+    af::array keys_ = reorder(keys, 1, 0, 2, 3);
+    af::array vals_ = reorder(vals, 1, 0, 2, 3);
+
+    af::array out_keys, out_vals;
+    af::sort(out_keys, out_vals, keys_, vals_, 1, dir);
+
+    out_keys = reorder(out_keys, 1, 0, 2, 3);
+    out_vals = reorder(out_vals, 1, 0, 2, 3);
+
+    size_t nElems = tests[resultIdx0].size();
+    // Get result
+    float* keyData = new float[tests[resultIdx0].size()];
+    out_keys.host((void*)keyData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    float* valData = new float[tests[resultIdx1].size()];
+    out_vals.host((void*)valData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] keyData;
+    delete[] valData;
+}
+
+TEST(SortByKey, CPPDim2)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = false;
+    const unsigned resultIdx0 = 2;
+    const unsigned resultIdx1 = 3;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array keys(idims, &(in[0].front()));
+    af::array vals(idims, &(in[1].front()));
+
+    af::array keys_ = reorder(keys, 1, 2, 0, 3);
+    af::array vals_ = reorder(vals, 1, 2, 0, 3);
+
+    af::array out_keys, out_vals;
+    af::sort(out_keys, out_vals, keys_, vals_, 2, dir);
+
+    out_keys = reorder(out_keys, 2, 0, 1, 3);
+    out_vals = reorder(out_vals, 2, 0, 1, 3);
+
+    size_t nElems = tests[resultIdx0].size();
+    // Get result
+    float* keyData = new float[tests[resultIdx0].size()];
+    out_keys.host((void*)keyData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    float* valData = new float[tests[resultIdx1].size()];
+    out_vals.host((void*)valData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] keyData;
+    delete[] valData;
+}
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 6aa240d5a5..0df4744c02 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -119,17 +119,16 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     SORT_INIT(SortMedTrue,    sort_med1,  true,  0, 1);
     SORT_INIT(Sort1000False,  sort_1000,  false, 2, 3);
     SORT_INIT(SortMedFalse,   sort_med1,  false, 2, 3);
-    // Takes too much time in current implementation. Enable when everything is parallel
-    //SORT_INIT(SortMed5True,   sort_med,   true,  0, 1);
-    //SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);
-    //SORT_INIT(SortLargeTrue,  sort_large, true,  0, 1);
-    //SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
-;
+
+    SORT_INIT(SortMed5True,   sort_med,   true,  0, 1);
+    SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);
+    SORT_INIT(SortLargeTrue,  sort_large, true,  0, 1);
+    SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
 
 
 //////////////////////////////////// CPP /////////////////////////////////
 //
-TEST(SortIndex, CPP)
+TEST(SortIndex, CPPDim0)
 {
     if (noDoubleTests<float>()) return;
 
@@ -171,3 +170,98 @@ TEST(SortIndex, CPP)
     delete[] sxData;
     delete[] ixData;
 }
+
+TEST(SortIndex, CPPDim1)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = true;
+    const unsigned resultIdx0 = 0;
+    const unsigned resultIdx1 = 1;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input_(idims, &(in[0].front()));
+    af::array input = reorder(input_, 1, 0, 2, 3);
+
+    af::array outValues, outIndices;
+    af::sort(outValues, outIndices, input, 1, dir);
+
+    outValues  = reorder(outValues,  1, 0, 2, 3);
+    outIndices = reorder(outIndices, 1, 0, 2, 3);
+
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    outValues.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Get result
+    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
+    outIndices.host((void*)ixData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+    delete[] ixData;
+}
+
+TEST(SortIndex, CPPDim2)
+{
+    if (noDoubleTests<float>()) return;
+
+    const bool dir = false;
+    const unsigned resultIdx0 = 2;
+    const unsigned resultIdx1 = 3;
+
+    vector<af::dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::array input_(idims, &(in[0].front()));
+    af::array input = reorder(input_, 1, 2, 0, 3);
+
+    af::array outValues, outIndices;
+    af::sort(outValues, outIndices, input, 2, dir);
+
+    outValues  = reorder(outValues,  2, 0, 1, 3);
+    outIndices = reorder(outIndices, 2, 0, 1, 3);
+    size_t nElems = tests[resultIdx0].size();
+
+    // Get result
+    float* sxData = new float[tests[resultIdx0].size()];
+    outValues.host((void*)sxData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Get result
+    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
+    outIndices.host((void*)ixData);
+
+    // Compare result
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] sxData;
+    delete[] ixData;
+}
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 7f1ac4e893..dc8598121e 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -47,7 +47,7 @@ void transformCoordinatesTest(string pTestFile)
     af_array outArray = 0;
     ASSERT_EQ(AF_SUCCESS, af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)af::dtype_traits<T>::af_type));
 
-    size_t nTests = in.size();
+    int nTests = in.size();
 
     for (int test = 1; test < nTests; test++) {
         dim_t d0 = (dim_t)in[test][0];
@@ -63,7 +63,7 @@ void transformCoordinatesTest(string pTestFile)
 
         const float thr = 1.f;
 
-        for (size_t elIter = 0; elIter < outEl; elIter++) {
+        for (dim_t elIter = 0; elIter < outEl; elIter++) {
             ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << std::endl;
         }
 

From a9ab3645ea3dbcb477c860bcd0650fa54be0116a Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Sat, 7 May 2016 00:25:45 -0400
Subject: [PATCH 0511/2677] Removed unnecessary comments

---
 src/backend/cuda/kernel/scan_first.hpp | 32 --------------------------
 1 file changed, 32 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 937f7053fc..596f6c32b7 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -184,38 +184,6 @@ namespace kernel
                 out, tmp, in, blocks_x, blocks_y, lim); break;
         }
 
-        //if (inclusive_scan) {
-        //    switch (threads_x) {
-        //    case 32:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32, true>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    case 64:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64, true>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    case 128:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128, true>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    case 256:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256, true>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    }
-        //} else {
-        //    switch (threads_x) {
-        //    case 32:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32, false>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    case 64:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64, false>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    case 128:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128, false>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    case 256:
-        //        CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256, false>), blocks, threads,
-        //            out, tmp, in, blocks_x, blocks_y, lim); break;
-        //    }
-        //}
-
         POST_LAUNCH_CHECK();
     }
 

From 9bb1f9c6b50ffe63bd93a8700c5002616bfc53fc Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Sat, 7 May 2016 00:29:26 -0400
Subject: [PATCH 0512/2677] Removed unnecessary comments

---
 src/api/c/scan.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index ebd9d410cc..743c61f635 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -115,7 +115,6 @@ af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
         case s16:  res = scan_op<short  , int    >(in, dim, op, inclusive_scan); break;
         case u8:   res = scan_op<uchar  , uint   >(in, dim, op, inclusive_scan); break;
         case b8:   res = scan_op<char   , uint   >(in, dim, op, inclusive_scan); break;
-        // Make sure you are adding only "1" for every non zero value, even if op == af_add_t
         default:
             TYPE_ERROR(1, type);
         }

From 7a4675196c62e979270e9d9b88e3da25847cd6a9 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 7 May 2016 20:34:47 -0400
Subject: [PATCH 0513/2677] Fixes to build arrayfire with gcc 6.1.1

- CUDA backend being forced to use c++98
---
 src/api/cpp/features.cpp          | 5 +++--
 src/api/cpp/graphics.cpp          | 5 ++++-
 src/backend/cuda/CMakeLists.txt   | 9 ++++++++-
 src/backend/opencl/Array.hpp      | 3 ++-
 src/backend/opencl/CMakeLists.txt | 7 ++++++-
 src/backend/opencl/fft.cpp        | 3 ++-
 6 files changed, 25 insertions(+), 7 deletions(-)

diff --git a/src/api/cpp/features.cpp b/src/api/cpp/features.cpp
index 9cf23699b0..d9001ba412 100644
--- a/src/api/cpp/features.cpp
+++ b/src/api/cpp/features.cpp
@@ -39,8 +39,9 @@ namespace af
 
     features::~features()
     {
-        if(AF_SUCCESS != af_release_features(feat)) {
-            fprintf(stderr, "Error: Couldn't release af::features: %p\n", this);
+        // THOU SHALL NOT THROW IN DESTRUCTORS
+        if (feat) {
+            af_release_features(feat);
         }
     }
 
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 8b53825c25..01d8c614d0 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -44,7 +44,10 @@ Window::Window(const af_window window)
 
 Window::~Window()
 {
-    AF_THROW(af_destroy_window(wnd));
+    // THOU SHALL NOT THROW IN DESTRUCTORS
+    if (wnd) {
+        af_destroy_window(wnd);
+    }
 }
 
 void Window::setPos(const unsigned x, const unsigned y)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 5a6f588741..d324babfa5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -64,7 +64,14 @@ IF(UNIX)
     # GCC 5.3 and above give errors for mempcy from <string.h>
     # This is a (temporary) fix for that
     IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
-        ADD_DEFINITIONS(-D_FORCE_INLINES)
+      ADD_DEFINITIONS(-D_FORCE_INLINES)
+    ENDIF()
+
+    # GCC 6.0 and above default to g++14, enabling c++11 features by default
+    # Enabling c++11 with nvcc 7.5 + gcc 6.x doesn't seem to work
+    # Only solution for now is to force use c++03 for gcc 6.x
+    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
+      SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -Xcompiler -std=c++98")
     ENDIF()
 
     # Forcing STRICT ANSI should resolve a bunch of issues that NVIDIA seems to face with GCC compilers.
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 5683546367..feb3e2e0fa 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -231,9 +231,10 @@ namespace opencl
         {
             auto func = [=] (void* ptr) {
                 try {
-                    if(ptr != nullptr)
+                    if(ptr != nullptr) {
                         getQueue().enqueueUnmapMemObject(*data, ptr);
                         ptr = nullptr;
+                    }
                 } catch(cl::Error err) {
                     CL_TO_AF_ERROR(err);
                 }
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 9e4918a5ff..0e29c7b9d6 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -233,7 +233,12 @@ CL_KERNEL_TO_H(
 
 # OS Definitions
 IF(UNIX)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
+  SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
+    # GCC 6.0 and above enable -Wignored-attributes by default causing a lot of warnings
+    # Disable the trigger for gcc >= 6.0.0
+    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
+        ADD_DEFINITIONS(-Wno-ignored-attributes)
+    ENDIF()
 ENDIF()
 
 INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index bd337a43f0..cd3a5c22f5 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -52,7 +52,8 @@ class clFFTPlanner
             #ifndef OS_WIN
             static bool flag = true;
             if(flag) {
-                CLFFT_CHECK(clfftTeardown());
+                // THOU SHALL NOT THROW IN DESTRUCTORS
+                clfftTeardown();
                 flag = false;
             }
             #endif

From 935e7823de3fb8733c474a2ddd602bcafd159930 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 24 Mar 2016 15:21:24 -0400
Subject: [PATCH 0514/2677] Rename ConvolveBatchKind to a more general
 AF_BATCH_KIND

* Moved to defines.hpp
---
 src/api/c/convolve.cpp                        | 23 +++++++-------
 src/api/c/convolve_common.hpp                 | 19 ------------
 src/api/c/dog.cpp                             |  2 +-
 src/api/c/fftconvolve.cpp                     | 23 +++++++-------
 src/backend/cpu/convolve.cpp                  | 20 ++++++-------
 src/backend/cpu/convolve.hpp                  |  3 +-
 src/backend/cpu/fftconvolve.cpp               | 13 ++++----
 src/backend/cpu/fftconvolve.hpp               |  3 +-
 src/backend/cpu/iir.cpp                       |  4 +--
 src/backend/cpu/kernel/convolve.hpp           |  8 ++---
 src/backend/cpu/kernel/fftconvolve.hpp        | 19 ++++++------
 src/backend/cuda/convolve.cpp                 | 18 +++++------
 src/backend/cuda/convolve.hpp                 |  3 +-
 src/backend/cuda/fftconvolve.cu               | 14 ++++-----
 src/backend/cuda/fftconvolve.hpp              |  3 +-
 src/backend/cuda/iir.cu                       |  4 +--
 src/backend/cuda/kernel/convolve.cu           | 20 ++++++-------
 src/backend/cuda/kernel/convolve.hpp          |  2 +-
 src/backend/cuda/kernel/fftconvolve.hpp       | 30 +++++++++----------
 src/backend/cuda/kernel/harris.hpp            |  1 -
 src/backend/cuda/kernel/orb.hpp               |  1 -
 src/backend/cuda/kernel/sift_nonfree.hpp      |  1 -
 src/backend/defines.hpp                       |  8 +++++
 src/backend/opencl/convolve.cpp               | 18 +++++------
 src/backend/opencl/convolve.hpp               |  3 +-
 src/backend/opencl/fftconvolve.cpp            | 14 ++++-----
 src/backend/opencl/fftconvolve.hpp            |  3 +-
 src/backend/opencl/iir.cpp                    |  4 +--
 src/backend/opencl/kernel/convolve.hpp        |  8 ++---
 .../opencl/kernel/convolve/conv_common.hpp    |  1 -
 src/backend/opencl/kernel/fftconvolve.hpp     | 20 ++++++-------
 .../opencl/kernel/fftconvolve_multiply.cl     |  6 ++--
 src/backend/opencl/kernel/harris.hpp          |  1 -
 src/backend/opencl/kernel/orb.hpp             |  1 -
 src/backend/opencl/kernel/sift_nonfree.hpp    |  1 -
 35 files changed, 147 insertions(+), 175 deletions(-)
 delete mode 100644 src/api/c/convolve_common.hpp

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 750552db88..fa6d5831f4 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -14,7 +14,6 @@
 #include <backend.hpp>
 #include <convolve.hpp>
 #include <fftconvolve.hpp>
-#include <convolve_common.hpp>
 
 #include <cstdio>
 
@@ -22,7 +21,7 @@ using af::dim4;
 using namespace detail;
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-inline static af_array convolve(const af_array &s, const af_array &f, ConvolveBatchKind kind)
+inline static af_array convolve(const af_array &s, const af_array &f, AF_BATCH_KIND kind)
 {
     return getHandle(convolve<T, accT, baseDim, expand>(getArray<T>(s), castArray<accT>(f), kind));
 }
@@ -36,17 +35,17 @@ inline static af_array convolve2(const af_array &s, const af_array &c_f, const a
 }
 
 template<dim_t baseDim>
-ConvolveBatchKind identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
+AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
 {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
     if (sn==baseDim && fn==baseDim)
-        return CONVOLVE_BATCH_NONE;
+        return AF_BATCH_NONE;
     else if (sn==baseDim && (fn>baseDim && fn<=4))
-        return CONVOLVE_BATCH_KERNEL;
+        return AF_BATCH_KERNEL;
     else if ((sn>baseDim && sn<=4) && fn==baseDim)
-        return CONVOLVE_BATCH_SIGNAL;
+        return AF_BATCH_SIGNAL;
     else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved = true;
@@ -54,11 +53,11 @@ ConvolveBatchKind identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
             isInterleaved &= (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
         }
-        if (doesDimensionsMatch) return CONVOLVE_BATCH_SAME;
-        return (isInterleaved ? CONVOLVE_BATCH_DIFF : CONVOLVE_BATCH_UNSUPPORTED);
+        if (doesDimensionsMatch) return AF_BATCH_SAME;
+        return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
     }
     else
-        return CONVOLVE_BATCH_UNSUPPORTED;
+        return AF_BATCH_UNSUPPORTED;
 }
 
 template<dim_t baseDim, bool expand>
@@ -73,9 +72,9 @@ af_err convolve(af_array *out, const af_array signal, const af_array filter)
         dim4 sdims = sInfo.dims();
         dim4 fdims = fInfo.dims();
 
-        ConvolveBatchKind convBT = identifyBatchKind<baseDim>(sdims, fdims);
+        AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
 
-        ARG_ASSERT(1, (convBT != CONVOLVE_BATCH_UNSUPPORTED && convBT != CONVOLVE_BATCH_DIFF));
+        ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED && convBT != AF_BATCH_DIFF));
 
         af_array output;
         switch(stype) {
@@ -152,7 +151,7 @@ bool isFreqDomain(const af_array &signal, const af_array filter, af_conv_domain
     dim4 sdims = sInfo.dims();
     dim4 fdims = fInfo.dims();
 
-    if (identifyBatchKind<baseDim>(sdims, fdims) == CONVOLVE_BATCH_DIFF) return true;
+    if (identifyBatchKind<baseDim>(sdims, fdims) == AF_BATCH_DIFF) return true;
 
     int kbatch = 1;
     for(int i = 3; i >= baseDim; i--) {
diff --git a/src/api/c/convolve_common.hpp b/src/api/c/convolve_common.hpp
deleted file mode 100644
index 2fb4445a49..0000000000
--- a/src/api/c/convolve_common.hpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-typedef enum {
-    CONVOLVE_BATCH_UNSUPPORTED = -1, /* invalid inputs */
-    CONVOLVE_BATCH_NONE,          /* one signal, one filter   */
-    CONVOLVE_BATCH_SIGNAL,        /* many signal, one filter  */
-    CONVOLVE_BATCH_KERNEL,        /* one signal, many filter  */
-    CONVOLVE_BATCH_SAME,          /* signal and filter have same batch size */
-    CONVOLVE_BATCH_DIFF,          /* signal and filter have different batch size */
-} ConvolveBatchKind;
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index 3cf793cca5..ffe7d4e178 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -31,7 +31,7 @@ static af_array dog(const af_array& in, const int radius1, const int radius2)
     Array<accT> input  = castArray<accT>(in);
     dim4 iDims      = input.dims();
 
-    ConvolveBatchKind bkind = iDims[2] > 1 ? CONVOLVE_BATCH_SIGNAL : CONVOLVE_BATCH_NONE;
+    AF_BATCH_KIND bkind = iDims[2] > 1 ? AF_BATCH_SIGNAL : AF_BATCH_NONE;
 
     Array<accT> smth1 = convolve<accT, accT, 2, false>(input, castArray<accT>(g1), bkind);
     Array<accT> smth2 = convolve<accT, accT, 2, false>(input, castArray<accT>(g2), bkind);
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index a7401058f0..45b1da94ee 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -14,7 +14,6 @@
 #include <backend.hpp>
 #include <arith.hpp>
 #include <fftconvolve.hpp>
-#include <convolve_common.hpp>
 #include <dispatch.hpp>
 #include <complex.hpp>
 #include <fft_common.hpp>
@@ -89,24 +88,24 @@ af_array fftconvolve_fallback(const af_array signal, const af_array filter, bool
 }
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-inline static af_array fftconvolve(const af_array &s, const af_array &f, const bool expand, ConvolveBatchKind kind)
+inline static af_array fftconvolve(const af_array &s, const af_array &f, const bool expand, AF_BATCH_KIND kind)
 {
-    if (kind == CONVOLVE_BATCH_DIFF) return fftconvolve_fallback<T, convT, cT, baseDim>(s, f, expand);
+    if (kind == AF_BATCH_DIFF) return fftconvolve_fallback<T, convT, cT, baseDim>(s, f, expand);
     else return getHandle(fftconvolve<T, convT, cT, isDouble, roundOut, baseDim>(getArray<T>(s), castArray<T>(f), expand, kind));
 }
 
 template<dim_t baseDim>
-ConvolveBatchKind identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
+AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
 {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
     if (sn==baseDim && fn==baseDim)
-        return CONVOLVE_BATCH_NONE;
+        return AF_BATCH_NONE;
     else if (sn==baseDim && (fn>baseDim && fn<=4))
-        return CONVOLVE_BATCH_KERNEL;
+        return AF_BATCH_KERNEL;
     else if ((sn>baseDim && sn<=4) && fn==baseDim)
-        return CONVOLVE_BATCH_SIGNAL;
+        return AF_BATCH_SIGNAL;
     else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
        bool doesDimensionsMatch = true;
         bool isInterleaved = true;
@@ -114,11 +113,11 @@ ConvolveBatchKind identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
             isInterleaved &= (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
         }
-        if (doesDimensionsMatch) return CONVOLVE_BATCH_SAME;
-        return (isInterleaved ? CONVOLVE_BATCH_DIFF : CONVOLVE_BATCH_UNSUPPORTED);
+        if (doesDimensionsMatch) return AF_BATCH_SAME;
+        return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
     }
     else
-        return CONVOLVE_BATCH_UNSUPPORTED;
+        return AF_BATCH_UNSUPPORTED;
 }
 
 template<dim_t baseDim>
@@ -133,9 +132,9 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
         dim4 sdims = sInfo.dims();
         dim4 fdims = fInfo.dims();
 
-        ConvolveBatchKind convBT = identifyBatchKind<baseDim>(sdims, fdims);
+        AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
 
-        ARG_ASSERT(1, (convBT != CONVOLVE_BATCH_UNSUPPORTED));
+        ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED));
 
         af_array output;
         switch(stype) {
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index e3e2486cd6..2941347ffc 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -22,7 +22,7 @@ namespace cpu
 {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind)
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
 {
     signal.eval();
     filter.eval();
@@ -33,7 +33,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==CONVOLVE_BATCH_NONE || kind==CONVOLVE_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -41,7 +41,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
         }
     } else {
         oDims = sDims;
-        if (kind==CONVOLVE_BATCH_KERNEL) {
+        if (kind==AF_BATCH_KERNEL) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -71,7 +71,7 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 
         dim_t cflen = (dim_t)cfDims.elements();
         dim_t rflen = (dim_t)rfDims.elements();
-        // separable convolve only does CONVOLVE_BATCH_NONE and standard batch(CONVOLVE_BATCH_SIGNAL)
+        // separable convolve only does AF_BATCH_NONE and standard batch(AF_BATCH_SIGNAL)
         tDims[0] += cflen - 1;
         oDims[0] += cflen - 1;
         oDims[1] += rflen - 1;
@@ -85,12 +85,12 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 }
 
 #define INSTANTIATE(T, accT)                                            \
-    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
+    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
     template Array<T> convolve2<T, accT, true >(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter); \
     template Array<T> convolve2<T, accT, false>(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
 
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index 95d7625c90..3b87843376 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -8,13 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace cpu
 {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind);
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index ef5830409e..8452f827b7 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -14,7 +14,6 @@
 #include <err_cpu.hpp>
 #include <fftw3.h>
 #include <copy.hpp>
-#include <convolve_common.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <kernel/fftconvolve.hpp>
@@ -24,7 +23,7 @@ namespace cpu
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, ConvolveBatchKind kind)
+                     const bool expand, AF_BATCH_KIND kind)
 {
     signal.eval();
     filter.eval();
@@ -193,7 +192,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==CONVOLVE_BATCH_NONE || kind==CONVOLVE_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
                 oDims[d] = sd[d]+fd[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sd[d]+fd[d]-1 : sd[d]);
@@ -201,7 +200,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         }
     } else {
         oDims = sd;
-        if (kind==CONVOLVE_BATCH_KERNEL) {
+        if (kind==AF_BATCH_KERNEL) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fd[i];
         }
@@ -218,11 +217,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
 #define INSTANTIATE(T, convT, cT, isDouble, roundOut)                                                   \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 1>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);    \
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 2>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);    \
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 3>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
 
 INSTANTIATE(double, double, cdouble, true , false)
 INSTANTIATE(float , float,  cfloat,  false, false)
diff --git a/src/backend/cpu/fftconvolve.hpp b/src/backend/cpu/fftconvolve.hpp
index db76b69c8a..f00d5d3468 100644
--- a/src/backend/cpu/fftconvolve.hpp
+++ b/src/backend/cpu/fftconvolve.hpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace cpu
 {
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
 
 }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index a835e8208d..79758c7641 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -27,9 +27,9 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
     a.eval();
     x.eval();
 
-    ConvolveBatchKind type = x.ndims() == 1 ? CONVOLVE_BATCH_NONE : CONVOLVE_BATCH_SAME;
+    AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
     if (x.ndims() != b.ndims()) {
-        type = (x.ndims() < b.ndims()) ? CONVOLVE_BATCH_KERNEL : CONVOLVE_BATCH_SIGNAL;
+        type = (x.ndims() < b.ndims()) ? AF_BATCH_KERNEL : AF_BATCH_SIGNAL;
     }
 
     // Extract the first N elements
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index af9b9cd3f6..27c0c8df9e 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -122,7 +122,7 @@ void one2one_3d(InT *optr, InT const * const iptr, AccT const * const fptr, af::
 }
 
 template<typename InT, typename AccT, dim_t baseDim, bool Expand>
-void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filter, ConvolveBatchKind kind)
+void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filter, AF_BATCH_KIND kind)
 {
     InT * optr = out.get();
     InT const * const iptr = signal.get();
@@ -143,18 +143,18 @@ void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filt
 
     for (dim_t i=1; i<4; ++i) {
         switch(kind) {
-            case CONVOLVE_BATCH_SIGNAL:
+            case AF_BATCH_SIGNAL:
                 out_step[i] = oStrides[i];
                 in_step[i]  = sStrides[i];
                 if (i>=baseDim) batch[i] = sDims[i];
                 break;
-            case CONVOLVE_BATCH_SAME:
+            case AF_BATCH_SAME:
                 out_step[i]  = oStrides[i];
                 in_step[i]   = sStrides[i];
                 filt_step[i] = fStrides[i];
                 if (i>=baseDim) batch[i] = sDims[i];
                 break;
-            case CONVOLVE_BATCH_KERNEL:
+            case AF_BATCH_KERNEL:
                 out_step[i]  = oStrides[i];
                 filt_step[i] = fStrides[i];
                 if (i>=baseDim) batch[i] = fDims[i];
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index ca192f5626..c78ecd062a 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace cpu
 {
@@ -88,14 +87,14 @@ void padArray(Array<To> out, const af::dim4 od, const af::dim4 os,
 template<typename T>
 void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
                      const af::dim4 fit_dims, const af::dim4 fit_strides,
-                     ConvolveBatchKind kind, const dim_t offset)
+                     AF_BATCH_KIND kind, const dim_t offset)
 {
-    T* out_ptr = packed.get() + (kind==CONVOLVE_BATCH_KERNEL? offset : 0);
+    T* out_ptr = packed.get() + (kind==AF_BATCH_KERNEL? offset : 0);
     T* in1_ptr = packed.get();
     T* in2_ptr = packed.get() + offset;
 
-    const af::dim4& od = (kind==CONVOLVE_BATCH_KERNEL ? fit_dims : sig_dims);
-    const af::dim4& os = (kind==CONVOLVE_BATCH_KERNEL ? fit_strides : sig_strides);
+    const af::dim4& od = (kind==AF_BATCH_KERNEL ? fit_dims : sig_dims);
+    const af::dim4& os = (kind==AF_BATCH_KERNEL ? fit_strides : sig_strides);
     const af::dim4& i1d = sig_dims;
     const af::dim4& i2d = fit_dims;
     const af::dim4& i1s = sig_strides;
@@ -105,7 +104,7 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
             for (int d1 = 0; d1 < (int)od[1]; d1++) {
                 for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
-                    if (kind == CONVOLVE_BATCH_NONE || kind == CONVOLVE_BATCH_SAME) {
+                    if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
                         // Complex multiply each signal to equivalent filter
                         const int ridx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
                         const int iidx = ridx + 1;
@@ -121,7 +120,7 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         out_ptr[ridx] = ac - bd;
                         out_ptr[iidx] = (a+b) * (c+d) - ac - bd;
                     }
-                    else if (kind == CONVOLVE_BATCH_SIGNAL) {
+                    else if (kind == AF_BATCH_SIGNAL) {
                         // Complex multiply all signals to filter
                         const int ridx1 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
                         const int iidx1 = ridx1 + 1;
@@ -139,7 +138,7 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         out_ptr[ridx1] = ac - bd;
                         out_ptr[iidx1] = (a+b) * (c+d) - ac - bd;
                     }
-                    else if (kind == CONVOLVE_BATCH_KERNEL) {
+                    else if (kind == AF_BATCH_KERNEL) {
                         // Complex multiply signal to all filters
                         const int ridx2 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
                         const int iidx2 = ridx2 + 1;
@@ -227,7 +226,7 @@ void reorder(Array<T> out, Array<convT> packed,
              const Array<T> filter, const dim_t sig_half_d0, const dim_t fftScale,
              const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
              const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
-             bool expand, ConvolveBatchKind kind)
+             bool expand, AF_BATCH_KIND kind)
 {
     T* out_ptr = out.get();
     const af::dim4 out_dims = out.dims();
@@ -240,7 +239,7 @@ void reorder(Array<T> out, Array<convT> packed,
     convT* filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
 
     // Reorder the output
-    if (kind == CONVOLVE_BATCH_KERNEL) {
+    if (kind == AF_BATCH_KERNEL) {
         reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
                 filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
                 filter_dims, sig_half_d0, baseDim, fftScale, expand);
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index a96e358371..fb274c3e02 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -19,7 +19,7 @@ namespace cuda
 {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind)
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
 {
     const dim4 sDims    = signal.dims();
     const dim4 fDims    = filter.dims();
@@ -27,7 +27,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==CONVOLVE_BATCH_NONE || kind==CONVOLVE_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -35,7 +35,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
         }
     } else {
         oDims = sDims;
-        if (kind==CONVOLVE_BATCH_KERNEL) {
+        if (kind==AF_BATCH_KERNEL) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -77,12 +77,12 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 }
 
 #define INSTANTIATE(T, accT)                                            \
-    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
+    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
     template Array<T> convolve2<T, accT, true >(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter); \
     template Array<T> convolve2<T, accT, false>(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
 
diff --git a/src/backend/cuda/convolve.hpp b/src/backend/cuda/convolve.hpp
index 513745736d..6ee841bfb5 100644
--- a/src/backend/cuda/convolve.hpp
+++ b/src/backend/cuda/convolve.hpp
@@ -8,13 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace cuda
 {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind);
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cu
index 74c8bb088d..7d3549de56 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cu
@@ -47,7 +47,7 @@ static const dim4 calcPackedSize(Array<T> const& i1,
 }
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind)
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind)
 {
     const dim4 sDims = signal.dims();
     const dim4 fDims = filter.dims();
@@ -55,7 +55,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==CONVOLVE_BATCH_NONE || kind==CONVOLVE_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -63,7 +63,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
         }
     } else {
         oDims = sDims;
-        if (kind==CONVOLVE_BATCH_KERNEL) {
+        if (kind==AF_BATCH_KERNEL) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -86,7 +86,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     else
         kernel::complexMultiplyHelper<T, cT>(out, signal_packed, filter_packed, signal, filter, kind);
 
-    if (kind == CONVOLVE_BATCH_KERNEL) {
+    if (kind == AF_BATCH_KERNEL) {
         fft_inplace<cT, baseDim, false>(filter_packed);
         if (expand)
             kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, filter_packed, signal, filter, kind);
@@ -105,11 +105,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
 
 #define INSTANTIATE(T, convT, cT, isDouble, roundOut)                                                   \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 1>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);    \
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 2>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);    \
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 3>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
 
 INSTANTIATE(double, double, cdouble, true , false)
 INSTANTIATE(float , float,  cfloat,  false, false)
diff --git a/src/backend/cuda/fftconvolve.hpp b/src/backend/cuda/fftconvolve.hpp
index 5eea28d376..66597c40df 100644
--- a/src/backend/cuda/fftconvolve.hpp
+++ b/src/backend/cuda/fftconvolve.hpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace cuda
 {
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
 
 }
diff --git a/src/backend/cuda/iir.cu b/src/backend/cuda/iir.cu
index c03c15f4aa..7b3a217659 100644
--- a/src/backend/cuda/iir.cu
+++ b/src/backend/cuda/iir.cu
@@ -24,9 +24,9 @@ namespace cuda
     Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
     {
 
-        ConvolveBatchKind type = x.ndims() == 1 ? CONVOLVE_BATCH_NONE : CONVOLVE_BATCH_SAME;
+        AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
         if (x.ndims() != b.ndims()) {
-            type = (x.ndims() < b.ndims()) ? CONVOLVE_BATCH_KERNEL : CONVOLVE_BATCH_SIGNAL;
+            type = (x.ndims() < b.ndims()) ? AF_BATCH_KERNEL : AF_BATCH_SIGNAL;
         }
 
         // Extract the first N elements
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index c01453c00c..9557b7a2e4 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -451,7 +451,7 @@ void convolve_3d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
 }
 
 template<typename T, typename aT, int baseDim, bool expand>
-void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, ConvolveBatchKind kind)
+void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, AF_BATCH_KIND kind)
 {
     bool callKernel = true;
 
@@ -470,9 +470,9 @@ void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, ConvolveBatchK
         param.o[i] = 0;
         param.s[i] = 0;
     }
-    param.launchMoreBlocks = kind==CONVOLVE_BATCH_SAME || kind==CONVOLVE_BATCH_KERNEL;
-    param.outHasNoOffset = kind==CONVOLVE_BATCH_SIGNAL || kind==CONVOLVE_BATCH_NONE;
-    param.inHasNoOffset  = kind!=CONVOLVE_BATCH_SAME;
+    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_KERNEL;
+    param.outHasNoOffset = kind==AF_BATCH_SIGNAL || kind==AF_BATCH_NONE;
+    param.inHasNoOffset  = kind!=AF_BATCH_SAME;
 
     switch(baseDim) {
         case 1: convolve_1d<T, aT, expand>(param, out, signal, filt); break;
@@ -484,12 +484,12 @@ void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, ConvolveBatchK
 }
 
 #define INSTANTIATE(T, aT)  \
-    template void convolve_nd<T, aT, 1, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, ConvolveBatchKind kind);\
-    template void convolve_nd<T, aT, 1, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, ConvolveBatchKind kind);\
-    template void convolve_nd<T, aT, 2, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, ConvolveBatchKind kind);\
-    template void convolve_nd<T, aT, 2, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, ConvolveBatchKind kind);\
-    template void convolve_nd<T, aT, 3, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, ConvolveBatchKind kind);\
-    template void convolve_nd<T, aT, 3, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, ConvolveBatchKind kind);\
+    template void convolve_nd<T, aT, 1, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
+    template void convolve_nd<T, aT, 1, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
+    template void convolve_nd<T, aT, 2, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
+    template void convolve_nd<T, aT, 2, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
+    template void convolve_nd<T, aT, 3, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
+    template void convolve_nd<T, aT, 3, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
 
 
 INSTANTIATE(cdouble, cdouble)
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 521a45cd1e..06bd314296 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -21,7 +21,7 @@ namespace kernel
 {
 
 template<typename T, typename accType, int baseDim, bool expand>
-void convolve_nd(Param<T> out, CParam<T> signal, CParam<accType> filter, ConvolveBatchKind kind);
+void convolve_nd(Param<T> out, CParam<T> signal, CParam<accType> filter, AF_BATCH_KIND kind);
 
 template<typename T, typename accType, int conv_dim, bool expand>
 void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter);
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 8684c1e643..f16c692ebf 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -130,7 +130,7 @@ __global__ void padArray(
     }
 }
 
-template<typename convT, ConvolveBatchKind kind>
+template<typename convT, AF_BATCH_KIND kind>
 __global__ void complexMultiply(
     Param<convT> out,
     Param<convT> in1,
@@ -142,7 +142,7 @@ __global__ void complexMultiply(
     if (t >= nelem)
         return;
 
-    if (kind == CONVOLVE_BATCH_NONE || kind == CONVOLVE_BATCH_SAME) {
+    if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
         // Complex multiply each signal to equivalent filter
         const int ridx = t;
 
@@ -152,7 +152,7 @@ __global__ void complexMultiply(
         out.ptr[ridx].x = c1.x*c2.x - c1.y*c2.y;
         out.ptr[ridx].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
     }
-    else if (kind == CONVOLVE_BATCH_SIGNAL) {
+    else if (kind == AF_BATCH_SIGNAL) {
         // Complex multiply all signals to filter
         const int ridx1 = t;
         const int ridx2 = t % (in2.strides[3] * in2.dims[3]);
@@ -163,7 +163,7 @@ __global__ void complexMultiply(
         out.ptr[ridx1].x = c1.x*c2.x - c1.y*c2.y;
         out.ptr[ridx1].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
     }
-    else if (kind == CONVOLVE_BATCH_KERNEL) {
+    else if (kind == AF_BATCH_KERNEL) {
         // Complex multiply signal to all filters
         const int ridx1 = t % (in1.strides[3] * in1.dims[3]);
         const int ridx2 = t;
@@ -294,7 +294,7 @@ void complexMultiplyHelper(Param<T> out,
                            Param<convT> filter_packed,
                            CParam<T> sig,
                            CParam<T> filter,
-                           ConvolveBatchKind kind)
+                           AF_BATCH_KIND kind)
 {
     int sig_packed_elem = 1;
     int filter_packed_elem = 1;
@@ -313,23 +313,23 @@ void complexMultiplyHelper(Param<T> out,
 
     // Multiply filter and signal FFT arrays
     switch(kind) {
-        case CONVOLVE_BATCH_NONE:
-            CUDA_LAUNCH((complexMultiply<convT, CONVOLVE_BATCH_NONE>), blocks, threads,
+        case AF_BATCH_NONE:
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_NONE>), blocks, threads,
                     sig_packed, sig_packed, filter_packed, mul_elem);
             break;
-        case CONVOLVE_BATCH_SIGNAL:
-            CUDA_LAUNCH((complexMultiply<convT, CONVOLVE_BATCH_SIGNAL>), blocks, threads,
+        case AF_BATCH_SIGNAL:
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_SIGNAL>), blocks, threads,
                         sig_packed, sig_packed, filter_packed, mul_elem);
             break;
-        case CONVOLVE_BATCH_KERNEL:
-            CUDA_LAUNCH((complexMultiply<convT, CONVOLVE_BATCH_KERNEL>), blocks, threads,
+        case AF_BATCH_KERNEL:
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_KERNEL>), blocks, threads,
                     filter_packed, sig_packed, filter_packed, mul_elem);
             break;
-        case CONVOLVE_BATCH_SAME:
-            CUDA_LAUNCH((complexMultiply<convT, CONVOLVE_BATCH_SAME>), blocks, threads,
+        case AF_BATCH_SAME:
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_SAME>), blocks, threads,
                     sig_packed, sig_packed, filter_packed, mul_elem);
             break;
-        case CONVOLVE_BATCH_UNSUPPORTED:
+        case AF_BATCH_UNSUPPORTED:
         default:
             break;
     }
@@ -341,7 +341,7 @@ void reorderOutputHelper(Param<T> out,
                          Param<convT> packed,
                          CParam<T> sig,
                          CParam<T> filter,
-                         ConvolveBatchKind kind)
+                         AF_BATCH_KIND kind)
 {
     dim_t *sd = sig.dims;
     int fftScale = 1;
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 4935c8e92a..f5a696a622 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -14,7 +14,6 @@
 #include <memory.hpp>
 
 #include "config.hpp"
-#include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "gradient.hpp"
 #include "sort_by_key.hpp"
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index f116c5962b..38b7ea9710 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -12,7 +12,6 @@
 #include <debug_cuda.hpp>
 #include <memory.hpp>
 
-#include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "orb_patch.hpp"
 #include "sort_by_key.hpp"
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index a8ed251f2f..54b2a715db 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -76,7 +76,6 @@
 #include <memory.hpp>
 #include "shared.hpp"
 
-#include <convolve_common.hpp>
 #include "convolve.hpp"
 #include "resize.hpp"
 
diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index 2ad71f3afd..3816da7e52 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -41,3 +41,11 @@ clipFilePath(std::string path, std::string str)
     #define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
 #endif
 
+typedef enum {
+    AF_BATCH_UNSUPPORTED = -1, /* invalid inputs */
+    AF_BATCH_NONE,             /* one signal, one filter   */
+    AF_BATCH_SIGNAL,           /* many signal, one filter  */
+    AF_BATCH_KERNEL,           /* one signal, many filter  */
+    AF_BATCH_SAME,             /* signal and filter have same batch size */
+    AF_BATCH_DIFF,             /* signal and filter have different batch size */
+} AF_BATCH_KIND;
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 2ed8e27637..00593e688b 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -19,7 +19,7 @@ namespace opencl
 {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind)
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
 {
     const dim4 sDims    = signal.dims();
     const dim4 fDims    = filter.dims();
@@ -27,7 +27,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==CONVOLVE_BATCH_NONE || kind==CONVOLVE_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -35,7 +35,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
         }
     } else {
         oDims = sDims;
-        if (kind==CONVOLVE_BATCH_KERNEL) {
+        if (kind==AF_BATCH_KERNEL) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -60,12 +60,12 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBat
 }
 
 #define INSTANTIATE(T, accT)                                            \
-    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
-    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind); \
+    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat ,  cfloat)
diff --git a/src/backend/opencl/convolve.hpp b/src/backend/opencl/convolve.hpp
index a4eace6c5f..285f848a5a 100644
--- a/src/backend/opencl/convolve.hpp
+++ b/src/backend/opencl/convolve.hpp
@@ -8,13 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace opencl
 {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, ConvolveBatchKind kind);
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 827a9802e6..5d4a102f11 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -49,7 +49,7 @@ static const dim4 calcPackedSize(Array<T> const& i1,
 }
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind)
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind)
 {
     const dim4 sDims = signal.dims();
     const dim4 fDims = filter.dims();
@@ -57,7 +57,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==CONVOLVE_BATCH_NONE || kind==CONVOLVE_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -65,7 +65,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
         }
     } else {
         oDims = sDims;
-        if (kind==CONVOLVE_BATCH_KERNEL) {
+        if (kind==AF_BATCH_KERNEL) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -81,7 +81,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     kernel::complexMultiplyHelper<cT, T, isDouble, convT>(packed, signal, filter, baseDim, kind);
 
     // Compute inverse FFT only on complex-multiplied data
-    if (kind == CONVOLVE_BATCH_KERNEL) {
+    if (kind == AF_BATCH_KERNEL) {
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim)
@@ -122,11 +122,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
 
 #define INSTANTIATE(T, convT, cT, isDouble, roundOut)                                                   \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 1>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);    \
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 2>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);    \
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
     template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 3>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);
+        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
 
 INSTANTIATE(double, double, cdouble, true , false)
 INSTANTIATE(float , float,  cfloat,  false, false)
diff --git a/src/backend/opencl/fftconvolve.hpp b/src/backend/opencl/fftconvolve.hpp
index ebaa504443..b32abf973f 100644
--- a/src/backend/opencl/fftconvolve.hpp
+++ b/src/backend/opencl/fftconvolve.hpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <convolve_common.hpp>
 
 namespace opencl
 {
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, ConvolveBatchKind kind);
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
 
 }
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 12d088fd2c..efc79f4752 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -25,9 +25,9 @@ namespace opencl
     {
         try {
 
-            ConvolveBatchKind type = x.ndims() == 1 ? CONVOLVE_BATCH_NONE : CONVOLVE_BATCH_SAME;
+            AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
             if (x.ndims() != b.ndims()) {
-                type = (x.ndims() < b.ndims()) ? CONVOLVE_BATCH_KERNEL : CONVOLVE_BATCH_SIGNAL;
+                type = (x.ndims() < b.ndims()) ? AF_BATCH_KERNEL : AF_BATCH_SIGNAL;
             }
 
             // Extract the first N elements
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index 6d1d7de7ee..4aefafb9d7 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -32,7 +32,7 @@ static const int MAX_CONV3_FILTER_LEN = 5;
  * written in corresponding conv[1|2|3].cpp files under the same folder.
  */
 template<typename T, typename accType, int baseDim, bool expand>
-void convolve_nd(Param out, const Param signal, const Param filter, ConvolveBatchKind kind)
+void convolve_nd(Param out, const Param signal, const Param filter, AF_BATCH_KIND kind)
 {
     conv_kparam_t param;
 
@@ -40,9 +40,9 @@ void convolve_nd(Param out, const Param signal, const Param filter, ConvolveBatc
         param.o[i] = 0;
         param.s[i] = 0;
     }
-    param.launchMoreBlocks = kind==CONVOLVE_BATCH_SAME || kind==CONVOLVE_BATCH_KERNEL;
-    param.outHasNoOffset = kind==CONVOLVE_BATCH_SIGNAL || kind==CONVOLVE_BATCH_NONE;
-    param.inHasNoOffset  = kind!=CONVOLVE_BATCH_SAME;
+    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_KERNEL;
+    param.outHasNoOffset = kind==AF_BATCH_SIGNAL || kind==AF_BATCH_NONE;
+    param.inHasNoOffset  = kind!=AF_BATCH_SAME;
 
     prepareKernelArgs<T>(param, out.info.dims, filter.info.dims, baseDim);
 
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 36bda38335..4c2cfa1c26 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -10,7 +10,6 @@
 #pragma once
 #include <af/defines.h>
 
-#include <convolve_common.hpp>
 #include <kernel_headers/convolve.hpp>
 
 #include <map>
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index bc65ee78d7..77ac836317 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -40,7 +40,7 @@ void calcParamSizes(Param& sig_tmp,
                     Param& sig,
                     Param& filter,
                     const int baseDim,
-                    ConvolveBatchKind kind)
+                    AF_BATCH_KIND kind)
 {
     sig_tmp.info.dims[0] = filter_tmp.info.dims[0] = packed.info.dims[0];
     sig_tmp.info.strides[0] = filter_tmp.info.strides[0] = 1;
@@ -63,7 +63,7 @@ void calcParamSizes(Param& sig_tmp,
     sig_tmp.data = packed.data;
     filter_tmp.data = packed.data;
 
-    if (kind == CONVOLVE_BATCH_KERNEL) {
+    if (kind == AF_BATCH_KERNEL) {
         filter_tmp.info.offset = 0;
         sig_tmp.info.offset = filter_tmp.info.strides[3] * filter_tmp.info.dims[3] * 2;
     }
@@ -78,7 +78,7 @@ void packDataHelper(Param packed,
                     Param sig,
                     Param filter,
                     const int baseDim,
-                    ConvolveBatchKind kind)
+                    AF_BATCH_KIND kind)
 {
     try {
         static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
@@ -158,7 +158,7 @@ void complexMultiplyHelper(Param packed,
                            Param sig,
                            Param filter,
                            const int baseDim,
-                           ConvolveBatchKind kind)
+                           AF_BATCH_KIND kind)
 {
     try {
         static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
@@ -171,10 +171,10 @@ void complexMultiplyHelper(Param packed,
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D CONVOLVE_BATCH_NONE=" << (int)CONVOLVE_BATCH_NONE
-                        << " -D CONVOLVE_BATCH_SIGNAL=" << (int)CONVOLVE_BATCH_SIGNAL
-                        << " -D CONVOLVE_BATCH_KERNEL=" << (int)CONVOLVE_BATCH_KERNEL
-                        << " -D CONVOLVE_BATCH_SAME=" << (int)CONVOLVE_BATCH_SAME;
+                        << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
+                        << " -D AF_BATCH_SIGNAL=" << (int)AF_BATCH_SIGNAL
+                        << " -D AF_BATCH_KERNEL=" << (int)AF_BATCH_KERNEL
+                        << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
 
                 if ((af_dtype) dtype_traits<convT>::af_type == c32) {
                     options << " -D CONVT=float";
@@ -231,7 +231,7 @@ void reorderOutputHelper(Param out,
                          Param sig,
                          Param filter,
                          const int baseDim,
-                         ConvolveBatchKind kind)
+                         AF_BATCH_KIND kind)
 {
     try {
         static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
@@ -287,7 +287,7 @@ void reorderOutputHelper(Param out,
                                 KParam, const int,
                                 const int, const int> (*roKernel[device]);
 
-        if (kind == CONVOLVE_BATCH_KERNEL) {
+        if (kind == AF_BATCH_KERNEL) {
             roOp(EnqueueArgs(getQueue(), global, local),
                  *out.data, out.info,
                  *filter_tmp.data, filter_tmp.info,
diff --git a/src/backend/opencl/kernel/fftconvolve_multiply.cl b/src/backend/opencl/kernel/fftconvolve_multiply.cl
index eae1e8259c..5eb540d72b 100644
--- a/src/backend/opencl/kernel/fftconvolve_multiply.cl
+++ b/src/backend/opencl/kernel/fftconvolve_multiply.cl
@@ -23,7 +23,7 @@ void complex_multiply(
     if (t >= nelem)
         return;
 
-    if (kind == CONVOLVE_BATCH_NONE || kind == CONVOLVE_BATCH_SAME) {
+    if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
         // Complex multiply each signal to equivalent filter
         const int ridx = t * 2;
         const int iidx = t * 2 + 1;
@@ -39,7 +39,7 @@ void complex_multiply(
         d_out[oInfo.offset + ridx] = ac - bd;
         d_out[oInfo.offset + iidx] = (a+b) * (c+d) - ac - bd;
     }
-    else if (kind == CONVOLVE_BATCH_SIGNAL) {
+    else if (kind == AF_BATCH_SIGNAL) {
         // Complex multiply all signals to filter
         const int ridx1 = t * 2;
         const int iidx1 = t * 2 + 1;
@@ -60,7 +60,7 @@ void complex_multiply(
         d_out[oInfo.offset + ridx1] = ac - bd;
         d_out[oInfo.offset + iidx1] = (a+b) * (c+d) - ac - bd;
     }
-    else if (kind == CONVOLVE_BATCH_KERNEL) {
+    else if (kind == AF_BATCH_KERNEL) {
         // Complex multiply signal to all filters
         const int ridx2 = t * 2;
         const int iidx2 = t * 2 + 1;
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 442275d326..7f25f9b03b 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -13,7 +13,6 @@
 #include <dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
-#include <convolve_common.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/gradient.hpp>
 #include <kernel/sort_by_key.hpp>
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 0c752d2c21..51fb2665a4 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -12,7 +12,6 @@
 #include <dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
-#include <convolve_common.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index 0b78ef02f3..b5dab785c7 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -88,7 +88,6 @@
 
 #pragma GCC diagnostic pop
 
-#include <convolve_common.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>

From 18fea469e4de5edd931625bca525d0e7a43cc633 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:10:10 -0400
Subject: [PATCH 0515/2677] PERF Use int instead of dim_t in unwrap CUDA kernel

---
 src/backend/cuda/kernel/unwrap.hpp | 58 +++++++++++++++---------------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 410d94f044..edceb3b61e 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -24,33 +24,33 @@ namespace cuda
         template<typename T, bool is_column>
         __global__
         void unwrap_kernel(Param<T> out, CParam<T> in,
-                           const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-                           const dim_t px, const dim_t py, const dim_t nx, dim_t reps)
+                           const int wx, const int wy, const int sx, const int sy,
+                           const int px, const int py, const int nx, int reps)
         {
             // Compute channel and volume
-            const dim_t w = blockIdx.y / in.dims[2];
-            const dim_t z = blockIdx.y % in.dims[2];
+            const int w = blockIdx.y / in.dims[2];
+            const int z = blockIdx.y % in.dims[2];
 
             if(w >= in.dims[3] || z >= in.dims[2])
                 return;
 
             // Compute offset for channel and volume
-            const dim_t cOut = w * out.strides[3] + z * out.strides[2];
-            const dim_t cIn  = w *  in.strides[3] + z *  in.strides[2];
+            const int cOut = w * out.strides[3] + z * out.strides[2];
+            const int cIn  = w *  in.strides[3] + z *  in.strides[2];
 
             // Compute the output column index
-            const dim_t id = is_column ?
+            const int id = is_column ?
                 (blockIdx.x * blockDim.y + threadIdx.y) :
                 (blockIdx.x * blockDim.x + threadIdx.x);
 
             if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
 
             // Compute the starting index of window in x and y of input
-            const dim_t startx = (id % nx) * sx;
-            const dim_t starty = (id / nx) * sy;
+            const int startx = (id % nx) * sx;
+            const int starty = (id / nx) * sy;
 
-            const dim_t spx = startx - px;
-            const dim_t spy = starty - py;
+            const int spx = startx - px;
+            const int spy = starty - py;
 
             // Offset the global pointers to the respective starting indices
             T* optr = out.ptr + cOut + id * (is_column ? out.strides[1] : 1);
@@ -61,7 +61,7 @@ namespace cuda
             for(int i = 0; i < reps; i++) {
 
                 // Compute output index local to column
-                const dim_t outIdx = is_column ?
+                const int outIdx = is_column ?
                     (i * blockDim.x + threadIdx.x) :
                     (i * blockDim.y + threadIdx.y);
 
@@ -69,16 +69,16 @@ namespace cuda
                     return;
 
                 // Compute input index local to window
-                const dim_t x = outIdx % wx;
-                const dim_t y = outIdx / wx;
+                const int x = outIdx % wx;
+                const int y = outIdx / wx;
 
-                const dim_t xpad = spx + x;
-                const dim_t ypad = spy + y;
+                const int xpad = spx + x;
+                const int ypad = spy + y;
 
                 // Copy
                 T val = scalar<T>(0.0);
                 if(cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 && ypad < in.dims[1])) {
-                    const dim_t inIdx = ypad * in.strides[1] + xpad;
+                    const int inIdx = ypad * in.strides[1] + xpad;
                     val = iptr[inIdx];
                 }
 
@@ -94,16 +94,16 @@ namespace cuda
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template <typename T>
-        void unwrap_col(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-                        const dim_t sx, const dim_t sy,
-                        const dim_t px, const dim_t py, const dim_t nx)
+        void unwrap_col(Param<T> out, CParam<T> in, const int wx, const int wy,
+                        const int sx, const int sy,
+                        const int px, const int py, const int nx)
         {
-            dim_t TX = std::min(THREADS_PER_BLOCK, nextpow2(out.dims[0]));
+            int TX = std::min(THREADS_PER_BLOCK, nextpow2(out.dims[0]));
 
             dim3 threads(TX, THREADS_PER_BLOCK / TX);
             dim3 blocks(divup(out.dims[1], threads.y), out.dims[2] * out.dims[3]);
 
-            dim_t reps = divup((wx * wy), threads.x); // is > 1 only when TX == 256 && wx * wy > 256
+            int reps = divup((wx * wy), threads.x); // is > 1 only when TX == 256 && wx * wy > 256
 
             CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads,
                         out, in, wx, wy, sx, sy, px, py, nx, reps);
@@ -112,14 +112,14 @@ namespace cuda
         }
 
         template<typename T>
-        void unwrap_row(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-                        const dim_t sx, const dim_t sy,
-                        const dim_t px, const dim_t py, const dim_t nx)
+        void unwrap_row(Param<T> out, CParam<T> in, const int wx, const int wy,
+                        const int sx, const int sy,
+                        const int px, const int py, const int nx)
         {
             dim3 threads(THREADS_X, THREADS_Y);
             dim3 blocks(divup(out.dims[0], threads.x), out.dims[2] * out.dims[3]);
 
-            dim_t reps = divup((wx * wy), threads.y);
+            int reps = divup((wx * wy), threads.y);
 
             CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads,
                         out, in, wx, wy, sx, sy, px, py, nx, reps);
@@ -128,9 +128,9 @@ namespace cuda
         }
 
         template <typename T>
-        void unwrap(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy,
-                    const dim_t px, const dim_t py, const dim_t nx, const bool is_column)
+        void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
+                    const int sx, const int sy,
+                    const int px, const int py, const int nx, const bool is_column)
         {
 
             if (is_column) {

From 9fc41586d743db3e8a683837c0e4b481a2121202 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:14:03 -0400
Subject: [PATCH 0516/2677] PERF Use int instead of dim_t in approx CUDA kernel

---
 src/backend/cuda/kernel/approx.hpp | 76 +++++++++++++++---------------
 1 file changed, 38 insertions(+), 38 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index b1437ba201..53831da975 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -27,13 +27,13 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         template<typename Ty, typename Tp>
         __device__ inline static
-        void core_nearest1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+        void core_nearest1(const int idx, const int idy, const int idz, const int idw,
                            Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
                            const float offGrid, const bool pBatch)
         {
-            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
                              + idy * out.strides[1] + idx;
-            dim_t pmId = idx;
+            int pmId = idx;
             if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
 
             const Tp x = pos.ptr[pmId];
@@ -42,8 +42,8 @@ namespace cuda
                 return;
             }
 
-            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
-            const dim_t iMem = round(x) + ioff;
+            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
+            const int iMem = round(x) + ioff;
 
             Ty yt = in.ptr[iMem];
             out.ptr[omId] = yt;
@@ -51,14 +51,14 @@ namespace cuda
 
         template<typename Ty, typename Tp>
         __device__ inline static
-        void core_nearest2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+        void core_nearest2(const int idx, const int idy, const int idz, const int idw,
                            Param<Ty> out, CParam<Ty> in,
                            CParam<Tp> pos, CParam<Tp> qos, const float offGrid, const bool pBatch)
         {
-            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
                              + idy * out.strides[1] + idx;
-            dim_t pmId = idy * pos.strides[1] + idx;
-            dim_t qmId = idy * qos.strides[1] + idx;
+            int pmId = idy * pos.strides[1] + idx;
+            int qmId = idy * qos.strides[1] + idx;
             if(pBatch) {
                 pmId += idw * pos.strides[3] + idz * pos.strides[2];
                 qmId += idw * qos.strides[3] + idz * qos.strides[2];
@@ -70,8 +70,8 @@ namespace cuda
                 return;
             }
 
-            const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
-            const dim_t imId = idw * in.strides[3] + idz * in.strides[2]
+            const int grid_x = round(x), grid_y = round(y); // nearest grid
+            const int imId = idw * in.strides[3] + idz * in.strides[2]
                           + grid_y * in.strides[1] + grid_x;
 
             Ty val = in.ptr[imId];
@@ -83,13 +83,13 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         template<typename Ty, typename Tp>
         __device__ inline static
-        void core_linear1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+        void core_linear1(const int idx, const int idy, const int idz, const int idw,
                           Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
                           const float offGrid, const bool pBatch)
         {
-            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
                              + idy * out.strides[1] + idx;
-            dim_t pmId = idx;
+            int pmId = idx;
             if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
 
             const Tp pVal = pos.ptr[pmId];
@@ -98,10 +98,10 @@ namespace cuda
                 return;
             }
 
-            const dim_t grid_x = floor(pVal);  // nearest grid
+            const int grid_x = floor(pVal);  // nearest grid
             const Tp off_x = pVal - grid_x; // fractional offset
 
-            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
+            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
 
             // Check if pVal and pVal + 1 are both valid indices
             bool cond = (pVal < in.dims[0] - 1);
@@ -117,14 +117,14 @@ namespace cuda
 
         template<typename Ty, typename Tp>
         __device__ inline static
-        void core_linear2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+        void core_linear2(const int idx, const int idy, const int idz, const int idw,
                            Param<Ty> out, CParam<Ty> in,
                            CParam<Tp> pos, CParam<Tp> qos, const float offGrid, const bool pBatch)
         {
-            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
                              + idy * out.strides[1] + idx;
-            dim_t pmId = idy * pos.strides[1] + idx;
-            dim_t qmId = idy * qos.strides[1] + idx;
+            int pmId = idy * pos.strides[1] + idx;
+            int qmId = idy * qos.strides[1] + idx;
             if(pBatch) {
                 pmId += idw * pos.strides[3] + idz * pos.strides[2];
                 qmId += idw * qos.strides[3] + idz * qos.strides[2];
@@ -137,10 +137,10 @@ namespace cuda
                 return;
             }
 
-            const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+            const int grid_x = floor(x),   grid_y = floor(y);   // nearest grid
             const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
 
-            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
+            int ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
 
             // Check if pVal and pVal + 1 are both valid indices
             bool condY = (y < in.dims[1] - 1);
@@ -172,14 +172,14 @@ namespace cuda
         template<typename Ty, typename Tp, af_interp_type method>
         __global__
         void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
-                            const float offGrid, const dim_t blocksMatX, const bool pBatch)
+                            const float offGrid, const int blocksMatX, const bool pBatch)
         {
-            const dim_t idw = blockIdx.y / out.dims[2];
-            const dim_t idz = blockIdx.y - idw * out.dims[2];
+            const int idw = blockIdx.y / out.dims[2];
+            const int idz = blockIdx.y - idw * out.dims[2];
 
-            const dim_t idy = blockIdx.x / blocksMatX;
-            const dim_t blockIdx_x = blockIdx.x - idy * blocksMatX;
-            const dim_t idx = blockIdx_x * blockDim.x + threadIdx.x;
+            const int idy = blockIdx.x / blocksMatX;
+            const int blockIdx_x = blockIdx.x - idy * blocksMatX;
+            const int idx = blockIdx_x * blockDim.x + threadIdx.x;
 
             if (idx >= out.dims[0] || idy >= out.dims[1] ||
                 idz >= out.dims[2] || idw >= out.dims[3])
@@ -201,16 +201,16 @@ namespace cuda
         __global__
         void approx2_kernel(Param<Ty> out, CParam<Ty> in,
                       CParam<Tp> pos, CParam<Tp> qos, const float offGrid,
-                      const dim_t blocksMatX, const dim_t blocksMatY, const bool pBatch)
+                      const int blocksMatX, const int blocksMatY, const bool pBatch)
         {
-            const dim_t idz = blockIdx.x / blocksMatX;
-            const dim_t idw = blockIdx.y / blocksMatY;
+            const int idz = blockIdx.x / blocksMatX;
+            const int idw = blockIdx.y / blocksMatY;
 
-            dim_t blockIdx_x = blockIdx.x - idz * blocksMatX;
-            dim_t blockIdx_y = blockIdx.y - idw * blocksMatY;
+            int blockIdx_x = blockIdx.x - idz * blocksMatX;
+            int blockIdx_y = blockIdx.y - idw * blocksMatY;
 
-            dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
-            dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
+            int idx = threadIdx.x + blockIdx_x * blockDim.x;
+            int idy = threadIdx.y + blockIdx_y * blockDim.y;
 
             if (idx >= out.dims[0] || idy >= out.dims[1] ||
                 idz >= out.dims[2] || idw >= out.dims[3])
@@ -236,7 +236,7 @@ namespace cuda
                CParam<Tp> pos, const float offGrid)
         {
             dim3 threads(THREADS, 1, 1);
-            dim_t blocksPerMat = divup(out.dims[0], threads.x);
+            int blocksPerMat = divup(out.dims[0], threads.x);
             dim3 blocks(blocksPerMat * out.dims[1], out.dims[2] * out.dims[3]);
 
             bool pBatch = !(pos.dims[1] == 1 && pos.dims[2] == 1 && pos.dims[3] == 1);
@@ -251,8 +251,8 @@ namespace cuda
                     CParam<Tp> pos, CParam<Tp> qos, const float offGrid)
         {
             dim3 threads(TX, TY, 1);
-            dim_t blocksPerMatX = divup(out.dims[0], threads.x);
-            dim_t blocksPerMatY = divup(out.dims[1], threads.y);
+            int blocksPerMatX = divup(out.dims[0], threads.x);
+            int blocksPerMatY = divup(out.dims[1], threads.y);
             dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3]);
 
             bool pBatch = !(pos.dims[2] == 1 && pos.dims[3] == 1);

From ab4f0f248ee6da1453ce7ccb8e9bbb57126793c2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:14:16 -0400
Subject: [PATCH 0517/2677] PERF Use int instead of dim_t in wrap CUDA kernel

---
 src/backend/cuda/kernel/wrap.hpp | 64 ++++++++++++++++----------------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index e02dcb2962..0004f79e8b 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -26,21 +26,21 @@ namespace cuda
         template<typename T, bool is_column>
         __global__
         void wrap_kernel(Param<T> out, CParam<T> in,
-                         const dim_t wx, const dim_t wy,
-                         const dim_t sx, const dim_t sy,
-                         const dim_t px, const dim_t py,
-                         const dim_t nx, const dim_t ny,
-                         dim_t blocks_x,
-                         dim_t blocks_y)
+                         const int wx, const int wy,
+                         const int sx, const int sy,
+                         const int px, const int py,
+                         const int nx, const int ny,
+                         int blocks_x,
+                         int blocks_y)
         {
-            dim_t idx2 = blockIdx.x / blocks_x;
-            dim_t idx3 = blockIdx.y / blocks_y;
+            int idx2 = blockIdx.x / blocks_x;
+            int idx3 = blockIdx.y / blocks_y;
 
-            dim_t blockIdx_x = blockIdx.x - idx2 * blocks_x;
-            dim_t blockIdx_y = blockIdx.y - idx3 * blocks_y;
+            int blockIdx_x = blockIdx.x - idx2 * blocks_x;
+            int blockIdx_y = blockIdx.y - idx3 * blocks_y;
 
-            dim_t oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
-            dim_t oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
+            int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
+            int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
 
                   T *optr = out.ptr + idx2 * out.strides[2] + idx3 * out.strides[3];
             const T *iptr =  in.ptr + idx2 *  in.strides[2] + idx3 *  in.strides[3];
@@ -48,30 +48,30 @@ namespace cuda
 
             if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1]) return;
 
-            dim_t pidx0 = oidx0 + px;
-            dim_t pidx1 = oidx1 + py;
+            int pidx0 = oidx0 + px;
+            int pidx1 = oidx1 + py;
 
             // The last time a value appears in the unwrapped index is padded_index / stride
             // Each previous index has the value appear "stride" locations earlier
             // We work our way back from the last index
 
-            const dim_t x_end = min(pidx0 / sx, nx - 1);
-            const dim_t y_end = min(pidx1 / sy, ny - 1);
+            const int x_end = min(pidx0 / sx, nx - 1);
+            const int y_end = min(pidx1 / sy, ny - 1);
 
-            const dim_t x_off = pidx0 - sx * x_end;
-            const dim_t y_off = pidx1 - sy * y_end;
+            const int x_off = pidx0 - sx * x_end;
+            const int y_off = pidx1 - sy * y_end;
 
             T val = scalar<T>(0);
-            dim_t idx = 1;
+            int idx = 1;
 
-            for (dim_t y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
-                dim_t win_end_y = yo * wx;
-                dim_t dim_end_y = y * nx;
+            for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
+                int win_end_y = yo * wx;
+                int dim_end_y = y * nx;
 
-                for (dim_t x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
+                for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
 
-                    dim_t win_end = win_end_y + xo;
-                    dim_t dim_end = dim_end_y + x;
+                    int win_end = win_end_y + xo;
+                    int dim_end = dim_end_y + x;
 
                     if (is_column) {
                         idx = dim_end * in.strides[1] + win_end;
@@ -87,17 +87,17 @@ namespace cuda
         }
 
         template <typename T>
-        void wrap(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
+        void wrap(Param<T> out, CParam<T> in, const int wx, const int wy,
+                  const int sx, const int sy,
+                  const int px, const int py,
                   const bool is_column)
         {
-            dim_t nx = (out.dims[0] + 2 * px - wx) / sx + 1;
-            dim_t ny = (out.dims[1] + 2 * py - wy) / sy + 1;
+            int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
+            int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
 
             dim3 threads(THREADS_X, THREADS_Y);
-            dim_t blocks_x = divup(out.dims[0], threads.x);
-            dim_t blocks_y = divup(out.dims[1], threads.y);
+            int blocks_x = divup(out.dims[0], threads.x);
+            int blocks_y = divup(out.dims[1], threads.y);
 
             dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 

From 85f3b0dcf83e23d6cbabf2f4b883da1b69f955fd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:22:51 -0400
Subject: [PATCH 0518/2677] PERF Use int instead of dim_t in approx1 OpenCL
 kernel

---
 src/backend/opencl/kernel/approx1.cl | 32 ++++++++++++++--------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index 5693fc3907..434570930d 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -32,15 +32,15 @@ Ty div(Ty a, Tp b) { a.x = a.x / b; a.y = a.y / b; return a; }
 ///////////////////////////////////////////////////////////////////////////
 // nearest-neighbor resampling
 ///////////////////////////////////////////////////////////////////////////
-void core_nearest1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+void core_nearest1(const int idx, const int idy, const int idz, const int idw,
                    __global       Ty *d_out, const KParam out,
                    __global const Ty *d_in,  const KParam in,
                    __global const Tp *d_pos, const KParam pos,
                    const float offGrid, const bool pBatch)
 {
-    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+    const int omId = idw * out.strides[3] + idz * out.strides[2]
                      + idy * out.strides[1] + idx;
-    dim_t pmId = idx;
+    int pmId = idx;
     if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
 
     const Tp pVal = d_pos[pmId];
@@ -49,8 +49,8 @@ void core_nearest1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_
         return;
     }
 
-    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
-    const dim_t imId = round(pVal) + ioff;
+    int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
+    const int imId = round(pVal) + ioff;
 
     Ty y;
     set(y, d_in[imId]);
@@ -60,15 +60,15 @@ void core_nearest1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_
 ///////////////////////////////////////////////////////////////////////////
 // linear resampling
 ///////////////////////////////////////////////////////////////////////////
-void core_linear1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+void core_linear1(const int idx, const int idy, const int idz, const int idw,
                    __global       Ty *d_out, const KParam out,
                    __global const Ty *d_in,  const KParam in,
                    __global const Tp *d_pos, const KParam pos,
                    const float offGrid, const bool pBatch)
 {
-    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+    const int omId = idw * out.strides[3] + idz * out.strides[2]
                      + idy * out.strides[1] + idx;
-    dim_t pmId = idx;
+    int pmId = idx;
     if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
 
     const Tp pVal = d_pos[pmId];
@@ -77,10 +77,10 @@ void core_linear1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t
         return;
     }
 
-    const dim_t grid_x = floor(pVal);  // nearest grid
+    const int grid_x = floor(pVal);  // nearest grid
     const Tp off_x = pVal - grid_x; // fractional offset
 
-    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
+    int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
 
     // Check if pVal and pVal + 1 are both valid indices
     bool cond = (pVal < in.dims[0] - 1);
@@ -106,14 +106,14 @@ __kernel
 void approx1_kernel(__global       Ty *d_out, const KParam out,
                     __global const Ty *d_in,  const KParam in,
                     __global const Tp *d_pos, const KParam pos,
-                    const float offGrid, const dim_t blocksMatX, const int pBatch)
+                    const float offGrid, const int blocksMatX, const int pBatch)
 {
-    const dim_t idw = get_group_id(1) / out.dims[2];
-    const dim_t idz = get_group_id(1)  - idw * out.dims[2];
+    const int idw = get_group_id(1) / out.dims[2];
+    const int idz = get_group_id(1)  - idw * out.dims[2];
 
-    const dim_t idy = get_group_id(0) / blocksMatX;
-    const dim_t blockIdx_x = get_group_id(0) - idy * blocksMatX;
-    const dim_t idx = get_local_id(0) + blockIdx_x * get_local_size(0);
+    const int idy = get_group_id(0) / blocksMatX;
+    const int blockIdx_x = get_group_id(0) - idy * blocksMatX;
+    const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
 
     if(idx >= out.dims[0] ||
        idy >= out.dims[1] ||

From 6d395c2764d5e33108eb043b259812c9e8b35976 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:23:03 -0400
Subject: [PATCH 0519/2677] PERF Use int instead of dim_t in approx2 OpenCL
 kernel

---
 src/backend/opencl/kernel/approx2.cl | 38 ++++++++++++++--------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 1066f55d41..eb719b1aeb 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -32,17 +32,17 @@ Ty div(Ty a, Tp b) { a.x = a.x / b; a.y = a.y / b; return a; }
 ///////////////////////////////////////////////////////////////////////////
 // nearest-neighbor resampling
 ///////////////////////////////////////////////////////////////////////////
-void core_nearest2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+void core_nearest2(const int idx, const int idy, const int idz, const int idw,
                    __global       Ty *d_out, const KParam out,
                    __global const Ty *d_in,  const KParam in,
                    __global const Tp *d_pos, const KParam pos,
                    __global const Tp *d_qos, const KParam qos,
                    const float offGrid, const bool pBatch)
 {
-    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+    const int omId = idw * out.strides[3] + idz * out.strides[2]
                      + idy * out.strides[1] + idx;
-    dim_t pmId = idy * pos.strides[1] + idx;
-    dim_t qmId = idy * qos.strides[1] + idx;
+    int pmId = idy * pos.strides[1] + idx;
+    int qmId = idy * qos.strides[1] + idx;
     if(pBatch) {
         pmId += idw * pos.strides[3] + idz * pos.strides[2];
         qmId += idw * qos.strides[3] + idz * qos.strides[2];
@@ -54,8 +54,8 @@ void core_nearest2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_
         return;
     }
 
-    const dim_t grid_x = round(x), grid_y = round(y); // nearest grid
-    const dim_t imId = idw * in.strides[3] + idz * in.strides[2]
+    const int grid_x = round(x), grid_y = round(y); // nearest grid
+    const int imId = idw * in.strides[3] + idz * in.strides[2]
                   + grid_y * in.strides[1] + grid_x;
 
     Ty z;
@@ -66,17 +66,17 @@ void core_nearest2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_
 ///////////////////////////////////////////////////////////////////////////
 // linear resampling
 ///////////////////////////////////////////////////////////////////////////
-void core_linear2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+void core_linear2(const int idx, const int idy, const int idz, const int idw,
                   __global       Ty *d_out, const KParam out,
                   __global const Ty *d_in,  const KParam in,
                   __global const Tp *d_pos, const KParam pos,
                   __global const Tp *d_qos, const KParam qos,
                   const float offGrid, const bool pBatch)
 {
-    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+    const int omId = idw * out.strides[3] + idz * out.strides[2]
                      + idy * out.strides[1] + idx;
-    dim_t pmId = idy * pos.strides[1] + idx;
-    dim_t qmId = idy * qos.strides[1] + idx;
+    int pmId = idy * pos.strides[1] + idx;
+    int qmId = idy * qos.strides[1] + idx;
     if(pBatch) {
         pmId += idw * pos.strides[3] + idz * pos.strides[2];
         qmId += idw * qos.strides[3] + idz * qos.strides[2];
@@ -88,10 +88,10 @@ void core_linear2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t
         return;
     }
 
-    const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+    const int grid_x = floor(x),   grid_y = floor(y);   // nearest grid
     const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
 
-    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
+    int ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
 
     // Check if pVal and pVal + 1 are both valid indices
     bool condY = (y < in.dims[1] - 1);
@@ -126,17 +126,17 @@ void approx2_kernel(__global       Ty *d_out, const KParam out,
                     __global const Ty *d_in,  const KParam in,
                     __global const Tp *d_pos, const KParam pos,
                     __global const Tp *d_qos, const KParam qos,
-                    const float offGrid, const dim_t blocksMatX, const dim_t blocksMatY,
+                    const float offGrid, const int blocksMatX, const int blocksMatY,
                     const int pBatch)
 {
-    const dim_t idz = get_group_id(0) / blocksMatX;
-    const dim_t idw = get_group_id(1) / blocksMatY;
+    const int idz = get_group_id(0) / blocksMatX;
+    const int idw = get_group_id(1) / blocksMatY;
 
-    const dim_t blockIdx_x = get_group_id(0) - idz * blocksMatX;
-    const dim_t blockIdx_y = get_group_id(1) - idw * blocksMatY;
+    const int blockIdx_x = get_group_id(0) - idz * blocksMatX;
+    const int blockIdx_y = get_group_id(1) - idw * blocksMatY;
 
-    const dim_t idx = get_local_id(0) + blockIdx_x * get_local_size(0);
-    const dim_t idy = get_local_id(1) + blockIdx_y * get_local_size(1);
+    const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
+    const int idy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
     if(idx >= out.dims[0] ||
        idy >= out.dims[1] ||

From 7a35dc2f7baf9e4de4cfeb995a37b67259023999 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:23:18 -0400
Subject: [PATCH 0520/2677] PERF Use int instead of dim_t in unwrap OpenCL
 kernel

---
 src/backend/opencl/kernel/unwrap.cl | 34 ++++++++++++++---------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/backend/opencl/kernel/unwrap.cl b/src/backend/opencl/kernel/unwrap.cl
index 6ffd1e468b..ddd990f1a5 100644
--- a/src/backend/opencl/kernel/unwrap.cl
+++ b/src/backend/opencl/kernel/unwrap.cl
@@ -10,33 +10,33 @@
 __kernel
 void unwrap_kernel(__global T *d_out, const KParam out,
                    __global const T *d_in, const KParam in,
-                   const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-                   const dim_t px, const dim_t py, const dim_t nx, const dim_t reps)
+                   const int wx, const int wy, const int sx, const int sy,
+                   const int px, const int py, const int nx, const int reps)
 {
     // Compute channel and volume
-    const dim_t w = get_group_id(1) / in.dims[2];
-    const dim_t z = get_group_id(1) - w * in.dims[2]; // get_group_id(1) % in.dims[2];
+    const int w = get_group_id(1) / in.dims[2];
+    const int z = get_group_id(1) - w * in.dims[2]; // get_group_id(1) % in.dims[2];
 
     if(w >= in.dims[3] || z >= in.dims[2])
         return;
 
     // Compute offset for channel and volume
-    const dim_t cOut = w * out.strides[3] + z * out.strides[2];
-    const dim_t cIn  = w *  in.strides[3] + z *  in.strides[2];
+    const int cOut = w * out.strides[3] + z * out.strides[2];
+    const int cIn  = w *  in.strides[3] + z *  in.strides[2];
 
     // Compute the output column index
-    const dim_t id = is_column ?
+    const int id = is_column ?
         (get_group_id(0) * get_local_size(1) + get_local_id(1)) :
         get_global_id(0);
 
     if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
 
     // Compute the starting index of window in x and y of input
-    const dim_t startx = (id % nx) * sx;
-    const dim_t starty = (id / nx) * sy;
+    const int startx = (id % nx) * sx;
+    const int starty = (id / nx) * sy;
 
-    const dim_t spx = startx - px;
-    const dim_t spy = starty - py;
+    const int spx = startx - px;
+    const int spy = starty - py;
 
     // Offset the global pointers to the respective starting indices
     __global       T* optr = d_out + cOut + id * (is_column ? out.strides[1] : 1);
@@ -47,7 +47,7 @@ void unwrap_kernel(__global T *d_out, const KParam out,
     for(int i = 0; i < reps; i++) {
 
         // Compute output index local to column
-        const dim_t outIdx = is_column ?
+        const int outIdx = is_column ?
             (i * get_local_size(0) + get_local_id(0)) :
             (i * get_local_size(1) + get_local_id(1));
 
@@ -55,16 +55,16 @@ void unwrap_kernel(__global T *d_out, const KParam out,
             return;
 
         // Compute input index local to window
-        const dim_t y = outIdx / wx;
-        const dim_t x = outIdx % wx;
+        const int y = outIdx / wx;
+        const int x = outIdx % wx;
 
-        const dim_t xpad = spx + x;
-        const dim_t ypad = spy + y;
+        const int xpad = spx + x;
+        const int ypad = spy + y;
 
         // Copy
         T val = ZERO;
         if(cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 && ypad < in.dims[1])) {
-            const dim_t inIdx = ypad * in.strides[1] + xpad * in.strides[0];
+            const int inIdx = ypad * in.strides[1] + xpad * in.strides[0];
             val = iptr[inIdx];
         }
 

From c537d85abd4babc021d3648c6f99a82e7321aeca Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 9 May 2016 19:23:29 -0400
Subject: [PATCH 0521/2677] PERF Use int instead of dim_t in wrap OpenCL kernel

---
 src/backend/opencl/kernel/wrap.cl | 50 +++++++++++++++----------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/src/backend/opencl/kernel/wrap.cl b/src/backend/opencl/kernel/wrap.cl
index c8242a7485..88e14e26cf 100644
--- a/src/backend/opencl/kernel/wrap.cl
+++ b/src/backend/opencl/kernel/wrap.cl
@@ -10,21 +10,21 @@
 __kernel
 void wrap_kernel(__global T *optr, KParam out,
                  __global T *iptr, KParam in,
-                 const dim_t wx, const dim_t wy,
-                 const dim_t sx, const dim_t sy,
-                 const dim_t px, const dim_t py,
-                 const dim_t nx, const dim_t ny,
-                 dim_t groups_x,
-                 dim_t groups_y)
+                 const int wx, const int wy,
+                 const int sx, const int sy,
+                 const int px, const int py,
+                 const int nx, const int ny,
+                 int groups_x,
+                 int groups_y)
 {
-    dim_t idx2 = get_group_id(0) / groups_x;
-    dim_t idx3 = get_group_id(1) / groups_y;
+    int idx2 = get_group_id(0) / groups_x;
+    int idx3 = get_group_id(1) / groups_y;
 
-    dim_t groupId_x = get_group_id(0) - idx2 * groups_x;
-    dim_t groupId_y = get_group_id(1) - idx3 * groups_y;
+    int groupId_x = get_group_id(0) - idx2 * groups_x;
+    int groupId_y = get_group_id(1) - idx3 * groups_y;
 
-    dim_t oidx0 = get_local_id(0) + get_local_size(0) * groupId_x;
-    dim_t oidx1 = get_local_id(1) + get_local_size(1) * groupId_y;
+    int oidx0 = get_local_id(0) + get_local_size(0) * groupId_x;
+    int oidx1 = get_local_id(1) + get_local_size(1) * groupId_y;
 
     optr += idx2 * out.strides[2] + idx3 * out.strides[3];
     iptr += idx2 *  in.strides[2] + idx3 *  in.strides[3] + in.offset;
@@ -32,30 +32,30 @@ void wrap_kernel(__global T *optr, KParam out,
 
     if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1]) return;
 
-    dim_t pidx0 = oidx0 + px;
-    dim_t pidx1 = oidx1 + py;
+    int pidx0 = oidx0 + px;
+    int pidx1 = oidx1 + py;
 
     // The last time a value appears in the unwrapped index is padded_index / stride
     // Each previous index has the value appear "stride" locations earlier
     // We work our way back from the last index
 
-    const dim_t x_end = min(pidx0 / sx, nx - 1);
-    const dim_t y_end = min(pidx1 / sy, ny - 1);
+    const int x_end = min(pidx0 / sx, nx - 1);
+    const int y_end = min(pidx1 / sy, ny - 1);
 
-    const dim_t x_off = pidx0 - sx * x_end;
-    const dim_t y_off = pidx1 - sy * y_end;
+    const int x_off = pidx0 - sx * x_end;
+    const int y_off = pidx1 - sy * y_end;
 
     T val = ZERO;
-    dim_t idx = 1;
+    int idx = 1;
 
-    for (dim_t y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
-        dim_t win_end_y = yo * wx;
-        dim_t dim_end_y = y * nx;
+    for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
+        int win_end_y = yo * wx;
+        int dim_end_y = y * nx;
 
-        for (dim_t x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
+        for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
 
-            dim_t win_end = win_end_y + xo;
-            dim_t dim_end = dim_end_y + x;
+            int win_end = win_end_y + xo;
+            int dim_end = dim_end_y + x;
 
             if (is_column) {
                 idx = dim_end * in.strides[1] + win_end;

From 09e701790afb2221d592f91602ffd4ff4e36a886 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 10 May 2016 18:49:30 -0400
Subject: [PATCH 0522/2677] IMPROV Batching in transform

* Now supports one-to-one batching.
  * Old form of batching (N images, K kernels = NxK images) now requires the K
    kernels to be on the 4th dimension
* Batching style is similar to convolve
* On dim 2 and dim 3, the image and kernels can:
  * All ones
  * If image has dim[d] = N > 1, then, the kernel dim[d] can either be 1 or N
  * Similarly, if kernel has dim[d] = K > 1, then, the image dim[d] can either be 1 or K
  * Where d is 2 or 3
* If transforms are batched, then output dimensions has to be specified
---
 include/af/image.h                           |   3 +-
 src/api/c/transform.cpp                      |  86 ++++++++++-
 src/backend/cpu/kernel/transform.hpp         |  65 ++++++---
 src/backend/cpu/transform.cpp                |  12 +-
 src/backend/cuda/kernel/transform.hpp        | 143 +++++++++++++------
 src/backend/cuda/kernel/transform_interp.hpp |   9 +-
 src/backend/cuda/transform.cu                |  12 +-
 src/backend/opencl/kernel/transform.cl       |  86 ++++++++---
 src/backend/opencl/kernel/transform.hpp      |  39 +++--
 src/backend/opencl/transform.cpp             |  28 ++--
 10 files changed, 343 insertions(+), 140 deletions(-)

diff --git a/include/af/image.h b/include/af/image.h
index 0e0c0ba901..def7fc9d05 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -221,7 +221,8 @@ AFAPI array rotate(const array& in, const float theta, const bool crop=true, con
 
     \ingroup transform_func_transform
 */
-AFAPI array transform(const array& in, const array& transform, const dim_t odim0 = 0, const dim_t odim1 = 0, const interpType method=AF_INTERP_NEAREST, const bool inverse=true);
+AFAPI array transform(const array& in, const array& transform, const dim_t odim0 = 0, const dim_t odim1 = 0,
+                      const interpType method=AF_INTERP_NEAREST, const bool inverse=true);
 
 #if AF_API_VERSION >= 33
 /**
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 785a05438e..6574cf4bbb 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -25,6 +25,33 @@ static inline af_array transform(const af_array in, const af_array tf, const af:
     return getHandle(transform<T>(getArray<T>(in), getArray<float>(tf), odims, method, inverse, perspective));
 }
 
+AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims)
+{
+    static const int baseDim = 2;
+
+    dim_t iNd = iDims.ndims();
+    dim_t tNd = tDims.ndims();
+
+    if (iNd == baseDim && tNd == baseDim)
+        return AF_BATCH_NONE;
+    else if (iNd == baseDim && tNd <= 4)
+        return AF_BATCH_KERNEL;
+    else if (iNd <= 4 && tNd == baseDim)
+        return AF_BATCH_SIGNAL;
+    else if (iNd <= 4 && tNd <= 4) {
+        bool dimsMatch = true;
+        bool isInterleaved = true;
+        for (dim_t i = baseDim; i < 4; i++) {
+            dimsMatch &= (iDims[i] == tDims[i]);
+            isInterleaved &= (iDims[i] == 1 || tDims[i] == 1 || iDims[i] == tDims[i]);
+        }
+        if (dimsMatch) return AF_BATCH_SAME;
+        return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
+    }
+    else
+        return AF_BATCH_UNSUPPORTED;
+}
+
 af_err af_transform(af_array *out, const af_array in, const af_array tf,
                     const dim_t odim0, const dim_t odim1,
                     const af_interp_type method, const bool inverse)
@@ -37,23 +64,70 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
         af::dim4 tdims = t_info.dims();
         af_dtype itype = i_info.getType();
 
+        // Assert type and interpolation
         ARG_ASSERT(2, t_info.getType() == f32);
         ARG_ASSERT(5, method == AF_INTERP_NEAREST  ||
                       method == AF_INTERP_BILINEAR ||
                       method == AF_INTERP_LOWER);
-        DIM_ASSERT(2, (tdims[0] == 3 && (tdims[1] == 2 || tdims[1] == 3)));
+
+        // Assert dimesions
+        // Image can be 2D or higher
         DIM_ASSERT(1, idims.elements() > 0);
-        DIM_ASSERT(1, (idims.ndims() == 2 || idims.ndims() == 3));
+        DIM_ASSERT(1, idims.ndims() >= 2);
 
-        const bool perspective = (tdims[1] == 3);
+        // Transform can be 3x2 for affine transform or 3x3 for perspective transform
+        DIM_ASSERT(2, (tdims[0] == 3 && (tdims[1] == 2 || tdims[1] == 3)));
+
+        // If transform is batched, the output dimensions must be specified
+        if(tdims[2] * tdims[3] > 1) {
+            ARG_ASSERT(3, odim0 > 0);
+            ARG_ASSERT(4, odim1 > 0);
+        }
+
+        // If idims[2] > 1 and tdims[2] > 1, then both must be equal
+        // else at least one of them must be 1
+        if(tdims[2] != 1 && idims[2] != 1)
+            DIM_ASSERT(2, idims[2] == tdims[2]);
+        else
+            DIM_ASSERT(2, idims[2] == 1 || tdims[2] == 1);
+
+        // If idims[3] > 1 and tdims[3] > 1, then both must be equal
+        // else at least one of them must be 1
+        if(tdims[3] != 1 && idims[3] != 1)
+            DIM_ASSERT(2, idims[3] == tdims[3]);
+        else
+            DIM_ASSERT(2, idims[3] == 1 || tdims[3] == 1);
 
-        dim_t o0 = odim0, o1 = odim1;
-        dim_t o2 = idims[2] * tdims[2];
+        const bool perspective = (tdims[1] == 3);
+        dim_t o0 = odim0, o1 = odim1, o2 = 0, o3 = 0;
         if (odim0 * odim1 == 0) {
             o0 = idims[0];
             o1 = idims[1];
         }
-        af::dim4 odims(o0, o1, o2, 1);
+
+        switch(getTransformBatchKind(idims, tdims)) {
+            case AF_BATCH_NONE:     // Both are exactly 2D
+            case AF_BATCH_SIGNAL:   // Image is 3/4D, transform is 2D
+            case AF_BATCH_SAME:     // Both are 3/4D and have the same dims
+                o2 = idims[2];
+                o3 = idims[3];
+                break;
+            case AF_BATCH_KERNEL:   // Image is 2D, transform is 3/4D
+                o2 = tdims[2];
+                o3 = tdims[3];
+                break;
+            case AF_BATCH_DIFF:     // Both are 3/4D, but have different dims
+                o2 = idims[2] == 1 ? tdims[2] : idims[2];
+                o3 = idims[3] == 1 ? tdims[3] : idims[3];
+                break;
+            case AF_BATCH_UNSUPPORTED:
+            default:
+                AF_ERROR("Unsupported combination of batching parameters in transform",
+                          AF_ERR_NOT_SUPPORTED);
+                break;
+        }
+
+        af::dim4 odims(o0, o1, o2, o3);
 
         af_array output = 0;
         switch(itype) {
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index 5c7233d28e..65c61a725b 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -73,6 +73,7 @@ void transform(Array<T> output, const Array<T> input,
 {
     const af::dim4 idims    = input.dims();
     const af::dim4 odims    = output.dims();
+    const af::dim4 tdims    = transform.dims();
     const af::dim4 istrides = input.strides();
     const af::dim4 ostrides = output.strides();
 
@@ -80,9 +81,10 @@ void transform(Array<T> output, const Array<T> input,
     const T * in = input.get();
     const float* tf = transform.get();
 
-    dim_t nimages     = idims[2];
-    // Multiplied in src/backend/transform.cpp
-    dim_t ntransforms = odims[2] / idims[2];
+    int nImg2 = idims[2];
+    int nImg3 = idims[3];
+    int nTfs2 = tdims[2];
+    int nTfs3 = tdims[3];
 
     void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
                  const af::dim4 &, const af::dim4 &,
@@ -106,23 +108,52 @@ void transform(Array<T> output, const Array<T> input,
 
     const int transf_len = (perspective) ? 9 : 6;
 
+    int batchImg2 = 1;
+    int batchImg3 = 1;
+    if(nImg2 != nTfs2 && nImg2 > 1)
+        batchImg2 = nImg2;
+    if(nImg3 != nTfs3 && nImg3 > 1)
+        batchImg3 = nImg3;
+
+    af::dim4 idims_ = idims;
+    idims_[3] = batchImg3;
+    idims_[2] = batchImg2;
+    const dim_t nimages  = batchImg2;
+
     // For each transform channel
-    for(int t_idx = 0; t_idx < (int)ntransforms; t_idx++) {
-        // Compute inverse if required
-        const float *tmat_ptr = tf + t_idx * transf_len;
-        float* tmat = new float[transf_len];
-        calc_transform_inverse(tmat, tmat_ptr, inverse, perspective, transf_len);
-
-        // Offset for output pointer
-        dim_t o_offset = t_idx * nimages * ostrides[2];
-
-        // Do transform for image
-        for(int yy = 0; yy < (int)odims[1]; yy++) {
-            for(int xx = 0; xx < (int)odims[0]; xx++) {
-                t_fn(out, in, tmat, idims, ostrides, istrides, nimages, o_offset, xx, yy, perspective);
+    for(int t_idx3 = 0; t_idx3 < nTfs3; t_idx3++) {
+        int offset3 = 0;
+        int i_offset3 = 0;
+        if(nTfs3 > 1) {       // Not Image Batched
+            offset3 = t_idx3 * ostrides[3];
+            if(nImg3 > 1) i_offset3 = t_idx3 * istrides[3]; // One to one batching
+        }
+
+        for(int t_idx2 = 0; t_idx2 < nTfs2; t_idx2++) {
+
+            // Compute inverse if required
+            const float *tmat_ptr = tf + (t_idx3 * nTfs2 + t_idx2) * transf_len;
+            float* tmat = new float[transf_len];
+            calc_transform_inverse(tmat, tmat_ptr, inverse, perspective, transf_len);
+
+            int offset2 = 0;
+            int i_offset2 = 0;
+            if(nTfs2 > 1) {       // Not Image Batched
+                offset2 = t_idx2 * ostrides[2];
+                if(nImg2 > 1) i_offset2 = t_idx2 * istrides[2]; // One to one batching
+            }
+
+            int i_offset = i_offset3 + i_offset2;
+
+            // Do transform for image
+            for(int yy = 0; yy < (int)odims[1]; yy++) {
+                for(int xx = 0; xx < (int)odims[0]; xx++) {
+                    t_fn(out, in + i_offset, tmat, idims_, ostrides, istrides,
+                         nimages, offset3 + offset2, xx, yy, perspective);
+                }
             }
+            delete[] tmat;
         }
-        delete[] tmat;
     }
 }
 
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 3a76fb2f24..4f3884e2e1 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -18,25 +18,25 @@ namespace cpu
 {
 
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
+Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
                     const af_interp_type method, const bool inverse, const bool perspective)
 {
     in.eval();
-    transform.eval();
+    tf.eval();
 
     Array<T> out = createEmptyArray<T>(odims);
 
     switch(method) {
         case AF_INTERP_NEAREST :
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_NEAREST >, out, in, transform,
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_NEAREST >, out, in, tf,
                     inverse, perspective);
             break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, transform,
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, tf,
                     inverse, perspective);
             break;
         case AF_INTERP_LOWER   :
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_LOWER   >, out, in, transform,
+            getQueue().enqueue(kernel::transform<T, AF_INTERP_LOWER   >, out, in, tf,
                     inverse, perspective);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
@@ -47,7 +47,7 @@ Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::
 
 
 #define INSTANTIATE(T)                                                              \
-template Array<T> transform(const Array<T> &in, const Array<float> &transform,      \
+template Array<T> transform(const Array<T> &in, const Array<float> &tf,             \
                             const af::dim4 &odims, const af_interp_type method,     \
                             const bool inverse, const bool perspective);
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 0dddb1ae1c..edafcd9b83 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -24,7 +24,7 @@ namespace cuda
         // Used for batching images
         static const unsigned TI = 4;
 
-        __constant__ float c_tmat[9 * 256];
+        __constant__ float c_tmat[3072]; // Allows 512 Affine Transforms and 340 Persp. Transforms
 
         template <typename T>
         __host__ __device__
@@ -67,36 +67,78 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         template<typename T, bool inverse, af_interp_type method>
         __global__ static void
-        transform_kernel(Param<T> out, CParam<T> in, const int nimages,
-                         const int ntransforms, const int blocksXPerImage,
-                         const bool perspective)
+        transform_kernel(Param<T> out, CParam<T> in,
+                const int nImg2, const int nImg3, const int nTfs2, const int nTfs3, const int batchImg2,
+                const int blocksXPerImage, const int blocksYPerImage,
+                const bool perspective)
         {
-            // Compute which image set
-            const int setId = blockIdx.x / blocksXPerImage;
-            const int blockIdx_x = blockIdx.x - setId * blocksXPerImage;
+            // Image Ids
+            const int imgId2 = blockIdx.x / blocksXPerImage;
+            const int imgId3 = blockIdx.y / blocksYPerImage;
 
-            // Get thread indices
+            // Block in local image
+            const int blockIdx_x = blockIdx.x - imgId2 * blocksXPerImage;
+            const int blockIdx_y = blockIdx.y - imgId3 * blocksYPerImage;
+
+            // Get thread indices in local image
             const int xx = blockIdx_x * blockDim.x + threadIdx.x;
-            const int yy = blockIdx.y * blockDim.y + threadIdx.y;
+            const int yy = blockIdx_y * blockDim.y + threadIdx.y;
 
-            const int limages = min(out.dims[2] - setId * nimages, nimages);
+            // Image iteration loop count for image batching
+            int limages = min(max(out.dims[2] - imgId2 * nImg2, 1), batchImg2);
 
-            if(xx >= out.dims[0] || yy >= out.dims[1] * ntransforms)
+            if(xx >= out.dims[0] || yy >= out.dims[1])
                 return;
 
-            // Index of channel of images and transform
-            //const int i_idx = xx / out.dims[0];
-            const int t_idx = yy / out.dims[1];
+            // Index of transform
+            const int eTfs2 = max((nTfs2 / nImg2), 1);
+            const int eTfs3 = max((nTfs3 / nImg3), 1);
+
+            int t_idx3 = -1;    // init
+            int t_idx2 = -1;    // init
+            int t_idx2_offset = 0;
+
+            if(nTfs3 == 1) {
+                t_idx3 = 0;     // Always 0 as only 1 transform defined
+            } else {
+                if(nTfs3 == nImg3) {
+                    t_idx3 = imgId3;    // One to one batch with all transforms defined
+                } else {
+                    t_idx3 = blockIdx.z / eTfs2;    // Transform batched, calculate
+                    t_idx2_offset = t_idx3 * nTfs2;
+                }
+            }
+
+            if(nTfs2 == 1) {
+                t_idx2 = 0;     // Always 0 as only 1 transform defined
+            } else {
+                if(nTfs2 == nImg2) {
+                    t_idx2 = imgId2;    // One to one batch with all transforms defined
+                } else {
+                    t_idx2 = blockIdx.z - t_idx2_offset;   // Transform batched, calculate
+                }
+            }
+
+            // Linear transform index
+            const int t_idx = t_idx2 + t_idx3 * nTfs2;
+            int offset = 0;
+
+            // Global offsets
+            const T *iptr = in.ptr + imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
+            if(nImg2 == nTfs2 || nImg2 > 1) {   // One-to-One or Image on dim2
+                offset += imgId2 * batchImg2 * out.strides[2];
+            } else {                            // Transform batched on dim2
+                offset += t_idx2 * out.strides[2];
+            }
+
+            if(nImg3 == nTfs3 || nImg3 > 1) {   // One-to-One or Image on dim3
+                offset += imgId3 * out.strides[3];
+            } else {                            // Transform batched on dim2
+                offset += t_idx3 * out.strides[3];
+            }
 
-            // Index in local channel -> This is output index
-            //const int xido = xx - i_idx * out.dims[0];
-            const int xido = xx;
-            const int yido = yy - t_idx * out.dims[1];
+            T *optr = out.ptr + offset;
 
-            // Global offset
-            //          Offset for transform channel + Offset for image channel.
-                  T *optr = out.ptr + t_idx * nimages * out.strides[2] + setId * nimages * out.strides[2];
-            const T *iptr = in.ptr  + setId * nimages * in.strides[2];
 
             // Transform is in constant memory.
             const int transf_len = (perspective ? 9 : 6);
@@ -113,15 +155,13 @@ namespace cuda
                 calc_transf_inverse(tmat, tmat_ptr, perspective);
             }
 
-            if (xido >= out.dims[0] && yido >= out.dims[1]) return;
-
             switch(method) {
                 case AF_INTERP_NEAREST:
-                    transform_n(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
+                    transform_n(optr, out, iptr, in, tmat, xx, yy, limages, perspective); break;
                 case AF_INTERP_BILINEAR:
-                    transform_b(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
+                    transform_b(optr, out, iptr, in, tmat, xx, yy, limages, perspective); break;
                 case AF_INTERP_LOWER:
-                    transform_l(optr, out, iptr, in, tmat, xido, yido, limages, perspective); break;
+                    transform_l(optr, out, iptr, in, tmat, xx, yy, limages, perspective); break;
                 default: break;
             }
         }
@@ -131,39 +171,50 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         template <typename T, af_interp_type method>
         void transform(Param<T> out, CParam<T> in, CParam<float> tf,
-                       const bool inverse, const bool perspective)
+                              const bool inverse, const bool perspective)
         {
-            int nimages = in.dims[2];
-            // Multiplied in src/backend/transform.cpp
-            const int ntransforms = out.dims[2] / in.dims[2];
-
+            const int nImg2 = in.dims[2];
+            const int nImg3 = in.dims[3];
+            const int nTfs2 = tf.dims[2];
+            const int nTfs3 = tf.dims[3];
 
-            const int transf_len = (perspective) ? 9 : 6;
+            const int tf_len = (perspective) ? 9 : 6;
 
             // Copy transform to constant memory.
-            CUDA_CHECK(cudaMemcpyToSymbolAsync(c_tmat, tf.ptr, ntransforms * transf_len * sizeof(float),
-                                          0, cudaMemcpyDeviceToDevice,
-                                          cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaMemcpyToSymbolAsync(c_tmat, tf.ptr,
+                                               nTfs2 * nTfs3 * tf_len * sizeof(float),
+                                               0, cudaMemcpyDeviceToDevice,
+                                               cuda::getStream(cuda::getActiveDeviceId())));
 
             dim3 threads(TX, TY, 1);
             dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
 
             const int blocksXPerImage = blocks.x;
-            if(nimages > TI) {
-                int tile_images = divup(nimages, TI);
-                nimages = TI;
-                blocks.x = blocks.x * tile_images;
-            }
+            const int blocksYPerImage = blocks.y;
+
+            // Takes care of all types of batching
+            // One-to-one batching is only done on blocks.x
+            // TODO If dim2 is not one-to-one batched, then divide blocks.x by factor
+            int batchImg2 = 1;
+            if(nImg2 != nTfs2)
+                batchImg2 = min(nImg2, TI);
+
+            blocks.x *= (nImg2 / batchImg2);
+            blocks.y *= nImg3;
 
-            if (ntransforms > 1) { blocks.y *= ntransforms; }
+            // Use blocks.z for transforms
+            blocks.z *= max((nTfs2 / nImg2), 1)
+                     *  max((nTfs3 / nImg3), 1);
 
             if(inverse) {
-                CUDA_LAUNCH((transform_kernel<T, true, method>), blocks, threads,
-                                out, in, nimages, ntransforms, blocksXPerImage,
+                CUDA_LAUNCH((transform_kernel<T, true, method>), blocks, threads, out, in,
+                                nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+                                blocksXPerImage, blocksYPerImage,
                                 perspective);
             } else {
-                CUDA_LAUNCH((transform_kernel<T, false, method>), blocks, threads,
-                                out, in, nimages, ntransforms, blocksXPerImage,
+                CUDA_LAUNCH((transform_kernel<T, false, method>), blocks, threads, out, in,
+                                nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+                                blocksXPerImage, blocksYPerImage,
                                 perspective);
             }
             POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/transform_interp.hpp b/src/backend/cuda/kernel/transform_interp.hpp
index 1554b8ec62..964477a61f 100644
--- a/src/backend/cuda/kernel/transform_interp.hpp
+++ b/src/backend/cuda/kernel/transform_interp.hpp
@@ -55,8 +55,7 @@ namespace cuda
                 yidi = round((xido * tmat[3]
                             + yido * tmat[4]
                                    + tmat[5]) / W);
-            }
-            else {
+            } else {
                 xidi = round(xido * tmat[0]
                            + yido * tmat[1]
                                   + tmat[2]);
@@ -104,8 +103,7 @@ namespace cuda
                 yidi = (xido * tmat[3]
                       + yido * tmat[4]
                              + tmat[5]) / W;
-            }
-            else {
+            } else {
                 xidi = xido * tmat[0]
                      + yido * tmat[1]
                             + tmat[2];
@@ -172,8 +170,7 @@ namespace cuda
                 yidi = floor((xido * tmat[3]
                             + yido * tmat[4]
                                    + tmat[5]) / W);
-            }
-            else {
+            } else {
                 xidi = floor(xido * tmat[0]
                            + yido * tmat[1]
                                   + tmat[2]);
diff --git a/src/backend/cuda/transform.cu b/src/backend/cuda/transform.cu
index 07c312353c..98ab58dee4 100644
--- a/src/backend/cuda/transform.cu
+++ b/src/backend/cuda/transform.cu
@@ -15,22 +15,20 @@
 namespace cuda
 {
     template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &transform, const af::dim4 &odims,
+    Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
                         const af_interp_type method, const bool inverse, const bool perspective)
     {
-        const af::dim4 idims = in.dims();
-
         Array<T> out = createEmptyArray<T>(odims);
 
         switch(method) {
             case AF_INTERP_NEAREST:
-                kernel::transform<T, AF_INTERP_NEAREST> (out, in, transform, inverse, perspective);
+                kernel::transform<T, AF_INTERP_NEAREST> (out, in, tf, inverse, perspective);
                 break;
             case AF_INTERP_BILINEAR:
-                kernel::transform<T, AF_INTERP_BILINEAR>(out, in, transform, inverse, perspective);
+                kernel::transform<T, AF_INTERP_BILINEAR>(out, in, tf, inverse, perspective);
                 break;
             case AF_INTERP_LOWER:
-                kernel::transform<T, AF_INTERP_LOWER>   (out, in, transform, inverse, perspective);
+                kernel::transform<T, AF_INTERP_LOWER>   (out, in, tf, inverse, perspective);
                 break;
             default:
                 AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -41,7 +39,7 @@ namespace cuda
 
 
 #define INSTANTIATE(T)                                                                      \
-    template Array<T> transform(const Array<T> &in, const Array<float> &transform,          \
+    template Array<T> transform(const Array<T> &in, const Array<float> &tf,                 \
                                 const af::dim4 &odims, const af_interp_type method,         \
                                 const bool inverse, const bool perspective);
 
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index b5be2977c0..3b0dff6597 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -48,34 +48,78 @@ __kernel
 void transform_kernel(__global T *d_out, const KParam out,
                       __global const T *d_in, const KParam in,
                       __global const float *c_tmat, const KParam tf,
-                      const int nimages, const int ntransforms,
-                      const int blocksXPerImage)
+                      const int nImg2, const int nImg3,
+                      const int nTfs2, const int nTfs3,
+                      const int batchImg2,
+                      const int blocksXPerImage, const int blocksYPerImage)
 {
-    // Compute which image set
-    const int setId = get_group_id(0) / blocksXPerImage;
-    const int blockIdx_x = get_group_id(0) - setId * blocksXPerImage;
+    // Image Ids
+    const int imgId2 = get_group_id(0) / blocksXPerImage;
+    const int imgId3 = get_group_id(1) / blocksYPerImage;
 
-    // Get thread indices
-    const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
-    const int yy = get_global_id(1);
+    // Block in local image
+    const int blockIdx_x = get_group_id(0) - imgId2 * blocksXPerImage;
+    const int blockIdx_y = get_group_id(1) - imgId3 * blocksYPerImage;
 
-    if(xx >= out.dims[0] * nimages || yy >= out.dims[1] * ntransforms)
+    // Get thread indices in local image
+    const int xx = blockIdx_x * get_local_size(0) + get_local_id(0);
+    const int yy = blockIdx_y * get_local_size(1) + get_local_id(1);
+
+    // Image iteration loop count for image batching
+    int limages = min(max((int)(out.dims[2] - imgId2 * nImg2), 1), batchImg2);
+
+    if(xx >= out.dims[0] || yy >= out.dims[1])
         return;
 
-    // Index of channel of images and transform
-    //int i_idx = xx / out.dims[0];
-    const int t_idx = yy / out.dims[1];
+    // Index of transform
+    const int eTfs2 = max((nTfs2 / nImg2), 1);
+    const int eTfs3 = max((nTfs3 / nImg3), 1);
 
-    const int limages = min((int)out.dims[2] - setId * nimages, nimages);
+    int t_idx3 = -1;    // init
+    int t_idx2 = -1;    // init
+    int t_idx2_offset = 0;
 
-    // Index in local channel -> This is output index
-    const int xido = xx; // - i_idx * out.dims[0];
-    const int yido = yy - t_idx * out.dims[1];
+    const int blockIdx_z = get_group_id(2);
+
+    if(nTfs3 == 1) {
+        t_idx3 = 0;     // Always 0 as only 1 transform defined
+    } else {
+        if(nTfs3 == nImg3) {
+            t_idx3 = imgId3;    // One to one batch with all transforms defined
+        } else {
+            t_idx3 = blockIdx_z / eTfs2;    // Transform batched, calculate
+            t_idx2_offset = t_idx3 * nTfs2;
+        }
+    }
+
+    if(nTfs2 == 1) {
+        t_idx2 = 0;     // Always 0 as only 1 transform defined
+    } else {
+        if(nTfs2 == nImg2) {
+            t_idx2 = imgId2;    // One to one batch with all transforms defined
+        } else {
+            t_idx2 = blockIdx_z - t_idx2_offset;   // Transform batched, calculate
+        }
+    }
+
+    // Linear transform index
+    const int t_idx = t_idx2 + t_idx3 * nTfs2;
 
     // Global offset
-    //          Offset for transform channel + Offset for image channel.
-    d_out += t_idx * nimages * out.strides[2] + setId * nimages * out.strides[2];
-    d_in  += setId * nimages * in.strides[2] + in.offset;
+    int offset = 0;
+    d_in += imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3] + in.offset;
+    if(nImg2 == nTfs2 || nImg2 > 1) {   // One-to-One or Image on dim2
+          offset += imgId2 * batchImg2 * out.strides[2];
+    } else {                            // Transform batched on dim2
+          offset += t_idx2 * out.strides[2];
+    }
+
+    if(nImg3 == nTfs3 || nImg3 > 1) {   // One-to-One or Image on dim3
+          offset += imgId3 * out.strides[3];
+    } else {                            // Transform batched on dim2
+          offset += t_idx3 * out.strides[3];
+    }
+    d_out += offset;
 
     // Transform is in global memory.
     // Needs offset to correct transform being processed.
@@ -98,7 +142,5 @@ void transform_kernel(__global T *d_out, const KParam out,
         calc_transf_inverse(tmat, tmat_ptr);
     }
 
-    if (xido >= out.dims[0] && yido >= out.dims[1]) return;
-
-    INTERP(d_out, out, d_in, in, tmat, xido, yido, limages);
+    INTERP(d_out, out, d_in, in, tmat, xx, yy, limages);
 }
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 3334d9aa41..d6a25639e3 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -104,31 +104,40 @@ namespace opencl
 
                 auto transformOp = make_kernel<Buffer, const KParam,
                                          const Buffer, const KParam, const Buffer, const KParam,
+                                         const int, const int, const int, const int,
                                          const int, const int, const int>
                                          (*transformKernels[device]);
 
+                const int nImg2 = in.info.dims[2];
+                const int nImg3 = in.info.dims[3];
+                const int nTfs2 = tf.info.dims[2];
+                const int nTfs3 = tf.info.dims[3];
+
                 NDRange local(TX, TY, 1);
 
-                int nimages = in.info.dims[2];
-                int global_x = local[0] * divup(out.info.dims[0], local[0]);
-                const int blocksXPerImage = global_x / local[0];
+                int batchImg2 = 1;
+                if(nImg2 != nTfs2)
+                    batchImg2 = min(nImg2, TI);
 
-                if(nimages > TI) {
-                    int tile_images = divup(nimages, TI);
-                    nimages = TI;
-                    global_x = global_x * tile_images;
-                }
+                const int blocksXPerImage = divup(out.info.dims[0], local[0]);
+                const int blocksYPerImage = divup(out.info.dims[1], local[1]);
 
-                // Multiplied in src/backend/transform.cpp
-                const int ntransforms = out.info.dims[2] / in.info.dims[2];
+                int global_x = local[0]
+                             * blocksXPerImage
+                             * (nImg2 / batchImg2);
+                int global_y = local[1]
+                             * blocksYPerImage
+                             * nImg3;
+                int global_z = local[2]
+                             * max((nTfs2 / nImg2), 1)
+                             * max((nTfs3 / nImg3), 1);
 
-                NDRange global(global_x,
-                               local[1] * divup(out.info.dims[1], local[1]) * ntransforms,
-                               1);
+                NDRange global(global_x, global_y, global_z);
 
                 transformOp(EnqueueArgs(getQueue(), global, local),
-                            *out.data, out.info, *in.data, in.info,
-                            *tf.data, tf.info, nimages, ntransforms, blocksXPerImage);
+                            *out.data, out.info, *in.data, in.info, *tf.data, tf.info,
+                            nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+                            blocksXPerImage, blocksYPerImage);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 8046573dee..ac8f86346e 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -16,7 +16,7 @@
 namespace opencl
 {
     template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &transform,
+    Array<T> transform(const Array<T> &in, const Array<float> &tf,
                        const af::dim4 &odims, const af_interp_type method,
                        const bool inverse, const bool perspective)
     {
@@ -27,15 +27,15 @@ namespace opencl
                 switch(method) {
                     case AF_INTERP_NEAREST:
                         kernel::transform<T, true, true, AF_INTERP_NEAREST>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_BILINEAR:
                         kernel::transform<T, true, true, AF_INTERP_BILINEAR>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_LOWER:
                         kernel::transform<T, true, true, AF_INTERP_LOWER>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     default:
                         AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -45,15 +45,15 @@ namespace opencl
                 switch(method) {
                     case AF_INTERP_NEAREST:
                         kernel::transform<T, true, false, AF_INTERP_NEAREST>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_BILINEAR:
                         kernel::transform<T, true, false, AF_INTERP_BILINEAR>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_LOWER:
                         kernel::transform<T, true, false, AF_INTERP_LOWER>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     default:
                         AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -65,15 +65,15 @@ namespace opencl
                 switch(method) {
                     case AF_INTERP_NEAREST:
                         kernel::transform<T, false, true, AF_INTERP_NEAREST>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_BILINEAR:
                         kernel::transform<T, false, true, AF_INTERP_BILINEAR>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_LOWER:
                         kernel::transform<T, false, true, AF_INTERP_LOWER>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     default:
                         AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -83,15 +83,15 @@ namespace opencl
                 switch(method) {
                     case AF_INTERP_NEAREST:
                         kernel::transform<T, false, false, AF_INTERP_NEAREST>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_BILINEAR:
                         kernel::transform<T, false, false, AF_INTERP_BILINEAR>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     case AF_INTERP_LOWER:
                         kernel::transform<T, false, false, AF_INTERP_LOWER>
-                                         (out, in, transform);
+                                         (out, in, tf);
                         break;
                     default:
                         AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
@@ -105,7 +105,7 @@ namespace opencl
 
 
 #define INSTANTIATE(T)                                                                  \
-    template Array<T> transform(const Array<T> &in, const Array<float> &transform,      \
+    template Array<T> transform(const Array<T> &in, const Array<float> &tf,             \
                                 const af::dim4 &odims, const af_interp_type method,     \
                                 const bool inverse, const bool perspective);
 

From d4381ff912753a751dd365c6bb8f27462bb522a0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 11 May 2016 12:19:47 -0400
Subject: [PATCH 0523/2677] TEST Add tests for transform batching

---
 test/data          |  2 +-
 test/transform.cpp | 76 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 77 insertions(+), 1 deletion(-)

diff --git a/test/data b/test/data
index cec85080f1..29c9ae2888 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit cec85080f12c25486d025d1fb1cf69e1beb03e58
+Subproject commit 29c9ae28883eeed76d3adc1dad050f58f15a2fc4
diff --git a/test/transform.cpp b/test/transform.cpp
index 1950284c2d..7a8e8d6742 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -266,3 +266,79 @@ TEST(Transform, CPP)
     delete[] h_gold_img;
     delete[] h_out_img;
 }
+
+// This tests batching of different forms
+// tf0 rotates by 90 clockwise
+// tf1 rotates by 90 counter clockwise
+// This test simply makes sure the batching is working correctly
+TEST(TransformBatching, CPP)
+{
+    vector<af::dim4>        vDims;
+    vector<vector<float> >  in;
+    vector<vector<float> >  gold;
+
+    readTests<float, float, int>(string(TEST_DIR"/transform/transform_batching.test"), vDims, in, gold);
+
+    af::array img0     (vDims[0], &(in[0].front()));
+    af::array img1     (vDims[1], &(in[1].front()));
+    af::array ip_tile  (vDims[2], &(in[2].front()));
+    af::array ip_quad  (vDims[3], &(in[3].front()));
+    af::array ip_mult  (vDims[4], &(in[4].front()));
+    af::array ip_tile3 (vDims[5], &(in[5].front()));
+    af::array ip_quad3 (vDims[6], &(in[6].front()));
+
+    af::array tf0      (vDims[7 + 0], &(in[7 + 0].front()));
+    af::array tf1      (vDims[7 + 1], &(in[7 + 1].front()));
+    af::array tf_tile  (vDims[7 + 2], &(in[7 + 2].front()));
+    af::array tf_quad  (vDims[7 + 3], &(in[7 + 3].front()));
+    af::array tf_mult  (vDims[7 + 4], &(in[7 + 4].front()));
+    af::array tf_mult3 (vDims[7 + 5], &(in[7 + 5].front()));
+    af::array tf_mult3x(vDims[7 + 6], &(in[7 + 6].front()));
+
+    const int X = img0.dims(0);
+    const int Y = img0.dims(1);
+
+    ASSERT_EQ(gold.size(), 21u);
+    vector<af::array> out(gold.size());
+    out[0 ] = transform(img0    , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+    out[1 ] = transform(img0    , tf1      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+    out[2 ] = transform(img1    , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+    out[3 ] = transform(img1    , tf1      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+
+    out[4 ] = transform(img0    , tf_tile  , Y, X, AF_INTERP_NEAREST);  // 1,1 x N,1
+    out[5 ] = transform(img0    , tf_mult  , Y, X, AF_INTERP_NEAREST);  // 1,1 x N,N
+    out[6 ] = transform(img0    , tf_quad  , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,N
+
+    out[7 ] = transform(ip_tile , tf0      , Y, X, AF_INTERP_NEAREST);  // N,1 x 1,1
+    out[8 ] = transform(ip_tile , tf_tile  , Y, X, AF_INTERP_NEAREST);  // N,1 x N,1
+    out[9 ] = transform(ip_tile , tf_mult  , Y, X, AF_INTERP_NEAREST);  // N,N x N,N
+    out[10] = transform(ip_tile , tf_quad  , Y, X, AF_INTERP_NEAREST);  // N,1 x 1,N
+
+    out[11] = transform(ip_quad , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,N x 1,1
+    out[12] = transform(ip_quad , tf_quad  , Y, X, AF_INTERP_NEAREST);  // 1,N x 1,N
+    out[13] = transform(ip_quad , tf_mult  , Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
+    out[14] = transform(ip_quad , tf_tile  , Y, X, AF_INTERP_NEAREST);  // 1,N x N,1
+
+    out[15] = transform(ip_mult , tf0      , Y, X, AF_INTERP_NEAREST);  // N,N x 1,1
+    out[16] = transform(ip_mult , tf_tile  , Y, X, AF_INTERP_NEAREST);  // N,N x N,1
+    out[17] = transform(ip_mult , tf_mult  , Y, X, AF_INTERP_NEAREST);  // N,N x N,N
+    out[18] = transform(ip_mult , tf_quad  , Y, X, AF_INTERP_NEAREST);  // N,N x 1,N
+
+    out[19] = transform(ip_tile3, tf_mult3 , Y, X, AF_INTERP_NEAREST);  // N,1 x N,N
+    out[20] = transform(ip_quad3, tf_mult3x, Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
+
+    af::array x_(af::dim4(35, 40, 1, 1), &(gold[1].front()));
+
+    for(int i = 0; i < (int)gold.size(); i++) {
+        // Get result
+        float *outData = new float[out[i].elements()];
+        out[i].host((void*)outData);
+
+        for(int iter = 0; iter < (int)gold[i].size(); iter++) {
+            ASSERT_EQ(gold[i][iter], outData[iter]) << "at: " << iter << std::endl
+                    << "for " << i << "-th operation"<< std::endl;
+        }
+
+        delete[] outData;
+    }
+}

From e08d31021dcf690e148678bc0f6452a622285859 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 12 May 2016 17:48:20 -0400
Subject: [PATCH 0524/2677] FEAT Add fallback to CUDA OpenGL Interop

This comes into play when devices are not graphics capable but OpenGL is
available.

On Windows, when all devices are in TCC mode or not connected to display, CUDA
throws an error
`CUDA Error (63): OS call failed or operation not supported on this OS`.

On Linux, if all devices are in TCC mode, the graphics part will still run as
long as OpenGL is available.
---
 src/backend/cuda/hist_graphics.cu   | 38 +++++++++++++++---------
 src/backend/cuda/image.cu           | 45 +++++++++++++++++++----------
 src/backend/cuda/interopManager.cu  | 22 ++++++++++++++
 src/backend/cuda/interopManager.hpp |  2 ++
 src/backend/cuda/plot.cu            | 38 +++++++++++++++---------
 src/backend/cuda/plot3.cu           | 38 +++++++++++++++---------
 src/backend/cuda/surface.cu         | 38 +++++++++++++++---------
 7 files changed, 153 insertions(+), 68 deletions(-)

diff --git a/src/backend/cuda/hist_graphics.cu b/src/backend/cuda/hist_graphics.cu
index 2ce0c199de..cee40c7f00 100644
--- a/src/backend/cuda/hist_graphics.cu
+++ b/src/backend/cuda/hist_graphics.cu
@@ -21,23 +21,35 @@ namespace cuda
 template<typename T>
 void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
 {
-    const T *d_P = data.get();
+    if(InteropManager::checkGraphicsInteropCapability()) {
+        const T *d_P = data.get();
 
-    InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = InteropManager::getInstance();
 
-    cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(hist);
-    // Map resource. Copy data to VBO. Unmap resource.
-    size_t num_bytes = hist->size();
-    T* d_vbo = NULL;
-    cudaGraphicsMapResources(1, &cudaVBOResource, 0);
-    cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
-    cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                    cuda::getStream(cuda::getActiveDeviceId()));
-    cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(hist);
+        // Map resource. Copy data to VBO. Unmap resource.
+        size_t num_bytes = hist->size();
+        T* d_vbo = NULL;
+        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
+                        cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
 
-    CheckGL("After cuda resource copy");
+        CheckGL("After cuda resource copy");
 
-    POST_LAUNCH_CHECK();
+        POST_LAUNCH_CHECK();
+    } else {
+        CheckGL("Begin CUDA fallback-resource copy");
+        glBindBuffer(GL_ARRAY_BUFFER, hist->vbo());
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpy(ptr, data.get(), hist->size(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        CheckGL("End CUDA fallback-resource copy");
+    }
 }
 
 #define INSTANTIATE(T)  \
diff --git a/src/backend/cuda/image.cu b/src/backend/cuda/image.cu
index a99c79207d..292f80110a 100644
--- a/src/backend/cuda/image.cu
+++ b/src/backend/cuda/image.cu
@@ -26,22 +26,35 @@ namespace cuda
 template<typename T>
 void copy_image(const Array<T> &in, const fg::Image* image)
 {
-    InteropManager& intrpMngr = InteropManager::getInstance();
-
-    cudaGraphicsResource *cudaPBOResource = intrpMngr.getBufferResource(image);
-
-    const T *d_X = in.get();
-    // Map resource. Copy data to PBO. Unmap resource.
-    size_t num_bytes;
-    T* d_pbo = NULL;
-    cudaGraphicsMapResources(1, &cudaPBOResource, 0);
-    cudaGraphicsResourceGetMappedPointer((void **)&d_pbo, &num_bytes, cudaPBOResource);
-    cudaMemcpyAsync(d_pbo, d_X, num_bytes, cudaMemcpyDeviceToDevice,
-                    cuda::getStream(cuda::getActiveDeviceId()));
-    cudaGraphicsUnmapResources(1, &cudaPBOResource, 0);
-
-    POST_LAUNCH_CHECK();
-    CheckGL("After cuda resource copy");
+    if(InteropManager::checkGraphicsInteropCapability()) {
+        InteropManager& intrpMngr = InteropManager::getInstance();
+
+        cudaGraphicsResource *cudaPBOResource = intrpMngr.getBufferResource(image);
+
+        const T *d_X = in.get();
+        // Map resource. Copy data to PBO. Unmap resource.
+        size_t num_bytes;
+        T* d_pbo = NULL;
+        cudaGraphicsMapResources(1, &cudaPBOResource, 0);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_pbo, &num_bytes, cudaPBOResource);
+        cudaMemcpyAsync(d_pbo, d_X, num_bytes, cudaMemcpyDeviceToDevice,
+                        cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, &cudaPBOResource, 0);
+
+        POST_LAUNCH_CHECK();
+        CheckGL("After cuda resource copy");
+    } else {
+        CheckGL("Begin CUDA fallback-resource copy");
+        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pbo());
+        glBufferData(GL_PIXEL_UNPACK_BUFFER, image->size(), 0, GL_STREAM_DRAW);
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpy(ptr, in.get(), image->size(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
+        }
+        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
+        CheckGL("End CUDA fallback-resource copy");
+    }
 }
 
 #define INSTANTIATE(T)      \
diff --git a/src/backend/cuda/interopManager.cu b/src/backend/cuda/interopManager.cu
index a6e2fcf9bd..cf83dc7a90 100644
--- a/src/backend/cuda/interopManager.cu
+++ b/src/backend/cuda/interopManager.cu
@@ -134,6 +134,28 @@ cudaGraphicsResource* InteropManager::getBufferResource(const fg::Surface* key)
     return interop_maps[device][key_value];
 }
 
+bool InteropManager::checkGraphicsInteropCapability()
+{
+    static bool run_once = true;
+    static bool capable  = true;
+
+    if(run_once) {
+        unsigned int pCudaEnabledDeviceCount = 0;
+        int pCudaGraphicsEnabledDeviceIds = 0;
+        cudaGetLastError(); // Reset Errors
+        cudaError_t err = cudaGLGetDevices(&pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds, getDeviceCount(), cudaGLDeviceListAll);
+        if(err == 63) { // OS Support Failure - Happens when devices are only Tesla
+            capable = false;
+            printf("Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL Interop will use CPU fallback.\n");
+            printf("Corresponding CUDA Error (%d): %s.\n", err, cudaGetErrorString(err));
+            printf("This may happen if all CUDA Devices are in TCC Mode and/or not connected to a display.\n");
+        }
+        cudaGetLastError(); // Reset Errors
+        run_once = false;
+    }
+    return capable;
+}
+
 }
 
 #endif
diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index e586d384a1..58799147c3 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -37,6 +37,8 @@ class InteropManager
 
     public:
         static InteropManager& getInstance();
+        static bool checkGraphicsInteropCapability();
+
         ~InteropManager();
         cudaGraphicsResource* getBufferResource(const fg::Image* handle);
         cudaGraphicsResource* getBufferResource(const fg::Plot* handle);
diff --git a/src/backend/cuda/plot.cu b/src/backend/cuda/plot.cu
index 20f899323d..74a8363648 100644
--- a/src/backend/cuda/plot.cu
+++ b/src/backend/cuda/plot.cu
@@ -26,23 +26,35 @@ namespace cuda
 template<typename T>
 void copy_plot(const Array<T> &P, fg::Plot* plot)
 {
-    const T *d_P = P.get();
+    if(InteropManager::checkGraphicsInteropCapability()) {
+        const T *d_P = P.get();
 
-    InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = InteropManager::getInstance();
 
-    cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot);
-    // Map resource. Copy data to VBO. Unmap resource.
-    size_t num_bytes = plot->size();
-    T* d_vbo = NULL;
-    cudaGraphicsMapResources(1, &cudaVBOResource, 0);
-    cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
-    cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-               cuda::getStream(cuda::getActiveDeviceId()));
-    cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot);
+        // Map resource. Copy data to VBO. Unmap resource.
+        size_t num_bytes = plot->size();
+        T* d_vbo = NULL;
+        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
+                cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
 
-    CheckGL("After cuda resource copy");
+        CheckGL("After cuda resource copy");
 
-    POST_LAUNCH_CHECK();
+        POST_LAUNCH_CHECK();
+    } else {
+        CheckGL("Begin CUDA fallback-resource copy");
+        glBindBuffer(GL_ARRAY_BUFFER, plot->vbo());
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot->size(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        CheckGL("End CUDA fallback-resource copy");
+    }
 }
 
 #define INSTANTIATE(T)  \
diff --git a/src/backend/cuda/plot3.cu b/src/backend/cuda/plot3.cu
index 378a6ec27f..ba94bcab0e 100644
--- a/src/backend/cuda/plot3.cu
+++ b/src/backend/cuda/plot3.cu
@@ -26,23 +26,35 @@ namespace cuda
 template<typename T>
 void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
 {
-    const T *d_P = P.get();
+    if(InteropManager::checkGraphicsInteropCapability()) {
+        const T *d_P = P.get();
 
-    InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = InteropManager::getInstance();
 
-    cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot3);
-    // Map resource. Copy data to VBO. Unmap resource.
-    size_t num_bytes = plot3->size();
-    T* d_vbo = NULL;
-    cudaGraphicsMapResources(1, &cudaVBOResource, 0);
-    cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
-    cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-               cuda::getStream(cuda::getActiveDeviceId()));
-    cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot3);
+        // Map resource. Copy data to VBO. Unmap resource.
+        size_t num_bytes = plot3->size();
+        T* d_vbo = NULL;
+        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
+                cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
 
-    CheckGL("After cuda resource copy");
+        CheckGL("After cuda resource copy");
 
-    POST_LAUNCH_CHECK();
+        POST_LAUNCH_CHECK();
+    } else {
+        CheckGL("Begin CUDA fallback-resource copy");
+        glBindBuffer(GL_ARRAY_BUFFER, plot3->vbo());
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot3->size(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        CheckGL("End CUDA fallback-resource copy");
+    }
 }
 
 #define INSTANTIATE(T)  \
diff --git a/src/backend/cuda/surface.cu b/src/backend/cuda/surface.cu
index fcb9f81975..9222b49de0 100644
--- a/src/backend/cuda/surface.cu
+++ b/src/backend/cuda/surface.cu
@@ -26,23 +26,35 @@ namespace cuda
 template<typename T>
 void copy_surface(const Array<T> &P, fg::Surface* surface)
 {
-    const T *d_P = P.get();
+    if(InteropManager::checkGraphicsInteropCapability()) {
+        const T *d_P = P.get();
 
-    InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = InteropManager::getInstance();
 
-    cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(surface);
-    // Map resource. Copy data to VBO. Unmap resource.
-    size_t num_bytes = surface->size();
-    T* d_vbo = NULL;
-    cudaGraphicsMapResources(1, &cudaVBOResource, 0);
-    cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
-    cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-               cuda::getStream(cuda::getActiveDeviceId()));
-    cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(surface);
+        // Map resource. Copy data to VBO. Unmap resource.
+        size_t num_bytes = surface->size();
+        T* d_vbo = NULL;
+        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
+                cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
 
-    CheckGL("After cuda resource copy");
+        CheckGL("After cuda resource copy");
 
-    POST_LAUNCH_CHECK();
+        POST_LAUNCH_CHECK();
+    } else {
+        CheckGL("Begin CUDA fallback-resource copy");
+        glBindBuffer(GL_ARRAY_BUFFER, surface->vbo());
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpy(ptr, P.get(), surface->size(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        CheckGL("End CUDA fallback-resource copy");
+    }
 }
 
 #define INSTANTIATE(T)  \

From 44dd4b8ca9e800004559141b1195ce28800a669f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 13 May 2016 16:58:14 -0400
Subject: [PATCH 0525/2677] RENAME AF_BATCH_KIND: SIGNAL -> LHS, KERNEL -> RHS

---
 src/api/c/convolve.cpp                            |  4 ++--
 src/api/c/dog.cpp                                 |  2 +-
 src/api/c/fftconvolve.cpp                         |  4 ++--
 src/api/c/transform.cpp                           |  8 ++++----
 src/backend/cpu/convolve.cpp                      |  6 +++---
 src/backend/cpu/fftconvolve.cpp                   |  4 ++--
 src/backend/cpu/iir.cpp                           |  2 +-
 src/backend/cpu/kernel/convolve.hpp               |  4 ++--
 src/backend/cpu/kernel/fftconvolve.hpp            | 12 ++++++------
 src/backend/cuda/convolve.cpp                     |  4 ++--
 src/backend/cuda/fftconvolve.cu                   |  6 +++---
 src/backend/cuda/iir.cu                           |  2 +-
 src/backend/cuda/kernel/convolve.cu               |  6 +++---
 src/backend/cuda/kernel/fftconvolve.hpp           | 12 ++++++------
 src/backend/defines.hpp                           |  4 ++--
 src/backend/opencl/convolve.cpp                   |  4 ++--
 src/backend/opencl/fftconvolve.cpp                |  6 +++---
 src/backend/opencl/iir.cpp                        |  2 +-
 src/backend/opencl/kernel/convolve.hpp            |  6 +++---
 src/backend/opencl/kernel/fftconvolve.hpp         |  8 ++++----
 src/backend/opencl/kernel/fftconvolve_multiply.cl |  4 ++--
 21 files changed, 55 insertions(+), 55 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index fa6d5831f4..fbdb862cb4 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -43,9 +43,9 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
     if (sn==baseDim && fn==baseDim)
         return AF_BATCH_NONE;
     else if (sn==baseDim && (fn>baseDim && fn<=4))
-        return AF_BATCH_KERNEL;
+        return AF_BATCH_RHS;
     else if ((sn>baseDim && sn<=4) && fn==baseDim)
-        return AF_BATCH_SIGNAL;
+        return AF_BATCH_LHS;
     else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved = true;
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index ffe7d4e178..953db19a49 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -31,7 +31,7 @@ static af_array dog(const af_array& in, const int radius1, const int radius2)
     Array<accT> input  = castArray<accT>(in);
     dim4 iDims      = input.dims();
 
-    AF_BATCH_KIND bkind = iDims[2] > 1 ? AF_BATCH_SIGNAL : AF_BATCH_NONE;
+    AF_BATCH_KIND bkind = iDims[2] > 1 ? AF_BATCH_LHS : AF_BATCH_NONE;
 
     Array<accT> smth1 = convolve<accT, accT, 2, false>(input, castArray<accT>(g1), bkind);
     Array<accT> smth2 = convolve<accT, accT, 2, false>(input, castArray<accT>(g2), bkind);
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 45b1da94ee..355720e09c 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -103,9 +103,9 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
     if (sn==baseDim && fn==baseDim)
         return AF_BATCH_NONE;
     else if (sn==baseDim && (fn>baseDim && fn<=4))
-        return AF_BATCH_KERNEL;
+        return AF_BATCH_RHS;
     else if ((sn>baseDim && sn<=4) && fn==baseDim)
-        return AF_BATCH_SIGNAL;
+        return AF_BATCH_LHS;
     else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
        bool doesDimensionsMatch = true;
         bool isInterleaved = true;
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 6574cf4bbb..074868463d 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -35,9 +35,9 @@ AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims)
     if (iNd == baseDim && tNd == baseDim)
         return AF_BATCH_NONE;
     else if (iNd == baseDim && tNd <= 4)
-        return AF_BATCH_KERNEL;
+        return AF_BATCH_RHS;
     else if (iNd <= 4 && tNd == baseDim)
-        return AF_BATCH_SIGNAL;
+        return AF_BATCH_LHS;
     else if (iNd <= 4 && tNd <= 4) {
         bool dimsMatch = true;
         bool isInterleaved = true;
@@ -107,12 +107,12 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
 
         switch(getTransformBatchKind(idims, tdims)) {
             case AF_BATCH_NONE:     // Both are exactly 2D
-            case AF_BATCH_SIGNAL:   // Image is 3/4D, transform is 2D
+            case AF_BATCH_LHS:   // Image is 3/4D, transform is 2D
             case AF_BATCH_SAME:     // Both are 3/4D and have the same dims
                 o2 = idims[2];
                 o3 = idims[3];
                 break;
-            case AF_BATCH_KERNEL:   // Image is 2D, transform is 3/4D
+            case AF_BATCH_RHS:   // Image is 2D, transform is 3/4D
                 o2 = tdims[2];
                 o3 = tdims[3];
                 break;
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 2941347ffc..7bc77e1a29 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -33,7 +33,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -41,7 +41,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_KERNEL) {
+        if (kind==AF_BATCH_RHS) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -71,7 +71,7 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 
         dim_t cflen = (dim_t)cfDims.elements();
         dim_t rflen = (dim_t)rfDims.elements();
-        // separable convolve only does AF_BATCH_NONE and standard batch(AF_BATCH_SIGNAL)
+        // separable convolve only does AF_BATCH_NONE and standard batch(AF_BATCH_LHS)
         tDims[0] += cflen - 1;
         oDims[0] += cflen - 1;
         oDims[1] += rflen - 1;
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 8452f827b7..bd86a53062 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -192,7 +192,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
                 oDims[d] = sd[d]+fd[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sd[d]+fd[d]-1 : sd[d]);
@@ -200,7 +200,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         }
     } else {
         oDims = sd;
-        if (kind==AF_BATCH_KERNEL) {
+        if (kind==AF_BATCH_RHS) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fd[i];
         }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 79758c7641..f37ae4795b 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -29,7 +29,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
 
     AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
     if (x.ndims() != b.ndims()) {
-        type = (x.ndims() < b.ndims()) ? AF_BATCH_KERNEL : AF_BATCH_SIGNAL;
+        type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
     }
 
     // Extract the first N elements
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 27c0c8df9e..4855f94b4f 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -143,7 +143,7 @@ void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filt
 
     for (dim_t i=1; i<4; ++i) {
         switch(kind) {
-            case AF_BATCH_SIGNAL:
+            case AF_BATCH_LHS:
                 out_step[i] = oStrides[i];
                 in_step[i]  = sStrides[i];
                 if (i>=baseDim) batch[i] = sDims[i];
@@ -154,7 +154,7 @@ void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filt
                 filt_step[i] = fStrides[i];
                 if (i>=baseDim) batch[i] = sDims[i];
                 break;
-            case AF_BATCH_KERNEL:
+            case AF_BATCH_RHS:
                 out_step[i]  = oStrides[i];
                 filt_step[i] = fStrides[i];
                 if (i>=baseDim) batch[i] = fDims[i];
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index c78ecd062a..b8b3696fd5 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -89,12 +89,12 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                      const af::dim4 fit_dims, const af::dim4 fit_strides,
                      AF_BATCH_KIND kind, const dim_t offset)
 {
-    T* out_ptr = packed.get() + (kind==AF_BATCH_KERNEL? offset : 0);
+    T* out_ptr = packed.get() + (kind==AF_BATCH_RHS? offset : 0);
     T* in1_ptr = packed.get();
     T* in2_ptr = packed.get() + offset;
 
-    const af::dim4& od = (kind==AF_BATCH_KERNEL ? fit_dims : sig_dims);
-    const af::dim4& os = (kind==AF_BATCH_KERNEL ? fit_strides : sig_strides);
+    const af::dim4& od  = (kind==AF_BATCH_RHS ? fit_dims : sig_dims);
+    const af::dim4& os  = (kind==AF_BATCH_RHS ? fit_strides : sig_strides);
     const af::dim4& i1d = sig_dims;
     const af::dim4& i2d = fit_dims;
     const af::dim4& i1s = sig_strides;
@@ -120,7 +120,7 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         out_ptr[ridx] = ac - bd;
                         out_ptr[iidx] = (a+b) * (c+d) - ac - bd;
                     }
-                    else if (kind == AF_BATCH_SIGNAL) {
+                    else if (kind == AF_BATCH_LHS) {
                         // Complex multiply all signals to filter
                         const int ridx1 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
                         const int iidx1 = ridx1 + 1;
@@ -138,7 +138,7 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         out_ptr[ridx1] = ac - bd;
                         out_ptr[iidx1] = (a+b) * (c+d) - ac - bd;
                     }
-                    else if (kind == AF_BATCH_KERNEL) {
+                    else if (kind == AF_BATCH_RHS) {
                         // Complex multiply signal to all filters
                         const int ridx2 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
                         const int iidx2 = ridx2 + 1;
@@ -239,7 +239,7 @@ void reorder(Array<T> out, Array<convT> packed,
     convT* filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
 
     // Reorder the output
-    if (kind == AF_BATCH_KERNEL) {
+    if (kind == AF_BATCH_RHS) {
         reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
                 filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
                 filter_dims, sig_half_d0, baseDim, fftScale, expand);
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index fb274c3e02..8a512f52e6 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -27,7 +27,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -35,7 +35,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_KERNEL) {
+        if (kind==AF_BATCH_RHS) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cu
index 7d3549de56..3c18e9401e 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cu
@@ -55,7 +55,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -63,7 +63,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_KERNEL) {
+        if (kind==AF_BATCH_RHS) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -86,7 +86,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     else
         kernel::complexMultiplyHelper<T, cT>(out, signal_packed, filter_packed, signal, filter, kind);
 
-    if (kind == AF_BATCH_KERNEL) {
+    if (kind == AF_BATCH_RHS) {
         fft_inplace<cT, baseDim, false>(filter_packed);
         if (expand)
             kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, filter_packed, signal, filter, kind);
diff --git a/src/backend/cuda/iir.cu b/src/backend/cuda/iir.cu
index 7b3a217659..eced5a3aee 100644
--- a/src/backend/cuda/iir.cu
+++ b/src/backend/cuda/iir.cu
@@ -26,7 +26,7 @@ namespace cuda
 
         AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
         if (x.ndims() != b.ndims()) {
-            type = (x.ndims() < b.ndims()) ? AF_BATCH_KERNEL : AF_BATCH_SIGNAL;
+            type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
         }
 
         // Extract the first N elements
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 9557b7a2e4..0a7f5425cf 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -470,9 +470,9 @@ void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, AF_BATCH_KIND
         param.o[i] = 0;
         param.s[i] = 0;
     }
-    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_KERNEL;
-    param.outHasNoOffset = kind==AF_BATCH_SIGNAL || kind==AF_BATCH_NONE;
-    param.inHasNoOffset  = kind!=AF_BATCH_SAME;
+    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_RHS;
+    param.outHasNoOffset   = kind==AF_BATCH_LHS  || kind==AF_BATCH_NONE;
+    param.inHasNoOffset    = kind!=AF_BATCH_SAME;
 
     switch(baseDim) {
         case 1: convolve_1d<T, aT, expand>(param, out, signal, filt); break;
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index f16c692ebf..5b926ba6da 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -152,7 +152,7 @@ __global__ void complexMultiply(
         out.ptr[ridx].x = c1.x*c2.x - c1.y*c2.y;
         out.ptr[ridx].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
     }
-    else if (kind == AF_BATCH_SIGNAL) {
+    else if (kind == AF_BATCH_LHS) {
         // Complex multiply all signals to filter
         const int ridx1 = t;
         const int ridx2 = t % (in2.strides[3] * in2.dims[3]);
@@ -163,7 +163,7 @@ __global__ void complexMultiply(
         out.ptr[ridx1].x = c1.x*c2.x - c1.y*c2.y;
         out.ptr[ridx1].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
     }
-    else if (kind == AF_BATCH_KERNEL) {
+    else if (kind == AF_BATCH_RHS) {
         // Complex multiply signal to all filters
         const int ridx1 = t % (in1.strides[3] * in1.dims[3]);
         const int ridx2 = t;
@@ -317,12 +317,12 @@ void complexMultiplyHelper(Param<T> out,
             CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_NONE>), blocks, threads,
                     sig_packed, sig_packed, filter_packed, mul_elem);
             break;
-        case AF_BATCH_SIGNAL:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_SIGNAL>), blocks, threads,
+        case AF_BATCH_LHS:
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_LHS>), blocks, threads,
                         sig_packed, sig_packed, filter_packed, mul_elem);
             break;
-        case AF_BATCH_KERNEL:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_KERNEL>), blocks, threads,
+        case AF_BATCH_RHS:
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_RHS>), blocks, threads,
                     filter_packed, sig_packed, filter_packed, mul_elem);
             break;
         case AF_BATCH_SAME:
diff --git a/src/backend/defines.hpp b/src/backend/defines.hpp
index 3816da7e52..b0e97d50ac 100644
--- a/src/backend/defines.hpp
+++ b/src/backend/defines.hpp
@@ -44,8 +44,8 @@ clipFilePath(std::string path, std::string str)
 typedef enum {
     AF_BATCH_UNSUPPORTED = -1, /* invalid inputs */
     AF_BATCH_NONE,             /* one signal, one filter   */
-    AF_BATCH_SIGNAL,           /* many signal, one filter  */
-    AF_BATCH_KERNEL,           /* one signal, many filter  */
+    AF_BATCH_LHS,              /* many signal, one filter  */
+    AF_BATCH_RHS,              /* one signal, many filter  */
     AF_BATCH_SAME,             /* signal and filter have same batch size */
     AF_BATCH_DIFF,             /* signal and filter have different batch size */
 } AF_BATCH_KIND;
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 00593e688b..f17b99563a 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -27,7 +27,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -35,7 +35,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_KERNEL) {
+        if (kind==AF_BATCH_RHS) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 5d4a102f11..16cdb0dd55 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -57,7 +57,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     dim4 oDims(1);
     if (expand) {
         for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_KERNEL) {
+            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
                 oDims[d] = sDims[d]+fDims[d]-1;
             } else {
                 oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
@@ -65,7 +65,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_KERNEL) {
+        if (kind==AF_BATCH_RHS) {
             for (dim_t i=baseDim; i<4; ++i)
                 oDims[i] = fDims[i];
         }
@@ -81,7 +81,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     kernel::complexMultiplyHelper<cT, T, isDouble, convT>(packed, signal, filter, baseDim, kind);
 
     // Compute inverse FFT only on complex-multiplied data
-    if (kind == AF_BATCH_KERNEL) {
+    if (kind == AF_BATCH_RHS) {
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim)
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index efc79f4752..1ee7398204 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -27,7 +27,7 @@ namespace opencl
 
             AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
             if (x.ndims() != b.ndims()) {
-                type = (x.ndims() < b.ndims()) ? AF_BATCH_KERNEL : AF_BATCH_SIGNAL;
+                type = (x.ndims() < b.ndims()) ?  AF_BATCH_RHS  : AF_BATCH_LHS;
             }
 
             // Extract the first N elements
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index 4aefafb9d7..879c0c7e20 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -40,9 +40,9 @@ void convolve_nd(Param out, const Param signal, const Param filter, AF_BATCH_KIN
         param.o[i] = 0;
         param.s[i] = 0;
     }
-    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_KERNEL;
-    param.outHasNoOffset = kind==AF_BATCH_SIGNAL || kind==AF_BATCH_NONE;
-    param.inHasNoOffset  = kind!=AF_BATCH_SAME;
+    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_RHS;
+    param.outHasNoOffset   = kind==AF_BATCH_LHS  || kind==AF_BATCH_NONE;
+    param.inHasNoOffset    = kind!=AF_BATCH_SAME;
 
     prepareKernelArgs<T>(param, out.info.dims, filter.info.dims, baseDim);
 
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 77ac836317..0690e13cb3 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -63,7 +63,7 @@ void calcParamSizes(Param& sig_tmp,
     sig_tmp.data = packed.data;
     filter_tmp.data = packed.data;
 
-    if (kind == AF_BATCH_KERNEL) {
+    if (kind == AF_BATCH_RHS) {
         filter_tmp.info.offset = 0;
         sig_tmp.info.offset = filter_tmp.info.strides[3] * filter_tmp.info.dims[3] * 2;
     }
@@ -172,8 +172,8 @@ void complexMultiplyHelper(Param packed,
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
-                        << " -D AF_BATCH_SIGNAL=" << (int)AF_BATCH_SIGNAL
-                        << " -D AF_BATCH_KERNEL=" << (int)AF_BATCH_KERNEL
+                        << " -D AF_BATCH_LHS="  << (int)AF_BATCH_LHS
+                        << " -D AF_BATCH_RHS="  << (int)AF_BATCH_RHS
                         << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
 
                 if ((af_dtype) dtype_traits<convT>::af_type == c32) {
@@ -287,7 +287,7 @@ void reorderOutputHelper(Param out,
                                 KParam, const int,
                                 const int, const int> (*roKernel[device]);
 
-        if (kind == AF_BATCH_KERNEL) {
+        if (kind == AF_BATCH_RHS) {
             roOp(EnqueueArgs(getQueue(), global, local),
                  *out.data, out.info,
                  *filter_tmp.data, filter_tmp.info,
diff --git a/src/backend/opencl/kernel/fftconvolve_multiply.cl b/src/backend/opencl/kernel/fftconvolve_multiply.cl
index 5eb540d72b..d0310ca0d2 100644
--- a/src/backend/opencl/kernel/fftconvolve_multiply.cl
+++ b/src/backend/opencl/kernel/fftconvolve_multiply.cl
@@ -39,7 +39,7 @@ void complex_multiply(
         d_out[oInfo.offset + ridx] = ac - bd;
         d_out[oInfo.offset + iidx] = (a+b) * (c+d) - ac - bd;
     }
-    else if (kind == AF_BATCH_SIGNAL) {
+    else if (kind == AF_BATCH_LHS) {
         // Complex multiply all signals to filter
         const int ridx1 = t * 2;
         const int iidx1 = t * 2 + 1;
@@ -60,7 +60,7 @@ void complex_multiply(
         d_out[oInfo.offset + ridx1] = ac - bd;
         d_out[oInfo.offset + iidx1] = (a+b) * (c+d) - ac - bd;
     }
-    else if (kind == AF_BATCH_KERNEL) {
+    else if (kind == AF_BATCH_RHS) {
         // Complex multiply signal to all filters
         const int ridx2 = t * 2;
         const int iidx2 = t * 2 + 1;

From 2242ce953c0a51c337d67b0001c308da6d3b3a5a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 14 May 2016 10:00:10 -0400
Subject: [PATCH 0526/2677] Fix OpenCL kernel compilation for dim_t -> int
 change

---
 src/backend/opencl/kernel/approx.hpp |  4 ++--
 src/backend/opencl/kernel/unwrap.hpp |  8 ++++----
 src/backend/opencl/kernel/wrap.hpp   | 10 +++++-----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index d7b5997a9e..275d8fe68e 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -87,7 +87,7 @@ namespace opencl
 
 
                 auto approx1Op = make_kernel<Buffer, const KParam, const Buffer, const KParam,
-                                       const Buffer, const KParam, const float, const dim_t, const int>
+                                       const Buffer, const KParam, const float, const int, const int>
                                       (*approxKernels[device]);
 
                 NDRange local(THREADS, 1, 1);
@@ -155,7 +155,7 @@ namespace opencl
 
                 auto approx2Op = make_kernel<Buffer, const KParam, const Buffer, const KParam,
                                        const Buffer, const KParam, const Buffer, const KParam,
-                                       const float, const dim_t, const dim_t, const int>
+                                       const float, const int, const int, const int>
                                        (*approxKernels[device]);
 
                 NDRange local(TX, TY, 1);
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 7e2b571e93..a6705b3cf3 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -99,10 +99,10 @@ namespace opencl
 
                 auto unwrapOp = make_kernel<Buffer, const KParam,
                                             const Buffer, const KParam,
-                                            const dim_t, const dim_t,
-                                            const dim_t, const dim_t,
-                                            const dim_t, const dim_t,
-                                            const dim_t, const dim_t> (*entry.ker);
+                                            const int, const int,
+                                            const int, const int,
+                                            const int, const int,
+                                            const int, const int> (*entry.ker);
 
                 unwrapOp(EnqueueArgs(getQueue(), global, local),
                        *out.data, out.info, *in.data, in.info, wx, wy, sx, sy, px, py, nx, reps);
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index b99c06dfd9..f16dbe9b37 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -91,11 +91,11 @@ namespace opencl
 
                 auto wrapOp = make_kernel<Buffer, const KParam,
                                           const Buffer, const KParam,
-                                          const dim_t, const dim_t,
-                                          const dim_t, const dim_t,
-                                          const dim_t, const dim_t,
-                                          const dim_t, const dim_t,
-                                          const dim_t, const dim_t> (*entry.ker);
+                                          const int, const int,
+                                          const int, const int,
+                                          const int, const int,
+                                          const int, const int,
+                                          const int, const int> (*entry.ker);
 
                 wrapOp(EnqueueArgs(getQueue(), global, local),
                        *out.data, out.info, *in.data, in.info,

From de06f5a458cc37fb7684325d02be9956be27fb70 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 15 May 2016 22:29:46 -0400
Subject: [PATCH 0527/2677] Moving af_err_to_string to a common file

---
 src/api/c/err_common.cpp  | 29 +++++++++++++++++++++++++++++
 src/api/c/error.cpp       | 29 -----------------------------
 src/api/unified/error.cpp |  7 -------
 3 files changed, 29 insertions(+), 36 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index e95ece6d4b..96c212c684 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -224,3 +224,32 @@ std::string& get_global_error_string()
     static std::string global_error_string = std::string("");
     return global_error_string;
 }
+
+const char *af_err_to_string(const af_err err)
+{
+    switch (err) {
+    case AF_SUCCESS:                return "Success";
+    case AF_ERR_NO_MEM:             return "Device out of memory";
+    case AF_ERR_DRIVER:             return "Driver not available or incompatible";
+    case AF_ERR_RUNTIME:            return "Runtime error ";
+    case AF_ERR_INVALID_ARRAY:      return "Invalid array";
+    case AF_ERR_ARG:                return "Invalid input argument";
+    case AF_ERR_SIZE:               return "Invalid input size";
+    case AF_ERR_TYPE:               return "Function does not support this data type";
+    case AF_ERR_DIFF_TYPE:          return "Input types are not the same";
+    case AF_ERR_BATCH:              return "Invalid batch configuration";
+    case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
+    case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
+    case AF_ERR_NONFREE:            return "Function unavailable. "
+                                           "ArrayFire compiled without Non-Free algorithms support";
+    case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
+    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
+                                           "ArrayFire compiled without Graphics support";
+    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. ";
+    case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
+    case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
+    case AF_ERR_INTERNAL:           return "Internal error";
+    case AF_ERR_UNKNOWN:
+    default:                        return "Unknown error";
+    }
+}
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 521ca9bef5..99d99803fd 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -32,32 +32,3 @@ void af_get_last_error(char **str, dim_t *len)
 
     if(len) *len = slen;
 }
-
-const char *af_err_to_string(const af_err err)
-{
-    switch (err) {
-    case AF_SUCCESS:                return "Success";
-    case AF_ERR_NO_MEM:             return "Device out of memory";
-    case AF_ERR_DRIVER:             return "Driver not available or incompatible";
-    case AF_ERR_RUNTIME:            return "Runtime error ";
-    case AF_ERR_INVALID_ARRAY:      return "Invalid array";
-    case AF_ERR_ARG:                return "Invalid input argument";
-    case AF_ERR_SIZE:               return "Invalid input size";
-    case AF_ERR_TYPE:               return "Function does not support this data type";
-    case AF_ERR_DIFF_TYPE:          return "Input types are not the same";
-    case AF_ERR_BATCH:              return "Invalid batch configuration";
-    case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
-    case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
-    case AF_ERR_NONFREE:            return "Function unavailable. "
-                                           "ArrayFire compiled without Non-Free algorithms support";
-    case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
-    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
-                                           "ArrayFire compiled without Graphics support";
-    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. ";
-    case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
-    case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
-    case AF_ERR_INTERNAL:           return "Internal error";
-    case AF_ERR_UNKNOWN:
-    default:                        return "Unknown error";
-    }
-}
diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index 0224876ec3..8fb04b21d1 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -42,10 +42,3 @@ void af_get_last_error(char **str, dim_t *len)
         func(str, len);
     }
 }
-
-const char *af_err_to_string(const af_err err)
-{
-    typedef char *(*af_func)(af_err);
-    af_func func = (af_func)LOAD_SYMBOL();
-    return func(err);
-}

From b09bee6caa41f678ba7eae7e5137e924874e20c2 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 15 May 2016 22:30:19 -0400
Subject: [PATCH 0528/2677] BUGFIX: Make the unified backend have its own alloc
 and free

---
 src/api/unified/device.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index ed8e6a37f6..5dcf1ce3b5 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -114,12 +114,14 @@ af_err af_free_pinned(void *ptr)
 
 af_err af_alloc_host(void **ptr, const dim_t bytes)
 {
-    return CALL(ptr, bytes);
+    *ptr = malloc(bytes);
+    return (*ptr == NULL) ? AF_ERR_NO_MEM : AF_SUCCESS;
 }
 
 af_err af_free_host(void *ptr)
 {
-    return CALL(ptr);
+    free(ptr);
+    return AF_SUCCESS;
 }
 
 af_err af_device_array(af_array *arr, const void *data, const unsigned ndims, const dim_t * const dims, const af_dtype type)

From ce4ed240208cd0b1e9b0c566af1491ec132b7e31 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 11 May 2016 20:07:17 -0400
Subject: [PATCH 0529/2677] BUGFIX fix incorrect results from timeit

* Timeit now computes the median time from a select number of trial runs that
  are synced after each iteration
* The median time is then used to compute how many batches of the functions to
  run based on some minimum time
* It now runs batch of trial runs and then averages the times from each batch
---
 src/api/cpp/timing.cpp | 40 ++++++++++++++++++++++++++--------------
 1 file changed, 26 insertions(+), 14 deletions(-)

diff --git a/src/api/cpp/timing.cpp b/src/api/cpp/timing.cpp
index 2758021beb..caf77fff60 100644
--- a/src/api/cpp/timing.cpp
+++ b/src/api/cpp/timing.cpp
@@ -10,6 +10,7 @@
 #include <af/timing.h>
 #include <af/device.h>
 #include <math.h>
+#include <vector>
 #include <algorithm>
 
 using namespace af;
@@ -83,32 +84,43 @@ double timer::stop()
 double timeit(void(*fn)())
 {
     // parameters
-    int sample_trials = 3;
-    double min_time = 1;
+    static const int    trials   = 10;  // trial runs
+    static const int    s_trials = 5;   // trial runs
+    static const double min_time = 1;   // seconds
+
+    std::vector<double> sample_times(s_trials);
 
     // estimate time for a few samples
-    double sample_time = 1e99; // INF
-    for (int i = 0; i < sample_trials; ++i) {
+    for (int i = 0; i < s_trials; ++i) {
         sync();
         timer start = timer::start();
         fn();
         sync();
-        sample_time = std::min(sample_time, timer::stop(start));
+        sample_times[i] = timer::stop(start);
     }
 
-    double seconds = std::max(sample_time, min_time); // at least minimum time
-    double elapsed = 0;
-    while (elapsed + sample_time < seconds) {
-        int r = ceilf((seconds - elapsed) / sample_time);
+    // Sort sample times and select the median time
+    std::sort(sample_times.begin(), sample_times.end());
+
+    double median_time = sample_times[s_trials / 2];
+
+    // Run a bunch of batches of fn
+    // Each batch runs trial runs before sync
+    // If trials * median_time < min time,
+    //   then run (min time / (trials * median_time)) batches
+    // else
+    //   run 1 batch
+    int batches = (int)ceilf(min_time / (trials * median_time));
+    double run_time = 0;
+
+    for(int b = 0; b < batches; b++) {
         timer start = timer::start();
-        for (int i = 0; i < r; ++i)
+        for (int i = 0; i < trials; ++i)
             fn();
         sync();
-        double t = timer::stop(start);
-        elapsed += t;
-        sample_time = std::min(sample_time, t / r);
+        run_time += timer::stop(start) / trials;
     }
-    return sample_time;
+    return run_time / batches;
 }
 
 } // namespace af

From 4fa35933d807e4c4a4209662014bdff8db7ebebd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 11 May 2016 21:16:39 -0400
Subject: [PATCH 0530/2677] Set the correct stream for cusolver manager

---
 src/backend/cuda/cusolverDnManager.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cuda/cusolverDnManager.cpp b/src/backend/cuda/cusolverDnManager.cpp
index 7b4564182f..cc243ea338 100644
--- a/src/backend/cuda/cusolverDnManager.cpp
+++ b/src/backend/cuda/cusolverDnManager.cpp
@@ -49,6 +49,7 @@ namespace cusolver {
             : handle(0)
         {
             CUSOLVER_CHECK(cusolverDnCreate(&handle));
+            CUSOLVER_CHECK(cusolverDnSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
         }
 
         ~cusolverDnHandle()

From a31d1c35790e96ee849be8d7406f032e9e9960dd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 16 May 2016 18:06:39 -0400
Subject: [PATCH 0531/2677] Change cusolver back to default stream

* Synchronize the ArrayFire stream when cusolver getHandle is done
* The stream form of cusolver require using cudaDeviceSynchronize and
cudaStreamSynchronize all over the place
* Even then, getrs in solve_lu failed on any stream other than 0
* patch available at: https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
---
 src/backend/cuda/cusolverDnManager.cpp | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/cusolverDnManager.cpp b/src/backend/cuda/cusolverDnManager.cpp
index cc243ea338..3fa1f9ce11 100644
--- a/src/backend/cuda/cusolverDnManager.cpp
+++ b/src/backend/cuda/cusolverDnManager.cpp
@@ -11,6 +11,7 @@
 
 #include <cusolverDnManager.hpp>
 #include <platform.hpp>
+#include <debug_cuda.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -49,7 +50,6 @@ namespace cusolver {
             : handle(0)
         {
             CUSOLVER_CHECK(cusolverDnCreate(&handle));
-            CUSOLVER_CHECK(cusolverDnSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
         }
 
         ~cusolverDnHandle()
@@ -74,6 +74,21 @@ namespace cusolver {
             handle[id].reset(new cusolverDnHandle());
         }
 
+        // FIXME
+        // This is not an ideal case. It's just a hack.
+        // The correct way to do is to use
+        // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
+        // in the class constructor.
+        // However, this is causing a lot of the cusolver functions to fail.
+        // The only way to fix them is to use cudaDeviceSynchronize() and cudaStreamSynchronize()
+        // all over the place, but even then some calls like getrs in solve_lu
+        // continue to fail on any stream other than 0.
+        //
+        // cuSolver Streams patch:
+        // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
+        //
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
+
         return handle[id]->get();
     }
 

From b3fc48563167f3b4363ab7ad0fac9c0c44225c84 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 16 May 2016 18:19:07 -0400
Subject: [PATCH 0532/2677] Reorganize the solve tests macros

---
 test/solve_dense.cpp | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 183afdbcc8..171551e8cf 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -130,7 +130,7 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
 }
 
-#define SOLVE_TESTS(T, eps)                             \
+#define SOLVE_LU_TESTS(T, eps)                          \
     TEST(SOLVE_LU, T##Reg)                              \
     {                                                   \
         solveLUTester<T>(1000, 100, eps);               \
@@ -139,6 +139,9 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
     {                                                   \
         solveLUTester<T>(2048, 512, eps);               \
     }                                                   \
+
+
+#define SOLVE_TRIANGLE_TESTS(T, eps)                    \
     TEST(SOLVE_Upper, T##Reg)                           \
     {                                                   \
         solveTriangleTester<T>(1000, 100, true, eps);   \
@@ -155,6 +158,8 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
     {                                                   \
         solveTriangleTester<T>(2048, 512, false, eps);  \
     }                                                   \
+
+#define SOLVE_GENERAL_TESTS(T, eps)                     \
     TEST(SOLVE, T##Square)                              \
     {                                                   \
         solveTester<T>(1000, 1000, 100, eps);           \
@@ -163,6 +168,8 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
     {                                                   \
         solveTester<T>(2048, 2048, 512, eps);           \
     }                                                   \
+
+#define SOLVE_LEASTSQ_TESTS(T, eps)                     \
     TEST(SOLVE, T##RectUnder)                           \
     {                                                   \
         solveTester<T>(800, 1000, 200, eps);            \
@@ -171,23 +178,21 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
     {                                                   \
         solveTester<T>(1536, 2048, 400, eps);           \
     }                                                   \
+    TEST(SOLVE, T##RectOver)                            \
+    {                                                   \
+        solveTester<T>(800, 600, 64, eps);              \
+    }                                                   \
     TEST(SOLVE, T##RectOverMultiple)                    \
     {                                                   \
         solveTester<T>(1536, 1024, 1, eps);             \
-    }
-
-SOLVE_TESTS(float, 0.01)
-SOLVE_TESTS(double, 1E-5)
-SOLVE_TESTS(cfloat, 0.01)
-SOLVE_TESTS(cdouble, 1E-5)
+    }                                                   \
 
-#undef SOLVE_TESTS
+#define SOLVE_TESTS(T, eps)                             \
+    SOLVE_GENERAL_TESTS(T, eps)                         \
+    SOLVE_LEASTSQ_TESTS(T, eps)                         \
+    SOLVE_LU_TESTS(T, eps)                              \
+    SOLVE_TRIANGLE_TESTS(T, eps)                        \
 
-#define SOLVE_TESTS(T, eps)                     \
-    TEST(SOLVE, T##RectOver)                    \
-    {                                           \
-        solveTester<T>(800, 600, 64, eps);      \
-    }
 
 SOLVE_TESTS(float, 0.01)
 SOLVE_TESTS(double, 1E-5)

From ff1e1125386d3da4ae10a74fffb80394950f8d9c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 16 May 2016 18:24:47 -0400
Subject: [PATCH 0533/2677] Remove requirement of 2D matrices for rgb <-> gray

---
 src/api/c/rgb_gray.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index da7ebb2bf3..85cf938b33 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -109,8 +109,13 @@ af_err convert(af_array* out, const af_array in, const float r, const float g, c
         af_dtype iType     = info.getType();
         af::dim4 inputDims = info.dims();
 
-        ARG_ASSERT(1, (inputDims.ndims()>=2));
+        // 2D is not required.
+        ARG_ASSERT(1, info.elements() > 0);
+
+        // If RGB is input, then assert 3 channels
+        // else 1 channel
         if (isRGB2GRAY) ARG_ASSERT(1, (inputDims[2]==3));
+        else            ARG_ASSERT(1, (inputDims[2]==1));
 
         af_array output = 0;
         switch(iType) {

From 2ddbb1dbd38ade0b68c9403d66995cd8df629e5f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 17 May 2016 14:35:23 -0400
Subject: [PATCH 0534/2677] Update Boost.Compute tag to Boost 1.61.0

---
 CMakeModules/build_boost_compute.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
index 03c20435a8..37f8fc3ad3 100644
--- a/CMakeModules/build_boost_compute.cmake
+++ b/CMakeModules/build_boost_compute.cmake
@@ -1,9 +1,9 @@
 # If using a commit, remove the v prefix to VER in URL.
 # If using a tag, don't use v in VER
 # This is because of how github handles it's release tar balls
-SET(VER 0.5)
-SET(URL https://github.com/boostorg/compute/archive/v${VER}.tar.gz)
-SET(MD5 69a52598ac539d3b7f6005a3dd2b6f58)
+SET(VER boost-1.61.0)
+SET(URL https://github.com/boostorg/compute/archive/${VER}.tar.gz)
+SET(MD5 7e1c433b48825d8cb2effa963823aec8)
 
 SET(thirdPartyDir "${CMAKE_BINARY_DIR}/third_party")
 SET(srcDir "${thirdPartyDir}/compute-${VER}")

From d272bfe68fb6f73966097faef1c0cb3c8a9f4e45 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Tue, 17 May 2016 17:10:59 -0400
Subject: [PATCH 0535/2677] [Docs] Fix functions left out of documentation.

---
 include/af/array.h  | 10 +++++-----
 include/af/opencl.h | 16 +++++++++++-----
 2 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index de746d9384..b2c5dd4f35 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -330,7 +330,7 @@ namespace af
 
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer.
+            \note If \p src is \ref afHost, the first \p dim0 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership ownership of the underlying memory.
 
         */
         template<typename T>
@@ -354,7 +354,7 @@ namespace af
 
             \image html 2dArray.png
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
         */
         template<typename T>
         array(dim_t dim0, dim_t dim1,
@@ -378,7 +378,7 @@ namespace af
             array A(3, 3, 2,  h_buffer);   // copy host data to 3D device array
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 * \p dim2 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 * \p dim2 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer and does not take ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
 
             \image html 3dArray.png
         */
@@ -407,7 +407,7 @@ namespace af
             array A(2, 2, 2, 2, h_buffer);   // copy host data to 4D device array
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 * \p dim2 * \p dim3 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 * \p dim2 * \p dim3 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer and does not take ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
         */
         template<typename T>
         array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,
@@ -444,7 +444,7 @@ namespace af
                                              // used in ArrayFire
             \endcode
 
-            \note If \p src is \ref afHost, the first dims.elements() elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first dims.elements() elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer and does not take ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
         */
         template<typename T>
         explicit
diff --git a/include/af/opencl.h b/include/af/opencl.h
index 34206325eb..9d8f7c36c8 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -163,13 +163,11 @@ AFAPI af_err afcl_get_platform(afcl_platform *res);
 namespace afcl
 {
 
-/**
-
- */
  /**
-     \ingroup opencl_mat
+     \addtogroup opencl_mat
      @{
  */
+
  /**
  Get a handle to ArrayFire's OpenCL context
 
@@ -304,7 +302,7 @@ static inline deviceType getDeviceType()
 
 #if AF_API_VERSION >= 33
 /**
-   Get the type of the current device
+   Get the type of the current platform
 */
 static inline platform getPlatform()
 {
@@ -438,6 +436,10 @@ static inline platform getPlatform()
 
 namespace af
 {
+ /**
+     \addtogroup opencl_mat
+     @{
+ */
 
 #if !defined(AF_OPENCL)
 template<> AFAPI cl_mem *array::device() const
@@ -449,6 +451,10 @@ template<> AFAPI cl_mem *array::device() const
 }
 #endif
 
+/**
+   @}
+*/
+
 }
 
 #endif

From 5eeafe611fad5c3e9e6f1ffebe3b5f744388f23e Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Tue, 17 May 2016 17:11:35 -0400
Subject: [PATCH 0536/2677] [Docs] Clarify getting started text.

---
 docs/pages/getting_started.md | 115 +++++++++++++++++++++++++---------
 test/getting_started.cpp      |   8 +++
 2 files changed, 93 insertions(+), 30 deletions(-)

diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index c1ae05e9d2..02997ee868 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -3,10 +3,22 @@ Getting Started {#gettingstarted}
 
 [TOC]
 
+# Introduction
+
+ArrayFire is a high performance software library for parallel computing with
+an easy-to-use API. ArrayFire abstracts away much of the details of
+programming parallel architectures by providing a high-level container object,
+the [array](\ref af::array), that represents data stored on a CPU, GPU, FPGA,
+or other type of accelerator. This abstraction permits developers to write
+massively parallel applications in a high-level language where they need
+not be concerned about low-level optimizations that are frequently required to
+achieve high throughput on most parallel architectures.
+
 # Supported data types {#gettingstarted_datatypes}
 
-There is one generic [array](\ref af::array) container object while the
-underlying data may be one of various [basic types](\ref af::af_dtype):
+ArrayFire provides one generic container object, the [array](\ref af::array)
+on which functions and mathematical operations are performed. The `array`
+can represent one of many different [basic data types](\ref af::af_dtype):
 
 * [b8](\ref b8) 8-bit boolean values (`bool`)
 * [f32](\ref f32) real single-precision (`float`)
@@ -20,67 +32,108 @@ underlying data may be one of various [basic types](\ref af::af_dtype):
 * [s16](\ref s16) 16-bit signed integer (`short`)
 * [u16](\ref u16) 16-bit unsigned integer (`unsigned short`)
 
-Older devices may not support double precision operations.
+Most of these data types are supported on all modern GPUs; however, some
+older devices may lack support for double precision arrays. In this case,
+a runtime error will be generated when the array is constructed.
+
+If not specified otherwise, `array`s are created as single precision floating
+point numbers (`f32`).
 
 # Creating and populating an ArrayFire array {#getting_started_af_arrays}
 
-ArrayFire [array](\ref af::array)s always exist on the device. They
-may be populated with data using an ArrayFire function, or filled with data
-found on the host. For example:
+ArrayFire [array](\ref af::array)s represent memory stored on the device.
+As such, creation and population of an array will consume memory on the device
+which cannot freed until the `array` object goes out of scope. As device memory
+allocation can be expensive, ArrayFire also includes a memory manager which
+will re-use device memory whenever possible.
+
+Arrays can be created using one of the [array constructors](\ref #construct_mat).
+Below we show how to create 1D, 2D, and 3D arrays with uninitialized values:
+
+\snippet test/getting_started.cpp ex_getting_started_constructors
+
+However, uninitialized memory is likely not useful in your application.
+ArrayFire provides several convenient functions for creating arrays that contain
+pre-populated values including constants, uniform random numbers, uniform
+normally distributed numbers, and the identity matrix:
 
 \snippet test/getting_started.cpp ex_getting_started_gen
 
 A complete list of ArrayFire functions that automatically generate data
 on the device may be found on the [functions to create arrays](\ref data_mat)
-page. The default data type for arrays is [f32](\ref f32) (a
+page. As stated above, the default data type for arrays is [f32](\ref f32) (a
 32-bit floating point number) unless specified otherwise.
 
-ArrayFire arrays may also be populated from data found on the host.
+ArrayFire `array`s may also be populated from data found on the host.
 For example:
 
 \snippet test/getting_started.cpp ex_getting_started_init
 
-ArrayFire also supports array initialization from a device pointer.
-For example ArrayFire can be populated directly by a call to `cudaMemcpy`
+ArrayFire also supports array initialization from memory already on the GPU.
+For example, with CUDA one can populate an `array` directly using a call
+to `cudaMemcpy`:
 
 \snippet test/getting_started.cpp ex_getting_started_dev_ptr
 
+Similar functionality exists for OpenCL too. If you wish to intermingle
+ArrayFire with CUDA or OpenCL code, we suggest you consult the
+[CUDA interoperability](\ref interop_cuda) or
+[OpenCL interoperability](\ref interop_opencl) pages for detailed instructions.
+
 # ArrayFire array contents, dimensions, and properties {#getting_started_array_properties}
 
+ArrayFire provides several functions to determine various aspects of arrays.
+This includes functions to print the contents, query the dimensions, and
+determine various other aspects of arrays.
+
 The [af_print](\ref af::af_print) function can be used to print arrays that
-have already been generated or an expression involving arrays:
+have already been generated or any expression involving arrays:
 
 \snippet test/getting_started.cpp ex_getting_started_print
 
-ArrayFire provides several convenient methods for accessing the dimensions.
-You may use either a [dim4](\ref af::dim4) object or access the dimensions
-directly using the [dims()](\ref af::array::dims) and
-[numdims()](\ref af::array::numdims) functions:
+The dimensions of an array may be determined using either a
+[dim4](\ref af::dim4) object or by accessing the dimensions directly using the
+[dims()](\ref af::array::dims) and [numdims()](\ref af::array::numdims)
+functions:
 
 \snippet test/getting_started.cpp ex_getting_started_dims
 
-Arrays also provide functions to determine their properties including:
+In addition to dimensions, arrays also carry several properties including
+methods to determine the underlying type and size (in bytes). You can even
+determine whether the array is empty, real/complex, a row/column, or a scalar
+or a vector:
 
 \snippet test/getting_started.cpp ex_getting_started_prop
 
+For further information on these capabilities, we suggest you consult the
+full documentation on the [array](\ref af::array).
+
 # Writing mathematical expressions in ArrayFire {#getting_started_writing_math}
 
-Most of ArrayFire's functions operate on an element-wise basis.
-This means that function like `c[i] = a[i] + b[i]` could simply be written
-as `c = a + b`.
-ArrayFire has an intelligent runtime JIT compliation engine which converts
-array expressions into the smallest number of OpenCL/CUDA kernels.
-This "kernel fusion" technology not only decreases the number of kernel calls,
-but, more importantly, avoids extraneous global memory operations.
+ArrayFire features an intelligent Just-In-Time (JIT) compilation engine that
+converts expressions using arrays into the smallest number of CUDA/OpenCL
+kernels. For most operations on arrays, ArrayFire functions like a vector library.
+That means that an element-wise operation, like `c[i] = a[i] + b[i]` in C,
+would be written more concisely without indexing, like `c = a + b`.
+When there are multiple expressions involving arrays, ArrayFire's JIT engine
+will merge them together. This "kernel fusion" technology not only decreases
+the number of kernel calls, but, more importantly, avoids extraneous global
+memory operations.
 Our JIT functionality extends across C/C++ function boundaries and only ends
 when a non-JIT function is encountered or a synchronization operation is
 explicitly called by the code.
 
-ArrayFire has [hundreds of functions](\ref arith_mat) for element-wise
-arithmetic. Here are a few examples:
+ArrayFire provides [hundreds of functions](\ref arith_mat) for element-wise
+operations. All of the standard operators (e.g. +,-,*,/) are supported
+as are most transcendental functions (sin, cos, log, sqrt, etc.).
+Here are a few examples:
 
 \snippet test/getting_started.cpp ex_getting_started_arith
 
+To see the complete list of functions please consult the documentation on
+[mathematical](\ref mathfunc_mat), [linear algebra](\ref linalg_mat),
+[signal processing](\ref signal_mat), and [statistics](\ref stats_mat).
+
 # Mathematical constants {#getting_started_constants}
 
 ArrayFire contains several platform-independent constants, like
@@ -119,11 +172,13 @@ use these functions.
 # Getting access to ArrayFire array memory on the host and device {#getting_started_memory_access}
 
 Memory in `af::array`s may be accessed using the [host()](\ref af::array::host)
-and device()](\ref af::array::device) functions.
+and [device()](\ref af::array::device) functions.
 The `host` function *copies* the data from the device and makes it available
-in a C-style array on the host.
-The `device` function returns a pointer to device memory for interoperability
-with external CUDA/OpenCL kernels.
+in a C-style array on the host. As such, it is up to the developer to manage
+any memory returned by `host`.
+The `device` function returns a pointer/reference to device memory for
+interoperability with external CUDA/OpenCL kernels. As this memory belongs to
+ArrayFire, the programmer should not attempt to free/deallocate the pointer.
 For example, here is how we can interact with both OpenCL and CUDA:
 
 \snippet test/getting_started.cpp ex_getting_started_ptr
@@ -139,7 +194,7 @@ get it using the [scalar()](\ref af::array::scalar) function:
 
 # Bitwise operators {#getting_started_bitwise_operators}
 
-In addition to supporting standard mathematical functions, `af::array`s
+In addition to supporting standard mathematical functions, arrays
 that contain integer data types also support bitwise operators including
 and, or, and shift:
 
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index 9d77af2b30..32983a6e63 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -20,6 +20,14 @@ using std::abs;
 TEST(GettingStarted, SNIPPET_getting_started_gen)
 {
 
+    //! [ex_getting_started_constructors]
+    // Arrays may be created using the af::array constructor and dimensioned
+    // as 1D, 2D, 3D; however, the values in these arrays will be undefined
+    array undefined_1D = array(100);        // 1D array with 100 elements
+    array undefined_2D = array(10, 100);    // 2D array of size 10 x 100
+    array undefined_3D = array(10, 10, 10); // 3D array of size 10 x 10 x 10
+    //! [ex_getting_started_constructors]
+
     //! [ex_getting_started_gen]
     // Generate an array of size three filled with zeros.
     // If no data type is specified, ArrayFire defaults to f32.

From 0a25d36238aa1eee3b775d3584937ca65b0a1807 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Tue, 17 May 2016 17:12:03 -0400
Subject: [PATCH 0537/2677] [Docs] Rewrite vectorization and array manipulation
 documentation.

---
 docs/pages/matrix_manipulation.md | 181 ++++++++++++++++++++----------
 docs/pages/vectorization.md       | 147 +++++++++++++++---------
 2 files changed, 213 insertions(+), 115 deletions(-)

diff --git a/docs/pages/matrix_manipulation.md b/docs/pages/matrix_manipulation.md
index 8fde4882d9..38e7219069 100644
--- a/docs/pages/matrix_manipulation.md
+++ b/docs/pages/matrix_manipulation.md
@@ -1,21 +1,27 @@
-Matrix Manipulation {#matrixmanipulation}
+Array and Matrix Manipulation {#matrixmanipulation}
 ===================
 
-Many different kinds of [matrix manipulation routines](\ref manip_mat) are available:
+ArrayFire provides several different methods for
+[manipulating arrays and matrices](\ref manip_mat). The functionality includes:
 
+* moddims() - change the dimensions of an array without changing the data
+* array() - create a (shallow) copy of an array with different dimensions.
 * flat() - flatten an array to one dimension
 * flip() - flip an array along a dimension
 * join() - join up to 4 arrays
-* moddims() - change the dimensions of an array without changing the data
 * reorder() - changes the dimension order within the array
 * shift() - shifts data along a dimension
 * tile() - repeats an array along a dimension
 * transpose() - performs a matrix transpose
-* [array()](\ref af::array) to adjust the dimensions of an array
-* [transpose](\ref af::array::T) a matrix or vector with shorthand notation
+* [T()](\ref af::array::T) - transpose a matrix or vector (shorthand notation)
+* [H()](\ref af::array::H) - Hermitian Transpose (conjugate-transpose) a matrix
+
+Below we provide several examples of these functions and their use.
 
 ## flat()
-The __flat()__ function flattens an array to one dimension.
+
+The __flat()__ function flattens an array to one dimension:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 3 1 1]
     1.0000     4.0000     7.0000
@@ -34,16 +40,20 @@ flat(a) [9 1 1 1]
     9.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The flat function has the following overloads:
-> __array af::flat(const array& in)__
-> --  flatten an array
+The flat function can be called from C and C++ as follows:
 
 > __af_err af_flat(af_array* out, const af_array in)__
 > --  C interface for flat() function
 
+> __array af::flat(const array& in)__
+> --  C++ interface for flat() function
 
 ## flip()
+
 The __flip()__ function flips the contents of an array along a chosen dimension.
+In the example below, we show the 5x2 array flipped along the zeroth (i.e.
+within a column) and first (e.g. across rows) axes:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [5 2 1 1]
     1.0000     6.0000
@@ -66,14 +76,21 @@ flip(a, 1) [5 2 1 1]
     9.0000     4.0000
    10.0000     5.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The flip function has the following overloads:
-> __array af::flip(const array &in, const unsigned dim)__
-> --  flips an array along a dimension 
+
+The flip function can be called from C and C++ as follows:
+
 > __af_err af_flip(af_array *out, const af_array in, const unsigned dim)__
 > --  C interface for flip()
 
+> __array af::flip(const array &in, const unsigned dim)__
+> --  C++ interface for flip()
+
 ## join()
-The __join()__ function can join up to 4 arrays together.
+
+The __join()__ function joins arrays along a specific dimension. The C++
+interface can join up to four arrays whereas the C interface supports up to 10
+arrays. Here is an example of how to use join an array to itself:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [5 1 1 1]
     1.0000
@@ -101,7 +118,17 @@ join(1, a, a) [5 2 1 1]
     4.0000     4.0000
     5.0000     5.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The join function has several overloads:
+
+The join function has several candidate functions in C:
+
+> __af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)__
+> --  C interface function to join 2 arrays along a dimension
+
+> __af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs)__
+> --  C interface function to join up to 10 arrays along a dimension
+
+and in C++:
+
 > __array af::join(const int dim, const array &first, const array &second)__
 > --  Joins 2 arrays along a dimension
 
@@ -111,14 +138,15 @@ The join function has several overloads:
 > __array af::join(const int dim, const array &first, const array &second, const array &third, const array &fourth)__
 > --  Joins 4 arrays along a dimension
 
-> __af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)__
-> --  C interface function to join 2 arrays along a dimension
-
-> __af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs)__
-> --  C interface function to join up to 10 arrays along a dimension
 
 ## moddims()
-The __moddims()__ function changes the dimensions of an array without changing its data or order. It is important to remember that the function only modifies the _metadata_ associated with the array and does not actually modify the content of the array.
+
+The __moddims()__ function changes the dimensions of an array without changing
+its data or order. Note that this function modifies only the _metadata_
+associated with the array. It does not modify the content of the array.
+Here is an example of moddims() converting an 8x1 array into a 2x4 and then
+back to a 8x1:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [8 1 1 1]
     1.0000
@@ -145,7 +173,14 @@ moddims(a, a.elements(), 1, 1, 1) [8 1 1 1]
     1.0000
     2.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The moddims function has several overloads:
+
+The moddims function has a single form in the C API:
+
+> __af_err af_moddims(af_array *out, const af_array in, const unsigned ndims, const dim_t *const dims)__
+> --  C interface to mod dimensions of an array
+
+And several overloaded candidates in the C++ API:
+
 > __array af::moddims(const array &in, const unsigned ndims, const dim_t *const dims)__
 > --  mods number of dimensions to match _ndims_ as specidied in the array _dims_
 
@@ -155,12 +190,12 @@ The moddims function has several overloads:
 > __array af::moddims(const array &in, const dim_t d0, const dim_t d1=1, const dim_t d2=1, const dim_t d3=1)__
 > --  mods dimensions of an array
 
-> __af_err af_moddims(af_array *out, const af_array in, const unsigned ndims, const dim_t *const dims)__
-> --  C interface to mod dimensions of an array
-
 ## reorder()
-The __reorder()__ function changes the order of the dimensions within the array.
-This actually alters the underlying data of the array.
+
+The __reorder()__ function modifies the order of data within an array by
+exchanging data according to the change in dimensionality. The linear ordering
+of data within the array is preserved.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [2 2 3 1]
     1.0000     3.0000
@@ -193,15 +228,20 @@ reorder(a, 2, 0, 1) [3 2 2 1]
     3.0000     4.0000
     3.0000     4.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The reorder function the following several overloads:
-> __array af::reorder(const array &in, const unsigned x, const unsigned y=1, const unsigned z=2, const unsigned w=3)__
-> --  Reorders dimensions of an array
+
+The reorder function has several candidates functions in the C/C++ APIs:
 
 > __af_err af_reorder(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__
 > --  C interface for reordering function
 
+> __array af::reorder(const array &in, const unsigned x, const unsigned y=1, const unsigned z=2, const unsigned w=3)__
+> --  Reorders dimensions of an array
+
 ## shift()
-The __shift()__ function shifts data in a circular buffer fashion along a chosen dimension.
+
+The __shift()__ function shifts data in a circular buffer fashion along a
+chosen dimension. Consider the following example:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 5 1 1]
     0.0000     0.0000     0.0000     0.0000     0.0000
@@ -218,21 +258,29 @@ shift(a, -1, 2 ) [3 5 1 1]
     1.0000     2.0000     3.0000     4.0000     5.0000
     0.0000     0.0000     0.0000     0.0000     0.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The shift function has the following overloads:
-> __array af::shift(const array &in, const int x, const int y=0, const int z=0, const int w=0)__
-> --  Shifts array along specified dimensions
+
+The shift function can be called from C and C++ as follows:
+
 
 > __af_err af_shift(af_array *out, const af_array in, const int x, const int y, const int z, const int w)__
 > --  C interface for shifting an array
 
+> __array af::shift(const array &in, const int x, const int y=0, const int z=0, const int w=0)__
+> --  Shifts array along specified dimensions
+
 ## tile()
-The __tile()__ function repeats an array along a dimension
+
+The __tile()__ function repeats an array along the specified dimension.
+For example below we show how to tile an array along the zeroth and first
+dimensions of an array:
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 1 1 1]
     1.0000
     2.0000
     3.0000
 
+// Repeat array a twice in the zeroth dimension
 tile(a, 2) [6 1 1 1]
     1.0000
     2.0000
@@ -241,6 +289,7 @@ tile(a, 2) [6 1 1 1]
     2.0000
     3.0000
 
+// Repeat array a twice along both the zeroth and first dimensions
 tile(a, 2, 2) [6 2 1 1]
     1.0000     1.0000
     2.0000     2.0000
@@ -249,6 +298,8 @@ tile(a, 2, 2) [6 2 1 1]
     2.0000     2.0000
     3.0000     3.0000
 
+// Repeat array a twice along the first and three times along the second
+// dimension.
 af::dim4 tile_dims(1, 2, 3);
 tile(a, tile_dims) [3 2 3 1]
     1.0000     1.0000
@@ -264,18 +315,24 @@ tile(a, tile_dims) [3 2 3 1]
     3.0000     3.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The tile function has several overloads:
+The C interface for tile is as follows:
+
+> __af_err af_tile(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__
+> --  C interface for tiling an array
+
+The C++ interface has two overloads
+
 > __array af::tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)__
 > --  Tiles array along specified dimensions
 
-> __array af::tile(const array &in, const dim4 &dims)__  
+> __array af::tile(const array &in, const dim4 &dims)__
 > --  Tile an array according to a dim4 object
 
-> __af_err af_tile(af_array *out, const af_array in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)__
-> --  C interface for tiling an array
-
 ## transpose()
-The __transpose()__ function performs a standard matrix transpose. The input array must have the dimensions of a 2D-matrix.
+
+The __transpose()__ function performs a standard matrix transpose. The input
+array must have the dimensions of a 2D-matrix.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 a [3 3 1 1]
     1.0000     3.0000     3.0000
@@ -288,12 +345,7 @@ transpose(a) [3 3 1 1]
     3.0000     3.0000     1.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The transpose function has several overloads:
-> __array af::transpose(const array &in, const bool conjugate=false)__
-> --   Transposes a matrix.
-
-> __void af::transposeInPlace(array &in, const bool conjugate=false)__
-> --   Transposes a matrix in-place.
+The C interfaces for transpose are as follows:
 
 > __af_err af_transpose(af_array *out, af_array in, const bool conjugate)__
 > --   C interface to transpose a matrix.
@@ -301,27 +353,42 @@ The transpose function has several overloads:
 > __af_err af_transpose_inplace(af_array in, const bool conjugate)__
 > --   C interface to transpose a matrix in-place.
 
-[array()](\ref af::array) can be used to create a (shallow) copy of a matrix
-with different dimensions.  The number of elements must remain the same as
-the original array.
+The C++ interface has two primary functions and two shorthand versions:
+
+> __array af::transpose(const array &in, const bool conjugate=false)__
+> --   Transposes a matrix.
+
+> __void af::transposeInPlace(array &in, const bool conjugate=false)__
+> --   Transposes a matrix in-place.
+
+> __array af::T()
+> --   Transpose a matrix
 
-\snippet test/matrix_manipulation.cpp ex_matrix_manipulation_moddims
+> __array af::H()
+> --   Conjugate Transpose (Hermitian transpose) of a matrix
 
-The [T()](\ref af::array::T) and [H()](\ref af::array::H) methods can be
-used to form the [matrix or vector transpose](\ref af::array::T) .
+Here is an example of how the shorthand versions might be used:
 
 \snippet test/matrix_manipulation.cpp ex_matrix_manipulation_transpose
 
+## array()
+
+[array()](\ref af::array) can be used to create a (shallow) copy of a matrix
+with different dimensions. The total number of elements must remain the same.
+This function is a wrapper over the moddims() function discussed earlier.
+
 # Combining re-ordering functions to enumerate grid coordinates
-By using a combination of the array restructuring functions, we can quickly code
+
+By using a combination of the array restructuring functions, one can quickly code
 complex manipulation patterns with a few lines of code. For example, consider
 generating (*x,y*) coordinates for a grid where each axis goes from *1 to n*.
 Instead of using several loops to populate our arrays we can just use a small
 combination of the above functions.
+
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 unsigned n=3;
-af::array xy = join(1
-                tile(seq(1, n), n)
+af::array xy = join(1,
+                tile(seq(1, n), n),
                 flat( transpose(tile(seq(1, n), 1, n)) )
                    );
 xy [9 2 1 1]
@@ -335,7 +402,3 @@ xy [9 2 1 1]
     2.0000     3.0000
     3.0000     3.0000
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# Conclusion
-Functions provided by arrayfire offer ease and flexibility for efficiently
-manipulating the structure of arrays. The provided functions can be used as 
-building blocks to generate, shift, or prepare data to any form imaginable!
diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index 8805ecf369..ea3279c6f8 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -2,19 +2,19 @@ Introduction to Vectorization {#vectorization}
 ===================
 
 Programmers and Data Scientists want to take advantage of fast and parallel
-computational devices. Writing vectorized code is becoming a necessity to get
+computational devices. Writing vectorized code is necessary to get
 the best performance out of the current generation parallel hardware and
 scientific computing software. However, writing vectorized code may not be
-intuitive immediately. Arrayfire provides many ways to vectorize a given code
-segment. In this tutorial, we will be presenting various ways to vectorize code
-using ArrayFire and the benefits and drawbacks associated with each method.
+immediately intuitive. ArrayFire provides many ways to vectorize a given code
+segment. In this tutorial, we present several methods to vectorize code
+using ArrayFire and discuss the benefits and drawbacks associated with each method.
 
 # Generic/Default vectorization
-By its very nature, Arrayfire is a vectorized library. Most functions operate on
+
+By its very nature, ArrayFire is a vectorized library. Most functions operate on
 arrays as a whole -- on all elements in parallel. Wherever possible, existing
 vectorized functions should be used opposed to manually indexing into arrays.
-For example, consider this valid, yet mislead code that attempts to increment
-each element of an array:
+For example consider the following code:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10); // [0,  9]
@@ -24,36 +24,38 @@ for(int i = 0; i < a.dims(0); ++i)
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Instead, the existing vectorized Arrayfire overload of the + operator should have been used:
+Although completely valid, the code is very inefficient as it results in
+a kernel kernels that operate on one datum.
+Instead, the developer should have used ArrayFire's overload of the + operator:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10);  // [0,  9]
 a = a + 1;                    // [1, 10]
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Most Arrayfire functions are vectorized. A small subset of these include:
+This code will result in a single kernel that operates on all 10 elements
+of `a` in  parallel.
+
+Most ArrayFire functions are vectorized. A small subset of these include:
 
 Operator Category                                           | Functions
 ------------------------------------------------------------|--------------------------
 [Arithmetic operations](\ref arith_mat)                     | [+](\ref arith_func_add), [-](\ref arith_func_sub), [*](\ref arith_func_mul), [/](\ref arith_func_div), [%](\ref arith_func_mod), [>>](\ref arith_func_shiftr), [<<](\ref arith_func_shiftl)
-[Complex operations](\ref complex_mat)                      | real(), imag(), conj(), etc.
-[Exponential and logarithmic functions](\ref explog_mat)    | exp(), log(), expm1(), log1p(), etc.
-[Hyperbolic functions](\ref hyper_mat)                      | sinh(), cosh(), tanh(), etc.
 [Logical operations](\ref logic_mat)                        | [&&](\ref arith_func_and), \|\|[(or)](\ref arith_func_or), [<](\ref arith_func_lt), [>](\ref arith_func_gt), [==](\ref arith_func_eq), [!=](\ref arith_func_neq) etc.
 [Numeric functions](\ref numeric_mat)                       | abs(), floor(), round(), min(), max(), etc.
+[Complex operations](\ref complex_mat)                      | real(), imag(), conj(), etc.
+[Exponential and logarithmic functions](\ref explog_mat)    | exp(), log(), expm1(), log1p(), etc.
 [Trigonometric functions](\ref trig_mat)                    | sin(), cos(), tan(), etc.
+[Hyperbolic functions](\ref hyper_mat)                      | sinh(), cosh(), tanh(), etc.
 
 In addition to element-wise operations, many other functions are also
-vectorized in Arrayfire.
+vectorized in ArrayFire.
 
-Vector operations such as min() support vectorization:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array arr = randn(100);
-std::cout << min<float>(arr) << std::endl;
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Signal processing functions like convolve() support vectorization:
+Notice that even that perform some form of aggregation (e.g. `sum()` or `min()`),
+signal processing (like `convolve()`), and even image processing functions
+(i.e. `rotate()`) all support vectorization on different columns or images.
+For example, if we have `NUM` images of size `WIDTH` by `HEIGHT`, one could
+convolve each image in a vector fashion as follows:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 float g_coef[] = { 1, 2, 1,
@@ -66,19 +68,26 @@ af::array signal = randu(WIDTH, HEIGHT, NUM);
 af::array conv = convolve2(signal, filter);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Image processing functions such as rotate() support vectorization:
+Similarly, one can rotate 100 images by 45 degrees in a single call using
+code like the following:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-af::array imgs = randu(WIDTH, HEIGHT, 100); // 100 (WIDTH x HEIGHT) images
-af::array rot_imgs = rotate(imgs, 45); // 100 rotated images
+// Construct an array of 100 WIDTH x HEIGHT images of random numbers
+af::array imgs = randu(WIDTH, HEIGHT, 100);
+// Rotate all of the images in a single command
+af::array rot_imgs = rotate(imgs, 45);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-One class of functions that does not support vectorization is the set of linear
-algebra functions. Using the built in vectorized operations should be the first
-and preferred method of vectorizing any code written with Arrayfire.
+Although *most* functions in ArrayFire do support vectorization, some do not.
+Most notably, all linear algebra functions. Even though they are not vectorized
+linear algebra operations still execute in parallel on your hardware.
+
+Using the built in vectorized operations should be the first
+and preferred method of vectorizing any code written with ArrayFire.
 
 # GFOR: Parallel for-loops
-Another novel method of vectorization present in Arrayfire is the GFOR loop
+
+Another novel method of vectorization present in ArrayFire is the GFOR loop
 replacement construct. GFOR allows launching all iterations of a loop in parallel
 on the GPU or device, as long as the iterations are independent. While the
 standard for-loop performs each iteration sequentially, ArrayFire's gfor-loop
@@ -89,7 +98,8 @@ of your code, e.g. you write a gfor-loop that increments every element of a vect
 but behind the scenes ArrayFire rewrites it to operate on the entire vector in
 parallel.
 
-We can remedy our first example with GFOR:
+The original for-loop example at the beginning of this document could be
+rewritten using GFOR as follows:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 af::array a = af::range(10);
@@ -97,14 +107,21 @@ gfor(seq i, n)
     a(i) = a(i) + 1;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+In this case, each instance of the gfor loop is independent, thus ArrayFire
+will automatically tile out the `a` array in device memory and execute the
+increment kernels in parallel.
+
 To see another example, you could run an accum() on every slice of a matrix in a
 for-loop, or you could "vectorize" and simply do it all in one gfor-loop operation:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// runs each accum() in sequence
 for (int i = 0; i < N; ++i)
-   B(span,i) = accum(A(span,i)); // runs each accum() in sequence
+   B(span,i) = accum(A(span,i));
+
+// runs N accums in parallel
 gfor (seq i, N)
-   B(span,i) = accum(A(span,i)); // runs N accums in parallel
+   B(span,i) = accum(A(span,i));
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 However, returning to our previous vectorization technique, accum() is already
@@ -130,51 +147,62 @@ gfor(seq i, n)
     combination(span, i) = consts * var_terms(span, i);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Using GFOR requires following several rules and multiple guidelines for optimal performance.
-The details of this vectorization method can be found in the [GFOR documentation](\ref gfor).
+Using GFOR requires following several rules and multiple guidelines for optimal
+performance. The details of this vectorization method can be found in the
+[GFOR documentation](\ref gfor).
 
 # Batching
-The batchFunc() function allows the broad application of existing Arrayfire
-functions to multiple sets of data. Effectively, batchFunc() allows Arrayfire
+
+The batchFunc() function allows the broad application of existing ArrayFire
+functions to multiple sets of data. Effectively, batchFunc() allows ArrayFire
 functions to execute in "batch processing" mode. In this mode, functions will
 find a dimension which contains "batches" of data to be processed and will
 parallelize the procedure.
 
-Consider the following example:
+Consider the following example. Here we create a filter which we would like
+to apply to each of the weight vectors. The naive solution would be using a
+for-loop as we have seen previously:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// Create the filter and the weight vectors
 af::array filter = randn(1, 5);
 af::array weights = randu(5, 5);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-We have a filter that we would like to apply to each of several weights vectors.
-The naive solution would be using a loop as we've seen before:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// Apply the filter using a for-loop
 af::array filtered_weights = constant(0, 5, 5);
 for(int i=0; i<weights.dims(1); ++i){
     filtered_weights.col(i) = filter * weights.col(i);
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-However we would like a vectorized solution. The following syntax begs to be used:
+However, as we have discussed above, this solution will be very inefficient.
+One may be tempted to implement a vectorized solution as follows:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// Create the filter and the weight vectors
+af::array filter = randn(1, 5);
+af::array weights = randu(5, 5);
+
 af::array filtered_weights = filter * weights; // fails due to dimension mismatch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-This fails due to the (5x1), (5x5) dimension mismatch. Wouldn't it be nice if
-Arrayfire could figure out along which dimension we intend to apply the batch
-operation? That is exactly what batchFunc() does!
-The signature of the function is:
+However, the dimensions of `filter` and `weights` do not match, thus ArrayFire
+will generate a runtime error.
+
+`batchfunc()` was created to solve this specific problem.
+The signature of the function is as follows:
 
-`array batchFunc(const array &lhs, const array &rhs, batchFunc_t func);`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array batchFunc(const array &lhs, const array &rhs, batchFunc_t func);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-where __batchFunc_t__ is a function pointer of the form:
-`typedef array (*batchFunc_t) (const array &lhs, const array &rhs);`
+where `__batchFunc_t__` is a function pointer of the form:
 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+typedef array (*batchFunc_t) (const array &lhs, const array &rhs);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-So, to use batchFunc(), we need to provide the function we will be applying as a
+So, to use batchFunc(), we need to provide the function we wish to apply as a
 batch operation. For illustration's sake, let's "implement" a multiplication
 function following the format.
 
@@ -188,16 +216,23 @@ Our final batch call is not much more difficult than the ideal
 syntax we imagined.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// Create the filter and the weight vectors
+af::array filter = randn(1, 5);
+af::array weights = randu(5, 5);
+
+// Apply the batch function
 af::array filtered_weights = batchFunc( filter, weights, my_mult );
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The batch function will work with many previously mentioned vectorized Arrayfire
+The batch function will work with many previously mentioned vectorized ArrayFire
 functions. It can even work with a combination of those functions if they are
-wrapped inside a helper function matching the __batchFunc_t__ signature. Unfortunately,
-the batch function cannot be used within a gfor() construct at this moment.
+wrapped inside a helper function matching the `__batchFunc_t__` signature.
+One limitation of `batchfunc()` is that it cannot be used from within a
+`gfor()` loop at the present time.
 
 # Advanced Vectorization
-We have seen the different methods Arrayfire provides to vectorize our code. Tying
+
+We have seen the different methods ArrayFire provides to vectorize our code. Tying
 them all together is a slightly more involved process that needs to consider data
 dimensionality and layout, memory usage, nesting order, etc. An excellent example
 and discussion of these factors can be found on our blog:
@@ -206,7 +241,7 @@ http://arrayfire.com/how-to-write-vectorized-code/
 
 It's worth noting that the content discussed in the blog has since been transformed
 into a convenient af::nearestNeighbour() function. Before writing something from
-scratch, check that Arrayfire doesn't already have an implementation. The default
-vectorized nature of Arrayfire and an extensive collection of functions will
+scratch, check that ArrayFire doesn't already have an implementation. The default
+vectorized nature of ArrayFire and an extensive collection of functions will
 speed things up in addition to replacing dozens of lines of code!
 

From 12cc51d53a21c44d25f55d81ffb421105c60c7af Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Tue, 17 May 2016 17:12:26 -0400
Subject: [PATCH 0538/2677] [Docs] Rewrite of CUDA and OpenCL interop pages.

---
 docs/pages/interop_cuda.md   | 299 +++++++++++++++++------------
 docs/pages/interop_opencl.md | 359 +++++++++++++++++++++--------------
 2 files changed, 394 insertions(+), 264 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index a131bbcb0b..42e5f282e2 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -1,163 +1,228 @@
 Interoperability with CUDA {#interop_cuda}
 ========
 
-As extensive as ArrayFire is, there are a few cases where you are still working
-with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels.
-For example, you may want to integrate ArrayFire into an existing code base for
-productivity or you may want to keep it around the old implementation for testing
-purposes. Arrayfire provides a number of functions that allow it to work alongside
-native CUDA commands. In this tutorial we are going to talk about how to use native
-CUDA memory operations and integrate custom CUDA kernels into ArrayFire in a seamless fashion.
+Although ArrayFire is quite extensive, there remain many cases in which you
+may want to write custom kernels in CUDA or [OpenCL](\ref interop_opencl).
+For example, you may wish to add ArrayFire to an existing code base to increase
+your productivity, or you may need to supplement ArrayFire's functionality
+with your own custom implementation of specific algorithms.
 
-# In and Out of Arrayfire
-First, let's consider the following code and then break it down bit by bit.
+ArrayFire manages its own memory, runs within its own CUDA stream, and
+creates custom IDs for devices. As such, most of the interoperability functions
+focus on reducing potential synchronization conflicts between ArrayFire and CUDA.
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-int main() {
-    af::array x = randu(num);
-    af::array y = randu(num);
+# Basics
 
-    float *d_x = x.device<float>();
-    float *d_y = y.device<float>();
+It is fairly straightforward to interface ArrayFire with your own custom CUDA
+code. ArrayFire provides several functions to ease this process including:
 
-    // Launch kernel to do the following operations
-    // y = sin(x)^2 + cos(x)^2
-    launch_simple_kernel(d_x, d_y, num);
+| Function              | Purpose                                             |
+|-----------------------|-----------------------------------------------------|
+| af::array(...)        | Construct an ArrayFire Array from device memory     |
+| af::array.device()    | Obtain a pointer to the device memory (implies lock() |
+| af::array.lock()      | Removes ArrayFire's control of a device memory pointer |
+| af::array.unlock()    | Restore's ArrayFire's control over a device memory pointer |
+| af::getDevice()       | Gets the current ArrayFire device ID                |
+| af::setDevice()       | Switches ArrayFire to the specified device          |
+| afcu::getNativeId()   | Converts an ArrayFire device ID to a CUDA device ID |
+| afcu::setNativeId()   | Switches ArrayFire to the specified CUDA device ID  |
+| afcu::getStream()     | Get the current CUDA stream used by ArrayFire       |
 
-    x.unlock();
-    y.unlock();
 
-    // check for errors, should be 0,
-    // since sin(x)^2 + cos(x)^2 == 1
-    float err = af::sum(af::abs(y-1));
-    printf("Error: %f\n", err);
-    return 0;
-}
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Below we provide two worked examples on how ArrayFire can be integrated
+into new and existing projects.
+
+# Adding custom CUDA kernels to an existing ArrayFire application
 
-## Breakdown
-Most kernels require an input. In this case, we created a random uniform array `x`.
-We also go ahead and prepare the output array. The necessary memory required is
-allocated in array `y` before the kernel launch.
+By default, ArrayFire manages its own memory and operates in its own CUDA
+stream. Thus there is a slight amount of bookkeeping that needs to be done
+in order to integrate your custom CUDA kernel.
+
+If your kernels can share the ArrayFire CUDA stream, you should:
+
+1. Add an include for `af/cuda.h` to your project
+2. Obtain a device pointer from ArrayFire af::array objects
+3. Determine ArrayFire's CUDA stream
+4. Set arguments and launch your kernel in ArrayFire's CUDA stream
+5. Return control of af::array memory to ArrayFire
+6. Compile your application using `nvcc` with the appropriate paths.
+
+Notice that since ArrayFire and your kernels are sharing the same CUDA
+stream, there is no need to perform any synchronization operations as
+operations within a stream are executed in order.
+
+This process is best illustrated with a fully worked example:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// 1. Add includes
+#include <arrayfire.h>
+#include <af/cuda.h>
+
+int main() {
+
+    // Create ArrayFire array objects:
     af::array x = randu(num);
     af::array y = randu(num);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-In this example, the output is the same size as in the input. Note that the actual
-output data type is not specified. For such cases, ArrayFire assumes the data type
-is single precision floating point (\ref af::f32). If necessary, the data type can be
-specified at the end of the array(..) constructor. Once you have the input and
-output arrays, you will need to extract the device pointers / objects using
-af::array::device() method in the following manner.
+    // ... many ArrayFire operations here
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    // Run a custom CUDA kernel in the ArrayFire CUDA stream
+
+    // 2. Obtain device pointers from ArrayFire array objects using
+    //    the array::device() function:
     float *d_x = x.device<float>();
     float *d_y = y.device<float>();
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Accesing the device pointer in this manner internally sets a flag prohibiting the
-arrayfire object from further managing the memory. Ownership will need to be
-returned to the af::array object once we are finished using it.
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    // Launch kernel to do the following operations
-    // y = sin(x)^2 + cos(x)^2
-    launch_simple_kernel(d_x, d_y, num);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The function `launch_simple_kernel` handles the launching of your custom kernel.
-We will have a look at how to do this in CUDA later in the post.
+    // 3. Determine ArrayFire's CUDA stream
+    int af_id = af::getDevice();
+    cudaStream_t af_cuda_stream = afcu::getStream(af_id);
 
-Once you have finished your computations, you have to tell ArrayFire to take
-control of the memory objects.
+    // 4. Set arguments and run your kernel in ArrayFire's stream
+    run_custom_kernel<blocks, threads, 0, stream>(d_x, d_y);
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+    // 5. Return control of af::array memory to ArrayFire using
+    //    the array::unlock() function:
     x.unlock();
     y.unlock();
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-This is a very crucial step as ArrayFire believes the user is still in control
-of the pointer. This means that ArrayFire will not perform garbage collection on
-these objects resulting in memory leaks. You can now proceed with the rest of the
-program.
+    // ... resume ArrayFire operations
+
+    // Because the device pointers, d_x and d_y, were returned to ArrayFire's
+    // control by the unlock function, there is no need to free them using
+    // cudaFree()
+
+    return 0;
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-In our particular example, we are just performing an error check and exiting.
+If your kernels needs to operate in their own CUDA stream, the process is
+essentially identical, except you need to instruct ArrayFire to complete
+its computations using the af::sync() function prior to launching your
+own kernel and ensure your kernels are complete using `cudaDeviceSynchronize()`
+(or similar) commands prior to returning control of the memory to ArrayFire:
+
+1. Add an include for `af/cuda.h` to your project.
+2. Instruct ArrayFire to finish operations using af::sync()
+3. Obtain a device pointer from ArrayFire af::array objects
+4. Determine ArrayFire's CUDA stream using afcu::getStream()
+5. Set arguments and launch your kernel in ArrayFire's CUDA stream
+6. Ensure CUDA operations have finished using `cudaDeviceSyncronize()`
+   or similar commands.
+7. Return control of af::array memory to ArrayFire
+8. Compile your application using `nvcc` with the appropriate paths.
+
+# Adding ArrayFire to an existing CUDA application
+
+Adding ArrayFire to an existing CUDA application is slightly more involved
+and can be somewhat tricky due to several optimizations we implement. The
+most important are as follows:
+
+* ArrayFire assumes control of all memory provided to it.
+* ArrayFire does not (in general) support in-place memory transactions.
+
+We will discuss the implications of these items below. To add ArrayFire
+to existing code you need to:
+
+1. Include `arrayfire.h` and `af/cuda.h` in your source file
+2. Finish any pending CUDA operations
+   (e.g. use cudaDeviceSynchronize() or similar stream functions)
+3. Create ArrayFire arrays from existing CUDA pointers
+4. Perform operations on ArrayFire arrays
+5. Instruct ArrayFire to finish operations using af::sync()
+6. Obtain pointers to important memory
+7. Continue your CUDA application.
+8. Free non-managed memory
+9. Compile and link with the appropriate paths and the `-lafcuda` flags.
+
+To create the af::array objects, you should use one of the following
+constructors with `src=afDevice`:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    // check for errors, should be 0,
-    // since sin(x)^2 + cos(x)^2 == 1
-    float err = af::sum(af::abs(y-1));
-    printf("Error: %f\n", err);
+// 1D - 3D af::array constructors
+af::array (dim_t dim0, const T *pointer, af::source src=afHost)
+af::array (dim_t dim0, dim_t dim1, const T *pointer, af::source src=afHost)
+af::array (dim_t dim0, dim_t dim1, dim_t dim2, const T *pointer, af::source src=afHost)
+af::array (dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, const T *pointer, af::source src=afHost)
+
+// af::array constructor using a dim4 object
+af::array (const dim4 &dims, const T *pointer, af::source src=afHost)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-# Launching a CUDA kernel
-Arrayfire provides a collection of CUDA interoperability functions for additional
-capabilities when working with custom CUDA code. To use them, we need to include
-the cuda.h header.
+*NOTE*: With all of these constructors, ArrayFire's memory manager automatically
+assumes responsibility for any memory provided to it. Thus ArrayFire could free
+or reuse the memory at any later time. If this behavior is not desired, you
+may call `array::unlock()` and manage the memory yourself. However, if you do
+so, please be cautious not to free memory when ArrayFire might be using it!
+
+The seven steps above are best illustrated using a fully-worked example:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// 1. Add includes
+#include <arrayfire.h>
 #include <af/cuda.h>
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The first thing these headers allow us to do are to get and set the active device
-using native CUDA device ids. This is achieved through the following functions:
+int main() {
 
-> `static int afcu::getNativeId (int id)`
-> -- Get the native device id of the CUDA device with `id` in the ArrayFire context.
+    // Create CUDA memory objects
+    const int elements = 100;
+    size_t size = elements * sizeof(float);
+    float *inputSignal;
+    cudaMalloc((void**) &inputSignal, size);
 
-> `static void afcu::setNativeId (int nativeId)`
-> -- Set the CUDA device with given native `id` as the active device for ArrayFire.
+    // ... perform many CUDA operations here
 
-The headers also allow us to retrieve the CUDA stream used internally inside Arrayfire.
+    // 2. Finish any pending CUDA operations
+    cudaDeviceSynchronize();
 
-> `static cudaStream_t afcu::getStream(int id)`
-> -- Get the stream for the CUDA device with `id` in ArrayFire context.
+    // 3. Create ArrayFire arrays from existing CUDA pointers.
+    //    Be sure to specify that the memory type is afDevice.
+    af::array d_A(size, inputSignal, afDevice);
 
-These functions are available within the \ref afcu namespace and equal C variants
-can be found in the full [af/cuda.h documentation](\ref cuda_mat).
+    // NOTE: ArrayFire now manages inputSignal
 
-To integrate a CUDA kernel into an ArrayFire code base, we first need to get the
-CUDA stream associated with arrayfire. Once we have this stream, we need to make
-sure Arrayfire is done with all computation before we can call our custom kernel
-to avoid out of order execution. We can do this with some variant of
-`cudaStreamQuery(af_stream)` or `cudaStreamSynchronize(af_stream)` or instead,
-we could add our kernel launch to Arrayfire's stream as shown below. Once we get
-the associated stream, all that is left is setting up the usual launch configuration
-parameters, launching the kernel and wait for the computations to finish:
+    // 4. Perform operations on the ArrayFire Arrays.
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-__global__
-static void simple_kernel(float *d_y,
-                          const float *d_x,
-                          const int num)
-{
-    const int id = blockIdx.x * blockDim.x + threadIdx.x;
-
-    if (id < num) {
-        float x = d_x[id];
-        float sin_x = sin(x);
-        float cos_x = cos(x);
-        d_y[id] = (sin_x * sin_x) + (cos_x * cos_x);
-    }
-}
+    // For example, add uniformly distributed noise to a signal
+    d_A = d_A + randu(elements);
 
-void inline launch_simple_kernel(float *d_y,
-                                 const float *d_x,
-                                 const int num)
-{
-    // Get Arrayfire's internal CUDA stream
-    int af_id = af::getDevice();
-    cudaStream_t af_stream = afcu::getStream(af_id);
+    // NOTE: ArrayFire does not perform the above transaction using
+    // in-place memory, thus the pointers containing memory to d_A have
+    // likely changed.
 
-    // Set launch configuration
-    const int threads = 256;
-    const int blocks = (num / threads) + ((num % threads) ? 1 : 0);
+    // 5. Instruct ArrayFire to finish pending operations
+    af::sync()
 
-    // execute kernel on Arrayfire's stream,
-    // ensuring all previous arrayfire operations complete
-    simple_kernel<<<blocks, threads, 0, af_stream>>>(d_y, d_x, num);
+    // 6. Get pointers to important memory objects.
+    //    Once device is called, ArrayFire will not manage the memory.
+    float * outputSignal = d_A.device<float>();
+
+    // 7. continue CUDA application as normal
+
+    // 8. Free non-managed memroy
+    //    We removed outputSignal from ArrayFire's control, we need to free it
+    cudaFree(outputSignal);
+
+    return 0;
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+# Using multiple devices
+
+If you are using multiple devices with ArrayFire and CUDA kernels, there is
+one "gotcha" of which you should be aware. ArrayFire implements its own internal
+order of compute devices, thus a CUDA device ID may not be the same as an
+ArrayFire device ID. Thus when switching between devices it is important
+that you use our interoperability functions to get/set the correct device
+IDs. Below is a quick listing of the various functions needed to switch
+between devices along with some disambiguation as to the device identifiers
+used with each function:
+
+| Function            | ID Type     | Purpose                                 |
+|---------------------|-------------|-----------------------------------------|
+| cudaGetDevice()     | CUDA        | Gets the current CUDA device ID         |
+| cudaSetDevice()     | CUDA        |Sets the current CUDA device             |
+| af::getDevice()     | AF          | Gets the current ArrayFire device ID    |
+| af::setDevice()     | AF          | Sets the current ArrayFire device       |
+| afcu::getNativeId() | AF -> CUDA  | Convert an ArrayFire device ID to a CUDA device ID |
+| afcu::setNativeId() | CUDA -> AF  |Set the current ArrayFire device from a CUDA ID |
+
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 74c7167b67..1f31076be8 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -1,189 +1,254 @@
 Interoperability with OpenCL {#interop_opencl}
 ========
 
-As extensive as ArrayFire is, there are a few cases where you are still working
-with custom [CUDA] (@ref interop_cuda) or [OpenCL] (@ref interop_opencl) kernels.
-For example, you may want to integrate ArrayFire into an existing code base for
-productivity or you may want to keep it around the old implementation for testing
-purposes. Arrayfire provides a number of functions that allow it to work alongside
-native OpenCL commands. In this tutorial we are going to talk about how to use
-native OpenCL memory operations and custom OpenCL kernels alongside ArrayFire
-in a seamless fashion.
-
-# OpenCL Kernels with Arrayfire arrays
-First, we will see how custom OpenCL kernels can be integrated into Arrayfire code.
-Let's consider the following code and then break it down bit by bit.
+Although ArrayFire is quite extensive, there remain many cases in which you
+may want to write custom kernels in OpenCL or [CUDA](\ref interop_cuda).
+For example, you may wish to add ArrayFire to an existing code base to increase
+your productivity, or you may need to supplement ArrayFire's functionality
+with your own custom implementation of specific algorithms.
+
+ArrayFire manages its own context, queue, memory, and creates custom IDs
+for devices. As such, most of the interoperability functions focus on reducing
+potential synchronization conflicts between ArrayFire and OpenCL.
+
+# Basics
+
+It is fairly straightforward to interface ArrayFire with your own custom OpenCL
+code. ArrayFire provides several functions to ease this process including:
+
+| Function              | Purpose                                             |
+|-----------------------|-----------------------------------------------------|
+| af::array(...)        | Construct an ArrayFire array from cl_mem references or cl::Buffer objects |
+| af::array.device()    | Obtain a pointer to the cl_mem reference (implies lock()) |
+| af::array.lock()      | Removes ArrayFire's control of a cl_mem buffer            |
+| af::array.unlock()    | Restore's ArrayFire's control over a cl_mem buffer        |
+| afcl::getPlatform()   | Get ArrayFire's current cl_platform                       |
+| af::getDevice()       | Get the current ArrayFire Device ID                       |
+| afcl::getDeviceId()   | Get ArrayFire's current cl_device_id                      |
+| af::setDevice()       | Set ArrayFire's device from an ArrayFire device ID        |
+| afcl::setDeviceId()   | Set ArrayFire's device from a cl_device_id                |
+| afcl::setDevice()     | Set ArrayFire's device from a cl_device_id and cl_context |
+| afcl::getContext()    | Get ArrayFire's current cl_context                        |
+| afcl::getQueue()      | Get ArrayFire's current cl_command_queue                  |
+| afcl::getDeviceType() | Get the current afcl_device_type                          |
+
+Additionally, the OpenCL backend permits the programmer to add and remove custom
+devices from the ArrayFire device manager. These permit you to attach ArrayFire
+directly to the OpenCL queue used by other portions of your application.
+
+| Function              | Purpose                                           |
+|-----------------------|---------------------------------------------------|
+| afcl::addDevice()     | Add a new device to ArrayFire's device manager    |
+| afcl::deleteDevice()  | Remove a device from ArrayFire's device manager   |
+
+Below we provide two worked examples on how ArrayFire can be integrated
+into new and existing projects.
+
+# Adding custom OpenCL kernels to an existing ArrayFire application
+
+By default, ArrayFire manages its own context, queue, memory, and creates custom
+IDs for devices. Thus there is some bookkeeping that needs to be done to
+integrate your custom OpenCL kernel.
+
+If your kernels can share operate in the same queue as ArrayFire, you should:
+
+1. Add an include for `af/opencl.h` to your project
+2. Obtain the OpenCL context, device, and queue used by ArrayFire
+3. Obtain cl_mem references to af::array objects
+4. Load, build, and use your kernels
+5. Return control of af::array memory to ArrayFire
+
+Note, ArrayFire uses an in-order queue, thus when ArrayFire and your kernels
+are operating in the same queue, there is no need to perform any
+synchronization operations.
+
+This process is best illustrated with a fully worked example:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-int main() {
-    af::array x = randu(num);
-    af::array y = randu(num);
-
-    float *d_x = x.device<float>();
-    float *d_y = y.device<float>();
-
-    // Launch kernel to do the following operations
-    // y = sin(x)^2 + cos(x)^2
-    launch_simple_kernel(d_x, d_y, num);
+#include <arrayfire.h>
+// 1. Add the af/opencl.h include to your project
+#include <af/opencl.h>
 
-    x.unlock();
-    y.unlock();
+int main() {
+    size_t length = 10;
+
+    // Create ArrayFire array objects:
+    af::array A = af::randu(length, f32);
+    af::array B = af::constant(0, length, f32);
+
+    // ... additional ArrayFire operations here
+
+    // 2. Obtain the device, context, and queue used by ArrayFire
+    static cl_context af_context = afcl::getContext();
+    static cl_device_id af_device_id = afcl::getDeviceId();
+    static cl_command_queue af_queue = afcl::getQueue();
+
+    // 3. Obtain cl_mem references to af::array objects
+    cl_mem * d_A = A.device<cl_mem>();
+    cl_mem * d_B = B.device<cl_mem>();
+
+    // 4. Load, build, and use your kernels.
+    //    For the sake of readability, we have omitted error checking.
+    int status = CL_SUCCESS;
+
+    // A simple copy kernel, uses C++11 syntax for multi-line strings.
+    const char * kernel_name = "copy_kernel";
+    const char * source = R"(
+        void __kernel
+        copy_kernel(__global float * gA, __global float * gB)
+        {
+            int id = get_global_id(0);
+            gB[id] = gA[id];
+        }
+    )";
+
+    // Create the program, build the executable, and extract the entry point
+    // for the kernel.
+    cl_program program = clCreateProgramWithSource(af_context, 1, &source, NULL, &status);
+	status = clBuildProgram(program, 1, &af_device_id, NULL, NULL, NULL);
+    cl_kernel kernel = clCreateKernel(program, kernel_name, &status);
+
+    // Set arguments and launch your kernels
+    clSetKernelArg(kernel, 0, sizeof(cl_mem), d_A);
+    clSetKernelArg(kernel, 1, sizeof(cl_mem), d_B);
+	clEnqueueNDRangeKernel(af_queue, kernel, 1, NULL, &length, NULL, 0, NULL, NULL);
+
+    // 5. Return control of af::array memory to ArrayFire
+    A.unlock();
+    B.unlock();
+
+    // ... resume ArrayFire operations
+
+    // Because the device pointers, d_x and d_y, were returned to ArrayFire's
+    // control by the unlock function, there is no need to free them using
+    // clReleaseMemObject()
 
-    // check for errors, should be 0,
-    // since sin(x)^2 + cos(x)^2 == 1
-    float err = af::sum<float>(af::abs(y-1));
-    printf("Error: %f\n", err);
     return 0;
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-## Breakdown
-Most kernels require an input. In this case, we created a random uniform array `x`
-We also go ahead and prepare the output array. The necessary memory required is
-allocated in array `y` before the kernel launch.
+If your kernels needs to operate in their own OpenCL queue, the process is
+essentially identical, except you need to instruct ArrayFire to complete
+its computations using the af::sync() function prior to launching your
+own kernel and ensure your kernels are complete using `clFinish`
+(or similar) commands prior to returning control of the memory to ArrayFire:
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    af::array x = randu(num);
-    af::array y = randu(num);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+1. Add an include for `af/opencl.h` to your project
+2. Obtain the OpenCL context, device, and queue used by ArrayFire
+3. Obtain cl_mem references to af::array objects
+4. Instruct ArrayFire to finish operations using af::sync()
+5. Load, build, and use your kernels
+6. Instruct OpenCL to finish operations using clFinish() or similar commands.
+5. Return control of af::array memory to ArrayFire
 
-In this example, the output is the same size as in the input. Note that the actual
-output data type is not specified. For such cases, ArrayFire assumes the data type
-is single precision floating point (\ref af::f32). If necessary, the data type can
-be specified at the end of the array(..) constructor. Once you have the input and
-output arrays, you will need to extract the device pointers / objects using
-af::array::device() method in the following manner.
+# Adding ArrayFire to an existing OpenCL application
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    float *d_x = x.device<float>();
-    float *d_y = y.device<float>();
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Adding ArrayFire to an existing OpenCL application is slightly more involved
+and can be somewhat tricky due to several optimizations we implement. The
+most important are as follows:
 
-Accesing the device pointer in this manner internally sets a flag prohibiting
-the arrayfire object from further managing the memory. Ownership will need to be
-returned to the af::array object once we are finished using it.
+* ArrayFire assumes control of all memory provided to it.
+* ArrayFire does not (in general) support in-place memory transactions.
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    // Launch kernel to do the following operations
-    // y = sin(x)^2 + cos(x)^2
-    launch_simple_kernel(d_x, d_y, num);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+We will discuss the implications of these items below. To add ArrayFire
+to existing code you need to:
 
-The function `launch_simple_kernel` handles the launching of your custom kernel.
-We will have a look at the specific functions Arrayfire provides to interface with
-OpenCL later in the post.
+1. Add includes
+2. Instruct OpenCL to complete its operations using clFinish (or similar)
+3. Instruct ArrayFire to use the user-created OpenCL Context
+4. Create ArrayFire arrays from OpenCL memory objects
+5. Perform ArrayFire operations on the Arrays
+6. Instruct ArrayFire to finish operations using af::sync()
+7. Obtain cl_mem references for important memory
+8. Continue your OpenCL application
 
-Once you have finished your computations, you have to tell ArrayFire to take control
-of the memory objects.
+To create the af::array objects, you should use one of the following
+constructors:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    x.unlock();
-    y.unlock();
+// 1D - 3D af::array constructors
+static af::array    array (dim_t dim0, cl_mem buf, af::dtype type, bool retain=false)
+static af::array    array (dim_t dim0, dim_t dim1, cl_mem buf, af::dtype type, bool retain=false)
+static af::array    array (dim_t dim0, dim_t dim1, dim_t dim2, cl_mem buf, af::dtype type, bool retain=false)
+static af::array    array (dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, cl_mem buf, af::dtype type, bool retain=false)
+
+// af::array constructor using a dim4 object
+static af::array    array (af::dim4 idims, cl_mem buf, af::dtype type, bool retain=false)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-This is a very crucial step as ArrayFire believes the user is still in control
-of the pointer. This means that ArrayFire will not perform garbage collection
-on these objects resulting in memory leaks. You can now proceed with the rest of
-the program. In our particular example, we are just performing an error check and exiting.
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-    // check for errors, should be 0,
-    // since sin(x)^2 + cos(x)^2 == 1
-    float err = af::sum(af::abs(y-1));
-    printf("Error: %f\n", err);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*NOTE*: With all of these constructors, ArrayFire's memory manager automatically
+assumes responsibility for any memory provided to it. If you are creating
+an array from a `cl::Buffer`, you should specify `retain=true` to ensure your
+memory is not deallocated if your `cl::Buffer` were to go out of scope.
+We use this technique in the example below.
+If you do not wish for ArrayFire to manage your memory, you may call the
+`array::unlock()` function and manage the memory yourself; however, if you do
+so, please be cautious not to call `clReleaseMemObj` on a `cl_mem`  when
+ArrayFire might be using it!
 
-## Launching an OpenCL kernel
-If you are integrating an OpenCL kernel into your ArrayFire code base you will
-need several additional steps to access Arrayfire's internal OpenCL context.
-Once you have access to the same context ArrayFire is using, the rest of the
-process is exactly the same as launching a stand alone OpenCL context.
+The eight steps above are best illustrated using a fully-worked example. Below we
+use the OpenCL 2.0 C++ API and omit error checking to keep the code readable. 
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-void inline launch_simple_kernel(float *d_y,
-                                 const float *d_x,
-                                 const int num)
-{
-    std::string simple_kernel_str = CONST_KERNEL_STRING;
+#include <CL/cl2.hpp>
 
-    // Get OpenCL context from memory buffer and create a Queue
-    cl::Context context(afcl::getContext(true));
-    cl::CommandQueue queue(afcl::getQueue(true));
+// 1. Add arrayfire.h and af/opencl.h to your application
+#include "arrayfire.h"
+#include "af/opencl.h"
 
-    //Build program and get the required kernel
-    cl::Program prog = cl::Program(context, simple_kernel_str, true);
-    cl::Kernel  kern = cl::Kernel(prog, "simple_kernel");
+#include <cstdio>
+#include <vector>
 
-    //set global work dimensions
-    static const cl::NDRange global(num);
+int main() {
 
-    //prepare argumenst
-    kern.setArg(0, d_y);
-    kern.setArg(1, d_x);
-    kern.setArg(2, num);
+  // Set up the OpenCL context, device, and queues
+  cl::Context context(CL_DEVICE_TYPE_ALL);
+  vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
+  cl::Device device = devices[0];
+  cl::CommandQueue queue(context, device);
 
-    //run kernel
-    queue.enqueueNDRangeKernel(kern, cl::NullRange, global);
-    queue.finish();
+  // Create a buffer of size 10 filled with ones, copy it to the device
+  int length = 10;
+  vector<float> h_A(length, 1);
+  cl::Buffer cl_A(context, CL_MEM_READ_WRITE, length * sizeof(float), h_A.data());
 
-    return;
-}
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // 2. Instruct OpenCL to complete its operations using clFinish (or similar)
+  queue.finish();
 
-First of all, to access to OpenCL and the interoperability functions we need to
-include the appropriate headers.
+  // 3. Instruct ArrayFire to use the user-created context
+  //    First, create a device from the current OpenCL device + context + queue
+  afcl::addDevice(device(), context(), queue());
+  //    Next switch ArrayFire to the device using the device and context as 
+  //    identifiers:
+  afcl::setDevice(device(), context());
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-#include <af/opencl.h>
-#include <CL/cl.hpp>
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // 4. Create ArrayFire arrays from OpenCL memory objects
+  af::array af_A = afcl::array(length, cl_A(), f32, true);
 
-The opencl.h header includes a number of functions for getting and setting the
-context, queue, and device ids used internally in Arrayfire. There are also a
-number of methods to construct an af::array from an OpenCL `cl_mem` buffer
-object.  There are both C and C++ versions of these functions, and the C++
-versions are wrapped inside the \ref afcl namespace. See full datails of these
-functions in the [af/opencl.h documentation] (\ref opencl_mat).
+  // 5. Perform ArrayFire operations on the Arrays
+  af_A = af_A + af::randu(length);
+    
+  // NOTE: ArrayFire does not perform the above transaction using in-place memory, 
+  // thus the underlying OpenCL buffers containing the memory containing memory to 
+  // probably have changed
 
+  // 6. Instruct ArrayFire to finish operations using af::sync
+  af::sync();
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-cl::Context context(afcl::getContext(true));
-cl::CommandQueue queue(afcl::getQueue(true));
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // 7. Obtain cl_mem references for important memory
+  cl_A = *af_A.device<cl_mem>();
 
-We start to use these functions by getting Arrayfire's context and queue. For
-the C++ api, a `true` flag must be passed for the retain parameter which calls
-the `clRetainQueue()` and `clRetainContext()` functions before returning. This
-allows us to use Arrayfire's internal OpenCL structures inside of the
-cl::Context and cl::CommandQueue objects from the C++ api. Once we have them,
-we can proceed to set up and enqueue the kernel like we would in any other
-OpenCL program.  The kernel we are using is actually simple and can be seen
-below.
+  // 8. Continue your OpenCL application
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-std::string CONST_KERNEL_STRING = R"(
-__kernel
-void simple_kernel(__global float *d_y,
-                   __global const float *d_x,
-                   const int num)
-{
-    const int id = get_global_id(0);
-
-    if (id < num) {
-        float x = d_x[id];
-        float sin_x = sin(x);
-        float cos_x = cos(x);
-        d_y[id] = (sin_x * sin_x) + (cos_x * cos_x);
-    }
+  // ... 
+  
+  return 0;
 }
-)";
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-# Reversing the workflow: Arrayfire arrays from OpenCL Memory
-Unfortunately, Arrayfire's interoperability functions don't yet allow us to work with
-external OpenCL contexts. This is currently an open issue and can be tracked here:
-https://github.com/arrayfire/arrayfire/issues/1002.
-
-Once the issue is addressed, it will be possible to take the reverse route and start with
-completely custom OpenCL code, then transfer our results into af::array objects.
+# Using multiple devices
 
+If you are using ArrayFire and OpenCL with multiple devices be sure to use
+`afcl::addDevice` to add your custom context + device + queue to ArrayFire's
+device manager. This will let you switch ArrayFire devices using your current
+`cl_device_id` and `cl_context`.

From 05ed11a00581ce84c962ddc34145c3ca39e706fd Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Tue, 17 May 2016 17:14:27 -0400
Subject: [PATCH 0539/2677] [Docs] Reorder default ordering, rewrite reorder
 example to be more clear.

---
 docs/details/data.dox | 73 +++++++++++++++++++------------------------
 docs/layout.xml       |  6 ++--
 2 files changed, 36 insertions(+), 43 deletions(-)

diff --git a/docs/details/data.dox b/docs/details/data.dox
index d3f470d113..4b0557b837 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -209,49 +209,42 @@ Creates copys of the array a specified number of times within the output array
 
 \defgroup manip_func_reorder reorder
 
-\brief Reorder the input by in the specified order
+\brief Reorder an array according to the specified dimensions.
 
-Exchanges dimensions within an array. The order of the data along each
-dimension does not change.
+Exchanges data within an array such that the requested change in dimension
+is satisfied. The linear ordering of data within the array is preserved.
 
 \code
-array a = randu(5, 4, 3);
-// a [5 4 3 1]
-// 0.0000     0.2190     0.3835     0.5297
-// 0.1315     0.0470     0.5194     0.6711
-// 0.7556     0.6789     0.8310     0.0077
-// 0.4587     0.6793     0.0346     0.3834
-// 0.5328     0.9347     0.0535     0.0668
-
-// 0.4175     0.5269     0.9103     0.3282
-// 0.6868     0.0920     0.7622     0.6326
-// 0.5890     0.6539     0.2625     0.7564
-// 0.9304     0.4160     0.0475     0.9910
-// 0.8462     0.7012     0.7361     0.3653
-
-// 0.2470     0.0727     0.7665     0.1665
-// 0.9826     0.6316     0.4777     0.4865
-// 0.7227     0.8847     0.2378     0.8977
-// 0.7534     0.2727     0.2749     0.9092
-// 0.6515     0.4364     0.3593     0.0606
-
-array b = reorder(a, 2, 0, 1)
-// b [3 5 4 1]
-// 0.0000     0.1315     0.7556     0.4587     0.5328
-// 0.4175     0.6868     0.5890     0.9304     0.8462
-// 0.2470     0.9826     0.7227     0.7534     0.6515
-
-// 0.2190     0.0470     0.6789     0.6793     0.9347
-// 0.5269     0.0920     0.6539     0.4160     0.7012
-// 0.0727     0.6316     0.8847     0.2727     0.4364
-
-// 0.3835     0.5194     0.8310     0.0346     0.0535
-// 0.9103     0.7622     0.2625     0.0475     0.7361
-// 0.7665     0.4777     0.2378     0.2749     0.3593
-
-// 0.5297     0.6711     0.0077     0.3834     0.0668
-// 0.3282     0.6326     0.7564     0.9910     0.3653
-// 0.1665     0.4865     0.8977     0.9092     0.0606
+a [2 2 3 1]
+    1.0000     3.0000
+    2.0000     4.0000
+
+    1.0000     3.0000
+    2.0000     4.0000
+
+    1.0000     3.0000
+    2.0000     4.0000
+
+
+reorder(a, 1, 0, 2) [2 2 3 1]  //equivalent to a transpose
+    1.0000     2.0000
+    3.0000     4.0000
+
+    1.0000     2.0000
+    3.0000     4.0000
+
+    1.0000     2.0000
+    3.0000     4.0000
+
+
+reorder(a, 2, 0, 1) [3 2 2 1]
+    1.0000     2.0000
+    1.0000     2.0000
+    1.0000     2.0000
+
+    3.0000     4.0000
+    3.0000     4.0000
+    3.0000     4.0000
 \endcode
 
 \ingroup manip_mat
diff --git a/docs/layout.xml b/docs/layout.xml
index 0b272f65f8..d2f18bc324 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -8,12 +8,12 @@
       <tab type="user" url="\ref using_on_windows" visible="yes" title="Using on Windows"/>
       <tab type="user" url="\ref using_on_osx" visible="yes" title="Using on OSX"/>
       <tab type="user" url="\ref gettingstarted" visible="yes" title="Getting Started"/>
-      <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
-      <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Matrix Manipulation"/>
       <tab type="user" url="\ref vectorization" visible="yes" title="Introduction to Vectorization"/>
-      <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
+      <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Array and Matrix Manipulation"/>
       <tab type="user" url="\ref interop_cuda" visible="yes" title="CUDA Interoperability"/>
       <tab type="user" url="\ref interop_opencl" visible="yes" title="OpenCL Interoperability"/>
+      <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
+      <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
       <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
       <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
       <tab type="user" url="\ref configuring_environment" visible="yes" title="Configuring ArrayFire Environment"/>

From 279e92a65e3942021ada30c8f7630b17a87f719f Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Wed, 18 May 2016 07:49:58 -0400
Subject: [PATCH 0540/2677] [Docs] Minor clarifications.

---
 docs/details/data.dox         | 2 +-
 docs/pages/getting_started.md | 2 +-
 include/af/array.h            | 4 ++--
 include/af/opencl.h           | 2 +-
 test/getting_started.cpp      | 6 +++---
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/details/data.dox b/docs/details/data.dox
index 4b0557b837..73580eca4d 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -211,7 +211,7 @@ Creates copys of the array a specified number of times within the output array
 
 \brief Reorder an array according to the specified dimensions.
 
-Exchanges data within an array such that the requested change in dimension
+Exchanges data of an array such that the requested change in dimension
 is satisfied. The linear ordering of data within the array is preserved.
 
 \code
diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index 02997ee868..7ea9a75216 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -124,7 +124,7 @@ when a non-JIT function is encountered or a synchronization operation is
 explicitly called by the code.
 
 ArrayFire provides [hundreds of functions](\ref arith_mat) for element-wise
-operations. All of the standard operators (e.g. +,-,*,/) are supported
+operations. All of the standard operators (e.g. +,-,\*,/) are supported
 as are most transcendental functions (sin, cos, log, sqrt, etc.).
 Here are a few examples:
 
diff --git a/include/af/array.h b/include/af/array.h
index b2c5dd4f35..03500640c6 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -330,7 +330,7 @@ namespace af
 
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership ownership of the underlying memory.
+            \note If \p src is \ref afHost, the first \p dim0 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership of the underlying memory.
 
         */
         template<typename T>
@@ -354,7 +354,7 @@ namespace af
 
             \image html 2dArray.png
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
         */
         template<typename T>
         array(dim_t dim0, dim_t dim1,
diff --git a/include/af/opencl.h b/include/af/opencl.h
index 9d8f7c36c8..30ad555a41 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -302,7 +302,7 @@ static inline deviceType getDeviceType()
 
 #if AF_API_VERSION >= 33
 /**
-   Get the type of the current platform
+   Get a vendor enumeration for the current platform
 */
 static inline platform getPlatform()
 {
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index 32983a6e63..26fecfb4bf 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -23,9 +23,9 @@ TEST(GettingStarted, SNIPPET_getting_started_gen)
     //! [ex_getting_started_constructors]
     // Arrays may be created using the af::array constructor and dimensioned
     // as 1D, 2D, 3D; however, the values in these arrays will be undefined
-    array undefined_1D = array(100);        // 1D array with 100 elements
-    array undefined_2D = array(10, 100);    // 2D array of size 10 x 100
-    array undefined_3D = array(10, 10, 10); // 3D array of size 10 x 10 x 10
+    array undefined_1D(100);        // 1D array with 100 elements
+    array undefined_2D(10, 100);    // 2D array of size 10 x 100
+    array undefined_3D(10, 10, 10); // 3D array of size 10 x 10 x 10
     //! [ex_getting_started_constructors]
 
     //! [ex_getting_started_gen]

From 2691d99693d6185d4f322a7776b1ced0e76877d2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 18 May 2016 10:18:34 -0400
Subject: [PATCH 0541/2677] BUGFIX Correctly handle lapacke found by package
 config

---
 CMakeModules/FindLAPACKE.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 5ecf7be55d..2ebd8ddbc5 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -143,7 +143,7 @@ ELSE(PC_LAPACKE_FOUND)
     ENDIF(LAPACKE_ROOT_DIR)
 ENDIF(PC_LAPACKE_FOUND)
 
-IF(LAPACKE_LIB AND LAPACK_LIB)
+IF(PC_LAPACKE_FOUND OR (LAPACKE_LIB AND LAPACK_LIB))
     SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
 ENDIF()
 IF(LAPACKE_INCLUDES)

From 1811024ecb4a934dd9c1c50b3a63474f6a19e1bb Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Wed, 18 May 2016 12:08:21 -0400
Subject: [PATCH 0542/2677] [Docs] Fix missing eval.

---
 docs/pages/interop_cuda.md | 92 +++++++++++++++++++++-----------------
 1 file changed, 51 insertions(+), 41 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index 42e5f282e2..a7dbe95f3c 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -40,12 +40,14 @@ in order to integrate your custom CUDA kernel.
 
 If your kernels can share the ArrayFire CUDA stream, you should:
 
-1. Add an include for `af/cuda.h` to your project
-2. Obtain a device pointer from ArrayFire af::array objects
-3. Determine ArrayFire's CUDA stream
-4. Set arguments and launch your kernel in ArrayFire's CUDA stream
-5. Return control of af::array memory to ArrayFire
-6. Compile your application using `nvcc` with the appropriate paths.
+1. Include the 'af/afcuda.h' header in your source code
+2. Use ArrayFire as normal
+3. Ensure any JIT kernels have executed using ``af::eval()`
+4. Obtain device pointers from ArrayFire array objects using
+5. Determine ArrayFire's CUDA stream
+6. Set arguments and run your kernel in ArrayFire's stream
+7. Return control of af::array memory to ArrayFire
+8. Compile with `nvcc`, linking with the `afcuda` library.
 
 Notice that since ArrayFire and your kernels are sharing the same CUDA
 stream, there is no need to perform any synchronization operations as
@@ -60,32 +62,37 @@ This process is best illustrated with a fully worked example:
 
 int main() {
 
-    // Create ArrayFire array objects:
-    af::array x = randu(num);
-    af::array y = randu(num);
+    // 2. Use ArrayFire as normal
+    size_t num = 10;
+    af::array x = af::constant(0, num);
+    
+    // ... many ArrayFire operaitons here
 
-    // ... many ArrayFire operations here
+    // 3. Ensure any JIT kernels have executed
+    x.eval();
+    af_print(x);
 
     // Run a custom CUDA kernel in the ArrayFire CUDA stream
 
-    // 2. Obtain device pointers from ArrayFire array objects using
+    // 4. Obtain device pointers from ArrayFire array objects using
     //    the array::device() function:
     float *d_x = x.device<float>();
-    float *d_y = y.device<float>();
 
-    // 3. Determine ArrayFire's CUDA stream
+    // 5. Determine ArrayFire's CUDA stream
     int af_id = af::getDevice();
-    cudaStream_t af_cuda_stream = afcu::getStream(af_id);
+    int cuda_id = afcu::getNativeId(af_id);
+    cudaStream_t af_cuda_stream = afcu::getStream(cuda_id);
 
-    // 4. Set arguments and run your kernel in ArrayFire's stream
-    run_custom_kernel<blocks, threads, 0, stream>(d_x, d_y);
+    // 6. Set arguments and run your kernel in ArrayFire's stream
+    //    Here launch with 10 blocks of 10 threads
+    increment<<<1, num, 0, af_cuda_stream>>>(d_x);
 
-    // 5. Return control of af::array memory to ArrayFire using
+    // 7. Return control of af::array memory to ArrayFire using
     //    the array::unlock() function:
     x.unlock();
-    y.unlock();
 
     // ... resume ArrayFire operations
+    af_print(x);
 
     // Because the device pointers, d_x and d_y, were returned to ArrayFire's
     // control by the unlock function, there is no need to free them using
@@ -101,15 +108,17 @@ its computations using the af::sync() function prior to launching your
 own kernel and ensure your kernels are complete using `cudaDeviceSynchronize()`
 (or similar) commands prior to returning control of the memory to ArrayFire:
 
-1. Add an include for `af/cuda.h` to your project.
-2. Instruct ArrayFire to finish operations using af::sync()
-3. Obtain a device pointer from ArrayFire af::array objects
-4. Determine ArrayFire's CUDA stream using afcu::getStream()
-5. Set arguments and launch your kernel in ArrayFire's CUDA stream
-6. Ensure CUDA operations have finished using `cudaDeviceSyncronize()`
+1. Include the 'af/afcuda.h' header in your source code
+2. Use ArrayFire as normal
+3. Ensure any JIT kernels have executed using ``af::eval()`
+4. Instruct ArrayFire to finish operations using af::sync()
+5. Obtain device pointers from ArrayFire array objects using
+6. Determine ArrayFire's CUDA stream
+7. Set arguments and run your kernel in your custom stream
+8. Ensure CUDA operations have finished using `cudaDeviceSyncronize()`
    or similar commands.
-7. Return control of af::array memory to ArrayFire
-8. Compile your application using `nvcc` with the appropriate paths.
+9. Return control of af::array memory to ArrayFire
+10. Compile with `nvcc`, linking with the `afcuda` library.
 
 # Adding ArrayFire to an existing CUDA application
 
@@ -128,7 +137,7 @@ to existing code you need to:
    (e.g. use cudaDeviceSynchronize() or similar stream functions)
 3. Create ArrayFire arrays from existing CUDA pointers
 4. Perform operations on ArrayFire arrays
-5. Instruct ArrayFire to finish operations using af::sync()
+5. Instruct ArrayFire to finish operations using af::eval() and af::sync()
 6. Obtain pointers to important memory
 7. Continue your CUDA application.
 8. Free non-managed memory
@@ -161,13 +170,15 @@ The seven steps above are best illustrated using a fully-worked example:
 #include <arrayfire.h>
 #include <af/cuda.h>
 
+using namespace std;
+
 int main() {
 
-    // Create CUDA memory objects
+    // Create and populate CUDA memory objects
     const int elements = 100;
     size_t size = elements * sizeof(float);
-    float *inputSignal;
-    cudaMalloc((void**) &inputSignal, size);
+    float *cuda_A;
+    cudaMalloc((void**) &cuda_A, size);
 
     // ... perform many CUDA operations here
 
@@ -176,31 +187,30 @@ int main() {
 
     // 3. Create ArrayFire arrays from existing CUDA pointers.
     //    Be sure to specify that the memory type is afDevice.
-    af::array d_A(size, inputSignal, afDevice);
+    af::array d_A(elements, cuda_A, afDevice);
 
-    // NOTE: ArrayFire now manages inputSignal
+    // NOTE: ArrayFire now manages cuda_A
 
     // 4. Perform operations on the ArrayFire Arrays.
-
-    // For example, add uniformly distributed noise to a signal
-    d_A = d_A + randu(elements);
+    d_A = d_A * 2;
 
     // NOTE: ArrayFire does not perform the above transaction using
     // in-place memory, thus the pointers containing memory to d_A have
     // likely changed.
 
-    // 5. Instruct ArrayFire to finish pending operations
-    af::sync()
+    // 5. Instruct ArrayFire to finish pending operations using eval and sync.
+    af::eval(d_A);
+    af::sync();
 
     // 6. Get pointers to important memory objects.
     //    Once device is called, ArrayFire will not manage the memory.
-    float * outputSignal = d_A.device<float>();
+    float * outputValue = d_A.device<float>();
 
     // 7. continue CUDA application as normal
 
-    // 8. Free non-managed memroy
-    //    We removed outputSignal from ArrayFire's control, we need to free it
-    cudaFree(outputSignal);
+    // 8. Free non-managed memory
+    //    We removed outputValue from ArrayFire's control, we need to free it
+    cudaFree(outputValue);
 
     return 0;
 }

From b3b42c69cf9d1b94310c4aa87522f2cdf13259a0 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Wed, 18 May 2016 14:17:33 -0400
Subject: [PATCH 0543/2677] [Docs] Fix minor typos

---
 docs/pages/configuring_arrayfire_environment.md | 2 +-
 docs/pages/interop_cuda.md                      | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index d554046f1e..8f32fb2fd9 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -134,7 +134,7 @@ paths, then those paths are shown in full.
 AF_MEM_DEBUG {#af_mem_debug}
 -------------------------------------------------------------------------------
 
-When AF_MEM_DEBUG is set to 1 (or anything not equal to 0), the caching mechanism in the memory manager.
+When AF_MEM_DEBUG is set to 1 (or anything not equal to 0), the caching mechanism in the memory manager is disabled.
 The device buffers are allocated using native functions as needed and freed when going out of scope.
 
 When the environment variable is not set, it is treated to be non zero.
diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index a7dbe95f3c..2ef88af3ff 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -42,7 +42,7 @@ If your kernels can share the ArrayFire CUDA stream, you should:
 
 1. Include the 'af/afcuda.h' header in your source code
 2. Use ArrayFire as normal
-3. Ensure any JIT kernels have executed using ``af::eval()`
+3. Ensure any JIT kernels have executed using `af::eval()`
 4. Obtain device pointers from ArrayFire array objects using
 5. Determine ArrayFire's CUDA stream
 6. Set arguments and run your kernel in ArrayFire's stream
@@ -65,7 +65,7 @@ int main() {
     // 2. Use ArrayFire as normal
     size_t num = 10;
     af::array x = af::constant(0, num);
-    
+
     // ... many ArrayFire operaitons here
 
     // 3. Ensure any JIT kernels have executed
@@ -110,7 +110,7 @@ own kernel and ensure your kernels are complete using `cudaDeviceSynchronize()`
 
 1. Include the 'af/afcuda.h' header in your source code
 2. Use ArrayFire as normal
-3. Ensure any JIT kernels have executed using ``af::eval()`
+3. Ensure any JIT kernels have executed using `af::eval()`
 4. Instruct ArrayFire to finish operations using af::sync()
 5. Obtain device pointers from ArrayFire array objects using
 6. Determine ArrayFire's CUDA stream

From d8b6246768ca612c878e93d6405ecfbddcc8e7a4 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Wed, 18 May 2016 16:03:26 -0400
Subject: [PATCH 0544/2677] [Docs] Update Forge page

* Clean up the intro to the Forge page
* Change order of items in the side menu
---
 docs/layout.xml                   |   2 +-
 docs/pages/forge_visualization.md | 161 +-----------------------------
 2 files changed, 2 insertions(+), 161 deletions(-)

diff --git a/docs/layout.xml b/docs/layout.xml
index d2f18bc324..ee2da85256 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -12,8 +12,8 @@
       <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Array and Matrix Manipulation"/>
       <tab type="user" url="\ref interop_cuda" visible="yes" title="CUDA Interoperability"/>
       <tab type="user" url="\ref interop_opencl" visible="yes" title="OpenCL Interoperability"/>
+      <tab type="user" url="\ref forge_visualization" visible="yes" title="Visualizing arrays with Forge"/>
       <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
-      <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
       <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
       <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
       <tab type="user" url="\ref configuring_environment" visible="yes" title="Configuring ArrayFire Environment"/>
diff --git a/docs/pages/forge_visualization.md b/docs/pages/forge_visualization.md
index 72901dc681..3c1d4f9cce 100644
--- a/docs/pages/forge_visualization.md
+++ b/docs/pages/forge_visualization.md
@@ -1,160 +1 @@
-Visualizing af::array with Forge {#forge_visualization}
-===================
-
-Arrayfire as a library aims to provide a robust and easy to use platform for
-high-performance, parallel and GPU computing.
-
-[TOC]
-
-The goal of [Forge](https://github.com/arrayfire/forge), an OpenGL visualization
-library, is to provide equally robust visualizations that are interoperable
-between Arrayfire data-structures and an OpenGL context.
-
-Arrayfire provides wrapper functions that are designed to be a simple interface
-to visualize af::arrays. These functions perform various interop tasks. One in
-particular is that instead of wasting time copying and reformatting data from
-the GPU to the host and back to the GPU, we can draw directly from GPU-data to
-GPU-framebuffers! This saves 2 memory copies.
-
-Let's see exactly what visuals we can illuminate with forge and how Arrayfire
-anneals the data between the two libraries.
-
-# Setup {#setup}
-Before we can call Forge functions, we need to set up the related "canvas" classes.
-Forge functions are tied to the af::Window class. First let's create a window:
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-const static int width = 512, height = 512;
-af::Window window(width, height, "2D plot example title");
-
-do{
-
-//drawing functions here
-
-} while( !window.close() );
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We also added a drawing loop, so now we can use Forge's drawing functions to 
-draw to the window.
-The drawing functions present in Forge are listed below.
-
-# Rendering Functions {#render_func}
-
-Documentation for rendering functions can be found [here](\ref gfx_func_draw).
-
-## Image {#image}
-The af::Window::image() function can be used to plot grayscale or color images.
-To plot a grayscale image a 2d array should be passed into the function.
-Let's see this on a static noise example:
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array img = constant(0, width, height); //make a black image
-array random = randu(width, height);      //make random [0,1] distribution
-img(random > 0.5) = 1; //set all pixels where distribution > 0.5 to white
-
-window.image(img);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-<img src="gfx_docs_images/noise.png" alt="Forge image plot of noise" width="20%" />
-Tweaking the previous example by giving our image a depth of 3 for the RGB values
-allows us to generate colorful noise:
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array img = 255 * randu(width, height, 3);      //make random [0, 255] distribution
-window.image( img.as(u8) );
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-<img src="gfx_docs_images/color_noise.png" alt="Forge image plot of color noise" width="20%" />
-Note that Forge automatically handles any af::array type passed from Arrayfire.
-In the first example we passed in an image of floats in the range [0, 1].
-In the last example we cast our array to an unsigned byte array with the range
-[0, 255]. The type-handling properties are consistent for all Forge drawing functions.
-
-## Plot {#plot}
-The af::Window::plot() function visualizes an array as a 2d-line plot. Let's see
-a simple example:
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array X = seq(-af::Pi, af::Pi, 0.01);
-array Y = sin(X);
-window.plot(X, Y);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-<img src="gfx_docs_images/sin_plot.png" alt="Forge 2d line plot of sin() function" width="30%" />
-The plot function has the signature:
-
-> **void plot( const array &X, const array &Y, const char * const title = NULL );**
-
-Both the x and y coordinates of the points are required to plot. This allows for
-non-uniform, or parametric plots:
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array t = seq(0, 100, 0.01);
-array X = sin(t) * (exp(cos(t)) - 2 * cos(4 * t) - pow(sin(t / 12), 5));
-array Y = cos(t) * (exp(cos(t)) - 2 * cos(4 * t) - pow(sin(t / 12), 5));
-window.plot(X, Y);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-<img src="gfx_docs_images/butterfly_plot.png" alt="Forge 2d line plot of butterfly function" width="30%" />
-
-## Plot3 {#plot3}
-The af::Window::plot3() function will plot a curve in 3d-space.
-Its signature is:
-> **void plot3 (const array &in, const char * title = NULL);**
-The input array expects xyz-triplets in sequential order. The points can be in a
-flattened one dimensional (*3n x 1*) array, or in one of the (*3 x n*), (*n x 3*) matrix forms.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array Z = seq(0.1f, 10.f, 0.01);
-array Y = sin(10 * Z) / Z;
-array X = cos(10 * Z) / Z;
-
-array Pts = join(1, X, Y, Z);
-//Pts can be passed in as a matrix in the from n x 3, 3 x n
-//or in the flattened xyz-triplet array with size 3n x 1
-window.plot3(Pts);
-//both of the following are equally valid
-//window.plot3(transpose(Pts));
-//window.plot3(flat(Pts));
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-<img src="gfx_docs_images/spiral_plot3.png" alt="Forge 3d line plot" width="40%" />
-
-## Histogram {#histogram}
-The af::Window::hist() function renders an input array as a histogram.
-In our example, the input array will be created with Arrayfire's histogram()
-function, which actually counts and bins each sample. The output from histogram()
-can directly be fed into the af::Window::hist() rendering function.
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-const int BINS = 128; SAMPLES = 9162;
-array norm = randn(SAMPLES);
-array hist_arr = histogram(norm, BINS);
-
-win.hist(hist_arr, 0, BINS);
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-In addition to the histogram array with the number of samples in each bin, the
-af::Window::hist() function takes two additional parameters -- the minimum and
-maximum values of all datapoints in the histogram array. This effectively sets
-the range of the binned data. The full signature of af::Window::hist() is:
-> **void hist(const array & X, const double minval, const double maxval, const char * const title = NULL);**
-<img src="gfx_docs_images/norm_histogram.png" alt="Forge 3d scatter plot" width="40%" />
-
-
-## Surface {#surface}
-The af::Window::surface() function will plot af::arrays as a 3d surface.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-array Z = randu(21, 21);
-window.surface(Z, "Random Surface");    //equal to next function call
-//window.surface( seq(-1, 1, 0.1), seq(-1, 1, 0.1), Z, "Random Surface");
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-<img src="gfx_docs_images/rand_surface.png" alt="Forge random surface plot" width="30%" />
-There are two overloads for the af::Window::surface() function:
-> **void surface (const array & S, const char * const title )**
-> // Accepts a 2d matrix with the z values of the surface
-
-> **void surface (const array &xVals, const array &yVals, const array &S, const char * const title)**
-> // accepts additional vectors that define the x,y coordinates for the surface points.
-
-The second overload has two options for the x, y coordinate vectors. Assuming a surface grid of size **m x n**:
- 1. Short vectors defining the spacing along each axis. Vectors will have sizes **m x 1** and **n x 1**.
- 2. Vectors containing the coordinates of each and every point.
- Each of the vectors will have length **mn x 1**.
- This can be used for completely non-uniform or parametric surfaces.
-
-# Conclusion {#conclusion}
-There is a fairly comprehensive collection of methods to visualize data in Arrayfire.
-Thanks to the high-performance gpu plotting library Forge, the provided Arrayfire
-functions not only make visualizations as simple as possible, but keep them as 
-robust as the rest of the Arrayfire library.
+robust as the rest of the ArrayFire library.

From d4d9d8e52768956fb1668c69c9bce5bbfa0faf30 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 19 May 2016 13:59:42 -0400
Subject: [PATCH 0545/2677] Fix GLEWmx after using find package handle standard
 args (6159382)

---
 CMakeLists.txt                |  2 +-
 CMakeModules/FindForge.cmake  | 10 +++++-----
 CMakeModules/FindGLEWmx.cmake |  1 +
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 795df3edb4..7e93ba32fb 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -63,7 +63,7 @@ IF(BUILD_GRAPHICS)
 
     IF(FORGE_FOUND)
         ADD_DEFINITIONS(-DGLEW_MX -DWITH_GRAPHICS)
-        INCLUDE("${CMAKE_MODULE_PATH}/FindGLEWmx.cmake")
+        FIND_PACKAGE(GLEWmx REQUIRED)
 
         INCLUDE_DIRECTORIES(
             ${FORGE_INCLUDE_DIRECTORIES}
diff --git a/CMakeModules/FindForge.cmake b/CMakeModules/FindForge.cmake
index 9ad5cde73f..117451da97 100644
--- a/CMakeModules/FindForge.cmake
+++ b/CMakeModules/FindForge.cmake
@@ -73,9 +73,9 @@ FIND_LIBRARY(FORGE_LIBRARY
     NAMES forge
     HINTS "${FORGE_PACKAGE_DIR}/lib")
 
-INCLUDE("${CMAKE_MODULE_PATH}/FindGLEWmx.cmake")
+FIND_PACKAGE(GLEWmx REQUIRED)
 
-IF(GLEWmx_FOUND AND OPENGL_FOUND)
+IF(GLEWMX_FOUND AND OPENGL_FOUND)
     IF(FORGE_INCLUDE_DIRECTORIES)
         SET(FORGE_INCLUDE_DIRECTORIES ${FORGE_INCLUDE_DIRECTORIES} ${GLEW_INCLUDE_DIR}
             CACHE INTERNAL "All include dirs required for FORGE'")
@@ -90,10 +90,10 @@ IF(GLEWmx_FOUND AND OPENGL_FOUND)
     FIND_PACKAGE_HANDLE_STANDARD_ARGS(FORGE DEFAULT_MSG FORGE_LIBRARIES FORGE_INCLUDE_DIRECTORIES)
     MARK_AS_ADVANCED(FORGE_LIBRARIES FORGE_INCLUDE_DIRECTORIES)
 
-ELSE(GLEWmx_FOUND AND OPENGL_FOUND)
-    IF(NOT GLEWmx_FOUND)
+ELSE(GLEWMX_FOUND AND OPENGL_FOUND)
+    IF(NOT GLEWMX_FOUND)
         MESSAGE(FATAL_ERROR "GLEW-MX Not Found")
     ELSEIF(NOT OPENGL_FOUND)
         MESSAGE(FATAL_ERROR "OpenGL Not Found")
     ENDIF()
-ENDIF(GLEWmx_FOUND AND OPENGL_FOUND)
+ENDIF(GLEWMX_FOUND AND OPENGL_FOUND)
diff --git a/CMakeModules/FindGLEWmx.cmake b/CMakeModules/FindGLEWmx.cmake
index 8231d33e15..587a6ff208 100644
--- a/CMakeModules/FindGLEWmx.cmake
+++ b/CMakeModules/FindGLEWmx.cmake
@@ -87,5 +87,6 @@ ENDIF(USE_GLEWmx_STATIC)
 MARK_AS_ADVANCED(GLEWmxs_LIBRARY GLEWmxd_LIBRARY GLEWmx_LIBRARY GLEW_INCLUDE_DIR)
 
 INCLUDE(FindPackageHandleStandardArgs)
+# Sets GLEWMX_FOUND
 FIND_PACKAGE_HANDLE_STANDARD_ARGS(GLEWmx DEFAULT_MSG
     GLEW_INCLUDE_DIR GLEWmx_LIBRARY)

From a1f6439640a1afc45df4f7c1cf22705665d12451 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Fri, 20 May 2016 20:19:11 -0400
Subject: [PATCH 0546/2677] Revert "[Docs] Update Forge page"

This reverts commit d8b6246768ca612c878e93d6405ecfbddcc8e7a4.
---
 docs/layout.xml                   |   2 +-
 docs/pages/forge_visualization.md | 161 +++++++++++++++++++++++++++++-
 2 files changed, 161 insertions(+), 2 deletions(-)

diff --git a/docs/layout.xml b/docs/layout.xml
index ee2da85256..d2f18bc324 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -12,8 +12,8 @@
       <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Array and Matrix Manipulation"/>
       <tab type="user" url="\ref interop_cuda" visible="yes" title="CUDA Interoperability"/>
       <tab type="user" url="\ref interop_opencl" visible="yes" title="OpenCL Interoperability"/>
-      <tab type="user" url="\ref forge_visualization" visible="yes" title="Visualizing arrays with Forge"/>
       <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
+      <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
       <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
       <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
       <tab type="user" url="\ref configuring_environment" visible="yes" title="Configuring ArrayFire Environment"/>
diff --git a/docs/pages/forge_visualization.md b/docs/pages/forge_visualization.md
index 3c1d4f9cce..72901dc681 100644
--- a/docs/pages/forge_visualization.md
+++ b/docs/pages/forge_visualization.md
@@ -1 +1,160 @@
-robust as the rest of the ArrayFire library.
+Visualizing af::array with Forge {#forge_visualization}
+===================
+
+Arrayfire as a library aims to provide a robust and easy to use platform for
+high-performance, parallel and GPU computing.
+
+[TOC]
+
+The goal of [Forge](https://github.com/arrayfire/forge), an OpenGL visualization
+library, is to provide equally robust visualizations that are interoperable
+between Arrayfire data-structures and an OpenGL context.
+
+Arrayfire provides wrapper functions that are designed to be a simple interface
+to visualize af::arrays. These functions perform various interop tasks. One in
+particular is that instead of wasting time copying and reformatting data from
+the GPU to the host and back to the GPU, we can draw directly from GPU-data to
+GPU-framebuffers! This saves 2 memory copies.
+
+Let's see exactly what visuals we can illuminate with forge and how Arrayfire
+anneals the data between the two libraries.
+
+# Setup {#setup}
+Before we can call Forge functions, we need to set up the related "canvas" classes.
+Forge functions are tied to the af::Window class. First let's create a window:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+const static int width = 512, height = 512;
+af::Window window(width, height, "2D plot example title");
+
+do{
+
+//drawing functions here
+
+} while( !window.close() );
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We also added a drawing loop, so now we can use Forge's drawing functions to 
+draw to the window.
+The drawing functions present in Forge are listed below.
+
+# Rendering Functions {#render_func}
+
+Documentation for rendering functions can be found [here](\ref gfx_func_draw).
+
+## Image {#image}
+The af::Window::image() function can be used to plot grayscale or color images.
+To plot a grayscale image a 2d array should be passed into the function.
+Let's see this on a static noise example:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array img = constant(0, width, height); //make a black image
+array random = randu(width, height);      //make random [0,1] distribution
+img(random > 0.5) = 1; //set all pixels where distribution > 0.5 to white
+
+window.image(img);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/noise.png" alt="Forge image plot of noise" width="20%" />
+Tweaking the previous example by giving our image a depth of 3 for the RGB values
+allows us to generate colorful noise:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array img = 255 * randu(width, height, 3);      //make random [0, 255] distribution
+window.image( img.as(u8) );
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/color_noise.png" alt="Forge image plot of color noise" width="20%" />
+Note that Forge automatically handles any af::array type passed from Arrayfire.
+In the first example we passed in an image of floats in the range [0, 1].
+In the last example we cast our array to an unsigned byte array with the range
+[0, 255]. The type-handling properties are consistent for all Forge drawing functions.
+
+## Plot {#plot}
+The af::Window::plot() function visualizes an array as a 2d-line plot. Let's see
+a simple example:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array X = seq(-af::Pi, af::Pi, 0.01);
+array Y = sin(X);
+window.plot(X, Y);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+<img src="gfx_docs_images/sin_plot.png" alt="Forge 2d line plot of sin() function" width="30%" />
+The plot function has the signature:
+
+> **void plot( const array &X, const array &Y, const char * const title = NULL );**
+
+Both the x and y coordinates of the points are required to plot. This allows for
+non-uniform, or parametric plots:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array t = seq(0, 100, 0.01);
+array X = sin(t) * (exp(cos(t)) - 2 * cos(4 * t) - pow(sin(t / 12), 5));
+array Y = cos(t) * (exp(cos(t)) - 2 * cos(4 * t) - pow(sin(t / 12), 5));
+window.plot(X, Y);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+<img src="gfx_docs_images/butterfly_plot.png" alt="Forge 2d line plot of butterfly function" width="30%" />
+
+## Plot3 {#plot3}
+The af::Window::plot3() function will plot a curve in 3d-space.
+Its signature is:
+> **void plot3 (const array &in, const char * title = NULL);**
+The input array expects xyz-triplets in sequential order. The points can be in a
+flattened one dimensional (*3n x 1*) array, or in one of the (*3 x n*), (*n x 3*) matrix forms.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array Z = seq(0.1f, 10.f, 0.01);
+array Y = sin(10 * Z) / Z;
+array X = cos(10 * Z) / Z;
+
+array Pts = join(1, X, Y, Z);
+//Pts can be passed in as a matrix in the from n x 3, 3 x n
+//or in the flattened xyz-triplet array with size 3n x 1
+window.plot3(Pts);
+//both of the following are equally valid
+//window.plot3(transpose(Pts));
+//window.plot3(flat(Pts));
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/spiral_plot3.png" alt="Forge 3d line plot" width="40%" />
+
+## Histogram {#histogram}
+The af::Window::hist() function renders an input array as a histogram.
+In our example, the input array will be created with Arrayfire's histogram()
+function, which actually counts and bins each sample. The output from histogram()
+can directly be fed into the af::Window::hist() rendering function.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+const int BINS = 128; SAMPLES = 9162;
+array norm = randn(SAMPLES);
+array hist_arr = histogram(norm, BINS);
+
+win.hist(hist_arr, 0, BINS);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+In addition to the histogram array with the number of samples in each bin, the
+af::Window::hist() function takes two additional parameters -- the minimum and
+maximum values of all datapoints in the histogram array. This effectively sets
+the range of the binned data. The full signature of af::Window::hist() is:
+> **void hist(const array & X, const double minval, const double maxval, const char * const title = NULL);**
+<img src="gfx_docs_images/norm_histogram.png" alt="Forge 3d scatter plot" width="40%" />
+
+
+## Surface {#surface}
+The af::Window::surface() function will plot af::arrays as a 3d surface.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+array Z = randu(21, 21);
+window.surface(Z, "Random Surface");    //equal to next function call
+//window.surface( seq(-1, 1, 0.1), seq(-1, 1, 0.1), Z, "Random Surface");
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+<img src="gfx_docs_images/rand_surface.png" alt="Forge random surface plot" width="30%" />
+There are two overloads for the af::Window::surface() function:
+> **void surface (const array & S, const char * const title )**
+> // Accepts a 2d matrix with the z values of the surface
+
+> **void surface (const array &xVals, const array &yVals, const array &S, const char * const title)**
+> // accepts additional vectors that define the x,y coordinates for the surface points.
+
+The second overload has two options for the x, y coordinate vectors. Assuming a surface grid of size **m x n**:
+ 1. Short vectors defining the spacing along each axis. Vectors will have sizes **m x 1** and **n x 1**.
+ 2. Vectors containing the coordinates of each and every point.
+ Each of the vectors will have length **mn x 1**.
+ This can be used for completely non-uniform or parametric surfaces.
+
+# Conclusion {#conclusion}
+There is a fairly comprehensive collection of methods to visualize data in Arrayfire.
+Thanks to the high-performance gpu plotting library Forge, the provided Arrayfire
+functions not only make visualizations as simple as possible, but keep them as 
+robust as the rest of the Arrayfire library.

From 41d121e034f9528e4d6f1320a5084dfd3699ba77 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 23 May 2016 14:57:53 -0400
Subject: [PATCH 0547/2677] Handle 1 element cases in median

---
 src/api/c/median.cpp | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index b5c033f461..a8268f5ff4 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -27,14 +27,30 @@ template<typename T>
 static double median(const af_array& in)
 {
     dim_t nElems = getInfo(in).elements();
-    double mid      = (nElems + 1) / 2;
-    af_seq mdSpan[1]= {af_make_seq(mid-1, mid, 1)};
     dim4 dims(nElems, 1, 1, 1);
 
     af_array temp = 0;
     AF_CHECK(af_moddims(&temp, in, 1, dims.get()));
     const Array<T> input  = getArray<T>(temp);
 
+    // Shortcut cases for 1 or 2 elements
+    if(nElems == 1) {
+        T result;
+        AF_CHECK(af_get_data_ptr((void*)&result, in));
+        return result;
+    } else if(nElems == 2) {
+        T result[2];
+        AF_CHECK(af_get_data_ptr((void*)&result, in));
+        if (input.isFloating()) {
+            return division(result[0] + result[1], 2.0);
+        } else {
+            return division(result[0] + result[1], 2.0);
+        }
+    }
+
+    double mid      = (nElems + 1) / 2;
+    af_seq mdSpan[1]= {af_make_seq(mid-1, mid, 1)};
+
     Array<T> sortedArr = sort<T, true>(input, 0);
 
     af_array sarrHandle = getHandle<T>(sortedArr);
@@ -66,6 +82,13 @@ template<typename T>
 static af_array median(const af_array& in, const dim_t dim)
 {
     const Array<T> input = getArray<T>(in);
+
+    // Shortcut cases for 1 element along selected dimension
+    if(input.dims()[dim] == 1) {
+        Array<T> result = copyArray<T>(input);
+        return getHandle<T>(result);
+    }
+
     Array<T> sortedIn   = sort<T, true>(input, dim);
 
     int dimLength = input.dims()[dim];

From 34d6f5d677c8c0c7cfbb6705cec051d1796ca45b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 25 May 2016 19:35:08 +0530
Subject: [PATCH 0548/2677] set af_index_t to af_span in af_create_indexers
 function

---
 src/api/c/index.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 4a20ca2b34..348932a570 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -241,6 +241,10 @@ af_err af_create_indexers(af_index_t** indexers)
 {
     try {
         af_index_t* out = new af_index_t[4];
+        for (int i=0; i<4; ++i) {
+            out[i].idx.seq = af_span;
+            out[i].isSeq = true;
+        }
         std::swap(*indexers, out);
     }
     CATCHALL;

From bf6f670c9a081d2bddf83d3d84340234bbe12599 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 25 May 2016 20:58:03 +0530
Subject: [PATCH 0549/2677] Document indexing helper functions

These functions are mostly used by FFI interfaces
---
 docs/details/index.dox |  2 +-
 include/af/index.h     | 45 +++++++++++++++++++++++++++++++++++++-----
 2 files changed, 41 insertions(+), 6 deletions(-)

diff --git a/docs/details/index.dox b/docs/details/index.dox
index 90b9924d5e..6125dd50ea 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -4,7 +4,7 @@
 
 \defgroup index_func_index index
 
-\brief lookup values on array based on sequences
+\brief lookup values on array based on sequences and/or arrays
 
 \ingroup index_mat
 
diff --git a/include/af/index.h b/include/af/index.h
index 79bf1229a5..c794bca3ac 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -293,10 +293,45 @@ extern "C" {
     ///
     /// \brief Create an quadruple of af_index_t array
     ///
+    /// \code
+    /// af_index_t* indexers = 0;
+    /// af_err err = af_create_indexers(&indexers); // Memory is allocated on heap by the callee
+    /// // by default all the indexers span all the elements along the given dimension
+    ///
+    /// //Create array
+    /// af_array a;
+    /// unsigned ndims = 2;
+    /// dim_t dim[] = {10,10};
+    /// af_randu(&a, ndims, dim, f32);
+    ///
+    /// //Create index array
+    /// af_array idx;
+    /// unsigned n = 1;
+    /// dim_t d[] = {5};
+    /// af_range(&idx, n, d, 0, s64);
+    ///
+    /// af_print_array(a);
+    /// af_print_array(idx);
+    ///
+    /// //create array indexer
+    /// err = af_set_array_indexer(indexers, idx, 1);
+    /// if (err != AF_SUCCESS) {
+    ///     printf("Error from set array indexer: %d \n", err2);
+    ///     exit(1);
+    /// }
+    ///
+    /// //index with indexers
+    /// af_array out;
+    /// af_index_gen(&out, a, 2, indexers); // number of indexers should be two since
+    ///                                     // we have set only second af_index_t
+    /// af_print_array(out);
+    /// af_release_indexers(indexers);
+    /// \endcode
+    ///
     /// \param[out] indexers pointer to location where quadruple af_index_t array is created
     /// \returns \ref af_err error code
     ///
-    /// \ingroup index_func_util
+    /// \ingroup index_func_index
     ///
     AFAPI af_err af_create_indexers(af_index_t** indexers);
 #endif
@@ -310,7 +345,7 @@ extern "C" {
     /// \param[in] dim is the dimension to be indexed
     /// \returns \ref af_err error code
     ///
-    /// \ingroup index_func_util
+    /// \ingroup index_func_index
     ///
     AFAPI af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim);
 #endif
@@ -324,7 +359,7 @@ extern "C" {
     /// \param[in] dim is the dimension to be indexed
     /// \param[in] is_batch indicates if the sequence based indexing is inside a batch operation
     ///
-    /// \ingroup index_func_util
+    /// \ingroup index_func_index
     ///
     AFAPI af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx,
                                   const dim_t dim, const bool is_batch);
@@ -342,7 +377,7 @@ extern "C" {
     /// \param[in] is_batch indicates if the sequence based indexing is inside a batch operation
     /// \returns \ref af_err error code
     ///
-    /// \ingroup index_func_util
+    /// \ingroup index_func_index
     ///
     AFAPI af_err af_set_seq_param_indexer(af_index_t* indexer,
                                         const double begin, const double end, const double step,
@@ -356,7 +391,7 @@ extern "C" {
     /// \param[in] indexers is pointer to location where quadruple af_index_t array is created
     //  \returns \ref af_err error code
     ///
-    /// \ingroup index_func_util
+    /// \ingroup index_func_index
     ///
     AFAPI af_err af_release_indexers(af_index_t* indexers);
 #endif

From ea61dd475fdbdd77f4520a78dee01250d91ea1c9 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 25 May 2016 11:32:41 -0400
Subject: [PATCH 0550/2677] CUDA Backend for scan by key

---
 include/af/algorithm.h                        |  41 +-
 include/af/defines.h                          |  10 +-
 src/api/c/scan.cpp                            |  85 ++-
 src/api/cpp/scan.cpp                          |   7 +
 src/backend/cpu/scan.cpp                      |  73 ++-
 src/backend/cpu/scan.hpp                      |   3 +
 src/backend/cuda/CMakeLists.txt               |   3 +
 .../cuda/kernel/scan_by_key/CMakeLists.txt    |  23 +
 .../kernel/scan_by_key/scan_by_key_impl.cu.in |  26 +
 src/backend/cuda/kernel/scan_dim_by_key.hpp   |  21 +
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 575 ++++++++++++++++++
 src/backend/cuda/kernel/scan_first_by_key.hpp |  21 +
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 517 ++++++++++++++++
 src/backend/cuda/math.hpp                     |   1 +
 src/backend/cuda/scan.cu                      |  44 +-
 src/backend/cuda/scan_by_key.cu               |  73 +++
 src/backend/cuda/scan_by_key.hpp              |  17 +
 src/backend/opencl/scan.cpp                   |  73 ++-
 src/backend/opencl/scan.hpp                   |   3 +
 19 files changed, 1530 insertions(+), 86 deletions(-)
 create mode 100644 src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
 create mode 100644 src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in
 create mode 100644 src/backend/cuda/kernel/scan_dim_by_key.hpp
 create mode 100644 src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
 create mode 100644 src/backend/cuda/kernel/scan_first_by_key.hpp
 create mode 100644 src/backend/cuda/kernel/scan_first_by_key_impl.hpp
 create mode 100644 src/backend/cuda/scan_by_key.cu
 create mode 100644 src/backend/cuda/scan_by_key.hpp

diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 1ae9c7903d..bc39b1deac 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -318,17 +318,31 @@ namespace af
 
 #if AF_API_VERSION >=34
     /**
-       C++ Interface exclusive sum (cumulative sum) of an array
+       C++ Interface generalized scan of an array
 
        \param[in] in is the input array
-       \param[in] dim The dimension along which exclusive sum is performed
+       \param[in] dim The dimension along which scan is performed
+       \param[in] op is the type of binary operation used
+       \param[in] inclusive_scan is flag specifying whether scan is inclusive
+       \return the output containing scan of the input
+
+       \ingroup scan_func_scan
+    */
+    AFAPI array scan(const array &in, const int dim = 0, af_binary_op op = AF_BINARY_ADD, bool inclusive_scan = true);
+
+    /**
+       C++ Interface generalized scan by key of an array
+
+       \param[in] key is the key array
+       \param[in] in is the input array
+       \param[in] dim The dimension along which scan is performed
        \param[in] op is the type of binary operations used
        \param[in] inclusive_scan is flag specifying whether scan is inclusive
-       \return the output containing exclusive sums of the input
+       \return the output containing scan of the input
 
        \ingroup scan_func_scan
     */
-    AFAPI array scan(const array &in, const int dim = 0, af_binary_op op = AF_ADD, bool inclusive_scan = true);
+    AFAPI array scanByKey(const array &key, const array& in, const int dim = 0, af_binary_op op = AF_BINARY_ADD, bool inclusive_scan = true);
 #endif
 
     /**
@@ -762,9 +776,9 @@ extern "C" {
     /**
        C Interface generalized scan of an array
 
-       \param[out] out will contain exclusive sums of the input
+       \param[out] out will contain scan of the input
        \param[in] in is the input array
-       \param[in] dim The dimension along which exclusive sum is performed
+       \param[in] dim The dimension along which scan is performed
        \param[in] op is the type of binary operations used
        \param[in] inclusive_scan is flag specifying whether scan is inclusive
        \return \ref AF_SUCCESS if the execution completes properly
@@ -772,6 +786,21 @@ extern "C" {
        \ingroup scan_func_scan
     */
     AFAPI af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan);
+
+    /**
+       C Interface generalized scan by key of an array
+
+       \param[out] out will contain scan of the input
+       \param[in] key is the key array
+       \param[in] in is the input array
+       \param[in] dim The dimension along which scan is performed
+       \param[in] op is the type of binary operations used
+       \param[in] inclusive_scan is flag specifying whether scan is inclusive
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup scan_func_scan
+    */
+    AFAPI af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan);
 #endif
 
     /**
diff --git a/include/af/defines.h b/include/af/defines.h
index 04e06ea38d..7191c32194 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -383,12 +383,10 @@ typedef enum {
 
 #if AF_API_VERSION >=34
 typedef enum {
-    AF_ADD  = 0,
-    AF_SUB  = 1,
-    AF_MUL  = 2,
-    AF_DIV  = 3,
-    AF_MIN  = 4,
-    AF_MAX  = 5
+    AF_BINARY_ADD  = 0,
+    AF_BINARY_MUL  = 1,
+    AF_BINARY_MIN  = 2,
+    AF_BINARY_MAX  = 3
 } af_binary_op;
 #endif
 
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 743c61f635..6bc70f6442 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -15,6 +15,7 @@
 #include <handle.hpp>
 #include <ops.hpp>
 #include <scan.hpp>
+#include <scan_by_key.hpp>
 #include <backend.hpp>
 
 using af::dim4;
@@ -26,18 +27,49 @@ static inline af_array scan(const af_array in, const int dim, bool inclusive_sca
     return getHandle(scan<op,Ti,To>(getArray<Ti>(in), dim, inclusive_scan));
 }
 
+template<af_op_t op, typename Ti, typename To>
+static inline af_array scan_key(const af_array key, const af_array in, const int dim, bool inclusive_scan = true)
+{
+    const ArrayInfo& key_info = getInfo(key);
+    af_dtype type = key_info.getType();
+    af_array out;
+
+    switch(type) {
+        case    s32:   out = getHandle(scan<op, Ti,   int, To>(getArray<  int>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
+        case    u32:   out = getHandle(scan<op, Ti,  uint, To>(getArray< uint>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
+        case    s64:   out = getHandle(scan<op, Ti,  intl, To>(getArray< intl>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
+        case    u64:   out = getHandle(scan<op, Ti, uintl, To>(getArray<uintl>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
+        default:
+            TYPE_ERROR(1, type);
+    }
+    return out;
+}
+
+template<typename Ti, typename To>
+static inline af_array scan_op(const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan = true)
+{
+    af_array out;
+
+    switch(op) {
+    case AF_BINARY_ADD:    out = scan_key<af_add_t, Ti, To>(key, in, dim, inclusive_scan); break;
+    case AF_BINARY_MUL:    out = scan_key<af_mul_t, Ti, To>(key, in, dim, inclusive_scan); break;
+    case AF_BINARY_MIN:    out = scan_key<af_min_t, Ti, To>(key, in, dim, inclusive_scan); break;
+    case AF_BINARY_MAX:    out = scan_key<af_max_t, Ti, To>(key, in, dim, inclusive_scan); break;
+                    //TODO Error for op in default case
+    }
+    return out;
+}
+
 template<typename Ti, typename To>
 static inline af_array scan_op(const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
 {
     af_array out;
 
     switch(op) {
-    case AF_ADD:    out = scan<af_add_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_SUB:    out = scan<af_sub_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_MUL:    out = scan<af_mul_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_DIV:    out = scan<af_div_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_MIN:    out = scan<af_min_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_MAX:    out = scan<af_max_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_BINARY_ADD:    out = scan<af_add_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_BINARY_MUL:    out = scan<af_mul_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_BINARY_MIN:    out = scan<af_min_t, Ti, To>(in, dim, inclusive_scan); break;
+    case AF_BINARY_MAX:    out = scan<af_max_t, Ti, To>(in, dim, inclusive_scan); break;
                     //TODO Error for op in default case
     }
     return out;
@@ -125,3 +157,44 @@ af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
 
     return AF_SUCCESS;
 }
+
+af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
+{
+    ARG_ASSERT(2, dim >= 0);
+    ARG_ASSERT(2, dim <  4);
+
+    try {
+
+        const ArrayInfo& in_info = getInfo(in);
+
+        if (dim >= (int)in_info.ndims()) {
+            *out = retain(in);
+            return AF_SUCCESS;
+        }
+
+        af_dtype type = in_info.getType();
+        af_array res;
+
+        switch(type) {
+        case f32:  res = scan_op<float  , float  >(key, in, dim, op, inclusive_scan); break;
+        case f64:  res = scan_op<double , double >(key, in, dim, op, inclusive_scan); break;
+        case c32:  res = scan_op<cfloat , cfloat >(key, in, dim, op, inclusive_scan); break;
+        case c64:  res = scan_op<cdouble, cdouble>(key, in, dim, op, inclusive_scan); break;
+        case u32:  res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
+        case s32:  res = scan_op<int    , int    >(key, in, dim, op, inclusive_scan); break;
+        case u64:  res = scan_op<uintl  , uintl  >(key, in, dim, op, inclusive_scan); break;
+        case s64:  res = scan_op<intl   , intl   >(key, in, dim, op, inclusive_scan); break;
+        case u16:  res = scan_op<ushort , uint   >(key, in, dim, op, inclusive_scan); break;
+        case s16:  res = scan_op<short  , int    >(key, in, dim, op, inclusive_scan); break;
+        case u8:   res = scan_op<uchar  , uint   >(key, in, dim, op, inclusive_scan); break;
+        case b8:   res = scan_op<char   , uint   >(key, in, dim, op, inclusive_scan); break;
+        default:
+            TYPE_ERROR(1, type);
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/scan.cpp b/src/api/cpp/scan.cpp
index 7fbbf39efd..3d2222e178 100644
--- a/src/api/cpp/scan.cpp
+++ b/src/api/cpp/scan.cpp
@@ -26,4 +26,11 @@ namespace af
         AF_THROW(af_scan(&out, in.get(), dim, op, inclusive_scan));
         return array(out);
     }
+
+    array scanByKey(const array& key, const array& in, const int dim, af_binary_op op, bool inclusive_scan)
+    {
+        af_array out = 0;
+        AF_THROW(af_scan_by_key(&out, key.get(), in.get(), dim, op, inclusive_scan));
+        return array(out);
+    }
 }
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 81ede16c83..f89ba54d35 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -71,30 +71,59 @@ namespace cpu
         return out;
     }
 
-#define INSTANTIATE(ROp, Ti, To)\
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
+    {
+        return scan(in, dim, inclusive_scan);
+    }
+
+#define INSTANTIATE_SCAN(ROp, Ti, To)\
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
-#define INSTANTIATE_SCAN(ROp)           \
-    INSTANTIATE(ROp, float  , float  )  \
-    INSTANTIATE(ROp, double , double )  \
-    INSTANTIATE(ROp, cfloat , cfloat )  \
-    INSTANTIATE(ROp, cdouble, cdouble)  \
-    INSTANTIATE(ROp, int    , int    )  \
-    INSTANTIATE(ROp, uint   , uint   )  \
-    INSTANTIATE(ROp, intl   , intl   )  \
-    INSTANTIATE(ROp, uintl  , uintl  )  \
-    INSTANTIATE(ROp, char   , int    )  \
-    INSTANTIATE(ROp, char   , uint   )  \
-    INSTANTIATE(ROp, uchar  , uint   )  \
-    INSTANTIATE(ROp, short  , int    )  \
-    INSTANTIATE(ROp, ushort , uint   )
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
+    template Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_ALL(ROp)                       \
+    INSTANTIATE_SCAN(ROp, float  , float  )             \
+    INSTANTIATE_SCAN(ROp, double , double )             \
+    INSTANTIATE_SCAN(ROp, cfloat , cfloat )             \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble)             \
+    INSTANTIATE_SCAN(ROp, int    , int    )             \
+    INSTANTIATE_SCAN(ROp, uint   , uint   )             \
+    INSTANTIATE_SCAN(ROp, intl   , intl   )             \
+    INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
+    INSTANTIATE_SCAN(ROp, char   , int    )             \
+    INSTANTIATE_SCAN(ROp, char   , uint   )             \
+    INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
+    INSTANTIATE_SCAN(ROp, short  , int    )             \
+    INSTANTIATE_SCAN(ROp, ushort , uint   )
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_OP(ROp)            \
+    INSTANTIATE_SCAN_ALL(ROp)               \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)   \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, long)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, ulong)
 
     //accum
-    INSTANTIATE(af_notzero_t, char  , uint)
-    INSTANTIATE_SCAN(af_add_t)
-    INSTANTIATE_SCAN(af_sub_t)
-    INSTANTIATE_SCAN(af_mul_t)
-    INSTANTIATE_SCAN(af_div_t)
-    INSTANTIATE_SCAN(af_min_t)
-    INSTANTIATE_SCAN(af_max_t)
+    INSTANTIATE_SCAN(af_notzero_t, char, uint)
+    INSTANTIATE_SCAN_OP(af_add_t)
+    INSTANTIATE_SCAN_OP(af_mul_t)
+    INSTANTIATE_SCAN_OP(af_min_t)
+    INSTANTIATE_SCAN_OP(af_max_t)
 }
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index 5620e44cd8..7adf5ac3ac 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -14,4 +14,7 @@ namespace cpu
 {
     template<af_op_t op, typename Ti, typename To>
     Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Ti>& in, const Array<Tk>& key, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6f3dc39ed0..cab0b25ddd 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -260,6 +260,8 @@ SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/CMakeLists.txt")
+
 LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
 IF(${COMPUTE_COUNT} EQUAL 1)
     SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} ${CUDA_GENERATE_CODE}")
@@ -391,6 +393,7 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${c_sources}
                 ${cpp_sources}
                 ${sort_by_key_sources}
+                ${scan_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE})
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..cf7557899b
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -0,0 +1,23 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu.in" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_BINARY_OPS")
+        STRING(REPLACE "// SBK_BINARY_OPS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_BINARY_OPS ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
+    CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu.in"
+        "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu")
+    ADD_CUSTOM_COMMAND(
+        OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
+        COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
+      DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp" "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp")
+ENDFOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
+
+FILE(GLOB scan_by_key_sources
+  "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/*.cu"
+)
+
+LIST(SORT scan_by_key_sources)
diff --git a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in
new file mode 100644
index 0000000000..45fe7f8d05
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <ops.hpp>
+#include <backend.hpp>
+#include <kernel/scan_first_by_key_impl.hpp>
+#include <kernel/scan_dim_by_key_impl.hpp>
+
+// This file instantiates scan_dim_by_key as separate object files from CMake
+// The line below is read by CMake to determenine the instantiations
+// SBK_BINARY_OPS:af_add_t af_mul_t af_max_t af_min_t
+
+namespace cuda
+{
+namespace kernel
+{
+    INSTANTIATE_SCAN_FIRST_BY_KEY_OP(@SBK_BINARY_OP@)
+    INSTANTIATE_SCAN_DIM_BY_KEY_OP(@SBK_BINARY_OP@)
+}
+}
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.hpp b/src/backend/cuda/kernel/scan_dim_by_key.hpp
new file mode 100644
index 0000000000..5ed4daad91
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_dim_by_key.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <ops.hpp>
+#include <Param.hpp>
+
+namespace cuda
+{
+    namespace kernel
+    {
+        template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool inclusive_scan>
+        void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+    }
+}
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
new file mode 100644
index 0000000000..60ab8d9eb2
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -0,0 +1,575 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <ops.hpp>
+#include <backend.hpp>
+#include <Param.hpp>
+#include <dispatch.hpp>
+#include <math.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <memory.hpp>
+#include "config.hpp"
+
+#include <kernel/scan_dim_by_key.hpp>
+
+namespace cuda
+{
+namespace kernel
+{
+
+    template<typename Tk>
+    __device__
+    inline static char calculate_head_flags_dim(const Tk *kptr, int id, int stride)
+    {
+        char flag;
+        if (id == 0) {
+            flag = 1;
+        } else {
+            flag = ((*kptr) != (*(kptr - stride)));
+        }
+        return flag;
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, uint DIMY, bool inclusive_scan>
+    __global__
+    static void scan_dim_nonfinal_kernel(Param<To> out,
+                                Param<To> tmp,
+                                Param<char> tflg,
+                                Param<int> tlid,
+                                CParam<Ti>  in,
+                                CParam<Tk> key,
+                                uint blocks_x,
+                                uint blocks_y,
+                                uint lim)
+    {
+        const int tidx = threadIdx.x;
+        const int tidy = threadIdx.y;
+        const int tid  = tidy * THREADS_X + tidx;
+
+        const int zid = blockIdx.x / blocks_x;
+        const int wid = blockIdx.y / blocks_y;
+        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int xid = blockIdx_x * blockDim.x + tidx;
+        const int yid = blockIdx_y; // yid  of output. updated for input later.
+
+        int ids[4] = {xid, yid, zid, wid};
+
+        const Ti *iptr = in.ptr;
+        const Tk *kptr = key.ptr;
+        To *optr = out.ptr;
+        To *tptr = tmp.ptr;
+        char *tfptr = tflg.ptr;
+        int *tiptr = tlid.ptr;
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before offsetting in
+        tptr    += ids[3] *  tmp.strides[3] + ids[2] *  tmp.strides[2] + ids[1] *  tmp.strides[1] + ids[0];
+        tfptr   += ids[3] * tflg.strides[3] + ids[2] * tflg.strides[2] + ids[1] * tflg.strides[1] + ids[0];
+        tiptr   += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] + ids[1] * tlid.strides[1] + ids[0];
+        const int blockIdx_dim = ids[dim];
+
+        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        iptr  += ids[3] *  in.strides[3] + ids[2] *  in.strides[2] + ids[1] *  in.strides[1] + ids[0];
+        kptr  += ids[3] * key.strides[3] + ids[2] * key.strides[2] + ids[1] * key.strides[1] + ids[0];
+        int id_dim = ids[dim];
+        const int out_dim = out.dims[dim];
+
+        bool is_valid =
+            (ids[0] < out.dims[0]) &&
+            (ids[1] < out.dims[1]) &&
+            (ids[2] < out.dims[2]) &&
+            (ids[3] < out.dims[3]);
+
+        const int ostride_dim = out.strides[dim];
+        const int istride_dim =  in.strides[dim];
+
+        __shared__ char s_flg[THREADS_X * DIMY * 2];
+        __shared__ To s_val[THREADS_X * DIMY * 2];
+        __shared__ char s_ftmp[THREADS_X];
+        __shared__ To s_tmp[THREADS_X];
+        To *sptr =  s_val + tid;
+        char *sfptr = s_flg + tid;
+
+        Transform<Ti, To, op> transform;
+        Binary<To, op> binop;
+
+        const To init = binop.init();
+        To val = init;
+
+        const bool isLast = (tidy == (DIMY - 1));
+        if (isLast) {
+            s_tmp[tidx] = val;
+            s_ftmp[tidx] = 0;
+        }
+        __syncthreads();
+
+        char *prev;
+        if (tidy == 0) {
+            prev = &s_ftmp[tidx];
+        } else {
+            prev = sfptr - THREADS_X;
+        }
+        char *curr = &sfptr[tidy];
+
+        char flag = 0;
+        int boundaryid = -1;
+        for (int k = 0; k < lim; k++) {
+
+            if (id_dim < out_dim) {
+                flag = calculate_head_flags_dim(kptr, id_dim, istride_dim);
+            } else {
+                flag = 0;
+            }
+
+            //Load val from global in
+            if (inclusive_scan) {
+                if (id_dim >= out_dim) {
+                    val = init;
+                } else {
+                    val = transform(*iptr);
+                }
+            } else {
+                if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
+                    val = init;
+                } else {
+                    val = transform(*(iptr - istride_dim));
+                }
+            }
+
+            if ((tidy == 0) && (flag == 0)) {
+                val = binop(val, s_tmp[tidx]);
+                flag = flag | s_ftmp[tidx];
+            }
+
+            *sptr = val;
+            *sfptr = flag;
+            __syncthreads();
+
+            int start = 0;
+#pragma unroll
+            for (int off = 1; off < DIMY; off *= 2) {
+
+                if (tidy >= off) {
+                    val = sfptr[start * THREADS_X] ? val : binop(val, sptr[(start - off) * THREADS_X]);
+                    flag = sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
+                }
+                start = DIMY - start;
+                sptr[start * THREADS_X] = val;
+                sfptr[start * THREADS_X] = flag;
+
+                __syncthreads();
+            }
+
+            if ((*prev == 0) && (*curr == 1)) {
+                boundaryid = id_dim;
+            }
+
+            if (is_valid && (id_dim < out_dim)) *optr = val;
+            if (isLast) {
+                s_tmp[tidx] = val;
+                s_ftmp[tidx] = flag;
+            }
+            id_dim += blockDim.y;
+            iptr += blockDim.y * istride_dim;
+            optr += blockDim.y * ostride_dim;
+            __syncthreads();
+        }
+
+        if (is_valid &&
+            (blockIdx_dim < tmp.dims[dim]) &&
+            isLast) {
+            *tptr = val;
+            *tfptr = flag;
+            *tiptr = boundaryid;
+            }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, uint DIMY, bool calculateFlags, bool inclusive_scan>
+    __global__
+    static void scan_dim_final_kernel(Param<To> out,
+                                CParam<Ti>  in,
+                                CParam<Tk> key,
+                                uint blocks_x,
+                                uint blocks_y,
+                                uint lim)
+    {
+        const int tidx = threadIdx.x;
+        const int tidy = threadIdx.y;
+        const int tid  = tidy * THREADS_X + tidx;
+
+        const int zid = blockIdx.x / blocks_x;
+        const int wid = blockIdx.y / blocks_y;
+        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int xid = blockIdx_x * blockDim.x + tidx;
+        const int yid = blockIdx_y; // yid  of output. updated for input later.
+
+        int ids[4] = {xid, yid, zid, wid};
+
+        const Ti *iptr = in.ptr;
+        const Tk *kptr = key.ptr;
+        To *optr = out.ptr;
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before offsetting in
+
+        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        iptr  += ids[3] *  in.strides[3] + ids[2] *  in.strides[2] + ids[1] *  in.strides[1] + ids[0];
+        kptr  += ids[3] * key.strides[3] + ids[2] * key.strides[2] + ids[1] * key.strides[1] + ids[0];
+        int id_dim = ids[dim];
+        const int out_dim = out.dims[dim];
+
+        bool is_valid =
+            (ids[0] < out.dims[0]) &&
+            (ids[1] < out.dims[1]) &&
+            (ids[2] < out.dims[2]) &&
+            (ids[3] < out.dims[3]);
+
+        const int ostride_dim = out.strides[dim];
+        const int istride_dim =  in.strides[dim];
+
+        __shared__ char s_flg[THREADS_X * DIMY * 2];
+        __shared__ To s_val[THREADS_X * DIMY * 2];
+        __shared__ char s_ftmp[THREADS_X];
+        __shared__ To s_tmp[THREADS_X];
+        To *sptr =  s_val + tid;
+        char *sfptr = s_flg + tid;
+
+        Transform<Ti, To, op> transform;
+        Binary<To, op> binop;
+
+        const To init = binop.init();
+        To val = init;
+
+        const bool isLast = (tidy == (DIMY - 1));
+        if (isLast) {
+            s_tmp[tidx] = val;
+            s_ftmp[tidx] = 0;
+        }
+        __syncthreads();
+
+        char flag = 0;
+        for (int k = 0; k < lim; k++) {
+
+            if (calculateFlags) {
+                if (id_dim < out_dim) {
+                    flag = calculate_head_flags_dim(kptr, id_dim, istride_dim);
+                } else {
+                    flag = 0;
+                }
+            } else {
+                flag = *kptr;
+            }
+
+            //Load val from global in
+            if (inclusive_scan) {
+                if (id_dim >= out_dim) {
+                    val = init;
+                } else {
+                    val = transform(*iptr);
+                }
+            } else {
+                if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
+                    val = init;
+                } else {
+                    val = transform(*(iptr - istride_dim));
+                }
+            }
+
+            if ((tidy == 0) && (flag == 0)) {
+                val = binop(val, s_tmp[tidx]);
+                flag = flag | s_ftmp[tidx];
+            }
+
+            *sptr = val;
+            *sfptr = flag;
+            __syncthreads();
+
+            int start = 0;
+#pragma unroll
+            for (int off = 1; off < DIMY; off *= 2) {
+
+                if (tidy >= off) {
+                    val = sfptr[start * THREADS_X] ? val : binop(val, sptr[(start - off) * THREADS_X]);
+                    flag = sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
+                }
+                start = DIMY - start;
+                sptr[start * THREADS_X] = val;
+                sfptr[start * THREADS_X] = flag;
+
+                __syncthreads();
+            }
+
+            if (is_valid && (id_dim < out_dim)) *optr = val;
+            if (isLast) {
+                s_tmp[tidx] = val;
+                s_ftmp[tidx] = flag;
+            }
+            id_dim += blockDim.y;
+            iptr += blockDim.y * istride_dim;
+            optr += blockDim.y * ostride_dim;
+            __syncthreads();
+        }
+
+    }
+
+    template<typename To, af_op_t op, int dim>
+    __global__
+    static void bcast_dim_kernel(Param<To> out,
+                                 CParam<To> tmp,
+                                 Param<int> tlid,
+                                 uint blocks_x,
+                                 uint blocks_y,
+                                 uint blocks_dim,
+                                 uint lim)
+    {
+        const int tidx = threadIdx.x;
+        const int tidy = threadIdx.y;
+
+        const int zid = blockIdx.x / blocks_x;
+        const int wid = blockIdx.y / blocks_y;
+        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int xid = blockIdx_x * blockDim.x + tidx;
+        const int yid = blockIdx_y; // yid  of output. updated for input later.
+
+        int ids[4] = {xid, yid, zid, wid};
+
+        const To *tptr = tmp.ptr;
+        To *optr = out.ptr;
+        const int *iptr = tlid.ptr;
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before offsetting in
+        tptr += ids[3] *  tmp.strides[3] + ids[2] *  tmp.strides[2] + ids[1] *  tmp.strides[1] + ids[0];
+        iptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] + ids[1] * tlid.strides[1] + ids[0];
+        const int blockIdx_dim = ids[dim];
+
+        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        const int id_dim = ids[dim];
+
+        bool is_valid =
+            (ids[0] < out.dims[0]) &&
+            (ids[1] < out.dims[1]) &&
+            (ids[2] < out.dims[2]) &&
+            (ids[3] < out.dims[3]);
+
+        if (!is_valid) return;
+        if (blockIdx_dim == 0) return;
+
+        int boundary = *iptr;
+        To accum = *(tptr - tmp.strides[dim]);
+
+        Binary<To, op> binop;
+        const int ostride_dim = out.strides[dim];
+
+        for (int k = 0, id = id_dim;
+             is_valid && k < lim && (id < boundary);
+             k++, id += blockDim.y) {
+
+            *optr = binop(*optr,accum);
+            optr += blockDim.y * ostride_dim;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool calculateFlags, bool inclusive_scan>
+    static void scan_dim_final_launcher(Param<To> out,
+                           CParam<Ti> in,
+                           CParam<Tk> key,
+                           const uint threads_y,
+                           const uint blocks_all[4])
+    {
+        dim3 threads(THREADS_X, threads_y);
+
+        dim3 blocks(blocks_all[0] * blocks_all[2],
+                    blocks_all[1] * blocks_all[3]);
+
+        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+
+        switch (threads_y) {
+        case 8:
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 8, calculateFlags, inclusive_scan>), blocks, threads,
+                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+        case 4:
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 4, calculateFlags, inclusive_scan>), blocks, threads,
+                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+        case 2:
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 2, calculateFlags, inclusive_scan>), blocks, threads,
+                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+        case 1:
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 1, calculateFlags, inclusive_scan>), blocks, threads,
+                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+        }
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool inclusive_scan>
+    static void scan_dim_nonfinal_launcher(Param<To> out,
+                           Param<To> tmp,
+                           Param<char> tflg,
+                           Param<int> tlid,
+                           CParam<Ti> in,
+                           CParam<Tk> key,
+                           const uint threads_y,
+                           const uint blocks_all[4])
+    {
+        dim3 threads(THREADS_X, threads_y);
+
+        dim3 blocks(blocks_all[0] * blocks_all[2],
+                    blocks_all[1] * blocks_all[3]);
+
+        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+
+        switch (threads_y) {
+        case 8:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 8, inclusive_scan>), blocks, threads,
+                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+        case 4:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 4, inclusive_scan>), blocks, threads,
+                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+        case 2:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 2, inclusive_scan>), blocks, threads,
+                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+        case 1:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 1, inclusive_scan>), blocks, threads,
+                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+        }
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename To, af_op_t op, int dim>
+    static void bcast_dim_launcher(Param<To> out,
+                                   CParam<To> tmp,
+                                   Param<int> tlid,
+                                   const uint threads_y,
+                                   const uint blocks_all[4])
+    {
+
+        dim3 threads(THREADS_X, threads_y);
+
+        dim3 blocks(blocks_all[0] * blocks_all[2],
+                    blocks_all[1] * blocks_all[3]);
+
+        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+
+        CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads,
+            out, tmp, tlid, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool inclusive_scan>
+    void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key)
+    {
+        uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
+        uint threads_x = THREADS_X;
+
+        uint blocks_all[] = {divup(out.dims[0], threads_x),
+                             out.dims[1], out.dims[2], out.dims[3]};
+
+        blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
+
+        if (blocks_all[dim] == 1) {
+
+            scan_dim_final_launcher<Ti, Tk, To, op, dim, true, inclusive_scan>(out, in, key,
+                                                     threads_y,
+                                                     blocks_all);
+
+        } else {
+
+            Param<To> tmp = out;
+            Param<char> tmpflg;
+            Param<int> tmpid;
+
+            tmp.dims[dim] = blocks_all[dim];
+            tmpflg.dims[dim] = blocks_all[dim];
+            tmpid.dims[dim] = blocks_all[dim];
+            tmp.strides[0] = 1;
+            tmpflg.strides[0] = 1;
+            tmpid.strides[0] = 1;
+            for (int k = 1; k < 4; k++) {
+                tmpflg.dims[k] = out.dims[k];
+                tmpid.dims[k] = out.dims[k];
+                tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
+                tmpflg.strides[k] = tmpflg.strides[k - 1] * tmpflg.dims[k - 1];
+                tmpid.strides[k] = tmpid.strides[k - 1] * tmpid.dims[k - 1];
+            }
+
+            int tmp_elements = tmp.strides[3] * tmp.dims[3];
+            tmp.ptr = memAlloc<To>(tmp_elements);
+            tmpflg.ptr = memAlloc<char>(tmp_elements);
+            tmpid.ptr = memAlloc<int>(tmp_elements);
+
+            scan_dim_nonfinal_launcher<Ti, Tk, To, op, dim, inclusive_scan>(
+                                                    out, tmp, tmpflg, tmpid, in, key,
+                                                    threads_y, blocks_all);
+
+            int bdim = blocks_all[dim];
+            blocks_all[dim] = 1;
+
+            //FIXME: Is there an alternative to the if condition ?
+            if (op == af_notzero_t) {
+                scan_dim_final_launcher<To, char, To, af_add_t, dim, false, true>(tmp, tmp, tmpflg,
+                                                               threads_y,
+                                                               blocks_all);
+            } else {
+                scan_dim_final_launcher<To, char, To,       op, dim, false, true>(tmp, tmp, tmpflg,
+                                                               threads_y,
+                                                               blocks_all);
+            }
+
+            blocks_all[dim] = bdim;
+            bcast_dim_launcher<To, op, dim>(out, tmp, tmpid, threads_y, blocks_all);
+
+            memFree(tmp.ptr);
+        }
+    }
+
+}
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)\
+    template void scan_dim_by_key<Ti, Tk, To, ROp, 1,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
+    template void scan_dim_by_key<Ti, Tk, To, ROp, 1, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
+    template void scan_dim_by_key<Ti, Tk, To, ROp, 2,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
+    template void scan_dim_by_key<Ti, Tk, To, ROp, 2, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
+    template void scan_dim_by_key<Ti, Tk, To, ROp, 3,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
+    template void scan_dim_by_key<Ti, Tk, To, ROp, 3, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)         \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int  )   \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uint )   \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl )   \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uintl)
+}
diff --git a/src/backend/cuda/kernel/scan_first_by_key.hpp b/src/backend/cuda/kernel/scan_first_by_key.hpp
new file mode 100644
index 0000000000..1292dd341f
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_first_by_key.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <ops.hpp>
+#include <Param.hpp>
+
+namespace cuda
+{
+    namespace kernel
+    {
+        template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+        void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+    }
+}
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
new file mode 100644
index 0000000000..ee49f14d04
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -0,0 +1,517 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <ops.hpp>
+#include <backend.hpp>
+#include <Param.hpp>
+#include <dispatch.hpp>
+#include <math.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <memory.hpp>
+#include "config.hpp"
+
+namespace cuda
+{
+namespace kernel
+{
+    template<typename Tk>
+    __device__
+    inline static char calculate_head_flags(const Tk *kptr, int id, int previd)
+    {
+        char flag;
+        if (id == 0) {
+            flag = 1;
+        } else {
+            flag = (kptr[id] != kptr[previd]);
+        }
+        return flag;
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX, bool inclusive_scan>
+    __global__
+    static void scan_nonfinal_kernel(Param<To> out,
+                                  Param<To> tmp,
+                                  Param<char> tflg,
+                                  Param<int> tlid,
+                                  CParam<Ti> in,
+                                  CParam<Tk> key,
+                                  uint blocks_x,
+                                  uint blocks_y,
+                                  uint lim)
+    {
+        //parallel segmented scan
+        //calculate flags from keys
+        //write to tmp
+        //write to temporary flag
+        //write to temporary last id
+        Transform<Ti, To, op> transform;
+        Binary<To, op> binop;
+        const To init = binop.init();
+        To val = init;
+
+        const int istride = in.strides[0];
+        const int DIMY = THREADS_PER_BLOCK / DIMX;
+        const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+        __shared__ char s_flg[SHARED_MEM_SIZE];
+        __shared__ To s_val[SHARED_MEM_SIZE];
+        __shared__ char s_ftmp[DIMY];
+        __shared__ To s_tmp[DIMY];
+
+        const int tidx = threadIdx.x;
+        const int tidy = threadIdx.y;
+        const int zid = blockIdx.x / blocks_x;
+        const int wid = blockIdx.y / blocks_y;
+        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int xid = blockIdx_x * blockDim.x * lim + tidx;
+        const int yid = blockIdx_y * blockDim.y + tidy;
+        bool cond_yzw = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+        if (!cond_yzw) return; // retire warps early
+
+        To *sptr = s_val + tidy * (2 * DIMX + 1);
+        char *sfptr = s_flg + tidy * (2 * DIMX + 1);
+        int id = xid;
+
+        const bool isLast = (tidx == (DIMX - 1));
+        if (isLast) {
+            s_tmp[tidy] = init;
+            s_ftmp[tidy] = 0;
+        }
+        __syncthreads();
+
+        const Ti *iptr = in.ptr;
+        const Tk *kptr = key.ptr;
+        To *optr = out.ptr;
+        To *tptr = tmp.ptr;
+        char *tfptr = tflg.ptr;
+        int *tiptr = tlid.ptr;
+        iptr  += wid *   in.strides[3] + zid *   in.strides[2] + yid *   in.strides[1];
+        kptr  += wid *  key.strides[3] + zid *  key.strides[2] + yid *  key.strides[1];
+        optr  += wid *  out.strides[3] + zid *  out.strides[2] + yid *  out.strides[1];
+        tptr  += wid *  tmp.strides[3] + zid *  tmp.strides[2] + yid *  tmp.strides[1];
+        tfptr += wid * tflg.strides[3] + zid * tflg.strides[2] + yid * tflg.strides[1];
+        tiptr += wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+        char *prev;
+        if (tidx == 0) {
+            prev = &s_ftmp[tidy];
+        } else {
+            prev = &sfptr[tidx-1];
+        }
+        char *curr = &sfptr[tidx];
+
+        char flag = 0;
+        int boundaryid = -1;
+        for (int k = 0; k < lim; k++) {
+            if (id < out.dims[0]) {
+                flag = calculate_head_flags(kptr, id, id - istride);
+            } else {
+                flag = 0;
+            }
+
+            //Load val from global in
+            if (inclusive_scan) {
+                if (id >= out.dims[0]) {
+                    val = init;
+                } else {
+                    val = transform(iptr[id]);
+                }
+            } else {
+                if ((id == 0) || (id >= out.dims[0]) || flag) {
+                    val = init;
+                } else {
+                    val = transform(iptr[id-istride]);
+                }
+            }
+
+            //Add partial result from last iteration before scan operation
+            if ((tidx == 0) && (flag == 0)) {
+                val = binop(val, s_tmp[tidy]);
+                flag = flag | s_ftmp[tidy];
+            }
+
+            //Write to shared memory
+            sptr[tidx] = val;
+            sfptr[tidx] = flag;
+            __syncthreads();
+
+            //Segmented Scan
+            int start = 0;
+#pragma unroll
+            for (int off = 1; off < DIMX; off *= 2) {
+                if (tidx >= off) {
+                    val = sfptr[start + tidx]? val : binop(val, sptr[(start - off) + tidx]);
+                    flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
+                }
+                start = DIMX - start;
+                sptr[start + tidx] = val;
+                sfptr[start + tidx] = flag;
+
+                __syncthreads();
+            }
+
+            //Identify segment boundary
+            if ((*prev == 0) && (*curr == 1)) {
+                boundaryid = id;
+            }
+
+            if (id < out.dims[0]) optr[id] = val;
+            if (isLast) {
+                s_tmp[tidy] = val;
+                s_ftmp[tidy] = flag;
+            }
+            id += blockDim.x;
+            __syncthreads();
+        }
+        if (isLast) {
+            tptr[blockIdx_x] = val;
+            tfptr[blockIdx_x] = flag;
+            tiptr[blockIdx_x] = boundaryid;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX, bool calculateFlags, bool inclusive_scan>
+    __global__
+    static void scan_final_kernel(Param<To> out,
+                                  CParam<Ti> in,
+                                  CParam<Tk> key,
+                                  uint blocks_x,
+                                  uint blocks_y,
+                                  uint lim)
+    {
+        //parallel segmented scan
+        //calculate flags from keys
+        //write to tmp
+        //write to temporary flag
+        //write to temporary last id
+        Transform<Ti, To, op> transform;
+        Binary<To, op> binop;
+        const To init = binop.init();
+        To val = init;
+
+        const int istride = in.strides[0];
+        const int DIMY = THREADS_PER_BLOCK / DIMX;
+        const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+        __shared__ char s_flg[SHARED_MEM_SIZE];
+        __shared__ To s_val[SHARED_MEM_SIZE];
+        __shared__ char s_ftmp[DIMY];
+        __shared__ To s_tmp[DIMY];
+
+        const int tidx = threadIdx.x;
+        const int tidy = threadIdx.y;
+        const int zid = blockIdx.x / blocks_x;
+        const int wid = blockIdx.y / blocks_y;
+        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int xid = blockIdx_x * blockDim.x * lim + tidx;
+        const int yid = blockIdx_y * blockDim.y + tidy;
+        bool cond_yzw = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+        if (!cond_yzw) return; // retire warps early
+
+        To *sptr = s_val + tidy * (2 * DIMX + 1);
+        char *sfptr = s_flg + tidy * (2 * DIMX + 1);
+        int id = xid;
+
+        const bool isLast = (tidx == (DIMX - 1));
+        if (isLast) {
+            s_tmp[tidy] = init;
+            s_ftmp[tidy] = 0;
+        }
+        __syncthreads();
+
+        const Ti *iptr = in.ptr;
+        const Tk *kptr = key.ptr;
+        To *optr = out.ptr;
+        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
+        kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
+        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+        for (int k = 0; k < lim; k++) {
+            char flag = 0;
+            if (calculateFlags) {
+                if (id < out.dims[0]) {
+                    flag = calculate_head_flags(kptr, id, id - istride);
+                }
+            } else {
+                flag = kptr[id];
+            }
+
+            //Load val from global in
+            if (inclusive_scan) {
+                if (id >= out.dims[0]) {
+                    val = init;
+                } else {
+                    val = transform(iptr[id]);
+                }
+            } else {
+                if ((id == 0) || (id >= out.dims[0]) || flag) {
+                    val = init;
+                } else {
+                    val = transform(iptr[id-istride]);
+                }
+            }
+
+            //Add partial result from last iteration before scan operation
+            if ((tidx == 0) && (flag == 0)) {
+                val = binop(val, s_tmp[tidy]);
+                flag = flag | s_ftmp[tidy];
+            }
+
+            //Write to shared memory
+            sptr[tidx] = val;
+            sfptr[tidx] = flag;
+            __syncthreads();
+
+            //Segmented Scan
+            int start = 0;
+#pragma unroll
+            for (int off = 1; off < DIMX; off *= 2) {
+                if (tidx >= off) {
+                    val = sfptr[start + tidx]? val : binop(val, sptr[(start - off) + tidx]);
+                    flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
+                }
+                start = DIMX - start;
+                sptr[start + tidx] = val;
+                sfptr[start + tidx] = flag;
+
+                __syncthreads();
+            }
+
+            if (id < out.dims[0]) optr[id] = val;
+            if (isLast) {
+                s_tmp[tidy] = val;
+                s_ftmp[tidy] = flag;
+            }
+            id += blockDim.x;
+            __syncthreads();
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void scan_nonfinal_launcher(Param<To> out,
+                             Param<To> tmp,
+                             Param<char> tflg,
+                             Param<int> tlid,
+                             CParam<Ti> in,
+                             CParam<Tk> key,
+                             const uint blocks_x,
+                             const uint blocks_y,
+                             const uint threads_x)
+    {
+
+        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+        dim3 blocks(blocks_x * out.dims[2],
+                    blocks_y * out.dims[3]);
+
+        uint lim = divup(out.dims[0], (threads_x * blocks_x));
+
+        switch (threads_x) {
+        case 32:
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 32, inclusive_scan>),
+                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+        case 64:
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 64, inclusive_scan>),
+                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+        case 128:
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 128, inclusive_scan>),
+                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+        case 256:
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 256, inclusive_scan>),
+                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+        }
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool calculateFlags, bool inclusive_scan>
+    static void scan_final_launcher(Param<To> out,
+                             CParam<Ti> in,
+                             CParam<Tk> key,
+                             const uint blocks_x,
+                             const uint blocks_y,
+                             const uint threads_x)
+    {
+
+        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+        dim3 blocks(blocks_x * out.dims[2],
+                    blocks_y * out.dims[3]);
+
+        uint lim = divup(out.dims[0], (threads_x * blocks_x));
+
+        switch (threads_x) {
+        case 32:
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 32, calculateFlags, inclusive_scan>),
+                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+        case 64:
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 64, calculateFlags, inclusive_scan>),
+                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+        case 128:
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 128, calculateFlags, inclusive_scan>),
+                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+        case 256:
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 256, calculateFlags, inclusive_scan>),
+                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+        }
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename To, af_op_t op>
+    __global__
+    static void bcast_first_kernel(Param<To> out,
+                                   Param<To> tmp,
+                                   Param<int> tlid,
+                                   uint blocks_x,
+                                   uint blocks_y,
+                                   uint lim)
+    {
+        const int tidx = threadIdx.x;
+        const int tidy = threadIdx.y;
+
+        const int zid = blockIdx.x / blocks_x;
+        const int wid = blockIdx.y / blocks_y;
+        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int xid = blockIdx_x * blockDim.x * lim + tidx;
+        const int yid = blockIdx_y * blockDim.y + tidy;
+
+        if (blockIdx_x == 0) return;
+
+        bool cond = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+        if (!cond) return;
+
+        To *optr = out.ptr;
+        const To *tptr = tmp.ptr;
+        const int *iptr = tlid.ptr;
+
+        optr += wid *  out.strides[3] + zid *  out.strides[2] + yid *  out.strides[1];
+        tptr += wid *  tmp.strides[3] + zid *  tmp.strides[2] + yid *  tmp.strides[1];
+        iptr += wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+        Binary<To, op> binop;
+        int boundary = iptr[blockIdx_x];
+        To accum = tptr[blockIdx_x - 1];
+
+        for (int k = 0, id = xid;
+             k < lim && id < boundary;
+             k++, id += blockDim.x) {
+
+            optr[id] = binop(accum, optr[id]);
+        }
+    }
+
+    template<typename To, af_op_t op>
+    static void bcast_first_launcher(Param<To> out,
+                                     Param<To> tmp,
+                                     Param<int> tlid,
+                                     const uint blocks_x,
+                                     const uint blocks_y,
+                                     const uint threads_x)
+    {
+
+        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+        dim3 blocks(blocks_x * out.dims[2],
+                    blocks_y * out.dims[3]);
+        uint lim = divup(out.dims[0], (threads_x * blocks_x));
+        CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp, tlid, blocks_x, blocks_y, lim);
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key)
+    {
+        uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+        uint blocks_x = divup(out.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(out.dims[1], threads_y);
+
+        if (blocks_x == 1) {
+            scan_final_launcher<Ti, Tk, To, op, true, inclusive_scan>(
+                                                out, in, key,
+                                                blocks_x, blocks_y, threads_x);
+
+        } else {
+
+            Param<To> tmp = out;
+            Param<char> tmpflg;
+            Param<int> tmpid;
+
+            tmp.dims[0] = blocks_x;
+            tmpflg.dims[0] = blocks_x;
+            tmpid.dims[0] = blocks_x;
+            tmp.strides[0] = 1;
+            tmpflg.strides[0] = 1;
+            tmpid.strides[0] = 1;
+            for (int k = 1; k < 4; k++) {
+                tmpflg.dims[k] = out.dims[k];
+                tmpid.dims[k] = out.dims[k];
+                tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
+                tmpflg.strides[k] = tmpflg.strides[k - 1] * tmpflg.dims[k - 1];
+                tmpid.strides[k] = tmpid.strides[k - 1] * tmpid.dims[k - 1];
+            }
+
+            int tmp_elements = tmp.strides[3] * tmp.dims[3];
+            tmp.ptr = memAlloc<To>(tmp_elements);
+            tmpflg.ptr = memAlloc<char>(tmp_elements);
+            tmpid.ptr = memAlloc<int>(tmp_elements);
+
+            scan_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(
+                                                out, tmp, tmpflg, tmpid, in, key,
+                                                blocks_x, blocks_y, threads_x);
+
+            //FIXME: Is there an alternative to the if condition ?
+            if (op == af_notzero_t) {
+                scan_final_launcher<To, char, To, af_add_t, false, true>(
+                                                tmp, tmp, tmpflg,
+                                                1, blocks_y, threads_x);
+            } else {
+                scan_final_launcher<To, char, To, op, false, true>(
+                                                tmp, tmp, tmpflg,
+                                                1, blocks_y, threads_x);
+            }
+
+            bcast_first_launcher<To, op>(out, tmp, tmpid, blocks_x, blocks_y, threads_x);
+
+            memFree(tmp.ptr);
+            memFree(tmpflg.ptr);
+            memFree(tmpid.ptr);
+        }
+    }
+}
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)\
+    template void scan_first_by_key<Ti, Tk, To, ROp,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key);  \
+    template void scan_first_by_key<Ti, Tk, To, ROp, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)        \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float  , Tk, float  )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, double , Tk, double )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cfloat , Tk, cfloat )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cdouble, Tk, cdouble)\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, int    , Tk, int    )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, int    )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, uint   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uchar  , Tk, uint   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, short  , Tk, int    )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)       \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int  ) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uint ) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl ) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uintl)
+}
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 44ae45b98c..ad7563f672 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <af/defines.h>
 #include <defines.hpp>
 #include <limits>
 #include <algorithm>
diff --git a/src/backend/cuda/scan.cu b/src/backend/cuda/scan.cu
index ed2cc7b99c..d6f6d8908b 100644
--- a/src/backend/cuda/scan.cu
+++ b/src/backend/cuda/scan.cu
@@ -43,31 +43,27 @@ namespace cuda
         return out;
     }
 
-
-#define INSTANTIATE(ROp, Ti, To)\
+#define INSTANTIATE_SCAN(ROp, Ti, To)\
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
-#define INSTANTIATE_SCAN(ROp)           \
-    INSTANTIATE(ROp, float  , float  )  \
-    INSTANTIATE(ROp, double , double )  \
-    INSTANTIATE(ROp, cfloat , cfloat )  \
-    INSTANTIATE(ROp, cdouble, cdouble)  \
-    INSTANTIATE(ROp, int    , int    )  \
-    INSTANTIATE(ROp, uint   , uint   )  \
-    INSTANTIATE(ROp, intl   , intl   )  \
-    INSTANTIATE(ROp, uintl  , uintl  )  \
-    INSTANTIATE(ROp, char   , int    )  \
-    INSTANTIATE(ROp, char   , uint   )  \
-    INSTANTIATE(ROp, uchar  , uint   )  \
-    INSTANTIATE(ROp, short  , int    )  \
-    INSTANTIATE(ROp, ushort , uint   )
+#define INSTANTIATE_SCAN_ALL(ROp)                       \
+    INSTANTIATE_SCAN(ROp, float  , float  )             \
+    INSTANTIATE_SCAN(ROp, double , double )             \
+    INSTANTIATE_SCAN(ROp, cfloat , cfloat )             \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble)             \
+    INSTANTIATE_SCAN(ROp, int    , int    )             \
+    INSTANTIATE_SCAN(ROp, uint   , uint   )             \
+    INSTANTIATE_SCAN(ROp, intl   , intl   )             \
+    INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
+    INSTANTIATE_SCAN(ROp, char   , int    )             \
+    INSTANTIATE_SCAN(ROp, char   , uint   )             \
+    INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
+    INSTANTIATE_SCAN(ROp, short  , int    )             \
+    INSTANTIATE_SCAN(ROp, ushort , uint   )
 
-    //accum
-    INSTANTIATE(af_notzero_t, char  , uint)
-    INSTANTIATE_SCAN(af_add_t)
-    INSTANTIATE_SCAN(af_sub_t)
-    INSTANTIATE_SCAN(af_mul_t)
-    INSTANTIATE_SCAN(af_div_t)
-    INSTANTIATE_SCAN(af_min_t)
-    INSTANTIATE_SCAN(af_max_t)
+    INSTANTIATE_SCAN(af_notzero_t, char, uint)
+    INSTANTIATE_SCAN_ALL(af_add_t)
+    INSTANTIATE_SCAN_ALL(af_mul_t)
+    INSTANTIATE_SCAN_ALL(af_min_t)
+    INSTANTIATE_SCAN_ALL(af_max_t)
 }
diff --git a/src/backend/cuda/scan_by_key.cu b/src/backend/cuda/scan_by_key.cu
new file mode 100644
index 0000000000..bdca0ee199
--- /dev/null
+++ b/src/backend/cuda/scan_by_key.cu
@@ -0,0 +1,73 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <ops.hpp>
+
+#undef _GLIBCXX_USE_INT128
+#include <scan_by_key.hpp>
+#include <complex>
+#include <kernel/scan_first_by_key.hpp>
+#include <kernel/scan_dim_by_key.hpp>
+
+namespace cuda
+{
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
+    {
+        Array<To> out = createEmptyArray<To>(in.dims());
+
+        if (inclusive_scan) {
+            switch (dim) {
+            case 0: kernel::scan_first_by_key<Ti, Tk, To, op   , true>(out, in, key); break;
+            case 1: kernel::scan_dim_by_key  <Ti, Tk, To, op, 1, true>(out, in, key); break;
+            case 2: kernel::scan_dim_by_key  <Ti, Tk, To, op, 2, true>(out, in, key); break;
+            case 3: kernel::scan_dim_by_key  <Ti, Tk, To, op, 3, true>(out, in, key); break;
+            }
+        } else {
+            switch (dim) {
+            case 0: kernel::scan_first_by_key<Ti, Tk, To, op   , false>(out, in, key); break;
+            case 1: kernel::scan_dim_by_key  <Ti, Tk, To, op, 1, false>(out, in, key); break;
+            case 2: kernel::scan_dim_by_key  <Ti, Tk, To, op, 2, false>(out, in, key); break;
+            case 3: kernel::scan_dim_by_key  <Ti, Tk, To, op, 3, false>(out, in, key); break;
+            }
+        }
+
+        return out;
+    }
+
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
+    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_OP(ROp)            \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
+
+    INSTANTIATE_SCAN_OP(af_add_t)
+    INSTANTIATE_SCAN_OP(af_mul_t)
+    INSTANTIATE_SCAN_OP(af_min_t)
+    INSTANTIATE_SCAN_OP(af_max_t)
+}
diff --git a/src/backend/cuda/scan_by_key.hpp b/src/backend/cuda/scan_by_key.hpp
new file mode 100644
index 0000000000..d876332f17
--- /dev/null
+++ b/src/backend/cuda/scan_by_key.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <ops.hpp>
+
+namespace cuda
+{
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+}
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 8c8eafecc7..99733c8c56 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -47,30 +47,59 @@ namespace opencl
         return out;
     }
 
-#define INSTANTIATE(ROp, Ti, To)\
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
+    {
+        return scan(in, dim, inclusive_scan);
+    }
+
+#define INSTANTIATE_SCAN(ROp, Ti, To)\
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
-#define INSTANTIATE_SCAN(ROp)           \
-    INSTANTIATE(ROp, float  , float  )  \
-    INSTANTIATE(ROp, double , double )  \
-    INSTANTIATE(ROp, cfloat , cfloat )  \
-    INSTANTIATE(ROp, cdouble, cdouble)  \
-    INSTANTIATE(ROp, int    , int    )  \
-    INSTANTIATE(ROp, uint   , uint   )  \
-    INSTANTIATE(ROp, intl   , intl   )  \
-    INSTANTIATE(ROp, uintl  , uintl  )  \
-    INSTANTIATE(ROp, char   , int    )  \
-    INSTANTIATE(ROp, char   , uint   )  \
-    INSTANTIATE(ROp, uchar  , uint   )  \
-    INSTANTIATE(ROp, short  , int    )  \
-    INSTANTIATE(ROp, ushort , uint   )
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
+    template Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_ALL(ROp)                       \
+    INSTANTIATE_SCAN(ROp, float  , float  )             \
+    INSTANTIATE_SCAN(ROp, double , double )             \
+    INSTANTIATE_SCAN(ROp, cfloat , cfloat )             \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble)             \
+    INSTANTIATE_SCAN(ROp, int    , int    )             \
+    INSTANTIATE_SCAN(ROp, uint   , uint   )             \
+    INSTANTIATE_SCAN(ROp, intl   , intl   )             \
+    INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
+    INSTANTIATE_SCAN(ROp, char   , int    )             \
+    INSTANTIATE_SCAN(ROp, char   , uint   )             \
+    INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
+    INSTANTIATE_SCAN(ROp, short  , int    )             \
+    INSTANTIATE_SCAN(ROp, ushort , uint   )
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_OP(ROp)            \
+    INSTANTIATE_SCAN_ALL(ROp)               \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)   \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, long)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, ulong)
 
     //accum
-    INSTANTIATE(af_notzero_t, char  , uint)
-    INSTANTIATE_SCAN(af_add_t)
-    INSTANTIATE_SCAN(af_sub_t)
-    INSTANTIATE_SCAN(af_mul_t)
-    INSTANTIATE_SCAN(af_div_t)
-    INSTANTIATE_SCAN(af_min_t)
-    INSTANTIATE_SCAN(af_max_t)
+    INSTANTIATE_SCAN(af_notzero_t, char, uint)
+    INSTANTIATE_SCAN_OP(af_add_t)
+    INSTANTIATE_SCAN_OP(af_mul_t)
+    INSTANTIATE_SCAN_OP(af_min_t)
+    INSTANTIATE_SCAN_OP(af_max_t)
 }
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index c8a62ff547..afd284575a 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -14,4 +14,7 @@ namespace opencl
 {
     template<af_op_t op, typename Ti, typename To>
     Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }

From 58ac59497b50257631713e689a6b0ddffb73361a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 25 May 2016 13:29:06 -0400
Subject: [PATCH 0551/2677] BUGFIX: Fixes to index and array_proxy classes when
 using c++11

---
 src/api/cpp/array.cpp | 1 +
 src/api/cpp/index.cpp | 6 +++++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index c32704b08a..8911154155 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -539,6 +539,7 @@ namespace af
     array::array_proxy&
     af::array::array_proxy::operator=(array_proxy &&other) {
         array out = other;
+        other.impl = nullptr;
         return *this = out;
     }
 #endif
diff --git a/src/api/cpp/index.cpp b/src/api/cpp/index.cpp
index d7a0cd1c76..ea23a41fc4 100644
--- a/src/api/cpp/index.cpp
+++ b/src/api/cpp/index.cpp
@@ -80,8 +80,10 @@ index::index(const af::index& idx0) {
 }
 
 index::~index() {
-    if (!impl.isSeq)
+    if (!impl.isSeq && impl.idx.arr)
         af_release_array(impl.idx.arr);
+
+
 }
 
 index & index::operator=(const index& idx0) {
@@ -97,10 +99,12 @@ index & index::operator=(const index& idx0) {
 #if __cplusplus > 199711L
 index::index(index &&idx0) {
     impl = idx0.impl;
+    idx0.impl.idx.arr = nullptr;
 }
 
 index& index::operator=(index &&idx0) {
     impl = idx0.impl;
+    idx0.impl.idx.arr = nullptr;
     return *this;
 }
 #endif

From 0317e70c5ad4846e3e87c475d35aef5239c3cf78 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 26 May 2016 11:18:32 +0530
Subject: [PATCH 0552/2677] Move index utility fn tests to unit tests as doc
 test

---
 include/af/index.h | 46 ++++++++++++----------------------------------
 test/index.cpp     | 43 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 55 insertions(+), 34 deletions(-)

diff --git a/include/af/index.h b/include/af/index.h
index c794bca3ac..07787cdc46 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -293,40 +293,7 @@ extern "C" {
     ///
     /// \brief Create an quadruple of af_index_t array
     ///
-    /// \code
-    /// af_index_t* indexers = 0;
-    /// af_err err = af_create_indexers(&indexers); // Memory is allocated on heap by the callee
-    /// // by default all the indexers span all the elements along the given dimension
-    ///
-    /// //Create array
-    /// af_array a;
-    /// unsigned ndims = 2;
-    /// dim_t dim[] = {10,10};
-    /// af_randu(&a, ndims, dim, f32);
-    ///
-    /// //Create index array
-    /// af_array idx;
-    /// unsigned n = 1;
-    /// dim_t d[] = {5};
-    /// af_range(&idx, n, d, 0, s64);
-    ///
-    /// af_print_array(a);
-    /// af_print_array(idx);
-    ///
-    /// //create array indexer
-    /// err = af_set_array_indexer(indexers, idx, 1);
-    /// if (err != AF_SUCCESS) {
-    ///     printf("Error from set array indexer: %d \n", err2);
-    ///     exit(1);
-    /// }
-    ///
-    /// //index with indexers
-    /// af_array out;
-    /// af_index_gen(&out, a, 2, indexers); // number of indexers should be two since
-    ///                                     // we have set only second af_index_t
-    /// af_print_array(out);
-    /// af_release_indexers(indexers);
-    /// \endcode
+    /// \snippet test/index.cpp ex_index_util_0
     ///
     /// \param[out] indexers pointer to location where quadruple af_index_t array is created
     /// \returns \ref af_err error code
@@ -340,6 +307,8 @@ extern "C" {
     ///
     /// \brief set \p dim to given indexer af_array \p idx
     ///
+    /// \snippet test/index.cpp ex_index_util_0
+    ///
     /// \param[in] indexer pointer to location where quadruple af_index_t array was created
     /// \param[in] idx is the af_array indexer for given dimension \p dim
     /// \param[in] dim is the dimension to be indexed
@@ -354,6 +323,9 @@ extern "C" {
     ///
     /// \brief set \p dim to given indexer af_array \p idx
     ///
+    /// This function is similar to \ref af_set_array_indexer in terms of functionality except
+    /// that this version accepts object of type \ref af_seq instead of \ref af_array.
+    ///
     /// \param[in] indexer pointer to location where quadruple af_index_t array was created
     /// \param[in] idx is the af_seq indexer for given dimension \p dim
     /// \param[in] dim is the dimension to be indexed
@@ -369,6 +341,10 @@ extern "C" {
     ///
     /// \brief set \p dim to given indexer af_array \p idx
     ///
+    ///  This function is alternative to \ref af_set_seq_indexer where instead of passing
+    ///  in an already prepared \ref af_seq object, you pass the arguments necessary for
+    ///  creating an af_seq directly.
+    ///
     /// \param[in] indexer pointer to location where quadruple af_index_t array was created
     /// \param[in] begin is the beginning index of along dimension \p dim
     /// \param[in] end is the beginning index of along dimension \p dim
@@ -388,6 +364,8 @@ extern "C" {
     ///
     /// \brief Release's the memory resource used by the quadruple af_index_t array
     ///
+    /// \snippet test/index.cpp ex_index_util_0
+    ///
     /// \param[in] indexers is pointer to location where quadruple af_index_t array is created
     //  \returns \ref af_err error code
     ///
diff --git a/test/index.cpp b/test/index.cpp
index 0bfd71a835..cdfaf5a265 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -495,6 +495,49 @@ TYPED_TEST(Indexing, 3D_to_1D)
     DimCheckND<TypeParam>(this->continuous3d_to_1d, TEST_DIR"/index/Continuous3Dto1D.test", 3);
 }
 
+
+TEST(Index, Docs_Util_C_API)
+{
+    //![ex_index_util_0]
+    af_index_t* indexers = 0;
+    af_err err = af_create_indexers(&indexers); // Memory is allocated on heap by the callee
+    // by default all the indexers span all the elements along the given dimension
+
+    //Create array
+    af_array a;
+    unsigned ndims = 2;
+    dim_t dim[] = {10,10};
+    af_randu(&a, ndims, dim, f32);
+
+    //Create index array
+    af_array idx;
+    unsigned n = 1;
+    dim_t d[] = {5};
+    af_range(&idx, n, d, 0, s64);
+
+    af_print_array(a);
+    af_print_array(idx);
+
+    //create array indexer
+    err = af_set_array_indexer(indexers, idx, 1);
+    if (err != AF_SUCCESS) {
+        printf("Error from set array indexer: %d \n", err);
+        exit(1);
+    }
+
+    //index with indexers
+    af_array out;
+    af_index_gen(&out, a, 2, indexers); // number of indexers should be two since
+                                        // we have set only second af_index_t
+    af_print_array(out);
+
+    af_release_indexers(indexers);
+    af_release_array(a);
+    af_release_array(idx);
+    af_release_array(out);
+    //![ex_index_util_0]
+}
+
 //////////////////////////////// CPP ////////////////////////////////
 TEST(Indexing2D, ColumnContiniousCPP)
 {

From 6ff198d0942dde22e7baa8a9826a9e63c7925a9f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 26 May 2016 15:13:50 +0530
Subject: [PATCH 0553/2677] Default af_index_t::isBatch to false in
 af_create_indexers

---
 src/api/c/index.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 348932a570..2df3575400 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -244,6 +244,7 @@ af_err af_create_indexers(af_index_t** indexers)
         for (int i=0; i<4; ++i) {
             out[i].idx.seq = af_span;
             out[i].isSeq = true;
+            out[i].isBatch = false;
         }
         std::swap(*indexers, out);
     }

From 914bc8fff094739d2a8a71062f26e083be713165 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 26 May 2016 15:15:50 +0530
Subject: [PATCH 0554/2677] Style changes to docs of utility index functions

---
 include/af/index.h |  2 ++
 test/index.cpp     | 25 ++++++++++++++++++++-----
 2 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/include/af/index.h b/include/af/index.h
index 07787cdc46..d49acb4aa8 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -326,6 +326,8 @@ extern "C" {
     /// This function is similar to \ref af_set_array_indexer in terms of functionality except
     /// that this version accepts object of type \ref af_seq instead of \ref af_array.
     ///
+    /// \snippet test/index.cpp ex_index_util_0
+    ///
     /// \param[in] indexer pointer to location where quadruple af_index_t array was created
     /// \param[in] idx is the af_seq indexer for given dimension \p dim
     /// \param[in] dim is the dimension to be indexed
diff --git a/test/index.cpp b/test/index.cpp
index cdfaf5a265..3cb0ab1785 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -513,22 +513,37 @@ TEST(Index, Docs_Util_C_API)
     af_array idx;
     unsigned n = 1;
     dim_t d[] = {5};
-    af_range(&idx, n, d, 0, s64);
+    af_range(&idx, n, d, 0, s32);
 
     af_print_array(a);
     af_print_array(idx);
 
     //create array indexer
     err = af_set_array_indexer(indexers, idx, 1);
-    if (err != AF_SUCCESS) {
-        printf("Error from set array indexer: %d \n", err);
-        exit(1);
-    }
 
     //index with indexers
     af_array out;
     af_index_gen(&out, a, 2, indexers); // number of indexers should be two since
                                         // we have set only second af_index_t
+    if (err != AF_SUCCESS) {
+        printf("Failed in af_index_gen: %d\n", err);
+        throw;
+    }
+    af_print_array(out);
+    af_release_array(out);
+
+    af_seq zeroIndices = af_make_seq(0.0, 9.0, 2.0);
+
+    err = af_set_seq_indexer(indexers, &zeroIndices, 0, false);
+
+    af_print_array(a);
+    af_print_array(out);
+
+    err = af_index_gen(&out, a, 2, indexers);
+    if (err != AF_SUCCESS) {
+        printf("Failed in af_index_gen: %d\n", err);
+        throw;
+    }
     af_print_array(out);
 
     af_release_indexers(indexers);

From a6ca6e65c8f40b79155de6cb961904fe948faa46 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Thu, 26 May 2016 10:16:41 -0400
Subject: [PATCH 0555/2677] Move template specialization to the OpenCL backend.

The presence of the template specialization in af/opencl.h results
in multiple definitions of af::array::device<cl_mem>() if the header
is included in more than one source file. The proposed commit
simply moves the definition into a source file in the backend.

Fixes issue #1429
---
 include/af/opencl.h        | 22 ----------------------
 src/backend/opencl/api.cpp | 12 ++++++++++++
 2 files changed, 12 insertions(+), 22 deletions(-)
 create mode 100644 src/backend/opencl/api.cpp

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 30ad555a41..27cc73e181 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -434,27 +434,5 @@ static inline platform getPlatform()
 */
 }
 
-namespace af
-{
- /**
-     \addtogroup opencl_mat
-     @{
- */
-
-#if !defined(AF_OPENCL)
-template<> AFAPI cl_mem *array::device() const
-{
-    cl_mem *mem_ptr = new cl_mem;
-    af_err err = af_get_device_ptr((void **)mem_ptr, get());
-    if (err != AF_SUCCESS) throw af::exception("Failed to get cl_mem from array object");
-    return mem_ptr;
-}
-#endif
-
-/**
-   @}
-*/
-
-}
 
 #endif
diff --git a/src/backend/opencl/api.cpp b/src/backend/opencl/api.cpp
new file mode 100644
index 0000000000..1508308c98
--- /dev/null
+++ b/src/backend/opencl/api.cpp
@@ -0,0 +1,12 @@
+#include <af/array.h>
+#include <af/opencl.h>
+
+namespace af {
+    template<> AFAPI cl_mem *array::device() const
+    {
+        cl_mem *mem_ptr = new cl_mem;
+        af_err err = af_get_device_ptr((void **)mem_ptr, get());
+        if (err != AF_SUCCESS) throw af::exception("Failed to get cl_mem from array object");
+        return mem_ptr;
+    }
+}

From f75877fdbc41c77e9e5c68214cde73e04dce50a1 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 27 May 2016 17:16:41 -0400
Subject: [PATCH 0556/2677] Fixes for CUDA 8 - Add OpenMP and
 --keep-device-functions flag

---
 src/backend/cuda/CMakeLists.txt | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6f3dc39ed0..f2e6a92e08 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -267,7 +267,13 @@ ELSE()
     SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch sm_20")
 ENDIF()
 
+# PUSH/POP --keep-device-functions flag. Only available in CUDA 8 or newer
+SET(OLD_CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS})
+IF(${CUDA_VERSION_MAJOR} GREATER 7) # CUDA 8 or newer
+    SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} --keep-device-functions")
+ENDIF()
 CUDA_COMPILE_PTX(ptx_files ${ptx_sources})
+SET(CUDA_NVCC_FLAGS ${OLD_CUDA_NVCC_FLAGS})
 
 set(cuda_ptx "")
 foreach(ptx_src_file ${ptx_sources})
@@ -316,6 +322,14 @@ IF("${APPLE}")
     ENDIF()
 ENDIF()
 
+IF(UNIX)
+    FIND_PACKAGE(OpenMP)
+    IF(OPENMP_FOUND)
+        SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
+        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+    ENDIF()
+ENDIF()
+
 ## Copied from FindCUDA.cmake
 ## The target_link_library needs to link with the cuda libraries using
 ## PRIVATE

From 0aaad6597acee8e2567af2455f93e7b39dbaaa56 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 27 May 2016 17:17:03 -0400
Subject: [PATCH 0557/2677] Add new computes for Pascal

---
 src/backend/cuda/CMakeLists.txt | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f2e6a92e08..f916657ea3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -28,6 +28,9 @@ IF(    CUDA_COMPUTE_20
     OR CUDA_COMPUTE_50
     OR CUDA_COMPUTE_52
     OR CUDA_COMPUTE_53
+    OR CUDA_COMPUTE_60
+    OR CUDA_COMPUTE_61
+    OR CUDA_COMPUTE_62
     )
     SET(FALLBACK OFF)
 ELSE()
@@ -36,10 +39,10 @@ ENDIF()
 
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
 IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
-    MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50")
+    MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50, 60")
     MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon separated list of CUDA Compute versions to enable the specified computes")
     MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
-    LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50")
+    LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50" "60")
 ENDIF()
 
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)

From 7f60f910c5938000a78d6075884011596b4a7eda Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 27 May 2016 18:53:43 -0400
Subject: [PATCH 0558/2677] Remove OpenMP requirement added in f75877f

---
 src/backend/cuda/CMakeLists.txt | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f916657ea3..665ebf8444 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -325,14 +325,6 @@ IF("${APPLE}")
     ENDIF()
 ENDIF()
 
-IF(UNIX)
-    FIND_PACKAGE(OpenMP)
-    IF(OPENMP_FOUND)
-        SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
-        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
-    ENDIF()
-ENDIF()
-
 ## Copied from FindCUDA.cmake
 ## The target_link_library needs to link with the cuda libraries using
 ## PRIVATE

From d45a10b3b0bd52e459a7af3caa0c00b8bcabf605 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 27 May 2016 19:34:18 -0400
Subject: [PATCH 0559/2677] Use FORCE_INLINES for GCC > 5.3 only for CUDA < 8

---
 src/backend/cuda/CMakeLists.txt | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 665ebf8444..ee1dce8cc8 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -72,15 +72,18 @@ ENDFOREACH()
 IF(UNIX)
     # GCC 5.3 and above give errors for mempcy from <string.h>
     # This is a (temporary) fix for that
+    # This was fixed in CUDA 8.0
     IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
-      ADD_DEFINITIONS(-D_FORCE_INLINES)
-    ENDIF()
-
-    # GCC 6.0 and above default to g++14, enabling c++11 features by default
-    # Enabling c++11 with nvcc 7.5 + gcc 6.x doesn't seem to work
-    # Only solution for now is to force use c++03 for gcc 6.x
-    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
-      SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -Xcompiler -std=c++98")
+        IF(${CUDA_VERSION_MAJOR} LESS 8)
+            ADD_DEFINITIONS(-D_FORCE_INLINES)
+        ENDIF(${CUDA_VERSION_MAJOR} LESS 8)
+
+        # GCC 6.0 and above default to g++14, enabling c++11 features by default
+        # Enabling c++11 with nvcc 7.5 + gcc 6.x doesn't seem to work
+        # Only solution for now is to force use c++03 for gcc 6.x
+        IF(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
+          SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -Xcompiler -std=c++98")
+        ENDIF()
     ENDIF()
 
     # Forcing STRICT ANSI should resolve a bunch of issues that NVIDIA seems to face with GCC compilers.

From ee8a2cf4ec34566e87c63df0c81a91a37dad9ed6 Mon Sep 17 00:00:00 2001
From: Gallagher Pryor <gallagher.pryor@gmail.com>
Date: Sun, 29 May 2016 22:43:30 -0400
Subject: [PATCH 0560/2677] correct cmake args for local example compile

The suggested cmake invocation for a local arrayfire installation only worked when utilizing `ArrayFire_DIR` as opposed to `ArrayFire_ROOT` and also by specifying the explicit directory containing the referred cmake file. cmake was version 3.5.2 -- perhaps a versioning issue?
---
 examples/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/README.md b/examples/README.md
index d1020978e0..a34581fcac 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -40,7 +40,7 @@ the directory which contains the `ArrayFireConfig.cmake` as an argument to the
 if you were to install ArrayFire to the `local` directory within your home
 folder, the invocation of `cmake` above would be replaced with the following:
 
-    cmake -DArrayFire_ROOT=~/local/share/ArrayFire/ ..
+    cmake -DArrayFire_DIR=$HOME/local/share/ArrayFire/cmake ..
     
 ### Support and Contact Info
 

From cd66ce9db559667d0dd198315c2c8c246e0edac4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 30 May 2016 22:28:27 +0530
Subject: [PATCH 0561/2677] update ocl cpp headers

* Removed old cl.hpp header file
* Added `OpenCL-CLHPP` header only repository as external project
---
 CMakeModules/build_cl2hpp.cmake   |    28 +
 src/backend/opencl/CMakeLists.txt |     3 +
 src/backend/opencl/cl.hpp         | 12911 ----------------------------
 src/backend/opencl/platform.cpp   |     2 -
 src/backend/opencl/platform.hpp   |     5 +-
 5 files changed, 35 insertions(+), 12914 deletions(-)
 create mode 100644 CMakeModules/build_cl2hpp.cmake
 delete mode 100644 src/backend/opencl/cl.hpp

diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
new file mode 100644
index 0000000000..269d663e4d
--- /dev/null
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -0,0 +1,28 @@
+INCLUDE(ExternalProject)
+
+SET(prefix ${CMAKE_BINARY_DIR}/third_party/cl2hpp/src/cl2hpp-ext-build)
+
+ExternalProject_Add(
+    cl2hpp-ext
+    GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
+    GIT_TAG 2b415bf8fc6ab035b6de6a14f3c579f91199fa2a
+    PREFIX "${prefix}"
+    INSTALL_COMMAND ""
+    INSTALL_DIR "${prefix}"
+    UPDATE_COMMAND ""
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
+    -DCMAKE_SOURCE_DIR:PATH=<SOURCE_DIR>
+    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+    -DBUILD_DOCS:BOOL=OFF
+    -DBUILD_EXAMPLES:BOOL=ON
+    -DBUILD_TESTS:BOOL=OFF
+    )
+
+ADD_CUSTOM_TARGET(cl2hpp DEPENDS "${prefix}/include/CL/cl2.hpp")
+
+ADD_DEPENDENCIES(cl2hpp cl2hpp-ext)
+
+SET(CL2HPP_INCLUDE_DIRECTORY ${prefix}/include)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index c81610b6e6..cbf1f08a8e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -3,6 +3,7 @@ PROJECT(ARRAYFIRE)
 
 FIND_PACKAGE(OpenCL REQUIRED)
 
+INCLUDE("${CMAKE_MODULE_PATH}/build_cl2hpp.cmake")
 INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
 
 IF(USE_OPENCL_F77_BLAS)
@@ -94,6 +95,7 @@ INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
     "${CMAKE_SOURCE_DIR}/src/backend/opencl"
     ${OpenCL_INCLUDE_DIRS}
+    ${CL2HPP_INCLUDE_DIRECTORY}
     "${CMAKE_CURRENT_BINARY_DIR}"
     ${CLBLAS_INCLUDE_DIRS}
     ${CLFFT_INCLUDE_DIRS}
@@ -304,6 +306,7 @@ ELSE(DEFINED BLAS_SYM_FILE)
 
 ENDIF()
 
+ADD_DEPENDENCIES(afopencl cl2hpp)
 ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
 
 TARGET_LINK_LIBRARIES(afopencl
diff --git a/src/backend/opencl/cl.hpp b/src/backend/opencl/cl.hpp
deleted file mode 100644
index ce56c66e56..0000000000
--- a/src/backend/opencl/cl.hpp
+++ /dev/null
@@ -1,12911 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2008-2015 The Khronos Group Inc.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a
- * copy of this software and/or associated documentation files (the
- * "Materials"), to deal in the Materials without restriction, including
- * without limitation the rights to use, copy, modify, merge, publish,
- * distribute, sublicense, and/or sell copies of the Materials, and to
- * permit persons to whom the Materials are furnished to do so, subject to
- * the following conditions:
- *
- * The above copyright notice and this permission notice shall be included
- * in all copies or substantial portions of the Materials.
- *
- * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
- * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
- * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
- * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
- ******************************************************************************/
-
-/*! \file
- *
- *   \brief C++ bindings for OpenCL 1.0 (rev 48), OpenCL 1.1 (rev 33) and 
- *       OpenCL 1.2 (rev 15)    
- *   \author Benedict R. Gaster, Laurent Morichetti and Lee Howes
- *   
- *   Additions and fixes from:
- *       Brian Cole, March 3rd 2010 and April 2012 
- *       Matt Gruenke, April 2012.
- *       Bruce Merry, February 2013.
- *       Tom Deakin and Simon McIntosh-Smith, July 2013
- *   
- *   \version 1.2.7
- *   \date January 2015
- *
- *   Optional extension support
- *
- *         cl
- *         cl_ext_device_fission
- *				#define USE_CL_DEVICE_FISSION
- */
-
-/*! \mainpage
- * \section intro Introduction
- * For many large applications C++ is the language of choice and so it seems
- * reasonable to define C++ bindings for OpenCL.
- *
- *
- * The interface is contained with a single C++ header file \em cl.hpp and all
- * definitions are contained within the namespace \em cl. There is no additional
- * requirement to include \em cl.h and to use either the C++ or original C
- * bindings it is enough to simply include \em cl.hpp.
- *
- * The bindings themselves are lightweight and correspond closely to the
- * underlying C API. Using the C++ bindings introduces no additional execution
- * overhead.
- *
- * For detail documentation on the bindings see:
- *
- * The OpenCL C++ Wrapper API 1.2 (revision 09)
- *  http://www.khronos.org/registry/cl/specs/opencl-cplusplus-1.2.pdf
- *
- * \section example Example
- *
- * The following example shows a general use case for the C++
- * bindings, including support for the optional exception feature and
- * also the supplied vector and string classes, see following sections for
- * decriptions of these features.
- *
- * \code
- * #define __CL_ENABLE_EXCEPTIONS
- * 
- * #if defined(__APPLE__) || defined(__MACOSX)
- * #include <OpenCL/cl.hpp>
- * #else
- * #include <CL/cl.hpp>
- * #endif
- * #include <cstdio>
- * #include <cstdlib>
- * #include <iostream>
- * 
- *  const char * helloStr  = "__kernel void "
- *                           "hello(void) "
- *                           "{ "
- *                           "  "
- *                           "} ";
- * 
- *  int
- *  main(void)
- *  {
- *     cl_int err = CL_SUCCESS;
- *     try {
- *
- *       std::vector<cl::Platform> platforms;
- *       cl::Platform::get(&platforms);
- *       if (platforms.size() == 0) {
- *           std::cout << "Platform size 0\n";
- *           return -1;
- *       }
- *
- *       cl_context_properties properties[] = 
- *          { CL_CONTEXT_PLATFORM, (cl_context_properties)(platforms[0])(), 0};
- *       cl::Context context(CL_DEVICE_TYPE_CPU, properties); 
- * 
- *       std::vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
- * 
- *       cl::Program::Sources source(1,
- *           std::make_pair(helloStr,strlen(helloStr)));
- *       cl::Program program_ = cl::Program(context, source);
- *       program_.build(devices);
- * 
- *       cl::Kernel kernel(program_, "hello", &err);
- * 
- *       cl::Event event;
- *       cl::CommandQueue queue(context, devices[0], 0, &err);
- *       queue.enqueueNDRangeKernel(
- *           kernel, 
- *           cl::NullRange, 
- *           cl::NDRange(4,4),
- *           cl::NullRange,
- *           NULL,
- *           &event); 
- * 
- *       event.wait();
- *     }
- *     catch (cl::Error err) {
- *        std::cerr 
- *           << "ERROR: "
- *           << err.what()
- *           << "("
- *           << err.err()
- *           << ")"
- *           << std::endl;
- *     }
- * 
- *    return EXIT_SUCCESS;
- *  }
- * 
- * \endcode
- *
- */
-
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
-#ifndef CL_HPP_
-#define CL_HPP_
-
-#ifdef _WIN32
-
-#include <malloc.h>
-
-#if defined(USE_DX_INTEROP)
-#include <CL/cl_d3d10.h>
-#include <CL/cl_dx9_media_sharing.h>
-#endif
-#endif // _WIN32
-
-#if defined(_MSC_VER)
-#include <intrin.h>
-#endif // _MSC_VER
-
-// 
-#if defined(USE_CL_DEVICE_FISSION)
-#include <CL/cl_ext.h>
-#endif
-
-#if defined(__APPLE__) || defined(__MACOSX)
-#include <OpenCL/opencl.h>
-#else
-#include <CL/opencl.h>
-#endif // !__APPLE__
-
-#if (_MSC_VER >= 1700) || (__cplusplus >= 201103L)
-#define CL_HPP_RVALUE_REFERENCES_SUPPORTED
-#define CL_HPP_CPP11_ATOMICS_SUPPORTED
-#include <atomic>
-#endif
-
-#if (__cplusplus >= 201103L)
-#define CL_HPP_NOEXCEPT noexcept
-#else
-#define CL_HPP_NOEXCEPT
-#endif
-
-
-// To avoid accidentally taking ownership of core OpenCL types
-// such as cl_kernel constructors are made explicit
-// under OpenCL 1.2
-#if defined(CL_VERSION_1_2) && !defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-#define __CL_EXPLICIT_CONSTRUCTORS explicit
-#else // #if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-#define __CL_EXPLICIT_CONSTRUCTORS 
-#endif // #if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-
-// Define deprecated prefixes and suffixes to ensure compilation
-// in case they are not pre-defined
-#if !defined(CL_EXT_PREFIX__VERSION_1_1_DEPRECATED)
-#define CL_EXT_PREFIX__VERSION_1_1_DEPRECATED  
-#endif // #if !defined(CL_EXT_PREFIX__VERSION_1_1_DEPRECATED)
-#if !defined(CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED)
-#define CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED
-#endif // #if !defined(CL_EXT_PREFIX__VERSION_1_1_DEPRECATED)
-
-#if !defined(CL_CALLBACK)
-#define CL_CALLBACK
-#endif //CL_CALLBACK
-
-#include <utility>
-#include <limits>
-#include <iterator>
-
-#if defined(__CL_ENABLE_EXCEPTIONS)
-#include <exception>
-#endif // #if defined(__CL_ENABLE_EXCEPTIONS)
-
-#if !defined(__NO_STD_VECTOR)
-#include <vector>
-#endif
-
-#if !defined(__NO_STD_STRING)
-#include <string>
-#endif 
-
-#if defined(__ANDROID__) || defined(linux) || defined(__APPLE__) || defined(__MACOSX)
-#include <alloca.h>
-#endif // linux
-
-#include <cstring>
-
-
-/*! \namespace cl
- *
- * \brief The OpenCL C++ bindings are defined within this namespace.
- *
- */
-namespace cl {
-
-class Memory;
-
-/**
- * Deprecated APIs for 1.2
- */
-#if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS) || (defined(CL_VERSION_1_1) && !defined(CL_VERSION_1_2)) 
-#define __INIT_CL_EXT_FCN_PTR(name) \
-    if(!pfn_##name) { \
-        pfn_##name = (PFN_##name) \
-            clGetExtensionFunctionAddress(#name); \
-        if(!pfn_##name) { \
-        } \
-    }
-#endif // #if defined(CL_VERSION_1_1)
-
-#if defined(CL_VERSION_1_2)
-#define __INIT_CL_EXT_FCN_PTR_PLATFORM(platform, name) \
-    if(!pfn_##name) { \
-        pfn_##name = (PFN_##name) \
-            clGetExtensionFunctionAddressForPlatform(platform, #name); \
-        if(!pfn_##name) { \
-        } \
-    }
-#endif // #if defined(CL_VERSION_1_1)
-
-class Program;
-class Device;
-class Context;
-class CommandQueue;
-class Memory;
-class Buffer;
-
-#if defined(__CL_ENABLE_EXCEPTIONS)
-/*! \brief Exception class 
- * 
- *  This may be thrown by API functions when __CL_ENABLE_EXCEPTIONS is defined.
- */
-class Error : public std::exception
-{
-private:
-    cl_int err_;
-    const char * errStr_;
-public:
-    /*! \brief Create a new CL error exception for a given error code
-     *  and corresponding message.
-     * 
-     *  \param err error code value.
-     *
-     *  \param errStr a descriptive string that must remain in scope until
-     *                handling of the exception has concluded.  If set, it
-     *                will be returned by what().
-     */
-    Error(cl_int err, const char * errStr = NULL) : err_(err), errStr_(errStr)
-    {}
-
-    ~Error() throw() {}
-
-    /*! \brief Get error string associated with exception
-     *
-     * \return A memory pointer to the error message string.
-     */
-    virtual const char * what() const throw ()
-    {
-        if (errStr_ == NULL) {
-            return "empty";
-        }
-        else {
-            return errStr_;
-        }
-    }
-
-    /*! \brief Get error code associated with exception
-     *
-     *  \return The error code.
-     */
-    cl_int err(void) const { return err_; }
-};
-
-#define __ERR_STR(x) #x
-#else
-#define __ERR_STR(x) NULL
-#endif // __CL_ENABLE_EXCEPTIONS
-
-
-namespace detail
-{
-#if defined(__CL_ENABLE_EXCEPTIONS)
-static inline cl_int errHandler (
-    cl_int err,
-    const char * errStr = NULL)
-{
-    if (err != CL_SUCCESS) {
-        throw Error(err, errStr);
-    }
-    return err;
-}
-#else
-static inline cl_int errHandler (cl_int err, const char * errStr = NULL)
-{
-    (void) errStr; // suppress unused variable warning
-    return err;
-}
-#endif // __CL_ENABLE_EXCEPTIONS
-}
-
-
-
-//! \cond DOXYGEN_DETAIL
-#if !defined(__CL_USER_OVERRIDE_ERROR_STRINGS)
-#define __GET_DEVICE_INFO_ERR               __ERR_STR(clGetDeviceInfo)
-#define __GET_PLATFORM_INFO_ERR             __ERR_STR(clGetPlatformInfo)
-#define __GET_DEVICE_IDS_ERR                __ERR_STR(clGetDeviceIDs)
-#define __GET_PLATFORM_IDS_ERR              __ERR_STR(clGetPlatformIDs)
-#define __GET_CONTEXT_INFO_ERR              __ERR_STR(clGetContextInfo)
-#define __GET_EVENT_INFO_ERR                __ERR_STR(clGetEventInfo)
-#define __GET_EVENT_PROFILE_INFO_ERR        __ERR_STR(clGetEventProfileInfo)
-#define __GET_MEM_OBJECT_INFO_ERR           __ERR_STR(clGetMemObjectInfo)
-#define __GET_IMAGE_INFO_ERR                __ERR_STR(clGetImageInfo)
-#define __GET_SAMPLER_INFO_ERR              __ERR_STR(clGetSamplerInfo)
-#define __GET_KERNEL_INFO_ERR               __ERR_STR(clGetKernelInfo)
-#if defined(CL_VERSION_1_2)
-#define __GET_KERNEL_ARG_INFO_ERR               __ERR_STR(clGetKernelArgInfo)
-#endif // #if defined(CL_VERSION_1_2)
-#define __GET_KERNEL_WORK_GROUP_INFO_ERR    __ERR_STR(clGetKernelWorkGroupInfo)
-#define __GET_PROGRAM_INFO_ERR              __ERR_STR(clGetProgramInfo)
-#define __GET_PROGRAM_BUILD_INFO_ERR        __ERR_STR(clGetProgramBuildInfo)
-#define __GET_COMMAND_QUEUE_INFO_ERR        __ERR_STR(clGetCommandQueueInfo)
-
-#define __CREATE_CONTEXT_ERR                __ERR_STR(clCreateContext)
-#define __CREATE_CONTEXT_FROM_TYPE_ERR      __ERR_STR(clCreateContextFromType)
-#define __GET_SUPPORTED_IMAGE_FORMATS_ERR   __ERR_STR(clGetSupportedImageFormats)
-
-#define __CREATE_BUFFER_ERR                 __ERR_STR(clCreateBuffer)
-#define __COPY_ERR                          __ERR_STR(cl::copy)
-#define __CREATE_SUBBUFFER_ERR              __ERR_STR(clCreateSubBuffer)
-#define __CREATE_GL_BUFFER_ERR              __ERR_STR(clCreateFromGLBuffer)
-#define __CREATE_GL_RENDER_BUFFER_ERR       __ERR_STR(clCreateFromGLBuffer)
-#define __GET_GL_OBJECT_INFO_ERR            __ERR_STR(clGetGLObjectInfo)
-#if defined(CL_VERSION_1_2)
-#define __CREATE_IMAGE_ERR                  __ERR_STR(clCreateImage)
-#define __CREATE_GL_TEXTURE_ERR             __ERR_STR(clCreateFromGLTexture)
-#define __IMAGE_DIMENSION_ERR               __ERR_STR(Incorrect image dimensions)
-#endif // #if defined(CL_VERSION_1_2)
-#define __CREATE_SAMPLER_ERR                __ERR_STR(clCreateSampler)
-#define __SET_MEM_OBJECT_DESTRUCTOR_CALLBACK_ERR __ERR_STR(clSetMemObjectDestructorCallback)
-
-#define __CREATE_USER_EVENT_ERR             __ERR_STR(clCreateUserEvent)
-#define __SET_USER_EVENT_STATUS_ERR         __ERR_STR(clSetUserEventStatus)
-#define __SET_EVENT_CALLBACK_ERR            __ERR_STR(clSetEventCallback)
-#define __WAIT_FOR_EVENTS_ERR               __ERR_STR(clWaitForEvents)
-
-#define __CREATE_KERNEL_ERR                 __ERR_STR(clCreateKernel)
-#define __SET_KERNEL_ARGS_ERR               __ERR_STR(clSetKernelArg)
-#define __CREATE_PROGRAM_WITH_SOURCE_ERR    __ERR_STR(clCreateProgramWithSource)
-#define __CREATE_PROGRAM_WITH_BINARY_ERR    __ERR_STR(clCreateProgramWithBinary)
-#if defined(CL_VERSION_1_2)
-#define __CREATE_PROGRAM_WITH_BUILT_IN_KERNELS_ERR    __ERR_STR(clCreateProgramWithBuiltInKernels)
-#endif // #if defined(CL_VERSION_1_2)
-#define __BUILD_PROGRAM_ERR                 __ERR_STR(clBuildProgram)
-#if defined(CL_VERSION_1_2)
-#define __COMPILE_PROGRAM_ERR                  __ERR_STR(clCompileProgram)
-#define __LINK_PROGRAM_ERR                  __ERR_STR(clLinkProgram)
-#endif // #if defined(CL_VERSION_1_2)
-#define __CREATE_KERNELS_IN_PROGRAM_ERR     __ERR_STR(clCreateKernelsInProgram)
-
-#define __CREATE_COMMAND_QUEUE_ERR          __ERR_STR(clCreateCommandQueue)
-#define __SET_COMMAND_QUEUE_PROPERTY_ERR    __ERR_STR(clSetCommandQueueProperty)
-#define __ENQUEUE_READ_BUFFER_ERR           __ERR_STR(clEnqueueReadBuffer)
-#define __ENQUEUE_READ_BUFFER_RECT_ERR      __ERR_STR(clEnqueueReadBufferRect)
-#define __ENQUEUE_WRITE_BUFFER_ERR          __ERR_STR(clEnqueueWriteBuffer)
-#define __ENQUEUE_WRITE_BUFFER_RECT_ERR     __ERR_STR(clEnqueueWriteBufferRect)
-#define __ENQEUE_COPY_BUFFER_ERR            __ERR_STR(clEnqueueCopyBuffer)
-#define __ENQEUE_COPY_BUFFER_RECT_ERR       __ERR_STR(clEnqueueCopyBufferRect)
-#define __ENQUEUE_FILL_BUFFER_ERR           __ERR_STR(clEnqueueFillBuffer)
-#define __ENQUEUE_READ_IMAGE_ERR            __ERR_STR(clEnqueueReadImage)
-#define __ENQUEUE_WRITE_IMAGE_ERR           __ERR_STR(clEnqueueWriteImage)
-#define __ENQUEUE_COPY_IMAGE_ERR            __ERR_STR(clEnqueueCopyImage)
-#define __ENQUEUE_FILL_IMAGE_ERR           __ERR_STR(clEnqueueFillImage)
-#define __ENQUEUE_COPY_IMAGE_TO_BUFFER_ERR  __ERR_STR(clEnqueueCopyImageToBuffer)
-#define __ENQUEUE_COPY_BUFFER_TO_IMAGE_ERR  __ERR_STR(clEnqueueCopyBufferToImage)
-#define __ENQUEUE_MAP_BUFFER_ERR            __ERR_STR(clEnqueueMapBuffer)
-#define __ENQUEUE_MAP_IMAGE_ERR             __ERR_STR(clEnqueueMapImage)
-#define __ENQUEUE_UNMAP_MEM_OBJECT_ERR      __ERR_STR(clEnqueueUnMapMemObject)
-#define __ENQUEUE_NDRANGE_KERNEL_ERR        __ERR_STR(clEnqueueNDRangeKernel)
-#define __ENQUEUE_TASK_ERR                  __ERR_STR(clEnqueueTask)
-#define __ENQUEUE_NATIVE_KERNEL             __ERR_STR(clEnqueueNativeKernel)
-#if defined(CL_VERSION_1_2)
-#define __ENQUEUE_MIGRATE_MEM_OBJECTS_ERR   __ERR_STR(clEnqueueMigrateMemObjects)
-#endif // #if defined(CL_VERSION_1_2)
-
-#define __ENQUEUE_ACQUIRE_GL_ERR            __ERR_STR(clEnqueueAcquireGLObjects)
-#define __ENQUEUE_RELEASE_GL_ERR            __ERR_STR(clEnqueueReleaseGLObjects)
-
-
-#define __RETAIN_ERR                        __ERR_STR(Retain Object)
-#define __RELEASE_ERR                       __ERR_STR(Release Object)
-#define __FLUSH_ERR                         __ERR_STR(clFlush)
-#define __FINISH_ERR                        __ERR_STR(clFinish)
-#define __VECTOR_CAPACITY_ERR               __ERR_STR(Vector capacity error)
-
-/**
- * CL 1.2 version that uses device fission.
- */
-#if defined(CL_VERSION_1_2)
-#define __CREATE_SUB_DEVICES                __ERR_STR(clCreateSubDevices)
-#else
-#define __CREATE_SUB_DEVICES                __ERR_STR(clCreateSubDevicesEXT)
-#endif // #if defined(CL_VERSION_1_2)
-
-/**
- * Deprecated APIs for 1.2
- */
-#if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS) || (defined(CL_VERSION_1_1) && !defined(CL_VERSION_1_2)) 
-#define __ENQUEUE_MARKER_ERR                __ERR_STR(clEnqueueMarker)
-#define __ENQUEUE_WAIT_FOR_EVENTS_ERR       __ERR_STR(clEnqueueWaitForEvents)
-#define __ENQUEUE_BARRIER_ERR               __ERR_STR(clEnqueueBarrier)
-#define __UNLOAD_COMPILER_ERR               __ERR_STR(clUnloadCompiler)
-#define __CREATE_GL_TEXTURE_2D_ERR          __ERR_STR(clCreateFromGLTexture2D)
-#define __CREATE_GL_TEXTURE_3D_ERR          __ERR_STR(clCreateFromGLTexture3D)
-#define __CREATE_IMAGE2D_ERR                __ERR_STR(clCreateImage2D)
-#define __CREATE_IMAGE3D_ERR                __ERR_STR(clCreateImage3D)
-#endif // #if defined(CL_VERSION_1_1)
-
-#endif // __CL_USER_OVERRIDE_ERROR_STRINGS
-//! \endcond
-
-/**
- * CL 1.2 marker and barrier commands
- */
-#if defined(CL_VERSION_1_2)
-#define __ENQUEUE_MARKER_WAIT_LIST_ERR                __ERR_STR(clEnqueueMarkerWithWaitList)
-#define __ENQUEUE_BARRIER_WAIT_LIST_ERR               __ERR_STR(clEnqueueBarrierWithWaitList)
-#endif // #if defined(CL_VERSION_1_2)
-
-#if !defined(__USE_DEV_STRING) && !defined(__NO_STD_STRING)
-typedef std::string STRING_CLASS;
-#elif !defined(__USE_DEV_STRING) 
-
-/*! \class string
- * \brief Simple string class, that provides a limited subset of std::string
- * functionality but avoids many of the issues that come with that class.
- 
- *  \note Deprecated. Please use std::string as default or
- *  re-define the string class to match the std::string
- *  interface by defining STRING_CLASS
- */
-class CL_EXT_PREFIX__VERSION_1_1_DEPRECATED string CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED
-{
-private:
-    ::size_t size_;
-    char * str_;
-public:
-    //! \brief Constructs an empty string, allocating no memory.
-    string(void) : size_(0), str_(NULL)
-    {
-    }
-
-    /*! \brief Constructs a string populated from an arbitrary value of
-     *  specified size.
-     * 
-     *  An extra '\0' is added, in case none was contained in str.
-     *
-     *  \param str the initial value of the string instance.  Note that '\0'     
-     *             characters receive no special treatment.  If NULL,
-     *             the string is left empty, with a size of 0.
-     *
-     *  \param size the number of characters to copy from str.
-     */
-    string(const char * str, ::size_t size) :
-        size_(size),
-        str_(NULL)
-    {
-        if( size > 0 ) {
-            str_ = new char[size_+1];
-            if (str_ != NULL) {
-                memcpy(str_, str, size_  * sizeof(char));
-                str_[size_] = '\0';
-            }
-            else {
-                size_ = 0;
-            }
-        }
-    }
-
-    /*! \brief Constructs a string populated from a null-terminated value.
-     *
-     *  \param str the null-terminated initial value of the string instance.
-     *             If NULL, the string is left empty, with a size of 0.
-     */
-    string(const char * str) :
-        size_(0),
-        str_(NULL)
-    {
-        if( str ) {
-            size_= ::strlen(str);
-        }
-        if( size_ > 0 ) {
-            str_ = new char[size_ + 1];
-            if (str_ != NULL) {
-                memcpy(str_, str, (size_ + 1) * sizeof(char));
-            }
-        }
-    }
-
-    void resize( ::size_t n )
-    {
-        if( size_ == n ) {
-            return;
-        }
-        if (n == 0) {
-            if( str_ ) {
-                delete [] str_;
-            }
-            str_ = NULL;
-            size_ = 0;
-        } 
-        else {
-            char *newString = new char[n + 1];
-            ::size_t copySize = n;
-            if( size_ < n ) {
-                copySize = size_;
-            }
-            size_ = n;
-            
-            if(str_) {
-                memcpy(newString, str_, (copySize + 1) * sizeof(char));
-            }
-            if( copySize < size_ ) {
-                memset(newString + copySize, 0, size_ - copySize);
-            }
-            newString[size_] = '\0';
-
-            delete [] str_;
-            str_ = newString;
-        }
-    }
-
-    const char& operator[] ( ::size_t pos ) const
-    {
-        return str_[pos];
-    }
-
-    char& operator[] ( ::size_t pos )
-    {
-        return str_[pos];
-    }
-
-    /*! \brief Copies the value of another string to this one.
-     *
-     *  \param rhs the string to copy.
-     *
-     *  \returns a reference to the modified instance.
-     */
-    string& operator=(const string& rhs)
-    {
-        if (this == &rhs) {
-            return *this;
-        }
-
-        if( str_ != NULL ) {
-            delete [] str_;
-            str_ = NULL;
-            size_ = 0;
-        }
-
-        if (rhs.size_ == 0 || rhs.str_ == NULL) {
-            str_ = NULL;
-            size_ = 0;
-        } 
-        else {
-            str_ = new char[rhs.size_ + 1];
-            size_ = rhs.size_;
-            
-            if (str_ != NULL) {
-                memcpy(str_, rhs.str_, (size_ + 1) * sizeof(char));
-            }
-            else {
-                size_ = 0;
-            }
-        }
-
-        return *this;
-    }
-
-    /*! \brief Constructs a string by copying the value of another instance.
-     *
-     *  \param rhs the string to copy.
-     */
-    string(const string& rhs) :
-        size_(0),
-        str_(NULL)
-    {
-        *this = rhs;
-    }
-
-    //! \brief Destructor - frees memory used to hold the current value.
-    ~string()
-    {
-        delete[] str_;
-        str_ = NULL;
-    }
-    
-    //! \brief Queries the length of the string, excluding any added '\0's.
-    ::size_t size(void) const   { return size_; }
-
-    //! \brief Queries the length of the string, excluding any added '\0's.
-    ::size_t length(void) const { return size(); }
-
-    /*! \brief Returns a pointer to the private copy held by this instance,
-     *  or "" if empty/unset.
-     */
-    const char * c_str(void) const { return (str_) ? str_ : "";}
-};
-typedef cl::string STRING_CLASS;
-#endif // #elif !defined(__USE_DEV_STRING) 
-
-#if !defined(__USE_DEV_VECTOR) && !defined(__NO_STD_VECTOR)
-#define VECTOR_CLASS std::vector
-#elif !defined(__USE_DEV_VECTOR) 
-#define VECTOR_CLASS cl::vector 
-
-#if !defined(__MAX_DEFAULT_VECTOR_SIZE)
-#define __MAX_DEFAULT_VECTOR_SIZE 10
-#endif
-
-/*! \class vector
- * \brief Fixed sized vector implementation that mirroring 
- *
- *  \note Deprecated. Please use std::vector as default or
- *  re-define the vector class to match the std::vector
- *  interface by defining VECTOR_CLASS
-
- *  \note Not recommended for use with custom objects as
- *  current implementation will construct N elements
- *
- * std::vector functionality.
- *  \brief Fixed sized vector compatible with std::vector.
- *
- *  \note
- *  This differs from std::vector<> not just in memory allocation,
- *  but also in terms of when members are constructed, destroyed,
- *  and assigned instead of being copy constructed.
- *
- *  \param T type of element contained in the vector.
- *
- *  \param N maximum size of the vector.
- */
-template <typename T, unsigned int N = __MAX_DEFAULT_VECTOR_SIZE>
-class CL_EXT_PREFIX__VERSION_1_1_DEPRECATED vector
-{
-private:
-    T data_[N];
-    unsigned int size_;
-
-public:
-    //! \brief Constructs an empty vector with no memory allocated.
-    vector() :  
-        size_(static_cast<unsigned int>(0))
-    {}
-
-    //! \brief Deallocates the vector's memory and destroys all of its elements.
-    ~vector() 
-    {
-        clear();
-    }
-
-    //! \brief Returns the number of elements currently contained.
-    unsigned int size(void) const
-    {
-        return size_;
-    }
-    
-    /*! \brief Empties the vector of all elements.
-     *  \note
-     *  This does not deallocate memory but will invoke destructors
-     *  on contained elements.
-     */
-    void clear()
-    {
-        while(!empty()) {
-            pop_back();
-        }
-    }
-
-    /*! \brief Appends an element after the last valid element.
-     * Calling this on a vector that has reached capacity will throw an 
-     * exception if exceptions are enabled.
-     */
-    void push_back (const T& x)
-    { 
-        if (size() < N) {
-            new (&data_[size_]) T(x);
-            size_++;
-        } else {
-            detail::errHandler(CL_MEM_OBJECT_ALLOCATION_FAILURE, __VECTOR_CAPACITY_ERR);
-        }
-    }
-
-    /*! \brief Removes the last valid element from the vector.
-     * Calling this on an empty vector will throw an exception
-     * if exceptions are enabled.
-     */
-    void pop_back(void)
-    {
-        if (size_ != 0) {
-            --size_;
-            data_[size_].~T();
-        } else {
-            detail::errHandler(CL_MEM_OBJECT_ALLOCATION_FAILURE, __VECTOR_CAPACITY_ERR);
-        }
-    }
-
-    /*! \brief Constructs with a value copied from another.
-     *
-     *  \param vec the vector to copy.
-     */
-    vector(const vector<T, N>& vec) : 
-        size_(vec.size_)
-    {
-        if (size_ != 0) {
-            assign(vec.begin(), vec.end());
-        }
-    } 
-
-    /*! \brief Constructs with a specified number of initial elements.
-     *
-     *  \param size number of initial elements.
-     *
-     *  \param val value of initial elements.
-     */
-    vector(unsigned int size, const T& val = T()) :
-        size_(0)
-    {
-        for (unsigned int i = 0; i < size; i++) {
-            push_back(val);
-        }
-    }
-
-    /*! \brief Overwrites the current content with that copied from another
-     *         instance.
-     *
-     *  \param rhs vector to copy.
-     *
-     *  \returns a reference to this.
-     */
-    vector<T, N>& operator=(const vector<T, N>& rhs)
-    {
-        if (this == &rhs) {
-            return *this;
-        }
-
-        if (rhs.size_ != 0) {	
-            assign(rhs.begin(), rhs.end());
-        } else {
-            clear();
-        }
-
-        return *this;
-    }
-
-    /*! \brief Tests equality against another instance.
-     *
-     *  \param vec the vector against which to compare.
-     */
-    bool operator==(vector<T,N> &vec)
-    {
-        if (size() != vec.size()) {
-            return false;
-        }
-
-        for( unsigned int i = 0; i < size(); ++i ) {
-            if( operator[](i) != vec[i] ) {
-                return false;
-            }
-        }
-        return true;
-    }
-  
-    //! \brief Conversion operator to T*.
-    operator T* ()             { return data_; }
-
-    //! \brief Conversion operator to const T*.
-    operator const T* () const { return data_; }
-   
-    //! \brief Tests whether this instance has any elements.
-    bool empty (void) const
-    {
-        return size_==0;
-    }
-  
-    //! \brief Returns the maximum number of elements this instance can hold.
-    unsigned int max_size (void) const
-    {
-        return N;
-    }
-
-    //! \brief Returns the maximum number of elements this instance can hold.
-    unsigned int capacity () const
-    {
-        return N;
-    }
-
-    //! \brief Resizes the vector to the given size
-    void resize(unsigned int newSize, T fill = T())
-    {
-        if (newSize > N)
-        {
-            detail::errHandler(CL_MEM_OBJECT_ALLOCATION_FAILURE, __VECTOR_CAPACITY_ERR);
-        }
-        else
-        {
-            while (size_ < newSize)
-            {
-                new (&data_[size_]) T(fill);
-                size_++;
-            }
-            while (size_ > newSize)
-            {
-                --size_;
-                data_[size_].~T();
-            }
-        }
-    }
-
-    /*! \brief Returns a reference to a given element.
-     *
-     *  \param index which element to access.     *
-     *  \note
-     *  The caller is responsible for ensuring index is >= 0 and < size().
-     */
-    T& operator[](int index)
-    {
-        return data_[index];
-    }
-  
-    /*! \brief Returns a const reference to a given element.
-     *
-     *  \param index which element to access.
-     *
-     *  \note
-     *  The caller is responsible for ensuring index is >= 0 and < size().
-     */
-    const T& operator[](int index) const
-    {
-        return data_[index];
-    }
-  
-    /*! \brief Assigns elements of the vector based on a source iterator range.
-     *
-     *  \param start Beginning iterator of source range
-     *  \param end Enditerator of source range
-     *
-     *  \note
-     *  Will throw an exception if exceptions are enabled and size exceeded.
-     */
-    template<class I>
-    void assign(I start, I end)
-    {
-        clear();   
-        while(start != end) {
-            push_back(*start);
-            start++;
-        }
-    }
-
-    /*! \class iterator
-     * \brief Const iterator class for vectors
-     */
-    class iterator
-    {
-    private:
-        const vector<T,N> *vec_;
-        int index_;
-
-        /**
-         * Internal iterator constructor to capture reference
-         * to the vector it iterates over rather than taking 
-         * the vector by copy.
-         */
-        iterator (const vector<T,N> &vec, int index) :
-            vec_(&vec)
-        {            
-            if( !vec.empty() ) {
-                index_ = index;
-            } else {
-                index_ = -1;
-            }
-        }
-
-    public:
-        iterator(void) : 
-            index_(-1),
-            vec_(NULL)
-        {
-        }
-
-        iterator(const iterator& rhs) :
-            vec_(rhs.vec_),
-            index_(rhs.index_)
-        {
-        }
-
-        ~iterator(void) {}
-
-        static iterator begin(const cl::vector<T,N> &vec)
-        {
-            iterator i(vec, 0);
-
-            return i;
-        }
-
-        static iterator end(const cl::vector<T,N> &vec)
-        {
-            iterator i(vec, vec.size());
-
-            return i;
-        }
-    
-        bool operator==(iterator i)
-        {
-            return ((vec_ == i.vec_) && 
-                    (index_ == i.index_));
-        }
-
-        bool operator!=(iterator i)
-        {
-            return (!(*this==i));
-        }
-
-        iterator& operator++()
-        {
-            ++index_;
-            return *this;
-        }
-
-        iterator operator++(int)
-        {
-            iterator retVal(*this);
-            ++index_;
-            return retVal;
-        }
-
-        iterator& operator--()
-        {
-            --index_;
-            return *this;
-        }
-
-        iterator operator--(int)
-        {
-            iterator retVal(*this);
-            --index_;
-            return retVal;
-        }
-
-        const T& operator *() const
-        {
-            return (*vec_)[index_];
-        }
-    };
-
-    iterator begin(void)
-    {
-        return iterator::begin(*this);
-    }
-
-    iterator begin(void) const
-    {
-        return iterator::begin(*this);
-    }
-
-    iterator end(void)
-    {
-        return iterator::end(*this);
-    }
-
-    iterator end(void) const
-    {
-        return iterator::end(*this);
-    }
-
-    T& front(void)
-    {
-        return data_[0];
-    }
-
-    T& back(void)
-    {
-        return data_[size_];
-    }
-
-    const T& front(void) const
-    {
-        return data_[0];
-    }
-
-    const T& back(void) const
-    {
-        return data_[size_-1];
-    }
-} CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED;
-#endif // #if !defined(__USE_DEV_VECTOR) && !defined(__NO_STD_VECTOR)
-
-
-
-
-
-namespace detail {
-#define __DEFAULT_NOT_INITIALIZED 1 
-#define __DEFAULT_BEING_INITIALIZED 2
-#define __DEFAULT_INITIALIZED 4
-
-    /*
-     * Compare and exchange primitives are needed for handling of defaults
-    */
-
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-    inline int compare_exchange(std::atomic<int> * dest, int exchange, int comparand)
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    inline int compare_exchange(volatile int * dest, int exchange, int comparand)
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    {
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-        std::atomic_compare_exchange_strong(dest, &comparand, exchange);
-        return comparand;
-#elif _MSC_VER
-        return (int)(_InterlockedCompareExchange(
-            (volatile long*)dest,
-            (long)exchange,
-            (long)comparand));
-#else // !_MSC_VER && !CL_HPP_CPP11_ATOMICS_SUPPORTED
-        return (__sync_val_compare_and_swap(
-            dest,
-            comparand,
-            exchange));
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    }
-
-    inline void fence() {
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-        std::atomic_thread_fence(std::memory_order_seq_cst);
-#elif _MSC_VER // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-        _ReadWriteBarrier();
-#else // !_MSC_VER && !CL_HPP_CPP11_ATOMICS_SUPPORTED
-        __sync_synchronize();
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    }
-} // namespace detail
-
-    
-/*! \brief class used to interface between C++ and
- *  OpenCL C calls that require arrays of size_t values, whose
- *  size is known statically.
- */
-template <int N>
-class size_t
-{ 
-private:
-    ::size_t data_[N];
-
-public:
-    //! \brief Initialize size_t to all 0s
-    size_t()
-    {
-        for( int i = 0; i < N; ++i ) {
-            data_[i] = 0;
-        }
-    }
-
-    ::size_t& operator[](int index)
-    {
-        return data_[index];
-    }
-
-    const ::size_t& operator[](int index) const
-    {
-        return data_[index];
-    }
-
-    //! \brief Conversion operator to T*.
-    operator ::size_t* ()             { return data_; }
-
-    //! \brief Conversion operator to const T*.
-    operator const ::size_t* () const { return data_; }
-};
-
-namespace detail {
-
-// Generic getInfoHelper. The final parameter is used to guide overload
-// resolution: the actual parameter passed is an int, which makes this
-// a worse conversion sequence than a specialization that declares the
-// parameter as an int.
-template<typename Functor, typename T>
-inline cl_int getInfoHelper(Functor f, cl_uint name, T* param, long)
-{
-    return f(name, sizeof(T), param, NULL);
-}
-
-// Specialized getInfoHelper for VECTOR_CLASS params
-template <typename Func, typename T>
-inline cl_int getInfoHelper(Func f, cl_uint name, VECTOR_CLASS<T>* param, long)
-{
-    ::size_t required;
-    cl_int err = f(name, 0, NULL, &required);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    T* value = (T*) alloca(required);
-    err = f(name, required, value, NULL);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    param->assign(&value[0], &value[required/sizeof(T)]);
-    return CL_SUCCESS;
-}
-
-/* Specialization for reference-counted types. This depends on the
- * existence of Wrapper<T>::cl_type, and none of the other types having the
- * cl_type member. Note that simplify specifying the parameter as Wrapper<T>
- * does not work, because when using a derived type (e.g. Context) the generic
- * template will provide a better match.
- */
-template <typename Func, typename T>
-inline cl_int getInfoHelper(Func f, cl_uint name, VECTOR_CLASS<T>* param, int, typename T::cl_type = 0)
-{
-    ::size_t required;
-    cl_int err = f(name, 0, NULL, &required);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    typename T::cl_type * value = (typename T::cl_type *) alloca(required);
-    err = f(name, required, value, NULL);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    ::size_t elements = required / sizeof(typename T::cl_type);
-    param->assign(&value[0], &value[elements]);
-    for (::size_t i = 0; i < elements; i++)
-    {
-        if (value[i] != NULL)
-        {
-            err = (*param)[i].retain();
-            if (err != CL_SUCCESS) {
-                return err;
-            }
-        }
-    }
-    return CL_SUCCESS;
-}
-
-// Specialized for getInfo<CL_PROGRAM_BINARIES>
-template <typename Func>
-inline cl_int getInfoHelper(Func f, cl_uint name, VECTOR_CLASS<char *>* param, int)
-{
-    cl_int err = f(name, param->size() * sizeof(char *), &(*param)[0], NULL);
-
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    return CL_SUCCESS;
-}
-
-// Specialized GetInfoHelper for STRING_CLASS params
-template <typename Func>
-inline cl_int getInfoHelper(Func f, cl_uint name, STRING_CLASS* param, long)
-{
-    ::size_t required;
-    cl_int err = f(name, 0, NULL, &required);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    // std::string has a constant data member
-    // a char vector does not
-    VECTOR_CLASS<char> value(required);
-    err = f(name, required, value.data(), NULL);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-    if (param) {
-        param->assign(value.begin(), value.end());
-    }
-    return CL_SUCCESS;
-}
-
-// Specialized GetInfoHelper for cl::size_t params
-template <typename Func, ::size_t N>
-inline cl_int getInfoHelper(Func f, cl_uint name, size_t<N>* param, long)
-{
-    ::size_t required;
-    cl_int err = f(name, 0, NULL, &required);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    ::size_t* value = (::size_t*) alloca(required);
-    err = f(name, required, value, NULL);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-
-    for(int i = 0; i < N; ++i) {
-        (*param)[i] = value[i];
-    }
-
-    return CL_SUCCESS;
-}
-
-template<typename T> struct ReferenceHandler;
-
-/* Specialization for reference-counted types. This depends on the
- * existence of Wrapper<T>::cl_type, and none of the other types having the
- * cl_type member. Note that simplify specifying the parameter as Wrapper<T>
- * does not work, because when using a derived type (e.g. Context) the generic
- * template will provide a better match.
- */
-template<typename Func, typename T>
-inline cl_int getInfoHelper(Func f, cl_uint name, T* param, int, typename T::cl_type = 0)
-{
-    typename T::cl_type value;
-    cl_int err = f(name, sizeof(value), &value, NULL);
-    if (err != CL_SUCCESS) {
-        return err;
-    }
-    *param = value;
-    if (value != NULL)
-    {
-        err = param->retain();
-        if (err != CL_SUCCESS) {
-            return err;
-        }
-    }
-    return CL_SUCCESS;
-}
-
-#define __PARAM_NAME_INFO_1_0(F) \
-    F(cl_platform_info, CL_PLATFORM_PROFILE, STRING_CLASS) \
-    F(cl_platform_info, CL_PLATFORM_VERSION, STRING_CLASS) \
-    F(cl_platform_info, CL_PLATFORM_NAME, STRING_CLASS) \
-    F(cl_platform_info, CL_PLATFORM_VENDOR, STRING_CLASS) \
-    F(cl_platform_info, CL_PLATFORM_EXTENSIONS, STRING_CLASS) \
-    \
-    F(cl_device_info, CL_DEVICE_TYPE, cl_device_type) \
-    F(cl_device_info, CL_DEVICE_VENDOR_ID, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_COMPUTE_UNITS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_WORK_GROUP_SIZE, ::size_t) \
-    F(cl_device_info, CL_DEVICE_MAX_WORK_ITEM_SIZES, VECTOR_CLASS< ::size_t>) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR, cl_uint) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG, cl_uint) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_CLOCK_FREQUENCY, cl_uint) \
-    F(cl_device_info, CL_DEVICE_ADDRESS_BITS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_READ_IMAGE_ARGS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_WRITE_IMAGE_ARGS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MAX_MEM_ALLOC_SIZE, cl_ulong) \
-    F(cl_device_info, CL_DEVICE_IMAGE2D_MAX_WIDTH, ::size_t) \
-    F(cl_device_info, CL_DEVICE_IMAGE2D_MAX_HEIGHT, ::size_t) \
-    F(cl_device_info, CL_DEVICE_IMAGE3D_MAX_WIDTH, ::size_t) \
-    F(cl_device_info, CL_DEVICE_IMAGE3D_MAX_HEIGHT, ::size_t) \
-    F(cl_device_info, CL_DEVICE_IMAGE3D_MAX_DEPTH, ::size_t) \
-    F(cl_device_info, CL_DEVICE_IMAGE_SUPPORT, cl_bool) \
-    F(cl_device_info, CL_DEVICE_MAX_PARAMETER_SIZE, ::size_t) \
-    F(cl_device_info, CL_DEVICE_MAX_SAMPLERS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MEM_BASE_ADDR_ALIGN, cl_uint) \
-    F(cl_device_info, CL_DEVICE_MIN_DATA_TYPE_ALIGN_SIZE, cl_uint) \
-    F(cl_device_info, CL_DEVICE_SINGLE_FP_CONFIG, cl_device_fp_config) \
-    F(cl_device_info, CL_DEVICE_GLOBAL_MEM_CACHE_TYPE, cl_device_mem_cache_type) \
-    F(cl_device_info, CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE, cl_uint)\
-    F(cl_device_info, CL_DEVICE_GLOBAL_MEM_CACHE_SIZE, cl_ulong) \
-    F(cl_device_info, CL_DEVICE_GLOBAL_MEM_SIZE, cl_ulong) \
-    F(cl_device_info, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE, cl_ulong) \
-    F(cl_device_info, CL_DEVICE_MAX_CONSTANT_ARGS, cl_uint) \
-    F(cl_device_info, CL_DEVICE_LOCAL_MEM_TYPE, cl_device_local_mem_type) \
-    F(cl_device_info, CL_DEVICE_LOCAL_MEM_SIZE, cl_ulong) \
-    F(cl_device_info, CL_DEVICE_ERROR_CORRECTION_SUPPORT, cl_bool) \
-    F(cl_device_info, CL_DEVICE_PROFILING_TIMER_RESOLUTION, ::size_t) \
-    F(cl_device_info, CL_DEVICE_ENDIAN_LITTLE, cl_bool) \
-    F(cl_device_info, CL_DEVICE_AVAILABLE, cl_bool) \
-    F(cl_device_info, CL_DEVICE_COMPILER_AVAILABLE, cl_bool) \
-    F(cl_device_info, CL_DEVICE_EXECUTION_CAPABILITIES, cl_device_exec_capabilities) \
-    F(cl_device_info, CL_DEVICE_QUEUE_PROPERTIES, cl_command_queue_properties) \
-    F(cl_device_info, CL_DEVICE_PLATFORM, cl_platform_id) \
-    F(cl_device_info, CL_DEVICE_NAME, STRING_CLASS) \
-    F(cl_device_info, CL_DEVICE_VENDOR, STRING_CLASS) \
-    F(cl_device_info, CL_DRIVER_VERSION, STRING_CLASS) \
-    F(cl_device_info, CL_DEVICE_PROFILE, STRING_CLASS) \
-    F(cl_device_info, CL_DEVICE_VERSION, STRING_CLASS) \
-    F(cl_device_info, CL_DEVICE_EXTENSIONS, STRING_CLASS) \
-    \
-    F(cl_context_info, CL_CONTEXT_REFERENCE_COUNT, cl_uint) \
-    F(cl_context_info, CL_CONTEXT_DEVICES, VECTOR_CLASS<Device>) \
-    F(cl_context_info, CL_CONTEXT_PROPERTIES, VECTOR_CLASS<cl_context_properties>) \
-    \
-    F(cl_event_info, CL_EVENT_COMMAND_QUEUE, cl::CommandQueue) \
-    F(cl_event_info, CL_EVENT_COMMAND_TYPE, cl_command_type) \
-    F(cl_event_info, CL_EVENT_REFERENCE_COUNT, cl_uint) \
-    F(cl_event_info, CL_EVENT_COMMAND_EXECUTION_STATUS, cl_int) \
-    \
-    F(cl_profiling_info, CL_PROFILING_COMMAND_QUEUED, cl_ulong) \
-    F(cl_profiling_info, CL_PROFILING_COMMAND_SUBMIT, cl_ulong) \
-    F(cl_profiling_info, CL_PROFILING_COMMAND_START, cl_ulong) \
-    F(cl_profiling_info, CL_PROFILING_COMMAND_END, cl_ulong) \
-    \
-    F(cl_mem_info, CL_MEM_TYPE, cl_mem_object_type) \
-    F(cl_mem_info, CL_MEM_FLAGS, cl_mem_flags) \
-    F(cl_mem_info, CL_MEM_SIZE, ::size_t) \
-    F(cl_mem_info, CL_MEM_HOST_PTR, void*) \
-    F(cl_mem_info, CL_MEM_MAP_COUNT, cl_uint) \
-    F(cl_mem_info, CL_MEM_REFERENCE_COUNT, cl_uint) \
-    F(cl_mem_info, CL_MEM_CONTEXT, cl::Context) \
-    \
-    F(cl_image_info, CL_IMAGE_FORMAT, cl_image_format) \
-    F(cl_image_info, CL_IMAGE_ELEMENT_SIZE, ::size_t) \
-    F(cl_image_info, CL_IMAGE_ROW_PITCH, ::size_t) \
-    F(cl_image_info, CL_IMAGE_SLICE_PITCH, ::size_t) \
-    F(cl_image_info, CL_IMAGE_WIDTH, ::size_t) \
-    F(cl_image_info, CL_IMAGE_HEIGHT, ::size_t) \
-    F(cl_image_info, CL_IMAGE_DEPTH, ::size_t) \
-    \
-    F(cl_sampler_info, CL_SAMPLER_REFERENCE_COUNT, cl_uint) \
-    F(cl_sampler_info, CL_SAMPLER_CONTEXT, cl::Context) \
-    F(cl_sampler_info, CL_SAMPLER_NORMALIZED_COORDS, cl_addressing_mode) \
-    F(cl_sampler_info, CL_SAMPLER_ADDRESSING_MODE, cl_filter_mode) \
-    F(cl_sampler_info, CL_SAMPLER_FILTER_MODE, cl_bool) \
-    \
-    F(cl_program_info, CL_PROGRAM_REFERENCE_COUNT, cl_uint) \
-    F(cl_program_info, CL_PROGRAM_CONTEXT, cl::Context) \
-    F(cl_program_info, CL_PROGRAM_NUM_DEVICES, cl_uint) \
-    F(cl_program_info, CL_PROGRAM_DEVICES, VECTOR_CLASS<Device>) \
-    F(cl_program_info, CL_PROGRAM_SOURCE, STRING_CLASS) \
-    F(cl_program_info, CL_PROGRAM_BINARY_SIZES, VECTOR_CLASS< ::size_t>) \
-    F(cl_program_info, CL_PROGRAM_BINARIES, VECTOR_CLASS<char *>) \
-    \
-    F(cl_program_build_info, CL_PROGRAM_BUILD_STATUS, cl_build_status) \
-    F(cl_program_build_info, CL_PROGRAM_BUILD_OPTIONS, STRING_CLASS) \
-    F(cl_program_build_info, CL_PROGRAM_BUILD_LOG, STRING_CLASS) \
-    \
-    F(cl_kernel_info, CL_KERNEL_FUNCTION_NAME, STRING_CLASS) \
-    F(cl_kernel_info, CL_KERNEL_NUM_ARGS, cl_uint) \
-    F(cl_kernel_info, CL_KERNEL_REFERENCE_COUNT, cl_uint) \
-    F(cl_kernel_info, CL_KERNEL_CONTEXT, cl::Context) \
-    F(cl_kernel_info, CL_KERNEL_PROGRAM, cl::Program) \
-    \
-    F(cl_kernel_work_group_info, CL_KERNEL_WORK_GROUP_SIZE, ::size_t) \
-    F(cl_kernel_work_group_info, CL_KERNEL_COMPILE_WORK_GROUP_SIZE, cl::size_t<3>) \
-    F(cl_kernel_work_group_info, CL_KERNEL_LOCAL_MEM_SIZE, cl_ulong) \
-    \
-    F(cl_command_queue_info, CL_QUEUE_CONTEXT, cl::Context) \
-    F(cl_command_queue_info, CL_QUEUE_DEVICE, cl::Device) \
-    F(cl_command_queue_info, CL_QUEUE_REFERENCE_COUNT, cl_uint) \
-    F(cl_command_queue_info, CL_QUEUE_PROPERTIES, cl_command_queue_properties)
-
-#if defined(CL_VERSION_1_1)
-#define __PARAM_NAME_INFO_1_1(F) \
-    F(cl_context_info, CL_CONTEXT_NUM_DEVICES, cl_uint)\
-    F(cl_device_info, CL_DEVICE_PREFERRED_VECTOR_WIDTH_HALF, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_CHAR, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_SHORT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_INT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_LONG, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_FLOAT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_DOUBLE, cl_uint) \
-    F(cl_device_info, CL_DEVICE_NATIVE_VECTOR_WIDTH_HALF, cl_uint) \
-    F(cl_device_info, CL_DEVICE_DOUBLE_FP_CONFIG, cl_device_fp_config) \
-    F(cl_device_info, CL_DEVICE_HALF_FP_CONFIG, cl_device_fp_config) \
-    F(cl_device_info, CL_DEVICE_HOST_UNIFIED_MEMORY, cl_bool) \
-    F(cl_device_info, CL_DEVICE_OPENCL_C_VERSION, STRING_CLASS) \
-    \
-    F(cl_mem_info, CL_MEM_ASSOCIATED_MEMOBJECT, cl::Memory) \
-    F(cl_mem_info, CL_MEM_OFFSET, ::size_t) \
-    \
-    F(cl_kernel_work_group_info, CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE, ::size_t) \
-    F(cl_kernel_work_group_info, CL_KERNEL_PRIVATE_MEM_SIZE, cl_ulong) \
-    \
-    F(cl_event_info, CL_EVENT_CONTEXT, cl::Context)
-#endif // CL_VERSION_1_1
-
-    
-#if defined(CL_VERSION_1_2)
-#define __PARAM_NAME_INFO_1_2(F) \
-    F(cl_image_info, CL_IMAGE_BUFFER, cl::Buffer) \
-    \
-    F(cl_program_info, CL_PROGRAM_NUM_KERNELS, ::size_t) \
-    F(cl_program_info, CL_PROGRAM_KERNEL_NAMES, STRING_CLASS) \
-    \
-    F(cl_program_build_info, CL_PROGRAM_BINARY_TYPE, cl_program_binary_type) \
-    \
-    F(cl_kernel_info, CL_KERNEL_ATTRIBUTES, STRING_CLASS) \
-    \
-    F(cl_kernel_arg_info, CL_KERNEL_ARG_ADDRESS_QUALIFIER, cl_kernel_arg_address_qualifier) \
-    F(cl_kernel_arg_info, CL_KERNEL_ARG_ACCESS_QUALIFIER, cl_kernel_arg_access_qualifier) \
-    F(cl_kernel_arg_info, CL_KERNEL_ARG_TYPE_NAME, STRING_CLASS) \
-    F(cl_kernel_arg_info, CL_KERNEL_ARG_NAME, STRING_CLASS) \
-    \
-    F(cl_device_info, CL_DEVICE_PARENT_DEVICE, cl_device_id) \
-    F(cl_device_info, CL_DEVICE_PARTITION_PROPERTIES, VECTOR_CLASS<cl_device_partition_property>) \
-    F(cl_device_info, CL_DEVICE_PARTITION_TYPE, VECTOR_CLASS<cl_device_partition_property>)  \
-    F(cl_device_info, CL_DEVICE_REFERENCE_COUNT, cl_uint) \
-    F(cl_device_info, CL_DEVICE_PREFERRED_INTEROP_USER_SYNC, ::size_t) \
-    F(cl_device_info, CL_DEVICE_PARTITION_AFFINITY_DOMAIN, cl_device_affinity_domain) \
-    F(cl_device_info, CL_DEVICE_BUILT_IN_KERNELS, STRING_CLASS)
-#endif // #if defined(CL_VERSION_1_2)
-
-#if defined(USE_CL_DEVICE_FISSION)
-#define __PARAM_NAME_DEVICE_FISSION(F) \
-    F(cl_device_info, CL_DEVICE_PARENT_DEVICE_EXT, cl_device_id) \
-    F(cl_device_info, CL_DEVICE_PARTITION_TYPES_EXT, VECTOR_CLASS<cl_device_partition_property_ext>) \
-    F(cl_device_info, CL_DEVICE_AFFINITY_DOMAINS_EXT, VECTOR_CLASS<cl_device_partition_property_ext>) \
-    F(cl_device_info, CL_DEVICE_REFERENCE_COUNT_EXT , cl_uint) \
-    F(cl_device_info, CL_DEVICE_PARTITION_STYLE_EXT, VECTOR_CLASS<cl_device_partition_property_ext>)
-#endif // USE_CL_DEVICE_FISSION
-
-template <typename enum_type, cl_int Name>
-struct param_traits {};
-
-#define __CL_DECLARE_PARAM_TRAITS(token, param_name, T) \
-struct token;                                        \
-template<>                                           \
-struct param_traits<detail:: token,param_name>       \
-{                                                    \
-    enum { value = param_name };                     \
-    typedef T param_type;                            \
-};
-
-__PARAM_NAME_INFO_1_0(__CL_DECLARE_PARAM_TRAITS)
-#if defined(CL_VERSION_1_1)
-__PARAM_NAME_INFO_1_1(__CL_DECLARE_PARAM_TRAITS)
-#endif // CL_VERSION_1_1
-#if defined(CL_VERSION_1_2)
-__PARAM_NAME_INFO_1_2(__CL_DECLARE_PARAM_TRAITS)
-#endif // CL_VERSION_1_1
-
-#if defined(USE_CL_DEVICE_FISSION)
-__PARAM_NAME_DEVICE_FISSION(__CL_DECLARE_PARAM_TRAITS);
-#endif // USE_CL_DEVICE_FISSION
-
-#ifdef CL_PLATFORM_ICD_SUFFIX_KHR
-__CL_DECLARE_PARAM_TRAITS(cl_platform_info, CL_PLATFORM_ICD_SUFFIX_KHR, STRING_CLASS)
-#endif
-
-#ifdef CL_DEVICE_PROFILING_TIMER_OFFSET_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_PROFILING_TIMER_OFFSET_AMD, cl_ulong)
-#endif
-
-#ifdef CL_DEVICE_GLOBAL_FREE_MEMORY_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_GLOBAL_FREE_MEMORY_AMD, VECTOR_CLASS< ::size_t>)
-#endif
-#ifdef CL_DEVICE_SIMD_PER_COMPUTE_UNIT_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_SIMD_PER_COMPUTE_UNIT_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_SIMD_WIDTH_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_SIMD_WIDTH_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_SIMD_INSTRUCTION_WIDTH_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_SIMD_INSTRUCTION_WIDTH_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_WAVEFRONT_WIDTH_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_WAVEFRONT_WIDTH_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_GLOBAL_MEM_CHANNELS_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_GLOBAL_MEM_CHANNELS_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_GLOBAL_MEM_CHANNEL_BANKS_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_GLOBAL_MEM_CHANNEL_BANKS_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_GLOBAL_MEM_CHANNEL_BANK_WIDTH_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_GLOBAL_MEM_CHANNEL_BANK_WIDTH_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_LOCAL_MEM_SIZE_PER_COMPUTE_UNIT_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_LOCAL_MEM_SIZE_PER_COMPUTE_UNIT_AMD, cl_uint)
-#endif
-#ifdef CL_DEVICE_LOCAL_MEM_BANKS_AMD
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_LOCAL_MEM_BANKS_AMD, cl_uint)
-#endif
-
-#ifdef CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV, cl_uint)
-#endif
-#ifdef CL_DEVICE_COMPUTE_CAPABILITY_MINOR_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_COMPUTE_CAPABILITY_MINOR_NV, cl_uint)
-#endif
-#ifdef CL_DEVICE_REGISTERS_PER_BLOCK_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_REGISTERS_PER_BLOCK_NV, cl_uint)
-#endif
-#ifdef CL_DEVICE_WARP_SIZE_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_WARP_SIZE_NV, cl_uint)
-#endif
-#ifdef CL_DEVICE_GPU_OVERLAP_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_GPU_OVERLAP_NV, cl_bool)
-#endif
-#ifdef CL_DEVICE_KERNEL_EXEC_TIMEOUT_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_KERNEL_EXEC_TIMEOUT_NV, cl_bool)
-#endif
-#ifdef CL_DEVICE_INTEGRATED_MEMORY_NV
-__CL_DECLARE_PARAM_TRAITS(cl_device_info, CL_DEVICE_INTEGRATED_MEMORY_NV, cl_bool)
-#endif
-
-// Convenience functions
-
-template <typename Func, typename T>
-inline cl_int
-getInfo(Func f, cl_uint name, T* param)
-{
-    return getInfoHelper(f, name, param, 0);
-}
-
-template <typename Func, typename Arg0>
-struct GetInfoFunctor0
-{
-    Func f_; const Arg0& arg0_;
-    cl_int operator ()(
-        cl_uint param, ::size_t size, void* value, ::size_t* size_ret)
-    { return f_(arg0_, param, size, value, size_ret); }
-};
-
-template <typename Func, typename Arg0, typename Arg1>
-struct GetInfoFunctor1
-{
-    Func f_; const Arg0& arg0_; const Arg1& arg1_;
-    cl_int operator ()(
-        cl_uint param, ::size_t size, void* value, ::size_t* size_ret)
-    { return f_(arg0_, arg1_, param, size, value, size_ret); }
-};
-
-template <typename Func, typename Arg0, typename T>
-inline cl_int
-getInfo(Func f, const Arg0& arg0, cl_uint name, T* param)
-{
-    GetInfoFunctor0<Func, Arg0> f0 = { f, arg0 };
-    return getInfoHelper(f0, name, param, 0);
-}
-
-template <typename Func, typename Arg0, typename Arg1, typename T>
-inline cl_int
-getInfo(Func f, const Arg0& arg0, const Arg1& arg1, cl_uint name, T* param)
-{
-    GetInfoFunctor1<Func, Arg0, Arg1> f0 = { f, arg0, arg1 };
-    return getInfoHelper(f0, name, param, 0);
-}
-
-template<typename T>
-struct ReferenceHandler
-{ };
-
-#if defined(CL_VERSION_1_2)
-/**
- * OpenCL 1.2 devices do have retain/release.
- */
-template <>
-struct ReferenceHandler<cl_device_id>
-{
-    /**
-     * Retain the device.
-     * \param device A valid device created using createSubDevices
-     * \return 
-     *   CL_SUCCESS if the function executed successfully.
-     *   CL_INVALID_DEVICE if device was not a valid subdevice
-     *   CL_OUT_OF_RESOURCES
-     *   CL_OUT_OF_HOST_MEMORY
-     */
-    static cl_int retain(cl_device_id device)
-    { return ::clRetainDevice(device); }
-    /**
-     * Retain the device.
-     * \param device A valid device created using createSubDevices
-     * \return 
-     *   CL_SUCCESS if the function executed successfully.
-     *   CL_INVALID_DEVICE if device was not a valid subdevice
-     *   CL_OUT_OF_RESOURCES
-     *   CL_OUT_OF_HOST_MEMORY
-     */
-    static cl_int release(cl_device_id device)
-    { return ::clReleaseDevice(device); }
-};
-#else // #if defined(CL_VERSION_1_2)
-/**
- * OpenCL 1.1 devices do not have retain/release.
- */
-template <>
-struct ReferenceHandler<cl_device_id>
-{
-    // cl_device_id does not have retain().
-    static cl_int retain(cl_device_id)
-    { return CL_SUCCESS; }
-    // cl_device_id does not have release().
-    static cl_int release(cl_device_id)
-    { return CL_SUCCESS; }
-};
-#endif // #if defined(CL_VERSION_1_2)
-
-template <>
-struct ReferenceHandler<cl_platform_id>
-{
-    // cl_platform_id does not have retain().
-    static cl_int retain(cl_platform_id)
-    { return CL_SUCCESS; }
-    // cl_platform_id does not have release().
-    static cl_int release(cl_platform_id)
-    { return CL_SUCCESS; }
-};
-
-template <>
-struct ReferenceHandler<cl_context>
-{
-    static cl_int retain(cl_context context)
-    { return ::clRetainContext(context); }
-    static cl_int release(cl_context context)
-    { return ::clReleaseContext(context); }
-};
-
-template <>
-struct ReferenceHandler<cl_command_queue>
-{
-    static cl_int retain(cl_command_queue queue)
-    { return ::clRetainCommandQueue(queue); }
-    static cl_int release(cl_command_queue queue)
-    { return ::clReleaseCommandQueue(queue); }
-};
-
-template <>
-struct ReferenceHandler<cl_mem>
-{
-    static cl_int retain(cl_mem memory)
-    { return ::clRetainMemObject(memory); }
-    static cl_int release(cl_mem memory)
-    { return ::clReleaseMemObject(memory); }
-};
-
-template <>
-struct ReferenceHandler<cl_sampler>
-{
-    static cl_int retain(cl_sampler sampler)
-    { return ::clRetainSampler(sampler); }
-    static cl_int release(cl_sampler sampler)
-    { return ::clReleaseSampler(sampler); }
-};
-
-template <>
-struct ReferenceHandler<cl_program>
-{
-    static cl_int retain(cl_program program)
-    { return ::clRetainProgram(program); }
-    static cl_int release(cl_program program)
-    { return ::clReleaseProgram(program); }
-};
-
-template <>
-struct ReferenceHandler<cl_kernel>
-{
-    static cl_int retain(cl_kernel kernel)
-    { return ::clRetainKernel(kernel); }
-    static cl_int release(cl_kernel kernel)
-    { return ::clReleaseKernel(kernel); }
-};
-
-template <>
-struct ReferenceHandler<cl_event>
-{
-    static cl_int retain(cl_event event)
-    { return ::clRetainEvent(event); }
-    static cl_int release(cl_event event)
-    { return ::clReleaseEvent(event); }
-};
-
-
-// Extracts version number with major in the upper 16 bits, minor in the lower 16
-static cl_uint getVersion(const char *versionInfo)
-{
-    int highVersion = 0;
-    int lowVersion = 0;
-    int index = 7;
-    while(versionInfo[index] != '.' ) {
-        highVersion *= 10;
-        highVersion += versionInfo[index]-'0';
-        ++index;
-    }
-    ++index;
-    while(versionInfo[index] != ' ' &&  versionInfo[index] != '\0') {
-        lowVersion *= 10;
-        lowVersion += versionInfo[index]-'0';
-        ++index;
-    }
-    return (highVersion << 16) | lowVersion;
-}
-
-static cl_uint getPlatformVersion(cl_platform_id platform)
-{
-    ::size_t size = 0;
-    clGetPlatformInfo(platform, CL_PLATFORM_VERSION, 0, NULL, &size);
-    char *versionInfo = (char *) alloca(size);
-    clGetPlatformInfo(platform, CL_PLATFORM_VERSION, size, &versionInfo[0], &size);
-    return getVersion(versionInfo);
-}
-
-static cl_uint getDevicePlatformVersion(cl_device_id device)
-{
-    cl_platform_id platform;
-    clGetDeviceInfo(device, CL_DEVICE_PLATFORM, sizeof(platform), &platform, NULL);
-    return getPlatformVersion(platform);
-}
-
-#if defined(CL_VERSION_1_2) && defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-static cl_uint getContextPlatformVersion(cl_context context)
-{
-    // The platform cannot be queried directly, so we first have to grab a
-    // device and obtain its context
-    ::size_t size = 0;
-    clGetContextInfo(context, CL_CONTEXT_DEVICES, 0, NULL, &size);
-    if (size == 0)
-        return 0;
-    cl_device_id *devices = (cl_device_id *) alloca(size);
-    clGetContextInfo(context, CL_CONTEXT_DEVICES, size, devices, NULL);
-    return getDevicePlatformVersion(devices[0]);
-}
-#endif // #if defined(CL_VERSION_1_2) && defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-
-template <typename T>
-class Wrapper
-{
-public:
-    typedef T cl_type;
-
-protected:
-    cl_type object_;
-
-public:
-    Wrapper() : object_(NULL) { }
-
-    Wrapper(const cl_type &obj) : object_(obj) { }
-
-    ~Wrapper()
-    {
-        if (object_ != NULL) { release(); }
-    }
-
-    Wrapper(const Wrapper<cl_type>& rhs)
-    {
-        object_ = rhs.object_;
-        if (object_ != NULL) { detail::errHandler(retain(), __RETAIN_ERR); }
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    Wrapper(Wrapper<cl_type>&& rhs) CL_HPP_NOEXCEPT
-    {
-        object_ = rhs.object_;
-        rhs.object_ = NULL;
-    }
-#endif
-
-    Wrapper<cl_type>& operator = (const Wrapper<cl_type>& rhs)
-    {
-        if (this != &rhs) {
-            if (object_ != NULL) { detail::errHandler(release(), __RELEASE_ERR); }
-            object_ = rhs.object_;
-            if (object_ != NULL) { detail::errHandler(retain(), __RETAIN_ERR); }
-        }
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    Wrapper<cl_type>& operator = (Wrapper<cl_type>&& rhs)
-    {
-        if (this != &rhs) {
-            if (object_ != NULL) { detail::errHandler(release(), __RELEASE_ERR); }
-            object_ = rhs.object_;
-            rhs.object_ = NULL;
-        }
-        return *this;
-    }
-#endif
-
-    Wrapper<cl_type>& operator = (const cl_type &rhs)
-    {
-        if (object_ != NULL) { detail::errHandler(release(), __RELEASE_ERR); }
-        object_ = rhs;
-        return *this;
-    }
-
-    cl_type operator ()() const { return object_; }
-
-    cl_type& operator ()() { return object_; }
-
-protected:
-    template<typename Func, typename U>
-    friend inline cl_int getInfoHelper(Func, cl_uint, U*, int, typename U::cl_type);
-
-    cl_int retain() const
-    {
-        return ReferenceHandler<cl_type>::retain(object_);
-    }
-
-    cl_int release() const
-    {
-        return ReferenceHandler<cl_type>::release(object_);
-    }
-};
-
-template <>
-class Wrapper<cl_device_id>
-{
-public:
-    typedef cl_device_id cl_type;
-
-protected:
-    cl_type object_;
-    bool referenceCountable_;
-
-    static bool isReferenceCountable(cl_device_id device)
-    {
-        bool retVal = false;
-        if (device != NULL) {
-            int version = getDevicePlatformVersion(device);
-            if(version > ((1 << 16) + 1)) {
-                retVal = true;
-            }
-        }
-        return retVal;
-    }
-
-public:
-    Wrapper() : object_(NULL), referenceCountable_(false) 
-    { 
-    }
-    
-    Wrapper(const cl_type &obj) : object_(obj), referenceCountable_(false) 
-    {
-        referenceCountable_ = isReferenceCountable(obj); 
-    }
-
-    ~Wrapper()
-    {
-        if (object_ != NULL) { release(); }
-    }
-    
-    Wrapper(const Wrapper<cl_type>& rhs)
-    {
-        object_ = rhs.object_;
-        referenceCountable_ = isReferenceCountable(object_); 
-        if (object_ != NULL) { detail::errHandler(retain(), __RETAIN_ERR); }
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    Wrapper(Wrapper<cl_type>&& rhs) CL_HPP_NOEXCEPT
-    {
-        object_ = rhs.object_;
-        referenceCountable_ = rhs.referenceCountable_;
-        rhs.object_ = NULL;
-        rhs.referenceCountable_ = false;
-    }
-#endif
-
-    Wrapper<cl_type>& operator = (const Wrapper<cl_type>& rhs)
-    {
-        if (this != &rhs) {
-            if (object_ != NULL) { detail::errHandler(release(), __RELEASE_ERR); }
-            object_ = rhs.object_;
-            referenceCountable_ = rhs.referenceCountable_;
-            if (object_ != NULL) { detail::errHandler(retain(), __RETAIN_ERR); }
-        }
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    Wrapper<cl_type>& operator = (Wrapper<cl_type>&& rhs)
-    {
-        if (this != &rhs) {
-            if (object_ != NULL) { detail::errHandler(release(), __RELEASE_ERR); }
-            object_ = rhs.object_;
-            referenceCountable_ = rhs.referenceCountable_;
-            rhs.object_ = NULL;
-            rhs.referenceCountable_ = false;
-        }
-        return *this;
-    }
-#endif
-
-    Wrapper<cl_type>& operator = (const cl_type &rhs)
-    {
-        if (object_ != NULL) { detail::errHandler(release(), __RELEASE_ERR); }
-        object_ = rhs;
-        referenceCountable_ = isReferenceCountable(object_); 
-        return *this;
-    }
-
-    cl_type operator ()() const { return object_; }
-
-    cl_type& operator ()() { return object_; }
-
-protected:
-    template<typename Func, typename U>
-    friend inline cl_int getInfoHelper(Func, cl_uint, U*, int, typename U::cl_type);
-
-    template<typename Func, typename U>
-    friend inline cl_int getInfoHelper(Func, cl_uint, VECTOR_CLASS<U>*, int, typename U::cl_type);
-
-    cl_int retain() const
-    {
-        if( referenceCountable_ ) {
-            return ReferenceHandler<cl_type>::retain(object_);
-        }
-        else {
-            return CL_SUCCESS;
-        }
-    }
-
-    cl_int release() const
-    {
-        if( referenceCountable_ ) {
-            return ReferenceHandler<cl_type>::release(object_);
-        }
-        else {
-            return CL_SUCCESS;
-        }
-    }
-};
-
-} // namespace detail
-//! \endcond
-
-/*! \stuct ImageFormat
- *  \brief Adds constructors and member functions for cl_image_format.
- *
- *  \see cl_image_format
- */
-struct ImageFormat : public cl_image_format
-{
-    //! \brief Default constructor - performs no initialization.
-    ImageFormat(){}
-
-    //! \brief Initializing constructor.
-    ImageFormat(cl_channel_order order, cl_channel_type type)
-    {
-        image_channel_order = order;
-        image_channel_data_type = type;
-    }
-
-    //! \brief Assignment operator.
-    ImageFormat& operator = (const ImageFormat& rhs)
-    {
-        if (this != &rhs) {
-            this->image_channel_data_type = rhs.image_channel_data_type;
-            this->image_channel_order     = rhs.image_channel_order;
-        }
-        return *this;
-    }
-};
-
-/*! \brief Class interface for cl_device_id.
- *
- *  \note Copies of these objects are inexpensive, since they don't 'own'
- *        any underlying resources or data structures.
- *
- *  \see cl_device_id
- */
-class Device : public detail::Wrapper<cl_device_id>
-{
-public:
-    //! \brief Default constructor - initializes to NULL.
-    Device() : detail::Wrapper<cl_type>() { }
-
-    /*! \brief Constructor from cl_device_id.
-     * 
-     *  This simply copies the device ID value, which is an inexpensive operation.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Device(const cl_device_id &device) : detail::Wrapper<cl_type>(device) { }
-
-    /*! \brief Returns the first device on the default context.
-     *
-     *  \see Context::getDefault()
-     */
-    static Device getDefault(cl_int * err = NULL);
-
-    /*! \brief Assignment operator from cl_device_id.
-     * 
-     *  This simply copies the device ID value, which is an inexpensive operation.
-     */
-    Device& operator = (const cl_device_id& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Device(const Device& dev) : detail::Wrapper<cl_type>(dev) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Device& operator = (const Device &dev)
-    {
-        detail::Wrapper<cl_type>::operator=(dev);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Device(Device&& dev) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(dev)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Device& operator = (Device &&dev)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(dev));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    //! \brief Wrapper for clGetDeviceInfo().
-    template <typename T>
-    cl_int getInfo(cl_device_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetDeviceInfo, object_, name, param),
-            __GET_DEVICE_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetDeviceInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_device_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_device_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    /**
-     * CL 1.2 version
-     */
-#if defined(CL_VERSION_1_2)
-    //! \brief Wrapper for clCreateSubDevicesEXT().
-    cl_int createSubDevices(
-        const cl_device_partition_property * properties,
-        VECTOR_CLASS<Device>* devices)
-    {
-        cl_uint n = 0;
-        cl_int err = clCreateSubDevices(object_, properties, 0, NULL, &n);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __CREATE_SUB_DEVICES);
-        }
-
-        cl_device_id* ids = (cl_device_id*) alloca(n * sizeof(cl_device_id));
-        err = clCreateSubDevices(object_, properties, n, ids, NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __CREATE_SUB_DEVICES);
-        }
-
-        devices->assign(&ids[0], &ids[n]);
-        return CL_SUCCESS;
-    }
-#endif // #if defined(CL_VERSION_1_2)
-
-/**
- * CL 1.1 version that uses device fission.
- */
-#if defined(CL_VERSION_1_1)
-#if defined(USE_CL_DEVICE_FISSION)
-    cl_int createSubDevices(
-        const cl_device_partition_property_ext * properties,
-        VECTOR_CLASS<Device>* devices)
-    {
-        typedef CL_API_ENTRY cl_int 
-            ( CL_API_CALL * PFN_clCreateSubDevicesEXT)(
-                cl_device_id /*in_device*/,
-                const cl_device_partition_property_ext * /* properties */,
-                cl_uint /*num_entries*/,
-                cl_device_id * /*out_devices*/,
-                cl_uint * /*num_devices*/ ) CL_EXT_SUFFIX__VERSION_1_1;
-
-        static PFN_clCreateSubDevicesEXT pfn_clCreateSubDevicesEXT = NULL;
-        __INIT_CL_EXT_FCN_PTR(clCreateSubDevicesEXT);
-
-        cl_uint n = 0;
-        cl_int err = pfn_clCreateSubDevicesEXT(object_, properties, 0, NULL, &n);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __CREATE_SUB_DEVICES);
-        }
-
-        cl_device_id* ids = (cl_device_id*) alloca(n * sizeof(cl_device_id));
-        err = pfn_clCreateSubDevicesEXT(object_, properties, n, ids, NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __CREATE_SUB_DEVICES);
-        }
-
-        devices->assign(&ids[0], &ids[n]);
-        return CL_SUCCESS;
-    }
-#endif // #if defined(USE_CL_DEVICE_FISSION)
-#endif // #if defined(CL_VERSION_1_1)
-};
-
-/*! \brief Class interface for cl_platform_id.
- *
- *  \note Copies of these objects are inexpensive, since they don't 'own'
- *        any underlying resources or data structures.
- *
- *  \see cl_platform_id
- */
-class Platform : public detail::Wrapper<cl_platform_id>
-{
-public:
-    //! \brief Default constructor - initializes to NULL.
-    Platform() : detail::Wrapper<cl_type>()  { }
-
-    /*! \brief Constructor from cl_platform_id.
-     * 
-     *  This simply copies the platform ID value, which is an inexpensive operation.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Platform(const cl_platform_id &platform) : detail::Wrapper<cl_type>(platform) { }
-
-    /*! \brief Assignment operator from cl_platform_id.
-     * 
-     *  This simply copies the platform ID value, which is an inexpensive operation.
-     */
-    Platform& operator = (const cl_platform_id& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    //! \brief Wrapper for clGetPlatformInfo().
-    cl_int getInfo(cl_platform_info name, STRING_CLASS* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetPlatformInfo, object_, name, param),
-            __GET_PLATFORM_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetPlatformInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_platform_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_platform_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    /*! \brief Gets a list of devices for this platform.
-     * 
-     *  Wraps clGetDeviceIDs().
-     */
-    cl_int getDevices(
-        cl_device_type type,
-        VECTOR_CLASS<Device>* devices) const
-    {
-        cl_uint n = 0;
-        if( devices == NULL ) {
-            return detail::errHandler(CL_INVALID_ARG_VALUE, __GET_DEVICE_IDS_ERR);
-        }
-        cl_int err = ::clGetDeviceIDs(object_, type, 0, NULL, &n);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_DEVICE_IDS_ERR);
-        }
-
-        cl_device_id* ids = (cl_device_id*) alloca(n * sizeof(cl_device_id));
-        err = ::clGetDeviceIDs(object_, type, n, ids, NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_DEVICE_IDS_ERR);
-        }
-
-        devices->assign(&ids[0], &ids[n]);
-        return CL_SUCCESS;
-    }
-
-#if defined(USE_DX_INTEROP)
-   /*! \brief Get the list of available D3D10 devices.
-     *
-     *  \param d3d_device_source.
-     *
-     *  \param d3d_object.
-     *
-     *  \param d3d_device_set.
-     *
-     *  \param devices returns a vector of OpenCL D3D10 devices found. The cl::Device
-     *  values returned in devices can be used to identify a specific OpenCL
-     *  device. If \a devices argument is NULL, this argument is ignored.
-     *
-     *  \return One of the following values:
-     *    - CL_SUCCESS if the function is executed successfully.
-     *
-     *  The application can query specific capabilities of the OpenCL device(s)
-     *  returned by cl::getDevices. This can be used by the application to
-     *  determine which device(s) to use.
-     *
-     * \note In the case that exceptions are enabled and a return value
-     * other than CL_SUCCESS is generated, then cl::Error exception is
-     * generated.
-     */
-    cl_int getDevices(
-        cl_d3d10_device_source_khr d3d_device_source,
-        void *                     d3d_object,
-        cl_d3d10_device_set_khr    d3d_device_set,
-        VECTOR_CLASS<Device>* devices) const
-    {
-        typedef CL_API_ENTRY cl_int (CL_API_CALL *PFN_clGetDeviceIDsFromD3D10KHR)(
-            cl_platform_id platform, 
-            cl_d3d10_device_source_khr d3d_device_source, 
-            void * d3d_object,
-            cl_d3d10_device_set_khr d3d_device_set,
-            cl_uint num_entries,
-            cl_device_id * devices,
-            cl_uint* num_devices);
-
-        if( devices == NULL ) {
-            return detail::errHandler(CL_INVALID_ARG_VALUE, __GET_DEVICE_IDS_ERR);
-        }
-
-        static PFN_clGetDeviceIDsFromD3D10KHR pfn_clGetDeviceIDsFromD3D10KHR = NULL;
-        __INIT_CL_EXT_FCN_PTR_PLATFORM(object_, clGetDeviceIDsFromD3D10KHR);
-
-        cl_uint n = 0;
-        cl_int err = pfn_clGetDeviceIDsFromD3D10KHR(
-            object_, 
-            d3d_device_source, 
-            d3d_object,
-            d3d_device_set, 
-            0, 
-            NULL, 
-            &n);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_DEVICE_IDS_ERR);
-        }
-
-        cl_device_id* ids = (cl_device_id*) alloca(n * sizeof(cl_device_id));
-        err = pfn_clGetDeviceIDsFromD3D10KHR(
-            object_, 
-            d3d_device_source, 
-            d3d_object,
-            d3d_device_set,
-            n, 
-            ids, 
-            NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_DEVICE_IDS_ERR);
-        }
-
-        devices->assign(&ids[0], &ids[n]);
-        return CL_SUCCESS;
-    }
-#endif
-
-    /*! \brief Gets a list of available platforms.
-     * 
-     *  Wraps clGetPlatformIDs().
-     */
-    static cl_int get(
-        VECTOR_CLASS<Platform>* platforms)
-    {
-        cl_uint n = 0;
-
-        if( platforms == NULL ) {
-            return detail::errHandler(CL_INVALID_ARG_VALUE, __GET_PLATFORM_IDS_ERR);
-        }
-
-        cl_int err = ::clGetPlatformIDs(0, NULL, &n);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_PLATFORM_IDS_ERR);
-        }
-
-        cl_platform_id* ids = (cl_platform_id*) alloca(
-            n * sizeof(cl_platform_id));
-        err = ::clGetPlatformIDs(n, ids, NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_PLATFORM_IDS_ERR);
-        }
-
-        platforms->assign(&ids[0], &ids[n]);
-        return CL_SUCCESS;
-    }
-
-    /*! \brief Gets the first available platform.
-     * 
-     *  Wraps clGetPlatformIDs(), returning the first result.
-     */
-    static cl_int get(
-        Platform * platform)
-    {
-        cl_uint n = 0;
-
-        if( platform == NULL ) {
-            return detail::errHandler(CL_INVALID_ARG_VALUE, __GET_PLATFORM_IDS_ERR);
-        }
-
-        cl_int err = ::clGetPlatformIDs(0, NULL, &n);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_PLATFORM_IDS_ERR);
-        }
-
-        cl_platform_id* ids = (cl_platform_id*) alloca(
-            n * sizeof(cl_platform_id));
-        err = ::clGetPlatformIDs(n, ids, NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_PLATFORM_IDS_ERR);
-        }
-
-        *platform = ids[0];
-        return CL_SUCCESS;
-    }
-
-    /*! \brief Gets the first available platform, returning it by value.
-     * 
-     *  Wraps clGetPlatformIDs(), returning the first result.
-     */
-    static Platform get(
-        cl_int * errResult = NULL)
-    {
-        Platform platform;
-        cl_uint n = 0;
-        cl_int err = ::clGetPlatformIDs(0, NULL, &n);
-        if (err != CL_SUCCESS) {
-            detail::errHandler(err, __GET_PLATFORM_IDS_ERR);
-            if (errResult != NULL) {
-                *errResult = err;
-            }
-            return Platform();
-        }
-
-        cl_platform_id* ids = (cl_platform_id*) alloca(
-            n * sizeof(cl_platform_id));
-        err = ::clGetPlatformIDs(n, ids, NULL);
-
-        if (err != CL_SUCCESS) {
-            detail::errHandler(err, __GET_PLATFORM_IDS_ERR);
-            if (errResult != NULL) {
-                *errResult = err;
-            }
-            return Platform();
-        }
-
-        
-        return Platform(ids[0]);
-    }
-
-    static Platform getDefault( 
-        cl_int *errResult = NULL )
-    {
-        return get(errResult);
-    }
-
-    
-#if defined(CL_VERSION_1_2)
-    //! \brief Wrapper for clUnloadCompiler().
-    cl_int
-    unloadCompiler()
-    {
-        return ::clUnloadPlatformCompiler(object_);
-    }
-#endif // #if defined(CL_VERSION_1_2)
-}; // class Platform
-
-/**
- * Deprecated APIs for 1.2
- */
-#if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS) || (defined(CL_VERSION_1_1) && !defined(CL_VERSION_1_2))
-/**
- * Unload the OpenCL compiler.
- * \note Deprecated for OpenCL 1.2. Use Platform::unloadCompiler instead.
- */
-inline CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_int
-UnloadCompiler() CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED;
-inline cl_int
-UnloadCompiler()
-{
-    return ::clUnloadCompiler();
-}
-#endif // #if defined(CL_VERSION_1_1)
-
-/*! \brief Class interface for cl_context.
- *
- *  \note Copies of these objects are shallow, meaning that the copy will refer
- *        to the same underlying cl_context as the original.  For details, see
- *        clRetainContext() and clReleaseContext().
- *
- *  \see cl_context
- */
-class Context 
-    : public detail::Wrapper<cl_context>
-{
-private:
-
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-    static std::atomic<int> default_initialized_;
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    static volatile int default_initialized_;
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    static Context default_;
-    static volatile cl_int default_error_;
-public:
-    /*! \brief Constructs a context including a list of specified devices.
-     *
-     *  Wraps clCreateContext().
-     */
-    Context(
-        const VECTOR_CLASS<Device>& devices,
-        cl_context_properties* properties = NULL,
-        void (CL_CALLBACK * notifyFptr)(
-            const char *,
-            const void *,
-            ::size_t,
-            void *) = NULL,
-        void* data = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        ::size_t numDevices = devices.size();
-        cl_device_id* deviceIDs = (cl_device_id*) alloca(numDevices * sizeof(cl_device_id));
-        for( ::size_t deviceIndex = 0; deviceIndex < numDevices; ++deviceIndex ) {
-            deviceIDs[deviceIndex] = (devices[deviceIndex])();
-        }
-
-        object_ = ::clCreateContext(
-            properties, (cl_uint) numDevices,
-            deviceIDs,
-            notifyFptr, data, &error);
-
-        detail::errHandler(error, __CREATE_CONTEXT_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    Context(
-        const Device& device,
-        cl_context_properties* properties = NULL,
-        void (CL_CALLBACK * notifyFptr)(
-            const char *,
-            const void *,
-            ::size_t,
-            void *) = NULL,
-        void* data = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        cl_device_id deviceID = device();
-
-        object_ = ::clCreateContext(
-            properties, 1,
-            &deviceID,
-            notifyFptr, data, &error);
-
-        detail::errHandler(error, __CREATE_CONTEXT_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /*! \brief Constructs a context including all or a subset of devices of a specified type.
-     *
-     *  Wraps clCreateContextFromType().
-     */
-    Context(
-        cl_device_type type,
-        cl_context_properties* properties = NULL,
-        void (CL_CALLBACK * notifyFptr)(
-            const char *,
-            const void *,
-            ::size_t,
-            void *) = NULL,
-        void* data = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-#if !defined(__APPLE__) && !defined(__MACOS)
-        cl_context_properties prop[4] = {CL_CONTEXT_PLATFORM, 0, 0, 0 };
-
-        if (properties == NULL) {
-            // Get a valid platform ID as we cannot send in a blank one
-            VECTOR_CLASS<Platform> platforms;
-            error = Platform::get(&platforms);
-            if (error != CL_SUCCESS) {
-                detail::errHandler(error, __CREATE_CONTEXT_FROM_TYPE_ERR);
-                if (err != NULL) {
-                    *err = error;
-                }
-                return;
-            }
-
-            // Check the platforms we found for a device of our specified type
-            cl_context_properties platform_id = 0;
-            for (unsigned int i = 0; i < platforms.size(); i++) {
-
-                VECTOR_CLASS<Device> devices;
-
-#if defined(__CL_ENABLE_EXCEPTIONS)
-                try {
-#endif
-
-                    error = platforms[i].getDevices(type, &devices);
-
-#if defined(__CL_ENABLE_EXCEPTIONS)
-                } catch (Error) {}
-    // Catch if exceptions are enabled as we don't want to exit if first platform has no devices of type
-    // We do error checking next anyway, and can throw there if needed
-#endif
-
-                // Only squash CL_SUCCESS and CL_DEVICE_NOT_FOUND
-                if (error != CL_SUCCESS && error != CL_DEVICE_NOT_FOUND) {
-                    detail::errHandler(error, __CREATE_CONTEXT_FROM_TYPE_ERR);
-                    if (err != NULL) {
-                        *err = error;
-                    }
-                }
-
-                if (devices.size() > 0) {
-                    platform_id = (cl_context_properties)platforms[i]();
-                    break;
-                }
-            }
-
-            if (platform_id == 0) {
-                detail::errHandler(CL_DEVICE_NOT_FOUND, __CREATE_CONTEXT_FROM_TYPE_ERR);
-                if (err != NULL) {
-                    *err = CL_DEVICE_NOT_FOUND;
-                }
-                return;
-            }
-
-            prop[1] = platform_id;
-            properties = &prop[0];
-        }
-#endif
-        object_ = ::clCreateContextFromType(
-            properties, type, notifyFptr, data, &error);
-
-        detail::errHandler(error, __CREATE_CONTEXT_FROM_TYPE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Context(const Context& ctx) : detail::Wrapper<cl_type>(ctx) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Context& operator = (const Context &ctx)
-    {
-        detail::Wrapper<cl_type>::operator=(ctx);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Context(Context&& ctx) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(ctx)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Context& operator = (Context &&ctx)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(ctx));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    /*! \brief Returns a singleton context including all devices of CL_DEVICE_TYPE_DEFAULT.
-     *
-     *  \note All calls to this function return the same cl_context as the first.
-     */
-    static Context getDefault(cl_int * err = NULL) 
-    {
-        int state = detail::compare_exchange(
-            &default_initialized_, 
-            __DEFAULT_BEING_INITIALIZED, __DEFAULT_NOT_INITIALIZED);
-        
-        if (state & __DEFAULT_INITIALIZED) {
-            if (err != NULL) {
-                *err = default_error_;
-            }
-            return default_;
-        }
-
-        if (state & __DEFAULT_BEING_INITIALIZED) {
-              // Assume writes will propagate eventually...
-              while(default_initialized_ != __DEFAULT_INITIALIZED) {
-                  detail::fence();
-              }
-
-            if (err != NULL) {
-                *err = default_error_;
-            }
-            return default_;
-        }
-
-        cl_int error;
-        default_ = Context(
-            CL_DEVICE_TYPE_DEFAULT,
-            NULL,
-            NULL,
-            NULL,
-            &error);
-
-        detail::fence();
-
-        default_error_ = error;
-        // Assume writes will propagate eventually...
-        default_initialized_ = __DEFAULT_INITIALIZED;
-
-        detail::fence();
-
-        if (err != NULL) {
-            *err = default_error_;
-        }
-        return default_;
-
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    Context() : detail::Wrapper<cl_type>() { }
-
-    /*! \brief Constructor from cl_context - takes ownership.
-     * 
-     *  This effectively transfers ownership of a refcount on the cl_context
-     *  into the new Context object.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Context(const cl_context& context) : detail::Wrapper<cl_type>(context) { }
-
-    /*! \brief Assignment operator from cl_context - takes ownership.
-     * 
-     *  This effectively transfers ownership of a refcount on the rhs and calls
-     *  clReleaseContext() on the value previously held by this instance.
-     */
-    Context& operator = (const cl_context& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    //! \brief Wrapper for clGetContextInfo().
-    template <typename T>
-    cl_int getInfo(cl_context_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetContextInfo, object_, name, param),
-            __GET_CONTEXT_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetContextInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_context_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_context_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    /*! \brief Gets a list of supported image formats.
-     *  
-     *  Wraps clGetSupportedImageFormats().
-     */
-    cl_int getSupportedImageFormats(
-        cl_mem_flags flags,
-        cl_mem_object_type type,
-        VECTOR_CLASS<ImageFormat>* formats) const
-    {
-        cl_uint numEntries;
-        cl_int err = ::clGetSupportedImageFormats(
-           object_, 
-           flags,
-           type, 
-           0, 
-           NULL, 
-           &numEntries);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_SUPPORTED_IMAGE_FORMATS_ERR);
-        }
-
-        ImageFormat* value = (ImageFormat*)
-            alloca(numEntries * sizeof(ImageFormat));
-        err = ::clGetSupportedImageFormats(
-            object_, 
-            flags, 
-            type, 
-            numEntries,
-            (cl_image_format*) value, 
-            NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __GET_SUPPORTED_IMAGE_FORMATS_ERR);
-        }
-
-        formats->assign(&value[0], &value[numEntries]);
-        return CL_SUCCESS;
-    }
-};
-
-inline Device Device::getDefault(cl_int * err)
-{
-    cl_int error;
-    Device device;
-
-    Context context = Context::getDefault(&error);
-    detail::errHandler(error, __CREATE_CONTEXT_ERR);
-
-    if (error != CL_SUCCESS) {
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-    else {
-        device = context.getInfo<CL_CONTEXT_DEVICES>()[0];
-        if (err != NULL) {
-            *err = CL_SUCCESS;
-        }
-    }
-
-    return device;
-}
-
-
-#ifdef _WIN32
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-__declspec(selectany) std::atomic<int> Context::default_initialized_;
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__declspec(selectany) volatile int Context::default_initialized_ = __DEFAULT_NOT_INITIALIZED;
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__declspec(selectany) Context Context::default_;
-__declspec(selectany) volatile cl_int Context::default_error_ = CL_SUCCESS;
-#else // !_WIN32
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-__attribute__((weak)) std::atomic<int> Context::default_initialized_;
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__attribute__((weak)) volatile int Context::default_initialized_ = __DEFAULT_NOT_INITIALIZED;
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__attribute__((weak)) Context Context::default_;
-__attribute__((weak)) volatile cl_int Context::default_error_ = CL_SUCCESS;
-#endif // !_WIN32
-
-/*! \brief Class interface for cl_event.
- *
- *  \note Copies of these objects are shallow, meaning that the copy will refer
- *        to the same underlying cl_event as the original.  For details, see
- *        clRetainEvent() and clReleaseEvent().
- *
- *  \see cl_event
- */
-class Event : public detail::Wrapper<cl_event>
-{
-public:
-    //! \brief Default constructor - initializes to NULL.
-    Event() : detail::Wrapper<cl_type>() { }
-
-    /*! \brief Constructor from cl_event - takes ownership.
-     * 
-     *  This effectively transfers ownership of a refcount on the cl_event
-     *  into the new Event object.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Event(const cl_event& event) : detail::Wrapper<cl_type>(event) { }
-
-    /*! \brief Assignment operator from cl_event - takes ownership.
-     *
-     *  This effectively transfers ownership of a refcount on the rhs and calls
-     *  clReleaseEvent() on the value previously held by this instance.
-     */
-    Event& operator = (const cl_event& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    //! \brief Wrapper for clGetEventInfo().
-    template <typename T>
-    cl_int getInfo(cl_event_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetEventInfo, object_, name, param),
-            __GET_EVENT_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetEventInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_event_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_event_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    //! \brief Wrapper for clGetEventProfilingInfo().
-    template <typename T>
-    cl_int getProfilingInfo(cl_profiling_info name, T* param) const
-    {
-        return detail::errHandler(detail::getInfo(
-            &::clGetEventProfilingInfo, object_, name, param),
-            __GET_EVENT_PROFILE_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetEventProfilingInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_profiling_info, name>::param_type
-    getProfilingInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_profiling_info, name>::param_type param;
-        cl_int result = getProfilingInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    /*! \brief Blocks the calling thread until this event completes.
-     * 
-     *  Wraps clWaitForEvents().
-     */
-    cl_int wait() const
-    {
-        return detail::errHandler(
-            ::clWaitForEvents(1, &object_),
-            __WAIT_FOR_EVENTS_ERR);
-    }
-
-#if defined(CL_VERSION_1_1)
-    /*! \brief Registers a user callback function for a specific command execution status.
-     *
-     *  Wraps clSetEventCallback().
-     */
-    cl_int setCallback(
-        cl_int type,
-        void (CL_CALLBACK * pfn_notify)(cl_event, cl_int, void *),		
-        void * user_data = NULL)
-    {
-        return detail::errHandler(
-            ::clSetEventCallback(
-                object_,
-                type,
-                pfn_notify,
-                user_data), 
-            __SET_EVENT_CALLBACK_ERR);
-    }
-#endif
-
-    /*! \brief Blocks the calling thread until every event specified is complete.
-     * 
-     *  Wraps clWaitForEvents().
-     */
-    static cl_int
-    waitForEvents(const VECTOR_CLASS<Event>& events)
-    {
-        return detail::errHandler(
-            ::clWaitForEvents(
-                (cl_uint) events.size(), (events.size() > 0) ? (cl_event*)&events.front() : NULL),
-            __WAIT_FOR_EVENTS_ERR);
-    }
-};
-
-#if defined(CL_VERSION_1_1)
-/*! \brief Class interface for user events (a subset of cl_event's).
- * 
- *  See Event for details about copy semantics, etc.
- */
-class UserEvent : public Event
-{
-public:
-    /*! \brief Constructs a user event on a given context.
-     *
-     *  Wraps clCreateUserEvent().
-     */
-    UserEvent(
-        const Context& context,
-        cl_int * err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateUserEvent(
-            context(),
-            &error);
-
-        detail::errHandler(error, __CREATE_USER_EVENT_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    UserEvent() : Event() { }
-
-    /*! \brief Sets the execution status of a user event object.
-     *
-     *  Wraps clSetUserEventStatus().
-     */
-    cl_int setStatus(cl_int status)
-    {
-        return detail::errHandler(
-            ::clSetUserEventStatus(object_,status), 
-            __SET_USER_EVENT_STATUS_ERR);
-    }
-};
-#endif
-
-/*! \brief Blocks the calling thread until every event specified is complete.
- * 
- *  Wraps clWaitForEvents().
- */
-inline static cl_int
-WaitForEvents(const VECTOR_CLASS<Event>& events)
-{
-    return detail::errHandler(
-        ::clWaitForEvents(
-            (cl_uint) events.size(), (events.size() > 0) ? (cl_event*)&events.front() : NULL),
-        __WAIT_FOR_EVENTS_ERR);
-}
-
-/*! \brief Class interface for cl_mem.
- *
- *  \note Copies of these objects are shallow, meaning that the copy will refer
- *        to the same underlying cl_mem as the original.  For details, see
- *        clRetainMemObject() and clReleaseMemObject().
- *
- *  \see cl_mem
- */
-class Memory : public detail::Wrapper<cl_mem>
-{
-public:
-    //! \brief Default constructor - initializes to NULL.
-    Memory() : detail::Wrapper<cl_type>() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     * 
-     *  This effectively transfers ownership of a refcount on the cl_mem
-     *  into the new Memory object.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Memory(const cl_mem& memory) : detail::Wrapper<cl_type>(memory) { }
-
-    /*! \brief Assignment operator from cl_mem - takes ownership.
-     *
-     *  This effectively transfers ownership of a refcount on the rhs and calls
-     *  clReleaseMemObject() on the value previously held by this instance.
-     */
-    Memory& operator = (const cl_mem& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Memory(const Memory& mem) : detail::Wrapper<cl_type>(mem) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Memory& operator = (const Memory &mem)
-    {
-        detail::Wrapper<cl_type>::operator=(mem);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Memory(Memory&& mem) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(mem)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Memory& operator = (Memory &&mem)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(mem));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    //! \brief Wrapper for clGetMemObjectInfo().
-    template <typename T>
-    cl_int getInfo(cl_mem_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetMemObjectInfo, object_, name, param),
-            __GET_MEM_OBJECT_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetMemObjectInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_mem_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_mem_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-#if defined(CL_VERSION_1_1)
-    /*! \brief Registers a callback function to be called when the memory object
-     *         is no longer needed.
-     *
-     *  Wraps clSetMemObjectDestructorCallback().
-     *
-     *  Repeated calls to this function, for a given cl_mem value, will append
-     *  to the list of functions called (in reverse order) when memory object's
-     *  resources are freed and the memory object is deleted.
-     *
-     *  \note
-     *  The registered callbacks are associated with the underlying cl_mem
-     *  value - not the Memory class instance.
-     */
-    cl_int setDestructorCallback(
-        void (CL_CALLBACK * pfn_notify)(cl_mem, void *),		
-        void * user_data = NULL)
-    {
-        return detail::errHandler(
-            ::clSetMemObjectDestructorCallback(
-                object_,
-                pfn_notify,
-                user_data), 
-            __SET_MEM_OBJECT_DESTRUCTOR_CALLBACK_ERR);
-    }
-#endif
-
-};
-
-// Pre-declare copy functions
-class Buffer;
-template< typename IteratorType >
-cl_int copy( IteratorType startIterator, IteratorType endIterator, cl::Buffer &buffer );
-template< typename IteratorType >
-cl_int copy( const cl::Buffer &buffer, IteratorType startIterator, IteratorType endIterator );
-template< typename IteratorType >
-cl_int copy( const CommandQueue &queue, IteratorType startIterator, IteratorType endIterator, cl::Buffer &buffer );
-template< typename IteratorType >
-cl_int copy( const CommandQueue &queue, const cl::Buffer &buffer, IteratorType startIterator, IteratorType endIterator );
-
-
-/*! \brief Class interface for Buffer Memory Objects.
- * 
- *  See Memory for details about copy semantics, etc.
- *
- *  \see Memory
- */
-class Buffer : public Memory
-{
-public:
-
-    /*! \brief Constructs a Buffer in a specified context.
-     *
-     *  Wraps clCreateBuffer().
-     *
-     *  \param host_ptr Storage to be used if the CL_MEM_USE_HOST_PTR flag was
-     *                  specified.  Note alignment & exclusivity requirements.
-     */
-    Buffer(
-        const Context& context,
-        cl_mem_flags flags,
-        ::size_t size,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateBuffer(context(), flags, size, host_ptr, &error);
-
-        detail::errHandler(error, __CREATE_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /*! \brief Constructs a Buffer in the default context.
-     *
-     *  Wraps clCreateBuffer().
-     *
-     *  \param host_ptr Storage to be used if the CL_MEM_USE_HOST_PTR flag was
-     *                  specified.  Note alignment & exclusivity requirements.
-     *
-     *  \see Context::getDefault()
-     */
-    Buffer(
-         cl_mem_flags flags,
-        ::size_t size,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        Context context = Context::getDefault(err);
-
-        object_ = ::clCreateBuffer(context(), flags, size, host_ptr, &error);
-
-        detail::errHandler(error, __CREATE_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /*!
-     * \brief Construct a Buffer from a host container via iterators.
-     * IteratorType must be random access.
-     * If useHostPtr is specified iterators must represent contiguous data.
-     */
-    template< typename IteratorType >
-    Buffer(
-        IteratorType startIterator,
-        IteratorType endIterator,
-        bool readOnly,
-        bool useHostPtr = false,
-        cl_int* err = NULL)
-    {
-        typedef typename std::iterator_traits<IteratorType>::value_type DataType;
-        cl_int error;
-
-        cl_mem_flags flags = 0;
-        if( readOnly ) {
-            flags |= CL_MEM_READ_ONLY;
-        }
-        else {
-            flags |= CL_MEM_READ_WRITE;
-        }
-        if( useHostPtr ) {
-            flags |= CL_MEM_USE_HOST_PTR;
-        }
-        
-        ::size_t size = sizeof(DataType)*(endIterator - startIterator);
-
-        Context context = Context::getDefault(err);
-
-        if( useHostPtr ) {
-            object_ = ::clCreateBuffer(context(), flags, size, static_cast<DataType*>(&*startIterator), &error);
-        } else {
-            object_ = ::clCreateBuffer(context(), flags, size, 0, &error);
-        }
-
-        detail::errHandler(error, __CREATE_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-
-        if( !useHostPtr ) {
-            error = cl::copy(startIterator, endIterator, *this);
-            detail::errHandler(error, __CREATE_BUFFER_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-    }
-
-    /*!
-     * \brief Construct a Buffer from a host container via iterators using a specified context.
-     * IteratorType must be random access.
-     * If useHostPtr is specified iterators must represent contiguous data.
-     */
-    template< typename IteratorType >
-    Buffer(const Context &context, IteratorType startIterator, IteratorType endIterator,
-        bool readOnly, bool useHostPtr = false, cl_int* err = NULL);
-
-    /*!
-    * \brief Construct a Buffer from a host container via iterators using a specified queue.
-    * If useHostPtr is specified iterators must represent contiguous data.
-    */
-    template< typename IteratorType >
-    Buffer(const CommandQueue &queue, IteratorType startIterator, IteratorType endIterator,
-        bool readOnly, bool useHostPtr = false, cl_int* err = NULL);
-
-    //! \brief Default constructor - initializes to NULL.
-    Buffer() : Memory() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Buffer(const cl_mem& buffer) : Memory(buffer) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Buffer& operator = (const cl_mem& rhs)
-    {
-        Memory::operator=(rhs);
-        return *this;
-    }
-    
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Buffer(const Buffer& buf) : Memory(buf) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Buffer& operator = (const Buffer &buf)
-    {
-        Memory::operator=(buf);
-        return *this;
-    }
-    
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Buffer(Buffer&& buf) CL_HPP_NOEXCEPT : Memory(std::move(buf)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Buffer& operator = (Buffer &&buf)
-    {
-        Memory::operator=(std::move(buf));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-#if defined(CL_VERSION_1_1)
-    /*! \brief Creates a new buffer object from this.
-     *
-     *  Wraps clCreateSubBuffer().
-     */
-    Buffer createSubBuffer(
-        cl_mem_flags flags,
-        cl_buffer_create_type buffer_create_type,
-        const void * buffer_create_info,
-        cl_int * err = NULL)
-    {
-        Buffer result;
-        cl_int error;
-        result.object_ = ::clCreateSubBuffer(
-            object_, 
-            flags, 
-            buffer_create_type, 
-            buffer_create_info, 
-            &error);
-
-        detail::errHandler(error, __CREATE_SUBBUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-
-        return result;
-    }		
-#endif
-};
-
-#if defined (USE_DX_INTEROP)
-/*! \brief Class interface for creating OpenCL buffers from ID3D10Buffer's.
- *
- *  This is provided to facilitate interoperability with Direct3D.
- * 
- *  See Memory for details about copy semantics, etc.
- *
- *  \see Memory
- */
-class BufferD3D10 : public Buffer
-{
-public:
-    typedef CL_API_ENTRY cl_mem (CL_API_CALL *PFN_clCreateFromD3D10BufferKHR)(
-    cl_context context, cl_mem_flags flags, ID3D10Buffer*  buffer,
-    cl_int* errcode_ret);
-
-    /*! \brief Constructs a BufferD3D10, in a specified context, from a
-     *         given ID3D10Buffer.
-     *
-     *  Wraps clCreateFromD3D10BufferKHR().
-     */
-    BufferD3D10(
-        const Context& context,
-        cl_mem_flags flags,
-        ID3D10Buffer* bufobj,
-        cl_int * err = NULL)
-    {
-        static PFN_clCreateFromD3D10BufferKHR pfn_clCreateFromD3D10BufferKHR = NULL;
-
-#if defined(CL_VERSION_1_2)
-        vector<cl_context_properties> props = context.getInfo<CL_CONTEXT_PROPERTIES>();
-        cl_platform platform = -1;
-        for( int i = 0; i < props.size(); ++i ) {
-            if( props[i] == CL_CONTEXT_PLATFORM ) {
-                platform = props[i+1];
-            }
-        }
-        __INIT_CL_EXT_FCN_PTR_PLATFORM(platform, clCreateFromD3D10BufferKHR);
-#endif
-#if defined(CL_VERSION_1_1)
-        __INIT_CL_EXT_FCN_PTR(clCreateFromD3D10BufferKHR);
-#endif
-
-        cl_int error;
-        object_ = pfn_clCreateFromD3D10BufferKHR(
-            context(),
-            flags,
-            bufobj,
-            &error);
-
-        detail::errHandler(error, __CREATE_GL_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    BufferD3D10() : Buffer() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS BufferD3D10(const cl_mem& buffer) : Buffer(buffer) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    BufferD3D10& operator = (const cl_mem& rhs)
-    {
-        Buffer::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferD3D10(const BufferD3D10& buf) : Buffer(buf) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferD3D10& operator = (const BufferD3D10 &buf)
-    {
-        Buffer::operator=(buf);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferD3D10(BufferD3D10&& buf) CL_HPP_NOEXCEPT : Buffer(std::move(buf)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferD3D10& operator = (BufferD3D10 &&buf)
-    {
-        Buffer::operator=(std::move(buf));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-#endif
-
-/*! \brief Class interface for GL Buffer Memory Objects.
- *
- *  This is provided to facilitate interoperability with OpenGL.
- * 
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- */
-class BufferGL : public Buffer
-{
-public:
-    /*! \brief Constructs a BufferGL in a specified context, from a given
-     *         GL buffer.
-     *
-     *  Wraps clCreateFromGLBuffer().
-     */
-    BufferGL(
-        const Context& context,
-        cl_mem_flags flags,
-        cl_GLuint bufobj,
-        cl_int * err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateFromGLBuffer(
-            context(),
-            flags,
-            bufobj,
-            &error);
-
-        detail::errHandler(error, __CREATE_GL_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    BufferGL() : Buffer() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS BufferGL(const cl_mem& buffer) : Buffer(buffer) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    BufferGL& operator = (const cl_mem& rhs)
-    {
-        Buffer::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferGL(const BufferGL& buf) : Buffer(buf) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferGL& operator = (const BufferGL &buf)
-    {
-        Buffer::operator=(buf);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferGL(BufferGL&& buf) CL_HPP_NOEXCEPT : Buffer(std::move(buf)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferGL& operator = (BufferGL &&buf)
-    {
-        Buffer::operator=(std::move(buf));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    //! \brief Wrapper for clGetGLObjectInfo().
-    cl_int getObjectInfo(
-        cl_gl_object_type *type,
-        cl_GLuint * gl_object_name)
-    {
-        return detail::errHandler(
-            ::clGetGLObjectInfo(object_,type,gl_object_name),
-            __GET_GL_OBJECT_INFO_ERR);
-    }
-};
-
-/*! \brief C++ base class for Image Memory objects.
- *
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- */
-class Image : public Memory
-{
-protected:
-    //! \brief Default constructor - initializes to NULL.
-    Image() : Memory() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Image(const cl_mem& image) : Memory(image) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Image& operator = (const cl_mem& rhs)
-    {
-        Memory::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image(const Image& img) : Memory(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image& operator = (const Image &img)
-    {
-        Memory::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image(Image&& img) CL_HPP_NOEXCEPT : Memory(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image& operator = (Image &&img)
-    {
-        Memory::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-public:
-    //! \brief Wrapper for clGetImageInfo().
-    template <typename T>
-    cl_int getImageInfo(cl_image_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetImageInfo, object_, name, param),
-            __GET_IMAGE_INFO_ERR);
-    }
-    
-    //! \brief Wrapper for clGetImageInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_image_info, name>::param_type
-    getImageInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_image_info, name>::param_type param;
-        cl_int result = getImageInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-};
-
-#if defined(CL_VERSION_1_2)
-/*! \brief Class interface for 1D Image Memory objects.
- *
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- */
-class Image1D : public Image
-{
-public:
-    /*! \brief Constructs a 1D Image in a specified context.
-     *
-     *  Wraps clCreateImage().
-     */
-    Image1D(
-        const Context& context,
-        cl_mem_flags flags,
-        ImageFormat format,
-        ::size_t width,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        cl_image_desc desc =
-        {
-            CL_MEM_OBJECT_IMAGE1D,
-            width,
-            0, 0, 0, 0, 0, 0, 0, 0
-        };
-        object_ = ::clCreateImage(
-            context(), 
-            flags, 
-            &format, 
-            &desc, 
-            host_ptr, 
-            &error);
-
-        detail::errHandler(error, __CREATE_IMAGE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    Image1D() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Image1D(const cl_mem& image1D) : Image(image1D) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Image1D& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1D(const Image1D& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1D& operator = (const Image1D &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1D(Image1D&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1D& operator = (Image1D &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-
-/*! \class Image1DBuffer
- * \brief Image interface for 1D buffer images.
- */
-class Image1DBuffer : public Image
-{
-public:
-    Image1DBuffer(
-        const Context& context,
-        cl_mem_flags flags,
-        ImageFormat format,
-        ::size_t width,
-        const Buffer &buffer,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        cl_image_desc desc =
-        {
-            CL_MEM_OBJECT_IMAGE1D_BUFFER,
-            width,
-            0, 0, 0, 0, 0, 0, 0,
-            buffer()
-        };
-        object_ = ::clCreateImage(
-            context(), 
-            flags, 
-            &format, 
-            &desc, 
-            NULL, 
-            &error);
-
-        detail::errHandler(error, __CREATE_IMAGE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    Image1DBuffer() { }
-
-    __CL_EXPLICIT_CONSTRUCTORS Image1DBuffer(const cl_mem& image1D) : Image(image1D) { }
-
-    Image1DBuffer& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-    
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DBuffer(const Image1DBuffer& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DBuffer& operator = (const Image1DBuffer &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DBuffer(Image1DBuffer&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DBuffer& operator = (Image1DBuffer &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-
-/*! \class Image1DArray
- * \brief Image interface for arrays of 1D images.
- */
-class Image1DArray : public Image
-{
-public:
-    Image1DArray(
-        const Context& context,
-        cl_mem_flags flags,
-        ImageFormat format,
-        ::size_t arraySize,
-        ::size_t width,
-        ::size_t rowPitch,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        cl_image_desc desc =
-        {
-            CL_MEM_OBJECT_IMAGE1D_ARRAY,
-            width,
-            0, 0,  // height, depth (unused)
-            arraySize,
-            rowPitch,
-            0, 0, 0, 0
-        };
-        object_ = ::clCreateImage(
-            context(), 
-            flags, 
-            &format, 
-            &desc, 
-            host_ptr, 
-            &error);
-
-        detail::errHandler(error, __CREATE_IMAGE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    Image1DArray() { }
-
-    __CL_EXPLICIT_CONSTRUCTORS Image1DArray(const cl_mem& imageArray) : Image(imageArray) { }
-
-    Image1DArray& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-    
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DArray(const Image1DArray& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DArray& operator = (const Image1DArray &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DArray(Image1DArray&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image1DArray& operator = (Image1DArray &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-#endif // #if defined(CL_VERSION_1_2)
-
-
-/*! \brief Class interface for 2D Image Memory objects.
- *
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- */
-class Image2D : public Image
-{
-public:
-    /*! \brief Constructs a 1D Image in a specified context.
-     *
-     *  Wraps clCreateImage().
-     */
-    Image2D(
-        const Context& context,
-        cl_mem_flags flags,
-        ImageFormat format,
-        ::size_t width,
-        ::size_t height,
-        ::size_t row_pitch = 0,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        bool useCreateImage;
-
-#if defined(CL_VERSION_1_2) && defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-        // Run-time decision based on the actual platform
-        {
-            cl_uint version = detail::getContextPlatformVersion(context());
-            useCreateImage = (version >= 0x10002); // OpenCL 1.2 or above
-        }
-#elif defined(CL_VERSION_1_2)
-        useCreateImage = true;
-#else
-        useCreateImage = false;
-#endif
-
-#if defined(CL_VERSION_1_2)
-        if (useCreateImage)
-        {
-            cl_image_desc desc =
-            {
-                CL_MEM_OBJECT_IMAGE2D,
-                width,
-                height,
-                0, 0, // depth, array size (unused)
-                row_pitch,
-                0, 0, 0, 0
-            };
-            object_ = ::clCreateImage(
-                context(),
-                flags,
-                &format,
-                &desc,
-                host_ptr,
-                &error);
-
-            detail::errHandler(error, __CREATE_IMAGE_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-#endif // #if defined(CL_VERSION_1_2)
-#if !defined(CL_VERSION_1_2) || defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-        if (!useCreateImage)
-        {
-            object_ = ::clCreateImage2D(
-                context(), flags,&format, width, height, row_pitch, host_ptr, &error);
-
-            detail::errHandler(error, __CREATE_IMAGE2D_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-#endif // #if !defined(CL_VERSION_1_2) || defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    Image2D() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Image2D(const cl_mem& image2D) : Image(image2D) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Image2D& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2D(const Image2D& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2D& operator = (const Image2D &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2D(Image2D&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2D& operator = (Image2D &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-
-
-#if !defined(CL_VERSION_1_2)
-/*! \brief Class interface for GL 2D Image Memory objects.
- *
- *  This is provided to facilitate interoperability with OpenGL.
- * 
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- *  \note Deprecated for OpenCL 1.2. Please use ImageGL instead.
- */
-class CL_EXT_PREFIX__VERSION_1_1_DEPRECATED Image2DGL CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED : public Image2D
-{
-public:
-    /*! \brief Constructs an Image2DGL in a specified context, from a given
-     *         GL Texture.
-     *
-     *  Wraps clCreateFromGLTexture2D().
-     */
-    Image2DGL(
-        const Context& context,
-        cl_mem_flags flags,
-        cl_GLenum target,
-        cl_GLint  miplevel,
-        cl_GLuint texobj,
-        cl_int * err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateFromGLTexture2D(
-            context(),
-            flags,
-            target,
-            miplevel,
-            texobj,
-            &error);
-
-        detail::errHandler(error, __CREATE_GL_TEXTURE_2D_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-
-    }
-    
-    //! \brief Default constructor - initializes to NULL.
-    Image2DGL() : Image2D() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Image2DGL(const cl_mem& image) : Image2D(image) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Image2DGL& operator = (const cl_mem& rhs)
-    {
-        Image2D::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DGL(const Image2DGL& img) : Image2D(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DGL& operator = (const Image2DGL &img)
-    {
-        Image2D::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DGL(Image2DGL&& img) CL_HPP_NOEXCEPT : Image2D(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DGL& operator = (Image2DGL &&img)
-    {
-        Image2D::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-#endif // #if !defined(CL_VERSION_1_2)
-
-#if defined(CL_VERSION_1_2)
-/*! \class Image2DArray
- * \brief Image interface for arrays of 2D images.
- */
-class Image2DArray : public Image
-{
-public:
-    Image2DArray(
-        const Context& context,
-        cl_mem_flags flags,
-        ImageFormat format,
-        ::size_t arraySize,
-        ::size_t width,
-        ::size_t height,
-        ::size_t rowPitch,
-        ::size_t slicePitch,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        cl_image_desc desc =
-        {
-            CL_MEM_OBJECT_IMAGE2D_ARRAY,
-            width,
-            height,
-            0,       // depth (unused)
-            arraySize,
-            rowPitch,
-            slicePitch,
-            0, 0, 0
-        };
-        object_ = ::clCreateImage(
-            context(), 
-            flags, 
-            &format, 
-            &desc, 
-            host_ptr, 
-            &error);
-
-        detail::errHandler(error, __CREATE_IMAGE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    Image2DArray() { }
-
-    __CL_EXPLICIT_CONSTRUCTORS Image2DArray(const cl_mem& imageArray) : Image(imageArray) { }
-
-    Image2DArray& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-    
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DArray(const Image2DArray& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DArray& operator = (const Image2DArray &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DArray(Image2DArray&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image2DArray& operator = (Image2DArray &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-#endif // #if defined(CL_VERSION_1_2)
-
-/*! \brief Class interface for 3D Image Memory objects.
- *
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- */
-class Image3D : public Image
-{
-public:
-    /*! \brief Constructs a 3D Image in a specified context.
-     *
-     *  Wraps clCreateImage().
-     */
-    Image3D(
-        const Context& context,
-        cl_mem_flags flags,
-        ImageFormat format,
-        ::size_t width,
-        ::size_t height,
-        ::size_t depth,
-        ::size_t row_pitch = 0,
-        ::size_t slice_pitch = 0,
-        void* host_ptr = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        bool useCreateImage;
-
-#if defined(CL_VERSION_1_2) && defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-        // Run-time decision based on the actual platform
-        {
-            cl_uint version = detail::getContextPlatformVersion(context());
-            useCreateImage = (version >= 0x10002); // OpenCL 1.2 or above
-        }
-#elif defined(CL_VERSION_1_2)
-        useCreateImage = true;
-#else
-        useCreateImage = false;
-#endif
-
-#if defined(CL_VERSION_1_2)
-        if (useCreateImage)
-        {
-            cl_image_desc desc =
-            {
-                CL_MEM_OBJECT_IMAGE3D,
-                width,
-                height,
-                depth,
-                0,      // array size (unused)
-                row_pitch,
-                slice_pitch,
-                0, 0, 0
-            };
-            object_ = ::clCreateImage(
-                context(), 
-                flags, 
-                &format, 
-                &desc, 
-                host_ptr, 
-                &error);
-
-            detail::errHandler(error, __CREATE_IMAGE_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-#endif  // #if defined(CL_VERSION_1_2)
-#if !defined(CL_VERSION_1_2) || defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-        if (!useCreateImage)
-        {
-            object_ = ::clCreateImage3D(
-                context(), flags, &format, width, height, depth, row_pitch,
-                slice_pitch, host_ptr, &error);
-
-            detail::errHandler(error, __CREATE_IMAGE3D_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-#endif // #if !defined(CL_VERSION_1_2) || defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS)
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    Image3D() : Image() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Image3D(const cl_mem& image3D) : Image(image3D) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Image3D& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3D(const Image3D& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3D& operator = (const Image3D &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3D(Image3D&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3D& operator = (Image3D &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-
-#if !defined(CL_VERSION_1_2)
-/*! \brief Class interface for GL 3D Image Memory objects.
- *
- *  This is provided to facilitate interoperability with OpenGL.
- * 
- *  See Memory for details about copy semantics, etc.
- * 
- *  \see Memory
- */
-class Image3DGL : public Image3D
-{
-public:
-    /*! \brief Constructs an Image3DGL in a specified context, from a given
-     *         GL Texture.
-     *
-     *  Wraps clCreateFromGLTexture3D().
-     */
-    Image3DGL(
-        const Context& context,
-        cl_mem_flags flags,
-        cl_GLenum target,
-        cl_GLint  miplevel,
-        cl_GLuint texobj,
-        cl_int * err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateFromGLTexture3D(
-            context(),
-            flags,
-            target,
-            miplevel,
-            texobj,
-            &error);
-
-        detail::errHandler(error, __CREATE_GL_TEXTURE_3D_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-    Image3DGL() : Image3D() { }
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-     *
-     *  See Memory for further details.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Image3DGL(const cl_mem& image) : Image3D(image) { }
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-     *
-     *  See Memory for further details.
-     */
-    Image3DGL& operator = (const cl_mem& rhs)
-    {
-        Image3D::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3DGL(const Image3DGL& img) : Image3D(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3DGL& operator = (const Image3DGL &img)
-    {
-        Image3D::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3DGL(Image3DGL&& img) CL_HPP_NOEXCEPT : Image3D(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Image3DGL& operator = (Image3DGL &&img)
-    {
-        Image3D::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-#endif // #if !defined(CL_VERSION_1_2)
-
-#if defined(CL_VERSION_1_2)
-/*! \class ImageGL
- * \brief general image interface for GL interop.
- * We abstract the 2D and 3D GL images into a single instance here
- * that wraps all GL sourced images on the grounds that setup information
- * was performed by OpenCL anyway.
- */
-class ImageGL : public Image
-{
-public:
-    ImageGL(
-        const Context& context,
-        cl_mem_flags flags,
-        cl_GLenum target,
-        cl_GLint  miplevel,
-        cl_GLuint texobj,
-        cl_int * err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateFromGLTexture(
-            context(), 
-            flags, 
-            target,
-            miplevel,
-            texobj,
-            &error);
-
-        detail::errHandler(error, __CREATE_GL_TEXTURE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    ImageGL() : Image() { }
-
-    __CL_EXPLICIT_CONSTRUCTORS ImageGL(const cl_mem& image) : Image(image) { }
-
-    ImageGL& operator = (const cl_mem& rhs)
-    {
-        Image::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    ImageGL(const ImageGL& img) : Image(img) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    ImageGL& operator = (const ImageGL &img)
-    {
-        Image::operator=(img);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    ImageGL(ImageGL&& img) CL_HPP_NOEXCEPT : Image(std::move(img)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    ImageGL& operator = (ImageGL &&img)
-    {
-        Image::operator=(std::move(img));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-};
-#endif // #if defined(CL_VERSION_1_2)
-
-/*! \brief Class interface for GL Render Buffer Memory Objects.
-*
-*  This is provided to facilitate interoperability with OpenGL.
-*
-*  See Memory for details about copy semantics, etc.
-*
-*  \see Memory
-*/
-class BufferRenderGL : 
-#if defined(CL_VERSION_1_2)
-    public ImageGL
-#else // #if defined(CL_VERSION_1_2)
-    public Image2DGL
-#endif //#if defined(CL_VERSION_1_2)
-{
-public:
-    /*! \brief Constructs a BufferRenderGL in a specified context, from a given
-    *         GL Renderbuffer.
-    *
-    *  Wraps clCreateFromGLRenderbuffer().
-    */
-    BufferRenderGL(
-        const Context& context,
-        cl_mem_flags flags,
-        cl_GLuint bufobj,
-        cl_int * err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateFromGLRenderbuffer(
-            context(),
-            flags,
-            bufobj,
-            &error);
-
-        detail::errHandler(error, __CREATE_GL_RENDER_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    //! \brief Default constructor - initializes to NULL.
-#if defined(CL_VERSION_1_2)
-    BufferRenderGL() : ImageGL() {};
-#else // #if defined(CL_VERSION_1_2)
-    BufferRenderGL() : Image2DGL() {};
-#endif //#if defined(CL_VERSION_1_2)
-
-    /*! \brief Constructor from cl_mem - takes ownership.
-    *
-    *  See Memory for further details.
-    */
-#if defined(CL_VERSION_1_2)
-    __CL_EXPLICIT_CONSTRUCTORS BufferRenderGL(const cl_mem& buffer) : ImageGL(buffer) { }
-#else // #if defined(CL_VERSION_1_2)
-    __CL_EXPLICIT_CONSTRUCTORS BufferRenderGL(const cl_mem& buffer) : Image2DGL(buffer) { }
-#endif //#if defined(CL_VERSION_1_2)
-
-
-    /*! \brief Assignment from cl_mem - performs shallow copy.
-    *
-    *  See Memory for further details.
-    */
-    BufferRenderGL& operator = (const cl_mem& rhs)
-    {
-#if defined(CL_VERSION_1_2)
-        ImageGL::operator=(rhs);
-#else // #if defined(CL_VERSION_1_2)
-        Image2DGL::operator=(rhs);
-#endif //#if defined(CL_VERSION_1_2)
-        
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-    * Required for MSVC.
-    */
-#if defined(CL_VERSION_1_2)
-    BufferRenderGL(const BufferRenderGL& buf) : ImageGL(buf) {}
-#else // #if defined(CL_VERSION_1_2)
-    BufferRenderGL(const BufferRenderGL& buf) : Image2DGL(buf) {}
-#endif //#if defined(CL_VERSION_1_2)
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferRenderGL& operator = (const BufferRenderGL &rhs)
-    {
-#if defined(CL_VERSION_1_2)
-        ImageGL::operator=(rhs);
-#else // #if defined(CL_VERSION_1_2)
-        Image2DGL::operator=(rhs);
-#endif //#if defined(CL_VERSION_1_2)
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-    * Required for MSVC.
-    */
-#if defined(CL_VERSION_1_2)
-    BufferRenderGL(BufferRenderGL&& buf) CL_HPP_NOEXCEPT : ImageGL(std::move(buf)) {}
-#else // #if defined(CL_VERSION_1_2)
-    BufferRenderGL(BufferRenderGL&& buf) CL_HPP_NOEXCEPT : Image2DGL(std::move(buf)) {}
-#endif //#if defined(CL_VERSION_1_2)
-    
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-    * Required for MSVC.
-    */
-    BufferRenderGL& operator = (BufferRenderGL &&buf)
-    {
-#if defined(CL_VERSION_1_2)
-        ImageGL::operator=(std::move(buf));
-#else // #if defined(CL_VERSION_1_2)
-        Image2DGL::operator=(std::move(buf));
-#endif //#if defined(CL_VERSION_1_2)
-        
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    //! \brief Wrapper for clGetGLObjectInfo().
-    cl_int getObjectInfo(
-        cl_gl_object_type *type,
-        cl_GLuint * gl_object_name)
-    {
-        return detail::errHandler(
-            ::clGetGLObjectInfo(object_, type, gl_object_name),
-            __GET_GL_OBJECT_INFO_ERR);
-    }
-};
-
-/*! \brief Class interface for cl_sampler.
- *
- *  \note Copies of these objects are shallow, meaning that the copy will refer
- *        to the same underlying cl_sampler as the original.  For details, see
- *        clRetainSampler() and clReleaseSampler().
- *
- *  \see cl_sampler 
- */
-class Sampler : public detail::Wrapper<cl_sampler>
-{
-public:
-    //! \brief Default constructor - initializes to NULL.
-    Sampler() { }
-
-    /*! \brief Constructs a Sampler in a specified context.
-     *
-     *  Wraps clCreateSampler().
-     */
-    Sampler(
-        const Context& context,
-        cl_bool normalized_coords,
-        cl_addressing_mode addressing_mode,
-        cl_filter_mode filter_mode,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateSampler(
-            context(), 
-            normalized_coords,
-            addressing_mode,
-            filter_mode,
-            &error);
-
-        detail::errHandler(error, __CREATE_SAMPLER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /*! \brief Constructor from cl_sampler - takes ownership.
-     * 
-     *  This effectively transfers ownership of a refcount on the cl_sampler
-     *  into the new Sampler object.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Sampler(const cl_sampler& sampler) : detail::Wrapper<cl_type>(sampler) { }
-
-    /*! \brief Assignment operator from cl_sampler - takes ownership.
-     *
-     *  This effectively transfers ownership of a refcount on the rhs and calls
-     *  clReleaseSampler() on the value previously held by this instance.
-     */
-    Sampler& operator = (const cl_sampler& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Sampler(const Sampler& sam) : detail::Wrapper<cl_type>(sam) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Sampler& operator = (const Sampler &sam)
-    {
-        detail::Wrapper<cl_type>::operator=(sam);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Sampler(Sampler&& sam) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(sam)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Sampler& operator = (Sampler &&sam)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(sam));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    //! \brief Wrapper for clGetSamplerInfo().
-    template <typename T>
-    cl_int getInfo(cl_sampler_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetSamplerInfo, object_, name, param),
-            __GET_SAMPLER_INFO_ERR);
-    }
-
-    //! \brief Wrapper for clGetSamplerInfo() that returns by value.
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_sampler_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_sampler_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-};
-
-class Program;
-class CommandQueue;
-class Kernel;
-
-//! \brief Class interface for specifying NDRange values.
-class NDRange
-{
-private:
-    size_t<3> sizes_;
-    cl_uint dimensions_;
-
-public:
-    //! \brief Default constructor - resulting range has zero dimensions.
-    NDRange()
-        : dimensions_(0)
-    { }
-
-    //! \brief Constructs one-dimensional range.
-    NDRange(::size_t size0)
-        : dimensions_(1)
-    {
-        sizes_[0] = size0;
-    }
-
-    //! \brief Constructs two-dimensional range.
-    NDRange(::size_t size0, ::size_t size1)
-        : dimensions_(2)
-    {
-        sizes_[0] = size0;
-        sizes_[1] = size1;
-    }
-
-    //! \brief Constructs three-dimensional range.
-    NDRange(::size_t size0, ::size_t size1, ::size_t size2)
-        : dimensions_(3)
-    {
-        sizes_[0] = size0;
-        sizes_[1] = size1;
-        sizes_[2] = size2;
-    }
-
-    /*! \brief Conversion operator to const ::size_t *.
-     *  
-     *  \returns a pointer to the size of the first dimension.
-     */
-    operator const ::size_t*() const { 
-        return (const ::size_t*) sizes_; 
-    }
-
-    //! \brief Queries the number of dimensions in the range.
-    ::size_t dimensions() const { return dimensions_; }
-};
-
-//! \brief A zero-dimensional range.
-static const NDRange NullRange;
-
-//! \brief Local address wrapper for use with Kernel::setArg
-struct LocalSpaceArg
-{
-    ::size_t size_;
-};
-
-namespace detail {
-
-template <typename T>
-struct KernelArgumentHandler
-{
-    static ::size_t size(const T&) { return sizeof(T); }
-    static const T* ptr(const T& value) { return &value; }
-};
-
-template <>
-struct KernelArgumentHandler<LocalSpaceArg>
-{
-    static ::size_t size(const LocalSpaceArg& value) { return value.size_; }
-    static const void* ptr(const LocalSpaceArg&) { return NULL; }
-};
-
-} 
-//! \endcond
-
-/*! __local
- * \brief Helper function for generating LocalSpaceArg objects.
- * Deprecated. Replaced with Local.
- */
-inline CL_EXT_PREFIX__VERSION_1_1_DEPRECATED LocalSpaceArg
-__local(::size_t size) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED;
-inline LocalSpaceArg
-__local(::size_t size)
-{
-    LocalSpaceArg ret = { size };
-    return ret;
-}
-
-/*! Local
- * \brief Helper function for generating LocalSpaceArg objects.
- */
-inline LocalSpaceArg
-Local(::size_t size)
-{
-    LocalSpaceArg ret = { size };
-    return ret;
-}
-
-//class KernelFunctor;
-
-/*! \brief Class interface for cl_kernel.
- *
- *  \note Copies of these objects are shallow, meaning that the copy will refer
- *        to the same underlying cl_kernel as the original.  For details, see
- *        clRetainKernel() and clReleaseKernel().
- *
- *  \see cl_kernel
- */
-class Kernel : public detail::Wrapper<cl_kernel>
-{
-public:
-    inline Kernel(const Program& program, const char* name, cl_int* err = NULL);
-
-    //! \brief Default constructor - initializes to NULL.
-    Kernel() { }
-
-    /*! \brief Constructor from cl_kernel - takes ownership.
-     * 
-     *  This effectively transfers ownership of a refcount on the cl_kernel
-     *  into the new Kernel object.
-     */
-    __CL_EXPLICIT_CONSTRUCTORS Kernel(const cl_kernel& kernel) : detail::Wrapper<cl_type>(kernel) { }
-
-    /*! \brief Assignment operator from cl_kernel - takes ownership.
-     *
-     *  This effectively transfers ownership of a refcount on the rhs and calls
-     *  clReleaseKernel() on the value previously held by this instance.
-     */
-    Kernel& operator = (const cl_kernel& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Kernel(const Kernel& kernel) : detail::Wrapper<cl_type>(kernel) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Kernel& operator = (const Kernel &kernel)
-    {
-        detail::Wrapper<cl_type>::operator=(kernel);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Kernel(Kernel&& kernel) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(kernel)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Kernel& operator = (Kernel &&kernel)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(kernel));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    template <typename T>
-    cl_int getInfo(cl_kernel_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetKernelInfo, object_, name, param),
-            __GET_KERNEL_INFO_ERR);
-    }
-
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_kernel_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_kernel_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-#if defined(CL_VERSION_1_2)
-    template <typename T>
-    cl_int getArgInfo(cl_uint argIndex, cl_kernel_arg_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetKernelArgInfo, object_, argIndex, name, param),
-            __GET_KERNEL_ARG_INFO_ERR);
-    }
-
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_kernel_arg_info, name>::param_type
-    getArgInfo(cl_uint argIndex, cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_kernel_arg_info, name>::param_type param;
-        cl_int result = getArgInfo(argIndex, name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-#endif // #if defined(CL_VERSION_1_2)
-
-    template <typename T>
-    cl_int getWorkGroupInfo(
-        const Device& device, cl_kernel_work_group_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(
-                &::clGetKernelWorkGroupInfo, object_, device(), name, param),
-                __GET_KERNEL_WORK_GROUP_INFO_ERR);
-    }
-
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_kernel_work_group_info, name>::param_type
-        getWorkGroupInfo(const Device& device, cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-        detail::cl_kernel_work_group_info, name>::param_type param;
-        cl_int result = getWorkGroupInfo(device, name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    template <typename T>
-    cl_int setArg(cl_uint index, const T &value)
-    {
-        return detail::errHandler(
-            ::clSetKernelArg(
-                object_,
-                index,
-                detail::KernelArgumentHandler<T>::size(value),
-                detail::KernelArgumentHandler<T>::ptr(value)),
-            __SET_KERNEL_ARGS_ERR);
-    }
-
-    cl_int setArg(cl_uint index, ::size_t size, const void* argPtr)
-    {
-        return detail::errHandler(
-            ::clSetKernelArg(object_, index, size, argPtr),
-            __SET_KERNEL_ARGS_ERR);
-    }
-};
-
-/*! \class Program
- * \brief Program interface that implements cl_program.
- */
-class Program : public detail::Wrapper<cl_program>
-{
-public:
-    typedef VECTOR_CLASS<std::pair<const void*, ::size_t> > Binaries;
-    typedef VECTOR_CLASS<std::pair<const char*, ::size_t> > Sources;
-
-    Program(
-        const STRING_CLASS& source,
-        bool build = false,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        const char * strings = source.c_str();
-        const ::size_t length  = source.size();
-
-        Context context = Context::getDefault(err);
-
-        object_ = ::clCreateProgramWithSource(
-            context(), (cl_uint)1, &strings, &length, &error);
-
-        detail::errHandler(error, __CREATE_PROGRAM_WITH_SOURCE_ERR);
-
-        if (error == CL_SUCCESS && build) {
-
-            error = ::clBuildProgram(
-                object_,
-                0,
-                NULL,
-                "",
-                NULL,
-                NULL);
-
-            detail::errHandler(error, __BUILD_PROGRAM_ERR);
-        }
-
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    Program(
-        const Context& context,
-        const STRING_CLASS& source,
-        bool build = false,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        const char * strings = source.c_str();
-        const ::size_t length  = source.size();
-
-        object_ = ::clCreateProgramWithSource(
-            context(), (cl_uint)1, &strings, &length, &error);
-
-        detail::errHandler(error, __CREATE_PROGRAM_WITH_SOURCE_ERR);
-
-        if (error == CL_SUCCESS && build) {
-
-            error = ::clBuildProgram(
-                object_,
-                0,
-                NULL,
-                "",
-                NULL,
-                NULL);
-
-            detail::errHandler(error, __BUILD_PROGRAM_ERR);
-        }
-
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    Program(
-        const Context& context,
-        const Sources& sources,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        const ::size_t n = (::size_t)sources.size();
-        ::size_t* lengths = (::size_t*) alloca(n * sizeof(::size_t));
-        const char** strings = (const char**) alloca(n * sizeof(const char*));
-
-        for (::size_t i = 0; i < n; ++i) {
-            strings[i] = sources[(int)i].first;
-            lengths[i] = sources[(int)i].second;
-        }
-
-        object_ = ::clCreateProgramWithSource(
-            context(), (cl_uint)n, strings, lengths, &error);
-
-        detail::errHandler(error, __CREATE_PROGRAM_WITH_SOURCE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /**
-     * Construct a program object from a list of devices and a per-device list of binaries.
-     * \param context A valid OpenCL context in which to construct the program.
-     * \param devices A vector of OpenCL device objects for which the program will be created.
-     * \param binaries A vector of pairs of a pointer to a binary object and its length.
-     * \param binaryStatus An optional vector that on completion will be resized to
-     *   match the size of binaries and filled with values to specify if each binary
-     *   was successfully loaded.
-     *   Set to CL_SUCCESS if the binary was successfully loaded.
-     *   Set to CL_INVALID_VALUE if the length is 0 or the binary pointer is NULL.
-     *   Set to CL_INVALID_BINARY if the binary provided is not valid for the matching device.
-     * \param err if non-NULL will be set to CL_SUCCESS on successful operation or one of the following errors:
-     *   CL_INVALID_CONTEXT if context is not a valid context.
-     *   CL_INVALID_VALUE if the length of devices is zero; or if the length of binaries does not match the length of devices; 
-     *     or if any entry in binaries is NULL or has length 0.
-     *   CL_INVALID_DEVICE if OpenCL devices listed in devices are not in the list of devices associated with context.
-     *   CL_INVALID_BINARY if an invalid program binary was encountered for any device. binaryStatus will return specific status for each device.
-     *   CL_OUT_OF_HOST_MEMORY if there is a failure to allocate resources required by the OpenCL implementation on the host.
-     */
-    Program(
-        const Context& context,
-        const VECTOR_CLASS<Device>& devices,
-        const Binaries& binaries,
-        VECTOR_CLASS<cl_int>* binaryStatus = NULL,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        
-        const ::size_t numDevices = devices.size();
-        
-        // Catch size mismatch early and return
-        if(binaries.size() != numDevices) {
-            error = CL_INVALID_VALUE;
-            detail::errHandler(error, __CREATE_PROGRAM_WITH_BINARY_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-            return;
-        }
-
-        ::size_t* lengths = (::size_t*) alloca(numDevices * sizeof(::size_t));
-        const unsigned char** images = (const unsigned char**) alloca(numDevices * sizeof(const unsigned char**));
-
-        for (::size_t i = 0; i < numDevices; ++i) {
-            images[i] = (const unsigned char*)binaries[i].first;
-            lengths[i] = binaries[(int)i].second;
-        }
-
-        cl_device_id* deviceIDs = (cl_device_id*) alloca(numDevices * sizeof(cl_device_id));
-        for( ::size_t deviceIndex = 0; deviceIndex < numDevices; ++deviceIndex ) {
-            deviceIDs[deviceIndex] = (devices[deviceIndex])();
-        }
-
-        if(binaryStatus) {
-            binaryStatus->resize(numDevices);
-        }
-        
-        object_ = ::clCreateProgramWithBinary(
-            context(), (cl_uint) devices.size(),
-            deviceIDs,
-            lengths, images, (binaryStatus != NULL && numDevices > 0)
-               ? &binaryStatus->front()
-               : NULL, &error);
-
-        detail::errHandler(error, __CREATE_PROGRAM_WITH_BINARY_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    
-#if defined(CL_VERSION_1_2)
-    /**
-     * Create program using builtin kernels.
-     * \param kernelNames Semi-colon separated list of builtin kernel names
-     */
-    Program(
-        const Context& context,
-        const VECTOR_CLASS<Device>& devices,
-        const STRING_CLASS& kernelNames,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-
-        ::size_t numDevices = devices.size();
-        cl_device_id* deviceIDs = (cl_device_id*) alloca(numDevices * sizeof(cl_device_id));
-        for( ::size_t deviceIndex = 0; deviceIndex < numDevices; ++deviceIndex ) {
-            deviceIDs[deviceIndex] = (devices[deviceIndex])();
-        }
-        
-        object_ = ::clCreateProgramWithBuiltInKernels(
-            context(), 
-            (cl_uint) devices.size(),
-            deviceIDs,
-            kernelNames.c_str(), 
-            &error);
-
-        detail::errHandler(error, __CREATE_PROGRAM_WITH_BUILT_IN_KERNELS_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-#endif // #if defined(CL_VERSION_1_2)
-
-    Program() { }
-
-    __CL_EXPLICIT_CONSTRUCTORS Program(const cl_program& program) : detail::Wrapper<cl_type>(program) { }
-
-    Program& operator = (const cl_program& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Program(const Program& program) : detail::Wrapper<cl_type>(program) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    Program& operator = (const Program &program)
-    {
-        detail::Wrapper<cl_type>::operator=(program);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Program(Program&& program) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(program)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    Program& operator = (Program &&program)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(program));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    cl_int build(
-        const VECTOR_CLASS<Device>& devices,
-        const char* options = NULL,
-        void (CL_CALLBACK * notifyFptr)(cl_program, void *) = NULL,
-        void* data = NULL) const
-    {
-        ::size_t numDevices = devices.size();
-        cl_device_id* deviceIDs = (cl_device_id*) alloca(numDevices * sizeof(cl_device_id));
-        for( ::size_t deviceIndex = 0; deviceIndex < numDevices; ++deviceIndex ) {
-            deviceIDs[deviceIndex] = (devices[deviceIndex])();
-        }
-
-        return detail::errHandler(
-            ::clBuildProgram(
-                object_,
-                (cl_uint)
-                devices.size(),
-                deviceIDs,
-                options,
-                notifyFptr,
-                data),
-                __BUILD_PROGRAM_ERR);
-    }
-
-    cl_int build(
-        const char* options = NULL,
-        void (CL_CALLBACK * notifyFptr)(cl_program, void *) = NULL,
-        void* data = NULL) const
-    {
-        return detail::errHandler(
-            ::clBuildProgram(
-                object_,
-                0,
-                NULL,
-                options,
-                notifyFptr,
-                data),
-                __BUILD_PROGRAM_ERR);
-    }
-
-#if defined(CL_VERSION_1_2)
-    cl_int compile(
-        const char* options = NULL,
-        void (CL_CALLBACK * notifyFptr)(cl_program, void *) = NULL,
-        void* data = NULL) const
-    {
-        return detail::errHandler(
-            ::clCompileProgram(
-                object_,
-                0,
-                NULL,
-                options,
-                0,
-                NULL,
-                NULL,
-                notifyFptr,
-                data),
-                __COMPILE_PROGRAM_ERR);
-    }
-#endif
-
-    template <typename T>
-    cl_int getInfo(cl_program_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(&::clGetProgramInfo, object_, name, param),
-            __GET_PROGRAM_INFO_ERR);
-    }
-
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_program_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_program_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    template <typename T>
-    cl_int getBuildInfo(
-        const Device& device, cl_program_build_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(
-                &::clGetProgramBuildInfo, object_, device(), name, param),
-                __GET_PROGRAM_BUILD_INFO_ERR);
-    }
-
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_program_build_info, name>::param_type
-    getBuildInfo(const Device& device, cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_program_build_info, name>::param_type param;
-        cl_int result = getBuildInfo(device, name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    cl_int createKernels(VECTOR_CLASS<Kernel>* kernels)
-    {
-        cl_uint numKernels;
-        cl_int err = ::clCreateKernelsInProgram(object_, 0, NULL, &numKernels);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __CREATE_KERNELS_IN_PROGRAM_ERR);
-        }
-
-        Kernel* value = (Kernel*) alloca(numKernels * sizeof(Kernel));
-        err = ::clCreateKernelsInProgram(
-            object_, numKernels, (cl_kernel*) value, NULL);
-        if (err != CL_SUCCESS) {
-            return detail::errHandler(err, __CREATE_KERNELS_IN_PROGRAM_ERR);
-        }
-
-        kernels->assign(&value[0], &value[numKernels]);
-        return CL_SUCCESS;
-    }
-};
-
-#if defined(CL_VERSION_1_2)
-inline Program linkProgram(
-    Program input1,
-    Program input2,
-    const char* options = NULL,
-    void (CL_CALLBACK * notifyFptr)(cl_program, void *) = NULL,
-    void* data = NULL,
-    cl_int* err = NULL) 
-{
-    cl_int error_local = CL_SUCCESS;
-
-    cl_program programs[2] = { input1(), input2() };
-
-    Context ctx = input1.getInfo<CL_PROGRAM_CONTEXT>(&error_local);
-    if(error_local!=CL_SUCCESS) {
-        detail::errHandler(error_local, __LINK_PROGRAM_ERR);
-    }
-
-    cl_program prog = ::clLinkProgram(
-        ctx(),
-        0,
-        NULL,
-        options,
-        2,
-        programs,
-        notifyFptr,
-        data,
-        &error_local);
-
-    detail::errHandler(error_local,__COMPILE_PROGRAM_ERR);
-    if (err != NULL) {
-        *err = error_local;
-    }
-
-    return Program(prog);
-}
-
-inline Program linkProgram(
-    VECTOR_CLASS<Program> inputPrograms,
-    const char* options = NULL,
-    void (CL_CALLBACK * notifyFptr)(cl_program, void *) = NULL,
-    void* data = NULL,
-    cl_int* err = NULL) 
-{
-    cl_int error_local = CL_SUCCESS;
-
-    cl_program * programs = (cl_program*) alloca(inputPrograms.size() * sizeof(cl_program));
-
-    if (programs != NULL) {
-        for (unsigned int i = 0; i < inputPrograms.size(); i++) {
-          programs[i] = inputPrograms[i]();
-        }
-    } 
-
-    Context ctx;
-    if(inputPrograms.size() > 0) {
-        ctx = inputPrograms[0].getInfo<CL_PROGRAM_CONTEXT>(&error_local);
-        if(error_local!=CL_SUCCESS) {
-            detail::errHandler(error_local, __LINK_PROGRAM_ERR);
-        }
-    }
-    cl_program prog = ::clLinkProgram(
-        ctx(),
-        0,
-        NULL,
-        options,
-        (cl_uint)inputPrograms.size(),
-        programs,
-        notifyFptr,
-        data,
-        &error_local);
-
-    detail::errHandler(error_local,__COMPILE_PROGRAM_ERR);
-    if (err != NULL) {
-        *err = error_local;
-    }
-
-    return Program(prog);
-}
-#endif
-
-template<>
-inline VECTOR_CLASS<char *> cl::Program::getInfo<CL_PROGRAM_BINARIES>(cl_int* err) const
-{
-    VECTOR_CLASS< ::size_t> sizes = getInfo<CL_PROGRAM_BINARY_SIZES>();
-    VECTOR_CLASS<char *> binaries;
-    for (VECTOR_CLASS< ::size_t>::iterator s = sizes.begin(); s != sizes.end(); ++s) 
-    {
-        char *ptr = NULL;
-        if (*s != 0) 
-            ptr = new char[*s];
-        binaries.push_back(ptr);
-    }
-    
-    cl_int result = getInfo(CL_PROGRAM_BINARIES, &binaries);
-    if (err != NULL) {
-        *err = result;
-    }
-    return binaries;
-}
-
-inline Kernel::Kernel(const Program& program, const char* name, cl_int* err)
-{
-    cl_int error;
-
-    object_ = ::clCreateKernel(program(), name, &error);
-    detail::errHandler(error, __CREATE_KERNEL_ERR);
-
-    if (err != NULL) {
-        *err = error;
-    }
-
-}
-
-/*! \class CommandQueue
- * \brief CommandQueue interface for cl_command_queue.
- */
-class CommandQueue : public detail::Wrapper<cl_command_queue>
-{
-private:
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-    static std::atomic<int> default_initialized_;
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    static volatile int default_initialized_;
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-    static CommandQueue default_;
-    static volatile cl_int default_error_;
-public:
-   CommandQueue(
-        cl_command_queue_properties properties,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-
-        Context context = Context::getDefault(&error);
-        detail::errHandler(error, __CREATE_CONTEXT_ERR);
-
-        if (error != CL_SUCCESS) {
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-        else {
-            Device device = context.getInfo<CL_CONTEXT_DEVICES>()[0];
-
-            object_ = ::clCreateCommandQueue(
-                context(), device(), properties, &error);
-
-            detail::errHandler(error, __CREATE_COMMAND_QUEUE_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-    }
-    /*!
-    * \brief Constructs a CommandQueue for an implementation defined device in the given context
-    */
-    explicit CommandQueue(
-        const Context& context,
-        cl_command_queue_properties properties = 0,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        VECTOR_CLASS<cl::Device> devices;
-        error = context.getInfo(CL_CONTEXT_DEVICES, &devices);
-
-        detail::errHandler(error, __CREATE_CONTEXT_ERR);
-
-        if (error != CL_SUCCESS)
-        {
-            if (err != NULL) {
-                *err = error;
-            }
-            return;
-        }
-
-        object_ = ::clCreateCommandQueue(context(), devices[0](), properties, &error);
-
-        detail::errHandler(error, __CREATE_COMMAND_QUEUE_ERR);
-
-        if (err != NULL) {
-            *err = error;
-        }
-
-    }
-
-    CommandQueue(
-        const Context& context,
-        const Device& device,
-        cl_command_queue_properties properties = 0,
-        cl_int* err = NULL)
-    {
-        cl_int error;
-        object_ = ::clCreateCommandQueue(
-            context(), device(), properties, &error);
-
-        detail::errHandler(error, __CREATE_COMMAND_QUEUE_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-
-    /*! \brief Copy constructor to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    CommandQueue(const CommandQueue& queue) : detail::Wrapper<cl_type>(queue) {}
-
-    /*! \brief Copy assignment to forward copy to the superclass correctly.
-     * Required for MSVC.
-     */
-    CommandQueue& operator = (const CommandQueue &queue)
-    {
-        detail::Wrapper<cl_type>::operator=(queue);
-        return *this;
-    }
-
-#if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-    /*! \brief Move constructor to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    CommandQueue(CommandQueue&& queue) CL_HPP_NOEXCEPT : detail::Wrapper<cl_type>(std::move(queue)) {}
-
-    /*! \brief Move assignment to forward move to the superclass correctly.
-     * Required for MSVC.
-     */
-    CommandQueue& operator = (CommandQueue &&queue)
-    {
-        detail::Wrapper<cl_type>::operator=(std::move(queue));
-        return *this;
-    }
-#endif // #if defined(CL_HPP_RVALUE_REFERENCES_SUPPORTED)
-
-    static CommandQueue getDefault(cl_int * err = NULL) 
-    {
-        int state = detail::compare_exchange(
-            &default_initialized_, 
-            __DEFAULT_BEING_INITIALIZED, __DEFAULT_NOT_INITIALIZED);
-        
-        if (state & __DEFAULT_INITIALIZED) {
-            if (err != NULL) {
-                *err = default_error_;
-            }
-            return default_;
-        }
-
-        if (state & __DEFAULT_BEING_INITIALIZED) {
-              // Assume writes will propagate eventually...
-              while(default_initialized_ != __DEFAULT_INITIALIZED) {
-                  detail::fence();
-              }
-
-            if (err != NULL) {
-                *err = default_error_;
-            }
-            return default_;
-        }
-
-        cl_int error;
-
-        Context context = Context::getDefault(&error);
-        detail::errHandler(error, __CREATE_COMMAND_QUEUE_ERR);
-
-        if (error != CL_SUCCESS) {
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-        else {
-            Device device = context.getInfo<CL_CONTEXT_DEVICES>()[0];
-
-            default_ = CommandQueue(context, device, 0, &error);
-
-            detail::errHandler(error, __CREATE_COMMAND_QUEUE_ERR);
-            if (err != NULL) {
-                *err = error;
-            }
-        }
-
-        detail::fence();
-
-        default_error_ = error;
-        // Assume writes will propagate eventually...
-        default_initialized_ = __DEFAULT_INITIALIZED;
-
-        detail::fence();
-
-        if (err != NULL) {
-            *err = default_error_;
-        }
-        return default_;
-
-    }
-
-    CommandQueue() { }
-
-    __CL_EXPLICIT_CONSTRUCTORS CommandQueue(const cl_command_queue& commandQueue) : detail::Wrapper<cl_type>(commandQueue) { }
-
-    CommandQueue& operator = (const cl_command_queue& rhs)
-    {
-        detail::Wrapper<cl_type>::operator=(rhs);
-        return *this;
-    }
-
-    template <typename T>
-    cl_int getInfo(cl_command_queue_info name, T* param) const
-    {
-        return detail::errHandler(
-            detail::getInfo(
-                &::clGetCommandQueueInfo, object_, name, param),
-                __GET_COMMAND_QUEUE_INFO_ERR);
-    }
-
-    template <cl_int name> typename
-    detail::param_traits<detail::cl_command_queue_info, name>::param_type
-    getInfo(cl_int* err = NULL) const
-    {
-        typename detail::param_traits<
-            detail::cl_command_queue_info, name>::param_type param;
-        cl_int result = getInfo(name, &param);
-        if (err != NULL) {
-            *err = result;
-        }
-        return param;
-    }
-
-    cl_int enqueueReadBuffer(
-        const Buffer& buffer,
-        cl_bool blocking,
-        ::size_t offset,
-        ::size_t size,
-        void* ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueReadBuffer(
-                object_, buffer(), blocking, offset, size,
-                ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_READ_BUFFER_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueWriteBuffer(
-        const Buffer& buffer,
-        cl_bool blocking,
-        ::size_t offset,
-        ::size_t size,
-        const void* ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueWriteBuffer(
-                object_, buffer(), blocking, offset, size,
-                ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_WRITE_BUFFER_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueCopyBuffer(
-        const Buffer& src,
-        const Buffer& dst,
-        ::size_t src_offset,
-        ::size_t dst_offset,
-        ::size_t size,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueCopyBuffer(
-                object_, src(), dst(), src_offset, dst_offset, size,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQEUE_COPY_BUFFER_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueReadBufferRect(
-        const Buffer& buffer,
-        cl_bool blocking,
-        const size_t<3>& buffer_offset,
-        const size_t<3>& host_offset,
-        const size_t<3>& region,
-        ::size_t buffer_row_pitch,
-        ::size_t buffer_slice_pitch,
-        ::size_t host_row_pitch,
-        ::size_t host_slice_pitch,
-        void *ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueReadBufferRect(
-                object_, 
-                buffer(), 
-                blocking, 
-                (const ::size_t *)buffer_offset,
-                (const ::size_t *)host_offset,
-                (const ::size_t *)region,
-                buffer_row_pitch,
-                buffer_slice_pitch,
-                host_row_pitch,
-                host_slice_pitch,
-                ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_READ_BUFFER_RECT_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueWriteBufferRect(
-        const Buffer& buffer,
-        cl_bool blocking,
-        const size_t<3>& buffer_offset,
-        const size_t<3>& host_offset,
-        const size_t<3>& region,
-        ::size_t buffer_row_pitch,
-        ::size_t buffer_slice_pitch,
-        ::size_t host_row_pitch,
-        ::size_t host_slice_pitch,
-        void *ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueWriteBufferRect(
-                object_, 
-                buffer(), 
-                blocking, 
-                (const ::size_t *)buffer_offset,
-                (const ::size_t *)host_offset,
-                (const ::size_t *)region,
-                buffer_row_pitch,
-                buffer_slice_pitch,
-                host_row_pitch,
-                host_slice_pitch,
-                ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_WRITE_BUFFER_RECT_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueCopyBufferRect(
-        const Buffer& src,
-        const Buffer& dst,
-        const size_t<3>& src_origin,
-        const size_t<3>& dst_origin,
-        const size_t<3>& region,
-        ::size_t src_row_pitch,
-        ::size_t src_slice_pitch,
-        ::size_t dst_row_pitch,
-        ::size_t dst_slice_pitch,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueCopyBufferRect(
-                object_, 
-                src(), 
-                dst(), 
-                (const ::size_t *)src_origin, 
-                (const ::size_t *)dst_origin, 
-                (const ::size_t *)region,
-                src_row_pitch,
-                src_slice_pitch,
-                dst_row_pitch,
-                dst_slice_pitch,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQEUE_COPY_BUFFER_RECT_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-#if defined(CL_VERSION_1_2)
-    /**
-     * Enqueue a command to fill a buffer object with a pattern
-     * of a given size. The pattern is specified a as vector.
-     * \tparam PatternType The datatype of the pattern field. 
-     *     The pattern type must be an accepted OpenCL data type.
-     */
-    template<typename PatternType>
-    cl_int enqueueFillBuffer(
-        const Buffer& buffer,
-        PatternType pattern,
-        ::size_t offset,
-        ::size_t size,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueFillBuffer(
-                object_, 
-                buffer(),
-                static_cast<void*>(&pattern),
-                sizeof(PatternType), 
-                offset, 
-                size,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_FILL_BUFFER_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-#endif // #if defined(CL_VERSION_1_2)
-
-    cl_int enqueueReadImage(
-        const Image& image,
-        cl_bool blocking,
-        const size_t<3>& origin,
-        const size_t<3>& region,
-        ::size_t row_pitch,
-        ::size_t slice_pitch,
-        void* ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueReadImage(
-                object_, image(), blocking, (const ::size_t *) origin,
-                (const ::size_t *) region, row_pitch, slice_pitch, ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_READ_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueWriteImage(
-        const Image& image,
-        cl_bool blocking,
-        const size_t<3>& origin,
-        const size_t<3>& region,
-        ::size_t row_pitch,
-        ::size_t slice_pitch,
-        void* ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueWriteImage(
-                object_, image(), blocking, (const ::size_t *) origin,
-                (const ::size_t *) region, row_pitch, slice_pitch, ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_WRITE_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueCopyImage(
-        const Image& src,
-        const Image& dst,
-        const size_t<3>& src_origin,
-        const size_t<3>& dst_origin,
-        const size_t<3>& region,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueCopyImage(
-                object_, src(), dst(), (const ::size_t *) src_origin,
-                (const ::size_t *)dst_origin, (const ::size_t *) region,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_COPY_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-#if defined(CL_VERSION_1_2)
-    /**
-     * Enqueue a command to fill an image object with a specified color.
-     * \param fillColor is the color to use to fill the image.
-     *     This is a four component RGBA floating-point color value if
-     *     the image channel data type is not an unnormalized signed or
-     *     unsigned data type.
-     */
-    cl_int enqueueFillImage(
-        const Image& image,
-        cl_float4 fillColor,
-        const size_t<3>& origin,
-        const size_t<3>& region,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueFillImage(
-                object_, 
-                image(),
-                static_cast<void*>(&fillColor), 
-                (const ::size_t *) origin, 
-                (const ::size_t *) region,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_FILL_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    /**
-     * Enqueue a command to fill an image object with a specified color.
-     * \param fillColor is the color to use to fill the image.
-     *     This is a four component RGBA signed integer color value if
-     *     the image channel data type is an unnormalized signed integer
-     *     type.
-     */
-    cl_int enqueueFillImage(
-        const Image& image,
-        cl_int4 fillColor,
-        const size_t<3>& origin,
-        const size_t<3>& region,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueFillImage(
-                object_, 
-                image(),
-                static_cast<void*>(&fillColor), 
-                (const ::size_t *) origin, 
-                (const ::size_t *) region,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_FILL_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    /**
-     * Enqueue a command to fill an image object with a specified color.
-     * \param fillColor is the color to use to fill the image.
-     *     This is a four component RGBA unsigned integer color value if
-     *     the image channel data type is an unnormalized unsigned integer
-     *     type.
-     */
-    cl_int enqueueFillImage(
-        const Image& image,
-        cl_uint4 fillColor,
-        const size_t<3>& origin,
-        const size_t<3>& region,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueFillImage(
-                object_, 
-                image(),
-                static_cast<void*>(&fillColor), 
-                (const ::size_t *) origin, 
-                (const ::size_t *) region,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-                __ENQUEUE_FILL_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-#endif // #if defined(CL_VERSION_1_2)
-
-    cl_int enqueueCopyImageToBuffer(
-        const Image& src,
-        const Buffer& dst,
-        const size_t<3>& src_origin,
-        const size_t<3>& region,
-        ::size_t dst_offset,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueCopyImageToBuffer(
-                object_, src(), dst(), (const ::size_t *) src_origin,
-                (const ::size_t *) region, dst_offset,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_COPY_IMAGE_TO_BUFFER_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueCopyBufferToImage(
-        const Buffer& src,
-        const Image& dst,
-        ::size_t src_offset,
-        const size_t<3>& dst_origin,
-        const size_t<3>& region,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueCopyBufferToImage(
-                object_, src(), dst(), src_offset,
-                (const ::size_t *) dst_origin, (const ::size_t *) region,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_COPY_BUFFER_TO_IMAGE_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    void* enqueueMapBuffer(
-        const Buffer& buffer,
-        cl_bool blocking,
-        cl_map_flags flags,
-        ::size_t offset,
-        ::size_t size,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL,
-        cl_int* err = NULL) const
-    {
-        cl_event tmp;
-        cl_int error;
-        void * result = ::clEnqueueMapBuffer(
-            object_, buffer(), blocking, flags, offset, size,
-            (events != NULL) ? (cl_uint) events->size() : 0,
-            (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-            (event != NULL) ? &tmp : NULL,
-            &error);
-
-        detail::errHandler(error, __ENQUEUE_MAP_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-        if (event != NULL && error == CL_SUCCESS)
-            *event = tmp;
-
-        return result;
-    }
-
-    void* enqueueMapImage(
-        const Image& buffer,
-        cl_bool blocking,
-        cl_map_flags flags,
-        const size_t<3>& origin,
-        const size_t<3>& region,
-        ::size_t * row_pitch,
-        ::size_t * slice_pitch,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL,
-        cl_int* err = NULL) const
-    {
-        cl_event tmp;
-        cl_int error;
-        void * result = ::clEnqueueMapImage(
-            object_, buffer(), blocking, flags,
-            (const ::size_t *) origin, (const ::size_t *) region,
-            row_pitch, slice_pitch,
-            (events != NULL) ? (cl_uint) events->size() : 0,
-            (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-            (event != NULL) ? &tmp : NULL,
-            &error);
-
-        detail::errHandler(error, __ENQUEUE_MAP_IMAGE_ERR);
-        if (err != NULL) {
-              *err = error;
-        }
-        if (event != NULL && error == CL_SUCCESS)
-            *event = tmp;
-        return result;
-    }
-
-    cl_int enqueueUnmapMemObject(
-        const Memory& memory,
-        void* mapped_ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueUnmapMemObject(
-                object_, memory(), mapped_ptr,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_UNMAP_MEM_OBJECT_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-#if defined(CL_VERSION_1_2)
-    /**
-     * Enqueues a marker command which waits for either a list of events to complete, 
-     * or all previously enqueued commands to complete.
-     *
-     * Enqueues a marker command which waits for either a list of events to complete, 
-     * or if the list is empty it waits for all commands previously enqueued in command_queue 
-     * to complete before it completes. This command returns an event which can be waited on, 
-     * i.e. this event can be waited on to insure that all events either in the event_wait_list 
-     * or all previously enqueued commands, queued before this command to command_queue, 
-     * have completed.
-     */
-    cl_int enqueueMarkerWithWaitList(
-        const VECTOR_CLASS<Event> *events = 0,
-        Event *event = 0)
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueMarkerWithWaitList(
-                object_,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_MARKER_WAIT_LIST_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    /**
-     * A synchronization point that enqueues a barrier operation.
-     *
-     * Enqueues a barrier command which waits for either a list of events to complete, 
-     * or if the list is empty it waits for all commands previously enqueued in command_queue 
-     * to complete before it completes. This command blocks command execution, that is, any 
-     * following commands enqueued after it do not execute until it completes. This command 
-     * returns an event which can be waited on, i.e. this event can be waited on to insure that 
-     * all events either in the event_wait_list or all previously enqueued commands, queued 
-     * before this command to command_queue, have completed.
-     */
-    cl_int enqueueBarrierWithWaitList(
-        const VECTOR_CLASS<Event> *events = 0,
-        Event *event = 0)
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueBarrierWithWaitList(
-                object_,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_BARRIER_WAIT_LIST_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-    
-    /**
-     * Enqueues a command to indicate with which device a set of memory objects
-     * should be associated.
-     */
-    cl_int enqueueMigrateMemObjects(
-        const VECTOR_CLASS<Memory> &memObjects,
-        cl_mem_migration_flags flags,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL
-        )
-    {
-        cl_event tmp;
-        
-        cl_mem* localMemObjects = static_cast<cl_mem*>(alloca(memObjects.size() * sizeof(cl_mem)));
-        for( int i = 0; i < (int)memObjects.size(); ++i ) {
-            localMemObjects[i] = memObjects[i]();
-        }
-
-
-        cl_int err = detail::errHandler(
-            ::clEnqueueMigrateMemObjects(
-                object_, 
-                (cl_uint)memObjects.size(), 
-                static_cast<const cl_mem*>(localMemObjects),
-                flags,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_UNMAP_MEM_OBJECT_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-#endif // #if defined(CL_VERSION_1_2)
-
-    cl_int enqueueNDRangeKernel(
-        const Kernel& kernel,
-        const NDRange& offset,
-        const NDRange& global,
-        const NDRange& local = NullRange,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueNDRangeKernel(
-                object_, kernel(), (cl_uint) global.dimensions(),
-                offset.dimensions() != 0 ? (const ::size_t*) offset : NULL,
-                (const ::size_t*) global,
-                local.dimensions() != 0 ? (const ::size_t*) local : NULL,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_NDRANGE_KERNEL_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueTask(
-        const Kernel& kernel,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueTask(
-                object_, kernel(),
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_TASK_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    cl_int enqueueNativeKernel(
-        void (CL_CALLBACK *userFptr)(void *),
-        std::pair<void*, ::size_t> args,
-        const VECTOR_CLASS<Memory>* mem_objects = NULL,
-        const VECTOR_CLASS<const void*>* mem_locs = NULL,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL) const
-    {
-        cl_mem * mems = (mem_objects != NULL && mem_objects->size() > 0) 
-            ? (cl_mem*) alloca(mem_objects->size() * sizeof(cl_mem))
-            : NULL;
-
-        if (mems != NULL) {
-            for (unsigned int i = 0; i < mem_objects->size(); i++) {
-                mems[i] = ((*mem_objects)[i])();
-            }
-        }
-
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueNativeKernel(
-                object_, userFptr, args.first, args.second,
-                (mem_objects != NULL) ? (cl_uint) mem_objects->size() : 0,
-                mems,
-                (mem_locs != NULL && mem_locs->size() > 0) ? (const void **) &mem_locs->front() : NULL,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_NATIVE_KERNEL);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-/**
- * Deprecated APIs for 1.2
- */
-#if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS) || (defined(CL_VERSION_1_1) && !defined(CL_VERSION_1_2)) 
-    CL_EXT_PREFIX__VERSION_1_1_DEPRECATED 
-    cl_int enqueueMarker(Event* event = NULL) const CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED
-    {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            ::clEnqueueMarker(
-                object_, 
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_MARKER_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-
-    CL_EXT_PREFIX__VERSION_1_1_DEPRECATED
-    cl_int enqueueWaitForEvents(const VECTOR_CLASS<Event>& events) const CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED
-    {
-        return detail::errHandler(
-            ::clEnqueueWaitForEvents(
-                object_,
-                (cl_uint) events.size(),
-                events.size() > 0 ? (const cl_event*) &events.front() : NULL),
-            __ENQUEUE_WAIT_FOR_EVENTS_ERR);
-    }
-#endif // #if defined(CL_VERSION_1_1)
-
-    cl_int enqueueAcquireGLObjects(
-         const VECTOR_CLASS<Memory>* mem_objects = NULL,
-         const VECTOR_CLASS<Event>* events = NULL,
-         Event* event = NULL) const
-     {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-             ::clEnqueueAcquireGLObjects(
-                 object_,
-                 (mem_objects != NULL) ? (cl_uint) mem_objects->size() : 0,
-                 (mem_objects != NULL && mem_objects->size() > 0) ? (const cl_mem *) &mem_objects->front(): NULL,
-                 (events != NULL) ? (cl_uint) events->size() : 0,
-                 (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                 (event != NULL) ? &tmp : NULL),
-             __ENQUEUE_ACQUIRE_GL_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-     }
-
-    cl_int enqueueReleaseGLObjects(
-         const VECTOR_CLASS<Memory>* mem_objects = NULL,
-         const VECTOR_CLASS<Event>* events = NULL,
-         Event* event = NULL) const
-     {
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-             ::clEnqueueReleaseGLObjects(
-                 object_,
-                 (mem_objects != NULL) ? (cl_uint) mem_objects->size() : 0,
-                 (mem_objects != NULL && mem_objects->size() > 0) ? (const cl_mem *) &mem_objects->front(): NULL,
-                 (events != NULL) ? (cl_uint) events->size() : 0,
-                 (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                 (event != NULL) ? &tmp : NULL),
-             __ENQUEUE_RELEASE_GL_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-     }
-
-#if defined (USE_DX_INTEROP)
-typedef CL_API_ENTRY cl_int (CL_API_CALL *PFN_clEnqueueAcquireD3D10ObjectsKHR)(
-    cl_command_queue command_queue, cl_uint num_objects,
-    const cl_mem* mem_objects, cl_uint num_events_in_wait_list,
-    const cl_event* event_wait_list, cl_event* event);
-typedef CL_API_ENTRY cl_int (CL_API_CALL *PFN_clEnqueueReleaseD3D10ObjectsKHR)(
-    cl_command_queue command_queue, cl_uint num_objects,
-    const cl_mem* mem_objects,  cl_uint num_events_in_wait_list,
-    const cl_event* event_wait_list, cl_event* event);
-
-    cl_int enqueueAcquireD3D10Objects(
-         const VECTOR_CLASS<Memory>* mem_objects = NULL,
-         const VECTOR_CLASS<Event>* events = NULL,
-         Event* event = NULL) const
-    {
-        static PFN_clEnqueueAcquireD3D10ObjectsKHR pfn_clEnqueueAcquireD3D10ObjectsKHR = NULL;
-#if defined(CL_VERSION_1_2)
-        cl_context context = getInfo<CL_QUEUE_CONTEXT>();
-        cl::Device device(getInfo<CL_QUEUE_DEVICE>());
-        cl_platform_id platform = device.getInfo<CL_DEVICE_PLATFORM>();
-        __INIT_CL_EXT_FCN_PTR_PLATFORM(platform, clEnqueueAcquireD3D10ObjectsKHR);
-#endif
-#if defined(CL_VERSION_1_1)
-        __INIT_CL_EXT_FCN_PTR(clEnqueueAcquireD3D10ObjectsKHR);
-#endif
-        
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-             pfn_clEnqueueAcquireD3D10ObjectsKHR(
-                 object_,
-                 (mem_objects != NULL) ? (cl_uint) mem_objects->size() : 0,
-                 (mem_objects != NULL && mem_objects->size() > 0) ? (const cl_mem *) &mem_objects->front(): NULL,
-                 (events != NULL) ? (cl_uint) events->size() : 0,
-                 (events != NULL) ? (cl_event*) &events->front() : NULL,
-                 (event != NULL) ? &tmp : NULL),
-             __ENQUEUE_ACQUIRE_GL_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-     }
-
-    cl_int enqueueReleaseD3D10Objects(
-         const VECTOR_CLASS<Memory>* mem_objects = NULL,
-         const VECTOR_CLASS<Event>* events = NULL,
-         Event* event = NULL) const
-    {
-        static PFN_clEnqueueReleaseD3D10ObjectsKHR pfn_clEnqueueReleaseD3D10ObjectsKHR = NULL;
-#if defined(CL_VERSION_1_2)
-        cl_context context = getInfo<CL_QUEUE_CONTEXT>();
-        cl::Device device(getInfo<CL_QUEUE_DEVICE>());
-        cl_platform_id platform = device.getInfo<CL_DEVICE_PLATFORM>();
-        __INIT_CL_EXT_FCN_PTR_PLATFORM(platform, clEnqueueReleaseD3D10ObjectsKHR);
-#endif // #if defined(CL_VERSION_1_2)
-#if defined(CL_VERSION_1_1)
-        __INIT_CL_EXT_FCN_PTR(clEnqueueReleaseD3D10ObjectsKHR);
-#endif // #if defined(CL_VERSION_1_1)
-
-        cl_event tmp;
-        cl_int err = detail::errHandler(
-            pfn_clEnqueueReleaseD3D10ObjectsKHR(
-                object_,
-                (mem_objects != NULL) ? (cl_uint) mem_objects->size() : 0,
-                (mem_objects != NULL && mem_objects->size() > 0) ? (const cl_mem *) &mem_objects->front(): NULL,
-                (events != NULL) ? (cl_uint) events->size() : 0,
-                (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-                (event != NULL) ? &tmp : NULL),
-            __ENQUEUE_RELEASE_GL_ERR);
-
-        if (event != NULL && err == CL_SUCCESS)
-            *event = tmp;
-
-        return err;
-    }
-#endif
-
-/**
- * Deprecated APIs for 1.2
- */
-#if defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS) || (defined(CL_VERSION_1_1) && !defined(CL_VERSION_1_2)) 
-    CL_EXT_PREFIX__VERSION_1_1_DEPRECATED
-    cl_int enqueueBarrier() const CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED
-    {
-        return detail::errHandler(
-            ::clEnqueueBarrier(object_),
-            __ENQUEUE_BARRIER_ERR);
-    }
-#endif // #if defined(CL_VERSION_1_1)
-
-    cl_int flush() const
-    {
-        return detail::errHandler(::clFlush(object_), __FLUSH_ERR);
-    }
-
-    cl_int finish() const
-    {
-        return detail::errHandler(::clFinish(object_), __FINISH_ERR);
-    }
-};
-
-#ifdef _WIN32
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-__declspec(selectany) std::atomic<int> CommandQueue::default_initialized_;
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__declspec(selectany) volatile int CommandQueue::default_initialized_ = __DEFAULT_NOT_INITIALIZED;
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__declspec(selectany) CommandQueue CommandQueue::default_;
-__declspec(selectany) volatile cl_int CommandQueue::default_error_ = CL_SUCCESS;
-#else // !_WIN32
-#ifdef CL_HPP_CPP11_ATOMICS_SUPPORTED
-__attribute__((weak)) std::atomic<int> CommandQueue::default_initialized_;
-#else // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__attribute__((weak)) volatile int CommandQueue::default_initialized_ = __DEFAULT_NOT_INITIALIZED;
-#endif // !CL_HPP_CPP11_ATOMICS_SUPPORTED
-__attribute__((weak)) CommandQueue CommandQueue::default_;
-__attribute__((weak)) volatile cl_int CommandQueue::default_error_ = CL_SUCCESS;
-#endif // !_WIN32
-
-template< typename IteratorType >
-Buffer::Buffer(
-    const Context &context,
-    IteratorType startIterator,
-    IteratorType endIterator,
-    bool readOnly,
-    bool useHostPtr,
-    cl_int* err)
-{
-    typedef typename std::iterator_traits<IteratorType>::value_type DataType;
-    cl_int error;
-
-    cl_mem_flags flags = 0;
-    if( readOnly ) {
-        flags |= CL_MEM_READ_ONLY;
-    }
-    else {
-        flags |= CL_MEM_READ_WRITE;
-    }
-    if( useHostPtr ) {
-        flags |= CL_MEM_USE_HOST_PTR;
-    }
-    
-    ::size_t size = sizeof(DataType)*(endIterator - startIterator);
-
-    if( useHostPtr ) {
-        object_ = ::clCreateBuffer(context(), flags, size, static_cast<DataType*>(&*startIterator), &error);
-    } else {
-        object_ = ::clCreateBuffer(context(), flags, size, 0, &error);
-    }
-
-    detail::errHandler(error, __CREATE_BUFFER_ERR);
-    if (err != NULL) {
-        *err = error;
-    }
-
-    if( !useHostPtr ) {
-        CommandQueue queue(context, 0, &error);
-        detail::errHandler(error, __CREATE_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-
-        error = cl::copy(queue, startIterator, endIterator, *this);
-        detail::errHandler(error, __CREATE_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-}
-
-template< typename IteratorType >
-Buffer::Buffer(
-    const CommandQueue &queue,
-    IteratorType startIterator,
-    IteratorType endIterator,
-    bool readOnly,
-    bool useHostPtr,
-    cl_int* err)
-{
-    typedef typename std::iterator_traits<IteratorType>::value_type DataType;
-    cl_int error;
-
-    cl_mem_flags flags = 0;
-    if (readOnly) {
-        flags |= CL_MEM_READ_ONLY;
-    }
-    else {
-        flags |= CL_MEM_READ_WRITE;
-    }
-    if (useHostPtr) {
-        flags |= CL_MEM_USE_HOST_PTR;
-    }
-
-    ::size_t size = sizeof(DataType)*(endIterator - startIterator);
-
-    Context context = queue.getInfo<CL_QUEUE_CONTEXT>();
-
-    if (useHostPtr) {
-        object_ = ::clCreateBuffer(context(), flags, size, static_cast<DataType*>(&*startIterator), &error);
-    }
-    else {
-        object_ = ::clCreateBuffer(context(), flags, size, 0, &error);
-    }
-
-    detail::errHandler(error, __CREATE_BUFFER_ERR);
-    if (err != NULL) {
-        *err = error;
-    }
-
-    if (!useHostPtr) {
-        error = cl::copy(queue, startIterator, endIterator, *this);
-        detail::errHandler(error, __CREATE_BUFFER_ERR);
-        if (err != NULL) {
-            *err = error;
-        }
-    }
-}
-
-inline cl_int enqueueReadBuffer(
-    const Buffer& buffer,
-    cl_bool blocking,
-    ::size_t offset,
-    ::size_t size,
-    void* ptr,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueReadBuffer(buffer, blocking, offset, size, ptr, events, event);
-}
-
-inline cl_int enqueueWriteBuffer(
-        const Buffer& buffer,
-        cl_bool blocking,
-        ::size_t offset,
-        ::size_t size,
-        const void* ptr,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueWriteBuffer(buffer, blocking, offset, size, ptr, events, event);
-}
-
-inline void* enqueueMapBuffer(
-        const Buffer& buffer,
-        cl_bool blocking,
-        cl_map_flags flags,
-        ::size_t offset,
-        ::size_t size,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL,
-        cl_int* err = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-    detail::errHandler(error, __ENQUEUE_MAP_BUFFER_ERR);
-    if (err != NULL) {
-        *err = error;
-    }
-
-    void * result = ::clEnqueueMapBuffer(
-            queue(), buffer(), blocking, flags, offset, size,
-            (events != NULL) ? (cl_uint) events->size() : 0,
-            (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-            (cl_event*) event,
-            &error);
-
-    detail::errHandler(error, __ENQUEUE_MAP_BUFFER_ERR);
-    if (err != NULL) {
-        *err = error;
-    }
-    return result;
-}
-
-inline cl_int enqueueUnmapMemObject(
-    const Memory& memory,
-    void* mapped_ptr,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-    detail::errHandler(error, __ENQUEUE_MAP_BUFFER_ERR);
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    cl_event tmp;
-    cl_int err = detail::errHandler(
-        ::clEnqueueUnmapMemObject(
-            queue(), memory(), mapped_ptr,
-            (events != NULL) ? (cl_uint) events->size() : 0,
-            (events != NULL && events->size() > 0) ? (cl_event*) &events->front() : NULL,
-            (event != NULL) ? &tmp : NULL),
-        __ENQUEUE_UNMAP_MEM_OBJECT_ERR);
-
-    if (event != NULL && err == CL_SUCCESS)
-        *event = tmp;
-
-    return err;
-}
-
-inline cl_int enqueueCopyBuffer(
-        const Buffer& src,
-        const Buffer& dst,
-        ::size_t src_offset,
-        ::size_t dst_offset,
-        ::size_t size,
-        const VECTOR_CLASS<Event>* events = NULL,
-        Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueCopyBuffer(src, dst, src_offset, dst_offset, size, events, event);
-}
-
-/**
- * Blocking copy operation between iterators and a buffer.
- * Host to Device.
- * Uses default command queue.
- */
-template< typename IteratorType >
-inline cl_int copy( IteratorType startIterator, IteratorType endIterator, cl::Buffer &buffer )
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-    if (error != CL_SUCCESS)
-        return error;
-
-    return cl::copy(queue, startIterator, endIterator, buffer);
-}
-
-/**
- * Blocking copy operation between iterators and a buffer.
- * Device to Host.
- * Uses default command queue.
- */
-template< typename IteratorType >
-inline cl_int copy( const cl::Buffer &buffer, IteratorType startIterator, IteratorType endIterator )
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-    if (error != CL_SUCCESS)
-        return error;
-
-    return cl::copy(queue, buffer, startIterator, endIterator);
-}
-
-/**
- * Blocking copy operation between iterators and a buffer.
- * Host to Device.
- * Uses specified queue.
- */
-template< typename IteratorType >
-inline cl_int copy( const CommandQueue &queue, IteratorType startIterator, IteratorType endIterator, cl::Buffer &buffer )
-{
-    typedef typename std::iterator_traits<IteratorType>::value_type DataType;
-    cl_int error;
-    
-    ::size_t length = endIterator-startIterator;
-    ::size_t byteLength = length*sizeof(DataType);
-
-    DataType *pointer = 
-        static_cast<DataType*>(queue.enqueueMapBuffer(buffer, CL_TRUE, CL_MAP_WRITE, 0, byteLength, 0, 0, &error));
-    // if exceptions enabled, enqueueMapBuffer will throw
-    if( error != CL_SUCCESS ) {
-        return error;
-    }
-#if defined(_MSC_VER)
-    std::copy(
-        startIterator, 
-        endIterator, 
-        stdext::checked_array_iterator<DataType*>(
-            pointer, length));
-#else
-    std::copy(startIterator, endIterator, pointer);
-#endif
-    Event endEvent;
-    error = queue.enqueueUnmapMemObject(buffer, pointer, 0, &endEvent);
-    // if exceptions enabled, enqueueUnmapMemObject will throw
-    if( error != CL_SUCCESS ) { 
-        return error;
-    }
-    endEvent.wait();
-    return CL_SUCCESS;
-}
-
-/**
- * Blocking copy operation between iterators and a buffer.
- * Device to Host.
- * Uses specified queue.
- */
-template< typename IteratorType >
-inline cl_int copy( const CommandQueue &queue, const cl::Buffer &buffer, IteratorType startIterator, IteratorType endIterator )
-{
-    typedef typename std::iterator_traits<IteratorType>::value_type DataType;
-    cl_int error;
-        
-    ::size_t length = endIterator-startIterator;
-    ::size_t byteLength = length*sizeof(DataType);
-
-    DataType *pointer = 
-        static_cast<DataType*>(queue.enqueueMapBuffer(buffer, CL_TRUE, CL_MAP_READ, 0, byteLength, 0, 0, &error));
-    // if exceptions enabled, enqueueMapBuffer will throw
-    if( error != CL_SUCCESS ) {
-        return error;
-    }
-    std::copy(pointer, pointer + length, startIterator);
-    Event endEvent;
-    error = queue.enqueueUnmapMemObject(buffer, pointer, 0, &endEvent);
-    // if exceptions enabled, enqueueUnmapMemObject will throw
-    if( error != CL_SUCCESS ) { 
-        return error;
-    }
-    endEvent.wait();
-    return CL_SUCCESS;
-}
-
-#if defined(CL_VERSION_1_1)
-inline cl_int enqueueReadBufferRect(
-    const Buffer& buffer,
-    cl_bool blocking,
-    const size_t<3>& buffer_offset,
-    const size_t<3>& host_offset,
-    const size_t<3>& region,
-    ::size_t buffer_row_pitch,
-    ::size_t buffer_slice_pitch,
-    ::size_t host_row_pitch,
-    ::size_t host_slice_pitch,
-    void *ptr,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueReadBufferRect(
-        buffer, 
-        blocking, 
-        buffer_offset, 
-        host_offset,
-        region,
-        buffer_row_pitch,
-        buffer_slice_pitch,
-        host_row_pitch,
-        host_slice_pitch,
-        ptr, 
-        events, 
-        event);
-}
-
-inline cl_int enqueueWriteBufferRect(
-    const Buffer& buffer,
-    cl_bool blocking,
-    const size_t<3>& buffer_offset,
-    const size_t<3>& host_offset,
-    const size_t<3>& region,
-    ::size_t buffer_row_pitch,
-    ::size_t buffer_slice_pitch,
-    ::size_t host_row_pitch,
-    ::size_t host_slice_pitch,
-    void *ptr,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueWriteBufferRect(
-        buffer, 
-        blocking, 
-        buffer_offset, 
-        host_offset,
-        region,
-        buffer_row_pitch,
-        buffer_slice_pitch,
-        host_row_pitch,
-        host_slice_pitch,
-        ptr, 
-        events, 
-        event);
-}
-
-inline cl_int enqueueCopyBufferRect(
-    const Buffer& src,
-    const Buffer& dst,
-    const size_t<3>& src_origin,
-    const size_t<3>& dst_origin,
-    const size_t<3>& region,
-    ::size_t src_row_pitch,
-    ::size_t src_slice_pitch,
-    ::size_t dst_row_pitch,
-    ::size_t dst_slice_pitch,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueCopyBufferRect(
-        src,
-        dst,
-        src_origin,
-        dst_origin,
-        region,
-        src_row_pitch,
-        src_slice_pitch,
-        dst_row_pitch,
-        dst_slice_pitch,
-        events, 
-        event);
-}
-#endif
-
-inline cl_int enqueueReadImage(
-    const Image& image,
-    cl_bool blocking,
-    const size_t<3>& origin,
-    const size_t<3>& region,
-    ::size_t row_pitch,
-    ::size_t slice_pitch,
-    void* ptr,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL) 
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueReadImage(
-        image,
-        blocking,
-        origin,
-        region,
-        row_pitch,
-        slice_pitch,
-        ptr,
-        events, 
-        event);
-}
-
-inline cl_int enqueueWriteImage(
-    const Image& image,
-    cl_bool blocking,
-    const size_t<3>& origin,
-    const size_t<3>& region,
-    ::size_t row_pitch,
-    ::size_t slice_pitch,
-    void* ptr,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueWriteImage(
-        image,
-        blocking,
-        origin,
-        region,
-        row_pitch,
-        slice_pitch,
-        ptr,
-        events, 
-        event);
-}
-
-inline cl_int enqueueCopyImage(
-    const Image& src,
-    const Image& dst,
-    const size_t<3>& src_origin,
-    const size_t<3>& dst_origin,
-    const size_t<3>& region,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueCopyImage(
-        src,
-        dst,
-        src_origin,
-        dst_origin,
-        region,
-        events,
-        event);
-}
-
-inline cl_int enqueueCopyImageToBuffer(
-    const Image& src,
-    const Buffer& dst,
-    const size_t<3>& src_origin,
-    const size_t<3>& region,
-    ::size_t dst_offset,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueCopyImageToBuffer(
-        src,
-        dst,
-        src_origin,
-        region,
-        dst_offset,
-        events,
-        event);
-}
-
-inline cl_int enqueueCopyBufferToImage(
-    const Buffer& src,
-    const Image& dst,
-    ::size_t src_offset,
-    const size_t<3>& dst_origin,
-    const size_t<3>& region,
-    const VECTOR_CLASS<Event>* events = NULL,
-    Event* event = NULL)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.enqueueCopyBufferToImage(
-        src,
-        dst,
-        src_offset,
-        dst_origin,
-        region,
-        events,
-        event);
-}
-
-
-inline cl_int flush(void)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    }
-
-    return queue.flush();
-}
-
-inline cl_int finish(void)
-{
-    cl_int error;
-    CommandQueue queue = CommandQueue::getDefault(&error);
-
-    if (error != CL_SUCCESS) {
-        return error;
-    } 
-
-
-    return queue.finish();
-}
-
-// Kernel Functor support
-// New interface as of September 2011
-// Requires the C++11 std::tr1::function (note do not support TR1)
-// Visual Studio 2010 and GCC 4.2
-
-struct EnqueueArgs
-{
-    CommandQueue queue_;
-    const NDRange offset_;
-    const NDRange global_;
-    const NDRange local_;
-    VECTOR_CLASS<Event> events_;
-
-    EnqueueArgs(NDRange global) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(NullRange), 
-      global_(global),
-      local_(NullRange)
-    {
-
-    }
-
-    EnqueueArgs(NDRange global, NDRange local) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(NullRange), 
-      global_(global),
-      local_(local)
-    {
-
-    }
-
-    EnqueueArgs(NDRange offset, NDRange global, NDRange local) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(offset), 
-      global_(global),
-      local_(local)
-    {
-
-    }
-
-    EnqueueArgs(Event e, NDRange global) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(NullRange), 
-      global_(global),
-      local_(NullRange)
-    {
-        events_.push_back(e);
-    }
-
-    EnqueueArgs(Event e, NDRange global, NDRange local) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(NullRange), 
-      global_(global),
-      local_(local)
-    {
-        events_.push_back(e);
-    }
-
-    EnqueueArgs(Event e, NDRange offset, NDRange global, NDRange local) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(offset), 
-      global_(global),
-      local_(local)
-    {
-        events_.push_back(e);
-    }
-
-    EnqueueArgs(const VECTOR_CLASS<Event> &events, NDRange global) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(NullRange), 
-      global_(global),
-      local_(NullRange),
-      events_(events)
-    {
-
-    }
-
-    EnqueueArgs(const VECTOR_CLASS<Event> &events, NDRange global, NDRange local) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(NullRange), 
-      global_(global),
-      local_(local),
-      events_(events)
-    {
-
-    }
-
-    EnqueueArgs(const VECTOR_CLASS<Event> &events, NDRange offset, NDRange global, NDRange local) : 
-      queue_(CommandQueue::getDefault()),
-      offset_(offset), 
-      global_(global),
-      local_(local),
-      events_(events)
-    {
-
-    }
-
-    EnqueueArgs(CommandQueue &queue, NDRange global) : 
-      queue_(queue),
-      offset_(NullRange), 
-      global_(global),
-      local_(NullRange)
-    {
-
-    }
-
-    EnqueueArgs(CommandQueue &queue, NDRange global, NDRange local) : 
-      queue_(queue),
-      offset_(NullRange), 
-      global_(global),
-      local_(local)
-    {
-
-    }
-
-    EnqueueArgs(CommandQueue &queue, NDRange offset, NDRange global, NDRange local) : 
-      queue_(queue),
-      offset_(offset), 
-      global_(global),
-      local_(local)
-    {
-
-    }
-
-    EnqueueArgs(CommandQueue &queue, Event e, NDRange global) : 
-      queue_(queue),
-      offset_(NullRange), 
-      global_(global),
-      local_(NullRange)
-    {
-        events_.push_back(e);
-    }
-
-    EnqueueArgs(CommandQueue &queue, Event e, NDRange global, NDRange local) : 
-      queue_(queue),
-      offset_(NullRange), 
-      global_(global),
-      local_(local)
-    {
-        events_.push_back(e);
-    }
-
-    EnqueueArgs(CommandQueue &queue, Event e, NDRange offset, NDRange global, NDRange local) : 
-      queue_(queue),
-      offset_(offset), 
-      global_(global),
-      local_(local)
-    {
-        events_.push_back(e);
-    }
-
-    EnqueueArgs(CommandQueue &queue, const VECTOR_CLASS<Event> &events, NDRange global) : 
-      queue_(queue),
-      offset_(NullRange), 
-      global_(global),
-      local_(NullRange),
-      events_(events)
-    {
-
-    }
-
-    EnqueueArgs(CommandQueue &queue, const VECTOR_CLASS<Event> &events, NDRange global, NDRange local) : 
-      queue_(queue),
-      offset_(NullRange), 
-      global_(global),
-      local_(local),
-      events_(events)
-    {
-
-    }
-
-    EnqueueArgs(CommandQueue &queue, const VECTOR_CLASS<Event> &events, NDRange offset, NDRange global, NDRange local) : 
-      queue_(queue),
-      offset_(offset), 
-      global_(global),
-      local_(local),
-      events_(events)
-    {
-
-    }
-};
-
-namespace detail {
-
-class NullType {};
-
-template<int index, typename T0>
-struct SetArg
-{
-    static void set (Kernel kernel, T0 arg)
-    {
-        kernel.setArg(index, arg);
-    }
-};  
-
-template<int index>
-struct SetArg<index, NullType>
-{
-    static void set (Kernel, NullType)
-    { 
-    }
-};
-
-template <
-   typename T0,   typename T1,   typename T2,   typename T3,
-   typename T4,   typename T5,   typename T6,   typename T7,
-   typename T8,   typename T9,   typename T10,   typename T11,
-   typename T12,   typename T13,   typename T14,   typename T15,
-   typename T16,   typename T17,   typename T18,   typename T19,
-   typename T20,   typename T21,   typename T22,   typename T23,
-   typename T24,   typename T25,   typename T26,   typename T27,
-   typename T28,   typename T29,   typename T30,   typename T31
->
-class KernelFunctorGlobal
-{
-private:
-    Kernel kernel_;
-
-public:
-   KernelFunctorGlobal(
-        Kernel kernel) :
-            kernel_(kernel)
-    {}
-
-   KernelFunctorGlobal(
-        const Program& program,
-        const STRING_CLASS name,
-        cl_int * err = NULL) :
-            kernel_(program, name.c_str(), err)
-    {}
-
-    Event operator() (
-        const EnqueueArgs& args,
-        T0 t0,
-        T1 t1 = NullType(),
-        T2 t2 = NullType(),
-        T3 t3 = NullType(),
-        T4 t4 = NullType(),
-        T5 t5 = NullType(),
-        T6 t6 = NullType(),
-        T7 t7 = NullType(),
-        T8 t8 = NullType(),
-        T9 t9 = NullType(),
-        T10 t10 = NullType(),
-        T11 t11 = NullType(),
-        T12 t12 = NullType(),
-        T13 t13 = NullType(),
-        T14 t14 = NullType(),
-        T15 t15 = NullType(),
-        T16 t16 = NullType(),
-        T17 t17 = NullType(),
-        T18 t18 = NullType(),
-        T19 t19 = NullType(),
-        T20 t20 = NullType(),
-        T21 t21 = NullType(),
-        T22 t22 = NullType(),
-        T23 t23 = NullType(),
-        T24 t24 = NullType(),
-        T25 t25 = NullType(),
-        T26 t26 = NullType(),
-        T27 t27 = NullType(),
-        T28 t28 = NullType(),
-        T29 t29 = NullType(),
-        T30 t30 = NullType(),
-        T31 t31 = NullType()
-        )
-    {
-        Event event;
-        SetArg<0, T0>::set(kernel_, t0);
-        SetArg<1, T1>::set(kernel_, t1);
-        SetArg<2, T2>::set(kernel_, t2);
-        SetArg<3, T3>::set(kernel_, t3);
-        SetArg<4, T4>::set(kernel_, t4);
-        SetArg<5, T5>::set(kernel_, t5);
-        SetArg<6, T6>::set(kernel_, t6);
-        SetArg<7, T7>::set(kernel_, t7);
-        SetArg<8, T8>::set(kernel_, t8);
-        SetArg<9, T9>::set(kernel_, t9);
-        SetArg<10, T10>::set(kernel_, t10);
-        SetArg<11, T11>::set(kernel_, t11);
-        SetArg<12, T12>::set(kernel_, t12);
-        SetArg<13, T13>::set(kernel_, t13);
-        SetArg<14, T14>::set(kernel_, t14);
-        SetArg<15, T15>::set(kernel_, t15);
-        SetArg<16, T16>::set(kernel_, t16);
-        SetArg<17, T17>::set(kernel_, t17);
-        SetArg<18, T18>::set(kernel_, t18);
-        SetArg<19, T19>::set(kernel_, t19);
-        SetArg<20, T20>::set(kernel_, t20);
-        SetArg<21, T21>::set(kernel_, t21);
-        SetArg<22, T22>::set(kernel_, t22);
-        SetArg<23, T23>::set(kernel_, t23);
-        SetArg<24, T24>::set(kernel_, t24);
-        SetArg<25, T25>::set(kernel_, t25);
-        SetArg<26, T26>::set(kernel_, t26);
-        SetArg<27, T27>::set(kernel_, t27);
-        SetArg<28, T28>::set(kernel_, t28);
-        SetArg<29, T29>::set(kernel_, t29);
-        SetArg<30, T30>::set(kernel_, t30);
-        SetArg<31, T31>::set(kernel_, t31);
-        
-        args.queue_.enqueueNDRangeKernel(
-            kernel_,
-            args.offset_,
-            args.global_,
-            args.local_,
-            &args.events_,
-            &event);
-        
-        return event;
-    }
-
-};
-
-//------------------------------------------------------------------------------------------------------
-
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25,
-	typename T26,
-	typename T27,
-	typename T28,
-	typename T29,
-	typename T30,
-	typename T31>
-struct functionImplementation_
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		T29,
-		T30,
-		T31> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 32))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		T29,
-		T30,
-		T31);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25,
-		T26 arg26,
-		T27 arg27,
-		T28 arg28,
-		T29 arg29,
-		T30 arg30,
-		T31 arg31)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25,
-			arg26,
-			arg27,
-			arg28,
-			arg29,
-			arg30,
-			arg31);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25,
-	typename T26,
-	typename T27,
-	typename T28,
-	typename T29,
-	typename T30>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	T25,
-	T26,
-	T27,
-	T28,
-	T29,
-	T30,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		T29,
-		T30,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 31))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		T29,
-		T30);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25,
-		T26 arg26,
-		T27 arg27,
-		T28 arg28,
-		T29 arg29,
-		T30 arg30)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25,
-			arg26,
-			arg27,
-			arg28,
-			arg29,
-			arg30);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25,
-	typename T26,
-	typename T27,
-	typename T28,
-	typename T29>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	T25,
-	T26,
-	T27,
-	T28,
-	T29,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		T29,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 30))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		T29);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25,
-		T26 arg26,
-		T27 arg27,
-		T28 arg28,
-		T29 arg29)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25,
-			arg26,
-			arg27,
-			arg28,
-			arg29);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25,
-	typename T26,
-	typename T27,
-	typename T28>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	T25,
-	T26,
-	T27,
-	T28,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 29))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		T28);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25,
-		T26 arg26,
-		T27 arg27,
-		T28 arg28)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25,
-			arg26,
-			arg27,
-			arg28);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25,
-	typename T26,
-	typename T27>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	T25,
-	T26,
-	T27,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 28))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		T27);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25,
-		T26 arg26,
-		T27 arg27)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25,
-			arg26,
-			arg27);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25,
-	typename T26>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	T25,
-	T26,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 27))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		T26);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25,
-		T26 arg26)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25,
-			arg26);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24,
-	typename T25>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	T25,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 26))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		T25);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24,
-		T25 arg25)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24,
-			arg25);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23,
-	typename T24>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	T24,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 25))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		T24);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23,
-		T24 arg24)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23,
-			arg24);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22,
-	typename T23>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	T23,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 24))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		T23);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22,
-		T23 arg23)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22,
-			arg23);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21,
-	typename T22>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	T22,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 23))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		T22);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21,
-		T22 arg22)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21,
-			arg22);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20,
-	typename T21>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	T21,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 22))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		T21);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20,
-		T21 arg21)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20,
-			arg21);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19,
-	typename T20>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	T20,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 21))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		T20);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19,
-		T20 arg20)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19,
-			arg20);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18,
-	typename T19>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	T19,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 20))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		T19);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18,
-		T19 arg19)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18,
-			arg19);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17,
-	typename T18>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	T18,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 19))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		T18);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17,
-		T18 arg18)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17,
-			arg18);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16,
-	typename T17>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	T17,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 18))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		T17);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16,
-		T17 arg17)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16,
-			arg17);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15,
-	typename T16>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	T16,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 17))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		T16);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15,
-		T16 arg16)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15,
-			arg16);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14,
-	typename T15>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	T15,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 16))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		T15);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14,
-		T15 arg15)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14,
-			arg15);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13,
-	typename T14>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	T14,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 15))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		T14);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13,
-		T14 arg14)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13,
-			arg14);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12,
-	typename T13>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	T13,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 14))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		T13);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12,
-		T13 arg13)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12,
-			arg13);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11,
-	typename T12>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	T12,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 13))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		T12);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11,
-		T12 arg12)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11,
-			arg12);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10,
-	typename T11>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	T11,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 12))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		T11);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10,
-		T11 arg11)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10,
-			arg11);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9,
-	typename T10>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	T10,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 11))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		T10);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9,
-		T10 arg10)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9,
-			arg10);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8,
-	typename T9>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	T9,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 10))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		T9);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8,
-		T9 arg9)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8,
-			arg9);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7,
-	typename T8>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	T8,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 9))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		T8);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7,
-		T8 arg8)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7,
-			arg8);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6,
-	typename T7>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	T7,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 8))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		T7);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6,
-		T7 arg7)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6,
-			arg7);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5,
-	typename T6>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	T6,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 7))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		T6);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5,
-		T6 arg6)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5,
-			arg6);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4,
-	typename T5>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	T5,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 6))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		T5);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4,
-		T5 arg5)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4,
-			arg5);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3,
-	typename T4>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	T4,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		T4,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 5))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3,
-		T4);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3,
-		T4 arg4)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3,
-			arg4);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2,
-	typename T3>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	T3,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		T3,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 4))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2,
-		T3);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2,
-		T3 arg3)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2,
-			arg3);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1,
-	typename T2>
-struct functionImplementation_
-<	T0,
-	T1,
-	T2,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		T2,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 3))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1,
-		T2);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1,
-		T2 arg2)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1,
-			arg2);
-	}
-
-
-};
-
-template<
-	typename T0,
-	typename T1>
-struct functionImplementation_
-<	T0,
-	T1,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		T1,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 2))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0,
-		T1);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0,
-		T1 arg1)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0,
-			arg1);
-	}
-
-
-};
-
-template<
-	typename T0>
-struct functionImplementation_
-<	T0,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType,
-	NullType>
-{
-	typedef detail::KernelFunctorGlobal<
-		T0,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType,
-		NullType> FunctorType;
-
-    FunctorType functor_;
-
-    functionImplementation_(const FunctorType &functor) :
-        functor_(functor)
-    {
-    
-        #if (defined(_WIN32) && defined(_VARIADIC_MAX) && (_VARIADIC_MAX < 1))
-        // Fail variadic expansion for dev11
-        static_assert(0, "Visual Studio has a hard limit of argument count for a std::function expansion. Please define _VARIADIC_MAX to be 10. If you need more arguments than that VC12 and below cannot support it.");
-        #endif
-            
-    }
-
-	//! \brief Return type of the functor
-	typedef Event result_type;
-
-	//! \brief Function signature of kernel functor with no event dependency.
-	typedef Event type_(
-		const EnqueueArgs&,
-		T0);
-
-	Event operator()(
-		const EnqueueArgs& enqueueArgs,
-		T0 arg0)
-	{
-		return functor_(
-			enqueueArgs,
-			arg0);
-	}
-
-
-};
-
-
-
-
-
-} // namespace detail
-
-//----------------------------------------------------------------------------------------------
-
-template <
-   typename T0,   typename T1 = detail::NullType,   typename T2 = detail::NullType,
-   typename T3 = detail::NullType,   typename T4 = detail::NullType,
-   typename T5 = detail::NullType,   typename T6 = detail::NullType,
-   typename T7 = detail::NullType,   typename T8 = detail::NullType,
-   typename T9 = detail::NullType,   typename T10 = detail::NullType,
-   typename T11 = detail::NullType,   typename T12 = detail::NullType,
-   typename T13 = detail::NullType,   typename T14 = detail::NullType,
-   typename T15 = detail::NullType,   typename T16 = detail::NullType,
-   typename T17 = detail::NullType,   typename T18 = detail::NullType,
-   typename T19 = detail::NullType,   typename T20 = detail::NullType,
-   typename T21 = detail::NullType,   typename T22 = detail::NullType,
-   typename T23 = detail::NullType,   typename T24 = detail::NullType,
-   typename T25 = detail::NullType,   typename T26 = detail::NullType,
-   typename T27 = detail::NullType,   typename T28 = detail::NullType,
-   typename T29 = detail::NullType,   typename T30 = detail::NullType,
-   typename T31 = detail::NullType
->
-struct make_kernel :
-    public detail::functionImplementation_<
-               T0,   T1,   T2,   T3,
-               T4,   T5,   T6,   T7,
-               T8,   T9,   T10,   T11,
-               T12,   T13,   T14,   T15,
-               T16,   T17,   T18,   T19,
-               T20,   T21,   T22,   T23,
-               T24,   T25,   T26,   T27,
-               T28,   T29,   T30,   T31
-    >
-{
-public:
-    typedef detail::KernelFunctorGlobal<             
-               T0,   T1,   T2,   T3,
-               T4,   T5,   T6,   T7,
-               T8,   T9,   T10,   T11,
-               T12,   T13,   T14,   T15,
-               T16,   T17,   T18,   T19,
-               T20,   T21,   T22,   T23,
-               T24,   T25,   T26,   T27,
-               T28,   T29,   T30,   T31
-    > FunctorType;
-
-    make_kernel(
-        const Program& program,
-        const STRING_CLASS name,
-        cl_int * err = NULL) :
-           detail::functionImplementation_<
-                    T0,   T1,   T2,   T3,
-                       T4,   T5,   T6,   T7,
-                       T8,   T9,   T10,   T11,
-                       T12,   T13,   T14,   T15,
-                       T16,   T17,   T18,   T19,
-                       T20,   T21,   T22,   T23,
-                       T24,   T25,   T26,   T27,
-                       T28,   T29,   T30,   T31
-           >(
-            FunctorType(program, name, err)) 
-    {}
-
-    make_kernel(
-        const Kernel kernel) :
-           detail::functionImplementation_<
-                    T0,   T1,   T2,   T3,
-                       T4,   T5,   T6,   T7,
-                       T8,   T9,   T10,   T11,
-                       T12,   T13,   T14,   T15,
-                       T16,   T17,   T18,   T19,
-                       T20,   T21,   T22,   T23,
-                       T24,   T25,   T26,   T27,
-                       T28,   T29,   T30,   T31
-           >(
-            FunctorType(kernel)) 
-    {}    
-};
-
-
-//----------------------------------------------------------------------------------------------------------------------
-
-#undef __ERR_STR
-#if !defined(__CL_USER_OVERRIDE_ERROR_STRINGS)
-#undef __GET_DEVICE_INFO_ERR
-#undef __GET_PLATFORM_INFO_ERR
-#undef __GET_DEVICE_IDS_ERR
-#undef __GET_CONTEXT_INFO_ERR
-#undef __GET_EVENT_INFO_ERR
-#undef __GET_EVENT_PROFILE_INFO_ERR
-#undef __GET_MEM_OBJECT_INFO_ERR
-#undef __GET_IMAGE_INFO_ERR
-#undef __GET_SAMPLER_INFO_ERR
-#undef __GET_KERNEL_INFO_ERR
-#undef __GET_KERNEL_ARG_INFO_ERR
-#undef __GET_KERNEL_WORK_GROUP_INFO_ERR
-#undef __GET_PROGRAM_INFO_ERR
-#undef __GET_PROGRAM_BUILD_INFO_ERR
-#undef __GET_COMMAND_QUEUE_INFO_ERR
-
-#undef __CREATE_CONTEXT_ERR
-#undef __CREATE_CONTEXT_FROM_TYPE_ERR
-#undef __GET_SUPPORTED_IMAGE_FORMATS_ERR
-
-#undef __CREATE_BUFFER_ERR
-#undef __CREATE_SUBBUFFER_ERR
-#undef __CREATE_IMAGE2D_ERR
-#undef __CREATE_IMAGE3D_ERR
-#undef __CREATE_SAMPLER_ERR
-#undef __SET_MEM_OBJECT_DESTRUCTOR_CALLBACK_ERR
-
-#undef __CREATE_USER_EVENT_ERR
-#undef __SET_USER_EVENT_STATUS_ERR
-#undef __SET_EVENT_CALLBACK_ERR
-#undef __SET_PRINTF_CALLBACK_ERR
-
-#undef __WAIT_FOR_EVENTS_ERR
-
-#undef __CREATE_KERNEL_ERR
-#undef __SET_KERNEL_ARGS_ERR
-#undef __CREATE_PROGRAM_WITH_SOURCE_ERR
-#undef __CREATE_PROGRAM_WITH_BINARY_ERR
-#undef __CREATE_PROGRAM_WITH_BUILT_IN_KERNELS_ERR
-#undef __BUILD_PROGRAM_ERR
-#undef __CREATE_KERNELS_IN_PROGRAM_ERR
-
-#undef __CREATE_COMMAND_QUEUE_ERR
-#undef __SET_COMMAND_QUEUE_PROPERTY_ERR
-#undef __ENQUEUE_READ_BUFFER_ERR
-#undef __ENQUEUE_WRITE_BUFFER_ERR
-#undef __ENQUEUE_READ_BUFFER_RECT_ERR
-#undef __ENQUEUE_WRITE_BUFFER_RECT_ERR
-#undef __ENQEUE_COPY_BUFFER_ERR
-#undef __ENQEUE_COPY_BUFFER_RECT_ERR
-#undef __ENQUEUE_READ_IMAGE_ERR
-#undef __ENQUEUE_WRITE_IMAGE_ERR
-#undef __ENQUEUE_COPY_IMAGE_ERR
-#undef __ENQUEUE_COPY_IMAGE_TO_BUFFER_ERR
-#undef __ENQUEUE_COPY_BUFFER_TO_IMAGE_ERR
-#undef __ENQUEUE_MAP_BUFFER_ERR
-#undef __ENQUEUE_MAP_IMAGE_ERR
-#undef __ENQUEUE_UNMAP_MEM_OBJECT_ERR
-#undef __ENQUEUE_NDRANGE_KERNEL_ERR
-#undef __ENQUEUE_TASK_ERR
-#undef __ENQUEUE_NATIVE_KERNEL
-
-#undef __CL_EXPLICIT_CONSTRUCTORS
-
-#undef __UNLOAD_COMPILER_ERR
-#endif //__CL_USER_OVERRIDE_ERROR_STRINGS
-
-#undef __CL_FUNCTION_TYPE
-
-// Extensions
-/**
- * Deprecated APIs for 1.2
- */
-#if defined(CL_VERSION_1_1)
-#undef __INIT_CL_EXT_FCN_PTR
-#endif // #if defined(CL_VERSION_1_1)
-#undef __CREATE_SUB_DEVICES
-
-#if defined(USE_CL_DEVICE_FISSION)
-#undef __PARAM_NAME_DEVICE_FISSION
-#endif // USE_CL_DEVICE_FISSION
-
-#undef __DEFAULT_NOT_INITIALIZED 
-#undef __DEFAULT_BEING_INITIALIZED 
-#undef __DEFAULT_INITIALIZED
-
-#undef CL_HPP_RVALUE_REFERENCES_SUPPORTED
-#undef CL_HPP_NOEXCEPT
-
-} // namespace cl
-
-#endif // CL_HPP_
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d5d70af8fa..464c99dc5a 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -22,8 +22,6 @@
 
 #endif
 
-#include <cl.hpp>
-
 #include <af/version.h>
 #include <af/opencl.h>
 #include <defines.hpp>
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 42579f89d1..cc19e13bf8 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -12,7 +12,10 @@
 #include <fg/window.h>
 #endif
 
-#include <cl.hpp>
+#define CL_HPP_ENABLE_EXCEPTIONS
+#define CL_HPP_TARGET_OPENCL_VERSION 120
+#include <CL/cl.hpp>
+
 #include <vector>
 #include <string>
 

From 5b97f75bf6fb6a220877072c2b87889a9a3ad5d5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Mar 2016 15:41:04 -0500
Subject: [PATCH 0562/2677] FEAT Add Sparse operations to CUDA Backend

---
 examples/CMakeLists.txt              |   2 +-
 include/af/defines.h                 |  13 ++
 include/af/sparse.h                  |  70 ++++++
 include/arrayfire.h                  |   1 +
 src/api/c/sparse.cpp                 | 320 +++++++++++++++++++++++++++
 src/api/c/sparse_matmul.cpp          |  79 +++++++
 src/api/c/sparse_t.hpp               |  24 ++
 src/backend/cuda/CMakeLists.txt      |   1 +
 src/backend/cuda/cusparseManager.cpp |  75 +++++++
 src/backend/cuda/cusparseManager.hpp |  34 +++
 src/backend/cuda/sparse.cpp          | 218 ++++++++++++++++++
 src/backend/cuda/sparse.hpp          |  19 ++
 src/backend/cuda/sparse_matmul.cpp   | 197 +++++++++++++++++
 src/backend/cuda/sparse_matmul.hpp   |  21 ++
 test/CMakeLists.txt                  |   8 +-
 15 files changed, 1077 insertions(+), 5 deletions(-)
 create mode 100644 include/af/sparse.h
 create mode 100644 src/api/c/sparse.cpp
 create mode 100644 src/api/c/sparse_matmul.cpp
 create mode 100644 src/api/c/sparse_t.hpp
 create mode 100644 src/backend/cuda/cusparseManager.cpp
 create mode 100644 src/backend/cuda/cusparseManager.hpp
 create mode 100644 src/backend/cuda/sparse.cpp
 create mode 100644 src/backend/cuda/sparse.hpp
 create mode 100644 src/backend/cuda/sparse_matmul.cpp
 create mode 100644 src/backend/cuda/sparse_matmul.hpp

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 3aa609d796..2220a3014f 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -130,7 +130,7 @@ IF (${CUDA_FOUND})
         ENDIF(NOT CUDA_CUDA_LIBRARY)
 
         OPTION(BUILD_CUDA "Build ArrayFire Examples for CUDA backend" ON)
-        BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
+        BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
         BUILD_ALL("${FILES}" cuda afcuda "")
     ELSE()
diff --git a/include/af/defines.h b/include/af/defines.h
index d3ba5fdfa7..d470a31ac6 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -394,6 +394,15 @@ typedef enum {
 } af_marker_type;
 #endif
 
+#if AF_API_VERSION >=34
+typedef enum {
+    AF_SPARSE_DENSE     = 0,
+    AF_SPARSE_COO       = 1,
+    AF_SPARSE_CSR       = 2,
+    AF_SPARSE_CSC       = 3,
+} af_sparse_storage;
+#endif
+
 #ifdef __cplusplus
 namespace af
 {
@@ -423,6 +432,10 @@ namespace af
 #if AF_API_VERSION >= 32
     typedef af_marker_type markerType;
 #endif
+#if AF_API_VERSION >= 34
+    typedef af_sparse_storage sparseStorage;
+#endif
+
 }
 
 #endif
diff --git a/include/af/sparse.h b/include/af/sparse.h
new file mode 100644
index 0000000000..9bdf9ec8ae
--- /dev/null
+++ b/include/af/sparse.h
@@ -0,0 +1,70 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+
+typedef void * af_sparse_array;
+
+#ifdef __cplusplus
+namespace af
+{
+    class array;
+
+}
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    AFAPI af_err af_create_sparse_array(
+                 af_sparse_array *out,
+                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const af_array values, const af_array rowIdx, const af_array colIdx,
+                 const af_sparse_storage storage);
+
+    AFAPI af_err af_create_sparse_array_from_host(
+                 af_sparse_array *out,
+                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const void * const values,
+                 const int * const rowIdx, const int * const colIdx,
+                 const af_dtype type, const af_sparse_storage storage);
+
+    AFAPI af_err af_create_sparse_array_from_dense(
+                 af_sparse_array *out, const af_array in,
+                 const af_sparse_storage storage);
+
+    AFAPI af_err af_retain_sparse_array(af_sparse_array *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_values(af_array *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_rows(af_array *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_cols(af_array *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_num_values(dim_t *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_num_rows(dim_t *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_num_cols(dim_t *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_get_storage(af_sparse_storage *out, const af_sparse_array in);
+
+    AFAPI af_err af_sparse_convert_storage(af_sparse_array *out, const af_sparse_array in,
+                                           const af_sparse_storage destStorage);
+
+    AFAPI af_err af_release_sparse_array(af_sparse_array in);
+
+    AFAPI af_err af_sparse_matmul(af_sparse_array *out,
+                            const af_sparse_array lhs, const af_sparse_array rhs,
+                            const af_mat_prop optLhs, const af_mat_prop optRhs);
+#ifdef __cplusplus
+}
+#endif
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 60df3176d1..085db86140 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -270,6 +270,7 @@
 #include "af/lapack.h"
 #include "af/seq.h"
 #include "af/signal.h"
+#include "af/sparse.h"
 #include "af/statistics.h"
 #include "af/timing.h"
 #include "af/util.h"
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
new file mode 100644
index 0000000000..10108ba0e9
--- /dev/null
+++ b/src/api/c/sparse.cpp
@@ -0,0 +1,320 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/sparse.h>
+#include <af/array.h>
+#include <af/algorithm.h>
+#include <sparse_t.hpp>
+#include <sparse.hpp>
+#include <handle.hpp>
+#include <backend.hpp>
+#include <err_common.hpp>
+#include <arith.hpp>
+#include <lookup.hpp>
+
+using namespace detail;
+using af::dim4;
+
+af_sparse_array getSparseHandle(const af_sparse_t sparse)
+{
+    af_sparse_t *sparseHandle = new af_sparse_t;
+    *sparseHandle = sparse;
+    return (af_sparse_array)sparseHandle;
+}
+
+af_sparse_t getSparse(const af_sparse_array sparseHandle)
+{
+    return *(af_sparse_t *)sparseHandle;
+}
+
+af_err af_release_sparse_array(af_sparse_array arr)
+{
+
+    try {
+        af_sparse_t sparse = *(af_sparse_t *)arr;
+        if (sparse.storage > 0) {
+            if (sparse.rowIdx != 0)     AF_CHECK(af_release_array(sparse.rowIdx));
+            if (sparse.colIdx != 0)     AF_CHECK(af_release_array(sparse.colIdx));
+            if (sparse.values != 0)     AF_CHECK(af_release_array(sparse.values));
+        }
+        delete (af_sparse_t *)arr;
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_retain_sparse_array(af_sparse_array *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t input = getSparse(in);
+        af_sparse_t output;
+
+        output.storage  = input.storage;
+        output.nRows    = input.nRows;
+        output.nCols    = input.nCols;
+        output.nNZ      = input.nNZ;
+
+        AF_CHECK(af_retain_array(&output.values, input.values));
+        AF_CHECK(af_retain_array(&output.rowIdx, input.rowIdx));
+        AF_CHECK(af_retain_array(&output.colIdx, input.colIdx));
+
+        *out = getSparseHandle(output);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Sparse Creation
+////////////////////////////////////////////////////////////////////////////////
+af_err af_create_sparse_array(
+                 af_sparse_array *out,
+                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const af_array values, const af_array rowIdx, const af_array colIdx,
+                 const af_sparse_storage storage)
+{
+    try {
+        // Checks:
+        // rowIdx and colIdx arrays are of s32 type
+        // values is of floating point type
+        // if COO, rowIdx, colIdx and values should have same dims
+        // if CRS, colIdx and values should have same dims, rowIdx.dims = nRows
+        // if CRC, rowIdx and values should have same dims, colIdx.dims = nCols
+        // storage is within acceptable range
+        // type is floating type
+
+        if(!(storage == AF_SPARSE_COO
+          || storage == AF_SPARSE_CSR
+          || storage == AF_SPARSE_CSC)) {
+            AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
+        }
+
+        ArrayInfo vInfo = getInfo(values);
+        ArrayInfo rInfo = getInfo(rowIdx);
+        ArrayInfo cInfo = getInfo(colIdx);
+
+        TYPE_ASSERT(vInfo.isFloating());
+        DIM_ASSERT(4, vInfo.isLinear());
+        DIM_ASSERT(5, rInfo.isLinear());
+        DIM_ASSERT(6, cInfo.isLinear());
+
+        af_sparse_t sparse;
+        sparse.storage = storage;
+        sparse.nRows = nRows;
+        sparse.nCols = nCols;
+        sparse.nNZ = nNZ;
+
+        AF_CHECK(af_retain_array(&sparse.rowIdx, rowIdx));
+        AF_CHECK(af_retain_array(&sparse.colIdx, colIdx));
+        AF_CHECK(af_retain_array(&sparse.values, values));
+
+        *out = getSparseHandle(sparse);
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_create_sparse_array_from_host(
+                 af_sparse_array *out,
+                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const void * const values,
+                 const int * const rowIdx, const int * const colIdx,
+                 const af_dtype type, const af_sparse_storage storage)
+{
+    try {
+        // Checks:
+        // rowIdx and colIdx arrays are of s32 type
+        // values is of floating point type
+        // if COO, rowIdx, colIdx and values should have same dims
+        // if CRS, colIdx and values should have same dims, rowIdx.dims = nRows
+        // if CRC, rowIdx and values should have same dims, colIdx.dims = nCols
+        // storage is within acceptable range
+        // type is floating type
+        if(!(storage == AF_SPARSE_COO
+          || storage == AF_SPARSE_CSR
+          || storage == AF_SPARSE_CSC)) {
+            AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
+        }
+
+        TYPE_ASSERT(type == f32 || type == f64
+                 || type == c32 || type == c64);
+
+        af_sparse_t sparse;
+        sparse.storage = storage;
+        sparse.nRows = nRows;
+        sparse.nCols = nCols;
+        sparse.nNZ = nNZ;
+
+        AF_CHECK(af_create_array(&sparse.values, values, 1, &nNZ, type));
+
+        if(storage == AF_SPARSE_COO) {
+            AF_CHECK(af_create_array(&sparse.rowIdx, rowIdx, 1, &nNZ, s32));
+            AF_CHECK(af_create_array(&sparse.colIdx, colIdx, 1, &nNZ, s32));
+        } else if(storage == AF_SPARSE_CSR) {
+            AF_CHECK(af_create_array(&sparse.rowIdx, rowIdx, 1, &nRows, s32));
+            AF_CHECK(af_create_array(&sparse.colIdx, colIdx, 1, &nNZ, s32));
+        } else if(storage == AF_SPARSE_CSC) {
+            AF_CHECK(af_create_array(&sparse.rowIdx, rowIdx, 1, &nNZ, s32));
+            AF_CHECK(af_create_array(&sparse.colIdx, colIdx, 1, &nCols, s32));
+        }
+
+        *out = getSparseHandle(sparse);
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+template<typename T>
+void create_sparse_array_from_dense(af_sparse_t *out, const af_array in_,
+                                    const af_array nonZeroIdx_, const af_sparse_storage storage)
+{
+    Array<int> nonZeroIdx = castArray<int>(nonZeroIdx_);
+
+    const Array<T> in = getArray<T>(in_);
+
+    dim_t nNZ = nonZeroIdx.elements();
+    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
+
+    Array<int> rowIdx = *initArray<int>();
+    Array<int> colIdx = *initArray<int>();
+    Array<T>   values = *initArray<T>();
+
+    if(storage == AF_SPARSE_COO) {
+
+        rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+        colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+        values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    } else if(storage == AF_SPARSE_CSR) {
+        dense2storage<T, AF_SPARSE_CSR>(values, rowIdx, colIdx, in);
+
+    } else if(storage == AF_SPARSE_CSC) {
+        dense2storage<T, AF_SPARSE_CSC>(values, rowIdx, colIdx, in);
+    }
+
+    out->rowIdx = getHandle(rowIdx);
+    out->colIdx = getHandle(colIdx);
+    out->values = getHandle(values);
+
+}
+
+af_err af_create_sparse_array_from_dense(af_sparse_array *out, const af_array in,
+                                         const af_sparse_storage storage)
+{
+    try {
+        // Checks:
+        // storage is within acceptable range
+        // values is of floating point type
+
+        ArrayInfo info = getInfo(in);
+
+        if(!(storage == AF_SPARSE_COO
+          || storage == AF_SPARSE_CSR
+          || storage == AF_SPARSE_CSC)) {
+            AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
+        }
+
+        TYPE_ASSERT(info.isFloating());
+
+        af_sparse_t sparse;
+
+        af_array nonZeroIdx = 0; // Yes I know how this looks
+        AF_CHECK(af_where(&nonZeroIdx, in));
+
+        ArrayInfo nNZInfo = getInfo(nonZeroIdx);
+        dim_t nNZ = nNZInfo.elements();
+
+        sparse.storage = storage;
+        sparse.nRows = info.dims()[0];
+        sparse.nCols = info.dims()[1];
+        sparse.nNZ = nNZ;
+
+        switch(info.getType()) {
+            case f32: create_sparse_array_from_dense<float  >(&sparse, in, nonZeroIdx, storage); break;
+            case f64: create_sparse_array_from_dense<double >(&sparse, in, nonZeroIdx, storage); break;
+            case c32: create_sparse_array_from_dense<cfloat >(&sparse, in, nonZeroIdx, storage); break;
+            case c64: create_sparse_array_from_dense<cdouble>(&sparse, in, nonZeroIdx, storage); break;
+            default: TYPE_ERROR(1, info.getType());
+        }
+
+        // Call the conversion in the backend here
+
+        *out = getSparseHandle(sparse);
+
+        if(nonZeroIdx != 0) AF_CHECK(af_release_array(nonZeroIdx));
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Get Functions
+////////////////////////////////////////////////////////////////////////////////
+af_err af_sparse_get_values(af_array *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.values;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_sparse_get_rows(af_array *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.rowIdx;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_sparse_get_cols(af_array *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.colIdx;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_sparse_get_num_values(dim_t *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.nNZ;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_sparse_get_num_rows(dim_t *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.nRows;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_sparse_get_num_cols(dim_t *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.nCols;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_sparse_get_storage(af_sparse_storage *out, const af_sparse_array in)
+{
+    try {
+        af_sparse_t sparse = getSparse(in);
+        *out = sparse.storage;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/sparse_matmul.cpp b/src/api/c/sparse_matmul.cpp
new file mode 100644
index 0000000000..261b1b1eaf
--- /dev/null
+++ b/src/api/c/sparse_matmul.cpp
@@ -0,0 +1,79 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/sparse.h>
+#include <af/array.h>
+#include <sparse_t.hpp>
+#include <sparse_matmul.hpp>
+#include <handle.hpp>
+#include <backend.hpp>
+#include <err_common.hpp>
+
+using namespace detail;
+using af::dim4;
+
+template<typename T>
+static inline af_array matmul(const af_sparse_t lhs, const af_array rhs,
+                              af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    return getHandle(detail::matmul<T>(
+                lhs.nRows, lhs.nCols, lhs.nNZ,
+                getArray<T>(lhs.values), getArray<int>(lhs.rowIdx), getArray<int>(lhs.colIdx),
+                getArray<T>(rhs), optLhs, optRhs));
+}
+
+af_err af_sparse_matmul(af_array *out,
+                        const af_sparse_array lhs_, const af_sparse_array rhs,
+                        const af_mat_prop optLhs, const af_mat_prop optRhs)
+{
+    try {
+        af_sparse_t lhs = getSparse(lhs_);
+
+        ArrayInfo lhsInfo = getInfo(lhs.values);
+        ArrayInfo rhsInfo = getInfo(rhs);
+
+        af_dtype lhs_type = lhsInfo.getType();
+        af_dtype rhs_type = rhsInfo.getType();
+
+        ARG_ASSERT(1, lhs.storage == AF_SPARSE_CSR);
+
+        if (!(optLhs == AF_MAT_NONE ||
+              optLhs == AF_MAT_TRANS ||
+              optLhs == AF_MAT_CTRANS)) {
+            AF_ERROR("Using this property is not yet supported in sparse matmul", AF_ERR_NOT_SUPPORTED);
+        }
+        if (optRhs != AF_MAT_NONE) {
+            AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
+        }
+
+        if (rhsInfo.ndims() > 2) {
+            AF_ERROR("Sparse matmul can not be used in batch mode", AF_ERR_BATCH);
+        }
+
+        TYPE_ASSERT(lhs_type == rhs_type);
+
+        dim4 ldims(lhs.nRows, lhs.nCols);
+        int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+        int rRowDim = (optRhs == AF_MAT_NONE) ? 0 : 1;
+
+        DIM_ASSERT(1, ldims[lColDim] == rhsInfo.dims()[rRowDim]);
+
+        af_array output = 0;
+        switch(lhs_type) {
+            case f32: output = matmul<float  >(lhs, rhs, optLhs, optRhs);   break;
+            case c32: output = matmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
+            case f64: output = matmul<double >(lhs, rhs, optLhs, optRhs);   break;
+            case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
+            default:  TYPE_ERROR(1, lhs_type);
+        }
+        std::swap(*out, output);
+
+    } CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/sparse_t.hpp b/src/api/c/sparse_t.hpp
new file mode 100644
index 0000000000..82f2f0882c
--- /dev/null
+++ b/src/api/c/sparse_t.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <af/sparse.h>
+
+typedef struct {
+    dim_t nRows, nCols, nNZ;
+    af_sparse_storage storage;
+    af_array rowIdx;
+    af_array colIdx;
+    af_array values;
+} af_sparse_t;
+
+af_sparse_array getSparseHandle(const af_sparse_t sparse);
+
+af_sparse_t getSparse(const af_sparse_array sparseHandle);
+
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ee1dce8cc8..a97343c841 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -411,6 +411,7 @@ TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
+                                PRIVATE ${CUDA_cusparse_LIBRARY}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
                                 PRIVATE ${CUDA_CUDA_LIBRARY})
 
diff --git a/src/backend/cuda/cusparseManager.cpp b/src/backend/cuda/cusparseManager.cpp
new file mode 100644
index 0000000000..dfb8bf729e
--- /dev/null
+++ b/src/backend/cuda/cusparseManager.cpp
@@ -0,0 +1,75 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cusparseManager.hpp>
+#include <platform.hpp>
+
+#include <stdexcept>
+#include <string>
+#include <iostream>
+#include <boost/scoped_ptr.hpp>
+
+namespace cusparse {
+
+    const char *errorString(cusparseStatus_t err)
+    {
+        switch(err) {
+        case    CUSPARSE_STATUS_SUCCESS                     :   return "CUSPARSE_STATUS_SUCCESS"                    ;
+        case    CUSPARSE_STATUS_NOT_INITIALIZED             :   return "CUSPARSE_STATUS_NOT_INITIALIZED"            ;
+        case    CUSPARSE_STATUS_ALLOC_FAILED                :   return "CUSPARSE_STATUS_ALLOC_FAILED"               ;
+        case    CUSPARSE_STATUS_INVALID_VALUE               :   return "CUSPARSE_STATUS_INVALID_VALUE"              ;
+        case    CUSPARSE_STATUS_ARCH_MISMATCH               :   return "CUSPARSE_STATUS_ARCH_MISMATCH"              ;
+        case    CUSPARSE_STATUS_MAPPING_ERROR               :   return "CUSPARSE_STATUS_MAPPING_ERROR"              ;
+        case    CUSPARSE_STATUS_EXECUTION_FAILED            :   return "CUSPARSE_STATUS_EXECUTION_FAILED"           ;
+        case    CUSPARSE_STATUS_INTERNAL_ERROR              :   return "CUSPARSE_STATUS_INTERNAL_ERROR"             ;
+        case    CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
+        case    CUSPARSE_STATUS_ZERO_PIVOT                  :   return "CUSPARSE_STATUS_ZERO_PIVOT"                 ;
+        default                                             :   return "UNKNOWN";
+        }
+    }
+
+
+//RAII class around the cusparse Handle
+    class cusparseHandle
+    {
+        cusparseHandle_t handle;
+    public:
+
+        cusparseHandle()
+            : handle(0)
+        {
+            CUSPARSE_CHECK(cusparseCreate(&handle));
+        }
+
+        ~cusparseHandle()
+        {
+            cusparseDestroy(handle);
+        }
+
+        cusparseHandle_t get() const
+        {
+            return handle;
+        }
+    };
+
+    cusparseHandle_t getHandle()
+    {
+        using boost::scoped_ptr;
+        static scoped_ptr<cusparseHandle> handle[cuda::DeviceManager::MAX_DEVICES];
+
+        int id = cuda::getActiveDeviceId();
+
+        if(!handle[id]) {
+            handle[id].reset(new cusparseHandle());
+        }
+
+        return handle[id]->get();
+    }
+
+}
diff --git a/src/backend/cuda/cusparseManager.hpp b/src/backend/cuda/cusparseManager.hpp
new file mode 100644
index 0000000000..23fcbf12c9
--- /dev/null
+++ b/src/backend/cuda/cusparseManager.hpp
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <stdio.h>
+#include <err_common.hpp>
+#include <defines.hpp>
+#include <cusparse_v2.h>
+
+namespace cusparse {
+
+    const char * errorString(cusparseStatus_t err);
+    cusparseHandle_t getHandle();
+}
+
+
+#define CUSPARSE_CHECK(fn) do {                         \
+        cusparseStatus_t _error = fn;                   \
+        if (_error != CUSPARSE_STATUS_SUCCESS) {        \
+            char _err_msg[1024];                        \
+            snprintf(_err_msg, sizeof(_err_msg),        \
+                     "CUSPARSE Error (%d): %s\n",       \
+                     (int)(_error),                     \
+                     cusparse::errorString( _error));   \
+                                                        \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);        \
+        }                                               \
+    } while(0)
diff --git a/src/backend/cuda/sparse.cpp b/src/backend/cuda/sparse.cpp
new file mode 100644
index 0000000000..fc93e58220
--- /dev/null
+++ b/src/backend/cuda/sparse.cpp
@@ -0,0 +1,218 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse.hpp>
+#include <cusparseManager.hpp>
+#include <cuda_runtime.h>
+#include <platform.hpp>
+
+#include <stdexcept>
+#include <string>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <complex.hpp>
+
+namespace cuda
+{
+
+using cusparse::getHandle;
+using namespace std;
+
+//cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
+//                                  int m, int n, int nnz,
+//                                  const cuDoubleComplex *csrSortedVal,
+//                                  const int *csrSortedRowPtr, const int *csrSortedColInd,
+//                                  cuDoubleComplex *cscSortedVal,
+//                                  int *cscSortedRowInd, int *cscSortedColPtr,
+//                                  cusparseAction_t copyValues,
+//                                  cusparseIndexBase_t idxBase);
+
+template<typename T>
+struct csr2csc_func_def_t
+{
+    typedef cusparseStatus_t (*csr2csc_func_def)( cusparseHandle_t,
+                                                  int, int, int,
+                                                  const T *, const int *, const int *,
+                                                  T *, int *, int *,
+                                                  cusparseAction_t,
+                                                  cusparseIndexBase_t);
+};
+
+//cusparseStatus_t cusparseZdense2csr(cusparseHandle_t handle,
+//                                    int m, int n,
+//                                    const cusparseMatDescr_t descrA,
+//                                    const cuDoubleComplex *A, int lda,
+//                                    const int *nnzPerRow,
+//                                    cuDoubleComplex *csrValA,
+//                                    int *csrRowPtrA, int *csrColIndA)
+template<typename T>
+struct dense2csr_func_def_t
+{
+    typedef cusparseStatus_t (*dense2csr_func_def)( cusparseHandle_t,
+                                                    int, int,
+                                                    const cusparseMatDescr_t,
+                                                    const T *, int,
+                                                    const int *,
+                                                    T *,
+                                                    int *, int *);
+};
+
+//cusparseStatus_t cusparseZdense2csc(cusparseHandle_t handle,
+//                                    int m, int n,
+//                                    const cusparseMatDescr_t descrA,
+//                                    const cuDoubleComplex *A, int lda,
+//                                    const int *nnzPerCol,
+//                                    cuDoubleComplex *cscValA,
+//                                    int *cscRowIndA, int *cscColPtrA)
+template<typename T>
+struct dense2csc_func_def_t
+{
+    typedef cusparseStatus_t (*dense2csc_func_def)( cusparseHandle_t,
+                                                    int, int,
+                                                    const cusparseMatDescr_t,
+                                                    const T *, int,
+                                                    const int *,
+                                                    T *,
+                                                    int *, int *);
+};
+
+//cusparseStatus_t cusparseZnnz(cusparseHandle_t handle,
+//                              cusparseDirection_t dirA,
+//                              int m, int n,
+//                              const cusparseMatDescr_t descrA,
+//                              const cuDoubleComplex *A, int lda,
+//                              int *nnzPerRowColumn,
+//                              int *nnzTotalDevHostPtr)
+template<typename T>
+struct nnz_func_def_t
+{
+    typedef cusparseStatus_t (*nnz_func_def)( cusparseHandle_t,
+                                              cusparseDirection_t,
+                                              int, int,
+                                              const cusparseMatDescr_t,
+                                              const T *, int,
+                                              int *, int *);
+};
+
+#define SPARSE_FUNC_DEF( FUNC )                     \
+template<typename T>                                \
+typename FUNC##_func_def_t<T>::FUNC##_func_def      \
+FUNC##_func();
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )                           \
+template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def        \
+FUNC##_func<TYPE>()                                                 \
+{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusparse##PREFIX##FUNC; }
+
+SPARSE_FUNC_DEF(csr2csc)
+SPARSE_FUNC(csr2csc, float,  S)
+SPARSE_FUNC(csr2csc, double, D)
+SPARSE_FUNC(csr2csc, cfloat, C)
+SPARSE_FUNC(csr2csc, cdouble,Z)
+
+SPARSE_FUNC_DEF(dense2csr)
+SPARSE_FUNC(dense2csr, float,  S)
+SPARSE_FUNC(dense2csr, double, D)
+SPARSE_FUNC(dense2csr, cfloat, C)
+SPARSE_FUNC(dense2csr, cdouble,Z)
+
+SPARSE_FUNC_DEF(dense2csc)
+SPARSE_FUNC(dense2csc, float,  S)
+SPARSE_FUNC(dense2csc, double, D)
+SPARSE_FUNC(dense2csc, cfloat, C)
+SPARSE_FUNC(dense2csc, cdouble,Z)
+
+SPARSE_FUNC_DEF(nnz)
+SPARSE_FUNC(nnz, float,  S)
+SPARSE_FUNC(nnz, double, D)
+SPARSE_FUNC(nnz, cfloat, C)
+SPARSE_FUNC(nnz, cdouble,Z)
+
+#undef SPARSE_FUNC
+#undef SPARSE_FUNC_DEF
+
+template<typename T, af_sparse_storage storage>
+void dense2storage(Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,
+                   const Array<T> in)
+{
+    const int M = in.dims()[0];
+    const int N = in.dims()[1];
+
+    // Create Sparse Matrix Descriptor
+    cusparseMatDescr_t descr = 0;
+    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
+    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+
+    int d = -1;
+    cusparseDirection_t dir = CUSPARSE_DIRECTION_ROW;
+
+    if(storage == AF_SPARSE_CSR) {
+        d = M;
+        dir = CUSPARSE_DIRECTION_ROW;
+    } else {
+        d = N;
+        dir = CUSPARSE_DIRECTION_COLUMN;
+    }
+    Array<int> nnzPerDir = createEmptyArray<int>(dim4(d));
+
+    int nNZ = -1;
+    CUSPARSE_CHECK(nnz_func<T>()(
+                        getHandle(),
+                        dir,
+                        M, N,
+                        descr,
+                        in.get(), in.strides()[1],
+                        nnzPerDir.get(), &nNZ));
+
+    if(storage == AF_SPARSE_CSR) {
+        rowIdx = createEmptyArray<int>(dim4(M+1));
+        colIdx = createEmptyArray<int>(dim4(nNZ));
+    } else {
+        rowIdx = createEmptyArray<int>(dim4(nNZ));
+        colIdx = createEmptyArray<int>(dim4(N+1));
+    }
+    values = createEmptyArray<T>(dim4(nNZ));
+
+    if(storage == AF_SPARSE_CSR)
+        CUSPARSE_CHECK(dense2csr_func<T>()(
+                        getHandle(),
+                        M, N,
+                        descr,
+                        in.get(), in.strides()[1],
+                        nnzPerDir.get(),
+                        values.get(), rowIdx.get(), colIdx.get()));
+    else
+        CUSPARSE_CHECK(dense2csc_func<T>()(
+                        getHandle(),
+                        M, N,
+                        descr,
+                        in.get(), in.strides()[1],
+                        nnzPerDir.get(),
+                        values.get(), rowIdx.get(), colIdx.get()));
+
+    // Destory Sparse Matrix Descriptor
+    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+}
+
+#define INSTANTIATE_SPARSE(T)                                                           \
+    template void dense2storage<T, AF_SPARSE_CSR>(                                      \
+        Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,                       \
+        const Array<T> in);                                                             \
+    template void dense2storage<T, AF_SPARSE_CSC>(                                      \
+        Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,                       \
+        const Array<T> in);                                                             \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+}
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
new file mode 100644
index 0000000000..bc39f340b5
--- /dev/null
+++ b/src/backend/cuda/sparse.hpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cuda
+{
+
+template<typename T, af_sparse_storage>
+void dense2storage(Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,
+                   const Array<T> in);
+
+}
diff --git a/src/backend/cuda/sparse_matmul.cpp b/src/backend/cuda/sparse_matmul.cpp
new file mode 100644
index 0000000000..e6ddfd1852
--- /dev/null
+++ b/src/backend/cuda/sparse_matmul.cpp
@@ -0,0 +1,197 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse_matmul.hpp>
+#include <cusparseManager.hpp>
+#include <cuda_runtime.h>
+#include <platform.hpp>
+
+#include <stdexcept>
+#include <string>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <complex.hpp>
+
+namespace cuda
+{
+
+using cusparse::getHandle;
+using namespace std;
+
+cusparseOperation_t
+toCusparseTranspose(af_mat_prop opt)
+{
+    cusparseOperation_t out = CUSPARSE_OPERATION_NON_TRANSPOSE;
+    switch(opt) {
+        case AF_MAT_NONE        : out = CUSPARSE_OPERATION_NON_TRANSPOSE;       break;
+        case AF_MAT_TRANS       : out = CUSPARSE_OPERATION_TRANSPOSE;           break;
+        case AF_MAT_CTRANS      : out = CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE; break;
+        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+    return out;
+}
+
+//cusparseStatus_t cusparseZcsrmm(  cusparseHandle_t handle,
+//                                  cusparseOperation_t transA,
+//                                  int m, int n, int k, int nnz,
+//                                  const cuDoubleComplex *alpha,
+//                                  const cusparseMatDescr_t descrA,
+//                                  const cuDoubleComplex *csrValA,
+//                                  const int *csrRowPtrA, const int *csrColIndA,
+//                                  const cuDoubleComplex *B, int ldb,
+//                                  const cuDoubleComplex *beta,
+//                                  cuDoubleComplex *C, int ldc);
+
+template<typename T>
+struct csrmm_func_def_t
+{
+    typedef cusparseStatus_t (*csrmm_func_def)( cusparseHandle_t,
+                                                cusparseOperation_t,
+                                                int, int, int, int,
+                                                const T *,
+                                                const cusparseMatDescr_t,
+                                                const T *, const int *, const int *,
+                                                const T *, int,
+                                                const T *,
+                                                T *, int);
+};
+
+//cusparseStatus_t cusparseZcsrmv(  cusparseHandle_t handle,
+//                                  cusparseOperation_t transA,
+//                                  int m, int n, int nnz,
+//                                  const cuDoubleComplex *alpha,
+//                                  const cusparseMatDescr_t descrA,
+//                                  const cuDoubleComplex *csrValA,
+//                                  const int *csrRowPtrA, const int *csrColIndA,
+//                                  const cuDoubleComplex *x,
+//                                  const cuDoubleComplex *beta,
+//                                  cuDoubleComplex *y)
+
+template<typename T>
+struct csrmv_func_def_t
+{
+    typedef cusparseStatus_t (*csrmv_func_def)( cusparseHandle_t,
+                                                cusparseOperation_t,
+                                                int, int, int,
+                                                const T *,
+                                                const cusparseMatDescr_t,
+                                                const T *, const int *, const int *,
+                                                const T *,
+                                                const T *,
+                                                T *);
+};
+
+//cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
+//                                  int m, int n, int nnz,
+//                                  const cuDoubleComplex *csrSortedVal,
+//                                  const int *csrSortedRowPtr, const int *csrSortedColInd,
+//                                  cuDoubleComplex *cscSortedVal,
+//                                  int *cscSortedRowInd, int *cscSortedColPtr,
+//                                  cusparseAction_t copyValues,
+//                                  cusparseIndexBase_t idxBase);
+
+#define SPARSE_FUNC_DEF( FUNC )                     \
+template<typename T>                                \
+typename FUNC##_func_def_t<T>::FUNC##_func_def      \
+FUNC##_func();
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )                           \
+template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def        \
+FUNC##_func<TYPE>()                                                 \
+{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusparse##PREFIX##FUNC; }
+
+SPARSE_FUNC_DEF(csrmm)
+SPARSE_FUNC(csrmm, float,  S)
+SPARSE_FUNC(csrmm, double, D)
+SPARSE_FUNC(csrmm, cfloat, C)
+SPARSE_FUNC(csrmm, cdouble,Z)
+
+SPARSE_FUNC_DEF(csrmv)
+SPARSE_FUNC(csrmv, float,  S)
+SPARSE_FUNC(csrmv, double, D)
+SPARSE_FUNC(csrmv, cfloat, C)
+SPARSE_FUNC(csrmv, cdouble,Z)
+
+#undef SPARSE_FUNC
+#undef SPARSE_FUNC_DEF
+
+template<typename T>
+Array<T> matmul(
+         const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+         const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
+         const Array<T> rhs, af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    // Similar Operations to GEMM
+    cusparseOperation_t lOpts = toCusparseTranspose(optLhs);
+
+    int lRowDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
+    int lColDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    static const int rColDim = 1; //Unsupported : (rOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+
+    dim4 lDims(nRows, nRows);
+    dim4 rDims = rhs.dims();
+    int M = lDims[lRowDim];
+    int N = rDims[rColDim];
+    int K = lDims[lColDim];
+
+    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    T alpha = scalar<T>(1);
+    T beta  = scalar<T>(0);
+
+    dim4 rStrides = rhs.strides();
+
+    // Create Sparse Matrix Descriptor
+    cusparseMatDescr_t descr = 0;
+    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
+    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+
+    // Call Matrix-Vector or Matrix-Matrix
+    if(rDims[rColDim] == 1) {
+        CUSPARSE_CHECK(csrmv_func<T>()(
+                       getHandle(),
+                       lOpts,
+                       M, K, nNZ,
+                       &alpha,
+                       descr, values.get(), rowIdx.get(), colIdx.get(),
+                       rhs.get(),
+                       &beta,
+                       out.get()));
+    } else {
+        CUSPARSE_CHECK(csrmm_func<T>()(
+                       getHandle(),
+                       lOpts,
+                       M, N, K, nNZ,
+                       &alpha,
+                       descr, values.get(), rowIdx.get(), colIdx.get(),
+                       rhs.get(), rStrides[1],
+                       &beta,
+                       out.get(),
+                       out.dims()[0]));
+    }
+
+    // Destory Sparse Matrix Descriptor
+    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+
+    return out;
+}
+
+#define INSTANTIATE_SPARSE(T)                                                           \
+    template Array<T> matmul<T>(                                                        \
+        const dim_t nRows, const dim_t nCols, const dim_t nNZ,                          \
+        const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,        \
+        const Array<T> rhs, af_mat_prop optLhs, af_mat_prop optRhs);                    \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+}
diff --git a/src/backend/cuda/sparse_matmul.hpp b/src/backend/cuda/sparse_matmul.hpp
new file mode 100644
index 0000000000..fd638c76f5
--- /dev/null
+++ b/src/backend/cuda/sparse_matmul.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cuda
+{
+
+template<typename T>
+Array<T> matmul(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
+                const Array<T> rhs, af_mat_prop optLhs, af_mat_prop optRhs);
+
+}
+
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 863353dcbb..f8ceaa7aca 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -224,7 +224,7 @@ IF (${CUDA_FOUND})
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
             OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
-            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
             FOREACH(FILE ${FILES})
                 GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -236,14 +236,14 @@ IF (${CUDA_FOUND})
         # ELSE OSX && CLANG && CUDA < 7
         ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
             OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
-            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
         ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
 
     ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
         # If OSX && CLANG && CUDA < 7
         IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
             FOREACH(FILE ${FILES})
                 GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -254,7 +254,7 @@ IF (${CUDA_FOUND})
 
         # ELSE OSX && CLANG && CUDA < 7
         ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
+            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
 
         ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
     ELSE()

From b50dfb87ef0b7216ce9d163e440986edcbc79355 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 24 May 2016 15:50:22 -0400
Subject: [PATCH 0563/2677] Added sparse function to C++/C API and ArrayInfo,
 CUDA Array

---
 include/af/array.h         | 16 ++++++++++++
 src/api/c/array.cpp        | 53 ++++++++++++++++++++++++++------------
 src/api/c/handle.hpp       |  2 ++
 src/api/cpp/array.cpp      |  2 ++
 src/backend/ArrayInfo.cpp  |  5 ++++
 src/backend/ArrayInfo.hpp  | 24 ++++++++++++++++-
 src/backend/cuda/Array.hpp |  1 +
 7 files changed, 86 insertions(+), 17 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 03500640c6..a11a000442 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -122,6 +122,7 @@ namespace af
             bool isfloating() const;
             bool isinteger() const;
             bool isbool() const;
+            bool issparse() const;
             void eval() const;
             array as(dtype type) const;
             array T() const;
@@ -649,6 +650,11 @@ namespace af
         */
         bool isbool() const;
 
+        /**
+           \brief Returns true if the array is a sparse array
+        */
+        bool issparse() const;
+
         /**
            \brief Evaluate any JIT expressions to generate data for the array
         */
@@ -1528,6 +1534,16 @@ extern "C" {
         \returns error codes
     */
     AFAPI af_err af_is_bool         (bool *result, const af_array arr);
+
+    /**
+        \brief Check if an array is sparse
+
+        \param[out] result is true if arr is sparse, otherwise false
+        \param[in] arr is the input array
+
+        \returns error codes
+    */
+    AFAPI af_err af_is_sparse       (bool *result, const af_array arr);
     /**
         @}
     */
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 80b0d85e60..ec023d0999 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -19,6 +19,26 @@ getInfo(const af_array arr, bool check)
 {
     const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
 
+    // Check Sparse
+    ARG_ASSERT(0, info->isSparse() == false);
+
+    if (check && info->getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    }
+
+    return *info;
+}
+
+const ArrayInfo&
+getSparseInfo(const af_array arr, bool sparseCheck, bool check)
+{
+    const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
+
+    // Check Sparse -> If false, then both standard Array<T> and SparseArray<T> are accepted
+    if(sparseCheck) {
+        ARG_ASSERT(0, info->isSparse() == true);
+    }
+
     if (check && info->getDevId() != detail::getActiveDeviceId()) {
         AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
     }
@@ -149,7 +169,7 @@ af_err af_copy_array(af_array *out, const af_array in)
 af_err af_get_data_ref_count(int *use_count, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        ArrayInfo info = getSparseInfo(in, false, false);
         const af_dtype type = info.getType();
 
         int res;
@@ -179,7 +199,7 @@ af_err af_release_array(af_array arr)
     try {
         int dev = getActiveDeviceId();
 
-        ArrayInfo info = getInfo(arr, false);
+        ArrayInfo info = getSparseInfo(arr, false, false);
 
         setDevice(info.getDevId());
 
@@ -220,7 +240,7 @@ static af_array retainHandle(const af_array in)
 
 af_array retain(const af_array in)
 {
-    af_dtype ty = getInfo(in).getType();
+    af_dtype ty = getSparseInfo(in, false, false).getType();
     switch(ty) {
     case f32: return retainHandle<float           >(in);
     case f64: return retainHandle<double          >(in);
@@ -289,7 +309,7 @@ af_err af_get_elements(dim_t *elems, const af_array arr)
 {
     try {
         // Do not check for device mismatch
-        *elems =  getInfo(arr, false).elements();
+        *elems =  getSparseInfo(arr, false, false).elements();
     } CATCHALL
     return AF_SUCCESS;
 }
@@ -298,7 +318,7 @@ af_err af_get_type(af_dtype *type, const af_array arr)
 {
     try {
         // Do not check for device mismatch
-        *type = getInfo(arr, false).getType();
+        *type = getInfo(arr, false, false).getType();
     } CATCHALL
     return AF_SUCCESS;
 }
@@ -308,7 +328,7 @@ af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
 {
     try {
         // Do not check for device mismatch
-        ArrayInfo info = getInfo(in, false);
+        ArrayInfo info = getInfo(in, false, false);
         *d0 = info.dims()[0];
         *d1 = info.dims()[1];
         *d2 = info.dims()[2];
@@ -322,7 +342,7 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
 {
     try {
         // Do not check for device mismatch
-        ArrayInfo info = getInfo(in, false);
+        ArrayInfo info = getInfo(in, false, false);
         *nd = info.ndims();
     }
     CATCHALL
@@ -331,15 +351,15 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
 
 
 #undef INSTANTIATE
-#define INSTANTIATE(fn1, fn2)                           \
-    af_err fn1(bool *result, const af_array in)         \
-    {                                                   \
-        try {                                           \
-            ArrayInfo info = getInfo(in, false);   \
-            *result = info.fn2();                       \
-        }                                               \
-        CATCHALL                                        \
-            return AF_SUCCESS;                          \
+#define INSTANTIATE(fn1, fn2)                                   \
+    af_err fn1(bool *result, const af_array in)                 \
+    {                                                           \
+        try {                                                   \
+            ArrayInfo info = getSparseInfo(in, false, false);   \
+            *result = info.fn2();                               \
+        }                                                       \
+        CATCHALL                                                \
+            return AF_SUCCESS;                                  \
     }
 
 INSTANTIATE(af_is_empty       , isEmpty       )
@@ -355,5 +375,6 @@ INSTANTIATE(af_is_realfloating, isRealFloating)
 INSTANTIATE(af_is_floating    , isFloating    )
 INSTANTIATE(af_is_integer     , isInteger     )
 INSTANTIATE(af_is_bool        , isBool        )
+INSTANTIATE(af_is_sparse      , isSparse      )
 
 #undef INSTANTIATE
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index e5dc3f43fe..a7c23a2abf 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -24,6 +24,7 @@ static const detail::Array<T> &
 getArray(const af_array &arr)
 {
     detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
+    ARG_ASSERT(0, A->isSparse() == false);
     return *A;
 }
 
@@ -59,6 +60,7 @@ static detail::Array<T> &
 getWritableArray(const af_array &arr)
 {
     const detail::Array<T> &A = getArray<T>(arr);
+    ARG_ASSERT(0, A.isSparse() == false);
     return const_cast<detail::Array<T>&>(A);
 }
 
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 8911154155..12cd04b147 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -294,6 +294,7 @@ namespace af
     INSTANTIATE(floating)
     INSTANTIATE(integer)
     INSTANTIATE(bool)
+    INSTANTIATE(sparse)
 
 #undef INSTANTIATE
 
@@ -609,6 +610,7 @@ namespace af
     MEM_FUNC(bool                   , isfloating)
     MEM_FUNC(bool                   , isinteger)
     MEM_FUNC(bool                   , isbool)
+    MEM_FUNC(bool                   , issparse)
     MEM_FUNC(void                   , eval)
     //MEM_FUNC(void                   , unlock)
 #undef MEM_FUNC
diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index 98a2264b5c..a95754346a 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -167,6 +167,11 @@ bool ArrayInfo::isLinear() const
     return true;
 }
 
+bool ArrayInfo::isSparse() const
+{
+    return is_sparse;
+}
+
 dim4 getOutDims(const dim4 &ldims, const dim4 &rdims, bool batchMode)
 {
     if (!batchMode) {
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/ArrayInfo.hpp
index d61dccf0af..aefd53775e 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/ArrayInfo.hpp
@@ -45,6 +45,7 @@ class ArrayInfo
     af::dim4        dim_size;
     dim_t           offset;
     af::dim4        dim_strides;
+    bool            is_sparse;
 
 public:
     ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride, af_dtype af_type):
@@ -52,7 +53,26 @@ class ArrayInfo
         type(af_type),
         dim_size(size),
         offset(offset_),
-        dim_strides(stride)
+        dim_strides(stride),
+        is_sparse(false)
+    {
+        af_init();
+        setId(id);
+#if __cplusplus > 199711l
+    static_assert(offsetof(ArrayInfo, devId) == 0,
+                  "ArrayInfo::devId must be the first member variable of ArrayInfo. \
+                   devId is used to encode the backend into the integer. \
+                   This is then used in the unified backend to check mismatched arrays.");
+#endif
+    }
+
+    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride, af_dtype af_type, bool sparse):
+        devId(id),
+        type(af_type),
+        dim_size(size),
+        offset(offset_),
+        dim_strides(stride),
+        is_sparse(sparse)
     {
         af_init();
         setId(id);
@@ -133,6 +153,8 @@ class ArrayInfo
     bool isBool() const;
 
     bool isLinear() const;
+
+    bool isSparse() const;
 };
 #if __cplusplus > 199711l
     static_assert(std::is_standard_layout<ArrayInfo>::value, "ArrayInfo must be a standard layout type");
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 2adbd35a84..b622ef8cdb 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -152,6 +152,7 @@ namespace cuda
         INFO_IS_FUNC(isInteger);
         INFO_IS_FUNC(isBool);
         INFO_IS_FUNC(isLinear);
+        INFO_IS_FUNC(isSparse);
 
 #undef INFO_IS_FUNC
 

From b6de1e7baeae3c8876dcd7113560eebe9c45db2b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 26 May 2016 18:15:49 -0400
Subject: [PATCH 0564/2677] Refactored sparse class to use af_array

* Added SparseArray and SparseArrayBase wrapper classes
* Added functions to handle/get etc
---
 include/af/sparse.h                           |  36 +--
 src/api/c/array.cpp                           | 124 ++++----
 src/api/c/blas.cpp                            |  86 +++++-
 src/api/c/handle.hpp                          |   2 +-
 src/api/c/sparse.cpp                          | 266 +++++++++---------
 src/api/c/sparse_handle.hpp                   |  63 +++++
 src/api/c/sparse_matmul.cpp                   |  79 ------
 src/api/c/sparse_t.hpp                        |  24 --
 src/api/cpp/array.cpp                         |   2 +-
 src/backend/SparseArray.cpp                   | 254 +++++++++++++++++
 src/backend/SparseArray.hpp                   | 228 +++++++++++++++
 src/backend/cuda/cusparseManager.cpp          |   1 +
 .../{sparse_matmul.cpp => sparse_blas.cpp}    |  30 +-
 .../{sparse_matmul.hpp => sparse_blas.hpp}    |   6 +-
 src/backend/sparse_helpers.hpp                |  55 ++++
 15 files changed, 918 insertions(+), 338 deletions(-)
 create mode 100644 src/api/c/sparse_handle.hpp
 delete mode 100644 src/api/c/sparse_matmul.cpp
 delete mode 100644 src/api/c/sparse_t.hpp
 create mode 100644 src/backend/SparseArray.cpp
 create mode 100644 src/backend/SparseArray.hpp
 rename src/backend/cuda/{sparse_matmul.cpp => sparse_blas.cpp} (87%)
 rename src/backend/cuda/{sparse_matmul.hpp => sparse_blas.hpp} (62%)
 create mode 100644 src/backend/sparse_helpers.hpp

diff --git a/include/af/sparse.h b/include/af/sparse.h
index 9bdf9ec8ae..6ea17d5471 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -10,8 +10,6 @@
 #pragma once
 #include <af/defines.h>
 
-typedef void * af_sparse_array;
-
 #ifdef __cplusplus
 namespace af
 {
@@ -25,46 +23,42 @@ extern "C" {
 #endif
 
     AFAPI af_err af_create_sparse_array(
-                 af_sparse_array *out,
+                 af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                  const af_array values, const af_array rowIdx, const af_array colIdx,
                  const af_sparse_storage storage);
 
-    AFAPI af_err af_create_sparse_array_from_host(
-                 af_sparse_array *out,
+    AFAPI af_err af_create_sparse_array_from_ptr(
+                 af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                  const void * const values,
                  const int * const rowIdx, const int * const colIdx,
-                 const af_dtype type, const af_sparse_storage storage);
+                 const af_dtype type, const af_sparse_storage storage,
+                 const af_source source);
 
     AFAPI af_err af_create_sparse_array_from_dense(
-                 af_sparse_array *out, const af_array in,
+                 af_array *out, const af_array in,
                  const af_sparse_storage storage);
 
-    AFAPI af_err af_retain_sparse_array(af_sparse_array *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols, const af_array in);
 
-    AFAPI af_err af_sparse_get_values(af_array *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_values(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_rows(af_array *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_rows(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_cols(af_array *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_cols(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_num_values(dim_t *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_num_values(dim_t *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_num_rows(dim_t *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_num_rows(dim_t *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_num_cols(dim_t *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_num_cols(dim_t *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_storage(af_sparse_storage *out, const af_sparse_array in);
+    AFAPI af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in);
 
-    AFAPI af_err af_sparse_convert_storage(af_sparse_array *out, const af_sparse_array in,
+    AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
                                            const af_sparse_storage destStorage);
 
-    AFAPI af_err af_release_sparse_array(af_sparse_array in);
-
-    AFAPI af_err af_sparse_matmul(af_sparse_array *out,
-                            const af_sparse_array lhs, const af_sparse_array rhs,
-                            const af_mat_prop optLhs, const af_mat_prop optRhs);
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index ec023d0999..d029561f20 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -11,35 +11,22 @@
 #include <platform.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
+#include <sparse_handle.hpp>
 
 using namespace detail;
 
 const ArrayInfo&
-getInfo(const af_array arr, bool check)
-{
-    const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
-
-    // Check Sparse
-    ARG_ASSERT(0, info->isSparse() == false);
-
-    if (check && info->getDevId() != detail::getActiveDeviceId()) {
-        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
-    }
-
-    return *info;
-}
-
-const ArrayInfo&
-getSparseInfo(const af_array arr, bool sparseCheck, bool check)
+getInfo(const af_array arr, bool sparse_check, bool device_check)
 {
     const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
 
     // Check Sparse -> If false, then both standard Array<T> and SparseArray<T> are accepted
-    if(sparseCheck) {
-        ARG_ASSERT(0, info->isSparse() == true);
+    // Otherwise only regular Array<T> is accepted
+    if(sparse_check) {
+        ARG_ASSERT(0, info->isSparse() == false);
     }
 
-    if (check && info->getDevId() != detail::getActiveDeviceId()) {
+    if (device_check && info->getDevId() != detail::getActiveDeviceId()) {
         AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
     }
 
@@ -169,7 +156,7 @@ af_err af_copy_array(af_array *out, const af_array in)
 af_err af_get_data_ref_count(int *use_count, const af_array in)
 {
     try {
-        ArrayInfo info = getSparseInfo(in, false, false);
+        ArrayInfo info = getInfo(in, false, false);
         const af_dtype type = info.getType();
 
         int res;
@@ -199,29 +186,39 @@ af_err af_release_array(af_array arr)
     try {
         int dev = getActiveDeviceId();
 
-        ArrayInfo info = getSparseInfo(arr, false, false);
-
-        setDevice(info.getDevId());
-
+        ArrayInfo info = getInfo(arr, false, false);
         af_dtype type = info.getType();
 
-        switch(type) {
-        case f32:   releaseHandle<float   >(arr); break;
-        case c32:   releaseHandle<cfloat  >(arr); break;
-        case f64:   releaseHandle<double  >(arr); break;
-        case c64:   releaseHandle<cdouble >(arr); break;
-        case b8:    releaseHandle<char    >(arr); break;
-        case s32:   releaseHandle<int     >(arr); break;
-        case u32:   releaseHandle<uint    >(arr); break;
-        case u8:    releaseHandle<uchar   >(arr); break;
-        case s64:   releaseHandle<intl    >(arr); break;
-        case u64:   releaseHandle<uintl   >(arr); break;
-        case s16:   releaseHandle<short   >(arr); break;
-        case u16:   releaseHandle<ushort  >(arr); break;
-        default:    TYPE_ERROR(0, type);
+        if(info.isSparse()) {
+            switch(type) {
+                case f32: releaseSparseHandle<float  >(arr); break;
+                case f64: releaseSparseHandle<double >(arr); break;
+                case c32: releaseSparseHandle<cfloat >(arr); break;
+                case c64: releaseSparseHandle<cdouble>(arr); break;
+                default : TYPE_ERROR(0, type);
+            }
+        } else {
+
+            setDevice(info.getDevId());
+
+            switch(type) {
+            case f32:   releaseHandle<float   >(arr); break;
+            case c32:   releaseHandle<cfloat  >(arr); break;
+            case f64:   releaseHandle<double  >(arr); break;
+            case c64:   releaseHandle<cdouble >(arr); break;
+            case b8:    releaseHandle<char    >(arr); break;
+            case s32:   releaseHandle<int     >(arr); break;
+            case u32:   releaseHandle<uint    >(arr); break;
+            case u8:    releaseHandle<uchar   >(arr); break;
+            case s64:   releaseHandle<intl    >(arr); break;
+            case u64:   releaseHandle<uintl   >(arr); break;
+            case s16:   releaseHandle<short   >(arr); break;
+            case u16:   releaseHandle<ushort  >(arr); break;
+            default:    TYPE_ERROR(0, type);
+            }
+
+            setDevice(dev);
         }
-
-        setDevice(dev);
     }
     CATCHALL
 
@@ -240,22 +237,33 @@ static af_array retainHandle(const af_array in)
 
 af_array retain(const af_array in)
 {
-    af_dtype ty = getSparseInfo(in, false, false).getType();
-    switch(ty) {
-    case f32: return retainHandle<float           >(in);
-    case f64: return retainHandle<double          >(in);
-    case s32: return retainHandle<int             >(in);
-    case u32: return retainHandle<uint            >(in);
-    case u8:  return retainHandle<uchar           >(in);
-    case c32: return retainHandle<detail::cfloat  >(in);
-    case c64: return retainHandle<detail::cdouble >(in);
-    case b8:  return retainHandle<char            >(in);
-    case s64: return retainHandle<intl            >(in);
-    case u64: return retainHandle<uintl           >(in);
-    case s16: return retainHandle<short           >(in);
-    case u16: return retainHandle<ushort          >(in);
-    default:
-        TYPE_ERROR(1, ty);
+    ArrayInfo info = getInfo(in, false, false);
+    af_dtype ty = info.getType();
+
+    if(info.isSparse()) {
+        switch(ty) {
+        case f32: return retainSparseHandle<float          >(in);
+        case f64: return retainSparseHandle<double         >(in);
+        case c32: return retainSparseHandle<detail::cfloat >(in);
+        case c64: return retainSparseHandle<detail::cdouble>(in);
+        default: TYPE_ERROR(1, ty);
+        }
+    } else {
+        switch(ty) {
+        case f32: return retainHandle<float           >(in);
+        case f64: return retainHandle<double          >(in);
+        case s32: return retainHandle<int             >(in);
+        case u32: return retainHandle<uint            >(in);
+        case u8:  return retainHandle<uchar           >(in);
+        case c32: return retainHandle<detail::cfloat  >(in);
+        case c64: return retainHandle<detail::cdouble >(in);
+        case b8:  return retainHandle<char            >(in);
+        case s64: return retainHandle<intl            >(in);
+        case u64: return retainHandle<uintl           >(in);
+        case s16: return retainHandle<short           >(in);
+        case u16: return retainHandle<ushort          >(in);
+        default: TYPE_ERROR(1, ty);
+        }
     }
 }
 
@@ -309,7 +317,7 @@ af_err af_get_elements(dim_t *elems, const af_array arr)
 {
     try {
         // Do not check for device mismatch
-        *elems =  getSparseInfo(arr, false, false).elements();
+        *elems =  getInfo(arr, false, false).elements();
     } CATCHALL
     return AF_SUCCESS;
 }
@@ -355,7 +363,7 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
     af_err fn1(bool *result, const af_array in)                 \
     {                                                           \
         try {                                                   \
-            ArrayInfo info = getSparseInfo(in, false, false);   \
+            ArrayInfo info = getInfo(in, false, false);         \
             *result = info.fn2();                               \
         }                                                       \
         CATCHALL                                                \
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 21fb44fe4a..d2c116ed2f 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -14,9 +14,19 @@
 #include <af/array.h>
 #include <af/defines.h>
 #include <ArrayInfo.hpp>
+#include <sparse_handle.hpp>
+#include <sparse_blas.hpp>
 #include <err_common.hpp>
 #include <backend.hpp>
 
+template<typename T>
+static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
+                                    af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    return getHandle(detail::matmul<T>(getSparseArray<T>(lhs), getArray<T>(rhs),
+                     optLhs, optRhs));
+}
+
 template<typename T>
 static inline af_array matmul(const af_array lhs, const af_array rhs,
                     af_mat_prop optLhs, af_mat_prop optRhs)
@@ -31,16 +41,74 @@ static inline af_array dot(const af_array lhs, const af_array rhs,
     return getHandle(detail::dot<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
-af_err af_matmul(   af_array *out,
-                    const af_array lhs, const af_array rhs,
-                    const af_mat_prop optLhs, const af_mat_prop optRhs)
+af_err af_sparse_matmul(af_array *out,
+                        const af_array lhs, const af_array rhs,
+                        const af_mat_prop optLhs, const af_mat_prop optRhs)
 {
     using namespace detail;
 
     try {
-        ArrayInfo lhsInfo = getInfo(lhs);
+        common::SparseArrayBase lhsInfo = getSparseArrayBase(lhs);
         ArrayInfo rhsInfo = getInfo(rhs);
 
+        ARG_ASSERT(2, lhsInfo.isSparse() == true && rhsInfo.isSparse() == false);
+
+        af_dtype lhs_type = lhsInfo.getType();
+        af_dtype rhs_type = rhsInfo.getType();
+
+        ARG_ASSERT(1, lhsInfo.getStorage() == AF_SPARSE_CSR);
+
+        if (!(optLhs == AF_MAT_NONE ||
+              optLhs == AF_MAT_TRANS ||
+              optLhs == AF_MAT_CTRANS)) {   // Note the ! operator.
+            AF_ERROR("Using this property is not yet supported in sparse matmul", AF_ERR_NOT_SUPPORTED);
+        }
+
+        // No transpose options for RHS
+        if (optRhs != AF_MAT_NONE) {
+            AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
+        }
+
+        if (rhsInfo.ndims() > 2) {
+            AF_ERROR("Sparse matmul can not be used in batch mode", AF_ERR_BATCH);
+        }
+
+        TYPE_ASSERT(lhs_type == rhs_type);
+
+        af::dim4 ldims = lhsInfo.dims();
+        int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+        int rRowDim = (optRhs == AF_MAT_NONE) ? 0 : 1;
+
+        DIM_ASSERT(1, ldims[lColDim] == rhsInfo.dims()[rRowDim]);
+
+        af_array output = 0;
+        switch(lhs_type) {
+            case f32: output = sparseMatmul<float  >(lhs, rhs, optLhs, optRhs);   break;
+            case c32: output = sparseMatmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
+            case f64: output = sparseMatmul<double >(lhs, rhs, optLhs, optRhs);   break;
+            case c64: output = sparseMatmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
+            default:  TYPE_ERROR(1, lhs_type);
+        }
+        std::swap(*out, output);
+
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_matmul(af_array *out,
+                 const af_array lhs, const af_array rhs,
+                 const af_mat_prop optLhs, const af_mat_prop optRhs)
+{
+    using namespace detail;
+
+    try {
+        ArrayInfo lhsInfo = getInfo(lhs, false, false);
+        ArrayInfo rhsInfo = getInfo(rhs, true, false);
+
+        if(lhsInfo.isSparse())
+            return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
+
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
 
@@ -71,11 +139,11 @@ af_err af_matmul(   af_array *out,
         DIM_ASSERT(1, lhsInfo.dims()[aColDim] == rhsInfo.dims()[bRowDim]);
 
         switch(lhs_type) {
-        case f32: output = matmul<float  >(lhs, rhs, optLhs, optRhs);   break;
-        case c32: output = matmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
-        case f64: output = matmul<double >(lhs, rhs, optLhs, optRhs);   break;
-        case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
-        default:  TYPE_ERROR(1, lhs_type);
+            case f32: output = matmul<float  >(lhs, rhs, optLhs, optRhs);   break;
+            case c32: output = matmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
+            case f64: output = matmul<double >(lhs, rhs, optLhs, optRhs);   break;
+            case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
+            default:  TYPE_ERROR(1, lhs_type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index a7c23a2abf..3c7be92013 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <af/dim4.hpp>
 
-const ArrayInfo& getInfo(const af_array arr, bool check = true);
+const ArrayInfo& getInfo(const af_array arr, bool device_check = true, bool sparse_check = true);
 
 template<typename T>
 static const detail::Array<T> &
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 10108ba0e9..0035eda4c3 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -10,71 +10,52 @@
 #include <af/sparse.h>
 #include <af/array.h>
 #include <af/algorithm.h>
-#include <sparse_t.hpp>
+#include <sparse_handle.hpp>
 #include <sparse.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <err_common.hpp>
 #include <arith.hpp>
 #include <lookup.hpp>
+#include <platform.hpp>
 
 using namespace detail;
+using namespace common;
 using af::dim4;
 
-af_sparse_array getSparseHandle(const af_sparse_t sparse)
+const SparseArrayBase& getSparseArrayBase(const af_array in, bool device_check)
 {
-    af_sparse_t *sparseHandle = new af_sparse_t;
-    *sparseHandle = sparse;
-    return (af_sparse_array)sparseHandle;
-}
-
-af_sparse_t getSparse(const af_sparse_array sparseHandle)
-{
-    return *(af_sparse_t *)sparseHandle;
-}
+    const SparseArrayBase *base = static_cast<SparseArrayBase*>(reinterpret_cast<void *>(in));
 
-af_err af_release_sparse_array(af_sparse_array arr)
-{
-
-    try {
-        af_sparse_t sparse = *(af_sparse_t *)arr;
-        if (sparse.storage > 0) {
-            if (sparse.rowIdx != 0)     AF_CHECK(af_release_array(sparse.rowIdx));
-            if (sparse.colIdx != 0)     AF_CHECK(af_release_array(sparse.colIdx));
-            if (sparse.values != 0)     AF_CHECK(af_release_array(sparse.values));
-        }
-        delete (af_sparse_t *)arr;
+    if(!base->isSparse()) {
+        AF_ERROR("Input is not a SparseArray and cannot be used in Sparse functions",
+                 AF_ERR_ARG);
     }
-    CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_retain_sparse_array(af_sparse_array *out, const af_sparse_array in)
-{
-    try {
-        af_sparse_t input = getSparse(in);
-        af_sparse_t output;
 
-        output.storage  = input.storage;
-        output.nRows    = input.nRows;
-        output.nCols    = input.nCols;
-        output.nNZ      = input.nNZ;
-
-        AF_CHECK(af_retain_array(&output.values, input.values));
-        AF_CHECK(af_retain_array(&output.rowIdx, input.rowIdx));
-        AF_CHECK(af_retain_array(&output.colIdx, input.colIdx));
-
-        *out = getSparseHandle(output);
+    if (device_check && base->getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
     }
-    CATCHALL;
-    return AF_SUCCESS;
+
+    return *base;
 }
 
 ////////////////////////////////////////////////////////////////////////////////
 // Sparse Creation
 ////////////////////////////////////////////////////////////////////////////////
+template<typename T>
+af_array createSparseArray(const af::dim4 &dims, const af_array values,
+                           const af_array rowIdx, const af_array colIdx,
+                           const af::sparseStorage storage)
+{
+    SparseArray<T> sparse = common::createArrayDataSparseArray(
+                            dims, getArray<T>(values),
+                            getArray<int>(rowIdx), getArray<int>(colIdx),
+                            storage);
+    return getHandle(sparse);
+}
+
 af_err af_create_sparse_array(
-                 af_sparse_array *out,
+                 af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                  const af_array values, const af_array rowIdx, const af_array colIdx,
                  const af_sparse_storage storage)
@@ -104,28 +85,48 @@ af_err af_create_sparse_array(
         DIM_ASSERT(5, rInfo.isLinear());
         DIM_ASSERT(6, cInfo.isLinear());
 
-        af_sparse_t sparse;
-        sparse.storage = storage;
-        sparse.nRows = nRows;
-        sparse.nCols = nCols;
-        sparse.nNZ = nNZ;
+        af_array output = 0;
+
+        af::dim4 dims(nRows, nCols);
 
-        AF_CHECK(af_retain_array(&sparse.rowIdx, rowIdx));
-        AF_CHECK(af_retain_array(&sparse.colIdx, colIdx));
-        AF_CHECK(af_retain_array(&sparse.values, values));
+        switch(vInfo.getType()) {
+            case f32: output = createSparseArray<float  >(dims, values, rowIdx, colIdx, storage); break;
+            case f64: output = createSparseArray<double >(dims, values, rowIdx, colIdx, storage); break;
+            case c32: output = createSparseArray<cfloat >(dims, values, rowIdx, colIdx, storage); break;
+            case c64: output = createSparseArray<cdouble>(dims, values, rowIdx, colIdx, storage); break;
+            default : TYPE_ERROR(1, vInfo.getType());
+        }
+        std::swap(*out, output);
 
-        *out = getSparseHandle(sparse);
     } CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_create_sparse_array_from_host(
-                 af_sparse_array *out,
+template<typename T>
+af_array createSparseArrayFromPtr(
+        const af::dim4 &dims, const dim_t nNZ,
+        const T * const values, const int * const rowIdx, const int * const colIdx,
+        const af::sparseStorage storage, const af::source source)
+{
+    SparseArray<T> sparse = createEmptySparseArray<T>(dims, nNZ, storage);
+
+    if(source == afHost)
+        sparse = common::createHostDataSparseArray(
+                         dims, nNZ, values, rowIdx, colIdx, storage);
+    else if (source == afDevice)
+        sparse = common::createDeviceDataSparseArray(
+                         dims, nNZ, values, rowIdx, colIdx, storage);
+
+    return getHandle(sparse);
+}
+
+af_err af_create_sparse_array_from_ptr(
+                 af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-                 const void * const values,
-                 const int * const rowIdx, const int * const colIdx,
-                 const af_dtype type, const af_sparse_storage storage)
+                 const void * const values, const int * const rowIdx, const int * const colIdx,
+                 const af_dtype type, const af_sparse_storage storage,
+                 const af_source source)
 {
     try {
         // Checks:
@@ -145,40 +146,42 @@ af_err af_create_sparse_array_from_host(
         TYPE_ASSERT(type == f32 || type == f64
                  || type == c32 || type == c64);
 
-        af_sparse_t sparse;
-        sparse.storage = storage;
-        sparse.nRows = nRows;
-        sparse.nCols = nCols;
-        sparse.nNZ = nNZ;
-
-        AF_CHECK(af_create_array(&sparse.values, values, 1, &nNZ, type));
-
-        if(storage == AF_SPARSE_COO) {
-            AF_CHECK(af_create_array(&sparse.rowIdx, rowIdx, 1, &nNZ, s32));
-            AF_CHECK(af_create_array(&sparse.colIdx, colIdx, 1, &nNZ, s32));
-        } else if(storage == AF_SPARSE_CSR) {
-            AF_CHECK(af_create_array(&sparse.rowIdx, rowIdx, 1, &nRows, s32));
-            AF_CHECK(af_create_array(&sparse.colIdx, colIdx, 1, &nNZ, s32));
-        } else if(storage == AF_SPARSE_CSC) {
-            AF_CHECK(af_create_array(&sparse.rowIdx, rowIdx, 1, &nNZ, s32));
-            AF_CHECK(af_create_array(&sparse.colIdx, colIdx, 1, &nCols, s32));
+
+        af_array output = 0;
+
+        af::dim4 dims(nRows, nCols);
+
+        switch(type) {
+            case f32: output = createSparseArrayFromPtr<float  >
+                               (dims, nNZ, static_cast<const float  *>(values), rowIdx, colIdx, storage, source);
+                      break;
+            case f64: output = createSparseArrayFromPtr<double >
+                               (dims, nNZ, static_cast<const double *>(values), rowIdx, colIdx, storage, source);
+                      break;
+            case c32: output = createSparseArrayFromPtr<cfloat >
+                               (dims, nNZ, static_cast<const cfloat *>(values), rowIdx, colIdx, storage, source);
+                      break;
+            case c64: output = createSparseArrayFromPtr<cdouble>
+                               (dims, nNZ, static_cast<const cdouble *>(values), rowIdx, colIdx, storage, source);
+                      break;
+            default : TYPE_ERROR(1, type);
         }
+        std::swap(*out, output);
 
-        *out = getSparseHandle(sparse);
     } CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-void create_sparse_array_from_dense(af_sparse_t *out, const af_array in_,
-                                    const af_array nonZeroIdx_, const af_sparse_storage storage)
+af_array createSparseArrayFromDense(
+        const af::dim4 &dims, const dim_t nNZ,
+        const af_array _in, const af_array _nonZeroIdx,
+        const af_sparse_storage storage)
 {
-    Array<int> nonZeroIdx = castArray<int>(nonZeroIdx_);
-
-    const Array<T> in = getArray<T>(in_);
+    Array<int> nonZeroIdx = castArray<int>(_nonZeroIdx);
+    const Array<T> in = getArray<T>(_in);
 
-    dim_t nNZ = nonZeroIdx.elements();
     Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
 
     Array<int> rowIdx = *initArray<int>();
@@ -186,25 +189,22 @@ void create_sparse_array_from_dense(af_sparse_t *out, const af_array in_,
     Array<T>   values = *initArray<T>();
 
     if(storage == AF_SPARSE_COO) {
-
         rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
         colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
         values = lookup<T, int>(in, nonZeroIdx, 0);
-
     } else if(storage == AF_SPARSE_CSR) {
         dense2storage<T, AF_SPARSE_CSR>(values, rowIdx, colIdx, in);
-
     } else if(storage == AF_SPARSE_CSC) {
         dense2storage<T, AF_SPARSE_CSC>(values, rowIdx, colIdx, in);
     }
 
-    out->rowIdx = getHandle(rowIdx);
-    out->colIdx = getHandle(colIdx);
-    out->values = getHandle(values);
+    SparseArray<T> sparse = common::createArrayDataSparseArray(
+                            dims, values, rowIdx, colIdx, storage);
 
+    return getHandle(sparse);
 }
 
-af_err af_create_sparse_array_from_dense(af_sparse_array *out, const af_array in,
+af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
                                          const af_sparse_storage storage)
 {
     try {
@@ -220,9 +220,10 @@ af_err af_create_sparse_array_from_dense(af_sparse_array *out, const af_array in
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
-        TYPE_ASSERT(info.isFloating());
+        // Only matrices allowed
+        DIM_ASSERT(1, info.ndims() == 2);
 
-        af_sparse_t sparse;
+        TYPE_ASSERT(info.isFloating());
 
         af_array nonZeroIdx = 0; // Yes I know how this looks
         AF_CHECK(af_where(&nonZeroIdx, in));
@@ -230,22 +231,18 @@ af_err af_create_sparse_array_from_dense(af_sparse_array *out, const af_array in
         ArrayInfo nNZInfo = getInfo(nonZeroIdx);
         dim_t nNZ = nNZInfo.elements();
 
-        sparse.storage = storage;
-        sparse.nRows = info.dims()[0];
-        sparse.nCols = info.dims()[1];
-        sparse.nNZ = nNZ;
+        af::dim4 dims(info.dims()[0], info.dims()[1]);
+
+        af_array output = 0;
 
         switch(info.getType()) {
-            case f32: create_sparse_array_from_dense<float  >(&sparse, in, nonZeroIdx, storage); break;
-            case f64: create_sparse_array_from_dense<double >(&sparse, in, nonZeroIdx, storage); break;
-            case c32: create_sparse_array_from_dense<cfloat >(&sparse, in, nonZeroIdx, storage); break;
-            case c64: create_sparse_array_from_dense<cdouble>(&sparse, in, nonZeroIdx, storage); break;
+            case f32: output = createSparseArrayFromDense<float  >(dims, nNZ, in, nonZeroIdx, storage); break;
+            case f64: output = createSparseArrayFromDense<double >(dims, nNZ, in, nonZeroIdx, storage); break;
+            case c32: output = createSparseArrayFromDense<cfloat >(dims, nNZ, in, nonZeroIdx, storage); break;
+            case c64: output = createSparseArrayFromDense<cdouble>(dims, nNZ, in, nonZeroIdx, storage); break;
             default: TYPE_ERROR(1, info.getType());
         }
-
-        // Call the conversion in the backend here
-
-        *out = getSparseHandle(sparse);
+        std::swap(*out, output);
 
         if(nonZeroIdx != 0) AF_CHECK(af_release_array(nonZeroIdx));
     } CATCHALL;
@@ -256,65 +253,82 @@ af_err af_create_sparse_array_from_dense(af_sparse_array *out, const af_array in
 ////////////////////////////////////////////////////////////////////////////////
 // Get Functions
 ////////////////////////////////////////////////////////////////////////////////
-af_err af_sparse_get_values(af_array *out, const af_sparse_array in)
+template<typename T>
+af_array getSparseValues(const af_array in)
 {
-    try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.values;
-    } CATCHALL;
+    return getHandle(getSparseArray<T>(in).getValues());
+}
+
+af_err af_sparse_get_values(af_array *out, const af_array in)
+{
+    try{
+        const SparseArrayBase base = getSparseArrayBase(in);
+
+        af_array output = 0;
+
+        switch(base.getType()) {
+            case f32: output = getSparseValues<float  >(in); break;
+            case f64: output = getSparseValues<double >(in); break;
+            case c32: output = getSparseValues<cfloat >(in); break;
+            case c64: output = getSparseValues<cdouble>(in); break;
+            default : TYPE_ERROR(1, base.getType());
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_rows(af_array *out, const af_sparse_array in)
+af_err af_sparse_get_rows(af_array *out, const af_array in)
 {
     try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.rowIdx;
+        const SparseArrayBase base = getSparseArrayBase(in);
+        *out = getHandle(base.getRows());
     } CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_cols(af_array *out, const af_sparse_array in)
+af_err af_sparse_get_cols(af_array *out, const af_array in)
 {
     try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.colIdx;
+        const SparseArrayBase base = getSparseArrayBase(in);
+        *out = getHandle(base.getColumns());
     } CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_num_values(dim_t *out, const af_sparse_array in)
+af_err af_sparse_get_num_values(dim_t *out, const af_array in)
 {
     try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.nNZ;
+        const SparseArrayBase base = getSparseArrayBase(in);
+        *out = base.getNNZ();
     } CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_num_rows(dim_t *out, const af_sparse_array in)
+af_err af_sparse_get_num_rows(dim_t *out, const af_array in)
 {
     try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.nRows;
+        const SparseArrayBase base = getSparseArrayBase(in);
+        *out = base.getRows().elements();
     } CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_num_cols(dim_t *out, const af_sparse_array in)
+af_err af_sparse_get_num_cols(dim_t *out, const af_array in)
 {
     try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.nCols;
+        const SparseArrayBase base = getSparseArrayBase(in);
+        *out = base.getColumns().elements();
     } CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_storage(af_sparse_storage *out, const af_sparse_array in)
+af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in)
 {
     try {
-        af_sparse_t sparse = getSparse(in);
-        *out = sparse.storage;
+        const SparseArrayBase base = getSparseArrayBase(in);
+        *out = base.getStorage();
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
new file mode 100644
index 0000000000..605042f787
--- /dev/null
+++ b/src/api/c/sparse_handle.hpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/array.h>
+#include <Array.hpp>
+#include <backend.hpp>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <copy.hpp>
+#include <cast.hpp>
+#include <af/dim4.hpp>
+
+#include <SparseArray.hpp>
+
+const common::SparseArrayBase& getSparseArrayBase(const af_array arr, bool device_check = true);
+
+template<typename T>
+const common::SparseArray<T>& getSparseArray(const af_array &arr)
+{
+    common::SparseArray<T> *A = reinterpret_cast<common::SparseArray<T>*>(arr);
+    ARG_ASSERT(0, A->isSparse() == true);
+    return *A;
+}
+
+template<typename T>
+common::SparseArray<T>& getWritableSparseArray(const af_array &arr)
+{
+    const common::SparseArray<T> &A = getSparseArray<T>(arr);
+    ARG_ASSERT(0, A.isSparse() == true);
+    return const_cast<common::SparseArray<T>&>(A);
+}
+
+template<typename T>
+static af_array
+getHandle(const common::SparseArray<T> &A)
+{
+    common::SparseArray<T> *ret = common::initSparseArray<T>();
+    *ret = A;
+    af_array arr = reinterpret_cast<af_array>(ret);
+    return arr;
+}
+
+template<typename T>
+static void releaseSparseHandle(const af_array arr)
+{
+    common::destroySparseArray(reinterpret_cast<common::SparseArray<T>*>(arr));
+}
+
+template<typename T>
+af_array retainSparseHandle(const af_array in)
+{
+    common::SparseArray<T> *sparse = reinterpret_cast<common::SparseArray<T> *>(in);
+    common::SparseArray<T> *out = common::initSparseArray<T>();
+    *out = *sparse;
+    return reinterpret_cast<af_array>(out);
+}
diff --git a/src/api/c/sparse_matmul.cpp b/src/api/c/sparse_matmul.cpp
deleted file mode 100644
index 261b1b1eaf..0000000000
--- a/src/api/c/sparse_matmul.cpp
+++ /dev/null
@@ -1,79 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/sparse.h>
-#include <af/array.h>
-#include <sparse_t.hpp>
-#include <sparse_matmul.hpp>
-#include <handle.hpp>
-#include <backend.hpp>
-#include <err_common.hpp>
-
-using namespace detail;
-using af::dim4;
-
-template<typename T>
-static inline af_array matmul(const af_sparse_t lhs, const af_array rhs,
-                              af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    return getHandle(detail::matmul<T>(
-                lhs.nRows, lhs.nCols, lhs.nNZ,
-                getArray<T>(lhs.values), getArray<int>(lhs.rowIdx), getArray<int>(lhs.colIdx),
-                getArray<T>(rhs), optLhs, optRhs));
-}
-
-af_err af_sparse_matmul(af_array *out,
-                        const af_sparse_array lhs_, const af_sparse_array rhs,
-                        const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
-    try {
-        af_sparse_t lhs = getSparse(lhs_);
-
-        ArrayInfo lhsInfo = getInfo(lhs.values);
-        ArrayInfo rhsInfo = getInfo(rhs);
-
-        af_dtype lhs_type = lhsInfo.getType();
-        af_dtype rhs_type = rhsInfo.getType();
-
-        ARG_ASSERT(1, lhs.storage == AF_SPARSE_CSR);
-
-        if (!(optLhs == AF_MAT_NONE ||
-              optLhs == AF_MAT_TRANS ||
-              optLhs == AF_MAT_CTRANS)) {
-            AF_ERROR("Using this property is not yet supported in sparse matmul", AF_ERR_NOT_SUPPORTED);
-        }
-        if (optRhs != AF_MAT_NONE) {
-            AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
-        }
-
-        if (rhsInfo.ndims() > 2) {
-            AF_ERROR("Sparse matmul can not be used in batch mode", AF_ERR_BATCH);
-        }
-
-        TYPE_ASSERT(lhs_type == rhs_type);
-
-        dim4 ldims(lhs.nRows, lhs.nCols);
-        int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
-        int rRowDim = (optRhs == AF_MAT_NONE) ? 0 : 1;
-
-        DIM_ASSERT(1, ldims[lColDim] == rhsInfo.dims()[rRowDim]);
-
-        af_array output = 0;
-        switch(lhs_type) {
-            case f32: output = matmul<float  >(lhs, rhs, optLhs, optRhs);   break;
-            case c32: output = matmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
-            case f64: output = matmul<double >(lhs, rhs, optLhs, optRhs);   break;
-            case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
-            default:  TYPE_ERROR(1, lhs_type);
-        }
-        std::swap(*out, output);
-
-    } CATCHALL;
-    return AF_SUCCESS;
-}
diff --git a/src/api/c/sparse_t.hpp b/src/api/c/sparse_t.hpp
deleted file mode 100644
index 82f2f0882c..0000000000
--- a/src/api/c/sparse_t.hpp
+++ /dev/null
@@ -1,24 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-#pragma once
-
-#include <af/sparse.h>
-
-typedef struct {
-    dim_t nRows, nCols, nNZ;
-    af_sparse_storage storage;
-    af_array rowIdx;
-    af_array colIdx;
-    af_array values;
-} af_sparse_t;
-
-af_sparse_array getSparseHandle(const af_sparse_t sparse);
-
-af_sparse_t getSparse(const af_sparse_array sparseHandle);
-
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 12cd04b147..e3cc33b526 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -165,7 +165,7 @@ namespace af
                 dims[3]);                                               \
     }                                                                   \
     template<> AFAPI                                                    \
-    array::array(dim_t d0, const T *ptr, af::source src) \
+    array::array(dim_t d0, const T *ptr, af::source src)                \
         : arr(0)                                                        \
     {                                                                   \
         initDataArray<T>(&arr, ptr, src, d0);                           \
diff --git a/src/backend/SparseArray.cpp b/src/backend/SparseArray.cpp
new file mode 100644
index 0000000000..57380b51bd
--- /dev/null
+++ b/src/backend/SparseArray.cpp
@@ -0,0 +1,254 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <SparseArray.hpp>
+#include <backend.hpp>
+#include <platform.hpp>
+#include <copy.hpp>
+
+namespace common
+{
+
+using namespace detail;
+
+////////////////////////////////////////////////////////////////////////////
+// Sparse Array Base Implementations
+////////////////////////////////////////////////////////////////////////////
+
+// ROW_LENGTH and column length expect standard variable names of
+// SparseArraBase::storage
+// _nNZ  -> Constructor Argument
+// _dims -> Constructor Argument
+#define ROW_LENGTH ((storage == AF_SPARSE_COO || storage == AF_SPARSE_CSC) ? _nNZ : (_dims[0] + 1))
+#define COL_LENGTH ((storage == AF_SPARSE_COO || storage == AF_SPARSE_CSR) ? _nNZ : (_dims[1] + 1))
+
+SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage, af_dtype _type):
+    info(getActiveDeviceId(), _dims, _nNZ, calcStrides(_dims), _type, true),
+    storage(_storage),
+    rowIdx(createEmptyArray<int>(dim4(ROW_LENGTH))),
+    colIdx(createEmptyArray<int>(dim4(COL_LENGTH)))
+{
+#if __cplusplus > 199711l
+    static_assert(offsetof(SparseArrayBase, info) == 0,
+                  "SparseArrayBase::info must be the first member variable of SparseArrayBase.");
+#endif
+}
+
+SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
+                    const int * const _rowIdx, const int * const _colIdx,
+                    const af::sparseStorage _storage, af_dtype _type,
+                    bool _is_device, bool _copy_device):
+    info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
+    storage(_storage),
+    rowIdx(_is_device ?
+          (!_copy_device ? createDeviceDataArray<int>(dim4(ROW_LENGTH), _rowIdx)
+                         : createEmptyArray<int>(dim4(ROW_LENGTH)))
+        : createHostDataArray<int>(dim4(ROW_LENGTH), _rowIdx)),
+    colIdx(_is_device ?
+          (!_copy_device ? createDeviceDataArray<int>(dim4(COL_LENGTH), _colIdx)
+                         : createEmptyArray<int>(dim4(COL_LENGTH)))
+        : createHostDataArray<int>(dim4(COL_LENGTH), _colIdx))
+{
+#if __cplusplus > 199711L
+    static_assert(offsetof(SparseArrayBase, info) == 0,
+                  "SparseArrayBase::info must be the first member variable of SparseArrayBase.");
+#endif
+    if(_is_device && _copy_device) {
+        writeDeviceDataArray<int>(rowIdx, _rowIdx, ROW_LENGTH * sizeof(int));
+        writeDeviceDataArray<int>(colIdx, _colIdx, COL_LENGTH * sizeof(int));
+    }
+}
+
+SparseArrayBase::SparseArrayBase(af::dim4 _dims,
+                    const Array<int> &_rowIdx, const Array<int> &_colIdx,
+                    const af::sparseStorage _storage, af_dtype _type,
+                    bool _copy):
+    info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
+    storage(_storage),
+    rowIdx(_copy ? copyArray<int>(_rowIdx): _rowIdx),
+    colIdx(_copy ? copyArray<int>(_colIdx): _colIdx)
+{
+#if __cplusplus > 199711L
+    static_assert(offsetof(SparseArrayBase, info) == 0,
+                  "SparseArrayBase::info must be the first member variable of SparseArrayBase.");
+#endif
+}
+
+SparseArrayBase::~SparseArrayBase()
+{
+}
+
+dim_t SparseArrayBase::getNNZ() const
+{
+    if(storage == AF_SPARSE_COO || storage == AF_SPARSE_CSC)
+        return rowIdx.elements();
+    else if(storage == AF_SPARSE_CSR)
+        return colIdx.elements();
+
+    // This is to ensure future storages are properly configured
+    return 0;
+}
+
+#undef ROW_LENGTH
+#undef COL_LENGTH
+
+////////////////////////////////////////////////////////////////////////////
+// Friend functions for Sparse Array Creation Implementations
+////////////////////////////////////////////////////////////////////////////
+template<typename T>
+SparseArray<T> createEmptySparseArray(
+        const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage)
+{
+    return SparseArray<T>(_dims, _nNZ, _storage);
+}
+
+template<typename T>
+SparseArray<T> createHostDataSparseArray(
+        const af::dim4 &_dims, const dim_t nNZ,
+        const T * const _values,
+        const int * const _rowIdx, const int * const _colIdx,
+        const af::sparseStorage _storage)
+{
+    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, false);
+}
+
+template<typename T>
+SparseArray<T> createDeviceDataSparseArray(
+        const af::dim4 &_dims, const dim_t nNZ,
+        const T * const _values,
+        const int * const _rowIdx, const int * const _colIdx,
+        const af::sparseStorage _storage)
+{
+    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, false);
+}
+
+template<typename T>
+SparseArray<T> createArrayDataSparseArray(
+        const af::dim4 &_dims,
+        const Array<T> &_values,
+        const Array<int> &_rowIdx, const Array<int> &_colIdx,
+        const af::sparseStorage _storage)
+{
+    return SparseArray<T>(_dims, _values, _rowIdx, _colIdx, _storage, false);
+}
+
+template<typename T>
+SparseArray<T> *initSparseArray()
+{
+    return new SparseArray<T>(dim4(), 0,  (af::sparseStorage)0);
+}
+
+template<typename T>
+void destroySparseArray(SparseArray<T> *sparse)
+{
+    delete sparse;
+}
+
+////////////////////////////////////////////////////////////////////////////
+// Sparse Array Class Implementations
+////////////////////////////////////////////////////////////////////////////
+template<typename T>
+SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::sparseStorage _storage):
+    base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type),
+    values(createEmptyArray<T>(dim4(_nNZ)))
+{
+#if __cplusplus > 199711L
+        static_assert(std::is_standard_layout<SparseArray<T>>::value,
+                      "SparseArray<T> must be a standard layout type");
+        static_assert(offsetof(SparseArray<T>, base) == 0,
+                      "SparseArray<T>::base must be the first member variable of SparseArray<T>");
+#endif
+}
+
+template<typename T>
+SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,
+                     const T * const _values,
+                     const int * const _rowIdx, const int * const _colIdx,
+                     const af::sparseStorage _storage,
+                     bool _is_device, bool _copy_device):
+    base(_dims, _nNZ, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _is_device, _copy_device),
+    values(_is_device ?
+          (!_copy_device ? createDeviceDataArray<T>(dim4(_nNZ), _values)
+                         : createEmptyArray<T>(dim4(_nNZ)))
+        : createHostDataArray<T>(dim4(_nNZ), _values))
+{
+#if __cplusplus > 199711L
+        static_assert(std::is_standard_layout<SparseArray<T>>::value,
+                      "SparseArray<T> must be a standard layout type");
+        static_assert(offsetof(SparseArray<T>, base) == 0,
+                      "SparseArray<T>::base must be the first member variable of SparseArray<T>");
+#endif
+    if(_is_device && _copy_device) {
+        writeDeviceDataArray<T>(values, _values, _nNZ * sizeof(T));
+    }
+}
+
+template<typename T>
+SparseArray<T>::SparseArray(af::dim4 _dims,
+            const Array<T> &_values,
+            const Array<int> &_rowIdx, const Array<int> &_colIdx,
+            const af::sparseStorage _storage, bool _copy):
+    base(_dims, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _copy),
+    values(_copy ? copyArray<T>(_values): _values)
+{
+#if __cplusplus > 199711L
+        static_assert(std::is_standard_layout<SparseArray<T>>::value,
+                      "SparseArray<T> must be a standard layout type");
+        static_assert(offsetof(SparseArray<T>, base) == 0,
+                      "SparseArray<T>::base must be the first member variable of SparseArray<T>");
+#endif
+}
+
+template<typename T>
+SparseArray<T>::~SparseArray()
+{
+}
+
+#define INSTANTIATE(T)                                                                              \
+    template SparseArray<T> createEmptySparseArray<T>(                                              \
+            const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage);                   \
+    template SparseArray<T> createHostDataSparseArray<T>(                                           \
+            const af::dim4 &_dims, const dim_t _nNZ,                                                \
+            const T * const _values,                                                                \
+            const int * const _rowIdx, const int * const _colIdx,                                   \
+            const af::sparseStorage _storage);                                                      \
+    template SparseArray<T> createDeviceDataSparseArray<T>(                                         \
+            const af::dim4 &_dims, const dim_t _nNZ,                                                \
+            const T * const _values,                                                                \
+            const int * const _rowIdx, const int * const _colIdx,                                   \
+            const af::sparseStorage _storage);                                                      \
+    template SparseArray<T> createArrayDataSparseArray<T>(                                          \
+            const af::dim4 &_dims,                                                                  \
+            const Array<T> &_values,                                                                \
+            const Array<int> &_rowIdx, const Array<int> &_colIdx,                                   \
+            const af::sparseStorage _storage);                                                      \
+    template SparseArray<T> *initSparseArray<T>();                                                  \
+    template void destroySparseArray<T>(SparseArray<T> *sparse);                                    \
+                                                                                                    \
+    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage);   \
+    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,                                \
+                        const T * const _values,                                                    \
+                        const int * const _rowIdx, const int * const _colIdx,                       \
+                        const af::sparseStorage _storage,                                           \
+                        bool _is_device, bool _copy_device);                                        \
+    template SparseArray<T>::SparseArray(af::dim4 _dims,                                            \
+                        const Array<T> &_values,                                                    \
+                        const Array<int> &_rowIdx, const Array<int> &_colIdx,                       \
+                        const af::sparseStorage _storage, bool _copy);                              \
+    template SparseArray<T>::~SparseArray();
+
+// Instantiate only floating types
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(cfloat);
+INSTANTIATE(cdouble);
+
+#undef INSTANTIATE
+
+} // namespace common
diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
new file mode 100644
index 0000000000..f8779fe173
--- /dev/null
+++ b/src/backend/SparseArray.hpp
@@ -0,0 +1,228 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <sparse_helpers.hpp>
+#include <ArrayInfo.hpp>
+#include <Array.hpp>
+#include <backend.hpp>
+#include <vector>
+#include <cstddef>
+
+namespace common
+{
+
+// SparseArray Arrayementation Info class
+// This class is the base class to all SparseArray objects. The purpose of this class
+// was to have a way to retrieve basic information of an Array object without
+// specifying what type the object is at compile time.
+//
+// Early declaration
+
+using namespace detail;
+
+template<typename T> class SparseArray;
+
+////////////////////////////////////////////////////////////////////////////
+// Sparse Array Base Class
+// No templates
+// Contains all data except values array
+////////////////////////////////////////////////////////////////////////////
+class SparseArrayBase
+{
+private:
+    ArrayInfo  info;    // This must be the first element of SparseArray<T>.
+    af::sparseStorage storage;  // Storage format: CSR, CSC, COO
+    Array<int> rowIdx;  // Linear array containing row indices
+    Array<int> colIdx;  // Linear array containing col indices
+
+public:
+    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage, af_dtype _type);
+
+    SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
+                    const int * const _rowIdx, const int * const _colIdx,
+                    const af::sparseStorage _storage, af_dtype _type,
+                    bool _is_device = false, bool _copy_device = false);
+
+    SparseArrayBase(af::dim4 _dims,
+                    const Array<int> &_rowIdx, const Array<int> &_colIdx,
+                    const af::sparseStorage _storage, af_dtype _type,
+                    bool _copy = false);
+
+    ~SparseArrayBase();
+
+    ////////////////////////////////////////////////////////////////////////////
+    // Functions that call ArrayInfo object's functions
+    ////////////////////////////////////////////////////////////////////////////
+#define INSTANTIATE_INFO(return_type, func)         \
+    return_type func() const { return info.func();  }
+
+    INSTANTIATE_INFO(const af_dtype&, getType       )
+    INSTANTIATE_INFO(size_t         , elements      )
+    INSTANTIATE_INFO(size_t         , ndims         )
+    INSTANTIATE_INFO(const af::dim4&, dims          )
+    INSTANTIATE_INFO(size_t         , total         )
+    INSTANTIATE_INFO(int            , getDevId      )
+    INSTANTIATE_INFO(af_backend     , getBackendId  )
+    INSTANTIATE_INFO(bool           , isEmpty       )
+    INSTANTIATE_INFO(bool           , isScalar      )
+    INSTANTIATE_INFO(bool           , isRow         )
+    INSTANTIATE_INFO(bool           , isColumn      )
+    INSTANTIATE_INFO(bool           , isVector      )
+    INSTANTIATE_INFO(bool           , isComplex     )
+    INSTANTIATE_INFO(bool           , isReal        )
+    INSTANTIATE_INFO(bool           , isDouble      )
+    INSTANTIATE_INFO(bool           , isSingle      )
+    INSTANTIATE_INFO(bool           , isRealFloating)
+    INSTANTIATE_INFO(bool           , isFloating    )
+    INSTANTIATE_INFO(bool           , isInteger     )
+    INSTANTIATE_INFO(bool           , isBool        )
+    INSTANTIATE_INFO(bool           , isLinear      )
+    INSTANTIATE_INFO(bool           , isSparse      )
+
+#undef INSTANTIATE_INFO
+
+    // setId of info, values, rowIdx, colIdx
+    void setId(int id)
+    {
+        info.setId(id);
+        rowIdx.setId(id);
+        colIdx.setId(id);
+    }
+
+    ////////////////////////////////////////////////////////////////////////////
+    // Specialized functions for SparseArray
+    ////////////////////////////////////////////////////////////////////////////
+    // Get the internal arrays
+    Array<int>& getRows()                 { return rowIdx;              }
+    Array<int>& getColumns()              { return colIdx;              }
+
+    const Array<int>& getRows()     const { return rowIdx;              }
+    const Array<int>& getColumns()  const { return colIdx;              }
+
+    // Dims, types etc
+    dim_t getNNZ()                  const;
+    af::sparseStorage getStorage()  const { return storage;             }
+};
+#if __cplusplus > 199711L
+        static_assert(std::is_standard_layout<SparseArrayBase>::value,
+                      "SparseArrayBase must be a standard layout type");
+#endif
+
+////////////////////////////////////////////////////////////////////////////
+// Sparse Array Class
+////////////////////////////////////////////////////////////////////////////
+template<typename T>
+class SparseArray
+{
+private:
+    SparseArrayBase  base;    // This must be the first element of SparseArray<T>.
+    Array<T>         values;  // Linear array containing actual values
+
+    SparseArray(af::dim4 _dims, dim_t _nNZ, af::sparseStorage storage);
+
+    explicit
+    SparseArray(af::dim4 _dims, dim_t _nNZ,
+                const T * const _values,
+                const int * const _rowIdx, const int * const _colIdx,
+                const af::sparseStorage _storage,
+                bool _is_device = false, bool _copy_device = false);
+
+    SparseArray(af::dim4 _dims,
+                const Array<T> &_values,
+                const Array<int> &_rowIdx, const Array<int> &_colIdx,
+                const af::sparseStorage _storage, bool _copy = false);
+
+public:
+
+    ~SparseArray();
+
+    ////////////////////////////////////////////////////////////////////////////
+    // Functions that call ArrayInfo object's functions
+    ////////////////////////////////////////////////////////////////////////////
+
+#define INSTANTIATE_INFO(return_type, func)         \
+    return_type func() const { return base.func();  }
+
+    INSTANTIATE_INFO(const af_dtype&, getType       )
+    INSTANTIATE_INFO(size_t         , elements      )
+    INSTANTIATE_INFO(size_t         , ndims         )
+    INSTANTIATE_INFO(const af::dim4&, dims          )
+    INSTANTIATE_INFO(size_t         , total         )
+    INSTANTIATE_INFO(int            , getDevId      )
+    INSTANTIATE_INFO(af_backend     , getBackendId  )
+    INSTANTIATE_INFO(bool           , isEmpty       )
+    INSTANTIATE_INFO(bool           , isScalar      )
+    INSTANTIATE_INFO(bool           , isRow         )
+    INSTANTIATE_INFO(bool           , isColumn      )
+    INSTANTIATE_INFO(bool           , isVector      )
+    INSTANTIATE_INFO(bool           , isComplex     )
+    INSTANTIATE_INFO(bool           , isReal        )
+    INSTANTIATE_INFO(bool           , isDouble      )
+    INSTANTIATE_INFO(bool           , isSingle      )
+    INSTANTIATE_INFO(bool           , isRealFloating)
+    INSTANTIATE_INFO(bool           , isFloating    )
+    INSTANTIATE_INFO(bool           , isInteger     )
+    INSTANTIATE_INFO(bool           , isBool        )
+    INSTANTIATE_INFO(bool           , isLinear      )
+    INSTANTIATE_INFO(bool           , isSparse      )
+
+    // Function from Base but not in ArrayInfo
+    INSTANTIATE_INFO(dim_t              , getNNZ    )
+    INSTANTIATE_INFO(af::sparseStorage  , getStorage)
+
+    Array<int>& getRows()                { return base.getRows();    }
+    Array<int>& getColumns()             { return base.getColumns(); }
+    const Array<int>& getRows()    const { return base.getRows();    }
+    const Array<int>& getColumns() const { return base.getColumns(); }
+
+#undef INSTANTIATE_INFO
+
+    void setId(int id)
+    {
+        base.setId(id);
+        values.setId(id);
+    }
+
+    // Return the values array
+    Array<T>& getValues()                 { return values;  }
+    const Array<T>& getValues()     const { return values;  }
+
+    ////////////////////////////////////////////////////////////////////////////
+    // Friend functions for Sparse Array Creation
+    ////////////////////////////////////////////////////////////////////////////
+
+    friend SparseArray<T> createEmptySparseArray<T>(
+            const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage);
+
+    friend SparseArray<T> createHostDataSparseArray<T>(
+            const af::dim4 &_dims, const dim_t nNZ,
+            const T * const _values,
+            const int * const _rowIdx, const int * const _colIdx,
+            const af::sparseStorage _storage);
+
+    friend SparseArray<T> createDeviceDataSparseArray<T>(
+            const af::dim4 &_dims, const dim_t nNZ,
+            const T * const _values,
+            const int * const _rowIdx, const int * const _colIdx,
+            const af::sparseStorage _storage);
+
+    friend SparseArray<T> createArrayDataSparseArray<T>(
+            const af::dim4 &_dims,
+            const Array<T> &_values,
+            const Array<int> &_rowIdx, const Array<int> &_colIdx,
+            const af::sparseStorage _storage);
+
+    friend SparseArray<T> *initSparseArray<T>();
+
+    friend void destroySparseArray<T>(SparseArray<T> *sparse);
+
+};
+
+} // namespace common
diff --git a/src/backend/cuda/cusparseManager.cpp b/src/backend/cuda/cusparseManager.cpp
index dfb8bf729e..24f0c40045 100644
--- a/src/backend/cuda/cusparseManager.cpp
+++ b/src/backend/cuda/cusparseManager.cpp
@@ -45,6 +45,7 @@ namespace cusparse {
             : handle(0)
         {
             CUSPARSE_CHECK(cusparseCreate(&handle));
+            CUSPARSE_CHECK(cusparseSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
         }
 
         ~cusparseHandle()
diff --git a/src/backend/cuda/sparse_matmul.cpp b/src/backend/cuda/sparse_blas.cpp
similarity index 87%
rename from src/backend/cuda/sparse_matmul.cpp
rename to src/backend/cuda/sparse_blas.cpp
index e6ddfd1852..69a773df71 100644
--- a/src/backend/cuda/sparse_matmul.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse_matmul.hpp>
+#include <sparse_blas.hpp>
 #include <cusparseManager.hpp>
 #include <cuda_runtime.h>
 #include <platform.hpp>
@@ -122,10 +122,8 @@ SPARSE_FUNC(csrmv, cdouble,Z)
 #undef SPARSE_FUNC_DEF
 
 template<typename T>
-Array<T> matmul(
-         const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-         const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
-         const Array<T> rhs, af_mat_prop optLhs, af_mat_prop optRhs)
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
 {
     // Similar Operations to GEMM
     cusparseOperation_t lOpts = toCusparseTranspose(optLhs);
@@ -134,7 +132,7 @@ Array<T> matmul(
     int lColDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
     static const int rColDim = 1; //Unsupported : (rOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
-    dim4 lDims(nRows, nRows);
+    dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
@@ -149,17 +147,18 @@ Array<T> matmul(
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
-    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
-    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+    CUSPARSE_CHECK(cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL));
+    CUSPARSE_CHECK(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
 
     // Call Matrix-Vector or Matrix-Matrix
     if(rDims[rColDim] == 1) {
         CUSPARSE_CHECK(csrmv_func<T>()(
                        getHandle(),
                        lOpts,
-                       M, K, nNZ,
+                       M, K, lhs.getNNZ(),
                        &alpha,
-                       descr, values.get(), rowIdx.get(), colIdx.get(),
+                       descr, lhs.getValues().get(),
+                       lhs.getRows().get(), lhs.getColumns().get(),
                        rhs.get(),
                        &beta,
                        out.get()));
@@ -167,9 +166,10 @@ Array<T> matmul(
         CUSPARSE_CHECK(csrmm_func<T>()(
                        getHandle(),
                        lOpts,
-                       M, N, K, nNZ,
+                       M, N, K, lhs.getNNZ(),
                        &alpha,
-                       descr, values.get(), rowIdx.get(), colIdx.get(),
+                       descr, lhs.getValues().get(),
+                       lhs.getRows().get(), lhs.getColumns().get(),
                        rhs.get(), rStrides[1],
                        &beta,
                        out.get(),
@@ -183,10 +183,8 @@ Array<T> matmul(
 }
 
 #define INSTANTIATE_SPARSE(T)                                                           \
-    template Array<T> matmul<T>(                                                        \
-        const dim_t nRows, const dim_t nCols, const dim_t nNZ,                          \
-        const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,        \
-        const Array<T> rhs, af_mat_prop optLhs, af_mat_prop optRhs);                    \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
+                                af_mat_prop optLhs, af_mat_prop optRhs);                \
 
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/cuda/sparse_matmul.hpp b/src/backend/cuda/sparse_blas.hpp
similarity index 62%
rename from src/backend/cuda/sparse_matmul.hpp
rename to src/backend/cuda/sparse_blas.hpp
index fd638c76f5..ee2d18227b 100644
--- a/src/backend/cuda/sparse_matmul.hpp
+++ b/src/backend/cuda/sparse_blas.hpp
@@ -8,14 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <SparseArray.hpp>
 
 namespace cuda
 {
 
 template<typename T>
-Array<T> matmul(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-                const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
-                const Array<T> rhs, af_mat_prop optLhs, af_mat_prop optRhs);
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
 
diff --git a/src/backend/sparse_helpers.hpp b/src/backend/sparse_helpers.hpp
new file mode 100644
index 0000000000..a2e83a3616
--- /dev/null
+++ b/src/backend/sparse_helpers.hpp
@@ -0,0 +1,55 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+
+namespace common
+{
+
+using namespace detail;
+
+class SparseArrayBase;
+template<typename T> class SparseArray;
+
+////////////////////////////////////////////////////////////////////////////
+// Friend functions for Sparse Array Creation
+////////////////////////////////////////////////////////////////////////////
+template<typename T>
+SparseArray<T> createEmptySparseArray(
+        const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage);
+
+template<typename T>
+SparseArray<T> createHostDataSparseArray(
+        const af::dim4 &_dims, const dim_t nNZ,
+        const T * const _values,
+        const int * const _rowIdx, const int * const _colIdx,
+        const af::sparseStorage _storage);
+
+template<typename T>
+SparseArray<T> createDeviceDataSparseArray(
+        const af::dim4 &_dims, const dim_t nNZ,
+        const T * const _values,
+        const int * const _rowIdx, const int * const _colIdx,
+        const af::sparseStorage _storage);
+
+template<typename T>
+SparseArray<T> createArrayDataSparseArray(
+        const af::dim4 &_dims,
+        const Array<T> &_values,
+        const Array<int> &_rowIdx, const Array<int> &_colIdx,
+        const af::sparseStorage _storage);
+
+template<typename T>
+SparseArray<T> *initSparseArray();
+
+template<typename T>
+void destroySparseArray(SparseArray<T> *sparse);
+
+} // namespace common

From 5ca147574b685e943f086c161684d3354b25d3b0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 31 May 2016 11:42:42 +0530
Subject: [PATCH 0565/2677] Update OpenCL CPP HEADERS: use cl2.hpp instead of
 cl.hpp

---
 src/backend/opencl/jit.cpp                       |  2 +-
 src/backend/opencl/kernel/approx.hpp             |  6 +++---
 src/backend/opencl/kernel/assign.hpp             |  4 ++--
 src/backend/opencl/kernel/bilateral.hpp          |  4 ++--
 .../opencl/kernel/convolve/conv2_impl.hpp        |  2 +-
 .../opencl/kernel/convolve/conv_common.hpp       | 13 ++++++-------
 src/backend/opencl/kernel/convolve_separable.cpp |  4 ++--
 src/backend/opencl/kernel/diagonal.hpp           |  6 +++---
 src/backend/opencl/kernel/diff.hpp               |  4 ++--
 src/backend/opencl/kernel/exampleFunction.hpp    |  4 ++--
 src/backend/opencl/kernel/fast.hpp               |  7 ++++---
 src/backend/opencl/kernel/fftconvolve.hpp        |  9 +++++----
 src/backend/opencl/kernel/gradient.hpp           |  4 ++--
 src/backend/opencl/kernel/harris.hpp             |  8 ++++----
 src/backend/opencl/kernel/histogram.hpp          |  3 ++-
 src/backend/opencl/kernel/homography.hpp         | 10 +++++-----
 src/backend/opencl/kernel/hsv_rgb.hpp            |  4 ++--
 src/backend/opencl/kernel/identity.hpp           |  4 ++--
 src/backend/opencl/kernel/iir.hpp                |  4 ++--
 src/backend/opencl/kernel/index.hpp              |  4 ++--
 src/backend/opencl/kernel/iota.hpp               |  4 ++--
 src/backend/opencl/kernel/ireduce.hpp            |  6 +++---
 src/backend/opencl/kernel/join.hpp               |  4 ++--
 src/backend/opencl/kernel/laset.hpp              |  9 ++++++---
 src/backend/opencl/kernel/laset_band.hpp         |  4 ++--
 src/backend/opencl/kernel/laswp.hpp              |  9 ++++++---
 src/backend/opencl/kernel/lookup.hpp             |  4 ++--
 src/backend/opencl/kernel/lu_split.hpp           |  4 ++--
 src/backend/opencl/kernel/match_template.hpp     |  4 ++--
 src/backend/opencl/kernel/meanshift.hpp          |  4 ++--
 src/backend/opencl/kernel/medfilt.hpp            |  4 ++--
 src/backend/opencl/kernel/memcopy.hpp            |  6 +++---
 src/backend/opencl/kernel/morph.hpp              |  6 +++---
 src/backend/opencl/kernel/nearest_neighbour.hpp  |  7 ++++---
 src/backend/opencl/kernel/orb.hpp                |  8 ++++----
 src/backend/opencl/kernel/random.hpp             |  4 ++--
 src/backend/opencl/kernel/range.hpp              |  4 ++--
 src/backend/opencl/kernel/reduce.hpp             |  6 +++---
 src/backend/opencl/kernel/regions.hpp            |  7 ++++---
 src/backend/opencl/kernel/reorder.hpp            |  4 ++--
 src/backend/opencl/kernel/resize.hpp             |  4 ++--
 src/backend/opencl/kernel/rotate.hpp             |  4 ++--
 src/backend/opencl/kernel/scan_dim.hpp           |  6 +++---
 src/backend/opencl/kernel/scan_first.hpp         |  6 +++---
 src/backend/opencl/kernel/select.hpp             |  6 +++---
 src/backend/opencl/kernel/shift.hpp              |  4 ++--
 src/backend/opencl/kernel/sift_nonfree.hpp       | 14 +++++++-------
 src/backend/opencl/kernel/sobel.hpp              |  4 ++--
 src/backend/opencl/kernel/sort.hpp               |  2 +-
 src/backend/opencl/kernel/sort_by_key_impl.hpp   |  6 +++---
 src/backend/opencl/kernel/susan.hpp              |  5 +++--
 src/backend/opencl/kernel/swapdblk.hpp           | 16 ++++++++++------
 src/backend/opencl/kernel/tile.hpp               |  4 ++--
 src/backend/opencl/kernel/transform.hpp          |  4 ++--
 src/backend/opencl/kernel/transpose.hpp          |  4 ++--
 src/backend/opencl/kernel/transpose_inplace.hpp  |  4 ++--
 src/backend/opencl/kernel/triangle.hpp           |  4 ++--
 src/backend/opencl/kernel/unwrap.hpp             |  4 ++--
 src/backend/opencl/kernel/where.hpp              |  4 ++--
 src/backend/opencl/kernel/wrap.hpp               |  4 ++--
 src/backend/opencl/platform.hpp                  |  3 ++-
 src/backend/opencl/program.cpp                   |  1 -
 src/backend/opencl/program.hpp                   |  1 -
 63 files changed, 171 insertions(+), 157 deletions(-)

diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index d6ab240fd6..7981d82edf 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -29,7 +29,7 @@ using JIT::Node;
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 275d8fe68e..8d82dc91c8 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -25,7 +25,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -86,7 +86,7 @@ namespace opencl
                 });
 
 
-                auto approx1Op = make_kernel<Buffer, const KParam, const Buffer, const KParam,
+                auto approx1Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
                                        const Buffer, const KParam, const float, const int, const int>
                                       (*approxKernels[device]);
 
@@ -153,7 +153,7 @@ namespace opencl
                     approxKernels[device] = new Kernel(*approxProgs[device], "approx2_kernel");
                 });
 
-                auto approx2Op = make_kernel<Buffer, const KParam, const Buffer, const KParam,
+                auto approx2Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
                                        const Buffer, const KParam, const Buffer, const KParam,
                                        const float, const int, const int, const int>
                                        (*approxKernels[device]);
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 2b6b517799..d46049ab75 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -74,7 +74,7 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p, Buffer *bPt
         NDRange global(blk_x * in.info.dims[2] * THREADS_X,
                 blk_y * in.info.dims[3] * THREADS_Y);
 
-        auto assignOp = make_kernel<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
+        auto assignOp = KernelFunctor<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
              Buffer, Buffer, Buffer, Buffer, int, int>(*agnKernels[device]);
 
         assignOp(EnqueueArgs(getQueue(), global, local),
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 76533ae894..b96b91d55a 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
@@ -63,7 +63,7 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
                     bilKernels[device] = new Kernel(*bilProgs[device], "bilateral");
                 });
 
-        auto bilateralOp = make_kernel<Buffer, KParam,
+        auto bilateralOp = KernelFunctor<Buffer, KParam,
                                        Buffer, KParam,
                                        LocalSpaceArg,
                                        LocalSpaceArg,
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index ee2555ebe6..7f2693ce41 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -64,7 +64,7 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, cons
             entry = idx->second;
         }
 
-        auto convOp = make_kernel<Buffer, KParam, Buffer, KParam,
+        auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
                                   Buffer, KParam, int, int,
                                   int, int,
                                   int, int
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 4c2cfa1c26..e5d8b233ba 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -27,7 +27,6 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -121,12 +120,12 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, co
                     convKernels[device] = new Kernel(*convProgs[device], "convolve");
                 });
 
-        auto convOp = make_kernel<Buffer, KParam, Buffer, KParam,
-                                  cl::LocalSpaceArg, Buffer, KParam,
-                                  int, int,
-                                  int, int, int,
-                                  int, int, int
-                                 >(*convKernels[device]);
+        auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                        cl::LocalSpaceArg, Buffer, KParam,
+                                        int, int,
+                                        int, int, int,
+                                        int, int, int
+                                       >(*convKernels[device]);
 
         convOp(EnqueueArgs(getQueue(), param.global, param.local),
                 *out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 73dd220a5b..e0b0877498 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -86,7 +86,7 @@ void convSep(Param out, const Param signal, const Param filter)
             entry = idx->second;
         }
 
-        auto convOp = make_kernel<Buffer, KParam, Buffer, KParam, Buffer,
+        auto convOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
                                   int, int>(*entry.ker);
 
         NDRange local(THREADS_X, THREADS_Y);
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index ab5da11b76..581658c0d6 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -65,7 +65,7 @@ namespace kernel
             NDRange global(groups_x * local[0] * out.info.dims[2],
                            groups_y * local[1]);
 
-            auto diagCreateOp = make_kernel<Buffer, const KParam,
+            auto diagCreateOp = KernelFunctor<Buffer, const KParam,
                                             Buffer, const KParam,
                                             int, int> (*diagCreateKernels[device]);
 
@@ -109,7 +109,7 @@ namespace kernel
             NDRange global(groups_x * local[0],
                            groups_z * local[1] * out.info.dims[3]);
 
-            auto diagExtractOp = make_kernel<Buffer, const KParam,
+            auto diagExtractOp = KernelFunctor<Buffer, const KParam,
                                              Buffer, const KParam,
                                              int, int> (*diagExtractKernels[device]);
 
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index a1f9318540..f8eaa7fc36 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -58,7 +58,7 @@ namespace opencl
                     diffKernels[device] = new Kernel(*diffProgs[device], "diff_kernel");
                 });
 
-                auto diffOp = make_kernel<Buffer, const Buffer, const KParam, const KParam,
+                auto diffOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
                                           const int, const int, const int>
                                           (*diffKernels[device]);
 
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index e2e953c9c8..ce900e663a 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -36,7 +36,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -105,7 +105,7 @@ void exampleFunc(Param out, const Param in, const af_someenum_t p)
         // create a kernel functor from the cl::Kernel object
         // corresponding to the device on which current execution
         // is happending.
-        auto exampleFuncOp = make_kernel<Buffer, KParam,
+        auto exampleFuncOp = KernelFunctor<Buffer, KParam,
                                      Buffer, KParam, int>(*egKernels[device]);
 
         // launch the kernel
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 1a1354fe4a..3143bf4407 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -20,6 +20,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
@@ -105,7 +106,7 @@ void fast(const unsigned arc_length,
         const NDRange local(FAST_THREADS_X, FAST_THREADS_Y);
         const NDRange global(blk_x * FAST_THREADS_X, blk_y * FAST_THREADS_Y);
 
-        auto lfOp = make_kernel<Buffer, KParam,
+        auto lfOp = KernelFunctor<Buffer, KParam,
                                 Buffer, const float, const unsigned,
                                 LocalSpaceArg> (entry.ker[0]);
 
@@ -130,7 +131,7 @@ void fast(const unsigned arc_length,
         cl::Buffer *d_counts  = bufferAlloc(blocks_sz);
         cl::Buffer *d_offsets = bufferAlloc(blocks_sz);
 
-        auto nmOp = make_kernel<Buffer, Buffer, Buffer,
+        auto nmOp = KernelFunctor<Buffer, Buffer, Buffer,
                                 Buffer, Buffer,
                                 KParam, const unsigned> (entry.ker[1]);
         nmOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
@@ -147,7 +148,7 @@ void fast(const unsigned arc_length,
             y_out.data = bufferAlloc(out_sz);
             score_out.data = bufferAlloc(out_sz);
 
-            auto gfOp = make_kernel<Buffer, Buffer, Buffer,
+            auto gfOp = KernelFunctor<Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer,
                                     KParam, const unsigned,
                                     const unsigned> (entry.ker[2]);
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 0690e13cb3..7cc39c6002 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -22,6 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
@@ -127,7 +128,7 @@ void packDataHelper(Param packed,
 
         // Pack signal in a complex matrix where first dimension is half the input
         // (allows faster FFT computation) and pad array to a power of 2 with 0s
-        auto pdOp = make_kernel<Buffer, KParam,
+        auto pdOp = KernelFunctor<Buffer, KParam,
                                 Buffer, KParam,
                                 const int, const int> (*pdKernel[device]);
 
@@ -140,7 +141,7 @@ void packDataHelper(Param packed,
         global = NDRange(blocks * THREADS);
 
         // Pad filter array with 0s
-        auto paOp = make_kernel<Buffer, KParam,
+        auto paOp = KernelFunctor<Buffer, KParam,
                                 Buffer, KParam> (*paKernel[device]);
 
         paOp(EnqueueArgs(getQueue(), global, local),
@@ -208,7 +209,7 @@ void complexMultiplyHelper(Param packed,
         NDRange global(blocks * THREADS);
 
         // Multiply filter and signal FFT arrays
-        auto cmOp = make_kernel<Buffer, KParam,
+        auto cmOp = KernelFunctor<Buffer, KParam,
                                 Buffer, KParam,
                                 Buffer, KParam,
                                 const int, const int> (*cmKernel[device]);
@@ -282,7 +283,7 @@ void reorderOutputHelper(Param out,
         NDRange local(THREADS);
         NDRange global(blocks * THREADS);
 
-        auto roOp = make_kernel<Buffer, KParam,
+        auto roOp = KernelFunctor<Buffer, KParam,
                                 Buffer, KParam,
                                 KParam, const int,
                                 const int, const int> (*roKernel[device]);
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index d7ab1f5a67..bea33c9718 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -24,7 +24,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -71,7 +71,7 @@ namespace opencl
                     gradKernels[device] = new Kernel(*gradProgs[device], "gradient_kernel");
                 });
 
-                auto gradOp = make_kernel<Buffer, const KParam, Buffer, const KParam,
+                auto gradOp = KernelFunctor<Buffer, const KParam, Buffer, const KParam,
                                     const Buffer, const KParam, const int, const int>
                                         (*gradKernels[device]);
 
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 7f25f9b03b..5634366262 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -182,7 +182,7 @@ void harris(unsigned* corners_out,
         const NDRange local_so(HARRIS_THREADS_PER_GROUP, 1);
         const NDRange global_so(blk_x_so * HARRIS_THREADS_PER_GROUP, 1);
 
-        auto soOp = make_kernel<Buffer, Buffer, Buffer,
+        auto soOp = KernelFunctor<Buffer, Buffer, Buffer,
                                 unsigned, Buffer, Buffer> (*soKernel[device]);
 
         // Compute second-order derivatives
@@ -206,7 +206,7 @@ void harris(unsigned* corners_out,
         const NDRange local_hr(HARRIS_THREADS_X, HARRIS_THREADS_Y);
         const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X, blk_y_hr * HARRIS_THREADS_Y);
 
-        auto hrOp = make_kernel<Buffer, unsigned, unsigned,
+        auto hrOp = KernelFunctor<Buffer, unsigned, unsigned,
                                 Buffer, Buffer, Buffer,
                                 float, unsigned> (*hrKernel[device]);
 
@@ -234,7 +234,7 @@ void harris(unsigned* corners_out,
 
         const float min_r = (max_corners > 0) ? 0.f : min_response;
 
-        auto nmOp = make_kernel<Buffer, Buffer, Buffer, Buffer,
+        auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
                                 Buffer, unsigned, unsigned,
                                 float, unsigned, unsigned> (*nmKernel[device]);
 
@@ -300,7 +300,7 @@ void harris(unsigned* corners_out,
             const NDRange local_kc(HARRIS_THREADS_PER_GROUP, 1);
             const NDRange global_kc(blk_x_kc * HARRIS_THREADS_PER_GROUP, 1);
 
-            auto kcOp = make_kernel<Buffer, Buffer, Buffer,
+            auto kcOp = KernelFunctor<Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer, Buffer,
                                     unsigned> (*kcKernel[device]);
 
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index fb48023134..fa0321bd8b 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -19,6 +19,7 @@
 #include <debug_opencl.hpp>
 
 using cl::Kernel;
+using cl::KernelFunctor;
 
 namespace opencl
 {
@@ -58,7 +59,7 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval)
                     histKernels[device] = new Kernel(*histProgs[device], "histogram");
                 });
 
-        auto histogramOp = make_kernel<Buffer, KParam, Buffer, KParam,
+        auto histogramOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
                                        cl::LocalSpaceArg,
                                        int, int, float, float, int
                                       >(*histKernels[device]);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index bd4896fc36..159bd241f5 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -94,7 +94,7 @@ int computeH(
         const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
 
         // Build linear system and solve SVD
-        auto chOp = make_kernel<Buffer, KParam,
+        auto chOp = KernelFunctor<Buffer, KParam,
                                 Buffer, Buffer, Buffer, Buffer,
                                 Buffer, KParam, unsigned>(*chKernel[device]);
 
@@ -129,7 +129,7 @@ int computeH(
             median.data = bufferAlloc(sizeof(float));
 
         // Compute (and for RANSAC, evaluate) homographies
-        auto ehOp = make_kernel<Buffer, Buffer, Buffer, KParam,
+        auto ehOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
                                 Buffer, KParam,
                                 Buffer, Buffer, Buffer, Buffer,
                                 Buffer, unsigned, unsigned, float>(*ehKernel[device]);
@@ -151,7 +151,7 @@ int computeH(
             float minMedian;
 
             // Compute median of every iteration
-            auto cmOp = make_kernel<Buffer, Buffer, Buffer, KParam,
+            auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
                                     unsigned>(*cmKernel[device]);
 
             cmOp(EnqueueArgs(getQueue(), global_eh, local_eh),
@@ -167,7 +167,7 @@ int computeH(
                 cl::Buffer* finalMedian = bufferAlloc(sizeof(float));
                 cl::Buffer* finalIdx = bufferAlloc(sizeof(unsigned));
 
-                auto fmOp = make_kernel<Buffer, Buffer, Buffer, KParam,
+                auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
                                         Buffer>(*fmKernel[device]);
 
                 fmOp(EnqueueArgs(getQueue(), global_fm, local_fm),
@@ -193,7 +193,7 @@ int computeH(
             const NDRange local_cl(HG_THREADS);
             const NDRange global_cl(blk_x_cl * HG_THREADS);
 
-            auto clOp = make_kernel<Buffer, Buffer,
+            auto clOp = KernelFunctor<Buffer, Buffer,
                                     Buffer, Buffer, Buffer, Buffer,
                                     float, unsigned>(*clKernel[device]);
 
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 7f28fb0a8f..62460311b9 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -70,7 +70,7 @@ void hsv2rgb_convert(Param out, const Param in)
         // parameter would be along 4th dimension
         NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
 
-        auto hsvrgbOp = make_kernel<Buffer, KParam, Buffer, KParam, int> (*hrKernels[device]);
+        auto hsvrgbOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int> (*hrKernels[device]);
 
         hsvrgbOp(EnqueueArgs(getQueue(), global, local),
                     *out.data, out.info, *in.data, in.info, blk_x);
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 8991eb22ec..cb1a677eeb 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -65,7 +65,7 @@ namespace kernel
             NDRange global(groups_x * out.info.dims[2] * local[0],
                            groups_y * out.info.dims[3] * local[1]);
 
-            auto identityOp = make_kernel<Buffer, const KParam,
+            auto identityOp = KernelFunctor<Buffer, const KParam,
                                           int, int> (*identityKernels[device]);
 
             identityOp(EnqueueArgs(getQueue(), global, local),
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index cbf1768935..d9b220195b 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -78,7 +78,7 @@ namespace opencl
             NDRange global(groups_x * local[0],
                            groups_y * y.info.dims[3] * local[1]);
 
-            auto iirOp = make_kernel<Buffer, KParam,
+            auto iirOp = KernelFunctor<Buffer, KParam,
                                      Buffer, KParam,
                                      Buffer, KParam,
                                      int>(*iirKernels[device]);
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index f87960ea8a..6266a251cd 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -74,7 +74,7 @@ void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[
         NDRange global(blk_x * out.info.dims[2] * THREADS_X,
                 blk_y * out.info.dims[3] * THREADS_Y);
 
-        auto indexOp = make_kernel<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
+        auto indexOp = KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
              Buffer, Buffer, Buffer, Buffer, int, int>(*idxKernels[device]);
 
         indexOp(EnqueueArgs(getQueue(), global, local),
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 7cd8046d68..54d79c3dc7 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -60,7 +60,7 @@ namespace opencl
                     iotaKernels[device] = new Kernel(*iotaProgs[device], "iota_kernel");
                 });
 
-                auto iotaOp = make_kernel<Buffer, const KParam,
+                auto iotaOp = KernelFunctor<Buffer, const KParam,
                                           const int, const int, const int, const int,
                                           const int, const int, const int, const int,
                                           const int, const int> (*iotaKernels[device]);
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 17fc460970..d752bacd34 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -29,7 +29,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -101,7 +101,7 @@ namespace kernel
         NDRange global(groups_all[0] * groups_all[2] * local[0],
                        groups_all[1] * groups_all[3] * local[1]);
 
-        auto ireduceOp = make_kernel<Buffer, KParam, Buffer,
+        auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer,
                                      Buffer, KParam, Buffer,
                                      uint, uint, uint>(*entry.ker);
 
@@ -214,7 +214,7 @@ namespace kernel
 
         uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-        auto ireduceOp = make_kernel<Buffer, KParam, Buffer,
+        auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer,
                                      Buffer, KParam, Buffer,
                                      uint, uint, uint>(*entry.ker);
 
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 8e3e95a788..878d95b7cd 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -66,7 +66,7 @@ namespace opencl
                     joinKernels[device] = new Kernel(*joinProgs[device], "join_kernel");
                 });
 
-                auto joinOp = make_kernel<Buffer, const KParam, const Buffer, const KParam,
+                auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
                               const int, const int, const int, const int,
                               const int, const int> (*joinKernels[device]);
 
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 9982285391..8f5fc1f432 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -23,7 +23,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -82,9 +82,12 @@ void laset(int m, int  n,
     NDRange global(groups_x * local[0],
                    groups_y * local[1]);
 
-    auto lasetOp = make_kernel<int, int, T, T, cl_mem, unsigned long long, int>(*setKernels[device]);
+    // retain the cl_mem object during cl::Buffer creation
+    cl::Buffer dAObj(dA, true);
+
+    auto lasetOp = KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(*setKernels[device]);
     lasetOp(EnqueueArgs(getQueue(), global, local),
-            m, n, offdiag, diag, dA, dA_offset, ldda);
+            m, n, offdiag, diag, dAObj, dA_offset, ldda);
 }
 
 }
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index b6ba692776..915be6f560 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -23,7 +23,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -86,7 +86,7 @@ void laset_band(int m, int  n, int k,
     NDRange local(threads, 1);
     NDRange global(threads * groups, 1);
 
-    auto lasetBandOp = make_kernel<int, int, T, T, cl_mem, unsigned long long, int>(*setKernels[device]);
+    auto lasetBandOp = KernelFunctor<int, int, T, T, cl_mem, unsigned long long, int>(*setKernels[device]);
 
     lasetBandOp(EnqueueArgs(getQueue(), global, local),
                 m, n, offdiag, diag, dA, dA_offset, ldda);
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 970572e01e..99eb4096ed 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -77,7 +77,10 @@ void laswp(int n, cl_mem in, size_t offset, int ldda,
     NDRange global(groups * local[0]);
     zlaswp_params_t params;
 
-    auto laswpOp = make_kernel<int, cl_mem, unsigned long long,
+    //retain the cl_mem object during cl::Buffer creation
+    cl::Buffer inObj(in, true);
+
+    auto laswpOp = KernelFunctor<int, Buffer, unsigned long long,
                                int, zlaswp_params_t>(*swpKernels[device]);
 
     for( int k = k1-1; k < k2; k += MAX_PIVOTS ) {
@@ -93,7 +96,7 @@ void laswp(int n, cl_mem in, size_t offset, int ldda,
         unsigned long long k_offset = offset + k*ldda;
 
         laswpOp(EnqueueArgs(getQueue(), global, local),
-                n, in, k_offset, ldda, params);
+                n, inObj, k_offset, ldda, params);
     }
 
 }
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 756c0ea9d0..9ee6d9cfb7 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -71,7 +71,7 @@ void lookup(Param out, const Param in, const Param indices, int nDims)
         NDRange global(blk_x * out.info.dims[2] * THREADS_X,
                        blk_y * out.info.dims[3] * THREADS_Y);
 
-        auto arrIdxOp = make_kernel<Buffer, KParam,
+        auto arrIdxOp = KernelFunctor<Buffer, KParam,
                                     Buffer, KParam,
                                     Buffer, KParam,
                                     int, int>(*aiKernels[device]);
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 5cf210365c..db6fc58c7f 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -23,7 +23,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -80,7 +80,7 @@ void lu_split_launcher(Param lower, Param upper, const Param in)
         NDRange global(groups_x * local[0] * in.info.dims[2],
                        groups_y * local[1] * in.info.dims[3]);
 
-        auto lu_split_op = make_kernel<Buffer, const KParam,
+        auto lu_split_op = KernelFunctor<Buffer, const KParam,
                                        Buffer, const KParam,
                                        const Buffer, const KParam,
                                        const int, const int> (*splitKernels[device]);
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index bfbe4b0178..b1bdd49236 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -77,7 +77,7 @@ void matchTemplate(Param out, const Param srch, const Param tmplt)
 
         NDRange global(blk_x * srch.info.dims[2] * THREADS_X, blk_y * srch.info.dims[3] * THREADS_Y);
 
-        auto matchImgOp = make_kernel<Buffer, KParam,
+        auto matchImgOp = KernelFunctor<Buffer, KParam,
                                        Buffer, KParam,
                                        Buffer, KParam,
                                        int, int> (*mtKernels[device]);
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index c3ea8bc4b4..0df2cc2abf 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
@@ -61,7 +61,7 @@ void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint ite
                     msKernels[device] = new Kernel(*msProgs[device], "meanshift");
                 });
 
-        auto meanshiftOp = make_kernel<Buffer, KParam,
+        auto meanshiftOp = KernelFunctor<Buffer, KParam,
                                        Buffer, KParam,
                                        LocalSpaceArg,
                                        int, float,
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index e4e75df767..992586aab1 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -77,7 +77,7 @@ void medfilt(Param out, const Param in)
         NDRange global(blk_x * in.info.dims[2] * THREADS_X,
                        blk_y * in.info.dims[3] * THREADS_Y);
 
-        auto medfiltOp = make_kernel<Buffer, KParam,
+        auto medfiltOp = KernelFunctor<Buffer, KParam,
                                      Buffer, KParam,
                                      cl::LocalSpaceArg,
                                      int, int> (*mfKernels[device]);
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 95f61c8869..d44bc20cb0 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -22,7 +22,7 @@
 
 using cl::Buffer;
 using cl::Program;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -83,7 +83,7 @@ namespace kernel
             NDRange global(groups_0 * idims[2] * local_size[0],
                            groups_1 * idims[3] * local_size[1]);
 
-            auto memcopy_kernel = make_kernel< Buffer, dims_t,
+            auto memcopy_kernel = KernelFunctor< Buffer, dims_t,
                                                Buffer, dims_t,
                                                dims_t, int,
                                                int, int >(*cpyKernels[device]);
@@ -154,7 +154,7 @@ namespace kernel
                 trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
             }
 
-            auto copyOp = make_kernel<Buffer, KParam, Buffer, KParam,
+            auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
                                       outType, float, dims_t,
                                       int, int
                                      >(*cpyKernels[device]);
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 1a93826581..bde71bd335 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
@@ -68,7 +68,7 @@ void morph(Param         out,
                     morKernels[device] = new Kernel(*morProgs[device], "morph");
                 });
 
-        auto morphOp = make_kernel<Buffer, KParam,
+        auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
                                    Buffer, cl::LocalSpaceArg,
                                    int, int
@@ -133,7 +133,7 @@ void morph3d(Param       out,
                     morKernels[device] = new Kernel(*morProgs[device], "morph3d");
                 });
 
-        auto morphOp = make_kernel<Buffer, KParam,
+        auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
                                    Buffer, cl::LocalSpaceArg, int
                                   >(*morKernels[device]);
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 34688b26ac..fac9c9feff 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -18,6 +18,7 @@
 #include <dispatch.hpp>
 #include <cache.hpp>
 
+using cl::KernelFunctor;
 using cl::LocalSpaceArg;
 
 namespace opencl
@@ -120,7 +121,7 @@ void nearest_neighbour(Param idx,
         // For each query vector, find training vector with smallest Hamming
         // distance per CUDA block
         if (unroll_len > 0) {
-            auto huOp = make_kernel<Buffer, Buffer,
+            auto huOp = KernelFunctor<Buffer, Buffer,
                                     Buffer, KParam,
                                     Buffer, KParam,
                                     const To,
@@ -132,7 +133,7 @@ void nearest_neighbour(Param idx,
                  max_dist, cl::Local(lmem_sz));
         }
         else {
-            auto hmOp = make_kernel<Buffer, Buffer,
+            auto hmOp = KernelFunctor<Buffer, Buffer,
                                     Buffer, KParam,
                                     Buffer, KParam,
                                     const To, const unsigned,
@@ -150,7 +151,7 @@ void nearest_neighbour(Param idx,
 
         // Reduce all smallest Hamming distances from each block and store final
         // best match
-        auto smOp = make_kernel<Buffer, Buffer, Buffer, Buffer,
+        auto smOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
                                 const unsigned, const unsigned,
                                 const To> (entry.ker[2]);
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 51fb2665a4..7cf77a4fbc 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -251,7 +251,7 @@ void orb(unsigned* out_feat,
             unsigned block_size = 7;
             float k_thr = 0.04f;
 
-            auto hrOp = make_kernel<Buffer, Buffer, Buffer,
+            auto hrOp = KernelFunctor<Buffer, Buffer, Buffer,
                                     Buffer, Buffer, const unsigned,
                                     Buffer, Buffer, KParam,
                                     const unsigned, const float, const unsigned> (*hrKernel[device]);
@@ -320,7 +320,7 @@ void orb(unsigned* out_feat,
             const NDRange local_keep(ORB_THREADS, 1);
             const NDRange global_keep(keep_blk * ORB_THREADS, 1);
 
-            auto kfOp = make_kernel<Buffer, Buffer, Buffer,
+            auto kfOp = KernelFunctor<Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer, Buffer,
                                     const unsigned> (*kfKernel[device]);
 
@@ -343,7 +343,7 @@ void orb(unsigned* out_feat,
             const NDRange local_centroid(ORB_THREADS_X, ORB_THREADS_Y);
             const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X, ORB_THREADS_Y);
 
-            auto caOp = make_kernel<Buffer, Buffer, Buffer,
+            auto caOp = KernelFunctor<Buffer, Buffer, Buffer,
                                     const unsigned, Buffer, KParam,
                                     const unsigned> (*caKernel[device]);
 
@@ -394,7 +394,7 @@ void orb(unsigned* out_feat,
                 getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0, usable_feat * 8 * sizeof(unsigned), h_desc_lvl.data());
             }
 
-            auto eoOp = make_kernel<Buffer, const unsigned,
+            auto eoOp = KernelFunctor<Buffer, const unsigned,
                                     Buffer, Buffer, Buffer, Buffer,
                                     Buffer, KParam,
                                     const float, const unsigned> (*eoKernel[device]);
diff --git a/src/backend/opencl/kernel/random.hpp b/src/backend/opencl/kernel/random.hpp
index f951797e97..8d6a017030 100644
--- a/src/backend/opencl/kernel/random.hpp
+++ b/src/backend/opencl/kernel/random.hpp
@@ -25,7 +25,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -127,7 +127,7 @@ namespace opencl
                         ranKernels[device] = new Kernel(*ranProgs[device], "random");
                     });
 
-                auto randomOp = make_kernel<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
+                auto randomOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
 
                 uint groups = divup(elements, THREADS * REPEAT);
                 counter += divup(elements, THREADS * groups);
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 0299c030d4..b57e01ab4b 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -59,7 +59,7 @@ namespace opencl
                     rangeKernels[device] = new Kernel(*rangeProgs[device], "range_kernel");
                 });
 
-                auto rangeOp = make_kernel<Buffer, const KParam, const int,
+                auto rangeOp = KernelFunctor<Buffer, const KParam, const int,
                                            const int, const int> (*rangeKernels[device]);
 
                 NDRange local(RANGE_TX, RANGE_TY, 1);
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index a35f5eab21..c48cea4072 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -29,7 +29,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -101,7 +101,7 @@ namespace kernel
         NDRange global(groups_all[0] * groups_all[2] * local[0],
                        groups_all[1] * groups_all[3] * local[1]);
 
-        auto reduceOp = make_kernel<Buffer, KParam,
+        auto reduceOp = KernelFunctor<Buffer, KParam,
                                     Buffer, KParam,
                                     uint, uint, uint,
                                     int, To>(*entry.ker);
@@ -220,7 +220,7 @@ namespace kernel
 
         uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-        auto reduceOp = make_kernel<Buffer, KParam,
+        auto reduceOp = KernelFunctor<Buffer, KParam,
                                     Buffer, KParam,
                                     uint, uint, uint,
                                     int, To>(*entry.ker);
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index d814d6db55..11f82d4535 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -36,6 +36,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 namespace compute = boost::compute;
@@ -103,7 +104,7 @@ void regions(Param out, Param in)
 
         const NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
 
-        auto ilOp = make_kernel<Buffer, KParam,
+        auto ilOp = KernelFunctor<Buffer, KParam,
                                 Buffer, KParam> (*ilKernel[device]);
 
         ilOp(EnqueueArgs(getQueue(), global, local),
@@ -118,7 +119,7 @@ void regions(Param out, Param in)
             h_continue = 0;
             getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
 
-            auto ueOp = make_kernel<Buffer, KParam,
+            auto ueOp = KernelFunctor<Buffer, KParam,
                                     Buffer> (*ueKernel[device]);
 
             ueOp(EnqueueArgs(getQueue(), global, local),
@@ -204,7 +205,7 @@ void regions(Param out, Param in)
                                 c_queue);
 
         // Apply the correct labels to the equivalency map
-        auto frOp = make_kernel<Buffer, KParam,
+        auto frOp = KernelFunctor<Buffer, KParam,
                                 Buffer, KParam,
                                 Buffer> (*frKernel[device]);
 
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 0ec436302e..1aa576350a 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -59,7 +59,7 @@ namespace opencl
                     reorderKernels[device] = new Kernel(*reorderProgs[device], "reorder_kernel");
                 });
 
-                auto reorderOp = make_kernel<Buffer, const Buffer, const KParam, const KParam,
+                auto reorderOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
                                           const int, const int, const int, const int,
                                           const int, const int> (*reorderKernels[device]);
 
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 53a952bd9c..0f659f1761 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -87,7 +87,7 @@ namespace opencl
                     resizeKernels[device] = new Kernel(*resizeProgs[device], "resize_kernel");
                 });
 
-                auto resizeOp = make_kernel<Buffer, const KParam,
+                auto resizeOp = KernelFunctor<Buffer, const KParam,
                                       const Buffer, const KParam,
                                       const int, const int, const float, const float>
                                       (*resizeKernels[device]);
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index e68d0edad5..5839c0ec12 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -25,7 +25,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -103,7 +103,7 @@ namespace opencl
                     rotateKernels[device] = new Kernel(*rotateProgs[device], "rotate_kernel");
                 });
 
-                auto rotateOp = make_kernel<Buffer, const KParam, const Buffer, const KParam, const tmat_t,
+                auto rotateOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam, const tmat_t,
                                             const int, const int, const int, const int>
                                            (*rotateKernels[device]);
 
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 84cc722bbd..33da836adf 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -26,7 +26,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -113,7 +113,7 @@ namespace kernel
 
             uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto scanOp = make_kernel<Buffer, KParam,
+            auto scanOp = KernelFunctor<Buffer, KParam,
                                       Buffer, KParam,
                                       Buffer, KParam,
                                       uint, uint,
@@ -146,7 +146,7 @@ namespace kernel
 
             uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto bcastOp = make_kernel<Buffer, KParam,
+            auto bcastOp = KernelFunctor<Buffer, KParam,
                                        Buffer, KParam,
                                        uint, uint,
                                        uint, uint>(ker);
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index d7a284da9b..11c4fbd10f 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -27,7 +27,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -118,7 +118,7 @@ namespace kernel
 
         uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        auto scanOp = make_kernel<Buffer, KParam,
+        auto scanOp = KernelFunctor<Buffer, KParam,
                                   Buffer, KParam,
                                   Buffer, KParam,
                                   uint, uint, uint>(ker);
@@ -147,7 +147,7 @@ namespace kernel
 
         uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        auto bcastOp = make_kernel<Buffer, KParam,
+        auto bcastOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
                                    uint, uint, uint>(ker);
 
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 8f17cf93b4..a0271712b5 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -23,7 +23,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -80,7 +80,7 @@ namespace opencl
             NDRange global(groups_0 * out.info.dims[2] * local[0],
                            groups_1 * out.info.dims[3] * local[1]);
 
-            auto selectOp = make_kernel<Buffer, KParam,
+            auto selectOp = KernelFunctor<Buffer, KParam,
                                         Buffer, KParam,
                                         Buffer, KParam,
                                         Buffer, KParam,
@@ -158,7 +158,7 @@ namespace opencl
             NDRange global(groups_0 * out.info.dims[2] * local[0],
                            groups_1 * out.info.dims[3] * local[1]);
 
-            auto selectOp = make_kernel<Buffer, KParam,
+            auto selectOp = KernelFunctor<Buffer, KParam,
                                         Buffer, KParam,
                                         Buffer, KParam,
                                         T,
diff --git a/src/backend/opencl/kernel/shift.hpp b/src/backend/opencl/kernel/shift.hpp
index 3ff63ee19d..ed40038d6a 100644
--- a/src/backend/opencl/kernel/shift.hpp
+++ b/src/backend/opencl/kernel/shift.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -60,7 +60,7 @@ namespace opencl
                     shiftKernels[device] = new Kernel(*shiftProgs[device], "shift_kernel");
                 });
 
-                auto shiftOp = make_kernel<Buffer, const Buffer, const KParam, const KParam,
+                auto shiftOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
                                           const int, const int, const int, const int,
                                           const int, const int> (*shiftKernels[device]);
 
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index b5dab785c7..a76d46882c 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -363,7 +363,7 @@ std::vector<Param> buildDoGPyr(
         const NDRange local(SIFT_THREADS, 1);
         const NDRange global(blk_x * SIFT_THREADS, 1);
 
-        auto suOp = make_kernel<Buffer, Buffer, unsigned, unsigned> (*suKernel);
+        auto suOp = KernelFunctor<Buffer, Buffer, unsigned, unsigned> (*suKernel);
 
         suOp(EnqueueArgs(getQueue(), global, local),
              *dog_pyr[o].data, *gauss_pyr[o].data, nel, dog_layers);
@@ -504,7 +504,7 @@ void sift(unsigned* out_feat,
 
             float extrema_thr = 0.5f * contrast_thr / n_layers;
 
-            auto deOp = make_kernel<Buffer, Buffer, Buffer, Buffer,
+            auto deOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
                                     Buffer, KParam, unsigned, float,
                                     LocalSpaceArg> (*deKernel[device]);
 
@@ -538,7 +538,7 @@ void sift(unsigned* out_feat,
             const NDRange local_interp(SIFT_THREADS, 1);
             const NDRange global_interp(blk_x_interp * SIFT_THREADS, 1);
 
-            auto ieOp = make_kernel<Buffer, Buffer, Buffer,
+            auto ieOp = KernelFunctor<Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer, unsigned,
                                     Buffer, KParam, unsigned, unsigned, unsigned,
@@ -612,7 +612,7 @@ void sift(unsigned* out_feat,
             const NDRange local_nodup(SIFT_THREADS, 1);
             const NDRange global_nodup(blk_x_nodup * SIFT_THREADS, 1);
 
-            auto rdOp = make_kernel<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+            auto rdOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer, Buffer, Buffer,
                                     unsigned> (*rdKernel[device]);
 
@@ -647,7 +647,7 @@ void sift(unsigned* out_feat,
             const NDRange local_ori(SIFT_THREADS_X, SIFT_THREADS_Y);
             const NDRange global_ori(SIFT_THREADS_X, blk_x_ori * SIFT_THREADS_Y);
 
-            auto coOp = make_kernel<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+            auto coOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
                                     Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
                                     Buffer, KParam, unsigned, unsigned, int,
                                     LocalSpaceArg> (*coKernel[device]);
@@ -692,7 +692,7 @@ void sift(unsigned* out_feat,
             const unsigned histsz = 8;
 
             if (compute_GLOH) {
-                auto cgOp = make_kernel<Buffer, unsigned, unsigned,
+                auto cgOp = KernelFunctor<Buffer, unsigned, unsigned,
                                         Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
                                         Buffer, KParam, int, unsigned, unsigned, unsigned, float, int,
                                         LocalSpaceArg> (*cgKernel[device]);
@@ -705,7 +705,7 @@ void sift(unsigned* out_feat,
                      cl::Local(desc_len * (histsz+1) * sizeof(float)));
             }
             else {
-                auto cdOp = make_kernel<Buffer, unsigned, unsigned,
+                auto cdOp = KernelFunctor<Buffer, unsigned, unsigned,
                                         Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
                                         Buffer, KParam, int, int, float, int,
                                         LocalSpaceArg> (*cdKernel[device]);
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 522dc4969a..12c3ba7894 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -68,7 +68,7 @@ void sobel(Param dx, Param dy, const Param in)
         NDRange global(blk_x * in.info.dims[2] * THREADS_X,
                        blk_y * in.info.dims[3] * THREADS_Y);
 
-        auto sobelOp = make_kernel<Buffer, KParam,
+        auto sobelOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
                                    Buffer, KParam,
                                    cl::LocalSpaceArg,
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 98ba75977a..2938af6f4e 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -32,7 +32,7 @@ namespace compute = boost::compute;
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index f9b16ac0c4..ded8ef604e 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -41,7 +41,7 @@ namespace compute = boost::compute;
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -135,7 +135,7 @@ namespace opencl
                     sortPairKernels[device] = new Kernel(*sortPairProgs[device], "make_pair_kernel");
                 });
 
-                auto makePairOp = make_kernel<Buffer, const Buffer, const Buffer, const unsigned>
+                auto makePairOp = KernelFunctor<Buffer, const Buffer, const Buffer, const unsigned>
                                           (*sortPairKernels[device]);
 
                 NDRange local(256, 1, 1);
@@ -177,7 +177,7 @@ namespace opencl
                     sortPairKernels[device] = new Kernel(*sortPairProgs[device], "split_pair_kernel");
                 });
 
-                auto splitPairOp = make_kernel<Buffer, Buffer, const Buffer, const unsigned>
+                auto splitPairOp = KernelFunctor<Buffer, Buffer, const Buffer, const unsigned>
                                           (*sortPairKernels[device]);
 
                 NDRange local(256, 1, 1);
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index a3f669d275..3e638023fc 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -20,6 +20,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
@@ -69,7 +70,7 @@ void susan(cl::Buffer* out, const cl::Buffer* in,
                 suKernel[device] = new Kernel(*suProg[device], "susan_responses");
             });
 
-        auto susanOp = make_kernel<Buffer, Buffer,
+        auto susanOp = KernelFunctor<Buffer, Buffer,
                                    unsigned,
                                    unsigned, unsigned,
                                    float, float, unsigned>(*suKernel[device]);
@@ -119,7 +120,7 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
         cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
         getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
 
-        auto nonMaximalOp = make_kernel<Buffer, Buffer, Buffer, Buffer,
+        auto nonMaximalOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
                                         unsigned, unsigned, Buffer,
                                         unsigned, unsigned>(*nmKernel[device]);
 
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 4577bdd259..0e0f4b16c5 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -90,14 +90,18 @@ void swapdblk(int n, int nb,
 
     NDRange local(nb);
     NDRange global(nblocks * nb);
-    auto swapdOp = make_kernel<int,
-                               cl_mem, unsigned long long, int, int,
-                               cl_mem, unsigned long long, int, int>(*swpKernels[device]);
+
+    cl::Buffer dAObj(dA, true);
+    cl::Buffer dBObj(dB, true);
+
+    auto swapdOp = KernelFunctor<int,
+                               Buffer, unsigned long long, int, int,
+                               Buffer, unsigned long long, int, int>(*swpKernels[device]);
 
     swapdOp(EnqueueArgs(getQueue(), global, local),
             nb,
-            dA, dA_offset, ldda, inca,
-            dB, dB_offset, lddb, incb);
+            dAObj, dA_offset, ldda, inca,
+            dBObj, dB_offset, lddb, incb);
 
 }
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index 710aefc0b1..f96765850b 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -21,7 +21,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -59,7 +59,7 @@ namespace opencl
                     tileKernels[device] = new Kernel(*tileProgs[device], "tile_kernel");
                 });
 
-                auto tileOp = make_kernel<Buffer, const Buffer, const KParam, const KParam,
+                auto tileOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
                                           const int, const int> (*tileKernels[device]);
 
                 NDRange local(TX, TY, 1);
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index d6a25639e3..cedcf0fc26 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -25,7 +25,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -102,7 +102,7 @@ namespace opencl
                     transformKernels[device] = new Kernel(*transformProgs[device], "transform_kernel");
                 });
 
-                auto transformOp = make_kernel<Buffer, const KParam,
+                auto transformOp = KernelFunctor<Buffer, const KParam,
                                          const Buffer, const KParam, const Buffer, const KParam,
                                          const int, const int, const int, const int,
                                          const int, const int, const int>
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 7975e67e6f..4643aba7a5 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -78,7 +78,7 @@ void transpose(Param out, const Param in)
         NDRange global(blk_x * local[0] * in.info.dims[2],
                        blk_y * local[1] * in.info.dims[3]);
 
-        auto transposeOp = make_kernel<Buffer, const KParam,
+        auto transposeOp = KernelFunctor<Buffer, const KParam,
                                        const Buffer, const KParam,
                                        const int, const int> (*trsKernels[device]);
 
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 4206e2bb34..239a909512 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -22,7 +22,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -78,7 +78,7 @@ void transpose_inplace(Param in)
         NDRange global(blk_x * local[0] * in.info.dims[2],
                        blk_y * local[1] * in.info.dims[3]);
 
-        auto transposeOp = make_kernel<Buffer, const KParam,
+        auto transposeOp = KernelFunctor<Buffer, const KParam,
                                        const int, const int> (*transposeKernels[device]);
 
         transposeOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 2b00117ebf..7fa6240aee 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -23,7 +23,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -80,7 +80,7 @@ void triangle(Param out, const Param in)
         NDRange global(groups_x * out.info.dims[2] * local[0],
                        groups_y * out.info.dims[3] * local[1]);
 
-        auto triangleOp = make_kernel<Buffer, KParam,
+        auto triangleOp = KernelFunctor<Buffer, KParam,
                                       const Buffer, KParam,
                                       const int, const int> (*trgKernels[device]);
 
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index a6705b3cf3..345cb0c108 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -25,7 +25,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -97,7 +97,7 @@ namespace opencl
                 NDRange global(local[0] * BX,
                                local[1] * BY);
 
-                auto unwrapOp = make_kernel<Buffer, const KParam,
+                auto unwrapOp = KernelFunctor<Buffer, const KParam,
                                             const Buffer, const KParam,
                                             const int, const int,
                                             const int, const int,
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 2b1308fcec..aadbb08efd 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -26,7 +26,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -72,7 +72,7 @@ namespace kernel
 
         uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
 
-        auto whereOp = make_kernel<Buffer,
+        auto whereOp = KernelFunctor<Buffer,
                                    Buffer, KParam,
                                    Buffer, KParam,
                                    Buffer, KParam,
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index f16dbe9b37..a04b5ad90e 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -25,7 +25,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -89,7 +89,7 @@ namespace opencl
                                local[1] * groups_y * out.info.dims[3]);
 
 
-                auto wrapOp = make_kernel<Buffer, const KParam,
+                auto wrapOp = KernelFunctor<Buffer, const KParam,
                                           const Buffer, const KParam,
                                           const int, const int,
                                           const int, const int,
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index cc19e13bf8..020c8494f1 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -13,8 +13,9 @@
 #endif
 
 #define CL_HPP_ENABLE_EXCEPTIONS
+#define CL_HPP_MINIMUM_OPENCL_VERSION 120
 #define CL_HPP_TARGET_OPENCL_VERSION 120
-#include <CL/cl.hpp>
+#include <CL/cl2.hpp>
 
 #include <vector>
 #include <string>
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 6b49730708..52409b0502 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -16,7 +16,6 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 6a2af45131..cce3d1dd59 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -16,7 +16,6 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;

From 4eb07a9b841289599e0079fb051d0b9ac552c7c8 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 31 May 2016 10:46:30 -0400
Subject: [PATCH 0566/2677] OpenCL Backend for scan

---
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |   9 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |   7 +-
 src/backend/opencl/kernel/scan_dim.cl         |   1 -
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 366 ++++++++++++++++++
 src/backend/opencl/kernel/scan_dim_by_key.hpp | 287 ++++++++++++++
 .../opencl/kernel/scan_first_by_key.cl        | 315 +++++++++++++++
 .../opencl/kernel/scan_first_by_key.hpp       | 268 +++++++++++++
 src/backend/opencl/program.cpp                |   2 +-
 src/backend/opencl/scan.cpp                   |  35 +-
 src/backend/opencl/scan.hpp                   |   3 -
 src/backend/opencl/scan_by_key.hpp            |  17 +
 11 files changed, 1295 insertions(+), 15 deletions(-)
 create mode 100644 src/backend/opencl/kernel/scan_dim_by_key.cl
 create mode 100644 src/backend/opencl/kernel/scan_dim_by_key.hpp
 create mode 100644 src/backend/opencl/kernel/scan_first_by_key.cl
 create mode 100644 src/backend/opencl/kernel/scan_first_by_key.hpp
 create mode 100644 src/backend/opencl/scan_by_key.hpp

diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 60ab8d9eb2..10cc20db52 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -98,6 +98,7 @@ namespace kernel
         __shared__ To s_val[THREADS_X * DIMY * 2];
         __shared__ char s_ftmp[THREADS_X];
         __shared__ To s_tmp[THREADS_X];
+        __shared__ int boundaryid;
         To *sptr =  s_val + tid;
         char *sfptr = s_flg + tid;
 
@@ -111,6 +112,7 @@ namespace kernel
         if (isLast) {
             s_tmp[tidx] = val;
             s_ftmp[tidx] = 0;
+            boundaryid = -1;
         }
         __syncthreads();
 
@@ -123,11 +125,10 @@ namespace kernel
         char *curr = &sfptr[tidy];
 
         char flag = 0;
-        int boundaryid = -1;
         for (int k = 0; k < lim; k++) {
 
             if (id_dim < out_dim) {
-                flag = calculate_head_flags_dim(kptr, id_dim, istride_dim);
+                flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
             } else {
                 flag = 0;
             }
@@ -181,6 +182,7 @@ namespace kernel
                 s_ftmp[tidx] = flag;
             }
             id_dim += blockDim.y;
+            kptr += blockDim.y * key.strides[dim];
             iptr += blockDim.y * istride_dim;
             optr += blockDim.y * ostride_dim;
             __syncthreads();
@@ -266,7 +268,7 @@ namespace kernel
 
             if (calculateFlags) {
                 if (id_dim < out_dim) {
-                    flag = calculate_head_flags_dim(kptr, id_dim, istride_dim);
+                    flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
                 } else {
                     flag = 0;
                 }
@@ -319,6 +321,7 @@ namespace kernel
                 s_ftmp[tidx] = flag;
             }
             id_dim += blockDim.y;
+            kptr += blockDim.y * key.strides[dim];
             iptr += blockDim.y * istride_dim;
             optr += blockDim.y * ostride_dim;
             __syncthreads();
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index ee49f14d04..687bac3c6f 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -64,6 +64,7 @@ namespace kernel
         __shared__ To s_val[SHARED_MEM_SIZE];
         __shared__ char s_ftmp[DIMY];
         __shared__ To s_tmp[DIMY];
+        __shared__ int boundaryid;
 
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -84,6 +85,7 @@ namespace kernel
         if (isLast) {
             s_tmp[tidy] = init;
             s_ftmp[tidy] = 0;
+            boundaryid = -1;
         }
         __syncthreads();
 
@@ -109,10 +111,9 @@ namespace kernel
         char *curr = &sfptr[tidx];
 
         char flag = 0;
-        int boundaryid = -1;
         for (int k = 0; k < lim; k++) {
             if (id < out.dims[0]) {
-                flag = calculate_head_flags(kptr, id, id - istride);
+                flag = calculate_head_flags(kptr, id, id - key.strides[0]);
             } else {
                 flag = 0;
             }
@@ -238,7 +239,7 @@ namespace kernel
             char flag = 0;
             if (calculateFlags) {
                 if (id < out.dims[0]) {
-                    flag = calculate_head_flags(kptr, id, id - istride);
+                    flag = calculate_head_flags(kptr, id, id - key.strides[0]);
                 }
             } else {
                 flag = kptr[id];
diff --git a/src/backend/opencl/kernel/scan_dim.cl b/src/backend/opencl/kernel/scan_dim.cl
index cd3ad6887d..625f14800a 100644
--- a/src/backend/opencl/kernel/scan_dim.cl
+++ b/src/backend/opencl/kernel/scan_dim.cl
@@ -71,7 +71,6 @@ void scan_dim_kernel(__global To *oData, KParam oInfo,
         l_val[lid] = val;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        int start = 0;
         for (int off = 1; off < DIMY; off *= 2) {
 
             if (lidy >= off) val = binOp(val, l_val[lid - off * THREADS_X]);
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
new file mode 100644
index 0000000000..2f76ec4d69
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -0,0 +1,366 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+static char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
+{
+    char flag;
+    if (id == 0) {
+        flag = 1;
+    } else {
+        flag = ((*kptr) != (*(kptr - stride)));
+    }
+    return flag;
+}
+
+__kernel
+void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
+                     __global To *tData, KParam tInfo,
+                     __global char *tfData, KParam tfInfo,
+                     __global int *tiData, KParam tiInfo,
+                     const __global Ti *iData, KParam iInfo,
+                     const __global Tk *kData, KParam kInfo,
+                     uint groups_x,
+                     uint groups_y,
+                     uint groups_dim,
+                     uint lim)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int lid  = lidy * THREADS_X + lidx;
+
+    const int zid = get_group_id(0) / groups_x;
+    const int wid = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x) * zid;
+    const int groupId_y = get_group_id(1) - (groups_y) * wid;
+    const int xid = groupId_x * get_local_size(0) + lidx;
+    const int yid = groupId_y;
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    // There is only one element per group for out
+    // There are DIMY elements per group for in
+    // Hence increment ids[dim] just after offseting out and before offsetting in
+    tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] + ids[1] * tInfo.strides[1] + ids[0];
+    tfData += ids[3] * tfInfo.strides[3] + ids[2] * tfInfo.strides[2] + ids[1] * tfInfo.strides[1] + ids[0];
+    tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] + ids[1] * tiInfo.strides[1] + ids[0];
+    const int groupId_dim = ids[dim];
+
+    ids[dim] = ids[dim] * DIMY * lim + lidy;
+    oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+    iData  += ids[3] *  iInfo.strides[3] + ids[2] *  iInfo.strides[2] + ids[1] *  iInfo.strides[1] + ids[0];
+    kData  += ids[3] *  kInfo.strides[3] + ids[2] *  kInfo.strides[2] + ids[1] *  kInfo.strides[1] + ids[0];
+    iData  += iInfo.offset;
+
+    int id_dim = ids[dim];
+    const int out_dim = oInfo.dims[dim];
+
+    bool is_valid =
+        (ids[0] < oInfo.dims[0]) &&
+        (ids[1] < oInfo.dims[1]) &&
+        (ids[2] < oInfo.dims[2]) &&
+        (ids[3] < oInfo.dims[3]);
+
+    const int ostride_dim = oInfo.strides[dim];
+    const int istride_dim =  iInfo.strides[dim];
+
+    __local To l_val0[THREADS_X * DIMY];
+    __local To l_val1[THREADS_X * DIMY];
+    __local char l_flg0[THREADS_X * DIMY];
+    __local char l_flg1[THREADS_X * DIMY];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[THREADS_X];
+    __local char l_ftmp[THREADS_X];
+    __local int boundaryid;
+
+    bool flip = 0;
+    const To init_val  = init;
+    To val = init_val;
+    const bool isLast = (lidy == (DIMY - 1));
+
+    if (isLast) {
+        l_tmp[lidy] = val;
+        l_ftmp[lidy] = 0;
+        boundaryid = -1;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    __local char *prev;
+    if (lidy == 0) {
+        prev = &l_ftmp[lidx];
+    } else {
+        prev = &l_flg[lid-THREADS_X];
+    }
+    __local char *curr = &l_flg[lid];
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+
+        //if (isLast) l_tmp[lidx] = val;
+
+        bool cond = (is_valid) && (id_dim < out_dim);
+
+        if (cond) {
+            flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
+        } else {
+            flag = 0;
+        }
+
+        //val = cond ? transform(*iData) : init_val;
+
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(*iData);
+            }
+        } else {
+            if ((id_dim == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(*(iData - iInfo.strides[dim]));
+            }
+        }
+
+        if ((lidy == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidx]);
+            flag = flag | l_ftmp[lidx];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int off = 1; off < DIMY; off *= 2) {
+
+            if (lidy >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
+                flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+
+        if ((*prev == 0) && (*curr == 1)) {
+            boundaryid = id_dim;
+        }
+
+        if (cond) *oData = val;
+        if (isLast) {
+            l_tmp[lidx] = val;
+            l_ftmp[lidx] = flag;
+        }
+        id_dim += DIMY;
+        kData += DIMY * kInfo.strides[dim];
+        iData += DIMY * istride_dim;
+        oData += DIMY * ostride_dim;
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (is_valid &&
+        (groupId_dim < tInfo.dims[dim]) &&
+        isLast) {
+        *tData = val;
+        *tfData = flag;
+        *tiData = boundaryid;
+    }
+}
+
+__kernel
+void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
+                     const __global Ti *iData, KParam iInfo,
+                     const __global Tk *kData, KParam kInfo,
+                     uint groups_x,
+                     uint groups_y,
+                     uint groups_dim,
+                     uint lim)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int lid  = lidy * THREADS_X + lidx;
+
+    const int zid = get_group_id(0) / groups_x;
+    const int wid = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x) * zid;
+    const int groupId_y = get_group_id(1) - (groups_y) * wid;
+    const int xid = groupId_x * get_local_size(0) + lidx;
+    const int yid = groupId_y;
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    // There is only one element per group for out
+    // There are DIMY elements per group for in
+    // Hence increment ids[dim] just after offseting out and before offsetting in
+    const int groupId_dim = ids[dim];
+
+    ids[dim] = ids[dim] * DIMY * lim + lidy;
+    oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+    iData  += ids[3] *  iInfo.strides[3] + ids[2] *  iInfo.strides[2] + ids[1] *  iInfo.strides[1] + ids[0];
+    kData  += ids[3] *  kInfo.strides[3] + ids[2] *  kInfo.strides[2] + ids[1] *  kInfo.strides[1] + ids[0];
+    iData  += iInfo.offset;
+
+    int id_dim = ids[dim];
+    const int out_dim = oInfo.dims[dim];
+
+    bool is_valid =
+        (ids[0] < oInfo.dims[0]) &&
+        (ids[1] < oInfo.dims[1]) &&
+        (ids[2] < oInfo.dims[2]) &&
+        (ids[3] < oInfo.dims[3]);
+
+    const int ostride_dim = oInfo.strides[dim];
+    const int istride_dim =  iInfo.strides[dim];
+
+    __local To l_val0[THREADS_X * DIMY];
+    __local To l_val1[THREADS_X * DIMY];
+    __local char l_flg0[THREADS_X * DIMY];
+    __local char l_flg1[THREADS_X * DIMY];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[THREADS_X];
+    __local char l_ftmp[THREADS_X];
+
+    bool flip = 0;
+    const To init_val  = init;
+    To val = init_val;
+    const bool isLast = (lidy == (DIMY - 1));
+
+    if (isLast) {
+        l_tmp[lidy] = val;
+        l_ftmp[lidy] = 0;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+
+        bool cond = (is_valid) && (id_dim < out_dim);
+
+        if (calculateFlags) {
+            if (cond) {
+                flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
+            } else {
+                flag = 0;
+            }
+        } else {
+            flag = *kData;
+        }
+
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(*iData);
+            }
+        } else {
+            if ((id_dim == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(*(iData - iInfo.strides[dim]));
+            }
+        }
+
+        if ((lidy == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidx]);
+            flag = flag | l_ftmp[lidx];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int off = 1; off < DIMY; off *= 2) {
+
+            if (lidy >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
+                flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+
+        if (cond) *oData = val;
+        if (isLast) {
+            l_tmp[lidx] = val;
+            l_ftmp[lidx] = flag;
+        }
+        id_dim += DIMY;
+        kData += DIMY * kInfo.strides[dim];
+        iData += DIMY * istride_dim;
+        oData += DIMY * ostride_dim;
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+}
+
+__kernel
+void bcast_dim_kernel(__global To *oData, KParam oInfo,
+                      const __global To *tData, KParam tInfo,
+                      const __global int *tiData, KParam tiInfo,
+                      uint groups_x,
+                      uint groups_y,
+                      uint groups_dim,
+                      uint lim)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int lid  = lidy * THREADS_X + lidx;
+
+    const int zid = get_group_id(0) / groups_x;
+    const int wid = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x) * zid;
+    const int groupId_y = get_group_id(1) - (groups_y) * wid;
+    const int xid = groupId_x * get_local_size(0) + lidx;
+    const int yid = groupId_y;
+
+    int ids[4] = {xid, yid, zid, wid};
+    const int groupId_dim = ids[dim];
+
+    if (groupId_dim != 0) {
+
+        // There is only one element per group for out
+        // There are DIMY elements per group for in
+        // Hence increment ids[dim] just after offseting out and before offsetting in
+        tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] + ids[1] * tiInfo.strides[1] + ids[0];
+        tData  += ids[3] * tInfo.strides[3]  + ids[2] * tInfo.strides[2]  + ids[1] * tInfo.strides[1]  + ids[0];
+
+        ids[dim] = ids[dim] * DIMY * lim + lidy;
+        oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+
+        const int id_dim = ids[dim];
+        const int out_dim = oInfo.dims[dim];
+
+        bool is_valid =
+            (ids[0] < oInfo.dims[0]) &&
+            (ids[1] < oInfo.dims[1]) &&
+            (ids[2] < oInfo.dims[2]) &&
+            (ids[3] < oInfo.dims[3]);
+
+        if (is_valid) {
+
+            int boundary = *tiData;
+            To accum = *(tData - tInfo.strides[dim]);
+
+            const int ostride_dim = oInfo.strides[dim];
+
+            for (int k = 0, id = id_dim;
+                 is_valid && k < lim && (id < boundary);
+                 k++, id += DIMY) {
+
+                *oData = binOp(*oData, accum);
+                oData += DIMY * ostride_dim;
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
new file mode 100644
index 0000000000..1abd9d44e1
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -0,0 +1,287 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+#include <mutex>
+#include <map>
+#include <kernel_headers/scan_dim_by_key.hpp>
+#include <kernel_headers/ops.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+#include <cache.hpp>
+#include "names.hpp"
+#include "config.hpp"
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+namespace kernel
+{
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags, uint threads_y)
+    {
+        std::string ref_name =
+            std::string("scan_") +
+            std::to_string(dim) +
+            std::string("_") +
+            std::to_string(calculateFlags) +
+            std::string("_") +
+            std::string(dtype_traits<Ti>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<Tk>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<To>::getName()) +
+            std::string("_") +
+            std::to_string(op) +
+            std::string("_") +
+            std::to_string(threads_y) +
+            std::string("_") +
+            std::to_string(int(inclusive_scan));
+
+        int device = getActiveDeviceId();
+        kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+        kc_entry_t entry;
+        if (idx == kernelCaches[device].end()) {
+
+            Binary<To, op> scan;
+            ToNum<To> toNum;
+
+            std::ostringstream options;
+            options << " -D To=" << dtype_traits<To>::getName()
+                    << " -D Ti=" << dtype_traits<Ti>::getName()
+                    << " -D Tk=" << dtype_traits<Ti>::getName()
+                    << " -D T=To"
+                    << " -D dim=" << dim
+                    << " -D DIMY=" << threads_y
+                    << " -D THREADS_X=" << THREADS_X
+                    << " -D init=" << toNum(scan.init())
+                    << " -D " << binOpName<op>()
+                    << " -D CPLX=" << af::iscplx<Ti>()
+                    << " -D calculateFlags=" << calculateFlags
+                    << " -D inclusive_scan=" << inclusive_scan;
+            if (std::is_same<Ti, double>::value ||
+                std::is_same<Ti, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
+            const int   ker_lens[] = {ops_cl_len, scan_dim_by_key_cl_len};
+            cl::Program prog;
+            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel[3];
+
+            entry.ker[0] = Kernel(*entry.prog, "scan_dim_by_key_final_kernel");
+            entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
+            entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
+
+            kernelCaches[device][ref_name] = entry;
+
+        } else {
+            entry = idx->second;
+        }
+
+        return entry.ker[kerIdx];
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void scan_dim_nonfinal_launcher(Param &out,
+                                  Param &tmp,
+                                  Param &tmpflg,
+                                  Param &tmpid,
+                                  const Param &in,
+                                  const Param &key,
+                                  int dim, uint threads_y,
+                                  const uint groups_all[4])
+    {
+        try {
+            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(1, dim, false, threads_y);
+
+            NDRange local(THREADS_X, threads_y);
+            NDRange global(groups_all[0] * groups_all[2] * local[0],
+                           groups_all[1] * groups_all[3] * local[1]);
+
+            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+            auto scanOp = make_kernel<Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      uint, uint,
+                                      uint, uint>(ker);
+
+            scanOp(EnqueueArgs(getQueue(), global, local),
+                   *out.data, out.info,
+                   *tmp.data, tmp.info,
+                   *tmpflg.data, tmpflg.info,
+                   *tmpid.data, tmpid.info,
+                   *in.data, in.info, *key.data, key.info,
+                   groups_all[0], groups_all[1], groups_all[dim], lim);
+
+            CL_DEBUG_FINISH(getQueue());
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void scan_dim_final_launcher(Param &out,
+                                  const Param &in,
+                                  const Param &key,
+                                  int dim, const bool calculateFlags, uint threads_y,
+                                  const uint groups_all[4])
+    {
+        try {
+            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(0, dim, calculateFlags, threads_y);
+
+            NDRange local(THREADS_X, threads_y);
+            NDRange global(groups_all[0] * groups_all[2] * local[0],
+                           groups_all[1] * groups_all[3] * local[1]);
+
+            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+            auto scanOp = make_kernel<Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      uint, uint,
+                                      uint, uint>(ker);
+
+            scanOp(EnqueueArgs(getQueue(), global, local),
+                   *out.data, out.info, *in.data, in.info, *key.data, key.info,
+                   groups_all[0], groups_all[1], groups_all[dim], lim);
+
+            CL_DEBUG_FINISH(getQueue());
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void bcast_dim_launcher(Param &out,
+                                   Param &tmp,
+                                   Param &tmpid,
+                                   int dim, uint threads_y,
+                                   const uint groups_all[4])
+    {
+        try {
+            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(2, dim, false, threads_y);
+
+            NDRange local(THREADS_X, threads_y);
+            NDRange global(groups_all[0] * groups_all[2] * local[0],
+                           groups_all[1] * groups_all[3] * local[1]);
+
+            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+            auto bcastOp = make_kernel<Buffer, KParam,
+                                       Buffer, KParam,
+                                       Buffer, KParam,
+                                       uint, uint,
+                                       uint, uint>(ker);
+
+            bcastOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
+                    groups_all[0], groups_all[1], groups_all[dim], lim);
+
+            CL_DEBUG_FINISH(getQueue());
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
+    static void scan_dim(Param &out, const Param &in, const Param &key, int dim)
+    {
+        try {
+            uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+            uint threads_x = THREADS_X;
+
+            uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
+                                 (uint)out.info.dims[1],
+                                 (uint)out.info.dims[2],
+                                 (uint)out.info.dims[3]};
+
+            groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+
+            if (groups_all[dim] == 1) {
+
+                scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
+                                              dim, true,
+                                              threads_y,
+                                              groups_all);
+            } else {
+
+                Param tmp = out;
+
+                tmp.info.dims[dim] = groups_all[dim];
+                tmp.info.strides[0] = 1;
+                for (int k = 1; k < 4; k++) {
+                    tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+                }
+                Param tmpflg = tmp;
+                Param tmpid = tmp;
+
+                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+                // FIXME: Do I need to free this ?
+                tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+                tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+                tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
+
+                scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
+                                              dim,
+                                              threads_y,
+                                              groups_all);
+
+                int gdim = groups_all[dim];
+                groups_all[dim] = 1;
+
+                if (op == af_notzero_t) {
+                    scan_dim_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
+                                                        dim, false,
+                                                        threads_y,
+                                                        groups_all);
+                } else {
+                    scan_dim_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
+                                                        dim, false,
+                                                        threads_y,
+                                                        groups_all);
+                }
+
+                groups_all[dim] = gdim;
+                bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
+                                               dim,
+                                               threads_y,
+                                               groups_all);
+                bufferFree(tmp.data);
+            }
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+}
+}
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
new file mode 100644
index 0000000000..d5a75a6db3
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -0,0 +1,315 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+static char calculate_head_flags(const __global Tk *kptr, int id, int previd)
+{
+    char flag;
+    if (id == 0) {
+        flag = 1;
+    } else {
+        flag = (kptr[id] != kptr[previd]);
+    }
+    return flag;
+}
+
+__kernel
+void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
+                       __global To *tData, KParam tInfo,
+                       __global char *tfData, KParam tfInfo,
+                       __global int *tiData, KParam tiInfo,
+                       const __global Ti *iData, KParam iInfo,
+                       const __global Tk *kData, KParam kInfo,
+                       uint groups_x, uint groups_y,
+                       uint lim)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int lid  = lidy * get_local_size(0) + lidx;
+
+    const int zid = get_group_id(0) / groups_x;
+    const int wid = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x) * zid;
+    const int groupId_y = get_group_id(1) - (groups_y) * wid;
+    const int xid = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid = groupId_y * get_local_size(1) + lidy;
+
+    bool cond_yzw = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+
+    iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
+        yid * iInfo.strides[1] + iInfo.offset;
+
+    kData += wid * kInfo.strides[3] + zid * kInfo.strides[2] +
+        yid * kInfo.strides[1] + kInfo.offset;
+
+    tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
+        yid * tInfo.strides[1] + tInfo.offset;
+
+    tfData += wid * tfInfo.strides[3] + zid * tfInfo.strides[2] +
+        yid * tfInfo.strides[1] + tfInfo.offset;
+
+    tiData += wid * tiInfo.strides[3] + zid * tiInfo.strides[2] +
+        yid * tiInfo.strides[1] + tiInfo.offset;
+
+    oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
+        yid * oInfo.strides[1] + oInfo.offset;
+
+    __local To l_val0[SHARED_MEM_SIZE];
+    __local To l_val1[SHARED_MEM_SIZE];
+    __local char l_flg0[SHARED_MEM_SIZE];
+    __local char l_flg1[SHARED_MEM_SIZE];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[DIMY];
+    __local char l_ftmp[DIMY];
+    __local int boundaryid;
+
+    bool flip = 0;
+
+    const To init_val = init;
+    int id = xid;
+    To val = init_val;
+
+    const bool isLast = (lidx == (DIMX - 1));
+
+    if (isLast) {
+        l_tmp[lidy] = val;
+        l_ftmp[lidy] = 0;
+        boundaryid = -1;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    __local char *prev;
+    if (lidx == 0) {
+        prev = &l_ftmp[lidy];
+    } else {
+        prev = &l_flg[lidx-1];
+    }
+    __local char *curr = &l_flg[lidx];
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+
+        //if (isLast) l_tmp[lidy] = val;
+
+        bool cond = ((id < oInfo.dims[0]) && cond_yzw);
+
+        if (cond) {
+            flag = calculate_head_flags(kData, id, id - kInfo.strides[0]);
+        } else {
+            flag = 0;
+        }
+        //val = cond ? transform(iData[id]) : init_val;
+
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(iData[id]);
+            }
+        } else {
+            if ((id == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(iData[id - iInfo.strides[0]]);
+            }
+        }
+
+        if ((lidx == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidy]);
+            flag = flag | l_ftmp[lidy];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (lidx >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
+                flag = l_flg[lid] | l_flg[lid - off];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+
+        if ((*prev == 0) && (*curr == 1)) {
+            boundaryid = id;
+        }
+
+        if (cond) oData[id] = val;
+        if (isLast) {
+            l_tmp[lidy] = val;
+            l_ftmp[lidy] = flag;
+        }
+        id += DIMX;
+        barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
+    }
+
+    if (isLast && cond_yzw) {
+        tData[groupId_x] = val;
+        tfData[groupId_x] = flag;
+        tiData[groupId_x] = boundaryid;
+    }
+}
+
+__kernel
+void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
+                       const __global Ti *iData, KParam iInfo,
+                       const __global Tk *kData, KParam kInfo,
+                       uint groups_x, uint groups_y,
+                       uint lim)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int lid  = lidy * get_local_size(0) + lidx;
+
+    const int zid = get_group_id(0) / groups_x;
+    const int wid = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x) * zid;
+    const int groupId_y = get_group_id(1) - (groups_y) * wid;
+    const int xid = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid = groupId_y * get_local_size(1) + lidy;
+
+    bool cond_yzw = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+
+    iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
+        yid * iInfo.strides[1] + iInfo.offset;
+
+    kData += wid * kInfo.strides[3] + zid * kInfo.strides[2] +
+        yid * kInfo.strides[1] + kInfo.offset;
+
+    oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
+        yid * oInfo.strides[1] + oInfo.offset;
+
+    __local To l_val0[SHARED_MEM_SIZE];
+    __local To l_val1[SHARED_MEM_SIZE];
+    __local char l_flg0[SHARED_MEM_SIZE];
+    __local char l_flg1[SHARED_MEM_SIZE];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[DIMY];
+    __local char l_ftmp[DIMY];
+
+    bool flip = 0;
+
+    const To init_val = init;
+    int id = xid;
+    To val = init_val;
+
+    const bool isLast = (lidx == (DIMX - 1));
+
+    for (int k = 0; k < lim; k++) {
+        char flag = 0;
+
+        //if (isLast) l_tmp[lidy] = val;
+
+        bool cond = ((id < oInfo.dims[0]) && cond_yzw);
+
+        if (calculateFlags) {
+            if (cond) {
+                flag = calculate_head_flags(kData, id, id - 1);
+            } else {
+                flag = 0;
+            }
+        } else {
+            flag = kData[id];
+        }
+        //val = cond ? transform(iData[id]) : init_val;
+
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(iData[id]);
+            }
+        } else {
+            if ((id == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(iData[id - 1]);
+            }
+        }
+
+        if ((lidx == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidy]);
+            flag = flag | l_ftmp[lidy];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (lidx >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
+                flag = l_flg[lid] | l_flg[lid - off];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+
+        if (cond) oData[id] = val;
+        if (isLast) {
+            l_tmp[lidy] = val;
+            l_ftmp[lidy] = flag;
+        }
+        id += DIMX;
+        barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
+    }
+}
+
+__kernel
+void bcast_first_kernel(__global To *oData, KParam oInfo,
+                        const __global To *tData, KParam tInfo,
+                        const __global int *tiData, KParam tiInfo,
+                        uint groups_x, uint groups_y, uint lim)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int lid  = lidy * get_local_size(0) + lidx;
+
+    const int zid = get_group_id(0) / groups_x;
+    const int wid = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x) * zid;
+    const int groupId_y = get_group_id(1) - (groups_y) * wid;
+    const int xid = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid = groupId_y * get_local_size(1) + lidy;
+
+    if (groupId_x != 0) {
+        bool cond = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+
+        if (cond) {
+
+            tiData += wid * tiInfo.strides[3] + zid * tiInfo.strides[2] +
+                yid * tiInfo.strides[1] + tiInfo.offset;
+
+            tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
+                yid * tInfo.strides[1] + tInfo.offset;
+
+            oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
+                yid * oInfo.strides[1] + oInfo.offset;
+
+            int boundary = tiData[groupId_x];
+            To accum = tData[groupId_x - 1];
+
+            for (int k = 0, id = xid;
+                 k < lim && id < boundary;
+                 k++, id += DIMX) {
+
+                oData[id] = binOp(accum, oData[id]);
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/scan_first_by_key.hpp b/src/backend/opencl/kernel/scan_first_by_key.hpp
new file mode 100644
index 0000000000..ba5f3bc88e
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_first_by_key.hpp
@@ -0,0 +1,268 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+#include <mutex>
+#include <map>
+#include <kernel_headers/scan_first_by_key.hpp>
+#include <kernel_headers/ops.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+#include <cache.hpp>
+#include "names.hpp"
+#include "config.hpp"
+#include <memory.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+namespace kernel
+{
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags, uint threads_x)
+    {
+        std::string ref_name =
+            std::string("scan_0_") +
+            std::string("_") +
+            std::to_string(calculateFlags) +
+            std::string("_") +
+            std::string(dtype_traits<Ti>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<Tk>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<To>::getName()) +
+            std::string("_") +
+            std::to_string(op) +
+            std::string("_") +
+            std::to_string(threads_x) +
+            std::string("_") +
+            std::to_string(int(inclusive_scan));
+
+        int device = getActiveDeviceId();
+        kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+        kc_entry_t entry;
+        if (idx == kernelCaches[device].end()) {
+
+            const uint threads_y = THREADS_PER_GROUP / threads_x;
+            const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
+
+            Binary<To, op> scan;
+            ToNum<To> toNum;
+
+            std::ostringstream options;
+            options << " -D To=" << dtype_traits<To>::getName()
+                    << " -D Ti=" << dtype_traits<Ti>::getName()
+                    << " -D Tk=" << dtype_traits<Tk>::getName()
+                    << " -D T=To"
+                    << " -D DIMX=" << threads_x
+                    << " -D DIMY=" << threads_y
+                    << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
+                    << " -D init=" << toNum(scan.init())
+                    << " -D " << binOpName<op>()
+                    << " -D CPLX=" << af::iscplx<Ti>()
+                    << " -D calculateFlags=" << calculateFlags
+                    << " -D inclusive_scan=" << inclusive_scan;
+            if (std::is_same<Ti, double>::value ||
+                std::is_same<Ti, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
+            const int   ker_lens[] = {ops_cl_len, scan_first_by_key_cl_len};
+            cl::Program prog;
+            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel[3];
+
+            entry.ker[0] = Kernel(*entry.prog, "scan_first_by_key_final_kernel");
+            entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
+            entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
+
+            kernelCaches[device][ref_name] = entry;
+
+        } else {
+            entry = idx->second;
+        }
+
+        return entry.ker[kerIdx];
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
+    static void scan_first_nonfinal_launcher(Param &out,
+                                    Param &tmp,
+                                    Param &tmpflg,
+                                    Param &tmpid,
+                                    const Param &in,
+                                    const Param &key,
+                                    const uint groups_x,
+                                    const uint groups_y,
+                                    const uint threads_x)
+    {
+        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(1, false, threads_x);
+
+        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
+
+        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+        auto scanOp = make_kernel<Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  uint, uint, uint>(ker);
+
+        scanOp(EnqueueArgs(getQueue(), global, local),
+               *out.data, out.info,
+               *tmp.data, tmp.info,
+               *tmpflg.data, tmpflg.info,
+               *tmpid.data, tmpid.info,
+               *in.data, in.info, *key.data, key.info,
+               groups_x, groups_y, lim);
+
+        CL_DEBUG_FINISH(getQueue());
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
+    static void scan_first_final_launcher(Param &out,
+                                    const Param &in,
+                                    const Param &key,
+                                    const bool calculateFlags,
+                                    const uint groups_x,
+                                    const uint groups_y,
+                                    const uint threads_x)
+    {
+        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(0, calculateFlags, threads_x);
+
+        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
+
+        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+        auto scanOp = make_kernel<Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  uint, uint, uint>(ker);
+
+        scanOp(EnqueueArgs(getQueue(), global, local),
+               *out.data, out.info, *in.data, in.info, *key.data, key.info,
+               groups_x, groups_y, lim);
+
+        CL_DEBUG_FINISH(getQueue());
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void bcast_first_launcher(Param &out,
+                                     Param &tmp,
+                                     Param &tmpid,
+                                     const uint groups_x,
+                                     const uint groups_y,
+                                     const uint threads_x)
+    {
+
+        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(2, false, threads_x);
+
+        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
+
+        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+        auto bcastOp = make_kernel<Buffer, KParam,
+                                   Buffer, KParam,
+                                   Buffer, KParam,
+                                   uint, uint, uint>(ker);
+
+        bcastOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
+                groups_x, groups_y, lim);
+
+        CL_DEBUG_FINISH(getQueue());
+    }
+
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
+    static void scan_first(Param &out, const Param &in, const Param &key)
+    {
+        uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
+
+        uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(out.info.dims[1], threads_y);
+
+        if (groups_x == 1) {
+            scan_first_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
+                                            true,
+                                            groups_x, groups_y,
+                                            threads_x);
+
+        } else {
+
+            Param tmp = out;
+            tmp.info.dims[0] = groups_x;
+            tmp.info.strides[0] = 1;
+            for (int k = 1; k < 4; k++) {
+                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims   [k - 1];
+            }
+            Param tmpflg = tmp;
+            Param tmpid = tmp;
+
+            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+
+            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+            tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+            tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
+
+            scan_first_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
+                                            groups_x, groups_y,
+                                            threads_x);
+
+            if (op == af_notzero_t) {
+                scan_first_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
+                                                      false,
+                                                      1, groups_y,
+                                                      threads_x);
+            } else {
+                scan_first_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
+                                                      false,
+                                                      1, groups_y,
+                                                      threads_x);
+            }
+
+            bcast_first_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
+                                             groups_x,
+                                             groups_y,
+                                             threads_x);
+
+            bufferFree(tmp.data);
+
+        }
+    }
+
+}
+}
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 6b49730708..36a8972f80 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -55,7 +55,7 @@ namespace opencl
             prog.build(targetDevices, (defaults + options).c_str());
 
         } catch (...) {
-            SHOW_DEBUG_BUILD_INFO(prog);
+            SHOW_BUILD_INFO(prog);
             throw;
         }
     }
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 99733c8c56..fe5d39aeab 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -16,6 +16,9 @@
 #include <kernel/scan_first.hpp>
 #include <kernel/scan_dim.hpp>
 
+#include <kernel/scan_first_by_key.hpp>
+#include <kernel/scan_dim_by_key.hpp>
+
 namespace opencl
 {
     template<af_op_t op, typename Ti, typename To>
@@ -50,14 +53,38 @@ namespace opencl
     template<af_op_t op, typename Ti, typename Tk, typename To>
     Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
     {
-        return scan(in, dim, inclusive_scan);
+        Array<To> out = createEmptyArray<To>(in.dims());
+
+        try {
+            Param Out = out;
+            Param Key = key;
+            Param In  =   in;
+
+            if (inclusive_scan) {
+                if (dim == 0)
+                    kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
+                else
+                    kernel::scan_dim  <Ti, Tk, To, op, true>(Out, In, Key, dim);
+            } else {
+                if (dim == 0)
+                    kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
+                else
+                    kernel::scan_dim  <Ti, Tk, To, op, false>(Out, In, Key, dim);
+            }
+
+        } catch (cl::Error &ex) {
+
+            CL_TO_AF_ERROR(ex);
+        }
+
+        return out;
     }
 
 #define INSTANTIATE_SCAN(ROp, Ti, To)\
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
 #define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
-    template Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
 
 #define INSTANTIATE_SCAN_ALL(ROp)                       \
     INSTANTIATE_SCAN(ROp, float  , float  )             \
@@ -93,8 +120,8 @@ namespace opencl
     INSTANTIATE_SCAN_ALL(ROp)               \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)   \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint)  \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, long)  \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, ulong)
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
 
     //accum
     INSTANTIATE_SCAN(af_notzero_t, char, uint)
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index afd284575a..c8a62ff547 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -14,7 +14,4 @@ namespace opencl
 {
     template<af_op_t op, typename Ti, typename To>
     Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
-
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/opencl/scan_by_key.hpp b/src/backend/opencl/scan_by_key.hpp
new file mode 100644
index 0000000000..9bce51bf1e
--- /dev/null
+++ b/src/backend/opencl/scan_by_key.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <ops.hpp>
+
+namespace opencl
+{
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+}

From 0b92f2547db2a0bf360539a6d15086f5c552cbbc Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 31 May 2016 14:45:12 -0400
Subject: [PATCH 0567/2677] More improvements to sparse

* Reorder storage enum. CSR=1, CSC=2, COO=3
* Cleanup af_create_sparse_array_from_dense function
* Add function to convert storage types
  * Currently backend limitation only accepts dense
  * In C-API implementation is complete
* Internal API changes
  * SparseArray getRows/Columns -> getRowIdx/getColIdx
  * Rename dense2storage to sparseConvertDenseToStorage
---
 include/af/defines.h               |   8 +-
 include/af/sparse.h                |   6 +-
 src/api/c/sparse.cpp               | 185 ++++++++++----
 src/backend/SparseArray.hpp        |  16 +-
 src/backend/cuda/kernel/sparse.hpp |  63 +++++
 src/backend/cuda/sparse.cpp        | 218 ----------------
 src/backend/cuda/sparse.cu         | 390 +++++++++++++++++++++++++++++
 src/backend/cuda/sparse.hpp        |  12 +-
 src/backend/cuda/sparse_blas.cpp   |   4 +-
 9 files changed, 617 insertions(+), 285 deletions(-)
 create mode 100644 src/backend/cuda/kernel/sparse.hpp
 delete mode 100644 src/backend/cuda/sparse.cpp
 create mode 100644 src/backend/cuda/sparse.cu

diff --git a/include/af/defines.h b/include/af/defines.h
index d470a31ac6..08852c529e 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -394,12 +394,12 @@ typedef enum {
 } af_marker_type;
 #endif
 
-#if AF_API_VERSION >=34
+#if AF_API_VERSION >= 34
 typedef enum {
     AF_SPARSE_DENSE     = 0,
-    AF_SPARSE_COO       = 1,
-    AF_SPARSE_CSR       = 2,
-    AF_SPARSE_CSC       = 3,
+    AF_SPARSE_CSR       = 1,
+    AF_SPARSE_CSC       = 2,
+    AF_SPARSE_COO       = 3,
 } af_sparse_storage;
 #endif
 
diff --git a/include/af/sparse.h b/include/af/sparse.h
index 6ea17d5471..d26ffc65bb 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -40,6 +40,9 @@ extern "C" {
                  af_array *out, const af_array in,
                  const af_sparse_storage storage);
 
+    AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
+                                           const af_sparse_storage destStorage);
+
     AFAPI af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols, const af_array in);
 
     AFAPI af_err af_sparse_get_values(af_array *out, const af_array in);
@@ -56,9 +59,6 @@ extern "C" {
 
     AFAPI af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in);
 
-    AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                                           const af_sparse_storage destStorage);
-
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 0035eda4c3..d61a813281 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -70,9 +70,9 @@ af_err af_create_sparse_array(
         // storage is within acceptable range
         // type is floating type
 
-        if(!(storage == AF_SPARSE_COO
-          || storage == AF_SPARSE_CSR
-          || storage == AF_SPARSE_CSC)) {
+        if(!(storage == AF_SPARSE_CSR
+          || storage == AF_SPARSE_CSC
+          || storage == AF_SPARSE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -137,9 +137,9 @@ af_err af_create_sparse_array_from_ptr(
         // if CRC, rowIdx and values should have same dims, colIdx.dims = nCols
         // storage is within acceptable range
         // type is floating type
-        if(!(storage == AF_SPARSE_COO
-          || storage == AF_SPARSE_CSR
-          || storage == AF_SPARSE_CSC)) {
+        if(!(storage == AF_SPARSE_CSR
+          || storage == AF_SPARSE_CSC
+          || storage == AF_SPARSE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -175,33 +175,20 @@ af_err af_create_sparse_array_from_ptr(
 
 template<typename T>
 af_array createSparseArrayFromDense(
-        const af::dim4 &dims, const dim_t nNZ,
-        const af_array _in, const af_array _nonZeroIdx,
+        const af::dim4 &dims, const af_array _in,
         const af_sparse_storage storage)
 {
-    Array<int> nonZeroIdx = castArray<int>(_nonZeroIdx);
     const Array<T> in = getArray<T>(_in);
 
-    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
-
-    Array<int> rowIdx = *initArray<int>();
-    Array<int> colIdx = *initArray<int>();
-    Array<T>   values = *initArray<T>();
-
-    if(storage == AF_SPARSE_COO) {
-        rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-        colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-        values = lookup<T, int>(in, nonZeroIdx, 0);
-    } else if(storage == AF_SPARSE_CSR) {
-        dense2storage<T, AF_SPARSE_CSR>(values, rowIdx, colIdx, in);
-    } else if(storage == AF_SPARSE_CSC) {
-        dense2storage<T, AF_SPARSE_CSC>(values, rowIdx, colIdx, in);
+    switch(storage) {
+        case AF_SPARSE_CSR:
+            return getHandle(sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(in));
+        case AF_SPARSE_CSC:
+            return getHandle(sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(in));
+        case AF_SPARSE_COO:
+            return getHandle(sparseConvertDenseToStorage<T, AF_SPARSE_COO>(in));
+        default: AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
     }
-
-    SparseArray<T> sparse = common::createArrayDataSparseArray(
-                            dims, values, rowIdx, colIdx, storage);
-
-    return getHandle(sparse);
 }
 
 af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
@@ -214,9 +201,9 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
 
         ArrayInfo info = getInfo(in);
 
-        if(!(storage == AF_SPARSE_COO
-          || storage == AF_SPARSE_CSR
-          || storage == AF_SPARSE_CSC)) {
+        if(!(storage == AF_SPARSE_CSR
+          || storage == AF_SPARSE_CSC
+          || storage == AF_SPARSE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -225,31 +212,122 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
 
         TYPE_ASSERT(info.isFloating());
 
-        af_array nonZeroIdx = 0; // Yes I know how this looks
-        AF_CHECK(af_where(&nonZeroIdx, in));
-
-        ArrayInfo nNZInfo = getInfo(nonZeroIdx);
-        dim_t nNZ = nNZInfo.elements();
-
         af::dim4 dims(info.dims()[0], info.dims()[1]);
 
         af_array output = 0;
 
         switch(info.getType()) {
-            case f32: output = createSparseArrayFromDense<float  >(dims, nNZ, in, nonZeroIdx, storage); break;
-            case f64: output = createSparseArrayFromDense<double >(dims, nNZ, in, nonZeroIdx, storage); break;
-            case c32: output = createSparseArrayFromDense<cfloat >(dims, nNZ, in, nonZeroIdx, storage); break;
-            case c64: output = createSparseArrayFromDense<cdouble>(dims, nNZ, in, nonZeroIdx, storage); break;
+            case f32: output = createSparseArrayFromDense<float  >(dims, in, storage); break;
+            case f64: output = createSparseArrayFromDense<double >(dims, in, storage); break;
+            case c32: output = createSparseArrayFromDense<cfloat >(dims, in, storage); break;
+            case c64: output = createSparseArrayFromDense<cdouble>(dims, in, storage); break;
             default: TYPE_ERROR(1, info.getType());
         }
         std::swap(*out, output);
 
-        if(nonZeroIdx != 0) AF_CHECK(af_release_array(nonZeroIdx));
     } CATCHALL;
 
     return AF_SUCCESS;
 }
 
+template<typename T>
+af_array sparseConvertStorage(const af_array in_, const af_sparse_storage destStorage)
+{
+    const SparseArray<T> in = getSparseArray<T>(in_);
+
+    // Only destStorage == AF_SPARSE_DENSE is supported
+    // All the other calls are for future when conversions are supported in
+    // the backend
+    if(destStorage == AF_SPARSE_DENSE) {
+        // Returns a regular af_array, not sparse
+        switch(in.getStorage()) {
+            case AF_SPARSE_CSR:
+                return getHandle(detail::sparseConvertStorageToDense<T, AF_SPARSE_CSR>(in));
+            case AF_SPARSE_CSC:
+                return getHandle(detail::sparseConvertStorageToDense<T, AF_SPARSE_CSC>(in));
+            case AF_SPARSE_COO:
+                return getHandle(detail::sparseConvertStorageToDense<T, AF_SPARSE_COO>(in));
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    } else if(destStorage == AF_SPARSE_CSR) {
+        // Returns a sparse af_array
+        switch(in.getStorage()) {
+            case AF_SPARSE_CSR:
+                return retainSparseHandle<T>(in_);
+            case AF_SPARSE_CSC:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSR, AF_SPARSE_CSC>(in));
+            case AF_SPARSE_COO:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSR, AF_SPARSE_COO>(in));
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    } else if(destStorage == AF_SPARSE_CSC) {
+        // Returns a sparse af_array
+        switch(in.getStorage()) {
+            case AF_SPARSE_CSR:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSC, AF_SPARSE_CSR>(in));
+            case AF_SPARSE_CSC:
+                return retainSparseHandle<T>(in_);
+            case AF_SPARSE_COO:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSC, AF_SPARSE_COO>(in));
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    } else if(destStorage == AF_SPARSE_COO) {
+        // Returns a sparse af_array
+        switch(in.getStorage()) {
+            case AF_SPARSE_CSR:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_COO, AF_SPARSE_CSR>(in));
+            case AF_SPARSE_CSC:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_COO, AF_SPARSE_CSC>(in));
+            case AF_SPARSE_COO:
+                return retainSparseHandle<T>(in_);
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    }
+
+    // Shoud never come here
+    return NULL;
+}
+
+af_err af_sparse_convert_storage(af_array *out, const af_array in,
+                           const af_sparse_storage destStorage)
+{
+    // Right now dest_storage can only be AF_SPARSE_DENSE
+    try {
+        af_array output = 0;
+
+        const SparseArrayBase base = getSparseArrayBase(in);
+
+        // Dense not allowed as input -> Should never happen
+        // To convert from dense to type, use the create* functions
+        ARG_ASSERT(1, base.getStorage() != AF_SPARSE_DENSE);
+
+        // Right now dest_storage can only be AF_SPARSE_DENSE
+        // TODO: Add support for [CSR, CSC, COO] <-> [CSR, CSC, COO] in backends
+        ARG_ASSERT(1, destStorage == AF_SPARSE_DENSE);
+
+        if(base.getStorage() == destStorage) {
+            // Return a reference
+            AF_CHECK(af_retain_array(out, in));
+            return AF_SUCCESS;
+        }
+
+        switch(base.getType()) {
+            case f32: output = sparseConvertStorage<float  >(in, destStorage); break;
+            case f64: output = sparseConvertStorage<double >(in, destStorage); break;
+            case c32: output = sparseConvertStorage<cfloat >(in, destStorage); break;
+            case c64: output = sparseConvertStorage<cdouble>(in, destStorage); break;
+            default : AF_ERROR("Output storage type is not valid", AF_ERR_ARG);
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 // Get Functions
 ////////////////////////////////////////////////////////////////////////////////
@@ -259,6 +337,19 @@ af_array getSparseValues(const af_array in)
     return getHandle(getSparseArray<T>(in).getValues());
 }
 
+af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols,
+                      const af_array in)
+{
+    try {
+        if(values != NULL) AF_CHECK(af_sparse_get_values(values, in));
+        if(rows   != NULL) AF_CHECK(af_sparse_get_rows  (rows  , in));
+        if(cols   != NULL) AF_CHECK(af_sparse_get_cols  (cols  , in));
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 af_err af_sparse_get_values(af_array *out, const af_array in)
 {
     try{
@@ -275,7 +366,7 @@ af_err af_sparse_get_values(af_array *out, const af_array in)
         }
         std::swap(*out, output);
     }
-    CATCHALL
+    CATCHALL;
     return AF_SUCCESS;
 }
 
@@ -283,7 +374,7 @@ af_err af_sparse_get_rows(af_array *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = getHandle(base.getRows());
+        *out = getHandle(base.getRowIdx());
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -292,7 +383,7 @@ af_err af_sparse_get_cols(af_array *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = getHandle(base.getColumns());
+        *out = getHandle(base.getColIdx());
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -310,7 +401,7 @@ af_err af_sparse_get_num_rows(dim_t *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = base.getRows().elements();
+        *out = base.getRowIdx().elements();
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -319,7 +410,7 @@ af_err af_sparse_get_num_cols(dim_t *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = base.getColumns().elements();
+        *out = base.getColIdx().elements();
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
index f8779fe173..bd33ad8b13 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/SparseArray.hpp
@@ -100,11 +100,11 @@ class SparseArrayBase
     // Specialized functions for SparseArray
     ////////////////////////////////////////////////////////////////////////////
     // Get the internal arrays
-    Array<int>& getRows()                 { return rowIdx;              }
-    Array<int>& getColumns()              { return colIdx;              }
+    Array<int>& getRowIdx()                 { return rowIdx;            }
+    Array<int>& getColIdx()                 { return colIdx;            }
 
-    const Array<int>& getRows()     const { return rowIdx;              }
-    const Array<int>& getColumns()  const { return colIdx;              }
+    const Array<int>& getRowIdx()     const { return rowIdx;            }
+    const Array<int>& getColIdx()     const { return colIdx;            }
 
     // Dims, types etc
     dim_t getNNZ()                  const;
@@ -177,10 +177,10 @@ class SparseArray
     INSTANTIATE_INFO(dim_t              , getNNZ    )
     INSTANTIATE_INFO(af::sparseStorage  , getStorage)
 
-    Array<int>& getRows()                { return base.getRows();    }
-    Array<int>& getColumns()             { return base.getColumns(); }
-    const Array<int>& getRows()    const { return base.getRows();    }
-    const Array<int>& getColumns() const { return base.getColumns(); }
+    Array<int>& getRowIdx()                 { return base.getRowIdx(); }
+    Array<int>& getColIdx()                 { return base.getColIdx(); }
+    const Array<int>& getRowIdx()   const   { return base.getRowIdx(); }
+    const Array<int>& getColIdx()   const   { return base.getColIdx(); }
 
 #undef INSTANTIATE_INFO
 
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
new file mode 100644
index 0000000000..58ffa91666
--- /dev/null
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <math.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+
+namespace cuda
+{
+    namespace kernel
+    {
+        static const int reps = 4;
+
+        /////////////////////////////////////////////////////////////////////////////
+        // Kernel to convert COO into Dense
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename T>
+        __global__
+        void coo2dense_kernel(Param<T> output, CParam<T> values,
+                              CParam<int> rowIdx, CParam<int> colIdx)
+        {
+            int id = blockIdx.x * blockDim.x * reps + threadIdx.x;
+            if(id >= values.dims[0])
+                return;
+
+            for(int i = threadIdx.x; i <= reps * blockDim.x; i += blockDim.x) {
+                if(i >= values.dims[0])
+                    return;
+
+                T   v = values.ptr[i];
+                int r = rowIdx.ptr[i];
+                int c = colIdx.ptr[i];
+
+                int offset = r + c * output.strides[1];
+
+                output.ptr[offset] = v;
+            }
+        }
+
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename T>
+        void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
+        {
+            dim3 threads(256, 1, 1);
+
+            dim3 blocks(divup(output.dims[0], threads.x * reps), 1, 1);
+
+            CUDA_LAUNCH((coo2dense_kernel<T>), blocks, threads, output, values, rowIdx, colIdx);
+
+            POST_LAUNCH_CHECK();
+        }
+    }
+}
diff --git a/src/backend/cuda/sparse.cpp b/src/backend/cuda/sparse.cpp
deleted file mode 100644
index fc93e58220..0000000000
--- a/src/backend/cuda/sparse.cpp
+++ /dev/null
@@ -1,218 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <sparse.hpp>
-#include <cusparseManager.hpp>
-#include <cuda_runtime.h>
-#include <platform.hpp>
-
-#include <stdexcept>
-#include <string>
-#include <err_common.hpp>
-#include <math.hpp>
-#include <complex.hpp>
-
-namespace cuda
-{
-
-using cusparse::getHandle;
-using namespace std;
-
-//cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
-//                                  int m, int n, int nnz,
-//                                  const cuDoubleComplex *csrSortedVal,
-//                                  const int *csrSortedRowPtr, const int *csrSortedColInd,
-//                                  cuDoubleComplex *cscSortedVal,
-//                                  int *cscSortedRowInd, int *cscSortedColPtr,
-//                                  cusparseAction_t copyValues,
-//                                  cusparseIndexBase_t idxBase);
-
-template<typename T>
-struct csr2csc_func_def_t
-{
-    typedef cusparseStatus_t (*csr2csc_func_def)( cusparseHandle_t,
-                                                  int, int, int,
-                                                  const T *, const int *, const int *,
-                                                  T *, int *, int *,
-                                                  cusparseAction_t,
-                                                  cusparseIndexBase_t);
-};
-
-//cusparseStatus_t cusparseZdense2csr(cusparseHandle_t handle,
-//                                    int m, int n,
-//                                    const cusparseMatDescr_t descrA,
-//                                    const cuDoubleComplex *A, int lda,
-//                                    const int *nnzPerRow,
-//                                    cuDoubleComplex *csrValA,
-//                                    int *csrRowPtrA, int *csrColIndA)
-template<typename T>
-struct dense2csr_func_def_t
-{
-    typedef cusparseStatus_t (*dense2csr_func_def)( cusparseHandle_t,
-                                                    int, int,
-                                                    const cusparseMatDescr_t,
-                                                    const T *, int,
-                                                    const int *,
-                                                    T *,
-                                                    int *, int *);
-};
-
-//cusparseStatus_t cusparseZdense2csc(cusparseHandle_t handle,
-//                                    int m, int n,
-//                                    const cusparseMatDescr_t descrA,
-//                                    const cuDoubleComplex *A, int lda,
-//                                    const int *nnzPerCol,
-//                                    cuDoubleComplex *cscValA,
-//                                    int *cscRowIndA, int *cscColPtrA)
-template<typename T>
-struct dense2csc_func_def_t
-{
-    typedef cusparseStatus_t (*dense2csc_func_def)( cusparseHandle_t,
-                                                    int, int,
-                                                    const cusparseMatDescr_t,
-                                                    const T *, int,
-                                                    const int *,
-                                                    T *,
-                                                    int *, int *);
-};
-
-//cusparseStatus_t cusparseZnnz(cusparseHandle_t handle,
-//                              cusparseDirection_t dirA,
-//                              int m, int n,
-//                              const cusparseMatDescr_t descrA,
-//                              const cuDoubleComplex *A, int lda,
-//                              int *nnzPerRowColumn,
-//                              int *nnzTotalDevHostPtr)
-template<typename T>
-struct nnz_func_def_t
-{
-    typedef cusparseStatus_t (*nnz_func_def)( cusparseHandle_t,
-                                              cusparseDirection_t,
-                                              int, int,
-                                              const cusparseMatDescr_t,
-                                              const T *, int,
-                                              int *, int *);
-};
-
-#define SPARSE_FUNC_DEF( FUNC )                     \
-template<typename T>                                \
-typename FUNC##_func_def_t<T>::FUNC##_func_def      \
-FUNC##_func();
-
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def        \
-FUNC##_func<TYPE>()                                                 \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusparse##PREFIX##FUNC; }
-
-SPARSE_FUNC_DEF(csr2csc)
-SPARSE_FUNC(csr2csc, float,  S)
-SPARSE_FUNC(csr2csc, double, D)
-SPARSE_FUNC(csr2csc, cfloat, C)
-SPARSE_FUNC(csr2csc, cdouble,Z)
-
-SPARSE_FUNC_DEF(dense2csr)
-SPARSE_FUNC(dense2csr, float,  S)
-SPARSE_FUNC(dense2csr, double, D)
-SPARSE_FUNC(dense2csr, cfloat, C)
-SPARSE_FUNC(dense2csr, cdouble,Z)
-
-SPARSE_FUNC_DEF(dense2csc)
-SPARSE_FUNC(dense2csc, float,  S)
-SPARSE_FUNC(dense2csc, double, D)
-SPARSE_FUNC(dense2csc, cfloat, C)
-SPARSE_FUNC(dense2csc, cdouble,Z)
-
-SPARSE_FUNC_DEF(nnz)
-SPARSE_FUNC(nnz, float,  S)
-SPARSE_FUNC(nnz, double, D)
-SPARSE_FUNC(nnz, cfloat, C)
-SPARSE_FUNC(nnz, cdouble,Z)
-
-#undef SPARSE_FUNC
-#undef SPARSE_FUNC_DEF
-
-template<typename T, af_sparse_storage storage>
-void dense2storage(Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,
-                   const Array<T> in)
-{
-    const int M = in.dims()[0];
-    const int N = in.dims()[1];
-
-    // Create Sparse Matrix Descriptor
-    cusparseMatDescr_t descr = 0;
-    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
-    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
-    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
-
-    int d = -1;
-    cusparseDirection_t dir = CUSPARSE_DIRECTION_ROW;
-
-    if(storage == AF_SPARSE_CSR) {
-        d = M;
-        dir = CUSPARSE_DIRECTION_ROW;
-    } else {
-        d = N;
-        dir = CUSPARSE_DIRECTION_COLUMN;
-    }
-    Array<int> nnzPerDir = createEmptyArray<int>(dim4(d));
-
-    int nNZ = -1;
-    CUSPARSE_CHECK(nnz_func<T>()(
-                        getHandle(),
-                        dir,
-                        M, N,
-                        descr,
-                        in.get(), in.strides()[1],
-                        nnzPerDir.get(), &nNZ));
-
-    if(storage == AF_SPARSE_CSR) {
-        rowIdx = createEmptyArray<int>(dim4(M+1));
-        colIdx = createEmptyArray<int>(dim4(nNZ));
-    } else {
-        rowIdx = createEmptyArray<int>(dim4(nNZ));
-        colIdx = createEmptyArray<int>(dim4(N+1));
-    }
-    values = createEmptyArray<T>(dim4(nNZ));
-
-    if(storage == AF_SPARSE_CSR)
-        CUSPARSE_CHECK(dense2csr_func<T>()(
-                        getHandle(),
-                        M, N,
-                        descr,
-                        in.get(), in.strides()[1],
-                        nnzPerDir.get(),
-                        values.get(), rowIdx.get(), colIdx.get()));
-    else
-        CUSPARSE_CHECK(dense2csc_func<T>()(
-                        getHandle(),
-                        M, N,
-                        descr,
-                        in.get(), in.strides()[1],
-                        nnzPerDir.get(),
-                        values.get(), rowIdx.get(), colIdx.get()));
-
-    // Destory Sparse Matrix Descriptor
-    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
-}
-
-#define INSTANTIATE_SPARSE(T)                                                           \
-    template void dense2storage<T, AF_SPARSE_CSR>(                                      \
-        Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,                       \
-        const Array<T> in);                                                             \
-    template void dense2storage<T, AF_SPARSE_CSC>(                                      \
-        Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,                       \
-        const Array<T> in);                                                             \
-
-
-INSTANTIATE_SPARSE(float)
-INSTANTIATE_SPARSE(double)
-INSTANTIATE_SPARSE(cfloat)
-INSTANTIATE_SPARSE(cdouble)
-
-}
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
new file mode 100644
index 0000000000..10177f98f6
--- /dev/null
+++ b/src/backend/cuda/sparse.cu
@@ -0,0 +1,390 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse.hpp>
+#include <cusparseManager.hpp>
+#include <kernel/sparse.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <complex.hpp>
+#include <err_common.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <where.hpp>
+
+namespace cuda
+{
+
+using cusparse::getHandle;
+using namespace common;
+using namespace std;
+
+//cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
+//                                  int m, int n, int nnz,
+//                                  const cuDoubleComplex *csrSortedVal,
+//                                  const int *csrSortedRowPtr, const int *csrSortedColInd,
+//                                  cuDoubleComplex *cscSortedVal,
+//                                  int *cscSortedRowInd, int *cscSortedColPtr,
+//                                  cusparseAction_t copyValues,
+//                                  cusparseIndexBase_t idxBase);
+
+template<typename T>
+struct csr2csc_func_def_t
+{
+    typedef cusparseStatus_t (*csr2csc_func_def)( cusparseHandle_t,
+                                                  int, int, int,
+                                                  const T *, const int *, const int *,
+                                                  T *, int *, int *,
+                                                  cusparseAction_t,
+                                                  cusparseIndexBase_t);
+};
+
+//cusparseStatus_t cusparseZdense2csr(cusparseHandle_t handle,
+//                                    int m, int n,
+//                                    const cusparseMatDescr_t descrA,
+//                                    const cuDoubleComplex *A, int lda,
+//                                    const int *nnzPerRow,
+//                                    cuDoubleComplex *csrValA,
+//                                    int *csrRowPtrA, int *csrColIndA)
+template<typename T>
+struct dense2csr_func_def_t
+{
+    typedef cusparseStatus_t (*dense2csr_func_def)( cusparseHandle_t,
+                                                    int, int,
+                                                    const cusparseMatDescr_t,
+                                                    const T *, int,
+                                                    const int *,
+                                                    T *,
+                                                    int *, int *);
+};
+
+//cusparseStatus_t cusparseZdense2csc(cusparseHandle_t handle,
+//                                    int m, int n,
+//                                    const cusparseMatDescr_t descrA,
+//                                    const cuDoubleComplex *A, int lda,
+//                                    const int *nnzPerCol,
+//                                    cuDoubleComplex *cscValA,
+//                                    int *cscRowIndA, int *cscColPtrA)
+template<typename T>
+struct dense2csc_func_def_t
+{
+    typedef cusparseStatus_t (*dense2csc_func_def)( cusparseHandle_t,
+                                                    int, int,
+                                                    const cusparseMatDescr_t,
+                                                    const T *, int,
+                                                    const int *,
+                                                    T *,
+                                                    int *, int *);
+};
+
+//cusparseStatus_t cusparseZcsr2dense(cusparseHandle_t handle,
+//                                    int m, int n,
+//                                    const cusparseMatDescr_t descrA,
+//                                    const cuDoubleComplex *csrValA,
+//                                    const int *csrRowPtrA,
+//                                    const int *csrColIndA,
+//                                    cuDoubleComplex *A, int lda)
+template<typename T>
+struct csr2dense_func_def_t
+{
+    typedef cusparseStatus_t (*csr2dense_func_def)( cusparseHandle_t,
+                                                    int, int,
+                                                    const cusparseMatDescr_t,
+                                                    const T *,
+                                                    const int *,
+                                                    const int *,
+                                                    T *, int);
+};
+
+//cusparseStatus_t cusparseZcsc2dense(cusparseHandle_t handle,
+//                                    int m, int n,
+//                                    const cusparseMatDescr_t descrA,
+//                                    const cuDoubleComplex *cscValA,
+//                                    const int *cscRowIndA,
+//                                    const int *cscColPtrA,
+//                                    cuDoubleComplex *A, int lda)
+template<typename T>
+struct csc2dense_func_def_t
+{
+    typedef cusparseStatus_t (*csc2dense_func_def)( cusparseHandle_t,
+                                                    int, int,
+                                                    const cusparseMatDescr_t,
+                                                    const T *,
+                                                    const int *,
+                                                    const int *,
+                                                    T *, int);
+};
+
+//cusparseStatus_t cusparseZnnz(cusparseHandle_t handle,
+//                              cusparseDirection_t dirA,
+//                              int m, int n,
+//                              const cusparseMatDescr_t descrA,
+//                              const cuDoubleComplex *A, int lda,
+//                              int *nnzPerRowColumn,
+//                              int *nnzTotalDevHostPtr)
+template<typename T>
+struct nnz_func_def_t
+{
+    typedef cusparseStatus_t (*nnz_func_def)( cusparseHandle_t,
+                                              cusparseDirection_t,
+                                              int, int,
+                                              const cusparseMatDescr_t,
+                                              const T *, int,
+                                              int *, int *);
+};
+
+#define SPARSE_FUNC_DEF( FUNC )                     \
+template<typename T>                                \
+typename FUNC##_func_def_t<T>::FUNC##_func_def      \
+FUNC##_func();
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )                           \
+template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def        \
+FUNC##_func<TYPE>()                                                 \
+{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusparse##PREFIX##FUNC; }
+
+SPARSE_FUNC_DEF(csr2csc)
+SPARSE_FUNC(csr2csc, float,  S)
+SPARSE_FUNC(csr2csc, double, D)
+SPARSE_FUNC(csr2csc, cfloat, C)
+SPARSE_FUNC(csr2csc, cdouble,Z)
+
+SPARSE_FUNC_DEF(dense2csr)
+SPARSE_FUNC(dense2csr, float,  S)
+SPARSE_FUNC(dense2csr, double, D)
+SPARSE_FUNC(dense2csr, cfloat, C)
+SPARSE_FUNC(dense2csr, cdouble,Z)
+
+SPARSE_FUNC_DEF(dense2csc)
+SPARSE_FUNC(dense2csc, float,  S)
+SPARSE_FUNC(dense2csc, double, D)
+SPARSE_FUNC(dense2csc, cfloat, C)
+SPARSE_FUNC(dense2csc, cdouble,Z)
+
+SPARSE_FUNC_DEF(csr2dense)
+SPARSE_FUNC(csr2dense, float,  S)
+SPARSE_FUNC(csr2dense, double, D)
+SPARSE_FUNC(csr2dense, cfloat, C)
+SPARSE_FUNC(csr2dense, cdouble,Z)
+
+SPARSE_FUNC_DEF(csc2dense)
+SPARSE_FUNC(csc2dense, float,  S)
+SPARSE_FUNC(csc2dense, double, D)
+SPARSE_FUNC(csc2dense, cfloat, C)
+SPARSE_FUNC(csc2dense, cdouble,Z)
+
+SPARSE_FUNC_DEF(nnz)
+SPARSE_FUNC(nnz, float,  S)
+SPARSE_FUNC(nnz, double, D)
+SPARSE_FUNC(nnz, cfloat, C)
+SPARSE_FUNC(nnz, cdouble,Z)
+
+#undef SPARSE_FUNC
+#undef SPARSE_FUNC_DEF
+
+// Partial template specialization of sparseConvertDenseToStorage for COO
+// However, template specialization is not allowed
+template<typename T>
+SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
+{
+    Array<uint> nonZeroIdx_ = where<T>(in);
+    Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
+
+    dim_t nNZ = nonZeroIdx.elements();
+
+    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
+
+    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
+}
+
+template<typename T, af_sparse_storage storage>
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
+{
+    //if(storage == AF_SPARSE_COO)
+    //    return sparseConvertDenseToCOO(in);
+
+    const int M = in.dims()[0];
+    const int N = in.dims()[1];
+
+    // Create Sparse Matrix Descriptor
+    cusparseMatDescr_t descr = 0;
+    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
+    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+
+    int d = -1;
+    cusparseDirection_t dir = CUSPARSE_DIRECTION_ROW;
+
+    if(storage == AF_SPARSE_CSR) {
+        d = M;
+        dir = CUSPARSE_DIRECTION_ROW;
+    } else {
+        d = N;
+        dir = CUSPARSE_DIRECTION_COLUMN;
+    }
+    Array<int> nnzPerDir = createEmptyArray<int>(dim4(d));
+
+    int nNZ = -1;
+    CUSPARSE_CHECK(nnz_func<T>()(
+                        getHandle(),
+                        dir,
+                        M, N,
+                        descr,
+                        in.get(), in.strides()[1],
+                        nnzPerDir.get(), &nNZ));
+
+    Array<int> *rowIdx = initArray<int>();
+    Array<int> *colIdx = initArray<int>();
+
+    if(storage == AF_SPARSE_CSR) {
+        *rowIdx = createEmptyArray<int>(dim4(M+1));
+        *colIdx = createEmptyArray<int>(dim4(nNZ));
+    } else {
+        *rowIdx = createEmptyArray<int>(dim4(nNZ));
+        *colIdx = createEmptyArray<int>(dim4(N+1));
+    }
+    Array<T> values = createEmptyArray<T>(dim4(nNZ));
+
+    if(storage == AF_SPARSE_CSR)
+        CUSPARSE_CHECK(dense2csr_func<T>()(
+                        getHandle(),
+                        M, N,
+                        descr,
+                        in.get(), in.strides()[1],
+                        nnzPerDir.get(),
+                        values.get(), (*rowIdx).get(), (*colIdx).get()));
+    else
+        CUSPARSE_CHECK(dense2csc_func<T>()(
+                        getHandle(),
+                        M, N,
+                        descr,
+                        in.get(), in.strides()[1],
+                        nnzPerDir.get(),
+                        values.get(), (*rowIdx).get(), (*colIdx).get()));
+
+    // Destory Sparse Matrix Descriptor
+    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+
+    return createArrayDataSparseArray<T>(in.dims(), values, *rowIdx, *colIdx, storage);
+}
+
+
+// Partial template specialization of sparseConvertStorageToDense for COO
+// However, template specialization is not allowed
+template<typename T>
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
+{
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+
+    Array<T>   values = in.getValues();
+    Array<int> rowIdx = in.getRowIdx();
+    Array<int> colIdx = in.getColIdx();
+
+    kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
+
+    return dense;
+}
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
+{
+    //if(storage == AF_SPARSE_COO)
+    //    return sparseConvertCOOToDense(in);
+
+    // Create Sparse Matrix Descriptor
+    cusparseMatDescr_t descr = 0;
+    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
+    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+
+    int M = in.dims()[0];
+    int N = in.dims()[1];
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    int d_strides1 = dense.strides()[1];
+
+    if(storage == AF_SPARSE_CSR)
+        CUSPARSE_CHECK(csr2dense_func<T>()(
+                        getHandle(),
+                        M, N,
+                        descr,
+                        in.getValues().get(),
+                        in.getRowIdx().get(),
+                        in.getColIdx().get(),
+                        dense.get(), d_strides1));
+    else
+        CUSPARSE_CHECK(csc2dense_func<T>()(
+                        getHandle(),
+                        M, N,
+                        descr,
+                        in.getValues().get(),
+                        in.getRowIdx().get(),
+                        in.getColIdx().get(),
+                        dense.get(), d_strides1));
+
+    // Destory Sparse Matrix Descriptor
+    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+
+    return dense;
+}
+
+template<typename T, af_sparse_storage src, af_sparse_storage dest>
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
+{
+    // Dummy function
+    // TODO finish this function when support is required
+    SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), in.getNNZ(), dest);
+
+    return dense;
+}
+
+
+#define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_COO>(const SparseArray<T> &in);   \
+
+#define INSTANTIATE_COO_SPECIAL(T)                                                                      \
+    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_COO>(const Array<T> &in)         \
+    { return sparseConvertDenseToCOO<T>(in); }                                                          \
+    template<> Array<T> sparseConvertStorageToDense<T, AF_SPARSE_COO>(const SparseArray<T> &in)         \
+    { return sparseConvertCOOToDense<T>(in); }                                                          \
+
+#define INSTANTIATE_SPARSE(T)                                                                           \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(const Array<T> &in);          \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(const Array<T> &in);          \
+                                                                                                        \
+    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSR>(const SparseArray<T> &in);          \
+    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSC>(const SparseArray<T> &in);          \
+                                                                                                        \
+    INSTANTIATE_COO_SPECIAL(T)                                                                          \
+                                                                                                        \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSR)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSC)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_COO)                                                            \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+#undef INSTANTIATE_TO_STORAGE
+#undef INSTANTIATE_COO_SPECIAL
+#undef INSTANTIATE_SPARSE
+
+}
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
index bc39f340b5..7b9bb473ac 100644
--- a/src/backend/cuda/sparse.hpp
+++ b/src/backend/cuda/sparse.hpp
@@ -8,12 +8,18 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <SparseArray.hpp>
 
 namespace cuda
 {
 
-template<typename T, af_sparse_storage>
-void dense2storage(Array<T> &values, Array<int> &rowIdx, Array<int> &colIdx,
-                   const Array<T> in);
+template<typename T, af_sparse_storage storage>
+common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
+
+template<typename T, af_sparse_storage src, af_sparse_storage dest>
+common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 69a773df71..629bc4ca65 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -158,7 +158,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                        M, K, lhs.getNNZ(),
                        &alpha,
                        descr, lhs.getValues().get(),
-                       lhs.getRows().get(), lhs.getColumns().get(),
+                       lhs.getRowIdx().get(), lhs.getColIdx().get(),
                        rhs.get(),
                        &beta,
                        out.get()));
@@ -169,7 +169,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                        M, N, K, lhs.getNNZ(),
                        &alpha,
                        descr, lhs.getValues().get(),
-                       lhs.getRows().get(), lhs.getColumns().get(),
+                       lhs.getRowIdx().get(), lhs.getColIdx().get(),
                        rhs.get(), rStrides[1],
                        &beta,
                        out.get(),

From 2f5b2f0470793fdc113567898d195927819e85d5 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 31 May 2016 17:35:11 -0400
Subject: [PATCH 0568/2677] Core common function

Scan first by key and scan dim by key each have a small common core
function.
---
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 174 ++++++++----------
 .../opencl/kernel/scan_first_by_key.cl        | 173 ++++++++---------
 2 files changed, 152 insertions(+), 195 deletions(-)

diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index 2f76ec4d69..58d73fc105 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-static char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
+char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
 {
     char flag;
     if (id == 0) {
@@ -18,6 +18,43 @@ static char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride
     return flag;
 }
 
+void scan_dim_by_key_core(const bool invalid,
+        To *val, char *flag,
+        const __global Ti *in, const To init_val,
+        __local To *l_val0, __local To *l_val1,
+        __local char *l_flg0, __local char *l_flg1,
+        __local To *last_val, __local char *last_flag,
+        const int lid, const int lidx, const int lidy)
+{
+    bool flip = 0;
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    *val = invalid? init_val : transform(*in);
+
+    if ((lidy == 0) && (*flag == 0)) {
+        *val = binOp(*val, last_val[lidx]);
+        *flag = *flag | last_flag[lidx];
+    }
+
+    l_val0[lid] = *val;
+    l_flg0[lid] = *flag;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int off = 1; off < DIMY; off *= 2) {
+
+        if (lidy >= off) {
+            *val = l_flg[lid] ? *val : binOp(*val, l_val[lid - off * THREADS_X]);
+            *flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
+        }
+        flip = 1 - flip;
+        l_val = flip ? l_val1 : l_val0;
+        l_flg = flip ? l_flg1 : l_flg0;
+        l_val[lid] = *val;
+        l_flg[lid] = *flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+}
+
 __kernel
 void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
                      __global To *tData, KParam tInfo,
@@ -68,87 +105,56 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
     const int ostride_dim = oInfo.strides[dim];
     const int istride_dim =  iInfo.strides[dim];
+    const int kstride_dim = kInfo.strides[dim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
     __local char l_flg0[THREADS_X * DIMY];
     __local char l_flg1[THREADS_X * DIMY];
-    __local To *l_val = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[THREADS_X];
-    __local char l_ftmp[THREADS_X];
+    __local To last_val[THREADS_X];
+    __local char last_flag[THREADS_X];
     __local int boundaryid;
 
-    bool flip = 0;
     const To init_val  = init;
     To val = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
+    char flag = 0;
+    if (!inclusive_scan) {
+        iData -= istride_dim;
+    }
+
     if (isLast) {
-        l_tmp[lidy] = val;
-        l_ftmp[lidy] = 0;
+        last_val[lidy] = val;
+        last_flag[lidy] = 0;
         boundaryid = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     __local char *prev;
     if (lidy == 0) {
-        prev = &l_ftmp[lidx];
+        prev = &last_flag[lidx];
     } else {
-        prev = &l_flg[lid-THREADS_X];
+        prev = &l_flg0[lid-THREADS_X];
     }
-    __local char *curr = &l_flg[lid];
+    __local char *curr = &l_flg0[lid];
 
-    char flag = 0;
     for (int k = 0; k < lim; k++) {
 
-        //if (isLast) l_tmp[lidx] = val;
-
         bool cond = (is_valid) && (id_dim < out_dim);
 
         if (cond) {
-            flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
+            flag = calculate_head_flags_dim(kData, id_dim, kstride_dim);
         } else {
             flag = 0;
         }
 
-        //val = cond ? transform(*iData) : init_val;
-
-        if (inclusive_scan) {
-            if (!cond) {
-                val = init_val;
-            } else {
-                val = transform(*iData);
-            }
-        } else {
-            if ((id_dim == 0) || (!cond) || flag) {
-                val = init_val;
-            } else {
-                val = transform(*(iData - iInfo.strides[dim]));
-            }
-        }
-
-        if ((lidy == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidx]);
-            flag = flag | l_ftmp[lidx];
-        }
-        l_val[lid] = val;
-        l_flg[lid] = flag;
-        barrier(CLK_LOCAL_MEM_FENCE);
-
-        for (int off = 1; off < DIMY; off *= 2) {
+        bool invalid = !cond;
+        if (!inclusive_scan) invalid = invalid || (id_dim == 0) || flag;
 
-            if (lidy >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
-                flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
-            }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
-            l_val[lid] = val;
-            l_flg[lid] = flag;
-            barrier(CLK_LOCAL_MEM_FENCE);
-        }
+        scan_dim_by_key_core(invalid, &val, &flag, iData, init_val,
+                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
+                lid, lidx, lidy);
 
         if ((*prev == 0) && (*curr == 1)) {
             boundaryid = id_dim;
@@ -156,11 +162,11 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if (cond) *oData = val;
         if (isLast) {
-            l_tmp[lidx] = val;
-            l_ftmp[lidx] = flag;
+            last_val[lidx] = val;
+            last_flag[lidx] = flag;
         }
         id_dim += DIMY;
-        kData += DIMY * kInfo.strides[dim];
+        kData += DIMY * kstride_dim;
         iData += DIMY * istride_dim;
         oData += DIMY * ostride_dim;
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -224,23 +230,24 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
     __local To l_val1[THREADS_X * DIMY];
     __local char l_flg0[THREADS_X * DIMY];
     __local char l_flg1[THREADS_X * DIMY];
-    __local To *l_val = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[THREADS_X];
-    __local char l_ftmp[THREADS_X];
+    __local To last_val[THREADS_X];
+    __local char last_flag[THREADS_X];
 
-    bool flip = 0;
     const To init_val  = init;
     To val = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
+    char flag = 0;
+    if (!inclusive_scan) {
+        iData -= istride_dim;
+    }
+
     if (isLast) {
-        l_tmp[lidy] = val;
-        l_ftmp[lidy] = 0;
+        last_val[lidy] = val;
+        last_flag[lidy] = 0;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    char flag = 0;
     for (int k = 0; k < lim; k++) {
 
         bool cond = (is_valid) && (id_dim < out_dim);
@@ -255,46 +262,17 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
             flag = *kData;
         }
 
-        if (inclusive_scan) {
-            if (!cond) {
-                val = init_val;
-            } else {
-                val = transform(*iData);
-            }
-        } else {
-            if ((id_dim == 0) || (!cond) || flag) {
-                val = init_val;
-            } else {
-                val = transform(*(iData - iInfo.strides[dim]));
-            }
-        }
-
-        if ((lidy == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidx]);
-            flag = flag | l_ftmp[lidx];
-        }
-        l_val[lid] = val;
-        l_flg[lid] = flag;
-        barrier(CLK_LOCAL_MEM_FENCE);
+        bool invalid = !cond;
+        if (!inclusive_scan) invalid = invalid || (id_dim == 0) || flag;
 
-        for (int off = 1; off < DIMY; off *= 2) {
-
-            if (lidy >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
-                flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
-            }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
-            l_val[lid] = val;
-            l_flg[lid] = flag;
-            barrier(CLK_LOCAL_MEM_FENCE);
-        }
+        scan_dim_by_key_core(invalid, &val, &flag, iData, init_val,
+                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
+                lid, lidx, lidy);
 
         if (cond) *oData = val;
         if (isLast) {
-            l_tmp[lidx] = val;
-            l_ftmp[lidx] = flag;
+            last_val[lidx] = val;
+            last_flag[lidx] = flag;
         }
         id_dim += DIMY;
         kData += DIMY * kInfo.strides[dim];
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index d5a75a6db3..0be5c9be24 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -18,6 +18,43 @@ static char calculate_head_flags(const __global Tk *kptr, int id, int previd)
     return flag;
 }
 
+void scan_first_by_key_core(const bool invalid,
+        To *val, char *flag,
+        const __global Ti *in, const To init_val,
+        __local To *l_val0, __local To *l_val1,
+        __local char *l_flg0, __local char *l_flg1,
+        __local To *last_val, __local char *last_flag,
+        const int lid, const int lidx, const int lidy)
+{
+    bool flip = 0;
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    *val = invalid? init_val : transform(*in);
+
+    if ((lidx == 0) && (flag == 0)) {
+        *val = binOp(*val, last_val[lidy]);
+        *flag = *flag | last_flag[lidy];
+    }
+
+    l_val0[lid] = *val;
+    l_flg0[lid] = *flag;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int off = 1; off < DIMY; off *= 2) {
+
+        if (lidy >= off) {
+            *val = l_flg[lid] ? *val : binOp(*val, l_val[lid - off]);
+            *flag = l_flg[lid] | l_flg[lid - off];
+        }
+        flip = 1 - flip;
+        l_val = flip ? l_val1 : l_val0;
+        l_flg = flip ? l_flg1 : l_flg0;
+        l_val[lid] = *val;
+        l_flg[lid] = *flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+}
+
 __kernel
 void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
                        __global To *tData, KParam tInfo,
@@ -63,83 +100,51 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     __local To l_val1[SHARED_MEM_SIZE];
     __local char l_flg0[SHARED_MEM_SIZE];
     __local char l_flg1[SHARED_MEM_SIZE];
-    __local To *l_val = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[DIMY];
-    __local char l_ftmp[DIMY];
+    __local To last_val[DIMY];
+    __local char last_flag[DIMY];
     __local int boundaryid;
 
-    bool flip = 0;
-
     const To init_val = init;
     int id = xid;
     To val = init_val;
-
     const bool isLast = (lidx == (DIMX - 1));
 
+    char flag = 0;
+    if (!inclusive_scan) {
+        iData -= 1;
+    }
+
     if (isLast) {
-        l_tmp[lidy] = val;
-        l_ftmp[lidy] = 0;
+        last_val[lidy] = val;
+        last_flag[lidy] = 0;
         boundaryid = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     __local char *prev;
     if (lidx == 0) {
-        prev = &l_ftmp[lidy];
+        prev = &last_flag[lidy];
     } else {
-        prev = &l_flg[lidx-1];
+        prev = &l_flg0[lidx-1];
     }
-    __local char *curr = &l_flg[lidx];
+    __local char *curr = &l_flg0[lidx];
 
-    char flag = 0;
     for (int k = 0; k < lim; k++) {
 
-        //if (isLast) l_tmp[lidy] = val;
-
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (cond) {
-            flag = calculate_head_flags(kData, id, id - kInfo.strides[0]);
+            flag = calculate_head_flags(kData, id, id - 1);
         } else {
             flag = 0;
         }
-        //val = cond ? transform(iData[id]) : init_val;
 
-        if (inclusive_scan) {
-            if (!cond) {
-                val = init_val;
-            } else {
-                val = transform(iData[id]);
-            }
-        } else {
-            if ((id == 0) || (!cond) || flag) {
-                val = init_val;
-            } else {
-                val = transform(iData[id - iInfo.strides[0]]);
-            }
-        }
-
-        if ((lidx == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidy]);
-            flag = flag | l_ftmp[lidy];
-        }
-        l_val[lid] = val;
-        l_flg[lid] = flag;
-        barrier(CLK_LOCAL_MEM_FENCE);
+        bool invalid = !cond;
+        if (!inclusive_scan) invalid = invalid || (id == 0) || flag;
 
-        for (int off = 1; off < DIMX; off *= 2) {
-            if (lidx >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
-                flag = l_flg[lid] | l_flg[lid - off];
-            }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
-            l_val[lid] = val;
-            l_flg[lid] = flag;
-            barrier(CLK_LOCAL_MEM_FENCE);
-        }
+        scan_first_by_key_core(invalid, &val, &flag, iData, init_val,
+                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
+                lid, lidx, lidy);
 
         if ((*prev == 0) && (*curr == 1)) {
             boundaryid = id;
@@ -147,8 +152,8 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if (cond) oData[id] = val;
         if (isLast) {
-            l_tmp[lidy] = val;
-            l_ftmp[lidy] = flag;
+            last_val[lidy] = val;
+            last_flag[lidy] = flag;
         }
         id += DIMX;
         barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
@@ -194,24 +199,27 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     __local To l_val1[SHARED_MEM_SIZE];
     __local char l_flg0[SHARED_MEM_SIZE];
     __local char l_flg1[SHARED_MEM_SIZE];
-    __local To *l_val = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[DIMY];
-    __local char l_ftmp[DIMY];
-
-    bool flip = 0;
+    __local To last_val[DIMY];
+    __local char last_flag[DIMY];
 
     const To init_val = init;
     int id = xid;
     To val = init_val;
-
     const bool isLast = (lidx == (DIMX - 1));
 
+    if (!inclusive_scan) {
+        iData -= 1;
+    }
+
+    if (isLast) {
+        last_val[lidy] = val;
+        last_flag[lidy] = 0;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
     for (int k = 0; k < lim; k++) {
         char flag = 0;
 
-        //if (isLast) l_tmp[lidy] = val;
-
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (calculateFlags) {
@@ -223,47 +231,18 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
         } else {
             flag = kData[id];
         }
-        //val = cond ? transform(iData[id]) : init_val;
-
-        if (inclusive_scan) {
-            if (!cond) {
-                val = init_val;
-            } else {
-                val = transform(iData[id]);
-            }
-        } else {
-            if ((id == 0) || (!cond) || flag) {
-                val = init_val;
-            } else {
-                val = transform(iData[id - 1]);
-            }
-        }
 
-        if ((lidx == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidy]);
-            flag = flag | l_ftmp[lidy];
-        }
-        l_val[lid] = val;
-        l_flg[lid] = flag;
-        barrier(CLK_LOCAL_MEM_FENCE);
+        bool invalid = !cond;
+        if (!inclusive_scan) invalid = invalid || (id == 0) || flag;
 
-        for (int off = 1; off < DIMX; off *= 2) {
-            if (lidx >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
-                flag = l_flg[lid] | l_flg[lid - off];
-            }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
-            l_val[lid] = val;
-            l_flg[lid] = flag;
-            barrier(CLK_LOCAL_MEM_FENCE);
-        }
+        scan_first_by_key_core(invalid, &val, &flag, iData, init_val,
+                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
+                lid, lidx, lidy);
 
         if (cond) oData[id] = val;
         if (isLast) {
-            l_tmp[lidy] = val;
-            l_ftmp[lidy] = flag;
+            last_val[lidy] = val;
+            last_flag[lidy] = flag;
         }
         id += DIMX;
         barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus

From 704a19a856668826315fb93a9ed20726c2123e24 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 31 May 2016 17:06:19 -0400
Subject: [PATCH 0569/2677] Add eval to SparseArray

---
 src/backend/SparseArray.hpp |  7 +++++++
 src/backend/cuda/sparse.cu  | 12 +++---------
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
index bd33ad8b13..5cad6ffc22 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/SparseArray.hpp
@@ -194,6 +194,13 @@ class SparseArray
     Array<T>& getValues()                 { return values;  }
     const Array<T>& getValues()     const { return values;  }
 
+    void eval() const
+    {
+        getValues().eval();
+        getRowIdx().eval();
+        getColIdx().eval();
+    }
+
     ////////////////////////////////////////////////////////////////////////////
     // Friend functions for Sparse Array Creation
     ////////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 10177f98f6..31dc81a68e 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -215,9 +215,6 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 template<typename T, af_sparse_storage storage>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 {
-    //if(storage == AF_SPARSE_COO)
-    //    return sparseConvertDenseToCOO(in);
-
     const int M = in.dims()[0];
     const int N = in.dims()[1];
 
@@ -291,9 +288,9 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
 {
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
 
-    Array<T>   values = in.getValues();
-    Array<int> rowIdx = in.getRowIdx();
-    Array<int> colIdx = in.getColIdx();
+    const Array<T>   values = in.getValues();
+    const Array<int> rowIdx = in.getRowIdx();
+    const Array<int> colIdx = in.getColIdx();
 
     kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
 
@@ -303,9 +300,6 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
 template<typename T, af_sparse_storage storage>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 {
-    //if(storage == AF_SPARSE_COO)
-    //    return sparseConvertCOOToDense(in);
-
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));

From d73f787e4f354913c0f624fcc64f3cab4e7a1b00 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 1 Jun 2016 19:52:14 +0530
Subject: [PATCH 0570/2677] Remove Array print statements

---
 test/index.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/test/index.cpp b/test/index.cpp
index 3cb0ab1785..3ab2037d6d 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -536,9 +536,6 @@ TEST(Index, Docs_Util_C_API)
 
     err = af_set_seq_indexer(indexers, &zeroIndices, 0, false);
 
-    af_print_array(a);
-    af_print_array(out);
-
     err = af_index_gen(&out, a, 2, indexers);
     if (err != AF_SUCCESS) {
         printf("Failed in af_index_gen: %d\n", err);

From 5f8fc76cc4c22f7c4e181c21df033e15fffa472a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 14:45:19 -0400
Subject: [PATCH 0571/2677] SparseArray Const: Use createValueArray instead of
 createEmptyArray

---
 src/backend/SparseArray.cpp | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/src/backend/SparseArray.cpp b/src/backend/SparseArray.cpp
index 57380b51bd..3cda7b0735 100644
--- a/src/backend/SparseArray.cpp
+++ b/src/backend/SparseArray.cpp
@@ -9,8 +9,9 @@
 
 #include <SparseArray.hpp>
 #include <backend.hpp>
-#include <platform.hpp>
 #include <copy.hpp>
+#include <math.hpp>
+#include <platform.hpp>
 
 namespace common
 {
@@ -29,10 +30,10 @@ using namespace detail;
 #define COL_LENGTH ((storage == AF_SPARSE_COO || storage == AF_SPARSE_CSR) ? _nNZ : (_dims[1] + 1))
 
 SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage, af_dtype _type):
-    info(getActiveDeviceId(), _dims, _nNZ, calcStrides(_dims), _type, true),
+    info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
     storage(_storage),
-    rowIdx(createEmptyArray<int>(dim4(ROW_LENGTH))),
-    colIdx(createEmptyArray<int>(dim4(COL_LENGTH)))
+    rowIdx(createValueArray<int>(dim4(ROW_LENGTH), 0)),
+    colIdx(createValueArray<int>(dim4(COL_LENGTH), 0))
 {
 #if __cplusplus > 199711l
     static_assert(offsetof(SparseArrayBase, info) == 0,
@@ -48,11 +49,11 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
     storage(_storage),
     rowIdx(_is_device ?
           (!_copy_device ? createDeviceDataArray<int>(dim4(ROW_LENGTH), _rowIdx)
-                         : createEmptyArray<int>(dim4(ROW_LENGTH)))
+                         : createValueArray<int>(dim4(ROW_LENGTH), 0))
         : createHostDataArray<int>(dim4(ROW_LENGTH), _rowIdx)),
     colIdx(_is_device ?
           (!_copy_device ? createDeviceDataArray<int>(dim4(COL_LENGTH), _colIdx)
-                         : createEmptyArray<int>(dim4(COL_LENGTH)))
+                         : createValueArray<int>(dim4(COL_LENGTH), 0))
         : createHostDataArray<int>(dim4(COL_LENGTH), _colIdx))
 {
 #if __cplusplus > 199711L
@@ -156,7 +157,7 @@ void destroySparseArray(SparseArray<T> *sparse)
 template<typename T>
 SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::sparseStorage _storage):
     base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type),
-    values(createEmptyArray<T>(dim4(_nNZ)))
+    values(createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
 {
 #if __cplusplus > 199711L
         static_assert(std::is_standard_layout<SparseArray<T>>::value,
@@ -175,7 +176,7 @@ SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,
     base(_dims, _nNZ, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _is_device, _copy_device),
     values(_is_device ?
           (!_copy_device ? createDeviceDataArray<T>(dim4(_nNZ), _values)
-                         : createEmptyArray<T>(dim4(_nNZ)))
+                         : createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
         : createHostDataArray<T>(dim4(_nNZ), _values))
 {
 #if __cplusplus > 199711L

From 50d789799d594c51d2f235f665da4f2e3400c102 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 14:46:17 -0400
Subject: [PATCH 0572/2677] CUDA Sparse: use createEmptyArray instead of
 initArray

---
 src/backend/cuda/sparse.cu | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 31dc81a68e..966e312ef0 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -245,15 +245,15 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
                         in.get(), in.strides()[1],
                         nnzPerDir.get(), &nNZ));
 
-    Array<int> *rowIdx = initArray<int>();
-    Array<int> *colIdx = initArray<int>();
+    Array<int> rowIdx = createEmptyArray<int>(dim4());
+    Array<int> colIdx = createEmptyArray<int>(dim4());
 
     if(storage == AF_SPARSE_CSR) {
-        *rowIdx = createEmptyArray<int>(dim4(M+1));
-        *colIdx = createEmptyArray<int>(dim4(nNZ));
+        rowIdx = createEmptyArray<int>(dim4(M+1));
+        colIdx = createEmptyArray<int>(dim4(nNZ));
     } else {
-        *rowIdx = createEmptyArray<int>(dim4(nNZ));
-        *colIdx = createEmptyArray<int>(dim4(N+1));
+        rowIdx = createEmptyArray<int>(dim4(nNZ));
+        colIdx = createEmptyArray<int>(dim4(N+1));
     }
     Array<T> values = createEmptyArray<T>(dim4(nNZ));
 
@@ -264,7 +264,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
                         descr,
                         in.get(), in.strides()[1],
                         nnzPerDir.get(),
-                        values.get(), (*rowIdx).get(), (*colIdx).get()));
+                        values.get(), rowIdx.get(), colIdx.get()));
     else
         CUSPARSE_CHECK(dense2csc_func<T>()(
                         getHandle(),
@@ -272,12 +272,12 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
                         descr,
                         in.get(), in.strides()[1],
                         nnzPerDir.get(),
-                        values.get(), (*rowIdx).get(), (*colIdx).get()));
+                        values.get(), rowIdx.get(), colIdx.get()));
 
     // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
 
-    return createArrayDataSparseArray<T>(in.dims(), values, *rowIdx, *colIdx, storage);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, storage);
 }
 
 

From c0f7ee16063a7e514d9031612e49dc51d85b3731 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 14:46:45 -0400
Subject: [PATCH 0573/2677] Add sparse array support for print

---
 src/api/c/print.cpp | 158 ++++++++++++++++++++++++++++++--------------
 1 file changed, 110 insertions(+), 48 deletions(-)

diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 66133503ef..dad9032dc4 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -22,6 +22,7 @@
 #include <backend.hpp>
 #include <type_util.hpp>
 #include <handle.hpp>
+#include <sparse_handle.hpp>
 
 #include <af/index.h>
 
@@ -104,26 +105,65 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
     os.flags(backup);
 }
 
+template<typename T>
+static void printSparse(const char *exp, af_array arr, const int precision,
+                        std::ostream &os = std::cout, bool transpose = true)
+{
+    common::SparseArray<T> sparse = getSparseArray<T>(arr);
+    std::string name("No Name Sparse Array");
+
+    if(exp != NULL) {
+        name = std::string(exp);
+    }
+    os << name << std::endl;
+    os << "Storage Format : ";
+    switch(sparse.getStorage()) {
+        case AF_SPARSE_DENSE: os << "AF_SPARSE_DENSE\n";    break;
+        case AF_SPARSE_CSR  : os << "AF_SPARSE_CSR\n";      break;
+        case AF_SPARSE_CSC  : os << "AF_SPARSE_CSC\n";      break;
+        case AF_SPARSE_COO  : os << "AF_SPARSE_COO\n";      break;
+    }
+    os << "[" << sparse.dims() << "]\n";
+
+    print<T  >(std::string(name + ": Values").c_str(), getHandle(sparse.getValues()),
+               precision, os, transpose);
+    print<int>(std::string(name + ": RowIdx").c_str(), getHandle(sparse.getRowIdx()),
+               precision, os, transpose);
+    print<int>(std::string(name + ": ColIdx").c_str(), getHandle(sparse.getColIdx()),
+               precision, os, transpose);
+}
+
 af_err af_print_array(af_array arr)
 {
     try {
-        ArrayInfo info = getInfo(arr);
+        ArrayInfo info = getInfo(arr, false);   // Don't assert sparse/dense
         af_dtype type = info.getType();
-        switch(type)
-        {
-        case f32:   print<float>   (NULL, arr, 4);   break;
-        case c32:   print<cfloat>  (NULL, arr, 4);   break;
-        case f64:   print<double>  (NULL, arr, 4);   break;
-        case c64:   print<cdouble> (NULL, arr, 4);   break;
-        case b8:    print<char>    (NULL, arr, 4);   break;
-        case s32:   print<int>     (NULL, arr, 4);   break;
-        case u32:   print<unsigned>(NULL, arr, 4);   break;
-        case u8:    print<uchar>   (NULL, arr, 4);   break;
-        case s64:   print<intl>    (NULL, arr, 4);   break;
-        case u64:   print<uintl>   (NULL, arr, 4);   break;
-        case s16:   print<short>   (NULL, arr, 4);   break;
-        case u16:   print<ushort>  (NULL, arr, 4);   break;
-        default:    TYPE_ERROR(1, type);
+
+        if(info.isSparse()) {
+            switch(type) {
+                case f32: printSparse<float  >(NULL, arr, 4); break;
+                case f64: printSparse<double >(NULL, arr, 4); break;
+                case c32: printSparse<cfloat >(NULL, arr, 4); break;
+                case c64: printSparse<cdouble>(NULL, arr, 4); break;
+                default : TYPE_ERROR(0, type);
+            }
+        } else {
+            switch(type)
+            {
+                case f32:   print<float>   (NULL, arr, 4);   break;
+                case c32:   print<cfloat>  (NULL, arr, 4);   break;
+                case f64:   print<double>  (NULL, arr, 4);   break;
+                case c64:   print<cdouble> (NULL, arr, 4);   break;
+                case b8:    print<char>    (NULL, arr, 4);   break;
+                case s32:   print<int>     (NULL, arr, 4);   break;
+                case u32:   print<unsigned>(NULL, arr, 4);   break;
+                case u8:    print<uchar>   (NULL, arr, 4);   break;
+                case s64:   print<intl>    (NULL, arr, 4);   break;
+                case u64:   print<uintl>   (NULL, arr, 4);   break;
+                case s16:   print<short>   (NULL, arr, 4);   break;
+                case u16:   print<ushort>  (NULL, arr, 4);   break;
+                default:    TYPE_ERROR(1, type);
+            }
         }
     }
     CATCHALL;
@@ -134,23 +174,34 @@ af_err af_print_array_gen(const char *exp, const af_array arr, const int precisi
 {
     try {
         ARG_ASSERT(0, exp != NULL);
-        ArrayInfo info = getInfo(arr);
+        ArrayInfo info = getInfo(arr, false);   // Don't assert sparse/dense
         af_dtype type = info.getType();
-        switch(type)
-        {
-        case f32:   print<float   >(exp, arr, precision);   break;
-        case c32:   print<cfloat  >(exp, arr, precision);   break;
-        case f64:   print<double  >(exp, arr, precision);   break;
-        case c64:   print<cdouble >(exp, arr, precision);   break;
-        case b8:    print<char    >(exp, arr, precision);   break;
-        case s32:   print<int     >(exp, arr, precision);   break;
-        case u32:   print<unsigned>(exp, arr, precision);   break;
-        case u8:    print<uchar   >(exp, arr, precision);   break;
-        case s64:   print<intl    >(exp, arr, precision);   break;
-        case u64:   print<uintl   >(exp, arr, precision);   break;
-        case s16:   print<short   >(exp, arr, precision);   break;
-        case u16:   print<ushort  >(exp, arr, precision);   break;
-        default:    TYPE_ERROR(1, type);
+
+        if(info.isSparse()) {
+            switch(type) {
+                case f32: printSparse<float  >(exp, arr, precision); break;
+                case f64: printSparse<double >(exp, arr, precision); break;
+                case c32: printSparse<cfloat >(exp, arr, precision); break;
+                case c64: printSparse<cdouble>(exp, arr, precision); break;
+                default : TYPE_ERROR(0, type);
+            }
+        } else {
+            switch(type)
+            {
+                case f32:   print<float   >(exp, arr, precision);   break;
+                case c32:   print<cfloat  >(exp, arr, precision);   break;
+                case f64:   print<double  >(exp, arr, precision);   break;
+                case c64:   print<cdouble >(exp, arr, precision);   break;
+                case b8:    print<char    >(exp, arr, precision);   break;
+                case s32:   print<int     >(exp, arr, precision);   break;
+                case u32:   print<unsigned>(exp, arr, precision);   break;
+                case u8:    print<uchar   >(exp, arr, precision);   break;
+                case s64:   print<intl    >(exp, arr, precision);   break;
+                case u64:   print<uintl   >(exp, arr, precision);   break;
+                case s16:   print<short   >(exp, arr, precision);   break;
+                case u16:   print<ushort  >(exp, arr, precision);   break;
+                default:    TYPE_ERROR(1, type);
+            }
         }
     }
     CATCHALL;
@@ -162,24 +213,35 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
 {
     try {
         ARG_ASSERT(0, exp != NULL);
-        ArrayInfo info = getInfo(arr);
+        ArrayInfo info = getInfo(arr, false);   // Don't assert sparse/dense
         af_dtype type = info.getType();
         std::stringstream ss;
-        switch(type)
-        {
-        case f32:   print<float   >(exp, arr, precision, ss, transpose);   break;
-        case c32:   print<cfloat  >(exp, arr, precision, ss, transpose);   break;
-        case f64:   print<double  >(exp, arr, precision, ss, transpose);   break;
-        case c64:   print<cdouble >(exp, arr, precision, ss, transpose);   break;
-        case b8:    print<char    >(exp, arr, precision, ss, transpose);   break;
-        case s32:   print<int     >(exp, arr, precision, ss, transpose);   break;
-        case u32:   print<unsigned>(exp, arr, precision, ss, transpose);   break;
-        case u8:    print<uchar   >(exp, arr, precision, ss, transpose);   break;
-        case s64:   print<intl    >(exp, arr, precision, ss, transpose);   break;
-        case u64:   print<uintl   >(exp, arr, precision, ss, transpose);   break;
-        case s16:   print<short   >(exp, arr, precision, ss, transpose);   break;
-        case u16:   print<ushort  >(exp, arr, precision, ss, transpose);   break;
-        default:    TYPE_ERROR(1, type);
+
+        if(info.isSparse()) {
+            switch(type) {
+                case f32: printSparse<float  >(exp, arr, precision, ss, transpose); break;
+                case f64: printSparse<double >(exp, arr, precision, ss, transpose); break;
+                case c32: printSparse<cfloat >(exp, arr, precision, ss, transpose); break;
+                case c64: printSparse<cdouble>(exp, arr, precision, ss, transpose); break;
+                default : TYPE_ERROR(0, type);
+            }
+        } else {
+            switch(type)
+            {
+                case f32:   print<float   >(exp, arr, precision, ss, transpose);   break;
+                case c32:   print<cfloat  >(exp, arr, precision, ss, transpose);   break;
+                case f64:   print<double  >(exp, arr, precision, ss, transpose);   break;
+                case c64:   print<cdouble >(exp, arr, precision, ss, transpose);   break;
+                case b8:    print<char    >(exp, arr, precision, ss, transpose);   break;
+                case s32:   print<int     >(exp, arr, precision, ss, transpose);   break;
+                case u32:   print<unsigned>(exp, arr, precision, ss, transpose);   break;
+                case u8:    print<uchar   >(exp, arr, precision, ss, transpose);   break;
+                case s64:   print<intl    >(exp, arr, precision, ss, transpose);   break;
+                case u64:   print<uintl   >(exp, arr, precision, ss, transpose);   break;
+                case s16:   print<short   >(exp, arr, precision, ss, transpose);   break;
+                case u16:   print<ushort  >(exp, arr, precision, ss, transpose);   break;
+                default:    TYPE_ERROR(1, type);
+            }
         }
         std::string str = ss.str();
         af_alloc_host((void**)output, sizeof(char) * (str.size() + 1));

From ebf00b05efe5d87af8e1785b6615f6c383b48db1 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 1 Jun 2016 16:42:56 -0400
Subject: [PATCH 0574/2677] Split template instatiations ALA sort_by_key

Minor change in cuda/kernel/scan_dim_by_key_impl.hpp where an
unnecessary include was removed.
---
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |   2 -
 src/backend/opencl/CMakeLists.txt             |   7 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  21 ++
 .../kernel/scan_by_key/scan_by_key_impl.cpp   |  26 ++
 src/backend/opencl/kernel/scan_dim_by_key.hpp | 267 +--------------
 .../opencl/kernel/scan_dim_by_key_impl.hpp    | 312 ++++++++++++++++++
 .../opencl/kernel/scan_first_by_key.hpp       | 247 +-------------
 .../opencl/kernel/scan_first_by_key_impl.hpp  | 293 ++++++++++++++++
 src/backend/opencl/scan.cpp                   |  67 +---
 src/backend/opencl/scan_by_key.cpp            |  79 +++++
 10 files changed, 742 insertions(+), 579 deletions(-)
 create mode 100644 src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
 create mode 100644 src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
 create mode 100644 src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
 create mode 100644 src/backend/opencl/kernel/scan_first_by_key_impl.hpp
 create mode 100644 src/backend/opencl/scan_by_key.cpp

diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 10cc20db52..f3cab51984 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -18,8 +18,6 @@
 #include <memory.hpp>
 #include "config.hpp"
 
-#include <kernel/scan_dim_by_key.hpp>
-
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index c81610b6e6..691d3c7716 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -239,6 +239,7 @@ ENDIF()
 
 INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/CMakeLists.txt")
 IF(DEFINED BLAS_SYM_FILE)
 
     ADD_LIBRARY(afopencl_static STATIC
@@ -256,7 +257,8 @@ IF(DEFINED BLAS_SYM_FILE)
                 ${backend_sources}
                 ${magma_sources}
                 ${magma_headers}
-                ${SORT_BY_KEY_OBJECTS})
+                ${SORT_BY_KEY_OBJECTS}
+                ${SCAN_BY_KEY_OBJECTS})
 
     ADD_LIBRARY(afopencl SHARED
                ${c_headers}
@@ -300,7 +302,8 @@ ELSE(DEFINED BLAS_SYM_FILE)
                 ${cpp_sources}
                 ${magma_sources}
                 ${magma_headers}
-                ${SORT_BY_KEY_OBJECTS})
+                ${SORT_BY_KEY_OBJECTS}
+                ${SCAN_BY_KEY_OBJECTS})
 
 ENDIF()
 
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..791fa766bb
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -0,0 +1,21 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_BINARY_OPS")
+        STRING(REPLACE "// SBK_BINARY_OPS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_BINARY_OPS ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
+    ADD_LIBRARY(opencl_scan_by_key_${SBK_BINARY_OP} OBJECT
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp"
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp")
+    ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} ${cl_kernel_targets})
+    IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
+        ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} forge)
+    ENDIF()
+    SET_TARGET_PROPERTIES(opencl_scan_by_key_${SBK_BINARY_OP} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_BINARY_OP}")
+    LIST(APPEND SCAN_BY_KEY_OBJECTS $<TARGET_OBJECTS:opencl_scan_by_key_${SBK_BINARY_OP}>)
+ENDFOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
diff --git a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
new file mode 100644
index 0000000000..dd5f9e1382
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <ops.hpp>
+#include <backend.hpp>
+#include <kernel/scan_first_by_key_impl.hpp>
+#include <kernel/scan_dim_by_key_impl.hpp>
+
+// This file instantiates scan_dim_by_key as separate object files from CMake
+// The line below is read by CMake to determenine the instantiations
+// SBK_BINARY_OPS:af_add_t af_mul_t af_max_t af_min_t
+
+namespace opencl
+{
+namespace kernel
+{
+    INSTANTIATE_SCAN_FIRST_BY_KEY_OP(TYPE)
+    INSTANTIATE_SCAN_DIM_BY_KEY_OP(TYPE)
+}
+}
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
index 1abd9d44e1..691552f465 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -8,280 +8,15 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/scan_dim_by_key.hpp>
-#include <kernel_headers/ops.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
-#include "config.hpp"
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::make_kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
 namespace opencl
 {
 namespace kernel
 {
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags, uint threads_y)
-    {
-        std::string ref_name =
-            std::string("scan_") +
-            std::to_string(dim) +
-            std::string("_") +
-            std::to_string(calculateFlags) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<Tk>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_y) +
-            std::string("_") +
-            std::to_string(int(inclusive_scan));
-
-        int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
-
-            Binary<To, op> scan;
-            ToNum<To> toNum;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D Tk=" << dtype_traits<Ti>::getName()
-                    << " -D T=To"
-                    << " -D dim=" << dim
-                    << " -D DIMY=" << threads_y
-                    << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNum(scan.init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D calculateFlags=" << calculateFlags
-                    << " -D inclusive_scan=" << inclusive_scan;
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
-            const int   ker_lens[] = {ops_cl_len, scan_dim_by_key_cl_len};
-            cl::Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[3];
-
-            entry.ker[0] = Kernel(*entry.prog, "scan_dim_by_key_final_kernel");
-            entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
-            entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
-
-            kernelCaches[device][ref_name] = entry;
-
-        } else {
-            entry = idx->second;
-        }
-
-        return entry.ker[kerIdx];
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_nonfinal_launcher(Param &out,
-                                  Param &tmp,
-                                  Param &tmpflg,
-                                  Param &tmpid,
-                                  const Param &in,
-                                  const Param &key,
-                                  int dim, uint threads_y,
-                                  const uint groups_all[4])
-    {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(1, dim, false, threads_y);
-
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
-
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
-
-            auto scanOp = make_kernel<Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      uint, uint,
-                                      uint, uint>(ker);
-
-            scanOp(EnqueueArgs(getQueue(), global, local),
-                   *out.data, out.info,
-                   *tmp.data, tmp.info,
-                   *tmpflg.data, tmpflg.info,
-                   *tmpid.data, tmpid.info,
-                   *in.data, in.info, *key.data, key.info,
-                   groups_all[0], groups_all[1], groups_all[dim], lim);
-
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_final_launcher(Param &out,
-                                  const Param &in,
-                                  const Param &key,
-                                  int dim, const bool calculateFlags, uint threads_y,
-                                  const uint groups_all[4])
-    {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(0, dim, calculateFlags, threads_y);
-
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
-
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
-
-            auto scanOp = make_kernel<Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      uint, uint,
-                                      uint, uint>(ker);
-
-            scanOp(EnqueueArgs(getQueue(), global, local),
-                   *out.data, out.info, *in.data, in.info, *key.data, key.info,
-                   groups_all[0], groups_all[1], groups_all[dim], lim);
-
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_dim_launcher(Param &out,
-                                   Param &tmp,
-                                   Param &tmpid,
-                                   int dim, uint threads_y,
-                                   const uint groups_all[4])
-    {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(2, dim, false, threads_y);
-
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
-
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
-
-            auto bcastOp = make_kernel<Buffer, KParam,
-                                       Buffer, KParam,
-                                       Buffer, KParam,
-                                       uint, uint,
-                                       uint, uint>(ker);
-
-            bcastOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
-                    groups_all[0], groups_all[1], groups_all[dim], lim);
-
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_dim(Param &out, const Param &in, const Param &key, int dim)
-    {
-        try {
-            uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
-            uint threads_x = THREADS_X;
-
-            uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
-                                 (uint)out.info.dims[1],
-                                 (uint)out.info.dims[2],
-                                 (uint)out.info.dims[3]};
-
-            groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
-
-            if (groups_all[dim] == 1) {
-
-                scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
-                                              dim, true,
-                                              threads_y,
-                                              groups_all);
-            } else {
-
-                Param tmp = out;
-
-                tmp.info.dims[dim] = groups_all[dim];
-                tmp.info.strides[0] = 1;
-                for (int k = 1; k < 4; k++) {
-                    tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
-                }
-                Param tmpflg = tmp;
-                Param tmpid = tmp;
-
-                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-                // FIXME: Do I need to free this ?
-                tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-                tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
-                tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
-
-                scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
-                                              dim,
-                                              threads_y,
-                                              groups_all);
-
-                int gdim = groups_all[dim];
-                groups_all[dim] = 1;
-
-                if (op == af_notzero_t) {
-                    scan_dim_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
-                                                        dim, false,
-                                                        threads_y,
-                                                        groups_all);
-                } else {
-                    scan_dim_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
-                                                        dim, false,
-                                                        threads_y,
-                                                        groups_all);
-                }
-
-                groups_all[dim] = gdim;
-                bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
-                                               dim,
-                                               threads_y,
-                                               groups_all);
-                bufferFree(tmp.data);
-            }
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
-    }
+    void scan_dim(Param &out, const Param &in, const Param &key, int dim);
 }
 }
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
new file mode 100644
index 0000000000..61b6ed3b1c
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -0,0 +1,312 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+#include <mutex>
+#include <map>
+#include <kernel_headers/scan_dim_by_key.hpp>
+#include <kernel_headers/ops.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+#include <cache.hpp>
+#include "names.hpp"
+#include "config.hpp"
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+namespace kernel
+{
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags, uint threads_y)
+    {
+        std::string ref_name =
+            std::string("scan_") +
+            std::to_string(dim) +
+            std::string("_") +
+            std::to_string(calculateFlags) +
+            std::string("_") +
+            std::string(dtype_traits<Ti>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<Tk>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<To>::getName()) +
+            std::string("_") +
+            std::to_string(op) +
+            std::string("_") +
+            std::to_string(threads_y) +
+            std::string("_") +
+            std::to_string(int(inclusive_scan));
+
+        int device = getActiveDeviceId();
+        kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+        kc_entry_t entry;
+        if (idx == kernelCaches[device].end()) {
+
+            Binary<To, op> scan;
+            ToNum<To> toNum;
+
+            std::ostringstream options;
+            options << " -D To=" << dtype_traits<To>::getName()
+                    << " -D Ti=" << dtype_traits<Ti>::getName()
+                    << " -D Tk=" << dtype_traits<Ti>::getName()
+                    << " -D T=To"
+                    << " -D dim=" << dim
+                    << " -D DIMY=" << threads_y
+                    << " -D THREADS_X=" << THREADS_X
+                    << " -D init=" << toNum(scan.init())
+                    << " -D " << binOpName<op>()
+                    << " -D CPLX=" << af::iscplx<Ti>()
+                    << " -D calculateFlags=" << calculateFlags
+                    << " -D inclusive_scan=" << inclusive_scan;
+            if (std::is_same<Ti, double>::value ||
+                std::is_same<Ti, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
+            const int   ker_lens[] = {ops_cl_len, scan_dim_by_key_cl_len};
+            cl::Program prog;
+            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel[3];
+
+            entry.ker[0] = Kernel(*entry.prog, "scan_dim_by_key_final_kernel");
+            entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
+            entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
+
+            kernelCaches[device][ref_name] = entry;
+
+        } else {
+            entry = idx->second;
+        }
+
+        return entry.ker[kerIdx];
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void scan_dim_nonfinal_launcher(Param &out,
+                                  Param &tmp,
+                                  Param &tmpflg,
+                                  Param &tmpid,
+                                  const Param &in,
+                                  const Param &key,
+                                  int dim, uint threads_y,
+                                  const uint groups_all[4])
+    {
+        try {
+            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(1, dim, false, threads_y);
+
+            NDRange local(THREADS_X, threads_y);
+            NDRange global(groups_all[0] * groups_all[2] * local[0],
+                           groups_all[1] * groups_all[3] * local[1]);
+
+            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+            auto scanOp = make_kernel<Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      uint, uint,
+                                      uint, uint>(ker);
+
+            scanOp(EnqueueArgs(getQueue(), global, local),
+                   *out.data, out.info,
+                   *tmp.data, tmp.info,
+                   *tmpflg.data, tmpflg.info,
+                   *tmpid.data, tmpid.info,
+                   *in.data, in.info, *key.data, key.info,
+                   groups_all[0], groups_all[1], groups_all[dim], lim);
+
+            CL_DEBUG_FINISH(getQueue());
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void scan_dim_final_launcher(Param &out,
+                                  const Param &in,
+                                  const Param &key,
+                                  int dim, const bool calculateFlags, uint threads_y,
+                                  const uint groups_all[4])
+    {
+        try {
+            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(0, dim, calculateFlags, threads_y);
+
+            NDRange local(THREADS_X, threads_y);
+            NDRange global(groups_all[0] * groups_all[2] * local[0],
+                           groups_all[1] * groups_all[3] * local[1]);
+
+            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+            auto scanOp = make_kernel<Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      uint, uint,
+                                      uint, uint>(ker);
+
+            scanOp(EnqueueArgs(getQueue(), global, local),
+                   *out.data, out.info, *in.data, in.info, *key.data, key.info,
+                   groups_all[0], groups_all[1], groups_all[dim], lim);
+
+            CL_DEBUG_FINISH(getQueue());
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void bcast_dim_launcher(Param &out,
+                                   Param &tmp,
+                                   Param &tmpid,
+                                   int dim, uint threads_y,
+                                   const uint groups_all[4])
+    {
+        try {
+            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(2, dim, false, threads_y);
+
+            NDRange local(THREADS_X, threads_y);
+            NDRange global(groups_all[0] * groups_all[2] * local[0],
+                           groups_all[1] * groups_all[3] * local[1]);
+
+            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+            auto bcastOp = make_kernel<Buffer, KParam,
+                                       Buffer, KParam,
+                                       Buffer, KParam,
+                                       uint, uint,
+                                       uint, uint>(ker);
+
+            bcastOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
+                    groups_all[0], groups_all[1], groups_all[dim], lim);
+
+            CL_DEBUG_FINISH(getQueue());
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    void scan_dim(Param &out, const Param &in, const Param &key, int dim)
+    {
+        try {
+            uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+            uint threads_x = THREADS_X;
+
+            uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
+                                 (uint)out.info.dims[1],
+                                 (uint)out.info.dims[2],
+                                 (uint)out.info.dims[3]};
+
+            groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+
+            if (groups_all[dim] == 1) {
+
+                scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
+                                              dim, true,
+                                              threads_y,
+                                              groups_all);
+            } else {
+
+                Param tmp = out;
+
+                tmp.info.dims[dim] = groups_all[dim];
+                tmp.info.strides[0] = 1;
+                for (int k = 1; k < 4; k++) {
+                    tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+                }
+                Param tmpflg = tmp;
+                Param tmpid = tmp;
+
+                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+                // FIXME: Do I need to free this ?
+                tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+                tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+                tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
+
+                scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
+                                              dim,
+                                              threads_y,
+                                              groups_all);
+
+                int gdim = groups_all[dim];
+                groups_all[dim] = 1;
+
+                if (op == af_notzero_t) {
+                    scan_dim_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
+                                                        dim, false,
+                                                        threads_y,
+                                                        groups_all);
+                } else {
+                    scan_dim_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
+                                                        dim, false,
+                                                        threads_y,
+                                                        groups_all);
+                }
+
+                groups_all[dim] = gdim;
+                bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
+                                               dim,
+                                               threads_y,
+                                               groups_all);
+                bufferFree(tmp.data);
+            }
+        } catch (cl::Error err) {
+            CL_TO_AF_ERROR(err);
+            throw;
+        }
+    }
+}
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)\
+    template void scan_dim<Ti, Tk, To, ROp,  true>(Param &out, const Param &in, const Param &key, int dim);\
+    template void scan_dim<Ti, Tk, To, ROp, false>(Param &out, const Param &in, const Param &key, int dim);
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)         \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int  )   \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uint )   \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl )   \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uintl)
+}
diff --git a/src/backend/opencl/kernel/scan_first_by_key.hpp b/src/backend/opencl/kernel/scan_first_by_key.hpp
index ba5f3bc88e..7652929385 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key.hpp
@@ -8,261 +8,16 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/scan_first_by_key.hpp>
-#include <kernel_headers/ops.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
-#include "config.hpp"
-#include <memory.hpp>
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::make_kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
 
 namespace opencl
 {
 namespace kernel
 {
-
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags, uint threads_x)
-    {
-        std::string ref_name =
-            std::string("scan_0_") +
-            std::string("_") +
-            std::to_string(calculateFlags) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<Tk>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_x) +
-            std::string("_") +
-            std::to_string(int(inclusive_scan));
-
-        int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
-
-            const uint threads_y = THREADS_PER_GROUP / threads_x;
-            const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
-
-            Binary<To, op> scan;
-            ToNum<To> toNum;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D Tk=" << dtype_traits<Tk>::getName()
-                    << " -D T=To"
-                    << " -D DIMX=" << threads_x
-                    << " -D DIMY=" << threads_y
-                    << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNum(scan.init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D calculateFlags=" << calculateFlags
-                    << " -D inclusive_scan=" << inclusive_scan;
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
-            const int   ker_lens[] = {ops_cl_len, scan_first_by_key_cl_len};
-            cl::Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[3];
-
-            entry.ker[0] = Kernel(*entry.prog, "scan_first_by_key_final_kernel");
-            entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
-            entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
-
-            kernelCaches[device][ref_name] = entry;
-
-        } else {
-            entry = idx->second;
-        }
-
-        return entry.ker[kerIdx];
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first_nonfinal_launcher(Param &out,
-                                    Param &tmp,
-                                    Param &tmpflg,
-                                    Param &tmpid,
-                                    const Param &in,
-                                    const Param &key,
-                                    const uint groups_x,
-                                    const uint groups_y,
-                                    const uint threads_x)
-    {
-        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(1, false, threads_x);
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
-
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
-
-        auto scanOp = make_kernel<Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  uint, uint, uint>(ker);
-
-        scanOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info,
-               *tmp.data, tmp.info,
-               *tmpflg.data, tmpflg.info,
-               *tmpid.data, tmpid.info,
-               *in.data, in.info, *key.data, key.info,
-               groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first_final_launcher(Param &out,
-                                    const Param &in,
-                                    const Param &key,
-                                    const bool calculateFlags,
-                                    const uint groups_x,
-                                    const uint groups_y,
-                                    const uint threads_x)
-    {
-        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(0, calculateFlags, threads_x);
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
-
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
-
-        auto scanOp = make_kernel<Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  uint, uint, uint>(ker);
-
-        scanOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info, *in.data, in.info, *key.data, key.info,
-               groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
-    }
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_first_launcher(Param &out,
-                                     Param &tmp,
-                                     Param &tmpid,
-                                     const uint groups_x,
-                                     const uint groups_y,
-                                     const uint threads_x)
-    {
-
-        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(2, false, threads_x);
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
-
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
-
-        auto bcastOp = make_kernel<Buffer, KParam,
-                                   Buffer, KParam,
-                                   Buffer, KParam,
-                                   uint, uint, uint>(ker);
-
-        bcastOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
-                groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
-    }
-
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first(Param &out, const Param &in, const Param &key)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
-
-        uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(out.info.dims[1], threads_y);
-
-        if (groups_x == 1) {
-            scan_first_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
-                                            true,
-                                            groups_x, groups_y,
-                                            threads_x);
-
-        } else {
-
-            Param tmp = out;
-            tmp.info.dims[0] = groups_x;
-            tmp.info.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims   [k - 1];
-            }
-            Param tmpflg = tmp;
-            Param tmpid = tmp;
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-            tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
-            tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
-
-            scan_first_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
-                                            groups_x, groups_y,
-                                            threads_x);
-
-            if (op == af_notzero_t) {
-                scan_first_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
-                                                      false,
-                                                      1, groups_y,
-                                                      threads_x);
-            } else {
-                scan_first_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
-                                                      false,
-                                                      1, groups_y,
-                                                      threads_x);
-            }
-
-            bcast_first_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
-                                             groups_x,
-                                             groups_y,
-                                             threads_x);
-
-            bufferFree(tmp.data);
-
-        }
-    }
-
+    void scan_first(Param &out, const Param &in, const Param &key);
 }
 }
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
new file mode 100644
index 0000000000..49eed4118f
--- /dev/null
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -0,0 +1,293 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+#include <mutex>
+#include <map>
+#include <kernel_headers/scan_first_by_key.hpp>
+#include <kernel_headers/ops.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+#include <cache.hpp>
+#include "names.hpp"
+#include "config.hpp"
+#include <memory.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+namespace kernel
+{
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags, uint threads_x)
+    {
+        std::string ref_name =
+            std::string("scan_0_") +
+            std::string("_") +
+            std::to_string(calculateFlags) +
+            std::string("_") +
+            std::string(dtype_traits<Ti>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<Tk>::getName()) +
+            std::string("_") +
+            std::string(dtype_traits<To>::getName()) +
+            std::string("_") +
+            std::to_string(op) +
+            std::string("_") +
+            std::to_string(threads_x) +
+            std::string("_") +
+            std::to_string(int(inclusive_scan));
+
+        int device = getActiveDeviceId();
+        kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+        kc_entry_t entry;
+        if (idx == kernelCaches[device].end()) {
+
+            const uint threads_y = THREADS_PER_GROUP / threads_x;
+            const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
+
+            Binary<To, op> scan;
+            ToNum<To> toNum;
+
+            std::ostringstream options;
+            options << " -D To=" << dtype_traits<To>::getName()
+                    << " -D Ti=" << dtype_traits<Ti>::getName()
+                    << " -D Tk=" << dtype_traits<Tk>::getName()
+                    << " -D T=To"
+                    << " -D DIMX=" << threads_x
+                    << " -D DIMY=" << threads_y
+                    << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
+                    << " -D init=" << toNum(scan.init())
+                    << " -D " << binOpName<op>()
+                    << " -D CPLX=" << af::iscplx<Ti>()
+                    << " -D calculateFlags=" << calculateFlags
+                    << " -D inclusive_scan=" << inclusive_scan;
+            if (std::is_same<Ti, double>::value ||
+                std::is_same<Ti, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
+            const int   ker_lens[] = {ops_cl_len, scan_first_by_key_cl_len};
+            cl::Program prog;
+            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel[3];
+
+            entry.ker[0] = Kernel(*entry.prog, "scan_first_by_key_final_kernel");
+            entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
+            entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
+
+            kernelCaches[device][ref_name] = entry;
+
+        } else {
+            entry = idx->second;
+        }
+
+        return entry.ker[kerIdx];
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
+    static void scan_first_nonfinal_launcher(Param &out,
+                                    Param &tmp,
+                                    Param &tmpflg,
+                                    Param &tmpid,
+                                    const Param &in,
+                                    const Param &key,
+                                    const uint groups_x,
+                                    const uint groups_y,
+                                    const uint threads_x)
+    {
+        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(1, false, threads_x);
+
+        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
+
+        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+        auto scanOp = make_kernel<Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  uint, uint, uint>(ker);
+
+        scanOp(EnqueueArgs(getQueue(), global, local),
+               *out.data, out.info,
+               *tmp.data, tmp.info,
+               *tmpflg.data, tmpflg.info,
+               *tmpid.data, tmpid.info,
+               *in.data, in.info, *key.data, key.info,
+               groups_x, groups_y, lim);
+
+        CL_DEBUG_FINISH(getQueue());
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
+    static void scan_first_final_launcher(Param &out,
+                                    const Param &in,
+                                    const Param &key,
+                                    const bool calculateFlags,
+                                    const uint groups_x,
+                                    const uint groups_y,
+                                    const uint threads_x)
+    {
+        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(0, calculateFlags, threads_x);
+
+        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
+
+        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+        auto scanOp = make_kernel<Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  uint, uint, uint>(ker);
+
+        scanOp(EnqueueArgs(getQueue(), global, local),
+               *out.data, out.info, *in.data, in.info, *key.data, key.info,
+               groups_x, groups_y, lim);
+
+        CL_DEBUG_FINISH(getQueue());
+    }
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    static void bcast_first_launcher(Param &out,
+                                     Param &tmp,
+                                     Param &tmpid,
+                                     const uint groups_x,
+                                     const uint groups_y,
+                                     const uint threads_x)
+    {
+
+        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(2, false, threads_x);
+
+        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
+
+        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+        auto bcastOp = make_kernel<Buffer, KParam,
+                                   Buffer, KParam,
+                                   Buffer, KParam,
+                                   uint, uint, uint>(ker);
+
+        bcastOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
+                groups_x, groups_y, lim);
+
+        CL_DEBUG_FINISH(getQueue());
+    }
+
+
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    void scan_first(Param &out, const Param &in, const Param &key)
+    {
+        uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
+
+        uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(out.info.dims[1], threads_y);
+
+        if (groups_x == 1) {
+            scan_first_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
+                                            true,
+                                            groups_x, groups_y,
+                                            threads_x);
+
+        } else {
+
+            Param tmp = out;
+            tmp.info.dims[0] = groups_x;
+            tmp.info.strides[0] = 1;
+            for (int k = 1; k < 4; k++) {
+                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims   [k - 1];
+            }
+            Param tmpflg = tmp;
+            Param tmpid = tmp;
+
+            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+
+            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+            tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+            tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
+
+            scan_first_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
+                                            groups_x, groups_y,
+                                            threads_x);
+
+            if (op == af_notzero_t) {
+                scan_first_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
+                                                      false,
+                                                      1, groups_y,
+                                                      threads_x);
+            } else {
+                scan_first_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
+                                                      false,
+                                                      1, groups_y,
+                                                      threads_x);
+            }
+
+            bcast_first_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
+                                             groups_x,
+                                             groups_y,
+                                             threads_x);
+
+            bufferFree(tmp.data);
+
+        }
+    }
+
+}
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)                                                  \
+    template void scan_first<Ti, Tk, To, ROp,  true>(Param &out, const Param &in, const Param &key);    \
+    template void scan_first<Ti, Tk, To, ROp, false>(Param &out, const Param &in, const Param &key);
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)        \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float  , Tk, float  )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, double , Tk, double )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cfloat , Tk, cfloat )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cdouble, Tk, cdouble)\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, int    , Tk, int    )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, int    )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, uint   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uchar  , Tk, uint   )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, short  , Tk, int    )\
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)       \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int  ) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uint ) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl ) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uintl)
+}
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index fe5d39aeab..39bdd2fb5e 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -16,9 +16,6 @@
 #include <kernel/scan_first.hpp>
 #include <kernel/scan_dim.hpp>
 
-#include <kernel/scan_first_by_key.hpp>
-#include <kernel/scan_dim_by_key.hpp>
-
 namespace opencl
 {
     template<af_op_t op, typename Ti, typename To>
@@ -50,42 +47,9 @@ namespace opencl
         return out;
     }
 
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        Array<To> out = createEmptyArray<To>(in.dims());
-
-        try {
-            Param Out = out;
-            Param Key = key;
-            Param In  =   in;
-
-            if (inclusive_scan) {
-                if (dim == 0)
-                    kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
-                else
-                    kernel::scan_dim  <Ti, Tk, To, op, true>(Out, In, Key, dim);
-            } else {
-                if (dim == 0)
-                    kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
-                else
-                    kernel::scan_dim  <Ti, Tk, To, op, false>(Out, In, Key, dim);
-            }
-
-        } catch (cl::Error &ex) {
-
-            CL_TO_AF_ERROR(ex);
-        }
-
-        return out;
-    }
-
 #define INSTANTIATE_SCAN(ROp, Ti, To)\
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
-#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
-    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
-
 #define INSTANTIATE_SCAN_ALL(ROp)                       \
     INSTANTIATE_SCAN(ROp, float  , float  )             \
     INSTANTIATE_SCAN(ROp, double , double )             \
@@ -101,32 +65,9 @@ namespace opencl
     INSTANTIATE_SCAN(ROp, short  , int    )             \
     INSTANTIATE_SCAN(ROp, ushort , uint   )
 
-#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
-    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
-
-#define INSTANTIATE_SCAN_OP(ROp)            \
-    INSTANTIATE_SCAN_ALL(ROp)               \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)   \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint)  \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl)  \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
-
-    //accum
     INSTANTIATE_SCAN(af_notzero_t, char, uint)
-    INSTANTIATE_SCAN_OP(af_add_t)
-    INSTANTIATE_SCAN_OP(af_mul_t)
-    INSTANTIATE_SCAN_OP(af_min_t)
-    INSTANTIATE_SCAN_OP(af_max_t)
+    INSTANTIATE_SCAN_ALL(af_add_t)
+    INSTANTIATE_SCAN_ALL(af_mul_t)
+    INSTANTIATE_SCAN_ALL(af_min_t)
+    INSTANTIATE_SCAN_ALL(af_max_t)
 }
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
new file mode 100644
index 0000000000..a589de1c01
--- /dev/null
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -0,0 +1,79 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <scan.hpp>
+#include <complex>
+#include <err_opencl.hpp>
+
+#include <kernel/scan_first_by_key.hpp>
+#include <kernel/scan_dim_by_key.hpp>
+
+namespace opencl
+{
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
+    {
+        Array<To> out = createEmptyArray<To>(in.dims());
+
+        try {
+            Param Out = out;
+            Param Key = key;
+            Param In  =   in;
+
+            if (inclusive_scan) {
+                if (dim == 0)
+                    kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
+                else
+                    kernel::scan_dim  <Ti, Tk, To, op, true>(Out, In, Key, dim);
+            } else {
+                if (dim == 0)
+                    kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
+                else
+                    kernel::scan_dim  <Ti, Tk, To, op, false>(Out, In, Key, dim);
+            }
+
+        } catch (cl::Error &ex) {
+
+            CL_TO_AF_ERROR(ex);
+        }
+
+        return out;
+    }
+
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
+    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_BY_KEY_OP(ROp)     \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
+
+    INSTANTIATE_SCAN_BY_KEY_OP(af_add_t)
+    INSTANTIATE_SCAN_BY_KEY_OP(af_mul_t)
+    INSTANTIATE_SCAN_BY_KEY_OP(af_min_t)
+    INSTANTIATE_SCAN_BY_KEY_OP(af_max_t)
+}

From 7225e7bf97e2dd20f5774c75365f8cb5ca514ae7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 18:09:07 -0400
Subject: [PATCH 0575/2677] Add Sparse functionality to CPU backend

* CSC not supported.
* BLAS is implemented but not working
---
 src/backend/cpu/Array.hpp         |   1 +
 src/backend/cpu/kernel/sparse.hpp |  44 +++++
 src/backend/cpu/sparse.cpp        | 309 ++++++++++++++++++++++++++++++
 src/backend/cpu/sparse.hpp        |  32 ++++
 src/backend/cpu/sparse_blas.cpp   | 223 +++++++++++++++++++++
 src/backend/cpu/sparse_blas.hpp   |  22 +++
 6 files changed, 631 insertions(+)
 create mode 100644 src/backend/cpu/kernel/sparse.hpp
 create mode 100644 src/backend/cpu/sparse.cpp
 create mode 100644 src/backend/cpu/sparse.hpp
 create mode 100644 src/backend/cpu/sparse_blas.cpp
 create mode 100644 src/backend/cpu/sparse_blas.hpp

diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 091ef540a0..8d05bb5b55 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -158,6 +158,7 @@ namespace cpu
         INFO_IS_FUNC(isInteger);
         INFO_IS_FUNC(isBool);
         INFO_IS_FUNC(isLinear);
+        INFO_IS_FUNC(isSparse);
 
 #undef INFO_IS_FUNC
 
diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
new file mode 100644
index 0000000000..6d0268ce4c
--- /dev/null
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void coo2dense(Array<T> output,
+               Array<T> const values, Array<int> const rowIdx, Array<int> const colIdx)
+{
+    T   const * const vPtr = values.get();
+    int const * const rPtr = rowIdx.get();
+    int const * const cPtr = colIdx.get();
+
+    T * outPtr = output.get();
+
+    af::dim4 ostrides = output.strides();
+
+    int nNZ = values.dims()[0];
+    for(int i = 0; i < nNZ; i++) {
+        T   v = vPtr[i];
+        int r = rPtr[i];
+        int c = cPtr[i];
+
+        int offset = r + c * ostrides[1];
+
+        outPtr[offset] = v;
+    }
+}
+
+}
+}
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
new file mode 100644
index 0000000000..154cfce37d
--- /dev/null
+++ b/src/backend/cpu/sparse.cpp
@@ -0,0 +1,309 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse.hpp>
+#include <kernel/sparse.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <complex.hpp>
+#include <err_common.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+#include <reduce.hpp>
+#include <where.hpp>
+
+namespace cpu
+{
+
+using namespace common;
+
+using std::add_const;
+using std::add_pointer;
+using std::enable_if;
+using std::is_floating_point;
+using std::remove_const;
+using std::conditional;
+using std::is_same;
+
+template<typename T, class Enable = void>
+struct blas_base {
+    using type = T;
+};
+
+template<typename T>
+struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
+    using type = typename conditional<is_same<T, cdouble>::value,
+                                      sp_cdouble, sp_cfloat>
+                                     ::type;
+};
+
+template<typename T>
+using cptr_type     =   typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T*>::type;
+template<typename T>
+using ptr_type     =    typename conditional<   is_complex<T>::value,
+                                                typename blas_base<T>::type *,
+                                                T*>::type;
+template<typename T>
+using scale_type   =    typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T *>::type;
+
+// void mkl_zdnscsr (const MKL_INT *job ,
+//                   const MKL_INT *m , const MKL_INT *n ,
+//                   MKL_Complex16 *adns , const MKL_INT *lda ,
+//                   MKL_Complex16 *acsr ,
+//                   MKL_INT *ja , MKL_INT *ia ,
+//                   MKL_INT *info );
+template<typename T>
+using dnscsr_func_def = void (*)(const int *,
+                                 const int *, const int *,
+                                 ptr_type<T>, const int *,
+                                 ptr_type<T>,
+                                 int *, int *,
+                                 int *);
+
+//void mkl_zcsrcsc (const MKL_INT *job ,
+//                  const MKL_INT *n ,
+//                  MKL_Complex16 *acsr ,
+//                  MKL_INT *ja , MKL_INT *ia ,
+//                  MKL_Complex16 *acsc ,
+//                  MKL_INT *ja1 , MKL_INT *ia1 ,
+//                  MKL_INT *info );
+template<typename T>
+using csrcsc_func_def = void (*)(const int *,
+                                 const int *,
+                                 ptr_type<T>, int *, int *,
+                                 ptr_type<T>,
+                                 int *, int *,
+                                 int *);
+
+#define SPARSE_FUNC_DEF( FUNC )                         \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
+  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
+{ return &mkl_##PREFIX##FUNC; }
+
+SPARSE_FUNC_DEF(dnscsr)
+SPARSE_FUNC(dnscsr, float,  s)
+SPARSE_FUNC(dnscsr, double, d)
+SPARSE_FUNC(dnscsr, cfloat, c)
+SPARSE_FUNC(dnscsr, cdouble,z)
+
+SPARSE_FUNC_DEF(csrcsc)
+SPARSE_FUNC(csrcsc, float,  s)
+SPARSE_FUNC(csrcsc, double, d)
+SPARSE_FUNC(csrcsc, cfloat, c)
+SPARSE_FUNC(csrcsc, cdouble,z)
+
+#undef SPARSE_FUNC
+#undef SPARSE_FUNC_DEF
+
+// Partial template specialization of sparseConvertDenseToStorage for COO
+// However, template specialization is not allowed
+template<typename T>
+SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
+{
+    in.eval();
+
+    Array<uint> nonZeroIdx_ = where<T>(in);
+    Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
+
+    dim_t nNZ = nonZeroIdx.elements();
+
+    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
+    constNNZ.eval();
+
+    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
+}
+
+template<typename T, af_sparse_storage storage>
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
+{
+    in_.eval();
+
+    // MKL only has dns->csr.
+    // CSR <-> CSC is only supported if input is square
+    uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
+
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_SPARSE_CSR);
+    sparse_.eval();
+
+    auto func = [=] (SparseArray<T> sparse, const Array<T> in) {
+        // Read: https://software.intel.com/en-us/node/520848
+        // But job description is incorrect with regards to job[1]
+        // 0 implies row major and 1 implies column major
+        int j1 = 1, j2 = 0;
+        const int job[] = {0, j1, j2, 2, (int)sparse.elements(), 1};
+
+        const int M = in.dims()[0];
+        const int N = in.dims()[1];
+
+        int ldd = in.strides()[1];
+
+        int info = 0;
+
+        // Have to mess up all const correctness because MKL dnscsr function
+        // is bidirectional and has input/output on all pointers
+        Array<T  > &values = sparse.getValues();
+        Array<int> &rowIdx = sparse.getRowIdx();
+        Array<int> &colIdx = sparse.getColIdx();
+
+        dnscsr_func<T>()(
+                job, &M, &N,
+                reinterpret_cast<ptr_type<T>>(const_cast<T*>(in.get())), &ldd,
+                reinterpret_cast<ptr_type<T>>(values.get()),
+                colIdx.get(),
+                rowIdx.get(),
+                &info);
+    };
+
+    getQueue().enqueue(func, sparse_, in_);
+
+    if(storage == AF_SPARSE_CSR)
+        return sparse_;
+    else
+        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
+    return sparse_;
+}
+
+
+// Partial template specialization of sparseConvertStorageToDense for COO
+// However, template specialization is not allowed
+template<typename T>
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
+{
+    in.eval();
+
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    dense.eval();
+
+    const Array<T>   values = in.getValues();
+    const Array<int> rowIdx = in.getRowIdx();
+    const Array<int> colIdx = in.getColIdx();
+
+    getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
+
+    return dense;
+}
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
+{
+    // MKL only has dns<->csr.
+    // CSR <-> CSC is only supported if input is square
+
+    if(storage == AF_SPARSE_CSC)
+        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
+    in_.eval();
+
+    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
+    dense_.eval();
+
+    auto func = [=] (Array<T> dense, const SparseArray<T> in) {
+        // Read: https://software.intel.com/en-us/node/520848
+        // But job description is incorrect with regards to job[1]
+        // 0 implies row major and 1 implies column major
+        int j1 = 1, j2 = 0;
+        const int job[] = {1, j1, j2, 2, (int)dense.elements(), 1};
+
+        const int M = dense.dims()[0];
+        const int N = dense.dims()[1];
+
+        int ldd = dense.strides()[1];
+
+        int info = 0;
+
+        Array<T  > values = in.getValues();
+        Array<int> rowIdx = in.getRowIdx();
+        Array<int> colIdx = in.getColIdx();
+
+        // Have to mess up all const correctness because MKL dnscsr function
+        // is bidirectional and has input/output on all pointers
+        dnscsr_func<T>()(
+                job, &M, &N,
+                reinterpret_cast<ptr_type<T>>(dense.get()), &ldd,
+                reinterpret_cast<ptr_type<T>>(const_cast<T*>(values.get())),
+                const_cast<int*>(colIdx.get()),
+                const_cast<int*>(rowIdx.get()),
+                &info);
+    };
+
+    getQueue().enqueue(func, dense_, in_);
+
+    if(storage == AF_SPARSE_CSR)
+        return dense_;
+    else
+        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
+    return dense_;
+}
+
+template<typename T, af_sparse_storage src, af_sparse_storage dest>
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
+{
+    in.eval();
+
+    // Dummy function
+    // TODO finish this function when support is required
+    SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+
+    return dense;
+}
+
+
+#define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_COO>(const SparseArray<T> &in);   \
+
+#define INSTANTIATE_COO_SPECIAL(T)                                                                      \
+    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_COO>(const Array<T> &in)         \
+    { return sparseConvertDenseToCOO<T>(in); }                                                          \
+    template<> Array<T> sparseConvertStorageToDense<T, AF_SPARSE_COO>(const SparseArray<T> &in)         \
+    { return sparseConvertCOOToDense<T>(in); }                                                          \
+
+#define INSTANTIATE_SPARSE(T)                                                                           \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(const Array<T> &in);          \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(const Array<T> &in);          \
+                                                                                                        \
+    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSR>(const SparseArray<T> &in);          \
+    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSC>(const SparseArray<T> &in);          \
+                                                                                                        \
+    INSTANTIATE_COO_SPECIAL(T)                                                                          \
+                                                                                                        \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSR)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSC)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_COO)                                                            \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+#undef INSTANTIATE_TO_STORAGE
+#undef INSTANTIATE_COO_SPECIAL
+#undef INSTANTIATE_SPARSE
+
+}
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
new file mode 100644
index 0000000000..4ee8fd5962
--- /dev/null
+++ b/src/backend/cpu/sparse.hpp
@@ -0,0 +1,32 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+#include <mkl_spblas.h>
+
+namespace cpu
+{
+
+#ifdef USE_MKL
+typedef char sp_op_t;
+typedef MKL_Complex8  sp_cfloat;
+typedef MKL_Complex16 sp_cdouble;
+#endif
+
+template<typename T, af_sparse_storage storage>
+common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
+
+template<typename T, af_sparse_storage src, af_sparse_storage dest>
+common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
+
+}
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
new file mode 100644
index 0000000000..9ba9683434
--- /dev/null
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -0,0 +1,223 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse_blas.hpp>
+#include <mkl_spblas.h>
+
+#include <stdexcept>
+#include <string>
+#include <cassert>
+
+#include <af/dim4.hpp>
+#include <complex.hpp>
+#include <handle.hpp>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+
+namespace cpu
+{
+
+using namespace common;
+
+using std::add_const;
+using std::add_pointer;
+using std::enable_if;
+using std::is_floating_point;
+using std::remove_const;
+using std::conditional;
+using std::is_same;
+
+template<typename T, class Enable = void>
+struct blas_base {
+    using type = T;
+};
+
+template<typename T>
+struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
+    using type = typename conditional<is_same<T, cdouble>::value,
+                                      sp_cdouble, sp_cfloat>
+                                     ::type;
+};
+
+template<typename T>
+using cptr_type     =   typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T*>::type;
+template<typename T>
+using ptr_type     =    typename conditional<   is_complex<T>::value,
+                                                typename blas_base<T>::type *,
+                                                T*>::type;
+template<typename T>
+using scale_type   =    typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T *>::type;
+// MKL
+// void mkl_zcsrmm (const char *transa ,
+//                  const MKL_INT *m , const MKL_INT *n , const MKL_INT *k ,
+//                  const MKL_Complex16 *alpha , const char *matdescra ,
+//                  const MKL_Complex16 *val , const MKL_INT *indx ,
+//                  const MKL_INT *pntrb , const MKL_INT *pntre ,
+//                  const MKL_Complex16 *b , const MKL_INT *ldb ,
+//                  const MKL_Complex16 *beta ,
+//                  MKL_Complex16 *c , const MKL_INT *ldc );
+//
+// void mkl_zcsrmv (const char *transa ,
+//                  const MKL_INT *m , const MKL_INT *k ,
+//                  const MKL_Complex16 *alpha , const char *matdescra ,
+//                  const MKL_Complex16 *val , const MKL_INT *indx ,
+//                  const MKL_INT *pntrb , const MKL_INT *pntre ,
+//                  const MKL_Complex16 *x ,
+//                  const MKL_Complex16 *beta ,
+//                  MKL_Complex16 *y );
+//
+
+template<typename T>
+using csrmm_func_def = void (*)( const sp_op_t *,
+                                 const int *, const int *, const int *,
+                                 const scale_type<T>, const sp_op_t *,
+                                 cptr_type<T>, const int *,
+                                 const int *, const int *,
+                                 cptr_type<T>, const int *,
+                                 scale_type<T>,
+                                 ptr_type<T>, const int *);
+
+template<typename T>
+using csrmv_func_def = void (*)( const sp_op_t *,
+                                 const int *, const int *,
+                                 const scale_type<T>, const sp_op_t *,
+                                 cptr_type<T>, const int *,
+                                 const int *, const int *,
+                                 cptr_type<T>,
+                                 scale_type<T>,
+                                 ptr_type<T>);
+
+#define SPARSE_FUNC_DEF( FUNC )                         \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
+  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
+{ return &mkl_##PREFIX##FUNC; }
+
+SPARSE_FUNC_DEF( csrmm )
+SPARSE_FUNC(csrmm , float   , s)
+SPARSE_FUNC(csrmm , double  , d)
+SPARSE_FUNC(csrmm , cfloat  , c)
+SPARSE_FUNC(csrmm , cdouble , z)
+
+SPARSE_FUNC_DEF( csrmv )
+SPARSE_FUNC(csrmv , float   , s)
+SPARSE_FUNC(csrmv , double  , d)
+SPARSE_FUNC(csrmv , cfloat  , c)
+SPARSE_FUNC(csrmv , cdouble , z)
+
+template<typename T, int value>
+scale_type<T> getScale()
+{
+    static T val(value);
+    return (const typename blas_base<T>::type*)&val;
+}
+
+sp_op_t
+toSparseTranspose(af_mat_prop opt)
+{
+    sp_op_t out = 'N';
+    switch(opt) {
+        case AF_MAT_NONE        : out = 'N';    break;
+        case AF_MAT_TRANS       : out = 'T';    break;
+        case AF_MAT_CTRANS      : out = 'C';    break;
+        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+    return out;
+}
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    // MKL: CSRMM Does not support optRhs
+
+    lhs.eval();
+    rhs.eval();
+
+    // Similar Operations to GEMM
+    sp_op_t lOpts = toSparseTranspose(optLhs);
+
+    int lRowDim = (lOpts == 'N') ? 0 : 1;
+    int lColDim = (lOpts == 'N') ? 1 : 0;
+    static const int rColDim = 1; //Unsupported : (rOpts == 'N;) ? 1 : 0;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhs.dims();
+    int M = lDims[lRowDim];
+    int N = rDims[rColDim];
+    int K = lDims[lColDim];
+
+    Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
+    out.eval();
+
+    auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
+        // Mat Descr
+        // When 0 is 'G', 1, 2 are ignored
+        // 4 and 5 are unused
+        static const sp_op_t descra[] = {'G', '0', '0', 'C', '0', '0'};
+
+        auto alpha = getScale<T, 1>();
+        auto beta  = getScale<T, 0>();
+
+        int ldb = rhs.strides()[1];
+        int ldc = out.strides()[1];
+
+        Array<T  > values = lhs.getValues();
+        Array<int> rowIdx = lhs.getRowIdx();
+        Array<int> colIdx = lhs.getColIdx();
+
+        const int *pB = rowIdx.get();
+        const int *pE = rowIdx.get() + 1;
+
+        if(rDims[rColDim] == 1) {
+            csrmv_func<T>()(
+                &lOpts, &M, &K,
+                reinterpret_cast<scale_type<T>>(&alpha), descra,
+                reinterpret_cast<cptr_type<T>>(values.get()),
+                reinterpret_cast<const int*>(colIdx.get()),
+                pB, pE,
+                reinterpret_cast<cptr_type<T>>(rhs.get()),
+                reinterpret_cast<scale_type<T>>(&beta),
+                reinterpret_cast<ptr_type<T>>(const_cast<T*>(out.get())));
+        } else {
+            csrmm_func<T>()(
+                &lOpts, &M, &N, &K,
+                reinterpret_cast<scale_type<T>>(&alpha), descra,
+                reinterpret_cast<cptr_type<T>>(values.get()),
+                reinterpret_cast<const int*>(colIdx.get()),
+                pB, pE,
+                reinterpret_cast<cptr_type<T>>(rhs.get()), &ldb,
+                reinterpret_cast<scale_type<T>>(&beta),
+                reinterpret_cast<ptr_type<T>>(const_cast<T*>(out.get())), &ldc);
+        }
+    };
+
+    getQueue().enqueue(func, out, lhs, rhs);
+
+    return out;
+}
+
+#define INSTANTIATE_SPARSE(T)                                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
+                                af_mat_prop optLhs, af_mat_prop optRhs);                \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+}
diff --git a/src/backend/cpu/sparse_blas.hpp b/src/backend/cpu/sparse_blas.hpp
new file mode 100644
index 0000000000..3b544dabaa
--- /dev/null
+++ b/src/backend/cpu/sparse_blas.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+#include <sparse.hpp>
+
+namespace cpu
+{
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs);
+
+}
+

From 992b720f3180aa857e5feccea7f5d6be81bf3430 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 18:25:17 -0400
Subject: [PATCH 0576/2677] Added sparse placeholders for OpenCL

---
 src/backend/opencl/Array.hpp       |   1 +
 src/backend/opencl/sparse.cpp      | 142 +++++++++++++++++++++++++++++
 src/backend/opencl/sparse.hpp      |  25 +++++
 src/backend/opencl/sparse_blas.cpp |  46 ++++++++++
 src/backend/opencl/sparse_blas.hpp |  22 +++++
 5 files changed, 236 insertions(+)
 create mode 100644 src/backend/opencl/sparse.cpp
 create mode 100644 src/backend/opencl/sparse.hpp
 create mode 100644 src/backend/opencl/sparse_blas.cpp
 create mode 100644 src/backend/opencl/sparse_blas.hpp

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index feb3e2e0fa..d18d8fc754 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -151,6 +151,7 @@ namespace opencl
         INFO_IS_FUNC(isInteger);
         INFO_IS_FUNC(isBool);
         INFO_IS_FUNC(isLinear);
+        INFO_IS_FUNC(isSparse);
 
 #undef INFO_IS_FUNC
         ~Array();
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
new file mode 100644
index 0000000000..84dbb3d8e0
--- /dev/null
+++ b/src/backend/opencl/sparse.cpp
@@ -0,0 +1,142 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <complex.hpp>
+#include <err_common.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <reduce.hpp>
+#include <where.hpp>
+
+namespace opencl
+{
+
+using namespace common;
+
+// Partial template specialization of sparseConvertDenseToStorage for COO
+// However, template specialization is not allowed
+template<typename T>
+SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
+{
+    in.eval();
+
+    Array<uint> nonZeroIdx_ = where<T>(in);
+    Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
+
+    dim_t nNZ = nonZeroIdx.elements();
+
+    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
+    constNNZ.eval();
+
+    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
+}
+
+template<typename T, af_sparse_storage storage>
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
+{
+    in_.eval();
+
+    uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
+
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_SPARSE_CSR);
+    sparse_.eval();
+
+    return sparse_;
+}
+
+
+// Partial template specialization of sparseConvertStorageToDense for COO
+// However, template specialization is not allowed
+template<typename T>
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
+{
+    in.eval();
+
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    dense.eval();
+
+    const Array<T>   values = in.getValues();
+    const Array<int> rowIdx = in.getRowIdx();
+    const Array<int> colIdx = in.getColIdx();
+
+    return dense;
+}
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
+{
+    in_.eval();
+
+    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
+    dense_.eval();
+
+    return dense_;
+}
+
+template<typename T, af_sparse_storage src, af_sparse_storage dest>
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
+{
+    in.eval();
+
+    // Dummy function
+    // TODO finish this function when support is required
+    SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    dense.eval();
+
+    return dense;
+}
+
+
+#define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_COO>(const SparseArray<T> &in);   \
+
+#define INSTANTIATE_COO_SPECIAL(T)                                                                      \
+    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_COO>(const Array<T> &in)         \
+    { return sparseConvertDenseToCOO<T>(in); }                                                          \
+    template<> Array<T> sparseConvertStorageToDense<T, AF_SPARSE_COO>(const SparseArray<T> &in)         \
+    { return sparseConvertCOOToDense<T>(in); }                                                          \
+
+#define INSTANTIATE_SPARSE(T)                                                                           \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(const Array<T> &in);          \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(const Array<T> &in);          \
+                                                                                                        \
+    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSR>(const SparseArray<T> &in);          \
+    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSC>(const SparseArray<T> &in);          \
+                                                                                                        \
+    INSTANTIATE_COO_SPECIAL(T)                                                                          \
+                                                                                                        \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSR)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSC)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_COO)                                                            \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+#undef INSTANTIATE_TO_STORAGE
+#undef INSTANTIATE_COO_SPECIAL
+#undef INSTANTIATE_SPARSE
+
+}
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
new file mode 100644
index 0000000000..8dc98fdf13
--- /dev/null
+++ b/src/backend/opencl/sparse.hpp
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+
+namespace opencl
+{
+
+template<typename T, af_sparse_storage storage>
+common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
+
+template<typename T, af_sparse_storage src, af_sparse_storage dest>
+common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
+
+}
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
new file mode 100644
index 0000000000..70523b5e87
--- /dev/null
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -0,0 +1,46 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse_blas.hpp>
+
+#include <stdexcept>
+#include <string>
+#include <cassert>
+
+#include <af/dim4.hpp>
+#include <complex.hpp>
+#include <handle.hpp>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+
+namespace opencl
+{
+
+using namespace common;
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    Array<T> out = createValueArray<T>(af::dim4(lhs.dims()[0], rhs.dims()[0], 1, 1), scalar<T>(0));
+    return out;
+}
+
+#define INSTANTIATE_SPARSE(T)                                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
+                                af_mat_prop optLhs, af_mat_prop optRhs);                \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+}
diff --git a/src/backend/opencl/sparse_blas.hpp b/src/backend/opencl/sparse_blas.hpp
new file mode 100644
index 0000000000..b78616fb11
--- /dev/null
+++ b/src/backend/opencl/sparse_blas.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+#include <sparse.hpp>
+
+namespace opencl
+{
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs);
+
+}
+

From 08881d90e33a78df5ad3ceffe5bb94a619f00408 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 19:10:04 -0400
Subject: [PATCH 0577/2677] Added sparse API for unified

---
 src/api/unified/array.cpp  |  1 +
 src/api/unified/sparse.cpp | 94 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 95 insertions(+)
 create mode 100644 src/api/unified/sparse.cpp

diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index 809c9d4e6b..37caceb3fb 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -115,3 +115,4 @@ ARRAY_HAPI_DEF(af_is_realfloating)
 ARRAY_HAPI_DEF(af_is_floating)
 ARRAY_HAPI_DEF(af_is_integer)
 ARRAY_HAPI_DEF(af_is_bool)
+ARRAY_HAPI_DEF(af_is_sparse)
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
new file mode 100644
index 0000000000..a7bd2708d4
--- /dev/null
+++ b/src/api/unified/sparse.cpp
@@ -0,0 +1,94 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/sparse.h>
+#include "symbol_manager.hpp"
+
+af_err af_create_sparse_array(
+       af_array *out,
+       const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+       const af_array values, const af_array rowIdx, const af_array colIdx,
+       const af_sparse_storage storage)
+{
+    CHECK_ARRAYS(values, rowIdx, colIdx); return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, storage);
+}
+
+af_err af_create_sparse_array_from_ptr(
+       af_array *out,
+       const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+       const void * const values,
+       const int * const rowIdx, const int * const colIdx,
+       const af_dtype type, const af_sparse_storage storage,
+       const af_source source)
+{
+    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, type, storage, source);
+}
+
+af_err af_create_sparse_array_from_dense(
+       af_array *out, const af_array in,
+       const af_sparse_storage storage)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, storage);
+}
+
+af_err af_sparse_convert_storage(af_array *out, const af_array in,
+                                 const af_sparse_storage destStorage)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, destStorage);
+}
+
+af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(values, rows, cols, in);
+}
+
+af_err af_sparse_get_values(af_array *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
+af_err af_sparse_get_rows(af_array *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
+af_err af_sparse_get_cols(af_array *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
+af_err af_sparse_get_num_values(dim_t *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
+af_err af_sparse_get_num_rows(dim_t *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
+af_err af_sparse_get_num_cols(dim_t *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
+af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}

From 5858961dadc0eb5cd66791319ca5e61706124548 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 1 Jun 2016 22:35:24 -0400
Subject: [PATCH 0578/2677] Fix getInfo assert arguments for device/blas

---
 src/api/c/blas.cpp   | 4 ++--
 src/api/c/device.cpp | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index d2c116ed2f..be4946941b 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -103,8 +103,8 @@ af_err af_matmul(af_array *out,
     using namespace detail;
 
     try {
-        ArrayInfo lhsInfo = getInfo(lhs, false, false);
-        ArrayInfo rhsInfo = getInfo(rhs, true, false);
+        ArrayInfo lhsInfo = getInfo(lhs, false, true);
+        ArrayInfo rhsInfo = getInfo(rhs, true, true);
 
         if(lhsInfo.isSparse())
             return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index b93907d55e..765719c70e 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -48,7 +48,7 @@ af_err af_get_backend_id(af_backend *result, const af_array in)
 {
     try {
         ARG_ASSERT(1, in != 0);
-        ArrayInfo info = getInfo(in, false);
+        ArrayInfo info = getInfo(in, false, false);
         *result = info.getBackendId();
     } CATCHALL;
     return AF_SUCCESS;
@@ -58,7 +58,7 @@ af_err af_get_device_id(int *device, const af_array in)
 {
     try {
         ARG_ASSERT(1, in != 0);
-        ArrayInfo info = getInfo(in, false);
+        ArrayInfo info = getInfo(in, false, false);
         *device = info.getDevId();
     } CATCHALL;
     return AF_SUCCESS;

From 7292ec9baf067af337bccb94faf20a11bf9aacc8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Jun 2016 11:24:11 +0530
Subject: [PATCH 0579/2677] Add missing cl2hpp dependency in sort_by_key
 project

---
 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 0ea404baf9..3ee39eaaae 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -11,7 +11,7 @@ FOREACH(SBK_TYPE ${SBK_TYPES})
     ADD_LIBRARY(opencl_sort_by_key_${SBK_TYPE} OBJECT
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
-    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets})
+    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets} cl2hpp)
     IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
         ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} forge)
     ENDIF()

From 2548896f4aab4a6c7cd5450061b2db47c8f20ea5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Jun 2016 11:24:37 +0530
Subject: [PATCH 0580/2677] Update install location for cl2hpp external project

---
 CMakeModules/build_cl2hpp.cmake | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 269d663e4d..0e91ed94e8 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -1,14 +1,13 @@
 INCLUDE(ExternalProject)
 
-SET(prefix ${CMAKE_BINARY_DIR}/third_party/cl2hpp/src/cl2hpp-ext-build)
+SET(prefix ${CMAKE_BINARY_DIR}/third_party/cl2hpp)
 
 ExternalProject_Add(
     cl2hpp-ext
-    GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
-    GIT_TAG 2b415bf8fc6ab035b6de6a14f3c579f91199fa2a
+    GIT_REPOSITORY https://github.com/9prady9/OpenCL-CLHPP.git
+    GIT_TAG install_targets
     PREFIX "${prefix}"
-    INSTALL_COMMAND ""
-    INSTALL_DIR "${prefix}"
+    INSTALL_DIR "${prefix}/package"
     UPDATE_COMMAND ""
     CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
     -DCMAKE_SOURCE_DIR:PATH=<SOURCE_DIR>
@@ -17,12 +16,14 @@ ExternalProject_Add(
     -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
     -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
     -DBUILD_DOCS:BOOL=OFF
-    -DBUILD_EXAMPLES:BOOL=ON
+    -DBUILD_EXAMPLES:BOOL=OFF
     -DBUILD_TESTS:BOOL=OFF
     )
 
-ADD_CUSTOM_TARGET(cl2hpp DEPENDS "${prefix}/include/CL/cl2.hpp")
+ExternalProject_Get_Property(cl2hpp-ext install_dir)
+
+ADD_CUSTOM_TARGET(cl2hpp DEPENDS "${prefix}/package/CL/cl2.hpp")
 
 ADD_DEPENDENCIES(cl2hpp cl2hpp-ext)
 
-SET(CL2HPP_INCLUDE_DIRECTORY ${prefix}/include)
+SET(CL2HPP_INCLUDE_DIRECTORY ${install_dir})

From 745e47cabfdc8519ce1e5ed3c4b79baa6e6accd3 Mon Sep 17 00:00:00 2001
From: Filip Matzner <floop@floop.cz>
Date: Thu, 2 Jun 2016 16:12:16 +0200
Subject: [PATCH 0581/2677] CPU copy kernel improvements.

---
 src/backend/cpu/copy.cpp        |  9 ++--
 src/backend/cpu/kernel/copy.hpp | 91 ++++++++++++++++++++++++++++++++-
 2 files changed, 95 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 93088aefd7..9220f33237 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -43,7 +43,7 @@ Array<T> copyArray(const Array<T> &A)
 {
     A.eval();
     Array<T> out = createEmptyArray<T>(A.dims());
-    getQueue().enqueue(kernel::copy<T, T>, out, A, scalar<T>(0), 1.0);
+    getQueue().enqueue(kernel::copy<T, T>, out, A);
     return out;
 }
 
@@ -51,7 +51,7 @@ template<typename T>
 void multiply_inplace(Array<T> &in, double val)
 {
     in.eval();
-    getQueue().enqueue(kernel::copy<T, T>, in, in, 0, val);
+    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, 0, val);
 }
 
 template<typename inType, typename outType>
@@ -61,7 +61,8 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
     Array<outType> ret = createValueArray<outType>(dims, default_value);
     ret.eval();
     in.eval();
-    getQueue().enqueue(kernel::copy<outType, inType>, ret, in, outType(default_value), factor);
+    getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in, outType(default_value), factor);
+
     return ret;
 }
 
@@ -70,7 +71,7 @@ void copyArray(Array<outType> &out, Array<inType> const &in)
 {
     out.eval();
     in.eval();
-    getQueue().enqueue(kernel::copy<outType, inType>, out, in, scalar<outType>(0), 1.0);
+    getQueue().enqueue(kernel::copy<outType, inType>, out, in);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index fad122cdce..e9f3424ecb 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Array.hpp>
+#include <math.hpp>
 
 namespace cpu
 {
@@ -38,7 +39,7 @@ void stridedCopy(T* dst, af::dim4 const & ostrides, T const * src,
 }
 
 template<typename OutT, typename InT>
-void copy(Array<OutT> dst, Array<InT> const src, OutT default_value, double factor)
+void copyElemwise(Array<OutT> dst, Array<InT> const src, OutT default_value, double factor)
 {
     af::dim4 src_dims       = src.dims();
     af::dim4 dst_dims       = dst.dims();
@@ -85,5 +86,93 @@ void copy(Array<OutT> dst, Array<InT> const src, OutT default_value, double fact
     }
 }
 
+template<typename OutT, typename InT>
+struct CopyImpl
+{
+    static void copy(Array<OutT> dst, Array<InT> const src)
+    {
+        copyElemwise(dst, src, scalar<OutT>(0), 1.0);
+    }
+};
+
+template<typename T>
+struct CopyImpl<T, T>
+{
+    static void copy(Array<T> dst, Array<T> const src)
+    {
+        af::dim4 src_dims       = src.dims();
+        af::dim4 dst_dims       = dst.dims();
+        af::dim4 src_strides    = src.strides();
+        af::dim4 dst_strides    = dst.strides();
+
+        T const * const src_ptr = src.get();
+        T * dst_ptr = dst.get();
+
+        // find the major-most dimension, which is linear in both arrays
+        int linear_end = 0;
+        dim_t count = 1;
+        while (linear_end < 4
+                && count == src_strides[linear_end]
+                && count == dst_strides[linear_end]) {
+            ++linear_end;
+            count *= src_dims[linear_end];
+        }
+
+        // traverse through the array using strides only until neccessary
+        if (linear_end == 4) {
+            std::memcpy(dst_ptr, src_ptr, sizeof(T) * src.elements());
+
+        } else {
+            for(dim_t l=0; l<dst_dims[3]; ++l) {
+                dim_t src_loff = l*src_strides[3];
+                dim_t dst_loff = l*dst_strides[3];
+
+                if (linear_end == 3) {
+                    dim_t dst_idx = dst_loff;
+                    dim_t src_idx = src_loff;
+                    std::memcpy(dst_ptr + dst_idx, src_ptr + src_idx, sizeof(T) * src_strides[3]);
+
+                } else {
+                    for(dim_t k=0; k<dst_dims[2]; ++k) {
+                        dim_t src_koff = k*src_strides[2];
+                        dim_t dst_koff = k*dst_strides[2];
+
+                        if (linear_end == 2) {
+                            dim_t dst_idx = dst_koff + dst_loff;
+                            dim_t src_idx = src_koff + src_loff;
+                            std::memcpy(dst_ptr + dst_idx, src_ptr + src_idx, sizeof(T) * src_strides[2]);
+
+                        } else {
+                            for(dim_t j=0; j<dst_dims[1]; ++j) {
+                                dim_t src_joff = j*src_strides[1];
+                                dim_t dst_joff = j*dst_strides[1];
+
+                                if (linear_end == 1) {
+                                    dim_t dst_idx = dst_joff + dst_koff + dst_loff;
+                                    dim_t src_idx = src_joff + src_koff + src_loff;
+                                    std::memcpy(dst_ptr + dst_idx, src_ptr + src_idx, sizeof(T) * src_strides[1]);
+
+                                } else {
+                                    for(dim_t i=0; i<dst_dims[0]; ++i) {
+                                        dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
+                                        dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
+                                        dst_ptr[dst_idx] = src_ptr[src_idx];
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+};
+
+template<typename OutT, typename InT>
+void copy(Array<OutT> dst, Array<InT> const src)
+{
+    CopyImpl<OutT, InT>::copy(dst, src);
+}
+
 }
 }

From 9589dd4f2f71fedac6af358f14c114385a192a85 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Jun 2016 22:02:46 +0530
Subject: [PATCH 0582/2677] BUGFIX: Removed null terminator pop back in
 getPlatformName function

Switch from cl.hpp to cl2.hpp has made this removal obsolete.
---
 src/backend/opencl/platform.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 464c99dc5a..cee53ad38c 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -431,8 +431,13 @@ std::string getPlatformName(const cl::Device &device)
 {
     const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
     std::string platStr = platform.getInfo<CL_PLATFORM_NAME>();
+
+    // BELOW NULL TERMINATION character removal was required with
+    // cl.hpp header, however with cl2.hpp this is not needed anymore.
+    //
     // Remove null termination character from the strings
-    platStr.pop_back();
+    //platStr.pop_back();
+
     return platformMap(platStr);
 }
 

From 728c133e11bf8dc5bb72b7a18800f5408f020619 Mon Sep 17 00:00:00 2001
From: Filip Matzner <floop@floop.cz>
Date: Thu, 2 Jun 2016 19:44:04 +0200
Subject: [PATCH 0583/2677] Simplify code using recursion instead of nested
 loops.

---
 src/backend/cpu/kernel/copy.hpp | 72 +++++++++++++--------------------
 1 file changed, 27 insertions(+), 45 deletions(-)

diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index e9f3424ecb..f556f0a10d 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -105,8 +105,8 @@ struct CopyImpl<T, T>
         af::dim4 src_strides    = src.strides();
         af::dim4 dst_strides    = dst.strides();
 
-        T const * const src_ptr = src.get();
-        T * dst_ptr = dst.get();
+        T const * src_ptr = src.get();
+        T * dst_ptr       = dst.get();
 
         // find the major-most dimension, which is linear in both arrays
         int linear_end = 0;
@@ -119,51 +119,33 @@ struct CopyImpl<T, T>
         }
 
         // traverse through the array using strides only until neccessary
-        if (linear_end == 4) {
+        if (linear_end == 4)
             std::memcpy(dst_ptr, src_ptr, sizeof(T) * src.elements());
+        else
+            copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, 3, linear_end);
+    }
 
-        } else {
-            for(dim_t l=0; l<dst_dims[3]; ++l) {
-                dim_t src_loff = l*src_strides[3];
-                dim_t dst_loff = l*dst_strides[3];
-
-                if (linear_end == 3) {
-                    dim_t dst_idx = dst_loff;
-                    dim_t src_idx = src_loff;
-                    std::memcpy(dst_ptr + dst_idx, src_ptr + src_idx, sizeof(T) * src_strides[3]);
-
-                } else {
-                    for(dim_t k=0; k<dst_dims[2]; ++k) {
-                        dim_t src_koff = k*src_strides[2];
-                        dim_t dst_koff = k*dst_strides[2];
-
-                        if (linear_end == 2) {
-                            dim_t dst_idx = dst_koff + dst_loff;
-                            dim_t src_idx = src_koff + src_loff;
-                            std::memcpy(dst_ptr + dst_idx, src_ptr + src_idx, sizeof(T) * src_strides[2]);
-
-                        } else {
-                            for(dim_t j=0; j<dst_dims[1]; ++j) {
-                                dim_t src_joff = j*src_strides[1];
-                                dim_t dst_joff = j*dst_strides[1];
-
-                                if (linear_end == 1) {
-                                    dim_t dst_idx = dst_joff + dst_koff + dst_loff;
-                                    dim_t src_idx = src_joff + src_koff + src_loff;
-                                    std::memcpy(dst_ptr + dst_idx, src_ptr + src_idx, sizeof(T) * src_strides[1]);
-
-                                } else {
-                                    for(dim_t i=0; i<dst_dims[0]; ++i) {
-                                        dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
-                                        dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
-                                        dst_ptr[dst_idx] = src_ptr[src_idx];
-                                    }
-                                }
-                            }
-                        }
-                    }
-                }
-            }
+    static void copy_go(
+            T * dst_ptr,       const af::dim4 & dst_strides, const af::dim4 & dst_dims,
+            T const * src_ptr, const af::dim4 & src_strides, const af::dim4 & src_dims,
+            int dim, int linear_end)
+    {
+        for(dim_t i=0; i<dst_dims[dim]; ++i) {
+
+            // 0th dimension is recursion bottom - copy element by element
+            if (dim == 0)
+                *dst_ptr = *src_ptr;
+
+            // if we are in a higher dimension, copy the entire stride if possible
+            else if (linear_end == dim)
+                std::memcpy(dst_ptr, src_ptr, sizeof(T) * src_strides[dim]);
+
+            // otherwise recurse to a lower dimenstion
+            else
+                copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, dim - 1, linear_end);
+
+            dst_ptr += dst_strides[dim];
+            src_ptr += src_strides[dim];
         }
     }
 };

From 4f74ff27b757c072076d5e11a4493751cf2de7ad Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 2 Jun 2016 14:31:09 -0400
Subject: [PATCH 0584/2677] CPU Backend for scan by key

---
 src/backend/cpu/kernel/scan_by_key.hpp |  90 +++++++++++++++++++++
 src/backend/cpu/scan.cpp               |  40 +---------
 src/backend/cpu/scan.hpp               |   3 -
 src/backend/cpu/scan_by_key.cpp        | 103 +++++++++++++++++++++++++
 src/backend/cpu/scan_by_key.hpp        |  17 ++++
 5 files changed, 214 insertions(+), 39 deletions(-)
 create mode 100644 src/backend/cpu/kernel/scan_by_key.hpp
 create mode 100644 src/backend/cpu/scan_by_key.cpp
 create mode 100644 src/backend/cpu/scan_by_key.hpp

diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
new file mode 100644
index 0000000000..80da543529
--- /dev/null
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -0,0 +1,90 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<af_op_t op, typename Ti, typename Tk, typename To, int D, bool inclusive_scan>
+struct scan_dim_by_key
+{
+    void operator()(Array<To> out, dim_t outOffset,
+                    const Array<Tk> key, dim_t keyOffset,
+                    const Array<Ti> in, dim_t inOffset,
+                    const int dim) const
+    {
+        const dim4 odims    = out.dims();
+        const dim4 ostrides = out.strides();
+        const dim4 kstrides = key.strides();
+        const dim4 istrides = in.strides();
+
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            scan_dim_by_key<op, Ti, Tk, To, D1, inclusive_scan> func;
+            getQueue().enqueue(func,
+                    out, outOffset + i * ostrides[D1],
+                    key, keyOffset + i * kstrides[D1],
+                    in, inOffset + i * istrides[D1], dim);
+            if (D1 == dim) break;
+        }
+    }
+};
+
+template<af_op_t op, typename Ti, typename Tk, typename To, bool inclusive_scan>
+struct scan_dim_by_key<op, Ti, Tk, To, 0, inclusive_scan>
+{
+    void operator()(Array<To> output, dim_t outOffset,
+                    const Array<Tk> keyinput, dim_t keyOffset,
+                    const Array<Ti> input, dim_t inOffset,
+                    const int dim) const
+    {
+        const Ti* in  = input.get()    + inOffset;
+        const Tk* key = keyinput.get() + keyOffset;
+              To* out = output.get()   + outOffset;
+
+        const dim4 ostrides = output.strides();
+        const dim4 kstrides = keyinput.strides();
+        const dim4 istrides = input.strides();
+        const dim4 idims    = input.dims();
+
+        dim_t istride = istrides[dim];
+        dim_t kstride = kstrides[dim];
+        dim_t ostride = ostrides[dim];
+
+        Transform<Ti, To, op> transform;
+        // FIXME: Change the name to something better
+        Binary<To, op> scan;
+
+        To out_val = scan.init();
+        Tk key_val = key[0];
+
+        dim_t k = !inclusive_scan;
+        if (!inclusive_scan) {
+            out[0] = scan.init();
+        }
+
+        for (dim_t i = 0; i < idims[dim] - (!inclusive_scan); i++, k++) {
+            To in_val = transform(in[i * istride]);
+            if (key[k * kstride] != key_val) {
+                out_val = !inclusive_scan? scan.init() : in_val;
+                key_val = key[k * kstride];
+            } else {
+                out_val = scan(in_val, out_val);
+            }
+            out[k * ostride] = out_val;
+        }
+    }
+};
+
+}
+}
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index f89ba54d35..4f71220273 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -71,18 +71,9 @@ namespace cpu
         return out;
     }
 
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        return scan(in, dim, inclusive_scan);
-    }
-
 #define INSTANTIATE_SCAN(ROp, Ti, To)\
     template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
 
-#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
-    template Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
-
 #define INSTANTIATE_SCAN_ALL(ROp)                       \
     INSTANTIATE_SCAN(ROp, float  , float  )             \
     INSTANTIATE_SCAN(ROp, double , double )             \
@@ -98,32 +89,9 @@ namespace cpu
     INSTANTIATE_SCAN(ROp, short  , int    )             \
     INSTANTIATE_SCAN(ROp, ushort , uint   )
 
-#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
-    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
-
-#define INSTANTIATE_SCAN_OP(ROp)            \
-    INSTANTIATE_SCAN_ALL(ROp)               \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)   \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint)  \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, long)  \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, ulong)
-
-    //accum
     INSTANTIATE_SCAN(af_notzero_t, char, uint)
-    INSTANTIATE_SCAN_OP(af_add_t)
-    INSTANTIATE_SCAN_OP(af_mul_t)
-    INSTANTIATE_SCAN_OP(af_min_t)
-    INSTANTIATE_SCAN_OP(af_max_t)
+    INSTANTIATE_SCAN_ALL(af_add_t)
+    INSTANTIATE_SCAN_ALL(af_mul_t)
+    INSTANTIATE_SCAN_ALL(af_min_t)
+    INSTANTIATE_SCAN_ALL(af_max_t)
 }
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index 7adf5ac3ac..5620e44cd8 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -14,7 +14,4 @@ namespace cpu
 {
     template<af_op_t op, typename Ti, typename To>
     Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
-
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Ti>& in, const Array<Tk>& key, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
new file mode 100644
index 0000000000..8f7d86d4b2
--- /dev/null
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -0,0 +1,103 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <complex>
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <scan_by_key.hpp>
+#include <ops.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+#include <kernel/scan_by_key.hpp>
+
+using af::dim4;
+
+namespace cpu
+{
+
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
+    {
+        dim4 dims     = in.dims();
+        Array<To> out = createEmptyArray<To>(dims);
+        in.eval();
+
+        if (inclusive_scan) {
+            switch (in.ndims()) {
+                case 1:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 1, true> func1;
+                    getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
+                    break;
+                case 2:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 2, true> func2;
+                    getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
+                    break;
+                case 3:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 3, true> func3;
+                    getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
+                    break;
+                case 4:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 4, true> func4;
+                    getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
+                    break;
+            }
+        } else {
+            switch (in.ndims()) {
+                case 1:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 1, false> func1;
+                    getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
+                    break;
+                case 2:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 2, false> func2;
+                    getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
+                    break;
+                case 3:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 3, false> func3;
+                    getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
+                    break;
+                case 4:
+                    kernel::scan_dim_by_key<op, Ti, Tk, To, 4, false> func4;
+                    getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
+                    break;
+            }
+        }
+
+        return out;
+
+    }
+
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
+    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
+    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL_OP(ROp) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
+
+    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_add_t)
+    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_mul_t)
+    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_min_t)
+    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_max_t)
+}
diff --git a/src/backend/cpu/scan_by_key.hpp b/src/backend/cpu/scan_by_key.hpp
new file mode 100644
index 0000000000..6b0cb1b5bd
--- /dev/null
+++ b/src/backend/cpu/scan_by_key.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <ops.hpp>
+
+namespace cpu
+{
+    template<af_op_t op, typename Ti, typename Tk, typename To>
+    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+}

From bc0096c324a06f11a95c9195f8f0e296fa0af49b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 2 Jun 2016 14:36:28 -0400
Subject: [PATCH 0585/2677] Add C++ API for sparse, remove unneccesary API

* Add C++ API as functions
* Remove num_rows and num_cols C API
* Rename get_rows/cols to get_row/col_idx
* Rename get_num_values to get_num_nonzero
---
 include/af/sparse.h        | 37 ++++++++++----
 src/api/c/sparse.cpp       | 28 ++---------
 src/api/cpp/sparse.cpp     | 98 ++++++++++++++++++++++++++++++++++++++
 src/api/unified/sparse.cpp | 25 +++-------
 4 files changed, 139 insertions(+), 49 deletions(-)
 create mode 100644 src/api/cpp/sparse.cpp

diff --git a/include/af/sparse.h b/include/af/sparse.h
index d26ffc65bb..8f5b40b21e 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -15,6 +15,31 @@ namespace af
 {
     class array;
 
+    AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                                  const array values, const array rowIdx, const array colIdx,
+                                  const af::sparseStorage storage = AF_SPARSE_CSR);
+
+    AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                                  const void* const values,
+                                  const int * const rowIdx, const int * const colIdx,
+                                  const dtype type = f32, const af::sparseStorage storage = AF_SPARSE_CSR,
+                                  const af::source src = afHost);
+
+    AFAPI array createSparseArray(const array dense, const af::sparseStorage storage = AF_SPARSE_CSR);
+
+    AFAPI array sparseConvertStorage(const array in, const af::sparseStorage storage);
+
+    AFAPI void sparseGetArrays(array &values, array &rowIdx, array &colIdx, const array in);
+
+    AFAPI array sparseGetValues(const array in);
+
+    AFAPI array sparseGetRowIdx(const array in);
+
+    AFAPI array sparseGetColIdx(const array in);
+
+    AFAPI dim_t sparseGetNumNonZero(const array in);
+
+    AFAPI af::sparseStorage sparseGetStorage(const array in);
 }
 #endif
 
@@ -43,19 +68,15 @@ extern "C" {
     AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
                                            const af_sparse_storage destStorage);
 
-    AFAPI af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols, const af_array in);
+    AFAPI af_err af_sparse_get_arrays(af_array *values, af_array *rowIdx, af_array *colIdx, const af_array in);
 
     AFAPI af_err af_sparse_get_values(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_rows(af_array *out, const af_array in);
-
-    AFAPI af_err af_sparse_get_cols(af_array *out, const af_array in);
-
-    AFAPI af_err af_sparse_get_num_values(dim_t *out, const af_array in);
+    AFAPI af_err af_sparse_get_row_idx(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_num_rows(dim_t *out, const af_array in);
+    AFAPI af_err af_sparse_get_col_idx(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_num_cols(dim_t *out, const af_array in);
+    AFAPI af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in);
 
     AFAPI af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in);
 
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index d61a813281..3fb8cc4982 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -342,8 +342,8 @@ af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols,
 {
     try {
         if(values != NULL) AF_CHECK(af_sparse_get_values(values, in));
-        if(rows   != NULL) AF_CHECK(af_sparse_get_rows  (rows  , in));
-        if(cols   != NULL) AF_CHECK(af_sparse_get_cols  (cols  , in));
+        if(rows   != NULL) AF_CHECK(af_sparse_get_row_idx(rows  , in));
+        if(cols   != NULL) AF_CHECK(af_sparse_get_col_idx(cols  , in));
     }
     CATCHALL;
 
@@ -370,7 +370,7 @@ af_err af_sparse_get_values(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_rows(af_array *out, const af_array in)
+af_err af_sparse_get_row_idx(af_array *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
@@ -379,7 +379,7 @@ af_err af_sparse_get_rows(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_cols(af_array *out, const af_array in)
+af_err af_sparse_get_col_idx(af_array *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
@@ -388,7 +388,7 @@ af_err af_sparse_get_cols(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_num_values(dim_t *out, const af_array in)
+af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
@@ -397,24 +397,6 @@ af_err af_sparse_get_num_values(dim_t *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_num_rows(dim_t *out, const af_array in)
-{
-    try {
-        const SparseArrayBase base = getSparseArrayBase(in);
-        *out = base.getRowIdx().elements();
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_sparse_get_num_cols(dim_t *out, const af_array in)
-{
-    try {
-        const SparseArrayBase base = getSparseArrayBase(in);
-        *out = base.getColIdx().elements();
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
 af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in)
 {
     try {
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
new file mode 100644
index 0000000000..900c66caf4
--- /dev/null
+++ b/src/api/cpp/sparse.cpp
@@ -0,0 +1,98 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/sparse.h>
+#include "error.hpp"
+
+namespace af
+{
+    array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                            const array values, const array rowIdx, const array colIdx,
+                            const af::sparseStorage storage)
+    {
+        af_array out = 0;
+        AF_THROW(af_create_sparse_array(&out, nRows, nCols, nNZ,
+                            values.get(), rowIdx.get(), colIdx.get(), storage));
+        return array(out);
+    }
+
+    array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                            const void * const values,
+                            const int * const rowIdx, const int * const colIdx,
+                            const dtype type, const af::sparseStorage storage,
+                            const af::source src)
+    {
+        af_array out = 0;
+        AF_THROW(af_create_sparse_array_from_ptr(&out, nRows, nCols, nNZ,
+                            values, rowIdx, colIdx, type, storage, src));
+        return array(out);
+    }
+
+
+    array createSparseArray(const array dense, const af::sparseStorage storage)
+    {
+        af_array out = 0;
+        AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), storage));
+        return array(out);
+    }
+
+    array sparseConvertStorage(const array in, const af::sparseStorage storage)
+    {
+        af_array out = 0;
+        AF_THROW(af_sparse_convert_storage(&out, in.get(), storage));
+        return array(out);
+    }
+
+    void sparseGetArrays(array &values, array &rowIdx, array &colIdx,
+                         const array in)
+    {
+        af_array values_ = 0, rowIdx_ = 0, colIdx_ = 0;
+        AF_THROW(af_sparse_get_arrays(&values_, &rowIdx_, &colIdx_, in.get()));
+        values = array(values_);
+        rowIdx = array(rowIdx_);
+        colIdx = array(colIdx_);
+        return;
+    }
+
+    array sparseGetValues(const array in)
+    {
+        af_array out = 0;
+        AF_THROW(af_sparse_get_values(&out, in.get()));
+        return array(out);
+    }
+
+    array sparseGetRowIdx(const array in)
+    {
+        af_array out = 0;
+        AF_THROW(af_sparse_get_row_idx(&out, in.get()));
+        return array(out);
+    }
+
+    array sparseGetColIdx(const array in)
+    {
+        af_array out = 0;
+        AF_THROW(af_sparse_get_col_idx(&out, in.get()));
+        return array(out);
+    }
+
+    dim_t sparseGetNumNonZero(const array in)
+    {
+        dim_t out = 0;
+        AF_THROW(af_sparse_get_num_nonzero(&out, in.get()));
+        return out;
+    }
+
+    af::sparseStorage sparseGetStorage(const array in)
+    {
+        af::sparseStorage out;
+        AF_THROW(af_sparse_get_storage(&out, in.get()));
+        return out;
+    }
+}
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index a7bd2708d4..bd84be294d 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -16,7 +16,8 @@ af_err af_create_sparse_array(
        const af_array values, const af_array rowIdx, const af_array colIdx,
        const af_sparse_storage storage)
 {
-    CHECK_ARRAYS(values, rowIdx, colIdx); return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, storage);
+    CHECK_ARRAYS(values, rowIdx, colIdx);
+    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, storage);
 }
 
 af_err af_create_sparse_array_from_ptr(
@@ -45,10 +46,10 @@ af_err af_sparse_convert_storage(af_array *out, const af_array in,
     return CALL(out, in, destStorage);
 }
 
-af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols, const af_array in)
+af_err af_sparse_get_arrays(af_array *values, af_array *rowIdx, af_array *colIdx, const af_array in)
 {
     CHECK_ARRAYS(in);
-    return CALL(values, rows, cols, in);
+    return CALL(values, rowIdx, colIdx, in);
 }
 
 af_err af_sparse_get_values(af_array *out, const af_array in)
@@ -57,31 +58,19 @@ af_err af_sparse_get_values(af_array *out, const af_array in)
     return CALL(out, in);
 }
 
-af_err af_sparse_get_rows(af_array *out, const af_array in)
+af_err af_sparse_get_row_idx(af_array *out, const af_array in)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_cols(af_array *out, const af_array in)
+af_err af_sparse_get_col_idx(af_array *out, const af_array in)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_num_values(dim_t *out, const af_array in)
-{
-    CHECK_ARRAYS(in);
-    return CALL(out, in);
-}
-
-af_err af_sparse_get_num_rows(dim_t *out, const af_array in)
-{
-    CHECK_ARRAYS(in);
-    return CALL(out, in);
-}
-
-af_err af_sparse_get_num_cols(dim_t *out, const af_array in)
+af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in);

From 25650d1fa3adeeca2dc789d5f93665537ef09f28 Mon Sep 17 00:00:00 2001
From: Filip Matzner <floop@floop.cz>
Date: Thu, 2 Jun 2016 20:44:21 +0200
Subject: [PATCH 0586/2677] Additional code improvements based on review.

---
 src/backend/cpu/kernel/copy.hpp | 32 +++++++++++++++++---------------
 1 file changed, 17 insertions(+), 15 deletions(-)

diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index f556f0a10d..3b9e4abae8 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -119,10 +119,7 @@ struct CopyImpl<T, T>
         }
 
         // traverse through the array using strides only until neccessary
-        if (linear_end == 4)
-            std::memcpy(dst_ptr, src_ptr, sizeof(T) * src.elements());
-        else
-            copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, 3, linear_end);
+        copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, 3, linear_end);
     }
 
     static void copy_go(
@@ -130,20 +127,25 @@ struct CopyImpl<T, T>
             T const * src_ptr, const af::dim4 & src_strides, const af::dim4 & src_dims,
             int dim, int linear_end)
     {
-        for(dim_t i=0; i<dst_dims[dim]; ++i) {
+        // if we are in a higher dimension, copy the entire stride if possible
+        if (linear_end == dim + 1) {
+            std::memcpy(dst_ptr, src_ptr, sizeof(T) * src_strides[dim] * src_dims[dim]);
+            return;
+        }
 
-            // 0th dimension is recursion bottom - copy element by element
-            if (dim == 0)
+        // 0th dimension is recursion bottom - copy element by element
+        if (dim == 0) {
+            for(dim_t i=0; i<dst_dims[0]; ++i) {
                 *dst_ptr = *src_ptr;
+                dst_ptr += dst_strides[0];
+                src_ptr += src_strides[0];
+            }
+            return;
+        }
 
-            // if we are in a higher dimension, copy the entire stride if possible
-            else if (linear_end == dim)
-                std::memcpy(dst_ptr, src_ptr, sizeof(T) * src_strides[dim]);
-
-            // otherwise recurse to a lower dimenstion
-            else
-                copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, dim - 1, linear_end);
-
+        // otherwise recurse to a lower dimenstion
+        for(dim_t i=0; i<dst_dims[dim]; ++i) {
+            copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, dim - 1, linear_end);
             dst_ptr += dst_strides[dim];
             src_ptr += src_strides[dim];
         }

From 411c53a6dcfca3efe5b881c8791f0a601d414e6e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 3 Jun 2016 00:40:58 +0530
Subject: [PATCH 0587/2677] Add CMake option to use system cl2.hpp header

---
 CMakeLists.txt                                       | 2 ++
 src/backend/opencl/CMakeLists.txt                    | 8 ++++++--
 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt | 5 ++++-
 3 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7e93ba32fb..a55e697857 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -53,6 +53,8 @@ ELSE(FREEIMAGE_FOUND)
     MESSAGE(WARNING, "FreeImage not found!")
 ENDIF(FREEIMAGE_FOUND)
 
+OPTION(USE_SYSTEM_CL2HPP "Use cl2.hpp installed on system" OFF)
+
 IF(BUILD_GRAPHICS)
     OPTION(USE_SYSTEM_FORGE "Use system Forge" OFF)
     IF(USE_SYSTEM_FORGE)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index cbf1f08a8e..29713daa04 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -3,7 +3,9 @@ PROJECT(ARRAYFIRE)
 
 FIND_PACKAGE(OpenCL REQUIRED)
 
-INCLUDE("${CMAKE_MODULE_PATH}/build_cl2hpp.cmake")
+IF(NOT USE_SYSTEM_CL2HPP)
+    INCLUDE("${CMAKE_MODULE_PATH}/build_cl2hpp.cmake")
+ENDIF(NOT USE_SYSTEM_CL2HPP)
 INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
 
 IF(USE_OPENCL_F77_BLAS)
@@ -306,7 +308,9 @@ ELSE(DEFINED BLAS_SYM_FILE)
 
 ENDIF()
 
-ADD_DEPENDENCIES(afopencl cl2hpp)
+IF(NOT USE_SYSTEM_CL2HPP)
+    ADD_DEPENDENCIES(afopencl cl2hpp)
+ENDIF(NOT USE_SYSTEM_CL2HPP)
 ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
 
 TARGET_LINK_LIBRARIES(afopencl
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 3ee39eaaae..983753644b 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -11,7 +11,10 @@ FOREACH(SBK_TYPE ${SBK_TYPES})
     ADD_LIBRARY(opencl_sort_by_key_${SBK_TYPE} OBJECT
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
-    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets} cl2hpp)
+    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets})
+    IF(NOT USE_SYSTEM_CL2HPP)
+        ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} cl2hpp)
+    ENDIF(NOT USE_SYSTEM_CL2HPP)
     IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
         ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} forge)
     ENDIF()

From 0fb572c3482ab513f5cd61c4e01747c427182cac Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 2 Jun 2016 17:16:47 -0400
Subject: [PATCH 0588/2677] af_scan_by_key in unified

Added some checks for validity of binary op enum and key dimensions in
api/scan.cpp
---
 src/api/c/scan.cpp            | 24 +++++++++++++-----------
 src/api/unified/algorithm.cpp |  6 ++++++
 2 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 6bc70f6442..022561510c 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -55,7 +55,8 @@ static inline af_array scan_op(const af_array key, const af_array in, const int
     case AF_BINARY_MUL:    out = scan_key<af_mul_t, Ti, To>(key, in, dim, inclusive_scan); break;
     case AF_BINARY_MIN:    out = scan_key<af_min_t, Ti, To>(key, in, dim, inclusive_scan); break;
     case AF_BINARY_MAX:    out = scan_key<af_max_t, Ti, To>(key, in, dim, inclusive_scan); break;
-                    //TODO Error for op in default case
+    default:
+        AF_ERROR("Incorrect binary operation enum for argument number 3", AF_ERR_ARG); break;
     }
     return out;
 }
@@ -70,17 +71,17 @@ static inline af_array scan_op(const af_array in, const int dim, af_binary_op op
     case AF_BINARY_MUL:    out = scan<af_mul_t, Ti, To>(in, dim, inclusive_scan); break;
     case AF_BINARY_MIN:    out = scan<af_min_t, Ti, To>(in, dim, inclusive_scan); break;
     case AF_BINARY_MAX:    out = scan<af_max_t, Ti, To>(in, dim, inclusive_scan); break;
-                    //TODO Error for op in default case
+    default:
+        AF_ERROR("Incorrect binary operation enum for argument number 2", AF_ERR_ARG); break;
     }
     return out;
 }
 
 af_err af_accum(af_array *out, const af_array in, const int dim)
 {
-    ARG_ASSERT(2, dim >= 0);
-    ARG_ASSERT(2, dim <  4);
-
     try {
+        ARG_ASSERT(2, dim >= 0);
+        ARG_ASSERT(2, dim <  4);
 
         const ArrayInfo& in_info = getInfo(in);
 
@@ -119,10 +120,9 @@ af_err af_accum(af_array *out, const af_array in, const int dim)
 
 af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
 {
-    ARG_ASSERT(2, dim >= 0);
-    ARG_ASSERT(2, dim <  4);
-
     try {
+        ARG_ASSERT(2, dim >= 0);
+        ARG_ASSERT(2, dim <  4);
 
         const ArrayInfo& in_info = getInfo(in);
 
@@ -160,18 +160,20 @@ af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
 
 af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
 {
-    ARG_ASSERT(2, dim >= 0);
-    ARG_ASSERT(2, dim <  4);
-
     try {
+        ARG_ASSERT(2, dim >= 0);
+        ARG_ASSERT(2, dim <  4);
 
         const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo& key_info = getInfo(key);
 
         if (dim >= (int)in_info.ndims()) {
             *out = retain(in);
             return AF_SUCCESS;
         }
 
+        ARG_ASSERT(2, in_info.dims() == key_info.dims());
+
         af_dtype type = in_info.getType();
         af_array res;
 
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index fd06f53b48..fe41f316d8 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -110,6 +110,12 @@ af_err af_scan(af_array* out, const af_array in, const int dim, af_binary_op op,
     return CALL(out, in, dim, op, inclusive_scan);
 }
 
+af_err af_scan_by_key(af_array* out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
+{
+    CHECK_ARRAYS(in, key);
+    return CALL(out, key, in, dim, op, inclusive_scan);
+}
+
 af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending)
 {
     CHECK_ARRAYS(in);

From 1fa816df98bfea35c2c8b915c6a798fe3f4ee4fb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 3 Jun 2016 21:10:23 +0530
Subject: [PATCH 0589/2677] Delegate convolve[3|2] calls to lower dim versions
 when appropriate

af_convolve2 & af_convolve3 will now delegate calls to af_convolve1 &
af_convolve2 respectively, when the input arrays are of lower dimension
than their's.

For example, if the input arrays of af_convolve3 are two dimensional,
the function call will be forwarded to af_convolve2.
---
 src/api/c/convolve.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index fbdb862cb4..f9395f8b6d 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -198,6 +198,10 @@ af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
         if (isFreqDomain<2>(signal, filter, domain))
             return af_fft_convolve2(out, signal, filter, mode);
 
+        if (getInfo(signal).dims().ndims()<2 && getInfo(filter).dims().ndims()<2) {
+            return af_convolve1(out, signal, filter, mode, domain);
+        }
+
         if (mode == AF_CONV_EXPAND)
             return convolve<2, true >(out, signal, filter);
         else
@@ -211,6 +215,10 @@ af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
         if (isFreqDomain<3>(signal, filter, domain))
             return af_fft_convolve3(out, signal, filter, mode);
 
+        if (getInfo(signal).dims().ndims()<3 && getInfo(filter).dims().ndims()<3) {
+            return af_convolve2(out, signal, filter, mode, domain);
+        }
+
         if (mode == AF_CONV_EXPAND)
             return convolve<3, true >(out, signal, filter);
         else

From 221d77c30171995c22a72d890937249faae4d785 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 3 Jun 2016 22:04:58 +0530
Subject: [PATCH 0590/2677] Add lower dim delegations for fftconvolve

---
 src/api/c/convolve.cpp    | 12 ++++++------
 src/api/c/fftconvolve.cpp | 12 ++++++++++--
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index f9395f8b6d..accbc606a8 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -195,13 +195,13 @@ af_err af_convolve1(af_array *out, const af_array signal, const af_array filter,
 af_err af_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
 {
     try {
-        if (isFreqDomain<2>(signal, filter, domain))
-            return af_fft_convolve2(out, signal, filter, mode);
-
         if (getInfo(signal).dims().ndims()<2 && getInfo(filter).dims().ndims()<2) {
             return af_convolve1(out, signal, filter, mode, domain);
         }
 
+        if (isFreqDomain<2>(signal, filter, domain))
+            return af_fft_convolve2(out, signal, filter, mode);
+
         if (mode == AF_CONV_EXPAND)
             return convolve<2, true >(out, signal, filter);
         else
@@ -212,13 +212,13 @@ af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
 af_err af_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
 {
     try {
-        if (isFreqDomain<3>(signal, filter, domain))
-            return af_fft_convolve3(out, signal, filter, mode);
-
         if (getInfo(signal).dims().ndims()<3 && getInfo(filter).dims().ndims()<3) {
             return af_convolve2(out, signal, filter, mode, domain);
         }
 
+        if (isFreqDomain<3>(signal, filter, domain))
+            return af_fft_convolve3(out, signal, filter, mode);
+
         if (mode == AF_CONV_EXPAND)
             return convolve<3, true >(out, signal, filter);
         else
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 355720e09c..a7c17da929 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -166,10 +166,18 @@ af_err af_fft_convolve1(af_array *out, const af_array signal, const af_array fil
 
 af_err af_fft_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode)
 {
-    return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
+    if (getInfo(signal).dims().ndims()<2 && getInfo(filter).dims().ndims()<2) {
+        return fft_convolve<1>(out, signal, filter, mode == AF_CONV_EXPAND);
+    } else {
+        return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
+    }
 }
 
 af_err af_fft_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode)
 {
-    return fft_convolve<3>(out, signal, filter, mode == AF_CONV_EXPAND);
+    if (getInfo(signal).dims().ndims()<3 && getInfo(filter).dims().ndims()<3) {
+        return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
+    } else {
+        return fft_convolve<3>(out, signal, filter, mode == AF_CONV_EXPAND);
+    }
 }

From 6a0886cfd31773629389e1b9e61def70449a6a69 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 3 Jun 2016 22:37:02 +0530
Subject: [PATCH 0591/2677] typo fix in signal header documentation

---
 include/af/signal.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index f2dccb8ea7..ec61f51917 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -1055,7 +1055,7 @@ AFAPI af_err af_fft_convolve2(af_array *out, const af_array signal, const af_arr
 AFAPI af_err af_fft_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode);
 
 /**
-   C++ Interface for finite impulse response  filter
+   C Interface for finite impulse response  filter
 
    \param[out] y is the output signal from the filter
    \param[in] b is the array containing the coefficients of the filter
@@ -1066,7 +1066,7 @@ AFAPI af_err af_fft_convolve3(af_array *out, const af_array signal, const af_arr
 AFAPI af_err af_fir(af_array *y, const af_array b, const af_array x);
 
 /**
-   C++ Interface for infinite impulse response filter
+   C Interface for infinite impulse response filter
 
    \param[out] y is the output signal from the filter
    \param[in] b is the array containing the feedforward coefficients

From 1688d41841ee687f5f704f8251ccd620add2b474 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 3 Jun 2016 20:57:18 -0400
Subject: [PATCH 0592/2677] CPU Copy had too many instantiations - Splitting
 into 2 files

* copy.cpp and padarray.cpp
* The copy.hpp file remains the same
---
 src/backend/cpu/copy.cpp     | 65 ++++++------------------
 src/backend/cpu/padarray.cpp | 96 ++++++++++++++++++++++++++++++++++++
 2 files changed, 111 insertions(+), 50 deletions(-)
 create mode 100644 src/backend/cpu/padarray.cpp

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 9220f33237..97f4514eb9 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -47,25 +47,6 @@ Array<T> copyArray(const Array<T> &A)
     return out;
 }
 
-template<typename T>
-void multiply_inplace(Array<T> &in, double val)
-{
-    in.eval();
-    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, 0, val);
-}
-
-template<typename inType, typename outType>
-Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                        outType default_value, double factor)
-{
-    Array<outType> ret = createValueArray<outType>(dims, default_value);
-    ret.eval();
-    in.eval();
-    getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in, outType(default_value), factor);
-
-    return ret;
-}
-
 template<typename inType, typename outType>
 void copyArray(Array<outType> &out, Array<inType> const &in)
 {
@@ -77,7 +58,6 @@ void copyArray(Array<outType> &out, Array<inType> const &in)
 #define INSTANTIATE(T)                                                  \
     template void      copyData<T> (T *data, const Array<T> &from);     \
     template Array<T>  copyArray<T>(const Array<T> &A);                 \
-    template void      multiply_inplace<T> (Array<T> &in, double norm); \
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
@@ -92,20 +72,7 @@ INSTANTIATE(uintl  )
 INSTANTIATE(short  )
 INSTANTIATE(ushort )
 
-
-#define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
-    template Array<float  > padArray<SRC_T, float  >(Array<SRC_T> const &src, dim4 const &dims, float   default_value, double factor); \
-    template Array<double > padArray<SRC_T, double >(Array<SRC_T> const &src, dim4 const &dims, double  default_value, double factor); \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
-    template Array<int    > padArray<SRC_T, int    >(Array<SRC_T> const &src, dim4 const &dims, int     default_value, double factor); \
-    template Array<uint   > padArray<SRC_T, uint   >(Array<SRC_T> const &src, dim4 const &dims, uint    default_value, double factor); \
-    template Array<intl   > padArray<SRC_T, intl   >(Array<SRC_T> const &src, dim4 const &dims, intl    default_value, double factor); \
-    template Array<uintl  > padArray<SRC_T, uintl  >(Array<SRC_T> const &src, dim4 const &dims, uintl   default_value, double factor); \
-    template Array<short  > padArray<SRC_T, short  >(Array<SRC_T> const &src, dim4 const &dims, short   default_value, double factor); \
-    template Array<ushort > padArray<SRC_T, ushort >(Array<SRC_T> const &src, dim4 const &dims, ushort  default_value, double factor); \
-    template Array<uchar  > padArray<SRC_T, uchar  >(Array<SRC_T> const &src, dim4 const &dims, uchar   default_value, double factor); \
-    template Array<char   > padArray<SRC_T, char   >(Array<SRC_T> const &src, dim4 const &dims, char    default_value, double factor); \
+#define INSTANTIATE_COPY_ARRAY(SRC_T)                                    \
     template void copyArray<SRC_T, float  >(Array<float  > &dst, Array<SRC_T> const &src);  \
     template void copyArray<SRC_T, double >(Array<double > &dst, Array<SRC_T> const &src);  \
     template void copyArray<SRC_T, cfloat >(Array<cfloat > &dst, Array<SRC_T> const &src);  \
@@ -119,25 +86,23 @@ INSTANTIATE(ushort )
     template void copyArray<SRC_T, uchar  >(Array<uchar  > &dst, Array<SRC_T> const &src);  \
     template void copyArray<SRC_T, char   >(Array<char   > &dst, Array<SRC_T> const &src);
 
-INSTANTIATE_PAD_ARRAY(float )
-INSTANTIATE_PAD_ARRAY(double)
-INSTANTIATE_PAD_ARRAY(int   )
-INSTANTIATE_PAD_ARRAY(uint  )
-INSTANTIATE_PAD_ARRAY(intl  )
-INSTANTIATE_PAD_ARRAY(uintl )
-INSTANTIATE_PAD_ARRAY(uchar )
-INSTANTIATE_PAD_ARRAY(char  )
-INSTANTIATE_PAD_ARRAY(ushort)
-INSTANTIATE_PAD_ARRAY(short )
-
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
+INSTANTIATE_COPY_ARRAY(float )
+INSTANTIATE_COPY_ARRAY(double)
+INSTANTIATE_COPY_ARRAY(int   )
+INSTANTIATE_COPY_ARRAY(uint  )
+INSTANTIATE_COPY_ARRAY(intl  )
+INSTANTIATE_COPY_ARRAY(uintl )
+INSTANTIATE_COPY_ARRAY(uchar )
+INSTANTIATE_COPY_ARRAY(char  )
+INSTANTIATE_COPY_ARRAY(ushort)
+INSTANTIATE_COPY_ARRAY(short )
+
+#define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                            \
     template void copyArray<SRC_T, cfloat  >(Array<cfloat  > &dst, Array<SRC_T> const &src);    \
     template void copyArray<SRC_T, cdouble   >(Array<cdouble > &dst, Array<SRC_T> const &src);
 
-INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
-INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat )
+INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 #define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
     template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
new file mode 100644
index 0000000000..b190cb45ec
--- /dev/null
+++ b/src/backend/cpu/padarray.cpp
@@ -0,0 +1,96 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <type_traits>
+#include <Array.hpp>
+#include <copy.hpp>
+#include <cstring>
+#include <algorithm>
+#include <complex>
+#include <vector>
+#include <cassert>
+#include <err_cpu.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+#include <kernel/copy.hpp>
+
+namespace cpu
+{
+
+template<typename T>
+void multiply_inplace(Array<T> &in, double val)
+{
+    in.eval();
+    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, 0, val);
+}
+
+template<typename inType, typename outType>
+Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                        outType default_value, double factor)
+{
+    Array<outType> ret = createValueArray<outType>(dims, default_value);
+    ret.eval();
+    in.eval();
+    getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in, outType(default_value), factor);
+
+    return ret;
+}
+
+#define INSTANTIATE(T)                                                  \
+    template void      multiply_inplace<T> (Array<T> &in, double norm); \
+
+INSTANTIATE(float  )
+INSTANTIATE(double )
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
+INSTANTIATE(int    )
+INSTANTIATE(uint   )
+INSTANTIATE(uchar  )
+INSTANTIATE(char   )
+INSTANTIATE(intl   )
+INSTANTIATE(uintl  )
+INSTANTIATE(short  )
+INSTANTIATE(ushort )
+
+
+#define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
+    template Array<float  > padArray<SRC_T, float  >(Array<SRC_T> const &src, dim4 const &dims, float   default_value, double factor); \
+    template Array<double > padArray<SRC_T, double >(Array<SRC_T> const &src, dim4 const &dims, double  default_value, double factor); \
+    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
+    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
+    template Array<int    > padArray<SRC_T, int    >(Array<SRC_T> const &src, dim4 const &dims, int     default_value, double factor); \
+    template Array<uint   > padArray<SRC_T, uint   >(Array<SRC_T> const &src, dim4 const &dims, uint    default_value, double factor); \
+    template Array<intl   > padArray<SRC_T, intl   >(Array<SRC_T> const &src, dim4 const &dims, intl    default_value, double factor); \
+    template Array<uintl  > padArray<SRC_T, uintl  >(Array<SRC_T> const &src, dim4 const &dims, uintl   default_value, double factor); \
+    template Array<short  > padArray<SRC_T, short  >(Array<SRC_T> const &src, dim4 const &dims, short   default_value, double factor); \
+    template Array<ushort > padArray<SRC_T, ushort >(Array<SRC_T> const &src, dim4 const &dims, ushort  default_value, double factor); \
+    template Array<uchar  > padArray<SRC_T, uchar  >(Array<SRC_T> const &src, dim4 const &dims, uchar   default_value, double factor); \
+    template Array<char   > padArray<SRC_T, char   >(Array<SRC_T> const &src, dim4 const &dims, char    default_value, double factor); \
+
+INSTANTIATE_PAD_ARRAY(float )
+INSTANTIATE_PAD_ARRAY(double)
+INSTANTIATE_PAD_ARRAY(int   )
+INSTANTIATE_PAD_ARRAY(uint  )
+INSTANTIATE_PAD_ARRAY(intl  )
+INSTANTIATE_PAD_ARRAY(uintl )
+INSTANTIATE_PAD_ARRAY(uchar )
+INSTANTIATE_PAD_ARRAY(char  )
+INSTANTIATE_PAD_ARRAY(ushort)
+INSTANTIATE_PAD_ARRAY(short )
+
+#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
+    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
+    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
+
+INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
+INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+
+}
+

From 7a666a3f4bccf5d9c9b0dccc2093f0b7817af61d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 3 Jun 2016 21:03:11 -0400
Subject: [PATCH 0593/2677] Use /bigobj flag for all Windows build types

---
 CMakeLists.txt | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7e93ba32fb..1c9f5757d1 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -148,12 +148,8 @@ ELSE(${UNIX}) #Windows
         # MP is multiprocess compilation. Gm- disables minimal rebuilds
         # http://stackoverflow.com/questions/6172205/how-can-i-do-a-parallel-build-in-visual-studio-2010vvvvvvvv
         # http://www.kitware.com/blog/home/post/434
-        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP /Gm-")
+        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP /Gm- /bigobj")
         SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /MP /Gm-")
-
-        # Builds that contain debug info require /bigobj
-        SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj")
-        SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /bigobj")
     ENDIF(MSVC)
 ENDIF()
 

From 9a169ad12b08acfaa9bb07836ac4fa7afd3670a5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 4 Jun 2016 15:23:02 +0530
Subject: [PATCH 0594/2677] FEATURE: af_set_fft_plan_cache_size

This function helps the user control the number of fft plans that
will be cached by fft functions.
---
 include/af/signal.h        |  13 +++++
 src/api/c/fft.cpp          |  11 ++++
 src/backend/cuda/fft.cpp   | 110 ++++++++++++++++++++++++-----------
 src/backend/cuda/fft.hpp   |   2 +
 src/backend/opencl/fft.cpp | 114 ++++++++++++++++++++++++-------------
 src/backend/opencl/fft.hpp |   2 +
 6 files changed, 180 insertions(+), 72 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index ec61f51917..29cdf42d00 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -1078,6 +1078,19 @@ AFAPI af_err af_fir(af_array *y, const af_array b, const af_array x);
    \ingroup signal_func_iir
 */
 AFAPI af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x);
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface for setting plan cache size
+
+   This function is doesn't do anything if called when CPU backend is active. The plans associated with
+   the most recently used array sizes are cached.
+
+   \param[in] cache_size is the number of plans that shall be cached
+*/
+AFAPI af_err af_set_fft_plan_cache_size(size_t cache_size);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index d472cdbf54..a763150bb1 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -247,3 +247,14 @@ af_err af_fft3_c2r(af_array *out, const af_array in, const double norm_factor, c
 {
     return fft_c2r<3>(out, in, norm_factor, is_odd);
 }
+
+af_err af_set_fft_plan_cache_size(size_t cache_size)
+{
+#ifndef AF_CPU
+    try {
+        detail::setFFTPlanCacheSize(cache_size);
+    }
+    CATCHALL;
+#endif
+    return AF_SUCCESS;
+}
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index c8fc020769..30e61084ce 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -16,6 +16,8 @@
 #include <cufft.h>
 #include <math.hpp>
 #include <string>
+#include <deque>
+#include <utility>
 #include <cstdio>
 #include <memory.hpp>
 
@@ -25,11 +27,15 @@ using std::string;
 namespace cuda
 {
 
+typedef std::pair<string, cufftHandle> FFTPlanPair;
+typedef std::deque<FFTPlanPair> FFTPlanCache;
 
-// cuFFTPlanner will do very basic plan caching.
-// it looks for required candidate in mHandles array and returns if found one.
-// otherwise, it will create a plan and set it at the mAvailSlotIndex and increment
-// the slot index variable in ciruclar fashion 0 to MAX_PLAN_CACHE, then back to zero and repeat.
+// cuFFTPlanner caches fft plans
+//
+// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
+//          |
+//          |--> ELSE just push the plan
+// existing plan -> reuse a plan
 class cuFFTPlanner
 {
     friend void find_cufft_plan(cufftHandle &plan, int rank, int *n,
@@ -43,16 +49,56 @@ class cuFFTPlanner
             return instances[cuda::getActiveDeviceId()];
         }
 
+        inline void setMaxCacheSize(size_t size) {
+            mCache.resize(size, FFTPlanPair(std::string(""), 0));
+        }
+
+        inline size_t getMaxCacheSize() const {
+            return mMaxCacheSize;
+        }
+
+        inline cufftHandle getPlan(int index) const {
+            return mCache[index].second;
+        }
+
+        // iterates through plan cache from front to back
+        // of the cache(queue)
+        int findIfPlanExists(std::string keyString) const {
+            int retVal = -1;
+            for(uint i=0; i<mCache.size(); ++i) {
+                if (keyString == mCache[i].first) {
+                    retVal = i;
+                }
+            }
+            return retVal;
+        }
+
+        // pops plan from the back of cache(queue)
+        void popPlan() {
+            if (!mCache.empty()) {
+                // destroy the cufft plan associated with the
+                // least recently used plan
+                cufftDestroy(mCache.back().second);
+                // now pop the entry from cache
+                mCache.pop_back();
+            }
+        }
+
+        // pushes plan to the front of cache(queue)
+        void pushPlan(std::string keyString, cufftHandle plan) {
+            if (mCache.size()>mMaxCacheSize) {
+                popPlan();
+            }
+            mCache.push_front(FFTPlanPair(keyString, plan));
+        }
+
     private:
-        cuFFTPlanner() : mAvailSlotIndex(0) {}
+        cuFFTPlanner() : mMaxCacheSize(5) {}
         cuFFTPlanner(cuFFTPlanner const&);
         void operator=(cuFFTPlanner const&);
 
-        static const int MAX_PLAN_CACHE = 5;
-
-        int                  mAvailSlotIndex;
-        cufftHandle mHandles[MAX_PLAN_CACHE];
-        string         mKeys[MAX_PLAN_CACHE];
+        size_t       mMaxCacheSize;
+        FFTPlanCache mCache;
 };
 
 void find_cufft_plan(cufftHandle &plan, int rank, int *n,
@@ -60,7 +106,6 @@ void find_cufft_plan(cufftHandle &plan, int rank, int *n,
                      int *onembed, int ostride, int odist,
                      cufftType type, int batch)
 {
-    cuFFTPlanner &planner = cuFFTPlanner::getInstance();
     // create the key string
     char key_str_temp[64];
     sprintf(key_str_temp, "%d:", rank);
@@ -94,40 +139,41 @@ void find_cufft_plan(cufftHandle &plan, int rank, int *n,
     key_string.append(std::string(key_str_temp));
 
     // find the matching plan_index in the array cuFFTPlanner::mKeys
-    int plan_index = -1;
-    for (int i=0; i<cuFFTPlanner::MAX_PLAN_CACHE; ++i) {
-        if (key_string==planner.mKeys[i]) {
-            plan_index = i;
-            break;
-        }
-    }
+    cuFFTPlanner &planner = cuFFTPlanner::getInstance();
+
+    int planIndex = planner.findIfPlanExists(key_string);
 
-    // return mHandles[plan_index] if plan_index valid
-    if (plan_index!=-1) {
-        plan = planner.mHandles[plan_index];
+    // if found a valid plan, return it
+    if (planIndex!=-1) {
+        plan = planner.getPlan(planIndex);
         return;
     }
 
-    // otherwise create a new plan and set it at mAvailSlotIndex
-    // and finally set it to output plan variable
-    int slot_index = planner.mAvailSlotIndex;
-    cufftDestroy(planner.mHandles[slot_index]); // We ignore both return values
-
     cufftHandle temp;
-    cufftResult res = cufftPlanMany(&temp, rank, n, inembed, istride, idist, onembed, ostride, odist, type, batch);
-
+    cufftResult res = cufftPlanMany(&temp, rank, n,
+                                    inembed, istride, idist,
+                                    onembed, ostride, odist,
+                                    type, batch);
 
     // If plan creation fails, clean up the memory we hold on to and try again
     if (res != CUFFT_SUCCESS) {
         garbageCollect();
-        CUFFT_CHECK(cufftPlanMany(&temp, rank, n, inembed, istride, idist, onembed, ostride, odist, type, batch));
+        CUFFT_CHECK(cufftPlanMany(&temp, rank, n,
+                                  inembed, istride, idist,
+                                  onembed, ostride, odist,
+                                  type, batch));
     }
 
     plan = temp;
     cufftSetStream(plan, cuda::getStream(cuda::getActiveDeviceId()));
-    planner.mHandles[slot_index] = temp;
-    planner.mKeys[slot_index] = key_string;
-    planner.mAvailSlotIndex = (slot_index + 1)%cuFFTPlanner::MAX_PLAN_CACHE;
+
+    // push the plan into plan cache
+    planner.pushPlan(key_string, plan);
+}
+
+void setFFTPlanCacheSize(size_t numPlans)
+{
+    cuFFTPlanner::getInstance().setMaxCacheSize(numPlans);
 }
 
 template<typename T>
diff --git a/src/backend/cuda/fft.hpp b/src/backend/cuda/fft.hpp
index 2d0ee2ca62..1d85b8ad84 100644
--- a/src/backend/cuda/fft.hpp
+++ b/src/backend/cuda/fft.hpp
@@ -12,6 +12,8 @@
 namespace cuda
 {
 
+void setFFTPlanCacheSize(size_t numPlans);
+
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &out);
 
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index cd3a5c22f5..239fc74bff 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -16,6 +16,8 @@
 #include <clFFT.h>
 #include <math.hpp>
 #include <string>
+#include <deque>
+#include <utility>
 #include <cstdio>
 #include <memory.hpp>
 
@@ -25,10 +27,15 @@ using std::string;
 namespace opencl
 {
 
-// clFFTPlanner will do very basic plan caching.
-// it looks for required candidate in mHandles array and returns if found one.
-// otherwise, it will create a plan and set it at the mAvailSlotIndex and increment
-// the slot index variable in ciruclar fashion 0 to MAX_PLAN_CACHE, then back to zero and repeat.
+typedef std::pair<string, clfftPlanHandle> FFTPlanPair;
+typedef std::deque<FFTPlanPair> FFTPlanCache;
+
+// clFFTPlanner caches fft plans
+//
+// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
+//          |
+//          |--> ELSE just push the plan
+// existing plan -> reuse a plan
 class clFFTPlanner
 {
     friend void find_clfft_plan(clfftPlanHandle &plan,
@@ -59,24 +66,61 @@ class clFFTPlanner
             #endif
         }
 
+        inline void setMaxCacheSize(size_t size) {
+            mCache.resize(size, FFTPlanPair(std::string(""), 0));
+        }
+
+        inline size_t getMaxCacheSize() const {
+            return mMaxCacheSize;
+        }
+
+        inline clfftPlanHandle getPlan(int index) const {
+            return mCache[index].second;
+        }
+
+        // iterates through plan cache from front to back
+        // of the cache(queue)
+        int findIfPlanExists(std::string keyString) const {
+            int retVal = -1;
+            for(uint i=0; i<mCache.size(); ++i) {
+                if (keyString == mCache[i].first) {
+                    retVal = i;
+                }
+            }
+            return retVal;
+        }
+
+        // pops plan from the back of cache(queue)
+        void popPlan() {
+            if (!mCache.empty()) {
+                // destroy the clfft plan associated with the
+                // least recently used plan
+                CLFFT_CHECK(clfftDestroyPlan(&mCache.back().second));
+                // now pop the entry from cache
+                mCache.pop_back();
+            }
+        }
+
+        // pushes plan to the front of cache(queue)
+        void pushPlan(std::string keyString, clfftPlanHandle plan) {
+            if (mCache.size()>mMaxCacheSize) {
+                popPlan();
+            }
+            mCache.push_front(FFTPlanPair(keyString, plan));
+        }
+
     private:
-        clFFTPlanner() : mAvailSlotIndex(0) {
-            CLFFT_CHECK(clfftInitSetupData(&fftSetup));
-            CLFFT_CHECK(clfftSetup(&fftSetup));
-            for(int p=0; p<MAX_PLAN_CACHE; ++p)
-                mHandles[p] = 0;
+        clFFTPlanner() : mMaxCacheSize(5) {
+            CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
+            CLFFT_CHECK(clfftSetup(&mFFTSetup));
         }
         clFFTPlanner(clFFTPlanner const&);
         void operator=(clFFTPlanner const&);
 
-        static const int MAX_PLAN_CACHE = 5;
+        clfftSetupData  mFFTSetup;
 
-        int          mAvailSlotIndex;
-        string mKeys[MAX_PLAN_CACHE];
-
-        clfftPlanHandle mHandles[MAX_PLAN_CACHE];
-
-        clfftSetupData  fftSetup;
+        size_t       mMaxCacheSize;
+        FFTPlanCache mCache;
 };
 
 void find_clfft_plan(clfftPlanHandle &plan,
@@ -86,8 +130,6 @@ void find_clfft_plan(clfftPlanHandle &plan,
                      size_t *ostrides, size_t odist,
                      clfftPrecision precision, size_t batch)
 {
-    clFFTPlanner &planner = clFFTPlanner::getInstance();
-
     // create the key string
     char key_str_temp[64];
     sprintf(key_str_temp, "%d:%d:%d:", iLayout, oLayout, rank);
@@ -122,27 +164,14 @@ void find_clfft_plan(clfftPlanHandle &plan,
     key_string.append(std::string(key_str_temp));
 
     // find the matching plan_index in the array clFFTPlanner::mKeys
-    int plan_index = -1;
-    for (int i=0; i<clFFTPlanner::MAX_PLAN_CACHE; ++i) {
-        if (key_string==planner.mKeys[i]) {
-            plan_index = i;
-            break;
-        }
-    }
-
-    // return mHandles[plan_index] if plan_index valid
-    if (plan_index!=-1) {
-        plan = planner.mHandles[plan_index];
-        return;
-    }
+    clFFTPlanner &planner = clFFTPlanner::getInstance();
 
-    // otherwise create a new plan and set it at mAvailSlotIndex
-    // and finally set it to output plan variable
-    int slot_index = planner.mAvailSlotIndex;
+    int planIndex = planner.findIfPlanExists(key_string);
 
-    if (planner.mHandles[slot_index]) {
-        CLFFT_CHECK(clfftDestroyPlan(&planner.mHandles[slot_index]));
-        planner.mHandles[slot_index] = 0;
+    // if found a valid plan, return it
+    if (planIndex!=-1) {
+        plan = planner.getPlan(planIndex);
+        return;
     }
 
     clfftPlanHandle temp;
@@ -171,9 +200,14 @@ void find_clfft_plan(clfftPlanHandle &plan,
     CLFFT_CHECK(clfftBakePlan(temp, 1, &(getQueue()()), NULL, NULL));
 
     plan = temp;
-    planner.mHandles[slot_index] = temp;
-    planner.mKeys[slot_index] = key_string;
-    planner.mAvailSlotIndex = (slot_index + 1)%clFFTPlanner::MAX_PLAN_CACHE;
+
+    // push the plan into plan cache
+    planner.pushPlan(key_string, plan);
+}
+
+void setFFTPlanCacheSize(size_t numPlans)
+{
+    clFFTPlanner::getInstance().setMaxCacheSize(numPlans);
 }
 
 template<typename T> struct Precision;
diff --git a/src/backend/opencl/fft.hpp b/src/backend/opencl/fft.hpp
index a1d9e6efa2..b6155b9987 100644
--- a/src/backend/opencl/fft.hpp
+++ b/src/backend/opencl/fft.hpp
@@ -12,6 +12,8 @@
 namespace opencl
 {
 
+void setFFTPlanCacheSize(size_t numPlans);
+
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in);
 

From 25ce554b1002cb4fec3ddd154423550e031c4397 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 6 Jun 2016 09:38:04 +0530
Subject: [PATCH 0595/2677] Correct type in fft helper fn documentation

---
 include/af/signal.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index 29cdf42d00..d5b1dc1f4d 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -1083,7 +1083,7 @@ AFAPI af_err af_iir(af_array *y, const af_array b, const af_array a, const af_ar
 /**
    C Interface for setting plan cache size
 
-   This function is doesn't do anything if called when CPU backend is active. The plans associated with
+   This function doesn't do anything if called when CPU backend is active. The plans associated with
    the most recently used array sizes are cached.
 
    \param[in] cache_size is the number of plans that shall be cached

From 15e5177aa6afccb6ba1e9c07c2d03a04a679c6a2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 6 Jun 2016 09:38:32 +0530
Subject: [PATCH 0596/2677] cpu backend placeholder for
 af_set_fft_plan_cache_size

---
 src/api/c/fft.cpp       | 2 --
 src/backend/cpu/fft.cpp | 4 ++++
 src/backend/cpu/fft.hpp | 2 ++
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index a763150bb1..3f092379b4 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -250,11 +250,9 @@ af_err af_fft3_c2r(af_array *out, const af_array in, const double norm_factor, c
 
 af_err af_set_fft_plan_cache_size(size_t cache_size)
 {
-#ifndef AF_CPU
     try {
         detail::setFFTPlanCacheSize(cache_size);
     }
     CATCHALL;
-#endif
     return AF_SUCCESS;
 }
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 59ccee6246..0c94280bd8 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -21,6 +21,10 @@ using af::dim4;
 namespace cpu
 {
 
+void setFFTPlanCacheSize(size_t numPlans)
+{
+}
+
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in)
 {
diff --git a/src/backend/cpu/fft.hpp b/src/backend/cpu/fft.hpp
index 02f4c9ac0f..7b4313b0e3 100644
--- a/src/backend/cpu/fft.hpp
+++ b/src/backend/cpu/fft.hpp
@@ -12,6 +12,8 @@
 namespace cpu
 {
 
+void setFFTPlanCacheSize(size_t numPlans);
+
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in);
 

From a23e0f1876b01c2299e385503519bb558d664310 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 7 Jun 2016 18:38:37 -0400
Subject: [PATCH 0597/2677] Minor corrections to scan by key first kernel

---
 src/backend/opencl/kernel/scan_first_by_key.cl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 0be5c9be24..bdefee3795 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -40,9 +40,9 @@ void scan_first_by_key_core(const bool invalid,
     l_flg0[lid] = *flag;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    for (int off = 1; off < DIMY; off *= 2) {
+    for (int off = 1; off < DIMX; off *= 2) {
 
-        if (lidy >= off) {
+        if (lidx >= off) {
             *val = l_flg[lid] ? *val : binOp(*val, l_val[lid - off]);
             *flag = l_flg[lid] | l_flg[lid - off];
         }

From 8d404a960bbe8f164413581907cc3f8f982e3dac Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 8 Jun 2016 16:41:21 -0400
Subject: [PATCH 0598/2677] Removed common function in scanbykey in OpenCL

Minor changes in CUDA backend. Missing free.
---
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |   2 +
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 174 ++++++++++--------
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   2 +
 .../opencl/kernel/scan_first_by_key.cl        | 174 ++++++++++--------
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   3 +-
 test/binary_ops.hpp                           | 141 ++++++++++++++
 test/scan.cpp                                 | 154 +++++++++++++++-
 7 files changed, 495 insertions(+), 155 deletions(-)
 create mode 100644 test/binary_ops.hpp

diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index f3cab51984..9c0a2286da 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -540,6 +540,8 @@ namespace kernel
             bcast_dim_launcher<To, op, dim>(out, tmp, tmpid, threads_y, blocks_all);
 
             memFree(tmp.ptr);
+            memFree(tmpflg.ptr);
+            memFree(tmpid.ptr);
         }
     }
 
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index 58d73fc105..2f76ec4d69 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
+static char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
 {
     char flag;
     if (id == 0) {
@@ -18,43 +18,6 @@ char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
     return flag;
 }
 
-void scan_dim_by_key_core(const bool invalid,
-        To *val, char *flag,
-        const __global Ti *in, const To init_val,
-        __local To *l_val0, __local To *l_val1,
-        __local char *l_flg0, __local char *l_flg1,
-        __local To *last_val, __local char *last_flag,
-        const int lid, const int lidx, const int lidy)
-{
-    bool flip = 0;
-    __local To *l_val = l_val0;
-    __local char *l_flg = l_flg0;
-    *val = invalid? init_val : transform(*in);
-
-    if ((lidy == 0) && (*flag == 0)) {
-        *val = binOp(*val, last_val[lidx]);
-        *flag = *flag | last_flag[lidx];
-    }
-
-    l_val0[lid] = *val;
-    l_flg0[lid] = *flag;
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    for (int off = 1; off < DIMY; off *= 2) {
-
-        if (lidy >= off) {
-            *val = l_flg[lid] ? *val : binOp(*val, l_val[lid - off * THREADS_X]);
-            *flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
-        }
-        flip = 1 - flip;
-        l_val = flip ? l_val1 : l_val0;
-        l_flg = flip ? l_flg1 : l_flg0;
-        l_val[lid] = *val;
-        l_flg[lid] = *flag;
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-}
-
 __kernel
 void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
                      __global To *tData, KParam tInfo,
@@ -105,56 +68,87 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
     const int ostride_dim = oInfo.strides[dim];
     const int istride_dim =  iInfo.strides[dim];
-    const int kstride_dim = kInfo.strides[dim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
     __local char l_flg0[THREADS_X * DIMY];
     __local char l_flg1[THREADS_X * DIMY];
-    __local To last_val[THREADS_X];
-    __local char last_flag[THREADS_X];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[THREADS_X];
+    __local char l_ftmp[THREADS_X];
     __local int boundaryid;
 
+    bool flip = 0;
     const To init_val  = init;
     To val = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
-    char flag = 0;
-    if (!inclusive_scan) {
-        iData -= istride_dim;
-    }
-
     if (isLast) {
-        last_val[lidy] = val;
-        last_flag[lidy] = 0;
+        l_tmp[lidy] = val;
+        l_ftmp[lidy] = 0;
         boundaryid = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     __local char *prev;
     if (lidy == 0) {
-        prev = &last_flag[lidx];
+        prev = &l_ftmp[lidx];
     } else {
-        prev = &l_flg0[lid-THREADS_X];
+        prev = &l_flg[lid-THREADS_X];
     }
-    __local char *curr = &l_flg0[lid];
+    __local char *curr = &l_flg[lid];
 
+    char flag = 0;
     for (int k = 0; k < lim; k++) {
 
+        //if (isLast) l_tmp[lidx] = val;
+
         bool cond = (is_valid) && (id_dim < out_dim);
 
         if (cond) {
-            flag = calculate_head_flags_dim(kData, id_dim, kstride_dim);
+            flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
         } else {
             flag = 0;
         }
 
-        bool invalid = !cond;
-        if (!inclusive_scan) invalid = invalid || (id_dim == 0) || flag;
+        //val = cond ? transform(*iData) : init_val;
+
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(*iData);
+            }
+        } else {
+            if ((id_dim == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(*(iData - iInfo.strides[dim]));
+            }
+        }
+
+        if ((lidy == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidx]);
+            flag = flag | l_ftmp[lidx];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int off = 1; off < DIMY; off *= 2) {
 
-        scan_dim_by_key_core(invalid, &val, &flag, iData, init_val,
-                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
-                lid, lidx, lidy);
+            if (lidy >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
+                flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
 
         if ((*prev == 0) && (*curr == 1)) {
             boundaryid = id_dim;
@@ -162,11 +156,11 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if (cond) *oData = val;
         if (isLast) {
-            last_val[lidx] = val;
-            last_flag[lidx] = flag;
+            l_tmp[lidx] = val;
+            l_ftmp[lidx] = flag;
         }
         id_dim += DIMY;
-        kData += DIMY * kstride_dim;
+        kData += DIMY * kInfo.strides[dim];
         iData += DIMY * istride_dim;
         oData += DIMY * ostride_dim;
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -230,24 +224,23 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
     __local To l_val1[THREADS_X * DIMY];
     __local char l_flg0[THREADS_X * DIMY];
     __local char l_flg1[THREADS_X * DIMY];
-    __local To last_val[THREADS_X];
-    __local char last_flag[THREADS_X];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[THREADS_X];
+    __local char l_ftmp[THREADS_X];
 
+    bool flip = 0;
     const To init_val  = init;
     To val = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
-    char flag = 0;
-    if (!inclusive_scan) {
-        iData -= istride_dim;
-    }
-
     if (isLast) {
-        last_val[lidy] = val;
-        last_flag[lidy] = 0;
+        l_tmp[lidy] = val;
+        l_ftmp[lidy] = 0;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
+    char flag = 0;
     for (int k = 0; k < lim; k++) {
 
         bool cond = (is_valid) && (id_dim < out_dim);
@@ -262,17 +255,46 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
             flag = *kData;
         }
 
-        bool invalid = !cond;
-        if (!inclusive_scan) invalid = invalid || (id_dim == 0) || flag;
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(*iData);
+            }
+        } else {
+            if ((id_dim == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(*(iData - iInfo.strides[dim]));
+            }
+        }
+
+        if ((lidy == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidx]);
+            flag = flag | l_ftmp[lidx];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
 
-        scan_dim_by_key_core(invalid, &val, &flag, iData, init_val,
-                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
-                lid, lidx, lidy);
+        for (int off = 1; off < DIMY; off *= 2) {
+
+            if (lidy >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
+                flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
 
         if (cond) *oData = val;
         if (isLast) {
-            last_val[lidx] = val;
-            last_flag[lidx] = flag;
+            l_tmp[lidx] = val;
+            l_ftmp[lidx] = flag;
         }
         id_dim += DIMY;
         kData += DIMY * kInfo.strides[dim];
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 61b6ed3b1c..1806716e4c 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -277,6 +277,8 @@ namespace kernel
                                                threads_y,
                                                groups_all);
                 bufferFree(tmp.data);
+                bufferFree(tmpflg.data);
+                bufferFree(tmpid.data);
             }
         } catch (cl::Error err) {
             CL_TO_AF_ERROR(err);
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index bdefee3795..0a4144301d 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -18,43 +18,6 @@ static char calculate_head_flags(const __global Tk *kptr, int id, int previd)
     return flag;
 }
 
-void scan_first_by_key_core(const bool invalid,
-        To *val, char *flag,
-        const __global Ti *in, const To init_val,
-        __local To *l_val0, __local To *l_val1,
-        __local char *l_flg0, __local char *l_flg1,
-        __local To *last_val, __local char *last_flag,
-        const int lid, const int lidx, const int lidy)
-{
-    bool flip = 0;
-    __local To *l_val = l_val0;
-    __local char *l_flg = l_flg0;
-    *val = invalid? init_val : transform(*in);
-
-    if ((lidx == 0) && (flag == 0)) {
-        *val = binOp(*val, last_val[lidy]);
-        *flag = *flag | last_flag[lidy];
-    }
-
-    l_val0[lid] = *val;
-    l_flg0[lid] = *flag;
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    for (int off = 1; off < DIMX; off *= 2) {
-
-        if (lidx >= off) {
-            *val = l_flg[lid] ? *val : binOp(*val, l_val[lid - off]);
-            *flag = l_flg[lid] | l_flg[lid - off];
-        }
-        flip = 1 - flip;
-        l_val = flip ? l_val1 : l_val0;
-        l_flg = flip ? l_flg1 : l_flg0;
-        l_val[lid] = *val;
-        l_flg[lid] = *flag;
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-}
-
 __kernel
 void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
                        __global To *tData, KParam tInfo,
@@ -100,37 +63,40 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     __local To l_val1[SHARED_MEM_SIZE];
     __local char l_flg0[SHARED_MEM_SIZE];
     __local char l_flg1[SHARED_MEM_SIZE];
-    __local To last_val[DIMY];
-    __local char last_flag[DIMY];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[DIMY];
+    __local char l_ftmp[DIMY];
     __local int boundaryid;
 
+    bool flip = 0;
+
     const To init_val = init;
     int id = xid;
     To val = init_val;
-    const bool isLast = (lidx == (DIMX - 1));
 
-    char flag = 0;
-    if (!inclusive_scan) {
-        iData -= 1;
-    }
+    const bool isLast = (lidx == (DIMX - 1));
 
     if (isLast) {
-        last_val[lidy] = val;
-        last_flag[lidy] = 0;
+        l_tmp[lidy] = val;
+        l_ftmp[lidy] = 0;
         boundaryid = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     __local char *prev;
     if (lidx == 0) {
-        prev = &last_flag[lidy];
+        prev = &l_ftmp[lidy];
     } else {
-        prev = &l_flg0[lidx-1];
+        prev = &l_flg[lidx-1];
     }
-    __local char *curr = &l_flg0[lidx];
+    __local char *curr = &l_flg[lidx];
 
+    char flag = 0;
     for (int k = 0; k < lim; k++) {
 
+        //if (isLast) l_tmp[lidy] = val;
+
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (cond) {
@@ -138,13 +104,42 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
         } else {
             flag = 0;
         }
+        //val = cond ? transform(iData[id]) : init_val;
+
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(iData[id]);
+            }
+        } else {
+            if ((id == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(iData[id - 1]);
+            }
+        }
 
-        bool invalid = !cond;
-        if (!inclusive_scan) invalid = invalid || (id == 0) || flag;
+        if ((lidx == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidy]);
+            flag = flag | l_ftmp[lidy];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
 
-        scan_first_by_key_core(invalid, &val, &flag, iData, init_val,
-                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
-                lid, lidx, lidy);
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (lidx >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
+                flag = l_flg[lid] | l_flg[lid - off];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
 
         if ((*prev == 0) && (*curr == 1)) {
             boundaryid = id;
@@ -152,14 +147,15 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if (cond) oData[id] = val;
         if (isLast) {
-            last_val[lidy] = val;
-            last_flag[lidy] = flag;
+            l_tmp[lidy] = val;
+            l_ftmp[lidy] = flag;
         }
         id += DIMX;
         barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
     }
 
-    if (isLast && cond_yzw) {
+    if (isLast) {
+    //if (isLast && cond_yzw)
         tData[groupId_x] = val;
         tfData[groupId_x] = flag;
         tiData[groupId_x] = boundaryid;
@@ -199,27 +195,24 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     __local To l_val1[SHARED_MEM_SIZE];
     __local char l_flg0[SHARED_MEM_SIZE];
     __local char l_flg1[SHARED_MEM_SIZE];
-    __local To last_val[DIMY];
-    __local char last_flag[DIMY];
+    __local To *l_val = l_val0;
+    __local char *l_flg = l_flg0;
+    __local To l_tmp[DIMY];
+    __local char l_ftmp[DIMY];
+
+    bool flip = 0;
 
     const To init_val = init;
     int id = xid;
     To val = init_val;
-    const bool isLast = (lidx == (DIMX - 1));
 
-    if (!inclusive_scan) {
-        iData -= 1;
-    }
-
-    if (isLast) {
-        last_val[lidy] = val;
-        last_flag[lidy] = 0;
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
+    const bool isLast = (lidx == (DIMX - 1));
 
     for (int k = 0; k < lim; k++) {
         char flag = 0;
 
+        //if (isLast) l_tmp[lidy] = val;
+
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (calculateFlags) {
@@ -231,18 +224,47 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
         } else {
             flag = kData[id];
         }
+        //val = cond ? transform(iData[id]) : init_val;
 
-        bool invalid = !cond;
-        if (!inclusive_scan) invalid = invalid || (id == 0) || flag;
+        if (inclusive_scan) {
+            if (!cond) {
+                val = init_val;
+            } else {
+                val = transform(iData[id]);
+            }
+        } else {
+            if ((id == 0) || (!cond) || flag) {
+                val = init_val;
+            } else {
+                val = transform(iData[id - 1]);
+            }
+        }
 
-        scan_first_by_key_core(invalid, &val, &flag, iData, init_val,
-                l_val0, l_val1, l_flg0, l_flg1, last_val, last_flag,
-                lid, lidx, lidy);
+        if ((lidx == 0) && (flag == 0)) {
+            val = binOp(val, l_tmp[lidy]);
+            flag = flag | l_ftmp[lidy];
+        }
+        l_val[lid] = val;
+        l_flg[lid] = flag;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (lidx >= off) {
+                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
+                flag = l_flg[lid] | l_flg[lid - off];
+            }
+            flip = 1 - flip;
+            l_val = flip ? l_val1 : l_val0;
+            l_flg = flip ? l_flg1 : l_flg0;
+            l_val[lid] = val;
+            l_flg[lid] = flag;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
 
         if (cond) oData[id] = val;
         if (isLast) {
-            last_val[lidy] = val;
-            last_flag[lidy] = flag;
+            l_tmp[lidy] = val;
+            l_ftmp[lidy] = flag;
         }
         id += DIMX;
         barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 49eed4118f..ba523dac06 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -260,7 +260,8 @@ namespace kernel
                                              threads_x);
 
             bufferFree(tmp.data);
-
+            bufferFree(tmpflg.data);
+            bufferFree(tmpid.data);
         }
     }
 
diff --git a/test/binary_ops.hpp b/test/binary_ops.hpp
new file mode 100644
index 0000000000..3c9dbc0c24
--- /dev/null
+++ b/test/binary_ops.hpp
@@ -0,0 +1,141 @@
+#include <af/defines.h>
+#include <algorithm>
+#include <numeric>
+#include <complex>
+
+template<typename T> static inline T min(T lhs, T rhs) { return std::min(lhs, rhs); }
+std::complex<float> min(std::complex<float> lhs, std::complex<float> rhs);
+std::complex<double> min(std::complex<double> lhs, std::complex<double> rhs);
+
+template<typename T> static inline T max(T lhs, T rhs) { return std::max(lhs, rhs); }
+std::complex<float> max(std::complex<float> lhs, std::complex<float> rhs);
+std::complex<double> max(std::complex<double> lhs, std::complex<double> rhs);
+
+template<typename T, af_binary_op op>
+struct Binary
+{
+    T init()
+    {
+        return (T)(0);
+    }
+
+    T operator() (T lhs, T rhs)
+    {
+        return lhs + rhs;
+    }
+};
+
+template<typename T>
+struct Binary<T, AF_BINARY_ADD>
+{
+    T init()
+    {
+        return (T)(0);
+    }
+
+    T operator() (T lhs, T rhs)
+    {
+        return lhs + rhs;
+    }
+};
+
+template<typename T>
+struct Binary<T, AF_BINARY_MUL>
+{
+    T init()
+    {
+        return (T)(1);
+    }
+
+    T operator() (T lhs, T rhs)
+    {
+        return lhs * rhs;
+    }
+};
+
+template<typename T>
+struct Binary<T, AF_BINARY_MIN>
+{
+    T init()
+    {
+        return std::numeric_limits<T>::max();
+    }
+
+    T operator() (T lhs, T rhs)
+    {
+        return min(lhs, rhs);
+    }
+};
+
+template<typename T>
+struct Binary<T, AF_BINARY_MAX>
+{
+    T init()
+    {
+        return std::numeric_limits<T>::min();
+    }
+
+    T operator() (T lhs, T rhs)
+    {
+        return max(lhs, rhs);
+    }
+};
+
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)               \
+    template<>                                      \
+    struct Binary<T, AF_BINARY_MIN>                 \
+    {                                               \
+        T init()                                    \
+        {                                           \
+            return                                  \
+            (T)(std::numeric_limits<Tr>::max());    \
+        }                                           \
+                                                    \
+        T operator() (T lhs, T rhs)                 \
+        {                                           \
+            return min(lhs, rhs);                   \
+        }                                           \
+    };                                              \
+
+SPECIALIZE_COMPLEX_MIN(std::complex<float>, float)
+SPECIALIZE_COMPLEX_MIN(std::complex<double>, double)
+#undef SPECIALIZE_COMPLEX_MIN
+
+#define SPECIALIZE_COMPLEX_MAX(T, Tr)               \
+    template<>                                      \
+    struct Binary<T, AF_BINARY_MAX>                 \
+    {                                               \
+        T init()                                    \
+        {                                           \
+            return (T)((Tr)(0));                    \
+        }                                           \
+                                                    \
+        T operator() (T lhs, T rhs)                 \
+        {                                           \
+            return max(lhs, rhs);                   \
+        }                                           \
+    };                                              \
+
+SPECIALIZE_COMPLEX_MAX(std::complex<float>, float)
+SPECIALIZE_COMPLEX_MAX(std::complex<double>, double)
+#undef SPECIALIZE_COMPLEX_MAX
+
+#define SPECIALIZE_FLOATING_MAX(T, Tr)              \
+    template<>                                      \
+    struct Binary<T, AF_BINARY_MAX>                 \
+    {                                               \
+        T init()                                    \
+        {                                           \
+            return                                  \
+            (T)(-std::numeric_limits<Tr>::max());   \
+        }                                           \
+                                                    \
+        T operator() (T lhs, T rhs)                 \
+        {                                           \
+            return max(lhs, rhs);                   \
+        }                                           \
+    };                                              \
+
+SPECIALIZE_FLOATING_MAX(float, float)
+SPECIALIZE_FLOATING_MAX(double, double)
+#undef SPECIALIZE_FLOATING_MAX
diff --git a/test/scan.cpp b/test/scan.cpp
index 34a077f122..1b88104468 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -17,6 +17,8 @@
 #include <string>
 #include <testHelpers.hpp>
 #include <af/device.h>
+#include "binary_ops.hpp"
+#include <utility>
 
 using std::vector;
 using std::string;
@@ -82,6 +84,155 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
 
+float randomInterval(float start, float end)
+{
+    return start + (end - start)*(std::rand()/float(RAND_MAX));
+}
+
+int randomInterval(int start, int end)
+{
+    return start + std::rand()%(end - start);
+}
+
+template <typename T>
+std::vector<T> createScanKey(af::dim4 dims, int scanDim,
+        const std::vector<int> &nodeLengths,
+        T keyStart, T keyEnd)
+{
+    int elemCount = dims.elements();
+    std::vector<T> key(elemCount);
+
+    int stride = 1;
+    for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
+
+    for (int start = 0; start < stride; ++start) {
+        T keyval = (T)(0);
+        for (int index = start, i = 0;
+                index < elemCount;
+                index += stride, i = (i+1)%dims[scanDim]) {
+            bool isNode = false;
+            for (unsigned n = 0; n < nodeLengths.size(); ++n) {
+                if (i % nodeLengths[n] == 0) {
+                    isNode = true;
+                }
+            }
+            if (isNode) {
+                if (std::rand()%2) keyval =
+                    randomInterval(keyStart, keyEnd);
+            }
+            key[index] = keyval;
+        }
+    }
+    return key;
+}
+
+template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive_scan>
+std::pair<std::vector<Ti>, std::vector<To> > createData(af::dim4 dims, const std::vector<Tk> &key,
+                int scanDim, Ti dataStart, Ti dataEnd)
+{
+    Binary<To, op> binOp;
+    int elemCount = dims.elements();
+    std::vector<To> out(elemCount);
+    std::vector<Ti> in(elemCount);
+
+    int stride = 1;
+    for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
+
+    for (int start = 0; start < stride; ++start) {
+        Ti keyval = key[start];
+        if (!inclusive_scan) {
+            out[start] = binOp.init();
+            in[start] = randomInterval(dataStart, dataEnd);
+        }
+        for (int index = start + (!inclusive_scan)*stride, i = (!inclusive_scan);
+                index < elemCount;
+                index += stride, i = (i+1)%dims[scanDim]) {
+            in[index] = randomInterval(dataStart, dataEnd);
+            if ((key[index] != keyval) || (i == 0)) {
+                keyval = key[index];
+                out[index] = inclusive_scan? (To)in[index] : binOp.init();
+            } else {
+                To dataval = (To)in[index - (!inclusive_scan)*stride];
+                out[index] = binOp(out[index - stride], dataval);
+            }
+        }
+    }
+    return std::make_pair(in, out);
+}
+
+template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
+void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
+        int keyStart, int keyEnd, Ti dataStart, Ti dataEnd)
+{
+    std::vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
+    std::pair<std::vector<Ti>, std::vector<To> > data =
+        createData<Ti, int, To, op, inclusive_scan>(dims, key, scanDim, dataStart, dataEnd);
+    std::vector<Ti> &in = data.first;
+    std::vector<To> &outgold = data.second;
+    af::array afkey(dims, key.data());
+    af::array afin(dims, in.data());
+    af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
+
+    std::vector<To> out(afout.elements());
+    afout.host(out.data());
+    for(unsigned i = 0; i < out.size(); ++i) {
+        ASSERT_NEAR(out[i], outgold[i], 1e-5);
+    }
+}
+
+#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND)    \
+TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                \
+{                                                                           \
+    af::dim4 dims(X, Y, Z, W);                                            \
+    int scanDim = DIM;                                                      \
+    int nodel[] = {37, 256};                                                \
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));   \
+    int keyStart = 0;                                                       \
+    int keyEnd = 15;                                                        \
+    int dataStart = DSTART;                                                 \
+    int dataEnd = DEND;                                                     \
+    scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths,              \
+            keyStart, keyEnd, dataStart, dataEnd);                          \
+}
+
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int,  true, 0,   -15, 15);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int, false, 0,   -15, 15);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float,  true, 0, -0.25, 0.25);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float, false, 0, -0.25, 0.25);
+
+TEST(ScanByKey,Test_Scan_By_key_Simple_0)
+{
+    af::dim4 dims(16, 8, 2, 1);
+    int scanDim = 0;
+    int nodel[] = {4, 8};
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+    int keyStart = 0;
+    int keyEnd = 15;
+    int dataStart = 2;
+    int dataEnd = 4;
+    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
+            keyStart, keyEnd, dataStart, dataEnd);
+}
+
+//TEST(ScanByKey,Test_Scan_By_key_Simple_1)
+//{
+//    af::dim4 dims(8, 256+128, 1, 1);
+//    int scanDim = 1;
+//    int nodel[] = {4, 8};
+//    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+//    int keyStart = 0;
+//    int keyEnd = 15;
+//    int dataStart = 2;
+//    int dataEnd = 4;
+//    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
+//            keyStart, keyEnd, dataStart, dataEnd);
+//}
+
+//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1,   int,   int,  true, 1,   -15, 15);
+//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1,   int,   int, false, 1,   -15, 15);
+//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1, float, float,  true, 1, -0.25, 0.25);
+//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1, float, float, false, 1, -0.25, 0.25);
+
 #define SCAN_TESTS(FN, TAG, Ti, To)             \
     TEST(Scan,Test_##FN##_##TAG)                \
     {                                           \
@@ -119,8 +270,7 @@ TEST(Scan,Test_Scan_Big1)
 }
 
 ///////////////////////////////// CPP ////////////////////////////////////
-//
-TEST(Scan, CPP)
+TEST(Accum, CPP)
 {
     vector<af::dim4> numDims;
 

From d71ef5aba7b67eb50b069ea1c8fbffe6510eda91 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 8 Jun 2016 17:05:58 -0400
Subject: [PATCH 0599/2677] Using MKL sparse handle style function for matmul

---
 src/backend/cpu/sparse.hpp      |   1 -
 src/backend/cpu/sparse_blas.cpp | 202 ++++++++++++++++++--------------
 2 files changed, 116 insertions(+), 87 deletions(-)

diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 4ee8fd5962..923345ec94 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -15,7 +15,6 @@ namespace cpu
 {
 
 #ifdef USE_MKL
-typedef char sp_op_t;
 typedef MKL_Complex8  sp_cfloat;
 typedef MKL_Complex16 sp_cdouble;
 #endif
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 9ba9683434..4cb61af0b0 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -57,82 +57,110 @@ using ptr_type     =    typename conditional<   is_complex<T>::value,
                                                 T*>::type;
 template<typename T>
 using scale_type   =    typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T *>::type;
+                                                const typename blas_base<T>::type,
+                                                const T>::type;
 // MKL
-// void mkl_zcsrmm (const char *transa ,
-//                  const MKL_INT *m , const MKL_INT *n , const MKL_INT *k ,
-//                  const MKL_Complex16 *alpha , const char *matdescra ,
-//                  const MKL_Complex16 *val , const MKL_INT *indx ,
-//                  const MKL_INT *pntrb , const MKL_INT *pntre ,
-//                  const MKL_Complex16 *b , const MKL_INT *ldb ,
-//                  const MKL_Complex16 *beta ,
-//                  MKL_Complex16 *c , const MKL_INT *ldc );
+// sparse_status_t mkl_sparse_z_create_csr (
+//                 sparse_matrix_t *A,
+//                 sparse_index_base_t indexing,
+//                 MKL_INT rows, MKL_INT cols,
+//                 MKL_INT *rows_start, MKL_INT *rows_end,
+//                 MKL_INT *col_indx,
+//                 MKL_Complex16 *values);
 //
-// void mkl_zcsrmv (const char *transa ,
-//                  const MKL_INT *m , const MKL_INT *k ,
-//                  const MKL_Complex16 *alpha , const char *matdescra ,
-//                  const MKL_Complex16 *val , const MKL_INT *indx ,
-//                  const MKL_INT *pntrb , const MKL_INT *pntre ,
-//                  const MKL_Complex16 *x ,
-//                  const MKL_Complex16 *beta ,
-//                  MKL_Complex16 *y );
+// sparse_status_t mkl_sparse_z_mv (
+//                 sparse_operation_t operation,
+//                 MKL_Complex16 alpha,
+//                 const sparse_matrix_t A,
+//                 struct matrix_descr descr,
+//                 const MKL_Complex16 *x,
+//                 MKL_Complex16 beta,
+//                 MKL_Complex16 *y);
 //
+// sparse_status_t mkl_sparse_z_mm (
+//                 sparse_operation_t operation,
+//                 MKL_Complex16 alpha,
+//                 const sparse_matrix_t A,
+//                 struct matrix_descr descr,
+//                 sparse_layout_t layout,
+//                 const MKL_Complex16 *x,
+//                 MKL_INT columns, MKL_INT ldx,
+//                 MKL_Complex16 beta,
+//                 MKL_Complex16 *y,
+//                 MKL_INT ldy);
+
+template<typename T>
+using create_csr_func_def = sparse_status_t (*)
+                           (sparse_matrix_t *,
+                            sparse_index_base_t,
+                            int, int,
+                            int *, int *, int*,
+                            ptr_type<T>);
 
 template<typename T>
-using csrmm_func_def = void (*)( const sp_op_t *,
-                                 const int *, const int *, const int *,
-                                 const scale_type<T>, const sp_op_t *,
-                                 cptr_type<T>, const int *,
-                                 const int *, const int *,
-                                 cptr_type<T>, const int *,
-                                 scale_type<T>,
-                                 ptr_type<T>, const int *);
+using mv_func_def         = sparse_status_t (*)
+                           (sparse_operation_t,
+                            scale_type<T>,
+                            const sparse_matrix_t,
+                            struct matrix_descr,
+                            cptr_type<T>,
+                            scale_type<T>,
+                            ptr_type<T>);
 
 template<typename T>
-using csrmv_func_def = void (*)( const sp_op_t *,
-                                 const int *, const int *,
-                                 const scale_type<T>, const sp_op_t *,
-                                 cptr_type<T>, const int *,
-                                 const int *, const int *,
-                                 cptr_type<T>,
-                                 scale_type<T>,
-                                 ptr_type<T>);
+using mm_func_def         = sparse_status_t (*)
+                           (sparse_operation_t,
+                            scale_type<T>,
+                            const sparse_matrix_t,
+                            struct matrix_descr,
+                            sparse_layout_t,
+                            cptr_type<T>,
+                            int, int,
+                            scale_type<T>,
+                            ptr_type<T>, int);
+
 
 #define SPARSE_FUNC_DEF( FUNC )                         \
 template<typename T> FUNC##_func_def<T> FUNC##_func();
 
 #define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
   template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &mkl_##PREFIX##FUNC; }
-
-SPARSE_FUNC_DEF( csrmm )
-SPARSE_FUNC(csrmm , float   , s)
-SPARSE_FUNC(csrmm , double  , d)
-SPARSE_FUNC(csrmm , cfloat  , c)
-SPARSE_FUNC(csrmm , cdouble , z)
-
-SPARSE_FUNC_DEF( csrmv )
-SPARSE_FUNC(csrmv , float   , s)
-SPARSE_FUNC(csrmv , double  , d)
-SPARSE_FUNC(csrmv , cfloat  , c)
-SPARSE_FUNC(csrmv , cdouble , z)
+{ return &mkl_sparse_##PREFIX##_##FUNC; }
+
+SPARSE_FUNC_DEF( create_csr )
+SPARSE_FUNC(create_csr , float   , s)
+SPARSE_FUNC(create_csr , double  , d)
+SPARSE_FUNC(create_csr , cfloat  , c)
+SPARSE_FUNC(create_csr , cdouble , z)
+
+SPARSE_FUNC_DEF( mv )
+SPARSE_FUNC(mv , float   , s)
+SPARSE_FUNC(mv , double  , d)
+SPARSE_FUNC(mv , cfloat  , c)
+SPARSE_FUNC(mv , cdouble , z)
+
+SPARSE_FUNC_DEF( mm )
+SPARSE_FUNC(mm , float   , s)
+SPARSE_FUNC(mm , double  , d)
+SPARSE_FUNC(mm , cfloat  , c)
+SPARSE_FUNC(mm , cdouble , z)
 
 template<typename T, int value>
 scale_type<T> getScale()
 {
     static T val(value);
-    return (const typename blas_base<T>::type*)&val;
+    //return (const typename blas_base<T>::type *)&val;
+    return *(const scale_type<T>*)&val;
 }
 
-sp_op_t
+sparse_operation_t
 toSparseTranspose(af_mat_prop opt)
 {
-    sp_op_t out = 'N';
+    sparse_operation_t out = SPARSE_OPERATION_NON_TRANSPOSE;
     switch(opt) {
-        case AF_MAT_NONE        : out = 'N';    break;
-        case AF_MAT_TRANS       : out = 'T';    break;
-        case AF_MAT_CTRANS      : out = 'C';    break;
+        case AF_MAT_NONE        : out = SPARSE_OPERATION_NON_TRANSPOSE;         break;
+        case AF_MAT_TRANS       : out = SPARSE_OPERATION_TRANSPOSE;             break;
+        case AF_MAT_CTRANS      : out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE;   break;
         default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
@@ -148,10 +176,10 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     rhs.eval();
 
     // Similar Operations to GEMM
-    sp_op_t lOpts = toSparseTranspose(optLhs);
+    sparse_operation_t lOpts = toSparseTranspose(optLhs);
 
-    int lRowDim = (lOpts == 'N') ? 0 : 1;
-    int lColDim = (lOpts == 'N') ? 1 : 0;
+    int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
+    int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
     static const int rColDim = 1; //Unsupported : (rOpts == 'N;) ? 1 : 0;
 
     dim4 lDims = lhs.dims();
@@ -164,45 +192,47 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     out.eval();
 
     auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
-        // Mat Descr
-        // When 0 is 'G', 1, 2 are ignored
-        // 4 and 5 are unused
-        static const sp_op_t descra[] = {'G', '0', '0', 'C', '0', '0'};
-
         auto alpha = getScale<T, 1>();
         auto beta  = getScale<T, 0>();
 
-        int ldb = rhs.strides()[1];
-        int ldc = out.strides()[1];
+        int ldb = right.strides()[1];
+        int ldc = output.strides()[1];
+
+        Array<T  > values = left.getValues();
+        Array<int> rowIdx = left.getRowIdx();
+        Array<int> colIdx = left.getColIdx();
+
+        int *pB = rowIdx.get();
+        int *pE = rowIdx.get() + 1;
+
+        sparse_matrix_t csrLhs;
+        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, M, K,
+                             pB, pE, colIdx.get(),
+                             reinterpret_cast<ptr_type<T>>(values.get()));
 
-        Array<T  > values = lhs.getValues();
-        Array<int> rowIdx = lhs.getRowIdx();
-        Array<int> colIdx = lhs.getColIdx();
+        struct matrix_descr descrLhs;
+        descrLhs.type = SPARSE_MATRIX_TYPE_GENERAL;
 
-        const int *pB = rowIdx.get();
-        const int *pE = rowIdx.get() + 1;
+        mkl_sparse_optimize(csrLhs);
 
         if(rDims[rColDim] == 1) {
-            csrmv_func<T>()(
-                &lOpts, &M, &K,
-                reinterpret_cast<scale_type<T>>(&alpha), descra,
-                reinterpret_cast<cptr_type<T>>(values.get()),
-                reinterpret_cast<const int*>(colIdx.get()),
-                pB, pE,
-                reinterpret_cast<cptr_type<T>>(rhs.get()),
-                reinterpret_cast<scale_type<T>>(&beta),
-                reinterpret_cast<ptr_type<T>>(const_cast<T*>(out.get())));
+            mkl_sparse_set_mv_hint(csrLhs, lOpts, descrLhs, 1);
+            mv_func<T>()(
+                lOpts, alpha,
+                csrLhs, descrLhs,
+                reinterpret_cast<cptr_type<T>>(right.get()),
+                beta,
+                reinterpret_cast<ptr_type<T>>(output.get()));
         } else {
-            csrmm_func<T>()(
-                &lOpts, &M, &N, &K,
-                reinterpret_cast<scale_type<T>>(&alpha), descra,
-                reinterpret_cast<cptr_type<T>>(values.get()),
-                reinterpret_cast<const int*>(colIdx.get()),
-                pB, pE,
-                reinterpret_cast<cptr_type<T>>(rhs.get()), &ldb,
-                reinterpret_cast<scale_type<T>>(&beta),
-                reinterpret_cast<ptr_type<T>>(const_cast<T*>(out.get())), &ldc);
+            mkl_sparse_set_mm_hint(csrLhs, lOpts, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR, N, 1);
+            mm_func<T>()(
+                lOpts, alpha,
+                csrLhs, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR,
+                reinterpret_cast<cptr_type<T>>(right.get()),
+                N, ldb, beta,
+                reinterpret_cast<ptr_type<T>>(output.get()), ldc);
         }
+        mkl_sparse_destroy(csrLhs);
     };
 
     getQueue().enqueue(func, out, lhs, rhs);

From b9a21ac88a17caa66045f682f1ff940e27cd82b0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 8 Jun 2016 17:55:34 -0400
Subject: [PATCH 0600/2677] Add basic test for sparse matmul

---
 test/sparse.cpp | 121 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)
 create mode 100644 test/sparse.cpp

diff --git a/test/sparse.cpp b/test/sparse.cpp
new file mode 100644
index 0000000000..3641458b57
--- /dev/null
+++ b/test/sparse.cpp
@@ -0,0 +1,121 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <complex>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using std::abs;
+using af::cfloat;
+using af::cdouble;
+
+///////////////////////////////// CPP ////////////////////////////////////
+//
+
+template<typename T>
+af::array makeSparse(af::array A, int factor)
+{
+    A = floor(A * 1000);
+    A = A * ((A % factor) == 0) / 1000;
+    return A;
+}
+
+template<>
+af::array makeSparse<cfloat>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = real(A);
+    i = floor(i * 1000);
+    i = i * ((i % factor) == 0) / 1000;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+template<>
+af::array makeSparse<cdouble>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = real(A);
+    i = floor(i * 1000);
+    i = i * ((i % factor) == 0) / 1000;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+template<typename T>
+void sparseTester(const int m, const int n, const int k, int factor, double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(n, k));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    // Result of GEMM
+    af::array dRes = matmul(A, B);
+
+    // Create Sparse Array From Dense
+    af::array sA = af::createSparseArray(A, AF_SPARSE_CSR);
+
+    // Sparse Matmul
+    af::array sRes = matmul(sA, B);
+
+    // Verify Results
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes - sRes))) / (m * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes - sRes))) / (m * k), eps);
+}
+
+
+#define SPARSE_TESTS(T, eps)                            \
+    TEST(SPARSE, T##Square)                             \
+    {                                                   \
+        sparseTester<T>(1000, 1000, 100, 5, eps);       \
+    }                                                   \
+    TEST(SPARSE, T##RectMultiple)                       \
+    {                                                   \
+        sparseTester<T>(2048, 1024, 512, 3, eps);       \
+    }                                                   \
+    TEST(SPARSE, T##RectDense)                          \
+    {                                                   \
+        sparseTester<T>(500, 1000, 250, 1, eps);        \
+    }                                                   \
+
+SPARSE_TESTS(float, 0.01)
+SPARSE_TESTS(double, 1E-5)
+SPARSE_TESTS(cfloat, 0.01)
+SPARSE_TESTS(cdouble, 1E-5)
+
+#undef SPARSE_TESTS

From e229abd3bd967db3049119d25d2762b41d6f7959 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 9 Jun 2016 14:23:04 -0400
Subject: [PATCH 0601/2677] Add CPU placeholders for implementation without MKL

---
 src/backend/cpu/sparse.cpp      | 113 +++++++++++++++++++++++++-------
 src/backend/cpu/sparse.hpp      |   6 ++
 src/backend/cpu/sparse_blas.cpp |  97 ++++++++++++++++++++++++---
 3 files changed, 184 insertions(+), 32 deletions(-)

diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 154cfce37d..19b09771f5 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -62,6 +62,8 @@ using scale_type   =    typename conditional<   is_complex<T>::value,
                                                 const typename blas_base<T>::type *,
                                                 const T *>::type;
 
+#ifdef USE_MKL
+
 // void mkl_zdnscsr (const MKL_INT *job ,
 //                   const MKL_INT *m , const MKL_INT *n ,
 //                   MKL_Complex16 *adns , const MKL_INT *lda ,
@@ -113,6 +115,12 @@ SPARSE_FUNC(csrcsc, cdouble,z)
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
 
+#endif // USE_MKL
+
+////////////////////////////////////////////////////////////////////////////////
+// Common to MKL and Not MKL
+////////////////////////////////////////////////////////////////////////////////
+
 // Partial template specialization of sparseConvertDenseToStorage for COO
 // However, template specialization is not allowed
 template<typename T>
@@ -135,6 +143,29 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
 }
 
+// Partial template specialization of sparseConvertStorageToDense for COO
+// However, template specialization is not allowed
+template<typename T>
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
+{
+    in.eval();
+
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    dense.eval();
+
+    const Array<T>   values = in.getValues();
+    const Array<int> rowIdx = in.getRowIdx();
+    const Array<int> colIdx = in.getColIdx();
+
+    getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
+
+    return dense;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#ifdef USE_MKL // Implementation using MKL
+////////////////////////////////////////////////////////////////////////////////
+
 template<typename T, af_sparse_storage storage>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
@@ -186,26 +217,6 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     return sparse_;
 }
 
-
-// Partial template specialization of sparseConvertStorageToDense for COO
-// However, template specialization is not allowed
-template<typename T>
-Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
-{
-    in.eval();
-
-    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
-    dense.eval();
-
-    const Array<T>   values = in.getValues();
-    const Array<int> rowIdx = in.getRowIdx();
-    const Array<int> colIdx = in.getColIdx();
-
-    getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
-
-    return dense;
-}
-
 template<typename T, af_sparse_storage storage>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
@@ -259,19 +270,75 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     return dense_;
 }
 
+////////////////////////////////////////////////////////////////////////////////
+#else // Implementation without using MKL
+////////////////////////////////////////////////////////////////////////////////
+
+template<typename T, af_sparse_storage storage>
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
+{
+    // TODO: Make an implementation without MKL
+    // Support CSC as well. MKL does not support Dense->CSC. So this will be
+    // used as fallback
+    // Make these implementations like a struct. See approx1
+    AF_ERROR("CPU Implementation Without MKL Currently Not Supported", AF_ERR_NOT_SUPPORTED);
+    in_.eval();
+
+    uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
+
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_SPARSE_CSR);
+
+    if(storage == AF_SPARSE_CSR)
+        return sparse_;
+    else
+        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
+    return sparse_;
+}
+
+template<typename T, af_sparse_storage storage>
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
+{
+    // TODO: Make an implementation without MKL
+    // Support CSC as well. MKL does not support CSC->Dense. So this will be
+    // used as fallback
+    // Make these implementations like a struct. See approx1
+
+    AF_ERROR("CPU Implementation Without MKL Currently Not Supported", AF_ERR_NOT_SUPPORTED);
+    in_.eval();
+
+    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
+    dense_.eval();
+
+    if(storage == AF_SPARSE_CSR)
+        return dense_;
+    else
+        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
+    return dense_;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#endif //USE_MKL
+////////////////////////////////////////////////////////////////////////////////
+
+////////////////////////////////////////////////////////////////////////////////
+// Common to MKL and Not MKL
+////////////////////////////////////////////////////////////////////////////////
 template<typename T, af_sparse_storage src, af_sparse_storage dest>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
-    in.eval();
-
     // Dummy function
     // TODO finish this function when support is required
+    AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
+    in.eval();
+
     SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
 
     return dense;
 }
 
-
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 923345ec94..0274a87f0a 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -9,7 +9,10 @@
 
 #include <Array.hpp>
 #include <SparseArray.hpp>
+
+#ifdef USE_MKL
 #include <mkl_spblas.h>
+#endif
 
 namespace cpu
 {
@@ -17,6 +20,9 @@ namespace cpu
 #ifdef USE_MKL
 typedef MKL_Complex8  sp_cfloat;
 typedef MKL_Complex16 sp_cdouble;
+#else
+typedef cfloat        sp_cfloat;
+typedef cdouble       sp_cdouble;
 #endif
 
 template<typename T, af_sparse_storage storage>
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 4cb61af0b0..81957a3362 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <sparse_blas.hpp>
-#include <mkl_spblas.h>
 
 #include <stdexcept>
 #include <string>
@@ -59,6 +58,8 @@ template<typename T>
 using scale_type   =    typename conditional<   is_complex<T>::value,
                                                 const typename blas_base<T>::type,
                                                 const T>::type;
+#ifdef USE_MKL
+
 // MKL
 // sparse_status_t mkl_sparse_z_create_csr (
 //                 sparse_matrix_t *A,
@@ -119,7 +120,6 @@ using mm_func_def         = sparse_status_t (*)
                             scale_type<T>,
                             ptr_type<T>, int);
 
-
 #define SPARSE_FUNC_DEF( FUNC )                         \
 template<typename T> FUNC##_func_def<T> FUNC##_func();
 
@@ -145,13 +145,17 @@ SPARSE_FUNC(mm , double  , d)
 SPARSE_FUNC(mm , cfloat  , c)
 SPARSE_FUNC(mm , cdouble , z)
 
-template<typename T, int value>
-scale_type<T> getScale()
+#else   // USE_MKL
+
+// From mkl_spblas.h
+typedef enum
 {
-    static T val(value);
-    //return (const typename blas_base<T>::type *)&val;
-    return *(const scale_type<T>*)&val;
-}
+    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
+    SPARSE_OPERATION_TRANSPOSE              = 11,
+    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
+} sparse_operation_t;
+
+#endif  // USE_MKL
 
 sparse_operation_t
 toSparseTranspose(af_mat_prop opt)
@@ -166,6 +170,17 @@ toSparseTranspose(af_mat_prop opt)
     return out;
 }
 
+template<typename T, int value>
+scale_type<T> getScale()
+{
+    static T val(value);
+    //return (const typename blas_base<T>::type *)&val;
+    return *(const scale_type<T>*)&val;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#ifdef USE_MKL // Implementation using MKL
+////////////////////////////////////////////////////////////////////////////////
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
@@ -180,7 +195,9 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
     int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
-    static const int rColDim = 1; //Unsupported : (rOpts == 'N;) ? 1 : 0;
+
+    //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
+    static const int rColDim = 1;
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
@@ -240,6 +257,68 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     return out;
 }
 
+////////////////////////////////////////////////////////////////////////////////
+#else // Implementation without using MKL
+////////////////////////////////////////////////////////////////////////////////
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    // TODO: Make a CPU Implementation for this
+    // Make separate function for MV and MM
+    // No need to support optRhs
+    lhs.eval();
+    rhs.eval();
+
+    // Similar Operations to GEMM
+    sparse_operation_t lOpts = toSparseTranspose(optLhs);
+
+    int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
+    // Commenting to avoid unused variable warnings
+    //int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+
+    //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
+    static const int rColDim = 1;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhs.dims();
+    int M = lDims[lRowDim];
+    int N = rDims[rColDim];
+    // Commenting to avoid unused variable warnings
+    //int K = lDims[lColDim];
+
+    Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
+    out.eval();
+
+    // Commenting to avoid unused variable warnings
+    //auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
+    //    auto alpha = getScale<T, 1>();
+    //    auto beta  = getScale<T, 0>();
+
+    //    int ldb = right.strides()[1];
+    //    int ldc = output.strides()[1];
+
+    //    Array<T  > values = left.getValues();
+    //    Array<int> rowIdx = left.getRowIdx();
+    //    Array<int> colIdx = left.getColIdx();
+
+    //    if(rDims[rColDim] == 1) {
+    //        // Call MV
+    //    } else {
+    //        // Call MM
+    //    }
+    //};
+
+    //getQueue().enqueue(func, out, lhs, rhs);
+
+    return out;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#endif
+////////////////////////////////////////////////////////////////////////////////
+
 #define INSTANTIATE_SPARSE(T)                                                           \
     template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
                                 af_mat_prop optLhs, af_mat_prop optRhs);                \

From 322c21253a210fc35ed00504c6f70bf6dbdb80dc Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 9 Jun 2016 15:16:03 -0400
Subject: [PATCH 0602/2677] adds image moments function to all backends

---
 include/af/defines.h                  |  12 +++
 include/af/image.h                    |   1 +
 include/af/moments.h                  |  68 +++++++++++++++
 include/arrayfire.h                   |   1 +
 src/api/c/moments.cpp                 |  62 +++++++++++++
 src/api/cpp/moments.cpp               |  49 +++++++++++
 src/api/unified/moments.cpp           |  18 ++++
 src/backend/cpu/image.cpp             |   2 +
 src/backend/cpu/image.hpp             |   1 +
 src/backend/cpu/kernel/moments.hpp    | 101 ++++++++++++++++++++++
 src/backend/cpu/moments.cpp           |  66 ++++++++++++++
 src/backend/cpu/moments.hpp           |  18 ++++
 src/backend/cuda/image.cu             |   3 +-
 src/backend/cuda/kernel/moments.hpp   | 120 ++++++++++++++++++++++++++
 src/backend/cuda/moments.cu           |  67 ++++++++++++++
 src/backend/cuda/moments.hpp          |  16 ++++
 src/backend/opencl/image.cpp          |   3 +-
 src/backend/opencl/kernel/moments.cl  | 104 ++++++++++++++++++++++
 src/backend/opencl/kernel/moments.hpp | 103 ++++++++++++++++++++++
 src/backend/opencl/moments.cpp        |  66 ++++++++++++++
 src/backend/opencl/moments.hpp        |  16 ++++
 21 files changed, 895 insertions(+), 2 deletions(-)
 create mode 100644 include/af/moments.h
 create mode 100644 src/api/c/moments.cpp
 create mode 100644 src/api/cpp/moments.cpp
 create mode 100644 src/api/unified/moments.cpp
 create mode 100644 src/backend/cpu/kernel/moments.hpp
 create mode 100644 src/backend/cpu/moments.cpp
 create mode 100644 src/backend/cpu/moments.hpp
 create mode 100644 src/backend/cuda/kernel/moments.hpp
 create mode 100644 src/backend/cuda/moments.cu
 create mode 100644 src/backend/cuda/moments.hpp
 create mode 100644 src/backend/opencl/kernel/moments.cl
 create mode 100644 src/backend/opencl/kernel/moments.hpp
 create mode 100644 src/backend/opencl/moments.cpp
 create mode 100644 src/backend/opencl/moments.hpp

diff --git a/include/af/defines.h b/include/af/defines.h
index d3ba5fdfa7..c0cc85e870 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -357,6 +357,15 @@ typedef enum {
 } af_image_format;
 #endif
 
+#if AF_API_VERSION >=34
+typedef enum {
+    AF_MOMENT_M00 = 0,
+    AF_MOMENT_M01 = 1,
+    AF_MOMENT_M10 = 2,
+    AF_MOMENT_M11 = 3,
+} af_moment_type;
+#endif
+
 #if AF_API_VERSION >= 32
 typedef enum {
     AF_HOMOGRAPHY_RANSAC = 0,   ///< Computes homography using RANSAC
@@ -423,6 +432,9 @@ namespace af
 #if AF_API_VERSION >= 32
     typedef af_marker_type markerType;
 #endif
+#if AF_API_VERSION >=34
+    typedef af_moment_type momentType;
+#endif
 }
 
 #endif
diff --git a/include/af/image.h b/include/af/image.h
index def7fc9d05..da0a099b86 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -1348,6 +1348,7 @@ extern "C" {
     */
     AFAPI af_err af_rgb2ycbcr(af_array* out, const af_array in, const af_ycc_std standard);
 #endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/include/af/moments.h b/include/af/moments.h
new file mode 100644
index 0000000000..a2a6328f04
--- /dev/null
+++ b/include/af/moments.h
@@ -0,0 +1,68 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+
+#ifdef __cplusplus
+namespace af
+{
+class array;
+
+#if AF_API_VERSION >= 34
+/**
+   C++ Interface for calculating an image moment
+
+   \param[in]  in is the input image
+   \param[moment] is the moment to calculate
+   \return      the value of the moment
+
+   \ingroup image_func_moments
+ */
+template<typename T> T moment(const array& in, const af_moment_type moment);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C++ Interface for calculating an image moment
+
+   \param[in]  in contains the input image(s)
+   \param[moment] is the moment to calculate
+   \return array containing the requested moment of each image
+
+   \ingroup image_func_moments
+ */
+AFAPI array moments(const array& in, const af_moment_type moment);
+#endif
+
+}
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       C Interface for finding image moments
+
+       \param[out] out is an array containing the calculated moments
+       \param[in]  in is an array of image(s)
+       \param[moment] is the moment to calculate
+       \return     ref AF_SUCCESS if the moment calculation is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_moments
+    */
+    AFAPI af_err af_moments(af_array *out, const af_array in, const af_moment_type moment);
+#endif
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 60df3176d1..5cd487b1dc 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -268,6 +268,7 @@
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
+#include "af/moments.h"
 #include "af/seq.h"
 #include "af/signal.h"
 #include "af/statistics.h"
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
new file mode 100644
index 0000000000..650bcea348
--- /dev/null
+++ b/src/api/c/moments.cpp
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/moments.h>
+#include <af/index.h>
+#include <af/data.h>
+
+#include <ArrayInfo.hpp>
+#include <graphics_common.hpp>
+#include <err_common.hpp>
+#include <backend.hpp>
+#include <moments.hpp>
+#include <handle.hpp>
+#include <reorder.hpp>
+#include <tile.hpp>
+#include <join.hpp>
+#include <cast.hpp>
+#include <arith.hpp>
+
+#include <iostream>
+#include <limits>
+
+using af::dim4;
+using namespace detail;
+
+template<typename T>
+static inline void moments(af_array *out, const af_array in, af_moment_type moment)
+{
+    Array<float> temp = moments<T>(getArray<T>(in), moment);
+    af_array tarr = getHandle<float>(temp);
+    Array<T> output = castArray<T>(tarr);
+
+    *out = getHandle<T>(output);
+}
+
+af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
+{
+    try {
+        const ArrayInfo in_info = getInfo(in);
+        af_dtype type = in_info.getType();
+
+        switch(type) {
+            case f32: moments<float>          (out, in, moment); break;
+            case f64: moments<double>         (out, in, moment); break;
+            case u32: moments<unsigned>       (out, in, moment); break;
+            case s32: moments<int>            (out, in, moment); break;
+            case u16: moments<unsigned short> (out, in, moment); break;
+            case s16: moments<short>          (out, in, moment); break;
+            case b8:  moments<char>           (out, in, moment); break;
+            default:  TYPE_ERROR(1, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/moments.cpp b/src/api/cpp/moments.cpp
new file mode 100644
index 0000000000..d4a163a08a
--- /dev/null
+++ b/src/api/cpp/moments.cpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/defines.h>
+#include <af/moments.h>
+#include <af/array.h>
+#include "error.hpp"
+
+namespace af
+{
+
+array moments(const array& in, const af_moment_type moment)
+{
+    af_array out = 0;
+    AF_THROW(af_moments(&out, in.get(), moment));
+    return array(out);
+}
+
+
+#define INSTANTIATE_REAL(T)                                 \
+    template<> AFAPI                                        \
+    T moment(const array &in, const af_moment_type moment)      \
+    {                                                       \
+        af_array out;                                       \
+        AF_THROW(af_moments(&out, in.get(), moment));       \
+        return array(out).scalar<T>();                      \
+    }                                                       \
+
+
+INSTANTIATE_REAL(float)
+INSTANTIATE_REAL(double)
+INSTANTIATE_REAL(int)
+INSTANTIATE_REAL(unsigned)
+INSTANTIATE_REAL(long long)
+INSTANTIATE_REAL(unsigned long long)
+INSTANTIATE_REAL(short)
+INSTANTIATE_REAL(unsigned short)
+INSTANTIATE_REAL(char)
+INSTANTIATE_REAL(unsigned char)
+
+#undef INSTANTIATE_REAL
+
+}
diff --git a/src/api/unified/moments.cpp b/src/api/unified/moments.cpp
new file mode 100644
index 0000000000..2ba2fbaca7
--- /dev/null
+++ b/src/api/unified/moments.cpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/moments.h>
+#include "symbol_manager.hpp"
+
+af_err af_moments(af_array* out, const af_array in, const af_moment_type moment)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, moment);
+}
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index b71ba23c12..738b4d01a8 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -12,11 +12,13 @@
 
 #if defined (WITH_GRAPHICS)
 
+#include <af/defines.h>
 #include <Array.hpp>
 #include <image.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
 #include <platform.hpp>
+#include <kernel/moments.hpp>
 #include <queue.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index dc6cc62f09..e9ed0ed371 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -11,6 +11,7 @@
 
 #include <Array.hpp>
 #include <graphics_common.hpp>
+#include <af/defines.h>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
new file mode 100644
index 0000000000..6a24fbfa66
--- /dev/null
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -0,0 +1,101 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <Array.hpp>
+#include <utility.hpp>
+#include <math.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+
+template<typename T, af_moment_type MM>
+struct moments_op
+{
+    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
+    {
+        return;
+    }
+};
+
+template<typename T>
+struct moments_op<T, AF_MOMENT_M00>
+{
+    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
+    {
+        return in[mId];
+    }
+};
+
+template<typename T>
+struct moments_op<T, AF_MOMENT_M01>
+{
+    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
+    {
+        return idx * in[mId];
+    }
+};
+
+template<typename T>
+struct moments_op<T, AF_MOMENT_M10>
+{
+    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
+    {
+        return idy * in[mId];
+    }
+};
+
+template<typename T>
+struct moments_op<T, AF_MOMENT_M11>
+{
+    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
+    {
+        return idx * idy * in[mId];
+    }
+};
+
+template<typename T, af_moment_type Method>
+void moments(Array<float> &output, Array<T> const input)
+{
+    T const * const in       = input.get();
+    af::dim4  const idims    = input.dims();
+    af::dim4  const istrides = input.strides();
+    dim_t     const iElems   = input.elements();
+
+    af::dim4  const odims    = output.dims();
+
+    moments_op<T, Method> op;
+    bool pBatch = !(idims[2] == 1 && idims[3] == 1);
+    bool tDim   = (idims[3] != 1);
+    bool zDim   = (idims[2] != 1);
+
+    float *out = output.get();
+
+    dim_t mId = 0;
+    for(dim_t w = 0; w < idims[3]; w++) {
+        for(dim_t z = 0; z < idims[2]; z++) {
+            T val = scalar<T>(0);
+            for(dim_t y = 0; y < idims[1]; y++) {
+                for(dim_t x = 0; x < idims[0]; x++) {
+                    val += op(in, mId, x, y);
+                    mId++;
+                }
+            }
+            out[w * odims[0] + z] = (float)val;
+        }
+    }
+}
+
+
+}
+}
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
new file mode 100644
index 0000000000..d4905b3b74
--- /dev/null
+++ b/src/backend/cpu/moments.cpp
@@ -0,0 +1,66 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/defines.h>
+#include <Array.hpp>
+#include <err_cpu.hpp>
+#include <platform.hpp>
+#include <kernel/moments.hpp>
+#include <queue.hpp>
+
+using af::dim4;
+
+namespace cpu
+{
+
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment)
+{
+    dim4 odims, idims = in.dims();
+    odims[0] = idims[2];
+    odims[1] = idims[3];
+    odims[2] = odims[3] = 1;
+
+    in.eval();
+    Array<float> out = createEmptyArray<float>(odims);
+
+    switch(moment) {
+        case AF_MOMENT_M00:
+            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M00>, out, in);
+            break;
+        case AF_MOMENT_M01:
+            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M01>, out, in);
+            break;
+        case AF_MOMENT_M10:
+            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M10>, out, in);
+            break;
+        case AF_MOMENT_M11:
+            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M11>, out, in);
+            break;
+        default:  break;
+    }
+    getQueue().sync();
+    return out;
+}
+
+
+#define INSTANTIATE(T)  \
+    template Array<float> moments<T>(const Array<T> &in, const af_moment_type moment);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
+}
+
diff --git a/src/backend/cpu/moments.hpp b/src/backend/cpu/moments.hpp
new file mode 100644
index 0000000000..f3627fad54
--- /dev/null
+++ b/src/backend/cpu/moments.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/defines.h>
+
+namespace cpu
+{
+    template<typename T>
+    Array<float> moments(const Array<T> &in, const af_moment_type moment);
+}
+
diff --git a/src/backend/cuda/image.cu b/src/backend/cuda/image.cu
index 292f80110a..45a26a9d3b 100644
--- a/src/backend/cuda/image.cu
+++ b/src/backend/cuda/image.cu
@@ -17,6 +17,7 @@
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <interopManager.hpp>
+#include <kernel/moments.hpp>
 
 using af::dim4;
 
@@ -57,7 +58,7 @@ void copy_image(const Array<T> &in, const fg::Image* image)
     }
 }
 
-#define INSTANTIATE(T)      \
+#define INSTANTIATE(T)                                                          \
     template void copy_image<T>(const Array<T> &in, const fg::Image* image);
 
 INSTANTIATE(float)
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
new file mode 100644
index 0000000000..4b48157a03
--- /dev/null
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -0,0 +1,120 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/defines.h>
+#include <backend.hpp>
+#include <Param.hpp>
+#include <dispatch.hpp>
+#include <math.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+
+namespace cuda
+{
+namespace kernel
+{
+
+    // Kernel Launch Config Values
+    static const int THREADS = 128;
+
+    // Moment functions
+    template<typename T>
+    __device__ inline static
+    T moments_m00(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
+    {
+        return in.ptr[mId];
+    }
+
+    template<typename T>
+    __device__ inline static
+    T moments_m01(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
+    {
+        return idx * in.ptr[mId];
+    }
+
+    template<typename T>
+    __device__ inline static
+    T moments_m10(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
+    {
+        return idy * in.ptr[mId];
+    }
+
+    template<typename T>
+    __device__ inline static
+    T moments_m11(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
+    {
+
+        return idx * idy * in.ptr[mId];
+    }
+
+    template<typename T, af_moment_type moment>
+    __global__
+    void moments_kernel(CParam<float> out, CParam<T> in,
+                  const dim_t blocksMatX, const bool pBatch)
+    {
+        const dim_t idw = blockIdx.y / in.dims[2];
+        const dim_t idz = blockIdx.y - idw * in.dims[2];
+
+        const dim_t idy = blockIdx.x / blocksMatX;
+        const dim_t blockIdx_x = blockIdx.x - idy * blocksMatX;
+        const dim_t idx = blockIdx_x * blockDim.x + threadIdx.x;
+
+        dim_t mId = idy * in.strides[1] + idx;
+        if(pBatch) {
+            mId += idw * in.strides[3] + idz * in.strides[2];
+        }
+
+        if (idx >= in.dims[0] || idy >= in.dims[1] ||
+            idz >= in.dims[2] || idw >= in.dims[3] )
+            return;
+
+        __shared__ float blk_moment_sum;
+        blk_moment_sum = 0.f;
+        __syncthreads();
+
+        switch(moment) {
+            case AF_MOMENT_M00:
+                atomicAdd(&blk_moment_sum, (float)moments_m00(mId, idx, idy, in));
+                break;
+            case AF_MOMENT_M01:
+                atomicAdd(&blk_moment_sum, (float)moments_m01(mId, idx, idy, in));
+                break;
+            case AF_MOMENT_M10:
+                atomicAdd(&blk_moment_sum, (float)moments_m10(mId, idx, idy, in));
+                break;
+            case AF_MOMENT_M11:
+                atomicAdd(&blk_moment_sum, (float)moments_m11(mId, idx, idy, in));
+                break;
+            default:
+                break;
+        }
+        __syncthreads();
+
+        float *offset = const_cast<float *>(out.ptr + (idw * out.dims[0] + idz));
+        if(threadIdx.x == 0)
+            atomicAdd(offset, blk_moment_sum);
+    }
+
+    // Wrapper functions
+    template <typename T, af_moment_type moment>
+    void moments(Param<float> out, CParam<T> in) {
+        dim3 threads(THREADS, 1, 1);
+        dim_t blocksMatX = divup(in.dims[0], threads.x);
+        dim3 blocks(blocksMatX * in.dims[1], in.dims[2] * in.dims[3]);
+
+        bool pBatch = !(in.dims[2] == 1 && in.dims[3] == 1);
+
+        CUDA_LAUNCH((moments_kernel<T, moment>), blocks, threads,
+                     out, in, blocksMatX, pBatch);
+        POST_LAUNCH_CHECK();
+        CUDA_CHECK(cudaDeviceSynchronize());
+    }
+
+}
+}
diff --git a/src/backend/cuda/moments.cu b/src/backend/cuda/moments.cu
new file mode 100644
index 0000000000..34bd5c181b
--- /dev/null
+++ b/src/backend/cuda/moments.cu
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <interopManager.hpp>
+#include <kernel/moments.hpp>
+
+using af::dim4;
+
+namespace cuda
+{
+
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment)
+{
+    dim4 odims, idims = in.dims();
+    odims[0] = odims[1] = odims[2] = odims[3] = 1;
+    if(idims[2] != 1) {
+        odims[0] = idims[2];
+    }
+    if(idims[3] != 1) {
+        odims[1] = idims[3];
+    }
+
+    in.eval();
+    Array<float> out = createValueArray<float>(odims, 0.f);
+
+    switch(moment) {
+        case AF_MOMENT_M00:
+            kernel::moments<T, AF_MOMENT_M00>(out, in);
+            break;
+        case AF_MOMENT_M01:
+            kernel::moments<T, AF_MOMENT_M01>(out, in);
+            break;
+        case AF_MOMENT_M10:
+            kernel::moments<T, AF_MOMENT_M10>(out, in);
+            break;
+        case AF_MOMENT_M11:
+            kernel::moments<T, AF_MOMENT_M11>(out, in);
+            break;
+        default:  break;
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                          \
+    template Array<float>  moments<T>(const Array<T> &in, const af_moment_type moment);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
+}
diff --git a/src/backend/cuda/moments.hpp b/src/backend/cuda/moments.hpp
new file mode 100644
index 0000000000..78142e0c18
--- /dev/null
+++ b/src/backend/cuda/moments.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cuda
+{
+    template<typename T>
+    Array<float> moments(const Array<T> &in, const af_moment_type moment);
+}
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 7f6b054739..11a479b0be 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -12,6 +12,7 @@
 #include <interopManager.hpp>
 #include <Array.hpp>
 #include <image.hpp>
+#include <kernel/moments.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <stdexcept>
@@ -62,7 +63,7 @@ void copy_image(const Array<T> &in, const fg::Image* image)
     }
 }
 
-#define INSTANTIATE(T)      \
+#define INSTANTIATE(T)                                                              \
     template void copy_image<T>(const Array<T> &in, const fg::Image* image);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/kernel/moments.cl b/src/backend/opencl/kernel/moments.cl
new file mode 100644
index 0000000000..1f836df4bb
--- /dev/null
+++ b/src/backend/opencl/kernel/moments.cl
@@ -0,0 +1,104 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#define M00 moments_m00
+#define M01 moments_m01
+#define M10 moments_m10
+#define M11 moments_m11
+
+////////////////////////////////////////////////////////////////////////////////////
+// Helper Functions
+////////////////////////////////////////////////////////////////////////////////////
+inline void fatomic_add_g(volatile __global float *source, const float operand) {
+    union {
+        unsigned int intVal;
+        float floatVal;
+    } newVal, prevVal, expVal;
+
+    prevVal.floatVal = *source;
+    do {
+        expVal.floatVal = prevVal.floatVal;
+        newVal.floatVal = expVal.floatVal + operand;
+        prevVal.intVal  = atomic_cmpxchg((volatile __global unsigned int *)source, expVal.intVal, newVal.intVal);
+    } while (expVal.intVal != prevVal.intVal);
+}
+
+inline void fatomic_add_l(volatile __local float *source, const float operand) {
+    union {
+        unsigned int intVal;
+        float floatVal;
+    } newVal, prevVal, expVal;
+
+    prevVal.floatVal = *source;
+    do {
+        expVal.floatVal = prevVal.floatVal;
+        newVal.floatVal = expVal.floatVal + operand;
+        prevVal.intVal  = atomic_cmpxchg((volatile __local unsigned int *)source, expVal.intVal, newVal.intVal);
+    } while (expVal.intVal != prevVal.intVal);
+}
+
+///////////////////////////////////////////////////////////////////////////
+// moments
+///////////////////////////////////////////////////////////////////////////
+float moments_m00(const int mId, const int idx, const int idy,
+                   __global const T *d_in,  const KParam in) {
+    return d_in[mId];
+}
+
+float moments_m01(const int mId, const int idx, const int idy,
+                   __global const T *d_in,  const KParam in) {
+    return idx * d_in[mId];
+}
+
+float moments_m10(const int mId, const int idx, const int idy,
+                   __global const T *d_in,  const KParam in) {
+    return idy * d_in[mId];
+}
+
+float moments_m11(const int mId, const int idx, const int idy,
+                   __global const T *d_in,  const KParam in) {
+    return idx * idy * d_in[mId];
+}
+
+////////////////////////////////////////////////////////////////////////////////////
+// Wrapper Kernel
+////////////////////////////////////////////////////////////////////////////////////
+__kernel
+void moments_kernel(__global  float *d_out, const KParam out,
+                    __global const T *d_in,  const KParam in,
+                    const int blocksMatX, const int pBatch)
+{
+    const int idw = get_group_id(1) / in.dims[2];
+    const int idz = get_group_id(1)  - idw * in.dims[2];
+
+    const int idy = get_group_id(0) / blocksMatX;
+    const int blockIdx_x = get_group_id(0) - idy * blocksMatX;
+    const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
+
+    int mId = idy * in.strides[1] + idx;
+    if(pBatch) {
+       mId += idw * in.strides[3] + idz * in.strides[2];
+    }
+
+    if(idx >= in.dims[0] ||
+       idy >= in.dims[1] ||
+       idz >= in.dims[2] ||
+       idw >= in.dims[3])
+        return;
+
+    __local float wkg_moment_sum;
+    wkg_moment_sum = 0;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    fatomic_add_l(&wkg_moment_sum, MOMENT(mId, idx, idy, d_in, in));
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if(get_local_id(0) == 0)
+        fatomic_add_g(d_out + (idw * out.dims[0] + idz), wkg_moment_sum);
+
+}
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
new file mode 100644
index 0000000000..c53b954eb1
--- /dev/null
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -0,0 +1,103 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/moments.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+#include <math.hpp>
+#include "config.hpp"
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::make_kernel;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        static const int THREADS = 128;
+
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template <typename T, af_moment_type moment>
+        void moments(Param out, const Param in)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>  momentsProgs;
+                static std::map<int, Kernel*> momentsKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="        << dtype_traits<T>::getName();
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    switch(moment) {
+                        case AF_MOMENT_M00:   options << " -D MOMENT=M00";
+                            break;
+                        case AF_MOMENT_M01:   options << " -D MOMENT=M01";
+                            break;
+                        case AF_MOMENT_M10:   options << " -D MOMENT=M10";
+                            break;
+                        case AF_MOMENT_M11:   options << " -D MOMENT=M11";
+                            break;
+                        default:
+                            break;
+                    }
+
+                    Program prog;
+                    buildProgram(prog, moments_cl, moments_cl_len, options.str());
+                    momentsProgs[device] = new Program(prog);
+
+                    momentsKernels[device] = new Kernel(*momentsProgs[device], "moments_kernel");
+                });
+
+
+                auto momentsp = make_kernel<Buffer, const KParam, const Buffer, const KParam,
+                                       const int, const int>
+                                      (*momentsKernels[device]);
+
+                NDRange local(THREADS, 1, 1);
+                dim_t blocksMatX = divup(in.info.dims[0], local[0]);
+                NDRange global(blocksMatX * in.info.dims[1] * local[0] ,
+                               in.info.dims[2] * in.info.dims[3] * local[1] );
+
+                bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
+
+                momentsp(EnqueueArgs(getQueue(), global, local),
+                          *out.data, out.info, *in.data, in.info,
+                          blocksMatX, (int)pBatch);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
new file mode 100644
index 0000000000..08bb88e2a5
--- /dev/null
+++ b/src/backend/opencl/moments.cpp
@@ -0,0 +1,66 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <kernel/moments.hpp>
+#include <err_opencl.hpp>
+#include <debug_opencl.hpp>
+#include <stdexcept>
+#include <vector>
+
+namespace opencl
+{
+
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment)
+{
+    dim4 odims, idims = in.dims();
+    odims[0] = odims[1] = odims[2] = odims[3] = 1;
+    if(idims[2] != 1) {
+        odims[0] = idims[2];
+    }
+    if(idims[3] != 1) {
+        odims[1] = idims[3];
+    }
+
+    in.eval();
+    Array<float> out = createValueArray<float>(odims, 0.f);
+
+    switch(moment) {
+        case AF_MOMENT_M00:
+            kernel::moments<T, AF_MOMENT_M00>(out, in);
+            break;
+        case AF_MOMENT_M01:
+            kernel::moments<T, AF_MOMENT_M01>(out, in);
+            break;
+        case AF_MOMENT_M10:
+            kernel::moments<T, AF_MOMENT_M10>(out, in);
+            break;
+        case AF_MOMENT_M11:
+            kernel::moments<T, AF_MOMENT_M11>(out, in);
+            break;
+        default:  break;
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                              \
+    template Array<float> moments<T>(const Array<T> &in, const af_moment_type moment);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
+}
diff --git a/src/backend/opencl/moments.hpp b/src/backend/opencl/moments.hpp
new file mode 100644
index 0000000000..e2ad2e2a01
--- /dev/null
+++ b/src/backend/opencl/moments.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+    template<typename T>
+    Array<float> moments(const Array<T> &in, const af_moment_type moment);
+}

From 4e73f8a769062696e3f153c730626ec95c4ffab7 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 9 Jun 2016 15:17:40 -0400
Subject: [PATCH 0603/2677] add tests for image moments

---
 test/moments.cpp | 145 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 145 insertions(+)
 create mode 100644 test/moments.cpp

diff --git a/test/moments.cpp b/test/moments.cpp
new file mode 100644
index 0000000000..1d64abf7ac
--- /dev/null
+++ b/test/moments.cpp
@@ -0,0 +1,145 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/array.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using af::cfloat;
+using af::cdouble;
+
+template<typename T>
+class Image : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {
+        }
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, double, int> TestTypes;
+
+// register the type list
+TYPED_TEST_CASE(Image, TestTypes);
+
+template<typename T>
+void momentsTest(string pTestFile)
+{
+    vector<af::dim4> numDims;
+
+    vector<vector<T> >   in;
+    vector<vector<float> >   tests;
+    readTests<T, float, float>(pTestFile, numDims, in, tests);
+
+    af::array imgArray(numDims.front(), &in.front()[0]);
+
+    af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
+    T *mData = momentsArray.host<T>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_M01);
+    mData = momentsArray.host<T>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_M10);
+    mData = momentsArray.host<T>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_M11);
+    mData = momentsArray.host<T>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+}
+
+void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
+{
+    vector<af::dim4> numDims;
+
+    vector<vector<float> >   in;
+    vector<vector<float> >   tests;
+    readTests<float, float, float>(pTestFile, numDims, in, tests);
+
+    af::array imgArray = af::loadImage(pImageFile.c_str(), isColor);
+
+    double maxVal = af::max<double>(imgArray);
+    double minVal = af::min<double>(imgArray);
+    imgArray /= maxVal - minVal;
+
+    af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
+
+    float *mData = momentsArray.host<float>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_M01);
+    mData = momentsArray.host<float>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_M10);
+    mData = momentsArray.host<float>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_M11);
+    mData = momentsArray.host<float>();
+    for(int i=0; i<momentsArray.elements();++i) {
+        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << std::endl;
+    }
+    delete [] mData;
+}
+
+TEST(IMAGE, MomentsImage)
+{
+    momentsOnImageTest(string(TEST_DIR"/image/gray_seq_16_moments.test"), string(TEST_DIR"/imageio/gray_seq_16.png"), false);
+}
+
+TEST(Image, MomentsImageBatch)
+{
+    momentsTest<float>(string(TEST_DIR"/image/simple_mat_batch_moments.test"));
+}
+
+TEST(Image, MomentsBatch2D)
+{
+    momentsOnImageTest(string(TEST_DIR"/image/color_seq_16_moments.test"), string(TEST_DIR"/imageio/color_seq_16.png"), true);
+}
+
+TYPED_TEST(Image, MomentsSynthTypes)
+{
+    momentsTest<TypeParam>(string(TEST_DIR"/image/simple_mat_moments.test"));
+}
+
+

From 0510329baa3ef211d97072c7c227f840a1a04340 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 9 Jun 2016 17:28:04 -0400
Subject: [PATCH 0604/2677] add af_moment function to return single moment from
 c api

---
 include/af/moments.h    | 17 ++++++++++++++++-
 src/api/c/moments.cpp   | 37 +++++++++++++++++++++++++++++++++++++
 src/api/cpp/moments.cpp | 10 +++++-----
 3 files changed, 58 insertions(+), 6 deletions(-)

diff --git a/include/af/moments.h b/include/af/moments.h
index a2a6328f04..98a4eb01b1 100644
--- a/include/af/moments.h
+++ b/include/af/moments.h
@@ -30,7 +30,7 @@ template<typename T> T moment(const array& in, const af_moment_type moment);
 
 #if AF_API_VERSION >= 34
 /**
-   C++ Interface for calculating an image moment
+   C++ Interface for calculating image moments
 
    \param[in]  in contains the input image(s)
    \param[moment] is the moment to calculate
@@ -63,6 +63,21 @@ extern "C" {
     AFAPI af_err af_moments(af_array *out, const af_array in, const af_moment_type moment);
 #endif
 
+#if AF_API_VERSION >= 34
+    /**
+       C Interface for calculating an image moment
+
+       \param[out] out is a pointer to the outputted moment
+       \param[in]  in is an array of image(s)
+       \param[moment] is the moment to calculate
+       \return     ref AF_SUCCESS if the moment calculation is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_moments
+    */
+    AFAPI af_err af_moment(double *out, const af_array in, const af_moment_type moment);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 650bcea348..98efd2b8b5 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -60,3 +60,40 @@ af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
 
     return AF_SUCCESS;
 }
+
+template<typename T>
+static inline void moment_copy(double *out, const af_array moments)
+{
+    dim_t elems;
+    af_get_elements(&elems, moments);
+    T *h_ptr = new T[elems];
+    af_get_data_ptr((void *)h_ptr, moments);
+
+    *out = (double)h_ptr[0];
+    delete[] h_ptr;
+}
+
+af_err af_moment(double *out, const af_array in, const af_moment_type moment)
+{
+    try {
+        af_array moments_arr;
+        af_moments(&moments_arr, in, moment);
+
+        const ArrayInfo m_info = getInfo(moments_arr);
+        af_dtype type = m_info.getType();
+
+        switch(type) {
+            case f32: moment_copy<float>          (out, moments_arr); break;
+            case f64: moment_copy<double>         (out, moments_arr); break;
+            case u32: moment_copy<unsigned>       (out, moments_arr); break;
+            case s32: moment_copy<int>            (out, moments_arr); break;
+            case u16: moment_copy<unsigned short> (out, moments_arr); break;
+            case s16: moment_copy<short>          (out, moments_arr); break;
+            case b8:  moment_copy<char>           (out, moments_arr); break;
+            default:  TYPE_ERROR(1, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/moments.cpp b/src/api/cpp/moments.cpp
index d4a163a08a..3802337e36 100644
--- a/src/api/cpp/moments.cpp
+++ b/src/api/cpp/moments.cpp
@@ -25,12 +25,12 @@ array moments(const array& in, const af_moment_type moment)
 
 #define INSTANTIATE_REAL(T)                                 \
     template<> AFAPI                                        \
-    T moment(const array &in, const af_moment_type moment)      \
+    T moment(const array &in, const af_moment_type moment)  \
     {                                                       \
-        af_array out;                                       \
-        AF_THROW(af_moments(&out, in.get(), moment));       \
-        return array(out).scalar<T>();                      \
-    }                                                       \
+        double out;                                         \
+        AF_THROW(af_moment(&out, in.get(), moment));        \
+        return (T)out;                                      \
+    }
 
 
 INSTANTIATE_REAL(float)

From 9c7df03e01b41b45db4a5afc18b8539823ad45af Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 9 Jun 2016 18:46:59 -0400
Subject: [PATCH 0605/2677] Sparse Cleanup

* AF_SPARSE_FOO -> AF_STORAGE_FOO
* af_sparse_storage -> af_storage, af::sparseStorage ->af::storage
* Remove unnecessary nNZ argument from createSparseArray
* Change the storage identifier to stype
---
 include/af/defines.h           |  12 +--
 include/af/sparse.h            |  24 ++---
 src/api/c/blas.cpp             |  10 +--
 src/api/c/print.cpp            |   8 +-
 src/api/c/sparse.cpp           | 154 ++++++++++++++++-----------------
 src/api/cpp/sparse.cpp         |  24 ++---
 src/api/unified/sparse.cpp     |  18 ++--
 src/backend/SparseArray.cpp    |  52 +++++------
 src/backend/SparseArray.hpp    |  34 ++++----
 src/backend/cpu/sparse.cpp     |  50 +++++------
 src/backend/cpu/sparse.hpp     |   6 +-
 src/backend/cuda/sparse.cu     |  42 ++++-----
 src/backend/cuda/sparse.hpp    |   6 +-
 src/backend/opencl/sparse.cpp  |  34 ++++----
 src/backend/opencl/sparse.hpp  |   6 +-
 src/backend/sparse_helpers.hpp |   8 +-
 test/sparse.cpp                |   2 +-
 17 files changed, 245 insertions(+), 245 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 08852c529e..61b7653ff2 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -396,11 +396,11 @@ typedef enum {
 
 #if AF_API_VERSION >= 34
 typedef enum {
-    AF_SPARSE_DENSE     = 0,
-    AF_SPARSE_CSR       = 1,
-    AF_SPARSE_CSC       = 2,
-    AF_SPARSE_COO       = 3,
-} af_sparse_storage;
+    AF_STORAGE_DENSE     = 0,
+    AF_STORAGE_CSR       = 1,
+    AF_STORAGE_CSC       = 2,
+    AF_STORAGE_COO       = 3,
+} af_storage;
 #endif
 
 #ifdef __cplusplus
@@ -433,7 +433,7 @@ namespace af
     typedef af_marker_type markerType;
 #endif
 #if AF_API_VERSION >= 34
-    typedef af_sparse_storage sparseStorage;
+    typedef af_storage storage;
 #endif
 
 }
diff --git a/include/af/sparse.h b/include/af/sparse.h
index 8f5b40b21e..44e30ea020 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -15,19 +15,19 @@ namespace af
 {
     class array;
 
-    AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+    AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols,
                                   const array values, const array rowIdx, const array colIdx,
-                                  const af::sparseStorage storage = AF_SPARSE_CSR);
+                                  const af::storage stype = AF_STORAGE_CSR);
 
     AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                                   const void* const values,
                                   const int * const rowIdx, const int * const colIdx,
-                                  const dtype type = f32, const af::sparseStorage storage = AF_SPARSE_CSR,
+                                  const dtype type = f32, const af::storage stype = AF_STORAGE_CSR,
                                   const af::source src = afHost);
 
-    AFAPI array createSparseArray(const array dense, const af::sparseStorage storage = AF_SPARSE_CSR);
+    AFAPI array createSparseArray(const array dense, const af::storage stype = AF_STORAGE_CSR);
 
-    AFAPI array sparseConvertStorage(const array in, const af::sparseStorage storage);
+    AFAPI array sparseConvertStorage(const array in, const af::storage stype);
 
     AFAPI void sparseGetArrays(array &values, array &rowIdx, array &colIdx, const array in);
 
@@ -39,7 +39,7 @@ namespace af
 
     AFAPI dim_t sparseGetNumNonZero(const array in);
 
-    AFAPI af::sparseStorage sparseGetStorage(const array in);
+    AFAPI af::storage sparseGetStorage(const array in);
 }
 #endif
 
@@ -49,24 +49,24 @@ extern "C" {
 
     AFAPI af_err af_create_sparse_array(
                  af_array *out,
-                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const dim_t nRows, const dim_t nCols,
                  const af_array values, const af_array rowIdx, const af_array colIdx,
-                 const af_sparse_storage storage);
+                 const af_storage stype);
 
     AFAPI af_err af_create_sparse_array_from_ptr(
                  af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                  const void * const values,
                  const int * const rowIdx, const int * const colIdx,
-                 const af_dtype type, const af_sparse_storage storage,
+                 const af_dtype type, const af_storage stype,
                  const af_source source);
 
     AFAPI af_err af_create_sparse_array_from_dense(
                  af_array *out, const af_array in,
-                 const af_sparse_storage storage);
+                 const af_storage stype);
 
     AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                                           const af_sparse_storage destStorage);
+                                           const af_storage destStorage);
 
     AFAPI af_err af_sparse_get_arrays(af_array *values, af_array *rowIdx, af_array *colIdx, const af_array in);
 
@@ -78,7 +78,7 @@ extern "C" {
 
     AFAPI af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in);
+    AFAPI af_err af_sparse_get_storage(af_storage *out, const af_array in);
 
 #ifdef __cplusplus
 }
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index be4946941b..ba49e3064b 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -48,15 +48,15 @@ af_err af_sparse_matmul(af_array *out,
     using namespace detail;
 
     try {
-        common::SparseArrayBase lhsInfo = getSparseArrayBase(lhs);
+        common::SparseArrayBase lhsBase = getSparseArrayBase(lhs);
         ArrayInfo rhsInfo = getInfo(rhs);
 
-        ARG_ASSERT(2, lhsInfo.isSparse() == true && rhsInfo.isSparse() == false);
+        ARG_ASSERT(2, lhsBase.isSparse() == true && rhsInfo.isSparse() == false);
 
-        af_dtype lhs_type = lhsInfo.getType();
+        af_dtype lhs_type = lhsBase.getType();
         af_dtype rhs_type = rhsInfo.getType();
 
-        ARG_ASSERT(1, lhsInfo.getStorage() == AF_SPARSE_CSR);
+        ARG_ASSERT(1, lhsBase.getStorage() == AF_STORAGE_CSR);
 
         if (!(optLhs == AF_MAT_NONE ||
               optLhs == AF_MAT_TRANS ||
@@ -75,7 +75,7 @@ af_err af_sparse_matmul(af_array *out,
 
         TYPE_ASSERT(lhs_type == rhs_type);
 
-        af::dim4 ldims = lhsInfo.dims();
+        af::dim4 ldims = lhsBase.dims();
         int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
         int rRowDim = (optRhs == AF_MAT_NONE) ? 0 : 1;
 
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index dad9032dc4..81df34ced3 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -118,10 +118,10 @@ static void printSparse(const char *exp, af_array arr, const int precision,
     os << name << std::endl;
     os << "Storage Format : ";
     switch(sparse.getStorage()) {
-        case AF_SPARSE_DENSE: os << "AF_SPARSE_DENSE\n";    break;
-        case AF_SPARSE_CSR  : os << "AF_SPARSE_CSR\n";      break;
-        case AF_SPARSE_CSC  : os << "AF_SPARSE_CSC\n";      break;
-        case AF_SPARSE_COO  : os << "AF_SPARSE_COO\n";      break;
+        case AF_STORAGE_DENSE: os << "AF_STORAGE_DENSE\n";    break;
+        case AF_STORAGE_CSR  : os << "AF_STORAGE_CSR\n";      break;
+        case AF_STORAGE_CSC  : os << "AF_STORAGE_CSC\n";      break;
+        case AF_STORAGE_COO  : os << "AF_STORAGE_COO\n";      break;
     }
     os << "[" << sparse.dims() << "]\n";
 
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 3fb8cc4982..deefc27c36 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -43,22 +43,22 @@ const SparseArrayBase& getSparseArrayBase(const af_array in, bool device_check)
 // Sparse Creation
 ////////////////////////////////////////////////////////////////////////////////
 template<typename T>
-af_array createSparseArray(const af::dim4 &dims, const af_array values,
-                           const af_array rowIdx, const af_array colIdx,
-                           const af::sparseStorage storage)
+af_array createSparseArrayFromData(const af::dim4 &dims, const af_array values,
+                                   const af_array rowIdx, const af_array colIdx,
+                                   const af::storage stype)
 {
     SparseArray<T> sparse = common::createArrayDataSparseArray(
                             dims, getArray<T>(values),
                             getArray<int>(rowIdx), getArray<int>(colIdx),
-                            storage);
+                            stype);
     return getHandle(sparse);
 }
 
 af_err af_create_sparse_array(
                  af_array *out,
-                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const dim_t nRows, const dim_t nCols,
                  const af_array values, const af_array rowIdx, const af_array colIdx,
-                 const af_sparse_storage storage)
+                 const af_storage stype)
 {
     try {
         // Checks:
@@ -67,12 +67,12 @@ af_err af_create_sparse_array(
         // if COO, rowIdx, colIdx and values should have same dims
         // if CRS, colIdx and values should have same dims, rowIdx.dims = nRows
         // if CRC, rowIdx and values should have same dims, colIdx.dims = nCols
-        // storage is within acceptable range
+        // stype is within acceptable range
         // type is floating type
 
-        if(!(storage == AF_SPARSE_CSR
-          || storage == AF_SPARSE_CSC
-          || storage == AF_SPARSE_COO)) {
+        if(!(stype == AF_STORAGE_CSR
+          || stype == AF_STORAGE_CSC
+          || stype == AF_STORAGE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -90,10 +90,10 @@ af_err af_create_sparse_array(
         af::dim4 dims(nRows, nCols);
 
         switch(vInfo.getType()) {
-            case f32: output = createSparseArray<float  >(dims, values, rowIdx, colIdx, storage); break;
-            case f64: output = createSparseArray<double >(dims, values, rowIdx, colIdx, storage); break;
-            case c32: output = createSparseArray<cfloat >(dims, values, rowIdx, colIdx, storage); break;
-            case c64: output = createSparseArray<cdouble>(dims, values, rowIdx, colIdx, storage); break;
+            case f32: output = createSparseArrayFromData<float  >(dims, values, rowIdx, colIdx, stype); break;
+            case f64: output = createSparseArrayFromData<double >(dims, values, rowIdx, colIdx, stype); break;
+            case c32: output = createSparseArrayFromData<cfloat >(dims, values, rowIdx, colIdx, stype); break;
+            case c64: output = createSparseArrayFromData<cdouble>(dims, values, rowIdx, colIdx, stype); break;
             default : TYPE_ERROR(1, vInfo.getType());
         }
         std::swap(*out, output);
@@ -107,16 +107,16 @@ template<typename T>
 af_array createSparseArrayFromPtr(
         const af::dim4 &dims, const dim_t nNZ,
         const T * const values, const int * const rowIdx, const int * const colIdx,
-        const af::sparseStorage storage, const af::source source)
+        const af::storage stype, const af::source source)
 {
-    SparseArray<T> sparse = createEmptySparseArray<T>(dims, nNZ, storage);
+    SparseArray<T> sparse = createEmptySparseArray<T>(dims, nNZ, stype);
 
     if(source == afHost)
         sparse = common::createHostDataSparseArray(
-                         dims, nNZ, values, rowIdx, colIdx, storage);
+                         dims, nNZ, values, rowIdx, colIdx, stype);
     else if (source == afDevice)
         sparse = common::createDeviceDataSparseArray(
-                         dims, nNZ, values, rowIdx, colIdx, storage);
+                         dims, nNZ, values, rowIdx, colIdx, stype);
 
     return getHandle(sparse);
 }
@@ -125,7 +125,7 @@ af_err af_create_sparse_array_from_ptr(
                  af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                  const void * const values, const int * const rowIdx, const int * const colIdx,
-                 const af_dtype type, const af_sparse_storage storage,
+                 const af_dtype type, const af_storage stype,
                  const af_source source)
 {
     try {
@@ -135,11 +135,11 @@ af_err af_create_sparse_array_from_ptr(
         // if COO, rowIdx, colIdx and values should have same dims
         // if CRS, colIdx and values should have same dims, rowIdx.dims = nRows
         // if CRC, rowIdx and values should have same dims, colIdx.dims = nCols
-        // storage is within acceptable range
+        // stype is within acceptable range
         // type is floating type
-        if(!(storage == AF_SPARSE_CSR
-          || storage == AF_SPARSE_CSC
-          || storage == AF_SPARSE_COO)) {
+        if(!(stype == AF_STORAGE_CSR
+          || stype == AF_STORAGE_CSC
+          || stype == AF_STORAGE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -153,16 +153,16 @@ af_err af_create_sparse_array_from_ptr(
 
         switch(type) {
             case f32: output = createSparseArrayFromPtr<float  >
-                               (dims, nNZ, static_cast<const float  *>(values), rowIdx, colIdx, storage, source);
+                               (dims, nNZ, static_cast<const float  *>(values), rowIdx, colIdx, stype, source);
                       break;
             case f64: output = createSparseArrayFromPtr<double >
-                               (dims, nNZ, static_cast<const double *>(values), rowIdx, colIdx, storage, source);
+                               (dims, nNZ, static_cast<const double *>(values), rowIdx, colIdx, stype, source);
                       break;
             case c32: output = createSparseArrayFromPtr<cfloat >
-                               (dims, nNZ, static_cast<const cfloat *>(values), rowIdx, colIdx, storage, source);
+                               (dims, nNZ, static_cast<const cfloat *>(values), rowIdx, colIdx, stype, source);
                       break;
             case c64: output = createSparseArrayFromPtr<cdouble>
-                               (dims, nNZ, static_cast<const cdouble *>(values), rowIdx, colIdx, storage, source);
+                               (dims, nNZ, static_cast<const cdouble*>(values), rowIdx, colIdx, stype, source);
                       break;
             default : TYPE_ERROR(1, type);
         }
@@ -176,34 +176,34 @@ af_err af_create_sparse_array_from_ptr(
 template<typename T>
 af_array createSparseArrayFromDense(
         const af::dim4 &dims, const af_array _in,
-        const af_sparse_storage storage)
+        const af_storage stype)
 {
     const Array<T> in = getArray<T>(_in);
 
-    switch(storage) {
-        case AF_SPARSE_CSR:
-            return getHandle(sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(in));
-        case AF_SPARSE_CSC:
-            return getHandle(sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(in));
-        case AF_SPARSE_COO:
-            return getHandle(sparseConvertDenseToStorage<T, AF_SPARSE_COO>(in));
+    switch(stype) {
+        case AF_STORAGE_CSR:
+            return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(in));
+        case AF_STORAGE_CSC:
+            return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(in));
+        case AF_STORAGE_COO:
+            return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_COO>(in));
         default: AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
     }
 }
 
 af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
-                                         const af_sparse_storage storage)
+                                         const af_storage stype)
 {
     try {
         // Checks:
-        // storage is within acceptable range
+        // stype is within acceptable range
         // values is of floating point type
 
         ArrayInfo info = getInfo(in);
 
-        if(!(storage == AF_SPARSE_CSR
-          || storage == AF_SPARSE_CSC
-          || storage == AF_SPARSE_COO)) {
+        if(!(stype == AF_STORAGE_CSR
+          || stype == AF_STORAGE_CSC
+          || stype == AF_STORAGE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -217,10 +217,10 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
         af_array output = 0;
 
         switch(info.getType()) {
-            case f32: output = createSparseArrayFromDense<float  >(dims, in, storage); break;
-            case f64: output = createSparseArrayFromDense<double >(dims, in, storage); break;
-            case c32: output = createSparseArrayFromDense<cfloat >(dims, in, storage); break;
-            case c64: output = createSparseArrayFromDense<cdouble>(dims, in, storage); break;
+            case f32: output = createSparseArrayFromDense<float  >(dims, in, stype); break;
+            case f64: output = createSparseArrayFromDense<double >(dims, in, stype); break;
+            case c32: output = createSparseArrayFromDense<cfloat >(dims, in, stype); break;
+            case c64: output = createSparseArrayFromDense<cdouble>(dims, in, stype); break;
             default: TYPE_ERROR(1, info.getType());
         }
         std::swap(*out, output);
@@ -231,57 +231,57 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
 }
 
 template<typename T>
-af_array sparseConvertStorage(const af_array in_, const af_sparse_storage destStorage)
+af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
 {
     const SparseArray<T> in = getSparseArray<T>(in_);
 
-    // Only destStorage == AF_SPARSE_DENSE is supported
+    // Only destStorage == AF_STORAGE_DENSE is supported
     // All the other calls are for future when conversions are supported in
     // the backend
-    if(destStorage == AF_SPARSE_DENSE) {
+    if(destStorage == AF_STORAGE_DENSE) {
         // Returns a regular af_array, not sparse
         switch(in.getStorage()) {
-            case AF_SPARSE_CSR:
-                return getHandle(detail::sparseConvertStorageToDense<T, AF_SPARSE_CSR>(in));
-            case AF_SPARSE_CSC:
-                return getHandle(detail::sparseConvertStorageToDense<T, AF_SPARSE_CSC>(in));
-            case AF_SPARSE_COO:
-                return getHandle(detail::sparseConvertStorageToDense<T, AF_SPARSE_COO>(in));
+            case AF_STORAGE_CSR:
+                return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_CSR>(in));
+            case AF_STORAGE_CSC:
+                return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_CSC>(in));
+            case AF_STORAGE_COO:
+                return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_COO>(in));
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
-    } else if(destStorage == AF_SPARSE_CSR) {
+    } else if(destStorage == AF_STORAGE_CSR) {
         // Returns a sparse af_array
         switch(in.getStorage()) {
-            case AF_SPARSE_CSR:
+            case AF_STORAGE_CSR:
                 return retainSparseHandle<T>(in_);
-            case AF_SPARSE_CSC:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSR, AF_SPARSE_CSC>(in));
-            case AF_SPARSE_COO:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSR, AF_SPARSE_COO>(in));
+            case AF_STORAGE_CSC:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR, AF_STORAGE_CSC>(in));
+            case AF_STORAGE_COO:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR, AF_STORAGE_COO>(in));
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
-    } else if(destStorage == AF_SPARSE_CSC) {
+    } else if(destStorage == AF_STORAGE_CSC) {
         // Returns a sparse af_array
         switch(in.getStorage()) {
-            case AF_SPARSE_CSR:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSC, AF_SPARSE_CSR>(in));
-            case AF_SPARSE_CSC:
+            case AF_STORAGE_CSR:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSC, AF_STORAGE_CSR>(in));
+            case AF_STORAGE_CSC:
                 return retainSparseHandle<T>(in_);
-            case AF_SPARSE_COO:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_CSC, AF_SPARSE_COO>(in));
+            case AF_STORAGE_COO:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSC, AF_STORAGE_COO>(in));
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
-    } else if(destStorage == AF_SPARSE_COO) {
+    } else if(destStorage == AF_STORAGE_COO) {
         // Returns a sparse af_array
         switch(in.getStorage()) {
-            case AF_SPARSE_CSR:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_COO, AF_SPARSE_CSR>(in));
-            case AF_SPARSE_CSC:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_SPARSE_COO, AF_SPARSE_CSC>(in));
-            case AF_SPARSE_COO:
+            case AF_STORAGE_CSR:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO, AF_STORAGE_CSR>(in));
+            case AF_STORAGE_CSC:
+                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO, AF_STORAGE_CSC>(in));
+            case AF_STORAGE_COO:
                 return retainSparseHandle<T>(in_);
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
@@ -293,9 +293,9 @@ af_array sparseConvertStorage(const af_array in_, const af_sparse_storage destSt
 }
 
 af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                           const af_sparse_storage destStorage)
+                           const af_storage destStorage)
 {
-    // Right now dest_storage can only be AF_SPARSE_DENSE
+    // Right now dest_storage can only be AF_STORAGE_DENSE
     try {
         af_array output = 0;
 
@@ -303,11 +303,11 @@ af_err af_sparse_convert_storage(af_array *out, const af_array in,
 
         // Dense not allowed as input -> Should never happen
         // To convert from dense to type, use the create* functions
-        ARG_ASSERT(1, base.getStorage() != AF_SPARSE_DENSE);
+        ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE);
 
-        // Right now dest_storage can only be AF_SPARSE_DENSE
+        // Right now dest_storage can only be AF_STORAGE_DENSE
         // TODO: Add support for [CSR, CSC, COO] <-> [CSR, CSC, COO] in backends
-        ARG_ASSERT(1, destStorage == AF_SPARSE_DENSE);
+        ARG_ASSERT(1, destStorage == AF_STORAGE_DENSE);
 
         if(base.getStorage() == destStorage) {
             // Return a reference
@@ -397,7 +397,7 @@ af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in)
+af_err af_sparse_get_storage(af_storage *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index 900c66caf4..c72220dc43 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -13,40 +13,40 @@
 
 namespace af
 {
-    array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+    array createSparseArray(const dim_t nRows, const dim_t nCols,
                             const array values, const array rowIdx, const array colIdx,
-                            const af::sparseStorage storage)
+                            const af::storage stype)
     {
         af_array out = 0;
-        AF_THROW(af_create_sparse_array(&out, nRows, nCols, nNZ,
-                            values.get(), rowIdx.get(), colIdx.get(), storage));
+        AF_THROW(af_create_sparse_array(&out, nRows, nCols,
+                            values.get(), rowIdx.get(), colIdx.get(), stype));
         return array(out);
     }
 
     array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                             const void * const values,
                             const int * const rowIdx, const int * const colIdx,
-                            const dtype type, const af::sparseStorage storage,
+                            const dtype type, const af::storage stype,
                             const af::source src)
     {
         af_array out = 0;
         AF_THROW(af_create_sparse_array_from_ptr(&out, nRows, nCols, nNZ,
-                            values, rowIdx, colIdx, type, storage, src));
+                            values, rowIdx, colIdx, type, stype, src));
         return array(out);
     }
 
 
-    array createSparseArray(const array dense, const af::sparseStorage storage)
+    array createSparseArray(const array dense, const af::storage stype)
     {
         af_array out = 0;
-        AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), storage));
+        AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), stype));
         return array(out);
     }
 
-    array sparseConvertStorage(const array in, const af::sparseStorage storage)
+    array sparseConvertStorage(const array in, const af::storage stype)
     {
         af_array out = 0;
-        AF_THROW(af_sparse_convert_storage(&out, in.get(), storage));
+        AF_THROW(af_sparse_convert_storage(&out, in.get(), stype));
         return array(out);
     }
 
@@ -89,9 +89,9 @@ namespace af
         return out;
     }
 
-    af::sparseStorage sparseGetStorage(const array in)
+    af::storage sparseGetStorage(const array in)
     {
-        af::sparseStorage out;
+        af::storage out;
         AF_THROW(af_sparse_get_storage(&out, in.get()));
         return out;
     }
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index bd84be294d..9d46380917 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -12,12 +12,12 @@
 
 af_err af_create_sparse_array(
        af_array *out,
-       const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+       const dim_t nRows, const dim_t nCols,
        const af_array values, const af_array rowIdx, const af_array colIdx,
-       const af_sparse_storage storage)
+       const af_storage stype)
 {
     CHECK_ARRAYS(values, rowIdx, colIdx);
-    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, storage);
+    return CALL(out, nRows, nCols, values, rowIdx, colIdx, stype);
 }
 
 af_err af_create_sparse_array_from_ptr(
@@ -25,22 +25,22 @@ af_err af_create_sparse_array_from_ptr(
        const dim_t nRows, const dim_t nCols, const dim_t nNZ,
        const void * const values,
        const int * const rowIdx, const int * const colIdx,
-       const af_dtype type, const af_sparse_storage storage,
+       const af_dtype type, const af_storage stype,
        const af_source source)
 {
-    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, type, storage, source);
+    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, type, stype, source);
 }
 
 af_err af_create_sparse_array_from_dense(
        af_array *out, const af_array in,
-       const af_sparse_storage storage)
+       const af_storage stype)
 {
     CHECK_ARRAYS(in);
-    return CALL(out, in, storage);
+    return CALL(out, in, stype);
 }
 
 af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                                 const af_sparse_storage destStorage)
+                                 const af_storage destStorage)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in, destStorage);
@@ -76,7 +76,7 @@ af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in)
     return CALL(out, in);
 }
 
-af_err af_sparse_get_storage(af_sparse_storage *out, const af_array in)
+af_err af_sparse_get_storage(af_storage *out, const af_array in)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in);
diff --git a/src/backend/SparseArray.cpp b/src/backend/SparseArray.cpp
index 3cda7b0735..eba69c411d 100644
--- a/src/backend/SparseArray.cpp
+++ b/src/backend/SparseArray.cpp
@@ -23,15 +23,15 @@ using namespace detail;
 ////////////////////////////////////////////////////////////////////////////
 
 // ROW_LENGTH and column length expect standard variable names of
-// SparseArraBase::storage
+// SparseArrayBase::stype
 // _nNZ  -> Constructor Argument
 // _dims -> Constructor Argument
-#define ROW_LENGTH ((storage == AF_SPARSE_COO || storage == AF_SPARSE_CSC) ? _nNZ : (_dims[0] + 1))
-#define COL_LENGTH ((storage == AF_SPARSE_COO || storage == AF_SPARSE_CSR) ? _nNZ : (_dims[1] + 1))
+#define ROW_LENGTH ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC) ? _nNZ : (_dims[0] + 1))
+#define COL_LENGTH ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSR) ? _nNZ : (_dims[1] + 1))
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage, af_dtype _type):
+SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage, af_dtype _type):
     info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
-    storage(_storage),
+    stype(_storage),
     rowIdx(createValueArray<int>(dim4(ROW_LENGTH), 0)),
     colIdx(createValueArray<int>(dim4(COL_LENGTH), 0))
 {
@@ -43,10 +43,10 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _
 
 SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
                     const int * const _rowIdx, const int * const _colIdx,
-                    const af::sparseStorage _storage, af_dtype _type,
+                    const af::storage _storage, af_dtype _type,
                     bool _is_device, bool _copy_device):
     info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
-    storage(_storage),
+    stype(_storage),
     rowIdx(_is_device ?
           (!_copy_device ? createDeviceDataArray<int>(dim4(ROW_LENGTH), _rowIdx)
                          : createValueArray<int>(dim4(ROW_LENGTH), 0))
@@ -68,10 +68,10 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
 
 SparseArrayBase::SparseArrayBase(af::dim4 _dims,
                     const Array<int> &_rowIdx, const Array<int> &_colIdx,
-                    const af::sparseStorage _storage, af_dtype _type,
+                    const af::storage _storage, af_dtype _type,
                     bool _copy):
     info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
-    storage(_storage),
+    stype(_storage),
     rowIdx(_copy ? copyArray<int>(_rowIdx): _rowIdx),
     colIdx(_copy ? copyArray<int>(_colIdx): _colIdx)
 {
@@ -87,9 +87,9 @@ SparseArrayBase::~SparseArrayBase()
 
 dim_t SparseArrayBase::getNNZ() const
 {
-    if(storage == AF_SPARSE_COO || storage == AF_SPARSE_CSC)
+    if(stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC)
         return rowIdx.elements();
-    else if(storage == AF_SPARSE_CSR)
+    else if(stype == AF_STORAGE_CSR)
         return colIdx.elements();
 
     // This is to ensure future storages are properly configured
@@ -104,7 +104,7 @@ dim_t SparseArrayBase::getNNZ() const
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
 SparseArray<T> createEmptySparseArray(
-        const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage)
+        const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage)
 {
     return SparseArray<T>(_dims, _nNZ, _storage);
 }
@@ -114,7 +114,7 @@ SparseArray<T> createHostDataSparseArray(
         const af::dim4 &_dims, const dim_t nNZ,
         const T * const _values,
         const int * const _rowIdx, const int * const _colIdx,
-        const af::sparseStorage _storage)
+        const af::storage _storage)
 {
     return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, false);
 }
@@ -124,7 +124,7 @@ SparseArray<T> createDeviceDataSparseArray(
         const af::dim4 &_dims, const dim_t nNZ,
         const T * const _values,
         const int * const _rowIdx, const int * const _colIdx,
-        const af::sparseStorage _storage)
+        const af::storage _storage)
 {
     return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, false);
 }
@@ -134,7 +134,7 @@ SparseArray<T> createArrayDataSparseArray(
         const af::dim4 &_dims,
         const Array<T> &_values,
         const Array<int> &_rowIdx, const Array<int> &_colIdx,
-        const af::sparseStorage _storage)
+        const af::storage _storage)
 {
     return SparseArray<T>(_dims, _values, _rowIdx, _colIdx, _storage, false);
 }
@@ -142,7 +142,7 @@ SparseArray<T> createArrayDataSparseArray(
 template<typename T>
 SparseArray<T> *initSparseArray()
 {
-    return new SparseArray<T>(dim4(), 0,  (af::sparseStorage)0);
+    return new SparseArray<T>(dim4(), 0,  (af::storage)0);
 }
 
 template<typename T>
@@ -155,7 +155,7 @@ void destroySparseArray(SparseArray<T> *sparse)
 // Sparse Array Class Implementations
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
-SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::sparseStorage _storage):
+SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage):
     base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type),
     values(createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
 {
@@ -171,7 +171,7 @@ template<typename T>
 SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,
                      const T * const _values,
                      const int * const _rowIdx, const int * const _colIdx,
-                     const af::sparseStorage _storage,
+                     const af::storage _storage,
                      bool _is_device, bool _copy_device):
     base(_dims, _nNZ, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _is_device, _copy_device),
     values(_is_device ?
@@ -194,7 +194,7 @@ template<typename T>
 SparseArray<T>::SparseArray(af::dim4 _dims,
             const Array<T> &_values,
             const Array<int> &_rowIdx, const Array<int> &_colIdx,
-            const af::sparseStorage _storage, bool _copy):
+            const af::storage _storage, bool _copy):
     base(_dims, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _copy),
     values(_copy ? copyArray<T>(_values): _values)
 {
@@ -213,35 +213,35 @@ SparseArray<T>::~SparseArray()
 
 #define INSTANTIATE(T)                                                                              \
     template SparseArray<T> createEmptySparseArray<T>(                                              \
-            const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage);                   \
+            const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);                         \
     template SparseArray<T> createHostDataSparseArray<T>(                                           \
             const af::dim4 &_dims, const dim_t _nNZ,                                                \
             const T * const _values,                                                                \
             const int * const _rowIdx, const int * const _colIdx,                                   \
-            const af::sparseStorage _storage);                                                      \
+            const af::storage _storage);                                                            \
     template SparseArray<T> createDeviceDataSparseArray<T>(                                         \
             const af::dim4 &_dims, const dim_t _nNZ,                                                \
             const T * const _values,                                                                \
             const int * const _rowIdx, const int * const _colIdx,                                   \
-            const af::sparseStorage _storage);                                                      \
+            const af::storage _storage);                                                            \
     template SparseArray<T> createArrayDataSparseArray<T>(                                          \
             const af::dim4 &_dims,                                                                  \
             const Array<T> &_values,                                                                \
             const Array<int> &_rowIdx, const Array<int> &_colIdx,                                   \
-            const af::sparseStorage _storage);                                                      \
+            const af::storage _storage);                                                            \
     template SparseArray<T> *initSparseArray<T>();                                                  \
     template void destroySparseArray<T>(SparseArray<T> *sparse);                                    \
                                                                                                     \
-    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage);   \
+    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage _storage);         \
     template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,                                \
                         const T * const _values,                                                    \
                         const int * const _rowIdx, const int * const _colIdx,                       \
-                        const af::sparseStorage _storage,                                           \
+                        const af::storage _storage,                                                 \
                         bool _is_device, bool _copy_device);                                        \
     template SparseArray<T>::SparseArray(af::dim4 _dims,                                            \
                         const Array<T> &_values,                                                    \
                         const Array<int> &_rowIdx, const Array<int> &_colIdx,                       \
-                        const af::sparseStorage _storage, bool _copy);                              \
+                        const af::storage _storage, bool _copy);                                    \
     template SparseArray<T>::~SparseArray();
 
 // Instantiate only floating types
diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
index 5cad6ffc22..42667fedd9 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/SparseArray.hpp
@@ -37,22 +37,22 @@ template<typename T> class SparseArray;
 class SparseArrayBase
 {
 private:
-    ArrayInfo  info;    // This must be the first element of SparseArray<T>.
-    af::sparseStorage storage;  // Storage format: CSR, CSC, COO
-    Array<int> rowIdx;  // Linear array containing row indices
-    Array<int> colIdx;  // Linear array containing col indices
+    ArrayInfo  info;        // This must be the first element of SparseArray<T>.
+    af::storage stype;      // Storage format: CSR, CSC, COO
+    Array<int> rowIdx;      // Linear array containing row indices
+    Array<int> colIdx;      // Linear array containing col indices
 
 public:
-    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::sparseStorage _storage, af_dtype _type);
+    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage, af_dtype _type);
 
     SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
                     const int * const _rowIdx, const int * const _colIdx,
-                    const af::sparseStorage _storage, af_dtype _type,
+                    const af::storage _storage, af_dtype _type,
                     bool _is_device = false, bool _copy_device = false);
 
     SparseArrayBase(af::dim4 _dims,
                     const Array<int> &_rowIdx, const Array<int> &_colIdx,
-                    const af::sparseStorage _storage, af_dtype _type,
+                    const af::storage _storage, af_dtype _type,
                     bool _copy = false);
 
     ~SparseArrayBase();
@@ -107,8 +107,8 @@ class SparseArrayBase
     const Array<int>& getColIdx()     const { return colIdx;            }
 
     // Dims, types etc
-    dim_t getNNZ()                  const;
-    af::sparseStorage getStorage()  const { return storage;             }
+    dim_t getNNZ()                    const;
+    af::storage getStorage()          const { return stype;             }
 };
 #if __cplusplus > 199711L
         static_assert(std::is_standard_layout<SparseArrayBase>::value,
@@ -125,19 +125,19 @@ class SparseArray
     SparseArrayBase  base;    // This must be the first element of SparseArray<T>.
     Array<T>         values;  // Linear array containing actual values
 
-    SparseArray(af::dim4 _dims, dim_t _nNZ, af::sparseStorage storage);
+    SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage stype);
 
     explicit
     SparseArray(af::dim4 _dims, dim_t _nNZ,
                 const T * const _values,
                 const int * const _rowIdx, const int * const _colIdx,
-                const af::sparseStorage _storage,
+                const af::storage _storage,
                 bool _is_device = false, bool _copy_device = false);
 
     SparseArray(af::dim4 _dims,
                 const Array<T> &_values,
                 const Array<int> &_rowIdx, const Array<int> &_colIdx,
-                const af::sparseStorage _storage, bool _copy = false);
+                const af::storage _storage, bool _copy = false);
 
 public:
 
@@ -175,7 +175,7 @@ class SparseArray
 
     // Function from Base but not in ArrayInfo
     INSTANTIATE_INFO(dim_t              , getNNZ    )
-    INSTANTIATE_INFO(af::sparseStorage  , getStorage)
+    INSTANTIATE_INFO(af::storage        , getStorage)
 
     Array<int>& getRowIdx()                 { return base.getRowIdx(); }
     Array<int>& getColIdx()                 { return base.getColIdx(); }
@@ -206,25 +206,25 @@ class SparseArray
     ////////////////////////////////////////////////////////////////////////////
 
     friend SparseArray<T> createEmptySparseArray<T>(
-            const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage);
+            const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);
 
     friend SparseArray<T> createHostDataSparseArray<T>(
             const af::dim4 &_dims, const dim_t nNZ,
             const T * const _values,
             const int * const _rowIdx, const int * const _colIdx,
-            const af::sparseStorage _storage);
+            const af::storage _storage);
 
     friend SparseArray<T> createDeviceDataSparseArray<T>(
             const af::dim4 &_dims, const dim_t nNZ,
             const T * const _values,
             const int * const _rowIdx, const int * const _colIdx,
-            const af::sparseStorage _storage);
+            const af::storage _storage);
 
     friend SparseArray<T> createArrayDataSparseArray<T>(
             const af::dim4 &_dims,
             const Array<T> &_values,
             const Array<int> &_rowIdx, const Array<int> &_colIdx,
-            const af::sparseStorage _storage);
+            const af::storage _storage);
 
     friend SparseArray<T> *initSparseArray<T>();
 
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 19b09771f5..c7b2411ec6 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -140,7 +140,7 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
     Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
     Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
 }
 
 // Partial template specialization of sparseConvertStorageToDense for COO
@@ -166,7 +166,7 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
 #ifdef USE_MKL // Implementation using MKL
 ////////////////////////////////////////////////////////////////////////////////
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
     in_.eval();
@@ -175,7 +175,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     // CSR <-> CSC is only supported if input is square
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
 
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_SPARSE_CSR);
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
     sparse_.eval();
 
     auto func = [=] (SparseArray<T> sparse, const Array<T> in) {
@@ -209,7 +209,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 
     getQueue().enqueue(func, sparse_, in_);
 
-    if(storage == AF_SPARSE_CSR)
+    if(stype == AF_STORAGE_CSR)
         return sparse_;
     else
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
@@ -217,13 +217,13 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     return sparse_;
 }
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
     // MKL only has dns<->csr.
     // CSR <-> CSC is only supported if input is square
 
-    if(storage == AF_SPARSE_CSC)
+    if(stype == AF_STORAGE_CSC)
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
 
     in_.eval();
@@ -262,7 +262,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 
     getQueue().enqueue(func, dense_, in_);
 
-    if(storage == AF_SPARSE_CSR)
+    if(stype == AF_STORAGE_CSR)
         return dense_;
     else
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
@@ -274,7 +274,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 #else // Implementation without using MKL
 ////////////////////////////////////////////////////////////////////////////////
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
     // TODO: Make an implementation without MKL
@@ -286,9 +286,9 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
 
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_SPARSE_CSR);
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
 
-    if(storage == AF_SPARSE_CSR)
+    if(stype == AF_STORAGE_CSR)
         return sparse_;
     else
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
@@ -296,7 +296,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     return sparse_;
 }
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
     // TODO: Make an implementation without MKL
@@ -310,7 +310,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
-    if(storage == AF_SPARSE_CSR)
+    if(stype == AF_STORAGE_CSR)
         return dense_;
     else
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
@@ -325,7 +325,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 ////////////////////////////////////////////////////////////////////////////////
 // Common to MKL and Not MKL
 ////////////////////////////////////////////////////////////////////////////////
-template<typename T, af_sparse_storage src, af_sparse_storage dest>
+template<typename T, af_storage src, af_storage dest>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
     // Dummy function
@@ -340,28 +340,28 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 }
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_COO>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T> &in);  \
 
 #define INSTANTIATE_COO_SPECIAL(T)                                                                      \
-    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_COO>(const Array<T> &in)         \
+    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in)        \
     { return sparseConvertDenseToCOO<T>(in); }                                                          \
-    template<> Array<T> sparseConvertStorageToDense<T, AF_SPARSE_COO>(const SparseArray<T> &in)         \
+    template<> Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in)        \
     { return sparseConvertCOOToDense<T>(in); }                                                          \
 
 #define INSTANTIATE_SPARSE(T)                                                                           \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(const Array<T> &in);          \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(const Array<T> &in);          \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in);         \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in);         \
                                                                                                         \
-    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSR>(const SparseArray<T> &in);          \
-    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSC>(const SparseArray<T> &in);          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in);         \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in);         \
                                                                                                         \
     INSTANTIATE_COO_SPECIAL(T)                                                                          \
                                                                                                         \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSR)                                                            \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSC)                                                            \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_COO)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                           \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                           \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                           \
 
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 0274a87f0a..291376e00b 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -25,13 +25,13 @@ typedef cfloat        sp_cfloat;
 typedef cdouble       sp_cdouble;
 #endif
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
-template<typename T, af_sparse_storage src, af_sparse_storage dest>
+template<typename T, af_storage src, af_storage dest>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 966e312ef0..bcdd8b0a5e 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -209,10 +209,10 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
     Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
     Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
 }
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 {
     const int M = in.dims()[0];
@@ -227,7 +227,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
     int d = -1;
     cusparseDirection_t dir = CUSPARSE_DIRECTION_ROW;
 
-    if(storage == AF_SPARSE_CSR) {
+    if(stype == AF_STORAGE_CSR) {
         d = M;
         dir = CUSPARSE_DIRECTION_ROW;
     } else {
@@ -248,7 +248,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
     Array<int> rowIdx = createEmptyArray<int>(dim4());
     Array<int> colIdx = createEmptyArray<int>(dim4());
 
-    if(storage == AF_SPARSE_CSR) {
+    if(stype == AF_STORAGE_CSR) {
         rowIdx = createEmptyArray<int>(dim4(M+1));
         colIdx = createEmptyArray<int>(dim4(nNZ));
     } else {
@@ -257,7 +257,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
     }
     Array<T> values = createEmptyArray<T>(dim4(nNZ));
 
-    if(storage == AF_SPARSE_CSR)
+    if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(dense2csr_func<T>()(
                         getHandle(),
                         M, N,
@@ -277,7 +277,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
     // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, storage);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, stype);
 }
 
 
@@ -297,7 +297,7 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
     return dense;
 }
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 {
     // Create Sparse Matrix Descriptor
@@ -311,7 +311,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
     int d_strides1 = dense.strides()[1];
 
-    if(storage == AF_SPARSE_CSR)
+    if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(csr2dense_func<T>()(
                         getHandle(),
                         M, N,
@@ -336,7 +336,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     return dense;
 }
 
-template<typename T, af_sparse_storage src, af_sparse_storage dest>
+template<typename T, af_storage src, af_storage dest>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
     // Dummy function
@@ -348,28 +348,28 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_COO>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T> &in);  \
 
 #define INSTANTIATE_COO_SPECIAL(T)                                                                      \
-    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_COO>(const Array<T> &in)         \
+    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in)        \
     { return sparseConvertDenseToCOO<T>(in); }                                                          \
-    template<> Array<T> sparseConvertStorageToDense<T, AF_SPARSE_COO>(const SparseArray<T> &in)         \
+    template<> Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in)        \
     { return sparseConvertCOOToDense<T>(in); }                                                          \
 
 #define INSTANTIATE_SPARSE(T)                                                                           \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(const Array<T> &in);          \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(const Array<T> &in);          \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in);         \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in);         \
                                                                                                         \
-    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSR>(const SparseArray<T> &in);          \
-    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSC>(const SparseArray<T> &in);          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in);         \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in);         \
                                                                                                         \
     INSTANTIATE_COO_SPECIAL(T)                                                                          \
                                                                                                         \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSR)                                                            \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSC)                                                            \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_COO)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                           \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                           \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                           \
 
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
index 7b9bb473ac..575e616d12 100644
--- a/src/backend/cuda/sparse.hpp
+++ b/src/backend/cuda/sparse.hpp
@@ -13,13 +13,13 @@
 namespace cuda
 {
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
-template<typename T, af_sparse_storage src, af_sparse_storage dest>
+template<typename T, af_storage src, af_storage dest>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 84dbb3d8e0..5338ebfed4 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -46,17 +46,17 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
     Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
     Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_SPARSE_COO);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
 }
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
     in_.eval();
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
 
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_SPARSE_CSR);
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
     sparse_.eval();
 
     return sparse_;
@@ -80,7 +80,7 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
     return dense;
 }
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
     in_.eval();
@@ -91,7 +91,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     return dense_;
 }
 
-template<typename T, af_sparse_storage src, af_sparse_storage dest>
+template<typename T, af_storage src, af_storage dest>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
     in.eval();
@@ -106,28 +106,28 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSR>(const SparseArray<T> &in);   \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_CSC>(const SparseArray<T> &in);   \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_SPARSE_COO>(const SparseArray<T> &in);   \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \
+    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T> &in);  \
 
 #define INSTANTIATE_COO_SPECIAL(T)                                                                      \
-    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_COO>(const Array<T> &in)         \
+    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in)        \
     { return sparseConvertDenseToCOO<T>(in); }                                                          \
-    template<> Array<T> sparseConvertStorageToDense<T, AF_SPARSE_COO>(const SparseArray<T> &in)         \
+    template<> Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in)        \
     { return sparseConvertCOOToDense<T>(in); }                                                          \
 
 #define INSTANTIATE_SPARSE(T)                                                                           \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSR>(const Array<T> &in);          \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_SPARSE_CSC>(const Array<T> &in);          \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in);         \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in);         \
                                                                                                         \
-    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSR>(const SparseArray<T> &in);          \
-    template Array<T> sparseConvertStorageToDense<T, AF_SPARSE_CSC>(const SparseArray<T> &in);          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in);         \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in);         \
                                                                                                         \
     INSTANTIATE_COO_SPECIAL(T)                                                                          \
                                                                                                         \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSR)                                                            \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_CSC)                                                            \
-    INSTANTIATE_TO_STORAGE(T, AF_SPARSE_COO)                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                           \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                           \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                           \
 
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index 8dc98fdf13..f27d88fa93 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -13,13 +13,13 @@
 namespace opencl
 {
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 
-template<typename T, af_sparse_storage storage>
+template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
-template<typename T, af_sparse_storage src, af_sparse_storage dest>
+template<typename T, af_storage src, af_storage dest>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }
diff --git a/src/backend/sparse_helpers.hpp b/src/backend/sparse_helpers.hpp
index a2e83a3616..31fced6e87 100644
--- a/src/backend/sparse_helpers.hpp
+++ b/src/backend/sparse_helpers.hpp
@@ -23,28 +23,28 @@ template<typename T> class SparseArray;
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
 SparseArray<T> createEmptySparseArray(
-        const af::dim4 &_dims, dim_t _nNZ, const af::sparseStorage _storage);
+        const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);
 
 template<typename T>
 SparseArray<T> createHostDataSparseArray(
         const af::dim4 &_dims, const dim_t nNZ,
         const T * const _values,
         const int * const _rowIdx, const int * const _colIdx,
-        const af::sparseStorage _storage);
+        const af::storage _storage);
 
 template<typename T>
 SparseArray<T> createDeviceDataSparseArray(
         const af::dim4 &_dims, const dim_t nNZ,
         const T * const _values,
         const int * const _rowIdx, const int * const _colIdx,
-        const af::sparseStorage _storage);
+        const af::storage _storage);
 
 template<typename T>
 SparseArray<T> createArrayDataSparseArray(
         const af::dim4 &_dims,
         const Array<T> &_values,
         const Array<int> &_rowIdx, const Array<int> &_colIdx,
-        const af::sparseStorage _storage);
+        const af::storage _storage);
 
 template<typename T>
 SparseArray<T> *initSparseArray();
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 3641458b57..d40a0fb32f 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -88,7 +88,7 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     af::array dRes = matmul(A, B);
 
     // Create Sparse Array From Dense
-    af::array sA = af::createSparseArray(A, AF_SPARSE_CSR);
+    af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
 
     // Sparse Matmul
     af::array sRes = matmul(sA, B);

From b1544109a96096ee741db126b39e4c4821735c4a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 13 Jun 2016 13:20:11 +0530
Subject: [PATCH 0606/2677] Change cl2hpp external project github url

---
 CMakeModules/build_cl2hpp.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 0e91ed94e8..d8bd046613 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -4,7 +4,7 @@ SET(prefix ${CMAKE_BINARY_DIR}/third_party/cl2hpp)
 
 ExternalProject_Add(
     cl2hpp-ext
-    GIT_REPOSITORY https://github.com/9prady9/OpenCL-CLHPP.git
+    GIT_REPOSITORY https://github.com/arrayfire/OpenCL-CLHPP.git
     GIT_TAG install_targets
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}/package"

From c1dd8f4b8f99b35d8c4f6427cddd9eba5ca173fc Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 10 Jun 2016 18:04:49 -0400
Subject: [PATCH 0607/2677] rename c-api af_moments_all and minor code cleanup

---
 include/af/image.h                   | 56 +++++++++++++++++++
 include/af/moments.h                 | 83 ----------------------------
 include/arrayfire.h                  |  1 -
 src/api/c/moments.cpp                |  2 +-
 src/api/cpp/moments.cpp              |  6 +-
 src/api/unified/moments.cpp          |  8 ++-
 src/backend/cpu/image.cpp            |  2 -
 src/backend/cpu/image.hpp            |  1 -
 src/backend/cpu/kernel/moments.hpp   |  5 +-
 src/backend/cpu/moments.cpp          |  4 +-
 src/backend/cuda/image.cu            |  3 +-
 src/backend/cuda/kernel/moments.hpp  |  3 +-
 src/backend/cuda/moments.cu          | 21 ++-----
 src/backend/opencl/image.cpp         |  3 +-
 src/backend/opencl/kernel/moments.cl |  2 +-
 src/backend/opencl/moments.cpp       | 18 ++----
 test/moments.cpp                     |  8 +--
 17 files changed, 90 insertions(+), 136 deletions(-)
 delete mode 100644 include/af/moments.h

diff --git a/include/af/image.h b/include/af/image.h
index da0a099b86..76a7cca6a4 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -677,6 +677,32 @@ AFAPI array ycbcr2rgb(const array& in, const YCCStd standard=AF_YCC_601);
 AFAPI array rgb2ycbcr(const array& in, const YCCStd standard=AF_YCC_601);
 #endif
 
+#if AF_API_VERSION >= 34
+/**
+   C++ Interface for calculating an image moment
+
+   \param[in]  in is the input image
+   \param[moment] is the moment to calculate
+   \return      the value of the moment
+
+   \ingroup image_func_moments
+ */
+template<typename T> T moment(const array& in, const af_moment_type moment);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C++ Interface for calculating image moments
+
+   \param[in]  in contains the input image(s)
+   \param[moment] is the moment to calculate
+   \return array containing the requested moment of each image
+
+   \ingroup image_func_moments
+ */
+AFAPI array moments(const array& in, const af_moment_type moment);
+#endif
+
 }
 #endif
 
@@ -1349,6 +1375,36 @@ extern "C" {
     AFAPI af_err af_rgb2ycbcr(af_array* out, const af_array in, const af_ycc_std standard);
 #endif
 
+#if AF_API_VERSION >= 34
+    /**
+       C Interface for finding image moments
+
+       \param[out] out is an array containing the calculated moments
+       \param[in]  in is an array of image(s)
+       \param[moment] is the moment to calculate
+       \return     ref AF_SUCCESS if the moment calculation is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_moments
+    */
+    AFAPI af_err af_moments(af_array *out, const af_array in, const af_moment_type moment);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       C Interface for calculating an image moment
+
+       \param[out] out is a pointer to the outputted moment
+       \param[in]  in is an array of image(s)
+       \param[moment] is the moment to calculate
+       \return     ref AF_SUCCESS if the moment calculation is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_moments
+    */
+    AFAPI af_err af_moments_all(double *out, const af_array in, const af_moment_type moment);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/include/af/moments.h b/include/af/moments.h
deleted file mode 100644
index 98a4eb01b1..0000000000
--- a/include/af/moments.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <af/defines.h>
-
-#ifdef __cplusplus
-namespace af
-{
-class array;
-
-#if AF_API_VERSION >= 34
-/**
-   C++ Interface for calculating an image moment
-
-   \param[in]  in is the input image
-   \param[moment] is the moment to calculate
-   \return      the value of the moment
-
-   \ingroup image_func_moments
- */
-template<typename T> T moment(const array& in, const af_moment_type moment);
-#endif
-
-#if AF_API_VERSION >= 34
-/**
-   C++ Interface for calculating image moments
-
-   \param[in]  in contains the input image(s)
-   \param[moment] is the moment to calculate
-   \return array containing the requested moment of each image
-
-   \ingroup image_func_moments
- */
-AFAPI array moments(const array& in, const af_moment_type moment);
-#endif
-
-}
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#if AF_API_VERSION >= 34
-    /**
-       C Interface for finding image moments
-
-       \param[out] out is an array containing the calculated moments
-       \param[in]  in is an array of image(s)
-       \param[moment] is the moment to calculate
-       \return     ref AF_SUCCESS if the moment calculation is successful,
-       otherwise an appropriate error code is returned.
-
-       \ingroup image_func_moments
-    */
-    AFAPI af_err af_moments(af_array *out, const af_array in, const af_moment_type moment);
-#endif
-
-#if AF_API_VERSION >= 34
-    /**
-       C Interface for calculating an image moment
-
-       \param[out] out is a pointer to the outputted moment
-       \param[in]  in is an array of image(s)
-       \param[moment] is the moment to calculate
-       \return     ref AF_SUCCESS if the moment calculation is successful,
-       otherwise an appropriate error code is returned.
-
-       \ingroup image_func_moments
-    */
-    AFAPI af_err af_moment(double *out, const af_array in, const af_moment_type moment);
-#endif
-
-#ifdef __cplusplus
-}
-#endif
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 5cd487b1dc..60df3176d1 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -268,7 +268,6 @@
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
-#include "af/moments.h"
 #include "af/seq.h"
 #include "af/signal.h"
 #include "af/statistics.h"
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 98efd2b8b5..afd41ea47c 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -73,7 +73,7 @@ static inline void moment_copy(double *out, const af_array moments)
     delete[] h_ptr;
 }
 
-af_err af_moment(double *out, const af_array in, const af_moment_type moment)
+af_err af_moments_all(double *out, const af_array in, const af_moment_type moment)
 {
     try {
         af_array moments_arr;
diff --git a/src/api/cpp/moments.cpp b/src/api/cpp/moments.cpp
index 3802337e36..3d91717fbb 100644
--- a/src/api/cpp/moments.cpp
+++ b/src/api/cpp/moments.cpp
@@ -28,7 +28,7 @@ array moments(const array& in, const af_moment_type moment)
     T moment(const array &in, const af_moment_type moment)  \
     {                                                       \
         double out;                                         \
-        AF_THROW(af_moment(&out, in.get(), moment));        \
+        AF_THROW(af_moments_all(&out, in.get(), moment));   \
         return (T)out;                                      \
     }
 
@@ -37,8 +37,8 @@ INSTANTIATE_REAL(float)
 INSTANTIATE_REAL(double)
 INSTANTIATE_REAL(int)
 INSTANTIATE_REAL(unsigned)
-INSTANTIATE_REAL(long long)
-INSTANTIATE_REAL(unsigned long long)
+INSTANTIATE_REAL(intl)
+INSTANTIATE_REAL(uintl)
 INSTANTIATE_REAL(short)
 INSTANTIATE_REAL(unsigned short)
 INSTANTIATE_REAL(char)
diff --git a/src/api/unified/moments.cpp b/src/api/unified/moments.cpp
index 2ba2fbaca7..51d74552c1 100644
--- a/src/api/unified/moments.cpp
+++ b/src/api/unified/moments.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2016, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -16,3 +16,9 @@ af_err af_moments(af_array* out, const af_array in, const af_moment_type moment)
     CHECK_ARRAYS(in);
     return CALL(out, in, moment);
 }
+
+af_err af_moments_all(double* out, const af_array in, const af_moment_type moment)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, moment);
+}
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 738b4d01a8..b71ba23c12 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -12,13 +12,11 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <af/defines.h>
 #include <Array.hpp>
 #include <image.hpp>
 #include <err_cpu.hpp>
 #include <graphics_common.hpp>
 #include <platform.hpp>
-#include <kernel/moments.hpp>
 #include <queue.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index e9ed0ed371..dc6cc62f09 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -11,7 +11,6 @@
 
 #include <Array.hpp>
 #include <graphics_common.hpp>
-#include <af/defines.h>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index 6a24fbfa66..fe984b3507 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -65,7 +65,7 @@ struct moments_op<T, AF_MOMENT_M11>
 };
 
 template<typename T, af_moment_type Method>
-void moments(Array<float> &output, Array<T> const input)
+void moments(Array<float> &output, Array<T> const &input)
 {
     T const * const in       = input.get();
     af::dim4  const idims    = input.dims();
@@ -73,6 +73,7 @@ void moments(Array<float> &output, Array<T> const input)
     dim_t     const iElems   = input.elements();
 
     af::dim4  const odims    = output.dims();
+    af::dim4  const ostrides = output.strides();
 
     moments_op<T, Method> op;
     bool pBatch = !(idims[2] == 1 && idims[3] == 1);
@@ -91,7 +92,7 @@ void moments(Array<float> &output, Array<T> const input)
                     mId++;
                 }
             }
-            out[w * odims[0] + z] = (float)val;
+            out[w * ostrides[1] + z] = (float)val;
         }
     }
 }
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index d4905b3b74..081406dfee 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -14,11 +14,11 @@
 #include <kernel/moments.hpp>
 #include <queue.hpp>
 
-using af::dim4;
-
 namespace cpu
 {
 
+using af::dim4;
+
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment)
 {
diff --git a/src/backend/cuda/image.cu b/src/backend/cuda/image.cu
index 45a26a9d3b..292f80110a 100644
--- a/src/backend/cuda/image.cu
+++ b/src/backend/cuda/image.cu
@@ -17,7 +17,6 @@
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <interopManager.hpp>
-#include <kernel/moments.hpp>
 
 using af::dim4;
 
@@ -58,7 +57,7 @@ void copy_image(const Array<T> &in, const fg::Image* image)
     }
 }
 
-#define INSTANTIATE(T)                                                          \
+#define INSTANTIATE(T)      \
     template void copy_image<T>(const Array<T> &in, const fg::Image* image);
 
 INSTANTIATE(float)
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 4b48157a03..22c58f90b6 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -96,7 +96,7 @@ namespace kernel
         }
         __syncthreads();
 
-        float *offset = const_cast<float *>(out.ptr + (idw * out.dims[0] + idz));
+        float *offset = const_cast<float *>(out.ptr + (idw * out.strides[1] + idz));
         if(threadIdx.x == 0)
             atomicAdd(offset, blk_moment_sum);
     }
@@ -113,7 +113,6 @@ namespace kernel
         CUDA_LAUNCH((moments_kernel<T, moment>), blocks, threads,
                      out, in, blocksMatX, pBatch);
         POST_LAUNCH_CHECK();
-        CUDA_CHECK(cudaDeviceSynchronize());
     }
 
 }
diff --git a/src/backend/cuda/moments.cu b/src/backend/cuda/moments.cu
index 34bd5c181b..f82f9a542f 100644
--- a/src/backend/cuda/moments.cu
+++ b/src/backend/cuda/moments.cu
@@ -10,14 +10,13 @@
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <interopManager.hpp>
 #include <kernel/moments.hpp>
 
-using af::dim4;
-
 namespace cuda
 {
 
+using af::dim4;
+
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment)
 {
@@ -34,18 +33,10 @@ Array<float> moments(const Array<T> &in, const af_moment_type moment)
     Array<float> out = createValueArray<float>(odims, 0.f);
 
     switch(moment) {
-        case AF_MOMENT_M00:
-            kernel::moments<T, AF_MOMENT_M00>(out, in);
-            break;
-        case AF_MOMENT_M01:
-            kernel::moments<T, AF_MOMENT_M01>(out, in);
-            break;
-        case AF_MOMENT_M10:
-            kernel::moments<T, AF_MOMENT_M10>(out, in);
-            break;
-        case AF_MOMENT_M11:
-            kernel::moments<T, AF_MOMENT_M11>(out, in);
-            break;
+        case AF_MOMENT_M00: kernel::moments<T, AF_MOMENT_M00>(out, in); break;
+        case AF_MOMENT_M01: kernel::moments<T, AF_MOMENT_M01>(out, in); break;
+        case AF_MOMENT_M10: kernel::moments<T, AF_MOMENT_M10>(out, in); break;
+        case AF_MOMENT_M11: kernel::moments<T, AF_MOMENT_M11>(out, in); break;
         default:  break;
     }
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 11a479b0be..7f6b054739 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -12,7 +12,6 @@
 #include <interopManager.hpp>
 #include <Array.hpp>
 #include <image.hpp>
-#include <kernel/moments.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <stdexcept>
@@ -63,7 +62,7 @@ void copy_image(const Array<T> &in, const fg::Image* image)
     }
 }
 
-#define INSTANTIATE(T)                                                              \
+#define INSTANTIATE(T)      \
     template void copy_image<T>(const Array<T> &in, const fg::Image* image);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/kernel/moments.cl b/src/backend/opencl/kernel/moments.cl
index 1f836df4bb..d26c8b6e94 100644
--- a/src/backend/opencl/kernel/moments.cl
+++ b/src/backend/opencl/kernel/moments.cl
@@ -99,6 +99,6 @@ void moments_kernel(__global  float *d_out, const KParam out,
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if(get_local_id(0) == 0)
-        fatomic_add_g(d_out + (idw * out.dims[0] + idz), wkg_moment_sum);
+        fatomic_add_g(d_out + (idw * out.strides[1] + idz), wkg_moment_sum);
 
 }
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index 08bb88e2a5..b91a5a2335 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -11,8 +11,6 @@
 #include <kernel/moments.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
-#include <stdexcept>
-#include <vector>
 
 namespace opencl
 {
@@ -33,18 +31,10 @@ Array<float> moments(const Array<T> &in, const af_moment_type moment)
     Array<float> out = createValueArray<float>(odims, 0.f);
 
     switch(moment) {
-        case AF_MOMENT_M00:
-            kernel::moments<T, AF_MOMENT_M00>(out, in);
-            break;
-        case AF_MOMENT_M01:
-            kernel::moments<T, AF_MOMENT_M01>(out, in);
-            break;
-        case AF_MOMENT_M10:
-            kernel::moments<T, AF_MOMENT_M10>(out, in);
-            break;
-        case AF_MOMENT_M11:
-            kernel::moments<T, AF_MOMENT_M11>(out, in);
-            break;
+        case AF_MOMENT_M00: kernel::moments<T, AF_MOMENT_M00>(out, in); break;
+        case AF_MOMENT_M01: kernel::moments<T, AF_MOMENT_M01>(out, in); break;
+        case AF_MOMENT_M10: kernel::moments<T, AF_MOMENT_M10>(out, in); break;
+        case AF_MOMENT_M11: kernel::moments<T, AF_MOMENT_M11>(out, in); break;
         default:  break;
     }
 
diff --git a/test/moments.cpp b/test/moments.cpp
index 1d64abf7ac..c63be2e3f3 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -124,22 +124,22 @@ void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
 
 TEST(IMAGE, MomentsImage)
 {
-    momentsOnImageTest(string(TEST_DIR"/image/gray_seq_16_moments.test"), string(TEST_DIR"/imageio/gray_seq_16.png"), false);
+    momentsOnImageTest(string(TEST_DIR"/moments/gray_seq_16_moments.test"), string(TEST_DIR"/imageio/gray_seq_16.png"), false);
 }
 
 TEST(Image, MomentsImageBatch)
 {
-    momentsTest<float>(string(TEST_DIR"/image/simple_mat_batch_moments.test"));
+    momentsTest<float>(string(TEST_DIR"/moments/simple_mat_batch_moments.test"));
 }
 
 TEST(Image, MomentsBatch2D)
 {
-    momentsOnImageTest(string(TEST_DIR"/image/color_seq_16_moments.test"), string(TEST_DIR"/imageio/color_seq_16.png"), true);
+    momentsOnImageTest(string(TEST_DIR"/moments/color_seq_16_moments.test"), string(TEST_DIR"/imageio/color_seq_16.png"), true);
 }
 
 TYPED_TEST(Image, MomentsSynthTypes)
 {
-    momentsTest<TypeParam>(string(TEST_DIR"/image/simple_mat_moments.test"));
+    momentsTest<TypeParam>(string(TEST_DIR"/moments/simple_mat_moments.test"));
 }
 
 

From f7db11df6b92ee33918bf07e31d0c1231c030773 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 14 Jun 2016 11:33:42 +0530
Subject: [PATCH 0608/2677] Fix for OCL 1.2 version use warnings

---
 src/backend/opencl/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 29713daa04..403e99030a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -3,6 +3,8 @@ PROJECT(ARRAYFIRE)
 
 FIND_PACKAGE(OpenCL REQUIRED)
 
+ADD_DEFINITIONS(-DCL_USE_DEPRECATED_OPENCL_1_2_APIS)
+
 IF(NOT USE_SYSTEM_CL2HPP)
     INCLUDE("${CMAKE_MODULE_PATH}/build_cl2hpp.cmake")
 ENDIF(NOT USE_SYSTEM_CL2HPP)

From 1ddc740f0938a8beef5bcb8419d41031fbc823d9 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 14 Jun 2016 11:50:40 -0400
Subject: [PATCH 0609/2677] Working OpenCL and CUDA backend

Debugged cuda and opencl backends.
---
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 42 ++++-----
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 37 +++-----
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 38 ++++----
 .../opencl/kernel/scan_first_by_key.cl        | 35 +++-----
 src/backend/opencl/math.hpp                   |  5 +-
 test/scan.cpp                                 | 88 +++++++++++--------
 6 files changed, 111 insertions(+), 134 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 9c0a2286da..8793cc7147 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -96,7 +96,7 @@ namespace kernel
         __shared__ To s_val[THREADS_X * DIMY * 2];
         __shared__ char s_ftmp[THREADS_X];
         __shared__ To s_tmp[THREADS_X];
-        __shared__ int boundaryid;
+        __shared__ int boundaryid[THREADS_X];
         To *sptr =  s_val + tid;
         char *sfptr = s_flg + tid;
 
@@ -110,18 +110,10 @@ namespace kernel
         if (isLast) {
             s_tmp[tidx] = val;
             s_ftmp[tidx] = 0;
-            boundaryid = -1;
+            boundaryid[tidx] = -1;
         }
         __syncthreads();
 
-        char *prev;
-        if (tidy == 0) {
-            prev = &s_ftmp[tidx];
-        } else {
-            prev = sfptr - THREADS_X;
-        }
-        char *curr = &sfptr[tidy];
-
         char flag = 0;
         for (int k = 0; k < lim; k++) {
 
@@ -170,8 +162,14 @@ namespace kernel
                 __syncthreads();
             }
 
-            if ((*prev == 0) && (*curr == 1)) {
-                boundaryid = id_dim;
+            if (tidy == 0) {
+                if ((s_ftmp[tidx] == 0) && (sfptr[start * THREADS_X] == 1)) {
+                    boundaryid[tidx] = id_dim;
+                }
+            } else {
+                if ((sfptr[(start - 1) * THREADS_X] == 0) && (sfptr[start * THREADS_X] == 1)) {
+                    boundaryid[tidx] = id_dim;
+                }
             }
 
             if (is_valid && (id_dim < out_dim)) *optr = val;
@@ -191,7 +189,8 @@ namespace kernel
             isLast) {
             *tptr = val;
             *tfptr = flag;
-            *tiptr = boundaryid;
+            int boundary = boundaryid[tidx];
+            *tiptr = (boundary == -1) ? id_dim : boundary;
             }
     }
 
@@ -494,23 +493,18 @@ namespace kernel
                                                      blocks_all);
 
         } else {
-
             Param<To> tmp = out;
             Param<char> tmpflg;
             Param<int> tmpid;
 
             tmp.dims[dim] = blocks_all[dim];
-            tmpflg.dims[dim] = blocks_all[dim];
-            tmpid.dims[dim] = blocks_all[dim];
             tmp.strides[0] = 1;
-            tmpflg.strides[0] = 1;
-            tmpid.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmpflg.dims[k] = out.dims[k];
-                tmpid.dims[k] = out.dims[k];
-                tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
-                tmpflg.strides[k] = tmpflg.strides[k - 1] * tmpflg.dims[k - 1];
-                tmpid.strides[k] = tmpid.strides[k - 1] * tmpid.dims[k - 1];
+            for (int k = 1; k < 4; k++) tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
+            for (int k = 0; k < 4; k++) {
+                tmpflg.strides[k] = tmp.strides[k];
+                tmpid.strides[k] = tmp.strides[k];
+                tmpflg.dims[k] = tmp.dims[k];
+                tmpid.dims[k] = tmp.dims[k];
             }
 
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 687bac3c6f..4b76ca9222 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -47,11 +47,6 @@ namespace kernel
                                   uint blocks_y,
                                   uint lim)
     {
-        //parallel segmented scan
-        //calculate flags from keys
-        //write to tmp
-        //write to temporary flag
-        //write to temporary last id
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
         const To init = binop.init();
@@ -64,7 +59,7 @@ namespace kernel
         __shared__ To s_val[SHARED_MEM_SIZE];
         __shared__ char s_ftmp[DIMY];
         __shared__ To s_tmp[DIMY];
-        __shared__ int boundaryid;
+        __shared__ int boundaryid[DIMY];
 
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -85,7 +80,7 @@ namespace kernel
         if (isLast) {
             s_tmp[tidy] = init;
             s_ftmp[tidy] = 0;
-            boundaryid = -1;
+            boundaryid[tidy] = -1;
         }
         __syncthreads();
 
@@ -102,18 +97,10 @@ namespace kernel
         tfptr += wid * tflg.strides[3] + zid * tflg.strides[2] + yid * tflg.strides[1];
         tiptr += wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
 
-        char *prev;
-        if (tidx == 0) {
-            prev = &s_ftmp[tidy];
-        } else {
-            prev = &sfptr[tidx-1];
-        }
-        char *curr = &sfptr[tidx];
-
         char flag = 0;
         for (int k = 0; k < lim; k++) {
             if (id < out.dims[0]) {
-                flag = calculate_head_flags(kptr, id, id - key.strides[0]);
+                flag = calculate_head_flags(kptr, id, id - 1);
             } else {
                 flag = 0;
             }
@@ -160,8 +147,14 @@ namespace kernel
             }
 
             //Identify segment boundary
-            if ((*prev == 0) && (*curr == 1)) {
-                boundaryid = id;
+            if (tidx == 0) {
+                if ((s_ftmp[tidy] == 0) && (sfptr[tidx] == 1)) {
+                    boundaryid[tidy] = id;
+                }
+            } else {
+                if ((sfptr[tidx-1] == 0) && (sfptr[tidx] == 1)) {
+                    boundaryid[tidy] = id;
+                }
             }
 
             if (id < out.dims[0]) optr[id] = val;
@@ -175,7 +168,8 @@ namespace kernel
         if (isLast) {
             tptr[blockIdx_x] = val;
             tfptr[blockIdx_x] = flag;
-            tiptr[blockIdx_x] = boundaryid;
+            int boundary = boundaryid[tidy];
+            tiptr[blockIdx_x] = (boundary == -1)? id : boundary;
         }
     }
 
@@ -188,11 +182,6 @@ namespace kernel
                                   uint blocks_y,
                                   uint lim)
     {
-        //parallel segmented scan
-        //calculate flags from keys
-        //write to tmp
-        //write to temporary flag
-        //write to temporary last id
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
         const To init = binop.init();
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index 2f76ec4d69..f020ef779a 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -77,7 +77,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     __local char *l_flg = l_flg0;
     __local To l_tmp[THREADS_X];
     __local char l_ftmp[THREADS_X];
-    __local int boundaryid;
+    __local int boundaryid[THREADS_X];
 
     bool flip = 0;
     const To init_val  = init;
@@ -85,25 +85,15 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     const bool isLast = (lidy == (DIMY - 1));
 
     if (isLast) {
-        l_tmp[lidy] = val;
-        l_ftmp[lidy] = 0;
-        boundaryid = -1;
+        l_tmp[lidx] = val;
+        l_ftmp[lidx] = 0;
+        boundaryid[lidx] = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local char *prev;
-    if (lidy == 0) {
-        prev = &l_ftmp[lidx];
-    } else {
-        prev = &l_flg[lid-THREADS_X];
-    }
-    __local char *curr = &l_flg[lid];
-
     char flag = 0;
     for (int k = 0; k < lim; k++) {
 
-        //if (isLast) l_tmp[lidx] = val;
-
         bool cond = (is_valid) && (id_dim < out_dim);
 
         if (cond) {
@@ -112,8 +102,6 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             flag = 0;
         }
 
-        //val = cond ? transform(*iData) : init_val;
-
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -150,8 +138,14 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
-        if ((*prev == 0) && (*curr == 1)) {
-            boundaryid = id_dim;
+        if (lidy == 0) {
+            if ((l_ftmp[lidx] == 0) && (l_flg[lid] == 1)) {
+                boundaryid[lidx] = id_dim;
+            }
+        } else {
+            if ((l_flg[lid - THREADS_X] == 0) && (l_flg[lid] == 1)) {
+                boundaryid[lidx] = id_dim;
+            }
         }
 
         if (cond) *oData = val;
@@ -171,7 +165,8 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
         isLast) {
         *tData = val;
         *tfData = flag;
-        *tiData = boundaryid;
+        int boundary = boundaryid[lidx];
+        *tiData = (boundary == -1)? id_dim : boundary;
     }
 }
 
@@ -235,8 +230,8 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
     const bool isLast = (lidy == (DIMY - 1));
 
     if (isLast) {
-        l_tmp[lidy] = val;
-        l_ftmp[lidy] = 0;
+        l_tmp[lidx] = val;
+        l_ftmp[lidx] = 0;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
@@ -339,7 +334,6 @@ void bcast_dim_kernel(__global To *oData, KParam oInfo,
         oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
 
         const int id_dim = ids[dim];
-        const int out_dim = oInfo.dims[dim];
 
         bool is_valid =
             (ids[0] < oInfo.dims[0]) &&
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 0a4144301d..584cf743aa 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -67,7 +67,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     __local char *l_flg = l_flg0;
     __local To l_tmp[DIMY];
     __local char l_ftmp[DIMY];
-    __local int boundaryid;
+    __local int boundaryid[DIMY];
 
     bool flip = 0;
 
@@ -80,23 +80,13 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     if (isLast) {
         l_tmp[lidy] = val;
         l_ftmp[lidy] = 0;
-        boundaryid = -1;
+        boundaryid[lidy] = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local char *prev;
-    if (lidx == 0) {
-        prev = &l_ftmp[lidy];
-    } else {
-        prev = &l_flg[lidx-1];
-    }
-    __local char *curr = &l_flg[lidx];
-
     char flag = 0;
     for (int k = 0; k < lim; k++) {
 
-        //if (isLast) l_tmp[lidy] = val;
-
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (cond) {
@@ -104,7 +94,6 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
         } else {
             flag = 0;
         }
-        //val = cond ? transform(iData[id]) : init_val;
 
         if (inclusive_scan) {
             if (!cond) {
@@ -140,9 +129,14 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             l_flg[lid] = flag;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
-
-        if ((*prev == 0) && (*curr == 1)) {
-            boundaryid = id;
+        if (lidx == 0) {
+            if ((l_ftmp[lidy] == 0) && (l_flg[lid] == 1)) {
+                boundaryid[lidy] = id;
+            }
+        } else {
+            if ((l_flg[lid-1] == 0) && (l_flg[lid] == 1)) {
+                boundaryid[lidy] = id;
+            }
         }
 
         if (cond) oData[id] = val;
@@ -154,11 +148,11 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
     }
 
-    if (isLast) {
-    //if (isLast && cond_yzw)
+    if (isLast && cond_yzw) {
         tData[groupId_x] = val;
         tfData[groupId_x] = flag;
-        tiData[groupId_x] = boundaryid;
+        int boundary = boundaryid[lidy];
+        tiData[groupId_x] = (boundary == -1)? id : boundary;
     }
 }
 
@@ -211,8 +205,6 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     for (int k = 0; k < lim; k++) {
         char flag = 0;
 
-        //if (isLast) l_tmp[lidy] = val;
-
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (calculateFlags) {
@@ -224,7 +216,6 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
         } else {
             flag = kData[id];
         }
-        //val = cond ? transform(iData[id]) : init_val;
 
         if (inclusive_scan) {
             if (!cond) {
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index f090062b03..da052547d8 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <af/defines.h>
+#include "defines.hpp"
 
 #include <complex>
 #include <limits>
@@ -39,10 +40,6 @@ namespace opencl
     cfloat division(cfloat lhs, double rhs);
     cdouble division(cdouble lhs, double rhs);
 
-#ifndef STATIC_
-#define STATIC_
-#endif
-
     template<> STATIC_
     cfloat max<cfloat>(cfloat lhs, cfloat rhs)
     {
diff --git a/test/scan.cpp b/test/scan.cpp
index 1b88104468..c14db219fc 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -99,6 +99,7 @@ std::vector<T> createScanKey(af::dim4 dims, int scanDim,
         const std::vector<int> &nodeLengths,
         T keyStart, T keyEnd)
 {
+    std::srand(0);
     int elemCount = dims.elements();
     std::vector<T> key(elemCount);
 
@@ -130,6 +131,7 @@ template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive
 std::pair<std::vector<Ti>, std::vector<To> > createData(af::dim4 dims, const std::vector<Tk> &key,
                 int scanDim, Ti dataStart, Ti dataEnd)
 {
+    std::srand(1);
     Binary<To, op> binOp;
     int elemCount = dims.elements();
     std::vector<To> out(elemCount);
@@ -162,7 +164,7 @@ std::pair<std::vector<Ti>, std::vector<To> > createData(af::dim4 dims, const std
 
 template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
 void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
-        int keyStart, int keyEnd, Ti dataStart, Ti dataEnd)
+        int keyStart, int keyEnd, Ti dataStart, Ti dataEnd, double eps)
 {
     std::vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
     std::pair<std::vector<Ti>, std::vector<To> > data =
@@ -173,32 +175,42 @@ void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
     af::array afin(dims, in.data());
     af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
 
+#if 0
+    if (scanDim == 1) {
+        af_print(afkey.T());
+        af_print(afin.T());
+        af_print(afout.T());
+        af::array afgold(dims, outgold.data());
+        af_print(afgold.T());
+    }
+#endif
+
     std::vector<To> out(afout.elements());
     afout.host(out.data());
     for(unsigned i = 0; i < out.size(); ++i) {
-        ASSERT_NEAR(out[i], outgold[i], 1e-5);
+        ASSERT_NEAR(out[i], outgold[i], eps);
     }
 }
 
-#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND)    \
-TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                \
-{                                                                           \
-    af::dim4 dims(X, Y, Z, W);                                            \
-    int scanDim = DIM;                                                      \
-    int nodel[] = {37, 256};                                                \
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));   \
-    int keyStart = 0;                                                       \
-    int keyEnd = 15;                                                        \
-    int dataStart = DSTART;                                                 \
-    int dataEnd = DEND;                                                     \
-    scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths,              \
-            keyStart, keyEnd, dataStart, dataEnd);                          \
+#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS)   \
+TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                    \
+{                                                                               \
+    af::dim4 dims(X, Y, Z, W);                                                  \
+    int scanDim = DIM;                                                          \
+    int nodel[] = {37, 256};                                                    \
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));       \
+    int keyStart = 0;                                                           \
+    int keyEnd = 15;                                                            \
+    int dataStart = DSTART;                                                     \
+    int dataEnd = DEND;                                                         \
+    scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths,                  \
+            keyStart, keyEnd, dataStart, dataEnd, EPS);                         \
 }
 
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int,  true, 0,   -15, 15);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int, false, 0,   -15, 15);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float,  true, 0, -0.25, 0.25);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float, false, 0, -0.25, 0.25);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1,   int,   int,  true, 0,   -15,   15, 1e-5);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1,   int,   int, false, 0,   -15,   15, 1e-5);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1, float, float,  true, 0, -0.25, 0.25, 1e-5);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1, float, float, false, 0, -0.25, 0.25, 1e-5);
 
 TEST(ScanByKey,Test_Scan_By_key_Simple_0)
 {
@@ -211,27 +223,27 @@ TEST(ScanByKey,Test_Scan_By_key_Simple_0)
     int dataStart = 2;
     int dataEnd = 4;
     scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
-            keyStart, keyEnd, dataStart, dataEnd);
+            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
+}
+
+TEST(ScanByKey,Test_Scan_By_key_Simple_1)
+{
+    af::dim4 dims(8, 256+128, 1, 1);
+    int scanDim = 1;
+    int nodel[] = {4, 8};
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+    int keyStart = 0;
+    int keyEnd = 15;
+    int dataStart = 2;
+    int dataEnd = 4;
+    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
+            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
 }
 
-//TEST(ScanByKey,Test_Scan_By_key_Simple_1)
-//{
-//    af::dim4 dims(8, 256+128, 1, 1);
-//    int scanDim = 1;
-//    int nodel[] = {4, 8};
-//    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
-//    int keyStart = 0;
-//    int keyEnd = 15;
-//    int dataStart = 2;
-//    int dataEnd = 4;
-//    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
-//            keyStart, keyEnd, dataStart, dataEnd);
-//}
-
-//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1,   int,   int,  true, 1,   -15, 15);
-//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1,   int,   int, false, 1,   -15, 15);
-//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1, float, float,  true, 1, -0.25, 0.25);
-//SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 256, 1, 1, float, float, false, 1, -0.25, 0.25);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1,   int,   int,  true, 1, -15, 15, 1e-4);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1,   int,   int, false, 1, -15, 15, 1e-4);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1, float, float,  true, 1,  -1,  1, 1e-4);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1, float, float, false, 1,  -1,  1, 1e-4);
 
 #define SCAN_TESTS(FN, TAG, Ti, To)             \
     TEST(Scan,Test_##FN##_##TAG)                \

From ce90699ec891ce97df78acffd0ea6696dbf0656f Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 14 Jun 2016 16:18:06 -0400
Subject: [PATCH 0610/2677] Design changes

Some ifs converted to ternary. Added comment explaining CPU inclusive
scan. CPU scan_by_key function is templated with inclusive_scan bool for
cleanliness.
---
 src/backend/cpu/kernel/scan.hpp               |  2 +
 src/backend/cpu/scan_by_key.cpp               | 61 ++++++++-----------
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 10 +--
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 10 +--
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 14 ++---
 .../opencl/kernel/scan_first_by_key.cl        | 12 +---
 test/scan.cpp                                 |  5 +-
 7 files changed, 39 insertions(+), 75 deletions(-)

diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index 5071db0a58..e8db08ac25 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -63,6 +63,8 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan>
             To in_val = transform(in[i * istride]);
             out_val = scan(in_val, out_val);
             if (!inclusive_scan) {
+                //The loop shifts the output index by 1.
+                //The last index wraps around and writes the first element.
                 if (i == (idims[dim] - 1)) {
                     out[0] = scan.init();
                 } else {
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index 8f7d86d4b2..3c2fc35129 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -20,6 +20,28 @@ using af::dim4;
 
 namespace cpu
 {
+    template<af_op_t op, typename Ti, typename Tk, typename To, bool inclusive_scan>
+    void scan_by_key(int ndims, Array<To>& out, const Array<Tk>& key, const Array<Ti>& in, const int dim)
+    {
+        switch (ndims) {
+            case 1:
+                kernel::scan_dim_by_key<op, Ti, Tk, To, 1, inclusive_scan> func1;
+                getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
+                break;
+            case 2:
+                kernel::scan_dim_by_key<op, Ti, Tk, To, 2, inclusive_scan> func2;
+                getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
+                break;
+            case 3:
+                kernel::scan_dim_by_key<op, Ti, Tk, To, 3, inclusive_scan> func3;
+                getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
+                break;
+            case 4:
+                kernel::scan_dim_by_key<op, Ti, Tk, To, 4, inclusive_scan> func4;
+                getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
+                break;
+        }
+    }
 
     template<af_op_t op, typename Ti, typename Tk, typename To>
     Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
@@ -29,47 +51,12 @@ namespace cpu
         in.eval();
 
         if (inclusive_scan) {
-            switch (in.ndims()) {
-                case 1:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 1, true> func1;
-                    getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
-                    break;
-                case 2:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 2, true> func2;
-                    getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
-                    break;
-                case 3:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 3, true> func3;
-                    getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
-                    break;
-                case 4:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 4, true> func4;
-                    getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
-                    break;
-            }
+            scan_by_key<op, Ti, Tk, To,  true>(in.ndims(), out, key, in, dim);
         } else {
-            switch (in.ndims()) {
-                case 1:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 1, false> func1;
-                    getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
-                    break;
-                case 2:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 2, false> func2;
-                    getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
-                    break;
-                case 3:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 3, false> func3;
-                    getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
-                    break;
-                case 4:
-                    kernel::scan_dim_by_key<op, Ti, Tk, To, 4, false> func4;
-                    getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
-                    break;
-            }
+            scan_by_key<op, Ti, Tk, To, false>(in.ndims(), out, key, in, dim);
         }
 
         return out;
-
     }
 
 #define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 8793cc7147..3d18102888 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -27,13 +27,7 @@ namespace kernel
     __device__
     inline static char calculate_head_flags_dim(const Tk *kptr, int id, int stride)
     {
-        char flag;
-        if (id == 0) {
-            flag = 1;
-        } else {
-            flag = ((*kptr) != (*(kptr - stride)));
-        }
-        return flag;
+        return (id == 0)? 1 : ((*kptr) != (*(kptr - stride)));
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, int dim, uint DIMY, bool inclusive_scan>
@@ -140,7 +134,7 @@ namespace kernel
 
             if ((tidy == 0) && (flag == 0)) {
                 val = binop(val, s_tmp[tidx]);
-                flag = flag | s_ftmp[tidx];
+                flag = s_ftmp[tidx];
             }
 
             *sptr = val;
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 4b76ca9222..49b2c06d74 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -26,13 +26,7 @@ namespace kernel
     __device__
     inline static char calculate_head_flags(const Tk *kptr, int id, int previd)
     {
-        char flag;
-        if (id == 0) {
-            flag = 1;
-        } else {
-            flag = (kptr[id] != kptr[previd]);
-        }
-        return flag;
+        return (id == 0)? 1 : (kptr[id] != kptr[previd]);
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX, bool inclusive_scan>
@@ -123,7 +117,7 @@ namespace kernel
             //Add partial result from last iteration before scan operation
             if ((tidx == 0) && (flag == 0)) {
                 val = binop(val, s_tmp[tidy]);
-                flag = flag | s_ftmp[tidy];
+                flag = s_ftmp[tidy];
             }
 
             //Write to shared memory
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index f020ef779a..c4aebd5401 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -7,15 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-static char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
+char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
 {
-    char flag;
-    if (id == 0) {
-        flag = 1;
-    } else {
-        flag = ((*kptr) != (*(kptr - stride)));
-    }
-    return flag;
+    return (id == 0)? 1 : ((*kptr) != (*(kptr - stride)));
 }
 
 __kernel
@@ -118,7 +112,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if ((lidy == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidx]);
-            flag = flag | l_ftmp[lidx];
+            flag = l_ftmp[lidx];
         }
         l_val[lid] = val;
         l_flg[lid] = flag;
@@ -266,7 +260,7 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
 
         if ((lidy == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidx]);
-            flag = flag | l_ftmp[lidx];
+            flag = l_ftmp[lidx];
         }
         l_val[lid] = val;
         l_flg[lid] = flag;
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 584cf743aa..26831c26e3 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -7,15 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-static char calculate_head_flags(const __global Tk *kptr, int id, int previd)
+char calculate_head_flags(const __global Tk *kptr, int id, int previd)
 {
-    char flag;
-    if (id == 0) {
-        flag = 1;
-    } else {
-        flag = (kptr[id] != kptr[previd]);
-    }
-    return flag;
+    return (id == 0)? 1 : (kptr[id] != kptr[previd]);
 }
 
 __kernel
@@ -111,7 +105,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if ((lidx == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidy]);
-            flag = flag | l_ftmp[lidy];
+            flag = l_ftmp[lidy];
         }
         l_val[lid] = val;
         l_flg[lid] = flag;
diff --git a/test/scan.cpp b/test/scan.cpp
index c14db219fc..ac20cf20f1 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -117,9 +117,8 @@ std::vector<T> createScanKey(af::dim4 dims, int scanDim,
                     isNode = true;
                 }
             }
-            if (isNode) {
-                if (std::rand()%2) keyval =
-                    randomInterval(keyStart, keyEnd);
+            if (isNode && (std::rand()%2)) {
+                keyval = randomInterval(keyStart, keyEnd);
             }
             key[index] = keyval;
         }

From 4779873d543f7e16caf0aba65631d52b0525298c Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 14 Jun 2016 16:43:27 -0400
Subject: [PATCH 0611/2677] Added some comments

---
 src/backend/cuda/kernel/scan_dim_by_key_impl.hpp |  7 +++++++
 src/backend/opencl/kernel/scan_dim_by_key.cl     | 11 +++++++++++
 src/backend/opencl/kernel/scan_first_by_key.cl   | 12 ++++++++++++
 3 files changed, 30 insertions(+)

diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 3d18102888..a4243477c3 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -132,15 +132,18 @@ namespace kernel
                 }
             }
 
+            //Add partial result from last iteration before scan operation
             if ((tidy == 0) && (flag == 0)) {
                 val = binop(val, s_tmp[tidx]);
                 flag = s_ftmp[tidx];
             }
 
+            //Write to shared memory
             *sptr = val;
             *sfptr = flag;
             __syncthreads();
 
+            //Segmented Scan
             int start = 0;
 #pragma unroll
             for (int off = 1; off < DIMY; off *= 2) {
@@ -156,6 +159,7 @@ namespace kernel
                 __syncthreads();
             }
 
+            //Identify segment boundary
             if (tidy == 0) {
                 if ((s_ftmp[tidx] == 0) && (sfptr[start * THREADS_X] == 1)) {
                     boundaryid[tidx] = id_dim;
@@ -282,15 +286,18 @@ namespace kernel
                 }
             }
 
+            //Add partial result from last iteration before scan operation
             if ((tidy == 0) && (flag == 0)) {
                 val = binop(val, s_tmp[tidx]);
                 flag = flag | s_ftmp[tidx];
             }
 
+            //Write to shared memory
             *sptr = val;
             *sfptr = flag;
             __syncthreads();
 
+            //Segmented Scan
             int start = 0;
 #pragma unroll
             for (int off = 1; off < DIMY; off *= 2) {
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index c4aebd5401..da648b7271 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -96,6 +96,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             flag = 0;
         }
 
+        //Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -110,14 +111,18 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             }
         }
 
+        //Add partial result from last iteration before scan operation
         if ((lidy == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidx]);
             flag = l_ftmp[lidx];
         }
+
+        //Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        //Segmented Scan
         for (int off = 1; off < DIMY; off *= 2) {
 
             if (lidy >= off) {
@@ -132,6 +137,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
+        //Identify segment boundary
         if (lidy == 0) {
             if ((l_ftmp[lidx] == 0) && (l_flg[lid] == 1)) {
                 boundaryid[lidx] = id_dim;
@@ -244,6 +250,7 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
             flag = *kData;
         }
 
+        //Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -258,14 +265,18 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
             }
         }
 
+        //Add partial result from last iteration before scan operation
         if ((lidy == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidx]);
             flag = l_ftmp[lidx];
         }
+
+        //Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        //Segmented Scan
         for (int off = 1; off < DIMY; off *= 2) {
 
             if (lidy >= off) {
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 26831c26e3..8561483fcd 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -89,6 +89,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             flag = 0;
         }
 
+        //Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -103,14 +104,18 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             }
         }
 
+        //Add partial result from last iteration before scan operation
         if ((lidx == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidy]);
             flag = l_ftmp[lidy];
         }
+
+        //Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        //Segmented Scan
         for (int off = 1; off < DIMX; off *= 2) {
             if (lidx >= off) {
                 val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
@@ -123,6 +128,8 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             l_flg[lid] = flag;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
+
+        //Identify segment boundary
         if (lidx == 0) {
             if ((l_ftmp[lidy] == 0) && (l_flg[lid] == 1)) {
                 boundaryid[lidy] = id;
@@ -211,6 +218,7 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
             flag = kData[id];
         }
 
+        //Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -225,14 +233,18 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
             }
         }
 
+        //Add partial result from last iteration before scan operation
         if ((lidx == 0) && (flag == 0)) {
             val = binOp(val, l_tmp[lidy]);
             flag = flag | l_ftmp[lidy];
         }
+
+        //Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        //Write to shared memory
         for (int off = 1; off < DIMX; off *= 2) {
             if (lidx >= off) {
                 val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);

From 35ce73b47cb288153dc9478c5b214479f4e5705b Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 14 Jun 2016 17:29:54 -0400
Subject: [PATCH 0612/2677] Replaced make_kernel with KernelFunctor

Also removed debug #if in test/scan.cpp
---
 .../opencl/kernel/scan_dim_by_key_impl.hpp    | 38 +++++++++----------
 .../opencl/kernel/scan_first_by_key_impl.hpp  | 32 ++++++++--------
 test/scan.cpp                                 | 10 -----
 3 files changed, 35 insertions(+), 45 deletions(-)

diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 1806716e4c..ccd47e407b 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -26,7 +26,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -123,14 +123,14 @@ namespace kernel
 
             uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto scanOp = make_kernel<Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      uint, uint,
-                                      uint, uint>(ker);
+            auto scanOp = KernelFunctor<Buffer, KParam,
+                                        Buffer, KParam,
+                                        Buffer, KParam,
+                                        Buffer, KParam,
+                                        Buffer, KParam,
+                                        Buffer, KParam,
+                                        uint, uint,
+                                        uint, uint>(ker);
 
             scanOp(EnqueueArgs(getQueue(), global, local),
                    *out.data, out.info,
@@ -163,11 +163,11 @@ namespace kernel
 
             uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto scanOp = make_kernel<Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      uint, uint,
-                                      uint, uint>(ker);
+            auto scanOp = KernelFunctor<Buffer, KParam,
+                                        Buffer, KParam,
+                                        Buffer, KParam,
+                                        uint, uint,
+                                        uint, uint>(ker);
 
             scanOp(EnqueueArgs(getQueue(), global, local),
                    *out.data, out.info, *in.data, in.info, *key.data, key.info,
@@ -196,11 +196,11 @@ namespace kernel
 
             uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto bcastOp = make_kernel<Buffer, KParam,
-                                       Buffer, KParam,
-                                       Buffer, KParam,
-                                       uint, uint,
-                                       uint, uint>(ker);
+            auto bcastOp = KernelFunctor<Buffer, KParam,
+                                         Buffer, KParam,
+                                         Buffer, KParam,
+                                         uint, uint,
+                                         uint, uint>(ker);
 
             bcastOp(EnqueueArgs(getQueue(), global, local),
                     *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index ba523dac06..489dbe1ec1 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -27,7 +27,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -127,13 +127,13 @@ namespace kernel
 
         uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        auto scanOp = make_kernel<Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  uint, uint, uint>(ker);
+        auto scanOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    uint, uint, uint>(ker);
 
         scanOp(EnqueueArgs(getQueue(), global, local),
                *out.data, out.info,
@@ -163,10 +163,10 @@ namespace kernel
 
         uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        auto scanOp = make_kernel<Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  uint, uint, uint>(ker);
+        auto scanOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    uint, uint, uint>(ker);
 
         scanOp(EnqueueArgs(getQueue(), global, local),
                *out.data, out.info, *in.data, in.info, *key.data, key.info,
@@ -192,10 +192,10 @@ namespace kernel
 
         uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        auto bcastOp = make_kernel<Buffer, KParam,
-                                   Buffer, KParam,
-                                   Buffer, KParam,
-                                   uint, uint, uint>(ker);
+        auto bcastOp = KernelFunctor<Buffer, KParam,
+                                     Buffer, KParam,
+                                     Buffer, KParam,
+                                     uint, uint, uint>(ker);
 
         bcastOp(EnqueueArgs(getQueue(), global, local),
                 *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
diff --git a/test/scan.cpp b/test/scan.cpp
index ac20cf20f1..133fc3f455 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -174,16 +174,6 @@ void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
     af::array afin(dims, in.data());
     af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
 
-#if 0
-    if (scanDim == 1) {
-        af_print(afkey.T());
-        af_print(afin.T());
-        af_print(afout.T());
-        af::array afgold(dims, outgold.data());
-        af_print(afgold.T());
-    }
-#endif
-
     std::vector<To> out(afout.elements());
     afout.host(out.data());
     for(unsigned i = 0; i < out.size(); ++i) {

From f76920a841cf647bb05e6127017acffe0e9cb409 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 10 Jun 2016 03:29:19 -0400
Subject: [PATCH 0613/2677] Fixes to CL-GL interop with different platforms

Fixes issues where multiple devices from different platforms
have cl_khr_gl_sharing availble but the gl context is created
on only of the devices
---
 src/backend/opencl/platform.cpp | 50 ++++++++++++++++++++++++++++++---
 src/backend/opencl/platform.hpp |  4 +++
 2 files changed, 50 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index cee53ad38c..80cf1be3cd 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -647,14 +647,12 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
         if (device >= (int)mQueues.size() ||
                 device>= (int)DeviceManager::MAX_DEVICES) {
             throw cl::Error(CL_INVALID_DEVICE, "Invalid device passed for CL-GL Interop");
-        }
-        else {
+        } else {
             mQueues[device]->finish();
 
             // check if the device has CL_GL sharing extension enabled
             bool temp = checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
             if (!temp) {
-                printf("Device[%d] has no support for OpenGL Interoperation\n",device);
                 /* return silently if given device has not OpenGL sharing extension
                  * enabled so that regular queue is used for it */
                 return;
@@ -662,6 +660,7 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
 
             // call forge to get OpenGL sharing context and details
             cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
+
 #ifdef OS_MAC
             CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
             CGLShareGroupObj cgl_share_group = CGLGetShareGroup(cgl_current_ctx);
@@ -681,17 +680,60 @@ void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHa
                 CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
                 0
             };
+
+            // Check if current OpenCL device is belongs to the OpenGL context
+            {
+                cl_context_properties test_cps[] = {
+                    CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
+                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
+                    0
+                };
+
+                // Load the extension
+                // If cl_khr_gl_sharing is available, this function should be present
+                // This has been checked earlier, it comes to this point only if it is found
+                auto func = (clGetGLContextInfoKHR_fn)
+                    clGetExtensionFunctionAddressForPlatform(plat(), "clGetGLContextInfoKHR");
+
+                // If the function doesn't load, bail early
+                if (!func) return;
+
+                // Get all devices associated with opengl context
+                std::vector<cl_device_id> devices(16);
+                size_t ret = 0;
+                cl_int err = func(test_cps,
+                                  CL_DEVICES_FOR_GL_CONTEXT_KHR,
+                                  devices.size() * sizeof(cl_device_id),
+                                  &devices[0],
+                                  &ret);
+                if (err != CL_SUCCESS) return;
+                int num = ret / sizeof(cl_device_id);
+                devices.resize(num);
+
+                // Check if current device is present in the associated devices
+                cl_device_id current_device = (*mDevices[device])();
+                auto res = std::find(std::begin(devices),
+                                     std::end(devices),
+                                     current_device);
+
+                if (res == std::end(devices)) return;
+            }
 #endif
+
+            // Change current device to use GL sharing
             Context * ctx = new Context(*mDevices[device], cps);
             CommandQueue * cq = new CommandQueue(*ctx, *mDevices[device]);
 
+            // May be fixes the AMD GL issues we see on windows?
+#if !defined(_WIN32) && !defined(_MSC_VER)
             delete mContexts[device];
             delete mQueues[device];
+#endif
 
             mContexts[device] = ctx;
             mQueues[device] = cq;
+            mIsGLSharingOn[device] = true;
         }
-        mIsGLSharingOn[device] = true;
     } catch (const cl::Error &ex) {
         /* If replacing the original context with GL shared context
          * failes, don't throw an error and instead fall back to
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 020c8494f1..be9670eb26 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -15,7 +15,11 @@
 #define CL_HPP_ENABLE_EXCEPTIONS
 #define CL_HPP_MINIMUM_OPENCL_VERSION 120
 #define CL_HPP_TARGET_OPENCL_VERSION 120
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
 #include <CL/cl2.hpp>
+#pragma GCC diagnostic pop
 
 #include <vector>
 #include <string>

From 8d9e93ffe21461ad0d3a0d53310aee733454dc7a Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 15 Jun 2016 03:04:39 -0400
Subject: [PATCH 0614/2677] Testing for more operators

Testing takes care of MIN and MAX operator types.
---
 test/scan.cpp | 89 ++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 59 insertions(+), 30 deletions(-)

diff --git a/test/scan.cpp b/test/scan.cpp
index 133fc3f455..3d0c885f2a 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -126,39 +126,52 @@ std::vector<T> createScanKey(af::dim4 dims, int scanDim,
     return key;
 }
 
+template <typename T>
+std::vector<T> createScanData(af::dim4 dims, T dataStart, T dataEnd)
+{
+    int elemCount = dims.elements();
+    std::vector<T> in(elemCount);
+    for (int i = 0; i < elemCount; ++i) {
+        in[i] = randomInterval(dataStart, dataEnd);
+    }
+    return in;
+}
+
 template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive_scan>
-std::pair<std::vector<Ti>, std::vector<To> > createData(af::dim4 dims, const std::vector<Tk> &key,
-                int scanDim, Ti dataStart, Ti dataEnd)
+void verify(af::dim4 dims,
+        const std::vector<Ti> &in,
+        const std::vector<Tk> &key,
+        const std::vector<To> &out,
+        int scanDim, double eps)
 {
     std::srand(1);
     Binary<To, op> binOp;
     int elemCount = dims.elements();
-    std::vector<To> out(elemCount);
-    std::vector<Ti> in(elemCount);
 
     int stride = 1;
     for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
 
     for (int start = 0; start < stride; ++start) {
-        Ti keyval = key[start];
-        if (!inclusive_scan) {
-            out[start] = binOp.init();
-            in[start] = randomInterval(dataStart, dataEnd);
-        }
+        Tk keyval = key[start];
+        To gold = binOp.init();
         for (int index = start + (!inclusive_scan)*stride, i = (!inclusive_scan);
                 index < elemCount;
                 index += stride, i = (i+1)%dims[scanDim]) {
-            in[index] = randomInterval(dataStart, dataEnd);
             if ((key[index] != keyval) || (i == 0)) {
                 keyval = key[index];
-                out[index] = inclusive_scan? (To)in[index] : binOp.init();
+                if (inclusive_scan) {
+                    gold = (To)in[index];
+                    ASSERT_NEAR(gold, out[index], eps);
+                } else {
+                    gold = binOp.init();
+                }
             } else {
                 To dataval = (To)in[index - (!inclusive_scan)*stride];
-                out[index] = binOp(out[index - stride], dataval);
+                gold = binOp(gold, dataval);
+                ASSERT_NEAR(gold, out[index], eps);
             }
         }
     }
-    return std::make_pair(in, out);
 }
 
 template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
@@ -166,19 +179,15 @@ void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
         int keyStart, int keyEnd, Ti dataStart, Ti dataEnd, double eps)
 {
     std::vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
-    std::pair<std::vector<Ti>, std::vector<To> > data =
-        createData<Ti, int, To, op, inclusive_scan>(dims, key, scanDim, dataStart, dataEnd);
-    std::vector<Ti> &in = data.first;
-    std::vector<To> &outgold = data.second;
+    std::vector<Ti> in = createScanData<Ti>(dims, dataStart, dataEnd);
+
     af::array afkey(dims, key.data());
     af::array afin(dims, in.data());
     af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
-
     std::vector<To> out(afout.elements());
     afout.host(out.data());
-    for(unsigned i = 0; i < out.size(); ++i) {
-        ASSERT_NEAR(out[i], outgold[i], eps);
-    }
+
+    verify<Ti, int, To, op, inclusive_scan>(dims, in, key, out, scanDim, eps);
 }
 
 #define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS)   \
@@ -196,10 +205,35 @@ TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)
             keyStart, keyEnd, dataStart, dataEnd, EPS);                         \
 }
 
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1,   int,   int,  true, 0,   -15,   15, 1e-5);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1,   int,   int, false, 0,   -15,   15, 1e-5);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1, float, float,  true, 0, -0.25, 0.25, 1e-5);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024+17, 1024, 1, 1, float, float, false, 0, -0.25, 0.25, 1e-5);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
 
 TEST(ScanByKey,Test_Scan_By_key_Simple_0)
 {
@@ -229,11 +263,6 @@ TEST(ScanByKey,Test_Scan_By_key_Simple_1)
             keyStart, keyEnd, dataStart, dataEnd, 1e-5);
 }
 
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1,   int,   int,  true, 1, -15, 15, 1e-4);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1,   int,   int, false, 1, -15, 15, 1e-4);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1, float, float,  true, 1,  -1,  1, 1e-4);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4*1024, 512, 1, 1, float, float, false, 1,  -1,  1, 1e-4);
-
 #define SCAN_TESTS(FN, TAG, Ti, To)             \
     TEST(Scan,Test_##FN##_##TAG)                \
     {                                           \

From 076925136d0e59ef9a20cc44c3fed5db4f2997e4 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 15 Jun 2016 03:28:07 -0400
Subject: [PATCH 0615/2677] C++ enum type for C++ API

---
 include/af/algorithm.h | 4 ++--
 src/api/cpp/scan.cpp   | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index bc39b1deac..62afdd5b03 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -328,7 +328,7 @@ namespace af
 
        \ingroup scan_func_scan
     */
-    AFAPI array scan(const array &in, const int dim = 0, af_binary_op op = AF_BINARY_ADD, bool inclusive_scan = true);
+    AFAPI array scan(const array &in, const int dim = 0, binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
 
     /**
        C++ Interface generalized scan by key of an array
@@ -342,7 +342,7 @@ namespace af
 
        \ingroup scan_func_scan
     */
-    AFAPI array scanByKey(const array &key, const array& in, const int dim = 0, af_binary_op op = AF_BINARY_ADD, bool inclusive_scan = true);
+    AFAPI array scanByKey(const array &key, const array& in, const int dim = 0, binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
 #endif
 
     /**
diff --git a/src/api/cpp/scan.cpp b/src/api/cpp/scan.cpp
index 3d2222e178..0adf255041 100644
--- a/src/api/cpp/scan.cpp
+++ b/src/api/cpp/scan.cpp
@@ -20,14 +20,14 @@ namespace af
         return array(out);
     }
 
-    array scan(const array& in, const int dim, af_binary_op op, bool inclusive_scan)
+    array scan(const array& in, const int dim, binaryOp op, bool inclusive_scan)
     {
         af_array out = 0;
         AF_THROW(af_scan(&out, in.get(), dim, op, inclusive_scan));
         return array(out);
     }
 
-    array scanByKey(const array& key, const array& in, const int dim, af_binary_op op, bool inclusive_scan)
+    array scanByKey(const array& key, const array& in, const int dim, binaryOp op, bool inclusive_scan)
     {
         af_array out = 0;
         AF_THROW(af_scan_by_key(&out, key.get(), in.get(), dim, op, inclusive_scan));

From de10367e7069e3590be03fdff985f15f2097092a Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 15 Jun 2016 10:29:27 -0400
Subject: [PATCH 0616/2677] Changed helloworld to reflect correct Binary Enum

---
 examples/helloworld/helloworld.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/helloworld/helloworld.cpp b/examples/helloworld/helloworld.cpp
index ad34e58fa2..b4958e920d 100644
--- a/examples/helloworld/helloworld.cpp
+++ b/examples/helloworld/helloworld.cpp
@@ -49,7 +49,7 @@ int main(int argc, char *argv[])
         af_print(r);
 
         printf("Scan\n");
-        array S = af::scan(r, 0, AF_MUL);
+        array S = af::scan(r, 0, AF_BINARY_MUL);
         af_print(S);
 
         printf("Create 2-by-3 matrix from host data\n");

From 4c20315216918d296cf690c403647229de15b9c5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 15 Jun 2016 17:00:24 -0400
Subject: [PATCH 0617/2677] allows requesting of multiple moments. restructures
 blocks for performance improvements

---
 include/af/defines.h                  |   9 ++-
 include/af/image.h                    |  18 ++---
 src/api/c/moments.cpp                 |  13 +++-
 src/api/cpp/moments.cpp               |  29 ++-----
 src/api/unified/moments.cpp           |   2 +-
 src/backend/cpu/kernel/moments.hpp    |  73 +++++-------------
 src/backend/cpu/moments.cpp           |  36 ++++-----
 src/backend/cuda/kernel/moments.hpp   | 107 ++++++++++----------------
 src/backend/cuda/moments.cu           |  31 ++++----
 src/backend/opencl/kernel/moments.cl  | 103 ++++++++++++-------------
 src/backend/opencl/kernel/moments.hpp |  23 ++----
 src/backend/opencl/moments.cpp        |  31 ++++----
 12 files changed, 190 insertions(+), 285 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index c0cc85e870..93cc07aa1a 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -359,10 +359,11 @@ typedef enum {
 
 #if AF_API_VERSION >=34
 typedef enum {
-    AF_MOMENT_M00 = 0,
-    AF_MOMENT_M01 = 1,
-    AF_MOMENT_M10 = 2,
-    AF_MOMENT_M11 = 3,
+    AF_MOMENT_M00 = 1,
+    AF_MOMENT_M01 = 2,
+    AF_MOMENT_M10 = 4,
+    AF_MOMENT_M11 = 8,
+    AF_MOMENT_FIRST_ORDER = 0xF
 } af_moment_type;
 #endif
 
diff --git a/include/af/image.h b/include/af/image.h
index 76a7cca6a4..76521b2de9 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -682,12 +682,12 @@ AFAPI array rgb2ycbcr(const array& in, const YCCStd standard=AF_YCC_601);
    C++ Interface for calculating an image moment
 
    \param[in]  in is the input image
-   \param[moment] is the moment to calculate
-   \return      the value of the moment
+   \param[out] out is a pointer to the outputted moment(s)
+   \param[moment] is the moment(s) to calculate
 
    \ingroup image_func_moments
  */
-template<typename T> T moment(const array& in, const af_moment_type moment);
+AFAPI void moments(double* out, const array& in, const af_moment_type moment);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -1381,7 +1381,7 @@ extern "C" {
 
        \param[out] out is an array containing the calculated moments
        \param[in]  in is an array of image(s)
-       \param[moment] is the moment to calculate
+       \param[moment] is the moment(s) to calculate
        \return     ref AF_SUCCESS if the moment calculation is successful,
        otherwise an appropriate error code is returned.
 
@@ -1392,17 +1392,17 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for calculating an image moment
+       C Interface for calculating image moment(s) of a single image
 
-       \param[out] out is a pointer to the outputted moment
-       \param[in]  in is an array of image(s)
-       \param[moment] is the moment to calculate
+       \param[out] out is  a pointer to the outputted moment(s)
+       \param[in] in is the input image
+       \param[moment] is the moment(s) to calculate
        \return     ref AF_SUCCESS if the moment calculation is successful,
        otherwise an appropriate error code is returned.
 
        \ingroup image_func_moments
     */
-    AFAPI af_err af_moments_all(double *out, const af_array in, const af_moment_type moment);
+    AFAPI af_err af_moments_all(double* out, const af_array in, const af_moment_type moment);
 #endif
 
 #ifdef __cplusplus
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index afd41ea47c..b280600ff8 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/moments.h>
+#include <af/image.h>
 #include <af/index.h>
 #include <af/data.h>
 
@@ -62,20 +62,25 @@ af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
 }
 
 template<typename T>
-static inline void moment_copy(double *out, const af_array moments)
+static inline void moment_copy(double* out, const af_array moments)
 {
     dim_t elems;
     af_get_elements(&elems, moments);
     T *h_ptr = new T[elems];
     af_get_data_ptr((void *)h_ptr, moments);
 
-    *out = (double)h_ptr[0];
+    for(unsigned i=0; i<elems; ++i)
+        out[i] = (double)h_ptr[i];
     delete[] h_ptr;
 }
 
-af_err af_moments_all(double *out, const af_array in, const af_moment_type moment)
+af_err af_moments_all(double* out, const af_array in, const af_moment_type moment)
 {
     try {
+        const ArrayInfo in_info = getInfo(in);
+        dim4 idims = in_info.dims();
+        DIM_ASSERT(1, idims[2] == 1 && idims[3] == 1);
+
         af_array moments_arr;
         af_moments(&moments_arr, in, moment);
 
diff --git a/src/api/cpp/moments.cpp b/src/api/cpp/moments.cpp
index 3d91717fbb..a572b59c55 100644
--- a/src/api/cpp/moments.cpp
+++ b/src/api/cpp/moments.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <af/moments.h>
+#include <af/image.h>
 #include <af/array.h>
 #include "error.hpp"
 
@@ -22,28 +22,9 @@ array moments(const array& in, const af_moment_type moment)
     return array(out);
 }
 
-
-#define INSTANTIATE_REAL(T)                                 \
-    template<> AFAPI                                        \
-    T moment(const array &in, const af_moment_type moment)  \
-    {                                                       \
-        double out;                                         \
-        AF_THROW(af_moments_all(&out, in.get(), moment));   \
-        return (T)out;                                      \
-    }
-
-
-INSTANTIATE_REAL(float)
-INSTANTIATE_REAL(double)
-INSTANTIATE_REAL(int)
-INSTANTIATE_REAL(unsigned)
-INSTANTIATE_REAL(intl)
-INSTANTIATE_REAL(uintl)
-INSTANTIATE_REAL(short)
-INSTANTIATE_REAL(unsigned short)
-INSTANTIATE_REAL(char)
-INSTANTIATE_REAL(unsigned char)
-
-#undef INSTANTIATE_REAL
+void moments(double* out, const array& in, const af_moment_type moment)
+{
+    AF_THROW(af_moments_all(out, in.get(), moment));
+}
 
 }
diff --git a/src/api/unified/moments.cpp b/src/api/unified/moments.cpp
index 51d74552c1..d568bb5369 100644
--- a/src/api/unified/moments.cpp
+++ b/src/api/unified/moments.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/array.h>
-#include <af/moments.h>
+#include <af/image.h>
 #include "symbol_manager.hpp"
 
 af_err af_moments(af_array* out, const af_array in, const af_moment_type moment)
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index fe984b3507..a1cda5dd3f 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -19,53 +19,8 @@ namespace kernel
 {
 
 
-template<typename T, af_moment_type MM>
-struct moments_op
-{
-    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
-    {
-        return;
-    }
-};
-
-template<typename T>
-struct moments_op<T, AF_MOMENT_M00>
-{
-    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
-    {
-        return in[mId];
-    }
-};
-
 template<typename T>
-struct moments_op<T, AF_MOMENT_M01>
-{
-    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
-    {
-        return idx * in[mId];
-    }
-};
-
-template<typename T>
-struct moments_op<T, AF_MOMENT_M10>
-{
-    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
-    {
-        return idy * in[mId];
-    }
-};
-
-template<typename T>
-struct moments_op<T, AF_MOMENT_M11>
-{
-    T operator()(T const * const in, dim_t mId, dim_t const idx, dim_t const idy)
-    {
-        return idx * idy * in[mId];
-    }
-};
-
-template<typename T, af_moment_type Method>
-void moments(Array<float> &output, Array<T> const &input)
+void moments(Array<float> &output, Array<T> const &input, af_moment_type moment)
 {
     T const * const in       = input.get();
     af::dim4  const idims    = input.dims();
@@ -75,24 +30,34 @@ void moments(Array<float> &output, Array<T> const &input)
     af::dim4  const odims    = output.dims();
     af::dim4  const ostrides = output.strides();
 
-    moments_op<T, Method> op;
-    bool pBatch = !(idims[2] == 1 && idims[3] == 1);
-    bool tDim   = (idims[3] != 1);
-    bool zDim   = (idims[2] != 1);
-
     float *out = output.get();
 
     dim_t mId = 0;
     for(dim_t w = 0; w < idims[3]; w++) {
         for(dim_t z = 0; z < idims[2]; z++) {
-            T val = scalar<T>(0);
             for(dim_t y = 0; y < idims[1]; y++) {
                 for(dim_t x = 0; x < idims[0]; x++) {
-                    val += op(in, mId, x, y);
+                    dim_t m_off=0;
+                    float val = in[mId];
+                    if((moment & AF_MOMENT_M00) > 0) {
+                        out[w * ostrides[3] + z * ostrides[2] + m_off] += val;
+                        m_off++;
+                    }
+                    if((moment & AF_MOMENT_M01) > 0) {
+                        out[w * ostrides[3] + z * ostrides[2] + m_off] += x * val;
+                        m_off++;
+                    }
+                    if((moment & AF_MOMENT_M10) > 0) {
+                        out[w * ostrides[3] + z * ostrides[2] + m_off] += y * val;
+                        m_off++;
+                    }
+                    if((moment & AF_MOMENT_M11) > 0) {
+                        out[w * ostrides[3] + z * ostrides[2] + m_off] += x * y * val;
+                        m_off++;
+                    }
                     mId++;
                 }
             }
-            out[w * ostrides[1] + z] = (float)val;
         }
     }
 }
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index 081406dfee..74f5a85171 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -17,34 +17,30 @@
 namespace cpu
 {
 
+static inline int bitCount(int v) {
+    v = v - ((v >> 1) & 0x55555555);
+    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
+    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+}
+
 using af::dim4;
 
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment)
 {
+    in.eval();
     dim4 odims, idims = in.dims();
-    odims[0] = idims[2];
-    odims[1] = idims[3];
-    odims[2] = odims[3] = 1;
+    dim_t moments_dim = bitCount(moment);
 
-    in.eval();
-    Array<float> out = createEmptyArray<float>(odims);
+    odims[0] = moments_dim;
+    odims[1] = 1;
+    odims[2] = idims[2];
+    odims[3] = idims[3];
+
+    Array<float> out = createValueArray<float>(odims, 0.f);
+    out.eval();
 
-    switch(moment) {
-        case AF_MOMENT_M00:
-            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M00>, out, in);
-            break;
-        case AF_MOMENT_M01:
-            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M01>, out, in);
-            break;
-        case AF_MOMENT_M10:
-            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M10>, out, in);
-            break;
-        case AF_MOMENT_M11:
-            getQueue().enqueue(kernel::moments<T, AF_MOMENT_M11>, out, in);
-            break;
-        default:  break;
-    }
+    getQueue().enqueue(kernel::moments<T>, out, in, moment);
     getQueue().sync();
     return out;
 }
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 22c58f90b6..7c12a6c6b6 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -23,95 +23,70 @@ namespace kernel
     // Kernel Launch Config Values
     static const int THREADS = 128;
 
-    // Moment functions
     template<typename T>
-    __device__ inline static
-    T moments_m00(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
-    {
-        return in.ptr[mId];
-    }
-
-    template<typename T>
-    __device__ inline static
-    T moments_m01(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
-    {
-        return idx * in.ptr[mId];
-    }
-
-    template<typename T>
-    __device__ inline static
-    T moments_m10(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
-    {
-        return idy * in.ptr[mId];
-    }
-
-    template<typename T>
-    __device__ inline static
-    T moments_m11(const dim_t mId, const dim_t idx, const dim_t idy, CParam<T> in)
-    {
-
-        return idx * idy * in.ptr[mId];
-    }
-
-    template<typename T, af_moment_type moment>
     __global__
-    void moments_kernel(CParam<float> out, CParam<T> in,
+    void moments_kernel(CParam<float> out, CParam<T> in, af_moment_type moment,
                   const dim_t blocksMatX, const bool pBatch)
     {
         const dim_t idw = blockIdx.y / in.dims[2];
         const dim_t idz = blockIdx.y - idw * in.dims[2];
 
-        const dim_t idy = blockIdx.x / blocksMatX;
-        const dim_t blockIdx_x = blockIdx.x - idy * blocksMatX;
-        const dim_t idx = blockIdx_x * blockDim.x + threadIdx.x;
+        const dim_t idy = blockIdx.x;
+        dim_t idx = threadIdx.x;
 
-        dim_t mId = idy * in.strides[1] + idx;
-        if(pBatch) {
-            mId += idw * in.strides[3] + idz * in.strides[2];
+        __shared__ float blk_moment_sum[4];
+        if(threadIdx.x < 4) {
+            blk_moment_sum[threadIdx.x] = 0.f;
         }
-
-        if (idx >= in.dims[0] || idy >= in.dims[1] ||
-            idz >= in.dims[2] || idw >= in.dims[3] )
-            return;
-
-        __shared__ float blk_moment_sum;
-        blk_moment_sum = 0.f;
         __syncthreads();
 
-        switch(moment) {
-            case AF_MOMENT_M00:
-                atomicAdd(&blk_moment_sum, (float)moments_m00(mId, idx, idy, in));
-                break;
-            case AF_MOMENT_M01:
-                atomicAdd(&blk_moment_sum, (float)moments_m01(mId, idx, idy, in));
-                break;
-            case AF_MOMENT_M10:
-                atomicAdd(&blk_moment_sum, (float)moments_m10(mId, idx, idy, in));
-                break;
-            case AF_MOMENT_M11:
-                atomicAdd(&blk_moment_sum, (float)moments_m11(mId, idx, idy, in));
-                break;
-            default:
+        for(unsigned i=0; i<blocksMatX; ++i) {
+            dim_t mId = idy * in.strides[1] + idx;
+            if(pBatch) {
+                mId += idw * in.strides[3] + idz * in.strides[2];
+            }
+
+            if (idx >= in.dims[0] || idy >= in.dims[1] ||
+                idz >= in.dims[2] || idw >= in.dims[3] )
                 break;
+
+            dim_t m_off = 0;
+            float val = (float)in.ptr[mId];
+
+            if((moment & AF_MOMENT_M00) > 0) {
+                atomicAdd(blk_moment_sum + m_off++, val);
+            }
+            if((moment & AF_MOMENT_M01) > 0) {
+                atomicAdd(blk_moment_sum + m_off++, idx * val);
+            }
+            if((moment & AF_MOMENT_M10) > 0) {
+                atomicAdd(blk_moment_sum + m_off++, idy * val);
+            }
+            if((moment & AF_MOMENT_M11) > 0) {
+                atomicAdd(blk_moment_sum + m_off, idx * idy * val);
+            }
+
+            idx += blockDim.x;
         }
+
         __syncthreads();
 
-        float *offset = const_cast<float *>(out.ptr + (idw * out.strides[1] + idz));
-        if(threadIdx.x == 0)
-            atomicAdd(offset, blk_moment_sum);
+        float *offset = const_cast<float *>(out.ptr + (idw * out.strides[3] + idz * out.strides[2]) + threadIdx.x);
+        if(threadIdx.x < out.dims[0])
+            atomicAdd(offset, blk_moment_sum[threadIdx.x]);
     }
 
     // Wrapper functions
-    template <typename T, af_moment_type moment>
-    void moments(Param<float> out, CParam<T> in) {
+    template <typename T>
+    void moments(Param<float> out, CParam<T> in, const af_moment_type moment) {
         dim3 threads(THREADS, 1, 1);
         dim_t blocksMatX = divup(in.dims[0], threads.x);
-        dim3 blocks(blocksMatX * in.dims[1], in.dims[2] * in.dims[3]);
+        dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
 
         bool pBatch = !(in.dims[2] == 1 && in.dims[3] == 1);
 
-        CUDA_LAUNCH((moments_kernel<T, moment>), blocks, threads,
-                     out, in, blocksMatX, pBatch);
+        CUDA_LAUNCH((moments_kernel<T>), blocks, threads,
+                     out, in, moment, blocksMatX, pBatch);
         POST_LAUNCH_CHECK();
     }
 
diff --git a/src/backend/cuda/moments.cu b/src/backend/cuda/moments.cu
index f82f9a542f..46d5e1f6dc 100644
--- a/src/backend/cuda/moments.cu
+++ b/src/backend/cuda/moments.cu
@@ -15,31 +15,30 @@
 namespace cuda
 {
 
+static inline int bitCount(int v) {
+    v = v - ((v >> 1) & 0x55555555);
+    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
+    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+}
+
 using af::dim4;
 
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment)
 {
+    in.eval();
     dim4 odims, idims = in.dims();
-    odims[0] = odims[1] = odims[2] = odims[3] = 1;
-    if(idims[2] != 1) {
-        odims[0] = idims[2];
-    }
-    if(idims[3] != 1) {
-        odims[1] = idims[3];
-    }
+    dim_t moments_dim = bitCount(moment);
 
-    in.eval();
-    Array<float> out = createValueArray<float>(odims, 0.f);
+    odims[0] = moments_dim;
+    odims[1] = 1;
+    odims[2] = idims[2];
+    odims[3] = idims[3];
 
-    switch(moment) {
-        case AF_MOMENT_M00: kernel::moments<T, AF_MOMENT_M00>(out, in); break;
-        case AF_MOMENT_M01: kernel::moments<T, AF_MOMENT_M01>(out, in); break;
-        case AF_MOMENT_M10: kernel::moments<T, AF_MOMENT_M10>(out, in); break;
-        case AF_MOMENT_M11: kernel::moments<T, AF_MOMENT_M11>(out, in); break;
-        default:  break;
-    }
+    Array<float> out = createValueArray<float>(odims, 0.f);
+    out.eval();
 
+    kernel::moments<T>(out, in, moment);
     return out;
 }
 
diff --git a/src/backend/opencl/kernel/moments.cl b/src/backend/opencl/kernel/moments.cl
index d26c8b6e94..87b6d74abd 100644
--- a/src/backend/opencl/kernel/moments.cl
+++ b/src/backend/opencl/kernel/moments.cl
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define M00 moments_m00
-#define M01 moments_m01
-#define M10 moments_m10
-#define M11 moments_m11
+#define AF_MOMENT_M00 1
+#define AF_MOMENT_M01 2
+#define AF_MOMENT_M10 4
+#define AF_MOMENT_M11 8
 
 ////////////////////////////////////////////////////////////////////////////////////
 // Helper Functions
 ////////////////////////////////////////////////////////////////////////////////////
-inline void fatomic_add_g(volatile __global float *source, const float operand) {
+inline void fatomic_add_l(volatile __local float *source, const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -25,11 +25,11 @@ inline void fatomic_add_g(volatile __global float *source, const float operand)
     do {
         expVal.floatVal = prevVal.floatVal;
         newVal.floatVal = expVal.floatVal + operand;
-        prevVal.intVal  = atomic_cmpxchg((volatile __global unsigned int *)source, expVal.intVal, newVal.intVal);
+        prevVal.intVal  = atomic_cmpxchg((volatile __local unsigned int *)source, expVal.intVal, newVal.intVal);
     } while (expVal.intVal != prevVal.intVal);
 }
 
-inline void fatomic_add_l(volatile __local float *source, const float operand) {
+inline void fatomic_add_g(volatile __global float *source, const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -39,66 +39,63 @@ inline void fatomic_add_l(volatile __local float *source, const float operand) {
     do {
         expVal.floatVal = prevVal.floatVal;
         newVal.floatVal = expVal.floatVal + operand;
-        prevVal.intVal  = atomic_cmpxchg((volatile __local unsigned int *)source, expVal.intVal, newVal.intVal);
+        prevVal.intVal  = atomic_cmpxchg((volatile __global unsigned int *)source, expVal.intVal, newVal.intVal);
     } while (expVal.intVal != prevVal.intVal);
 }
 
-///////////////////////////////////////////////////////////////////////////
-// moments
-///////////////////////////////////////////////////////////////////////////
-float moments_m00(const int mId, const int idx, const int idy,
-                   __global const T *d_in,  const KParam in) {
-    return d_in[mId];
-}
-
-float moments_m01(const int mId, const int idx, const int idy,
-                   __global const T *d_in,  const KParam in) {
-    return idx * d_in[mId];
-}
-
-float moments_m10(const int mId, const int idx, const int idy,
-                   __global const T *d_in,  const KParam in) {
-    return idy * d_in[mId];
-}
-
-float moments_m11(const int mId, const int idx, const int idy,
-                   __global const T *d_in,  const KParam in) {
-    return idx * idy * d_in[mId];
-}
 
-////////////////////////////////////////////////////////////////////////////////////
-// Wrapper Kernel
-////////////////////////////////////////////////////////////////////////////////////
 __kernel
 void moments_kernel(__global  float *d_out, const KParam out,
                     __global const T *d_in,  const KParam in,
-                    const int blocksMatX, const int pBatch)
+                    const int moment, const int blocksMatX,
+                    const int pBatch)
 {
-    const int idw = get_group_id(1) / in.dims[2];
-    const int idz = get_group_id(1)  - idw * in.dims[2];
+    const dim_t idw = get_group_id(1) / in.dims[2];
+    const dim_t idz = get_group_id(1)  - idw * in.dims[2];
 
-    const int idy = get_group_id(0) / blocksMatX;
-    const int blockIdx_x = get_group_id(0) - idy * blocksMatX;
-    const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
+    const dim_t idy = get_group_id(0);
+    dim_t idx = get_local_id(0);
 
-    int mId = idy * in.strides[1] + idx;
-    if(pBatch) {
-       mId += idw * in.strides[3] + idz * in.strides[2];
+    __local float wkg_moment_sum[4];
+    if(get_local_id(0) < 4) {
+         wkg_moment_sum[get_local_id(0)] = 0.f;
     }
+    barrier(CLK_LOCAL_MEM_FENCE);
 
-    if(idx >= in.dims[0] ||
-       idy >= in.dims[1] ||
-       idz >= in.dims[2] ||
-       idw >= in.dims[3])
-        return;
+    for(unsigned i=0; i<blocksMatX; ++i) {
+        int mId = idy * in.strides[1] + idx;
+        if(pBatch) {
+           mId += idw * in.strides[3] + idz * in.strides[2];
+        }
+
+        if(idx >= in.dims[0] ||
+           idy >= in.dims[1] ||
+           idz >= in.dims[2] ||
+           idw >= in.dims[3])
+            break;
+
+
+        dim_t m_off = 0;
+        float val = d_in[mId];
+
+        if((moment & AF_MOMENT_M00) > 0) {
+            fatomic_add_l(wkg_moment_sum + m_off++, val);
+        }
+        if((moment & AF_MOMENT_M01) > 0) {
+            fatomic_add_l(wkg_moment_sum + m_off++, idx * val);
+        }
+        if((moment & AF_MOMENT_M10) > 0) {
+            fatomic_add_l(wkg_moment_sum + m_off++, idy * val);
+        }
+        if((moment & AF_MOMENT_M11) > 0) {
+            fatomic_add_l(wkg_moment_sum + m_off, idx * idy * val);
+        }
+        idx += get_local_size(0);
+    }
 
-    __local float wkg_moment_sum;
-    wkg_moment_sum = 0;
-    barrier(CLK_LOCAL_MEM_FENCE);
-    fatomic_add_l(&wkg_moment_sum, MOMENT(mId, idx, idy, d_in, in));
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if(get_local_id(0) == 0)
-        fatomic_add_g(d_out + (idw * out.strides[1] + idz), wkg_moment_sum);
+    if(get_local_id(0) < out.dims[0])
+        fatomic_add_g(d_out + (idw * out.strides[3] + idz * out.strides[2]) + get_local_id(0), wkg_moment_sum[get_local_id(0)]);
 
 }
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index c53b954eb1..15120a1437 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -38,8 +38,8 @@ namespace opencl
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template <typename T, af_moment_type moment>
-        void moments(Param out, const Param in)
+        template <typename T>
+        void moments(Param out, const Param in, af_moment_type moment)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -57,18 +57,6 @@ namespace opencl
                         options << " -D USE_DOUBLE";
                     }
 
-                    switch(moment) {
-                        case AF_MOMENT_M00:   options << " -D MOMENT=M00";
-                            break;
-                        case AF_MOMENT_M01:   options << " -D MOMENT=M01";
-                            break;
-                        case AF_MOMENT_M10:   options << " -D MOMENT=M10";
-                            break;
-                        case AF_MOMENT_M11:   options << " -D MOMENT=M11";
-                            break;
-                        default:
-                            break;
-                    }
 
                     Program prog;
                     buildProgram(prog, moments_cl, moments_cl_len, options.str());
@@ -78,20 +66,19 @@ namespace opencl
                 });
 
 
-                auto momentsp = make_kernel<Buffer, const KParam, const Buffer, const KParam,
-                                       const int, const int>
+                auto momentsp = make_kernel<Buffer, const KParam, const Buffer, const KParam, const int, const int, const int>
                                       (*momentsKernels[device]);
 
                 NDRange local(THREADS, 1, 1);
                 dim_t blocksMatX = divup(in.info.dims[0], local[0]);
-                NDRange global(blocksMatX * in.info.dims[1] * local[0] ,
+                NDRange global(in.info.dims[1] * local[0] ,
                                in.info.dims[2] * in.info.dims[3] * local[1] );
 
                 bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
 
                 momentsp(EnqueueArgs(getQueue(), global, local),
                           *out.data, out.info, *in.data, in.info,
-                          blocksMatX, (int)pBatch);
+                          (int)moment, blocksMatX, (int)pBatch);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index b91a5a2335..b38db4cdcc 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -15,29 +15,28 @@
 namespace opencl
 {
 
+static inline int bitCount(int v) {
+    v = v - ((v >> 1) & 0x55555555);
+    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
+    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+}
+
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment)
 {
+    in.eval();
     dim4 odims, idims = in.dims();
-    odims[0] = odims[1] = odims[2] = odims[3] = 1;
-    if(idims[2] != 1) {
-        odims[0] = idims[2];
-    }
-    if(idims[3] != 1) {
-        odims[1] = idims[3];
-    }
+    dim_t moments_dim = bitCount(moment);
 
-    in.eval();
-    Array<float> out = createValueArray<float>(odims, 0.f);
+    odims[0] = moments_dim;
+    odims[1] = 1;
+    odims[2] = idims[2];
+    odims[3] = idims[3];
 
-    switch(moment) {
-        case AF_MOMENT_M00: kernel::moments<T, AF_MOMENT_M00>(out, in); break;
-        case AF_MOMENT_M01: kernel::moments<T, AF_MOMENT_M01>(out, in); break;
-        case AF_MOMENT_M10: kernel::moments<T, AF_MOMENT_M10>(out, in); break;
-        case AF_MOMENT_M11: kernel::moments<T, AF_MOMENT_M11>(out, in); break;
-        default:  break;
-    }
+    Array<float> out = createValueArray<float>(odims, 0.f);
+    out.eval();
 
+    kernel::moments<T>(out, in, moment);
     return out;
 }
 

From 58c9cb747e8ba3d347984fa3d07cf3d464257e16 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 15 Jun 2016 17:24:47 -0400
Subject: [PATCH 0618/2677] update moments to match new ocl header

---
 src/backend/opencl/kernel/moments.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 15120a1437..1ea3ce9fac 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -24,7 +24,7 @@
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
-using cl::make_kernel;
+using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
@@ -66,7 +66,7 @@ namespace opencl
                 });
 
 
-                auto momentsp = make_kernel<Buffer, const KParam, const Buffer, const KParam, const int, const int, const int>
+                auto momentsp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam, const int, const int, const int>
                                       (*momentsKernels[device]);
 
                 NDRange local(THREADS, 1, 1);

From a7a297ba814743a696b3c113df5444193f9cab27 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 01:55:16 -0400
Subject: [PATCH 0619/2677] BUGFIX: Add missing syncthreads to scan_by_key

---
 src/backend/cuda/kernel/scan_dim_by_key_impl.hpp   | 1 +
 src/backend/cuda/kernel/scan_first_by_key_impl.hpp | 1 +
 src/backend/opencl/kernel/scan_dim_by_key.cl       | 1 +
 src/backend/opencl/kernel/scan_first.cl            | 2 +-
 src/backend/opencl/kernel/scan_first_by_key.cl     | 5 +++--
 5 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index a4243477c3..e61346575a 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -169,6 +169,7 @@ namespace kernel
                     boundaryid[tidx] = id_dim;
                 }
             }
+            __syncthreads();
 
             if (is_valid && (id_dim < out_dim)) *optr = val;
             if (isLast) {
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 49b2c06d74..cb4b0bb3dd 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -150,6 +150,7 @@ namespace kernel
                     boundaryid[tidy] = id;
                 }
             }
+            __syncthreads();
 
             if (id < out.dims[0]) optr[id] = val;
             if (isLast) {
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index da648b7271..9ea1f1cd2b 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -147,6 +147,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
                 boundaryid[lidx] = id_dim;
             }
         }
+        barrier(CLK_LOCAL_MEM_FENCE);
 
         if (cond) *oData = val;
         if (isLast) {
diff --git a/src/backend/opencl/kernel/scan_first.cl b/src/backend/opencl/kernel/scan_first.cl
index ecda3f90f9..79917a5cd5 100644
--- a/src/backend/opencl/kernel/scan_first.cl
+++ b/src/backend/opencl/kernel/scan_first.cl
@@ -81,7 +81,7 @@ void scan_first_kernel(__global To *oData, KParam oInfo,
             }
         }
         id += DIMX;
-        barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
+        barrier(CLK_LOCAL_MEM_FENCE);
     }
 
     if (!isFinalPass && isLast && cond_yzw) {
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 8561483fcd..ac843d8c1e 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -139,6 +139,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
                 boundaryid[lidy] = id;
             }
         }
+        barrier(CLK_LOCAL_MEM_FENCE);
 
         if (cond) oData[id] = val;
         if (isLast) {
@@ -146,7 +147,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             l_ftmp[lidy] = flag;
         }
         id += DIMX;
-        barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
+        barrier(CLK_LOCAL_MEM_FENCE);
     }
 
     if (isLast && cond_yzw) {
@@ -264,7 +265,7 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
             l_ftmp[lidy] = flag;
         }
         id += DIMX;
-        barrier(CLK_LOCAL_MEM_FENCE); //FIXME: May be needed only for non nvidia gpus
+        barrier(CLK_LOCAL_MEM_FENCE);
     }
 }
 

From 5a45791bb9e726a9d68d7c61ed4f6fb1109793cb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 01:55:48 -0400
Subject: [PATCH 0620/2677] BUGFIX: Fixing a typo on OpenCL scan_by_key types

---
 src/backend/opencl/kernel/scan_dim_by_key_impl.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index ccd47e407b..8319be52af 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -68,7 +68,7 @@ namespace kernel
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
                     << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D Tk=" << dtype_traits<Ti>::getName()
+                    << " -D Tk=" << dtype_traits<Tk>::getName()
                     << " -D T=To"
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y

From bb087d0fcc5f3d716ceb5f66feae02a14dd48f95 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 02:37:31 -0400
Subject: [PATCH 0621/2677] Remove dim as template parameter in scan_by_key

- Reduces compilation time
- Only affects CUDA backend
---
 src/backend/cuda/kernel/scan_dim_by_key.hpp   |   4 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 147 +++++++++---------
 src/backend/cuda/scan_by_key.cu               |  19 +--
 3 files changed, 87 insertions(+), 83 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_dim_by_key.hpp b/src/backend/cuda/kernel/scan_dim_by_key.hpp
index 5ed4daad91..fd50fb5b67 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key.hpp
@@ -15,7 +15,7 @@ namespace cuda
 {
     namespace kernel
     {
-        template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool inclusive_scan>
-        void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+        template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+        void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim);
     }
 }
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index e61346575a..e2d2d75f5e 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -30,17 +30,18 @@ namespace kernel
         return (id == 0)? 1 : ((*kptr) != (*(kptr - stride)));
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, uint DIMY, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY, bool inclusive_scan>
     __global__
     static void scan_dim_nonfinal_kernel(Param<To> out,
-                                Param<To> tmp,
-                                Param<char> tflg,
-                                Param<int> tlid,
-                                CParam<Ti>  in,
-                                CParam<Tk> key,
-                                uint blocks_x,
-                                uint blocks_y,
-                                uint lim)
+                                         Param<To> tmp,
+                                         Param<char> tflg,
+                                         Param<int> tlid,
+                                         CParam<Ti>  in,
+                                         CParam<Tk> key,
+                                         int dim,
+                                         uint blocks_x,
+                                         uint blocks_y,
+                                         uint lim)
     {
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -193,14 +194,15 @@ namespace kernel
             }
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, uint DIMY, bool calculateFlags, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY, bool calculateFlags, bool inclusive_scan>
     __global__
     static void scan_dim_final_kernel(Param<To> out,
-                                CParam<Ti>  in,
-                                CParam<Tk> key,
-                                uint blocks_x,
-                                uint blocks_y,
-                                uint lim)
+                                      CParam<Ti>  in,
+                                      CParam<Tk> key,
+                                      int dim,
+                                      uint blocks_x,
+                                      uint blocks_y,
+                                      uint lim)
     {
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -290,7 +292,7 @@ namespace kernel
             //Add partial result from last iteration before scan operation
             if ((tidy == 0) && (flag == 0)) {
                 val = binop(val, s_tmp[tidx]);
-                flag = flag | s_ftmp[tidx];
+                flag = s_ftmp[tidx];
             }
 
             //Write to shared memory
@@ -328,11 +330,12 @@ namespace kernel
 
     }
 
-    template<typename To, af_op_t op, int dim>
+    template<typename To, af_op_t op>
     __global__
     static void bcast_dim_kernel(Param<To> out,
                                  CParam<To> tmp,
                                  Param<int> tlid,
+                                 int dim,
                                  uint blocks_x,
                                  uint blocks_y,
                                  uint blocks_dim,
@@ -389,12 +392,13 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool calculateFlags, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool calculateFlags, bool inclusive_scan>
     static void scan_dim_final_launcher(Param<To> out,
-                           CParam<Ti> in,
-                           CParam<Tk> key,
-                           const uint threads_y,
-                           const uint blocks_all[4])
+                                        CParam<Ti> in,
+                                        CParam<Tk> key,
+                                        const int dim,
+                                        const uint threads_y,
+                                        const uint blocks_all[4])
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -405,31 +409,32 @@ namespace kernel
 
         switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 8, calculateFlags, inclusive_scan>), blocks, threads,
-                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 8, calculateFlags, inclusive_scan>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         case 4:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 4, calculateFlags, inclusive_scan>), blocks, threads,
-                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 4, calculateFlags, inclusive_scan>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         case 2:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 2, calculateFlags, inclusive_scan>), blocks, threads,
-                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 2, calculateFlags, inclusive_scan>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         case 1:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, dim, 1, calculateFlags, inclusive_scan>), blocks, threads,
-                out, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 1, calculateFlags, inclusive_scan>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         }
 
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
     static void scan_dim_nonfinal_launcher(Param<To> out,
-                           Param<To> tmp,
-                           Param<char> tflg,
-                           Param<int> tlid,
-                           CParam<Ti> in,
-                           CParam<Tk> key,
-                           const uint threads_y,
-                           const uint blocks_all[4])
+                                           Param<To> tmp,
+                                           Param<char> tflg,
+                                           Param<int> tlid,
+                                           CParam<Ti> in,
+                                           CParam<Tk> key,
+                                           const int dim,
+                                           const uint threads_y,
+                                           const uint blocks_all[4])
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -440,26 +445,27 @@ namespace kernel
 
         switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 8, inclusive_scan>), blocks, threads,
-                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 8, inclusive_scan>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         case 4:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 4, inclusive_scan>), blocks, threads,
-                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 4, inclusive_scan>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         case 2:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 2, inclusive_scan>), blocks, threads,
-                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 2, inclusive_scan>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         case 1:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, dim, 1, inclusive_scan>), blocks, threads,
-                out, tmp, tflg, tlid, in, key, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 1, inclusive_scan>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
         }
 
         POST_LAUNCH_CHECK();
     }
 
-    template<typename To, af_op_t op, int dim>
+    template<typename To, af_op_t op>
     static void bcast_dim_launcher(Param<To> out,
                                    CParam<To> tmp,
                                    Param<int> tlid,
+                                   const int dim,
                                    const uint threads_y,
                                    const uint blocks_all[4])
     {
@@ -471,14 +477,14 @@ namespace kernel
 
         uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-        CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads,
-            out, tmp, tlid, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
+        CUDA_LAUNCH((bcast_dim_kernel<To, op>), blocks, threads,
+                    out, tmp, tlid, dim, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
 
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, int dim, bool inclusive_scan>
-    void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key)
+    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim)
     {
         uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
         uint threads_x = THREADS_X;
@@ -490,9 +496,10 @@ namespace kernel
 
         if (blocks_all[dim] == 1) {
 
-            scan_dim_final_launcher<Ti, Tk, To, op, dim, true, inclusive_scan>(out, in, key,
-                                                     threads_y,
-                                                     blocks_all);
+            scan_dim_final_launcher<Ti, Tk, To, op, true, inclusive_scan>(out, in, key,
+                                                                          dim,
+                                                                          threads_y,
+                                                                          blocks_all);
 
         } else {
             Param<To> tmp = out;
@@ -514,26 +521,30 @@ namespace kernel
             tmpflg.ptr = memAlloc<char>(tmp_elements);
             tmpid.ptr = memAlloc<int>(tmp_elements);
 
-            scan_dim_nonfinal_launcher<Ti, Tk, To, op, dim, inclusive_scan>(
-                                                    out, tmp, tmpflg, tmpid, in, key,
-                                                    threads_y, blocks_all);
+            scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg,
+                                                                       tmpid, in, key,
+                                                                       dim,
+                                                                       threads_y,
+                                                                       blocks_all);
 
             int bdim = blocks_all[dim];
             blocks_all[dim] = 1;
 
             //FIXME: Is there an alternative to the if condition ?
             if (op == af_notzero_t) {
-                scan_dim_final_launcher<To, char, To, af_add_t, dim, false, true>(tmp, tmp, tmpflg,
-                                                               threads_y,
-                                                               blocks_all);
+                scan_dim_final_launcher<To, char, To, af_add_t, false, true>(tmp, tmp, tmpflg,
+                                                                             dim,
+                                                                             threads_y,
+                                                                             blocks_all);
             } else {
-                scan_dim_final_launcher<To, char, To,       op, dim, false, true>(tmp, tmp, tmpflg,
-                                                               threads_y,
-                                                               blocks_all);
+                scan_dim_final_launcher<To, char, To,       op, false, true>(tmp, tmp, tmpflg,
+                                                                             dim,
+                                                                             threads_y,
+                                                                             blocks_all);
             }
 
             blocks_all[dim] = bdim;
-            bcast_dim_launcher<To, op, dim>(out, tmp, tmpid, threads_y, blocks_all);
+            bcast_dim_launcher<To, op>(out, tmp, tmpid, dim, threads_y, blocks_all);
 
             memFree(tmp.ptr);
             memFree(tmpflg.ptr);
@@ -544,12 +555,8 @@ namespace kernel
 }
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)\
-    template void scan_dim_by_key<Ti, Tk, To, ROp, 1,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
-    template void scan_dim_by_key<Ti, Tk, To, ROp, 1, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
-    template void scan_dim_by_key<Ti, Tk, To, ROp, 2,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
-    template void scan_dim_by_key<Ti, Tk, To, ROp, 2, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
-    template void scan_dim_by_key<Ti, Tk, To, ROp, 3,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key); \
-    template void scan_dim_by_key<Ti, Tk, To, ROp, 3, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+    template void scan_dim_by_key<Ti, Tk, To, ROp,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim); \
+    template void scan_dim_by_key<Ti, Tk, To, ROp, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim); \
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
diff --git a/src/backend/cuda/scan_by_key.cu b/src/backend/cuda/scan_by_key.cu
index bdca0ee199..e5c2af3ad3 100644
--- a/src/backend/cuda/scan_by_key.cu
+++ b/src/backend/cuda/scan_by_key.cu
@@ -24,21 +24,18 @@ namespace cuda
         Array<To> out = createEmptyArray<To>(in.dims());
 
         if (inclusive_scan) {
-            switch (dim) {
-            case 0: kernel::scan_first_by_key<Ti, Tk, To, op   , true>(out, in, key); break;
-            case 1: kernel::scan_dim_by_key  <Ti, Tk, To, op, 1, true>(out, in, key); break;
-            case 2: kernel::scan_dim_by_key  <Ti, Tk, To, op, 2, true>(out, in, key); break;
-            case 3: kernel::scan_dim_by_key  <Ti, Tk, To, op, 3, true>(out, in, key); break;
+            if (dim == 0) {
+                kernel::scan_first_by_key<Ti, Tk, To, op, true>(out, in, key);
+            } else {
+                kernel::scan_dim_by_key  <Ti, Tk, To, op, true>(out, in, key, dim);
             }
         } else {
-            switch (dim) {
-            case 0: kernel::scan_first_by_key<Ti, Tk, To, op   , false>(out, in, key); break;
-            case 1: kernel::scan_dim_by_key  <Ti, Tk, To, op, 1, false>(out, in, key); break;
-            case 2: kernel::scan_dim_by_key  <Ti, Tk, To, op, 2, false>(out, in, key); break;
-            case 3: kernel::scan_dim_by_key  <Ti, Tk, To, op, 3, false>(out, in, key); break;
+            if (dim == 0) {
+                kernel::scan_first_by_key<Ti, Tk, To, op, false>(out, in, key);
+            } else {
+                kernel::scan_dim_by_key  <Ti, Tk, To, op, false>(out, in, key, dim);
             }
         }
-
         return out;
     }
 

From 4d4462d1b64565bba508a2137e47232bc7e29c5d Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 02:38:41 -0400
Subject: [PATCH 0622/2677] Remove unnecessary instantiation

---
 src/backend/cpu/scan_by_key.cpp                      | 1 -
 src/backend/cuda/kernel/scan_dim_by_key_impl.hpp     | 1 -
 src/backend/cuda/kernel/scan_first_by_key_impl.hpp   | 1 -
 src/backend/cuda/scan_by_key.cu                      | 1 -
 src/backend/opencl/kernel/scan_dim_by_key_impl.hpp   | 1 -
 src/backend/opencl/kernel/scan_first_by_key_impl.hpp | 1 -
 src/backend/opencl/scan.cpp                          | 1 -
 src/backend/opencl/scan_by_key.cpp                   | 1 -
 8 files changed, 8 deletions(-)

diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index 3c2fc35129..ac66028b71 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -71,7 +71,6 @@ namespace cpu
     INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index e2d2d75f5e..91da713d6b 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -567,7 +567,6 @@ namespace kernel
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, int    )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uchar  , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, short  , Tk, int    )  \
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index cb4b0bb3dd..d0c23eb24c 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -488,7 +488,6 @@ namespace kernel
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, int    )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uchar  , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, short  , Tk, int    )\
diff --git a/src/backend/cuda/scan_by_key.cu b/src/backend/cuda/scan_by_key.cu
index e5c2af3ad3..022a95aed5 100644
--- a/src/backend/cuda/scan_by_key.cu
+++ b/src/backend/cuda/scan_by_key.cu
@@ -51,7 +51,6 @@ namespace cuda
     INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 8319be52af..5e07d9d5e2 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -300,7 +300,6 @@ namespace kernel
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, int    )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uchar  , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, short  , Tk, int    )  \
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 489dbe1ec1..89e5f7ebdc 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -280,7 +280,6 @@ namespace kernel
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, int    )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uchar  , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, short  , Tk, int    )\
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 39bdd2fb5e..29c256894a 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -59,7 +59,6 @@ namespace opencl
     INSTANTIATE_SCAN(ROp, uint   , uint   )             \
     INSTANTIATE_SCAN(ROp, intl   , intl   )             \
     INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
-    INSTANTIATE_SCAN(ROp, char   , int    )             \
     INSTANTIATE_SCAN(ROp, char   , uint   )             \
     INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
     INSTANTIATE_SCAN(ROp, short  , int    )             \
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index a589de1c01..d82718f2b5 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -60,7 +60,6 @@ namespace opencl
     INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, int    )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \

From f03f734d584634b4a8fc981c4807e3bbb0d9eee8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 30 May 2016 22:26:34 -0400
Subject: [PATCH 0623/2677] OpenCL: Modifying evalNodes to handle multiple
 outputs

---
 src/backend/opencl/jit.cpp | 233 ++++++++++++++++++++++---------------
 1 file changed, 140 insertions(+), 93 deletions(-)

diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 7981d82edf..756956afb7 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -10,6 +10,7 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <map>
+#include <vector>
 #include <stdexcept>
 #include <copy.hpp>
 #include <JIT/Node.hpp>
@@ -35,10 +36,8 @@ using cl::NDRange;
 using std::string;
 using std::stringstream;
 
-static string getFuncName(Node *node, bool is_linear, bool *is_double)
+static string getFuncName(std::vector<Node *> nodes, bool is_linear, bool *is_double)
 {
-    node->setId(0);
-
     stringstream hashName;
     stringstream funcName;
 
@@ -48,100 +47,120 @@ static string getFuncName(Node *node, bool is_linear, bool *is_double)
         funcName << "G_";
     }
 
-    std::string outName = node->getNameStr();
-    funcName << outName;
-
-    node->genKerName(funcName);
-
-    string nameStr = funcName.str();
-    funcName << nameStr;
+    bool is_dbl = false;
+    int id = 0;
+    for (auto node :  nodes) {
+        id = node->setId(id);
+        std::string outName = node->getNameStr();
+        funcName << outName;
+
+        node->genKerName(funcName);
+        string nameStr = funcName.str();
+        funcName << nameStr;
+
+        nameStr = nameStr + outName;
+        string dblChars = "dDzZ";
+        size_t loc = nameStr.find_first_of(dblChars);
+        is_dbl |= (loc != std::string::npos);
+    }
 
-    nameStr = nameStr + outName;
-    string dblChars = "dDzZ";
-    size_t loc = nameStr.find_first_of(dblChars);
-    *is_double = (loc != std::string::npos);
+    *is_double = is_dbl;
 
     std::hash<std::string> hash_fn;
     hashName << "KER" << hash_fn(funcName.str());
     return hashName.str();
 }
 
-static string getKernelString(string funcName, Node *node, bool is_linear)
+static string getKernelString(string funcName, std::vector<Node *> nodes, bool is_linear)
 {
-    stringstream kerStream;
-    int id = node->getId();
 
-    kerStream << "__kernel void" << "\n";
+    // Common OpenCL code
+    // This part of the code does not change with the kernel.
+
+    static const char *kernelVoid =  "__kernel void\n";
+    static const char *dimParams = "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
+    static const char *blockStart = "{\n\n";
+    static const char *blockEnd = "\n\n}";
+
+    static const char *linearIndex = "\n"
+        "uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);\n"
+        "uint threadId = get_local_id(0);\n"
+        "int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;\n"
+        "if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;\n";
+
+    static const char *generalIndex = "\n"
+        "uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;\n"
+        "if (num_odims > 2) {\n"
+        "id2 = get_group_id(0) / groups_0;\n"
+        "id0 = get_group_id(0) - id2 * groups_0;\n"
+        "id0 = get_local_id(0) + id0 * get_local_size(0);\n"
+        "if (num_odims > 3) {\n"
+        "id3 = get_group_id(1) / groups_1;\n"
+        "id1 = get_group_id(1) - id3 * groups_1;\n"
+        "id1 = get_local_id(1) + id1 * get_local_size(1);\n"
+        "} else {\n"
+        "id1 = get_global_id(1);\n"
+        "}\n"
+        " } else {\n"
+        "id3 = 0;\n"
+        "id2 = 0;\n"
+        "id1 = get_global_id(1);\n"
+        "id0 = get_global_id(0);\n"
+        "}\n"
+        "bool cond = \n"
+        "id0 < oInfo.dims[0] && \n"
+        "id1 < oInfo.dims[1] && \n"
+        "id2 < oInfo.dims[2] && \n"
+        "id3 < oInfo.dims[3];\n\n"
+        "if (!cond) return;\n\n"
+        "int idx = "
+        "oInfo.strides[3] * id3 + oInfo.strides[2] * id2 + "
+        "oInfo.strides[1] * id1 + id0 + oInfo.offset;\n\n";
+
+
+    stringstream inParamStream;
+    stringstream outParamStream;
+    stringstream outWriteStream;
+    stringstream offsetsStream;
+    stringstream opsStream;
+
+    for (auto node : nodes) {
+        int id = node->getId();
+        node->genParams(inParamStream);
+        outParamStream << "__global " << node->getTypeStr() << " *out" << id << ", \n";
+        outWriteStream << "out" << id << "[idx] = " << "val" << id << ";\n";
+        node->genOffsets(offsetsStream, is_linear);
+        node->genFuncs(opsStream);
+    }
 
+    // Put various blocks into a single stream
+    stringstream kerStream;
+    kerStream << kernelVoid;
     kerStream << funcName;
-    kerStream << "(" << "\n";
-
-    node->genParams(kerStream);
-    kerStream << "__global " << node->getTypeStr() << " *out, KParam oInfo," << "\n";
-    kerStream << "uint groups_0, uint groups_1, uint num_odims)" << "\n";
-
-    kerStream << "{" << "\n" << "\n";
-
-    if (!is_linear) {
-
-        kerStream << "uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;\n";
-        kerStream << "if (num_odims > 2) {\n";
-
-        kerStream << "id2 = get_group_id(0) / groups_0;" << "\n";
-        kerStream << "id0 = get_group_id(0) - id2 * groups_0;" << "\n";
-        kerStream << "id0 = get_local_id(0) + id0 * get_local_size(0);" << "\n";
-
-        kerStream << "if (num_odims > 3) {\n";
-        kerStream << "id3 = get_group_id(1) / groups_1;" << "\n";
-        kerStream << "id1 = get_group_id(1) - id3 * groups_1;" << "\n";
-        kerStream << "id1 = get_local_id(1) + id1 * get_local_size(1);" << "\n";
-        kerStream << "} else {\n";
-        kerStream << "id1 = get_global_id(1);" << "\n";
-        kerStream << "}\n";
-        kerStream << " } else {\n";
-        kerStream << "id3 = 0;" << "\n";
-        kerStream << "id2 = 0;" << "\n";
-        kerStream << "id1 = get_global_id(1);" << "\n";
-        kerStream << "id0 = get_global_id(0);" << "\n";
-        kerStream << "}\n";
-
-        kerStream << "bool cond = " << "\n";
-        kerStream << "id0 < oInfo.dims[0] && " << "\n";
-        kerStream << "id1 < oInfo.dims[1] && " << "\n";
-        kerStream << "id2 < oInfo.dims[2] && " << "\n";
-        kerStream << "id3 < oInfo.dims[3];" << "\n" << "\n";
-
-        kerStream << "if (!cond) return;" << "\n" << "\n";
-
-        kerStream << "int idx = ";
-        kerStream << "oInfo.strides[3] * id3 + oInfo.strides[2] * id2 + ";
-        kerStream << "oInfo.strides[1] * id1 + id0 + oInfo.offset;" << "\n" << "\n";
-
+    kerStream << "(\n";
+    kerStream << inParamStream.str();
+    kerStream << outParamStream.str();
+    kerStream << dimParams;
+    kerStream << ")\n";
+    kerStream << blockStart;
+    if (is_linear) {
+        kerStream << linearIndex;
     } else {
-
-        kerStream << "uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);" << "\n";
-        kerStream << "uint threadId = get_local_id(0);" << "\n";
-        kerStream << "int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;" << "\n";
-        kerStream << "if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;" << "\n";
+        kerStream << generalIndex;
     }
-
-    node->genOffsets(kerStream, is_linear);
-    node->genFuncs(kerStream);
-    kerStream << "\n";
-
-    kerStream << "out[idx] = val"
-           << id << ";"  << "\n";
-
-    kerStream << "}" << "\n";
+    kerStream << offsetsStream.str();
+    kerStream << opsStream.str();
+    kerStream << outWriteStream.str();
+    kerStream << blockEnd;
 
     return kerStream.str();
 }
 
-static Kernel getKernel(Node *node, bool is_linear)
+static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
 {
 
     bool is_dbl = false;
-    string funcName = getFuncName(node, is_linear, &is_dbl);
+    string funcName = getFuncName(nodes, is_linear, &is_dbl);
 
     int device = getActiveDeviceId();
 
@@ -149,7 +168,7 @@ static Kernel getKernel(Node *node, bool is_linear)
     kc_entry_t entry;
 
     if (idx == kernelCaches[device].end()) {
-        string jit_ker = getKernelString(funcName, node, is_linear);
+        string jit_ker = getKernelString(funcName, nodes, is_linear);
 
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
@@ -166,12 +185,21 @@ static Kernel getKernel(Node *node, bool is_linear)
     return *entry.ker;
 }
 
-void evalNodes(Param &out, Node *node)
+void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
 {
     try {
 
-        bool is_linear = node->isLinear(out.info.dims);
-        Kernel ker = getKernel(node, is_linear);
+        // Assume all ouputs are of same size
+        //FIXME: Add assert to check if all outputs are same size?
+        KParam out_info = outputs[0].info;
+
+        // Verify if all ASTs hold Linear Arrays
+        bool is_linear = true;
+        for (auto node : nodes) {
+            is_linear &= node->isLinear(out_info.dims);
+        }
+
+        Kernel ker = getKernel(nodes, is_linear);
 
         uint local_0 = 1;
         uint local_1 = 1;
@@ -185,13 +213,13 @@ void evalNodes(Param &out, Node *node)
         const int work_group_size = (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
 
         while (num_odims >= 1) {
-            if (out.info.dims[num_odims - 1] == 1) num_odims--;
+            if (out_info.dims[num_odims - 1] == 1) num_odims--;
             else break;
         }
 
         if (is_linear) {
             local_0 = work_group_size;
-            uint out_elements = out.info.dims[3] * out.info.strides[3];
+            uint out_elements = out_info.dims[3] * out_info.strides[3];
             uint groups = divup(out_elements, local_0);
 
             global_1 = divup(groups,     1000) * local_1;
@@ -201,22 +229,34 @@ void evalNodes(Param &out, Node *node)
             local_1 =  4;
             local_0 = work_group_size / local_1;
 
-            groups_0 = divup(out.info.dims[0], local_0);
-            groups_1 = divup(out.info.dims[1], local_1);
+            groups_0 = divup(out_info.dims[0], local_0);
+            groups_1 = divup(out_info.dims[1], local_1);
 
-            global_0 = groups_0 * local_0 * out.info.dims[2];
-            global_1 = groups_1 * local_1 * out.info.dims[3];
+            global_0 = groups_0 * local_0 * out_info.dims[2];
+            global_1 = groups_1 * local_1 * out_info.dims[3];
         }
 
         NDRange local(local_0, local_1);
         NDRange global(global_0, global_1);
 
-        int args = node->setArgs(ker, 0);
-        ker.setArg(args + 0, *out.data);
-        ker.setArg(args + 1,  out.info);
-        ker.setArg(args + 2,  groups_0);
-        ker.setArg(args + 3,  groups_1);
-        ker.setArg(args + 4,  num_odims);
+        int args = 0;
+        for (auto node : nodes) {
+            args = node->setArgs(ker, args);
+        }
+
+        // Set output parameters
+        for (auto output : outputs) {
+            ker.setArg(args, *(output.data));
+            ++args;
+        }
+
+        // Set dimensions
+        // All outputs are asserted to be of same size
+        // Just use the size from the first output
+        ker.setArg(args + 0,  out_info);
+        ker.setArg(args + 1,  groups_0);
+        ker.setArg(args + 2,  groups_1);
+        ker.setArg(args + 3,  num_odims);
 
         getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
 
@@ -226,4 +266,11 @@ void evalNodes(Param &out, Node *node)
 
 }
 
+void evalNodes(Param &out, Node *node)
+{
+    std::vector<Param>  outputs{out};
+    std::vector<Node *> nodes{node};
+    return evalNodes(outputs, nodes);
+}
+
 }

From c5e44562e6a411f41c1730aceefade3644ba2060 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 30 May 2016 22:28:11 -0400
Subject: [PATCH 0624/2677] FEAT: Adding API evaluating multiple outputs

---
 include/af/array.h           | 21 ++++++++++----
 src/api/c/device.cpp         | 55 ++++++++++++++++++++++++++++++++++++
 src/api/cpp/array.cpp        | 39 +++++++++++++++++++++++++
 src/api/unified/device.cpp   |  8 ++++++
 src/backend/cpu/Array.cpp    | 13 +++++++++
 src/backend/cpu/Array.hpp    |  5 ++++
 src/backend/cuda/Array.cpp   | 12 ++++++++
 src/backend/cuda/Array.hpp   |  4 +++
 src/backend/opencl/Array.cpp | 44 +++++++++++++++++++++++++----
 src/backend/opencl/Array.hpp |  7 +++++
 10 files changed, 197 insertions(+), 11 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 03500640c6..1691d9c7f5 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1243,11 +1243,12 @@ namespace af
        @{
     */
     inline array &eval(array &a) { a.eval(); return a; }
-    inline void eval(array &a, array &b) { eval(a); b.eval(); }
-    inline void eval(array &a, array &b, array &c) { eval(a, b); c.eval(); }
-    inline void eval(array &a, array &b, array &c, array &d) { eval(a, b, c); d.eval(); }
-    inline void eval(array &a, array &b, array &c, array &d, array &e) { eval(a, b, c, d); e.eval(); }
-    inline void eval(array &a, array &b, array &c, array &d, array &e, array &f) { eval(a, b, c, d, e); f.eval(); }
+    AFAPI void eval(array &a, array &b);
+    AFAPI void eval(array &a, array &b, array &c);
+    AFAPI void eval(array &a, array &b, array &c, array &d);
+    AFAPI void eval(array &a, array &b, array &c, array &d, array &e);
+    AFAPI void eval(array &a, array &b, array &c, array &d, array &e, array &f);
+    AFAPI void eval(int num, array *arrays);
     /**
        @}
     */
@@ -1345,6 +1346,16 @@ extern "C" {
       @}
     */
 
+
+    /**
+       Evaluate multiple arrays together
+    */
+    AFAPI af_err af_eval_multiple(const int num, af_array *arrays);
+    /**
+      @}
+    */
+
+
     /**
         \ingroup method_mat
         @{
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index b93907d55e..079a04752d 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -189,3 +189,58 @@ af_err af_eval(af_array arr)
 
     return AF_SUCCESS;
 }
+
+template<typename T>
+static inline void evalMultiple(int num, af_array *arrayPtrs)
+{
+    Array<T> empty = createEmptyArray<T>(dim4());
+    std::vector<Array<T>*> arrays(num, &empty);
+
+    for (int i = 0; i < num; i++) {
+        arrays[i] = reinterpret_cast<Array<T>*>(arrayPtrs[i]);
+    }
+
+    evalMultiple<T>(arrays);
+    return;
+}
+
+af_err af_eval_multiple(int num, af_array *arrays)
+{
+    try {
+        ArrayInfo info = getInfo(arrays[0]);
+        af_dtype type = info.getType();
+        dim4 dims = info.dims();
+
+        for (int i = 1; i < num; i++) {
+            ArrayInfo currInfo = getInfo(arrays[i]);
+
+            // FIXME: This needs to be removed when new functionality is added
+            if (type != currInfo.getType()) {
+                AF_ERROR("All arrays must be of same type", AF_ERR_TYPE);
+            }
+
+            if (dims != currInfo.dims()) {
+                AF_ERROR("All arrays must be of same size", AF_ERR_SIZE);
+            }
+        }
+
+        switch (type) {
+        case f32: evalMultiple<float  >(num, arrays); break;
+        case f64: evalMultiple<double >(num, arrays); break;
+        case c32: evalMultiple<cfloat >(num, arrays); break;
+        case c64: evalMultiple<cdouble>(num, arrays); break;
+        case s32: evalMultiple<int    >(num, arrays); break;
+        case u32: evalMultiple<uint   >(num, arrays); break;
+        case u8 : evalMultiple<uchar  >(num, arrays); break;
+        case b8 : evalMultiple<char   >(num, arrays); break;
+        case s64: evalMultiple<intl   >(num, arrays); break;
+        case u64: evalMultiple<uintl  >(num, arrays); break;
+        case s16: evalMultiple<short  >(num, arrays); break;
+        case u16: evalMultiple<ushort >(num, arrays); break;
+        default:
+            TYPE_ERROR(0, type);
+        }
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 8911154155..adee829283 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1046,4 +1046,43 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     {
         AF_THROW(af_unlock_array(get()));
     }
+
+    void eval(array &a, array &b)
+    {
+        af_array arrays[] = {a.get(), b.get()};
+        AF_THROW(af_eval_multiple(2, arrays));
+    }
+
+    void eval(array &a, array &b, array &c)
+    {
+        af_array arrays[] = {a.get(), b.get(), c.get()};
+        AF_THROW(af_eval_multiple(3, arrays));
+    }
+
+    void eval(array &a, array &b, array &c, array &d)
+    {
+        af_array arrays[] = {a.get(), b.get(), c.get(), d.get()};
+        AF_THROW(af_eval_multiple(4, arrays));
+    }
+
+    void eval(array &a, array &b, array &c, array &d, array &e)
+    {
+        af_array arrays[] = {a.get(), b.get(), c.get(), d.get(), e.get()};
+        AF_THROW(af_eval_multiple(5, arrays));
+    }
+
+    void eval(array &a, array &b, array &c, array &d, array &e, array &f)
+    {
+        af_array arrays[] = {a.get(), b.get(), c.get(), d.get(), e.get(), f.get()};
+        AF_THROW(af_eval_multiple(6, arrays));
+    }
+
+    void eval(int num, array *arrays)
+    {
+        std::vector<af_array> outputs(num);
+        for (int i = 0; i < num; i++) {
+            outputs[i] = arrays[i].get();
+        }
+        AF_THROW(af_eval_multiple(num, &outputs[0]));
+    }
 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 5dcf1ce3b5..d735b242d1 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -184,3 +184,11 @@ af_err af_get_device_ptr(void **ptr, const af_array arr)
     CHECK_ARRAYS(arr);
     return CALL(ptr, arr);
 }
+
+af_err af_eval_multiple(int num, af_array *arrays)
+{
+    for (int i = 0; i < num; i++) {
+        CHECK_ARRAYS(arrays[i]);
+    }
+    return CALL(num, arrays);
+}
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 2c296d02d3..ebd5457e90 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -108,6 +108,18 @@ void Array<T>::eval() const
     const_cast<Array<T> *>(this)->eval();
 }
 
+
+template<typename T>
+void evalMultiple(std::vector<Array<T>*> arrays)
+{
+    //FIXME: implement this correctly
+    //Using fallback for now
+    for (auto array : arrays) {
+        array->eval();
+    }
+    return;
+}
+
 template<typename T>
 Node_ptr Array<T>::getNode() const
 {
@@ -265,6 +277,7 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     template       TNJ::Node_ptr Array<T>::getNode() const;             \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
+    template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 091ef540a0..9579093e75 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -39,6 +39,9 @@ namespace cpu
     using std::shared_ptr;
     using af::dim4;
 
+    template<typename T>
+    void evalMultiple(std::vector<Array<T> *> arrays);
+
     template<typename T> class Array;
 
     // Creates a new Array object on the heap and returns a reference to it.
@@ -217,6 +220,8 @@ namespace cpu
 
         TNJ::Node_ptr getNode() const;
 
+        friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
+
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 786574129b..bf2e40a38a 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -137,6 +137,17 @@ namespace cuda
         const_cast<Array<T> *>(this)->eval();
     }
 
+    template<typename T>
+    void evalMultiple(std::vector<Array<T>*> arrays)
+    {
+        //FIXME: implement this correctly
+        //Using fallback for now
+        for (int i = 0; i < (int)arrays.size(); i++) {
+            arrays[i]->eval();
+        }
+        return;
+    }
+
     template<typename T>
     Array<T>::~Array() {}
 
@@ -308,6 +319,7 @@ namespace cuda
     template       void Array<T>::eval() const;                         \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
+    template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 2adbd35a84..3b3d299d9f 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -39,6 +39,9 @@ namespace cuda
     template<typename T>
     void evalNodes(Param<T> &out, JIT::Node *node);
 
+    template<typename T>
+    void evalMultiple(std::vector<Array<T> *> arrays);
+
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
@@ -228,6 +231,7 @@ namespace cuda
 
         JIT::Node_ptr getNode() const;
 
+        friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 002c1d5b82..9c63277ac5 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -116,7 +116,6 @@ namespace opencl
         }
     }
 
-
     template<typename T>
     void Array<T>::eval()
     {
@@ -135,8 +134,7 @@ namespace opencl
         evalNodes(res, this->getNode().get());
         ready = true;
 
-        Node_ptr prev = node;
-        prev->resetFlags();
+        node->resetFlags();
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
         node.reset();
     }
@@ -148,6 +146,40 @@ namespace opencl
         const_cast<Array<T> *>(this)->eval();
     }
 
+    template<typename T>
+    void evalMultiple(std::vector<Array<T>*> arrays)
+    {
+        std::vector<Param> outputs;
+        std::vector<Node *> nodes;
+
+        for (auto array : arrays) {
+            if (array->isReady()) continue;
+
+            const ArrayInfo info = array->info;
+
+            array->setId(getActiveDeviceId());
+            array->data = Buffer_ptr(bufferAlloc(info.elements() * sizeof(T)), bufferFree);
+
+            // Do not replace this with cast operator
+            KParam kInfo = {{info.dims()[0], info.dims()[1], info.dims()[2], info.dims()[3]},
+                            {info.strides()[0], info.strides()[1],
+                             info.strides()[2], info.strides()[3]},
+                            0};
+
+            Param res = {array->data.get(), kInfo};
+            outputs.push_back(res);
+            nodes.push_back(array->getNode().get());
+        }
+        evalNodes(outputs, nodes);
+        for (auto array : arrays) {
+            if (array->isReady()) continue;
+            array->ready = true;
+            array->node->resetFlags();
+            // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
+            array->node.reset();
+        }
+    }
+
     template<typename T>
     Array<T>::~Array()
     { }
@@ -317,14 +349,13 @@ namespace opencl
         return;
     }
 
-
 #define INSTANTIATE(T)                                                  \
     template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
     template       Array<T>  *initArray<T      >      ();               \
-    template       Array<T>  createParamArray<T>      (Param &tmp);  \
+    template       Array<T>  createParamArray<T>      (Param &tmp);     \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
@@ -335,11 +366,12 @@ namespace opencl
                                    bool is_device);                     \
     template       Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
     template       Array<T>::~Array        ();                          \
-    template       Node_ptr Array<T>::getNode() const;             \
+    template       Node_ptr Array<T>::getNode() const;                  \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
+    template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index feb3e2e0fa..aee3088df1 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -29,7 +29,11 @@ namespace opencl
 
     template<typename T> class Array;
 
+    template<typename T>
+    void evalMultiple(std::vector<Array<T> *> arrays);
+
     void evalNodes(Param &out, JIT::Node *node);
+    void evalNodes(std::vector<Param> &outputs, std::vector<JIT::Node *> nodes);
 
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
@@ -256,6 +260,9 @@ namespace opencl
             return std::shared_ptr<T>(ptr, func);
         }
 
+
+        friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
+
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);

From 3247988dc8d4fbbca8ac37636693f92a38d09073 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 2 Jun 2016 02:27:24 -0400
Subject: [PATCH 0625/2677] Fixes to remove duplicate BufferNodes from AST

- This has side affect on reference counts.
---
 src/backend/opencl/Array.cpp | 35 +++++++++++++++++++++++------------
 src/backend/opencl/Array.hpp |  2 ++
 2 files changed, 25 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 9c63277ac5..8a96a24b8a 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -28,6 +28,19 @@ namespace opencl
     using JIT::Node;
     using JIT::Node_ptr;
 
+    template<typename T>
+    void Array<T>::genBufferNode() const
+    {
+        bool is_linear = isLinear();
+        unsigned bytes = this->getDataDims().elements() * sizeof(T);
+        BufferNode *buf_node = new BufferNode(dtype_traits<T>::getName(),
+                                              shortname<T>(true),
+                                              *this, data,
+                                              bytes,
+                                              is_linear);
+        const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
+    }
+
     template<typename T>
     Array<T>::Array(af::dim4 dims) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
@@ -35,6 +48,7 @@ namespace opencl
         data_dims(dims),
         node(), ready(true), owner(true)
     {
+        this->genBufferNode();
     }
 
     template<typename T>
@@ -44,6 +58,7 @@ namespace opencl
         data_dims(dims),
         node(n), ready(false), owner(true)
     {
+        //this->genBufferNode();
     }
 
     template<typename T>
@@ -56,6 +71,7 @@ namespace opencl
         static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
         static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
         getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T)*info.elements(), in_data);
+        this->genBufferNode();
     }
 
     template<typename T>
@@ -72,6 +88,7 @@ namespace opencl
                                          src_offset, 0,
                                          sizeof(T) * info.elements());
         }
+        this->genBufferNode();
     }
 
     template<typename T>
@@ -82,7 +99,9 @@ namespace opencl
         node(),
         ready(true),
         owner(false)
-    { }
+    {
+        this->genBufferNode();
+    }
 
 
     template<typename T>
@@ -97,6 +116,7 @@ namespace opencl
         data_dims(af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
         node(), ready(true), owner(true)
     {
+        this->genBufferNode();
     }
 
     template<typename T>
@@ -114,6 +134,7 @@ namespace opencl
         if (!is_device) {
             getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T) * info.total(), in_data);
         }
+        this->genBufferNode();
     }
 
     template<typename T>
@@ -137,6 +158,7 @@ namespace opencl
         node->resetFlags();
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
         node.reset();
+        this->genBufferNode();
     }
 
     template<typename T>
@@ -187,17 +209,6 @@ namespace opencl
     template<typename T>
     Node_ptr Array<T>::getNode() const
     {
-        if (!node) {
-            bool is_linear = isLinear();
-            unsigned bytes = this->getDataDims().elements() * sizeof(T);
-            BufferNode *buf_node = new BufferNode(dtype_traits<T>::getName(),
-                                                  shortname<T>(true),
-                                                  *this, data,
-                                                  bytes,
-                                                  is_linear);
-            const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
-        }
-
         return node;
     }
 
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index aee3088df1..af83eaf6b8 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -115,6 +115,8 @@ namespace opencl
         explicit Array(af::dim4 dims, const T * const in_data);
         explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
 
+        void genBufferNode() const;
+
     public:
 
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,

From 312eca90dd50b0ce8e2287f8a9d14d54fdc3cb3f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 4 Jun 2016 00:00:47 -0400
Subject: [PATCH 0626/2677] More fixes to multi eval in OpenCL

---
 src/backend/opencl/Array.cpp          | 27 ++++++++++++++++++-------
 src/backend/opencl/Array.hpp          | 13 ++++++++++--
 src/backend/opencl/JIT/BufferNode.hpp | 29 ++++++++++++++++++---------
 src/backend/opencl/JIT/Node.hpp       |  3 +++
 src/backend/opencl/jit.cpp            | 25 +++++++++++------------
 5 files changed, 65 insertions(+), 32 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 8a96a24b8a..ea3f643d4d 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -35,7 +35,6 @@ namespace opencl
         unsigned bytes = this->getDataDims().elements() * sizeof(T);
         BufferNode *buf_node = new BufferNode(dtype_traits<T>::getName(),
                                               shortname<T>(true),
-                                              *this, data,
                                               bytes,
                                               is_linear);
         const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
@@ -58,7 +57,6 @@ namespace opencl
         data_dims(dims),
         node(n), ready(false), owner(true)
     {
-        //this->genBufferNode();
     }
 
     template<typename T>
@@ -152,7 +150,7 @@ namespace opencl
 
         Param res = {data.get(), info};
 
-        evalNodes(res, this->getNode().get());
+        evalNodes(res, node.get());
         ready = true;
 
         node->resetFlags();
@@ -175,7 +173,9 @@ namespace opencl
         std::vector<Node *> nodes;
 
         for (auto array : arrays) {
-            if (array->isReady()) continue;
+            if (array->isReady()) {
+                continue;
+            }
 
             const ArrayInfo info = array->info;
 
@@ -190,7 +190,7 @@ namespace opencl
 
             Param res = {array->data.get(), kInfo};
             outputs.push_back(res);
-            nodes.push_back(array->getNode().get());
+            nodes.push_back(array->node.get());
         }
         evalNodes(outputs, nodes);
         for (auto array : arrays) {
@@ -204,14 +204,27 @@ namespace opencl
 
     template<typename T>
     Array<T>::~Array()
-    { }
+    {
+    }
 
     template<typename T>
-    Node_ptr Array<T>::getNode() const
+    Node_ptr Array<T>::getNode()
     {
+        if (node->isBuffer()) {
+            KParam kinfo = *this;
+            BufferNode *bufNode = reinterpret_cast<BufferNode *>(node.get());
+            bufNode->setData(kinfo, data);
+        }
         return node;
     }
 
+
+    template<typename T>
+    Node_ptr Array<T>::getNode() const
+    {
+        return const_cast<Array<T> *>(this)->getNode();
+    }
+
     using af::dim4;
 
     template<typename T>
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index af83eaf6b8..9490c130a8 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -24,9 +24,8 @@
 
 namespace opencl
 {
-    using af::dim4;
     typedef std::shared_ptr<cl::Buffer> Buffer_ptr;
-
+    using af::dim4;
     template<typename T> class Array;
 
     template<typename T>
@@ -230,7 +229,17 @@ namespace opencl
             return out;
         }
 
+        operator KParam() const
+        {
+            KParam kinfo = {{dims()[0], dims()[1], dims()[2], dims()[3]},
+                            {strides()[0], strides()[1], strides()[2], strides()[3]},
+                            getOffset()};
+
+            return kinfo;
+        }
+
         JIT::Node_ptr getNode() const;
+        JIT::Node_ptr getNode();
 
     public:
         std::shared_ptr<T> getMappedPtr() const
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 9306d59ef5..87d07893ed 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -21,8 +21,8 @@ namespace JIT
     class BufferNode : public Node
     {
     private:
-        const std::shared_ptr<cl::Buffer> m_data;
-        const Param m_param;
+        std::shared_ptr<cl::Buffer> m_data;
+        KParam m_info;
         const unsigned m_bytes;
         bool m_linear;
 
@@ -30,22 +30,31 @@ namespace JIT
 
         BufferNode(const char *type_str,
                    const char *name_str,
-                   const Param param,
-                   const std::shared_ptr<cl::Buffer> data,
                    const unsigned bytes,
                    const bool is_linear)
             : Node(type_str, name_str),
-              m_data(data),
-              m_param(param),
               m_bytes(bytes),
               m_linear(is_linear)
-        {}
+        {
+        }
+
+        bool isBuffer() { return true; }
+
+        ~BufferNode()
+        {
+        }
+
+        void setData(KParam info, std::shared_ptr<cl::Buffer> data)
+        {
+            m_info = info;
+            m_data = data;
+        }
 
         bool isLinear(dim_t dims[4])
         {
             bool same_dims = true;
             for (int i = 0; same_dims && i < 4; i++) {
-                same_dims &= (dims[i] == m_param.info.dims[i]);
+                same_dims &= (dims[i] == m_info.dims[i]);
             }
             return m_linear && same_dims;
         }
@@ -71,8 +80,8 @@ namespace JIT
         {
             if (m_set_arg) return id;
 
-            ker.setArg(id + 0, *m_param.data);
-            ker.setArg(id + 1,  m_param.info);
+            ker.setArg(id + 0, *m_data);
+            ker.setArg(id + 1,  m_info);
 
             m_set_arg = true;
             return id + 2;
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index 4437432e39..447e5b6fc5 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -27,6 +27,7 @@ namespace JIT
         std::string m_type_str;
         std::string m_name_str;
         int m_id;
+        int m_level;
         bool m_set_id;
         bool m_gen_func;
         bool m_gen_param;
@@ -75,6 +76,8 @@ namespace JIT
             bytes = 0;
         }
 
+        virtual bool isBuffer() { return false; }
+
 
         virtual void resetFlags()
         {
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 756956afb7..dbc1fc6b90 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -47,24 +47,19 @@ static string getFuncName(std::vector<Node *> nodes, bool is_linear, bool *is_do
         funcName << "G_";
     }
 
-    bool is_dbl = false;
     int id = 0;
     for (auto node :  nodes) {
+        funcName << "[";
         id = node->setId(id);
-        std::string outName = node->getNameStr();
-        funcName << outName;
-
+        funcName << node->getNameStr();
         node->genKerName(funcName);
-        string nameStr = funcName.str();
-        funcName << nameStr;
-
-        nameStr = nameStr + outName;
-        string dblChars = "dDzZ";
-        size_t loc = nameStr.find_first_of(dblChars);
-        is_dbl |= (loc != std::string::npos);
+        funcName << "]";
     }
 
-    *is_double = is_dbl;
+    string nameStr = funcName.str();
+    string dblChars = "dDzZ";
+    size_t loc = nameStr.find_first_of(dblChars);
+    *is_double = (loc != std::string::npos);
 
     std::hash<std::string> hash_fn;
     hashName << "KER" << hash_fn(funcName.str());
@@ -124,6 +119,8 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
     stringstream offsetsStream;
     stringstream opsStream;
 
+    int count  = 0;
+
     for (auto node : nodes) {
         int id = node->getId();
         node->genParams(inParamStream);
@@ -131,6 +128,7 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
         outWriteStream << "out" << id << "[idx] = " << "val" << id << ";\n";
         node->genOffsets(offsetsStream, is_linear);
         node->genFuncs(opsStream);
+        opsStream << "//" << ++count << std::endl << std::endl;
     }
 
     // Put various blocks into a single stream
@@ -161,7 +159,6 @@ static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
 
     bool is_dbl = false;
     string funcName = getFuncName(nodes, is_linear, &is_dbl);
-
     int device = getActiveDeviceId();
 
     kc_t::iterator idx = kernelCaches[device].find(funcName);
@@ -170,6 +167,8 @@ static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
     if (idx == kernelCaches[device].end()) {
         string jit_ker = getKernelString(funcName, nodes, is_linear);
 
+        std::cout << jit_ker << std::endl;
+
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
         cl::Program prog;

From e4f93f6c492bcfc2f622c5a7c88a095f56f0564f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 6 Jun 2016 20:59:16 -0400
Subject: [PATCH 0627/2677] PERF: Remove redundant traversals of the JIT Trees

---
 src/backend/cpu/TNJ/BinaryNode.hpp    | 26 ++++++++++-----
 src/backend/cpu/TNJ/BufferNode.hpp    | 46 +++++++++++++++++----------
 src/backend/cpu/TNJ/Node.hpp          | 45 ++++++++++++++++++++++++--
 src/backend/cpu/TNJ/ScalarNode.hpp    |  2 +-
 src/backend/cpu/TNJ/UnaryNode.hpp     | 20 +++++++++---
 src/backend/cuda/JIT/BinaryNode.hpp   | 23 +++++++-------
 src/backend/cuda/JIT/BufferNode.hpp   | 18 +++++++----
 src/backend/cuda/JIT/Node.hpp         |  8 ++++-
 src/backend/cuda/JIT/ScalarNode.hpp   |  4 +--
 src/backend/cuda/JIT/UnaryNode.hpp    | 21 ++++++------
 src/backend/opencl/JIT/BinaryNode.hpp | 16 +++++++---
 src/backend/opencl/JIT/BufferNode.hpp | 18 +++++++----
 src/backend/opencl/JIT/Node.hpp       |  8 ++++-
 src/backend/opencl/JIT/ScalarNode.hpp |  4 +--
 src/backend/opencl/JIT/UnaryNode.hpp  | 16 ++++++----
 15 files changed, 188 insertions(+), 87 deletions(-)

diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index f183698e1b..c1247aaa8a 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -49,15 +49,19 @@ namespace TNJ
 
         void *calc(int x, int y, int z, int w)
         {
-            m_val = m_op.eval(*(Ti *)m_lhs->calc(x, y, z, w),
-                              *(Ti *)m_rhs->calc(x, y, z, w));
+            if (calcCurrent(x, y, z, w)) {
+                m_val = m_op.eval(*(Ti *)m_lhs->calc(x, y, z, w),
+                                  *(Ti *)m_rhs->calc(x, y, z, w));
+            }
             return  (void *)&m_val;
         }
 
         void *calc(int idx)
         {
-            m_val = m_op.eval(*(Ti *)m_lhs->calc(idx),
-                              *(Ti *)m_rhs->calc(idx));
+            if (calcCurrent(idx)) {
+                m_val = m_op.eval(*(Ti *)m_lhs->calc(idx),
+                                  *(Ti *)m_rhs->calc(idx));
+            }
             return (void *)&m_val;
         }
 
@@ -75,14 +79,20 @@ namespace TNJ
 
         void reset()
         {
-            m_lhs->reset();
-            m_rhs->reset();
-            m_is_eval = false;
+            if (m_is_eval) {
+                resetCommonFlags();
+                m_lhs->reset();
+                m_rhs->reset();
+            }
         }
 
         bool isLinear(const dim_t *dims)
         {
-            return m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
+            if (!m_set_is_linear) {
+                m_linear = m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
     };
 
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index ada1aba54c..3e97980176 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -26,10 +26,11 @@ namespace TNJ
     protected:
         shared_ptr<T> ptr;
         unsigned m_bytes;
-        bool m_is_linear;
+        bool m_linear_buffer;
         dim_t m_off;
         dim_t m_strides[4];
         dim_t m_dims[4];
+        T m_val;
     public:
 
         BufferNode(shared_ptr<T> data,
@@ -41,8 +42,9 @@ namespace TNJ
             Node(),
             ptr(data),
             m_bytes(bytes),
-            m_is_linear(is_linear),
-            m_off(data_off)
+            m_linear_buffer(is_linear),
+            m_off(data_off),
+            m_val(0)
         {
             for (int i = 0; i < 4; i++) {
                 m_strides[i] = strs[i];
@@ -52,17 +54,23 @@ namespace TNJ
 
         void *calc(int x, int y, int z, int w)
         {
-            dim_t l_off = 0;
-            l_off += (w < (int)m_dims[3]) * w * m_strides[3];
-            l_off += (z < (int)m_dims[2]) * z * m_strides[2];
-            l_off += (y < (int)m_dims[1]) * y * m_strides[1];
-            l_off += (x < (int)m_dims[0]) * x;
-            return (void *)(ptr.get() + m_off + l_off);
+            if (calcCurrent(x, y, z, w)) {
+                dim_t l_off = 0;
+                l_off += (w < (int)m_dims[3]) * w * m_strides[3];
+                l_off += (z < (int)m_dims[2]) * z * m_strides[2];
+                l_off += (y < (int)m_dims[1]) * y * m_strides[1];
+                l_off += (x < (int)m_dims[0]) * x;
+                m_val = *(ptr.get() + m_off + l_off);
+            }
+            return (void *)&m_val;
         }
 
         void *calc(int idx)
         {
-            return (void *)(ptr.get() + idx + m_off);
+            if (calcCurrent(idx)) {
+                m_val = *(ptr.get() + idx + m_off);
+            }
+            return (void *)&m_val;
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
@@ -78,16 +86,22 @@ namespace TNJ
 
         void reset()
         {
-            m_is_eval = false;
+            if (m_is_eval) {
+                resetCommonFlags();
+            }
         }
 
         bool isLinear(const dim_t *dims)
         {
-            return m_is_linear &&
-                dims[0] == m_dims[0] &&
-                dims[1] == m_dims[1] &&
-                dims[2] == m_dims[2] &&
-                dims[3] == m_dims[3];
+            if (!m_set_is_linear) {
+                m_linear = m_linear_buffer &&
+                    dims[0] == m_dims[0] &&
+                    dims[1] == m_dims[1] &&
+                    dims[2] == m_dims[2] &&
+                    dims[3] == m_dims[3];
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
     };
 
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index c6b48d49f6..a6488991bb 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -22,10 +22,51 @@ namespace TNJ
     {
 
     protected:
+
+        int x, y, z, w;
         bool m_is_eval;
+        bool m_linear;
+        bool m_set_is_linear;
+
+
+        void resetCommonFlags()
+        {
+            x = -1;
+            y = -1;
+            z = -1;
+            w = -1;
+            m_is_eval = false;
+            m_linear = false;
+            m_set_is_linear = false;
+        }
+
+        bool calcCurrent(int xc)
+        {
+            bool res = (x == xc);
+            x = xc;
+            return !res;
+        }
+
+        bool calcCurrent(int xc, int yc, int zc, int wc)
+        {
+            bool res = (xc == x) && (yc == y) && (zc == z) && (wc == w);
+            x = xc;
+            y = yc;
+            z = zc;
+            w = wc;
+            return !res;
+        }
 
     public:
-        Node() : m_is_eval(false) {}
+        Node() :
+            x(-1),
+            y(-1),
+            z(-1),
+            w(-1),
+            m_is_eval(false),
+            m_linear(false),
+            m_set_is_linear(false)
+        {}
 
         virtual void *calc(int x, int y, int z, int w)
         {
@@ -47,7 +88,7 @@ namespace TNJ
         }
 
         virtual bool isLinear(const dim_t *dims) { return true; }
-        virtual void reset() { m_is_eval = false;}
+        virtual void reset() { resetCommonFlags(); }
 
         virtual ~Node() {}
     };
diff --git a/src/backend/cpu/TNJ/ScalarNode.hpp b/src/backend/cpu/TNJ/ScalarNode.hpp
index a85dfdae02..2498318736 100644
--- a/src/backend/cpu/TNJ/ScalarNode.hpp
+++ b/src/backend/cpu/TNJ/ScalarNode.hpp
@@ -46,7 +46,7 @@ namespace TNJ
             return;
         }
 
-        void reset() { m_is_eval = false; }
+        void reset() { resetCommonFlags(); }
 
         bool isLinear(const dim_t *dims) { return true; }
     };
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 7217164ae0..98a40ff167 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -47,13 +47,17 @@ namespace TNJ
 
         void *calc(int x, int y, int z, int w)
         {
-            m_val = m_op.eval(*(Ti *)m_child->calc(x, y, z, w));
+            if (calcCurrent(x, y, z, w)) {
+                m_val = m_op.eval(*(Ti *)m_child->calc(x, y, z, w));
+            }
             return (void *)(&m_val);
         }
 
         void *calc(int idx)
         {
-            m_val = m_op.eval(*(Ti *)m_child->calc(idx));
+            if (calcCurrent(idx)) {
+                m_val = m_op.eval(*(Ti *)m_child->calc(idx));
+            }
             return (void *)&m_val;
         }
 
@@ -70,13 +74,19 @@ namespace TNJ
 
         void reset()
         {
-            m_child->reset();
-            m_is_eval = false;
+            if (m_is_eval) {
+                resetCommonFlags();
+                m_child->reset();
+            }
         }
 
         bool isLinear(const dim_t *dims)
         {
-            return m_child->isLinear(dims);
+            if (!m_set_is_linear) {
+                m_linear = m_child->isLinear(dims);
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
     };
 
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index f916d85576..dee15dca41 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -38,7 +38,11 @@ namespace JIT
 
         bool isLinear(dim_t dims[4])
         {
-            return m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
+            if (!m_set_is_linear) {
+                m_linear = m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
 
         void genParams(std::stringstream &kerStream,
@@ -60,10 +64,10 @@ namespace JIT
 
         void genKerName(std::stringstream &kerStream)
         {
+            if (m_gen_name) return;
             m_lhs->genKerName(kerStream);
             m_rhs->genKerName(kerStream);
 
-            if (m_gen_name) return;
             // Make the hex representation of enum part of the Kernel name
             kerStream << "_" << std::setw(2) << std::setfill('0') << std::hex << m_op;
             kerStream << std::setw(2) << std::setfill('0') << std::hex << m_lhs->getId();
@@ -102,42 +106,37 @@ namespace JIT
         int setId(int id)
         {
             if (m_set_id) return id;
-
             id = m_lhs->setId(id);
             id = m_rhs->setId(id);
-
             m_id = id;
             m_set_id = true;
-
             return m_id + 1;
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
             if (m_set_id) return;
-
             m_lhs->getInfo(len, buf_count, bytes);
             m_rhs->getInfo(len, buf_count, bytes);
             len++;
-
             m_set_id = true;
             return;
         }
 
         void resetFlags()
         {
-            resetCommonFlags();
-            m_lhs->resetFlags();
-            m_rhs->resetFlags();
+            if (m_set_id) {
+                resetCommonFlags();
+                m_lhs->resetFlags();
+                m_rhs->resetFlags();
+            }
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
         {
             if (m_set_arg) return;
-
             m_lhs->setArgs(args, is_linear);
             m_rhs->setArgs(args, is_linear);
-
             m_set_arg = true;
         }
     };
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 342e1ed0b7..0570e78332 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -34,7 +34,7 @@ namespace JIT
         CParam<T> m_param;
         unsigned m_bytes;
 
-        bool m_linear;
+        bool m_linear_buffer;
     public:
 
         BufferNode(const char *type_str,
@@ -47,17 +47,21 @@ namespace JIT
               sptr(data),
               m_param(param),
               m_bytes(bytes),
-              m_linear(is_linear)
+              m_linear_buffer(is_linear)
         {
         }
 
         bool isLinear(dim_t dims[4])
         {
-            bool same_dims = true;
-            for (int i = 0; same_dims && i < 4; i++) {
-                same_dims &= (dims[i] == m_param.dims[i]);
+            if (!m_set_is_linear) {
+                bool same_dims = true;
+                for (int i = 0; same_dims && i < 4; i++) {
+                    same_dims &= (dims[i] == m_param.dims[i]);
+                }
+                m_linear = m_linear_buffer && same_dims;
+                m_set_is_linear = true;
             }
-            return m_linear && same_dims;
+            return m_linear;
         }
 
         void genKerName(std::stringstream &kerStream)
@@ -178,7 +182,7 @@ namespace JIT
 
         void resetFlags()
         {
-            resetCommonFlags();
+            if (m_set_id) resetCommonFlags();
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 90f6273be2..971e605085 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -35,6 +35,8 @@ namespace JIT
         bool m_gen_offset;
         bool m_set_arg;
         bool m_gen_name;
+        bool m_linear;
+        bool m_set_is_linear;
 
     protected:
 
@@ -46,6 +48,8 @@ namespace JIT
             m_gen_offset = false;
             m_set_arg = false;
             m_gen_name = false;
+            m_linear = false;
+            m_set_is_linear = false;
         }
 
 
@@ -60,7 +64,9 @@ namespace JIT
               m_gen_param(false),
               m_gen_offset(false),
               m_set_arg(false),
-              m_gen_name(false)
+              m_gen_name(false),
+              m_linear(false),
+              m_set_is_linear(false)
         {}
 
         virtual void genKerName(std::stringstream &kerStream) {}
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index 34f316d34b..1765843bb6 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -70,10 +70,8 @@ namespace JIT
         int setId(int id)
         {
             if (m_set_id) return id;
-
             m_id = id;
             m_set_id = true;
-
             return m_id + 1;
         }
 
@@ -87,7 +85,7 @@ namespace JIT
 
         void resetFlags()
         {
-            resetCommonFlags();
+            if (m_set_id) resetCommonFlags();
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 94ee96ece7..cc3f02556a 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -37,7 +37,11 @@ namespace JIT
 
         bool isLinear(dim_t dims[4])
         {
-            return m_child->isLinear(dims);
+            if (!m_set_is_linear) {
+                m_linear = m_child->isLinear(dims);
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
 
         void genParams(std::stringstream &kerStream,
@@ -58,10 +62,10 @@ namespace JIT
 
         void genKerName(std::stringstream &kerStream)
         {
-            m_child->genKerName(kerStream);
-
             if (m_gen_name) return;
 
+            m_child->genKerName(kerStream);
+
             // Make the hex representation of enum part of the Kernel name
             kerStream << "_" << std::setw(2) << std::setfill('0') << std::hex << m_op;
             kerStream << std::setw(2) << std::setfill('0') << std::hex << m_child->getId();
@@ -96,30 +100,27 @@ namespace JIT
         int setId(int id)
         {
             if (m_set_id) return id;
-
             id = m_child->setId(id);
-
             m_id = id;
             m_set_id = true;
-
             return m_id + 1;
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
             if (m_set_id) return;
-
             m_child->getInfo(len, buf_count, bytes);
             len++;
-
             m_set_id = true;
             return;
         }
 
         void resetFlags()
         {
-            resetCommonFlags();
-            m_child->resetFlags();
+            if (m_set_id) {
+                resetCommonFlags();
+                m_child->resetFlags();
+            }
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index b1f6d112b7..6b5a6d05d6 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -38,7 +38,11 @@ namespace JIT
 
         bool isLinear(dim_t dims[4])
         {
-            return m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
+            if (!m_set_is_linear) {
+                m_linear = m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
 
         void genParams(std::stringstream &kerStream)
@@ -69,10 +73,10 @@ namespace JIT
 
         void genKerName(std::stringstream &kerStream)
         {
+            if (m_gen_name) return;
             m_lhs->genKerName(kerStream);
             m_rhs->genKerName(kerStream);
 
-            if (m_gen_name) return;
             // Make the dec representation of enum part of the Kernel name
             kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << m_lhs->getId();
@@ -123,9 +127,11 @@ namespace JIT
 
         void resetFlags()
         {
-            resetCommonFlags();
-            m_lhs->resetFlags();
-            m_rhs->resetFlags();
+            if (m_set_id) {
+                resetCommonFlags();
+                m_lhs->resetFlags();
+                m_rhs->resetFlags();
+            }
         }
     };
 
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 87d07893ed..54d7fa1d68 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -24,7 +24,7 @@ namespace JIT
         std::shared_ptr<cl::Buffer> m_data;
         KParam m_info;
         const unsigned m_bytes;
-        bool m_linear;
+        bool m_linear_buffer;
 
     public:
 
@@ -34,7 +34,7 @@ namespace JIT
                    const bool is_linear)
             : Node(type_str, name_str),
               m_bytes(bytes),
-              m_linear(is_linear)
+              m_linear_buffer(is_linear)
         {
         }
 
@@ -52,11 +52,15 @@ namespace JIT
 
         bool isLinear(dim_t dims[4])
         {
-            bool same_dims = true;
-            for (int i = 0; same_dims && i < 4; i++) {
-                same_dims &= (dims[i] == m_info.dims[i]);
+            if (!m_set_is_linear) {
+                bool same_dims = true;
+                for (int i = 0; same_dims && i < 4; i++) {
+                    same_dims &= (dims[i] == m_info.dims[i]);
+                }
+                m_set_is_linear = true;
+                m_linear = m_linear_buffer && same_dims;
             }
-            return m_linear && same_dims;
+            return m_linear;
         }
 
         void genKerName(std::stringstream &kerStream)
@@ -147,7 +151,7 @@ namespace JIT
 
         void resetFlags()
         {
-            resetCommonFlags();
+            if (m_set_id) resetCommonFlags();
         }
     };
 
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index 447e5b6fc5..215290c76d 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -34,6 +34,8 @@ namespace JIT
         bool m_gen_offset;
         bool m_set_arg;
         bool m_gen_name;
+        bool m_linear;
+        bool m_set_is_linear;
 
     protected:
         void resetCommonFlags()
@@ -44,6 +46,8 @@ namespace JIT
             m_gen_offset = false;
             m_set_arg = false;
             m_gen_name = false;
+            m_linear = false;
+            m_set_is_linear = false;
         }
 
     public:
@@ -57,7 +61,9 @@ namespace JIT
               m_gen_param(false),
               m_gen_offset(false),
               m_set_arg(false),
-              m_gen_name(false)
+              m_gen_name(false),
+              m_linear(false),
+              m_set_is_linear(false)
         {}
 
         virtual void genKerName(std::stringstream &kerStream) {}
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index 0bba7a2fc9..57d9f91d12 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -82,10 +82,8 @@ namespace JIT
         int setId(int id)
         {
             if (m_set_id) return id;
-
             m_id = id;
             m_set_id = true;
-
             return m_id + 1;
         }
 
@@ -99,7 +97,7 @@ namespace JIT
 
         void resetFlags()
         {
-            resetCommonFlags();
+            if (m_set_id) resetCommonFlags();
         }
     };
 
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index e1f32ded8f..c035426686 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -37,7 +37,11 @@ namespace JIT
 
         bool isLinear(dim_t dims[4])
         {
-            return m_child->isLinear(dims);
+            if (!m_set_is_linear) {
+                m_linear = m_child->isLinear(dims);
+                m_set_is_linear = true;
+            }
+            return m_linear;
         }
 
         void genParams(std::stringstream &kerStream)
@@ -63,6 +67,7 @@ namespace JIT
 
         void genKerName(std::stringstream &kerStream)
         {
+            if (m_gen_name) return;
             m_child->genKerName(kerStream);
 
             // Make the dec representation of enum part of the Kernel name
@@ -88,12 +93,9 @@ namespace JIT
         int setId(int id)
         {
             if (m_set_id) return id;
-
             id = m_child->setId(id);
-
             m_id = id;
             m_set_id = true;
-
             return m_id + 1;
         }
 
@@ -110,8 +112,10 @@ namespace JIT
 
         void resetFlags()
         {
-            resetCommonFlags();
-            m_child->resetFlags();
+            if (m_set_id) {
+                resetCommonFlags();
+                m_child->resetFlags();
+            }
         }
     };
 

From cdc27cdaa6d628516cb6d57b806681e053479e3d Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 6 Jun 2016 22:56:32 -0400
Subject: [PATCH 0628/2677] Changing af::eval() to use pointers

---
 include/af/array.h    | 2 +-
 src/api/cpp/array.cpp | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 1691d9c7f5..720d7a2c13 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1248,7 +1248,7 @@ namespace af
     AFAPI void eval(array &a, array &b, array &c, array &d);
     AFAPI void eval(array &a, array &b, array &c, array &d, array &e);
     AFAPI void eval(array &a, array &b, array &c, array &d, array &e, array &f);
-    AFAPI void eval(int num, array *arrays);
+    AFAPI void eval(int num, array **arrays);
     /**
        @}
     */
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index adee829283..708bfec022 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1077,11 +1077,11 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
         AF_THROW(af_eval_multiple(6, arrays));
     }
 
-    void eval(int num, array *arrays)
+    void eval(int num, array **arrays)
     {
         std::vector<af_array> outputs(num);
         for (int i = 0; i < num; i++) {
-            outputs[i] = arrays[i].get();
+            outputs[i] = arrays[i]->get();
         }
         AF_THROW(af_eval_multiple(num, &outputs[0]));
     }

From 86da2539fb98b1b0e05c54eec7378d4efd6c9654 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 6 Jun 2016 22:59:33 -0400
Subject: [PATCH 0629/2677] FEAT: Adding flags to set internal eval flags

---
 include/af/array.h              | 21 +++++++++++++++++++++
 src/api/c/device.cpp            | 19 +++++++++++++++++++
 src/api/cpp/array.cpp           | 12 ++++++++++++
 src/api/unified/device.cpp      | 13 ++++++++++++-
 src/backend/cpu/Array.cpp       | 22 ++++++++++++----------
 src/backend/cpu/platform.cpp    |  7 +++++++
 src/backend/cpu/platform.hpp    |  2 ++
 src/backend/cuda/Array.cpp      | 22 ++++++++++++----------
 src/backend/cuda/platform.cpp   |  6 ++++++
 src/backend/cuda/platform.hpp   |  2 ++
 src/backend/opencl/Array.cpp    | 18 ++++++++++--------
 src/backend/opencl/platform.cpp |  6 ++++++
 src/backend/opencl/platform.hpp |  2 ++
 13 files changed, 123 insertions(+), 29 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 720d7a2c13..dc4e77cda5 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1249,6 +1249,10 @@ namespace af
     AFAPI void eval(array &a, array &b, array &c, array &d, array &e);
     AFAPI void eval(array &a, array &b, array &c, array &d, array &e, array &f);
     AFAPI void eval(int num, array **arrays);
+
+    AFAPI void setInternalEvalFlag(bool flag);
+    AFAPI bool getInternalEvalFlag();
+
     /**
        @}
     */
@@ -1355,6 +1359,23 @@ extern "C" {
       @}
     */
 
+    /**
+       Manually set the internal eval flag
+    */
+    AFAPI af_err af_set_internal_eval_flag(bool flag);
+    /**
+      @}
+    */
+
+
+    /**
+       Get the current internal eval flag
+    */
+    AFAPI af_err af_get_internal_eval_flag(bool *flag);
+    /**
+      @}
+    */
+
 
     /**
         \ingroup method_mat
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 079a04752d..ca45ef0ac3 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -244,3 +244,22 @@ af_err af_eval_multiple(int num, af_array *arrays)
 
     return AF_SUCCESS;
 }
+
+af_err af_set_internal_eval_flag(bool flag)
+{
+    try {
+        bool& backendFlag = evalFlag();
+        backendFlag = flag;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+
+af_err af_get_internal_eval_flag(bool *flag)
+{
+    try {
+        bool backendFlag = evalFlag();
+        *flag = backendFlag;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 708bfec022..35739e5c8f 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1085,4 +1085,16 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
         }
         AF_THROW(af_eval_multiple(num, &outputs[0]));
     }
+
+    void setInternalEvalFlag(bool flag)
+    {
+        AF_THROW(af_set_internal_eval_flag(flag));
+    }
+
+    bool getInternalEvalFlag()
+    {
+        bool flag;
+        AF_THROW(af_get_internal_eval_flag(&flag));
+        return flag;
+    }
 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index d735b242d1..c82887f0ec 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -185,10 +185,21 @@ af_err af_get_device_ptr(void **ptr, const af_array arr)
     return CALL(ptr, arr);
 }
 
-af_err af_eval_multiple(int num, af_array *arrays)
+af_err af_eval_multiple(const int num, af_array *arrays)
 {
     for (int i = 0; i < num; i++) {
         CHECK_ARRAYS(arrays[i]);
     }
     return CALL(num, arrays);
 }
+
+af_err af_set_internal_eval_flag(bool flag)
+{
+    return CALL(flag);
+}
+
+
+af_err af_get_internal_eval_flag(bool *flag)
+{
+    return CALL(flag);
+}
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index ebd5457e90..7427680be5 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -179,16 +179,18 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 {
     Array<T> out =  Array<T>(dims, node);
 
-    unsigned length =0, buf_count = 0, bytes = 0;
-
-    Node *n = node.get();
-    n->getInfo(length, buf_count, bytes);
-    n->reset();
-
-    if (length > getMaxJitSize() ||
-        buf_count >= getMaxBuffers() ||
-        bytes >= getMaxBytes()) {
-        out.eval();
+    if (evalFlag()) {
+        unsigned length =0, buf_count = 0, bytes = 0;
+
+        Node *n = node.get();
+        n->getInfo(length, buf_count, bytes);
+        n->reset();
+
+        if (length > getMaxJitSize() ||
+            buf_count >= getMaxBuffers() ||
+            bytes >= getMaxBytes()) {
+            out.eval();
+        }
     }
 
     return out;
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 947f0c2c46..38fdfac990 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -311,4 +311,11 @@ void sync(int device)
     getQueue().sync();
 }
 
+bool& evalFlag()
+{
+    static bool flag = true;
+    return flag;
+}
+
+
 }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 7caddccc72..edf7a072fc 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -37,4 +37,6 @@ namespace cpu {
     queue& getQueue(int idx = 0);
 
     unsigned getMaxJitSize();
+
+    bool& evalFlag();
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index bf2e40a38a..2b8cbdc6f7 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -171,16 +171,18 @@ namespace cuda
     {
         Array<T> out =  Array<T>(dims, node);
 
-        unsigned length =0, buf_count = 0, bytes = 0;
-
-        Node *n = node.get();
-        n->getInfo(length, buf_count, bytes);
-        n->resetFlags();
-
-        if (length > getMaxJitSize() ||
-            buf_count >= getMaxBuffers() ||
-            bytes >= getMaxBytes()) {
-            out.eval();
+        if (evalFlag()) {
+            unsigned length =0, buf_count = 0, bytes = 0;
+
+            Node *n = node.get();
+            n->getInfo(length, buf_count, bytes);
+            n->resetFlags();
+
+            if (length > getMaxJitSize() ||
+                buf_count >= getMaxBuffers() ||
+                bytes >= getMaxBytes()) {
+                out.eval();
+            }
         }
 
         return out;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 23735389e5..2fa3eb93cf 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -488,6 +488,12 @@ bool synchronize_calls() {
     return sync;
 }
 
+bool& evalFlag()
+{
+    static bool flag = true;
+    return flag;
+}
+
 }
 
 af_err afcu_get_stream(cudaStream_t* stream, int id)
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 3fcc67ea5b..fa73ccf5e0 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -64,6 +64,8 @@ struct cudaDevice_t {
     int nativeId;
 };
 
+bool& evalFlag();
+
 class DeviceManager
 {
     public:
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index ea3f643d4d..df4a11395c 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -234,16 +234,18 @@ namespace opencl
 
         Array<T> out =  Array<T>(dims, node);
 
-        unsigned length =0, buf_count = 0, bytes = 0;
+        if (evalFlag()) {
+            unsigned length =0, buf_count = 0, bytes = 0;
 
-        Node *n = node.get();
-        n->getInfo(length, buf_count, bytes);
-        n->resetFlags();
+            Node *n = node.get();
+            n->getInfo(length, buf_count, bytes);
+            n->resetFlags();
 
-        if (length > getMaxJitSize() ||
-            buf_count >= getMaxBuffers() ||
-            bytes >= getMaxBytes()) {
-            out.eval();
+            if (length > getMaxJitSize() ||
+                buf_count >= getMaxBuffers() ||
+                bytes >= getMaxBytes()) {
+                out.eval();
+            }
         }
 
         return out;
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 80cf1be3cd..8dd2db63fe 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -864,6 +864,12 @@ unsigned getMaxJitSize()
     return length;
 }
 
+bool& evalFlag()
+{
+    static bool flag = true;
+    return flag;
+}
+
 }
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index be9670eb26..157d304425 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -147,4 +147,6 @@ bool synchronize_calls();
 int getActiveDeviceType();
 int getActivePlatform();
 
+bool& evalFlag();
+
 }

From 941d4b57d894507547b55474938d53be17c894dc Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 7 Jun 2016 16:55:51 -0400
Subject: [PATCH 0630/2677] BUGFIX: Fix to multi_eval in opencl backend

---
 src/backend/opencl/Array.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index df4a11395c..24e2f30213 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -199,6 +199,7 @@ namespace opencl
             array->node->resetFlags();
             // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
             array->node.reset();
+            array->genBufferNode();
         }
     }
 

From fe087155bec53484510b963369cdbaf5b0d5ec9a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 7 Jun 2016 16:56:18 -0400
Subject: [PATCH 0631/2677] Fix for missing symbols in unified backend

---
 src/api/unified/device.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index c82887f0ec..030a29fb51 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -9,6 +9,7 @@
 
 #include <af/backend.h>
 #include <af/device.h>
+#include <af/array.h>
 #include "symbol_manager.hpp"
 
 af_err af_set_backend(const af_backend bknd)

From 7aa8e7b58d55950e2bad9d690f7adc80b1723d97 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 7 Jun 2016 18:48:20 -0400
Subject: [PATCH 0632/2677] BUGFIX: Do not error out when all inputs are
 evaluated

---
 src/backend/opencl/Array.cpp          | 5 ++---
 src/backend/opencl/JIT/BufferNode.hpp | 8 ++++----
 src/backend/opencl/jit.cpp            | 4 ++--
 3 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 24e2f30213..52919e4e42 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -32,10 +32,8 @@ namespace opencl
     void Array<T>::genBufferNode() const
     {
         bool is_linear = isLinear();
-        unsigned bytes = this->getDataDims().elements() * sizeof(T);
         BufferNode *buf_node = new BufferNode(dtype_traits<T>::getName(),
                                               shortname<T>(true),
-                                              bytes,
                                               is_linear);
         const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
     }
@@ -214,7 +212,8 @@ namespace opencl
         if (node->isBuffer()) {
             KParam kinfo = *this;
             BufferNode *bufNode = reinterpret_cast<BufferNode *>(node.get());
-            bufNode->setData(kinfo, data);
+            unsigned bytes = this->getDataDims().elements() * sizeof(T);
+            bufNode->setData(kinfo, data, bytes);
         }
         return node;
     }
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 54d7fa1d68..964d28aae8 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -23,17 +23,16 @@ namespace JIT
     private:
         std::shared_ptr<cl::Buffer> m_data;
         KParam m_info;
-        const unsigned m_bytes;
+        unsigned m_bytes;
         bool m_linear_buffer;
 
     public:
 
         BufferNode(const char *type_str,
                    const char *name_str,
-                   const unsigned bytes,
                    const bool is_linear)
             : Node(type_str, name_str),
-              m_bytes(bytes),
+              m_bytes(0),
               m_linear_buffer(is_linear)
         {
         }
@@ -44,10 +43,11 @@ namespace JIT
         {
         }
 
-        void setData(KParam info, std::shared_ptr<cl::Buffer> data)
+        void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes)
         {
             m_info = info;
             m_data = data;
+            m_bytes = bytes;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index dbc1fc6b90..37686528f9 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -167,8 +167,6 @@ static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
     if (idx == kernelCaches[device].end()) {
         string jit_ker = getKernelString(funcName, nodes, is_linear);
 
-        std::cout << jit_ker << std::endl;
-
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
         cl::Program prog;
@@ -188,6 +186,8 @@ void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
 {
     try {
 
+        if (outputs.size() == 0) return;
+
         // Assume all ouputs are of same size
         //FIXME: Add assert to check if all outputs are same size?
         KParam out_info = outputs[0].info;

From 3917ffae211e616dcc36ee66f6744e3a4498a133 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 16 Jun 2016 14:38:56 -0400
Subject: [PATCH 0633/2677] cleanup and optimization optimize loops in kernels.
 dynamically-sized shared memory. varname corrections

---
 include/af/defines.h                  |  2 +-
 include/af/image.h                    | 18 ++++++++-------
 src/backend/cpu/kernel/moments.hpp    |  9 ++++----
 src/backend/cuda/kernel/moments.hpp   | 32 ++++++++++++---------------
 src/backend/opencl/kernel/moments.cl  | 31 ++++++++++++--------------
 src/backend/opencl/kernel/moments.hpp | 32 ++++++++++++++++++---------
 6 files changed, 65 insertions(+), 59 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 93cc07aa1a..848cd510b0 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -363,7 +363,7 @@ typedef enum {
     AF_MOMENT_M01 = 2,
     AF_MOMENT_M10 = 4,
     AF_MOMENT_M11 = 8,
-    AF_MOMENT_FIRST_ORDER = 0xF
+    AF_MOMENT_FIRST_ORDER = AF_MOMENT_M00 | AF_MOMENT_M01 | AF_MOMENT_M10 | AF_MOMENT_M11
 } af_moment_type;
 #endif
 
diff --git a/include/af/image.h b/include/af/image.h
index 76521b2de9..fcd68e73fd 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -681,13 +681,14 @@ AFAPI array rgb2ycbcr(const array& in, const YCCStd standard=AF_YCC_601);
 /**
    C++ Interface for calculating an image moment
 
+   \param[out] out is a pointer to a pre-allocated array where the calculated moment(s) will be placed.
+   User is responsible for ensuring enough space to hold all requested moments
    \param[in]  in is the input image
-   \param[out] out is a pointer to the outputted moment(s)
-   \param[moment] is the moment(s) to calculate
+   \param[in] moment is moment(s) to calculate
 
    \ingroup image_func_moments
  */
-AFAPI void moments(double* out, const array& in, const af_moment_type moment);
+AFAPI void moments(double* out, const array& in, const momentType moment=AF_MOMENT_FIRST_ORDER);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -695,12 +696,12 @@ AFAPI void moments(double* out, const array& in, const af_moment_type moment);
    C++ Interface for calculating image moments
 
    \param[in]  in contains the input image(s)
-   \param[moment] is the moment to calculate
+   \param[in] moment is moment(s) to calculate
    \return array containing the requested moment of each image
 
    \ingroup image_func_moments
  */
-AFAPI array moments(const array& in, const af_moment_type moment);
+AFAPI array moments(const array& in, const momentType moment=AF_MOMENT_FIRST_ORDER);
 #endif
 
 }
@@ -1381,7 +1382,7 @@ extern "C" {
 
        \param[out] out is an array containing the calculated moments
        \param[in]  in is an array of image(s)
-       \param[moment] is the moment(s) to calculate
+       \param[in] moment is moment(s) to calculate
        \return     ref AF_SUCCESS if the moment calculation is successful,
        otherwise an appropriate error code is returned.
 
@@ -1394,9 +1395,10 @@ extern "C" {
     /**
        C Interface for calculating image moment(s) of a single image
 
-       \param[out] out is  a pointer to the outputted moment(s)
+       \param[out] out is a pointer to a pre-allocated array where the calculated moment(s) will be placed.
+       User is responsible for ensuring enough space to hold all requested moments
        \param[in] in is the input image
-       \param[moment] is the moment(s) to calculate
+       \param[in] moment is moment(s) to calculate
        \return     ref AF_SUCCESS if the moment calculation is successful,
        otherwise an appropriate error code is returned.
 
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index a1cda5dd3f..6466986442 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -35,24 +35,25 @@ void moments(Array<float> &output, Array<T> const &input, af_moment_type moment)
     dim_t mId = 0;
     for(dim_t w = 0; w < idims[3]; w++) {
         for(dim_t z = 0; z < idims[2]; z++) {
+            dim_t out_off = w * ostrides[3] + z * ostrides[2];
             for(dim_t y = 0; y < idims[1]; y++) {
                 for(dim_t x = 0; x < idims[0]; x++) {
                     dim_t m_off=0;
                     float val = in[mId];
                     if((moment & AF_MOMENT_M00) > 0) {
-                        out[w * ostrides[3] + z * ostrides[2] + m_off] += val;
+                        out[out_off + m_off] += val;
                         m_off++;
                     }
                     if((moment & AF_MOMENT_M01) > 0) {
-                        out[w * ostrides[3] + z * ostrides[2] + m_off] += x * val;
+                        out[out_off + m_off] += x * val;
                         m_off++;
                     }
                     if((moment & AF_MOMENT_M10) > 0) {
-                        out[w * ostrides[3] + z * ostrides[2] + m_off] += y * val;
+                        out[out_off + m_off] += y * val;
                         m_off++;
                     }
                     if((moment & AF_MOMENT_M11) > 0) {
-                        out[w * ostrides[3] + z * ostrides[2] + m_off] += x * y * val;
+                        out[out_off + m_off] += x * y * val;
                         m_off++;
                     }
                     mId++;
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 7c12a6c6b6..f9c61cf1e2 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -25,8 +25,7 @@ namespace kernel
 
     template<typename T>
     __global__
-    void moments_kernel(CParam<float> out, CParam<T> in, af_moment_type moment,
-                  const dim_t blocksMatX, const bool pBatch)
+    void moments_kernel(Param<float> out, CParam<T> in, af_moment_type moment, const bool pBatch)
     {
         const dim_t idw = blockIdx.y / in.dims[2];
         const dim_t idz = blockIdx.y - idw * in.dims[2];
@@ -34,24 +33,24 @@ namespace kernel
         const dim_t idy = blockIdx.x;
         dim_t idx = threadIdx.x;
 
-        __shared__ float blk_moment_sum[4];
-        if(threadIdx.x < 4) {
+        if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3] )
+            return;
+
+        extern __shared__ float blk_moment_sum[];
+        if(threadIdx.x < out.dims[0]) {
             blk_moment_sum[threadIdx.x] = 0.f;
         }
         __syncthreads();
 
-        for(unsigned i=0; i<blocksMatX; ++i) {
-            dim_t mId = idy * in.strides[1] + idx;
-            if(pBatch) {
-                mId += idw * in.strides[3] + idz * in.strides[2];
-            }
-
-            if (idx >= in.dims[0] || idy >= in.dims[1] ||
-                idz >= in.dims[2] || idw >= in.dims[3] )
-                break;
+        dim_t mId = idy * in.strides[1] + idx;
+        if(pBatch) {
+            mId += idw * in.strides[3] + idz * in.strides[2];
+        }
 
+        for(; idx<in.dims[0]; idx+=blockDim.x) {
             dim_t m_off = 0;
             float val = (float)in.ptr[mId];
+            mId += blockDim.x;
 
             if((moment & AF_MOMENT_M00) > 0) {
                 atomicAdd(blk_moment_sum + m_off++, val);
@@ -65,8 +64,6 @@ namespace kernel
             if((moment & AF_MOMENT_M11) > 0) {
                 atomicAdd(blk_moment_sum + m_off, idx * idy * val);
             }
-
-            idx += blockDim.x;
         }
 
         __syncthreads();
@@ -80,13 +77,12 @@ namespace kernel
     template <typename T>
     void moments(Param<float> out, CParam<T> in, const af_moment_type moment) {
         dim3 threads(THREADS, 1, 1);
-        dim_t blocksMatX = divup(in.dims[0], threads.x);
         dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
 
         bool pBatch = !(in.dims[2] == 1 && in.dims[3] == 1);
 
-        CUDA_LAUNCH((moments_kernel<T>), blocks, threads,
-                     out, in, moment, blocksMatX, pBatch);
+        CUDA_LAUNCH_SMEM((moments_kernel<T>), blocks, threads, sizeof(float) * out.dims[0],
+                     out, in, moment, pBatch);
         POST_LAUNCH_CHECK();
     }
 
diff --git a/src/backend/opencl/kernel/moments.cl b/src/backend/opencl/kernel/moments.cl
index 87b6d74abd..ea19371729 100644
--- a/src/backend/opencl/kernel/moments.cl
+++ b/src/backend/opencl/kernel/moments.cl
@@ -47,8 +47,7 @@ inline void fatomic_add_g(volatile __global float *source, const float operand)
 __kernel
 void moments_kernel(__global  float *d_out, const KParam out,
                     __global const T *d_in,  const KParam in,
-                    const int moment, const int blocksMatX,
-                    const int pBatch)
+                    const int moment, const int pBatch)
 {
     const dim_t idw = get_group_id(1) / in.dims[2];
     const dim_t idz = get_group_id(1)  - idw * in.dims[2];
@@ -56,27 +55,26 @@ void moments_kernel(__global  float *d_out, const KParam out,
     const dim_t idy = get_group_id(0);
     dim_t idx = get_local_id(0);
 
-    __local float wkg_moment_sum[4];
-    if(get_local_id(0) < 4) {
+    if(idy >= in.dims[1] ||
+       idz >= in.dims[2] ||
+       idw >= in.dims[3] )
+        return;
+
+    __local float wkg_moment_sum[MOMENTS_SZ];
+    if(get_local_id(0) < MOMENTS_SZ) {
          wkg_moment_sum[get_local_id(0)] = 0.f;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    for(unsigned i=0; i<blocksMatX; ++i) {
-        int mId = idy * in.strides[1] + idx;
-        if(pBatch) {
-           mId += idw * in.strides[3] + idz * in.strides[2];
-        }
-
-        if(idx >= in.dims[0] ||
-           idy >= in.dims[1] ||
-           idz >= in.dims[2] ||
-           idw >= in.dims[3])
-            break;
-
+    int mId = idy * in.strides[1] + idx;
+    if(pBatch) {
+       mId += idw * in.strides[3] + idz * in.strides[2];
+    }
 
+    for(; idx<in.dims[0]; idx+=get_local_size(0)) {
         dim_t m_off = 0;
         float val = d_in[mId];
+        mId += get_local_size(0);
 
         if((moment & AF_MOMENT_M00) > 0) {
             fatomic_add_l(wkg_moment_sum + m_off++, val);
@@ -90,7 +88,6 @@ void moments_kernel(__global  float *d_out, const KParam out,
         if((moment & AF_MOMENT_M11) > 0) {
             fatomic_add_l(wkg_moment_sum + m_off, idx * idy * val);
         }
-        idx += get_local_size(0);
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 1ea3ce9fac..7ed24da55b 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -19,6 +19,7 @@
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
 #include <math.hpp>
+#include <cache.hpp>
 #include "config.hpp"
 
 using cl::Buffer;
@@ -42,15 +43,20 @@ namespace opencl
         void moments(Param out, const Param in, af_moment_type moment)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  momentsProgs;
-                static std::map<int, Kernel*> momentsKernels;
+                std::string ref_name =
+                std::string("moments_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(out.info.dims[0]);
 
                 int device = getActiveDeviceId();
+                kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-                std::call_once( compileFlags[device], [device] () {
+                kc_entry_t entry;
+                if (idx == kernelCaches[device].end()) {
                     std::ostringstream options;
                     options << " -D T="        << dtype_traits<T>::getName();
+                    options << " -D MOMENTS_SZ=" << out.info.dims[0];
 
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
@@ -60,17 +66,21 @@ namespace opencl
 
                     Program prog;
                     buildProgram(prog, moments_cl, moments_cl_len, options.str());
-                    momentsProgs[device] = new Program(prog);
 
-                    momentsKernels[device] = new Kernel(*momentsProgs[device], "moments_kernel");
-                });
+                    entry.prog = new Program(prog);
+                    entry.ker = new Kernel(*entry.prog, "moments_kernel");
 
+                    kernelCaches[device][ref_name] = entry;
+                } else {
+                    entry = idx->second;
+                }
 
-                auto momentsp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam, const int, const int, const int>
-                                      (*momentsKernels[device]);
+
+                auto momentsp = KernelFunctor<Buffer, const KParam,
+                                              const Buffer, const KParam,
+                                              const int, const int>(*entry.ker);
 
                 NDRange local(THREADS, 1, 1);
-                dim_t blocksMatX = divup(in.info.dims[0], local[0]);
                 NDRange global(in.info.dims[1] * local[0] ,
                                in.info.dims[2] * in.info.dims[3] * local[1] );
 
@@ -78,7 +88,7 @@ namespace opencl
 
                 momentsp(EnqueueArgs(getQueue(), global, local),
                           *out.data, out.info, *in.data, in.info,
-                          (int)moment, blocksMatX, (int)pBatch);
+                          (int)moment, (int)pBatch);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {

From 6d0d62b81e5d84dad1c57e9191520006c40b78ec Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 15:40:48 -0400
Subject: [PATCH 0634/2677] Fixes to OpenCL JIT for buffer nodes

---
 src/backend/opencl/Array.cpp          |  6 ++----
 src/backend/opencl/JIT/BufferNode.hpp | 10 ++++------
 2 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 52919e4e42..811fe88b57 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -31,10 +31,8 @@ namespace opencl
     template<typename T>
     void Array<T>::genBufferNode() const
     {
-        bool is_linear = isLinear();
         BufferNode *buf_node = new BufferNode(dtype_traits<T>::getName(),
-                                              shortname<T>(true),
-                                              is_linear);
+                                              shortname<T>(true));
         const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
     }
 
@@ -213,7 +211,7 @@ namespace opencl
             KParam kinfo = *this;
             BufferNode *bufNode = reinterpret_cast<BufferNode *>(node.get());
             unsigned bytes = this->getDataDims().elements() * sizeof(T);
-            bufNode->setData(kinfo, data, bytes);
+            bufNode->setData(kinfo, data, bytes, isLinear());
         }
         return node;
     }
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 964d28aae8..3d992ce5e4 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -29,11 +29,8 @@ namespace JIT
     public:
 
         BufferNode(const char *type_str,
-                   const char *name_str,
-                   const bool is_linear)
-            : Node(type_str, name_str),
-              m_bytes(0),
-              m_linear_buffer(is_linear)
+                   const char *name_str)
+            : Node(type_str, name_str)
         {
         }
 
@@ -43,11 +40,12 @@ namespace JIT
         {
         }
 
-        void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes)
+        void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
         {
             m_info = info;
             m_data = data;
             m_bytes = bytes;
+            m_linear_buffer = is_linear;
         }
 
         bool isLinear(dim_t dims[4])

From 83ea4465e225e4d6556ced697ad1344a80c0bd65 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 16:13:53 -0400
Subject: [PATCH 0635/2677] Cleaning up BufferNode creation in OpenCL backend

---
 src/backend/opencl/Array.cpp | 35 +++++++++++++++--------------------
 src/backend/opencl/Array.hpp |  2 --
 2 files changed, 15 insertions(+), 22 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 811fe88b57..14f90b3f32 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -29,11 +29,10 @@ namespace opencl
     using JIT::Node_ptr;
 
     template<typename T>
-    void Array<T>::genBufferNode() const
+    Node_ptr bufferNodePtr()
     {
-        BufferNode *buf_node = new BufferNode(dtype_traits<T>::getName(),
-                                              shortname<T>(true));
-        const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
+        return Node_ptr(reinterpret_cast<Node *>(new BufferNode(dtype_traits<T>::getName(),
+                                                                shortname<T>(true))));
     }
 
     template<typename T>
@@ -41,9 +40,8 @@ namespace opencl
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(bufferAlloc(info.elements() * sizeof(T)), bufferFree),
         data_dims(dims),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {
-        this->genBufferNode();
     }
 
     template<typename T>
@@ -60,12 +58,11 @@ namespace opencl
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(bufferAlloc(info.elements()*sizeof(T)), bufferFree),
         data_dims(dims),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {
         static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
         static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
         getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T)*info.elements(), in_data);
-        this->genBufferNode();
     }
 
     template<typename T>
@@ -73,7 +70,7 @@ namespace opencl
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new cl::Buffer(mem), bufferFree),
         data_dims(dims),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {
         if (copy) {
             clRetainMemObject(mem);
@@ -82,7 +79,6 @@ namespace opencl
                                          src_offset, 0,
                                          sizeof(T) * info.elements());
         }
-        this->genBufferNode();
     }
 
     template<typename T>
@@ -90,11 +86,10 @@ namespace opencl
         info(parent.getDevId(), dims, offset_, stride, (af_dtype)dtype_traits<T>::af_type),
         data(parent.getData()),
         data_dims(parent.getDataDims()),
-        node(),
+        node(bufferNodePtr<T>()),
         ready(true),
         owner(false)
     {
-        this->genBufferNode();
     }
 
 
@@ -108,9 +103,8 @@ namespace opencl
              (af_dtype)dtype_traits<T>::af_type),
         data(tmp.data, bufferFree),
         data_dims(af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {
-        this->genBufferNode();
     }
 
     template<typename T>
@@ -121,14 +115,13 @@ namespace opencl
              (new cl::Buffer((cl_mem)in_data)) :
              (bufferAlloc(info.total() * sizeof(T))), bufferFree),
         data_dims(dims),
-        node(),
+        node(bufferNodePtr<T>()),
         ready(true),
         owner(true)
     {
         if (!is_device) {
             getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T) * info.total(), in_data);
         }
-        this->genBufferNode();
     }
 
     template<typename T>
@@ -152,7 +145,7 @@ namespace opencl
         node->resetFlags();
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
         node.reset();
-        this->genBufferNode();
+        node = bufferNodePtr<T>();
     }
 
     template<typename T>
@@ -195,7 +188,7 @@ namespace opencl
             array->node->resetFlags();
             // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
             array->node.reset();
-            array->genBufferNode();
+            array->node = bufferNodePtr<T>();
         }
     }
 
@@ -216,11 +209,13 @@ namespace opencl
         return node;
     }
 
-
     template<typename T>
     Node_ptr Array<T>::getNode() const
     {
-        return const_cast<Array<T> *>(this)->getNode();
+        if (node->isBuffer()) {
+            return const_cast<Array<T> *>(this)->getNode();
+        }
+        return node;
     }
 
     using af::dim4;
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 9490c130a8..15586893a3 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -114,8 +114,6 @@ namespace opencl
         explicit Array(af::dim4 dims, const T * const in_data);
         explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
 
-        void genBufferNode() const;
-
     public:
 
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,

From bccac45bb4c046896cd252c03a084abfbc67e4d1 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 16:39:23 -0400
Subject: [PATCH 0636/2677] Fixes to reuse buffer nodes for CUDA backend

---
 src/backend/cuda/Array.cpp          | 38 ++++++++++++++++++++---------
 src/backend/cuda/Array.hpp          |  1 +
 src/backend/cuda/JIT/BufferNode.hpp | 26 +++++++++++---------
 src/backend/cuda/JIT/Node.hpp       |  2 ++
 4 files changed, 43 insertions(+), 24 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 2b8cbdc6f7..8016b58203 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -28,11 +28,18 @@ namespace cuda
     using JIT::Node;
     using JIT::Node_ptr;
 
+    template<typename T>
+    Node_ptr bufferNodePtr()
+    {
+        Node_ptr node(reinterpret_cast<Node *>(new BufferNode<T>(irname<T>(), afShortName<T>())));
+        return node;
+    }
+
     template<typename T>
     Array<T>::Array(af::dim4 dims) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {}
 
     template<typename T>
@@ -40,7 +47,7 @@ namespace cuda
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(((is_device & !copy_device) ? (T *)in_data : memAlloc<T>(dims.elements())), memFree<T>),
         data_dims(dims),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {
 #if __cplusplus > 199711L
         static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
@@ -61,7 +68,7 @@ namespace cuda
     Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &strides) :
         info(parent.getDevId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(parent.getData()), data_dims(parent.getDataDims()),
-        node(),
+        node(bufferNodePtr<T>()),
         ready(true), owner(false)
     { }
 
@@ -74,7 +81,7 @@ namespace cuda
              (af_dtype)dtype_traits<T>::af_type),
         data(tmp.ptr, memFree<T>),
         data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3])),
-        node(), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(true)
     {
     }
 
@@ -92,7 +99,7 @@ namespace cuda
         info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(is_device ? (T*)in_data : memAlloc<T>(info.total()), memFree<T>),
         data_dims(dims),
-        node(),
+        node(bufferNodePtr<T>()),
         ready(true),
         owner(true)
     {
@@ -128,6 +135,7 @@ namespace cuda
         prev->resetFlags();
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
         node.reset();
+        node = bufferNodePtr<T>();
     }
 
     template<typename T>
@@ -152,17 +160,23 @@ namespace cuda
     Array<T>::~Array() {}
 
     template<typename T>
-    Node_ptr Array<T>::getNode() const
+    Node_ptr Array<T>::getNode()
     {
-        if (!node) {
-            bool is_linear = isLinear();
+        if (node->isBuffer()) {
             unsigned bytes = this->getDataDims().elements() * sizeof(T);
-            BufferNode<T> *buf_node = new BufferNode<T>(irname<T>(),
-                                                        afShortName<T>(), data,
-                                                        *this, bytes, is_linear);
-            const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
+            BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
+            Param<T> param = *this;
+            bufNode->setData(param, data, bytes, isLinear());
         }
+        return node;
+    }
 
+    template<typename T>
+    Node_ptr Array<T>::getNode() const
+    {
+        if (node->isBuffer()) {
+            return const_cast<Array<T> *>(this)->getNode();
+        }
         return node;
     }
 
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 3b3d299d9f..72d5ea1fba 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -229,6 +229,7 @@ namespace cuda
             return out;
         }
 
+        JIT::Node_ptr getNode();
         JIT::Node_ptr getNode() const;
 
         friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 0570e78332..3f02214a11 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -30,27 +30,27 @@ namespace JIT
     {
     private:
         // Keep the shared pointer for reference counting
-        shared_ptr<T> sptr;
-        CParam<T> m_param;
+        shared_ptr<T> m_data;
+        Param<T> m_param;
         unsigned m_bytes;
 
         bool m_linear_buffer;
     public:
 
         BufferNode(const char *type_str,
-                   const char *name_str,
-                   shared_ptr<T> data,
-                   CParam<T> param,
-                   unsigned bytes,
-                   bool is_linear)
-            : Node(type_str, name_str),
-              sptr(data),
-              m_param(param),
-              m_bytes(bytes),
-              m_linear_buffer(is_linear)
+                   const char *name_str)
+            : Node(type_str, name_str)
         {
         }
 
+        void setData(Param<T> param, shared_ptr<T> data, const unsigned bytes, bool is_linear)
+        {
+            m_param = param;
+            m_data = data;
+            m_bytes = bytes;
+            m_linear_buffer = is_linear;
+        }
+
         bool isLinear(dim_t dims[4])
         {
             if (!m_set_is_linear) {
@@ -64,6 +64,8 @@ namespace JIT
             return m_linear;
         }
 
+        bool isBuffer() { return true; }
+
         void genKerName(std::stringstream &kerStream)
         {
             if (m_gen_name) return;
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 971e605085..4ceac273b3 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -92,6 +92,8 @@ namespace JIT
             bytes = 0;
         }
 
+        virtual bool isBuffer() { return false; }
+
         std::string getTypeStr() { return m_type_str; }
 
         bool isGenFunc() { return m_gen_func; }

From 6ff379f7afff627929e964f68138d01e66016f19 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 16 Jun 2016 16:16:28 -0400
Subject: [PATCH 0637/2677] additional tests for AF_MOMENT_FIRST_ORDER

---
 test/moments.cpp | 55 ++++++++++++++++++++++++++++++------------------
 1 file changed, 35 insertions(+), 20 deletions(-)

diff --git a/test/moments.cpp b/test/moments.cpp
index c63be2e3f3..83286aada1 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -50,32 +50,39 @@ void momentsTest(string pTestFile)
     af::array imgArray(numDims.front(), &in.front()[0]);
 
     af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
-    T *mData = momentsArray.host<T>();
+    vector<T> mData(momentsArray.elements());
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
         ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
 
     momentsArray = af::moments(imgArray, AF_MOMENT_M01);
-    mData = momentsArray.host<T>();
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
         ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
 
     momentsArray = af::moments(imgArray, AF_MOMENT_M10);
-    mData = momentsArray.host<T>();
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
         ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
 
     momentsArray = af::moments(imgArray, AF_MOMENT_M11);
-    mData = momentsArray.host<T>();
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
         ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_FIRST_ORDER);
+    mData.resize(momentsArray.elements());
+    momentsArray.host(&mData[0]);
+    for(int i=0; i<momentsArray.elements()/4;i+=4) {
+        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-3 * tests[0][i] ) << "at: " << i   << std::endl;
+        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-3 * tests[1][i] ) << "at: " << i+1 << std::endl;
+        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-3 * tests[2][i] ) << "at: " << i+2 << std::endl;
+        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-3 * tests[3][i] ) << "at: " << i+3 << std::endl;
+    }
 }
 
 void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
@@ -90,36 +97,44 @@ void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
 
     double maxVal = af::max<double>(imgArray);
     double minVal = af::min<double>(imgArray);
+    imgArray -= minVal;
     imgArray /= maxVal - minVal;
 
     af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
 
-    float *mData = momentsArray.host<float>();
+    vector<float> mData(momentsArray.elements());
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[0][i], mData[i], 1e-2 * tests[0][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
 
     momentsArray = af::moments(imgArray, AF_MOMENT_M01);
-    mData = momentsArray.host<float>();
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[1][i], mData[i], 1e-2 * tests[1][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
 
     momentsArray = af::moments(imgArray, AF_MOMENT_M10);
-    mData = momentsArray.host<float>();
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[2][i], mData[i], 1e-2 * tests[2][i] ) << "at: " << i << std::endl;
     }
-    delete [] mData;
 
     momentsArray = af::moments(imgArray, AF_MOMENT_M11);
-    mData = momentsArray.host<float>();
+    momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[3][i], mData[i], 1e-2 * tests[3][i] ) << "at: " << i << std::endl;
+    }
+
+    momentsArray = af::moments(imgArray, AF_MOMENT_FIRST_ORDER);
+    mData.resize(momentsArray.elements());
+    momentsArray.host(&mData[0]);
+    for(int i=0; i<momentsArray.elements()/4;i+=4) {
+        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-2 * tests[0][i] ) << "at: " << i   << std::endl;
+        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-2 * tests[1][i] ) << "at: " << i+1 << std::endl;
+        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-2 * tests[2][i] ) << "at: " << i+2 << std::endl;
+        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-2 * tests[3][i] ) << "at: " << i+3 << std::endl;
     }
-    delete [] mData;
 }
 
 TEST(IMAGE, MomentsImage)

From 7dd2a6d33754babf936885d8538e376368e04856 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 18:27:44 -0400
Subject: [PATCH 0638/2677] Adding multi_eval functionality for CUDA backend

---
 src/backend/cuda/Array.cpp |  43 +++-
 src/backend/cuda/Array.hpp |   3 +
 src/backend/cuda/jit.cpp   | 419 +++++++++++++++++++++----------------
 3 files changed, 282 insertions(+), 183 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 8016b58203..1945df0c04 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -130,9 +130,7 @@ namespace cuda
 
         evalNodes(res, this->getNode().get());
         ready = true;
-
-        Node_ptr prev = node;
-        prev->resetFlags();
+        node->resetFlags();
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
         node.reset();
         node = bufferNodePtr<T>();
@@ -148,10 +146,43 @@ namespace cuda
     template<typename T>
     void evalMultiple(std::vector<Array<T>*> arrays)
     {
-        //FIXME: implement this correctly
-        //Using fallback for now
+        std::vector<Param<T> > outputs;
+        std::vector<JIT::Node *> nodes;
+
+        for (int i = 0; i < (int)arrays.size(); i++) {
+            Array<T> *array = arrays[i];
+
+            if (array->isReady()) {
+                continue;
+            }
+
+            array->setId(getActiveDeviceId());
+            array->data = shared_ptr<T>(memAlloc<T>(array->elements()),
+                                        memFree<T>);
+
+            Param<T> res;
+            res.ptr = array->data.get();
+
+            for (int  i = 0; i < 4; i++) {
+                res.dims[i] = array->dims()[i];
+                res.strides[i] = array->strides()[i];
+            }
+
+            outputs.push_back(res);
+            nodes.push_back(array->node.get());
+        }
+
+        evalNodes(outputs, nodes);
+
         for (int i = 0; i < (int)arrays.size(); i++) {
-            arrays[i]->eval();
+            Array<T> *array = arrays[i];
+
+            if (array->isReady()) continue;
+            array->ready = true;
+            array->node->resetFlags();
+            // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
+            array->node.reset();
+            array->node = bufferNodePtr<T>();
         }
         return;
     }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 72d5ea1fba..eebbaf8434 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -39,6 +39,9 @@ namespace cuda
     template<typename T>
     void evalNodes(Param<T> &out, JIT::Node *node);
 
+    template<typename T>
+    void evalNodes(std::vector<Param<T> > &out, std::vector<JIT::Node *> nodes);
+
     template<typename T>
     void evalMultiple(std::vector<Array<T> *> arrays);
 
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index af5f2d6b68..b51aca8bba 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -47,19 +47,23 @@ const char *layout32 = "target datalayout = \"e-p:32:32:32-i1:8:8-i8:8:8-i16:16:
 const char *triple64 = "target triple = \"nvptx64-unknown-cuda\"\n\n";
 const char *triple32 = "target triple = \"nvptx-unknown-cuda\"\n\n";
 
-static string getFuncName(Node *node, bool is_linear)
+static string getFuncName(std::vector<Node *> nodes, bool is_linear)
 {
-    node->setId(0);
-
     stringstream funcName;
     stringstream hashName;
 
     if (is_linear) funcName << "L_"; //Kernel Linear
     else           funcName << "G_"; //Kernel General
 
-    funcName << node->getNameStr();
-    node->genKerName(funcName);
-    funcName.str();
+    int id = 0;
+
+    for (int i = 0; i < (int)nodes.size(); i++) {
+        funcName << "[";
+        id = nodes[i]->setId(id);
+        funcName << nodes[i]->getNameStr();
+        nodes[i]->genKerName(funcName);
+        funcName << "]";
+    }
 
     boost::hash<std::string> hash_fn;
 
@@ -68,13 +72,151 @@ static string getFuncName(Node *node, bool is_linear)
     return hashName.str();
 }
 
-static string getKernelString(string funcName, Node *node, bool is_linear)
+static string getKernelString(string funcName, std::vector<Node *> nodes, bool is_linear)
 {
+    static const char *defineVoid = "define void ";
+    static const char *dimParams = "\n"
+        "i32 %ostr0, i32 %ostr1, i32 %ostr2, i32 %ostr3,\n"
+        "i32 %odim0, i32 %odim1, i32 %odim2, i32 %odim3,\n"
+        "i32 %blkx, i32 %blky, i32 %ndims";
+
+    static const char *blockStart = "\n{\n\n"
+        "entry:\n\n";
+    static const char *blockEnd = "\n\n"
+        "ret void\n"
+        "\n\n}\n";
+
+    static const char *idAlias = "\n"
+        "%tidx = call i32 @llvm.nvvm.read.ptx.sreg.tid.x()\n"
+        "%bdmx = call i32 @llvm.nvvm.read.ptx.sreg.ntid.x()\n"
+        "%bidx = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()\n"
+        "%bidy = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.y()\n"
+        "%gdmx = call i32 @llvm.nvvm.read.ptx.sreg.nctaid.x()\n"
+        "\n\n";
+    static const char *earlyExit = "\n"
+        "end:\n\n"
+        "ret void\n";
+    static const char *core = "\n"
+        "core:\n\n";
+
+    static const char *generalIndex = "\n"
+        "%tidy = call i32 @llvm.nvvm.read.ptx.sreg.tid.y()\n"
+        "%bdmy = call i32 @llvm.nvvm.read.ptx.sreg.ntid.y()\n"
+        "%blk_x = alloca i32, align 4\n"
+        "%blk_y = alloca i32, align 4\n"
+        "%id_3 = alloca i32, align 4\n"
+        "%id_2 = alloca i32, align 4\n"
+        "store i32 %bidx, i32* %blk_x, align 4\n"
+        "store i32 %bidy, i32* %blk_y, align 4\n"
+        "store i32 0, i32* %id_2, align 4\n"
+        "store i32 0, i32* %id_3, align 4\n"
+        "%two = alloca i32, align 4\n"
+        "store i32 2, i32* %two, align 4\n"
+        "%twoval = load i32* %two, align 4\n"
+        "%is34 = icmp sgt i32 %ndims, %twoval\n"
+        "br i1 %is34, label %do34, label %do2\n"
+        "\ndo34:\n"
+        "%id2t = sdiv i32 %bidx, %blkx\n"
+        "store i32 %id2t, i32* %id_2, align 4\n"
+        "%id2m = mul i32 %id2t, %blkx\n"
+        "%blk_xx = sub i32 %bidx, %id2m\n"
+        "store i32 %blk_xx, i32* %blk_x, align 4\n"
+        "%three = alloca i32, align 4\n"
+        "store i32 3, i32* %three, align 4\n"
+        "%threeval = load i32* %three, align 4\n"
+        "%is4 = icmp sgt i32 %ndims, %threeval\n"
+        "br i1 %is4, label %do4, label %do2\n"
+        "\ndo4:\n"
+        "%id3t = sdiv i32 %bidy, %blky\n"
+        "store i32 %id3t, i32* %id_3, align 4\n"
+        "%id3m = mul i32 %id3t, %blky\n"
+        "%blk_yy = sub i32 %bidy, %id3m\n"
+        "store i32 %blk_yy, i32* %blk_y, align 4\n"
+        "br label %do2\n"
+        "\ndo2:\n"
+        "%id2 = load i32* %id_2, align 4\n"
+        "%id3 = load i32* %id_3, align 4\n"
+        "%tmp_x = load i32* %blk_x, align 4\n"
+        "%id0m = mul i32 %tmp_x, %bdmx\n"
+        "%id0 = add i32 %tidx, %id0m\n"
+        "%tmp_y = load i32* %blk_y, align 4\n"
+        "%id1m = mul i32 %tmp_y, %bdmy\n"
+        "%id1 = add i32 %tidy, %id1m\n"
+        "\n\n"
+        "%off3o = mul i32 %id3, %ostr3\n"
+        "%off2o = mul i32 %id2, %ostr2\n"
+        "%off1o = mul i32 %id1, %ostr1\n"
+        "%off23o = add i32 %off3o, %off2o\n"
+        "%off123o = add i32 %off23o, %off1o\n"
+        "%idxa = add i32 %off123o, %id0\n"
+        "%idx = sext i32 %idxa to i64\n"
+        "\n\n"
+        "%cmp3 = icmp slt i32 %id3, %odim3\n"
+        "%cmp2 = icmp slt i32 %id2, %odim2\n"
+        "%cmp1 = icmp slt i32 %id1, %odim1\n"
+        "%cmp0 = icmp slt i32 %id0, %odim0\n"
+        "br i1 %cmp3, label %check2, label %end\n"
+        "\ncheck2:\n"
+        "br i1 %cmp2, label %check1, label %end\n"
+        "\ncheck1:\n"
+        "br i1 %cmp1, label %check0, label %end\n"
+        "\ncheck0:\n"
+        "br i1 %cmp0, label %core, label %end\n";
+
+    static const char *linearIndex = "\n"
+        "%boff = mul i32 %bidy, %gdmx\n"
+        "%bid  = add i32 %boff, %bidx\n"
+        "%goff = mul i32 %bid , %bdmx\n"
+        "%gid  = add i32 %goff ,%tidx\n"
+        "%idx  = sext i32 %gid to i64\n"
+        "%el1  = mul i32 %odim0, %odim1\n"
+        "%el2  = mul i32 %el1  , %odim2\n"
+        "%el3  = mul i32 %el2  , %odim3\n"
+        "%cmp0 = icmp slt i32 %gid, %el3\n"
+        "br i1 %cmp0, label %core, label %end\n";
+
+    static const char *functionLoad = "\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() nounwind readnone\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.tid.y() nounwind readnone\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x() nounwind readnone\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.ntid.y() nounwind readnone\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x() nounwind readnone\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.y() nounwind readnone\n"
+        "declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.x() nounwind readnone\n"
+        "\n";
+
+
     stringstream kerStream;
-    stringstream annStream;
+    stringstream inAnnStream;
+    stringstream outAnnStream;
+    stringstream inParamStream;
+    stringstream outParamStream;
+    stringstream funcBodyStream;
+    stringstream offsetsStream;
+    stringstream outWriteStream;
     str_map_t declStrs;
 
-    int id = node->getId();
+    for (int i = 0; i < (int)nodes.size(); i++) {
+        std::string outTypeStr = nodes[i]->getTypeStr();
+        int id = nodes[i]->getId();
+
+        nodes[i]->genParams(inParamStream, inAnnStream, is_linear);
+        outParamStream << outTypeStr << "* %out" << id << ",\n";
+        nodes[i]->genOffsets(offsetsStream, is_linear);
+        nodes[i]->genFuncs(funcBodyStream, declStrs, is_linear);
+
+        outWriteStream << "%outIdx" << id
+                       << "= getelementptr inbounds "
+                       << outTypeStr
+                       << "* %out" << id
+                       << ", i64 %idx\n";
+        outWriteStream << "store "
+                       << outTypeStr
+                       << " %val" << id << ", "
+                       << outTypeStr
+                       << "* %outIdx" << id << "\n";
+        outAnnStream << outTypeStr << "*,\n";
+    }
 
     if (sizeof(void *) == 8) {
         kerStream << layout64;
@@ -84,152 +226,34 @@ static string getKernelString(string funcName, Node *node, bool is_linear)
         kerStream << triple32;
     }
 
-    kerStream << "define void " << funcName << " (" << std::endl;
-    node->genParams(kerStream, annStream, is_linear);
-    kerStream << node->getTypeStr() <<"* %out,\n"
-              << "i32 %ostr0, i32 %ostr1, i32 %ostr2, i32 %ostr3,\n"
-              << "i32 %odim0, i32 %odim1, i32 %odim2, i32 %odim3,\n"
-              << "i32 %blkx, i32 %blky, i32 %ndims) {"
-              << "\n\n";
-
-    kerStream << "entry:\n\n";
-    kerStream << "%tidx = call i32 @llvm.nvvm.read.ptx.sreg.tid.x()\n";
-    kerStream << "%bdmx = call i32 @llvm.nvvm.read.ptx.sreg.ntid.x()\n";
-    kerStream << "%bidx = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()\n";
-    kerStream << "%bidy = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.y()\n";
-    kerStream << "%gdmx = call i32 @llvm.nvvm.read.ptx.sreg.nctaid.x()\n";
-    kerStream << "\n\n";
-
-    if (!is_linear) {
-
-        kerStream << "%tidy = call i32 @llvm.nvvm.read.ptx.sreg.tid.y()\n";
-        kerStream << "%bdmy = call i32 @llvm.nvvm.read.ptx.sreg.ntid.y()\n";
-
-        kerStream << "%blk_x = alloca i32, align 4\n";
-        kerStream << "%blk_y = alloca i32, align 4\n";
-        kerStream << "%id_3 = alloca i32, align 4\n";
-        kerStream << "%id_2 = alloca i32, align 4\n";
-        kerStream << "store i32 %bidx, i32* %blk_x, align 4\n";
-        kerStream << "store i32 %bidy, i32* %blk_y, align 4\n";
-        kerStream << "store i32 0, i32* %id_2, align 4\n";
-        kerStream << "store i32 0, i32* %id_3, align 4\n";
-
-        kerStream << "%two = alloca i32, align 4\n";
-        kerStream << "store i32 2, i32* %two, align 4\n";
-        kerStream << "%twoval = load i32* %two, align 4\n";
-        kerStream << "%is34 = icmp sgt i32 %ndims, %twoval\n";
-        kerStream << "br i1 %is34, label %do34, label %do2\n";
-
-        kerStream << "\ndo34:\n";
-
-        kerStream << "%id2t = sdiv i32 %bidx, %blkx\n";
-        kerStream << "store i32 %id2t, i32* %id_2, align 4\n";
-        kerStream << "%id2m = mul i32 %id2t, %blkx\n";
-        kerStream << "%blk_xx = sub i32 %bidx, %id2m\n";
-        kerStream << "store i32 %blk_xx, i32* %blk_x, align 4\n";
-
-        kerStream << "%three = alloca i32, align 4\n";
-        kerStream << "store i32 3, i32* %three, align 4\n";
-        kerStream << "%threeval = load i32* %three, align 4\n";
-        kerStream << "%is4 = icmp sgt i32 %ndims, %threeval\n";
-        kerStream << "br i1 %is4, label %do4, label %do2\n";
-
-        kerStream << "\ndo4:\n";
-        kerStream << "%id3t = sdiv i32 %bidy, %blky\n";
-        kerStream << "store i32 %id3t, i32* %id_3, align 4\n";
-        kerStream << "%id3m = mul i32 %id3t, %blky\n";
-        kerStream << "%blk_yy = sub i32 %bidy, %id3m\n";
-        kerStream << "store i32 %blk_yy, i32* %blk_y, align 4\n";
-        kerStream << "br label %do2\n";
-
-        kerStream << "\ndo2:\n";
-        kerStream << "%id2 = load i32* %id_2, align 4\n";
-        kerStream << "%id3 = load i32* %id_3, align 4\n";
-
-        kerStream << "%tmp_x = load i32* %blk_x, align 4\n";
-        kerStream << "%id0m = mul i32 %tmp_x, %bdmx\n";
-        kerStream << "%id0 = add i32 %tidx, %id0m\n";
-
-        kerStream << "%tmp_y = load i32* %blk_y, align 4\n";
-        kerStream << "%id1m = mul i32 %tmp_y, %bdmy\n";
-        kerStream << "%id1 = add i32 %tidy, %id1m\n";
-        kerStream << "\n\n";
-
-        kerStream << "%off3o = mul i32 %id3, %ostr3\n";
-        kerStream << "%off2o = mul i32 %id2, %ostr2\n";
-        kerStream << "%off1o = mul i32 %id1, %ostr1\n";
-        kerStream << "%off23o = add i32 %off3o, %off2o\n";
-        kerStream << "%off123o = add i32 %off23o, %off1o\n";
-        kerStream << "%idxa = add i32 %off123o, %id0\n";
-        kerStream << "%idx = sext i32 %idxa to i64\n";
-        kerStream << "\n\n";
-
-        kerStream << "%cmp3 = icmp slt i32 %id3, %odim3\n";
-        kerStream << "%cmp2 = icmp slt i32 %id2, %odim2\n";
-        kerStream << "%cmp1 = icmp slt i32 %id1, %odim1\n";
-        kerStream << "%cmp0 = icmp slt i32 %id0, %odim0\n";
-
-        kerStream << "br i1 %cmp3, label %check2, label %end\n";
-        kerStream << "\ncheck2:\n";
-        kerStream << "br i1 %cmp2, label %check1, label %end\n";
-        kerStream << "\ncheck1:\n";
-        kerStream << "br i1 %cmp1, label %check0, label %end\n";
-        kerStream << "\ncheck0:\n";
-        kerStream << "br i1 %cmp0, label %core, label %end\n";
-
-    } else {
-
-        kerStream << "%boff = mul i32 %bidy, %gdmx\n";
-        kerStream << "%bid  = add i32 %boff, %bidx\n";
-        kerStream << "%goff = mul i32 %bid , %bdmx\n";
-        kerStream << "%gid  = add i32 %goff ,%tidx\n";
-        kerStream << "%idx  = sext i32 %gid to i64\n";
-        kerStream << "%el1  = mul i32 %odim0, %odim1\n";
-        kerStream << "%el2  = mul i32 %el1  , %odim2\n";
-        kerStream << "%el3  = mul i32 %el2  , %odim3\n";
-        kerStream << "%cmp0 = icmp slt i32 %gid, %el3\n";
-        kerStream << "br i1 %cmp0, label %core, label %end\n";
-    }
-
-    kerStream << "\n";
-    kerStream << "end:\n\n";
-    kerStream << "ret void\n";
-
-    kerStream <<"\n";
-    kerStream << "core:\n\n";
-    node->genOffsets(kerStream, is_linear);
-
-    node->genFuncs(kerStream, declStrs, is_linear);
-
-    kerStream << "%outIdx = getelementptr inbounds " << node->getTypeStr() << "* %out, i64 %idx\n";
-    kerStream << "store "
-              << node->getTypeStr()
-              << " %val" << id << ", "
-              << node->getTypeStr()
-              << "* %outIdx\n";
-
-    kerStream << "\nret void\n";
-    kerStream << "\n}\n\n";
+    const char *index = is_linear ? linearIndex : generalIndex;
+
+    kerStream << defineVoid
+              << funcName
+              << " (\n"
+              << inParamStream.str()
+              << outParamStream.str()
+              << dimParams
+              << " )\n"
+              << blockStart
+              << idAlias
+              << index
+              << earlyExit
+              << core
+              << offsetsStream.str()
+              << funcBodyStream.str()
+              << outWriteStream.str()
+              << blockEnd;
 
     for(str_map_iter iterator = declStrs.begin(); iterator != declStrs.end(); iterator++) {
         kerStream << iterator->first << "\n";
     }
-
-    kerStream
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() nounwind readnone\n"
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.tid.y() nounwind readnone\n"
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x() nounwind readnone\n"
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.ntid.y() nounwind readnone\n"
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x() nounwind readnone\n"
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.y() nounwind readnone\n"
-        << "declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.x() nounwind readnone\n";
-
-    kerStream << "\n";
+    kerStream << functionLoad;
 
     kerStream << "!nvvm.annotations = !{!1}\n"
               << "!1 = metadata !{void (\n"
-              << annStream.str()
-              << node->getTypeStr() << "*,\n"
+              << inAnnStream.str()
+              << outAnnStream.str()
               << "i32, i32, i32, i32,\n"
               << "i32, i32, i32, i32,\n"
               << "i32, i32, i32\n"
@@ -388,10 +412,10 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
     return entry;
 }
 
-static CUfunction getKernel(Node *node, bool is_linear)
+static CUfunction getKernel(std::vector<Node *> nodes, bool is_linear)
 {
 
-    string funcName = getFuncName(node, is_linear);
+    string funcName = getFuncName(nodes, is_linear);
 
     typedef std::map<string, kc_entry_t> kc_t;
     static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
@@ -401,7 +425,7 @@ static CUfunction getKernel(Node *node, bool is_linear)
     kc_entry_t entry = {NULL, NULL};
 
     if (idx == kernelCaches[device].end()) {
-        string jit_ker = getKernelString(funcName, node, is_linear);
+        string jit_ker = getKernelString(funcName, nodes, is_linear);
         entry = compileKernel(funcName.c_str(), jit_ker);
         kernelCaches[device][funcName] = entry;
     } else {
@@ -412,27 +436,19 @@ static CUfunction getKernel(Node *node, bool is_linear)
 }
 
 template<typename T>
-void evalNodes(Param<T> &out, Node *node)
+void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
 {
-    bool is_linear = node->isLinear(out.dims);
-    CUfunction ker = getKernel(node, is_linear);
-    vector<void *> args;
-    node->setArgs(args, is_linear);
+    int num_outputs = (int)outputs.size();
 
-    void *ptr = (void *)out.ptr;
-    int strides[] = {(int)out.strides[0],
-                     (int)out.strides[1],
-                     (int)out.strides[2],
-                     (int)out.strides[3]};
+    if (num_outputs == 0) return;
 
-    int dims[] = {(int)out.dims[0],
-                  (int)out.dims[1],
-                  (int)out.dims[2],
-                  (int)out.dims[3]};
+    bool is_linear = true;
 
-    args.push_back((void *)&ptr);
-    for (int i = 0; i < 4; i++) args.push_back((void *)(strides + i));
-    for (int i = 0; i < 4; i++) args.push_back((void *)(dims + i));
+    for (int i = 0; i < num_outputs; i++) {
+        is_linear &= nodes[i]->isLinear(outputs[0].dims);
+    }
+
+    CUfunction ker = getKernel(nodes, is_linear);
 
     int threads_x = 1, threads_y = 1;
     int blocks_x_ = 1, blocks_y_ = 1;
@@ -441,7 +457,7 @@ void evalNodes(Param<T> &out, Node *node)
     int num_odims = 4;
 
     while (num_odims >= 1) {
-        if (out.dims[num_odims - 1] == 1) num_odims--;
+        if (outputs[0].dims[num_odims - 1] == 1) num_odims--;
         else break;
     }
 
@@ -450,10 +466,10 @@ void evalNodes(Param<T> &out, Node *node)
         threads_x = 256;
         threads_y =  1;
 
-        int blocks = divup((out.dims[0] *
-                            out.dims[1] *
-                            out.dims[2] *
-                            out.dims[3]), threads_x);
+        int blocks = divup((outputs[0].dims[0] *
+                            outputs[0].dims[1] *
+                            outputs[0].dims[2] *
+                            outputs[0].dims[3]), threads_x);
 
         blocks_y_ = divup(blocks, 65535);
         blocks_x_ = divup(blocks, blocks_y_);
@@ -466,13 +482,36 @@ void evalNodes(Param<T> &out, Node *node)
         threads_x = 32;
         threads_y =  8;
 
-        blocks_x_ = divup(out.dims[0], threads_x);
-        blocks_y_ = divup(out.dims[1], threads_y);
+        blocks_x_ = divup(outputs[0].dims[0], threads_x);
+        blocks_y_ = divup(outputs[0].dims[1], threads_y);
+
+        blocks_x = blocks_x_ * outputs[0].dims[2];
+        blocks_y = blocks_y_ * outputs[0].dims[3];
+    }
+
+    vector<void *> args;
 
-        blocks_x = blocks_x_ * out.dims[2];
-        blocks_y = blocks_y_ * out.dims[3];
+    for (int i = 0; i < num_outputs; i++) {
+        nodes[i]->setArgs(args, is_linear);
     }
 
+    int strides[] = {(int)outputs[0].strides[0],
+                     (int)outputs[0].strides[1],
+                     (int)outputs[0].strides[2],
+                     (int)outputs[0].strides[3]};
+
+    int dims[] = {(int)outputs[0].dims[0],
+                  (int)outputs[0].dims[1],
+                  (int)outputs[0].dims[2],
+                  (int)outputs[0].dims[3]};
+
+    for (int i = 0; i < num_outputs; i++) {
+        args.push_back(&outputs[i].ptr);
+    }
+
+    for (int i = 0; i < 4; i++) args.push_back((void *)(strides + i));
+    for (int i = 0; i < 4; i++) args.push_back((void *)(dims + i));
+
     args.push_back((void *)&blocks_x_);
     args.push_back((void *)&blocks_y_);
     args.push_back((void *)&num_odims);
@@ -490,6 +529,18 @@ void evalNodes(Param<T> &out, Node *node)
                             NULL));
 }
 
+template<typename T>
+void evalNodes(Param<T> &out, Node *node)
+{
+    std::vector<Param<T> > outputs;
+    std::vector<Node *> nodes;
+
+    outputs.push_back(out);
+    nodes.push_back(node);
+    evalNodes(outputs, nodes);
+    return;
+}
+
 template void evalNodes<float  >(Param<float  > &out, Node *node);
 template void evalNodes<double >(Param<double > &out, Node *node);
 template void evalNodes<cfloat >(Param<cfloat > &out, Node *node);
@@ -503,5 +554,19 @@ template void evalNodes<uintl  >(Param<uintl  > &out, Node *node);
 template void evalNodes<short  >(Param<short  > &out, Node *node);
 template void evalNodes<ushort >(Param<ushort > &out, Node *node);
 
+template void evalNodes<float  >(std::vector<Param<float  > > &out, std::vector<Node *> node);
+template void evalNodes<double >(std::vector<Param<double > > &out, std::vector<Node *> node);
+template void evalNodes<cfloat >(std::vector<Param<cfloat > > &out, std::vector<Node *> node);
+template void evalNodes<cdouble>(std::vector<Param<cdouble> > &out, std::vector<Node *> node);
+template void evalNodes<int    >(std::vector<Param<int    > > &out, std::vector<Node *> node);
+template void evalNodes<uint   >(std::vector<Param<uint   > > &out, std::vector<Node *> node);
+template void evalNodes<char   >(std::vector<Param<char   > > &out, std::vector<Node *> node);
+template void evalNodes<uchar  >(std::vector<Param<uchar  > > &out, std::vector<Node *> node);
+template void evalNodes<intl   >(std::vector<Param<intl   > > &out, std::vector<Node *> node);
+template void evalNodes<uintl  >(std::vector<Param<uintl  > > &out, std::vector<Node *> node);
+template void evalNodes<short  >(std::vector<Param<short  > > &out, std::vector<Node *> node);
+template void evalNodes<ushort >(std::vector<Param<ushort > > &out, std::vector<Node *> node);
+
+
 
 }

From 3486817e66d063d52363ce347bb29685a16b9d89 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 20:11:08 -0400
Subject: [PATCH 0639/2677] TEST: Adding tests for multi_eval

---
 test/jit.cpp | 125 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 125 insertions(+)

diff --git a/test/jit.cpp b/test/jit.cpp
index a20b0f4b19..35e5ff61a4 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -115,3 +115,128 @@ TEST(JIT, CPP_JIT_Reset_Unary)
         ASSERT_EQ(hf[i], -hg[i]);
     }
 }
+
+TEST(JIT, CPP_Multi_linear)
+{
+    using af::array;
+
+    const int num = 1 << 16;
+    af::array a = af::randu(num, s32);
+    af::array b = af::randu(num, s32);
+    af::array x = a + b;
+    af::array y = a - b;
+    af::eval(x, y);
+
+    std::vector<int> ha(num);
+    std::vector<int> hb(num);
+    std::vector<int> hx(num);
+    std::vector<int> hy(num);
+
+    a.host(&ha[0]);
+    b.host(&hb[0]);
+    x.host(&hx[0]);
+    y.host(&hy[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_EQ((ha[i] + hb[i]), hx[i]);
+        ASSERT_EQ((ha[i] - hb[i]), hy[i]);
+    }
+}
+
+TEST(JIT, CPP_strided)
+{
+    using af::array;
+
+    const int num = 1024;
+    af::gforSet(true);
+    af::array a = af::randu(num, 1, s32);
+    af::array b = af::randu(1, num, s32);
+    af::array x = a + b;
+    af::array y = a - b;
+    af::eval(x);
+    af::eval(y);
+    af::gforSet(false);
+
+    std::vector<int> ha(num);
+    std::vector<int> hb(num);
+    std::vector<int> hx(num * num);
+    std::vector<int> hy(num * num);
+
+    a.host(&ha[0]);
+    b.host(&hb[0]);
+    x.host(&hx[0]);
+    y.host(&hy[0]);
+
+    for (int j = 0; j < num; j++) {
+        for (int i = 0; i < num; i++) {
+            ASSERT_EQ((ha[i] + hb[j]), hx[j*num + i]);
+            ASSERT_EQ((ha[i] - hb[j]), hy[j*num + i]);
+        }
+    }
+}
+
+TEST(JIT, CPP_Multi_strided)
+{
+    using af::array;
+
+    const int num = 1024;
+    af::gforSet(true);
+    af::array a = af::randu(num, 1, s32);
+    af::array b = af::randu(1, num, s32);
+    af::array x = a + b;
+    af::array y = a - b;
+    af::eval(x, y);
+    af::gforSet(false);
+
+    std::vector<int> ha(num);
+    std::vector<int> hb(num);
+    std::vector<int> hx(num * num);
+    std::vector<int> hy(num * num);
+
+    a.host(&ha[0]);
+    b.host(&hb[0]);
+    x.host(&hx[0]);
+    y.host(&hy[0]);
+
+    for (int j = 0; j < num; j++) {
+        for (int i = 0; i < num; i++) {
+            ASSERT_EQ((ha[i] + hb[j]), hx[j*num + i]);
+            ASSERT_EQ((ha[i] - hb[j]), hy[j*num + i]);
+        }
+    }
+}
+
+TEST(JIT, CPP_Multi_pre_eval)
+{
+    using af::array;
+
+    const int num = 1 << 16;
+    af::array a = af::randu(num, s32);
+    af::array b = af::randu(num, s32);
+    af::array x = a + b;
+    af::array y = a - b;
+
+    af::eval(x);
+
+    // Should evaluate only y
+    af::eval(x, y);
+
+    // Should not evaluate anything
+    // Should not error out
+    af::eval(x, y);
+
+    std::vector<int> ha(num);
+    std::vector<int> hb(num);
+    std::vector<int> hx(num);
+    std::vector<int> hy(num);
+
+    a.host(&ha[0]);
+    b.host(&hb[0]);
+    x.host(&hx[0]);
+    y.host(&hy[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_EQ((ha[i] + hb[i]), hx[i]);
+        ASSERT_EQ((ha[i] - hb[i]), hy[i]);
+    }
+}

From 6f859859b87b2fdac1a30035d26cb5f6c5e586f8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 16 Jun 2016 20:11:45 -0400
Subject: [PATCH 0640/2677] Renaming functions to control manualEvalFalgs

---
 include/af/array.h         | 17 +++++++++++------
 src/api/c/device.cpp       |  8 ++++----
 src/api/cpp/array.cpp      |  8 ++++----
 src/api/unified/device.cpp |  4 ++--
 4 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index dc4e77cda5..87b16e35dc 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1250,8 +1250,13 @@ namespace af
     AFAPI void eval(array &a, array &b, array &c, array &d, array &e, array &f);
     AFAPI void eval(int num, array **arrays);
 
-    AFAPI void setInternalEvalFlag(bool flag);
-    AFAPI bool getInternalEvalFlag();
+    ///
+    /// Turn the manual eval flag on or off
+    ///
+    AFAPI void setManualEvalFlag(bool flag);
+
+    /// Get the manual eval flag
+    AFAPI bool getManualEvalFlag();
 
     /**
        @}
@@ -1360,18 +1365,18 @@ extern "C" {
     */
 
     /**
-       Manually set the internal eval flag
+       Turn the manual eval flag on or off
     */
-    AFAPI af_err af_set_internal_eval_flag(bool flag);
+    AFAPI af_err af_set_manual_eval_flag(bool flag);
     /**
       @}
     */
 
 
     /**
-       Get the current internal eval flag
+       Get the manual eval flag
     */
-    AFAPI af_err af_get_internal_eval_flag(bool *flag);
+    AFAPI af_err af_get_manual_eval_flag(bool *flag);
     /**
       @}
     */
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index ca45ef0ac3..a172c07354 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -245,21 +245,21 @@ af_err af_eval_multiple(int num, af_array *arrays)
     return AF_SUCCESS;
 }
 
-af_err af_set_internal_eval_flag(bool flag)
+af_err af_set_manual_eval_flag(bool flag)
 {
     try {
         bool& backendFlag = evalFlag();
-        backendFlag = flag;
+        backendFlag = !flag;
     } CATCHALL;
     return AF_SUCCESS;
 }
 
 
-af_err af_get_internal_eval_flag(bool *flag)
+af_err af_get_manual_eval_flag(bool *flag)
 {
     try {
         bool backendFlag = evalFlag();
-        *flag = backendFlag;
+        *flag = !backendFlag;
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 35739e5c8f..56ec5ff86c 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1086,15 +1086,15 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
         AF_THROW(af_eval_multiple(num, &outputs[0]));
     }
 
-    void setInternalEvalFlag(bool flag)
+    void setManualEvalFlag(bool flag)
     {
-        AF_THROW(af_set_internal_eval_flag(flag));
+        AF_THROW(af_set_manual_eval_flag(flag));
     }
 
-    bool getInternalEvalFlag()
+    bool getManualEvalFlag()
     {
         bool flag;
-        AF_THROW(af_get_internal_eval_flag(&flag));
+        AF_THROW(af_get_manual_eval_flag(&flag));
         return flag;
     }
 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 030a29fb51..157d80e9b0 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -194,13 +194,13 @@ af_err af_eval_multiple(const int num, af_array *arrays)
     return CALL(num, arrays);
 }
 
-af_err af_set_internal_eval_flag(bool flag)
+af_err af_set_manual_eval_flag(bool flag)
 {
     return CALL(flag);
 }
 
 
-af_err af_get_internal_eval_flag(bool *flag)
+af_err af_get_manual_eval_flag(bool *flag)
 {
     return CALL(flag);
 }

From 6e765ac3cced0189595c138de3b962736353a1fd Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 16 Jun 2016 21:11:22 -0400
Subject: [PATCH 0641/2677] update submodule tag

---
 test/data | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/data b/test/data
index 29c9ae2888..855b458f51 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 29c9ae28883eeed76d3adc1dad050f58f15a2fc4
+Subproject commit 855b458f511853b28987f632d4be0f174fa2feea

From 08dc227554d6bf0a8aaacd700a701f1b30ce967e Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 02:19:50 +0200
Subject: [PATCH 0642/2677] Prepare for adding more complex math functions

---
 src/api/c/unary.cpp | 88 ++++++++++++++++++++++++++-------------------
 1 file changed, 51 insertions(+), 37 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index a92df7b06d..cdc9a5bc2c 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -33,6 +33,9 @@ static inline af_array unaryOp(const af_array in)
     return res;
 }
 
+template<typename Tc, typename Tr, af_op_t op>
+struct unaryOpCplx;
+
 template<af_op_t op>
 static af_err af_unary(af_array *out, const af_array in)
 {
@@ -60,12 +63,44 @@ static af_err af_unary(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
+template<af_op_t op>
+static af_err af_unary_complex(af_array *out, const af_array in)
+{
+    try {
+        ArrayInfo in_info = getInfo(in);
+
+        af_dtype in_type = in_info.getType();
+        af_array res;
+
+        // Convert all inputs to floats / doubles
+        af_dtype type = implicit(in_type, f32);
+
+        switch (type) {
+        case f32 : res = unaryOp<float  , op>(in); break;
+        case f64 : res = unaryOp<double , op>(in); break;
+        case c32 : res = unaryOpCplx<cfloat , float , op>(in); break;
+        case c64 : res = unaryOpCplx<cdouble, double, op>(in); break;
+        default:
+            TYPE_ERROR(1, in_type); break;
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 #define UNARY(fn)                                       \
     af_err af_##fn(af_array *out, const af_array in)    \
     {                                                   \
         return af_unary<af_##fn##_t>(out, in);          \
     }
 
+#define UNARY_COMPLEX(fn)                               \
+    af_err af_##fn(af_array *out, const af_array in)    \
+    {                                                   \
+        return af_unary_complex<af_##fn##_t>(out, in);  \
+    }
 
 UNARY(sin)
 UNARY(cos)
@@ -105,50 +140,29 @@ UNARY(cbrt)
 UNARY(tgamma)
 UNARY(lgamma)
 
-template<typename Tc, typename Tr>
-af_array expCplx(const af_array a)
-{
-    Array<Tc> In = getArray<Tc>(a);
-    Array<Tr> Real = real<Tr, Tc>(In);
-    Array<Tr> Imag = imag<Tr, Tc>(In);
-
-    Array<Tr> ExpReal = unaryOp<Tr, af_exp_t>(Real);
-    Array<Tr> CosImag = unaryOp<Tr, af_cos_t>(Imag);
-    Array<Tr> SinImag = unaryOp<Tr, af_sin_t>(Imag);
-
-    Array<Tc> Unit  = cplx<Tc, Tr>(CosImag, SinImag, CosImag.dims());
-    Array<Tc> Scale = cast<Tc, Tr>(ExpReal);
-
-    Array<Tc> Result = arithOp<Tc, af_mul_t>(Scale, Unit, Scale.dims());
+UNARY_COMPLEX(exp)
 
-    return getHandle(Result);
-}
-
-af_err af_exp(af_array *out, const af_array in)
+template<typename Tc, typename Tr>
+struct unaryOpCplx<Tc, Tr, af_exp_t>
 {
-    try {
+    af_array operator()(const af_array a)
+    {
+        Array<Tc> In = getArray<Tc>(a);
+        Array<Tr> Real = real<Tr, Tc>(In);
+        Array<Tr> Imag = imag<Tr, Tc>(In);
 
-        ArrayInfo in_info = getInfo(in);
-        af_dtype in_type = in_info.getType();
-        af_array res;
+        Array<Tr> ExpReal = unaryOp<Tr, af_exp_t>(Real);
+        Array<Tr> CosImag = unaryOp<Tr, af_cos_t>(Imag);
+        Array<Tr> SinImag = unaryOp<Tr, af_sin_t>(Imag);
 
-        // Convert all inputs to floats / doubles
-        af_dtype type = implicit(in_type, f32);
+        Array<Tc> Unit  = cplx<Tc, Tr>(CosImag, SinImag, CosImag.dims());
+        Array<Tc> Scale = cast<Tc, Tr>(ExpReal);
 
-        switch (type) {
-        case f32 : res = unaryOp<float  , af_exp_t>(in); break;
-        case f64 : res = unaryOp<double , af_exp_t>(in); break;
-        case c32 : res = expCplx<cfloat , float >(in); break;
-        case c64 : res = expCplx<cdouble, double>(in); break;
-        default:
-            TYPE_ERROR(1, in_type); break;
-        }
+        Array<Tc> Result = arithOp<Tc, af_mul_t>(Scale, Unit, Scale.dims());
 
-        std::swap(*out, res);
+        return getHandle(Result);
     }
-    CATCHALL;
-    return AF_SUCCESS;
-}
+};
 
 af_err af_not(af_array *out, const af_array in)
 {

From 0cbfe70c1cd5bd4cb741e801ebc955e1629efb99 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 02:59:36 +0200
Subject: [PATCH 0643/2677] Add complex sqrt

---
 src/api/c/unary.cpp | 30 +++++++++++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index cdc9a5bc2c..c762ce1530 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -134,13 +134,13 @@ UNARY(log10)
 UNARY(log1p)
 UNARY(log2)
 
-UNARY(sqrt)
 UNARY(cbrt)
 
 UNARY(tgamma)
 UNARY(lgamma)
 
 UNARY_COMPLEX(exp)
+UNARY_COMPLEX(sqrt)
 
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_exp_t>
@@ -164,6 +164,34 @@ struct unaryOpCplx<Tc, Tr, af_exp_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplx<Tc, Tr, af_sqrt_t>
+{
+    af_array operator()(const af_array in)
+    {
+        // convert cartesian to polar
+        Array<Tc> z = getArray<Tc>(a);
+        Array<Tr> a = real<Tr, Tc>(In);
+        Array<Tr> b = imag<Tr, Tc>(In);
+        Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
+        Array<Tr> phi = abs<Tr>(z);
+
+        // compute sqrt
+        Array<Tr> two = createValueArray<Tr>(phi.dims(), 2.0);
+        Array<Tr> r_out = unaryOp<Tr, af_sqrt_t>(r);
+        Array<Tr> phi_out = arithOp<Tr, af_div_t>(phi, two, phi.dims());
+
+        // convert polar to cartesian
+        Array<Tr> a_out_unit = unaryOp<Tr, af_cos_t>(phi_out);
+        Array<Tr> b_out_unit = unaryOp<Tr, af_sin_t>(phi_out);
+        Array<Tr> a_out = arithOp<Tc, af_mul_t>(r_out, a_out_unit, SqrtAbs.dims());
+        Array<Tr> b_out = arithOp<Tc, af_mul_t>(r_out, b_out_unit, SqrtAbs.dims());
+        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
+
+        return getHandle(z_out);
+    }
+};
+
 af_err af_not(af_array *out, const af_array in)
 {
     try {

From 50600305a53d37436ed75fea3874b35b0c17e995 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 03:02:01 +0200
Subject: [PATCH 0644/2677] Add complex log

---
 src/api/c/unary.cpp | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index c762ce1530..53166ef4ae 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -164,6 +164,27 @@ struct unaryOpCplx<Tc, Tr, af_exp_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplx<Tc, Tr, af_log_t>
+{
+    af_array operator()(const af_array in)
+    {
+        // convert cartesian to polar
+        Array<Tc> z = getArray<Tc>(a);
+        Array<Tr> a = real<Tr, Tc>(In);
+        Array<Tr> b = imag<Tr, Tc>(In);
+        Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
+        Array<Tr> phi = abs<Tr>(z);
+
+        // compute log
+        Array<Tr> a_out = unaryOp<Tr, af_log_t>(r);
+        Array<Tr> b_out = phi;
+        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
+
+        return getHandle(z_out);
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_sqrt_t>
 {

From e9cc002098a8104774b6bb9e2bbb8fd8ed8c964e Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 04:08:35 +0200
Subject: [PATCH 0645/2677] Add complex sin

---
 src/api/c/unary.cpp | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 53166ef4ae..81f091699e 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -101,8 +101,7 @@ static af_err af_unary_complex(af_array *out, const af_array in)
     {                                                   \
         return af_unary_complex<af_##fn##_t>(out, in);  \
     }
-
-UNARY(sin)
+    
 UNARY(cos)
 UNARY(tan)
 
@@ -140,6 +139,7 @@ UNARY(tgamma)
 UNARY(lgamma)
 
 UNARY_COMPLEX(exp)
+UNARY_COMPLEX(sin)
 UNARY_COMPLEX(sqrt)
 
 template<typename Tc, typename Tr>
@@ -185,6 +185,29 @@ struct unaryOpCplx<Tc, Tr, af_log_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplx<Tc, Tr, af_sin_t>
+{
+    af_array operator()(const af_array in)
+    {
+        Array<Tc> z = getArray<Tc>(a);
+        Array<Tr> a = real<Tr, Tc>(In);
+        Array<Tr> b = imag<Tr, Tc>(In);
+
+        // compute sin
+        Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
+        Array<Tr> cos_a = unaryOp<Tr, af_sin_t>(a);
+        Array<Tr> sinh_b = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> cosh_b = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> a_out = arithOp<Tr, af_mul_t>(sin_a, cosh_b, sin_a.dims());
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(cos_a, sinh_b, cos_a.dims());
+
+        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
+
+        return getHandle(z_out);
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_sqrt_t>
 {

From c9090fa8aafc7f5fc272c80052eaeb3dc26858d6 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 04:13:42 +0200
Subject: [PATCH 0646/2677] Fix misspelled variables

---
 src/api/c/unary.cpp | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 81f091699e..f7eb4f0c2d 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -101,7 +101,7 @@ static af_err af_unary_complex(af_array *out, const af_array in)
     {                                                   \
         return af_unary_complex<af_##fn##_t>(out, in);  \
     }
-    
+
 UNARY(cos)
 UNARY(tan)
 
@@ -171,8 +171,8 @@ struct unaryOpCplx<Tc, Tr, af_log_t>
     {
         // convert cartesian to polar
         Array<Tc> z = getArray<Tc>(a);
-        Array<Tr> a = real<Tr, Tc>(In);
-        Array<Tr> b = imag<Tr, Tc>(In);
+        Array<Tr> a = real<Tr, Tc>(in);
+        Array<Tr> b = imag<Tr, Tc>(in);
         Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
         Array<Tr> phi = abs<Tr>(z);
 
@@ -191,8 +191,8 @@ struct unaryOpCplx<Tc, Tr, af_sin_t>
     af_array operator()(const af_array in)
     {
         Array<Tc> z = getArray<Tc>(a);
-        Array<Tr> a = real<Tr, Tc>(In);
-        Array<Tr> b = imag<Tr, Tc>(In);
+        Array<Tr> a = real<Tr, Tc>(in);
+        Array<Tr> b = imag<Tr, Tc>(in);
 
         // compute sin
         Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
@@ -215,8 +215,8 @@ struct unaryOpCplx<Tc, Tr, af_sqrt_t>
     {
         // convert cartesian to polar
         Array<Tc> z = getArray<Tc>(a);
-        Array<Tr> a = real<Tr, Tc>(In);
-        Array<Tr> b = imag<Tr, Tc>(In);
+        Array<Tr> a = real<Tr, Tc>(in);
+        Array<Tr> b = imag<Tr, Tc>(in);
         Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
         Array<Tr> phi = abs<Tr>(z);
 
@@ -228,8 +228,8 @@ struct unaryOpCplx<Tc, Tr, af_sqrt_t>
         // convert polar to cartesian
         Array<Tr> a_out_unit = unaryOp<Tr, af_cos_t>(phi_out);
         Array<Tr> b_out_unit = unaryOp<Tr, af_sin_t>(phi_out);
-        Array<Tr> a_out = arithOp<Tc, af_mul_t>(r_out, a_out_unit, SqrtAbs.dims());
-        Array<Tr> b_out = arithOp<Tc, af_mul_t>(r_out, b_out_unit, SqrtAbs.dims());
+        Array<Tr> a_out = arithOp<Tc, af_mul_t>(r_out, a_out_unit, r_out.dims());
+        Array<Tr> b_out = arithOp<Tc, af_mul_t>(r_out, b_out_unit, r_out.dims());
         Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
 
         return getHandle(z_out);

From b90919d9a8b2443df49a7340ec20498e09bd89b7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 16 Jun 2016 23:37:56 -0400
Subject: [PATCH 0647/2677] Add cl2hpp dependency to scan by key kernels

---
 src/backend/opencl/kernel/scan_by_key/CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 791fa766bb..664ef57c1e 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -13,6 +13,9 @@ FOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp")
     ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} ${cl_kernel_targets})
+    IF(NOT USE_SYSTEM_CL2HPP)
+        ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} cl2hpp)
+    ENDIF()
     IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
         ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} forge)
     ENDIF()

From b6765b21f2fcebfb52c5ff725d9a1535aa883f9f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 20 Jun 2016 22:35:23 -0400
Subject: [PATCH 0648/2677] OPENCL: Add device opencl version to build options

Also provides as a work around for failures on AMD devices
when compiling large kernels
---
 src/backend/opencl/program.cpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 41b4b282b6..9e680865f7 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -49,9 +49,14 @@ namespace opencl
                 std::string(dtype_traits<dim_t>::getName());
 
             prog = cl::Program(getContext(), setSrc);
-            std::vector<cl::Device> targetDevices;
-            targetDevices.push_back(getDevice());
-            prog.build(targetDevices, (defaults + options).c_str());
+            auto device = getDevice();
+
+            std::string cl_std =
+                std::string(" -cl-std=CL") +
+                device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
+
+            // Braces needed to list initialize the vector for the first argument
+            prog.build({device}, (cl_std + defaults + options).c_str());
 
         } catch (...) {
             SHOW_BUILD_INFO(prog);

From afd755a0af4ef8cca30d807285562e2fca065ed6 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 20 Jun 2016 22:37:16 -0400
Subject: [PATCH 0649/2677] OPENCL: Removing AMD specific MAX_JIT_LEN

- Every backend now has the same MAX_JIT_LEN by default
---
 docs/pages/configuring_arrayfire_environment.md | 2 +-
 src/backend/opencl/platform.cpp                 | 5 -----
 2 files changed, 1 insertion(+), 6 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 8f32fb2fd9..edb59d56cb 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -156,7 +156,7 @@ When not set, the default value is 1000.
 AF_OPENCL_MAX_JIT_LEN {#af_opencl_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum length of the OpenCL JIT tree after which evaluation is forced. The default value for this is 16 for AMD devices and 20 otherwise.
+When set, this environment variable specifies the maximum length of the OpenCL JIT tree after which evaluation is forced. The default value for this is 20.
 
 AF_CUDA_MAX_JIT_LEN {#af_cuda_max_jit_len}
 -------------------------------------------------------------------------------
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 8dd2db63fe..3d373e1319 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -846,7 +846,6 @@ bool synchronize_calls() {
 unsigned getMaxJitSize()
 {
     const int MAX_JIT_LEN = 20;
-    const int MAX_JIT_LEN_AMD = 16; //FIXME: Change this when bug is fixed
 
     static int length = 0;
     if (length == 0) {
@@ -857,10 +856,6 @@ unsigned getMaxJitSize()
             length = MAX_JIT_LEN;
         }
     }
-
-    if (getActivePlatform() == AFCL_PLATFORM_AMD) {
-        return std::min(length, MAX_JIT_LEN_AMD);
-    }
     return length;
 }
 

From 421e75f845126cd579ca18d2a7a4e3faccfaf5bf Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 06:06:21 +0200
Subject: [PATCH 0650/2677] Fix more misspelled variables

---
 src/api/c/unary.cpp | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index f7eb4f0c2d..07f377ed9e 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -170,9 +170,9 @@ struct unaryOpCplx<Tc, Tr, af_log_t>
     af_array operator()(const af_array in)
     {
         // convert cartesian to polar
-        Array<Tc> z = getArray<Tc>(a);
-        Array<Tr> a = real<Tr, Tc>(in);
-        Array<Tr> b = imag<Tr, Tc>(in);
+        Array<Tc> z = getArray<Tc>(in);
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
         Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
         Array<Tr> phi = abs<Tr>(z);
 
@@ -190,9 +190,9 @@ struct unaryOpCplx<Tc, Tr, af_sin_t>
 {
     af_array operator()(const af_array in)
     {
-        Array<Tc> z = getArray<Tc>(a);
-        Array<Tr> a = real<Tr, Tc>(in);
-        Array<Tr> b = imag<Tr, Tc>(in);
+        Array<Tc> z = getArray<Tc>(in);
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
 
         // compute sin
         Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
@@ -214,9 +214,9 @@ struct unaryOpCplx<Tc, Tr, af_sqrt_t>
     af_array operator()(const af_array in)
     {
         // convert cartesian to polar
-        Array<Tc> z = getArray<Tc>(a);
-        Array<Tr> a = real<Tr, Tc>(in);
-        Array<Tr> b = imag<Tr, Tc>(in);
+        Array<Tc> z = getArray<Tc>(in);
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
         Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
         Array<Tr> phi = abs<Tr>(z);
 

From 76c66e09a61b61d364da3c5180a22ac8150610bf Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 06:07:01 +0200
Subject: [PATCH 0651/2677] Reorder use of macro

---
 src/api/c/unary.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 07f377ed9e..fee7afafd9 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -128,7 +128,6 @@ UNARY(expm1)
 UNARY(erf)
 UNARY(erfc)
 
-UNARY(log)
 UNARY(log10)
 UNARY(log1p)
 UNARY(log2)
@@ -138,10 +137,6 @@ UNARY(cbrt)
 UNARY(tgamma)
 UNARY(lgamma)
 
-UNARY_COMPLEX(exp)
-UNARY_COMPLEX(sin)
-UNARY_COMPLEX(sqrt)
-
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_exp_t>
 {
@@ -236,6 +231,11 @@ struct unaryOpCplx<Tc, Tr, af_sqrt_t>
     }
 };
 
+UNARY_COMPLEX(exp)
+UNARY_COMPLEX(log)
+UNARY_COMPLEX(sin)
+UNARY_COMPLEX(sqrt)
+
 af_err af_not(af_array *out, const af_array in)
 {
     try {

From 179f65ee764290dd2c3a2cc38fed98883794480b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 21 Jun 2016 02:01:56 -0400
Subject: [PATCH 0652/2677] Fix License grammar

---
 LICENSE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LICENSE b/LICENSE
index ec2f10c01f..18109a9a61 100644
--- a/LICENSE
+++ b/LICENSE
@@ -11,7 +11,7 @@ are permitted provided that the following conditions are met:
   list of conditions and the following disclaimer in the documentation and/or
   other materials provided with the distribution.
 
-* Neither the name of the ArrayFire nor the names of its
+* Neither the name ArrayFire nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.
 

From a014835b0f1438773465f9242fe7d89df47e54fe Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Jun 2016 16:44:33 +0530
Subject: [PATCH 0653/2677] Fix convolution forward check for 2d & 3d
 convolutions

---
 src/api/c/convolve.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index accbc606a8..ef3786a457 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -195,7 +195,7 @@ af_err af_convolve1(af_array *out, const af_array signal, const af_array filter,
 af_err af_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
 {
     try {
-        if (getInfo(signal).dims().ndims()<2 && getInfo(filter).dims().ndims()<2) {
+        if (getInfo(signal).dims().ndims()<2 || getInfo(filter).dims().ndims()<2) {
             return af_convolve1(out, signal, filter, mode, domain);
         }
 
@@ -212,7 +212,7 @@ af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
 af_err af_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
 {
     try {
-        if (getInfo(signal).dims().ndims()<3 && getInfo(filter).dims().ndims()<3) {
+        if (getInfo(signal).dims().ndims()<3 || getInfo(filter).dims().ndims()<3) {
             return af_convolve2(out, signal, filter, mode, domain);
         }
 

From 5e7b0d6d5a692417fe106b7c3df163ed47b54445 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 22:52:55 +0200
Subject: [PATCH 0654/2677] Fix compilation errors

---
 src/api/c/unary.cpp | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index fee7afafd9..c8dc38e025 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -78,8 +78,8 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         switch (type) {
         case f32 : res = unaryOp<float  , op>(in); break;
         case f64 : res = unaryOp<double , op>(in); break;
-        case c32 : res = unaryOpCplx<cfloat , float , op>(in); break;
-        case c64 : res = unaryOpCplx<cdouble, double, op>(in); break;
+        case c32 : res = unaryOpCplx<cfloat , float , op>()(in); break;
+        case c64 : res = unaryOpCplx<cdouble, double, op>()(in); break;
         default:
             TYPE_ERROR(1, in_type); break;
         }
@@ -137,6 +137,12 @@ UNARY(cbrt)
 UNARY(tgamma)
 UNARY(lgamma)
 
+UNARY_COMPLEX(exp)
+UNARY_COMPLEX(log)
+UNARY_COMPLEX(sin)
+UNARY_COMPLEX(sqrt)
+
+
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_exp_t>
 {
@@ -223,19 +229,14 @@ struct unaryOpCplx<Tc, Tr, af_sqrt_t>
         // convert polar to cartesian
         Array<Tr> a_out_unit = unaryOp<Tr, af_cos_t>(phi_out);
         Array<Tr> b_out_unit = unaryOp<Tr, af_sin_t>(phi_out);
-        Array<Tr> a_out = arithOp<Tc, af_mul_t>(r_out, a_out_unit, r_out.dims());
-        Array<Tr> b_out = arithOp<Tc, af_mul_t>(r_out, b_out_unit, r_out.dims());
+        Array<Tr> a_out = arithOp<Tr, af_mul_t>(r_out, a_out_unit, r_out.dims());
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(r_out, b_out_unit, r_out.dims());
         Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
 
         return getHandle(z_out);
     }
 };
 
-UNARY_COMPLEX(exp)
-UNARY_COMPLEX(log)
-UNARY_COMPLEX(sin)
-UNARY_COMPLEX(sqrt)
-
 af_err af_not(af_array *out, const af_array in)
 {
     try {

From 78b1731b674a220a34640a2b1247ad47199ff9ef Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 23:07:15 +0200
Subject: [PATCH 0655/2677] Fix bug in complex sin

---
 src/api/c/unary.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index c8dc38e025..3531d03872 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -197,9 +197,9 @@ struct unaryOpCplx<Tc, Tr, af_sin_t>
 
         // compute sin
         Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
-        Array<Tr> cos_a = unaryOp<Tr, af_sin_t>(a);
-        Array<Tr> sinh_b = unaryOp<Tr, af_sin_t>(b);
-        Array<Tr> cosh_b = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> cos_a = unaryOp<Tr, af_cos_t>(a);
+        Array<Tr> sinh_b = unaryOp<Tr, af_sinh_t>(b);
+        Array<Tr> cosh_b = unaryOp<Tr, af_cosh_t>(b);
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(sin_a, cosh_b, sin_a.dims());
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(cos_a, sinh_b, cos_a.dims());
 

From 865a018fc378de3d5406f3e6d6ef22d8864fb410 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Tue, 21 Jun 2016 23:21:42 +0200
Subject: [PATCH 0656/2677] Add complex cos

---
 src/api/c/unary.cpp | 27 ++++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 3531d03872..6ccee7a420 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -102,7 +102,6 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         return af_unary_complex<af_##fn##_t>(out, in);  \
     }
 
-UNARY(cos)
 UNARY(tan)
 
 UNARY(asin)
@@ -137,6 +136,7 @@ UNARY(cbrt)
 UNARY(tgamma)
 UNARY(lgamma)
 
+UNARY_COMPLEX(cos)
 UNARY_COMPLEX(exp)
 UNARY_COMPLEX(log)
 UNARY_COMPLEX(sin)
@@ -209,6 +209,31 @@ struct unaryOpCplx<Tc, Tr, af_sin_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplx<Tc, Tr, af_cos_t>
+{
+    af_array operator()(const af_array in)
+    {
+        Array<Tc> z = getArray<Tc>(in);
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
+
+        // compute cos
+        Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
+        Array<Tr> cos_a = unaryOp<Tr, af_cos_t>(a);
+        Array<Tr> sinh_b = unaryOp<Tr, af_sinh_t>(b);
+        Array<Tr> cosh_b = unaryOp<Tr, af_cosh_t>(b);
+        Array<Tr> a_out = arithOp<Tr, af_mul_t>(cos_a, cosh_b, sin_a.dims());
+        Array<Tr> neg_one = createValueArray<Tr>(a_out.dims(), -1);
+        Array<Tr> b_out_neg = arithOp<Tr, af_mul_t>(sin_a, sinh_b, cos_a.dims());
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(b_out_neg, b_out_neg, b_out_neg.dims());
+
+        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
+
+        return getHandle(z_out);
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_sqrt_t>
 {

From d0433da4bef2c230ad08f5296d6ff5169bcc651e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 22 Jun 2016 15:09:38 +0530
Subject: [PATCH 0657/2677] Change cl2hpp external project url to KhronosGroup
 Repository

---
 CMakeModules/build_cl2hpp.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index d8bd046613..bd83432521 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -4,8 +4,8 @@ SET(prefix ${CMAKE_BINARY_DIR}/third_party/cl2hpp)
 
 ExternalProject_Add(
     cl2hpp-ext
-    GIT_REPOSITORY https://github.com/arrayfire/OpenCL-CLHPP.git
-    GIT_TAG install_targets
+    GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
+    GIT_TAG 75bb7d0d8b2ffc6aac0a3dcaa22f6622cab81f7c
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}/package"
     UPDATE_COMMAND ""

From 39d5b67d9cda74ed769db25e74cf283eb907c321 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 01:39:56 +0200
Subject: [PATCH 0658/2677] Add complex tan

---
 src/api/c/unary.cpp | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 6ccee7a420..bb9bce6729 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -102,8 +102,6 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         return af_unary_complex<af_##fn##_t>(out, in);  \
     }
 
-UNARY(tan)
-
 UNARY(asin)
 UNARY(acos)
 UNARY(atan)
@@ -141,7 +139,7 @@ UNARY_COMPLEX(exp)
 UNARY_COMPLEX(log)
 UNARY_COMPLEX(sin)
 UNARY_COMPLEX(sqrt)
-
+UNARY_COMPLEX(tan)
 
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_exp_t>
@@ -234,6 +232,18 @@ struct unaryOpCplx<Tc, Tr, af_cos_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplx<Tc, Tr, af_tan_t>
+{
+    af_array operator()(const af_array in)
+    {
+        Array<Tc> sin_z = getArray<Tc>(unaryOpCplx<Tc, Tr, af_sin_t>()(in));
+        Array<Tc> cos_z = getArray<Tc>(unaryOpCplx<Tc, Tr, af_cos_t>()(in));
+        Array<Tc> tan_z = arithOp<Tc, af_div_t>(sin_z, cos_z, sin_z.dims());
+        return getHandle(tan_z);
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplx<Tc, Tr, af_sqrt_t>
 {

From f0d297f4257f24c4af669bf4175eb0a5e6a59de8 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 02:17:33 +0200
Subject: [PATCH 0659/2677] Change functor usage and add helpers

---
 src/api/c/unary.cpp | 87 ++++++++++++++++++++++-----------------------
 1 file changed, 42 insertions(+), 45 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index bb9bce6729..178b03e86a 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -34,7 +34,19 @@ static inline af_array unaryOp(const af_array in)
 }
 
 template<typename Tc, typename Tr, af_op_t op>
-struct unaryOpCplx;
+struct unaryOpCplxFun;
+
+template<typename Tc, typename Tr, af_op_t op>
+static inline Array<Tc> unaryOpCplx(const Array<Tc> &in)
+{
+    return unaryOpCplxFun<Tc, Tr, op>()(in);
+}
+
+template<typename Tc, typename Tr, af_op_t op>
+static inline af_array unaryOpCplx(const af_array in)
+{
+    return getHandle(unaryOpCplx<Tc, Tr, op>(castArray<Tc>(in)));
+}
 
 template<af_op_t op>
 static af_err af_unary(af_array *out, const af_array in)
@@ -78,8 +90,8 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         switch (type) {
         case f32 : res = unaryOp<float  , op>(in); break;
         case f64 : res = unaryOp<double , op>(in); break;
-        case c32 : res = unaryOpCplx<cfloat , float , op>()(in); break;
-        case c64 : res = unaryOpCplx<cdouble, double, op>()(in); break;
+        case c32 : res = unaryOpCplx<cfloat , float , op>(in); break;
+        case c64 : res = unaryOpCplx<cdouble, double, op>(in); break;
         default:
             TYPE_ERROR(1, in_type); break;
         }
@@ -142,34 +154,29 @@ UNARY_COMPLEX(sqrt)
 UNARY_COMPLEX(tan)
 
 template<typename Tc, typename Tr>
-struct unaryOpCplx<Tc, Tr, af_exp_t>
+struct unaryOpCplxFun<Tc, Tr, af_exp_t>
 {
-    af_array operator()(const af_array a)
+    Array<Tc> operator()(const Array<Tc> &z)
     {
-        Array<Tc> In = getArray<Tc>(a);
-        Array<Tr> Real = real<Tr, Tc>(In);
-        Array<Tr> Imag = imag<Tr, Tc>(In);
-
-        Array<Tr> ExpReal = unaryOp<Tr, af_exp_t>(Real);
-        Array<Tr> CosImag = unaryOp<Tr, af_cos_t>(Imag);
-        Array<Tr> SinImag = unaryOp<Tr, af_sin_t>(Imag);
-
-        Array<Tc> Unit  = cplx<Tc, Tr>(CosImag, SinImag, CosImag.dims());
-        Array<Tc> Scale = cast<Tc, Tr>(ExpReal);
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
 
-        Array<Tc> Result = arithOp<Tc, af_mul_t>(Scale, Unit, Scale.dims());
+        Array<Tr> exp_a = unaryOp<Tr, af_exp_t>(a);
+        Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+        Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> a_out = arithOp<Tr, af_mul_t>(exp_a, cos_b, exp_a.dims());
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(exp_a, sin_b, exp_a.dims());
 
-        return getHandle(Result);
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplx<Tc, Tr, af_log_t>
+struct unaryOpCplxFun<Tc, Tr, af_log_t>
 {
-    af_array operator()(const af_array in)
+    Array<Tc> operator()(const Array<Tc> &z)
     {
         // convert cartesian to polar
-        Array<Tc> z = getArray<Tc>(in);
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
         Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
@@ -178,18 +185,16 @@ struct unaryOpCplx<Tc, Tr, af_log_t>
         // compute log
         Array<Tr> a_out = unaryOp<Tr, af_log_t>(r);
         Array<Tr> b_out = phi;
-        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
 
-        return getHandle(z_out);
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplx<Tc, Tr, af_sin_t>
+struct unaryOpCplxFun<Tc, Tr, af_sin_t>
 {
-    af_array operator()(const af_array in)
+    Array<Tc> operator()(const Array<Tc> &z)
     {
-        Array<Tc> z = getArray<Tc>(in);
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
@@ -201,18 +206,15 @@ struct unaryOpCplx<Tc, Tr, af_sin_t>
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(sin_a, cosh_b, sin_a.dims());
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(cos_a, sinh_b, cos_a.dims());
 
-        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
-
-        return getHandle(z_out);
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplx<Tc, Tr, af_cos_t>
+struct unaryOpCplxFun<Tc, Tr, af_cos_t>
 {
-    af_array operator()(const af_array in)
+    Array<Tc> operator()(const Array<Tc> &z)
     {
-        Array<Tc> z = getArray<Tc>(in);
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
@@ -226,31 +228,27 @@ struct unaryOpCplx<Tc, Tr, af_cos_t>
         Array<Tr> b_out_neg = arithOp<Tr, af_mul_t>(sin_a, sinh_b, cos_a.dims());
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(b_out_neg, b_out_neg, b_out_neg.dims());
 
-        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
-
-        return getHandle(z_out);
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplx<Tc, Tr, af_tan_t>
+struct unaryOpCplxFun<Tc, Tr, af_tan_t>
 {
-    af_array operator()(const af_array in)
+    Array<Tc> operator()(const Array<Tc> &z)
     {
-        Array<Tc> sin_z = getArray<Tc>(unaryOpCplx<Tc, Tr, af_sin_t>()(in));
-        Array<Tc> cos_z = getArray<Tc>(unaryOpCplx<Tc, Tr, af_cos_t>()(in));
-        Array<Tc> tan_z = arithOp<Tc, af_div_t>(sin_z, cos_z, sin_z.dims());
-        return getHandle(tan_z);
+        Array<Tc> sin_z = unaryOpCplx<Tc, Tr, af_sin_t>(z);
+        Array<Tc> cos_z = unaryOpCplx<Tc, Tr, af_cos_t>(z);
+        return arithOp<Tc, af_div_t>(sin_z, cos_z, sin_z.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplx<Tc, Tr, af_sqrt_t>
+struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {
-    af_array operator()(const af_array in)
+    Array<Tc> operator()(const Array<Tc> &z)
     {
         // convert cartesian to polar
-        Array<Tc> z = getArray<Tc>(in);
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
         Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
@@ -266,9 +264,8 @@ struct unaryOpCplx<Tc, Tr, af_sqrt_t>
         Array<Tr> b_out_unit = unaryOp<Tr, af_sin_t>(phi_out);
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(r_out, a_out_unit, r_out.dims());
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(r_out, b_out_unit, r_out.dims());
-        Array<Tc> z_out  = cplx<Tc, Tr>(a_out, b_out, a_out.dims());
 
-        return getHandle(z_out);
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 

From 52a52038503c7172f69333b6fd832887626e62ee Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 02:29:24 +0200
Subject: [PATCH 0660/2677] Add complex sinh, cosh, and tanh

---
 src/api/c/unary.cpp | 58 +++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 54 insertions(+), 4 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 178b03e86a..9d7528a233 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -118,10 +118,6 @@ UNARY(asin)
 UNARY(acos)
 UNARY(atan)
 
-UNARY(sinh)
-UNARY(cosh)
-UNARY(tanh)
-
 UNARY(asinh)
 UNARY(acosh)
 UNARY(atanh)
@@ -147,11 +143,14 @@ UNARY(tgamma)
 UNARY(lgamma)
 
 UNARY_COMPLEX(cos)
+UNARY_COMPLEX(cosh)
 UNARY_COMPLEX(exp)
 UNARY_COMPLEX(log)
 UNARY_COMPLEX(sin)
+UNARY_COMPLEX(sinh)
 UNARY_COMPLEX(sqrt)
 UNARY_COMPLEX(tan)
+UNARY_COMPLEX(tanh)
 
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_exp_t>
@@ -243,6 +242,57 @@ struct unaryOpCplxFun<Tc, Tr, af_tan_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_sinh_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
+
+        // compute sinh
+        Array<Tr> sinh_a = unaryOp<Tr, af_sinh_t>(a);
+        Array<Tr> cosh_a = unaryOp<Tr, af_cosh_t>(a);
+        Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+        Array<Tr> a_out = arithOp<Tr, af_mul_t>(sinh_a, cos_b, sinh_a.dims());
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(cosh_a, sin_b, cosh_a.dims());
+
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
+    }
+};
+
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_cosh_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
+
+        // compute cosh
+        Array<Tr> sinh_a = unaryOp<Tr, af_sinh_t>(a);
+        Array<Tr> cosh_a = unaryOp<Tr, af_cosh_t>(a);
+        Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+        Array<Tr> a_out = arithOp<Tr, af_mul_t>(cosh_a, cos_b, cosh_a.dims());
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(sinh_a, sin_b, sinh_a.dims());
+
+        return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
+    }
+};
+
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_tanh_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        Array<Tc> sinh_z = unaryOpCplx<Tc, Tr, af_sinh_t>(z);
+        Array<Tc> cosh_z = unaryOpCplx<Tc, Tr, af_cosh_t>(z);
+        return arithOp<Tc, af_div_t>(sinh_z, cosh_z, sinh_z.dims());
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From a34278eaea752b31c833ebd7bb3bd4561cde0843 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 03:17:54 +0200
Subject: [PATCH 0661/2677] Add complex asinh

---
 src/api/c/unary.cpp | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 9d7528a233..aff4951667 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -118,7 +118,6 @@ UNARY(asin)
 UNARY(acos)
 UNARY(atan)
 
-UNARY(asinh)
 UNARY(acosh)
 UNARY(atanh)
 
@@ -142,6 +141,7 @@ UNARY(cbrt)
 UNARY(tgamma)
 UNARY(lgamma)
 
+UNARY_COMPLEX(asinh)
 UNARY_COMPLEX(cos)
 UNARY_COMPLEX(cosh)
 UNARY_COMPLEX(exp)
@@ -293,6 +293,28 @@ struct unaryOpCplxFun<Tc, Tr, af_tanh_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        // dont simplify this expression, as it might lead to branch cuts
+        // acosh(z) = log(z+sqrt(z+1)*sqrt(z-1))
+        Array<Tr> a = real<Tr, Tc>(z);
+        Array<Tr> b = imag<Tr, Tc>(z);
+        Array<Tr> one = createValueArray<Tr>(a.dims(), 1);
+        Array<Tr> a_plus_one = arithOp<Tr, af_add_t>(a, one, a.dims());
+        Array<Tr> a_minus_one = arithOp<Tr, af_sub_t>(a, one, a.dims());
+        Array<Tc> z_plus_one = cplx<Tc, Tr>(a_plus_one, b, b.dims());
+        Array<Tc> z_minus_one = cplx<Tc, Tr>(a_minus_one, b, b.dims());
+        Array<Tc> sqrt_z_plus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z_plus_one);
+        Array<Tc> sqrt_z_minus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z_minus_one);
+        Array<Tc> sqrt_prod = arithOp<Tc, af_mul_t>(sqrt_z_plus_one, sqrt_z_minus_one, sqrt_z_plus_one.dims());
+        Array<Tc> w = arithOp<Tc, af_add_t>(z, sqrt_prod, z.dims());
+        return unaryOpCplx<Tc, Tr, af_log_t>(w);
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From 5885cdcea8e73020e0265b1638b20132fbe5dcbc Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 03:19:42 +0200
Subject: [PATCH 0662/2677] Add complex acosh

---
 src/api/c/unary.cpp | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index aff4951667..a000618b0a 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -118,7 +118,6 @@ UNARY(asin)
 UNARY(acos)
 UNARY(atan)
 
-UNARY(acosh)
 UNARY(atanh)
 
 UNARY(trunc)
@@ -141,6 +140,7 @@ UNARY(cbrt)
 UNARY(tgamma)
 UNARY(lgamma)
 
+UNARY_COMPLEX(acosh)
 UNARY_COMPLEX(asinh)
 UNARY_COMPLEX(cos)
 UNARY_COMPLEX(cosh)
@@ -315,6 +315,24 @@ struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        // asinh(z) = log(z+sqrt(z^2+1))
+        Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
+        Array<Tr> a = real<Tr, Tc>(z2);
+        Array<Tr> b = imag<Tr, Tc>(z2);
+        Array<Tr> one = createValueArray<Tr>(a.dims(), 1);
+        Array<Tr> a_plus_one = arithOp<Tr, af_add_t>(a, one, a.dims());
+        Array<Tc> z2_plus_one = cplx<Tc, Tr>(a_plus_one, b, b.dims());
+        Array<Tc> sqrt_z2_plus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z2_plus_one);
+        Array<Tc> w = arithOp<Tc, af_add_t>(z, sqrt_z2_plus_one, z.dims());
+        return unaryOpCplx<Tc, Tr, af_log_t>(w);
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From 7ffb21e759fc20111b21e63d18438d9be48b2049 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 03:36:04 +0200
Subject: [PATCH 0663/2677] Add complex atanh

---
 src/api/c/unary.cpp | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index a000618b0a..7061ab7521 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -118,8 +118,6 @@ UNARY(asin)
 UNARY(acos)
 UNARY(atan)
 
-UNARY(atanh)
-
 UNARY(trunc)
 UNARY(sign)
 UNARY(round)
@@ -142,6 +140,7 @@ UNARY(lgamma)
 
 UNARY_COMPLEX(acosh)
 UNARY_COMPLEX(asinh)
+UNARY_COMPLEX(atanh)
 UNARY_COMPLEX(cos)
 UNARY_COMPLEX(cosh)
 UNARY_COMPLEX(exp)
@@ -333,6 +332,23 @@ struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        // atanh(z) = 0.5*(log(1+z)-log(1-z))
+        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
+        Array<Tc> one_plus_z = arithOp<Tc, af_add_t>(one, z, one.dims());
+        Array<Tc> one_minus_z = arithOp<Tc, af_min_t>(one, z, one.dims());
+        Array<Tc> log_one_plus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_z);
+        Array<Tc> log_one_minus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_z);
+        Array<Tc> w = arithOp<Tc, af_min_t>(log_one_plus_z, log_one_minus_z, log_one_plus_z.dims());
+        Array<Tc> two = createValueArray<Tr>(z.dims(), Tc(2, 0));
+        return arithOp<Tc, af_div_t>(w, two, w.dims());
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From 040f067ad426df73c7ecf73e91db22548fcd936a Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 03:55:31 +0200
Subject: [PATCH 0664/2677] Fix bug with atanh

---
 src/api/c/unary.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 7061ab7521..55c33e463b 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -344,7 +344,7 @@ struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
         Array<Tc> log_one_plus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_z);
         Array<Tc> log_one_minus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_z);
         Array<Tc> w = arithOp<Tc, af_min_t>(log_one_plus_z, log_one_minus_z, log_one_plus_z.dims());
-        Array<Tc> two = createValueArray<Tr>(z.dims(), Tc(2, 0));
+        Array<Tc> two = createValueArray<Tc>(z.dims(), Tc(2, 0));
         return arithOp<Tc, af_div_t>(w, two, w.dims());
     }
 };

From f75478bcef920d28b5be6b624b2a9b9c9cc41a42 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 04:02:06 +0200
Subject: [PATCH 0665/2677] Add complex acos

---
 src/api/c/unary.cpp | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 55c33e463b..cf68c7e645 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -115,7 +115,6 @@ static af_err af_unary_complex(af_array *out, const af_array in)
     }
 
 UNARY(asin)
-UNARY(acos)
 UNARY(atan)
 
 UNARY(trunc)
@@ -139,6 +138,7 @@ UNARY(tgamma)
 UNARY(lgamma)
 
 UNARY_COMPLEX(acosh)
+UNARY_COMPLEX(acos)
 UNARY_COMPLEX(asinh)
 UNARY_COMPLEX(atanh)
 UNARY_COMPLEX(cos)
@@ -349,6 +349,26 @@ struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_acos_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        // acos(z) = pi/2+i*log(i*z+sqrt(1-z.^2))
+        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
+        Array<Tc> i = createValueArray<Tc>(z.dims(), Tc(0, 1));
+        Array<Tc> pi_half = createValueArray<Tc>(z.dims(), Tc(M_PI / 2.0, 0));
+
+        Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
+        Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
+        Array<Tc> sqrt_one_minus_z2 = unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
+        Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
+        Array<Tc> w = arithOp<Tc, af_add_t>(iz, sqrt_one_minus_z2, iz.dims());
+        Array<Tc> log_w = unaryOpCplx<Tc, Tr, af_log_t>(w);
+        return arithOp<Tc, af_add_t>(pi_half, log_w, pi_half.dims());
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From 9bc5d047fb3b4981b1bde25af3b42c09cf83ddac Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 10:56:16 +0200
Subject: [PATCH 0666/2677] Fix bug with acos

---
 src/api/c/unary.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index cf68c7e645..d357a4f4ff 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -365,7 +365,8 @@ struct unaryOpCplxFun<Tc, Tr, af_acos_t>
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
         Array<Tc> w = arithOp<Tc, af_add_t>(iz, sqrt_one_minus_z2, iz.dims());
         Array<Tc> log_w = unaryOpCplx<Tc, Tr, af_log_t>(w);
-        return arithOp<Tc, af_add_t>(pi_half, log_w, pi_half.dims());
+        Array<Tc> i_log_w = arithOp<Tc, af_mul_t>(i, w, i.dims());
+        return arithOp<Tc, af_add_t>(pi_half, i_log_w, pi_half.dims());
     }
 };
 

From 612553a3bab566301a7a92dd32c1ed3bd1a30f6c Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 10:58:13 +0200
Subject: [PATCH 0667/2677] Add complex asin

---
 src/api/c/unary.cpp | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index d357a4f4ff..58d4d1413b 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -114,7 +114,6 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         return af_unary_complex<af_##fn##_t>(out, in);  \
     }
 
-UNARY(asin)
 UNARY(atan)
 
 UNARY(trunc)
@@ -139,6 +138,7 @@ UNARY(lgamma)
 
 UNARY_COMPLEX(acosh)
 UNARY_COMPLEX(acos)
+UNARY_COMPLEX(asin)
 UNARY_COMPLEX(asinh)
 UNARY_COMPLEX(atanh)
 UNARY_COMPLEX(cos)
@@ -370,6 +370,25 @@ struct unaryOpCplxFun<Tc, Tr, af_acos_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_asin_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        // asin(z) = -i*log(i*z+sqrt(1-z^2))
+        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
+        Array<Tc> i = createValueArray<Tc>(z.dims(), Tc(0, 1));
+        Array<Tc> minus_i = createValueArray<Tc>(z.dims(), Tc(0, -1));
+
+        Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
+        Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
+        Array<Tc> sqrt_one_minus_z2 = unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
+        Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
+        Array<Tc> w = arithOp<Tc, af_add_t>(iz, sqrt_one_minus_z2, iz.dims());
+        return arithOp<Tc, af_mul_t>(minus_i, w, minus_i.dims());
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From 9a4ec852abbd1bcfea0f13de153c88a1e2492716 Mon Sep 17 00:00:00 2001
From: Godisemo <carl.akerlindh@gmail.com>
Date: Thu, 23 Jun 2016 11:06:57 +0200
Subject: [PATCH 0668/2677] Add complex atan

---
 src/api/c/unary.cpp | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 58d4d1413b..89dfe1e1e0 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -114,8 +114,6 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         return af_unary_complex<af_##fn##_t>(out, in);  \
     }
 
-UNARY(atan)
-
 UNARY(trunc)
 UNARY(sign)
 UNARY(round)
@@ -140,6 +138,7 @@ UNARY_COMPLEX(acosh)
 UNARY_COMPLEX(acos)
 UNARY_COMPLEX(asin)
 UNARY_COMPLEX(asinh)
+UNARY_COMPLEX(atan)
 UNARY_COMPLEX(atanh)
 UNARY_COMPLEX(cos)
 UNARY_COMPLEX(cosh)
@@ -389,6 +388,25 @@ struct unaryOpCplxFun<Tc, Tr, af_asin_t>
     }
 };
 
+template<typename Tc, typename Tr>
+struct unaryOpCplxFun<Tc, Tr, af_atan_t>
+{
+    Array<Tc> operator()(const Array<Tc> &z)
+    {
+        // atan(z) = 0.5*i*(log(1-i*z)-log(1+i*z))
+        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
+        Array<Tc> i = createValueArray<Tc>(z.dims(), Tc(0, 1));
+        Array<Tc> i_half = createValueArray<Tc>(z.dims(), Tc(0, 0.5));
+        Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
+        Array<Tc> one_minus_i2 = arithOp<Tc, af_sub_t>(one, iz, z.dims());
+        Array<Tc> one_plus_i2 = arithOp<Tc, af_add_t>(one, iz, z.dims());
+        Array<Tc> log_minus = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_i2);
+        Array<Tc> log_plus = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_i2);
+        Array<Tc> log_diff = arithOp<Tc, af_sub_t>(log_minus, log_plus, z.dims());
+        return arithOp<Tc, af_mul_t>(i_half, log_diff, z.dims());
+    }
+};
+
 template<typename Tc, typename Tr>
 struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {

From 451f09cc2498320fe51fee8b6f24dce12eb9d904 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 25 Jun 2016 17:41:11 -0400
Subject: [PATCH 0669/2677] PERF: Removing the need to check for maxJitLen()

---
 .../configuring_arrayfire_environment.md      | 12 +++++++---
 src/backend/cpu/Array.cpp                     | 24 ++++++++++++-------
 src/backend/cuda/Array.cpp                    | 24 ++++++++++++-------
 src/backend/opencl/Array.cpp                  | 24 ++++++++++++-------
 4 files changed, 56 insertions(+), 28 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index edb59d56cb..467e627cb4 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -156,14 +156,20 @@ When not set, the default value is 1000.
 AF_OPENCL_MAX_JIT_LEN {#af_opencl_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum length of the OpenCL JIT tree after which evaluation is forced. The default value for this is 20.
+This flag is no longer supported as of 3.4
+
+In older versions, When set, this environment variable specifies the maximum length of the OpenCL JIT tree after which evaluation is forced. The default value for this is 20.
 
 AF_CUDA_MAX_JIT_LEN {#af_cuda_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum length of the CUDA JIT tree after which evaluation is forced. The default value for this is 20.
+This flag is no longer supported as of 3.4
+
+In older versions, When set, this environment variable specifies the maximum length of the CUDA JIT tree after which evaluation is forced. The default value for this is 20.
 
 AF_CPU_MAX_JIT_LEN {#af_cpu_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced. The default value for this is 20.
+This flag is no longer supported as of 3.4
+
+In older versions, When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced. The default value for this is 20.
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 7427680be5..9f50f89582 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -180,16 +180,24 @@ createNodeArray(const dim4 &dims, Node_ptr node)
     Array<T> out =  Array<T>(dims, node);
 
     if (evalFlag()) {
-        unsigned length =0, buf_count = 0, bytes = 0;
+        size_t alloc_bytes, alloc_buffers;
+        size_t lock_bytes, lock_buffers;
 
-        Node *n = node.get();
-        n->getInfo(length, buf_count, bytes);
-        n->reset();
+        deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
+                         &lock_bytes, &lock_buffers);
 
-        if (length > getMaxJitSize() ||
-            buf_count >= getMaxBuffers() ||
-            bytes >= getMaxBytes()) {
-            out.eval();
+        // Check if approaching the memory limit
+        if (lock_bytes > getMaxBytes() ||
+            lock_buffers > getMaxBuffers()) {
+
+            unsigned length =0, buf_count = 0, bytes = 0;
+            Node *n = node.get();
+            n->getInfo(length, buf_count, bytes);
+            n->reset();
+
+            if (2 * bytes > lock_bytes) {
+                out.eval();
+            }
         }
     }
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 1945df0c04..6f7694f465 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -217,16 +217,24 @@ namespace cuda
         Array<T> out =  Array<T>(dims, node);
 
         if (evalFlag()) {
-            unsigned length =0, buf_count = 0, bytes = 0;
+            size_t alloc_bytes, alloc_buffers;
+            size_t lock_bytes, lock_buffers;
 
-            Node *n = node.get();
-            n->getInfo(length, buf_count, bytes);
-            n->resetFlags();
+            deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
+                             &lock_bytes, &lock_buffers);
 
-            if (length > getMaxJitSize() ||
-                buf_count >= getMaxBuffers() ||
-                bytes >= getMaxBytes()) {
-                out.eval();
+            // Check if approaching the memory limit
+            if (lock_bytes > getMaxBytes() ||
+                lock_buffers > getMaxBuffers()) {
+
+                unsigned length =0, buf_count = 0, bytes = 0;
+                Node *n = node.get();
+                n->getInfo(length, buf_count, bytes);
+                n->resetFlags();
+
+                if (2 * bytes > lock_bytes) {
+                    out.eval();
+                }
             }
         }
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 14f90b3f32..8a2f0037d4 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -224,20 +224,26 @@ namespace opencl
     Array<T> createNodeArray(const dim4 &dims, Node_ptr node)
     {
         verifyDoubleSupport<T>();
-
         Array<T> out =  Array<T>(dims, node);
 
         if (evalFlag()) {
-            unsigned length =0, buf_count = 0, bytes = 0;
+            size_t alloc_bytes, alloc_buffers;
+            size_t lock_bytes, lock_buffers;
+
+            deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
+                             &lock_bytes, &lock_buffers);
+
+            if (lock_bytes > getMaxBytes() ||
+                lock_buffers > getMaxBuffers()) {
 
-            Node *n = node.get();
-            n->getInfo(length, buf_count, bytes);
-            n->resetFlags();
+                unsigned length =0, buf_count = 0, bytes = 0;
+                Node *n = node.get();
+                n->getInfo(length, buf_count, bytes);
+                n->resetFlags();
 
-            if (length > getMaxJitSize() ||
-                buf_count >= getMaxBuffers() ||
-                bytes >= getMaxBytes()) {
-                out.eval();
+                if (2 * bytes > lock_bytes) {
+                    out.eval();
+                }
             }
         }
 

From b1319be4b7b0ae8b70fb1bbcca73c7521226a34f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 25 Jun 2016 17:54:09 -0400
Subject: [PATCH 0670/2677] PERF: Only pass offsets to linear OpenCL JIT
 kernels

---
 src/backend/opencl/JIT/BinaryNode.hpp | 12 ++++++------
 src/backend/opencl/JIT/BufferNode.hpp | 23 +++++++++++++++++------
 src/backend/opencl/JIT/Node.hpp       |  4 ++--
 src/backend/opencl/JIT/ScalarNode.hpp |  4 ++--
 src/backend/opencl/JIT/UnaryNode.hpp  |  8 ++++----
 src/backend/opencl/jit.cpp            |  4 ++--
 6 files changed, 33 insertions(+), 22 deletions(-)

diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index 6b5a6d05d6..9239c74522 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -45,21 +45,21 @@ namespace JIT
             return m_linear;
         }
 
-        void genParams(std::stringstream &kerStream)
+        void genParams(std::stringstream &kerStream, bool is_linear)
         {
             if (m_gen_param) return;
-            if (!(m_lhs->isGenParam())) m_lhs->genParams(kerStream);
-            if (!(m_rhs->isGenParam())) m_rhs->genParams(kerStream);
+            if (!(m_lhs->isGenParam())) m_lhs->genParams(kerStream, is_linear);
+            if (!(m_rhs->isGenParam())) m_rhs->genParams(kerStream, is_linear);
             m_gen_param = true;
         }
 
-        int setArgs(cl::Kernel &ker, int id)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear)
         {
             if (m_set_arg) return id;
             m_set_arg = true;
 
-            id = m_lhs->setArgs(ker, id);
-            id = m_rhs->setArgs(ker, id);
+            id = m_lhs->setArgs(ker, id, is_linear);
+            id = m_rhs->setArgs(ker, id, is_linear);
             return id;
         }
 
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 3d992ce5e4..4fa8134ed0 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -70,20 +70,31 @@ namespace JIT
             m_gen_name = true;
         }
 
-        void genParams(std::stringstream &kerStream)
+        void genParams(std::stringstream &kerStream, bool is_linear)
         {
             if (m_gen_param) return;
-            kerStream << "__global " << m_type_str << " *in" << m_id
-                      << ", KParam iInfo" << m_id << ", " << "\n";
+
+            if (!is_linear) {
+                kerStream << "__global " << m_type_str << " *in" << m_id
+                          << ", KParam iInfo" << m_id << ", " << "\n";
+            } else {
+                kerStream << "__global " << m_type_str << " *in" << m_id
+                          << ", dim_t iInfo" << m_id << "_offset, " << "\n";
+            }
             m_gen_param = true;
         }
 
-        int setArgs(cl::Kernel &ker, int id)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear)
         {
             if (m_set_arg) return id;
 
             ker.setArg(id + 0, *m_data);
-            ker.setArg(id + 1,  m_info);
+
+            if (!is_linear) {
+                ker.setArg(id + 1,  m_info);
+            } else {
+                ker.setArg(id + 1, m_info.offset);
+            }
 
             m_set_arg = true;
             return id + 2;
@@ -108,7 +119,7 @@ namespace JIT
                           << "id0 + " << info_str << ".offset;"
                           << "\n";
             } else {
-                kerStream << idx_str << " = idx + " << info_str << ".offset;" << "\n";
+                kerStream << idx_str << " = idx + " << info_str << "_offset;" << "\n";
             }
 
             m_gen_offset = true;
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index 215290c76d..2bc0147120 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -67,11 +67,11 @@ namespace JIT
         {}
 
         virtual void genKerName(std::stringstream &kerStream) {}
-        virtual void genParams  (std::stringstream &kerStream) {}
+        virtual void genParams  (std::stringstream &kerStream, bool is_linear) {}
         virtual void genOffsets (std::stringstream &kerStream, bool is_linear) {}
         virtual void genFuncs   (std::stringstream &kerStream) { m_gen_func = true;}
 
-        virtual int setArgs (cl::Kernel &ker, int id) { return id; }
+        virtual int setArgs (cl::Kernel &ker, int id, bool is_linear) { return id; }
 
         virtual int setId(int id) { m_set_id = true; return id; }
 
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index 57d9f91d12..0bc8664e54 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -47,14 +47,14 @@ namespace JIT
             m_gen_name = true;
         }
 
-        void genParams(std::stringstream &kerStream)
+        void genParams(std::stringstream &kerStream, bool is_linear)
         {
             if (m_gen_param) return;
             kerStream << m_type_str << " scalar" << m_id << ", " << "\n";
             m_gen_param = true;
         }
 
-        int setArgs(cl::Kernel &ker, int id)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear)
         {
             if (m_set_arg) return id;
             ker.setArg(id, m_val);
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index c035426686..18a3441f81 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -44,18 +44,18 @@ namespace JIT
             return m_linear;
         }
 
-        void genParams(std::stringstream &kerStream)
+        void genParams(std::stringstream &kerStream, bool is_linear)
         {
             if (m_gen_param) return;
-            if (!(m_child->isGenParam())) m_child->genParams(kerStream);
+            if (!(m_child->isGenParam())) m_child->genParams(kerStream, is_linear);
             m_gen_param = true;
         }
 
-        int setArgs(cl::Kernel &ker, int id)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear)
         {
             if (m_set_arg) return id;
             m_set_arg = true;
-            return m_child->setArgs(ker, id);
+            return m_child->setArgs(ker, id, is_linear);
         }
 
         void genOffsets(std::stringstream &kerStream, bool is_linear)
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 37686528f9..055b4c347e 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -123,7 +123,7 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
 
     for (auto node : nodes) {
         int id = node->getId();
-        node->genParams(inParamStream);
+        node->genParams(inParamStream, is_linear);
         outParamStream << "__global " << node->getTypeStr() << " *out" << id << ", \n";
         outWriteStream << "out" << id << "[idx] = " << "val" << id << ";\n";
         node->genOffsets(offsetsStream, is_linear);
@@ -240,7 +240,7 @@ void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
 
         int args = 0;
         for (auto node : nodes) {
-            args = node->setArgs(ker, args);
+            args = node->setArgs(ker, args, is_linear);
         }
 
         // Set output parameters

From 19508739098f5a4f640e71bbbe721f887f569d84 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 25 Jun 2016 19:54:57 -0400
Subject: [PATCH 0671/2677] PERF: Use unordered_map for memory manager

- Also use const references when possible
---
 src/backend/MemoryManager.cpp | 6 +++---
 src/backend/MemoryManager.hpp | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index d5be32f9ae..04bd46783f 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -242,7 +242,7 @@ size_t MemoryManager::getMaxBytes()
 void MemoryManager::printInfo(const char *msg, const int device)
 {
     lock_guard_t lock(this->memory_mutex);
-    memory_info& current = this->getCurrentMemoryInfo();
+    const memory_info& current = this->getCurrentMemoryInfo();
 
     std::cout << msg << std::endl;
 
@@ -298,7 +298,7 @@ void MemoryManager::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                                size_t *lock_bytes,  size_t *lock_buffers)
 {
     lock_guard_t lock(this->memory_mutex);
-    memory_info current = this->getCurrentMemoryInfo();
+    const memory_info& current = this->getCurrentMemoryInfo();
     if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
     if (alloc_buffers ) *alloc_buffers = current.total_buffers;
     if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
@@ -312,7 +312,7 @@ unsigned MemoryManager::getMaxBuffers()
 
 bool MemoryManager::checkMemoryLimit()
 {
-    memory_info& current = this->getCurrentMemoryInfo();
+    const memory_info& current = this->getCurrentMemoryInfo();
     return current.lock_bytes >= current.max_bytes || current.total_buffers >= this->max_buffers;
 }
 
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index 0db70b572d..cd991d3529 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -10,8 +10,8 @@
 #pragma once
 
 #include <vector>
-#include <map>
 #include <mutex>
+#include <unordered_map>
 
 namespace common
 {
@@ -31,10 +31,10 @@ class MemoryManager
         size_t bytes;
     } locked_info;
 
-    typedef std::map<void *, locked_info> locked_t;
+    typedef std::unordered_map<void *, locked_info> locked_t;
     typedef locked_t::iterator locked_iter;
 
-    typedef std::map<size_t, std::vector<void *> >free_t;
+    typedef std::unordered_map<size_t, std::vector<void *> >free_t;
     typedef free_t::iterator free_iter;
 
     typedef struct

From c70d0427d7902e57d031efbc947e00e81e781901 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 27 Jun 2016 05:13:42 -0400
Subject: [PATCH 0672/2677] CPU Offload is enabled by default for Unified Mem
 devices

---
 src/backend/opencl/platform.cpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 3d373e1319..cb726c2e0a 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -519,7 +519,7 @@ bool isHostUnifiedMemory(const cl::Device &device)
 
 bool OpenCLCPUOffload(bool forceOffloadOSX)
 {
-    static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") == "1";
+    static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") != "0";
     bool offload = false;
     if(offloadEnv) offload = isHostUnifiedMemory(getDevice());
 #if OS_MAC
@@ -531,7 +531,11 @@ bool OpenCLCPUOffload(bool forceOffloadOSX)
     // variable inconsequential to the returned result.
     //
     // Issue https://github.com/arrayfire/arrayfire/issues/662
-    offload = offload || forceOffloadOSX;
+    //
+    // Make sure device has unified memory
+    bool osx_offload = isHostUnifiedMemory(getDevice());
+    // Force condition
+    offload = osx_offload && (offload || forceOffloadOSX);
 #endif
     return offload;
 }

From a53ba5c3728b9d2981ecfe474d199f65f3d20275 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 27 Jun 2016 05:20:50 -0400
Subject: [PATCH 0673/2677] Documenting behaviro change for OpenCL CPU Offload

---
 .../configuring_arrayfire_environment.md      | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index edb59d56cb..95f891582c 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -88,14 +88,25 @@ AF_OPENCL_DEVICE_TYPE=CPU ./myprogram_opencl
 AF_OPENCL_CPU_OFFLOAD {#af_opencl_cpu_offload}
 -------------------------------------------------------------------------------
 
-When this variable is set to 1, and the selected OpenCL device has unified
-memory with the host (ie. `CL_DEVICE_HOST_UNIFIED_MEMORY` is true for device),
-then certain functions are offloaded to run on the CPU using mapped buffers.
+When ArrayFire runs on devices with unified memory with the host (ie.
+`CL_DEVICE_HOST_UNIFIED_MENORY` is true for the device) then certain functions
+are offloaded to run on the CPU using mapped buffers.
 
-This takes advantage of fast libraries such as MKL while spending no time
+ArrayFire takes advantage of fast libraries such as MKL while spending no time
 copying memory from device to host. The device memory is mapped to a host
 pointer which can be used in the offloaded functions.
 
+This functionality can be disabled by using the environment variable
+`AF_OPENCL_CPU_OFFLOAD=0`.
+
+The default bevaior of this has changed in version 3.4.
+
+Prior to v3.4, CPU Offload functionality was used only when the user set
+`AF_OPENCL_CPU_OFFLOAD=1` and disabled otherwise.
+
+From v3.4 onwards, CPU Offload is enabled by default and is disabled only when
+`AF_OPENCL_CPU_OFFLOAD=0` is set.
+
 AF_OPENCL_SHOW_BUILD_INFO {#af_opencl_show_build_info}
 -------------------------------------------------------------------------------
 

From c61b5237674596de6737be966fd44b16120d3381 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 17 Jun 2016 18:09:41 -0400
Subject: [PATCH 0674/2677] additional checks in tests

---
 test/moments.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/test/moments.cpp b/test/moments.cpp
index 83286aada1..1ce666ff34 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -41,6 +41,8 @@ TYPED_TEST_CASE(Image, TestTypes);
 template<typename T>
 void momentsTest(string pTestFile)
 {
+    if (noDoubleTests<T>()) return;
+
     vector<af::dim4> numDims;
 
     vector<vector<T> >   in;
@@ -87,6 +89,7 @@ void momentsTest(string pTestFile)
 
 void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
 {
+    if (noImageIOTests()) return;
     vector<af::dim4> numDims;
 
     vector<vector<float> >   in;

From c5563d718fc7cd2eca7ef209ea3ab0f6da09159f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 27 Jun 2016 16:14:36 -0400
Subject: [PATCH 0675/2677] A faster implementation of black scholes example

---
 examples/financial/black_scholes_options.cpp | 43 +++++++++++++++-----
 1 file changed, 32 insertions(+), 11 deletions(-)

diff --git a/examples/financial/black_scholes_options.cpp b/examples/financial/black_scholes_options.cpp
index a8cdc07724..5d979fd6b9 100644
--- a/examples/financial/black_scholes_options.cpp
+++ b/examples/financial/black_scholes_options.cpp
@@ -16,15 +16,36 @@
 #include "input.h"
 using namespace af;
 
-static array cnd(const array& x)
+// The following function is a modified version of http://www.johndcook.com/blog/cpp_phi/
+// The example above references Handbook of Mathematical Functions by Abramowitz and Stegun
+
+array cnd(array x)
 {
-    static float sqrt2 = sqrtf(2.0f);
-    array temp = (x > 0);
-    array y = temp * (0.5f + erf(x/sqrt2)/2) + (1-temp) * (0.5f - erf((-x)/sqrt2)/2);
-    return y;
+    // constants
+    const float a1 =  0.254829592;
+    const float a2 = -0.284496736;
+    const float a3 =  1.421413741;
+    const float a4 = -1.453152027;
+    const float a5 =  1.061405429;
+    const float p  =  0.3275911;
+    const float sqrt2 = sqrt(2.0);
+
+    // Save the sign of x
+    array xSign = sign(x);
+
+    x = abs(x) / sqrt2;
+
+    // A&S formula 7.1.26
+    array t = 1.0f / (1.0f + p*x);
+    array y = 1.0f + 0.5f * (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);
+
+    return xSign * y + !xSign * (1 - y); // equivalent of (x >= 0) ? y : (1 - y);
 }
 
-static void black_scholes(array& C, array& P, const array& S, const array& X, const array& R, const array& V, const array& T)
+static void black_scholes(array& C, array& P,
+                          const array& S, const array& X,
+                          const array& R, const array& V,
+                          const array& T)
 {
     // This function computes the call and put option prices based on
     // Black-Scholes Model
@@ -82,7 +103,7 @@ int main(int argc, char **argv)
         af::sync();
 
 
-        int iter = 100;
+        int iter = 1000;
         for (int n = 50; n <= 500; n += 50) {
 
             // Create GPU copies of the data
@@ -91,21 +112,21 @@ int main(int argc, char **argv)
             Rg = tile(GC3, n, 1);
             Vg = tile(GC4, n, 1);
             Tg = tile(GC5, n, 1);
+            af::eval(Sg, Xg, Rg, Vg, Tg);
 
             dim4 dims = Xg.dims();
-            printf("Input Data Size = %d x %d\n", (int)dims[0], (int)dims[1]);
-
             // Force compute on the GPU
             af::sync();
 
             timer::start();
             for (int i = 0; i < iter; i++) {
                 black_scholes(Cg, Pg, Sg,Xg,Rg,Vg,Tg);
-                eval(Cg,Pg);
+                eval(Cg, Pg);
             }
             af::sync();
 
-            printf("Mean GPU Time = %0.6fms\n\n\n", 1000 * timer::stop()/iter);
+            double t = timer::stop() / iter;
+            printf("Input Data Size = %8d. Mean GPU Time: %0.6f ms\n", (int)dims[0], 1000 * t);
         }
     } catch (af::exception& e){
         fprintf(stderr, "%s\n", e.what());

From 736c62d1b3756e4b0f9f96c13872eae7e968434b Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 29 Jun 2016 18:32:36 -0400
Subject: [PATCH 0676/2677] 1-D median filter implementation

---
 include/af/image.h                    |  35 ++++++
 src/api/c/filters.cpp                 |  46 +++++++
 src/api/cpp/filters.cpp               |   7 ++
 src/api/unified/image.cpp             |   6 +
 src/backend/cpu/kernel/medfilt.hpp    |  86 ++++++++++++-
 src/backend/cpu/medfilt.cpp           |  18 ++-
 src/backend/cpu/medfilt.hpp           |   3 +
 src/backend/cuda/kernel/medfilt.hpp   | 152 +++++++++++++++++++++++
 src/backend/cuda/medfilt.cu           |  22 +++-
 src/backend/cuda/medfilt.hpp          |   3 +
 src/backend/opencl/kernel/medfilt.hpp |  57 +++++++++
 src/backend/opencl/medfilt.cpp        |  28 ++++-
 src/backend/opencl/medfilt.hpp        |   3 +
 test/medfilt.cpp                      | 166 ++++++++++++++++++++++++++
 14 files changed, 622 insertions(+), 10 deletions(-)

diff --git a/include/af/image.h b/include/af/image.h
index def7fc9d05..76b3c84fe8 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -355,6 +355,24 @@ AFAPI array meanShift(const array& in, const float spatial_sigma, const float ch
 */
 AFAPI array medfilt(const array& in, const dim_t wind_length = 3, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
 
+#if AF_API_VERSION >= 34
+/**
+    C++ Interface for median filter
+
+    \snippet test/medfilt.cpp ex_image_medfilt
+
+    \param[in]  in array is the input signal
+    \param[in]  wind_width is the kernel width
+    \param[in]  edge_pad value will decide what happens to border when running
+                filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+    \return     the processed signal
+
+    \ingroup image_func_medfilt
+*/
+AFAPI array medfilt_1d(const array& in, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
+
+#endif
+
 /**
     C++ Interface for minimum filter
 
@@ -1070,6 +1088,23 @@ extern "C" {
     */
     AFAPI af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad);
 
+#if AF_API_VERSION >= 34
+    /**
+        C Interface for 1D median filter
+
+        \param[out] out array is the processed signal
+        \param[in]  in array is the input signal
+        \param[in]  wind_width is the kernel width
+        \param[in]  edge_pad value will decide what happens to border when running
+                    filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+        \return     \ref AF_SUCCESS if the median filter is applied successfully,
+        otherwise an appropriate error code is returned.
+
+        \ingroup image_func_medfilt
+    */
+    AFAPI af_err af_medfilt_1d(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad);
+
+#endif
     /**
         C Interface for minimum filter
 
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index 5be7322d98..fe8908a871 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -67,6 +67,52 @@ af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, con
     return AF_SUCCESS;
 }
 
+template<typename T>
+static af_array medfilt_1d(af_array const &in, dim_t w_wid, af_border_type edge_pad)
+{
+    switch(edge_pad) {
+        case AF_PAD_ZERO : return getHandle<T>(medfilt_1d<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
+        case AF_PAD_SYM  : return getHandle<T>(medfilt_1d<T, AF_PAD_SYM >(getArray<T>(in), w_wid)); break;
+        default          : return getHandle<T>(medfilt_1d<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
+    }
+}
+
+af_err af_medfilt_1d(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
+{
+    try {
+        ARG_ASSERT(2, (wind_width>0));
+        ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
+
+        ArrayInfo info = getInfo(in);
+        af::dim4 dims  = info.dims();
+
+        dim_t input_ndims = dims.ndims();
+        DIM_ASSERT(1, (input_ndims >= 1));
+
+        if (wind_width==1) {
+            *out = retain(in);
+        } else {
+            af_array output;
+            af_dtype type  = info.getType();
+            switch(type) {
+                case f32: output = medfilt_1d<float >(in, wind_width, edge_pad); break;
+                case f64: output = medfilt_1d<double>(in, wind_width, edge_pad); break;
+                case b8 : output = medfilt_1d<char  >(in, wind_width, edge_pad); break;
+                case s32: output = medfilt_1d<int   >(in, wind_width, edge_pad); break;
+                case u32: output = medfilt_1d<uint  >(in, wind_width, edge_pad); break;
+                case s16: output = medfilt_1d<short >(in, wind_width, edge_pad); break;
+                case u16: output = medfilt_1d<ushort>(in, wind_width, edge_pad); break;
+                case u8 : output = medfilt_1d<uchar >(in, wind_width, edge_pad); break;
+                default : TYPE_ERROR(1, type);
+            }
+            std::swap(*out, output);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad)
 {
diff --git a/src/api/cpp/filters.cpp b/src/api/cpp/filters.cpp
index 900bd4e19d..ecd6b7894b 100644
--- a/src/api/cpp/filters.cpp
+++ b/src/api/cpp/filters.cpp
@@ -21,6 +21,13 @@ array medfilt(const array& in, const dim_t wind_length, const dim_t wind_width,
     return array(out);
 }
 
+array medfilt_1d(const array& in, const dim_t wind_width, const borderType edge_pad)
+{
+    af_array out = 0;
+    AF_THROW(af_medfilt_1d(&out, in.get(), wind_width, edge_pad));
+    return array(out);
+}
+
 array minfilt(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
 {
     af_array out = 0;
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 0ee211d585..5e4453c5e0 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -158,6 +158,12 @@ af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, con
     return CALL(out, in, wind_length, wind_width, edge_pad);
 }
 
+af_err af_medfilt_1d(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, wind_width, edge_pad);
+}
+
 af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
 {
     CHECK_ARRAYS(in);
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index e6e1a24499..3cfe2f1259 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -17,6 +17,88 @@ namespace cpu
 namespace kernel
 {
 
+template<typename T, af_border_type Pad>
+void medfilt_1d(Array<T> out, const Array<T> in, dim_t w_wid)
+{
+    const af::dim4 dims     = in.dims();
+    const af::dim4 istrides = in.strides();
+    const af::dim4 ostrides = out.strides();
+
+    std::vector<T> wind_vals;
+    wind_vals.reserve(w_wid);
+
+    T const * in_ptr = in.get();
+    T * out_ptr = out.get();
+
+    for(int b3=0; b3<(int)dims[3]; b3++) {
+
+        for(int b2=0; b2<(int)dims[2]; b2++) {
+
+            for(int col=0; col<(int)dims[1]; col++) {
+
+                int ocol_off = col*ostrides[1];
+
+                for(int row=0; row<(int)dims[0]; row++) {
+
+                    wind_vals.clear();
+                    for(int wi=0; wi<(int)w_wid; ++wi) {
+                        bool isRowOff = false;
+
+                        int im_row = row + wi-w_wid/2;
+                        int im_roff;
+                        switch(Pad) {
+                            case AF_PAD_ZERO:
+                                im_roff = im_row * istrides[0];
+                                if (im_row < 0 || im_row>=(int)dims[0])
+                                    isRowOff = true;
+                                break;
+                            case AF_PAD_SYM:
+                                {
+                                    if (im_row < 0) {
+                                        im_row *= -1;
+                                        isRowOff = true;
+                                    }
+
+                                    if (im_row>=(int)dims[0]) {
+                                        im_row = 2*((int)dims[0]-1) - im_row;
+                                        isRowOff = true;
+                                    }
+
+                                    im_roff = im_row * istrides[0];
+                                }
+                                break;
+                        }
+
+                        if(isRowOff) {
+                            switch(Pad) {
+                                case AF_PAD_ZERO:
+                                    wind_vals.push_back(0);
+                                    break;
+                                case AF_PAD_SYM:
+                                    wind_vals.push_back(in_ptr[im_roff]);
+                                    break;
+                            }
+                        } else {
+                            wind_vals.push_back(in_ptr[im_roff]);
+                        }
+                    }
+
+                    int off = wind_vals.size()/2;
+                    std::stable_sort(wind_vals.begin(),wind_vals.end());
+                    if (wind_vals.size()%2==0)
+                        out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
+                    else {
+                        out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
+                    }
+                }
+            }
+            in_ptr  += istrides[2];
+            out_ptr += ostrides[2];
+        }
+    }
+}
+
+
 template<typename T, af_border_type Pad>
 void medfilt(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
 {
@@ -118,9 +200,8 @@ void medfilt(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
                     int off = wind_vals.size()/2;
                     if (wind_vals.size()%2==0)
                         out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
-                    else {
+                    else
                         out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
-                    }
                 }
             }
             in_ptr  += istrides[2];
@@ -129,6 +210,5 @@ void medfilt(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
     }
 }
 
-
 }
 }
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 8c50a98263..d188e2d8f7 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -31,9 +31,23 @@ Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
     return out;
 }
 
+template<typename T, af_border_type pad>
+Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid)
+{
+    in.eval();
+
+    Array<T> out = createEmptyArray<T>(in.dims());
+
+    getQueue().enqueue(kernel::medfilt_1d<T, pad>, out, in, w_wid);
+
+    return out;
+}
+
 #define INSTANTIATE(T)\
-    template Array<T> medfilt<T, AF_PAD_ZERO     >(const Array<T> &in, dim_t w_len, dim_t w_wid); \
-    template Array<T> medfilt<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
+    template Array<T> medfilt_1d<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt_1d<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
diff --git a/src/backend/cpu/medfilt.hpp b/src/backend/cpu/medfilt.hpp
index 81234ee997..62a64003ae 100644
--- a/src/backend/cpu/medfilt.hpp
+++ b/src/backend/cpu/medfilt.hpp
@@ -15,4 +15,7 @@ namespace cpu
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
+template<typename T, af_border_type edge_pad>
+Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid);
+
 }
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 9b2e39f0ae..aeb37e2500 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -64,6 +64,31 @@ void load2ShrdMem(T * shrd, const T * in,
     }
 }
 
+template<typename T, af_border_type pad>
+__device__
+void load2ShrdMem_1d(T * shrd, const T * in,
+                    int lx, int dim0, int gx, int inStride0)
+{
+    switch(pad) {
+        case AF_PAD_ZERO:
+        {
+            if (gx<0 || gx>=dim0)
+                shrd[lx] = T(0);
+            else
+                shrd[lx] = in[gx];
+        }
+        break;
+        case AF_PAD_SYM:
+        {
+            if (gx<0) gx *= -1;
+            if (gx>=dim0) gx = 2*(dim0-1) - gx;
+
+            shrd[lx] = in[gx];
+        }
+        break;
+    }
+}
+
 template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
 __global__
 void medfilt(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
@@ -181,6 +206,111 @@ void medfilt(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
     }
 }
 
+template<typename T, af_border_type pad, unsigned w_wid>
+__global__
+void medfilt_1d(Param<T> out, CParam<T> in, int nBBS0)
+{
+    __shared__ T shrdMem[(THREADS_X+w_wid-1)];
+
+    // calculate necessary offset and window parameters
+    const int padding = w_wid-1;
+    const int halo    = padding/2;
+    const int shrdLen = blockDim.x + padding;
+
+    // batch offsets
+    unsigned b1 = blockIdx.x / nBBS0;
+    unsigned b2 = blockIdx.y;
+    unsigned b3 = blockIdx.z;
+
+    const T* iptr    = (const T *) in.ptr + (b1 * in.strides[1] + b2 *  in.strides[2] + b3 *  in.strides[3]);
+    T*       optr    = (T *      )out.ptr + (b1 * in.strides[1] + b2 * out.strides[2] + b3 * out.strides[3]);
+
+    // local neighborhood indices
+    int lx = threadIdx.x;
+
+    // global indices
+    int gx = blockDim.x * (blockIdx.x - b1 * nBBS0) + lx;
+
+    // pull signal to local memory
+    for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
+        load2ShrdMem_1d<T, pad>(shrdMem, iptr, a, in.dims[0], gx2-halo, in.strides[0]);
+    }
+
+    __syncthreads();
+
+    // Only continue if we're at a valid location
+    if (gx < in.dims[0]) {
+        const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+        // pull top half from shared memory into local memory
+        T v[ARR_SIZE];
+
+#pragma unroll
+        for(int k = 0; k <= w_wid/2 + 1; k++) {
+            v[k] = shrdMem[lx+k];
+        }
+        // with each pass, remove min and max values and add new value
+        // initial sort
+        // ensure min in first half, max in second half
+#pragma unroll
+        for(int i = 0; i < ARR_SIZE/2; i++) {
+            swap(v[i], v[ARR_SIZE-1-i]);
+        }
+        // move min in first half to first pos
+#pragma unroll
+        for(int i = 1; i < (ARR_SIZE+1)/2; i++) {
+            swap(v[0], v[i]);
+        }
+        // move max in second half to last pos
+#pragma unroll
+        for(int i = ARR_SIZE-2; i >= ARR_SIZE/2; i--) {
+            swap(v[i], v[ARR_SIZE-1]);
+        }
+
+        int last = ARR_SIZE-1;
+
+        for(int k = w_wid/2 + 2; k < w_wid; k++) {
+            // add new contestant to first position in array
+            v[0] = shrdMem[lx + k];
+
+            last--;
+
+            // place max in last half, min in first half
+            for(int i = 0; i < (last+1)/2; i++) {
+                swap(v[i], v[last-i]);
+            }
+            // now perform swaps on each half such that
+            // max is in last pos, min is in first pos
+            for(int i = 1; i <= last/2; i++) {
+                swap(v[0], v[i]);
+            }
+            for(int i = last-1; i >= (last+1)/2; i--) {
+                swap(v[i], v[last]);
+            }
+        }
+
+        // no more new contestants
+        // may still have to sort the last row
+        // each outer loop drops the min and max
+        for(int k = 0; k < last; k++) {
+            // move max/min into respective halves
+            for(int i = k; i < ARR_SIZE/2; i++) {
+                swap(v[i], v[ARR_SIZE-1-i]);
+            }
+            // move min into first pos
+            for(int i = k+1; i <= ARR_SIZE/2; i++) {
+                swap(v[k], v[i]);
+            }
+            // move max into last pos
+            for(int i = ARR_SIZE-k-2; i >= ARR_SIZE/2; i--) {
+                swap(v[i], v[ARR_SIZE-1-k]);
+            }
+        }
+
+        // pick the middle element of the first row
+        optr[gx*out.strides[0]] = v[last/2];
+    }
+}
+
 template<typename T, af_border_type pad>
 void medfilt(Param<T> out, CParam<T> in, int w_len, int w_wid)
 {
@@ -204,6 +334,28 @@ void medfilt(Param<T> out, CParam<T> in, int w_len, int w_wid)
     POST_LAUNCH_CHECK();
 }
 
+template<typename T, af_border_type pad>
+void medfilt_1d(Param<T> out, CParam<T> in, int w_wid)
+{
+    const dim3 threads(THREADS_X);
+
+    int blk_x = divup(in.dims[0], threads.x);
+
+    dim3 blocks(blk_x*in.dims[1], in.dims[2], in.dims[3] );
+
+    switch(w_wid) {
+        case  3: CUDA_LAUNCH((medfilt_1d<T,pad, 3>), blocks, threads, out, in, blk_x); break;
+        case  5: CUDA_LAUNCH((medfilt_1d<T,pad, 5>), blocks, threads, out, in, blk_x); break;
+        case  7: CUDA_LAUNCH((medfilt_1d<T,pad, 7>), blocks, threads, out, in, blk_x); break;
+        case  9: CUDA_LAUNCH((medfilt_1d<T,pad, 9>), blocks, threads, out, in, blk_x); break;
+        case 11: CUDA_LAUNCH((medfilt_1d<T,pad,11>), blocks, threads, out, in, blk_x); break;
+        case 13: CUDA_LAUNCH((medfilt_1d<T,pad,13>), blocks, threads, out, in, blk_x); break;
+        case 15: CUDA_LAUNCH((medfilt_1d<T,pad,15>), blocks, threads, out, in, blk_x); break;
+    }
+
+    POST_LAUNCH_CHECK();
+}
+
 }
 
 }
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cu
index 7f4d386177..db1f7590c6 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cu
@@ -32,9 +32,25 @@ Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
     return out;
 }
 
-#define INSTANTIATE(T)\
-    template Array<T> medfilt<T, AF_PAD_ZERO     >(const Array<T> &in, dim_t w_len, dim_t w_wid); \
-    template Array<T> medfilt<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
+template<typename T, af_border_type pad>
+Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid)
+{
+    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
+
+    const dim4 dims = in.dims();
+
+    Array<T> out    = createEmptyArray<T>(dims);
+
+    kernel::medfilt_1d<T, pad>(out, in, w_wid);
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                          \
+    template Array<T> medfilt_1d<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt_1d<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
diff --git a/src/backend/cuda/medfilt.hpp b/src/backend/cuda/medfilt.hpp
index f13935b794..4e01041218 100644
--- a/src/backend/cuda/medfilt.hpp
+++ b/src/backend/cuda/medfilt.hpp
@@ -15,4 +15,7 @@ namespace cuda
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
+template<typename T, af_border_type edge_pad>
+Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid);
+
 }
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index e4e75df767..5e84940fbd 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <kernel_headers/medfilt.hpp>
+#include <kernel_headers/medfilt_1d.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -94,6 +95,62 @@ void medfilt(Param out, const Param in)
     }
 }
 
+template<typename T, af_border_type pad, unsigned w_wid>
+void medfilt_1d(Param out, const Param in)
+{
+    try {
+        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+        static std::map<int, Program*>  mfProgs;
+        static std::map<int, Kernel*> mfKernels;
+
+        int device = getActiveDeviceId();
+
+        std::call_once( compileFlags[device], [device] () {
+
+                const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D pad="<< pad
+                        << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
+                        << " -D AF_PAD_SYM="<< AF_PAD_SYM
+                        << " -D ARR_SIZE="<< ARR_SIZE
+                        << " -D w_wid=" << w_wid;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, medfilt_1d_cl, medfilt_1d_cl_len, options.str());
+                mfProgs[device]   = new Program(prog);
+                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt_1d");
+            });
+
+        NDRange local(THREADS_X, 1, 1);
+
+        int blk_x = divup(in.info.dims[0], THREADS_X);
+
+        NDRange global(blk_x * in.info.dims[1] * THREADS_X,
+                                           in.info.dims[2],
+                                           in.info.dims[3]);
+
+        auto medfiltOp = make_kernel<Buffer, KParam,
+                                     Buffer, KParam,
+                                     cl::LocalSpaceArg,
+                                     int> (*mfKernels[device]);
+
+        size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
+
+        medfiltOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
+
+        CL_DEBUG_FINISH(getQueue());
+    } catch (cl::Error err) {
+        CL_TO_AF_ERROR(err);
+        throw;
+    }
+}
+
 }
 
 }
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 2e561f44fc..af52b97ce5 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -39,8 +39,32 @@ Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
     return out;
 }
 
-#define INSTANTIATE(T)\
-    template Array<T> medfilt<T, AF_PAD_ZERO     >(const Array<T> &in, dim_t w_len, dim_t w_wid); \
+template<typename T, af_border_type pad>
+Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid)
+{
+    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
+
+    const dim4 dims = in.dims();
+
+    Array<T> out    = createEmptyArray<T>(dims);
+
+    switch(w_wid) {
+        case  3: kernel::medfilt_1d<T, pad,  3>(out, in); break;
+        case  5: kernel::medfilt_1d<T, pad,  5>(out, in); break;
+        case  7: kernel::medfilt_1d<T, pad,  7>(out, in); break;
+        case  9: kernel::medfilt_1d<T, pad,  9>(out, in); break;
+        case 11: kernel::medfilt_1d<T, pad, 11>(out, in); break;
+        case 13: kernel::medfilt_1d<T, pad, 13>(out, in); break;
+        case 15: kernel::medfilt_1d<T, pad, 15>(out, in); break;
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                          \
+    template Array<T> medfilt_1d<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt_1d<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
     template Array<T> medfilt<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
diff --git a/src/backend/opencl/medfilt.hpp b/src/backend/opencl/medfilt.hpp
index d1ba7d388f..49e5aaab53 100644
--- a/src/backend/opencl/medfilt.hpp
+++ b/src/backend/opencl/medfilt.hpp
@@ -15,4 +15,7 @@ namespace opencl
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
+template<typename T, af_border_type edge_pad>
+Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid);
+
 }
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 2e3a1fcb6b..fe86c22015 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -26,11 +26,19 @@ class MedianFilter : public ::testing::Test
         virtual void SetUp() {}
 };
 
+template<typename T>
+class MedianFilter1d : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {}
+};
+
 // create a list of types to be tested
 typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(MedianFilter, TestTypes);
+TYPED_TEST_CASE(MedianFilter1d, TestTypes);
 
 template<typename T>
 void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
@@ -88,6 +96,63 @@ TYPED_TEST(MedianFilter, BATCH_SYMMETRIC_PAD_3x3)
     medfiltTest<TypeParam>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x3_window.test"), 3, 3, AF_PAD_SYM);
 }
 
+
+template<typename T>
+void medfilt1d_Test(string pTestFile, dim_t w_wid, af_border_type pad)
+{
+    if (noDoubleTests<T>()) return;
+
+    vector<af::dim4>  numDims;
+    vector<vector<T> >      in;
+    vector<vector<T> >   tests;
+
+    readTests<T,T,int>(pTestFile, numDims, in, tests);
+
+    af::dim4 dims      = numDims[0];
+    af_array outArray  = 0;
+    af_array inArray   = 0;
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+
+    ASSERT_EQ(AF_SUCCESS, af_medfilt_1d(&outArray, inArray, w_wid, pad));
+
+    T *outData = new T[dims.elements()];
+
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+    vector<T> currGoldBar = tests[0];
+    size_t nElems        = currGoldBar.size();
+    for (size_t elIter=0; elIter<nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+    }
+
+    // cleanup
+    delete[] outData;
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+}
+
+TYPED_TEST(MedianFilter1d, ZERO_PAD_3)
+{
+    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
+}
+
+TYPED_TEST(MedianFilter1d, SYMMETRIC_PAD_3)
+{
+    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
+}
+
+TYPED_TEST(MedianFilter1d, BATCH_ZERO_PAD_3)
+{
+    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
+}
+
+TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3)
+{
+    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
+}
+
 template<typename T,bool isColor>
 void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
 {
@@ -187,6 +252,33 @@ TYPED_TEST(MedianFilter, InvalidWindow)
     medfiltWindowTest<TypeParam>();
 }
 
+
+template<typename T>
+void medfilt1d_WindowTest(void)
+{
+    if (noDoubleTests<T>()) return;
+
+    af_array inArray   = 0;
+    af_array outArray  = 0;
+
+    vector<T>   in(100, 1);
+
+    // Check for 4D inputs
+    af::dim4 dims(10, 10, 1, 1);
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt_1d(&outArray, inArray, -1, AF_PAD_ZERO));
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+}
+
+TYPED_TEST(MedianFilter1d, InvalidWindow)
+{
+    medfilt1d_WindowTest<TypeParam>();
+}
+
 template<typename T>
 void medfiltPadTest(void)
 {
@@ -215,6 +307,33 @@ TYPED_TEST(MedianFilter, InvalidPadType)
     medfiltPadTest<TypeParam>();
 }
 
+template<typename T>
+void medfilt1d_PadTest(void)
+{
+    if (noDoubleTests<T>()) return;
+
+    af_array inArray   = 0;
+    af_array outArray  = 0;
+
+    vector<T>   in(100, 1);
+
+    // Check for 4D inputs
+    af::dim4 dims(10, 10, 1, 1);
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt_1d(&outArray, inArray, 3, af_border_type(3)));
+
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt_1d(&outArray, inArray, 3, af_border_type(-1)));
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+}
+
+TYPED_TEST(MedianFilter1d, InvalidPadType)
+{
+    medfilt1d_PadTest<TypeParam>();
+}
 
 //////////////////////////////////// CPP ////////////////////////////////////
 //
@@ -249,6 +368,36 @@ TEST(MedianFilter, CPP)
     delete[] outData;
 }
 
+TEST(MedianFilter1d, CPP)
+{
+    if (noDoubleTests<float>()) return;
+
+    const dim_t w_wid = 3;
+
+    vector<af::dim4>  numDims;
+    vector<vector<float> >      in;
+    vector<vector<float> >   tests;
+
+    readTests<float,float,int>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"),
+                               numDims, in, tests);
+
+    af::dim4 dims    = numDims[0];
+    af::array input(dims, &(in[0].front()));
+    af::array output = af::medfilt_1d(input, w_wid, AF_PAD_SYM);
+
+    float *outData = new float[dims.elements()];
+    output.host((void*)outData);
+
+    vector<float> currGoldBar = tests[0];
+    size_t nElems = currGoldBar.size();
+    for (size_t elIter=0; elIter<nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+    }
+
+    // cleanup
+    delete[] outData;
+}
+
 
 TEST(MedianFilter, Docs)
 {
@@ -309,3 +458,20 @@ TEST(MedianFilter, GFOR)
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
+
+TEST(MedianFilter1d, GFOR)
+{
+    dim4 dims = dim4(10, 10, 3);
+    array A = iota(dims);
+    array B = constant(0, dims);
+
+    gfor(seq ii, 3) {
+        B(span, ii) = medfilt_1d(A(span, ii));
+    }
+
+    for(int ii = 0; ii < 3; ii++) {
+        array c_ii = medfilt_1d(A(span, ii));
+        array b_ii = B(span, ii);
+        ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
+    }
+}

From a35806f7417aebae89bbddf89f39cbe7d666da4f Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 5 Jul 2016 15:02:30 -0400
Subject: [PATCH 0677/2677] CPU Sparse Matrix Product

Sparse Matrix X Dense Vector.
Sparse Matrix X Dense Matrix.
Sparse Matrix^Transpose X Dense Vector.
Sparse Matrix^Transpose X Dense Matrix.
---
 src/backend/cpu/kernel/sparse.hpp |  53 +++++++++
 src/backend/cpu/sparse.cpp        |  25 +++-
 src/backend/cpu/sparse_blas.cpp   | 184 ++++++++++++++++++++++++++----
 test/sparse.cpp                   |  14 ++-
 4 files changed, 243 insertions(+), 33 deletions(-)

diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index 6d0268ce4c..a059c341e0 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 #include <utility.hpp>
+#include <math.hpp>
 
 namespace cpu
 {
@@ -40,5 +41,57 @@ void coo2dense(Array<T> output,
     }
 }
 
+template<typename T>
+struct dns_csr
+{
+    void operator()(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
+                    Array<T> const in)
+    {
+        T const * const iPtr = in.get();
+        T       * const vPtr = values.get();
+        int     * const rPtr = rowIdx.get();
+        int     * const cPtr = colIdx.get();
+
+        int stride = in.strides()[1];
+        af::dim4 dims = in.dims();
+
+        int offset = 0;
+        for (int i = 0; i < dims[0]; ++i) {
+            rPtr[i] = offset;
+            for (int j = 0; j < dims[1]; ++j) {
+                if (iPtr[j*stride + i] != scalar<T>(0)) {
+                    vPtr[offset] = iPtr[j*stride + i];
+                    cPtr[offset++] = j;
+                }
+            }
+        }
+        rPtr[dims[0]] = offset;
+    }
+};
+
+template<typename T>
+struct csr_dns
+{
+    void operator()(Array<T> out,
+                    Array<T> const values, Array<int> const rowIdx, Array<int> const colIdx)
+    {
+        T         * const oPtr = out.get();
+        T   const * const vPtr = values.get();
+        int const * const rPtr = rowIdx.get();
+        int const * const cPtr = colIdx.get();
+
+        int stride = out.strides()[1];
+
+        int r = rowIdx.dims()[0];
+        for (int i = 0; i < r; i++) {
+            for (int ii = rPtr[i]; ii < rPtr[i+1]; ++ii) {
+                int j = cPtr[ii];
+                T v = vPtr[ii];
+                oPtr[j*stride + i] = v;
+            }
+        }
+    }
+};
+
 }
 }
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index c7b2411ec6..9f86a9647c 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -280,13 +280,22 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     // TODO: Make an implementation without MKL
     // Support CSC as well. MKL does not support Dense->CSC. So this will be
     // used as fallback
-    // Make these implementations like a struct. See approx1
-    AF_ERROR("CPU Implementation Without MKL Currently Not Supported", AF_ERR_NOT_SUPPORTED);
     in_.eval();
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
 
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
+    sparse_.eval();
+
+    auto func = [=] (SparseArray<T> sparse, const Array<T> in) {
+        Array<T  > values = sparse.getValues();
+        Array<int> rowIdx = sparse.getRowIdx();
+        Array<int> colIdx = sparse.getColIdx();
+
+        kernel::dns_csr<T>()(values, rowIdx, colIdx, in);
+    };
+
+    getQueue().enqueue(func, sparse_, in_);
 
     if(stype == AF_STORAGE_CSR)
         return sparse_;
@@ -303,13 +312,21 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     // Support CSC as well. MKL does not support CSC->Dense. So this will be
     // used as fallback
     // Make these implementations like a struct. See approx1
-
-    AF_ERROR("CPU Implementation Without MKL Currently Not Supported", AF_ERR_NOT_SUPPORTED);
     in_.eval();
 
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
+    auto func = [=] (Array<T> dense, const SparseArray<T> in) {
+        Array<T  > values = in.getValues();
+        Array<int> rowIdx = in.getRowIdx();
+        Array<int> colIdx = in.getColIdx();
+
+        kernel::csr_dns<T>()(dense, values, rowIdx, colIdx);
+    };
+
+    getQueue().enqueue(func, dense_, in_);
+
     if(stype == AF_STORAGE_CSR)
         return dense_;
     else
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 81957a3362..51e27c657d 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -261,13 +261,144 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 #else // Implementation without using MKL
 ////////////////////////////////////////////////////////////////////////////////
 
+template<typename T>
+T getConjugate(const T &in)
+{
+    // For non-complex types return same
+    return in;
+}
+
+template<>
+cfloat getConjugate(const cfloat &in)
+{
+    return std::conj(in);
+}
+
+template<>
+cdouble getConjugate(const cdouble &in)
+{
+    return std::conj(in);
+}
+
+template<typename T>
+void mv(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M)
+{
+    T   const * const   valPtr = values.get();
+    int const * const   rowPtr = rowIdx.get();
+    int const * const   colPtr = colIdx.get();
+    T   const * const rightPtr = right.get();
+    T         * const   outPtr = output.get();
+
+    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        outPtr[i] = scalar<T>(0);
+        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+            //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
+            outPtr[i] += valPtr[j] * rightPtr[colPtr[j]];
+        }
+    }
+}
+
+template<typename T, bool conjugate>
+void mtv(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M)
+{
+    T   const * const   valPtr = values.get();
+    int const * const   rowPtr = rowIdx.get();
+    int const * const   colPtr = colIdx.get();
+    T   const * const rightPtr = right.get();
+    T         * const   outPtr = output.get();
+
+    for (int i = 0; i < M; ++i) {
+        outPtr[i] = scalar<T>(0);
+    }
+
+    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+            //If stride[0] of right is not 1 then rightPtr[i*stride]
+            if (conjugate) {
+                outPtr[colPtr[j]] += getConjugate(valPtr[j]) * rightPtr[i];
+            } else {
+                outPtr[colPtr[j]] += valPtr[j] * rightPtr[i];
+            }
+        }
+    }
+}
+
+template<typename T>
+void mm(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M, int N,
+        int ldb, int ldc)
+{
+    T   const * const   valPtr = values.get();
+    int const * const   rowPtr = rowIdx.get();
+    int const * const   colPtr = colIdx.get();
+    T   const *       rightPtr = right.get();
+    T         *         outPtr = output.get();
+
+    for (int o = 0; o < N; ++o) {
+        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+            outPtr[i] = scalar<T>(0);
+            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+                //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
+                outPtr[i] += valPtr[j] * rightPtr[colPtr[j]];
+            }
+        }
+        rightPtr += ldb;
+        outPtr += ldc;
+    }
+}
+
+template<typename T, bool conjugate>
+void mtm(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M, int N,
+        int ldb, int ldc)
+{
+    T   const * const   valPtr = values.get();
+    int const * const   rowPtr = rowIdx.get();
+    int const * const   colPtr = colIdx.get();
+    T   const *       rightPtr = right.get();
+    T         *         outPtr = output.get();
+
+    for (int o = 0; o < N; ++o) {
+        for (int i = 0; i < M; ++i) {
+            outPtr[i] = scalar<T>(0);
+        }
+
+        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+                //If stride[0] of right is not 1 then rightPtr[i*stride]
+                if (conjugate) {
+                    outPtr[colPtr[j]] += getConjugate(valPtr[j]) * rightPtr[i];
+                } else {
+                    outPtr[colPtr[j]] += valPtr[j] * rightPtr[i];
+                }
+            }
+        }
+        rightPtr += ldb;
+        outPtr += ldc;
+    }
+}
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
-    // TODO: Make a CPU Implementation for this
-    // Make separate function for MV and MM
-    // No need to support optRhs
     lhs.eval();
     rhs.eval();
 
@@ -275,42 +406,45 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     sparse_operation_t lOpts = toSparseTranspose(optLhs);
 
     int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
-    // Commenting to avoid unused variable warnings
-    //int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
-    //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
     static const int rColDim = 1;
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
-    // Commenting to avoid unused variable warnings
-    //int K = lDims[lColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
 
-    // Commenting to avoid unused variable warnings
-    //auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
-    //    auto alpha = getScale<T, 1>();
-    //    auto beta  = getScale<T, 0>();
-
-    //    int ldb = right.strides()[1];
-    //    int ldc = output.strides()[1];
+    auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
+        int ldb = right.strides()[1];
+        int ldc = output.strides()[1];
 
-    //    Array<T  > values = left.getValues();
-    //    Array<int> rowIdx = left.getRowIdx();
-    //    Array<int> colIdx = left.getColIdx();
+        Array<T  > values = left.getValues();
+        Array<int> rowIdx = left.getRowIdx();
+        Array<int> colIdx = left.getColIdx();
 
-    //    if(rDims[rColDim] == 1) {
-    //        // Call MV
-    //    } else {
-    //        // Call MM
-    //    }
-    //};
+        if(rDims[rColDim] == 1) {
+            if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
+                mv(output, values, rowIdx, colIdx, right, M);
+            } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
+                mtv<T, false>(output, values, rowIdx, colIdx, right, M);
+            } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
+                mtv<T, true>(output, values, rowIdx, colIdx, right, M);
+            }
+        } else {
+            if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
+                mm(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+            } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
+                mtm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+            } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
+                mtm<T, true>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+            }
+        }
+    };
 
-    //getQueue().enqueue(func, out, lhs, rhs);
+    getQueue().enqueue(func, out, lhs, rhs);
 
     return out;
 }
diff --git a/test/sparse.cpp b/test/sparse.cpp
index d40a0fb32f..f165e7c333 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -77,6 +77,7 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
 #if 1
     af::array A = cpu_randu<T>(af::dim4(m, n));
     af::array B = cpu_randu<T>(af::dim4(n, k));
+    af::array C = cpu_randu<T>(af::dim4(m, k));
 #else
     af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
     af::array B = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
@@ -85,17 +86,22 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     A = makeSparse<T>(A, factor);
 
     // Result of GEMM
-    af::array dRes = matmul(A, B);
+    af::array dRes1 = matmul(A, B);
+    af::array dRes2 = matmul(A, C, AF_MAT_TRANS, AF_MAT_NONE);
 
     // Create Sparse Array From Dense
     af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
 
     // Sparse Matmul
-    af::array sRes = matmul(sA, B);
+    af::array sRes1 = matmul(sA, B);
+    af::array sRes2 = matmul(sA, C, AF_MAT_TRANS, AF_MAT_NONE);
 
     // Verify Results
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes - sRes))) / (m * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes - sRes))) / (m * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes1 - sRes1))) / (m * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes1 - sRes1))) / (m * k), eps);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes2 - sRes2))) / (n * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes2 - sRes2))) / (n * k), eps);
 }
 
 

From b0710fdff5dc1b7419fbd1f20fbd29168f853f3d Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 5 Jul 2016 15:21:16 -0400
Subject: [PATCH 0678/2677] Test for AF_MAT_CTRANS

---
 src/backend/cpu/sparse_blas.cpp | 20 ++++++++++++++------
 test/sparse.cpp                 |  5 +++++
 2 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 51e27c657d..2fe84846d9 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -280,7 +280,7 @@ cdouble getConjugate(const cdouble &in)
     return std::conj(in);
 }
 
-template<typename T>
+template<typename T, bool conjugate>
 void mv(Array<T> output,
         const Array<T> values,
         const Array<int> rowIdx,
@@ -298,7 +298,11 @@ void mv(Array<T> output,
         outPtr[i] = scalar<T>(0);
         for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
             //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
-            outPtr[i] += valPtr[j] * rightPtr[colPtr[j]];
+            if (conjugate) {
+                outPtr[i] += getConjugate(valPtr[j]) * rightPtr[colPtr[j]];
+            } else {
+                outPtr[i] += valPtr[j] * rightPtr[colPtr[j]];
+            }
         }
     }
 }
@@ -333,7 +337,7 @@ void mtv(Array<T> output,
     }
 }
 
-template<typename T>
+template<typename T, bool conjugate>
 void mm(Array<T> output,
         const Array<T> values,
         const Array<int> rowIdx,
@@ -353,7 +357,11 @@ void mm(Array<T> output,
             outPtr[i] = scalar<T>(0);
             for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
                 //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
-                outPtr[i] += valPtr[j] * rightPtr[colPtr[j]];
+                if (conjugate) {
+                    outPtr[i] += getConjugate(valPtr[j]) * rightPtr[colPtr[j]];
+                } else {
+                    outPtr[i] += valPtr[j] * rightPtr[colPtr[j]];
+                }
             }
         }
         rightPtr += ldb;
@@ -427,7 +435,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
         if(rDims[rColDim] == 1) {
             if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
-                mv(output, values, rowIdx, colIdx, right, M);
+                mv<T, false>(output, values, rowIdx, colIdx, right, M);
             } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
                 mtv<T, false>(output, values, rowIdx, colIdx, right, M);
             } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
@@ -435,7 +443,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
             }
         } else {
             if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
-                mm(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+                mm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
             } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
                 mtm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
             } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
diff --git a/test/sparse.cpp b/test/sparse.cpp
index f165e7c333..badfb28290 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -88,6 +88,7 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     // Result of GEMM
     af::array dRes1 = matmul(A, B);
     af::array dRes2 = matmul(A, C, AF_MAT_TRANS, AF_MAT_NONE);
+    af::array dRes3 = matmul(A, C, AF_MAT_CTRANS, AF_MAT_NONE);
 
     // Create Sparse Array From Dense
     af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
@@ -95,6 +96,7 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     // Sparse Matmul
     af::array sRes1 = matmul(sA, B);
     af::array sRes2 = matmul(sA, C, AF_MAT_TRANS, AF_MAT_NONE);
+    af::array sRes3 = matmul(sA, C, AF_MAT_CTRANS, AF_MAT_NONE);
 
     // Verify Results
     ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes1 - sRes1))) / (m * k), eps);
@@ -102,6 +104,9 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
 
     ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes2 - sRes2))) / (n * k), eps);
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes2 - sRes2))) / (n * k), eps);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes3 - sRes3))) / (n * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes3 - sRes3))) / (n * k), eps);
 }
 
 

From e73ab8417a56882a612696c1dde07a6128df3f10 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 5 Jul 2016 15:23:25 -0400
Subject: [PATCH 0679/2677] Removed TODO

---
 src/backend/cpu/sparse.cpp | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 9f86a9647c..fdee0e5729 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -277,9 +277,6 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
-    // TODO: Make an implementation without MKL
-    // Support CSC as well. MKL does not support Dense->CSC. So this will be
-    // used as fallback
     in_.eval();
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
@@ -308,10 +305,6 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
-    // TODO: Make an implementation without MKL
-    // Support CSC as well. MKL does not support CSC->Dense. So this will be
-    // used as fallback
-    // Make these implementations like a struct. See approx1
     in_.eval();
 
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));

From 89962b62c3c64b5895188ccaae80be2686e056f8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 8 Jul 2016 14:34:51 -0400
Subject: [PATCH 0680/2677] Fixes to build with MKL

---
 CMakeModules/FindCBLAS.cmake | 2 ++
 CMakeModules/FindFFTW.cmake  | 6 +++---
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index 52fa44879b..57512c6473 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -181,6 +181,8 @@ MACRO(CHECK_ALL_LIBRARIES
 
   IF(_bug_search_include)
     FIND_PATH(${_prefix}${_combined_name}_INCLUDE ${_include}
+      PATHS
+      ${CBLAS_ROOT_DIR}/include
       /opt/intel/mkl/include
       /usr/include
       /usr/local/include
diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index a7ce3e7826..225058992c 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -20,7 +20,7 @@
 ######## This FindFFTW.cmake file is a copy of the file from the eigen library
 ######## http://code.metager.de/source/xref/lib/eigen/cmake/FindFFTW.cmake
 
-IF(NOT FFTW_ROOT AND ENV{FFTWDIR})
+IF(NOT FFTW_ROOT)
     SET(FFTW_ROOT $ENV{FFTWDIR})
 ENDIF()
 
@@ -65,14 +65,14 @@ IF(FFTW_ROOT)
         FFTW_LIB
         NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "lib" "lib64"
+        PATH_SUFFIXES "lib" "lib64" "lib/intel64"
         NO_DEFAULT_PATH
         )
     FIND_LIBRARY(
         FFTWF_LIB
         NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "lib" "lib64"
+        PATH_SUFFIXES "lib" "lib64" "lib/intel64"
         NO_DEFAULT_PATH
         )
 

From a6224549f5406b8d00e5f8be976c640becc12bcb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 8 Jul 2016 17:51:00 -0400
Subject: [PATCH 0681/2677] Changing LU test to only check for reconstructed
 values

The earlier tests were problematic in some
multi-threaded environments where the number
of threads avaialble may change.
---
 test/lu_dense.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 0783fb3425..9bf8d720d3 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -159,11 +159,12 @@ void luTester(const int m, const int n, double eps)
     l2 = l2(af::span, af::seq(mn));
     u2 = u2(af::seq(mn), af::span);
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(l2 - l))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(l2 - l))), eps);
+    af::array a_recon2 = af::matmul(l2, u2);
+    af::array a_perm2 = a_orig(pivot2, af::span);
+
+    ASSERT_NEAR(0, af::max<double>(af::abs(real(a_recon2 - a_perm2))), eps);
+    ASSERT_NEAR(0, af::max<double>(af::abs(imag(a_recon2 - a_perm2))), eps);
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(u2 - u))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(u2 - u))), eps);
 }
 
 #define LU_BIG_TESTS(T, eps)                    \

From 8dc1f4295b156bdfe00a486060737c03ca738f01 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 8 Jul 2016 18:05:15 -0400
Subject: [PATCH 0682/2677] Moving resetFlags to be inside evalNode functions

---
 src/backend/cpu/Array.cpp        | 7 ++-----
 src/backend/cpu/kernel/Array.hpp | 3 +++
 src/backend/cuda/Array.cpp       | 4 ----
 src/backend/cuda/jit.cpp         | 4 ++++
 src/backend/opencl/Array.cpp     | 7 -------
 src/backend/opencl/jit.cpp       | 4 ++++
 6 files changed, 13 insertions(+), 16 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 9f50f89582..67161cbd76 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -93,12 +93,9 @@ void Array<T>::eval()
     data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
     getQueue().enqueue(kernel::evalArray<T>, *this);
-
+    // Reset shared_ptr
+    this->node.reset();
     ready = true;
-    Node_ptr prev = node;
-    prev->reset();
-    // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-    node.reset();
 }
 
 template<typename T>
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 1bbb7512f6..3c4a736298 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -51,6 +51,9 @@ void evalArray(Array<T> in)
             }
         }
     }
+
+    // Reset TNJ flags
+    in.node->reset();
 }
 
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 6f7694f465..3da916d1af 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -130,9 +130,7 @@ namespace cuda
 
         evalNodes(res, this->getNode().get());
         ready = true;
-        node->resetFlags();
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-        node.reset();
         node = bufferNodePtr<T>();
     }
 
@@ -179,9 +177,7 @@ namespace cuda
 
             if (array->isReady()) continue;
             array->ready = true;
-            array->node->resetFlags();
             // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-            array->node.reset();
             array->node = bufferNodePtr<T>();
         }
         return;
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index b51aca8bba..f6a470f00a 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -527,6 +527,10 @@ void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
                             getStream(getActiveDeviceId()),
                             &args.front(),
                             NULL));
+
+    for (int i = 0; i < num_outputs; i++) {
+        nodes[i]->resetFlags();
+    }
 }
 
 template<typename T>
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 8a2f0037d4..4cfee0d107 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -141,10 +141,6 @@ namespace opencl
 
         evalNodes(res, node.get());
         ready = true;
-
-        node->resetFlags();
-        // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-        node.reset();
         node = bufferNodePtr<T>();
     }
 
@@ -185,9 +181,6 @@ namespace opencl
         for (auto array : arrays) {
             if (array->isReady()) continue;
             array->ready = true;
-            array->node->resetFlags();
-            // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-            array->node.reset();
             array->node = bufferNodePtr<T>();
         }
     }
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 055b4c347e..258f57fa50 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -259,6 +259,10 @@ void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
 
         getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
 
+        for (auto node : nodes) {
+            node->resetFlags();
+        }
+
     } catch (const cl::Error &ex) {
         CL_TO_AF_ERROR(ex);
     }

From 9110b1f3e19bad28bbc420ce9826deffe8770aea Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 10 Jul 2016 01:07:11 -0400
Subject: [PATCH 0683/2677] BUGFIX: Remove race condition in CPU backend

---
 src/backend/cpu/Array.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 67161cbd76..3634b22383 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -187,6 +187,11 @@ createNodeArray(const dim4 &dims, Node_ptr node)
         if (lock_bytes > getMaxBytes() ||
             lock_buffers > getMaxBuffers()) {
 
+            // Calling sync to ensure the TNJ calls below
+            // don't overwrite the same nodes being evaluated
+            // FIXME: This should ideally be JIT specific mutex
+            getQueue().sync();
+
             unsigned length =0, buf_count = 0, bytes = 0;
             Node *n = node.get();
             n->getInfo(length, buf_count, bytes);

From cbb94436dcdd68d866dfddd8b55ea9c780082bec Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 11 Jul 2016 01:11:15 -0400
Subject: [PATCH 0684/2677] Fixes for CUDA computes for 6x

---
 src/backend/cuda/CMakeLists.txt | 2 +-
 src/backend/cuda/platform.cpp   | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 5dd3a20c88..d542c3195b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -61,7 +61,7 @@ MACRO(SET_COMPUTE VERSION)
 ENDMACRO(SET_COMPUTE)
 
 # Iterate over compute versions. Create variables and enable computes if needed
-FOREACH(VER 20 30 32 35 37 50 52 53)
+FOREACH(VER 20 30 32 35 37 50 52 53 60 61 62)
     OPTION(CUDA_COMPUTE_${VER} "CUDA Compute Capability ${VER}" OFF)
     MARK_AS_ADVANCED(CUDA_COMPUTE_${VER})
     IF(${CUDA_COMPUTE_${VER}})
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 2fa3eb93cf..e8573d18a5 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -53,6 +53,9 @@ static inline int compute2cores(int major, int minor)
         { 0x50, 128 },
         { 0x52, 128 },
         { 0x53, 128 },
+        { 0x60, 128 },
+        { 0x61, 64  },
+        { 0x62, 128 },
         {   -1, -1  },
     };
 

From 072d507358bfa6ba45e77ad9b93c0374ad17e549 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 11 Jul 2016 02:32:24 -0400
Subject: [PATCH 0685/2677] Enabling OpenMP flags when building with CUDA 8

---
 src/backend/cuda/CMakeLists.txt | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index d542c3195b..4aa45c6970 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -328,6 +328,16 @@ IF("${APPLE}")
         SET(CMAKE_STATIC_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${STD_LIB_BINDING}")
         SET(CUDA_HOST_COMPILER "/usr/bin/clang++")
     ENDIF()
+ELSE()
+  IF(UNIX)
+    IF(${CUDA_VERSION_MAJOR} GREATER 7)
+      FIND_PACKAGE(OpenMP)
+      IF(OPENMP_FOUND)
+        SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
+        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+      ENDIF()
+    ENDIF()
+  ENDIF()
 ENDIF()
 
 ## Copied from FindCUDA.cmake

From d3eb693849ee745c3785316a8c4d10908f225990 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 12 Jul 2016 16:47:32 -0400
Subject: [PATCH 0686/2677] CUDA Backend Philox and Threefry

---
 include/af/defines.h                          |   6 +
 include/af/random_engine.h                    |  89 ++++
 include/arrayfire.h                           |   1 +
 src/api/c/random_engine.cpp                   | 102 ++++
 src/api/cpp/random_engine.cpp                 |  60 +++
 src/api/unified/random_engine.cpp             |  27 ++
 src/backend/cpu/random_engine.cpp             |  43 ++
 src/backend/cpu/random_engine.hpp             |  18 +
 src/backend/cuda/kernel/random_engine.hpp     | 438 ++++++++++++++++++
 .../cuda/kernel/random_engine_philox.hpp      |  58 +++
 .../cuda/kernel/random_engine_threefry.hpp    | 118 +++++
 src/backend/cuda/random_engine.cu             |  42 ++
 src/backend/cuda/random_engine.hpp            |  18 +
 src/backend/opencl/kernel/random_engine.hpp   |  51 ++
 src/backend/opencl/random_engine.cpp          |  43 ++
 src/backend/opencl/random_engine.hpp          |  18 +
 test/random.cpp                               |  26 ++
 17 files changed, 1158 insertions(+)
 create mode 100644 include/af/random_engine.h
 create mode 100644 src/api/c/random_engine.cpp
 create mode 100644 src/api/cpp/random_engine.cpp
 create mode 100644 src/api/unified/random_engine.cpp
 create mode 100644 src/backend/cpu/random_engine.cpp
 create mode 100644 src/backend/cpu/random_engine.hpp
 create mode 100644 src/backend/cuda/kernel/random_engine.hpp
 create mode 100644 src/backend/cuda/kernel/random_engine_philox.hpp
 create mode 100644 src/backend/cuda/kernel/random_engine_threefry.hpp
 create mode 100644 src/backend/cuda/random_engine.cu
 create mode 100644 src/backend/cuda/random_engine.hpp
 create mode 100644 src/backend/opencl/kernel/random_engine.hpp
 create mode 100644 src/backend/opencl/random_engine.cpp
 create mode 100644 src/backend/opencl/random_engine.hpp

diff --git a/include/af/defines.h b/include/af/defines.h
index 9f09ff4238..e061a0c559 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -398,6 +398,11 @@ typedef enum {
     AF_BINARY_MIN  = 2,
     AF_BINARY_MAX  = 3
 } af_binary_op;
+
+typedef enum {
+    AF_RANDOM_PHILOX = 0,
+    AF_RANDOM_THREEFRY = 1
+} af_random_type;
 #endif
 
 #if AF_API_VERSION >=32
@@ -447,6 +452,7 @@ namespace af
 #endif
 #if AF_API_VERSION >= 34
     typedef af_binary_op binaryOp;
+    typedef af_random_type randomType;
 #endif
 }
 
diff --git a/include/af/random_engine.h b/include/af/random_engine.h
new file mode 100644
index 0000000000..22a591dc6b
--- /dev/null
+++ b/include/af/random_engine.h
@@ -0,0 +1,89 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+
+typedef void * af_random_engine;
+
+#ifdef __cplusplus
+namespace af
+{
+    class array;
+    class dim4;
+
+    class AFAPI randomEngine {
+        private:
+            af_random_engine engine;
+        public:
+            explicit
+            randomEngine(randomType typeIn = AF_RANDOM_PHILOX, unsigned long long seedIn = 0);
+            ~randomEngine();
+
+            array uniform(const dim_t dim0, const dtype ty = f32);
+            array uniform(const dim_t dim0, const dim_t dim1, const dtype ty = f32);
+            array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
+            array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
+            array uniform(const dim4& dims, const dtype ty = f32);
+    };
+}
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    /**
+       C Interface for creating random engine
+
+       \param[out]  engine is the pointer to the returned random engine object
+       \param[in]   rtype is the type of the random number generator
+       \param[in]   seed is the initializing seed of the random number generator
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_type rtype, unsigned long long seed);
+
+    /**
+       C Interface for creating an array of uniform numbers using a random engine
+
+       \param[out]  arr The pointer to the returned object.
+       \param[in]   engine is the random engine object
+       \param[in]   ndims The number of dimensions read from the \p dims parameter
+       \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
+       \param[in]   type The type of the \ref af_array object
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+
+    /**
+       C Interface for creating an array of normal numbers using a random engine
+
+       \param[out]  arr The pointer to the returned object.
+       \param[in]   engine is the random engine object
+       \param[in]   ndims The number of dimensions read from the \p dims parameter
+       \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
+       \param[in]   type The type of the \ref af_array object
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    //AFAPI af_err af_random_engine_normal(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+
+    /**
+       C Interface for releasing random engine
+
+       \param[in] engine is the random engine object
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_release_random_engine(af_random_engine engine);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 60df3176d1..419c74b6ae 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -268,6 +268,7 @@
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
+#include "af/random_engine.h"
 #include "af/seq.h"
 #include "af/signal.h"
 #include "af/statistics.h"
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
new file mode 100644
index 0000000000..d3be5f5b77
--- /dev/null
+++ b/src/api/c/random_engine.cpp
@@ -0,0 +1,102 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/random_engine.h>
+
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <backend.hpp>
+#include <err_common.hpp>
+#include <handle.hpp>
+#include <random_engine.hpp>
+#include <iostream>
+
+using detail::cfloat;
+using detail::cdouble;
+using detail::uchar;
+using detail::uniformDistribution;
+
+typedef struct {
+    af_random_type type;
+    unsigned long long seed;
+    unsigned long long counter;
+} af_random_engine_t;
+
+//TODO : static pointer to random_engine_t that wil hold the default random engine
+//protect this object with mutex
+
+af_random_engine getRandomEngineHandle(const af_random_engine_t engine)
+{
+    af_random_engine_t *engineHandle = new af_random_engine_t;
+    *engineHandle = engine;
+    return static_cast<af_random_engine>(engineHandle);
+}
+
+af_random_engine_t* getRandomEngine(const af_random_engine engineHandle)
+{
+    return (af_random_engine_t *)engineHandle;
+}
+
+template<typename T>
+static inline af_array uniformDistribution_(const af::dim4 &dims,
+        const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+{
+    return getHandle(uniformDistribution<T>(dims, type, seed, counter));
+}
+
+af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
+{
+    try {
+        af_random_engine_t engine{rtype, seed, 0};
+        *engineHandle = getRandomEngineHandle(engine);
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    try {
+        af_array result;
+        AF_CHECK(af_init());
+
+        af::dim4 d = verifyDims(ndims, dims);
+        af_random_engine_t *e = getRandomEngine(engine);
+
+        switch(type) {
+        case f32:   result = uniformDistribution_<float  >(d, e->type, e->seed, e->counter);    break;
+        case c32:   result = uniformDistribution_<cfloat >(d, e->type, e->seed, e->counter);    break;
+        case f64:   result = uniformDistribution_<double >(d, e->type, e->seed, e->counter);    break;
+        case c64:   result = uniformDistribution_<cdouble>(d, e->type, e->seed, e->counter);    break;
+        case s32:   result = uniformDistribution_<int    >(d, e->type, e->seed, e->counter);    break;
+        case u32:   result = uniformDistribution_<uint   >(d, e->type, e->seed, e->counter);    break;
+        case s64:   result = uniformDistribution_<intl   >(d, e->type, e->seed, e->counter);    break;
+        case u64:   result = uniformDistribution_<uintl  >(d, e->type, e->seed, e->counter);    break;
+        case s16:   result = uniformDistribution_<short  >(d, e->type, e->seed, e->counter);    break;
+        case u16:   result = uniformDistribution_<ushort >(d, e->type, e->seed, e->counter);    break;
+        case u8:    result = uniformDistribution_<uchar  >(d, e->type, e->seed, e->counter);    break;
+        case b8:    result = uniformDistribution_<char   >(d, e->type, e->seed, e->counter);    break;
+        default:    TYPE_ERROR(4, type);
+        }
+        std::swap(*out, result);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_release_random_engine(af_random_engine engineHandle)
+{
+    try {
+        delete (af_random_engine_t *)engineHandle;
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
new file mode 100644
index 0000000000..dfd1b5fa87
--- /dev/null
+++ b/src/api/cpp/random_engine.cpp
@@ -0,0 +1,60 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/random_engine.h>
+#include <af/array.h>
+#include <af/dim4.hpp>
+#include <af/data.h>
+#include "error.hpp"
+
+namespace af
+{
+    randomEngine::randomEngine(randomType type, unsigned long long seed)
+    {
+        AF_THROW(af_create_random_engine(&engine, type, seed));
+    }
+
+    randomEngine::~randomEngine()
+    {
+        if (engine) {
+            af_release_random_engine(engine);
+        }
+    }
+
+    array randomEngine::uniform(const dim_t dim0, const dtype ty)
+    {
+        dim4 d(dim0, 1, 1, 1);
+        return uniform(d, ty);
+    }
+
+    array randomEngine::uniform(const dim_t dim0, const dim_t dim1, const dtype ty)
+    {
+        dim4 d(dim0, dim1, 1, 1);
+        return uniform(d, ty);
+    }
+
+    array randomEngine::uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty)
+    {
+        dim4 d(dim0, dim1, dim2, 1);
+        return uniform(d, ty);
+    }
+
+    array randomEngine::uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty)
+    {
+        dim4 d(dim0, dim1, dim2, dim3);
+        return uniform(d, ty);
+    }
+
+    array randomEngine::uniform(const dim4& dims, const dtype ty)
+    {
+        af_array out;
+        AF_THROW(af_random_engine_uniform(&out, engine, dims.ndims(), dims.get(), ty));
+        return array(out);
+    }
+}
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
new file mode 100644
index 0000000000..7ac26549c0
--- /dev/null
+++ b/src/api/unified/random_engine.cpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright(c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/random_engine.h>
+#include "symbol_manager.hpp"
+
+af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
+{
+    return CALL(engineHandle, rtype, seed);
+}
+
+af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    return CALL(arr, engine, ndims, dims, type);
+}
+
+af_err af_release_random_engine(af_random_engine engineHandle)
+{
+    return CALL(engineHandle);
+}
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
new file mode 100644
index 0000000000..eebc101a37
--- /dev/null
+++ b/src/backend/cpu/random_engine.cpp
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <random.hpp>
+//#include <kernel/random_engine.hpp>
+#include <cassert>
+
+namespace cpu
+{
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+
+        switch(type) {
+        case AF_RANDOM_PHILOX: break;//kernel::uniformDistribution<T, AF_RANDOM_PHILOX>(dims, type, seed, counter); break;
+        case AF_RANDOM_THREEFRY: break;
+        }
+        return out;
+    }
+
+    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<cfloat>  uniformDistribution<cfloat>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<cdouble> uniformDistribution<cdouble> (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+}
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
new file mode 100644
index 0000000000..b052541908
--- /dev/null
+++ b/src/backend/cpu/random_engine.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/defines.h>
+#include <backend.hpp>
+
+namespace cpu
+{
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+}
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
new file mode 100644
index 0000000000..c08dcd8379
--- /dev/null
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -0,0 +1,438 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <dispatch.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <af/defines.h>
+#include <kernel/random_engine_philox.hpp>
+#include <kernel/random_engine_threefry.hpp>
+
+namespace cuda
+{
+namespace kernel
+{
+    //Utils
+    static const int THREADS = 256;
+    #define UINTMAXFLOAT 4294967296.0f
+    #define UINTLMAXDOUBLE 4294967296.0*4294967296.0
+    #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
+
+    __device__ static float getFloat(const uint &num)
+    {
+        return float(num)/UINTMAXFLOAT;
+    }
+
+    __device__ static double getDouble(const uint &num1, const uint &num2)
+    {
+        uintl num = (((uintl)num1)<<32) | ((uintl)num2);
+        return double(num)/UINTLMAXDOUBLE;
+    }
+
+    template <typename T>
+    __device__ static void normalize(T * const out1, T * const out2, const T &r1, const T &r2)
+    {
+        T r = sqrt((T)(-2.0) * log(r1));
+        T theta = 2 * (T)PI_VAL * r2;
+        *out1 = r*sin(theta);
+        *out2 = r*cos(theta);
+    }
+
+    //Writes without boundary checking
+
+    __device__ static void writeOut256Bytes(uchar *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index]                 =     r1;
+        out[index +    blockDim.x] =  r1>>8;
+        out[index +  2*blockDim.x] = r1>>16;
+        out[index +  3*blockDim.x] = r1>>24;
+        out[index +  4*blockDim.x] =     r2;
+        out[index +  5*blockDim.x] =  r2>>8;
+        out[index +  6*blockDim.x] = r2>>16;
+        out[index +  7*blockDim.x] = r2>>24;
+        out[index +  8*blockDim.x] =     r3;
+        out[index +  9*blockDim.x] =  r3>>8;
+        out[index + 10*blockDim.x] = r3>>16;
+        out[index + 11*blockDim.x] = r3>>24;
+        out[index + 12*blockDim.x] =     r4;
+        out[index + 13*blockDim.x] =  r4>>8;
+        out[index + 14*blockDim.x] = r4>>16;
+        out[index + 15*blockDim.x] = r4>>24;
+    }
+
+    __device__ static void writeOut256Bytes(char *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index]                 = (r1   )&0x1;
+        out[index +    blockDim.x] = (r1>>1)&0x1;
+        out[index +  2*blockDim.x] = (r1>>2)&0x1;
+        out[index +  3*blockDim.x] = (r1>>3)&0x1;
+        out[index +  4*blockDim.x] = (r2   )&0x1;
+        out[index +  5*blockDim.x] = (r2>>1)&0x1;
+        out[index +  6*blockDim.x] = (r2>>2)&0x1;
+        out[index +  7*blockDim.x] = (r2>>3)&0x1;
+        out[index +  8*blockDim.x] = (r3   )&0x1;
+        out[index +  9*blockDim.x] = (r3>>1)&0x1;
+        out[index + 10*blockDim.x] = (r3>>2)&0x1;
+        out[index + 11*blockDim.x] = (r3>>3)&0x1;
+        out[index + 12*blockDim.x] = (r4   )&0x1;
+        out[index + 13*blockDim.x] = (r4>>1)&0x1;
+        out[index + 14*blockDim.x] = (r4>>2)&0x1;
+        out[index + 15*blockDim.x] = (r4>>3)&0x1;
+    }
+
+    __device__ static void writeOut256Bytes(short *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index]                =     r1;
+        out[index +   blockDim.x] = r1>>16;
+        out[index + 2*blockDim.x] =     r2;
+        out[index + 3*blockDim.x] = r2>>16;
+        out[index + 4*blockDim.x] =     r3;
+        out[index + 5*blockDim.x] = r3>>16;
+        out[index + 6*blockDim.x] =     r4;
+        out[index + 7*blockDim.x] = r4>>16;
+    }
+
+    __device__ static void writeOut256Bytes(ushort *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        writeOut256Bytes((short*)(out), index, r1, r2, r3, r4);
+    }
+
+    __device__ static void writeOut256Bytes(int *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index]                = r1;
+        out[index +   blockDim.x] = r2;
+        out[index + 2*blockDim.x] = r3;
+        out[index + 3*blockDim.x] = r4;
+    }
+
+    __device__ static void writeOut256Bytes(uint *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        writeOut256Bytes((int*)(out), index, r1, r2, r3, r4);
+    }
+
+    __device__ static void writeOut256Bytes(intl *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        intl c1 = r2;
+        c1 = (c1<<32) | r1;
+        intl c2 = r4;
+        c2 = (c2<<32) | r3;
+        out[index]              = c1;
+        out[index + blockDim.x] = c2;
+    }
+
+    __device__ static void writeOut256Bytes(uintl *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        writeOut256Bytes((intl*)(out), index, r1, r2, r3, r4);
+    }
+
+    __device__ static void writeOut256Bytes(float *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index]                = getFloat(r1);
+        out[index +   blockDim.x] = getFloat(r2);
+        out[index + 2*blockDim.x] = getFloat(r3);
+        out[index + 3*blockDim.x] = getFloat(r4);
+    }
+
+    __device__ static void writeOut256Bytes(cfloat *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index].x              = getFloat(r1);
+        out[index].y              = getFloat(r2);
+        out[index + blockDim.x].x = getFloat(r3);
+        out[index + blockDim.x].y = getFloat(r4);
+    }
+
+    __device__ static void writeOut256Bytes(double *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index]              = getDouble(r1, r2);
+        out[index + blockDim.x] = getDouble(r3, r4);
+    }
+
+    __device__ static void writeOut256Bytes(cdouble *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        out[index].x = getDouble(r1, r2);
+        out[index].y = getDouble(r3, r4);
+    }
+
+    //Normalized writes without boundary checking
+
+    __device__ static void normalizedWriteOut256Bytes(float *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        normalize(&out[index]               , &out[index +   blockDim.x], getFloat(r1), getFloat(r2));
+        normalize(&out[index + 2*blockDim.x], &out[index + 3*blockDim.x], getFloat(r1), getFloat(r2));
+    }
+
+    __device__ static void normalizedWriteOut256Bytes(cfloat *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        normalize(&out[index].x             , &out[index].y             , getFloat(r1), getFloat(r2));
+        normalize(&out[index + blockDim.x].x, &out[index + blockDim.x].y, getFloat(r3), getFloat(r4));
+    }
+
+    __device__ static void normalizedWriteOut256Bytes(double *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        normalize(&out[index], &out[index + blockDim.x], getDouble(r1, r2), getDouble(r3, r4));
+    }
+
+    __device__ static void normalizedWriteOut256Bytes(cdouble *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
+    {
+        normalize(&out[index].x, &out[index].y, getDouble(r1, r2), getDouble(r3, r4));
+    }
+
+    //Writes with boundary checking
+
+    __device__ static void partialWriteOut256Bytes(uchar *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index                 < elements) {out[index]                 =     r1;}
+        if (index +    blockDim.x < elements) {out[index +    blockDim.x] =  r1>>8;}
+        if (index +  2*blockDim.x < elements) {out[index +  2*blockDim.x] = r1>>16;}
+        if (index +  3*blockDim.x < elements) {out[index +  3*blockDim.x] = r1>>24;}
+        if (index +  4*blockDim.x < elements) {out[index +  4*blockDim.x] =     r2;}
+        if (index +  5*blockDim.x < elements) {out[index +  5*blockDim.x] =  r2>>8;}
+        if (index +  6*blockDim.x < elements) {out[index +  6*blockDim.x] = r2>>16;}
+        if (index +  7*blockDim.x < elements) {out[index +  7*blockDim.x] = r2>>24;}
+        if (index +  8*blockDim.x < elements) {out[index +  8*blockDim.x] =     r3;}
+        if (index +  9*blockDim.x < elements) {out[index +  9*blockDim.x] =  r3>>8;}
+        if (index + 10*blockDim.x < elements) {out[index + 10*blockDim.x] = r3>>16;}
+        if (index + 11*blockDim.x < elements) {out[index + 11*blockDim.x] = r3>>24;}
+        if (index + 12*blockDim.x < elements) {out[index + 12*blockDim.x] =     r4;}
+        if (index + 13*blockDim.x < elements) {out[index + 13*blockDim.x] =  r4>>8;}
+        if (index + 14*blockDim.x < elements) {out[index + 14*blockDim.x] = r4>>16;}
+        if (index + 15*blockDim.x < elements) {out[index + 15*blockDim.x] = r4>>24;}
+    }
+
+    __device__ static void partialWriteOut256Bytes(char *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index                 < elements) {out[index]                 = (r1   )&0x1;}
+        if (index +    blockDim.x < elements) {out[index +    blockDim.x] = (r1>>1)&0x1;}
+        if (index +  2*blockDim.x < elements) {out[index +  2*blockDim.x] = (r1>>2)&0x1;}
+        if (index +  3*blockDim.x < elements) {out[index +  3*blockDim.x] = (r1>>3)&0x1;}
+        if (index +  4*blockDim.x < elements) {out[index +  4*blockDim.x] = (r2   )&0x1;}
+        if (index +  5*blockDim.x < elements) {out[index +  5*blockDim.x] = (r2>>1)&0x1;}
+        if (index +  6*blockDim.x < elements) {out[index +  6*blockDim.x] = (r2>>2)&0x1;}
+        if (index +  7*blockDim.x < elements) {out[index +  7*blockDim.x] = (r2>>3)&0x1;}
+        if (index +  8*blockDim.x < elements) {out[index +  8*blockDim.x] = (r3   )&0x1;}
+        if (index +  9*blockDim.x < elements) {out[index +  9*blockDim.x] = (r3>>1)&0x1;}
+        if (index + 10*blockDim.x < elements) {out[index + 10*blockDim.x] = (r3>>2)&0x1;}
+        if (index + 11*blockDim.x < elements) {out[index + 11*blockDim.x] = (r3>>3)&0x1;}
+        if (index + 12*blockDim.x < elements) {out[index + 12*blockDim.x] = (r4   )&0x1;}
+        if (index + 13*blockDim.x < elements) {out[index + 13*blockDim.x] = (r4>>1)&0x1;}
+        if (index + 14*blockDim.x < elements) {out[index + 14*blockDim.x] = (r4>>2)&0x1;}
+        if (index + 15*blockDim.x < elements) {out[index + 15*blockDim.x] = (r4>>3)&0x1;}
+    }
+
+    __device__ static void partialWriteOut256Bytes(short *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index                < elements) {out[index]                =     r1;}
+        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = r1>>16;}
+        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] =     r2;}
+        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = r2>>16;}
+        if (index + 4*blockDim.x < elements) {out[index + 4*blockDim.x] =     r3;}
+        if (index + 5*blockDim.x < elements) {out[index + 5*blockDim.x] = r3>>16;}
+        if (index + 6*blockDim.x < elements) {out[index + 6*blockDim.x] =     r4;}
+        if (index + 7*blockDim.x < elements) {out[index + 7*blockDim.x] = r4>>16;}
+    }
+
+    __device__ static void partialWriteOut256Bytes(ushort *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        partialWriteOut256Bytes((short*)(out), index, r1, r2, r3, r4, elements);
+    }
+
+    __device__ static void partialWriteOut256Bytes(int *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index                < elements) {out[index]                = r1;}
+        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = r2;}
+        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = r3;}
+        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = r4;}
+    }
+
+    __device__ static void partialWriteOut256Bytes(uint *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        partialWriteOut256Bytes((int*)(out), index, r1, r2, r3, r4, elements);
+    }
+
+    __device__ static void partialWriteOut256Bytes(intl *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        intl c1 = r2;
+        c1 = (c1<<32) | r1;
+        intl c2 = r4;
+        c2 = (c2<<32) | r3;
+        if (index              < elements) {out[index]              = c1;}
+        if (index + blockDim.x < elements) {out[index + blockDim.x] = c2;}
+    }
+
+    __device__ static void partialWriteOut256Bytes(uintl *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        partialWriteOut256Bytes((intl*)(out), index, r1, r2, r3, r4, elements);
+    }
+
+    __device__ static void partialWriteOut256Bytes(float *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index                < elements) {out[index]                = getFloat(r1);}
+        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = getFloat(r2);}
+        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = getFloat(r3);}
+        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = getFloat(r4);}
+    }
+
+    __device__ static void partialWriteOut256Bytes(cfloat *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index              < elements) {
+            out[index].x              = getFloat(r1);
+            out[index].y              = getFloat(r2);
+        }
+        if (index + blockDim.x < elements) {
+            out[index + blockDim.x].x = getFloat(r3);
+            out[index + blockDim.x].y = getFloat(r4);
+        }
+    }
+
+    __device__ static void partialWriteOut256Bytes(double *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index              < elements) {out[index]              = getDouble(r1, r2);}
+        if (index + blockDim.x < elements) {out[index + blockDim.x] = getDouble(r3, r4);}
+    }
+
+    __device__ static void partialWriteOut256Bytes(cdouble *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        if (index < elements) {
+            out[index].x = getDouble(r1, r2);
+            out[index].y = getDouble(r3, r4);
+        }
+    }
+
+    //Normalized writes with boundary checking
+
+    __device__ static void partialNormalizedWriteOut256Bytes(float *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        float n1, n2, n3, n4;
+        normalize(&n1, &n2, getFloat(r1), getFloat(r2));
+        normalize(&n3, &n4, getFloat(r3), getFloat(r4));
+        if (index                < elements) {out[index]                = n1;}
+        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = n2;}
+        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = n3;}
+        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = n4;}
+    }
+
+    __device__ static void partialNormalizedWriteOut256Bytes(cfloat *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        float n1, n2, n3, n4;
+        normalize(&n1, &n2, getFloat(r1), getFloat(r2));
+        normalize(&n3, &n4, getFloat(r3), getFloat(r4));
+        if (index              < elements) {
+            out[index].x              = n1;
+            out[index].y              = n2;
+        }
+        if (index + blockDim.x < elements) {
+            out[index + blockDim.x].x = n3;
+            out[index + blockDim.x].y = n4;
+        }
+    }
+
+    __device__ static void partialNormalizedWriteOut256Bytes(double *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        double n1, n2;
+        normalize(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+        if (index              < elements) {out[index]              = n1;}
+        if (index + blockDim.x < elements) {out[index + blockDim.x] = n2;}
+    }
+
+    __device__ static void partialNormalizedWriteOut256Bytes(cdouble *out, const uint &index,
+            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
+    {
+        double n1, n2;
+        normalize(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+        if (index < elements) {
+            out[index].x = n1;
+            out[index].y = n2;
+        }
+    }
+
+    template <typename T>
+    __global__ void uniformPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    {
+        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
+        uint key[2] = {index, hi};
+        uint ctr[4] = {index+counter, 0, 0, lo};
+        if (blockIdx.x != (gridDim.x - 1)) {
+            philox(key, ctr);
+            writeOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
+        } else {
+            philox(key, ctr);
+            partialWriteOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
+        }
+    }
+
+    template <typename T>
+    __global__ void uniformThreefry(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    {
+        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
+        uint key[2] = {index, hi};
+        uint ctr[2] = {index+counter, lo};
+        uint o[4];
+        if (blockIdx.x != (gridDim.x - 1)) {
+            threefry(key, ctr, o);
+            ctr[1] += elements;
+            threefry(key, ctr, o + 2);
+            writeOut256Bytes(out, index, o[0], o[1], o[2], o[3]);
+        } else {
+            threefry(key, ctr, o);
+            ctr[1] += elements;
+            threefry(key, ctr, o + 2);
+            partialWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3], elements);
+        }
+    }
+
+    template <typename T, af_random_type Type>
+    void uniformDistribution(T *out, size_t elements, const uintl seed, uintl &counter)
+    {
+        int threads = THREADS;
+        int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
+        int blocks = divup(elements, elementsPerBlock);
+        uint hi = seed>>32;
+        uint lo = seed;
+        uintl count = counter;
+        switch (Type) {
+        case AF_RANDOM_PHILOX : CUDA_LAUNCH(uniformPhilox, blocks, threads,
+                out, hi, lo, count, elementsPerBlock, elements); break;
+        case AF_RANDOM_THREEFRY : CUDA_LAUNCH(uniformThreefry, blocks, threads,
+                out, hi, lo, count, elementsPerBlock, elements); break;
+        }
+        counter += elements;
+    }
+}
+}
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
new file mode 100644
index 0000000000..049713ce1a
--- /dev/null
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace cuda
+{
+namespace kernel
+{
+    //Utils
+#define m4x32_0 uint(0xD2511F53)
+#define m4x32_1 uint(0xCD9E8D57)
+#define w32_0 uint(0x9E3779B9)
+#define w32_1 uint(0xBB67AE85)
+
+    static inline __device__ void mulhilo(const uint &a, const uint &b, uint &hi, uint &lo)
+    {
+        hi = __umulhi(a,b);
+        lo = a*b;
+    }
+
+    static inline __device__ void philoxBump(uint k[2])
+    {
+        k[0] += w32_0;
+        k[1] += w32_1;
+    }
+
+    static inline __device__ void philoxRound(const uint k[2], uint c[4])
+    {
+        uint hi0, lo0, hi1, lo1;
+        mulhilo(m4x32_0, c[0], hi0, lo0);
+        mulhilo(m4x32_1, c[2], hi1, lo1);
+        c[0] = hi1^c[1]^k[0];
+        c[1] = lo1;
+        c[2] = hi0^c[3]^k[1];
+        c[3] = lo0;
+    }
+
+    static inline __device__ void philox(uint key[2], uint ctr[4])
+    {
+        //10 Rounds
+                           philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+    }
+}
+}
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
new file mode 100644
index 0000000000..2f066909d6
--- /dev/null
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -0,0 +1,118 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace cuda
+{
+namespace kernel
+{
+    //Utils
+#define SKEIN_KS_PARITY32 0x1BD11BDA
+#define SKEIN_KS_PARITY64 0x1BD11BDAA9FC1A22
+
+    static const uint R0_32=13;
+    static const uint R1_32=15;
+    static const uint R2_32=26;
+    static const uint R3_32= 6;
+    static const uint R4_32=17;
+    static const uint R5_32=29;
+    static const uint R6_32=16;
+    static const uint R7_32=24;
+
+    static const uint R0_64=16;
+    static const uint R1_64=42;
+    static const uint R2_64=12;
+    static const uint R3_64=31;
+    static const uint R4_64=16;
+    static const uint R5_64=32;
+    static const uint R6_64=24;
+    static const uint R7_64=21;
+
+    static inline __device__ void setSkeinParity(uint *ptr)
+    {
+        *ptr = SKEIN_KS_PARITY32;
+    }
+
+    static inline __device__ void setSkeinParity(uintl *ptr)
+    {
+        *ptr = SKEIN_KS_PARITY64;
+    }
+
+    static inline __device__ uintl rotL(uintl x, uint N)
+    {
+        return (x << (N & 63)) | (x >> ((64-N) & 63));
+    }
+
+    static inline __device__ uint rotL(uint x, uint N)
+    {
+        return (x << (N & 31)) | (x >> ((32-N) & 31));
+    }
+
+    template <typename T, uint R0, uint R1, uint R2, uint R3, uint R4, uint R5, uint R6, uint R7>
+    static inline __device__ void threefry_kernel(T k[2], T c[2], T X[2])
+    {
+        T ks[3];
+
+        setSkeinParity(&ks[2]);
+        ks[0] = k[0];
+        X[0] = c[0];
+        ks[2] ^= k[0];
+        ks[1] = k[1];
+        X[1] = c[1];
+        ks[2] ^= k[1];
+
+        X[0] += ks[0]; X[1] += ks[1];
+
+        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+        /* InjectKey(r=1) */
+        X[0] += ks[1]; X[1] += ks[2];
+        X[1] += 1;     /* X[2-1] += r  */
+
+        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+        /* InjectKey(r=2) */
+        X[0] += ks[2]; X[1] += ks[0];
+        X[1] += 2;
+
+        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+        /* InjectKey(r=3) */
+        X[0] += ks[0]; X[1] += ks[1];
+        X[1] += 3;
+
+        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+        /* InjectKey(r=4) */
+        X[0] += ks[1]; X[1] += ks[2];
+        X[1] += 4;
+    }
+
+    __device__ void threefry(uint k[2], uint c[2], uint X[2])
+    {
+        threefry_kernel<uint, R0_32, R1_32, R2_32, R3_32, R4_32, R5_32, R6_32, R7_32>(k, c, X);
+    }
+
+    __device__ void threefry(uintl k[2], uintl c[2], uintl X[2])
+    {
+        threefry_kernel<uintl, R0_64, R1_64, R2_64, R3_64, R4_64, R5_64, R6_64, R7_64>(k, c, X);
+    }
+}
+}
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
new file mode 100644
index 0000000000..17443d837c
--- /dev/null
+++ b/src/backend/cuda/random_engine.cu
@@ -0,0 +1,42 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <kernel/random_engine.hpp>
+#include <cassert>
+
+namespace cuda
+{
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+
+        switch(type) {
+        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<T,    AF_RANDOM_PHILOX>(out.get(), out.elements(), seed, counter); break;
+        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<T,  AF_RANDOM_THREEFRY>(out.get(), out.elements(), seed, counter); break;
+        }
+        return out;
+    }
+
+    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<cfloat>  uniformDistribution<cfloat>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<cdouble> uniformDistribution<cdouble> (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+}
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
new file mode 100644
index 0000000000..4be4be97cb
--- /dev/null
+++ b/src/backend/cuda/random_engine.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/defines.h>
+#include <backend.hpp>
+
+namespace cuda
+{
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+}
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
new file mode 100644
index 0000000000..aac31640f1
--- /dev/null
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -0,0 +1,51 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <platform.hpp>
+#include <af/defines.h>
+//#include <kernel_headers/random.hpp>
+#include <traits.hpp>
+#include <sstream>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <err_opencl.hpp>
+#include <debug_opencl.hpp>
+#include <program.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        template <typename T, af_random_type Type>
+        void uniformDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>  ranProgs;
+                static std::map<int, Kernel*>   ranKernels;
+                int device = getActiveDeviceId();
+
+            } catch (cl::Error ex) {
+                CL_TO_AF_ERROR(ex);
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
new file mode 100644
index 0000000000..b3102136ea
--- /dev/null
+++ b/src/backend/opencl/random_engine.cpp
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <kernel/random_engine.hpp>
+#include <cassert>
+
+namespace opencl
+{
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+    {
+        verifyDoubleSupport<T>();
+        Array<T> out = createEmptyArray<T>(dims);
+
+        switch(type) {
+        case AF_RANDOM_PHILOX: kernel::uniformDistribution<T, AF_RANDOM_PHILOX>(*out.get(), out.elements(), seed, counter); break;
+        case AF_RANDOM_THREEFRY: break;
+        }
+        return out;
+    }
+
+    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<cfloat>  uniformDistribution<cfloat>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<cdouble> uniformDistribution<cdouble> (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+}
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
new file mode 100644
index 0000000000..b4045bb5a2
--- /dev/null
+++ b/src/backend/opencl/random_engine.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/defines.h>
+#include <backend.hpp>
+
+namespace opencl
+{
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+}
diff --git a/test/random.cpp b/test/random.cpp
index 74f7e6541b..6a75e14e3c 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -23,6 +23,10 @@ using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::array;
+using af::randomEngine;
+using af::mean;
+using af::stdev;
 
 template<typename T>
 class Random : public ::testing::Test
@@ -235,3 +239,25 @@ TYPED_TEST(Random, getSeed)
 {
     testGetSeed<TypeParam>(1234, 9876);
 }
+
+TEST(Random, philoxEngine)
+{
+    int elem = 16*1024*1024;
+    af::randomEngine r(AF_RANDOM_PHILOX, 0);
+    array A = r.uniform(elem, f32);
+    float m = mean<float>(A);
+    float s = stdev<float>(A);
+    ASSERT_NEAR(m, 0.5, 1e-3);
+    ASSERT_NEAR(s, 0.2887, 1e-3);
+}
+
+TEST(Random, threefryEngine)
+{
+    int elem = 16*1024*1024;
+    af::randomEngine r(AF_RANDOM_THREEFRY, 0);
+    array A = r.uniform(elem, f32);
+    float m = mean<float>(A);
+    float s = stdev<float>(A);
+    ASSERT_NEAR(m, 0.5, 1e-3);
+    ASSERT_NEAR(s, 0.2887, 1e-3);
+}

From d10568d5b7834a4e8b5aaeea811520f0cfe4b0b7 Mon Sep 17 00:00:00 2001
From: Andreas Schuh <andreas.schuh.84@gmail.com>
Date: Wed, 13 Jul 2016 17:27:31 +0100
Subject: [PATCH 0687/2677] FIX: Add include directories BEFORE system paths

---
 CMakeLists.txt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 71ca1d787a..dd03c72433 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -48,7 +48,7 @@ FIND_PACKAGE(FreeImage)
 IF(FREEIMAGE_FOUND)
     ADD_DEFINITIONS(-DWITH_FREEIMAGE)
     SET(FreeImage_LIBS ${FREEIMAGE_LIBRARY})
-    INCLUDE_DIRECTORIES(${FREEIMAGE_INCLUDE_PATH})
+    INCLUDE_DIRECTORIES(BEFORE ${FREEIMAGE_INCLUDE_PATH})
 ELSE(FREEIMAGE_FOUND)
     MESSAGE(WARNING, "FreeImage not found!")
 ENDIF(FREEIMAGE_FOUND)
@@ -67,7 +67,7 @@ IF(BUILD_GRAPHICS)
         ADD_DEFINITIONS(-DGLEW_MX -DWITH_GRAPHICS)
         FIND_PACKAGE(GLEWmx REQUIRED)
 
-        INCLUDE_DIRECTORIES(
+        INCLUDE_DIRECTORIES(BEFORE
             ${FORGE_INCLUDE_DIRECTORIES}
             ${GLEW_INCLUDE_DIR}
             )
@@ -79,7 +79,7 @@ IF(BUILD_GRAPHICS)
 
         IF(APPLE)
             FIND_PACKAGE(X11 REQUIRED)
-            INCLUDE_DIRECTORIES(${X11_INCLUDE_DIR})
+            INCLUDE_DIRECTORIES(BEFORE ${X11_INCLUDE_DIR})
         ENDIF(APPLE)
 
     ELSE(FORGE_FOUND)
@@ -110,7 +110,7 @@ IF(${BUILD_NONFREE_SIFT})
     "Columbia.")
 ENDIF(${BUILD_NONFREE_SIFT})
 
-INCLUDE_DIRECTORIES(
+INCLUDE_DIRECTORIES(BEFORE
     "${CMAKE_CURRENT_SOURCE_DIR}/include"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/backend"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/api/c"

From 770df618c33001486208f00cae1dbc39e9b7f0db Mon Sep 17 00:00:00 2001
From: Andreas Schuh <andreas.schuh.84@gmail.com>
Date: Wed, 13 Jul 2016 18:04:14 +0100
Subject: [PATCH 0688/2677] FIX: xforge-ext library file path when build with
 Xcode

---
 CMakeModules/build_forge.cmake | 23 +++++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 17aafcee9d..667b5a452a 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -2,13 +2,31 @@ INCLUDE(ExternalProject)
 
 SET(prefix ${CMAKE_BINARY_DIR}/third_party/forge)
 
+IF(CMAKE_GENERATOR MATCHES "Xcode") # TODO: Also for "Visual Studio"?
+    # FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
+    #        does not yet support it for the OUTPUT argument, see also:
+    #        - Old "duplicate":   https://cmake.org/Bug/view.php?id=12877
+    #        - Old issue tracker: https://cmake.org/Bug/view.php?id=13840
+    #        - New issue tracker: https://gitlab.kitware.com/cmake/cmake/issues/13840
+    #        In the meantime, use CMAKE_BUILD_TYPE if set by user, assuming that it
+    #        is the primary build configuration used. Otherwise, default to Release.
+    IF(CMAKE_BUILD_TYPE)
+        SET(forge_lib_config ${CMAKE_BUILD_TYPE})
+    ELSE()
+        SET(forge_lib_config Release)
+    ENDIF()
+    SET(forge_lib_infix "/${forge_lib_config}")
+ELSE()
+    SET(forge_lib_config "$<CONFIG>")
+    SET(forge_lib_infix)
+ENDIF()
 IF(WIN32)
     SET(forge_lib_prefix "${prefix}/lib")
 ELSE(WIN32)
     SET(forge_lib_prefix "${prefix}/src/forge-ext-build/src")
 ENDIF(WIN32)
 
-SET(forge_location "${forge_lib_prefix}/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
+SET(forge_location "${forge_lib_prefix}${forge_lib_infix}/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
 IF(CMAKE_VERSION VERSION_LESS 3.2)
     IF(CMAKE_GENERATOR MATCHES "Ninja")
         MESSAGE(WARNING "Building forge with Ninja has known issues with CMake older than 3.2")
@@ -36,6 +54,7 @@ ExternalProject_Add(
     -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
     -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
     -DBUILD_EXAMPLES:BOOL=OFF
+    BUILD_COMMAND ${CMAKE_COMMAND} --build . --config ${forge_lib_config}
     ${byproducts}
     )
 
@@ -46,7 +65,7 @@ SET_TARGET_PROPERTIES(forge PROPERTIES IMPORTED_LOCATION ${forge_location})
 IF(WIN32)
     SET_TARGET_PROPERTIES(forge PROPERTIES IMPORTED_IMPLIB ${forge_lib_prefix}/forge.lib)
 ELSE(WIN32)
-    SET(forge_bindir_location ${binary_dir}/src/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX})
+    SET(forge_bindir_location ${binary_dir}/src${forge_lib_infix}/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX})
     IF(NOT (${forge_bindir_location} STREQUAL ${forge_location}))
         MESSAGE(WARNING "Did the forge binary location move? (Have ${forge_bindir_location} vs ${forge_location})")
     ENDIF()

From ae925072550b30d5680099b00c651dc8c917cc02 Mon Sep 17 00:00:00 2001
From: Andreas Schuh <andreas.schuh.84@gmail.com>
Date: Thu, 14 Jul 2016 11:25:10 +0100
Subject: [PATCH 0689/2677] FIX: Add -framework CUDA linker flags to test
 targets for Clang on OS X

---
 test/CMakeLists.txt | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 863353dcbb..ec82ee9ae0 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -64,6 +64,12 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
       SET(TEST_FILES ${FILES})
     ENDIF(${BACKEND} STREQUAL "unified")
 
+    # libcuda.dylib depends on @rpath/CUDA.framework/Versions/A/CUDA in /Library/Frameworks
+    SET(TEST_LINK_FLAGS)
+    IF(${DEF_NAME} STREQUAL "CUDA" AND "${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
+        SET(TEST_LINK_FLAGS -F/Library/Frameworks -Xlinker -framework -Xlinker CUDA)
+    ENDIF(${DEF_NAME} STREQUAL "CUDA" AND "${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
+
     IF (${BUILD_SINGLE_TEST_FILE})
       SET(TEST_NAME test_${BACKEND})
       SET(TEST_NAME_BASIC test_basic_${BACKEND})
@@ -85,6 +91,10 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
         COMPILE_FLAGS -DAF_${DEF_NAME}
         FOLDER "Tests/${BACKEND}")
 
+      IF(TEST_LINK_FLAGS)
+        SET_TARGET_PROPERTIES(${TEST_NAME_BASIC} PROPERTIES LINK_FLAGS ${TEST_LINK_FLAGS})
+      ENDIF(TEST_LINK_FLAGS)
+
     ELSE()
       FOREACH(FILE ${TEST_FILES})
         GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
@@ -109,6 +119,10 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
           PROPERTIES
           COMPILE_FLAGS -DAF_${DEF_NAME}
           FOLDER "Tests/${BACKEND}")
+
+        IF(TEST_LINK_FLAGS)
+          SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS ${TEST_LINK_FLAGS})
+        ENDIF(TEST_LINK_FLAGS)
       ENDFOREACH()
     ENDIF()
 

From 4345f39938857fcd924c06cd5016929ca427fb0b Mon Sep 17 00:00:00 2001
From: Andreas Schuh <andreas.schuh.84@gmail.com>
Date: Thu, 14 Jul 2016 11:23:10 +0100
Subject: [PATCH 0690/2677] FIX: TEST_DIR setting for Xcode generator and CMake
 >=3.5

---
 test/CMakeLists.txt | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 863353dcbb..5534f53955 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -139,11 +139,13 @@ ELSE(${USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
     SET(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
 ENDIF(${USE_RELATIVE_TEST_DIR})
 
-IF (${CMAKE_GENERATOR} STREQUAL "Xcode")
+# Workaround for Xcode generator escaping issue, see
+# - https://cmake.org/cmake/help/v3.5/release/3.5.html#deprecated-and-removed-features
+IF (CMAKE_VERSION VERSION_LESS 3.5 AND ${CMAKE_GENERATOR} STREQUAL "Xcode")
     ADD_DEFINITIONS("-D TEST_DIR=\"\\\\\"${TESTDATA_SOURCE_DIR}\\\\\"\"")
-ELSE (${CMAKE_GENERATOR} STREQUAL "Xcode")
+ELSE (CMAKE_VERSION VERSION_LESS 3.5 AND ${CMAKE_GENERATOR} STREQUAL "Xcode")
     ADD_DEFINITIONS("-D TEST_DIR=\"\\\"${TESTDATA_SOURCE_DIR}\\\"\"")
-ENDIF (${CMAKE_GENERATOR} STREQUAL "Xcode")
+ENDIF (CMAKE_VERSION VERSION_LESS 3.5 AND ${CMAKE_GENERATOR} STREQUAL "Xcode")
 
 IF(NOT ${USE_RELATIVE_TEST_DIR})
     # Check if data exists

From 8f241023760f97f455c06f7b7e4ad6f7dd47c588 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 14 Jul 2016 18:04:39 -0400
Subject: [PATCH 0691/2677] OpenCL Threefry and Philox

---
 src/backend/cuda/kernel/random_engine.hpp     |   9 +-
 src/backend/opencl/kernel/random_engine.hpp   |  52 +++
 .../opencl/kernel/random_engine_philox.cl     | 109 +++++
 .../opencl/kernel/random_engine_threefry.cl   | 138 ++++++
 .../opencl/kernel/random_engine_write.cl      | 397 ++++++++++++++++++
 src/backend/opencl/random_engine.cpp          |  21 +-
 6 files changed, 719 insertions(+), 7 deletions(-)
 create mode 100644 src/backend/opencl/kernel/random_engine_philox.cl
 create mode 100644 src/backend/opencl/kernel/random_engine_threefry.cl
 create mode 100644 src/backend/opencl/kernel/random_engine_write.cl

diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index c08dcd8379..2dd8f7a3b5 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -386,7 +386,7 @@ namespace kernel
     __global__ void uniformPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
     {
         uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {index, hi};
+        uint key[2] = {index+counter, hi};
         uint ctr[4] = {index+counter, 0, 0, lo};
         if (blockIdx.x != (gridDim.x - 1)) {
             philox(key, ctr);
@@ -401,17 +401,17 @@ namespace kernel
     __global__ void uniformThreefry(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
     {
         uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {index, hi};
+        uint key[2] = {index+counter, hi};
         uint ctr[2] = {index+counter, lo};
         uint o[4];
         if (blockIdx.x != (gridDim.x - 1)) {
             threefry(key, ctr, o);
-            ctr[1] += elements;
+            ctr[0] += elements;
             threefry(key, ctr, o + 2);
             writeOut256Bytes(out, index, o[0], o[1], o[2], o[3]);
         } else {
             threefry(key, ctr, o);
-            ctr[1] += elements;
+            ctr[0] += elements;
             threefry(key, ctr, o + 2);
             partialWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3], elements);
         }
@@ -431,6 +431,7 @@ namespace kernel
                 out, hi, lo, count, elementsPerBlock, elements); break;
         case AF_RANDOM_THREEFRY : CUDA_LAUNCH(uniformThreefry, blocks, threads,
                 out, hi, lo, count, elementsPerBlock, elements); break;
+                                  //THROW
         }
         counter += elements;
     }
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index aac31640f1..babbc6f10f 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -12,6 +12,9 @@
 #include <platform.hpp>
 #include <af/defines.h>
 //#include <kernel_headers/random.hpp>
+#include <kernel_headers/random_engine_philox.hpp>
+#include <kernel_headers/random_engine_threefry.hpp>
+#include <kernel_headers/random_engine_write.hpp>
 #include <traits.hpp>
 #include <sstream>
 #include <string>
@@ -22,6 +25,8 @@
 #include <debug_opencl.hpp>
 #include <program.hpp>
 
+#include <iostream>
+
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
@@ -34,6 +39,8 @@ namespace opencl
 {
     namespace kernel
     {
+        static const uint THREADS = 256;
+
         template <typename T, af_random_type Type>
         void uniformDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
         {
@@ -41,8 +48,53 @@ namespace opencl
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
                 static std::map<int, Program*>  ranProgs;
                 static std::map<int, Kernel*>   ranKernels;
+
                 int device = getActiveDeviceId();
 
+                std::call_once( compileFlags[device], [device] () {
+                        std::string kernelString;
+                        switch (Type) {
+                            case AF_RANDOM_PHILOX : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
+                                                    std::string(random_engine_philox_cl, random_engine_philox_cl_len); break;
+                            case AF_RANDOM_THREEFRY : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
+                                                    std::string(random_engine_threefry_cl, random_engine_threefry_cl_len); break;
+                                                    //THROW
+                        }
+                        uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
+
+                        Program::Sources setSrc;
+                        setSrc.emplace_back(kernelString.c_str(), kernelString.length());
+
+                        std::ostringstream options;
+                        options << " -D T=" << dtype_traits<T>::getName()
+                                << " -D THREADS=" << THREADS
+                                << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
+#if defined(OS_MAC) // Because apple is "special"
+                        options << " -D IS_APPLE"
+                                << " -D log10_val=" << std::log(10.0);
+#endif
+
+                        cl::Program prog;
+                        buildProgram(prog, kernelString.c_str(), kernelString.length(), options.str());
+                        ranProgs[device] = new Program(prog);
+                        ranKernels[device] = new Kernel(*ranProgs[device], "uniformDistribution");
+                    });
+
+                    auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
+
+                    uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
+                    uint groups = divup(elements, elementsPerBlock);
+                    counter += elements;
+
+                    NDRange local(THREADS, 1);
+                    NDRange global(THREADS * groups, 1);
+
+                    uint hi = seed>>32;
+                    uint lo = seed;
+
+                    randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                            out, elements, counter, lo, hi);
+                    CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error ex) {
                 CL_TO_AF_ERROR(ex);
             }
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
new file mode 100644
index 0000000000..628ff78dc1
--- /dev/null
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -0,0 +1,109 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+#define m4x32_0 0xD2511F53
+#define m4x32_1 0xCD9E8D57
+#define w32_0 0x9E3779B9
+#define w32_1 0xBB67AE85
+
+inline void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
+{
+    *hi = mul_hi(a, b);
+    *lo = a*b;
+}
+
+inline void philoxBump(uint k[2])
+{
+    k[0] += w32_0;
+    k[1] += w32_1;
+}
+
+inline void philoxRound(const uint k[2], uint c[4])
+{
+    uint hi0, lo0, hi1, lo1;
+    mulhilo(m4x32_0, c[0], &hi0, &lo0);
+    mulhilo(m4x32_1, c[2], &hi1, &lo1);
+    c[0] = hi1^c[1]^k[0];
+    c[1] = lo1;
+    c[2] = hi0^c[3]^k[1];
+    c[3] = lo0;
+}
+
+inline void philox(uint key[2], uint ctr[4])
+{
+    //10 Rounds
+                       philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+}
+
+__kernel void uniformDistribution(__global T *output, unsigned elements,
+        unsigned counter, unsigned lo, unsigned hi)
+{
+    unsigned gid = get_group_id(0);
+    unsigned off = get_local_size(0);
+    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
+
+    uint key[2] = {index+counter, hi};
+    uint ctr[4] = {index+counter, 0, 0, lo};
+
+    if (gid != get_num_groups(0) - 1) {
+        philox(key, ctr);
+        WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3]);
+    } else {
+        philox(key, ctr);
+        PARTIALWRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3], &elements);
+    }
+}
+
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
new file mode 100644
index 0000000000..e53ba86bb9
--- /dev/null
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -0,0 +1,138 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+#define SKEIN_KS_PARITY 0x1BD11BDA
+
+#define R0 13
+#define R1 15
+#define R2 26
+#define R3  6
+#define R4 17
+#define R5 29
+#define R6 16
+#define R7 24
+
+inline uint rotL(uint x, uint N)
+{
+    return (x << (N & 31)) | (x >> ((32-N) & 31));
+}
+
+inline void threefry(uint k[2], uint c[2], uint X[2])
+{
+    uint ks[3];
+
+    ks[2] = SKEIN_KS_PARITY;
+    ks[0] = k[0];
+    X[0] = c[0];
+    ks[2] ^= k[0];
+    ks[1] = k[1];
+    X[1] = c[1];
+    ks[2] ^= k[1];
+
+    X[0] += ks[0]; X[1] += ks[1];
+
+    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+    /* InjectKey(r=1) */
+    X[0] += ks[1]; X[1] += ks[2];
+    X[1] += 1;     /* X[2-1] += r  */
+
+    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+    /* InjectKey(r=2) */
+    X[0] += ks[2]; X[1] += ks[0];
+    X[1] += 2;
+
+    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+    /* InjectKey(r=3) */
+    X[0] += ks[0]; X[1] += ks[1];
+    X[1] += 3;
+
+    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+    /* InjectKey(r=4) */
+    X[0] += ks[1]; X[1] += ks[2];
+    X[1] += 4;
+}
+
+__kernel void uniformDistribution(__global T *output, unsigned elements,
+        unsigned counter, unsigned lo, unsigned hi)
+{
+    unsigned gid = get_group_id(0);
+    unsigned off = get_local_size(0);
+    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
+
+    uint key[2] = {index+counter, hi};
+    uint ctr[2] = {index+counter, lo};
+    uint o[4];
+
+    if (gid != get_num_groups(0) - 1) {
+        threefry(key, ctr, o);
+        ctr[0] += elements;
+        threefry(key, ctr, o+2);
+        WRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
+    } else {
+        threefry(key, ctr, o);
+        ctr[0] += elements;
+        threefry(key, ctr, o+2);
+        PARTIALWRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
+    }
+}
+
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
new file mode 100644
index 0000000000..51690a945c
--- /dev/null
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -0,0 +1,397 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+typedef ulong uintl;
+typedef long intl;
+#define UINTMAXFLOAT 4294967296.0f
+#define UINTLMAXDOUBLE 4294967296.0*4294967296.0
+#define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
+
+float getFloat(const uint * const num)
+{
+    return ((float)(*num))/UINTMAXFLOAT;
+}
+
+float getFloatSimple(uint num)
+{
+    return ((float)(num))/UINTMAXFLOAT;
+}
+
+double getDouble(const uint * const num1, const uint * const num2)
+{
+    uintl num = (((uintl)*num1)<<32) | ((uintl)*num2);
+    return ((double)num)/UINTLMAXDOUBLE;
+}
+
+void normalizePairFloat(float * const out1, float * const out2, const float r1, const float r2)
+{
+#if defined(IS_APPLE) // Because Apple is.. "special"
+    float r = sqrt((T)(-2.0) * log10(r1) * (float)log10_val);
+#else
+    float r = sqrt((T)(-2.0) * log(r1));
+#endif
+    float theta = 2 * (T)PI_VAL * (r2);
+    *out1 = r*sin(theta);
+    *out2 = r*cos(theta);
+}
+
+void normalizePairDouble(double * const out1, double * const out2, const double r1, const double r2)
+{
+#if defined(IS_APPLE) // Because Apple is.. "special"
+    double r = sqrt((T)(-2.0) * log10(r1) * (double)log10_val);
+#else
+    double r = sqrt((T)(-2.0) * log(r1));
+#endif
+    double theta = 2 * (T)PI_VAL * (r2);
+    *out1 = r*sin(theta);
+    *out2 = r*cos(theta);
+}
+
+//Writes without boundary checking
+
+void writeOut256Bytes_uchar(__global uchar *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]              =     *r1;
+    out[*index +    THREADS] =  *r1>>8;
+    out[*index +  2*THREADS] = *r1>>16;
+    out[*index +  3*THREADS] = *r1>>24;
+    out[*index +  4*THREADS] =     *r2;
+    out[*index +  5*THREADS] =  *r2>>8;
+    out[*index +  6*THREADS] = *r2>>16;
+    out[*index +  7*THREADS] = *r2>>24;
+    out[*index +  8*THREADS] =     *r3;
+    out[*index +  9*THREADS] =  *r3>>8;
+    out[*index + 10*THREADS] = *r3>>16;
+    out[*index + 11*THREADS] = *r3>>24;
+    out[*index + 12*THREADS] =     *r4;
+    out[*index + 13*THREADS] =  *r4>>8;
+    out[*index + 14*THREADS] = *r4>>16;
+    out[*index + 15*THREADS] = *r4>>24;
+}
+
+void writeOut256Bytes_char(char *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]              = (*r1   )&0x1;
+    out[*index +    THREADS] = (*r1>>1)&0x1;
+    out[*index +  2*THREADS] = (*r1>>2)&0x1;
+    out[*index +  3*THREADS] = (*r1>>3)&0x1;
+    out[*index +  4*THREADS] = (*r2   )&0x1;
+    out[*index +  5*THREADS] = (*r2>>1)&0x1;
+    out[*index +  6*THREADS] = (*r2>>2)&0x1;
+    out[*index +  7*THREADS] = (*r2>>3)&0x1;
+    out[*index +  8*THREADS] = (*r3   )&0x1;
+    out[*index +  9*THREADS] = (*r3>>1)&0x1;
+    out[*index + 10*THREADS] = (*r3>>2)&0x1;
+    out[*index + 11*THREADS] = (*r3>>3)&0x1;
+    out[*index + 12*THREADS] = (*r4   )&0x1;
+    out[*index + 13*THREADS] = (*r4>>1)&0x1;
+    out[*index + 14*THREADS] = (*r4>>2)&0x1;
+    out[*index + 15*THREADS] = (*r4>>3)&0x1;
+}
+
+void writeOut256Bytes_short(__global short *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]             =     *r1;
+    out[*index +   THREADS] = *r1>>16;
+    out[*index + 2*THREADS] =     *r2;
+    out[*index + 3*THREADS] = *r2>>16;
+    out[*index + 4*THREADS] =     *r3;
+    out[*index + 5*THREADS] = *r3>>16;
+    out[*index + 6*THREADS] =     *r4;
+    out[*index + 7*THREADS] = *r4>>16;
+}
+
+void writeOut256Bytes_ushort(__global ushort *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]             =     *r1;
+    out[*index +   THREADS] = *r1>>16;
+    out[*index + 2*THREADS] =     *r2;
+    out[*index + 3*THREADS] = *r2>>16;
+    out[*index + 4*THREADS] =     *r3;
+    out[*index + 5*THREADS] = *r3>>16;
+    out[*index + 6*THREADS] =     *r4;
+    out[*index + 7*THREADS] = *r4>>16;
+}
+
+void writeOut256Bytes_int(__global int *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]             = *r1;
+    out[*index +   THREADS] = *r2;
+    out[*index + 2*THREADS] = *r3;
+    out[*index + 3*THREADS] = *r4;
+}
+
+void writeOut256Bytes_uint(__global uint *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]             = *r1;
+    out[*index +   THREADS] = *r2;
+    out[*index + 2*THREADS] = *r3;
+    out[*index + 3*THREADS] = *r4;
+}
+
+void writeOut256Bytes_intl(__global intl *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    intl c1 = *r2;
+    c1 = (c1<<32) | *r1;
+    intl c2 = *r4;
+    c2 = (c2<<32) | *r3;
+    out[*index]           = c1;
+    out[*index + THREADS] = c2;
+}
+
+void writeOut256Bytes_uintl(__global uintl *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    intl c1 = *r2;
+    c1 = (c1<<32) | *r1;
+    intl c2 = *r4;
+    c2 = (c2<<32) | *r3;
+    out[*index]           = c1;
+    out[*index + THREADS] = c2;
+}
+
+void writeOut256Bytes_float(__global float *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]             = getFloat(r1);
+    out[*index +   THREADS] = getFloat(r2);
+    out[*index + 2*THREADS] = getFloat(r3);
+    out[*index + 3*THREADS] = getFloat(r4);
+}
+
+void writeOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]           = getDouble(r1, r2);
+    out[*index + THREADS] = getDouble(r3, r4);
+}
+
+//Normalized writes without boundary checking
+
+void normalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    float n1, n2, n3, n4;
+    normalizePairFloat(&n1, &n2, getFloat(r1), getFloat(r2));
+    normalizePairFloat(&n3, &n4, getFloat(r1), getFloat(r2));
+    out[*index]             = n1;
+    out[*index +   THREADS] = n2;
+    out[*index + 2*THREADS] = n3;
+    out[*index + 3*THREADS] = n4;
+}
+
+void normalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    double n1, n2;
+    normalizePairDouble(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    out[*index]           = n1;
+    out[*index + THREADS] = n2;
+}
+
+//Writes with boundary checking
+
+void partialWriteOut256Bytes_uchar(__global uchar *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index              < *elements) {out[*index]              =     *r1;}
+    if (*index +    THREADS < *elements) {out[*index +    THREADS] =  *r1>>8;}
+    if (*index +  2*THREADS < *elements) {out[*index +  2*THREADS] = *r1>>16;}
+    if (*index +  3*THREADS < *elements) {out[*index +  3*THREADS] = *r1>>24;}
+    if (*index +  4*THREADS < *elements) {out[*index +  4*THREADS] =     *r2;}
+    if (*index +  5*THREADS < *elements) {out[*index +  5*THREADS] =  *r2>>8;}
+    if (*index +  6*THREADS < *elements) {out[*index +  6*THREADS] = *r2>>16;}
+    if (*index +  7*THREADS < *elements) {out[*index +  7*THREADS] = *r2>>24;}
+    if (*index +  8*THREADS < *elements) {out[*index +  8*THREADS] =     *r3;}
+    if (*index +  9*THREADS < *elements) {out[*index +  9*THREADS] =  *r3>>8;}
+    if (*index + 10*THREADS < *elements) {out[*index + 10*THREADS] = *r3>>16;}
+    if (*index + 11*THREADS < *elements) {out[*index + 11*THREADS] = *r3>>24;}
+    if (*index + 12*THREADS < *elements) {out[*index + 12*THREADS] =     *r4;}
+    if (*index + 13*THREADS < *elements) {out[*index + 13*THREADS] =  *r4>>8;}
+    if (*index + 14*THREADS < *elements) {out[*index + 14*THREADS] = *r4>>16;}
+    if (*index + 15*THREADS < *elements) {out[*index + 15*THREADS] = *r4>>24;}
+}
+
+void partialWriteOut256Bytes_char(__global char *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index              < *elements) {out[*index]              = (*r1   )&0x1;}
+    if (*index +    THREADS < *elements) {out[*index +    THREADS] = (*r1>>1)&0x1;}
+    if (*index +  2*THREADS < *elements) {out[*index +  2*THREADS] = (*r1>>2)&0x1;}
+    if (*index +  3*THREADS < *elements) {out[*index +  3*THREADS] = (*r1>>3)&0x1;}
+    if (*index +  4*THREADS < *elements) {out[*index +  4*THREADS] = (*r2   )&0x1;}
+    if (*index +  5*THREADS < *elements) {out[*index +  5*THREADS] = (*r2>>1)&0x1;}
+    if (*index +  6*THREADS < *elements) {out[*index +  6*THREADS] = (*r2>>2)&0x1;}
+    if (*index +  7*THREADS < *elements) {out[*index +  7*THREADS] = (*r2>>3)&0x1;}
+    if (*index +  8*THREADS < *elements) {out[*index +  8*THREADS] = (*r3   )&0x1;}
+    if (*index +  9*THREADS < *elements) {out[*index +  9*THREADS] = (*r3>>1)&0x1;}
+    if (*index + 10*THREADS < *elements) {out[*index + 10*THREADS] = (*r3>>2)&0x1;}
+    if (*index + 11*THREADS < *elements) {out[*index + 11*THREADS] = (*r3>>3)&0x1;}
+    if (*index + 12*THREADS < *elements) {out[*index + 12*THREADS] = (*r4   )&0x1;}
+    if (*index + 13*THREADS < *elements) {out[*index + 13*THREADS] = (*r4>>1)&0x1;}
+    if (*index + 14*THREADS < *elements) {out[*index + 14*THREADS] = (*r4>>2)&0x1;}
+    if (*index + 15*THREADS < *elements) {out[*index + 15*THREADS] = (*r4>>3)&0x1;}
+}
+
+void partialWriteOut256Bytes_short(__global short *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index             < *elements) {out[*index]             =     *r1;}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r1>>16;}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] =     *r2;}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r2>>16;}
+    if (*index + 4*THREADS < *elements) {out[*index + 4*THREADS] =     *r3;}
+    if (*index + 5*THREADS < *elements) {out[*index + 5*THREADS] = *r3>>16;}
+    if (*index + 6*THREADS < *elements) {out[*index + 6*THREADS] =     *r4;}
+    if (*index + 7*THREADS < *elements) {out[*index + 7*THREADS] = *r4>>16;}
+}
+
+void partialWriteOut256Bytes_ushort(__global ushort *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index             < *elements) {out[*index]             =     *r1;}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r1>>16;}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] =     *r2;}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r2>>16;}
+    if (*index + 4*THREADS < *elements) {out[*index + 4*THREADS] =     *r3;}
+    if (*index + 5*THREADS < *elements) {out[*index + 5*THREADS] = *r3>>16;}
+    if (*index + 6*THREADS < *elements) {out[*index + 6*THREADS] =     *r4;}
+    if (*index + 7*THREADS < *elements) {out[*index + 7*THREADS] = *r4>>16;}
+}
+
+void partialWriteOut256Bytes_int(__global int *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index             < *elements) {out[*index]             = *r1;}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r2;}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = *r3;}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
+}
+
+void partialWriteOut256Bytes_uint(__global uint *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index             < *elements) {out[*index]             = *r1;}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r2;}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = *r3;}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
+}
+
+void partialWriteOut256Bytes_intl(__global intl *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    intl c1 = *r2;
+    c1 = (c1<<32) | *r1;
+    intl c2 = *r4;
+    c2 = (c2<<32) | *r3;
+    if (*index           < *elements) {out[*index]           = c1;}
+    if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
+}
+
+void partialWriteOut256Bytes_uintl(__global uintl *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    intl c1 = *r2;
+    c1 = (c1<<32) | *r1;
+    intl c2 = *r4;
+    c2 = (c2<<32) | *r3;
+    if (*index           < *elements) {out[*index]           = c1;}
+    if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
+}
+
+void partialWriteOut256Bytes_float(__global float *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index             < *elements) {out[*index]             = getFloat(r1);}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = getFloat(r2);}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = getFloat(r3);}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = getFloat(r4);}
+}
+
+void partialWriteOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index           < *elements) {out[*index]           = getDouble(r1, r2);}
+    if (*index + THREADS < *elements) {out[*index + THREADS] = getDouble(r3, r4);}
+}
+
+//Normalized writes with boundary checking
+
+void partialNormalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    float n1, n2, n3, n4;
+    normalizePairFloat(&n1, &n2, getFloat(r1), getFloat(r2));
+    normalizePairFloat(&n3, &n4, getFloat(r3), getFloat(r4));
+    if (*index             < *elements) {out[*index]             = n1;}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = n2;}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = n3;}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = n4;}
+}
+
+void partialNormalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    double n1, n2;
+    normalizePairDouble(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    if (*index           < *elements) {out[*index]           = n1;}
+    if (*index + THREADS < *elements) {out[*index + THREADS] = n2;}
+}
+
+#define PASTER(x,y) x ## _ ## y
+#define EVALUATOR(x,y) PASTER(x,y)
+#define EVALUATE_T(function) EVALUATOR(function, T)
+#define WRITE EVALUATE_T(writeOut256Bytes)
+#define PARTIALWRITE EVALUATE_T(partialWriteOut256Bytes)
+
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index b3102136ea..1f01ddde05 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -22,15 +22,30 @@ namespace opencl
 
         switch(type) {
         case AF_RANDOM_PHILOX: kernel::uniformDistribution<T, AF_RANDOM_PHILOX>(*out.get(), out.elements(), seed, counter); break;
-        case AF_RANDOM_THREEFRY: break;
+        case AF_RANDOM_THREEFRY: kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements(), seed, counter); break;
         }
         return out;
     }
 
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)\
+    {\
+        verifyDoubleSupport<T>();\
+        Array<T> out = createEmptyArray<T>(dims);\
+\
+        switch(type) {\
+        case AF_RANDOM_PHILOX: kernel::uniformDistribution<TR, AF_RANDOM_PHILOX>(*out.get(), out.elements()*2, seed, counter); break;\
+        case AF_RANDOM_THREEFRY: break;\
+        }\
+        return out;\
+    }\
+
+    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+
     template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
     template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<cfloat>  uniformDistribution<cfloat>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<cdouble> uniformDistribution<cdouble> (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
     template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
     template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
     template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);

From 398a8f9b0011755670fcd0a5e8f9acc29534ba38 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 15 Jul 2016 15:53:24 -0400
Subject: [PATCH 0692/2677] Changes required to build the library

---
 src/api/c/unary.cpp | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 89dfe1e1e0..65aeff46f7 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -337,13 +337,13 @@ struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // atanh(z) = 0.5*(log(1+z)-log(1-z))
-        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
+        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
         Array<Tc> one_plus_z = arithOp<Tc, af_add_t>(one, z, one.dims());
         Array<Tc> one_minus_z = arithOp<Tc, af_min_t>(one, z, one.dims());
         Array<Tc> log_one_plus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_z);
         Array<Tc> log_one_minus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_z);
         Array<Tc> w = arithOp<Tc, af_min_t>(log_one_plus_z, log_one_minus_z, log_one_plus_z.dims());
-        Array<Tc> two = createValueArray<Tc>(z.dims(), Tc(2, 0));
+        Array<Tc> two = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(2.0, 0.0));
         return arithOp<Tc, af_div_t>(w, two, w.dims());
     }
 };
@@ -354,9 +354,10 @@ struct unaryOpCplxFun<Tc, Tr, af_acos_t>
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // acos(z) = pi/2+i*log(i*z+sqrt(1-z.^2))
-        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
-        Array<Tc> i = createValueArray<Tc>(z.dims(), Tc(0, 1));
-        Array<Tc> pi_half = createValueArray<Tc>(z.dims(), Tc(M_PI / 2.0, 0));
+        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+
+        Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
+        Array<Tc> pi_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(M_PI / 2.0, 0.0));
 
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
         Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
@@ -375,9 +376,9 @@ struct unaryOpCplxFun<Tc, Tr, af_asin_t>
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // asin(z) = -i*log(i*z+sqrt(1-z^2))
-        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
-        Array<Tc> i = createValueArray<Tc>(z.dims(), Tc(0, 1));
-        Array<Tc> minus_i = createValueArray<Tc>(z.dims(), Tc(0, -1));
+        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
+        Array<Tc> minus_i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, -1.0));
 
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
         Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
@@ -394,9 +395,9 @@ struct unaryOpCplxFun<Tc, Tr, af_atan_t>
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // atan(z) = 0.5*i*(log(1-i*z)-log(1+i*z))
-        Array<Tc> one = createValueArray<Tc>(z.dims(), Tc(1, 0));
-        Array<Tc> i = createValueArray<Tc>(z.dims(), Tc(0, 1));
-        Array<Tc> i_half = createValueArray<Tc>(z.dims(), Tc(0, 0.5));
+        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
+        Array<Tc> i_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 0.5));
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
         Array<Tc> one_minus_i2 = arithOp<Tc, af_sub_t>(one, iz, z.dims());
         Array<Tc> one_plus_i2 = arithOp<Tc, af_add_t>(one, iz, z.dims());

From 455214bdb42d66af167cf32fcacea7e4b9c9f713 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 15 Jul 2016 16:26:11 -0400
Subject: [PATCH 0693/2677] TEST: Writing tests for complex math functions

---
 test/math.cpp | 206 +++++++++++++++++++++++++++++---------------------
 1 file changed, 120 insertions(+), 86 deletions(-)

diff --git a/test/math.cpp b/test/math.cpp
index e286e2a202..0af39a48ef 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
+#include <complex>
 #include <gtest/gtest.h>
 #include <af/arith.h>
 #include <af/data.h>
@@ -19,6 +19,11 @@ using std::abs;
 const int num = 10000;
 const float flt_err = 1e-3;
 const double dbl_err = 1e-10;
+const float cflt_err = 1e-3;
+const double cdbl_err = 1e-8;
+
+typedef std::complex<float> complex_float;
+typedef std::complex<double> complex_double;
 
 template<typename T>
 T sigmoid(T in)
@@ -26,93 +31,122 @@ T sigmoid(T in)
     return 1.0 / (1.0 + std::exp(-in));
 }
 
-#define MATH_TESTS_LIMITS(Ti, To, func, err, lo, hi)            \
-    TEST(MathTests, Test_##func##_##Ti)                         \
-    {                                                           \
-        if (noDoubleTests<Ti>()) return;                        \
-        af_dtype ty = (af_dtype)dtype_traits<Ti>::af_type;      \
-        af::array a = (hi - lo) * randu(num, ty) + lo + err;    \
-        af::eval(a);                                            \
-        af::array b = af::func(a);                              \
-        Ti *h_a = a.host<Ti>();                                 \
-        To *h_b = b.host<To>();                                 \
-                                                                \
-        for (int i = 0; i < num; i++)                           \
-            ASSERT_NEAR(h_b[i], func(h_a[i]), err) <<           \
-                "for value: " << h_a[i] << std::endl;           \
-        delete[] h_a;                                           \
-        delete[] h_b;                                           \
-    }                                                           \
-
-#define MATH_TESTS_FLOAT(func) MATH_TESTS_LIMITS(float, float, func, flt_err, 0.05f, 0.95f)
-#define MATH_TESTS_DOUBLE(func) MATH_TESTS_LIMITS(double, double, func, dbl_err, 0.05, 0.95)
-
-MATH_TESTS_FLOAT(sin)
-MATH_TESTS_FLOAT(cos)
-MATH_TESTS_FLOAT(tan)
-MATH_TESTS_FLOAT(asin)
-MATH_TESTS_FLOAT(acos)
-MATH_TESTS_FLOAT(atan)
-
-MATH_TESTS_FLOAT(sinh)
-MATH_TESTS_FLOAT(cosh)
-MATH_TESTS_FLOAT(tanh)
-
-
-MATH_TESTS_FLOAT(sqrt)
-
-MATH_TESTS_FLOAT(exp)
-MATH_TESTS_FLOAT(sigmoid)
-MATH_TESTS_FLOAT(log)
-MATH_TESTS_FLOAT(log10)
-MATH_TESTS_FLOAT(log2)
-
-MATH_TESTS_LIMITS(float, float, abs, flt_err, -10, 10)
-MATH_TESTS_LIMITS(float, float, ceil, flt_err, -10, 10)
-MATH_TESTS_LIMITS(float, float, floor, flt_err, -10, 10)
-
-MATH_TESTS_DOUBLE(sin)
-MATH_TESTS_DOUBLE(cos)
-MATH_TESTS_DOUBLE(tan)
-MATH_TESTS_DOUBLE(asin)
-MATH_TESTS_DOUBLE(acos)
-MATH_TESTS_DOUBLE(atan)
-
-MATH_TESTS_DOUBLE(sinh)
-MATH_TESTS_DOUBLE(cosh)
-MATH_TESTS_DOUBLE(tanh)
-#if __cplusplus > 199711L
-MATH_TESTS_FLOAT(asinh)
-MATH_TESTS_FLOAT(atanh)
-MATH_TESTS_LIMITS(float, float, acosh, flt_err, 1, 5)
-MATH_TESTS_LIMITS(float, float, round, flt_err, -10, 10)
-MATH_TESTS_FLOAT(cbrt)
-MATH_TESTS_FLOAT(expm1)
-MATH_TESTS_FLOAT(log1p)
-MATH_TESTS_FLOAT(erf)
-MATH_TESTS_FLOAT(erfc)
-
-MATH_TESTS_DOUBLE(asinh)
-MATH_TESTS_DOUBLE(atanh)
-MATH_TESTS_LIMITS(double, double, acosh, dbl_err, 1, 5)
-MATH_TESTS_LIMITS(double, double, round, dbl_err, -10, 10)
-MATH_TESTS_DOUBLE(cbrt)
-MATH_TESTS_DOUBLE(expm1)
-MATH_TESTS_DOUBLE(erf)
-MATH_TESTS_DOUBLE(log1p)
-MATH_TESTS_DOUBLE(erfc)
-#endif
+#define TEST_REAL(T, func, err, lo, hi)                             \
+    TEST(MathTests, Test_##func##_##T)                              \
+    {                                                               \
+        try {                                                       \
+            if (noDoubleTests<T>()) return;                         \
+            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;       \
+            af::array a = (hi - lo) * randu(num, ty) + lo + err;    \
+            af::eval(a);                                            \
+            af::array b = af::func(a);                              \
+            std::vector<T> h_a(a.elements());                       \
+            std::vector<T> h_b(b.elements());                       \
+            a.host(&h_a[0]);                                        \
+            b.host(&h_b[0]);                                        \
+                                                                    \
+            for (int i = 0; i < num; i++) {                         \
+                ASSERT_NEAR(h_b[i], func(h_a[i]), err) <<           \
+                    "for value: " << h_a[i] << std::endl;           \
+            }                                                       \
+        } catch (af::exception &ex) {                               \
+            FAIL() << ex.what();                                    \
+        }                                                           \
+    }                                                               \
+
+#define TEST_CPLX(T, func, err, lo, hi)                             \
+    TEST(MathTests, Test_##func##_##T)                              \
+    {                                                               \
+        try {                                                       \
+            if (noDoubleTests<T>()) return;                         \
+            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;       \
+            af::array a = (hi - lo) * randu(num, ty) + lo + err;    \
+            af::eval(a);                                            \
+            af::array b = af::func(a);                              \
+            std::vector<T> h_a(a.elements());                       \
+            std::vector<T> h_b(b.elements());                       \
+            a.host(&h_a[0]);                                        \
+            b.host(&h_b[0]);                                        \
+                                                                    \
+            for (int i = 0; i < num; i++) {                         \
+                T res = func(h_a[i]);                               \
+                ASSERT_NEAR(real(h_b[i]), real(res), err) <<        \
+                    "for real value: " << h_a[i] << std::endl;      \
+                ASSERT_NEAR(imag(h_b[i]), imag(res), err) <<        \
+                    "for imag value: " << h_a[i] << std::endl;      \
+            }                                                       \
+        } catch (af::exception &ex) {                               \
+            FAIL() << ex.what();                                    \
+        }                                                           \
+    }                                                               \
+
+#define MATH_TESTS_FLOAT(func) TEST_REAL(float, func, flt_err, 0.05f, 0.95f)
+#define MATH_TESTS_DOUBLE(func) TEST_REAL(double, func, dbl_err, 0.05, 0.95)
+
+#define MATH_TESTS_CFLOAT(func) TEST_CPLX(complex_float, func, flt_err, 0.05f, 0.95f)
+#define MATH_TESTS_CDOUBLE(func) TEST_CPLX(complex_double, func, dbl_err, 0.05, 0.95)
+
+#define MATH_TESTS_REAL(func)                   \
+    MATH_TESTS_FLOAT(func)                      \
+    MATH_TESTS_DOUBLE(func)                     \
+
+#define MATH_TESTS_CPLX(func)                   \
+    MATH_TESTS_CFLOAT(func)                     \
+    MATH_TESTS_CDOUBLE(func)                    \
+
+#define MATH_TESTS_ALL(func)                    \
+    MATH_TESTS_REAL(func)                       \
+    MATH_TESTS_CPLX(func)                       \
+
+#define MATH_TESTS_LIMITS_REAL(func, lo, hi)    \
+    TEST_REAL(float, func, flt_err, lo, hi)     \
+    TEST_REAL(double, func, dbl_err, lo, hi)    \
+
+#define MATH_TESTS_LIMITS_CPLX(func, lo, hi)            \
+    TEST_CPLX(complex_float, func, flt_err, lo, hi)     \
+    TEST_CPLX(complex_double, func, dbl_err, lo, hi)    \
+
+MATH_TESTS_ALL(sin)
+MATH_TESTS_ALL(cos)
+MATH_TESTS_ALL(tan)
+
+MATH_TESTS_REAL(asin)
+MATH_TESTS_REAL(acos)
+MATH_TESTS_REAL(atan)
+
+MATH_TESTS_ALL(sinh)
+MATH_TESTS_ALL(cosh)
+MATH_TESTS_ALL(tanh)
+
+MATH_TESTS_ALL(sqrt)
+MATH_TESTS_ALL(exp)
+MATH_TESTS_ALL(log)
+MATH_TESTS_REAL(log10)
+MATH_TESTS_REAL(log2)
+
+MATH_TESTS_REAL(sigmoid)
+
+MATH_TESTS_LIMITS_REAL(abs, -10, 10)
+MATH_TESTS_LIMITS_REAL(ceil, -10, 10)
+MATH_TESTS_LIMITS_REAL(floor, -10, 10)
 
-MATH_TESTS_DOUBLE(sqrt)
-
-MATH_TESTS_DOUBLE(exp)
-MATH_TESTS_DOUBLE(log)
-MATH_TESTS_DOUBLE(log10)
-MATH_TESTS_DOUBLE(log2)
+#if __cplusplus > 199711L
 
-MATH_TESTS_LIMITS(double, double, abs, dbl_err, -10, 10)
-MATH_TESTS_LIMITS(double, double, ceil, dbl_err, -10, 10)
-MATH_TESTS_LIMITS(double, double, floor, dbl_err, -10, 10)
+MATH_TESTS_CPLX(asin)
+MATH_TESTS_CPLX(acos)
+MATH_TESTS_CPLX(atan)
+
+MATH_TESTS_ALL(asinh)
+MATH_TESTS_ALL(atanh)
+MATH_TESTS_LIMITS_REAL(acosh, 1, 5)
+MATH_TESTS_LIMITS_CPLX(acosh, 1, 5)
+MATH_TESTS_LIMITS_REAL(round, -10, 10)
+MATH_TESTS_REAL(cbrt)
+MATH_TESTS_REAL(expm1)
+MATH_TESTS_REAL(log1p)
+MATH_TESTS_REAL(erf)
+MATH_TESTS_REAL(erfc)
+#endif
 
 TEST(MathTests, Not)
 {

From d7049a36395e69da1653cb0f9431156bf94ec1ca Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 15 Jul 2016 16:26:31 -0400
Subject: [PATCH 0694/2677] BUGFIX: Fixing bugs for complex math functions

Also added step by step comments to reduce future mistakes
---
 src/api/c/unary.cpp | 181 ++++++++++++++++++++++++++++++++++++--------
 1 file changed, 149 insertions(+), 32 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 65aeff46f7..bd8d31d76e 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -155,15 +155,24 @@ struct unaryOpCplxFun<Tc, Tr, af_exp_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // exp(a + ib)
+        // --> exp(a) * exp(ib)
+        // --> exp(a) * (cos(a) + i * sin(b))
+        // --> exp(a) * cos(a) + i * exp(a) * sin(b)
+
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
         Array<Tr> exp_a = unaryOp<Tr, af_exp_t>(a);
         Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
         Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
+
+        // exp(a) * cos(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(exp_a, cos_b, exp_a.dims());
+        // exp(a) * sin(b)
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(exp_a, sin_b, exp_a.dims());
 
+        // exp(a) * cos(b) + i * exp(a) * sin(b)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
@@ -173,16 +182,28 @@ struct unaryOpCplxFun<Tc, Tr, af_log_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // log(a + ib)
+        // using r = abs(a + ib), phi == arg(a + ib)
+        // --> log(r * exp(i * phi))
+        // --> log(r) + i * phi
+
         // convert cartesian to polar
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
-        Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
-        Array<Tr> phi = abs<Tr>(z);
+
+        // phi = arg(a + ib)
+        // --> phi = atan2(b, a)
+        Array<Tr> phi = arithOp<Tr, af_atan2_t>(b, a, b.dims());
+
+        Array<Tr> r = abs<Tr>(z);
 
         // compute log
+        // log(r)
         Array<Tr> a_out = unaryOp<Tr, af_log_t>(r);
+        // phi
         Array<Tr> b_out = phi;
 
+        // log(r) + i * phi
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
@@ -192,6 +213,10 @@ struct unaryOpCplxFun<Tc, Tr, af_sin_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // sin(a + ib)
+        // --> sin(a) * cos(ib) + cos(a) * sin(ib)
+        // --> sin(a) * cosh(b) + i * cos(a) * sinh(b)
+
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
@@ -200,9 +225,13 @@ struct unaryOpCplxFun<Tc, Tr, af_sin_t>
         Array<Tr> cos_a = unaryOp<Tr, af_cos_t>(a);
         Array<Tr> sinh_b = unaryOp<Tr, af_sinh_t>(b);
         Array<Tr> cosh_b = unaryOp<Tr, af_cosh_t>(b);
+
+        // sin(a) * cosh(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(sin_a, cosh_b, sin_a.dims());
+        // cos(a) * sinh(b)
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(cos_a, sinh_b, cos_a.dims());
 
+        // sin(a) * cosh(b) + i * cos(a) * sinh(b)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
@@ -212,6 +241,10 @@ struct unaryOpCplxFun<Tc, Tr, af_cos_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // cos(a + ib)
+        // --> cos(a) * cos(ib) - sin(a) * sin(ib)
+        // --> cos(a) * cosh(b) - i * sin(a) * sinh(b)
+
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
@@ -220,11 +253,16 @@ struct unaryOpCplxFun<Tc, Tr, af_cos_t>
         Array<Tr> cos_a = unaryOp<Tr, af_cos_t>(a);
         Array<Tr> sinh_b = unaryOp<Tr, af_sinh_t>(b);
         Array<Tr> cosh_b = unaryOp<Tr, af_cosh_t>(b);
+
+        // cos(a) * cosh(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(cos_a, cosh_b, sin_a.dims());
+        // -1
         Array<Tr> neg_one = createValueArray<Tr>(a_out.dims(), -1);
+        // sin(a) * sinh(b)
         Array<Tr> b_out_neg = arithOp<Tr, af_mul_t>(sin_a, sinh_b, cos_a.dims());
-        Array<Tr> b_out = arithOp<Tr, af_mul_t>(b_out_neg, b_out_neg, b_out_neg.dims());
-
+        // -1 * sin(a) * sinh(b)
+        Array<Tr> b_out = arithOp<Tr, af_mul_t>(neg_one, b_out_neg, b_out_neg.dims());
+        // cos(a) * cosh(b) - i * sin(a) * sinh(b)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
@@ -234,6 +272,7 @@ struct unaryOpCplxFun<Tc, Tr, af_tan_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // tan(a + ib) = sin(a + ib) / cos(a + ib)
         Array<Tc> sin_z = unaryOpCplx<Tc, Tr, af_sin_t>(z);
         Array<Tc> cos_z = unaryOpCplx<Tc, Tr, af_cos_t>(z);
         return arithOp<Tc, af_div_t>(sin_z, cos_z, sin_z.dims());
@@ -245,6 +284,10 @@ struct unaryOpCplxFun<Tc, Tr, af_sinh_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // sinh(a + ib)
+        // --> sinh(a) * cosh(ib) + cosh(a) * sinh(ib)
+        // --> sinh(a) * cos(b) + i * cosh(a) * sin(b)
+
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
@@ -253,9 +296,13 @@ struct unaryOpCplxFun<Tc, Tr, af_sinh_t>
         Array<Tr> cosh_a = unaryOp<Tr, af_cosh_t>(a);
         Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
         Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+
+        // sinh(a) * cos(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(sinh_a, cos_b, sinh_a.dims());
+        // cosh(a) * sin(b)
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(cosh_a, sin_b, cosh_a.dims());
 
+        // sinh(a) * cos(b) + i * cosh(a) * sin(b)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
@@ -265,6 +312,9 @@ struct unaryOpCplxFun<Tc, Tr, af_cosh_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // cosh(a + ib)
+        // --> cosh(a) * cosh(ib) + sinh(a) * sinh(ib)
+        // --> cosh(a) * cos(b) + i * sinh(a) * sin(b)
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
@@ -273,9 +323,13 @@ struct unaryOpCplxFun<Tc, Tr, af_cosh_t>
         Array<Tr> cosh_a = unaryOp<Tr, af_cosh_t>(a);
         Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
         Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+
+        // cosh(a) * cos(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(cosh_a, cos_b, cosh_a.dims());
+        // sinh(a) * sin(b)
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(sinh_a, sin_b, sinh_a.dims());
 
+        // cosh(a) * cos(b) + i * sinh(a) * sin(b)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
@@ -285,6 +339,7 @@ struct unaryOpCplxFun<Tc, Tr, af_tanh_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // tanh(a + ib) = sinh(a + ib) / cosh(a + ib)
         Array<Tc> sinh_z = unaryOpCplx<Tc, Tr, af_sinh_t>(z);
         Array<Tc> cosh_z = unaryOpCplx<Tc, Tr, af_cosh_t>(z);
         return arithOp<Tc, af_div_t>(sinh_z, cosh_z, sinh_z.dims());
@@ -292,41 +347,49 @@ struct unaryOpCplxFun<Tc, Tr, af_tanh_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
+struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // dont simplify this expression, as it might lead to branch cuts
         // acosh(z) = log(z+sqrt(z+1)*sqrt(z-1))
-        Array<Tr> a = real<Tr, Tc>(z);
-        Array<Tr> b = imag<Tr, Tc>(z);
-        Array<Tr> one = createValueArray<Tr>(a.dims(), 1);
-        Array<Tr> a_plus_one = arithOp<Tr, af_add_t>(a, one, a.dims());
-        Array<Tr> a_minus_one = arithOp<Tr, af_sub_t>(a, one, a.dims());
-        Array<Tc> z_plus_one = cplx<Tc, Tr>(a_plus_one, b, b.dims());
-        Array<Tc> z_minus_one = cplx<Tc, Tr>(a_minus_one, b, b.dims());
+
+        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc>(1.0));
+
+        // (z + 1)
+        Array<Tc> z_plus_one = arithOp<Tc, af_add_t>(z, one, z.dims());
+        // (z - 1)
+        Array<Tc> z_minus_one = arithOp<Tc, af_sub_t>(z, one, z.dims());
+        // sqrt(z + 1)
         Array<Tc> sqrt_z_plus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z_plus_one);
+        // sqrt(z - 1)
         Array<Tc> sqrt_z_minus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z_minus_one);
+        // sqrt(z + 1) * sqrt(z - 1)
         Array<Tc> sqrt_prod = arithOp<Tc, af_mul_t>(sqrt_z_plus_one, sqrt_z_minus_one, sqrt_z_plus_one.dims());
+        // z + sqrt(z + 1) * sqrt(z - 1)
         Array<Tc> w = arithOp<Tc, af_add_t>(z, sqrt_prod, z.dims());
+        // log(z + sqrt(z + 1) * sqrt(z - 1))
         return unaryOpCplx<Tc, Tr, af_log_t>(w);
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
+struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // asinh(z) = log(z+sqrt(z^2+1))
+        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc>(1.0));
+
+        // z^2
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
-        Array<Tr> a = real<Tr, Tc>(z2);
-        Array<Tr> b = imag<Tr, Tc>(z2);
-        Array<Tr> one = createValueArray<Tr>(a.dims(), 1);
-        Array<Tr> a_plus_one = arithOp<Tr, af_add_t>(a, one, a.dims());
-        Array<Tc> z2_plus_one = cplx<Tc, Tr>(a_plus_one, b, b.dims());
+        // ((a + 1) + i * b) --> z^2 + 1
+        Array<Tc> z2_plus_one = arithOp<Tc, af_add_t>(z2, one, z.dims());
+        // sqrt(z^2 + 1)
         Array<Tc> sqrt_z2_plus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z2_plus_one);
+        // z + sqrt(z^2 + 1)
         Array<Tc> w = arithOp<Tc, af_add_t>(z, sqrt_z2_plus_one, z.dims());
+        // log(z + sqrt(z^2 + 1))
         return unaryOpCplx<Tc, Tr, af_log_t>(w);
     }
 };
@@ -338,13 +401,20 @@ struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
     {
         // atanh(z) = 0.5*(log(1+z)-log(1-z))
         Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.5, 0.0));
+
+        // (1 + z)
         Array<Tc> one_plus_z = arithOp<Tc, af_add_t>(one, z, one.dims());
-        Array<Tc> one_minus_z = arithOp<Tc, af_min_t>(one, z, one.dims());
+        // (1 - z)
+        Array<Tc> one_minus_z = arithOp<Tc, af_sub_t>(one, z, one.dims());
+        // log(1 + z)
         Array<Tc> log_one_plus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_z);
+        // log(1 - z)
         Array<Tc> log_one_minus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_z);
-        Array<Tc> w = arithOp<Tc, af_min_t>(log_one_plus_z, log_one_minus_z, log_one_plus_z.dims());
-        Array<Tc> two = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(2.0, 0.0));
-        return arithOp<Tc, af_div_t>(w, two, w.dims());
+        // (log(1 + z) - log(1 - z))
+        Array<Tc> w = arithOp<Tc, af_sub_t>(log_one_plus_z, log_one_minus_z, log_one_plus_z.dims());
+        // 0.5 * (log(1 + z) - log(1 - z))
+        return arithOp<Tc, af_mul_t>(w, half, w.dims());
     }
 };
 
@@ -353,19 +423,29 @@ struct unaryOpCplxFun<Tc, Tr, af_acos_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
-        // acos(z) = pi/2+i*log(i*z+sqrt(1-z.^2))
+        // acos(z) = pi/2 + i*log(i*z+sqrt(1-z.^2))
+        // --> pi/2 - asinz(z)
+
         Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
 
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
         Array<Tc> pi_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(M_PI / 2.0, 0.0));
 
+        // z^2
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
+        // 1 - z^2
         Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
+        // sqrt(1 - z^2)
         Array<Tc> sqrt_one_minus_z2 = unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
+        // i*z
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
+        // (i*z - sqrt(1 - z^2))
         Array<Tc> w = arithOp<Tc, af_add_t>(iz, sqrt_one_minus_z2, iz.dims());
+        // log(i*z - sqrt(1 - z^2))
         Array<Tc> log_w = unaryOpCplx<Tc, Tr, af_log_t>(w);
-        Array<Tc> i_log_w = arithOp<Tc, af_mul_t>(i, w, i.dims());
+        // i*log(i*z - sqrt(1 - z^2))
+        Array<Tc> i_log_w = arithOp<Tc, af_mul_t>(i, log_w, i.dims());
+        // pi/2 + i*log(i*z - sqrt(1 - z^2))
         return arithOp<Tc, af_add_t>(pi_half, i_log_w, pi_half.dims());
     }
 };
@@ -376,16 +456,25 @@ struct unaryOpCplxFun<Tc, Tr, af_asin_t>
     Array<Tc> operator()(const Array<Tc> &z)
     {
         // asin(z) = -i*log(i*z+sqrt(1-z^2))
+
         Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
         Array<Tc> minus_i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, -1.0));
 
+        // z^2
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
+        // 1 - z^2
         Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
+        // sqrt(1 - z^2)
         Array<Tc> sqrt_one_minus_z2 = unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
+        // i*z
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
+        // (i*z + sqrt(1 - z^2))
         Array<Tc> w = arithOp<Tc, af_add_t>(iz, sqrt_one_minus_z2, iz.dims());
-        return arithOp<Tc, af_mul_t>(minus_i, w, minus_i.dims());
+        // log(i*z + sqrt(1 - z^2))
+        Array<Tc> log_w = unaryOpCplx<Tc, Tr, af_log_t>(w);
+        // i*log(i*z + sqrt(1 - z^2))
+        return arithOp<Tc, af_mul_t>(minus_i, log_w, minus_i.dims());
     }
 };
 
@@ -394,16 +483,25 @@ struct unaryOpCplxFun<Tc, Tr, af_atan_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
-        // atan(z) = 0.5*i*(log(1-i*z)-log(1+i*z))
+        // atan(z) = 0.5 * i * (log(1-i*z)-log(1+i*z))
         Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
+
+        // 0.5 * i
         Array<Tc> i_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 0.5));
+        // i*z
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
-        Array<Tc> one_minus_i2 = arithOp<Tc, af_sub_t>(one, iz, z.dims());
-        Array<Tc> one_plus_i2 = arithOp<Tc, af_add_t>(one, iz, z.dims());
-        Array<Tc> log_minus = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_i2);
-        Array<Tc> log_plus = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_i2);
+        // 1 - i*z
+        Array<Tc> one_minus_iz = arithOp<Tc, af_sub_t>(one, iz, z.dims());
+        // 1 + i*z
+        Array<Tc> one_plus_iz = arithOp<Tc, af_add_t>(one, iz, z.dims());
+        // log(1 - i*z)
+        Array<Tc> log_minus = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_iz);
+        // log(1 + i*z)
+        Array<Tc> log_plus = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_iz);
+        // log(1 - i*z) - log(1 + i*z)
         Array<Tc> log_diff = arithOp<Tc, af_sub_t>(log_minus, log_plus, z.dims());
+        // 0.5 * i * (log(1 - i*z) - log(1 + i*z))
         return arithOp<Tc, af_mul_t>(i_half, log_diff, z.dims());
     }
 };
@@ -413,23 +511,42 @@ struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
 {
     Array<Tc> operator()(const Array<Tc> &z)
     {
+        // sqrt(a + ib)
+        // using r = abs(a + ib), phi == arg(a + ib)
+        // --> sqrt(r * exp(i * phi))
+        // --> sqrt(r) * exp(i * phi / 2)
+        // --> sqrt(r) * cos(phi/2) + i * sqrt(r) * sin(phi/2)
+
         // convert cartesian to polar
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
-        Array<Tr> r = arithOp<Tr, af_atan2_t>(b, a, b.dims());
-        Array<Tr> phi = abs<Tr>(z);
+
+
+        // phi = arg(a + ib)
+        // --> phi = atan2(b, a)
+        Array<Tr> phi = arithOp<Tr, af_atan2_t>(b, a, b.dims());
+        Array<Tr> r = abs<Tr>(z);
 
         // compute sqrt
         Array<Tr> two = createValueArray<Tr>(phi.dims(), 2.0);
+
+        // sqrt(r)
         Array<Tr> r_out = unaryOp<Tr, af_sqrt_t>(r);
+
+        // phi/2
         Array<Tr> phi_out = arithOp<Tr, af_div_t>(phi, two, phi.dims());
 
         // convert polar to cartesian
+        // cos(phi/2)
         Array<Tr> a_out_unit = unaryOp<Tr, af_cos_t>(phi_out);
+        // sin(phi/2)
         Array<Tr> b_out_unit = unaryOp<Tr, af_sin_t>(phi_out);
+        // sqrt(r) * cos(phi/2)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(r_out, a_out_unit, r_out.dims());
+        // sqrt(r) * sin(phi/2)
         Array<Tr> b_out = arithOp<Tr, af_mul_t>(r_out, b_out_unit, r_out.dims());
 
+        // sqrt(r) * cos(phi/2) + i * sqrt(r) * sin(phi/2)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };

From 8839d85844f2bb46787d7e7e1ce94d8e59580517 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 15 Jul 2016 18:31:03 -0400
Subject: [PATCH 0695/2677] FEAT,TEST: Adding support for complex pow, root

---
 src/api/c/binary.cpp | 16 +++++++++++++--
 test/binary.cpp      | 46 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 60 insertions(+), 2 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 95a133557f..3e3dceba23 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -143,7 +143,13 @@ af_err af_pow(af_array *out, const af_array lhs, const af_array rhs, const bool
         ArrayInfo linfo = getInfo(lhs);
         ArrayInfo rinfo = getInfo(rhs);
         if (linfo.isComplex() || rinfo.isComplex()) {
-            AF_ERROR("Powers of Complex numbers not supported", AF_ERR_NOT_SUPPORTED);
+            af_array log_lhs, log_res;
+            af_array res;
+            AF_CHECK(af_log(&log_lhs, lhs));
+            AF_CHECK(af_mul(&log_res, log_lhs, rhs, batchMode));
+            AF_CHECK(af_exp(&res, log_res));
+            std::swap(*out, res);
+            return AF_SUCCESS;
         }
     } CATCHALL;
 
@@ -156,7 +162,13 @@ af_err af_root(af_array *out, const af_array lhs, const af_array rhs, const bool
         ArrayInfo linfo = getInfo(lhs);
         ArrayInfo rinfo = getInfo(rhs);
         if (linfo.isComplex() || rinfo.isComplex()) {
-            AF_ERROR("Powers of Complex numbers not supported", AF_ERR_NOT_SUPPORTED);
+            af_array log_lhs, log_res;
+            af_array res;
+            AF_CHECK(af_log(&log_lhs, lhs));
+            AF_CHECK(af_div(&log_res, log_lhs, rhs, batchMode));
+            AF_CHECK(af_exp(&res, log_res));
+            std::swap(*out, res);
+            return AF_SUCCESS;
         }
 
         af_array one;
diff --git a/test/binary.cpp b/test/binary.cpp
index 91ebcbc8b2..64b8267835 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -24,6 +24,9 @@ const int num = 10000;
 #define mul(left, right) (left) * (right)
 #define div(left, right) (left) / (right)
 
+typedef std::complex<float> complex_float;
+typedef std::complex<double> complex_double;
+
 template<typename T> T mod(T a, T b)
 {
     return std::fmod(a, b);
@@ -289,3 +292,46 @@ BITOP(bitand, uintl, &)
 BITOP(bitxor, uintl, ^)
 BITOP(bitshiftl, uintl, <<)
 BITOP(bitshiftr, uintl, >>)
+
+TEST(BinaryTests, Test_pow_cfloat_float)
+{
+    af::array a = randgen(num, c32);
+    af::array b = randgen(num, f32);
+    af::array c = af::pow(a, b);
+    complex_float *h_a = (complex_float *)a.host<cfloat>();
+    float *h_b = b.host<float>();
+    complex_float *h_c = (complex_float *)c.host<cfloat>();
+    for (int i = 0; i < num; i++) {
+        complex_float res = std::pow(h_a[i], h_b[i]);
+        ASSERT_NEAR(real(h_c[i]), real(res), 1E-5)
+            << "for real values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+        ASSERT_NEAR(imag(h_c[i]), imag(res), 1E-5)
+            << "for imag values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+
+    }
+    delete[] h_a;
+    delete[] h_b;
+    delete[] h_c;
+}
+
+TEST(BinaryTests, Test_pow_cdouble_cdouble)
+{
+    if (noDoubleTests<cdouble>()) return;
+    af::array a = randgen(num, c64);
+    af::array b = randgen(num, c64);
+    af::array c = af::pow(a, b);
+    complex_double *h_a = (complex_double *)a.host<cdouble>();
+    complex_double *h_b = (complex_double *)b.host<cdouble>();
+    complex_double *h_c = (complex_double *)c.host<cdouble>();
+    for (int i = 0; i < num; i++) {
+        complex_double res = std::pow(h_a[i], h_b[i]);
+        ASSERT_NEAR(real(h_c[i]), real(res), 1E-10)
+            << "for real values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+        ASSERT_NEAR(imag(h_c[i]), imag(res), 1E-10)
+            << "for imag values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+
+    }
+    delete[] h_a;
+    delete[] h_b;
+    delete[] h_c;
+}

From 8fc6ae240e9f8741fc48b96fd1ef9b8d2fdb7fc6 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 15 Jul 2016 21:40:33 -0400
Subject: [PATCH 0696/2677] TEST: Fixing noDoubleTests function

---
 test/testHelpers.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 83f2552e08..999a71b97d 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -382,8 +382,8 @@ inline double imag<af::cfloat> (af::cfloat val) { return imag(val); }
 template<typename T>
 bool noDoubleTests()
 {
-    bool isTypeDouble = is_same_type<T, double>::value || is_same_type<T, af::cdouble>::value;
-
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    bool isTypeDouble = (ty == f64) || (ty == c64);
     int dev = af::getDevice();
     bool isDoubleSupported = af::isDoubleAvailable(dev);
 

From 351217a51c5dc2bab47cfaeb9fb8fb174f725134 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 18 Jul 2016 15:58:29 -0400
Subject: [PATCH 0697/2677] CPU Backend for THREEFRY AND PHILOX

Minor edits in OpenCL and CUDA backend for setting key and counter.
Changed unsigned long long to uintl.
---
 src/backend/cpu/kernel/random_engine.hpp      | 105 +++++++++++
 .../cpu/kernel/random_engine_philox.hpp       | 142 +++++++++++++++
 .../cpu/kernel/random_engine_threefry.hpp     | 163 ++++++++++++++++++
 src/backend/cpu/random_engine.cpp             |  51 ++++--
 src/backend/cuda/random_engine.cu             |  41 +++--
 src/backend/opencl/kernel/random_engine.hpp   |   2 +-
 .../opencl/kernel/random_engine_philox.cl     |   2 +-
 .../opencl/kernel/random_engine_threefry.cl   |   2 +-
 src/backend/opencl/random_engine.cpp          |  24 +--
 test/random.cpp                               |   4 +-
 10 files changed, 489 insertions(+), 47 deletions(-)
 create mode 100644 src/backend/cpu/kernel/random_engine.hpp
 create mode 100644 src/backend/cpu/kernel/random_engine_philox.hpp
 create mode 100644 src/backend/cpu/kernel/random_engine_threefry.hpp

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
new file mode 100644
index 0000000000..b7ac1d4b86
--- /dev/null
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -0,0 +1,105 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <kernel/random_engine_philox.hpp>
+#include <kernel/random_engine_threefry.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+    //Utils
+    #define UINTMAXFLOAT 4294967296.0f
+    #define UINTLMAXDOUBLE 4294967296.0*4294967296.0
+    #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
+
+    template <typename T>
+    T transform(uint *val, int index)
+    {
+        T *oval = (T*)val;
+        return oval[index];
+    }
+
+    template <> char transform<char>(uint *val, int index)
+    {
+        char v = transform<char>(val, index);
+        v = (v&0x1) ? 1 : 0;
+        return v;
+    }
+
+    template <> float transform<float>(uint *val, int index)
+    {
+        return (float)val[index]/UINTMAXFLOAT;
+    }
+
+    template <> double transform<double>(uint *val, int index)
+    {
+        uintl v = transform<uintl>(val, index);
+        return (double)v/UINTLMAXDOUBLE;
+    }
+
+    template <typename T>
+    void philoxUniform(T* out, size_t elements, const uintl seed, uintl &counter)
+    {
+        uint hi = seed>>32;
+        uint lo = seed;
+        uint key[2] = {(uint)counter, hi};
+        uint ctr[4] = {(uint)counter, 0, 0, lo};
+
+        int fresh = 0;
+        int reset = (4*sizeof(uint))/sizeof(T);
+        philox(key, ctr);
+        for (int i = 0; i < (int)elements; ++i) {
+            if (fresh == reset) {
+                philox(key, ctr);
+                ++ctr[0]; ++key[0];
+                fresh = 0;
+            }
+            out[i] = transform<T>(ctr, fresh);
+            fresh++;
+        }
+    }
+
+    template <typename T>
+    void threefryUniform(T* out, size_t elements, const uintl seed, uintl &counter)
+    {
+        uint hi = seed>>32;
+        uint lo = seed;
+        uint key[2] = {(uint)counter, hi};
+        uint ctr[2] = {(uint)counter, lo};
+        uint val[2];
+
+        int fresh = 0;
+        int reset = (2*sizeof(uint))/sizeof(T);
+        threefry(key, ctr, val);
+        ++ctr[0]; ++key[0];
+        for (int i = 0; i < (int)elements; ++i) {
+            if (fresh == reset) {
+                threefry(key, ctr, val);
+                ++ctr[0]; ++key[0];
+                fresh = 0;
+            }
+            out[i] = transform<T>(val, fresh);
+            fresh++;
+        }
+    }
+
+    template <typename T, af_random_type Type>
+    void uniformDistribution(T* out, size_t elements, const uintl seed, uintl &counter)
+    {
+        switch(Type) {
+            case AF_RANDOM_PHILOX   :   philoxUniform(out, elements, seed, counter); break;
+            case AF_RANDOM_THREEFRY : threefryUniform(out, elements, seed, counter); break;
+        }
+    }
+
+}
+}
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
new file mode 100644
index 0000000000..a894107b2d
--- /dev/null
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -0,0 +1,142 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+#pragma once
+namespace cpu
+{
+namespace kernel
+{
+
+#define m4x32_0 0xD2511F53
+#define m4x32_1 0xCD9E8D57
+#define w32_0 0x9E3779B9
+#define w32_1 0xBB67AE85
+
+static inline void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
+{
+    *hi = (((uintl)a) * ((uintl)b))>>32;
+    *lo = a*b;
+}
+
+static inline void philoxBump(uint k[2])
+{
+    k[0] += w32_0;
+    k[1] += w32_1;
+}
+
+static inline void philoxRound(const uint k[2], uint c[4])
+{
+    uint hi0, lo0, hi1, lo1;
+    mulhilo(m4x32_0, c[0], &hi0, &lo0);
+    mulhilo(m4x32_1, c[2], &hi1, &lo1);
+    c[0] = hi1^c[1]^k[0];
+    c[1] = lo1;
+    c[2] = hi0^c[3]^k[1];
+    c[3] = lo0;
+}
+
+static inline void philox(uint key[2], uint ctr[4])
+{
+    //10 Rounds
+                       philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+    philoxBump(key);   philoxRound(key, ctr);
+}
+
+/*
+template <> struct Random<AF_RANDOM_PHILOX>
+{
+    uint hi;
+    uint lo;
+    uintl counter;
+    uint key[2];
+    uint ctr[4];
+    int reset;
+
+    template <typename T>
+    T uniform(void);
+
+    Random(uintl seed, uintl counter);
+};
+
+Random<AF_RANDOM_PHILOX>::Random(uintl seed, uintl counterInput) : hi(seed>>32), lo(seed), counter(counterInput), reset(0)
+{
+    key[0] = counter;
+    key[1] = hi;
+    ctr[0] = counter;
+    ctr[1] = 0;
+    ctr[2] = 0;
+    ctr[3] = lo;
+}
+
+template <typename T>
+void Random<AF_RANDOM_PHILOX>::uniform(T* out, size_t elements)
+{
+    int reset = (4*sizeof(uint))/sizeof(T);
+    philox(key, ctr);
+    for (int i = 0; i < (int)out.elements(); ++i) {
+        if (fresh == reset) {
+            philox(key, ctr);
+            ctr[0] += 4;
+            fresh = 0;
+        }
+        out[i] = transform<T>(ctr, fresh);
+        fresh++;
+    }
+
+}
+*/
+
+}
+}
diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
new file mode 100644
index 0000000000..f948869112
--- /dev/null
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -0,0 +1,163 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+#pragma once
+namespace cpu
+{
+namespace kernel
+{
+
+#define SKEIN_KS_PARITY 0x1BD11BDA
+
+#define R0 13
+#define R1 15
+#define R2 26
+#define R3  6
+#define R4 17
+#define R5 29
+#define R6 16
+#define R7 24
+
+static inline uint rotL(uint x, uint N)
+{
+    return (x << (N & 31)) | (x >> ((32-N) & 31));
+}
+
+static inline void threefry(uint k[2], uint c[2], uint X[2])
+{
+    uint ks[3];
+
+    ks[2] = SKEIN_KS_PARITY;
+    ks[0] = k[0];
+    X[0] = c[0];
+    ks[2] ^= k[0];
+    ks[1] = k[1];
+    X[1] = c[1];
+    ks[2] ^= k[1];
+
+    X[0] += ks[0]; X[1] += ks[1];
+
+    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+    /* InjectKey(r=1) */
+    X[0] += ks[1]; X[1] += ks[2];
+    X[1] += 1;     /* X[2-1] += r  */
+
+    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+    /* InjectKey(r=2) */
+    X[0] += ks[2]; X[1] += ks[0];
+    X[1] += 2;
+
+    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+    /* InjectKey(r=3) */
+    X[0] += ks[0]; X[1] += ks[1];
+    X[1] += 3;
+
+    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+    /* InjectKey(r=4) */
+    X[0] += ks[1]; X[1] += ks[2];
+    X[1] += 4;
+}
+
+/*
+template <> struct Random<AF_RANDOM_THREEFRY>
+{
+    uint hi;
+    uint lo;
+    uintl counter;
+    uint key[2];
+    uint ctr[2];
+    uint val[2];
+    int reset;
+
+    template <typename T>
+    T uniform(void);
+
+    Random(uintl seed, uintl counter);
+};
+
+Random<AF_RANDOM_THREEFRY>::Random(uintl seed, uintl counterInput) : hi(seed>>32), lo(seed), counter(counterInput), reset(0)
+{
+    key[0] = counter;
+    key[1] = hi;
+    ctr[0] = counter;
+    ctr[2] = lo;
+}
+
+template <typename T>
+void Random<AF_RANDOM_THREEFRY>::uniform(T* out, size_t elements)
+{
+    int reset = (2*sizeof(uint))/sizeof(T);
+    threefry(key, ctr, val);
+    for (int i = 0; i < (int)out.elements(); ++i) {
+        if (fresh == reset) {
+            threefry(key, ctr, val);
+            ctr[0] += 2;
+            fresh = 0;
+        }
+        out[i] = transform<T>(ctr, fresh);
+        fresh++;
+    }
+}
+*/
+}
+}
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index eebc101a37..c8bf785c6b 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -10,34 +10,51 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <random.hpp>
-//#include <kernel/random_engine.hpp>
+#include <kernel/random_engine.hpp>
 #include <cassert>
 
 namespace cpu
 {
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-
+        T *outPtr = out.get();
+        size_t elements = out.elements();
         switch(type) {
-        case AF_RANDOM_PHILOX: break;//kernel::uniformDistribution<T, AF_RANDOM_PHILOX>(dims, type, seed, counter); break;
-        case AF_RANDOM_THREEFRY: break;
+        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::uniformDistribution<T,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;
+        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;
         }
+        counter += elements;
         return out;
     }
 
-    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<cfloat>  uniformDistribution<cfloat>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<cdouble> uniformDistribution<cdouble> (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements();\
+        switch(type) {\
+        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::uniformDistribution<TR,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;\
+        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::uniformDistribution<TR, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;\
+        }\
+        return out;\
+    }\
+
+    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+
+    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
 }
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 17443d837c..f3e1506691 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -15,7 +15,7 @@
 namespace cuda
 {
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
 
@@ -26,17 +26,32 @@ namespace cuda
         return out;
     }
 
-    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<cfloat>  uniformDistribution<cfloat>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<cdouble> uniformDistribution<cdouble> (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        switch(type) {\
+        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<TR,    AF_RANDOM_PHILOX>(outPtr, elements, seed, counter); break;\
+        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<TR,  AF_RANDOM_THREEFRY>(outPtr, elements, seed, counter); break;\
+        }\
+        return out;\
+    }\
+
+    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+
+    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
 }
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index babbc6f10f..c92d641a1d 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -93,7 +93,7 @@ namespace opencl
                     uint lo = seed;
 
                     randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                            out, elements, counter, lo, hi);
+                            out, elements, counter, hi, lo);
                     CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error ex) {
                 CL_TO_AF_ERROR(ex);
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index 628ff78dc1..c65be21451 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -89,7 +89,7 @@ inline void philox(uint key[2], uint ctr[4])
 }
 
 __kernel void uniformDistribution(__global T *output, unsigned elements,
-        unsigned counter, unsigned lo, unsigned hi)
+        unsigned counter, unsigned hi, unsigned lo)
 {
     unsigned gid = get_group_id(0);
     unsigned off = get_local_size(0);
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index e53ba86bb9..63672c3d92 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -113,7 +113,7 @@ inline void threefry(uint k[2], uint c[2], uint X[2])
 }
 
 __kernel void uniformDistribution(__global T *output, unsigned elements,
-        unsigned counter, unsigned lo, unsigned hi)
+        unsigned counter, unsigned hi, unsigned lo)
 {
     unsigned gid = get_group_id(0);
     unsigned off = get_local_size(0);
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 1f01ddde05..0228a2c556 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -15,7 +15,7 @@
 namespace opencl
 {
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         verifyDoubleSupport<T>();
         Array<T> out = createEmptyArray<T>(dims);
@@ -29,7 +29,7 @@ namespace opencl
 
 #define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter)\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
     {\
         verifyDoubleSupport<T>();\
         Array<T> out = createEmptyArray<T>(dims);\
@@ -44,15 +44,15 @@ namespace opencl
     COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
     COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
-    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
-    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
 }
diff --git a/test/random.cpp b/test/random.cpp
index 6a75e14e3c..24ab999b3b 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -248,7 +248,7 @@ TEST(Random, philoxEngine)
     float m = mean<float>(A);
     float s = stdev<float>(A);
     ASSERT_NEAR(m, 0.5, 1e-3);
-    ASSERT_NEAR(s, 0.2887, 1e-3);
+    ASSERT_NEAR(s, 0.2887, 1e-2);
 }
 
 TEST(Random, threefryEngine)
@@ -259,5 +259,5 @@ TEST(Random, threefryEngine)
     float m = mean<float>(A);
     float s = stdev<float>(A);
     ASSERT_NEAR(m, 0.5, 1e-3);
-    ASSERT_NEAR(s, 0.2887, 1e-3);
+    ASSERT_NEAR(s, 0.2887, 1e-2);
 }

From 58baec5d4490dcee431dcb5e35e0c38ebcf89969 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 19 Jul 2016 14:30:15 -0400
Subject: [PATCH 0698/2677] Normal random functions for all backends

Minor corrections for double and cdouble kernels. Test for normal
distribution.
---
 include/af/random_engine.h                    | 14 ++-
 src/api/c/random_engine.cpp                   | 30 +++++++
 src/api/cpp/random_engine.cpp                 | 31 +++++++
 src/api/unified/random_engine.cpp             |  5 ++
 src/backend/cpu/kernel/random_engine.hpp      | 89 ++++++++++++++++++-
 src/backend/cpu/random_engine.cpp             | 56 +++++++++---
 src/backend/cpu/random_engine.hpp             |  3 +
 src/backend/cuda/kernel/random_engine.hpp     | 57 +++++++++++-
 src/backend/cuda/random_engine.cu             | 60 ++++++++++---
 src/backend/cuda/random_engine.hpp            |  3 +
 src/backend/opencl/kernel/random_engine.hpp   | 59 ++++++++++++
 .../opencl/kernel/random_engine_philox.cl     | 19 ++++
 .../opencl/kernel/random_engine_threefry.cl   | 24 +++++
 .../opencl/kernel/random_engine_write.cl      | 16 ++--
 src/backend/opencl/random_engine.cpp          | 35 +++++++-
 src/backend/opencl/random_engine.hpp          |  3 +
 test/random.cpp                               | 26 +++++-
 17 files changed, 489 insertions(+), 41 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 22a591dc6b..482eead463 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -31,6 +31,12 @@ namespace af
             array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
             array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
             array uniform(const dim4& dims, const dtype ty = f32);
+
+            array normal(const dim_t dim0, const dtype ty = f32);
+            array normal(const dim_t dim0, const dim_t dim1, const dtype ty = f32);
+            array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
+            array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
+            array normal(const dim4& dims, const dtype ty = f32);
     };
 }
 #endif
@@ -53,7 +59,7 @@ extern "C" {
     /**
        C Interface for creating an array of uniform numbers using a random engine
 
-       \param[out]  arr The pointer to the returned object.
+       \param[out]  out The pointer to the returned object.
        \param[in]   engine is the random engine object
        \param[in]   ndims The number of dimensions read from the \p dims parameter
        \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
@@ -61,12 +67,12 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
        C Interface for creating an array of normal numbers using a random engine
 
-       \param[out]  arr The pointer to the returned object.
+       \param[out]  out The pointer to the returned object.
        \param[in]   engine is the random engine object
        \param[in]   ndims The number of dimensions read from the \p dims parameter
        \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
@@ -74,7 +80,7 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    //AFAPI af_err af_random_engine_normal(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_random_engine_normal(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
        C Interface for releasing random engine
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index d3be5f5b77..3d8139a3d7 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -23,6 +23,7 @@ using detail::cfloat;
 using detail::cdouble;
 using detail::uchar;
 using detail::uniformDistribution;
+using detail::normalDistribution;
 
 typedef struct {
     af_random_type type;
@@ -52,6 +53,13 @@ static inline af_array uniformDistribution_(const af::dim4 &dims,
     return getHandle(uniformDistribution<T>(dims, type, seed, counter));
 }
 
+template<typename T>
+static inline af_array normalDistribution_(const af::dim4 &dims,
+        const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+{
+    return getHandle(normalDistribution<T>(dims, type, seed, counter));
+}
+
 af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
 {
     try {
@@ -92,6 +100,28 @@ af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const un
     return AF_SUCCESS;
 }
 
+af_err af_random_engine_normal(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    try {
+        af_array result;
+        AF_CHECK(af_init());
+
+        af::dim4 d = verifyDims(ndims, dims);
+        af_random_engine_t *e = getRandomEngine(engine);
+
+        switch(type) {
+        case f32:   result = normalDistribution_<float  >(d, e->type, e->seed, e->counter);    break;
+        case c32:   result = normalDistribution_<cfloat >(d, e->type, e->seed, e->counter);    break;
+        case f64:   result = normalDistribution_<double >(d, e->type, e->seed, e->counter);    break;
+        case c64:   result = normalDistribution_<cdouble>(d, e->type, e->seed, e->counter);    break;
+        default:    TYPE_ERROR(4, type);
+        }
+        std::swap(*out, result);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index dfd1b5fa87..aa594cebc0 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -57,4 +57,35 @@ namespace af
         AF_THROW(af_random_engine_uniform(&out, engine, dims.ndims(), dims.get(), ty));
         return array(out);
     }
+
+    array randomEngine::normal(const dim_t dim0, const dtype ty)
+    {
+        dim4 d(dim0, 1, 1, 1);
+        return normal(d, ty);
+    }
+
+    array randomEngine::normal(const dim_t dim0, const dim_t dim1, const dtype ty)
+    {
+        dim4 d(dim0, dim1, 1, 1);
+        return normal(d, ty);
+    }
+
+    array randomEngine::normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty)
+    {
+        dim4 d(dim0, dim1, dim2, 1);
+        return normal(d, ty);
+    }
+
+    array randomEngine::normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty)
+    {
+        dim4 d(dim0, dim1, dim2, dim3);
+        return normal(d, ty);
+    }
+
+    array randomEngine::normal(const dim4& dims, const dtype ty)
+    {
+        af_array out;
+        AF_THROW(af_random_engine_normal(&out, engine, dims.ndims(), dims.get(), ty));
+        return array(out);
+    }
 }
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
index 7ac26549c0..f8febe5314 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random_engine.cpp
@@ -21,6 +21,11 @@ af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const un
     return CALL(arr, engine, ndims, dims, type);
 }
 
+af_err af_random_engine_normal(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    return CALL(arr, engine, ndims, dims, type);
+}
+
 af_err af_release_random_engine(af_random_engine engineHandle)
 {
     return CALL(engineHandle);
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index b7ac1d4b86..3584bf4c27 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -18,7 +18,7 @@ namespace kernel
 {
     //Utils
     #define UINTMAXFLOAT 4294967296.0f
-    #define UINTLMAXDOUBLE 4294967296.0*4294967296.0
+    #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
     #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
 
     template <typename T>
@@ -101,5 +101,92 @@ namespace kernel
         }
     }
 
+    template <typename T>
+    void normalizePair(T * const out1, T * const out2, const T r1, const T r2)
+    {
+#if defined(IS_APPLE) // Because Apple is.. "special"
+        T r = sqrt((T)(-2.0) * log10(r1) * (float)log10_val);
+#else
+        T r = sqrt((T)(-2.0) * log(r1));
+#endif
+        T theta = 2 * (T)PI_VAL * (r2);
+        *out1 = r*sin(theta);
+        *out2 = r*cos(theta);
+    }
+
+    void normalize(uint val[4], double *temp)
+    {
+        uintl *v = (uintl*)val;
+        normalizePair(&temp[0], &temp[1], v[0]/UINTLMAXDOUBLE, v[1]/UINTLMAXDOUBLE);
+    }
+
+    void normalize(uint val[4], float *temp)
+    {
+        normalizePair(&temp[0], &temp[1], val[0]/UINTMAXFLOAT, val[1]/UINTMAXFLOAT);
+        normalizePair(&temp[2], &temp[3], val[2]/UINTMAXFLOAT, val[3]/UINTMAXFLOAT);
+    }
+
+    template <typename T>
+    void threefryNormal(T* out, size_t elements, const uintl seed, uintl &counter)
+    {
+        uint hi = seed>>32;
+        uint lo = seed;
+        uint key[2] = {(uint)counter, hi};
+        uint ctr[2] = {(uint)counter, lo};
+        uint val[4];
+        T temp[(4*sizeof(uint))/sizeof(T)];
+
+        int fresh = 0;
+        int reset = (4*sizeof(uint))/sizeof(T);
+        threefry(key, ctr, val);
+        normalize(val, temp);
+        ++ctr[0]; ++key[0];
+        for (int i = 0; i < (int)elements; ++i) {
+            if (fresh == reset) {
+                threefry(key, ctr, val);
+                ++ctr[0]; ++key[0];
+                threefry(key, ctr, val+2);
+                fresh = 0;
+                normalize(val, temp);
+            }
+            out[i] = temp[fresh];
+            fresh++;
+        }
+    }
+
+    template <typename T>
+    void philoxNormal(T* out, size_t elements, const uintl seed, uintl &counter)
+    {
+        uint hi = seed>>32;
+        uint lo = seed;
+        uint key[2] = {(uint)counter, hi};
+        uint ctr[4] = {(uint)counter, 0, 0, lo};
+        T temp[(4*sizeof(uint))/sizeof(T)];
+
+        int fresh = 0;
+        int reset = (4*sizeof(uint))/sizeof(T);
+        philox(key, ctr);
+        normalize(ctr, temp);
+        for (int i = 0; i < (int)elements; ++i) {
+            if (fresh == reset) {
+                philox(key, ctr);
+                ++ctr[0]; ++key[0];
+                fresh = 0;
+                normalize(ctr, temp);
+            }
+            out[i] = temp[fresh];
+            fresh++;
+        }
+    }
+
+    template <typename T, af_random_type Type>
+    void normalDistribution(T* out, size_t elements, const uintl seed, uintl &counter)
+    {
+        switch(Type) {
+            case AF_RANDOM_PHILOX   :   philoxNormal(out, elements, seed, counter); break;
+            case AF_RANDOM_THREEFRY : threefryNormal(out, elements, seed, counter); break;
+        }
+    }
+
 }
 }
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index c8bf785c6b..a648d0c676 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -35,7 +35,7 @@ namespace cpu
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements();\
+        size_t elements = out.elements()*2;\
         switch(type) {\
         case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::uniformDistribution<TR,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;\
         case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::uniformDistribution<TR, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;\
@@ -46,15 +46,49 @@ namespace cpu
     COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
     COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
-    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uint>   uniformDistribution<uint>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<int>    uniformDistribution<int>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<intl>   uniformDistribution<intl>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uintl>  uniformDistribution<uintl> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<char>   uniformDistribution<char>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uchar>  uniformDistribution<uchar> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        T *outPtr = out.get();
+        size_t elements = out.elements();
+        switch(type) {
+        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::normalDistribution<T,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;
+        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::normalDistribution<T, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;
+        }
+        counter += elements;
+        return out;
+    }
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        switch(type) {\
+        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::normalDistribution<TR,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;\
+        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::normalDistribution<TR, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;\
+        }\
+        return out;\
+    }\
+
+    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
+    template Array<float>  normalDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> normalDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
 }
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index b052541908..4fbbae55fc 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -15,4 +15,7 @@ namespace cpu
 {
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
 }
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 2dd8f7a3b5..fa1d2fa4d9 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -21,7 +21,7 @@ namespace kernel
     //Utils
     static const int THREADS = 256;
     #define UINTMAXFLOAT 4294967296.0f
-    #define UINTLMAXDOUBLE 4294967296.0*4294967296.0
+    #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
     #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
 
     __device__ static float getFloat(const uint &num)
@@ -435,5 +435,60 @@ namespace kernel
         }
         counter += elements;
     }
+
+    template <typename T>
+    __global__ void normalPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    {
+        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
+        uint key[2] = {index+counter, hi};
+        uint ctr[4] = {index+counter, 0, 0, lo};
+        if (blockIdx.x != (gridDim.x - 1)) {
+            philox(key, ctr);
+            normalizedWriteOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
+        } else {
+            philox(key, ctr);
+            partialNormalizedWriteOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
+        }
+    }
+
+    template <typename T>
+    __global__ void normalThreefry(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    {
+        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
+        uint key[2] = {index+counter, hi};
+        uint ctr[2] = {index+counter, lo};
+        uint o[4];
+        if (blockIdx.x != (gridDim.x - 1)) {
+            threefry(key, ctr, o);
+            ctr[0] += elements;
+            threefry(key, ctr, o + 2);
+            normalizedWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3]);
+        } else {
+            threefry(key, ctr, o);
+            ctr[0] += elements;
+            threefry(key, ctr, o + 2);
+            partialNormalizedWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3], elements);
+        }
+    }
+
+    template <typename T, af_random_type Type>
+    void normalDistribution(T *out, size_t elements, const uintl seed, uintl &counter)
+    {
+        int threads = THREADS;
+        int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
+        int blocks = divup(elements, elementsPerBlock);
+        uint hi = seed>>32;
+        uint lo = seed;
+        uintl count = counter;
+        switch (Type) {
+        case AF_RANDOM_PHILOX : CUDA_LAUNCH(normalPhilox, blocks, threads,
+                out, hi, lo, count, elementsPerBlock, elements); break;
+        case AF_RANDOM_THREEFRY : CUDA_LAUNCH(normalThreefry, blocks, threads,
+                out, hi, lo, count, elementsPerBlock, elements); break;
+                                  //THROW
+        }
+        counter += elements;
+    }
+
 }
 }
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index f3e1506691..dea31063da 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -20,8 +20,8 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(dims);
 
         switch(type) {
-        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<T,    AF_RANDOM_PHILOX>(out.get(), out.elements(), seed, counter); break;
-        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<T,  AF_RANDOM_THREEFRY>(out.get(), out.elements(), seed, counter); break;
+        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<T,   AF_RANDOM_PHILOX>(out.get(), out.elements(), seed, counter); break;
+        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>(out.get(), out.elements(), seed, counter); break;
         }
         return out;
     }
@@ -34,8 +34,8 @@ namespace cuda
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
         switch(type) {\
-        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<TR,    AF_RANDOM_PHILOX>(outPtr, elements, seed, counter); break;\
-        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<TR,  AF_RANDOM_THREEFRY>(outPtr, elements, seed, counter); break;\
+        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<TR,   AF_RANDOM_PHILOX>(outPtr, elements, seed, counter); break;\
+        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<TR, AF_RANDOM_THREEFRY>(outPtr, elements, seed, counter); break;\
         }\
         return out;\
     }\
@@ -43,15 +43,47 @@ namespace cuda
     COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
     COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
-    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<int>    uniformDistribution<int>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uint>   uniformDistribution<uint>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<intl>   uniformDistribution<intl>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uintl>  uniformDistribution<uintl> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<char>   uniformDistribution<char>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uchar>  uniformDistribution<uchar> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+
+        switch(type) {
+        case AF_RANDOM_PHILOX   : kernel::normalDistribution<T,   AF_RANDOM_PHILOX>(out.get(), out.elements(), seed, counter); break;
+        case AF_RANDOM_THREEFRY : kernel::normalDistribution<T, AF_RANDOM_THREEFRY>(out.get(), out.elements(), seed, counter); break;
+        }
+        return out;
+    }
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        switch(type) {\
+        case AF_RANDOM_PHILOX   : kernel::normalDistribution<TR,   AF_RANDOM_PHILOX>(outPtr, elements, seed, counter); break;\
+        case AF_RANDOM_THREEFRY : kernel::normalDistribution<TR, AF_RANDOM_THREEFRY>(outPtr, elements, seed, counter); break;\
+        }\
+        return out;\
+    }\
+
+    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
+    template Array<float>  normalDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> normalDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
 }
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index 4be4be97cb..bada8a2f09 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -15,4 +15,7 @@ namespace cuda
 {
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
 }
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index c92d641a1d..3064d56d64 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -99,5 +99,64 @@ namespace opencl
                 CL_TO_AF_ERROR(ex);
             }
         }
+
+        template <typename T, af_random_type Type>
+        void normalDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>  ranProgs;
+                static std::map<int, Kernel*>   ranKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                        std::string kernelString;
+                        switch (Type) {
+                            case AF_RANDOM_PHILOX : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
+                                                    std::string(random_engine_philox_cl, random_engine_philox_cl_len); break;
+                            case AF_RANDOM_THREEFRY : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
+                                                    std::string(random_engine_threefry_cl, random_engine_threefry_cl_len); break;
+                                                    //THROW
+                        }
+                        uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
+
+                        Program::Sources setSrc;
+                        setSrc.emplace_back(kernelString.c_str(), kernelString.length());
+
+                        std::ostringstream options;
+                        options << " -D T=" << dtype_traits<T>::getName()
+                                << " -D THREADS=" << THREADS
+                                << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
+#if defined(OS_MAC) // Because apple is "special"
+                        options << " -D IS_APPLE"
+                                << " -D log10_val=" << std::log(10.0);
+#endif
+
+                        cl::Program prog;
+                        buildProgram(prog, kernelString.c_str(), kernelString.length(), options.str());
+                        ranProgs[device] = new Program(prog);
+                        ranKernels[device] = new Kernel(*ranProgs[device], "normalDistribution");
+                    });
+
+                    auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
+
+                    uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
+                    uint groups = divup(elements, elementsPerBlock);
+                    counter += elements;
+
+                    NDRange local(THREADS, 1);
+                    NDRange global(THREADS * groups, 1);
+
+                    uint hi = seed>>32;
+                    uint lo = seed;
+
+                    randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                            out, elements, counter, hi, lo);
+                    CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error ex) {
+                CL_TO_AF_ERROR(ex);
+            }
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index c65be21451..b47f4380e7 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -107,3 +107,22 @@ __kernel void uniformDistribution(__global T *output, unsigned elements,
     }
 }
 
+__kernel void normalDistribution(__global T *output, unsigned elements,
+        unsigned counter, unsigned hi, unsigned lo)
+{
+    unsigned gid = get_group_id(0);
+    unsigned off = get_local_size(0);
+    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
+
+    uint key[2] = {index+counter, hi};
+    uint ctr[4] = {index+counter, 0, 0, lo};
+
+    if (gid != get_num_groups(0) - 1) {
+        philox(key, ctr);
+        NORMALWRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3]);
+    } else {
+        philox(key, ctr);
+        NORMALPARTIALWRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3], &elements);
+    }
+}
+
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index 63672c3d92..b225dcde60 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -136,3 +136,27 @@ __kernel void uniformDistribution(__global T *output, unsigned elements,
     }
 }
 
+__kernel void normalDistribution(__global T *output, unsigned elements,
+        unsigned counter, unsigned hi, unsigned lo)
+{
+    unsigned gid = get_group_id(0);
+    unsigned off = get_local_size(0);
+    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
+
+    uint key[2] = {index+counter, hi};
+    uint ctr[2] = {index+counter, lo};
+    uint o[4];
+
+    if (gid != get_num_groups(0) - 1) {
+        threefry(key, ctr, o);
+        ctr[0] += elements;
+        threefry(key, ctr, o+2);
+        NORMALWRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
+    } else {
+        threefry(key, ctr, o);
+        ctr[0] += elements;
+        threefry(key, ctr, o+2);
+        NORMALPARTIALWRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
+    }
+}
+
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 51690a945c..9b2bcf7680 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -48,7 +48,7 @@
 typedef ulong uintl;
 typedef long intl;
 #define UINTMAXFLOAT 4294967296.0f
-#define UINTLMAXDOUBLE 4294967296.0*4294967296.0
+#define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
 #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
 
 float getFloat(const uint * const num)
@@ -70,11 +70,11 @@ double getDouble(const uint * const num1, const uint * const num2)
 void normalizePairFloat(float * const out1, float * const out2, const float r1, const float r2)
 {
 #if defined(IS_APPLE) // Because Apple is.. "special"
-    float r = sqrt((T)(-2.0) * log10(r1) * (float)log10_val);
+    float r = sqrt((float)(-2.0) * log10(r1) * (float)log10_val);
 #else
-    float r = sqrt((T)(-2.0) * log(r1));
+    float r = sqrt((float)(-2.0) * log(r1));
 #endif
-    float theta = 2 * (T)PI_VAL * (r2);
+    float theta = 2 * (float)PI_VAL * (r2);
     *out1 = r*sin(theta);
     *out2 = r*cos(theta);
 }
@@ -82,11 +82,11 @@ void normalizePairFloat(float * const out1, float * const out2, const float r1,
 void normalizePairDouble(double * const out1, double * const out2, const double r1, const double r2)
 {
 #if defined(IS_APPLE) // Because Apple is.. "special"
-    double r = sqrt((T)(-2.0) * log10(r1) * (double)log10_val);
+    double r = sqrt((double)(-2.0) * log10(r1) * (double)log10_val);
 #else
-    double r = sqrt((T)(-2.0) * log(r1));
+    double r = sqrt((double)(-2.0) * log(r1));
 #endif
-    double theta = 2 * (T)PI_VAL * (r2);
+    double theta = 2 * (double)PI_VAL * (r2);
     *out1 = r*sin(theta);
     *out2 = r*cos(theta);
 }
@@ -394,4 +394,6 @@ void partialNormalizedWriteOut256Bytes_double(__global double *out, const uint *
 #define EVALUATE_T(function) EVALUATOR(function, T)
 #define WRITE EVALUATE_T(writeOut256Bytes)
 #define PARTIALWRITE EVALUATE_T(partialWriteOut256Bytes)
+#define NORMALWRITE EVALUATE_T(normalizedWriteOut256Bytes)
+#define NORMALPARTIALWRITE EVALUATE_T(partialNormalizedWriteOut256Bytes)
 
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 0228a2c556..7e4bcdf370 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -36,7 +36,7 @@ namespace opencl
 \
         switch(type) {\
         case AF_RANDOM_PHILOX: kernel::uniformDistribution<TR, AF_RANDOM_PHILOX>(*out.get(), out.elements()*2, seed, counter); break;\
-        case AF_RANDOM_THREEFRY: break;\
+        case AF_RANDOM_THREEFRY: kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements()*2, seed, counter); break;\
         }\
         return out;\
     }\
@@ -55,4 +55,37 @@ namespace opencl
     template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
     template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    {
+        verifyDoubleSupport<T>();
+        Array<T> out = createEmptyArray<T>(dims);
+
+        switch(type) {
+        case AF_RANDOM_PHILOX: kernel::normalDistribution<T, AF_RANDOM_PHILOX>(*out.get(), out.elements(), seed, counter); break;
+        case AF_RANDOM_THREEFRY: kernel::normalDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements(), seed, counter); break;
+        }
+        return out;
+    }
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        verifyDoubleSupport<T>();\
+        Array<T> out = createEmptyArray<T>(dims);\
+\
+        switch(type) {\
+        case AF_RANDOM_PHILOX: kernel::normalDistribution<TR, AF_RANDOM_PHILOX>(*out.get(), out.elements()*2, seed, counter); break;\
+        case AF_RANDOM_THREEFRY: kernel::normalDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements()*2, seed, counter); break;\
+        }\
+        return out;\
+    }\
+
+    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
+    template Array<float>   normalDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double>  normalDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+
 }
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index b4045bb5a2..2cafa3e214 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -15,4 +15,7 @@ namespace opencl
 {
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
 }
diff --git a/test/random.cpp b/test/random.cpp
index 24ab999b3b..35d26200a3 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -240,7 +240,7 @@ TYPED_TEST(Random, getSeed)
     testGetSeed<TypeParam>(1234, 9876);
 }
 
-TEST(Random, philoxEngine)
+TEST(Random, philoxEngineUniform)
 {
     int elem = 16*1024*1024;
     af::randomEngine r(AF_RANDOM_PHILOX, 0);
@@ -251,7 +251,7 @@ TEST(Random, philoxEngine)
     ASSERT_NEAR(s, 0.2887, 1e-2);
 }
 
-TEST(Random, threefryEngine)
+TEST(Random, threefryEngineUniform)
 {
     int elem = 16*1024*1024;
     af::randomEngine r(AF_RANDOM_THREEFRY, 0);
@@ -261,3 +261,25 @@ TEST(Random, threefryEngine)
     ASSERT_NEAR(m, 0.5, 1e-3);
     ASSERT_NEAR(s, 0.2887, 1e-2);
 }
+
+TEST(Random, philoxEngineNormal)
+{
+    int elem = 16*1024*1024;
+    af::randomEngine r(AF_RANDOM_PHILOX, 0);
+    array A = r.normal(elem, f32);
+    float m = mean<float>(A);
+    float s = stdev<float>(A);
+    ASSERT_NEAR(m, 0, 1e-2);
+    ASSERT_NEAR(s, 1, 1e-1);
+}
+
+TEST(Random, threefryEngineNormal)
+{
+    int elem = 16*1024*1024;
+    af::randomEngine r(AF_RANDOM_THREEFRY, 0);
+    array A = r.normal(elem, f32);
+    float m = mean<float>(A);
+    float s = stdev<float>(A);
+    ASSERT_NEAR(m, 0, 1e-2);
+    ASSERT_NEAR(s, 1, 1e-1);
+}

From 4030f21bf1c33665003f858a34f74ace7d3bf2ef Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 20 Jul 2016 16:03:57 -0400
Subject: [PATCH 0699/2677] OpenCL kernel building using kernel caches

Improved test for normal and uniform.
---
 src/backend/cpu/kernel/random_engine.hpp    |  17 +-
 src/backend/opencl/kernel/random_engine.hpp | 177 +++++++++-----------
 test/random.cpp                             |  77 +++++----
 3 files changed, 140 insertions(+), 131 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 3584bf4c27..c436e016ab 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -55,8 +55,9 @@ namespace kernel
         uint ctr[4] = {(uint)counter, 0, 0, lo};
 
         int fresh = 0;
-        int reset = (4*sizeof(uint))/sizeof(T);
+        unsigned reset = (4*sizeof(uint))/sizeof(T);
         philox(key, ctr);
+        ++ctr[0]; ++key[0];
         for (int i = 0; i < (int)elements; ++i) {
             if (fresh == reset) {
                 philox(key, ctr);
@@ -65,6 +66,12 @@ namespace kernel
             }
             out[i] = transform<T>(ctr, fresh);
             fresh++;
+            ////philox(key, ctr);
+            ////++ctr[0]; ++key[0];
+            ////int lim = (reset < elements - i)? reset : elements - i;
+            ////for (int j = 0; j < lim; ++j) {
+            ////    out[i + j] = transform<T>(ctr, j);
+            ////}
         }
     }
 
@@ -78,7 +85,7 @@ namespace kernel
         uint val[2];
 
         int fresh = 0;
-        int reset = (2*sizeof(uint))/sizeof(T);
+        unsigned reset = (2*sizeof(uint))/sizeof(T);
         threefry(key, ctr, val);
         ++ctr[0]; ++key[0];
         for (int i = 0; i < (int)elements; ++i) {
@@ -89,6 +96,12 @@ namespace kernel
             }
             out[i] = transform<T>(val, fresh);
             fresh++;
+            ////threefry(key, ctr, val);
+            ////++ctr[0]; ++key[0];
+            ////int lim = (reset < elements - i)? reset : elements - i;
+            ////for (int j = 0; j < lim; ++j) {
+            ////    out[i + j] = transform<T>(ctr, j);
+            ////}
         }
     }
 
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 3064d56d64..c1c532bca7 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -24,6 +24,10 @@
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <program.hpp>
+#include <type_util.hpp>
+#include <cache.hpp>
+#include "names.hpp"
+#include "config.hpp"
 
 #include <iostream>
 
@@ -41,122 +45,93 @@ namespace opencl
     {
         static const uint THREADS = 256;
 
-        template <typename T, af_random_type Type>
-        void uniformDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        template <typename T>
+        static Kernel get_random_engine_kernel(af_random_type type, std::string distribution, uint elementsPerBlock)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  ranProgs;
-                static std::map<int, Kernel*>   ranKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                        std::string kernelString;
-                        switch (Type) {
-                            case AF_RANDOM_PHILOX : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
-                                                    std::string(random_engine_philox_cl, random_engine_philox_cl_len); break;
-                            case AF_RANDOM_THREEFRY : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
-                                                    std::string(random_engine_threefry_cl, random_engine_threefry_cl_len); break;
-                                                    //THROW
-                        }
-                        uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
-
-                        Program::Sources setSrc;
-                        setSrc.emplace_back(kernelString.c_str(), kernelString.length());
-
-                        std::ostringstream options;
-                        options << " -D T=" << dtype_traits<T>::getName()
-                                << " -D THREADS=" << THREADS
-                                << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
+            int kerIdx;
+            std::string engineName;
+            std::string kernelString;
+            if (distribution == std::string("uniformDistribution")) {
+                kerIdx = 0;
+            } if (distribution == std::string("normalDistribution")) {
+                kerIdx = 1;
+            }
+            switch (type) {
+                case AF_RANDOM_PHILOX : engineName = "Philox";
+                                        kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
+                                        std::string(random_engine_philox_cl, random_engine_philox_cl_len); break;
+                case AF_RANDOM_THREEFRY : engineName = "Threefry";
+                                        kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
+                                        std::string(random_engine_threefry_cl, random_engine_threefry_cl_len); break;
+                                        //THROW
+            }
+            std::string ref_name =
+                std::string("random_engine_kernel_") + engineName +
+                std::string("_") + std::string(dtype_traits<T>::getName());
+            int device = getActiveDeviceId();
+            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+            if (idx == kernelCaches[device].end()) {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D THREADS=" << THREADS
+                        << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
 #if defined(OS_MAC) // Because apple is "special"
-                        options << " -D IS_APPLE"
-                                << " -D log10_val=" << std::log(10.0);
+                options << " -D IS_APPLE"
+                        << " -D log10_val=" << std::log(10.0);
 #endif
+                cl::Program prog;
+                buildProgram(prog, kernelString.c_str(), kernelString.length(), options.str());
+                entry.prog = new Program(prog);
+                entry.ker = new Kernel[2];
+                entry.ker[0] = Kernel(*entry.prog, "uniformDistribution");
+                entry.ker[1] = Kernel(*entry.prog, "normalDistribution");
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
+            }
 
-                        cl::Program prog;
-                        buildProgram(prog, kernelString.c_str(), kernelString.length(), options.str());
-                        ranProgs[device] = new Program(prog);
-                        ranKernels[device] = new Kernel(*ranProgs[device], "uniformDistribution");
-                    });
+            return entry.ker[kerIdx];
+        }
 
-                    auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
+        template <typename T>
+        static void randomDistribution(af_random_type type, cl::Buffer out, size_t elements, const uintl seed, uintl &counter, std::string distribution)
+        {
+            try {
+                uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
+                uint groups = divup(elements, elementsPerBlock);
+
+                uint hi = seed>>32;
+                uint lo = seed;
 
-                    uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
-                    uint groups = divup(elements, elementsPerBlock);
-                    counter += elements;
+                NDRange local(THREADS, 1);
+                NDRange global(THREADS * groups, 1);
 
-                    NDRange local(THREADS, 1);
-                    NDRange global(THREADS * groups, 1);
+                Kernel ker = get_random_engine_kernel<T>(type, distribution, elementsPerBlock);
+                auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
 
-                    uint hi = seed>>32;
-                    uint lo = seed;
+                randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                        out, elements, counter, hi, lo);
 
-                    randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                            out, elements, counter, hi, lo);
-                    CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error ex) {
-                CL_TO_AF_ERROR(ex);
+                counter += elements;
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
             }
         }
 
         template <typename T, af_random_type Type>
-        void normalDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        void uniformDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  ranProgs;
-                static std::map<int, Kernel*>   ranKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                        std::string kernelString;
-                        switch (Type) {
-                            case AF_RANDOM_PHILOX : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
-                                                    std::string(random_engine_philox_cl, random_engine_philox_cl_len); break;
-                            case AF_RANDOM_THREEFRY : kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
-                                                    std::string(random_engine_threefry_cl, random_engine_threefry_cl_len); break;
-                                                    //THROW
-                        }
-                        uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
-
-                        Program::Sources setSrc;
-                        setSrc.emplace_back(kernelString.c_str(), kernelString.length());
-
-                        std::ostringstream options;
-                        options << " -D T=" << dtype_traits<T>::getName()
-                                << " -D THREADS=" << THREADS
-                                << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
-#if defined(OS_MAC) // Because apple is "special"
-                        options << " -D IS_APPLE"
-                                << " -D log10_val=" << std::log(10.0);
-#endif
-
-                        cl::Program prog;
-                        buildProgram(prog, kernelString.c_str(), kernelString.length(), options.str());
-                        ranProgs[device] = new Program(prog);
-                        ranKernels[device] = new Kernel(*ranProgs[device], "normalDistribution");
-                    });
-
-                    auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
-
-                    uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
-                    uint groups = divup(elements, elementsPerBlock);
-                    counter += elements;
-
-                    NDRange local(THREADS, 1);
-                    NDRange global(THREADS * groups, 1);
-
-                    uint hi = seed>>32;
-                    uint lo = seed;
+            randomDistribution<T>(Type, out, elements, seed, counter, "uniformDistribution");
+        }
 
-                    randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                            out, elements, counter, hi, lo);
-                    CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error ex) {
-                CL_TO_AF_ERROR(ex);
-            }
+        template <typename T, af_random_type Type>
+        void normalDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        {
+            randomDistribution<T>(Type, out, elements, seed, counter, "normalDistribution");
         }
+
     }
 }
diff --git a/test/random.cpp b/test/random.cpp
index 35d26200a3..f0c9ed39e2 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -25,6 +25,7 @@ using af::cfloat;
 using af::cdouble;
 using af::array;
 using af::randomEngine;
+using af::randomType;
 using af::mean;
 using af::stdev;
 
@@ -53,9 +54,23 @@ class Random_norm : public ::testing::Test
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypesNorm;
 
+template<typename T>
+class RandomEngine : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {
+        }
+};
+
 // register the type list
 TYPED_TEST_CASE(Random_norm, TestTypesNorm);
 
+// create a list of types to be tested
+typedef ::testing::Types<float, double> TestTypesEngine;
+
+// register the type list
+TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
+
 template<typename T>
 void randuTest(af::dim4 & dims)
 {
@@ -240,46 +255,52 @@ TYPED_TEST(Random, getSeed)
     testGetSeed<TypeParam>(1234, 9876);
 }
 
-TEST(Random, philoxEngineUniform)
+template <typename T>
+void testRandomEngineUniform(af_random_type type)
 {
+    if (noDoubleTests<T>()) return;
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+
     int elem = 16*1024*1024;
-    af::randomEngine r(AF_RANDOM_PHILOX, 0);
-    array A = r.uniform(elem, f32);
-    float m = mean<float>(A);
-    float s = stdev<float>(A);
+    af::randomEngine r(type, 0);
+    array A = r.uniform(elem, ty);
+    T m = mean<T>(A);
+    T s = stdev<T>(A);
     ASSERT_NEAR(m, 0.5, 1e-3);
     ASSERT_NEAR(s, 0.2887, 1e-2);
 }
 
-TEST(Random, threefryEngineUniform)
+template <typename T>
+void testRandomEngineNormal(af_random_type type)
 {
+    if (noDoubleTests<T>()) return;
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+
     int elem = 16*1024*1024;
-    af::randomEngine r(AF_RANDOM_THREEFRY, 0);
-    array A = r.uniform(elem, f32);
-    float m = mean<float>(A);
-    float s = stdev<float>(A);
-    ASSERT_NEAR(m, 0.5, 1e-3);
-    ASSERT_NEAR(s, 0.2887, 1e-2);
+    af::randomEngine r(type, 0);
+    array A = r.normal(elem, ty);
+    T m = mean<T>(A);
+    T s = stdev<T>(A);
+    ASSERT_NEAR(m, 0, 1e-1);
+    ASSERT_NEAR(s, 1, 1e-1);
 }
 
-TEST(Random, philoxEngineNormal)
+TYPED_TEST(RandomEngine, philoxRandomEngineUniform)
 {
-    int elem = 16*1024*1024;
-    af::randomEngine r(AF_RANDOM_PHILOX, 0);
-    array A = r.normal(elem, f32);
-    float m = mean<float>(A);
-    float s = stdev<float>(A);
-    ASSERT_NEAR(m, 0, 1e-2);
-    ASSERT_NEAR(s, 1, 1e-1);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_PHILOX);
 }
 
-TEST(Random, threefryEngineNormal)
+TYPED_TEST(RandomEngine, threefryRandomEngineUniform)
 {
-    int elem = 16*1024*1024;
-    af::randomEngine r(AF_RANDOM_THREEFRY, 0);
-    array A = r.normal(elem, f32);
-    float m = mean<float>(A);
-    float s = stdev<float>(A);
-    ASSERT_NEAR(m, 0, 1e-2);
-    ASSERT_NEAR(s, 1, 1e-1);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_THREEFRY);
+}
+
+TYPED_TEST(RandomEngine, philoxRandomEngineNormal)
+{
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_PHILOX);
+}
+
+TYPED_TEST(RandomEngine, threefryRandomEngineNormal)
+{
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_THREEFRY);
 }

From e353ea86bb44d8a8c3dc0aed7c5ac81cd18ee460 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 22 Jul 2016 17:13:35 -0400
Subject: [PATCH 0700/2677] Changed CPU backend to address undefined behavior

Removed af_random_type as template parameter in cuda and opencl
backends.
---
 src/backend/cpu/kernel/random_engine.hpp      | 178 +++++++++---------
 .../cpu/kernel/random_engine_philox.hpp       |   9 +-
 src/backend/cpu/random_engine.cpp             |  72 +++----
 src/backend/cuda/kernel/random_engine.hpp     |  46 ++---
 src/backend/cuda/random_engine.cu             |  60 +++---
 src/backend/opencl/kernel/random_engine.hpp   |  53 +++---
 src/backend/opencl/random_engine.cpp          |  73 +++----
 7 files changed, 224 insertions(+), 267 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index c436e016ab..f928837cba 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Array.hpp>
+#include <err_cpu.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
 
@@ -30,11 +31,49 @@ namespace kernel
 
     template <> char transform<char>(uint *val, int index)
     {
-        char v = transform<char>(val, index);
+        char v = val[index>>2]>>(8<<(index & 3));
         v = (v&0x1) ? 1 : 0;
         return v;
     }
 
+    template <> uchar transform<uchar>(uint *val, int index)
+    {
+        uchar v = val[index>>2]>>(8<<(index & 3));
+        return v;
+    }
+
+    template <> ushort transform<ushort>(uint *val, int index)
+    {
+        ushort v = val[index>>1]>>(16<<(index & 1));
+        return v;
+    }
+
+    template <> short transform<short>(uint *val, int index)
+    {
+        return transform<ushort>(val, index);
+    }
+
+    template <> uint transform<uint>(uint *val, int index)
+    {
+        return val[index];
+    }
+
+    template <> int transform<int>(uint *val, int index)
+    {
+        return transform<uint>(val, index);
+    }
+
+    template <> uintl transform<uintl>(uint *val, int index)
+    {
+        uintl v = (((uintl)val[index<<1])<<32) | ((uintl)val[(index<<1)+1]);
+        return v;
+    }
+
+    template <> intl transform<intl>(uint *val, int index)
+    {
+        return transform<uintl>(val, index);
+    }
+
     template <> float transform<float>(uint *val, int index)
     {
         return (float)val[index]/UINTMAXFLOAT;
@@ -47,36 +86,25 @@ namespace kernel
     }
 
     template <typename T>
-    void philoxUniform(T* out, size_t elements, const uintl seed, uintl &counter)
+    void philoxUniform(T* out, size_t elements, const uintl seed, uintl counter)
     {
         uint hi = seed>>32;
         uint lo = seed;
         uint key[2] = {(uint)counter, hi};
         uint ctr[4] = {(uint)counter, 0, 0, lo};
 
-        int fresh = 0;
-        unsigned reset = (4*sizeof(uint))/sizeof(T);
-        philox(key, ctr);
-        ++ctr[0]; ++key[0];
-        for (int i = 0; i < (int)elements; ++i) {
-            if (fresh == reset) {
-                philox(key, ctr);
-                ++ctr[0]; ++key[0];
-                fresh = 0;
+        int reset = (4*sizeof(uint))/sizeof(T);
+        for (int i = 0; i < (int)elements; i += reset) {
+            philox(key, ctr);
+            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
+            for (int j = 0; j < lim; ++j) {
+                out[i + j] = transform<T>(ctr, j);
             }
-            out[i] = transform<T>(ctr, fresh);
-            fresh++;
-            ////philox(key, ctr);
-            ////++ctr[0]; ++key[0];
-            ////int lim = (reset < elements - i)? reset : elements - i;
-            ////for (int j = 0; j < lim; ++j) {
-            ////    out[i + j] = transform<T>(ctr, j);
-            ////}
         }
     }
 
     template <typename T>
-    void threefryUniform(T* out, size_t elements, const uintl seed, uintl &counter)
+    void threefryUniform(T* out, size_t elements, const uintl seed, uintl counter)
     {
         uint hi = seed>>32;
         uint lo = seed;
@@ -84,44 +112,21 @@ namespace kernel
         uint ctr[2] = {(uint)counter, lo};
         uint val[2];
 
-        int fresh = 0;
-        unsigned reset = (2*sizeof(uint))/sizeof(T);
-        threefry(key, ctr, val);
-        ++ctr[0]; ++key[0];
-        for (int i = 0; i < (int)elements; ++i) {
-            if (fresh == reset) {
-                threefry(key, ctr, val);
-                ++ctr[0]; ++key[0];
-                fresh = 0;
+        int reset = (2*sizeof(uint))/sizeof(T);
+        for (int i = 0; i < (int)elements; i += reset) {
+            threefry(key, ctr, val);
+            ++ctr[0]; ++key[0];
+            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
+            for (int j = 0; j < lim; ++j) {
+                out[i + j] = transform<T>(val, j);
             }
-            out[i] = transform<T>(val, fresh);
-            fresh++;
-            ////threefry(key, ctr, val);
-            ////++ctr[0]; ++key[0];
-            ////int lim = (reset < elements - i)? reset : elements - i;
-            ////for (int j = 0; j < lim; ++j) {
-            ////    out[i + j] = transform<T>(ctr, j);
-            ////}
-        }
-    }
-
-    template <typename T, af_random_type Type>
-    void uniformDistribution(T* out, size_t elements, const uintl seed, uintl &counter)
-    {
-        switch(Type) {
-            case AF_RANDOM_PHILOX   :   philoxUniform(out, elements, seed, counter); break;
-            case AF_RANDOM_THREEFRY : threefryUniform(out, elements, seed, counter); break;
         }
     }
 
     template <typename T>
     void normalizePair(T * const out1, T * const out2, const T r1, const T r2)
     {
-#if defined(IS_APPLE) // Because Apple is.. "special"
-        T r = sqrt((T)(-2.0) * log10(r1) * (float)log10_val);
-#else
         T r = sqrt((T)(-2.0) * log(r1));
-#endif
         T theta = 2 * (T)PI_VAL * (r2);
         *out1 = r*sin(theta);
         *out2 = r*cos(theta);
@@ -129,75 +134,76 @@ namespace kernel
 
     void normalize(uint val[4], double *temp)
     {
-        uintl *v = (uintl*)val;
-        normalizePair(&temp[0], &temp[1], v[0]/UINTLMAXDOUBLE, v[1]/UINTLMAXDOUBLE);
+        normalizePair(&temp[0], &temp[1], transform<double>(val, 0), transform<double>(val,1));
     }
 
     void normalize(uint val[4], float *temp)
     {
-        normalizePair(&temp[0], &temp[1], val[0]/UINTMAXFLOAT, val[1]/UINTMAXFLOAT);
-        normalizePair(&temp[2], &temp[3], val[2]/UINTMAXFLOAT, val[3]/UINTMAXFLOAT);
+        normalizePair(&temp[0], &temp[1], transform<float>(val, 0), transform<float>(val, 1));
+        normalizePair(&temp[2], &temp[3], transform<float>(val, 2), transform<float>(val, 3));
     }
 
     template <typename T>
-    void threefryNormal(T* out, size_t elements, const uintl seed, uintl &counter)
+    void philoxNormal(T* out, size_t elements, const uintl seed, uintl counter)
     {
         uint hi = seed>>32;
         uint lo = seed;
         uint key[2] = {(uint)counter, hi};
-        uint ctr[2] = {(uint)counter, lo};
-        uint val[4];
+        uint ctr[4] = {(uint)counter, 0, 0, lo};
         T temp[(4*sizeof(uint))/sizeof(T)];
 
-        int fresh = 0;
         int reset = (4*sizeof(uint))/sizeof(T);
-        threefry(key, ctr, val);
-        normalize(val, temp);
-        ++ctr[0]; ++key[0];
-        for (int i = 0; i < (int)elements; ++i) {
-            if (fresh == reset) {
-                threefry(key, ctr, val);
-                ++ctr[0]; ++key[0];
-                threefry(key, ctr, val+2);
-                fresh = 0;
-                normalize(val, temp);
+        for (int i = 0; i < (int)elements; i += reset) {
+            philox(key, ctr);
+            normalize(ctr, temp);
+            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
+            for (int j = 0; j < lim; ++j) {
+                out[i + j] = temp[j];
             }
-            out[i] = temp[fresh];
-            fresh++;
         }
     }
 
     template <typename T>
-    void philoxNormal(T* out, size_t elements, const uintl seed, uintl &counter)
+    void threefryNormal(T* out, size_t elements, const uintl seed, uintl counter)
     {
         uint hi = seed>>32;
         uint lo = seed;
         uint key[2] = {(uint)counter, hi};
-        uint ctr[4] = {(uint)counter, 0, 0, lo};
+        uint ctr[2] = {(uint)counter, lo};
+        uint val[4];
         T temp[(4*sizeof(uint))/sizeof(T)];
 
-        int fresh = 0;
         int reset = (4*sizeof(uint))/sizeof(T);
-        philox(key, ctr);
-        normalize(ctr, temp);
-        for (int i = 0; i < (int)elements; ++i) {
-            if (fresh == reset) {
-                philox(key, ctr);
-                ++ctr[0]; ++key[0];
-                fresh = 0;
-                normalize(ctr, temp);
+        for (int i = 0; i < (int)elements; i += reset) {
+            threefry(key, ctr, val);
+            ++ctr[0]; ++key[0];
+            threefry(key, ctr, val+2);
+            ++ctr[0]; ++key[0];
+            normalize(val, temp);
+            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
+            for (int j = 0; j < lim; ++j) {
+                out[i + j] = temp[j];
             }
-            out[i] = temp[fresh];
-            fresh++;
         }
     }
 
-    template <typename T, af_random_type Type>
-    void normalDistribution(T* out, size_t elements, const uintl seed, uintl &counter)
+    template <typename T>
+    void uniformDistribution(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
+    {
+        switch(type) {
+            case AF_RANDOM_PHILOX   :   philoxUniform(out, elements, seed, counter); break;
+            case AF_RANDOM_THREEFRY : threefryUniform(out, elements, seed, counter); break;
+            default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
+    }
+
+    template <typename T>
+    void normalDistribution(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
     {
-        switch(Type) {
+        switch(type) {
             case AF_RANDOM_PHILOX   :   philoxNormal(out, elements, seed, counter); break;
             case AF_RANDOM_THREEFRY : threefryNormal(out, elements, seed, counter); break;
+            default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }
 
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index a894107b2d..55c5b41e59 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -56,19 +56,19 @@ namespace kernel
 #define w32_0 0x9E3779B9
 #define w32_1 0xBB67AE85
 
-static inline void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
+void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
 {
     *hi = (((uintl)a) * ((uintl)b))>>32;
     *lo = a*b;
 }
 
-static inline void philoxBump(uint k[2])
+void philoxBump(uint * const k)
 {
     k[0] += w32_0;
     k[1] += w32_1;
 }
 
-static inline void philoxRound(const uint k[2], uint c[4])
+void philoxRound(const uint * const k, uint * const c)
 {
     uint hi0, lo0, hi1, lo1;
     mulhilo(m4x32_0, c[0], &hi0, &lo0);
@@ -79,8 +79,9 @@ static inline void philoxRound(const uint k[2], uint c[4])
     c[3] = lo0;
 }
 
-static inline void philox(uint key[2], uint ctr[4])
+void philox(uint * const key, uint * const ctr)
 {
+    ctr[0] = -1;
     //10 Rounds
                        philoxRound(key, ctr);
     philoxBump(key);   philoxRound(key, ctr);
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index a648d0c676..5d1899a9eb 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -19,13 +19,19 @@ namespace cpu
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        T *outPtr = out.get();
-        size_t elements = out.elements();
-        switch(type) {
-        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::uniformDistribution<T,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;
-        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;
-        }
-        counter += elements;
+        getQueue().enqueue(kernel::uniformDistribution<T>, out.get(), out.elements(), type, seed, counter);
+        out.eval();
+        getQueue().sync();
+        counter += out.elements();
+        return out;
+    }
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        getQueue().enqueue(kernel::normalDistribution<T>, out.get(), out.elements(), type, seed, counter);
+        counter += out.elements();
         return out;
     }
 
@@ -36,15 +42,22 @@ namespace cpu
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        switch(type) {\
-        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::uniformDistribution<TR,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;\
-        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::uniformDistribution<TR, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;\
-        }\
+        getQueue().enqueue(kernel::uniformDistribution<TR>, outPtr, elements, type, seed, counter);\
+        counter += elements;\
         return out;\
     }\
 
-    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        getQueue().enqueue(kernel::normalDistribution<TR>, outPtr, elements, type, seed, counter);\
+        counter += elements;\
+        return out;\
+    }\
 
     template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
     template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
@@ -57,38 +70,13 @@ namespace cpu
     template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
     template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        T *outPtr = out.get();
-        size_t elements = out.elements();
-        switch(type) {
-        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::normalDistribution<T,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;
-        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::normalDistribution<T, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;
-        }
-        counter += elements;
-        return out;
-    }
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        switch(type) {\
-        case AF_RANDOM_PHILOX   : getQueue().enqueue(kernel::normalDistribution<TR,   AF_RANDOM_PHILOX>, outPtr, elements, seed, counter); break;\
-        case AF_RANDOM_THREEFRY : getQueue().enqueue(kernel::normalDistribution<TR, AF_RANDOM_THREEFRY>, outPtr, elements, seed, counter); break;\
-        }\
-        return out;\
-    }\
+    template Array<float>  normalDistribution<float>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> normalDistribution<double> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
     COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
     COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
-    template Array<float>  normalDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> normalDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
 }
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index fa1d2fa4d9..2c5df8789f 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -417,25 +417,6 @@ namespace kernel
         }
     }
 
-    template <typename T, af_random_type Type>
-    void uniformDistribution(T *out, size_t elements, const uintl seed, uintl &counter)
-    {
-        int threads = THREADS;
-        int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
-        int blocks = divup(elements, elementsPerBlock);
-        uint hi = seed>>32;
-        uint lo = seed;
-        uintl count = counter;
-        switch (Type) {
-        case AF_RANDOM_PHILOX : CUDA_LAUNCH(uniformPhilox, blocks, threads,
-                out, hi, lo, count, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY : CUDA_LAUNCH(uniformThreefry, blocks, threads,
-                out, hi, lo, count, elementsPerBlock, elements); break;
-                                  //THROW
-        }
-        counter += elements;
-    }
-
     template <typename T>
     __global__ void normalPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
     {
@@ -471,8 +452,27 @@ namespace kernel
         }
     }
 
-    template <typename T, af_random_type Type>
-    void normalDistribution(T *out, size_t elements, const uintl seed, uintl &counter)
+    template <typename T>
+    void uniformDistribution(T *out, size_t elements, af_random_type type, const uintl seed, uintl &counter)
+    {
+        int threads = THREADS;
+        int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
+        int blocks = divup(elements, elementsPerBlock);
+        uint hi = seed>>32;
+        uint lo = seed;
+        uintl count = counter;
+        switch (type) {
+        case AF_RANDOM_PHILOX : CUDA_LAUNCH(uniformPhilox, blocks, threads,
+                out, hi, lo, count, elementsPerBlock, elements); break;
+        case AF_RANDOM_THREEFRY : CUDA_LAUNCH(uniformThreefry, blocks, threads,
+                out, hi, lo, count, elementsPerBlock, elements); break;
+        default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
+        counter += elements;
+    }
+
+    template <typename T>
+    void normalDistribution(T *out, size_t elements, af_random_type type, const uintl seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
@@ -480,12 +480,12 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         uintl count = counter;
-        switch (Type) {
+        switch (type) {
         case AF_RANDOM_PHILOX : CUDA_LAUNCH(normalPhilox, blocks, threads,
                 out, hi, lo, count, elementsPerBlock, elements); break;
         case AF_RANDOM_THREEFRY : CUDA_LAUNCH(normalThreefry, blocks, threads,
                 out, hi, lo, count, elementsPerBlock, elements); break;
-                                  //THROW
+        default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
     }
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index dea31063da..00ebc0844b 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -18,11 +18,15 @@ namespace cuda
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
+        kernel::uniformDistribution<T>(out.get(), out.elements(), type, seed, counter);
+        return out;
+    }
 
-        switch(type) {
-        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<T,   AF_RANDOM_PHILOX>(out.get(), out.elements(), seed, counter); break;
-        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>(out.get(), out.elements(), seed, counter); break;
-        }
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        kernel::normalDistribution<T>(out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
@@ -33,15 +37,20 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        switch(type) {\
-        case AF_RANDOM_PHILOX   : kernel::uniformDistribution<TR,   AF_RANDOM_PHILOX>(outPtr, elements, seed, counter); break;\
-        case AF_RANDOM_THREEFRY : kernel::uniformDistribution<TR, AF_RANDOM_THREEFRY>(outPtr, elements, seed, counter); break;\
-        }\
+        kernel::uniformDistribution<TR>(outPtr, elements, type, seed, counter);\
         return out;\
     }\
 
-    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
+    template<>\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        kernel::uniformDistribution<TR>(outPtr, elements, type, seed, counter);\
+        return out;\
+    }\
 
     template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
     template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
@@ -54,36 +63,13 @@ namespace cuda
     template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
     template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-
-        switch(type) {
-        case AF_RANDOM_PHILOX   : kernel::normalDistribution<T,   AF_RANDOM_PHILOX>(out.get(), out.elements(), seed, counter); break;
-        case AF_RANDOM_THREEFRY : kernel::normalDistribution<T, AF_RANDOM_THREEFRY>(out.get(), out.elements(), seed, counter); break;
-        }
-        return out;
-    }
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        switch(type) {\
-        case AF_RANDOM_PHILOX   : kernel::normalDistribution<TR,   AF_RANDOM_PHILOX>(outPtr, elements, seed, counter); break;\
-        case AF_RANDOM_THREEFRY : kernel::normalDistribution<TR, AF_RANDOM_THREEFRY>(outPtr, elements, seed, counter); break;\
-        }\
-        return out;\
-    }\
+    template Array<float>  normalDistribution<float>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> normalDistribution<double> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
     COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
     COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
-    template Array<float>  normalDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> normalDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
 }
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index c1c532bca7..fe5ced8c7d 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -11,25 +11,18 @@
 
 #include <platform.hpp>
 #include <af/defines.h>
-//#include <kernel_headers/random.hpp>
 #include <kernel_headers/random_engine_philox.hpp>
 #include <kernel_headers/random_engine_threefry.hpp>
 #include <kernel_headers/random_engine_write.hpp>
 #include <traits.hpp>
 #include <sstream>
 #include <string>
-#include <mutex>
-#include <map>
 #include <dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <program.hpp>
 #include <type_util.hpp>
 #include <cache.hpp>
-#include "names.hpp"
-#include "config.hpp"
-
-#include <iostream>
 
 using cl::Buffer;
 using cl::Program;
@@ -46,25 +39,25 @@ namespace opencl
         static const uint THREADS = 256;
 
         template <typename T>
-        static Kernel get_random_engine_kernel(af_random_type type, std::string distribution, uint elementsPerBlock)
+        static Kernel get_random_engine_kernel(const af_random_type type, const int kerIdx, const uint elementsPerBlock)
         {
-            int kerIdx;
             std::string engineName;
-            std::string kernelString;
-            if (distribution == std::string("uniformDistribution")) {
-                kerIdx = 0;
-            } if (distribution == std::string("normalDistribution")) {
-                kerIdx = 1;
-            }
+            const char *ker_strs[2];
+            int ker_lens[2];
+            ker_strs[0] = random_engine_write_cl;
+            ker_lens[0] = random_engine_write_cl_len;
             switch (type) {
-                case AF_RANDOM_PHILOX : engineName = "Philox";
-                                        kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
-                                        std::string(random_engine_philox_cl, random_engine_philox_cl_len); break;
+                case AF_RANDOM_PHILOX   : engineName = "Philox";
+                                        ker_strs[1] = random_engine_philox_cl;
+                                        ker_lens[1] = random_engine_philox_cl_len;
+                                        break;
                 case AF_RANDOM_THREEFRY : engineName = "Threefry";
-                                        kernelString = std::string(random_engine_write_cl, random_engine_write_cl_len) +
-                                        std::string(random_engine_threefry_cl, random_engine_threefry_cl_len); break;
-                                        //THROW
+                                        ker_strs[1] = random_engine_threefry_cl;
+                                        ker_lens[1] = random_engine_threefry_cl_len;
+                                        break;
+                default                 : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
             }
+
             std::string ref_name =
                 std::string("random_engine_kernel_") + engineName +
                 std::string("_") + std::string(dtype_traits<T>::getName());
@@ -81,7 +74,7 @@ namespace opencl
                         << " -D log10_val=" << std::log(10.0);
 #endif
                 cl::Program prog;
-                buildProgram(prog, kernelString.c_str(), kernelString.length(), options.str());
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel[2];
                 entry.ker[0] = Kernel(*entry.prog, "uniformDistribution");
@@ -95,7 +88,7 @@ namespace opencl
         }
 
         template <typename T>
-        static void randomDistribution(af_random_type type, cl::Buffer out, size_t elements, const uintl seed, uintl &counter, std::string distribution)
+        static void randomDistribution(cl::Buffer out, const size_t elements, const af_random_type type, const uintl seed, uintl &counter, int kerIdx)
         {
             try {
                 uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
@@ -107,7 +100,7 @@ namespace opencl
                 NDRange local(THREADS, 1);
                 NDRange global(THREADS * groups, 1);
 
-                Kernel ker = get_random_engine_kernel<T>(type, distribution, elementsPerBlock);
+                Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
                 auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
 
                 randomEngineOp(EnqueueArgs(getQueue(), global, local),
@@ -121,16 +114,16 @@ namespace opencl
             }
         }
 
-        template <typename T, af_random_type Type>
-        void uniformDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        template <typename T>
+        void uniformDistribution(cl::Buffer out, const size_t elements, const af_random_type type, const uintl seed, uintl &counter)
         {
-            randomDistribution<T>(Type, out, elements, seed, counter, "uniformDistribution");
+            randomDistribution<T>(out, elements, type, seed, counter, 0);
         }
 
-        template <typename T, af_random_type Type>
-        void normalDistribution(cl::Buffer out, size_t elements, const uintl seed, uintl &counter)
+        template <typename T>
+        void normalDistribution(cl::Buffer out, const size_t elements, const af_random_type type, const uintl seed, uintl &counter)
         {
-            randomDistribution<T>(Type, out, elements, seed, counter, "normalDistribution");
+            randomDistribution<T>(out, elements, type, seed, counter, 1);
         }
 
     }
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 7e4bcdf370..161526b066 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -19,11 +19,16 @@ namespace opencl
     {
         verifyDoubleSupport<T>();
         Array<T> out = createEmptyArray<T>(dims);
+        kernel::uniformDistribution<T>(*out.get(), out.elements(), type, seed, counter);
+        return out;
+    }
 
-        switch(type) {
-        case AF_RANDOM_PHILOX: kernel::uniformDistribution<T, AF_RANDOM_PHILOX>(*out.get(), out.elements(), seed, counter); break;
-        case AF_RANDOM_THREEFRY: kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements(), seed, counter); break;
-        }
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    {
+        verifyDoubleSupport<T>();
+        Array<T> out = createEmptyArray<T>(dims);
+        kernel::normalDistribution<T>(*out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
@@ -33,59 +38,37 @@ namespace opencl
     {\
         verifyDoubleSupport<T>();\
         Array<T> out = createEmptyArray<T>(dims);\
-\
-        switch(type) {\
-        case AF_RANDOM_PHILOX: kernel::uniformDistribution<TR, AF_RANDOM_PHILOX>(*out.get(), out.elements()*2, seed, counter); break;\
-        case AF_RANDOM_THREEFRY: kernel::uniformDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements()*2, seed, counter); break;\
-        }\
+        kernel::uniformDistribution<TR>(*out.get(), out.elements()*2, type, seed, counter);\
         return out;\
     }\
 
-    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
-
-    template Array<float>   uniformDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double>  uniformDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<int>     uniformDistribution<int>     (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uint>    uniformDistribution<uint>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<intl>    uniformDistribution<intl>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uintl>   uniformDistribution<uintl>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<char>    uniformDistribution<char>    (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uchar>   uniformDistribution<uchar>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<short>   uniformDistribution<short>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<ushort>  uniformDistribution<ushort>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
-    {
-        verifyDoubleSupport<T>();
-        Array<T> out = createEmptyArray<T>(dims);
-
-        switch(type) {
-        case AF_RANDOM_PHILOX: kernel::normalDistribution<T, AF_RANDOM_PHILOX>(*out.get(), out.elements(), seed, counter); break;
-        case AF_RANDOM_THREEFRY: kernel::normalDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements(), seed, counter); break;
-        }
-        return out;
-    }
-
 #define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
     template<>\
     Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
     {\
         verifyDoubleSupport<T>();\
         Array<T> out = createEmptyArray<T>(dims);\
-\
-        switch(type) {\
-        case AF_RANDOM_PHILOX: kernel::normalDistribution<TR, AF_RANDOM_PHILOX>(*out.get(), out.elements()*2, seed, counter); break;\
-        case AF_RANDOM_THREEFRY: kernel::normalDistribution<T, AF_RANDOM_THREEFRY>(*out.get(), out.elements()*2, seed, counter); break;\
-        }\
+        kernel::normalDistribution<TR>(*out.get(), out.elements()*2, type, seed, counter);\
         return out;\
     }\
 
-    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+    template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<int>    uniformDistribution<int>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uint>   uniformDistribution<uint>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<intl>   uniformDistribution<intl>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uintl>  uniformDistribution<uintl> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<char>   uniformDistribution<char>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<uchar>  uniformDistribution<uchar> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+
+    template Array<float>  normalDistribution<float>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    template Array<double> normalDistribution<double> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
 
-    template Array<float>   normalDistribution<float>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double>  normalDistribution<double>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
+    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 }

From f0507ec93c22a4cfdb1d71e40afcd60a7c7a5428 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 27 Jul 2016 16:20:32 -0400
Subject: [PATCH 0701/2677] Improved OpenCL backend

---
 src/backend/opencl/kernel/random_engine.hpp   |  21 +-
 .../opencl/kernel/random_engine_philox.cl     |  26 +--
 .../opencl/kernel/random_engine_threefry.cl   |  38 +---
 .../opencl/kernel/random_engine_write.cl      | 180 +++++++++---------
 src/backend/opencl/random_engine.cpp          |   1 +
 5 files changed, 112 insertions(+), 154 deletions(-)

diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index fe5ced8c7d..eda4651e01 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -41,7 +41,9 @@ namespace opencl
         template <typename T>
         static Kernel get_random_engine_kernel(const af_random_type type, const int kerIdx, const uint elementsPerBlock)
         {
-            std::string engineName;
+            using std::string;
+            using std::to_string;
+            string engineName;
             const char *ker_strs[2];
             int ker_lens[2];
             ker_strs[0] = random_engine_write_cl;
@@ -58,9 +60,10 @@ namespace opencl
                 default                 : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
-            std::string ref_name =
-                std::string("random_engine_kernel_") + engineName +
-                std::string("_") + std::string(dtype_traits<T>::getName());
+            string ref_name =
+                "random_engine_kernel_" + engineName +
+                "_" + string(dtype_traits<T>::getName()) +
+                "_" + to_string(kerIdx);
             int device = getActiveDeviceId();
             kc_t::iterator idx = kernelCaches[device].find(ref_name);
             kc_entry_t entry;
@@ -68,7 +71,11 @@ namespace opencl
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D THREADS=" << THREADS
-                        << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
+                        << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock
+                        << " -D RAND_DIST=" << kerIdx;
+                if (std::is_same<T, double>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 #if defined(OS_MAC) // Because apple is "special"
                 options << " -D IS_APPLE"
                         << " -D log10_val=" << std::log(10.0);
@@ -76,9 +83,7 @@ namespace opencl
                 cl::Program prog;
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
-                entry.ker = new Kernel[2];
-                entry.ker[0] = Kernel(*entry.prog, "uniformDistribution");
-                entry.ker[1] = Kernel(*entry.prog, "normalDistribution");
+                entry.ker = new Kernel(*entry.prog, "generate");
                 kernelCaches[device][ref_name] = entry;
             } else {
                 entry = idx->second;
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index b47f4380e7..44ddd0639e 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -88,7 +88,7 @@ inline void philox(uint key[2], uint ctr[4])
     philoxBump(key);   philoxRound(key, ctr);
 }
 
-__kernel void uniformDistribution(__global T *output, unsigned elements,
+__kernel void generate(__global T *output, unsigned elements,
         unsigned counter, unsigned hi, unsigned lo)
 {
     unsigned gid = get_group_id(0);
@@ -97,32 +97,12 @@ __kernel void uniformDistribution(__global T *output, unsigned elements,
 
     uint key[2] = {index+counter, hi};
     uint ctr[4] = {index+counter, 0, 0, lo};
+    philox(key, ctr);
 
     if (gid != get_num_groups(0) - 1) {
-        philox(key, ctr);
         WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3]);
     } else {
-        philox(key, ctr);
-        PARTIALWRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3], &elements);
-    }
-}
-
-__kernel void normalDistribution(__global T *output, unsigned elements,
-        unsigned counter, unsigned hi, unsigned lo)
-{
-    unsigned gid = get_group_id(0);
-    unsigned off = get_local_size(0);
-    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
-
-    uint key[2] = {index+counter, hi};
-    uint ctr[4] = {index+counter, 0, 0, lo};
-
-    if (gid != get_num_groups(0) - 1) {
-        philox(key, ctr);
-        NORMALWRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3]);
-    } else {
-        philox(key, ctr);
-        NORMALPARTIALWRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3], &elements);
+        PARTIAL_WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3], &elements);
     }
 }
 
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index b225dcde60..6d4af83f28 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -112,7 +112,7 @@ inline void threefry(uint k[2], uint c[2], uint X[2])
     X[1] += 4;
 }
 
-__kernel void uniformDistribution(__global T *output, unsigned elements,
+__kernel void generate(__global T *output, unsigned elements,
         unsigned counter, unsigned hi, unsigned lo)
 {
     unsigned gid = get_group_id(0);
@@ -123,40 +123,14 @@ __kernel void uniformDistribution(__global T *output, unsigned elements,
     uint ctr[2] = {index+counter, lo};
     uint o[4];
 
-    if (gid != get_num_groups(0) - 1) {
-        threefry(key, ctr, o);
-        ctr[0] += elements;
-        threefry(key, ctr, o+2);
-        WRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
-    } else {
-        threefry(key, ctr, o);
-        ctr[0] += elements;
-        threefry(key, ctr, o+2);
-        PARTIALWRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
-    }
-}
-
-__kernel void normalDistribution(__global T *output, unsigned elements,
-        unsigned counter, unsigned hi, unsigned lo)
-{
-    unsigned gid = get_group_id(0);
-    unsigned off = get_local_size(0);
-    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
-
-    uint key[2] = {index+counter, hi};
-    uint ctr[2] = {index+counter, lo};
-    uint o[4];
+    threefry(key, ctr, o);
+    ctr[0] += elements;
+    threefry(key, ctr, o+2);
 
     if (gid != get_num_groups(0) - 1) {
-        threefry(key, ctr, o);
-        ctr[0] += elements;
-        threefry(key, ctr, o+2);
-        NORMALWRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
+        WRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
     } else {
-        threefry(key, ctr, o);
-        ctr[0] += elements;
-        threefry(key, ctr, o+2);
-        NORMALPARTIALWRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
+        PARTIAL_WRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
     }
 }
 
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 9b2bcf7680..edf8ed8f8d 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -45,8 +45,8 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
-typedef ulong uintl;
-typedef long intl;
+//typedef ulong uintl;
+//typedef long intl;
 #define UINTMAXFLOAT 4294967296.0f
 #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
 #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
@@ -56,41 +56,6 @@ float getFloat(const uint * const num)
     return ((float)(*num))/UINTMAXFLOAT;
 }
 
-float getFloatSimple(uint num)
-{
-    return ((float)(num))/UINTMAXFLOAT;
-}
-
-double getDouble(const uint * const num1, const uint * const num2)
-{
-    uintl num = (((uintl)*num1)<<32) | ((uintl)*num2);
-    return ((double)num)/UINTLMAXDOUBLE;
-}
-
-void normalizePairFloat(float * const out1, float * const out2, const float r1, const float r2)
-{
-#if defined(IS_APPLE) // Because Apple is.. "special"
-    float r = sqrt((float)(-2.0) * log10(r1) * (float)log10_val);
-#else
-    float r = sqrt((float)(-2.0) * log(r1));
-#endif
-    float theta = 2 * (float)PI_VAL * (r2);
-    *out1 = r*sin(theta);
-    *out2 = r*cos(theta);
-}
-
-void normalizePairDouble(double * const out1, double * const out2, const double r1, const double r2)
-{
-#if defined(IS_APPLE) // Because Apple is.. "special"
-    double r = sqrt((double)(-2.0) * log10(r1) * (double)log10_val);
-#else
-    double r = sqrt((double)(-2.0) * log(r1));
-#endif
-    double theta = 2 * (double)PI_VAL * (r2);
-    *out1 = r*sin(theta);
-    *out2 = r*cos(theta);
-}
-
 //Writes without boundary checking
 
 void writeOut256Bytes_uchar(__global uchar *out, const uint * const index,
@@ -114,7 +79,7 @@ void writeOut256Bytes_uchar(__global uchar *out, const uint * const index,
     out[*index + 15*THREADS] = *r4>>24;
 }
 
-void writeOut256Bytes_char(char *out, const uint * const index,
+void writeOut256Bytes_char(__global char *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]              = (*r1   )&0x1;
@@ -179,23 +144,23 @@ void writeOut256Bytes_uint(__global uint *out, const uint * const index,
     out[*index + 3*THREADS] = *r4;
 }
 
-void writeOut256Bytes_intl(__global intl *out, const uint * const index,
+void writeOut256Bytes_long(__global long *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
-    intl c1 = *r2;
+    long c1 = *r2;
     c1 = (c1<<32) | *r1;
-    intl c2 = *r4;
+    long c2 = *r4;
     c2 = (c2<<32) | *r3;
     out[*index]           = c1;
     out[*index + THREADS] = c2;
 }
 
-void writeOut256Bytes_uintl(__global uintl *out, const uint * const index,
+void writeOut256Bytes_ulong(__global ulong *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
-    intl c1 = *r2;
+    long c1 = *r2;
     c1 = (c1<<32) | *r1;
-    intl c2 = *r4;
+    long c2 = *r4;
     c2 = (c2<<32) | *r3;
     out[*index]           = c1;
     out[*index + THREADS] = c2;
@@ -210,35 +175,9 @@ void writeOut256Bytes_float(__global float *out, const uint * const index,
     out[*index + 3*THREADS] = getFloat(r4);
 }
 
-void writeOut256Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]           = getDouble(r1, r2);
-    out[*index + THREADS] = getDouble(r3, r4);
-}
-
-//Normalized writes without boundary checking
-
-void normalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    float n1, n2, n3, n4;
-    normalizePairFloat(&n1, &n2, getFloat(r1), getFloat(r2));
-    normalizePairFloat(&n3, &n4, getFloat(r1), getFloat(r2));
-    out[*index]             = n1;
-    out[*index +   THREADS] = n2;
-    out[*index + 2*THREADS] = n3;
-    out[*index + 3*THREADS] = n4;
-}
 
-void normalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    double n1, n2;
-    normalizePairDouble(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
-    out[*index]           = n1;
-    out[*index + THREADS] = n2;
-}
+#if RAND_DIST == 1
+#endif
 
 //Writes with boundary checking
 
@@ -328,23 +267,23 @@ void partialWriteOut256Bytes_uint(__global uint *out, const uint * const index,
     if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
 }
 
-void partialWriteOut256Bytes_intl(__global intl *out, const uint * const index,
+void partialWriteOut256Bytes_long(__global long *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
-    intl c1 = *r2;
+    long c1 = *r2;
     c1 = (c1<<32) | *r1;
-    intl c2 = *r4;
+    long c2 = *r4;
     c2 = (c2<<32) | *r3;
     if (*index           < *elements) {out[*index]           = c1;}
     if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
 }
 
-void partialWriteOut256Bytes_uintl(__global uintl *out, const uint * const index,
+void partialWriteOut256Bytes_ulong(__global ulong *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
-    intl c1 = *r2;
+    long c1 = *r2;
     c1 = (c1<<32) | *r1;
-    intl c2 = *r4;
+    long c2 = *r4;
     c2 = (c2<<32) | *r3;
     if (*index           < *elements) {out[*index]           = c1;}
     if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
@@ -359,41 +298,100 @@ void partialWriteOut256Bytes_float(__global float *out, const uint * const index
     if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = getFloat(r4);}
 }
 
-void partialWriteOut256Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+#if RAND_DIST == 1
+void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
 {
-    if (*index           < *elements) {out[*index]           = getDouble(r1, r2);}
-    if (*index + THREADS < *elements) {out[*index + THREADS] = getDouble(r3, r4);}
+#if defined(IS_APPLE) // Because Apple is.. "special"
+    T r = sqrt((T)(-2.0) * log10(r1) * (T)log10_val);
+#else
+    T r = sqrt((T)(-2.0) * log(r1));
+#endif
+    T theta = 2 * (T)PI_VAL * (r2);
+    *out1 = r*sin(theta);
+    *out2 = r*cos(theta);
 }
 
-//Normalized writes with boundary checking
+//Normalized writes without boundary checking
+void normalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    float n1, n2, n3, n4;
+    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform(&n3, &n4, getFloat(r1), getFloat(r2));
+    out[*index]             = n1;
+    out[*index +   THREADS] = n2;
+    out[*index + 2*THREADS] = n3;
+    out[*index + 3*THREADS] = n4;
+}
 
+//Normalized writes with boundary checking
 void partialNormalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     float n1, n2, n3, n4;
-    normalizePairFloat(&n1, &n2, getFloat(r1), getFloat(r2));
-    normalizePairFloat(&n3, &n4, getFloat(r3), getFloat(r4));
+    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
     if (*index             < *elements) {out[*index]             = n1;}
     if (*index +   THREADS < *elements) {out[*index +   THREADS] = n2;}
     if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = n3;}
     if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = n4;}
 }
+#endif
+
+#ifdef USE_DOUBLE
+double getDouble(const uint * const num1, const uint * const num2)
+{
+    ulong num = (((ulong)*num1)<<32) | ((ulong)*num2);
+    return ((double)num)/UINTLMAXDOUBLE;
+}
+
+void writeOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    out[*index]           = getDouble(r1, r2);
+    out[*index + THREADS] = getDouble(r3, r4);
+}
+
+void partialWriteOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
+{
+    if (*index           < *elements) {out[*index]           = getDouble(r1, r2);}
+    if (*index + THREADS < *elements) {out[*index + THREADS] = getDouble(r3, r4);}
+}
+
+#if RAND_DIST == 1
+void normalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
+        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
+{
+    double n1, n2;
+    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    out[*index]           = n1;
+    out[*index + THREADS] = n2;
+}
 
 void partialNormalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     double n1, n2;
-    normalizePairDouble(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
     if (*index           < *elements) {out[*index]           = n1;}
     if (*index + THREADS < *elements) {out[*index + THREADS] = n2;}
 }
+#endif
+#endif
 
 #define PASTER(x,y) x ## _ ## y
 #define EVALUATOR(x,y) PASTER(x,y)
 #define EVALUATE_T(function) EVALUATOR(function, T)
-#define WRITE EVALUATE_T(writeOut256Bytes)
-#define PARTIALWRITE EVALUATE_T(partialWriteOut256Bytes)
-#define NORMALWRITE EVALUATE_T(normalizedWriteOut256Bytes)
-#define NORMALPARTIALWRITE EVALUATE_T(partialNormalizedWriteOut256Bytes)
-
+#define UNIFORM_WRITE EVALUATE_T(writeOut256Bytes)
+#define UNIFORM_PARTIAL_WRITE EVALUATE_T(partialWriteOut256Bytes)
+#define NORMAL_WRITE EVALUATE_T(normalizedWriteOut256Bytes)
+#define NORMAL_PARTIAL_WRITE EVALUATE_T(partialNormalizedWriteOut257Bytes)
+
+#if RAND_DIST == 0
+#define WRITE UNIFORM_WRITE
+#define PARTIAL_WRITE UNIFORM_PARTIAL_WRITE
+#elif RAND_DIST == 1
+#define WRITE NORMAL_WRITE
+#define PARTIAL_WRITE NORMAL_PARTIAL_WRITE
+#endif
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 161526b066..7217a8dc58 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -71,4 +71,5 @@ namespace opencl
 
     COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
     COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
 }

From 735f2ea0455600aea4c3d0b470166196725635d7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jul 2016 20:11:47 -0400
Subject: [PATCH 0702/2677] BUGFIX: Fixing bug in getMappedPtr in OpenCL
 backend

---
 src/backend/opencl/Array.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 15586893a3..efa24376f0 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -258,7 +258,7 @@ namespace opencl
                 if(ptr == nullptr) {
                     ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()),
                                                           true, CL_MAP_READ|CL_MAP_WRITE,
-                                                          getOffset(),
+                                                          getOffset() * sizeof(T),
                                                           (getDataDims().elements() - getOffset())
                                                           * sizeof(T));
                 }

From ca32cf5083ea7317a5af669369b14f74e928566b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jul 2016 20:12:08 -0400
Subject: [PATCH 0703/2677] Cleanup: Use internal API for transform_coordinates

---
 src/api/c/transform_coordinates.cpp | 70 ++++++++++++-----------------
 1 file changed, 29 insertions(+), 41 deletions(-)

diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index 79b448db5d..e1e1dfa15b 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -9,69 +9,57 @@
 
 #include <af/dim4.hpp>
 #include <af/defines.h>
-#include <af/vision.h>
 #include <af/image.h>
-#include <af/arith.h>
-#include <af/blas.h>
-#include <af/data.h>
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <convolve.hpp>
 #include <arith.hpp>
+#include <blas.hpp>
+#include <join.hpp>
+#include <vector>
 
 using af::dim4;
 using namespace detail;
 
+template<typename T>
+Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs, std::vector<af_seq> idx)
+{
+    return matmul(lhs, createSubArray(rhs, idx), AF_MAT_NONE, AF_MAT_NONE);
+}
+
 template<typename T>
 static af_array transform_coordinates(const af_array& tf, const float d0, const float d1)
 {
-    dim_t in_dims[2] = { 4, 3 };
+    af::dim4 h_dims(4, 3);
     T h_in[4*3] = { (T)0, (T)0,  (T)d1, (T)d1,
                     (T)0, (T)d0, (T)d0, (T)0,
                     (T)1, (T)1,  (T)1,  (T)1 };
 
-    af_array in  = 0;
-    af_array w   = 0;
-    af_array tmp = 0;
-    af_array xt  = 0;
-    af_array yt  = 0;
-    af_array t   = 0;
-
-    AF_CHECK(af_create_array(&in, h_in, 2, in_dims, (af_dtype) af::dtype_traits<T>::af_type));
-
-    af_array tfIdx = 0;
-    af_index_t tfIndexs[2];
-    tfIndexs[0].isSeq = true;
-    tfIndexs[1].isSeq = true;
-    tfIndexs[0].idx.seq = af_make_seq(0, 2, 1);
-    tfIndexs[1].idx.seq = af_make_seq(2, 2, 1);
-    AF_CHECK(af_index_gen(&tfIdx, tf, 2, tfIndexs));
-
-    AF_CHECK(af_matmul(&tmp, in, tfIdx, AF_MAT_NONE, AF_MAT_NONE));
-    T h_w[4] = { 1, 1, 1, 1 };
-    dim_t w_dims = 4;
-    AF_CHECK(af_create_array(&w, h_w, 1, &w_dims, (af_dtype) af::dtype_traits<T>::af_type));
-    AF_CHECK(af_div(&w, w, tmp, false));
+    const Array<T> TF = getArray<T>(tf);
+    Array<T> IN = createHostDataArray<T>(h_dims, h_in);
 
-    tfIndexs[1].idx.seq = af_make_seq(0, 0, 1);
-    AF_CHECK(af_index_gen(&tfIdx, tf, 2, tfIndexs));
-    AF_CHECK(af_matmul(&tmp, in, tfIdx, AF_MAT_NONE, AF_MAT_NONE));
-    AF_CHECK(af_mul(&xt, tmp, w, false));
+    std::vector<af_seq> idx(2);
+    idx[0] = af_make_seq(0, 2, 1);
 
-    tfIndexs[1].idx.seq = af_make_seq(1, 1, 1);
-    AF_CHECK(af_index_gen(&tfIdx, tf, 2, tfIndexs));
-    AF_CHECK(af_matmul(&tmp, in, tfIdx, AF_MAT_NONE, AF_MAT_NONE));
-    AF_CHECK(af_mul(&yt, tmp, w, false));
+    // w = 1.0 / matmul(TF, IN(span, 2));
+    // iw = matmul(TF, IN(span, 2));
+    idx[1] = af_make_seq(2, 2, 1);
+    Array<T> IW = multiplyIndexed(IN, TF, idx);
 
-    AF_CHECK(af_join(&t, 1, xt, yt));
+    // xt = w * matmul(TF, IN(span, 0));
+    // xt = matmul(TF, IN(span, 0)) / iw;
+    idx[1] = af_make_seq(0, 0, 1);
+    Array<T> XT = arithOp<T, af_div_t>(multiplyIndexed(IN, TF, idx), IW, IW.dims());
 
-    AF_CHECK(af_release_array(w));
-    AF_CHECK(af_release_array(tmp));
-    AF_CHECK(af_release_array(xt));
-    AF_CHECK(af_release_array(yt));
+    // yt = w * matmul(TF, IN(span, 1));
+    // yt = matmul(TF, IN(span, 1)) / iw;
+    idx[1] = af_make_seq(1, 1, 1);
+    Array<T> YT = arithOp<T, af_div_t>(multiplyIndexed(IN, TF, idx), IW, IW.dims());
 
-    return t;
+    // return join(1, xt, yt)
+    Array<T> R = join(1, XT, YT);
+    return getHandle(R);
 }
 
 af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0, const float d1)

From a5eed2218f3be5afcc1d7faa918b9b28127654cd Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jul 2016 18:30:34 -0400
Subject: [PATCH 0704/2677] BUILD: Add missing cmath header

- Also use proper constant for pi/2
---
 src/api/c/unary.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index bd8d31d76e..4eb58bfdcc 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -7,6 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+// This needs to be the first thing in the file
+#if defined(_WIN32) || defined(_MSC_VER)
+#define _USE_MATH_DEFINES
+#endif
+#include <cmath>
+
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/arith.h>
@@ -429,7 +435,7 @@ struct unaryOpCplxFun<Tc, Tr, af_acos_t>
         Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
 
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
-        Array<Tc> pi_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(M_PI / 2.0, 0.0));
+        Array<Tc> pi_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(M_PI_2, 0.0));
 
         // z^2
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());

From e772ebc49012849b3a5e39f7c6d60c9ab0f13197 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jul 2016 20:12:37 -0400
Subject: [PATCH 0705/2677] TEST: Use transformCoordinates for homography test

- Change the way test is being handled
---
 test/homography.cpp | 28 ++++++++++------------------
 1 file changed, 10 insertions(+), 18 deletions(-)

diff --git a/test/homography.cpp b/test/homography.cpp
index 1bd24425be..c623d9c3c4 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -39,20 +39,7 @@ af::array perspectiveTransform(af::dim4 inDims, af::array H)
 {
     T d0 = (T)inDims[0];
     T d1 = (T)inDims[1];
-    af::dim4 dims(4, 3);
-    T h_in[4*3] = { (T)0, (T)0,  (T)d1, (T)d1,
-                    (T)0, (T)d0, (T)d0, (T)0,
-                    (T)1, (T)1,  (T)1,  (T)1 };
-
-    af::array in(dims, h_in);
-
-    af::array w = 1.f / af::matmul(in, H(af::span, 2));
-    af::array xt = af::matmul(in, H(af::span, 0)) * w;
-    af::array yt = af::matmul(in, H(af::span, 1)) * w;
-
-    af::array t = join(1, xt, yt);
-
-    return t;
+    return transformCoordinates(H, d0, d1);
 }
 
 template<typename T>
@@ -105,6 +92,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     const float theta = af::Pi * 0.5f;
     const dim_t test_d0 = inDims[0][0] * size_ratio;
     const dim_t test_d1 = inDims[0][1] * size_ratio;
+    const dim_t tDims[] = {test_d0, test_d1};
     if (rotate)
         ASSERT_EQ(AF_SUCCESS, af_rotate(&queryArray, trainArray, theta, false, AF_INTERP_NEAREST));
     else
@@ -171,8 +159,10 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     T* out_t = new T[8];
     t.host(out_t);
 
-    for (int elIter = 0; elIter < 8; elIter++)
-        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]), 70.f) << "at: " << elIter << std::endl;
+    for (int elIter = 0; elIter < 8; elIter++) {
+        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.1f)
+            << "at: " << elIter << std::endl;
+    }
 
     delete[] gold_t;
     delete[] out_t;
@@ -273,8 +263,10 @@ TEST(Homography, CPP)
     float* out_t = new float[4*2];
     t.host(out_t);
 
-    for (int elIter = 0; elIter < 8; elIter++)
-        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]), 70.f) << "at: " << elIter << std::endl;
+    for (int elIter = 0; elIter < 8; elIter++) {
+        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.1f)
+            << "at: " << elIter << std::endl;
+    }
 
     delete[] gold_t;
     delete[] out_t;

From 71da292bc42e29639443b6873a5de364fa7a45b9 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 28 Jul 2016 17:32:28 -0400
Subject: [PATCH 0706/2677] BUILD: Fixing build issues for older cmake versions

---
 CMakeModules/build_forge.cmake | 25 ++++++++++++-------------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 667b5a452a..83892477f8 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -2,22 +2,21 @@ INCLUDE(ExternalProject)
 
 SET(prefix ${CMAKE_BINARY_DIR}/third_party/forge)
 
+# FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
+#        does not yet support it for the OUTPUT argument, see also:
+#        - Old "duplicate":   https://cmake.org/Bug/view.php?id=12877
+#        - Old issue tracker: https://cmake.org/Bug/view.php?id=13840
+#        - New issue tracker: https://gitlab.kitware.com/cmake/cmake/issues/13840
+#        In the meantime, use CMAKE_BUILD_TYPE if set by user, assuming that it
+#        is the primary build configuration used. Otherwise, default to Release.
+IF(CMAKE_BUILD_TYPE)
+    SET(forge_lib_config ${CMAKE_BUILD_TYPE})
+ELSE()
+    SET(forge_lib_config Release)
+ENDIF()
 IF(CMAKE_GENERATOR MATCHES "Xcode") # TODO: Also for "Visual Studio"?
-    # FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
-    #        does not yet support it for the OUTPUT argument, see also:
-    #        - Old "duplicate":   https://cmake.org/Bug/view.php?id=12877
-    #        - Old issue tracker: https://cmake.org/Bug/view.php?id=13840
-    #        - New issue tracker: https://gitlab.kitware.com/cmake/cmake/issues/13840
-    #        In the meantime, use CMAKE_BUILD_TYPE if set by user, assuming that it
-    #        is the primary build configuration used. Otherwise, default to Release.
-    IF(CMAKE_BUILD_TYPE)
-        SET(forge_lib_config ${CMAKE_BUILD_TYPE})
-    ELSE()
-        SET(forge_lib_config Release)
-    ENDIF()
     SET(forge_lib_infix "/${forge_lib_config}")
 ELSE()
-    SET(forge_lib_config "$<CONFIG>")
     SET(forge_lib_infix)
 ENDIF()
 IF(WIN32)

From 77716978fcaf977270f1323bf99f3acdc5c05eb9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 29 Jul 2016 08:57:53 -0400
Subject: [PATCH 0707/2677] Rename dns to dense in sparse

---
 src/backend/cpu/kernel/sparse.hpp | 4 ++--
 src/backend/cpu/sparse.cpp        | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index a059c341e0..66ef5e9f27 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -42,7 +42,7 @@ void coo2dense(Array<T> output,
 }
 
 template<typename T>
-struct dns_csr
+struct dense_csr
 {
     void operator()(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
                     Array<T> const in)
@@ -70,7 +70,7 @@ struct dns_csr
 };
 
 template<typename T>
-struct csr_dns
+struct csr_dense
 {
     void operator()(Array<T> out,
                     Array<T> const values, Array<int> const rowIdx, Array<int> const colIdx)
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index fdee0e5729..56fda0fac6 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -289,7 +289,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
         Array<int> rowIdx = sparse.getRowIdx();
         Array<int> colIdx = sparse.getColIdx();
 
-        kernel::dns_csr<T>()(values, rowIdx, colIdx, in);
+        kernel::dense_csr<T>()(values, rowIdx, colIdx, in);
     };
 
     getQueue().enqueue(func, sparse_, in_);
@@ -315,7 +315,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
         Array<int> rowIdx = in.getRowIdx();
         Array<int> colIdx = in.getColIdx();
 
-        kernel::csr_dns<T>()(dense, values, rowIdx, colIdx);
+        kernel::csr_dense<T>()(dense, values, rowIdx, colIdx);
     };
 
     getQueue().enqueue(func, dense_, in_);

From 45f10f104d0d963dfceda080717d6c632b7ea05e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 29 Jul 2016 16:16:40 -0400
Subject: [PATCH 0708/2677] Avoid instantiating templates for sort direction

---
 src/api/c/median.cpp                          |  4 +-
 src/api/c/sort.cpp                            | 18 +----
 src/backend/cpu/harris.cpp                    |  2 +-
 src/backend/cpu/kernel/sort.hpp               |  4 +-
 src/backend/cpu/kernel/sort_by_key.hpp        | 12 ++--
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  3 +-
 src/backend/cpu/kernel/sort_by_key_impl.hpp   | 67 +++++++++--------
 src/backend/cpu/orb.cpp                       |  2 +-
 src/backend/cpu/set.cpp                       |  2 +-
 src/backend/cpu/sort.cpp                      | 31 ++++----
 src/backend/cpu/sort.hpp                      |  4 +-
 src/backend/cpu/sort_by_key.cpp               | 20 +++---
 src/backend/cpu/sort_by_key.hpp               |  4 +-
 src/backend/cpu/sort_index.cpp                | 18 +++--
 src/backend/cpu/sort_index.hpp                |  4 +-
 src/backend/cuda/kernel/harris.hpp            |  2 +-
 src/backend/cuda/kernel/homography.hpp        |  2 +-
 src/backend/cuda/kernel/orb.hpp               |  2 +-
 src/backend/cuda/kernel/sort.hpp              | 16 ++---
 src/backend/cuda/kernel/sort_by_key.hpp       | 12 ++--
 .../cuda/kernel/sort_by_key/CMakeLists.txt    | 21 +++---
 .../kernel/sort_by_key/sort_by_key_impl.cu.in |  2 +-
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 72 ++++++++++---------
 src/backend/cuda/sort.cu                      | 17 +++--
 src/backend/cuda/sort.hpp                     |  4 +-
 src/backend/cuda/sort_by_key.cu               | 18 +++--
 src/backend/cuda/sort_by_key.hpp              |  4 +-
 src/backend/cuda/sort_index.cu                | 18 +++--
 src/backend/cuda/sort_index.hpp               |  4 +-
 src/backend/opencl/kernel/harris.hpp          |  2 +-
 src/backend/opencl/kernel/homography.hpp      |  2 +-
 src/backend/opencl/kernel/orb.hpp             |  2 +-
 src/backend/opencl/kernel/sort.hpp            | 16 ++---
 src/backend/opencl/kernel/sort_by_key.hpp     | 12 ++--
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  3 +-
 .../opencl/kernel/sort_by_key_impl.hpp        | 52 +++++++-------
 src/backend/opencl/sort.cpp                   | 15 ++--
 src/backend/opencl/sort.hpp                   |  4 +-
 src/backend/opencl/sort_by_key.cpp            | 40 +++++------
 src/backend/opencl/sort_by_key.hpp            |  4 +-
 src/backend/opencl/sort_index.cpp             | 19 +++--
 src/backend/opencl/sort_index.hpp             |  4 +-
 42 files changed, 275 insertions(+), 289 deletions(-)

diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index a8268f5ff4..e41239ac35 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -51,7 +51,7 @@ static double median(const af_array& in)
     double mid      = (nElems + 1) / 2;
     af_seq mdSpan[1]= {af_make_seq(mid-1, mid, 1)};
 
-    Array<T> sortedArr = sort<T, true>(input, 0);
+    Array<T> sortedArr = sort<T>(input, 0, true);
 
     af_array sarrHandle = getHandle<T>(sortedArr);
 
@@ -89,7 +89,7 @@ static af_array median(const af_array& in, const dim_t dim)
         return getHandle<T>(result);
     }
 
-    Array<T> sortedIn   = sort<T, true>(input, dim);
+    Array<T> sortedIn   = sort<T>(input, dim, true);
 
     int dimLength = input.dims()[dim];
     double mid    = (dimLength + 1) / 2;
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index dd58175936..dd9afe024e 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -28,11 +28,7 @@ template<typename T>
 static inline af_array sort(const af_array in, const unsigned dim, const bool isAscending)
 {
     const Array<T> &inArray = getArray<T>(in);
-    if(isAscending) {
-        return getHandle(sort<T, 1>(inArray, dim));
-    } else {
-        return getHandle(sort<T, 0>(inArray, dim));
-    }
+    return getHandle(sort<T>(inArray, dim, isAscending));
 }
 
 af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending)
@@ -75,11 +71,7 @@ static inline void sort_index(af_array *val, af_array *idx, const af_array in,
     Array<T> valArray = createEmptyArray<T>(af::dim4());
     Array<uint> idxArray = createEmptyArray<uint>(af::dim4());
 
-    if(isAscending) {
-        sort_index<T, 1>(valArray, idxArray, inArray, dim);
-    } else {
-        sort_index<T, 0>(valArray, idxArray, inArray, dim);
-    }
+    sort_index<T>(valArray, idxArray, inArray, dim, isAscending);
     *val = getHandle(valArray);
     *idx = getHandle(idxArray);
 }
@@ -127,11 +119,7 @@ static inline void sort_by_key(af_array *okey, af_array *oval, const af_array ik
     Array<Tk> okeyArray = createEmptyArray<Tk>(af::dim4());
     Array<Tv> ovalArray = createEmptyArray<Tv>(af::dim4());
 
-    if(isAscending) {
-        sort_by_key<Tk, Tv, 1>(okeyArray, ovalArray, ikeyArray, ivalArray, dim);
-    } else {
-        sort_by_key<Tk, Tv, 0>(okeyArray, ovalArray, ikeyArray, ivalArray, dim);
-    }
+    sort_by_key<Tk, Tv>(okeyArray, ovalArray, ikeyArray, ivalArray, dim, isAscending);
     *okey = getHandle(okeyArray);
     *oval = getHandle(ovalArray);
 }
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index cf55395255..770bd11c20 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -95,7 +95,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(dim4(corners_found));
 
         // Sort Harris responses
-        sort_index<float, false>(harris_sorted, harris_idx, respCorners, 0);
+        sort_index<float>(harris_sorted, harris_idx, respCorners, 0, false);
 
         x_out = createEmptyArray<float>(dim4(corners_out));
         y_out = createEmptyArray<float>(dim4(corners_out));
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index db82d4159c..afc5b5adaa 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -21,8 +21,8 @@ namespace kernel
 {
 
 // Based off of http://stackoverflow.com/a/12399290
-template<typename T, bool isAscending>
-void sort0Iterative(Array<T> val)
+template<typename T>
+void sort0Iterative(Array<T> val, bool isAscending)
 {
     // initialize original index locations
     T *val_ptr = val.get();
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index dc8a543430..0ff8881a8b 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -16,14 +16,14 @@ namespace cpu
 namespace kernel
 {
 
-template<typename Tk, typename Tv, bool isAscending>
-void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval);
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending);
 
-template<typename Tk, typename Tv, bool isAscending>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim);
+template<typename Tk, typename Tv>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim, bool isAscending);
 
-template<typename Tk, typename Tv, bool isAscending>
-void sort0ByKey(Array<Tk> okey, Array<Tv> oval);
+template<typename Tk, typename Tv>
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval, bool isAscending);
 
 }
 }
diff --git a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index fbdedfde39..e3cca6f663 100644
--- a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -15,7 +15,6 @@ namespace cpu
 {
 namespace kernel
 {
-    INSTANTIATE1(TYPE,true)
-    INSTANTIATE1(TYPE,false)
+    INSTANTIATE1(TYPE)
 }
 }
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index dde75b50bf..eea05e8198 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -23,8 +23,8 @@ namespace cpu
 namespace kernel
 {
 
-template<typename Tk, typename Tv, bool isAscending>
-void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending)
 {
     // Get pointers and initialize original index locations
     Tk *okey_ptr = okey.get();
@@ -56,7 +56,11 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
                    pairKeyVal[x] = std::make_tuple(okey_col_ptr[x], oval_col_ptr[x]);
                 }
 
-                std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, isAscending>());
+                if(isAscending) {
+                    std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, true>());
+                } else {
+                    std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, false>());
+                }
 
                 for(unsigned x = 0; x < size; x++) {
                     okey_ptr[okeyOffset + x] = std::get<0>(pairKeyVal[x]);
@@ -70,8 +74,8 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval)
     return;
 }
 
-template<typename Tk, typename Tv, bool isAscending>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim)
+template<typename Tk, typename Tv>
+void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim, bool isAscending)
 {
     af::dim4 inDims = okey.dims();
 
@@ -122,7 +126,12 @@ void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim)
 
     memFree(key); // key is no longer required
 
-    std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, isAscending>());
+    if(isAscending) {
+      std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, true>());
+    }
+    else {
+      std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, false>());
+    }
 
     std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareK<Tk, Tv, true>());
 
@@ -135,34 +144,36 @@ void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim)
     return;
 }
 
-template<typename Tk, typename Tv, bool isAscending>
-void sort0ByKey(Array<Tk> okey, Array<Tv> oval)
+template<typename Tk, typename Tv>
+void sort0ByKey(Array<Tk> okey, Array<Tv> oval, bool isAscending)
 {
     int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
     // TODO Make a better heurisitic
     if(higherDims > 4)
-        kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 0);
+        kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
     else
-        kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+        kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
 }
 
-#define INSTANTIATE(Tk, Tv, dr)                                                                 \
-    template void sort0ByKey<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);                       \
-    template void sort0ByKeyIterative<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval);              \
-    template void sortByKeyBatched<Tk, Tv, dr>(Array<Tk> okey, Array<Tv> oval, const int dim);
-
-#define INSTANTIATE1(Tk    , dr) \
-    INSTANTIATE(Tk, float  , dr) \
-    INSTANTIATE(Tk, double , dr) \
-    INSTANTIATE(Tk, cfloat , dr) \
-    INSTANTIATE(Tk, cdouble, dr) \
-    INSTANTIATE(Tk, int    , dr) \
-    INSTANTIATE(Tk, uint   , dr) \
-    INSTANTIATE(Tk, short  , dr) \
-    INSTANTIATE(Tk, ushort , dr) \
-    INSTANTIATE(Tk, char   , dr) \
-    INSTANTIATE(Tk, uchar  , dr) \
-    INSTANTIATE(Tk, intl   , dr) \
-    INSTANTIATE(Tk, uintl  , dr)
+#define INSTANTIATE(Tk, Tv)                                                             \
+    template void sort0ByKey<Tk, Tv>(Array<Tk> okey, Array<Tv> oval, bool isAscending); \
+    template void sort0ByKeyIterative<Tk, Tv>(Array<Tk> okey, Array<Tv> oval,           \
+                                              bool isAscending);                        \
+    template void sortByKeyBatched<Tk, Tv>(Array<Tk> okey, Array<Tv> oval,              \
+                                           const int dim, bool isAscending);
+
+#define INSTANTIATE1(Tk) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
 }
 }
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 1279400d21..19ae60a0bf 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -158,7 +158,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
         Array<float> harris_sorted = createEmptyArray<float>(af::dim4());
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(af::dim4());
 
-        sort_index<float, false>(harris_sorted, harris_idx, score_harris, 0);
+        sort_index<float>(harris_sorted, harris_idx, score_harris, 0, false);
         getQueue().sync();
 
         usable_feat = std::min(usable_feat, lvl_best[i]);
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 7c970e787f..659ef1e490 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -33,7 +33,7 @@ Array<T> setUnique(const Array<T> &in,
 
     Array<T> out = createEmptyArray<T>(af::dim4());
     if (is_sorted) out = copyArray<T>(in);
-    else           out = sort<T, 1>(in, 0);
+    else           out = sort<T>(in, 0, true);
 
     // Need to sync old jobs since we need to
     // operator on pointers directly in std::unique
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 4a649e0b23..de8a42b85a 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -25,8 +25,8 @@
 namespace cpu
 {
 
-template<typename T, bool isAscending, int dim>
-void sortBatched(Array<T>& val)
+template<typename T, int dim>
+void sortBatched(Array<T>& val, bool isAscending)
 {
     af::dim4 inDims = val.dims();
 
@@ -44,37 +44,37 @@ void sortBatched(Array<T>& val)
     val.setDataDims(inDims.elements());
     key.setDataDims(inDims.elements());
 
-    sort_by_key<T, uint, isAscending>(resVal, resKey, val, key, 0);
+    sort_by_key<T, uint>(resVal, resKey, val, key, 0, isAscending);
 
     // Needs to be ascending (true) in order to maintain the indices properly
-    sort_by_key<uint, T, true>(key, val, resKey, resVal, 0);
+    sort_by_key<uint, T>(key, val, resKey, resVal, 0, true);
     val.eval();
 
     val.setDataDims(inDims); // This is correct only for dim0
 }
 
-template<typename T, bool isAscending>
-void sort0(Array<T>& val)
+template<typename T>
+void sort0(Array<T>& val, bool isAscending)
 {
     int higherDims = val.elements() / val.dims()[0];
     // TODO Make a better heurisitic
     if(higherDims > 10)
-        sortBatched<T, isAscending, 0>(val);
+        sortBatched<T, 0>(val, isAscending);
     else
-        getQueue().enqueue(kernel::sort0Iterative<T, isAscending>, val);
+        getQueue().enqueue(kernel::sort0Iterative<T>, val, isAscending);
 }
 
-template<typename T, bool isAscending>
-Array<T> sort(const Array<T> &in, const unsigned dim)
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
 {
     in.eval();
 
     Array<T> out = copyArray<T>(in);
     switch(dim) {
-        case 0: sort0<T, isAscending>(out); break;
-        case 1: sortBatched<T, isAscending, 1>(out); break;
-        case 2: sortBatched<T, isAscending, 2>(out); break;
-        case 3: sortBatched<T, isAscending, 3>(out); break;
+        case 0: sort0<T>(out, isAscending); break;
+        case 1: sortBatched<T, 1>(out, isAscending); break;
+        case 2: sortBatched<T, 2>(out, isAscending); break;
+        case 3: sortBatched<T, 3>(out, isAscending); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
@@ -95,8 +95,7 @@ Array<T> sort(const Array<T> &in, const unsigned dim)
 }
 
 #define INSTANTIATE(T)                                                  \
-    template Array<T> sort<T, true>(const Array<T> &in, const unsigned dim); \
-    template Array<T> sort<T,false>(const Array<T> &in, const unsigned dim); \
+    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/sort.hpp b/src/backend/cpu/sort.hpp
index 645f5a1f10..cb924873a1 100644
--- a/src/backend/cpu/sort.hpp
+++ b/src/backend/cpu/sort.hpp
@@ -11,6 +11,6 @@
 
 namespace cpu
 {
-    template<typename T, bool isAscending>
-    Array<T> sort(const Array<T> &in, const unsigned dim);
+    template<typename T>
+    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index df0648d47f..0a139a319f 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -19,9 +19,9 @@
 namespace cpu
 {
 
-template<typename Tk, typename Tv, bool isAscending>
+template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-           const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
+                 const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim, bool isAscending)
 {
     ikey.eval();
     ival.eval();
@@ -30,10 +30,10 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     oval = copyArray<Tv>(ival);
 
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0ByKey<Tk, Tv, isAscending>, okey, oval); break;
-        case 1: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending>, okey, oval, 1); break;
-        case 2: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending>, okey, oval, 2); break;
-        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv, isAscending>, okey, oval, 3); break;
+        case 0: getQueue().enqueue(kernel::sort0ByKey<Tk, Tv>, okey, oval, isAscending); break;
+        case 1:
+        case 2:
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv>, okey, oval, dim, isAscending); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
@@ -57,11 +57,9 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
 
 #define INSTANTIATE(Tk, Tv)                                             \
     template void                                                       \
-    sort_by_key<Tk, Tv, true>(Array<Tk> &okey, Array<Tv> &oval,         \
-                              const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim); \
-    template void                                                       \
-    sort_by_key<Tk, Tv,false>(Array<Tk> &okey, Array<Tv> &oval,         \
-                              const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim); \
+    sort_by_key<Tk, Tv>(Array<Tk> &okey, Array<Tv> &oval,               \
+                        const Array<Tk> &ikey, const Array<Tv> &ival,   \
+                        const uint dim, bool isAscending);
 
 #define INSTANTIATE1(Tk)       \
     INSTANTIATE(Tk, float)     \
diff --git a/src/backend/cpu/sort_by_key.hpp b/src/backend/cpu/sort_by_key.hpp
index 20908e6014..c18d14be6f 100644
--- a/src/backend/cpu/sort_by_key.hpp
+++ b/src/backend/cpu/sort_by_key.hpp
@@ -11,7 +11,7 @@
 
 namespace cpu
 {
-    template<typename Tk, typename Tv, bool isAscending>
+    template<typename Tk, typename Tv>
     void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim);
+                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index e8058a7233..fa43ce589f 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -22,8 +22,8 @@
 namespace cpu
 {
 
-template<typename T, bool isAscending>
-void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
+template<typename T>
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim, bool isAscending)
 {
     in.eval();
 
@@ -33,10 +33,10 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uin
     oval.eval();
 
     switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0ByKey<T, uint, isAscending>, okey, oval); break;
-        case 1: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending>, okey, oval, 1); break;
-        case 2: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending>, okey, oval, 2); break;
-        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint, isAscending>, okey, oval, 3); break;
+        case 0: getQueue().enqueue(kernel::sort0ByKey<T, uint>, okey, oval, isAscending); break;
+        case 1:
+        case 2:
+        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint>, okey, oval, dim, isAscending); break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
@@ -59,10 +59,8 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uin
 }
 
 #define INSTANTIATE(T)                                                  \
-    template void sort_index<T, true>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim);                  \
-    template void sort_index<T,false>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim);                  \
+    template void sort_index<T>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
+                                      const uint dim, bool isAscending);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/sort_index.hpp b/src/backend/cpu/sort_index.hpp
index 0dd2ca80f3..2052752eb0 100644
--- a/src/backend/cpu/sort_index.hpp
+++ b/src/backend/cpu/sort_index.hpp
@@ -11,6 +11,6 @@
 
 namespace cpu
 {
-    template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim);
+    template<typename T>
+    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index f5a696a622..11e6003158 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -340,7 +340,7 @@ void harris(unsigned* corners_out,
         kernel::range<uint>(harris_idx, 0);
 
         // Sort Harris responses
-        sort0ByKey<float, uint, false>(harris_responses, harris_idx);
+        sort0ByKey<float, uint>(harris_responses, harris_idx, false);
 
         *x_out = memAlloc<float>(*corners_out);
         *y_out = memAlloc<float>(*corners_out);
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index b414825d9d..d24261ea53 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -617,7 +617,7 @@ int computeH(
     if (htype == AF_HOMOGRAPHY_LMEDS) {
         // TODO: Improve this sorting, if the number of iterations is
         // sufficiently large, this can be *very* slow
-        kernel::sort0<float, true>(err);
+        kernel::sort0<float>(err, true);
 
         unsigned minIdx;
         float minMedian;
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 38b7ea9710..be24ec2393 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -398,7 +398,7 @@ void orb(unsigned* out_feat,
         kernel::range<uint>(harris_idx, 0);
 
         // Sort features according to Harris responses
-        kernel::sort0ByKey<float, uint, false>(harris_sorted, harris_idx);
+        kernel::sort0ByKey<float, uint>(harris_sorted, harris_idx, false);
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index f0095b144d..f14583a2fc 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -23,8 +23,8 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool isAscending>
-        void sort0Iterative(Param<T> val)
+        template<typename T>
+        void sort0Iterative(Param<T> val, bool isAscending)
         {
             thrust::device_ptr<T> val_ptr = thrust::device_pointer_cast(val.ptr);
 
@@ -47,8 +47,8 @@ namespace cuda
             POST_LAUNCH_CHECK();
         }
 
-        template<typename T, bool isAscending, int dim>
-        void sortBatched(Param<T> pVal)
+        template<typename T, int dim>
+        void sortBatched(Param<T> pVal, bool isAscending)
         {
             af::dim4 inDims;
             for(int i = 0; i < 4; i++)
@@ -124,15 +124,15 @@ namespace cuda
             memFree(key);
         }
 
-        template<typename T, bool isAscending>
-        void sort0(Param<T> val)
+        template<typename T>
+        void sort0(Param<T> val, bool isAscending)
         {
             int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 10)
-                sortBatched<T, isAscending, 0>(val);
+              sortBatched<T, 0>(val, isAscending);
             else
-                kernel::sort0Iterative<T, isAscending>(val);
+              kernel::sort0Iterative<T>(val, isAscending);
         }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 082368ab22..f05386825d 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -17,14 +17,14 @@ namespace cuda
 {
     namespace kernel
     {
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval);
+        template<typename Tk, typename Tv>
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim);
+        template<typename Tk, typename Tv>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim, bool isAscending);
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKey(Param<Tk> okey, Param<Tv> oval);
+        template<typename Tk, typename Tv>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
index 61529754c4..d4b2855edc 100644
--- a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
@@ -4,9 +4,6 @@ FOREACH(STR ${FILESTRINGS})
     IF(${STR} MATCHES "// SBK_TYPES")
         STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
         STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
-    ELSEIF(${STR} MATCHES "// SBK_DIRS:")
-        STRING(REPLACE "// SBK_DIRS:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_DIRS ${TEMP})
     ELSEIF(${STR} MATCHES "// SBK_INSTS:")
         STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
         STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
@@ -14,16 +11,14 @@ FOREACH(STR ${FILESTRINGS})
 ENDFOREACH()
 
 FOREACH(SBK_TYPE ${SBK_TYPES})
-    FOREACH(SBK_DIR ${SBK_DIRS})
-        FOREACH(SBK_INST ${SBK_INSTS})
-            CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in"
-            "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu")
-            ADD_CUSTOM_COMMAND(
-              OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu"
-              COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_DIR}_${SBK_INST}.cu"
-              DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
-        ENDFOREACH(SBK_INST ${SBK_INSTS})
-    ENDFOREACH(SBK_DIR ${SBK_DIRS})
+    FOREACH(SBK_INST ${SBK_INSTS})
+        CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in"
+        "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
+        ADD_CUSTOM_COMMAND(
+          OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
+          COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
+          DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
+    ENDFOREACH(SBK_INST ${SBK_INSTS})
 ENDFOREACH(SBK_TYPE ${SBK_TYPES})
 
 FILE(GLOB sort_by_key_sources
diff --git a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
index 94168df1de..b7911b6de0 100644
--- a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
+++ b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
@@ -19,6 +19,6 @@ namespace cuda
 {
 namespace kernel
 {
-    INSTANTIATE@SBK_INST@(@SBK_TYPE@, @SBK_DIR@)
+    INSTANTIATE@SBK_INST@(@SBK_TYPE@)
 }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index 20a4b50ffd..aff6cfdc18 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -76,8 +76,8 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval)
+        template<typename Tk, typename Tv>
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
         {
             thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
             thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
@@ -110,8 +110,8 @@ namespace cuda
             POST_LAUNCH_CHECK();
         }
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim)
+        template<typename Tk, typename Tv>
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim, bool isAscending)
         {
             af::dim4 inDims;
             for(int i = 0; i < 4; i++)
@@ -154,10 +154,18 @@ namespace cuda
             // Need to convert pSeq to thrust::device_ptr, otherwise thrust
             // throws weird errors for all *64 data types (double, intl, uintl etc)
             thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          X, X + elements,
-                          dSeq,
-                          IPCompare<Tk, Tv, isAscending>());
+            if(isAscending) {
+              THRUST_SELECT(thrust::stable_sort_by_key,
+                            X, X + elements,
+                            dSeq,
+                            IPCompare<Tk, Tv, true>());
+            }
+            else {
+              THRUST_SELECT(thrust::stable_sort_by_key,
+                            X, X + elements,
+                            dSeq,
+                            IPCompare<Tk, Tv, false>());
+            }
             POST_LAUNCH_CHECK();
 
             // Needs to be ascending (true) in order to maintain the indices properly
@@ -179,37 +187,37 @@ namespace cuda
             memFree((char*)Xptr);
         }
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKey(Param<Tk> okey, Param<Tv> oval)
+        template<typename Tk, typename Tv>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
         {
             int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 4)
-                kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 0);
+                kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
             else
-                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(okey, oval);
+                kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
         }
 
-#define INSTANTIATE(Tk, Tv, dr)                                                                 \
-    template void sort0ByKey<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);                       \
-    template void sort0ByKeyIterative<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval);              \
-    template void sortByKeyBatched<Tk, Tv, dr>(Param<Tk> okey, Param<Tv> oval, const int dim);
-
-#define INSTANTIATE0(Tk    , dr) \
-    INSTANTIATE(Tk, float  , dr) \
-    INSTANTIATE(Tk, double , dr) \
-    INSTANTIATE(Tk, cfloat , dr) \
-    INSTANTIATE(Tk, cdouble, dr) \
-    INSTANTIATE(Tk, char   , dr) \
-    INSTANTIATE(Tk, uchar  , dr)
-
-#define INSTANTIATE1(Tk    , dr) \
-    INSTANTIATE(Tk, int    , dr) \
-    INSTANTIATE(Tk, uint   , dr) \
-    INSTANTIATE(Tk, short  , dr) \
-    INSTANTIATE(Tk, ushort , dr) \
-    INSTANTIATE(Tk, intl   , dr) \
-    INSTANTIATE(Tk, uintl  , dr)
+#define INSTANTIATE(Tk, Tv)                                                                 \
+      template void sort0ByKey<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, bool);               \
+      template void sort0ByKeyIterative<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, bool);      \
+      template void sortByKeyBatched<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, const int dim, bool);
+
+#define INSTANTIATE0(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  )
+
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
 
     }
 }
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 9b0f4c53af..6c070017c5 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -18,15 +18,15 @@
 
 namespace cuda
 {
-    template<typename T, bool isAscending>
-    Array<T> sort(const Array<T> &in, const unsigned dim)
+    template<typename T>
+    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
     {
         Array<T> out = copyArray<T>(in);
         switch(dim) {
-            case 0: kernel::sort0<T, isAscending>(out); break;
-            case 1: kernel::sortBatched<T, isAscending, 1>(out); break;
-            case 2: kernel::sortBatched<T, isAscending, 2>(out); break;
-            case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
+            case 0: kernel::sort0<T>(out, isAscending); break;
+            case 1: kernel::sortBatched<T, 1>(out, isAscending); break;
+            case 2: kernel::sortBatched<T, 2>(out, isAscending); break;
+            case 3: kernel::sortBatched<T, 3>(out, isAscending); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
@@ -45,10 +45,9 @@ namespace cuda
         }
         return out;
     }
-
+  
 #define INSTANTIATE(T)                                                  \
-    template Array<T> sort<T, true>(const Array<T> &in, const unsigned dim); \
-    template Array<T>  sort<T,false>(const Array<T> &in, const unsigned dim); \
+    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/cuda/sort.hpp b/src/backend/cuda/sort.hpp
index ad191bb22a..5ea6309868 100644
--- a/src/backend/cuda/sort.hpp
+++ b/src/backend/cuda/sort.hpp
@@ -11,6 +11,6 @@
 
 namespace cuda
 {
-    template<typename T, bool isAscending>
-    Array<T> sort(const Array<T> &in, const unsigned dim);
+    template<typename T>
+    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cuda/sort_by_key.cu b/src/backend/cuda/sort_by_key.cu
index be5557c939..86f5b23a18 100644
--- a/src/backend/cuda/sort_by_key.cu
+++ b/src/backend/cuda/sort_by_key.cu
@@ -18,18 +18,18 @@
 
 namespace cuda
 {
-    template<typename Tk, typename Tv, bool isAscending>
+    template<typename Tk, typename Tv>
     void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim)
+                     const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim, bool isAscending)
     {
         okey = copyArray<Tk>(ikey);
         oval = copyArray<Tv>(ival);
 
         switch(dim) {
-            case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
-            case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 1); break;
-            case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 2); break;
-            case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 3); break;
+            case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
+            case 1:
+            case 2:
+            case 3: kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
@@ -52,10 +52,8 @@ namespace cuda
     }
 
 #define INSTANTIATE(Tk, Tv)                                                         \
-    template void sort_by_key<Tk, Tv, true >(Array<Tk> &okey, Array<Tv> &oval,      \
-                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
-    template void sort_by_key<Tk, Tv, false>(Array<Tk> &okey, Array<Tv> &oval,      \
-                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+    template void sort_by_key<Tk, Tv>(Array<Tk> &okey, Array<Tv> &oval,      \
+                                      const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim, bool);
 
 #define INSTANTIATE1(Tk    ) \
     INSTANTIATE(Tk, float  ) \
diff --git a/src/backend/cuda/sort_by_key.hpp b/src/backend/cuda/sort_by_key.hpp
index f752dfaf3a..ac3840bea6 100644
--- a/src/backend/cuda/sort_by_key.hpp
+++ b/src/backend/cuda/sort_by_key.hpp
@@ -11,7 +11,7 @@
 
 namespace cuda
 {
-    template<typename Tk, typename Tv, bool isAscending>
+    template<typename Tk, typename Tv>
     void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim);
+                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 02485b9e1e..707b428b35 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -19,18 +19,18 @@
 
 namespace cuda
 {
-    template<typename T, bool isAscending>
-    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
+    template<typename T>
+    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim, bool isAscending)
     {
         okey = copyArray<T>(in);
         oval = range<uint>(in.dims(), dim);
         oval.eval();
 
         switch(dim) {
-            case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
-            case 1: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 1); break;
-            case 2: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 2); break;
-            case 3: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 3); break;
+            case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
+            case 1:
+            case 2:
+            case 3: kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
@@ -53,10 +53,8 @@ namespace cuda
     }
 
 #define INSTANTIATE(T)                                                  \
-    template void sort_index<T, true>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim);                  \
-    template void sort_index<T,false>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim);                  \
+    template void sort_index<T>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
+                                      const uint dim,bool isAscending);
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/cuda/sort_index.hpp b/src/backend/cuda/sort_index.hpp
index 19736bd435..5520014b02 100644
--- a/src/backend/cuda/sort_index.hpp
+++ b/src/backend/cuda/sort_index.hpp
@@ -11,6 +11,6 @@
 
 namespace cuda
 {
-    template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim);
+    template<typename T>
+    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 5634366262..fe7126c0f1 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -289,7 +289,7 @@ void harris(unsigned* corners_out,
             kernel::range<uint>(harris_idx, 0);
 
             // Sort Harris responses
-            kernel::sort0ByKey<float, uint, false>(harris_resp, harris_idx);
+            kernel::sort0ByKey<float, uint>(harris_resp, harris_idx, false);
 
             x_out.data = bufferAlloc(*corners_out * sizeof(float));
             y_out.data = bufferAlloc(*corners_out * sizeof(float));
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 159bd241f5..1029d1b3f4 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -145,7 +145,7 @@ int computeH(
         if (htype == AF_HOMOGRAPHY_LMEDS) {
             // TODO: Improve this sorting, if the number of iterations is
             // sufficiently large, this can be *very* slow
-            kernel::sort0<float, true>(err);
+            kernel::sort0<float>(err, true);
 
             unsigned minIdx;
             float minMedian;
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 7cf77a4fbc..b7350821ae 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -307,7 +307,7 @@ void orb(unsigned* out_feat,
             d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
             kernel::range<uint>(d_harris_idx, 0);
 
-            kernel::sort0ByKey<float, uint, false>(d_harris_sorted, d_harris_idx);
+            kernel::sort0ByKey<float, uint>(d_harris_sorted, d_harris_idx, false);
 
             cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
             cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 2938af6f4e..de9d77786b 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -41,8 +41,8 @@ namespace opencl
 {
     namespace kernel
     {
-        template<typename T, bool isAscending>
-        void sort0Iterative(Param val)
+        template<typename T>
+        void sort0Iterative(Param val, bool isAscending)
         {
             try {
                 compute::command_queue c_queue(getQueue()());
@@ -79,8 +79,8 @@ namespace opencl
             }
         }
 
-        template<typename T, bool isAscending, int dim>
-        void sortBatched(Param pVal)
+        template<typename T, int dim>
+        void sortBatched(Param pVal, bool isAscending)
         {
             try{
                 af::dim4 inDims;
@@ -157,15 +157,15 @@ namespace opencl
             }
         }
 
-        template<typename T, bool isAscending>
-        void sort0(Param val)
+        template<typename T>
+        void sort0(Param val, bool isAscending)
         {
             int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 10)
-                sortBatched<T, isAscending, 0>(val);
+                sortBatched<T, 0>(val, isAscending);
             else
-                kernel::sort0Iterative<T, isAscending>(val);
+                kernel::sort0Iterative<T>(val, isAscending);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 3aff6fb4e9..18f96cdc7c 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -17,13 +17,13 @@ namespace opencl
 {
     namespace kernel
     {
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKeyIterative(Param pKey, Param pVal);
+        template<typename Tk, typename Tv>
+        void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending);
 
-        template<typename Tk_, typename Tv_, bool isAscending>
-        void sortByKeyBatched(Param pKey, Param pVal, const int dim);
+        template<typename Tk_, typename Tv_>
+        void sortByKeyBatched(Param pKey, Param pVal, const int dim, bool isAscending);
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKey(Param pKey, Param pVal);
+        template<typename Tk, typename Tv>
+        void sort0ByKey(Param pKey, Param pVal, bool isAscending);
     }
 }
diff --git a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index bf4c96bbb2..43732771cd 100644
--- a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -15,7 +15,6 @@ namespace opencl
 {
 namespace kernel
 {
-    INSTANTIATE1(TYPE,true)
-    INSTANTIATE1(TYPE,false)
+    INSTANTIATE1(TYPE)
 }
 }
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index ded8ef604e..4c54d84d60 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -192,8 +192,8 @@ namespace opencl
             }
         }
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKeyIterative(Param pKey, Param pVal)
+        template<typename Tk, typename Tv>
+        void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending)
         {
             try {
                 compute::command_queue c_queue(getQueue()());
@@ -232,8 +232,8 @@ namespace opencl
             }
         }
 
-        template<typename Tk_, typename Tv_, bool isAscending>
-        void sortByKeyBatched(Param pKey, Param pVal, const int dim)
+        template<typename Tk_, typename Tv_>
+        void sortByKeyBatched(Param pKey, Param pVal, const int dim, bool isAscending)
         {
             typedef type_t<Tk_> Tk;
             typedef type_t<Tv_> Tv;
@@ -335,35 +335,35 @@ namespace opencl
             }
         }
 
-        template<typename Tk, typename Tv, bool isAscending>
-        void sort0ByKey(Param pKey, Param pVal)
+        template<typename Tk, typename Tv>
+        void sort0ByKey(Param pKey, Param pVal, bool isAscending)
         {
             int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 5)
-                kernel::sortByKeyBatched<Tk, Tv, isAscending>(pKey, pVal, 0);
+                kernel::sortByKeyBatched<Tk, Tv>(pKey, pVal, 0, isAscending);
             else
-                kernel::sort0ByKeyIterative<Tk, Tv, isAscending>(pKey, pVal);
+                kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
         }
 
-#define INSTANTIATE(Tk, Tv, dr)                                                         \
-    template void sort0ByKey<Tk, Tv, dr>(Param okey, Param oval);                       \
-    template void sort0ByKeyIterative<Tk, Tv, dr>(Param okey, Param oval);              \
-    template void sortByKeyBatched<Tk, Tv, dr>(Param okey, Param oval, const int dim);
-
-#define INSTANTIATE1(Tk    , dr) \
-    INSTANTIATE(Tk, float  , dr) \
-    INSTANTIATE(Tk, double , dr) \
-    INSTANTIATE(Tk, cfloat , dr) \
-    INSTANTIATE(Tk, cdouble, dr) \
-    INSTANTIATE(Tk, int    , dr) \
-    INSTANTIATE(Tk, uint   , dr) \
-    INSTANTIATE(Tk, short  , dr) \
-    INSTANTIATE(Tk, ushort , dr) \
-    INSTANTIATE(Tk, char   , dr) \
-    INSTANTIATE(Tk, uchar  , dr) \
-    INSTANTIATE(Tk, intl   , dr) \
-    INSTANTIATE(Tk, uintl  , dr)
+#define INSTANTIATE(Tk, Tv)                                                         \
+  template void sort0ByKey<Tk, Tv>(Param okey, Param oval, bool isAscending);        \
+  template void sort0ByKeyIterative<Tk, Tv>(Param okey, Param oval, bool isAscending); \
+  template void sortByKeyBatched<Tk, Tv>(Param okey, Param oval, const int dim, bool isAscending);
+
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
     }
 }
 
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 1548f27472..b20b9fb15c 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -18,16 +18,16 @@
 
 namespace opencl
 {
-    template<typename T, bool isAscending>
-    Array<T> sort(const Array<T> &in, const unsigned dim)
+    template<typename T>
+    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
     {
         try {
             Array<T> out = copyArray<T>(in);
             switch(dim) {
-                case 0: kernel::sort0<T, isAscending>(out); break;
-                case 1: kernel::sortBatched<T, isAscending, 1>(out); break;
-                case 2: kernel::sortBatched<T, isAscending, 2>(out); break;
-                case 3: kernel::sortBatched<T, isAscending, 3>(out); break;
+                case 0: kernel::sort0<T>(out, isAscending); break;
+                case 1: kernel::sortBatched<T, 1>(out, isAscending); break;
+                case 2: kernel::sortBatched<T, 2>(out, isAscending); break;
+                case 3: kernel::sortBatched<T, 3>(out, isAscending); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
@@ -51,8 +51,7 @@ namespace opencl
     }
 
 #define INSTANTIATE(T)                                                  \
-    template Array<T> sort<T, true>(const Array<T> &in, const unsigned dim); \
-    template Array<T> sort<T,false>(const Array<T> &in, const unsigned dim); \
+    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/opencl/sort.hpp b/src/backend/opencl/sort.hpp
index 5bb74f52a9..82f6385e2e 100644
--- a/src/backend/opencl/sort.hpp
+++ b/src/backend/opencl/sort.hpp
@@ -11,6 +11,6 @@
 
 namespace opencl
 {
-    template<typename T, bool isAscending>
-    Array<T> sort(const Array<T> &in, const unsigned dim);
+    template<typename T>
+    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
index 53809c92e7..9452311d11 100644
--- a/src/backend/opencl/sort_by_key.cpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -18,19 +18,19 @@
 
 namespace opencl
 {
-    template<typename Tk, typename Tv, bool isAscending>
+    template<typename Tk, typename Tv>
     void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim)
+                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending)
     {
         try {
             okey = copyArray<Tk>(ikey);
             oval = copyArray<Tv>(ival);
 
             switch(dim) {
-                case 0: kernel::sort0ByKey<Tk, Tv, isAscending>(okey, oval); break;
-                case 1: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 1); break;
-                case 2: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 2); break;
-                case 3: kernel::sortByKeyBatched<Tk, Tv, isAscending>(okey, oval, 3); break;
+                case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
+                case 1:
+                case 2:
+                case 3: kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
@@ -56,10 +56,9 @@ namespace opencl
     }
 
 #define INSTANTIATE(Tk, Tv)                                                         \
-    template void sort_by_key<Tk, Tv, true >(Array<Tk> &okey, Array<Tv> &oval,      \
-                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
-    template void sort_by_key<Tk, Tv, false>(Array<Tk> &okey, Array<Tv> &oval,      \
-                const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim);      \
+    template void sort_by_key<Tk, Tv>(Array<Tk> &okey, Array<Tv> &oval,             \
+                                      const Array<Tk> &ikey, const Array<Tv> &ival, \
+                                      const uint dim, bool isAscending);
 
 #define INSTANTIATE1(Tk    ) \
     INSTANTIATE(Tk, float  ) \
@@ -76,15 +75,14 @@ namespace opencl
     INSTANTIATE(Tk, uintl  )
 
 
-INSTANTIATE1(float )
-INSTANTIATE1(double)
-INSTANTIATE1(int   )
-INSTANTIATE1(uint  )
-INSTANTIATE1(short )
-INSTANTIATE1(ushort)
-INSTANTIATE1(char  )
-INSTANTIATE1(uchar )
-INSTANTIATE1(intl  )
-INSTANTIATE1(uintl )
-
+  INSTANTIATE1(float )
+  INSTANTIATE1(double)
+  INSTANTIATE1(int   )
+  INSTANTIATE1(uint  )
+  INSTANTIATE1(short )
+  INSTANTIATE1(ushort)
+  INSTANTIATE1(char  )
+  INSTANTIATE1(uchar )
+  INSTANTIATE1(intl  )
+  INSTANTIATE1(uintl )
 }
diff --git a/src/backend/opencl/sort_by_key.hpp b/src/backend/opencl/sort_by_key.hpp
index 712f0be615..0b8577f1a3 100644
--- a/src/backend/opencl/sort_by_key.hpp
+++ b/src/backend/opencl/sort_by_key.hpp
@@ -11,7 +11,7 @@
 
 namespace opencl
 {
-    template<typename Tk, typename Tv, bool isAscending>
+    template<typename Tk, typename Tv>
     void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-               const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim);
+                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index bf4c031027..20a92e6d45 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -19,8 +19,8 @@
 
 namespace opencl
 {
-    template<typename T, bool isAscending>
-    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim)
+    template<typename T>
+    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim, bool isAscending)
     {
         try {
             // okey contains values, oval contains indices
@@ -29,10 +29,10 @@ namespace opencl
             oval.eval();
 
             switch(dim) {
-                case 0: kernel::sort0ByKey<T, uint, isAscending>(okey, oval); break;
-                case 1: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 1); break;
-                case 2: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 2); break;
-                case 3: kernel::sortByKeyBatched<T, uint, isAscending>(okey, oval, 3); break;
+                case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
+                case 1:
+                case 2:
+                case 3: kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
@@ -58,10 +58,9 @@ namespace opencl
     }
 
 #define INSTANTIATE(T)                                                  \
-    template void sort_index<T, true>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim);                  \
-    template void sort_index<T,false>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim);                  \
+  template void sort_index<T>(Array<T> &val, Array<uint> &idx,          \
+                              const Array<T> &in, const uint dim,       \
+                              bool isAscending);
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/opencl/sort_index.hpp b/src/backend/opencl/sort_index.hpp
index 995d57bc06..cfa3366906 100644
--- a/src/backend/opencl/sort_index.hpp
+++ b/src/backend/opencl/sort_index.hpp
@@ -11,6 +11,6 @@
 
 namespace opencl
 {
-    template<typename T, bool isAscending>
-    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim);
+    template<typename T>
+    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim, bool isAscending);
 }

From 7f1e79eabe9c5d7467e95a75f0327bb3c75b006f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 30 Jul 2016 20:29:35 -0400
Subject: [PATCH 0709/2677] Reduce compile times in CUDA and OpenCL backends

This function reduces compilation times of scan_by_key
---
 src/api/c/scan.cpp                            |  16 +-
 src/backend/cpu/scan_by_key.cpp               |   4 -
 src/backend/cuda/kernel/scan_dim_by_key.hpp   |   4 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 104 +++++++------
 src/backend/cuda/kernel/scan_first_by_key.hpp |   4 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 137 +++++++++---------
 src/backend/cuda/kernel/sort.hpp              |   2 +-
 src/backend/cuda/kernel/sort_by_key_impl.hpp  |  24 ++-
 src/backend/cuda/scan_by_key.cu               |  18 +--
 src/backend/cuda/sort.cu                      |   2 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   4 -
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   4 -
 src/backend/opencl/scan_by_key.cpp            |   4 -
 13 files changed, 147 insertions(+), 180 deletions(-)

diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 022561510c..31811142ca 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -35,10 +35,10 @@ static inline af_array scan_key(const af_array key, const af_array in, const int
     af_array out;
 
     switch(type) {
-        case    s32:   out = getHandle(scan<op, Ti,   int, To>(getArray<  int>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
-        case    u32:   out = getHandle(scan<op, Ti,  uint, To>(getArray< uint>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
-        case    s64:   out = getHandle(scan<op, Ti,  intl, To>(getArray< intl>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
-        case    u64:   out = getHandle(scan<op, Ti, uintl, To>(getArray<uintl>(key), getArray<Ti>(in), dim, inclusive_scan)); break;
+        case    s32:   out = getHandle(scan<op, Ti,   int, To>(getArray<  int>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
+        case    u32:   out = getHandle(scan<op, Ti,  uint, To>(getArray< uint>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
+        case    s64:   out = getHandle(scan<op, Ti,  intl, To>(getArray< intl>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
+        case    u64:   out = getHandle(scan<op, Ti, uintl, To>(getArray<uintl>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
         default:
             TYPE_ERROR(1, type);
     }
@@ -186,10 +186,10 @@ af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, cons
         case s32:  res = scan_op<int    , int    >(key, in, dim, op, inclusive_scan); break;
         case u64:  res = scan_op<uintl  , uintl  >(key, in, dim, op, inclusive_scan); break;
         case s64:  res = scan_op<intl   , intl   >(key, in, dim, op, inclusive_scan); break;
-        case u16:  res = scan_op<ushort , uint   >(key, in, dim, op, inclusive_scan); break;
-        case s16:  res = scan_op<short  , int    >(key, in, dim, op, inclusive_scan); break;
-        case u8:   res = scan_op<uchar  , uint   >(key, in, dim, op, inclusive_scan); break;
-        case b8:   res = scan_op<char   , uint   >(key, in, dim, op, inclusive_scan); break;
+        case u16:  res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
+        case s16:  res = scan_op<int    , int    >(key, in, dim, op, inclusive_scan); break;
+        case u8:   res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
+        case b8:   res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
         default:
             TYPE_ERROR(1, type);
         }
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index ac66028b71..c90a2b94ab 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -71,10 +71,6 @@ namespace cpu
     INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_BY_KEY_ALL_OP(ROp) \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.hpp b/src/backend/cuda/kernel/scan_dim_by_key.hpp
index fd50fb5b67..a609510b92 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key.hpp
@@ -15,7 +15,7 @@ namespace cuda
 {
     namespace kernel
     {
-        template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-        void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim);
+        template<typename Ti, typename Tk, typename To, af_op_t op>
+        void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, bool inclusive_scan);
     }
 }
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 91da713d6b..7badd3066b 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -30,7 +30,7 @@ namespace kernel
         return (id == 0)? 1 : ((*kptr) != (*(kptr - stride)));
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
     __global__
     static void scan_dim_nonfinal_kernel(Param<To> out,
                                          Param<To> tmp,
@@ -41,7 +41,8 @@ namespace kernel
                                          int dim,
                                          uint blocks_x,
                                          uint blocks_y,
-                                         uint lim)
+                                         uint lim,
+                                         bool inclusive_scan)
     {
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -194,7 +195,7 @@ namespace kernel
             }
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY, bool calculateFlags, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
     __global__
     static void scan_dim_final_kernel(Param<To> out,
                                       CParam<Ti>  in,
@@ -202,7 +203,9 @@ namespace kernel
                                       int dim,
                                       uint blocks_x,
                                       uint blocks_y,
-                                      uint lim)
+                                      uint lim,
+                                      bool calculateFlags,
+                                      bool inclusive_scan)
     {
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -392,13 +395,15 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool calculateFlags, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op>
     static void scan_dim_final_launcher(Param<To> out,
                                         CParam<Ti> in,
                                         CParam<Tk> key,
                                         const int dim,
                                         const uint threads_y,
-                                        const uint blocks_all[4])
+                                        const uint blocks_all[4],
+                                        bool calculateFlags,
+                                        bool inclusive_scan)
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -409,23 +414,23 @@ namespace kernel
 
         switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 8, calculateFlags, inclusive_scan>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 8>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
         case 4:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 4, calculateFlags, inclusive_scan>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 4>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
         case 2:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 2, calculateFlags, inclusive_scan>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 2>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
         case 1:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 1, calculateFlags, inclusive_scan>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 1>), blocks, threads,
+                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
         }
 
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op>
     static void scan_dim_nonfinal_launcher(Param<To> out,
                                            Param<To> tmp,
                                            Param<char> tflg,
@@ -434,7 +439,8 @@ namespace kernel
                                            CParam<Tk> key,
                                            const int dim,
                                            const uint threads_y,
-                                           const uint blocks_all[4])
+                                           const uint blocks_all[4],
+                                           bool inclusive_scan)
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -445,17 +451,17 @@ namespace kernel
 
         switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 8, inclusive_scan>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 8>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
         case 4:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 4, inclusive_scan>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 4>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
         case 2:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 2, inclusive_scan>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 2>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
         case 1:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 1, inclusive_scan>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim); break;
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 1>), blocks, threads,
+                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
         }
 
         POST_LAUNCH_CHECK();
@@ -483,8 +489,8 @@ namespace kernel
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim)
+    template<typename Ti, typename Tk, typename To, af_op_t op>
+    void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, bool inclusive_scan)
     {
         uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
         uint threads_x = THREADS_X;
@@ -496,10 +502,11 @@ namespace kernel
 
         if (blocks_all[dim] == 1) {
 
-            scan_dim_final_launcher<Ti, Tk, To, op, true, inclusive_scan>(out, in, key,
-                                                                          dim,
-                                                                          threads_y,
-                                                                          blocks_all);
+            scan_dim_final_launcher<Ti, Tk, To, op>(out, in, key,
+                                                    dim,
+                                                    threads_y,
+                                                    blocks_all,
+                                                    true, inclusive_scan);
 
         } else {
             Param<To> tmp = out;
@@ -521,27 +528,19 @@ namespace kernel
             tmpflg.ptr = memAlloc<char>(tmp_elements);
             tmpid.ptr = memAlloc<int>(tmp_elements);
 
-            scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg,
-                                                                       tmpid, in, key,
-                                                                       dim,
-                                                                       threads_y,
-                                                                       blocks_all);
+            scan_dim_nonfinal_launcher<Ti, Tk, To, op>(out, tmp, tmpflg,
+                                                       tmpid, in, key,
+                                                       dim,
+                                                       threads_y,
+                                                       blocks_all,
+                                                       inclusive_scan);
 
             int bdim = blocks_all[dim];
             blocks_all[dim] = 1;
-
-            //FIXME: Is there an alternative to the if condition ?
-            if (op == af_notzero_t) {
-                scan_dim_final_launcher<To, char, To, af_add_t, false, true>(tmp, tmp, tmpflg,
-                                                                             dim,
-                                                                             threads_y,
-                                                                             blocks_all);
-            } else {
-                scan_dim_final_launcher<To, char, To,       op, false, true>(tmp, tmp, tmpflg,
-                                                                             dim,
-                                                                             threads_y,
-                                                                             blocks_all);
-            }
+            scan_dim_final_launcher<To, char, To,       op>(tmp, tmp, tmpflg,
+                                                            dim,
+                                                            threads_y,
+                                                            blocks_all, false, true);
 
             blocks_all[dim] = bdim;
             bcast_dim_launcher<To, op>(out, tmp, tmpid, dim, threads_y, blocks_all);
@@ -554,9 +553,8 @@ namespace kernel
 
 }
 
-#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)\
-    template void scan_dim_by_key<Ti, Tk, To, ROp,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim); \
-    template void scan_dim_by_key<Ti, Tk, To, ROp, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim); \
+#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)                    \
+    template void scan_dim_by_key<Ti, Tk, To, ROp>(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, bool inclusive_scan); \
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
@@ -567,10 +565,6 @@ namespace kernel
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)         \
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int  )   \
diff --git a/src/backend/cuda/kernel/scan_first_by_key.hpp b/src/backend/cuda/kernel/scan_first_by_key.hpp
index 1292dd341f..2acdd9f782 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key.hpp
@@ -15,7 +15,7 @@ namespace cuda
 {
     namespace kernel
     {
-        template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-        void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+        template<typename Ti, typename Tk, typename To, af_op_t op>
+        void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan);
     }
 }
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index d0c23eb24c..aecd8b957c 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -29,17 +29,18 @@ namespace kernel
         return (id == 0)? 1 : (kptr[id] != kptr[previd]);
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
     __global__
     static void scan_nonfinal_kernel(Param<To> out,
-                                  Param<To> tmp,
-                                  Param<char> tflg,
-                                  Param<int> tlid,
-                                  CParam<Ti> in,
-                                  CParam<Tk> key,
-                                  uint blocks_x,
-                                  uint blocks_y,
-                                  uint lim)
+                                     Param<To> tmp,
+                                     Param<char> tflg,
+                                     Param<int> tlid,
+                                     CParam<Ti> in,
+                                     CParam<Tk> key,
+                                     uint blocks_x,
+                                     uint blocks_y,
+                                     uint lim,
+                                     bool inclusive_scan)
     {
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
@@ -168,14 +169,16 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX, bool calculateFlags, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
     __global__
     static void scan_final_kernel(Param<To> out,
                                   CParam<Ti> in,
                                   CParam<Tk> key,
                                   uint blocks_x,
                                   uint blocks_y,
-                                  uint lim)
+                                  uint lim,
+                                  bool calculateFlags,
+                                  bool inclusive_scan)
     {
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
@@ -280,16 +283,17 @@ namespace kernel
         }
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op>
     static void scan_nonfinal_launcher(Param<To> out,
-                             Param<To> tmp,
-                             Param<char> tflg,
-                             Param<int> tlid,
-                             CParam<Ti> in,
-                             CParam<Tk> key,
-                             const uint blocks_x,
-                             const uint blocks_y,
-                             const uint threads_x)
+                                       Param<To> tmp,
+                                       Param<char> tflg,
+                                       Param<int> tlid,
+                                       CParam<Ti> in,
+                                       CParam<Tk> key,
+                                       const uint blocks_x,
+                                       const uint blocks_y,
+                                       const uint threads_x,
+                                       bool inclusive_scan)
     {
 
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
@@ -300,29 +304,35 @@ namespace kernel
 
         switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 32, inclusive_scan>),
-                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 32>),
+                        blocks, threads, out, tmp, tflg, tlid, in, key,
+                        blocks_x, blocks_y, lim, inclusive_scan); break;
         case 64:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 64, inclusive_scan>),
-                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 64>),
+                        blocks, threads, out, tmp, tflg, tlid, in, key,
+                        blocks_x, blocks_y, lim, inclusive_scan); break;
         case 128:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 128, inclusive_scan>),
-                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 128>),
+                        blocks, threads, out, tmp, tflg, tlid, in, key,
+                        blocks_x, blocks_y, lim, inclusive_scan); break;
         case 256:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 256, inclusive_scan>),
-                    blocks, threads, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 256>),
+                        blocks, threads, out, tmp, tflg, tlid, in, key,
+                        blocks_x, blocks_y, lim, inclusive_scan); break;
         }
 
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool calculateFlags, bool inclusive_scan>
+    template<typename Ti, typename Tk, typename To, af_op_t op>
     static void scan_final_launcher(Param<To> out,
-                             CParam<Ti> in,
-                             CParam<Tk> key,
-                             const uint blocks_x,
-                             const uint blocks_y,
-                             const uint threads_x)
+                                    CParam<Ti> in,
+                                    CParam<Tk> key,
+                                    const uint blocks_x,
+                                    const uint blocks_y,
+                                    const uint threads_x,
+                                    bool calculateFlags,
+                                    bool inclusive_scan)
     {
 
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
@@ -333,17 +343,21 @@ namespace kernel
 
         switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 32, calculateFlags, inclusive_scan>),
-                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 32>),
+                        blocks, threads, out, in, key, blocks_x,
+                        blocks_y, lim, calculateFlags, inclusive_scan); break;
         case 64:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 64, calculateFlags, inclusive_scan>),
-                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 64>),
+                        blocks, threads, out, in, key, blocks_x,
+                        blocks_y, lim, calculateFlags, inclusive_scan); break;
         case 128:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 128, calculateFlags, inclusive_scan>),
-                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 128>),
+                        blocks, threads, out, in, key, blocks_x,
+                        blocks_y, lim, calculateFlags, inclusive_scan); break;
         case 256:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 256, calculateFlags, inclusive_scan>),
-                    blocks, threads, out, in, key, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 256>),
+                        blocks, threads, out, in, key, blocks_x,
+                        blocks_y, lim, calculateFlags, inclusive_scan); break;
         }
 
         POST_LAUNCH_CHECK();
@@ -411,8 +425,8 @@ namespace kernel
         POST_LAUNCH_CHECK();
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key)
+    template<typename Ti, typename Tk, typename To, af_op_t op>
+    void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan)
     {
         uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
         threads_x = std::min(threads_x, THREADS_PER_BLOCK);
@@ -422,9 +436,10 @@ namespace kernel
         uint blocks_y = divup(out.dims[1], threads_y);
 
         if (blocks_x == 1) {
-            scan_final_launcher<Ti, Tk, To, op, true, inclusive_scan>(
-                                                out, in, key,
-                                                blocks_x, blocks_y, threads_x);
+            scan_final_launcher<Ti, Tk, To, op>(
+                out, in, key,
+                blocks_x, blocks_y, threads_x,
+                true, inclusive_scan);
 
         } else {
 
@@ -451,20 +466,15 @@ namespace kernel
             tmpflg.ptr = memAlloc<char>(tmp_elements);
             tmpid.ptr = memAlloc<int>(tmp_elements);
 
-            scan_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(
-                                                out, tmp, tmpflg, tmpid, in, key,
-                                                blocks_x, blocks_y, threads_x);
+            scan_nonfinal_launcher<Ti, Tk, To, op>(
+                out, tmp, tmpflg, tmpid, in, key,
+                blocks_x, blocks_y, threads_x,
+                inclusive_scan);
 
-            //FIXME: Is there an alternative to the if condition ?
-            if (op == af_notzero_t) {
-                scan_final_launcher<To, char, To, af_add_t, false, true>(
-                                                tmp, tmp, tmpflg,
-                                                1, blocks_y, threads_x);
-            } else {
-                scan_final_launcher<To, char, To, op, false, true>(
-                                                tmp, tmp, tmpflg,
-                                                1, blocks_y, threads_x);
-            }
+            scan_final_launcher<To, char, To, op>(
+                tmp, tmp, tmpflg,
+                1, blocks_y, threads_x,
+                false, true);
 
             bcast_first_launcher<To, op>(out, tmp, tmpid, blocks_x, blocks_y, threads_x);
 
@@ -476,8 +486,7 @@ namespace kernel
 }
 
 #define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)\
-    template void scan_first_by_key<Ti, Tk, To, ROp,  true>(Param<To> out, CParam<Ti> in, CParam<Tk> key);  \
-    template void scan_first_by_key<Ti, Tk, To, ROp, false>(Param<To> out, CParam<Ti> in, CParam<Tk> key);
+    template void scan_first_by_key<Ti, Tk, To, ROp>(Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan); \
 
 #define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)        \
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float  , Tk, float  )\
@@ -488,10 +497,6 @@ namespace kernel
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, uint   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uchar  , Tk, uint   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, short  , Tk, int    )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)       \
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int  ) \
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index f14583a2fc..3d8a121566 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -129,7 +129,7 @@ namespace cuda
         {
             int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
             // TODO Make a better heurisitic
-            if(higherDims > 10)
+            if(higherDims > 16)
               sortBatched<T, 0>(val, isAscending);
             else
               kernel::sort0Iterative<T>(val, isAscending);
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index aff6cfdc18..24a9b528aa 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -29,10 +29,14 @@ struct IndexPair
     Tv second;
 };
 
-template <typename Tk, typename Tv, bool isAscending>
+template <typename Tk, typename Tv>
 struct IPCompare
 {
-    __host__ __device__
+    bool isAscending;
+    IPCompare(bool ascendFlag) : isAscending(ascendFlag)
+    {
+    }
+    __device__
     bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs) const
     {
         // Check stable sort condition
@@ -154,18 +158,10 @@ namespace cuda
             // Need to convert pSeq to thrust::device_ptr, otherwise thrust
             // throws weird errors for all *64 data types (double, intl, uintl etc)
             thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
-            if(isAscending) {
-              THRUST_SELECT(thrust::stable_sort_by_key,
-                            X, X + elements,
-                            dSeq,
-                            IPCompare<Tk, Tv, true>());
-            }
-            else {
-              THRUST_SELECT(thrust::stable_sort_by_key,
-                            X, X + elements,
-                            dSeq,
-                            IPCompare<Tk, Tv, false>());
-            }
+            THRUST_SELECT(thrust::stable_sort_by_key,
+                          X, X + elements,
+                          dSeq,
+                          IPCompare<Tk, Tv>(isAscending));
             POST_LAUNCH_CHECK();
 
             // Needs to be ascending (true) in order to maintain the indices properly
diff --git a/src/backend/cuda/scan_by_key.cu b/src/backend/cuda/scan_by_key.cu
index 022a95aed5..93d43c17ed 100644
--- a/src/backend/cuda/scan_by_key.cu
+++ b/src/backend/cuda/scan_by_key.cu
@@ -23,18 +23,10 @@ namespace cuda
     {
         Array<To> out = createEmptyArray<To>(in.dims());
 
-        if (inclusive_scan) {
-            if (dim == 0) {
-                kernel::scan_first_by_key<Ti, Tk, To, op, true>(out, in, key);
-            } else {
-                kernel::scan_dim_by_key  <Ti, Tk, To, op, true>(out, in, key, dim);
-            }
+        if (dim == 0) {
+            kernel::scan_first_by_key<Ti, Tk, To, op>(out, in, key, inclusive_scan);
         } else {
-            if (dim == 0) {
-                kernel::scan_first_by_key<Ti, Tk, To, op, false>(out, in, key);
-            } else {
-                kernel::scan_dim_by_key  <Ti, Tk, To, op, false>(out, in, key, dim);
-            }
+            kernel::scan_dim_by_key  <Ti, Tk, To, op>(out, in, key, dim, inclusive_scan);
         }
         return out;
     }
@@ -51,10 +43,6 @@ namespace cuda
     INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_OP(ROp)            \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 6c070017c5..f42ea37da5 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -45,7 +45,7 @@ namespace cuda
         }
         return out;
     }
-  
+
 #define INSTANTIATE(T)                                                  \
     template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
 
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 5e07d9d5e2..83998516be 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -300,10 +300,6 @@ namespace kernel
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)         \
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int  )   \
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 89e5f7ebdc..76964ca200 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -280,10 +280,6 @@ namespace kernel
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, char   , Tk, uint   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uchar  , Tk, uint   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, short  , Tk, int    )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)       \
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int  ) \
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index d82718f2b5..56fab0bfd4 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -60,10 +60,6 @@ namespace opencl
     INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
     INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, char   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uchar  , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, short  , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, ushort , Tk, uint   )
 
 #define INSTANTIATE_SCAN_BY_KEY_OP(ROp)     \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \

From 565caf5df8c6941410ad025bf88ad9c230b4b06c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 30 Jul 2016 23:55:33 -0400
Subject: [PATCH 0710/2677] PERF: Improve performance for sort_by_key

- Has added benefit of cutting build times by half
---
 src/backend/cuda/kernel/sort.hpp              |   4 +-
 .../kernel/sort_by_key/sort_by_key_impl.cu.in |   1 -
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 141 ++++++------------
 .../opencl/kernel/sort_by_key_impl.hpp        |  98 +-----------
 src/backend/opencl/kernel/sort_pair.cl        |  43 ------
 5 files changed, 54 insertions(+), 233 deletions(-)
 delete mode 100644 src/backend/opencl/kernel/sort_pair.cl

diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index 3d8a121566..d8ef559f21 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -128,8 +128,8 @@ namespace cuda
         void sort0(Param<T> val, bool isAscending)
         {
             int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 16)
+
+            if(higherDims > 10)
               sortBatched<T, 0>(val, isAscending);
             else
               kernel::sort0Iterative<T>(val, isAscending);
diff --git a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
index b7911b6de0..7402aab5c7 100644
--- a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
+++ b/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
@@ -12,7 +12,6 @@
 // This file instantiates sort_by_key as separate object files from CMake
 // The 3 lines below are read by CMake to determenine the instantiations
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar
-// SBK_DIRS:true false
 // SBK_INSTS:0 1
 
 namespace cuda
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
index 24a9b528aa..d7eb664cc2 100644
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/sort_by_key_impl.hpp
@@ -21,71 +21,34 @@
 #include <thrust/device_vector.h>
 #include <thrust/sort.h>
 
-// This needs to be in global namespace as it is used by thrust
-template<typename Tk, typename Tv>
-struct IndexPair
-{
-    Tk first;
-    Tv second;
-};
-
-template <typename Tk, typename Tv>
-struct IPCompare
-{
-    bool isAscending;
-    IPCompare(bool ascendFlag) : isAscending(ascendFlag)
-    {
-    }
-    __device__
-    bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs) const
-    {
-        // Check stable sort condition
-        if(isAscending) return (lhs.first < rhs.first);
-        else return (lhs.first > rhs.first);
-    }
-};
-
 namespace cuda
 {
     namespace kernel
     {
         static const int copyPairIter = 4;
 
-        template <typename Tk, typename Tv>
-        __global__
-        void makeIndexPair(IndexPair<Tk, Tv> *out, const Tk *first, const Tv *second, const int N)
-        {
-            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
-
-            for(int i = tIdx; i < N; i += blockDim.x)
-            {
-                out[i].first = first[i];
-                out[i].second = second[i];
-            }
-        }
-
-        template <typename Tk, typename Tv>
-        __global__
-        void splitIndexPair(Tk *first, Tv *second, const IndexPair<Tk, Tv> *out, const int N)
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename Tk, typename Tv>
+        void sortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending)
         {
-            int tIdx = blockIdx.x * blockDim.x * copyPairIter + threadIdx.x;
-
-            for(int i = tIdx; i < N; i += blockDim.x)
-            {
-                first[i]  = out[i].first;
-                second[i] = out[i].second;
+            if (isAscending) {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              keyPtr,
+                              keyPtr + elements,
+                              valPtr);
+            } else {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              keyPtr,
+                              keyPtr + elements,
+                              valPtr, thrust::greater<Tk>());
             }
         }
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv>
         void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
         {
-            thrust::device_ptr<Tk>       okey_ptr = thrust::device_pointer_cast(okey.ptr);
-            thrust::device_ptr<Tv>       oval_ptr = thrust::device_pointer_cast(oval.ptr);
-
             for(int w = 0; w < okey.dims[3]; w++) {
                 int okeyW = w * okey.strides[3];
                 int ovalW = w * oval.strides[3];
@@ -97,17 +60,10 @@ namespace cuda
                         int okeyOffset = okeyWZ + y * okey.strides[1];
                         int ovalOffset = ovalWZ + y * oval.strides[1];
 
-                        if(isAscending) {
-                            THRUST_SELECT(thrust::stable_sort_by_key,
-                                          okey_ptr + okeyOffset,
-                                          okey_ptr + okeyOffset + okey.dims[0],
-                                          oval_ptr + ovalOffset);
-                        } else {
-                            THRUST_SELECT(thrust::stable_sort_by_key,
-                                          okey_ptr + okeyOffset,
-                                          okey_ptr + okeyOffset + okey.dims[0],
-                                          oval_ptr + ovalOffset, thrust::greater<Tk>());
-                        }
+                        sortByKey<Tk, Tv>(okey.ptr + okeyOffset,
+                                          oval.ptr + ovalOffset,
+                                          okey.dims[0],
+                                          isAscending);
                     }
                 }
             }
@@ -132,9 +88,9 @@ namespace cuda
 
             // Create/call iota
             // Array<uint> key = iota<uint>(seqDims, tileDims);
-            uint* key = memAlloc<uint>(elements);
+            uint* Seq = memAlloc<uint>(elements);
             Param<uint> pSeq;
-            pSeq.ptr = key;
+            pSeq.ptr = Seq;
             pSeq.strides[0] = 1;
             pSeq.dims[0] = inDims[0];
             for(int i = 1; i < 4; i++) {
@@ -143,52 +99,43 @@ namespace cuda
             }
             cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
 
-            // Make pkey, pVal into a pair
-            IndexPair<Tk, Tv> *Xptr = (IndexPair<Tk, Tv>*)memAlloc<char>(sizeof(IndexPair<Tk, Tv>) * elements);
-
-            const int threads = 256;
-            int blocks = divup(elements, threads * copyPairIter);
-            CUDA_LAUNCH((makeIndexPair<Tk, Tv>), blocks, threads,
-                        Xptr, pKey.ptr, pVal.ptr, elements);
-            POST_LAUNCH_CHECK();
+            Tk *Key = pKey.ptr;
+            Tk *cKey = memAlloc<Tk>(elements);
+            CUDA_CHECK(cudaMemcpyAsync(cKey, Key, elements * sizeof(Tk),
+                                       cudaMemcpyDeviceToDevice,
+                                       getStream(cuda::getActiveDeviceId())));
 
-            thrust::device_ptr<IndexPair<Tk, Tv> > X = thrust::device_pointer_cast(Xptr);
+            Tv *Val = pVal.ptr;
+            sortByKey(Key, Val, elements, isAscending);
+            sortByKey(cKey, Seq, elements, isAscending);
 
-            // Sort indices
-            // Need to convert pSeq to thrust::device_ptr, otherwise thrust
-            // throws weird errors for all *64 data types (double, intl, uintl etc)
-            thrust::device_ptr<uint> dSeq = thrust::device_pointer_cast(pSeq.ptr);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          X, X + elements,
-                          dSeq,
-                          IPCompare<Tk, Tv>(isAscending));
-            POST_LAUNCH_CHECK();
+            uint *cSeq = memAlloc<uint>(elements);
+            CUDA_CHECK(cudaMemcpyAsync(cSeq, Seq, elements * sizeof(uint),
+                                       cudaMemcpyDeviceToDevice,
+                                       getStream(cuda::getActiveDeviceId())));
 
-            // Needs to be ascending (true) in order to maintain the indices properly
-            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          dSeq, dSeq + elements,
-                          X);
-            POST_LAUNCH_CHECK();
-
-            CUDA_LAUNCH((splitIndexPair<Tk, Tv>), blocks, threads,
-                        pKey.ptr, pVal.ptr, Xptr, elements);
-            POST_LAUNCH_CHECK();
+            // This always needs to be ascending
+            sortByKey(Seq, Val, elements, true);
+            sortByKey(cSeq, Key, elements, true);
 
             // No need of doing moddims here because the original Array<T>
             // dimensions have not been changed
             //val.modDims(inDims);
 
-            memFree(key);
-            memFree((char*)Xptr);
+            memFree(Seq);
+            memFree(cSeq);
+            memFree(cKey);
         }
 
         template<typename Tk, typename Tv>
         void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
         {
             int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 4)
+            // Batced sort performs 4x sort by keys
+            // But this is only useful before GPU is saturated
+            // The GPU is saturated at around 100,000 integers
+            // Call batched sort only if both conditions are met
+            if(higherDims > 4 && okey.dims[0] < 100000)
                 kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 4c54d84d60..575f676ca2 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/sort_pair.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <kernel/sort_helper.hpp>
 #include <program.hpp>
@@ -108,90 +107,6 @@ namespace opencl
     {
         static const int copyPairIter = 4;
 
-        template<typename Tk, typename Tv>
-        void makePair(cl::Buffer *out, const cl::Buffer *first, const cl::Buffer *second, const unsigned N)
-        {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sortPairProgs;
-                static std::map<int, Kernel*>  sortPairKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D Tk="      << dtype_traits<Tk>::getName()
-                            << " -D Tv="      << dtype_traits<Tv>::getName()
-                            << " -D copyPairIter=" << copyPairIter;
-                    if (std::is_same<Tk, double >::value ||
-                        std::is_same<Tk, cdouble>::value ||
-                        std::is_same<Tv, double >::value ||
-                        std::is_same<Tv, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
-                    sortPairProgs[device]   = new Program(prog);
-                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "make_pair_kernel");
-                });
-
-                auto makePairOp = KernelFunctor<Buffer, const Buffer, const Buffer, const unsigned>
-                                          (*sortPairKernels[device]);
-
-                NDRange local(256, 1, 1);
-                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
-
-                makePairOp(EnqueueArgs(getQueue(), global, local), *out, *first, *second, N);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
-
-        template<typename Tk, typename Tv>
-        void splitPair(cl::Buffer *first, cl::Buffer *second, const cl::Buffer *in, const unsigned N)
-        {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sortPairProgs;
-                static std::map<int, Kernel*>  sortPairKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D Tk="      << dtype_traits<Tk>::getName()
-                            << " -D Tv="      << dtype_traits<Tv>::getName()
-                            << " -D copyPairIter=" << copyPairIter;
-                    if (std::is_same<Tk, double >::value ||
-                        std::is_same<Tk, cdouble>::value ||
-                        std::is_same<Tv, double >::value ||
-                        std::is_same<Tv, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, sort_pair_cl, sort_pair_cl_len, options.str());
-                    sortPairProgs[device]   = new Program(prog);
-                    sortPairKernels[device] = new Kernel(*sortPairProgs[device], "split_pair_kernel");
-                });
-
-                auto splitPairOp = KernelFunctor<Buffer, Buffer, const Buffer, const unsigned>
-                                          (*sortPairKernels[device]);
-
-                NDRange local(256, 1, 1);
-                NDRange global(local[0] * divup(N, local[0] * copyPairIter), 1, 1);
-
-                splitPairOp(EnqueueArgs(getQueue(), global, local), *first, *second, *in, N);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
-        }
-
         template<typename Tk, typename Tv>
         void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending)
         {
@@ -252,9 +167,9 @@ namespace opencl
 
                 // Create/call iota
                 // Array<Tv> key = iota<Tv>(seqDims, tileDims);
-                cl::Buffer* key = bufferAlloc(inDims.elements() * sizeof(unsigned));
+                cl::Buffer* Seq = bufferAlloc(inDims.elements() * sizeof(unsigned));
                 Param pSeq;
-                pSeq.data = key;
+                pSeq.data = Seq;
                 pSeq.info.offset = 0;
                 pSeq.info.dims[0] = inDims[0];
                 pSeq.info.strides[0] = 1;
@@ -326,7 +241,7 @@ namespace opencl
                 ////val.modDims(inDims);
 
                 CL_DEBUG_FINISH(getQueue());
-                bufferFree(key);
+                bufferFree(Seq);
                 bufferFree(cSeq);
                 bufferFree(cKey);
             } catch (cl::Error err) {
@@ -339,8 +254,11 @@ namespace opencl
         void sort0ByKey(Param pKey, Param pVal, bool isAscending)
         {
             int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 5)
+            // Batced sort performs 4x sort by keys
+            // But this is only useful before GPU is saturated
+            // The GPU is saturated at around 1000,000 integers
+            // Call batched sort only if both conditions are met
+            if(higherDims > 4 && pKey.info.dims[0] < 1000000)
                 kernel::sortByKeyBatched<Tk, Tv>(pKey, pVal, 0, isAscending);
             else
                 kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
diff --git a/src/backend/opencl/kernel/sort_pair.cl b/src/backend/opencl/kernel/sort_pair.cl
deleted file mode 100644
index f5e5413d73..0000000000
--- a/src/backend/opencl/kernel/sort_pair.cl
+++ /dev/null
@@ -1,43 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-struct IndexPair
-{
-    Tk first;
-    Tv second;
-};
-
-typedef struct IndexPair IndexPair_t;
-
-__kernel
-void make_pair_kernel(__global IndexPair_t *out,
-                      __global const Tk *first, __global const Tv *second,
-                      const unsigned N)
-{
-    int tIdx = get_group_id(0) * get_local_size(0) * copyPairIter + get_local_id(0);
-    const int blockDimX = get_local_size(0);
-
-    for(int i = tIdx; i < N; i += blockDimX) {
-        out[i].first  = first[i];
-        out[i].second = second[i];
-    }
-}
-
-__kernel
-void split_pair_kernel( __global Tk *first, __global Tv *second,
-                       __global const IndexPair_t *out, const unsigned N)
-{
-    int tIdx = get_group_id(0) * get_local_size(0) * copyPairIter + get_local_id(0);
-    const int blockDimX = get_local_size(0);
-
-    for(int i = tIdx; i < N; i += blockDimX) {
-        first[i]  = out[i].first;
-        second[i] = out[i].second;
-    }
-}

From f76ce99670b652559674a4d9af730be4ab53d6ef Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 31 Jul 2016 01:12:12 -0400
Subject: [PATCH 0711/2677] BUILD: Instantiate thrust sort kernels only once

---
 src/backend/cuda/CMakeLists.txt               |   4 +-
 src/backend/cuda/kernel/sort.hpp              |  36 ++--
 src/backend/cuda/kernel/sort_by_key.hpp       | 101 ++++++++++-
 .../cuda/kernel/sort_by_key/CMakeLists.txt    |  28 ---
 src/backend/cuda/kernel/sort_by_key_impl.hpp  | 166 ------------------
 .../cuda/kernel/thrust_sort_by_key.hpp        |  13 ++
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |  28 +++
 .../thrust_sort_by_key_impl.cu.in}            |   2 +-
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |  51 ++++++
 9 files changed, 202 insertions(+), 227 deletions(-)
 delete mode 100644 src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
 delete mode 100644 src/backend/cuda/kernel/sort_by_key_impl.hpp
 create mode 100644 src/backend/cuda/kernel/thrust_sort_by_key.hpp
 create mode 100644 src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
 rename src/backend/cuda/kernel/{sort_by_key/sort_by_key_impl.cu.in => thrust_sort_by_key/thrust_sort_by_key_impl.cu.in} (93%)
 create mode 100644 src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4aa45c6970..f8c39b47c2 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -264,7 +264,7 @@ LIST(SORT cpp_sources)
 
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
-INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/CMakeLists.txt")
 
 INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/CMakeLists.txt")
 
@@ -414,7 +414,7 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${c_headers}
                 ${c_sources}
                 ${cpp_sources}
-                ${sort_by_key_sources}
+                ${thrust_sort_by_key_sources}
                 ${scan_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE})
 
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index d8ef559f21..45dc023206 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -13,8 +13,8 @@
 #include <kernel/iota.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <thrust/device_ptr.h>
 #include <thrust/sort.h>
+#include <kernel/thrust_sort_by_key.hpp>
 
 namespace cuda
 {
@@ -26,8 +26,6 @@ namespace cuda
         template<typename T>
         void sort0Iterative(Param<T> val, bool isAscending)
         {
-            thrust::device_ptr<T> val_ptr = thrust::device_pointer_cast(val.ptr);
-
             for(int w = 0; w < val.dims[3]; w++) {
                 int valW = w * val.strides[3];
                 for(int z = 0; z < val.dims[2]; z++) {
@@ -37,9 +35,14 @@ namespace cuda
                         int valOffset = valWZ + y * val.strides[1];
 
                         if(isAscending) {
-                            THRUST_SELECT(thrust::sort, val_ptr + valOffset, val_ptr + valOffset + val.dims[0]);
+                            THRUST_SELECT(thrust::sort,
+                                          val.ptr + valOffset,
+                                          val.ptr + valOffset + val.dims[0]);
                         } else {
-                            THRUST_SELECT(thrust::sort, val_ptr + valOffset, val_ptr + valOffset + val.dims[0], thrust::greater<T>());
+                            THRUST_SELECT(thrust::sort,
+                                          val.ptr + valOffset,
+                                          val.ptr + valOffset + val.dims[0],
+                                          thrust::greater<T>());
                         }
                     }
                 }
@@ -91,29 +94,10 @@ namespace cuda
 
             // Sort indices
             // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-            //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
-            thrust::device_ptr<T>    pVal_ptr = thrust::device_pointer_cast(pVal.ptr);
-            thrust::device_ptr<uint> pKey_ptr = thrust::device_pointer_cast(pKey.ptr);
-            if(isAscending) {
-                THRUST_SELECT(thrust::stable_sort_by_key,
-                              pVal_ptr,
-                              pVal_ptr + pVal.dims[0],
-                              pKey_ptr);
-            } else {
-                THRUST_SELECT(thrust::stable_sort_by_key,
-                              pVal_ptr,
-                              pVal_ptr + pVal.dims[0],
-                              pKey_ptr, thrust::greater<T>());
-            }
-            POST_LAUNCH_CHECK();
+            thrustSortByKey(pVal.ptr, pKey.ptr, pVal.dims[0], isAscending);
 
             // Needs to be ascending (true) in order to maintain the indices properly
-            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
-            THRUST_SELECT(thrust::stable_sort_by_key,
-                          pKey_ptr,
-                          pKey_ptr + pVal.dims[0],
-                          pVal_ptr);
-            POST_LAUNCH_CHECK();
+            thrustSortByKey(pKey.ptr, pVal.ptr, pVal.dims[0], true);
 
             // No need of doing moddims here because the original Array<T>
             // dimensions have not been changed
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index f05386825d..a84e41c53d 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -7,24 +7,117 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/dim4.hpp>
+#include <memory.hpp>
 #include <math.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <kernel/iota.hpp>
+#include <kernel/thrust_sort_by_key.hpp>
 
 namespace cuda
 {
     namespace kernel
     {
-        template<typename Tk, typename Tv>
-        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim, bool isAscending);
+        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
+        {
+            for(int w = 0; w < okey.dims[3]; w++) {
+                int okeyW = w * okey.strides[3];
+                int ovalW = w * oval.strides[3];
+                for(int z = 0; z < okey.dims[2]; z++) {
+                    int okeyWZ = okeyW + z * okey.strides[2];
+                    int ovalWZ = ovalW + z * oval.strides[2];
+                    for(int y = 0; y < okey.dims[1]; y++) {
+
+                        int okeyOffset = okeyWZ + y * okey.strides[1];
+                        int ovalOffset = ovalWZ + y * oval.strides[1];
+
+                        thrustSortByKey<Tk, Tv>(okey.ptr + okeyOffset,
+                                                oval.ptr + ovalOffset,
+                                                okey.dims[0],
+                                                isAscending);
+                    }
+                }
+            }
+            POST_LAUNCH_CHECK();
+        }
 
         template<typename Tk, typename Tv>
-        void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending);
+        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim, bool isAscending)
+        {
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pKey.dims[i];
+
+            const dim_t elements = inDims.elements();
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            uint* Seq = memAlloc<uint>(elements);
+            Param<uint> pSeq;
+            pSeq.ptr = Seq;
+            pSeq.strides[0] = 1;
+            pSeq.dims[0] = inDims[0];
+            for(int i = 1; i < 4; i++) {
+                pSeq.dims[i] = inDims[i];
+                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
+            }
+            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
+
+            Tk *Key = pKey.ptr;
+            Tk *cKey = memAlloc<Tk>(elements);
+            CUDA_CHECK(cudaMemcpyAsync(cKey, Key, elements * sizeof(Tk),
+                                       cudaMemcpyDeviceToDevice,
+                                       getStream(cuda::getActiveDeviceId())));
 
+            Tv *Val = pVal.ptr;
+            thrustSortByKey(Key, Val, elements, isAscending);
+            thrustSortByKey(cKey, Seq, elements, isAscending);
+
+            uint *cSeq = memAlloc<uint>(elements);
+            CUDA_CHECK(cudaMemcpyAsync(cSeq, Seq, elements * sizeof(uint),
+                                       cudaMemcpyDeviceToDevice,
+                                       getStream(cuda::getActiveDeviceId())));
+
+            // This always needs to be ascending
+            thrustSortByKey(Seq, Val, elements, true);
+            thrustSortByKey(cSeq, Key, elements, true);
+
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
+
+            memFree(Seq);
+            memFree(cSeq);
+            memFree(cKey);
+        }
+
+        template<typename Tk, typename Tv>
+        void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
+        {
+            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
+            // Batced sort performs 4x sort by keys
+            // But this is only useful before GPU is saturated
+            // The GPU is saturated at around 100,000 integers
+            // Call batched sort only if both conditions are met
+            if(higherDims > 4 && okey.dims[0] < 100000)
+                kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
+            else
+                kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
+        }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
deleted file mode 100644
index d4b2855edc..0000000000
--- a/src/backend/cuda/kernel/sort_by_key/CMakeLists.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in" FILESTRINGS)
-
-FOREACH(STR ${FILESTRINGS})
-    IF(${STR} MATCHES "// SBK_TYPES")
-        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
-    ELSEIF(${STR} MATCHES "// SBK_INSTS:")
-        STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
-    ENDIF()
-ENDFOREACH()
-
-FOREACH(SBK_TYPE ${SBK_TYPES})
-    FOREACH(SBK_INST ${SBK_INSTS})
-        CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cu.in"
-        "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
-        ADD_CUSTOM_COMMAND(
-          OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
-          COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
-          DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
-    ENDFOREACH(SBK_INST ${SBK_INSTS})
-ENDFOREACH(SBK_TYPE ${SBK_TYPES})
-
-FILE(GLOB sort_by_key_sources
-  "${CMAKE_CURRENT_BINARY_DIR}/sort_by_key/*.cu"
-)
-
-LIST(SORT sort_by_key_sources)
diff --git a/src/backend/cuda/kernel/sort_by_key_impl.hpp b/src/backend/cuda/kernel/sort_by_key_impl.hpp
deleted file mode 100644
index d7eb664cc2..0000000000
--- a/src/backend/cuda/kernel/sort_by_key_impl.hpp
+++ /dev/null
@@ -1,166 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/dim4.hpp>
-#include <memory.hpp>
-#include <math.hpp>
-#include <dispatch.hpp>
-#include <Param.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
-#include <kernel/sort_by_key.hpp>
-#include <kernel/iota.hpp>
-
-#include <thrust/device_ptr.h>
-#include <thrust/device_vector.h>
-#include <thrust/sort.h>
-
-namespace cuda
-{
-    namespace kernel
-    {
-        static const int copyPairIter = 4;
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename Tk, typename Tv>
-        void sortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending)
-        {
-            if (isAscending) {
-                THRUST_SELECT(thrust::stable_sort_by_key,
-                              keyPtr,
-                              keyPtr + elements,
-                              valPtr);
-            } else {
-                THRUST_SELECT(thrust::stable_sort_by_key,
-                              keyPtr,
-                              keyPtr + elements,
-                              valPtr, thrust::greater<Tk>());
-            }
-        }
-
-        template<typename Tk, typename Tv>
-        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
-        {
-            for(int w = 0; w < okey.dims[3]; w++) {
-                int okeyW = w * okey.strides[3];
-                int ovalW = w * oval.strides[3];
-                for(int z = 0; z < okey.dims[2]; z++) {
-                    int okeyWZ = okeyW + z * okey.strides[2];
-                    int ovalWZ = ovalW + z * oval.strides[2];
-                    for(int y = 0; y < okey.dims[1]; y++) {
-
-                        int okeyOffset = okeyWZ + y * okey.strides[1];
-                        int ovalOffset = ovalWZ + y * oval.strides[1];
-
-                        sortByKey<Tk, Tv>(okey.ptr + okeyOffset,
-                                          oval.ptr + ovalOffset,
-                                          okey.dims[0],
-                                          isAscending);
-                    }
-                }
-            }
-            POST_LAUNCH_CHECK();
-        }
-
-        template<typename Tk, typename Tv>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim, bool isAscending)
-        {
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pKey.dims[i];
-
-            const dim_t elements = inDims.elements();
-
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
-
-            // Create/call iota
-            // Array<uint> key = iota<uint>(seqDims, tileDims);
-            uint* Seq = memAlloc<uint>(elements);
-            Param<uint> pSeq;
-            pSeq.ptr = Seq;
-            pSeq.strides[0] = 1;
-            pSeq.dims[0] = inDims[0];
-            for(int i = 1; i < 4; i++) {
-                pSeq.dims[i] = inDims[i];
-                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
-            }
-            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
-
-            Tk *Key = pKey.ptr;
-            Tk *cKey = memAlloc<Tk>(elements);
-            CUDA_CHECK(cudaMemcpyAsync(cKey, Key, elements * sizeof(Tk),
-                                       cudaMemcpyDeviceToDevice,
-                                       getStream(cuda::getActiveDeviceId())));
-
-            Tv *Val = pVal.ptr;
-            sortByKey(Key, Val, elements, isAscending);
-            sortByKey(cKey, Seq, elements, isAscending);
-
-            uint *cSeq = memAlloc<uint>(elements);
-            CUDA_CHECK(cudaMemcpyAsync(cSeq, Seq, elements * sizeof(uint),
-                                       cudaMemcpyDeviceToDevice,
-                                       getStream(cuda::getActiveDeviceId())));
-
-            // This always needs to be ascending
-            sortByKey(Seq, Val, elements, true);
-            sortByKey(cSeq, Key, elements, true);
-
-            // No need of doing moddims here because the original Array<T>
-            // dimensions have not been changed
-            //val.modDims(inDims);
-
-            memFree(Seq);
-            memFree(cSeq);
-            memFree(cKey);
-        }
-
-        template<typename Tk, typename Tv>
-        void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
-        {
-            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
-            // Batced sort performs 4x sort by keys
-            // But this is only useful before GPU is saturated
-            // The GPU is saturated at around 100,000 integers
-            // Call batched sort only if both conditions are met
-            if(higherDims > 4 && okey.dims[0] < 100000)
-                kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
-            else
-                kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
-        }
-
-#define INSTANTIATE(Tk, Tv)                                                                 \
-      template void sort0ByKey<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, bool);               \
-      template void sort0ByKeyIterative<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, bool);      \
-      template void sortByKeyBatched<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, const int dim, bool);
-
-#define INSTANTIATE0(Tk    ) \
-    INSTANTIATE(Tk, float  ) \
-    INSTANTIATE(Tk, double ) \
-    INSTANTIATE(Tk, cfloat ) \
-    INSTANTIATE(Tk, cdouble) \
-    INSTANTIATE(Tk, char   ) \
-    INSTANTIATE(Tk, uchar  )
-
-#define INSTANTIATE1(Tk    ) \
-    INSTANTIATE(Tk, int    ) \
-    INSTANTIATE(Tk, uint   ) \
-    INSTANTIATE(Tk, short  ) \
-    INSTANTIATE(Tk, ushort ) \
-    INSTANTIATE(Tk, intl   ) \
-    INSTANTIATE(Tk, uintl  )
-
-    }
-}
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key.hpp b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
new file mode 100644
index 0000000000..0fcb013a34
--- /dev/null
+++ b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
@@ -0,0 +1,13 @@
+#pragma once
+#include <Param.hpp>
+namespace cuda
+{
+    namespace kernel
+    {
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename Tk, typename Tv>
+        void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending);
+    }
+}
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..d032a27e53
--- /dev/null
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -0,0 +1,28 @@
+FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in" FILESTRINGS)
+
+FOREACH(STR ${FILESTRINGS})
+    IF(${STR} MATCHES "// SBK_TYPES")
+        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+    ELSEIF(${STR} MATCHES "// SBK_INSTS:")
+        STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
+        STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
+    ENDIF()
+ENDFOREACH()
+
+FOREACH(SBK_TYPE ${SBK_TYPES})
+    FOREACH(SBK_INST ${SBK_INSTS})
+        CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in"
+        "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
+        ADD_CUSTOM_COMMAND(
+          OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
+          COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
+          DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp")
+    ENDFOREACH(SBK_INST ${SBK_INSTS})
+ENDFOREACH(SBK_TYPE ${SBK_TYPES})
+
+FILE(GLOB thrust_sort_by_key_sources
+  "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/*.cu"
+)
+
+LIST(SORT thrust_sort_by_key_sources)
diff --git a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in
similarity index 93%
rename from src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
rename to src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in
index 7402aab5c7..fd5b27463f 100644
--- a/src/backend/cuda/kernel/sort_by_key/sort_by_key_impl.cu.in
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/sort_by_key_impl.hpp>
+#include <kernel/thrust_sort_by_key_impl.hpp>
 
 // This file instantiates sort_by_key as separate object files from CMake
 // The 3 lines below are read by CMake to determenine the instantiations
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
new file mode 100644
index 0000000000..7d5cf7beba
--- /dev/null
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -0,0 +1,51 @@
+#include <debug_cuda.hpp>
+#include <kernel/thrust_sort_by_key.hpp>
+#include <thrust/sort.h>
+
+namespace cuda
+{
+    namespace kernel
+    {
+        ///////////////////////////////////////////////////////////////////////////
+        // Wrapper functions
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename Tk, typename Tv>
+        void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending)
+        {
+            if (isAscending) {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              keyPtr,
+                              keyPtr + elements,
+                              valPtr);
+            } else {
+                THRUST_SELECT(thrust::stable_sort_by_key,
+                              keyPtr,
+                              keyPtr + elements,
+                              valPtr, thrust::greater<Tk>());
+            }
+            POST_LAUNCH_CHECK();
+        }
+
+#define INSTANTIATE(Tk, Tv)                                             \
+        template void thrustSortByKey<Tk, Tv>(Tk *keyPtr, Tv *valPtr,   \
+                                              int elements,             \
+                                              bool isAscending);        \
+
+#define INSTANTIATE0(Tk    ) \
+    INSTANTIATE(Tk, float  ) \
+    INSTANTIATE(Tk, double ) \
+    INSTANTIATE(Tk, cfloat ) \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, char   ) \
+    INSTANTIATE(Tk, uchar  )
+
+#define INSTANTIATE1(Tk    ) \
+    INSTANTIATE(Tk, int    ) \
+    INSTANTIATE(Tk, uint   ) \
+    INSTANTIATE(Tk, short  ) \
+    INSTANTIATE(Tk, ushort ) \
+    INSTANTIATE(Tk, intl   ) \
+    INSTANTIATE(Tk, uintl  )
+
+    }
+}

From ff0f2f957529c2693bd8f7ee0f3add523c523c49 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 31 Jul 2016 03:01:55 -0400
Subject: [PATCH 0712/2677] Removing warnings when building cpu backend

---
 src/backend/cpu/kernel/moments.hpp | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index 6466986442..9f30f95c23 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -25,21 +25,18 @@ void moments(Array<float> &output, Array<T> const &input, af_moment_type moment)
     T const * const in       = input.get();
     af::dim4  const idims    = input.dims();
     af::dim4  const istrides = input.strides();
-    dim_t     const iElems   = input.elements();
-
-    af::dim4  const odims    = output.dims();
     af::dim4  const ostrides = output.strides();
 
     float *out = output.get();
 
-    dim_t mId = 0;
     for(dim_t w = 0; w < idims[3]; w++) {
         for(dim_t z = 0; z < idims[2]; z++) {
             dim_t out_off = w * ostrides[3] + z * ostrides[2];
             for(dim_t y = 0; y < idims[1]; y++) {
+                dim_t in_off = y * istrides[1] + z * istrides[2] + w * istrides[1];
                 for(dim_t x = 0; x < idims[0]; x++) {
                     dim_t m_off=0;
-                    float val = in[mId];
+                    float val = in[in_off + x];
                     if((moment & AF_MOMENT_M00) > 0) {
                         out[out_off + m_off] += val;
                         m_off++;
@@ -56,7 +53,6 @@ void moments(Array<float> &output, Array<T> const &input, af_moment_type moment)
                         out[out_off + m_off] += x * y * val;
                         m_off++;
                     }
-                    mId++;
                 }
             }
         }

From 59f365f7005732a2999f8a2700b9d57a53ee009c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 31 Jul 2016 03:02:40 -0400
Subject: [PATCH 0713/2677] CPU: Reducing compilation times of Scan by key

---
 src/backend/cpu/kernel/scan_by_key.hpp | 14 +++++---
 src/backend/cpu/scan_by_key.cpp        | 46 ++++++++++----------------
 2 files changed, 28 insertions(+), 32 deletions(-)

diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index 80da543529..8e5c332567 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -15,9 +15,12 @@ namespace cpu
 namespace kernel
 {
 
-template<af_op_t op, typename Ti, typename Tk, typename To, int D, bool inclusive_scan>
+template<af_op_t op, typename Ti, typename Tk, typename To, int D>
 struct scan_dim_by_key
 {
+    bool inclusive_scan;
+    scan_dim_by_key(bool inclusiveSanKey) : inclusive_scan(inclusiveSanKey) {}
+
     void operator()(Array<To> out, dim_t outOffset,
                     const Array<Tk> key, dim_t keyOffset,
                     const Array<Ti> in, dim_t inOffset,
@@ -30,7 +33,7 @@ struct scan_dim_by_key
 
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
-            scan_dim_by_key<op, Ti, Tk, To, D1, inclusive_scan> func;
+            scan_dim_by_key<op, Ti, Tk, To, D1> func(inclusive_scan);
             getQueue().enqueue(func,
                     out, outOffset + i * ostrides[D1],
                     key, keyOffset + i * kstrides[D1],
@@ -40,9 +43,12 @@ struct scan_dim_by_key
     }
 };
 
-template<af_op_t op, typename Ti, typename Tk, typename To, bool inclusive_scan>
-struct scan_dim_by_key<op, Ti, Tk, To, 0, inclusive_scan>
+template<af_op_t op, typename Ti, typename Tk, typename To>
+struct scan_dim_by_key<op, Ti, Tk, To, 0>
 {
+    bool inclusive_scan;
+    scan_dim_by_key(bool inclusiveSanKey) : inclusive_scan(inclusiveSanKey) {}
+
     void operator()(Array<To> output, dim_t outOffset,
                     const Array<Tk> keyinput, dim_t keyOffset,
                     const Array<Ti> input, dim_t inOffset,
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index c90a2b94ab..d00db6cceb 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -20,40 +20,30 @@ using af::dim4;
 
 namespace cpu
 {
-    template<af_op_t op, typename Ti, typename Tk, typename To, bool inclusive_scan>
-    void scan_by_key(int ndims, Array<To>& out, const Array<Tk>& key, const Array<Ti>& in, const int dim)
-    {
-        switch (ndims) {
-            case 1:
-                kernel::scan_dim_by_key<op, Ti, Tk, To, 1, inclusive_scan> func1;
-                getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
-                break;
-            case 2:
-                kernel::scan_dim_by_key<op, Ti, Tk, To, 2, inclusive_scan> func2;
-                getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
-                break;
-            case 3:
-                kernel::scan_dim_by_key<op, Ti, Tk, To, 3, inclusive_scan> func3;
-                getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
-                break;
-            case 4:
-                kernel::scan_dim_by_key<op, Ti, Tk, To, 4, inclusive_scan> func4;
-                getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
-                break;
-        }
-    }
-
     template<af_op_t op, typename Ti, typename Tk, typename To>
     Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
     {
         dim4 dims     = in.dims();
         Array<To> out = createEmptyArray<To>(dims);
-        in.eval();
+        kernel::scan_dim_by_key<op, Ti, Tk, To, 1> func1(inclusive_scan);
+        kernel::scan_dim_by_key<op, Ti, Tk, To, 2> func2(inclusive_scan);
+        kernel::scan_dim_by_key<op, Ti, Tk, To, 3> func3(inclusive_scan);
+        kernel::scan_dim_by_key<op, Ti, Tk, To, 4> func4(inclusive_scan);
 
-        if (inclusive_scan) {
-            scan_by_key<op, Ti, Tk, To,  true>(in.ndims(), out, key, in, dim);
-        } else {
-            scan_by_key<op, Ti, Tk, To, false>(in.ndims(), out, key, in, dim);
+        in.eval();
+        switch (in.ndims()) {
+        case 1:
+            getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
+            break;
+        case 2:
+            getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
+            break;
+        case 3:
+            getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
+            break;
+        case 4:
+            getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
+            break;
         }
 
         return out;

From b722ff2412ed6a5aecdcfd99ff7e94f4e5bc6089 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 31 Jul 2016 03:43:01 -0400
Subject: [PATCH 0714/2677] Directives to remove warnings from OpenCL backend

---
 src/backend/opencl/platform.hpp | 2 ++
 src/backend/opencl/types.hpp    | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 157d304425..6473e12027 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -18,8 +18,10 @@
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
+#pragma GCC diagnostic pop
 
 #include <vector>
 #include <string>
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index f0ed13382c..44b7921473 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -8,11 +8,14 @@
  ********************************************************/
 
 #pragma once
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 #if __APPLE__
 #include <OpenCL/cl.h>
 #else
 #include <CL/cl.h>
 #endif
+#pragma GCC diagnostic pop
 
 namespace opencl
 {

From a2f61f05ef17082cdde8dbcecdc9c51a5821cc1c Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 1 Jul 2016 13:02:23 -0400
Subject: [PATCH 0715/2677] fixes compiler warnings

---
 src/backend/cpu/moments.cpp    | 2 +-
 src/backend/cuda/moments.cu    | 2 +-
 src/backend/opencl/moments.cpp | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index 74f5a85171..63549bc084 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -20,7 +20,7 @@ namespace cpu
 static inline int bitCount(int v) {
     v = v - ((v >> 1) & 0x55555555);
     v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
-    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+    return (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
 }
 
 using af::dim4;
diff --git a/src/backend/cuda/moments.cu b/src/backend/cuda/moments.cu
index 46d5e1f6dc..2314b1cc97 100644
--- a/src/backend/cuda/moments.cu
+++ b/src/backend/cuda/moments.cu
@@ -18,7 +18,7 @@ namespace cuda
 static inline int bitCount(int v) {
     v = v - ((v >> 1) & 0x55555555);
     v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
-    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+    return (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
 }
 
 using af::dim4;
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index b38db4cdcc..950c36de87 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -18,7 +18,7 @@ namespace opencl
 static inline int bitCount(int v) {
     v = v - ((v >> 1) & 0x55555555);
     v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
-    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+    return (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
 }
 
 template<typename T>

From ed83cb49de8046002f76176732ad0b9843a78528 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 31 Jul 2016 04:01:22 -0400
Subject: [PATCH 0716/2677] Removing warnings from examples

---
 examples/graphics/gravity_sim.cpp | 11 +++++------
 examples/pde/swe.cpp              |  3 ---
 2 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 6acccf8813..0fb2e39075 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -27,7 +27,7 @@ float mass_range = 0;
 float min_mass   = 0;
 
 void initial_conditions_rand(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
-    for(int i=0; i<pos.size(); ++i) {
+    for(int i=0; i< (int)pos.size(); ++i) {
         pos[i]    = af::randn(total_particles) * width + width;
         vels[i]   = 0 * af::randu(total_particles) - 0.5;
         forces[i] = af::constant(0, total_particles);
@@ -39,7 +39,7 @@ void initial_conditions_galaxy(af::array &mass, vector<af::array> &pos, vector<a
     af::array initial_cond_consts(af::dim4(7, total_particles), hbd);
     initial_cond_consts = initial_cond_consts.T();
 
-    for(int i=0; i<pos.size(); ++i) {
+    for(int i=0; i< (int)pos.size(); ++i) {
         pos[i]    = af::randn(total_particles) * width + width;
         vels[i]   = 0 * (af::randu(total_particles) - 0.5);
         forces[i] = af::constant(0, total_particles);
@@ -101,7 +101,7 @@ af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz, af::
 
 
 void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces, float dt) {
-    for(int i=0; i<pos.size(); ++i) {
+    for(int i=0; i< (int)pos.size(); ++i) {
         pos[i] += vels[i] * dt;
         pos[i].eval();
     }
@@ -110,7 +110,7 @@ void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels,
     vector<af::array> diff(pos.size());
     af::array dist = af::constant(0, pos[0].dims(0),pos[0].dims(0));
 
-    for(int i=0; i<pos.size(); ++i) {
+    for(int i=0; i< (int)pos.size(); ++i) {
         diff[i] = tile(pos[i], 1, pos[i].dims(0)) - transpose(tile(pos[i], 1, pos[i].dims(0)));
         dist += (diff[i]*diff[i]);
     }
@@ -119,7 +119,7 @@ void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels,
     dist = af::max(min_dist, dist);
     dist *= dist * dist;
 
-    for(int i=0; i<pos.size(); ++i) {
+    for(int i=0; i< (int)pos.size(); ++i) {
         //calculate force vectors
         forces[i] = diff[i] / dist;
         forces[i].eval();
@@ -240,4 +240,3 @@ int main(int argc, char *argv[])
 
     return 0;
 }
-
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index db89ff99bd..bcb545a3e0 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -4,7 +4,6 @@
 #include <stdlib.h>
 #include <math.h>
 #include <arrayfire.h>
-#include "../common/progress.h"
 
 using namespace af;
 
@@ -19,7 +18,6 @@ array normalize(array a, float max)
 
 static void swe(bool console)
 {
-    double time_total = 40; // run for N seconds
     // Grid length, number and spacing
     const unsigned Lx = 1600, nx = Lx + 1;
     const unsigned Ly = 1600, ny = Ly + 1;
@@ -55,7 +53,6 @@ static void swe(bool console)
         win->grid(2, 2);
     }
 
-    timer t = timer::start();
     unsigned iter = 0;
     unsigned random_interval = 30;
 

From 84e6fbca4ee711ed8d79efcfd91dfa8b0fc1b4b1 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 16 Jun 2016 23:52:36 -0400
Subject: [PATCH 0717/2677] Remove manual definition of CMAKE_SOURCE_DIR from
 cl2hpp

---
 CMakeModules/build_cl2hpp.cmake | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index bd83432521..34ce9ef80e 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -10,7 +10,6 @@ ExternalProject_Add(
     INSTALL_DIR "${prefix}/package"
     UPDATE_COMMAND ""
     CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
-    -DCMAKE_SOURCE_DIR:PATH=<SOURCE_DIR>
     -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
     -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
     -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}

From 60cc9c7673258f7142cfa9a92893365ddc16e768 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 28 Jun 2016 14:00:02 -0400
Subject: [PATCH 0718/2677] Add noImageIO Tests to transform test

---
 test/transform.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/test/transform.cpp b/test/transform.cpp
index 7a8e8d6742..80504d76e7 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -47,6 +47,7 @@ template<typename T>
 void transformTest(string pTestFile, string pHomographyFile, const af_interp_type method, const bool invert)
 {
     if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
 
     vector<af::dim4> inNumDims;
     vector<string>   inFiles;
@@ -213,6 +214,8 @@ TYPED_TEST(TransformInt, PerspectiveLowerInvert)
 //
 TEST(Transform, CPP)
 {
+    if (noImageIOTests()) return;
+
     vector<af::dim4>   inDims;
     vector<string> inFiles;
     vector<dim_t>  goldDim;

From d34d52ae8420a9236cbdc557390afa6597f387e8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 28 Jul 2016 17:33:36 -0400
Subject: [PATCH 0719/2677] BUILD: Fixes to MKL builds for FFT and LAPACK libs

---
 CMakeModules/FindFFTW.cmake    | 10 ++++++++--
 CMakeModules/FindLAPACKE.cmake | 16 ++++++++++------
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index 225058992c..b8f8fa6039 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -59,20 +59,26 @@ ELSE()
     SET(CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_SHARED_LIBRARY_SUFFIX})
 ENDIF()
 
+IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+  SET(MKL_LIB_DIR_SUFFIX "intel64")
+ELSE()
+  SET(MKL_LIB_DIR_SUFFIX "ia32")
+ENDIF()
+
 IF(FFTW_ROOT)
     #find libs
     FIND_LIBRARY(
         FFTW_LIB
         NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "lib" "lib64" "lib/intel64"
+        PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
         NO_DEFAULT_PATH
         )
     FIND_LIBRARY(
         FFTWF_LIB
         NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_rt"
         PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "lib" "lib64" "lib/intel64"
+        PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
         NO_DEFAULT_PATH
         )
 
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 2ebd8ddbc5..b75797269f 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -66,13 +66,19 @@ IF(PC_LAPACKE_FOUND)
 
 ELSE(PC_LAPACKE_FOUND)
 
+    IF ("${SIZE_OF_VOIDP}" EQUAL 8)
+        SET(MKL_LIB_DIR_SUFFIX "intel64")
+    ELSE()
+        SET(MKL_LIB_DIR_SUFFIX "ia32")
+    ENDIF()
+
     IF(LAPACKE_ROOT_DIR)
         #find libs
         FIND_LIBRARY(
             LAPACKE_LIB
             NAMES "lapacke" "LAPACKE" "liblapacke" "mkl_rt"
             PATHS ${LAPACKE_ROOT_DIR}
-            PATH_SUFFIXES "lib" "lib64" "lib/ia32" "lib/intel64"
+            PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
             DOC "LAPACKE Library"
             NO_DEFAULT_PATH
             )
@@ -80,7 +86,7 @@ ELSE(PC_LAPACKE_FOUND)
             LAPACK_LIB
             NAMES "lapack" "LAPACK" "liblapack" "mkl_rt"
             PATHS ${LAPACKE_ROOT_DIR}
-            PATH_SUFFIXES "lib" "lib64" "lib/ia32" "lib/intel64"
+            PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
             DOC "LAPACK Library"
             NO_DEFAULT_PATH
             )
@@ -99,8 +105,7 @@ ELSE(PC_LAPACKE_FOUND)
             PATHS
             ${PC_LAPACKE_LIBRARY_DIRS}
             ${LIB_INSTALL_DIR}
-            /opt/intel/mkl/lib/ia32
-            /opt/intel/mkl/lib/intel64
+            /opt/intel/mkl/lib/${MKL_LIB_DIR_SUFFIX}
             /usr/lib64
             /usr/lib
             /usr/local/lib64
@@ -115,8 +120,7 @@ ELSE(PC_LAPACKE_FOUND)
             PATHS
             ${PC_LAPACKE_LIBRARY_DIRS}
             ${LIB_INSTALL_DIR}
-            /opt/intel/mkl/lib/ia32
-            /opt/intel/mkl/lib/intel64
+            /opt/intel/mkl/lib/${MKL_LIB_DIR_SUFFIX}
             /usr/lib64
             /usr/lib
             /usr/local/lib64

From fa68743005b1045fde0945e1761ba4852b8e34e1 Mon Sep 17 00:00:00 2001
From: Andreas Schuh <andreas.schuh.84@gmail.com>
Date: Thu, 14 Jul 2016 16:30:48 +0100
Subject: [PATCH 0720/2677] FIX: find_backend(Unified) in build tree
 ArrayFireConfig.cmake file

---
 CMakeLists.txt                        | 2 ++
 CMakeModules/ArrayFireConfig.cmake.in | 6 +++++-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index dd03c72433..6b9336a712 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -220,6 +220,7 @@ ENDIF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
 ## configuration to be used from the binary directory directly
 SET(INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 SET(BACKEND_DIR "src/backend/\${lowerbackend}")
+SET(UNIFIED_DIR "src/api/unified")
 CONFIGURE_FILE(
     ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfig.cmake
@@ -230,6 +231,7 @@ CONFIGURE_FILE(
 STRING(REGEX REPLACE "[^/]+" ".." reldir "${AF_INSTALL_CMAKE_DIR}")
 SET(INCLUDE_DIR "\${CMAKE_CURRENT_LIST_DIR}/${reldir}/include")
 set(BACKEND_DIR)
+set(UNIFIED_DIR)
 CONFIGURE_FILE(
     ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index c34b5a22c4..d95a39ca20 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -51,7 +51,11 @@
 get_filename_component(ArrayFire_INCLUDE_DIRS "@INCLUDE_DIR@" ABSOLUTE)
 
 macro(find_backend backend libname)
-  set(targetFile ${CMAKE_CURRENT_LIST_DIR}/@BACKEND_DIR@/ArrayFire${backend}.cmake)
+  if (${backend} STREQUAL "Unified")
+    set(targetFile ${CMAKE_CURRENT_LIST_DIR}/@UNIFIED_DIR@/ArrayFire${backend}.cmake)
+  else ()
+    set(targetFile ${CMAKE_CURRENT_LIST_DIR}/@BACKEND_DIR@/ArrayFire${backend}.cmake)
+  endif ()
   if(EXISTS ${targetFile})
     include(${targetFile})
     set(ArrayFire_${backend}_FOUND ON)

From 81ebbe4a5d3f8487956c7a4691970a0325a69c3d Mon Sep 17 00:00:00 2001
From: Miha Lunar <mlunar@gmail.com>
Date: Sun, 31 Jul 2016 21:59:41 +0200
Subject: [PATCH 0721/2677] Documentation typo fixes

---
 docs/details/data.dox | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/details/data.dox b/docs/details/data.dox
index 73580eca4d..453612f19b 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -134,7 +134,7 @@ array b = iota(dim4(5, 3), dim4(1, 2))
 =======================================================================
 
 \defgroup data_func_diag diag
-\brief Extract diagonal from a matrix when \p extract is set to true. Create a diagonal marix from input array when \p extract is set to false
+\brief Extract diagonal from a matrix when \p extract is set to true. Create a diagonal matrix from input array when \p extract is set to false
 
 \code
 // Extraction
@@ -287,7 +287,7 @@ Simply returns the array as a vector. This is a noop.
 
 \defgroup manip_func_flip flip
 
-\brief Flip the input along sepcified dimension
+\brief Flip the input along specified dimension
 
 Mirrors the array along the specified dimensions.
 
@@ -298,7 +298,7 @@ Mirrors the array along the specified dimensions.
 
 \defgroup data_func_lower lower
 
-\brief Create a lower triangular marix from input array
+\brief Create a lower triangular matrix from input array
 
 \ingroup data_mat
 \ingroup arrayfire_func
@@ -307,7 +307,7 @@ Mirrors the array along the specified dimensions.
 
 \defgroup data_func_upper upper
 
-\brief Create a upper triangular marix from input array
+\brief Create a upper triangular matrix from input array
 
 \ingroup data_mat
 \ingroup arrayfire_func

From a7e4c3510441a97a4e4df45dcde388258bb08b51 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 1 Aug 2016 10:56:24 -0400
Subject: [PATCH 0722/2677] BUGFIX: Create sparse handle using original
 dimensions

---
 src/backend/cpu/sparse_blas.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 2fe84846d9..389af54d4f 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -194,7 +194,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     sparse_operation_t lOpts = toSparseTranspose(optLhs);
 
     int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
-    int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    //int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
     //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
     static const int rColDim = 1;
@@ -203,7 +203,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     dim4 rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
-    int K = lDims[lColDim];
+    //int K = lDims[lColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
@@ -223,7 +223,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
         int *pE = rowIdx.get() + 1;
 
         sparse_matrix_t csrLhs;
-        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, M, K,
+        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, left.dims()[0], left.dims()[1],
                              pB, pE, colIdx.get(),
                              reinterpret_cast<ptr_type<T>>(values.get()));
 

From dfba8776d695fe2a0dea8c2079b1ce82aa12a97f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 1 Aug 2016 11:00:58 -0400
Subject: [PATCH 0723/2677] TESTS: Improvements to sparse tests

* Generation of complex data
* Split transpose tests
* Add vector tests
---
 test/sparse.cpp | 89 ++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 65 insertions(+), 24 deletions(-)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index badfb28290..1f09a18412 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -44,9 +44,7 @@ af::array makeSparse<cfloat>(af::array A, int factor)
     r = floor(r * 1000);
     r = r * ((r % factor) == 0) / 1000;
 
-    af::array i = real(A);
-    i = floor(i * 1000);
-    i = i * ((i % factor) == 0) / 1000;
+    af::array i = r / 2;
 
     A = af::complex(r, i);
     return A;
@@ -59,9 +57,7 @@ af::array makeSparse<cdouble>(af::array A, int factor)
     r = floor(r * 1000);
     r = r * ((r % factor) == 0) / 1000;
 
-    af::array i = real(A);
-    i = floor(i * 1000);
-    i = i * ((i % factor) == 0) / 1000;
+    af::array i = r / 2;
 
     A = af::complex(r, i);
     return A;
@@ -77,7 +73,6 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
 #if 1
     af::array A = cpu_randu<T>(af::dim4(m, n));
     af::array B = cpu_randu<T>(af::dim4(n, k));
-    af::array C = cpu_randu<T>(af::dim4(m, k));
 #else
     af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
     af::array B = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
@@ -87,21 +82,47 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
 
     // Result of GEMM
     af::array dRes1 = matmul(A, B);
-    af::array dRes2 = matmul(A, C, AF_MAT_TRANS, AF_MAT_NONE);
-    af::array dRes3 = matmul(A, C, AF_MAT_CTRANS, AF_MAT_NONE);
 
     // Create Sparse Array From Dense
     af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
 
     // Sparse Matmul
     af::array sRes1 = matmul(sA, B);
-    af::array sRes2 = matmul(sA, C, AF_MAT_TRANS, AF_MAT_NONE);
-    af::array sRes3 = matmul(sA, C, AF_MAT_CTRANS, AF_MAT_NONE);
 
     // Verify Results
     ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes1 - sRes1))) / (m * k), eps);
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes1 - sRes1))) / (m * k), eps);
+}
+
+template<typename T>
+void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
 
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(m, k));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(m, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    // Result of GEMM
+    af::array dRes2 = matmul(A, B, AF_MAT_TRANS, AF_MAT_NONE);
+    af::array dRes3 = matmul(A, B, AF_MAT_CTRANS, AF_MAT_NONE);
+
+    // Create Sparse Array From Dense
+    af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
+
+    // Sparse Matmul
+    af::array sRes2 = matmul(sA, B, AF_MAT_TRANS, AF_MAT_NONE);
+    af::array sRes3 = matmul(sA, B, AF_MAT_CTRANS, AF_MAT_NONE);
+
+    // Verify Results
     ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes2 - sRes2))) / (n * k), eps);
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes2 - sRes2))) / (n * k), eps);
 
@@ -109,20 +130,40 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes3 - sRes3))) / (n * k), eps);
 }
 
+#define SPARSE_TESTS(T, eps)                                    \
+    TEST(SPARSE, T##Square)                                     \
+    {                                                           \
+        sparseTester<T>(1000, 1000, 100, 5, eps);               \
+    }                                                           \
+    TEST(SPARSE, T##RectMultiple)                               \
+    {                                                           \
+        sparseTester<T>(2048, 1024, 512, 3, eps);               \
+    }                                                           \
+    TEST(SPARSE, T##RectDense)                                  \
+    {                                                           \
+        sparseTester<T>(500, 1000, 250, 1, eps);                \
+    }                                                           \
+    TEST(SPARSE, T##MatVec)                                     \
+    {                                                           \
+        sparseTester<T>(625, 1331, 1, 2, eps);                  \
+    }                                                           \
+    TEST(SPARSE_TRANSPOSE, T##Square)                           \
+    {                                                           \
+        sparseTransposeTester<T>(1000, 1000, 100, 5, eps);      \
+    }                                                           \
+    TEST(SPARSE_TRANSPOSE, T##RectMultiple)                     \
+    {                                                           \
+        sparseTransposeTester<T>(2048, 1024, 512, 3, eps);      \
+    }                                                           \
+    TEST(SPARSE_TRANSPOSE, T##RectDense)                        \
+    {                                                           \
+        sparseTransposeTester<T>(453, 751, 397, 1, eps);        \
+    }                                                           \
+    TEST(SPARSE_TRANSPOSE, T##MatVec)                           \
+    {                                                           \
+        sparseTransposeTester<T>(625, 1331, 1, 2, eps);         \
+    }                                                           \
 
-#define SPARSE_TESTS(T, eps)                            \
-    TEST(SPARSE, T##Square)                             \
-    {                                                   \
-        sparseTester<T>(1000, 1000, 100, 5, eps);       \
-    }                                                   \
-    TEST(SPARSE, T##RectMultiple)                       \
-    {                                                   \
-        sparseTester<T>(2048, 1024, 512, 3, eps);       \
-    }                                                   \
-    TEST(SPARSE, T##RectDense)                          \
-    {                                                   \
-        sparseTester<T>(500, 1000, 250, 1, eps);        \
-    }                                                   \
 
 SPARSE_TESTS(float, 0.01)
 SPARSE_TESTS(double, 1E-5)

From 89c2f8fd7cc6caab9d3f428577f3bb8aa44a8803 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 1 Aug 2016 14:25:30 -0400
Subject: [PATCH 0724/2677] BUGFIX: Use dimensions of A in CUDA sparse blas

---
 src/backend/cuda/sparse_blas.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 629bc4ca65..a8394abdcb 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -129,14 +129,14 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     cusparseOperation_t lOpts = toCusparseTranspose(optLhs);
 
     int lRowDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
-    int lColDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    //int lColDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
     static const int rColDim = 1; //Unsupported : (rOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
-    int K = lDims[lColDim];
+    //int K = lDims[lColDim];
 
     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
     T alpha = scalar<T>(1);
@@ -151,11 +151,15 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     CUSPARSE_CHECK(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
 
     // Call Matrix-Vector or Matrix-Matrix
+    // Note:
+    // Do not use M, N, K here. Use lDims and rDims instead.
+    // This is because the function wants row/col of A
+    // and not OP(A) (gemm wants row/col of OP(A)).
     if(rDims[rColDim] == 1) {
         CUSPARSE_CHECK(csrmv_func<T>()(
                        getHandle(),
                        lOpts,
-                       M, K, lhs.getNNZ(),
+                       lDims[0], lDims[1], lhs.getNNZ(),
                        &alpha,
                        descr, lhs.getValues().get(),
                        lhs.getRowIdx().get(), lhs.getColIdx().get(),
@@ -166,7 +170,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
         CUSPARSE_CHECK(csrmm_func<T>()(
                        getHandle(),
                        lOpts,
-                       M, N, K, lhs.getNNZ(),
+                       lDims[0], rDims[rColDim], lDims[1], lhs.getNNZ(),
                        &alpha,
                        descr, lhs.getValues().get(),
                        lhs.getRowIdx().get(), lhs.getColIdx().get(),

From 01082033575fc3650272b6c925d6ee61aae9100a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 3 Aug 2016 14:08:18 -0400
Subject: [PATCH 0725/2677] BUGFIX: Fix to moments in CPU backend

---
 src/backend/cpu/kernel/moments.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index 9f30f95c23..bf1302a64f 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -33,7 +33,7 @@ void moments(Array<float> &output, Array<T> const &input, af_moment_type moment)
         for(dim_t z = 0; z < idims[2]; z++) {
             dim_t out_off = w * ostrides[3] + z * ostrides[2];
             for(dim_t y = 0; y < idims[1]; y++) {
-                dim_t in_off = y * istrides[1] + z * istrides[2] + w * istrides[1];
+                dim_t in_off = y * istrides[1] + z * istrides[2] + w * istrides[3];
                 for(dim_t x = 0; x < idims[0]; x++) {
                     dim_t m_off=0;
                     float val = in[in_off + x];

From 1e3decc6622019876575bd8bda56b7238343ea52 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 3 Aug 2016 05:06:15 -0400
Subject: [PATCH 0726/2677] Add clSPARSE external build

---
 CMakeModules/build_clSPARSE.cmake | 43 +++++++++++++++++++++++++++++++
 src/backend/opencl/CMakeLists.txt | 11 ++++++++
 2 files changed, 54 insertions(+)
 create mode 100644 CMakeModules/build_clSPARSE.cmake

diff --git a/CMakeModules/build_clSPARSE.cmake b/CMakeModules/build_clSPARSE.cmake
new file mode 100644
index 0000000000..781ef10571
--- /dev/null
+++ b/CMakeModules/build_clSPARSE.cmake
@@ -0,0 +1,43 @@
+INCLUDE(ExternalProject)
+
+SET(prefix ${CMAKE_BINARY_DIR}/third_party/clSPARSE)
+SET(clSPARSE_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clSPARSE${CMAKE_STATIC_LIBRARY_SUFFIX})
+IF(CMAKE_VERSION VERSION_LESS 3.2)
+    IF(CMAKE_GENERATOR MATCHES "Ninja")
+        MESSAGE(WARNING "Building clSPARSE with Ninja has known issues with CMake older than 3.2")
+    endif()
+    SET(byproducts)
+ELSE()
+    SET(byproducts BYPRODUCTS ${clSPARSE_location})
+ENDIF()
+
+# Builds the src directory of clSPARSE and not the wrapper top level directory
+ExternalProject_Add(
+    clSPARSE-ext
+    GIT_REPOSITORY https://github.com/arrayfire/clSPARSE.git
+    GIT_TAG arrayfire-release-test
+    PREFIX "${prefix}"
+    INSTALL_DIR "${prefix}"
+    UPDATE_COMMAND ""
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>/src
+    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+    "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+    "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
+    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+    -DclSPARSE_LIBRARY_TYPE:STRING=STATIC
+    -DLIBRARY_DEBUG_POSTFIX:STRING=
+    -DSUFFIX_LIB:STRING=
+    -DBUILD_BENCHMARKS:BOOL=OFF
+    -DBUILD_TESTS:BOOL=OFF
+    ${byproducts}
+    )
+
+ExternalProject_Get_Property(clSPARSE-ext prefix)
+ADD_LIBRARY(clSPARSE IMPORTED STATIC)
+SET_TARGET_PROPERTIES(clSPARSE PROPERTIES IMPORTED_LOCATION ${clSPARSE_location})
+ADD_DEPENDENCIES(clSPARSE clSPARSE-ext)
+SET(CLSPARSE_INCLUDE_DIRS ${prefix}/include)
+SET(CLSPARSE_LIBRARIES clSPARSE)
+SET(CLSPARSE_FOUND ON)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index d0e4794831..98512fd57e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -81,6 +81,15 @@ ENDIF()
 INCLUDE_DIRECTORIES(${CLFFT_INCLUDE_DIRS})
 LINK_DIRECTORIES(${CLFFT_LIBRARY_DIR})
 
+OPTION(USE_SYSTEM_CLSPARSE "Use system clSPARSE" OFF)
+IF(USE_SYSTEM_CLSPARSE)
+    FIND_PACKAGE(clSPARSE REQUIRED)
+ELSE()
+  INCLUDE("${CMAKE_MODULE_PATH}/build_clSPARSE.cmake")
+ENDIF()
+INCLUDE_DIRECTORIES(${CLSPARSE_INCLUDE_DIRS})
+LINK_DIRECTORIES(${CLSPARSE_LIBRARY_DIR})
+
 ADD_DEFINITIONS( -DBOOST_ALL_NO_LIB )
 SET(Boost_USE_STATIC_LIBS OFF)
 FIND_PACKAGE(Boost 1.48 REQUIRED)
@@ -103,6 +112,7 @@ INCLUDE_DIRECTORIES(
     "${CMAKE_CURRENT_BINARY_DIR}"
     ${CLBLAS_INCLUDE_DIRS}
     ${CLFFT_INCLUDE_DIRS}
+    ${CLSPARSE_INCLUDE_DIRS}
     ${Boost_INCLUDE_DIR}
     ${BoostCompute_INCLUDE_DIRS}
     ${CBLAS_INCLUDE_DIR}
@@ -322,6 +332,7 @@ TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${OpenCL_LIBRARIES}
                                 PRIVATE ${CLBLAS_LIBRARIES}
                                 PRIVATE ${CLFFT_LIBRARIES}
+                                PRIVATE ${CLSPARSE_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
                      )
 

From b93bc6ab4e53642ed9fe8cd80853fd2ce5e1b3b6 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Mon, 1 Aug 2016 10:38:10 -0400
Subject: [PATCH 0727/2677] Explain ArrayFire and espouse benefits.

---
 README.md | 42 +++++++++++++++++++++++++++++++-----------
 1 file changed, 31 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 5b4a0548fa..067ae9923d 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,29 @@
 <a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="300"></a>
 
-ArrayFire is a high performance software library for parallel computing with an
-easy-to-use API. Its **array** based function set makes parallel programming
-simple.
-
-ArrayFire's multiple backends (**CUDA**, **OpenCL** and native **CPU**) make it
-platform independent and highly portable. ArrayFire provides visualization
-capabilities using our OpenGL-based,
-[high performance visualization library](https://github.com/arrayfire/forge).
-
-A few lines of code in ArrayFire can replace dozens of lines of parallel
-computing code, saving you valuable time and lowering development costs.
+ArrayFire is a general-purpose library that simplifies the process of developing 
+software that targets parallel and massively-parallel architectures including 
+CPUs, GPUs, and other hardware acceleration devices.
+To achieve this goal, ArrayFire provides software developers with a high-level 
+abstraction of data which resides on the accelerator, the `af::array` object 
+(or C-style struct).
+Developers write code which performs operations on ArrayFire arrays which, in turn,
+are automatically translated into near-optimal kernels that execute on the computational
+device. 
+ArrayFire is successfully used on devices ranging from low-power mobile phones to 
+high-power GPU-enabled supercomputers including CPUs from all major vendors (Intel, AMD, Arm), 
+GPUs from the dominant manufacturers (NVIDIA, AMD, and Qualcomm), as well as a variety 
+of other accelerator devices on Windows, Mac, and Linux.
+
+Several of ArrayFire's benefits include:
+
+* [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable, 
+  [well-documented](http://arrayfire.org/docs) API.
+* Rigorously Tested for Performance and Accuracy 
+* Commercially Friendly Open-Source Licensing
+* Commercial support from [ArrayFire](http://arrayfire.com)
+* [Read about more benefits on Arrayfire.com](http://arrayfire.com/the-arrayfire-library/)
+ 
+### Build and Test Status
 
 |         | Linux x86_64 | Linux armv7l | Linux aarch64 | Windows | OSX |
 |:-------:|:------------:|:------------:|:-------------:|:-------:|:---:|
@@ -126,3 +139,10 @@ details.
 
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
 * ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
+
+### Trademark Policy
+
+The literal mark “ArrayFire” and ArrayFire logos are trademarks of 
+AccelerEyes LLC DBA ArrayFire.
+If you wish to use either of these marks in your own project, please consult
+[ArrayFire's Trademark Policy](http://arrayfire.com/trademark-policy/)

From 03b60c5b100c3599ab457f0d854194df1b27da18 Mon Sep 17 00:00:00 2001
From: Brian Kloppenborg <brian@kloppenborg.net>
Date: Wed, 3 Aug 2016 14:12:10 -0400
Subject: [PATCH 0728/2677] Update README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 067ae9923d..07bf01a47c 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@
 ArrayFire is a general-purpose library that simplifies the process of developing 
 software that targets parallel and massively-parallel architectures including 
 CPUs, GPUs, and other hardware acceleration devices.
+
 To achieve this goal, ArrayFire provides software developers with a high-level 
 abstraction of data which resides on the accelerator, the `af::array` object 
 (or C-style struct).

From d001252430011c07059a27339285f941990ebdb5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 3 Aug 2016 18:22:56 -0400
Subject: [PATCH 0729/2677] handle empty arrays in a variety of functions allow
 indexing with empty arrays

---
 src/api/c/assign.cpp      |  11 +-
 src/api/c/binary.cpp      |   5 +
 src/api/c/blas.cpp        |   3 +
 src/api/c/cast.cpp        |   7 +
 src/api/c/cholesky.cpp    |   9 ++
 src/api/c/convolve.cpp    |   4 +
 src/api/c/data.cpp        |  34 ++++-
 src/api/c/det.cpp         |   5 +
 src/api/c/diff.cpp        |   9 ++
 src/api/c/fft.cpp         |  13 ++
 src/api/c/histogram.cpp   |   4 +
 src/api/c/hsv_rgb.cpp     |   5 +
 src/api/c/index.cpp       |  11 +-
 src/api/c/inverse.cpp     |   4 +
 src/api/c/join.cpp        |   8 ++
 src/api/c/lu.cpp          |  13 ++
 src/api/c/median.cpp      |   5 +
 src/api/c/moddims.cpp     |   3 +
 src/api/c/qr.cpp          |  13 ++
 src/api/c/rank.cpp        |   4 +
 src/api/c/reduce.cpp      |   6 +-
 src/api/c/reorder.cpp     |   4 +
 src/api/c/replace.cpp     |   4 +
 src/api/c/rgb_gray.cpp    |   5 +-
 src/api/c/rotate.cpp      |   3 +
 src/api/c/select.cpp      |   4 +
 src/api/c/set.cpp         |  22 ++-
 src/api/c/shift.cpp       |   3 +
 src/api/c/solve.cpp       |  10 ++
 src/api/c/sort.cpp        |  17 ++-
 src/api/c/svd.cpp         |  18 ++-
 src/api/c/tile.cpp        |   3 +
 src/api/cpp/histogram.cpp |   3 +
 test/empty.cpp            | 284 ++++++++++++++++++++++++++++++++++++++
 34 files changed, 542 insertions(+), 14 deletions(-)
 create mode 100644 test/empty.cpp

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 8ff37630e8..a09fa65c9b 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -206,7 +206,6 @@ af_err af_assign_gen(af_array *out,
     spanner.isSeq = true;
 
     try {
-        ARG_ASSERT(2, (ndims>0));
         ARG_ASSERT(3, (indexs!=NULL));
 
         int track = 0;
@@ -233,6 +232,15 @@ af_err af_assign_gen(af_array *out,
         af_dtype lhsType= lInfo.getType();
         af_dtype rhsType= rInfo.getType();
 
+        if(rhsDims.ndims() == 0) {
+            return af_retain_array(out, lhs);
+        }
+
+        if(lhsDims.ndims() == 0) {
+            dim_t my_dims[] = { 0, 0, 0, 0 };
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, lhsType);
+        }
+
         ARG_ASSERT(2, (ndims == 1) || (ndims == (dim_t)lInfo.ndims()));
 
         if (ndims == 1 && ndims != (dim_t)lInfo.ndims()) {
@@ -246,7 +254,6 @@ af_err af_assign_gen(af_array *out,
         }
 
         ARG_ASSERT(1, (lhsType==rhsType));
-        ARG_ASSERT(3, (rhsDims.ndims()>0));
         ARG_ASSERT(1, (lhsDims.ndims()>=rhsDims.ndims()));
         ARG_ASSERT(2, (lhsDims.ndims()>=ndims));
 
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 95a133557f..2858eb9260 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -332,6 +332,11 @@ static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs,
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        if(odims.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         af_array res;
         switch (type) {
         case s32: res = bitOp<int    , op>(lhs, rhs, odims); break;
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 21fb44fe4a..e7d74ffffa 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -105,6 +105,9 @@ af_err af_dot(      af_array *out,
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
 
+        if(lhsInfo.ndims() == 0) {
+            return af_retain_array(out, lhs);
+        }
         if (lhsInfo.ndims() > 1 ||
             rhsInfo.ndims() > 1) {
             AF_ERROR("dot can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 872ace27c5..343310d462 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -48,6 +48,13 @@ static af_array cast(const af_array in, const af_dtype type)
 af_err af_cast(af_array *out, const af_array in, const af_dtype type)
 {
     try {
+        const ArrayInfo info = getInfo(in);
+        dim4 idims = info.dims();
+        if(idims.elements() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         af_array res = cast(in, type);
         std::swap(*out, res);
     }
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index d738568e06..bd1a38e8ae 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -45,6 +45,11 @@ af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_up
         ARG_ASSERT(2, i_info.isFloating());                  // Only floating and complex types
         DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
 
+        if(i_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         af_array output;
         switch(type) {
             case f32: output = cholesky<float  >(info, in, is_upper);  break;
@@ -74,6 +79,10 @@ af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper)
         ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
         DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
 
+        if(i_info.ndims() == 0) {
+            return AF_SUCCESS;
+        }
+
         int out;
 
         switch(type) {
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index ef3786a457..a6858ddfc1 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -72,6 +72,10 @@ af_err convolve(af_array *out, const af_array signal, const af_array filter)
         dim4 sdims = sInfo.dims();
         dim4 fdims = fInfo.dims();
 
+        if(fdims.ndims() == 0 || sdims.ndims() ==  0) {
+            return af_retain_array(out, signal);
+        }
+
         AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
 
         ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED && convBT != AF_BATCH_DIFF));
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 295fa83cc7..b9e2364fa9 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -28,7 +28,6 @@
 
 using af::dim4;
 using namespace detail;
-using namespace std;
 
 dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
 {
@@ -54,7 +53,14 @@ af_err af_constant(af_array *result, const double value,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
+
 
         switch(type) {
         case f32:   out = createHandleFromValue<float  >(d, value); break;
@@ -229,6 +235,11 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
         af_array result;
         AF_CHECK(af_init());
 
+        if(ndims == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         dim4 d = verifyDims(ndims, dims);
 
         switch(type) {
@@ -301,6 +312,11 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
         af_array out;
         AF_CHECK(af_init());
 
+        if(ndims == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+        }
+
         DIM_ASSERT(1, ndims > 0 && ndims <= 4);
         DIM_ASSERT(3, t_ndims > 0 && t_ndims <= 4);
 
@@ -345,6 +361,12 @@ af_err af_diag_create(af_array *out, const af_array in, const int num)
         af_dtype type = in_info.getType();
 
         af_array result;
+
+        if(in_info.dims()[0] == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         switch(type) {
         case f32:   result = diagCreate<float  >(in, num);    break;
         case c32:   result = diagCreate<cfloat >(in, num);    break;
@@ -372,9 +394,15 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num)
 
     try {
         ArrayInfo in_info = getInfo(in);
-        DIM_ASSERT(1, in_info.ndims() >= 2);
         af_dtype type = in_info.getType();
 
+        if(in_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
+        DIM_ASSERT(1, in_info.ndims() >= 2);
+
         af_array result;
         switch(type) {
         case f32:   result = diagExtract<float  >(in, num);    break;
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index bdad4b665e..7ef2e5296e 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -29,6 +29,11 @@ T det(const af_array a)
 
     const int num = A.dims()[0];
 
+    if(num == 0) {
+        T res = scalar<T>(1.0);
+        return res;
+    }
+
     std::vector<T> hD(num);
     std::vector<int> hP(num);
 
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index 8bc4d07da5..7f341f5d72 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -40,6 +40,11 @@ af_err af_diff1(af_array *out, const af_array in, const int dim)
         af_dtype type = info.getType();
 
         af::dim4 in_dims = info.dims();
+        if(in_dims[dim] < 2) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         DIM_ASSERT(1, in_dims[dim] >= 2);
 
         af_array output;
@@ -77,6 +82,10 @@ af_err af_diff2(af_array *out, const af_array in, const int dim)
         af_dtype type = info.getType();
 
         af::dim4 in_dims = info.dims();
+        if(in_dims[dim] < 3) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
         DIM_ASSERT(1, in_dims[dim] >= 3);
 
         af_array output;
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 3f092379b4..55df61001a 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -33,6 +33,10 @@ static af_err fft(af_array *out, const af_array in, const double norm_factor, co
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
+        if(dims.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
+
         DIM_ASSERT(1, (dims.ndims()>=rank));
 
         af_array output;
@@ -104,6 +108,9 @@ static af_err fft_inplace(af_array in, const double norm_factor)
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
+        if(dims.ndims() == 0) {
+            return AF_SUCCESS;
+        }
         DIM_ASSERT(1, (dims.ndims()>=rank));
 
         switch(type) {
@@ -163,6 +170,9 @@ static af_err fft_r2c(af_array *out, const af_array in, const double norm_factor
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
+        if(dims.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
         DIM_ASSERT(1, (dims.ndims()>=rank));
 
         af_array output;
@@ -215,6 +225,9 @@ static af_err fft_c2r(af_array *out, const af_array in, const double norm_factor
         af_dtype type  = info.getType();
         af::dim4 idims  = info.dims();
 
+        if(idims.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
         DIM_ASSERT(1, (idims.ndims()>=rank));
 
         dim4 odims = idims;
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index cd6dee8e30..827c5b1d3e 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -35,6 +35,10 @@ af_err af_histogram(af_array *out, const af_array in,
         ArrayInfo info = getInfo(in);
         af_dtype type  = info.getType();
 
+        if(info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
+
         af_array output;
         switch(type) {
             case f32: output = histogram<float , uint>(in, nbins, minval, maxval, info.isLinear()); break;
diff --git a/src/api/c/hsv_rgb.cpp b/src/api/c/hsv_rgb.cpp
index 9188e03e60..585e6776c4 100644
--- a/src/api/c/hsv_rgb.cpp
+++ b/src/api/c/hsv_rgb.cpp
@@ -38,6 +38,11 @@ af_err convert(af_array* out, const af_array& in)
         af_dtype iType = info.getType();
         af::dim4 inputDims = info.dims();
 
+        if(info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, iType);
+        }
+
         ARG_ASSERT(1, (inputDims.ndims() >= 3));
 
         af_array output = 0;
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 2df3575400..e4c7059996 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -107,6 +107,10 @@ af_err af_lookup(af_array *out, const af_array in, const af_array indices, const
 
         ArrayInfo idxInfo= getInfo(indices);
 
+        if(idxInfo.ndims() == 0) {
+            return af_retain_array(out, indices);
+        }
+
         ARG_ASSERT(2, idxInfo.isVector() || idxInfo.isScalar());
 
         af_dtype idxType = idxInfo.getType();
@@ -204,10 +208,13 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
         }
 
         dim4 iDims = iInfo.dims();
+        af_dtype inType = getInfo(in).getType();
 
-        ARG_ASSERT(1, (iDims.ndims()>0));
+        if(iDims.ndims() <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, inType);
+        }
 
-        af_dtype inType = getInfo(in).getType();
         switch(inType) {
             case c64: output = genIndex<cdouble>(in, idxrs); break;
             case f64: output = genIndex<double >(in, idxrs); break;
diff --git a/src/api/c/inverse.cpp b/src/api/c/inverse.cpp
index 04603db26e..e3dd68d0b4 100644
--- a/src/api/c/inverse.cpp
+++ b/src/api/c/inverse.cpp
@@ -45,6 +45,10 @@ af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options)
 
         af_array output;
 
+        if(i_info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
+
         switch(type) {
             case f32: output = inverse<float  >(in);  break;
             case f64: output = inverse<double >(in);  break;
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 2a2b93dd36..8de18df12a 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -46,6 +46,14 @@ af_err af_join(af_array *out, const int dim, const af_array first, const af_arra
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
         ARG_ASSERT(2, finfo.getType() == sinfo.getType());
+        if(sinfo.elements() == 0) {
+            return af_retain_array(out, first);
+        }
+
+        if(finfo.elements() == 0) {
+            return af_retain_array(out, second);
+        }
+
         DIM_ASSERT(2, sinfo.elements() > 0);
         DIM_ASSERT(3, finfo.elements() > 0);
 
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index 1d98e02490..59989bd932 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -53,6 +53,14 @@ af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array i
 
         ARG_ASSERT(3, i_info.isFloating());                       // Only floating and complex types
 
+        if(i_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            AF_CHECK(af_create_handle(lower, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(upper, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(pivot, AF_MAX_DIMS, my_dims, type));
+            return AF_SUCCESS;
+        }
+
         switch(type) {
             case f32: lu<float  >(lower, upper, pivot, in);  break;
             case f64: lu<double >(lower, upper, pivot, in);  break;
@@ -79,6 +87,11 @@ af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv)
 
         ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
 
+        if(i_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(pivot, AF_MAX_DIMS, my_dims, type);
+        }
+
         af_array out;
 
         switch(type) {
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index a8268f5ff4..b714ab1ddf 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -28,6 +28,7 @@ static double median(const af_array& in)
 {
     dim_t nElems = getInfo(in).elements();
     dim4 dims(nElems, 1, 1, 1);
+    ARG_ASSERT(0, nElems > 0);
 
     af_array temp = 0;
     AF_CHECK(af_moddims(&temp, in, 1, dims.get()));
@@ -154,6 +155,8 @@ af_err af_median_all(double *realVal, double *imagVal, const af_array in)
     try {
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
+
+        ARG_ASSERT(2, info.ndims() > 0);
         switch(type) {
             case f64: *realVal = median<double>(in); break;
             case f32: *realVal = median<float >(in); break;
@@ -176,6 +179,8 @@ af_err af_median(af_array* out, const af_array in, const dim_t dim)
 
         af_array output = 0;
         ArrayInfo info = getInfo(in);
+
+        ARG_ASSERT(1, info.ndims() > 0);
         af_dtype type = info.getType();
         switch(type) {
             case f64: output = median<double>(in, dim); break;
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 1d326c0846..3fd4edb154 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -53,6 +53,9 @@ af_err af_moddims(af_array *out, const af_array in,
                   const unsigned ndims, const dim_t * const dims)
 {
     try {
+        if(ndims == 0) {
+            return af_retain_array(out, in);
+        }
         ARG_ASSERT(2, ndims >= 1);
         ARG_ASSERT(3, dims != NULL);
 
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index 95eb53adab..cd3f142455 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -50,6 +50,14 @@ af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
 
         af_dtype type = i_info.getType();
 
+        if(i_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            AF_CHECK(af_create_handle(q,   AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(r,   AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(tau, AF_MAX_DIMS, my_dims, type));
+            return AF_SUCCESS;
+        }
+
         ARG_ASSERT(3, i_info.isFloating());                       // Only floating and complex types
 
         switch(type) {
@@ -78,6 +86,11 @@ af_err af_qr_inplace(af_array *tau, af_array in)
 
         ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
 
+        if(i_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(tau, AF_MAX_DIMS, my_dims, type);
+        }
+
         af_array out;
 
         switch(type) {
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 197d2c8974..208b967bc5 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -60,6 +60,10 @@ af_err af_rank(uint *out, const af_array in, const double tol)
         ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
 
         uint output;
+        if(i_info.ndims() == 0) {
+            output = 0;
+            return AF_SUCCESS;
+        }
 
         switch(type) {
             case f32: output = rank<float  >(in, tol);  break;
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 3fe30be9c0..26dc66de66 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -81,8 +81,7 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim)
         const ArrayInfo in_info = getInfo(in);
 
         if (dim >= (int)in_info.ndims()) {
-            *out = retain(in);
-            return AF_SUCCESS;
+            return af_retain_array(out, in);
         }
 
         af_dtype type = in_info.getType();
@@ -246,6 +245,7 @@ static af_err reduce_all_common(double *real_val, double *imag_val, const af_arr
         const ArrayInfo in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
+        ARG_ASSERT(2, in_info.ndims() > 0);
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
         if (!imag_val) *imag_val = 0;
@@ -399,6 +399,7 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in, co
         ARG_ASSERT(2, dim <  4);
 
         const ArrayInfo in_info = getInfo(in);
+        ARG_ASSERT(2, in_info.ndims() > 0);
 
         if (dim >= (int)in_info.ndims()) {
             *val = retain(in);
@@ -457,6 +458,7 @@ static af_err ireduce_all_common(double *real_val, double *imag_val,
         const ArrayInfo in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
+        ARG_ASSERT(3, in_info.ndims() > 0);
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
         if (!imag_val) *imag_val = 0;
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 10d2cc31d1..23adad9834 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -30,6 +30,10 @@ af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims)
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
 
+        if(info.elements() == 0) {
+            return af_retain_array(out, in);
+        }
+
         DIM_ASSERT(1, info.elements() > 0);
 
         // Check that dimensions are not repeated
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 7c0a3cf863..0464e306cc 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -35,6 +35,10 @@ af_err af_replace(af_array a, const af_array cond, const af_array b)
         ArrayInfo binfo = getInfo(b);
         ArrayInfo cinfo = getInfo(cond);
 
+        if(cinfo.ndims() == 0) {
+            return AF_SUCCESS;
+        }
+
         ARG_ASSERT(2, ainfo.getType() == binfo.getType());
         ARG_ASSERT(1, cinfo.getType() == b8);
 
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 85cf938b33..7e127440a3 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -110,7 +110,10 @@ af_err convert(af_array* out, const af_array in, const float r, const float g, c
         af::dim4 inputDims = info.dims();
 
         // 2D is not required.
-        ARG_ASSERT(1, info.elements() > 0);
+        if(info.elements() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, iType);
+        }
 
         // If RGB is input, then assert 3 channels
         // else 1 channel
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index a5978e3e61..cf5f7699a1 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -49,6 +49,9 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
                       method == AF_INTERP_BILINEAR ||
                       method == AF_INTERP_LOWER);
 
+        if(idims.elements() == 0) {
+            return af_retain_array(out, in);
+        }
         DIM_ASSERT(1, idims.elements() > 0);
 
         af::dim4 odims(odims0, odims1, idims[2], idims[3]);
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 42eb91b806..162de25fdd 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -36,6 +36,10 @@ af_err af_select(af_array *out, const af_array cond, const af_array a, const af_
         ArrayInfo binfo = getInfo(b);
         ArrayInfo cinfo = getInfo(cond);
 
+        if(cinfo.ndims() == 0) {
+            return af_retain_array(out, cond);
+        }
+
         ARG_ASSERT(2, ainfo.getType() == binfo.getType());
         ARG_ASSERT(1, cinfo.getType() == b8);
 
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index db9b5782e5..097b608945 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -29,6 +29,9 @@ af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
     try {
 
         ArrayInfo in_info = getInfo(in);
+        if(in_info.isEmpty()) {
+            return af_retain_array(out, in);
+        }
         ARG_ASSERT(1, in_info.isVector());
         af_dtype type = in_info.getType();
 
@@ -67,6 +70,15 @@ af_err af_set_union(af_array *out, const af_array first, const af_array second,
         ArrayInfo first_info = getInfo(first);
         ArrayInfo second_info = getInfo(second);
 
+        af_array res;
+        if(first_info.isEmpty()) {
+            return af_retain_array(out, second);
+        }
+
+        if(second_info.isEmpty()) {
+            return af_retain_array(out, first);
+        }
+
         ARG_ASSERT(1, first_info.isVector());
         ARG_ASSERT(1, second_info.isVector());
 
@@ -75,7 +87,6 @@ af_err af_set_union(af_array *out, const af_array first, const af_array second,
 
         ARG_ASSERT(1, first_type == second_type);
 
-        af_array res;
         switch(first_type) {
         case f32: res = setUnion<float  >(first, second, is_unique); break;
         case f64: res = setUnion<double >(first, second, is_unique); break;
@@ -109,6 +120,15 @@ af_err af_set_intersect(af_array *out, const af_array first, const af_array seco
         ArrayInfo first_info = getInfo(first);
         ArrayInfo second_info = getInfo(second);
 
+        //TODO: fix for set intersect from union
+        if(first_info.isEmpty()) {
+            return af_retain_array(out, first);
+        }
+
+        if(second_info.isEmpty()) {
+            return af_retain_array(out, second);
+        }
+
         ARG_ASSERT(1, first_info.isVector());
         ARG_ASSERT(1, second_info.isVector());
 
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index e383915e0a..e027ba5d44 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -29,6 +29,9 @@ af_err af_shift(af_array *out, const af_array in, const int sdims[4])
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
 
+        if(info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
         DIM_ASSERT(1, info.elements() > 0);
 
         af_array output;
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index 857ee18688..7035495af0 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -51,6 +51,11 @@ af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_
         DIM_ASSERT(1, bdims[2] == adims[2]);
         DIM_ASSERT(1, bdims[3] == adims[3]);
 
+        if(a_info.ndims() == 0 || b_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, a_type);
+        }
+
         bool is_triangle_solve = (options & AF_MAT_LOWER) || (options & AF_MAT_UPPER);
 
         if (options != AF_MAT_NONE && !is_triangle_solve) {
@@ -117,6 +122,11 @@ af_err af_solve_lu(af_array *out, const af_array a,
         DIM_ASSERT(1, bdims[2] == adims[2]);
         DIM_ASSERT(1, bdims[3] == adims[3]);
 
+        if(a_info.ndims() == 0 || b_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, a_type);
+        }
+
         if (options != AF_MAT_NONE) {
             AF_ERROR("Using this property is not yet supported in solveLU", AF_ERR_NOT_SUPPORTED);
         }
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index dd58175936..32404df1b4 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -41,6 +41,9 @@ af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
 
+        if(info.elements() == 0) {
+            return af_retain_array(out, in);
+        }
         DIM_ASSERT(1, info.elements() > 0);
 
         af_array val;
@@ -90,7 +93,12 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
 
-        DIM_ASSERT(2, info.elements() > 0);
+        if(info.elements() <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            AF_CHECK(af_create_handle(out,     AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(indices, AF_MAX_DIMS, my_dims, type));
+            return AF_SUCCESS;
+        }
 
         af_array val;
         af_array idx;
@@ -172,8 +180,13 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
 
         ArrayInfo vinfo = getInfo(values);
 
-        DIM_ASSERT(3, kinfo.elements() > 0);
         DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
+        if(kinfo.elements() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            AF_CHECK(af_create_handle(out_keys,   AF_MAX_DIMS, my_dims, ktype));
+            AF_CHECK(af_create_handle(out_values, AF_MAX_DIMS, my_dims, ktype));
+            return AF_SUCCESS;
+        }
 
         TYPE_ASSERT(kinfo.isReal());
 
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index 244579aefe..ad1e0265b3 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -73,6 +73,14 @@ af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
         af_dtype type = info.getType();
 
+        if(dims.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            AF_CHECK(af_create_handle(u, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(s, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(vt, AF_MAX_DIMS, my_dims, type));
+            return AF_SUCCESS;
+        }
+
         switch (type) {
         case f64:
             svd<double>(s, u, vt, in);
@@ -102,9 +110,17 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
 
         DIM_ASSERT(3, dims[0] <= dims[1]);
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
-
         af_dtype type = info.getType();
 
+        if(dims.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            AF_CHECK(af_create_handle(u, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(s, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(vt, AF_MAX_DIMS, my_dims, type));
+            return AF_SUCCESS;
+        }
+
+
         switch (type) {
         case f64:
             svdInPlace<double>(s, u, vt, in);
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index f722f89892..9b8e8b24b1 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -55,6 +55,9 @@ af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims)
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
 
+        if(info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
         DIM_ASSERT(1, info.dims().elements() > 0);
         DIM_ASSERT(2, tileDims.elements() > 0);
 
diff --git a/src/api/cpp/histogram.cpp b/src/api/cpp/histogram.cpp
index 0bd7d90bf7..17bd7f0e08 100644
--- a/src/api/cpp/histogram.cpp
+++ b/src/api/cpp/histogram.cpp
@@ -26,6 +26,9 @@ array histogram(const array &in, const unsigned nbins, const double minval, cons
 array histogram(const array &in, const unsigned nbins)
 {
     af_array out = 0;
+    if(in.numdims() == 0) {
+        return in;
+    }
     AF_THROW(af_histogram(&out, in.get(), nbins, min<double>(in), max<double>(in)));
     return array(out);
 }
diff --git a/test/empty.cpp b/test/empty.cpp
new file mode 100644
index 0000000000..a4f41cdcd8
--- /dev/null
+++ b/test/empty.cpp
@@ -0,0 +1,284 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <cstdio>
+#include <iostream>
+#include <cstdlib>
+
+using namespace af;
+using namespace std;
+
+template<typename T>
+class Array : public ::testing::Test
+{
+
+};
+
+TEST(Array, TestEmptyAssignment) {
+    array A = randu(5, f32);
+    array C = constant(0,0);
+    array B = A(isNaN(A));
+    A(isNaN(A)) = C;
+    ASSERT_EQ(B.numdims(), 0);
+    ASSERT_EQ(A.numdims(), 1);
+    ASSERT_EQ(lookup(constant(1,9), constant(0,0)).numdims(), 0);
+}
+
+TEST(Array, TestEmptySigProc) {
+    ASSERT_EQ(convolve(constant(1,1), constant(0,0)).numdims(), 1);
+    ASSERT_EQ(convolve(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(convolve2(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(convolve3(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(iir(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(approx1(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(approx1(constant(0,0), seq(0,10)).numdims(), 0);
+    ASSERT_EQ(approx2(constant(0,0), constant(0,0),  constant(0,0)).numdims(), 0);
+    ASSERT_EQ(approx2(constant(0,0), seq(0,10), seq(0,10)).numdims(), 0);
+}
+
+TEST(Array, TestEmptySet) {
+    ASSERT_EQ(setIntersect(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(setUnique(constant(0,0)).numdims(), 0);
+    array A = randu(5, f32);
+    array B = constant(0, 0);
+    ASSERT_EQ(setUnion(A, B).elements(), 5);
+    ASSERT_EQ(setUnion(B, A).elements(), 5);
+}
+
+TEST(Array, TestEmptyOperators) {
+    ASSERT_EQ((constant(0,0) +  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) && constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) -  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) &  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) |  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) ^  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) << constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) >> constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) /  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) == constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) <= constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) >= constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) >  constant(0,0)).numdims(), 0);
+    ASSERT_EQ((constant(0,0) <  constant(0,0)).numdims(), 0);
+    ASSERT_EQ(-constant(0,0).numdims(), 0 );
+    ASSERT_EQ((!constant(0,0)).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) != constant(0,0)).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) += 1).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) -= 1).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) *= 1).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) /= 1).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) || constant(0,0)).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) %  constant(0,0)).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) *  constant(0,0)).numdims(), 0 );
+}
+
+TEST(Array, TestEmptyFFT) {
+    array arr = constant(0,0);
+    fftInPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+    fft2InPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+    fft3InPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+    ifftInPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+    ifft2InPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+    ifft3InPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+
+    ASSERT_EQ((fft(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fftNorm(constant(0,0), 0.5)).numdims(), 0);
+    ASSERT_EQ((fft2(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fft2Norm(constant(0,0), 0.5)).numdims(), 0);
+    ASSERT_EQ((fft3(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fft3Norm(constant(0,0), 0.5)).numdims(), 0);
+    ASSERT_EQ((fftC2R<1>(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fftR2C<1>(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fftC2R<2>(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fftR2C<2>(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fftC2R<3>(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((fftR2C<3>(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((ifft(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((ifftNorm(constant(0,0), 0.5)).numdims(), 0);
+    ASSERT_EQ((ifft2(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((ifft2Norm(constant(0,0), 0.5)).numdims(), 0);
+    ASSERT_EQ((ifft3(constant(0,0))).numdims(), 0);
+    ASSERT_EQ((ifft3Norm(constant(0,0), 0.5)).numdims(), 0);
+}
+
+TEST(Array, TestEmptyDiff) {
+    ASSERT_EQ(diff1(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(diff1(constant(1,1)).numdims(), 0);
+    ASSERT_EQ(diff1(constant(1,2)).numdims(), 1);
+    ASSERT_EQ(diff2(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(diff2(constant(1,1)).numdims(), 0);
+    ASSERT_EQ(diff2(constant(1,2)).numdims(), 0);
+    ASSERT_EQ(diff2(constant(1,3)).numdims(), 1);
+}
+
+TEST(Array, TestEmptyLinAlg) {
+    ASSERT_EQ( det<float>(constant(0,0)), 1);
+    ASSERT_EQ( det<cfloat>(constant(0,0)).real, 1);
+    ASSERT_EQ( det<cdouble>(constant(0,0)).real, 1);
+    ASSERT_EQ( norm(constant(0,0)), 0);
+    ASSERT_EQ( rank(constant(0,0)), 0);
+    array tau_qr, arr = constant(0,0);
+    qrInPlace(tau_qr, arr);
+    ASSERT_EQ(tau_qr.numdims(), 0);
+
+
+    array out_qr;
+    qr(out_qr, tau_qr, constant(0,0));
+    ASSERT_EQ(out_qr.numdims(), 0);
+    ASSERT_EQ(tau_qr.numdims(), 0);
+    ASSERT_EQ(solve(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(solveLU(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0);
+
+    array out_lu, piv_lu;
+    lu(out_lu, piv_lu, constant(0,0));
+    ASSERT_EQ(out_lu.numdims(), 0);
+    ASSERT_EQ(piv_lu.numdims(), 0);
+
+    array low_lu, up_lu;
+    lu(low_lu, up_lu, piv_lu, constant(0,0));
+    ASSERT_EQ(low_lu.numdims(), 0);
+    ASSERT_EQ(up_lu.numdims(), 0);
+    ASSERT_EQ(piv_lu.numdims(), 0);
+
+    luInPlace(piv_lu, arr, true);
+    ASSERT_EQ(piv_lu.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0);
+
+    array u, s, v;
+    svd(u,s,v, constant(0,0));
+    svdInPlace(u,s,v, arr);
+    ASSERT_EQ(dot(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(transpose(constant(0,0)).numdims(), 0);
+    choleskyInPlace(arr);
+    ASSERT_EQ(arr.numdims(), 0);
+    array out;
+    cholesky(out, constant(0,0));
+    ASSERT_EQ(out.numdims(), 0);
+}
+
+TEST(Array, TestEmptyMath) {
+    ASSERT_EQ(acos(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(acosh(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(abs(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(asin(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(asinh(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(atan(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(atan2(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(atanh(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(cos(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(cosh(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(log(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(log10(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(log1p(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(sin(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(sinh(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(tan(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(tanh(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(sqrt(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(real(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(imag(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(conjg(constant(0,0)).numdims(), 0);
+    //ASSERT_EQ(complex(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(erf(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(erfc(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(exp(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(expm1(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(cbrt(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(ceil(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(factorial(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(lgamma(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(pow(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(root(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(tgamma(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(arg(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(floor(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(hypot(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(rem(constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(round(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(sign(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(trunc(constant(0,0)).numdims(), 0);
+}
+
+TEST(Array, TestEmptyVecOp) {
+    ASSERT_EQ(accum(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(allTrue(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(anyTrue(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(count(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(where(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(max(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(min(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(product(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(sum(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(sort(constant(0,0)).numdims(), 0);
+
+    array skeys, svals;
+    sort(skeys, svals, constant(0,0), constant(0,0));
+    ASSERT_EQ(skeys.numdims(), 0);
+    ASSERT_EQ(svals.numdims(), 0);
+
+
+    array sout, sind;
+    sort(sout, sind, constant(0,0));
+    ASSERT_EQ(sout.numdims(), 0);
+    ASSERT_EQ(sind.numdims(), 0);
+}
+
+TEST(Array, TestEmptyArrMod) {
+    ASSERT_EQ(diag(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(diag(constant(0,0), true).numdims(), 0);
+    ASSERT_EQ(identity(0).numdims(), 0);
+    ASSERT_EQ(iota(dim4(0)).numdims(), 0);
+    ASSERT_EQ(lower(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(upper(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(constant(0,0).as(u8).numdims(), 0);
+    ASSERT_EQ(isNaN(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(isInf(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(iszero(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(flat(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(flip(constant(0,0), 0).numdims(), 0);
+    ASSERT_EQ(join(0, constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(join(0, randu(3), constant(0,0)).elements(), 3);
+    ASSERT_EQ(join(0, constant(0,0), randn(3)).elements(), 3);
+    ASSERT_EQ(moddims(constant(0,0), dim4(0)).numdims(), 0);
+    ASSERT_EQ(reorder(constant(0,0),0).numdims(), 0);
+    ASSERT_EQ(select(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(shift(constant(0,0), 1).numdims(), 0);
+    ASSERT_EQ(tile(constant(0,0), 1).numdims(), 0);
+
+    array arr = constant(0,0);
+    replace(arr, constant(0,0), constant(0,0));
+    ASSERT_EQ(arr.numdims(), 0);
+
+}
+
+TEST(Array, TestEmptyImage) {
+    ASSERT_EQ(histogram(constant(0,0) , 1).numdims(), 0);
+    ASSERT_EQ(hsv2rgb(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(gray2rgb(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(rotate(constant(0,0),0).numdims(), 0);
+
+    af_array h, hout;
+    dim_t ds[1];
+    af_constant (&h, 0, 0, ds, f32);
+    af_histogram(&hout, h, 10, 0.0, 1.0);
+
+    unsigned nd; af_get_numdims(&nd, h);
+    ASSERT_EQ(nd, 0);
+    af_get_numdims(&nd, hout);
+    ASSERT_EQ(nd, 0);
+}
+

From 88f3eac8dcd4b82b811a62996de9c8e57a60232f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 27 Jul 2016 18:18:32 -0400
Subject: [PATCH 0730/2677] PERF: improvements to nvvm ir based CUDA JIT

---
 src/backend/cuda/JIT/BinaryNode.hpp |  60 +++++++----
 src/backend/cuda/binary.hpp         | 159 ++++++++++++++++++++++++----
 src/backend/cuda/complex.hpp        |   4 +-
 src/backend/cuda/jit.cpp            |   4 +-
 src/backend/cuda/unary.hpp          |  18 ++++
 5 files changed, 203 insertions(+), 42 deletions(-)

diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index dee15dca41..73ed571ee5 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -23,16 +23,18 @@ namespace JIT
         std::string m_op_str;
         Node_ptr m_lhs, m_rhs;
         int m_op;
+        int m_call_type;
 
     public:
         BinaryNode(const char *out_type_str, const char *name_str,
                    const std::string &op_str,
-                   Node_ptr lhs, Node_ptr rhs, int op)
+                   Node_ptr lhs, Node_ptr rhs, int op, int call_type)
             : Node(out_type_str, name_str),
               m_op_str(op_str),
               m_lhs(lhs),
               m_rhs(rhs),
-              m_op(op)
+              m_op(op),
+              m_call_type(call_type)
         {
         }
 
@@ -83,23 +85,45 @@ namespace JIT
             if (!(m_lhs->isGenFunc())) m_lhs->genFuncs(kerStream, declStrs, is_linear);
             if (!(m_rhs->isGenFunc())) m_rhs->genFuncs(kerStream, declStrs, is_linear);
 
-            std::stringstream declStream;
-            declStream << "declare " << m_type_str << " " << m_op_str
-                       << "(" << m_lhs->getTypeStr() << " , " << m_rhs->getTypeStr() << ")\n";
-
-            str_map_iter loc = declStrs.find(declStream.str());
-            if (loc == declStrs.end()) {
-                declStrs[declStream.str()] = true;
+            if (m_call_type == 0) {
+                std::stringstream declStream;
+                declStream << "declare " << m_type_str << " " << m_op_str
+                           << "(" << m_lhs->getTypeStr() << " , " << m_rhs->getTypeStr() << ")\n";
+
+                str_map_iter loc = declStrs.find(declStream.str());
+                if (loc == declStrs.end()) {
+                    declStrs[declStream.str()] = true;
+                }
+
+                kerStream << "%val" << m_id << " = call "
+                          << m_type_str << " "
+                          << m_op_str << "("
+                          << m_lhs->getTypeStr() << " "
+                          << "%val" << m_lhs->getId() << ", "
+                          << m_rhs->getTypeStr() << " "
+                          << "%val" << m_rhs->getId() << ")\n";
+
+            } else {
+                if (m_call_type == 1) {
+                    // arithmetic operations
+                    kerStream << "%val" << m_id << " = "
+                              << m_op_str << " "
+                              << m_type_str << " "
+                              << "%val" << m_lhs->getId() << ", "
+                              << "%val" << m_rhs->getId() << "\n";
+                } else {
+                    // logical operators
+                    kerStream << "%tmp" << m_id << " = "
+                              << m_op_str << " "
+                              << m_lhs->getTypeStr() << " "
+                              << "%val" << m_lhs->getId() << ", "
+                              << "%val" << m_rhs->getId() << "\n";
+
+                    kerStream << "%val" << m_id << " = "
+                              << "zext i1 %tmp" << m_id << " to i8\n";
+
+                }
             }
-
-            kerStream << "%val" << m_id << " = call "
-                      << m_type_str << " "
-                      << m_op_str << "("
-                      << m_lhs->getTypeStr() << " "
-                      << "%val" << m_lhs->getId() << ", "
-                      << m_rhs->getTypeStr() << " "
-                      << "%val" << m_rhs->getId() << ")\n";
-
             m_gen_func = true;
         }
 
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index d5f742eec3..db02926187 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -21,44 +21,159 @@ namespace cuda
 template<typename To, typename Ti, af_op_t op>
 struct BinOp
 {
-    const char *name()
-    {
-        return "noop";
-    }
+    std::string name;
+    int call_type;
+    BinOp() :
+        name("noop"),
+        call_type(0)
+    {}
 };
-
-#define BINARY(fn)                                  \
-    template<typename To, typename Ti>              \
-    struct BinOp<To, Ti, af_##fn##_t>               \
-    {                                               \
-        std::string res;                            \
-        BinOp() :                                   \
-            res(cuMangledName<Ti, true>("___"#fn))  \
-        {}                                          \
-        const std::string name()                    \
-        {                                           \
-            return res;                             \
-        }                                           \
+#define BINARY(fn)                                      \
+    template<typename To, typename Ti>                  \
+    struct BinOp<To, Ti, af_##fn##_t>                   \
+    {                                                   \
+        std::string name;                               \
+        int call_type;                                  \
+        BinOp() :                                       \
+            name(cuMangledName<Ti, true>("___"#fn)),    \
+            call_type(0)                                \
+            {}                                          \
     };
 
+#define SPECIALIZE_ARITH(T, fn, fname)          \
+    template<>                                  \
+    struct BinOp<T, T, af_##fn##_t>             \
+    {                                           \
+        std::string name;                       \
+        int call_type;                          \
+        BinOp() :                               \
+            name(fname),                        \
+            call_type(1)                        \
+            {}                                  \
+    };                                          \
+
+#define SPECIALIZE_ARITH_INT(fn, fname)         \
+    SPECIALIZE_ARITH(int, fn, fname)            \
+    SPECIALIZE_ARITH(short, fn, fname)          \
+    SPECIALIZE_ARITH(intl, fn, fname)           \
+
+#define SPECIALIZE_ARITH_UINT(fn, fname)        \
+    SPECIALIZE_ARITH(uint, fn, fname)           \
+    SPECIALIZE_ARITH(ushort, fn, fname)         \
+    SPECIALIZE_ARITH(uintl, fn, fname)          \
+
+#define SPECIALIZE_ARITH_FLOAT(fn, fname)       \
+    SPECIALIZE_ARITH(float, fn, fname)          \
+    SPECIALIZE_ARITH(double, fn, fname)         \
+
+#define SPECIALIZE_ARITH_CPLX(fn, fname)        \
+    SPECIALIZE_ARITH(cfloat, fn, fname)         \
+    SPECIALIZE_ARITH(cdouble, fn, fname)        \
+
+
 BINARY(add)
+SPECIALIZE_ARITH_INT(add, "add")
+SPECIALIZE_ARITH_UINT(add, "add")
+SPECIALIZE_ARITH_FLOAT(add, "fadd")
+SPECIALIZE_ARITH_CPLX(add, "fadd")
+
 BINARY(sub)
+SPECIALIZE_ARITH_INT(sub, "sub")
+SPECIALIZE_ARITH_UINT(sub, "sub")
+SPECIALIZE_ARITH_FLOAT(sub, "fsub")
+SPECIALIZE_ARITH_CPLX(sub, "fsub")
+
 BINARY(mul)
+SPECIALIZE_ARITH_INT(mul, "mul")
+SPECIALIZE_ARITH_UINT(mul, "mul")
+SPECIALIZE_ARITH_FLOAT(mul, "fmul")
+
 BINARY(div)
-BINARY(and)
-BINARY(or)
+SPECIALIZE_ARITH_INT(div, "sdiv")
+SPECIALIZE_ARITH_UINT(div, "udiv")
+SPECIALIZE_ARITH_FLOAT(div, "fdiv")
+
 BINARY(bitand)
+SPECIALIZE_ARITH_INT(bitand, "and")
+SPECIALIZE_ARITH_UINT(bitand, "and")
+
 BINARY(bitor)
+SPECIALIZE_ARITH_INT(bitor, "or")
+SPECIALIZE_ARITH_UINT(bitor, "or")
+
 BINARY(bitxor)
+SPECIALIZE_ARITH_INT(bitxor, "xor")
+SPECIALIZE_ARITH_UINT(bitxor, "xor")
+
 BINARY(bitshiftl)
+SPECIALIZE_ARITH_INT(bitshiftl, "shl")
+SPECIALIZE_ARITH_UINT(bitshiftl, "shl")
+
 BINARY(bitshiftr)
+SPECIALIZE_ARITH_INT(bitshiftr, "lshr")
+SPECIALIZE_ARITH_UINT(bitshiftr, "lshr")
+
+
+BINARY(and)
+BINARY(or)
+
+
+#define SPECIALIZE_COMPARE(T, fn, fname)        \
+    template<>                                  \
+    struct BinOp<char, T, af_##fn##_t>          \
+    {                                           \
+        std::string name;                       \
+        int call_type;                          \
+        BinOp() :                               \
+            name(fname),                        \
+            call_type(2)                        \
+            {}                                  \
+    };                                          \
+
+#define SPECIALIZE_COMPARE_INT(fn, fname)       \
+    SPECIALIZE_COMPARE(int, fn, fname)          \
+    SPECIALIZE_COMPARE(short, fn, fname)        \
+    SPECIALIZE_COMPARE(intl, fn, fname)         \
+
+#define SPECIALIZE_COMPARE_UINT(fn, fname)      \
+    SPECIALIZE_COMPARE(uint, fn, fname)         \
+    SPECIALIZE_COMPARE(ushort, fn, fname)       \
+    SPECIALIZE_COMPARE(uintl, fn, fname)        \
+
+#define SPECIALIZE_COMPARE_FLOAT(fn, fname)     \
+    SPECIALIZE_COMPARE(float, fn, fname)        \
+    SPECIALIZE_COMPARE(double, fn, fname)       \
+
 
 BINARY(lt)
+SPECIALIZE_COMPARE_INT(lt, "icmp ult")
+SPECIALIZE_COMPARE_UINT(lt, "icmp slt")
+SPECIALIZE_COMPARE_FLOAT(lt, "fcmp olt")
+
 BINARY(gt)
+SPECIALIZE_COMPARE_INT(gt, "icmp ugt")
+SPECIALIZE_COMPARE_UINT(gt, "icmp sgt")
+SPECIALIZE_COMPARE_FLOAT(gt, "fcmp ogt")
+
 BINARY(le)
+SPECIALIZE_COMPARE_INT(le, "icmp ule")
+SPECIALIZE_COMPARE_UINT(le, "icmp sle")
+SPECIALIZE_COMPARE_FLOAT(le, "fcmp ole")
+
 BINARY(ge)
+SPECIALIZE_COMPARE_INT(ge, "icmp uge")
+SPECIALIZE_COMPARE_UINT(ge, "icmp sge")
+SPECIALIZE_COMPARE_FLOAT(ge, "fcmp oge")
+
 BINARY(eq)
+SPECIALIZE_COMPARE_INT(eq, "icmp ueq")
+SPECIALIZE_COMPARE_UINT(eq, "icmp seq")
+SPECIALIZE_COMPARE_FLOAT(eq, "fcmp oeq")
+
 BINARY(neq)
+SPECIALIZE_COMPARE_INT(neq, "icmp une")
+SPECIALIZE_COMPARE_UINT(neq, "icmp sne")
+SPECIALIZE_COMPARE_FLOAT(neq, "fcmp one")
 
 BINARY(max)
 BINARY(min)
@@ -80,9 +195,11 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af:
 
     JIT::BinaryNode *node = new JIT::BinaryNode(irname<To>(),
                                                 afShortName<To>(),
-                                                bop.name(),
+                                                bop.name,
                                                 lhs_node,
-                                                rhs_node, (int)(op));
+                                                rhs_node,
+                                                (int)(op),
+                                                bop.call_type);
 
     return createNodeArray<To>(odims, JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
 }
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index e67806fceb..73af320640 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -47,7 +47,9 @@ namespace cuda
                                                     afShortName<To>(),
                                                     cplx_name<To>(),
                                                     lhs_node,
-                                                    rhs_node, (int)(af_cplx2_t));
+                                                    rhs_node,
+                                                    (int)(af_cplx2_t),
+                                                    0);
 
         return createNodeArray<To>(odims, JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index f6a470f00a..d4f20ec8e0 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -289,7 +289,8 @@ static char *irToPtx(string IR, size_t *ptx_size)
 #if 0
     NVVM_CHECK(nvvmCompileProgram(prog, 0, NULL), "Failed to compile program");
 #else
-    nvvmResult comp_res = nvvmCompileProgram(prog, 0, NULL);
+    const char *options = "";
+    nvvmResult comp_res = nvvmCompileProgram(prog, 1, &options);
     if (comp_res != NVVM_SUCCESS) {
         size_t log_size = 0;
         nvvmGetProgramLogSize(prog, &log_size);
@@ -305,7 +306,6 @@ static char *irToPtx(string IR, size_t *ptx_size)
 
     char *ptx = new char[*ptx_size];
     NVVM_CHECK(nvvmGetCompiledResult(prog, ptx), "Can not get ptx from NVVM IR");
-
     NVVM_CHECK(nvvmDestroyProgram(&prog), "Failed to destroy program");
     return ptx;
 }
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 85597ccfe5..48cd021e1d 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -40,6 +40,21 @@ struct UnOp
         }                                           \
     };                                              \
 
+#define UNARY_FN_LLVM(T, fn, fname)             \
+    template<>                                  \
+    struct UnOp<T, af_##fn##_t>                 \
+    {                                           \
+        std::string res;                        \
+        UnOp() :                                \
+            res(fname)                          \
+        {                                       \
+        }                                       \
+        const std::string name()                \
+        {                                       \
+            return res;                         \
+        }                                       \
+    };                                          \
+
 UNARY_FN(sin)
 UNARY_FN(cos)
 UNARY_FN(tan)
@@ -71,6 +86,9 @@ UNARY_FN(log10)
 UNARY_FN(log2)
 
 UNARY_FN(sqrt)
+UNARY_FN_LLVM(float, sqrt, "@llvm.sqrt.f32")
+UNARY_FN_LLVM(double, sqrt, "@llvm.sqrt.f64")
+
 UNARY_FN(cbrt)
 
 UNARY_FN(sign )

From 5b5582e81a8c9a4dab465414c0d18dc5c4bba4fe Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 4 Aug 2016 19:50:46 -0400
Subject: [PATCH 0731/2677] Updating bin2cpp with latest updates

---
 CMakeModules/bin2cpp.cpp | 23 +++++++++--------------
 1 file changed, 9 insertions(+), 14 deletions(-)

diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index 66fab53fdf..ac82710846 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -13,7 +13,6 @@
 using namespace std;
 typedef map<string, string> opt_t;
 
-static
 void print_usage() {
     cout << R"delimiter(BIN2CPP
 Converts files from a binary file to C++ headers. It is similar to bin2c and
@@ -34,23 +33,21 @@ Example
 ./bin2cpp --file blah.txt --namespace blah detail --formatted --name blah_var
 
 Will produce:
-#pragma once
 #include <cstddef>
 namespace blah {
-    namespace detail {
-        static const char blah_var[] = {
-            0x2f,    0x2f,    0x20,    0x62,    0x6c,    0x61,    0x68,    0x2e,    0x74,    0x78,
-            0x74,    0xa,    0x62,    0x6c,    0x61,    0x68,    0x20,    0x62,    0x6c,    0x61,
-            0x68,    0x20,    0x62,    0x6c,    0x61,    0x68,    0xa,    };
-        static const size_t blah_var_len = 27;
-    }
+	namespace detail {
+		static const char blah_var[] = {
+			0x2f,	0x2f,	0x20,	0x62,	0x6c,	0x61,	0x68,	0x2e,	0x74,	0x78,
+			0x74,	0xa,	0x62,	0x6c,	0x61,	0x68,	0x20,	0x62,	0x6c,	0x61,
+			0x68,	0x20,	0x62,	0x6c,	0x61,	0x68,	0xa,	};
+		static const size_t blah_var_len = 27;
+	}
 })delimiter";
         exit(0);
 }
 
 static bool formatted;
 
-static
 void add_tabs(const int level ){
     if(formatted) {
         for(int i =0; i < level; i++) {
@@ -59,7 +56,6 @@ void add_tabs(const int level ){
     }
 }
 
-static
 opt_t
 parse_options(const vector<string>& args) {
     opt_t options;
@@ -69,7 +65,7 @@ parse_options(const vector<string>& args) {
     options["--file"]       = "";
     options["--output"]     = "";
     options["--namespace"]  = "";
-    options["--eof"]        = "";
+    options["--eof"]        = "0";
 
     //Parse Arguments
     string curr_opt;
@@ -113,7 +109,6 @@ parse_options(const vector<string>& args) {
 
 int main(int argc, const char * const * const argv)
 {
-
     vector<string> args(argv, argv+argc);
 
     opt_t&& options = parse_options(args);
@@ -158,7 +153,7 @@ int main(int argc, const char * const * const argv)
     size_t char_cnt = 0;
     add_tabs(++level);
     for(char i; input.get(i);) {
-        cout << "0x" << std::hex << static_cast<int>(i) << ",\t";
+        cout << "0x" << std::hex << static_cast<int>(i & 0xff) << ",\t";
         char_cnt++;
         if(!(char_cnt % 10)) {
             cout << endl;

From 4e0b8608015aceef60f8f1ae80ad41b047308650 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 4 Aug 2016 23:08:45 -0400
Subject: [PATCH 0732/2677] Fixing bin2cpp to avoid narrowing

---
 CMakeModules/bin2cpp.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index ac82710846..8a0429540b 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -146,8 +146,9 @@ int main(int argc, const char * const * const argv)
         options["--type"]     = "char";
     }
     add_tabs(level);
-    cout << "static const " << options["--type"] << " " << options["--name"] << "[] = {\n";
 
+    // Always create unsigned char to avoid narrowing
+    cout << "static const " << "unsigned char" << " " << options["--name"] << "_uchar [] = {\n";
 
     ifstream input(options["--file"]);
     size_t char_cnt = 0;
@@ -169,6 +170,14 @@ int main(int argc, const char * const * const argv)
 
     cout << "};\n";
     add_tabs(--level);
+
+    // Cast to proper output type
+    cout << "static const "
+         << options["--type"] << " *"
+         << options["--name"] << " = (const "
+         << options["--type"] << " *)"
+         << options["--name"] << "_uchar;\n";
+
     cout << "static const size_t " << options["--name"] << "_len" << " = " << std::dec << char_cnt << ";\n";
 
     while(ns_cnt--) {

From 40a0f6dbbbc5e48b8d6365cc6f30e151735a7b2a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 4 Aug 2016 23:09:26 -0400
Subject: [PATCH 0733/2677] Add libdevice to the build process

- Also fixes CLKernelToH.cmake targets
---
 CMakeModules/CLKernelToH.cmake  |  4 ++--
 src/backend/cuda/CMakeLists.txt | 42 +++++++++++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/CLKernelToH.cmake b/CMakeModules/CLKernelToH.cmake
index c4df5d5b3e..61928db328 100644
--- a/CMakeModules/CLKernelToH.cmake
+++ b/CMakeModules/CLKernelToH.cmake
@@ -56,8 +56,8 @@ function(CL_KERNEL_TO_H)
 
         list(APPEND _output_files ${_output_file})
     endforeach()
-    ADD_CUSTOM_TARGET(${RTCS_NAMESPACE}_bin_target DEPENDS ${_output_files})
+    ADD_CUSTOM_TARGET(${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target DEPENDS ${_output_files})
 
     set("${RTCS_VARNAME}" ${_output_files} PARENT_SCOPE)
-    set("${RTCS_TARGETS}" ${RTCS_NAMESPACE}_bin_target PARENT_SCOPE)
+    set("${RTCS_TARGETS}" ${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target PARENT_SCOPE)
 endfunction(CL_KERNEL_TO_H)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f8c39b47c2..6aa2d51bff 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -314,6 +314,44 @@ CL_KERNEL_TO_H(
     EOF "1"
     )
 
+SET(libdevice_bc "")
+SET(libdevice_computes "")
+LIST(APPEND libdevice_computes "20" "30" "35" "50")
+FOREACH(libdevice_compute ${libdevice_computes})
+  SET(_libdevice_bc_file "${CUDA_NVVM_HOME}/libdevice/libdevice.compute_${libdevice_compute}.10.bc")
+  SET(_libdevice_bc_copy "${CMAKE_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
+  IF (EXISTS ${_libdevice_bc_file})
+    ADD_CUSTOM_COMMAND(
+      OUTPUT "${_libdevice_bc_copy}"
+      DEPENDS "${_libdevice_bc_file}"
+      COMMAND ${CMAKE_COMMAND} -E copy "${_libdevice_bc_file}" "${_libdevice_bc_copy}")
+    LIST(APPEND libdevice_bc ${_libdevice_bc_copy})
+    ADD_DEFINITIONS(-D"__LIBDEVICE_COMPUTE_${libdevice_compute}")
+  ENDIF()
+ENDFOREACH()
+
+LIST(LENGTH libdevice_bc libdevice_bc_len)
+
+IF (${libdevice_bc_len} GREATER 0)
+
+  SET(libdevice_headers
+    "libdevice_headers")
+
+  CL_KERNEL_TO_H(
+    SOURCES ${libdevice_bc}
+    VARNAME libdevice_files
+    EXTENSION "hpp"
+    OUTPUT_DIR ${libdevice_headers}
+    TARGETS libdevice_targets
+    NAMESPACE "cuda"
+    EOF "1"
+    )
+
+  ADD_DEFINITIONS(-DUSE_LIBDEVICE)
+ELSE()
+  MESSAGE(STATUS "LIBDEVICE not found on system. CUDA JIT may be slower")
+ENDIF()
+
 IF("${APPLE}")
     ADD_DEFINITIONS(-D__STRICT_ANSI__)
     IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
@@ -420,6 +458,10 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
 
+IF (${libdevice_bc_len} GREATER 0)
+  ADD_DEPENDENCIES(afcuda ${libdevice_targets})
+ENDIF()
+
 TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}

From 925f403d3431c7859d81c1dd07ab0523587e019c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 4 Aug 2016 23:16:23 -0400
Subject: [PATCH 0734/2677] Adding specialized libdevice calls for CUDA JIT

---
 src/backend/cuda/CMakeLists.txt |  33 ++---
 src/backend/cuda/binary.hpp     | 217 +++++++++++++++++++-------------
 src/backend/cuda/complex.hpp    |   9 +-
 src/backend/cuda/jit.cpp        |  35 +++++-
 src/backend/cuda/unary.hpp      | 128 ++++++++++---------
 5 files changed, 258 insertions(+), 164 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6aa2d51bff..e743bbbbd8 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -6,6 +6,8 @@ FIND_PACKAGE(Boost REQUIRED)
 INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
 INCLUDE("${CMAKE_MODULE_PATH}/FindNVVM.cmake")
 
+OPTION(USE_LIBDEVICE "Use libdevice for CUDA JIT" ON)
+
 MARK_AS_ADVANCED(
     CUDA_BUILD_CUBIN
     CUDA_BUILD_EMULATION
@@ -315,20 +317,22 @@ CL_KERNEL_TO_H(
     )
 
 SET(libdevice_bc "")
-SET(libdevice_computes "")
-LIST(APPEND libdevice_computes "20" "30" "35" "50")
-FOREACH(libdevice_compute ${libdevice_computes})
-  SET(_libdevice_bc_file "${CUDA_NVVM_HOME}/libdevice/libdevice.compute_${libdevice_compute}.10.bc")
-  SET(_libdevice_bc_copy "${CMAKE_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
-  IF (EXISTS ${_libdevice_bc_file})
-    ADD_CUSTOM_COMMAND(
-      OUTPUT "${_libdevice_bc_copy}"
-      DEPENDS "${_libdevice_bc_file}"
-      COMMAND ${CMAKE_COMMAND} -E copy "${_libdevice_bc_file}" "${_libdevice_bc_copy}")
-    LIST(APPEND libdevice_bc ${_libdevice_bc_copy})
-    ADD_DEFINITIONS(-D"__LIBDEVICE_COMPUTE_${libdevice_compute}")
-  ENDIF()
-ENDFOREACH()
+IF (USE_LIBDEVICE)
+  SET(libdevice_computes "")
+  LIST(APPEND libdevice_computes "20" "30" "35" "50")
+  FOREACH(libdevice_compute ${libdevice_computes})
+    SET(_libdevice_bc_file "${CUDA_NVVM_HOME}/libdevice/libdevice.compute_${libdevice_compute}.10.bc")
+    SET(_libdevice_bc_copy "${CMAKE_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
+    IF (EXISTS ${_libdevice_bc_file})
+      ADD_CUSTOM_COMMAND(
+        OUTPUT "${_libdevice_bc_copy}"
+        DEPENDS "${_libdevice_bc_file}"
+        COMMAND ${CMAKE_COMMAND} -E copy "${_libdevice_bc_file}" "${_libdevice_bc_copy}")
+      LIST(APPEND libdevice_bc ${_libdevice_bc_copy})
+      ADD_DEFINITIONS(-D"__LIBDEVICE_COMPUTE_${libdevice_compute}")
+    ENDIF()
+  ENDFOREACH()
+ENDIF()
 
 LIST(LENGTH libdevice_bc libdevice_bc_len)
 
@@ -347,6 +351,7 @@ IF (${libdevice_bc_len} GREATER 0)
     EOF "1"
     )
 
+  MESSAGE(STATUS "LIBDEVICE found.")
   ADD_DEFINITIONS(-DUSE_LIBDEVICE)
 ELSE()
   MESSAGE(STATUS "LIBDEVICE not found on system. CUDA JIT may be slower")
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index db02926187..68b14891d8 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -28,6 +28,7 @@ struct BinOp
         call_type(0)
     {}
 };
+
 #define BINARY(fn)                                      \
     template<typename To, typename Ti>                  \
     struct BinOp<To, Ti, af_##fn##_t>                   \
@@ -40,7 +41,8 @@ struct BinOp
             {}                                          \
     };
 
-#define SPECIALIZE_ARITH(T, fn, fname)          \
+#if defined(USE_LIBDEVICE)
+#define NVVM_ARITH_OP(T, fn, fname)             \
     template<>                                  \
     struct BinOp<T, T, af_##fn##_t>             \
     {                                           \
@@ -52,136 +54,181 @@ struct BinOp
             {}                                  \
     };                                          \
 
-#define SPECIALIZE_ARITH_INT(fn, fname)         \
-    SPECIALIZE_ARITH(int, fn, fname)            \
-    SPECIALIZE_ARITH(short, fn, fname)          \
-    SPECIALIZE_ARITH(intl, fn, fname)           \
+#define NVVM_COMPARE_OP(T, fn, fname)           \
+    template<>                                  \
+    struct BinOp<char, T, af_##fn##_t>          \
+    {                                           \
+        std::string name;                       \
+        int call_type;                          \
+        BinOp() :                               \
+            name(fname),                        \
+            call_type(2)                        \
+            {}                                  \
+    };                                          \
+
+#define NVVM_BINARY_FUNC(T, fn, fname)          \
+    template<>                                  \
+    struct BinOp<T, T, af_##fn##_t>             \
+    {                                           \
+        std::string name;                       \
+        int call_type;                          \
+        BinOp() :                               \
+            name("@__nv_"#fname),               \
+            call_type(0)                        \
+            {}                                  \
+    };                                          \
+
+#else
+
+#define NVVM_ARITH_OP(T, fn, fname)    // No specialization
+#define NVVM_COMPARE_OP(T, fn, fname)  // No specialization
+#define NVVM_BINARY_FUNC(T, fn, fname) // No specialization
+
+#endif
+
+#define NVVM_ARITH_OP_INT(fn, fname)         \
+    NVVM_ARITH_OP(int, fn, fname)            \
+    NVVM_ARITH_OP(short, fn, fname)          \
+    NVVM_ARITH_OP(intl, fn, fname)           \
 
-#define SPECIALIZE_ARITH_UINT(fn, fname)        \
-    SPECIALIZE_ARITH(uint, fn, fname)           \
-    SPECIALIZE_ARITH(ushort, fn, fname)         \
-    SPECIALIZE_ARITH(uintl, fn, fname)          \
+#define NVVM_ARITH_OP_UINT(fn, fname)        \
+    NVVM_ARITH_OP(uint, fn, fname)           \
+    NVVM_ARITH_OP(ushort, fn, fname)         \
+    NVVM_ARITH_OP(uintl, fn, fname)          \
 
-#define SPECIALIZE_ARITH_FLOAT(fn, fname)       \
-    SPECIALIZE_ARITH(float, fn, fname)          \
-    SPECIALIZE_ARITH(double, fn, fname)         \
+#define NVVM_ARITH_OP_FLOAT(fn, fname)       \
+    NVVM_ARITH_OP(float, fn, fname)          \
+    NVVM_ARITH_OP(double, fn, fname)         \
 
-#define SPECIALIZE_ARITH_CPLX(fn, fname)        \
-    SPECIALIZE_ARITH(cfloat, fn, fname)         \
-    SPECIALIZE_ARITH(cdouble, fn, fname)        \
+#define NVVM_ARITH_OP_CPLX(fn, fname)        \
+    NVVM_ARITH_OP(cfloat, fn, fname)         \
+    NVVM_ARITH_OP(cdouble, fn, fname)        \
 
+#define NVVM_COMPARE_OP_INT(fn, fname)       \
+    NVVM_COMPARE_OP(int, fn, fname)          \
+    NVVM_COMPARE_OP(short, fn, fname)        \
+    NVVM_COMPARE_OP(intl, fn, fname)         \
+
+#define NVVM_COMPARE_OP_UINT(fn, fname)      \
+    NVVM_COMPARE_OP(uint, fn, fname)         \
+    NVVM_COMPARE_OP(ushort, fn, fname)       \
+    NVVM_COMPARE_OP(uintl, fn, fname)        \
+
+#define NVVM_COMPARE_OP_FLOAT(fn, fname)     \
+    NVVM_COMPARE_OP(float, fn, fname)        \
+    NVVM_COMPARE_OP(double, fn, fname)       \
 
 BINARY(add)
-SPECIALIZE_ARITH_INT(add, "add")
-SPECIALIZE_ARITH_UINT(add, "add")
-SPECIALIZE_ARITH_FLOAT(add, "fadd")
-SPECIALIZE_ARITH_CPLX(add, "fadd")
+NVVM_ARITH_OP_INT(add, "add")
+NVVM_ARITH_OP_UINT(add, "add")
+NVVM_ARITH_OP_FLOAT(add, "fadd")
+NVVM_ARITH_OP_CPLX(add, "fadd")
 
 BINARY(sub)
-SPECIALIZE_ARITH_INT(sub, "sub")
-SPECIALIZE_ARITH_UINT(sub, "sub")
-SPECIALIZE_ARITH_FLOAT(sub, "fsub")
-SPECIALIZE_ARITH_CPLX(sub, "fsub")
+NVVM_ARITH_OP_INT(sub, "sub")
+NVVM_ARITH_OP_UINT(sub, "sub")
+NVVM_ARITH_OP_FLOAT(sub, "fsub")
+NVVM_ARITH_OP_CPLX(sub, "fsub")
 
 BINARY(mul)
-SPECIALIZE_ARITH_INT(mul, "mul")
-SPECIALIZE_ARITH_UINT(mul, "mul")
-SPECIALIZE_ARITH_FLOAT(mul, "fmul")
+NVVM_ARITH_OP_INT(mul, "mul")
+NVVM_ARITH_OP_UINT(mul, "mul")
+NVVM_ARITH_OP_FLOAT(mul, "fmul")
 
 BINARY(div)
-SPECIALIZE_ARITH_INT(div, "sdiv")
-SPECIALIZE_ARITH_UINT(div, "udiv")
-SPECIALIZE_ARITH_FLOAT(div, "fdiv")
+NVVM_ARITH_OP_INT(div, "sdiv")
+NVVM_ARITH_OP_UINT(div, "udiv")
+NVVM_ARITH_OP_FLOAT(div, "fdiv")
 
 BINARY(bitand)
-SPECIALIZE_ARITH_INT(bitand, "and")
-SPECIALIZE_ARITH_UINT(bitand, "and")
+NVVM_ARITH_OP_INT(bitand, "and")
+NVVM_ARITH_OP_UINT(bitand, "and")
 
 BINARY(bitor)
-SPECIALIZE_ARITH_INT(bitor, "or")
-SPECIALIZE_ARITH_UINT(bitor, "or")
+NVVM_ARITH_OP_INT(bitor, "or")
+NVVM_ARITH_OP_UINT(bitor, "or")
 
 BINARY(bitxor)
-SPECIALIZE_ARITH_INT(bitxor, "xor")
-SPECIALIZE_ARITH_UINT(bitxor, "xor")
+NVVM_ARITH_OP_INT(bitxor, "xor")
+NVVM_ARITH_OP_UINT(bitxor, "xor")
 
 BINARY(bitshiftl)
-SPECIALIZE_ARITH_INT(bitshiftl, "shl")
-SPECIALIZE_ARITH_UINT(bitshiftl, "shl")
+NVVM_ARITH_OP_INT(bitshiftl, "shl")
+NVVM_ARITH_OP_UINT(bitshiftl, "shl")
 
 BINARY(bitshiftr)
-SPECIALIZE_ARITH_INT(bitshiftr, "lshr")
-SPECIALIZE_ARITH_UINT(bitshiftr, "lshr")
+NVVM_ARITH_OP_INT(bitshiftr, "lshr")
+NVVM_ARITH_OP_UINT(bitshiftr, "lshr")
 
 
 BINARY(and)
 BINARY(or)
 
-
-#define SPECIALIZE_COMPARE(T, fn, fname)        \
-    template<>                                  \
-    struct BinOp<char, T, af_##fn##_t>          \
-    {                                           \
-        std::string name;                       \
-        int call_type;                          \
-        BinOp() :                               \
-            name(fname),                        \
-            call_type(2)                        \
-            {}                                  \
-    };                                          \
-
-#define SPECIALIZE_COMPARE_INT(fn, fname)       \
-    SPECIALIZE_COMPARE(int, fn, fname)          \
-    SPECIALIZE_COMPARE(short, fn, fname)        \
-    SPECIALIZE_COMPARE(intl, fn, fname)         \
-
-#define SPECIALIZE_COMPARE_UINT(fn, fname)      \
-    SPECIALIZE_COMPARE(uint, fn, fname)         \
-    SPECIALIZE_COMPARE(ushort, fn, fname)       \
-    SPECIALIZE_COMPARE(uintl, fn, fname)        \
-
-#define SPECIALIZE_COMPARE_FLOAT(fn, fname)     \
-    SPECIALIZE_COMPARE(float, fn, fname)        \
-    SPECIALIZE_COMPARE(double, fn, fname)       \
-
-
 BINARY(lt)
-SPECIALIZE_COMPARE_INT(lt, "icmp ult")
-SPECIALIZE_COMPARE_UINT(lt, "icmp slt")
-SPECIALIZE_COMPARE_FLOAT(lt, "fcmp olt")
+NVVM_COMPARE_OP_INT(lt, "icmp ult")
+NVVM_COMPARE_OP_UINT(lt, "icmp slt")
+NVVM_COMPARE_OP_FLOAT(lt, "fcmp olt")
 
 BINARY(gt)
-SPECIALIZE_COMPARE_INT(gt, "icmp ugt")
-SPECIALIZE_COMPARE_UINT(gt, "icmp sgt")
-SPECIALIZE_COMPARE_FLOAT(gt, "fcmp ogt")
+NVVM_COMPARE_OP_INT(gt, "icmp ugt")
+NVVM_COMPARE_OP_UINT(gt, "icmp sgt")
+NVVM_COMPARE_OP_FLOAT(gt, "fcmp ogt")
 
 BINARY(le)
-SPECIALIZE_COMPARE_INT(le, "icmp ule")
-SPECIALIZE_COMPARE_UINT(le, "icmp sle")
-SPECIALIZE_COMPARE_FLOAT(le, "fcmp ole")
+NVVM_COMPARE_OP_INT(le, "icmp ule")
+NVVM_COMPARE_OP_UINT(le, "icmp sle")
+NVVM_COMPARE_OP_FLOAT(le, "fcmp ole")
 
 BINARY(ge)
-SPECIALIZE_COMPARE_INT(ge, "icmp uge")
-SPECIALIZE_COMPARE_UINT(ge, "icmp sge")
-SPECIALIZE_COMPARE_FLOAT(ge, "fcmp oge")
+NVVM_COMPARE_OP_INT(ge, "icmp uge")
+NVVM_COMPARE_OP_UINT(ge, "icmp sge")
+NVVM_COMPARE_OP_FLOAT(ge, "fcmp oge")
 
 BINARY(eq)
-SPECIALIZE_COMPARE_INT(eq, "icmp ueq")
-SPECIALIZE_COMPARE_UINT(eq, "icmp seq")
-SPECIALIZE_COMPARE_FLOAT(eq, "fcmp oeq")
+NVVM_COMPARE_OP_INT(eq, "icmp ueq")
+NVVM_COMPARE_OP_UINT(eq, "icmp seq")
+NVVM_COMPARE_OP_FLOAT(eq, "fcmp oeq")
 
 BINARY(neq)
-SPECIALIZE_COMPARE_INT(neq, "icmp une")
-SPECIALIZE_COMPARE_UINT(neq, "icmp sne")
-SPECIALIZE_COMPARE_FLOAT(neq, "fcmp one")
+NVVM_COMPARE_OP_INT(neq, "icmp une")
+NVVM_COMPARE_OP_UINT(neq, "icmp sne")
+NVVM_COMPARE_OP_FLOAT(neq, "fcmp one")
 
 BINARY(max)
+NVVM_BINARY_FUNC(float, max, fmaxf)
+NVVM_BINARY_FUNC(double, max, fmax)
+NVVM_BINARY_FUNC(int, max, max)
+NVVM_BINARY_FUNC(uint, max, umax)
+NVVM_BINARY_FUNC(intl, max, llmax)
+NVVM_BINARY_FUNC(uintl, max, ullmax)
+
 BINARY(min)
+NVVM_BINARY_FUNC(float, min, fminf)
+NVVM_BINARY_FUNC(double, min, fmin)
+NVVM_BINARY_FUNC(int, min, min)
+NVVM_BINARY_FUNC(uint, min, umin)
+NVVM_BINARY_FUNC(intl, min, llmin)
+NVVM_BINARY_FUNC(uintl, min, ullmin)
+
 BINARY(pow)
+NVVM_BINARY_FUNC(float, pow, powf)
+NVVM_BINARY_FUNC(double, pow, pow)
+
 BINARY(mod)
+NVVM_BINARY_FUNC(float, mod, fmodf)
+NVVM_BINARY_FUNC(double, mod, fmod)
+
 BINARY(rem)
+NVVM_BINARY_FUNC(float, rem, remainderf)
+NVVM_BINARY_FUNC(double, rem, remainder)
+
 BINARY(atan2)
+NVVM_BINARY_FUNC(float, atan2, atan2f)
+NVVM_BINARY_FUNC(double, atan2, atan2)
+
 BINARY(hypot)
+NVVM_BINARY_FUNC(float, hypot, hypotf)
+NVVM_BINARY_FUNC(double, hypot, hypot)
 
 #undef BINARY
 
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 73af320640..6082a5e194 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -28,8 +28,13 @@ namespace cuda
     template<> STATIC_ const std::string imag_name<cdouble>() { return cuMangledName<cdouble, false>("___imag"); }
 
     template<typename T> static const std::string abs_name()  { return cuMangledName<T, false>("___noop");       }
-    template<> STATIC_ const std::string abs_name<float  >()  { return cuMangledName<float  , false>("___abs");  }
-    template<> STATIC_ const std::string abs_name<double >()  { return cuMangledName<double , false>("___abs");  }
+#if defined(USE_LIBDEVICE)
+    template<> STATIC_ const std::string abs_name<float  >()  { return "@__nv_fabsf";  }
+    template<> STATIC_ const std::string abs_name<double >()  { return "@__nv_fabs" ;  }
+#else
+    template<> STATIC_ const std::string abs_name<float >()  { return cuMangledName<float , false>("___abs");  }
+    template<> STATIC_ const std::string abs_name<double>()  { return cuMangledName<double, false>("___abs");  }
+#endif
     template<> STATIC_ const std::string abs_name<cfloat >()  { return cuMangledName<cfloat , false>("___abs");  }
     template<> STATIC_ const std::string abs_name<cdouble>()  { return cuMangledName<cdouble, false>("___abs");  }
 
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index d4f20ec8e0..535d89ff8e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -13,6 +13,7 @@
 #include <stdexcept>
 #include <copy.hpp>
 #include <JIT/Node.hpp>
+
 #include <ptx_headers/arith.hpp>
 #include <ptx_headers/logic.hpp>
 #include <ptx_headers/exp.hpp>
@@ -20,6 +21,23 @@
 #include <ptx_headers/trig.hpp>
 #include <ptx_headers/hyper.hpp>
 #include <ptx_headers/cast.hpp>
+
+#if defined(__LIBDEVICE_COMPUTE_20)
+#include <libdevice_headers/compute_20.hpp>
+#endif
+
+#if defined(__LIBDEVICE_COMPUTE_30)
+#include <libdevice_headers/compute_30.hpp>
+#endif
+
+#if defined(__LIBDEVICE_COMPUTE_35)
+#include <libdevice_headers/compute_35.hpp>
+#endif
+
+#if defined(__LIBDEVICE_COMPUTE_50)
+#include <libdevice_headers/compute_50.hpp>
+#endif
+
 #include <platform.hpp>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
@@ -267,7 +285,7 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
         nvvmResult res = fn;                    \
         if (res == NVVM_SUCCESS) break;         \
         char nvvm_err_msg[1024];                \
-        snprintf(nvvm_err_msg,                    \
+        snprintf(nvvm_err_msg,                  \
                  sizeof(nvvm_err_msg),          \
                  "NVVM Error (%d): %s\n",       \
                  (int)(res), msg);              \
@@ -282,15 +300,24 @@ static char *irToPtx(string IR, size_t *ptx_size)
 
     NVVM_CHECK(nvvmCreateProgram(&prog), "Failed to create program");
 
+#if defined(USE_LIBDEVICE)
+    //FIXME: Use proper compute
+    NVVM_CHECK(nvvmAddModuleToProgram(prog, compute_20_bc, compute_20_bc_len, "libdevice kernels"),
+               "Failed to add libdevice");
+#endif
+
     NVVM_CHECK(nvvmAddModuleToProgram(prog, IR.c_str(), IR.size(), "generated kernel"),
                "Failed to add module");
 
+    //FIXME: Use proper compute
+    const char *options = NULL;
+    const int noptions = 0;
+
 //#ifdef NDEBUG
 #if 0
-    NVVM_CHECK(nvvmCompileProgram(prog, 0, NULL), "Failed to compile program");
+    NVVM_CHECK(nvvmCompileProgram(prog, noptions, &options), "Failed to compile program");
 #else
-    const char *options = "";
-    nvvmResult comp_res = nvvmCompileProgram(prog, 1, &options);
+    nvvmResult comp_res = nvvmCompileProgram(prog, noptions, &options);
     if (comp_res != NVVM_SUCCESS) {
         size_t log_size = 0;
         nvvmGetProgramLogSize(prog, &log_size);
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 48cd021e1d..f40e4fa9a2 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -40,13 +40,29 @@ struct UnOp
         }                                           \
     };                                              \
 
-#define UNARY_FN_LLVM(T, fn, fname)             \
+#define UNARY_FN_NAME(op, fn)                       \
+    template<typename T>                            \
+    struct UnOp<T, af_##op##_t>                     \
+    {                                               \
+        std::string res;                            \
+        UnOp() :                                    \
+            res(cuMangledName<T, false>("___"#fn))  \
+        {                                           \
+        }                                           \
+        const std::string name()                    \
+        {                                           \
+            return res;                             \
+        }                                           \
+    };                                              \
+
+#if defined(USE_LIBDEVICE)
+#define NVVM_SPECIALIZE_TYPE(T, fn, fname)      \
     template<>                                  \
     struct UnOp<T, af_##fn##_t>                 \
     {                                           \
         std::string res;                        \
         UnOp() :                                \
-            res(fname)                          \
+            res("@__nv_"#fname)                 \
         {                                       \
         }                                       \
         const std::string name()                \
@@ -55,47 +71,62 @@ struct UnOp
         }                                       \
     };                                          \
 
-UNARY_FN(sin)
-UNARY_FN(cos)
-UNARY_FN(tan)
+#else
+#define #define NVVM_SPECIALIZE_TYPE(T, fn, fname)  // no specialization
+#endif
+
+#define NVVM_SPECIALIZE_FLOATING_NAME(fn, fname)    \
+    UNARY_FN(fn)                                    \
+    NVVM_SPECIALIZE_TYPE(float, fn, fname##f)       \
+    NVVM_SPECIALIZE_TYPE(double, fn, fname)         \
+
+
+#define NVVM_SPECIALIZE_FLOATING(fn)            \
+    NVVM_SPECIALIZE_FLOATING_NAME(fn, fn)
+
+NVVM_SPECIALIZE_FLOATING(sin)
+NVVM_SPECIALIZE_FLOATING(cos)
+NVVM_SPECIALIZE_FLOATING(tan)
+NVVM_SPECIALIZE_FLOATING(asin)
+NVVM_SPECIALIZE_FLOATING(acos)
+NVVM_SPECIALIZE_FLOATING(atan)
+NVVM_SPECIALIZE_FLOATING(sinh)
+NVVM_SPECIALIZE_FLOATING(cosh)
+NVVM_SPECIALIZE_FLOATING(tanh)
+NVVM_SPECIALIZE_FLOATING(asinh)
+NVVM_SPECIALIZE_FLOATING(acosh)
+NVVM_SPECIALIZE_FLOATING(atanh)
+NVVM_SPECIALIZE_FLOATING(exp)
+NVVM_SPECIALIZE_FLOATING(expm1)
+NVVM_SPECIALIZE_FLOATING(erf)
+NVVM_SPECIALIZE_FLOATING(erfc)
+NVVM_SPECIALIZE_FLOATING(tgamma)
+NVVM_SPECIALIZE_FLOATING(lgamma)
+NVVM_SPECIALIZE_FLOATING(log)
+NVVM_SPECIALIZE_FLOATING(log1p)
+NVVM_SPECIALIZE_FLOATING(log10)
+NVVM_SPECIALIZE_FLOATING(log2)
+NVVM_SPECIALIZE_FLOATING(sqrt)
+NVVM_SPECIALIZE_FLOATING(cbrt)
+NVVM_SPECIALIZE_FLOATING(round)
+NVVM_SPECIALIZE_FLOATING(trunc)
+NVVM_SPECIALIZE_FLOATING(ceil)
+NVVM_SPECIALIZE_FLOATING(floor)
 
-UNARY_FN(asin)
-UNARY_FN(acos)
-UNARY_FN(atan)
+UNARY_FN(sign )
+NVVM_SPECIALIZE_TYPE(float , sign, signbitf)
+NVVM_SPECIALIZE_TYPE(double, sign, signbitd)
 
-UNARY_FN(sinh)
-UNARY_FN(cosh)
-UNARY_FN(tanh)
+UNARY_FN_NAME(isnan, isNaN)
+NVVM_SPECIALIZE_TYPE(float , isnan, isnand)
+NVVM_SPECIALIZE_TYPE(double, isnan, isnanf)
 
-UNARY_FN(asinh)
-UNARY_FN(acosh)
-UNARY_FN(atanh)
+UNARY_FN_NAME(isinf, isINF)
+NVVM_SPECIALIZE_TYPE(float , isinf, isinfd)
+NVVM_SPECIALIZE_TYPE(double, isinf, isinff)
 
-UNARY_FN(exp)
+UNARY_FN_NAME(iszero, iszero)
 UNARY_FN(sigmoid)
-UNARY_FN(expm1)
-UNARY_FN(erf)
-UNARY_FN(erfc)
-
-UNARY_FN(tgamma)
-UNARY_FN(lgamma)
-
-UNARY_FN(log)
-UNARY_FN(log1p)
-UNARY_FN(log10)
-UNARY_FN(log2)
-
-UNARY_FN(sqrt)
-UNARY_FN_LLVM(float, sqrt, "@llvm.sqrt.f32")
-UNARY_FN_LLVM(double, sqrt, "@llvm.sqrt.f64")
-
-UNARY_FN(cbrt)
-
-UNARY_FN(sign )
-UNARY_FN(round)
-UNARY_FN(trunc)
-UNARY_FN(ceil)
-UNARY_FN(floor)
 
 #undef UNARY_FN
 
@@ -115,27 +146,6 @@ UNARY_FN(floor)
         return createNodeArray<T>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
-
-#define UNARY2_FN(op, fn)                           \
-    template<typename T>                            \
-    struct UnOp<T, af_##op##_t>                     \
-    {                                               \
-        std::string res;                            \
-        UnOp() :                                    \
-            res(cuMangledName<T, false>("___"#fn))  \
-        {                                           \
-        }                                           \
-        const std::string name()                    \
-        {                                           \
-            return res;                             \
-        }                                           \
-    };                                              \
-
-
-UNARY2_FN(isnan, isNaN)
-UNARY2_FN(isinf, isINF)
-UNARY2_FN(iszero, iszero)
-
     template<typename T, af_op_t op>
     Array<char> checkOp(const Array<T> &in)
     {

From 6ad2cf4acc9e211df47402ebaeaa263e594d1731 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 5 Aug 2016 00:03:11 -0400
Subject: [PATCH 0735/2677] performance improvements for Linear JIT CUDA
 kernels

---
 src/backend/cuda/jit.cpp | 66 ++++++++++++++++++++++++++--------------
 1 file changed, 43 insertions(+), 23 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 535d89ff8e..f90e137887 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -93,11 +93,16 @@ static string getFuncName(std::vector<Node *> nodes, bool is_linear)
 static string getKernelString(string funcName, std::vector<Node *> nodes, bool is_linear)
 {
     static const char *defineVoid = "define void ";
-    static const char *dimParams = "\n"
+    static const char *generalDimParams = "\n"
         "i32 %ostr0, i32 %ostr1, i32 %ostr2, i32 %ostr3,\n"
         "i32 %odim0, i32 %odim1, i32 %odim2, i32 %odim3,\n"
         "i32 %blkx, i32 %blky, i32 %ndims";
 
+    static const char *linearDimParams = "\n"
+        "i32 %nelem, i32 %blkx, i32 %blky";
+
+    const char *dimParams = is_linear ? linearDimParams : generalDimParams;
+
     static const char *blockStart = "\n{\n\n"
         "entry:\n\n";
     static const char *blockEnd = "\n\n"
@@ -187,10 +192,7 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
         "%goff = mul i32 %bid , %bdmx\n"
         "%gid  = add i32 %goff ,%tidx\n"
         "%idx  = sext i32 %gid to i64\n"
-        "%el1  = mul i32 %odim0, %odim1\n"
-        "%el2  = mul i32 %el1  , %odim2\n"
-        "%el3  = mul i32 %el2  , %odim3\n"
-        "%cmp0 = icmp slt i32 %gid, %el3\n"
+        "%cmp0 = icmp slt i32 %gid, %nelem\n"
         "br i1 %cmp0, label %core, label %end\n";
 
     static const char *functionLoad = "\n"
@@ -271,11 +273,17 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
     kerStream << "!nvvm.annotations = !{!1}\n"
               << "!1 = metadata !{void (\n"
               << inAnnStream.str()
-              << outAnnStream.str()
-              << "i32, i32, i32, i32,\n"
-              << "i32, i32, i32, i32,\n"
-              << "i32, i32, i32\n"
-              << ")* " << funcName << ",\n "
+              << outAnnStream.str();
+
+    if (is_linear) {
+        kerStream << "i32, i32, i32\n";
+    } else {
+        kerStream  << "i32, i32, i32, i32,\n"
+                   << "i32, i32, i32, i32,\n"
+                   << "i32, i32, i32\n";
+    }
+
+    kerStream << ")* " << funcName << ",\n "
               << "metadata !\"kernel\", i32 1}\n";
 
     return kerStream.str();
@@ -522,26 +530,38 @@ void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
         nodes[i]->setArgs(args, is_linear);
     }
 
-    int strides[] = {(int)outputs[0].strides[0],
-                     (int)outputs[0].strides[1],
-                     (int)outputs[0].strides[2],
-                     (int)outputs[0].strides[3]};
-
-    int dims[] = {(int)outputs[0].dims[0],
-                  (int)outputs[0].dims[1],
-                  (int)outputs[0].dims[2],
-                  (int)outputs[0].dims[3]};
-
     for (int i = 0; i < num_outputs; i++) {
         args.push_back(&outputs[i].ptr);
     }
 
-    for (int i = 0; i < 4; i++) args.push_back((void *)(strides + i));
-    for (int i = 0; i < 4; i++) args.push_back((void *)(dims + i));
+
+    if (is_linear) {
+        int nelem = 1;
+        for (int i = 0; i < 4; i++) {
+            nelem *= outputs[0].dims[i];
+        }
+        args.push_back((void *)&nelem);
+    } else {
+        int strides[] = {(int)outputs[0].strides[0],
+                         (int)outputs[0].strides[1],
+                         (int)outputs[0].strides[2],
+                         (int)outputs[0].strides[3]};
+
+        int dims[] = {(int)outputs[0].dims[0],
+                      (int)outputs[0].dims[1],
+                      (int)outputs[0].dims[2],
+                      (int)outputs[0].dims[3]};
+
+        for (int i = 0; i < 4; i++) args.push_back((void *)(strides + i));
+        for (int i = 0; i < 4; i++) args.push_back((void *)(dims + i));
+    }
 
     args.push_back((void *)&blocks_x_);
     args.push_back((void *)&blocks_y_);
-    args.push_back((void *)&num_odims);
+
+    if (!is_linear) {
+        args.push_back((void *)&num_odims);
+    }
 
     CU_CHECK(cuLaunchKernel(ker,
                             blocks_x,

From 7b9caf7d841b16997accc8256cb0a4849319344f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 5 Aug 2016 02:52:21 -0400
Subject: [PATCH 0736/2677] Fixing bugs in CUDA JIT

---
 src/backend/cuda/JIT/UnaryNode.hpp | 39 +++++++++++++++++++------
 src/backend/cuda/binary.hpp        | 24 ++++++++--------
 src/backend/cuda/unary.hpp         | 46 +++++++++++++++++++++++-------
 3 files changed, 77 insertions(+), 32 deletions(-)

diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index cc3f02556a..1579688f8c 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -23,16 +23,19 @@ namespace JIT
         std::string m_op_str;
         Node_ptr m_child;
         int m_op;
+        bool m_is_check;
 
     public:
         UnaryNode(const char *out_type_str, const char *name_str,
                   const std::string &op_str,
-                  Node_ptr child, int op)
+                  Node_ptr child, int op, bool is_check=false)
             : Node(out_type_str, name_str),
               m_op_str(op_str),
               m_child(child),
-              m_op(op)
+              m_op(op),
+              m_is_check(is_check)
         {
+
         }
 
         bool isLinear(dim_t dims[4])
@@ -80,19 +83,36 @@ namespace JIT
             if (!(m_child->isGenFunc())) m_child->genFuncs(kerStream, declStrs, is_linear);
 
             std::stringstream declStream;
-            declStream << "declare " << m_type_str << " " << m_op_str
-                       << "(" << m_child->getTypeStr() << ")\n";
+
+            if (m_is_check) {
+                declStream << "declare " << "i32 " << m_op_str
+                           << "(" << m_child->getTypeStr() << ")\n";
+            } else {
+                declStream << "declare " << m_type_str << " " << m_op_str
+                           << "(" << m_child->getTypeStr() << ")\n";
+            }
 
             str_map_iter loc = declStrs.find(declStream.str());
             if (loc == declStrs.end()) {
                 declStrs[declStream.str()] = true;
             }
 
-            kerStream << "%val" << m_id << " = call "
-                      << m_type_str << " "
-                      << m_op_str << "("
-                      << m_child->getTypeStr() << " "
-                      << "%val" << m_child->getId() << ")\n";
+            if (m_is_check) {
+                kerStream << "%tmp" << m_id << " = call i32 "
+                          << m_op_str << "("
+                          << m_child->getTypeStr() << " "
+                          << "%val" << m_child->getId() << ")\n";
+
+                kerStream << "%val" << m_id << " = "
+                          << "trunc i32 %tmp" << m_id << " to " << m_type_str << "\n";
+
+            } else {
+                kerStream << "%val" << m_id << " = call "
+                          << m_type_str << " "
+                          << m_op_str << "("
+                          << m_child->getTypeStr() << " "
+                          << "%val" << m_child->getId() << ")\n";
+            }
 
             m_gen_func = true;
         }
@@ -118,6 +138,7 @@ namespace JIT
         void resetFlags()
         {
             if (m_set_id) {
+                m_is_check = false;
                 resetCommonFlags();
                 m_child->resetFlags();
             }
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index 68b14891d8..bb81c19ffa 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -165,33 +165,33 @@ BINARY(and)
 BINARY(or)
 
 BINARY(lt)
-NVVM_COMPARE_OP_INT(lt, "icmp ult")
-NVVM_COMPARE_OP_UINT(lt, "icmp slt")
+NVVM_COMPARE_OP_INT(lt, "icmp slt")
+NVVM_COMPARE_OP_UINT(lt, "icmp ult")
 NVVM_COMPARE_OP_FLOAT(lt, "fcmp olt")
 
 BINARY(gt)
-NVVM_COMPARE_OP_INT(gt, "icmp ugt")
-NVVM_COMPARE_OP_UINT(gt, "icmp sgt")
+NVVM_COMPARE_OP_INT(gt, "icmp sgt")
+NVVM_COMPARE_OP_UINT(gt, "icmp ugt")
 NVVM_COMPARE_OP_FLOAT(gt, "fcmp ogt")
 
 BINARY(le)
-NVVM_COMPARE_OP_INT(le, "icmp ule")
-NVVM_COMPARE_OP_UINT(le, "icmp sle")
+NVVM_COMPARE_OP_INT(le, "icmp sle")
+NVVM_COMPARE_OP_UINT(le, "icmp ule")
 NVVM_COMPARE_OP_FLOAT(le, "fcmp ole")
 
 BINARY(ge)
-NVVM_COMPARE_OP_INT(ge, "icmp uge")
-NVVM_COMPARE_OP_UINT(ge, "icmp sge")
+NVVM_COMPARE_OP_INT(ge, "icmp sge")
+NVVM_COMPARE_OP_UINT(ge, "icmp uge")
 NVVM_COMPARE_OP_FLOAT(ge, "fcmp oge")
 
 BINARY(eq)
-NVVM_COMPARE_OP_INT(eq, "icmp ueq")
-NVVM_COMPARE_OP_UINT(eq, "icmp seq")
+NVVM_COMPARE_OP_INT(eq, "icmp eq")
+NVVM_COMPARE_OP_UINT(eq, "icmp eq")
 NVVM_COMPARE_OP_FLOAT(eq, "fcmp oeq")
 
 BINARY(neq)
-NVVM_COMPARE_OP_INT(neq, "icmp une")
-NVVM_COMPARE_OP_UINT(neq, "icmp sne")
+NVVM_COMPARE_OP_INT(neq, "icmp ne")
+NVVM_COMPARE_OP_UINT(neq, "icmp ne")
 NVVM_COMPARE_OP_FLOAT(neq, "fcmp one")
 
 BINARY(max)
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index f40e4fa9a2..a858c82995 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -30,8 +30,10 @@ struct UnOp
     struct UnOp<T, af_##fn##_t>                     \
     {                                               \
         std::string res;                            \
+        bool is_check;                              \
         UnOp() :                                    \
-            res(cuMangledName<T, false>("___"#fn))  \
+            res(cuMangledName<T, false>("___"#fn)), \
+            is_check(false)                         \
         {                                           \
         }                                           \
         const std::string name()                    \
@@ -45,8 +47,10 @@ struct UnOp
     struct UnOp<T, af_##op##_t>                     \
     {                                               \
         std::string res;                            \
+        bool is_check;                              \
         UnOp() :                                    \
-            res(cuMangledName<T, false>("___"#fn))  \
+            res(cuMangledName<T, false>("___"#fn)), \
+            is_check(false)                         \
         {                                           \
         }                                           \
         const std::string name()                    \
@@ -61,8 +65,27 @@ struct UnOp
     struct UnOp<T, af_##fn##_t>                 \
     {                                           \
         std::string res;                        \
+        bool is_check;                          \
         UnOp() :                                \
-            res("@__nv_"#fname)                 \
+            res("@__nv_"#fname),                \
+            is_check(false)                     \
+        {                                       \
+        }                                       \
+        const std::string name()                \
+        {                                       \
+            return res;                         \
+        }                                       \
+    };                                          \
+
+#define NVVM_SPECIALIZE_CHECK(T, fn, fname)     \
+    template<>                                  \
+    struct UnOp<T, af_##fn##_t>                 \
+    {                                           \
+        std::string res;                        \
+        bool is_check;                          \
+        UnOp() :                                \
+            res("@__nv_"#fname),                \
+            is_check(true)                      \
         {                                       \
         }                                       \
         const std::string name()                \
@@ -73,6 +96,7 @@ struct UnOp
 
 #else
 #define #define NVVM_SPECIALIZE_TYPE(T, fn, fname)  // no specialization
+#define #define NVVM_SPECIALIZE_CHECK(T, fn, fname)  // no specialization
 #endif
 
 #define NVVM_SPECIALIZE_FLOATING_NAME(fn, fname)    \
@@ -114,16 +138,16 @@ NVVM_SPECIALIZE_FLOATING(ceil)
 NVVM_SPECIALIZE_FLOATING(floor)
 
 UNARY_FN(sign )
-NVVM_SPECIALIZE_TYPE(float , sign, signbitf)
-NVVM_SPECIALIZE_TYPE(double, sign, signbitd)
+NVVM_SPECIALIZE_CHECK(float , sign, signbitf)
+NVVM_SPECIALIZE_CHECK(double, sign, signbitd)
 
 UNARY_FN_NAME(isnan, isNaN)
-NVVM_SPECIALIZE_TYPE(float , isnan, isnand)
-NVVM_SPECIALIZE_TYPE(double, isnan, isnanf)
+NVVM_SPECIALIZE_CHECK(float , isnan, isnanf)
+NVVM_SPECIALIZE_CHECK(double, isnan, isnand)
 
 UNARY_FN_NAME(isinf, isINF)
-NVVM_SPECIALIZE_TYPE(float , isinf, isinfd)
-NVVM_SPECIALIZE_TYPE(double, isinf, isinff)
+NVVM_SPECIALIZE_CHECK(float , isinf, isinff)
+NVVM_SPECIALIZE_CHECK(double, isinf, isinfd)
 
 UNARY_FN_NAME(iszero, iszero)
 UNARY_FN(sigmoid)
@@ -141,7 +165,7 @@ UNARY_FN(sigmoid)
         JIT::UnaryNode *node = new JIT::UnaryNode(irname<T>(),
                                                   afShortName<T>(),
                                                   uop.name(),
-                                                  in_node, op);
+                                                  in_node, op, uop.is_check);
 
         return createNodeArray<T>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
@@ -155,7 +179,7 @@ UNARY_FN(sigmoid)
         JIT::UnaryNode *node = new JIT::UnaryNode(irname<char>(),
                                                   afShortName<char>(),
                                                   uop.name(),
-                                                  in_node, op);
+                                                  in_node, op, uop.is_check);
         return createNodeArray<char>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 }

From 6e1442caca61392bf63429cdafa13c45b3c6e7c5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 5 Aug 2016 03:14:19 -0400
Subject: [PATCH 0737/2677] TEST: Adding compare tests

---
 test/compare.cpp | 49 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 test/compare.cpp

diff --git a/test/compare.cpp b/test/compare.cpp
new file mode 100644
index 0000000000..ab2fa6c7d0
--- /dev/null
+++ b/test/compare.cpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <af/array.h>
+#include <af/arith.h>
+#include <af/data.h>
+#include <testHelpers.hpp>
+
+template<typename T>
+class Compare : public ::testing::Test
+{
+};
+
+typedef ::testing::Types<float, double, uint, int, intl, uintl, uchar, short, ushort> TestTypes;
+TYPED_TEST_CASE(Compare, TestTypes);
+
+#define COMPARE(OP, Name)                                       \
+    TYPED_TEST(Compare, Test_##Name)                            \
+    {                                                           \
+        typedef TypeParam T;                                    \
+        const int num = 1 << 20;                                \
+        af_dtype ty = (af_dtype) af::dtype_traits<T>::af_type;  \
+        af::array a = af::randu(num, ty);                       \
+        af::array b = af::randu(num, ty);                       \
+        af::array c = a OP b;                                   \
+        std::vector<T> ha(num), hb(num);                        \
+        std::vector<char> hc(num);                              \
+        a.host(&ha[0]);                                         \
+        b.host(&hb[0]);                                         \
+        c.host(&hc[0]);                                         \
+        for (int i = 0; i < num; i++) {                         \
+            char res = ha[i] OP hb[i];                          \
+            ASSERT_EQ((int)res, (int)hc[i]);                    \
+        }                                                       \
+    }                                                           \
+
+COMPARE(==, eq)
+COMPARE(!=, ne)
+COMPARE(<=, le)
+COMPARE(>=, ge)
+COMPARE(<, lt)
+COMPARE(>, gt)

From 9f88ab36e8a5053ece1819fd56924f8f4abe81fb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 5 Aug 2016 13:19:09 -0400
Subject: [PATCH 0738/2677] BUGFIX: Fixing bug in sign function for cuda
 backend

---
 src/backend/cuda/JIT/UnaryNode.hpp | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 1579688f8c..87679efa20 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -103,8 +103,13 @@ namespace JIT
                           << m_child->getTypeStr() << " "
                           << "%val" << m_child->getId() << ")\n";
 
-                kerStream << "%val" << m_id << " = "
-                          << "trunc i32 %tmp" << m_id << " to " << m_type_str << "\n";
+                if (m_type_str[0] == 'i') {
+                    kerStream << "%val" << m_id << " = "
+                              << "trunc i32 %tmp" << m_id << " to " << m_type_str << "\n";
+                } else {
+                    kerStream << "%val" << m_id << " = "
+                              << "sitofp i32 %tmp" << m_id << " to " << m_type_str << "\n";
+                }
 
             } else {
                 kerStream << "%val" << m_id << " = call "

From 2b0f3d192763387422c10a6978e8f8cc6b4d7353 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 6 Jul 2016 10:54:53 -0400
Subject: [PATCH 0739/2677] rebase cubic interp

---
 src/api/c/approx.cpp                 |   8 +-
 src/backend/cpu/approx.cpp           |   8 ++
 src/backend/cpu/kernel/approx1.hpp   |  55 ++++++++++-
 src/backend/cpu/kernel/approx2.hpp   |  94 ++++++++++++++++++-
 src/backend/cuda/approx.cu           |   6 ++
 src/backend/cuda/kernel/approx.hpp   | 132 ++++++++++++++++++++++++++-
 src/backend/opencl/approx.cpp        |   6 ++
 src/backend/opencl/kernel/approx.hpp |   4 +
 src/backend/opencl/kernel/approx1.cl |  49 ++++++++++
 src/backend/opencl/kernel/approx2.cl |  78 ++++++++++++++++
 10 files changed, 434 insertions(+), 6 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 7c2935ac1b..c3fcc03c6d 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -53,7 +53,9 @@ af_err af_approx1(af_array *out, const af_array in, const af_array pos,
         // POS should either be (x, 1, 1, 1) or (1, idims[1], idims[2], idims[3])
         DIM_ASSERT(2, p_info.isColumn() ||
                       (pdims[1] == idims[1] && pdims[2] == idims[2] && pdims[3] == idims[3]));
-        ARG_ASSERT(3, (method == AF_INTERP_LINEAR || method == AF_INTERP_NEAREST));
+        ARG_ASSERT(3, (method == AF_INTERP_LINEAR  ||
+                       method == AF_INTERP_NEAREST ||
+                       method == AF_INTERP_CUBIC));
 
         af_array output;
 
@@ -96,7 +98,9 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
         // POS should either be (x, y, 1, 1) or (x, y, idims[2], idims[3])
         DIM_ASSERT(2, (pdims[2] == 1        && pdims[3] == 1) ||
                       (pdims[2] == idims[2] && pdims[3] == idims[3]));
-        ARG_ASSERT(3, (method == AF_INTERP_LINEAR || method == AF_INTERP_NEAREST));
+        ARG_ASSERT(3, (method == AF_INTERP_LINEAR  ||
+                       method == AF_INTERP_NEAREST ||
+                       method == AF_INTERP_CUBIC));
 
         af_array output;
 
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index b817b840b4..c3736e44f3 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -38,6 +38,10 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
             getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
                                out, in, pos, offGrid);
             break;
+        case AF_INTERP_CUBIC:
+            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_CUBIC>,
+                               out, in, pos, offGrid);
+            break;
         default:
             break;
     }
@@ -68,6 +72,10 @@ Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &p
             getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
                                out, in, pos0, pos1, offGrid);
             break;
+        case AF_INTERP_CUBIC:
+            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_CUBIC>,
+                               out, in, pos0, pos1, offGrid);
+            break;
         default:
             break;
     }
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
index 2ba1fd40b5..737a64d9c9 100644
--- a/src/backend/cpu/kernel/approx1.hpp
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -83,7 +83,7 @@ struct approx1_op<InT, LocT, AF_INTERP_LINEAR>
         }
 
         dim_t const grid_x = floor(x);  // nearest grid
-        LocT const off_x = x - grid_x; // fractional offset
+        LocT const off_x = x - grid_x;  // fractional offset
 
         dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
                          + idy * ostrides[1] + idx;
@@ -106,6 +106,59 @@ struct approx1_op<InT, LocT, AF_INTERP_LINEAR>
     }
 };
 
+template<typename InT, typename LocT>
+struct approx1_op<InT, LocT, AF_INTERP_CUBIC>
+{
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
+    {
+        dim_t pmId = idx;
+        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
+
+        LocT const x = pos[pmId];
+        bool gFlag = false;
+        if (x < 0 || idims[0] < x+1) { //check index of bounds
+            gFlag = true;
+        }
+
+        dim_t const grid_x = floor(x);  // nearest grid
+        LocT  const off_x  = x - grid_x;  // fractional offset
+
+        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+
+        if(gFlag) {
+            out[omId] = scalar<InT>(offGrid);
+        } else {
+            dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
+
+            //compute basis function values
+            InT h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
+            InT h10 = off_x * (1 - off_x) * (1 - off_x);
+            InT h01 = off_x * off_x * (3 - 2 * off_x);
+            InT h11 = off_x * off_x * (off_x - 1);
+            // Check if x-1, x, and x+1, x+2 are both valid indices
+            bool condr  = (x > 0);
+            bool condl1 = (x < idims[0] - 1);
+            bool condl2 = (x < idims[0] - 2);
+            // Compute Left and Right points and tangents
+            InT pl = condr  ? in[ioff]     : scalar<InT>(offGrid);
+            InT pr = condl1 ? in[ioff + 1] : scalar<InT>(offGrid);
+            InT tl = condr  ? scalar<InT>(0.5)  * ((in[ioff + 1] - in[ioff - 1])) :
+                              scalar<InT>(0.5)  * ((in[ioff + 1] - scalar<InT>(offGrid)));
+            InT tr = condl2 ? scalar<InT>(0.5)  * ((in[ioff + 2] - in[ioff])) :
+                              scalar<InT>(0.5)  * ((scalar<InT>(offGrid) - in[ioff]));
+
+            // Write final value
+            out[omId] = h00 * pl + h10 * tl + h01 * pr + h11 * tr;
+        }
+    }
+};
+
 template<typename InT, typename LocT, af_interp_type Method>
 void approx1(Array<InT> output, Array<InT> const input,
              Array<LocT> const position, float const offGrid)
diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
index a29a11d9f9..706f140312 100644
--- a/src/backend/cpu/kernel/approx2.hpp
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2015,  ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #pragma once
+#include <af/defines.h>
+#include <handle.hpp>
+#include <cast.hpp>
 #include <Array.hpp>
 #include <math.hpp>
 
@@ -16,6 +19,7 @@ namespace cpu
 namespace kernel
 {
 
+
 template<typename InT, typename LocT, af_interp_type Method>
 struct approx2_op
 {
@@ -99,7 +103,7 @@ struct approx2_op<InT, LocT, AF_INTERP_LINEAR>
         bool condY = (y < idims[1] - 1);
         bool condX = (x < idims[0] - 1);
 
-        // Compute wieghts used
+        // Compute weights used
         LocT wt00 = ((LocT)1.0 - off_x) * ((LocT)1.0 - off_y);
         LocT wt10 = (condY) ? ((LocT)1.0 - off_x) * (off_y) : 0;
         LocT wt01 = (condX) ? (off_x) * ((LocT)1.0 - off_y) : 0;
@@ -130,6 +134,92 @@ struct approx2_op<InT, LocT, AF_INTERP_LINEAR>
     }
 };
 
+template<typename InT, typename LocT> inline static
+InT cubicInterpolate(InT p[4], LocT x) {
+    return p[1] + scalar<InT>(0.5) * x * (p[2] - p[0] + x * (scalar<InT>(2.0) * p[0] - scalar<InT>(5.0) * p[1] + scalar<InT>(4.0) * p[2] - p[3] + x*(scalar<InT>(3.0)*(p[1] - p[2]) + p[3] - p[0])));
+}
+
+template<typename InT, typename LocT> inline static
+InT bicubicInterpolate(InT p[4][4], LocT x, LocT y) {
+    InT arr[4];
+    arr[0] = cubicInterpolate(p[0], x);
+    arr[1] = cubicInterpolate(p[1], x);
+    arr[2] = cubicInterpolate(p[2], x);
+    arr[3] = cubicInterpolate(p[3], x);
+    return cubicInterpolate(arr, y);
+}
+
+template<typename InT, typename LocT>
+struct approx2_op<InT, LocT, AF_INTERP_CUBIC>
+{
+    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
+              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
+              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
+              af::dim4 const & ostrides, af::dim4 const & istrides,
+              af::dim4 const & pstrides, af::dim4 const & qstrides,
+              float const offGrid, bool const pBatch,
+              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
+    {
+        dim_t pmId = idy * pstrides[1] + idx;
+        dim_t qmId = idy * qstrides[1] + idx;
+        if(pBatch) {
+            pmId += idw * pstrides[3] + idz * pstrides[2];
+            qmId += idw * qstrides[3] + idz * qstrides[2];
+        }
+
+        LocT const x = pos[pmId], y = qos[qmId];
+
+
+        dim_t const grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+        LocT  const off_x  = x - grid_x, off_y  = y - grid_y; // 0-1 fractional offset
+
+        //input and output indices
+        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
+                         + idy * ostrides[1] + idx;
+        dim_t ioff = idw * istrides[3] + idz * istrides[2]
+                + grid_y * istrides[1] + grid_x;
+
+        // Check if x,y are in bounds
+        if (x < 0 || y < 0 || x > idims[0] - 1 || y > idims[1] - 1) {
+            out[omId] = scalar<InT>(offGrid);
+            return;
+        }
+
+        // used for setting values at boundaries
+        bool condXl = (x < 1);
+        bool condYl = (y < 1);
+        bool condXg = (x > idims[0] - 3);
+        bool condYg = (y > idims[1] - 3);
+
+        //for bicubic interpolation, work with 4x4 patch at a time
+        InT patch[4][4];
+
+        //assumption is that inner patch consisting of 4 points is minimum requirement for bicubic interpolation
+        //inner square
+        patch[1][1] = in[ioff];
+        patch[1][2] = in[ioff + 1];
+        patch[2][1] = in[ioff + istrides[1]];
+        patch[2][2] = in[ioff + istrides[1] + 1];
+        //outer sides
+        patch[0][1] = (condYl)? scalar<InT>(offGrid) : in[ioff - istrides[1]];
+        patch[0][2] = (condYl)? scalar<InT>(offGrid) : in[ioff - istrides[1] + 1];
+        patch[3][1] = (condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1]];
+        patch[3][2] = (condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1] + 1];
+        patch[1][0] = (condXl)? scalar<InT>(offGrid) : in[ioff - 1];
+        patch[2][0] = (condXl)? scalar<InT>(offGrid) : in[ioff + istrides[1] -1];
+        patch[1][3] = (condXg)? scalar<InT>(offGrid) : in[ioff + 2];
+        patch[2][3] = (condXg)? scalar<InT>(offGrid) : in[ioff + istrides[1] + 2];
+        //corners
+        patch[0][0] =(condXl || condYl)? scalar<InT>(offGrid) : in[ioff - istrides[1] - 1]    ;
+        patch[0][3] =(condYl || condXg)? scalar<InT>(offGrid) : in[ioff - istrides[1] + 1]    ;
+        patch[3][0] =(condXl || condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1] - 1];
+        patch[3][3] =(condXg || condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1] + 2];
+
+        // Write Final Value
+        out[omId] = bicubicInterpolate(patch, off_x, off_y);
+    }
+};
+
 template<typename InT, typename LocT, af_interp_type Method>
 void approx2(Array<InT> output, Array<InT> const input,
              Array<LocT> const position, Array<LocT> const qosition,
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cu
index 34e9d43139..6ae3a50f12 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cu
@@ -33,6 +33,9 @@ namespace cuda
             case AF_INTERP_LINEAR:
                 kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
                 break;
+            case AF_INTERP_CUBIC:
+                kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
+                break;
             default:
                 break;
         }
@@ -58,6 +61,9 @@ namespace cuda
             case AF_INTERP_LINEAR:
                 kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
                 break;
+            case AF_INTERP_CUBIC:
+                kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
+                break;
             default:
                 break;
         }
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 53831da975..40435f851b 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -146,7 +146,7 @@ namespace cuda
             bool condY = (y < in.dims[1] - 1);
             bool condX = (x < in.dims[0] - 1);
 
-            // Compute wieghts used
+            // Compute weights used
             Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
             Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
             Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
@@ -166,6 +166,130 @@ namespace cuda
             out.ptr[omId] = (yo / wt);
         }
 
+        ///////////////////////////////////////////////////////////////////////////
+        // cubic resampling
+        ///////////////////////////////////////////////////////////////////////////
+        template<typename Ty, typename Tp>
+        __device__ inline static
+        void core_cubic1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+                          Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
+                          const float offGrid, const bool pBatch)
+        {
+            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+                             + idy * out.strides[1] + idx;
+            dim_t pmId = idx;
+            if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
+            const Tp pVal = pos.ptr[pmId];
+            if (pVal < 0 || in.dims[0] < pVal+1) {
+                out.ptr[omId] = scalar<Ty>(offGrid);
+                return;
+            }
+
+            const dim_t grid_x = floor(pVal);  // nearest grid
+            const Tp off_x = pVal - grid_x;    // fractional offset
+
+            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
+
+            // Check if x-1, x, and x+1, x+2 are both valid indices
+            bool condr  = (pVal > 0);
+            bool condl1 = (pVal < in.dims[0] - 1);
+            bool condl2 = (pVal < in.dims[0] - 2);
+
+            //compute basis function values
+            Tp h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
+            Tp h10 = off_x * (1 - off_x) * (1 - off_x);
+            Tp h01 = off_x * off_x * (3 - 2 * off_x);
+            Tp h11 = off_x * off_x * (off_x - 1);
+
+            // Compute Left and Right points and tangents
+            Ty pl = condr  ? in.ptr[ioff]     : scalar<Ty>(offGrid);
+            Ty pr = condl1 ? in.ptr[ioff + 1] : scalar<Ty>(offGrid);
+            Ty tl = condr  ? scalar<Ty>(0.5)  * ((in.ptr[ioff + 1] - in.ptr[ioff - 1])) :
+                             scalar<Ty>(0.5)  * ((in.ptr[ioff + 1] - scalar<Ty>(offGrid)));
+            Ty tr = condl2 ? scalar<Ty>(0.5)  * ((in.ptr[ioff + 2] - in.ptr[ioff])) :
+                             scalar<Ty>(0.5)  * ((scalar<Ty>(offGrid) - in.ptr[ioff]));
+
+            // Write final value
+            out.ptr[omId] = h00 * pl + h10 * tl + h01 * pr + h11 * tr;
+        }
+
+        template<typename Ty, typename Tp>
+        __device__ inline static
+        Ty cubicInterpolate(Ty p[4], Tp x) {
+            return p[1] + scalar<Ty>(0.5) * x * (p[2] - p[0] + x * (scalar<Ty>(2.0) * p[0] - scalar<Ty>(5.0) * p[1] + scalar<Ty>(4.0) * p[2] - p[3] + x*(scalar<Ty>(3.0)*(p[1] - p[2]) + p[3] - p[0])));
+        }
+
+        template<typename Ty, typename Tp>
+        __device__ inline static
+        Ty bicubicInterpolate(Ty p[4][4], Tp x, Tp y) {
+            Ty arr[4];
+            arr[0] = cubicInterpolate(p[0], x);
+            arr[1] = cubicInterpolate(p[1], x);
+            arr[2] = cubicInterpolate(p[2], x);
+            arr[3] = cubicInterpolate(p[3], x);
+            return cubicInterpolate(arr, y);
+        }
+
+        template<typename Ty, typename Tp>
+        __device__ inline static
+        void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+                           Param<Ty> out, CParam<Ty> in,
+                           CParam<Tp> pos, CParam<Tp> qos, const float offGrid, const bool pBatch)
+        {
+            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+                             + idy * out.strides[1] + idx;
+            dim_t pmId = idy * pos.strides[1] + idx;
+            dim_t qmId = idy * qos.strides[1] + idx;
+            if(pBatch) {
+                pmId += idw * pos.strides[3] + idz * pos.strides[2];
+                qmId += idw * qos.strides[3] + idz * qos.strides[2];
+            }
+
+
+            const Tp x = pos.ptr[pmId], y = qos.ptr[qmId];
+            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
+                out.ptr[omId] = scalar<Ty>(offGrid);
+                return;
+            }
+
+            const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+            const Tp off_x  = x - grid_x, off_y  = y - grid_y;    // fractional offset
+
+            // used for setting values at boundaries
+            bool condXl = (x < 1);
+            bool condYl = (y < 1);
+            bool condXg = (x > in.dims[0] - 3);
+            bool condYg = (y > in.dims[1] - 3);
+
+            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
+
+            //for bicubic interpolation, work with 4x4 patch at a time
+            Ty patch[4][4];
+
+            //assumption is that inner patch consisting of 4 points is minimum requirement for bicubic interpolation
+            //inner square
+            patch[1][1] = in.ptr[ioff];
+            patch[1][2] = in.ptr[ioff + 1];
+            patch[2][1] = in.ptr[ioff + in.strides[1]];
+            patch[2][2] = in.ptr[ioff + in.strides[1] + 1];
+            //outer sides
+            patch[0][1] = (condYl)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1]];
+            patch[0][2] = (condYl)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1] + 1];
+            patch[3][1] = (condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1]];
+            patch[3][2] = (condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1] + 1];
+            patch[1][0] = (condXl)? scalar<Ty>(offGrid) : in.ptr[ioff - 1];
+            patch[2][0] = (condXl)? scalar<Ty>(offGrid) : in.ptr[ioff + in.strides[1] -1];
+            patch[1][3] = (condXg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2];
+            patch[2][3] = (condXg)? scalar<Ty>(offGrid) : in.ptr[ioff + in.strides[1] + 2];
+            //corners
+            patch[0][0] = (condXl || condYl)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1] - 1]    ;
+            patch[0][3] = (condYl || condXg)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1] + 1]    ;
+            patch[3][0] = (condXl || condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1] - 1];
+            patch[3][3] = (condXg || condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1] + 2];
+
+            out.ptr[omId] = bicubicInterpolate<Ty, Tp>(patch, off_x, off_y);
+        }
+
         ///////////////////////////////////////////////////////////////////////////
         // Approx Kernel
         ///////////////////////////////////////////////////////////////////////////
@@ -192,6 +316,9 @@ namespace cuda
                 case AF_INTERP_LINEAR:
                     core_linear1(idx, idy, idz, idw, out, in, pos, offGrid, pBatch);
                     break;
+                case AF_INTERP_CUBIC:
+                    core_cubic1(idx, idy, idz, idw, out, in, pos, offGrid, pBatch);
+                    break;
                 default:
                     break;
             }
@@ -223,6 +350,9 @@ namespace cuda
                 case AF_INTERP_LINEAR:
                     core_linear2(idx, idy, idz, idw, out, in, pos, qos, offGrid, pBatch);
                     break;
+                case AF_INTERP_CUBIC:
+                    core_cubic2(idx, idy, idz, idw, out, in, pos, qos, offGrid, pBatch);
+                    break;
                 default:
                     break;
             }
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index 8933ce0ccf..dfbae5efdb 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -32,6 +32,9 @@ namespace opencl
             case AF_INTERP_LINEAR:
                 kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
                 break;
+            case AF_INTERP_CUBIC:
+                kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
+                break;
             default:
                 break;
         }
@@ -56,6 +59,9 @@ namespace opencl
             case AF_INTERP_LINEAR:
                 kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
                 break;
+            case AF_INTERP_CUBIC:
+                kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
+                break;
             default:
                 break;
         }
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 8d82dc91c8..47f03fbdeb 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -75,6 +75,8 @@ namespace opencl
                             break;
                         case AF_INTERP_LINEAR:  options << " -D INTERP=LINEAR";
                             break;
+                        case AF_INTERP_CUBIC:  options << " -D INTERP=CUBIC";
+                            break;
                         default:
                             break;
                     }
@@ -143,6 +145,8 @@ namespace opencl
                             break;
                         case AF_INTERP_LINEAR:  options << " -D INTERP=LINEAR";
                             break;
+                        case AF_INTERP_CUBIC:  options << " -D INTERP=CUBIC";
+                            break;
                         default:
                             break;
                     }
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index 434570930d..d80efa849f 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -9,6 +9,7 @@
 
 #define NEAREST core_nearest1
 #define LINEAR core_linear1
+#define CUBIC core_cubic
 
 #if CPLX
 #define set(a, b) a = b
@@ -99,6 +100,54 @@ void core_linear1(const int idx, const int idy, const int idz, const int idw,
     set(d_out[omId], div(yo, wt));
 }
 
+///////////////////////////////////////////////////////////////////////////
+// cubic resampling
+///////////////////////////////////////////////////////////////////////////
+void core_cubic(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+                   __global       Ty *d_out, const KParam out,
+                   __global const Ty *d_in,  const KParam in,
+                   __global const Tp *d_pos, const KParam pos,
+                   const float offGrid, const bool pBatch)
+{
+    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+                     + idy * out.strides[1] + idx;
+    dim_t pmId = idx;
+    if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
+
+    const Tp pVal = d_pos[pmId];
+    if (pVal < 0 || in.dims[0] < pVal+1) {
+        set_scalar(d_out[omId], offGrid);
+        return;
+    }
+
+    const dim_t grid_x = floor(pVal);  // nearest grid
+    const Tp off_x = pVal - grid_x; // fractional offset
+
+    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
+
+    // Check if x-1, x, and x+1, x+2 are both valid indices
+    bool condr  = (pVal > 0);
+    bool condl1 = (pVal < in.dims[0] - 1);
+    bool condl2 = (pVal < in.dims[0] - 2);
+
+    //compute basis function values
+    Ty h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
+    Ty h10 = off_x * (1 - off_x) * (1 - off_x);
+    Ty h01 = off_x * off_x * (3 - 2 * off_x);
+    Ty h11 = off_x * off_x * (off_x - 1);
+
+    // Compute Left and Right Weighted Values
+    Ty pl = condr  ? d_in[ioff]     : offGrid;
+    Ty pr = condl1 ? d_in[ioff + 1] : offGrid;
+    Ty tl = condr  ? 0.5 * (d_in[ioff + 1] - d_in[ioff - 1]) :
+                     0.5 * (d_in[ioff + 1] - offGrid);
+    Ty tr = condl2 ? 0.5 * (d_in[ioff + 2] - d_in[ioff]) :
+                     0.5 * (offGrid - d_in[ioff]);
+
+    // Write final value
+    set(d_out[omId], h00 * pl + h10 * tl + h01 * pr + h11 * tr);
+}
+
 ////////////////////////////////////////////////////////////////////////////////////
 // Wrapper Kernel
 ////////////////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index eb719b1aeb..3537fca849 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -9,6 +9,7 @@
 
 #define NEAREST core_nearest2
 #define LINEAR core_linear2
+#define CUBIC core_cubic2
 
 #if CPLX
 #define set(a, b) a = b
@@ -20,6 +21,7 @@
 Ty mul(Ty a, Tp b) { a.x = a.x * b; a.y = a.y * b; return a; }
 Ty div(Ty a, Tp b) { a.x = a.x / b; a.y = a.y / b; return a; }
 
+
 #else
 
 #define set(a, b) a = b
@@ -118,6 +120,82 @@ void core_linear2(const int idx, const int idy, const int idz, const int idw,
     set(d_out[omId], div(yo, wt));
 }
 
+///////////////////////////////////////////////////////////////////////////
+// cubic resampling
+///////////////////////////////////////////////////////////////////////////
+
+Ty cubicInterpolate(Ty p[4], Tp x) {
+    return p[1] + (Ty)0.5 * x * (p[2] - p[0] + x * ((Ty)2.0 * p[0] - (Ty)5.0 * p[1] + (Ty)4.0 * p[2] - p[3] + x*((Ty)3.0*(p[1] - p[2]) + p[3] - p[0])));
+}
+
+Ty bicubicInterpolate(Ty p[4][4], Tp x, Tp y) {
+    Ty arr[4];
+    arr[0] = cubicInterpolate(p[0], x);
+    arr[1] = cubicInterpolate(p[1], x);
+    arr[2] = cubicInterpolate(p[2], x);
+    arr[3] = cubicInterpolate(p[3], x);
+    return cubicInterpolate(arr, y);
+}
+
+void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
+                  __global       Ty *d_out, const KParam out,
+                  __global const Ty *d_in,  const KParam in,
+                  __global const Tp *d_pos, const KParam pos,
+                  __global const Tp *d_qos, const KParam qos,
+                  const float offGrid, const bool pBatch)
+{
+    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
+                     + idy * out.strides[1] + idx;
+    dim_t pmId = idy * pos.strides[1] + idx;
+    dim_t qmId = idy * qos.strides[1] + idx;
+    if(pBatch) {
+        pmId += idw * pos.strides[3] + idz * pos.strides[2];
+        qmId += idw * qos.strides[3] + idz * qos.strides[2];
+    }
+
+    const Tp x = d_pos[pmId], y = d_qos[qmId];
+    if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
+        set_scalar(d_out[omId], offGrid);
+        return;
+    }
+
+    const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
+    const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
+
+    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
+    // used for setting values at boundaries
+    bool condXl = (x < 1);
+    bool condYl = (y < 1);
+    bool condXg = (x > in.dims[0] - 3);
+    bool condYg = (y > in.dims[1] - 3);
+
+    //for bicubic interpolation, work with 4x4 patch at a time
+    Ty patch[4][4];
+
+    //assumption is that inner patch consisting of 4 points is minimum requirement for bicubic interpolation
+    //inner square
+    patch[1][1] = d_in[ioff];
+    patch[1][2] = d_in[ioff + 1];
+    patch[2][1] = d_in[ioff + in.strides[1]];
+    patch[2][2] = d_in[ioff + in.strides[1] + 1];
+    //outer sides
+    patch[0][1] = (condYl)? (Ty)offGrid : d_in[ioff - in.strides[1]];
+    patch[0][2] = (condYl)? (Ty)offGrid : d_in[ioff - in.strides[1] + 1];
+    patch[3][1] = (condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1]];
+    patch[3][2] = (condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1] + 1];
+    patch[1][0] = (condXl)? (Ty)offGrid : d_in[ioff - 1];
+    patch[2][0] = (condXl)? (Ty)offGrid : d_in[ioff + in.strides[1] -1];
+    patch[1][3] = (condXg)? (Ty)offGrid : d_in[ioff + 2];
+    patch[2][3] = (condXg)? (Ty)offGrid : d_in[ioff + in.strides[1] + 2];
+    //corners
+    patch[0][0] = (condXl || condYl)? (Ty)offGrid : d_in[ioff - in.strides[1] - 1]     ;
+    patch[0][3] = (condYl || condXg)? (Ty)offGrid : d_in[ioff - in.strides[1] + 1]     ;
+    patch[3][0] = (condXl || condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1] - 1] ;
+    patch[3][3] = (condXg || condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1] + 2] ;
+
+    set(d_out[omId], bicubicInterpolate(patch, off_x, off_y));
+}
+
 ////////////////////////////////////////////////////////////////////////////////////
 // Wrapper Kernel
 ////////////////////////////////////////////////////////////////////////////////////

From 012e483036621d0c91a41463a26b740fd8c3dd49 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 5 Aug 2016 20:12:13 -0400
Subject: [PATCH 0740/2677] BUGFIX: Fixing bugs with variables going out of
 scope

And that is why you have to be careful with pointers
---
 src/backend/cuda/jit.cpp | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index f90e137887..e6979ec229 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -534,6 +534,19 @@ void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
         args.push_back(&outputs[i].ptr);
     }
 
+    // DO NOT PUT THESE IN A SCOPE.
+    // The pointers are used later.
+    // Scoping them results in undefined behavior.
+
+    int strides[] = {(int)outputs[0].strides[0],
+                     (int)outputs[0].strides[1],
+                     (int)outputs[0].strides[2],
+                     (int)outputs[0].strides[3]};
+
+    int dims[] = {(int)outputs[0].dims[0],
+                  (int)outputs[0].dims[1],
+                  (int)outputs[0].dims[2],
+                  (int)outputs[0].dims[3]};
 
     if (is_linear) {
         int nelem = 1;
@@ -542,16 +555,6 @@ void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
         }
         args.push_back((void *)&nelem);
     } else {
-        int strides[] = {(int)outputs[0].strides[0],
-                         (int)outputs[0].strides[1],
-                         (int)outputs[0].strides[2],
-                         (int)outputs[0].strides[3]};
-
-        int dims[] = {(int)outputs[0].dims[0],
-                      (int)outputs[0].dims[1],
-                      (int)outputs[0].dims[2],
-                      (int)outputs[0].dims[3]};
-
         for (int i = 0; i < 4; i++) args.push_back((void *)(strides + i));
         for (int i = 0; i < 4; i++) args.push_back((void *)(dims + i));
     }

From ef6c3363abaca5a779bbe08810b355b528b807bb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 8 Aug 2016 09:20:13 -0400
Subject: [PATCH 0741/2677] CMake Error when CUDA Compute 6x and CUDA < 8

---
 src/backend/cuda/CMakeLists.txt | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f8c39b47c2..42bb5dddab 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -69,6 +69,20 @@ FOREACH(VER 20 30 32 35 37 50 52 53 60 61 62)
     ENDIF()
 ENDFOREACH()
 
+# Error out if Compute 6x is enabled but CUDA version is less than 8
+IF(${CUDA_VERSION_MAJOR} LESS 8)
+    IF(   CUDA_COMPUTE_60
+       OR CUDA_COMPUTE_61
+       OR CUDA_COMPUTE_62
+      )
+        MESSAGE(FATAL_ERROR
+                "CUDA Compute 6x was enabled.\
+                CUDA Compute 6x (Pascal) GPUs require CUDA 8 or greater.\
+                Your CUDA Version is ${CUDA_VERSION}."
+                )
+    ENDIF()
+ENDIF(${CUDA_VERSION_MAJOR} LESS 8)
+
 IF(UNIX)
     # GCC 5.3 and above give errors for mempcy from <string.h>
     # This is a (temporary) fix for that

From e5dcc2e53928b1decbe02e3bfe0e3b00fa2b9541 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 8 Aug 2016 15:17:08 -0400
Subject: [PATCH 0742/2677] correct boundaries add tests

---
 src/backend/cpu/kernel/approx1.hpp   |  18 ++---
 src/backend/cuda/kernel/approx.hpp   |  16 ++--
 src/backend/opencl/kernel/approx1.cl |  16 ++--
 test/approx1.cpp                     | 116 ++++++++++++++++++++++++---
 4 files changed, 127 insertions(+), 39 deletions(-)

diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
index 737a64d9c9..cb3778c0cd 100644
--- a/src/backend/cpu/kernel/approx1.hpp
+++ b/src/backend/cpu/kernel/approx1.hpp
@@ -121,7 +121,7 @@ struct approx1_op<InT, LocT, AF_INTERP_CUBIC>
 
         LocT const x = pos[pmId];
         bool gFlag = false;
-        if (x < 0 || idims[0] < x+1) { //check index of bounds
+        if (x < 0 || idims[0] < x + 1) { //check index of bounds
             gFlag = true;
         }
 
@@ -142,16 +142,14 @@ struct approx1_op<InT, LocT, AF_INTERP_CUBIC>
             InT h01 = off_x * off_x * (3 - 2 * off_x);
             InT h11 = off_x * off_x * (off_x - 1);
             // Check if x-1, x, and x+1, x+2 are both valid indices
-            bool condr  = (x > 0);
-            bool condl1 = (x < idims[0] - 1);
-            bool condl2 = (x < idims[0] - 2);
+            bool condr  = (grid_x > 0);
+            bool condl1 = (grid_x < idims[0] - 1);
+            bool condl2 = (grid_x < idims[0] - 2);
             // Compute Left and Right points and tangents
-            InT pl = condr  ? in[ioff]     : scalar<InT>(offGrid);
-            InT pr = condl1 ? in[ioff + 1] : scalar<InT>(offGrid);
-            InT tl = condr  ? scalar<InT>(0.5)  * ((in[ioff + 1] - in[ioff - 1])) :
-                              scalar<InT>(0.5)  * ((in[ioff + 1] - scalar<InT>(offGrid)));
-            InT tr = condl2 ? scalar<InT>(0.5)  * ((in[ioff + 2] - in[ioff])) :
-                              scalar<InT>(0.5)  * ((scalar<InT>(offGrid) - in[ioff]));
+            InT pl = in[ioff];
+            InT pr = condl1  ? in[ioff + 1] : in[ioff];
+            InT tl = condr   ? scalar<InT>(0.5) * (in[ioff + 1] - in[ioff - 1]) : (in[ioff + 1] - in[ioff]);
+            InT tr = condl2  ? scalar<InT>(0.5) * (in[ioff + 2] - in[ioff]) : (condl1) ? in[ioff + 1] - in[ioff] : (in[ioff] - in[ioff - 1]);
 
             // Write final value
             out[omId] = h00 * pl + h10 * tl + h01 * pr + h11 * tr;
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 40435f851b..f8a7169585 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -191,9 +191,9 @@ namespace cuda
             dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
 
             // Check if x-1, x, and x+1, x+2 are both valid indices
-            bool condr  = (pVal > 0);
-            bool condl1 = (pVal < in.dims[0] - 1);
-            bool condl2 = (pVal < in.dims[0] - 2);
+            bool condr  = (grid_x > 0);
+            bool condl1 = (grid_x < in.dims[0] - 1);
+            bool condl2 = (grid_x < in.dims[0] - 2);
 
             //compute basis function values
             Tp h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
@@ -202,12 +202,10 @@ namespace cuda
             Tp h11 = off_x * off_x * (off_x - 1);
 
             // Compute Left and Right points and tangents
-            Ty pl = condr  ? in.ptr[ioff]     : scalar<Ty>(offGrid);
-            Ty pr = condl1 ? in.ptr[ioff + 1] : scalar<Ty>(offGrid);
-            Ty tl = condr  ? scalar<Ty>(0.5)  * ((in.ptr[ioff + 1] - in.ptr[ioff - 1])) :
-                             scalar<Ty>(0.5)  * ((in.ptr[ioff + 1] - scalar<Ty>(offGrid)));
-            Ty tr = condl2 ? scalar<Ty>(0.5)  * ((in.ptr[ioff + 2] - in.ptr[ioff])) :
-                             scalar<Ty>(0.5)  * ((scalar<Ty>(offGrid) - in.ptr[ioff]));
+            Ty pl = in.ptr[ioff];
+            Ty pr = condl1  ? in.ptr[ioff + 1] : in.ptr[ioff];
+            Ty tl = condr   ? scalar<Ty>(0.5) * (in.ptr[ioff + 1] - in.ptr[ioff - 1]) : (in.ptr[ioff + 1] - in.ptr[ioff]);
+            Ty tr = condl2  ? scalar<Ty>(0.5) * (in.ptr[ioff + 2] - in.ptr[ioff]) : (condl1) ? in.ptr[ioff + 1] - in.ptr[ioff] : (in.ptr[ioff] - in.ptr[ioff - 1]);
 
             // Write final value
             out.ptr[omId] = h00 * pl + h10 * tl + h01 * pr + h11 * tr;
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index d80efa849f..a3d5719487 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -126,9 +126,9 @@ void core_cubic(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t i
     dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
 
     // Check if x-1, x, and x+1, x+2 are both valid indices
-    bool condr  = (pVal > 0);
-    bool condl1 = (pVal < in.dims[0] - 1);
-    bool condl2 = (pVal < in.dims[0] - 2);
+    bool condr  = (grid_x > 0);
+    bool condl1 = (grid_x < in.dims[0] - 1);
+    bool condl2 = (grid_x < in.dims[0] - 2);
 
     //compute basis function values
     Ty h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
@@ -137,12 +137,10 @@ void core_cubic(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t i
     Ty h11 = off_x * off_x * (off_x - 1);
 
     // Compute Left and Right Weighted Values
-    Ty pl = condr  ? d_in[ioff]     : offGrid;
-    Ty pr = condl1 ? d_in[ioff + 1] : offGrid;
-    Ty tl = condr  ? 0.5 * (d_in[ioff + 1] - d_in[ioff - 1]) :
-                     0.5 * (d_in[ioff + 1] - offGrid);
-    Ty tr = condl2 ? 0.5 * (d_in[ioff + 2] - d_in[ioff]) :
-                     0.5 * (offGrid - d_in[ioff]);
+    Ty pl = d_in[ioff];
+    Ty pr = condl1  ? d_in[ioff + 1] : d_in[ioff];
+    Ty tl = condr   ? 0.5 * (d_in[ioff + 1] - d_in[ioff - 1]) : (d_in[ioff + 1] - d_in[ioff]);
+    Ty tr = condl2  ? 0.5 * (d_in[ioff + 2] - d_in[ioff]) : (condl1) ? d_in[ioff + 1] - d_in[ioff] : (d_in[ioff] - d_in[ioff - 1]);
 
     // Write final value
     set(d_out[omId], h00 * pl + h10 * tl + h01 * pr + h11 * tr);
diff --git a/test/approx1.cpp b/test/approx1.cpp
index e7ea94e51e..e5508e53bb 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -13,6 +13,7 @@
 #include <af/signal.h>
 #include <af/index.h>
 #include <af/traits.hpp>
+#include <af/dim4.hpp>
 #include <vector>
 #include <iostream>
 #include <complex>
@@ -86,7 +87,7 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     size_t nElems = tests[resultIdx].size();
     bool ret = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ret = (abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005);
+        ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005;
         ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
     }
 
@@ -99,14 +100,84 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define APPROX1_INIT(desc, file, resultIdx, method)                               \
-    TYPED_TEST(Approx1, desc)                                                                    \
-    {                                                                                           \
-        approx1Test<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method);\
+#define APPROX1_INIT(desc, file, resultIdx, method)                                         \
+    TYPED_TEST(Approx1, desc)                                                               \
+    {                                                                                       \
+        approx1Test<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method);  \
     }
 
     APPROX1_INIT(Approx1Nearest, approx1, 0, AF_INTERP_NEAREST);
-    APPROX1_INIT(Approx1Linear, approx1, 1, AF_INTERP_LINEAR);
+    APPROX1_INIT(Approx1Linear,  approx1, 1, AF_INTERP_LINEAR);
+
+template<typename T>
+void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
+{
+    if (noDoubleTests<T>()) return;
+
+    typedef typename af::dtype_traits<T>::base_type BT;
+    vector<af::dim4> numDims;
+    vector<vector<BT> > in;
+    vector<vector<T> > tests;
+    readTests<BT, T, float>(pTestFile,numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::dim4 pdims = numDims[1];
+
+    af_array inArray = 0;
+    af_array posArray = 0;
+    af_array outArray = 0;
+    af_array tempArray = 0;
+
+    vector<T> input(in[0].begin(), in[0].end());
+
+    if (isSubRef) {
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+    } else {
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    }
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
+
+    // Get result
+    T* outData = new T[tests[resultIdx].size()];
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+    // Compare result
+    size_t nElems = tests[resultIdx].size();
+    bool ret = true;
+
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        double integral;
+        //test that control points are exact
+        if((std::modf(in[1][elIter], &integral) < 0.001) || (std::modf(in[1][elIter], &integral) > 0.999))  {
+            ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001;
+            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        } else {
+            //match intermediate values withing a threshold
+            ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 8;
+            //ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.05 * range; //TODO: percentage
+            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        }
+    }
+
+    // Delete
+    delete[] outData;
+
+    if(inArray   != 0) af_release_array(inArray);
+    if(posArray  != 0) af_release_array(posArray);
+    if(outArray  != 0) af_release_array(outArray);
+    if(tempArray != 0) af_release_array(tempArray);
+}
+
+#define APPROX1_INIT_CUBIC(desc, file, resultIdx, method)                                       \
+    TYPED_TEST(Approx1, desc)                                                                   \
+    {                                                                                           \
+        approx1CubicTest<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method); \
+    }
+
+APPROX1_INIT_CUBIC(Approx1Cubic, approx1_cubic, 0, AF_INTERP_CUBIC);
 
 ///////////////////////////////////////////////////////////////////////////////
 // Test Argument Failure Cases
@@ -150,7 +221,6 @@ void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
     APPROX1_ARGS(Approx1NearestArgsPos2D, approx1_pos2d, 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
     APPROX1_ARGS(Approx1LinearArgsPos2D, approx1_pos2d, 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
     APPROX1_ARGS(Approx1ArgsInterpBilinear, approx1, 0, AF_INTERP_BILINEAR, AF_ERR_ARG);
-    APPROX1_ARGS(Approx1ArgsInterpCubic, approx1, 0, AF_INTERP_CUBIC, AF_ERR_ARG);
 
 template<typename T>
 void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
@@ -186,14 +256,15 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
     if(outArray  != 0) af_release_array(outArray);
 }
 
-#define APPROX1_ARGSP(desc, file, resultIdx, method)                                           \
-    TYPED_TEST(Approx1, desc)                                                                    \
-    {                                                                                           \
-        approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method);\
+#define APPROX1_ARGSP(desc, file, resultIdx, method)                                                    \
+    TYPED_TEST(Approx1, desc)                                                                           \
+    {                                                                                                   \
+        approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method); \
     }
 
     APPROX1_ARGSP(Approx1NearestArgsPrecision, approx1, 0, AF_INTERP_NEAREST);
     APPROX1_ARGSP(Approx1LinearArgsPrecision, approx1, 1, AF_INTERP_LINEAR);
+    APPROX1_ARGSP(Approx1CubicArgsPrecision, approx1_cubic, 2, AF_INTERP_CUBIC);
 
 
 //////////////////////////////////////// CPP //////////////////////////////////
@@ -280,3 +351,26 @@ TEST(Approx1, CPPLinearBatch)
     ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
 }
+
+TEST(Approx1, CPPCubicBatch)
+{
+    if (noDoubleTests<float>()) return;
+
+    af::array input = af::iota(af::dim4(10000, 20), c32);
+    af::array pos   = input.dims(0) * af::randu(50000, 20);
+
+    af::array outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+
+    af::array outSerial(pos.dims());
+    for(int i = 0; i < pos.dims(1); i++) {
+        outSerial(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC);
+    }
+
+    af::array outGFOR(pos.dims());
+    gfor(af::seq i, pos.dims(1)) {
+        outGFOR(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC);
+    }
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
+}

From 1db2c19a9eec65eb0933b0bcd5cbaf347982d7ba Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 15 Aug 2016 13:06:59 -0400
Subject: [PATCH 0743/2677] Fixes to bin2cpp when reading binary files on
 windows

---
 CMakeModules/CLKernelToH.cmake  | 9 +++++++--
 CMakeModules/bin2cpp.cpp        | 7 ++++++-
 src/backend/cuda/CMakeLists.txt | 1 +
 3 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/CLKernelToH.cmake b/CMakeModules/CLKernelToH.cmake
index 61928db328..8c55676c18 100644
--- a/CMakeModules/CLKernelToH.cmake
+++ b/CMakeModules/CLKernelToH.cmake
@@ -28,7 +28,7 @@ include(CMakeParseArguments)
 set(BIN2CPP_PROGRAM "bin2cpp")
 
 function(CL_KERNEL_TO_H)
-    cmake_parse_arguments(RTCS "" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;EOF" "SOURCES" ${ARGN})
+    cmake_parse_arguments(RTCS "" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;BINARY;EOF" "SOURCES" ${ARGN})
 
     set(_output_files "")
     foreach(_input_file ${RTCS_SOURCES})
@@ -38,6 +38,11 @@ function(CL_KERNEL_TO_H)
         get_filename_component(_name_we "${_input_file}" NAME_WE)
 
         set(_namespace "${RTCS_NAMESPACE}")
+        set(_binary "")
+        if(${RTCS_BINARY})
+            set(_binary "--binary")
+        endif(${RTCS_BINARY})
+
         string(REPLACE "." "_" var_name ${var_name})
 
         set(_output_path "${CMAKE_CURRENT_BINARY_DIR}/${RTCS_OUTPUT_DIR}")
@@ -48,7 +53,7 @@ function(CL_KERNEL_TO_H)
             DEPENDS ${_input_file} ${BIN2CPP_PROGRAM}
             COMMAND ${CMAKE_COMMAND} -E make_directory "${_output_path}"
             COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\<${_path}/${_name_we}.hpp\\>"  >>"${_output_file}"
-            COMMAND ${BIN2CPP_PROGRAM} --file ${_name} --namespace ${_namespace} --output ${_output_file} --name ${var_name} --eof ${RTCS_EOF}
+            COMMAND ${BIN2CPP_PROGRAM} --file ${_name} --namespace ${_namespace} --output ${_output_file} --name ${var_name} ${_binary} --eof ${RTCS_EOF}
             WORKING_DIRECTORY "${_path}"
             COMMENT "Compiling ${_input_file} to C++ source"
         )
diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index 8a0429540b..758a5f2716 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -22,6 +22,7 @@ xxd but adds support for namespaces.
 | --file        | input file                                                        |
 | --output      | output file (If no output is specified then it prints to stdout   |
 | --type        | Type of variable (default: char)                                  |
+| --binary      | If the file contents are in binary form                           |
 | --namespace   | A space seperated list of namespaces                              |
 | --formatted   | Tabs for formatting                                               |
 | --version     | Prints my name                                                    |
@@ -47,6 +48,7 @@ namespace blah {
 }
 
 static bool formatted;
+static bool binary = false;
 
 void add_tabs(const int level ){
     if(formatted) {
@@ -74,6 +76,9 @@ parse_options(const vector<string>& args) {
         if(arg == "--verbose") {
             verbose = true;
         }
+        else if(arg == "--binary") {
+            binary = true;
+        }
         else if(arg == "--formatted") {
             formatted = true;
         }
@@ -150,7 +155,7 @@ int main(int argc, const char * const * const argv)
     // Always create unsigned char to avoid narrowing
     cout << "static const " << "unsigned char" << " " << options["--name"] << "_uchar [] = {\n";
 
-    ifstream input(options["--file"]);
+    ifstream input(options["--file"], (binary ? std::ios::binary : std::ios::in));
     size_t char_cnt = 0;
     add_tabs(++level);
     for(char i; input.get(i);) {
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e743bbbbd8..9b43c28d7b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -348,6 +348,7 @@ IF (${libdevice_bc_len} GREATER 0)
     OUTPUT_DIR ${libdevice_headers}
     TARGETS libdevice_targets
     NAMESPACE "cuda"
+    BINARY TRUE
     EOF "1"
     )
 

From dd8bd91b31047426479816bc89ea70d4b4e385e7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 15 Aug 2016 13:34:52 -0400
Subject: [PATCH 0744/2677] Skip double tests when double precision not
 supported

---
 test/compare.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/test/compare.cpp b/test/compare.cpp
index ab2fa6c7d0..f886356b42 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -25,6 +25,7 @@ TYPED_TEST_CASE(Compare, TestTypes);
     TYPED_TEST(Compare, Test_##Name)                            \
     {                                                           \
         typedef TypeParam T;                                    \
+        if (noDoubleTests<T>()) return;                         \
         const int num = 1 << 20;                                \
         af_dtype ty = (af_dtype) af::dtype_traits<T>::af_type;  \
         af::array a = af::randu(num, ty);                       \

From 14413c0bf22a2e3fe5fd38394e964eeefc86a767 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 15 Aug 2016 16:39:38 -0400
Subject: [PATCH 0745/2677] Added code to handle sparse conversion in OpenCL
 (untested)

---
 src/backend/opencl/err_clsparse.hpp  |  72 ++++++++++
 src/backend/opencl/kernel/sparse.cl  |  35 +++++
 src/backend/opencl/kernel/sparse.hpp |  88 ++++++++++++
 src/backend/opencl/sparse.cpp        | 206 ++++++++++++++++++++++++++-
 src/backend/opencl/sparse.hpp        |   5 +
 5 files changed, 399 insertions(+), 7 deletions(-)
 create mode 100644 src/backend/opencl/err_clsparse.hpp
 create mode 100644 src/backend/opencl/kernel/sparse.cl
 create mode 100644 src/backend/opencl/kernel/sparse.hpp

diff --git a/src/backend/opencl/err_clsparse.hpp b/src/backend/opencl/err_clsparse.hpp
new file mode 100644
index 0000000000..c314ca8cbf
--- /dev/null
+++ b/src/backend/opencl/err_clsparse.hpp
@@ -0,0 +1,72 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <stdio.h>
+#include <err_common.hpp>
+#include <clSPARSE.h>
+
+static const char * _clsparseGetResultString(clsparseStatus st)
+{
+    switch (st)
+    {
+        case clsparseSuccess:                       return "Success";
+        case clsparseInvalidValue:                  return "Invalid value";
+        case clsparseInvalidCommandQueue:           return "Invalid queue";
+        case clsparseInvalidContext:                return "Invalid context";
+        case clsparseInvalidMemObject:              return "Invalid memory object";
+        case clsparseInvalidDevice:                 return "Invalid device";
+        case clsparseInvalidEventWaitList:          return "Invalid event list";
+        case clsparseInvalidEvent:                  return "Invalid event";
+        case clsparseOutOfResources:                return "Out of resources";
+        case clsparseOutOfHostMemory:               return "Out of host memory";
+        case clsparseInvalidOperation:              return "Invalid operation";
+        case clsparseCompilerNotAvailable:          return "Compiler not available";
+        case clsparseBuildProgramFailure:           return "Build program failure";
+        case clsparseInvalidKernelArgs:             return "Invalid kernel arguments";
+
+        case clsparseNotImplemented:                return "Not implemented";
+        case clsparseNotInitialized:                return "clSPARSE Not initialized";
+        case clsparseStructInvalid:                 return "Struct invalid";
+        case clsparseInvalidSize:                   return "Invalid size";
+        case clsparseInvalidMemObj:                 return "Invalid memory object";
+        case clsparseInsufficientMemory:            return "Insufficient Memory";
+        case clsparseInvalidControlObject:          return "Invalid control object";
+        case clsparseInvalidFile:                   return "Invalid file";
+        case clsparseInvalidFileFormat:             return "Invalid file format";
+        case clsparseInvalidKernelExecution:        return "Invalid kernel execution";
+        case clsparseInvalidType:                   return "Invalid type";
+
+        case clsparseInvalidSolverControlObject:    return "Invalid solver control object";
+        case clsparseInvalidSystemSize:             return "Invalid system size";
+        case clsparseIterationsExceeded:            return "Iterations exceeded";
+        case clsparseToleranceNotReached:           return "Tolerance not reached";
+        case clsparseSolverError:                   return "Solver error";
+        default:                                    return "Unknown clSPARSE Error";
+    }
+
+    return "Unknown error";
+}
+
+#define CLSPARSE_CHECK(fn) do {                     \
+        clsparseStatus _clsparse_st = fn;           \
+        if (_clsparse_st != clsparseSuccess) {      \
+            char clsparse_st_msg[1024];             \
+            snprintf(clsparse_st_msg,               \
+                     sizeof(clsparse_st_msg),       \
+                     "clsparse Error (%d): %s\n",   \
+                     (int)(_clsparse_st),           \
+                     _clsparseGetResultString(      \
+                         _clsparse_st));            \
+                                                    \
+            AF_ERROR(clsparse_st_msg,               \
+                     AF_ERR_INTERNAL);              \
+        }                                           \
+    } while(0)
+
diff --git a/src/backend/opencl/kernel/sparse.cl b/src/backend/opencl/kernel/sparse.cl
new file mode 100644
index 0000000000..fb86ebd82b
--- /dev/null
+++ b/src/backend/opencl/kernel/sparse.cl
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void coo2dense_kernel(__global T *oPtr, const KParam output,
+                      __global const T *vPtr, const KParam values,
+                      __global const int *rPtr, const KParam rowIdx,
+                      __global const int *cPtr, const KParam colIdx)
+{
+    const int id = get_group_id(0) * get_local_size(0) * reps + get_local_id(0);
+
+    if(id >= values.dims[0])
+        return;
+
+    const int dimSize = get_local_size(0);
+
+    for(int i = get_local_id(0); i < reps * dimSize; i += dimSize) {
+        if(i >= values.dims[0])
+            return;
+
+        T   v = vPtr[i];
+        int r = rPtr[i];
+        int c = cPtr[i];
+
+        int offset = r + c * output.strides[1];
+
+        oPtr[offset] = v;
+    }
+}
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
new file mode 100644
index 0000000000..a5b34d9089
--- /dev/null
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -0,0 +1,88 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/sparse.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        static const int TX = 16;
+        static const int TY = 16;
+        static const int THREADS = 256;
+        static const int reps = 4;
+
+        template<typename T>
+        void coo2dense(Param out, const Param values, const Param rowIdx, const Param colIdx)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   coo2denseProgs;
+                static std::map<int, Kernel*>  coo2denseKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="        << dtype_traits<T>::getName()
+                            << " -D reps="     << reps
+                            ;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    Program prog;
+                    buildProgram(prog, sparse_cl, sparse_cl_len, options.str());
+                    coo2denseProgs[device]   = new Program(prog);
+                    coo2denseKernels[device] = new Kernel(*coo2denseProgs[device], "coo2dense_kernel");
+                });
+
+                auto coo2denseOp = KernelFunctor<Buffer, const KParam,
+                                           const Buffer, const KParam,
+                                           const Buffer, const KParam,
+                                           const Buffer, const KParam>
+                                          (*coo2denseKernels[device]);
+
+                NDRange local(THREADS, 1, 1);
+
+                NDRange global(divup(out.info.dims[0], local[0] * reps) * THREADS, 1, 1);
+
+                coo2denseOp(EnqueueArgs(getQueue(), global, local),
+                       *out.data, out.info,
+                       *values.data, values.info,
+                       *rowIdx.data, rowIdx.info,
+                       *colIdx.data, colIdx.info);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 5338ebfed4..e219cc7aa4 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <sparse.hpp>
+#include <kernel/sparse.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -27,6 +28,97 @@ namespace opencl
 
 using namespace common;
 
+////////////////////////////////////////////////////////////////////////////////
+// clSPARSE Setup and Teardown Manager
+// This gets initialized in opencl/sparse.cpp
+////////////////////////////////////////////////////////////////////////////////
+class clSPARSEManager
+{
+    public:
+    static clsparseControl control;
+
+    clSPARSEManager()
+    {
+        CLSPARSE_CHECK(clsparseSetup());
+        clsparseCreateResult createResult = clsparseCreateControl(getQueue()());
+        control = (createResult.status == clsparseSuccess) ? createResult.control : nullptr;
+    }
+
+    ~clSPARSEManager()
+    {
+        CLSPARSE_CHECK(clsparseReleaseControl(control));
+        control = nullptr;
+        CLSPARSE_CHECK(clsparseTeardown());
+    }
+};
+////////////////////////////////////////////////////////////////////////////////
+
+clsparseControl clSPARSEManager::control = nullptr;
+
+// Instantiate clSPARSEManager
+void clSPARSEInit()
+{
+    static clSPARSEManager manager = clSPARSEManager();
+}
+
+clsparseControl getControl()
+{
+    return clSPARSEManager::control;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#define SPARSE_FUNC_DEF(NAME)                                               \
+template<typename T>                                                        \
+struct NAME##_func;
+
+#define SPARSE_FUNC(NAME, TYPE, PREFIX)                                     \
+template<>                                                                  \
+struct NAME##_func<TYPE>                                                    \
+{                                                                           \
+    template<typename... Args>                                              \
+    clsparseStatus                                                          \
+    operator() (Args... args) { return clsparse##PREFIX##NAME(args...); }   \
+};
+
+// Dense -> CSR
+SPARSE_FUNC_DEF(dense2csr)
+SPARSE_FUNC(dense2csr, float,      S)
+SPARSE_FUNC(dense2csr, double,     D)
+// TODO
+// Fix this. clSPARSE does not have functions for C and Z
+SPARSE_FUNC(dense2csr, cfloat,     S)
+SPARSE_FUNC(dense2csr, cdouble,    D)
+
+// CSR -> Dense
+SPARSE_FUNC_DEF(csr2dense)
+SPARSE_FUNC(csr2dense, float,      S)
+SPARSE_FUNC(csr2dense, double,     D)
+// TODO
+// Fix this. clSPARSE does not have functions for C and Z
+SPARSE_FUNC(csr2dense, cfloat,     S)
+SPARSE_FUNC(csr2dense, cdouble,    D)
+
+// CSR -> COO
+SPARSE_FUNC_DEF(csr2coo)
+SPARSE_FUNC(csr2coo, float,      S)
+SPARSE_FUNC(csr2coo, double,     D)
+// TODO
+// Fix this. clSPARSE does not have functions for C and Z
+SPARSE_FUNC(csr2coo, cfloat,     S)
+SPARSE_FUNC(csr2coo, cdouble,    D)
+
+// COO -> CSR
+SPARSE_FUNC_DEF(coo2csr)
+SPARSE_FUNC(coo2csr, float,      S)
+SPARSE_FUNC(coo2csr, double,     D)
+// TODO
+// Fix this. clSPARSE does not have functions for C and Z
+SPARSE_FUNC(coo2csr, cfloat,     S)
+SPARSE_FUNC(coo2csr, cdouble,    D)
+
+#undef SPARSE_FUNC_DEF
+#undef SPARSE_FUNC
+
 // Partial template specialization of sparseConvertDenseToStorage for COO
 // However, template specialization is not allowed
 template<typename T>
@@ -52,17 +144,42 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
+    clSPARSEInit();
+
     in_.eval();
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
 
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
     sparse_.eval();
 
+    // Assign to clSparse Dense
+    cldenseMatrix clDenseMat;
+    CLSPARSE_CHECK(cldenseInitMatrix(&clDenseMat));
+    clDenseMat.values = (*in_.get())();
+    clDenseMat.num_rows = in_.dims()[0];
+    clDenseMat.num_cols = in_.dims()[1];
+    clDenseMat.lead_dim = in_.strides()[1];
+
+    // Assign to clSparse CSR
+    clsparseCsrMatrix clSparseMat;
+    CLSPARSE_CHECK(clsparseInitCsrMatrix(&clSparseMat));
+
+    clSparseMat.values      = (*sparse_.getValues().get())();
+    clSparseMat.row_pointer = (*sparse_.getRowIdx().get())();
+    clSparseMat.col_indices = (*sparse_.getColIdx().get())();
+    clSparseMat.num_rows = in_.dims()[0];
+    clSparseMat.num_cols = in_.dims()[1];
+    clSparseMat.num_nonzeros = nNZ;
+
+    if(stype == AF_STORAGE_CSR)
+        CLSPARSE_CHECK(dense2csr_func<T>()(&clDenseMat, &clSparseMat, getControl()));
+    else
+        AF_ERROR("OpenCL Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+
     return sparse_;
 }
 
-
 // Partial template specialization of sparseConvertStorageToDense for COO
 // However, template specialization is not allowed
 template<typename T>
@@ -77,31 +194,106 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
     const Array<int> rowIdx = in.getRowIdx();
     const Array<int> colIdx = in.getColIdx();
 
+    kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
+
     return dense;
 }
 
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
+    clSPARSEInit();
+
+    if(stype != AF_STORAGE_CSR)
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+
     in_.eval();
 
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
+    // Assign to clSparse CSR
+    clsparseCsrMatrix clSparseMat;
+    CLSPARSE_CHECK(clsparseInitCsrMatrix(&clSparseMat));
+
+    clSparseMat.values      = (*in_.getValues().get())();
+    clSparseMat.row_pointer = (*in_.getRowIdx().get())();
+    clSparseMat.col_indices = (*in_.getColIdx().get())();
+    clSparseMat.num_rows = in_.dims()[0];
+    clSparseMat.num_cols = in_.dims()[1];
+    clSparseMat.num_nonzeros = in_.getNNZ();
+
+    // Assign to clSparse Dense
+    cldenseMatrix clDenseMat;
+    CLSPARSE_CHECK(cldenseInitMatrix(&clDenseMat));
+    clDenseMat.values = (*dense_.get())();
+    clDenseMat.num_rows = dense_.dims()[0];
+    clDenseMat.num_cols = dense_.dims()[1];
+    clDenseMat.lead_dim = dense_.strides()[1];
+
+    if(stype == AF_STORAGE_CSR)
+        CLSPARSE_CHECK(csr2dense_func<T>()(&clSparseMat, &clDenseMat, getControl()));
+    else
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+
     return dense_;
 }
 
 template<typename T, af_storage src, af_storage dest>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
+    // TODO
+    // Convert CSR <-> CSC <-> COO <-> CSR
+    // Currently supports CSR <-> COO
+
+    // If src and dest are the same, simply return.
+    if(src == dest)
+        return in;
+
+    clSPARSEInit();
+
     in.eval();
 
-    // Dummy function
-    // TODO finish this function when support is required
-    SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
-    dense.eval();
+    SparseArray<T> out = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    out.eval();
 
-    return dense;
+    clsparseCsrMatrix csrMat;
+    CLSPARSE_CHECK(clsparseInitCsrMatrix(&csrMat));
+
+    clsparseCooMatrix cooMat;
+    CLSPARSE_CHECK(clsparseInitCooMatrix(&cooMat));
+
+    csrMat.num_rows = in.dims()[0];
+    csrMat.num_cols = in.dims()[1];
+    csrMat.num_nonzeros = in.getNNZ();
+
+    cooMat.num_rows = in.dims()[0];
+    cooMat.num_cols = in.dims()[1];
+    cooMat.num_nonzeros = in.getNNZ();
+
+    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+        csrMat.values      = (*in.getValues().get())();
+        csrMat.row_pointer = (*in.getRowIdx().get())();
+        csrMat.col_indices = (*in.getColIdx().get())();
+
+        cooMat.values      = (*out.getValues().get())();
+        cooMat.row_indices = (*out.getRowIdx().get())();
+        cooMat.col_indices = (*out.getColIdx().get())();
+
+        CLSPARSE_CHECK(csr2coo_func<T>()(&csrMat, &cooMat, getControl()));
+    } else if(src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+        cooMat.values      = (*in.getValues().get())();
+        cooMat.row_indices = (*in.getRowIdx().get())();
+        cooMat.col_indices = (*in.getColIdx().get())();
+
+        csrMat.values      = (*out.getValues().get())();
+        csrMat.row_pointer = (*out.getRowIdx().get())();
+        csrMat.col_indices = (*out.getColIdx().get())();
+
+        CLSPARSE_CHECK(coo2csr_func<T>()(&cooMat, &csrMat, getControl()));
+    }
+
+    return out;
 }
 
 
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index f27d88fa93..ae7f3327bc 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <SparseArray.hpp>
+#include <err_clsparse.hpp>
 
 namespace opencl
 {
@@ -22,4 +23,8 @@ Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 template<typename T, af_storage src, af_storage dest>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
+void clSPARSEInit();
+
+clsparseControl getControl();
+
 }

From 64ab97931ce79c7a6be7f2eead63173c306a3fca Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 15 Aug 2016 17:34:24 -0400
Subject: [PATCH 0746/2677] Remove unnecessary pragma pop (#6)

---
 src/backend/opencl/platform.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 6473e12027..73abe66f68 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -21,7 +21,6 @@
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
-#pragma GCC diagnostic pop
 
 #include <vector>
 #include <string>

From bcaea3a8fb8fe9cd1a8fca0a63492fa61271c63d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 16 Aug 2016 16:44:48 -0400
Subject: [PATCH 0747/2677] Fix bin2cpp null termination

It was affecting ptx files as they were failing to compile.
CUDA cuLinkAddData requires the string to be NULL Terminated.
---
 CMakeModules/CLKernelToH.cmake    | 7 +++++--
 CMakeModules/bin2cpp.cpp          | 8 ++++++--
 src/backend/cuda/CMakeLists.txt   | 3 +--
 src/backend/opencl/CMakeLists.txt | 1 -
 4 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/CMakeModules/CLKernelToH.cmake b/CMakeModules/CLKernelToH.cmake
index 8c55676c18..23b5f0e1bc 100644
--- a/CMakeModules/CLKernelToH.cmake
+++ b/CMakeModules/CLKernelToH.cmake
@@ -28,7 +28,7 @@ include(CMakeParseArguments)
 set(BIN2CPP_PROGRAM "bin2cpp")
 
 function(CL_KERNEL_TO_H)
-    cmake_parse_arguments(RTCS "" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;BINARY;EOF" "SOURCES" ${ARGN})
+    cmake_parse_arguments(RTCS "" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;BINARY;NULLTERM" "SOURCES" ${ARGN})
 
     set(_output_files "")
     foreach(_input_file ${RTCS_SOURCES})
@@ -42,6 +42,9 @@ function(CL_KERNEL_TO_H)
         if(${RTCS_BINARY})
             set(_binary "--binary")
         endif(${RTCS_BINARY})
+        if(${RTCS_NULLTERM})
+            set(_nullterm "--nullterm")
+        endif(${RTCS_NULLTERM})
 
         string(REPLACE "." "_" var_name ${var_name})
 
@@ -53,7 +56,7 @@ function(CL_KERNEL_TO_H)
             DEPENDS ${_input_file} ${BIN2CPP_PROGRAM}
             COMMAND ${CMAKE_COMMAND} -E make_directory "${_output_path}"
             COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\<${_path}/${_name_we}.hpp\\>"  >>"${_output_file}"
-            COMMAND ${BIN2CPP_PROGRAM} --file ${_name} --namespace ${_namespace} --output ${_output_file} --name ${var_name} ${_binary} --eof ${RTCS_EOF}
+            COMMAND ${BIN2CPP_PROGRAM} --file ${_name} --namespace ${_namespace} --output ${_output_file} --name ${var_name} ${_binary} ${_nullterm}
             WORKING_DIRECTORY "${_path}"
             COMMENT "Compiling ${_input_file} to C++ source"
         )
diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index 758a5f2716..273d7f0baf 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -23,6 +23,7 @@ xxd but adds support for namespaces.
 | --output      | output file (If no output is specified then it prints to stdout   |
 | --type        | Type of variable (default: char)                                  |
 | --binary      | If the file contents are in binary form                           |
+| --nullterm    | Add a null character to the end of the file                       |
 | --namespace   | A space seperated list of namespaces                              |
 | --formatted   | Tabs for formatting                                               |
 | --version     | Prints my name                                                    |
@@ -49,6 +50,7 @@ namespace blah {
 
 static bool formatted;
 static bool binary = false;
+static bool nullterm = false;
 
 void add_tabs(const int level ){
     if(formatted) {
@@ -67,7 +69,6 @@ parse_options(const vector<string>& args) {
     options["--file"]       = "";
     options["--output"]     = "";
     options["--namespace"]  = "";
-    options["--eof"]        = "0";
 
     //Parse Arguments
     string curr_opt;
@@ -79,6 +80,9 @@ parse_options(const vector<string>& args) {
         else if(arg == "--binary") {
             binary = true;
         }
+        else if(arg == "--nullterm") {
+            nullterm = true;
+        }
         else if(arg == "--formatted") {
             formatted = true;
         }
@@ -167,7 +171,7 @@ int main(int argc, const char * const * const argv)
         }
     }
 
-    if (options["--eof"].c_str()[0] == '1') {
+    if (nullterm) {
         // Add end of file character
         cout << "0x0";
         char_cnt++;
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 95523872b1..84c17b251d 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -327,7 +327,7 @@ CL_KERNEL_TO_H(
     OUTPUT_DIR ${ptx_headers}
     TARGETS ptx_targets
     NAMESPACE "cuda"
-    EOF "1"
+    NULLTERM TRUE
     )
 
 SET(libdevice_bc "")
@@ -363,7 +363,6 @@ IF (${libdevice_bc_len} GREATER 0)
     TARGETS libdevice_targets
     NAMESPACE "cuda"
     BINARY TRUE
-    EOF "1"
     )
 
   MESSAGE(STATUS "LIBDEVICE found.")
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index d0e4794831..cb27e8408d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -230,7 +230,6 @@ CL_KERNEL_TO_H(
     OUTPUT_DIR ${cl_kernel_headers}
     TARGETS cl_kernel_targets
     NAMESPACE "opencl"
-    EOF "0"
     )
 
 # OS Definitions

From bb32f6a2be0204140dd2a08903c3672dae78db11 Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@users.noreply.github.com>
Date: Wed, 17 Aug 2016 15:25:45 +0100
Subject: [PATCH 0748/2677] Use compute library from Boost 1.61. (#1542)

* Use compute library from Boost 1.61.
---
 src/backend/opencl/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index cb27e8408d..fea5d6324e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -87,7 +87,9 @@ FIND_PACKAGE(Boost 1.48 REQUIRED)
 
 OPTION(USE_SYSTEM_BOOST_COMPUTE "Use system BoostCompute" OFF)
 IF(USE_SYSTEM_BOOST_COMPUTE)
-    FIND_PACKAGE(BoostCompute REQUIRED)
+    IF(Boost_VERSION VERSION_LESS "1.61")
+        FIND_PACKAGE(BoostCompute REQUIRED)
+    ENDIF()
 ELSE()
     INCLUDE("${CMAKE_MODULE_PATH}/build_boost_compute.cmake")
 ENDIF()

From 143f716fcf493d8dfe432c550b1339ec397108b6 Mon Sep 17 00:00:00 2001
From: Ghislain Antony Vaillant <ghisvail@gmail.com>
Date: Wed, 17 Aug 2016 20:01:03 +0100
Subject: [PATCH 0749/2677] Fix typo in LAPACKE_ROOT_DIR envvar.

---
 CMakeModules/FindLAPACKE.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index b75797269f..419186918c 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -22,7 +22,7 @@ IF(NOT LAPACKE_ROOT_DIR)
     SET(LAPACKE_ROOT_DIR $ENV{LAPACKEDIR})
   ENDIF()
 
-  IF (ENV{LAPACKE_ROOT_DIR_DIR})
+  IF (ENV{LAPACKE_ROOT_DIR})
     SET(LAPACKE_ROOT_DIR $ENV{LAPACKE_ROOT_DIR})
   ENDIF()
 

From 31a92efd425d85ee300a324d96aa50c557f41604 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 17 Aug 2016 13:19:43 -0700
Subject: [PATCH 0750/2677] Fixes initial values for erode and dilate  (#1547)

* fix initial value for morph

* Fixing initial values in morph functions for all backends
---
 src/backend/cpu/kernel/morph.hpp    | 10 ++++-
 src/backend/cuda/kernel/morph.hpp   |  6 ++-
 src/backend/opencl/kernel/morph.cl  |  4 +-
 src/backend/opencl/kernel/morph.hpp | 60 ++++++++++++++++-------------
 4 files changed, 48 insertions(+), 32 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index d990bb873b..4cec3b363a 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -8,8 +8,10 @@
  ********************************************************/
 
 #pragma once
+#include <limits>
 #include <Array.hpp>
 #include <utility.hpp>
+#include <ops.hpp>
 
 namespace cpu
 {
@@ -30,6 +32,8 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
     const dim_t R0      = window[0]/2;
     const dim_t R1      = window[1]/2;
 
+    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+
     for(dim_t b3=0; b3<dims[3]; ++b3) {
         for(dim_t b2=0; b2<dims[2]; ++b2) {
             // either channels or batch is handled by outer most loop
@@ -37,7 +41,7 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = inData[ getIdx(istrides, i, j) ];
+                    T filterResult = init;
 
                     // wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wj=0; wj<window[1]; wj++) {
@@ -88,6 +92,8 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
     const T*   inData   = in.get();
     const T*   filter   = mask.get();
 
+    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+
     for(dim_t batchId=0; batchId<bCount; ++batchId) {
         // either channels or batch is handled by outer most loop
         for(dim_t k=0; k<dims[2]; ++k) {
@@ -96,7 +102,7 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = inData[ getIdx(istrides, i, j, k) ];
+                    T filterResult = init;
 
                     // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wk=0; wk<window[2]; wk++) {
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index f92ed01c26..beef1dbbb9 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -7,11 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <limits>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
+#include <ops.hpp>
 #include "shared.hpp"
 
 namespace cuda
@@ -102,7 +104,7 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     __syncthreads();
 
     const T * d_filt = (const T *)cFilter;
-    T acc = shrdMem[ lIdx(i, j, shrdLen, 1) ];
+    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -196,7 +198,7 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     int k  = lz + halo;
 
     const T * d_filt = (const T *)cFilter;
-    T acc = shrdMem[ lIdx3D(i, j, k, shrdArea, shrdLen, 1) ];
+    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index cbbbb84707..4b5a24f9e2 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -69,7 +69,7 @@ void morph(__global T *              out,
     int j = ly + halo;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    T acc = localMem[ lIdx(i, j, shrdLen, 1) ];
+    T acc = init;
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -169,7 +169,7 @@ void morph3d(__global T *         out,
     int j  = ly + halo;
     int k  = lz + halo;
 
-    T acc = localMem[ lIdx3D(i, j, k, shrdArea, shrdLen, 1) ];
+    T acc = init;
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index bde71bd335..fa7b81618a 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -18,6 +18,8 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
+#include <type_util.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -54,19 +56,22 @@ void morph(Param         out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D isDilation="<< isDilation
-                            << " -D windLen=" << windLen;
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                    morProgs[device]   = new Program(prog);
-                    morKernels[device] = new Kernel(*morProgs[device], "morph");
-                });
+                ToNum<T> toNum;
+                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D isDilation="<< isDilation
+                        << " -D init=" << toNum(init)
+                        << " -D windLen=" << windLen;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, morph_cl, morph_cl_len, options.str());
+                morProgs[device]   = new Program(prog);
+                morKernels[device] = new Kernel(*morProgs[device], "morph");
+            });
 
         auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
@@ -119,19 +124,22 @@ void morph3d(Param       out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D isDilation="<< isDilation
-                            << " -D windLen=" << windLen;
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                    morProgs[device]   = new Program(prog);
-                    morKernels[device] = new Kernel(*morProgs[device], "morph3d");
-                });
+                ToNum<T> toNum;
+                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D isDilation="<< isDilation
+                        << " -D init=" << toNum(init)
+                        << " -D windLen=" << windLen;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, morph_cl, morph_cl_len, options.str());
+                morProgs[device]   = new Program(prog);
+                morKernels[device] = new Kernel(*morProgs[device], "morph3d");
+            });
 
         auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,

From 5fccabfdf09e43c16ff58766a3f24285b93105ec Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 17 Aug 2016 16:24:13 -0400
Subject: [PATCH 0751/2677] Revert "Fixes initial values for erode and dilate 
 (#1547)"

This reverts commit 31a92efd425d85ee300a324d96aa50c557f41604.
---
 src/backend/cpu/kernel/morph.hpp    | 10 +----
 src/backend/cuda/kernel/morph.hpp   |  6 +--
 src/backend/opencl/kernel/morph.cl  |  4 +-
 src/backend/opencl/kernel/morph.hpp | 60 +++++++++++++----------------
 4 files changed, 32 insertions(+), 48 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 4cec3b363a..d990bb873b 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -8,10 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <limits>
 #include <Array.hpp>
 #include <utility.hpp>
-#include <ops.hpp>
 
 namespace cpu
 {
@@ -32,8 +30,6 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
     const dim_t R0      = window[0]/2;
     const dim_t R1      = window[1]/2;
 
-    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-
     for(dim_t b3=0; b3<dims[3]; ++b3) {
         for(dim_t b2=0; b2<dims[2]; ++b2) {
             // either channels or batch is handled by outer most loop
@@ -41,7 +37,7 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = init;
+                    T filterResult = inData[ getIdx(istrides, i, j) ];
 
                     // wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wj=0; wj<window[1]; wj++) {
@@ -92,8 +88,6 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
     const T*   inData   = in.get();
     const T*   filter   = mask.get();
 
-    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-
     for(dim_t batchId=0; batchId<bCount; ++batchId) {
         // either channels or batch is handled by outer most loop
         for(dim_t k=0; k<dims[2]; ++k) {
@@ -102,7 +96,7 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = init;
+                    T filterResult = inData[ getIdx(istrides, i, j, k) ];
 
                     // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wk=0; wk<window[2]; wk++) {
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index beef1dbbb9..f92ed01c26 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -7,13 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <limits>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
-#include <ops.hpp>
 #include "shared.hpp"
 
 namespace cuda
@@ -104,7 +102,7 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     __syncthreads();
 
     const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T acc = shrdMem[ lIdx(i, j, shrdLen, 1) ];
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -198,7 +196,7 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     int k  = lz + halo;
 
     const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T acc = shrdMem[ lIdx3D(i, j, k, shrdArea, shrdLen, 1) ];
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index 4b5a24f9e2..cbbbb84707 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -69,7 +69,7 @@ void morph(__global T *              out,
     int j = ly + halo;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    T acc = init;
+    T acc = localMem[ lIdx(i, j, shrdLen, 1) ];
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -169,7 +169,7 @@ void morph3d(__global T *         out,
     int j  = ly + halo;
     int k  = lz + halo;
 
-    T acc = init;
+    T acc = localMem[ lIdx3D(i, j, k, shrdArea, shrdLen, 1) ];
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index fa7b81618a..bde71bd335 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -18,8 +18,6 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
-#include <type_util.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -56,22 +54,19 @@ void morph(Param         out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                ToNum<T> toNum;
-                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D isDilation="<< isDilation
-                        << " -D init=" << toNum(init)
-                        << " -D windLen=" << windLen;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                morProgs[device]   = new Program(prog);
-                morKernels[device] = new Kernel(*morProgs[device], "morph");
-            });
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName()
+                            << " -D isDilation="<< isDilation
+                            << " -D windLen=" << windLen;
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, morph_cl, morph_cl_len, options.str());
+                    morProgs[device]   = new Program(prog);
+                    morKernels[device] = new Kernel(*morProgs[device], "morph");
+                });
 
         auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
@@ -124,22 +119,19 @@ void morph3d(Param       out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                ToNum<T> toNum;
-                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D isDilation="<< isDilation
-                        << " -D init=" << toNum(init)
-                        << " -D windLen=" << windLen;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                morProgs[device]   = new Program(prog);
-                morKernels[device] = new Kernel(*morProgs[device], "morph3d");
-            });
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName()
+                            << " -D isDilation="<< isDilation
+                            << " -D windLen=" << windLen;
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, morph_cl, morph_cl_len, options.str());
+                    morProgs[device]   = new Program(prog);
+                    morKernels[device] = new Kernel(*morProgs[device], "morph3d");
+                });
 
         auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,

From 627a4017dee134cbc899739c4edae52742de31e5 Mon Sep 17 00:00:00 2001
From: Vardan Akopian <vakopian@sustain.net>
Date: Tue, 16 Aug 2016 15:08:06 -0700
Subject: [PATCH 0752/2677] fix initial value for morph

---
 src/backend/cpu/kernel/morph.hpp  | 7 +++++--
 src/backend/cuda/kernel/morph.hpp | 7 +++++--
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index d990bb873b..0589dd222f 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <limits>
 #include <Array.hpp>
 #include <utility.hpp>
 
@@ -37,7 +38,8 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = inData[ getIdx(istrides, i, j) ];
+                    T filterResult = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
+			: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
 
                     // wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wj=0; wj<window[1]; wj++) {
@@ -96,7 +98,8 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = inData[ getIdx(istrides, i, j, k) ];
+                    T filterResult = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
+			: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
 
                     // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wk=0; wk<window[2]; wk++) {
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index f92ed01c26..697f8760ec 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <limits>
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
@@ -102,7 +103,8 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     __syncthreads();
 
     const T * d_filt = (const T *)cFilter;
-    T acc = shrdMem[ lIdx(i, j, shrdLen, 1) ];
+    T acc = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
+	: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -196,7 +198,8 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     int k  = lz + halo;
 
     const T * d_filt = (const T *)cFilter;
-    T acc = shrdMem[ lIdx3D(i, j, k, shrdArea, shrdLen, 1) ];
+    T acc = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
+	: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;

From 63f025629405d579df064925b528353cef944230 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 17 Aug 2016 13:59:40 -0400
Subject: [PATCH 0753/2677] Fixing initial values in morph functions for all
 backends

---
 src/backend/cpu/kernel/morph.hpp    | 11 ++++--
 src/backend/cuda/kernel/morph.hpp   |  7 ++--
 src/backend/opencl/kernel/morph.cl  |  4 +-
 src/backend/opencl/kernel/morph.hpp | 60 ++++++++++++++++-------------
 4 files changed, 46 insertions(+), 36 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 0589dd222f..4cec3b363a 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -11,6 +11,7 @@
 #include <limits>
 #include <Array.hpp>
 #include <utility.hpp>
+#include <ops.hpp>
 
 namespace cpu
 {
@@ -31,6 +32,8 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
     const dim_t R0      = window[0]/2;
     const dim_t R1      = window[1]/2;
 
+    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+
     for(dim_t b3=0; b3<dims[3]; ++b3) {
         for(dim_t b2=0; b2<dims[2]; ++b2) {
             // either channels or batch is handled by outer most loop
@@ -38,8 +41,7 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
-			: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
+                    T filterResult = init;
 
                     // wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wj=0; wj<window[1]; wj++) {
@@ -90,6 +92,8 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
     const T*   inData   = in.get();
     const T*   filter   = mask.get();
 
+    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+
     for(dim_t batchId=0; batchId<bCount; ++batchId) {
         // either channels or batch is handled by outer most loop
         for(dim_t k=0; k<dims[2]; ++k) {
@@ -98,8 +102,7 @@ void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
                 // j steps along 2nd dimension
                 for(dim_t i=0; i<dims[0]; ++i) {
                     // i steps along 1st dimension
-                    T filterResult = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
-			: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
+                    T filterResult = init;
 
                     // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
                     for(dim_t wk=0; wk<window[2]; wk++) {
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 697f8760ec..beef1dbbb9 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -13,6 +13,7 @@
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
+#include <ops.hpp>
 #include "shared.hpp"
 
 namespace cuda
@@ -103,8 +104,7 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     __syncthreads();
 
     const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
-	: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
+    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -198,8 +198,7 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     int k  = lz + halo;
 
     const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? (std::is_integral<T>::value ? std::numeric_limits<T>::lowest() : -std::numeric_limits<T>::infinity())
-	: (std::is_integral<T>::value ? std::numeric_limits<T>::max() : std::numeric_limits<T>::infinity());
+    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index cbbbb84707..4b5a24f9e2 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -69,7 +69,7 @@ void morph(__global T *              out,
     int j = ly + halo;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    T acc = localMem[ lIdx(i, j, shrdLen, 1) ];
+    T acc = init;
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -169,7 +169,7 @@ void morph3d(__global T *         out,
     int j  = ly + halo;
     int k  = lz + halo;
 
-    T acc = localMem[ lIdx3D(i, j, k, shrdArea, shrdLen, 1) ];
+    T acc = init;
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index bde71bd335..fa7b81618a 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -18,6 +18,8 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
+#include <type_util.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -54,19 +56,22 @@ void morph(Param         out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D isDilation="<< isDilation
-                            << " -D windLen=" << windLen;
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                    morProgs[device]   = new Program(prog);
-                    morKernels[device] = new Kernel(*morProgs[device], "morph");
-                });
+                ToNum<T> toNum;
+                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D isDilation="<< isDilation
+                        << " -D init=" << toNum(init)
+                        << " -D windLen=" << windLen;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, morph_cl, morph_cl_len, options.str());
+                morProgs[device]   = new Program(prog);
+                morKernels[device] = new Kernel(*morProgs[device], "morph");
+            });
 
         auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,
@@ -119,19 +124,22 @@ void morph3d(Param       out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D isDilation="<< isDilation
-                            << " -D windLen=" << windLen;
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                    morProgs[device]   = new Program(prog);
-                    morKernels[device] = new Kernel(*morProgs[device], "morph3d");
-                });
+                ToNum<T> toNum;
+                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D isDilation="<< isDilation
+                        << " -D init=" << toNum(init)
+                        << " -D windLen=" << windLen;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, morph_cl, morph_cl_len, options.str());
+                morProgs[device]   = new Program(prog);
+                morKernels[device] = new Kernel(*morProgs[device], "morph3d");
+            });
 
         auto morphOp = KernelFunctor<Buffer, KParam,
                                    Buffer, KParam,

From dd296bed19984b01201767c4a460d55afa1d2562 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 18 Aug 2016 18:19:16 -0400
Subject: [PATCH 0754/2677] update api

---
 include/af/image.h                            |  67 ---------
 include/af/signal.h                           | 103 +++++++++++++
 src/api/c/filters.cpp                         |  74 +++++-----
 src/api/cpp/filters.cpp                       |  12 +-
 src/api/unified/image.cpp                     |  12 --
 src/api/unified/signal.cpp                    |  19 +++
 src/backend/cpu/kernel/medfilt.hpp            |   4 +-
 src/backend/cpu/medfilt.cpp                   |  16 +-
 src/backend/cpu/medfilt.hpp                   |   4 +-
 src/backend/cuda/kernel/medfilt.hpp           |  36 ++---
 src/backend/cuda/medfilt.cu                   |  28 ++--
 src/backend/cuda/medfilt.hpp                  |   4 +-
 src/backend/opencl/kernel/medfilt.hpp         |  54 +++----
 src/backend/opencl/kernel/medfilt1.cl         | 138 ++++++++++++++++++
 .../opencl/kernel/{medfilt.cl => medfilt2.cl} |  14 +-
 src/backend/opencl/medfilt.cpp                |  58 ++++----
 src/backend/opencl/medfilt.hpp                |   4 +-
 test/medfilt.cpp                              |  36 ++---
 18 files changed, 438 insertions(+), 245 deletions(-)
 create mode 100644 src/backend/opencl/kernel/medfilt1.cl
 rename src/backend/opencl/kernel/{medfilt.cl => medfilt2.cl} (95%)

diff --git a/include/af/image.h b/include/af/image.h
index 76b3c84fe8..f00d55935f 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -339,40 +339,6 @@ AFAPI array histogram(const array &in, const unsigned nbins);
 */
 AFAPI array meanShift(const array& in, const float spatial_sigma, const float chromatic_sigma, const unsigned iter, const bool is_color=false);
 
-/**
-    C++ Interface for median filter
-
-    \snippet test/medfilt.cpp ex_image_medfilt
-
-    \param[in]  in array is the input image
-    \param[in]  wind_length is the kernel height
-    \param[in]  wind_width is the kernel width
-    \param[in]  edge_pad value will decide what happens to border when running
-                filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
-    \return     the processed image
-
-    \ingroup image_func_medfilt
-*/
-AFAPI array medfilt(const array& in, const dim_t wind_length = 3, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
-
-#if AF_API_VERSION >= 34
-/**
-    C++ Interface for median filter
-
-    \snippet test/medfilt.cpp ex_image_medfilt
-
-    \param[in]  in array is the input signal
-    \param[in]  wind_width is the kernel width
-    \param[in]  edge_pad value will decide what happens to border when running
-                filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
-    \return     the processed signal
-
-    \ingroup image_func_medfilt
-*/
-AFAPI array medfilt_1d(const array& in, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
-
-#endif
-
 /**
     C++ Interface for minimum filter
 
@@ -1072,39 +1038,6 @@ extern "C" {
     */
     AFAPI af_err af_mean_shift(af_array *out, const af_array in, const float spatial_sigma, const float chromatic_sigma, const unsigned iter, const bool is_color);
 
-    /**
-        C Interface for median filter
-
-        \param[out] out array is the processed image
-        \param[in]  in array is the input image
-        \param[in]  wind_length is the kernel height
-        \param[in]  wind_width is the kernel width
-        \param[in]  edge_pad value will decide what happens to border when running
-                    filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
-        \return     \ref AF_SUCCESS if the median filter is applied successfully,
-        otherwise an appropriate error code is returned.
-
-        \ingroup image_func_medfilt
-    */
-    AFAPI af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad);
-
-#if AF_API_VERSION >= 34
-    /**
-        C Interface for 1D median filter
-
-        \param[out] out array is the processed signal
-        \param[in]  in array is the input signal
-        \param[in]  wind_width is the kernel width
-        \param[in]  edge_pad value will decide what happens to border when running
-                    filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
-        \return     \ref AF_SUCCESS if the median filter is applied successfully,
-        otherwise an appropriate error code is returned.
-
-        \ingroup image_func_medfilt
-    */
-    AFAPI af_err af_medfilt_1d(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad);
-
-#endif
     /**
         C Interface for minimum filter
 
diff --git a/include/af/signal.h b/include/af/signal.h
index d5b1dc1f4d..ffa0c108af 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -603,6 +603,57 @@ AFAPI array fir(const array &b, const array &x);
 */
 AFAPI array iir(const array &b, const array &a, const array &x);
 
+/**
+    C++ Interface for median filter
+
+    \snippet test/medfilt.cpp ex_image_medfilt
+
+    \param[in]  in array is the input image
+    \param[in]  wind_length is the kernel height
+    \param[in]  wind_width is the kernel width
+    \param[in]  edge_pad value will decide what happens to border when running
+                filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+    \return     the processed image
+
+    \ingroup image_func_medfilt
+*/
+AFAPI array medfilt(const array& in, const dim_t wind_length = 3, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
+
+#if AF_API_VERSION >= 34
+/**
+    C++ Interface for median filter
+
+    \snippet test/medfilt.cpp ex_image_medfilt
+
+    \param[in]  in array is the input signal
+    \param[in]  wind_width is the kernel width
+    \param[in]  edge_pad value will decide what happens to border when running
+                filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+    \return     the processed signal
+
+    \ingroup image_func_medfilt
+*/
+AFAPI array medfilt1(const array& in, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+    C++ Interface for median filter
+
+    \snippet test/medfilt.cpp ex_image_medfilt
+
+    \param[in]  in array is the input image
+    \param[in]  wind_length is the kernel height
+    \param[in]  wind_width is the kernel width
+    \param[in]  edge_pad value will decide what happens to border when running
+                filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+    \return     the processed image
+
+    \ingroup image_func_medfilt
+*/
+AFAPI array medfilt2(const array& in, const dim_t wind_length = 3, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
+#endif
+
 }
 #endif
 
@@ -1079,6 +1130,58 @@ AFAPI af_err af_fir(af_array *y, const af_array b, const af_array x);
 */
 AFAPI af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x);
 
+    /**
+        C Interface for median filter
+
+        \param[out] out array is the processed image
+        \param[in]  in array is the input image
+        \param[in]  wind_length is the kernel height
+        \param[in]  wind_width is the kernel width
+        \param[in]  edge_pad value will decide what happens to border when running
+                    filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+        \return     \ref AF_SUCCESS if the median filter is applied successfully,
+        otherwise an appropriate error code is returned.
+
+        \ingroup image_func_medfilt
+    */
+    AFAPI af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad);
+
+#if AF_API_VERSION >= 34
+    /**
+        C Interface for 1D median filter
+
+        \param[out] out array is the processed signal
+        \param[in]  in array is the input signal
+        \param[in]  wind_width is the kernel width
+        \param[in]  edge_pad value will decide what happens to border when running
+                    filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+        \return     \ref AF_SUCCESS if the median filter is applied successfully,
+        otherwise an appropriate error code is returned.
+
+        \ingroup image_func_medfilt
+    */
+    AFAPI af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+        C Interface for median filter
+
+        \param[out] out array is the processed image
+        \param[in]  in array is the input image
+        \param[in]  wind_length is the kernel height
+        \param[in]  wind_width is the kernel width
+        \param[in]  edge_pad value will decide what happens to border when running
+                    filter in their neighborhood. It takes one of the values [\ref AF_PAD_ZERO | \ref AF_PAD_SYM]
+        \return     \ref AF_SUCCESS if the median filter is applied successfully,
+        otherwise an appropriate error code is returned.
+
+        \ingroup image_func_medfilt
+    */
+    AFAPI af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad);
+#endif
+
+
 #if AF_API_VERSION >= 34
 /**
    C Interface for setting plan cache size
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index fe8908a871..a173f17132 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -10,6 +10,7 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/image.h>
+#include <af/signal.h>
 #include <af/data.h>
 #include <handle.hpp>
 #include <err_common.hpp>
@@ -19,44 +20,47 @@
 using af::dim4;
 using namespace detail;
 
+af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
+{
+    return af_medfilt2(out, in, wind_length, wind_width, edge_pad);
+}
+
 template<typename T>
-static af_array medfilt(af_array const &in, dim_t w_len, dim_t w_wid, af_border_type edge_pad)
+static af_array medfilt1(af_array const &in, dim_t w_wid, af_border_type edge_pad)
 {
     switch(edge_pad) {
-        case AF_PAD_ZERO : return getHandle<T>(medfilt<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid)); break;
-        case AF_PAD_SYM  : return getHandle<T>(medfilt<T, AF_PAD_SYM >(getArray<T>(in), w_len, w_wid)); break;
-        default          : return getHandle<T>(medfilt<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid)); break;
+        case AF_PAD_ZERO : return getHandle<T>(medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
+        case AF_PAD_SYM  : return getHandle<T>(medfilt1<T, AF_PAD_SYM >(getArray<T>(in), w_wid)); break;
+        default          : return getHandle<T>(medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
     }
 }
 
-af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
+af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
 {
     try {
-        ARG_ASSERT(2, (wind_length==wind_width));
-        ARG_ASSERT(2, (wind_length>0));
-        ARG_ASSERT(3, (wind_width>0));
+        ARG_ASSERT(2, (wind_width>0));
         ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
 
         ArrayInfo info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         dim_t input_ndims = dims.ndims();
-        DIM_ASSERT(1, (input_ndims >= 2));
+        DIM_ASSERT(1, (input_ndims >= 1));
 
-        if (wind_length==1) {
+        if (wind_width==1) {
             *out = retain(in);
         } else {
             af_array output;
             af_dtype type  = info.getType();
             switch(type) {
-                case f32: output = medfilt<float >(in, wind_length, wind_width, edge_pad); break;
-                case f64: output = medfilt<double>(in, wind_length, wind_width, edge_pad); break;
-                case b8 : output = medfilt<char  >(in, wind_length, wind_width, edge_pad); break;
-                case s32: output = medfilt<int   >(in, wind_length, wind_width, edge_pad); break;
-                case u32: output = medfilt<uint  >(in, wind_length, wind_width, edge_pad); break;
-                case s16: output = medfilt<short >(in, wind_length, wind_width, edge_pad); break;
-                case u16: output = medfilt<ushort>(in, wind_length, wind_width, edge_pad); break;
-                case u8 : output = medfilt<uchar >(in, wind_length, wind_width, edge_pad); break;
+                case f32: output = medfilt1<float >(in, wind_width, edge_pad); break;
+                case f64: output = medfilt1<double>(in, wind_width, edge_pad); break;
+                case b8 : output = medfilt1<char  >(in, wind_width, edge_pad); break;
+                case s32: output = medfilt1<int   >(in, wind_width, edge_pad); break;
+                case u32: output = medfilt1<uint  >(in, wind_width, edge_pad); break;
+                case s16: output = medfilt1<short >(in, wind_width, edge_pad); break;
+                case u16: output = medfilt1<ushort>(in, wind_width, edge_pad); break;
+                case u8 : output = medfilt1<uchar >(in, wind_width, edge_pad); break;
                 default : TYPE_ERROR(1, type);
             }
             std::swap(*out, output);
@@ -68,41 +72,43 @@ af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, con
 }
 
 template<typename T>
-static af_array medfilt_1d(af_array const &in, dim_t w_wid, af_border_type edge_pad)
+static af_array medfilt2(af_array const &in, dim_t w_len, dim_t w_wid, af_border_type edge_pad)
 {
     switch(edge_pad) {
-        case AF_PAD_ZERO : return getHandle<T>(medfilt_1d<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
-        case AF_PAD_SYM  : return getHandle<T>(medfilt_1d<T, AF_PAD_SYM >(getArray<T>(in), w_wid)); break;
-        default          : return getHandle<T>(medfilt_1d<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
+        case AF_PAD_ZERO : return getHandle<T>(medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid)); break;
+        case AF_PAD_SYM  : return getHandle<T>(medfilt2<T, AF_PAD_SYM >(getArray<T>(in), w_len, w_wid)); break;
+        default          : return getHandle<T>(medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid)); break;
     }
 }
 
-af_err af_medfilt_1d(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
+af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
 {
     try {
-        ARG_ASSERT(2, (wind_width>0));
+        ARG_ASSERT(2, (wind_length==wind_width));
+        ARG_ASSERT(2, (wind_length>0));
+        ARG_ASSERT(3, (wind_width>0));
         ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
 
         ArrayInfo info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         dim_t input_ndims = dims.ndims();
-        DIM_ASSERT(1, (input_ndims >= 1));
+        DIM_ASSERT(1, (input_ndims >= 2));
 
-        if (wind_width==1) {
+        if (wind_length==1) {
             *out = retain(in);
         } else {
             af_array output;
             af_dtype type  = info.getType();
             switch(type) {
-                case f32: output = medfilt_1d<float >(in, wind_width, edge_pad); break;
-                case f64: output = medfilt_1d<double>(in, wind_width, edge_pad); break;
-                case b8 : output = medfilt_1d<char  >(in, wind_width, edge_pad); break;
-                case s32: output = medfilt_1d<int   >(in, wind_width, edge_pad); break;
-                case u32: output = medfilt_1d<uint  >(in, wind_width, edge_pad); break;
-                case s16: output = medfilt_1d<short >(in, wind_width, edge_pad); break;
-                case u16: output = medfilt_1d<ushort>(in, wind_width, edge_pad); break;
-                case u8 : output = medfilt_1d<uchar >(in, wind_width, edge_pad); break;
+                case f32: output = medfilt2<float >(in, wind_length, wind_width, edge_pad); break;
+                case f64: output = medfilt2<double>(in, wind_length, wind_width, edge_pad); break;
+                case b8 : output = medfilt2<char  >(in, wind_length, wind_width, edge_pad); break;
+                case s32: output = medfilt2<int   >(in, wind_length, wind_width, edge_pad); break;
+                case u32: output = medfilt2<uint  >(in, wind_length, wind_width, edge_pad); break;
+                case s16: output = medfilt2<short >(in, wind_length, wind_width, edge_pad); break;
+                case u16: output = medfilt2<ushort>(in, wind_length, wind_width, edge_pad); break;
+                case u8 : output = medfilt2<uchar >(in, wind_length, wind_width, edge_pad); break;
                 default : TYPE_ERROR(1, type);
             }
             std::swap(*out, output);
diff --git a/src/api/cpp/filters.cpp b/src/api/cpp/filters.cpp
index ecd6b7894b..f950d60010 100644
--- a/src/api/cpp/filters.cpp
+++ b/src/api/cpp/filters.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/image.h>
+#include <af/signal.h>
 #include <af/array.h>
 #include "error.hpp"
 
@@ -15,16 +16,21 @@ namespace af
 {
 
 array medfilt(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
+{
+    return medfilt2(in, wind_length, wind_width, edge_pad);
+}
+
+array medfilt1(const array& in, const dim_t wind_width, const borderType edge_pad)
 {
     af_array out = 0;
-    AF_THROW(af_medfilt(&out, in.get(), wind_length, wind_width, edge_pad));
+    AF_THROW(af_medfilt1(&out, in.get(), wind_width, edge_pad));
     return array(out);
 }
 
-array medfilt_1d(const array& in, const dim_t wind_width, const borderType edge_pad)
+array medfilt2(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
 {
     af_array out = 0;
-    AF_THROW(af_medfilt_1d(&out, in.get(), wind_width, edge_pad));
+    AF_THROW(af_medfilt2(&out, in.get(), wind_length, wind_width, edge_pad));
     return array(out);
 }
 
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 5e4453c5e0..a01b8ff600 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -152,18 +152,6 @@ af_err af_mean_shift(af_array *out, const af_array in, const float spatial_sigma
     return CALL(out, in, spatial_sigma, chromatic_sigma, iter, is_color);
 }
 
-af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
-    CHECK_ARRAYS(in);
-    return CALL(out, in, wind_length, wind_width, edge_pad);
-}
-
-af_err af_medfilt_1d(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
-{
-    CHECK_ARRAYS(in);
-    return CALL(out, in, wind_width, edge_pad);
-}
-
 af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
 {
     CHECK_ARRAYS(in);
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 138a0d6905..d4cc372d4a 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -141,3 +141,22 @@ af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
     CHECK_ARRAYS(b, a, x);
     return CALL(y, b, a, x);
 }
+
+
+af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, wind_length, wind_width, edge_pad);
+}
+
+af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, wind_width, edge_pad);
+}
+
+af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, wind_length, wind_width, edge_pad);
+}
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index 3cfe2f1259..9587641465 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -18,7 +18,7 @@ namespace kernel
 {
 
 template<typename T, af_border_type Pad>
-void medfilt_1d(Array<T> out, const Array<T> in, dim_t w_wid)
+void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
 {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
@@ -100,7 +100,7 @@ void medfilt_1d(Array<T> out, const Array<T> in, dim_t w_wid)
 
 
 template<typename T, af_border_type Pad>
-void medfilt(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
+void medfilt2(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
 {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index d188e2d8f7..a1c44a4323 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -20,34 +20,34 @@ namespace cpu
 {
 
 template<typename T, af_border_type pad>
-Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::medfilt<T, pad>, out, in, w_len, w_wid);
+    getQueue().enqueue(kernel::medfilt1<T, pad>, out, in, w_wid);
 
     return out;
 }
 
 template<typename T, af_border_type pad>
-Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid)
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::medfilt_1d<T, pad>, out, in, w_wid);
+    getQueue().enqueue(kernel::medfilt2<T, pad>, out, in, w_len, w_wid);
 
     return out;
 }
 
 #define INSTANTIATE(T)\
-    template Array<T> medfilt_1d<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt_1d<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
-    template Array<T> medfilt<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt2<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
diff --git a/src/backend/cpu/medfilt.hpp b/src/backend/cpu/medfilt.hpp
index 62a64003ae..2bb5836841 100644
--- a/src/backend/cpu/medfilt.hpp
+++ b/src/backend/cpu/medfilt.hpp
@@ -13,9 +13,9 @@ namespace cpu
 {
 
 template<typename T, af_border_type edge_pad>
-Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid);
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
 
 template<typename T, af_border_type edge_pad>
-Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid);
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 }
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index aeb37e2500..fcbc70623e 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -91,7 +91,7 @@ void load2ShrdMem_1d(T * shrd, const T * in,
 
 template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
 __global__
-void medfilt(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
+void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
 {
     __shared__ T shrdMem[(THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)];
 
@@ -208,7 +208,7 @@ void medfilt(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
 
 template<typename T, af_border_type pad, unsigned w_wid>
 __global__
-void medfilt_1d(Param<T> out, CParam<T> in, int nBBS0)
+void medfilt1(Param<T> out, CParam<T> in, int nBBS0)
 {
     __shared__ T shrdMem[(THREADS_X+w_wid-1)];
 
@@ -312,7 +312,7 @@ void medfilt_1d(Param<T> out, CParam<T> in, int nBBS0)
 }
 
 template<typename T, af_border_type pad>
-void medfilt(Param<T> out, CParam<T> in, int w_len, int w_wid)
+void medfilt2(Param<T> out, CParam<T> in, int w_len, int w_wid)
 {
     const dim3 threads(THREADS_X, THREADS_Y);
 
@@ -322,20 +322,20 @@ void medfilt(Param<T> out, CParam<T> in, int w_len, int w_wid)
     dim3 blocks(blk_x*in.dims[2], blk_y*in.dims[3]);
 
     switch(w_len) {
-        case  3: CUDA_LAUNCH((medfilt<T,pad, 3, 3>), blocks, threads, out, in, blk_x, blk_y); break;
-        case  5: CUDA_LAUNCH((medfilt<T,pad, 5, 5>), blocks, threads, out, in, blk_x, blk_y); break;
-        case  7: CUDA_LAUNCH((medfilt<T,pad, 7, 7>), blocks, threads, out, in, blk_x, blk_y); break;
-        case  9: CUDA_LAUNCH((medfilt<T,pad, 9, 9>), blocks, threads, out, in, blk_x, blk_y); break;
-        case 11: CUDA_LAUNCH((medfilt<T,pad,11,11>), blocks, threads, out, in, blk_x, blk_y); break;
-        case 13: CUDA_LAUNCH((medfilt<T,pad,13,13>), blocks, threads, out, in, blk_x, blk_y); break;
-        case 15: CUDA_LAUNCH((medfilt<T,pad,15,15>), blocks, threads, out, in, blk_x, blk_y); break;
+        case  3: CUDA_LAUNCH((medfilt2<T,pad, 3, 3>), blocks, threads, out, in, blk_x, blk_y); break;
+        case  5: CUDA_LAUNCH((medfilt2<T,pad, 5, 5>), blocks, threads, out, in, blk_x, blk_y); break;
+        case  7: CUDA_LAUNCH((medfilt2<T,pad, 7, 7>), blocks, threads, out, in, blk_x, blk_y); break;
+        case  9: CUDA_LAUNCH((medfilt2<T,pad, 9, 9>), blocks, threads, out, in, blk_x, blk_y); break;
+        case 11: CUDA_LAUNCH((medfilt2<T,pad,11,11>), blocks, threads, out, in, blk_x, blk_y); break;
+        case 13: CUDA_LAUNCH((medfilt2<T,pad,13,13>), blocks, threads, out, in, blk_x, blk_y); break;
+        case 15: CUDA_LAUNCH((medfilt2<T,pad,15,15>), blocks, threads, out, in, blk_x, blk_y); break;
     }
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_border_type pad>
-void medfilt_1d(Param<T> out, CParam<T> in, int w_wid)
+void medfilt1(Param<T> out, CParam<T> in, int w_wid)
 {
     const dim3 threads(THREADS_X);
 
@@ -344,13 +344,13 @@ void medfilt_1d(Param<T> out, CParam<T> in, int w_wid)
     dim3 blocks(blk_x*in.dims[1], in.dims[2], in.dims[3] );
 
     switch(w_wid) {
-        case  3: CUDA_LAUNCH((medfilt_1d<T,pad, 3>), blocks, threads, out, in, blk_x); break;
-        case  5: CUDA_LAUNCH((medfilt_1d<T,pad, 5>), blocks, threads, out, in, blk_x); break;
-        case  7: CUDA_LAUNCH((medfilt_1d<T,pad, 7>), blocks, threads, out, in, blk_x); break;
-        case  9: CUDA_LAUNCH((medfilt_1d<T,pad, 9>), blocks, threads, out, in, blk_x); break;
-        case 11: CUDA_LAUNCH((medfilt_1d<T,pad,11>), blocks, threads, out, in, blk_x); break;
-        case 13: CUDA_LAUNCH((medfilt_1d<T,pad,13>), blocks, threads, out, in, blk_x); break;
-        case 15: CUDA_LAUNCH((medfilt_1d<T,pad,15>), blocks, threads, out, in, blk_x); break;
+        case  3: CUDA_LAUNCH((medfilt1<T,pad, 3>), blocks, threads, out, in, blk_x); break;
+        case  5: CUDA_LAUNCH((medfilt1<T,pad, 5>), blocks, threads, out, in, blk_x); break;
+        case  7: CUDA_LAUNCH((medfilt1<T,pad, 7>), blocks, threads, out, in, blk_x); break;
+        case  9: CUDA_LAUNCH((medfilt1<T,pad, 9>), blocks, threads, out, in, blk_x); break;
+        case 11: CUDA_LAUNCH((medfilt1<T,pad,11>), blocks, threads, out, in, blk_x); break;
+        case 13: CUDA_LAUNCH((medfilt1<T,pad,13>), blocks, threads, out, in, blk_x); break;
+        case 15: CUDA_LAUNCH((medfilt1<T,pad,15>), blocks, threads, out, in, blk_x); break;
     }
 
     POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cu
index db1f7590c6..0dcea3d16f 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cu
@@ -19,38 +19,38 @@ namespace cuda
 {
 
 template<typename T, af_border_type pad>
-Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
 
-    const dim4 dims     = in.dims();
+    const dim4 dims = in.dims();
 
-    Array<T> out      = createEmptyArray<T>(dims);
+    Array<T> out    = createEmptyArray<T>(dims);
 
-    kernel::medfilt<T, pad>(out, in, w_len, w_wid);
+    kernel::medfilt1<T, pad>(out, in, w_wid);
 
     return out;
 }
 
 template<typename T, af_border_type pad>
-Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid)
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER_LEN));
 
-    const dim4 dims = in.dims();
+    const dim4 dims     = in.dims();
 
-    Array<T> out    = createEmptyArray<T>(dims);
+    Array<T> out      = createEmptyArray<T>(dims);
 
-    kernel::medfilt_1d<T, pad>(out, in, w_wid);
+    kernel::medfilt2<T, pad>(out, in, w_len, w_wid);
 
     return out;
 }
 
 #define INSTANTIATE(T)                                                                          \
-    template Array<T> medfilt_1d<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt_1d<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
-    template Array<T> medfilt<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt2<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
diff --git a/src/backend/cuda/medfilt.hpp b/src/backend/cuda/medfilt.hpp
index 4e01041218..663c819012 100644
--- a/src/backend/cuda/medfilt.hpp
+++ b/src/backend/cuda/medfilt.hpp
@@ -13,9 +13,9 @@ namespace cuda
 {
 
 template<typename T, af_border_type edge_pad>
-Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid);
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
 
 template<typename T, af_border_type edge_pad>
-Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid);
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 }
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 5e84940fbd..3b00a8e5ab 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/medfilt.hpp>
-#include <kernel_headers/medfilt_1d.hpp>
+#include <kernel_headers/medfilt2.hpp>
+#include <kernel_headers/medfilt1.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -38,8 +38,8 @@ static const int MAX_MEDFILTER_LEN = 15;
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
-void medfilt(Param out, const Param in)
+template<typename T, af_border_type pad, unsigned w_wid>
+void medfilt1(Param out, const Param in)
 {
     try {
         static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -50,7 +50,7 @@ void medfilt(Param out, const Param in)
 
         std::call_once( compileFlags[device], [device] () {
 
-                const int ARR_SIZE = w_len * (w_wid-w_wid/2);
+                const int ARR_SIZE = (w_wid-w_wid/2) + 1;
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
@@ -58,35 +58,34 @@ void medfilt(Param out, const Param in)
                         << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
                         << " -D AF_PAD_SYM="<< AF_PAD_SYM
                         << " -D ARR_SIZE="<< ARR_SIZE
-                        << " -D w_len="<< w_len
                         << " -D w_wid=" << w_wid;
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
                     options << " -D USE_DOUBLE";
                 }
                 Program prog;
-                buildProgram(prog, medfilt_cl, medfilt_cl_len, options.str());
+                buildProgram(prog, medfilt1_cl, medfilt1_cl_len, options.str());
                 mfProgs[device]   = new Program(prog);
-                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt");
+                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt1");
             });
 
-        NDRange local(THREADS_X, THREADS_Y);
+        NDRange local(THREADS_X, 1, 1);
 
         int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-        NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                       blk_y * in.info.dims[3] * THREADS_Y);
+        NDRange global(blk_x * in.info.dims[1] * THREADS_X,
+                                           in.info.dims[2],
+                                           in.info.dims[3]);
 
         auto medfiltOp = make_kernel<Buffer, KParam,
                                      Buffer, KParam,
                                      cl::LocalSpaceArg,
-                                     int, int> (*mfKernels[device]);
+                                     int> (*mfKernels[device]);
 
-        size_t loc_size = (THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)*sizeof(T);
+        size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
 
         medfiltOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
 
         CL_DEBUG_FINISH(getQueue());
     } catch (cl::Error err) {
@@ -95,8 +94,8 @@ void medfilt(Param out, const Param in)
     }
 }
 
-template<typename T, af_border_type pad, unsigned w_wid>
-void medfilt_1d(Param out, const Param in)
+template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
+void medfilt2(Param out, const Param in)
 {
     try {
         static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -107,7 +106,7 @@ void medfilt_1d(Param out, const Param in)
 
         std::call_once( compileFlags[device], [device] () {
 
-                const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+                const int ARR_SIZE = w_len * (w_wid-w_wid/2);
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
@@ -115,34 +114,35 @@ void medfilt_1d(Param out, const Param in)
                         << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
                         << " -D AF_PAD_SYM="<< AF_PAD_SYM
                         << " -D ARR_SIZE="<< ARR_SIZE
+                        << " -D w_len="<< w_len
                         << " -D w_wid=" << w_wid;
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
                     options << " -D USE_DOUBLE";
                 }
                 Program prog;
-                buildProgram(prog, medfilt_1d_cl, medfilt_1d_cl_len, options.str());
+                buildProgram(prog, medfilt2_cl, medfilt2_cl_len, options.str());
                 mfProgs[device]   = new Program(prog);
-                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt_1d");
+                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt2");
             });
 
-        NDRange local(THREADS_X, 1, 1);
+        NDRange local(THREADS_X, THREADS_Y);
 
         int blk_x = divup(in.info.dims[0], THREADS_X);
+        int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-        NDRange global(blk_x * in.info.dims[1] * THREADS_X,
-                                           in.info.dims[2],
-                                           in.info.dims[3]);
+        NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                       blk_y * in.info.dims[3] * THREADS_Y);
 
         auto medfiltOp = make_kernel<Buffer, KParam,
                                      Buffer, KParam,
                                      cl::LocalSpaceArg,
-                                     int> (*mfKernels[device]);
+                                     int, int> (*mfKernels[device]);
 
-        size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
+        size_t loc_size = (THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)*sizeof(T);
 
         medfiltOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
+                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
 
         CL_DEBUG_FINISH(getQueue());
     } catch (cl::Error err) {
diff --git a/src/backend/opencl/kernel/medfilt1.cl b/src/backend/opencl/kernel/medfilt1.cl
new file mode 100644
index 0000000000..0459f69957
--- /dev/null
+++ b/src/backend/opencl/kernel/medfilt1.cl
@@ -0,0 +1,138 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+// Exchange trick: Morgan McGuire, ShaderX 2008
+#define swap(a,b)    { T tmp = a; a = min(a,b); b = max(tmp,b); }
+
+void load2ShrdMem_1d(__local T *      shrd,
+                     __global const T * in,
+                                    int lx,
+                                  int dim0,
+                                    int gx,
+                             int inStride0)
+{
+    if (pad==AF_PAD_ZERO) {
+        if (gx<0 || gx>=dim0)
+            shrd[lx] = (T)0;
+        else
+            shrd[lx] = in[gx];
+    } else if (pad==AF_PAD_SYM) {
+        if (gx<0) gx *= -1;
+        if (gx>=dim0) gx = 2*(dim0-1) - gx;
+        shrd[lx] = in[gx];
+    }
+}
+
+__kernel
+void medfilt1(__global T *       out,
+                KParam             oInfo,
+                __global const T * in,
+                KParam             iInfo,
+                __local T *        localMem,
+                int                nBBS0)
+{
+    // calculate necessary offset and window parameters
+    const int padding = w_wid-1;
+    const int halo    = padding/2;
+    const int shrdLen = get_local_size(0) + padding;
+
+    // batch offsets
+    unsigned b1 = get_group_id(0) / nBBS0;
+    unsigned b0 = get_group_id(0) - b1 * nBBS0;
+    unsigned b2 = get_group_id(1);
+    unsigned b3 = get_group_id(2);
+    __global const T* iptr =  in + (b1 * iInfo.strides[1] + b2 * iInfo.strides[2] + b3 * iInfo.strides[3]) + iInfo.offset;
+    __global T*       optr = out + (b1 * oInfo.strides[1] + b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) + oInfo.offset;
+
+    // local neighborhood indices
+    int lx = get_local_id(0);
+
+    // global indices
+    int gx = get_local_size(0) * b0  + lx;
+
+    int s0 = iInfo.strides[0];
+    int d0 = iInfo.dims[0];
+    for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
+        load2ShrdMem_1d(localMem, iptr, a, d0, gx2-halo, s0);
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Only continue if we're at a valid location
+    if (gx < iInfo.dims[0]) {
+        // pull top half from shared memory into local memory
+        T v[ARR_SIZE];
+
+#pragma unroll
+        for(int k = 0; k <= w_wid/2 + 1; k++) {
+            v[k] = localMem[lx+k];
+        }
+
+        // with each pass, remove min and max values and add new value
+        // initial sort
+        // ensure min in first half, max in second half
+#pragma unroll
+        for(int i = 0; i < ARR_SIZE/2; i++) {
+            swap(v[i], v[ARR_SIZE-1-i]);
+        }
+        // move min in first half to first pos
+#pragma unroll
+        for(int i = 1; i < (ARR_SIZE+1)/2; i++) {
+            swap(v[0], v[i]);
+        }
+        // move max in second half to last pos
+#pragma unroll
+        for(int i = ARR_SIZE-2; i >= ARR_SIZE/2; i--) {
+            swap(v[i], v[ARR_SIZE-1]);
+        }
+
+        int last = ARR_SIZE-1;
+
+        for(int k = w_wid/2 + 2; k < w_wid; k++) {
+            // add new contestant to first position in array
+            v[0] = localMem[lx+k];
+
+            last--;
+
+            // place max in last half, min in first half
+            for(int i = 0; i < (last+1)/2; i++) {
+                swap(v[i], v[last-i]);
+            }
+            // now perform swaps on each half such that
+            // max is in last pos, min is in first pos
+            for(int i = 1; i <= last/2; i++) {
+                swap(v[0], v[i]);
+            }
+            for(int i = last-1; i >= (last+1)/2; i--) {
+                swap(v[i], v[last]);
+            }
+        }
+
+        // no more new contestants
+        // may still have to sort the last row
+        // each outer loop drops the min and max
+        for(int k = 0; k < last; k++) {
+            // move max/min into respective halves
+            for(int i = k; i < ARR_SIZE/2; i++) {
+                swap(v[i], v[ARR_SIZE-1-i]);
+            }
+            // move min into first pos
+            for(int i = k+1; i <= ARR_SIZE/2; i++) {
+                swap(v[k], v[i]);
+            }
+            // move max into last pos
+            for(int i = ARR_SIZE-k-2; i >= ARR_SIZE/2; i--) {
+                swap(v[i], v[ARR_SIZE-1-k]);
+            }
+        }
+
+        // pick the middle element of the first row
+        optr[gx*oInfo.strides[0]] = v[last/2];
+    }
+}
diff --git a/src/backend/opencl/kernel/medfilt.cl b/src/backend/opencl/kernel/medfilt2.cl
similarity index 95%
rename from src/backend/opencl/kernel/medfilt.cl
rename to src/backend/opencl/kernel/medfilt2.cl
index 78a62b2bca..0fbf186969 100644
--- a/src/backend/opencl/kernel/medfilt.cl
+++ b/src/backend/opencl/kernel/medfilt2.cl
@@ -37,13 +37,13 @@ void load2ShrdMem(__local T *           shrd,
 }
 
 __kernel
-void medfilt(__global T *       out,
-             KParam             oInfo,
-             __global const T * in,
-             KParam             iInfo,
-             __local T *        localMem,
-             int           nBBS0,
-             int           nBBS1)
+void medfilt2(__global T *       out,
+              KParam             oInfo,
+              __global const T * in,
+              KParam             iInfo,
+              __local T *        localMem,
+              int           nBBS0,
+              int           nBBS1)
 {
     // calculate necessary offset and window parameters
     const int padding = w_len-1;
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index af52b97ce5..16a5d8f316 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -19,53 +19,53 @@ namespace opencl
 {
 
 template<typename T, af_border_type pad>
-Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid)
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
 
-    const dim4 dims     = in.dims();
+    const dim4 dims = in.dims();
 
-    Array<T> out      = createEmptyArray<T>(dims);
+    Array<T> out    = createEmptyArray<T>(dims);
 
-    switch(w_len) {
-        case  3: kernel::medfilt<T, pad,  3,  3>(out, in); break;
-        case  5: kernel::medfilt<T, pad,  5,  5>(out, in); break;
-        case  7: kernel::medfilt<T, pad,  7,  7>(out, in); break;
-        case  9: kernel::medfilt<T, pad,  9,  9>(out, in); break;
-        case 11: kernel::medfilt<T, pad, 11, 11>(out, in); break;
-        case 13: kernel::medfilt<T, pad, 13, 13>(out, in); break;
-        case 15: kernel::medfilt<T, pad, 15, 15>(out, in); break;
+    switch(w_wid) {
+        case  3: kernel::medfilt1<T, pad,  3>(out, in); break;
+        case  5: kernel::medfilt1<T, pad,  5>(out, in); break;
+        case  7: kernel::medfilt1<T, pad,  7>(out, in); break;
+        case  9: kernel::medfilt1<T, pad,  9>(out, in); break;
+        case 11: kernel::medfilt1<T, pad, 11>(out, in); break;
+        case 13: kernel::medfilt1<T, pad, 13>(out, in); break;
+        case 15: kernel::medfilt1<T, pad, 15>(out, in); break;
     }
+
     return out;
 }
 
 template<typename T, af_border_type pad>
-Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid)
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER_LEN));
 
-    const dim4 dims = in.dims();
+    const dim4 dims     = in.dims();
 
-    Array<T> out    = createEmptyArray<T>(dims);
+    Array<T> out      = createEmptyArray<T>(dims);
 
-    switch(w_wid) {
-        case  3: kernel::medfilt_1d<T, pad,  3>(out, in); break;
-        case  5: kernel::medfilt_1d<T, pad,  5>(out, in); break;
-        case  7: kernel::medfilt_1d<T, pad,  7>(out, in); break;
-        case  9: kernel::medfilt_1d<T, pad,  9>(out, in); break;
-        case 11: kernel::medfilt_1d<T, pad, 11>(out, in); break;
-        case 13: kernel::medfilt_1d<T, pad, 13>(out, in); break;
-        case 15: kernel::medfilt_1d<T, pad, 15>(out, in); break;
+    switch(w_len) {
+        case  3: kernel::medfilt2<T, pad,  3,  3>(out, in); break;
+        case  5: kernel::medfilt2<T, pad,  5,  5>(out, in); break;
+        case  7: kernel::medfilt2<T, pad,  7,  7>(out, in); break;
+        case  9: kernel::medfilt2<T, pad,  9,  9>(out, in); break;
+        case 11: kernel::medfilt2<T, pad, 11, 11>(out, in); break;
+        case 13: kernel::medfilt2<T, pad, 13, 13>(out, in); break;
+        case 15: kernel::medfilt2<T, pad, 15, 15>(out, in); break;
     }
-
     return out;
 }
 
 #define INSTANTIATE(T)                                                                          \
-    template Array<T> medfilt_1d<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt_1d<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
-    template Array<T> medfilt<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
diff --git a/src/backend/opencl/medfilt.hpp b/src/backend/opencl/medfilt.hpp
index 49e5aaab53..f3d13259d0 100644
--- a/src/backend/opencl/medfilt.hpp
+++ b/src/backend/opencl/medfilt.hpp
@@ -13,9 +13,9 @@ namespace opencl
 {
 
 template<typename T, af_border_type edge_pad>
-Array<T> medfilt(const Array<T> &in, dim_t w_len, dim_t w_wid);
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
 
 template<typename T, af_border_type edge_pad>
-Array<T> medfilt_1d(const Array<T> &in, dim_t w_wid);
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 }
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index fe86c22015..1dd4b0f9ce 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -58,7 +58,7 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_medfilt(&outArray, inArray, w_len, w_wid, pad));
+    ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, pad));
 
     T *outData = new T[dims.elements()];
 
@@ -98,7 +98,7 @@ TYPED_TEST(MedianFilter, BATCH_SYMMETRIC_PAD_3x3)
 
 
 template<typename T>
-void medfilt1d_Test(string pTestFile, dim_t w_wid, af_border_type pad)
+void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
 {
     if (noDoubleTests<T>()) return;
 
@@ -115,7 +115,7 @@ void medfilt1d_Test(string pTestFile, dim_t w_wid, af_border_type pad)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_medfilt_1d(&outArray, inArray, w_wid, pad));
+    ASSERT_EQ(AF_SUCCESS, af_medfilt1(&outArray, inArray, w_wid, pad));
 
     T *outData = new T[dims.elements()];
 
@@ -135,22 +135,22 @@ void medfilt1d_Test(string pTestFile, dim_t w_wid, af_border_type pad)
 
 TYPED_TEST(MedianFilter1d, ZERO_PAD_3)
 {
-    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
+    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
 }
 
 TYPED_TEST(MedianFilter1d, SYMMETRIC_PAD_3)
 {
-    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
+    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
 }
 
 TYPED_TEST(MedianFilter1d, BATCH_ZERO_PAD_3)
 {
-    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
+    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
 }
 
 TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3)
 {
-    medfilt1d_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
+    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
 }
 
 template<typename T,bool isColor>
@@ -184,7 +184,7 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
         ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_medfilt(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
+        ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
 
         T * outData = new T[nElems];
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
@@ -216,7 +216,7 @@ void medfiltInputTest(void)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_SIZE, af_medfilt(&outArray, inArray, 1, 1, AF_PAD_ZERO));
+    ASSERT_EQ(AF_ERR_SIZE, af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -242,7 +242,7 @@ void medfiltWindowTest(void)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt(&outArray, inArray, 3, 5, AF_PAD_ZERO));
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 5, AF_PAD_ZERO));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -269,7 +269,7 @@ void medfilt1d_WindowTest(void)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt_1d(&outArray, inArray, -1, AF_PAD_ZERO));
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, -1, AF_PAD_ZERO));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -295,9 +295,9 @@ void medfiltPadTest(void)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt(&outArray, inArray, 3, 3, af_border_type(3)));
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(3)));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt(&outArray, inArray, 3, 3, af_border_type(-1)));
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(-1)));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -323,9 +323,9 @@ void medfilt1d_PadTest(void)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt_1d(&outArray, inArray, 3, af_border_type(3)));
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(3)));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt_1d(&outArray, inArray, 3, af_border_type(-1)));
+    ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(-1)));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -383,7 +383,7 @@ TEST(MedianFilter1d, CPP)
 
     af::dim4 dims    = numDims[0];
     af::array input(dims, &(in[0].front()));
-    af::array output = af::medfilt_1d(input, w_wid, AF_PAD_SYM);
+    af::array output = af::medfilt1(input, w_wid, AF_PAD_SYM);
 
     float *outData = new float[dims.elements()];
     output.host((void*)outData);
@@ -466,11 +466,11 @@ TEST(MedianFilter1d, GFOR)
     array B = constant(0, dims);
 
     gfor(seq ii, 3) {
-        B(span, ii) = medfilt_1d(A(span, ii));
+        B(span, ii) = medfilt1(A(span, ii));
     }
 
     for(int ii = 0; ii < 3; ii++) {
-        array c_ii = medfilt_1d(A(span, ii));
+        array c_ii = medfilt1(A(span, ii));
         array b_ii = B(span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }

From 3adfb6727a7bf5c0dbe5d25674c8e053e94d7abd Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 19 Aug 2016 11:39:11 -0400
Subject: [PATCH 0755/2677] CUDA and OpenCL backend for Mersenne Twister

Small change in OpenCL kernel generation function to fix segfaults.
---
 include/af/defines.h                          |   3 +-
 src/api/c/random_engine.cpp                   | 119 +++++++++---
 src/backend/MersenneTwister.hpp               | 178 ++++++++++++++++++
 src/backend/cuda/kernel/random_engine.hpp     | 176 ++++++++++++++++-
 .../cuda/kernel/random_engine_mersenne.hpp    |  91 +++++++++
 src/backend/cuda/random_engine.cu             | 132 +++++++++++--
 src/backend/cuda/random_engine.hpp            |  17 +-
 src/backend/opencl/kernel/random_engine.hpp   | 127 ++++++++++++-
 .../opencl/kernel/random_engine_mersenne.cl   | 117 ++++++++++++
 .../kernel/random_engine_mersenne_init.cl     |  29 +++
 .../opencl/kernel/random_engine_write.cl      |   2 +-
 src/backend/opencl/random_engine.cpp          | 134 ++++++++++---
 src/backend/opencl/random_engine.hpp          |  17 +-
 13 files changed, 1046 insertions(+), 96 deletions(-)
 create mode 100644 src/backend/MersenneTwister.hpp
 create mode 100644 src/backend/cuda/kernel/random_engine_mersenne.hpp
 create mode 100644 src/backend/opencl/kernel/random_engine_mersenne.cl
 create mode 100644 src/backend/opencl/kernel/random_engine_mersenne_init.cl

diff --git a/include/af/defines.h b/include/af/defines.h
index e061a0c559..4b5f36a120 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -401,7 +401,8 @@ typedef enum {
 
 typedef enum {
     AF_RANDOM_PHILOX = 0,
-    AF_RANDOM_THREEFRY = 1
+    AF_RANDOM_THREEFRY = 1,
+    AF_RANDOM_MERSENNE = 2
 } af_random_type;
 #endif
 
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 3d8139a3d7..329e12f7f9 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -17,6 +17,9 @@
 #include <err_common.hpp>
 #include <handle.hpp>
 #include <random_engine.hpp>
+#include <MersenneTwister.hpp>
+
+#include <af/util.h>
 #include <iostream>
 
 using detail::cfloat;
@@ -25,15 +28,33 @@ using detail::uchar;
 using detail::uniformDistribution;
 using detail::normalDistribution;
 
+using common::MaxBlocks;
+using common::TableLength;
+using common::MtStateLength;
+using common::pos;
+using common::sh1;
+using common::sh2;
+using common::mask;
+using common::recursion_tbl;
+using common::temper_tbl;
+
+//TODO : static pointer to random_engine_t that wil hold the default random engine
+//protect this object with mutex
+
+//TODO : class and camel case
 typedef struct {
     af_random_type type;
     unsigned long long seed;
     unsigned long long counter;
+    af_array pos;
+    af_array sh1;
+    af_array sh2;
+    uint mask;
+    af_array recursion_table;
+    af_array temper_table;
+    af_array state;
 } af_random_engine_t;
 
-//TODO : static pointer to random_engine_t that wil hold the default random engine
-//protect this object with mutex
-
 af_random_engine getRandomEngineHandle(const af_random_engine_t engine)
 {
     af_random_engine_t *engineHandle = new af_random_engine_t;
@@ -47,24 +68,57 @@ af_random_engine_t* getRandomEngine(const af_random_engine engineHandle)
 }
 
 template<typename T>
-static inline af_array uniformDistribution_(const af::dim4 &dims,
-        const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+static inline af_array uniformDistribution_(const af::dim4 &dims, af_random_engine_t *e)
 {
-    return getHandle(uniformDistribution<T>(dims, type, seed, counter));
+    if (e->type == AF_RANDOM_MERSENNE) {
+        return getHandle(uniformDistribution<T>(dims,
+                    getArray<uint>(e->pos),
+                    getArray<uint>(e->sh1),
+                    getArray<uint>(e->sh2),
+                    e->mask,
+                    getArray<uint>(e->recursion_table),
+                    getArray<uint>(e->temper_table),
+                    getArray<uint>(e->state)));
+    } else {
+        return getHandle(uniformDistribution<T>(dims, e->type, e->seed, e->counter));
+    }
 }
 
 template<typename T>
-static inline af_array normalDistribution_(const af::dim4 &dims,
-        const af_random_type type, const unsigned long long seed, unsigned long long &counter)
+static inline af_array normalDistribution_(const af::dim4 &dims, af_random_engine_t *e)
 {
-    return getHandle(normalDistribution<T>(dims, type, seed, counter));
+    if (e->type == AF_RANDOM_MERSENNE) {
+        return getHandle(normalDistribution<T>(dims,
+                    getArray<uint>(e->pos),
+                    getArray<uint>(e->sh1),
+                    getArray<uint>(e->sh2),
+                    e->mask,
+                    getArray<uint>(e->recursion_table),
+                    getArray<uint>(e->temper_table),
+                    getArray<uint>(e->state)));
+    } else {
+        return getHandle(normalDistribution<T>(dims, e->type, e->seed, e->counter));
+    }
 }
 
 af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
 {
     try {
-        af_random_engine_t engine{rtype, seed, 0};
-        *engineHandle = getRandomEngineHandle(engine);
+        af_random_engine_t e;
+        e.type = rtype;
+        e.seed = seed;
+        e.counter = 0;
+        if (rtype == AF_RANDOM_MERSENNE) {
+            //TODO Add AF_CHECK(af_* calls)
+            af_create_array(&e.pos, pos, 1, &MaxBlocks, u32);
+            af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32);
+            af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32);
+            e.mask = mask;
+            af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32);
+            af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32);
+            e.state = getHandle(initMersenneState(seed, getArray<uint>(e.recursion_table)));
+        }
+        *engineHandle = getRandomEngineHandle(e);
     } CATCHALL;
 
     return AF_SUCCESS;
@@ -80,18 +134,18 @@ af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const un
         af_random_engine_t *e = getRandomEngine(engine);
 
         switch(type) {
-        case f32:   result = uniformDistribution_<float  >(d, e->type, e->seed, e->counter);    break;
-        case c32:   result = uniformDistribution_<cfloat >(d, e->type, e->seed, e->counter);    break;
-        case f64:   result = uniformDistribution_<double >(d, e->type, e->seed, e->counter);    break;
-        case c64:   result = uniformDistribution_<cdouble>(d, e->type, e->seed, e->counter);    break;
-        case s32:   result = uniformDistribution_<int    >(d, e->type, e->seed, e->counter);    break;
-        case u32:   result = uniformDistribution_<uint   >(d, e->type, e->seed, e->counter);    break;
-        case s64:   result = uniformDistribution_<intl   >(d, e->type, e->seed, e->counter);    break;
-        case u64:   result = uniformDistribution_<uintl  >(d, e->type, e->seed, e->counter);    break;
-        case s16:   result = uniformDistribution_<short  >(d, e->type, e->seed, e->counter);    break;
-        case u16:   result = uniformDistribution_<ushort >(d, e->type, e->seed, e->counter);    break;
-        case u8:    result = uniformDistribution_<uchar  >(d, e->type, e->seed, e->counter);    break;
-        case b8:    result = uniformDistribution_<char   >(d, e->type, e->seed, e->counter);    break;
+        case f32:   result = uniformDistribution_<float  >(d, e); break;
+        case c32:   result = uniformDistribution_<cfloat >(d, e); break;
+        case f64:   result = uniformDistribution_<double >(d, e); break;
+        case c64:   result = uniformDistribution_<cdouble>(d, e); break;
+        case s32:   result = uniformDistribution_<int    >(d, e); break;
+        case u32:   result = uniformDistribution_<uint   >(d, e); break;
+        case s64:   result = uniformDistribution_<intl   >(d, e); break;
+        case u64:   result = uniformDistribution_<uintl  >(d, e); break;
+        case s16:   result = uniformDistribution_<short  >(d, e); break;
+        case u16:   result = uniformDistribution_<ushort >(d, e); break;
+        case u8:    result = uniformDistribution_<uchar  >(d, e); break;
+        case b8:    result = uniformDistribution_<char   >(d, e); break;
         default:    TYPE_ERROR(4, type);
         }
         std::swap(*out, result);
@@ -110,10 +164,10 @@ af_err af_random_engine_normal(af_array *out, af_random_engine engine, const uns
         af_random_engine_t *e = getRandomEngine(engine);
 
         switch(type) {
-        case f32:   result = normalDistribution_<float  >(d, e->type, e->seed, e->counter);    break;
-        case c32:   result = normalDistribution_<cfloat >(d, e->type, e->seed, e->counter);    break;
-        case f64:   result = normalDistribution_<double >(d, e->type, e->seed, e->counter);    break;
-        case c64:   result = normalDistribution_<cdouble>(d, e->type, e->seed, e->counter);    break;
+        case f32:   result = normalDistribution_<float  >(d, e); break;
+        case c32:   result = normalDistribution_<cfloat >(d, e); break;
+        case f64:   result = normalDistribution_<double >(d, e); break;
+        case c64:   result = normalDistribution_<cdouble>(d, e); break;
         default:    TYPE_ERROR(4, type);
         }
         std::swap(*out, result);
@@ -125,7 +179,16 @@ af_err af_random_engine_normal(af_array *out, af_random_engine engine, const uns
 af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
-        delete (af_random_engine_t *)engineHandle;
+        af_random_engine_t *e = getRandomEngine(engineHandle);
+        if (e->type == AF_RANDOM_MERSENNE) {
+            af_release_array(e->pos);
+            af_release_array(e->sh1);
+            af_release_array(e->sh2);
+            af_release_array(e->recursion_table);
+            af_release_array(e->temper_table);
+            af_release_array(e->state);
+        }
+        delete e;
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/backend/MersenneTwister.hpp b/src/backend/MersenneTwister.hpp
new file mode 100644
index 0000000000..c6d820f680
--- /dev/null
+++ b/src/backend/MersenneTwister.hpp
@@ -0,0 +1,178 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <af/defines.h>
+
+namespace common
+{
+    const dim_t MaxBlocks = 32;
+    const dim_t TableLength = 16*MaxBlocks;
+    const dim_t MersenneN = 351;
+    const dim_t MtStateLength = MaxBlocks * MersenneN;
+
+    static const int pos[] = {
+    88, 84, 25, 42, 22, 11, 76, 11, 42, 60, 45, 80, 81, 16, 63, 38,
+    3, 55, 9, 75, 70, 63, 32, 70, 58, 33, 18, 9, 14, 91, 90, 86,
+    };
+
+    static const int sh1[] = {
+    19, 15, 4, 20, 1, 16, 16, 15, 6, 6, 12, 6, 8, 1, 14, 28,
+    30, 1, 9, 17, 15, 15, 7, 12, 21, 7, 7, 12, 16, 4, 10, 6,
+    };
+
+    static const int sh2[] = {
+    5, 12, 18, 9, 5, 1, 6, 16, 11, 11, 13, 9, 18, 19, 18, 1,
+    2, 16, 15, 6, 6, 17, 15, 10, 2, 10, 13, 13, 3, 2, 14, 7,
+    };
+
+    static const uint32_t mask = 4294443008;
+    //static const uint32_t mask[] = {
+    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+    //};
+
+    static const uint32_t recursion_tbl[] = {
+    0, 2879706668, 3137826695, 279165355, 570425344, 2309281324, 2567401351, 849590699,
+    38330, 2879669142, 3137862205, 279129105, 570463674, 2309243798, 2567436861, 849554449,
+    0, 2593609479, 1975655185, 4015344662, 357564441, 2412205854, 1620187912, 4194651151,
+    53865, 2593621358, 1975699832, 4015365759, 357618288, 2412217719, 1620232545, 4194672230,
+    0, 1350351572, 3879866427, 3074337519, 1908408359, 566016755, 2525106204, 3338578632,
+    56684, 1350330296, 3879914839, 3074324355, 1908464971, 565995423, 2525154672, 3338565540,
+    0, 1055977248, 2682116586, 2704094922, 271581238, 784396054, 2414729692, 2971481852,
+    23789, 1055962061, 2682094855, 2704108071, 271604955, 784380923, 2414708017, 2971494929,
+    0, 2953117369, 62376084, 3014866477, 122683469, 3075800820, 82299097, 3034789472,
+    18916, 2953099101, 62357872, 3014885321, 122702249, 3075782416, 82280765, 3034808196,
+    0, 1684682268, 3815655459, 2265218623, 723517535, 1330263619, 3360573564, 2888072800,
+    51884, 1684733104, 3815670415, 2265232531, 723569395, 1330314479, 3360588496, 2888086732,
+    0, 465136444, 2086871972, 1742358680, 574619745, 972647261, 1579361221, 1167739129,
+    19553, 465119069, 2086891461, 1742341369, 574639104, 972629820, 1579380644, 1167721624,
+    0, 2761653723, 3161842783, 418290052, 1969225846, 3522919853, 3373655081, 1838062066,
+    50425, 2761668898, 3161792678, 418274685, 1969276047, 3522935124, 3373605072, 1838046475,
+    0, 823868564, 2715863359, 2432431531, 1012924546, 226180118, 2642463165, 2895901993,
+    46626, 823888566, 2715844381, 2432385929, 1012971168, 226200116, 2642444191, 2895856395,
+    0, 3163477696, 1302313789, 4044451325, 2389704861, 855561821, 3287268256, 2137091424,
+    40970, 3163453130, 1302272823, 4044475895, 2389745815, 855537239, 3287227306, 2137116010,
+    0, 112997176, 3723016697, 3679750849, 4213178533, 4254872477, 650688860, 544508516,
+    42220, 113022932, 3722976533, 3679727149, 4213220425, 4254898033, 650649008, 544485000,
+    0, 612320333, 3070909104, 2473926397, 3292528821, 3762242808, 1934252549, 1463098952,
+    21839, 612307202, 3070889983, 2473937842, 3292550650, 3762229687, 1934233418, 1463110407,
+    0, 3452614784, 3739161126, 320187046, 3513778374, 481998918, 263131872, 3261442656,
+    46663, 3452571335, 3739198561, 320150753, 3513824897, 481955329, 263169191, 3261406247,
+    0, 588293362, 2445856652, 3000527742, 2311061713, 2865800227, 403230557, 991456175,
+    62105, 588273259, 2445819157, 3000539623, 2311123528, 2865780410, 403193284, 991467830,
+    0, 2177339548, 3971246860, 1836317584, 4080009455, 1928826995, 528772067, 2655255423,
+    59650, 2177333662, 3971252750, 1836257938, 4080069101, 1928821105, 528777953, 2655195773,
+    0, 3849091899, 1973261595, 2431774240, 442499322, 4279008193, 1878889953, 2324819674,
+    17524, 3849076559, 1973277039, 2431756884, 442516622, 4278992821, 1878905237, 2324802222,
+    0, 2208474059, 1390454348, 3510764935, 2555379979, 468886208, 3400574791, 1225917580,
+    43685, 2208434542, 1390415081, 3510808354, 2555423662, 468846693, 3400535522, 1225961001,
+    0, 753114312, 1662184071, 1341224527, 2666529043, 2987630043, 4259507092, 3506534236,
+    32220, 753131796, 1662162779, 1341197203, 2666560719, 2987646983, 4259485256, 3506506368,
+    0, 578983141, 3246792315, 3808725662, 1649410346, 1087542735, 2748718929, 2169801652,
+    22528, 578997477, 3246802555, 3808744094, 1649432874, 1087557071, 2748729169, 2169820084,
+    0, 2174674396, 1341825145, 3462677925, 3861905719, 1739515115, 2848629070, 676611218,
+    35444, 2174644136, 1341794829, 3462713297, 3861941059, 1739484831, 2848598842, 676646630,
+    0, 3312287941, 1270375145, 2396381740, 2464153923, 1468891526, 3646448554, 473293679,
+    38325, 3312260464, 1270413148, 2396354457, 2464191734, 1468863539, 3646486047, 473265882,
+    0, 1011438676, 4087471600, 3488123300, 455082329, 661214477, 3900824745, 3569912061,
+    52539, 1011456367, 4087419083, 3488105631, 455134306, 661231670, 3900772754, 3569894854,
+    0, 3529350863, 2972224887, 1668617144, 3278897504, 288202671, 1918405655, 2684687064,
+    37845, 3529313562, 2972196514, 1668644973, 3278934709, 288164986, 1918377922, 2684715277,
+    0, 1851876274, 2748239338, 3450842712, 3060793725, 3625018063, 364825751, 2078256933,
+    54612, 1851897574, 2748192958, 3450829580, 3060847657, 3625039771, 364779971, 2078243441,
+    0, 2798128189, 889378840, 2479543333, 3664773513, 2092436916, 4017281425, 1236981164,
+    52108, 2798176177, 889328532, 2479497129, 3664824837, 2092484152, 4017230365, 1236934176,
+    0, 1067241239, 3453461153, 4065029558, 4190110101, 3327970946, 873964340, 193686563,
+    27698, 1067229989, 3453472403, 4065001860, 4190137767, 3327959728, 873975558, 193658897,
+    0, 2213869621, 887682042, 3072068559, 4061135267, 1910831510, 3338203737, 1158417004,
+    40265, 2213832060, 887647923, 3072104070, 4061175018, 1910793439, 3338170128, 1158453029,
+    0, 3310321543, 1761913168, 2890651351, 2243953084, 1083145787, 3972311276, 697030507,
+    31607, 3310290160, 1761923623, 2890640800, 2243984075, 1083114828, 3972322203, 697019420,
+    0, 2168967706, 1652210191, 3812452373, 663749057, 2799162331, 1173011406, 3299699156,
+    62673, 2168923851, 1652182750, 3812465860, 663811344, 2799118090, 1172983583, 3299712261,
+    0, 2787116654, 1233955067, 4021071509, 3287286227, 1708132285, 2323425576, 744271686,
+    37500, 2787152914, 1233926791, 4021042409, 3287323567, 1708168641, 2323397460, 744242490,
+    0, 3709953686, 1938447361, 2930457239, 1075839468, 2634114938, 866803181, 4002102139,
+    50633, 3709969247, 1938463176, 2930507614, 1075889189, 2634130099, 866818084, 4002152114,
+    0, 1104625460, 154195956, 1223157952, 1706033657, 610746061, 1820382733, 760736057,
+    35538, 1104655846, 154164518, 1223123474, 1706068779, 610776095, 1820351711, 760701931,
+    };
+
+    static const uint32_t temper_tbl[] = {
+    0, 101711872, 634912768, 600309760, 673972224, 775684096, 234094592, 199491584,
+    855825920, 890428928, 383442432, 281730560, 456056320, 490659328, 1056366080, 954654208,
+    0, 6581248, 135327744, 141859840, 922910720, 929491968, 1058172928, 1064705024,
+    5266944, 3420672, 138456576, 136626688, 928177664, 926331392, 1061301760, 1059471872,
+    0, 69272064, 134479872, 203751936, 289406976, 358679040, 423886848, 493158912,
+    544153088, 609098752, 678108672, 743054336, 825171456, 890117120, 959127040, 1024072704,
+    0, 608635904, 2523648, 610373120, 5439488, 605293568, 7700992, 607292928,
+    274488832, 874205696, 276487168, 876466176, 269442560, 877154816, 271178752, 879677440,
+    0, 1214875648, 67174400, 1281918976, 1614020608, 677218304, 1681195008, 744261632,
+    537288192, 1752159744, 604462592, 1819203072, 1077042688, 140236288, 1144217088, 207279616,
+    0, 3567010304, 194572288, 3741626880, 604008960, 4036767744, 798523904, 4211392512,
+    1563500032, 2309839872, 1453977088, 2184555520, 2033282048, 2913807872, 1923718144, 2788548096,
+    0, 136677376, 272809984, 409421824, 2109440, 134592512, 274919424, 407336960,
+    1891638784, 2028312064, 1619189248, 1755796992, 1893740032, 2026219008, 1621290496, 1753703936,
+    0, 4026617856, 172764160, 4199382016, 75673600, 4102283264, 248421376, 4275031040,
+    1158700544, 3037793792, 1331458560, 3210551808, 1100148224, 2979249664, 1272889856, 3151991296,
+    0, 2147483648, 0, 2147483648, 3221225472, 1073741824, 3221225472, 1073741824,
+    536878592, 2684362240, 536878592, 2684362240, 3758104064, 1610620416, 3758104064, 1610620416,
+    0, 3225420800, 2228224, 3227649024, 809369600, 4034790400, 807141376, 4032562176,
+    1073880576, 2151815680, 1075846656, 2153781760, 1882988032, 2960923136, 1881021952, 2958957056,
+    0, 206130176, 1107561472, 1313685504, 537462784, 742409216, 1645020160, 1849968640,
+    272670208, 470405632, 1380225536, 1577967104, 810128896, 1006688768, 1917688320, 2114246144,
+    0, 807406080, 275513344, 541854208, 1573888, 808979968, 276038656, 542379520,
+    269098496, 539628544, 6692352, 809899008, 269621760, 540151808, 8264192, 811470848,
+    0, 1612447744, 142082048, 1751384064, 7602176, 1617428480, 135004160, 1745879040,
+    10493440, 1622941184, 148381184, 1757683200, 13901312, 1623727616, 145497600, 1756372480,
+    0, 1275199488, 2793406464, 3934388224, 352321536, 1493303296, 3011510272, 4286709760,
+    689970688, 1696734720, 2409635328, 3282181632, 1008737792, 1881284096, 2594184704, 3600948736,
+    0, 150999040, 33619968, 184619008, 2103296, 153094144, 35723264, 186714112,
+    805543424, 956534272, 839032320, 990023168, 807634432, 958633472, 841123328, 992122368,
+    0, 3225487872, 3876324352, 659361280, 4198400000, 981436928, 489849856, 3715337728,
+    545267200, 3770749952, 3347847680, 130879488, 3669925376, 452957184, 1035115008, 4260597760,
+    0, 3910139904, 2496659456, 2109734912, 3687579648, 853278720, 1327235072, 2785804288,
+    164634112, 3770686976, 2634030592, 1947213312, 3525058048, 990649856, 1187782144, 2950438400,
+    0, 201461760, 3812622336, 4014084096, 33554432, 235016192, 3779067904, 3980529664,
+    1352670720, 1554124288, 3017809408, 3219262976, 1386225152, 1587678720, 2984254976, 3185708544,
+    0, 1469123584, 3497837568, 2280507392, 2818795520, 4287783936, 2021633024, 804167680,
+    5381632, 1472401920, 3492731392, 2277496320, 2823910912, 4290804224, 2016260608, 800898560,
+    0, 2550235136, 537106432, 3087144960, 1074806784, 3625041920, 1611913216, 4161951744,
+    212480, 2550316544, 536913408, 3087083008, 1075019264, 3625123328, 1611720192, 4161889792,
+    0, 543432704, 623958016, 89454592, 27283968, 566522368, 613452288, 83143168,
+    5381632, 540425728, 627230208, 84338176, 32656384, 563506176, 616731648, 78033920,
+    0, 134377472, 2521945088, 2656281600, 4236288, 138597376, 2517725184, 2652045312,
+    7249408, 141356544, 2520730112, 2654812672, 3029504, 137120256, 2524966400, 2659032576,
+    0, 269697536, 42663936, 311968256, 1346895872, 1079722496, 1388511232, 1120944640,
+    284057088, 16587776, 308633088, 41294848, 1084644864, 1354046464, 1110269440, 1379802112,
+    0, 54056960, 5527552, 57442304, 22155264, 40552448, 17188864, 37654528,
+    2153984, 51906048, 7636480, 55336448, 24301056, 38409728, 19305984, 35540480,
+    0, 3597599744, 21800448, 3609436672, 4467200, 3593154048, 17337344, 3613886464,
+    209935872, 3672922624, 231733248, 3684760576, 214397952, 3668471808, 227267072, 3689207296,
+    0, 70910976, 7421952, 72041472, 272663552, 343572480, 271696896, 336314368,
+    1879350784, 1950259712, 1886772736, 1951390208, 1615075840, 1685986816, 1614109184, 1678728704,
+    0, 1761935360, 185210880, 1645156352, 1101029376, 681926656, 1252685824, 598702080,
+    74128896, 1835933184, 258016768, 1717831168, 1170963968, 751730176, 1321297408, 667182592,
+    0, 3338677248, 1516357632, 2640438272, 302069760, 3573617664, 1214312448, 2405489664,
+    3368033792, 264253952, 2460079616, 1436678656, 3670091264, 499190272, 2158030336, 1201717760,
+    0, 554461696, 7452672, 561893888, 3422689792, 3977146368, 3430130176, 3984574464,
+    1102241280, 1623110656, 1103324672, 1624181760, 2377171968, 2898046464, 2378267648, 2899121664,
+    0, 697340416, 7652864, 702829568, 1074688000, 1772020224, 1081783808, 1776952320,
+    1141022208, 1838287872, 1148606464, 1843841536, 67956224, 765230080, 74983424, 770226688,
+    0, 423231488, 128225280, 513708032, 6653952, 425691136, 130095104, 519772160,
+    1146551808, 1567424000, 1139961344, 1523084800, 1144223232, 1560901120, 1134028288, 1521346048,
+    0, 33619968, 271785984, 305274880, 268632064, 302120960, 3153920, 36773888,
+    1077583360, 1111203328, 1342815744, 1376304640, 1345953280, 1379442176, 1074445824, 1108065792,
+    };
+
+}
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 2c5df8789f..e74ebbeffa 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -13,6 +13,8 @@
 #include <af/defines.h>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
+#include <kernel/random_engine_mersenne.hpp>
+#include <random_engine.hpp>
 
 namespace cuda
 {
@@ -417,6 +419,67 @@ namespace kernel
         }
     }
 
+    template <typename T>
+    __global__ void uniformMersenne(T * const out,
+            uint * const gState,
+            uint const * const pos_tbl,
+            uint const * const sh1_tbl,
+            uint const * const sh2_tbl,
+            uint mask,
+            uint const * const g_recursion_table,
+            uint const * const g_temper_table,
+            uint elementsPerBlock, size_t elements)
+    {
+        __shared__ uint state[STATE_SIZE];
+        __shared__ uint recursion_table[TABLE_SIZE];
+        __shared__ uint temper_table[TABLE_SIZE];
+        uint start = blockIdx.x*elementsPerBlock;
+        uint end = start + elementsPerBlock;
+        end = (end > elements)? elements : end;
+        int iter = divup((end - start)*sizeof(T), blockDim.x*4*sizeof(uint));
+
+        uint pos = pos_tbl[blockIdx.x];
+        uint sh1 = sh1_tbl[blockIdx.x];
+        uint sh2 = sh2_tbl[blockIdx.x];
+        state_read(state, gState);
+        read_table(recursion_table, g_recursion_table);
+        read_table(temper_table, g_temper_table);
+        __syncthreads();
+
+        uint index = start;
+        int elementsPerBlockIteration = blockDim.x*4*sizeof(uint)/sizeof(T);
+        uint o[4];
+        int offsetX1 = (STATE_SIZE - N + threadIdx.x          ) % STATE_SIZE;
+        int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1      ) % STATE_SIZE;
+        int offsetY  = (STATE_SIZE - N + threadIdx.x + pos    ) % STATE_SIZE;
+        int offsetT  = (STATE_SIZE - N + threadIdx.x + pos - 1) % STATE_SIZE;
+        int offsetO  = threadIdx.x;
+
+        for (int i = 0; i < iter; ++i) {
+            for (int ii = 0; ii < 4; ++ii) {
+                uint r = recursion(recursion_table, mask, sh1, sh2,
+                        state[offsetX1],
+                        state[offsetX2],
+                        state[offsetY ]);
+                state[offsetO] = r;
+                o[ii] = temper(temper_table, r, state[offsetT]);
+                offsetX1 = (offsetX1 + blockDim.x) % STATE_SIZE;
+                offsetX2 = (offsetX2 + blockDim.x) % STATE_SIZE;
+                offsetY  = (offsetY  + blockDim.x) % STATE_SIZE;
+                offsetT  = (offsetT  + blockDim.x) % STATE_SIZE;
+                offsetO  = (offsetO  + blockDim.x) % STATE_SIZE;
+                __syncthreads();
+            }
+            if (i == iter - 1) {
+                partialWriteOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
+            } else {
+                writeOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
+            }
+            index += elementsPerBlockIteration;
+        }
+        state_write(gState, state);
+    }
+
     template <typename T>
     __global__ void normalPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
     {
@@ -453,42 +516,137 @@ namespace kernel
     }
 
     template <typename T>
-    void uniformDistribution(T *out, size_t elements, af_random_type type, const uintl seed, uintl &counter)
+    __global__ void normalMersenne(T * const out,
+            uint * const gState,
+            uint const * const pos_tbl,
+            uint const * const sh1_tbl,
+            uint const * const sh2_tbl,
+            uint mask,
+            uint const * const g_recursion_table,
+            uint const * const g_temper_table,
+            uint elementsPerBlock, uint elements)
+    {
+
+        __shared__ uint state[STATE_SIZE];
+        __shared__ uint recursion_table[TABLE_SIZE];
+        __shared__ uint temper_table[TABLE_SIZE];
+        uint start = blockIdx.x*elementsPerBlock;
+        uint end = start + elementsPerBlock;
+        end = (end > elements)? elements : end;
+        int iter = divup((end - start)*sizeof(T), blockDim.x*4*sizeof(uint));
+
+        uint pos = pos_tbl[blockIdx.x];
+        uint sh1 = sh1_tbl[blockIdx.x];
+        uint sh2 = sh2_tbl[blockIdx.x];
+        state_read(state, gState);
+        read_table(recursion_table, g_recursion_table);
+        read_table(temper_table, g_temper_table);
+        __syncthreads();
+
+        uint index = start;
+        int elementsPerBlockIteration = blockDim.x*4*sizeof(uint)/sizeof(T);
+        uint o[4];
+        int offsetX1 = (STATE_SIZE - N + threadIdx.x          ) % STATE_SIZE;
+        int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1      ) % STATE_SIZE;
+        int offsetY  = (STATE_SIZE - N + threadIdx.x + pos    ) % STATE_SIZE;
+        int offsetT  = (STATE_SIZE - N + threadIdx.x + pos - 1) % STATE_SIZE;
+        int offsetO  = threadIdx.x;
+
+        for (int i = 0; i < iter; ++i) {
+            for (int ii = 0; ii < 4; ++ii) {
+                uint r = recursion(recursion_table, mask, sh1, sh2,
+                        state[offsetX1],
+                        state[offsetX2],
+                        state[offsetY ]);
+                state[offsetO] = r;
+                o[ii] = temper(temper_table, r, state[offsetT]);
+                offsetX1 = (offsetX1 + blockDim.x) % STATE_SIZE;
+                offsetX2 = (offsetX2 + blockDim.x) % STATE_SIZE;
+                offsetY  = (offsetY  + blockDim.x) % STATE_SIZE;
+                offsetT  = (offsetT  + blockDim.x) % STATE_SIZE;
+                offsetO  = (offsetO  + blockDim.x) % STATE_SIZE;
+                __syncthreads();
+            }
+            if (i == iter - 1) {
+                partialNormalizedWriteOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
+            } else {
+                normalizedWriteOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
+            }
+            index += elementsPerBlockIteration;
+        }
+        state_write(gState, state);
+    }
+
+    template <typename T>
+    void uniformDistribution(T* out, size_t elements,
+            uint * const state,
+            uint const * const pos,
+            uint const * const sh1,
+            uint const * const sh2,
+            uint mask,
+            uint const * const recursion_table,
+            uint const * const temper_table)
+    {
+        int threads = THREADS;
+        int min_elements_per_block = 32*threads*4*sizeof(uint)/sizeof(T);
+        int blocks = divup(elements, min_elements_per_block);
+        blocks = (blocks > BLOCKS)? BLOCKS : blocks;
+        uint elementsPerBlock = divup(elements, blocks);
+        CUDA_LAUNCH(uniformMersenne, blocks, threads, out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
+    }
+
+    template <typename T>
+    void normalDistribution(T* out, size_t elements,
+            uint * const state,
+            uint const * const pos,
+            uint const * const sh1,
+            uint const * const sh2,
+            uint mask,
+            uint const * const recursion_table,
+            uint const * const temper_table)
+    {
+        int threads = THREADS;
+        int min_elements_per_block = 32*threads*4*sizeof(uint)/sizeof(T);
+        int blocks = divup(elements, min_elements_per_block);
+        blocks = (blocks > BLOCKS)? BLOCKS : blocks;
+        uint elementsPerBlock = divup(elements, blocks);
+        CUDA_LAUNCH(normalMersenne, blocks, threads, out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
+    }
+
+    template <typename T>
+    void uniformDistribution(T* out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
         int blocks = divup(elements, elementsPerBlock);
         uint hi = seed>>32;
         uint lo = seed;
-        uintl count = counter;
         switch (type) {
         case AF_RANDOM_PHILOX : CUDA_LAUNCH(uniformPhilox, blocks, threads,
-                out, hi, lo, count, elementsPerBlock, elements); break;
+                out, hi, lo, counter, elementsPerBlock, elements); break;
         case AF_RANDOM_THREEFRY : CUDA_LAUNCH(uniformThreefry, blocks, threads,
-                out, hi, lo, count, elementsPerBlock, elements); break;
+                out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
     }
 
     template <typename T>
-    void normalDistribution(T *out, size_t elements, af_random_type type, const uintl seed, uintl &counter)
+    void normalDistribution(T *out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
         int blocks = divup(elements, elementsPerBlock);
         uint hi = seed>>32;
         uint lo = seed;
-        uintl count = counter;
         switch (type) {
         case AF_RANDOM_PHILOX : CUDA_LAUNCH(normalPhilox, blocks, threads,
-                out, hi, lo, count, elementsPerBlock, elements); break;
+                out, hi, lo, counter, elementsPerBlock, elements); break;
         case AF_RANDOM_THREEFRY : CUDA_LAUNCH(normalThreefry, blocks, threads,
-                out, hi, lo, count, elementsPerBlock, elements); break;
+                out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
     }
-
 }
 }
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
new file mode 100644
index 0000000000..7d1111fe19
--- /dev/null
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -0,0 +1,91 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace cuda
+{
+namespace kernel
+{
+#define N 351
+#define BLOCKS 32
+#define STATE_SIZE 786
+#define TABLE_SIZE 16
+
+    //Utils
+    static inline __device__ void read_table(uint * const sharedTable, const uint * const table)
+    {
+        const uint * const t = table + (blockIdx.x * TABLE_SIZE);
+        if (threadIdx.x < TABLE_SIZE) {
+            sharedTable[threadIdx.x] = t[threadIdx.x];
+        }
+    }
+
+    static inline __device__ void state_read(uint * const state, const uint * const gState)
+    {
+        const uint * const g = gState + (blockIdx.x * N);
+        state[STATE_SIZE - N + threadIdx.x] = g[threadIdx.x];
+        if (threadIdx.x < N - blockDim.x) {
+            state[STATE_SIZE - N + blockDim.x + threadIdx.x] = g[blockDim.x + threadIdx.x];
+        }
+    }
+
+    static inline __device__ void state_write(uint * const gState, const uint * const state)
+    {
+        uint * const g = gState + (blockIdx.x * N);
+        g[threadIdx.x] = state[STATE_SIZE - N + threadIdx.x];
+        if (threadIdx.x < N - blockDim.x) {
+            g[blockDim.x + threadIdx.x] = state[STATE_SIZE - N + blockDim.x + threadIdx.x];
+        }
+    }
+
+    static inline __device__ uint recursion(uint const * const recursion_table,
+            const uint mask, const uint sh1, const uint sh2,
+            const uint x1, const uint x2, uint y)
+    {
+        uint x = (x1 & mask) ^ x2;
+        x ^= x << sh1;
+        y = x ^ (y >> sh2);
+        uint mat = recursion_table[y & 0x0f];
+        return y ^ mat;
+    }
+
+    static inline __device__ uint temper(const uint * const temper_table, const uint v, uint t)
+    {
+        t ^= t >> 16;
+        t ^= t >> 8;
+        uint mat = temper_table[t & 0x0f];
+        return v ^ mat;
+    }
+
+    //Initialization
+
+    __global__ void initState(uint *state, const uint *tbl, uintl seed)
+    {
+        __shared__ uint lstate[N];
+        const uint *ltbl = tbl + (TABLE_SIZE*blockIdx.x);
+        uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
+        uint tmp = hidden_seed;
+        tmp += tmp >> 16;
+        tmp += tmp >> 8;
+        if (threadIdx.x == 0) {
+            lstate[0] = seed;
+            lstate[1] = hidden_seed;
+            for (int i = 1; i < N; ++i) {
+                lstate[i] ^= (uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i;
+            }
+        }
+        __syncthreads();
+        state[N*blockIdx.x + threadIdx.x] = lstate[threadIdx.x];
+    }
+
+    void initMersenneState(uint *state, const uint *tbl, uintl seed)
+    {
+        CUDA_LAUNCH(initState, BLOCKS, N, state, tbl, seed);
+    }
+}
+}
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 00ebc0844b..fd31c3e4d3 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -11,11 +11,23 @@
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
 #include <cassert>
+#include <MersenneTwister.hpp>
+
+using common::MtStateLength;
+using common::MaxBlocks;
+using common::MersenneN;
 
 namespace cuda
 {
+    Array<uint> initMersenneState(const uintl seed, const Array<uint> tbl)
+    {
+        Array<uint> state = createEmptyArray<uint>(MtStateLength);
+        kernel::initMersenneState(state.get(), tbl.get(), seed);
+        return state;
+    }
+
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         kernel::uniformDistribution<T>(out.get(), out.elements(), type, seed, counter);
@@ -23,16 +35,62 @@ namespace cuda
     }
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         kernel::normalDistribution<T>(out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        kernel::uniformDistribution<T>(
+              out.get(), out.elements(),
+              state.get(), pos.get(),
+              sh1.get(), sh2.get(),
+              mask, recursion_table.get(),
+              temper_table.get());
+        return out;
+    }
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        kernel::normalDistribution<T>(
+              out.get(), out.elements(),
+              state.get(), pos.get(),
+              sh1.get(), sh2.get(),
+              mask, recursion_table.get(),
+              temper_table.get());
+        return out;
+    }
+
+#define INSTANTIATE_UNIFORM(T)\
+    template\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
+    template\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
+
+#define INSTANTIATE_NORMAL(T)\
+    template\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
+    template\
+    Array<T> normalDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
+
 #define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
@@ -40,36 +98,70 @@ namespace cuda
         kernel::uniformDistribution<TR>(outPtr, elements, type, seed, counter);\
         return out;\
     }\
+    \
+    template<>\
+    Array<T> uniformDistribution(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        kernel::uniformDistribution<TR>(\
+              outPtr, elements,\
+              state.get(), pos.get(),\
+              sh1.get(), sh2.get(),\
+              mask, recursion_table.get(),\
+              temper_table.get());\
+        return out;\
+    }\
 
 #define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        kernel::uniformDistribution<TR>(outPtr, elements, type, seed, counter);\
+        kernel::normalDistribution<TR>(outPtr, elements, type, seed, counter);\
+        return out;\
+    }\
+    \
+    template<>\
+    Array<T> normalDistribution(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        kernel::normalDistribution<TR>(\
+              outPtr, elements,\
+              state.get(), pos.get(),\
+              sh1.get(), sh2.get(),\
+              mask, recursion_table.get(),\
+              temper_table.get());\
         return out;\
     }\
 
-    template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<int>    uniformDistribution<int>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uint>   uniformDistribution<uint>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<intl>   uniformDistribution<intl>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uintl>  uniformDistribution<uintl> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<char>   uniformDistribution<char>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uchar>  uniformDistribution<uchar> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-
-    template Array<float>  normalDistribution<float>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> normalDistribution<double> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    INSTANTIATE_UNIFORM(float )
+    INSTANTIATE_UNIFORM(double)
+    INSTANTIATE_UNIFORM(int   )
+    INSTANTIATE_UNIFORM(uint  )
+    INSTANTIATE_UNIFORM(intl  )
+    INSTANTIATE_UNIFORM(uintl )
+    INSTANTIATE_UNIFORM(char  )
+    INSTANTIATE_UNIFORM(uchar )
+    INSTANTIATE_UNIFORM(short )
+    INSTANTIATE_UNIFORM(ushort)
 
-    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+    INSTANTIATE_NORMAL(float )
+    INSTANTIATE_NORMAL(double)
 
     COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
     COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
+    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
 }
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index bada8a2f09..c72695e643 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <Array.hpp>
 #include <af/defines.h>
@@ -13,9 +14,21 @@
 
 namespace cuda
 {
+    Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
+
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    Array<T> uniformDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    Array<T> normalDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
 }
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index eda4651e01..26d5b76c6f 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -23,6 +23,10 @@
 #include <program.hpp>
 #include <type_util.hpp>
 #include <cache.hpp>
+#include <random_engine.hpp>
+
+#include <kernel_headers/random_engine_mersenne_init.hpp>
+#include <kernel_headers/random_engine_mersenne.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -32,6 +36,11 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 
+#define N 351
+#define TABLE_SIZE 16
+#define MAX_BLOCKS 32
+#define STATE_SIZE 786
+
 namespace opencl
 {
     namespace kernel
@@ -57,6 +66,10 @@ namespace opencl
                                         ker_strs[1] = random_engine_threefry_cl;
                                         ker_lens[1] = random_engine_threefry_cl_len;
                                         break;
+                case AF_RANDOM_MERSENNE : engineName = "Mersenne";
+                                        ker_strs[1] = random_engine_mersenne_cl;
+                                        ker_lens[1] = random_engine_mersenne_cl_len;
+                                        break;
                 default                 : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
@@ -71,8 +84,14 @@ namespace opencl
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D THREADS=" << THREADS
-                        << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock
                         << " -D RAND_DIST=" << kerIdx;
+                if (type == AF_RANDOM_MERSENNE) {
+                    options << " -D STATE_SIZE=" << STATE_SIZE
+                            << " -D TABLE_SIZE=" << TABLE_SIZE
+                            << " -D N=" << N;
+                } else {
+                    options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
+                }
                 if (std::is_same<T, double>::value) {
                     options << " -D USE_DOUBLE";
                 }
@@ -89,11 +108,38 @@ namespace opencl
                 entry = idx->second;
             }
 
-            return entry.ker[kerIdx];
+            return *entry.ker;
+        }
+
+        static Kernel get_mersenne_init_kernel(void)
+        {
+            using std::string;
+            using std::to_string;
+            string engineName;
+            const char *ker_str = random_engine_mersenne_init_cl;
+            int ker_len = random_engine_mersenne_init_cl_len;
+            string ref_name = "mersenne_init";
+            int device = getActiveDeviceId();
+            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+            if (idx == kernelCaches[device].end()) {
+                std::ostringstream options;
+                options << " -D N=" << N << " -D TABLE_SIZE=" << TABLE_SIZE;
+                cl::Program prog;
+                buildProgram(prog, 1, &ker_str, &ker_len, options.str());
+                entry.prog = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "initState");
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
+            }
+
+            return *entry.ker;
         }
 
         template <typename T>
-        static void randomDistribution(cl::Buffer out, const size_t elements, const af_random_type type, const uintl seed, uintl &counter, int kerIdx)
+        static void randomDistribution(cl::Buffer out, const size_t elements,
+                const af_random_type type, const uintl &seed, uintl &counter, int kerIdx)
         {
             try {
                 uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
@@ -105,11 +151,12 @@ namespace opencl
                 NDRange local(THREADS, 1);
                 NDRange global(THREADS * groups, 1);
 
-                Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
-                auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
-
-                randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                        out, elements, counter, hi, lo);
+                if ((type == AF_RANDOM_PHILOX) || (type == AF_RANDOM_THREEFRY)) {
+                    Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
+                    auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
+                    randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                            out, elements, counter, hi, lo);
+                }
 
                 counter += elements;
                 CL_DEBUG_FINISH(getQueue());
@@ -120,16 +167,76 @@ namespace opencl
         }
 
         template <typename T>
-        void uniformDistribution(cl::Buffer out, const size_t elements, const af_random_type type, const uintl seed, uintl &counter)
+        void randomDistribution(cl::Buffer out, const size_t elements,
+                cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
+                const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table,
+                int kerIdx)
+        {
+            try {
+                int threads = THREADS;
+                int min_elements_per_block = 32*THREADS*4*sizeof(uint)/sizeof(T);
+                int blocks = divup(elements, min_elements_per_block);
+                blocks = (blocks > MAX_BLOCKS)? MAX_BLOCKS : blocks;
+                int elementsPerBlock = divup(elements, blocks);
+
+                NDRange local(threads, 1);
+                NDRange global(threads * blocks, 1);
+                Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_MERSENNE, kerIdx, elementsPerBlock);
+                auto randomEngineOp = KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
+                     uint, cl::Buffer, cl::Buffer, uint, uint>(ker);
+                randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                        out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template <typename T>
+        void uniformDistribution(cl::Buffer out, const size_t elements,
+                const af_random_type type, const uintl &seed, uintl &counter)
         {
             randomDistribution<T>(out, elements, type, seed, counter, 0);
         }
 
         template <typename T>
-        void normalDistribution(cl::Buffer out, const size_t elements, const af_random_type type, const uintl seed, uintl &counter)
+        void normalDistribution(cl::Buffer out, const size_t elements,
+                const af_random_type type, const uintl &seed, uintl &counter)
         {
             randomDistribution<T>(out, elements, type, seed, counter, 1);
         }
 
+        template <typename T>
+        void uniformDistribution(cl::Buffer out, const size_t elements,
+                cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
+                const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table)
+        {
+            randomDistribution<T>(out, elements, state, pos, sh1, sh2, mask, recursion_table, temper_table, 0);
+        }
+
+        template <typename T>
+        void normalDistribution(cl::Buffer out, const size_t elements,
+                cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
+                const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table)
+        {
+            randomDistribution<T>(out, elements, state, pos, sh1, sh2, mask, recursion_table, temper_table, 1);
+        }
+
+        void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed)
+        {
+            try{
+                NDRange local(N, 1);
+                NDRange global(N * MAX_BLOCKS, 1);
+
+                Kernel ker = get_mersenne_init_kernel();
+                auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
+                initOp(EnqueueArgs(getQueue(), global, local), state, table, seed);
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
new file mode 100644
index 0000000000..27c73ac3f8
--- /dev/null
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -0,0 +1,117 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+#define divup(N, D) (((N) + (D) - 1)/(D));
+
+void read_table(__local uint * const localTable, __global const uint * const table)
+{
+    __global const uint * const t = table + (get_group_id(0) * TABLE_SIZE);
+    if (get_local_id(0) < TABLE_SIZE) {
+        localTable[get_local_id(0)] = t[get_local_id(0)];
+    }
+}
+
+void state_read(__local uint * const localState, __global const uint * const state)
+{
+    __global const uint * const g = state + (get_group_id(0) * N);
+    localState[STATE_SIZE - N + get_local_id(0)] = g[get_local_id(0)];
+    if (get_local_id(0) < N - THREADS) {
+        localState[STATE_SIZE - N + THREADS + get_local_id(0)] = g[THREADS + get_local_id(0)];
+    }
+}
+
+void state_write(__global uint * const state, __local const uint * const localState)
+{
+    __global uint * const g = state + (get_group_id(0) * N);
+    g[get_local_id(0)] = localState[STATE_SIZE - N + get_local_id(0)];
+    if (get_local_id(0) < N - THREADS) {
+        g[THREADS + get_local_id(0)] = localState[STATE_SIZE - N + THREADS + get_local_id(0)];
+    }
+}
+
+uint recursion(__local const uint * const recursion_table, const uint mask,
+        const uint sh1, const uint sh2, const uint x1, const uint x2, uint y)
+{
+    uint x = (x1 & mask) ^ x2;
+    x ^= x << sh1;
+    y = x ^ (y >> sh2);
+    uint mat = recursion_table[y & 0x0f];
+    return y ^ mat;
+}
+
+uint temper(__local const uint * const temper_table, const uint v, uint t)
+{
+     t ^= t >> 16;
+     t ^= t >> 8;
+     uint mat = temper_table[t & 0x0f];
+     return v ^ mat;
+}
+
+__kernel void generate(__global T *output,
+        __global uint * const state,
+        __global const uint * const pos_tbl,
+        __global const uint * const sh1_tbl,
+        __global const uint * const sh2_tbl,
+        uint mask,
+        __global const uint * const recursion_table,
+        __global const uint * const temper_table,
+        uint elements_per_block, uint elements)
+{
+    __local uint l_state[STATE_SIZE];
+    __local uint l_recursion_table[TABLE_SIZE];
+    __local uint l_temper_table[TABLE_SIZE];
+    uint start = get_group_id(0)*elements_per_block;
+    uint end = start + elements_per_block;
+    end = (end > elements)? elements : end;
+    int iter = divup((end - start)*sizeof(T), THREADS*4*sizeof(uint));
+    uint pos = pos_tbl[get_group_id(0)];
+    uint sh1 = sh1_tbl[get_group_id(0)];
+    uint sh2 = sh2_tbl[get_group_id(0)];
+
+    state_read(l_state, state);
+    read_table(l_recursion_table, recursion_table);
+    read_table(l_temper_table, temper_table);
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    uint index = start;
+    int elementsPerBlockIteration = THREADS*4*sizeof(uint)/sizeof(T);
+    uint o[4];
+    int offsetX1 = (STATE_SIZE - N + get_local_id(0)          ) % STATE_SIZE;
+    int offsetX2 = (STATE_SIZE - N + get_local_id(0) + 1      ) % STATE_SIZE;
+    int offsetY  = (STATE_SIZE - N + get_local_id(0) + pos    ) % STATE_SIZE;
+    int offsetT  = (STATE_SIZE - N + get_local_id(0) + pos - 1) % STATE_SIZE;
+    int offsetO  = get_local_id(0);
+
+    for (int i = 0; i < iter; ++i) {
+        for (int ii = 0; ii < 4; ++ii) {
+            uint r = recursion(l_recursion_table, mask, sh1, sh2,
+                    l_state[offsetX1],
+                    l_state[offsetX2],
+                    l_state[offsetY ]);
+            l_state[offsetO] = r;
+            o[ii] = temper(l_temper_table, r, l_state[offsetT]);
+            offsetX1 = (offsetX1 + THREADS) % STATE_SIZE;
+            offsetX2 = (offsetX2 + THREADS) % STATE_SIZE;
+            offsetY  = (offsetY  + THREADS) % STATE_SIZE;
+            offsetT  = (offsetT  + THREADS) % STATE_SIZE;
+            offsetO  = (offsetO  + THREADS) % STATE_SIZE;
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+        uint writeIndex = index + get_local_id(0);
+        if (i == iter - 1) {
+            PARTIAL_WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3], &elements);
+        } else {
+            WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3]);
+        }
+        index += elementsPerBlockIteration;
+    }
+    state_write(state, l_state);
+}
+
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
new file mode 100644
index 0000000000..e5e8f372f7
--- /dev/null
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ *
+ ********************************************************/
+
+__kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
+{
+    __local uint lstate[N];
+    const __global uint *ltbl = tbl + (TABLE_SIZE*get_group_id(0));
+    uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
+    uint tmp = hidden_seed;
+    tmp += tmp >> 16;
+    tmp += tmp >> 8;
+    if (get_local_id(0) == 0) {
+        lstate[0] = seed;
+        lstate[1] = hidden_seed;
+            for (int i = 1; i < N; ++i) {
+                lstate[i] ^= (uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i;
+            }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+    state[N*get_group_id(0) + get_local_id(0)] = lstate[get_local_id(0)];
+}
+
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index edf8ed8f8d..0702d71437 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -386,7 +386,7 @@ void partialNormalizedWriteOut256Bytes_double(__global double *out, const uint *
 #define UNIFORM_WRITE EVALUATE_T(writeOut256Bytes)
 #define UNIFORM_PARTIAL_WRITE EVALUATE_T(partialWriteOut256Bytes)
 #define NORMAL_WRITE EVALUATE_T(normalizedWriteOut256Bytes)
-#define NORMAL_PARTIAL_WRITE EVALUATE_T(partialNormalizedWriteOut257Bytes)
+#define NORMAL_PARTIAL_WRITE EVALUATE_T(partialNormalizedWriteOut256Bytes)
 
 #if RAND_DIST == 0
 #define WRITE UNIFORM_WRITE
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 7217a8dc58..765b79a350 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -11,60 +11,148 @@
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
 #include <cassert>
+#include <MersenneTwister.hpp>
+
+using common::MtStateLength;
+using common::MaxBlocks;
+using common::MersenneN;
 
 namespace opencl
 {
+    Array<uint> initMersenneState(const uintl seed, const Array<uint> tbl)
+    {
+        Array<uint> state = createEmptyArray<uint>(MtStateLength);
+        kernel::initMersenneState(*state.get(), *tbl.get(), seed);
+        return state;
+    }
+
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
-        verifyDoubleSupport<T>();
         Array<T> out = createEmptyArray<T>(dims);
         kernel::uniformDistribution<T>(*out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
-        verifyDoubleSupport<T>();
         Array<T> out = createEmptyArray<T>(dims);
         kernel::normalDistribution<T>(*out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        kernel::uniformDistribution<T>(
+              *out.get(), out.elements(),
+              *state.get(), *pos.get(),
+              *sh1.get(), *sh2.get(),
+              mask, *recursion_table.get(),
+              *temper_table.get());
+        return out;
+    }
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        kernel::normalDistribution<T>(
+              *out.get(), out.elements(),
+              *state.get(), *pos.get(),
+              *sh1.get(), *sh2.get(),
+              mask, *recursion_table.get(),
+              *temper_table.get());
+        return out;
+    }
+
+#define INSTANTIATE_UNIFORM(T)\
+    template\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
+    template\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
+
+#define INSTANTIATE_NORMAL(T)\
+    template\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
+    template\
+    Array<T> normalDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
+
 #define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        size_t elements = out.elements()*2;\
+        kernel::uniformDistribution<TR>(*out.get(), elements, type, seed, counter);\
+        return out;\
+    }\
+    \
+    template<>\
+    Array<T> uniformDistribution(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
     {\
-        verifyDoubleSupport<T>();\
         Array<T> out = createEmptyArray<T>(dims);\
-        kernel::uniformDistribution<TR>(*out.get(), out.elements()*2, type, seed, counter);\
+        size_t elements = out.elements()*2;\
+        kernel::uniformDistribution<TR>(\
+              *out.get(), elements,\
+              *state.get(), *pos.get(),\
+              *sh1.get(), *sh2.get(),\
+              mask, *recursion_table.get(),\
+              *temper_table.get());\
         return out;\
     }\
 
 #define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
-        verifyDoubleSupport<T>();\
         Array<T> out = createEmptyArray<T>(dims);\
-        kernel::normalDistribution<TR>(*out.get(), out.elements()*2, type, seed, counter);\
+        size_t elements = out.elements()*2;\
+        kernel::normalDistribution<TR>(*out.get(), elements, type, seed, counter);\
         return out;\
     }\
+    \
+    template<>\
+    Array<T> normalDistribution(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        size_t elements = out.elements()*2;\
+        kernel::normalDistribution<TR>(\
+              *out.get(), elements,\
+              *state.get(), *pos.get(),\
+              *sh1.get(), *sh2.get(),\
+              mask, *recursion_table.get(),\
+              *temper_table.get());\
+        return out;\
+    }\
+
+    INSTANTIATE_UNIFORM(float )
+    INSTANTIATE_UNIFORM(double)
+    INSTANTIATE_UNIFORM(int   )
+    INSTANTIATE_UNIFORM(uint  )
+    INSTANTIATE_UNIFORM(intl  )
+    INSTANTIATE_UNIFORM(uintl )
+    INSTANTIATE_UNIFORM(char  )
+    INSTANTIATE_UNIFORM(uchar )
+    INSTANTIATE_UNIFORM(short )
+    INSTANTIATE_UNIFORM(ushort)
 
-    template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<int>    uniformDistribution<int>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uint>   uniformDistribution<uint>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<intl>   uniformDistribution<intl>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uintl>  uniformDistribution<uintl> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<char>   uniformDistribution<char>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uchar>  uniformDistribution<uchar> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-
-    template Array<float>  normalDistribution<float>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> normalDistribution<double> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    INSTANTIATE_NORMAL(float )
+    INSTANTIATE_NORMAL(double)
 
     COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
     COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index 2cafa3e214..6e8be57997 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <Array.hpp>
 #include <af/defines.h>
@@ -13,9 +14,21 @@
 
 namespace opencl
 {
+    Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
+
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    Array<T> uniformDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    Array<T> normalDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
 }

From c027dce73c838b040269492c693cd7337a9050d5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 19 Aug 2016 15:23:45 -0400
Subject: [PATCH 0756/2677] updates to tests and fixes for opencl,cuda backends

---
 src/api/c/approx.cpp         | 10 ++++++
 src/api/c/array.cpp          |  5 +++
 src/api/c/data.cpp           | 64 +++++++++++++++++++++++++++++++-----
 src/api/c/iir.cpp            |  4 +++
 src/api/c/index.cpp          | 17 +++++-----
 src/api/c/norm.cpp           |  2 ++
 src/api/c/transpose.cpp      |  4 +++
 src/api/c/where.cpp          |  9 ++++-
 src/backend/cuda/index.cu    |  1 +
 src/backend/opencl/index.cpp |  1 +
 test/constant.cpp            |  4 +--
 test/empty.cpp               |  8 ++---
 test/moddims.cpp             |  2 +-
 13 files changed, 106 insertions(+), 25 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 7c2935ac1b..58579b1cf5 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -55,6 +55,11 @@ af_err af_approx1(af_array *out, const af_array in, const af_array pos,
                       (pdims[1] == idims[1] && pdims[2] == idims[2] && pdims[3] == idims[3]));
         ARG_ASSERT(3, (method == AF_INTERP_LINEAR || method == AF_INTERP_NEAREST));
 
+        if(idims.ndims() == 0 || pdims.ndims() ==  0) {
+            dim_t my_dims[] = { 0, 0, 0, 0 };
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, itype);
+        }
+
         af_array output;
 
         switch(itype) {
@@ -98,6 +103,11 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
                       (pdims[2] == idims[2] && pdims[3] == idims[3]));
         ARG_ASSERT(3, (method == AF_INTERP_LINEAR || method == AF_INTERP_NEAREST));
 
+        if(idims.ndims() == 0 || pdims.ndims() ==  0 || qdims.ndims() == 0) {
+            dim_t my_dims[] = { 0, 0, 0, 0 };
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, itype);
+        }
+
         af_array output;
 
         switch(itype) {
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 80b0d85e60..6ce8022acb 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -123,6 +123,11 @@ af_err af_copy_array(af_array *out, const af_array in)
         ArrayInfo info = getInfo(in);
         const af_dtype type = info.getType();
 
+        if(info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        }
+
         af_array res;
         switch(type) {
         case f32:   res = copyArray<float   >(in); break;
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index b9e2364fa9..3bcdfdf40a 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -98,7 +98,13 @@ af_err af_constant_complex(af_array *result, const double real, const double ima
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
 
         switch (type) {
         case c32: out = createCplx<cfloat , float >(d, real, imag); break;
@@ -119,7 +125,13 @@ af_err af_constant_long(af_array *result, const intl val,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(result, AF_MAX_DIMS, my_dims, s64);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
 
         out = getHandle(createValueArray<intl>(d, val));
 
@@ -136,7 +148,13 @@ af_err af_constant_ulong(af_array *result, const uintl val,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(result, AF_MAX_DIMS, my_dims, u64);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
         out = getHandle(createValueArray<uintl>(d, val));
 
         std::swap(*result, out);
@@ -169,7 +187,13 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_array result;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
 
         switch(type) {
         case f32:   result = randu_<float  >(d);    break;
@@ -198,7 +222,13 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_array result;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
 
         switch(type) {
         case f32:   result = randn_<float  >(d);    break;
@@ -278,7 +308,13 @@ af_err af_range(af_array *result, const unsigned ndims, const dim_t * const dims
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d = verifyDims(ndims, dims);
+        dim4 d(1, 1, 1, 1);
+        if(ndims <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+        } else {
+            d = verifyDims(ndims, dims);
+        }
 
         switch(type) {
         case f32:   out = range_<float  >(d, seq_dim); break;
@@ -439,7 +475,13 @@ af_array triangle(const af_array in, bool is_unit_diag)
 af_err af_lower(af_array *out, const af_array in, bool is_unit_diag)
 {
     try {
-        af_dtype type = getInfo(in).getType();
+        ArrayInfo info = getInfo(in);
+        af_dtype type = info.getType();
+
+        if(info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
+
         af_array res;
         switch(type) {
         case f32: res = triangle<float   , false>(in, is_unit_diag); break;
@@ -465,7 +507,13 @@ af_err af_lower(af_array *out, const af_array in, bool is_unit_diag)
 af_err af_upper(af_array *out, const af_array in, bool is_unit_diag)
 {
     try {
-        af_dtype type = getInfo(in).getType();
+        ArrayInfo info = getInfo(in);
+        af_dtype type = info.getType();
+
+        if(info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
+
         af_array res;
         switch(type) {
         case f32: res = triangle<float   , true>(in, is_unit_diag); break;
diff --git a/src/api/c/iir.cpp b/src/api/c/iir.cpp
index 640d9a51c2..a70207de59 100644
--- a/src/api/c/iir.cpp
+++ b/src/api/c/iir.cpp
@@ -65,6 +65,10 @@ af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
         dim4 bdims = binfo.dims();
         dim4 xdims = xinfo.dims();
 
+        if(xinfo.ndims() == 0) {
+            return af_retain_array(y, x);
+        }
+
         if (xinfo.ndims() > 1) {
             if (binfo.ndims() > 1) {
                 for (int i = 1; i < 3; i++) {
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index e4c7059996..23cd9a509b 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -161,6 +161,15 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
         ARG_ASSERT(3, (indexs!=NULL));
 
         ArrayInfo iInfo = getInfo(in);
+
+        dim4 iDims = iInfo.dims();
+        af_dtype inType = getInfo(in).getType();
+
+        if(iDims.ndims() <= 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(out, AF_MAX_DIMS, my_dims, inType);
+        }
+
         if (ndims == 1 && ndims != (dim_t)iInfo.ndims()) {
             af_array tmp_in;
             AF_CHECK(af_flat(&tmp_in, in));
@@ -207,14 +216,6 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
             }
         }
 
-        dim4 iDims = iInfo.dims();
-        af_dtype inType = getInfo(in).getType();
-
-        if(iDims.ndims() <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, inType);
-        }
-
         switch(inType) {
             case c64: output = genIndex<cdouble>(in, idxrs); break;
             case f64: output = genIndex<double >(in, idxrs); break;
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index bf83353983..dbae2f35e9 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -138,6 +138,8 @@ af_err af_norm(double *out, const af_array in,
 
         *out = 0;
 
+        if(i_info.ndims() == 0) { return AF_SUCCESS; }
+
         switch(i_type) {
         case f32: *out = norm<float  >(in, type, p, q);  break;
         case f64: *out = norm<double >(in, type, p, q);  break;
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index 1418c290c4..6799651a19 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -33,6 +33,10 @@ af_err af_transpose(af_array *out, af_array in, const bool conjugate)
         af_dtype type = info.getType();
         af::dim4 dims = info.dims();
 
+        if (dims.elements() == 0) {
+            return af_retain_array(out, in);
+        }
+
         if (dims[0]==1 || dims[1]==1) {
             af::dim4 outDims(dims[1],dims[0],dims[2],dims[3]);
             if(conjugate) {
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 4aad8c4a75..30cb26ffbd 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -29,7 +29,14 @@ static inline af_array where(const af_array in)
 af_err af_where(af_array *idx, const af_array in)
 {
     try {
-        af_dtype type = getInfo(in).getType();
+        ArrayInfo i_info = getInfo(in);
+        af_dtype type = i_info.getType();
+
+        if(i_info.ndims() == 0) {
+            dim_t my_dims[] = {0, 0, 0, 0};
+            return af_create_handle(idx, AF_MAX_DIMS, my_dims, u32);
+        }
+
         af_array res;
         switch(type) {
         case f32: res = where<float  >(in); break;
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cu
index f2148840c7..7ebc6f1f97 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cu
@@ -60,6 +60,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
+    if(oDims.elements() == 0) { return out; }
 
     kernel::index<T>(out, in, p);
 
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 110f7e26b6..978b6f30b2 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -63,6 +63,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
+    if(oDims.elements() == 0) { return out; }
 
     kernel::index<T>(out, in, p, bPtrs);
 
diff --git a/test/constant.cpp b/test/constant.cpp
index d3244a0566..011cf24fab 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -130,10 +130,10 @@ void IdentityCPPError() {
         array out = af::identity(num, 0, 10, dty);
     }
     catch(const af::exception &ex) {
-        SUCCEED();
+        FAIL() << "Incorrectly thrown 0-length exception";
         return;
     }
-    FAIL() << "Failed to throw an exception";
+    SUCCEED();
 }
 
 TYPED_TEST(Constant, basicCPP)
diff --git a/test/empty.cpp b/test/empty.cpp
index a4f41cdcd8..64c5fb9a3f 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -10,11 +10,9 @@
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <cstdio>
-#include <iostream>
 #include <cstdlib>
 
 using namespace af;
-using namespace std;
 
 template<typename T>
 class Array : public ::testing::Test
@@ -129,8 +127,8 @@ TEST(Array, TestEmptyLinAlg) {
     ASSERT_EQ( det<float>(constant(0,0)), 1);
     ASSERT_EQ( det<cfloat>(constant(0,0)).real, 1);
     ASSERT_EQ( det<cdouble>(constant(0,0)).real, 1);
-    ASSERT_EQ( norm(constant(0,0)), 0);
-    ASSERT_EQ( rank(constant(0,0)), 0);
+    ASSERT_EQ( af::norm(constant(0,0)), 0);
+    ASSERT_EQ( af::rank(constant(0,0)), 0);
     array tau_qr, arr = constant(0,0);
     qrInPlace(tau_qr, arr);
     ASSERT_EQ(tau_qr.numdims(), 0);
@@ -240,7 +238,7 @@ TEST(Array, TestEmptyVecOp) {
 TEST(Array, TestEmptyArrMod) {
     ASSERT_EQ(diag(constant(0,0)).numdims(), 0);
     ASSERT_EQ(diag(constant(0,0), true).numdims(), 0);
-    ASSERT_EQ(identity(0).numdims(), 0);
+    ASSERT_EQ(af::identity(0).numdims(), 0);
     ASSERT_EQ(iota(dim4(0)).numdims(), 0);
     ASSERT_EQ(lower(constant(0,0)).numdims(), 0);
     ASSERT_EQ(upper(constant(0,0)).numdims(), 0);
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 053948dbe2..505f780ac3 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -136,7 +136,7 @@ void moddimsArgsTest(string pTestFile)
     af::dim4 newDims(1);
     newDims[0] = dims[1];
     newDims[1] = dims[0]*dims[2];
-    ASSERT_EQ(AF_ERR_ARG, af_moddims(&outArray,inArray,0,newDims.get()));
+    ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,inArray,0,newDims.get()));
     ASSERT_EQ(AF_ERR_ARG, af_moddims(&outArray,inArray,newDims.ndims(),NULL));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));

From 8d4a9825a51e5d534c9043244d10a3bf2d564d20 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 19 Aug 2016 15:56:33 -0400
Subject: [PATCH 0757/2677] Small changes to template instatiation macros

---
 src/backend/cuda/random_engine.cu    | 8 ++++----
 src/backend/opencl/random_engine.cpp | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index fd31c3e4d3..5a39030416 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -90,7 +90,7 @@ namespace cuda
 
 #define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
@@ -100,7 +100,7 @@ namespace cuda
     }\
     \
     template<>\
-    Array<T> uniformDistribution(const af::dim4 &dims,\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
             Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
     {\
@@ -118,7 +118,7 @@ namespace cuda
 
 #define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
@@ -128,7 +128,7 @@ namespace cuda
     }\
     \
     template<>\
-    Array<T> normalDistribution(const af::dim4 &dims,\
+    Array<T> normalDistribution<T>(const af::dim4 &dims,\
             Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
     {\
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 765b79a350..85fc3005c7 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -90,7 +90,7 @@ namespace opencl
 
 #define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         size_t elements = out.elements()*2;\
@@ -99,7 +99,7 @@ namespace opencl
     }\
     \
     template<>\
-    Array<T> uniformDistribution(const af::dim4 &dims,\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
             Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
     {\
@@ -116,7 +116,7 @@ namespace opencl
 
 #define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
     template<>\
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         size_t elements = out.elements()*2;\
@@ -125,7 +125,7 @@ namespace opencl
     }\
     \
     template<>\
-    Array<T> normalDistribution(const af::dim4 &dims,\
+    Array<T> normalDistribution<T>(const af::dim4 &dims,\
             Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
     {\

From 0b56aff18048ed1e23d07b0e10990654ad7898b8 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 19 Aug 2016 16:54:36 -0400
Subject: [PATCH 0758/2677] CPU backend done

Disambiguated overloaded functions in kernel folder.
---
 src/api/c/random_engine.cpp                   |  22 +--
 src/backend/cpu/kernel/random_engine.hpp      |  80 ++++++++++-
 .../cpu/kernel/random_engine_mersenne.hpp     |  80 +++++++++++
 .../cpu/kernel/random_engine_philox.hpp       | 112 +++++----------
 src/backend/cpu/random_engine.cpp             | 133 +++++++++++++++---
 src/backend/cpu/random_engine.hpp             |  12 ++
 src/backend/cuda/kernel/random_engine.hpp     |   8 +-
 src/backend/cuda/random_engine.cu             |  16 +--
 src/backend/opencl/kernel/random_engine.hpp   |   8 +-
 src/backend/opencl/random_engine.cpp          |  16 +--
 10 files changed, 350 insertions(+), 137 deletions(-)
 create mode 100644 src/backend/cpu/kernel/random_engine_mersenne.hpp

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 329e12f7f9..95686ead97 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -110,12 +110,12 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
         e.counter = 0;
         if (rtype == AF_RANDOM_MERSENNE) {
             //TODO Add AF_CHECK(af_* calls)
-            af_create_array(&e.pos, pos, 1, &MaxBlocks, u32);
-            af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32);
-            af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32);
+            AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
+            AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
+            AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
             e.mask = mask;
-            af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32);
-            af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32);
+            AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
+            AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
             e.state = getHandle(initMersenneState(seed, getArray<uint>(e.recursion_table)));
         }
         *engineHandle = getRandomEngineHandle(e);
@@ -181,12 +181,12 @@ af_err af_release_random_engine(af_random_engine engineHandle)
     try {
         af_random_engine_t *e = getRandomEngine(engineHandle);
         if (e->type == AF_RANDOM_MERSENNE) {
-            af_release_array(e->pos);
-            af_release_array(e->sh1);
-            af_release_array(e->sh2);
-            af_release_array(e->recursion_table);
-            af_release_array(e->temper_table);
-            af_release_array(e->state);
+            AF_CHECK(af_release_array(e->pos));
+            AF_CHECK(af_release_array(e->sh1));
+            AF_CHECK(af_release_array(e->sh2));
+            AF_CHECK(af_release_array(e->recursion_table));
+            AF_CHECK(af_release_array(e->temper_table));
+            AF_CHECK(af_release_array(e->state));
         }
         delete e;
     }
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index f928837cba..6534abad98 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -12,6 +12,7 @@
 #include <err_cpu.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
+#include <kernel/random_engine_mersenne.hpp>
 
 namespace cpu
 {
@@ -21,6 +22,7 @@ namespace kernel
     #define UINTMAXFLOAT 4294967296.0f
     #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
     #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
+    #define N 351
 
     template <typename T>
     T transform(uint *val, int index)
@@ -188,7 +190,69 @@ namespace kernel
     }
 
     template <typename T>
-    void uniformDistribution(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
+    void uniformDistributionMT(T* out, size_t elements,
+            uint * const state,
+            uint const * const pos,
+            uint const * const sh1,
+            uint const * const sh2,
+            uint mask,
+            uint const * const recursion_table,
+            uint const * const temper_table)
+    {
+        uint l_state[STATE_SIZE];
+        uint o[4];
+        uint lpos = pos[0];
+        uint lsh1 = sh1[0];
+        uint lsh2 = sh2[0];
+
+        state_read(l_state, state);
+
+        int reset = (4*sizeof(uint))/sizeof(T);
+        for (int i = 0; i < (int)elements; i += reset) {
+            mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table, temper_table);
+            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
+            for (int j = 0; j < lim; ++j) {
+                out[i + j] = transform<T>(o, j);
+            }
+        }
+
+        state_write(state, l_state);
+    }
+
+    template <typename T>
+    void normalDistributionMT(T* out, size_t elements,
+            uint * const state,
+            uint const * const pos,
+            uint const * const sh1,
+            uint const * const sh2,
+            uint mask,
+            uint const * const recursion_table,
+            uint const * const temper_table)
+    {
+        T temp[(4*sizeof(uint))/sizeof(T)];
+        uint l_state[STATE_SIZE];
+        uint o[4];
+        uint lpos = pos[0];
+        uint lsh1 = sh1[0];
+        uint lsh2 = sh2[0];
+
+        state_read(l_state, state);
+
+        int reset = (4*sizeof(uint))/sizeof(T);
+        for (int i = 0; i < (int)elements; i += reset) {
+            mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table, temper_table);
+            normalize(o, temp);
+            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
+            for (int j = 0; j < lim; ++j) {
+                out[i + j] = temp[j];
+            }
+        }
+
+        state_write(state, l_state);
+    }
+
+    template <typename T>
+    void uniformDistributionCBRNG(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
     {
         switch(type) {
             case AF_RANDOM_PHILOX   :   philoxUniform(out, elements, seed, counter); break;
@@ -198,7 +262,7 @@ namespace kernel
     }
 
     template <typename T>
-    void normalDistribution(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
+    void normalDistributionCBRNG(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
     {
         switch(type) {
             case AF_RANDOM_PHILOX   :   philoxNormal(out, elements, seed, counter); break;
@@ -207,5 +271,17 @@ namespace kernel
         }
     }
 
+    void initMersenneState(uint * const state, const uint * const tbl, const uintl seed)
+    {
+        uint hidden_seed = tbl[4] ^ (tbl[8] << 16);
+        uint tmp = hidden_seed;
+        tmp += tmp >> 16;
+        tmp += tmp >> 8;
+        state[0] = seed;
+        state[1] = hidden_seed;
+            for (int i = 1; i < N; ++i) {
+                state[i] ^= (uint)(1812433253) * (state[i-1] ^ (state[i-1] >> 30)) + i;
+            }
+    }
 }
 }
diff --git a/src/backend/cpu/kernel/random_engine_mersenne.hpp b/src/backend/cpu/kernel/random_engine_mersenne.hpp
new file mode 100644
index 0000000000..74b96389e4
--- /dev/null
+++ b/src/backend/cpu/kernel/random_engine_mersenne.hpp
@@ -0,0 +1,80 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace cpu
+{
+namespace kernel
+{
+
+    #define N 351
+    #define STATE_SIZE 786
+
+    uint recursion(const uint * const recursion_table, const uint mask,
+            const uint sh1, const uint sh2, const uint x1, const uint x2, uint y)
+    {
+        uint x = (x1 & mask) ^ x2;
+        x ^= x << sh1;
+        y = x ^ (y >> sh2);
+        uint mat = recursion_table[y & 0x0f];
+        return y ^ mat;
+    }
+
+    uint temper(const uint * const temper_table, const uint v, uint t)
+    {
+         t ^= t >> 16;
+         t ^= t >> 8;
+         uint mat = temper_table[t & 0x0f];
+         return v ^ mat;
+    }
+
+    void mersenne(uint * const out,
+            uint * const state,
+            int i,
+            uint pos,
+            uint sh1,
+            uint sh2,
+            uint mask,
+            uint const * const recursion_table,
+            uint const * const temper_table)
+    {
+        int index    = i % STATE_SIZE;
+        int offsetX1 = (STATE_SIZE - N + index          ) % STATE_SIZE;
+        int offsetX2 = (STATE_SIZE - N + index + 1      ) % STATE_SIZE;
+        int offsetY  = (STATE_SIZE - N + index + pos    ) % STATE_SIZE;
+        int offsetT  = (STATE_SIZE - N + index + pos - 1) % STATE_SIZE;
+        for (int i = 0; i < 4; ++i) {
+            state[index] = recursion(recursion_table, mask, sh1, sh2,
+                    state[offsetX1], state[offsetX2], state[offsetY]);
+            out[i] = temper(temper_table, state[index], state[offsetT]);
+            offsetX1 = (offsetX1 + 1) % STATE_SIZE;
+            offsetX2 = (offsetX2 + 1) % STATE_SIZE;
+            offsetY  = (offsetY  + 1) % STATE_SIZE;
+            offsetT  = (offsetT  + 1) % STATE_SIZE;
+            index    = (index    + 1) % STATE_SIZE;
+        }
+    }
+
+    void state_read(uint * const l_state, const uint * const state)
+    {
+        for (int i = 0; i < N; ++i) {
+            l_state[STATE_SIZE - N + i] = state[i];
+        }
+    }
+
+    void state_write(uint * const state, const uint * const l_state)
+    {
+        for (int i = 0; i < N; ++i) {
+            state[i] = l_state[STATE_SIZE - N + i];
+        }
+    }
+
+}
+}
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index 55c5b41e59..e9e59b7ba4 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -56,88 +56,44 @@ namespace kernel
 #define w32_0 0x9E3779B9
 #define w32_1 0xBB67AE85
 
-void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
-{
-    *hi = (((uintl)a) * ((uintl)b))>>32;
-    *lo = a*b;
-}
-
-void philoxBump(uint * const k)
-{
-    k[0] += w32_0;
-    k[1] += w32_1;
-}
-
-void philoxRound(const uint * const k, uint * const c)
-{
-    uint hi0, lo0, hi1, lo1;
-    mulhilo(m4x32_0, c[0], &hi0, &lo0);
-    mulhilo(m4x32_1, c[2], &hi1, &lo1);
-    c[0] = hi1^c[1]^k[0];
-    c[1] = lo1;
-    c[2] = hi0^c[3]^k[1];
-    c[3] = lo0;
-}
-
-void philox(uint * const key, uint * const ctr)
-{
-    ctr[0] = -1;
-    //10 Rounds
-                       philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-}
-
-/*
-template <> struct Random<AF_RANDOM_PHILOX>
-{
-    uint hi;
-    uint lo;
-    uintl counter;
-    uint key[2];
-    uint ctr[4];
-    int reset;
-
-    template <typename T>
-    T uniform(void);
-
-    Random(uintl seed, uintl counter);
-};
+    void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
+    {
+        *hi = (((uintl)a) * ((uintl)b))>>32;
+        *lo = a*b;
+    }
 
-Random<AF_RANDOM_PHILOX>::Random(uintl seed, uintl counterInput) : hi(seed>>32), lo(seed), counter(counterInput), reset(0)
-{
-    key[0] = counter;
-    key[1] = hi;
-    ctr[0] = counter;
-    ctr[1] = 0;
-    ctr[2] = 0;
-    ctr[3] = lo;
-}
+    void philoxBump(uint * const k)
+    {
+        k[0] += w32_0;
+        k[1] += w32_1;
+    }
 
-template <typename T>
-void Random<AF_RANDOM_PHILOX>::uniform(T* out, size_t elements)
-{
-    int reset = (4*sizeof(uint))/sizeof(T);
-    philox(key, ctr);
-    for (int i = 0; i < (int)out.elements(); ++i) {
-        if (fresh == reset) {
-            philox(key, ctr);
-            ctr[0] += 4;
-            fresh = 0;
-        }
-        out[i] = transform<T>(ctr, fresh);
-        fresh++;
+    void philoxRound(const uint * const k, uint * const c)
+    {
+        uint hi0, lo0, hi1, lo1;
+        mulhilo(m4x32_0, c[0], &hi0, &lo0);
+        mulhilo(m4x32_1, c[2], &hi1, &lo1);
+        c[0] = hi1^c[1]^k[0];
+        c[1] = lo1;
+        c[2] = hi0^c[3]^k[1];
+        c[3] = lo0;
     }
 
-}
-*/
+    void philox(uint * const key, uint * const ctr)
+    {
+        ctr[0] = -1;
+        //10 Rounds
+                           philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+        philoxBump(key);   philoxRound(key, ctr);
+    }
 
 }
 }
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 5d1899a9eb..a0c2d77d89 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -9,19 +9,28 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <random.hpp>
 #include <kernel/random_engine.hpp>
 #include <cassert>
+#include <MersenneTwister.hpp>
+
+using common::MtStateLength;
+using common::MaxBlocks;
+using common::MersenneN;
 
 namespace cpu
 {
+    Array<uint> initMersenneState(const uintl seed, const Array<uint> tbl)
+    {
+        Array<uint> state = createEmptyArray<uint>(MtStateLength);
+        getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
+        return state;
+    }
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        getQueue().enqueue(kernel::uniformDistribution<T>, out.get(), out.elements(), type, seed, counter);
-        out.eval();
-        getQueue().sync();
+        getQueue().enqueue(kernel::uniformDistributionCBRNG<T>, out.get(), out.elements(), type, seed, counter);
         counter += out.elements();
         return out;
     }
@@ -30,11 +39,57 @@ namespace cpu
     Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        getQueue().enqueue(kernel::normalDistribution<T>, out.get(), out.elements(), type, seed, counter);
+        getQueue().enqueue(kernel::normalDistributionCBRNG<T>, out.get(), out.elements(), type, seed, counter);
         counter += out.elements();
         return out;
     }
 
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        getQueue().enqueue(kernel::uniformDistributionMT<T>,
+              out.get(), out.elements(),
+              state.get(), pos.get(),
+              sh1.get(), sh2.get(),
+              mask, recursion_table.get(),
+              temper_table.get());
+        return out;
+    }
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
+    {
+        Array<T> out = createEmptyArray<T>(dims);
+        getQueue().enqueue(kernel::normalDistributionMT<T>,
+              out.get(), out.elements(),
+              state.get(), pos.get(),
+              sh1.get(), sh2.get(),
+              mask, recursion_table.get(),
+              temper_table.get());
+        return out;
+    }
+
+#define INSTANTIATE_UNIFORM(T)\
+    template\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter);\
+    template\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
+
+#define INSTANTIATE_NORMAL(T)\
+    template\
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter);\
+    template\
+    Array<T> normalDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
+
 #define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
     template<>\
     Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
@@ -42,10 +97,27 @@ namespace cpu
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        getQueue().enqueue(kernel::uniformDistribution<TR>, outPtr, elements, type, seed, counter);\
+        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);\
         counter += elements;\
         return out;\
     }\
+    \
+    template<>\
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        getQueue().enqueue(kernel::uniformDistributionMT<TR>,\
+              outPtr, elements,\
+              state.get(), pos.get(),\
+              sh1.get(), sh2.get(),\
+              mask, recursion_table.get(),\
+              temper_table.get());\
+        return out;\
+    }\
 
 #define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
     template<>\
@@ -54,29 +126,46 @@ namespace cpu
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        getQueue().enqueue(kernel::normalDistribution<TR>, outPtr, elements, type, seed, counter);\
+        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);\
         counter += elements;\
         return out;\
     }\
+    \
+    template<>\
+    Array<T> normalDistribution<T>(const af::dim4 &dims,\
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
+    {\
+        Array<T> out = createEmptyArray<T>(dims);\
+        TR *outPtr = (TR*)out.get();\
+        size_t elements = out.elements()*2;\
+        getQueue().enqueue(kernel::normalDistributionMT<TR>,\
+              outPtr, elements,\
+              state.get(), pos.get(),\
+              sh1.get(), sh2.get(),\
+              mask, recursion_table.get(),\
+              temper_table.get());\
+        return out;\
+    }\
 
-    template Array<float>  uniformDistribution<float> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> uniformDistribution<double>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uint>   uniformDistribution<uint>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<int>    uniformDistribution<int>   (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<intl>   uniformDistribution<intl>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uintl>  uniformDistribution<uintl> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<char>   uniformDistribution<char>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<uchar>  uniformDistribution<uchar> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<short>  uniformDistribution<short> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<ushort> uniformDistribution<ushort>(const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-
-    template Array<float>  normalDistribution<float>  (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
-    template Array<double> normalDistribution<double> (const af::dim4 &dim, const af_random_type type, const uintl seed, uintl &counter);
+    INSTANTIATE_UNIFORM(float )
+    INSTANTIATE_UNIFORM(double)
+    INSTANTIATE_UNIFORM(int   )
+    INSTANTIATE_UNIFORM(uint  )
+    INSTANTIATE_UNIFORM(intl  )
+    INSTANTIATE_UNIFORM(uintl )
+    INSTANTIATE_UNIFORM(char  )
+    INSTANTIATE_UNIFORM(uchar )
+    INSTANTIATE_UNIFORM(short )
+    INSTANTIATE_UNIFORM(ushort)
 
-    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+    INSTANTIATE_NORMAL(float )
+    INSTANTIATE_NORMAL(double)
 
     COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
     COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
+    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
 }
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index 4fbbae55fc..9bf541e297 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -13,9 +13,21 @@
 
 namespace cpu
 {
+    Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
 
     template<typename T>
     Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+
+    template<typename T>
+    Array<T> uniformDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
+
+    template<typename T>
+    Array<T> normalDistribution(const af::dim4 &dims,
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
 }
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index e74ebbeffa..30040076af 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -578,7 +578,7 @@ namespace kernel
     }
 
     template <typename T>
-    void uniformDistribution(T* out, size_t elements,
+    void uniformDistributionMT(T* out, size_t elements,
             uint * const state,
             uint const * const pos,
             uint const * const sh1,
@@ -596,7 +596,7 @@ namespace kernel
     }
 
     template <typename T>
-    void normalDistribution(T* out, size_t elements,
+    void normalDistributionMT(T* out, size_t elements,
             uint * const state,
             uint const * const pos,
             uint const * const sh1,
@@ -614,7 +614,7 @@ namespace kernel
     }
 
     template <typename T>
-    void uniformDistribution(T* out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
+    void uniformDistributionCBRNG(T* out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
@@ -632,7 +632,7 @@ namespace kernel
     }
 
     template <typename T>
-    void normalDistribution(T *out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
+    void normalDistributionCBRNG(T *out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 5a39030416..9bdf921920 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -30,7 +30,7 @@ namespace cuda
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistribution<T>(out.get(), out.elements(), type, seed, counter);
+        kernel::uniformDistributionCBRNG<T>(out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
@@ -38,7 +38,7 @@ namespace cuda
     Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistribution<T>(out.get(), out.elements(), type, seed, counter);
+        kernel::normalDistributionCBRNG<T>(out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
@@ -48,7 +48,7 @@ namespace cuda
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistribution<T>(
+        kernel::uniformDistributionMT<T>(
               out.get(), out.elements(),
               state.get(), pos.get(),
               sh1.get(), sh2.get(),
@@ -63,7 +63,7 @@ namespace cuda
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistribution<T>(
+        kernel::normalDistributionMT<T>(
               out.get(), out.elements(),
               state.get(), pos.get(),
               sh1.get(), sh2.get(),
@@ -95,7 +95,7 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        kernel::uniformDistribution<TR>(outPtr, elements, type, seed, counter);\
+        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);\
         return out;\
     }\
     \
@@ -107,7 +107,7 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        kernel::uniformDistribution<TR>(\
+        kernel::uniformDistributionMT<TR>(\
               outPtr, elements,\
               state.get(), pos.get(),\
               sh1.get(), sh2.get(),\
@@ -123,7 +123,7 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        kernel::normalDistribution<TR>(outPtr, elements, type, seed, counter);\
+        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);\
         return out;\
     }\
     \
@@ -135,7 +135,7 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(dims);\
         TR *outPtr = (TR*)out.get();\
         size_t elements = out.elements()*2;\
-        kernel::normalDistribution<TR>(\
+        kernel::normalDistributionMT<TR>(\
               outPtr, elements,\
               state.get(), pos.get(),\
               sh1.get(), sh2.get(),\
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 26d5b76c6f..a3a49c307c 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -194,21 +194,21 @@ namespace opencl
         }
 
         template <typename T>
-        void uniformDistribution(cl::Buffer out, const size_t elements,
+        void uniformDistributionCBRNG(cl::Buffer out, const size_t elements,
                 const af_random_type type, const uintl &seed, uintl &counter)
         {
             randomDistribution<T>(out, elements, type, seed, counter, 0);
         }
 
         template <typename T>
-        void normalDistribution(cl::Buffer out, const size_t elements,
+        void normalDistributionCBRNG(cl::Buffer out, const size_t elements,
                 const af_random_type type, const uintl &seed, uintl &counter)
         {
             randomDistribution<T>(out, elements, type, seed, counter, 1);
         }
 
         template <typename T>
-        void uniformDistribution(cl::Buffer out, const size_t elements,
+        void uniformDistributionMT(cl::Buffer out, const size_t elements,
                 cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
                 const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table)
         {
@@ -216,7 +216,7 @@ namespace opencl
         }
 
         template <typename T>
-        void normalDistribution(cl::Buffer out, const size_t elements,
+        void normalDistributionMT(cl::Buffer out, const size_t elements,
                 cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
                 const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table)
         {
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 85fc3005c7..5574b47b84 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -30,7 +30,7 @@ namespace opencl
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistribution<T>(*out.get(), out.elements(), type, seed, counter);
+        kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
@@ -38,7 +38,7 @@ namespace opencl
     Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistribution<T>(*out.get(), out.elements(), type, seed, counter);
+        kernel::normalDistributionCBRNG<T>(*out.get(), out.elements(), type, seed, counter);
         return out;
     }
 
@@ -48,7 +48,7 @@ namespace opencl
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistribution<T>(
+        kernel::uniformDistributionMT<T>(
               *out.get(), out.elements(),
               *state.get(), *pos.get(),
               *sh1.get(), *sh2.get(),
@@ -63,7 +63,7 @@ namespace opencl
             Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
     {
         Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistribution<T>(
+        kernel::normalDistributionMT<T>(
               *out.get(), out.elements(),
               *state.get(), *pos.get(),
               *sh1.get(), *sh2.get(),
@@ -94,7 +94,7 @@ namespace opencl
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         size_t elements = out.elements()*2;\
-        kernel::uniformDistribution<TR>(*out.get(), elements, type, seed, counter);\
+        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);\
         return out;\
     }\
     \
@@ -105,7 +105,7 @@ namespace opencl
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         size_t elements = out.elements()*2;\
-        kernel::uniformDistribution<TR>(\
+        kernel::uniformDistributionMT<TR>(\
               *out.get(), elements,\
               *state.get(), *pos.get(),\
               *sh1.get(), *sh2.get(),\
@@ -120,7 +120,7 @@ namespace opencl
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         size_t elements = out.elements()*2;\
-        kernel::normalDistribution<TR>(*out.get(), elements, type, seed, counter);\
+        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);\
         return out;\
     }\
     \
@@ -131,7 +131,7 @@ namespace opencl
     {\
         Array<T> out = createEmptyArray<T>(dims);\
         size_t elements = out.elements()*2;\
-        kernel::normalDistribution<TR>(\
+        kernel::normalDistributionMT<TR>(\
               *out.get(), elements,\
               *state.get(), *pos.get(),\
               *sh1.get(), *sh2.get(),\

From e782d8a13bcbce7765682cdda0469c9eb7eca9cc Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 19 Aug 2016 17:24:36 -0400
Subject: [PATCH 0759/2677] Changed internal struct to class

---
 src/api/c/random_engine.cpp | 35 +++++++++++++++++++++++++----------
 1 file changed, 25 insertions(+), 10 deletions(-)

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 95686ead97..8cff29269d 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -55,20 +55,35 @@ typedef struct {
     af_array state;
 } af_random_engine_t;
 
-af_random_engine getRandomEngineHandle(const af_random_engine_t engine)
+class RandomEngine
 {
-    af_random_engine_t *engineHandle = new af_random_engine_t;
+    public :
+    af_random_type type;
+    unsigned long long seed;
+    unsigned long long counter;
+    af_array pos;
+    af_array sh1;
+    af_array sh2;
+    uint mask;
+    af_array recursion_table;
+    af_array temper_table;
+    af_array state;
+};
+
+af_random_engine getRandomEngineHandle(const RandomEngine engine)
+{
+    RandomEngine *engineHandle = new RandomEngine;
     *engineHandle = engine;
     return static_cast<af_random_engine>(engineHandle);
 }
 
-af_random_engine_t* getRandomEngine(const af_random_engine engineHandle)
+RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 {
-    return (af_random_engine_t *)engineHandle;
+    return (RandomEngine *)engineHandle;
 }
 
 template<typename T>
-static inline af_array uniformDistribution_(const af::dim4 &dims, af_random_engine_t *e)
+static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
     if (e->type == AF_RANDOM_MERSENNE) {
         return getHandle(uniformDistribution<T>(dims,
@@ -85,7 +100,7 @@ static inline af_array uniformDistribution_(const af::dim4 &dims, af_random_engi
 }
 
 template<typename T>
-static inline af_array normalDistribution_(const af::dim4 &dims, af_random_engine_t *e)
+static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
     if (e->type == AF_RANDOM_MERSENNE) {
         return getHandle(normalDistribution<T>(dims,
@@ -104,7 +119,7 @@ static inline af_array normalDistribution_(const af::dim4 &dims, af_random_engin
 af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
 {
     try {
-        af_random_engine_t e;
+        RandomEngine e;
         e.type = rtype;
         e.seed = seed;
         e.counter = 0;
@@ -131,7 +146,7 @@ af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const un
         AF_CHECK(af_init());
 
         af::dim4 d = verifyDims(ndims, dims);
-        af_random_engine_t *e = getRandomEngine(engine);
+        RandomEngine *e = getRandomEngine(engine);
 
         switch(type) {
         case f32:   result = uniformDistribution_<float  >(d, e); break;
@@ -161,7 +176,7 @@ af_err af_random_engine_normal(af_array *out, af_random_engine engine, const uns
         AF_CHECK(af_init());
 
         af::dim4 d = verifyDims(ndims, dims);
-        af_random_engine_t *e = getRandomEngine(engine);
+        RandomEngine *e = getRandomEngine(engine);
 
         switch(type) {
         case f32:   result = normalDistribution_<float  >(d, e); break;
@@ -179,7 +194,7 @@ af_err af_random_engine_normal(af_array *out, af_random_engine engine, const uns
 af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
-        af_random_engine_t *e = getRandomEngine(engineHandle);
+        RandomEngine *e = getRandomEngine(engineHandle);
         if (e->type == AF_RANDOM_MERSENNE) {
             AF_CHECK(af_release_array(e->pos));
             AF_CHECK(af_release_array(e->sh1));

From 967640213df7e8f844d75525cd4f3f8be44a5bb8 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 22 Aug 2016 13:15:55 -0400
Subject: [PATCH 0760/2677] Minor commits to enum types

setSeed and getSeed
---
 include/af/defines.h                 | 10 ++++--
 include/af/random_engine.h           | 27 +++++++++++++--
 src/api/c/random_engine.cpp          | 50 ++++++++++++++++------------
 src/api/cpp/random_engine.cpp        | 15 ++++++++-
 src/api/unified/random_engine.cpp    | 10 ++++++
 src/backend/cpu/random_engine.cpp    |  5 +++
 src/backend/cpu/random_engine.hpp    |  2 ++
 src/backend/cuda/random_engine.cu    |  5 +++
 src/backend/cuda/random_engine.hpp   |  2 ++
 src/backend/opencl/random_engine.cpp |  5 +++
 src/backend/opencl/random_engine.hpp |  2 ++
 11 files changed, 106 insertions(+), 27 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 4b5f36a120..575a04c8d3 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -400,9 +400,13 @@ typedef enum {
 } af_binary_op;
 
 typedef enum {
-    AF_RANDOM_PHILOX = 0,
-    AF_RANDOM_THREEFRY = 1,
-    AF_RANDOM_MERSENNE = 2
+    AF_RANDOM_PHILOX_4X32_10 = 100,
+    AF_RANDOM_THREEFRY_2X32_16 = 200,
+    AF_RANDOM_MERSENNE_GP11213 = 300,
+    AF_RANDOM_PHILOX = AF_RANDOM_PHILOX_4X32_10,
+    AF_RANDOM_THREEFRY = AF_RANDOM_THREEFRY_2X32_16,
+    AF_RANDOM_MERSENNE = AF_RANDOM_MERSENNE_GP11213,
+    AF_RANDOM_DEFAULT = AF_RANDOM_PHILOX
 } af_random_type;
 #endif
 
diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 482eead463..8e28f34769 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -23,7 +23,7 @@ namespace af
             af_random_engine engine;
         public:
             explicit
-            randomEngine(randomType typeIn = AF_RANDOM_PHILOX, unsigned long long seedIn = 0);
+            randomEngine(randomType typeIn = AF_RANDOM_DEFAULT, uintl seedIn = 0);
             ~randomEngine();
 
             array uniform(const dim_t dim0, const dtype ty = f32);
@@ -37,6 +37,9 @@ namespace af
             array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
             array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
             array normal(const dim4& dims, const dtype ty = f32);
+
+            void setSeed(uintl seed);
+            uintl getSeed();
     };
 }
 #endif
@@ -54,7 +57,7 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_type rtype, unsigned long long seed);
+    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_type rtype, uintl seed);
 
     /**
        C Interface for creating an array of uniform numbers using a random engine
@@ -82,6 +85,26 @@ extern "C" {
     */
     AFAPI af_err af_random_engine_normal(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
+    /**
+       C Interface for setting the seed of a random engine
+
+       \param[in]   engine is the random engine object
+       \param[in]   seed is the initializing seed of the random number generator
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine);
+
+    /**
+       C Interface for getting the seed of a random engine
+
+       \param[out]  out The pointer to the returned seed.
+       \param[in]   engine is the random engine object
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine);
+
     /**
        C Interface for releasing random engine
 
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 8cff29269d..605b904a78 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -27,6 +27,7 @@ using detail::cdouble;
 using detail::uchar;
 using detail::uniformDistribution;
 using detail::normalDistribution;
+using detail::initMersenneState;
 
 using common::MaxBlocks;
 using common::TableLength;
@@ -38,29 +39,12 @@ using common::mask;
 using common::recursion_tbl;
 using common::temper_tbl;
 
-//TODO : static pointer to random_engine_t that wil hold the default random engine
-//protect this object with mutex
-
-//TODO : class and camel case
-typedef struct {
-    af_random_type type;
-    unsigned long long seed;
-    unsigned long long counter;
-    af_array pos;
-    af_array sh1;
-    af_array sh2;
-    uint mask;
-    af_array recursion_table;
-    af_array temper_table;
-    af_array state;
-} af_random_engine_t;
-
 class RandomEngine
 {
     public :
     af_random_type type;
-    unsigned long long seed;
-    unsigned long long counter;
+    uintl seed;
+    uintl counter;
     af_array pos;
     af_array sh1;
     af_array sh2;
@@ -116,7 +100,7 @@ static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e
     }
 }
 
-af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
+af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, uintl seed)
 {
     try {
         RandomEngine e;
@@ -124,7 +108,6 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
         e.seed = seed;
         e.counter = 0;
         if (rtype == AF_RANDOM_MERSENNE) {
-            //TODO Add AF_CHECK(af_* calls)
             AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
             AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
             AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
@@ -139,6 +122,31 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
     return AF_SUCCESS;
 }
 
+af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
+{
+    try {
+        AF_CHECK(af_init());
+        RandomEngine *e = getRandomEngine(engine);
+        e->seed = seed;
+        if (e->type == AF_RANDOM_MERSENNE) {
+            initMersenneState(getArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
+        }
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
+{
+    try {
+        AF_CHECK(af_init());
+        RandomEngine *e = getRandomEngine(engine);
+        *seed = e->seed;
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     try {
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index aa594cebc0..f47f33cefc 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -15,7 +15,7 @@
 
 namespace af
 {
-    randomEngine::randomEngine(randomType type, unsigned long long seed)
+    randomEngine::randomEngine(randomType type, uintl seed)
     {
         AF_THROW(af_create_random_engine(&engine, type, seed));
     }
@@ -88,4 +88,17 @@ namespace af
         AF_THROW(af_random_engine_normal(&out, engine, dims.ndims(), dims.get(), ty));
         return array(out);
     }
+
+    void randomEngine::setSeed(const uintl seed)
+    {
+        AF_THROW(af_random_engine_set_seed(seed, engine));
+    }
+
+    uintl randomEngine::getSeed(void)
+    {
+        uintl seed;
+        AF_THROW(af_random_engine_get_seed(&seed, engine));
+        return seed;
+    }
+
 }
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
index f8febe5314..907a129974 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random_engine.cpp
@@ -30,3 +30,13 @@ af_err af_release_random_engine(af_random_engine engineHandle)
 {
     return CALL(engineHandle);
 }
+
+af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
+{
+    return CALL(seed, engine);
+}
+
+af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
+{
+    return CALL(seed, engine);
+}
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index a0c2d77d89..81fee77b55 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -26,6 +26,11 @@ namespace cpu
         return state;
     }
 
+    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
+    {
+        kernel::initMersenneState(state.get(), tbl.get(), seed);
+    }
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
     {
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index 9bf541e297..3bdcbc1b5d 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -15,6 +15,8 @@ namespace cpu
 {
     Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
 
+    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl);
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
 
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 9bdf921920..3994063ba2 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -26,6 +26,11 @@ namespace cuda
         return state;
     }
 
+    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
+    {
+        kernel::initMersenneState(state.get(), tbl.get(), seed);
+    }
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index c72695e643..c34bcff6b4 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -16,6 +16,8 @@ namespace cuda
 {
     Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
 
+    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl);
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
 
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 5574b47b84..2c9b9e8a07 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -26,6 +26,11 @@ namespace opencl
         return state;
     }
 
+    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
+    {
+        kernel::initMersenneState(*state.get(), *tbl.get(), seed);
+    }
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
     {
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index 6e8be57997..a1564c3517 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -16,6 +16,8 @@ namespace opencl
 {
     Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
 
+    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl);
+
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
 

From b56fb98fab255dad77db99be4ad6cd359d54659d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 22 Aug 2016 14:20:30 -0400
Subject: [PATCH 0761/2677] removes extra templates

---
 src/api/c/filters.cpp                 |  4 ++
 src/api/cpp/filters.cpp               |  4 +-
 src/backend/cpu/kernel/medfilt.hpp    | 21 ++---------
 src/backend/cuda/kernel/medfilt.hpp   | 54 +++++++++++++++------------
 src/backend/cuda/medfilt.cu           | 14 ++++---
 src/backend/opencl/kernel/medfilt.hpp |  9 +++--
 src/backend/opencl/medfilt.cpp        | 22 ++++-------
 test/medfilt.cpp                      |  9 ++++-
 8 files changed, 70 insertions(+), 67 deletions(-)

diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index a173f17132..0f4f6891ad 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -92,6 +92,10 @@ af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, co
         ArrayInfo info = getInfo(in);
         af::dim4 dims  = info.dims();
 
+        if(info.isColumn()) {
+            return af_medfilt1(out, in, wind_width, edge_pad);
+        }
+
         dim_t input_ndims = dims.ndims();
         DIM_ASSERT(1, (input_ndims >= 2));
 
diff --git a/src/api/cpp/filters.cpp b/src/api/cpp/filters.cpp
index f950d60010..222aa99283 100644
--- a/src/api/cpp/filters.cpp
+++ b/src/api/cpp/filters.cpp
@@ -17,7 +17,9 @@ namespace af
 
 array medfilt(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
 {
-    return medfilt2(in, wind_length, wind_width, edge_pad);
+     af_array out = 0;
+     AF_THROW(af_medfilt(&out, in.get(), wind_length, wind_width, edge_pad));
+     return array(out);
 }
 
 array medfilt1(const array& in, const dim_t wind_width, const borderType edge_pad)
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index 9587641465..a11e1f0b6c 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -42,7 +42,6 @@ void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
 
                     wind_vals.clear();
                     for(int wi=0; wi<(int)w_wid; ++wi) {
-                        bool isRowOff = false;
 
                         int im_row = row + wi-w_wid/2;
                         int im_roff;
@@ -50,37 +49,25 @@ void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
                             case AF_PAD_ZERO:
                                 im_roff = im_row * istrides[0];
                                 if (im_row < 0 || im_row>=(int)dims[0])
-                                    isRowOff = true;
+                                    wind_vals.push_back(0);
+                                else
+                                    wind_vals.push_back(in_ptr[im_roff]);
                                 break;
                             case AF_PAD_SYM:
                                 {
                                     if (im_row < 0) {
                                         im_row *= -1;
-                                        isRowOff = true;
                                     }
 
                                     if (im_row>=(int)dims[0]) {
                                         im_row = 2*((int)dims[0]-1) - im_row;
-                                        isRowOff = true;
                                     }
 
                                     im_roff = im_row * istrides[0];
+                                    wind_vals.push_back(in_ptr[im_roff]);
                                 }
                                 break;
                         }
-
-                        if(isRowOff) {
-                            switch(Pad) {
-                                case AF_PAD_ZERO:
-                                    wind_vals.push_back(0);
-                                    break;
-                                case AF_PAD_SYM:
-                                    wind_vals.push_back(in_ptr[im_roff]);
-                                    break;
-                            }
-                        } else {
-                            wind_vals.push_back(in_ptr[im_roff]);
-                        }
                     }
 
                     int off = wind_vals.size()/2;
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index fcbc70623e..ffa7fcadb7 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -12,6 +12,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
+#include "shared.hpp"
 
 namespace cuda
 {
@@ -19,7 +20,8 @@ namespace cuda
 namespace kernel
 {
 
-static const int MAX_MEDFILTER_LEN = 15;
+static const int MAX_MEDFILTER1_LEN = 121;
+static const int MAX_MEDFILTER2_LEN = 15;
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
@@ -206,11 +208,12 @@ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
     }
 }
 
-template<typename T, af_border_type pad, unsigned w_wid>
+template<typename T, af_border_type pad, unsigned ARR_SIZE>
 __global__
-void medfilt1(Param<T> out, CParam<T> in, int nBBS0)
+void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0)
 {
-    __shared__ T shrdMem[(THREADS_X+w_wid-1)];
+    SharedMemory<T> shared;
+    T * shrdMem = shared.getPointer();
 
     // calculate necessary offset and window parameters
     const int padding = w_wid-1;
@@ -240,7 +243,7 @@ void medfilt1(Param<T> out, CParam<T> in, int nBBS0)
 
     // Only continue if we're at a valid location
     if (gx < in.dims[0]) {
-        const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+        const int ARR_BOUNDARY = (w_wid-w_wid/2) + 1;
         // pull top half from shared memory into local memory
         T v[ARR_SIZE];
 
@@ -252,21 +255,21 @@ void medfilt1(Param<T> out, CParam<T> in, int nBBS0)
         // initial sort
         // ensure min in first half, max in second half
 #pragma unroll
-        for(int i = 0; i < ARR_SIZE/2; i++) {
-            swap(v[i], v[ARR_SIZE-1-i]);
+        for(int i = 0; i < ARR_BOUNDARY/2; i++) {
+            swap(v[i], v[ARR_BOUNDARY-1-i]);
         }
         // move min in first half to first pos
 #pragma unroll
-        for(int i = 1; i < (ARR_SIZE+1)/2; i++) {
+        for(int i = 1; i < (ARR_BOUNDARY+1)/2; i++) {
             swap(v[0], v[i]);
         }
         // move max in second half to last pos
 #pragma unroll
-        for(int i = ARR_SIZE-2; i >= ARR_SIZE/2; i--) {
-            swap(v[i], v[ARR_SIZE-1]);
+        for(int i = ARR_BOUNDARY-2; i >= ARR_BOUNDARY/2; i--) {
+            swap(v[i], v[ARR_BOUNDARY-1]);
         }
 
-        int last = ARR_SIZE-1;
+        int last = ARR_BOUNDARY-1;
 
         for(int k = w_wid/2 + 2; k < w_wid; k++) {
             // add new contestant to first position in array
@@ -293,16 +296,16 @@ void medfilt1(Param<T> out, CParam<T> in, int nBBS0)
         // each outer loop drops the min and max
         for(int k = 0; k < last; k++) {
             // move max/min into respective halves
-            for(int i = k; i < ARR_SIZE/2; i++) {
-                swap(v[i], v[ARR_SIZE-1-i]);
+            for(int i = k; i < ARR_BOUNDARY/2; i++) {
+                swap(v[i], v[ARR_BOUNDARY-1-i]);
             }
             // move min into first pos
-            for(int i = k+1; i <= ARR_SIZE/2; i++) {
+            for(int i = k+1; i <= ARR_BOUNDARY/2; i++) {
                 swap(v[k], v[i]);
             }
             // move max into last pos
-            for(int i = ARR_SIZE-k-2; i >= ARR_SIZE/2; i--) {
-                swap(v[i], v[ARR_SIZE-1-k]);
+            for(int i = ARR_BOUNDARY-k-2; i >= ARR_BOUNDARY/2; i--) {
+                swap(v[i], v[ARR_BOUNDARY-1-k]);
             }
         }
 
@@ -343,14 +346,19 @@ void medfilt1(Param<T> out, CParam<T> in, int w_wid)
 
     dim3 blocks(blk_x*in.dims[1], in.dims[2], in.dims[3] );
 
+    const size_t shrdMemBytes = sizeof(T) * (THREADS_X + w_wid - 1);
+
     switch(w_wid) {
-        case  3: CUDA_LAUNCH((medfilt1<T,pad, 3>), blocks, threads, out, in, blk_x); break;
-        case  5: CUDA_LAUNCH((medfilt1<T,pad, 5>), blocks, threads, out, in, blk_x); break;
-        case  7: CUDA_LAUNCH((medfilt1<T,pad, 7>), blocks, threads, out, in, blk_x); break;
-        case  9: CUDA_LAUNCH((medfilt1<T,pad, 9>), blocks, threads, out, in, blk_x); break;
-        case 11: CUDA_LAUNCH((medfilt1<T,pad,11>), blocks, threads, out, in, blk_x); break;
-        case 13: CUDA_LAUNCH((medfilt1<T,pad,13>), blocks, threads, out, in, blk_x); break;
-        case 15: CUDA_LAUNCH((medfilt1<T,pad,15>), blocks, threads, out, in, blk_x); break;
+        case  3: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 3>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case  5: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 4>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case  7: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 5>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case  9: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 6>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case 11: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 7>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case 13: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 8>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case 15: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 9>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case 17: CUDA_LAUNCH_SMEM((medfilt1<T,pad,10>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case 19: CUDA_LAUNCH_SMEM((medfilt1<T,pad,11>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        default: CUDA_LAUNCH_SMEM((medfilt1<T,pad,62>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
     }
 
     POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cu
index 0dcea3d16f..c36edca4b0 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cu
@@ -21,7 +21,8 @@ namespace cuda
 template<typename T, af_border_type pad>
 Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER1_LEN));
+    ARG_ASSERT(2, (w_wid % 2 != 0));
 
     const dim4 dims = in.dims();
 
@@ -35,9 +36,10 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 template<typename T, af_border_type pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER2_LEN));
+    ARG_ASSERT(2, (w_len % 2 != 0));
 
-    const dim4 dims     = in.dims();
+    const dim4 dims   = in.dims();
 
     Array<T> out      = createEmptyArray<T>(dims);
 
@@ -47,9 +49,9 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 }
 
 #define INSTANTIATE(T)                                                                          \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);                \
+    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);                \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);   \
     template Array<T> medfilt2<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 3b00a8e5ab..c3e992a48c 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -33,13 +33,14 @@ namespace opencl
 namespace kernel
 {
 
-static const int MAX_MEDFILTER_LEN = 15;
+static const int MAX_MEDFILTER2_LEN = 15;
+static const int MAX_MEDFILTER1_LEN = 121;
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-template<typename T, af_border_type pad, unsigned w_wid>
-void medfilt1(Param out, const Param in)
+template<typename T, af_border_type pad>
+void medfilt1(Param out, const Param in, unsigned w_wid)
 {
     try {
         static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -48,7 +49,7 @@ void medfilt1(Param out, const Param in)
 
         int device = getActiveDeviceId();
 
-        std::call_once( compileFlags[device], [device] () {
+        std::call_once( compileFlags[device], [device, w_wid] () {
 
                 const int ARR_SIZE = (w_wid-w_wid/2) + 1;
 
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 16a5d8f316..43de95f066 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -21,21 +21,14 @@ namespace opencl
 template<typename T, af_border_type pad>
 Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER1_LEN));
+    ARG_ASSERT(2, (w_wid % 2 != 0));
 
     const dim4 dims = in.dims();
 
     Array<T> out    = createEmptyArray<T>(dims);
 
-    switch(w_wid) {
-        case  3: kernel::medfilt1<T, pad,  3>(out, in); break;
-        case  5: kernel::medfilt1<T, pad,  5>(out, in); break;
-        case  7: kernel::medfilt1<T, pad,  7>(out, in); break;
-        case  9: kernel::medfilt1<T, pad,  9>(out, in); break;
-        case 11: kernel::medfilt1<T, pad, 11>(out, in); break;
-        case 13: kernel::medfilt1<T, pad, 13>(out, in); break;
-        case 15: kernel::medfilt1<T, pad, 15>(out, in); break;
-    }
+    kernel::medfilt1<T, pad>(out, in, w_wid);
 
     return out;
 }
@@ -43,7 +36,8 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 template<typename T, af_border_type pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
-    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER_LEN));
+    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER2_LEN));
+    ARG_ASSERT(2, (w_len % 2 != 0));
 
     const dim4 dims     = in.dims();
 
@@ -62,9 +56,9 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 }
 
 #define INSTANTIATE(T)                                                                          \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);                \
+    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);                \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);   \
     template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
 INSTANTIATE(float )
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 1dd4b0f9ce..1296e22b0c 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -210,13 +210,18 @@ void medfiltInputTest(void)
 
     vector<T>   in(100, 1);
 
-    // Check for 1D inputs
+    // Check for 1D inputs -> medfilt1
     af::dim4 dims = af::dim4(100, 1, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_SIZE, af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
+    ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
+
+    bool medfilt1;
+    ASSERT_EQ(AF_SUCCESS, af_is_vector(&medfilt1, outArray));
+
+    ASSERT_EQ(true, medfilt1);
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }

From 2efd06fc056e23e87d1299a82636d7994dd79180 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 22 Aug 2016 16:15:27 -0400
Subject: [PATCH 0762/2677] Corrected state resetting after seed reset

Removed unneeded header files
---
 src/api/c/random_engine.cpp                   |  5 ++---
 src/backend/cpu/kernel/random_engine.hpp      | 12 ++++++----
 src/backend/cpu/random_engine.cpp             |  2 +-
 .../cuda/kernel/random_engine_mersenne.hpp    |  5 +++++
 .../kernel/random_engine_mersenne_init.cl     | 11 +++++++---
 test/random.cpp                               | 22 ++++++++++++++-----
 6 files changed, 40 insertions(+), 17 deletions(-)

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 605b904a78..4bf71d277f 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -19,9 +19,6 @@
 #include <random_engine.hpp>
 #include <MersenneTwister.hpp>
 
-#include <af/util.h>
-#include <iostream>
-
 using detail::cfloat;
 using detail::cdouble;
 using detail::uchar;
@@ -130,6 +127,8 @@ af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
         e->seed = seed;
         if (e->type == AF_RANDOM_MERSENNE) {
             initMersenneState(getArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
+        } else {
+            e->counter = 0;
         }
     }
     CATCHALL;
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 6534abad98..17cfbe2fcd 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -277,11 +277,15 @@ namespace kernel
         uint tmp = hidden_seed;
         tmp += tmp >> 16;
         tmp += tmp >> 8;
+        tmp &= 0xff;
+        tmp |= tmp << 8;
+        tmp |= tmp << 16;
         state[0] = seed;
-        state[1] = hidden_seed;
-            for (int i = 1; i < N; ++i) {
-                state[i] ^= (uint)(1812433253) * (state[i-1] ^ (state[i-1] >> 30)) + i;
-            }
+        state[1] = hidden_seed ^ ((uint)(1812433253) * (state[0] ^ (state[0] >> 30)) + 1);
+        for (int i = 2; i < N; ++i) {
+            state[i] = tmp;
+            state[i] ^= (uint)(1812433253) * (state[i-1] ^ (state[i-1] >> 30)) + i;
+        }
     }
 }
 }
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 81fee77b55..1b0ee2f086 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -28,7 +28,7 @@ namespace cpu
 
     void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
     {
-        kernel::initMersenneState(state.get(), tbl.get(), seed);
+        getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
     }
 
     template<typename T>
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index 7d1111fe19..d680b09a91 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -72,6 +72,11 @@ namespace kernel
         uint tmp = hidden_seed;
         tmp += tmp >> 16;
         tmp += tmp >> 8;
+        tmp &= 0xff;
+        tmp |= tmp << 8;
+        tmp |= tmp << 16;
+        lstate[threadIdx.x] = tmp;
+        __syncthreads();
         if (threadIdx.x == 0) {
             lstate[0] = seed;
             lstate[1] = hidden_seed;
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index e5e8f372f7..a64dd5cc85 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -16,12 +16,17 @@ __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
     uint tmp = hidden_seed;
     tmp += tmp >> 16;
     tmp += tmp >> 8;
+    tmp &= 0xff;
+    tmp |= tmp << 8;
+    tmp |= tmp << 16;
+    lstate[get_local_id(0)] = tmp;
+    barrier(CLK_LOCAL_MEM_FENCE);
     if (get_local_id(0) == 0) {
         lstate[0] = seed;
         lstate[1] = hidden_seed;
-            for (int i = 1; i < N; ++i) {
-                lstate[i] ^= (uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i;
-            }
+        for (int i = 1; i < N; ++i) {
+            lstate[i] ^= (uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i;
+        }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
     state[N*get_group_id(0) + get_local_id(0)] = lstate[get_local_id(0)];
diff --git a/test/random.cpp b/test/random.cpp
index f0c9ed39e2..db17aeab50 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -287,20 +287,30 @@ void testRandomEngineNormal(af_random_type type)
 
 TYPED_TEST(RandomEngine, philoxRandomEngineUniform)
 {
-    testRandomEngineUniform<TypeParam>(AF_RANDOM_PHILOX);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, threefryRandomEngineUniform)
+TYPED_TEST(RandomEngine, philoxRandomEngineNormal)
 {
-    testRandomEngineUniform<TypeParam>(AF_RANDOM_THREEFRY);
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, philoxRandomEngineNormal)
+TYPED_TEST(RandomEngine, threefryRandomEngineUniform)
 {
-    testRandomEngineNormal<TypeParam>(AF_RANDOM_PHILOX);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_THREEFRY_2X32_16);
 }
 
 TYPED_TEST(RandomEngine, threefryRandomEngineNormal)
 {
-    testRandomEngineNormal<TypeParam>(AF_RANDOM_THREEFRY);
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_THREEFRY_2X32_16);
+}
+
+TYPED_TEST(RandomEngine, mersenneRandomEngineUniform)
+{
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
+}
+
+TYPED_TEST(RandomEngine, mersenneRandomEngineNormal)
+{
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
 }

From f7562754e22296c5fa34c0fb5aa74c79645a9aa6 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 22 Aug 2016 16:16:50 -0400
Subject: [PATCH 0763/2677] Added test to check state reset

---
 test/random.cpp | 69 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 67 insertions(+), 2 deletions(-)

diff --git a/test/random.cpp b/test/random.cpp
index db17aeab50..e7dd1ffee4 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -256,7 +256,7 @@ TYPED_TEST(Random, getSeed)
 }
 
 template <typename T>
-void testRandomEngineUniform(af_random_type type)
+void testRandomEngineUniform(randomType type)
 {
     if (noDoubleTests<T>()) return;
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
@@ -271,7 +271,7 @@ void testRandomEngineUniform(af_random_type type)
 }
 
 template <typename T>
-void testRandomEngineNormal(af_random_type type)
+void testRandomEngineNormal(randomType type)
 {
     if (noDoubleTests<T>()) return;
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
@@ -314,3 +314,68 @@ TYPED_TEST(RandomEngine, mersenneRandomEngineNormal)
 {
     testRandomEngineNormal<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
 }
+
+template <typename T>
+void testRandomEngineSeed(randomType type, bool is_norm = false)
+{
+    int elem = 4*32*1024;
+    uintl orig_seed = 0;
+    uintl new_seed = 1;
+    af::randomEngine e(type, orig_seed);
+
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    array d1 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+    e.setSeed(new_seed);
+    array d2 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+    e.setSeed(orig_seed);
+    array d3 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+    array d4 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+
+    std::vector<T> h1(elem);
+    std::vector<T> h2(elem);
+    std::vector<T> h3(elem);
+    std::vector<T> h4(elem);
+
+    d1.host((void*)h1.data());
+    d2.host((void*)h2.data());
+    d3.host((void*)h3.data());
+    d4.host((void*)h4.data());
+
+    for (int i = 0; i < elem; i++) {
+        ASSERT_EQ(h1[i], h3[i]) << "at : " << i;
+        if (ty != b8 && ty != u8) {
+            ASSERT_NE(h1[i], h2[i]);
+            ASSERT_NE(h3[i], h4[i]);
+        }
+    }
+}
+
+TYPED_TEST(RandomEngine, philoxSeedUniform)
+{
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX, false);
+}
+
+TYPED_TEST(RandomEngine, threefrySeedUniform)
+{
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY, false);
+}
+
+TYPED_TEST(RandomEngine, mersenneSeedUniform)
+{
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE, false);
+}
+
+TYPED_TEST(RandomEngine, philoxSeedNormal)
+{
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX, true);
+}
+
+TYPED_TEST(RandomEngine, threefrySeedNormal)
+{
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY, true);
+}
+
+TYPED_TEST(RandomEngine, mersenneSeedNormal)
+{
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE, true);
+}

From 216130ca9096bd0a939f797d15b0ea5eab57b3d2 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 22 Aug 2016 16:41:01 -0400
Subject: [PATCH 0764/2677] Minor edit to a function signature of randomEngine

---
 include/af/random_engine.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 8e28f34769..98a94889e0 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -38,8 +38,8 @@ namespace af
             array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
             array normal(const dim4& dims, const dtype ty = f32);
 
-            void setSeed(uintl seed);
-            uintl getSeed();
+            void setSeed(const uintl seed);
+            uintl getSeed(void);
     };
 }
 #endif

From a7fe838411e0eb6a8a7e33a723f86469d758b8ec Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 23 Aug 2016 13:51:10 -0400
Subject: [PATCH 0765/2677] update opencl medfilt kernel

---
 src/backend/opencl/kernel/medfilt.hpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 0e02c25fdf..0e631f8f0a 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -78,10 +78,10 @@ void medfilt1(Param out, const Param in, unsigned w_wid)
                                            in.info.dims[2],
                                            in.info.dims[3]);
 
-        auto medfiltOp = make_kernel<Buffer, KParam,
-                                     Buffer, KParam,
-                                     cl::LocalSpaceArg,
-                                     int> (*mfKernels[device]);
+        auto medfiltOp = KernelFunctor<Buffer, KParam,
+                                       Buffer, KParam,
+                                       cl::LocalSpaceArg,
+                                       int> (*mfKernels[device]);
 
         size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
 

From a83687be49b762020e737b4df8a93479d60617bb Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 23 Aug 2016 17:35:46 -0400
Subject: [PATCH 0766/2677] update test reference

---
 test/data | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/data b/test/data
index 855b458f51..51e7ad9123 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 855b458f511853b28987f632d4be0f174fa2feea
+Subproject commit 51e7ad91239d6f366ee282b7cba5047b73063948

From 5fee5e98fb24f48a6eb9abdbe18f74725d767857 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 23 Aug 2016 17:24:12 -0400
Subject: [PATCH 0767/2677] fix boundary conditions for approx2 and add tests

---
 src/backend/cpu/kernel/approx2.hpp   | 32 +++++++++---------
 src/backend/cuda/kernel/approx.hpp   | 32 +++++++++---------
 src/backend/opencl/kernel/approx1.cl |  4 +--
 src/backend/opencl/kernel/approx2.cl | 32 +++++++++---------
 test/approx1.cpp                     | 17 +++++++---
 test/approx2.cpp                     | 50 +++++++++++++++++++++++++++-
 test/data                            |  2 +-
 7 files changed, 112 insertions(+), 57 deletions(-)

diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
index 706f140312..d7782c7e58 100644
--- a/src/backend/cpu/kernel/approx2.hpp
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -186,10 +186,10 @@ struct approx2_op<InT, LocT, AF_INTERP_CUBIC>
         }
 
         // used for setting values at boundaries
-        bool condXl = (x < 1);
-        bool condYl = (y < 1);
-        bool condXg = (x > idims[0] - 3);
-        bool condYg = (y > idims[1] - 3);
+        bool condXl = (grid_x < 1);
+        bool condYl = (grid_y < 1);
+        bool condXg = (grid_x > idims[0] - 3);
+        bool condYg = (grid_y > idims[1] - 3);
 
         //for bicubic interpolation, work with 4x4 patch at a time
         InT patch[4][4];
@@ -201,19 +201,19 @@ struct approx2_op<InT, LocT, AF_INTERP_CUBIC>
         patch[2][1] = in[ioff + istrides[1]];
         patch[2][2] = in[ioff + istrides[1] + 1];
         //outer sides
-        patch[0][1] = (condYl)? scalar<InT>(offGrid) : in[ioff - istrides[1]];
-        patch[0][2] = (condYl)? scalar<InT>(offGrid) : in[ioff - istrides[1] + 1];
-        patch[3][1] = (condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1]];
-        patch[3][2] = (condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1] + 1];
-        patch[1][0] = (condXl)? scalar<InT>(offGrid) : in[ioff - 1];
-        patch[2][0] = (condXl)? scalar<InT>(offGrid) : in[ioff + istrides[1] -1];
-        patch[1][3] = (condXg)? scalar<InT>(offGrid) : in[ioff + 2];
-        patch[2][3] = (condXg)? scalar<InT>(offGrid) : in[ioff + istrides[1] + 2];
+        patch[0][1] = (condYl)? in[ioff]     : in[ioff - istrides[1]];
+        patch[0][2] = (condYl)? in[ioff + 1] : in[ioff - istrides[1] + 1];
+        patch[3][1] = (condYg)? in[ioff + istrides[1]]     : in[ioff + 2 * istrides[1]];
+        patch[3][2] = (condYg)? in[ioff + istrides[1] + 1] : in[ioff + 2 * istrides[1] + 1];
+        patch[1][0] = (condXl)? in[ioff] : in[ioff - 1];
+        patch[2][0] = (condXl)? in[ioff + istrides[1]] : in[ioff + istrides[1] - 1];
+        patch[1][3] = (condXg)? in[ioff + 1] : in[ioff + 2];
+        patch[2][3] = (condXg)? in[ioff + istrides[1] + 1] : in[ioff + istrides[1] + 2];
         //corners
-        patch[0][0] =(condXl || condYl)? scalar<InT>(offGrid) : in[ioff - istrides[1] - 1]    ;
-        patch[0][3] =(condYl || condXg)? scalar<InT>(offGrid) : in[ioff - istrides[1] + 1]    ;
-        patch[3][0] =(condXl || condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1] - 1];
-        patch[3][3] =(condXg || condYg)? scalar<InT>(offGrid) : in[ioff + 2 * istrides[1] + 2];
+        patch[0][0] = (condXl || condYl)? in[ioff] : in[ioff - istrides[1] - 1]    ;
+        patch[0][3] = (condYl || condXg)? in[ioff + 1] : in[ioff - istrides[1] + 1]    ;
+        patch[3][0] = (condXl || condYg)? in[ioff + istrides[1]] : in[ioff + 2 * istrides[1] - 1];
+        patch[3][3] = (condXg || condYg)? in[ioff + istrides[1] + 1] : in[ioff + 2 * istrides[1] + 2];
 
         // Write Final Value
         out[omId] = bicubicInterpolate(patch, off_x, off_y);
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index f8a7169585..cc2a875194 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -254,10 +254,10 @@ namespace cuda
             const Tp off_x  = x - grid_x, off_y  = y - grid_y;    // fractional offset
 
             // used for setting values at boundaries
-            bool condXl = (x < 1);
-            bool condYl = (y < 1);
-            bool condXg = (x > in.dims[0] - 3);
-            bool condYg = (y > in.dims[1] - 3);
+            bool condXl = (grid_x < 1);
+            bool condYl = (grid_y < 1);
+            bool condXg = (grid_x > in.dims[0] - 3);
+            bool condYg = (grid_y > in.dims[1] - 3);
 
             dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
 
@@ -271,19 +271,19 @@ namespace cuda
             patch[2][1] = in.ptr[ioff + in.strides[1]];
             patch[2][2] = in.ptr[ioff + in.strides[1] + 1];
             //outer sides
-            patch[0][1] = (condYl)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1]];
-            patch[0][2] = (condYl)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1] + 1];
-            patch[3][1] = (condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1]];
-            patch[3][2] = (condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1] + 1];
-            patch[1][0] = (condXl)? scalar<Ty>(offGrid) : in.ptr[ioff - 1];
-            patch[2][0] = (condXl)? scalar<Ty>(offGrid) : in.ptr[ioff + in.strides[1] -1];
-            patch[1][3] = (condXg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2];
-            patch[2][3] = (condXg)? scalar<Ty>(offGrid) : in.ptr[ioff + in.strides[1] + 2];
+            patch[0][1] = (condYl)? in.ptr[ioff]     : in.ptr[ioff - in.strides[1]];
+            patch[0][2] = (condYl)? in.ptr[ioff + 1] : in.ptr[ioff - in.strides[1] + 1];
+            patch[3][1] = (condYg)? in.ptr[ioff + in.strides[1]]     : in.ptr[ioff + 2 * in.strides[1]];
+            patch[3][2] = (condYg)? in.ptr[ioff + in.strides[1] + 1] : in.ptr[ioff + 2 * in.strides[1] + 1];
+            patch[1][0] = (condXl)? in.ptr[ioff] : in.ptr[ioff - 1];
+            patch[2][0] = (condXl)? in.ptr[ioff + in.strides[1]] : in.ptr[ioff + in.strides[1] - 1];
+            patch[1][3] = (condXg)? in.ptr[ioff + 1] : in.ptr[ioff + 2];
+            patch[2][3] = (condXg)? in.ptr[ioff + in.strides[1] + 1] : in.ptr[ioff + in.strides[1] + 2];
             //corners
-            patch[0][0] = (condXl || condYl)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1] - 1]    ;
-            patch[0][3] = (condYl || condXg)? scalar<Ty>(offGrid) : in.ptr[ioff - in.strides[1] + 1]    ;
-            patch[3][0] = (condXl || condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1] - 1];
-            patch[3][3] = (condXg || condYg)? scalar<Ty>(offGrid) : in.ptr[ioff + 2 * in.strides[1] + 2];
+            patch[0][0] = (condXl || condYl)? in.ptr[ioff] : in.ptr[ioff - in.strides[1] - 1]    ;
+            patch[0][3] = (condYl || condXg)? in.ptr[ioff + 1] : in.ptr[ioff - in.strides[1] + 1]    ;
+            patch[3][0] = (condXl || condYg)? in.ptr[ioff + in.strides[1]] : in.ptr[ioff + 2 * in.strides[1] - 1];
+            patch[3][3] = (condXg || condYg)? in.ptr[ioff + in.strides[1] + 1] : in.ptr[ioff + 2 * in.strides[1] + 2];
 
             out.ptr[omId] = bicubicInterpolate<Ty, Tp>(patch, off_x, off_y);
         }
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index a3d5719487..ce9e2bed7a 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -139,8 +139,8 @@ void core_cubic(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t i
     // Compute Left and Right Weighted Values
     Ty pl = d_in[ioff];
     Ty pr = condl1  ? d_in[ioff + 1] : d_in[ioff];
-    Ty tl = condr   ? 0.5 * (d_in[ioff + 1] - d_in[ioff - 1]) : (d_in[ioff + 1] - d_in[ioff]);
-    Ty tr = condl2  ? 0.5 * (d_in[ioff + 2] - d_in[ioff]) : (condl1) ? d_in[ioff + 1] - d_in[ioff] : (d_in[ioff] - d_in[ioff - 1]);
+    Ty tl = condr   ? (Ty)0.5 * (d_in[ioff + 1] - d_in[ioff - 1]) : (d_in[ioff + 1] - d_in[ioff]);
+    Ty tr = condl2  ? (Ty)0.5 * (d_in[ioff + 2] - d_in[ioff]) : (condl1) ? d_in[ioff + 1] - d_in[ioff] : (d_in[ioff] - d_in[ioff - 1]);
 
     // Write final value
     set(d_out[omId], h00 * pl + h10 * tl + h01 * pr + h11 * tr);
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 3537fca849..4378734786 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -164,10 +164,10 @@ void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t
 
     dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
     // used for setting values at boundaries
-    bool condXl = (x < 1);
-    bool condYl = (y < 1);
-    bool condXg = (x > in.dims[0] - 3);
-    bool condYg = (y > in.dims[1] - 3);
+    bool condXl = (grid_x < 1);
+    bool condYl = (grid_y < 1);
+    bool condXg = (grid_x > in.dims[0] - 3);
+    bool condYg = (grid_y > in.dims[1] - 3);
 
     //for bicubic interpolation, work with 4x4 patch at a time
     Ty patch[4][4];
@@ -179,19 +179,19 @@ void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t
     patch[2][1] = d_in[ioff + in.strides[1]];
     patch[2][2] = d_in[ioff + in.strides[1] + 1];
     //outer sides
-    patch[0][1] = (condYl)? (Ty)offGrid : d_in[ioff - in.strides[1]];
-    patch[0][2] = (condYl)? (Ty)offGrid : d_in[ioff - in.strides[1] + 1];
-    patch[3][1] = (condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1]];
-    patch[3][2] = (condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1] + 1];
-    patch[1][0] = (condXl)? (Ty)offGrid : d_in[ioff - 1];
-    patch[2][0] = (condXl)? (Ty)offGrid : d_in[ioff + in.strides[1] -1];
-    patch[1][3] = (condXg)? (Ty)offGrid : d_in[ioff + 2];
-    patch[2][3] = (condXg)? (Ty)offGrid : d_in[ioff + in.strides[1] + 2];
+    patch[0][1] = (condYl)? d_in[ioff]     : d_in[ioff - in.strides[1]];
+    patch[0][2] = (condYl)? d_in[ioff + 1] : d_in[ioff - in.strides[1] + 1];
+    patch[3][1] = (condYg)? d_in[ioff + in.strides[1]]     : d_in[ioff + 2 * in.strides[1]];
+    patch[3][2] = (condYg)? d_in[ioff + in.strides[1] + 1] : d_in[ioff + 2 * in.strides[1] + 1];
+    patch[1][0] = (condXl)? d_in[ioff] : d_in[ioff - 1];
+    patch[2][0] = (condXl)? d_in[ioff + in.strides[1]] : d_in[ioff + in.strides[1] - 1];
+    patch[1][3] = (condXg)? d_in[ioff + 1] : d_in[ioff + 2];
+    patch[2][3] = (condXg)? d_in[ioff + in.strides[1] + 1] : d_in[ioff + in.strides[1] + 2];
     //corners
-    patch[0][0] = (condXl || condYl)? (Ty)offGrid : d_in[ioff - in.strides[1] - 1]     ;
-    patch[0][3] = (condYl || condXg)? (Ty)offGrid : d_in[ioff - in.strides[1] + 1]     ;
-    patch[3][0] = (condXl || condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1] - 1] ;
-    patch[3][3] = (condXg || condYg)? (Ty)offGrid : d_in[ioff + 2 * in.strides[1] + 2] ;
+    patch[0][0] = (condXl || condYl)? d_in[ioff] : d_in[ioff - in.strides[1] - 1]    ;
+    patch[0][3] = (condYl || condXg)? d_in[ioff + 1] : d_in[ioff - in.strides[1] + 1]    ;
+    patch[3][0] = (condXl || condYg)? d_in[ioff + in.strides[1]] : d_in[ioff + 2 * in.strides[1] - 1];
+    patch[3][3] = (condXg || condYg)? d_in[ioff + in.strides[1] + 1] : d_in[ioff + 2 * in.strides[1] + 2];
 
     set(d_out[omId], bicubicInterpolate(patch, off_x, off_y));
 }
diff --git a/test/approx1.cpp b/test/approx1.cpp
index e5508e53bb..69cface136 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -87,8 +87,9 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     size_t nElems = tests[resultIdx].size();
     bool ret = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005;
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        ret = (abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005);
+        if(!ret)printf("error: %f", abs(tests[resultIdx][elIter] - outData[elIter]));
+        //ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
     }
 
     // Delete
@@ -148,6 +149,14 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     size_t nElems = tests[resultIdx].size();
     bool ret = true;
 
+    float max = real(outData[0]), min = real(outData[0]);
+    for(int i=1; i < nElems; ++i) {
+        min = (real(outData[i]) < min) ? real(outData[i]) : min;
+        max = (real(outData[i]) > max) ? real(outData[i]) : max;
+    }
+    float range = max - min;
+    ASSERT_GT(range, 0.f);
+
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         double integral;
         //test that control points are exact
@@ -156,8 +165,7 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
             ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
         } else {
             //match intermediate values withing a threshold
-            ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 8;
-            //ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.05 * range; //TODO: percentage
+            ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.035 * range;
             ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
         }
     }
@@ -266,7 +274,6 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
     APPROX1_ARGSP(Approx1LinearArgsPrecision, approx1, 1, AF_INTERP_LINEAR);
     APPROX1_ARGSP(Approx1CubicArgsPrecision, approx1_cubic, 2, AF_INTERP_CUBIC);
 
-
 //////////////////////////////////////// CPP //////////////////////////////////
 //
 TEST(Approx1, CPP)
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 75a650631b..d15fcb3a06 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -159,7 +159,6 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
     APPROX2_ARGS(Approx2LinearArgsPos3D,       approx2_pos3d,   1, AF_INTERP_LINEAR,   AF_ERR_SIZE);
     APPROX2_ARGS(Approx2NearestArgsPosUnequal, approx2_unequal, 0, AF_INTERP_NEAREST,  AF_ERR_SIZE);
     APPROX2_ARGS(Approx2ArgsInterpBilinear,    approx2,         0, AF_INTERP_BILINEAR, AF_ERR_ARG);
-    APPROX2_ARGS(Approx2ArgsInterpCubic,       approx2,         0, AF_INTERP_CUBIC,    AF_ERR_ARG);
 
 template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
@@ -250,6 +249,55 @@ TEST(Approx2, CPP)
 #undef BT
 }
 
+TEST(Approx2Cubic, CPP)
+{
+    if (noDoubleTests<float>()) return;
+    const unsigned resultIdx = 0;
+#define BT af::dtype_traits<float>::base_type
+    vector<af::dim4> numDims;
+    vector<vector<BT> > in;
+    vector<vector<float> > tests;
+    readTests<BT, float, float>(string(TEST_DIR"/approx/approx2_cubic.test"),numDims,in,tests);
+
+    af::dim4 idims = numDims[0];
+    af::dim4 pdims = numDims[1];
+    af::dim4 qdims = numDims[2];
+
+    af::array input(idims,&(in[0].front()));
+    input = input.T();
+    af::array pos0(pdims,&(in[1].front()));
+    af::array pos1(qdims,&(in[2].front()));
+    pos0 = tile(pos0, 1, pos0.dims(0));
+    pos1 = tile(pos1.T(), pos1.dims(0));
+    af::array output = af::approx2(input, pos0, pos1, AF_INTERP_CUBIC, 0).T();
+
+    // Get result
+    float* outData = new float[tests[resultIdx].size()];
+    output.host((void*)outData);
+
+    // Compare result
+    size_t nElems = tests[resultIdx].size();
+    bool ret = true;
+
+    float max = real(outData[0]), min = real(outData[0]);
+    for(int i=1; i < nElems; ++i) {
+        min = (real(outData[i]) < min) ? real(outData[i]) : min;
+        max = (real(outData[i]) > max) ? real(outData[i]) : max;
+    }
+    float range = max - min;
+    ASSERT_GT(range, 0.f);
+
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.01 * range);
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+    }
+
+    // Delete
+    delete[] outData;
+
+#undef BT
+}
+
 TEST(Approx2, CPPNearestBatch)
 {
     if (noDoubleTests<float>()) return;
diff --git a/test/data b/test/data
index 855b458f51..b69986ebbd 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 855b458f511853b28987f632d4be0f174fa2feea
+Subproject commit b69986ebbd09a4308dec37287968ffa881c2beee

From 2f81fc9eda78e696a2ef42fa591f998a4fec6f5b Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 23 Aug 2016 18:29:40 -0400
Subject: [PATCH 0768/2677] Changed defines to const variables in kernel files

Minor edit to function signature in api/c/random_engine.cpp
---
 src/api/c/random_engine.cpp                   |   2 +-
 src/backend/cpu/kernel/random_engine.hpp      |   7 +-
 .../cpu/kernel/random_engine_mersenne.hpp     |   4 +-
 .../cpu/kernel/random_engine_philox.hpp       |   9 +-
 .../cpu/kernel/random_engine_threefry.hpp     | 171 +++++++-----------
 src/backend/cpu/random_engine.cpp             |   2 +-
 src/backend/cpu/random_engine.hpp             |   4 +-
 src/backend/cuda/kernel/random_engine.hpp     |   5 +-
 .../cuda/kernel/random_engine_mersenne.hpp    |  11 +-
 .../cuda/kernel/random_engine_philox.hpp      |  35 ++--
 .../cuda/kernel/random_engine_threefry.hpp    |   5 +-
 src/backend/cuda/random_engine.cu             |   2 +-
 src/backend/cuda/random_engine.hpp            |   2 +-
 src/backend/opencl/kernel/random_engine.hpp   |   8 +-
 .../opencl/kernel/random_engine_write.cl      |   2 -
 src/backend/opencl/random_engine.cpp          |   2 +-
 src/backend/opencl/random_engine.hpp          |   3 +-
 17 files changed, 118 insertions(+), 156 deletions(-)

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 4bf71d277f..4e560a3930 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -126,7 +126,7 @@ af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
         RandomEngine *e = getRandomEngine(engine);
         e->seed = seed;
         if (e->type == AF_RANDOM_MERSENNE) {
-            initMersenneState(getArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
+            initMersenneState(getWritableArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
         } else {
             e->counter = 0;
         }
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 17cfbe2fcd..9dfa5dec79 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -19,10 +19,9 @@ namespace cpu
 namespace kernel
 {
     //Utils
-    #define UINTMAXFLOAT 4294967296.0f
-    #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
-    #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
-    #define N 351
+    static const float UINTMAXFLOAT = 4294967296.0f;
+    static const float UINTLMAXDOUBLE = (4294967296.0*4294967296.0);
+    static const double PI_VAL = 3.1415926535897932384626433832795028841971693993751058209749445923078164;
 
     template <typename T>
     T transform(uint *val, int index)
diff --git a/src/backend/cpu/kernel/random_engine_mersenne.hpp b/src/backend/cpu/kernel/random_engine_mersenne.hpp
index 74b96389e4..0de6c91483 100644
--- a/src/backend/cpu/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cpu/kernel/random_engine_mersenne.hpp
@@ -14,8 +14,8 @@ namespace cpu
 namespace kernel
 {
 
-    #define N 351
-    #define STATE_SIZE 786
+    static const int N = 351;
+    static const int STATE_SIZE = 256*3;
 
     uint recursion(const uint * const recursion_table, const uint mask,
             const uint sh1, const uint sh2, const uint x1, const uint x2, uint y)
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index e9e59b7ba4..1afe3e2ece 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -51,10 +51,10 @@ namespace cpu
 namespace kernel
 {
 
-#define m4x32_0 0xD2511F53
-#define m4x32_1 0xCD9E8D57
-#define w32_0 0x9E3779B9
-#define w32_1 0xBB67AE85
+    static const uint m4x32_0 = 0xD2511F53;
+    static const uint m4x32_1 = 0xCD9E8D57;
+    static const uint w32_0   = 0x9E3779B9;
+    static const uint w32_1   = 0xBB67AE85;
 
     void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
     {
@@ -81,7 +81,6 @@ namespace kernel
 
     void philox(uint * const key, uint * const ctr)
     {
-        ctr[0] = -1;
         //10 Rounds
                            philoxRound(key, ctr);
         philoxBump(key);   philoxRound(key, ctr);
diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
index f948869112..3104829a82 100644
--- a/src/backend/cpu/kernel/random_engine_threefry.hpp
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -51,113 +51,72 @@ namespace cpu
 namespace kernel
 {
 
-#define SKEIN_KS_PARITY 0x1BD11BDA
-
-#define R0 13
-#define R1 15
-#define R2 26
-#define R3  6
-#define R4 17
-#define R5 29
-#define R6 16
-#define R7 24
-
-static inline uint rotL(uint x, uint N)
-{
-    return (x << (N & 31)) | (x >> ((32-N) & 31));
-}
-
-static inline void threefry(uint k[2], uint c[2], uint X[2])
-{
-    uint ks[3];
-
-    ks[2] = SKEIN_KS_PARITY;
-    ks[0] = k[0];
-    X[0] = c[0];
-    ks[2] ^= k[0];
-    ks[1] = k[1];
-    X[1] = c[1];
-    ks[2] ^= k[1];
-
-    X[0] += ks[0]; X[1] += ks[1];
-
-    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
-
-    /* InjectKey(r=1) */
-    X[0] += ks[1]; X[1] += ks[2];
-    X[1] += 1;     /* X[2-1] += r  */
-
-    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
-
-    /* InjectKey(r=2) */
-    X[0] += ks[2]; X[1] += ks[0];
-    X[1] += 2;
-
-    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
-
-    /* InjectKey(r=3) */
-    X[0] += ks[0]; X[1] += ks[1];
-    X[1] += 3;
-
-    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
-
-    /* InjectKey(r=4) */
-    X[0] += ks[1]; X[1] += ks[2];
-    X[1] += 4;
-}
-
-/*
-template <> struct Random<AF_RANDOM_THREEFRY>
-{
-    uint hi;
-    uint lo;
-    uintl counter;
-    uint key[2];
-    uint ctr[2];
-    uint val[2];
-    int reset;
-
-    template <typename T>
-    T uniform(void);
-
-    Random(uintl seed, uintl counter);
-};
-
-Random<AF_RANDOM_THREEFRY>::Random(uintl seed, uintl counterInput) : hi(seed>>32), lo(seed), counter(counterInput), reset(0)
-{
-    key[0] = counter;
-    key[1] = hi;
-    ctr[0] = counter;
-    ctr[2] = lo;
-}
+    static const uint SKEIN_KS_PARITY = 0x1BD11BDA;
+
+    static const uint R0 = 13;
+    static const uint R1 = 15;
+    static const uint R2 = 26;
+    static const uint R3 =  6;
+    static const uint R4 = 17;
+    static const uint R5 = 29;
+    static const uint R6 = 16;
+    static const uint R7 = 24;
+
+    static inline uint rotL(uint x, uint N)
+    {
+        return (x << (N & 31)) | (x >> ((32-N) & 31));
+    }
 
-template <typename T>
-void Random<AF_RANDOM_THREEFRY>::uniform(T* out, size_t elements)
-{
-    int reset = (2*sizeof(uint))/sizeof(T);
-    threefry(key, ctr, val);
-    for (int i = 0; i < (int)out.elements(); ++i) {
-        if (fresh == reset) {
-            threefry(key, ctr, val);
-            ctr[0] += 2;
-            fresh = 0;
-        }
-        out[i] = transform<T>(ctr, fresh);
-        fresh++;
+    static inline void threefry(uint k[2], uint c[2], uint X[2])
+    {
+        uint ks[3];
+
+        ks[2] = SKEIN_KS_PARITY;
+        ks[0] = k[0];
+        X[0] = c[0];
+        ks[2] ^= k[0];
+        ks[1] = k[1];
+        X[1] = c[1];
+        ks[2] ^= k[1];
+
+        X[0] += ks[0]; X[1] += ks[1];
+
+        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+        /* InjectKey(r=1) */
+        X[0] += ks[1]; X[1] += ks[2];
+        X[1] += 1;     /* X[2-1] += r  */
+
+        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+        /* InjectKey(r=2) */
+        X[0] += ks[2]; X[1] += ks[0];
+        X[1] += 2;
+
+        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+
+        /* InjectKey(r=3) */
+        X[0] += ks[0]; X[1] += ks[1];
+        X[1] += 3;
+
+        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
+        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+
+        /* InjectKey(r=4) */
+        X[0] += ks[1]; X[1] += ks[2];
+        X[1] += 4;
     }
-}
-*/
+
 }
 }
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 1b0ee2f086..659a331039 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -26,7 +26,7 @@ namespace cpu
         return state;
     }
 
-    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
+    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
     {
         getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
     }
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index 3bdcbc1b5d..10e9e240e6 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <af/defines.h>
 #include <backend.hpp>
@@ -15,7 +17,7 @@ namespace cpu
 {
     Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
 
-    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl);
+    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
 
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 30040076af..f44d5639a6 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -21,6 +21,7 @@ namespace cuda
 namespace kernel
 {
     //Utils
+
     static const int THREADS = 256;
     #define UINTMAXFLOAT 4294967296.0f
     #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
@@ -436,7 +437,8 @@ namespace kernel
         uint start = blockIdx.x*elementsPerBlock;
         uint end = start + elementsPerBlock;
         end = (end > elements)? elements : end;
-        int iter = divup((end - start)*sizeof(T), blockDim.x*4*sizeof(uint));
+        int elementsPerBlockIteration = (blockDim.x*4*sizeof(uint))/sizeof(T);
+        int iter = divup((end - start), elementsPerBlockIteration);
 
         uint pos = pos_tbl[blockIdx.x];
         uint sh1 = sh1_tbl[blockIdx.x];
@@ -447,7 +449,6 @@ namespace kernel
         __syncthreads();
 
         uint index = start;
-        int elementsPerBlockIteration = blockDim.x*4*sizeof(uint)/sizeof(T);
         uint o[4];
         int offsetX1 = (STATE_SIZE - N + threadIdx.x          ) % STATE_SIZE;
         int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1      ) % STATE_SIZE;
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index d680b09a91..6e283b746c 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -11,10 +11,11 @@ namespace cuda
 {
 namespace kernel
 {
-#define N 351
-#define BLOCKS 32
-#define STATE_SIZE 786
-#define TABLE_SIZE 16
+
+    static const uint N = 351;
+    static const uint BLOCKS = 32;
+    static const uint STATE_SIZE = (256*3);
+    static const uint TABLE_SIZE = 16;
 
     //Utils
     static inline __device__ void read_table(uint * const sharedTable, const uint * const table)
@@ -81,7 +82,7 @@ namespace kernel
             lstate[0] = seed;
             lstate[1] = hidden_seed;
             for (int i = 1; i < N; ++i) {
-                lstate[i] ^= (uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i;
+                lstate[i] ^= ((uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i);
             }
         }
         __syncthreads();
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index 049713ce1a..fa9985e589 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -12,10 +12,11 @@ namespace cuda
 namespace kernel
 {
     //Utils
-#define m4x32_0 uint(0xD2511F53)
-#define m4x32_1 uint(0xCD9E8D57)
-#define w32_0 uint(0x9E3779B9)
-#define w32_1 uint(0xBB67AE85)
+
+    static const uint m4x32_0 = 0xD2511F53;
+    static const uint m4x32_1 = 0xCD9E8D57;
+    static const uint w32_0   = 0x9E3779B9;
+    static const uint w32_1   = 0xBB67AE85;
 
     static inline __device__ void mulhilo(const uint &a, const uint &b, uint &hi, uint &lo)
     {
@@ -29,11 +30,11 @@ namespace kernel
         k[1] += w32_1;
     }
 
-    static inline __device__ void philoxRound(const uint k[2], uint c[4])
+    static inline __device__ void philoxRound(const uint m0, const uint m1, const uint k[2], uint c[4])
     {
         uint hi0, lo0, hi1, lo1;
-        mulhilo(m4x32_0, c[0], hi0, lo0);
-        mulhilo(m4x32_1, c[2], hi1, lo1);
+        mulhilo(m0, c[0], hi0, lo0);
+        mulhilo(m1, c[2], hi1, lo1);
         c[0] = hi1^c[1]^k[0];
         c[1] = lo1;
         c[2] = hi0^c[3]^k[1];
@@ -43,16 +44,16 @@ namespace kernel
     static inline __device__ void philox(uint key[2], uint ctr[4])
     {
         //10 Rounds
-                           philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
+                           philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
+        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
     }
 }
 }
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
index 2f066909d6..3cc70121bb 100644
--- a/src/backend/cuda/kernel/random_engine_threefry.hpp
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -12,8 +12,9 @@ namespace cuda
 namespace kernel
 {
     //Utils
-#define SKEIN_KS_PARITY32 0x1BD11BDA
-#define SKEIN_KS_PARITY64 0x1BD11BDAA9FC1A22
+
+    static const uint  SKEIN_KS_PARITY32 = 0x1BD11BDA;
+    static const uintl SKEIN_KS_PARITY64 = 0x1BD11BDAA9FC1A22;
 
     static const uint R0_32=13;
     static const uint R1_32=15;
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 3994063ba2..3826ad0146 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -26,7 +26,7 @@ namespace cuda
         return state;
     }
 
-    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
+    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
     {
         kernel::initMersenneState(state.get(), tbl.get(), seed);
     }
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index c34bcff6b4..c8362838e6 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -16,7 +16,7 @@ namespace cuda
 {
     Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
 
-    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl);
+    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
 
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index a3a49c307c..02f5733295 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -36,10 +36,10 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 
-#define N 351
-#define TABLE_SIZE 16
-#define MAX_BLOCKS 32
-#define STATE_SIZE 786
+static const int N = 351;
+static const int TABLE_SIZE = 16;
+static const int MAX_BLOCKS = 32;
+static const int STATE_SIZE = (256*3);
 
 namespace opencl
 {
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 0702d71437..b2c8781f21 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -45,8 +45,6 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
-//typedef ulong uintl;
-//typedef long intl;
 #define UINTMAXFLOAT 4294967296.0f
 #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
 #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 2c9b9e8a07..3e5d54f6a7 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -26,7 +26,7 @@ namespace opencl
         return state;
     }
 
-    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl)
+    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
     {
         kernel::initMersenneState(*state.get(), *tbl.get(), seed);
     }
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index a1564c3517..c11e695360 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #pragma once
 
 #include <Array.hpp>
@@ -16,7 +17,7 @@ namespace opencl
 {
     Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
 
-    void initMersenneState(Array<uint> state, const uintl seed, const Array<uint> tbl);
+    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
 
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);

From 6293728c8cb194c6e7db441a310f1521a3c0c3b6 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 23 Aug 2016 18:30:44 -0400
Subject: [PATCH 0769/2677] Separate test for seed resetting

---
 test/random.cpp | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/test/random.cpp b/test/random.cpp
index e7dd1ffee4..1bce160894 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -62,6 +62,14 @@ class RandomEngine : public ::testing::Test
         }
 };
 
+template<typename T>
+class RandomEngineSeed : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {
+        }
+};
+
 // register the type list
 TYPED_TEST_CASE(Random_norm, TestTypesNorm);
 
@@ -71,6 +79,10 @@ typedef ::testing::Types<float, double> TestTypesEngine;
 // register the type list
 TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
 
+typedef ::testing::Types<float, double> TestTypesEngineSeed;
+// register the type list
+TYPED_TEST_CASE(RandomEngineSeed, TestTypesEngineSeed);
+
 template<typename T>
 void randuTest(af::dim4 & dims)
 {
@@ -344,38 +356,38 @@ void testRandomEngineSeed(randomType type, bool is_norm = false)
     for (int i = 0; i < elem; i++) {
         ASSERT_EQ(h1[i], h3[i]) << "at : " << i;
         if (ty != b8 && ty != u8) {
-            ASSERT_NE(h1[i], h2[i]);
-            ASSERT_NE(h3[i], h4[i]);
+            ASSERT_NE(h1[i], h2[i]) << "at : " << i;
+            ASSERT_NE(h3[i], h4[i]) << "at : " << i;
         }
     }
 }
 
-TYPED_TEST(RandomEngine, philoxSeedUniform)
+TYPED_TEST(RandomEngineSeed, philoxSeedUniform)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX, false);
 }
 
-TYPED_TEST(RandomEngine, threefrySeedUniform)
+TYPED_TEST(RandomEngineSeed, threefrySeedUniform)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY, false);
 }
 
-TYPED_TEST(RandomEngine, mersenneSeedUniform)
+TYPED_TEST(RandomEngineSeed, mersenneSeedUniform)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE, false);
 }
 
-TYPED_TEST(RandomEngine, philoxSeedNormal)
+TYPED_TEST(RandomEngineSeed, philoxSeedNormal)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX, true);
 }
 
-TYPED_TEST(RandomEngine, threefrySeedNormal)
+TYPED_TEST(RandomEngineSeed, threefrySeedNormal)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY, true);
 }
 
-TYPED_TEST(RandomEngine, mersenneSeedNormal)
+TYPED_TEST(RandomEngineSeed, mersenneSeedNormal)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE, true);
 }

From 4f951c351fca32be57ceed71f4d98d4483d1781c Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 23 Aug 2016 18:44:34 -0400
Subject: [PATCH 0770/2677] Use full enum names in backend

---
 src/api/c/random_engine.cpp                 | 10 +++----
 src/backend/cpu/kernel/random_engine.hpp    |  8 +++---
 src/backend/cuda/kernel/random_engine.hpp   |  8 +++---
 src/backend/opencl/kernel/random_engine.hpp | 32 ++++++++++-----------
 test/random.cpp                             | 12 ++++----
 5 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 4e560a3930..668886ae06 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -66,7 +66,7 @@ RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 template<typename T>
 static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
-    if (e->type == AF_RANDOM_MERSENNE) {
+    if (e->type == AF_RANDOM_MERSENNE_GP11213) {
         return getHandle(uniformDistribution<T>(dims,
                     getArray<uint>(e->pos),
                     getArray<uint>(e->sh1),
@@ -83,7 +83,7 @@ static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *
 template<typename T>
 static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
-    if (e->type == AF_RANDOM_MERSENNE) {
+    if (e->type == AF_RANDOM_MERSENNE_GP11213) {
         return getHandle(normalDistribution<T>(dims,
                     getArray<uint>(e->pos),
                     getArray<uint>(e->sh1),
@@ -104,7 +104,7 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
         e.type = rtype;
         e.seed = seed;
         e.counter = 0;
-        if (rtype == AF_RANDOM_MERSENNE) {
+        if (rtype == AF_RANDOM_MERSENNE_GP11213) {
             AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
             AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
             AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
@@ -125,7 +125,7 @@ af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(engine);
         e->seed = seed;
-        if (e->type == AF_RANDOM_MERSENNE) {
+        if (e->type == AF_RANDOM_MERSENNE_GP11213) {
             initMersenneState(getWritableArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
         } else {
             e->counter = 0;
@@ -202,7 +202,7 @@ af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
         RandomEngine *e = getRandomEngine(engineHandle);
-        if (e->type == AF_RANDOM_MERSENNE) {
+        if (e->type == AF_RANDOM_MERSENNE_GP11213) {
             AF_CHECK(af_release_array(e->pos));
             AF_CHECK(af_release_array(e->sh1));
             AF_CHECK(af_release_array(e->sh2));
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 9dfa5dec79..05a87b0d99 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -254,8 +254,8 @@ namespace kernel
     void uniformDistributionCBRNG(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
     {
         switch(type) {
-            case AF_RANDOM_PHILOX   :   philoxUniform(out, elements, seed, counter); break;
-            case AF_RANDOM_THREEFRY : threefryUniform(out, elements, seed, counter); break;
+            case AF_RANDOM_PHILOX_4X32_10   :   philoxUniform(out, elements, seed, counter); break;
+            case AF_RANDOM_THREEFRY_2X32_16 : threefryUniform(out, elements, seed, counter); break;
             default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }
@@ -264,8 +264,8 @@ namespace kernel
     void normalDistributionCBRNG(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
     {
         switch(type) {
-            case AF_RANDOM_PHILOX   :   philoxNormal(out, elements, seed, counter); break;
-            case AF_RANDOM_THREEFRY : threefryNormal(out, elements, seed, counter); break;
+            case AF_RANDOM_PHILOX_4X32_10   :   philoxNormal(out, elements, seed, counter); break;
+            case AF_RANDOM_THREEFRY_2X32_16 : threefryNormal(out, elements, seed, counter); break;
             default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index f44d5639a6..846ea71971 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -623,9 +623,9 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         switch (type) {
-        case AF_RANDOM_PHILOX : CUDA_LAUNCH(uniformPhilox, blocks, threads,
+        case AF_RANDOM_PHILOX_4X32_10   : CUDA_LAUNCH(uniformPhilox, blocks, threads,
                 out, hi, lo, counter, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY : CUDA_LAUNCH(uniformThreefry, blocks, threads,
+        case AF_RANDOM_THREEFRY_2X32_16 : CUDA_LAUNCH(uniformThreefry, blocks, threads,
                 out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
@@ -641,9 +641,9 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         switch (type) {
-        case AF_RANDOM_PHILOX : CUDA_LAUNCH(normalPhilox, blocks, threads,
+        case AF_RANDOM_PHILOX_4X32_10   : CUDA_LAUNCH(normalPhilox, blocks, threads,
                 out, hi, lo, counter, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY : CUDA_LAUNCH(normalThreefry, blocks, threads,
+        case AF_RANDOM_THREEFRY_2X32_16 : CUDA_LAUNCH(normalThreefry, blocks, threads,
                 out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 02f5733295..70035bc430 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -58,19 +58,19 @@ namespace opencl
             ker_strs[0] = random_engine_write_cl;
             ker_lens[0] = random_engine_write_cl_len;
             switch (type) {
-                case AF_RANDOM_PHILOX   : engineName = "Philox";
-                                        ker_strs[1] = random_engine_philox_cl;
-                                        ker_lens[1] = random_engine_philox_cl_len;
-                                        break;
-                case AF_RANDOM_THREEFRY : engineName = "Threefry";
-                                        ker_strs[1] = random_engine_threefry_cl;
-                                        ker_lens[1] = random_engine_threefry_cl_len;
-                                        break;
-                case AF_RANDOM_MERSENNE : engineName = "Mersenne";
-                                        ker_strs[1] = random_engine_mersenne_cl;
-                                        ker_lens[1] = random_engine_mersenne_cl_len;
-                                        break;
-                default                 : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+                case AF_RANDOM_PHILOX_4X32_10   : engineName = "Philox";
+                                                ker_strs[1] = random_engine_philox_cl;
+                                                ker_lens[1] = random_engine_philox_cl_len;
+                                                break;
+                case AF_RANDOM_THREEFRY_2X32_16 : engineName = "Threefry";
+                                                ker_strs[1] = random_engine_threefry_cl;
+                                                ker_lens[1] = random_engine_threefry_cl_len;
+                                                break;
+                case AF_RANDOM_MERSENNE_GP11213 : engineName = "Mersenne";
+                                                ker_strs[1] = random_engine_mersenne_cl;
+                                                ker_lens[1] = random_engine_mersenne_cl_len;
+                                                break;
+                default                         : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
             string ref_name =
@@ -85,7 +85,7 @@ namespace opencl
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D THREADS=" << THREADS
                         << " -D RAND_DIST=" << kerIdx;
-                if (type == AF_RANDOM_MERSENNE) {
+                if (type == AF_RANDOM_MERSENNE_GP11213) {
                     options << " -D STATE_SIZE=" << STATE_SIZE
                             << " -D TABLE_SIZE=" << TABLE_SIZE
                             << " -D N=" << N;
@@ -151,7 +151,7 @@ namespace opencl
                 NDRange local(THREADS, 1);
                 NDRange global(THREADS * groups, 1);
 
-                if ((type == AF_RANDOM_PHILOX) || (type == AF_RANDOM_THREEFRY)) {
+                if ((type == AF_RANDOM_PHILOX_4X32_10) || (type == AF_RANDOM_THREEFRY_2X32_16)) {
                     Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
                     auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
                     randomEngineOp(EnqueueArgs(getQueue(), global, local),
@@ -181,7 +181,7 @@ namespace opencl
 
                 NDRange local(threads, 1);
                 NDRange global(threads * blocks, 1);
-                Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_MERSENNE, kerIdx, elementsPerBlock);
+                Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_MERSENNE_GP11213, kerIdx, elementsPerBlock);
                 auto randomEngineOp = KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
                      uint, cl::Buffer, cl::Buffer, uint, uint>(ker);
                 randomEngineOp(EnqueueArgs(getQueue(), global, local),
diff --git a/test/random.cpp b/test/random.cpp
index 1bce160894..f2919ec8ab 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -364,30 +364,30 @@ void testRandomEngineSeed(randomType type, bool is_norm = false)
 
 TYPED_TEST(RandomEngineSeed, philoxSeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX, false);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX_4X32_10, false);
 }
 
 TYPED_TEST(RandomEngineSeed, threefrySeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY, false);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY_2X32_16, false);
 }
 
 TYPED_TEST(RandomEngineSeed, mersenneSeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE, false);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE_GP11213, false);
 }
 
 TYPED_TEST(RandomEngineSeed, philoxSeedNormal)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX, true);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX_4X32_10, true);
 }
 
 TYPED_TEST(RandomEngineSeed, threefrySeedNormal)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY, true);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY_2X32_16, true);
 }
 
 TYPED_TEST(RandomEngineSeed, mersenneSeedNormal)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE, true);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE_GP11213, true);
 }

From 3a3852a03fc903e358d35bb64fa039784908d92e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 23 Aug 2016 18:10:10 -0400
Subject: [PATCH 0771/2677] OpenCL: Adding support for dense to csr conversion

---
 .../opencl/kernel/{sparse.cl => coo2dense.cl} |   0
 src/backend/opencl/kernel/dense2csr.cl        |  45 ++++++
 src/backend/opencl/kernel/sparse.hpp          | 153 +++++++++++++++---
 src/backend/opencl/sparse.cpp                 |  10 ++
 4 files changed, 189 insertions(+), 19 deletions(-)
 rename src/backend/opencl/kernel/{sparse.cl => coo2dense.cl} (100%)
 create mode 100644 src/backend/opencl/kernel/dense2csr.cl

diff --git a/src/backend/opencl/kernel/sparse.cl b/src/backend/opencl/kernel/coo2dense.cl
similarity index 100%
rename from src/backend/opencl/kernel/sparse.cl
rename to src/backend/opencl/kernel/coo2dense.cl
diff --git a/src/backend/opencl/kernel/dense2csr.cl b/src/backend/opencl/kernel/dense2csr.cl
new file mode 100644
index 0000000000..3843707392
--- /dev/null
+++ b/src/backend/opencl/kernel/dense2csr.cl
@@ -0,0 +1,45 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if IS_CPLX
+#define IS_ZERO(val) ((val.x == 0) && (val.y == 0))
+#else
+#define IS_ZERO(val) (val == 0)
+#endif
+
+__kernel
+void dense2csr_split_kernel(__global T *svalptr,
+                            __global int *scolptr,
+                            __global const T *dvalptr,
+                            const KParam valinfo,
+                            __global const int *dcolptr,
+                            const KParam colinfo,
+                            __global const int *rowptr)
+{
+    int gidx = get_global_id(0);
+    int gidy = get_global_id(1);
+
+    if (gidx >= valinfo.dims[0]) return;
+    if (gidy >= valinfo.dims[1]) return;
+
+    int rowoff = rowptr[gidx];
+    svalptr += rowoff;
+    scolptr += rowoff;
+
+    dvalptr += valinfo.offset;
+    dcolptr += colinfo.offset;
+
+    int idx = gidx + gidy * valinfo.strides[1];
+    T val = dvalptr[gidx + gidy * valinfo.strides[1]];
+    if (IS_ZERO(val)) return;
+
+    int oloc = dcolptr[gidx + gidy * colinfo.strides[1]];
+    svalptr[oloc - 1] = val;
+    scolptr[oloc - 1]  = gidy;
+}
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index a5b34d9089..46634025f8 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -8,7 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/sparse.hpp>
+#include <kernel_headers/coo2dense.hpp>
+#include <kernel_headers/dense2csr.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -17,6 +18,12 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <cache.hpp>
+#include <type_util.hpp>
+#include "scan_dim.hpp"
+#include "reduce.hpp"
+#include "scan_first.hpp"
+#include "config.hpp"
 
 using cl::Buffer;
 using cl::Program;
@@ -30,25 +37,25 @@ namespace opencl
 {
     namespace kernel
     {
-        static const int TX = 16;
-        static const int TY = 16;
-        static const int THREADS = 256;
-        static const int reps = 4;
-
         template<typename T>
         void coo2dense(Param out, const Param values, const Param rowIdx, const Param colIdx)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   coo2denseProgs;
-                static std::map<int, Kernel*>  coo2denseKernels;
+
+                std::string ref_name =
+                    std::string("coo2dense_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(REPEAT);
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
 
-                std::call_once( compileFlags[device], [device] () {
+                if (idx == kernelCaches[device].end()) {
                     std::ostringstream options;
                     options << " -D T="        << dtype_traits<T>::getName()
-                            << " -D reps="     << reps
+                            << " -D reps="     << REPEAT
                             ;
 
                     if (std::is_same<T, double>::value ||
@@ -57,20 +64,22 @@ namespace opencl
                     }
 
                     Program prog;
-                    buildProgram(prog, sparse_cl, sparse_cl_len, options.str());
-                    coo2denseProgs[device]   = new Program(prog);
-                    coo2denseKernels[device] = new Kernel(*coo2denseProgs[device], "coo2dense_kernel");
-                });
+                    buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
+                    entry.prog   = new Program(prog);
+                    entry.ker = new Kernel(*entry.prog, "coo2dense_kernel");
+                } else {
+                    entry = idx->second;
+                };
 
                 auto coo2denseOp = KernelFunctor<Buffer, const KParam,
                                            const Buffer, const KParam,
                                            const Buffer, const KParam,
                                            const Buffer, const KParam>
-                                          (*coo2denseKernels[device]);
+                                          (*entry.ker);
 
-                NDRange local(THREADS, 1, 1);
+                NDRange local(THREADS_PER_GROUP, 1, 1);
 
-                NDRange global(divup(out.info.dims[0], local[0] * reps) * THREADS, 1, 1);
+                NDRange global(divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
 
                 coo2denseOp(EnqueueArgs(getQueue(), global, local),
                        *out.data, out.info,
@@ -81,7 +90,113 @@ namespace opencl
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
-                throw;
+            }
+        }
+
+        template<typename T>
+        void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense)
+        {
+            try {
+                int num_rows = dense.info.dims[0];
+                int num_cols = dense.info.dims[1];
+                int dense_elements = num_rows * num_cols;
+                Param sd1, rd1, sd0;
+                // sd1 contains output of scan along dim 1 of dense
+                sd1.data = bufferAlloc(dense_elements * sizeof(int));
+                // rd1 contains output of nonzero count along dim 1 along dense
+                rd1.data = bufferAlloc(num_rows * sizeof(int));
+                // sd0 contains output of exclusive scan rd1
+                sd0 = rowIdx;
+
+                sd1.info.offset = 0;
+                rd1.info.offset = 0;
+
+                sd1.info.dims[0] = num_rows;
+                rd1.info.dims[0] = num_rows;
+
+                sd1.info.dims[1] = num_cols;
+                rd1.info.dims[1] = 1;
+
+                sd1.info.dims[2] = 1;
+                rd1.info.dims[2] = 1;
+
+                sd1.info.dims[3] = 1;
+                rd1.info.dims[3] = 1;
+
+                sd1.info.strides[0] = 1;
+                rd1.info.strides[0] = 1;
+                for (int i = 1; i < 4; i++) {
+                    sd1.info.strides[i] = sd1.info.dims[i - 1] * sd1.info.strides[i - 1];
+                    rd1.info.strides[i] = rd1.info.dims[i - 1] * rd1.info.strides[i - 1];
+                }
+
+                scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
+                reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
+                scan_first<int, int, af_add_t, false>(sd0, rd1);
+
+                int nnz = values.info.dims[0];
+                getQueue().enqueueWriteBuffer(*sd0.data, CL_TRUE,
+                                              sd0.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
+                                              sizeof(int),
+                                              (void *)&nnz);
+
+                std::string ref_name =
+                    std::string("dense2csr_") +
+                    std::string(dtype_traits<T>::getName());
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    if (std::is_same<T, cfloat>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+
+                    const char *ker_strs[] = {dense2csr_cl};
+                    const int   ker_lens[] = {dense2csr_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
+
+                    kernelCaches[device][ref_name] = entry;
+                } else {
+                    entry = idx->second;
+                }
+
+                NDRange local(THREADS_X, THREADS_Y);
+                int groups_x = divup(dense.info.dims[0], local[0]);
+                int groups_y = divup(dense.info.dims[1], local[1]);
+                NDRange global(groups_x * local[0], groups_y * local[1]);
+                auto dense2csr_split = KernelFunctor<Buffer, Buffer,
+                                                     Buffer, KParam,
+                                                     Buffer, KParam,
+                                                     Buffer>(*entry.ker);
+
+                dense2csr_split(EnqueueArgs(getQueue(), global, local),
+                                *values.data, *colIdx.data,
+                                *dense.data, dense.info,
+                                *sd1.data, sd1.info,
+                                *sd0.data);
+
+                CL_DEBUG_FINISH(getQueue());
+
+                bufferFree(rd1.data);
+                bufferFree(sd1.data);
+            } catch (cl::Error &err) {
+                CL_TO_AF_ERROR(err);
             }
         }
     }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index e219cc7aa4..bb9bb7360b 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -23,6 +23,8 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
+#define ENABLE_CLSPARSE 0
+
 namespace opencl
 {
 
@@ -153,6 +155,8 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
     sparse_.eval();
 
+#if ENABLE_CLSPARSE
+
     // Assign to clSparse Dense
     cldenseMatrix clDenseMat;
     CLSPARSE_CHECK(cldenseInitMatrix(&clDenseMat));
@@ -176,7 +180,13 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
         CLSPARSE_CHECK(dense2csr_func<T>()(&clDenseMat, &clSparseMat, getControl()));
     else
         AF_ERROR("OpenCL Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+#else
+    Array<T> &values = sparse_.getValues();
+    Array<int> &rowIdx = sparse_.getRowIdx();
+    Array<int> &colIdx = sparse_.getColIdx();
 
+    kernel::dense2csr<T>(values, rowIdx, colIdx, in_);
+#endif
     return sparse_;
 }
 

From 85ad01b02fcc8022241462f5e18e34df7b030d76 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 23 Aug 2016 23:10:08 -0400
Subject: [PATCH 0772/2677] OPENCL: Adding support for CSRMV

---
 src/backend/opencl/kernel/csrmm.hpp |  46 +++++++++++
 src/backend/opencl/kernel/csrmv.cl  | 105 +++++++++++++++++++++++++
 src/backend/opencl/kernel/csrmv.hpp | 118 ++++++++++++++++++++++++++++
 src/backend/opencl/sparse_blas.cpp  |  36 ++++++++-
 4 files changed, 303 insertions(+), 2 deletions(-)
 create mode 100644 src/backend/opencl/kernel/csrmm.hpp
 create mode 100644 src/backend/opencl/kernel/csrmv.cl
 create mode 100644 src/backend/opencl/kernel/csrmv.hpp

diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
new file mode 100644
index 0000000000..a0cb569b4c
--- /dev/null
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -0,0 +1,46 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#pragma once
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <cache.hpp>
+#include <type_util.hpp>
+#include "scan_dim.hpp"
+#include "reduce.hpp"
+#include "scan_first.hpp"
+#include "config.hpp"
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        template<typename T>
+        void csrmm_nt(Param out,
+                      const Param &values, const Param &rowIdx, const Param &colIdx,
+                      const Param &rhs, const T alpha, const T beta)
+        {
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
new file mode 100644
index 0000000000..46ce743f85
--- /dev/null
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -0,0 +1,105 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if IS_CPLX
+
+T __cmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x - lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y + lhs.y * rhs.x;
+    return out;
+}
+#define MUL(a, b) __cmul(a, b)
+#else
+#define MUL(a, b) (a) * (b)
+#endif
+
+
+__kernel void
+csrmv_thread(__global T *output,
+             __global const T *values,
+             __global const int *rowidx,
+             __global const int *colidx,
+             const int M,
+             __global const T *rhs,
+             const KParam rinfo,
+             const T alpha,
+             const T beta)
+{
+    int rid = get_global_id(0) + get_global_size(0) * get_global_id(1);
+    if (rid >= M) return;
+
+    int colStart = rowidx[rid];
+    int colEnd   = rowidx[rid + 1];
+    T val = 0;
+
+    for (int id = colStart; id < colEnd; id++) {
+        int cid = colidx[id];
+        val += MUL(values[id], rhs[cid]);
+    }
+
+#if USE_ALPHA
+    val *= alpa;
+#endif
+
+#if USE_BETA
+    output[rid] = val + beta * output[rid];
+#else
+    output[rid] = val;
+#endif
+}
+
+__kernel void
+csrmv_block(__global T *output,
+            __global const T *values,
+            __global const int *rowidx,
+            __global const int *colidx,
+            const int M,
+            __global const T *rhs,
+            const KParam rinfo,
+            const T alpha,
+            const T beta)
+{
+    int rid = get_group_id(0) + get_num_groups(0) * get_group_id(1);
+    int lid = get_local_id(0);
+    int off = get_local_size(0);
+    if (rid >= M) return;
+
+    __local T s_val[THREADS_PER_GROUP];
+
+    int colStart = rowidx[rid];
+    int colEnd   = rowidx[rid + 1];
+    T val = 0;
+    for (int id = colStart + lid; id < colEnd; id += off) {
+        int cid = colidx[id];
+        val += MUL(values[id], rhs[cid]);
+    }
+    s_val[lid] = val;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int n = off / 2; n > 0; n /= 2) {
+        if (lid < n) s_val[lid] += s_val[lid + n];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (lid == 0) {
+#if USE_ALPHA
+        val = alpha * s_val[0];
+#else
+        val = s_val[0];
+#endif
+
+#if USE_BETA
+        output[rid] = val + beta * output[rid];
+#else
+        output[rid] = val;
+#endif
+    }
+}
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
new file mode 100644
index 0000000000..ab5aa2becb
--- /dev/null
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -0,0 +1,118 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#pragma once
+#include <kernel_headers/csrmv.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <cache.hpp>
+#include <type_util.hpp>
+#include "scan_dim.hpp"
+#include "reduce.hpp"
+#include "scan_first.hpp"
+#include "config.hpp"
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        const int MAX_GROUPS_X = 4096 * 4;
+        template<typename T>
+        void csrmv(Param out,
+                   const Param &values, const Param &rowIdx, const Param &colIdx,
+                   const Param &rhs, const T alpha, const T beta)
+        {
+            try {
+                bool use_alpha = (alpha != scalar<T>(1.0));
+                bool use_beta = (beta != scalar<T>(0.0));
+                std::string ref_name =
+                    std::string("csrmv_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(use_alpha) +
+                    std::string("_") +
+                    std::to_string(use_beta);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    options << " -D USE_ALPHA=" << use_alpha;
+                    options << " -D USE_BETA=" << use_beta;
+                    options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    if (std::is_same<T, cfloat>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+
+                    const char *ker_strs[] = {csrmv_cl};
+                    const int   ker_lens[] = {csrmv_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel[2];
+                    entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
+                    entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
+                } else {
+                    entry = idx->second;
+                }
+
+                // TODO: Figure out the proper way to choose either csrmv_thread or csrmv_block
+                bool is_csrmv_block = true;
+                auto csrmv_kernel = is_csrmv_block ? entry.ker[1] : entry.ker[0];
+                auto csrmv_func = KernelFunctor<Buffer,
+                                                Buffer, Buffer, Buffer,
+                                                int,
+                                                Buffer, KParam, T, T>(csrmv_kernel);
+
+                NDRange local(THREADS_PER_GROUP, 1);
+                int M = rowIdx.info.dims[0] - 1;
+                int num_groups = is_csrmv_block ? M : divup(M, local[0]);
+                int groups_y = divup(num_groups, MAX_GROUPS_X);
+                int groups_x = divup(num_groups, groups_y);
+                NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+                csrmv_func(EnqueueArgs(getQueue(), global, local),
+                           *out.data, *values.data, *rowIdx.data, *colIdx.data,
+                           M, *rhs.data, rhs.info, alpha, beta);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error &ex) {
+                CL_TO_AF_ERROR(ex);
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 70523b5e87..41ed79257f 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -8,6 +8,8 @@
  ********************************************************/
 
 #include <sparse_blas.hpp>
+#include <kernel/csrmv.hpp>
+#include <kernel/csrmm.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -17,8 +19,10 @@
 #include <complex.hpp>
 #include <handle.hpp>
 #include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <transpose.hpp>
 
 namespace opencl
 {
@@ -26,10 +30,38 @@ namespace opencl
 using namespace common;
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
-    Array<T> out = createValueArray<T>(af::dim4(lhs.dims()[0], rhs.dims()[0], 1, 1), scalar<T>(0));
+
+    if (optLhs != AF_MAT_NONE) OPENCL_NOT_SUPPORTED();
+
+
+    int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+    //int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+    static const int rColDim = 1; //Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhsIn.dims();
+    int M = lDims[lRowDim];
+    int N = rDims[rColDim];
+    //int K = lDims[lColDim];
+
+    const Array<T> rhs = (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) : rhsIn;
+    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+
+    static const T alpha = scalar<T>(1.0);
+    static const T beta  = scalar<T>(0.0);
+
+    const Array<T> &values = lhs.getValues();
+    const Array<int> &rowIdx = lhs.getRowIdx();
+    const Array<int> &colIdx = lhs.getColIdx();
+
+    if (N == 1) {
+        kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
+    } else {
+        kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
+    }
     return out;
 }
 

From 98141dc9e67af007017f4b346ec73bdc249ebc88 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 24 Aug 2016 00:08:49 -0400
Subject: [PATCH 0773/2677] OPENCL: Better load balancing per group in csrmv

---
 src/backend/opencl/kernel/csrmv.cl  | 82 +++++++++++++++++------------
 src/backend/opencl/kernel/csrmv.hpp | 22 +++++---
 2 files changed, 63 insertions(+), 41 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index 46ce743f85..94801420ce 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -31,29 +31,32 @@ csrmv_thread(__global T *output,
              __global const T *rhs,
              const KParam rinfo,
              const T alpha,
-             const T beta)
+             const T beta,
+             __global int *counter)
 {
-    int rid = get_global_id(0) + get_global_size(0) * get_global_id(1);
-    if (rid >= M) return;
+    while (true) {
+        int rid = atomic_inc(counter);
+        if (rid >= M) return;
 
-    int colStart = rowidx[rid];
-    int colEnd   = rowidx[rid + 1];
-    T val = 0;
+        int colStart = rowidx[rid];
+        int colEnd   = rowidx[rid + 1];
+        T outval = 0;
 
-    for (int id = colStart; id < colEnd; id++) {
-        int cid = colidx[id];
-        val += MUL(values[id], rhs[cid]);
-    }
+        for (int id = colStart; id < colEnd; id++) {
+            int cid = colidx[id];
+            outval += MUL(values[id], rhs[cid]);
+        }
 
 #if USE_ALPHA
-    val *= alpa;
+        outval *= alpa;
 #endif
 
 #if USE_BETA
-    output[rid] = val + beta * output[rid];
+        output[rid] = outval + beta * output[rid];
 #else
-    output[rid] = val;
+        output[rid] = outval;
 #endif
+    }
 }
 
 __kernel void
@@ -65,41 +68,52 @@ csrmv_block(__global T *output,
             __global const T *rhs,
             const KParam rinfo,
             const T alpha,
-            const T beta)
+            const T beta,
+            __global int *counter)
 {
-    int rid = get_group_id(0) + get_num_groups(0) * get_group_id(1);
     int lid = get_local_id(0);
     int off = get_local_size(0);
-    if (rid >= M) return;
 
-    __local T s_val[THREADS_PER_GROUP];
+    __local int s_rid;
 
-    int colStart = rowidx[rid];
-    int colEnd   = rowidx[rid + 1];
-    T val = 0;
-    for (int id = colStart + lid; id < colEnd; id += off) {
-        int cid = colidx[id];
-        val += MUL(values[id], rhs[cid]);
-    }
-    s_val[lid] = val;
-    barrier(CLK_LOCAL_MEM_FENCE);
+    while (true) {
 
-    for (int n = off / 2; n > 0; n /= 2) {
-        if (lid < n) s_val[lid] += s_val[lid + n];
+        if (lid == 0) {
+            s_rid = atomic_inc(counter);
+        }
         barrier(CLK_LOCAL_MEM_FENCE);
-    }
+        int rid = s_rid;
+        if (rid >= M) return;
+
+        __local T s_outval[THREADS_PER_GROUP];
+
+        int colStart = rowidx[rid];
+        int colEnd   = rowidx[rid + 1];
+        T outval = 0;
+        for (int id = colStart + lid; id < colEnd; id += off) {
+            int cid = colidx[id];
+            outval += MUL(values[id], rhs[cid]);
+        }
+        s_outval[lid] = outval;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int n = off / 2; n > 0; n /= 2) {
+            if (lid < n) s_outval[lid] += s_outval[lid + n];
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
 
-    if (lid == 0) {
+        if (lid == 0) {
 #if USE_ALPHA
-        val = alpha * s_val[0];
+            outval = alpha * s_outval[0];
 #else
-        val = s_val[0];
+            outval = s_outval[0];
 #endif
 
 #if USE_BETA
-        output[rid] = val + beta * output[rid];
+            output[rid] = outval + beta * output[rid];
 #else
-        output[rid] = val;
+            output[rid] = outval;
 #endif
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index ab5aa2becb..9b22e9dd53 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -37,7 +37,7 @@ namespace opencl
 {
     namespace kernel
     {
-        const int MAX_GROUPS_X = 4096 * 4;
+        const int MAX_GROUPS = 4096;
         template<typename T>
         void csrmv(Param out,
                    const Param &values, const Param &rowIdx, const Param &colIdx,
@@ -90,26 +90,34 @@ namespace opencl
                     entry = idx->second;
                 }
 
+                int count = 0;
+                cl::Buffer *counter = bufferAlloc(sizeof(int));
+                getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
+                                              0,
+                                              sizeof(int),
+                                              (void *)&count);
+
                 // TODO: Figure out the proper way to choose either csrmv_thread or csrmv_block
                 bool is_csrmv_block = true;
                 auto csrmv_kernel = is_csrmv_block ? entry.ker[1] : entry.ker[0];
                 auto csrmv_func = KernelFunctor<Buffer,
                                                 Buffer, Buffer, Buffer,
                                                 int,
-                                                Buffer, KParam, T, T>(csrmv_kernel);
+                                                Buffer, KParam, T, T, Buffer>(csrmv_kernel);
 
                 NDRange local(THREADS_PER_GROUP, 1);
                 int M = rowIdx.info.dims[0] - 1;
-                int num_groups = is_csrmv_block ? M : divup(M, local[0]);
-                int groups_y = divup(num_groups, MAX_GROUPS_X);
-                int groups_x = divup(num_groups, groups_y);
-                NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+                int groups_x = is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
+                groups_x = std::min(groups_x, MAX_GROUPS);
+                NDRange global(local[0] * groups_x, 1);
 
                 csrmv_func(EnqueueArgs(getQueue(), global, local),
                            *out.data, *values.data, *rowIdx.data, *colIdx.data,
-                           M, *rhs.data, rhs.info, alpha, beta);
+                           M, *rhs.data, rhs.info, alpha, beta, *counter);
 
                 CL_DEBUG_FINISH(getQueue());
+                bufferFree(counter);
             } catch (cl::Error &ex) {
                 CL_TO_AF_ERROR(ex);
             }

From d158e227d879db7ac94cb590085e6f883b2704a7 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 11:25:27 -0400
Subject: [PATCH 0774/2677] Removed extraneous throw

---
 src/backend/opencl/kernel/random_engine.hpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 70035bc430..e51bc2f1ea 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -162,7 +162,6 @@ namespace opencl
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
-                throw;
             }
         }
 
@@ -189,7 +188,6 @@ namespace opencl
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
-                throw;
             }
         }
 
@@ -235,7 +233,6 @@ namespace opencl
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
-                throw;
             }
         }
     }

From 04ba2bb07731b88c61db0ed1df743bfe39686421 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 14:25:51 -0400
Subject: [PATCH 0775/2677] Stated why kernel cache names don't have defines

Mersenne Twister kernels do not need to have N, TABLE_LENGTH and
STATE_SIZE in the cache names because they are common to every kernel.
---
 src/backend/opencl/kernel/random_engine.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index e51bc2f1ea..9eaad38665 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -86,6 +86,8 @@ namespace opencl
                         << " -D THREADS=" << THREADS
                         << " -D RAND_DIST=" << kerIdx;
                 if (type == AF_RANDOM_MERSENNE_GP11213) {
+                    //These defines do not need to be a part of the hashing string
+                    //because they are the same for all Mersenne Twister kernels.
                     options << " -D STATE_SIZE=" << STATE_SIZE
                             << " -D TABLE_SIZE=" << TABLE_SIZE
                             << " -D N=" << N;
@@ -124,6 +126,8 @@ namespace opencl
             kc_entry_t entry;
             if (idx == kernelCaches[device].end()) {
                 std::ostringstream options;
+                //These defines do not need to be a part of the hashing string
+                //because they are the same for all Mersenne Twister kernels.
                 options << " -D N=" << N << " -D TABLE_SIZE=" << TABLE_SIZE;
                 cl::Program prog;
                 buildProgram(prog, 1, &ker_str, &ker_len, options.str());

From fe7f5cddc749ae2f3e4c6a669d455008d8e0cd20 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 24 Aug 2016 03:40:58 -0400
Subject: [PATCH 0776/2677] OPENCL: Initial implementation of csrmm

---
 src/backend/opencl/kernel/csrmm.cl  | 83 +++++++++++++++++++++++++++++
 src/backend/opencl/kernel/csrmm.hpp | 80 +++++++++++++++++++++++++++
 src/backend/opencl/kernel/csrmv.hpp |  4 +-
 3 files changed, 165 insertions(+), 2 deletions(-)
 create mode 100644 src/backend/opencl/kernel/csrmm.cl

diff --git a/src/backend/opencl/kernel/csrmm.cl b/src/backend/opencl/kernel/csrmm.cl
new file mode 100644
index 0000000000..69e6f882ab
--- /dev/null
+++ b/src/backend/opencl/kernel/csrmm.cl
@@ -0,0 +1,83 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if IS_CPLX
+
+T __cmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x - lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y + lhs.y * rhs.x;
+    return out;
+}
+#define MUL(a, b) __cmul(a, b)
+#else
+#define MUL(a, b) (a) * (b)
+#endif
+
+
+__kernel void
+csrmm_nt(__global T *output,
+         __global const T *values,
+         __global const int *rowidx,
+         __global const int *colidx,
+         const int M,
+         const int N,
+         const int K,
+         __global const T *rhs,
+         const KParam rinfo,
+         const T alpha,
+         const T beta,
+         __global int *counter)
+{
+    int gidx = get_global_id(0);
+    int lid = get_local_id(0);
+    int off = get_local_size(0);
+
+    rhs += gidx;
+    output += gidx * M;
+
+    bool within_K = (gidx < K);
+
+    __local T s_values[THREADS_PER_GROUP];
+    __local int s_colidx[THREADS_PER_GROUP];
+
+    // FIXME: Implement better load balancing using atomic counter
+    for (int rid = get_group_id(1); rid < M; rid += get_num_groups(1)) {
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        const int colStart = rowidx[rid];
+        const int colEnd   = rowidx[rid + 1];
+
+        T outval = 0;
+        for (int id = colStart; id < colEnd; id += off) {
+            int lim = min(colEnd - id, off);
+            s_values[lid] = lid < lim ? values[id + lid] : 0;
+            s_colidx[lid] = lid < lim ? colidx[id + lid] : -1;
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            for (int idy = 0; within_K && idy < lim; idy++) {
+                outval += MUL(s_values[idy], rhs[K * s_colidx[idy]]);
+            }
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+
+        if (within_K) {
+#if USE_ALPHA
+            outval = alpha * outval;
+#endif
+
+#if USE_BETA
+            output[rid] = outval + beta * output[rid];
+#else
+            output[rid] = outval;
+#endif
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index a0cb569b4c..011d18b4b6 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #pragma once
+#include <kernel_headers/csrmm.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -36,11 +37,90 @@ namespace opencl
 {
     namespace kernel
     {
+        static const int MAX_CSRMM_GROUPS = 4096;
         template<typename T>
         void csrmm_nt(Param out,
                       const Param &values, const Param &rowIdx, const Param &colIdx,
                       const Param &rhs, const T alpha, const T beta)
         {
+            try {
+                bool use_alpha = (alpha != scalar<T>(1.0));
+                bool use_beta = (beta != scalar<T>(0.0));
+                std::string ref_name =
+                    std::string("csrmm_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(use_alpha) +
+                    std::string("_") +
+                    std::to_string(use_beta);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    options << " -D USE_ALPHA=" << use_alpha;
+                    options << " -D USE_BETA=" << use_beta;
+                    options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    if (std::is_same<T, cfloat>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+
+                    const char *ker_strs[] = {csrmm_cl};
+                    const int   ker_lens[] = {csrmm_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel[2];
+                    entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
+                    // FIXME: Change this after adding another kernel
+                    entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
+                } else {
+                    entry = idx->second;
+                }
+
+                auto csrmm_nt_kernel = entry.ker[0];
+                auto csrmm_nt_func = KernelFunctor<Buffer,
+                                                   Buffer, Buffer, Buffer,
+                                                   int, int, int,
+                                                   Buffer, KParam, T, T, Buffer>(csrmm_nt_kernel);
+                NDRange local(THREADS_PER_GROUP, 1);
+                int M = rowIdx.info.dims[0] - 1;
+                int N = rhs.info.dims[1];
+                int K = rhs.info.dims[0];
+
+                int groups_x = divup(K, local[0]);
+                int groups_y = divup(M, REPEAT);
+                groups_y = std::min(groups_y, MAX_CSRMM_GROUPS);
+                NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+                std::vector<int> count(groups_x);
+                cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
+                getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
+                                              0,
+                                              count.size() * sizeof(int),
+                                              (void *)count.data());
+
+                csrmm_nt_func(EnqueueArgs(getQueue(), global, local),
+                              *out.data, *values.data, *rowIdx.data, *colIdx.data,
+                              M, N, K, *rhs.data, rhs.info, alpha, beta, *counter);
+
+                bufferFree(counter);
+            } catch (cl::Error &ex) {
+                CL_TO_AF_ERROR(ex);
+            }
         }
     }
 }
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 9b22e9dd53..1083a86798 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -37,7 +37,7 @@ namespace opencl
 {
     namespace kernel
     {
-        const int MAX_GROUPS = 4096;
+        static const int MAX_CSRMV_GROUPS = 4096;
         template<typename T>
         void csrmv(Param out,
                    const Param &values, const Param &rowIdx, const Param &colIdx,
@@ -109,7 +109,7 @@ namespace opencl
                 int M = rowIdx.info.dims[0] - 1;
 
                 int groups_x = is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
-                groups_x = std::min(groups_x, MAX_GROUPS);
+                groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
                 NDRange global(local[0] * groups_x, 1);
 
                 csrmv_func(EnqueueArgs(getQueue(), global, local),

From 185489e193c85f72335f829eaf1c7bd34c5e0808 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 15:16:57 -0400
Subject: [PATCH 0777/2677] Added license information for Random123

---
 .../cpu/kernel/random_engine_threefry.hpp     |  1 -
 .../cuda/kernel/random_engine_philox.hpp      | 37 +++++++++++++++++++
 .../cuda/kernel/random_engine_threefry.hpp    | 37 +++++++++++++++++++
 src/backend/cuda/random_engine.hpp            |  1 +
 .../opencl/kernel/random_engine_mersenne.cl   |  1 -
 .../kernel/random_engine_mersenne_init.cl     |  1 -
 .../opencl/kernel/random_engine_write.cl      | 37 -------------------
 7 files changed, 75 insertions(+), 40 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
index 3104829a82..1cd32abc20 100644
--- a/src/backend/cpu/kernel/random_engine_threefry.hpp
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -5,7 +5,6 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
- *
  ********************************************************/
 
 /*******************************************************
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index fa9985e589..96a1a50571 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -7,6 +7,43 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
index 3cc70121bb..a04af627ce 100644
--- a/src/backend/cuda/kernel/random_engine_threefry.hpp
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -7,6 +7,43 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index c8362838e6..86d8af78a1 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #pragma once
 
 #include <Array.hpp>
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index 27c73ac3f8..a62ff16852 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -5,7 +5,6 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
- *
  ********************************************************/
 
 #define divup(N, D) (((N) + (D) - 1)/(D));
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index a64dd5cc85..98c0d533a8 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -5,7 +5,6 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
- *
  ********************************************************/
 
 __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index b2c8781f21..8d99750d12 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -8,43 +8,6 @@
  *
  ********************************************************/
 
-/*******************************************************
- * Modified version of Random123 library:
- * https://www.deshawresearch.com/downloads/download_random123.cgi/
- * The original copyright can be seen here:
- *
- * RANDOM123 LICENSE AGREEMENT
- *
- * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * * Redistributions of source code must retain the above copyright notice,
- *   this list of conditions, and the following disclaimer.
- *
- * * Redistributions in binary form must reproduce the above copyright
- *   notice, this list of conditions, and the following disclaimer in the
- *   documentation and/or other materials provided with the distribution.
- *
- * Neither the name of D. E. Shaw Research nor the names of its contributors
- * may be used to endorse or promote products derived from this software
- * without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
- * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
- * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
- * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
- * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
- * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
- * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *********************************************************/
-
 #define UINTMAXFLOAT 4294967296.0f
 #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
 #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164

From 79c3d1656da867df4e72740f6ecdac3e6e528ff0 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 24 Aug 2016 15:15:13 -0400
Subject: [PATCH 0778/2677] OPENCL: Runtime checks for greedy or naive indexing
 for csrmv/csrmm

---
 src/backend/opencl/kernel/csrmm.cl  | 22 +++++++++++----
 src/backend/opencl/kernel/csrmm.hpp |  9 ++++++-
 src/backend/opencl/kernel/csrmv.cl  | 42 ++++++++++++++++++-----------
 src/backend/opencl/kernel/csrmv.hpp | 12 ++++++++-
 4 files changed, 63 insertions(+), 22 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmm.cl b/src/backend/opencl/kernel/csrmm.cl
index 69e6f882ab..d2c9713d49 100644
--- a/src/backend/opencl/kernel/csrmm.cl
+++ b/src/backend/opencl/kernel/csrmm.cl
@@ -49,11 +49,23 @@ csrmm_nt(__global T *output,
     __local int s_colidx[THREADS_PER_GROUP];
 
     // FIXME: Implement better load balancing using atomic counter
-    for (int rid = get_group_id(1); rid < M; rid += get_num_groups(1)) {
+    int rowNext = get_group_id(1);
+    __local int s_rowId;
+    while(true) {
+#if USE_GREEDY
+        if (lid == 0) {
+            s_rowId = atomic_inc(counter + get_group_id(0));
+        }
         barrier(CLK_LOCAL_MEM_FENCE);
+        int rowId = s_rowId;
+#else
+        int rowId = rowNext;
+        rowNext += get_num_groups(1);
+#endif
+        if (rowId >= M) return;
 
-        const int colStart = rowidx[rid];
-        const int colEnd   = rowidx[rid + 1];
+        const int colStart = rowidx[rowId];
+        const int colEnd   = rowidx[rowId + 1];
 
         T outval = 0;
         for (int id = colStart; id < colEnd; id += off) {
@@ -74,9 +86,9 @@ csrmm_nt(__global T *output,
 #endif
 
 #if USE_BETA
-            output[rid] = outval + beta * output[rid];
+            output[rowId] = outval + beta * output[rowId];
 #else
-            output[rid] = outval;
+            output[rowId] = outval;
 #endif
         }
     }
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 011d18b4b6..f5f2a24db5 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -46,13 +46,19 @@ namespace opencl
             try {
                 bool use_alpha = (alpha != scalar<T>(1.0));
                 bool use_beta = (beta != scalar<T>(0.0));
+                bool use_greedy = (getActiveDeviceType() == AFCL_DEVICE_TYPE_GPU) &&
+                    ((getActivePlatform() == AFCL_PLATFORM_AMD) ||
+                     (getActivePlatform() == AFCL_PLATFORM_NVIDIA));
+
                 std::string ref_name =
                     std::string("csrmm_") +
                     std::string(dtype_traits<T>::getName()) +
                     std::string("_") +
                     std::to_string(use_alpha) +
                     std::string("_") +
-                    std::to_string(use_beta);
+                    std::to_string(use_beta) +
+                    std::string("_") +
+                    std::to_string(use_greedy);
 
                 int device = getActiveDeviceId();
                 auto idx = kernelCaches[device].find(ref_name);
@@ -64,6 +70,7 @@ namespace opencl
                     options << " -D T=" << dtype_traits<T>::getName();
                     options << " -D USE_ALPHA=" << use_alpha;
                     options << " -D USE_BETA=" << use_beta;
+                    options << " -D USE_GREEDY=" << use_greedy;
                     options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
 
                     if (std::is_same<T, double>::value ||
diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index 94801420ce..f3c22f682c 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -34,12 +34,19 @@ csrmv_thread(__global T *output,
              const T beta,
              __global int *counter)
 {
+
+    int rowNext = get_global_id(0);
     while (true) {
-        int rid = atomic_inc(counter);
-        if (rid >= M) return;
+#if USE_GREEDY
+        int rowId = atomic_inc(counter);
+#else
+        int rowId = rowNext;
+        rowNext += get_global_size(0);
+#endif
+        if (rowId >= M) return;
 
-        int colStart = rowidx[rid];
-        int colEnd   = rowidx[rid + 1];
+        int colStart = rowidx[rowId];
+        int colEnd   = rowidx[rowId + 1];
         T outval = 0;
 
         for (int id = colStart; id < colEnd; id++) {
@@ -52,9 +59,9 @@ csrmv_thread(__global T *output,
 #endif
 
 #if USE_BETA
-        output[rid] = outval + beta * output[rid];
+        output[rowId] = outval + beta * output[rowId];
 #else
-        output[rid] = outval;
+        output[rowId] = outval;
 #endif
     }
 }
@@ -74,21 +81,26 @@ csrmv_block(__global T *output,
     int lid = get_local_id(0);
     int off = get_local_size(0);
 
-    __local int s_rid;
-
+    int rowNext = get_group_id(0);
+    __local int s_rowId;
     while (true) {
 
+#if USE_GREEDY
         if (lid == 0) {
-            s_rid = atomic_inc(counter);
+            s_rowId = atomic_inc(counter);
         }
         barrier(CLK_LOCAL_MEM_FENCE);
-        int rid = s_rid;
-        if (rid >= M) return;
+        int rowId = s_rowId;
+#else
+        int rowId = rowNext;
+        rowNext += get_num_groups(0);
+#endif
+        if (rowId >= M) return;
 
         __local T s_outval[THREADS_PER_GROUP];
 
-        int colStart = rowidx[rid];
-        int colEnd   = rowidx[rid + 1];
+        int colStart = rowidx[rowId];
+        int colEnd   = rowidx[rowId + 1];
         T outval = 0;
         for (int id = colStart + lid; id < colEnd; id += off) {
             int cid = colidx[id];
@@ -110,9 +122,9 @@ csrmv_block(__global T *output,
 #endif
 
 #if USE_BETA
-            output[rid] = outval + beta * output[rid];
+            output[rowId] = outval + beta * output[rowId];
 #else
-            output[rid] = outval;
+            output[rowId] = outval;
 #endif
         }
     }
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 1083a86798..ff284b51e6 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -24,6 +24,7 @@
 #include "reduce.hpp"
 #include "scan_first.hpp"
 #include "config.hpp"
+#include <af/opencl.h>
 
 using cl::Buffer;
 using cl::Program;
@@ -46,13 +47,21 @@ namespace opencl
             try {
                 bool use_alpha = (alpha != scalar<T>(1.0));
                 bool use_beta = (beta != scalar<T>(0.0));
+
+                // Use other metrics for this as well
+                bool use_greedy = (getActiveDeviceType() == AFCL_DEVICE_TYPE_GPU) &&
+                    ((getActivePlatform() == AFCL_PLATFORM_AMD) ||
+                     (getActivePlatform() == AFCL_PLATFORM_NVIDIA));
+
                 std::string ref_name =
                     std::string("csrmv_") +
                     std::string(dtype_traits<T>::getName()) +
                     std::string("_") +
                     std::to_string(use_alpha) +
                     std::string("_") +
-                    std::to_string(use_beta);
+                    std::to_string(use_beta) +
+                    std::string("_") +
+                    std::to_string(use_greedy);
 
                 int device = getActiveDeviceId();
                 auto idx = kernelCaches[device].find(ref_name);
@@ -64,6 +73,7 @@ namespace opencl
                     options << " -D T=" << dtype_traits<T>::getName();
                     options << " -D USE_ALPHA=" << use_alpha;
                     options << " -D USE_BETA=" << use_beta;
+                    options << " -D USE_GREEDY=" << use_greedy;
                     options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
 
                     if (std::is_same<T, double>::value ||

From 8ce941662313987752cd7364a74123aa01acaa04 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 24 Aug 2016 15:36:43 -0400
Subject: [PATCH 0779/2677] OPENCL: Change in launch config for csrmv

---
 src/backend/opencl/kernel/csrmv.cl  |  4 ++--
 src/backend/opencl/kernel/csrmv.hpp | 11 ++++++++---
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index f3c22f682c..11da95240c 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -97,7 +97,7 @@ csrmv_block(__global T *output,
 #endif
         if (rowId >= M) return;
 
-        __local T s_outval[THREADS_PER_GROUP];
+        __local T s_outval[THREADS];
 
         int colStart = rowidx[rowId];
         int colEnd   = rowidx[rowId + 1];
@@ -109,7 +109,7 @@ csrmv_block(__global T *output,
         s_outval[lid] = outval;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        for (int n = off / 2; n > 0; n /= 2) {
+        for (int n = THREADS / 2; n > 0; n /= 2) {
             if (lid < n) s_outval[lid] += s_outval[lid + n];
             barrier(CLK_LOCAL_MEM_FENCE);
         }
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index ff284b51e6..92f27c7108 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -53,6 +53,9 @@ namespace opencl
                     ((getActivePlatform() == AFCL_PLATFORM_AMD) ||
                      (getActivePlatform() == AFCL_PLATFORM_NVIDIA));
 
+                // FIXME: Find a better number based on average non zeros per row
+                int threads = 64;
+
                 std::string ref_name =
                     std::string("csrmv_") +
                     std::string(dtype_traits<T>::getName()) +
@@ -61,7 +64,9 @@ namespace opencl
                     std::string("_") +
                     std::to_string(use_beta) +
                     std::string("_") +
-                    std::to_string(use_greedy);
+                    std::to_string(use_greedy) +
+                    std::string("_") +
+                    std::to_string(threads);
 
                 int device = getActiveDeviceId();
                 auto idx = kernelCaches[device].find(ref_name);
@@ -74,7 +79,7 @@ namespace opencl
                     options << " -D USE_ALPHA=" << use_alpha;
                     options << " -D USE_BETA=" << use_beta;
                     options << " -D USE_GREEDY=" << use_greedy;
-                    options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
+                    options << " -D THREADS=" << threads;
 
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
@@ -115,7 +120,7 @@ namespace opencl
                                                 int,
                                                 Buffer, KParam, T, T, Buffer>(csrmv_kernel);
 
-                NDRange local(THREADS_PER_GROUP, 1);
+                NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
                 int M = rowIdx.info.dims[0] - 1;
 
                 int groups_x = is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);

From d8be571f36bc19b2be126529b76080b268fc773d Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 15:48:00 -0400
Subject: [PATCH 0780/2677] Added license information for MTGP

---
 src/backend/MersenneTwister.hpp               | 35 +++++++++++++
 src/backend/cpu/kernel/random_engine.hpp      | 17 +-----
 .../cpu/kernel/random_engine_mersenne.hpp     | 52 +++++++++++++++++++
 .../cpu/kernel/random_engine_philox.hpp       |  1 +
 .../cpu/kernel/random_engine_threefry.hpp     |  1 +
 src/backend/cuda/kernel/random_engine.hpp     |  2 +
 .../cuda/kernel/random_engine_mersenne.hpp    | 35 +++++++++++++
 .../cuda/kernel/random_engine_philox.hpp      |  2 +
 .../cuda/kernel/random_engine_threefry.hpp    |  2 +
 .../opencl/kernel/random_engine_mersenne.cl   | 35 +++++++++++++
 .../kernel/random_engine_mersenne_init.cl     | 35 +++++++++++++
 .../opencl/kernel/random_engine_write.cl      |  1 -
 12 files changed, 201 insertions(+), 17 deletions(-)

diff --git a/src/backend/MersenneTwister.hpp b/src/backend/MersenneTwister.hpp
index c6d820f680..f21ea92a89 100644
--- a/src/backend/MersenneTwister.hpp
+++ b/src/backend/MersenneTwister.hpp
@@ -7,6 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/********************************************************
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.
+ * Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University, The Uinversity
+ *       of Tokyo nor the names of its contributors may be used to
+ *       endorse or promote products derived from this software without
+ *       specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *******************************************************/
+
 #pragma once
 
 #include <af/defines.h>
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 05a87b0d99..3f6d683dbb 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <kernel/random_engine_philox.hpp>
@@ -270,21 +271,5 @@ namespace kernel
         }
     }
 
-    void initMersenneState(uint * const state, const uint * const tbl, const uintl seed)
-    {
-        uint hidden_seed = tbl[4] ^ (tbl[8] << 16);
-        uint tmp = hidden_seed;
-        tmp += tmp >> 16;
-        tmp += tmp >> 8;
-        tmp &= 0xff;
-        tmp |= tmp << 8;
-        tmp |= tmp << 16;
-        state[0] = seed;
-        state[1] = hidden_seed ^ ((uint)(1812433253) * (state[0] ^ (state[0] >> 30)) + 1);
-        for (int i = 2; i < N; ++i) {
-            state[i] = tmp;
-            state[i] ^= (uint)(1812433253) * (state[i-1] ^ (state[i-1] >> 30)) + i;
-        }
-    }
 }
 }
diff --git a/src/backend/cpu/kernel/random_engine_mersenne.hpp b/src/backend/cpu/kernel/random_engine_mersenne.hpp
index 0de6c91483..94e818b3ae 100644
--- a/src/backend/cpu/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cpu/kernel/random_engine_mersenne.hpp
@@ -7,6 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/********************************************************
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.
+ * Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University, The Uinversity
+ *       of Tokyo nor the names of its contributors may be used to
+ *       endorse or promote products derived from this software without
+ *       specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *******************************************************/
+
 #pragma once
 
 namespace cpu
@@ -76,5 +111,22 @@ namespace kernel
         }
     }
 
+    void initMersenneState(uint * const state, const uint * const tbl, const uintl seed)
+    {
+        uint hidden_seed = tbl[4] ^ (tbl[8] << 16);
+        uint tmp = hidden_seed;
+        tmp += tmp >> 16;
+        tmp += tmp >> 8;
+        tmp &= 0xff;
+        tmp |= tmp << 8;
+        tmp |= tmp << 16;
+        state[0] = seed;
+        state[1] = hidden_seed ^ ((uint)(1812433253) * (state[0] ^ (state[0] >> 30)) + 1);
+        for (int i = 2; i < N; ++i) {
+            state[i] = tmp;
+            state[i] ^= (uint)(1812433253) * (state[i-1] ^ (state[i-1] >> 30)) + i;
+        }
+    }
+
 }
 }
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index 1afe3e2ece..fed39d681f 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -46,6 +46,7 @@
  *********************************************************/
 
 #pragma once
+
 namespace cpu
 {
 namespace kernel
diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
index 1cd32abc20..7f914ec87a 100644
--- a/src/backend/cpu/kernel/random_engine_threefry.hpp
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -45,6 +45,7 @@
  *********************************************************/
 
 #pragma once
+
 namespace cpu
 {
 namespace kernel
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 846ea71971..4456935b7c 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index 6e283b746c..daa9b7579f 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -7,6 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/********************************************************
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.
+ * Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University, The Uinversity
+ *       of Tokyo nor the names of its contributors may be used to
+ *       endorse or promote products derived from this software without
+ *       specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *******************************************************/
+
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index 96a1a50571..17a6a9d6ea 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -44,6 +44,8 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
+#pragma once
+
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
index a04af627ce..bbecef44fe 100644
--- a/src/backend/cuda/kernel/random_engine_threefry.hpp
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -44,6 +44,8 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
+#pragma once
+
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index a62ff16852..b37520f1fd 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -7,6 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/********************************************************
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.
+ * Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University, The Uinversity
+ *       of Tokyo nor the names of its contributors may be used to
+ *       endorse or promote products derived from this software without
+ *       specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *******************************************************/
+
 #define divup(N, D) (((N) + (D) - 1)/(D));
 
 void read_table(__local uint * const localTable, __global const uint * const table)
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index 98c0d533a8..ef8f5e02fb 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -7,6 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+/********************************************************
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.
+ * Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University, The Uinversity
+ *       of Tokyo nor the names of its contributors may be used to
+ *       endorse or promote products derived from this software without
+ *       specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *******************************************************/
+
 __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
 {
     __local uint lstate[N];
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 8d99750d12..18d6a1628d 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -5,7 +5,6 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
- *
  ********************************************************/
 
 #define UINTMAXFLOAT 4294967296.0f

From d447c194b267932200b1376635c6f98e4942c6e5 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 16:43:33 -0400
Subject: [PATCH 0781/2677] Implemented af_retain_random_engine and operator=

---
 include/af/random_engine.h        | 14 +++++++++++++-
 src/api/c/random_engine.cpp       | 24 ++++++++++++++++++++++++
 src/api/cpp/random_engine.cpp     | 16 +++++++++++++++-
 src/api/unified/random_engine.cpp |  5 +++++
 4 files changed, 57 insertions(+), 2 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 98a94889e0..b73aa9f36a 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -26,6 +26,8 @@ namespace af
             randomEngine(randomType typeIn = AF_RANDOM_DEFAULT, uintl seedIn = 0);
             ~randomEngine();
 
+            randomEngine& operator= (const randomEngine& other);
+
             array uniform(const dim_t dim0, const dtype ty = f32);
             array uniform(const dim_t dim0, const dim_t dim1, const dtype ty = f32);
             array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
@@ -39,7 +41,8 @@ namespace af
             array normal(const dim4& dims, const dtype ty = f32);
 
             void setSeed(const uintl seed);
-            uintl getSeed(void);
+            uintl getSeed(void) const;
+            af_random_engine get() const;
     };
 }
 #endif
@@ -59,6 +62,15 @@ extern "C" {
     */
     AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_type rtype, uintl seed);
 
+    /**
+       C Interface for retaining random engine
+
+       \param[out]  out is the pointer to the returned random engine object
+       \param[in]   engine is the random engine object
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_retain_random_engine(af_random_engine *out, const af_random_engine engine);
     /**
        C Interface for creating an array of uniform numbers using a random engine
 
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 668886ae06..36d0861287 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -119,6 +119,30 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
     return AF_SUCCESS;
 }
 
+af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engine engineHandle)
+{
+    try {
+
+        RandomEngine engine = *(getRandomEngine(engineHandle));
+        RandomEngine out;
+
+        out.type = engine.type;
+        out.seed = engine.seed;
+        out.counter = engine.counter;
+        AF_CHECK(af_retain_array(&out.pos, engine.pos));
+        AF_CHECK(af_retain_array(&out.sh1, engine.sh1));
+        AF_CHECK(af_retain_array(&out.sh2, engine.sh2));
+        out.mask = engine.mask;
+        AF_CHECK(af_retain_array(&out.recursion_table, engine.recursion_table));
+        AF_CHECK(af_retain_array(&out.temper_table, engine.temper_table));
+        AF_CHECK(af_retain_array(&out.state, engine.state));
+
+        *outHandle = getRandomEngineHandle(out);
+
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
 {
     try {
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index f47f33cefc..01502a03ae 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -27,6 +27,15 @@ namespace af
         }
     }
 
+    randomEngine& randomEngine::operator= (const randomEngine& other)
+    {
+        if (this != &other) {
+            AF_THROW(af_release_random_engine(engine));
+            AF_THROW(af_retain_random_engine(&engine, other.get()));
+        }
+        return *this;
+    }
+
     array randomEngine::uniform(const dim_t dim0, const dtype ty)
     {
         dim4 d(dim0, 1, 1, 1);
@@ -94,11 +103,16 @@ namespace af
         AF_THROW(af_random_engine_set_seed(seed, engine));
     }
 
-    uintl randomEngine::getSeed(void)
+    uintl randomEngine::getSeed(void) const
     {
         uintl seed;
         AF_THROW(af_random_engine_get_seed(&seed, engine));
         return seed;
     }
 
+    af_random_engine randomEngine::get() const
+    {
+        return engine;
+    }
+
 }
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
index 907a129974..a63011190c 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random_engine.cpp
@@ -16,6 +16,11 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
     return CALL(engineHandle, rtype, seed);
 }
 
+af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engine engineHandle)
+{
+    return CALL(outHandle, engineHandle);
+}
+
 af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     return CALL(arr, engine, ndims, dims, type);

From 602c654547599bbd24be077ae11643ac0a36ff12 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 17:05:35 -0400
Subject: [PATCH 0782/2677] Replaced modulus with ternary in OpenCL Mersenne

---
 .../opencl/kernel/random_engine_mersenne.cl       | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index b37520f1fd..a2e16c9cf9 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -131,11 +131,16 @@ __kernel void generate(__global T *output,
                     l_state[offsetY ]);
             l_state[offsetO] = r;
             o[ii] = temper(l_temper_table, r, l_state[offsetT]);
-            offsetX1 = (offsetX1 + THREADS) % STATE_SIZE;
-            offsetX2 = (offsetX2 + THREADS) % STATE_SIZE;
-            offsetY  = (offsetY  + THREADS) % STATE_SIZE;
-            offsetT  = (offsetT  + THREADS) % STATE_SIZE;
-            offsetO  = (offsetO  + THREADS) % STATE_SIZE;
+            offsetX1 += THREADS;
+            offsetX2 += THREADS;
+            offsetY  += THREADS;
+            offsetT  += THREADS;
+            offsetO  += THREADS;
+            offsetX1 = (offsetX1 >= STATE_SIZE)? offsetX1 - STATE_SIZE : offsetX1;
+            offsetX2 = (offsetX2 >= STATE_SIZE)? offsetX2 - STATE_SIZE : offsetX2;
+            offsetY  = (offsetY  >= STATE_SIZE)? offsetY  - STATE_SIZE : offsetY ;
+            offsetT  = (offsetT  >= STATE_SIZE)? offsetT  - STATE_SIZE : offsetT ;
+            offsetO  = (offsetO  >= STATE_SIZE)? offsetO  - STATE_SIZE : offsetO ;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
         uint writeIndex = index + get_local_id(0);

From 45bd3a88897f03e31823f974586b7a35a272acfe Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 24 Aug 2016 17:54:24 -0400
Subject: [PATCH 0783/2677] Changed 'normalized' to boxMuller

---
 src/backend/cpu/kernel/random_engine.hpp      |  18 +--
 src/backend/cuda/kernel/random_engine.hpp     | 126 +++++++++---------
 .../opencl/kernel/random_engine_write.cl      |  60 ++++-----
 3 files changed, 102 insertions(+), 102 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 3f6d683dbb..de3d03c6c5 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -126,7 +126,7 @@ namespace kernel
     }
 
     template <typename T>
-    void normalizePair(T * const out1, T * const out2, const T r1, const T r2)
+    void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
     {
         T r = sqrt((T)(-2.0) * log(r1));
         T theta = 2 * (T)PI_VAL * (r2);
@@ -134,15 +134,15 @@ namespace kernel
         *out2 = r*cos(theta);
     }
 
-    void normalize(uint val[4], double *temp)
+    void boxMullerTransform(uint val[4], double *temp)
     {
-        normalizePair(&temp[0], &temp[1], transform<double>(val, 0), transform<double>(val,1));
+        boxMullerTransform(&temp[0], &temp[1], transform<double>(val, 0), transform<double>(val,1));
     }
 
-    void normalize(uint val[4], float *temp)
+    void boxMullerTransform(uint val[4], float *temp)
     {
-        normalizePair(&temp[0], &temp[1], transform<float>(val, 0), transform<float>(val, 1));
-        normalizePair(&temp[2], &temp[3], transform<float>(val, 2), transform<float>(val, 3));
+        boxMullerTransform(&temp[0], &temp[1], transform<float>(val, 0), transform<float>(val, 1));
+        boxMullerTransform(&temp[2], &temp[3], transform<float>(val, 2), transform<float>(val, 3));
     }
 
     template <typename T>
@@ -157,7 +157,7 @@ namespace kernel
         int reset = (4*sizeof(uint))/sizeof(T);
         for (int i = 0; i < (int)elements; i += reset) {
             philox(key, ctr);
-            normalize(ctr, temp);
+            boxMullerTransform(ctr, temp);
             int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
             for (int j = 0; j < lim; ++j) {
                 out[i + j] = temp[j];
@@ -181,7 +181,7 @@ namespace kernel
             ++ctr[0]; ++key[0];
             threefry(key, ctr, val+2);
             ++ctr[0]; ++key[0];
-            normalize(val, temp);
+            boxMullerTransform(val, temp);
             int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
             for (int j = 0; j < lim; ++j) {
                 out[i + j] = temp[j];
@@ -241,7 +241,7 @@ namespace kernel
         int reset = (4*sizeof(uint))/sizeof(T);
         for (int i = 0; i < (int)elements; i += reset) {
             mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table, temper_table);
-            normalize(o, temp);
+            boxMullerTransform(o, temp);
             int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
             for (int j = 0; j < lim; ++j) {
                 out[i + j] = temp[j];
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 4456935b7c..a8c97b04d2 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -41,7 +41,7 @@ namespace kernel
     }
 
     template <typename T>
-    __device__ static void normalize(T * const out1, T * const out2, const T &r1, const T &r2)
+    __device__ static void boxMullerTransform(T * const out1, T * const out2, const T &r1, const T &r2)
     {
         T r = sqrt((T)(-2.0) * log(r1));
         T theta = 2 * (T)PI_VAL * r2;
@@ -51,7 +51,7 @@ namespace kernel
 
     //Writes without boundary checking
 
-    __device__ static void writeOut256Bytes(uchar *out, const uint &index,
+    __device__ static void writeOut128Bytes(uchar *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index]                 =     r1;
@@ -72,7 +72,7 @@ namespace kernel
         out[index + 15*blockDim.x] = r4>>24;
     }
 
-    __device__ static void writeOut256Bytes(char *out, const uint &index,
+    __device__ static void writeOut128Bytes(char *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index]                 = (r1   )&0x1;
@@ -93,7 +93,7 @@ namespace kernel
         out[index + 15*blockDim.x] = (r4>>3)&0x1;
     }
 
-    __device__ static void writeOut256Bytes(short *out, const uint &index,
+    __device__ static void writeOut128Bytes(short *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index]                =     r1;
@@ -106,13 +106,13 @@ namespace kernel
         out[index + 7*blockDim.x] = r4>>16;
     }
 
-    __device__ static void writeOut256Bytes(ushort *out, const uint &index,
+    __device__ static void writeOut128Bytes(ushort *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        writeOut256Bytes((short*)(out), index, r1, r2, r3, r4);
+        writeOut128Bytes((short*)(out), index, r1, r2, r3, r4);
     }
 
-    __device__ static void writeOut256Bytes(int *out, const uint &index,
+    __device__ static void writeOut128Bytes(int *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index]                = r1;
@@ -121,13 +121,13 @@ namespace kernel
         out[index + 3*blockDim.x] = r4;
     }
 
-    __device__ static void writeOut256Bytes(uint *out, const uint &index,
+    __device__ static void writeOut128Bytes(uint *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        writeOut256Bytes((int*)(out), index, r1, r2, r3, r4);
+        writeOut128Bytes((int*)(out), index, r1, r2, r3, r4);
     }
 
-    __device__ static void writeOut256Bytes(intl *out, const uint &index,
+    __device__ static void writeOut128Bytes(intl *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         intl c1 = r2;
@@ -138,13 +138,13 @@ namespace kernel
         out[index + blockDim.x] = c2;
     }
 
-    __device__ static void writeOut256Bytes(uintl *out, const uint &index,
+    __device__ static void writeOut128Bytes(uintl *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        writeOut256Bytes((intl*)(out), index, r1, r2, r3, r4);
+        writeOut128Bytes((intl*)(out), index, r1, r2, r3, r4);
     }
 
-    __device__ static void writeOut256Bytes(float *out, const uint &index,
+    __device__ static void writeOut128Bytes(float *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index]                = getFloat(r1);
@@ -153,7 +153,7 @@ namespace kernel
         out[index + 3*blockDim.x] = getFloat(r4);
     }
 
-    __device__ static void writeOut256Bytes(cfloat *out, const uint &index,
+    __device__ static void writeOut128Bytes(cfloat *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index].x              = getFloat(r1);
@@ -162,14 +162,14 @@ namespace kernel
         out[index + blockDim.x].y = getFloat(r4);
     }
 
-    __device__ static void writeOut256Bytes(double *out, const uint &index,
+    __device__ static void writeOut128Bytes(double *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index]              = getDouble(r1, r2);
         out[index + blockDim.x] = getDouble(r3, r4);
     }
 
-    __device__ static void writeOut256Bytes(cdouble *out, const uint &index,
+    __device__ static void writeOut128Bytes(cdouble *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
         out[index].x = getDouble(r1, r2);
@@ -178,35 +178,35 @@ namespace kernel
 
     //Normalized writes without boundary checking
 
-    __device__ static void normalizedWriteOut256Bytes(float *out, const uint &index,
+    __device__ static void boxMullerWriteOut128Bytes(float *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        normalize(&out[index]               , &out[index +   blockDim.x], getFloat(r1), getFloat(r2));
-        normalize(&out[index + 2*blockDim.x], &out[index + 3*blockDim.x], getFloat(r1), getFloat(r2));
+        boxMullerTransform(&out[index]               , &out[index +   blockDim.x], getFloat(r1), getFloat(r2));
+        boxMullerTransform(&out[index + 2*blockDim.x], &out[index + 3*blockDim.x], getFloat(r1), getFloat(r2));
     }
 
-    __device__ static void normalizedWriteOut256Bytes(cfloat *out, const uint &index,
+    __device__ static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        normalize(&out[index].x             , &out[index].y             , getFloat(r1), getFloat(r2));
-        normalize(&out[index + blockDim.x].x, &out[index + blockDim.x].y, getFloat(r3), getFloat(r4));
+        boxMullerTransform(&out[index].x             , &out[index].y             , getFloat(r1), getFloat(r2));
+        boxMullerTransform(&out[index + blockDim.x].x, &out[index + blockDim.x].y, getFloat(r3), getFloat(r4));
     }
 
-    __device__ static void normalizedWriteOut256Bytes(double *out, const uint &index,
+    __device__ static void boxMullerWriteOut128Bytes(double *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        normalize(&out[index], &out[index + blockDim.x], getDouble(r1, r2), getDouble(r3, r4));
+        boxMullerTransform(&out[index], &out[index + blockDim.x], getDouble(r1, r2), getDouble(r3, r4));
     }
 
-    __device__ static void normalizedWriteOut256Bytes(cdouble *out, const uint &index,
+    __device__ static void boxMullerWriteOut128Bytes(cdouble *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        normalize(&out[index].x, &out[index].y, getDouble(r1, r2), getDouble(r3, r4));
+        boxMullerTransform(&out[index].x, &out[index].y, getDouble(r1, r2), getDouble(r3, r4));
     }
 
     //Writes with boundary checking
 
-    __device__ static void partialWriteOut256Bytes(uchar *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(uchar *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index                 < elements) {out[index]                 =     r1;}
@@ -227,7 +227,7 @@ namespace kernel
         if (index + 15*blockDim.x < elements) {out[index + 15*blockDim.x] = r4>>24;}
     }
 
-    __device__ static void partialWriteOut256Bytes(char *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(char *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index                 < elements) {out[index]                 = (r1   )&0x1;}
@@ -248,7 +248,7 @@ namespace kernel
         if (index + 15*blockDim.x < elements) {out[index + 15*blockDim.x] = (r4>>3)&0x1;}
     }
 
-    __device__ static void partialWriteOut256Bytes(short *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(short *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index                < elements) {out[index]                =     r1;}
@@ -261,13 +261,13 @@ namespace kernel
         if (index + 7*blockDim.x < elements) {out[index + 7*blockDim.x] = r4>>16;}
     }
 
-    __device__ static void partialWriteOut256Bytes(ushort *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(ushort *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
-        partialWriteOut256Bytes((short*)(out), index, r1, r2, r3, r4, elements);
+        partialWriteOut128Bytes((short*)(out), index, r1, r2, r3, r4, elements);
     }
 
-    __device__ static void partialWriteOut256Bytes(int *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(int *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index                < elements) {out[index]                = r1;}
@@ -276,13 +276,13 @@ namespace kernel
         if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = r4;}
     }
 
-    __device__ static void partialWriteOut256Bytes(uint *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(uint *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
-        partialWriteOut256Bytes((int*)(out), index, r1, r2, r3, r4, elements);
+        partialWriteOut128Bytes((int*)(out), index, r1, r2, r3, r4, elements);
     }
 
-    __device__ static void partialWriteOut256Bytes(intl *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(intl *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         intl c1 = r2;
@@ -293,13 +293,13 @@ namespace kernel
         if (index + blockDim.x < elements) {out[index + blockDim.x] = c2;}
     }
 
-    __device__ static void partialWriteOut256Bytes(uintl *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(uintl *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
-        partialWriteOut256Bytes((intl*)(out), index, r1, r2, r3, r4, elements);
+        partialWriteOut128Bytes((intl*)(out), index, r1, r2, r3, r4, elements);
     }
 
-    __device__ static void partialWriteOut256Bytes(float *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(float *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index                < elements) {out[index]                = getFloat(r1);}
@@ -308,7 +308,7 @@ namespace kernel
         if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = getFloat(r4);}
     }
 
-    __device__ static void partialWriteOut256Bytes(cfloat *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(cfloat *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index              < elements) {
@@ -321,14 +321,14 @@ namespace kernel
         }
     }
 
-    __device__ static void partialWriteOut256Bytes(double *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(double *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index              < elements) {out[index]              = getDouble(r1, r2);}
         if (index + blockDim.x < elements) {out[index + blockDim.x] = getDouble(r3, r4);}
     }
 
-    __device__ static void partialWriteOut256Bytes(cdouble *out, const uint &index,
+    __device__ static void partialWriteOut128Bytes(cdouble *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index < elements) {
@@ -339,24 +339,24 @@ namespace kernel
 
     //Normalized writes with boundary checking
 
-    __device__ static void partialNormalizedWriteOut256Bytes(float *out, const uint &index,
+    __device__ static void partialBoxMullerWriteOut128Bytes(float *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         float n1, n2, n3, n4;
-        normalize(&n1, &n2, getFloat(r1), getFloat(r2));
-        normalize(&n3, &n4, getFloat(r3), getFloat(r4));
+        boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
+        boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
         if (index                < elements) {out[index]                = n1;}
         if (index +   blockDim.x < elements) {out[index +   blockDim.x] = n2;}
         if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = n3;}
         if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = n4;}
     }
 
-    __device__ static void partialNormalizedWriteOut256Bytes(cfloat *out, const uint &index,
+    __device__ static void partialBoxMullerWriteOut128Bytes(cfloat *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         float n1, n2, n3, n4;
-        normalize(&n1, &n2, getFloat(r1), getFloat(r2));
-        normalize(&n3, &n4, getFloat(r3), getFloat(r4));
+        boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
+        boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
         if (index              < elements) {
             out[index].x              = n1;
             out[index].y              = n2;
@@ -367,20 +367,20 @@ namespace kernel
         }
     }
 
-    __device__ static void partialNormalizedWriteOut256Bytes(double *out, const uint &index,
+    __device__ static void partialBoxMullerWriteOut128Bytes(double *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         double n1, n2;
-        normalize(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+        boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
         if (index              < elements) {out[index]              = n1;}
         if (index + blockDim.x < elements) {out[index + blockDim.x] = n2;}
     }
 
-    __device__ static void partialNormalizedWriteOut256Bytes(cdouble *out, const uint &index,
+    __device__ static void partialBoxMullerWriteOut128Bytes(cdouble *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         double n1, n2;
-        normalize(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+        boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
         if (index < elements) {
             out[index].x = n1;
             out[index].y = n2;
@@ -395,10 +395,10 @@ namespace kernel
         uint ctr[4] = {index+counter, 0, 0, lo};
         if (blockIdx.x != (gridDim.x - 1)) {
             philox(key, ctr);
-            writeOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
+            writeOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
         } else {
             philox(key, ctr);
-            partialWriteOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
+            partialWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
         }
     }
 
@@ -413,12 +413,12 @@ namespace kernel
             threefry(key, ctr, o);
             ctr[0] += elements;
             threefry(key, ctr, o + 2);
-            writeOut256Bytes(out, index, o[0], o[1], o[2], o[3]);
+            writeOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
         } else {
             threefry(key, ctr, o);
             ctr[0] += elements;
             threefry(key, ctr, o + 2);
-            partialWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3], elements);
+            partialWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
         }
     }
 
@@ -474,9 +474,9 @@ namespace kernel
                 __syncthreads();
             }
             if (i == iter - 1) {
-                partialWriteOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
+                partialWriteOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
             } else {
-                writeOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
+                writeOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
             }
             index += elementsPerBlockIteration;
         }
@@ -491,10 +491,10 @@ namespace kernel
         uint ctr[4] = {index+counter, 0, 0, lo};
         if (blockIdx.x != (gridDim.x - 1)) {
             philox(key, ctr);
-            normalizedWriteOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
+            boxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
         } else {
             philox(key, ctr);
-            partialNormalizedWriteOut256Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
+            partialBoxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
         }
     }
 
@@ -509,12 +509,12 @@ namespace kernel
             threefry(key, ctr, o);
             ctr[0] += elements;
             threefry(key, ctr, o + 2);
-            normalizedWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3]);
+            boxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
         } else {
             threefry(key, ctr, o);
             ctr[0] += elements;
             threefry(key, ctr, o + 2);
-            partialNormalizedWriteOut256Bytes(out, index, o[0], o[1], o[2], o[3], elements);
+            partialBoxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
         }
     }
 
@@ -571,9 +571,9 @@ namespace kernel
                 __syncthreads();
             }
             if (i == iter - 1) {
-                partialNormalizedWriteOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
+                partialBoxMullerWriteOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
             } else {
-                normalizedWriteOut256Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
+                boxMullerWriteOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
             }
             index += elementsPerBlockIteration;
         }
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 18d6a1628d..61251442aa 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -18,7 +18,7 @@ float getFloat(const uint * const num)
 
 //Writes without boundary checking
 
-void writeOut256Bytes_uchar(__global uchar *out, const uint * const index,
+void writeOut128Bytes_uchar(__global uchar *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]              =     *r1;
@@ -39,7 +39,7 @@ void writeOut256Bytes_uchar(__global uchar *out, const uint * const index,
     out[*index + 15*THREADS] = *r4>>24;
 }
 
-void writeOut256Bytes_char(__global char *out, const uint * const index,
+void writeOut128Bytes_char(__global char *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]              = (*r1   )&0x1;
@@ -60,7 +60,7 @@ void writeOut256Bytes_char(__global char *out, const uint * const index,
     out[*index + 15*THREADS] = (*r4>>3)&0x1;
 }
 
-void writeOut256Bytes_short(__global short *out, const uint * const index,
+void writeOut128Bytes_short(__global short *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]             =     *r1;
@@ -73,7 +73,7 @@ void writeOut256Bytes_short(__global short *out, const uint * const index,
     out[*index + 7*THREADS] = *r4>>16;
 }
 
-void writeOut256Bytes_ushort(__global ushort *out, const uint * const index,
+void writeOut128Bytes_ushort(__global ushort *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]             =     *r1;
@@ -86,7 +86,7 @@ void writeOut256Bytes_ushort(__global ushort *out, const uint * const index,
     out[*index + 7*THREADS] = *r4>>16;
 }
 
-void writeOut256Bytes_int(__global int *out, const uint * const index,
+void writeOut128Bytes_int(__global int *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]             = *r1;
@@ -95,7 +95,7 @@ void writeOut256Bytes_int(__global int *out, const uint * const index,
     out[*index + 3*THREADS] = *r4;
 }
 
-void writeOut256Bytes_uint(__global uint *out, const uint * const index,
+void writeOut128Bytes_uint(__global uint *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]             = *r1;
@@ -104,7 +104,7 @@ void writeOut256Bytes_uint(__global uint *out, const uint * const index,
     out[*index + 3*THREADS] = *r4;
 }
 
-void writeOut256Bytes_long(__global long *out, const uint * const index,
+void writeOut128Bytes_long(__global long *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     long c1 = *r2;
@@ -115,7 +115,7 @@ void writeOut256Bytes_long(__global long *out, const uint * const index,
     out[*index + THREADS] = c2;
 }
 
-void writeOut256Bytes_ulong(__global ulong *out, const uint * const index,
+void writeOut128Bytes_ulong(__global ulong *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     long c1 = *r2;
@@ -126,7 +126,7 @@ void writeOut256Bytes_ulong(__global ulong *out, const uint * const index,
     out[*index + THREADS] = c2;
 }
 
-void writeOut256Bytes_float(__global float *out, const uint * const index,
+void writeOut128Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]             = getFloat(r1);
@@ -141,7 +141,7 @@ void writeOut256Bytes_float(__global float *out, const uint * const index,
 
 //Writes with boundary checking
 
-void partialWriteOut256Bytes_uchar(__global uchar *out, const uint * const index,
+void partialWriteOut128Bytes_uchar(__global uchar *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index              < *elements) {out[*index]              =     *r1;}
@@ -162,7 +162,7 @@ void partialWriteOut256Bytes_uchar(__global uchar *out, const uint * const index
     if (*index + 15*THREADS < *elements) {out[*index + 15*THREADS] = *r4>>24;}
 }
 
-void partialWriteOut256Bytes_char(__global char *out, const uint * const index,
+void partialWriteOut128Bytes_char(__global char *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index              < *elements) {out[*index]              = (*r1   )&0x1;}
@@ -183,7 +183,7 @@ void partialWriteOut256Bytes_char(__global char *out, const uint * const index,
     if (*index + 15*THREADS < *elements) {out[*index + 15*THREADS] = (*r4>>3)&0x1;}
 }
 
-void partialWriteOut256Bytes_short(__global short *out, const uint * const index,
+void partialWriteOut128Bytes_short(__global short *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index             < *elements) {out[*index]             =     *r1;}
@@ -196,7 +196,7 @@ void partialWriteOut256Bytes_short(__global short *out, const uint * const index
     if (*index + 7*THREADS < *elements) {out[*index + 7*THREADS] = *r4>>16;}
 }
 
-void partialWriteOut256Bytes_ushort(__global ushort *out, const uint * const index,
+void partialWriteOut128Bytes_ushort(__global ushort *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index             < *elements) {out[*index]             =     *r1;}
@@ -209,7 +209,7 @@ void partialWriteOut256Bytes_ushort(__global ushort *out, const uint * const ind
     if (*index + 7*THREADS < *elements) {out[*index + 7*THREADS] = *r4>>16;}
 }
 
-void partialWriteOut256Bytes_int(__global int *out, const uint * const index,
+void partialWriteOut128Bytes_int(__global int *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index             < *elements) {out[*index]             = *r1;}
@@ -218,7 +218,7 @@ void partialWriteOut256Bytes_int(__global int *out, const uint * const index,
     if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
 }
 
-void partialWriteOut256Bytes_uint(__global uint *out, const uint * const index,
+void partialWriteOut128Bytes_uint(__global uint *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index             < *elements) {out[*index]             = *r1;}
@@ -227,7 +227,7 @@ void partialWriteOut256Bytes_uint(__global uint *out, const uint * const index,
     if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
 }
 
-void partialWriteOut256Bytes_long(__global long *out, const uint * const index,
+void partialWriteOut128Bytes_long(__global long *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     long c1 = *r2;
@@ -238,7 +238,7 @@ void partialWriteOut256Bytes_long(__global long *out, const uint * const index,
     if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
 }
 
-void partialWriteOut256Bytes_ulong(__global ulong *out, const uint * const index,
+void partialWriteOut128Bytes_ulong(__global ulong *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     long c1 = *r2;
@@ -249,7 +249,7 @@ void partialWriteOut256Bytes_ulong(__global ulong *out, const uint * const index
     if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
 }
 
-void partialWriteOut256Bytes_float(__global float *out, const uint * const index,
+void partialWriteOut128Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index             < *elements) {out[*index]             = getFloat(r1);}
@@ -271,8 +271,8 @@ void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
     *out2 = r*cos(theta);
 }
 
-//Normalized writes without boundary checking
-void normalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
+//BoxMuller writes without boundary checking
+void boxMullerWriteOut128Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     float n1, n2, n3, n4;
@@ -284,8 +284,8 @@ void normalizedWriteOut256Bytes_float(__global float *out, const uint * const in
     out[*index + 3*THREADS] = n4;
 }
 
-//Normalized writes with boundary checking
-void partialNormalizedWriteOut256Bytes_float(__global float *out, const uint * const index,
+//BoxMuller writes with boundary checking
+void partialBoxMullerWriteOut128Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     float n1, n2, n3, n4;
@@ -305,14 +305,14 @@ double getDouble(const uint * const num1, const uint * const num2)
     return ((double)num)/UINTLMAXDOUBLE;
 }
 
-void writeOut256Bytes_double(__global double *out, const uint * const index,
+void writeOut128Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     out[*index]           = getDouble(r1, r2);
     out[*index + THREADS] = getDouble(r3, r4);
 }
 
-void partialWriteOut256Bytes_double(__global double *out, const uint * const index,
+void partialWriteOut128Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     if (*index           < *elements) {out[*index]           = getDouble(r1, r2);}
@@ -320,7 +320,7 @@ void partialWriteOut256Bytes_double(__global double *out, const uint * const ind
 }
 
 #if RAND_DIST == 1
-void normalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
+void boxMullerWriteOut128Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
     double n1, n2;
@@ -329,7 +329,7 @@ void normalizedWriteOut256Bytes_double(__global double *out, const uint * const
     out[*index + THREADS] = n2;
 }
 
-void partialNormalizedWriteOut256Bytes_double(__global double *out, const uint * const index,
+void partialBoxMullerWriteOut128Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
     double n1, n2;
@@ -343,10 +343,10 @@ void partialNormalizedWriteOut256Bytes_double(__global double *out, const uint *
 #define PASTER(x,y) x ## _ ## y
 #define EVALUATOR(x,y) PASTER(x,y)
 #define EVALUATE_T(function) EVALUATOR(function, T)
-#define UNIFORM_WRITE EVALUATE_T(writeOut256Bytes)
-#define UNIFORM_PARTIAL_WRITE EVALUATE_T(partialWriteOut256Bytes)
-#define NORMAL_WRITE EVALUATE_T(normalizedWriteOut256Bytes)
-#define NORMAL_PARTIAL_WRITE EVALUATE_T(partialNormalizedWriteOut256Bytes)
+#define UNIFORM_WRITE EVALUATE_T(writeOut128Bytes)
+#define UNIFORM_PARTIAL_WRITE EVALUATE_T(partialWriteOut128Bytes)
+#define NORMAL_WRITE EVALUATE_T(boxMullerWriteOut128Bytes)
+#define NORMAL_PARTIAL_WRITE EVALUATE_T(partialBoxMullerWriteOut128Bytes)
 
 #if RAND_DIST == 0
 #define WRITE UNIFORM_WRITE

From 8b9756e8f31303110a195326319f4782bf9cdb54 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 25 Aug 2016 01:53:08 -0400
Subject: [PATCH 0784/2677] OPENCL: Disabling greedy assignment for csrmm and
 csrmv

- Was causing performance issues on intel and amd devices
---
 src/backend/opencl/kernel/csrmm.hpp | 7 ++++---
 src/backend/opencl/kernel/csrmv.hpp | 7 +++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index f5f2a24db5..835fc85459 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -46,9 +46,10 @@ namespace opencl
             try {
                 bool use_alpha = (alpha != scalar<T>(1.0));
                 bool use_beta = (beta != scalar<T>(0.0));
-                bool use_greedy = (getActiveDeviceType() == AFCL_DEVICE_TYPE_GPU) &&
-                    ((getActivePlatform() == AFCL_PLATFORM_AMD) ||
-                     (getActivePlatform() == AFCL_PLATFORM_NVIDIA));
+
+                // Using greedy indexing is causing performance issues on many platforms
+                // FIXME: Figure out why
+                bool use_greedy = false;
 
                 std::string ref_name =
                     std::string("csrmm_") +
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 92f27c7108..2415c8e62b 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -48,10 +48,9 @@ namespace opencl
                 bool use_alpha = (alpha != scalar<T>(1.0));
                 bool use_beta = (beta != scalar<T>(0.0));
 
-                // Use other metrics for this as well
-                bool use_greedy = (getActiveDeviceType() == AFCL_DEVICE_TYPE_GPU) &&
-                    ((getActivePlatform() == AFCL_PLATFORM_AMD) ||
-                     (getActivePlatform() == AFCL_PLATFORM_NVIDIA));
+                // Using greedy indexing is causing performance issues on many platforms
+                // FIXME: Figure out why
+                bool use_greedy = false;
 
                 // FIXME: Find a better number based on average non zeros per row
                 int threads = 64;

From 07a612a2efd33810b0f45d08d23887218c0ad9f9 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 25 Aug 2016 13:03:53 -0400
Subject: [PATCH 0785/2677] Relevant defines in cl file

---
 src/backend/opencl/kernel/random_engine.hpp       | 15 +++------------
 .../opencl/kernel/random_engine_mersenne.cl       |  6 +++++-
 .../opencl/kernel/random_engine_mersenne_init.cl  |  3 +++
 3 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 9eaad38665..bbea41b224 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -85,13 +85,7 @@ namespace opencl
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D THREADS=" << THREADS
                         << " -D RAND_DIST=" << kerIdx;
-                if (type == AF_RANDOM_MERSENNE_GP11213) {
-                    //These defines do not need to be a part of the hashing string
-                    //because they are the same for all Mersenne Twister kernels.
-                    options << " -D STATE_SIZE=" << STATE_SIZE
-                            << " -D TABLE_SIZE=" << TABLE_SIZE
-                            << " -D N=" << N;
-                } else {
+                if (type != AF_RANDOM_MERSENNE_GP11213) {
                     options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
                 }
                 if (std::is_same<T, double>::value) {
@@ -125,12 +119,9 @@ namespace opencl
             kc_t::iterator idx = kernelCaches[device].find(ref_name);
             kc_entry_t entry;
             if (idx == kernelCaches[device].end()) {
-                std::ostringstream options;
-                //These defines do not need to be a part of the hashing string
-                //because they are the same for all Mersenne Twister kernels.
-                options << " -D N=" << N << " -D TABLE_SIZE=" << TABLE_SIZE;
+                std::string emptyOptionString;
                 cl::Program prog;
-                buildProgram(prog, 1, &ker_str, &ker_len, options.str());
+                buildProgram(prog, 1, &ker_str, &ker_len, emptyOptionString);
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "initState");
                 kernelCaches[device][ref_name] = entry;
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index a2e16c9cf9..05a328f25b 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -42,7 +42,11 @@
  * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *******************************************************/
 
-#define divup(N, D) (((N) + (D) - 1)/(D));
+#define N 351
+#define TABLE_SIZE 16
+#define STATE_SIZE (256*3)
+
+#define divup(NUM, DEN) (((NUM) + (DEN) - 1)/(DEN));
 
 void read_table(__local uint * const localTable, __global const uint * const table)
 {
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index ef8f5e02fb..9303a05c04 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -42,6 +42,9 @@
  * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *******************************************************/
 
+#define N 351
+#define TABLE_SIZE 16
+
 __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
 {
     __local uint lstate[N];

From db123a4b8cfb57d87af4d0d5782eda3c8295ff60 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 25 Aug 2016 14:14:56 -0400
Subject: [PATCH 0786/2677] Separate API conditions

---
 include/af/defines.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/include/af/defines.h b/include/af/defines.h
index 575a04c8d3..543d9d94d2 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -398,7 +398,9 @@ typedef enum {
     AF_BINARY_MIN  = 2,
     AF_BINARY_MAX  = 3
 } af_binary_op;
+#endif
 
+#if AF_API_VERSION >=34
 typedef enum {
     AF_RANDOM_PHILOX_4X32_10 = 100,
     AF_RANDOM_THREEFRY_2X32_16 = 200,

From 080ad19b4fbf2668bf170b8a276671ca6b9dcd8d Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 25 Aug 2016 14:38:11 -0400
Subject: [PATCH 0787/2677] Added link to source of Mersenne Twister constants

---
 src/backend/MersenneTwister.hpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/backend/MersenneTwister.hpp b/src/backend/MersenneTwister.hpp
index f21ea92a89..f35ccceedf 100644
--- a/src/backend/MersenneTwister.hpp
+++ b/src/backend/MersenneTwister.hpp
@@ -42,6 +42,11 @@
  * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *******************************************************/
 
+/*
+ * These numbers have been obtained from the following file :
+ * https://github.com/MersenneTwister-Lab/MTGP/blob/master/cuda-sample/mtgp32dc-param-11213.c
+ */
+
 #pragma once
 
 #include <af/defines.h>

From 7a7b37082e33e4486de78e2cf7bfa01cbfb6d488 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 26 Aug 2016 16:26:42 -0400
Subject: [PATCH 0788/2677] Default Random Engine

Changed testing for setSeed to uints
---
 include/af/data.h                 | 167 -------------------------
 include/af/random_engine.h        | 201 +++++++++++++++++++++++++++++-
 src/api/c/data.cpp                |  79 ------------
 src/api/c/random_engine.cpp       | 120 +++++++++++++++++-
 src/api/cpp/data.cpp              |  74 -----------
 src/api/cpp/random_engine.cpp     |  86 ++++++++++++-
 src/api/unified/data.cpp          |  20 ---
 src/api/unified/random_engine.cpp |  34 ++++-
 test/random.cpp                   |  42 ++++---
 9 files changed, 458 insertions(+), 365 deletions(-)

diff --git a/include/af/data.h b/include/af/data.h
index f402b9e085..53993ebbe7 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -85,139 +85,6 @@ namespace af
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3, const af_dtype ty=(af_dtype)dtype_traits<T>::ctype);
 
-    /**
-        \param[in] dims is the dimensions of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p dims
-
-        \ingroup data_func_randu
-    */
-    AFAPI array randu(const dim4 &dims, const dtype ty=f32);
-
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0
-
-        \ingroup data_func_randu
-    */
-    AFAPI array randu(const dim_t d0, const dtype ty=f32);
-
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x \p d1
-
-        \ingroup data_func_randu
-    */
-    AFAPI array randu(const dim_t d0,
-                      const dim_t d1, const dtype ty=f32);
-
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x \p d1 x \p d2
-
-        \ingroup data_func_randu
-    */
-    AFAPI array randu(const dim_t d0,
-                      const dim_t d1, const dim_t d2, const dtype ty=f32);
-
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] d3 is the size of the fourth dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x \p d1 x \p d2 x \p d3
-
-        \ingroup data_func_randu
-    */
-    AFAPI array randu(const dim_t d0,
-                      const dim_t d1, const dim_t d2,
-                      const dim_t d3, const dtype ty=f32);
-
-    /**
-        \param[in] dims is the dimensions of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p dims
-
-        \ingroup data_func_randn
-    */
-    AFAPI array randn(const dim4 &dims, const dtype ty=f32);
-
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0
-
-        \ingroup data_func_randn
-    */
-    AFAPI array randn(const dim_t d0, const dtype ty=f32);
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x \p d1
-
-        \ingroup data_func_randn
-    */
-    AFAPI array randn(const dim_t d0,
-                      const dim_t d1, const dtype ty=f32);
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x \p d1 x \p d2
-
-        \ingroup data_func_randn
-    */
-    AFAPI array randn(const dim_t d0,
-                      const dim_t d1, const dim_t d2, const dtype ty=f32);
-
-    /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] d3 is the size of the fourth dimension
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x \p d1 x \p d2 x \p d3
-
-        \ingroup data_func_randn
-    */
-    AFAPI array randn(const dim_t d0,
-                      const dim_t d1, const dim_t d2,
-                      const dim_t d3, const dtype ty=f32);
-
-    /**
-        \param[in] seed is a 64 bit unsigned integer
-
-        \ingroup data_func_setseed
-    */
-    AFAPI void setSeed(const uintl seed);
-
-    /**
-        \returns seed which is a 64 bit unsigned integer
-
-        \ingroup data_func_getseed
-    */
-    AFAPI uintl getSeed();
-
-
     /**
         \param[in] dims is dim4 for size of all dimensions
         \param[in] ty is the type of array to generate
@@ -627,40 +494,6 @@ extern "C" {
     AFAPI af_err af_iota(af_array *out, const unsigned ndims, const dim_t * const dims,
                          const unsigned t_ndims, const dim_t * const tdims, const af_dtype type);
 
-    /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
-
-       \ingroup data_func_randu
-    */
-    AFAPI af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
-
-    /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
-
-       \ingroup data_func_randn
-    */
-    AFAPI af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
-
-    /**
-        \param[in] seed is a 64 bit unsigned integer
-
-        \ingroup data_func_setseed
-    */
-    AFAPI af_err af_set_seed(const uintl seed);
-
-    /**
-        \param[out] seed which is a 64 bit unsigned integer
-
-        \ingroup data_func_getseed
-    */
-    AFAPI af_err af_get_seed(uintl *seed);
-
 
     /**
         \param[out] out is the generated array
diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index b73aa9f36a..742c19908a 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -40,10 +40,151 @@ namespace af
             array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
             array normal(const dim4& dims, const dtype ty = f32);
 
+            void setType(const randomType type);
             void setSeed(const uintl seed);
             uintl getSeed(void) const;
             af_random_engine get() const;
     };
+
+    /**
+        \param[in] dims is the dimensions of the array to be generated
+        \param[in] ty is the type of the array
+
+        \return array of size \p dims
+
+        \ingroup data_func_randu
+    */
+    AFAPI array randu(const dim4 &dims, const dtype ty=f32);
+
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0
+
+        \ingroup data_func_randu
+    */
+    AFAPI array randu(const dim_t d0, const dtype ty=f32);
+
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] d1 is the size of the second dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0 x \p d1
+
+        \ingroup data_func_randu
+    */
+    AFAPI array randu(const dim_t d0,
+                      const dim_t d1, const dtype ty=f32);
+
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] d1 is the size of the second dimension
+        \param[in] d2 is the size of the third dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0 x \p d1 x \p d2
+
+        \ingroup data_func_randu
+    */
+    AFAPI array randu(const dim_t d0,
+                      const dim_t d1, const dim_t d2, const dtype ty=f32);
+
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] d1 is the size of the second dimension
+        \param[in] d2 is the size of the third dimension
+        \param[in] d3 is the size of the fourth dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0 x \p d1 x \p d2 x \p d3
+
+        \ingroup data_func_randu
+    */
+    AFAPI array randu(const dim_t d0,
+                      const dim_t d1, const dim_t d2,
+                      const dim_t d3, const dtype ty=f32);
+
+    /**
+        \param[in] dims is the dimensions of the array to be generated
+        \param[in] ty is the type of the array
+
+        \return array of size \p dims
+
+        \ingroup data_func_randn
+    */
+    AFAPI array randn(const dim4 &dims, const dtype ty=f32);
+
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0
+
+        \ingroup data_func_randn
+    */
+    AFAPI array randn(const dim_t d0, const dtype ty=f32);
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] d1 is the size of the second dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0 x \p d1
+
+        \ingroup data_func_randn
+    */
+    AFAPI array randn(const dim_t d0,
+                      const dim_t d1, const dtype ty=f32);
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] d1 is the size of the second dimension
+        \param[in] d2 is the size of the third dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0 x \p d1 x \p d2
+
+        \ingroup data_func_randn
+    */
+    AFAPI array randn(const dim_t d0,
+                      const dim_t d1, const dim_t d2, const dtype ty=f32);
+
+    /**
+        \param[in] d0 is the size of the first dimension
+        \param[in] d1 is the size of the second dimension
+        \param[in] d2 is the size of the third dimension
+        \param[in] d3 is the size of the fourth dimension
+        \param[in] ty is the type of the array
+
+        \return array of size \p d0 x \p d1 x \p d2 x \p d3
+
+        \ingroup data_func_randn
+    */
+    AFAPI array randn(const dim_t d0,
+                      const dim_t d1, const dim_t d2,
+                      const dim_t d3, const dtype ty=f32);
+
+    /**
+        \param[in] rtype is the type of the random number generator
+
+        \ingroup data_func_set_type
+    */
+    AFAPI void setDefaultRandomEngine(randomType rtype);
+
+    /**
+        \param[in] seed is a 64 bit unsigned integer
+
+        \ingroup data_func_setseed
+    */
+    AFAPI void setSeed(const uintl seed);
+
+    /**
+        \returns seed which is a 64 bit unsigned integer
+
+        \ingroup data_func_getseed
+    */
+    AFAPI uintl getSeed();
+
 }
 #endif
 
@@ -71,6 +212,17 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_retain_random_engine(af_random_engine *out, const af_random_engine engine);
+
+    /**
+       C Interface for changing random engine type
+
+       \param[in]   engine is the random engine object
+       \param[in]   rtype is the type of the random number generator
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype);
+
     /**
        C Interface for creating an array of uniform numbers using a random engine
 
@@ -100,12 +252,21 @@ extern "C" {
     /**
        C Interface for setting the seed of a random engine
 
-       \param[in]   engine is the random engine object
+       \param[out]  engine is the random engine object
        \param[in]   seed is the initializing seed of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine);
+    AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed);
+
+    /**
+       C Interface for setting the type of the default random engine
+
+       \param[in]   rtype is the type of the random number generator
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_default_random_engine_set_type(const af_random_type rtype);
 
     /**
        C Interface for getting the seed of a random engine
@@ -125,6 +286,42 @@ extern "C" {
     */
     AFAPI af_err af_release_random_engine(af_random_engine engine);
 
+    //General rand calls
+
+    /**
+        \param[out] out is the generated array
+        \param[in] ndims is size of dimension array \p dims
+        \param[in] dims is the array containing sizes of the dimension
+        \param[in] type is the type of array to generate
+
+       \ingroup data_func_randu
+    */
+    AFAPI af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+
+    /**
+        \param[out] out is the generated array
+        \param[in] ndims is size of dimension array \p dims
+        \param[in] dims is the array containing sizes of the dimension
+        \param[in] type is the type of array to generate
+
+       \ingroup data_func_randn
+    */
+    AFAPI af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+
+    /**
+        \param[in] seed is a 64 bit unsigned integer
+
+        \ingroup data_func_setseed
+    */
+    AFAPI af_err af_set_seed(const uintl seed);
+
+    /**
+        \param[out] seed which is a 64 bit unsigned integer
+
+        \ingroup data_func_getseed
+    */
+    AFAPI af_err af_get_seed(uintl *seed);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 295fa83cc7..7e40fe2f94 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -17,7 +17,6 @@
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
-#include <random.hpp>
 #include <math.hpp>
 #include <range.hpp>
 #include <iota.hpp>
@@ -139,90 +138,12 @@ af_err af_constant_ulong(af_array *result, const uintl val,
     return AF_SUCCESS;
 }
 
-template<typename T>
-static inline af_array randn_(const af::dim4 &dims)
-{
-    return getHandle(randn<T>(dims));
-}
-
-template<typename T>
-static inline af_array randu_(const af::dim4 &dims)
-{
-    return getHandle(randu<T>(dims));
-}
-
 template<typename T>
 static inline af_array identity_(const af::dim4 &dims)
 {
     return getHandle(detail::identity<T>(dims));
 }
 
-af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
-    try {
-        af_array result;
-        AF_CHECK(af_init());
-
-        dim4 d = verifyDims(ndims, dims);
-
-        switch(type) {
-        case f32:   result = randu_<float  >(d);    break;
-        case c32:   result = randu_<cfloat >(d);    break;
-        case f64:   result = randu_<double >(d);    break;
-        case c64:   result = randu_<cdouble>(d);    break;
-        case s32:   result = randu_<int    >(d);    break;
-        case u32:   result = randu_<uint   >(d);    break;
-        case s64:   result = randu_<intl   >(d);    break;
-        case u64:   result = randu_<uintl  >(d);    break;
-        case s16:   result = randu_<short  >(d);    break;
-        case u16:   result = randu_<ushort >(d);    break;
-        case u8:    result = randu_<uchar  >(d);    break;
-        case b8:    result = randu_<char  >(d);    break;
-        default:    TYPE_ERROR(3, type);
-        }
-        std::swap(*out, result);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
-    try {
-        af_array result;
-        AF_CHECK(af_init());
-
-        dim4 d = verifyDims(ndims, dims);
-
-        switch(type) {
-        case f32:   result = randn_<float  >(d);    break;
-        case c32:   result = randn_<cfloat >(d);    break;
-        case f64:   result = randn_<double >(d);    break;
-        case c64:   result = randn_<cdouble>(d);    break;
-        default:    TYPE_ERROR(3, type);
-        }
-        std::swap(*out, result);
-    }
-    CATCHALL
-    return AF_SUCCESS;
-}
-
-af_err af_set_seed(const uintl seed)
-{
-    try {
-        setSeed(seed);
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
-af_err af_get_seed(uintl *seed)
-{
-    try {
-        *seed = getSeed();
-    } CATCHALL;
-    return AF_SUCCESS;
-}
-
 af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     try {
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 36d0861287..b16300a64f 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -17,6 +17,7 @@
 #include <err_common.hpp>
 #include <handle.hpp>
 #include <random_engine.hpp>
+#include <random.hpp>
 #include <MersenneTwister.hpp>
 
 using detail::cfloat;
@@ -25,6 +26,10 @@ using detail::uchar;
 using detail::uniformDistribution;
 using detail::normalDistribution;
 using detail::initMersenneState;
+using detail::randu;
+using detail::randn;
+using detail::setSeed;
+using detail::getSeed;
 
 using common::MaxBlocks;
 using common::TableLength;
@@ -49,6 +54,8 @@ class RandomEngine
     af_array recursion_table;
     af_array temper_table;
     af_array state;
+
+    RandomEngine(void) : type(AF_RANDOM_THREEFRY), seed(0), counter(0) {}
 };
 
 af_random_engine getRandomEngineHandle(const RandomEngine engine)
@@ -58,6 +65,12 @@ af_random_engine getRandomEngineHandle(const RandomEngine engine)
     return static_cast<af_random_engine>(engineHandle);
 }
 
+af_random_engine DefaultRandomEngine(void)
+{
+    static RandomEngine r;
+    return static_cast<af_random_engine>(&r);
+}
+
 RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 {
     return (RandomEngine *)engineHandle;
@@ -143,11 +156,44 @@ af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engi
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
+af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype)
+{
+    try {
+        RandomEngine e = *(getRandomEngine(engine));
+        if (rtype != e.type) {
+            bool empty;
+            if (rtype == AF_RANDOM_MERSENNE_GP11213) {
+                af_is_empty(&empty, e.state);
+                if (empty) {
+                    AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
+                    AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
+                    AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
+                    e.mask = mask;
+                    AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
+                    AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
+                    e.state = getHandle(initMersenneState(e.seed, getArray<uint>(e.recursion_table)));
+                }
+            }
+            e.type = rtype;
+        }
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_default_random_engine_set_type(const af_random_type rtype)
+{
+    try {
+        af_random_engine e = DefaultRandomEngine();
+        af_random_engine_set_type(&e, rtype);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
 {
     try {
         AF_CHECK(af_init());
-        RandomEngine *e = getRandomEngine(engine);
+        RandomEngine *e = getRandomEngine(*engine);
         e->seed = seed;
         if (e->type == AF_RANDOM_MERSENNE_GP11213) {
             initMersenneState(getWritableArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
@@ -239,3 +285,73 @@ af_err af_release_random_engine(af_random_engine engineHandle)
     CATCHALL;
     return AF_SUCCESS;
 }
+
+af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    try {
+        af_array result;
+        AF_CHECK(af_init());
+
+        RandomEngine *e = getRandomEngine(DefaultRandomEngine());
+        af::dim4 d = verifyDims(ndims, dims);
+
+        switch(type) {
+        case f32: result = uniformDistribution_<float  >(d, e); break;
+        case c32: result = uniformDistribution_<cfloat >(d, e); break;
+        case f64: result = uniformDistribution_<double >(d, e); break;
+        case c64: result = uniformDistribution_<cdouble>(d, e); break;
+        case s32: result = uniformDistribution_<int    >(d, e); break;
+        case u32: result = uniformDistribution_<uint   >(d, e); break;
+        case s64: result = uniformDistribution_<intl   >(d, e); break;
+        case u64: result = uniformDistribution_<uintl  >(d, e); break;
+        case s16: result = uniformDistribution_<short  >(d, e); break;
+        case u16: result = uniformDistribution_<ushort >(d, e); break;
+        case u8:  result = uniformDistribution_<uchar  >(d, e); break;
+        case b8:  result = uniformDistribution_<char   >(d, e); break;
+        default:  TYPE_ERROR(3, type);
+        }
+        std::swap(*out, result);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    try {
+        af_array result;
+        AF_CHECK(af_init());
+
+        RandomEngine *e = getRandomEngine(DefaultRandomEngine());
+        af::dim4 d = verifyDims(ndims, dims);
+
+        switch(type) {
+        case f32: result = normalDistribution_<float  >(d, e); break;
+        case c32: result = normalDistribution_<cfloat >(d, e); break;
+        case f64: result = normalDistribution_<double >(d, e); break;
+        case c64: result = normalDistribution_<cdouble>(d, e); break;
+        default:  TYPE_ERROR(3, type);
+        }
+        std::swap(*out, result);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_set_seed(const uintl seed)
+{
+    try {
+        af_random_engine e = DefaultRandomEngine();
+        af_random_engine_set_seed(&e, seed);
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_get_seed(uintl *seed)
+{
+    try {
+        af_random_engine_get_seed(seed, DefaultRandomEngine());
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 3b7854a20b..358853537d 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -122,80 +122,6 @@ namespace af
 
 #undef CONSTANT
 
-    array randu(const dim4 &dims, const af::dtype type)
-    {
-        af_array res;
-        AF_THROW(af_randu(&res, dims.ndims(), dims.get(), type));
-        return array(res);
-    }
-
-    array randu(const dim_t d0, const af::dtype ty)
-    {
-        return randu(dim4(d0), ty);
-    }
-
-    array randu(const dim_t d0,
-                const dim_t d1, const af::dtype ty)
-    {
-        return randu(dim4(d0, d1), ty);
-    }
-
-    array randu(const dim_t d0,
-                const dim_t d1, const dim_t d2, const af::dtype ty)
-    {
-        return randu(dim4(d0, d1, d2), ty);
-    }
-
-    array randu(const dim_t d0,
-                const dim_t d1, const dim_t d2,
-                const dim_t d3, const af::dtype ty)
-    {
-        return randu(dim4(d0, d1, d2, d3), ty);
-    }
-
-    array randn(const dim4 &dims, const af::dtype type)
-    {
-        af_array res;
-        AF_THROW(af_randn(&res, dims.ndims(), dims.get(), type));
-        return array(res);
-    }
-
-    array randn(const dim_t d0, const af::dtype ty)
-    {
-        return randn(dim4(d0), ty);
-    }
-
-    array randn(const dim_t d0,
-                const dim_t d1, const af::dtype ty)
-    {
-        return randn(dim4(d0, d1), ty);
-    }
-
-    array randn(const dim_t d0,
-                const dim_t d1, const dim_t d2, const af::dtype ty)
-    {
-        return randn(dim4(d0, d1, d2), ty);
-    }
-
-    array randn(const dim_t d0,
-                const dim_t d1, const dim_t d2,
-                const dim_t d3, const af::dtype ty)
-    {
-        return randn(dim4(d0, d1, d2, d3), ty);
-    }
-
-    void setSeed(const uintl seed)
-    {
-        AF_THROW(af_set_seed(seed));
-    }
-
-    uintl getSeed()
-    {
-        uintl seed = 0;
-        AF_THROW(af_get_seed(&seed));
-        return seed;
-    }
-
     array range(const dim4 &dims, const int seq_dim, const af::dtype ty)
     {
         af_array out;
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index 01502a03ae..6ceb04a4b7 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -36,6 +36,11 @@ namespace af
         return *this;
     }
 
+    void randomEngine::setType(const randomType type)
+    {
+        AF_THROW(af_random_engine_set_type(&engine, type));
+    }
+
     array randomEngine::uniform(const dim_t dim0, const dtype ty)
     {
         dim4 d(dim0, 1, 1, 1);
@@ -100,7 +105,7 @@ namespace af
 
     void randomEngine::setSeed(const uintl seed)
     {
-        AF_THROW(af_random_engine_set_seed(seed, engine));
+        AF_THROW(af_random_engine_set_seed(&engine, seed));
     }
 
     uintl randomEngine::getSeed(void) const
@@ -115,4 +120,83 @@ namespace af
         return engine;
     }
 
+    array randu(const dim4 &dims, const af::dtype type)
+    {
+        af_array res;
+        AF_THROW(af_randu(&res, dims.ndims(), dims.get(), type));
+        return array(res);
+    }
+
+    array randu(const dim_t d0, const af::dtype ty)
+    {
+        return randu(dim4(d0), ty);
+    }
+
+    array randu(const dim_t d0,
+                const dim_t d1, const af::dtype ty)
+    {
+        return randu(dim4(d0, d1), ty);
+    }
+
+    array randu(const dim_t d0,
+                const dim_t d1, const dim_t d2, const af::dtype ty)
+    {
+        return randu(dim4(d0, d1, d2), ty);
+    }
+
+    array randu(const dim_t d0,
+                const dim_t d1, const dim_t d2,
+                const dim_t d3, const af::dtype ty)
+    {
+        return randu(dim4(d0, d1, d2, d3), ty);
+    }
+
+    array randn(const dim4 &dims, const af::dtype type)
+    {
+        af_array res;
+        AF_THROW(af_randn(&res, dims.ndims(), dims.get(), type));
+        return array(res);
+    }
+
+    array randn(const dim_t d0, const af::dtype ty)
+    {
+        return randn(dim4(d0), ty);
+    }
+
+    array randn(const dim_t d0,
+                const dim_t d1, const af::dtype ty)
+    {
+        return randn(dim4(d0, d1), ty);
+    }
+
+    array randn(const dim_t d0,
+                const dim_t d1, const dim_t d2, const af::dtype ty)
+    {
+        return randn(dim4(d0, d1, d2), ty);
+    }
+
+    array randn(const dim_t d0,
+                const dim_t d1, const dim_t d2,
+                const dim_t d3, const af::dtype ty)
+    {
+        return randn(dim4(d0, d1, d2, d3), ty);
+    }
+
+    void setDefaultRandomEngine(randomType rtype)
+    {
+        AF_THROW(af_default_random_engine_set_type(rtype));
+    }
+
+    void setSeed(const uintl seed)
+    {
+        AF_THROW(af_set_seed(seed));
+    }
+
+    uintl getSeed()
+    {
+        uintl seed = 0;
+        AF_THROW(af_get_seed(&seed));
+        return seed;
+    }
+
 }
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index 9579e094e4..256dab27ca 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -49,26 +49,6 @@ af_err af_iota(af_array *out, const unsigned ndims, const dim_t * const dims,
     return CALL(out, ndims, dims, t_ndims, tdims, type);
 }
 
-af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
-    return CALL(out, ndims, dims, type);
-}
-
-af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
-    return CALL(out, ndims, dims, type);
-}
-
-af_err af_set_seed(const uintl seed)
-{
-    return CALL(seed);
-}
-
-af_err af_get_seed(uintl *seed)
-{
-    return CALL(seed);
-}
-
 af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     return CALL(out, ndims, dims, type);
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
index a63011190c..1910108089 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random_engine.cpp
@@ -21,6 +21,16 @@ af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engi
     return CALL(outHandle, engineHandle);
 }
 
+af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype)
+{
+    return CALL(engine, rtype);
+}
+
+af_err af_default_random_engine_set_type(const af_random_type rtype)
+{
+    return CALL(rtype);
+}
+
 af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     return CALL(arr, engine, ndims, dims, type);
@@ -36,12 +46,32 @@ af_err af_release_random_engine(af_random_engine engineHandle)
     return CALL(engineHandle);
 }
 
-af_err af_random_engine_set_seed(const uintl seed, af_random_engine engine)
+af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
 {
-    return CALL(seed, engine);
+    return CALL(engine, seed);
 }
 
 af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
 {
     return CALL(seed, engine);
 }
+
+af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    return CALL(out, ndims, dims, type);
+}
+
+af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+{
+    return CALL(out, ndims, dims, type);
+}
+
+af_err af_set_seed(const uintl seed)
+{
+    return CALL(seed);
+}
+
+af_err af_get_seed(uintl *seed)
+{
+    return CALL(seed);
+}
diff --git a/test/random.cpp b/test/random.cpp
index f2919ec8ab..f8c7476fa8 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -51,9 +51,6 @@ class Random_norm : public ::testing::Test
         }
 };
 
-// create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble> TestTypesNorm;
-
 template<typename T>
 class RandomEngine : public ::testing::Test
 {
@@ -70,12 +67,21 @@ class RandomEngineSeed : public ::testing::Test
         }
 };
 
+template<typename T>
+class RandomSeed : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {
+        }
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypesNorm;
 // register the type list
 TYPED_TEST_CASE(Random_norm, TestTypesNorm);
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double> TestTypesEngine;
-
 // register the type list
 TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
 
@@ -83,6 +89,11 @@ typedef ::testing::Types<float, double> TestTypesEngineSeed;
 // register the type list
 TYPED_TEST_CASE(RandomEngineSeed, TestTypesEngineSeed);
 
+// create a list of types to be tested
+typedef ::testing::Types<unsigned> TestTypesSeed;
+// register the type list
+TYPED_TEST_CASE(RandomSeed, TestTypesSeed);
+
 template<typename T>
 void randuTest(af::dim4 & dims)
 {
@@ -181,7 +192,7 @@ TEST(Random, CPP)
 }
 
 template<typename T>
-void testSetSeed(const uintl seed0, const uintl seed1, bool is_norm = false)
+void testSetSeed(const uintl seed0, const uintl seed1)
 {
 
     if (noDoubleTests<T>()) return;
@@ -192,14 +203,14 @@ void testSetSeed(const uintl seed0, const uintl seed1, bool is_norm = false)
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     af::setSeed(seed0);
-    af::array in0 = is_norm ? af::randn(num, ty) : af::randu(num, ty);
+    af::array in0 = af::randu(num, ty);
 
     af::setSeed(seed1);
-    af::array in1 = is_norm ? af::randn(num, ty) : af::randu(num, ty);
+    af::array in1 = af::randu(num, ty);
 
     af::setSeed(seed0);
-    af::array in2 = is_norm ? af::randn(num, ty) : af::randu(num, ty);
-    af::array in3 = is_norm ? af::randn(num, ty) : af::randu(num, ty);
+    af::array in2 = af::randu(num, ty);
+    af::array in3 = af::randu(num, ty);
 
     std::vector<T> h_in0(num);
     std::vector<T> h_in1(num);
@@ -217,24 +228,19 @@ void testSetSeed(const uintl seed0, const uintl seed1, bool is_norm = false)
 
         // Verify different arrays created with different seeds differ
         // b8 and u9 can clash because they generate a small set of values
-        if (ty != b8 && ty != u8) ASSERT_NE(h_in0[i], h_in1[i]);
+        if (ty != b8 && ty != u8) ASSERT_NE(h_in0[i], h_in1[i]) << "at : " << i;
 
         // Verify different arrays created one after the other with same seed differ
         // b8 and u9 can clash because they generate a small set of values
-        if (ty != b8 && ty != u8) ASSERT_NE(h_in2[i], h_in3[i]);
+        if (ty != b8 && ty != u8) ASSERT_NE(h_in2[i], h_in3[i]) << "at : " << i;
     }
 
     af::setSeed(orig_seed); // Reset the seed
 }
 
-TYPED_TEST(Random, setSeed)
-{
-    testSetSeed<TypeParam>(10101, 23232, false);
-}
-
-TYPED_TEST(Random_norm, setSeed)
+TYPED_TEST(RandomSeed, setSeed)
 {
-    testSetSeed<TypeParam>(456, 789, true);
+    testSetSeed<TypeParam>(10101, 23232);
 }
 
 template<typename T>

From b6a8215e6a4dd313945d795f3924cce7f8682baf Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 26 Aug 2016 17:38:10 -0400
Subject: [PATCH 0789/2677] Edit function signature to set default engine

---
 include/af/random_engine.h        | 2 +-
 src/api/c/random_engine.cpp       | 2 +-
 src/api/cpp/random_engine.cpp     | 2 +-
 src/api/unified/random_engine.cpp | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 742c19908a..f5a134fbc9 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -266,7 +266,7 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_default_random_engine_set_type(const af_random_type rtype);
+    AFAPI af_err af_set_default_random_engine(const af_random_type rtype);
 
     /**
        C Interface for getting the seed of a random engine
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index b16300a64f..bd632f7f01 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -180,7 +180,7 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type
     return AF_SUCCESS;
 }
 
-af_err af_default_random_engine_set_type(const af_random_type rtype)
+af_err af_set_default_random_engine(const af_random_type rtype)
 {
     try {
         af_random_engine e = DefaultRandomEngine();
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index 6ceb04a4b7..ded1ec6b24 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -184,7 +184,7 @@ namespace af
 
     void setDefaultRandomEngine(randomType rtype)
     {
-        AF_THROW(af_default_random_engine_set_type(rtype));
+        AF_THROW(af_set_default_random_engine(rtype));
     }
 
     void setSeed(const uintl seed)
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
index 1910108089..f2d94f4f02 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random_engine.cpp
@@ -26,7 +26,7 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type
     return CALL(engine, rtype);
 }
 
-af_err af_default_random_engine_set_type(const af_random_type rtype)
+af_err af_set_default_random_engine(const af_random_type rtype)
 {
     return CALL(rtype);
 }

From 64e7cc2b9848c704f6e359f864fda1e21ba0911b Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 26 Aug 2016 17:46:15 -0400
Subject: [PATCH 0790/2677] Comment clarifying -2 multiplier in box muller

---
 src/backend/cpu/kernel/random_engine.hpp         | 3 +++
 src/backend/cuda/kernel/random_engine.hpp        | 3 +++
 src/backend/opencl/kernel/random_engine_write.cl | 3 +++
 3 files changed, 9 insertions(+)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index de3d03c6c5..d50141c683 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -128,6 +128,9 @@ namespace kernel
     template <typename T>
     void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
     {
+        /*
+         * The log of a real value x where 0 < x < 1 is negative.
+         */
         T r = sqrt((T)(-2.0) * log(r1));
         T theta = 2 * (T)PI_VAL * (r2);
         *out1 = r*sin(theta);
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index a8c97b04d2..5fe9dcc16b 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -43,6 +43,9 @@ namespace kernel
     template <typename T>
     __device__ static void boxMullerTransform(T * const out1, T * const out2, const T &r1, const T &r2)
     {
+        /*
+         * The log of a real value x where 0 < x < 1 is negative.
+         */
         T r = sqrt((T)(-2.0) * log(r1));
         T theta = 2 * (T)PI_VAL * r2;
         *out1 = r*sin(theta);
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 61251442aa..6e76862b8d 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -261,6 +261,9 @@ void partialWriteOut128Bytes_float(__global float *out, const uint * const index
 #if RAND_DIST == 1
 void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
 {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
 #if defined(IS_APPLE) // Because Apple is.. "special"
     T r = sqrt((T)(-2.0) * log10(r1) * (T)log10_val);
 #else

From 13cf76250b72b95dc8ef27de2648846b7dfa72ab Mon Sep 17 00:00:00 2001
From: Dmitry Trifonov <dmitry.trifonov@hp.com>
Date: Thu, 25 Aug 2016 15:55:19 -0700
Subject: [PATCH 0791/2677] fix cmake configuration to build arrayfire in
 Conan.io environment:   * don't use CMAKE_SOURCE_DIR, CMAKE_BINARY_DIR   *
 don't use CMAKE_MODULE_PATH explicitly   * modify FindCBLAS.cmake to work
 with lapacke

---
 CMakeLists.txt                      | 26 +++++++++++++-------------
 CMakeModules/CPackConfig.cmake      |  2 +-
 CMakeModules/CUDACheckCompute.cmake |  4 ++--
 CMakeModules/FindCBLAS.cmake        |  4 ++--
 CMakeModules/Version.cmake          | 10 +++++-----
 docs/CMakeLists.txt                 |  8 ++++----
 examples/CMakeLists.txt             |  2 +-
 src/api/unified/CMakeLists.txt      |  2 +-
 src/backend/cpu/CMakeLists.txt      | 14 +++++++-------
 src/backend/cuda/CMakeLists.txt     | 16 ++++++++--------
 src/backend/opencl/CMakeLists.txt   | 23 ++++++++++-------------
 test/CMakeLists.txt                 |  4 ++--
 12 files changed, 56 insertions(+), 59 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6b9336a712..a246ee8a16 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -4,7 +4,7 @@ PROJECT(ARRAYFIRE)
 SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
 
 SET(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
-INCLUDE(${CMAKE_MODULE_PATH}/UploadCoveralls.cmake)
+INCLUDE(UploadCoveralls)
 INCLUDE(AFInstallDirs)
 
 OPTION(BUILD_TEST "Build Tests" ON)
@@ -42,7 +42,7 @@ if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
 endif()
 
 OPTION(MIN_BUILD_TIME "This flag compiles ArrayFire with O0, which is the fastest way to compile" OFF)
-INCLUDE(${CMAKE_MODULE_PATH}/MinBuildTime.cmake)
+INCLUDE(MinBuildTime)
 
 FIND_PACKAGE(FreeImage)
 IF(FREEIMAGE_FOUND)
@@ -60,7 +60,7 @@ IF(BUILD_GRAPHICS)
     IF(USE_SYSTEM_FORGE)
         FIND_PACKAGE(Forge)
     ELSE(USE_SYSTEM_FORGE)
-        INCLUDE("${CMAKE_MODULE_PATH}/build_forge.cmake")
+        INCLUDE(build_forge)
     ENDIF(USE_SYSTEM_FORGE)
 
     IF(FORGE_FOUND)
@@ -156,10 +156,10 @@ ELSE(${UNIX}) #Windows
 ENDIF()
 
 # Architechture Definitions
-INCLUDE(${CMAKE_MODULE_PATH}/TargetArch.cmake)
+INCLUDE(TargetArch)
 target_architecture(ARCH)
 
-INCLUDE(${CMAKE_MODULE_PATH}/Version.cmake)
+INCLUDE(Version)
 
 IF(${BUILD_CPU})
     ADD_SUBDIRECTORY(src/backend/cpu)
@@ -182,7 +182,7 @@ IF(${BUILD_DOCS})
     ADD_SUBDIRECTORY(docs)
 ENDIF()
 
-ADD_EXECUTABLE(bin2cpp ${CMAKE_MODULE_PATH}/bin2cpp.cpp)
+ADD_EXECUTABLE(bin2cpp ${PROJECT_SOURCE_DIR}/CMakeModules/bin2cpp.cpp)
 
 IF(${BUILD_TEST})
     ENABLE_TESTING()
@@ -207,12 +207,12 @@ INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include/" DESTINATION "${AF_INSTA
 ## The ArrayFire version file is generated and won't be included above, install
 ## it separately.
 INSTALL(FILES
-    ${CMAKE_SOURCE_DIR}/include/af/version.h DESTINATION "${AF_INSTALL_INC_DIR}/af/"
+    ${PROJECT_SOURCE_DIR}/include/af/version.h DESTINATION "${AF_INSTALL_INC_DIR}/af/"
     COMPONENT headers
 )
 
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-    INSTALL(DIRECTORY "${CMAKE_BINARY_DIR}/third_party/forge/lib/" DESTINATION "${AF_INSTALL_LIB_DIR}"
+    INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/lib/" DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT libraries
     )
 ENDIF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
@@ -222,7 +222,7 @@ SET(INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 SET(BACKEND_DIR "src/backend/\${lowerbackend}")
 SET(UNIFIED_DIR "src/api/unified")
 CONFIGURE_FILE(
-    ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+    ${PROJECT_SOURCE_DIR}/CMakeModules/ArrayFireConfig.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfig.cmake
     @ONLY)
 
@@ -233,11 +233,11 @@ SET(INCLUDE_DIR "\${CMAKE_CURRENT_LIST_DIR}/${reldir}/include")
 set(BACKEND_DIR)
 set(UNIFIED_DIR)
 CONFIGURE_FILE(
-    ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+    ${PROJECT_SOURCE_DIR}/CMakeModules/ArrayFireConfig.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
     @ONLY)
 CONFIGURE_FILE(
-    ${CMAKE_MODULE_PATH}/ArrayFireConfigVersion.cmake.in
+    ${PROJECT_SOURCE_DIR}/CMakeModules/ArrayFireConfigVersion.cmake.in
     ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfigVersion.cmake
     @ONLY)
 INSTALL(FILES ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
@@ -259,10 +259,10 @@ INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/assets/examples"
     COMPONENT examples)
 
 IF(APPLE)
-    INCLUDE("${CMAKE_MODULE_PATH}/osx_install/OSXInstaller.cmake")
+    INCLUDE(osx_install/OSXInstaller)
 ENDIF(APPLE)
 
 ##
 # Packaging
 ##
-include(${CMAKE_MODULE_PATH}/CPackConfig.cmake)
+include(CPackConfig)
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index deb154c6c0..7d95809485 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -1,6 +1,6 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
-INCLUDE("${CMAKE_MODULE_PATH}/Version.cmake")
+INCLUDE(Version)
 
 OPTION(CREATE_STGZ "Create .sh install file" ON)
 MARK_AS_ADVANCED(CREATE_STGZ)
diff --git a/CMakeModules/CUDACheckCompute.cmake b/CMakeModules/CUDACheckCompute.cmake
index f377c5c3f1..b55b40a62e 100644
--- a/CMakeModules/CUDACheckCompute.cmake
+++ b/CMakeModules/CUDACheckCompute.cmake
@@ -6,11 +6,11 @@
 # based on http://stackoverflow.com/questions/2285185/easiest-way-to-test-for-existence-of-cuda-capable-gpu-from-cmake/2297877#2297877 (Christopher Bruns)
 
 IF(CUDA_FOUND)
-    MESSAGE(STATUS "${CMAKE_MODULE_PATH}/cuda_compute_capability.cpp")
+    MESSAGE(STATUS "${PROJECT_SOURCE_DIR}/CMakeModules/cuda_compute_capability.cpp")
 
     TRY_RUN(RUN_RESULT_VAR COMPILE_RESULT_VAR
         ${CMAKE_BINARY_DIR}
-        ${CMAKE_MODULE_PATH}/cuda_compute_capability.cpp
+        ${PROJECT_SOURCE_DIR}/CMakeModules/cuda_compute_capability.cpp
         CMAKE_FLAGS
         -DINCLUDE_DIRECTORIES:STRING=${CUDA_TOOLKIT_INCLUDE}
         -DLINK_LIBRARIES:STRING=${CUDA_CUDART_LIBRARY}
diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index 57512c6473..fbb646bc47 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -151,7 +151,7 @@ MACRO(CHECK_ALL_LIBRARIES
           )
       ELSE(APPLE)
         FIND_LIBRARY(${_prefix}_${_library}_LIBRARY
-          NAMES ${_library}
+          NAMES ${_library} lib${_library}
           PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64
           ENV LD_LIBRARY_PATH
           "${CBLAS_LIB_DIR}" "${CBLAS_LIB32_DIR}" "${CBLAS_LIB64_DIR}"
@@ -319,7 +319,7 @@ IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
     CBLAS_LIBRARIES
     CBLAS
-    cblas_dgemm
+    dgemm_
     ""
     "blas"
     "cblas.h"
diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index a390593e74..a4ea1b3560 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -38,7 +38,7 @@ SET(AF_COMPILER_STRING "${COMPILER_NAME} ${COMPILER_VERSION}")
 
 EXECUTE_PROCESS(
     COMMAND git log -1 --format=%h
-    WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
     OUTPUT_VARIABLE GIT_COMMIT_HASH
     OUTPUT_STRIP_TRAILING_WHITESPACE
 )
@@ -49,13 +49,13 @@ IF(NOT GIT_COMMIT_HASH)
 ENDIF()
 
 CONFIGURE_FILE(
-    ${CMAKE_MODULE_PATH}/version.h.in
-    ${CMAKE_SOURCE_DIR}/include/af/version.h
+    ${PROJECT_SOURCE_DIR}/CMakeModules/version.h.in
+    ${PROJECT_SOURCE_DIR}/include/af/version.h
 )
 
 CONFIGURE_FILE(
-    ${CMAKE_MODULE_PATH}/version.hpp.in
-    ${CMAKE_SOURCE_DIR}/src/backend/version.hpp
+    ${PROJECT_SOURCE_DIR}/CMakeModules/version.hpp.in
+    ${PROJECT_SOURCE_DIR}/src/backend/version.hpp
 )
 
 CMAKE_POLICY(POP)
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index aa9a259275..4ca5c5b802 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -12,9 +12,9 @@ SET(AF_DOCS_LAYOUT_OUT "${CMAKE_CURRENT_BINARY_DIR}/layout.xml.out")
 
 SET(DOCS_DIR ${CMAKE_CURRENT_SOURCE_DIR})
 SET(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../assets")
-SET(INCLUDE_DIR "${CMAKE_SOURCE_DIR}/include")
-SET(EXAMPLES_DIR "${CMAKE_SOURCE_DIR}/examples")
-SET(SNIPPETS_DIR "${CMAKE_SOURCE_DIR}/test")
+SET(INCLUDE_DIR "${PROJECT_SOURCE_DIR}/include")
+SET(EXAMPLES_DIR "${PROJECT_SOURCE_DIR}/examples")
+SET(SNIPPETS_DIR "${PROJECT_SOURCE_DIR}/test")
 CONFIGURE_FILE(${AF_DOCS_CONFIG} ${AF_DOCS_CONFIG_OUT})
 CONFIGURE_FILE(${AF_DOCS_LAYOUT} ${AF_DOCS_LAYOUT_OUT})
 
@@ -40,7 +40,7 @@ ENDFOREACH(SRC ${EXAMPLES_CPP})
 
 # Write string containing file names to examples.dox
 CONFIGURE_FILE(
-    ${CMAKE_MODULE_PATH}/examples.dox.in
+    ${PROJECT_SOURCE_DIR}/CMakeModules/examples.dox.in
     ${DOCS_DIR}/details/examples.dox
 )
 ###########################################################
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 3aa609d796..70902a97ce 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -18,7 +18,7 @@ IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
         MESSAGE(STATUS "Assests submodule unavailable. Updating submodules.")
         EXECUTE_PROCESS(
             COMMAND git submodule update --init --recursive
-            WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+            WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
             OUTPUT_QUIET
         )
     ENDIF()
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 18d15c474c..c9c87409c1 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -69,7 +69,7 @@ INSTALL(TARGETS af EXPORT AF DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT libraries)
 
 IF(APPLE)
-    INSTALL(SCRIPT "${CMAKE_MODULE_PATH}/osx_install/InstallTool.cmake")
+    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
 ENDIF(APPLE)
 
 EXPORT(TARGETS af FILE ArrayFireUnified.cmake)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 7901715963..bae962fce7 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -90,14 +90,14 @@ ELSE()
     MESSAGE(STATUS "threads submodule unavailable. Updating submodules.")
     EXECUTE_PROCESS(
         COMMAND git submodule update --init --recursive
-        WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+        WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
         )
 ENDIF()
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
-    "${CMAKE_SOURCE_DIR}/src/backend/cpu"
-    "${CMAKE_SOURCE_DIR}/src/backend/cpu/threads"
+    "${PROJECT_SOURCE_DIR}/src/backend/cpu"
+    "${PROJECT_SOURCE_DIR}/src/backend/cpu/threads"
     ${FFTW_INCLUDES}
     ${CBLAS_INCLUDE_DIR}
     )
@@ -188,10 +188,10 @@ IF(DEFINED BLAS_SYM_FILE)
         PROPERTIES LINK_FLAGS -Wl,-exported_symbols_list,${BLAS_SYM_FILE})
     TARGET_LINK_LIBRARIES(afcpu PUBLIC $<TARGET_FILE:afcpu_static>)
   ELSE(APPLE)
-    add_custom_command(OUTPUT ${CMAKE_BINARY_DIR}/afcpu_static.renamed
-      COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afcpu_static> ${CMAKE_BINARY_DIR}/afcpu_static.renamed
+    add_custom_command(OUTPUT ${PROJECT_BINARY_DIR}/afcpu_static.renamed
+      COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afcpu_static> ${PROJECT_BINARY_DIR}/afcpu_static.renamed
       DEPENDS $<TARGET_FILE:afcpu_static>)
-      TARGET_LINK_LIBRARIES(afcpu PUBLIC ${CMAKE_BINARY_DIR}/afcpu_static.renamed)
+      TARGET_LINK_LIBRARIES(afcpu PUBLIC ${PROJECT_BINARY_DIR}/afcpu_static.renamed)
   ENDIF(APPLE)
 
 ELSE(DEFINED BLAS_SYM_FILE)
@@ -234,7 +234,7 @@ INSTALL(TARGETS afcpu EXPORT CPU DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT libraries)
 
 IF(APPLE)
-    INSTALL(SCRIPT "${CMAKE_MODULE_PATH}/osx_install/InstallTool.cmake")
+    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
 ENDIF(APPLE)
 
 export(TARGETS afcpu FILE ArrayFireCPU.cmake)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 84c17b251d..55700d4ec4 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -3,8 +3,8 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 FIND_PACKAGE(CUDA REQUIRED)
 FIND_PACKAGE(Boost REQUIRED)
 
-INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
-INCLUDE("${CMAKE_MODULE_PATH}/FindNVVM.cmake")
+INCLUDE(CLKernelToH)
+INCLUDE(FindNVVM)
 
 OPTION(USE_LIBDEVICE "Use libdevice for CUDA JIT" ON)
 
@@ -19,7 +19,7 @@ OPTION(CUDA_COMPUTE_DETECT "Run autodetection of CUDA Architecture" ON)
 MARK_AS_ADVANCED(CUDA_COMPUTE_DETECT)
 
 IF(CUDA_COMPUTE_DETECT AND NOT DEFINED COMPUTES_DETECTED_LIST)
-    INCLUDE("${CMAKE_MODULE_PATH}/CUDACheckCompute.cmake")
+    INCLUDE(CUDACheckCompute)
 ENDIF()
 
 IF(    CUDA_COMPUTE_20
@@ -192,7 +192,7 @@ INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
     ${Boost_INCLUDE_DIR}
     ${CUDA_INCLUDE_DIRS}
-    "${CMAKE_SOURCE_DIR}/src/backend/cuda"
+    "${PROJECT_SOURCE_DIR}/src/backend/cuda"
     "${CMAKE_CURRENT_BINARY_DIR}"
     ${CUDA_nvvm_INCLUDE_DIR}
     )
@@ -305,9 +305,9 @@ foreach(ptx_src_file ${ptx_sources})
       get_filename_component(_name "${ptx_src_file}" NAME_WE)
 
       set(_gen_file_name
-        "${CMAKE_BINARY_DIR}/src/backend/cuda/cuda_compile_ptx_generated_${_name}.cu.ptx")
+        "${PROJECT_BINARY_DIR}/src/backend/cuda/cuda_compile_ptx_generated_${_name}.cu.ptx")
       set(_out_file_name
-        "${CMAKE_BINARY_DIR}/src/backend/cuda/${_name}.ptx")
+        "${PROJECT_BINARY_DIR}/src/backend/cuda/${_name}.ptx")
 
       ADD_CUSTOM_COMMAND(
         OUTPUT "${_out_file_name}"
@@ -336,7 +336,7 @@ IF (USE_LIBDEVICE)
   LIST(APPEND libdevice_computes "20" "30" "35" "50")
   FOREACH(libdevice_compute ${libdevice_computes})
     SET(_libdevice_bc_file "${CUDA_NVVM_HOME}/libdevice/libdevice.compute_${libdevice_compute}.10.bc")
-    SET(_libdevice_bc_copy "${CMAKE_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
+    SET(_libdevice_bc_copy "${PROJECT_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
     IF (EXISTS ${_libdevice_bc_file})
       ADD_CUSTOM_COMMAND(
         OUTPUT "${_libdevice_bc_copy}"
@@ -506,7 +506,7 @@ INSTALL(TARGETS afcuda EXPORT CUDA DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT libraries)
 
 IF(APPLE)
-    INSTALL(SCRIPT "${CMAKE_MODULE_PATH}/osx_install/InstallTool.cmake")
+    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
 ENDIF(APPLE)
 
 export(TARGETS afcuda FILE ArrayFireCUDA.cmake)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index fea5d6324e..cf649dce0f 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -1,14 +1,11 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8.7)
-PROJECT(ARRAYFIRE)
-
 FIND_PACKAGE(OpenCL REQUIRED)
 
 ADD_DEFINITIONS(-DCL_USE_DEPRECATED_OPENCL_1_2_APIS)
 
 IF(NOT USE_SYSTEM_CL2HPP)
-    INCLUDE("${CMAKE_MODULE_PATH}/build_cl2hpp.cmake")
+    INCLUDE(build_cl2hpp)
 ENDIF(NOT USE_SYSTEM_CL2HPP)
-INCLUDE("${CMAKE_MODULE_PATH}/CLKernelToH.cmake")
+INCLUDE(CLKernelToH)
 
 IF(USE_OPENCL_F77_BLAS)
     ADD_DEFINITIONS(-DUSE_F77_BLAS)
@@ -67,7 +64,7 @@ OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
 IF(USE_SYSTEM_CLBLAS)
     FIND_PACKAGE(clBLAS REQUIRED)
 ELSE()
-    INCLUDE("${CMAKE_MODULE_PATH}/build_clBLAS.cmake")
+    INCLUDE(build_clBLAS)
 ENDIF()
 INCLUDE_DIRECTORIES(${CLBLAS_INCLUDE_DIRS})
 LINK_DIRECTORIES(${CLBLAS_LIBRARY_DIR})
@@ -76,7 +73,7 @@ OPTION(USE_SYSTEM_CLFFT "Use system clFFT" OFF)
 IF(USE_SYSTEM_CLFFT)
     FIND_PACKAGE(clFFT REQUIRED)
 ELSE()
-    INCLUDE("${CMAKE_MODULE_PATH}/build_clFFT.cmake")
+    INCLUDE(build_clFFT)
 ENDIF()
 INCLUDE_DIRECTORIES(${CLFFT_INCLUDE_DIRS})
 LINK_DIRECTORIES(${CLFFT_LIBRARY_DIR})
@@ -91,7 +88,7 @@ IF(USE_SYSTEM_BOOST_COMPUTE)
         FIND_PACKAGE(BoostCompute REQUIRED)
     ENDIF()
 ELSE()
-    INCLUDE("${CMAKE_MODULE_PATH}/build_boost_compute.cmake")
+    INCLUDE(build_boost_compute)
 ENDIF()
 
 SET( cl_kernel_headers
@@ -99,7 +96,7 @@ SET( cl_kernel_headers
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
-    "${CMAKE_SOURCE_DIR}/src/backend/opencl"
+    ${CMAKE_CURRENT_SOURCE_DIR}
     ${OpenCL_INCLUDE_DIRS}
     ${CL2HPP_INCLUDE_DIRECTORY}
     "${CMAKE_CURRENT_BINARY_DIR}"
@@ -282,10 +279,10 @@ IF(DEFINED BLAS_SYM_FILE)
         PROPERTIES LINK_FLAGS -Wl,-exported_symbols_list,${BLAS_SYM_FILE})
     TARGET_LINK_LIBRARIES(afopencl PUBLIC $<TARGET_FILE:afopencl_static>)
     ELSE(APPLE)
-    add_custom_command(OUTPUT ${CMAKE_BINARY_DIR}/afopencl_static.renamed
-      COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afopencl_static> ${CMAKE_BINARY_DIR}/afopencl_static.renamed
+    add_custom_command(OUTPUT ${PROJECT_BINARY_DIR}/afopencl_static.renamed
+      COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afopencl_static> ${PROJECT_BINARY_DIR}/afopencl_static.renamed
       DEPENDS $<TARGET_FILE:afopencl_static>)
-      TARGET_LINK_LIBRARIES(afopencl PUBLIC ${CMAKE_BINARY_DIR}/afopencl_static.renamed)
+      TARGET_LINK_LIBRARIES(afopencl PUBLIC ${PROJECT_BINARY_DIR}/afopencl_static.renamed)
     ENDIF(APPLE)
 
 
@@ -350,7 +347,7 @@ INSTALL(TARGETS afopencl EXPORT OpenCL DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT libraries)
 
 IF(APPLE)
-    INSTALL(SCRIPT "${CMAKE_MODULE_PATH}/osx_install/InstallTool.cmake")
+    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
 ENDIF(APPLE)
 
 export(TARGETS afopencl FILE ArrayFireOpenCL.cmake)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5f53c420a4..117c4ed1e9 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -172,7 +172,7 @@ IF(NOT ${USE_RELATIVE_TEST_DIR})
         MESSAGE(STATUS "Test submodules unavailable. Updating submodules.")
         EXECUTE_PROCESS(
             COMMAND git submodule update --init --recursive
-            WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+            WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
             OUTPUT_QUIET
         )
     ENDIF()
@@ -182,7 +182,7 @@ OPTION(USE_SYSTEM_GTEST "Use GTEST from system libraries" OFF)
 IF(USE_SYSTEM_GTEST)
     FIND_PACKAGE(GTest REQUIRED)
 ELSE(USE_SYSTEM_GTEST)
-    INCLUDE("${CMAKE_MODULE_PATH}/build_gtest.cmake")
+    INCLUDE("${PROJECT_SOURCE_DIR}/CMakeModules/build_gtest.cmake")
 ENDIF(USE_SYSTEM_GTEST)
 
 INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})

From bce7db5b959644de84c343eb41f3bb22ff57d332 Mon Sep 17 00:00:00 2001
From: Dmitry Trifonov <dmitry.trifonov@hp.com>
Date: Mon, 29 Aug 2016 11:19:16 -0700
Subject: [PATCH 0792/2677] documentation generator path fix

---
 docs/CMakeLists.txt | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 4ca5c5b802..9459f7a04b 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -1,6 +1,3 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
-PROJECT(ARRAYFIRE_DOCS)
-
 # Doxygen is required for the documentation to be built. Do not fail silently.
 FIND_PACKAGE(Doxygen REQUIRED)
 

From 15fe06a0cb728544c781983ee77facf3528465b7 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 29 Aug 2016 14:37:20 -0400
Subject: [PATCH 0793/2677] Changed data type for random engine seed test

---
 test/random.cpp | 33 +++++++++------------------------
 1 file changed, 9 insertions(+), 24 deletions(-)

diff --git a/test/random.cpp b/test/random.cpp
index f8c7476fa8..7e08934c06 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -85,7 +85,7 @@ typedef ::testing::Types<float, double> TestTypesEngine;
 // register the type list
 TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
 
-typedef ::testing::Types<float, double> TestTypesEngineSeed;
+typedef ::testing::Types<unsigned> TestTypesEngineSeed;
 // register the type list
 TYPED_TEST_CASE(RandomEngineSeed, TestTypesEngineSeed);
 
@@ -334,7 +334,7 @@ TYPED_TEST(RandomEngine, mersenneRandomEngineNormal)
 }
 
 template <typename T>
-void testRandomEngineSeed(randomType type, bool is_norm = false)
+void testRandomEngineSeed(randomType type)
 {
     int elem = 4*32*1024;
     uintl orig_seed = 0;
@@ -342,12 +342,12 @@ void testRandomEngineSeed(randomType type, bool is_norm = false)
     af::randomEngine e(type, orig_seed);
 
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
-    array d1 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+    array d1 = e.uniform(elem, ty);
     e.setSeed(new_seed);
-    array d2 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+    array d2 = e.uniform(elem, ty);
     e.setSeed(orig_seed);
-    array d3 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
-    array d4 = is_norm ? e.normal(elem, ty) : e.uniform(elem, ty);
+    array d3 = e.uniform(elem, ty);
+    array d4 = e.uniform(elem, ty);
 
     std::vector<T> h1(elem);
     std::vector<T> h2(elem);
@@ -370,30 +370,15 @@ void testRandomEngineSeed(randomType type, bool is_norm = false)
 
 TYPED_TEST(RandomEngineSeed, philoxSeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX_4X32_10, false);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX_4X32_10);
 }
 
 TYPED_TEST(RandomEngineSeed, threefrySeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY_2X32_16, false);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY_2X32_16);
 }
 
 TYPED_TEST(RandomEngineSeed, mersenneSeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE_GP11213, false);
-}
-
-TYPED_TEST(RandomEngineSeed, philoxSeedNormal)
-{
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX_4X32_10, true);
-}
-
-TYPED_TEST(RandomEngineSeed, threefrySeedNormal)
-{
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY_2X32_16, true);
-}
-
-TYPED_TEST(RandomEngineSeed, mersenneSeedNormal)
-{
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE_GP11213, true);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
 }

From 874dcca39b21c6680b9ee819ff3baaa30f7bbccc Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 29 Aug 2016 15:00:38 -0400
Subject: [PATCH 0794/2677] Removed const from Mersenne constant arrays

Because Apple is "special".
---
 src/backend/MersenneTwister.hpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/MersenneTwister.hpp b/src/backend/MersenneTwister.hpp
index f35ccceedf..5c1716f7bd 100644
--- a/src/backend/MersenneTwister.hpp
+++ b/src/backend/MersenneTwister.hpp
@@ -58,30 +58,30 @@ namespace common
     const dim_t MersenneN = 351;
     const dim_t MtStateLength = MaxBlocks * MersenneN;
 
-    static const int pos[] = {
+    static int pos[] = {
     88, 84, 25, 42, 22, 11, 76, 11, 42, 60, 45, 80, 81, 16, 63, 38,
     3, 55, 9, 75, 70, 63, 32, 70, 58, 33, 18, 9, 14, 91, 90, 86,
     };
 
-    static const int sh1[] = {
+    static int sh1[] = {
     19, 15, 4, 20, 1, 16, 16, 15, 6, 6, 12, 6, 8, 1, 14, 28,
     30, 1, 9, 17, 15, 15, 7, 12, 21, 7, 7, 12, 16, 4, 10, 6,
     };
 
-    static const int sh2[] = {
+    static int sh2[] = {
     5, 12, 18, 9, 5, 1, 6, 16, 11, 11, 13, 9, 18, 19, 18, 1,
     2, 16, 15, 6, 6, 17, 15, 10, 2, 10, 13, 13, 3, 2, 14, 7,
     };
 
-    static const uint32_t mask = 4294443008;
-    //static const uint32_t mask[] = {
+    static unsigned mask = 4294443008;
+    //static const unsigned mask[] = {
     //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
     //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
     //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
     //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
     //};
 
-    static const uint32_t recursion_tbl[] = {
+    static unsigned recursion_tbl[] = {
     0, 2879706668, 3137826695, 279165355, 570425344, 2309281324, 2567401351, 849590699,
     38330, 2879669142, 3137862205, 279129105, 570463674, 2309243798, 2567436861, 849554449,
     0, 2593609479, 1975655185, 4015344662, 357564441, 2412205854, 1620187912, 4194651151,
@@ -148,7 +148,7 @@ namespace common
     35538, 1104655846, 154164518, 1223123474, 1706068779, 610776095, 1820351711, 760701931,
     };
 
-    static const uint32_t temper_tbl[] = {
+    static unsigned temper_tbl[] = {
     0, 101711872, 634912768, 600309760, 673972224, 775684096, 234094592, 199491584,
     855825920, 890428928, 383442432, 281730560, 456056320, 490659328, 1056366080, 954654208,
     0, 6581248, 135327744, 141859840, 922910720, 929491968, 1058172928, 1064705024,

From 33a08a1a137388d93a22348c969b997d39bf42f6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 29 Aug 2016 15:29:28 -0400
Subject: [PATCH 0795/2677] BUGFIX: Fix type when libdevice is off

---
 src/backend/cuda/unary.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index a858c82995..291f805193 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -95,8 +95,8 @@ struct UnOp
     };                                          \
 
 #else
-#define #define NVVM_SPECIALIZE_TYPE(T, fn, fname)  // no specialization
-#define #define NVVM_SPECIALIZE_CHECK(T, fn, fname)  // no specialization
+#define NVVM_SPECIALIZE_TYPE(T, fn, fname)  // no specialization
+#define NVVM_SPECIALIZE_CHECK(T, fn, fname)  // no specialization
 #endif
 
 #define NVVM_SPECIALIZE_FLOATING_NAME(fn, fname)    \

From f9b1d2dcdb0fe13e1b6782799b18db38199c45a5 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 30 Aug 2016 15:03:52 -0400
Subject: [PATCH 0796/2677] Style changes

---
 include/af/random_engine.h                    |   1 +
 src/api/c/random_engine.cpp                   |  42 +++--
 src/api/cpp/random_engine.cpp                 |   7 +
 src/backend/cpu/kernel/random_engine.hpp      |  20 +--
 .../cpu/kernel/random_engine_mersenne.hpp     |   4 +-
 .../cpu/kernel/random_engine_philox.hpp       |   3 +
 .../cpu/kernel/random_engine_threefry.hpp     |   3 +
 src/backend/cpu/random_engine.cpp             | 144 +++++++++---------
 src/backend/cuda/kernel/random_engine.hpp     |  56 +++----
 .../cuda/kernel/random_engine_mersenne.hpp    |   2 +-
 .../cuda/kernel/random_engine_philox.hpp      |   2 +
 .../cuda/kernel/random_engine_threefry.hpp    |  54 ++-----
 src/backend/cuda/random_engine.cu             | 140 +++++++++--------
 src/backend/opencl/kernel/random_engine.hpp   |  30 ++--
 .../opencl/kernel/random_engine_philox.cl     |   4 +
 .../opencl/kernel/random_engine_threefry.cl   |   4 +
 src/backend/opencl/random_engine.cpp          | 132 ++++++++--------
 17 files changed, 331 insertions(+), 317 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index f5a134fbc9..1d365b5aa1 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -24,6 +24,7 @@ namespace af
         public:
             explicit
             randomEngine(randomType typeIn = AF_RANDOM_DEFAULT, uintl seedIn = 0);
+            randomEngine(const randomEngine& other);
             ~randomEngine();
 
             randomEngine& operator= (const randomEngine& other);
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index bd632f7f01..f4cae7a82e 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -19,7 +19,9 @@
 #include <random_engine.hpp>
 #include <random.hpp>
 #include <MersenneTwister.hpp>
+#include <types.hpp>
 
+using detail::uint;
 using detail::cfloat;
 using detail::cdouble;
 using detail::uchar;
@@ -65,7 +67,7 @@ af_random_engine getRandomEngineHandle(const RandomEngine engine)
     return static_cast<af_random_engine>(engineHandle);
 }
 
-af_random_engine DefaultRandomEngine(void)
+af_random_engine defaultRandomEngine(void)
 {
     static RandomEngine r;
     return static_cast<af_random_engine>(&r);
@@ -73,6 +75,9 @@ af_random_engine DefaultRandomEngine(void)
 
 RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 {
+    if (engineHandle == 0) {
+        AF_ERROR("Uninitialized random engine", AF_ERR_ARG);
+    }
     return (RandomEngine *)engineHandle;
 }
 
@@ -110,9 +115,23 @@ static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e
     }
 }
 
+static void validateRandomType(const af_random_type type)
+{
+    if ((type != AF_RANDOM_PHILOX_4X32_10)
+    &&  (type != AF_RANDOM_THREEFRY_2X32_16)
+    &&  (type != AF_RANDOM_MERSENNE_GP11213)
+    &&  (type != AF_RANDOM_PHILOX)
+    &&  (type != AF_RANDOM_THREEFRY)
+    &&  (type != AF_RANDOM_MERSENNE)
+    &&  (type != AF_RANDOM_DEFAULT)) {
+        AF_ERROR("Invalid random type", AF_ERR_ARG);
+    }
+}
+
 af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, uintl seed)
 {
     try {
+        validateRandomType(rtype);
         RandomEngine e;
         e.type = rtype;
         e.seed = seed;
@@ -159,11 +178,12 @@ af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engi
 af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype)
 {
     try {
+        validateRandomType(rtype);
         RandomEngine e = *(getRandomEngine(engine));
         if (rtype != e.type) {
-            bool empty;
             if (rtype == AF_RANDOM_MERSENNE_GP11213) {
-                af_is_empty(&empty, e.state);
+                bool empty;
+                AF_CHECK(af_is_empty(&empty, e.state));
                 if (empty) {
                     AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
                     AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
@@ -183,8 +203,9 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type
 af_err af_set_default_random_engine(const af_random_type rtype)
 {
     try {
-        af_random_engine e = DefaultRandomEngine();
-        af_random_engine_set_type(&e, rtype);
+        AF_CHECK(af_init());
+        af_random_engine e = defaultRandomEngine();
+        AF_CHECK(af_random_engine_set_type(&e, rtype));
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -292,7 +313,7 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_array result;
         AF_CHECK(af_init());
 
-        RandomEngine *e = getRandomEngine(DefaultRandomEngine());
+        RandomEngine *e = getRandomEngine(defaultRandomEngine());
         af::dim4 d = verifyDims(ndims, dims);
 
         switch(type) {
@@ -322,7 +343,7 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_array result;
         AF_CHECK(af_init());
 
-        RandomEngine *e = getRandomEngine(DefaultRandomEngine());
+        RandomEngine *e = getRandomEngine(defaultRandomEngine());
         af::dim4 d = verifyDims(ndims, dims);
 
         switch(type) {
@@ -341,8 +362,8 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
 af_err af_set_seed(const uintl seed)
 {
     try {
-        af_random_engine e = DefaultRandomEngine();
-        af_random_engine_set_seed(&e, seed);
+        af_random_engine e = defaultRandomEngine();
+        AF_CHECK(af_random_engine_set_seed(&e, seed));
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -350,8 +371,7 @@ af_err af_set_seed(const uintl seed)
 af_err af_get_seed(uintl *seed)
 {
     try {
-        af_random_engine_get_seed(seed, DefaultRandomEngine());
+        AF_CHECK(af_random_engine_get_seed(seed, defaultRandomEngine()));
     } CATCHALL;
     return AF_SUCCESS;
 }
-
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index ded1ec6b24..3b10c1e8a0 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -20,6 +20,13 @@ namespace af
         AF_THROW(af_create_random_engine(&engine, type, seed));
     }
 
+    randomEngine::randomEngine(const randomEngine& other)
+    {
+        if (this != &other) {
+            AF_THROW(af_retain_random_engine(&engine, other.get()));
+        }
+    }
+
     randomEngine::~randomEngine()
     {
         if (engine) {
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index d50141c683..43e53b6051 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -195,12 +195,12 @@ namespace kernel
     template <typename T>
     void uniformDistributionMT(T* out, size_t elements,
             uint * const state,
-            uint const * const pos,
-            uint const * const sh1,
-            uint const * const sh2,
+            const uint * const pos,
+            const uint * const sh1,
+            const uint * const sh2,
             uint mask,
-            uint const * const recursion_table,
-            uint const * const temper_table)
+            const uint * const recursion_table,
+            const uint * const temper_table)
     {
         uint l_state[STATE_SIZE];
         uint o[4];
@@ -225,12 +225,12 @@ namespace kernel
     template <typename T>
     void normalDistributionMT(T* out, size_t elements,
             uint * const state,
-            uint const * const pos,
-            uint const * const sh1,
-            uint const * const sh2,
+            const uint * const pos,
+            const uint * const sh1,
+            const uint * const sh2,
             uint mask,
-            uint const * const recursion_table,
-            uint const * const temper_table)
+            const uint * const recursion_table,
+            const uint * const temper_table)
     {
         T temp[(4*sizeof(uint))/sizeof(T)];
         uint l_state[STATE_SIZE];
diff --git a/src/backend/cpu/kernel/random_engine_mersenne.hpp b/src/backend/cpu/kernel/random_engine_mersenne.hpp
index 94e818b3ae..d4074a78e2 100644
--- a/src/backend/cpu/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cpu/kernel/random_engine_mersenne.hpp
@@ -77,8 +77,8 @@ namespace kernel
             uint sh1,
             uint sh2,
             uint mask,
-            uint const * const recursion_table,
-            uint const * const temper_table)
+            const uint * const recursion_table,
+            const uint * const temper_table)
     {
         int index    = i % STATE_SIZE;
         int offsetX1 = (STATE_SIZE - N + index          ) % STATE_SIZE;
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index fed39d681f..30e945d720 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -51,6 +51,9 @@ namespace cpu
 {
 namespace kernel
 {
+    //Utils
+    //Source of these constants :
+    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
 
     static const uint m4x32_0 = 0xD2511F53;
     static const uint m4x32_1 = 0xCD9E8D57;
diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
index 7f914ec87a..7c95d7e6a7 100644
--- a/src/backend/cpu/kernel/random_engine_threefry.hpp
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -50,6 +50,9 @@ namespace cpu
 {
 namespace kernel
 {
+    //Utils
+    //Source of these constants :
+    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
 
     static const uint SKEIN_KS_PARITY = 0x1BD11BDA;
 
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 659a331039..74d19f7309 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -79,79 +79,77 @@ namespace cpu
         return out;
     }
 
-#define INSTANTIATE_UNIFORM(T)\
-    template\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter);\
-    template\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
-
-#define INSTANTIATE_NORMAL(T)\
-    template\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter);\
-    template\
-    Array<T> normalDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
-
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);\
-        counter += elements;\
-        return out;\
-    }\
-    \
-    template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        getQueue().enqueue(kernel::uniformDistributionMT<TR>,\
-              outPtr, elements,\
-              state.get(), pos.get(),\
-              sh1.get(), sh2.get(),\
-              mask, recursion_table.get(),\
-              temper_table.get());\
-        return out;\
-    }\
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);\
-        counter += elements;\
-        return out;\
-    }\
-    \
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        getQueue().enqueue(kernel::normalDistributionMT<TR>,\
-              outPtr, elements,\
-              state.get(), pos.get(),\
-              sh1.get(), sh2.get(),\
-              mask, recursion_table.get(),\
-              temper_table.get());\
-        return out;\
-    }\
+#define INSTANTIATE_UNIFORM(T)                                                                                          \
+    template                                                                                                            \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter); \
+    template                                                                                                            \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                               \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                  \
+
+#define INSTANTIATE_NORMAL(T)                                                                                           \
+    template                                                                                                            \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter);  \
+    template                                                                                                            \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                  \
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                             \
+    template<>                                                                                                          \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)  \
+    {                                                                                                                   \
+        Array<T> out = createEmptyArray<T>(dims);                                                                       \
+        TR *outPtr = (TR*)out.get();                                                                                    \
+        size_t elements = out.elements()*2;                                                                             \
+        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                \
+        counter += elements;                                                                                            \
+        return out;                                                                                                     \
+    }                                                                                                                   \
+    template<>                                                                                                          \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                               \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                   \
+    {                                                                                                                   \
+        Array<T> out = createEmptyArray<T>(dims);                                                                       \
+        TR *outPtr = (TR*)out.get();                                                                                    \
+        size_t elements = out.elements()*2;                                                                             \
+        getQueue().enqueue(kernel::uniformDistributionMT<TR>,                                                           \
+              outPtr, elements,                                                                                         \
+              state.get(), pos.get(),                                                                                   \
+              sh1.get(), sh2.get(),                                                                                     \
+              mask, recursion_table.get(),                                                                              \
+              temper_table.get());                                                                                      \
+        return out;                                                                                                     \
+    }                                                                                                                   \
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                              \
+    template<>                                                                                                          \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)   \
+    {                                                                                                                   \
+        Array<T> out = createEmptyArray<T>(dims);                                                                       \
+        TR *outPtr = (TR*)out.get();                                                                                    \
+        size_t elements = out.elements()*2;                                                                             \
+        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                 \
+        counter += elements;                                                                                            \
+        return out;                                                                                                     \
+    }                                                                                                                   \
+    template<>                                                                                                          \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                   \
+    {                                                                                                                   \
+        Array<T> out = createEmptyArray<T>(dims);                                                                       \
+        TR *outPtr = (TR*)out.get();                                                                                    \
+        size_t elements = out.elements()*2;                                                                             \
+        getQueue().enqueue(kernel::normalDistributionMT<TR>,                                                            \
+              outPtr, elements,                                                                                         \
+              state.get(), pos.get(),                                                                                   \
+              sh1.get(), sh2.get(),                                                                                     \
+              mask, recursion_table.get(),                                                                              \
+              temper_table.get());                                                                                      \
+        return out;                                                                                                     \
+    }                                                                                                                   \
 
     INSTANTIATE_UNIFORM(float )
     INSTANTIATE_UNIFORM(double)
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 5fe9dcc16b..4e976c3eed 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -428,12 +428,12 @@ namespace kernel
     template <typename T>
     __global__ void uniformMersenne(T * const out,
             uint * const gState,
-            uint const * const pos_tbl,
-            uint const * const sh1_tbl,
-            uint const * const sh2_tbl,
+            const uint * const pos_tbl,
+            const uint * const sh1_tbl,
+            const uint * const sh2_tbl,
             uint mask,
-            uint const * const g_recursion_table,
-            uint const * const g_temper_table,
+            const uint * const g_recursion_table,
+            const uint * const g_temper_table,
             uint elementsPerBlock, size_t elements)
     {
         __shared__ uint state[STATE_SIZE];
@@ -524,12 +524,12 @@ namespace kernel
     template <typename T>
     __global__ void normalMersenne(T * const out,
             uint * const gState,
-            uint const * const pos_tbl,
-            uint const * const sh1_tbl,
-            uint const * const sh2_tbl,
+            const uint * const pos_tbl,
+            const uint * const sh1_tbl,
+            const uint * const sh2_tbl,
             uint mask,
-            uint const * const g_recursion_table,
-            uint const * const g_temper_table,
+            const uint * const g_recursion_table,
+            const uint * const g_temper_table,
             uint elementsPerBlock, uint elements)
     {
 
@@ -586,12 +586,12 @@ namespace kernel
     template <typename T>
     void uniformDistributionMT(T* out, size_t elements,
             uint * const state,
-            uint const * const pos,
-            uint const * const sh1,
-            uint const * const sh2,
+            const uint * const pos,
+            const uint * const sh1,
+            const uint * const sh2,
             uint mask,
-            uint const * const recursion_table,
-            uint const * const temper_table)
+            const uint * const recursion_table,
+            const uint * const temper_table)
     {
         int threads = THREADS;
         int min_elements_per_block = 32*threads*4*sizeof(uint)/sizeof(T);
@@ -604,12 +604,12 @@ namespace kernel
     template <typename T>
     void normalDistributionMT(T* out, size_t elements,
             uint * const state,
-            uint const * const pos,
-            uint const * const sh1,
-            uint const * const sh2,
+            const uint * const pos,
+            const uint * const sh1,
+            const uint * const sh2,
             uint mask,
-            uint const * const recursion_table,
-            uint const * const temper_table)
+            const uint * const recursion_table,
+            const uint * const temper_table)
     {
         int threads = THREADS;
         int min_elements_per_block = 32*threads*4*sizeof(uint)/sizeof(T);
@@ -628,10 +628,10 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         switch (type) {
-        case AF_RANDOM_PHILOX_4X32_10   : CUDA_LAUNCH(uniformPhilox, blocks, threads,
-                out, hi, lo, counter, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY_2X32_16 : CUDA_LAUNCH(uniformThreefry, blocks, threads,
-                out, hi, lo, counter, elementsPerBlock, elements); break;
+        case AF_RANDOM_PHILOX_4X32_10   :
+            CUDA_LAUNCH(uniformPhilox, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
+        case AF_RANDOM_THREEFRY_2X32_16 :
+            CUDA_LAUNCH(uniformThreefry, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
@@ -646,10 +646,10 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         switch (type) {
-        case AF_RANDOM_PHILOX_4X32_10   : CUDA_LAUNCH(normalPhilox, blocks, threads,
-                out, hi, lo, counter, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY_2X32_16 : CUDA_LAUNCH(normalThreefry, blocks, threads,
-                out, hi, lo, counter, elementsPerBlock, elements); break;
+        case AF_RANDOM_PHILOX_4X32_10   :
+            CUDA_LAUNCH(normalPhilox, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
+        case AF_RANDOM_THREEFRY_2X32_16 :
+            CUDA_LAUNCH(normalThreefry, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index daa9b7579f..c70f82a643 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -79,7 +79,7 @@ namespace kernel
         }
     }
 
-    static inline __device__ uint recursion(uint const * const recursion_table,
+    static inline __device__ uint recursion(const uint * const recursion_table,
             const uint mask, const uint sh1, const uint sh2,
             const uint x1, const uint x2, uint y)
     {
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index 17a6a9d6ea..bad322698c 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -51,6 +51,8 @@ namespace cuda
 namespace kernel
 {
     //Utils
+    //Source of these constants :
+    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
 
     static const uint m4x32_0 = 0xD2511F53;
     static const uint m4x32_1 = 0xCD9E8D57;
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
index bbecef44fe..6c0894060b 100644
--- a/src/backend/cuda/kernel/random_engine_threefry.hpp
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -51,52 +51,33 @@ namespace cuda
 namespace kernel
 {
     //Utils
+    //Source of these constants :
+    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
 
     static const uint  SKEIN_KS_PARITY32 = 0x1BD11BDA;
-    static const uintl SKEIN_KS_PARITY64 = 0x1BD11BDAA9FC1A22;
-
-    static const uint R0_32=13;
-    static const uint R1_32=15;
-    static const uint R2_32=26;
-    static const uint R3_32= 6;
-    static const uint R4_32=17;
-    static const uint R5_32=29;
-    static const uint R6_32=16;
-    static const uint R7_32=24;
-
-    static const uint R0_64=16;
-    static const uint R1_64=42;
-    static const uint R2_64=12;
-    static const uint R3_64=31;
-    static const uint R4_64=16;
-    static const uint R5_64=32;
-    static const uint R6_64=24;
-    static const uint R7_64=21;
+
+    static const uint R0=13;
+    static const uint R1=15;
+    static const uint R2=26;
+    static const uint R3= 6;
+    static const uint R4=17;
+    static const uint R5=29;
+    static const uint R6=16;
+    static const uint R7=24;
 
     static inline __device__ void setSkeinParity(uint *ptr)
     {
         *ptr = SKEIN_KS_PARITY32;
     }
 
-    static inline __device__ void setSkeinParity(uintl *ptr)
-    {
-        *ptr = SKEIN_KS_PARITY64;
-    }
-
-    static inline __device__ uintl rotL(uintl x, uint N)
-    {
-        return (x << (N & 63)) | (x >> ((64-N) & 63));
-    }
-
     static inline __device__ uint rotL(uint x, uint N)
     {
         return (x << (N & 31)) | (x >> ((32-N) & 31));
     }
 
-    template <typename T, uint R0, uint R1, uint R2, uint R3, uint R4, uint R5, uint R6, uint R7>
-    static inline __device__ void threefry_kernel(T k[2], T c[2], T X[2])
+    __device__ void threefry(uint k[2], uint c[2], uint X[2])
     {
-        T ks[3];
+        uint ks[3];
 
         setSkeinParity(&ks[2]);
         ks[0] = k[0];
@@ -145,14 +126,5 @@ namespace kernel
         X[1] += 4;
     }
 
-    __device__ void threefry(uint k[2], uint c[2], uint X[2])
-    {
-        threefry_kernel<uint, R0_32, R1_32, R2_32, R3_32, R4_32, R5_32, R6_32, R7_32>(k, c, X);
-    }
-
-    __device__ void threefry(uintl k[2], uintl c[2], uintl X[2])
-    {
-        threefry_kernel<uintl, R0_64, R1_64, R2_64, R3_64, R4_64, R5_64, R6_64, R7_64>(k, c, X);
-    }
 }
 }
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 3826ad0146..9a89d642a0 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -77,77 +77,75 @@ namespace cuda
         return out;
     }
 
-#define INSTANTIATE_UNIFORM(T)\
-    template\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
-    template\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
-
-#define INSTANTIATE_NORMAL(T)\
-    template\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
-    template\
-    Array<T> normalDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
-
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);\
-        return out;\
-    }\
-    \
-    template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        kernel::uniformDistributionMT<TR>(\
-              outPtr, elements,\
-              state.get(), pos.get(),\
-              sh1.get(), sh2.get(),\
-              mask, recursion_table.get(),\
-              temper_table.get());\
-        return out;\
-    }\
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);\
-        return out;\
-    }\
-    \
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        TR *outPtr = (TR*)out.get();\
-        size_t elements = out.elements()*2;\
-        kernel::normalDistributionMT<TR>(\
-              outPtr, elements,\
-              state.get(), pos.get(),\
-              sh1.get(), sh2.get(),\
-              mask, recursion_table.get(),\
-              temper_table.get());\
-        return out;\
-    }\
+#define INSTANTIATE_UNIFORM(T)                                                                                              \
+    template                                                                                                                \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);    \
+    template                                                                                                                \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
+
+#define INSTANTIATE_NORMAL(T)                                                                                               \
+    template                                                                                                                \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);     \
+    template                                                                                                                \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                 \
+    template<>                                                                                                              \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)     \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        TR *outPtr = (TR*)out.get();                                                                                        \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                        \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+    template<>                                                                                                              \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        TR *outPtr = (TR*)out.get();                                                                                        \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::uniformDistributionMT<TR>(                                                                                  \
+              outPtr, elements,                                                                                             \
+              state.get(), pos.get(),                                                                                       \
+              sh1.get(), sh2.get(),                                                                                         \
+              mask, recursion_table.get(),                                                                                  \
+              temper_table.get());                                                                                          \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                  \
+    template<>                                                                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)      \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        TR *outPtr = (TR*)out.get();                                                                                        \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                         \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+    template<>                                                                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        TR *outPtr = (TR*)out.get();                                                                                        \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::normalDistributionMT<TR>(                                                                                   \
+              outPtr, elements,                                                                                             \
+              state.get(), pos.get(),                                                                                       \
+              sh1.get(), sh2.get(),                                                                                         \
+              mask, recursion_table.get(),                                                                                  \
+              temper_table.get());                                                                                          \
+        return out;                                                                                                         \
+    }                                                                                                                       \
 
     INSTANTIATE_UNIFORM(float )
     INSTANTIATE_UNIFORM(double)
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index bbea41b224..9879bbed35 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -58,19 +58,23 @@ namespace opencl
             ker_strs[0] = random_engine_write_cl;
             ker_lens[0] = random_engine_write_cl_len;
             switch (type) {
-                case AF_RANDOM_PHILOX_4X32_10   : engineName = "Philox";
-                                                ker_strs[1] = random_engine_philox_cl;
-                                                ker_lens[1] = random_engine_philox_cl_len;
-                                                break;
-                case AF_RANDOM_THREEFRY_2X32_16 : engineName = "Threefry";
-                                                ker_strs[1] = random_engine_threefry_cl;
-                                                ker_lens[1] = random_engine_threefry_cl_len;
-                                                break;
-                case AF_RANDOM_MERSENNE_GP11213 : engineName = "Mersenne";
-                                                ker_strs[1] = random_engine_mersenne_cl;
-                                                ker_lens[1] = random_engine_mersenne_cl_len;
-                                                break;
-                default                         : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+                case AF_RANDOM_PHILOX_4X32_10   :
+                    engineName = "Philox";
+                    ker_strs[1] = random_engine_philox_cl;
+                    ker_lens[1] = random_engine_philox_cl_len;
+                    break;
+                case AF_RANDOM_THREEFRY_2X32_16 :
+                    engineName = "Threefry";
+                    ker_strs[1] = random_engine_threefry_cl;
+                    ker_lens[1] = random_engine_threefry_cl_len;
+                    break;
+                case AF_RANDOM_MERSENNE_GP11213 :
+                    engineName = "Mersenne";
+                    ker_strs[1] = random_engine_mersenne_cl;
+                    ker_lens[1] = random_engine_mersenne_cl_len;
+                    break;
+                default :
+                    AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
             string ref_name =
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index 44ddd0639e..f5720e3421 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -45,6 +45,10 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
+//Utils
+//Source of these constants :
+//github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
+
 #define m4x32_0 0xD2511F53
 #define m4x32_1 0xCD9E8D57
 #define w32_0 0x9E3779B9
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index 6d4af83f28..ff458c1d79 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -45,6 +45,10 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
+//Utils
+//Source of these constants :
+//github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
+
 #define SKEIN_KS_PARITY 0x1BD11BDA
 
 #define R0 13
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 3e5d54f6a7..6474821831 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -77,73 +77,71 @@ namespace opencl
         return out;
     }
 
-#define INSTANTIATE_UNIFORM(T)\
-    template\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
-    template\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
-
-#define INSTANTIATE_NORMAL(T)\
-    template\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);\
-    template\
-    Array<T> normalDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);\
-
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        size_t elements = out.elements()*2;\
-        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);\
-        return out;\
-    }\
-    \
-    template<>\
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        size_t elements = out.elements()*2;\
-        kernel::uniformDistributionMT<TR>(\
-              *out.get(), elements,\
-              *state.get(), *pos.get(),\
-              *sh1.get(), *sh2.get(),\
-              mask, *recursion_table.get(),\
-              *temper_table.get());\
-        return out;\
-    }\
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)\
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        size_t elements = out.elements()*2;\
-        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);\
-        return out;\
-    }\
-    \
-    template<>\
-    Array<T> normalDistribution<T>(const af::dim4 &dims,\
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,\
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)\
-    {\
-        Array<T> out = createEmptyArray<T>(dims);\
-        size_t elements = out.elements()*2;\
-        kernel::normalDistributionMT<TR>(\
-              *out.get(), elements,\
-              *state.get(), *pos.get(),\
-              *sh1.get(), *sh2.get(),\
-              mask, *recursion_table.get(),\
-              *temper_table.get());\
-        return out;\
-    }\
+#define INSTANTIATE_UNIFORM(T)                                                                                              \
+    template                                                                                                                \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);    \
+    template                                                                                                                \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
+
+#define INSTANTIATE_NORMAL(T)                                                                                               \
+    template                                                                                                                \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);     \
+    template                                                                                                                \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                 \
+    template<>                                                                                                              \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)     \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                    \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+    template<>                                                                                                              \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::uniformDistributionMT<TR>(                                                                                  \
+              *out.get(), elements,                                                                                         \
+              *state.get(), *pos.get(),                                                                                     \
+              *sh1.get(), *sh2.get(),                                                                                       \
+              mask, *recursion_table.get(),                                                                                 \
+              *temper_table.get());                                                                                         \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                  \
+    template<>                                                                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)      \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                     \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+    template<>                                                                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        size_t elements = out.elements()*2;                                                                                 \
+        kernel::normalDistributionMT<TR>(                                                                                   \
+              *out.get(), elements,                                                                                         \
+              *state.get(), *pos.get(),                                                                                     \
+              *sh1.get(), *sh2.get(),                                                                                       \
+              mask, *recursion_table.get(),                                                                                 \
+              *temper_table.get());                                                                                         \
+        return out;                                                                                                         \
+    }                                                                                                                       \
 
     INSTANTIATE_UNIFORM(float )
     INSTANTIATE_UNIFORM(double)

From b74d200855f2cccd1946214ebfadfccee2f36bf6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:27:21 -0400
Subject: [PATCH 0797/2677] Fix warnings in tests

---
 test/empty.cpp           | 4 ++--
 test/ocl_ext_context.cpp | 5 +++++
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/test/empty.cpp b/test/empty.cpp
index 64c5fb9a3f..1cff2a762f 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -25,8 +25,8 @@ TEST(Array, TestEmptyAssignment) {
     array C = constant(0,0);
     array B = A(isNaN(A));
     A(isNaN(A)) = C;
-    ASSERT_EQ(B.numdims(), 0);
-    ASSERT_EQ(A.numdims(), 1);
+    ASSERT_EQ(B.numdims(), 0u);
+    ASSERT_EQ(A.numdims(), 1u);
     ASSERT_EQ(lookup(constant(1,9), constant(0,0)).numdims(), 0);
 }
 
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index e711c631e4..6a6c0a1cb9 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -13,6 +13,9 @@
 #include <af/opencl.h>
 #include <iostream>
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+
 using namespace std;
 
 inline void checkErr(cl_int err, const char * name) {
@@ -129,3 +132,5 @@ TEST(OCLExtContext, NoopCPU)
 {
 }
 #endif
+
+#pragma GCC diagnostic pop

From de842e2f5d0dae0423176ce441f04bd5dfa56a83 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:27:42 -0400
Subject: [PATCH 0798/2677] Fix CMAKE_SOURCE/BINARY_DIR and CMAKE_MODULE_PATH
 Usage

* Continuation of PR #1554
---
 CMakeModules/CUDACheckCompute.cmake         |  2 +-
 CMakeModules/TargetArch.cmake               |  6 ++---
 CMakeModules/build_boost_compute.cmake      |  2 +-
 CMakeModules/build_cl2hpp.cmake             |  2 +-
 CMakeModules/build_clBLAS.cmake             |  2 +-
 CMakeModules/build_clFFT.cmake              |  2 +-
 CMakeModules/build_forge.cmake              |  2 +-
 CMakeModules/osx_install/OSXInstaller.cmake | 28 ++++++++++-----------
 CMakeModules/osx_install/distribution.dist  |  2 +-
 9 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/CMakeModules/CUDACheckCompute.cmake b/CMakeModules/CUDACheckCompute.cmake
index b55b40a62e..de379d3e28 100644
--- a/CMakeModules/CUDACheckCompute.cmake
+++ b/CMakeModules/CUDACheckCompute.cmake
@@ -9,7 +9,7 @@ IF(CUDA_FOUND)
     MESSAGE(STATUS "${PROJECT_SOURCE_DIR}/CMakeModules/cuda_compute_capability.cpp")
 
     TRY_RUN(RUN_RESULT_VAR COMPILE_RESULT_VAR
-        ${CMAKE_BINARY_DIR}
+        ${PROJECT_BINARY_DIR}
         ${PROJECT_SOURCE_DIR}/CMakeModules/cuda_compute_capability.cpp
         CMAKE_FLAGS
         -DINCLUDE_DIRECTORIES:STRING=${CUDA_TOOLKIT_INCLUDE}
diff --git a/CMakeModules/TargetArch.cmake b/CMakeModules/TargetArch.cmake
index bcf7d61a8b..65252f35e2 100644
--- a/CMakeModules/TargetArch.cmake
+++ b/CMakeModules/TargetArch.cmake
@@ -118,7 +118,7 @@ function(target_architecture output_var)
             list(APPEND ARCH ppc64)
         endif()
     else()
-        file(WRITE "${CMAKE_BINARY_DIR}/arch.c" "${archdetect_c_code}")
+        file(WRITE "${PROJECT_BINARY_DIR}/arch.c" "${archdetect_c_code}")
 
         enable_language(C)
 
@@ -133,8 +133,8 @@ function(target_architecture output_var)
         try_run(
             run_result_unused
             compile_result_unused
-            "${CMAKE_BINARY_DIR}"
-            "${CMAKE_BINARY_DIR}/arch.c"
+            "${PROJECT_BINARY_DIR}"
+            "${PROJECT_BINARY_DIR}/arch.c"
             COMPILE_OUTPUT_VARIABLE ARCH
             CMAKE_FLAGS CMAKE_OSX_ARCHITECTURES=${CMAKE_OSX_ARCHITECTURES}
         )
diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
index 37f8fc3ad3..fdcdc22029 100644
--- a/CMakeModules/build_boost_compute.cmake
+++ b/CMakeModules/build_boost_compute.cmake
@@ -5,7 +5,7 @@ SET(VER boost-1.61.0)
 SET(URL https://github.com/boostorg/compute/archive/${VER}.tar.gz)
 SET(MD5 7e1c433b48825d8cb2effa963823aec8)
 
-SET(thirdPartyDir "${CMAKE_BINARY_DIR}/third_party")
+SET(thirdPartyDir "${PROJECT_BINARY_DIR}/third_party")
 SET(srcDir "${thirdPartyDir}/compute-${VER}")
 SET(archive ${srcDir}.tar.gz)
 SET(inflated ${srcDir}-inflated)
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 34ce9ef80e..9c950af3fc 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -1,6 +1,6 @@
 INCLUDE(ExternalProject)
 
-SET(prefix ${CMAKE_BINARY_DIR}/third_party/cl2hpp)
+SET(prefix ${PROJECT_BINARY_DIR}/third_party/cl2hpp)
 
 ExternalProject_Add(
     cl2hpp-ext
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index d486b31801..76a65d1658 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -1,6 +1,6 @@
 INCLUDE(ExternalProject)
 
-SET(prefix ${CMAKE_BINARY_DIR}/third_party/clBLAS)
+SET(prefix ${PROJECT_BINARY_DIR}/third_party/clBLAS)
 SET(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${CMAKE_STATIC_LIBRARY_SUFFIX})
 IF(CMAKE_VERSION VERSION_LESS 3.2)
     IF(CMAKE_GENERATOR MATCHES "Ninja")
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 961347f913..e9b3ea979a 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -1,6 +1,6 @@
 INCLUDE(ExternalProject)
 
-SET(prefix "${CMAKE_BINARY_DIR}/third_party/clFFT")
+SET(prefix "${PROJECT_BINARY_DIR}/third_party/clFFT")
 SET(clFFT_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clFFT${CMAKE_STATIC_LIBRARY_SUFFIX})
 IF(CMAKE_VERSION VERSION_LESS 3.2)
     IF(CMAKE_GENERATOR MATCHES "Ninja")
diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 83892477f8..24f066d725 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -1,6 +1,6 @@
 INCLUDE(ExternalProject)
 
-SET(prefix ${CMAKE_BINARY_DIR}/third_party/forge)
+SET(prefix ${PROJECT_BINARY_DIR}/third_party/forge)
 
 # FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
 #        does not yet support it for the OUTPUT argument, see also:
diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 2b2a52be62..f916536874 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -2,16 +2,16 @@
 # Builds ArrayFire Installers for OSX
 #
 INCLUDE(CMakeParseArguments)
-INCLUDE(${CMAKE_MODULE_PATH}/Version.cmake)
+INCLUDE(Version)
 
 SET(BIN2CPP_PROGRAM "bin2cpp")
 
-SET(OSX_INSTALL_DIR ${CMAKE_MODULE_PATH}/osx_install)
+SET(OSX_INSTALL_SOURCE ${PROJECT_SOURCE_DIR}/CMakeModules/osx_install)
 
 ################################################################################
 ## Create Directory Structure
 ################################################################################
-SET(OSX_TEMP "${CMAKE_BINARY_DIR}/osx_install_files")
+SET(OSX_TEMP "${PROJECT_BINARY_DIR}/osx_install_files")
 
 # Common files - libforge, ArrayFireConfig*.cmake
 FILE(GLOB COMMONLIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
@@ -23,7 +23,7 @@ FOREACH(SRC ${COMMONLIB} ${COMMONCMAKE})
     ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_COMMON PRE_BUILD
                        COMMAND ${CMAKE_COMMAND} -E copy
                        ${SRC} "${OSX_TEMP}/common/${SRC_REL}"
-                       WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                       WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                        COMMENT "Copying Common files to temporary OSX Install Dir"
                        )
 ENDFOREACH()
@@ -39,7 +39,7 @@ MACRO(OSX_INSTALL_SETUP BACKEND LIB)
         ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
                            COMMAND ${CMAKE_COMMAND} -E copy
                            ${SRC} "${OSX_TEMP}/${BACKEND}/${SRC_REL}"
-                           WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                           WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                            COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir"
                            )
     ENDFOREACH()
@@ -54,7 +54,7 @@ OSX_INSTALL_SETUP(Unified af)
 ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_INCLUDE
                   COMMAND ${CMAKE_COMMAND} -E copy_directory
                   ${CMAKE_INSTALL_PREFIX}/include "${OSX_TEMP}/include"
-                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                   COMMENT "Copying header files to temporary OSX Install Dir"
                   )
 
@@ -62,7 +62,7 @@ ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_INCLUDE
 ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_EXAMPLES
                   COMMAND ${CMAKE_COMMAND} -E copy_directory
                   "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/examples" "${OSX_TEMP}/examples"
-                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                   COMMENT "Copying examples files to temporary OSX Install Dir"
                   )
 
@@ -70,7 +70,7 @@ ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_EXAMPLES
 ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
                   COMMAND ${CMAKE_COMMAND} -E copy_directory
                   "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/doc" "${OSX_TEMP}/doc"
-                  WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                   COMMENT "Copying documentation files to temporary OSX Install Dir"
                   )
 ################################################################################
@@ -105,13 +105,13 @@ ENDFUNCTION(PKG_BUILD)
 
 FUNCTION(PRODUCT_BUILD)
     CMAKE_PARSE_ARGUMENTS(ARGS "" "" "DEPENDS" ${ARGN})
-    SET(DISTRIBUTION_FILE       "${OSX_INSTALL_DIR}/distribution.dist")
+    SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution.dist")
     SET(DISTRIBUTION_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/distribution.dist.out")
 
-    SET(WELCOME_FILE       "${OSX_INSTALL_DIR}/welcome.html")
+    SET(WELCOME_FILE       "${OSX_INSTALL_SOURCE}/welcome.html")
     SET(WELCOME_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/welcome.html.out")
 
-    SET(README_FILE       "${OSX_INSTALL_DIR}/readme.html")
+    SET(README_FILE       "${OSX_INSTALL_SOURCE}/readme.html")
     SET(README_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/readme.html.out")
 
     SET(AF_TITLE    "ArrayFire ${AF_VERSION}")
@@ -140,7 +140,7 @@ PKG_BUILD(  PKG_NAME        ArrayFireCPU
             DEPENDS         OSX_INSTALL_SETUP_CPU
             TARGETS         cpu_package
             INSTALL_LOCATION /usr/local
-            SCRIPT_DIR      ${OSX_INSTALL_DIR}/cpu_scripts
+            SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/cpu_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cpu.lib
             PATH_TO_FILES   ${OSX_TEMP}/CPU
             FILTERS         opencl cuda unified)
@@ -149,7 +149,7 @@ PKG_BUILD(  PKG_NAME        ArrayFireCUDA
             DEPENDS         OSX_INSTALL_SETUP_CUDA
             TARGETS         cuda_package
             INSTALL_LOCATION /usr/local
-            SCRIPT_DIR      ${OSX_INSTALL_DIR}/cuda_scripts
+            SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/cuda_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cuda.lib
             PATH_TO_FILES   ${OSX_TEMP}/CUDA
             FILTERS         cpu opencl unified)
@@ -158,7 +158,7 @@ PKG_BUILD(  PKG_NAME        ArrayFireOPENCL
             DEPENDS         OSX_INSTALL_SETUP_OpenCL
             TARGETS         opencl_package
             INSTALL_LOCATION /usr/local
-            SCRIPT_DIR      ${OSX_INSTALL_DIR}/opencl_scripts
+            SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/opencl_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.opencl.lib
             PATH_TO_FILES   ${OSX_TEMP}/OpenCL
             FILTERS         cpu cuda unified)
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
index b476bf013f..1494e7efde 100644
--- a/CMakeModules/osx_install/distribution.dist
+++ b/CMakeModules/osx_install/distribution.dist
@@ -3,7 +3,7 @@
     <title>${AF_TITLE}</title>
     <welcome    file="${WELCOME_FILE_OUT}" />
     <readme     file="${README_FILE_OUT}" mime-type="test/html" />
-    <license    file="${CMAKE_MODULE_PATH}/../LICENSE" mime-type="test/plain" />
+    <license    file="${PROJECT_SOURCE_DIR}/LICENSE" mime-type="test/plain" />
     <script>
       function CheckBackendSelected() {
       return choices.opencl_lib.selected ||

From 831aa75471624894bc75632d97fbae5986f04b50 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:45:34 -0400
Subject: [PATCH 0799/2677] Rename CUDA-GL interop files from CU -> CPP

---
 .../{hist_graphics.cu => hist_graphics.cpp}   | 20 ++++++----
 src/backend/cuda/{image.cu => image.cpp}      | 18 +++++----
 .../{interopManager.cu => interopManager.cpp} | 37 +++++--------------
 src/backend/cuda/{plot.cu => plot.cpp}        | 20 ++++++----
 src/backend/cuda/{surface.cu => surface.cpp}  | 20 ++++++----
 5 files changed, 57 insertions(+), 58 deletions(-)
 rename src/backend/cuda/{hist_graphics.cu => hist_graphics.cpp} (72%)
 rename src/backend/cuda/{image.cu => image.cpp} (74%)
 rename src/backend/cuda/{interopManager.cu => interopManager.cpp} (75%)
 rename src/backend/cuda/{plot.cu => plot.cpp} (72%)
 rename src/backend/cuda/{surface.cu => surface.cpp} (71%)

diff --git a/src/backend/cuda/hist_graphics.cu b/src/backend/cuda/hist_graphics.cpp
similarity index 72%
rename from src/backend/cuda/hist_graphics.cu
rename to src/backend/cuda/hist_graphics.cpp
index cee40c7f00..e911debc6f 100644
--- a/src/backend/cuda/hist_graphics.cu
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -17,9 +17,10 @@
 
 namespace cuda
 {
+using namespace gl;
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
+void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 {
     if(InteropManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
@@ -28,7 +29,7 @@ void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
 
         cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = hist->size();
+        size_t num_bytes = hist->verticesSize();
         T* d_vbo = NULL;
         cudaGraphicsMapResources(1, &cudaVBOResource, 0);
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
@@ -40,20 +41,23 @@ void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
 
         POST_LAUNCH_CHECK();
     } else {
+        // Make sure to do this
+        glbinding::Binding::useCurrentContext();
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, hist->vbo());
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, hist->vertices());
+        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, data.get(), hist->size(), cudaMemcpyDeviceToHost));
-            glUnmapBuffer(GL_ARRAY_BUFFER);
+            CUDA_CHECK(cudaMemcpy(ptr, data.get(), hist->verticesSize(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_histogram<T>(const Array<T> &data, const fg::Histogram* hist);
+    template void copy_histogram<T>(const Array<T> &data, const forge::Histogram* hist);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
diff --git a/src/backend/cuda/image.cu b/src/backend/cuda/image.cpp
similarity index 74%
rename from src/backend/cuda/image.cu
rename to src/backend/cuda/image.cpp
index 292f80110a..ef7fbbe865 100644
--- a/src/backend/cuda/image.cu
+++ b/src/backend/cuda/image.cpp
@@ -22,9 +22,10 @@ using af::dim4;
 
 namespace cuda
 {
+using namespace gl;
 
 template<typename T>
-void copy_image(const Array<T> &in, const fg::Image* image)
+void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if(InteropManager::checkGraphicsInteropCapability()) {
         InteropManager& intrpMngr = InteropManager::getInstance();
@@ -44,21 +45,24 @@ void copy_image(const Array<T> &in, const fg::Image* image)
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
     } else {
+        // Make sure to do this
+        glbinding::Binding::useCurrentContext();
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pbo());
-        glBufferData(GL_PIXEL_UNPACK_BUFFER, image->size(), 0, GL_STREAM_DRAW);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
+        glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->pbo());
+        glBufferData((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->size(), 0, (gl::GLenum)GL_STREAM_DRAW);
+        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpy(ptr, in.get(), image->size(), cudaMemcpyDeviceToHost));
-            glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
+            glUnmapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER);
         }
-        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
+        glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)      \
-    template void copy_image<T>(const Array<T> &in, const fg::Image* image);
+    template void copy_image<T>(const Array<T> &in, const forge::Image* image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/interopManager.cu b/src/backend/cuda/interopManager.cpp
similarity index 75%
rename from src/backend/cuda/interopManager.cu
rename to src/backend/cuda/interopManager.cpp
index cf83dc7a90..cdc85cbcc7 100644
--- a/src/backend/cuda/interopManager.cu
+++ b/src/backend/cuda/interopManager.cpp
@@ -51,7 +51,7 @@ InteropManager& InteropManager::getInstance()
     return my_instance;
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const fg::Image* key)
+cudaGraphicsResource* InteropManager::getBufferResource(const forge::Image* key)
 {
     int device = getActiveDeviceId();
     void* key_value = (void*)key;
@@ -66,68 +66,51 @@ cudaGraphicsResource* InteropManager::getBufferResource(const fg::Image* key)
     return interop_maps[device][key_value];
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const fg::Plot* key)
+cudaGraphicsResource* InteropManager::getBufferResource(const forge::Plot* key)
 {
     int device = getActiveDeviceId();
     void* key_value = (void*)key;
 
     iter_t iter = interop_maps[device].find(key_value);
 
-    if(interop_maps[device].find(key_value) == interop_maps[device].end()) {
-        cudaGraphicsResource *cudaVBOResource;
-        // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vbo(), cudaGraphicsMapFlagsWriteDiscard));
-        interop_maps[device][key_value] = cudaVBOResource;
-    }
-
-    return interop_maps[device][key_value];
-}
-
-cudaGraphicsResource* InteropManager::getBufferResource(const fg::Plot3* key)
-{
-    int device = getActiveDeviceId();
-    void* key_value = (void*)key;
-
-    iter_t iter = interop_maps[device].find(key_value);
-
-    if(interop_maps[device].find(key_value) == interop_maps[device].end()) {
+    if(iter == interop_maps[device].end()) {
         cudaGraphicsResource *cudaVBOResource;
         // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vbo(), cudaGraphicsMapFlagsWriteDiscard));
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
         interop_maps[device][key_value] = cudaVBOResource;
     }
 
     return interop_maps[device][key_value];
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const fg::Histogram* key)
+cudaGraphicsResource* InteropManager::getBufferResource(const forge::Histogram* key)
 {
     int device = getActiveDeviceId();
     void* key_value = (void*)key;
 
     iter_t iter = interop_maps[device].find(key_value);
 
-    if(interop_maps[device].find(key_value) == interop_maps[device].end()) {
+    if(iter == interop_maps[device].end()) {
         cudaGraphicsResource *cudaVBOResource;
         // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vbo(), cudaGraphicsMapFlagsWriteDiscard));
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
         interop_maps[device][key_value] = cudaVBOResource;
     }
 
     return interop_maps[device][key_value];
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const fg::Surface* key)
+cudaGraphicsResource* InteropManager::getBufferResource(const forge::Surface* key)
 {
     int device = getActiveDeviceId();
     void* key_value = (void*)key;
 
     iter_t iter = interop_maps[device].find(key_value);
 
-    if(interop_maps[device].find(key_value) == interop_maps[device].end()) {
+    if(iter == interop_maps[device].end()) {
         cudaGraphicsResource *cudaVBOResource;
         // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vbo(), cudaGraphicsMapFlagsWriteDiscard));
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
         interop_maps[device][key_value] = cudaVBOResource;
     }
 
diff --git a/src/backend/cuda/plot.cu b/src/backend/cuda/plot.cpp
similarity index 72%
rename from src/backend/cuda/plot.cu
rename to src/backend/cuda/plot.cpp
index 74a8363648..fc0340589e 100644
--- a/src/backend/cuda/plot.cu
+++ b/src/backend/cuda/plot.cpp
@@ -22,9 +22,10 @@ using af::dim4;
 
 namespace cuda
 {
+using namespace gl;
 
 template<typename T>
-void copy_plot(const Array<T> &P, fg::Plot* plot)
+void copy_plot(const Array<T> &P, forge::Plot* plot)
 {
     if(InteropManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
@@ -33,7 +34,7 @@ void copy_plot(const Array<T> &P, fg::Plot* plot)
 
         cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = plot->size();
+        size_t num_bytes = plot->verticesSize();
         T* d_vbo = NULL;
         cudaGraphicsMapResources(1, &cudaVBOResource, 0);
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
@@ -45,20 +46,23 @@ void copy_plot(const Array<T> &P, fg::Plot* plot)
 
         POST_LAUNCH_CHECK();
     } else {
+        // Make sure to do this
+        glbinding::Binding::useCurrentContext();
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, plot->vbo());
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, plot->vertices());
+        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot->size(), cudaMemcpyDeviceToHost));
-            glUnmapBuffer(GL_ARRAY_BUFFER);
+            CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot->verticesSize(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_plot<T>(const Array<T> &P, fg::Plot* plot);
+    template void copy_plot<T>(const Array<T> &P, forge::Plot* plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/surface.cu b/src/backend/cuda/surface.cpp
similarity index 71%
rename from src/backend/cuda/surface.cu
rename to src/backend/cuda/surface.cpp
index 9222b49de0..4709264aff 100644
--- a/src/backend/cuda/surface.cu
+++ b/src/backend/cuda/surface.cpp
@@ -22,9 +22,10 @@ using af::dim4;
 
 namespace cuda
 {
+using namespace gl;
 
 template<typename T>
-void copy_surface(const Array<T> &P, fg::Surface* surface)
+void copy_surface(const Array<T> &P, forge::Surface* surface)
 {
     if(InteropManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
@@ -33,7 +34,7 @@ void copy_surface(const Array<T> &P, fg::Surface* surface)
 
         cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = surface->size();
+        size_t num_bytes = surface->verticesSize();
         T* d_vbo = NULL;
         cudaGraphicsMapResources(1, &cudaVBOResource, 0);
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
@@ -45,20 +46,23 @@ void copy_surface(const Array<T> &P, fg::Surface* surface)
 
         POST_LAUNCH_CHECK();
     } else {
+        // Make sure to do this
+        glbinding::Binding::useCurrentContext();
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, surface->vbo());
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, surface->vertices());
+        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, P.get(), surface->size(), cudaMemcpyDeviceToHost));
-            glUnmapBuffer(GL_ARRAY_BUFFER);
+            CUDA_CHECK(cudaMemcpy(ptr, P.get(), surface->verticesSize(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_surface<T>(const Array<T> &P, fg::Surface* surface);
+    template void copy_surface<T>(const Array<T> &P, forge::Surface* surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)

From f3532c52e0a9f1d4db7657371d50671f15dcb9a9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:46:26 -0400
Subject: [PATCH 0800/2677] Remove backend plot3 files

---
 src/backend/cpu/plot3.cpp    | 51 -----------------------
 src/backend/cpu/plot3.hpp    | 22 ----------
 src/backend/cuda/plot3.cu    | 73 ---------------------------------
 src/backend/cuda/plot3.hpp   | 22 ----------
 src/backend/opencl/plot3.cpp | 78 ------------------------------------
 src/backend/opencl/plot3.hpp | 22 ----------
 6 files changed, 268 deletions(-)
 delete mode 100644 src/backend/cpu/plot3.cpp
 delete mode 100644 src/backend/cpu/plot3.hpp
 delete mode 100644 src/backend/cuda/plot3.cu
 delete mode 100644 src/backend/cuda/plot3.hpp
 delete mode 100644 src/backend/opencl/plot3.cpp
 delete mode 100644 src/backend/opencl/plot3.hpp

diff --git a/src/backend/cpu/plot3.cpp b/src/backend/cpu/plot3.cpp
deleted file mode 100644
index 515fe0336c..0000000000
--- a/src/backend/cpu/plot3.cpp
+++ /dev/null
@@ -1,51 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_GRAPHICS)
-
-#include <Array.hpp>
-#include <plot3.hpp>
-#include <err_cpu.hpp>
-#include <graphics_common.hpp>
-#include <platform.hpp>
-#include <queue.hpp>
-
-using af::dim4;
-
-namespace cpu
-{
-
-template<typename T>
-void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
-{
-    P.eval();
-    getQueue().sync();
-    CheckGL("Before CopyArrayToVBO");
-
-    glBindBuffer(GL_ARRAY_BUFFER, plot3->vbo());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, plot3->size(), P.get());
-    glBindBuffer(GL_ARRAY_BUFFER, 0);
-
-    CheckGL("In CopyArrayToVBO");
-}
-
-#define INSTANTIATE(T)  \
-    template void copy_plot3<T>(const Array<T> &P, fg::Plot3* plot3);
-
-INSTANTIATE(float)
-INSTANTIATE(double)
-INSTANTIATE(int)
-INSTANTIATE(uint)
-INSTANTIATE(uchar)
-INSTANTIATE(short)
-INSTANTIATE(ushort)
-
-}
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/plot3.hpp b/src/backend/cpu/plot3.hpp
deleted file mode 100644
index 1e5c97ab0a..0000000000
--- a/src/backend/cpu/plot3.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined (WITH_GRAPHICS)
-
-#include <Array.hpp>
-#include <graphics_common.hpp>
-
-namespace cpu
-{
-    template<typename T>
-    void copy_plot3(const Array<T> &P, fg::Plot3* plot3);
-}
-
-#endif
-
diff --git a/src/backend/cuda/plot3.cu b/src/backend/cuda/plot3.cu
deleted file mode 100644
index ba94bcab0e..0000000000
--- a/src/backend/cuda/plot3.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined (WITH_GRAPHICS)
-
-#include <interopManager.hpp>
-#include <Array.hpp>
-#include <plot3.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
-#include <join.hpp>
-#include <reduce.hpp>
-#include <reorder.hpp>
-
-using af::dim4;
-
-namespace cuda
-{
-
-template<typename T>
-void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
-{
-    if(InteropManager::checkGraphicsInteropCapability()) {
-        const T *d_P = P.get();
-
-        InteropManager& intrpMngr = InteropManager::getInstance();
-
-        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot3);
-        // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = plot3->size();
-        T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
-
-        CheckGL("After cuda resource copy");
-
-        POST_LAUNCH_CHECK();
-    } else {
-        CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, plot3->vbo());
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
-        if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot3->size(), cudaMemcpyDeviceToHost));
-            glUnmapBuffer(GL_ARRAY_BUFFER);
-        }
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
-        CheckGL("End CUDA fallback-resource copy");
-    }
-}
-
-#define INSTANTIATE(T)  \
-    template void copy_plot3<T>(const Array<T> &P, fg::Plot3* plot3);
-
-INSTANTIATE(float)
-INSTANTIATE(double)
-INSTANTIATE(int)
-INSTANTIATE(uint)
-INSTANTIATE(short)
-INSTANTIATE(ushort)
-INSTANTIATE(uchar)
-
-}
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cuda/plot3.hpp b/src/backend/cuda/plot3.hpp
deleted file mode 100644
index 3badb331f3..0000000000
--- a/src/backend/cuda/plot3.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined (WITH_GRAPHICS)
-
-#include <Array.hpp>
-#include <graphics_common.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    void copy_plot3(const Array<T> &P, fg::Plot3* plot3);
-}
-
-#endif
-
diff --git a/src/backend/opencl/plot3.cpp b/src/backend/opencl/plot3.cpp
deleted file mode 100644
index de6769cabf..0000000000
--- a/src/backend/opencl/plot3.cpp
+++ /dev/null
@@ -1,78 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined (WITH_GRAPHICS)
-
-#include <interopManager.hpp>
-#include <Array.hpp>
-#include <plot3.hpp>
-#include <err_opencl.hpp>
-#include <debug_opencl.hpp>
-
-using af::dim4;
-
-namespace opencl
-{
-
-template<typename T>
-void copy_plot3(const Array<T> &P, fg::Plot3* plot3)
-{
-    if (isGLSharingSupported()) {
-        CheckGL("Begin OpenCL resource copy");
-        const cl::Buffer *d_P = P.get();
-        size_t bytes = plot3->size();
-
-        InteropManager& intrpMngr = InteropManager::getInstance();
-
-        cl::Buffer *clPBOResource = intrpMngr.getBufferResource(plot3);
-
-        std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*clPBOResource);
-
-        glFinish();
-
-        // Use of events:
-        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
-        cl::Event event;
-
-        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
-        event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
-        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
-        event.wait();
-
-        CL_DEBUG_FINISH(getQueue());
-        CheckGL("End OpenCL resource copy");
-    } else {
-        CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, plot3->vbo());
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
-        if (ptr) {
-            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, plot3->size(), ptr);
-            glUnmapBuffer(GL_ARRAY_BUFFER);
-        }
-        glBindBuffer(GL_ARRAY_BUFFER, 0);
-        CheckGL("End OpenCL fallback-resource copy");
-    }
-}
-
-#define INSTANTIATE(T)  \
-    template void copy_plot3<T>(const Array<T> &P, fg::Plot3* plot3);
-
-INSTANTIATE(float)
-INSTANTIATE(double)
-INSTANTIATE(int)
-INSTANTIATE(uint)
-INSTANTIATE(short)
-INSTANTIATE(ushort)
-INSTANTIATE(uchar)
-
-}
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/opencl/plot3.hpp b/src/backend/opencl/plot3.hpp
deleted file mode 100644
index 86093908a6..0000000000
--- a/src/backend/opencl/plot3.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined (WITH_GRAPHICS)
-
-#include <Array.hpp>
-#include <graphics_common.hpp>
-
-namespace opencl
-{
-    template<typename T>
-    void copy_plot3(const Array<T> &P, fg::Plot3* plot3);
-}
-
-#endif
-

From d026fd009ddae0cb97c6ca03993ff51af0247e8c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:47:19 -0400
Subject: [PATCH 0801/2677] Reorder forge related enums in defines.h

---
 include/af/defines.h | 29 +++++++++++++++++------------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 9f09ff4238..dfe77aa953 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -329,16 +329,6 @@ typedef enum {
     AF_NORM_EUCLID = AF_NORM_VECTOR_2, ///< The default. Same as AF_NORM_VECTOR_2
 } af_norm_type;
 
-typedef enum {
-    AF_COLORMAP_DEFAULT = 0,    ///< Default grayscale map
-    AF_COLORMAP_SPECTRUM= 1,    ///< Spectrum map
-    AF_COLORMAP_COLORS  = 2,    ///< Colors
-    AF_COLORMAP_RED     = 3,    ///< Red hue map
-    AF_COLORMAP_MOOD    = 4,    ///< Mood map
-    AF_COLORMAP_HEAT    = 5,    ///< Heat map
-    AF_COLORMAP_BLUE    = 6     ///< Blue hue map
-} af_colormap;
-
 #if AF_API_VERSION >= 31
 typedef enum {
     AF_FIF_BMP          = 0,    ///< FreeImage Enum for Bitmap File
@@ -400,7 +390,21 @@ typedef enum {
 } af_binary_op;
 #endif
 
-#if AF_API_VERSION >=32
+////////////////////////////////////////////////////////////////////////////////
+// FORGE / Graphics Related Enums
+// These enums have values corresponsding to Forge enums in forge defines.h
+////////////////////////////////////////////////////////////////////////////////
+typedef enum {
+    AF_COLORMAP_DEFAULT = 0,    ///< Default grayscale map
+    AF_COLORMAP_SPECTRUM= 1,    ///< Spectrum map
+    AF_COLORMAP_COLORS  = 2,    ///< Colors
+    AF_COLORMAP_RED     = 3,    ///< Red hue map
+    AF_COLORMAP_MOOD    = 4,    ///< Mood map
+    AF_COLORMAP_HEAT    = 5,    ///< Heat map
+    AF_COLORMAP_BLUE    = 6     ///< Blue hue map
+} af_colormap;
+
+#if AF_API_VERSION >= 32
 typedef enum {
     AF_MARKER_NONE         = 0,
     AF_MARKER_POINT        = 1,
@@ -412,6 +416,7 @@ typedef enum {
     AF_MARKER_STAR         = 7
 } af_marker_type;
 #endif
+////////////////////////////////////////////////////////////////////////////////
 
 #ifdef __cplusplus
 namespace af
@@ -442,7 +447,7 @@ namespace af
 #if AF_API_VERSION >= 32
     typedef af_marker_type markerType;
 #endif
-#if AF_API_VERSION >=34
+#if AF_API_VERSION >= 34
     typedef af_moment_type momentType;
 #endif
 #if AF_API_VERSION >= 34

From 1b460423b659040693e16be40ffafd8d1ea1e469 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:47:51 -0400
Subject: [PATCH 0802/2677] BUGFIX Fractal example failing because JIT tree was
 too long

---
 examples/graphics/fractal.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index 9781b61c90..74718eaa51 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -50,8 +50,8 @@ array mandelbrot(const array &in, int iter, float maxval)
         Z = Z * (1 - cond);
 
         // Ensuring the JIT does not become too large
-        C.eval();
-        Z.eval();
+        af::eval(C, Z);
+        mag.eval();
     }
 
     // Normalize

From cf1d733e672ce68384c431a2169ac325497a0594 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 15:48:25 -0400
Subject: [PATCH 0803/2677] Add CUFFT_NOT_SUPPORTED enum to err_cufft for CUDA
 8.0

---
 src/backend/cuda/err_cufft.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/cuda/err_cufft.hpp b/src/backend/cuda/err_cufft.hpp
index 492fcd9ed1..99fcd97cee 100644
--- a/src/backend/cuda/err_cufft.hpp
+++ b/src/backend/cuda/err_cufft.hpp
@@ -65,6 +65,10 @@ static const char * _cufftGetResultString(cufftResult res)
 
         case CUFFT_LICENSE_ERROR:
             return "cuFFT: license error";
+#endif
+#if CUDA_VERSION >= 8000
+        case CUFFT_NOT_SUPPORTED:
+            return "cuFFT: not supported";
 #endif
     }
 

From 7120e01e6512dbb9a504d0e442bba04ba6dd9b3c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 18:13:24 -0400
Subject: [PATCH 0804/2677] Remove obselete FindForge.cmake

---
 CMakeModules/FindForge.cmake | 99 ------------------------------------
 1 file changed, 99 deletions(-)
 delete mode 100644 CMakeModules/FindForge.cmake

diff --git a/CMakeModules/FindForge.cmake b/CMakeModules/FindForge.cmake
deleted file mode 100644
index 117451da97..0000000000
--- a/CMakeModules/FindForge.cmake
+++ /dev/null
@@ -1,99 +0,0 @@
-# - Find Forge
-
-# Defines the following variables:
-# FORGE_INCLUDE_DIRECTORIES    - Location of FORGE's include directory.
-# FORGE_LIBRARIES              - Location of FORGE's libraries.
-# FORGE_FOUND                  - True if FORGE has been located
-#
-# You may provide a hint to where FORGE's root directory may be located
-# by setting FORGE_ROOT_DIR before calling this script.
-#
-# ----------------------------------------------------------------------------
-#
-# FORGE_FOUND        - True of the FORGE library has been found.
-# FORGE_LIBRARIES    - Location of FORGE library, if found
-#
-# Variables used by this module, they can change the default behaviour and
-# need to be set before calling find_package:
-#
-#=============================================================================
-# Copyright (c) 2014, ArrayFire
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without modification,
-# are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright notice, this
-#   list of conditions and the following disclaimer in the documentation and/or
-#   other materials provided with the distribution.
-#
-# * Neither the name of the ArrayFire nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#=============================================================================
-
-IF(FORGE_INCLUDE_DIRECTORIES)
-  # Already in cache, be silent
-  set (FORGE_FIND_QUIETLY TRUE)
-ENDIF()
-
-# Find the FORGE install directories and headers:
-FIND_PATH(FORGE_ROOT_DIR
-    NAMES include/forge.h
-    PATH_SUFFIXES forge FORGE FORGE
-    HINTS "${CMAKE_INSTALL_PREFIX}" "${FORGE_ROOT_DIR}" "${FORGE_ROOT_DIR}/lib" "${FORGE_ROOT_DIR}/lib64" "${CMAKE_SOURCE_DIR}/.." "${CMAKE_SOURCE_DIR}/../.."
-    DOC "FORGE root directory.")
-
-FIND_PATH(FORGE_PACKAGE_DIR
-    NAMES include/forge.h lib
-    HINTS "${FORGE_ROOT_DIR}/package" "${FORGE_ROOT_DIR}/build/package" "${FORGE_ROOT_DIR}"
-    DOC "FORGE Package directory.")
-
-FIND_PATH(FORGE_INCLUDE_DIRECTORIES
-    NAMES forge.h
-    HINTS "${FORGE_PACKAGE_DIR}/include"
-    DOC "FORGE Include directory")
-
-# Find all libraries required for the FORGE
-FIND_LIBRARY(FORGE_LIBRARY
-    NAMES forge
-    HINTS "${FORGE_PACKAGE_DIR}/lib")
-
-FIND_PACKAGE(GLEWmx REQUIRED)
-
-IF(GLEWMX_FOUND AND OPENGL_FOUND)
-    IF(FORGE_INCLUDE_DIRECTORIES)
-        SET(FORGE_INCLUDE_DIRECTORIES ${FORGE_INCLUDE_DIRECTORIES} ${GLEW_INCLUDE_DIR}
-            CACHE INTERNAL "All include dirs required for FORGE'")
-    ENDIF()
-    IF(FORGE_LIBRARY)
-        SET(FORGE_LIBRARIES ${FORGE_LIBRARY} ${GLEWmx_LIBRARY} ${OPENGL_gl_LIBRARY}
-            CACHE INTERNAL "All libraries required for FORGE'")
-    ENDIF()
-    # handle the QUIETLY and REQUIRED arguments and set FORGE_FOUND to TRUE if
-    # all listed variables are TRUE
-    INCLUDE (FindPackageHandleStandardArgs)
-    FIND_PACKAGE_HANDLE_STANDARD_ARGS(FORGE DEFAULT_MSG FORGE_LIBRARIES FORGE_INCLUDE_DIRECTORIES)
-    MARK_AS_ADVANCED(FORGE_LIBRARIES FORGE_INCLUDE_DIRECTORIES)
-
-ELSE(GLEWMX_FOUND AND OPENGL_FOUND)
-    IF(NOT GLEWMX_FOUND)
-        MESSAGE(FATAL_ERROR "GLEW-MX Not Found")
-    ELSEIF(NOT OPENGL_FOUND)
-        MESSAGE(FATAL_ERROR "OpenGL Not Found")
-    ENDIF()
-ENDIF(GLEWMX_FOUND AND OPENGL_FOUND)

From 96a4a8253807c394a79f888a6283b80721652225 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 18:57:30 -0400
Subject: [PATCH 0805/2677] Add build_glbinding external build cmake file

---
 CMakeModules/build_glbinding.cmake | 62 ++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 CMakeModules/build_glbinding.cmake

diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
new file mode 100644
index 0000000000..c929778d92
--- /dev/null
+++ b/CMakeModules/build_glbinding.cmake
@@ -0,0 +1,62 @@
+INCLUDE(ExternalProject)
+
+SET(prefix ${PROJECT_BINARY_DIR}/third_party/glb)
+
+SET(LIB_POSTFIX "")
+IF (${CMAKE_BUILD_TYPE} STREQUAL "Debug")
+    SET(LIB_POSTFIX "d")
+ENDIF()
+
+SET(glbinding_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}glbinding${LIB_POSTFIX}${CMAKE_STATIC_LIBRARY_SUFFIX})
+
+IF(CMAKE_VERSION VERSION_LESS 3.2)
+    IF(CMAKE_GENERATOR MATCHES "Ninja")
+        MESSAGE(WARNING "Building with Ninja has known issues with CMake older than 3.2")
+    endif()
+    SET(byproducts)
+ELSE()
+    SET(byproducts BYPRODUCTS ${glbinding_location})
+ENDIF()
+
+IF(UNIX)
+    SET(CXXFLAGS "${CMAKE_CXX_FLAGS} -w -fPIC")
+    SET(CFLAGS "${CMAKE_C_FLAGS} -w -fPIC")
+ENDIF(UNIX)
+
+ExternalProject_Add(
+    glb-ext
+    GIT_REPOSITORY https://github.com/cginternals/glbinding.git
+    GIT_TAG v2.1.1
+    UPDATE_COMMAND ""
+    PREFIX "${prefix}"
+    INSTALL_DIR "${prefix}"
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
+    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+    -DCMAKE_CXX_FLAGS:STRING=${CXXFLAGS}
+    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+    -DCMAKE_C_FLAGS:STRING=${CFLAGS}
+    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+    -DBUILD_SHARED_LIBS:BOOL=OFF
+    -DOPTION_BUILD_TESTS:BOOL=OFF
+    # Leave these GLFW_LIBRARY Options as empty.
+    # They are only required for GLBINDING Executables.
+    # Leaving them empty will disable compilation of said executables.
+    -DGLFW_LIBRARY_RELEASE:PATH=
+    -DGLFW_LIBRARY_DEBUG:PATH=
+    ${byproducts}
+    )
+
+ADD_LIBRARY(glbinding IMPORTED STATIC)
+
+ExternalProject_Get_Property(glb-ext install_dir)
+
+SET_TARGET_PROPERTIES(glbinding PROPERTIES IMPORTED_LOCATION ${glbinding_location})
+
+ADD_DEPENDENCIES(glbinding glb-ext)
+
+SET(GLBINDING_INCLUDE_DIRS ${install_dir}/include CACHE INTERNAL "" FORCE)
+SET(GLBINDING_LIBRARIES ${glbinding_location} CACHE INTERNAL "" FORCE)
+# Use glbinding_DIR as is and don't change the case
+SET(glbinding_DIR ${install_dir} CACHE INTERNAL "" FORCE)
+SET(GLBINDING_FOUND ON CACHE INTERNAL "" FORCE)

From 06d0fa457fb4a54c75ae9a61a15b4b10c03f1994 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 19:01:25 -0400
Subject: [PATCH 0806/2677] Update build_forge.cmake for glbinding

* Variables now match ForgeConfig.cmake
* Add glbinding as an external project passed from ArrayFire
* Fix the config thing
---
 CMakeModules/build_forge.cmake | 34 +++++++++++++++++++++++-----------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 24f066d725..6975afdc99 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -1,5 +1,11 @@
 INCLUDE(ExternalProject)
 
+IF(USE_SYSTEM_GLBINDING)
+    SET(GLBINDING_TARGET "")
+ELSE(USE_SYSTEM_GLBINDING)
+    SET(GLBINDING_TARGET glbinding)
+ENDIF(USE_SYSTEM_GLBINDING)
+
 SET(prefix ${PROJECT_BINARY_DIR}/third_party/forge)
 
 # FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
@@ -14,18 +20,19 @@ IF(CMAKE_BUILD_TYPE)
 ELSE()
     SET(forge_lib_config Release)
 ENDIF()
-IF(CMAKE_GENERATOR MATCHES "Xcode") # TODO: Also for "Visual Studio"?
-    SET(forge_lib_infix "/${forge_lib_config}")
+
+IF(CMAKE_GENERATOR MATCHES "Xcode")
+    SET(forge_lib_infix "${forge_lib_config}/")
 ELSE()
-    SET(forge_lib_infix)
+    SET(forge_lib_infix "")
 ENDIF()
 IF(WIN32)
     SET(forge_lib_prefix "${prefix}/lib")
 ELSE(WIN32)
-    SET(forge_lib_prefix "${prefix}/src/forge-ext-build/src")
+    SET(forge_lib_prefix "${prefix}/src/forge-ext-build/src/backend/opengl")
 ENDIF(WIN32)
 
-SET(forge_location "${forge_lib_prefix}${forge_lib_infix}/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
+SET(forge_location "${forge_lib_prefix}/${forge_lib_infix}${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
 IF(CMAKE_VERSION VERSION_LESS 3.2)
     IF(CMAKE_GENERATOR MATCHES "Ninja")
         MESSAGE(WARNING "Building forge with Ninja has known issues with CMake older than 3.2")
@@ -39,17 +46,18 @@ ENDIF()
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG master
+    GIT_TAG devel
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
+    DEPENDS ${GLBINDING_TARGET}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
     -DCMAKE_SOURCE_DIR:PATH=<SOURCE_DIR>
     -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
     -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
     -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DUSE_GLEWmx_STATIC:BOOL=${USE_GLEWmx_STATIC}
-    -DGLEW_ROOT_DIR:STRING=${GLEW_ROOT_DIR}
+    -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
+    -Dglbinding_DIR:STRING=${glbinding_DIR}
     -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
     -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
     -DBUILD_EXAMPLES:BOOL=OFF
@@ -59,17 +67,21 @@ ExternalProject_Add(
 
 ExternalProject_Get_Property(forge-ext binary_dir)
 ExternalProject_Get_Property(forge-ext install_dir)
+
 ADD_LIBRARY(forge SHARED IMPORTED)
 SET_TARGET_PROPERTIES(forge PROPERTIES IMPORTED_LOCATION ${forge_location})
+
 IF(WIN32)
     SET_TARGET_PROPERTIES(forge PROPERTIES IMPORTED_IMPLIB ${forge_lib_prefix}/forge.lib)
 ELSE(WIN32)
-    SET(forge_bindir_location ${binary_dir}/src${forge_lib_infix}/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX})
+    SET(forge_bindir_location ${binary_dir}/src/backend/opengl/${forge_lib_infix}${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX})
     IF(NOT (${forge_bindir_location} STREQUAL ${forge_location}))
         MESSAGE(WARNING "Did the forge binary location move? (Have ${forge_bindir_location} vs ${forge_location})")
     ENDIF()
 ENDIF(WIN32)
-ADD_DEPENDENCIES(forge forge-ext)
-SET(FORGE_INCLUDE_DIRECTORIES ${install_dir}/include)
+
+ADD_DEPENDENCIES(forge forge-ext ${GLBINDING_TARGET})
+
+SET(FORGE_INCLUDE_DIRS ${install_dir}/include)
 SET(FORGE_LIBRARIES forge)
 SET(FORGE_FOUND ON)

From 7bfbb8a0f3e4e3874acedf50e26ca12b0d0ef5cf Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 19:57:11 -0400
Subject: [PATCH 0807/2677] CMake: Build config for Forge + glbinding

* Remove GLEW and GLU
* Cleanup
---
 CMakeLists.txt                    | 44 ++++++++++++++++++++-----------
 src/backend/cpu/CMakeLists.txt    | 11 ++++----
 src/backend/cuda/CMakeLists.txt   |  7 ++++-
 src/backend/opencl/CMakeLists.txt | 28 +++++++++++---------
 4 files changed, 56 insertions(+), 34 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a246ee8a16..102cdb41a9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -57,34 +57,48 @@ OPTION(USE_SYSTEM_CL2HPP "Use cl2.hpp installed on system" OFF)
 
 IF(BUILD_GRAPHICS)
     OPTION(USE_SYSTEM_FORGE "Use system Forge" OFF)
+    OPTION(USE_SYSTEM_GLBINDING "Use system glbinding" OFF)
+
+    FIND_PACKAGE(OpenGL REQUIRED)
+
+    IF(USE_SYSTEM_GLBINDING)
+        # Point glbinding_DIR (case sensitive) to the location of glbinding-config.cmake
+        # This file is generally at CMAKE_INSTALL_PREFIX/glbinding-config.cmake of
+        # the glbinding project
+        FIND_PACKAGE(glbinding REQUIRED)
+        SET(GLBINDING_FOUND "ON")
+        SET(GLBINDING_LIBRARIES glbinding::glbinding CACHE INTERNAL "glbinding library target")
+    ELSE(USE_SYSTEM_GLBINDING)
+        INCLUDE(build_glbinding)
+        LIST(APPEND GRAPHICS_DEPENDENCIES glbinding)
+    ENDIF(USE_SYSTEM_GLBINDING)
+
     IF(USE_SYSTEM_FORGE)
-        FIND_PACKAGE(Forge)
-    ELSE(USE_SYSTEM_FORGE)
+      FIND_PACKAGE(Forge REQUIRED)
+    ELSE(USE_SYSTEM_FORGE)  # Build Forge as an external Project
         INCLUDE(build_forge)
+        LIST(APPEND GRAPHICS_DEPENDENCIES forge)
     ENDIF(USE_SYSTEM_FORGE)
 
-    IF(FORGE_FOUND)
-        ADD_DEFINITIONS(-DGLEW_MX -DWITH_GRAPHICS)
-        FIND_PACKAGE(GLEWmx REQUIRED)
+    IF(FORGE_FOUND AND GLBINDING_FOUND)
+        ADD_DEFINITIONS(-DWITH_GRAPHICS)
 
         INCLUDE_DIRECTORIES(BEFORE
-            ${FORGE_INCLUDE_DIRECTORIES}
-            ${GLEW_INCLUDE_DIR}
-            )
+            ${FORGE_INCLUDE_DIRS}
+            ${GLBINDING_INCLUDE_DIRS})
 
-        SET(FORGE_LIBRARIES ${FORGE_LIBRARIES}
-                            ${GLEWmx_LIBRARY}
-                            ${OPENGL_gl_LIBRARY}
-                            ${OPENGL_glu_LIBRARY})
+        SET(GRAPHICS_LIBRARIES  ${FORGE_LIBRARIES}
+                                ${GLBINDING_LIBRARIES}
+                                ${OPENGL_gl_LIBRARY})
 
         IF(APPLE)
             FIND_PACKAGE(X11 REQUIRED)
             INCLUDE_DIRECTORIES(BEFORE ${X11_INCLUDE_DIR})
         ENDIF(APPLE)
 
-    ELSE(FORGE_FOUND)
-        MESSAGE(WARNING "Forge not found. Graphics will be disabled")
-    ENDIF(FORGE_FOUND)
+    ELSE(FORGE_FOUND AND GLBINDING_FOUND)
+        MESSAGE(WARNING "Graphics dependencies (Forge and/or glbinding) not found. Graphics will be disabled")
+    ENDIF(FORGE_FOUND AND GLBINDING_FOUND)
 
 ENDIF(BUILD_GRAPHICS)
 
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index bae962fce7..9559099351 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -214,16 +214,17 @@ TARGET_LINK_LIBRARIES(afcpu
                             PRIVATE ${FreeImage_LIBS}
                      )
 
-IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-  ADD_DEPENDENCIES(afcpu forge)
-ENDIF()
-
 IF(LAPACK_FOUND)
    TARGET_LINK_LIBRARIES(afcpu  PRIVATE ${LAPACK_LIBRARIES})
 ENDIF()
 
+LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
+IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+    ADD_DEPENDENCIES(afcpu ${GRAPHICS_DEPENDENCIES})
+ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+
 IF(FORGE_FOUND)
-    TARGET_LINK_LIBRARIES(afcpu PRIVATE ${FORGE_LIBRARIES})
+   TARGET_LINK_LIBRARIES(afcpu PRIVATE ${GRAPHICS_LIBRARIES})
 ENDIF()
 
 SET_TARGET_PROPERTIES(afcpu PROPERTIES
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 55700d4ec4..6b7bc08df9 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -488,8 +488,13 @@ TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
                                 PRIVATE ${CUDA_CUDA_LIBRARY})
 
+LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
+IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+    ADD_DEPENDENCIES(afcuda ${GRAPHICS_DEPENDENCIES})
+ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+
 IF(FORGE_FOUND)
-    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${FORGE_LIBRARIES})
+    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${GRAPHICS_LIBRARIES})
 ENDIF()
 
 IF(CUDA_cusolver_LIBRARY)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index cf649dce0f..37655cfd36 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -314,6 +314,7 @@ ENDIF()
 IF(NOT USE_SYSTEM_CL2HPP)
     ADD_DEPENDENCIES(afopencl cl2hpp)
 ENDIF(NOT USE_SYSTEM_CL2HPP)
+
 ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
 
 TARGET_LINK_LIBRARIES(afopencl
@@ -321,28 +322,29 @@ TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${CLBLAS_LIBRARIES}
                                 PRIVATE ${CLFFT_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
+                                PRIVATE ${FreeImage_LIBS}
                      )
 
-IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-    ADD_DEPENDENCIES(afopencl forge)
-ENDIF()
-
 IF(LAPACK_FOUND)
-  TARGET_LINK_LIBRARIES(afopencl
-    PRIVATE ${LAPACK_LIBRARIES}
-    PRIVATE ${CBLAS_LIBRARIES})
-ENDIF()
+    TARGET_LINK_LIBRARIES(afopencl
+                                PRIVATE ${LAPACK_LIBRARIES}
+                                PRIVATE ${CBLAS_LIBRARIES})
+ENDIF(LAPACK_FOUND)
 
-TARGET_LINK_LIBRARIES(afopencl PRIVATE ${FreeImage_LIBS})
+LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
+IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+    ADD_DEPENDENCIES(afopencl ${GRAPHICS_DEPENDENCIES})
+ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+
+IF(FORGE_FOUND)
+    TARGET_LINK_LIBRARIES(afopencl
+                                PRIVATE ${GRAPHICS_LIBRARIES})
+ENDIF(FORGE_FOUND)
 
 SET_TARGET_PROPERTIES(afopencl PROPERTIES
     VERSION "${AF_VERSION}"
     SOVERSION "${AF_VERSION_MAJOR}")
 
-IF(FORGE_FOUND)
-    TARGET_LINK_LIBRARIES(afopencl  PRIVATE ${FORGE_LIBRARIES})
-ENDIF()
-
 INSTALL(TARGETS afopencl EXPORT OpenCL DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT libraries)
 

From ae05c0f511fa270a2c440d6c75277fa4e4440075 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 30 Aug 2016 19:59:14 -0400
Subject: [PATCH 0808/2677] Compiling code with new Forge and glbinding (image
 works)

* Histogram, Plot and Surface do not work
---
 src/api/c/err_common.cpp              |   2 +-
 src/api/c/graphics_common.cpp         | 104 +++++++++++---------------
 src/api/c/graphics_common.hpp         |  46 ++++++------
 src/api/c/hist.cpp                    |  25 ++++---
 src/api/c/image.cpp                   |  34 ++++-----
 src/api/c/plot.cpp                    |  31 ++++----
 src/api/c/plot3.cpp                   |  38 +++++-----
 src/api/c/surface.cpp                 |  24 +++---
 src/backend/cpu/hist_graphics.cpp     |  13 +++-
 src/backend/cpu/hist_graphics.hpp     |   2 +-
 src/backend/cpu/image.cpp             |  11 ++-
 src/backend/cpu/image.hpp             |   2 +-
 src/backend/cpu/plot.cpp              |  13 +++-
 src/backend/cpu/plot.hpp              |   2 +-
 src/backend/cpu/surface.cpp           |  13 +++-
 src/backend/cpu/surface.hpp           |   2 +-
 src/backend/cuda/hist_graphics.hpp    |   2 +-
 src/backend/cuda/image.hpp            |   2 +-
 src/backend/cuda/interopManager.hpp   |  15 ++--
 src/backend/cuda/plot.hpp             |   2 +-
 src/backend/cuda/surface.hpp          |   2 +-
 src/backend/opencl/hist_graphics.cpp  |  14 ++--
 src/backend/opencl/hist_graphics.hpp  |   2 +-
 src/backend/opencl/image.cpp          |   8 +-
 src/backend/opencl/image.hpp          |   2 +-
 src/backend/opencl/interopManager.cpp |  26 ++-----
 src/backend/opencl/interopManager.hpp |  13 ++--
 src/backend/opencl/platform.cpp       |   6 +-
 src/backend/opencl/platform.hpp       |   2 +-
 src/backend/opencl/plot.cpp           |  14 ++--
 src/backend/opencl/plot.hpp           |   2 +-
 src/backend/opencl/surface.cpp        |  14 ++--
 src/backend/opencl/surface.hpp        |   2 +-
 33 files changed, 255 insertions(+), 235 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 96c212c684..6d56b65c1a 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -206,7 +206,7 @@ af_err processException()
         print_error(ss.str());
         err = ex.getError();
 #if defined(WITH_GRAPHICS) && !defined(AF_UNIFIED)
-    } catch (const fg::Error &ex) {
+    } catch (const forge::Error &ex) {
         ss << ex << "\n";
         print_error(ss.str());
         err = AF_ERR_INTERNAL;
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index dc5a46b5e1..044dfd0057 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -15,37 +15,40 @@
 #include <platform.hpp>
 #include <util.hpp>
 
+#include <glbinding/Meta.h>
+
 using namespace std;
+using namespace gl;
 
 template<typename T>
 GLenum getGLType() { return GL_FLOAT; }
 
-fg::MarkerType getFGMarker(const af_marker_type af_marker) {
-    fg::MarkerType fg_marker;
+forge::MarkerType getFGMarker(const af_marker_type af_marker) {
+    forge::MarkerType fg_marker;
     switch (af_marker) {
-        case AF_MARKER_NONE: fg_marker = fg::FG_NONE; break;
-        case AF_MARKER_POINT: fg_marker = fg::FG_POINT; break;
-        case AF_MARKER_CIRCLE: fg_marker = fg::FG_CIRCLE; break;
-        case AF_MARKER_SQUARE: fg_marker = fg::FG_SQUARE; break;
-        case AF_MARKER_TRIANGLE: fg_marker = fg::FG_TRIANGLE; break;
-        case AF_MARKER_CROSS: fg_marker = fg::FG_CROSS; break;
-        case AF_MARKER_PLUS: fg_marker = fg::FG_PLUS; break;
-        case AF_MARKER_STAR: fg_marker = fg::FG_STAR; break;
-        default: fg_marker = fg::FG_NONE; break;
+        case AF_MARKER_NONE     : fg_marker = FG_MARKER_NONE;        break;
+        case AF_MARKER_POINT    : fg_marker = FG_MARKER_POINT;       break;
+        case AF_MARKER_CIRCLE   : fg_marker = FG_MARKER_CIRCLE;      break;
+        case AF_MARKER_SQUARE   : fg_marker = FG_MARKER_SQUARE;      break;
+        case AF_MARKER_TRIANGLE : fg_marker = FG_MARKER_TRIANGLE;    break;
+        case AF_MARKER_CROSS    : fg_marker = FG_MARKER_CROSS;       break;
+        case AF_MARKER_PLUS     : fg_marker = FG_MARKER_PLUS;        break;
+        case AF_MARKER_STAR     : fg_marker = FG_MARKER_STAR;        break;
+        default                 : fg_marker = FG_MARKER_NONE;        break;
     }
     return fg_marker;
 }
 
 #define INSTANTIATE_GET_FG_TYPE(T, ForgeEnum)\
-    template<> fg::dtype getGLType<T>() { return ForgeEnum; }
+    template<> forge::dtype getGLType<T>() { return ForgeEnum; }
 
-INSTANTIATE_GET_FG_TYPE(float, fg::f32);
-INSTANTIATE_GET_FG_TYPE(int  , fg::s32);
-INSTANTIATE_GET_FG_TYPE(unsigned, fg::u32);
-INSTANTIATE_GET_FG_TYPE(char, fg::s8);
-INSTANTIATE_GET_FG_TYPE(unsigned char, fg::u8);
-INSTANTIATE_GET_FG_TYPE(unsigned short, fg::u16);
-INSTANTIATE_GET_FG_TYPE(short, fg::s16);
+INSTANTIATE_GET_FG_TYPE(float, forge::f32);
+INSTANTIATE_GET_FG_TYPE(int  , forge::s32);
+INSTANTIATE_GET_FG_TYPE(unsigned, forge::u32);
+INSTANTIATE_GET_FG_TYPE(char, forge::s8);
+INSTANTIATE_GET_FG_TYPE(unsigned char, forge::u8);
+INSTANTIATE_GET_FG_TYPE(unsigned short, forge::u16);
+INSTANTIATE_GET_FG_TYPE(short, forge::s16);
 
 GLenum glErrorSkip(const char *msg, const char* file, int line)
 {
@@ -53,12 +56,12 @@ GLenum glErrorSkip(const char *msg, const char* file, int line)
     GLenum x = glGetError();
     if (x != GL_NO_ERROR) {
         char buf[1024];
-        sprintf(buf, "GL Error Skipped at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, x, gluErrorString(x));
+        sprintf(buf, "GL Error Skipped at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
         AF_ERROR(buf, AF_ERR_INTERNAL);
     }
     return x;
 #else
-    return 0;
+    return (GLenum)0;
 #endif
 }
 
@@ -70,12 +73,12 @@ GLenum glErrorCheck(const char *msg, const char* file, int line)
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
-        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, x, gluErrorString(x));
+        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
         AF_ERROR(buf, AF_ERR_INTERNAL);
     }
     return x;
 #else
-    return 0;
+    return (GLenum)0;
 #endif
 }
 
@@ -85,7 +88,7 @@ GLenum glForceErrorCheck(const char *msg, const char* file, int line)
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
-        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, x, gluErrorString(x));
+        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
         AF_ERROR(buf, AF_ERR_INTERNAL);
     }
     return x;
@@ -119,19 +122,19 @@ ForgeManager::~ForgeManager()
     destroyResources();
 }
 
-fg::Font* ForgeManager::getFont(const bool dontCreate)
+forge::Font* ForgeManager::getFont(const bool dontCreate)
 {
     static bool flag = true;
-    static fg::Font* fnt = NULL;
+    static forge::Font* fnt = NULL;
 
     CheckGL("Begin ForgeManager::getFont");
 
     if (flag && !dontCreate) {
-        fnt = new fg::Font();
+        fnt = new forge::Font();
 #if defined(_WIN32) || defined(_MSC_VER)
-        fnt->loadSystemFont("Arial", 32);
+        fnt->loadSystemFont("Arial");
 #else
-        fnt->loadSystemFont("Vera", 32);
+        fnt->loadSystemFont("Vera");
 #endif
         CheckGL("End ForgeManager::getFont");
         flag = false;
@@ -140,16 +143,17 @@ fg::Font* ForgeManager::getFont(const bool dontCreate)
     return fnt;
 }
 
-fg::Window* ForgeManager::getMainWindow(const bool dontCreate)
+forge::Window* ForgeManager::getMainWindow(const bool dontCreate)
 {
     static bool flag = true;
-    static fg::Window* wnd = NULL;
+    static forge::Window* wnd = NULL;
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
     if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         if (flag && !dontCreate) {
-            wnd = new fg::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
+            wnd = new forge::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
+            wnd->makeCurrent();
             CheckGL("End ForgeManager::getMainWindow");
             flag = false;
         };
@@ -157,7 +161,7 @@ fg::Window* ForgeManager::getMainWindow(const bool dontCreate)
     return wnd;
 }
 
-fg::Image* ForgeManager::getImage(int w, int h, fg::ChannelFormat mode, fg::dtype type)
+forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, forge::dtype type)
 {
     /* w, h needs to fall in the range of [0, 2^16]
      * for the ForgeManager to correctly retrieve
@@ -171,14 +175,14 @@ fg::Image* ForgeManager::getImage(int w, int h, fg::ChannelFormat mode, fg::dtyp
 
     ImgMapIter iter = mImgMap.find(key);
     if (iter==mImgMap.end()) {
-        fg::Image* temp = new fg::Image(w, h, mode, type);
+        forge::Image* temp = new forge::Image(w, h, mode, type);
         mImgMap[key] = temp;
     }
 
     return mImgMap[key];
 }
 
-fg::Plot* ForgeManager::getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype)
+forge::Plot* ForgeManager::getPlot(int nPoints, forge::dtype dtype, forge::ChartType ctype, forge::PlotType ptype, forge::MarkerType mtype)
 {
     /* nPoints needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -191,34 +195,14 @@ fg::Plot* ForgeManager::getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype
 
     PltMapIter iter = mPltMap.find(key);
     if (iter==mPltMap.end()) {
-        fg::Plot* temp = new fg::Plot(nPoints, dtype, ptype, mtype);
+        forge::Plot* temp = new forge::Plot(nPoints, dtype, ctype, ptype, mtype);
         mPltMap[key] = temp;
     }
 
     return mPltMap[key];
 }
 
-fg::Plot3* ForgeManager::getPlot3(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype)
-{
-    /* nPoints needs to fall in the range of [0, 2^48]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Plot object. So, this implementation
-     * is a limitation on how big of an plot graph can be rendered
-     * using arrayfire graphics funtionality */
-    assert(nPoints <= 2ll<<48);
-    long long key = ((nPoints & _48BIT) << 48);
-    key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
-
-    Plt3MapIter iter = mPlt3Map.find(key);
-    if (iter==mPlt3Map.end()) {
-        fg::Plot3* temp = new fg::Plot3(nPoints, dtype, ptype, mtype);
-        mPlt3Map[key] = temp;
-    }
-
-    return mPlt3Map[key];
-}
-
-fg::Histogram* ForgeManager::getHistogram(int nBins, fg::dtype type)
+forge::Histogram* ForgeManager::getHistogram(int nBins, forge::dtype type)
 {
     /* nBins needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -230,14 +214,14 @@ fg::Histogram* ForgeManager::getHistogram(int nBins, fg::dtype type)
 
     HstMapIter iter = mHstMap.find(key);
     if (iter==mHstMap.end()) {
-        fg::Histogram* temp = new fg::Histogram(nBins, type);
+        forge::Histogram* temp = new forge::Histogram(nBins, type);
         mHstMap[key] = temp;
     }
 
     return mHstMap[key];
 }
 
-fg::Surface* ForgeManager::getSurface(int nX, int nY, fg::dtype type)
+forge::Surface* ForgeManager::getSurface(int nX, int nY, forge::dtype type)
 {
     /* nX * nY needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -249,7 +233,7 @@ fg::Surface* ForgeManager::getSurface(int nX, int nY, fg::dtype type)
 
     SfcMapIter iter = mSfcMap.find(key);
     if (iter==mSfcMap.end()) {
-        fg::Surface* temp = new fg::Surface(nX, nY, type);
+        forge::Surface* temp = new forge::Surface(nX, nY, type);
         mSfcMap[key] = temp;
     }
 
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 53d4629f68..4332fa1175 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -13,24 +13,25 @@
 
 #include <af/graphics.h>
 #include <forge.h>
-
+#include <glbinding/gl/gl.h>
+#include <glbinding/Binding.h>
 #include <map>
 
 // default to f32(float) type
 template<typename T>
-fg::dtype getGLType();
+forge::dtype getGLType();
 
 // Print for OpenGL errors
 // Returns 1 if an OpenGL error occurred, 0 otherwise.
-GLenum glErrorSkip(const char *msg, const char* file, int line);
-GLenum glErrorCheck(const char *msg, const char* file, int line);
-GLenum glForceErrorCheck(const char *msg, const char* file, int line);
+gl::GLenum glErrorSkip(const char *msg, const char* file, int line);
+gl::GLenum glErrorCheck(const char *msg, const char* file, int line);
+gl::GLenum glForceErrorCheck(const char *msg, const char* file, int line);
 
 #define CheckGL(msg)      glErrorCheck     (msg, __AF_FILENAME__, __LINE__)
 #define ForceCheckGL(msg) glForceErrorCheck(msg, __AF_FILENAME__, __LINE__)
 #define CheckGLSkip(msg)  glErrorSkip      (msg, __AF_FILENAME__, __LINE__)
 
-fg::MarkerType getFGMarker(const af_marker_type af_marker);
+forge::MarkerType getFGMarker(const af_marker_type af_marker);
 namespace graphics
 {
 
@@ -43,15 +44,13 @@ static const long long _16BIT = 0x000000000000FFFF;
 static const long long _32BIT = 0x00000000FFFFFFFF;
 static const long long _48BIT = 0x0000FFFFFFFFFFFF;
 
-typedef std::map<long long, fg::Image*> ImageMap_t;
-typedef std::map<long long, fg::Plot*> PlotMap_t;
-typedef std::map<long long, fg::Histogram*> HistogramMap_t;
-typedef std::map<long long, fg::Plot3*> Plot3Map_t;
-typedef std::map<long long, fg::Surface*> SurfaceMap_t;
+typedef std::map<long long, forge::Image*> ImageMap_t;
+typedef std::map<long long, forge::Plot*> PlotMap_t;
+typedef std::map<long long, forge::Histogram*> HistogramMap_t;
+typedef std::map<long long, forge::Surface*> SurfaceMap_t;
 
 typedef ImageMap_t::iterator ImgMapIter;
 typedef PlotMap_t::iterator PltMapIter;
-typedef Plot3Map_t::iterator Plt3MapIter;
 typedef HistogramMap_t::iterator HstMapIter;
 typedef SurfaceMap_t::iterator SfcMapIter;
 
@@ -61,18 +60,16 @@ typedef SurfaceMap_t::iterator SfcMapIter;
  * It manages the windows, and other renderables (given below) that are drawed
  * onto chosen window.
  * Renderables:
- *             fg::Image
- *             fg::Plot
- *             fg::Plot3
- *             fg::Histogram
- *             fg::Surface
+ *             forge::Image
+ *             forge::Plot
+ *             forge::Histogram
+ *             forge::Surface
  * */
 class ForgeManager
 {
     private:
         ImageMap_t      mImgMap;
         PlotMap_t       mPltMap;
-        Plot3Map_t      mPlt3Map;
         HistogramMap_t  mHstMap;
         SurfaceMap_t    mSfcMap;
 
@@ -80,13 +77,12 @@ class ForgeManager
         static ForgeManager& getInstance();
         ~ForgeManager();
 
-        fg::Font* getFont(const bool dontCreate=false);
-        fg::Window* getMainWindow(const bool dontCreate=false);
-        fg::Image* getImage(int w, int h, fg::ChannelFormat mode, fg::dtype type);
-        fg::Plot* getPlot(int nPoints, fg::dtype dtype, fg::PlotType ptype, fg::MarkerType mtype);
-        fg::Plot3* getPlot3(int nPoints, fg::dtype dtype,fg::PlotType ptype, fg::MarkerType mtype);
-        fg::Histogram* getHistogram(int nBins, fg::dtype type);
-        fg::Surface* getSurface(int nX, int nY, fg::dtype type);
+        forge::Font* getFont(const bool dontCreate=false);
+        forge::Window* getMainWindow(const bool dontCreate=false);
+        forge::Image* getImage(int w, int h, forge::ChannelFormat mode, forge::dtype type);
+        forge::Plot* getPlot(int nPoints, forge::dtype dtype, forge::ChartType ctype, forge::PlotType ptype, forge::MarkerType mtype);
+        forge::Histogram* getHistogram(int nBins, forge::dtype type);
+        forge::Surface* getSurface(int nX, int nY, forge::dtype type);
 
     protected:
         ForgeManager() {}
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 76731f360a..97197261e1 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -24,7 +24,7 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg::Histogram* setup_histogram(const af_array in, const double minval, const double maxval)
+forge::Histogram* setup_histogram(const af_array in, const double minval, const double maxval)
 {
     Array<T> histogramInput = getArray<T>(in);
     dim_t nBins = histogramInput.elements();
@@ -33,13 +33,14 @@ fg::Histogram* setup_histogram(const af_array in, const double minval, const dou
 
     /* retrieve Forge Histogram with nBins and array type */
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Histogram* hist = fgMngr.getHistogram(nBins, getGLType<T>());
+    forge::Histogram* hist = fgMngr.getHistogram(nBins, getGLType<T>());
     /* set histogram bar colors to orange */
-    hist->setBarColor(0.929f, 0.486f, 0.2745f);
+    hist->setColor(0.929f, 0.486f, 0.2745f, 1.0f);
     /* set x axis limits to maximum and minimum values of data
      * and y axis limits to range [0, nBins]*/
-    hist->setAxesLimits(maxval, minval, double(freqMax), 0.0f);
-    hist->setAxesTitles("Bins", "Frequency");
+    // FORGE FIX ME
+    //hist->setAxesLimits(maxval, minval, double(freqMax), 0.0f);
+    //hist->setAxesTitles("Bins", "Frequency");
 
     copy_histogram<T>(histogramInput, hist);
 
@@ -62,9 +63,9 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
 
         ARG_ASSERT(0, Xinfo.isVector());
 
-        fg::Window* window = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         window->makeCurrent();
-        fg::Histogram* hist = NULL;
+        forge::Histogram* hist = NULL;
 
         switch(Xtype) {
             case f32: hist = setup_histogram<float  >(X, minval, maxval); break;
@@ -76,10 +77,12 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
             default:  TYPE_ERROR(1, Xtype);
         }
 
-        if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *hist, props->title);
-        else
-            window->draw(*hist);
+        // FORGE FIX ME
+        // Window's draw function requires either image or chart
+        //if (props->col>-1 && props->row>-1)
+        //    //window->draw(props->col, props->row, *hist, props->title);
+        //else
+        //    window->draw(*hist);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 2c523d0947..c4a9453c5b 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -54,7 +54,7 @@ Array<float> normalizePerType<float>(const Array<float>& in)
 }
 
 template<typename T>
-static fg::Image* convert_and_copy_image(const af_array in)
+static forge::Image* convert_and_copy_image(const af_array in)
 {
     const Array<T> _in  = getArray<T>(in);
     dim4 inDims = _in.dims();
@@ -65,9 +65,9 @@ static fg::Image* convert_and_copy_image(const af_array in)
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
-    // The inDims[2] * 100 is a hack to convert to fg::ChannelFormat
+    // The inDims[2] * 100 is a hack to convert to forge::ChannelFormat
     // TODO Write a proper conversion function
-    fg::Image* ret_val = fgMngr.getImage(inDims[1], inDims[0], (fg::ChannelFormat)(inDims[2] * 100), getGLType<T>());
+    forge::Image* ret_val = fgMngr.getImage(inDims[1], inDims[0], (forge::ChannelFormat)(inDims[2] * 100), getGLType<T>());
 
     copy_image<T>(normalizePerType<T>(imgData), ret_val);
 
@@ -91,9 +91,9 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
         DIM_ASSERT(0, in_dims[2] == 1 || in_dims[2] == 3 || in_dims[2] == 4);
         DIM_ASSERT(0, in_dims[3] == 1);
 
-        fg::Window* window = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         window->makeCurrent();
-        fg::Image* image = NULL;
+        forge::Image* image = NULL;
 
         switch(type) {
             case f32: image = convert_and_copy_image<float >(in); break;
@@ -106,7 +106,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
             default:  TYPE_ERROR(1, type);
         }
 
-        window->setColorMap((fg::ColorMap)props->cmap);
+        window->setColorMap((forge::ColorMap)props->cmap);
         if (props->col>-1 && props->row>-1)
             window->draw(props->col, props->row, *image, props->title);
         else
@@ -123,10 +123,10 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
 af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
 {
 #if defined(WITH_GRAPHICS)
-    fg::Window* wnd;
+    forge::Window* wnd;
     try {
         graphics::ForgeManager& fgMngr = graphics::ForgeManager::getInstance();
-        fg::Window* mainWnd = NULL;
+        forge::Window* mainWnd = NULL;
 
         try {
             mainWnd = fgMngr.getMainWindow();
@@ -139,7 +139,7 @@ af_err af_create_window(af_window *out, const int width, const int height, const
             return AF_SUCCESS;
         }
 
-        wnd = new fg::Window(width, height, title, mainWnd);
+        wnd = new forge::Window(width, height, title, mainWnd);
         wnd->setFont(fgMngr.getFont());
         *out = reinterpret_cast<af_window>(wnd);
     }
@@ -159,7 +159,7 @@ af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         wnd->setPos(x, y);
     }
     CATCHALL;
@@ -178,7 +178,7 @@ af_err af_set_title(const af_window wind, const char* const title)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         wnd->setTitle(title);
     }
     CATCHALL;
@@ -197,7 +197,7 @@ af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         wnd->setSize(w, h);
     }
     CATCHALL;
@@ -216,7 +216,7 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         wnd->grid(rows, cols);
     }
     CATCHALL;
@@ -235,7 +235,7 @@ af_err af_show(const af_window wind)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         wnd->swapBuffers();
     }
     CATCHALL;
@@ -254,7 +254,7 @@ af_err af_is_window_closed(bool *out, const af_window wind)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         *out = wnd->close();
     }
     CATCHALL;
@@ -273,7 +273,7 @@ af_err af_set_visibility(const af_window wind, const bool is_visible)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         if (is_visible)
             wnd->show();
         else
@@ -295,7 +295,7 @@ af_err af_destroy_window(const af_window wind)
     }
 
     try {
-        fg::Window* wnd = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         delete wnd;
     }
     CATCHALL;
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index a812947228..9dd5770a01 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -27,7 +27,7 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg::Plot* setup_plot(const af_array X, const af_array Y, fg::PlotType type, fg::MarkerType marker)
+forge::Plot* setup_plot(const af_array X, const af_array Y, forge::PlotType type, forge::MarkerType marker)
 {
     Array<T> xIn = getArray<T>(X);
     Array<T> yIn = getArray<T>(Y);
@@ -51,17 +51,19 @@ fg::Plot* setup_plot(const af_array X, const af_array Y, fg::PlotType type, fg::
     Array<T> P = join(0, xIn, yIn);
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Plot* plot = fgMngr.getPlot(elements, getGLType<T>(), type, marker);
-    plot->setColor(1.0, 0.0, 0.0);
-    plot->setAxesLimits(xmax, xmin, ymax, ymin);
-    plot->setAxesTitles("X Axis", "Y Axis");
+    // FORGE FIX ME FG_CHART_2D
+    forge::Plot* plot = fgMngr.getPlot(elements, getGLType<T>(), FG_CHART_2D, type, marker);
+    plot->setColor(1.0, 0.0, 0.0, 1.0);
+    // FORGE FIX ME
+    //plot->setAxesLimits(xmax, xmin, ymax, ymin);
+    //plot->setAxesTitles("X Axis", "Y Axis");
 
     copy_plot<T>(P, plot);
 
     return plot;
 }
 
-af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, fg::PlotType type=fg::FG_LINE, fg::MarkerType marker=fg::FG_NONE)
+af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, forge::PlotType type=FG_PLOT_LINE, forge::MarkerType marker=FG_MARKER_NONE)
 {
     if(wind==0) {
         std::cerr<<"Not a valid window"<<std::endl;
@@ -83,9 +85,9 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
 
         TYPE_ASSERT(Xtype == Ytype);
 
-        fg::Window* window = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         window->makeCurrent();
-        fg::Plot* plot = NULL;
+        forge::Plot* plot = NULL;
 
         switch(Xtype) {
             case f32: plot = setup_plot<float  >(X, Y, type, marker); break;
@@ -97,10 +99,11 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
             default:  TYPE_ERROR(1, Xtype);
         }
 
-        if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *plot, props->title);
-        else
-            window->draw(*plot);
+        //FORGE FIX ME
+        //if (props->col>-1 && props->row>-1)
+        //    window->draw(props->col, props->row, *plot, props->title);
+        //else
+        //    window->draw(*plot);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -120,8 +123,8 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
 af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type af_marker, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    fg::MarkerType fg_marker = getFGMarker(af_marker);
-    return plotWrapper(wind, X, Y, props, fg::FG_SCATTER, fg_marker);
+    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 #else
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 2e18251b45..6383c89af4 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -14,7 +14,7 @@
 #include <graphics_common.hpp>
 #include <err_common.hpp>
 #include <backend.hpp>
-#include <plot3.hpp>
+#include <plot.hpp>
 #include <reduce.hpp>
 #include <join.hpp>
 #include <transpose.hpp>
@@ -30,7 +30,7 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtype)
+forge::Plot* setup_plot3(const af_array P, forge::PlotType ptype, forge::MarkerType mtype)
 {
     Array<T> pIn = getArray<T>(P);
     ArrayInfo Pinfo = getInfo(P);
@@ -55,17 +55,18 @@ fg::Plot3* setup_plot3(const af_array P, fg::PlotType ptype, fg::MarkerType mtyp
     copyData(min, reduce<af_min_t, T, T>(pIn, 1));
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Plot3* plot3 = fgMngr.getPlot3(P_dims.elements()/3, getGLType<T>(), ptype, mtype);
-    plot3->setColor(1.0, 0.0, 0.0);
-    plot3->setAxesLimits(max[0], min[0],
-                         max[1], min[1],
-                         max[2], min[2]);
-    plot3->setAxesTitles("X Axis", "Y Axis", "Z Axis");
-    copy_plot3<T>(pIn, plot3);
+    forge::Plot* plot3 = fgMngr.getPlot(P_dims.elements()/3, getGLType<T>(), FG_CHART_3D, ptype, mtype);
+    plot3->setColor(1.0, 0.0, 0.0, 1.0);
+    // FORGE FIX ME
+    //plot3->setAxesLimits(max[0], min[0],
+    //                     max[1], min[1],
+    //                     max[2], min[2]);
+    //plot3->setAxesTitles("X Axis", "Y Axis", "Z Axis");
+    copy_plot<T>(pIn, plot3);
     return plot3;
 }
 
-af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const props, const fg::PlotType type=fg::FG_LINE, const fg::MarkerType marker=fg::FG_NONE)
+af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const props, const forge::PlotType type=FG_PLOT_LINE, const forge::MarkerType marker=FG_MARKER_NONE)
 {
     if(wind==0) {
         std::cerr<<"Not a valid window"<<std::endl;
@@ -76,9 +77,9 @@ af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const
         ArrayInfo Pinfo = getInfo(P);
         af_dtype Ptype  = Pinfo.getType();
 
-        fg::Window* window = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         window->makeCurrent();
-        fg::Plot3* plot3 = NULL;
+        forge::Plot* plot3 = NULL;
 
         switch(Ptype) {
             case f32: plot3 = setup_plot3<float >(P, type, marker); break;
@@ -90,10 +91,11 @@ af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const
             default:  TYPE_ERROR(1, Ptype);
         }
 
-        if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *plot3, props->title);
-        else
-            window->draw(*plot3);
+        // FORGE FIX ME
+        //if (props->col>-1 && props->row>-1)
+        //    window->draw(props->col, props->row, *plot3, props->title);
+        //else
+        //    window->draw(*plot3);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -113,8 +115,8 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
 af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type af_marker, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    fg::MarkerType fg_marker = getFGMarker(af_marker);
-    return plot3Wrapper(wind, P, props, fg::FG_SCATTER, fg_marker);
+    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    return plot3Wrapper(wind, P, props, FG_PLOT_SCATTER, fg_marker);
 #else
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 2394f5f96c..4785c4e959 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -28,7 +28,7 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg::Surface* setup_surface(const af_array xVals, const af_array yVals, const af_array zVals)
+forge::Surface* setup_surface(const af_array xVals, const af_array yVals, const af_array zVals)
 {
     Array<T> xIn = getArray<T>(xVals);
     Array<T> yIn = getArray<T>(yVals);
@@ -74,10 +74,11 @@ fg::Surface* setup_surface(const af_array xVals, const af_array yVals, const af_
     Array<T> Z = join(0, inputs);
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    fg::Surface* surface = fgMngr.getSurface(Z_dims[0], Z_dims[1], getGLType<T>());
-    surface->setColor(1.0, 0.0, 0.0);
-    surface->setAxesLimits(xmax, xmin, ymax, ymin, zmax, zmin);
-    surface->setAxesTitles("X Axis", "Y Axis", "Z Axis");
+    forge::Surface* surface = fgMngr.getSurface(Z_dims[0], Z_dims[1], getGLType<T>());
+    surface->setColor(1.0, 0.0, 0.0, 1.0);
+    // FORGE FIX ME
+    //surface->setAxesLimits(xmax, xmin, ymax, ymin, zmax, zmin);
+    //surface->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
     copy_surface<T>(Z, surface);
 
@@ -116,9 +117,9 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
             DIM_ASSERT(3, ( X_dims[0] * Y_dims[0] == (dim_t)Sinfo.elements()));
         }
 
-        fg::Window* window = reinterpret_cast<fg::Window*>(wind);
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         window->makeCurrent();
-        fg::Surface* surface = NULL;
+        forge::Surface* surface = NULL;
 
         switch(Xtype) {
             case f32: surface = setup_surface<float  >(xVals, yVals , S); break;
@@ -130,10 +131,11 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
             default:  TYPE_ERROR(1, Xtype);
         }
 
-        if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *surface, props->title);
-        else
-            window->draw(*surface);
+        // FORGE FIX ME
+        //if (props->col>-1 && props->row>-1)
+        //    window->draw(props->col, props->row, *surface, props->title);
+        //else
+        //    window->draw(*surface);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index ad7d69067d..21298cf501 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -16,23 +16,28 @@
 
 namespace cpu
 {
+using namespace gl;
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
+void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 {
     data.eval();
     getQueue().sync();
+
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     CheckGL("Begin copy_histogram");
 
-    glBindBuffer(GL_ARRAY_BUFFER, hist->vbo());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, hist->size(), data.get());
+    glBindBuffer(GL_ARRAY_BUFFER, hist->vertices());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, hist->verticesSize(), data.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("End copy_histogram");
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_histogram<T>(const Array<T> &data, const fg::Histogram* hist);
+    template void copy_histogram<T>(const Array<T> &data, const forge::Histogram* hist);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
diff --git a/src/backend/cpu/hist_graphics.hpp b/src/backend/cpu/hist_graphics.hpp
index 909bd76099..b238ff9bff 100644
--- a/src/backend/cpu/hist_graphics.hpp
+++ b/src/backend/cpu/hist_graphics.hpp
@@ -18,7 +18,7 @@ namespace cpu
 {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const fg::Histogram* hist);
+void copy_histogram(const Array<T> &data, const forge::Histogram* hist);
 
 }
 
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index b71ba23c12..fe4beef2da 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -23,17 +23,22 @@ using af::dim4;
 
 namespace cpu
 {
+using namespace gl;
 
 template<typename T>
-void copy_image(const Array<T> &in, const fg::Image* image)
+void copy_image(const Array<T> &in, const forge::Image* image)
 {
     in.eval();
     getQueue().sync();
+
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     CheckGL("Before CopyArrayToPBO");
     const T *d_X = in.get();
     size_t data_size = image->size();
 
-    glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pbo());
+    glBindBuffer(gl::GL_PIXEL_UNPACK_BUFFER, image->pbo());
     glBufferSubData(GL_PIXEL_UNPACK_BUFFER, 0, data_size, d_X);
     glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
 
@@ -41,7 +46,7 @@ void copy_image(const Array<T> &in, const fg::Image* image)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_image<T>(const Array<T> &in, const fg::Image* image);
+    template void copy_image<T>(const Array<T> &in, const forge::Image* image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index dc6cc62f09..5f4b98a1fc 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
     template<typename T>
-    void copy_image(const Array<T> &in, const fg::Image* image);
+    void copy_image(const Array<T> &in, const forge::Image* image);
 }
 
 #endif
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 2ab69643c8..5689e186ad 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -20,23 +20,28 @@ using af::dim4;
 
 namespace cpu
 {
+using namespace gl;
 
 template<typename T>
-void copy_plot(const Array<T> &P, fg::Plot* plot)
+void copy_plot(const Array<T> &P, forge::Plot* plot)
 {
     P.eval();
     getQueue().sync();
+
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     CheckGL("Before CopyArrayToVBO");
 
-    glBindBuffer(GL_ARRAY_BUFFER, plot->vbo());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, plot->size(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, plot->vertices());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, plot->verticesSize(), P.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("In CopyArrayToVBO");
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_plot<T>(const Array<T> &P, fg::Plot* plot);
+    template void copy_plot<T>(const Array<T> &P, forge::Plot* plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/plot.hpp b/src/backend/cpu/plot.hpp
index 73c72101e2..a92b21d9b8 100644
--- a/src/backend/cpu/plot.hpp
+++ b/src/backend/cpu/plot.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
     template<typename T>
-    void copy_plot(const Array<T> &P, fg::Plot* plot);
+    void copy_plot(const Array<T> &P, forge::Plot* plot);
 }
 
 #endif
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 24c945c20b..d4a55b2b01 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -20,23 +20,28 @@ using af::dim4;
 
 namespace cpu
 {
+using namespace gl;
 
 template<typename T>
-void copy_surface(const Array<T> &P, fg::Surface* surface)
+void copy_surface(const Array<T> &P, forge::Surface* surface)
 {
     P.eval();
     getQueue().sync();
+
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     CheckGL("Before CopyArrayToVBO");
 
-    glBindBuffer(GL_ARRAY_BUFFER, surface->vbo());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, surface->size(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, surface->vertices());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, surface->verticesSize(), P.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("In CopyArrayToVBO");
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_surface<T>(const Array<T> &P, fg::Surface* surface);
+    template void copy_surface<T>(const Array<T> &P, forge::Surface* surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/surface.hpp b/src/backend/cpu/surface.hpp
index 46a4c4b652..3d668df33e 100644
--- a/src/backend/cpu/surface.hpp
+++ b/src/backend/cpu/surface.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
     template<typename T>
-    void copy_surface(const Array<T> &P, fg::Surface* surface);
+    void copy_surface(const Array<T> &P, forge::Surface* surface);
 }
 
 #endif
diff --git a/src/backend/cuda/hist_graphics.hpp b/src/backend/cuda/hist_graphics.hpp
index c8a6176b59..90969707e2 100644
--- a/src/backend/cuda/hist_graphics.hpp
+++ b/src/backend/cuda/hist_graphics.hpp
@@ -18,7 +18,7 @@ namespace cuda
 {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const fg::Histogram* hist);
+void copy_histogram(const Array<T> &data, const forge::Histogram* hist);
 
 }
 
diff --git a/src/backend/cuda/image.hpp b/src/backend/cuda/image.hpp
index ece664b3c2..c476f0aee1 100644
--- a/src/backend/cuda/image.hpp
+++ b/src/backend/cuda/image.hpp
@@ -15,7 +15,7 @@
 namespace cuda
 {
     template<typename T>
-    void copy_image(const Array<T> &in, const fg::Image* image);
+    void copy_image(const Array<T> &in, const forge::Image* image);
 }
 
 #endif
diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index 58799147c3..9bce7d3c0a 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -12,6 +12,12 @@
 
 #if defined(WITH_GRAPHICS)
 
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+//#if defined(__arm__) || defined (__aarch64__)
+#include <glbinding/gl/gl.h>
+#include <glbinding/Binding.h>
+//#endif
+
 #include <platform.hpp>
 #include <graphics_common.hpp>
 
@@ -40,11 +46,10 @@ class InteropManager
         static bool checkGraphicsInteropCapability();
 
         ~InteropManager();
-        cudaGraphicsResource* getBufferResource(const fg::Image* handle);
-        cudaGraphicsResource* getBufferResource(const fg::Plot* handle);
-        cudaGraphicsResource* getBufferResource(const fg::Plot3* handle);
-        cudaGraphicsResource* getBufferResource(const fg::Histogram* handle);
-        cudaGraphicsResource* getBufferResource(const fg::Surface* handle);
+        cudaGraphicsResource* getBufferResource(const forge::Image* handle);
+        cudaGraphicsResource* getBufferResource(const forge::Plot* handle);
+        cudaGraphicsResource* getBufferResource(const forge::Histogram* handle);
+        cudaGraphicsResource* getBufferResource(const forge::Surface* handle);
 
     protected:
         InteropManager() {}
diff --git a/src/backend/cuda/plot.hpp b/src/backend/cuda/plot.hpp
index d41e4fb595..a9c73cbca9 100644
--- a/src/backend/cuda/plot.hpp
+++ b/src/backend/cuda/plot.hpp
@@ -15,7 +15,7 @@
 namespace cuda
 {
     template<typename T>
-    void copy_plot(const Array<T> &P, fg::Plot* plot);
+    void copy_plot(const Array<T> &P, forge::Plot* plot);
 }
 
 #endif
diff --git a/src/backend/cuda/surface.hpp b/src/backend/cuda/surface.hpp
index d7019837e2..6a5da62449 100644
--- a/src/backend/cuda/surface.hpp
+++ b/src/backend/cuda/surface.hpp
@@ -15,7 +15,7 @@
 namespace cuda
 {
     template<typename T>
-    void copy_surface(const Array<T> &P, fg::Surface* surface);
+    void copy_surface(const Array<T> &P, forge::Surface* surface);
 }
 
 #endif
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index d26fcc4e6e..57b98d74d4 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -17,14 +17,18 @@
 
 namespace opencl
 {
+using namespace gl;
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
+void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 {
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = data.get();
-        size_t bytes = hist->size();
+        size_t bytes = hist->verticesSize();
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
@@ -49,10 +53,10 @@ void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
         CheckGL("End OpenCL resource copy");
     } else {
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, hist->vbo());
+        glBindBuffer(GL_ARRAY_BUFFER, hist->vertices());
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*data.get(), CL_TRUE, 0, hist->size(), ptr);
+            getQueue().enqueueReadBuffer(*data.get(), CL_TRUE, 0, hist->verticesSize(), ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -61,7 +65,7 @@ void copy_histogram(const Array<T> &data, const fg::Histogram* hist)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_histogram<T>(const Array<T> &data, const fg::Histogram* hist);
+    template void copy_histogram<T>(const Array<T> &data, const forge::Histogram* hist);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
diff --git a/src/backend/opencl/hist_graphics.hpp b/src/backend/opencl/hist_graphics.hpp
index 6bb3a9eb5b..318437059d 100644
--- a/src/backend/opencl/hist_graphics.hpp
+++ b/src/backend/opencl/hist_graphics.hpp
@@ -16,7 +16,7 @@ namespace opencl
 {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const fg::Histogram* hist);
+void copy_histogram(const Array<T> &data, const forge::Histogram* hist);
 
 }
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 7f6b054739..db31baebc9 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -19,10 +19,14 @@
 
 namespace opencl
 {
+using namespace gl;
 
 template<typename T>
-void copy_image(const Array<T> &in, const fg::Image* image)
+void copy_image(const Array<T> &in, const forge::Image* image)
 {
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
         InteropManager& intrpMngr = InteropManager::getInstance();
@@ -63,7 +67,7 @@ void copy_image(const Array<T> &in, const fg::Image* image)
 }
 
 #define INSTANTIATE(T)      \
-    template void copy_image<T>(const Array<T> &in, const fg::Image* image);
+    template void copy_image<T>(const Array<T> &in, const forge::Image* image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/image.hpp b/src/backend/opencl/image.hpp
index 778fde0949..3e74fe86f9 100644
--- a/src/backend/opencl/image.hpp
+++ b/src/backend/opencl/image.hpp
@@ -15,7 +15,7 @@
 namespace opencl
 {
     template<typename T>
-    void copy_image(const Array<T> &in, const fg::Image* image);
+    void copy_image(const Array<T> &in, const forge::Image* image);
 }
 
 #endif
diff --git a/src/backend/opencl/interopManager.cpp b/src/backend/opencl/interopManager.cpp
index 89487ec9ca..e390020244 100644
--- a/src/backend/opencl/interopManager.cpp
+++ b/src/backend/opencl/interopManager.cpp
@@ -35,7 +35,7 @@ InteropManager& InteropManager::getInstance()
     return my_instance;
 }
 
-cl::Buffer* InteropManager::getBufferResource(const fg::Image* image)
+cl::Buffer* InteropManager::getBufferResource(const forge::Image* image)
 {
     void * key = (void*)image;
     int device = getActiveDeviceId();
@@ -47,50 +47,38 @@ cl::Buffer* InteropManager::getBufferResource(const fg::Image* image)
     return interop_maps[device][key];
 }
 
-cl::Buffer* InteropManager::getBufferResource(const fg::Plot* plot)
+cl::Buffer* InteropManager::getBufferResource(const forge::Plot* plot)
 {
     void * key = (void*)plot;
     int device = getActiveDeviceId();
     iter_t iter = interop_maps[device].find(key);
 
     if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, plot->vbo(), NULL);
+        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, plot->vertices(), NULL);
 
     return interop_maps[device][key];
 }
 
-cl::Buffer* InteropManager::getBufferResource(const fg::Plot3* plot3)
-{
-    void * key = (void*)plot3;
-    int device = getActiveDeviceId();
-    iter_t iter = interop_maps[device].find(key);
-
-    if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, plot3->vbo(), NULL);
-
-    return interop_maps[device][key];
-}
-
-cl::Buffer* InteropManager::getBufferResource(const fg::Histogram* hist)
+cl::Buffer* InteropManager::getBufferResource(const forge::Histogram* hist)
 {
     void * key = (void*)hist;
     int device = getActiveDeviceId();
     iter_t iter = interop_maps[device].find(key);
 
     if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, hist->vbo(), NULL);
+        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, hist->vertices(), NULL);
 
     return interop_maps[device][key];
 }
 
-cl::Buffer* InteropManager::getBufferResource(const fg::Surface* surface)
+cl::Buffer* InteropManager::getBufferResource(const forge::Surface* surface)
 {
     void * key = (void*)surface;
     int device = getActiveDeviceId();
     iter_t iter = interop_maps[device].find(key);
 
     if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, surface->vbo(), NULL);
+        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, surface->vertices(), NULL);
 
     return interop_maps[device][key];
 }
diff --git a/src/backend/opencl/interopManager.hpp b/src/backend/opencl/interopManager.hpp
index c7a2c25868..d5128acab6 100644
--- a/src/backend/opencl/interopManager.hpp
+++ b/src/backend/opencl/interopManager.hpp
@@ -9,7 +9,9 @@
 
 #if defined(WITH_GRAPHICS)
 
-#include <GL/glew.h>
+#include <glbinding/gl/gl.h>
+#include <glbinding/Binding.h>
+
 #include <graphics_common.hpp>
 #include <platform.hpp>
 #include <map>
@@ -28,11 +30,10 @@ class InteropManager
     public:
         static InteropManager& getInstance();
         ~InteropManager();
-        cl::Buffer* getBufferResource(const fg::Image* image);
-        cl::Buffer* getBufferResource(const fg::Plot* plot);
-        cl::Buffer* getBufferResource(const fg::Plot3* plot3);
-        cl::Buffer* getBufferResource(const fg::Histogram* hist);
-        cl::Buffer* getBufferResource(const fg::Surface* surface);
+        cl::Buffer* getBufferResource(const forge::Image* image);
+        cl::Buffer* getBufferResource(const forge::Plot* plot);
+        cl::Buffer* getBufferResource(const forge::Histogram* hist);
+        cl::Buffer* getBufferResource(const forge::Surface* surface);
 
     protected:
         InteropManager() {}
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index cb726c2e0a..9978aad472 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -17,7 +17,7 @@
 #include <OpenGL/OpenGL.h>
 #include <libkern/OSAtomic.h>
 #else
-#include <GL/gl.h>
+#include <glbinding/gl/gl.h>
 #endif // !__APPLE__
 
 #endif
@@ -336,7 +336,7 @@ DeviceManager::DeviceManager()
             /* loop over devices and replace contexts with
              * OpenGL shared contexts whereever applicable */
             int devCount = mDevices.size();
-            fg::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
+            forge::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
             for(int i=0; i<devCount; ++i)
                 markDeviceForInterop(i, wHandle);
         } catch (...) {
@@ -645,7 +645,7 @@ bool checkExtnAvailability(const Device &pDevice, std::string pName)
 }
 
 #if defined(WITH_GRAPHICS)
-void DeviceManager::markDeviceForInterop(const int device, const fg::Window* wHandle)
+void DeviceManager::markDeviceForInterop(const int device, const forge::Window* wHandle)
 {
     try {
         if (device >= (int)mQueues.size() ||
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 73abe66f68..f1ed903974 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -82,7 +82,7 @@ class DeviceManager
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
 #if defined(WITH_GRAPHICS)
-        void markDeviceForInterop(const int device, const fg::Window* wHandle);
+        void markDeviceForInterop(const int device, const forge::Window* wHandle);
 #endif
 
     private:
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index ba7d49b8c0..2eb3e15396 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -22,14 +22,18 @@ using af::dim4;
 
 namespace opencl
 {
+using namespace gl;
 
 template<typename T>
-void copy_plot(const Array<T> &P, fg::Plot* plot)
+void copy_plot(const Array<T> &P, forge::Plot* plot)
 {
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
-        size_t bytes = plot->size();
+        size_t bytes = plot->verticesSize();
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
@@ -54,10 +58,10 @@ void copy_plot(const Array<T> &P, fg::Plot* plot)
         CheckGL("End OpenCL resource copy");
     } else {
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, plot->vbo());
+        glBindBuffer(GL_ARRAY_BUFFER, plot->vertices());
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, plot->size(), ptr);
+            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, plot->verticesSize(), ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -66,7 +70,7 @@ void copy_plot(const Array<T> &P, fg::Plot* plot)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_plot<T>(const Array<T> &P, fg::Plot* plot);
+    template void copy_plot<T>(const Array<T> &P, forge::Plot* plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/plot.hpp b/src/backend/opencl/plot.hpp
index c195694869..7bd4fbe8f9 100644
--- a/src/backend/opencl/plot.hpp
+++ b/src/backend/opencl/plot.hpp
@@ -15,7 +15,7 @@
 namespace opencl
 {
     template<typename T>
-    void copy_plot(const Array<T> &P, fg::Plot* plot);
+    void copy_plot(const Array<T> &P, forge::Plot* plot);
 }
 
 #endif
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 4bca24848b..10b08351b7 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -22,14 +22,18 @@ using af::dim4;
 
 namespace opencl
 {
+using namespace gl;
 
 template<typename T>
-void copy_surface(const Array<T> &P, fg::Surface* surface)
+void copy_surface(const Array<T> &P, forge::Surface* surface)
 {
+    // Make sure to do this
+    glbinding::Binding::useCurrentContext();
+
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
-        size_t bytes = surface->size();
+        size_t bytes = surface->verticesSize();
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
@@ -54,10 +58,10 @@ void copy_surface(const Array<T> &P, fg::Surface* surface)
         CheckGL("End OpenCL resource copy");
     } else {
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, surface->vbo());
+        glBindBuffer(GL_ARRAY_BUFFER, surface->vertices());
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, surface->size(), ptr);
+            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, surface->verticesSize(), ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -66,7 +70,7 @@ void copy_surface(const Array<T> &P, fg::Surface* surface)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_surface<T>(const Array<T> &P, fg::Surface* surface);
+    template void copy_surface<T>(const Array<T> &P, forge::Surface* surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/surface.hpp b/src/backend/opencl/surface.hpp
index 15079f0159..131a6a51dd 100644
--- a/src/backend/opencl/surface.hpp
+++ b/src/backend/opencl/surface.hpp
@@ -15,7 +15,7 @@
 namespace opencl
 {
     template<typename T>
-    void copy_surface(const Array<T> &P, fg::Surface* surface);
+    void copy_surface(const Array<T> &P, forge::Surface* surface);
 }
 
 #endif

From 3c1b5ee3fbf89af0dd34ebde04e05533a790605d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 11:13:28 -0400
Subject: [PATCH 0809/2677] GL Header fix for cuda_gl_interop on ARM/Tegra

---
 src/backend/cuda/interopManager.hpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index 9bce7d3c0a..4feb8389ed 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -13,10 +13,13 @@
 #if defined(WITH_GRAPHICS)
 
 // cuda_gl_interop.h does not include OpenGL headers for ARM
-//#if defined(__arm__) || defined (__aarch64__)
 #include <glbinding/gl/gl.h>
 #include <glbinding/Binding.h>
-//#endif
+
+#if defined(__arm__) || defined(__aarch64__)
+using namespace gl;
+#define GL_VERSION gl::GL_VERSION
+#endif
 
 #include <platform.hpp>
 #include <graphics_common.hpp>

From e266def68bbf0c78284b440282b14e0c2be858f0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 10:57:05 -0400
Subject: [PATCH 0810/2677] Add FreeImage CMake Variables to build_forge

---
 CMakeModules/build_forge.cmake | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 6975afdc99..0f6574a36e 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -56,11 +56,15 @@ ExternalProject_Add(
     -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
     -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
     -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+    -DBUILD_EXAMPLES:BOOL=OFF
     -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
     -Dglbinding_DIR:STRING=${glbinding_DIR}
     -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_EXAMPLES:BOOL=OFF
+    -DFREEIMAGE_INCLUDE_PATH:PATH=${FREEIMAGE_INCLUDE_PATH}
+    -DFREEIMAGE_DYNAMIC_LIBRARY:PATH=${FREEIMAGE_DYNAMIC_LIBRARY}
+    -DFREEIMAGE_STATIC_LIBRARY:PATH=${FREEIMAGE_STATIC_LIBRARY}
+    -DUSE_FREEIMAGE_STATIC:BOOL=${USE_FREEIMAGE_STATIC}
     BUILD_COMMAND ${CMAKE_COMMAND} --build . --config ${forge_lib_config}
     ${byproducts}
     )

From a9010bc4b96c4de9bf36470518ace8584c639334 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 11:45:58 -0400
Subject: [PATCH 0811/2677] Create a function that does window->makeCurrent and
 glbinding useCurrentContext

* Before we were calling glbinding from everywhere
* Now its done when window is set to current
---
 src/api/c/graphics_common.cpp        | 9 ++++++++-
 src/api/c/graphics_common.hpp        | 3 +++
 src/api/c/hist.cpp                   | 2 +-
 src/api/c/image.cpp                  | 2 +-
 src/api/c/plot.cpp                   | 2 +-
 src/api/c/plot3.cpp                  | 2 +-
 src/api/c/surface.cpp                | 2 +-
 src/backend/cpu/hist_graphics.cpp    | 3 ---
 src/backend/cpu/image.cpp            | 3 ---
 src/backend/cpu/plot.cpp             | 3 ---
 src/backend/cpu/surface.cpp          | 3 ---
 src/backend/cuda/hist_graphics.cpp   | 3 ---
 src/backend/cuda/image.cpp           | 3 ---
 src/backend/cuda/plot.cpp            | 3 ---
 src/backend/cuda/surface.cpp         | 3 ---
 src/backend/opencl/hist_graphics.cpp | 3 ---
 src/backend/opencl/image.cpp         | 3 ---
 src/backend/opencl/plot.cpp          | 3 ---
 src/backend/opencl/surface.cpp       | 3 ---
 19 files changed, 16 insertions(+), 42 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 044dfd0057..14ddcd8c3c 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -108,6 +108,13 @@ size_t getTypeSize(GLenum type)
     }
 }
 
+void makeContextCurrent(forge::Window *window)
+{
+    window->makeCurrent();
+    glbinding::Binding::useCurrentContext();
+    CheckGL("End makeContextCurrent");
+}
+
 namespace graphics
 {
 
@@ -153,7 +160,7 @@ forge::Window* ForgeManager::getMainWindow(const bool dontCreate)
     if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         if (flag && !dontCreate) {
             wnd = new forge::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
-            wnd->makeCurrent();
+            makeContextCurrent(wnd);
             CheckGL("End ForgeManager::getMainWindow");
             flag = false;
         };
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 4332fa1175..8cab3a9c40 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -32,6 +32,9 @@ gl::GLenum glForceErrorCheck(const char *msg, const char* file, int line);
 #define CheckGLSkip(msg)  glErrorSkip      (msg, __AF_FILENAME__, __LINE__)
 
 forge::MarkerType getFGMarker(const af_marker_type af_marker);
+
+void makeContextCurrent(forge::Window *window);
+
 namespace graphics
 {
 
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 97197261e1..9126a28563 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -64,7 +64,7 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
         ARG_ASSERT(0, Xinfo.isVector());
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
-        window->makeCurrent();
+        makeContextCurrent(window);
         forge::Histogram* hist = NULL;
 
         switch(Xtype) {
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index c4a9453c5b..0b6dd25041 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -92,7 +92,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
         DIM_ASSERT(0, in_dims[3] == 1);
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
-        window->makeCurrent();
+        makeContextCurrent(window);
         forge::Image* image = NULL;
 
         switch(type) {
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 9dd5770a01..25e07b3806 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -86,7 +86,7 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
         TYPE_ASSERT(Xtype == Ytype);
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
-        window->makeCurrent();
+        makeContextCurrent(window);
         forge::Plot* plot = NULL;
 
         switch(Xtype) {
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 6383c89af4..62b00903a6 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -78,7 +78,7 @@ af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const
         af_dtype Ptype  = Pinfo.getType();
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
-        window->makeCurrent();
+        makeContextCurrent(window);
         forge::Plot* plot3 = NULL;
 
         switch(Ptype) {
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 4785c4e959..25c609f805 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -118,7 +118,7 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
         }
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
-        window->makeCurrent();
+        makeContextCurrent(window);
         forge::Surface* surface = NULL;
 
         switch(Xtype) {
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 21298cf501..21ca622ec8 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -24,9 +24,6 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
     data.eval();
     getQueue().sync();
 
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     CheckGL("Begin copy_histogram");
 
     glBindBuffer(GL_ARRAY_BUFFER, hist->vertices());
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index fe4beef2da..3c06b6521b 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -31,9 +31,6 @@ void copy_image(const Array<T> &in, const forge::Image* image)
     in.eval();
     getQueue().sync();
 
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     CheckGL("Before CopyArrayToPBO");
     const T *d_X = in.get();
     size_t data_size = image->size();
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 5689e186ad..cef685bab9 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -28,9 +28,6 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
     P.eval();
     getQueue().sync();
 
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     CheckGL("Before CopyArrayToVBO");
 
     glBindBuffer(GL_ARRAY_BUFFER, plot->vertices());
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index d4a55b2b01..c4d8f4071d 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -28,9 +28,6 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
     P.eval();
     getQueue().sync();
 
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     CheckGL("Before CopyArrayToVBO");
 
     glBindBuffer(GL_ARRAY_BUFFER, surface->vertices());
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index e911debc6f..de2d9160a0 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -41,9 +41,6 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 
         POST_LAUNCH_CHECK();
     } else {
-        // Make sure to do this
-        glbinding::Binding::useCurrentContext();
-
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, hist->vertices());
         GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index ef7fbbe865..634eefe6a4 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -45,9 +45,6 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
     } else {
-        // Make sure to do this
-        glbinding::Binding::useCurrentContext();
-
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->pbo());
         glBufferData((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->size(), 0, (gl::GLenum)GL_STREAM_DRAW);
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index fc0340589e..65926277c5 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -46,9 +46,6 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
 
         POST_LAUNCH_CHECK();
     } else {
-        // Make sure to do this
-        glbinding::Binding::useCurrentContext();
-
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, plot->vertices());
         GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 4709264aff..2e0bcbfd53 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -46,9 +46,6 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
 
         POST_LAUNCH_CHECK();
     } else {
-        // Make sure to do this
-        glbinding::Binding::useCurrentContext();
-
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, surface->vertices());
         GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 57b98d74d4..9d153d51c5 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -22,9 +22,6 @@ using namespace gl;
 template<typename T>
 void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 {
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = data.get();
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index db31baebc9..d2f22e37e1 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -24,9 +24,6 @@ using namespace gl;
 template<typename T>
 void copy_image(const Array<T> &in, const forge::Image* image)
 {
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
         InteropManager& intrpMngr = InteropManager::getInstance();
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 2eb3e15396..ee35c3d9f1 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -27,9 +27,6 @@ using namespace gl;
 template<typename T>
 void copy_plot(const Array<T> &P, forge::Plot* plot)
 {
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 10b08351b7..b76badee06 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -27,9 +27,6 @@ using namespace gl;
 template<typename T>
 void copy_surface(const Array<T> &P, forge::Surface* surface)
 {
-    // Make sure to do this
-    glbinding::Binding::useCurrentContext();
-
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();

From e008e1bcbeb38fa400f4515c7a295a95389da637 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep@arrayfire.com>
Date: Wed, 31 Aug 2016 11:52:29 -0400
Subject: [PATCH 0812/2677] Compilation fixes for OSX OpenCL-GL interop

---
 src/api/c/graphics_common.cpp | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 14ddcd8c3c..b2795d6379 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -15,13 +15,14 @@
 #include <platform.hpp>
 #include <util.hpp>
 
+#include <glbinding/gl/gl.h>
 #include <glbinding/Meta.h>
 
 using namespace std;
 using namespace gl;
 
 template<typename T>
-GLenum getGLType() { return GL_FLOAT; }
+gl::GLenum getGLType() { return GL_FLOAT; }
 
 forge::MarkerType getFGMarker(const af_marker_type af_marker) {
     forge::MarkerType fg_marker;
@@ -50,10 +51,10 @@ INSTANTIATE_GET_FG_TYPE(unsigned char, forge::u8);
 INSTANTIATE_GET_FG_TYPE(unsigned short, forge::u16);
 INSTANTIATE_GET_FG_TYPE(short, forge::s16);
 
-GLenum glErrorSkip(const char *msg, const char* file, int line)
+gl::GLenum glErrorSkip(const char *msg, const char* file, int line)
 {
 #ifndef NDEBUG
-    GLenum x = glGetError();
+    gl::GLenum x = glGetError();
     if (x != GL_NO_ERROR) {
         char buf[1024];
         sprintf(buf, "GL Error Skipped at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
@@ -61,15 +62,15 @@ GLenum glErrorSkip(const char *msg, const char* file, int line)
     }
     return x;
 #else
-    return (GLenum)0;
+    return (gl::GLenum)0;
 #endif
 }
 
-GLenum glErrorCheck(const char *msg, const char* file, int line)
+gl::GLenum glErrorCheck(const char *msg, const char* file, int line)
 {
 // Skipped in release mode
 #ifndef NDEBUG
-    GLenum x = glGetError();
+    gl::GLenum x = glGetError();
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
@@ -78,13 +79,13 @@ GLenum glErrorCheck(const char *msg, const char* file, int line)
     }
     return x;
 #else
-    return (GLenum)0;
+    return (gl::GLenum)0;
 #endif
 }
 
-GLenum glForceErrorCheck(const char *msg, const char* file, int line)
+gl::GLenum glForceErrorCheck(const char *msg, const char* file, int line)
 {
-    GLenum x = glGetError();
+    gl::GLenum x = glGetError();
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
@@ -94,7 +95,7 @@ GLenum glForceErrorCheck(const char *msg, const char* file, int line)
     return x;
 }
 
-size_t getTypeSize(GLenum type)
+size_t getTypeSize(gl::GLenum type)
 {
     switch(type) {
         case GL_FLOAT:          return sizeof(float);

From e8d451d11cce785e8ccd3045d4d03b4e9b82fb94 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 14:38:38 -0400
Subject: [PATCH 0813/2677] Fix signed/unsigned warnings in empty test

---
 test/empty.cpp | 345 +++++++++++++++++++++++++------------------------
 1 file changed, 175 insertions(+), 170 deletions(-)

diff --git a/test/empty.cpp b/test/empty.cpp
index 1cff2a762f..df5820fc90 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -27,247 +27,252 @@ TEST(Array, TestEmptyAssignment) {
     A(isNaN(A)) = C;
     ASSERT_EQ(B.numdims(), 0u);
     ASSERT_EQ(A.numdims(), 1u);
-    ASSERT_EQ(lookup(constant(1,9), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(lookup(constant(1,9), constant(0,0)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptySigProc) {
-    ASSERT_EQ(convolve(constant(1,1), constant(0,0)).numdims(), 1);
-    ASSERT_EQ(convolve(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(convolve2(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(convolve3(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(iir(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(approx1(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(approx1(constant(0,0), seq(0,10)).numdims(), 0);
-    ASSERT_EQ(approx2(constant(0,0), constant(0,0),  constant(0,0)).numdims(), 0);
-    ASSERT_EQ(approx2(constant(0,0), seq(0,10), seq(0,10)).numdims(), 0);
+    ASSERT_EQ(convolve (constant(1,1), constant(0,0)).numdims(), 1u);
+    ASSERT_EQ(convolve (constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(convolve2(constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(convolve3(constant(0,0), constant(0,0)).numdims(), 0u);
+
+    ASSERT_EQ(iir(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0u);
+
+    ASSERT_EQ(approx1(constant(0,0), constant(0,0))                 .numdims(), 0u);
+    ASSERT_EQ(approx1(constant(0,0), seq(0,10))                     .numdims(), 0u);
+    ASSERT_EQ(approx2(constant(0,0), constant(0,0),  constant(0,0)) .numdims(), 0u);
+    ASSERT_EQ(approx2(constant(0,0), seq(0,10), seq(0,10))          .numdims(), 0u);
 }
 
 TEST(Array, TestEmptySet) {
-    ASSERT_EQ(setIntersect(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(setUnique(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(setIntersect(constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(setUnique(constant(0,0))                  .numdims(), 0u);
+
     array A = randu(5, f32);
     array B = constant(0, 0);
+
     ASSERT_EQ(setUnion(A, B).elements(), 5);
     ASSERT_EQ(setUnion(B, A).elements(), 5);
 }
 
 TEST(Array, TestEmptyOperators) {
-    ASSERT_EQ((constant(0,0) +  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) && constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) -  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) &  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) |  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) ^  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) << constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) >> constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) /  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) == constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) <= constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) >= constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) >  constant(0,0)).numdims(), 0);
-    ASSERT_EQ((constant(0,0) <  constant(0,0)).numdims(), 0);
-    ASSERT_EQ(-constant(0,0).numdims(), 0 );
-    ASSERT_EQ((!constant(0,0)).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) != constant(0,0)).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) += 1).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) -= 1).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) *= 1).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) /= 1).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) || constant(0,0)).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) %  constant(0,0)).numdims(), 0 );
-    ASSERT_EQ((constant(0,0) *  constant(0,0)).numdims(), 0 );
+    ASSERT_EQ((constant(0,0) +  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) && constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) -  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) &  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) |  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) ^  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) << constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) >> constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) /  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) == constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) <= constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) >= constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) >  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) <  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(-constant(0,0)                  .numdims(), 0u);
+    ASSERT_EQ((!constant(0,0))                .numdims(), 0u);
+    ASSERT_EQ((constant(0,0) != constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) += 1)            .numdims(), 0u);
+    ASSERT_EQ((constant(0,0) -= 1)            .numdims(), 0u);
+    ASSERT_EQ((constant(0,0) *= 1)            .numdims(), 0u);
+    ASSERT_EQ((constant(0,0) /= 1)            .numdims(), 0u);
+    ASSERT_EQ((constant(0,0) || constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) %  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0,0) *  constant(0,0)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptyFFT) {
     array arr = constant(0,0);
     fftInPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
     fft2InPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
     fft3InPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
     ifftInPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
     ifft2InPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
     ifft3InPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
-
-    ASSERT_EQ((fft(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fftNorm(constant(0,0), 0.5)).numdims(), 0);
-    ASSERT_EQ((fft2(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fft2Norm(constant(0,0), 0.5)).numdims(), 0);
-    ASSERT_EQ((fft3(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fft3Norm(constant(0,0), 0.5)).numdims(), 0);
-    ASSERT_EQ((fftC2R<1>(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fftR2C<1>(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fftC2R<2>(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fftR2C<2>(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fftC2R<3>(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((fftR2C<3>(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((ifft(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((ifftNorm(constant(0,0), 0.5)).numdims(), 0);
-    ASSERT_EQ((ifft2(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((ifft2Norm(constant(0,0), 0.5)).numdims(), 0);
-    ASSERT_EQ((ifft3(constant(0,0))).numdims(), 0);
-    ASSERT_EQ((ifft3Norm(constant(0,0), 0.5)).numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
+
+    ASSERT_EQ((fft(constant(0,0)))              .numdims(), 0u);
+    ASSERT_EQ((fftNorm(constant(0,0), 0.5))     .numdims(), 0u);
+    ASSERT_EQ((fft2(constant(0,0)))             .numdims(), 0u);
+    ASSERT_EQ((fft2Norm(constant(0,0), 0.5))    .numdims(), 0u);
+    ASSERT_EQ((fft3(constant(0,0)))             .numdims(), 0u);
+    ASSERT_EQ((fft3Norm(constant(0,0), 0.5))    .numdims(), 0u);
+    ASSERT_EQ((fftC2R<1>(constant(0,0)))        .numdims(), 0u);
+    ASSERT_EQ((fftR2C<1>(constant(0,0)))        .numdims(), 0u);
+    ASSERT_EQ((fftC2R<2>(constant(0,0)))        .numdims(), 0u);
+    ASSERT_EQ((fftR2C<2>(constant(0,0)))        .numdims(), 0u);
+    ASSERT_EQ((fftC2R<3>(constant(0,0)))        .numdims(), 0u);
+    ASSERT_EQ((fftR2C<3>(constant(0,0)))        .numdims(), 0u);
+    ASSERT_EQ((ifft(constant(0,0)))             .numdims(), 0u);
+    ASSERT_EQ((ifftNorm(constant(0,0), 0.5))    .numdims(), 0u);
+    ASSERT_EQ((ifft2(constant(0,0)))            .numdims(), 0u);
+    ASSERT_EQ((ifft2Norm(constant(0,0), 0.5))   .numdims(), 0u);
+    ASSERT_EQ((ifft3(constant(0,0)))            .numdims(), 0u);
+    ASSERT_EQ((ifft3Norm(constant(0,0), 0.5))   .numdims(), 0u);
 }
 
 TEST(Array, TestEmptyDiff) {
-    ASSERT_EQ(diff1(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(diff1(constant(1,1)).numdims(), 0);
-    ASSERT_EQ(diff1(constant(1,2)).numdims(), 1);
-    ASSERT_EQ(diff2(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(diff2(constant(1,1)).numdims(), 0);
-    ASSERT_EQ(diff2(constant(1,2)).numdims(), 0);
-    ASSERT_EQ(diff2(constant(1,3)).numdims(), 1);
+    ASSERT_EQ(diff1(constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(diff1(constant(1,1)).numdims(), 0u);
+    ASSERT_EQ(diff1(constant(1,2)).numdims(), 1u);
+    ASSERT_EQ(diff2(constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(diff2(constant(1,1)).numdims(), 0u);
+    ASSERT_EQ(diff2(constant(1,2)).numdims(), 0u);
+    ASSERT_EQ(diff2(constant(1,3)).numdims(), 1u);
 }
 
 TEST(Array, TestEmptyLinAlg) {
-    ASSERT_EQ( det<float>(constant(0,0)), 1);
-    ASSERT_EQ( det<cfloat>(constant(0,0)).real, 1);
-    ASSERT_EQ( det<cdouble>(constant(0,0)).real, 1);
-    ASSERT_EQ( af::norm(constant(0,0)), 0);
-    ASSERT_EQ( af::rank(constant(0,0)), 0);
+    ASSERT_EQ(det<float  >(constant(0,0)), 1);
+    ASSERT_EQ(det<cfloat >(constant(0,0)).real, 1);
+    ASSERT_EQ(det<cdouble>(constant(0,0)).real, 1);
+    ASSERT_EQ(af::norm(constant(0,0)), 0);
+    ASSERT_EQ(af::rank(constant(0,0)), 0u);
+
     array tau_qr, arr = constant(0,0);
     qrInPlace(tau_qr, arr);
-    ASSERT_EQ(tau_qr.numdims(), 0);
-
+    ASSERT_EQ(tau_qr.numdims(), 0u);
 
     array out_qr;
     qr(out_qr, tau_qr, constant(0,0));
-    ASSERT_EQ(out_qr.numdims(), 0);
-    ASSERT_EQ(tau_qr.numdims(), 0);
-    ASSERT_EQ(solve(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(solveLU(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(out_qr.numdims(), 0u);
+    ASSERT_EQ(tau_qr.numdims(), 0u);
+    ASSERT_EQ(solve(constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(solveLU(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0u);
 
     array out_lu, piv_lu;
     lu(out_lu, piv_lu, constant(0,0));
-    ASSERT_EQ(out_lu.numdims(), 0);
-    ASSERT_EQ(piv_lu.numdims(), 0);
+    ASSERT_EQ(out_lu.numdims(), 0u);
+    ASSERT_EQ(piv_lu.numdims(), 0u);
 
     array low_lu, up_lu;
     lu(low_lu, up_lu, piv_lu, constant(0,0));
-    ASSERT_EQ(low_lu.numdims(), 0);
-    ASSERT_EQ(up_lu.numdims(), 0);
-    ASSERT_EQ(piv_lu.numdims(), 0);
+    ASSERT_EQ(low_lu.numdims(), 0u);
+    ASSERT_EQ( up_lu.numdims(), 0u);
+    ASSERT_EQ(piv_lu.numdims(), 0u);
 
     luInPlace(piv_lu, arr, true);
-    ASSERT_EQ(piv_lu.numdims(), 0);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(piv_lu.numdims(), 0u);
+    ASSERT_EQ(arr.numdims(), 0u);
 
     array u, s, v;
     svd(u,s,v, constant(0,0));
     svdInPlace(u,s,v, arr);
-    ASSERT_EQ(dot(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(transpose(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(dot(constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(transpose(constant(0,0)).numdims(), 0u);
     choleskyInPlace(arr);
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
     array out;
     cholesky(out, constant(0,0));
-    ASSERT_EQ(out.numdims(), 0);
+    ASSERT_EQ(out.numdims(), 0u);
 }
 
 TEST(Array, TestEmptyMath) {
-    ASSERT_EQ(acos(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(acosh(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(abs(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(asin(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(asinh(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(atan(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(atan2(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(atanh(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(cos(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(cosh(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(log(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(log10(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(log1p(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(sin(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(sinh(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(tan(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(tanh(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(sqrt(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(real(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(imag(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(conjg(constant(0,0)).numdims(), 0);
-    //ASSERT_EQ(complex(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(erf(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(erfc(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(exp(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(expm1(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(cbrt(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(ceil(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(factorial(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(lgamma(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(pow(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(root(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(tgamma(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(arg(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(floor(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(hypot(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(rem(constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(round(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(sign(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(trunc(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(acos  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(acosh (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(abs   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(asin  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(asinh (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(atan  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(atan2 (constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(atanh (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(cos   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(cosh  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(log   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(log10 (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(log1p (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(sin   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(sinh  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(tan   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(tanh  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(sqrt  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(real  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(imag  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(conjg (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(erf   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(erfc  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(exp   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(expm1 (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(cbrt  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(ceil  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(lgamma(constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(pow   (constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(root  (constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(tgamma(constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(arg   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(floor (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(hypot (constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(rem   (constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(round (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(sign  (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(trunc (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(factorial(constant(0,0)).numdims(), 0u);
+    //ASSERT_EQ(complex(constant(0,0), constant(0,0)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptyVecOp) {
-    ASSERT_EQ(accum(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(allTrue(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(anyTrue(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(count(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(where(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(max(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(min(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(product(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(sum(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(sort(constant(0,0)).numdims(), 0);
+    ASSERT_EQ(accum     (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(allTrue   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(anyTrue   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(count     (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(where     (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(max       (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(min       (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(product   (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(sum       (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(sort      (constant(0,0)).numdims(), 0u);
 
     array skeys, svals;
     sort(skeys, svals, constant(0,0), constant(0,0));
-    ASSERT_EQ(skeys.numdims(), 0);
-    ASSERT_EQ(svals.numdims(), 0);
-
+    ASSERT_EQ(skeys.numdims(), 0u);
+    ASSERT_EQ(svals.numdims(), 0u);
 
     array sout, sind;
     sort(sout, sind, constant(0,0));
-    ASSERT_EQ(sout.numdims(), 0);
-    ASSERT_EQ(sind.numdims(), 0);
+    ASSERT_EQ(sout.numdims(), 0u);
+    ASSERT_EQ(sind.numdims(), 0u);
 }
 
 TEST(Array, TestEmptyArrMod) {
-    ASSERT_EQ(diag(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(diag(constant(0,0), true).numdims(), 0);
-    ASSERT_EQ(af::identity(0).numdims(), 0);
-    ASSERT_EQ(iota(dim4(0)).numdims(), 0);
-    ASSERT_EQ(lower(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(upper(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(constant(0,0).as(u8).numdims(), 0);
-    ASSERT_EQ(isNaN(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(isInf(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(iszero(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(flat(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(flip(constant(0,0), 0).numdims(), 0);
-    ASSERT_EQ(join(0, constant(0,0), constant(0,0)).numdims(), 0);
+    ASSERT_EQ(diag  (constant(0,0))             .numdims(), 0u);
+    ASSERT_EQ(diag  (constant(0,0), true)       .numdims(), 0u);
+    ASSERT_EQ(af::identity(0)                   .numdims(), 0u);
+    ASSERT_EQ(iota(dim4(0))                     .numdims(), 0u);
+    ASSERT_EQ(lower(constant(0,0))              .numdims(), 0u);
+    ASSERT_EQ(upper(constant(0,0))              .numdims(), 0u);
+    ASSERT_EQ(constant(0,0).as(u8)              .numdims(), 0u);
+    ASSERT_EQ(isNaN(constant(0,0))              .numdims(), 0u);
+    ASSERT_EQ(isInf(constant(0,0))              .numdims(), 0u);
+    ASSERT_EQ(iszero(constant(0,0))             .numdims(), 0u);
+    ASSERT_EQ(flat(constant(0,0))               .numdims(), 0u);
+    ASSERT_EQ(flip(constant(0,0), 0)            .numdims(), 0u);
+    ASSERT_EQ(moddims(constant(0,0), dim4(0))   .numdims(), 0u);
+    ASSERT_EQ(reorder(constant(0,0),0)          .numdims(), 0u);
+    ASSERT_EQ(shift(constant(0,0), 1)           .numdims(), 0u);
+    ASSERT_EQ(tile(constant(0,0), 1)            .numdims(), 0u);
+
+    ASSERT_EQ(join(0, constant(0,0), constant(0,0)).numdims(), 0u);
     ASSERT_EQ(join(0, randu(3), constant(0,0)).elements(), 3);
     ASSERT_EQ(join(0, constant(0,0), randn(3)).elements(), 3);
-    ASSERT_EQ(moddims(constant(0,0), dim4(0)).numdims(), 0);
-    ASSERT_EQ(reorder(constant(0,0),0).numdims(), 0);
-    ASSERT_EQ(select(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0);
-    ASSERT_EQ(shift(constant(0,0), 1).numdims(), 0);
-    ASSERT_EQ(tile(constant(0,0), 1).numdims(), 0);
+
+    ASSERT_EQ(select(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0u);
 
     array arr = constant(0,0);
     replace(arr, constant(0,0), constant(0,0));
-    ASSERT_EQ(arr.numdims(), 0);
+    ASSERT_EQ(arr.numdims(), 0u);
 
 }
 
 TEST(Array, TestEmptyImage) {
-    ASSERT_EQ(histogram(constant(0,0) , 1).numdims(), 0);
-    ASSERT_EQ(hsv2rgb(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(gray2rgb(constant(0,0)).numdims(), 0);
-    ASSERT_EQ(rotate(constant(0,0),0).numdims(), 0);
+    ASSERT_EQ(histogram(constant(0,0) , 1).numdims(), 0u);
+    ASSERT_EQ(hsv2rgb(constant(0,0)) .numdims(), 0u);
+    ASSERT_EQ(gray2rgb(constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(rotate(constant(0,0),0).numdims(), 0u);
 
     af_array h, hout;
     dim_t ds[1];
@@ -275,8 +280,8 @@ TEST(Array, TestEmptyImage) {
     af_histogram(&hout, h, 10, 0.0, 1.0);
 
     unsigned nd; af_get_numdims(&nd, h);
-    ASSERT_EQ(nd, 0);
+    ASSERT_EQ(nd, 0u);
     af_get_numdims(&nd, hout);
-    ASSERT_EQ(nd, 0);
+    ASSERT_EQ(nd, 0u);
 }
 

From 24712480a05c6066accb274db52a7827cca31435 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 18:00:22 -0400
Subject: [PATCH 0814/2677] Add required windows.h header for Windows

cuda_gl_interop.h includes gl.h which requires windows.h to be included before it
---
 src/backend/cuda/interopManager.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index 4feb8389ed..c98bd41bef 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -12,6 +12,10 @@
 
 #if defined(WITH_GRAPHICS)
 
+#if defined(OS_WIN)
+#include <windows.h>
+#endif
+
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <glbinding/gl/gl.h>
 #include <glbinding/Binding.h>

From 40ddee2fc05a0420340e03a78f15995067c45136 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 18:01:08 -0400
Subject: [PATCH 0815/2677] Fix ambiguous symbols from windows gl.h

---
 src/backend/cuda/hist_graphics.cpp | 2 +-
 src/backend/cuda/image.cpp         | 2 +-
 src/backend/cuda/plot.cpp          | 2 +-
 src/backend/cuda/surface.cpp       | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index de2d9160a0..48e7f214e8 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -43,7 +43,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
     } else {
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, hist->vertices());
-        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpy(ptr, data.get(), hist->verticesSize(), cudaMemcpyDeviceToHost));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 634eefe6a4..5abab92a05 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -48,7 +48,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->pbo());
         glBufferData((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->size(), 0, (gl::GLenum)GL_STREAM_DRAW);
-        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpy(ptr, in.get(), image->size(), cudaMemcpyDeviceToHost));
             glUnmapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER);
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 65926277c5..7434e09b7b 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -48,7 +48,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
     } else {
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, plot->vertices());
-        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot->verticesSize(), cudaMemcpyDeviceToHost));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 2e0bcbfd53..f200d537be 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -48,7 +48,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
     } else {
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, surface->vertices());
-        GLubyte* ptr = (GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpy(ptr, P.get(), surface->verticesSize(), cudaMemcpyDeviceToHost));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);

From 60bba65bd8abdd4fd46bff0c572f51ede569d170 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 31 Aug 2016 18:24:07 -0400
Subject: [PATCH 0816/2677] Minor layout fixes to conway pretty example

---
 examples/graphics/conway_pretty.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/examples/graphics/conway_pretty.cpp b/examples/graphics/conway_pretty.cpp
index 58e73a6b42..6980f7b27d 100644
--- a/examples/graphics/conway_pretty.cpp
+++ b/examples/graphics/conway_pretty.cpp
@@ -29,19 +29,21 @@ int main(int argc, char *argv[])
                   << "3. Any live cell with more than three live neighbours dies, as if by overcrowding." << std::endl
                   << "4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction." << std::endl
                   << "Each white block in the visualization represents 1 alive cell, black space represents dead cells" << std::endl
-                  ;
+                  << std::endl;
+
         std::cout << "The conway_pretty example visualizes all the states in Conway" << std::endl
                   << "Red   : Cells that have died due to under population"          << std::endl
                   << "Yellow: Cells that continue to live from previous state"       << std::endl
                   << "Green : Cells that are new as a result of reproduction"        << std::endl
                   << "Blue  : Cells that have died due to over population"           << std::endl
-                  ;
+                  << std::endl;
+
         std::cout << "This examples is throttled so as to be a better visualization" << std::endl;
 
         af::Window simpleWindow(512, 512, "Conway's Game Of Life - Current State");
         af::Window prettyWindow(512, 512, "Conway's Game Of Life - Visualizing States");
-        simpleWindow.setPos(25, 25);
-        prettyWindow.setPos(125, 15);
+        simpleWindow.setPos(32, 32);
+        prettyWindow.setPos(512 + 32, 32);
 
         int frame_count = 0;
 

From 6d793b5e1950e4f9484719167dddc4bb1240ac1f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 31 Aug 2016 19:28:25 -0400
Subject: [PATCH 0817/2677] OPENCL: cleaning up csrmm and csrmv

- Added comments
- Fixed typos and bugs
- Fixed terminology
---
 src/backend/opencl/kernel/csrmm.cl  | 54 ++++++++++++++++++------
 src/backend/opencl/kernel/csrmm.hpp |  9 ++--
 src/backend/opencl/kernel/csrmv.cl  | 65 ++++++++++++++++++++++++-----
 3 files changed, 100 insertions(+), 28 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmm.cl b/src/backend/opencl/kernel/csrmm.cl
index d2c9713d49..90cd74bebe 100644
--- a/src/backend/opencl/kernel/csrmm.cl
+++ b/src/backend/opencl/kernel/csrmm.cl
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #if IS_CPLX
-
 T __cmul(T lhs, T rhs)
 {
     T out;
@@ -16,11 +15,33 @@ T __cmul(T lhs, T rhs)
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
+
+T __ccmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x + lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y - lhs.y * rhs.x;
+    return out;
+}
+
 #define MUL(a, b) __cmul(a, b)
+
+#if IS_CONJ
+#define CMUL(a, b) __ccmul(a, b)
+#else
+#define CMUL(a, b) __cmul(a, b)
+#endif
+
 #else
 #define MUL(a, b) (a) * (b)
+#define CMUL(a, b) (a) * (b)
 #endif
 
+// This kernel expects the dense matrix to be transpose of column major (aka non transpose row major).
+
+// In this kernel, each block performs multiple "dot" operations.
+// In each outer facing iteration, the group performs a "dot" on (one sparse row, `THREADS_PER_GROUP` dense columns).
+// The threads in the block load the sparse row into local memmory and then perform individual "dot" operations.
 
 __kernel void
 csrmm_nt(__global T *output,
@@ -29,7 +50,6 @@ csrmm_nt(__global T *output,
          __global const int *colidx,
          const int M,
          const int N,
-         const int K,
          __global const T *rhs,
          const KParam rinfo,
          const T alpha,
@@ -38,55 +58,63 @@ csrmm_nt(__global T *output,
 {
     int gidx = get_global_id(0);
     int lid = get_local_id(0);
-    int off = get_local_size(0);
 
-    rhs += gidx;
+    rhs += gidx + rinfo.offset;
     output += gidx * M;
 
-    bool within_K = (gidx < K);
+    bool within_N = (gidx < N);
 
     __local T s_values[THREADS_PER_GROUP];
     __local int s_colidx[THREADS_PER_GROUP];
 
-    // FIXME: Implement better load balancing using atomic counter
     int rowNext = get_group_id(1);
     __local int s_rowId;
+
+    // Each iteration writes `THREADS_PER_GROUP` columns from one row of the output
     while(true) {
 #if USE_GREEDY
+        // If the hardware has decent atomic operation support, greediy get the next available row
         if (lid == 0) {
             s_rowId = atomic_inc(counter + get_group_id(0));
         }
         barrier(CLK_LOCAL_MEM_FENCE);
         int rowId = s_rowId;
 #else
+        // Fall back to the naive distribution of rows otherwise
         int rowId = rowNext;
         rowNext += get_num_groups(1);
 #endif
         if (rowId >= M) return;
 
+        // Load the nonzero column offsets for current row
         const int colStart = rowidx[rowId];
         const int colEnd   = rowidx[rowId + 1];
 
         T outval = 0;
-        for (int id = colStart; id < colEnd; id += off) {
-            int lim = min(colEnd - id, off);
+        // Since the number of nonzero elements might be greater than local memory available,
+        // Load only part of the row into local memory, perform partial dot, repeat until done.
+        for (int id = colStart; id < colEnd; id += THREADS_PER_GROUP) {
+            // Load the current chunk of the row into local memory
+            int lim = min(colEnd - id, THREADS_PER_GROUP);
             s_values[lid] = lid < lim ? values[id + lid] : 0;
             s_colidx[lid] = lid < lim ? colidx[id + lid] : -1;
             barrier(CLK_LOCAL_MEM_FENCE);
 
-            for (int idy = 0; within_K && idy < lim; idy++) {
-                outval += MUL(s_values[idy], rhs[K * s_colidx[idy]]);
+            // Perform partial "dot" operation for each thread
+            for (int idy = 0; within_N && idy < lim; idy++) {
+                outval += CMUL(s_values[idy], rhs[rinfo.strides[1] * s_colidx[idy]]);
             }
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
-        if (within_K) {
+        if (within_N) {
+            // Each thread writes the output for one column in the current row
 #if USE_ALPHA
-            outval = alpha * outval;
+            outval = MUL(alpha, outval);
 #endif
 
 #if USE_BETA
-            output[rowId] = outval + beta * output[rowId];
+            output[rowId] = outval + MUL(beta, output[rowId]);
 #else
             output[rowId] = outval;
 #endif
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 835fc85459..ac6e801b41 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -102,14 +102,13 @@ namespace opencl
                 auto csrmm_nt_kernel = entry.ker[0];
                 auto csrmm_nt_func = KernelFunctor<Buffer,
                                                    Buffer, Buffer, Buffer,
-                                                   int, int, int,
+                                                   int, int,
                                                    Buffer, KParam, T, T, Buffer>(csrmm_nt_kernel);
                 NDRange local(THREADS_PER_GROUP, 1);
                 int M = rowIdx.info.dims[0] - 1;
-                int N = rhs.info.dims[1];
-                int K = rhs.info.dims[0];
+                int N = rhs.info.dims[0];
 
-                int groups_x = divup(K, local[0]);
+                int groups_x = divup(N, local[0]);
                 int groups_y = divup(M, REPEAT);
                 groups_y = std::min(groups_y, MAX_CSRMM_GROUPS);
                 NDRange global(local[0] * groups_x, local[1] * groups_y);
@@ -123,7 +122,7 @@ namespace opencl
 
                 csrmm_nt_func(EnqueueArgs(getQueue(), global, local),
                               *out.data, *values.data, *rowIdx.data, *colIdx.data,
-                              M, N, K, *rhs.data, rhs.info, alpha, beta, *counter);
+                              M, N, *rhs.data, rhs.info, alpha, beta, *counter);
 
                 bufferFree(counter);
             } catch (cl::Error &ex) {
diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index 11da95240c..222b04e3df 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #if IS_CPLX
-
 T __cmul(T lhs, T rhs)
 {
     T out;
@@ -16,12 +15,32 @@ T __cmul(T lhs, T rhs)
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
+
+T __ccmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x + lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y - lhs.y * rhs.x;
+    return out;
+}
+
 #define MUL(a, b) __cmul(a, b)
+
+#if IS_CONJ
+#define CMUL(a, b) __ccmul(a, b)
+#else
+#define CMUL(a, b) __cmul(a, b)
+#endif
+
 #else
 #define MUL(a, b) (a) * (b)
+#define CMUL(a, b) (a) * (b)
 #endif
 
 
+// In this kernel, each thread performs one "dot" operation by reading nonzero elements from one row
+// and multiplying with the corresponding elements from the dense vector to produce a single output value.
+// This kernel should be used when the number of nonzero elements per block is fairly small
 __kernel void
 csrmv_thread(__global T *output,
              __global const T *values,
@@ -35,37 +54,52 @@ csrmv_thread(__global T *output,
              __global int *counter)
 {
 
+    rhs += rinfo.offset;
     int rowNext = get_global_id(0);
+
     while (true) {
+
+        // Each thread performs multiple "dot" operations
 #if USE_GREEDY
+        // Considering that the number of non zero elements per row can be uneven a greedy approach may be useful.
+        // This acheived by getting the next available row to perform the "dot" operation on.
         int rowId = atomic_inc(counter);
 #else
+        // Unfortunately atomic operations are costly on some architectures.
+        // The fallback is to use same number of rows on all threads.
         int rowId = rowNext;
         rowNext += get_global_size(0);
 #endif
         if (rowId >= M) return;
 
+        // Find the columns offsets for the current row
         int colStart = rowidx[rowId];
         int colEnd   = rowidx[rowId + 1];
-        T outval = 0;
 
+        T outval = 0;
+        // Performing the "dot" operation
         for (int id = colStart; id < colEnd; id++) {
             int cid = colidx[id];
-            outval += MUL(values[id], rhs[cid]);
+            outval += CMUL(values[id], rhs[cid]);
         }
 
+        // Writing out a single output
 #if USE_ALPHA
-        outval *= alpa;
+        outval = MUL(alpha, outval);
 #endif
 
 #if USE_BETA
-        output[rowId] = outval + beta * output[rowId];
+        output[rowId] = outval + MUL(beta, output[rowId]);
 #else
         output[rowId] = outval;
 #endif
     }
 }
 
+// In this kernel, each block performs one "dot" operation by having each thread read a nonzero element from a row
+// and multiplying with the corresponding elements from dense vector to produce a local output values.
+// Then the block performs a reduction operation to produce a single output value.
+// This kernel should be used when the number of nonzero elements per block is large
 __kernel void
 csrmv_block(__global T *output,
             __global const T *values,
@@ -78,51 +112,62 @@ csrmv_block(__global T *output,
             const T beta,
             __global int *counter)
 {
+    rhs += rinfo.offset;
     int lid = get_local_id(0);
-    int off = get_local_size(0);
-
     int rowNext = get_group_id(0);
     __local int s_rowId;
+
+    // Each groups performs multiple "dot" operations
     while (true) {
 
 #if USE_GREEDY
+        // Considering that the number of non zero elements per row can be uneven a greedy approach may be useful.
+        // This acheived by getting the next available row to perform the "dot" operation on.
+        // Since the rowId needs is the same across the block, only one thread needs to increment the counter.
         if (lid == 0) {
             s_rowId = atomic_inc(counter);
         }
         barrier(CLK_LOCAL_MEM_FENCE);
         int rowId = s_rowId;
 #else
+        // Unfortunately atomic operations are costly on some architectures.
+        // The fallback is to use same number of rows on all blocks.
         int rowId = rowNext;
         rowNext += get_num_groups(0);
 #endif
         if (rowId >= M) return;
 
+        // Each thread stores part of the output result
         __local T s_outval[THREADS];
 
         int colStart = rowidx[rowId];
         int colEnd   = rowidx[rowId + 1];
         T outval = 0;
-        for (int id = colStart + lid; id < colEnd; id += off) {
+
+        // Each thread performs "dot" on num_nonzero_elements / THREADS for a given row
+        for (int id = colStart + lid; id < colEnd; id += THREADS) {
             int cid = colidx[id];
             outval += MUL(values[id], rhs[cid]);
         }
         s_outval[lid] = outval;
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        // Perform a block reduce operation to get the single output value
         for (int n = THREADS / 2; n > 0; n /= 2) {
             if (lid < n) s_outval[lid] += s_outval[lid + n];
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
+        // A single thread writes the output value
         if (lid == 0) {
 #if USE_ALPHA
-            outval = alpha * s_outval[0];
+            outval = MUL(alpha, s_outval[0]);
 #else
             outval = s_outval[0];
 #endif
 
 #if USE_BETA
-            output[rowId] = outval + beta * output[rowId];
+            output[rowId] = outval + MUL(beta, output[rowId]);
 #else
             output[rowId] = outval;
 #endif

From dc086d62a231a97d965895318bec05d7c99f84df Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 10:47:24 -0400
Subject: [PATCH 0818/2677] Split C-api window functions into a separate file

---
 src/api/c/image.cpp  | 185 --------------------------------------
 src/api/c/window.cpp | 209 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 209 insertions(+), 185 deletions(-)
 create mode 100644 src/api/c/window.cpp

diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 0b6dd25041..07cd2d7c04 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -119,188 +119,3 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
-
-af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
-{
-#if defined(WITH_GRAPHICS)
-    forge::Window* wnd;
-    try {
-        graphics::ForgeManager& fgMngr = graphics::ForgeManager::getInstance();
-        forge::Window* mainWnd = NULL;
-
-        try {
-            mainWnd = fgMngr.getMainWindow();
-        } catch(...) {
-            std::cerr<<"OpenGL context creation failed"<<std::endl;
-        }
-
-        if(mainWnd==0) {
-            std::cerr<<"Not a valid window"<<std::endl;
-            return AF_SUCCESS;
-        }
-
-        wnd = new forge::Window(width, height, title, mainWnd);
-        wnd->setFont(fgMngr.getFont());
-        *out = reinterpret_cast<af_window>(wnd);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        wnd->setPos(x, y);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_set_title(const af_window wind, const char* const title)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        wnd->setTitle(title);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        wnd->setSize(w, h);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_grid(const af_window wind, const int rows, const int cols)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        wnd->grid(rows, cols);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_show(const af_window wind)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        wnd->swapBuffers();
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_is_window_closed(bool *out, const af_window wind)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        *out = wnd->close();
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_set_visibility(const af_window wind, const bool is_visible)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        if (is_visible)
-            wnd->show();
-        else
-            wnd->hide();
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
-
-af_err af_destroy_window(const af_window wind)
-{
-#if defined(WITH_GRAPHICS)
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        delete wnd;
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
new file mode 100644
index 0000000000..2cbfab2c8f
--- /dev/null
+++ b/src/api/c/window.cpp
@@ -0,0 +1,209 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+
+#include <af/graphics.h>
+
+#include <graphics_common.hpp>
+#include <err_common.hpp>
+#include <backend.hpp>
+
+using af::dim4;
+using namespace detail;
+
+#if defined(WITH_GRAPHICS)
+using namespace graphics;
+#endif
+
+
+af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
+{
+#if defined(WITH_GRAPHICS)
+    forge::Window* wnd;
+    try {
+        graphics::ForgeManager& fgMngr = graphics::ForgeManager::getInstance();
+        forge::Window* mainWnd = NULL;
+
+        try {
+            mainWnd = fgMngr.getMainWindow();
+        } catch(...) {
+            std::cerr<<"OpenGL context creation failed"<<std::endl;
+        }
+
+        if(mainWnd==0) {
+            std::cerr<<"Not a valid window"<<std::endl;
+            return AF_SUCCESS;
+        }
+
+        wnd = new forge::Window(width, height, title, mainWnd);
+        wnd->setFont(fgMngr.getFont());
+        *out = reinterpret_cast<af_window>(wnd);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        wnd->setPos(x, y);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_title(const af_window wind, const char* const title)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        wnd->setTitle(title);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        wnd->setSize(w, h);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_grid(const af_window wind, const int rows, const int cols)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        wnd->grid(rows, cols);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_show(const af_window wind)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        wnd->swapBuffers();
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_is_window_closed(bool *out, const af_window wind)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        *out = wnd->close();
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_visibility(const af_window wind, const bool is_visible)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        if (is_visible)
+            wnd->show();
+        else
+            wnd->hide();
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_destroy_window(const af_window wind)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        delete wnd;
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+

From 3e9ee663a301677bc85bc90078697d82b368a009 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 31 Aug 2016 19:41:07 -0400
Subject: [PATCH 0819/2677] OPENCL: Implementing cscmv for matmulTN

---
 src/backend/opencl/kernel/cscmv.cl  | 125 +++++++++++++++++++++++++++
 src/backend/opencl/kernel/cscmv.hpp | 129 ++++++++++++++++++++++++++++
 src/backend/opencl/sparse_blas.cpp  |  20 +++--
 3 files changed, 267 insertions(+), 7 deletions(-)
 create mode 100644 src/backend/opencl/kernel/cscmv.cl
 create mode 100644 src/backend/opencl/kernel/cscmv.hpp

diff --git a/src/backend/opencl/kernel/cscmv.cl b/src/backend/opencl/kernel/cscmv.cl
new file mode 100644
index 0000000000..96ae556e20
--- /dev/null
+++ b/src/backend/opencl/kernel/cscmv.cl
@@ -0,0 +1,125 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if IS_CPLX
+T __cmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x - lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y + lhs.y * rhs.x;
+    return out;
+}
+
+T __ccmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x + lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y - lhs.y * rhs.x;
+    return out;
+}
+
+#define MUL(a, b) __cmul(a, b)
+
+#if IS_CONJ
+#define CMUL(a, b) __ccmul(a, b)
+#else
+#define CMUL(a, b) __cmul(a, b)
+#endif
+
+#else
+#define MUL(a, b) (a) * (b)
+#define CMUL(a, b) (a) * (b)
+#endif
+
+int binary_search(__global int *ptr, int len, int val)
+{
+    int start = 0;
+    int end   = len;
+    while (end > start) {
+        int mid = start + (end - start) / 2;
+        if (val < ptr[mid]) {
+            end = mid;
+        } else if (val > ptr[mid]) {
+            start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    return start;
+}
+
+// Each thread performs Matrix Vector multiplications for ROWS_PER_GROUP rows and (K / THREAD) columns.
+// This generates a local output buffer of size ROWS_PER_THREAD for each thread.
+// The outputs from each thread are added up to generate the final result.
+__kernel void
+cscmv_block(__global T *output,
+            __global const T *values,
+            __global const int *colidx,  // rowidx from csr is colidx in csc
+            __global const int *rowidx,  // colidx from csr is rowidx in csc
+            const int M,                 // K from csr is M in csc
+            const int K,                 // M from csr is K in csc
+            __global const T *rhs,
+            const KParam rinfo,
+            const T alpha,
+            const T beta)
+{
+    int lid = get_local_id(0);
+    int loff = lid * ROWS_PER_GROUP;
+    __local T s_outvals[THREADS * ROWS_PER_GROUP];
+
+    // Get the row offset for the current group in the uncompressed matrix
+    int rowOff = get_group_id(0) * ROWS_PER_GROUP;
+
+    for (int i = 0; i < ROWS_PER_GROUP; i++) {
+        s_outvals[loff + i] = 0;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int colId = lid; colId < K; colId += THREADS) {
+
+        int rowStart = colidx[colId];
+        int rowEnd   = colidx[colId + 1];
+        int nonZeroCount = rowEnd - rowStart;
+
+        // Find the location of the next non zero element after rowOff
+        int rowPos   = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
+        T rhsval = rhs[colId];
+
+        for (int id =  rowPos + rowStart; id < rowEnd; id++) {
+            int rowId = rowidx[id];
+
+            // This work will be done by next few blocks
+            if (rowId >= rowOff + ROWS_PER_GROUP) break;
+
+            s_outvals[loff + rowId - rowOff] += CMUL(values[id], rhsval);
+        }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Each thread is adding up values from one row at a time.
+    for (int n = lid; n < ROWS_PER_GROUP; n += THREADS) {
+        if (rowOff + n >= M) break;
+        T outval = 0;
+        // Add up the partial results from all the threads.
+        for (int i = 0; i < THREADS; i++) {
+            outval += s_outvals[i * ROWS_PER_GROUP + n];
+        }
+
+#if USE_ALPHA
+        outval = MUL(alpha, outval);
+#endif
+
+#if USE_BETA
+        output[rowOff + n] = outval + MUL(beta, output[rowOff + n]);
+#else
+        output[rowOff + n] = outval;
+#endif
+    }
+}
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
new file mode 100644
index 0000000000..e578239b3e
--- /dev/null
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -0,0 +1,129 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#pragma once
+#include <kernel_headers/cscmv.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <cache.hpp>
+#include <type_util.hpp>
+#include "scan_dim.hpp"
+#include "reduce.hpp"
+#include "scan_first.hpp"
+#include "config.hpp"
+#include <af/opencl.h>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        template<typename T>
+        void cscmv(Param out,
+                   const Param &values, const Param &colIdx, const Param &rowIdx,
+                   const Param &rhs, const T alpha, const T beta, bool is_conj)
+        {
+            try {
+                bool use_alpha = (alpha != scalar<T>(1.0));
+                bool use_beta = (beta != scalar<T>(0.0));
+
+                int threads = 256;
+                auto dev = getDevice(getActiveDeviceId());
+                // Artificially limiting it to 32768 because some NVIDIA GPUs complain
+                // when we use all the available local memory
+                int  max_local_size= std::min(32768, (int)dev.getInfo<CL_DEVICE_LOCAL_MEM_SIZE>());
+                int rows_per_group = max_local_size / (threads * sizeof(T));
+
+                std::string ref_name =
+                    std::string("cscmv_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(use_alpha) +
+                    std::string("_") +
+                    std::to_string(use_beta) +
+                    std::string("_") +
+                    std::to_string(is_conj) +
+                    std::string("_") +
+                    std::to_string(rows_per_group) +
+                    std::string("_") +
+                    std::to_string(threads);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    options << " -D USE_ALPHA=" << use_alpha;
+                    options << " -D USE_BETA=" << use_beta;
+                    options << " -D IS_CONJ=" << is_conj;
+                    options << " -D THREADS=" << threads;
+                    options << " -D ROWS_PER_GROUP=" << rows_per_group;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    if (std::is_same<T, cfloat>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+
+                    const char *ker_strs[] = {cscmv_cl};
+                    const int   ker_lens[] = {cscmv_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "cscmv_block");
+                } else {
+                    entry = idx->second;
+                }
+
+                auto cscmv_kernel = *entry.ker;
+                auto cscmv_func = KernelFunctor<Buffer,
+                                                Buffer, Buffer, Buffer,
+                                                int, int,
+                                                Buffer, KParam, T, T>(cscmv_kernel);
+
+                NDRange local(threads);
+                int K = colIdx.info.dims[0] - 1;
+                int M = out.info.dims[0];
+                int groups_x = divup(M, rows_per_group);
+                NDRange global(local[0] * groups_x, 1);
+
+                cscmv_func(EnqueueArgs(getQueue(), global, local),
+                           *out.data, *values.data, *colIdx.data, *rowIdx.data,
+                           M, K, *rhs.data, rhs.info, alpha, beta);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error &ex) {
+                CL_TO_AF_ERROR(ex);
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 41ed79257f..f917994bd1 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -10,6 +10,7 @@
 #include <sparse_blas.hpp>
 #include <kernel/csrmv.hpp>
 #include <kernel/csrmm.hpp>
+#include <kernel/cscmv.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -33,10 +34,6 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
-
-    if (optLhs != AF_MAT_NONE) OPENCL_NOT_SUPPORTED();
-
-
     int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
     //int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
     static const int rColDim = 1; //Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
@@ -57,10 +54,19 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
     const Array<int> &rowIdx = lhs.getRowIdx();
     const Array<int> &colIdx = lhs.getColIdx();
 
-    if (N == 1) {
-        kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
+    if (optLhs == AF_MAT_NONE) {
+        if (N == 1) {
+            kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
+        } else {
+            kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
+        }
     } else {
-        kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
+        // CSR transpose is a CSC matrix
+        if (N == 1) {
+            kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta, optLhs == AF_MAT_CTRANS);
+        } else {
+            //kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
+        }
     }
     return out;
 }

From d936cbe4b8560a54d3557376f74c22d6fadab27f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 1 Sep 2016 15:55:58 -0400
Subject: [PATCH 0820/2677] OPENCL: Change cscmv to use registers instead of
 local memory

---
 src/backend/opencl/kernel/cscmv.cl  | 48 +++++++++++++++++++----------
 src/backend/opencl/kernel/cscmv.hpp |  7 ++---
 2 files changed, 33 insertions(+), 22 deletions(-)

diff --git a/src/backend/opencl/kernel/cscmv.cl b/src/backend/opencl/kernel/cscmv.cl
index 96ae556e20..e7128dd901 100644
--- a/src/backend/opencl/kernel/cscmv.cl
+++ b/src/backend/opencl/kernel/cscmv.cl
@@ -37,7 +37,7 @@ T __ccmul(T lhs, T rhs)
 #define CMUL(a, b) (a) * (b)
 #endif
 
-int binary_search(__global int *ptr, int len, int val)
+int binary_search(__global const int *ptr, int len, int val)
 {
     int start = 0;
     int end   = len;
@@ -70,16 +70,16 @@ cscmv_block(__global T *output,
             const T beta)
 {
     int lid = get_local_id(0);
-    int loff = lid * ROWS_PER_GROUP;
-    __local T s_outvals[THREADS * ROWS_PER_GROUP];
 
     // Get the row offset for the current group in the uncompressed matrix
     int rowOff = get_group_id(0) * ROWS_PER_GROUP;
+    int rowLim = min(ROWS_PER_GROUP, M - rowOff);
+    rhs += rinfo.offset;
 
-    for (int i = 0; i < ROWS_PER_GROUP; i++) {
-        s_outvals[loff + i] = 0;
+    T l_outvals[ROWS_PER_GROUP];
+    for (int i = 0; i < rowLim; i++) {
+        l_outvals[i] = 0;
     }
-    barrier(CLK_LOCAL_MEM_FENCE);
 
     for (int colId = lid; colId < K; colId += THREADS) {
 
@@ -97,29 +97,43 @@ cscmv_block(__global T *output,
             // This work will be done by next few blocks
             if (rowId >= rowOff + ROWS_PER_GROUP) break;
 
-            s_outvals[loff + rowId - rowOff] += CMUL(values[id], rhsval);
+            l_outvals[rowId - rowOff] += CMUL(values[id], rhsval);
         }
     }
 
-    barrier(CLK_LOCAL_MEM_FENCE);
+    // s_outvals is used for reduction
+    __local T s_outvals[THREADS];
+
+    // s_output is used to store the final output into local memory
+    __local T s_output[ROWS_PER_GROUP];
 
-    // Each thread is adding up values from one row at a time.
-    for (int n = lid; n < ROWS_PER_GROUP; n += THREADS) {
-        if (rowOff + n >= M) break;
-        T outval = 0;
-        // Add up the partial results from all the threads.
-        for (int i = 0; i < THREADS; i++) {
-            outval += s_outvals[i * ROWS_PER_GROUP + n];
+    for (int i = 0; i < rowLim; i++) {
+        s_outvals[lid] = l_outvals[i];
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int n = THREADS / 2; n > 0; n /= 2) {
+            if (lid < n) s_outvals[lid] += s_outvals[lid + n];
+            barrier(CLK_LOCAL_MEM_FENCE);
         }
 
+        if (lid == 0) {
+            s_output[i] = s_outvals[0];
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // s_output is used to write global output in coalesced manner
+    for (int i = lid; i < ROWS_PER_GROUP; i += THREADS) {
+        T outval = s_output[i];
+
 #if USE_ALPHA
         outval = MUL(alpha, outval);
 #endif
 
 #if USE_BETA
-        output[rowOff + n] = outval + MUL(beta, output[rowOff + n]);
+        output[rowOff + i] = outval + MUL(beta, output[j * M + rowOff + i]);
 #else
-        output[rowOff + n] = outval;
+        output[rowOff + i] = outval;
 #endif
     }
 }
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index e578239b3e..e121b387fb 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -48,11 +48,8 @@ namespace opencl
                 bool use_beta = (beta != scalar<T>(0.0));
 
                 int threads = 256;
-                auto dev = getDevice(getActiveDeviceId());
-                // Artificially limiting it to 32768 because some NVIDIA GPUs complain
-                // when we use all the available local memory
-                int  max_local_size= std::min(32768, (int)dev.getInfo<CL_DEVICE_LOCAL_MEM_SIZE>());
-                int rows_per_group = max_local_size / (threads * sizeof(T));
+                //TODO: rows_per_group limited by register pressure. Find better way to handle this.
+                int rows_per_group = 64;
 
                 std::string ref_name =
                     std::string("cscmv_") +

From f374fe549f62eb51e889ef39294449eeda0653f1 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 1 Sep 2016 15:56:33 -0400
Subject: [PATCH 0821/2677] OPENCL: Adding support for cscmm_nn

---
 src/backend/opencl/kernel/cscmm.cl  | 146 ++++++++++++++++++++++++++++
 src/backend/opencl/kernel/cscmm.hpp | 133 +++++++++++++++++++++++++
 src/backend/opencl/sparse_blas.cpp  |   4 +-
 3 files changed, 282 insertions(+), 1 deletion(-)
 create mode 100644 src/backend/opencl/kernel/cscmm.cl
 create mode 100644 src/backend/opencl/kernel/cscmm.hpp

diff --git a/src/backend/opencl/kernel/cscmm.cl b/src/backend/opencl/kernel/cscmm.cl
new file mode 100644
index 0000000000..f714a51340
--- /dev/null
+++ b/src/backend/opencl/kernel/cscmm.cl
@@ -0,0 +1,146 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if IS_CPLX
+T __cmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x - lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y + lhs.y * rhs.x;
+    return out;
+}
+
+T __ccmul(T lhs, T rhs)
+{
+    T out;
+    out.x = lhs.x * rhs.x + lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y - lhs.y * rhs.x;
+    return out;
+}
+
+#define MUL(a, b) __cmul(a, b)
+
+#if IS_CONJ
+#define CMUL(a, b) __ccmul(a, b)
+#else
+#define CMUL(a, b) __cmul(a, b)
+#endif
+
+#else
+#define MUL(a, b) (a) * (b)
+#define CMUL(a, b) (a) * (b)
+#endif
+
+int binary_search(__global const int *ptr, int len, int val)
+{
+    int start = 0;
+    int end   = len;
+    while (end > start) {
+        int mid = start + (end - start) / 2;
+        if (val < ptr[mid]) {
+            end = mid;
+        } else if (val > ptr[mid]) {
+            start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    return start;
+}
+
+// Each thread performs Matrix Vector multiplications for ROWS_PER_GROUP rows and (K / THREAD) columns.
+// This generates a local output buffer of size ROWS_PER_THREAD for each thread.
+// The outputs from each thread are added up to generate the final result.
+__kernel void
+cscmm_nn(__global T *output,
+         __global const T *values,
+         __global const int *colidx,  // rowidx from csr is colidx in csc
+         __global const int *rowidx,  // colidx from csr is rowidx in csc
+         const int M,                 // K from csr is M in csc
+         const int K,                 // M from csr is K in csc
+         const int N,                 // N is number of columns in dense matrix
+         __global const T *rhs,
+         const KParam rinfo,
+         const T alpha,
+         const T beta)
+{
+    int lid = get_local_id(0);
+
+    // Get the row offset for the current group in the uncompressed matrix
+    int rowOff = get_group_id(0) * ROWS_PER_GROUP;
+    int colOff = get_group_id(1) * COLS_PER_GROUP;
+
+    // Ensure you are not going out of bounds
+    int rowLim = min(ROWS_PER_GROUP, M - rowOff);
+    int colLim = min(COLS_PER_GROUP, N - colOff);
+
+    rhs += colOff * rinfo.strides[1] + rinfo.offset;
+    output += colOff * M;
+
+    T l_outvals[COLS_PER_GROUP][ROWS_PER_GROUP];
+    for (int j = 0; j < colLim; j++) {
+        for (int i = 0; i < rowLim; i++) {
+            l_outvals[j][i] = 0;
+        }
+    }
+
+    for (int colId = lid; colId < K; colId += THREADS) {
+
+        int rowStart = colidx[colId];
+        int rowEnd   = colidx[colId + 1];
+        int nonZeroCount = rowEnd - rowStart;
+
+        // Find the location of the next non zero element after rowOff
+        int rowPos   = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
+
+        T rhsvals[COLS_PER_GROUP];
+        for (int j = 0; j < colLim; j++) {
+            rhsvals[j] = rhs[colId + j * rinfo.strides[1]];
+        }
+
+        for (int id =  rowPos + rowStart; id < rowEnd; id++) {
+            int rowId = rowidx[id];
+
+            // This work will be done by next few blocks
+            if (rowId >= rowOff + ROWS_PER_GROUP) break;
+
+            T lhsval = values[id];
+            for (int j = 0; j < colLim; j++) {
+                l_outvals[j][rowId - rowOff] += CMUL(lhsval, rhsvals[j]);
+            }
+        }
+    }
+
+    __local T s_outvals[THREADS];
+    for (int j = 0; j < colLim; j++) {
+        for (int i = 0; i < rowLim; i++) {
+            s_outvals[lid] = l_outvals[j][i];
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            for (int n = THREADS / 2; n > 0; n /= 2) {
+                if (lid < n) s_outvals[lid] += s_outvals[lid + n];
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+
+            if (lid == 0) {
+                T outval = s_outvals[0];
+
+#if USE_ALPHA
+                outval = MUL(alpha, outval);
+#endif
+
+#if USE_BETA
+                output[j * M + rowOff + i] = outval + MUL(beta, output[j * M + rowOff + i]);
+#else
+                output[j * M + rowOff + i] = outval;
+#endif
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
new file mode 100644
index 0000000000..f480c65fc6
--- /dev/null
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -0,0 +1,133 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#pragma once
+#include <kernel_headers/cscmm.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <cache.hpp>
+#include <type_util.hpp>
+#include "scan_dim.hpp"
+#include "reduce.hpp"
+#include "scan_first.hpp"
+#include "config.hpp"
+#include <af/opencl.h>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        template<typename T>
+        void cscmm_nn(Param out,
+                      const Param &values, const Param &colIdx, const Param &rowIdx,
+                      const Param &rhs, const T alpha, const T beta, bool is_conj)
+        {
+            try {
+                bool use_alpha = (alpha != scalar<T>(1.0));
+                bool use_beta = (beta != scalar<T>(0.0));
+
+                int threads = 256;
+                // TODO: Find a better way to tune these parameters
+                int rows_per_group = 8;
+                int cols_per_group = 8;
+
+                std::string ref_name =
+                    std::string("cscmm_nn_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(use_alpha) +
+                    std::string("_") +
+                    std::to_string(use_beta) +
+                    std::string("_") +
+                    std::to_string(is_conj) +
+                    std::string("_") +
+                    std::to_string(rows_per_group) +
+                    std::string("_") +
+                    std::to_string(cols_per_group) +
+                    std::string("_") +
+                    std::to_string(threads);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    options << " -D USE_ALPHA=" << use_alpha;
+                    options << " -D USE_BETA=" << use_beta;
+                    options << " -D IS_CONJ=" << is_conj;
+                    options << " -D THREADS=" << threads;
+                    options << " -D ROWS_PER_GROUP=" << rows_per_group;
+                    options << " -D COLS_PER_GROUP=" << cols_per_group;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    if (std::is_same<T, cfloat>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+
+                    const char *ker_strs[] = {cscmm_cl};
+                    const int   ker_lens[] = {cscmm_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
+                } else {
+                    entry = idx->second;
+                }
+
+                auto cscmm_kernel = *entry.ker;
+                auto cscmm_func = KernelFunctor<Buffer,
+                                                Buffer, Buffer, Buffer,
+                                                int, int, int,
+                                                Buffer, KParam, T, T>(cscmm_kernel);
+
+                NDRange local(threads, 1);
+                int M = out.info.dims[0];
+                int N = out.info.dims[1];
+                int K = colIdx.info.dims[0] - 1;
+
+                int groups_x = divup(M, rows_per_group);
+                int groups_y = divup(N, cols_per_group);
+                NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+                cscmm_func(EnqueueArgs(getQueue(), global, local),
+                           *out.data, *values.data, *colIdx.data, *rowIdx.data,
+                           M, K, N, *rhs.data, rhs.info, alpha, beta);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error &ex) {
+                CL_TO_AF_ERROR(ex);
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index f917994bd1..7ace15fe86 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -8,9 +8,11 @@
  ********************************************************/
 
 #include <sparse_blas.hpp>
+
 #include <kernel/csrmv.hpp>
 #include <kernel/csrmm.hpp>
 #include <kernel/cscmv.hpp>
+#include <kernel/cscmm.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -65,7 +67,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
         if (N == 1) {
             kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta, optLhs == AF_MAT_CTRANS);
         } else {
-            //kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
+            kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha, beta, optLhs == AF_MAT_CTRANS);
         }
     }
     return out;

From e3b2aabcf8ed1c1cddc3e0ef561ba1560e01a2bd Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 1 Sep 2016 16:06:32 -0400
Subject: [PATCH 0822/2677] OPENCL: Fixing csrmm_nt to cache using proper tag

---
 src/backend/opencl/kernel/csrmm.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index ac6e801b41..73c37d3845 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -52,7 +52,7 @@ namespace opencl
                 bool use_greedy = false;
 
                 std::string ref_name =
-                    std::string("csrmm_") +
+                    std::string("csrmm_nt_") +
                     std::string(dtype_traits<T>::getName()) +
                     std::string("_") +
                     std::to_string(use_alpha) +

From ad4fa97b5bcfff074487f13ba7832195544707d6 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 1 Sep 2016 16:07:04 -0400
Subject: [PATCH 0823/2677] OPENCL: Adding proper comments for cscmv and cscmm

---
 src/backend/opencl/kernel/cscmm.cl | 17 ++++++++++++++---
 src/backend/opencl/kernel/cscmv.cl | 10 ++++++++--
 2 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/kernel/cscmm.cl b/src/backend/opencl/kernel/cscmm.cl
index f714a51340..6e72414c27 100644
--- a/src/backend/opencl/kernel/cscmm.cl
+++ b/src/backend/opencl/kernel/cscmm.cl
@@ -54,8 +54,8 @@ int binary_search(__global const int *ptr, int len, int val)
     return start;
 }
 
-// Each thread performs Matrix Vector multiplications for ROWS_PER_GROUP rows and (K / THREAD) columns.
-// This generates a local output buffer of size ROWS_PER_THREAD for each thread.
+// Each group computes an output of size ROWS_PER_GROUP x COLS_PER_GROUP
+// Each thread in a group maintains the partial outputs of size ROWS_PER_GROUP x COLS_PER_GROUP
 // The outputs from each thread are added up to generate the final result.
 __kernel void
 cscmm_nn(__global T *output,
@@ -83,6 +83,7 @@ cscmm_nn(__global T *output,
     rhs += colOff * rinfo.strides[1] + rinfo.offset;
     output += colOff * M;
 
+    // Initialize partial output to 0
     T l_outvals[COLS_PER_GROUP][ROWS_PER_GROUP];
     for (int j = 0; j < colLim; j++) {
         for (int i = 0; i < rowLim; i++) {
@@ -90,6 +91,7 @@ cscmm_nn(__global T *output,
         }
     }
 
+    // Dot requires you to traverse the entire inner dimension
     for (int colId = lid; colId < K; colId += THREADS) {
 
         int rowStart = colidx[colId];
@@ -99,17 +101,20 @@ cscmm_nn(__global T *output,
         // Find the location of the next non zero element after rowOff
         int rowPos   = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
 
+        // Read the rhs values from all the columns as they can be reused for all rows
         T rhsvals[COLS_PER_GROUP];
         for (int j = 0; j < colLim; j++) {
             rhsvals[j] = rhs[colId + j * rinfo.strides[1]];
         }
 
+        // Traversing through nonzero elements in the current chunk
         for (int id =  rowPos + rowStart; id < rowEnd; id++) {
             int rowId = rowidx[id];
 
-            // This work will be done by next few blocks
+            // Exit if going past current chunk
             if (rowId >= rowOff + ROWS_PER_GROUP) break;
 
+            // Read the row value and multiply with all columns
             T lhsval = values[id];
             for (int j = 0; j < colLim; j++) {
                 l_outvals[j][rowId - rowOff] += CMUL(lhsval, rhsvals[j]);
@@ -118,16 +123,22 @@ cscmm_nn(__global T *output,
     }
 
     __local T s_outvals[THREADS];
+
+    // For each row and col of output, copy registers to local memory, add results, write to output.
     for (int j = 0; j < colLim; j++) {
         for (int i = 0; i < rowLim; i++) {
+
+            // Copying to local memory
             s_outvals[lid] = l_outvals[j][i];
             barrier(CLK_LOCAL_MEM_FENCE);
 
+            // Adding the results through reduction
             for (int n = THREADS / 2; n > 0; n /= 2) {
                 if (lid < n) s_outvals[lid] += s_outvals[lid + n];
                 barrier(CLK_LOCAL_MEM_FENCE);
             }
 
+            // Writing to output
             if (lid == 0) {
                 T outval = s_outvals[0];
 
diff --git a/src/backend/opencl/kernel/cscmv.cl b/src/backend/opencl/kernel/cscmv.cl
index e7128dd901..b60956d9f5 100644
--- a/src/backend/opencl/kernel/cscmv.cl
+++ b/src/backend/opencl/kernel/cscmv.cl
@@ -91,10 +91,11 @@ cscmv_block(__global T *output,
         int rowPos   = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
         T rhsval = rhs[colId];
 
+        // Traversing through nonzero elements in the current chunk
         for (int id =  rowPos + rowStart; id < rowEnd; id++) {
             int rowId = rowidx[id];
 
-            // This work will be done by next few blocks
+            // Exit if moving past current chunk
             if (rowId >= rowOff + ROWS_PER_GROUP) break;
 
             l_outvals[rowId - rowOff] += CMUL(values[id], rhsval);
@@ -107,22 +108,27 @@ cscmv_block(__global T *output,
     // s_output is used to store the final output into local memory
     __local T s_output[ROWS_PER_GROUP];
 
+    // For each row of output, copy registers to local memory, add results, write to output.
     for (int i = 0; i < rowLim; i++) {
+
+        // Copying to local memory
         s_outvals[lid] = l_outvals[i];
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        // Adding the results through reduction
         for (int n = THREADS / 2; n > 0; n /= 2) {
             if (lid < n) s_outvals[lid] += s_outvals[lid + n];
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
+        // Store to another local buffer so it can be written in a coalesced manner later
         if (lid == 0) {
             s_output[i] = s_outvals[0];
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    // s_output is used to write global output in coalesced manner
+    // For each row in output, write output in coalesced manner
     for (int i = lid; i < ROWS_PER_GROUP; i += THREADS) {
         T outval = s_output[i];
 

From 2590894a4ba48b6bfc195e6436e6fb3f9fc891c4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 1 Sep 2016 16:07:23 -0400
Subject: [PATCH 0824/2677] TEST: Change the error checks for sparse blas

---
 test/sparse.cpp | 91 ++++++++++++++++++++++++++-----------------------
 1 file changed, 48 insertions(+), 43 deletions(-)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index 1f09a18412..12b90b7709 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -63,6 +63,11 @@ af::array makeSparse<cdouble>(af::array A, int factor)
     return A;
 }
 
+double calc_norm(af::array lhs, af::array rhs)
+{
+    return af::max<double>(af::abs(lhs - rhs) / (af::abs(lhs) + af::abs(rhs) + 1E-5));
+}
+
 template<typename T>
 void sparseTester(const int m, const int n, const int k, int factor, double eps)
 {
@@ -90,8 +95,8 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     af::array sRes1 = matmul(sA, B);
 
     // Verify Results
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes1 - sRes1))) / (m * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes1 - sRes1))) / (m * k), eps);
+    ASSERT_NEAR(0, calc_norm(real(dRes1), real(sRes1)), eps);
+    ASSERT_NEAR(0, calc_norm(imag(dRes1), imag(sRes1)), eps);
 }
 
 template<typename T>
@@ -123,51 +128,51 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
     af::array sRes3 = matmul(sA, B, AF_MAT_CTRANS, AF_MAT_NONE);
 
     // Verify Results
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes2 - sRes2))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes2 - sRes2))) / (n * k), eps);
+    ASSERT_NEAR(0, calc_norm(real(dRes2), real(sRes2)), eps);
+    ASSERT_NEAR(0, calc_norm(imag(dRes2), imag(sRes2)), eps);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(dRes3 - sRes3))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(dRes3 - sRes3))) / (n * k), eps);
+    ASSERT_NEAR(0, calc_norm(real(dRes3), real(sRes3)), eps);
+    ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
 }
 
-#define SPARSE_TESTS(T, eps)                                    \
-    TEST(SPARSE, T##Square)                                     \
-    {                                                           \
-        sparseTester<T>(1000, 1000, 100, 5, eps);               \
-    }                                                           \
-    TEST(SPARSE, T##RectMultiple)                               \
-    {                                                           \
-        sparseTester<T>(2048, 1024, 512, 3, eps);               \
-    }                                                           \
-    TEST(SPARSE, T##RectDense)                                  \
-    {                                                           \
-        sparseTester<T>(500, 1000, 250, 1, eps);                \
-    }                                                           \
-    TEST(SPARSE, T##MatVec)                                     \
-    {                                                           \
-        sparseTester<T>(625, 1331, 1, 2, eps);                  \
-    }                                                           \
-    TEST(SPARSE_TRANSPOSE, T##Square)                           \
-    {                                                           \
-        sparseTransposeTester<T>(1000, 1000, 100, 5, eps);      \
-    }                                                           \
-    TEST(SPARSE_TRANSPOSE, T##RectMultiple)                     \
-    {                                                           \
-        sparseTransposeTester<T>(2048, 1024, 512, 3, eps);      \
-    }                                                           \
-    TEST(SPARSE_TRANSPOSE, T##RectDense)                        \
-    {                                                           \
-        sparseTransposeTester<T>(453, 751, 397, 1, eps);        \
-    }                                                           \
-    TEST(SPARSE_TRANSPOSE, T##MatVec)                           \
-    {                                                           \
-        sparseTransposeTester<T>(625, 1331, 1, 2, eps);         \
-    }                                                           \
-
-
-SPARSE_TESTS(float, 0.01)
+#define SPARSE_TESTS(T, eps)                                \
+    TEST(SPARSE, T##Square)                                 \
+    {                                                       \
+        sparseTester<T>(1000, 1000, 100, 5, eps);           \
+    }                                                       \
+    TEST(SPARSE, T##RectMultiple)                           \
+    {                                                       \
+        sparseTester<T>(2048, 1024, 512, 3, eps);           \
+    }                                                       \
+    TEST(SPARSE, T##RectDense)                              \
+    {                                                       \
+        sparseTester<T>(500, 1000, 250, 1, eps);            \
+    }                                                       \
+    TEST(SPARSE, T##MatVec)                                 \
+    {                                                       \
+        sparseTester<T>(625, 1331, 1, 2, eps);              \
+    }                                                       \
+    TEST(SPARSE_TRANSPOSE, T##MatVec)                       \
+    {                                                       \
+        sparseTransposeTester<T>(625, 1331, 1, 2, eps);     \
+    }                                                       \
+    TEST(SPARSE_TRANSPOSE, T##Square)                       \
+    {                                                       \
+        sparseTransposeTester<T>(1000, 1000, 100, 5, eps);  \
+    }                                                       \
+    TEST(SPARSE_TRANSPOSE, T##RectMultiple)                 \
+    {                                                       \
+        sparseTransposeTester<T>(2048, 1024, 512, 3, eps);  \
+    }                                                       \
+    TEST(SPARSE_TRANSPOSE, T##RectDense)                    \
+    {                                                       \
+        sparseTransposeTester<T>(453, 751, 397, 1, eps);    \
+    }                                                       \
+
+
+SPARSE_TESTS(float, 1E-3)
 SPARSE_TESTS(double, 1E-5)
-SPARSE_TESTS(cfloat, 0.01)
+SPARSE_TESTS(cfloat, 1E-3)
 SPARSE_TESTS(cdouble, 1E-5)
 
 #undef SPARSE_TESTS

From d69036601f29518a52949396b5d6be9cda07c3b1 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 16:32:26 -0400
Subject: [PATCH 0825/2677] Add forge::Chart functionality to internal graphics
 code

* A vector of chart is created for each window
* Each time a chart object (histogram, plot, surface) gets called, the
  appropriate chart is fetch and the object is added to chart
* Window then calls the draw on the chart
* A map of all charts to windows is maintained for lookup
---
 examples/graphics/surface.cpp |   2 -
 src/api/c/graphics_common.cpp | 118 ++++++++++++++++++++++++++++++----
 src/api/c/graphics_common.hpp |  32 +++++++--
 src/api/c/hist.cpp            |  56 +++++++++-------
 src/api/c/plot.cpp            |  55 ++++++++++------
 src/api/c/plot3.cpp           |  54 ++++++++++------
 src/api/c/surface.cpp         |  50 ++++++++------
 src/api/c/window.cpp          |  13 ++++
 8 files changed, 281 insertions(+), 99 deletions(-)

diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index 0d2648a850..b48cb8fa47 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -27,8 +27,6 @@ int main(int argc, char *argv[])
         const array x = iota(dim4(N, 1), dim4(1, N)) / M - 1;
         const array y = iota(dim4(1, N), dim4(N, 1)) / M - 1;
 
-        std::cout << x.dims()  << y.dims() << std::endl;
-
         static float t=0;
         while(!myWindow.close()) {
             t+=0.07;
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index b2795d6379..e493e1ddc4 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -43,13 +43,13 @@ forge::MarkerType getFGMarker(const af_marker_type af_marker) {
 #define INSTANTIATE_GET_FG_TYPE(T, ForgeEnum)\
     template<> forge::dtype getGLType<T>() { return ForgeEnum; }
 
-INSTANTIATE_GET_FG_TYPE(float, forge::f32);
-INSTANTIATE_GET_FG_TYPE(int  , forge::s32);
-INSTANTIATE_GET_FG_TYPE(unsigned, forge::u32);
-INSTANTIATE_GET_FG_TYPE(char, forge::s8);
-INSTANTIATE_GET_FG_TYPE(unsigned char, forge::u8);
-INSTANTIATE_GET_FG_TYPE(unsigned short, forge::u16);
-INSTANTIATE_GET_FG_TYPE(short, forge::s16);
+INSTANTIATE_GET_FG_TYPE(float           , forge::f32);
+INSTANTIATE_GET_FG_TYPE(int             , forge::s32);
+INSTANTIATE_GET_FG_TYPE(unsigned        , forge::u32);
+INSTANTIATE_GET_FG_TYPE(char            , forge::s8);
+INSTANTIATE_GET_FG_TYPE(unsigned char   , forge::u8);
+INSTANTIATE_GET_FG_TYPE(unsigned short  , forge::u16);
+INSTANTIATE_GET_FG_TYPE(short           , forge::s16);
 
 gl::GLenum glErrorSkip(const char *msg, const char* file, int line)
 {
@@ -162,6 +162,10 @@ forge::Window* ForgeManager::getMainWindow(const bool dontCreate)
         if (flag && !dontCreate) {
             wnd = new forge::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
             makeContextCurrent(wnd);
+
+            ForgeManager& fgMngr = ForgeManager::getInstance();
+            fgMngr.setWindowChartGrid(wnd, 1, 1);
+
             CheckGL("End ForgeManager::getMainWindow");
             flag = false;
         };
@@ -169,6 +173,58 @@ forge::Window* ForgeManager::getMainWindow(const bool dontCreate)
     return wnd;
 }
 
+void ForgeManager::setWindowChartGrid(const forge::Window* window,
+                                      const int r, const int c)
+{
+    ChartMapIter iter = mChartMap.find(window);
+
+        if(iter != mChartMap.end()) {
+
+    }
+
+    if(iter != mChartMap.end()) {
+        // ChartVec found. Clear it.
+        // TODO: Should we clear this even if r = old_r and c = old_c?
+        for(int i = 0; i < (int)(iter->second).size(); i++)
+            if((iter->second)[i] != NULL) delete (iter->second)[i];
+        (iter->second).clear();
+    }
+
+    if(r == 0 || c == 0) {
+        mChartMap.erase(window);
+    } else {
+        mChartMap[window] = std::vector<forge::Chart*>(r * c);
+    }
+}
+
+forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, const int c,
+                                     const forge::ChartType ctype)
+{
+    forge::Chart* chart = NULL;
+    ChartMapIter iter = mChartMap.find(window);
+
+    if(iter != mChartMap.end()) {
+        int gRows = window->gridRows();
+        int gCols = window->gridCols();
+
+        if(c >= gCols || r >= gRows)
+            AF_ERROR("Grid points are out of bounds", AF_ERR_TYPE);
+
+        chart = (iter->second)[c * gRows + r];
+
+        if(chart == NULL) {
+            // Chart has not been created
+            chart = new forge::Chart(ctype);
+            (iter->second)[c * gRows + r] = chart;
+        }
+    } else {
+        // The chart map for this was never created
+        // Which should never happen
+    }
+
+    return chart;
+}
+
 forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, forge::dtype type)
 {
     /* w, h needs to fall in the range of [0, 2^16]
@@ -190,7 +246,34 @@ forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, fo
     return mImgMap[key];
 }
 
-forge::Plot* ForgeManager::getPlot(int nPoints, forge::dtype dtype, forge::ChartType ctype, forge::PlotType ptype, forge::MarkerType mtype)
+forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
+                                     forge::ChannelFormat mode, forge::dtype type)
+{
+    /* w, h needs to fall in the range of [0, 2^16]
+     * for the ForgeManager to correctly retrieve
+     * the necessary Forge Image object. So, this implementation
+     * is a limitation on how big of an image can be rendered
+     * using arrayfire graphics funtionality */
+    assert(w <= 2ll<<16);
+    assert(h <= 2ll<<16);
+    long long key = ((w & _16BIT) << 16) | (h & _16BIT);
+    key = (((key << 16) | mode) << 16) | type;
+
+    ImgMapIter iter = mImgMap.find(key);
+    if (iter==mImgMap.end()) {
+        if(chart->getChartType() != FG_CHART_2D)
+            AF_ERROR("Image can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
+
+        forge::Image* temp = new forge::Image(w, h, mode, type);
+        mImgMap[key] = temp;
+        chart->add(*mImgMap[key]);
+    }
+
+    return mImgMap[key];
+}
+
+forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtype dtype,
+                                   forge::PlotType ptype, forge::MarkerType mtype)
 {
     /* nPoints needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -203,14 +286,15 @@ forge::Plot* ForgeManager::getPlot(int nPoints, forge::dtype dtype, forge::Chart
 
     PltMapIter iter = mPltMap.find(key);
     if (iter==mPltMap.end()) {
-        forge::Plot* temp = new forge::Plot(nPoints, dtype, ctype, ptype, mtype);
+        forge::Plot* temp = new forge::Plot(nPoints, dtype, chart->getChartType(), ptype, mtype);
         mPltMap[key] = temp;
+        chart->add(*mPltMap[key]);
     }
 
     return mPltMap[key];
 }
 
-forge::Histogram* ForgeManager::getHistogram(int nBins, forge::dtype type)
+forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, forge::dtype type)
 {
     /* nBins needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -222,14 +306,18 @@ forge::Histogram* ForgeManager::getHistogram(int nBins, forge::dtype type)
 
     HstMapIter iter = mHstMap.find(key);
     if (iter==mHstMap.end()) {
+        if(chart->getChartType() != FG_CHART_2D)
+            AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
+
         forge::Histogram* temp = new forge::Histogram(nBins, type);
         mHstMap[key] = temp;
+        chart->add(*mHstMap[key]);
     }
 
     return mHstMap[key];
 }
 
-forge::Surface* ForgeManager::getSurface(int nX, int nY, forge::dtype type)
+forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, forge::dtype type)
 {
     /* nX * nY needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -241,8 +329,12 @@ forge::Surface* ForgeManager::getSurface(int nX, int nY, forge::dtype type)
 
     SfcMapIter iter = mSfcMap.find(key);
     if (iter==mSfcMap.end()) {
+        if(chart->getChartType() != FG_CHART_3D)
+            AF_ERROR("Surface can only be added to chart of type FG_CHART_3D", AF_ERR_TYPE);
+
         forge::Surface* temp = new forge::Surface(nX, nY, type);
         mSfcMap[key] = temp;
+        chart->add(*mSfcMap[key]);
     }
 
     return mSfcMap[key];
@@ -261,6 +353,10 @@ void ForgeManager::destroyResources()
     for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
         delete (iter->second);
 
+    for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++)
+        for(int i = 0; i < (int)(iter->second).size(); i++)
+            if((iter->second)[i] != NULL) delete (iter->second)[i];
+
     delete getFont(true);
     delete getMainWindow(true);
 }
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 8cab3a9c40..b19834b634 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -15,6 +15,7 @@
 #include <forge.h>
 #include <glbinding/gl/gl.h>
 #include <glbinding/Binding.h>
+#include <vector>
 #include <map>
 
 // default to f32(float) type
@@ -57,6 +58,12 @@ typedef PlotMap_t::iterator PltMapIter;
 typedef HistogramMap_t::iterator HstMapIter;
 typedef SurfaceMap_t::iterator SfcMapIter;
 
+typedef std::vector<forge::Chart*> ChartVec_t;
+typedef std::map<const forge::Window*, ChartVec_t> ChartMap_t;
+typedef ChartVec_t::iterator ChartVecIter;
+typedef ChartMap_t::iterator ChartMapIter;
+
+
 /**
  * ForgeManager class follows a single pattern. Any user of this class, has
  * to call ForgeManager::getInstance inorder to use Forge resources for rendering.
@@ -76,16 +83,29 @@ class ForgeManager
         HistogramMap_t  mHstMap;
         SurfaceMap_t    mSfcMap;
 
+        ChartMap_t      mChartMap;
+
     public:
         static ForgeManager& getInstance();
         ~ForgeManager();
 
-        forge::Font* getFont(const bool dontCreate=false);
-        forge::Window* getMainWindow(const bool dontCreate=false);
-        forge::Image* getImage(int w, int h, forge::ChannelFormat mode, forge::dtype type);
-        forge::Plot* getPlot(int nPoints, forge::dtype dtype, forge::ChartType ctype, forge::PlotType ptype, forge::MarkerType mtype);
-        forge::Histogram* getHistogram(int nBins, forge::dtype type);
-        forge::Surface* getSurface(int nX, int nY, forge::dtype type);
+        forge::Font*    getFont(const bool dontCreate=false);
+        forge::Window*  getMainWindow(const bool dontCreate=false);
+
+        void            setWindowChartGrid(const forge::Window* window,
+                                           const int r, const int c);
+
+        forge::Chart*   getChart(const forge::Window* window, const int r, const int c,
+                                 const forge::ChartType ctype);
+
+        forge::Image* getImage          (int w, int h, forge::ChannelFormat mode,
+                                         forge::dtype type);
+        forge::Image* getImage          (forge::Chart* chart, int w, int h,
+                                         forge::ChannelFormat mode, forge::dtype type);
+        forge::Plot * getPlot           (forge::Chart* chart, int nPoints, forge::dtype dtype,
+                                         forge::PlotType ptype, forge::MarkerType mtype);
+        forge::Histogram* getHistogram  (forge::Chart* chart, int nBins, forge::dtype type);
+        forge::Surface* getSurface      (forge::Chart* chart, int nX, int nY, forge::dtype type);
 
     protected:
         ForgeManager() {}
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 9126a28563..3e86b78623 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -24,27 +24,39 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Histogram* setup_histogram(const af_array in, const double minval, const double maxval)
+forge::Chart* setup_histogram(const forge::Window* const window,
+                              const af_array in, const double minval, const double maxval,
+                              const af_cell* const props)
 {
     Array<T> histogramInput = getArray<T>(in);
     dim_t nBins = histogramInput.elements();
 
     T freqMax = detail::reduce_all<af_max_t, T, T>(histogramInput);
 
-    /* retrieve Forge Histogram with nBins and array type */
+    // Retrieve Forge Histogram with nBins and array type
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    forge::Histogram* hist = fgMngr.getHistogram(nBins, getGLType<T>());
-    /* set histogram bar colors to orange */
+
+    // Get the chart for the current grid position (if any)
+    forge::Chart* chart = NULL;
+    if (props->col>-1 && props->row>-1)
+        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+    else
+        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+
+    // Create a histogram for the chart
+    forge::Histogram* hist = fgMngr.getHistogram(chart, nBins, getGLType<T>());
+
+    // Set histogram bar colors to orange
     hist->setColor(0.929f, 0.486f, 0.2745f, 1.0f);
-    /* set x axis limits to maximum and minimum values of data
-     * and y axis limits to range [0, nBins]*/
-    // FORGE FIX ME
-    //hist->setAxesLimits(maxval, minval, double(freqMax), 0.0f);
-    //hist->setAxesTitles("Bins", "Frequency");
+
+    // set x axis limits to maximum and minimum values of data
+    // and y axis limits to range [0, nBins]
+    chart->setAxesLimits(minval, maxval, 0.0f, double(freqMax));
+    chart->setAxesTitles("Bins", "Frequency");
 
     copy_histogram<T>(histogramInput, hist);
 
-    return hist;
+    return chart;
 }
 #endif
 
@@ -65,24 +77,24 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         makeContextCurrent(window);
-        forge::Histogram* hist = NULL;
+
+        forge::Chart* chart = NULL;
 
         switch(Xtype) {
-            case f32: hist = setup_histogram<float  >(X, minval, maxval); break;
-            case s32: hist = setup_histogram<int    >(X, minval, maxval); break;
-            case u32: hist = setup_histogram<uint   >(X, minval, maxval); break;
-            case s16: hist = setup_histogram<short  >(X, minval, maxval); break;
-            case u16: hist = setup_histogram<ushort >(X, minval, maxval); break;
-            case u8 : hist = setup_histogram<uchar  >(X, minval, maxval); break;
+            case f32: chart = setup_histogram<float  >(window, X, minval, maxval, props); break;
+            case s32: chart = setup_histogram<int    >(window, X, minval, maxval, props); break;
+            case u32: chart = setup_histogram<uint   >(window, X, minval, maxval, props); break;
+            case s16: chart = setup_histogram<short  >(window, X, minval, maxval, props); break;
+            case u16: chart = setup_histogram<ushort >(window, X, minval, maxval, props); break;
+            case u8 : chart = setup_histogram<uchar  >(window, X, minval, maxval, props); break;
             default:  TYPE_ERROR(1, Xtype);
         }
 
-        // FORGE FIX ME
         // Window's draw function requires either image or chart
-        //if (props->col>-1 && props->row>-1)
-        //    //window->draw(props->col, props->row, *hist, props->title);
-        //else
-        //    window->draw(*hist);
+        if (props->col > -1 && props->row > -1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 25e07b3806..7401e713f6 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -27,7 +27,10 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Plot* setup_plot(const af_array X, const af_array Y, forge::PlotType type, forge::MarkerType marker)
+forge::Chart* setup_plot(const forge::Window* const window,
+                         const af_array X, const af_array Y,
+                         const af_cell* const props,
+                         forge::PlotType type, forge::MarkerType marker)
 {
     Array<T> xIn = getArray<T>(X);
     Array<T> yIn = getArray<T>(Y);
@@ -51,19 +54,30 @@ forge::Plot* setup_plot(const af_array X, const af_array Y, forge::PlotType type
     Array<T> P = join(0, xIn, yIn);
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    // FORGE FIX ME FG_CHART_2D
-    forge::Plot* plot = fgMngr.getPlot(elements, getGLType<T>(), FG_CHART_2D, type, marker);
+
+    // Get the chart for the current grid position (if any)
+    forge::Chart* chart = NULL;
+    if (props->col>-1 && props->row>-1)
+        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+    else
+        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+
+    forge::Plot* plot = fgMngr.getPlot(chart, elements, getGLType<T>(), type, marker);
+
     plot->setColor(1.0, 0.0, 0.0, 1.0);
-    // FORGE FIX ME
-    //plot->setAxesLimits(xmax, xmin, ymax, ymin);
-    //plot->setAxesTitles("X Axis", "Y Axis");
+
+    chart->setAxesLimits(xmin, xmax, ymin, ymax);
+
+    chart->setAxesTitles("X Axis", "Y Axis");
 
     copy_plot<T>(P, plot);
 
-    return plot;
+    return chart;
 }
 
-af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_cell* const props, forge::PlotType type=FG_PLOT_LINE, forge::MarkerType marker=FG_MARKER_NONE)
+af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
+                   const af_cell* const props,
+                   forge::PlotType type = FG_PLOT_LINE, forge::MarkerType marker = FG_MARKER_NONE)
 {
     if(wind==0) {
         std::cerr<<"Not a valid window"<<std::endl;
@@ -87,23 +101,24 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         makeContextCurrent(window);
-        forge::Plot* plot = NULL;
+
+        forge::Chart* chart = NULL;
 
         switch(Xtype) {
-            case f32: plot = setup_plot<float  >(X, Y, type, marker); break;
-            case s32: plot = setup_plot<int    >(X, Y, type, marker); break;
-            case u32: plot = setup_plot<uint   >(X, Y, type, marker); break;
-            case s16: plot = setup_plot<short  >(X, Y, type, marker); break;
-            case u16: plot = setup_plot<ushort >(X, Y, type, marker); break;
-            case u8 : plot = setup_plot<uchar  >(X, Y, type, marker); break;
+            case f32: chart = setup_plot<float  >(window, X, Y, props, type, marker); break;
+            case s32: chart = setup_plot<int    >(window, X, Y, props, type, marker); break;
+            case u32: chart = setup_plot<uint   >(window, X, Y, props, type, marker); break;
+            case s16: chart = setup_plot<short  >(window, X, Y, props, type, marker); break;
+            case u16: chart = setup_plot<ushort >(window, X, Y, props, type, marker); break;
+            case u8 : chart = setup_plot<uchar  >(window, X, Y, props, type, marker); break;
             default:  TYPE_ERROR(1, Xtype);
         }
 
-        //FORGE FIX ME
-        //if (props->col>-1 && props->row>-1)
-        //    window->draw(props->col, props->row, *plot, props->title);
-        //else
-        //    window->draw(*plot);
+        // Window's draw function requires either image or chart
+        if (props->col>-1 && props->row>-1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 62b00903a6..1b45c22c8b 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -30,7 +30,9 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Plot* setup_plot3(const af_array P, forge::PlotType ptype, forge::MarkerType mtype)
+forge::Chart* setup_plot3(const forge::Window* const window, const af_array P,
+                          const af_cell* const props,
+                          forge::PlotType ptype, forge::MarkerType mtype)
 {
     Array<T> pIn = getArray<T>(P);
     ArrayInfo Pinfo = getInfo(P);
@@ -55,15 +57,27 @@ forge::Plot* setup_plot3(const af_array P, forge::PlotType ptype, forge::MarkerT
     copyData(min, reduce<af_min_t, T, T>(pIn, 1));
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    forge::Plot* plot3 = fgMngr.getPlot(P_dims.elements()/3, getGLType<T>(), FG_CHART_3D, ptype, mtype);
+
+    // Get the chart for the current grid position (if any)
+    forge::Chart* chart = NULL;
+    if (props->col>-1 && props->row>-1)
+        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+    else
+        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+
+    forge::Plot* plot3 = fgMngr.getPlot(chart, P_dims.elements()/3, getGLType<T>(), ptype, mtype);
+
     plot3->setColor(1.0, 0.0, 0.0, 1.0);
-    // FORGE FIX ME
-    //plot3->setAxesLimits(max[0], min[0],
-    //                     max[1], min[1],
-    //                     max[2], min[2]);
-    //plot3->setAxesTitles("X Axis", "Y Axis", "Z Axis");
+
+    chart->setAxesLimits(min[0], max[0],
+                         min[1], max[1],
+                         min[2], max[2]);
+
+    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
+
     copy_plot<T>(pIn, plot3);
-    return plot3;
+
+    return chart;
 }
 
 af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const props, const forge::PlotType type=FG_PLOT_LINE, const forge::MarkerType marker=FG_MARKER_NONE)
@@ -79,23 +93,23 @@ af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         makeContextCurrent(window);
-        forge::Plot* plot3 = NULL;
+
+        forge::Chart* chart = NULL;
 
         switch(Ptype) {
-            case f32: plot3 = setup_plot3<float >(P, type, marker); break;
-            case s32: plot3 = setup_plot3<int   >(P, type, marker); break;
-            case u32: plot3 = setup_plot3<uint  >(P, type, marker); break;
-            case s16: plot3 = setup_plot3<short >(P, type, marker); break;
-            case u16: plot3 = setup_plot3<ushort>(P, type, marker); break;
-            case u8 : plot3 = setup_plot3<uchar >(P, type, marker); break;
+            case f32: chart = setup_plot3<float >(window, P, props, type, marker); break;
+            case s32: chart = setup_plot3<int   >(window, P, props, type, marker); break;
+            case u32: chart = setup_plot3<uint  >(window, P, props, type, marker); break;
+            case s16: chart = setup_plot3<short >(window, P, props, type, marker); break;
+            case u16: chart = setup_plot3<ushort>(window, P, props, type, marker); break;
+            case u8 : chart = setup_plot3<uchar >(window, P, props, type, marker); break;
             default:  TYPE_ERROR(1, Ptype);
         }
 
-        // FORGE FIX ME
-        //if (props->col>-1 && props->row>-1)
-        //    window->draw(props->col, props->row, *plot3, props->title);
-        //else
-        //    window->draw(*plot3);
+        if (props->col>-1 && props->row>-1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 25c609f805..1efa7766b6 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -28,7 +28,9 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Surface* setup_surface(const af_array xVals, const af_array yVals, const af_array zVals)
+forge::Chart* setup_surface(const forge::Window* const window,
+                            const af_array xVals, const af_array yVals, const af_array zVals,
+                            const af_cell* const props)
 {
     Array<T> xIn = getArray<T>(xVals);
     Array<T> yIn = getArray<T>(yVals);
@@ -74,15 +76,27 @@ forge::Surface* setup_surface(const af_array xVals, const af_array yVals, const
     Array<T> Z = join(0, inputs);
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
-    forge::Surface* surface = fgMngr.getSurface(Z_dims[0], Z_dims[1], getGLType<T>());
+
+    // Get the chart for the current grid position (if any)
+    forge::Chart* chart = NULL;
+    if (props->col>-1 && props->row>-1)
+        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
+    else
+        chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
+
+    forge::Surface* surface = fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
+
     surface->setColor(1.0, 0.0, 0.0, 1.0);
-    // FORGE FIX ME
-    //surface->setAxesLimits(xmax, xmin, ymax, ymin, zmax, zmin);
-    //surface->setAxesTitles("X Axis", "Y Axis", "Z Axis");
+
+    chart->setAxesLimits(xmin, xmax,
+                         ymin, ymax,
+                         zmin, zmax);
+
+    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
     copy_surface<T>(Z, surface);
 
-    return surface;
+    return chart;
 }
 #endif
 
@@ -119,23 +133,23 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         makeContextCurrent(window);
-        forge::Surface* surface = NULL;
+
+        forge::Chart* chart = NULL;
 
         switch(Xtype) {
-            case f32: surface = setup_surface<float  >(xVals, yVals , S); break;
-            case s32: surface = setup_surface<int    >(xVals, yVals , S); break;
-            case u32: surface = setup_surface<uint   >(xVals, yVals , S); break;
-            case s16: surface = setup_surface<short  >(xVals, yVals , S); break;
-            case u16: surface = setup_surface<ushort >(xVals, yVals , S); break;
-            case u8 : surface = setup_surface<uchar  >(xVals, yVals , S); break;
+            case f32: chart = setup_surface<float  >(window, xVals, yVals , S, props); break;
+            case s32: chart = setup_surface<int    >(window, xVals, yVals , S, props); break;
+            case u32: chart = setup_surface<uint   >(window, xVals, yVals , S, props); break;
+            case s16: chart = setup_surface<short  >(window, xVals, yVals , S, props); break;
+            case u16: chart = setup_surface<ushort >(window, xVals, yVals , S, props); break;
+            case u8 : chart = setup_surface<uchar  >(window, xVals, yVals , S, props); break;
             default:  TYPE_ERROR(1, Xtype);
         }
 
-        // FORGE FIX ME
-        //if (props->col>-1 && props->row>-1)
-        //    window->draw(props->col, props->row, *surface, props->title);
-        //else
-        //    window->draw(*surface);
+        if (props->col>-1 && props->row>-1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 2cbfab2c8f..b3f2d3f98e 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -43,6 +43,10 @@ af_err af_create_window(af_window *out, const int width, const int height, const
 
         wnd = new forge::Window(width, height, title, mainWnd);
         wnd->setFont(fgMngr.getFont());
+
+        // Create a chart map
+        fgMngr.setWindowChartGrid(wnd, 1, 1);
+
         *out = reinterpret_cast<af_window>(wnd);
     }
     CATCHALL;
@@ -120,6 +124,10 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
     try {
         forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
         wnd->grid(rows, cols);
+
+        // Recreate a chart map
+        ForgeManager& fgMngr = ForgeManager::getInstance();
+        fgMngr.setWindowChartGrid(wnd, rows, cols);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -198,6 +206,11 @@ af_err af_destroy_window(const af_window wind)
 
     try {
         forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+
+        // Delete chart map
+        ForgeManager& fgMngr = ForgeManager::getInstance();
+        fgMngr.setWindowChartGrid(wnd, 0, 0);
+
         delete wnd;
     }
     CATCHALL;

From bd5713afb2ddae85effc0774b175e84a7f9649f8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 1 Sep 2016 17:30:12 -0400
Subject: [PATCH 0826/2677] OPENCL: Adding csr2dense

- Added necessary tests
---
 src/backend/opencl/kernel/csr2dense.cl | 25 +++++++++++
 src/backend/opencl/kernel/sparse.hpp   | 60 ++++++++++++++++++++++++++
 src/backend/opencl/sparse.cpp          | 12 ++++++
 test/sparse.cpp                        | 22 +++++++++-
 4 files changed, 118 insertions(+), 1 deletion(-)
 create mode 100644 src/backend/opencl/kernel/csr2dense.cl

diff --git a/src/backend/opencl/kernel/csr2dense.cl b/src/backend/opencl/kernel/csr2dense.cl
new file mode 100644
index 0000000000..a23d121cb6
--- /dev/null
+++ b/src/backend/opencl/kernel/csr2dense.cl
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void csr2dense(__global T *output,
+               __global const T *values,
+               __global const int *rowidx,
+               __global const int *colidx,
+               const int M)
+{
+    int lid = get_local_id(0);
+    for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
+        int colStart = rowidx[rowId];
+        int colEnd   = rowidx[rowId + 1];
+        for (int colId = colStart + lid;  colId < colEnd; colId += THREADS) {
+            output[rowId + colidx[colId] * M] = values[colId];
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 46634025f8..36c96a5f0e 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <kernel_headers/coo2dense.hpp>
+#include <kernel_headers/csr2dense.hpp>
 #include <kernel_headers/dense2csr.hpp>
 #include <program.hpp>
 #include <traits.hpp>
@@ -93,6 +94,65 @@ namespace opencl
             }
         }
 
+        template<typename T>
+        void csr2dense(Param output, const Param values, const Param rowIdx, const Param colIdx)
+        {
+            try {
+                const int MAX_GROUPS = 4096;
+                int M = rowIdx.info.dims[0] - 1;
+                //FIXME: This needs to be based non nonzeros per row
+                int threads = 64;
+
+                std::string ref_name =
+                    std::string("csr2dense_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(threads);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    options << " -D THREADS=" << threads;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    const char *ker_strs[] = {csr2dense_cl};
+                    const int   ker_lens[] = {csr2dense_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "csr2dense");
+                } else {
+                    entry = idx->second;
+                }
+
+                NDRange local(threads, 1);
+                int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+                NDRange global(local[0] * groups_x, 1);
+                auto csr2dense_kernel = *entry.ker;
+                auto csr2dense_func = KernelFunctor<Buffer,
+                                                    Buffer, Buffer, Buffer,
+                                                    int> (csr2dense_kernel);
+
+                csr2dense_func(EnqueueArgs(getQueue(), global, local),
+                               *output.data, *values.data, *rowIdx.data, *colIdx.data, M);
+
+                CL_DEBUG_FINISH(getQueue());
+
+            } catch(cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
+
         template<typename T>
         void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense)
         {
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index bb9bb7360b..6110e150f0 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -222,6 +222,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
+#if ENABLE_CLSPARSE
     // Assign to clSparse CSR
     clsparseCsrMatrix clSparseMat;
     CLSPARSE_CHECK(clsparseInitCsrMatrix(&clSparseMat));
@@ -245,6 +246,17 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
         CLSPARSE_CHECK(csr2dense_func<T>()(&clSparseMat, &clDenseMat, getControl()));
     else
         AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+#else
+    const Array<T> &values = in_.getValues();
+    const Array<int> &rowIdx = in_.getRowIdx();
+    const Array<int> &colIdx = in_.getColIdx();
+
+    if(stype == AF_STORAGE_CSR)
+        kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
+    else
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+
+#endif
 
     return dense_;
 }
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 12b90b7709..ecd872680a 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -135,6 +135,23 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
     ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
 }
 
+template<typename T>
+void convertCSR(const int M, const int N, const float ratio)
+{
+    if (noDoubleTests<T>()) return;
+#if 1
+    af::array a = cpu_randu<T>(af::dim4(M, N));
+#else
+    af::array a = af::randu(M, N);
+#endif
+    a = a * (a > ratio);
+
+    af::array s = af::createSparseArray(a, AF_STORAGE_CSR);
+    af::array aa = af::sparseConvertStorage(s, AF_STORAGE_DENSE);
+
+    ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));
+}
+
 #define SPARSE_TESTS(T, eps)                                \
     TEST(SPARSE, T##Square)                                 \
     {                                                       \
@@ -168,7 +185,10 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
     {                                                       \
         sparseTransposeTester<T>(453, 751, 397, 1, eps);    \
     }                                                       \
-
+    TEST(SPARSE, T##ConvertCSR)                             \
+    {                                                       \
+        convertCSR<T>(2345, 5678, 0.5);                     \
+    }                                                       \
 
 SPARSE_TESTS(float, 1E-3)
 SPARSE_TESTS(double, 1E-5)

From 3d6a4799f6496b0a07b3e732f04123b692771e3d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 17:57:43 -0400
Subject: [PATCH 0827/2677] Remove all usage of clSPARSE

---
 CMakeModules/build_clSPARSE.cmake   |  43 -------
 src/backend/opencl/CMakeLists.txt   |  11 --
 src/backend/opencl/err_clsparse.hpp |  72 -----------
 src/backend/opencl/sparse.cpp       | 192 +---------------------------
 src/backend/opencl/sparse.hpp       |   5 -
 5 files changed, 3 insertions(+), 320 deletions(-)
 delete mode 100644 CMakeModules/build_clSPARSE.cmake
 delete mode 100644 src/backend/opencl/err_clsparse.hpp

diff --git a/CMakeModules/build_clSPARSE.cmake b/CMakeModules/build_clSPARSE.cmake
deleted file mode 100644
index 781ef10571..0000000000
--- a/CMakeModules/build_clSPARSE.cmake
+++ /dev/null
@@ -1,43 +0,0 @@
-INCLUDE(ExternalProject)
-
-SET(prefix ${CMAKE_BINARY_DIR}/third_party/clSPARSE)
-SET(clSPARSE_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clSPARSE${CMAKE_STATIC_LIBRARY_SUFFIX})
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(CMAKE_GENERATOR MATCHES "Ninja")
-        MESSAGE(WARNING "Building clSPARSE with Ninja has known issues with CMake older than 3.2")
-    endif()
-    SET(byproducts)
-ELSE()
-    SET(byproducts BYPRODUCTS ${clSPARSE_location})
-ENDIF()
-
-# Builds the src directory of clSPARSE and not the wrapper top level directory
-ExternalProject_Add(
-    clSPARSE-ext
-    GIT_REPOSITORY https://github.com/arrayfire/clSPARSE.git
-    GIT_TAG arrayfire-release-test
-    PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}"
-    UPDATE_COMMAND ""
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>/src
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DclSPARSE_LIBRARY_TYPE:STRING=STATIC
-    -DLIBRARY_DEBUG_POSTFIX:STRING=
-    -DSUFFIX_LIB:STRING=
-    -DBUILD_BENCHMARKS:BOOL=OFF
-    -DBUILD_TESTS:BOOL=OFF
-    ${byproducts}
-    )
-
-ExternalProject_Get_Property(clSPARSE-ext prefix)
-ADD_LIBRARY(clSPARSE IMPORTED STATIC)
-SET_TARGET_PROPERTIES(clSPARSE PROPERTIES IMPORTED_LOCATION ${clSPARSE_location})
-ADD_DEPENDENCIES(clSPARSE clSPARSE-ext)
-SET(CLSPARSE_INCLUDE_DIRS ${prefix}/include)
-SET(CLSPARSE_LIBRARIES clSPARSE)
-SET(CLSPARSE_FOUND ON)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a05f95dd78..cf649dce0f 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -78,15 +78,6 @@ ENDIF()
 INCLUDE_DIRECTORIES(${CLFFT_INCLUDE_DIRS})
 LINK_DIRECTORIES(${CLFFT_LIBRARY_DIR})
 
-OPTION(USE_SYSTEM_CLSPARSE "Use system clSPARSE" OFF)
-IF(USE_SYSTEM_CLSPARSE)
-    FIND_PACKAGE(clSPARSE REQUIRED)
-ELSE()
-  INCLUDE("${CMAKE_MODULE_PATH}/build_clSPARSE.cmake")
-ENDIF()
-INCLUDE_DIRECTORIES(${CLSPARSE_INCLUDE_DIRS})
-LINK_DIRECTORIES(${CLSPARSE_LIBRARY_DIR})
-
 ADD_DEFINITIONS( -DBOOST_ALL_NO_LIB )
 SET(Boost_USE_STATIC_LIBS OFF)
 FIND_PACKAGE(Boost 1.48 REQUIRED)
@@ -111,7 +102,6 @@ INCLUDE_DIRECTORIES(
     "${CMAKE_CURRENT_BINARY_DIR}"
     ${CLBLAS_INCLUDE_DIRS}
     ${CLFFT_INCLUDE_DIRS}
-    ${CLSPARSE_INCLUDE_DIRS}
     ${Boost_INCLUDE_DIR}
     ${BoostCompute_INCLUDE_DIRS}
     ${CBLAS_INCLUDE_DIR}
@@ -330,7 +320,6 @@ TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${OpenCL_LIBRARIES}
                                 PRIVATE ${CLBLAS_LIBRARIES}
                                 PRIVATE ${CLFFT_LIBRARIES}
-                                PRIVATE ${CLSPARSE_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
                      )
 
diff --git a/src/backend/opencl/err_clsparse.hpp b/src/backend/opencl/err_clsparse.hpp
deleted file mode 100644
index c314ca8cbf..0000000000
--- a/src/backend/opencl/err_clsparse.hpp
+++ /dev/null
@@ -1,72 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <stdio.h>
-#include <err_common.hpp>
-#include <clSPARSE.h>
-
-static const char * _clsparseGetResultString(clsparseStatus st)
-{
-    switch (st)
-    {
-        case clsparseSuccess:                       return "Success";
-        case clsparseInvalidValue:                  return "Invalid value";
-        case clsparseInvalidCommandQueue:           return "Invalid queue";
-        case clsparseInvalidContext:                return "Invalid context";
-        case clsparseInvalidMemObject:              return "Invalid memory object";
-        case clsparseInvalidDevice:                 return "Invalid device";
-        case clsparseInvalidEventWaitList:          return "Invalid event list";
-        case clsparseInvalidEvent:                  return "Invalid event";
-        case clsparseOutOfResources:                return "Out of resources";
-        case clsparseOutOfHostMemory:               return "Out of host memory";
-        case clsparseInvalidOperation:              return "Invalid operation";
-        case clsparseCompilerNotAvailable:          return "Compiler not available";
-        case clsparseBuildProgramFailure:           return "Build program failure";
-        case clsparseInvalidKernelArgs:             return "Invalid kernel arguments";
-
-        case clsparseNotImplemented:                return "Not implemented";
-        case clsparseNotInitialized:                return "clSPARSE Not initialized";
-        case clsparseStructInvalid:                 return "Struct invalid";
-        case clsparseInvalidSize:                   return "Invalid size";
-        case clsparseInvalidMemObj:                 return "Invalid memory object";
-        case clsparseInsufficientMemory:            return "Insufficient Memory";
-        case clsparseInvalidControlObject:          return "Invalid control object";
-        case clsparseInvalidFile:                   return "Invalid file";
-        case clsparseInvalidFileFormat:             return "Invalid file format";
-        case clsparseInvalidKernelExecution:        return "Invalid kernel execution";
-        case clsparseInvalidType:                   return "Invalid type";
-
-        case clsparseInvalidSolverControlObject:    return "Invalid solver control object";
-        case clsparseInvalidSystemSize:             return "Invalid system size";
-        case clsparseIterationsExceeded:            return "Iterations exceeded";
-        case clsparseToleranceNotReached:           return "Tolerance not reached";
-        case clsparseSolverError:                   return "Solver error";
-        default:                                    return "Unknown clSPARSE Error";
-    }
-
-    return "Unknown error";
-}
-
-#define CLSPARSE_CHECK(fn) do {                     \
-        clsparseStatus _clsparse_st = fn;           \
-        if (_clsparse_st != clsparseSuccess) {      \
-            char clsparse_st_msg[1024];             \
-            snprintf(clsparse_st_msg,               \
-                     sizeof(clsparse_st_msg),       \
-                     "clsparse Error (%d): %s\n",   \
-                     (int)(_clsparse_st),           \
-                     _clsparseGetResultString(      \
-                         _clsparse_st));            \
-                                                    \
-            AF_ERROR(clsparse_st_msg,               \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
-
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 6110e150f0..e4aebbfaf8 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -23,104 +23,11 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
-#define ENABLE_CLSPARSE 0
-
 namespace opencl
 {
 
 using namespace common;
 
-////////////////////////////////////////////////////////////////////////////////
-// clSPARSE Setup and Teardown Manager
-// This gets initialized in opencl/sparse.cpp
-////////////////////////////////////////////////////////////////////////////////
-class clSPARSEManager
-{
-    public:
-    static clsparseControl control;
-
-    clSPARSEManager()
-    {
-        CLSPARSE_CHECK(clsparseSetup());
-        clsparseCreateResult createResult = clsparseCreateControl(getQueue()());
-        control = (createResult.status == clsparseSuccess) ? createResult.control : nullptr;
-    }
-
-    ~clSPARSEManager()
-    {
-        CLSPARSE_CHECK(clsparseReleaseControl(control));
-        control = nullptr;
-        CLSPARSE_CHECK(clsparseTeardown());
-    }
-};
-////////////////////////////////////////////////////////////////////////////////
-
-clsparseControl clSPARSEManager::control = nullptr;
-
-// Instantiate clSPARSEManager
-void clSPARSEInit()
-{
-    static clSPARSEManager manager = clSPARSEManager();
-}
-
-clsparseControl getControl()
-{
-    return clSPARSEManager::control;
-}
-
-////////////////////////////////////////////////////////////////////////////////
-#define SPARSE_FUNC_DEF(NAME)                                               \
-template<typename T>                                                        \
-struct NAME##_func;
-
-#define SPARSE_FUNC(NAME, TYPE, PREFIX)                                     \
-template<>                                                                  \
-struct NAME##_func<TYPE>                                                    \
-{                                                                           \
-    template<typename... Args>                                              \
-    clsparseStatus                                                          \
-    operator() (Args... args) { return clsparse##PREFIX##NAME(args...); }   \
-};
-
-// Dense -> CSR
-SPARSE_FUNC_DEF(dense2csr)
-SPARSE_FUNC(dense2csr, float,      S)
-SPARSE_FUNC(dense2csr, double,     D)
-// TODO
-// Fix this. clSPARSE does not have functions for C and Z
-SPARSE_FUNC(dense2csr, cfloat,     S)
-SPARSE_FUNC(dense2csr, cdouble,    D)
-
-// CSR -> Dense
-SPARSE_FUNC_DEF(csr2dense)
-SPARSE_FUNC(csr2dense, float,      S)
-SPARSE_FUNC(csr2dense, double,     D)
-// TODO
-// Fix this. clSPARSE does not have functions for C and Z
-SPARSE_FUNC(csr2dense, cfloat,     S)
-SPARSE_FUNC(csr2dense, cdouble,    D)
-
-// CSR -> COO
-SPARSE_FUNC_DEF(csr2coo)
-SPARSE_FUNC(csr2coo, float,      S)
-SPARSE_FUNC(csr2coo, double,     D)
-// TODO
-// Fix this. clSPARSE does not have functions for C and Z
-SPARSE_FUNC(csr2coo, cfloat,     S)
-SPARSE_FUNC(csr2coo, cdouble,    D)
-
-// COO -> CSR
-SPARSE_FUNC_DEF(coo2csr)
-SPARSE_FUNC(coo2csr, float,      S)
-SPARSE_FUNC(coo2csr, double,     D)
-// TODO
-// Fix this. clSPARSE does not have functions for C and Z
-SPARSE_FUNC(coo2csr, cfloat,     S)
-SPARSE_FUNC(coo2csr, cdouble,    D)
-
-#undef SPARSE_FUNC_DEF
-#undef SPARSE_FUNC
-
 // Partial template specialization of sparseConvertDenseToStorage for COO
 // However, template specialization is not allowed
 template<typename T>
@@ -146,8 +53,6 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 {
-    clSPARSEInit();
-
     in_.eval();
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
@@ -155,38 +60,12 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
     sparse_.eval();
 
-#if ENABLE_CLSPARSE
-
-    // Assign to clSparse Dense
-    cldenseMatrix clDenseMat;
-    CLSPARSE_CHECK(cldenseInitMatrix(&clDenseMat));
-    clDenseMat.values = (*in_.get())();
-    clDenseMat.num_rows = in_.dims()[0];
-    clDenseMat.num_cols = in_.dims()[1];
-    clDenseMat.lead_dim = in_.strides()[1];
-
-    // Assign to clSparse CSR
-    clsparseCsrMatrix clSparseMat;
-    CLSPARSE_CHECK(clsparseInitCsrMatrix(&clSparseMat));
-
-    clSparseMat.values      = (*sparse_.getValues().get())();
-    clSparseMat.row_pointer = (*sparse_.getRowIdx().get())();
-    clSparseMat.col_indices = (*sparse_.getColIdx().get())();
-    clSparseMat.num_rows = in_.dims()[0];
-    clSparseMat.num_cols = in_.dims()[1];
-    clSparseMat.num_nonzeros = nNZ;
-
-    if(stype == AF_STORAGE_CSR)
-        CLSPARSE_CHECK(dense2csr_func<T>()(&clDenseMat, &clSparseMat, getControl()));
-    else
-        AF_ERROR("OpenCL Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
-#else
     Array<T> &values = sparse_.getValues();
     Array<int> &rowIdx = sparse_.getRowIdx();
     Array<int> &colIdx = sparse_.getColIdx();
 
     kernel::dense2csr<T>(values, rowIdx, colIdx, in_);
-#endif
+
     return sparse_;
 }
 
@@ -212,8 +91,6 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 {
-    clSPARSEInit();
-
     if(stype != AF_STORAGE_CSR)
         AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
 
@@ -222,31 +99,6 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
-#if ENABLE_CLSPARSE
-    // Assign to clSparse CSR
-    clsparseCsrMatrix clSparseMat;
-    CLSPARSE_CHECK(clsparseInitCsrMatrix(&clSparseMat));
-
-    clSparseMat.values      = (*in_.getValues().get())();
-    clSparseMat.row_pointer = (*in_.getRowIdx().get())();
-    clSparseMat.col_indices = (*in_.getColIdx().get())();
-    clSparseMat.num_rows = in_.dims()[0];
-    clSparseMat.num_cols = in_.dims()[1];
-    clSparseMat.num_nonzeros = in_.getNNZ();
-
-    // Assign to clSparse Dense
-    cldenseMatrix clDenseMat;
-    CLSPARSE_CHECK(cldenseInitMatrix(&clDenseMat));
-    clDenseMat.values = (*dense_.get())();
-    clDenseMat.num_rows = dense_.dims()[0];
-    clDenseMat.num_cols = dense_.dims()[1];
-    clDenseMat.lead_dim = dense_.strides()[1];
-
-    if(stype == AF_STORAGE_CSR)
-        CLSPARSE_CHECK(csr2dense_func<T>()(&clSparseMat, &clDenseMat, getControl()));
-    else
-        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
-#else
     const Array<T> &values = in_.getValues();
     const Array<int> &rowIdx = in_.getRowIdx();
     const Array<int> &colIdx = in_.getColIdx();
@@ -256,8 +108,6 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     else
         AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
 
-#endif
-
     return dense_;
 }
 
@@ -268,53 +118,17 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
     // Convert CSR <-> CSC <-> COO <-> CSR
     // Currently supports CSR <-> COO
 
+    AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+
     // If src and dest are the same, simply return.
     if(src == dest)
         return in;
 
-    clSPARSEInit();
-
     in.eval();
 
     SparseArray<T> out = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
     out.eval();
 
-    clsparseCsrMatrix csrMat;
-    CLSPARSE_CHECK(clsparseInitCsrMatrix(&csrMat));
-
-    clsparseCooMatrix cooMat;
-    CLSPARSE_CHECK(clsparseInitCooMatrix(&cooMat));
-
-    csrMat.num_rows = in.dims()[0];
-    csrMat.num_cols = in.dims()[1];
-    csrMat.num_nonzeros = in.getNNZ();
-
-    cooMat.num_rows = in.dims()[0];
-    cooMat.num_cols = in.dims()[1];
-    cooMat.num_nonzeros = in.getNNZ();
-
-    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
-        csrMat.values      = (*in.getValues().get())();
-        csrMat.row_pointer = (*in.getRowIdx().get())();
-        csrMat.col_indices = (*in.getColIdx().get())();
-
-        cooMat.values      = (*out.getValues().get())();
-        cooMat.row_indices = (*out.getRowIdx().get())();
-        cooMat.col_indices = (*out.getColIdx().get())();
-
-        CLSPARSE_CHECK(csr2coo_func<T>()(&csrMat, &cooMat, getControl()));
-    } else if(src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-        cooMat.values      = (*in.getValues().get())();
-        cooMat.row_indices = (*in.getRowIdx().get())();
-        cooMat.col_indices = (*in.getColIdx().get())();
-
-        csrMat.values      = (*out.getValues().get())();
-        csrMat.row_pointer = (*out.getRowIdx().get())();
-        csrMat.col_indices = (*out.getColIdx().get())();
-
-        CLSPARSE_CHECK(coo2csr_func<T>()(&cooMat, &csrMat, getControl()));
-    }
-
     return out;
 }
 
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index ae7f3327bc..f27d88fa93 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -9,7 +9,6 @@
 
 #include <Array.hpp>
 #include <SparseArray.hpp>
-#include <err_clsparse.hpp>
 
 namespace opencl
 {
@@ -23,8 +22,4 @@ Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 template<typename T, af_storage src, af_storage dest>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
-void clSPARSEInit();
-
-clsparseControl getControl();
-
 }

From 709c82929385d0866c646e97281b25468ebcd77d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 18:28:53 -0400
Subject: [PATCH 0828/2677] Sparse API: createSparseArray() -> sparse()

---
 include/af/sparse.h    | 18 +++++++++---------
 src/api/cpp/sparse.cpp | 18 +++++++++---------
 test/sparse.cpp        |  6 +++---
 3 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/include/af/sparse.h b/include/af/sparse.h
index 44e30ea020..c2d596a5bd 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -15,17 +15,17 @@ namespace af
 {
     class array;
 
-    AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols,
-                                  const array values, const array rowIdx, const array colIdx,
-                                  const af::storage stype = AF_STORAGE_CSR);
+    AFAPI array sparse(const dim_t nRows, const dim_t nCols,
+                       const array values, const array rowIdx, const array colIdx,
+                       const af::storage stype = AF_STORAGE_CSR);
 
-    AFAPI array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-                                  const void* const values,
-                                  const int * const rowIdx, const int * const colIdx,
-                                  const dtype type = f32, const af::storage stype = AF_STORAGE_CSR,
-                                  const af::source src = afHost);
+    AFAPI array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                       const void* const values,
+                       const int * const rowIdx, const int * const colIdx,
+                       const dtype type = f32, const af::storage stype = AF_STORAGE_CSR,
+                       const af::source src = afHost);
 
-    AFAPI array createSparseArray(const array dense, const af::storage stype = AF_STORAGE_CSR);
+    AFAPI array sparse(const array dense, const af::storage stype = AF_STORAGE_CSR);
 
     AFAPI array sparseConvertStorage(const array in, const af::storage stype);
 
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index c72220dc43..d8c8f2533e 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -13,9 +13,9 @@
 
 namespace af
 {
-    array createSparseArray(const dim_t nRows, const dim_t nCols,
-                            const array values, const array rowIdx, const array colIdx,
-                            const af::storage stype)
+    array sparse(const dim_t nRows, const dim_t nCols,
+                 const array values, const array rowIdx, const array colIdx,
+                 const af::storage stype)
     {
         af_array out = 0;
         AF_THROW(af_create_sparse_array(&out, nRows, nCols,
@@ -23,11 +23,11 @@ namespace af
         return array(out);
     }
 
-    array createSparseArray(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-                            const void * const values,
-                            const int * const rowIdx, const int * const colIdx,
-                            const dtype type, const af::storage stype,
-                            const af::source src)
+    array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+                 const void * const values,
+                 const int * const rowIdx, const int * const colIdx,
+                 const dtype type, const af::storage stype,
+                 const af::source src)
     {
         af_array out = 0;
         AF_THROW(af_create_sparse_array_from_ptr(&out, nRows, nCols, nNZ,
@@ -36,7 +36,7 @@ namespace af
     }
 
 
-    array createSparseArray(const array dense, const af::storage stype)
+    array sparse(const array dense, const af::storage stype)
     {
         af_array out = 0;
         AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), stype));
diff --git a/test/sparse.cpp b/test/sparse.cpp
index ecd872680a..6d0e2082f4 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -89,7 +89,7 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
     af::array dRes1 = matmul(A, B);
 
     // Create Sparse Array From Dense
-    af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
+    af::array sA = af::sparse(A, AF_STORAGE_CSR);
 
     // Sparse Matmul
     af::array sRes1 = matmul(sA, B);
@@ -121,7 +121,7 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
     af::array dRes3 = matmul(A, B, AF_MAT_CTRANS, AF_MAT_NONE);
 
     // Create Sparse Array From Dense
-    af::array sA = af::createSparseArray(A, AF_STORAGE_CSR);
+    af::array sA = af::sparse(A, AF_STORAGE_CSR);
 
     // Sparse Matmul
     af::array sRes2 = matmul(sA, B, AF_MAT_TRANS, AF_MAT_NONE);
@@ -146,7 +146,7 @@ void convertCSR(const int M, const int N, const float ratio)
 #endif
     a = a * (a > ratio);
 
-    af::array s = af::createSparseArray(a, AF_STORAGE_CSR);
+    af::array s = af::sparse(a, AF_STORAGE_CSR);
     af::array aa = af::sparseConvertStorage(s, AF_STORAGE_DENSE);
 
     ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));

From bb1b73e1567e56af369ea5d921b13ff2d7d14683 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 18:30:41 -0400
Subject: [PATCH 0829/2677] Sparse API: getNumNonZero() -> getNNZ()

---
 include/af/sparse.h        | 4 ++--
 src/api/c/sparse.cpp       | 2 +-
 src/api/cpp/sparse.cpp     | 4 ++--
 src/api/unified/sparse.cpp | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/include/af/sparse.h b/include/af/sparse.h
index c2d596a5bd..903765c7e6 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -37,7 +37,7 @@ namespace af
 
     AFAPI array sparseGetColIdx(const array in);
 
-    AFAPI dim_t sparseGetNumNonZero(const array in);
+    AFAPI dim_t sparseGetNNZ(const array in);
 
     AFAPI af::storage sparseGetStorage(const array in);
 }
@@ -76,7 +76,7 @@ extern "C" {
 
     AFAPI af_err af_sparse_get_col_idx(af_array *out, const af_array in);
 
-    AFAPI af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in);
+    AFAPI af_err af_sparse_get_nnz(dim_t *out, const af_array in);
 
     AFAPI af_err af_sparse_get_storage(af_storage *out, const af_array in);
 
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index deefc27c36..dc423683ca 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -388,7 +388,7 @@ af_err af_sparse_get_col_idx(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in)
+af_err af_sparse_get_nnz(dim_t *out, const af_array in)
 {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index d8c8f2533e..60f3c7e0d2 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -82,10 +82,10 @@ namespace af
         return array(out);
     }
 
-    dim_t sparseGetNumNonZero(const array in)
+    dim_t sparseGetNNZ(const array in)
     {
         dim_t out = 0;
-        AF_THROW(af_sparse_get_num_nonzero(&out, in.get()));
+        AF_THROW(af_sparse_get_nnz(&out, in.get()));
         return out;
     }
 
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index 9d46380917..01dda239c8 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -70,7 +70,7 @@ af_err af_sparse_get_col_idx(af_array *out, const af_array in)
     return CALL(out, in);
 }
 
-af_err af_sparse_get_num_nonzero(dim_t *out, const af_array in)
+af_err af_sparse_get_nnz(dim_t *out, const af_array in)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in);

From dedae85b1f211bd4edc48cea86e059a841e38fa5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 18:34:59 -0400
Subject: [PATCH 0830/2677] Sparse API: getArrays() -> getInfo()

---
 include/af/sparse.h        | 6 ++++--
 src/api/c/sparse.cpp       | 9 +++++----
 src/api/cpp/sparse.cpp     | 8 +++++---
 src/api/unified/sparse.cpp | 5 +++--
 4 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/include/af/sparse.h b/include/af/sparse.h
index 903765c7e6..1706a57db7 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -29,7 +29,8 @@ namespace af
 
     AFAPI array sparseConvertStorage(const array in, const af::storage stype);
 
-    AFAPI void sparseGetArrays(array &values, array &rowIdx, array &colIdx, const array in);
+    AFAPI void sparseGetInfo(array &values, array &rowIdx, array &colIdx, af::storage &stype,
+                             const array in);
 
     AFAPI array sparseGetValues(const array in);
 
@@ -68,7 +69,8 @@ extern "C" {
     AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
                                            const af_storage destStorage);
 
-    AFAPI af_err af_sparse_get_arrays(af_array *values, af_array *rowIdx, af_array *colIdx, const af_array in);
+    AFAPI af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx, af_storage *stype,
+                                    const af_array in);
 
     AFAPI af_err af_sparse_get_values(af_array *out, const af_array in);
 
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index dc423683ca..0e149c63c5 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -337,13 +337,14 @@ af_array getSparseValues(const af_array in)
     return getHandle(getSparseArray<T>(in).getValues());
 }
 
-af_err af_sparse_get_arrays(af_array *values, af_array *rows, af_array *cols,
-                      const af_array in)
+af_err af_sparse_get_info(af_array *values, af_array *rows, af_array *cols, af_storage *stype,
+                          const af_array in)
 {
     try {
         if(values != NULL) AF_CHECK(af_sparse_get_values(values, in));
-        if(rows   != NULL) AF_CHECK(af_sparse_get_row_idx(rows  , in));
-        if(cols   != NULL) AF_CHECK(af_sparse_get_col_idx(cols  , in));
+        if(rows   != NULL) AF_CHECK(af_sparse_get_row_idx(rows , in));
+        if(cols   != NULL) AF_CHECK(af_sparse_get_col_idx(cols , in));
+        if(stype  != NULL) AF_CHECK(af_sparse_get_storage(stype, in));
     }
     CATCHALL;
 
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index 60f3c7e0d2..9d3ec9094f 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -50,14 +50,16 @@ namespace af
         return array(out);
     }
 
-    void sparseGetArrays(array &values, array &rowIdx, array &colIdx,
-                         const array in)
+    void sparseGetInfo(array &values, array &rowIdx, array &colIdx, storage &stype,
+                       const array in)
     {
         af_array values_ = 0, rowIdx_ = 0, colIdx_ = 0;
-        AF_THROW(af_sparse_get_arrays(&values_, &rowIdx_, &colIdx_, in.get()));
+        af_storage stype_ = AF_STORAGE_DENSE;
+        AF_THROW(af_sparse_get_info(&values_, &rowIdx_, &colIdx_, &stype_, in.get()));
         values = array(values_);
         rowIdx = array(rowIdx_);
         colIdx = array(colIdx_);
+        stype  = stype_;
         return;
     }
 
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index 01dda239c8..3d85f2f633 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -46,10 +46,11 @@ af_err af_sparse_convert_storage(af_array *out, const af_array in,
     return CALL(out, in, destStorage);
 }
 
-af_err af_sparse_get_arrays(af_array *values, af_array *rowIdx, af_array *colIdx, const af_array in)
+af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx, af_storage *stype,
+                          const af_array in)
 {
     CHECK_ARRAYS(in);
-    return CALL(values, rowIdx, colIdx, in);
+    return CALL(values, rowIdx, colIdx, stype, in);
 }
 
 af_err af_sparse_get_values(af_array *out, const af_array in)

From a1cc07f972e1dd0bc387ac0eb901fcf3db54b038 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Sep 2016 18:37:26 -0400
Subject: [PATCH 0831/2677] Sparse API: convertStorage() -> convertTo(), add
 dense()

---
 include/af/sparse.h        | 10 +++++++---
 src/api/c/sparse.cpp       | 28 ++++++++++++++++++++++++++--
 src/api/cpp/sparse.cpp     | 11 +++++++++--
 src/api/unified/sparse.cpp | 10 ++++++++--
 test/sparse.cpp            |  2 +-
 5 files changed, 51 insertions(+), 10 deletions(-)

diff --git a/include/af/sparse.h b/include/af/sparse.h
index 1706a57db7..f28282c2a4 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -27,7 +27,9 @@ namespace af
 
     AFAPI array sparse(const array dense, const af::storage stype = AF_STORAGE_CSR);
 
-    AFAPI array sparseConvertStorage(const array in, const af::storage stype);
+    AFAPI array sparseConvertTo(const array in, const af::storage stype);
+
+    AFAPI array dense(const array sparse);
 
     AFAPI void sparseGetInfo(array &values, array &rowIdx, array &colIdx, af::storage &stype,
                              const array in);
@@ -66,8 +68,10 @@ extern "C" {
                  af_array *out, const af_array in,
                  const af_storage stype);
 
-    AFAPI af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                                           const af_storage destStorage);
+    AFAPI af_err af_sparse_convert_to(af_array *out, const af_array in,
+                                      const af_storage destStorage);
+
+    AFAPI af_err af_sparse_to_dense(af_array *out, const af_array in);
 
     AFAPI af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx, af_storage *stype,
                                     const af_array in);
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 0e149c63c5..a88c7a64f9 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -292,8 +292,8 @@ af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
     return NULL;
 }
 
-af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                           const af_storage destStorage)
+af_err af_sparse_convert_to(af_array *out, const af_array in,
+                            const af_storage destStorage)
 {
     // Right now dest_storage can only be AF_STORAGE_DENSE
     try {
@@ -328,6 +328,30 @@ af_err af_sparse_convert_storage(af_array *out, const af_array in,
     return AF_SUCCESS;
 }
 
+af_err af_sparse_to_dense(af_array *out, const af_array in)
+{
+    try {
+        af_array output = 0;
+
+        const SparseArrayBase base = getSparseArrayBase(in);
+
+        // Dense not allowed as input -> Should never happen
+        // To convert from dense to type, use the create* functions
+        ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE);
+
+        switch(base.getType()) {
+            case f32: output = sparseConvertStorage<float  >(in, AF_STORAGE_DENSE); break;
+            case f64: output = sparseConvertStorage<double >(in, AF_STORAGE_DENSE); break;
+            case c32: output = sparseConvertStorage<cfloat >(in, AF_STORAGE_DENSE); break;
+            case c64: output = sparseConvertStorage<cdouble>(in, AF_STORAGE_DENSE); break;
+            default : AF_ERROR("Output storage type is not valid", AF_ERR_ARG);
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 // Get Functions
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index 9d3ec9094f..ab5a0b8158 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -43,10 +43,17 @@ namespace af
         return array(out);
     }
 
-    array sparseConvertStorage(const array in, const af::storage stype)
+    array sparseConvertTo(const array in, const af::storage stype)
     {
         af_array out = 0;
-        AF_THROW(af_sparse_convert_storage(&out, in.get(), stype));
+        AF_THROW(af_sparse_convert_to(&out, in.get(), stype));
+        return array(out);
+    }
+
+    array dense(const array sparse)
+    {
+        af_array out = 0;
+        AF_THROW(af_sparse_to_dense(&out, sparse.get()));
         return array(out);
     }
 
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index 3d85f2f633..a6a74dafc8 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -39,13 +39,19 @@ af_err af_create_sparse_array_from_dense(
     return CALL(out, in, stype);
 }
 
-af_err af_sparse_convert_storage(af_array *out, const af_array in,
-                                 const af_storage destStorage)
+af_err af_sparse_convert_to(af_array *out, const af_array in,
+                            const af_storage destStorage)
 {
     CHECK_ARRAYS(in);
     return CALL(out, in, destStorage);
 }
 
+af_err af_sparse_to_dense(af_array *out, const af_array in)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in);
+}
+
 af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx, af_storage *stype,
                           const af_array in)
 {
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 6d0e2082f4..826984ecdc 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -147,7 +147,7 @@ void convertCSR(const int M, const int N, const float ratio)
     a = a * (a > ratio);
 
     af::array s = af::sparse(a, AF_STORAGE_CSR);
-    af::array aa = af::sparseConvertStorage(s, AF_STORAGE_DENSE);
+    af::array aa = af::dense(s);
 
     ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));
 }

From f20b6ff099c00132374218903fdcc095094ef110 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 2 Sep 2016 10:26:13 -0400
Subject: [PATCH 0832/2677] BUGFIX: In csr->dense CPU fallback kernel, out of
 bounds issue

---
 src/backend/cpu/kernel/sparse.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index 66ef5e9f27..4a9362a15f 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -83,7 +83,7 @@ struct csr_dense
         int stride = out.strides()[1];
 
         int r = rowIdx.dims()[0];
-        for (int i = 0; i < r; i++) {
+        for (int i = 0; i < r - 1; i++) {
             for (int ii = rPtr[i]; ii < rPtr[i+1]; ++ii) {
                 int j = cPtr[ii];
                 T v = vPtr[ii];

From 3944293539ca3b87282bbb5f0a2f19018dd67327 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 2 Sep 2016 15:08:24 -0400
Subject: [PATCH 0833/2677] Style changes. API changes.

Removed an overloaded initMersenneTwister. Some documentation added.
---
 include/af/defines.h                        |  20 +-
 include/af/random_engine.h                  | 326 ++++++++++++++------
 src/api/c/random_engine.cpp                 | 134 ++++----
 src/api/cpp/random_engine.cpp               |  86 ++----
 src/api/unified/random_engine.cpp           |  24 +-
 src/backend/MersenneTwister.hpp             |   6 +-
 src/backend/cpu/kernel/random_engine.hpp    |  12 +-
 src/backend/cpu/random_engine.cpp           | 158 +++++-----
 src/backend/cpu/random_engine.hpp           |   4 +-
 src/backend/cuda/kernel/random_engine.hpp   |  12 +-
 src/backend/cuda/random_engine.cu           | 154 +++++----
 src/backend/cuda/random_engine.hpp          |   4 +-
 src/backend/opencl/kernel/random_engine.hpp |  20 +-
 src/backend/opencl/random_engine.cpp        | 146 ++++-----
 src/backend/opencl/random_engine.hpp        |   4 +-
 test/random.cpp                             |  38 +--
 16 files changed, 617 insertions(+), 531 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 543d9d94d2..d3365d732e 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -402,14 +402,14 @@ typedef enum {
 
 #if AF_API_VERSION >=34
 typedef enum {
-    AF_RANDOM_PHILOX_4X32_10 = 100,
-    AF_RANDOM_THREEFRY_2X32_16 = 200,
-    AF_RANDOM_MERSENNE_GP11213 = 300,
-    AF_RANDOM_PHILOX = AF_RANDOM_PHILOX_4X32_10,
-    AF_RANDOM_THREEFRY = AF_RANDOM_THREEFRY_2X32_16,
-    AF_RANDOM_MERSENNE = AF_RANDOM_MERSENNE_GP11213,
-    AF_RANDOM_DEFAULT = AF_RANDOM_PHILOX
-} af_random_type;
+    AF_RANDOM_ENGINE_PHILOX_4X32_10 = 100,
+    AF_RANDOM_ENGINE_THREEFRY_2X32_16 = 200,
+    AF_RANDOM_ENGINE_MERSENNE_GP11213 = 300,
+    AF_RANDOM_ENGINE_PHILOX = AF_RANDOM_ENGINE_PHILOX_4X32_10,
+    AF_RANDOM_ENGINE_THREEFRY = AF_RANDOM_ENGINE_THREEFRY_2X32_16,
+    AF_RANDOM_ENGINE_MERSENNE = AF_RANDOM_ENGINE_MERSENNE_GP11213,
+    AF_RANDOM_ENGINE_DEFAULT = AF_RANDOM_ENGINE_PHILOX
+} af_random_engine_type;
 #endif
 
 #if AF_API_VERSION >=32
@@ -459,7 +459,9 @@ namespace af
 #endif
 #if AF_API_VERSION >= 34
     typedef af_binary_op binaryOp;
-    typedef af_random_type randomType;
+#endif
+#if AF_API_VERSION >= 34
+    typedef af_random_engine_type randomEngineType;
 #endif
 }
 
diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 1d365b5aa1..51fe484828 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -18,171 +18,278 @@ namespace af
     class array;
     class dim4;
 
+    ///
+    /// \brief A random number generator class
+    ///
     class AFAPI randomEngine {
         private:
             af_random_engine engine;
         public:
+            /**
+                \ingroup construct_random
+                @{
+            */
+            /**
+                Create random number generator object
+
+                \code
+                randomEngine r(AF_RANDOM_ENGINE_DEFAULT, 1);   // creates random engine of default type with seed = 1
+                \endcode
+            */
             explicit
-            randomEngine(randomType typeIn = AF_RANDOM_DEFAULT, uintl seedIn = 0);
-            randomEngine(const randomEngine& other);
-            ~randomEngine();
+            randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT, uintl seedIn = 0);
+            /**
+                Create the default random number generator object
+
+                \code
+                randomEngine r;   // creates random engine of default type with default seed
+                \endcode
+            */
+            randomEngine(void);
+            /**
+                Creates a copy of the random engine object
+                \param in The input random engine object
+            */
+            randomEngine(const randomEngine& in);
+            /**
+                @}
+            */
 
-            randomEngine& operator= (const randomEngine& other);
+            ~randomEngine();
 
-            array uniform(const dim_t dim0, const dtype ty = f32);
-            array uniform(const dim_t dim0, const dim_t dim1, const dtype ty = f32);
-            array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
-            array uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
-            array uniform(const dim4& dims, const dtype ty = f32);
+            /**
+                \ingroup random_operator_eq
+                @{
+                \brief Assigns the internal state of randome engine
+
+                \param[in] in The object to be assigned to the random engine
+                \returns the reference to this
+
+            */
+            randomEngine& operator= (const randomEngine& in);
+            /**
+                @}
+            */
+
+            /**
+                \ingroup random_set_type
+                @{
+                \brief Sets the random type of the random engine
+
+                \param[in] type The type of the random number generator
+            */
+            void setType(const randomEngineType type);
+            /**
+                @}
+            */
+
+            /**
+                \ingroup random_get_type
+                @{
+                \brief Return the random type of the random engine
+
+                \returns the random type enum associated with random engine
+            */
+            randomEngineType getType(void);
+            /**
+                @}
+            */
+
+            /**
+                \ingroup random_set_seed
+                @{
+                \brief Sets the seed of the random engine
+
+                \param[in] seed The initializing seed of the random number generator
+            */
+            void setSeed(const uintl seed);
+            /**
+                @}
+            */
 
-            array normal(const dim_t dim0, const dtype ty = f32);
-            array normal(const dim_t dim0, const dim_t dim1, const dtype ty = f32);
-            array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty = f32);
-            array normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty = f32);
-            array normal(const dim4& dims, const dtype ty = f32);
+            /**
+                \ingroup random_get_seed
+                @{
+                \brief Returns the seed of the random engine
 
-            void setType(const randomType type);
-            void setSeed(const uintl seed);
+                \returns the seed associated with random engine
+            */
             uintl getSeed(void) const;
-            af_random_engine get() const;
+            /**
+                @}
+            */
+
+            /**
+                \ingroup random_get
+                @{
+                \brief Returns the internal state of the random engine
+
+                \returns the internal state associated with random engine
+            */
+            af_random_engine get(void) const;
+            /**
+                @}
+            */
     };
 
     /**
-        \param[in] dims is the dimensions of the array to be generated
-        \param[in] ty is the type of the array
+        \param[in] dims The dimensions of the array to be generated
+        \param[in] ty The type of the array
+        \param[in] r The random engine object
+
+        \return array of size \p dims
+
+        \ingroup random_func_randu
+    */
+    AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r);
+
+    /**
+        \param[in] dims The dimensions of the array to be generated
+        \param[in] ty The type of the array
+        \param[in] r The random engine object
+
+        \return array of size \p dims
+
+        \ingroup random_func_randn
+    */
+    AFAPI array randn(const dim4 &dims, const dtype ty, randomEngine &r);
+
+    /**
+        \param[in] dims The dimensions of the array to be generated
+        \param[in] ty The type of the array
 
         \return array of size \p dims
 
-        \ingroup data_func_randu
+        \ingroup random_func_randu
     */
     AFAPI array randu(const dim4 &dims, const dtype ty=f32);
 
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0
 
-        \ingroup data_func_randu
+        \ingroup random_func_randu
     */
     AFAPI array randu(const dim_t d0, const dtype ty=f32);
 
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] d1 The size of the second dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0 x \p d1
 
-        \ingroup data_func_randu
+        \ingroup random_func_randu
     */
     AFAPI array randu(const dim_t d0,
                       const dim_t d1, const dtype ty=f32);
 
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] d1 The size of the second dimension
+        \param[in] d2 The size of the third dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0 x \p d1 x \p d2
 
-        \ingroup data_func_randu
+        \ingroup random_func_randu
     */
     AFAPI array randu(const dim_t d0,
                       const dim_t d1, const dim_t d2, const dtype ty=f32);
 
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] d3 is the size of the fourth dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] d1 The size of the second dimension
+        \param[in] d2 The size of the third dimension
+        \param[in] d3 The size of the fourth dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0 x \p d1 x \p d2 x \p d3
 
-        \ingroup data_func_randu
+        \ingroup random_func_randu
     */
     AFAPI array randu(const dim_t d0,
                       const dim_t d1, const dim_t d2,
                       const dim_t d3, const dtype ty=f32);
 
     /**
-        \param[in] dims is the dimensions of the array to be generated
-        \param[in] ty is the type of the array
+        \param[in] dims The dimensions of the array to be generated
+        \param[in] ty The type of the array
 
         \return array of size \p dims
 
-        \ingroup data_func_randn
+        \ingroup random_func_randn
     */
     AFAPI array randn(const dim4 &dims, const dtype ty=f32);
 
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0
 
-        \ingroup data_func_randn
+        \ingroup random_func_randn
     */
     AFAPI array randn(const dim_t d0, const dtype ty=f32);
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] d1 The size of the second dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0 x \p d1
 
-        \ingroup data_func_randn
+        \ingroup random_func_randn
     */
     AFAPI array randn(const dim_t d0,
                       const dim_t d1, const dtype ty=f32);
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] d1 The size of the second dimension
+        \param[in] d2 The size of the third dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0 x \p d1 x \p d2
 
-        \ingroup data_func_randn
+        \ingroup random_func_randn
     */
     AFAPI array randn(const dim_t d0,
                       const dim_t d1, const dim_t d2, const dtype ty=f32);
 
     /**
-        \param[in] d0 is the size of the first dimension
-        \param[in] d1 is the size of the second dimension
-        \param[in] d2 is the size of the third dimension
-        \param[in] d3 is the size of the fourth dimension
-        \param[in] ty is the type of the array
+        \param[in] d0 The size of the first dimension
+        \param[in] d1 The size of the second dimension
+        \param[in] d2 The size of the third dimension
+        \param[in] d3 The size of the fourth dimension
+        \param[in] ty The type of the array
 
         \return array of size \p d0 x \p d1 x \p d2 x \p d3
 
-        \ingroup data_func_randn
+        \ingroup random_func_randn
     */
     AFAPI array randn(const dim_t d0,
                       const dim_t d1, const dim_t d2,
                       const dim_t d3, const dtype ty=f32);
 
     /**
-        \param[in] rtype is the type of the random number generator
+        \param[in] rtype The type of the random number generator
 
-        \ingroup data_func_set_type
+        \ingroup random_func_set_type
     */
-    AFAPI void setDefaultRandomEngine(randomType rtype);
+    AFAPI void setDefaultRandomEngine(randomEngineType rtype);
 
     /**
-        \param[in] seed is a 64 bit unsigned integer
+        \param[in] seed A 64 bit unsigned integer
 
-        \ingroup data_func_setseed
+        \ingroup random_func_setseed
     */
     AFAPI void setSeed(const uintl seed);
 
     /**
-        \returns seed which is a 64 bit unsigned integer
+        \returns seed A 64 bit unsigned integer
 
-        \ingroup data_func_getseed
+        \ingroup random_func_getseed
     */
     AFAPI uintl getSeed();
 
@@ -196,19 +303,19 @@ extern "C" {
     /**
        C Interface for creating random engine
 
-       \param[out]  engine is the pointer to the returned random engine object
-       \param[in]   rtype is the type of the random number generator
-       \param[in]   seed is the initializing seed of the random number generator
+       \param[out]  engine The pointer to the returned random engine object
+       \param[in]   rtype The type of the random number generator
+       \param[in]   seed The initializing seed of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_type rtype, uintl seed);
+    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_engine_type rtype, uintl seed);
 
     /**
        C Interface for retaining random engine
 
-       \param[out]  out is the pointer to the returned random engine object
-       \param[in]   engine is the random engine object
+       \param[out]  out The pointer to the returned random engine object
+       \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
@@ -217,63 +324,82 @@ extern "C" {
     /**
        C Interface for changing random engine type
 
-       \param[in]   engine is the random engine object
-       \param[in]   rtype is the type of the random number generator
+       \param[in]   engine The random engine object
+       \param[in]   rtype The type of the random number generator
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype);
+
+    /**
+       C Interface for getting random engine type
+
+       \param[out]  rtype The type of the random number generator
+       \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype);
+    AFAPI af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine);
 
     /**
        C Interface for creating an array of uniform numbers using a random engine
 
        \param[out]  out The pointer to the returned object.
-       \param[in]   engine is the random engine object
        \param[in]   ndims The number of dimensions read from the \p dims parameter
        \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
        \param[in]   type The type of the \ref af_array object
+       \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
 
     /**
        C Interface for creating an array of normal numbers using a random engine
 
        \param[out]  out The pointer to the returned object.
-       \param[in]   engine is the random engine object
        \param[in]   ndims The number of dimensions read from the \p dims parameter
        \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
        \param[in]   type The type of the \ref af_array object
+       \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_random_engine_normal(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
 
     /**
        C Interface for setting the seed of a random engine
 
-       \param[out]  engine is the random engine object
-       \param[in]   seed is the initializing seed of the random number generator
+       \param[out]  engine The pointer to the returned random engine object
+       \param[in]   seed The initializing seed of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed);
 
+    /**
+       C Interface for getting the default random engine
+
+       \param[out]  engine The pointer to returned default random engine object
+
+       \returns \ref AF_SUCCESS if the execution completes properly
+    */
+    AFAPI af_err af_get_default_random_engine(af_random_engine *engine);
+
     /**
        C Interface for setting the type of the default random engine
 
-       \param[in]   rtype is the type of the random number generator
+       \param[in]   rtype The type of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_set_default_random_engine(const af_random_type rtype);
+    AFAPI af_err af_set_default_random_engine(const af_random_engine_type rtype);
 
     /**
        C Interface for getting the seed of a random engine
 
-       \param[out]  out The pointer to the returned seed.
-       \param[in]   engine is the random engine object
+       \param[out]  seed The pointer to the returned seed.
+       \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
@@ -282,7 +408,7 @@ extern "C" {
     /**
        C Interface for releasing random engine
 
-       \param[in] engine is the random engine object
+       \param[in] engine The random engine object
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_release_random_engine(af_random_engine engine);
@@ -290,36 +416,36 @@ extern "C" {
     //General rand calls
 
     /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
+        \param[out] out The generated array
+        \param[in] ndims Size of dimension array \p dims
+        \param[in] dims The array containing sizes of the dimension
+        \param[in] type The type of array to generate
 
-       \ingroup data_func_randu
+       \ingroup random_func_randu
     */
     AFAPI af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
+        \param[out] out The generated array
+        \param[in] ndims Size of dimension array \p dims
+        \param[in] dims The array containing sizes of the dimension
+        \param[in] type The type of array to generate
 
-       \ingroup data_func_randn
+       \ingroup random_func_randn
     */
     AFAPI af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
-        \param[in] seed is a 64 bit unsigned integer
+        \param[in] seed A 64 bit unsigned integer
 
-        \ingroup data_func_setseed
+        \ingroup random_func_setseed
     */
     AFAPI af_err af_set_seed(const uintl seed);
 
     /**
-        \param[out] seed which is a 64 bit unsigned integer
+        \param[out] seed A 64 bit unsigned integer
 
-        \ingroup data_func_getseed
+        \ingroup random_func_getseed
     */
     AFAPI af_err af_get_seed(uintl *seed);
 
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index f4cae7a82e..433f0ee540 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -21,32 +21,13 @@
 #include <MersenneTwister.hpp>
 #include <types.hpp>
 
-using detail::uint;
-using detail::cfloat;
-using detail::cdouble;
-using detail::uchar;
-using detail::uniformDistribution;
-using detail::normalDistribution;
-using detail::initMersenneState;
-using detail::randu;
-using detail::randn;
-using detail::setSeed;
-using detail::getSeed;
-
-using common::MaxBlocks;
-using common::TableLength;
-using common::MtStateLength;
-using common::pos;
-using common::sh1;
-using common::sh2;
-using common::mask;
-using common::recursion_tbl;
-using common::temper_tbl;
+using namespace detail;
+using namespace common;
 
 class RandomEngine
 {
     public :
-    af_random_type type;
+    af_random_engine_type type;
     uintl seed;
     uintl counter;
     af_array pos;
@@ -57,7 +38,7 @@ class RandomEngine
     af_array temper_table;
     af_array state;
 
-    RandomEngine(void) : type(AF_RANDOM_THREEFRY), seed(0), counter(0) {}
+    RandomEngine(void) : type(AF_RANDOM_ENGINE_DEFAULT), seed(0), counter(0) {}
 };
 
 af_random_engine getRandomEngineHandle(const RandomEngine engine)
@@ -67,12 +48,6 @@ af_random_engine getRandomEngineHandle(const RandomEngine engine)
     return static_cast<af_random_engine>(engineHandle);
 }
 
-af_random_engine defaultRandomEngine(void)
-{
-    static RandomEngine r;
-    return static_cast<af_random_engine>(&r);
-}
-
 RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 {
     if (engineHandle == 0) {
@@ -84,7 +59,7 @@ RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 template<typename T>
 static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
-    if (e->type == AF_RANDOM_MERSENNE_GP11213) {
+    if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(uniformDistribution<T>(dims,
                     getArray<uint>(e->pos),
                     getArray<uint>(e->sh1),
@@ -101,7 +76,7 @@ static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *
 template<typename T>
 static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
-    if (e->type == AF_RANDOM_MERSENNE_GP11213) {
+    if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(normalDistribution<T>(dims,
                     getArray<uint>(e->pos),
                     getArray<uint>(e->sh1),
@@ -115,20 +90,27 @@ static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e
     }
 }
 
-static void validateRandomType(const af_random_type type)
+static void validateRandomType(const af_random_engine_type type)
 {
-    if ((type != AF_RANDOM_PHILOX_4X32_10)
-    &&  (type != AF_RANDOM_THREEFRY_2X32_16)
-    &&  (type != AF_RANDOM_MERSENNE_GP11213)
-    &&  (type != AF_RANDOM_PHILOX)
-    &&  (type != AF_RANDOM_THREEFRY)
-    &&  (type != AF_RANDOM_MERSENNE)
-    &&  (type != AF_RANDOM_DEFAULT)) {
+    if ((type != AF_RANDOM_ENGINE_PHILOX_4X32_10)
+    &&  (type != AF_RANDOM_ENGINE_THREEFRY_2X32_16)
+    &&  (type != AF_RANDOM_ENGINE_MERSENNE_GP11213)
+    &&  (type != AF_RANDOM_ENGINE_PHILOX)
+    &&  (type != AF_RANDOM_ENGINE_THREEFRY)
+    &&  (type != AF_RANDOM_ENGINE_MERSENNE)
+    &&  (type != AF_RANDOM_ENGINE_DEFAULT)) {
         AF_ERROR("Invalid random type", AF_ERR_ARG);
     }
 }
 
-af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, uintl seed)
+af_err af_get_default_random_engine(af_random_engine *r)
+{
+    static RandomEngine re;
+    *r = static_cast<af_random_engine> (&re);
+    return AF_SUCCESS;
+}
+
+af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_type rtype, uintl seed)
 {
     try {
         validateRandomType(rtype);
@@ -136,15 +118,27 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
         e.type = rtype;
         e.seed = seed;
         e.counter = 0;
-        if (rtype == AF_RANDOM_MERSENNE_GP11213) {
+
+        if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
             AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
             AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
             e.mask = mask;
             AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
             AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
-            e.state = getHandle(initMersenneState(seed, getArray<uint>(e.recursion_table)));
+            AF_CHECK(af_create_handle(&e.state, 1, &MtStateLength, u32));
+            initMersenneState(getWritableArray<uint>(e.state), seed, getArray<uint>(e.recursion_table));
+        } else {
+            dim_t empty = 0;
+            AF_CHECK(af_create_handle(&e.pos, 1, &empty, u32));
+            AF_CHECK(af_create_handle(&e.sh1, 1, &empty, u32));
+            AF_CHECK(af_create_handle(&e.sh2, 1, &empty, u32));
+            e.mask = 0;
+            AF_CHECK(af_create_handle(&e.recursion_table, 1, &empty, u32));
+            AF_CHECK(af_create_handle(&e.temper_table, 1, &empty, u32));
+            AF_CHECK(af_create_handle(&e.state, 1, &empty, u32));
         }
+
         *engineHandle = getRandomEngineHandle(e);
     } CATCHALL;
 
@@ -154,13 +148,13 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rt
 af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engine engineHandle)
 {
     try {
-
         RandomEngine engine = *(getRandomEngine(engineHandle));
         RandomEngine out;
 
         out.type = engine.type;
         out.seed = engine.seed;
         out.counter = engine.counter;
+
         AF_CHECK(af_retain_array(&out.pos, engine.pos));
         AF_CHECK(af_retain_array(&out.sh1, engine.sh1));
         AF_CHECK(af_retain_array(&out.sh2, engine.sh2));
@@ -175,23 +169,31 @@ af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engi
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype)
+af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype)
 {
     try {
+        AF_CHECK(af_init());
         validateRandomType(rtype);
         RandomEngine e = *(getRandomEngine(engine));
         if (rtype != e.type) {
-            if (rtype == AF_RANDOM_MERSENNE_GP11213) {
+            if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
                 bool empty;
                 AF_CHECK(af_is_empty(&empty, e.state));
                 if (empty) {
+                    AF_CHECK(af_release_array(e.pos));
+                    AF_CHECK(af_release_array(e.sh1));
+                    AF_CHECK(af_release_array(e.sh2));
+                    AF_CHECK(af_release_array(e.recursion_table));
+                    AF_CHECK(af_release_array(e.temper_table));
+                    AF_CHECK(af_release_array(e.state));
                     AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
                     AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
                     AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
                     e.mask = mask;
                     AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
                     AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
-                    e.state = getHandle(initMersenneState(e.seed, getArray<uint>(e.recursion_table)));
+                    AF_CHECK(af_create_handle(&e.state, 1, &MtStateLength, u32));
+                    initMersenneState(getWritableArray<uint>(e.state), e.seed, getArray<uint>(e.recursion_table));
                 }
             }
             e.type = rtype;
@@ -200,11 +202,22 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type
     return AF_SUCCESS;
 }
 
-af_err af_set_default_random_engine(const af_random_type rtype)
+af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine)
 {
     try {
         AF_CHECK(af_init());
-        af_random_engine e = defaultRandomEngine();
+        RandomEngine *e = getRandomEngine(engine);
+        *rtype = e->type;
+    } CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err af_set_default_random_engine(const af_random_engine_type rtype)
+{
+    try {
+        AF_CHECK(af_init());
+        af_random_engine e;
+        AF_CHECK(af_get_default_random_engine(&e));
         AF_CHECK(af_random_engine_set_type(&e, rtype));
     } CATCHALL;
     return AF_SUCCESS;
@@ -216,7 +229,7 @@ af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(*engine);
         e->seed = seed;
-        if (e->type == AF_RANDOM_MERSENNE_GP11213) {
+        if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             initMersenneState(getWritableArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
         } else {
             e->counter = 0;
@@ -237,7 +250,7 @@ af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
 {
     try {
         af_array result;
@@ -267,7 +280,7 @@ af_err af_random_engine_uniform(af_array *out, af_random_engine engine, const un
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_normal(af_array *out, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
 {
     try {
         af_array result;
@@ -293,7 +306,7 @@ af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
         RandomEngine *e = getRandomEngine(engineHandle);
-        if (e->type == AF_RANDOM_MERSENNE_GP11213) {
+        if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             AF_CHECK(af_release_array(e->pos));
             AF_CHECK(af_release_array(e->sh1));
             AF_CHECK(af_release_array(e->sh2));
@@ -313,7 +326,9 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_array result;
         AF_CHECK(af_init());
 
-        RandomEngine *e = getRandomEngine(defaultRandomEngine());
+        af_random_engine engine;
+        AF_CHECK(af_get_default_random_engine(&engine));
+        RandomEngine *e = getRandomEngine(engine);
         af::dim4 d = verifyDims(ndims, dims);
 
         switch(type) {
@@ -343,7 +358,9 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_array result;
         AF_CHECK(af_init());
 
-        RandomEngine *e = getRandomEngine(defaultRandomEngine());
+        af_random_engine engine;
+        AF_CHECK(af_get_default_random_engine(&engine));
+        RandomEngine *e = getRandomEngine(engine);
         af::dim4 d = verifyDims(ndims, dims);
 
         switch(type) {
@@ -362,8 +379,9 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
 af_err af_set_seed(const uintl seed)
 {
     try {
-        af_random_engine e = defaultRandomEngine();
-        AF_CHECK(af_random_engine_set_seed(&e, seed));
+        af_random_engine engine;
+        AF_CHECK(af_get_default_random_engine(&engine));
+        AF_CHECK(af_random_engine_set_seed(&engine, seed));
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -371,7 +389,9 @@ af_err af_set_seed(const uintl seed)
 af_err af_get_seed(uintl *seed)
 {
     try {
-        AF_CHECK(af_random_engine_get_seed(seed, defaultRandomEngine()));
+        af_random_engine e;
+        AF_CHECK(af_get_default_random_engine(&e));
+        AF_CHECK(af_random_engine_get_seed(seed, e));
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index 3b10c1e8a0..ddbed920cd 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -15,11 +15,16 @@
 
 namespace af
 {
-    randomEngine::randomEngine(randomType type, uintl seed)
+    randomEngine::randomEngine(randomEngineType type, uintl seed)
     {
         AF_THROW(af_create_random_engine(&engine, type, seed));
     }
 
+    randomEngine::randomEngine(void)
+    {
+        af_get_default_random_engine(&engine);
+    }
+
     randomEngine::randomEngine(const randomEngine& other)
     {
         if (this != &other) {
@@ -43,90 +48,49 @@ namespace af
         return *this;
     }
 
-    void randomEngine::setType(const randomType type)
+    randomEngineType randomEngine::getType(void)
     {
-        AF_THROW(af_random_engine_set_type(&engine, type));
+        af_random_engine_type type;
+        AF_THROW(af_random_engine_get_type(&type, engine));
+        return type;
     }
 
-    array randomEngine::uniform(const dim_t dim0, const dtype ty)
+    void randomEngine::setType(const randomEngineType type)
     {
-        dim4 d(dim0, 1, 1, 1);
-        return uniform(d, ty);
+        AF_THROW(af_random_engine_set_type(&engine, type));
     }
 
-    array randomEngine::uniform(const dim_t dim0, const dim_t dim1, const dtype ty)
+    void randomEngine::setSeed(const uintl seed)
     {
-        dim4 d(dim0, dim1, 1, 1);
-        return uniform(d, ty);
+        AF_THROW(af_random_engine_set_seed(&engine, seed));
     }
 
-    array randomEngine::uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty)
+    uintl randomEngine::getSeed(void) const
     {
-        dim4 d(dim0, dim1, dim2, 1);
-        return uniform(d, ty);
+        uintl seed;
+        AF_THROW(af_random_engine_get_seed(&seed, engine));
+        return seed;
     }
 
-    array randomEngine::uniform(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty)
+    af_random_engine randomEngine::get(void) const
     {
-        dim4 d(dim0, dim1, dim2, dim3);
-        return uniform(d, ty);
+        return engine;
     }
 
-    array randomEngine::uniform(const dim4& dims, const dtype ty)
+    AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r)
     {
         af_array out;
-        AF_THROW(af_random_engine_uniform(&out, engine, dims.ndims(), dims.get(), ty));
+        AF_THROW(af_random_uniform(&out, dims.ndims(), dims.get(), ty, r.get()));
         return array(out);
     }
 
-    array randomEngine::normal(const dim_t dim0, const dtype ty)
-    {
-        dim4 d(dim0, 1, 1, 1);
-        return normal(d, ty);
-    }
-
-    array randomEngine::normal(const dim_t dim0, const dim_t dim1, const dtype ty)
-    {
-        dim4 d(dim0, dim1, 1, 1);
-        return normal(d, ty);
-    }
-
-    array randomEngine::normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dtype ty)
-    {
-        dim4 d(dim0, dim1, dim2, 1);
-        return normal(d, ty);
-    }
-
-    array randomEngine::normal(const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3, const dtype ty)
-    {
-        dim4 d(dim0, dim1, dim2, dim3);
-        return normal(d, ty);
-    }
-
-    array randomEngine::normal(const dim4& dims, const dtype ty)
+    AFAPI array randn(const dim4 &dims, const dtype ty, randomEngine &r)
     {
         af_array out;
-        AF_THROW(af_random_engine_normal(&out, engine, dims.ndims(), dims.get(), ty));
+        AF_THROW(af_random_normal(&out, dims.ndims(), dims.get(), ty, r.get()));
         return array(out);
     }
 
-    void randomEngine::setSeed(const uintl seed)
-    {
-        AF_THROW(af_random_engine_set_seed(&engine, seed));
-    }
-
-    uintl randomEngine::getSeed(void) const
-    {
-        uintl seed;
-        AF_THROW(af_random_engine_get_seed(&seed, engine));
-        return seed;
-    }
-
-    af_random_engine randomEngine::get() const
-    {
-        return engine;
-    }
-
     array randu(const dim4 &dims, const af::dtype type)
     {
         af_array res;
@@ -189,7 +153,7 @@ namespace af
         return randn(dim4(d0, d1, d2, d3), ty);
     }
 
-    void setDefaultRandomEngine(randomType rtype)
+    void setDefaultRandomEngine(randomEngineType rtype)
     {
         AF_THROW(af_set_default_random_engine(rtype));
     }
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random_engine.cpp
index f2d94f4f02..27ee7e7f49 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random_engine.cpp
@@ -11,7 +11,12 @@
 #include <af/random_engine.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_random_engine(af_random_engine *engineHandle, af_random_type rtype, unsigned long long seed)
+af_err af_get_default_random_engine(af_random_engine *r)
+{
+    return CALL(r);
+}
+
+af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_type rtype, unsigned long long seed)
 {
     return CALL(engineHandle, rtype, seed);
 }
@@ -21,24 +26,29 @@ af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engi
     return CALL(outHandle, engineHandle);
 }
 
-af_err af_random_engine_set_type(af_random_engine *engine, const af_random_type rtype)
+af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine)
+{
+    return CALL(rtype, engine);
+}
+
+af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype)
 {
     return CALL(engine, rtype);
 }
 
-af_err af_set_default_random_engine(const af_random_type rtype)
+af_err af_set_default_random_engine(const af_random_engine_type rtype)
 {
     return CALL(rtype);
 }
 
-af_err af_random_engine_uniform(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+af_err af_random_uniform(af_array *arr, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
 {
-    return CALL(arr, engine, ndims, dims, type);
+    return CALL(arr, ndims, dims, type, engine);
 }
 
-af_err af_random_engine_normal(af_array *arr, af_random_engine engine, const unsigned ndims, const dim_t * const dims, const af_dtype type)
+af_err af_random_normal(af_array *arr, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
 {
-    return CALL(arr, engine, ndims, dims, type);
+    return CALL(arr, ndims, dims, type, engine);
 }
 
 af_err af_release_random_engine(af_random_engine engineHandle)
diff --git a/src/backend/MersenneTwister.hpp b/src/backend/MersenneTwister.hpp
index 5c1716f7bd..5bf847f990 100644
--- a/src/backend/MersenneTwister.hpp
+++ b/src/backend/MersenneTwister.hpp
@@ -58,17 +58,17 @@ namespace common
     const dim_t MersenneN = 351;
     const dim_t MtStateLength = MaxBlocks * MersenneN;
 
-    static int pos[] = {
+    static unsigned pos[] = {
     88, 84, 25, 42, 22, 11, 76, 11, 42, 60, 45, 80, 81, 16, 63, 38,
     3, 55, 9, 75, 70, 63, 32, 70, 58, 33, 18, 9, 14, 91, 90, 86,
     };
 
-    static int sh1[] = {
+    static unsigned sh1[] = {
     19, 15, 4, 20, 1, 16, 16, 15, 6, 6, 12, 6, 8, 1, 14, 28,
     30, 1, 9, 17, 15, 15, 7, 12, 21, 7, 7, 12, 16, 4, 10, 6,
     };
 
-    static int sh2[] = {
+    static unsigned sh2[] = {
     5, 12, 18, 9, 5, 1, 6, 16, 11, 11, 13, 9, 18, 19, 18, 1,
     2, 16, 15, 6, 6, 17, 15, 10, 2, 10, 13, 13, 3, 2, 14, 7,
     };
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 43e53b6051..bc8e998af6 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -255,21 +255,21 @@ namespace kernel
     }
 
     template <typename T>
-    void uniformDistributionCBRNG(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
+    void uniformDistributionCBRNG(T* out, size_t elements, af_random_engine_type type, const uintl seed, uintl counter)
     {
         switch(type) {
-            case AF_RANDOM_PHILOX_4X32_10   :   philoxUniform(out, elements, seed, counter); break;
-            case AF_RANDOM_THREEFRY_2X32_16 : threefryUniform(out, elements, seed, counter); break;
+            case AF_RANDOM_ENGINE_PHILOX_4X32_10   :   philoxUniform(out, elements, seed, counter); break;
+            case AF_RANDOM_ENGINE_THREEFRY_2X32_16 : threefryUniform(out, elements, seed, counter); break;
             default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }
 
     template <typename T>
-    void normalDistributionCBRNG(T* out, size_t elements, af_random_type type, const uintl seed, uintl counter)
+    void normalDistributionCBRNG(T* out, size_t elements, af_random_engine_type type, const uintl seed, uintl counter)
     {
         switch(type) {
-            case AF_RANDOM_PHILOX_4X32_10   :   philoxNormal(out, elements, seed, counter); break;
-            case AF_RANDOM_THREEFRY_2X32_16 : threefryNormal(out, elements, seed, counter); break;
+            case AF_RANDOM_ENGINE_PHILOX_4X32_10   :   philoxNormal(out, elements, seed, counter); break;
+            case AF_RANDOM_ENGINE_THREEFRY_2X32_16 : threefryNormal(out, elements, seed, counter); break;
             default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
     }
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 74d19f7309..a0e92162ad 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -11,28 +11,16 @@
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
 #include <cassert>
-#include <MersenneTwister.hpp>
-
-using common::MtStateLength;
-using common::MaxBlocks;
-using common::MersenneN;
 
 namespace cpu
 {
-    Array<uint> initMersenneState(const uintl seed, const Array<uint> tbl)
-    {
-        Array<uint> state = createEmptyArray<uint>(MtStateLength);
-        getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
-        return state;
-    }
-
     void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
     {
         getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
     }
 
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         getQueue().enqueue(kernel::uniformDistributionCBRNG<T>, out.get(), out.elements(), type, seed, counter);
@@ -41,7 +29,7 @@ namespace cpu
     }
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         getQueue().enqueue(kernel::normalDistributionCBRNG<T>, out.get(), out.elements(), type, seed, counter);
@@ -79,77 +67,77 @@ namespace cpu
         return out;
     }
 
-#define INSTANTIATE_UNIFORM(T)                                                                                          \
-    template                                                                                                            \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter); \
-    template                                                                                                            \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                               \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                  \
-
-#define INSTANTIATE_NORMAL(T)                                                                                           \
-    template                                                                                                            \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter);  \
-    template                                                                                                            \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                  \
-
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                             \
-    template<>                                                                                                          \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)  \
-    {                                                                                                                   \
-        Array<T> out = createEmptyArray<T>(dims);                                                                       \
-        TR *outPtr = (TR*)out.get();                                                                                    \
-        size_t elements = out.elements()*2;                                                                             \
-        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                \
-        counter += elements;                                                                                            \
-        return out;                                                                                                     \
-    }                                                                                                                   \
-    template<>                                                                                                          \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                               \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                   \
-    {                                                                                                                   \
-        Array<T> out = createEmptyArray<T>(dims);                                                                       \
-        TR *outPtr = (TR*)out.get();                                                                                    \
-        size_t elements = out.elements()*2;                                                                             \
-        getQueue().enqueue(kernel::uniformDistributionMT<TR>,                                                           \
-              outPtr, elements,                                                                                         \
-              state.get(), pos.get(),                                                                                   \
-              sh1.get(), sh2.get(),                                                                                     \
-              mask, recursion_table.get(),                                                                              \
-              temper_table.get());                                                                                      \
-        return out;                                                                                                     \
-    }                                                                                                                   \
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                              \
-    template<>                                                                                                          \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl seed, uintl &counter)   \
-    {                                                                                                                   \
-        Array<T> out = createEmptyArray<T>(dims);                                                                       \
-        TR *outPtr = (TR*)out.get();                                                                                    \
-        size_t elements = out.elements()*2;                                                                             \
-        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                 \
-        counter += elements;                                                                                            \
-        return out;                                                                                                     \
-    }                                                                                                                   \
-    template<>                                                                                                          \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                               \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                   \
-    {                                                                                                                   \
-        Array<T> out = createEmptyArray<T>(dims);                                                                       \
-        TR *outPtr = (TR*)out.get();                                                                                    \
-        size_t elements = out.elements()*2;                                                                             \
-        getQueue().enqueue(kernel::normalDistributionMT<TR>,                                                            \
-              outPtr, elements,                                                                                         \
-              state.get(), pos.get(),                                                                                   \
-              sh1.get(), sh2.get(),                                                                                     \
-              mask, recursion_table.get(),                                                                              \
-              temper_table.get());                                                                                      \
-        return out;                                                                                                     \
-    }                                                                                                                   \
+#define INSTANTIATE_UNIFORM(T)                                                                                                  \
+    template                                                                                                                    \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter);  \
+    template                                                                                                                    \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+
+#define INSTANTIATE_NORMAL(T)                                                                                                   \
+    template                                                                                                                    \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter);   \
+    template                                                                                                                    \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                     \
+    template<>                                                                                                                  \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)   \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        TR *outPtr = (TR*)out.get();                                                                                            \
+        size_t elements = out.elements()*2;                                                                                     \
+        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                        \
+        counter += elements;                                                                                                    \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+    template<>                                                                                                                  \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        TR *outPtr = (TR*)out.get();                                                                                            \
+        size_t elements = out.elements()*2;                                                                                     \
+        getQueue().enqueue(kernel::uniformDistributionMT<TR>,                                                                   \
+              outPtr, elements,                                                                                                 \
+              state.get(), pos.get(),                                                                                           \
+              sh1.get(), sh2.get(),                                                                                             \
+              mask, recursion_table.get(),                                                                                      \
+              temper_table.get());                                                                                              \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                  \
+    template<>                                                                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)\
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        TR *outPtr = (TR*)out.get();                                                                                        \
+        size_t elements = out.elements()*2;                                                                                 \
+        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                     \
+        counter += elements;                                                                                                \
+        return out;                                                                                                         \
+    }                                                                                                                       \
+    template<>                                                                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
+    {                                                                                                                       \
+        Array<T> out = createEmptyArray<T>(dims);                                                                           \
+        TR *outPtr = (TR*)out.get();                                                                                        \
+        size_t elements = out.elements()*2;                                                                                 \
+        getQueue().enqueue(kernel::normalDistributionMT<TR>,                                                                \
+              outPtr, elements,                                                                                             \
+              state.get(), pos.get(),                                                                                       \
+              sh1.get(), sh2.get(),                                                                                         \
+              mask, recursion_table.get(),                                                                                  \
+              temper_table.get());                                                                                          \
+        return out;                                                                                                         \
+    }                                                                                                                       \
 
     INSTANTIATE_UNIFORM(float )
     INSTANTIATE_UNIFORM(double)
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index 10e9e240e6..ac24ad277f 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -20,10 +20,10 @@ namespace cpu
     void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
 
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const unsigned long long seed, unsigned long long &counter);
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const unsigned long long seed, unsigned long long &counter);
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const unsigned long long seed, unsigned long long &counter);
 
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims,
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 4e976c3eed..56b03b872c 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -620,7 +620,7 @@ namespace kernel
     }
 
     template <typename T>
-    void uniformDistributionCBRNG(T* out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
+    void uniformDistributionCBRNG(T* out, size_t elements, const af_random_engine_type type, const uintl &seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
@@ -628,9 +628,9 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         switch (type) {
-        case AF_RANDOM_PHILOX_4X32_10   :
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
             CUDA_LAUNCH(uniformPhilox, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY_2X32_16 :
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
             CUDA_LAUNCH(uniformThreefry, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
@@ -638,7 +638,7 @@ namespace kernel
     }
 
     template <typename T>
-    void normalDistributionCBRNG(T *out, size_t elements, const af_random_type type, const uintl &seed, uintl &counter)
+    void normalDistributionCBRNG(T *out, size_t elements, const af_random_engine_type type, const uintl &seed, uintl &counter)
     {
         int threads = THREADS;
         int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
@@ -646,9 +646,9 @@ namespace kernel
         uint hi = seed>>32;
         uint lo = seed;
         switch (type) {
-        case AF_RANDOM_PHILOX_4X32_10   :
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
             CUDA_LAUNCH(normalPhilox, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
-        case AF_RANDOM_THREEFRY_2X32_16 :
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
             CUDA_LAUNCH(normalThreefry, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 9a89d642a0..1200801880 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -11,28 +11,16 @@
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
 #include <cassert>
-#include <MersenneTwister.hpp>
-
-using common::MtStateLength;
-using common::MaxBlocks;
-using common::MersenneN;
 
 namespace cuda
 {
-    Array<uint> initMersenneState(const uintl seed, const Array<uint> tbl)
-    {
-        Array<uint> state = createEmptyArray<uint>(MtStateLength);
-        kernel::initMersenneState(state.get(), tbl.get(), seed);
-        return state;
-    }
-
     void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
     {
         kernel::initMersenneState(state.get(), tbl.get(), seed);
     }
 
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         kernel::uniformDistributionCBRNG<T>(out.get(), out.elements(), type, seed, counter);
@@ -40,7 +28,7 @@ namespace cuda
     }
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         kernel::normalDistributionCBRNG<T>(out.get(), out.elements(), type, seed, counter);
@@ -77,75 +65,75 @@ namespace cuda
         return out;
     }
 
-#define INSTANTIATE_UNIFORM(T)                                                                                              \
-    template                                                                                                                \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);    \
-    template                                                                                                                \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
-
-#define INSTANTIATE_NORMAL(T)                                                                                               \
-    template                                                                                                                \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);     \
-    template                                                                                                                \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
-
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                 \
-    template<>                                                                                                              \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)     \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        TR *outPtr = (TR*)out.get();                                                                                        \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                        \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-    template<>                                                                                                              \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        TR *outPtr = (TR*)out.get();                                                                                        \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::uniformDistributionMT<TR>(                                                                                  \
-              outPtr, elements,                                                                                             \
-              state.get(), pos.get(),                                                                                       \
-              sh1.get(), sh2.get(),                                                                                         \
-              mask, recursion_table.get(),                                                                                  \
-              temper_table.get());                                                                                          \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                  \
-    template<>                                                                                                              \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)      \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        TR *outPtr = (TR*)out.get();                                                                                        \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                         \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-    template<>                                                                                                              \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        TR *outPtr = (TR*)out.get();                                                                                        \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::normalDistributionMT<TR>(                                                                                   \
-              outPtr, elements,                                                                                             \
-              state.get(), pos.get(),                                                                                       \
-              sh1.get(), sh2.get(),                                                                                         \
-              mask, recursion_table.get(),                                                                                  \
-              temper_table.get());                                                                                          \
-        return out;                                                                                                         \
-    }                                                                                                                       \
+#define INSTANTIATE_UNIFORM(T)                                                                                                  \
+    template                                                                                                                    \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter); \
+    template                                                                                                                    \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+
+#define INSTANTIATE_NORMAL(T)                                                                                                   \
+    template                                                                                                                    \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);  \
+    template                                                                                                                    \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                     \
+    template<>                                                                                                                  \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)  \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        TR *outPtr = (TR*)out.get();                                                                                            \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                            \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+    template<>                                                                                                                  \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        TR *outPtr = (TR*)out.get();                                                                                            \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::uniformDistributionMT<TR>(                                                                                      \
+              outPtr, elements,                                                                                                 \
+              state.get(), pos.get(),                                                                                           \
+              sh1.get(), sh2.get(),                                                                                             \
+              mask, recursion_table.get(),                                                                                      \
+              temper_table.get());                                                                                              \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                      \
+    template<>                                                                                                                  \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)   \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        TR *outPtr = (TR*)out.get();                                                                                            \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                             \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+    template<>                                                                                                                  \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        TR *outPtr = (TR*)out.get();                                                                                            \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::normalDistributionMT<TR>(                                                                                       \
+              outPtr, elements,                                                                                                 \
+              state.get(), pos.get(),                                                                                           \
+              sh1.get(), sh2.get(),                                                                                             \
+              mask, recursion_table.get(),                                                                                      \
+              temper_table.get());                                                                                              \
+        return out;                                                                                                             \
+    }                                                                                                                           \
 
     INSTANTIATE_UNIFORM(float )
     INSTANTIATE_UNIFORM(double)
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index 86d8af78a1..f33ba590df 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -20,10 +20,10 @@ namespace cuda
     void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
 
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
 
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims,
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 9879bbed35..29693a35fb 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -48,7 +48,7 @@ namespace opencl
         static const uint THREADS = 256;
 
         template <typename T>
-        static Kernel get_random_engine_kernel(const af_random_type type, const int kerIdx, const uint elementsPerBlock)
+        static Kernel get_random_engine_kernel(const af_random_engine_type type, const int kerIdx, const uint elementsPerBlock)
         {
             using std::string;
             using std::to_string;
@@ -58,17 +58,17 @@ namespace opencl
             ker_strs[0] = random_engine_write_cl;
             ker_lens[0] = random_engine_write_cl_len;
             switch (type) {
-                case AF_RANDOM_PHILOX_4X32_10   :
+                case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
                     engineName = "Philox";
                     ker_strs[1] = random_engine_philox_cl;
                     ker_lens[1] = random_engine_philox_cl_len;
                     break;
-                case AF_RANDOM_THREEFRY_2X32_16 :
+                case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
                     engineName = "Threefry";
                     ker_strs[1] = random_engine_threefry_cl;
                     ker_lens[1] = random_engine_threefry_cl_len;
                     break;
-                case AF_RANDOM_MERSENNE_GP11213 :
+                case AF_RANDOM_ENGINE_MERSENNE_GP11213 :
                     engineName = "Mersenne";
                     ker_strs[1] = random_engine_mersenne_cl;
                     ker_lens[1] = random_engine_mersenne_cl_len;
@@ -89,7 +89,7 @@ namespace opencl
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D THREADS=" << THREADS
                         << " -D RAND_DIST=" << kerIdx;
-                if (type != AF_RANDOM_MERSENNE_GP11213) {
+                if (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) {
                     options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
                 }
                 if (std::is_same<T, double>::value) {
@@ -138,7 +138,7 @@ namespace opencl
 
         template <typename T>
         static void randomDistribution(cl::Buffer out, const size_t elements,
-                const af_random_type type, const uintl &seed, uintl &counter, int kerIdx)
+                const af_random_engine_type type, const uintl &seed, uintl &counter, int kerIdx)
         {
             try {
                 uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
@@ -150,7 +150,7 @@ namespace opencl
                 NDRange local(THREADS, 1);
                 NDRange global(THREADS * groups, 1);
 
-                if ((type == AF_RANDOM_PHILOX_4X32_10) || (type == AF_RANDOM_THREEFRY_2X32_16)) {
+                if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) || (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
                     Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
                     auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
                     randomEngineOp(EnqueueArgs(getQueue(), global, local),
@@ -179,7 +179,7 @@ namespace opencl
 
                 NDRange local(threads, 1);
                 NDRange global(threads * blocks, 1);
-                Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_MERSENNE_GP11213, kerIdx, elementsPerBlock);
+                Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213, kerIdx, elementsPerBlock);
                 auto randomEngineOp = KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
                      uint, cl::Buffer, cl::Buffer, uint, uint>(ker);
                 randomEngineOp(EnqueueArgs(getQueue(), global, local),
@@ -192,14 +192,14 @@ namespace opencl
 
         template <typename T>
         void uniformDistributionCBRNG(cl::Buffer out, const size_t elements,
-                const af_random_type type, const uintl &seed, uintl &counter)
+                const af_random_engine_type type, const uintl &seed, uintl &counter)
         {
             randomDistribution<T>(out, elements, type, seed, counter, 0);
         }
 
         template <typename T>
         void normalDistributionCBRNG(cl::Buffer out, const size_t elements,
-                const af_random_type type, const uintl &seed, uintl &counter)
+                const af_random_engine_type type, const uintl &seed, uintl &counter)
         {
             randomDistribution<T>(out, elements, type, seed, counter, 1);
         }
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 6474821831..500ba904a2 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -11,28 +11,16 @@
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
 #include <cassert>
-#include <MersenneTwister.hpp>
-
-using common::MtStateLength;
-using common::MaxBlocks;
-using common::MersenneN;
 
 namespace opencl
 {
-    Array<uint> initMersenneState(const uintl seed, const Array<uint> tbl)
-    {
-        Array<uint> state = createEmptyArray<uint>(MtStateLength);
-        kernel::initMersenneState(*state.get(), *tbl.get(), seed);
-        return state;
-    }
-
     void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
     {
         kernel::initMersenneState(*state.get(), *tbl.get(), seed);
     }
 
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed, counter);
@@ -40,7 +28,7 @@ namespace opencl
     }
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
     {
         Array<T> out = createEmptyArray<T>(dims);
         kernel::normalDistributionCBRNG<T>(*out.get(), out.elements(), type, seed, counter);
@@ -77,71 +65,71 @@ namespace opencl
         return out;
     }
 
-#define INSTANTIATE_UNIFORM(T)                                                                                              \
-    template                                                                                                                \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);    \
-    template                                                                                                                \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
-
-#define INSTANTIATE_NORMAL(T)                                                                                               \
-    template                                                                                                                \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);     \
-    template                                                                                                                \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                      \
-
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                 \
-    template<>                                                                                                              \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)     \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                    \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-    template<>                                                                                                              \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                   \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::uniformDistributionMT<TR>(                                                                                  \
-              *out.get(), elements,                                                                                         \
-              *state.get(), *pos.get(),                                                                                     \
-              *sh1.get(), *sh2.get(),                                                                                       \
-              mask, *recursion_table.get(),                                                                                 \
-              *temper_table.get());                                                                                         \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                  \
-    template<>                                                                                                              \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter)      \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                     \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-    template<>                                                                                                              \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        size_t elements = out.elements()*2;                                                                                 \
-        kernel::normalDistributionMT<TR>(                                                                                   \
-              *out.get(), elements,                                                                                         \
-              *state.get(), *pos.get(),                                                                                     \
-              *sh1.get(), *sh2.get(),                                                                                       \
-              mask, *recursion_table.get(),                                                                                 \
-              *temper_table.get());                                                                                         \
-        return out;                                                                                                         \
-    }                                                                                                                       \
+#define INSTANTIATE_UNIFORM(T)                                                                                                  \
+    template                                                                                                                    \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter); \
+    template                                                                                                                    \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+
+#define INSTANTIATE_NORMAL(T)                                                                                                   \
+    template                                                                                                                    \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);  \
+    template                                                                                                                    \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                     \
+    template<>                                                                                                                  \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)  \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                        \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+    template<>                                                                                                                  \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::uniformDistributionMT<TR>(                                                                                      \
+              *out.get(), elements,                                                                                             \
+              *state.get(), *pos.get(),                                                                                         \
+              *sh1.get(), *sh2.get(),                                                                                           \
+              mask, *recursion_table.get(),                                                                                     \
+              *temper_table.get());                                                                                             \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                      \
+    template<>                                                                                                                  \
+    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)   \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                         \
+        return out;                                                                                                             \
+    }                                                                                                                           \
+    template<>                                                                                                                  \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
+            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
+            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
+    {                                                                                                                           \
+        Array<T> out = createEmptyArray<T>(dims);                                                                               \
+        size_t elements = out.elements()*2;                                                                                     \
+        kernel::normalDistributionMT<TR>(                                                                                       \
+              *out.get(), elements,                                                                                             \
+              *state.get(), *pos.get(),                                                                                         \
+              *sh1.get(), *sh2.get(),                                                                                           \
+              mask, *recursion_table.get(),                                                                                     \
+              *temper_table.get());                                                                                             \
+        return out;                                                                                                             \
+    }                                                                                                                           \
 
     INSTANTIATE_UNIFORM(float )
     INSTANTIATE_UNIFORM(double)
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index c11e695360..edd74c4558 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -20,10 +20,10 @@ namespace opencl
     void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
 
     template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
 
     template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_type type, const uintl &seed, uintl &counter);
+    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
 
     template<typename T>
     Array<T> uniformDistribution(const af::dim4 &dims,
diff --git a/test/random.cpp b/test/random.cpp
index 7e08934c06..03109f9974 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -25,7 +25,7 @@ using af::cfloat;
 using af::cdouble;
 using af::array;
 using af::randomEngine;
-using af::randomType;
+using af::randomEngineType;
 using af::mean;
 using af::stdev;
 
@@ -274,14 +274,14 @@ TYPED_TEST(Random, getSeed)
 }
 
 template <typename T>
-void testRandomEngineUniform(randomType type)
+void testRandomEngineUniform(randomEngineType type)
 {
     if (noDoubleTests<T>()) return;
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     int elem = 16*1024*1024;
     af::randomEngine r(type, 0);
-    array A = r.uniform(elem, ty);
+    array A = randu(elem, ty, r);
     T m = mean<T>(A);
     T s = stdev<T>(A);
     ASSERT_NEAR(m, 0.5, 1e-3);
@@ -289,14 +289,14 @@ void testRandomEngineUniform(randomType type)
 }
 
 template <typename T>
-void testRandomEngineNormal(randomType type)
+void testRandomEngineNormal(randomEngineType type)
 {
     if (noDoubleTests<T>()) return;
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
 
     int elem = 16*1024*1024;
     af::randomEngine r(type, 0);
-    array A = r.normal(elem, ty);
+    array A = randn(elem, ty, r);
     T m = mean<T>(A);
     T s = stdev<T>(A);
     ASSERT_NEAR(m, 0, 1e-1);
@@ -305,36 +305,36 @@ void testRandomEngineNormal(randomType type)
 
 TYPED_TEST(RandomEngine, philoxRandomEngineUniform)
 {
-    testRandomEngineUniform<TypeParam>(AF_RANDOM_PHILOX_4X32_10);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
 TYPED_TEST(RandomEngine, philoxRandomEngineNormal)
 {
-    testRandomEngineNormal<TypeParam>(AF_RANDOM_PHILOX_4X32_10);
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
 TYPED_TEST(RandomEngine, threefryRandomEngineUniform)
 {
-    testRandomEngineUniform<TypeParam>(AF_RANDOM_THREEFRY_2X32_16);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
 TYPED_TEST(RandomEngine, threefryRandomEngineNormal)
 {
-    testRandomEngineNormal<TypeParam>(AF_RANDOM_THREEFRY_2X32_16);
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
 TYPED_TEST(RandomEngine, mersenneRandomEngineUniform)
 {
-    testRandomEngineUniform<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
+    testRandomEngineUniform<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
 
 TYPED_TEST(RandomEngine, mersenneRandomEngineNormal)
 {
-    testRandomEngineNormal<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
+    testRandomEngineNormal<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
 
 template <typename T>
-void testRandomEngineSeed(randomType type)
+void testRandomEngineSeed(randomEngineType type)
 {
     int elem = 4*32*1024;
     uintl orig_seed = 0;
@@ -342,12 +342,12 @@ void testRandomEngineSeed(randomType type)
     af::randomEngine e(type, orig_seed);
 
     af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
-    array d1 = e.uniform(elem, ty);
+    array d1 = randu(elem, ty, e);
     e.setSeed(new_seed);
-    array d2 = e.uniform(elem, ty);
+    array d2 = randu(elem, ty, e);
     e.setSeed(orig_seed);
-    array d3 = e.uniform(elem, ty);
-    array d4 = e.uniform(elem, ty);
+    array d3 = randu(elem, ty, e);
+    array d4 = randu(elem, ty, e);
 
     std::vector<T> h1(elem);
     std::vector<T> h2(elem);
@@ -370,15 +370,15 @@ void testRandomEngineSeed(randomType type)
 
 TYPED_TEST(RandomEngineSeed, philoxSeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_PHILOX_4X32_10);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
 TYPED_TEST(RandomEngineSeed, threefrySeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_THREEFRY_2X32_16);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
 TYPED_TEST(RandomEngineSeed, mersenneSeedUniform)
 {
-    testRandomEngineSeed<TypeParam>(AF_RANDOM_MERSENNE_GP11213);
+    testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }

From a94c36e6bd41f697e9ac95b835869cde493046ba Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 2 Sep 2016 15:45:20 -0400
Subject: [PATCH 0834/2677] Describe af_random_engine_type enums

---
 include/af/defines.h | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index d3365d732e..0cd2e44919 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -402,13 +402,13 @@ typedef enum {
 
 #if AF_API_VERSION >=34
 typedef enum {
-    AF_RANDOM_ENGINE_PHILOX_4X32_10 = 100,
-    AF_RANDOM_ENGINE_THREEFRY_2X32_16 = 200,
-    AF_RANDOM_ENGINE_MERSENNE_GP11213 = 300,
-    AF_RANDOM_ENGINE_PHILOX = AF_RANDOM_ENGINE_PHILOX_4X32_10,
-    AF_RANDOM_ENGINE_THREEFRY = AF_RANDOM_ENGINE_THREEFRY_2X32_16,
-    AF_RANDOM_ENGINE_MERSENNE = AF_RANDOM_ENGINE_MERSENNE_GP11213,
-    AF_RANDOM_ENGINE_DEFAULT = AF_RANDOM_ENGINE_PHILOX
+    AF_RANDOM_ENGINE_PHILOX_4X32_10     = 100,                                  //Philox variant with N = 4, W = 32 and Rounds = 10
+    AF_RANDOM_ENGINE_THREEFRY_2X32_16   = 200,                                  //Threefry variant with N = 2, W = 32 and Rounds = 16
+    AF_RANDOM_ENGINE_MERSENNE_GP11213   = 300,                                  //Mersenne variant with MEXP = 11213
+    AF_RANDOM_ENGINE_PHILOX             = AF_RANDOM_ENGINE_PHILOX_4X32_10,      //Resolves to Philox 4x32_10
+    AF_RANDOM_ENGINE_THREEFRY           = AF_RANDOM_ENGINE_THREEFRY_2X32_16,    //Resolves to Threefry 2X32_16
+    AF_RANDOM_ENGINE_MERSENNE           = AF_RANDOM_ENGINE_MERSENNE_GP11213,    //Resolves to Mersenne GP 11213
+    AF_RANDOM_ENGINE_DEFAULT            = AF_RANDOM_ENGINE_PHILOX               //Resolves to Philox
 } af_random_engine_type;
 #endif
 

From 6aeba2a585fbffbd2368364b66659ce3735dd7ef Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 2 Sep 2016 15:55:05 -0400
Subject: [PATCH 0835/2677] Added missing af_init()

---
 src/api/c/random_engine.cpp | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 433f0ee540..e7242a2456 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -113,6 +113,7 @@ af_err af_get_default_random_engine(af_random_engine *r)
 af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_type rtype, uintl seed)
 {
     try {
+        AF_CHECK(af_init());
         validateRandomType(rtype);
         RandomEngine e;
         e.type = rtype;
@@ -148,6 +149,7 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_
 af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engine engineHandle)
 {
     try {
+        AF_CHECK(af_init());
         RandomEngine engine = *(getRandomEngine(engineHandle));
         RandomEngine out;
 
@@ -253,8 +255,8 @@ af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
 af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
 {
     try {
-        af_array result;
         AF_CHECK(af_init());
+        af_array result;
 
         af::dim4 d = verifyDims(ndims, dims);
         RandomEngine *e = getRandomEngine(engine);
@@ -283,8 +285,8 @@ af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * cons
 af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
 {
     try {
-        af_array result;
         AF_CHECK(af_init());
+        af_array result;
 
         af::dim4 d = verifyDims(ndims, dims);
         RandomEngine *e = getRandomEngine(engine);
@@ -305,6 +307,7 @@ af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const
 af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
+        AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(engineHandle);
         if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             AF_CHECK(af_release_array(e->pos));
@@ -323,8 +326,8 @@ af_err af_release_random_engine(af_random_engine engineHandle)
 af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     try {
-        af_array result;
         AF_CHECK(af_init());
+        af_array result;
 
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
@@ -355,8 +358,8 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, c
 af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
 {
     try {
-        af_array result;
         AF_CHECK(af_init());
+        af_array result;
 
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
@@ -379,6 +382,7 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
 af_err af_set_seed(const uintl seed)
 {
     try {
+        AF_CHECK(af_init());
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
         AF_CHECK(af_random_engine_set_seed(&engine, seed));
@@ -389,6 +393,7 @@ af_err af_set_seed(const uintl seed)
 af_err af_get_seed(uintl *seed)
 {
     try {
+        AF_CHECK(af_init());
         af_random_engine e;
         AF_CHECK(af_get_default_random_engine(&e));
         AF_CHECK(af_random_engine_get_seed(seed, e));

From 47f532f49287dc6b8ed19d7952656da0907b0a11 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 2 Sep 2016 15:58:14 -0400
Subject: [PATCH 0836/2677] BUGFIX: Fixing boundary conditions for approx2

- Also includes code cleanup
---
 src/backend/cpu/kernel/approx2.hpp   | 35 +++++++-----------------
 src/backend/cuda/kernel/approx.hpp   | 40 ++++++++++------------------
 src/backend/opencl/kernel/approx2.cl | 37 +++++++++----------------
 3 files changed, 37 insertions(+), 75 deletions(-)

diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
index 16d14aa0e0..5879cdd80c 100644
--- a/src/backend/cpu/kernel/approx2.hpp
+++ b/src/backend/cpu/kernel/approx2.hpp
@@ -191,34 +191,19 @@ struct approx2_op<InT, LocT, AF_INTERP_CUBIC>
         }
 
         // used for setting values at boundaries
-        bool condXl = (grid_x < 1);
-        bool condYl = (grid_y < 1);
-        bool condXg = (grid_x > idims[0] - 3);
-        bool condYg = (grid_y > idims[1] - 3);
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < idims[1], grid_y + 2 < idims[1]};
+        int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
         //for bicubic interpolation, work with 4x4 patch at a time
         InT patch[4][4];
-
-        //assumption is that inner patch consisting of 4 points is minimum requirement for bicubic interpolation
-        //inner square
-        patch[1][1] = in[ioff];
-        patch[1][2] = in[ioff + 1];
-        patch[2][1] = in[ioff + istrides[1]];
-        patch[2][2] = in[ioff + istrides[1] + 1];
-        //outer sides
-        patch[0][1] = (condYl)? in[ioff]     : in[ioff - istrides[1]];
-        patch[0][2] = (condYl)? in[ioff + 1] : in[ioff - istrides[1] + 1];
-        patch[3][1] = (condYg)? in[ioff + istrides[1]]     : in[ioff + 2 * istrides[1]];
-        patch[3][2] = (condYg)? in[ioff + istrides[1] + 1] : in[ioff + 2 * istrides[1] + 1];
-        patch[1][0] = (condXl)? in[ioff] : in[ioff - 1];
-        patch[2][0] = (condXl)? in[ioff + istrides[1]] : in[ioff + istrides[1] - 1];
-        patch[1][3] = (condXg)? in[ioff + 1] : in[ioff + 2];
-        patch[2][3] = (condXg)? in[ioff + istrides[1] + 1] : in[ioff + istrides[1] + 2];
-        //corners
-        patch[0][0] = (condXl || condYl)? in[ioff] : in[ioff - istrides[1] - 1]    ;
-        patch[0][3] = (condYl || condXg)? in[ioff + 1] : in[ioff - istrides[1] + 1]    ;
-        patch[3][0] = (condXl || condYg)? in[ioff + istrides[1]] : in[ioff + 2 * istrides[1] - 1];
-        patch[3][3] = (condXg || condYg)? in[ioff + istrides[1] + 1] : in[ioff + 2 * istrides[1] + 2];
+        for (int j = 0; j < 4; j++) {
+            int ioff_j = ioff + offY[j] * istrides[1];
+            for (int i = 0; i < 4; i++) {
+                patch[j][i] = in[ioff_j + offX[i]];
+            }
+        }
 
         // Write Final Value
         out[omId] = bicubicInterpolate(patch, off_x, off_y);
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index cc2a875194..e5f6349e4c 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -253,37 +253,25 @@ namespace cuda
             const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
             const Tp off_x  = x - grid_x, off_y  = y - grid_y;    // fractional offset
 
-            // used for setting values at boundaries
-            bool condXl = (grid_x < 1);
-            bool condYl = (grid_y < 1);
-            bool condXg = (grid_x > in.dims[0] - 3);
-            bool condYg = (grid_y > in.dims[1] - 3);
-
             dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
 
             //for bicubic interpolation, work with 4x4 patch at a time
             Ty patch[4][4];
 
-            //assumption is that inner patch consisting of 4 points is minimum requirement for bicubic interpolation
-            //inner square
-            patch[1][1] = in.ptr[ioff];
-            patch[1][2] = in.ptr[ioff + 1];
-            patch[2][1] = in.ptr[ioff + in.strides[1]];
-            patch[2][2] = in.ptr[ioff + in.strides[1] + 1];
-            //outer sides
-            patch[0][1] = (condYl)? in.ptr[ioff]     : in.ptr[ioff - in.strides[1]];
-            patch[0][2] = (condYl)? in.ptr[ioff + 1] : in.ptr[ioff - in.strides[1] + 1];
-            patch[3][1] = (condYg)? in.ptr[ioff + in.strides[1]]     : in.ptr[ioff + 2 * in.strides[1]];
-            patch[3][2] = (condYg)? in.ptr[ioff + in.strides[1] + 1] : in.ptr[ioff + 2 * in.strides[1] + 1];
-            patch[1][0] = (condXl)? in.ptr[ioff] : in.ptr[ioff - 1];
-            patch[2][0] = (condXl)? in.ptr[ioff + in.strides[1]] : in.ptr[ioff + in.strides[1] - 1];
-            patch[1][3] = (condXg)? in.ptr[ioff + 1] : in.ptr[ioff + 2];
-            patch[2][3] = (condXg)? in.ptr[ioff + in.strides[1] + 1] : in.ptr[ioff + in.strides[1] + 2];
-            //corners
-            patch[0][0] = (condXl || condYl)? in.ptr[ioff] : in.ptr[ioff - in.strides[1] - 1]    ;
-            patch[0][3] = (condYl || condXg)? in.ptr[ioff + 1] : in.ptr[ioff - in.strides[1] + 1]    ;
-            patch[3][0] = (condXl || condYg)? in.ptr[ioff + in.strides[1]] : in.ptr[ioff + 2 * in.strides[1] - 1];
-            patch[3][3] = (condXg || condYg)? in.ptr[ioff + in.strides[1] + 1] : in.ptr[ioff + 2 * in.strides[1] + 2];
+            // used for setting values at boundaries
+            bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+            bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+            int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+            int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+#pragma unroll
+            for (int j = 0; j < 4; j++) {
+                int ioff_j = ioff + offY[j] * in.strides[1];
+#pragma unroll
+                for (int i = 0; i < 4; i++) {
+                    patch[j][i] = in.ptr[ioff_j + offX[i]];
+                }
+            }
 
             out.ptr[omId] = bicubicInterpolate<Ty, Tp>(patch, off_x, off_y);
         }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 4378734786..7762bc9c67 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -163,35 +163,24 @@ void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t
     const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
 
     dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
+
     // used for setting values at boundaries
-    bool condXl = (grid_x < 1);
-    bool condYl = (grid_y < 1);
-    bool condXg = (grid_x > in.dims[0] - 3);
-    bool condYg = (grid_y > in.dims[1] - 3);
+    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+    int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+    int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
     //for bicubic interpolation, work with 4x4 patch at a time
     Ty patch[4][4];
 
-    //assumption is that inner patch consisting of 4 points is minimum requirement for bicubic interpolation
-    //inner square
-    patch[1][1] = d_in[ioff];
-    patch[1][2] = d_in[ioff + 1];
-    patch[2][1] = d_in[ioff + in.strides[1]];
-    patch[2][2] = d_in[ioff + in.strides[1] + 1];
-    //outer sides
-    patch[0][1] = (condYl)? d_in[ioff]     : d_in[ioff - in.strides[1]];
-    patch[0][2] = (condYl)? d_in[ioff + 1] : d_in[ioff - in.strides[1] + 1];
-    patch[3][1] = (condYg)? d_in[ioff + in.strides[1]]     : d_in[ioff + 2 * in.strides[1]];
-    patch[3][2] = (condYg)? d_in[ioff + in.strides[1] + 1] : d_in[ioff + 2 * in.strides[1] + 1];
-    patch[1][0] = (condXl)? d_in[ioff] : d_in[ioff - 1];
-    patch[2][0] = (condXl)? d_in[ioff + in.strides[1]] : d_in[ioff + in.strides[1] - 1];
-    patch[1][3] = (condXg)? d_in[ioff + 1] : d_in[ioff + 2];
-    patch[2][3] = (condXg)? d_in[ioff + in.strides[1] + 1] : d_in[ioff + in.strides[1] + 2];
-    //corners
-    patch[0][0] = (condXl || condYl)? d_in[ioff] : d_in[ioff - in.strides[1] - 1]    ;
-    patch[0][3] = (condYl || condXg)? d_in[ioff + 1] : d_in[ioff - in.strides[1] + 1]    ;
-    patch[3][0] = (condXl || condYg)? d_in[ioff + in.strides[1]] : d_in[ioff + 2 * in.strides[1] - 1];
-    patch[3][3] = (condXg || condYg)? d_in[ioff + in.strides[1] + 1] : d_in[ioff + 2 * in.strides[1] + 2];
+#pragma unroll
+    for (int j = 0; j < 4; j++) {
+        int ioff_j = ioff + offY[j] * in.strides[1];
+#pragma unroll
+        for (int i = 0; i < 4; i++) {
+            patch[j][i] = d_in[ioff_j + offX[i]];
+        }
+    }
 
     set(d_out[omId], bicubicInterpolate(patch, off_x, off_y));
 }

From 3979349c750c64718627ebfea135749603aa41ee Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 2 Sep 2016 16:00:10 -0400
Subject: [PATCH 0837/2677] FEAT: Adding bicubic interpolation enum

---
 include/af/defines.h | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/include/af/defines.h b/include/af/defines.h
index 9f09ff4238..89ba7e9d2b 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -226,6 +226,9 @@ typedef enum {
     AF_INTERP_LINEAR,   ///< Linear Interpolation
     AF_INTERP_BILINEAR, ///< Bilinear Interpolation
     AF_INTERP_CUBIC,    ///< Cubic Interpolation
+#if AF_API_VERSION >= 34
+    AF_INTERP_BICUBIC,  ///< Bicubic Interpolation
+#endif
     AF_INTERP_LOWER     ///< Floor Indexed
 } af_interp_type;
 

From f7e65f5aad93ac3f5d27d58232adc9fc8fa89513 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 2 Sep 2016 16:03:06 -0400
Subject: [PATCH 0838/2677] Fixing approx2 to accept bilinear and bicubic enums

---
 src/api/c/approx.cpp          |  4 ++-
 src/backend/cpu/approx.cpp    | 58 ++++++++++++++++++-----------------
 src/backend/cuda/approx.cu    | 46 ++++++++++++++-------------
 src/backend/opencl/approx.cpp | 46 ++++++++++++++-------------
 test/approx2.cpp              |  1 -
 5 files changed, 81 insertions(+), 74 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 56a5f851a8..542e94ffbd 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -105,7 +105,9 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
                       (pdims[2] == idims[2] && pdims[3] == idims[3]));
         ARG_ASSERT(3, (method == AF_INTERP_LINEAR  ||
                        method == AF_INTERP_NEAREST ||
-                       method == AF_INTERP_CUBIC));
+                       method == AF_INTERP_CUBIC   ||
+                       method == AF_INTERP_BILINEAR ||
+                       method == AF_INTERP_BICUBIC));
 
         if(idims.ndims() == 0 || pdims.ndims() ==  0 || qdims.ndims() == 0) {
             dim_t my_dims[] = { 0, 0, 0, 0 };
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index c3736e44f3..89d5c0f4ea 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -30,20 +30,20 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
     Array<Ty> out = createEmptyArray<Ty>(odims);
 
     switch(method) {
-        case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
-                               out, in, pos, offGrid);
-            break;
-        case AF_INTERP_LINEAR:
-            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
-                               out, in, pos, offGrid);
-            break;
-        case AF_INTERP_CUBIC:
-            getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_CUBIC>,
-                               out, in, pos, offGrid);
-            break;
-        default:
-            break;
+    case AF_INTERP_NEAREST:
+        getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
+                           out, in, pos, offGrid);
+        break;
+    case AF_INTERP_LINEAR:
+        getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
+                           out, in, pos, offGrid);
+        break;
+    case AF_INTERP_CUBIC:
+        getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_CUBIC>,
+                           out, in, pos, offGrid);
+        break;
+    default:
+        break;
     }
     return out;
 }
@@ -64,20 +64,22 @@ Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &p
     Array<Ty> out = createEmptyArray<Ty>(odims);
 
     switch(method) {
-        case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
-                               out, in, pos0, pos1, offGrid);
-            break;
-        case AF_INTERP_LINEAR:
-            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
-                               out, in, pos0, pos1, offGrid);
-            break;
-        case AF_INTERP_CUBIC:
-            getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_CUBIC>,
-                               out, in, pos0, pos1, offGrid);
-            break;
-        default:
-            break;
+    case AF_INTERP_NEAREST:
+        getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
+                           out, in, pos0, pos1, offGrid);
+        break;
+    case AF_INTERP_LINEAR:
+    case AF_INTERP_BILINEAR:
+        getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
+                           out, in, pos0, pos1, offGrid);
+        break;
+    case AF_INTERP_CUBIC:
+    case AF_INTERP_BICUBIC:
+        getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_CUBIC>,
+                           out, in, pos0, pos1, offGrid);
+        break;
+    default:
+        break;
     }
     return out;
 }
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cu
index 6ae3a50f12..fc276332b3 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cu
@@ -27,17 +27,17 @@ namespace cuda
         Array<Ty> out = createEmptyArray<Ty>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::approx1<Ty, Tp, AF_INTERP_NEAREST> (out, in, pos, offGrid);
-                break;
-            case AF_INTERP_LINEAR:
-                kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
-                break;
-            case AF_INTERP_CUBIC:
-                kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
-                break;
-            default:
-                break;
+        case AF_INTERP_NEAREST:
+            kernel::approx1<Ty, Tp, AF_INTERP_NEAREST> (out, in, pos, offGrid);
+            break;
+        case AF_INTERP_LINEAR:
+            kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
+            break;
+        case AF_INTERP_CUBIC:
+            kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
+            break;
+        default:
+            break;
         }
         return out;
     }
@@ -55,17 +55,19 @@ namespace cuda
         Array<Ty> out = createEmptyArray<Ty>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::approx2<Ty, Tp, AF_INTERP_NEAREST> (out, in, pos0, pos1, offGrid);
-                break;
-            case AF_INTERP_LINEAR:
-                kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
-                break;
-            case AF_INTERP_CUBIC:
-                kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
-                break;
-            default:
-                break;
+        case AF_INTERP_NEAREST:
+            kernel::approx2<Ty, Tp, AF_INTERP_NEAREST> (out, in, pos0, pos1, offGrid);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_BILINEAR:
+            kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_BICUBIC:
+            kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
+            break;
+        default:
+            break;
         }
         return out;
     }
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index dfbae5efdb..4e344d27e9 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -26,17 +26,17 @@ namespace opencl
         Array<Ty> out = createEmptyArray<Ty>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>(out, in, pos, offGrid);
-                break;
-            case AF_INTERP_LINEAR:
-                kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
-                break;
-            case AF_INTERP_CUBIC:
-                kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
-                break;
-            default:
-                break;
+        case AF_INTERP_NEAREST:
+            kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>(out, in, pos, offGrid);
+            break;
+        case AF_INTERP_LINEAR:
+            kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
+            break;
+        case AF_INTERP_CUBIC:
+            kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
+            break;
+        default:
+            break;
         }
         return out;
     }
@@ -53,17 +53,19 @@ namespace opencl
         Array<Ty> out = createEmptyArray<Ty>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>(out, in, pos0, pos1, offGrid);
-                break;
-            case AF_INTERP_LINEAR:
-                kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
-                break;
-            case AF_INTERP_CUBIC:
-                kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
-                break;
-            default:
-                break;
+        case AF_INTERP_NEAREST:
+            kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>(out, in, pos0, pos1, offGrid);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_BILINEAR:
+            kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_BICUBIC:
+            kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
+            break;
+        default:
+            break;
         }
         return out;
     }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index d15fcb3a06..6a37cb6dc3 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -158,7 +158,6 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
     APPROX2_ARGS(Approx2NearestArgsPos3D,      approx2_pos3d,   0, AF_INTERP_NEAREST,  AF_ERR_SIZE);
     APPROX2_ARGS(Approx2LinearArgsPos3D,       approx2_pos3d,   1, AF_INTERP_LINEAR,   AF_ERR_SIZE);
     APPROX2_ARGS(Approx2NearestArgsPosUnequal, approx2_unequal, 0, AF_INTERP_NEAREST,  AF_ERR_SIZE);
-    APPROX2_ARGS(Approx2ArgsInterpBilinear,    approx2,         0, AF_INTERP_BILINEAR, AF_ERR_ARG);
 
 template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)

From a3fcb8ecf24071e4e3611c25e0d1bde390ed5174 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 2 Sep 2016 20:33:36 -0400
Subject: [PATCH 0839/2677] CUDA: Cleaning up approx1 and apporx2

- Reuse as much code base as possible
- Moving interp functions into stand alone files
- Making it modular so it can be later used by resize / transform
---
 src/backend/cuda/kernel/approx.hpp | 331 ++++-------------------------
 src/backend/cuda/kernel/interp.hpp | 198 +++++++++++++++++
 2 files changed, 241 insertions(+), 288 deletions(-)
 create mode 100644 src/backend/cuda/kernel/interp.hpp

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index e5f6349e4c..b0b6ea09f9 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include "interp.hpp"
 
 namespace cuda
 {
@@ -22,267 +23,13 @@ namespace cuda
         static const int TY = 16;
         static const int THREADS = 256;
 
-        ///////////////////////////////////////////////////////////////////////////
-        // nearest-neighbor resampling
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        void core_nearest1(const int idx, const int idy, const int idz, const int idw,
-                           Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
-                           const float offGrid, const bool pBatch)
-        {
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                             + idy * out.strides[1] + idx;
-            int pmId = idx;
-            if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
-
-            const Tp x = pos.ptr[pmId];
-            if (x < 0 || in.dims[0] < x+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
-            const int iMem = round(x) + ioff;
-
-            Ty yt = in.ptr[iMem];
-            out.ptr[omId] = yt;
-        }
-
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        void core_nearest2(const int idx, const int idy, const int idz, const int idw,
-                           Param<Ty> out, CParam<Ty> in,
-                           CParam<Tp> pos, CParam<Tp> qos, const float offGrid, const bool pBatch)
-        {
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                             + idy * out.strides[1] + idx;
-            int pmId = idy * pos.strides[1] + idx;
-            int qmId = idy * qos.strides[1] + idx;
-            if(pBatch) {
-                pmId += idw * pos.strides[3] + idz * pos.strides[2];
-                qmId += idw * qos.strides[3] + idz * qos.strides[2];
-            }
-
-            const Tp x = pos.ptr[pmId], y = qos.ptr[qmId];
-            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            const int grid_x = round(x), grid_y = round(y); // nearest grid
-            const int imId = idw * in.strides[3] + idz * in.strides[2]
-                          + grid_y * in.strides[1] + grid_x;
-
-            Ty val = in.ptr[imId];
-            out.ptr[omId] = val;
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // linear resampling
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        void core_linear1(const int idx, const int idy, const int idz, const int idw,
-                          Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
-                          const float offGrid, const bool pBatch)
-        {
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                             + idy * out.strides[1] + idx;
-            int pmId = idx;
-            if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
-
-            const Tp pVal = pos.ptr[pmId];
-            if (pVal < 0 || in.dims[0] < pVal+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            const int grid_x = floor(pVal);  // nearest grid
-            const Tp off_x = pVal - grid_x; // fractional offset
-
-            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
-
-            // Check if pVal and pVal + 1 are both valid indices
-            bool cond = (pVal < in.dims[0] - 1);
-            // Compute Left and Right Weighted Values
-            Ty yl = ((Tp)1.0 - off_x) * in.ptr[ioff];
-            Ty yr = cond ? (off_x) * in.ptr[ioff + 1] : scalar<Ty>(0);
-            Ty yo = yl + yr;
-            // Compute Weight used
-            Tp wt = cond ? (Tp)1.0 : (Tp)(1.0 - off_x);
-            // Write final value
-            out.ptr[omId] = (yo / wt);
-        }
-
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        void core_linear2(const int idx, const int idy, const int idz, const int idw,
-                           Param<Ty> out, CParam<Ty> in,
-                           CParam<Tp> pos, CParam<Tp> qos, const float offGrid, const bool pBatch)
-        {
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                             + idy * out.strides[1] + idx;
-            int pmId = idy * pos.strides[1] + idx;
-            int qmId = idy * qos.strides[1] + idx;
-            if(pBatch) {
-                pmId += idw * pos.strides[3] + idz * pos.strides[2];
-                qmId += idw * qos.strides[3] + idz * qos.strides[2];
-            }
-
-
-            const Tp x = pos.ptr[pmId], y = qos.ptr[qmId];
-            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            const int grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-            const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
-
-            int ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
-
-            // Check if pVal and pVal + 1 are both valid indices
-            bool condY = (y < in.dims[1] - 1);
-            bool condX = (x < in.dims[0] - 1);
-
-            // Compute weights used
-            Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
-            Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
-            Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
-            Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-            Tp wt = wt00 + wt10 + wt01 + wt11;
-
-            // Compute Weighted Values
-            Ty zero = scalar<Ty>(0);
-            Ty y00 =                    wt00 * in.ptr[ioff];
-            Ty y10 = (condY) ?          wt10 * in.ptr[ioff + in.strides[1]]     : zero;
-            Ty y01 = (condX) ?          wt01 * in.ptr[ioff + 1]                 : zero;
-            Ty y11 = (condX && condY) ? wt11 * in.ptr[ioff + in.strides[1] + 1] : zero;
-            Ty yo = y00 + y10 + y01 + y11;
-
-            // Write Final Value
-            out.ptr[omId] = (yo / wt);
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // cubic resampling
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        void core_cubic1(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
-                          Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
-                          const float offGrid, const bool pBatch)
-        {
-            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
-                             + idy * out.strides[1] + idx;
-            dim_t pmId = idx;
-            if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
-            const Tp pVal = pos.ptr[pmId];
-            if (pVal < 0 || in.dims[0] < pVal+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            const dim_t grid_x = floor(pVal);  // nearest grid
-            const Tp off_x = pVal - grid_x;    // fractional offset
-
-            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
-
-            // Check if x-1, x, and x+1, x+2 are both valid indices
-            bool condr  = (grid_x > 0);
-            bool condl1 = (grid_x < in.dims[0] - 1);
-            bool condl2 = (grid_x < in.dims[0] - 2);
-
-            //compute basis function values
-            Tp h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
-            Tp h10 = off_x * (1 - off_x) * (1 - off_x);
-            Tp h01 = off_x * off_x * (3 - 2 * off_x);
-            Tp h11 = off_x * off_x * (off_x - 1);
-
-            // Compute Left and Right points and tangents
-            Ty pl = in.ptr[ioff];
-            Ty pr = condl1  ? in.ptr[ioff + 1] : in.ptr[ioff];
-            Ty tl = condr   ? scalar<Ty>(0.5) * (in.ptr[ioff + 1] - in.ptr[ioff - 1]) : (in.ptr[ioff + 1] - in.ptr[ioff]);
-            Ty tr = condl2  ? scalar<Ty>(0.5) * (in.ptr[ioff + 2] - in.ptr[ioff]) : (condl1) ? in.ptr[ioff + 1] - in.ptr[ioff] : (in.ptr[ioff] - in.ptr[ioff - 1]);
-
-            // Write final value
-            out.ptr[omId] = h00 * pl + h10 * tl + h01 * pr + h11 * tr;
-        }
-
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        Ty cubicInterpolate(Ty p[4], Tp x) {
-            return p[1] + scalar<Ty>(0.5) * x * (p[2] - p[0] + x * (scalar<Ty>(2.0) * p[0] - scalar<Ty>(5.0) * p[1] + scalar<Ty>(4.0) * p[2] - p[3] + x*(scalar<Ty>(3.0)*(p[1] - p[2]) + p[3] - p[0])));
-        }
-
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        Ty bicubicInterpolate(Ty p[4][4], Tp x, Tp y) {
-            Ty arr[4];
-            arr[0] = cubicInterpolate(p[0], x);
-            arr[1] = cubicInterpolate(p[1], x);
-            arr[2] = cubicInterpolate(p[2], x);
-            arr[3] = cubicInterpolate(p[3], x);
-            return cubicInterpolate(arr, y);
-        }
-
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
-                           Param<Ty> out, CParam<Ty> in,
-                           CParam<Tp> pos, CParam<Tp> qos, const float offGrid, const bool pBatch)
-        {
-            const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
-                             + idy * out.strides[1] + idx;
-            dim_t pmId = idy * pos.strides[1] + idx;
-            dim_t qmId = idy * qos.strides[1] + idx;
-            if(pBatch) {
-                pmId += idw * pos.strides[3] + idz * pos.strides[2];
-                qmId += idw * qos.strides[3] + idz * qos.strides[2];
-            }
-
-
-            const Tp x = pos.ptr[pmId], y = qos.ptr[qmId];
-            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-            const Tp off_x  = x - grid_x, off_y  = y - grid_y;    // fractional offset
-
-            dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
-
-            //for bicubic interpolation, work with 4x4 patch at a time
-            Ty patch[4][4];
-
-            // used for setting values at boundaries
-            bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-            bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
-            int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-            int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
-
-#pragma unroll
-            for (int j = 0; j < 4; j++) {
-                int ioff_j = ioff + offY[j] * in.strides[1];
-#pragma unroll
-                for (int i = 0; i < 4; i++) {
-                    patch[j][i] = in.ptr[ioff_j + offX[i]];
-                }
-            }
-
-            out.ptr[omId] = bicubicInterpolate<Ty, Tp>(patch, off_x, off_y);
-        }
-
         ///////////////////////////////////////////////////////////////////////////
         // Approx Kernel
         ///////////////////////////////////////////////////////////////////////////
         template<typename Ty, typename Tp, af_interp_type method>
         __global__
-        void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> pos,
-                            const float offGrid, const int blocksMatX, const bool pBatch)
+        void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> xpos,
+                            const float offGrid, const int blocksMatX, const bool batch)
         {
             const int idw = blockIdx.y / out.dims[2];
             const int idz = blockIdx.y - idw * out.dims[2];
@@ -295,26 +42,28 @@ namespace cuda
                 idz >= out.dims[2] || idw >= out.dims[3])
                 return;
 
-            switch(method) {
-                case AF_INTERP_NEAREST:
-                    core_nearest1(idx, idy, idz, idw, out, in, pos, offGrid, pBatch);
-                    break;
-                case AF_INTERP_LINEAR:
-                    core_linear1(idx, idy, idz, idw, out, in, pos, offGrid, pBatch);
-                    break;
-                case AF_INTERP_CUBIC:
-                    core_cubic1(idx, idy, idz, idw, out, in, pos, offGrid, pBatch);
-                    break;
-                default:
-                    break;
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
+                                               + idy * out.strides[1] + idx;
+            int xmid = idx;
+            if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
+
+            const Tp x = xpos.ptr[xmid];
+            if (x < 0 || in.dims[0] < x+1) {
+                out.ptr[omId] = scalar<Ty>(offGrid);
+                return;
             }
+
+            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
+
+            Interp1<Ty, Tp, method> interp;
+            out.ptr[omId] = interp(in, ioff, x);
         }
 
         template<typename Ty, typename Tp, af_interp_type method>
         __global__
         void approx2_kernel(Param<Ty> out, CParam<Ty> in,
-                      CParam<Tp> pos, CParam<Tp> qos, const float offGrid,
-                      const int blocksMatX, const int blocksMatY, const bool pBatch)
+                            CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
+                            const int blocksMatX, const int blocksMatY, const bool batch)
         {
             const int idz = blockIdx.x / blocksMatX;
             const int idw = blockIdx.y / blocksMatY;
@@ -329,19 +78,25 @@ namespace cuda
                 idz >= out.dims[2] || idw >= out.dims[3])
                 return;
 
-            switch(method) {
-                case AF_INTERP_NEAREST:
-                    core_nearest2(idx, idy, idz, idw, out, in, pos, qos, offGrid, pBatch);
-                    break;
-                case AF_INTERP_LINEAR:
-                    core_linear2(idx, idy, idz, idw, out, in, pos, qos, offGrid, pBatch);
-                    break;
-                case AF_INTERP_CUBIC:
-                    core_cubic2(idx, idy, idz, idw, out, in, pos, qos, offGrid, pBatch);
-                    break;
-                default:
-                    break;
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
+                + idy * out.strides[1] + idx;
+            int xmid = idy * xpos.strides[1] + idx;
+            int ymid = idy * ypos.strides[1] + idx;
+            if(batch) {
+                xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
+                ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
             }
+
+            const Tp x = xpos.ptr[xmid], y = ypos.ptr[ymid];
+            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
+                out.ptr[omId] = scalar<Ty>(offGrid);
+                return;
+            }
+
+            int ioff = idw * in.strides[3] + idz * in.strides[2];
+
+            Interp2<Ty, Tp, method> interp;
+            out.ptr[omId] = interp(in, ioff, x, y);
         }
 
         ///////////////////////////////////////////////////////////////////////////
@@ -349,32 +104,32 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         template <typename Ty, typename Tp, af_interp_type method>
         void approx1(Param<Ty> out, CParam<Ty> in,
-               CParam<Tp> pos, const float offGrid)
+               CParam<Tp> xpos, const float offGrid)
         {
             dim3 threads(THREADS, 1, 1);
             int blocksPerMat = divup(out.dims[0], threads.x);
             dim3 blocks(blocksPerMat * out.dims[1], out.dims[2] * out.dims[3]);
 
-            bool pBatch = !(pos.dims[1] == 1 && pos.dims[2] == 1 && pos.dims[3] == 1);
+            bool batch = !(xpos.dims[1] == 1 && xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
             CUDA_LAUNCH((approx1_kernel<Ty, Tp, method>), blocks, threads,
-                         out, in, pos, offGrid, blocksPerMat, pBatch);
+                         out, in, xpos, offGrid, blocksPerMat, batch);
             POST_LAUNCH_CHECK();
         }
 
         template <typename Ty, typename Tp, af_interp_type method>
         void approx2(Param<Ty> out, CParam<Ty> in,
-                    CParam<Tp> pos, CParam<Tp> qos, const float offGrid)
+                    CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid)
         {
             dim3 threads(TX, TY, 1);
             int blocksPerMatX = divup(out.dims[0], threads.x);
             int blocksPerMatY = divup(out.dims[1], threads.y);
             dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3]);
 
-            bool pBatch = !(pos.dims[2] == 1 && pos.dims[3] == 1);
+            bool batch = !(xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
             CUDA_LAUNCH((approx2_kernel<Ty, Tp, method>), blocks, threads,
-                         out, in, pos, qos, offGrid, blocksPerMatX, blocksPerMatY, pBatch);
+                         out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch);
             POST_LAUNCH_CHECK();
         }
     }
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
new file mode 100644
index 0000000000..be28534a67
--- /dev/null
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -0,0 +1,198 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+namespace cuda
+{
+    namespace kernel
+    {
+
+        template<typename Ty, typename Tp>
+        __device__
+        Ty linearInterpFunc(Ty val[2], Tp frac)
+        {
+            return (1 - frac) * val[0] + frac * val[1];
+        }
+
+        template<typename Ty, typename Tp>
+        __device__
+        Ty bilinearInterpFunc(Ty val[2][2], Tp xfrac, Tp yfrac)
+        {
+            Ty res[2];
+            res[0] = linearInterpFunc(val[0], xfrac);
+            res[1] = linearInterpFunc(val[1], xfrac);
+            return linearInterpFunc(res, yfrac);
+        }
+
+        template<typename Ty, typename Tp>
+        __device__ inline static
+        Ty cubicInterpFunc(Ty val[4], Tp xfrac)
+        {
+            Ty a0 =
+                scalar<Ty>(-0.5) * val[0] + scalar<Ty>( 1.5) * val[1] +
+                scalar<Ty>(-1.5) * val[2] + scalar<Ty>( 0.5) * val[3];
+
+            Ty a1 =
+                scalar<Ty>( 1.0) * val[0] + scalar<Ty>(-2.5) * val[1] +
+                scalar<Ty>( 2.0) * val[2] + scalar<Ty>(-0.5) * val[3];
+
+            Ty a2 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(0.5) * val[2];
+
+            Ty a3 = val[1];
+
+            Tp xfrac2 = xfrac * xfrac;
+            Tp xfrac3 = xfrac2 * xfrac;
+
+            return a0 * xfrac3 + a1 * xfrac2 + a2 * xfrac + a3;
+        }
+
+        template<typename Ty, typename Tp>
+        __device__ inline static
+        Ty bicubicInterpFunc(Ty val[4][4], Tp xfrac, Tp yfrac) {
+            Ty res[4];
+            res[0] = cubicInterpFunc(val[0], xfrac);
+            res[1] = cubicInterpFunc(val[1], xfrac);
+            res[2] = cubicInterpFunc(val[2], xfrac);
+            res[3] = cubicInterpFunc(val[3], xfrac);
+            return cubicInterpFunc(res, yfrac);
+        }
+
+        template<typename Ty, typename Tp, af_interp_type>
+        struct Interp1
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x)
+            {
+                const int idx = round(x) + ioff;
+                return in.ptr[idx];
+            }
+        };
+
+        template<typename Ty, typename Tp>
+        struct Interp1<Ty, Tp, AF_INTERP_LINEAR>
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x)
+            {
+                const int grid_x = floor(x);    // nearest grid
+                const Tp off_x = x - grid_x;    // fractional offset
+                const int idx = ioff + grid_x;
+                Ty val[2] = {in.ptr[idx], x + 1 < in.dims[0] ? in.ptr[idx + 1] : scalar<Ty>(0)};
+                return linearInterpFunc(val, off_x);
+            }
+        };
+
+        template<typename Ty, typename Tp>
+        struct Interp1<Ty, Tp, AF_INTERP_CUBIC>
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x)
+            {
+                const int grid_x = floor(x);    // nearest grid
+                const Tp off_x = x - grid_x;    // fractional offset
+                const int idx = ioff + grid_x;
+
+                bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+                int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+                Ty val[4];
+                for (int i = 0; i < 4; i++) {
+                    val[i] = in.ptr[idx + off[i]];
+                }
+                return cubicInterpFunc(val, off_x);
+            }
+        };
+
+        template<typename Ty, typename Tp, af_interp_type>
+        struct Interp2
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
+            {
+                const int idx = ioff + round(y) * in.strides[1] + round(x);
+                return in.ptr[idx];
+            }
+        };
+
+        template<typename Ty, typename Tp>
+        struct Interp2<Ty, Tp, AF_INTERP_BILINEAR>
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
+            {
+                const int grid_x = floor(x);
+                const Tp off_x = x - grid_x;
+
+                const int grid_y = floor(y);
+                const Tp off_y = y - grid_y;
+
+                const int idx = ioff + grid_y * in.strides[1] + grid_x;
+
+                bool condX[2] = {true, x + 1 < in.dims[0]};
+                bool condY[2] = {true, y + 1 < in.dims[1]};
+
+                Ty val[2][2];
+                for (int j = 0; j < 2; j++) {
+                    int off_y = idx + j * in.strides[1];
+                    for (int i = 0; i < 2; i++) {
+                        val[j][i] = condX[i] && condY[j] ? in.ptr[off_y + i] : scalar<Ty>(0);
+                    }
+                }
+
+                return bilinearInterpFunc(val, off_x, off_y);
+            }
+        };
+
+        template<typename Ty, typename Tp>
+        struct Interp2<Ty, Tp, AF_INTERP_LINEAR>
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
+            {
+                return Interp2<Ty, Tp, AF_INTERP_BILINEAR>()(in, ioff, x, y);
+            }
+        };
+
+        template<typename Ty, typename Tp>
+        struct Interp2<Ty, Tp, AF_INTERP_BICUBIC>
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
+            {
+                const int grid_x = floor(x);
+                const Tp off_x = x - grid_x;
+
+                const int grid_y = floor(y);
+                const Tp off_y = y - grid_y;
+
+                const int idx = ioff + grid_y * in.strides[1] + grid_x;
+
+                //for bicubic interpolation, work with 4x4 val at a time
+                Ty val[4][4];
+
+                // used for setting values at boundaries
+                bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+                bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+                int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+                int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+#pragma unroll
+                for (int j = 0; j < 4; j++) {
+                    int ioff_j = idx + offY[j] * in.strides[1];
+#pragma unroll
+                    for (int i = 0; i < 4; i++) {
+                        val[j][i] = in.ptr[ioff_j + offX[i]];
+                    }
+                }
+
+                return bicubicInterpFunc(val, off_x, off_y);
+            }
+        };
+
+        template<typename Ty, typename Tp>
+        struct Interp2<Ty, Tp, AF_INTERP_CUBIC>
+        {
+            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
+            {
+                return Interp2<Ty, Tp, AF_INTERP_BICUBIC>()(in, ioff, x, y);
+            }
+        };
+    }
+}

From 995b9cd5f693235e9ac7a1e7357fcab94b1ab46d Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 3 Sep 2016 02:55:15 -0400
Subject: [PATCH 0840/2677] CPU: cleaning up approx1 and approx2

- Reuse as much code base as possible
- Create interp.hpp that can be used by resize and transform
---
 src/backend/cpu/approx.cpp         |   3 +-
 src/backend/cpu/kernel/approx.hpp  | 115 +++++++++++++
 src/backend/cpu/kernel/approx1.hpp | 194 ----------------------
 src/backend/cpu/kernel/approx2.hpp | 249 -----------------------------
 src/backend/cpu/kernel/interp.hpp  | 214 +++++++++++++++++++++++++
 5 files changed, 330 insertions(+), 445 deletions(-)
 create mode 100644 src/backend/cpu/kernel/approx.hpp
 delete mode 100644 src/backend/cpu/kernel/approx1.hpp
 delete mode 100644 src/backend/cpu/kernel/approx2.hpp
 create mode 100644 src/backend/cpu/kernel/interp.hpp

diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 89d5c0f4ea..7ac9922c76 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <approx.hpp>
-#include <kernel/approx1.hpp>
-#include <kernel/approx2.hpp>
+#include <kernel/approx.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
new file mode 100644
index 0000000000..8ff47de5dc
--- /dev/null
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -0,0 +1,115 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <math.hpp>
+#include "interp.hpp"
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename InT, typename LocT, af_interp_type Method>
+void approx1(Array<InT> output, const Array<InT> input,
+             const Array<LocT> xposition, const float offGrid)
+{
+    InT * out = output.get();
+    const LocT *xpos = xposition.get();
+
+    const af::dim4 odims     = output.dims();
+    const af::dim4 idims     = input.dims();
+    const af::dim4 xdims     = xposition.dims();
+
+    const af::dim4 ostrides  = output.strides();
+    const af::dim4 istrides  = input.strides();
+    const af::dim4 xstrides  = xposition.strides();
+
+    Interp1<InT, LocT, Method> interp;
+    bool batch = !(xdims[1] == 1 && xdims[2] == 1 && xdims[3] == 1);
+
+    for(dim_t idw = 0; idw < odims[3]; idw++) {
+        for(dim_t idz = 0; idz < odims[2]; idz++) {
+            dim_t ooffzw = idw * ostrides[3] + idz * ostrides[2];
+            dim_t ioffzw = idw * istrides[3] + idz * istrides[2];
+            dim_t xoffzw = idw * xstrides[3] + idz * xstrides[2];
+
+            for(dim_t idy = 0; idy < odims[1]; idy++) {
+
+                dim_t ooff = ooffzw + idy * ostrides[1];
+                dim_t ioff = ioffzw + idy * istrides[1];
+                dim_t xoff = xoffzw + idy * xstrides[1];
+
+                for(dim_t idx = 0; idx < odims[0]; idx++) {
+
+                    const LocT x = xpos[batch * xoff + idx];
+
+                    if (x < 0 || idims[0] < x + 1) {
+                        out[ooff + idx] = scalar<InT>(offGrid);
+                    } else {
+                        out[ooff + idx] = interp(input, ioff, x);
+                    }
+                }
+            }
+        }
+    }
+}
+
+template<typename InT, typename LocT, af_interp_type Method>
+void approx2(Array<InT> output, const Array<InT> input,
+             const Array<LocT> xposition, const Array<LocT> yposition,
+             float const offGrid)
+{
+    InT * out = output.get();
+    const LocT *xpos = xposition.get();
+    const LocT *ypos = yposition.get();
+
+    af::dim4 const odims     = output.dims();
+    af::dim4 const idims     = input.dims();
+    af::dim4 const xdims     = xposition.dims();
+    af::dim4 const ostrides  = output.strides();
+    af::dim4 const istrides  = input.strides();
+    af::dim4 const xstrides  = xposition.strides();
+    af::dim4 const ystrides  = yposition.strides();
+
+    Interp2<InT, LocT, Method> interp;
+    bool batch = !(xdims[2] == 1 && xdims[3] == 1);
+
+    for(dim_t idw = 0; idw < odims[3]; idw++) {
+        for(dim_t idz = 0; idz < odims[2]; idz++) {
+
+            dim_t xoffzw = idw * xstrides[3] + idz * xstrides[2];
+            dim_t yoffzw = idw * ystrides[3] + idz * ystrides[2];
+            dim_t ooffzw = idw * ostrides[3] + idz * ostrides[2];
+            dim_t ioffzw = idw * istrides[3] + idz * istrides[2];
+
+            for(dim_t idy = 0; idy < odims[1]; idy++) {
+                dim_t xoff = xoffzw * batch + idy * xstrides[1];
+                dim_t yoff = yoffzw * batch + idy * ystrides[1];
+                dim_t ooff = ooffzw         + idy * ostrides[1];
+
+                for(dim_t idx = 0; idx < odims[0]; idx++) {
+
+                    const LocT x = xpos[xoff + idx];
+                    const LocT y = ypos[yoff + idx];
+
+                    if (x < 0 || idims[0] < x + 1 ||
+                        y < 0 || idims[1] < y + 1 ) {
+                        out[ooff + idx] = scalar<InT>(offGrid);
+                    } else {
+                        out[ooff + idx] = interp(input, ioffzw, x, y);
+                    }
+                }
+            }
+        }
+    }
+}
+}
+}
diff --git a/src/backend/cpu/kernel/approx1.hpp b/src/backend/cpu/kernel/approx1.hpp
deleted file mode 100644
index cb3778c0cd..0000000000
--- a/src/backend/cpu/kernel/approx1.hpp
+++ /dev/null
@@ -1,194 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <Array.hpp>
-#include <math.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-
-template<typename InT, typename LocT, af_interp_type Method>
-struct approx1_op
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        return;
-    }
-};
-
-template<typename InT, typename LocT>
-struct approx1_op<InT, LocT, AF_INTERP_NEAREST>
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        dim_t pmId = idx;
-        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-        LocT const x = pos[pmId];
-        bool gFlag = false;
-        if (x < 0 || idims[0] < x+1) {  // No need to check y
-            gFlag = true;
-        }
-
-        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<InT>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                       + idy * istrides[1];
-            dim_t const iMem = round(x) + ioff;
-
-            out[omId] = in[iMem];
-        }
-    }
-};
-
-template<typename InT, typename LocT>
-struct approx1_op<InT, LocT, AF_INTERP_LINEAR>
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        dim_t pmId = idx;
-        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-        LocT const x = pos[pmId];
-        bool gFlag = false;
-        if (x < 0 || idims[0] < x+1) {
-            gFlag = true;
-        }
-
-        dim_t const grid_x = floor(x);  // nearest grid
-        LocT const off_x = x - grid_x;  // fractional offset
-
-        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<InT>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
-
-            // Check if x and x + 1 are both valid indices
-            bool cond = (x < idims[0] - 1);
-            // Compute Left and Right Weighted Values
-            InT yl = ((LocT)1.0 - off_x) * in[ioff];
-            InT yr = cond ? (off_x) * in[ioff + 1] : scalar<InT>(0);
-            InT yo = yl + yr;
-            // Compute Weight used
-            LocT wt = cond ? (LocT)1.0 : (LocT)(1.0 - off_x);
-            // Write final value
-            out[omId] = (yo / wt);
-        }
-    }
-};
-
-template<typename InT, typename LocT>
-struct approx1_op<InT, LocT, AF_INTERP_CUBIC>
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides, af::dim4 const & pstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        dim_t pmId = idx;
-        if(pBatch) pmId += idw * pstrides[3] + idz * pstrides[2] + idy * pstrides[1];
-
-        LocT const x = pos[pmId];
-        bool gFlag = false;
-        if (x < 0 || idims[0] < x + 1) { //check index of bounds
-            gFlag = true;
-        }
-
-        dim_t const grid_x = floor(x);  // nearest grid
-        LocT  const off_x  = x - grid_x;  // fractional offset
-
-        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-
-        if(gFlag) {
-            out[omId] = scalar<InT>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2] + idy * istrides[1] + grid_x;
-
-            //compute basis function values
-            InT h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
-            InT h10 = off_x * (1 - off_x) * (1 - off_x);
-            InT h01 = off_x * off_x * (3 - 2 * off_x);
-            InT h11 = off_x * off_x * (off_x - 1);
-            // Check if x-1, x, and x+1, x+2 are both valid indices
-            bool condr  = (grid_x > 0);
-            bool condl1 = (grid_x < idims[0] - 1);
-            bool condl2 = (grid_x < idims[0] - 2);
-            // Compute Left and Right points and tangents
-            InT pl = in[ioff];
-            InT pr = condl1  ? in[ioff + 1] : in[ioff];
-            InT tl = condr   ? scalar<InT>(0.5) * (in[ioff + 1] - in[ioff - 1]) : (in[ioff + 1] - in[ioff]);
-            InT tr = condl2  ? scalar<InT>(0.5) * (in[ioff + 2] - in[ioff]) : (condl1) ? in[ioff + 1] - in[ioff] : (in[ioff] - in[ioff - 1]);
-
-            // Write final value
-            out[omId] = h00 * pl + h10 * tl + h01 * pr + h11 * tr;
-        }
-    }
-};
-
-template<typename InT, typename LocT, af_interp_type Method>
-void approx1(Array<InT> output, Array<InT> const input,
-             Array<LocT> const position, float const offGrid)
-{
-    InT * out = output.get();
-    InT const * const in  = input.get();
-    LocT const * const pos = position.get();
-
-    af::dim4 const odims     = output.dims();
-    af::dim4 const idims     = input.dims();
-    af::dim4 const pdims     = position.dims();
-    af::dim4 const ostrides  = output.strides();
-    af::dim4 const istrides  = input.strides();
-    af::dim4 const pstrides  = position.strides();
-
-    dim_t const oElems = output.elements();
-    dim_t const iElems = input.elements();
-
-    approx1_op<InT, LocT, Method> op;
-    bool pBatch = !(pdims[1] == 1 && pdims[2] == 1 && pdims[3] == 1);
-
-    for(dim_t w = 0; w < odims[3]; w++) {
-        for(dim_t z = 0; z < odims[2]; z++) {
-            for(dim_t y = 0; y < odims[1]; y++) {
-                for(dim_t x = 0; x < odims[0]; x++) {
-                    op(out, odims, oElems, in, idims, iElems, pos, pdims,
-                       ostrides, istrides, pstrides, offGrid, pBatch, x, y, z, w);
-                }
-            }
-        }
-    }
-}
-
-}
-}
diff --git a/src/backend/cpu/kernel/approx2.hpp b/src/backend/cpu/kernel/approx2.hpp
deleted file mode 100644
index 5879cdd80c..0000000000
--- a/src/backend/cpu/kernel/approx2.hpp
+++ /dev/null
@@ -1,249 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015,  ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <af/defines.h>
-#include <handle.hpp>
-#include <cast.hpp>
-#include <Array.hpp>
-#include <math.hpp>
-
-namespace cpu
-{
-namespace kernel
-{
-
-
-template<typename InT, typename LocT, af_interp_type Method>
-struct approx2_op
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides,
-              af::dim4 const & pstrides, af::dim4 const & qstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        return;
-    }
-};
-
-template<typename InT, typename LocT>
-struct approx2_op<InT, LocT, AF_INTERP_NEAREST>
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides,
-              af::dim4 const & pstrides, af::dim4 const & qstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        dim_t pmId = idy * pstrides[1] + idx;
-        dim_t qmId = idy * qstrides[1] + idx;
-        if(pBatch) {
-            pmId += idw * pstrides[3] + idz * pstrides[2];
-            qmId += idw * qstrides[3] + idz * qstrides[2];
-        }
-
-        bool gFlag = false;
-        LocT const x = pos[pmId], y = qos[qmId];
-        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
-            gFlag = true;
-        }
-
-        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<InT>(offGrid);
-        } else {
-            dim_t const grid_x = round(x), grid_y = round(y); // nearest grid
-            dim_t const imId = idw * istrides[3] + idz * istrides[2] +
-                            grid_y * istrides[1] + grid_x;
-            out[omId] = in[imId];
-        }
-    }
-};
-
-template<typename InT, typename LocT>
-struct approx2_op<InT, LocT, AF_INTERP_LINEAR>
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides,
-              af::dim4 const & pstrides, af::dim4 const & qstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        dim_t pmId = idy * pstrides[1] + idx;
-        dim_t qmId = idy * qstrides[1] + idx;
-        if(pBatch) {
-            pmId += idw * pstrides[3] + idz * pstrides[2];
-            qmId += idw * qstrides[3] + idz * qstrides[2];
-        }
-
-        bool gFlag = false;
-        LocT const x = pos[pmId], y = qos[qmId];
-        if (x < 0 || y < 0 || idims[0] < x+1 || idims[1] < y+1) {
-            gFlag = true;
-        }
-
-        dim_t const grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-        LocT const off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
-
-        // Check if pVal and pVal + 1 are both valid indices
-        bool condY = (y < idims[1] - 1);
-        bool condX = (x < idims[0] - 1);
-
-        // Compute weights used
-        LocT wt00 = ((LocT)1.0 - off_x) * ((LocT)1.0 - off_y);
-        LocT wt10 = (condY) ? ((LocT)1.0 - off_x) * (off_y) : 0;
-        LocT wt01 = (condX) ? (off_x) * ((LocT)1.0 - off_y) : 0;
-        LocT wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-        LocT wt = wt00 + wt10 + wt01 + wt11;
-        InT zero = scalar<InT>(0);
-
-        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        if(gFlag) {
-            out[omId] = scalar<InT>(offGrid);
-        } else {
-            dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                    + grid_y * istrides[1] + grid_x;
-
-            // Compute Weighted Values
-            InT y00 =                    wt00 * in[ioff];
-            InT y10 = (condY) ?          wt10 * in[ioff + istrides[1]]     : zero;
-            InT y01 = (condX) ?          wt01 * in[ioff + 1]               : zero;
-            InT y11 = (condX && condY) ? wt11 * in[ioff + istrides[1] + 1] : zero;
-
-            InT yo = y00 + y10 + y01 + y11;
-
-            // Write Final Value
-            out[omId] = (yo / wt);
-        }
-    }
-};
-
-template<typename InT, typename LocT> inline static
-InT cubicInterpolate(InT p[4], LocT x) {
-    return p[1] + scalar<InT>(0.5) * x *
-        (p[2] - p[0] + x * (scalar<InT>(2.0) *
-                     p[0] - scalar<InT>(5.0) *
-                     p[1] + scalar<InT>(4.0) *
-                     p[2] - p[3] + x * (scalar<InT>(3.0) *
-                     (p[1] - p[2]) + p[3] - p[0])));
-}
-
-template<typename InT, typename LocT> inline static
-InT bicubicInterpolate(InT p[4][4], LocT x, LocT y) {
-    InT arr[4];
-    arr[0] = cubicInterpolate(p[0], x);
-    arr[1] = cubicInterpolate(p[1], x);
-    arr[2] = cubicInterpolate(p[2], x);
-    arr[3] = cubicInterpolate(p[3], x);
-    return cubicInterpolate(arr, y);
-}
-
-template<typename InT, typename LocT>
-struct approx2_op<InT, LocT, AF_INTERP_CUBIC>
-{
-    void operator()(InT *out, af::dim4 const & odims, dim_t const oElems,
-              InT const * const in,  af::dim4 const & idims, dim_t const iElems,
-              LocT const * const pos, af::dim4 const & pdims, LocT const * const qos, af::dim4 const & qdims,
-              af::dim4 const & ostrides, af::dim4 const & istrides,
-              af::dim4 const & pstrides, af::dim4 const & qstrides,
-              float const offGrid, bool const pBatch,
-              dim_t const idx, dim_t const idy, dim_t const idz, dim_t const idw)
-    {
-        dim_t pmId = idy * pstrides[1] + idx;
-        dim_t qmId = idy * qstrides[1] + idx;
-        if(pBatch) {
-            pmId += idw * pstrides[3] + idz * pstrides[2];
-            qmId += idw * qstrides[3] + idz * qstrides[2];
-        }
-
-        LocT const x = pos[pmId], y = qos[qmId];
-
-
-        dim_t const grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-        LocT  const off_x  = x - grid_x, off_y  = y - grid_y; // 0-1 fractional offset
-
-        //input and output indices
-        dim_t const omId = idw * ostrides[3] + idz * ostrides[2]
-                         + idy * ostrides[1] + idx;
-        dim_t ioff = idw * istrides[3] + idz * istrides[2]
-                + grid_y * istrides[1] + grid_x;
-
-        // Check if x,y are in bounds
-        if (x < 0 || y < 0 || x > idims[0] - 1 || y > idims[1] - 1) {
-            out[omId] = scalar<InT>(offGrid);
-            return;
-        }
-
-        // used for setting values at boundaries
-        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
-        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < idims[1], grid_y + 2 < idims[1]};
-        int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-        int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
-
-        //for bicubic interpolation, work with 4x4 patch at a time
-        InT patch[4][4];
-        for (int j = 0; j < 4; j++) {
-            int ioff_j = ioff + offY[j] * istrides[1];
-            for (int i = 0; i < 4; i++) {
-                patch[j][i] = in[ioff_j + offX[i]];
-            }
-        }
-
-        // Write Final Value
-        out[omId] = bicubicInterpolate(patch, off_x, off_y);
-    }
-};
-
-template<typename InT, typename LocT, af_interp_type Method>
-void approx2(Array<InT> output, Array<InT> const input,
-             Array<LocT> const position, Array<LocT> const qosition,
-             float const offGrid)
-{
-    InT * out = output.get();
-    InT const * const in  = input.get();
-    LocT const * const pos = position.get();
-    LocT const * const qos = qosition.get();
-    af::dim4 const odims     = output.dims();
-    af::dim4 const idims     = input.dims();
-    af::dim4 const pdims     = position.dims();
-    af::dim4 const qdims     = qosition.dims();
-    af::dim4 const ostrides  = output.strides();
-    af::dim4 const istrides  = input.strides();
-    af::dim4 const pstrides  = position.strides();
-    af::dim4 const qstrides  = qosition.strides();
-    dim_t const oElems   = output.elements();
-    dim_t const iElems   = input.elements();
-
-    approx2_op<InT, LocT, Method> op;
-    bool pBatch = !(pdims[2] == 1 && pdims[3] == 1);
-
-    for(dim_t w = 0; w < odims[3]; w++) {
-        for(dim_t z = 0; z < odims[2]; z++) {
-            for(dim_t y = 0; y < odims[1]; y++) {
-                for(dim_t x = 0; x < odims[0]; x++) {
-                    op(out, odims, oElems, in, idims, iElems, pos, pdims, qos, qdims,
-                       ostrides, istrides, pstrides, qstrides, offGrid, pBatch, x, y, z, w);
-                }
-            }
-        }
-    }
-}
-
-}
-}
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
new file mode 100644
index 0000000000..5886132729
--- /dev/null
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -0,0 +1,214 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <math.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+
+template<typename InT, typename LocT>
+InT linearInterpFunc(InT val[2], LocT frac)
+{
+    return (1 - frac) * val[0] + frac * val[1];
+}
+
+template<typename InT, typename LocT>
+InT bilinearInterpFunc(InT val[2][2], LocT xfrac, LocT yfrac)
+{
+    InT res[2];
+    res[0] = linearInterpFunc(val[0], xfrac);
+    res[1] = linearInterpFunc(val[1], xfrac);
+    return linearInterpFunc(res, yfrac);
+}
+
+template<typename InT, typename LocT>
+InT cubicInterpFunc(InT val[4], LocT xfrac)
+{
+    InT a0 =
+        scalar<InT>(-0.5) * val[0] + scalar<InT>( 1.5) * val[1] +
+        scalar<InT>(-1.5) * val[2] + scalar<InT>( 0.5) * val[3];
+
+    InT a1 =
+        scalar<InT>( 1.0) * val[0] + scalar<InT>(-2.5) * val[1] +
+        scalar<InT>( 2.0) * val[2] + scalar<InT>(-0.5) * val[3];
+
+    InT a2 = scalar<InT>(-0.5) * val[0] + scalar<InT>(0.5) * val[2];
+
+    InT a3 = val[1];
+
+    LocT xfrac2 = xfrac * xfrac;
+    LocT xfrac3 = xfrac2 * xfrac;
+
+    return a0 * xfrac3 + a1 * xfrac2 + a2 * xfrac + a3;
+}
+
+template<typename InT, typename LocT>
+InT bicubicInterpFunc(InT val[4][4], LocT xfrac, LocT yfrac) {
+    InT res[4];
+    res[0] = cubicInterpFunc(val[0], xfrac);
+    res[1] = cubicInterpFunc(val[1], xfrac);
+    res[2] = cubicInterpFunc(val[2], xfrac);
+    res[3] = cubicInterpFunc(val[3], xfrac);
+    return cubicInterpFunc(res, yfrac);
+}
+
+template<typename InT, typename LocT, af_interp_type>
+struct Interp1
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x)
+    {
+        const InT *inptr = in.get();
+        const int idx = round(x) + ioff;
+        return inptr[idx];
+    }
+};
+
+template<typename InT, typename LocT>
+struct Interp1<InT, LocT, AF_INTERP_LINEAR>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x)
+    {
+        const int grid_x = floor(x);    // nearest grid
+        const LocT off_x = x - grid_x;    // fractional offset
+        const int idx = ioff + grid_x;
+        const InT *inptr = in.get();
+        const dim4 idims = in.dims();
+        InT val[2] = {inptr[idx], x + 1 < idims[0] ? inptr[idx + 1] : scalar<InT>(0)};
+        return linearInterpFunc(val, off_x);
+    }
+};
+
+template<typename InT, typename LocT>
+struct Interp1<InT, LocT, AF_INTERP_CUBIC>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x)
+    {
+        const int grid_x = floor(x);    // nearest grid
+        const LocT off_x = x - grid_x;    // fractional offset
+        const int idx = ioff + grid_x;
+        const InT *inptr = in.get();
+        const dim4 idims = in.dims();
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
+        int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+        InT val[4];
+        for (int i = 0; i < 4; i++) {
+            val[i] = inptr[idx + off[i]];
+        }
+        return cubicInterpFunc(val, off_x);
+    }
+};
+
+template<typename InT, typename LocT, af_interp_type>
+struct Interp2
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    {
+        const InT *inptr = in.get();
+        const dim4 istrides = in.strides();
+        const int idx = ioff + round(y) * istrides[1] + round(x);
+        return inptr[idx];
+    }
+};
+
+template<typename InT, typename LocT>
+struct Interp2<InT, LocT, AF_INTERP_BILINEAR>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    {
+        const InT *inptr = in.get();
+        const dim4 idims = in.dims();
+        const dim4 istrides = in.strides();
+
+        const int grid_x = floor(x);
+        const LocT off_x = x - grid_x;
+
+        const int grid_y = floor(y);
+        const LocT off_y = y - grid_y;
+
+        const int idx = ioff + grid_y * istrides[1] + grid_x;
+
+        bool condX[2] = {true, x + 1 < idims[0]};
+        bool condY[2] = {true, y + 1 < idims[1]};
+
+        InT val[2][2];
+        for (int j = 0; j < 2; j++) {
+            int off_y = idx + j * istrides[1];
+            for (int i = 0; i < 2; i++) {
+                val[j][i] = condX[i] && condY[j] ? inptr[off_y + i] : scalar<InT>(0);
+            }
+        }
+
+        return bilinearInterpFunc(val, off_x, off_y);
+    }
+};
+
+template<typename InT, typename LocT>
+struct Interp2<InT, LocT, AF_INTERP_LINEAR>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    {
+        return Interp2<InT, LocT, AF_INTERP_BILINEAR>()(in, ioff, x, y);
+    }
+};
+
+template<typename InT, typename LocT>
+struct Interp2<InT, LocT, AF_INTERP_BICUBIC>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    {
+        const InT *inptr = in.get();
+        const dim4 idims = in.dims();
+        const dim4 istrides = in.strides();
+
+        const int grid_x = floor(x);
+        const LocT off_x = x - grid_x;
+
+        const int grid_y = floor(y);
+        const LocT off_y = y - grid_y;
+
+        const int idx = ioff + grid_y * istrides[1] + grid_x;
+
+        //for bicubic interpolation, work with 4x4 val at a time
+        InT val[4][4];
+
+        // used for setting values at boundaries
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < idims[1], grid_y + 2 < idims[1]};
+        int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+        for (int j = 0; j < 4; j++) {
+            int ioff_j = idx + offY[j] * istrides[1];
+            for (int i = 0; i < 4; i++) {
+                val[j][i] = inptr[ioff_j + offX[i]];
+            }
+        }
+
+        return bicubicInterpFunc(val, off_x, off_y);
+    }
+};
+
+template<typename InT, typename LocT>
+struct Interp2<InT, LocT, AF_INTERP_CUBIC>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    {
+        return Interp2<InT, LocT, AF_INTERP_BICUBIC>()(in, ioff, x, y);
+    }
+};
+
+}
+}

From 8129e9f261ead161a03dd718ba0ec90e9896460c Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 3 Sep 2016 23:56:30 -0400
Subject: [PATCH 0841/2677] OPENCL: Cleaning up approx1 and approx2

- Reusing as much code as possible
- Create interp.cl that could be reused by resize and transform
---
 src/backend/opencl/kernel/approx.hpp |  59 ++++----
 src/backend/opencl/kernel/approx1.cl | 160 ++------------------
 src/backend/opencl/kernel/approx2.cl | 209 +++------------------------
 src/backend/opencl/kernel/interp.cl  | 160 ++++++++++++++++++++
 src/backend/opencl/kernel/interp.hpp |  30 ++++
 5 files changed, 252 insertions(+), 366 deletions(-)
 create mode 100644 src/backend/opencl/kernel/interp.cl
 create mode 100644 src/backend/opencl/kernel/interp.hpp

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 47f03fbdeb..36a1a3fe81 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <kernel_headers/approx1.hpp>
 #include <kernel_headers/approx2.hpp>
+#include <kernel_headers/interp.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -21,6 +22,7 @@
 #include <type_util.hpp>
 #include <math.hpp>
 #include "config.hpp"
+#include "interp.hpp"
 
 using cl::Buffer;
 using cl::Program;
@@ -43,7 +45,7 @@ namespace opencl
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template <typename Ty, typename Tp, af_interp_type method>
-        void approx1(Param out, const Param in, const Param pos, const float offGrid)
+        void approx1(Param out, const Param in, const Param xpos, const float offGrid)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -61,27 +63,21 @@ namespace opencl
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
                        (af_dtype) dtype_traits<Ty>::af_type == c64) {
-                        options << " -D CPLX=1";
+                        options << " -D IS_CPLX=1";
                     } else {
-                        options << " -D CPLX=0";
+                        options << " -D IS_CPLX=0";
                     }
                     if (std::is_same<Ty, double>::value ||
                         std::is_same<Ty, cdouble>::value) {
                         options << " -D USE_DOUBLE";
                     }
 
-                    switch(method) {
-                        case AF_INTERP_NEAREST: options << " -D INTERP=NEAREST";
-                            break;
-                        case AF_INTERP_LINEAR:  options << " -D INTERP=LINEAR";
-                            break;
-                        case AF_INTERP_CUBIC:  options << " -D INTERP=CUBIC";
-                            break;
-                        default:
-                            break;
-                    }
+                    options << " -D " << getInterpName<method>();
+
                     Program prog;
-                    buildProgram(prog, approx1_cl, approx1_cl_len, options.str());
+                    const char *ker_strs[] = {interp_cl, approx1_cl};
+                    const int   ker_lens[] = {interp_cl_len, approx1_cl_len};
+                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                     approxProgs[device] = new Program(prog);
 
                     approxKernels[device] = new Kernel(*approxProgs[device], "approx1_kernel");
@@ -89,7 +85,7 @@ namespace opencl
 
 
                 auto approx1Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                       const Buffer, const KParam, const float, const int, const int>
+                                       const Buffer, const KParam, const Ty, const int, const int>
                                       (*approxKernels[device]);
 
                 NDRange local(THREADS, 1, 1);
@@ -99,11 +95,12 @@ namespace opencl
                                1);
 
                 // Passing bools to opencl kernels is not allowed
-                bool pBatch = !(pos.info.dims[1] == 1 && pos.info.dims[2] == 1 && pos.info.dims[3] == 1);
+                bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 &&
+                                xpos.info.dims[3] == 1);
 
                 approx1Op(EnqueueArgs(getQueue(), global, local),
                           *out.data, out.info, *in.data, in.info,
-                          *pos.data, pos.info, offGrid, blocksPerMat, (int)pBatch);
+                          *xpos.data, xpos.info, scalar<Ty>(offGrid), blocksPerMat, (int)batch);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
@@ -113,7 +110,7 @@ namespace opencl
         }
 
         template <typename Ty, typename Tp, af_interp_type method>
-        void approx2(Param out, const Param in, const Param pos, const Param qos, const float offGrid)
+        void approx2(Param out, const Param in, const Param xpos, const Param ypos, const float offGrid)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -140,18 +137,12 @@ namespace opencl
                         options << " -D USE_DOUBLE";
                     }
 
-                    switch(method) {
-                        case AF_INTERP_NEAREST: options << " -D INTERP=NEAREST";
-                            break;
-                        case AF_INTERP_LINEAR:  options << " -D INTERP=LINEAR";
-                            break;
-                        case AF_INTERP_CUBIC:  options << " -D INTERP=CUBIC";
-                            break;
-                        default:
-                            break;
-                    }
+                    options << " -D " << getInterpName<method>();
+
                     Program prog;
-                    buildProgram(prog, approx2_cl, approx2_cl_len, options.str());
+                    const char *ker_strs[] = {interp_cl, approx2_cl};
+                    const int   ker_lens[] = {interp_cl_len, approx2_cl_len};
+                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                     approxProgs[device] = new Program(prog);
 
                     approxKernels[device] = new Kernel(*approxProgs[device], "approx2_kernel");
@@ -159,7 +150,7 @@ namespace opencl
 
                 auto approx2Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
                                        const Buffer, const KParam, const Buffer, const KParam,
-                                       const float, const int, const int, const int>
+                                       const Ty, const int, const int, const int>
                                        (*approxKernels[device]);
 
                 NDRange local(TX, TY, 1);
@@ -170,14 +161,14 @@ namespace opencl
                                1);
 
                 // Passing bools to opencl kernels is not allowed
-                bool pBatch = !(pos.info.dims[2] == 1 && pos.info.dims[3] == 1);
+                bool batch = !(xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
 
                 approx2Op(EnqueueArgs(getQueue(), global, local),
                           *out.data, out.info,
                           *in.data, in.info,
-                          *pos.data, pos.info,
-                          *qos.data, qos.info,
-                          offGrid, blocksPerMatX, blocksPerMatY, (int)pBatch);
+                          *xpos.data, xpos.info,
+                          *ypos.data, ypos.info,
+                          scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch);
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index ce9e2bed7a..b4aa4b99b7 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -7,153 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define NEAREST core_nearest1
-#define LINEAR core_linear1
-#define CUBIC core_cubic
-
-#if CPLX
-#define set(a, b) a = b
-#define set_scalar(a, b) do {                   \
-        a.x = b;                                \
-        a.y = 0;                                \
-    } while(0)
-
-Ty mul(Ty a, Tp b) { a.x = a.x * b; a.y = a.y * b; return a; }
-Ty div(Ty a, Tp b) { a.x = a.x / b; a.y = a.y / b; return a; }
-
-#else
-
-#define set(a, b) a = b
-#define set_scalar(a, b) a = b
-#define mul(a, b) ((a) * (b))
-#define div(a, b) ((a) / (b))
-
-#endif
-
-///////////////////////////////////////////////////////////////////////////
-// nearest-neighbor resampling
-///////////////////////////////////////////////////////////////////////////
-void core_nearest1(const int idx, const int idy, const int idz, const int idw,
-                   __global       Ty *d_out, const KParam out,
-                   __global const Ty *d_in,  const KParam in,
-                   __global const Tp *d_pos, const KParam pos,
-                   const float offGrid, const bool pBatch)
-{
-    const int omId = idw * out.strides[3] + idz * out.strides[2]
-                     + idy * out.strides[1] + idx;
-    int pmId = idx;
-    if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
-
-    const Tp pVal = d_pos[pmId];
-    if (pVal < 0 || in.dims[0] < pVal+1) {
-        set_scalar(d_out[omId], offGrid);
-        return;
-    }
-
-    int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
-    const int imId = round(pVal) + ioff;
-
-    Ty y;
-    set(y, d_in[imId]);
-    set(d_out[omId], y);
-}
-
-///////////////////////////////////////////////////////////////////////////
-// linear resampling
-///////////////////////////////////////////////////////////////////////////
-void core_linear1(const int idx, const int idy, const int idz, const int idw,
-                   __global       Ty *d_out, const KParam out,
-                   __global const Ty *d_in,  const KParam in,
-                   __global const Tp *d_pos, const KParam pos,
-                   const float offGrid, const bool pBatch)
-{
-    const int omId = idw * out.strides[3] + idz * out.strides[2]
-                     + idy * out.strides[1] + idx;
-    int pmId = idx;
-    if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
-
-    const Tp pVal = d_pos[pmId];
-    if (pVal < 0 || in.dims[0] < pVal+1) {
-        set_scalar(d_out[omId], offGrid);
-        return;
-    }
-
-    const int grid_x = floor(pVal);  // nearest grid
-    const Tp off_x = pVal - grid_x; // fractional offset
-
-    int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
-
-    // Check if pVal and pVal + 1 are both valid indices
-    bool cond = (pVal < in.dims[0] - 1);
-
-    Ty zero = ZERO;
-
-    // Compute Left and Right Weighted Values
-    Ty yl = mul(d_in[ioff] , (1 - off_x));
-    Ty yr = cond ? mul(d_in[ioff + 1], off_x) : zero;
-    Ty yo = yl + yr;
-
-    // Compute Weight used
-    Tp wt = cond ? 1 : (1 - off_x);
-
-    // Write final value
-    set(d_out[omId], div(yo, wt));
-}
-
-///////////////////////////////////////////////////////////////////////////
-// cubic resampling
-///////////////////////////////////////////////////////////////////////////
-void core_cubic(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
-                   __global       Ty *d_out, const KParam out,
-                   __global const Ty *d_in,  const KParam in,
-                   __global const Tp *d_pos, const KParam pos,
-                   const float offGrid, const bool pBatch)
-{
-    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
-                     + idy * out.strides[1] + idx;
-    dim_t pmId = idx;
-    if(pBatch) pmId += idw * pos.strides[3] + idz * pos.strides[2] + idy * pos.strides[1];
-
-    const Tp pVal = d_pos[pmId];
-    if (pVal < 0 || in.dims[0] < pVal+1) {
-        set_scalar(d_out[omId], offGrid);
-        return;
-    }
-
-    const dim_t grid_x = floor(pVal);  // nearest grid
-    const Tp off_x = pVal - grid_x; // fractional offset
-
-    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + grid_x;
-
-    // Check if x-1, x, and x+1, x+2 are both valid indices
-    bool condr  = (grid_x > 0);
-    bool condl1 = (grid_x < in.dims[0] - 1);
-    bool condl2 = (grid_x < in.dims[0] - 2);
-
-    //compute basis function values
-    Ty h00 = (1 + 2 * off_x) * (1 - off_x) * (1 - off_x);
-    Ty h10 = off_x * (1 - off_x) * (1 - off_x);
-    Ty h01 = off_x * off_x * (3 - 2 * off_x);
-    Ty h11 = off_x * off_x * (off_x - 1);
-
-    // Compute Left and Right Weighted Values
-    Ty pl = d_in[ioff];
-    Ty pr = condl1  ? d_in[ioff + 1] : d_in[ioff];
-    Ty tl = condr   ? (Ty)0.5 * (d_in[ioff + 1] - d_in[ioff - 1]) : (d_in[ioff + 1] - d_in[ioff]);
-    Ty tr = condl2  ? (Ty)0.5 * (d_in[ioff + 2] - d_in[ioff]) : (condl1) ? d_in[ioff + 1] - d_in[ioff] : (d_in[ioff] - d_in[ioff - 1]);
-
-    // Write final value
-    set(d_out[omId], h00 * pl + h10 * tl + h01 * pr + h11 * tr);
-}
-
-////////////////////////////////////////////////////////////////////////////////////
-// Wrapper Kernel
-////////////////////////////////////////////////////////////////////////////////////
 __kernel
 void approx1_kernel(__global       Ty *d_out, const KParam out,
                     __global const Ty *d_in,  const KParam in,
-                    __global const Tp *d_pos, const KParam pos,
-                    const float offGrid, const int blocksMatX, const int pBatch)
+                    __global const Tp *d_xpos, const KParam xpos,
+                    const Ty offGrid, const int blocksMatX, const int batch)
 {
     const int idw = get_group_id(1) / out.dims[2];
     const int idz = get_group_id(1)  - idw * out.dims[2];
@@ -168,5 +26,17 @@ void approx1_kernel(__global       Ty *d_out, const KParam out,
        idw >= out.dims[3])
         return;
 
-    INTERP(idx, idy, idz, idw, d_out, out, d_in + in.offset, in, d_pos + pos.offset, pos, offGrid, pBatch);
+    const int omId = idw * out.strides[3] + idz * out.strides[2]
+        + idy * out.strides[1] + idx + out.offset;
+    int xmid = idx + xpos.offset;
+    if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
+
+    const Tp x = d_xpos[xmid];
+    if (x < 0 || in.dims[0] < x+1) {
+        d_out[omId] = offGrid;
+        return;
+    }
+
+    int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + in.offset;
+    d_out[omId] = interp1(d_in, in, ioff, x);
 }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 7762bc9c67..88b5dbbec0 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -7,194 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define NEAREST core_nearest2
-#define LINEAR core_linear2
-#define CUBIC core_cubic2
-
-#if CPLX
-#define set(a, b) a = b
-#define set_scalar(a, b) do {                   \
-        a.x = b;                                \
-        a.y = 0;                                \
-    } while(0)
-
-Ty mul(Ty a, Tp b) { a.x = a.x * b; a.y = a.y * b; return a; }
-Ty div(Ty a, Tp b) { a.x = a.x / b; a.y = a.y / b; return a; }
-
-
-#else
-
-#define set(a, b) a = b
-#define set_scalar(a, b) a = b
-#define mul(a, b) ((a) * (b))
-#define div(a, b) ((a) / (b))
-
-#endif
-
-///////////////////////////////////////////////////////////////////////////
-// nearest-neighbor resampling
-///////////////////////////////////////////////////////////////////////////
-void core_nearest2(const int idx, const int idy, const int idz, const int idw,
-                   __global       Ty *d_out, const KParam out,
-                   __global const Ty *d_in,  const KParam in,
-                   __global const Tp *d_pos, const KParam pos,
-                   __global const Tp *d_qos, const KParam qos,
-                   const float offGrid, const bool pBatch)
-{
-    const int omId = idw * out.strides[3] + idz * out.strides[2]
-                     + idy * out.strides[1] + idx;
-    int pmId = idy * pos.strides[1] + idx;
-    int qmId = idy * qos.strides[1] + idx;
-    if(pBatch) {
-        pmId += idw * pos.strides[3] + idz * pos.strides[2];
-        qmId += idw * qos.strides[3] + idz * qos.strides[2];
-    }
-
-    const Tp x = d_pos[pmId], y = d_qos[qmId];
-    if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-        set_scalar(d_out[omId], offGrid);
-        return;
-    }
-
-    const int grid_x = round(x), grid_y = round(y); // nearest grid
-    const int imId = idw * in.strides[3] + idz * in.strides[2]
-                  + grid_y * in.strides[1] + grid_x;
-
-    Ty z;
-    set(z, d_in[imId]);
-    set(d_out[omId], z);
-}
-
-///////////////////////////////////////////////////////////////////////////
-// linear resampling
-///////////////////////////////////////////////////////////////////////////
-void core_linear2(const int idx, const int idy, const int idz, const int idw,
-                  __global       Ty *d_out, const KParam out,
-                  __global const Ty *d_in,  const KParam in,
-                  __global const Tp *d_pos, const KParam pos,
-                  __global const Tp *d_qos, const KParam qos,
-                  const float offGrid, const bool pBatch)
-{
-    const int omId = idw * out.strides[3] + idz * out.strides[2]
-                     + idy * out.strides[1] + idx;
-    int pmId = idy * pos.strides[1] + idx;
-    int qmId = idy * qos.strides[1] + idx;
-    if(pBatch) {
-        pmId += idw * pos.strides[3] + idz * pos.strides[2];
-        qmId += idw * qos.strides[3] + idz * qos.strides[2];
-    }
-
-    const Tp x = d_pos[pmId], y = d_qos[qmId];
-    if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-        set_scalar(d_out[omId], offGrid);
-        return;
-    }
-
-    const int grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-    const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
-
-    int ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
-
-    // Check if pVal and pVal + 1 are both valid indices
-    bool condY = (y < in.dims[1] - 1);
-    bool condX = (x < in.dims[0] - 1);
-
-    // Compute wieghts used
-    Tp wt00 = ((Tp)1.0 - off_x) * ((Tp)1.0 - off_y);
-    Tp wt10 = (condY) ? ((Tp)1.0 - off_x) * (off_y) : 0;
-    Tp wt01 = (condX) ? (off_x) * ((Tp)1.0 - off_y) : 0;
-    Tp wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-    Tp wt = wt00 + wt10 + wt01 + wt11;
-
-    // Compute Weighted Values
-    Ty zero = ZERO;
-    Ty y00 =                    mul(d_in[ioff],                     wt00)       ;
-    Ty y10 = (condY) ?          mul(d_in[ioff + in.strides[1]],     wt10) : zero;
-    Ty y01 = (condX) ?          mul(d_in[ioff + 1],                 wt01) : zero;
-    Ty y11 = (condX && condY) ? mul(d_in[ioff + in.strides[1] + 1], wt11) : zero;
-
-    Ty yo = y00 + y10 + y01 + y11;
-
-    // Write Final Value
-    set(d_out[omId], div(yo, wt));
-}
-
-///////////////////////////////////////////////////////////////////////////
-// cubic resampling
-///////////////////////////////////////////////////////////////////////////
-
-Ty cubicInterpolate(Ty p[4], Tp x) {
-    return p[1] + (Ty)0.5 * x * (p[2] - p[0] + x * ((Ty)2.0 * p[0] - (Ty)5.0 * p[1] + (Ty)4.0 * p[2] - p[3] + x*((Ty)3.0*(p[1] - p[2]) + p[3] - p[0])));
-}
-
-Ty bicubicInterpolate(Ty p[4][4], Tp x, Tp y) {
-    Ty arr[4];
-    arr[0] = cubicInterpolate(p[0], x);
-    arr[1] = cubicInterpolate(p[1], x);
-    arr[2] = cubicInterpolate(p[2], x);
-    arr[3] = cubicInterpolate(p[3], x);
-    return cubicInterpolate(arr, y);
-}
-
-void core_cubic2(const dim_t idx, const dim_t idy, const dim_t idz, const dim_t idw,
-                  __global       Ty *d_out, const KParam out,
-                  __global const Ty *d_in,  const KParam in,
-                  __global const Tp *d_pos, const KParam pos,
-                  __global const Tp *d_qos, const KParam qos,
-                  const float offGrid, const bool pBatch)
-{
-    const dim_t omId = idw * out.strides[3] + idz * out.strides[2]
-                     + idy * out.strides[1] + idx;
-    dim_t pmId = idy * pos.strides[1] + idx;
-    dim_t qmId = idy * qos.strides[1] + idx;
-    if(pBatch) {
-        pmId += idw * pos.strides[3] + idz * pos.strides[2];
-        qmId += idw * qos.strides[3] + idz * qos.strides[2];
-    }
-
-    const Tp x = d_pos[pmId], y = d_qos[qmId];
-    if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-        set_scalar(d_out[omId], offGrid);
-        return;
-    }
-
-    const dim_t grid_x = floor(x),   grid_y = floor(y);   // nearest grid
-    const Tp off_x  = x - grid_x, off_y  = y - grid_y; // fractional offset
-
-    dim_t ioff = idw * in.strides[3] + idz * in.strides[2] + grid_y * in.strides[1] + grid_x;
-
-    // used for setting values at boundaries
-    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
-    int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-    int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
-
-    //for bicubic interpolation, work with 4x4 patch at a time
-    Ty patch[4][4];
-
-#pragma unroll
-    for (int j = 0; j < 4; j++) {
-        int ioff_j = ioff + offY[j] * in.strides[1];
-#pragma unroll
-        for (int i = 0; i < 4; i++) {
-            patch[j][i] = d_in[ioff_j + offX[i]];
-        }
-    }
-
-    set(d_out[omId], bicubicInterpolate(patch, off_x, off_y));
-}
-
-////////////////////////////////////////////////////////////////////////////////////
-// Wrapper Kernel
-////////////////////////////////////////////////////////////////////////////////////
 __kernel
 void approx2_kernel(__global       Ty *d_out, const KParam out,
                     __global const Ty *d_in,  const KParam in,
-                    __global const Tp *d_pos, const KParam pos,
-                    __global const Tp *d_qos, const KParam qos,
-                    const float offGrid, const int blocksMatX, const int blocksMatY,
-                    const int pBatch)
+                    __global const Tp *d_xpos, const KParam xpos,
+                    __global const Tp *d_ypos, const KParam ypos,
+                    const Ty offGrid, const int blocksMatX, const int blocksMatY,
+                    const int batch)
 {
     const int idz = get_group_id(0) / blocksMatX;
     const int idw = get_group_id(1) / blocksMatY;
@@ -211,6 +30,22 @@ void approx2_kernel(__global       Ty *d_out, const KParam out,
        idw >= out.dims[3])
         return;
 
-    INTERP(idx, idy, idz, idw, d_out, out, d_in + in.offset, in,
-           d_pos + pos.offset, pos, d_qos + qos.offset, qos, offGrid, pBatch);
+    const int omId = idw * out.strides[3] + idz * out.strides[2]
+        + idy * out.strides[1] + idx + out.offset;
+    int xmid = idy * xpos.strides[1] + idx + xpos.offset;
+    int ymid = idy * ypos.strides[1] + idx + ypos.offset;
+    if(batch) {
+        xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
+        ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
+    }
+
+    const Tp x = d_xpos[xmid], y = d_ypos[ymid];
+    if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
+        d_out[omId] = offGrid;
+        return;
+    }
+
+    int ioff = idw * in.strides[3] + idz * in.strides[2] + in.offset;
+
+    d_out[omId] = interp2(d_in, in, ioff, x, y);
 }
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
new file mode 100644
index 0000000000..ddb84d3911
--- /dev/null
+++ b/src/backend/opencl/kernel/interp.cl
@@ -0,0 +1,160 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if IS_CPLX
+#if USE_DOUBLE
+typedef double TS;
+#else
+typedef float TS;
+#endif
+Ty __mulrc(TS s, Ty v)
+{
+    Ty out = {s * v.x, s * v.y};
+    return out;
+}
+#define MULRC(a, b) __mulrc(a, b)
+#define MULCR(a, b) __mulrc(b, a)
+#else
+#define MULRC(a, b) (a) * (b)
+#define MULCR(a, b) (a) * (b)
+#endif
+
+Ty linearInterpFunc(Ty val[2], Tp frac)
+{
+    return MULRC((1 - frac), val[0]) + MULRC(frac, val[1]);
+}
+
+Ty bilinearInterpFunc(Ty val[2][2], Tp xfrac, Tp yfrac)
+{
+    Ty res[2];
+    res[0] = linearInterpFunc(val[0], xfrac);
+    res[1] = linearInterpFunc(val[1], xfrac);
+    return linearInterpFunc(res, yfrac);
+}
+
+Ty cubicInterpFunc(Ty val[4], Tp xfrac)
+{
+    Ty a0 = MULRC(-0.5, val[0]) + MULRC( 1.5, val[1]) + MULRC(-1.5, val[2]) + MULRC( 0.5, val[3]);
+    Ty a1 = MULRC( 1.0, val[0]) + MULRC(-2.5, val[1]) + MULRC( 2.0, val[2]) + MULRC(-0.5, val[3]);
+    Ty a2 = MULRC(-0.5, val[0]) + MULRC( 0.5, val[2]);
+    Ty a3 = val[1];
+
+    Tp xfrac2 = xfrac * xfrac;
+    Tp xfrac3 = xfrac2 * xfrac;
+
+    return MULCR(a0, xfrac3) + MULCR(a1, xfrac2) + MULCR(a2, xfrac) + a3;
+}
+
+Ty bicubicInterpFunc(Ty val[4][4], Tp xfrac, Tp yfrac)
+{
+    Ty res[4];
+    res[0] = cubicInterpFunc(val[0], xfrac);
+    res[1] = cubicInterpFunc(val[1], xfrac);
+    res[2] = cubicInterpFunc(val[2], xfrac);
+    res[3] = cubicInterpFunc(val[3], xfrac);
+    return cubicInterpFunc(res, yfrac);
+}
+
+#if defined(NEAREST)
+Ty interp1(const Ty *d_in, KParam in, int ioff, Tp x)
+{
+    const int idx = round(x) + ioff;
+    return d_in[idx];
+}
+#elif defined(LINEAR)
+Ty interp1(const Ty *d_in, KParam in, int ioff, Tp x)
+{
+    const int grid_x = floor(x);    // nearest grid
+    const Tp off_x = x - grid_x;    // fractional offset
+    const int idx = ioff + grid_x;
+    Ty zero = ZERO;
+    Ty val[2] = {d_in[idx], x + 1 < in.dims[0] ? d_in[idx + 1] : zero};
+    return linearInterpFunc(val, off_x);
+}
+#elif defined(CUBIC)
+Ty interp1(const Ty *d_in, KParam in, int ioff, Tp x)
+{
+    const int grid_x = floor(x);    // nearest grid
+    const Tp off_x = x - grid_x;    // fractional offset
+    const int idx = ioff + grid_x;
+
+    bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+    int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+    Ty val[4];
+    for (int i = 0; i < 4; i++) {
+        val[i] = d_in[idx + off[i]];
+    }
+    return cubicInterpFunc(val, off_x);
+}
+#endif
+
+#if defined(NEAREST)
+Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
+{
+    const int idx = ioff + round(y) * in.strides[1] + round(x);
+    return d_in[idx];
+}
+#elif defined(LINEAR) || defined(BILINEAR)
+Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
+{
+    const int grid_x = floor(x);
+    const Tp off_x = x - grid_x;
+
+    const int grid_y = floor(y);
+    const Tp off_y = y - grid_y;
+
+    const int idx = ioff + grid_y * in.strides[1] + grid_x;
+
+    bool condX[2] = {true, x + 1 < in.dims[0]};
+    bool condY[2] = {true, y + 1 < in.dims[1]};
+
+    Ty zero = ZERO;
+    Ty val[2][2];
+    for (int j = 0; j < 2; j++) {
+        int off_y = idx + j * in.strides[1];
+        for (int i = 0; i < 2; i++) {
+            val[j][i] = condX[i] && condY[j] ? d_in[off_y + i] : zero;
+        }
+    }
+
+    return bilinearInterpFunc(val, off_x, off_y);
+}
+#elif defined(CUBIC) || defined(BICUBIC)
+Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
+{
+    const int grid_x = floor(x);
+    const Tp off_x = x - grid_x;
+
+    const int grid_y = floor(y);
+    const Tp off_y = y - grid_y;
+
+    const int idx = ioff + grid_y * in.strides[1] + grid_x;
+
+    //for bicubic interpolation, work with 4x4 val at a time
+    Ty val[4][4];
+
+    // used for setting values at boundaries
+    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+    int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+    int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+#pragma unroll
+    for (int j = 0; j < 4; j++) {
+        int ioff_j = idx + offY[j] * in.strides[1];
+#pragma unroll
+        for (int i = 0; i < 4; i++) {
+            val[j][i] = d_in[ioff_j + offX[i]];
+        }
+    }
+
+    return bicubicInterpFunc(val, off_x, off_y);
+}
+#endif
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
new file mode 100644
index 0000000000..306912a7d2
--- /dev/null
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/defines.h>
+
+namespace opencl {
+    namespace kernel {
+
+        template<af_interp_type method>
+        const char *getInterpName()
+        {
+            switch(method) {
+            case AF_INTERP_NEAREST:   return "NEAREST";
+            case AF_INTERP_LINEAR:    return "LINEAR";
+            case AF_INTERP_BILINEAR:  return "BILINEAR";
+            case AF_INTERP_CUBIC:     return "CUBIC";
+            case AF_INTERP_BICUBIC:   return "BICUBIC";
+            case AF_INTERP_LOWER:     return "LOWER";
+            default:                  return "NONE";
+            }
+        }
+
+    }
+}

From 75cfa6a25916bdde79ff77282b43721a67c594d5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sat, 3 Sep 2016 23:57:49 -0400
Subject: [PATCH 0842/2677] TEST: Removing warnings from approx1 and approx2

---
 test/approx1.cpp | 2 +-
 test/approx2.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/approx1.cpp b/test/approx1.cpp
index 5158fba36f..1ce22663b0 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -149,7 +149,7 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     bool ret = true;
 
     float max = real(outData[0]), min = real(outData[0]);
-    for(int i=1; i < nElems; ++i) {
+    for(int i=1; i < (int)nElems; ++i) {
         min = (real(outData[i]) < min) ? real(outData[i]) : min;
         max = (real(outData[i]) > max) ? real(outData[i]) : max;
     }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 6a37cb6dc3..05756ece75 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -279,7 +279,7 @@ TEST(Approx2Cubic, CPP)
     bool ret = true;
 
     float max = real(outData[0]), min = real(outData[0]);
-    for(int i=1; i < nElems; ++i) {
+    for(int i=1; i < (int)nElems; ++i) {
         min = (real(outData[i]) < min) ? real(outData[i]) : min;
         max = (real(outData[i]) > max) ? real(outData[i]) : max;
     }

From da7a9fc39c604540c6b704e49c3436b68fefb65b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 00:59:44 -0400
Subject: [PATCH 0843/2677] FEAT: Adding newer types of interpolation methods

- Renamed AF_INTERP_*CUBIC to AF_INTERP_*CUBIC_SPLINE
- Added a simpler methods for AF_INTERP_*CUBIC
- Added AF_INTERP_*LINEAR_COSINE
---
 include/af/defines.h                 |  25 +-
 src/api/c/approx.cpp                 |  10 +-
 src/backend/cpu/approx.cpp           |  32 ++-
 src/backend/cpu/kernel/approx.hpp    |  16 +-
 src/backend/cpu/kernel/interp.hpp    | 151 +++++++-----
 src/backend/cuda/approx.cu           |  20 +-
 src/backend/cuda/kernel/approx.hpp   |  36 +--
 src/backend/cuda/kernel/interp.hpp   | 341 ++++++++++++++-------------
 src/backend/opencl/approx.cpp        |  22 +-
 src/backend/opencl/kernel/approx.hpp |  36 ++-
 src/backend/opencl/kernel/approx1.cl |   4 +-
 src/backend/opencl/kernel/approx2.cl |   4 +-
 src/backend/opencl/kernel/interp.cl  | 146 +++++++-----
 src/backend/opencl/kernel/interp.hpp |  28 ++-
 test/approx1.cpp                     |  12 +-
 test/approx2.cpp                     |   2 +-
 16 files changed, 508 insertions(+), 377 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 0cc768d0d8..2a5b03ba3b 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -222,14 +222,27 @@ typedef enum {
 typedef void * af_array;
 
 typedef enum {
-    AF_INTERP_NEAREST,  ///< Nearest Interpolation
-    AF_INTERP_LINEAR,   ///< Linear Interpolation
-    AF_INTERP_BILINEAR, ///< Bilinear Interpolation
-    AF_INTERP_CUBIC,    ///< Cubic Interpolation
+    AF_INTERP_NEAREST,         ///< Nearest Interpolation
+    AF_INTERP_LINEAR,          ///< Linear Interpolation
+    AF_INTERP_BILINEAR,        ///< Bilinear Interpolation
+    AF_INTERP_CUBIC,           ///< Cubic Interpolation
+    AF_INTERP_LOWER,           ///< Floor Indexed
 #if AF_API_VERSION >= 34
-    AF_INTERP_BICUBIC,  ///< Bicubic Interpolation
+    AF_INTERP_LINEAR_COSINE,   ///< Linear Interpolation with cosine smoothing
 #endif
-    AF_INTERP_LOWER     ///< Floor Indexed
+#if AF_API_VERSION >= 34
+    AF_INTERP_BILINEAR_COSINE, ///< Bilinear Interpolation with cosine smoothing
+#endif
+#if AF_API_VERSION >= 34
+    AF_INTERP_BICUBIC,         ///< Bicubic Interpolation
+#endif
+#if AF_API_VERSION >= 34
+    AF_INTERP_CUBIC_SPLINE,    ///< Cubic Interpolation with Catmull-Rom splines
+#endif
+#if AF_API_VERSION >= 34
+    AF_INTERP_BICUBIC_SPLINE,  ///< Bicubic Interpolation with Catmull-Rom splines
+#endif
+
 } af_interp_type;
 
 typedef enum {
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 542e94ffbd..76f70a900c 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -55,7 +55,10 @@ af_err af_approx1(af_array *out, const af_array in, const af_array pos,
                       (pdims[1] == idims[1] && pdims[2] == idims[2] && pdims[3] == idims[3]));
         ARG_ASSERT(3, (method == AF_INTERP_LINEAR  ||
                        method == AF_INTERP_NEAREST ||
-                       method == AF_INTERP_CUBIC));
+                       method == AF_INTERP_CUBIC   ||
+                       method == AF_INTERP_CUBIC_SPLINE ||
+                       method == AF_INTERP_LINEAR_COSINE ||
+                       method == AF_INTERP_LOWER));
 
         if(idims.ndims() == 0 || pdims.ndims() ==  0) {
             dim_t my_dims[] = { 0, 0, 0, 0 };
@@ -103,11 +106,6 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
         // POS should either be (x, y, 1, 1) or (x, y, idims[2], idims[3])
         DIM_ASSERT(2, (pdims[2] == 1        && pdims[3] == 1) ||
                       (pdims[2] == idims[2] && pdims[3] == idims[3]));
-        ARG_ASSERT(3, (method == AF_INTERP_LINEAR  ||
-                       method == AF_INTERP_NEAREST ||
-                       method == AF_INTERP_CUBIC   ||
-                       method == AF_INTERP_BILINEAR ||
-                       method == AF_INTERP_BICUBIC));
 
         if(idims.ndims() == 0 || pdims.ndims() ==  0 || qdims.ndims() == 0) {
             dim_t my_dims[] = { 0, 0, 0, 0 };
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 7ac9922c76..4df1c180a2 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -30,16 +30,19 @@ Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
 
     switch(method) {
     case AF_INTERP_NEAREST:
-        getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>,
-                           out, in, pos, offGrid);
+    case AF_INTERP_LOWER:
+        getQueue().enqueue(kernel::approx1<Ty, Tp, 1>,
+                           out, in, pos, offGrid, method);
         break;
     case AF_INTERP_LINEAR:
-        getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_LINEAR>,
-                           out, in, pos, offGrid);
+    case AF_INTERP_LINEAR_COSINE:
+        getQueue().enqueue(kernel::approx1<Ty, Tp, 2>,
+                           out, in, pos, offGrid, method);
         break;
     case AF_INTERP_CUBIC:
-        getQueue().enqueue(kernel::approx1<Ty, Tp, AF_INTERP_CUBIC>,
-                           out, in, pos, offGrid);
+    case AF_INTERP_CUBIC_SPLINE:
+        getQueue().enqueue(kernel::approx1<Ty, Tp, 3>,
+                           out, in, pos, offGrid, method);
         break;
     default:
         break;
@@ -64,18 +67,23 @@ Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &p
 
     switch(method) {
     case AF_INTERP_NEAREST:
-        getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>,
-                           out, in, pos0, pos1, offGrid);
+    case AF_INTERP_LOWER:
+        getQueue().enqueue(kernel::approx2<Ty, Tp, 1>,
+                           out, in, pos0, pos1, offGrid, method);
         break;
     case AF_INTERP_LINEAR:
     case AF_INTERP_BILINEAR:
-        getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_LINEAR>,
-                           out, in, pos0, pos1, offGrid);
+    case AF_INTERP_LINEAR_COSINE:
+    case AF_INTERP_BILINEAR_COSINE:
+        getQueue().enqueue(kernel::approx2<Ty, Tp, 2>,
+                           out, in, pos0, pos1, offGrid, method);
         break;
     case AF_INTERP_CUBIC:
     case AF_INTERP_BICUBIC:
-        getQueue().enqueue(kernel::approx2<Ty, Tp, AF_INTERP_CUBIC>,
-                           out, in, pos0, pos1, offGrid);
+    case AF_INTERP_CUBIC_SPLINE:
+    case AF_INTERP_BICUBIC_SPLINE:
+        getQueue().enqueue(kernel::approx2<Ty, Tp, 3>,
+                           out, in, pos0, pos1, offGrid, method);
         break;
     default:
         break;
diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index 8ff47de5dc..8cc092c846 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -17,9 +17,9 @@ namespace cpu
 namespace kernel
 {
 
-template<typename InT, typename LocT, af_interp_type Method>
+template<typename InT, typename LocT, int order>
 void approx1(Array<InT> output, const Array<InT> input,
-             const Array<LocT> xposition, const float offGrid)
+             const Array<LocT> xposition, const float offGrid, af_interp_type method)
 {
     InT * out = output.get();
     const LocT *xpos = xposition.get();
@@ -32,7 +32,7 @@ void approx1(Array<InT> output, const Array<InT> input,
     const af::dim4 istrides  = input.strides();
     const af::dim4 xstrides  = xposition.strides();
 
-    Interp1<InT, LocT, Method> interp;
+    Interp1<InT, LocT, order> interp;
     bool batch = !(xdims[1] == 1 && xdims[2] == 1 && xdims[3] == 1);
 
     for(dim_t idw = 0; idw < odims[3]; idw++) {
@@ -54,7 +54,7 @@ void approx1(Array<InT> output, const Array<InT> input,
                     if (x < 0 || idims[0] < x + 1) {
                         out[ooff + idx] = scalar<InT>(offGrid);
                     } else {
-                        out[ooff + idx] = interp(input, ioff, x);
+                        out[ooff + idx] = interp(input, ioff, x, method);
                     }
                 }
             }
@@ -62,10 +62,10 @@ void approx1(Array<InT> output, const Array<InT> input,
     }
 }
 
-template<typename InT, typename LocT, af_interp_type Method>
+template<typename InT, typename LocT, int order>
 void approx2(Array<InT> output, const Array<InT> input,
              const Array<LocT> xposition, const Array<LocT> yposition,
-             float const offGrid)
+             float const offGrid, af_interp_type method)
 {
     InT * out = output.get();
     const LocT *xpos = xposition.get();
@@ -79,7 +79,7 @@ void approx2(Array<InT> output, const Array<InT> input,
     af::dim4 const xstrides  = xposition.strides();
     af::dim4 const ystrides  = yposition.strides();
 
-    Interp2<InT, LocT, Method> interp;
+    Interp2<InT, LocT, order> interp;
     bool batch = !(xdims[2] == 1 && xdims[3] == 1);
 
     for(dim_t idw = 0; idw < odims[3]; idw++) {
@@ -104,7 +104,7 @@ void approx2(Array<InT> output, const Array<InT> input,
                         y < 0 || idims[1] < y + 1 ) {
                         out[ooff + idx] = scalar<InT>(offGrid);
                     } else {
-                        out[ooff + idx] = interp(input, ioffzw, x, y);
+                        out[ooff + idx] = interp(input, ioffzw, x, y, method);
                     }
                 }
             }
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index 5886132729..c1439f3a1a 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -6,8 +6,9 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
 #pragma once
+#define _USE_MATH_DEFINES
+#include <cmath>
 #include <Array.hpp>
 #include <math.hpp>
 
@@ -18,66 +19,80 @@ namespace kernel
 
 
 template<typename InT, typename LocT>
-InT linearInterpFunc(InT val[2], LocT frac)
+InT linearInterpFunc(InT val[2], LocT ratio)
 {
-    return (1 - frac) * val[0] + frac * val[1];
+    return (1 - ratio) * val[0] + ratio * val[1];
 }
 
 template<typename InT, typename LocT>
-InT bilinearInterpFunc(InT val[2][2], LocT xfrac, LocT yfrac)
+InT bilinearInterpFunc(InT val[2][2], LocT xratio, LocT yratio)
 {
     InT res[2];
-    res[0] = linearInterpFunc(val[0], xfrac);
-    res[1] = linearInterpFunc(val[1], xfrac);
-    return linearInterpFunc(res, yfrac);
+    res[0] = linearInterpFunc(val[0], xratio);
+    res[1] = linearInterpFunc(val[1], xratio);
+    return linearInterpFunc(res, yratio);
 }
 
 template<typename InT, typename LocT>
-InT cubicInterpFunc(InT val[4], LocT xfrac)
+InT cubicInterpFunc(InT val[4], LocT xratio, bool spline)
 {
-    InT a0 =
-        scalar<InT>(-0.5) * val[0] + scalar<InT>( 1.5) * val[1] +
-        scalar<InT>(-1.5) * val[2] + scalar<InT>( 0.5) * val[3];
-
-    InT a1 =
-        scalar<InT>( 1.0) * val[0] + scalar<InT>(-2.5) * val[1] +
-        scalar<InT>( 2.0) * val[2] + scalar<InT>(-0.5) * val[3];
-
-    InT a2 = scalar<InT>(-0.5) * val[0] + scalar<InT>(0.5) * val[2];
-
-    InT a3 = val[1];
+    InT a0, a1, a2, a3;
+    if (spline) {
+        a0 =
+            scalar<InT>(-0.5) * val[0] + scalar<InT>( 1.5) * val[1] +
+            scalar<InT>(-1.5) * val[2] + scalar<InT>( 0.5) * val[3];
+
+        a1 =
+            scalar<InT>( 1.0) * val[0] + scalar<InT>(-2.5) * val[1] +
+            scalar<InT>( 2.0) * val[2] + scalar<InT>(-0.5) * val[3];
+
+        a2 = scalar<InT>(-0.5) * val[0] + scalar<InT>(0.5) * val[2];
+
+        a3 = val[1];
+    } else {
+        a0 = val[3] - val[2] - val[0] + val[1];
+        a1 = val[0] - val[1] - a0;
+        a2 = val[2] - val[0];
+        a3 = val[1];
+    }
 
-    LocT xfrac2 = xfrac * xfrac;
-    LocT xfrac3 = xfrac2 * xfrac;
+    LocT xratio2 = xratio * xratio;
+    LocT xratio3 = xratio2 * xratio;
 
-    return a0 * xfrac3 + a1 * xfrac2 + a2 * xfrac + a3;
+    return a0 * xratio3 + a1 * xratio2 + a2 * xratio + a3;
 }
 
 template<typename InT, typename LocT>
-InT bicubicInterpFunc(InT val[4][4], LocT xfrac, LocT yfrac) {
+InT bicubicInterpFunc(InT val[4][4], LocT xratio, LocT yratio, bool spline)
+{
     InT res[4];
-    res[0] = cubicInterpFunc(val[0], xfrac);
-    res[1] = cubicInterpFunc(val[1], xfrac);
-    res[2] = cubicInterpFunc(val[2], xfrac);
-    res[3] = cubicInterpFunc(val[3], xfrac);
-    return cubicInterpFunc(res, yfrac);
+    res[0] = cubicInterpFunc(val[0], xratio, spline);
+    res[1] = cubicInterpFunc(val[1], xratio, spline);
+    res[2] = cubicInterpFunc(val[2], xratio, spline);
+    res[3] = cubicInterpFunc(val[3], xratio, spline);
+    return cubicInterpFunc(res, yratio, spline);
 }
 
-template<typename InT, typename LocT, af_interp_type>
+template<typename InT, typename LocT, int order>
 struct Interp1
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x)
+};
+
+template<typename InT, typename LocT>
+struct Interp1<InT, LocT, 1>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method)
     {
         const InT *inptr = in.get();
-        const int idx = round(x) + ioff;
+        int idx = ioff + (method == AF_INTERP_LOWER ? floor(x) : round(x));
         return inptr[idx];
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp1<InT, LocT, AF_INTERP_LINEAR>
+struct Interp1<InT, LocT, 2>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type  method)
     {
         const int grid_x = floor(x);    // nearest grid
         const LocT off_x = x - grid_x;    // fractional offset
@@ -85,14 +100,21 @@ struct Interp1<InT, LocT, AF_INTERP_LINEAR>
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
         InT val[2] = {inptr[idx], x + 1 < idims[0] ? inptr[idx + 1] : scalar<InT>(0)};
-        return linearInterpFunc(val, off_x);
+
+        LocT ratio = off_x;
+        if (method == AF_INTERP_LINEAR_COSINE) {
+            // Smooth the factional part with cosine
+            ratio = (1 - std::cos(ratio * M_PI))/2;
+        }
+
+        return linearInterpFunc(val, ratio);
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp1<InT, LocT, AF_INTERP_CUBIC>
+struct Interp1<InT, LocT, 3>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method)
     {
         const int grid_x = floor(x);    // nearest grid
         const LocT off_x = x - grid_x;    // fractional offset
@@ -107,26 +129,36 @@ struct Interp1<InT, LocT, AF_INTERP_CUBIC>
         for (int i = 0; i < 4; i++) {
             val[i] = inptr[idx + off[i]];
         }
-        return cubicInterpFunc(val, off_x);
+        return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
     }
 };
 
-template<typename InT, typename LocT, af_interp_type>
+template<typename InT, typename LocT, int order>
 struct Interp2
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+};
+
+template<typename InT, typename LocT>
+struct Interp2<InT, LocT, 1>
+{
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method)
     {
         const InT *inptr = in.get();
         const dim4 istrides = in.strides();
-        const int idx = ioff + round(y) * istrides[1] + round(x);
-        return inptr[idx];
+        if (method == AF_INTERP_LOWER) {
+            const int idx = ioff + floor(y) * istrides[1] + floor(x);
+            return inptr[idx];
+        } else {
+            const int idx = ioff + round(y) * istrides[1] + round(x);
+            return inptr[idx];
+        }
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp2<InT, LocT, AF_INTERP_BILINEAR>
+struct Interp2<InT, LocT, 2>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method)
     {
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
@@ -151,23 +183,22 @@ struct Interp2<InT, LocT, AF_INTERP_BILINEAR>
             }
         }
 
-        return bilinearInterpFunc(val, off_x, off_y);
-    }
-};
+        LocT xratio = off_x, yratio = off_y;
+        if (method == AF_INTERP_LINEAR_COSINE ||
+            method == AF_INTERP_BILINEAR_COSINE) {
+            // Smooth the factional part with cosine
+            xratio = (1 - std::cos(xratio * M_PI))/2;
+            yratio = (1 - std::cos(yratio * M_PI))/2;
+        }
 
-template<typename InT, typename LocT>
-struct Interp2<InT, LocT, AF_INTERP_LINEAR>
-{
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
-    {
-        return Interp2<InT, LocT, AF_INTERP_BILINEAR>()(in, ioff, x, y);
+        return bilinearInterpFunc(val, off_x, off_y);
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp2<InT, LocT, AF_INTERP_BICUBIC>
+struct Interp2<InT, LocT, 3>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method)
     {
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
@@ -197,16 +228,8 @@ struct Interp2<InT, LocT, AF_INTERP_BICUBIC>
             }
         }
 
-        return bicubicInterpFunc(val, off_x, off_y);
-    }
-};
-
-template<typename InT, typename LocT>
-struct Interp2<InT, LocT, AF_INTERP_CUBIC>
-{
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y)
-    {
-        return Interp2<InT, LocT, AF_INTERP_BICUBIC>()(in, ioff, x, y);
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
+        return bicubicInterpFunc(val, off_x, off_y, spline);
     }
 };
 
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cu
index fc276332b3..8854cb8964 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cu
@@ -28,13 +28,16 @@ namespace cuda
 
         switch(method) {
         case AF_INTERP_NEAREST:
-            kernel::approx1<Ty, Tp, AF_INTERP_NEAREST> (out, in, pos, offGrid);
+        case AF_INTERP_LOWER:
+            kernel::approx1<Ty, Tp, 1> (out, in, pos, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
-            kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
+        case AF_INTERP_LINEAR_COSINE:
+            kernel::approx1<Ty, Tp, 2> (out, in, pos, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
-            kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
+        case AF_INTERP_CUBIC_SPLINE:
+            kernel::approx1<Ty, Tp, 3> (out, in, pos, offGrid, method);
             break;
         default:
             break;
@@ -56,15 +59,20 @@ namespace cuda
 
         switch(method) {
         case AF_INTERP_NEAREST:
-            kernel::approx2<Ty, Tp, AF_INTERP_NEAREST> (out, in, pos0, pos1, offGrid);
+        case AF_INTERP_LOWER:
+            kernel::approx2<Ty, Tp, 1> (out, in, pos0, pos1, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
-            kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
+        case AF_INTERP_LINEAR_COSINE:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::approx2<Ty, Tp, 2> (out, in, pos0, pos1, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
-            kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
+        case AF_INTERP_CUBIC_SPLINE:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::approx2<Ty, Tp, 3> (out, in, pos0, pos1, offGrid, method);
             break;
         default:
             break;
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index b0b6ea09f9..474682b24f 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -26,10 +26,11 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         // Approx Kernel
         ///////////////////////////////////////////////////////////////////////////
-        template<typename Ty, typename Tp, af_interp_type method>
+        template<typename Ty, typename Tp, int order>
         __global__
         void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> xpos,
-                            const float offGrid, const int blocksMatX, const bool batch)
+                            const float offGrid, const int blocksMatX, const bool batch,
+                            af_interp_type method)
         {
             const int idw = blockIdx.y / out.dims[2];
             const int idz = blockIdx.y - idw * out.dims[2];
@@ -55,15 +56,16 @@ namespace cuda
 
             int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
 
-            Interp1<Ty, Tp, method> interp;
-            out.ptr[omId] = interp(in, ioff, x);
+            Interp1<Ty, Tp, order> interp;
+            out.ptr[omId] = interp(in, ioff, x, method);
         }
 
-        template<typename Ty, typename Tp, af_interp_type method>
+        template<typename Ty, typename Tp, int order>
         __global__
         void approx2_kernel(Param<Ty> out, CParam<Ty> in,
                             CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
-                            const int blocksMatX, const int blocksMatY, const bool batch)
+                            const int blocksMatX, const int blocksMatY, const bool batch,
+                            af_interp_type method)
         {
             const int idz = blockIdx.x / blocksMatX;
             const int idw = blockIdx.y / blocksMatY;
@@ -95,16 +97,17 @@ namespace cuda
 
             int ioff = idw * in.strides[3] + idz * in.strides[2];
 
-            Interp2<Ty, Tp, method> interp;
-            out.ptr[omId] = interp(in, ioff, x, y);
+            Interp2<Ty, Tp, order> interp;
+            out.ptr[omId] = interp(in, ioff, x, y, method);
         }
 
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template <typename Ty, typename Tp, af_interp_type method>
+        template <typename Ty, typename Tp, int order>
         void approx1(Param<Ty> out, CParam<Ty> in,
-               CParam<Tp> xpos, const float offGrid)
+                     CParam<Tp> xpos, const float offGrid,
+                     af_interp_type method)
         {
             dim3 threads(THREADS, 1, 1);
             int blocksPerMat = divup(out.dims[0], threads.x);
@@ -112,14 +115,15 @@ namespace cuda
 
             bool batch = !(xpos.dims[1] == 1 && xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
-            CUDA_LAUNCH((approx1_kernel<Ty, Tp, method>), blocks, threads,
-                         out, in, xpos, offGrid, blocksPerMat, batch);
+            CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads,
+                        out, in, xpos, offGrid, blocksPerMat, batch, method);
             POST_LAUNCH_CHECK();
         }
 
-        template <typename Ty, typename Tp, af_interp_type method>
+        template <typename Ty, typename Tp, int order>
         void approx2(Param<Ty> out, CParam<Ty> in,
-                    CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid)
+                     CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
+                     af_interp_type method)
         {
             dim3 threads(TX, TY, 1);
             int blocksPerMatX = divup(out.dims[0], threads.x);
@@ -128,8 +132,8 @@ namespace cuda
 
             bool batch = !(xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
-            CUDA_LAUNCH((approx2_kernel<Ty, Tp, method>), blocks, threads,
-                         out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch);
+            CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads,
+                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method);
             POST_LAUNCH_CHECK();
         }
     }
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index be28534a67..c0f7068f3d 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -6,193 +6,218 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
+#include <math_constants.h>
+
 namespace cuda
 {
-    namespace kernel
-    {
+namespace kernel
+{
 
-        template<typename Ty, typename Tp>
-        __device__
-        Ty linearInterpFunc(Ty val[2], Tp frac)
-        {
-            return (1 - frac) * val[0] + frac * val[1];
-        }
+template<typename Ty, typename Tp>
+__device__
+Ty linearInterpFunc(Ty val[2], Tp ratio)
+{
+    return (1 - ratio) * val[0] + ratio * val[1];
+}
 
-        template<typename Ty, typename Tp>
-        __device__
-        Ty bilinearInterpFunc(Ty val[2][2], Tp xfrac, Tp yfrac)
-        {
-            Ty res[2];
-            res[0] = linearInterpFunc(val[0], xfrac);
-            res[1] = linearInterpFunc(val[1], xfrac);
-            return linearInterpFunc(res, yfrac);
-        }
+template<typename Ty, typename Tp>
+__device__
+Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio)
+{
+    Ty res[2];
+    res[0] = linearInterpFunc(val[0], xratio);
+    res[1] = linearInterpFunc(val[1], xratio);
+    return linearInterpFunc(res, yratio);
+}
 
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        Ty cubicInterpFunc(Ty val[4], Tp xfrac)
-        {
-            Ty a0 =
-                scalar<Ty>(-0.5) * val[0] + scalar<Ty>( 1.5) * val[1] +
-                scalar<Ty>(-1.5) * val[2] + scalar<Ty>( 0.5) * val[3];
+template<typename Ty, typename Tp>
+__device__ inline static
+Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline)
+{
+    Ty a0, a1, a2, a3;
+    if (spline) {
+        a0 =
+            scalar<Ty>(-0.5) * val[0] + scalar<Ty>( 1.5) * val[1] +
+            scalar<Ty>(-1.5) * val[2] + scalar<Ty>( 0.5) * val[3];
+
+        a1 =
+            scalar<Ty>( 1.0) * val[0] + scalar<Ty>(-2.5) * val[1] +
+            scalar<Ty>( 2.0) * val[2] + scalar<Ty>(-0.5) * val[3];
+
+        a2 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(0.5) * val[2];
+
+        a3 = val[1];
+    } else {
+        a0 = val[3] - val[2] - val[0] + val[1];
+        a1 = val[0] - val[1] - a0;
+        a2 = val[2] - val[0];
+        a3 = val[1];
+    }
 
-            Ty a1 =
-                scalar<Ty>( 1.0) * val[0] + scalar<Ty>(-2.5) * val[1] +
-                scalar<Ty>( 2.0) * val[2] + scalar<Ty>(-0.5) * val[3];
+    Tp xratio2 = xratio * xratio;
+    Tp xratio3 = xratio2 * xratio;
 
-            Ty a2 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(0.5) * val[2];
+    return a0 * xratio3 + a1 * xratio2 + a2 * xratio + a3;
+}
 
-            Ty a3 = val[1];
+template<typename Ty, typename Tp>
+__device__ inline static
+Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio, bool spline)
+{
+    Ty res[4];
+    res[0] = cubicInterpFunc(val[0], xratio, spline);
+    res[1] = cubicInterpFunc(val[1], xratio, spline);
+    res[2] = cubicInterpFunc(val[2], xratio, spline);
+    res[3] = cubicInterpFunc(val[3], xratio, spline);
+    return cubicInterpFunc(res, yratio, spline);
+}
 
-            Tp xfrac2 = xfrac * xfrac;
-            Tp xfrac3 = xfrac2 * xfrac;
+template<typename Ty, typename Tp, int order>
+struct Interp1
+{
+};
 
-            return a0 * xfrac3 + a1 * xfrac2 + a2 * xfrac + a3;
+template<typename Ty, typename Tp>
+struct Interp1<Ty, Tp, 1>
+{
+    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
+    {
+        const int idx = ioff + (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        return in.ptr[idx];
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp1<Ty, Tp, 2>
+{
+    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
+    {
+        const int grid_x = floor(x);    // nearest grid
+        const Tp off_x = x - grid_x;    // fractional offset
+        const int idx = ioff + grid_x;
+        Ty val[2] = {in.ptr[idx], x + 1 < in.dims[0] ? in.ptr[idx + 1] : scalar<Ty>(0)};
+
+        Tp ratio = off_x;
+        if (method == AF_INTERP_LINEAR_COSINE) {
+            // Smooth the factional part with cosine
+            ratio = (1 - cos(ratio * CUDART_PI))/2;
         }
 
-        template<typename Ty, typename Tp>
-        __device__ inline static
-        Ty bicubicInterpFunc(Ty val[4][4], Tp xfrac, Tp yfrac) {
-            Ty res[4];
-            res[0] = cubicInterpFunc(val[0], xfrac);
-            res[1] = cubicInterpFunc(val[1], xfrac);
-            res[2] = cubicInterpFunc(val[2], xfrac);
-            res[3] = cubicInterpFunc(val[3], xfrac);
-            return cubicInterpFunc(res, yfrac);
+        return linearInterpFunc(val, ratio);
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp1<Ty, Tp, 3>
+{
+    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
+    {
+        const int grid_x = floor(x);    // nearest grid
+        const Tp off_x = x - grid_x;    // fractional offset
+        const int idx = ioff + grid_x;
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+        int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+        Ty val[4];
+        for (int i = 0; i < 4; i++) {
+            val[i] = in.ptr[idx + off[i]];
         }
+        return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
+    }
+};
 
-        template<typename Ty, typename Tp, af_interp_type>
-        struct Interp1
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x)
-            {
-                const int idx = round(x) + ioff;
-                return in.ptr[idx];
-            }
-        };
-
-        template<typename Ty, typename Tp>
-        struct Interp1<Ty, Tp, AF_INTERP_LINEAR>
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x)
-            {
-                const int grid_x = floor(x);    // nearest grid
-                const Tp off_x = x - grid_x;    // fractional offset
-                const int idx = ioff + grid_x;
-                Ty val[2] = {in.ptr[idx], x + 1 < in.dims[0] ? in.ptr[idx + 1] : scalar<Ty>(0)};
-                return linearInterpFunc(val, off_x);
-            }
-        };
-
-        template<typename Ty, typename Tp>
-        struct Interp1<Ty, Tp, AF_INTERP_CUBIC>
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x)
-            {
-                const int grid_x = floor(x);    // nearest grid
-                const Tp off_x = x - grid_x;    // fractional offset
-                const int idx = ioff + grid_x;
-
-                bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-                int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
-
-                Ty val[4];
-                for (int i = 0; i < 4; i++) {
-                    val[i] = in.ptr[idx + off[i]];
-                }
-                return cubicInterpFunc(val, off_x);
-            }
-        };
-
-        template<typename Ty, typename Tp, af_interp_type>
-        struct Interp2
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
-            {
-                const int idx = ioff + round(y) * in.strides[1] + round(x);
-                return in.ptr[idx];
-            }
-        };
+template<typename Ty, typename Tp, int order>
+struct Interp2
+{
+};
 
-        template<typename Ty, typename Tp>
-        struct Interp2<Ty, Tp, AF_INTERP_BILINEAR>
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
-            {
-                const int grid_x = floor(x);
-                const Tp off_x = x - grid_x;
+template<typename Ty, typename Tp>
+struct Interp2<Ty, Tp, 1>
+{
+    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
+    {
+        if (method == AF_INTERP_LOWER) {
+            const int idx = ioff + floor(y) * in.strides[1] + floor(x);
+            return in.ptr[idx];
+        } else {
+            const int idx = ioff + round(y) * in.strides[1] + round(x);
+            return in.ptr[idx];
+        }
+    }
+};
 
-                const int grid_y = floor(y);
-                const Tp off_y = y - grid_y;
+template<typename Ty, typename Tp>
+struct Interp2<Ty, Tp, 2>
+{
+    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
+    {
+        const int grid_x = floor(x);
+        const Tp off_x = x - grid_x;
 
-                const int idx = ioff + grid_y * in.strides[1] + grid_x;
+        const int grid_y = floor(y);
+        const Tp off_y = y - grid_y;
 
-                bool condX[2] = {true, x + 1 < in.dims[0]};
-                bool condY[2] = {true, y + 1 < in.dims[1]};
+        const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
-                Ty val[2][2];
-                for (int j = 0; j < 2; j++) {
-                    int off_y = idx + j * in.strides[1];
-                    for (int i = 0; i < 2; i++) {
-                        val[j][i] = condX[i] && condY[j] ? in.ptr[off_y + i] : scalar<Ty>(0);
-                    }
-                }
+        bool condX[2] = {true, x + 1 < in.dims[0]};
+        bool condY[2] = {true, y + 1 < in.dims[1]};
 
-                return bilinearInterpFunc(val, off_x, off_y);
+        Ty val[2][2];
+        for (int j = 0; j < 2; j++) {
+            int off_y = idx + j * in.strides[1];
+            for (int i = 0; i < 2; i++) {
+                val[j][i] = condX[i] && condY[j] ? in.ptr[off_y + i] : scalar<Ty>(0);
             }
-        };
-
-        template<typename Ty, typename Tp>
-        struct Interp2<Ty, Tp, AF_INTERP_LINEAR>
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
-            {
-                return Interp2<Ty, Tp, AF_INTERP_BILINEAR>()(in, ioff, x, y);
-            }
-        };
+        }
 
-        template<typename Ty, typename Tp>
-        struct Interp2<Ty, Tp, AF_INTERP_BICUBIC>
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
-            {
-                const int grid_x = floor(x);
-                const Tp off_x = x - grid_x;
+        Tp xratio = off_x, yratio = off_y;
+        if (method == AF_INTERP_LINEAR_COSINE ||
+            method == AF_INTERP_BILINEAR_COSINE) {
+            // Smooth the factional part with cosine
+            xratio = (1 - cos(xratio * CUDART_PI))/2;
+            yratio = (1 - cos(yratio * CUDART_PI))/2;
+        }
+        return bilinearInterpFunc(val, xratio, yratio);
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp2<Ty, Tp, 3>
+{
+    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
+    {
+        const int grid_x = floor(x);
+        const Tp off_x = x - grid_x;
 
-                const int grid_y = floor(y);
-                const Tp off_y = y - grid_y;
+        const int grid_y = floor(y);
+        const Tp off_y = y - grid_y;
 
-                const int idx = ioff + grid_y * in.strides[1] + grid_x;
+        const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
-                //for bicubic interpolation, work with 4x4 val at a time
-                Ty val[4][4];
+        //for bicubic interpolation, work with 4x4 val at a time
+        Ty val[4][4];
 
-                // used for setting values at boundaries
-                bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-                bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
-                int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-                int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
+        // used for setting values at boundaries
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+        int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
 #pragma unroll
-                for (int j = 0; j < 4; j++) {
-                    int ioff_j = idx + offY[j] * in.strides[1];
+        for (int j = 0; j < 4; j++) {
+            int ioff_j = idx + offY[j] * in.strides[1];
 #pragma unroll
-                    for (int i = 0; i < 4; i++) {
-                        val[j][i] = in.ptr[ioff_j + offX[i]];
-                    }
-                }
-
-                return bicubicInterpFunc(val, off_x, off_y);
-            }
-        };
-
-        template<typename Ty, typename Tp>
-        struct Interp2<Ty, Tp, AF_INTERP_CUBIC>
-        {
-            __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y)
-            {
-                return Interp2<Ty, Tp, AF_INTERP_BICUBIC>()(in, ioff, x, y);
+            for (int i = 0; i < 4; i++) {
+                val[j][i] = in.ptr[ioff_j + offX[i]];
             }
-        };
+        }
+
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
+        return bicubicInterpFunc(val, off_x, off_y, spline);
     }
+};
+
+}
 }
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index 4e344d27e9..5b4e6acef9 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -24,16 +24,18 @@ namespace opencl
 
         // Create output placeholder
         Array<Ty> out = createEmptyArray<Ty>(odims);
-
         switch(method) {
         case AF_INTERP_NEAREST:
-            kernel::approx1<Ty, Tp, AF_INTERP_NEAREST>(out, in, pos, offGrid);
+        case AF_INTERP_LOWER:
+            kernel::approx1<Ty, Tp, 1> (out, in, pos, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
-            kernel::approx1<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos, offGrid);
+        case AF_INTERP_LINEAR_COSINE:
+            kernel::approx1<Ty, Tp, 2> (out, in, pos, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
-            kernel::approx1<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos, offGrid);
+        case AF_INTERP_CUBIC_SPLINE:
+            kernel::approx1<Ty, Tp, 3> (out, in, pos, offGrid, method);
             break;
         default:
             break;
@@ -54,19 +56,25 @@ namespace opencl
 
         switch(method) {
         case AF_INTERP_NEAREST:
-            kernel::approx2<Ty, Tp, AF_INTERP_NEAREST>(out, in, pos0, pos1, offGrid);
+        case AF_INTERP_LOWER:
+            kernel::approx2<Ty, Tp, 1> (out, in, pos0, pos1, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
-            kernel::approx2<Ty, Tp, AF_INTERP_LINEAR> (out, in, pos0, pos1, offGrid);
+        case AF_INTERP_LINEAR_COSINE:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::approx2<Ty, Tp, 2> (out, in, pos0, pos1, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
-            kernel::approx2<Ty, Tp, AF_INTERP_CUBIC> (out, in, pos0, pos1, offGrid);
+        case AF_INTERP_CUBIC_SPLINE:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::approx2<Ty, Tp, 3> (out, in, pos0, pos1, offGrid, method);
             break;
         default:
             break;
         }
+
         return out;
     }
 
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 36a1a3fe81..145c6cfd85 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -44,8 +44,9 @@ namespace opencl
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template <typename Ty, typename Tp, af_interp_type method>
-        void approx1(Param out, const Param in, const Param xpos, const float offGrid)
+        template <typename Ty, typename Tp, int order>
+        void approx1(Param out, const Param in, const Param xpos, const float offGrid,
+                     af_interp_type method)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -57,9 +58,11 @@ namespace opencl
                 std::call_once( compileFlags[device], [device] () {
                     ToNum<Ty> toNum;
                     std::ostringstream options;
-                    options << " -D Ty="        << dtype_traits<Ty>::getName()
-                            << " -D Tp="        << dtype_traits<Tp>::getName()
-                            << " -D ZERO="      << toNum(scalar<Ty>(0));
+                    options << " -D Ty="          << dtype_traits<Ty>::getName()
+                            << " -D Tp="          << dtype_traits<Tp>::getName()
+                            << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+                            << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
+                            << " -D ZERO="        << toNum(scalar<Ty>(0));
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
                        (af_dtype) dtype_traits<Ty>::af_type == c64) {
@@ -72,7 +75,8 @@ namespace opencl
                         options << " -D USE_DOUBLE";
                     }
 
-                    options << " -D " << getInterpName<method>();
+                    options << " -D INTERP_ORDER=" << order;
+                    addInterpEnumOptions(options);
 
                     Program prog;
                     const char *ker_strs[] = {interp_cl, approx1_cl};
@@ -85,7 +89,8 @@ namespace opencl
 
 
                 auto approx1Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                       const Buffer, const KParam, const Ty, const int, const int>
+                                               const Buffer, const KParam, const Ty,
+                                               const int, const int, const int>
                                       (*approxKernels[device]);
 
                 NDRange local(THREADS, 1, 1);
@@ -100,7 +105,8 @@ namespace opencl
 
                 approx1Op(EnqueueArgs(getQueue(), global, local),
                           *out.data, out.info, *in.data, in.info,
-                          *xpos.data, xpos.info, scalar<Ty>(offGrid), blocksPerMat, (int)batch);
+                          *xpos.data, xpos.info, scalar<Ty>(offGrid),
+                          blocksPerMat, (int)batch, (int)method);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
@@ -109,8 +115,9 @@ namespace opencl
             }
         }
 
-        template <typename Ty, typename Tp, af_interp_type method>
-        void approx2(Param out, const Param in, const Param xpos, const Param ypos, const float offGrid)
+        template <typename Ty, typename Tp, int order>
+        void approx2(Param out, const Param in, const Param xpos, const Param ypos,
+                     const float offGrid, af_interp_type method)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -124,6 +131,8 @@ namespace opencl
                     std::ostringstream options;
                     options << " -D Ty="        << dtype_traits<Ty>::getName()
                             << " -D Tp="        << dtype_traits<Tp>::getName()
+                            << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+                            << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
                             << " -D ZERO="      << toNum(scalar<Ty>(0));
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
@@ -137,7 +146,8 @@ namespace opencl
                         options << " -D USE_DOUBLE";
                     }
 
-                    options << " -D " << getInterpName<method>();
+                    options << " -D INTERP_ORDER=" << order;
+                    addInterpEnumOptions(options);
 
                     Program prog;
                     const char *ker_strs[] = {interp_cl, approx2_cl};
@@ -150,7 +160,7 @@ namespace opencl
 
                 auto approx2Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
                                        const Buffer, const KParam, const Buffer, const KParam,
-                                       const Ty, const int, const int, const int>
+                                               const Ty, const int, const int, const int, const int>
                                        (*approxKernels[device]);
 
                 NDRange local(TX, TY, 1);
@@ -168,7 +178,7 @@ namespace opencl
                           *in.data, in.info,
                           *xpos.data, xpos.info,
                           *ypos.data, ypos.info,
-                          scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch);
+                          scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index b4aa4b99b7..7087ad2341 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -11,7 +11,7 @@ __kernel
 void approx1_kernel(__global       Ty *d_out, const KParam out,
                     __global const Ty *d_in,  const KParam in,
                     __global const Tp *d_xpos, const KParam xpos,
-                    const Ty offGrid, const int blocksMatX, const int batch)
+                    const Ty offGrid, const int blocksMatX, const int batch, const int method)
 {
     const int idw = get_group_id(1) / out.dims[2];
     const int idz = get_group_id(1)  - idw * out.dims[2];
@@ -38,5 +38,5 @@ void approx1_kernel(__global       Ty *d_out, const KParam out,
     }
 
     int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + in.offset;
-    d_out[omId] = interp1(d_in, in, ioff, x);
+    d_out[omId] = interp1(d_in, in, ioff, x, method);
 }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 88b5dbbec0..1945fc2120 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -13,7 +13,7 @@ void approx2_kernel(__global       Ty *d_out, const KParam out,
                     __global const Tp *d_xpos, const KParam xpos,
                     __global const Tp *d_ypos, const KParam ypos,
                     const Ty offGrid, const int blocksMatX, const int blocksMatY,
-                    const int batch)
+                    const int batch, int method)
 {
     const int idz = get_group_id(0) / blocksMatX;
     const int idw = get_group_id(1) / blocksMatY;
@@ -47,5 +47,5 @@ void approx2_kernel(__global       Ty *d_out, const KParam out,
 
     int ioff = idw * in.strides[3] + idz * in.strides[2] + in.offset;
 
-    d_out[omId] = interp2(d_in, in, ioff, x, y);
+    d_out[omId] = interp2(d_in, in, ioff, x, y, method);
 }
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index ddb84d3911..fb25e6fd05 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -9,13 +9,13 @@
 
 #if IS_CPLX
 #if USE_DOUBLE
-typedef double TS;
+typedef double ScalarTy;
 #else
-typedef float TS;
+typedef float ScalarTy;
 #endif
-Ty __mulrc(TS s, Ty v)
+InterpInTy __mulrc(ScalarTy s, InterpInTy v)
 {
-    Ty out = {s * v.x, s * v.y};
+    InterpInTy out = {s * v.x, s * v.y};
     return out;
 }
 #define MULRC(a, b) __mulrc(a, b)
@@ -25,98 +25,121 @@ Ty __mulrc(TS s, Ty v)
 #define MULCR(a, b) (a) * (b)
 #endif
 
-Ty linearInterpFunc(Ty val[2], Tp frac)
+InterpInTy linearInterpFunc(InterpInTy val[2], InterpPosTy ratio)
 {
-    return MULRC((1 - frac), val[0]) + MULRC(frac, val[1]);
+    return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
 }
 
-Ty bilinearInterpFunc(Ty val[2][2], Tp xfrac, Tp yfrac)
+InterpInTy bilinearInterpFunc(InterpInTy val[2][2], InterpPosTy xratio, InterpPosTy yratio)
 {
-    Ty res[2];
-    res[0] = linearInterpFunc(val[0], xfrac);
-    res[1] = linearInterpFunc(val[1], xfrac);
-    return linearInterpFunc(res, yfrac);
+    InterpInTy res[2];
+    res[0] = linearInterpFunc(val[0], xratio);
+    res[1] = linearInterpFunc(val[1], xratio);
+    return linearInterpFunc(res, yratio);
 }
 
-Ty cubicInterpFunc(Ty val[4], Tp xfrac)
+InterpInTy cubicInterpFunc(InterpInTy val[4], InterpPosTy xratio, bool spline)
 {
-    Ty a0 = MULRC(-0.5, val[0]) + MULRC( 1.5, val[1]) + MULRC(-1.5, val[2]) + MULRC( 0.5, val[3]);
-    Ty a1 = MULRC( 1.0, val[0]) + MULRC(-2.5, val[1]) + MULRC( 2.0, val[2]) + MULRC(-0.5, val[3]);
-    Ty a2 = MULRC(-0.5, val[0]) + MULRC( 0.5, val[2]);
-    Ty a3 = val[1];
+    InterpInTy a0, a1, a2, a3;
+    if (spline) {
+        a0 = MULRC(-0.5, val[0]) + MULRC( 1.5, val[1]) + MULRC(-1.5, val[2]) + MULRC( 0.5, val[3]);
+        a1 = MULRC( 1.0, val[0]) + MULRC(-2.5, val[1]) + MULRC( 2.0, val[2]) + MULRC(-0.5, val[3]);
+        a2 = MULRC(-0.5, val[0]) + MULRC( 0.5, val[2]);
+        a3 = val[1];
+    } else {
+        a0 = val[3] - val[2] - val[0] + val[1];
+        a1 = val[0] - val[1] - a0;
+        a2 = val[2] - val[0];
+        a3 = val[1];
+    }
 
-    Tp xfrac2 = xfrac * xfrac;
-    Tp xfrac3 = xfrac2 * xfrac;
+    InterpPosTy xratio2 = xratio * xratio;
+    InterpPosTy xratio3 = xratio2 * xratio;
 
-    return MULCR(a0, xfrac3) + MULCR(a1, xfrac2) + MULCR(a2, xfrac) + a3;
+    return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
 }
 
-Ty bicubicInterpFunc(Ty val[4][4], Tp xfrac, Tp yfrac)
+InterpInTy bicubicInterpFunc(InterpInTy val[4][4], InterpPosTy xratio, InterpPosTy yratio, bool spline)
 {
-    Ty res[4];
-    res[0] = cubicInterpFunc(val[0], xfrac);
-    res[1] = cubicInterpFunc(val[1], xfrac);
-    res[2] = cubicInterpFunc(val[2], xfrac);
-    res[3] = cubicInterpFunc(val[3], xfrac);
-    return cubicInterpFunc(res, yfrac);
+    InterpInTy res[4];
+    res[0] = cubicInterpFunc(val[0], xratio, spline);
+    res[1] = cubicInterpFunc(val[1], xratio, spline);
+    res[2] = cubicInterpFunc(val[2], xratio, spline);
+    res[3] = cubicInterpFunc(val[3], xratio, spline);
+    return cubicInterpFunc(res, yratio, spline);
 }
 
-#if defined(NEAREST)
-Ty interp1(const Ty *d_in, KParam in, int ioff, Tp x)
+
+#if INTERP_ORDER == 1
+InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, int method)
 {
-    const int idx = round(x) + ioff;
-    return d_in[idx];
+    if (method == AF_INTERP_LOWER) {
+        const int idx = floor(x) + ioff;
+        return d_in[idx];
+    } else {
+        const int idx = round(x) + ioff;
+        return d_in[idx];
+    }
 }
-#elif defined(LINEAR)
-Ty interp1(const Ty *d_in, KParam in, int ioff, Tp x)
+#elif INTERP_ORDER == 2
+InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, int method)
 {
     const int grid_x = floor(x);    // nearest grid
-    const Tp off_x = x - grid_x;    // fractional offset
+    const InterpPosTy off_x = x - grid_x;    // fractional offset
     const int idx = ioff + grid_x;
-    Ty zero = ZERO;
-    Ty val[2] = {d_in[idx], x + 1 < in.dims[0] ? d_in[idx + 1] : zero};
-    return linearInterpFunc(val, off_x);
+    InterpInTy zero = ZERO;
+    InterpInTy val[2] = {d_in[idx], x + 1 < in.dims[0] ? d_in[idx + 1] : zero};
+    InterpPosTy ratio = off_x;
+    if (method == AF_INTERP_LINEAR_COSINE) {
+        ratio = (1 - cos(ratio * M_PI))/2;
+    }
+    return linearInterpFunc(val, ratio);
 }
-#elif defined(CUBIC)
-Ty interp1(const Ty *d_in, KParam in, int ioff, Tp x)
+#elif INTERP_ORDER == 3
+InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, int method)
 {
     const int grid_x = floor(x);    // nearest grid
-    const Tp off_x = x - grid_x;    // fractional offset
+    const InterpPosTy off_x = x - grid_x;    // fractional offset
     const int idx = ioff + grid_x;
 
     bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
     int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
-    Ty val[4];
+    InterpInTy val[4];
     for (int i = 0; i < 4; i++) {
         val[i] = d_in[idx + off[i]];
     }
-    return cubicInterpFunc(val, off_x);
+    return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
 }
 #endif
 
-#if defined(NEAREST)
-Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
+#if INTERP_ORDER == 1
+InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
 {
-    const int idx = ioff + round(y) * in.strides[1] + round(x);
-    return d_in[idx];
+    if (method == AF_INTERP_LOWER) {
+        const int idx = ioff + floor(y) * in.strides[1] + floor(x);
+        return d_in[idx];
+    } else {
+        const int idx = ioff + round(y) * in.strides[1] + round(x);
+        return d_in[idx];
+    }
 }
-#elif defined(LINEAR) || defined(BILINEAR)
-Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
+#elif INTERP_ORDER == 2
+InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
 {
     const int grid_x = floor(x);
-    const Tp off_x = x - grid_x;
+    const InterpPosTy off_x = x - grid_x;
 
     const int grid_y = floor(y);
-    const Tp off_y = y - grid_y;
+    const InterpPosTy off_y = y - grid_y;
 
     const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
     bool condX[2] = {true, x + 1 < in.dims[0]};
     bool condY[2] = {true, y + 1 < in.dims[1]};
 
-    Ty zero = ZERO;
-    Ty val[2][2];
+    InterpInTy zero = ZERO;
+    InterpInTy val[2][2];
     for (int j = 0; j < 2; j++) {
         int off_y = idx + j * in.strides[1];
         for (int i = 0; i < 2; i++) {
@@ -124,21 +147,27 @@ Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
         }
     }
 
-    return bilinearInterpFunc(val, off_x, off_y);
+    InterpPosTy xratio = off_x, yratio = off_y;
+    if (method == AF_INTERP_LINEAR_COSINE) {
+        xratio = (1 - cos(xratio * M_PI))/2;
+        yratio = (1 - cos(yratio * M_PI))/2;
+    }
+
+    return bilinearInterpFunc(val, xratio, yratio);
 }
-#elif defined(CUBIC) || defined(BICUBIC)
-Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
+#elif INTERP_ORDER == 3
+InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
 {
     const int grid_x = floor(x);
-    const Tp off_x = x - grid_x;
+    const InterpPosTy off_x = x - grid_x;
 
     const int grid_y = floor(y);
-    const Tp off_y = y - grid_y;
+    const InterpPosTy off_y = y - grid_y;
 
     const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
     //for bicubic interpolation, work with 4x4 val at a time
-    Ty val[4][4];
+    InterpInTy val[4][4];
 
     // used for setting values at boundaries
     bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
@@ -155,6 +184,7 @@ Ty interp2(const Ty *d_in, KParam in, int ioff, Tp x, Tp y)
         }
     }
 
-    return bicubicInterpFunc(val, off_x, off_y);
+    bool spline  = method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE;
+    return bicubicInterpFunc(val, off_x, off_y, spline);
 }
 #endif
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
index 306912a7d2..6fcc87adc8 100644
--- a/src/backend/opencl/kernel/interp.hpp
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -8,23 +8,27 @@
  ********************************************************/
 
 #include <af/defines.h>
+#include <string>
+
+#define ADD_ENUM_OPTION(options, name) do {      \
+        options << " -D " #name "=" << name;     \
+    } while(0)
 
 namespace opencl {
     namespace kernel {
 
-        template<af_interp_type method>
-        const char *getInterpName()
+        void addInterpEnumOptions(std::ostringstream &options)
         {
-            switch(method) {
-            case AF_INTERP_NEAREST:   return "NEAREST";
-            case AF_INTERP_LINEAR:    return "LINEAR";
-            case AF_INTERP_BILINEAR:  return "BILINEAR";
-            case AF_INTERP_CUBIC:     return "CUBIC";
-            case AF_INTERP_BICUBIC:   return "BICUBIC";
-            case AF_INTERP_LOWER:     return "LOWER";
-            default:                  return "NONE";
-            }
+            ADD_ENUM_OPTION(options, AF_INTERP_NEAREST);
+            ADD_ENUM_OPTION(options, AF_INTERP_LINEAR);
+            ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR);
+            ADD_ENUM_OPTION(options, AF_INTERP_CUBIC);
+            ADD_ENUM_OPTION(options, AF_INTERP_LOWER);
+            ADD_ENUM_OPTION(options, AF_INTERP_LINEAR_COSINE);
+            ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR_COSINE);
+            ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC);
+            ADD_ENUM_OPTION(options, AF_INTERP_CUBIC_SPLINE);
+            ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC_SPLINE);
         }
-
     }
 }
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 1ce22663b0..ae4df4f0f7 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -178,13 +178,13 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define APPROX1_INIT_CUBIC(desc, file, resultIdx, method)                                       \
+#define APPROX1_INIT_CUBIC_SPLINE(desc, file, resultIdx, method)                                       \
     TYPED_TEST(Approx1, desc)                                                                   \
     {                                                                                           \
         approx1CubicTest<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method); \
     }
 
-APPROX1_INIT_CUBIC(Approx1Cubic, approx1_cubic, 0, AF_INTERP_CUBIC);
+APPROX1_INIT_CUBIC_SPLINE(Approx1Cubic, approx1_cubic, 0, AF_INTERP_CUBIC_SPLINE);
 
 ///////////////////////////////////////////////////////////////////////////////
 // Test Argument Failure Cases
@@ -271,7 +271,7 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 
     APPROX1_ARGSP(Approx1NearestArgsPrecision, approx1, 0, AF_INTERP_NEAREST);
     APPROX1_ARGSP(Approx1LinearArgsPrecision, approx1, 1, AF_INTERP_LINEAR);
-    APPROX1_ARGSP(Approx1CubicArgsPrecision, approx1_cubic, 2, AF_INTERP_CUBIC);
+    APPROX1_ARGSP(Approx1CubicArgsPrecision, approx1_cubic, 2, AF_INTERP_CUBIC_SPLINE);
 
 //////////////////////////////////////// CPP //////////////////////////////////
 //
@@ -365,16 +365,16 @@ TEST(Approx1, CPPCubicBatch)
     af::array input = af::iota(af::dim4(10000, 20), c32);
     af::array pos   = input.dims(0) * af::randu(50000, 20);
 
-    af::array outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+    af::array outBatch = af::approx1(input, pos, AF_INTERP_CUBIC_SPLINE);
 
     af::array outSerial(pos.dims());
     for(int i = 0; i < pos.dims(1); i++) {
-        outSerial(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC);
+        outSerial(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC_SPLINE);
     }
 
     af::array outGFOR(pos.dims());
     gfor(af::seq i, pos.dims(1)) {
-        outGFOR(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC);
+        outGFOR(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC_SPLINE);
     }
 
     ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 05756ece75..b150840bb3 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -268,7 +268,7 @@ TEST(Approx2Cubic, CPP)
     af::array pos1(qdims,&(in[2].front()));
     pos0 = tile(pos0, 1, pos0.dims(0));
     pos1 = tile(pos1.T(), pos1.dims(0));
-    af::array output = af::approx2(input, pos0, pos1, AF_INTERP_CUBIC, 0).T();
+    af::array output = af::approx2(input, pos0, pos1, AF_INTERP_BICUBIC_SPLINE, 0).T();
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];

From d09150860349aa35d036dce12ca92447e26384f7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 14:07:24 -0400
Subject: [PATCH 0844/2677] OPENCL: Fixing bugs in interp.cl

---
 src/backend/opencl/kernel/approx.hpp |  4 ++--
 src/backend/opencl/kernel/interp.cl  | 18 ++++++++++++------
 2 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 145c6cfd85..7a17dab47e 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -137,9 +137,9 @@ namespace opencl
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
                        (af_dtype) dtype_traits<Ty>::af_type == c64) {
-                        options << " -D CPLX=1";
+                        options << " -D IS_CPLX=1";
                     } else {
-                        options << " -D CPLX=0";
+                        options << " -D IS_CPLX=0";
                     }
                     if (std::is_same<Ty, double>::value ||
                         std::is_same<Ty, cdouble>::value) {
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index fb25e6fd05..52c5963f23 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -71,7 +71,8 @@ InterpInTy bicubicInterpFunc(InterpInTy val[4][4], InterpPosTy xratio, InterpPos
 
 
 #if INTERP_ORDER == 1
-InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, int method)
+InterpInTy interp1(__global const InterpInTy *d_in,
+                   KParam in, int ioff, InterpPosTy x, int method)
 {
     if (method == AF_INTERP_LOWER) {
         const int idx = floor(x) + ioff;
@@ -82,7 +83,8 @@ InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, i
     }
 }
 #elif INTERP_ORDER == 2
-InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, int method)
+InterpInTy interp1(__global const InterpInTy *d_in,
+                   KParam in, int ioff, InterpPosTy x, int method)
 {
     const int grid_x = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;    // fractional offset
@@ -96,7 +98,8 @@ InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, i
     return linearInterpFunc(val, ratio);
 }
 #elif INTERP_ORDER == 3
-InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, int method)
+InterpInTy interp1(__global const InterpInTy *d_in,
+                   KParam in, int ioff, InterpPosTy x, int method)
 {
     const int grid_x = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;    // fractional offset
@@ -114,7 +117,8 @@ InterpInTy interp1(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, i
 #endif
 
 #if INTERP_ORDER == 1
-InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
+InterpInTy interp2(__global const InterpInTy *d_in,
+                   KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
 {
     if (method == AF_INTERP_LOWER) {
         const int idx = ioff + floor(y) * in.strides[1] + floor(x);
@@ -125,7 +129,8 @@ InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, I
     }
 }
 #elif INTERP_ORDER == 2
-InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
+InterpInTy interp2(__global const InterpInTy *d_in,
+                   KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
 {
     const int grid_x = floor(x);
     const InterpPosTy off_x = x - grid_x;
@@ -156,7 +161,8 @@ InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, I
     return bilinearInterpFunc(val, xratio, yratio);
 }
 #elif INTERP_ORDER == 3
-InterpInTy interp2(const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
+InterpInTy interp2(__global const InterpInTy *d_in,
+                   KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
 {
     const int grid_x = floor(x);
     const InterpPosTy off_x = x - grid_x;

From bb7f9478ecc9e0284d4af8533f7f49983f251c5a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 14:07:55 -0400
Subject: [PATCH 0845/2677] CUDA: transform and rotate now use interp.hpp

- Removed transform_interp.hpp
---
 src/backend/cuda/kernel/interp.hpp           |  86 ++++++--
 src/backend/cuda/kernel/rotate.hpp           |  47 +++--
 src/backend/cuda/kernel/transform.hpp        |  85 ++++----
 src/backend/cuda/kernel/transform_interp.hpp | 203 -------------------
 src/backend/cuda/rotate.cu                   |  25 ++-
 src/backend/cuda/transform.cu                |  25 ++-
 6 files changed, 168 insertions(+), 303 deletions(-)
 delete mode 100644 src/backend/cuda/kernel/transform_interp.hpp

diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index c0f7068f3d..ff8d9a6abf 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -14,6 +14,34 @@ namespace cuda
 namespace kernel
 {
 
+template<typename T>
+struct itype_t
+{
+    typedef float wtype;
+    typedef float vtype;
+};
+
+template<>
+struct itype_t<double>
+{
+    typedef double wtype;
+    typedef double vtype;
+};
+
+template<>
+struct itype_t<cfloat>
+{
+    typedef float  wtype;
+    typedef cfloat vtype;
+};
+
+template<>
+struct itype_t<cdouble>
+{
+    typedef double  wtype;
+    typedef cdouble vtype;
+};
+
 template<typename Ty, typename Tp>
 __device__
 Ty linearInterpFunc(Ty val[2], Tp ratio)
@@ -83,8 +111,10 @@ struct Interp1<Ty, Tp, 1>
 {
     __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
     {
-        const int idx = ioff + (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        return in.ptr[idx];
+        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        const int idx = ioff + xid;
+        Ty zero = scalar<Ty>(0);
+        return xid >= 0 && xid < in.dims[0] ? in.ptr[idx] : zero;
     }
 };
 
@@ -93,12 +123,15 @@ struct Interp1<Ty, Tp, 2>
 {
     __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
     {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
         const int grid_x = floor(x);    // nearest grid
-        const Tp off_x = x - grid_x;    // fractional offset
+        const WT off_x = x - grid_x;    // fractional offset
         const int idx = ioff + grid_x;
-        Ty val[2] = {in.ptr[idx], x + 1 < in.dims[0] ? in.ptr[idx + 1] : scalar<Ty>(0)};
+        VT val[2] = {in.ptr[idx], x + 1 < in.dims[0] ? in.ptr[idx + 1] : scalar<VT>(0)};
 
-        Tp ratio = off_x;
+        WT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
             ratio = (1 - cos(ratio * CUDART_PI))/2;
@@ -113,14 +146,17 @@ struct Interp1<Ty, Tp, 3>
 {
     __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
     {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
         const int grid_x = floor(x);    // nearest grid
-        const Tp off_x = x - grid_x;    // fractional offset
+        const WT off_x = x - grid_x;    // fractional offset
         const int idx = ioff + grid_x;
 
         bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
         int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
-        Ty val[4];
+        VT val[4];
         for (int i = 0; i < 4; i++) {
             val[i] = in.ptr[idx + off[i]];
         }
@@ -138,13 +174,15 @@ struct Interp2<Ty, Tp, 1>
 {
     __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
     {
-        if (method == AF_INTERP_LOWER) {
-            const int idx = ioff + floor(y) * in.strides[1] + floor(x);
-            return in.ptr[idx];
-        } else {
-            const int idx = ioff + round(y) * in.strides[1] + round(x);
-            return in.ptr[idx];
-        }
+        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+
+        bool condX = xid >= 0 && xid < in.dims[0];
+        bool condY = yid >= 0 && yid < in.dims[1];
+
+        int idx = ioff + yid * in.strides[1] + xid;
+        Ty zero = scalar<Ty>(0);
+        return condX && condY ? in.ptr[idx] : zero;
     }
 };
 
@@ -153,18 +191,21 @@ struct Interp2<Ty, Tp, 2>
 {
     __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
     {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
         const int grid_x = floor(x);
-        const Tp off_x = x - grid_x;
+        const WT off_x = x - grid_x;
 
         const int grid_y = floor(y);
-        const Tp off_y = y - grid_y;
+        const WT off_y = y - grid_y;
 
         const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
         bool condX[2] = {true, x + 1 < in.dims[0]};
         bool condY[2] = {true, y + 1 < in.dims[1]};
 
-        Ty val[2][2];
+        VT val[2][2];
         for (int j = 0; j < 2; j++) {
             int off_y = idx + j * in.strides[1];
             for (int i = 0; i < 2; i++) {
@@ -172,7 +213,7 @@ struct Interp2<Ty, Tp, 2>
             }
         }
 
-        Tp xratio = off_x, yratio = off_y;
+        WT xratio = off_x, yratio = off_y;
         if (method == AF_INTERP_LINEAR_COSINE ||
             method == AF_INTERP_BILINEAR_COSINE) {
             // Smooth the factional part with cosine
@@ -188,16 +229,19 @@ struct Interp2<Ty, Tp, 3>
 {
     __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
     {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
         const int grid_x = floor(x);
-        const Tp off_x = x - grid_x;
+        const WT off_x = x - grid_x;
 
         const int grid_y = floor(y);
-        const Tp off_y = y - grid_y;
+        const WT off_y = y - grid_y;
 
         const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
         //for bicubic interpolation, work with 4x4 val at a time
-        Ty val[4][4];
+        VT val[4][4];
 
         // used for setting values at boundaries
         bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 3cea7f2698..c5edbdfb07 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -12,7 +12,7 @@
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include "transform_interp.hpp"
+#include "interp.hpp"
 
 namespace cuda
 {
@@ -31,11 +31,12 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         // Rotate Kernel
         ///////////////////////////////////////////////////////////////////////////
-        template<typename T, af_interp_type method>
+        template<typename T, int order>
         __global__ static void
         rotate_kernel(Param<T> out, CParam<T> in, const tmat_t t,
                       const int nimages, const int nbatches,
-                      const int blocksXPerImage, const int blocksYPerImage)
+                      const int blocksXPerImage, const int blocksYPerImage,
+                      af_interp_type method)
         {
             // Compute which image set
             const int setId = blockIdx.x / blocksXPerImage;
@@ -45,35 +46,37 @@ namespace cuda
             const int blockIdx_y = blockIdx.y - batch * blocksYPerImage;
 
             // Get thread indices
-            const int xx = blockIdx_x * blockDim.x + threadIdx.x;
-            const int yy = blockIdx_y * blockDim.y + threadIdx.y;
+            const int xido = blockIdx_x * blockDim.x + threadIdx.x;
+            const int yido = blockIdx_y * blockDim.y + threadIdx.y;
 
             const int limages = min(out.dims[2] - setId * nimages, nimages);
 
-            if(xx >= out.dims[0] || yy >= out.dims[1])
+            if(xido >= out.dims[0] || yido >= out.dims[1])
                 return;
 
+            // Compute input index
+            typedef typename itype_t<T>::wtype WT;
+            WT xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
+            WT yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
+
             // Global offset
             //          Offset for transform channel + Offset for image channel.
-                  T *optr = out.ptr + setId * nimages * out.strides[2] + batch * out.strides[3];
-            const T *iptr = in.ptr  + setId * nimages * in.strides[2]  + batch * in.strides[3];
-
-            switch(method) {
-                case AF_INTERP_NEAREST:
-                    transform_n(optr, out, iptr, in, t.tmat, xx, yy, limages, false); break;
-                case AF_INTERP_BILINEAR:
-                    transform_b(optr, out, iptr, in, t.tmat, xx, yy, limages, false); break;
-                case AF_INTERP_LOWER:
-                    transform_l(optr, out, iptr, in, t.tmat, xx, yy, limages, false); break;
-                default: break;
+            int outoff =  setId * nimages * out.strides[2] + batch * out.strides[3];
+            int inoff  =  setId * nimages * in.strides[2]  + batch * in.strides[3];
+            const int loco = outoff + (yido * out.strides[1] + xido);
+
+            Interp2<T, WT, order> interp;
+            for(int i = 0; i < limages; i++) {
+                int ioff = inoff + (i * in.strides[2]);
+                out.ptr[loco + i * out.strides[2]] = interp(in, ioff, xidi, yidi, method);
             }
         }
 
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template <typename T, af_interp_type method>
-        void rotate(Param<T> out, CParam<T> in, const float theta)
+        template <typename T, int order>
+        void rotate(Param<T> out, CParam<T> in, const float theta, af_interp_type method)
         {
             const float c = cos(-theta), s = sin(-theta);
             float tx, ty;
@@ -114,11 +117,11 @@ namespace cuda
 
             blocks.y = blocks.y * nbatches;
 
-            CUDA_LAUNCH((rotate_kernel<T, method>), blocks, threads,
-                    out, in, t, nimages, nbatches, blocksXPerImage, blocksYPerImage);
+            CUDA_LAUNCH((rotate_kernel<T, order>), blocks, threads,
+                        out, in, t, nimages, nbatches,
+                        blocksXPerImage, blocksYPerImage, method);
 
             POST_LAUNCH_CHECK();
         }
     }
 }
-
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index edafcd9b83..6b453f5fab 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -12,7 +12,7 @@
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include "transform_interp.hpp"
+#include "interp.hpp"
 
 namespace cuda
 {
@@ -65,12 +65,13 @@ namespace cuda
         ///////////////////////////////////////////////////////////////////////////
         // Transform Kernel
         ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool inverse, af_interp_type method>
+        template<typename T, bool inverse, int order>
         __global__ static void
         transform_kernel(Param<T> out, CParam<T> in,
-                const int nImg2, const int nImg3, const int nTfs2, const int nTfs3, const int batchImg2,
-                const int blocksXPerImage, const int blocksYPerImage,
-                const bool perspective)
+                         const int nImg2, const int nImg3, const int nTfs2, const int nTfs3,
+                         const int batchImg2,
+                         const int blocksXPerImage, const int blocksYPerImage,
+                         const bool perspective, af_interp_type method)
         {
             // Image Ids
             const int imgId2 = blockIdx.x / blocksXPerImage;
@@ -81,13 +82,13 @@ namespace cuda
             const int blockIdx_y = blockIdx.y - imgId3 * blocksYPerImage;
 
             // Get thread indices in local image
-            const int xx = blockIdx_x * blockDim.x + threadIdx.x;
-            const int yy = blockIdx_y * blockDim.y + threadIdx.y;
+            const int xido = blockIdx_x * blockDim.x + threadIdx.x;
+            const int yido = blockIdx_y * blockDim.y + threadIdx.y;
 
             // Image iteration loop count for image batching
             int limages = min(max(out.dims[2] - imgId2 * nImg2, 1), batchImg2);
 
-            if(xx >= out.dims[0] || yy >= out.dims[1])
+            if(xido >= out.dims[0] || yido >= out.dims[1])
                 return;
 
             // Index of transform
@@ -121,25 +122,22 @@ namespace cuda
 
             // Linear transform index
             const int t_idx = t_idx2 + t_idx3 * nTfs2;
-            int offset = 0;
+            int outoff = 0;
 
             // Global offsets
-            const T *iptr = in.ptr + imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
+            const int inoff= imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
             if(nImg2 == nTfs2 || nImg2 > 1) {   // One-to-One or Image on dim2
-                offset += imgId2 * batchImg2 * out.strides[2];
+                outoff += imgId2 * batchImg2 * out.strides[2];
             } else {                            // Transform batched on dim2
-                offset += t_idx2 * out.strides[2];
+                outoff += t_idx2 * out.strides[2];
             }
 
             if(nImg3 == nTfs3 || nImg3 > 1) {   // One-to-One or Image on dim3
-                offset += imgId3 * out.strides[3];
+                outoff += imgId3 * out.strides[3];
             } else {                            // Transform batched on dim2
-                offset += t_idx3 * out.strides[3];
+                outoff += t_idx3 * out.strides[3];
             }
 
-            T *optr = out.ptr + offset;
-
-
             // Transform is in constant memory.
             const int transf_len = (perspective ? 9 : 6);
             const float *tmat_ptr = c_tmat + t_idx * transf_len;
@@ -155,23 +153,40 @@ namespace cuda
                 calc_transf_inverse(tmat, tmat_ptr, perspective);
             }
 
-            switch(method) {
-                case AF_INTERP_NEAREST:
-                    transform_n(optr, out, iptr, in, tmat, xx, yy, limages, perspective); break;
-                case AF_INTERP_BILINEAR:
-                    transform_b(optr, out, iptr, in, tmat, xx, yy, limages, perspective); break;
-                case AF_INTERP_LOWER:
-                    transform_l(optr, out, iptr, in, tmat, xx, yy, limages, perspective); break;
-                default: break;
+            const int loco = outoff + (yido * out.strides[1] + xido);
+
+            // Compute input index
+            typedef typename itype_t<T>::wtype WT;
+            WT xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
+            WT yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
+
+            if (perspective) {
+                const WT W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+                xidi /= W;
+                yidi /= W;
+            }
+
+            if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi || in.dims[1] <= yidi) {
+                for(int i = 0; i < limages; i++) {
+                    out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
+                }
+                return;
+            }
+
+            Interp2<T, WT, order> interp;
+            for(int i = 0; i < limages; i++) {
+                int ioff = inoff + (i * in.strides[2]);
+                out.ptr[loco + i * out.strides[2]] = interp(in, ioff, xidi, yidi, method);
             }
         }
 
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
-        template <typename T, af_interp_type method>
+        template <typename T, int order>
         void transform(Param<T> out, CParam<T> in, CParam<float> tf,
-                              const bool inverse, const bool perspective)
+                       const bool inverse, const bool perspective,
+                       af_interp_type method)
         {
             const int nImg2 = in.dims[2];
             const int nImg3 = in.dims[3];
@@ -207,15 +222,15 @@ namespace cuda
                      *  max((nTfs3 / nImg3), 1);
 
             if(inverse) {
-                CUDA_LAUNCH((transform_kernel<T, true, method>), blocks, threads, out, in,
-                                nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                                blocksXPerImage, blocksYPerImage,
-                                perspective);
+                CUDA_LAUNCH((transform_kernel<T, true, order>), blocks, threads, out, in,
+                            nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+                            blocksXPerImage, blocksYPerImage,
+                            perspective, method);
             } else {
-                CUDA_LAUNCH((transform_kernel<T, false, method>), blocks, threads, out, in,
-                                nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                                blocksXPerImage, blocksYPerImage,
-                                perspective);
+                CUDA_LAUNCH((transform_kernel<T, false, order>), blocks, threads, out, in,
+                            nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+                            blocksXPerImage, blocksYPerImage,
+                            perspective, method);
             }
             POST_LAUNCH_CHECK();
         }
diff --git a/src/backend/cuda/kernel/transform_interp.hpp b/src/backend/cuda/kernel/transform_interp.hpp
deleted file mode 100644
index 964477a61f..0000000000
--- a/src/backend/cuda/kernel/transform_interp.hpp
+++ /dev/null
@@ -1,203 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-namespace cuda
-{
-    namespace kernel
-    {
-        template<typename T>
-        struct itype_t
-        {
-            typedef float wtype;
-            typedef float vtype;
-        };
-
-        template<>
-        struct itype_t<double>
-        {
-            typedef double wtype;
-            typedef double vtype;
-        };
-
-        template<>
-        struct itype_t<cfloat>
-        {
-            typedef float  wtype;
-            typedef cfloat vtype;
-        };
-
-        template<>
-        struct itype_t<cdouble>
-        {
-            typedef double  wtype;
-            typedef cdouble vtype;
-        };
-
-        template<typename T>
-        __device__
-        void transform_n(T *optr, Param<T> out, const T *iptr, CParam<T> in, const float *tmat,
-                         const int xido, const int yido, const int nimages,
-                         const bool perspective)
-        {
-            // Compute input index
-            int xidi = 0, yidi = 0;
-            if (perspective) {
-                const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-                xidi = round((xido * tmat[0]
-                            + yido * tmat[1]
-                                   + tmat[2]) / W);
-                yidi = round((xido * tmat[3]
-                            + yido * tmat[4]
-                                   + tmat[5]) / W);
-            } else {
-                xidi = round(xido * tmat[0]
-                           + yido * tmat[1]
-                                  + tmat[2]);
-                yidi = round(xido * tmat[3]
-                           + yido * tmat[4]
-                                  + tmat[5]);
-            }
-
-            // Makes scale give same output as resize
-            // But fails rotate tests
-            //if (xidi >= in.dims[0]) { xidi = in.dims[0] - 1; }
-            //if (yidi >= in.dims[1]) { yidi = in.dims[1] - 1; }
-
-            const int loci = yidi * in.strides[1]  + xidi;
-            const int loco = yido * out.strides[1] + xido;
-
-            for(int i = 0; i < nimages; i++) {
-                // Compute memory location of indices
-                int ioff = loci + i * in.strides[2];
-                int ooff = loco + i * out.strides[2];
-
-                // Copy to output
-                T val = scalar<T>(0);
-                if (xidi < in.dims[0] && yidi < in.dims[1] && xidi >= 0 && yidi >= 0) val = iptr[ioff];
-
-                optr[ooff] = val;
-            }
-        }
-
-        template<typename T>
-        __device__
-        void transform_b(T *optr, Param<T> out, const T *iptr, CParam<T> in, const float *tmat,
-                         const int xido, const int yido, const int nimages,
-                         const bool perspective)
-        {
-            const int loco = (yido * out.strides[1] + xido);
-
-            // Compute input index
-            float xidi = 0.0f, yidi = 0.0f;
-            if (perspective) {
-                const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-                xidi = (xido * tmat[0]
-                      + yido * tmat[1]
-                             + tmat[2]) / W;
-                yidi = (xido * tmat[3]
-                      + yido * tmat[4]
-                             + tmat[5]) / W;
-            } else {
-                xidi = xido * tmat[0]
-                     + yido * tmat[1]
-                            + tmat[2];
-                yidi = xido * tmat[3]
-                     + yido * tmat[4]
-                            + tmat[5];
-            }
-
-            if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi || in.dims[1] < yidi) {
-                for(int i = 0; i < nimages; i++) {
-                    optr[loco + i * out.strides[2]] = scalar<T>(0.0f);
-                }
-                return;
-            }
-
-            typedef typename itype_t<T>::wtype WT;
-            typedef typename itype_t<T>::vtype VT;
-
-            const int grd_x = floor(xidi),  grd_y = floor(yidi);
-            const WT off_x = xidi - grd_x, off_y = yidi - grd_y;
-
-            // Check if pVal and pVal + 1 are both valid indices
-            const bool condY = (yidi < (in.dims[1] - 1));
-            const bool condX = (xidi < (in.dims[0] - 1));
-
-            // Compute weights used
-            const WT wt00 = (1.0 - off_x) * (1.0 - off_y);
-            const WT wt10 = (condY) ? (1.0 - off_x) * (off_y)     : 0;
-            const WT wt01 = (condX) ? (off_x) * (1.0 - off_y)     : 0;
-            const WT wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-            const WT wt = wt00 + wt10 + wt01 + wt11;
-
-            const int loci = grd_y * in.strides[1] + grd_x;
-            T zero = scalar<T>(0.0f);
-            for(int i = 0; i < nimages; i++) {
-                const int ioff = loci + (i * in.strides[2]);
-                const int ooff = loco + (i * out.strides[2]);
-
-                // Compute Weighted Values
-                VT v00 =                    wt00 * iptr[ioff];
-                VT v10 = (condY) ?          wt10 * iptr[ioff + in.strides[1]]     : zero;
-                VT v01 = (condX) ?          wt01 * iptr[ioff + 1]                 : zero;
-                VT v11 = (condX && condY) ? wt11 * iptr[ioff + in.strides[1] + 1] : zero;
-                VT vo  = v00 + v10 + v01 + v11;
-
-                optr[ooff] = (vo / wt);
-            }
-        }
-
-        template<typename T>
-        __device__
-        void transform_l(T *optr, Param<T> out, const T *iptr, CParam<T> in, const float *tmat,
-                         const int xido, const int yido, const int nimages,
-                         const bool perspective)
-        {
-            // Compute input index
-            int xidi = 0, yidi = 0;
-            if (perspective) {
-                const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-                xidi = floor((xido * tmat[0]
-                            + yido * tmat[1]
-                                   + tmat[2]) / W);
-                yidi = floor((xido * tmat[3]
-                            + yido * tmat[4]
-                                   + tmat[5]) / W);
-            } else {
-                xidi = floor(xido * tmat[0]
-                           + yido * tmat[1]
-                                  + tmat[2]);
-                yidi = floor(xido * tmat[3]
-                           + yido * tmat[4]
-                                  + tmat[5]);
-            }
-
-            // Makes scale give same output as resize
-            // But fails rotate tests
-            //if (xidi >= in.dims[0]) { xidi = in.dims[0] - 1; }
-            //if (yidi >= in.dims[1]) { yidi = in.dims[1] - 1; }
-
-            const int loci = yidi * in.strides[1]  + xidi;
-            const int loco = yido * out.strides[1] + xido;
-
-            for(int i = 0; i < nimages; i++) {
-                // Compute memory location of indices
-                int ioff = loci + i * in.strides[2];
-                int ooff = loco + i * out.strides[2];
-
-                // Copy to output
-                T val = scalar<T>(0);
-                if (xidi < in.dims[0] && yidi < in.dims[1] && xidi >= 0 && yidi >= 0) val = iptr[ioff];
-
-                optr[ooff] = val;
-            }
-        }
-    }
-}
diff --git a/src/backend/cuda/rotate.cu b/src/backend/cuda/rotate.cu
index 23c99e13f2..dc0ec4dc55 100644
--- a/src/backend/cuda/rotate.cu
+++ b/src/backend/cuda/rotate.cu
@@ -21,17 +21,20 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::rotate<T, AF_INTERP_NEAREST> (out, in, theta);
-                break;
-            case AF_INTERP_BILINEAR:
-                kernel::rotate<T, AF_INTERP_BILINEAR>(out, in, theta);
-                break;
-            case AF_INTERP_LOWER:
-                kernel::rotate<T, AF_INTERP_LOWER>   (out, in, theta);
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::rotate<T, 1>(out, in, theta, method);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::rotate<T, 2>(out, in, theta, method);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::rotate<T, 3>(out, in, theta, method);
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
         }
 
         return out;
diff --git a/src/backend/cuda/transform.cu b/src/backend/cuda/transform.cu
index 98ab58dee4..63745b304e 100644
--- a/src/backend/cuda/transform.cu
+++ b/src/backend/cuda/transform.cu
@@ -21,17 +21,20 @@ namespace cuda
         Array<T> out = createEmptyArray<T>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::transform<T, AF_INTERP_NEAREST> (out, in, tf, inverse, perspective);
-                break;
-            case AF_INTERP_BILINEAR:
-                kernel::transform<T, AF_INTERP_BILINEAR>(out, in, tf, inverse, perspective);
-                break;
-            case AF_INTERP_LOWER:
-                kernel::transform<T, AF_INTERP_LOWER>   (out, in, tf, inverse, perspective);
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::transform<T, 1>(out, in, tf, inverse, perspective, method);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::transform<T, 2>(out, in, tf, inverse, perspective, method);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::transform<T, 3>(out, in, tf, inverse, perspective, method);
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
         }
 
         return out;

From af94c7320d815a91fe7ac00b6f75522745adf915 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 15:50:08 -0400
Subject: [PATCH 0846/2677] BUGFIX: Making sure windows finds Pi constant

---
 src/backend/cpu/kernel/interp.hpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index c1439f3a1a..ed8d6aea62 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -7,10 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#define _USE_MATH_DEFINES
-#include <cmath>
 #include <Array.hpp>
 #include <math.hpp>
+#include <af/constants.h>
 
 namespace cpu
 {
@@ -104,7 +103,7 @@ struct Interp1<InT, LocT, 2>
         LocT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
-            ratio = (1 - std::cos(ratio * M_PI))/2;
+            ratio = (1 - std::cos(ratio * af::Pi))/2;
         }
 
         return linearInterpFunc(val, ratio);

From 914c60f5ba4ee1e194cab8518ec9184c7756da09 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 16:27:08 -0400
Subject: [PATCH 0847/2677] CUDA: Adding batch support for interp.hpp

- Move the batching loop from transform, rotate to interp
---
 src/backend/cuda/kernel/approx.hpp    |   4 +-
 src/backend/cuda/kernel/interp.hpp    | 114 ++++++++++++++++++--------
 src/backend/cuda/kernel/rotate.hpp    |   5 +-
 src/backend/cuda/kernel/transform.hpp |   5 +-
 4 files changed, 82 insertions(+), 46 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 474682b24f..9b2dca9af5 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -57,7 +57,7 @@ namespace cuda
             int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
 
             Interp1<Ty, Tp, order> interp;
-            out.ptr[omId] = interp(in, ioff, x, method);
+            interp(out, omId, in, ioff, x, method, 1);
         }
 
         template<typename Ty, typename Tp, int order>
@@ -98,7 +98,7 @@ namespace cuda
             int ioff = idw * in.strides[3] + idz * in.strides[2];
 
             Interp2<Ty, Tp, order> interp;
-            out.ptr[omId] = interp(in, ioff, x, y, method);
+            interp(out, omId, in, ioff, x, y, method, 1);
         }
 
         ///////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index ff8d9a6abf..21c742991c 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -109,19 +109,28 @@ struct Interp1
 template<typename Ty, typename Tp>
 struct Interp1<Ty, Tp, 1>
 {
-    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
+    __device__ void operator()(Param<Ty> out, int ooff,
+                               CParam<Ty> in, int ioff, Tp x,
+                               af_interp_type method, int batch)
     {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        const int idx = ioff + xid;
         Ty zero = scalar<Ty>(0);
-        return xid >= 0 && xid < in.dims[0] ? in.ptr[idx] : zero;
+        bool cond = xid >= 0 && xid < in.dims[0];
+        const int idx = ioff + xid;
+
+        for (int n = 0; n < batch; n++) {
+            Ty outval = cond ? in.ptr[idx + n * in.strides[1]] : zero;
+            out.ptr[ooff + n * out.strides[1]] = outval;
+        }
     }
 };
 
 template<typename Ty, typename Tp>
 struct Interp1<Ty, Tp, 2>
 {
-    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
+    __device__ void operator()(Param<Ty> out, int ooff,
+                               CParam<Ty> in, int ioff, Tp x,
+                               af_interp_type method, int batch)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -129,22 +138,31 @@ struct Interp1<Ty, Tp, 2>
         const int grid_x = floor(x);    // nearest grid
         const WT off_x = x - grid_x;    // fractional offset
         const int idx = ioff + grid_x;
-        VT val[2] = {in.ptr[idx], x + 1 < in.dims[0] ? in.ptr[idx + 1] : scalar<VT>(0)};
 
+        bool cond[2] = {grid_x >= 0, grid_x + 1 < in.dims[0]};
         WT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
             ratio = (1 - cos(ratio * CUDART_PI))/2;
         }
 
-        return linearInterpFunc(val, ratio);
+        Ty zero = scalar<Ty>(0);
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[1];
+            VT val[2] = {cond[0] ? in.ptr[idx_n] : zero,
+                         cond[1] ? in.ptr[idx_n + 1] : zero};
+            out.ptr[ooff + n * out.strides[1]] = linearInterpFunc(val, ratio);
+        }
     }
 };
 
 template<typename Ty, typename Tp>
 struct Interp1<Ty, Tp, 3>
 {
-    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, af_interp_type method)
+    __device__ void operator()(Param<Ty> out, int ooff,
+                               CParam<Ty> in, int ioff, Tp x,
+                               af_interp_type method, int batch)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -154,13 +172,17 @@ struct Interp1<Ty, Tp, 3>
         const int idx = ioff + grid_x;
 
         bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-        int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+        int  offx[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
-        VT val[4];
-        for (int i = 0; i < 4; i++) {
-            val[i] = in.ptr[idx + off[i]];
+        bool spline = method == AF_INTERP_CUBIC_SPLINE;
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[1];
+            VT val[4];
+            for (int i = 0; i < 4; i++) {
+                val[i] = in.ptr[idx_n + offx[i]];
+            }
+            out.ptr[ooff + n * out.strides[1]] = cubicInterpFunc(val, off_x, spline);
         }
-        return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
     }
 };
 
@@ -172,24 +194,36 @@ struct Interp2
 template<typename Ty, typename Tp>
 struct Interp2<Ty, Tp, 1>
 {
-    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
+    __device__ void operator()(Param<Ty> out, int ooff,
+                               CParam<Ty> in, int ioff, Tp x, Tp y,
+                               af_interp_type method,
+                               int nimages)
     {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+        int idx = ioff + yid * in.strides[1] + xid;
 
         bool condX = xid >= 0 && xid < in.dims[0];
         bool condY = yid >= 0 && yid < in.dims[1];
 
-        int idx = ioff + yid * in.strides[1] + xid;
         Ty zero = scalar<Ty>(0);
-        return condX && condY ? in.ptr[idx] : zero;
+        bool cond = condX && condY;
+
+        for (int n = 0; n < nimages; n++) {
+            int idx_n = idx + n * in.strides[2];
+            Ty val = cond ? in.ptr[idx_n] : zero;
+            out.ptr[ooff + n * out.strides[2]] = val;
+        }
     }
 };
 
 template<typename Ty, typename Tp>
 struct Interp2<Ty, Tp, 2>
 {
-    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
+    __device__ void operator()(Param<Ty> out, int ooff,
+                               CParam<Ty> in, int ioff, Tp x, Tp y,
+                               af_interp_type method,
+                               int nimages)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -205,14 +239,6 @@ struct Interp2<Ty, Tp, 2>
         bool condX[2] = {true, x + 1 < in.dims[0]};
         bool condY[2] = {true, y + 1 < in.dims[1]};
 
-        VT val[2][2];
-        for (int j = 0; j < 2; j++) {
-            int off_y = idx + j * in.strides[1];
-            for (int i = 0; i < 2; i++) {
-                val[j][i] = condX[i] && condY[j] ? in.ptr[off_y + i] : scalar<Ty>(0);
-            }
-        }
-
         WT xratio = off_x, yratio = off_y;
         if (method == AF_INTERP_LINEAR_COSINE ||
             method == AF_INTERP_BILINEAR_COSINE) {
@@ -220,14 +246,28 @@ struct Interp2<Ty, Tp, 2>
             xratio = (1 - cos(xratio * CUDART_PI))/2;
             yratio = (1 - cos(yratio * CUDART_PI))/2;
         }
-        return bilinearInterpFunc(val, xratio, yratio);
+
+        for (int n = 0; n < nimages; n++) {
+            int idx_n = idx + n * in.strides[2];
+            VT val[2][2];
+            for (int j = 0; j < 2; j++) {
+                int off_y = idx_n + j * in.strides[1];
+                for (int i = 0; i < 2; i++) {
+                    val[j][i] = condX[i] && condY[j] ? in.ptr[off_y + i] : scalar<Ty>(0);
+                }
+            }
+            out.ptr[ooff + n * out.strides[2]] = bilinearInterpFunc(val, xratio, yratio);
+        }
     }
 };
 
 template<typename Ty, typename Tp>
 struct Interp2<Ty, Tp, 3>
 {
-    __device__ Ty operator()(CParam<Ty> in, int ioff, Tp x, Tp y, af_interp_type method)
+    __device__ void operator()(Param<Ty> out, int ooff,
+                               CParam<Ty> in, int ioff, Tp x, Tp y,
+                               af_interp_type method,
+                               int nimages)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -240,26 +280,28 @@ struct Interp2<Ty, Tp, 3>
 
         const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
-        //for bicubic interpolation, work with 4x4 val at a time
-        VT val[4][4];
-
         // used for setting values at boundaries
         bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
         bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
         int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
         int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
+        //for bicubic interpolation, work with 4x4 val at a time
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
+        for (int n = 0; n < nimages; n++) {
+            int idx_n = idx + n * out.strides[2];
+            VT val[4][4];
 #pragma unroll
-        for (int j = 0; j < 4; j++) {
-            int ioff_j = idx + offY[j] * in.strides[1];
+            for (int j = 0; j < 4; j++) {
+                int ioff_j = idx_n + offY[j] * in.strides[1];
 #pragma unroll
-            for (int i = 0; i < 4; i++) {
-                val[j][i] = in.ptr[ioff_j + offX[i]];
+                for (int i = 0; i < 4; i++) {
+                    val[j][i] = in.ptr[ioff_j + offX[i]];
+                }
             }
-        }
 
-        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
-        return bicubicInterpFunc(val, off_x, off_y, spline);
+            out.ptr[ooff + n * out.strides[2]] = bicubicInterpFunc(val, off_x, off_y, spline);
+        }
     }
 };
 
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index c5edbdfb07..1b970ca3ae 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -66,10 +66,7 @@ namespace cuda
             const int loco = outoff + (yido * out.strides[1] + xido);
 
             Interp2<T, WT, order> interp;
-            for(int i = 0; i < limages; i++) {
-                int ioff = inoff + (i * in.strides[2]);
-                out.ptr[loco + i * out.strides[2]] = interp(in, ioff, xidi, yidi, method);
-            }
+            interp(out, loco, in, inoff, xidi, yidi, method, limages);
         }
 
         ///////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 6b453f5fab..fad5f279a3 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -174,10 +174,7 @@ namespace cuda
             }
 
             Interp2<T, WT, order> interp;
-            for(int i = 0; i < limages; i++) {
-                int ioff = inoff + (i * in.strides[2]);
-                out.ptr[loco + i * out.strides[2]] = interp(in, ioff, xidi, yidi, method);
-            }
+            interp(out, loco, in, inoff, xidi, yidi, method, limages);
         }
 
         ///////////////////////////////////////////////////////////////////////////

From 25b4afb42fa7f88cf94950f9677cf5099f30b4bb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 17:15:04 -0400
Subject: [PATCH 0848/2677] CUDA: Add clamp support for interp.hpp

- Fixes broken tests in translate and rotate_linear
---
 src/backend/cuda/kernel/approx.hpp    | 14 ++++++--
 src/backend/cuda/kernel/interp.hpp    | 46 ++++++++++++++++++---------
 src/backend/cuda/kernel/rotate.hpp    | 17 +++++++++-
 src/backend/cuda/kernel/transform.hpp |  5 ++-
 4 files changed, 63 insertions(+), 19 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 9b2dca9af5..f701bfdf78 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -56,8 +56,13 @@ namespace cuda
 
             int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
 
+            // FIXME: Only cubic interpolation is doing clamping
+            // We need to make it consistent across all methods
+            // Not changing the behavior because tests will fail
+            bool clamp = order == 3;
+
             Interp1<Ty, Tp, order> interp;
-            interp(out, omId, in, ioff, x, method, 1);
+            interp(out, omId, in, ioff, x, method, 1, clamp);
         }
 
         template<typename Ty, typename Tp, int order>
@@ -97,8 +102,13 @@ namespace cuda
 
             int ioff = idw * in.strides[3] + idz * in.strides[2];
 
+            // FIXME: Only cubic interpolation is doing clamping
+            // We need to make it consistent across all methods
+            // Not changing the behavior because tests will fail
+            bool clamp = order == 3;
+
             Interp2<Ty, Tp, order> interp;
-            interp(out, omId, in, ioff, x, y, method, 1);
+            interp(out, omId, in, ioff, x, y, method, 1, clamp);
         }
 
         ///////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index 21c742991c..e5612346f8 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -111,15 +111,17 @@ struct Interp1<Ty, Tp, 1>
 {
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch)
+                               af_interp_type method, int batch, bool clamp)
     {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         Ty zero = scalar<Ty>(0);
         bool cond = xid >= 0 && xid < in.dims[0];
+        if (clamp) xid = max(0, min(xid, in.dims[0]));
+
         const int idx = ioff + xid;
 
         for (int n = 0; n < batch; n++) {
-            Ty outval = cond ? in.ptr[idx + n * in.strides[1]] : zero;
+            Ty outval = (cond || clamp) ? in.ptr[idx + n * in.strides[1]] : zero;
             out.ptr[ooff + n * out.strides[1]] = outval;
         }
     }
@@ -130,7 +132,7 @@ struct Interp1<Ty, Tp, 2>
 {
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch)
+                               af_interp_type method, int batch, bool clamp)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -139,7 +141,8 @@ struct Interp1<Ty, Tp, 2>
         const WT off_x = x - grid_x;    // fractional offset
         const int idx = ioff + grid_x;
 
-        bool cond[2] = {grid_x >= 0, grid_x + 1 < in.dims[0]};
+        bool cond[2] = {true, grid_x + 1 < in.dims[0]};
+        int  offx[2]  = {0, cond[1] ? 1 : 0};
         WT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
@@ -150,8 +153,8 @@ struct Interp1<Ty, Tp, 2>
 
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * in.strides[1];
-            VT val[2] = {cond[0] ? in.ptr[idx_n] : zero,
-                         cond[1] ? in.ptr[idx_n + 1] : zero};
+            VT val[2] = {(clamp || cond[0]) ? in.ptr[idx_n + offx[0]] : zero,
+                         (clamp || cond[1]) ? in.ptr[idx_n + offx[1]] : zero};
             out.ptr[ooff + n * out.strides[1]] = linearInterpFunc(val, ratio);
         }
     }
@@ -162,7 +165,7 @@ struct Interp1<Ty, Tp, 3>
 {
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch)
+                               af_interp_type method, int batch, bool clamp)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -175,11 +178,12 @@ struct Interp1<Ty, Tp, 3>
         int  offx[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
         bool spline = method == AF_INTERP_CUBIC_SPLINE;
+        Ty zero = scalar<Ty>(0);
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * in.strides[1];
             VT val[4];
             for (int i = 0; i < 4; i++) {
-                val[i] = in.ptr[idx_n + offx[i]];
+                val[i] = (clamp || cond[i]) ? in.ptr[idx_n + offx[i]] : zero;
             }
             out.ptr[ooff + n * out.strides[1]] = cubicInterpFunc(val, off_x, spline);
         }
@@ -197,10 +201,15 @@ struct Interp2<Ty, Tp, 1>
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x, Tp y,
                                af_interp_type method,
-                               int nimages)
+                               int nimages, bool clamp)
     {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+
+        if (clamp) {
+            xid = max(0, min(xid, in.dims[0]));
+            yid = max(0, min(yid, in.dims[1]));
+        }
         int idx = ioff + yid * in.strides[1] + xid;
 
         bool condX = xid >= 0 && xid < in.dims[0];
@@ -211,7 +220,7 @@ struct Interp2<Ty, Tp, 1>
 
         for (int n = 0; n < nimages; n++) {
             int idx_n = idx + n * in.strides[2];
-            Ty val = cond ? in.ptr[idx_n] : zero;
+            Ty val = (clamp || cond) ? in.ptr[idx_n] : zero;
             out.ptr[ooff + n * out.strides[2]] = val;
         }
     }
@@ -223,7 +232,7 @@ struct Interp2<Ty, Tp, 2>
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x, Tp y,
                                af_interp_type method,
-                               int nimages)
+                               int nimages, bool clamp)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -238,6 +247,8 @@ struct Interp2<Ty, Tp, 2>
 
         bool condX[2] = {true, x + 1 < in.dims[0]};
         bool condY[2] = {true, y + 1 < in.dims[1]};
+        int  offx[2]  = {0, condX[1] ? 1 : 0};
+        int  offy[2]  = {0, condY[1] ? 1 : 0};
 
         WT xratio = off_x, yratio = off_y;
         if (method == AF_INTERP_LINEAR_COSINE ||
@@ -247,13 +258,16 @@ struct Interp2<Ty, Tp, 2>
             yratio = (1 - cos(yratio * CUDART_PI))/2;
         }
 
+        Ty zero = scalar<Ty>(0);
+
         for (int n = 0; n < nimages; n++) {
             int idx_n = idx + n * in.strides[2];
             VT val[2][2];
             for (int j = 0; j < 2; j++) {
-                int off_y = idx_n + j * in.strides[1];
+                int ioff_j = idx_n + offy[j] * in.strides[1];
                 for (int i = 0; i < 2; i++) {
-                    val[j][i] = condX[i] && condY[j] ? in.ptr[off_y + i] : scalar<Ty>(0);
+                    bool cond = clamp || (condX[i] && condY[j]);
+                    val[j][i] = (cond) ? in.ptr[ioff_j + offx[i]] : zero;
                 }
             }
             out.ptr[ooff + n * out.strides[2]] = bilinearInterpFunc(val, xratio, yratio);
@@ -267,7 +281,7 @@ struct Interp2<Ty, Tp, 3>
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x, Tp y,
                                af_interp_type method,
-                               int nimages)
+                               int nimages, bool clamp)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -287,6 +301,7 @@ struct Interp2<Ty, Tp, 3>
         int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
         //for bicubic interpolation, work with 4x4 val at a time
+        Ty zero = scalar<Ty>(0);
         bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
         for (int n = 0; n < nimages; n++) {
             int idx_n = idx + n * out.strides[2];
@@ -296,7 +311,8 @@ struct Interp2<Ty, Tp, 3>
                 int ioff_j = idx_n + offY[j] * in.strides[1];
 #pragma unroll
                 for (int i = 0; i < 4; i++) {
-                    val[j][i] = in.ptr[ioff_j + offX[i]];
+                    bool cond = clamp || (condX[i] && condY[j]);
+                    val[j][i] = (cond) ? in.ptr[ioff_j + offX[i]] : zero;
                 }
             }
 
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 1b970ca3ae..4a78a21353 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -65,8 +65,23 @@ namespace cuda
             int inoff  =  setId * nimages * in.strides[2]  + batch * in.strides[3];
             const int loco = outoff + (yido * out.strides[1] + xido);
 
+            if (order > 1) {
+                // Special conditions to deal with boundaries for bilinear and bicubic
+                // FIXME: Ideally this condition should be removed or be present for all methods
+                // But tests are expecting a different behavior for bilinear and nearest
+                if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi || in.dims[1] < yidi) {
+                    for(int i = 0; i < nimages; i++) {
+                        out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
+                    }
+                    return;
+                }
+            }
+
             Interp2<T, WT, order> interp;
-            interp(out, loco, in, inoff, xidi, yidi, method, limages);
+            // FIXME: Nearest and lower do not do clamping, but other methods do
+            // Make it consistent
+            bool clamp = order != 1;
+            interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
         }
 
         ///////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index fad5f279a3..735eaa5408 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -174,7 +174,10 @@ namespace cuda
             }
 
             Interp2<T, WT, order> interp;
-            interp(out, loco, in, inoff, xidi, yidi, method, limages);
+            // FIXME: Nearest and lower do not do clamping, but other methods do
+            // Make it consistent
+            bool clamp = order != 1;
+            interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
         }
 
         ///////////////////////////////////////////////////////////////////////////

From 8ed0ede1712c91baf652273a221100263a6c94b9 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 4 Sep 2016 20:36:16 -0400
Subject: [PATCH 0849/2677] OPENCL: Bugfix for kernels missing M_PI

---
 src/backend/opencl/program.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 9e680865f7..1362e87c44 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -25,7 +25,11 @@ namespace opencl
     const static std::string USE_DBL_SRC_STR("\n\
                                            #ifdef USE_DOUBLE\n\
                                            #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
-                                           #endif\n");
+                                           #endif\n                     \
+                                           #ifndef M_PI\n               \
+                                           #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
+                                           #endif\n                     \
+                                           ");
     void buildProgram(cl::Program &prog,
                       const char *ker_str, const int ker_len, std::string options)
     {

From 85f8fcd03d837425b234ffd91ff83f96f9174934 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 2 Sep 2016 17:44:30 -0400
Subject: [PATCH 0850/2677] Fix warning from type cast between MKL_Complex and
 stl complex

---
 src/backend/cpu/sparse_blas.cpp | 28 ++++++++++++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 389af54d4f..1c74ff9960 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -58,6 +58,13 @@ template<typename T>
 using scale_type   =    typename conditional<   is_complex<T>::value,
                                                 const typename blas_base<T>::type,
                                                 const T>::type;
+
+template<typename To, typename Ti>
+To getScaleValue(Ti val)
+{
+    return (To)(val);
+}
+
 #ifdef USE_MKL
 
 // MKL
@@ -145,6 +152,24 @@ SPARSE_FUNC(mm , double  , d)
 SPARSE_FUNC(mm , cfloat  , c)
 SPARSE_FUNC(mm , cdouble , z)
 
+template<>
+const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
+{
+    sp_cfloat ret;
+    ret.real = val.real();
+    ret.imag = val.imag();
+    return ret;
+}
+
+template<>
+const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
+{
+    sp_cdouble ret;
+    ret.real = val.real();
+    ret.imag = val.imag();
+    return ret;
+}
+
 #else   // USE_MKL
 
 // From mkl_spblas.h
@@ -174,8 +199,7 @@ template<typename T, int value>
 scale_type<T> getScale()
 {
     static T val(value);
-    //return (const typename blas_base<T>::type *)&val;
-    return *(const scale_type<T>*)&val;
+    return getScaleValue<scale_type<T>, T>(val);
 }
 
 ////////////////////////////////////////////////////////////////////////////////

From f579d8b34c918ca249c3fa1f2f2ea3489c28f629 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 5 Sep 2016 13:40:55 -0400
Subject: [PATCH 0851/2677] Documentation for sparse functions

---
 docs/details/blas.dox   |  10 +-
 docs/details/sparse.dox | 119 +++++++++++++++++
 include/af/blas.h       |  23 +++-
 include/af/defines.h    |   8 +-
 include/af/sparse.h     | 276 +++++++++++++++++++++++++++++++++++++++-
 include/arrayfire.h     |  43 ++++++-
 6 files changed, 467 insertions(+), 12 deletions(-)
 create mode 100644 docs/details/sparse.dox

diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index 2c63fa70c9..275efe9121 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -23,7 +23,15 @@ in either case.
 
 \brief Matrix multiplication using array
 
-Performs a matrix multiplication on the two input arrays after performing the operations specified in the options. The operations are done while reading the data from memory. This results in no additional memory being used for temporary buffers.
+Performs a matrix multiplication on the two input arrays after performing the
+operations specified in the options. The operations are done while reading the
+data from memory. This results in no additional memory being used for temporary
+buffers.
+
+\note Sparse support was added to ArrayFire in v3.4.0. This function can be use
+for Sparse-Dense matrix multiplication. See the notes of the function for usage
+and restrictions.
+
 
 =======================================================================
 
diff --git a/docs/details/sparse.dox b/docs/details/sparse.dox
new file mode 100644
index 0000000000..c01827cbbe
--- /dev/null
+++ b/docs/details/sparse.dox
@@ -0,0 +1,119 @@
+/**
+\addtogroup arrayfire_func
+@{
+
+\defgroup sparse_func_create sparse
+
+\brief Create a sparse array
+
+The sparse creation function has 3 different types of inputs it can accept.
+1. Independent \ref af::array for values, row indices and column indices.
+2. Independent host or device native arrays for values, row indices and column
+   indices.
+3. A dense \ref af::array.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_convert_to sparseConvertTo
+
+\brief Convert an existing sparse array into a different storage format
+
+Converting storage formats is allowed between \ref AF_STORAGE_CSR, \ref
+AF_STORAGE_COO and \ref AF_STORAGE_DENSE.
+
+When converting to \ref AF_STORAGE_DENSE, a dense array is returned.
+
+\note \ref AF_STORAGE_CSC is currently not supported.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_dense dense
+
+\brief Returns a dense array from a sparse input
+
+Converts the sparse matrix into a dense matrix and returns it
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_info sparseGetInfo
+
+\brief Returns reference to components of the input sparse array
+
+Returns reference to values, row indices, column indices and storage
+format of an input sparse array
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_values sparseGetValues
+
+\brief Returns reference to the values component of the sparse array
+
+Values is the \ref af::array containing the non-zero elements of the dense
+matrix.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_row_idx sparseGetRowIdx
+
+\brief Returns reference to the row indices component of the sparse array
+
+Row indices is the \ref af::array containing the row indices of the sparse
+array.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_col_idx sparseGetColdx
+
+\brief Returns reference to the column indices component of the sparse array
+
+Column indices is the \ref af::array containing the column indices of the sparse
+array.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_nnz sparseGetNNZ
+
+\brief Returns the number of non zero elements in the sparse array
+
+This is always equal to the size of the values array.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+\defgroup sparse_func_storage sparseGetStorage
+
+\brief Returns the storage type of a sparse array
+
+The \ref af::storage type of the format of data storage in the sparse array.
+
+\ingroup sparse_func
+\ingroup arrayfire_func
+
+=======================================================================
+
+@}
+*/
+
diff --git a/include/af/blas.h b/include/af/blas.h
index b7a8c63c50..605364d6be 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -34,8 +34,17 @@ namespace af
         \return The result of the matrix multiplication of lhs, rhs
 
         \note optLhs and optRhs can only be one of \ref AF_MAT_NONE, \ref
-                AF_MAT_TRANS, \ref AF_MAT_CTRANS \note This function is not supported
-                in GFOR
+              AF_MAT_TRANS, \ref AF_MAT_CTRANS \note This function is not supported
+              in GFOR
+
+        \note <b> The following applies for Sparse-Dense matrix multiplication.</b>
+        \note This function can be used with one sparse input. The sparse input
+              must always be the \p lhs and the dense matrix must be \p rhs.
+        \note The sparse array can only be of \ref AF_STORAGE_CSR format.
+        \note The returned array is always dense.
+        \note \p optLhs an only be one of \ref AF_MAT_NONE, \ref AF_MAT_TRANS,
+              \ref AF_MAT_CTRANS.
+        \note \p optRhs can only be \ref AF_MAT_NONE.
 
         \ingroup blas_func_matmul
 
@@ -199,6 +208,16 @@ extern "C" {
         \param[in] optRhs Transpose right hand side before the function is performed
 
         \return AF_SUCCESS if the process is successful.
+
+        \note <b> The following applies for Sparse-Dense matrix multiplication.</b>
+        \note This function can be used with one sparse input. The sparse input
+              must always be the \p lhs and the dense matrix must be \p rhs.
+        \note The sparse array can only be of \ref AF_STORAGE_CSR format.
+        \note The returned array is always dense.
+        \note \p optLhs an only be one of \ref AF_MAT_NONE, \ref AF_MAT_TRANS,
+              \ref AF_MAT_CTRANS.
+        \note \p optRhs can only be \ref AF_MAT_NONE.
+
         \ingroup blas_func_matmul
      */
     AFAPI af_err af_matmul( af_array *out ,
diff --git a/include/af/defines.h b/include/af/defines.h
index 0cc768d0d8..a81dd67526 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -423,10 +423,10 @@ typedef enum {
 
 #if AF_API_VERSION >= 34
 typedef enum {
-    AF_STORAGE_DENSE     = 0,
-    AF_STORAGE_CSR       = 1,
-    AF_STORAGE_CSC       = 2,
-    AF_STORAGE_COO       = 3,
+    AF_STORAGE_DENSE     = 0,   ///< Storage type is dense
+    AF_STORAGE_CSR       = 1,   ///< Storage type is CSR
+    AF_STORAGE_CSC       = 2,   ///< Storage type is CSC
+    AF_STORAGE_COO       = 3,   ///< Storage type is COO
 } af_storage;
 #endif
 
diff --git a/include/af/sparse.h b/include/af/sparse.h
index f28282c2a4..82a14952ef 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -15,34 +15,157 @@ namespace af
 {
     class array;
 
+#if AF_API_VERSION >= 34
+    /**
+       This function converts \ref af::array of values, row indices and column
+       indices into a sparse array.
+
+       \note This function only create references of these arrays into the
+             sparse data structure and does not do deep copies.
+
+       \param[in] nRows is the number of rows in the dense matrix
+       \param[in] nCols is the number of columns in the dense matrix
+       \param[in] values is the \ref af::array containing the non-zero elements
+                  of the matrix
+       \param[in] rowIdx is the row indices for the sparse array
+       \param[in] colIdx is the column indices for the sparse array
+       \param[in] stype is the storage format of the sparse array
+       \return \ref af::array for the sparse array
+
+       \ingroup sparse_func_create
+     */
     AFAPI array sparse(const dim_t nRows, const dim_t nCols,
                        const array values, const array rowIdx, const array colIdx,
                        const af::storage stype = AF_STORAGE_CSR);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       This function converts host or device arrays of values, row indices and
+       column indices into a sparse array on the device.
 
+       \note The rules for deep copy/shallow copy/reference are the same as for
+             creating a regular \ref af::array.
+
+       \param[in] nRows is the number of rows in the dense matrix
+       \param[in] nCols is the number of columns in the dense matrix
+       \param[in] nNZ is the number of non zero elements in the dense matrix
+       \param[in] values is the host array containing the non-zero elements
+                  of the matrix
+       \param[in] rowIdx is the row indices for the sparse array
+       \param[in] colIdx is the column indices for the sparse array
+       \param[in] type is the data type for the matrix
+       \param[in] stype is the storage format of the sparse array
+       \param[in] src is \ref afHost if inputs are host arrays and \ref afDevice
+                  if the arrays are device arrays.
+       \return \ref af::array for the sparse array
+
+       \ingroup sparse_func_create
+     */
     AFAPI array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                        const void* const values,
                        const int * const rowIdx, const int * const colIdx,
                        const dtype type = f32, const af::storage stype = AF_STORAGE_CSR,
                        const af::source src = afHost);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       This function converts a dense \ref af::array into a sparse array.
 
+       \param[in] dense is the source dense matrix
+       \param[in] stype is the storage format of the sparse array
+       \return \ref af::array for the sparse array with the given storage type
+
+       \ingroup sparse_func_create
+     */
     AFAPI array sparse(const array dense, const af::storage stype = AF_STORAGE_CSR);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] in is the source sparse matrix to be converted
+       \param[in] destStrorage is the storage format of the output sparse array
+       \return \ref af::array for the sparse array with the given storage type
+
+       \ingroup sparse_func_convert_to
+     */
+    AFAPI array sparseConvertTo(const array in, const af::storage destStrorage);
+#endif
 
-    AFAPI array sparseConvertTo(const array in, const af::storage stype);
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] sparse is the source sparse matrix
+       \return dense \ref af::array from sparse
 
+       \ingroup sparse_func_dense
+     */
     AFAPI array dense(const array sparse);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] values stores the non-zero elements component of the sparse array
+       \param[out] rowIdx stores the row indices component of the sparse array
+       \param[out] colIdx stores the column indices component of the sparse array
+       \param[out] stype stores the storage type of the sparse array
+       \param[in] in is the input sparse matrix
+
+       \ingroup sparse_func_info
+     */
     AFAPI void sparseGetInfo(array &values, array &rowIdx, array &colIdx, af::storage &stype,
                              const array in);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] in is the input sparse matrix
+       \return \ref af::array for the non-zero elements component of the sparse array
+
+       \ingroup sparse_func_values
+     */
     AFAPI array sparseGetValues(const array in);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] in is the input sparse matrix
+       \return \ref af::array for the row indices component of the sparse array
+
+       \ingroup sparse_func_row_idx
+     */
     AFAPI array sparseGetRowIdx(const array in);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] in is the input sparse matrix
+       \return \ref af::array for the column indices component of the sparse array
+
+       \ingroup sparse_func_col_idx
+     */
     AFAPI array sparseGetColIdx(const array in);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] in is the input sparse matrix
+       \return the number of non-zero elements of the sparse array
+
+       \ingroup sparse_func_nnz
+     */
     AFAPI dim_t sparseGetNNZ(const array in);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[in] in is the input sparse matrix
+       \return \ref af::storage for the storage type of the sparse array
+
+       \ingroup sparse_func_storage
+     */
     AFAPI af::storage sparseGetStorage(const array in);
+#endif
 }
 #endif
 
@@ -50,41 +173,186 @@ namespace af
 extern "C" {
 #endif
 
+#if AF_API_VERSION >= 34
+    /**
+       This function converts \ref af::array of values, row indices and column
+       indices into a sparse array.
+
+       \note This function only create references of these arrays into the
+             sparse data structure and does not do deep copies.
+
+       \param[out] out \ref af::array for the sparse array
+       \param[in] nRows is the number of rows in the dense matrix
+       \param[in] nCols is the number of columns in the dense matrix
+       \param[in] values is the \ref af_array containing the non-zero elements
+                  of the matrix
+       \param[in] rowIdx is the row indices for the sparse array
+       \param[in] colIdx is the column indices for the sparse array
+       \param[in] stype is the storage format of the sparse array
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_create
+     */
     AFAPI af_err af_create_sparse_array(
                  af_array *out,
                  const dim_t nRows, const dim_t nCols,
                  const af_array values, const af_array rowIdx, const af_array colIdx,
                  const af_storage stype);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       This function converts host or device arrays of values, row indices and
+       column indices into a sparse array on the device.
 
+       \note The rules for deep copy/shallow copy/reference are the same as for
+             creating a regular \ref af::array.
+
+       \param[out] out \ref af::array for the sparse array
+       \param[in] nRows is the number of rows in the dense matrix
+       \param[in] nCols is the number of columns in the dense matrix
+       \param[in] nNZ is the number of non zero elements in the dense matrix
+       \param[in] values is the host array containing the non-zero elements
+                  of the matrix
+       \param[in] rowIdx is the row indices for the sparse array
+       \param[in] colIdx is the column indices for the sparse array
+       \param[in] type is the data type for the matrix
+       \param[in] stype is the storage format of the sparse array
+       \param[in] src is \ref afHost if inputs are host arrays and \ref afDevice
+                  if the arrays are device arrays.
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_create
+     */
     AFAPI af_err af_create_sparse_array_from_ptr(
                  af_array *out,
                  const dim_t nRows, const dim_t nCols, const dim_t nNZ,
                  const void * const values,
                  const int * const rowIdx, const int * const colIdx,
                  const af_dtype type, const af_storage stype,
-                 const af_source source);
+                 const af_source src);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       This function converts a dense \ref af_array into a sparse array.
+
+       \param[out] out \ref af_array for the sparse array with the given storage type
+       \param[in] dense is the source dense matrix
+       \param[in] stype is the storage format of the sparse array
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_create
+     */
     AFAPI af_err af_create_sparse_array_from_dense(
-                 af_array *out, const af_array in,
+                 af_array *out, const af_array dense,
                  const af_storage stype);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out \ref af_array for the sparse array with the given storage type
+       \param[in] in is the source sparse matrix to be converted
+       \param[in] destStorage is the storage format of the output sparse array
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_convert_to
+     */
     AFAPI af_err af_sparse_convert_to(af_array *out, const af_array in,
                                       const af_storage destStorage);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out dense \ref af_array from sparse
+       \param[in] sparse is the source sparse matrix
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_dense
+     */
+    AFAPI af_err af_sparse_to_dense(af_array *out, const af_array sparse);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] values stores the non-zero elements component of the sparse array
+       \param[out] rowIdx stores the row indices component of the sparse array
+       \param[out] colIdx stores the column indices component of the sparse array
+       \param[out] stype stores the storage type of the sparse array
+       \param[in] in is the input sparse matrix
 
-    AFAPI af_err af_sparse_to_dense(af_array *out, const af_array in);
+       \return \ref AF_SUCCESS if the execution completes properly
 
+       \ingroup sparse_func_info
+     */
     AFAPI af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx, af_storage *stype,
                                     const af_array in);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out \ref af_array for the non-zero elements component of the sparse array
+       \param[in] in is the input sparse matrix
 
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_values
+     */
     AFAPI af_err af_sparse_get_values(af_array *out, const af_array in);
+#endif
 
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out \ref af_array for the row indices component of the sparse array
+       \param[in] in is the input sparse matrix
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_row_idx
+     */
     AFAPI af_err af_sparse_get_row_idx(af_array *out, const af_array in);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out \ref af_array for the column indices component of the sparse array
+       \param[in] in is the input sparse matrix
 
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_col_idx
+     */
     AFAPI af_err af_sparse_get_col_idx(af_array *out, const af_array in);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out the number of non-zero elements of the sparse array
+       \param[in] in is the input sparse matrix
+
+       \return \ref AF_SUCCESS if the execution completes properly
 
+       \ingroup sparse_func_nnz
+     */
     AFAPI af_err af_sparse_get_nnz(dim_t *out, const af_array in);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+       \param[out] out contains \ref af_storage for the storage type of the sparse array
+       \param[in] in is the input sparse matrix
 
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup sparse_func_storage
+     */
     AFAPI af_err af_sparse_get_storage(af_storage *out, const af_array in);
+#endif
 
 #ifdef __cplusplus
 }
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 085db86140..071fd4a6d7 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -100,7 +100,7 @@
    @defgroup linalg_mat Linear Algebra
    @{
 
-     Matrix multiply, solve, decompositions
+     Matrix multiply, solve, decompositions, sparse matrix
 
      @defgroup blas_mat BLAS operations
      Matrix multiply, dot product, etc.
@@ -115,6 +115,47 @@
      inverse, det, rank, norm etc.
 
      @defgroup lapack_helper LAPACK Helper functions
+
+     @defgroup sparse_func Sparse functions
+        \brief Functions to create and handle sparse arrays and matrix operations
+
+        Sparse array in ArrayFire use the same \ref af::array (or \ref af_array)
+        handle as normal. Internally, this handle is used to maintain a structure
+        of the sparse array (components listed below).
+
+        Description     | Data Type
+        ----------------|-------------------
+        Values          | T (one of \ref f32, \ref f64, \ref c32, \ref c64)
+        Row Indices     | Int (\ref s32)
+        Column Indices  | Int (\ref s32)
+        Storage         | \ref af::storage
+
+        The value array contains the non-zero elements of the matrix. The
+        \ref af::dtype of the value array is the same as that of the matrix.
+        The size of this array is the same as the number of non-zero elements
+        of the matrix.
+
+        The row indices and column indices contain the indices based on
+        \ref af::storage type. These \ref af::array are always of type \ref s32.
+
+        The \ref af::storage is used to determin the type of storage to use.
+        Currently \ref AF_STORAGE_CSR and \ref AF_STORAGE_COO are available.
+
+        A sparse array can be identied using the \ref af::array::issparse()
+        function.  This function will return true for a sparse array and false
+        for a regular \ref af::array.
+
+        The valid operations on sparse arrays are \ref af::matmul (sparse-dense).
+        When calling matmul for sparse matrices, the sparse array is required to
+        be the left hand side matrix and can be used with transposing options.
+        The dense matrix on the right hand side cannot be used with any transpose
+        options.
+
+        Most functions cannot use sparse arrays and will throw an error with
+        \ref AF_ERR_ARG if a sparse array is given as input.
+
+        \note Sparse functionality support was added to ArrayFire in v3.4.0.
+
    @}
 
    @defgroup image_mat Image Processing

From 3c3f7a40275eddf0f5e7445c0a6ea699f7b855cd Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 14:52:14 -0400
Subject: [PATCH 0852/2677] BUILD: Fixing compilation issues on windows

---
 src/backend/cpu/kernel/interp.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index ed8d6aea62..4d895e9015 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -186,8 +186,8 @@ struct Interp2<InT, LocT, 2>
         if (method == AF_INTERP_LINEAR_COSINE ||
             method == AF_INTERP_BILINEAR_COSINE) {
             // Smooth the factional part with cosine
-            xratio = (1 - std::cos(xratio * M_PI))/2;
-            yratio = (1 - std::cos(yratio * M_PI))/2;
+            xratio = (1 - std::cos(xratio * af::Pi))/2;
+            yratio = (1 - std::cos(yratio * af::Pi))/2;
         }
 
         return bilinearInterpFunc(val, off_x, off_y);

From e4fbe3416292d3cc758ad0a613f26b9e7908d8c6 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 17:27:36 -0400
Subject: [PATCH 0853/2677] CPU: transform and rotate now use interp.hpp

- Removed transform_interp.hpp
---
 src/backend/cpu/kernel/approx.hpp    |  14 +-
 src/backend/cpu/kernel/interp.hpp    |  93 ++++++++++---
 src/backend/cpu/kernel/rotate.hpp    |  63 +++++----
 src/backend/cpu/kernel/transform.hpp | 122 +++++++----------
 src/backend/cpu/rotate.cpp           |  28 ++--
 src/backend/cpu/transform.cpp        |  30 ++--
 src/backend/cpu/transform_interp.hpp | 198 ---------------------------
 7 files changed, 197 insertions(+), 351 deletions(-)
 delete mode 100644 src/backend/cpu/transform_interp.hpp

diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index 8cc092c846..0d32a0f95e 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -51,10 +51,15 @@ void approx1(Array<InT> output, const Array<InT> input,
 
                     const LocT x = xpos[batch * xoff + idx];
 
+                    // FIXME: Only cubic interpolation is doing clamping
+                    // We need to make it consistent across all methods
+                    // Not changing the behavior because tests will fail
+                    bool clamp = order == 3;
+
                     if (x < 0 || idims[0] < x + 1) {
                         out[ooff + idx] = scalar<InT>(offGrid);
                     } else {
-                        out[ooff + idx] = interp(input, ioff, x, method);
+                        out[ooff + idx] = interp(input, ioff, x, method, clamp);
                     }
                 }
             }
@@ -100,11 +105,16 @@ void approx2(Array<InT> output, const Array<InT> input,
                     const LocT x = xpos[xoff + idx];
                     const LocT y = ypos[yoff + idx];
 
+                    // FIXME: Only cubic interpolation is doing clamping
+                    // We need to make it consistent across all methods
+                    // Not changing the behavior because tests will fail
+                    bool clamp = order == 3;
+
                     if (x < 0 || idims[0] < x + 1 ||
                         y < 0 || idims[1] < y + 1 ) {
                         out[ooff + idx] = scalar<InT>(offGrid);
                     } else {
-                        out[ooff + idx] = interp(input, ioffzw, x, y, method);
+                        out[ooff + idx] = interp(input, ioffzw, x, y, method, clamp);
                     }
                 }
             }
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index 4d895e9015..e6d70d1afb 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -10,12 +10,23 @@
 #include <Array.hpp>
 #include <math.hpp>
 #include <af/constants.h>
+#include <type_traits>
 
 namespace cpu
 {
 namespace kernel
 {
 
+using std::conditional;
+using std::is_same;
+
+template<typename T>
+using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+
+template<typename T>
+using vtype_t = typename conditional<is_complex<T>::value,
+                                     T, wtype_t<T>
+                                     >::type;
 
 template<typename InT, typename LocT>
 InT linearInterpFunc(InT val[2], LocT ratio)
@@ -80,25 +91,34 @@ struct Interp1
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 1>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method, bool clamp)
     {
+        const dim4 idims = in.dims();
+        int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
+        bool cond = xid >= 0 && xid < idims[0];
+        if (clamp) xid = std::max(0, std::min(xid, (int)idims[0]));
+
         const InT *inptr = in.get();
-        int idx = ioff + (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        return inptr[idx];
+        int idx = ioff + xid;
+        return (cond || clamp) ? inptr[idx] : scalar<InT>(0);
     }
 };
 
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 2>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type  method)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type  method, bool clamp)
     {
+        typedef vtype_t<InT> VT;
+
         const int grid_x = floor(x);    // nearest grid
         const LocT off_x = x - grid_x;    // fractional offset
         const int idx = ioff + grid_x;
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
-        InT val[2] = {inptr[idx], x + 1 < idims[0] ? inptr[idx + 1] : scalar<InT>(0)};
+
+        bool cond[2] = {true, grid_x + 1 < idims[0]};
+        int  offx[2] = {0 , cond[1] ? 1 : 0};
 
         LocT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
@@ -106,6 +126,12 @@ struct Interp1<InT, LocT, 2>
             ratio = (1 - std::cos(ratio * af::Pi))/2;
         }
 
+        const VT zero = scalar<VT>(0);
+        VT val[2] = {zero, zero};
+        for (int i = 0; i < 2; i++) {
+            if (clamp || cond[i]) val[i] = inptr[idx + offx[i]];
+        }
+
         return linearInterpFunc(val, ratio);
     }
 };
@@ -113,8 +139,10 @@ struct Interp1<InT, LocT, 2>
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 3>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method, bool clamp)
     {
+        typedef vtype_t<InT> VT;
+
         const int grid_x = floor(x);    // nearest grid
         const LocT off_x = x - grid_x;    // fractional offset
         const int idx = ioff + grid_x;
@@ -124,9 +152,10 @@ struct Interp1<InT, LocT, 3>
         bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
         int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
-        InT val[4];
+        const VT zero = scalar<VT>(0);
+        VT val[4] = {zero, zero, zero, zero};
         for (int i = 0; i < 4; i++) {
-            val[i] = inptr[idx + off[i]];
+            if (clamp || cond[i]) val[i] = inptr[idx + off[i]];
         }
         return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
     }
@@ -140,25 +169,36 @@ struct Interp2
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 1>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method, bool clamp)
     {
         const InT *inptr = in.get();
         const dim4 istrides = in.strides();
-        if (method == AF_INTERP_LOWER) {
-            const int idx = ioff + floor(y) * istrides[1] + floor(x);
-            return inptr[idx];
-        } else {
-            const int idx = ioff + round(y) * istrides[1] + round(x);
-            return inptr[idx];
+        const dim4 idims = in.dims();
+
+        int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
+        int yid = (method == AF_INTERP_LOWER ? std::floor(y) : std::round(y));
+
+        bool condX = xid >= 0 && xid < idims[0];
+        bool condY = yid >= 0 && yid < idims[1];
+
+        if (clamp) {
+            xid = std::max(0, std::min(xid, (int)idims[0]));
+            yid = std::max(0, std::min(yid, (int)idims[1]));
         }
+
+        bool cond = condX && condY;
+        int idx = ioff + yid * istrides[1] + xid;
+        return (clamp || cond) ? inptr[idx] : scalar<InT>(0);
     }
 };
 
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 2>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method, bool clamp)
     {
+        typedef vtype_t<InT> VT;
+
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
@@ -174,11 +214,16 @@ struct Interp2<InT, LocT, 2>
         bool condX[2] = {true, x + 1 < idims[0]};
         bool condY[2] = {true, y + 1 < idims[1]};
 
-        InT val[2][2];
+        bool offX[2] = {0, condX[1] ? 1 : 0};
+        bool offY[2] = {0, condY[1] ? 1 : 0};
+
+        VT zero = scalar<VT>(0);
+        VT val[2][2];
         for (int j = 0; j < 2; j++) {
-            int off_y = idx + j * istrides[1];
+            int off_y = idx + offY[j] * istrides[1];
             for (int i = 0; i < 2; i++) {
-                val[j][i] = condX[i] && condY[j] ? inptr[off_y + i] : scalar<InT>(0);
+                bool cond = clamp || (condX[i] && condY[j]);
+                val[j][i] = cond ? inptr[off_y + offX[i]] : zero;
             }
         }
 
@@ -197,8 +242,10 @@ struct Interp2<InT, LocT, 2>
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 3>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method)
+    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method, bool clamp)
     {
+        typedef vtype_t<InT> VT;
+
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
@@ -212,7 +259,7 @@ struct Interp2<InT, LocT, 3>
         const int idx = ioff + grid_y * istrides[1] + grid_x;
 
         //for bicubic interpolation, work with 4x4 val at a time
-        InT val[4][4];
+        VT val[4][4];
 
         // used for setting values at boundaries
         bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
@@ -220,10 +267,12 @@ struct Interp2<InT, LocT, 3>
         int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
         int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
+        VT zero = scalar<VT>(0);
         for (int j = 0; j < 4; j++) {
             int ioff_j = idx + offY[j] * istrides[1];
             for (int i = 0; i < 4; i++) {
-                val[j][i] = inptr[ioff_j + offX[i]];
+                bool cond = clamp || (condX[i] && condY[j]);
+                val[j][i] = cond ? inptr[ioff_j + offX[i]] : zero;
             }
         }
 
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index 088d5aaac8..35de77654e 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -11,29 +11,26 @@
 #include <Array.hpp>
 #include <math.hpp>
 #include <err_cpu.hpp>
+#include "interp.hpp"
 
 namespace cpu
 {
 namespace kernel
 {
 
-template<typename T, af_interp_type method>
-void rotate(Array<T> output, const Array<T> input, const float theta)
+template<typename T, int order>
+void rotate(Array<T> output, const Array<T> input,
+            const float theta, af_interp_type method)
 {
+    typedef typename dtype_traits<T>::base_type BT;
+    typedef wtype_t<BT> WT;
+    Interp2<T, WT, order> interp;
+
     const af::dim4 odims    = output.dims();
     const af::dim4 idims    = input.dims();
     const af::dim4 ostrides = output.strides();
     const af::dim4 istrides = input.strides();
 
-    const T* in   = input.get();
-          T* out  = output.get();
-    dim_t nimages = idims[2];
-
-    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
-                 const af::dim4 &, const af::dim4 &,
-                 const dim_t, const dim_t, const dim_t, const dim_t,
-                 const bool);
-
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
     {
@@ -55,26 +52,34 @@ void rotate(Array<T> output, const Array<T> input, const float theta)
                            std::round(ty * 1000) / 1000.0f,
                           };
 
-    switch(method) {
-        case AF_INTERP_NEAREST:
-            t_fn = &transform_n;
-            break;
-        case AF_INTERP_BILINEAR:
-            t_fn = &transform_b;
-            break;
-        case AF_INTERP_LOWER:
-            t_fn = &transform_l;
-            break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-            break;
-    }
+    for (int idw = 0; idw < (int)odims[3]; idw++) {
+        for (int idz = 0; idz < (int)odims[2]; idz++) {
+
+            int out_off = idz * ostrides[2] + idw * ostrides[3];
+            int in_off  = idz * istrides[2] + idw * istrides[3];
+            T *out = output.get() + out_off;
 
+            // Do transform for image
+            for(int idy = 0; idy < (int)odims[1]; idy++) {
+                for(int idx = 0; idx < (int)odims[0]; idx++) {
+                    WT xidi = idx * tmat[0] + idy * tmat[1] + tmat[2];
+                    WT yidi = idx * tmat[3] + idy * tmat[4] + tmat[5];
 
-    // Do transform for image
-    for(int yy = 0; yy < (int)odims[1]; yy++) {
-        for(int xx = 0; xx < (int)odims[0]; xx++) {
-            t_fn(out, in, tmat, idims, ostrides, istrides, nimages, 0, xx, yy, false);
+                    // Special conditions to deal with boundaries for bilinear and bicubic
+                    // FIXME: Ideally this condition should be removed or be present for all methods
+                    // But tests are expecting a different behavior for bilinear and nearest
+                    bool condX = xidi >= -0.0001 && xidi < idims[0];
+                    bool condY = yidi >= -0.0001 && yidi < idims[1];
+                    T val = scalar<T>(0);
+                    if (order == 1 || (condX && condY)) {
+                        // FIXME: Nearest and lower do not do clamping, but other methods do
+                        // Make it consistent
+                        bool clamp = order != 1;
+                        val = interp(input, in_off, xidi, yidi, method, clamp);
+                    }
+                    out[idy * ostrides[1] + idx] = val;
+                }
+            }
         }
     }
 }
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index 65c61a725b..9e2dcc60ee 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -10,6 +10,8 @@
 #pragma once
 #include <Array.hpp>
 #include <err_cpu.hpp>
+#include <type_traits>
+#include "interp.hpp"
 
 namespace cpu
 {
@@ -66,93 +68,67 @@ void calc_transform_inverse(T *tmat, const T *tmat_ptr, const bool inverse,
     }
 }
 
-template<typename T, af_interp_type method>
+template<typename T, int order>
 void transform(Array<T> output, const Array<T> input,
                const Array<float> transform, const bool inverse,
-               const bool perspective)
+               const bool perspective,
+               af_interp_type method)
 {
+    typedef typename dtype_traits<T>::base_type BT;
+    typedef wtype_t<BT> WT;
+
     const af::dim4 idims    = input.dims();
     const af::dim4 odims    = output.dims();
     const af::dim4 tdims    = transform.dims();
+    const af::dim4 tstrides = transform.strides();
     const af::dim4 istrides = input.strides();
     const af::dim4 ostrides = output.strides();
 
     T * out = output.get();
-    const T * in = input.get();
     const float* tf = transform.get();
 
-    int nImg2 = idims[2];
-    int nImg3 = idims[3];
-    int nTfs2 = tdims[2];
-    int nTfs3 = tdims[3];
-
-    void (*t_fn)(T *, const T *, const float *, const af::dim4 &,
-                 const af::dim4 &, const af::dim4 &,
-                 const dim_t, const dim_t, const dim_t, const dim_t,
-                 const bool);
-
-    switch(method) {
-        case AF_INTERP_NEAREST:
-            t_fn = &transform_n;
-            break;
-        case AF_INTERP_BILINEAR:
-            t_fn = &transform_b;
-            break;
-        case AF_INTERP_LOWER:
-            t_fn = &transform_l;
-            break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-            break;
-    }
-
-    const int transf_len = (perspective) ? 9 : 6;
-
-    int batchImg2 = 1;
-    int batchImg3 = 1;
-    if(nImg2 != nTfs2 && nImg2 > 1)
-        batchImg2 = nImg2;
-    if(nImg3 != nTfs3 && nImg3 > 1)
-        batchImg3 = nImg3;
-
-    af::dim4 idims_ = idims;
-    idims_[3] = batchImg3;
-    idims_[2] = batchImg2;
-    const dim_t nimages  = batchImg2;
-
-    // For each transform channel
-    for(int t_idx3 = 0; t_idx3 < nTfs3; t_idx3++) {
-        int offset3 = 0;
-        int i_offset3 = 0;
-        if(nTfs3 > 1) {       // Not Image Batched
-            offset3 = t_idx3 * ostrides[3];
-            if(nImg3 > 1) i_offset3 = t_idx3 * istrides[3]; // One to one batching
-        }
-
-        for(int t_idx2 = 0; t_idx2 < nTfs2; t_idx2++) {
-
-            // Compute inverse if required
-            const float *tmat_ptr = tf + (t_idx3 * nTfs2 + t_idx2) * transf_len;
-            float* tmat = new float[transf_len];
-            calc_transform_inverse(tmat, tmat_ptr, inverse, perspective, transf_len);
-
-            int offset2 = 0;
-            int i_offset2 = 0;
-            if(nTfs2 > 1) {       // Not Image Batched
-                offset2 = t_idx2 * ostrides[2];
-                if(nImg2 > 1) i_offset2 = t_idx2 * istrides[2]; // One to one batching
-            }
-
-            int i_offset = i_offset3 + i_offset2;
-
-            // Do transform for image
-            for(int yy = 0; yy < (int)odims[1]; yy++) {
-                for(int xx = 0; xx < (int)odims[0]; xx++) {
-                    t_fn(out, in + i_offset, tmat, idims_, ostrides, istrides,
-                         nimages, offset3 + offset2, xx, yy, perspective);
+    Interp2<T, WT, order> interp;
+    for (int idw = 0; idw < (int)odims[3]; idw++) {
+        dim_t out_offw = idw * ostrides[3];
+        dim_t in_offw = (idims[3] > 1) * idw * istrides[3];
+        dim_t tf_offw = (tdims[3] > 1) * idw * tstrides[3];
+
+        for (int idz = 0; idz < (int)odims[2]; idz++) {
+            dim_t out_offzw = out_offw + idz * ostrides[2];
+            dim_t in_offzw = in_offw + (idims[2] > 1) * idz * istrides[2];
+            dim_t tf_offzw = tf_offw + (tdims[2] > 1) * idz * tstrides[2];
+
+            const float *tptr = tf + tf_offzw;
+
+            float tmat[9];
+            calc_transform_inverse(tmat, tptr, inverse, perspective, perspective ? 9 : 6);
+
+            T *optr = out + out_offzw;
+
+            for (int idy = 0; idy < (int)odims[1]; idy++) {
+                for (int idx = 0; idx < (int)odims[0]; idx++) {
+                    WT xidi = idx * tmat[0] + idy * tmat[1] + tmat[2];
+                    WT yidi = idx * tmat[3] + idy * tmat[4] + tmat[5];
+
+                    if (perspective) {
+                        WT W    = idx * tmat[6] + idy * tmat[7] + tmat[8];
+                        xidi /= W;
+                        yidi /= W;
+                    }
+
+                    // FIXME: Nearest and lower do not do clamping, but other methods do
+                    // Make it consistent
+                    bool clamp = order != 1;
+                    bool condX = xidi >= -0.0001 && xidi < idims[0];
+                    bool condY = yidi >= -0.0001 && yidi < idims[1];
+
+                    T val = scalar<T>(0);
+                    if (condX && condY) {
+                        val = interp(input, in_offzw, xidi, yidi, method, clamp);
+                    }
+                    optr[idy * ostrides[1] + idx] =  val;
                 }
             }
-            delete[] tmat;
         }
     }
 }
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 0fb9b17674..2f853a4a47 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -11,7 +11,6 @@
 #include <rotate.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include "transform_interp.hpp"
 #include <kernel/rotate.hpp>
 
 namespace cpu
@@ -26,18 +25,21 @@ Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
     Array<T> out = createEmptyArray<T>(odims);
 
     switch(method) {
-        case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::rotate<T, AF_INTERP_NEAREST>, out, in, theta);
-            break;
-        case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::rotate<T, AF_INTERP_BILINEAR>, out, in, theta);
-            break;
-        case AF_INTERP_LOWER:
-            getQueue().enqueue(kernel::rotate<T, AF_INTERP_LOWER>, out, in, theta);
-            break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-            break;
+    case AF_INTERP_NEAREST:
+    case AF_INTERP_LOWER:
+        getQueue().enqueue(kernel::rotate<T, 1>, out, in, theta, method);
+        break;
+    case AF_INTERP_BILINEAR:
+    case AF_INTERP_BILINEAR_COSINE:
+        getQueue().enqueue(kernel::rotate<T, 2>, out, in, theta, method);
+        break;
+    case AF_INTERP_BICUBIC:
+    case AF_INTERP_BICUBIC_SPLINE:
+        getQueue().enqueue(kernel::rotate<T, 3>, out, in, theta, method);
+        break;
+    default:
+        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+        break;
     }
 
     return out;
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 4f3884e2e1..1e43b325b2 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -11,7 +11,6 @@
 #include <transform.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include "transform_interp.hpp"
 #include <kernel/transform.hpp>
 
 namespace cpu
@@ -27,19 +26,22 @@ Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &o
     Array<T> out = createEmptyArray<T>(odims);
 
     switch(method) {
-        case AF_INTERP_NEAREST :
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_NEAREST >, out, in, tf,
-                    inverse, perspective);
-            break;
-        case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_BILINEAR>, out, in, tf,
-                    inverse, perspective);
-            break;
-        case AF_INTERP_LOWER   :
-            getQueue().enqueue(kernel::transform<T, AF_INTERP_LOWER   >, out, in, tf,
-                    inverse, perspective);
-            break;
-        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
+    case AF_INTERP_NEAREST:
+    case AF_INTERP_LOWER:
+        getQueue().enqueue(kernel::transform<T, 1>, out, in, tf,
+                           inverse, perspective, method);
+        break;
+    case AF_INTERP_BILINEAR:
+    case AF_INTERP_BILINEAR_COSINE:
+        getQueue().enqueue(kernel::transform<T, 2>, out, in, tf,
+                           inverse, perspective, method);
+        break;
+    case AF_INTERP_BICUBIC:
+    case AF_INTERP_BICUBIC_SPLINE:
+        getQueue().enqueue(kernel::transform<T, 3>, out, in, tf,
+                           inverse, perspective, method);
+        break;
+    default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
 
     return out;
diff --git a/src/backend/cpu/transform_interp.hpp b/src/backend/cpu/transform_interp.hpp
deleted file mode 100644
index 3728434028..0000000000
--- a/src/backend/cpu/transform_interp.hpp
+++ /dev/null
@@ -1,198 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <math.hpp>
-#include <types.hpp>
-#include <type_traits>
-
-namespace cpu
-{
-    using std::conditional;
-    using std::is_same;
-
-    template<typename T>
-    using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
-
-    template<typename T>
-    using vtype_t = typename conditional<is_complex<T>::value,
-                                         T, wtype_t<T>
-                                        >::type;
-
-    template<typename T>
-    void transform_n(T *out, const T *in, const float *tmat, const af::dim4 &idims,
-                      const af::dim4 &ostrides, const af::dim4 &istrides,
-                      const dim_t nimages, const dim_t o_offset,
-                      const dim_t xx, const dim_t yy, const bool perspective)
-    {
-        dim_t yi = 0, xi = 0;
-        // Compute output index
-        if (perspective) {
-            const float W = xx * tmat[6] + yy * tmat[7] + tmat[8];
-            xi = round((xx * tmat[0]
-                      + yy * tmat[1]
-                           + tmat[2]) / W);
-            yi = round((xx * tmat[3]
-                      + yy * tmat[4]
-                           + tmat[5]) / W);
-        }
-        else {
-            xi = round(xx * tmat[0]
-                     + yy * tmat[1]
-                          + tmat[2]);
-            yi = round(xx * tmat[3]
-                     + yy * tmat[4]
-                          + tmat[5]);
-        }
-
-        // Compute memory location of indices
-        dim_t loci = (yi * istrides[1] + xi);
-        dim_t loco = (yy * ostrides[1] + xx);
-
-        T val = scalar<T>(0.0f);
-        // Copy to output
-        for(int batch = 0; batch < (int)idims[3]; batch++) {
-            dim_t i__ = batch * istrides[3];
-            dim_t o__ = batch * ostrides[3];
-            for(int i_idx = 0; i_idx < (int)nimages; i_idx++) {
-                dim_t i_off = i_idx * istrides[2] + i__;
-                dim_t o_off = o_offset + i_idx * ostrides[2] + o__;
-
-                if (xi < idims[0] && yi < idims[1] && xi >= 0 && yi >= 0)
-                    val = in[i_off + loci];
-
-                out[o_off + loco] = val;
-            }
-        }
-    }
-
-    template<typename T>
-    void transform_b(T *out, const T *in, const float *tmat, const af::dim4 &idims,
-                      const af::dim4 &ostrides, const af::dim4 &istrides,
-                      const dim_t nimages, const dim_t o_offset,
-                      const dim_t xx, const dim_t yy, const bool perspective)
-    {
-        dim_t loco = (yy * ostrides[1] + xx);
-        // Compute input index
-        float xi = 0.0f, yi = 0.0f;
-        if (perspective) {
-            const float W = xx * tmat[6] + yy * tmat[7] + tmat[8];
-            xi = (xx * tmat[0]
-                + yy * tmat[1]
-                     + tmat[2]) / W;
-            yi = (xx * tmat[3]
-                + yy * tmat[4]
-                     + tmat[5]) / W;
-        }
-        else {
-            xi = xx * tmat[0]
-               + yy * tmat[1]
-                    + tmat[2];
-            yi = xx * tmat[3]
-               + yy * tmat[4]
-                    + tmat[5];
-        }
-
-        if (xi < -0.0001 || yi < -0.0001 || idims[0] < xi || idims[1] < yi) {
-            for(int i_idx = 0; i_idx < (int)nimages; i_idx++) {
-                const dim_t o_off = o_offset + i_idx * ostrides[2] + loco;
-                out[o_off] = scalar<T>(0.0f);
-            }
-            return;
-        }
-
-        typedef typename dtype_traits<T>::base_type BT;
-        typedef wtype_t<BT> WT;
-        typedef vtype_t<T> VT;
-
-        const dim_t grd_x = floor(xi),  grd_y = floor(yi);
-        const WT off_x = xi - grd_x, off_y = yi - grd_y;
-
-        dim_t loci = grd_y * istrides[1] + grd_x;
-
-        // Check if pVal and pVal + 1 are both valid indices
-        bool condY = (yi < idims[1] - 1);
-        bool condX = (xi < idims[0] - 1);
-
-        const T zero = scalar<T>(0.0f);
-
-        // Compute weights used
-        const WT wt00 = (1.0 - off_x) * (1.0 - off_y);
-        const WT wt10 = (condY) ? (1.0 - off_x) * (off_y)     : 0;
-        const WT wt01 = (condX) ? (off_x) * (1.0 - off_y)     : 0;
-        const WT wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-        const WT wt = wt00 + wt10 + wt01 + wt11;
-
-        for(int batch = 0; batch < (int)idims[3]; batch++) {
-            dim_t i__ = batch * istrides[3];
-            dim_t o__ = batch * ostrides[3];
-            for(int i_idx = 0; i_idx < (int)nimages; i_idx++) {
-                const dim_t i_off = i_idx * istrides[2] + loci + i__;
-                const dim_t o_off = o_offset + i_idx * ostrides[2] + loco + o__;
-                // Compute Weighted Values
-                VT v00 =                    in[i_off] * wt00;
-                VT v10 = (condY) ?          in[i_off + istrides[1]] * wt10     : zero;
-                VT v01 = (condX) ?          in[i_off + 1] * wt01               : zero;
-                VT v11 = (condX && condY) ? in[i_off + istrides[1] + 1] * wt11 : zero;
-                VT vo = v00 + v10 + v01 + v11;
-
-                out[o_off] = vo / wt;
-            }
-        }
-    }
-
-    template<typename T>
-    void transform_l(T *out, const T *in, const float *tmat, const af::dim4 &idims,
-                      const af::dim4 &ostrides, const af::dim4 &istrides,
-                      const dim_t nimages, const dim_t o_offset,
-                      const dim_t xx, const dim_t yy, const bool perspective)
-    {
-        // Compute output index
-        dim_t xi = 0, yi = 0;
-        if (perspective) {
-            const float W = xx * tmat[6] + yy * tmat[7] + tmat[8];
-            xi = floor((xx * tmat[0]
-                      + yy * tmat[1]
-                           + tmat[2]) / W);
-            yi = floor((xx * tmat[3]
-                      + yy * tmat[4]
-                           + tmat[5]) / W);
-        }
-        else {
-            xi = floor(xx * tmat[0]
-                     + yy * tmat[1]
-                          + tmat[2]);
-            yi = floor(xx * tmat[3]
-                     + yy * tmat[4]
-                          + tmat[5]);
-        }
-
-        // Compute memory location of indices
-        dim_t loci = (yi * istrides[1] + xi);
-        dim_t loco = (yy * ostrides[1] + xx);
-
-        T val = scalar<T>(0.0f);
-        // Copy to output
-        for(int batch = 0; batch < (int)idims[3]; batch++) {
-            dim_t i__ = batch * istrides[3];
-            dim_t o__ = batch * ostrides[3];
-            for(int i_idx = 0; i_idx < (int)nimages; i_idx++) {
-                dim_t i_off = i_idx * istrides[2] + i__;
-                dim_t o_off = o_offset + i_idx * ostrides[2] + o__;
-
-                if (xi < idims[0] && yi < idims[1] && xi >= 0 && yi >= 0)
-                    val = in[i_off + loci];
-
-                out[o_off + loco] = val;
-            }
-        }
-    }
-
-}

From 2eb0458f9603ce5f7222a0764caeaadc42786e11 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 19:03:05 -0400
Subject: [PATCH 0854/2677] OPENCL: transform and rotate now use interp.cl

- Removed transform_interp.cl
---
 src/backend/opencl/kernel/approx.hpp          |   8 +-
 src/backend/opencl/kernel/approx1.cl          |  10 +-
 src/backend/opencl/kernel/approx2.cl          |   9 +-
 src/backend/opencl/kernel/interp.cl           | 176 ++++++++++++------
 src/backend/opencl/kernel/interp.hpp          |   3 +-
 src/backend/opencl/kernel/rotate.cl           |  38 +++-
 src/backend/opencl/kernel/rotate.hpp          |  43 ++---
 src/backend/opencl/kernel/transform.cl        |  52 ++++--
 src/backend/opencl/kernel/transform.hpp       |  73 ++++----
 src/backend/opencl/kernel/transform_interp.cl | 162 ----------------
 src/backend/opencl/rotate.cpp                 |  26 +--
 src/backend/opencl/transform.cpp              |  92 ++-------
 12 files changed, 301 insertions(+), 391 deletions(-)
 delete mode 100644 src/backend/opencl/kernel/transform_interp.cl

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 7a17dab47e..9445befc5e 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -61,6 +61,7 @@ namespace opencl
                     options << " -D Ty="          << dtype_traits<Ty>::getName()
                             << " -D Tp="          << dtype_traits<Tp>::getName()
                             << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+                            << " -D InterpValTy=" << dtype_traits<Ty>::getName()
                             << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
                             << " -D ZERO="        << toNum(scalar<Ty>(0));
 
@@ -129,11 +130,12 @@ namespace opencl
                 std::call_once( compileFlags[device], [device] () {
                     ToNum<Ty> toNum;
                     std::ostringstream options;
-                    options << " -D Ty="        << dtype_traits<Ty>::getName()
-                            << " -D Tp="        << dtype_traits<Tp>::getName()
+                    options << " -D Ty="          << dtype_traits<Ty>::getName()
+                            << " -D Tp="          << dtype_traits<Tp>::getName()
                             << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+                            << " -D InterpValTy=" << dtype_traits<Ty>::getName()
                             << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                            << " -D ZERO="      << toNum(scalar<Ty>(0));
+                            << " -D ZERO="        << toNum(scalar<Ty>(0));
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
                        (af_dtype) dtype_traits<Ty>::af_type == c64) {
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index 7087ad2341..89c4bccc37 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -38,5 +38,13 @@ void approx1_kernel(__global       Ty *d_out, const KParam out,
     }
 
     int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + in.offset;
-    d_out[omId] = interp1(d_in, in, ioff, x, method);
+
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    bool clamp = INTERP_ORDER == 3;
+
+    interp1(d_out, out, omId,
+             d_in,  in, ioff,
+            x, method, 1, clamp);
 }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 1945fc2120..def691216b 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -47,5 +47,12 @@ void approx2_kernel(__global       Ty *d_out, const KParam out,
 
     int ioff = idw * in.strides[3] + idz * in.strides[2] + in.offset;
 
-    d_out[omId] = interp2(d_in, in, ioff, x, y, method);
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    bool clamp = INTERP_ORDER == 3;
+
+    interp2(d_out, out, omId,
+             d_in,  in, ioff,
+            x, y, method, 1, clamp);
 }
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index 52c5963f23..29f9145aab 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -25,22 +25,22 @@ InterpInTy __mulrc(ScalarTy s, InterpInTy v)
 #define MULCR(a, b) (a) * (b)
 #endif
 
-InterpInTy linearInterpFunc(InterpInTy val[2], InterpPosTy ratio)
+InterpValTy linearInterpFunc(InterpValTy val[2], InterpPosTy ratio)
 {
     return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
 }
 
-InterpInTy bilinearInterpFunc(InterpInTy val[2][2], InterpPosTy xratio, InterpPosTy yratio)
+InterpValTy bilinearInterpFunc(InterpValTy val[2][2], InterpPosTy xratio, InterpPosTy yratio)
 {
-    InterpInTy res[2];
+    InterpValTy res[2];
     res[0] = linearInterpFunc(val[0], xratio);
     res[1] = linearInterpFunc(val[1], xratio);
     return linearInterpFunc(res, yratio);
 }
 
-InterpInTy cubicInterpFunc(InterpInTy val[4], InterpPosTy xratio, bool spline)
+InterpValTy cubicInterpFunc(InterpValTy val[4], InterpPosTy xratio, bool spline)
 {
-    InterpInTy a0, a1, a2, a3;
+    InterpValTy a0, a1, a2, a3;
     if (spline) {
         a0 = MULRC(-0.5, val[0]) + MULRC( 1.5, val[1]) + MULRC(-1.5, val[2]) + MULRC( 0.5, val[3]);
         a1 = MULRC( 1.0, val[0]) + MULRC(-2.5, val[1]) + MULRC( 2.0, val[2]) + MULRC(-0.5, val[3]);
@@ -59,9 +59,9 @@ InterpInTy cubicInterpFunc(InterpInTy val[4], InterpPosTy xratio, bool spline)
     return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
 }
 
-InterpInTy bicubicInterpFunc(InterpInTy val[4][4], InterpPosTy xratio, InterpPosTy yratio, bool spline)
+InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio, InterpPosTy yratio, bool spline)
 {
-    InterpInTy res[4];
+    InterpValTy res[4];
     res[0] = cubicInterpFunc(val[0], xratio, spline);
     res[1] = cubicInterpFunc(val[1], xratio, spline);
     res[2] = cubicInterpFunc(val[2], xratio, spline);
@@ -69,37 +69,59 @@ InterpInTy bicubicInterpFunc(InterpInTy val[4][4], InterpPosTy xratio, InterpPos
     return cubicInterpFunc(res, yratio, spline);
 }
 
-
 #if INTERP_ORDER == 1
-InterpInTy interp1(__global const InterpInTy *d_in,
-                   KParam in, int ioff, InterpPosTy x, int method)
+void interp1(
+    __global InterpInTy *d_out,
+    KParam out, int ooff,
+    __global const InterpInTy *d_in,
+    KParam in, int ioff, InterpPosTy x,
+    int method, int batch, bool clamp)
 {
-    if (method == AF_INTERP_LOWER) {
-        const int idx = floor(x) + ioff;
-        return d_in[idx];
-    } else {
-        const int idx = round(x) + ioff;
-        return d_in[idx];
+    int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+    InterpInTy zero = ZERO;
+    bool cond = xid >= 0 && xid < in.dims[0];
+    if (clamp) xid = max(0, min(xid, (int)in.dims[0]));
+    const int idx = ioff + xid;
+    for (int n = 0; n < batch; n++) {
+        int idx_n = idx + n * in.strides[1];
+        d_out[ooff + n * out.strides[1]] = (clamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-InterpInTy interp1(__global const InterpInTy *d_in,
-                   KParam in, int ioff, InterpPosTy x, int method)
+void interp1(
+    __global InterpInTy *d_out,
+    KParam out, int ooff,
+    __global const InterpInTy *d_in,
+    KParam in, int ioff, InterpPosTy x,
+    int method, int batch, bool clamp)
 {
     const int grid_x = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;    // fractional offset
     const int idx = ioff + grid_x;
-    InterpInTy zero = ZERO;
-    InterpInTy val[2] = {d_in[idx], x + 1 < in.dims[0] ? d_in[idx + 1] : zero};
+    InterpValTy zero = ZERO;
+    bool cond[2] = {true, grid_x + 1 < in.dims[0]};
+    int  offx[2]  = {0, cond[1] ? 1 : 0};
+
     InterpPosTy ratio = off_x;
     if (method == AF_INTERP_LINEAR_COSINE) {
         ratio = (1 - cos(ratio * M_PI))/2;
     }
-    return linearInterpFunc(val, ratio);
+
+    for (int n = 0; n < batch; n++) {
+        int idx_n = idx + n * in.strides[1];
+        InterpValTy val[2] = {(clamp || cond[0]) ? d_in[idx_n + offx[0]] : zero,
+                              (clamp || cond[1]) ? d_in[idx_n + offx[1]] : zero};
+
+        d_out[ooff + n * out.strides[1]] = linearInterpFunc(val, ratio);
+    }
 }
 #elif INTERP_ORDER == 3
-InterpInTy interp1(__global const InterpInTy *d_in,
-                   KParam in, int ioff, InterpPosTy x, int method)
+void interp1(
+    __global InterpInTy *d_out,
+    KParam out, int ooff,
+    __global const InterpInTy *d_in,
+    KParam in, int ioff, InterpPosTy x,
+    int method, int batch, bool clamp)
 {
     const int grid_x = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;    // fractional offset
@@ -108,29 +130,54 @@ InterpInTy interp1(__global const InterpInTy *d_in,
     bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
     int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
-    InterpInTy val[4];
-    for (int i = 0; i < 4; i++) {
-        val[i] = d_in[idx + off[i]];
+    InterpValTy zero = ZERO;
+
+    for (int n = 0; n < batch; n++) {
+        InterpValTy val[4];
+        int idx_n = idx + n * in.strides[1];
+        for (int i = 0; i < 4; i++) {
+            val[i] = (clamp || cond[i]) ? d_in[idx_n + off[i]] : zero;
+        }
+        bool spline = method == AF_INTERP_CUBIC_SPLINE;
+        d_out[ooff + n * out.strides[1]] = cubicInterpFunc(val, off_x, spline);;
     }
-    return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
 }
 #endif
 
 #if INTERP_ORDER == 1
-InterpInTy interp2(__global const InterpInTy *d_in,
-                   KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
+void interp2(
+    __global InterpInTy *d_out,
+    KParam out, int ooff,
+    __global const InterpInTy *d_in,
+    KParam in, int ioff, InterpPosTy x, InterpPosTy y,
+    int method, int nimages, bool clamp)
 {
-    if (method == AF_INTERP_LOWER) {
-        const int idx = ioff + floor(y) * in.strides[1] + floor(x);
-        return d_in[idx];
-    } else {
-        const int idx = ioff + round(y) * in.strides[1] + round(x);
-        return d_in[idx];
+    int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+    int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+
+    if (clamp) {
+        xid = max(0, min(xid, (int)in.dims[0]));
+        yid = max(0, min(yid, (int)in.dims[1]));
+    }
+    int idx = ioff + yid * in.strides[1] + xid;
+
+    bool condX = xid >= 0 && xid < in.dims[0];
+    bool condY = yid >= 0 && yid < in.dims[1];
+
+    InterpInTy zero = ZERO;
+    bool cond = condX && condY;
+    for (int n = 0; n < nimages; n++) {
+        int idx_n = idx + n * in.strides[2];
+        d_out[ooff + n * out.strides[2]] =  (clamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-InterpInTy interp2(__global const InterpInTy *d_in,
-                   KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
+void interp2(
+    __global InterpInTy *d_out,
+    KParam out, int ooff,
+    __global const InterpInTy *d_in,
+    KParam in, int ioff, InterpPosTy x, InterpPosTy y,
+    int method, int nimages, bool clamp)
 {
     const int grid_x = floor(x);
     const InterpPosTy off_x = x - grid_x;
@@ -142,15 +189,8 @@ InterpInTy interp2(__global const InterpInTy *d_in,
 
     bool condX[2] = {true, x + 1 < in.dims[0]};
     bool condY[2] = {true, y + 1 < in.dims[1]};
-
-    InterpInTy zero = ZERO;
-    InterpInTy val[2][2];
-    for (int j = 0; j < 2; j++) {
-        int off_y = idx + j * in.strides[1];
-        for (int i = 0; i < 2; i++) {
-            val[j][i] = condX[i] && condY[j] ? d_in[off_y + i] : zero;
-        }
-    }
+    int  offx[2]  = {0, condX[1] ? 1 : 0};
+    int  offy[2]  = {0, condY[1] ? 1 : 0};
 
     InterpPosTy xratio = off_x, yratio = off_y;
     if (method == AF_INTERP_LINEAR_COSINE) {
@@ -158,11 +198,27 @@ InterpInTy interp2(__global const InterpInTy *d_in,
         yratio = (1 - cos(yratio * M_PI))/2;
     }
 
-    return bilinearInterpFunc(val, xratio, yratio);
+    InterpValTy zero = ZERO;
+    for (int n = 0; n < nimages; n++) {
+        int idx_n = idx + n * in.strides[2];
+        InterpValTy val[2][2];
+        for (int j = 0; j < 2; j++) {
+            int off_y = idx_n + offy[j] * in.strides[1];
+            for (int i = 0; i < 2; i++) {
+                bool cond = (clamp || (condX[i] && condY[j]));
+                val[j][i] = cond ? d_in[off_y + offx[i]] : zero;
+            }
+        }
+        d_out[ooff + n * out.strides[2]] =  bilinearInterpFunc(val, xratio, yratio);
+    }
 }
 #elif INTERP_ORDER == 3
-InterpInTy interp2(__global const InterpInTy *d_in,
-                   KParam in, int ioff, InterpPosTy x, InterpPosTy y, int method)
+void interp2(
+    __global InterpInTy *d_out,
+    KParam out, int ooff,
+    __global const InterpInTy *d_in,
+    KParam in, int ioff, InterpPosTy x, InterpPosTy y,
+    int method, int nimages, bool clamp)
 {
     const int grid_x = floor(x);
     const InterpPosTy off_x = x - grid_x;
@@ -172,8 +228,6 @@ InterpInTy interp2(__global const InterpInTy *d_in,
 
     const int idx = ioff + grid_y * in.strides[1] + grid_x;
 
-    //for bicubic interpolation, work with 4x4 val at a time
-    InterpInTy val[4][4];
 
     // used for setting values at boundaries
     bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
@@ -181,16 +235,22 @@ InterpInTy interp2(__global const InterpInTy *d_in,
     int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
     int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
+    InterpValTy zero = ZERO;
+    for (int n = 0; n < nimages; n++) {
+        int idx_n = idx + n * in.strides[2];
+        //for bicubic interpolation, work with 4x4 val at a time
+        InterpValTy val[4][4];
 #pragma unroll
-    for (int j = 0; j < 4; j++) {
-        int ioff_j = idx + offY[j] * in.strides[1];
+        for (int j = 0; j < 4; j++) {
+            int ioff_j = idx_n + offY[j] * in.strides[1];
 #pragma unroll
-        for (int i = 0; i < 4; i++) {
-            val[j][i] = d_in[ioff_j + offX[i]];
+            for (int i = 0; i < 4; i++) {
+                bool cond = (clamp || (condX[i] && condY[j]));
+                val[j][i] = cond ? d_in[ioff_j + offX[i]] : zero;
+            }
         }
+        bool spline  = method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE;
+        d_out[ooff +  n * out.strides[2]] = bicubicInterpFunc(val, off_x, off_y, spline);
     }
-
-    bool spline  = method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE;
-    return bicubicInterpFunc(val, off_x, off_y, spline);
 }
 #endif
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
index 6fcc87adc8..9bb23d9abf 100644
--- a/src/backend/opencl/kernel/interp.hpp
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <af/defines.h>
 #include <string>
@@ -17,7 +18,7 @@
 namespace opencl {
     namespace kernel {
 
-        void addInterpEnumOptions(std::ostringstream &options)
+        static void addInterpEnumOptions(std::ostringstream &options)
         {
             ADD_ENUM_OPTION(options, AF_INTERP_NEAREST);
             ADD_ENUM_OPTION(options, AF_INTERP_LINEAR);
diff --git a/src/backend/opencl/kernel/rotate.cl b/src/backend/opencl/kernel/rotate.cl
index 882f9a6c5c..cc593198fa 100644
--- a/src/backend/opencl/kernel/rotate.cl
+++ b/src/backend/opencl/kernel/rotate.cl
@@ -19,7 +19,7 @@ __kernel
 void rotate_kernel(__global T *d_out, const KParam out,
                    __global const T *d_in, const KParam in,
                    const tmat_t t, const int nimages, const int batches,
-                   const int blocksXPerImage, const int blocksYPerImage)
+                   const int blocksXPerImage, const int blocksYPerImage, int method)
 {
     // Compute which image set
     const int setId = get_group_id(0) / blocksXPerImage;
@@ -29,15 +29,39 @@ void rotate_kernel(__global T *d_out, const KParam out,
     const int blockIdx_y = get_group_id(1) - batch * blocksYPerImage;
 
     // Get thread indices
-    const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
-    const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
+    const int xido = get_local_id(0) + blockIdx_x * get_local_size(0);
+    const int yido = get_local_id(1) + blockIdx_y * get_local_size(1);
 
     const int limages = min((int)out.dims[2] - setId * nimages, nimages);
 
-    if(xx >= out.dims[0] || yy >= out.dims[1])
+    if(xido >= out.dims[0] || yido >= out.dims[1])
         return;
 
-    __global T *optr = d_out + setId * nimages * out.strides[2] + batch * out.strides[3];
-    __global const T *iptr = d_in + in.offset + setId * nimages * in.strides[2] + batch * in.strides[3];
-    INTERP(optr, out, iptr, in, t.tmat, xx, yy, limages);
+    InterpPosTy xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
+    InterpPosTy yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
+
+    int outoff = out.offset + setId * nimages * out.strides[2] + batch * out.strides[3];
+    int inoff = in.offset + setId * nimages * in.strides[2] + batch * in.strides[3];
+
+    const int loco = outoff + (yido * out.strides[1] + xido);
+
+    InterpInTy zero = ZERO;
+    if (INTERP_ORDER > 1) {
+        // Special conditions to deal with boundaries for bilinear and bicubic
+        // FIXME: Ideally this condition should be removed or be present for all methods
+        // But tests are expecting a different behavior for bilinear and nearest
+        if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi || in.dims[1] < yidi) {
+            for(int i = 0; i < nimages; i++) {
+                d_out[loco + i * out.strides[2]] = zero;
+            }
+            return;
+        }
+    }
+
+    // FIXME: Nearest and lower do not do clamping, but other methods do
+    // Make it consistent
+    bool clamp = INTERP_ORDER != 1;
+    interp2(d_out, out, loco,
+            d_in,  in, inoff,
+            xidi, yidi, method, limages, clamp);
 }
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 5839c0ec12..be35de2ec7 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/transform_interp.hpp>
+#include <kernel_headers/interp.hpp>
 #include <kernel_headers/rotate.hpp>
 #include <program.hpp>
 #include <traits.hpp>
@@ -21,6 +21,7 @@
 #include <type_util.hpp>
 #include <math.hpp>
 #include "config.hpp"
+#include "interp.hpp"
 
 using cl::Buffer;
 using cl::Program;
@@ -53,8 +54,8 @@ namespace opencl
                                              T, wtype_t<T>
                                             >::type;
 
-        template<typename T, af_interp_type method>
-        void rotate(Param out, const Param in, const float theta)
+        template<typename T, int order>
+        void rotate(Param out, const Param in, const float theta, af_interp_type method)
         {
             try {
                 static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -68,44 +69,40 @@ namespace opencl
                     ToNum<T> toNum;
                     std::ostringstream options;
                     options << " -D T="        << dtype_traits<T>::getName();
-                    options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
-                    options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
                     options << " -D ZERO="      << toNum(scalar<T>(0));
+                    options << " -D InterpInTy=" << dtype_traits<T>::getName();
+                    options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+                    options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
 
                     if((af_dtype) dtype_traits<T>::af_type == c32 ||
                        (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D CPLX=1";
+                        options << " -D IS_CPLX=1";
                         options << " -D TB=" << dtype_traits<BT>::getName();
                     } else {
-                        options << " -D CPLX=0";
+                        options << " -D IS_CPLX=0";
                     }
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
                         options << " -D USE_DOUBLE";
                     }
 
-                    switch(method) {
-                        case AF_INTERP_NEAREST : options << " -D INTERP=NEAREST";
-                            break;
-                        case AF_INTERP_BILINEAR: options << " -D INTERP=BILINEAR";
-                            break;
-                        case AF_INTERP_LOWER   : options << " -D INTERP=LOWER";
-                            break;
-                        default:
-                            break;
-                    }
+                    options << " -D INTERP_ORDER=" << order;
+                    addInterpEnumOptions(options);
 
-                    const char *ker_strs[] = {transform_interp_cl, rotate_cl};
-                    const int   ker_lens[] = {transform_interp_cl_len, rotate_cl_len};
+                    const char *ker_strs[] = {interp_cl, rotate_cl};
+                    const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
                     Program prog;
                     buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                     rotateProgs[device] = new Program(prog);
                     rotateKernels[device] = new Kernel(*rotateProgs[device], "rotate_kernel");
                 });
 
-                auto rotateOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam, const tmat_t,
-                                            const int, const int, const int, const int>
-                                           (*rotateKernels[device]);
+                auto rotateOp = KernelFunctor<Buffer, const KParam,
+                                              const Buffer, const KParam,
+                                              const tmat_t,
+                                              const int, const int,
+                                              const int, const int,
+                                              const int>(*rotateKernels[device]);
 
                 const float c = cos(-theta), s = sin(-theta);
                 float tx, ty;
@@ -150,7 +147,7 @@ namespace opencl
 
                 rotateOp(EnqueueArgs(getQueue(), global, local),
                          *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
-                         blocksXPerImage, blocksYPerImage);
+                         blocksXPerImage, blocksYPerImage, (int)method);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 3b0dff6597..303b0d7539 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -51,7 +51,9 @@ void transform_kernel(__global T *d_out, const KParam out,
                       const int nImg2, const int nImg3,
                       const int nTfs2, const int nTfs3,
                       const int batchImg2,
-                      const int blocksXPerImage, const int blocksYPerImage)
+                      const int blocksXPerImage,
+                      const int blocksYPerImage,
+                      const int method)
 {
     // Image Ids
     const int imgId2 = get_group_id(0) / blocksXPerImage;
@@ -62,13 +64,13 @@ void transform_kernel(__global T *d_out, const KParam out,
     const int blockIdx_y = get_group_id(1) - imgId3 * blocksYPerImage;
 
     // Get thread indices in local image
-    const int xx = blockIdx_x * get_local_size(0) + get_local_id(0);
-    const int yy = blockIdx_y * get_local_size(1) + get_local_id(1);
+    const int xido = blockIdx_x * get_local_size(0) + get_local_id(0);
+    const int yido = blockIdx_y * get_local_size(1) + get_local_id(1);
 
     // Image iteration loop count for image batching
     int limages = min(max((int)(out.dims[2] - imgId2 * nImg2), 1), batchImg2);
 
-    if(xx >= out.dims[0] || yy >= out.dims[1])
+    if(xido >= out.dims[0] || yido >= out.dims[1])
         return;
 
     // Index of transform
@@ -105,24 +107,23 @@ void transform_kernel(__global T *d_out, const KParam out,
     // Linear transform index
     const int t_idx = t_idx2 + t_idx3 * nTfs2;
 
-    // Global offset
-    int offset = 0;
-    d_in += imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3] + in.offset;
+    // Global outoff
+    int outoff = out.offset;
+    int inoff = imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3] + in.offset;
     if(nImg2 == nTfs2 || nImg2 > 1) {   // One-to-One or Image on dim2
-          offset += imgId2 * batchImg2 * out.strides[2];
+          outoff += imgId2 * batchImg2 * out.strides[2];
     } else {                            // Transform batched on dim2
-          offset += t_idx2 * out.strides[2];
+          outoff += t_idx2 * out.strides[2];
     }
 
     if(nImg3 == nTfs3 || nImg3 > 1) {   // One-to-One or Image on dim3
-          offset += imgId3 * out.strides[3];
+          outoff += imgId3 * out.strides[3];
     } else {                            // Transform batched on dim2
-          offset += t_idx3 * out.strides[3];
+          outoff += t_idx3 * out.strides[3];
     }
-    d_out += offset;
 
     // Transform is in global memory.
-    // Needs offset to correct transform being processed.
+    // Needs outoff to correct transform being processed.
 #if PERSPECTIVE
     const int transf_len = 9;
     float tmat[9];
@@ -142,5 +143,28 @@ void transform_kernel(__global T *d_out, const KParam out,
         calc_transf_inverse(tmat, tmat_ptr);
     }
 
-    INTERP(d_out, out, d_in, in, tmat, xx, yy, limages);
+    InterpPosTy xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
+    InterpPosTy yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
+
+#if PERSPECTIVE
+        const InterpPosTy W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+        xidi /= W;
+        yidi /= W;
+#endif
+    const int loco = outoff + (yido * out.strides[1] + xido);
+    // FIXME: Nearest and lower do not do clamping, but other methods do
+    // Make it consistent
+    bool clamp = INTERP_ORDER != 1;
+
+    T zero = ZERO;
+    if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi || in.dims[1] <= yidi) {
+        for(int n = 0; n < limages; n++) {
+            d_out[loco + n * out.strides[2]] = zero;
+        }
+        return;
+    }
+
+    interp2(d_out, out, loco,
+             d_in,  in, inoff,
+            xidi, yidi, method, limages, clamp);
 }
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index cedcf0fc26..b1dd6585aa 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/transform_interp.hpp>
+#include <kernel_headers/interp.hpp>
 #include <kernel_headers/transform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
@@ -17,10 +17,12 @@
 #include <map>
 #include <dispatch.hpp>
 #include <Param.hpp>
+#include <cache.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
 #include <math.hpp>
 #include "config.hpp"
+#include "interp.hpp"
 
 using cl::Buffer;
 using cl::Program;
@@ -50,63 +52,70 @@ namespace opencl
                                             >::type;
 
 
-        template<typename T, bool isInverse, bool isPerspective, af_interp_type method>
-        void transform(Param out, const Param in, const Param tf)
+        template<typename T, int order>
+        void transform(Param out, const Param in,
+                       const Param tf, bool isInverse,
+                       bool isPerspective, af_interp_type method)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   transformProgs;
-                static std::map<int, Kernel *> transformKernels;
 
-                int device = getActiveDeviceId();
                 typedef typename dtype_traits<T>::base_type BT;
 
-                std::call_once( compileFlags[device], [device] () {
+                std::string ref_name =
+                    std::string("transform_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(isInverse) +
+                    std::string("_") +
+                    std::to_string(isPerspective) +
+                    std::string("_") +
+                    std::to_string(order);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
                     ToNum<T> toNum;
                     std::ostringstream options;
                     options << " -D T="           << dtype_traits<T>::getName()
                             << " -D INVERSE="     << (isInverse ? 1 : 0)
                             << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
                             << " -D ZERO="        << toNum(scalar<T>(0));
-                    options << " -D VT="          << dtype_traits<vtype_t<T>>::getName();
-                    options << " -D WT="          << dtype_traits<wtype_t<BT>>::getName();
+                    options << " -D InterpInTy=" << dtype_traits<T>::getName();
+                    options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+                    options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
 
                     if((af_dtype) dtype_traits<T>::af_type == c32 ||
                        (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D CPLX=1";
+                        options << " -D IS_CPLX=1";
                         options << " -D TB=" << dtype_traits<BT>::getName();
                     } else {
-                        options << " -D CPLX=0";
+                        options << " -D IS_CPLX=0";
                     }
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
                         options << " -D USE_DOUBLE";
                     }
 
-                    switch(method) {
-                        case AF_INTERP_NEAREST : options << " -D INTERP=NEAREST";
-                            break;
-                        case AF_INTERP_BILINEAR: options << " -D INTERP=BILINEAR";
-                            break;
-                        case AF_INTERP_LOWER   : options << " -D INTERP=LOWER";
-                            break;
-                        default:
-                            break;
-                    }
+                    options << " -D INTERP_ORDER=" << order;
+                    addInterpEnumOptions(options);
 
-                    const char *ker_strs[] = {transform_interp_cl, transform_cl};
-                    const int   ker_lens[] = {transform_interp_cl_len, transform_cl_len};
+                    const char *ker_strs[] = {interp_cl, transform_cl};
+                    const int   ker_lens[] = {interp_cl_len, transform_cl_len};
                     Program prog;
                     buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    transformProgs[device] = new Program(prog);
-                    transformKernels[device] = new Kernel(*transformProgs[device], "transform_kernel");
-                });
+                    entry.prog = new Program(prog);
+                    entry.ker = new Kernel(*entry.prog, "transform_kernel");
+                } else {
+                    entry = idx->second;
+                }
 
                 auto transformOp = KernelFunctor<Buffer, const KParam,
-                                         const Buffer, const KParam, const Buffer, const KParam,
-                                         const int, const int, const int, const int,
-                                         const int, const int, const int>
-                                         (*transformKernels[device]);
+                                                 const Buffer, const KParam,
+                                                 const Buffer, const KParam,
+                                                 const int, const int, const int, const int,
+                                                 const int, const int, const int, const int>(*entry.ker);
 
                 const int nImg2 = in.info.dims[2];
                 const int nImg3 = in.info.dims[3];
@@ -137,7 +146,7 @@ namespace opencl
                 transformOp(EnqueueArgs(getQueue(), global, local),
                             *out.data, out.info, *in.data, in.info, *tf.data, tf.info,
                             nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                            blocksXPerImage, blocksYPerImage);
+                            blocksXPerImage, blocksYPerImage, (int)method);
 
                 CL_DEBUG_FINISH(getQueue());
             } catch (cl::Error err) {
diff --git a/src/backend/opencl/kernel/transform_interp.cl b/src/backend/opencl/kernel/transform_interp.cl
deleted file mode 100644
index a083df0ff6..0000000000
--- a/src/backend/opencl/kernel/transform_interp.cl
+++ /dev/null
@@ -1,162 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if CPLX
-#define set(a, b) a = b
-#define set_scalar(a, b) do {                   \
-        a.x = b;                                \
-        a.y = 0;                                \
-    } while(0)
-
-#else
-
-#define set(a, b) a = b
-#define set_scalar(a, b) a = b
-
-#endif
-
-void transform_n(__global T *d_out, const KParam out, __global const T *d_in, const KParam in,
-                 const float *tmat, const int xido, const int yido, const int nimages)
-{
-    // Compute input index
-    int xidi = 0, yidi = 0;
-#if PERSPECTIVE
-    const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-    xidi = round((xido * tmat[0]
-                + yido * tmat[1]
-                       + tmat[2]) / W);
-    yidi = round((xido * tmat[3]
-                + yido * tmat[4]
-                       + tmat[5]) / W);
-#else
-    xidi = round(xido * tmat[0]
-               + yido * tmat[1]
-                      + tmat[2]);
-    yidi = round(xido * tmat[3]
-               + yido * tmat[4]
-                      + tmat[5]);
-#endif
-
-    // Compute memory location of indices
-    const int loci = yidi * in.strides[1]  + xidi;
-    const int loco = yido * out.strides[1] + xido;
-
-    for(int i = 0; i < nimages; i++) {
-        // Compute memory location of indices
-        int ioff = loci + i * in.strides[2];
-        int ooff = loco + i * out.strides[2];
-
-        T val; set_scalar(val, 0);
-        if (xidi < in.dims[0] && yidi < in.dims[1] && xidi >= 0 && yidi >= 0) val = d_in[ioff];
-
-        d_out[ooff] = val;
-    }
-}
-
-void transform_b(__global T *d_out, const KParam out, __global const T *d_in, const KParam in,
-                 const float *tmat, const int xido, const int yido, const int nimages)
-{
-    const int loco = (yido * out.strides[1] + xido);
-
-    // Compute input index
-    float xid = 0.0f, yid = 0.0f;
-#if PERSPECTIVE
-    const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-    xid = (xido * tmat[0]
-         + yido * tmat[1]
-                + tmat[2]) / W;
-    yid = (xido * tmat[3]
-         + yido * tmat[4]
-                + tmat[5]) / W;
-#else
-    xid = xido * tmat[0]
-        + yido * tmat[1]
-               + tmat[2];
-    yid = xido * tmat[3]
-        + yido * tmat[4]
-               + tmat[5];
-#endif
-
-    T zero = ZERO;
-    if (xid < -0.001 || yid < -0.001 || in.dims[0] < xid || in.dims[1] < yid) {
-        for(int i = 0; i < nimages; i++) {
-            d_out[loco + i * out.strides[2]] = zero;
-        }
-        return;
-    }
-
-    const int grd_x = floor(xid),  grd_y = floor(yid);
-    const WT off_x = xid - grd_x, off_y = yid - grd_y;
-
-    // Check if pVal and pVal + 1 are both valid indices
-    const bool condY = (yid < in.dims[1] - 1);
-    const bool condX = (xid < in.dims[0] - 1);
-
-    // Compute weights used
-    const WT wt00 = (1.0 - off_x) * (1.0 - off_y);
-    const WT wt10 = (condY) ? (1.0 - off_x) * (off_y)     : 0;
-    const WT wt01 = (condX) ? (off_x) * (1.0 - off_y)     : 0;
-    const WT wt11 = (condX && condY) ? (off_x) * (off_y)  : 0;
-
-    const WT wt = wt00 + wt10 + wt01 + wt11;
-
-    const int loci = grd_y * in.strides[1] + grd_x;
-    for(int i = 0; i < nimages; i++) {
-        const int ioff = loci + (i * in.strides[2]);
-        const int ooff = loco + (i * out.strides[2]);
-
-        // Compute Weighted Values
-        VT v00 =                    (wt00 * d_in[ioff]);
-        VT v10 = (condY) ?          (wt10 * d_in[ioff + in.strides[1]])     : zero;
-        VT v01 = (condX) ?          (wt01 * d_in[ioff + 1])                 : zero;
-        VT v11 = (condX && condY) ? (wt11 * d_in[ioff + in.strides[1] + 1]) : zero;
-        VT vo  = v00 + v10 + v01 + v11;
-
-        d_out[ooff] = (T)(vo / wt);
-    }
-}
-
-void transform_l(__global T *d_out, const KParam out, __global const T *d_in, const KParam in,
-                 const float *tmat, const int xido, const int yido, const int nimages)
-{
-    // Compute input index
-    int xidi = 0, yidi = 0;
-#if PERSPECTIVE
-    const float W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-    xidi = floor((xido * tmat[0]
-                + yido * tmat[1]
-                       + tmat[2]) / W);
-    yidi = floor((xido * tmat[3]
-                + yido * tmat[4]
-                       + tmat[5]) / W);
-#else
-    xidi = floor(xido * tmat[0]
-               + yido * tmat[1]
-                      + tmat[2]);
-    yidi = floor(xido * tmat[3]
-               + yido * tmat[4]
-                      + tmat[5]);
-#endif
-
-    // Compute memory location of indices
-    const int loci = yidi * in.strides[1]  + xidi;
-    const int loco = yido * out.strides[1] + xido;
-
-    for(int i = 0; i < nimages; i++) {
-        // Compute memory location of indices
-        int ioff = loci + i * in.strides[2];
-        int ooff = loco + i * out.strides[2];
-
-        T val; set_scalar(val, 0);
-        if (xidi < in.dims[0] && yidi < in.dims[1] && xidi >= 0 && yidi >= 0) val = d_in[ioff];
-
-        d_out[ooff] = val;
-    }
-}
-
diff --git a/src/backend/opencl/rotate.cpp b/src/backend/opencl/rotate.cpp
index 404b79af91..c57b8208e4 100644
--- a/src/backend/opencl/rotate.cpp
+++ b/src/backend/opencl/rotate.cpp
@@ -22,18 +22,20 @@ namespace opencl
         Array<T> out = createEmptyArray<T>(odims);
 
         switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::rotate<T, AF_INTERP_NEAREST> (out, in, theta);
-                break;
-            case AF_INTERP_BILINEAR:
-                kernel::rotate<T, AF_INTERP_BILINEAR>(out, in, theta);
-                break;
-            case AF_INTERP_LOWER:
-                kernel::rotate<T, AF_INTERP_LOWER>   (out, in, theta);
-                break;
-            default:
-                AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                break;
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::rotate<T, 1>(out, in, theta, method);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::rotate<T, 2>(out, in, theta, method);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::rotate<T, 3>(out, in, theta, method);
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
         }
 
         return out;
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index ac8f86346e..608128fd0b 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -22,84 +22,22 @@ namespace opencl
     {
         Array<T> out = createEmptyArray<T>(odims);
 
-        if(inverse) {
-            if (perspective) {
-                switch(method) {
-                    case AF_INTERP_NEAREST:
-                        kernel::transform<T, true, true, AF_INTERP_NEAREST>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_BILINEAR:
-                        kernel::transform<T, true, true, AF_INTERP_BILINEAR>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_LOWER:
-                        kernel::transform<T, true, true, AF_INTERP_LOWER>
-                                         (out, in, tf);
-                        break;
-                    default:
-                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                        break;
-                }
-            } else {
-                switch(method) {
-                    case AF_INTERP_NEAREST:
-                        kernel::transform<T, true, false, AF_INTERP_NEAREST>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_BILINEAR:
-                        kernel::transform<T, true, false, AF_INTERP_BILINEAR>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_LOWER:
-                        kernel::transform<T, true, false, AF_INTERP_LOWER>
-                                         (out, in, tf);
-                        break;
-                    default:
-                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                        break;
-                }
-            }
-        } else {
-            if (perspective) {
-                switch(method) {
-                    case AF_INTERP_NEAREST:
-                        kernel::transform<T, false, true, AF_INTERP_NEAREST>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_BILINEAR:
-                        kernel::transform<T, false, true, AF_INTERP_BILINEAR>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_LOWER:
-                        kernel::transform<T, false, true, AF_INTERP_LOWER>
-                                         (out, in, tf);
-                        break;
-                    default:
-                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                        break;
-                }
-            } else {
-                switch(method) {
-                    case AF_INTERP_NEAREST:
-                        kernel::transform<T, false, false, AF_INTERP_NEAREST>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_BILINEAR:
-                        kernel::transform<T, false, false, AF_INTERP_BILINEAR>
-                                         (out, in, tf);
-                        break;
-                    case AF_INTERP_LOWER:
-                        kernel::transform<T, false, false, AF_INTERP_LOWER>
-                                         (out, in, tf);
-                        break;
-                    default:
-                        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-                        break;
-                }
-            }
+        switch(method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::transform<T, 1>(out, in, tf, inverse, perspective, method);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::transform<T, 2>(out, in, tf, inverse, perspective, method);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::transform<T, 3>(out, in, tf, inverse, perspective, method);
+            break;
+        default:
+            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
         }
-
         return out;
     }
 

From 270f15dfb261b84ecdac6873eb1a51faa531cbf0 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 19:03:50 -0400
Subject: [PATCH 0855/2677] BUG: Fixing strides for interp in CUDA backend

---
 src/backend/cuda/kernel/interp.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index e5612346f8..4f3bb6e785 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -304,7 +304,7 @@ struct Interp2<Ty, Tp, 3>
         Ty zero = scalar<Ty>(0);
         bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
         for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * out.strides[2];
+            int idx_n = idx + n * in.strides[2];
             VT val[4][4];
 #pragma unroll
             for (int j = 0; j < 4; j++) {

From a4c2d0526a7c7e38ccf4909aeab5c069a4e8c4af Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 19:05:03 -0400
Subject: [PATCH 0856/2677] FEAT: Enabling additional interpolation types

- Enabling cubic support for rotate and transform
- resize fallsback to use scale

Resize is not using common interp.cl because of compilation
issues that arose from using too much constant memory when
compiling FAST in CUDA backend.
---
 src/api/c/resize.cpp    | 20 ++++++++++++++++----
 src/api/c/rotate.cpp    |  3 +++
 src/api/c/transform.cpp |  3 +++
 3 files changed, 22 insertions(+), 4 deletions(-)

diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index 50992b9da9..7471707dba 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -33,13 +33,25 @@ af_err af_resize(af_array *out, const af_array in, const dim_t odim0, const dim_
         ArrayInfo info = getInfo(in);
         af_dtype type = info.getType();
 
-        ARG_ASSERT(4, (method == AF_INTERP_BILINEAR ||
-                       method == AF_INTERP_NEAREST  ||
-                       method == AF_INTERP_LOWER
-                       ));
+        ARG_ASSERT(4, method == AF_INTERP_NEAREST  ||
+                      method == AF_INTERP_BILINEAR ||
+                      method == AF_INTERP_BILINEAR_COSINE ||
+                      method == AF_INTERP_BICUBIC ||
+                      method == AF_INTERP_BICUBIC_SPLINE ||
+                      method == AF_INTERP_LOWER);
+
         DIM_ASSERT(2, odim0 > 0);
         DIM_ASSERT(3, odim1 > 0);
 
+        bool is_resize_supported = (method == AF_INTERP_LOWER ||
+                                    method == AF_INTERP_NEAREST ||
+                                    method == AF_INTERP_BILINEAR);
+
+        if (!is_resize_supported) {
+            // Fall back to scale for additional methods
+            return af_scale(out, in, 0, 0, odim0, odim1, method);
+        }
+
         af_array output;
 
         switch(type) {
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index cf5f7699a1..92627b9383 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -47,6 +47,9 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
 
         ARG_ASSERT(3, method == AF_INTERP_NEAREST  ||
                       method == AF_INTERP_BILINEAR ||
+                      method == AF_INTERP_BILINEAR_COSINE ||
+                      method == AF_INTERP_BICUBIC ||
+                      method == AF_INTERP_BICUBIC_SPLINE ||
                       method == AF_INTERP_LOWER);
 
         if(idims.elements() == 0) {
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 074868463d..cbe1240fc3 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -68,6 +68,9 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
         ARG_ASSERT(2, t_info.getType() == f32);
         ARG_ASSERT(5, method == AF_INTERP_NEAREST  ||
                       method == AF_INTERP_BILINEAR ||
+                      method == AF_INTERP_BILINEAR_COSINE ||
+                      method == AF_INTERP_BICUBIC ||
+                      method == AF_INTERP_BICUBIC_SPLINE ||
                       method == AF_INTERP_LOWER);
 
         // Assert dimesions

From d610c96a76e1f89450be4b0179d80498d6ebd45f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 22:13:45 -0400
Subject: [PATCH 0857/2677] BUILD: Fixing wrong datatype

---
 src/backend/cpu/kernel/interp.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index e6d70d1afb..c792cbf3f2 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -214,8 +214,8 @@ struct Interp2<InT, LocT, 2>
         bool condX[2] = {true, x + 1 < idims[0]};
         bool condY[2] = {true, y + 1 < idims[1]};
 
-        bool offX[2] = {0, condX[1] ? 1 : 0};
-        bool offY[2] = {0, condY[1] ? 1 : 0};
+        int offX[2] = {0, condX[1] ? 1 : 0};
+        int offY[2] = {0, condY[1] ? 1 : 0};
 
         VT zero = scalar<VT>(0);
         VT val[2][2];

From 6a919a56b6ed4f1547f28daf26b65bc20243ca8b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Sep 2016 22:56:37 -0400
Subject: [PATCH 0858/2677] CPU: Add batching support for interp.hpp

---
 src/backend/cpu/kernel/approx.hpp    |   4 +-
 src/backend/cpu/kernel/interp.hpp    | 124 +++++++++++++++++++--------
 src/backend/cpu/kernel/rotate.hpp    |  45 +++++-----
 src/backend/cpu/kernel/transform.hpp |  16 ++--
 4 files changed, 122 insertions(+), 67 deletions(-)

diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index 0d32a0f95e..6977b0dbc7 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -59,7 +59,7 @@ void approx1(Array<InT> output, const Array<InT> input,
                     if (x < 0 || idims[0] < x + 1) {
                         out[ooff + idx] = scalar<InT>(offGrid);
                     } else {
-                        out[ooff + idx] = interp(input, ioff, x, method, clamp);
+                        interp(output, ooff + idx, input, ioff, x, method, 1, clamp);
                     }
                 }
             }
@@ -114,7 +114,7 @@ void approx2(Array<InT> output, const Array<InT> input,
                         y < 0 || idims[1] < y + 1 ) {
                         out[ooff + idx] = scalar<InT>(offGrid);
                     } else {
-                        out[ooff + idx] = interp(input, ioffzw, x, y, method, clamp);
+                        interp(output, ooff + idx, input, ioffzw, x, y, method, 1, clamp);
                     }
                 }
             }
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index c792cbf3f2..008913c407 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -91,23 +91,34 @@ struct Interp1
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 1>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method, bool clamp)
+    void operator()(Array<InT> &out, int ooff,
+                    const Array<InT> &in, int ioff, LocT x,
+                    af_interp_type  method, int batch, bool clamp)
     {
+        const InT *inptr = in.get();
         const dim4 idims = in.dims();
+        const dim4 istrides = in.strides();
         int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
         bool cond = xid >= 0 && xid < idims[0];
         if (clamp) xid = std::max(0, std::min(xid, (int)idims[0]));
 
-        const InT *inptr = in.get();
+        InT *outptr = out.get();
+        const dim4 ostrides = out.strides();
         int idx = ioff + xid;
-        return (cond || clamp) ? inptr[idx] : scalar<InT>(0);
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * istrides[1];
+            outptr[ooff + n * ostrides[1]] = (cond || clamp) ? inptr[idx_n] : scalar<InT>(0);
+        }
     }
 };
 
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 2>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type  method, bool clamp)
+    void operator()(Array<InT> &out, int ooff,
+                    const Array<InT> &in, int ioff, LocT x,
+                    af_interp_type  method, int batch, bool clamp)
     {
         typedef vtype_t<InT> VT;
 
@@ -116,6 +127,9 @@ struct Interp1<InT, LocT, 2>
         const int idx = ioff + grid_x;
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
+        const dim4 istrides = in.strides();
+        InT *outptr = out.get();
+        const dim4 ostrides = out.strides();
 
         bool cond[2] = {true, grid_x + 1 < idims[0]};
         int  offx[2] = {0 , cond[1] ? 1 : 0};
@@ -127,19 +141,23 @@ struct Interp1<InT, LocT, 2>
         }
 
         const VT zero = scalar<VT>(0);
-        VT val[2] = {zero, zero};
-        for (int i = 0; i < 2; i++) {
-            if (clamp || cond[i]) val[i] = inptr[idx + offx[i]];
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * istrides[1];
+            VT val[2] = {zero, zero};
+            for (int i = 0; i < 2; i++) {
+                if (clamp || cond[i]) val[i] = inptr[idx_n + offx[i]];
+            }
+            outptr[ooff + n * ostrides[1]] = linearInterpFunc(val, ratio);
         }
-
-        return linearInterpFunc(val, ratio);
     }
 };
 
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 3>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, af_interp_type method, bool clamp)
+    void operator()(Array<InT> &out, int ooff,
+                    const Array<InT> &in, int ioff, LocT x,
+                    af_interp_type  method, int batch, bool clamp)
     {
         typedef vtype_t<InT> VT;
 
@@ -148,16 +166,23 @@ struct Interp1<InT, LocT, 3>
         const int idx = ioff + grid_x;
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
+        const dim4 istrides = in.strides();
+        InT *outptr = out.get();
+        const dim4 ostrides = out.strides();
 
         bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
         int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
         const VT zero = scalar<VT>(0);
-        VT val[4] = {zero, zero, zero, zero};
-        for (int i = 0; i < 4; i++) {
-            if (clamp || cond[i]) val[i] = inptr[idx + off[i]];
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * istrides[1];
+            VT val[4] = {zero, zero, zero, zero};
+            for (int i = 0; i < 4; i++) {
+                if (clamp || cond[i]) val[i] = inptr[idx_n + off[i]];
+            }
+            bool spline = method == AF_INTERP_CUBIC_SPLINE;
+            outptr[ooff + n * ostrides[2]] =  cubicInterpFunc(val, off_x, spline);
         }
-        return cubicInterpFunc(val, off_x, method == AF_INTERP_CUBIC_SPLINE);
     }
 };
 
@@ -169,12 +194,17 @@ struct Interp2
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 1>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method, bool clamp)
+    void operator()(Array<InT> &out, int ooff,
+                    const Array<InT> &in, int ioff, LocT x, LocT y,
+                    af_interp_type  method, int nimages, bool clamp)
     {
         const InT *inptr = in.get();
         const dim4 istrides = in.strides();
         const dim4 idims = in.dims();
 
+        InT *outptr = out.get();
+        const dim4 ostrides = out.strides();
+
         int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
         int yid = (method == AF_INTERP_LOWER ? std::floor(y) : std::round(y));
 
@@ -188,14 +218,19 @@ struct Interp2<InT, LocT, 1>
 
         bool cond = condX && condY;
         int idx = ioff + yid * istrides[1] + xid;
-        return (clamp || cond) ? inptr[idx] : scalar<InT>(0);
+        for (int n = 0; n < nimages; n++) {
+            int idx_n = idx + n * istrides[2];
+            outptr[ooff + n * ostrides[2]] = (clamp || cond) ? inptr[idx_n] : scalar<InT>(0);
+        }
     }
 };
 
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 2>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method, bool clamp)
+    void operator()(Array<InT> &out, int ooff,
+                    const Array<InT> &in, int ioff, LocT x, LocT y,
+                    af_interp_type  method, int nimages, bool clamp)
     {
         typedef vtype_t<InT> VT;
 
@@ -203,6 +238,9 @@ struct Interp2<InT, LocT, 2>
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
 
+        InT *outptr = out.get();
+        const dim4 ostrides = out.strides();
+
         const int grid_x = floor(x);
         const LocT off_x = x - grid_x;
 
@@ -218,14 +256,6 @@ struct Interp2<InT, LocT, 2>
         int offY[2] = {0, condY[1] ? 1 : 0};
 
         VT zero = scalar<VT>(0);
-        VT val[2][2];
-        for (int j = 0; j < 2; j++) {
-            int off_y = idx + offY[j] * istrides[1];
-            for (int i = 0; i < 2; i++) {
-                bool cond = clamp || (condX[i] && condY[j]);
-                val[j][i] = cond ? inptr[off_y + offX[i]] : zero;
-            }
-        }
 
         LocT xratio = off_x, yratio = off_y;
         if (method == AF_INTERP_LINEAR_COSINE ||
@@ -235,14 +265,27 @@ struct Interp2<InT, LocT, 2>
             yratio = (1 - std::cos(yratio * af::Pi))/2;
         }
 
-        return bilinearInterpFunc(val, off_x, off_y);
+        for (int n = 0; n < nimages; n++) {
+            int idx_n = idx + n * istrides[2];
+            VT val[2][2];
+            for (int j = 0; j < 2; j++) {
+                int off_y = idx_n + offY[j] * istrides[1];
+                for (int i = 0; i < 2; i++) {
+                    bool cond = clamp || (condX[i] && condY[j]);
+                    val[j][i] = cond ? inptr[off_y + offX[i]] : zero;
+                }
+            }
+            outptr[ooff + n * ostrides[2]] = bilinearInterpFunc(val, off_x, off_y);
+        }
     }
 };
 
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 3>
 {
-    InT operator()(const Array<InT> &in, int ioff, LocT x, LocT y, af_interp_type method, bool clamp)
+    void operator()(Array<InT> &out, int ooff,
+                    const Array<InT> &in, int ioff, LocT x, LocT y,
+                    af_interp_type  method, int nimages, bool clamp)
     {
         typedef vtype_t<InT> VT;
 
@@ -250,6 +293,9 @@ struct Interp2<InT, LocT, 3>
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
 
+        InT *outptr = out.get();
+        const dim4 ostrides = out.strides();
+
         const int grid_x = floor(x);
         const LocT off_x = x - grid_x;
 
@@ -258,26 +304,28 @@ struct Interp2<InT, LocT, 3>
 
         const int idx = ioff + grid_y * istrides[1] + grid_x;
 
-        //for bicubic interpolation, work with 4x4 val at a time
-        VT val[4][4];
-
         // used for setting values at boundaries
         bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
         bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < idims[1], grid_y + 2 < idims[1]};
         int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
         int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
         VT zero = scalar<VT>(0);
-        for (int j = 0; j < 4; j++) {
-            int ioff_j = idx + offY[j] * istrides[1];
-            for (int i = 0; i < 4; i++) {
-                bool cond = clamp || (condX[i] && condY[j]);
-                val[j][i] = cond ? inptr[ioff_j + offX[i]] : zero;
+        for (int n = 0; n < nimages; n++) {
+            int idx_n = idx + n * istrides[2];
+
+            //for bicubic interpolation, work with 4x4 val at a time
+            VT val[4][4];
+            for (int j = 0; j < 4; j++) {
+                int ioff_j = idx_n + offY[j] * istrides[1];
+                for (int i = 0; i < 4; i++) {
+                    bool cond = clamp || (condX[i] && condY[j]);
+                    val[j][i] = cond ? inptr[ioff_j + offX[i]] : zero;
+                }
             }
+            outptr[ooff + n * ostrides[2]] = bicubicInterpFunc(val, off_x, off_y, spline);
         }
-
-        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
-        return bicubicInterpFunc(val, off_x, off_y, spline);
     }
 };
 
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index 35de77654e..7776ff414d 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -52,32 +52,35 @@ void rotate(Array<T> output, const Array<T> input,
                            std::round(ty * 1000) / 1000.0f,
                           };
 
+    int nimages = odims[2];
+    T *out = output.get();
+
     for (int idw = 0; idw < (int)odims[3]; idw++) {
-        for (int idz = 0; idz < (int)odims[2]; idz++) {
 
-            int out_off = idz * ostrides[2] + idw * ostrides[3];
-            int in_off  = idz * istrides[2] + idw * istrides[3];
-            T *out = output.get() + out_off;
+        int out_offw = idw * ostrides[3];
+        int in_offw  = idw * istrides[3];
 
-            // Do transform for image
-            for(int idy = 0; idy < (int)odims[1]; idy++) {
-                for(int idx = 0; idx < (int)odims[0]; idx++) {
-                    WT xidi = idx * tmat[0] + idy * tmat[1] + tmat[2];
-                    WT yidi = idx * tmat[3] + idy * tmat[4] + tmat[5];
+        // Do transform for image
+        for(int idy = 0; idy < (int)odims[1]; idy++) {
+            for(int idx = 0; idx < (int)odims[0]; idx++) {
+                WT xidi = idx * tmat[0] + idy * tmat[1] + tmat[2];
+                WT yidi = idx * tmat[3] + idy * tmat[4] + tmat[5];
 
-                    // Special conditions to deal with boundaries for bilinear and bicubic
-                    // FIXME: Ideally this condition should be removed or be present for all methods
-                    // But tests are expecting a different behavior for bilinear and nearest
-                    bool condX = xidi >= -0.0001 && xidi < idims[0];
-                    bool condY = yidi >= -0.0001 && yidi < idims[1];
-                    T val = scalar<T>(0);
-                    if (order == 1 || (condX && condY)) {
-                        // FIXME: Nearest and lower do not do clamping, but other methods do
-                        // Make it consistent
-                        bool clamp = order != 1;
-                        val = interp(input, in_off, xidi, yidi, method, clamp);
+                // Special conditions to deal with boundaries for bilinear and bicubic
+                // FIXME: Ideally this condition should be removed or be present for all methods
+                // But tests are expecting a different behavior for bilinear and nearest
+                bool condX = xidi >= -0.0001 && xidi < idims[0];
+                bool condY = yidi >= -0.0001 && yidi < idims[1];
+                int ooff = out_offw + idy * ostrides[1] + idx;
+                if (order == 1 || (condX && condY)) {
+                    // FIXME: Nearest and lower do not do clamping, but other methods do
+                    // Make it consistent
+                    bool clamp = order != 1;
+                    interp(output, ooff, input, in_offw, xidi, yidi, method, nimages, clamp);
+                } else {
+                    for (int n = 0; n < nimages; n++) {
+                        out[ooff + n * ostrides[2]] = scalar<T>(0);
                     }
-                    out[idy * ostrides[1] + idx] = val;
                 }
             }
         }
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index 9e2dcc60ee..8a9ceacb07 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -87,13 +87,16 @@ void transform(Array<T> output, const Array<T> input,
     T * out = output.get();
     const float* tf = transform.get();
 
+    int batch_size = 1;
+    if (idims[2] != tdims[2]) batch_size = idims[2];
+
     Interp2<T, WT, order> interp;
     for (int idw = 0; idw < (int)odims[3]; idw++) {
         dim_t out_offw = idw * ostrides[3];
         dim_t in_offw = (idims[3] > 1) * idw * istrides[3];
         dim_t tf_offw = (tdims[3] > 1) * idw * tstrides[3];
 
-        for (int idz = 0; idz < (int)odims[2]; idz++) {
+        for (int idz = 0; idz < (int)odims[2]; idz += batch_size) {
             dim_t out_offzw = out_offw + idz * ostrides[2];
             dim_t in_offzw = in_offw + (idims[2] > 1) * idz * istrides[2];
             dim_t tf_offzw = tf_offw + (tdims[2] > 1) * idz * tstrides[2];
@@ -103,8 +106,6 @@ void transform(Array<T> output, const Array<T> input,
             float tmat[9];
             calc_transform_inverse(tmat, tptr, inverse, perspective, perspective ? 9 : 6);
 
-            T *optr = out + out_offzw;
-
             for (int idy = 0; idy < (int)odims[1]; idy++) {
                 for (int idx = 0; idx < (int)odims[0]; idx++) {
                     WT xidi = idx * tmat[0] + idy * tmat[1] + tmat[2];
@@ -122,11 +123,14 @@ void transform(Array<T> output, const Array<T> input,
                     bool condX = xidi >= -0.0001 && xidi < idims[0];
                     bool condY = yidi >= -0.0001 && yidi < idims[1];
 
-                    T val = scalar<T>(0);
+                    int ooff = out_offzw + idy * ostrides[1] + idx;
                     if (condX && condY) {
-                        val = interp(input, in_offzw, xidi, yidi, method, clamp);
+                        interp(output, ooff, input, in_offzw, xidi, yidi, method, batch_size, clamp);
+                    } else {
+                        for (int n = 0; n < batch_size; n++) {
+                            out[ooff + n * ostrides[2]] =  scalar<T>(0);
+                        }
                     }
-                    optr[idy * ostrides[1] + idx] =  val;
                 }
             }
         }

From 48f4e0e739369933a5bfa0db6d48bd4138e90849 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 6 Jul 2016 13:02:53 +0530
Subject: [PATCH 0859/2677] Add shared(cuda)/local(opencl) memory limit check
 for bilateral

---
 src/backend/cuda/kernel/bilateral.hpp   | 7 ++++++-
 src/backend/opencl/kernel/bilateral.hpp | 5 +++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index e8f6fb41da..55d7c91e7f 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -136,7 +136,12 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma, float c_sig
     int radius = (int)std::max(s_sigma * 1.5f, 1.f);
     int num_shrd_elems    = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
     int num_gauss_elems   = (2 * radius + 1)*(2 * radius + 1);
-    int total_shrd_size   = sizeof(outType) * (num_shrd_elems + num_gauss_elems);
+    size_t total_shrd_size   = sizeof(outType) * (num_shrd_elems + num_gauss_elems);
+
+    size_t MAX_SHRD_SIZE = cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock;
+    if (total_shrd_size > MAX_SHRD_SIZE) {
+        CUDA_NOT_SUPPORTED();
+    }
 
     CUDA_LAUNCH_SMEM((bilateralKernel<inType, outType>), blocks, threads, total_shrd_size,
         out, in, s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index b96b91d55a..0ee3058b6f 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -83,6 +83,11 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
         int radius = (int)std::max(s_sigma * 1.5f, 1.f);
         int num_shrd_elems    = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
         int num_gauss_elems   = (2*radius+1)*(2*radius+1);
+        size_t localMemSize   = (num_shrd_elems + num_gauss_elems)*sizeof(outType);
+        size_t MaxLocalSize   = getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
+        if (localMemSize>MaxLocalSize) {
+            OPENCL_NOT_SUPPORTED();
+        }
 
         bilateralOp(EnqueueArgs(getQueue(), global, local),
                     *out.data, out.info, *in.data, in.info,

From d0d87ab2525af162de48764a0e4634110ab7d00f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 5 Sep 2016 12:20:31 +0530
Subject: [PATCH 0860/2677] 28-30% improvement in cuda vs opencl speedup for
 bilateral

* Replacing exp cuda device function with __expf improved the cuda
  bilateral kernel performance vs opencl kernel.
* Removed redundant multiplication calculation in the for loop
  of cuda/opencl kernels
---
 src/backend/cuda/kernel/bilateral.hpp  | 8 ++++----
 src/backend/opencl/kernel/bilateral.cl | 6 +++---
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 55d7c91e7f..93711a99f6 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -38,7 +38,7 @@ int clamp(int f, int a, int b)
 inline __device__
 float gaussian1d(float x, float variance)
 {
-    return exp((x * x) / (-2.f * variance));
+    return __expf((x * x) / variance);
 }
 
 template<typename inType, typename outType>
@@ -66,8 +66,8 @@ void bilateralKernel(Param<outType> out, CParam<inType> in,
     const int padding     = 2 * radius;
     const int window_size = padding + 1;
     const int shrdLen     = THREADS_X + padding;
-    const float variance_range = sigma_color * sigma_color;
-    const float variance_space = sigma_space * sigma_space;
+    const float variance_range = -2.f * sigma_color * sigma_color;
+    const float variance_space = -2.f * sigma_space * sigma_space;
 
     // gfor batch offsets
     unsigned b2 = blockIdx.x / nBBS0;
@@ -85,7 +85,7 @@ void bilateralKernel(Param<outType> out, CParam<inType> in,
     if (lx<window_size && ly<window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = exp( ((x*x) + (y*y)) / (-2.f * variance_space));
+        gauss2d[ly*window_size+lx] = __expf(((x*x) + (y*y)) / variance_space);
     }
 
     // pull image to local memory
diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index 05c5ac6657..c6d8f94dda 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -44,8 +44,8 @@ void bilateral(__global outType *        d_dst,
     const int padding     = 2 * radius;
     const int window_size = padding + 1;
     const int shrdLen     = get_local_size(0) + padding;
-    const float variance_range = sigma_color * sigma_color;
-    const float variance_space = sigma_space * sigma_space;
+    const float variance_range = -2.f * sigma_color * sigma_color;
+    const float variance_space = -2.f * sigma_space * sigma_space;
 
     // gfor batch offsets
     unsigned b2 = get_group_id(0) / nBBS0;
@@ -63,7 +63,7 @@ void bilateral(__global outType *        d_dst,
     if (lx<window_size && ly<window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = exp( ((x*x) + (y*y)) / (-2.f * variance_space));
+        gauss2d[ly*window_size+lx] = exp( ((x*x) + (y*y)) / variance_space);
     }
 
     int s0 = iInfo.strides[0];

From a17be567e9fc7f105643e2264b8e85ca9e817d55 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 5 Sep 2016 13:40:18 +0530
Subject: [PATCH 0861/2677] Update example function code flow for better
 readability

---
 include/af/util.h                             |  6 ++-
 src/api/c/exampleFunction.cpp                 | 30 ++++++-------
 src/api/cpp/exampleFunction.cpp               |  4 +-
 src/api/unified/util.cpp                      |  6 +--
 src/backend/cpu/exampleFunction.cpp           | 42 +++++++++++++++----
 src/backend/cpu/exampleFunction.hpp           |  2 +-
 src/backend/cuda/exampleFunction.cu           |  6 +--
 src/backend/cuda/exampleFunction.hpp          |  2 +-
 src/backend/cuda/kernel/exampleFunction.hpp   | 33 ++++++++++++---
 src/backend/opencl/exampleFunction.cpp        |  6 +--
 src/backend/opencl/exampleFunction.hpp        |  2 +-
 src/backend/opencl/kernel/example.cl          | 25 +++++++++--
 src/backend/opencl/kernel/exampleFunction.hpp | 12 +++---
 13 files changed, 122 insertions(+), 54 deletions(-)

diff --git a/include/af/util.h b/include/af/util.h
index 75db59c605..259b03b5e2 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -128,7 +128,7 @@ namespace af
 #endif
 
     // Purpose of Addition: "How to add Function" documentation
-    AFAPI array exampleFunction(const array& in, const af_someenum_t param);
+    AFAPI array exampleFunction(const array& a, const array& b, const af_someenum_t param);
 
 #if AF_API_VERSION >= 34
     ///
@@ -252,7 +252,9 @@ extern "C" {
 #endif
 
     // Purpose of Addition: "How to add Function" documentation
-    AFAPI af_err af_example_function(af_array* out, const af_array in, const af_someenum_t param);
+    AFAPI af_err af_example_function(af_array* out,
+                                     const af_array a, const af_array b,
+                                     const af_someenum_t param);
 
     ///
     /// Get the version information of the library
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index f8fe157c04..b7ff405777 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -35,7 +35,7 @@ using namespace detail;         // detail is an alias to appropriate backend
                                 // change this
 
 template<typename T>
-af_array example(const af_array& in, const af_someenum_t& param)
+af_array example(const af_array& a, const af_array& b, const af_someenum_t& param)
 {
     // getArray<T> function is defined in handle.hpp
     // and it returns backend specific Array, namely one of the following
@@ -45,25 +45,23 @@ af_array example(const af_array& in, const af_someenum_t& param)
     // getHandle<T> function is defined in handle.hpp takes one of the
     // above backend specific detail::Array<T> and returns the
     // universal array handle af_array
-    return getHandle<T>( exampleFunction(getArray<T>(in), param) );
+    return getHandle<T>( exampleFunction(getArray<T>(a), getArray<T>(b), param) );
 }
 
-af_err af_example_function(af_array* out,
-                            const af_array in,
-                            const af_someenum_t param)
+af_err af_example_function(af_array* out, const af_array a, const af_array b, const af_someenum_t param)
 {
     try {
         af_array output = 0;
-        ArrayInfo info = getInfo(in);       // ArrayInfo is the base class which
+        ArrayInfo info = getInfo(a);        // ArrayInfo is the base class which
                                             // each backend specific Array inherits
                                             // This class stores the basic array meta-data
                                             // such as type of data, dimensions,
                                             // offsets and strides. This class is declared
                                             // in src/backend/ArrayInfo.hpp
+        ArrayInfo info2 = getInfo(b);
 
         af::dim4 dims = info.dims();
 
-
         ARG_ASSERT(2, (dims.ndims()>=0 && dims.ndims()<=3));
                                             // defined in err_common.hpp
                                             // there are other useful Macros
@@ -72,16 +70,18 @@ af_err af_example_function(af_array* out,
 
         af_dtype type = info.getType();
 
+        ARG_ASSERT(2, (info2.getType() == type));
+
         switch(type) {                      // Based on the data type, call backend specific
                                             // implementation
-            case f64: output = example<double >(in, param); break;
-            case f32: output = example<float  >(in, param); break;
-            case s32: output = example<int    >(in, param); break;
-            case u32: output = example<uint   >(in, param); break;
-            case  u8: output = example<uchar  >(in, param); break;
-            case  b8: output = example<char   >(in, param); break;
-            case c32: output = example<cfloat >(in, param); break;
-            case c64: output = example<cdouble>(in, param); break;
+            case f64: output = example<double >(a, b, param); break;
+            case f32: output = example<float  >(a, b, param); break;
+            case s32: output = example<int    >(a, b, param); break;
+            case u32: output = example<uint   >(a, b, param); break;
+            case  u8: output = example<uchar  >(a, b, param); break;
+            case  b8: output = example<char   >(a, b, param); break;
+            case c32: output = example<cfloat >(a, b, param); break;
+            case c64: output = example<cdouble>(a, b, param); break;
             default : TYPE_ERROR(1, type);  // Another helpful macro from err_common.hpp
                                             // that helps throw type based error messages
         }
diff --git a/src/api/cpp/exampleFunction.cpp b/src/api/cpp/exampleFunction.cpp
index 5236e60fee..066eca1b37 100644
--- a/src/api/cpp/exampleFunction.cpp
+++ b/src/api/cpp/exampleFunction.cpp
@@ -18,13 +18,13 @@
 namespace af
 {
 
-array exampleFunction(const array& in, const af_someenum_t p)
+array exampleFunction(const array& a, const array& b, const af_someenum_t p)
 {
     // create a temporary af_array handle
     af_array temp = 0;
 
     // call C-API function
-    AF_THROW( af_example_function(&temp, in.get(), p) );
+    AF_THROW( af_example_function(&temp, a.get(), b.get(), p) );
 
     // array::get() returns af_array handle for the corresponding cpp af::array
     return array(temp);
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 178ac87ad8..6b7d073de2 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -51,8 +51,8 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
     return CALL(output, exp, arr, precision, transpose);
 }
 
-af_err af_example_function(af_array* out, const af_array in, const af_someenum_t param)
+af_err af_example_function(af_array* out, const af_array a, const af_array b, const af_someenum_t param)
 {
-    CHECK_ARRAYS(in);
-    return CALL(out, in, param);
+    CHECK_ARRAYS(a, b);
+    return CALL(out, a, b, param);
 }
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index 0eb86462e1..c953528e8e 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -22,14 +22,15 @@ namespace cpu
 {
 
 template<typename T>
-Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method)
 {
-    in.eval();                          // All input Arrays should call eval mandatorily
+    a.eval();                           // All input Arrays should call eval mandatorily
                                         // in CPU backend function implementations. Since
                                         // the cpu fns are asynchronous launches, any Arrays
                                         // that are either views/JIT nodes needs to evaluated
                                         // before they are passed onto functions that are
                                         // enqueued onto the queues.
+    b.eval();
 
     dim4 outputDims;                    // this should be '= in.dims();' in most cases
                                         // but would definitely depend on the type of
@@ -42,27 +43,52 @@ Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
                                         // file to know what are the different types you
                                         // can create.
 
-    //dim4 in_dims    = in.dims();        // you can retrieve dimensions
+    dim4 aDims    = a.dims();           // you can retrieve dimensions
+    dim4 bDims    = b.dims();
+    dim4 oDims    = out.dims();
 
-    //dim_t in_offset = in.getOffset(); // you can retrieve the offset - used when given array
+    dim_t aOffset = a.getOffset();      // you can retrieve the offset - used when given array
                                         // is an sub-array pointing to some other array and
                                         // doesn't have memory of its own
+    dim_t bOffset = b.getOffset();
 
-    //dim4 in_strides = in.strides();     // you can retrieve strides
+    dim4 aStrides = a.strides();        // you can retrieve strides
+    dim4 bStrides = b.strides();
+    dim4 oStrides = out.strides();
 
-    //const T* src = in.get();            // cpu::Array<T>::get returns the pointer to the
+    const T* src1 = a.get();            // cpu::Array<T>::get returns the pointer to the
                                         // memory allocated for that Array
+    const T* src2 = b.get();            // cpu::Array<T>::get returns the pointer to the
+                                        // memory allocated for that Array
+    src1 += aOffset;
+    src2 += bOffset;
 
-    //T* dst = out.get();
+    T* dst = out.get();
 
     // Implement your algorithm and write results to dst
+    for(int j=0; j<aDims[1]; ++j) {
+        for (int i=0; i<aDims[0]; ++i) {
+
+            int src1Idx = i*aStrides[0] + j*aStrides[1];
+            int src2Idx = i*bStrides[0] + j*bStrides[1];
+            int dstIdx  = i*oStrides[0] + j*oStrides[1];
+
+            // kernel algorithm goes here
+            switch(method) {
+                case 1: dst[dstIdx] = src1[src1Idx] + src2[src2Idx]; break;
+                case 2: dst[dstIdx] = src1[src1Idx] - src2[src2Idx]; break;
+                case 3: dst[dstIdx] = src1[src1Idx] * src2[src2Idx]; break;
+                case 4: dst[dstIdx] = src1[src1Idx] / src2[src2Idx]; break;
+            }
+        }
+    }
 
     return out;                         // return the result
 }
 
 
 #define INSTANTIATE(T)  \
-    template Array<T> exampleFunction<T>(const Array<T> &in, const af_someenum_t method);
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
 
 // INSTANTIATIONS for all the types which
 // are present in the switch case statement
diff --git a/src/backend/cpu/exampleFunction.hpp b/src/backend/cpu/exampleFunction.hpp
index 107bd41a3d..d5c8139430 100644
--- a/src/backend/cpu/exampleFunction.hpp
+++ b/src/backend/cpu/exampleFunction.hpp
@@ -12,6 +12,6 @@
 namespace cpu
 {
     template<typename T>
-    Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method);
+    Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
 }
 
diff --git a/src/backend/cuda/exampleFunction.cu b/src/backend/cuda/exampleFunction.cu
index 4a7db44fb4..c97a46b5cf 100644
--- a/src/backend/cuda/exampleFunction.cu
+++ b/src/backend/cuda/exampleFunction.cu
@@ -27,7 +27,7 @@ namespace cuda
 {
 
 template<typename T>
-Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method)
 {
     dim4 outputDims;                    // this should be '= in.dims();' in most cases
                                         // but would definitely depend on the type of
@@ -41,14 +41,14 @@ Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
                                         // can create.
 
     // Relay the actual computation to CUDA kernel wrapper
-    kernel::exampleFunc<T>(out, in, method);
+    kernel::exampleFunc<T>(out, a, b, method);
 
     return out;                         // return the result
 }
 
 
 #define INSTANTIATE(T)  \
-    template Array<T> exampleFunction<T>(const Array<T> &in, const af_someenum_t method);
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
 
 // INSTANTIATIONS for all the types which
 // are present in the switch case statement
diff --git a/src/backend/cuda/exampleFunction.hpp b/src/backend/cuda/exampleFunction.hpp
index a297ef7ee4..1f97d7073d 100644
--- a/src/backend/cuda/exampleFunction.hpp
+++ b/src/backend/cuda/exampleFunction.hpp
@@ -12,5 +12,5 @@
 namespace cuda
 {
     template<typename T>
-    Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method);
+    Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
 }
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index c9e032b9db..e1339b552f 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -35,20 +35,41 @@ static const unsigned TY = 16;          // Kernel Launch Config Values
 
 template<typename T>
 __global__
-void exampleFuncKernel(Param<T> out, CParam<T> in, const af_someenum_t p)
+void exampleFuncKernel(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p)
 {
-    // kernel implementation goes here
+    // get current thread global identifiers along required dimensions
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+
+    if ( i<a.dims[0] && j<a.dims[1] ) {
+        // if needed use strides array to compute linear index of arrays
+        int src1Idx = i*a.strides[0] + j*a.strides[1];
+        int src2Idx = i*b.strides[0] + j*b.strides[1];
+        int dstIdx  = i*c.strides[0] + j*c.strides[1];
+
+        T* dst = c.ptr;
+        const T* src1 = a.ptr;
+        const T* src2 = b.ptr;
+
+        // kernel algorithm goes here
+        switch(p) {
+            case 1: dst[dstIdx] = src1[src1Idx] + src2[src2Idx]; break;
+            case 2: dst[dstIdx] = src1[src1Idx] - src2[src2Idx]; break;
+            case 3: dst[dstIdx] = src1[src1Idx] * src2[src2Idx]; break;
+            case 4: dst[dstIdx] = src1[src1Idx] / src2[src2Idx]; break;
+        }
+    }
 }
 
 
 template <typename T>                   // CUDA kernel wrapper function
-void exampleFunc(Param<T> out, CParam<T> in, const af_someenum_t p)
+void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p)
 {
 
     dim3 threads(TX, TY, 1);            // set your cuda launch config for blocks
 
-    int blk_x = divup(out.dims[0], threads.x);
-    int blk_y = divup(out.dims[1], threads.y);
+    int blk_x = divup(c.dims[0], threads.x);
+    int blk_y = divup(c.dims[1], threads.y);
     dim3 blocks(blk_x, blk_y);          // set your opencl launch config for grid
 
     // launch your kernel
@@ -61,7 +82,7 @@ void exampleFunc(Param<T> out, CParam<T> in, const af_someenum_t p)
     //
     // CUDA_LAUNCH_SMEM takes in an additional parameter, size of shared memory, after
     // threads paramters, which are then followed by kernel parameters
-    CUDA_LAUNCH((exampleFuncKernel<T>), blocks, threads, out, in, p);
+    CUDA_LAUNCH((exampleFuncKernel<T>), blocks, threads, c, a, b, p);
 
     POST_LAUNCH_CHECK();                // Macro for post kernel launch checks
                                         // these checks are carried  ONLY IN DEBUG mode
diff --git a/src/backend/opencl/exampleFunction.cpp b/src/backend/opencl/exampleFunction.cpp
index 082c223f30..6c477d6e77 100644
--- a/src/backend/opencl/exampleFunction.cpp
+++ b/src/backend/opencl/exampleFunction.cpp
@@ -27,7 +27,7 @@ namespace opencl
 {
 
 template<typename T>
-Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method)
 {
     dim4 outputDims;                    // this should be '= in.dims();' in most cases
                                         // but would definitely depend on the type of
@@ -41,14 +41,14 @@ Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method)
                                         // can create.
 
     // Relay the actual computation to OpenCL kernel wrapper
-    kernel::exampleFunc<T>(out, in, method);
+    kernel::exampleFunc<T>(out, a, b, method);
 
     return out;                         // return the result
 }
 
 
 #define INSTANTIATE(T)  \
-    template Array<T> exampleFunction<T>(const Array<T> &in, const af_someenum_t method);
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
 
 // INSTANTIATIONS for all the types which
 // are present in the switch case statement
diff --git a/src/backend/opencl/exampleFunction.hpp b/src/backend/opencl/exampleFunction.hpp
index 442cc09574..ee72c1cd74 100644
--- a/src/backend/opencl/exampleFunction.hpp
+++ b/src/backend/opencl/exampleFunction.hpp
@@ -12,6 +12,6 @@
 namespace opencl
 {
     template<typename T>
-    Array<T> exampleFunction(const Array<T> &in, const af_someenum_t method);
+    Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
 }
 
diff --git a/src/backend/opencl/kernel/example.cl b/src/backend/opencl/kernel/example.cl
index c8b8328580..7ca84560e1 100644
--- a/src/backend/opencl/kernel/example.cl
+++ b/src/backend/opencl/kernel/example.cl
@@ -10,9 +10,28 @@
 __kernel
 void example(__global T *       d_dst,
              KParam             oInfo,
-             __global const T * d_src,
-             KParam             iInfo,
+             __global const T * d_src1,
+             KParam             iInfo1,
+             __global const T * d_src2,
+             KParam             iInfo2,
              int                method);
 {
-    // kernel algorithm goes here
+    // get current thread global identifiers along required dimensions
+    int i = get_global_id(0);
+    int j = get_global_id(1);
+
+    if ( i<iInfo1.dims[0] && j<iInfo1.dims[1] ) {
+        // if needed use strides array to compute linear index of arrays
+        int src1Idx = i*iInfo1.strides[0] + j*iInfo1.strides[1];
+        int src2Idx = i*iInfo2.strides[0] + j*iInfo2.strides[1];
+        int dstIdx  = i* oInfo.strides[0] + j* oInfo.strides[1];
+
+        // kernel algorithm goes here
+        switch(method) {
+            case 1: d_dst[dstIdx] = d_src1[src1Idx] + d_src2[src2Idx]; break;
+            case 2: d_dst[dstIdx] = d_src1[src1Idx] - d_src2[src2Idx]; break;
+            case 3: d_dst[dstIdx] = d_src1[src1Idx] * d_src2[src2Idx]; break;
+            case 4: d_dst[dstIdx] = d_src1[src1Idx] / d_src2[src2Idx]; break;
+        }
+    }
 }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index ce900e663a..c044346c0f 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -51,7 +51,7 @@ static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T>
-void exampleFunc(Param out, const Param in, const af_someenum_t p)
+void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
 {
     try {
         static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
@@ -96,8 +96,8 @@ void exampleFunc(Param out, const Param in, const af_someenum_t p)
         // configure work group parameters
         NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(out.info.dims[0], THREADS_X);
-        int blk_y = divup(out.info.dims[1], THREADS_Y);
+        int blk_x = divup(c.info.dims[0], THREADS_X);
+        int blk_y = divup(c.info.dims[1], THREADS_Y);
 
         // configure global launch parameters
         NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
@@ -105,12 +105,12 @@ void exampleFunc(Param out, const Param in, const af_someenum_t p)
         // create a kernel functor from the cl::Kernel object
         // corresponding to the device on which current execution
         // is happending.
-        auto exampleFuncOp = KernelFunctor<Buffer, KParam,
-                                     Buffer, KParam, int>(*egKernels[device]);
+        auto exampleFuncOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                           Buffer, KParam, int>(*egKernels[device]);
 
         // launch the kernel
         exampleFuncOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, (int)p);
+                    *c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
 
         // Below Macro activates validations ONLY in DEBUG
         // mode as its name indicates

From ff38da27a7e606d6c8a70dd4d715dface693b2e3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 6 Sep 2016 20:23:28 +0530
Subject: [PATCH 0862/2677] fix init values for morphological ops in
 cuda/opencl along edges

---
 src/backend/cuda/kernel/morph.hpp  | 48 +++++++++++++++---------------
 src/backend/opencl/kernel/morph.cl | 18 +++++------
 test/morph.cpp                     | 47 +++++++++++++++++++++++++++++
 3 files changed, 80 insertions(+), 33 deletions(-)

diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index beef1dbbb9..70650b8e53 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -43,21 +43,18 @@ __forceinline__ __device__ int lIdx(int x, int y,
     return (y*stride1 + x*stride0);
 }
 
-__forceinline__ __device__ int clamp(int f, int a, int b)
-{
-    return max(a, min(f, b));
-}
-
-template<typename T>
+template<typename T, bool isDilation>
 inline __device__ void load2ShrdMem(T * shrd, const T * const in,
         int lx, int ly, int shrdStride,
         int dim0, int dim1,
         int gx, int gy,
         int inStride1, int inStride0)
 {
-    int gx_  = clamp(gx, 0, dim0-1);
-    int gy_  = clamp(gy, 0, dim1-1);
-    shrd[ lIdx(lx, ly, shrdStride, 1) ] = in[ lIdx(gx_, gy_, inStride1, inStride0) ];
+    T val = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    if (gx>=0 && gx<dim0 && gy>=0 && gy<dim1) {
+        val = in[ lIdx(gx, gy, inStride1, inStride0) ];
+    }
+    shrd[ lIdx(lx, ly, shrdStride, 1) ] = val;
 }
 
 // kernel assumes mask/filter is square and hence does the
@@ -93,8 +90,9 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     for (int b=ly, gy2=gy; b<shrdLen1; b+=blockDim.y, gy2+=blockDim.y) {
         // move row_set get_local_size(1) along coloumns
         for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-            load2ShrdMem(shrdMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
-                         gx2-halo, gy2-halo, in.strides[1], in.strides[0]);
+            load2ShrdMem<T, isDilation>(shrdMem, iptr, a, b, shrdLen,
+                                        in.dims[0], in.dims[1],
+                                        gx2-halo, gy2-halo, in.strides[1], in.strides[0]);
         }
     }
 
@@ -111,8 +109,8 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
         int w_joff = (j+wj-halo)*shrdLen;
 #pragma unroll
         for(int wi=0; wi<windLen; ++wi) {
-            T cur  = shrdMem[w_joff + (i+wi-halo)];
-            if (d_filt[joff+wi]) {
+            if (d_filt[joff+wi] > (T)0) {
+                T cur  = shrdMem[w_joff + (i+wi-halo)];
                 if (isDilation)
                     acc = max(acc, cur);
                 else
@@ -133,7 +131,7 @@ __forceinline__ __device__ int lIdx3D(int x, int y, int z,
     return (z*stride2 + y*stride1 + x*stride0);
 }
 
-template<typename T>
+template<typename T, bool isDilation>
 inline __device__ void load2ShrdVolume(T * shrd, const T * const in,
         int lx, int ly, int lz,
         int shrdStride1, int shrdStride2,
@@ -141,12 +139,13 @@ inline __device__ void load2ShrdVolume(T * shrd, const T * const in,
         int gx, int gy, int gz,
         int inStride2, int inStride1, int inStride0)
 {
-    int gx_  = clamp(gx,0,dim0-1);
-    int gy_  = clamp(gy,0,dim1-1);
-    int gz_  = clamp(gz,0,dim2-1);
-    int shrdIdx = lx + ly*shrdStride1 + lz*shrdStride2;
-    int inIdx   = gx_*inStride0 + gy_*inStride1 + gz_*inStride2;
-    shrd[ shrdIdx ] = in[ inIdx ];
+    T val = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    if (gx>=0 && gx<dim0 &&
+        gy>=0 && gy<dim1 &&
+        gz>=0 && gz<dim2) {
+        val = in[gx*inStride0 + gy*inStride1 + gz*inStride2];
+    }
+    shrd[lx + ly*shrdStride1 + lz*shrdStride2] = val;
 }
 
 // kernel assumes mask/filter is square and hence does the
@@ -184,9 +183,10 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     for (int c=lz, gz2=gz; c<shrdLen2; c+=blockDim.z, gz2+=blockDim.z) {
         for (int b=ly, gy2=gy; b<shrdLen1; b+=blockDim.y, gy2+=blockDim.y) {
             for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-                load2ShrdVolume(shrdMem, iptr, a, b, c, shrdLen, shrdArea,
-                        in.dims[0], in.dims[1], in.dims[2], gx2-halo, gy2-halo, gz2-halo,
-                        in.strides[2], in.strides[1], in.strides[0]);
+                load2ShrdVolume<T, isDilation>(shrdMem, iptr, a, b, c, shrdLen, shrdArea,
+                                               in.dims[0], in.dims[1], in.dims[2],
+                                               gx2-halo, gy2-halo, gz2-halo,
+                                               in.strides[2], in.strides[1], in.strides[0]);
             }
         }
     }
@@ -209,8 +209,8 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
         int w_joff = (j+wj-halo)*shrdLen;
 #pragma unroll
             for(int wi=0; wi<windLen; ++wi) {
-                T cur  = shrdMem[w_koff+w_joff + i+wi-halo];
                 if (d_filt[koff+joff+wi]) {
+                    T cur  = shrdMem[w_koff+w_joff + i+wi-halo];
                     if (isDilation)
                         acc = max(acc, cur);
                     else
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index 4b5a24f9e2..916d9567b6 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -20,9 +20,8 @@ void load2LocalMem(__local T *  shrd,
         int gx, int gy,
         int inStride1, int inStride0)
 {
-    int gx_  = clamp(gx, 0, dim0-1);
-    int gy_  = clamp(gy, 0, dim1-1);
-    shrd[ lIdx(lx, ly, shrdStride, 1) ] = in[ lIdx(gx_, gy_, inStride1, inStride0) ];
+    T val = gx>=0 && gx<dim0 && gy>=0 && gy<dim1 ? in[ lIdx(gx, gy, inStride1, inStride0) ] : init;
+    shrd[ lIdx(lx, ly, shrdStride, 1) ] = val;
 }
 
 //kernel assumes four dimensions
@@ -108,12 +107,13 @@ void load2LocVolume(__local T * shrd,
         int gx, int gy, int gz,
         int inStride2, int inStride1, int inStride0)
 {
-    int gx_  = clamp(gx, 0, dim0-1);
-    int gy_  = clamp(gy, 0, dim1-1);
-    int gz_  = clamp(gz, 0, dim2-1);
-    int shrdIdx = lx + ly*shrdStride1 + lz*shrdStride2;
-    int inIdx   = gx_*inStride0 + gy_*inStride1 + gz_*inStride2;
-    shrd[ shrdIdx ] = in[ inIdx ];
+    T val = (T)0;
+    if (gx>=0 && gx<dim0 && gy>=0 && gy<dim1 && gz>=0 && gz<dim2)
+       val = in[gx*inStride0 + gy*inStride1 + gz*inStride2];
+    else
+       val = init;
+
+    shrd[ lx + ly*shrdStride1 + lz*shrdStride2 ] = val;
 }
 
 __kernel
diff --git a/test/morph.cpp b/test/morph.cpp
index c42ddf0cba..8aa0885cc4 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -412,3 +412,50 @@ TEST(Morph, GFOR)
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
+
+TEST(Morph, EdgeIssue1564)
+{
+    int inputData[10 * 10] =
+    {
+        0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
+        0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
+        0, 0, 0, 0, 0, 1, 1, 1, 1, 1
+    };
+    int goldData[10 * 10] =
+    {
+        0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
+        0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
+        0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
+        0, 0, 1, 1, 1, 0, 0, 1, 1, 1,
+        0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
+        0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
+        0, 0, 0, 0, 1, 1, 1, 1, 1, 1
+    };
+    array input(10, 10, inputData);
+    int maskData[3 * 3] =
+    {
+        1, 1, 1,
+        1, 0, 1,
+        1, 1, 1
+    };
+    array mask(3, 3, maskData);
+    array dilated = dilate(input.as(b8), mask.as(b8));
+
+    size_t nElems = dilated.elements();
+    char * outData = new char[nElems];
+    dilated.host((void*)outData);
+
+    for (size_t i=0; i<nElems; ++i) {
+        ASSERT_EQ((int)outData[i], goldData[i]);
+    }
+}

From 90ed799fd8e2709eb50b8f860cefd3117d5a2bfb Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 6 Sep 2016 11:21:33 -0400
Subject: [PATCH 0863/2677] Removed default constructor of randomEngine

---
 include/af/random_engine.h    | 8 --------
 src/api/cpp/random_engine.cpp | 5 -----
 2 files changed, 13 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 51fe484828..21fc822c68 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -38,14 +38,6 @@ namespace af
             */
             explicit
             randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT, uintl seedIn = 0);
-            /**
-                Create the default random number generator object
-
-                \code
-                randomEngine r;   // creates random engine of default type with default seed
-                \endcode
-            */
-            randomEngine(void);
             /**
                 Creates a copy of the random engine object
                 \param in The input random engine object
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index ddbed920cd..28c47e7b70 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -20,11 +20,6 @@ namespace af
         AF_THROW(af_create_random_engine(&engine, type, seed));
     }
 
-    randomEngine::randomEngine(void)
-    {
-        af_get_default_random_engine(&engine);
-    }
-
     randomEngine::randomEngine(const randomEngine& other)
     {
         if (this != &other) {

From 21482fe638622e29bc27b7ebc729fcc8f36b8c9a Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 6 Sep 2016 14:54:53 -0400
Subject: [PATCH 0864/2677] Shared pointer implementation for counter and seed

---
 src/api/c/random_engine.cpp | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index e7242a2456..35ab178cca 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -20,6 +20,7 @@
 #include <random.hpp>
 #include <MersenneTwister.hpp>
 #include <types.hpp>
+#include <memory>
 
 using namespace detail;
 using namespace common;
@@ -28,8 +29,8 @@ class RandomEngine
 {
     public :
     af_random_engine_type type;
-    uintl seed;
-    uintl counter;
+    std::shared_ptr<uintl> seed;
+    std::shared_ptr<uintl> counter;
     af_array pos;
     af_array sh1;
     af_array sh2;
@@ -38,7 +39,10 @@ class RandomEngine
     af_array temper_table;
     af_array state;
 
-    RandomEngine(void) : type(AF_RANDOM_ENGINE_DEFAULT), seed(0), counter(0) {}
+    RandomEngine(void) : type(AF_RANDOM_ENGINE_DEFAULT), seed(new uintl), counter(new uintl) {
+        *seed = 0;
+        *counter = 0;
+    }
 };
 
 af_random_engine getRandomEngineHandle(const RandomEngine engine)
@@ -69,7 +73,7 @@ static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *
                     getArray<uint>(e->temper_table),
                     getArray<uint>(e->state)));
     } else {
-        return getHandle(uniformDistribution<T>(dims, e->type, e->seed, e->counter));
+        return getHandle(uniformDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
     }
 }
 
@@ -86,7 +90,7 @@ static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e
                     getArray<uint>(e->temper_table),
                     getArray<uint>(e->state)));
     } else {
-        return getHandle(normalDistribution<T>(dims, e->type, e->seed, e->counter));
+        return getHandle(normalDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
     }
 }
 
@@ -117,8 +121,8 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_
         validateRandomType(rtype);
         RandomEngine e;
         e.type = rtype;
-        e.seed = seed;
-        e.counter = 0;
+        *e.seed = seed;
+        *e.counter = 0;
 
         if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
@@ -195,7 +199,7 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engin
                     AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
                     AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
                     AF_CHECK(af_create_handle(&e.state, 1, &MtStateLength, u32));
-                    initMersenneState(getWritableArray<uint>(e.state), e.seed, getArray<uint>(e.recursion_table));
+                    initMersenneState(getWritableArray<uint>(e.state), *e.seed, getArray<uint>(e.recursion_table));
                 }
             }
             e.type = rtype;
@@ -230,11 +234,11 @@ af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
     try {
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(*engine);
-        e->seed = seed;
+        *(e->seed) = seed;
         if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             initMersenneState(getWritableArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
         } else {
-            e->counter = 0;
+            *(e->counter) = 0;
         }
     }
     CATCHALL;
@@ -246,7 +250,7 @@ af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
     try {
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(engine);
-        *seed = e->seed;
+        *seed = *(e->seed);
     }
     CATCHALL;
     return AF_SUCCESS;

From c549eda8b9dc941ebb84cc4824563d59c26934fd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 5 Sep 2016 13:56:34 +0530
Subject: [PATCH 0865/2677] fix chart type in scatter3 function

---
 src/api/c/plot3.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
index 1b45c22c8b..f8274279eb 100644
--- a/src/api/c/plot3.cpp
+++ b/src/api/c/plot3.cpp
@@ -61,9 +61,9 @@ forge::Chart* setup_plot3(const forge::Window* const window, const af_array P,
     // Get the chart for the current grid position (if any)
     forge::Chart* chart = NULL;
     if (props->col>-1 && props->row>-1)
-        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
     else
-        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+        chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
 
     forge::Plot* plot3 = fgMngr.getPlot(chart, P_dims.elements()/3, getGLType<T>(), ptype, mtype);
 

From 23e3d90832c800105adab837bf52fefe3d5fe786 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 11:51:29 -0400
Subject: [PATCH 0866/2677] Add vector field API to ArrayFire graphics

* Add vector field
* Improvements to CUDA and OpenCL interop
* Use cudaGraphicsResource_t instead of cudGraphicsResource*
---
 include/af/graphics.h                 | 145 +++++++++++-
 src/api/c/graphics_common.cpp         |  20 ++
 src/api/c/graphics_common.hpp         |  29 ++-
 src/api/c/vector_field.cpp            | 318 ++++++++++++++++++++++++++
 src/api/cpp/graphics.cpp              |  27 +++
 src/api/unified/graphics.cpp          |  28 +++
 src/backend/cpu/vector_field.cpp      |  60 +++++
 src/backend/cpu/vector_field.hpp      |  23 ++
 src/backend/cuda/interopManager.cpp   | 121 +++++++---
 src/backend/cuda/interopManager.hpp   |  16 +-
 src/backend/cuda/vector_field.cpp     |  88 +++++++
 src/backend/cuda/vector_field.hpp     |  23 ++
 src/backend/opencl/interopManager.cpp |  83 +++++--
 src/backend/opencl/interopManager.hpp |  14 +-
 src/backend/opencl/vector_field.cpp   |  94 ++++++++
 src/backend/opencl/vector_field.hpp   |  24 ++
 16 files changed, 1036 insertions(+), 77 deletions(-)
 create mode 100644 src/api/c/vector_field.cpp
 create mode 100644 src/backend/cpu/vector_field.cpp
 create mode 100644 src/backend/cpu/vector_field.hpp
 create mode 100644 src/backend/cuda/vector_field.cpp
 create mode 100644 src/backend/cuda/vector_field.hpp
 create mode 100644 src/backend/opencl/vector_field.cpp
 create mode 100644 src/backend/opencl/vector_field.hpp

diff --git a/include/af/graphics.h b/include/af/graphics.h
index b69a83854a..4d0c03a3c7 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -260,6 +260,66 @@ class AFAPI Window {
         void surface(const array& xVals, const array& yVals, const array& S, const char* const title = NULL);
 #endif
 
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 2D or 3D vector field plot to the window
+
+           \param[in] points is an \ref array with the points
+           \param[in] directions is an \ref array with the directions at the points
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note \p points and \p directions should have the same size and must
+           be 2D.
+           The number of rows (dim 0) determines are number of points and the
+           number columns determines the type of plot. If the number of columns
+           are 2, then the plot is 2D and if there are 3 columns, then the plot
+           is 3D.
+
+           \ingroup gfx_func_draw
+         */
+        void vectorField(const array& points, const array& directions, const char* const title = NULL);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 3D vector field plot to the window
+
+           \param[in] xPoints is an \ref array with the x-coordinate points
+           \param[in] yPoints is an \ref array with the y-coordinate points
+           \param[in] zPoints is an \ref array with the z-coordinate points
+           \param[in] xDirs is an \ref array with the x-coordinate directions at the points
+           \param[in] yDirs is an \ref array with the y-coordinate directions at the points
+           \param[in] zDirs is an \ref array with the z-coordinate directions at the points
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note All the array inputs must be vectors and must have the size sizes.
+
+           \ingroup gfx_func_draw
+         */
+        void vectorField(const array& xPoints, const array& yPoints, const array& zPoints,
+                         const array& xDirs  , const array& yDirs  , const array& zDirs  ,
+                         const char* const title = NULL);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 2D vector field plot to the window
+
+           \param[in] xPoints is an \ref array with the x-coordinate points
+           \param[in] yPoints is an \ref array with the y-coordinate points
+           \param[in] xDirs is an \ref array with the x-coordinate directions at the points
+           \param[in] yDirs is an \ref array with the y-coordinate directions at the points
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note All the array inputs must be vectors and must have the size sizes.
+
+           \ingroup gfx_func_draw
+         */
+        void vectorField(const array& xPoints, const array& yPoints,
+                         const array& xDirs  , const array& yDirs  ,
+                         const char* const title = NULL);
+#endif
+
         /**
            Setup grid layout for multiview mode in a window
 
@@ -499,7 +559,7 @@ AFAPI af_err af_draw_hist(const af_window wind, const af_array X, const double m
 
 #if AF_API_VERSION >= 32
 /**
-   C Interface wrapper for drawing arrayis as a surface
+   C Interface wrapper for drawing array's as a surface
 
    \param[in]   wind is the window handle
    \param[in]   xVals is an \ref af_array with the x-axis data points
@@ -518,6 +578,89 @@ AFAPI af_err af_draw_hist(const af_window wind, const af_array X, const double m
 AFAPI af_err af_draw_surface(const af_window wind, const af_array xVals, const af_array yVals, const af_array S, const af_cell* const props);
 #endif
 
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing array's as a 2D or 3D vector field
+
+   \param[in]   wind is the window handle
+   \param[in]   points is an \ref af_array with the points
+   \param[in]   directions is an \ref af_array with the directions
+   \param[in]   props is structure \ref af_cell that has the properties that
+                are used for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p points and \p directions should have the same size and must
+   be 2D.
+   The number of rows (dim 0) determines are number of points and the
+   number columns determines the type of plot. If the number of columns
+   are 2, then the plot is 2D and if there are 3 columns, then the plot
+   is 3D.
+
+   \note all the \ref af_array inputs should be vectors and the same size
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_vector_field(const af_window wind,
+                const af_array points, const af_array directions,
+                const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing array's as a 3D vector field
+
+   \param[in]   wind is the window handle
+   \param[in]   xPoints is an \ref af_array with the x-axis points
+   \param[in]   yPoints is an \ref af_array with the y-axis points
+   \param[in]   zPoints is an \ref af_array with the z-axis points
+   \param[in]   xDirs is an \ref af_array with the x-axis directions
+   \param[in]   yDirs is an \ref af_array with the y-axis directions
+   \param[in]   zDirs is an \ref af_array with the z-axis directions
+   \param[in]   props is structure \ref af_cell that has the properties that
+                are used for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note all the \ref af_array inputs should be vectors and the same size
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_vector_field_3d(
+                const af_window wind,
+                const af_array xPoints, const af_array yPoints, const af_array zPoints,
+                const af_array xDirs, const af_array yDirs, const af_array zDirs,
+                const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing array's as a 2D vector field
+
+   \param[in]   wind is the window handle
+   \param[in]   xPoints is an \ref af_array with the x-axis points
+   \param[in]   yPoints is an \ref af_array with the y-axis points
+   \param[in]   xDirs is an \ref af_array with the x-axis directions
+   \param[in]   yDirs is an \ref af_array with the y-axis directions
+   \param[in]   props is structure \ref af_cell that has the properties that
+                are used for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note all the \ref af_array inputs should be vectors and the same size
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_vector_field_2d(
+                const af_window wind,
+                const af_array xPoints, const af_array yPoints,
+                const af_array xDirs, const af_array yDirs,
+                const af_cell* const props);
+#endif
+
 /**
    C Interface wrapper for grid setup in a window
 
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index e493e1ddc4..106e8cbea8 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -340,6 +340,26 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
     return mSfcMap[key];
 }
 
+forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoints, forge::dtype type)
+{
+    /* nPoints needs to fall in the range of [0, 2^48]
+     * for the ForgeManager to correctly retrieve
+     * the necessary Forge Vector Field object. So, this implementation
+     * is a limitation on how big of an plot graph can be rendered
+     * using arrayfire graphics funtionality */
+    assert(nPoints <= 2ll<<48);
+    long long key = (((nPoints) & _48BIT) << 48) | (type & _16BIT);
+
+    VcfMapIter iter = mVcfMap.find(key);
+    if (iter==mVcfMap.end()) {
+        forge::VectorField* temp = new forge::VectorField(nPoints, type, chart->getChartType());
+        mVcfMap[key] = temp;
+        chart->add(*mVcfMap[key]);
+    }
+
+    return mVcfMap[key];
+}
+
 void ForgeManager::destroyResources()
 {
     /* clear all OpenGL resource objects (images, plots, histograms etc) first
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index b19834b634..361e3a4874 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -52,11 +52,13 @@ typedef std::map<long long, forge::Image*> ImageMap_t;
 typedef std::map<long long, forge::Plot*> PlotMap_t;
 typedef std::map<long long, forge::Histogram*> HistogramMap_t;
 typedef std::map<long long, forge::Surface*> SurfaceMap_t;
+typedef std::map<long long, forge::VectorField*> VectorFieldMap_t;
 
 typedef ImageMap_t::iterator ImgMapIter;
 typedef PlotMap_t::iterator PltMapIter;
 typedef HistogramMap_t::iterator HstMapIter;
 typedef SurfaceMap_t::iterator SfcMapIter;
+typedef VectorFieldMap_t::iterator VcfMapIter;
 
 typedef std::vector<forge::Chart*> ChartVec_t;
 typedef std::map<const forge::Window*, ChartVec_t> ChartMap_t;
@@ -74,14 +76,16 @@ typedef ChartMap_t::iterator ChartMapIter;
  *             forge::Plot
  *             forge::Histogram
  *             forge::Surface
+ *             forge::VectorField
  * */
 class ForgeManager
 {
     private:
-        ImageMap_t      mImgMap;
-        PlotMap_t       mPltMap;
-        HistogramMap_t  mHstMap;
-        SurfaceMap_t    mSfcMap;
+        ImageMap_t          mImgMap;
+        PlotMap_t           mPltMap;
+        HistogramMap_t      mHstMap;
+        SurfaceMap_t        mSfcMap;
+        VectorFieldMap_t    mVcfMap;
 
         ChartMap_t      mChartMap;
 
@@ -98,14 +102,15 @@ class ForgeManager
         forge::Chart*   getChart(const forge::Window* window, const int r, const int c,
                                  const forge::ChartType ctype);
 
-        forge::Image* getImage          (int w, int h, forge::ChannelFormat mode,
-                                         forge::dtype type);
-        forge::Image* getImage          (forge::Chart* chart, int w, int h,
-                                         forge::ChannelFormat mode, forge::dtype type);
-        forge::Plot * getPlot           (forge::Chart* chart, int nPoints, forge::dtype dtype,
-                                         forge::PlotType ptype, forge::MarkerType mtype);
-        forge::Histogram* getHistogram  (forge::Chart* chart, int nBins, forge::dtype type);
-        forge::Surface* getSurface      (forge::Chart* chart, int nX, int nY, forge::dtype type);
+        forge::Image*       getImage        (int w, int h, forge::ChannelFormat mode,
+                                             forge::dtype type);
+        forge::Image*       getImage        (forge::Chart* chart, int w, int h,
+                                             forge::ChannelFormat mode, forge::dtype type);
+        forge::Plot *       getPlot         (forge::Chart* chart, int nPoints, forge::dtype dtype,
+                                             forge::PlotType ptype, forge::MarkerType mtype);
+        forge::Histogram*   getHistogram    (forge::Chart* chart, int nBins, forge::dtype type);
+        forge::Surface*     getSurface      (forge::Chart* chart, int nX, int nY, forge::dtype type);
+        forge::VectorField* getVectorField  (forge::Chart* chart, int nPoints, forge::dtype type);
 
     protected:
         ForgeManager() {}
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
new file mode 100644
index 0000000000..32eac83933
--- /dev/null
+++ b/src/api/c/vector_field.cpp
@@ -0,0 +1,318 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/graphics.h>
+#include <af/data.h>
+
+#include <ArrayInfo.hpp>
+#include <graphics_common.hpp>
+#include <err_common.hpp>
+#include <backend.hpp>
+#include <vector_field.hpp>
+#include <transpose.hpp>
+#include <handle.hpp>
+
+using af::dim4;
+using namespace detail;
+
+#if defined(WITH_GRAPHICS)
+using namespace graphics;
+
+template<typename T>
+forge::Chart* setup_vector_field(const forge::Window* const window,
+                                 const af_array points, const af_array directions,
+                                 const af_cell* const props, const bool transpose_ = false)
+{
+    Array<T> pIn = getArray<T>(points);
+    Array<T> dIn = getArray<T>(directions);
+
+    // do transpose if required
+    if(transpose_) {
+        pIn = transpose<T>(pIn, false);
+        dIn = transpose<T>(dIn, false);
+    }
+
+    ForgeManager& fgMngr = ForgeManager::getInstance();
+
+    // Get the chart for the current grid position (if any)
+    forge::Chart* chart = NULL;
+
+    if(pIn.dims()[0] == 2) {
+        if (props->col>-1 && props->row>-1)
+            chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+        else
+            chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+    } else {
+        if (props->col>-1 && props->row>-1)
+            chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
+        else
+            chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
+    }
+
+    forge::VectorField* vectorfield = fgMngr.getVectorField(chart, pIn.elements(), getGLType<T>());
+
+    vectorfield->setColor(1.0, 0.0, 0.0, 1.0);
+
+    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
+
+    copy_vector_field<T>(pIn, dIn, vectorfield);
+
+    return chart;
+}
+
+af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_array directions,
+                          const af_cell* const props)
+{
+    if(wind==0) {
+        AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
+    }
+
+    try {
+        ArrayInfo pInfo = getInfo(points);
+        af::dim4 pDims  = pInfo.dims();
+        af_dtype pType  = pInfo.getType();
+
+        ArrayInfo dInfo = getInfo(directions);
+        af::dim4 dDims  = dInfo.dims();
+        af_dtype dType  = dInfo.getType();
+
+        DIM_ASSERT(0, pDims == dDims);
+        DIM_ASSERT(0, pDims.ndims() == 2);
+        DIM_ASSERT(0, pDims[1] == 2 || pDims[2] == 3); // Columns:P 2 means 2D and 3 means 3D
+
+        TYPE_ASSERT(pType == dType);
+
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        makeContextCurrent(window);
+
+        forge::Chart* chart = NULL;
+
+        switch(pType) {
+            case f32: chart = setup_vector_field<float  >(window, points, directions, props, true); break;
+            case s32: chart = setup_vector_field<int    >(window, points, directions, props, true); break;
+            case u32: chart = setup_vector_field<uint   >(window, points, directions, props, true); break;
+            case s16: chart = setup_vector_field<short  >(window, points, directions, props, true); break;
+            case u16: chart = setup_vector_field<ushort >(window, points, directions, props, true); break;
+            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props, true); break;
+            default:  TYPE_ERROR(1, pType);
+        }
+
+        // Window's draw function requires either image or chart
+        if (props->col > -1 && props->row > -1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err vectorFieldWrapper(const af_window wind,
+                          const af_array xPoints, const af_array yPoints, const af_array zPoints,
+                          const af_array xDirs, const af_array yDirs, const af_array zDirs,
+                          const af_cell* const props)
+{
+    if(wind==0) {
+        AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
+    }
+
+    try {
+        ArrayInfo xpInfo = getInfo(xPoints);
+        ArrayInfo ypInfo = getInfo(yPoints);
+        ArrayInfo zpInfo = getInfo(zPoints);
+
+        af::dim4 xpDims  = xpInfo.dims();
+        af::dim4 ypDims  = ypInfo.dims();
+        af::dim4 zpDims  = zpInfo.dims();
+
+        af_dtype xpType  = xpInfo.getType();
+        af_dtype ypType  = ypInfo.getType();
+        af_dtype zpType  = zpInfo.getType();
+
+        ArrayInfo xdInfo = getInfo(xDirs);
+        ArrayInfo ydInfo = getInfo(yDirs);
+        ArrayInfo zdInfo = getInfo(zDirs);
+
+        af::dim4 xdDims  = xdInfo.dims();
+        af::dim4 ydDims  = ydInfo.dims();
+        af::dim4 zdDims  = zdInfo.dims();
+
+        af_dtype xdType  = xdInfo.getType();
+        af_dtype ydType  = ydInfo.getType();
+        af_dtype zdType  = zdInfo.getType();
+
+        // Assert all arrays are equal dimensions
+        DIM_ASSERT(1, xpDims == xdDims);
+        DIM_ASSERT(2, ypDims == ydDims);
+        DIM_ASSERT(3, zpDims == zdDims);
+
+        DIM_ASSERT(1, xpDims == ypDims);
+        DIM_ASSERT(1, xpDims == zpDims);
+
+        // Verify vector
+        DIM_ASSERT(1, xpDims.ndims() == 1);
+
+        // Assert all arrays are equal types
+        DIM_ASSERT(1, xpType == xdType);
+        DIM_ASSERT(2, ypType == ydType);
+        DIM_ASSERT(3, zpType == zdType);
+
+        DIM_ASSERT(1, xpType == ypType);
+        DIM_ASSERT(1, xpType == zpType);
+
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        makeContextCurrent(window);
+
+        forge::Chart* chart = NULL;
+
+        // Join for set up vector
+        af_array points = 0, directions = 0;
+        af_array pIn[] = {xPoints, yPoints, zPoints};
+        af_array dIn[] = {xDirs, yDirs, zDirs};
+        AF_CHECK(af_join_many(&points, 0, 3, pIn));
+        AF_CHECK(af_join_many(&directions, 0, 3, dIn));
+
+        switch(xpType) {
+            case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
+            case s32: chart = setup_vector_field<int    >(window, points, directions, props); break;
+            case u32: chart = setup_vector_field<uint   >(window, points, directions, props); break;
+            case s16: chart = setup_vector_field<short  >(window, points, directions, props); break;
+            case u16: chart = setup_vector_field<ushort >(window, points, directions, props); break;
+            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
+            default:  TYPE_ERROR(1, xpType);
+        }
+
+        // Window's draw function requires either image or chart
+        if (props->col > -1 && props->row > -1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
+
+        AF_CHECK(af_release_array(points));
+        AF_CHECK(af_release_array(directions));
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err vectorFieldWrapper(const af_window wind,
+                          const af_array xPoints, const af_array yPoints,
+                          const af_array xDirs, const af_array yDirs,
+                          const af_cell* const props)
+{
+    if(wind==0) {
+        AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
+    }
+
+    try {
+        ArrayInfo xpInfo = getInfo(xPoints);
+        ArrayInfo ypInfo = getInfo(yPoints);
+
+        af::dim4 xpDims  = xpInfo.dims();
+        af::dim4 ypDims  = ypInfo.dims();
+
+        af_dtype xpType  = xpInfo.getType();
+        af_dtype ypType  = ypInfo.getType();
+
+        ArrayInfo xdInfo = getInfo(xDirs);
+        ArrayInfo ydInfo = getInfo(yDirs);
+
+        af::dim4 xdDims  = xdInfo.dims();
+        af::dim4 ydDims  = ydInfo.dims();
+
+        af_dtype xdType  = xdInfo.getType();
+        af_dtype ydType  = ydInfo.getType();
+
+        // Assert all arrays are equal dimensions
+        DIM_ASSERT(1, xpDims == xdDims);
+        DIM_ASSERT(2, ypDims == ydDims);
+
+        DIM_ASSERT(1, xpDims == ypDims);
+
+        // Verify vector
+        DIM_ASSERT(1, xpDims.ndims() == 1);
+
+        // Assert all arrays are equal types
+        DIM_ASSERT(1, xpType == xdType);
+        DIM_ASSERT(2, ypType == ydType);
+
+        DIM_ASSERT(1, xpType == ypType);
+
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        makeContextCurrent(window);
+
+        forge::Chart* chart = NULL;
+
+        // Join for set up vector
+        af_array points = 0, directions = 0;
+        AF_CHECK(af_join(&points, 0, xPoints, yPoints));
+        AF_CHECK(af_join(&directions, 0, xDirs, yDirs));
+
+        switch(xpType) {
+            case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
+            case s32: chart = setup_vector_field<int    >(window, points, directions, props); break;
+            case u32: chart = setup_vector_field<uint   >(window, points, directions, props); break;
+            case s16: chart = setup_vector_field<short  >(window, points, directions, props); break;
+            case u16: chart = setup_vector_field<ushort >(window, points, directions, props); break;
+            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
+            default:  TYPE_ERROR(1, xpType);
+        }
+
+        // Window's draw function requires either image or chart
+        if (props->col > -1 && props->row > -1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
+
+        AF_CHECK(af_release_array(points));
+        AF_CHECK(af_release_array(directions));
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+#endif // WITH_GRAPHICS
+
+// ADD THIS TO UNIFIED
+af_err af_draw_vector_field(const af_window wind,
+                const af_array points, const af_array directions,
+                const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    return vectorFieldWrapper(wind, points, directions, props);
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_draw_vector_field_3d(
+                const af_window wind,
+                const af_array xPoints, const af_array yPoints, const af_array zPoints,
+                const af_array xDirs, const af_array yDirs, const af_array zDirs,
+                const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    return vectorFieldWrapper(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_draw_vector_field_2d(
+                const af_window wind,
+                const af_array xPoints, const af_array yPoints,
+                const af_array xDirs, const af_array yDirs,
+                const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    return vectorFieldWrapper(wind, xPoints, yPoints, xDirs, yDirs, props);
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 01d8c614d0..bc49b0aff3 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -120,6 +120,33 @@ void Window::surface(const array& xVals, const array& yVals, const array& S, con
     AF_THROW(af_draw_surface(get(), xVals.get(), yVals.get(), S.get(), &temp));
 }
 
+void Window::vectorField(const array& points, const array& directions, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_vector_field(get(), points.get(), directions.get(), &temp));
+}
+
+void Window::vectorField(const array& xPoints, const array& yPoints, const array& zPoints,
+                         const array& xDirs  , const array& yDirs  , const array& zDirs  ,
+                         const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_vector_field_3d(get(),
+                xPoints.get(), yPoints.get(), zPoints.get(),
+                xDirs.get()  , yDirs.get()  , zDirs.get()  ,
+                &temp));
+}
+
+void Window::vectorField(const array& xPoints, const array& yPoints,
+                         const array& xDirs  , const array& yDirs  ,
+                         const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_vector_field_2d(get(),
+                xPoints.get(), yPoints.get(), xDirs.get(), yDirs.get(),
+                &temp));
+}
+
 void Window::grid(const int rows, const int cols)
 {
     AF_THROW(af_grid(get(), rows, cols));
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 9e3f1c8b38..ec3d7bd394 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -74,6 +74,34 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
     return CALL(wind, xVals, yVals, S, props);
 }
 
+af_err af_draw_vector_field(const af_window wind,
+                const af_array points, const af_array directions,
+                const af_cell* const props)
+{
+    CHECK_ARRAYS(points, directions);
+    return CALL(wind, points, directions, props);
+}
+
+af_err af_draw_vector_field_3d(
+                const af_window wind,
+                const af_array xPoints, const af_array yPoints, const af_array zPoints,
+                const af_array xDirs, const af_array yDirs, const af_array zDirs,
+                const af_cell* const props)
+{
+    CHECK_ARRAYS(xPoints, yPoints, zPoints, xDirs, yDirs, zDirs);
+    return CALL(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
+}
+
+af_err af_draw_vector_field_2d(
+                const af_window wind,
+                const af_array xPoints, const af_array yPoints,
+                const af_array xDirs, const af_array yDirs,
+                const af_cell* const props)
+{
+    CHECK_ARRAYS(xPoints, yPoints, xDirs, yDirs);
+    return CALL(wind, xPoints, yPoints, xDirs, yDirs, props);
+}
+
 af_err af_grid(const af_window wind, const int rows, const int cols)
 {
     return CALL(wind, rows, cols);
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
new file mode 100644
index 0000000000..425056758a
--- /dev/null
+++ b/src/backend/cpu/vector_field.cpp
@@ -0,0 +1,60 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(WITH_GRAPHICS)
+
+#include <Array.hpp>
+#include <vector_field.hpp>
+#include <err_cpu.hpp>
+#include <graphics_common.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+
+using af::dim4;
+
+namespace cpu
+{
+using namespace gl;
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       forge::VectorField* vector_field)
+{
+    points.eval();
+    directions.eval();
+    getQueue().sync();
+
+    CheckGL("Before CopyArrayToVBO");
+
+    glBindBuffer(GL_ARRAY_BUFFER, vector_field->vertices());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, vector_field->verticesSize(), points.get());
+    glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+    glBindBuffer(GL_ARRAY_BUFFER, vector_field->directions());
+    glBufferSubData(GL_ARRAY_BUFFER, 0, vector_field->directionsSize(), directions.get());
+    glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+    CheckGL("In CopyArrayToVBO");
+}
+
+#define INSTANTIATE(T)                                                                      \
+    template void copy_vector_field<T>(const Array<T> &points, const Array<T> &directions,  \
+                                       forge::VectorField* vector_field);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}
+
+#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/vector_field.hpp b/src/backend/cpu/vector_field.hpp
new file mode 100644
index 0000000000..aacb16caa7
--- /dev/null
+++ b/src/backend/cpu/vector_field.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined (WITH_GRAPHICS)
+
+#include <Array.hpp>
+#include <graphics_common.hpp>
+
+namespace cpu
+{
+    template<typename T>
+    void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                           forge::VectorField* vector_field);
+}
+
+#endif
+
diff --git a/src/backend/cuda/interopManager.cpp b/src/backend/cuda/interopManager.cpp
index cdc85cbcc7..645dfd2f73 100644
--- a/src/backend/cuda/interopManager.cpp
+++ b/src/backend/cuda/interopManager.cpp
@@ -22,9 +22,14 @@ namespace cuda
 
 void InteropManager::destroyResources()
 {
+    typedef std::vector<CGR_t>::iterator CGRIter_t;
+
     int n = getActiveDeviceId();
     for(iter_t iter = interop_maps[n].begin(); iter != interop_maps[n].end(); iter++) {
-        CUDA_CHECK(cudaGraphicsUnregisterResource(iter->second));
+        for(CGRIter_t ct = (iter->second).begin(); ct != (iter->second).end(); ct++) {
+            CUDA_CHECK(cudaGraphicsUnregisterResource(*ct));
+        }
+        (iter->second).clear();
     }
 }
 
@@ -51,70 +56,122 @@ InteropManager& InteropManager::getInstance()
     return my_instance;
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const forge::Image* key)
+interop_t& InteropManager::getDeviceMap(int device)
+{
+    return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
+}
+
+CGR_t InteropManager::getBufferResource(const forge::Image* key)
 {
-    int device = getActiveDeviceId();
     void* key_value = (void*)key;
+    interop_t i_map = getDeviceMap();
 
-    if(interop_maps[device].find(key_value) == interop_maps[device].end()) {
-        cudaGraphicsResource *cudaPBOResource;
+    if(i_map.find(key_value) == i_map.end()) {
+        CGR_t pboResource;
         // Register PBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaPBOResource, key->pbo(), cudaGraphicsMapFlagsWriteDiscard));
-        interop_maps[device][key_value] = cudaPBOResource;
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&pboResource, key->pbo(), cudaGraphicsMapFlagsWriteDiscard));
+        // TODO:
+        // A way to store multiple buffers and take PBO/CBO etc as
+        // argument and return the appropriate buffer
+        std::vector<CGR_t> vec(1);
+        vec[0] = pboResource;
+        i_map[key_value] = vec;
+    }
+
+    return i_map[key_value][0];
+}
+
+CGR_t InteropManager::getBufferResource(const forge::Plot* key)
+{
+    void* key_value = (void*)key;
+    interop_t i_map = getDeviceMap();
+
+    iter_t iter = i_map.find(key_value);
+
+    if(iter == i_map.end()) {
+        CGR_t vboResource;
+        // Register VBO with CUDA
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&vboResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
+        // TODO:
+        // A way to store multiple buffers and take PBO/CBO etc as
+        // argument and return the appropriate buffer
+        std::vector<CGR_t> vec(1);
+        vec[0] = vboResource;
+        i_map[key_value] = vec;
     }
 
-    return interop_maps[device][key_value];
+    return i_map[key_value][0];
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const forge::Plot* key)
+CGR_t InteropManager::getBufferResource(const forge::Histogram* key)
 {
-    int device = getActiveDeviceId();
     void* key_value = (void*)key;
+    interop_t i_map = getDeviceMap();
 
-    iter_t iter = interop_maps[device].find(key_value);
+    iter_t iter = i_map.find(key_value);
 
-    if(iter == interop_maps[device].end()) {
-        cudaGraphicsResource *cudaVBOResource;
+    if(iter == i_map.end()) {
+        CGR_t vboResource;
         // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        interop_maps[device][key_value] = cudaVBOResource;
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&vboResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
+        // TODO:
+        // A way to store multiple buffers and take PBO/CBO etc as
+        // argument and return the appropriate buffer
+        std::vector<CGR_t> vec(1);
+        vec[0] = vboResource;
+        i_map[key_value] = vec;
     }
 
-    return interop_maps[device][key_value];
+    return i_map[key_value][0];
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const forge::Histogram* key)
+CGR_t InteropManager::getBufferResource(const forge::Surface* key)
 {
-    int device = getActiveDeviceId();
     void* key_value = (void*)key;
+    interop_t i_map = getDeviceMap();
 
-    iter_t iter = interop_maps[device].find(key_value);
+    iter_t iter = i_map.find(key_value);
 
-    if(iter == interop_maps[device].end()) {
-        cudaGraphicsResource *cudaVBOResource;
+    if(iter == i_map.end()) {
+        CGR_t  vboResource;
         // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        interop_maps[device][key_value] = cudaVBOResource;
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&vboResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
+        // TODO:
+        // A way to store multiple buffers and take PBO/CBO etc as
+        // argument and return the appropriate buffer
+        std::vector<CGR_t> vec(1);
+        vec[0] = vboResource;
+        i_map[key_value] = vec;
     }
 
-    return interop_maps[device][key_value];
+    return i_map[key_value][0];
 }
 
-cudaGraphicsResource* InteropManager::getBufferResource(const forge::Surface* key)
+void InteropManager::getBufferResource(CGR_t points, CGR_t directions,
+                                       const forge::VectorField* key)
 {
-    int device = getActiveDeviceId();
     void* key_value = (void*)key;
+    interop_t i_map = getDeviceMap();
 
-    iter_t iter = interop_maps[device].find(key_value);
+    iter_t iter = i_map.find(key_value);
 
-    if(iter == interop_maps[device].end()) {
-        cudaGraphicsResource *cudaVBOResource;
+    if(iter == i_map.end()) {
+        CGR_t   pResource;
+        CGR_t   dResource;
         // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&cudaVBOResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        interop_maps[device][key_value] = cudaVBOResource;
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&pResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&dResource, key->directions(), cudaGraphicsMapFlagsWriteDiscard));
+        // TODO:
+        // A way to store multiple buffers and take PBO/CBO etc as
+        // argument and return the appropriate buffer
+        std::vector<CGR_t> vec(2);
+        vec[0] = pResource;
+        vec[1] = dResource;
+        i_map[key_value] = vec;
     }
 
-    return interop_maps[device][key_value];
+    points     = i_map[key_value][0];
+    directions = i_map[key_value][0];
 }
 
 bool InteropManager::checkGraphicsInteropCapability()
diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index c98bd41bef..f9c03e7259 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -33,14 +33,17 @@ using namespace gl;
 #include <cuda_gl_interop.h>
 
 #include <map>
+#include <vector>
 
 using af::dim4;
 
 namespace cuda
 {
 
-typedef std::map<void *, cudaGraphicsResource *> interop_t;
+typedef std::map<void *, std::vector<cudaGraphicsResource_t> > interop_t;
 typedef interop_t::iterator iter_t;
+typedef cudaGraphicsResource_t CGR_t;
+
 
 // Manager Class for cudaPBOResource: calls garbage collection at the end of the program
 class InteropManager
@@ -53,15 +56,18 @@ class InteropManager
         static bool checkGraphicsInteropCapability();
 
         ~InteropManager();
-        cudaGraphicsResource* getBufferResource(const forge::Image* handle);
-        cudaGraphicsResource* getBufferResource(const forge::Plot* handle);
-        cudaGraphicsResource* getBufferResource(const forge::Histogram* handle);
-        cudaGraphicsResource* getBufferResource(const forge::Surface* handle);
+        CGR_t getBufferResource(const forge::Image      *handle);
+        CGR_t getBufferResource(const forge::Plot       *handle);
+        CGR_t getBufferResource(const forge::Histogram  *handle);
+        CGR_t getBufferResource(const forge::Surface    *handle);
+        void  getBufferResource(CGR_t points, CGR_t directions,
+                                const forge::VectorField *handle);
 
     protected:
         InteropManager() {}
         InteropManager(InteropManager const&);
         void operator=(InteropManager const&);
+        interop_t& getDeviceMap(int device = -1); // default will return current device
         void destroyResources();
 };
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
new file mode 100644
index 0000000000..83efad7170
--- /dev/null
+++ b/src/backend/cuda/vector_field.cpp
@@ -0,0 +1,88 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined (WITH_GRAPHICS)
+
+#include <interopManager.hpp>
+#include <Array.hpp>
+#include <vector_field.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+
+using af::dim4;
+
+namespace cuda
+{
+using namespace gl;
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       forge::VectorField* vector_field)
+{
+    if(InteropManager::checkGraphicsInteropCapability()) {
+        InteropManager& intrpMngr = InteropManager::getInstance();
+
+        cudaGraphicsResource *resources[2];
+        intrpMngr.getBufferResource(resources[0], resources[1], vector_field);
+
+        // Map resource. Copy data to VBO. Unmap resource.
+        // Map all resources at once.
+        cudaGraphicsMapResources(2, resources, 0);
+
+        // Points
+        {
+            const T *ptr     = points.get();
+            size_t num_bytes = vector_field->verticesSize();
+            T* d_vbo = NULL;
+            cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
+            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
+                            cuda::getStream(cuda::getActiveDeviceId()));
+        }
+        // Directions
+        {
+            const T *ptr = directions.get();
+            size_t num_bytes = vector_field->directionsSize();
+            T* d_vbo = NULL;
+            cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[1]);
+            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
+                            cuda::getStream(cuda::getActiveDeviceId()));
+        }
+        cudaGraphicsUnmapResources(2, resources, 0);
+
+        CheckGL("After cuda resource copy");
+
+        POST_LAUNCH_CHECK();
+    } else {
+        CheckGL("Begin CUDA fallback-resource copy");
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, vector_field->vertices());
+        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpy(ptr, points.get(), vector_field->verticesSize(), cudaMemcpyDeviceToHost));
+            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
+        }
+        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
+        CheckGL("End CUDA fallback-resource copy");
+    }
+}
+
+#define INSTANTIATE(T)                                                                      \
+    template void copy_vector_field<T>(const Array<T> &points, const Array<T> &directions,  \
+                                       forge::VectorField* vector_field);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}
+
+#endif  // WITH_GRAPHICS
diff --git a/src/backend/cuda/vector_field.hpp b/src/backend/cuda/vector_field.hpp
new file mode 100644
index 0000000000..288f8a15b3
--- /dev/null
+++ b/src/backend/cuda/vector_field.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined (WITH_GRAPHICS)
+
+#include <Array.hpp>
+#include <graphics_common.hpp>
+
+namespace cuda
+{
+    template<typename T>
+    void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                           forge::VectorField* vector_field);
+}
+
+#endif
+
diff --git a/src/backend/opencl/interopManager.cpp b/src/backend/opencl/interopManager.cpp
index e390020244..2617167f0e 100644
--- a/src/backend/opencl/interopManager.cpp
+++ b/src/backend/opencl/interopManager.cpp
@@ -16,9 +16,14 @@ namespace opencl
 
 void InteropManager::destroyResources()
 {
+    typedef std::vector<cl::Buffer *>::iterator buffer_t;
     int n = getActiveDeviceId();
-    for(iter_t iter = interop_maps[n].begin(); iter != interop_maps[n].end(); iter++)
-        delete iter->second;
+    for(iter_t iter = interop_maps[n].begin(); iter != interop_maps[n].end(); iter++) {
+        for(buffer_t bt = (iter->second).begin(); bt != (iter->second).end(); bt++) {
+            delete *bt;
+        }
+        (iter->second).clear();
+    }
 }
 
 InteropManager::~InteropManager()
@@ -35,52 +40,86 @@ InteropManager& InteropManager::getInstance()
     return my_instance;
 }
 
+interop_t& InteropManager::getDeviceMap(int device)
+{
+    return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
+}
+
 cl::Buffer* InteropManager::getBufferResource(const forge::Image* image)
 {
     void * key = (void*)image;
-    int device = getActiveDeviceId();
-    iter_t iter = interop_maps[device].find(key);
+    interop_t i_map = getDeviceMap();
+    iter_t iter = i_map.find(key);
 
-    if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, image->pbo(), NULL);
+    if (iter == i_map.end()) {
+        std::vector<cl::Buffer *> vec(1);
+        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, image->pbo(), NULL);
+        i_map[key] = vec;
+    }
 
-    return interop_maps[device][key];
+    return i_map[key][0];
 }
 
 cl::Buffer* InteropManager::getBufferResource(const forge::Plot* plot)
 {
     void * key = (void*)plot;
-    int device = getActiveDeviceId();
-    iter_t iter = interop_maps[device].find(key);
+    interop_t i_map = getDeviceMap();
+    iter_t iter = i_map.find(key);
 
-    if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, plot->vertices(), NULL);
+    if (iter == i_map.end()) {
+        std::vector<cl::Buffer *> vec(1);
+        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, plot->vertices(), NULL);
+        i_map[key] = vec;
+    }
 
-    return interop_maps[device][key];
+    return i_map[key][0];
 }
 
 cl::Buffer* InteropManager::getBufferResource(const forge::Histogram* hist)
 {
     void * key = (void*)hist;
-    int device = getActiveDeviceId();
-    iter_t iter = interop_maps[device].find(key);
+    interop_t i_map = getDeviceMap();
+    iter_t iter = i_map.find(key);
 
-    if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, hist->vertices(), NULL);
+    if (iter == i_map.end()) {
+        std::vector<cl::Buffer *> vec(1);
+        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, hist->vertices(), NULL);
+        i_map[key] = vec;
+    }
 
-    return interop_maps[device][key];
+    return i_map[key][0];
 }
 
 cl::Buffer* InteropManager::getBufferResource(const forge::Surface* surface)
 {
     void * key = (void*)surface;
-    int device = getActiveDeviceId();
-    iter_t iter = interop_maps[device].find(key);
+    interop_t i_map = getDeviceMap();
+    iter_t iter = i_map.find(key);
+
+    if (iter == i_map.end()) {
+        std::vector<cl::Buffer *> vec(1);
+        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, surface->vertices(), NULL);
+        i_map[key] = vec;
+    }
 
-    if (iter == interop_maps[device].end())
-        interop_maps[device][key] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, surface->vertices(), NULL);
+    return i_map[key][0];
+}
 
-    return interop_maps[device][key];
+void InteropManager::getBufferResource(cl::Buffer *points, cl::Buffer *directions,
+                                       const forge::VectorField* vector_field)
+{
+    void * key = (void*)vector_field;
+    interop_t i_map = getDeviceMap();
+    iter_t iter = i_map.find(key);
+
+    if (iter == i_map.end()) {
+        std::vector<cl::Buffer *> vec(2);
+        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, vector_field->vertices(), NULL);
+        vec[1] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, vector_field->directions(), NULL);
+        i_map[key] = vec;
+    }
+    points     = i_map[key][0];
+    directions = i_map[key][1];
 }
 
 }
diff --git a/src/backend/opencl/interopManager.hpp b/src/backend/opencl/interopManager.hpp
index d5128acab6..52a121e23c 100644
--- a/src/backend/opencl/interopManager.hpp
+++ b/src/backend/opencl/interopManager.hpp
@@ -15,11 +15,12 @@
 #include <graphics_common.hpp>
 #include <platform.hpp>
 #include <map>
+#include <vector>
 
 namespace opencl
 {
 
-typedef std::map<void *, cl::Buffer*> interop_t;
+typedef std::map<void *, std::vector<cl::Buffer*> > interop_t;
 typedef interop_t::iterator iter_t;
 
 class InteropManager
@@ -30,15 +31,18 @@ class InteropManager
     public:
         static InteropManager& getInstance();
         ~InteropManager();
-        cl::Buffer* getBufferResource(const forge::Image* image);
-        cl::Buffer* getBufferResource(const forge::Plot* plot);
-        cl::Buffer* getBufferResource(const forge::Histogram* hist);
-        cl::Buffer* getBufferResource(const forge::Surface* surface);
+        cl::Buffer* getBufferResource(const forge::Image        *handle);
+        cl::Buffer* getBufferResource(const forge::Plot         *handle);
+        cl::Buffer* getBufferResource(const forge::Histogram    *handle);
+        cl::Buffer* getBufferResource(const forge::Surface      *handle);
+        void        getBufferResource(cl::Buffer *points, cl::Buffer *directions,
+                                      const forge::VectorField  *handle);
 
     protected:
         InteropManager() {}
         InteropManager(InteropManager const&);
         void operator=(InteropManager const&);
+        interop_t& getDeviceMap(int device = -1); // default will return current device
         void destroyResources();
 };
 
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
new file mode 100644
index 0000000000..00ee604e92
--- /dev/null
+++ b/src/backend/opencl/vector_field.cpp
@@ -0,0 +1,94 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined (WITH_GRAPHICS)
+
+#include <interopManager.hpp>
+#include <Array.hpp>
+#include <vector_field.hpp>
+#include <err_opencl.hpp>
+#include <debug_opencl.hpp>
+
+using af::dim4;
+
+namespace opencl
+{
+using namespace gl;
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       forge::VectorField* vector_field)
+{
+    if (isGLSharingSupported()) {
+        CheckGL("Begin OpenCL resource copy");
+        const cl::Buffer *d_points      = points.get();
+        const cl::Buffer *d_directions  = directions.get();
+        size_t pBytes = vector_field->verticesSize();
+        size_t dBytes = vector_field->directionsSize();
+
+        InteropManager& intrpMngr = InteropManager::getInstance();
+
+        cl::Buffer *resources[2] = { NULL };
+        intrpMngr.getBufferResource(resources[0], resources[1], vector_field);
+
+        std::vector<cl::Memory> shared_objects;
+        shared_objects.push_back(*resources[0]);
+        shared_objects.push_back(*resources[1]);
+
+        glFinish();
+
+        // Use of events:
+        // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+        cl::Event event;
+
+        getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+        getQueue().enqueueCopyBuffer(*d_points    , *resources[0], 0, 0, pBytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_directions, *resources[1], 0, 0, dBytes, NULL, &event);
+        getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+        event.wait();
+
+        CL_DEBUG_FINISH(getQueue());
+        CheckGL("End OpenCL resource copy");
+    } else {
+        CheckGL("Begin OpenCL fallback-resource copy");
+        glBindBuffer(GL_ARRAY_BUFFER, vector_field->vertices());
+        GLubyte* pPtr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (pPtr) {
+            getQueue().enqueueReadBuffer(*points.get(), CL_TRUE, 0, vector_field->verticesSize(), pPtr);
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+        glBindBuffer(GL_ARRAY_BUFFER, vector_field->directions());
+        GLubyte* dPtr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (dPtr) {
+            getQueue().enqueueReadBuffer(*directions.get(), CL_TRUE, 0, vector_field->directionsSize(), dPtr);
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+        CheckGL("End OpenCL fallback-resource copy");
+    }
+}
+
+#define INSTANTIATE(T)                                                                      \
+    template void copy_vector_field<T>(const Array<T> &points, const Array<T> &directions,  \
+                                       forge::VectorField* vector_field);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}
+
+#endif  // WITH_GRAPHICS
diff --git a/src/backend/opencl/vector_field.hpp b/src/backend/opencl/vector_field.hpp
new file mode 100644
index 0000000000..b75fd59b05
--- /dev/null
+++ b/src/backend/opencl/vector_field.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined (WITH_GRAPHICS)
+
+#include <Array.hpp>
+#include <graphics_common.hpp>
+
+namespace opencl
+{
+    template<typename T>
+    void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                           forge::VectorField* vector_field);
+}
+
+#endif
+
+

From 208895dfab81c15c6ba92c44b5fe9f835eb36357 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 11:55:12 -0400
Subject: [PATCH 0867/2677] Add stream param to cudaGraphicsMapResources/Unmap

---
 src/backend/cuda/hist_graphics.cpp | 4 ++--
 src/backend/cuda/image.cpp         | 4 ++--
 src/backend/cuda/plot.cpp          | 4 ++--
 src/backend/cuda/surface.cpp       | 4 ++--
 src/backend/cuda/vector_field.cpp  | 4 ++--
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 48e7f214e8..dd794ed6f3 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -31,11 +31,11 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = hist->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsMapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
                         cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 5abab92a05..46e7391502 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -36,11 +36,11 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         // Map resource. Copy data to PBO. Unmap resource.
         size_t num_bytes;
         T* d_pbo = NULL;
-        cudaGraphicsMapResources(1, &cudaPBOResource, 0);
+        cudaGraphicsMapResources(1, &cudaPBOResource, cuda::getStream(cuda::getActiveDeviceId()));
         cudaGraphicsResourceGetMappedPointer((void **)&d_pbo, &num_bytes, cudaPBOResource);
         cudaMemcpyAsync(d_pbo, d_X, num_bytes, cudaMemcpyDeviceToDevice,
                         cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaPBOResource, 0);
+        cudaGraphicsUnmapResources(1, &cudaPBOResource, cuda::getStream(cuda::getActiveDeviceId()));
 
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 7434e09b7b..7799cbed47 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -36,11 +36,11 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = plot->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsMapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
                 cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index f200d537be..b7a23b6d76 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -36,11 +36,11 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = surface->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsMapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
                 cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, 0);
+        cudaGraphicsUnmapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 83efad7170..cf844f2e53 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -33,7 +33,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Map resource. Copy data to VBO. Unmap resource.
         // Map all resources at once.
-        cudaGraphicsMapResources(2, resources, 0);
+        cudaGraphicsMapResources(2, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
         // Points
         {
@@ -53,7 +53,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
             cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
                             cuda::getStream(cuda::getActiveDeviceId()));
         }
-        cudaGraphicsUnmapResources(2, resources, 0);
+        cudaGraphicsUnmapResources(2, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 

From 975a6cef6dc809fdc77f75ce0e4da4839665d238 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 12:59:39 -0400
Subject: [PATCH 0868/2677] CUDA-GL Interop: Return pointers for
 cudaGraphicsResource arrays

---
 src/backend/cuda/hist_graphics.cpp  |  8 ++++----
 src/backend/cuda/image.cpp          |  8 ++++----
 src/backend/cuda/interopManager.cpp | 32 ++++++++++++++---------------
 src/backend/cuda/interopManager.hpp | 11 +++++-----
 src/backend/cuda/plot.cpp           |  8 ++++----
 src/backend/cuda/surface.cpp        |  8 ++++----
 src/backend/cuda/vector_field.cpp   |  3 +--
 7 files changed, 37 insertions(+), 41 deletions(-)

diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index dd794ed6f3..4e9017da88 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -27,15 +27,15 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(hist);
+        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = hist->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
                         cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 46e7391502..8aa247657a 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -30,17 +30,17 @@ void copy_image(const Array<T> &in, const forge::Image* image)
     if(InteropManager::checkGraphicsInteropCapability()) {
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cudaGraphicsResource *cudaPBOResource = intrpMngr.getBufferResource(image);
+        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
 
         const T *d_X = in.get();
         // Map resource. Copy data to PBO. Unmap resource.
         size_t num_bytes;
         T* d_pbo = NULL;
-        cudaGraphicsMapResources(1, &cudaPBOResource, cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsResourceGetMappedPointer((void **)&d_pbo, &num_bytes, cudaPBOResource);
+        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_pbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_pbo, d_X, num_bytes, cudaMemcpyDeviceToDevice,
                         cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaPBOResource, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
diff --git a/src/backend/cuda/interopManager.cpp b/src/backend/cuda/interopManager.cpp
index 645dfd2f73..981e87f785 100644
--- a/src/backend/cuda/interopManager.cpp
+++ b/src/backend/cuda/interopManager.cpp
@@ -61,10 +61,10 @@ interop_t& InteropManager::getDeviceMap(int device)
     return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
 }
 
-CGR_t InteropManager::getBufferResource(const forge::Image* key)
+CGR_t* InteropManager::getBufferResource(const forge::Image* key)
 {
     void* key_value = (void*)key;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
 
     if(i_map.find(key_value) == i_map.end()) {
         CGR_t pboResource;
@@ -78,13 +78,13 @@ CGR_t InteropManager::getBufferResource(const forge::Image* key)
         i_map[key_value] = vec;
     }
 
-    return i_map[key_value][0];
+    return &i_map[key_value].front();
 }
 
-CGR_t InteropManager::getBufferResource(const forge::Plot* key)
+CGR_t* InteropManager::getBufferResource(const forge::Plot* key)
 {
     void* key_value = (void*)key;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
 
     iter_t iter = i_map.find(key_value);
 
@@ -100,13 +100,13 @@ CGR_t InteropManager::getBufferResource(const forge::Plot* key)
         i_map[key_value] = vec;
     }
 
-    return i_map[key_value][0];
+    return &i_map[key_value].front();
 }
 
-CGR_t InteropManager::getBufferResource(const forge::Histogram* key)
+CGR_t* InteropManager::getBufferResource(const forge::Histogram* key)
 {
     void* key_value = (void*)key;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
 
     iter_t iter = i_map.find(key_value);
 
@@ -122,13 +122,13 @@ CGR_t InteropManager::getBufferResource(const forge::Histogram* key)
         i_map[key_value] = vec;
     }
 
-    return i_map[key_value][0];
+    return &i_map[key_value].front();
 }
 
-CGR_t InteropManager::getBufferResource(const forge::Surface* key)
+CGR_t* InteropManager::getBufferResource(const forge::Surface* key)
 {
     void* key_value = (void*)key;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
 
     iter_t iter = i_map.find(key_value);
 
@@ -144,14 +144,13 @@ CGR_t InteropManager::getBufferResource(const forge::Surface* key)
         i_map[key_value] = vec;
     }
 
-    return i_map[key_value][0];
+    return &i_map[key_value].front();
 }
 
-void InteropManager::getBufferResource(CGR_t points, CGR_t directions,
-                                       const forge::VectorField* key)
+CGR_t* InteropManager::getBufferResource(const forge::VectorField* key)
 {
     void* key_value = (void*)key;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
 
     iter_t iter = i_map.find(key_value);
 
@@ -170,8 +169,7 @@ void InteropManager::getBufferResource(CGR_t points, CGR_t directions,
         i_map[key_value] = vec;
     }
 
-    points     = i_map[key_value][0];
-    directions = i_map[key_value][0];
+    return &i_map[key_value].front();
 }
 
 bool InteropManager::checkGraphicsInteropCapability()
diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index f9c03e7259..deb4f126e9 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -56,12 +56,11 @@ class InteropManager
         static bool checkGraphicsInteropCapability();
 
         ~InteropManager();
-        CGR_t getBufferResource(const forge::Image      *handle);
-        CGR_t getBufferResource(const forge::Plot       *handle);
-        CGR_t getBufferResource(const forge::Histogram  *handle);
-        CGR_t getBufferResource(const forge::Surface    *handle);
-        void  getBufferResource(CGR_t points, CGR_t directions,
-                                const forge::VectorField *handle);
+        CGR_t* getBufferResource(const forge::Image       *handle);
+        CGR_t* getBufferResource(const forge::Plot        *handle);
+        CGR_t* getBufferResource(const forge::Histogram   *handle);
+        CGR_t* getBufferResource(const forge::Surface     *handle);
+        CGR_t* getBufferResource(const forge::VectorField *handle);
 
     protected:
         InteropManager() {}
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 7799cbed47..6c1aad70af 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -32,15 +32,15 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(plot);
+        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = plot->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
                 cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index b7a23b6d76..6d84d714b6 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -32,15 +32,15 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cudaGraphicsResource *cudaVBOResource = intrpMngr.getBufferResource(surface);
+        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = surface->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, cudaVBOResource);
+        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
                 cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, &cudaVBOResource, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index cf844f2e53..7ce587f199 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -28,8 +28,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
     if(InteropManager::checkGraphicsInteropCapability()) {
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cudaGraphicsResource *resources[2];
-        intrpMngr.getBufferResource(resources[0], resources[1], vector_field);
+        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(vector_field);
 
         // Map resource. Copy data to VBO. Unmap resource.
         // Map all resources at once.

From cc427f8ccaf46d374e74d3dbc8be0ccb68fef44d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 13:53:55 -0400
Subject: [PATCH 0869/2677] OpenCL-GL Interop: Return pointers for cl::Buffer's
 as arrays

---
 src/backend/opencl/hist_graphics.cpp  |  6 ++---
 src/backend/opencl/image.cpp          |  6 ++---
 src/backend/opencl/interopManager.cpp | 33 +++++++++++++--------------
 src/backend/opencl/interopManager.hpp | 11 ++++-----
 src/backend/opencl/plot.cpp           |  6 ++---
 src/backend/opencl/surface.cpp        |  6 ++---
 src/backend/opencl/vector_field.cpp   |  3 +--
 7 files changed, 34 insertions(+), 37 deletions(-)

diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 9d153d51c5..50a78be927 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -29,10 +29,10 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cl::Buffer *clPBOResource = intrpMngr.getBufferResource(hist);
+        cl::Buffer **resources = intrpMngr.getBufferResource(hist);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*clPBOResource);
+        shared_objects.push_back(*resources[0]);
 
         glFinish();
 
@@ -42,7 +42,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *resources[0], 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index d2f22e37e1..1b0e010b9c 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -28,12 +28,12 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         CheckGL("Begin opencl resource copy");
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cl::Buffer *clPBOResource = intrpMngr.getBufferResource(image);
+        cl::Buffer **resources = intrpMngr.getBufferResource(image);
         const cl::Buffer *d_X = in.get();
         size_t num_bytes = image->size();
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*clPBOResource);
+        shared_objects.push_back(*resources[0]);
 
         glFinish();
 
@@ -43,7 +43,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_X, *clPBOResource, 0, 0, num_bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_X, *resources[0], 0, 0, num_bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/interopManager.cpp b/src/backend/opencl/interopManager.cpp
index 2617167f0e..7e222a9e8a 100644
--- a/src/backend/opencl/interopManager.cpp
+++ b/src/backend/opencl/interopManager.cpp
@@ -45,10 +45,10 @@ interop_t& InteropManager::getDeviceMap(int device)
     return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
 }
 
-cl::Buffer* InteropManager::getBufferResource(const forge::Image* image)
+cl::Buffer** InteropManager::getBufferResource(const forge::Image* image)
 {
     void * key = (void*)image;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
     iter_t iter = i_map.find(key);
 
     if (iter == i_map.end()) {
@@ -57,13 +57,13 @@ cl::Buffer* InteropManager::getBufferResource(const forge::Image* image)
         i_map[key] = vec;
     }
 
-    return i_map[key][0];
+    return &i_map[key].front();
 }
 
-cl::Buffer* InteropManager::getBufferResource(const forge::Plot* plot)
+cl::Buffer** InteropManager::getBufferResource(const forge::Plot* plot)
 {
     void * key = (void*)plot;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
     iter_t iter = i_map.find(key);
 
     if (iter == i_map.end()) {
@@ -72,13 +72,13 @@ cl::Buffer* InteropManager::getBufferResource(const forge::Plot* plot)
         i_map[key] = vec;
     }
 
-    return i_map[key][0];
+    return &i_map[key].front();
 }
 
-cl::Buffer* InteropManager::getBufferResource(const forge::Histogram* hist)
+cl::Buffer** InteropManager::getBufferResource(const forge::Histogram* hist)
 {
     void * key = (void*)hist;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
     iter_t iter = i_map.find(key);
 
     if (iter == i_map.end()) {
@@ -87,13 +87,13 @@ cl::Buffer* InteropManager::getBufferResource(const forge::Histogram* hist)
         i_map[key] = vec;
     }
 
-    return i_map[key][0];
+    return &i_map[key].front();
 }
 
-cl::Buffer* InteropManager::getBufferResource(const forge::Surface* surface)
+cl::Buffer** InteropManager::getBufferResource(const forge::Surface* surface)
 {
     void * key = (void*)surface;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
     iter_t iter = i_map.find(key);
 
     if (iter == i_map.end()) {
@@ -102,14 +102,13 @@ cl::Buffer* InteropManager::getBufferResource(const forge::Surface* surface)
         i_map[key] = vec;
     }
 
-    return i_map[key][0];
+    return &i_map[key].front();
 }
 
-void InteropManager::getBufferResource(cl::Buffer *points, cl::Buffer *directions,
-                                       const forge::VectorField* vector_field)
+cl::Buffer** InteropManager::getBufferResource(const forge::VectorField* vector_field)
 {
     void * key = (void*)vector_field;
-    interop_t i_map = getDeviceMap();
+    interop_t& i_map = getDeviceMap();
     iter_t iter = i_map.find(key);
 
     if (iter == i_map.end()) {
@@ -118,8 +117,8 @@ void InteropManager::getBufferResource(cl::Buffer *points, cl::Buffer *direction
         vec[1] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, vector_field->directions(), NULL);
         i_map[key] = vec;
     }
-    points     = i_map[key][0];
-    directions = i_map[key][1];
+
+    return &i_map[key].front();
 }
 
 }
diff --git a/src/backend/opencl/interopManager.hpp b/src/backend/opencl/interopManager.hpp
index 52a121e23c..6858f51c4b 100644
--- a/src/backend/opencl/interopManager.hpp
+++ b/src/backend/opencl/interopManager.hpp
@@ -31,12 +31,11 @@ class InteropManager
     public:
         static InteropManager& getInstance();
         ~InteropManager();
-        cl::Buffer* getBufferResource(const forge::Image        *handle);
-        cl::Buffer* getBufferResource(const forge::Plot         *handle);
-        cl::Buffer* getBufferResource(const forge::Histogram    *handle);
-        cl::Buffer* getBufferResource(const forge::Surface      *handle);
-        void        getBufferResource(cl::Buffer *points, cl::Buffer *directions,
-                                      const forge::VectorField  *handle);
+        cl::Buffer** getBufferResource(const forge::Image        *handle);
+        cl::Buffer** getBufferResource(const forge::Plot         *handle);
+        cl::Buffer** getBufferResource(const forge::Histogram    *handle);
+        cl::Buffer** getBufferResource(const forge::Surface      *handle);
+        cl::Buffer** getBufferResource(const forge::VectorField  *handle);
 
     protected:
         InteropManager() {}
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index ee35c3d9f1..e597dff270 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -34,10 +34,10 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cl::Buffer *clPBOResource = intrpMngr.getBufferResource(plot);
+        cl::Buffer **resources = intrpMngr.getBufferResource(plot);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*clPBOResource);
+        shared_objects.push_back(*resources[0]);
 
         glFinish();
 
@@ -47,7 +47,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *resources[0], 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index b76badee06..8a54a8e3f6 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -34,10 +34,10 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cl::Buffer *clPBOResource = intrpMngr.getBufferResource(surface);
+        cl::Buffer **resources = intrpMngr.getBufferResource(surface);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*clPBOResource);
+        shared_objects.push_back(*resources[0]);
 
         glFinish();
 
@@ -47,7 +47,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *clPBOResource, 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *resources[0], 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 00ee604e92..5f35437e95 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -34,8 +34,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         InteropManager& intrpMngr = InteropManager::getInstance();
 
-        cl::Buffer *resources[2] = { NULL };
-        intrpMngr.getBufferResource(resources[0], resources[1], vector_field);
+        cl::Buffer **resources = intrpMngr.getBufferResource(vector_field);
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*resources[0]);

From 2e1a63afaa98b76337663037283b0938b68efd66 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 15:13:30 -0400
Subject: [PATCH 0870/2677] API Change af_draw_vector_field ->
 af_draw_vector_field_nd

---
 include/af/graphics.h        | 2 +-
 src/api/c/vector_field.cpp   | 2 +-
 src/api/cpp/graphics.cpp     | 2 +-
 src/api/unified/graphics.cpp | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 4d0c03a3c7..368d3a55af 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -602,7 +602,7 @@ AFAPI af_err af_draw_surface(const af_window wind, const af_array xVals, const a
 
    \ingroup gfx_func_draw
 */
-AFAPI af_err af_draw_vector_field(const af_window wind,
+AFAPI af_err af_draw_vector_field_nd(const af_window wind,
                 const af_array points, const af_array directions,
                 const af_cell* const props);
 #endif
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 32eac83933..b506ded8a1 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -280,7 +280,7 @@ af_err vectorFieldWrapper(const af_window wind,
 #endif // WITH_GRAPHICS
 
 // ADD THIS TO UNIFIED
-af_err af_draw_vector_field(const af_window wind,
+af_err af_draw_vector_field_nd(const af_window wind,
                 const af_array points, const af_array directions,
                 const af_cell* const props)
 {
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index bc49b0aff3..21be44b9d8 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -123,7 +123,7 @@ void Window::surface(const array& xVals, const array& yVals, const array& S, con
 void Window::vectorField(const array& points, const array& directions, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_vector_field(get(), points.get(), directions.get(), &temp));
+    AF_THROW(af_draw_vector_field_nd(get(), points.get(), directions.get(), &temp));
 }
 
 void Window::vectorField(const array& xPoints, const array& yPoints, const array& zPoints,
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index ec3d7bd394..8bdf860543 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -74,7 +74,7 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
     return CALL(wind, xVals, yVals, S, props);
 }
 
-af_err af_draw_vector_field(const af_window wind,
+af_err af_draw_vector_field_nd(const af_window wind,
                 const af_array points, const af_array directions,
                 const af_cell* const props)
 {

From 73506792636b3d5c2b4a6226d81967bc73dd73d2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 16:28:11 -0400
Subject: [PATCH 0871/2677] Fix element count in vector field

---
 src/api/c/vector_field.cpp | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index b506ded8a1..d99d3c2a83 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -27,7 +27,7 @@ using namespace graphics;
 template<typename T>
 forge::Chart* setup_vector_field(const forge::Window* const window,
                                  const af_array points, const af_array directions,
-                                 const af_cell* const props, const bool transpose_ = false)
+                                 const af_cell* const props, const bool transpose_ = true)
 {
     Array<T> pIn = getArray<T>(points);
     Array<T> dIn = getArray<T>(directions);
@@ -55,7 +55,7 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
             chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
     }
 
-    forge::VectorField* vectorfield = fgMngr.getVectorField(chart, pIn.elements(), getGLType<T>());
+    forge::VectorField* vectorfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
 
     vectorfield->setColor(1.0, 0.0, 0.0, 1.0);
 
@@ -84,7 +84,7 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
 
         DIM_ASSERT(0, pDims == dDims);
         DIM_ASSERT(0, pDims.ndims() == 2);
-        DIM_ASSERT(0, pDims[1] == 2 || pDims[2] == 3); // Columns:P 2 means 2D and 3 means 3D
+        DIM_ASSERT(0, pDims[1] == 2 || pDims[1] == 3); // Columns:P 2 means 2D and 3 means 3D
 
         TYPE_ASSERT(pType == dType);
 
@@ -94,12 +94,12 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
         forge::Chart* chart = NULL;
 
         switch(pType) {
-            case f32: chart = setup_vector_field<float  >(window, points, directions, props, true); break;
-            case s32: chart = setup_vector_field<int    >(window, points, directions, props, true); break;
-            case u32: chart = setup_vector_field<uint   >(window, points, directions, props, true); break;
-            case s16: chart = setup_vector_field<short  >(window, points, directions, props, true); break;
-            case u16: chart = setup_vector_field<ushort >(window, points, directions, props, true); break;
-            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props, true); break;
+            case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
+            case s32: chart = setup_vector_field<int    >(window, points, directions, props); break;
+            case u32: chart = setup_vector_field<uint   >(window, points, directions, props); break;
+            case s16: chart = setup_vector_field<short  >(window, points, directions, props); break;
+            case u16: chart = setup_vector_field<ushort >(window, points, directions, props); break;
+            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
             default:  TYPE_ERROR(1, pType);
         }
 
@@ -175,8 +175,8 @@ af_err vectorFieldWrapper(const af_window wind,
         af_array points = 0, directions = 0;
         af_array pIn[] = {xPoints, yPoints, zPoints};
         af_array dIn[] = {xDirs, yDirs, zDirs};
-        AF_CHECK(af_join_many(&points, 0, 3, pIn));
-        AF_CHECK(af_join_many(&directions, 0, 3, dIn));
+        AF_CHECK(af_join_many(&points, 1, 3, pIn));
+        AF_CHECK(af_join_many(&directions, 1, 3, dIn));
 
         switch(xpType) {
             case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
@@ -251,8 +251,8 @@ af_err vectorFieldWrapper(const af_window wind,
 
         // Join for set up vector
         af_array points = 0, directions = 0;
-        AF_CHECK(af_join(&points, 0, xPoints, yPoints));
-        AF_CHECK(af_join(&directions, 0, xDirs, yDirs));
+        AF_CHECK(af_join(&points, 1, xPoints, yPoints));
+        AF_CHECK(af_join(&directions, 1, xDirs, yDirs));
 
         switch(xpType) {
             case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;

From 177659c24fa326abd13ad9e36ca411449c31ee28 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 18:41:47 -0400
Subject: [PATCH 0872/2677] API: Revise plot/scatter API

* Deprecate plot3/scatter3
* Add af_draw_[plot, scatter]_[nd, 2d, 3d]
* Deprecate af_draw_plot3 and af_draw_scatter3
* Clean up wrapper functions for plot
---
 include/af/graphics.h        | 210 +++++++++++++++++-
 src/api/c/plot.cpp           | 412 +++++++++++++++++++++++++++++++----
 src/api/c/plot3.cpp          | 137 ------------
 src/api/cpp/graphics.cpp     |  39 +++-
 src/api/unified/graphics.cpp |  47 +++-
 5 files changed, 650 insertions(+), 195 deletions(-)
 delete mode 100644 src/api/c/plot3.cpp

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 368d3a55af..387e02c7b2 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -169,9 +169,41 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
+        DEPRECATED("Use plot instead")
         void plot3(const array& in, const char* title=NULL);
 #endif
 
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 2D or 3D plot to the window
+
+           \param[in] in is an \ref array with the data points
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note \p in must be 2d and of the form [n, order], where order is either 2 or 3.
+                 If order is 2, then chart is 2D and if order is 3, then chart is 3D.
+
+           \ingroup gfx_func_draw
+         */
+        void plot(const array& in, const char* const title=NULL);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 3D plot to the window
+
+           \param[in] X is an \ref array with the x-axis data points
+           \param[in] Y is an \ref array with the y-axis data points
+           \param[in] Z is an \ref array with the z-axis data points
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note \p X, \p Y and \p Z should be vectors.
+
+           \ingroup gfx_func_draw
+         */
+        void plot(const array& X, const array& Y, const array& Z, const char* const title=NULL);
+#endif
+
         /**
            Renders the input arrays as a 2D plot to the window
 
@@ -185,6 +217,41 @@ class AFAPI Window {
          */
         void plot(const array& X, const array& Y, const char* const title=NULL);
 
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 2D or 3D scatter-plot to the window
+
+           \param[in] in is an \ref array with the data points
+           \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note \p in must be 2d and of the form [n, order], where order is either 2 or 3.
+                 If order is 2, then chart is 2D and if order is 3, then chart is 3D.
+
+           \ingroup gfx_func_draw
+         */
+        void scatter(const array& in, const af::markerType marker = AF_MARKER_POINT,
+                     const char* const title = NULL);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Renders the input arrays as a 3D scatter-plot to the window
+
+           \param[in] X is an \ref array with the x-axis data points
+           \param[in] Y is an \ref array with the y-axis data points
+           \param[in] Z is an \ref array with the z-axis data points
+           \param[in] marker is an \ref markerType enum specifying which marker to use in the scatter plot
+           \param[in] title parameter is used when this function is called in grid mode
+
+           \note \p X, \p Y and \p Z should be vectors.
+
+           \ingroup gfx_func_draw
+         */
+        void scatter(const array& X, const array& Y, const array& Z,
+                     const af::markerType marker = AF_MARKER_POINT, const char* const title = NULL);
+#endif
+
 #if AF_API_VERSION >= 33
         /**
            Renders the input arrays as a 2D scatter-plot to the window
@@ -212,6 +279,7 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
+        DEPRECATED("Use scatter instead")
         void scatter3(const array& P, const af::markerType marker = AF_MARKER_POINT,
                       const char* const title = NULL);
 #endif
@@ -476,8 +544,93 @@ AFAPI af_err af_draw_image(const af_window wind, const af_array in, const af_cel
 
    \ingroup gfx_func_draw
 */
+DEPRECATED("Use af_draw_plot_nd or af_draw_plot_2d instead")
 AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props);
 
+#if AF_API_VERSION >= 32
+/**
+   C Interface wrapper for drawing an array as a plot
+
+   \param[in]   wind is the window handle
+   \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p P should be a 3n x 1 vector or one of a 3xn or nx3 matrices.
+
+   \ingroup gfx_func_draw
+*/
+DEPRECATED("Use af_draw_plot_nd or af_draw_plot_3d instead")
+AFAPI af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing an array as a 2D or 3D plot
+
+   \param[in]   wind is the window handle
+   \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p in must be 2d and of the form [n, order], where order is either 2 or 3.
+         If order is 2, then chart is 2D and if order is 3, then chart is 3D.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_plot_nd(const af_window wind, const af_array P, const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing an array as a 2D plot
+
+   \param[in]   wind is the window handle
+   \param[in]   X is an \ref af_array with the x-axis data points
+   \param[in]   Y is an \ref af_array with the y-axis data points
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p X and \p Y should be vectors.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
+                             const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing an array as a 3D plot
+
+   \param[in]   wind is the window handle
+   \param[in]   X is an \ref af_array with the x-axis data points
+   \param[in]   Y is an \ref af_array with the y-axis data points
+   \param[in]   Z is an \ref af_array with the z-axis data points
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p X, \p Y and \p Z should be vectors.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_plot_3d(const af_window wind,
+                             const af_array X, const af_array Y, const af_array Z,
+                             const af_cell* const props);
+#endif
+
 #if AF_API_VERSION >= 33
 /**
    C Interface wrapper for drawing an array as a plot
@@ -496,6 +649,7 @@ AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array
 
    \ingroup gfx_func_draw
 */
+DEPRECATED("Use af_draw_scatter_nd or af_draw_scatter_2d instead")
 AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
                              const af_marker_type marker, const af_cell* const props);
 #endif
@@ -515,27 +669,77 @@ AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_ar
 
    \ingroup gfx_func_draw
 */
+DEPRECATED("Use af_draw_scatter_nd or af_draw_scatter_3d instead")
 AFAPI af_err af_draw_scatter3(const af_window wind, const af_array P,
                               const af_marker_type marker, const af_cell* const props);
 #endif
 
-#if AF_API_VERSION >= 32
+#if AF_API_VERSION >= 34
 /**
    C Interface wrapper for drawing an array as a plot
 
    \param[in]   wind is the window handle
    \param[in]   P is an \ref af_array or matrix with the xyz-values of the points
+   \param[in]   marker is an \ref af_marker_type enum specifying which marker to use in the scatter plot
    \param[in]   props is structure \ref af_cell that has the properties that are used
    for the current rendering.
 
    \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
    is returned.
 
-   \note \p P should be a 3n x 1  vector or one of a 3xn or nx3 matrices.
+   \note \p in must be 2d and of the form [n, order], where order is either 2 or 3.
+         If order is 2, then chart is 2D and if order is 3, then chart is 3D.
 
    \ingroup gfx_func_draw
 */
-AFAPI af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props);
+AFAPI af_err af_draw_scatter_nd(const af_window wind, const af_array P,
+                                const af_marker_type marker, const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing an array as a 2D plot
+
+   \param[in]   wind is the window handle
+   \param[in]   X is an \ref af_array with the x-axis data points
+   \param[in]   Y is an \ref af_array with the y-axis data points
+   \param[in]   marker is an \ref af_marker_type enum specifying which marker to use in the scatter plot
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p X and \p Y should be vectors.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array Y,
+                                const af_marker_type marker, const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for drawing an array as a 3D plot
+
+   \param[in]   wind is the window handle
+   \param[in]   X is an \ref af_array with the x-axis data points
+   \param[in]   Y is an \ref af_array with the y-axis data points
+   \param[in]   Z is an \ref af_array with the z-axis data points
+   \param[in]   marker is an \ref af_marker_type enum specifying which marker to use in the scatter plot
+   \param[in]   props is structure \ref af_cell that has the properties that are used
+   for the current rendering.
+
+   \return     \ref AF_SUCCESS if rendering is successful, otherwise an appropriate error code
+   is returned.
+
+   \note \p X, \p Y and \p Z should be vectors.
+
+   \ingroup gfx_func_draw
+*/
+AFAPI af_err af_draw_scatter_3d(const af_window wind,
+                                const af_array X, const af_array Y, const af_array Z,
+                                const af_marker_type marker, const af_cell* const props);
 #endif
 
 /**
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 7401e713f6..de8f580500 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -9,6 +9,7 @@
 
 #include <af/graphics.h>
 #include <af/image.h>
+#include <af/data.h>
 
 #include <ArrayInfo.hpp>
 #include <graphics_common.hpp>
@@ -18,6 +19,7 @@
 #include <reduce.hpp>
 #include <join.hpp>
 #include <reorder.hpp>
+#include <transpose.hpp>
 #include <handle.hpp>
 
 using af::dim4;
@@ -26,58 +28,182 @@ using namespace detail;
 #if defined(WITH_GRAPHICS)
 using namespace graphics;
 
-template<typename T>
-forge::Chart* setup_plot(const forge::Window* const window,
-                         const af_array X, const af_array Y,
+// Requires in_ to be in either [order, n] or [n, order] format
+template<typename T, int order>
+forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
                          const af_cell* const props,
-                         forge::PlotType type, forge::MarkerType marker)
+                         forge::PlotType ptype, forge::MarkerType mtype)
 {
-    Array<T> xIn = getArray<T>(X);
-    Array<T> yIn = getArray<T>(Y);
+    Array<T> in = getArray<T>(in_);
 
-    T xmax = reduce_all<af_max_t, T, T>(xIn);
-    T xmin = reduce_all<af_min_t, T, T>(xIn);
-    T ymax = reduce_all<af_max_t, T, T>(yIn);
-    T ymin = reduce_all<af_min_t, T, T>(yIn);
+    af::dim4 dims = in.dims();
 
-    dim4 rdims(1, 0, 2, 3);
+    DIM_ASSERT(1, dims.ndims() == 2);
+    DIM_ASSERT(1, (dims[0] == order || dims[1] == order));
 
-    dim_t elements = xIn.elements();
-    dim4 rowDims = dim4(1, elements, 1, 1);
+    // The data expected by backend is 2D [order, n]
+    if(dims[1] == order) {
+        in = transpose(in, false);
+    }
 
-    // Force the vectors to be row vectors
-    // This ensures we can use join(0,..) and skip reorder
-    xIn.modDims(rowDims);
-    yIn.modDims(rowDims);
+    af::dim4 tdims = in.dims(); //transposed dimensions
 
-    // join along first dimension, skip reorder
-    Array<T> P = join(0, xIn, yIn);
+    T max[order], min[order];
+    copyData(max, reduce<af_max_t, T, T>(in, 1));
+    copyData(min, reduce<af_min_t, T, T>(in, 1));
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
     forge::Chart* chart = NULL;
-    if (props->col>-1 && props->row>-1)
-        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+    fg_chart_type ctype = order == 2 ? FG_CHART_2D : FG_CHART_3D;
+
+    if (props->col > -1 && props->row > -1)
+        chart = fgMngr.getChart(window, props->row, props->col, ctype);
     else
-        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+        chart = fgMngr.getChart(window, 0, 0, ctype);
 
-    forge::Plot* plot = fgMngr.getPlot(chart, elements, getGLType<T>(), type, marker);
+    forge::Plot* plot = fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
 
     plot->setColor(1.0, 0.0, 0.0, 1.0);
 
-    chart->setAxesLimits(xmin, xmax, ymin, ymax);
+    if(order == 2) {
+        chart->setAxesLimits(min[0], max[0],
+                             min[1], max[1]);
+    } else if(order == 3) {
+        chart->setAxesLimits(min[0], max[0],
+                             min[1], max[1],
+                             min[2], max[2]);
+    }
 
-    chart->setAxesTitles("X Axis", "Y Axis");
+    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
-    copy_plot<T>(P, plot);
+    copy_plot<T>(in, plot);
 
     return chart;
 }
 
+template<typename T>
+forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
+                         const int order, const af_cell* const props,
+                         forge::PlotType ptype, forge::MarkerType mtype)
+{
+    if(order == 2)
+        return setup_plot<T, 2>(window, in_, props, ptype, mtype);
+    else if(order == 3)
+        return setup_plot<T, 3>(window, in_, props, ptype, mtype);
+
+    // Dummy to avoid warnings
+    return NULL;
+}
+
+af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
+                   const af_cell* const props,
+                   forge::PlotType ptype = FG_PLOT_LINE, forge::MarkerType marker = FG_MARKER_NONE)
+{
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        ArrayInfo info = getInfo(in);
+        af::dim4  dims = info.dims();
+        af_dtype  type = info.getType();
+
+        DIM_ASSERT(0, dims.ndims() == 2);
+        DIM_ASSERT(0, dims[order_dim] == 2 || dims[order_dim] == 3);
+
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        makeContextCurrent(window);
+
+        forge::Chart* chart = NULL;
+
+        switch(type) {
+            case f32: chart = setup_plot<float  >(window, in, dims[order_dim], props, ptype, marker); break;
+            case s32: chart = setup_plot<int    >(window, in, dims[order_dim], props, ptype, marker); break;
+            case u32: chart = setup_plot<uint   >(window, in, dims[order_dim], props, ptype, marker); break;
+            case s16: chart = setup_plot<short  >(window, in, dims[order_dim], props, ptype, marker); break;
+            case u16: chart = setup_plot<ushort >(window, in, dims[order_dim], props, ptype, marker); break;
+            case u8 : chart = setup_plot<uchar  >(window, in, dims[order_dim], props, ptype, marker); break;
+            default:  TYPE_ERROR(1, type);
+        }
+
+        // Window's draw function requires either image or chart
+        if (props->col>-1 && props->row>-1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_array Z,
+                   const af_cell* const props,
+                   forge::PlotType ptype = FG_PLOT_LINE,
+                   forge::MarkerType marker = FG_MARKER_NONE)
+{
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        ArrayInfo xInfo = getInfo(X);
+        af::dim4  xDims = xInfo.dims();
+        af_dtype  xType = xInfo.getType();
+
+        ArrayInfo yInfo = getInfo(Y);
+        af::dim4  yDims = yInfo.dims();
+        af_dtype  yType = yInfo.getType();
+
+        ArrayInfo zInfo = getInfo(Z);
+        af::dim4  zDims = zInfo.dims();
+        af_dtype  zType = zInfo.getType();
+
+        DIM_ASSERT(0, xDims == yDims);
+        DIM_ASSERT(0, xDims == zDims);
+        DIM_ASSERT(0, xInfo.isVector());
+
+        TYPE_ASSERT(xType == yType);
+        TYPE_ASSERT(xType == zType);
+
+        // Join for set up vector
+        af_array in = 0;
+        af_array pIn[] = {X, Y, Z};
+        AF_CHECK(af_join_many(&in, 1, 3, pIn));
+
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        makeContextCurrent(window);
+
+        forge::Chart* chart = NULL;
+
+        switch(xType) {
+            case f32: chart = setup_plot<float  >(window, in, 3, props, ptype, marker); break;
+            case s32: chart = setup_plot<int    >(window, in, 3, props, ptype, marker); break;
+            case u32: chart = setup_plot<uint   >(window, in, 3, props, ptype, marker); break;
+            case s16: chart = setup_plot<short  >(window, in, 3, props, ptype, marker); break;
+            case u16: chart = setup_plot<ushort >(window, in, 3, props, ptype, marker); break;
+            case u8 : chart = setup_plot<uchar  >(window, in, 3, props, ptype, marker); break;
+            default:  TYPE_ERROR(1, xType);
+        }
+
+        // Window's draw function requires either image or chart
+        if (props->col>-1 && props->row>-1)
+            window->draw(props->col, props->row, *chart, props->title);
+        else
+            window->draw(*chart);
+
+        AF_CHECK(af_release_array(in));
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
                    const af_cell* const props,
-                   forge::PlotType type = FG_PLOT_LINE, forge::MarkerType marker = FG_MARKER_NONE)
+                   forge::PlotType ptype = FG_PLOT_LINE, forge::MarkerType marker = FG_MARKER_NONE)
 {
     if(wind==0) {
         std::cerr<<"Not a valid window"<<std::endl;
@@ -85,33 +211,36 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
     }
 
     try {
-        ArrayInfo Xinfo = getInfo(X);
-        af::dim4 X_dims = Xinfo.dims();
-        af_dtype Xtype  = Xinfo.getType();
+        ArrayInfo xInfo = getInfo(X);
+        af::dim4  xDims = xInfo.dims();
+        af_dtype  xType = xInfo.getType();
 
-        ArrayInfo Yinfo = getInfo(Y);
-        af::dim4 Y_dims = Yinfo.dims();
-        af_dtype Ytype  = Yinfo.getType();
+        ArrayInfo yInfo = getInfo(Y);
+        af::dim4  yDims = yInfo.dims();
+        af_dtype  yType = yInfo.getType();
 
-        DIM_ASSERT(0, X_dims == Y_dims);
-        DIM_ASSERT(0, X_dims == Y_dims);
-        DIM_ASSERT(0, Xinfo.isVector());
+        DIM_ASSERT(0, xDims == yDims);
+        DIM_ASSERT(0, xInfo.isVector());
 
-        TYPE_ASSERT(Xtype == Ytype);
+        TYPE_ASSERT(xType == yType);
+
+        // Join for set up vector
+        af_array in = 0;
+        AF_CHECK(af_join(&in, 1, X, Y));
 
         forge::Window* window = reinterpret_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
 
-        switch(Xtype) {
-            case f32: chart = setup_plot<float  >(window, X, Y, props, type, marker); break;
-            case s32: chart = setup_plot<int    >(window, X, Y, props, type, marker); break;
-            case u32: chart = setup_plot<uint   >(window, X, Y, props, type, marker); break;
-            case s16: chart = setup_plot<short  >(window, X, Y, props, type, marker); break;
-            case u16: chart = setup_plot<ushort >(window, X, Y, props, type, marker); break;
-            case u8 : chart = setup_plot<uchar  >(window, X, Y, props, type, marker); break;
-            default:  TYPE_ERROR(1, Xtype);
+        switch(xType) {
+            case f32: chart = setup_plot<float  >(window, in, 2, props, ptype, marker); break;
+            case s32: chart = setup_plot<int    >(window, in, 2, props, ptype, marker); break;
+            case u32: chart = setup_plot<uint   >(window, in, 2, props, ptype, marker); break;
+            case s16: chart = setup_plot<short  >(window, in, 2, props, ptype, marker); break;
+            case u16: chart = setup_plot<ushort >(window, in, 2, props, ptype, marker); break;
+            case u8 : chart = setup_plot<uchar  >(window, in, 2, props, ptype, marker); break;
+            default:  TYPE_ERROR(1, xType);
         }
 
         // Window's draw function requires either image or chart
@@ -119,6 +248,8 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
             window->draw(props->col, props->row, *chart, props->title);
         else
             window->draw(*chart);
+
+        AF_CHECK(af_release_array(in));
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -126,6 +257,94 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
 
 #endif // WITH_GRAPHICS
 
+//
+//template<typename T>
+//forge::Chart* setup_plot(const forge::Window* const window,
+//                         const af_array X, const af_array Y,
+//                         const af_cell* const props,
+//                         forge::PlotType type, forge::MarkerType marker)
+//{
+//    Array<T> xIn = getArray<T>(X);
+//    Array<T> yIn = getArray<T>(Y);
+//
+//    T xmax = reduce_all<af_max_t, T, T>(xIn);
+//    T xmin = reduce_all<af_min_t, T, T>(xIn);
+//    T ymax = reduce_all<af_max_t, T, T>(yIn);
+//    T ymin = reduce_all<af_min_t, T, T>(yIn);
+//
+//    dim4 rdims(1, 0, 2, 3);
+//
+//    dim_t elements = xIn.elements();
+//    dim4 rowDims = dim4(1, elements, 1, 1);
+//
+//    // Force the vectors to be row vectors
+//    // This ensures we can use join(0,..) and skip reorder
+//    xIn.modDims(rowDims);
+//    yIn.modDims(rowDims);
+//
+//    // join along first dimension, skip reorder
+//    Array<T> P = join(0, xIn, yIn);
+//
+//    ForgeManager& fgMngr = ForgeManager::getInstance();
+//
+//    // Get the chart for the current grid position (if any)
+//    forge::Chart* chart = NULL;
+//    if (props->col>-1 && props->row>-1)
+//        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+//    else
+//        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+//
+//    forge::Plot* plot = fgMngr.getPlot(chart, elements, getGLType<T>(), type, marker);
+//
+//    plot->setColor(1.0, 0.0, 0.0, 1.0);
+//
+//    chart->setAxesLimits(xmin, xmax, ymin, ymax);
+//
+//    chart->setAxesTitles("X Axis", "Y Axis");
+//
+//    copy_plot<T>(P, plot);
+//
+//    return chart;
+//}
+
+
+////////////////////////////////////////////////////////////////////////////////
+// Plot API
+////////////////////////////////////////////////////////////////////////////////
+af_err af_draw_plot_nd(const af_window wind, const af_array in,
+                       const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    return plotWrapper(wind, in, 1, props);
+#else
+    return AF_ERR_NO_GFX;
+#endif
+}
+
+af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
+                       const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    return plotWrapper(wind, X, Y, props);
+#else
+    return AF_ERR_NO_GFX;
+#endif
+}
+
+af_err af_draw_plot_3d(const af_window wind,
+                       const af_array X, const af_array Y, const af_array Z,
+                       const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    return plotWrapper(wind, X, Y, Z, props);
+#else
+    return AF_ERR_NO_GFX;
+#endif
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Deprecated Plot API
+////////////////////////////////////////////////////////////////////////////////
 af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
@@ -135,6 +354,77 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
 #endif
 }
 
+af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    try {
+        ArrayInfo info = getInfo(P);
+        af::dim4  dims = info.dims();
+
+        if(dims.ndims() == 2 && dims[1] == 3) {
+            return plotWrapper(wind, P, 1, props);
+        } else if(dims.ndims() == 2 && dims[0] == 3) {
+            return plotWrapper(wind, P, 0, props);
+        } else if(dims.ndims() == 1 && dims[0] % 3 == 0) {
+            dim4 rdims(dims.elements() / 3, 3, 1, 1);
+            af_array in = 0;
+            AF_CHECK(af_moddims(&in, P, rdims.ndims(), rdims.get()));
+            af_err err = plotWrapper(wind, in, 1, props);
+            AF_CHECK(af_release_array(in));
+            return err;
+        } else {
+            AF_RETURN_ERROR("Input needs to be either [n, 3] or [3, n] or [3n, 1]",
+                            AF_ERR_SIZE);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+#else
+    return AF_ERR_NO_GFX;
+#endif
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Scatter API
+////////////////////////////////////////////////////////////////////////////////
+af_err af_draw_scatter_nd(const af_window wind, const af_array in,
+                          const af_marker_type af_marker, const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array Y,
+                          const af_marker_type af_marker, const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_draw_scatter_3d(const af_window wind,
+                          const af_array X, const af_array Y, const af_array Z,
+                          const af_marker_type af_marker, const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Deprecated Scatter API
+////////////////////////////////////////////////////////////////////////////////
 af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type af_marker, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
@@ -144,3 +434,35 @@ af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
+
+af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type af_marker, const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    try {
+        ArrayInfo info = getInfo(P);
+        af::dim4  dims = info.dims();
+
+        if(dims.ndims() == 2 && dims[1] == 3) {
+            return plotWrapper(wind, P, 1, props, FG_PLOT_SCATTER, fg_marker);
+        } else if(dims.ndims() == 2 && dims[0] == 3) {
+            return plotWrapper(wind, P, 0, props, FG_PLOT_SCATTER, fg_marker);
+        } else if(dims.ndims() == 1 && dims[0] % 3 == 0) {
+            dim4 rdims(dims.elements() / 3, 3, 1, 1);
+            af_array in = 0;
+            AF_CHECK(af_moddims(&in, P, rdims.ndims(), rdims.get()));
+            af_err err = plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
+            AF_CHECK(af_release_array(in));
+            return err;
+        } else {
+            AF_RETURN_ERROR("Input needs to be either [n, 3] or [3, n] or [3n, 1]",
+                            AF_ERR_SIZE);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
diff --git a/src/api/c/plot3.cpp b/src/api/c/plot3.cpp
deleted file mode 100644
index f8274279eb..0000000000
--- a/src/api/c/plot3.cpp
+++ /dev/null
@@ -1,137 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/graphics.h>
-#include <af/image.h>
-
-#include <ArrayInfo.hpp>
-#include <graphics_common.hpp>
-#include <err_common.hpp>
-#include <backend.hpp>
-#include <plot.hpp>
-#include <reduce.hpp>
-#include <join.hpp>
-#include <transpose.hpp>
-#include <reorder.hpp>
-#include <handle.hpp>
-#include <af/data.h>
-#include <iostream>
-
-using af::dim4;
-using namespace detail;
-
-#if defined(WITH_GRAPHICS)
-using namespace graphics;
-
-template<typename T>
-forge::Chart* setup_plot3(const forge::Window* const window, const af_array P,
-                          const af_cell* const props,
-                          forge::PlotType ptype, forge::MarkerType mtype)
-{
-    Array<T> pIn = getArray<T>(P);
-    ArrayInfo Pinfo = getInfo(P);
-    af::dim4 P_dims = Pinfo.dims();
-
-    DIM_ASSERT(0, Pinfo.ndims() == 1 || Pinfo.ndims() == 2);
-    DIM_ASSERT(0, (P_dims[0] == 3 || P_dims[1] == 3) ||
-                    (Pinfo.isVector() && P_dims[0]%3 == 0));
-
-    if(Pinfo.isVector()){
-        dim4 rdims(P_dims.elements()/3, 3, 1, 1);
-        pIn.modDims(rdims);
-        P_dims = pIn.dims();
-    }
-
-    if(P_dims[1] == 3){
-        pIn = transpose(pIn, false);
-    }
-
-    T max[3], min[3];
-    copyData(max, reduce<af_max_t, T, T>(pIn, 1));
-    copyData(min, reduce<af_min_t, T, T>(pIn, 1));
-
-    ForgeManager& fgMngr = ForgeManager::getInstance();
-
-    // Get the chart for the current grid position (if any)
-    forge::Chart* chart = NULL;
-    if (props->col>-1 && props->row>-1)
-        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
-    else
-        chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
-
-    forge::Plot* plot3 = fgMngr.getPlot(chart, P_dims.elements()/3, getGLType<T>(), ptype, mtype);
-
-    plot3->setColor(1.0, 0.0, 0.0, 1.0);
-
-    chart->setAxesLimits(min[0], max[0],
-                         min[1], max[1],
-                         min[2], max[2]);
-
-    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
-
-    copy_plot<T>(pIn, plot3);
-
-    return chart;
-}
-
-af_err plot3Wrapper(const af_window wind, const af_array P, const af_cell* const props, const forge::PlotType type=FG_PLOT_LINE, const forge::MarkerType marker=FG_MARKER_NONE)
-{
-    if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
-    try {
-        ArrayInfo Pinfo = getInfo(P);
-        af_dtype Ptype  = Pinfo.getType();
-
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
-        makeContextCurrent(window);
-
-        forge::Chart* chart = NULL;
-
-        switch(Ptype) {
-            case f32: chart = setup_plot3<float >(window, P, props, type, marker); break;
-            case s32: chart = setup_plot3<int   >(window, P, props, type, marker); break;
-            case u32: chart = setup_plot3<uint  >(window, P, props, type, marker); break;
-            case s16: chart = setup_plot3<short >(window, P, props, type, marker); break;
-            case u16: chart = setup_plot3<ushort>(window, P, props, type, marker); break;
-            case u8 : chart = setup_plot3<uchar >(window, P, props, type, marker); break;
-            default:  TYPE_ERROR(1, Ptype);
-        }
-
-        if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *chart, props->title);
-        else
-            window->draw(*chart);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
-}
-
-#endif // WITH_GRAPHICS
-
-af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
-{
-#if defined(WITH_GRAPHICS)
-    return plot3Wrapper(wind, P, props);
-#else
-    return AF_ERR_NO_GFX;
-#endif
-}
-
-af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type af_marker, const af_cell* const props)
-{
-#if defined(WITH_GRAPHICS)
-    forge::MarkerType fg_marker = getFGMarker(af_marker);
-    return plot3Wrapper(wind, P, props, FG_PLOT_SCATTER, fg_marker);
-#else
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
-}
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 21be44b9d8..4404a0a421 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -76,29 +76,54 @@ void Window::image(const array& in, const char* const title)
     AF_THROW(af_draw_image(get(), in.get(), &temp));
 }
 
-void Window::plot(const array& X, const array& Y, const char* const title)
+void Window::plot(const array& in, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_plot(get(), X.get(), Y.get(), &temp));
+    AF_THROW(af_draw_plot_nd(get(), in.get(), &temp));
 }
 
-void Window::scatter(const array& X, const array& Y, af::markerType marker, const char* const title)
+void Window::plot(const array& X, const array& Y, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_scatter(get(), X.get(), Y.get(), marker, &temp));
+    AF_THROW(af_draw_plot_2d(get(), X.get(), Y.get(), &temp));
 }
 
-void Window::scatter3(const array& P, af::markerType marker, const char* const title)
+void Window::plot(const array& X, const array& Y, const array& Z, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_scatter3(get(), P.get(), marker, &temp));
+    AF_THROW(af_draw_plot_3d(get(), X.get(), Y.get(), Z.get(), &temp));
 }
 
 void Window::plot3(const array& P, const char* const title)
 {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     P.eval();
-    AF_THROW(af_draw_plot3(get(), P.get(), &temp));
+    AF_THROW(af_draw_plot_nd(get(), P.get(), &temp));
+}
+
+void Window::scatter(const array& in, af::markerType marker, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_scatter_nd(get(), in.get(), marker, &temp));
+}
+
+void Window::scatter(const array& X, const array& Y, af::markerType marker, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_scatter_2d(get(), X.get(), Y.get(), marker, &temp));
+}
+
+void Window::scatter(const array& X, const array& Y, const array& Z,
+                     af::markerType marker, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_scatter_3d(get(), X.get(), Y.get(), Z.get(), marker, &temp));
+}
+
+void Window::scatter3(const array& P, af::markerType marker, const char* const title)
+{
+    af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_draw_scatter_nd(get(), P.get(), marker, &temp));
 }
 
 void Window::hist(const array& X, const double minval, const double maxval, const char* const title)
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 8bdf860543..f46bb11cb2 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -44,6 +44,31 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
     return CALL(wind, X, Y, props);
 }
 
+af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
+{
+    CHECK_ARRAYS(P);
+    return CALL(wind, P, props);
+}
+
+af_err af_draw_plot_nd(const af_window wind, const af_array in, const af_cell* const props)
+{
+    CHECK_ARRAYS(in);
+    return CALL(wind, in, props);
+}
+
+af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
+{
+    CHECK_ARRAYS(X, Y);
+    return CALL(wind, X, Y, props);
+}
+
+af_err af_draw_plot_3d(const af_window wind, const af_array X, const af_array Y, const af_array Z,
+                       const af_cell* const props)
+{
+    CHECK_ARRAYS(X, Y, Z);
+    return CALL(wind, X, Y, Z, props);
+}
+
 af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type marker, const af_cell* const props)
 {
     CHECK_ARRAYS(X, Y);
@@ -56,10 +81,26 @@ af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_
     return CALL(wind, P, marker, props);
 }
 
-af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
+af_err af_draw_scatter_nd(const af_window wind, const af_array in,
+                          const af_marker_type marker, const af_cell* const props)
 {
-    CHECK_ARRAYS(P);
-    return CALL(wind, P, props);
+    CHECK_ARRAYS(in);
+    return CALL(wind, in, marker, props);
+}
+
+af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array Y,
+                          const af_marker_type marker, const af_cell* const props)
+{
+    CHECK_ARRAYS(X, Y);
+    return CALL(wind, X, Y, marker, props);
+}
+
+af_err af_draw_scatter_3d(const af_window wind,
+                          const af_array X, const af_array Y, const af_array Z,
+                          const af_marker_type marker, const af_cell* const props)
+{
+    CHECK_ARRAYS(X, Y, Z);
+    return CALL(wind, X, Y, Z, marker, props);
 }
 
 af_err af_draw_hist(const af_window wind, const af_array X, const double minval, const double maxval, const af_cell* const props)

From 83566d29fc436bed5a92ac230e14f29de813db3a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 18:43:55 -0400
Subject: [PATCH 0873/2677] API: Use new plot API in examples

---
 examples/graphics/plot3.cpp | 3 +--
 examples/pde/swe.cpp        | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index d9e58563da..4d7afd5af8 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -32,10 +32,9 @@ int main(int argc, char *argv[])
             X = max(min(X, 1.0), -1.0);
             Y = max(min(Y, 1.0), -1.0);
 
-            array Pts = join(1, X, Y, Z);
             //Pts can be passed in as a matrix in the form n x 3, 3 x n
             //or in the flattened xyz-triplet array with size 3n x 1
-            myWindow.plot3(Pts);
+            myWindow.plot(X, Y, Z);
 
             t+=0.01;
         } while(!myWindow.close());
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index bcb545a3e0..129b5193ed 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -88,7 +88,7 @@ static void swe(bool console)
             array hist_out = histogram(normalize(eta, m_eta), 15);
             (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
             (*win)(1,0).plot(seq(up.dims(1)), vp.col(0), "Pressure at left boundary");
-            (*win)(1,1).plot3(join(1, flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0))), "Gradients versus Magnitude at left boundary"); // viz
+            (*win)(1,1).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
             win->show();
         } else eval(eta, up, vp);
         iter++;

From 54a39fcba476705bbb5a9a3a379a08eb318c4405 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 6 Sep 2016 20:53:44 -0400
Subject: [PATCH 0874/2677] BUGFIX: Fixing initMersenneState function in OpenCL
 for AMD

---
 src/backend/opencl/kernel/random_engine.hpp   |  5 +++--
 .../kernel/random_engine_mersenne_init.cl     | 19 ++++++++++++++-----
 2 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 29693a35fb..4294b3b211 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -24,6 +24,7 @@
 #include <type_util.hpp>
 #include <cache.hpp>
 #include <random_engine.hpp>
+#include "config.hpp"
 
 #include <kernel_headers/random_engine_mersenne_init.hpp>
 #include <kernel_headers/random_engine_mersenne.hpp>
@@ -223,8 +224,8 @@ namespace opencl
         void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed)
         {
             try{
-                NDRange local(N, 1);
-                NDRange global(N * MAX_BLOCKS, 1);
+                NDRange local(THREADS_PER_GROUP, 1);
+                NDRange global(local[0] * MAX_BLOCKS, 1);
 
                 Kernel ker = get_mersenne_init_kernel();
                 auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index 9303a05c04..9b931e474f 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -47,8 +47,11 @@
 
 __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
 {
+    int tid = get_local_id(0);
+    int nthreads = get_local_size(0);
+    int gid = get_group_id(0);
     __local uint lstate[N];
-    const __global uint *ltbl = tbl + (TABLE_SIZE*get_group_id(0));
+    const __global uint *ltbl = tbl + (TABLE_SIZE*gid);
     uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
     uint tmp = hidden_seed;
     tmp += tmp >> 16;
@@ -56,9 +59,13 @@ __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
     tmp &= 0xff;
     tmp |= tmp << 8;
     tmp |= tmp << 16;
-    lstate[get_local_id(0)] = tmp;
+
+    for (int id = tid; id < N; id += nthreads) {
+        lstate[id] = tmp;
+    }
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (get_local_id(0) == 0) {
+
+    if (tid == 0) {
         lstate[0] = seed;
         lstate[1] = hidden_seed;
         for (int i = 1; i < N; ++i) {
@@ -66,6 +73,8 @@ __kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
-    state[N*get_group_id(0) + get_local_id(0)] = lstate[get_local_id(0)];
-}
 
+    for (int id = tid; id < N; id += nthreads) {
+        state[N*gid + id] = lstate[id];
+    }
+}

From 063832f15820d5c53aaf5dc5d1a9a39bcf64ea57 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 22:08:40 -0400
Subject: [PATCH 0875/2677] Add chart limits to vector field

---
 src/api/c/vector_field.cpp | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index d99d3c2a83..70aa11bcd0 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -15,6 +15,7 @@
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <vector_field.hpp>
+#include <reduce.hpp>
 #include <transpose.hpp>
 #include <handle.hpp>
 
@@ -38,6 +39,10 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
         dIn = transpose<T>(dIn, false);
     }
 
+    T max[3], min[3];
+    copyData(max, reduce<af_max_t, T, T>(pIn, 1));
+    copyData(min, reduce<af_min_t, T, T>(pIn, 1));
+
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
@@ -59,6 +64,15 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
 
     vectorfield->setColor(1.0, 0.0, 0.0, 1.0);
 
+    if(pIn.dims()[0] == 2) {
+        chart->setAxesLimits(min[0], max[0],
+                             min[1], max[1]);
+    } else if(pIn.dims()[0] == 3) {
+        chart->setAxesLimits(min[0], max[0],
+                             min[1], max[1],
+                             min[2], max[2]);
+    }
+
     chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
     copy_vector_field<T>(pIn, dIn, vectorfield);

From efb56d80424849a3cac4c22ee21165737c56443b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 7 Sep 2016 11:43:35 +0530
Subject: [PATCH 0876/2677] use native_exp function in opencl kernel for
 bilateral

---
 src/backend/opencl/kernel/bilateral.cl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index c6d8f94dda..bf734b88e9 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -27,7 +27,7 @@ void load2LocalMem(__local outType *  shrd,
 
 float gaussian1d(float x, float variance)
 {
-    return exp((x * x) / (-2.f * variance));
+    return native_exp((x * x) / (-2.f * variance));
 }
 
 __kernel
@@ -63,7 +63,7 @@ void bilateral(__global outType *        d_dst,
     if (lx<window_size && ly<window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = exp( ((x*x) + (y*y)) / variance_space);
+        gauss2d[ly*window_size+lx] = native_exp( ((x*x) + (y*y)) / variance_space);
     }
 
     int s0 = iInfo.strides[0];

From 02aaea21c93db3fb8fd151632b72fa696f202f2d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Sep 2016 22:09:23 -0400
Subject: [PATCH 0877/2677] Add 2d field example

---
 examples/graphics/field.cpp | 48 +++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 examples/graphics/field.cpp

diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
new file mode 100644
index 0000000000..7f50107b67
--- /dev/null
+++ b/examples/graphics/field.cpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <cstdio>
+#include <math.h>
+
+using namespace af;
+
+const float MINIMUM = 1.0f;
+const float MAXIMUM = 20.f;
+const float STEP    = 2.0f;
+const float NELEMS  = (MAXIMUM-MINIMUM+1)/STEP;
+
+int main(int argc, char *argv[])
+{
+    try {
+        // Initialize the kernel array just once
+        af::info();
+        af::Window myWindow(1024, 1024, "2D Vector Field example: ArrayFire");
+
+        float h_divPoints[] = {5, 5, 15, 15,
+                               5, 15, 5, 15};
+        array divPoints(4, 2, h_divPoints);
+
+        //array points = join(1, flat(range(dim4(10, 10)) * 2 + 1), flat(range(dim4(10, 10), 1) * 2 + 1));
+        array points = join(1, flat(range(dim4(10, 10), 1) * 2 + 1), flat(range(dim4(10, 10)) * 2 + 1));
+        array directions = sin(2 * Pi * points / 10.0f);
+
+        while(!myWindow.close()) {
+            myWindow.scatter(divPoints, AF_MARKER_CIRCLE);
+            myWindow.vectorField(points, directions);
+            myWindow.show();
+        }
+
+    } catch (af::exception& e) {
+        fprintf(stderr, "%s\n", e.what());
+        throw;
+    }
+    return 0;
+}
+

From 5f1a0b7762bcc57208496419a022cc1a880ed580 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 7 Sep 2016 10:24:45 -0400
Subject: [PATCH 0878/2677] Graphics: Change default colors of Surface and
 Vector Field

* Surface: Red -> Green
* Vector Field: Red -> Yellow
---
 src/api/c/surface.cpp      | 2 +-
 src/api/c/vector_field.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 1efa7766b6..ebcd399b19 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -86,7 +86,7 @@ forge::Chart* setup_surface(const forge::Window* const window,
 
     forge::Surface* surface = fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
 
-    surface->setColor(1.0, 0.0, 0.0, 1.0);
+    surface->setColor(0.0, 1.0, 0.0, 1.0);
 
     chart->setAxesLimits(xmin, xmax,
                          ymin, ymax,
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 70aa11bcd0..8890702f4e 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -62,7 +62,7 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
 
     forge::VectorField* vectorfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
 
-    vectorfield->setColor(1.0, 0.0, 0.0, 1.0);
+    vectorfield->setColor(1.0, 1.0, 0.0, 1.0);
 
     if(pIn.dims()[0] == 2) {
         chart->setAxesLimits(min[0], max[0],

From d73a0289c434ed4725f537022b7df0be93fa7f8c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 7 Sep 2016 10:25:48 -0400
Subject: [PATCH 0879/2677] Remove chart->setAxesLimits code from chart
 rendering objects

---
 src/api/c/hist.cpp         |  7 -------
 src/api/c/plot.cpp         | 13 -------------
 src/api/c/surface.cpp      | 11 -----------
 src/api/c/vector_field.cpp | 13 -------------
 4 files changed, 44 deletions(-)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 3e86b78623..f40acf5652 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -31,8 +31,6 @@ forge::Chart* setup_histogram(const forge::Window* const window,
     Array<T> histogramInput = getArray<T>(in);
     dim_t nBins = histogramInput.elements();
 
-    T freqMax = detail::reduce_all<af_max_t, T, T>(histogramInput);
-
     // Retrieve Forge Histogram with nBins and array type
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
@@ -49,11 +47,6 @@ forge::Chart* setup_histogram(const forge::Window* const window,
     // Set histogram bar colors to orange
     hist->setColor(0.929f, 0.486f, 0.2745f, 1.0f);
 
-    // set x axis limits to maximum and minimum values of data
-    // and y axis limits to range [0, nBins]
-    chart->setAxesLimits(minval, maxval, 0.0f, double(freqMax));
-    chart->setAxesTitles("Bins", "Frequency");
-
     copy_histogram<T>(histogramInput, hist);
 
     return chart;
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index de8f580500..792f2f5383 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -48,10 +48,6 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
 
     af::dim4 tdims = in.dims(); //transposed dimensions
 
-    T max[order], min[order];
-    copyData(max, reduce<af_max_t, T, T>(in, 1));
-    copyData(min, reduce<af_min_t, T, T>(in, 1));
-
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
@@ -67,15 +63,6 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
 
     plot->setColor(1.0, 0.0, 0.0, 1.0);
 
-    if(order == 2) {
-        chart->setAxesLimits(min[0], max[0],
-                             min[1], max[1]);
-    } else if(order == 3) {
-        chart->setAxesLimits(min[0], max[0],
-                             min[1], max[1],
-                             min[2], max[2]);
-    }
-
     chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
     copy_plot<T>(in, plot);
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index ebcd399b19..450057b69c 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -36,13 +36,6 @@ forge::Chart* setup_surface(const forge::Window* const window,
     Array<T> yIn = getArray<T>(yVals);
     Array<T> zIn = getArray<T>(zVals);
 
-    T xmax = reduce_all<af_max_t, T, T>(xIn);
-    T xmin = reduce_all<af_min_t, T, T>(xIn);
-    T ymax = reduce_all<af_max_t, T, T>(yIn);
-    T ymin = reduce_all<af_min_t, T, T>(yIn);
-    T zmax = reduce_all<af_max_t, T, T>(zIn);
-    T zmin = reduce_all<af_min_t, T, T>(zIn);
-
     ArrayInfo Xinfo = getInfo(xVals);
     ArrayInfo Yinfo = getInfo(yVals);
     ArrayInfo Zinfo = getInfo(zVals);
@@ -88,10 +81,6 @@ forge::Chart* setup_surface(const forge::Window* const window,
 
     surface->setColor(0.0, 1.0, 0.0, 1.0);
 
-    chart->setAxesLimits(xmin, xmax,
-                         ymin, ymax,
-                         zmin, zmax);
-
     chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
     copy_surface<T>(Z, surface);
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 8890702f4e..7a092d6633 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -39,10 +39,6 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
         dIn = transpose<T>(dIn, false);
     }
 
-    T max[3], min[3];
-    copyData(max, reduce<af_max_t, T, T>(pIn, 1));
-    copyData(min, reduce<af_min_t, T, T>(pIn, 1));
-
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
@@ -64,15 +60,6 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
 
     vectorfield->setColor(1.0, 1.0, 0.0, 1.0);
 
-    if(pIn.dims()[0] == 2) {
-        chart->setAxesLimits(min[0], max[0],
-                             min[1], max[1]);
-    } else if(pIn.dims()[0] == 3) {
-        chart->setAxesLimits(min[0], max[0],
-                             min[1], max[1],
-                             min[2], max[2]);
-    }
-
     chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
 
     copy_vector_field<T>(pIn, dIn, vectorfield);

From 2a7efaaf00f9f20f4e13c7c93354622c610d954f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 7 Sep 2016 20:39:43 +0530
Subject: [PATCH 0880/2677] fix API breakage in exampleFunction

---
 include/af/util.h               |  4 ++--
 src/api/c/exampleFunction.cpp   | 22 +++++++++-------------
 src/api/cpp/exampleFunction.cpp |  4 ++--
 src/api/unified/util.cpp        |  6 +++---
 4 files changed, 16 insertions(+), 20 deletions(-)

diff --git a/include/af/util.h b/include/af/util.h
index 259b03b5e2..95a7eea693 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -128,7 +128,7 @@ namespace af
 #endif
 
     // Purpose of Addition: "How to add Function" documentation
-    AFAPI array exampleFunction(const array& a, const array& b, const af_someenum_t param);
+    AFAPI array exampleFunction(const array& in, const af_someenum_t param);
 
 #if AF_API_VERSION >= 34
     ///
@@ -253,7 +253,7 @@ extern "C" {
 
     // Purpose of Addition: "How to add Function" documentation
     AFAPI af_err af_example_function(af_array* out,
-                                     const af_array a, const af_array b,
+                                     const af_array in,
                                      const af_someenum_t param);
 
     ///
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index b7ff405777..5234f0da8d 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -48,7 +48,7 @@ af_array example(const af_array& a, const af_array& b, const af_someenum_t& para
     return getHandle<T>( exampleFunction(getArray<T>(a), getArray<T>(b), param) );
 }
 
-af_err af_example_function(af_array* out, const af_array a, const af_array b, const af_someenum_t param)
+af_err af_example_function(af_array* out, const af_array a, const af_someenum_t param)
 {
     try {
         af_array output = 0;
@@ -58,8 +58,6 @@ af_err af_example_function(af_array* out, const af_array a, const af_array b, co
                                             // such as type of data, dimensions,
                                             // offsets and strides. This class is declared
                                             // in src/backend/ArrayInfo.hpp
-        ArrayInfo info2 = getInfo(b);
-
         af::dim4 dims = info.dims();
 
         ARG_ASSERT(2, (dims.ndims()>=0 && dims.ndims()<=3));
@@ -70,18 +68,16 @@ af_err af_example_function(af_array* out, const af_array a, const af_array b, co
 
         af_dtype type = info.getType();
 
-        ARG_ASSERT(2, (info2.getType() == type));
-
         switch(type) {                      // Based on the data type, call backend specific
                                             // implementation
-            case f64: output = example<double >(a, b, param); break;
-            case f32: output = example<float  >(a, b, param); break;
-            case s32: output = example<int    >(a, b, param); break;
-            case u32: output = example<uint   >(a, b, param); break;
-            case  u8: output = example<uchar  >(a, b, param); break;
-            case  b8: output = example<char   >(a, b, param); break;
-            case c32: output = example<cfloat >(a, b, param); break;
-            case c64: output = example<cdouble>(a, b, param); break;
+            case f64: output = example<double >(a, a, param); break;
+            case f32: output = example<float  >(a, a, param); break;
+            case s32: output = example<int    >(a, a, param); break;
+            case u32: output = example<uint   >(a, a, param); break;
+            case  u8: output = example<uchar  >(a, a, param); break;
+            case  b8: output = example<char   >(a, a, param); break;
+            case c32: output = example<cfloat >(a, a, param); break;
+            case c64: output = example<cdouble>(a, a, param); break;
             default : TYPE_ERROR(1, type);  // Another helpful macro from err_common.hpp
                                             // that helps throw type based error messages
         }
diff --git a/src/api/cpp/exampleFunction.cpp b/src/api/cpp/exampleFunction.cpp
index 066eca1b37..c3ec5b8442 100644
--- a/src/api/cpp/exampleFunction.cpp
+++ b/src/api/cpp/exampleFunction.cpp
@@ -18,13 +18,13 @@
 namespace af
 {
 
-array exampleFunction(const array& a, const array& b, const af_someenum_t p)
+array exampleFunction(const array& a, const af_someenum_t p)
 {
     // create a temporary af_array handle
     af_array temp = 0;
 
     // call C-API function
-    AF_THROW( af_example_function(&temp, a.get(), b.get(), p) );
+    AF_THROW( af_example_function(&temp, a.get(), p) );
 
     // array::get() returns af_array handle for the corresponding cpp af::array
     return array(temp);
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 6b7d073de2..1e83561306 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -51,8 +51,8 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
     return CALL(output, exp, arr, precision, transpose);
 }
 
-af_err af_example_function(af_array* out, const af_array a, const af_array b, const af_someenum_t param)
+af_err af_example_function(af_array* out, const af_array a, const af_someenum_t param)
 {
-    CHECK_ARRAYS(a, b);
-    return CALL(out, a, b, param);
+    CHECK_ARRAYS(a);
+    return CALL(out, a, param);
 }

From fdf1abe1fb94decf382f59d2f88f53a6b1b7c908 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 7 Sep 2016 14:16:01 -0400
Subject: [PATCH 0881/2677] explicitly instantiate void* type for device

---
 src/api/cpp/array.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 203589912c..a96b4ebb07 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1003,6 +1003,13 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
 
 #undef INSTANTIATE
 
+    template<> AFAPI void* array::device() const
+    {
+        void *ptr = NULL;
+        AF_THROW(af_get_device_ptr(&ptr, get()));
+        return (void *)ptr;
+    }
+
 
 // array_proxy instanciations
 #define TEMPLATE_MEM_FUNC(TYPE, RETURN_TYPE, FUNC)      \

From 67f2b107798bbdb7a2d2c2749682ee4f59dfb61a Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 7 Sep 2016 15:07:32 -0400
Subject: [PATCH 0882/2677] Removed older extraneous random implementation

---
 src/backend/cpu/kernel/random.hpp    | 199 ----------------
 src/backend/cpu/random.cpp           |  73 ------
 src/backend/cpu/random.hpp           |  22 --
 src/backend/cuda/kernel/random.hpp   | 239 -------------------
 src/backend/cuda/random.cu           |  69 ------
 src/backend/cuda/random.hpp          |  22 --
 src/backend/opencl/kernel/random.cl  | 331 ---------------------------
 src/backend/opencl/kernel/random.hpp | 146 ------------
 src/backend/opencl/random.cpp        |  81 -------
 src/backend/opencl/random.hpp        |  22 --
 10 files changed, 1204 deletions(-)
 delete mode 100644 src/backend/cpu/kernel/random.hpp
 delete mode 100644 src/backend/cpu/random.cpp
 delete mode 100644 src/backend/cpu/random.hpp
 delete mode 100644 src/backend/cuda/kernel/random.hpp
 delete mode 100644 src/backend/cuda/random.cu
 delete mode 100644 src/backend/cuda/random.hpp
 delete mode 100644 src/backend/opencl/kernel/random.cl
 delete mode 100644 src/backend/opencl/kernel/random.hpp
 delete mode 100644 src/backend/opencl/random.cpp
 delete mode 100644 src/backend/opencl/random.hpp

diff --git a/src/backend/cpu/kernel/random.hpp b/src/backend/cpu/kernel/random.hpp
deleted file mode 100644
index 9b2d311007..0000000000
--- a/src/backend/cpu/kernel/random.hpp
+++ /dev/null
@@ -1,199 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <Array.hpp>
-#include <type_traits>
-#include <random>
-#include <algorithm>
-#include <functional>
-#include <limits>
-#include <type_traits>
-
-namespace cpu
-{
-namespace kernel
-{
-
-using namespace std;
-
-#if defined(_WIN32)
-    #define __THREAD_LOCAL static __declspec(thread)
-#else
-    #define __THREAD_LOCAL static __thread
-#endif
-
-template<typename T>
-using is_arithmetic_t       = typename enable_if< is_arithmetic<T>::value,      function<T()>>::type;
-template<typename T>
-using is_complex_t          = typename enable_if< is_complex<T>::value,         function<T()>>::type;
-template<typename T>
-using is_floating_point_t   = typename enable_if< is_floating_point<T>::value,  function<T()>>::type;
-
-template<typename T, typename GenType>
-is_arithmetic_t<T>
-urand(GenType &generator)
-{
-    typedef typename conditional<   is_floating_point<T>::value,
-                                    uniform_real_distribution<T>,
-#if OS_WIN
-                                    uniform_int_distribution<unsigned>>::type dist;
-#else
-                                    uniform_int_distribution<T >> ::type dist;
-#endif
-    return bind(dist(), generator);
-}
-
-template<typename T, typename GenType>
-is_complex_t<T>
-urand(GenType &generator)
-{
-    auto func = urand<typename T::value_type>(generator);
-    return [func] () { return T(func(), func());};
-}
-
-template<typename T, typename GenType>
-is_floating_point_t<T>
-nrand(GenType &generator)
-{
-    return bind(normal_distribution<T>(), generator);
-}
-
-template<typename T, typename GenType>
-is_complex_t<T>
-nrand(GenType &generator)
-{
-    auto func = nrand<typename T::value_type>(generator);
-    return [func] () { return T(func(), func());};
-}
-
-mt19937& getGenerator()
-{
-    // FIXME: This abomination of a work around is brought to you
-    // by incomplete standards from Xcode and Visual Studio
-    // Should ideally be using thread_local on object instead of pointer
-    __THREAD_LOCAL mt19937 *generator = NULL;
-    if (generator == NULL) generator = new mt19937();
-    return *generator;
-}
-
-unsigned long long& getSeed()
-{
-    __THREAD_LOCAL unsigned long long gen_seed = 0;
-    return gen_seed;
-}
-
-void getSeedPtr(unsigned long long *seed)
-{
-    *seed = getSeed();
-}
-
-bool& isFirst()
-{
-    __THREAD_LOCAL bool is_first = true;
-    return is_first;
-}
-
-void setSeed(const uintl seed)
-{
-    getGenerator().seed(seed);
-    getSeed() = seed;
-    isFirst() = false;
-}
-
-//FIXME: See if we can use functors instead of function pointer directly
-template<typename T>
-struct RandomDistribution
-{
-    std::function<T()> func;
-    RandomDistribution(std::function<T()> dist_func) : func(dist_func)
-    {
-    }
-};
-
-template<typename T>
-void randn(Array<T> out)
-{
-    __THREAD_LOCAL unsigned long long my_seed = 0;
-    if (isFirst()) {
-        my_seed = getSeed();
-        setSeed(my_seed);
-    }
-
-    // FIXME: This abomination of a work around is brought to you
-    // by incomplete standards from Xcode and Visual Studio
-    // Should ideally be using thread_local on object instead of pointer
-    __THREAD_LOCAL RandomDistribution<T> *distPtr = NULL;
-
-    if (!distPtr || my_seed != getSeed()) {
-        if (distPtr) delete distPtr;
-        distPtr = new RandomDistribution<T>(nrand<T>(getGenerator()));
-        my_seed = getSeed();
-    }
-
-    T *outPtr = out.get();
-    for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = distPtr->func();
-    }
-}
-
-template<typename T>
-void randu(Array<T> out)
-{
-    __THREAD_LOCAL unsigned long long my_seed = 0;
-    if (isFirst()) {
-        my_seed = getSeed();
-        setSeed(my_seed);
-    }
-
-    // FIXME: This abomination of a work around is brought to you
-    // by incomplete standards from Xcode and Visual Studio
-    // Should ideally be using thread_local on object instead of pointer
-    __THREAD_LOCAL RandomDistribution<T> *distPtr = NULL;
-
-    if (!distPtr || my_seed != getSeed()) {
-        if (distPtr) delete distPtr;
-        distPtr = new RandomDistribution<T>(urand<T>(getGenerator()));
-        my_seed = getSeed();
-    }
-
-    T *outPtr = out.get();
-    for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = distPtr->func();
-    }
-}
-
-template<>
-void randu(Array<char> out)
-{
-    __THREAD_LOCAL unsigned long long my_seed = 0;
-    if (isFirst()) {
-        my_seed = getSeed();
-        setSeed(my_seed);
-    }
-
-    // FIXME: This abomination of a work around is brought to you
-    // by incomplete standards from Xcode and Visual Studio
-    // Should ideally be using thread_local on object instead of pointer
-    __THREAD_LOCAL RandomDistribution<float> *distPtr = NULL;
-
-    if (!distPtr || my_seed != getSeed()) {
-        if (distPtr) delete distPtr;
-        distPtr = new RandomDistribution<float>(nrand<float>(getGenerator()));
-        my_seed = getSeed();
-    }
-
-    char *outPtr = out.get();
-    for (int i = 0; i < (int)out.elements(); i++) {
-        outPtr[i] = distPtr->func() > 0.5;
-    }
-}
-
-}
-}
diff --git a/src/backend/cpu/random.cpp b/src/backend/cpu/random.cpp
deleted file mode 100644
index ec484fa0db..0000000000
--- a/src/backend/cpu/random.cpp
+++ /dev/null
@@ -1,73 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/dim4.hpp>
-#include <Array.hpp>
-#include <random.hpp>
-#include <platform.hpp>
-#include <queue.hpp>
-#include <kernel/random.hpp>
-
-namespace cpu
-{
-
-template<typename T>
-Array<T> randu(const af::dim4 &dims)
-{
-    Array<T> outArray = createEmptyArray<T>(dims);
-    getQueue().enqueue(kernel::randu<T>, outArray);
-    return outArray;
-}
-
-#define INSTANTIATE_UNIFORM(T)                              \
-    template Array<T>  randu<T>    (const af::dim4 &dims);
-
-INSTANTIATE_UNIFORM(float)
-INSTANTIATE_UNIFORM(double)
-INSTANTIATE_UNIFORM(cfloat)
-INSTANTIATE_UNIFORM(cdouble)
-INSTANTIATE_UNIFORM(int)
-INSTANTIATE_UNIFORM(uint)
-INSTANTIATE_UNIFORM(intl)
-INSTANTIATE_UNIFORM(uintl)
-INSTANTIATE_UNIFORM(uchar)
-INSTANTIATE_UNIFORM(char)
-INSTANTIATE_UNIFORM(short)
-INSTANTIATE_UNIFORM(ushort)
-
-template<typename T>
-Array<T> randn(const af::dim4 &dims)
-{
-    Array<T> outArray = createEmptyArray<T>(dims);
-    getQueue().enqueue(kernel::randn<T>, outArray);
-    return outArray;
-}
-
-#define INSTANTIATE_NORMAL(T)                              \
-    template Array<T>  randn<T>(const af::dim4 &dims);
-
-INSTANTIATE_NORMAL(float)
-INSTANTIATE_NORMAL(double)
-INSTANTIATE_NORMAL(cfloat)
-INSTANTIATE_NORMAL(cdouble)
-
-void setSeed(const uintl seed)
-{
-    getQueue().enqueue(kernel::setSeed, seed);
-}
-
-uintl getSeed()
-{
-    uintl seed = 0;
-    getQueue().enqueue(kernel::getSeedPtr, &seed);
-    getQueue().sync();
-    return seed;
-}
-
-}
diff --git a/src/backend/cpu/random.hpp b/src/backend/cpu/random.hpp
deleted file mode 100644
index 1f471627d7..0000000000
--- a/src/backend/cpu/random.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cpu
-{
-    template<typename T>
-    Array<T> randu(const af::dim4 &dims);
-
-    template<typename T>
-    Array<T> randn(const af::dim4 &dims);
-
-    void setSeed(const uintl seed);
-    uintl getSeed();
-}
diff --git a/src/backend/cuda/kernel/random.hpp b/src/backend/cuda/kernel/random.hpp
deleted file mode 100644
index 96cf098c03..0000000000
--- a/src/backend/cuda/kernel/random.hpp
+++ /dev/null
@@ -1,239 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <curand_kernel.h>
-#include <dispatch.hpp>
-#include <err_cuda.hpp>
-#include <platform.hpp>
-#include <debug_cuda.hpp>
-
-namespace cuda
-{
-namespace kernel
-{
-
-    static const int THREADS = 256;
-    static const int BLOCKS  = 64;
-    static unsigned long long seeds[DeviceManager::MAX_DEVICES] = {0};
-
-    __global__ static void
-    setup_kernel(curandState_t *states, unsigned long long seed)
-    {
-        unsigned tid = blockDim.x * blockIdx.x + threadIdx.x;
-        curand_init(seed, tid, 0, &states[tid]);
-    }
-
-    class curandStateManager
-    {
-        curandState_t *_state;
-        unsigned long long _seed;
-
-        void resetSeed()
-        {
-            CUDA_LAUNCH(setup_kernel, BLOCKS, THREADS, _state, _seed);
-
-            POST_LAUNCH_CHECK();
-        }
-
-        public:
-        curandStateManager()
-            : _state(NULL), _seed(0)
-        {
-        }
-
-        ~curandStateManager()
-        {
-            try {
-                if (_state != NULL) {
-                    cudaError_t err = cudaFree(_state);
-                    if (err != cudaErrorCudartUnloading) {
-                        CUDA_CHECK(err);
-                    }
-                }
-            } catch (AfError err) {
-                if (err.getError() != AF_ERR_DRIVER) { // Can happen from cudaErrorDevicesUnavailable
-                    throw err;
-                }
-            }
-        }
-
-        unsigned long long getSeed() const
-        {
-            return _seed;
-        }
-
-        void setSeed(const unsigned long long in_seed)
-        {
-            _seed = in_seed;
-            this->resetSeed();
-        }
-
-        curandState_t* getState()
-        {
-            if(_state)
-                return _state;
-
-            CUDA_CHECK(cudaMalloc((void **)&_state, BLOCKS * THREADS * sizeof(curandState_t)));
-            this->resetSeed();
-            return _state;
-        }
-    };
-
-    curandState_t* getcurandState()
-    {
-        static curandStateManager states[cuda::DeviceManager::MAX_DEVICES];
-
-        int id = cuda::getActiveDeviceId();
-
-        if(!(states[id].getState())) {
-            // states[id] was not initialized. Very bad.
-            // Throw an error here
-        }
-
-        if(states[id].getSeed() != seeds[id]) {
-            states[id].setSeed(seeds[id]);
-        }
-
-        return states[id].getState();
-    }
-
-    template<typename T>
-    __device__
-    void generate_uniform(T *val, curandState_t *state)
-    {
-        *val =  (T)curand(state);
-    }
-
-    template<> __device__
-    void generate_uniform<char>(char *val, curandState_t *state)
-    {
-        *val = curand_uniform(state) > 0.5;
-    }
-
-    template<> __device__
-    void generate_uniform<float>(float *val, curandState_t *state)
-    {
-        *val = curand_uniform(state);
-    }
-
-    template<> __device__
-    void generate_uniform<double>(double *val, curandState_t *state)
-    {
-        *val = curand_uniform_double(state);
-    }
-
-    template<> __device__
-    void generate_uniform<cfloat>(cfloat *cval, curandState_t *state)
-    {
-        cval->x = curand_uniform(state);
-        cval->y = curand_uniform(state);
-    }
-
-    template<> __device__
-    void generate_uniform<cdouble>(cdouble *cval, curandState_t *state)
-    {
-        cval->x = curand_uniform_double(state);
-        cval->y = curand_uniform_double(state);
-    }
-
-
-    __device__
-    void generate_normal(float *val, curandState_t *state)
-    {
-        *val = curand_normal(state);
-    }
-
-
-    __device__
-    void generate_normal(double *val, curandState_t *state)
-    {
-        *val = curand_normal_double(state);
-    }
-
-
-    __device__
-    void generate_normal(cfloat *cval, curandState_t *state)
-    {
-        cval->x = curand_normal(state);
-        cval->y = curand_normal(state);
-    }
-
-
-    __device__
-    void generate_normal(cdouble *cval, curandState_t *state)
-    {
-        cval->x = curand_normal_double(state);
-        cval->y = curand_normal_double(state);
-    }
-
-    template<typename T>
-    __global__ static void
-    uniform_kernel(T *out, curandState_t *states, size_t elements)
-    {
-        unsigned id = blockDim.x * blockIdx.x + threadIdx.x;
-        curandState_t state = states[id];
-        for (int tid = id; tid < elements; tid += blockDim.x * gridDim.x) {
-            T value;
-            generate_uniform<T>(&value, &state);
-            out[tid] = value;
-        }
-        states[id] = state;
-    }
-
-    template<typename T>
-    __global__ static void
-    normal_kernel(T *out, curandState_t *states, size_t elements)
-    {
-        unsigned id = blockDim.x * blockIdx.x + threadIdx.x;
-        curandState_t state = states[id];
-        for (int tid = id; tid < elements; tid += blockDim.x * gridDim.x) {
-            T value;
-            generate_normal(&value, &state);
-            out[tid] = value;
-        }
-        states[id] = state;
-    }
-
-    void setup_states()
-    {
-        curandState_t *state = getcurandState();
-    }
-
-    template<typename T>
-    void randu(T *out, size_t elements)
-    {
-        int device = getActiveDeviceId();
-
-        int threads = THREADS;
-        int blocks  = divup(elements, THREADS);
-        if (blocks > BLOCKS) blocks = BLOCKS;
-
-        curandState_t *state = getcurandState();
-
-        CUDA_LAUNCH(uniform_kernel, blocks, threads, out, state, elements);
-        POST_LAUNCH_CHECK();
-    }
-
-    template<typename T>
-    void randn(T *out, size_t elements)
-    {
-        int device = getActiveDeviceId();
-
-        int threads = THREADS;
-        int blocks  = divup(elements, THREADS);
-        if (blocks > BLOCKS) blocks = BLOCKS;
-
-        curandState_t *state = getcurandState();
-
-        CUDA_LAUNCH(normal_kernel, blocks, threads, out, state, elements);
-
-        POST_LAUNCH_CHECK();
-    }
-}
-}
diff --git a/src/backend/cuda/random.cu b/src/backend/cuda/random.cu
deleted file mode 100644
index 8c75af1c4e..0000000000
--- a/src/backend/cuda/random.cu
+++ /dev/null
@@ -1,69 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/dim4.hpp>
-#include <Array.hpp>
-#include <random.hpp>
-#include <kernel/random.hpp>
-#include <cassert>
-
-namespace cuda
-{
-    template<typename T>
-    Array<T> randu(const af::dim4 &dims)
-    {
-        kernel::setup_states();
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::randu(out.get(), out.elements());
-        return out;
-    }
-
-    template<typename T>
-    Array<T> randn(const af::dim4 &dims)
-    {
-        kernel::setup_states();
-        Array<T> out  = createEmptyArray<T>(dims);
-        kernel::randn(out.get(), out.elements());
-        return out;
-    }
-
-    template Array<float>   randu<float>   (const af::dim4 &dims);
-    template Array<double>  randu<double>  (const af::dim4 &dims);
-    template Array<cfloat>  randu<cfloat>  (const af::dim4 &dims);
-    template Array<cdouble> randu<cdouble> (const af::dim4 &dims);
-    template Array<int>     randu<int>     (const af::dim4 &dims);
-    template Array<uint>    randu<uint>    (const af::dim4 &dims);
-    template Array<intl>    randu<intl>    (const af::dim4 &dims);
-    template Array<uintl>   randu<uintl>   (const af::dim4 &dims);
-    template Array<char>    randu<char>    (const af::dim4 &dims);
-    template Array<uchar>   randu<uchar>   (const af::dim4 &dims);
-    template Array<short>   randu<short>   (const af::dim4 &dims);
-    template Array<ushort>  randu<ushort>  (const af::dim4 &dims);
-
-    template Array<float>   randn<float>   (const af::dim4 &dims);
-    template Array<double>  randn<double>  (const af::dim4 &dims);
-    template Array<cfloat>  randn<cfloat>  (const af::dim4 &dims);
-    template Array<cdouble> randn<cdouble> (const af::dim4 &dims);
-
-
-    void setSeed(const uintl seed)
-    {
-        int id = getActiveDeviceId();
-        kernel::seeds[id] = seed;
-        kernel::setup_states();
-    }
-
-    uintl getSeed()
-    {
-        int id = getActiveDeviceId();
-        return kernel::seeds[id];
-    }
-
-
-}
diff --git a/src/backend/cuda/random.hpp b/src/backend/cuda/random.hpp
deleted file mode 100644
index d7d996f425..0000000000
--- a/src/backend/cuda/random.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    Array<T> randu(const af::dim4 &dims);
-
-    template<typename T>
-    Array<T> randn(const af::dim4 &dims);
-
-    void setSeed(const uintl seed);
-    uintl getSeed();
-}
diff --git a/src/backend/opencl/kernel/random.cl b/src/backend/opencl/kernel/random.cl
deleted file mode 100644
index e956f897ae..0000000000
--- a/src/backend/opencl/kernel/random.cl
+++ /dev/null
@@ -1,331 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- *
- ********************************************************/
-
-/*******************************************************
- * Modified version of Random123 library:
- * https://www.deshawresearch.com/downloads/download_random123.cgi/
- * The original copyright can be seen here:
- *
- * RANDOM123 LICENSE AGREEMENT
- *
- * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * * Redistributions of source code must retain the above copyright notice,
- *   this list of conditions, and the following disclaimer.
- *
- * * Redistributions in binary form must reproduce the above copyright
- *   notice, this list of conditions, and the following disclaimer in the
- *   documentation and/or other materials provided with the distribution.
- *
- * Neither the name of D. E. Shaw Research nor the names of its contributors
- * may be used to endorse or promote products derived from this software
- * without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
- * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
- * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
- * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
- * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
- * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
- * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *********************************************************/
-typedef ulong uint64_t;
-typedef uint  uint32_t;
-
-#define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
-#define R123_STATIC_INLINE inline
-#define R123_0x1p_32f (1.f/4294967296.f)
-#define R123_0x1p_64 (1./(4294967296.*4294967296.))
-
-#ifdef IS_64
-#define SKEIN_KS_PARITY SKEIN_KS_PARITY64
-#define RotL RotL_64
-#define uint_t uint64_t
-
-#define result(a) (a)*R123_0x1p_64
-
-enum r123_enum_threefry64x2 {
-    /*
-    // Output from skein_rot_search: (srs64_B64-X1000)
-    // Random seed = 1. BlockSize = 128 bits. sampleCnt =  1024. rounds =  8, minHW_or=57
-    // Start: Tue Mar  1 10:07:48 2011
-    // rMin = 0.136. #0325[*15] [CRC=455A682F. hw_OR=64. cnt=16384. blkSize= 128].format
-    */
-    R_2_0_0=16,
-    R_2_1_0=42,
-    R_2_2_0=12,
-    R_2_3_0=31,
-    R_2_4_0=16,
-    R_2_5_0=32,
-    R_2_6_0=24,
-    R_2_7_0=21
-    /* 4 rounds: minHW =  4  [  4  4  4  4 ]
-    // 5 rounds: minHW =  8  [  8  8  8  8 ]
-    // 6 rounds: minHW = 16  [ 16 16 16 16 ]
-    // 7 rounds: minHW = 32  [ 32 32 32 32 ]
-    // 8 rounds: minHW = 64  [ 64 64 64 64 ]
-    // 9 rounds: minHW = 64  [ 64 64 64 64 ]
-    //10 rounds: minHW = 64  [ 64 64 64 64 ]
-    //11 rounds: minHW = 64  [ 64 64 64 64 ] */
-};
-#else
-#define SKEIN_KS_PARITY SKEIN_KS_PARITY32
-#define RotL RotL_32
-#define uint_t uint32_t
-
-#ifdef IS_BOOL
-#define result(a) ((a)*R123_0x1p_32f) > 0.5
-#else
-#define result(a) (a)*R123_0x1p_32f
-#endif
-
-enum r123_enum_threefry32x2 {
-    /* Output from skein_rot_search (srs2-X5000.out)
-    // Random seed = 1. BlockSize = 64 bits. sampleCnt =  1024. rounds =  8, minHW_or=28
-    // Start: Tue Jul 12 11:11:33 2011
-    // rMin = 0.334. #0206[*07] [CRC=1D9765C0. hw_OR=32. cnt=16384. blkSize=  64].format   */
-    R_2_0_0=13,
-    R_2_1_0=15,
-    R_2_2_0=26,
-    R_2_3_0= 6,
-    R_2_4_0=17,
-    R_2_5_0=29,
-    R_2_6_0=16,
-    R_2_7_0=24
-
-    /* 4 rounds: minHW =  4  [  4  4  4  4 ]
-    // 5 rounds: minHW =  6  [  6  8  6  8 ]
-    // 6 rounds: minHW =  9  [  9 12  9 12 ]
-    // 7 rounds: minHW = 16  [ 16 24 16 24 ]
-    // 8 rounds: minHW = 32  [ 32 32 32 32 ]
-    // 9 rounds: minHW = 32  [ 32 32 32 32 ]
-    //10 rounds: minHW = 32  [ 32 32 32 32 ]
-    //11 rounds: minHW = 32  [ 32 32 32 32 ] */
-};
-#endif
-
-enum r123_enum_threefry_wcnt {
-    WCNT2=2,
-    WCNT4=4
-};
-
-R123_STATIC_INLINE uint64_t RotL_64(uint64_t x, uint64_t N)
-{
-    return (x << (N & 63)) | (x >> ((64-N) & 63));
-}
-
-R123_STATIC_INLINE uint32_t RotL_32(uint32_t x, uint32_t N)
-{
-    return (x << (N & 31)) | (x >> ((32-N) & 31));
-}
-
-#define SKEIN_MK_64(hi32,lo32)  ((lo32) + (((uint64_t) (hi32)) << 32))
-#define SKEIN_KS_PARITY64         SKEIN_MK_64(0x1BD11BDA,0xA9FC1A22)
-#define SKEIN_KS_PARITY32         0x1BD11BDA
-
-
-// http://www.thesalmons.org/john/random123/releases/1.06/docs/structr123_1_1Threefry2x32__R.html#af5be46f8426cfcd86e75327e4b3750b0
-#define Nrounds 16
-
-struct r123array2
-{
-    uint_t v[2];
-};
-
-typedef struct r123array2 threefry2_ctr_t;
-typedef struct r123array2 threefry2_key_t;
-typedef struct r123array2 threefry2_ukey_t;
-
-R123_STATIC_INLINE
-threefry2_key_t threefry2keyinit(threefry2_ukey_t uk) { return uk; }
-
-R123_STATIC_INLINE
-threefry2_ctr_t threefry2_R(threefry2_ctr_t in, threefry2_key_t k)
-{
-    threefry2_ctr_t X;
-    uint_t ks[2+1];
-    int  i; /* avoid size_t to avoid need for stddef.h */
-    ks[2] =  SKEIN_KS_PARITY;
-    for (i=0;i < 2; i++)
-    {
-        ks[i] = k.v[i];
-        X.v[i]  = in.v[i];
-        ks[2] ^= k.v[i];
-    }
-
-    /* Insert initial key before round 0 */
-    X.v[0] += ks[0]; X.v[1] += ks[1];
-
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_0_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_1_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_2_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_3_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=1) */
-    X.v[0] += ks[1]; X.v[1] += ks[2];
-    X.v[1] += 1;     /* X.v[2-1] += r  */
-
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_4_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_5_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_6_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_7_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=2) */
-    X.v[0] += ks[2]; X.v[1] += ks[0];
-    X.v[1] += 2;
-
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_0_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_1_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_2_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_3_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=3) */
-    X.v[0] += ks[0]; X.v[1] += ks[1];
-    X.v[1] += 3;
-
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_4_0); X.v[1] ^= X.v[0];
-
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_5_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_6_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_7_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=4) */
-    X.v[0] += ks[1]; X.v[1] += ks[2];
-    X.v[1] += 4;
-
-#if Nrounds > 16
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_0_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_1_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_2_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_3_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=4) */
-    X.v[0] += ks[2]; X.v[1] += ks[0];
-    X.v[1] += 5;
-#endif
-
-#if Nrounds > 20
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_0_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_1_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_2_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_3_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=3) */
-    X.v[0] += ks[0]; X.v[1] += ks[1];
-    X.v[1] += 6;
-#endif
-
-#if Nrounds > 24
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_4_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_5_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_6_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_7_0); X.v[1] ^= X.v[0];
-
-    /* InjectKey(r=4) */
-    X.v[0] += ks[1]; X.v[1] += ks[2];
-    X.v[1] += 7;
-#endif
-
-#if Nrounds > 28
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_0_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_1_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_2_0); X.v[1] ^= X.v[0];
-    X.v[0] += X.v[1]; X.v[1] = RotL(X.v[1],R_2_3_0); X.v[1] ^= X.v[0];
-
-        /* InjectKey(r=4) */
-    X.v[0] += ks[2]; X.v[1] += ks[0];
-    X.v[1] += 8;
-#endif
-
-    return X;
-}
-
-#define threefry2(c,k) threefry2_R(c, k)
-
-#ifdef randu
-void generate(T *one, T *two, threefry2_ctr_t *c, threefry2_key_t k)
-{
-    threefry2_ctr_t r = threefry2(*c, k);
-    c->v[0] = c->v[0] + 1;
-
-    *one = result(r.v[0]);
-    *two = result(r.v[1]);
-}
-#endif
-
-#ifdef randn
-void generate(T *one, T *two, threefry2_ctr_t *c, threefry2_key_t k)
-{
-    threefry2_ctr_t r = threefry2(*c, k);
-    c->v[0] = c->v[0] + 1;
-
-    T u1 = result(r.v[0]);
-    T u2 = result(r.v[1]);
-
-#if defined(IS_APPLE) // Because Apple is.. "special"
-    T R     = sqrt((T)(-2.0) * log10(u1) * (T)log10_val);
-#else
-    T R     = sqrt((T)(-2.0) * log(u1));
-#endif
-
-    T Theta = 2 * (T)PI_VAL * u2;
-
-    *one = R * sin(Theta);
-    *two = R * cos(Theta);
-}
-#endif
-
-#ifdef randi
-void generate(T *one, T *two, threefry2_ctr_t *c, threefry2_key_t k)
-{
-    threefry2_ctr_t r = threefry2(*c, k);
-    c->v[0] = c->v[0] + 1;
-
-    *one = (T)r.v[0];
-    *two = (T)r.v[1];
-}
-#endif
-
-__kernel void random(__global T *output, unsigned numel,
-                    unsigned counter, unsigned lo, unsigned hi)
-{
-    unsigned gid = get_group_id(0);
-    unsigned off = get_local_size(0);
-    unsigned tid =  off * gid * repeat + get_local_id(0);
-
-    threefry2_key_t k = {{tid, lo}};
-    threefry2_ctr_t c = {{counter, hi}};
-
-    T one, two;
-
-    if (gid < get_num_groups(0) - 1) {
-        for(int i = 0; i < repeat; i+=2) {
-            generate(&one, &two, &c, k);
-            output[tid      ] = one;
-            output[tid + off] = two;
-            tid += 2 * off;
-        }
-    } else {
-        for(int i = 0; i < repeat; i+=2) {
-            generate(&one, &two, &c, k);
-            if (tid       < numel) output[tid      ] = one;
-            if (tid + off < numel) output[tid + off] = two;
-            tid += 2 * off;
-        }
-    }
-}
diff --git a/src/backend/opencl/kernel/random.hpp b/src/backend/opencl/kernel/random.hpp
deleted file mode 100644
index 8d6a017030..0000000000
--- a/src/backend/opencl/kernel/random.hpp
+++ /dev/null
@@ -1,146 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <platform.hpp>
-#include <af/defines.h>
-#include <kernel_headers/random.hpp>
-#include <traits.hpp>
-#include <sstream>
-#include <string>
-#include <mutex>
-#include <map>
-#include <dispatch.hpp>
-#include <err_opencl.hpp>
-#include <debug_opencl.hpp>
-#include <program.hpp>
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
-namespace opencl
-{
-    namespace kernel
-    {
-        static const uint REPEAT  = 32;
-        static const uint THREADS = 256;
-
-        static uint random_seed[2] = {0, 0};
-        static unsigned counter;
-
-        template<typename T, bool isRandu>
-        struct random_name
-        {
-            const char *name()
-            {
-                return "randi";
-            }
-        };
-
-        template<typename T>
-        struct random_name<T, false>
-        {
-            const char *name()
-            {
-                return "randn";
-            }
-        };
-
-        template<>
-        struct random_name<float, true>
-        {
-            const char *name()
-            {
-                return "randu";
-            }
-        };
-
-        template<>
-        struct random_name<char, true>
-        {
-            const char *name()
-            {
-                return "randu";
-            }
-        };
-
-        template<>
-        struct random_name<double, true>
-        {
-            const char *name()
-            {
-                return "randu";
-            }
-        };
-
-        template<typename> static bool isDouble() { return false; }
-        template<> STATIC_ bool isDouble<double>() { return true; }
-        template<> STATIC_ bool isDouble<cdouble>() { return true; }
-
-        template<typename T, bool isRandu>
-        void random(cl::Buffer out, int elements)
-        {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  ranProgs;
-                static std::map<int, Kernel*>   ranKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                        Program::Sources setSrc;
-                        setSrc.emplace_back(random_cl, random_cl_len);
-
-                        std::ostringstream options;
-                        options << " -D T=" << dtype_traits<T>::getName()
-                                << " -D repeat="<< REPEAT
-#if defined(OS_MAC) // Because apple is "special"
-                                << " -D IS_APPLE"
-                                << " -D log10_val=" << std::log(10.0)
-#endif
-                                << " -D " << random_name<T, isRandu>().name();
-
-                        if (std::is_same<T, double>::value) {
-                            options << " -D USE_DOUBLE";
-                            options << " -D IS_64";
-                        }
-
-                        if (std::is_same<T, char>::value) {
-                            options << " -D IS_BOOL";
-                        }
-
-                        cl::Program prog;
-                        buildProgram(prog, random_cl, random_cl_len, options.str());
-                        ranProgs[device] = new Program(prog);
-                        ranKernels[device] = new Kernel(*ranProgs[device], "random");
-                    });
-
-                auto randomOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(*ranKernels[device]);
-
-                uint groups = divup(elements, THREADS * REPEAT);
-                counter += divup(elements, THREADS * groups);
-
-                NDRange local(THREADS, 1);
-                NDRange global(THREADS * groups, 1);
-
-                randomOp(EnqueueArgs(getQueue(), global, local),
-                         out, elements, counter, random_seed[0], random_seed[1]);
-                CL_DEBUG_FINISH(getQueue());
-            } catch(cl::Error ex) {
-                CL_TO_AF_ERROR(ex);
-            }
-        }
-    }
-}
diff --git a/src/backend/opencl/random.cpp b/src/backend/opencl/random.cpp
deleted file mode 100644
index 39d8503384..0000000000
--- a/src/backend/opencl/random.cpp
+++ /dev/null
@@ -1,81 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <af/dim4.hpp>
-#include <Array.hpp>
-#include <random.hpp>
-#include <cassert>
-#include <kernel/random.hpp>
-#include <err_opencl.hpp>
-
-namespace opencl
-{
-    template<typename T>
-    Array<T> randu(const af::dim4 &dims)
-    {
-        verifyDoubleSupport<T>();
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::random<T, true>(*out.get(), out.elements());
-        return out;
-    }
-
-    template<typename T>
-    Array<T> randn(const af::dim4 &dims)
-    {
-        verifyDoubleSupport<T>();
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::random<T, false>(*out.get(), out.elements());
-        return out;
-    }
-
-    template Array<float>  randu<float>   (const af::dim4 &dims);
-    template Array<double> randu<double>  (const af::dim4 &dims);
-    template Array<int>    randu<int>     (const af::dim4 &dims);
-    template Array<uint>   randu<uint>    (const af::dim4 &dims);
-    template Array<intl>   randu<intl>    (const af::dim4 &dims);
-    template Array<uintl>  randu<uintl>   (const af::dim4 &dims);
-    template Array<short>  randu<short>   (const af::dim4 &dims);
-    template Array<ushort> randu<ushort>  (const af::dim4 &dims);
-    template Array<char>   randu<char>    (const af::dim4 &dims);
-    template Array<uchar>  randu<uchar>   (const af::dim4 &dims);
-
-    template Array<float>  randn<float>   (const af::dim4 &dims);
-    template Array<double> randn<double>  (const af::dim4 &dims);
-
-#define COMPLEX_RANDOM(fn, T, TR, is_randu)                 \
-    template<> Array<T> fn<T>(const af::dim4 &dims)         \
-    {                                                       \
-        Array<T> out = createEmptyArray<T>(dims);           \
-        dim_t elements = out.elements() * 2;             \
-        kernel::random<TR, is_randu>(*out.get(), elements); \
-        return out;                                         \
-    }                                                       \
-
-    COMPLEX_RANDOM(randu, cfloat, float, true)
-    COMPLEX_RANDOM(randu, cdouble, double, true)
-    COMPLEX_RANDOM(randn, cfloat, float, false)
-    COMPLEX_RANDOM(randn, cdouble, double, false)
-
-
-    void setSeed(const uintl seed)
-    {
-        uintl hi = (seed & 0xffffffff00000000) >> 32;
-        uintl lo = (seed & 0x00000000ffffffff);
-        kernel::random_seed[0] = (unsigned)hi;
-        kernel::random_seed[1] = (unsigned)lo;
-        kernel::counter = 0;
-    }
-
-    uintl getSeed()
-    {
-        uintl hi = kernel::random_seed[0];
-        uintl lo = kernel::random_seed[1];
-        return hi << 32 | lo;
-    }
-}
diff --git a/src/backend/opencl/random.hpp b/src/backend/opencl/random.hpp
deleted file mode 100644
index e7c812d040..0000000000
--- a/src/backend/opencl/random.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace opencl
-{
-    template<typename T>
-    Array<T> randu(const af::dim4 &dims);
-
-    template<typename T>
-    Array<T> randn(const af::dim4 &dims);
-
-    void setSeed(const uintl seed);
-    uintl getSeed();
-}

From 3321400701f93a7cab4f622ed1ba4e6e82111702 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 7 Sep 2016 15:06:35 -0400
Subject: [PATCH 0883/2677] BUGFIX: Fix condition when imag is set for
 reduce_all

---
 src/api/c/reduce.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 26dc66de66..d1e0a74967 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -248,7 +248,7 @@ static af_err reduce_all_common(double *real_val, double *imag_val, const af_arr
         ARG_ASSERT(2, in_info.ndims() > 0);
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
-        if (!imag_val) *imag_val = 0;
+        if (imag_val != NULL) *imag_val = 0;
 
         cfloat  cfval;
         cdouble cdval;
@@ -299,7 +299,7 @@ static af_err reduce_all_promote(double *real_val, double *imag_val, const af_ar
 
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
-        if (!imag_val) *imag_val = 0;
+        if (imag_val) *imag_val = 0;
 
         cfloat  cfval;
         cdouble cdval;

From ba68877f62a106a955cd6aead93a23363fd8ff70 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 7 Sep 2016 15:08:48 -0400
Subject: [PATCH 0884/2677] Add setAxesLimit and setAxesTitle functions to
 graphics

---
 include/af/graphics.h        | 132 +++++++++++++++++++++++++++++++++++
 src/api/c/window.cpp         | 126 +++++++++++++++++++++++++++++++++
 src/api/cpp/graphics.cpp     |  28 ++++++++
 src/api/unified/graphics.cpp |  27 +++++++
 4 files changed, 313 insertions(+)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 387e02c7b2..440fb16452 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -388,6 +388,71 @@ class AFAPI Window {
                          const char* const title = NULL);
 #endif
 
+#if AF_API_VERSION >= 34
+        /**
+           Setup the axes limits for a 2D histogram/plot/vector field
+
+           This function computes the minimum and maximum for each dimension
+
+           \param[in] x the data to compute the limits for x-axis.
+           \param[in] y the data to compute the limits for y-axis.
+
+           \ingroup gfx_func_window
+        */
+        void setAxesLimits(const array &x, const array &y);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Setup the axes limits for a histogram/plot/surface/vector field
+
+           This function computes the minimum and maximum for each dimension
+
+           \param[in] x the data to compute the limits for x-axis.
+           \param[in] y the data to compute the limits for y-axis.
+           \param[in] z the data to compute the limits for z-axis.
+
+           \ingroup gfx_func_window
+        */
+        void setAxesLimits(const array &x, const array &y, const array &z);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Setup the axes limits for a histogram/plot/surface/vector field
+
+           This function sets the axes limits to the ones provided by the user.
+
+           \param[in] xmin is the minimum on x-axis
+           \param[in] xmax is the maximum on x-axis
+           \param[in] ymin is the minimum on y-axis
+           \param[in] ymax is the maximum on y-axis
+           \param[in] zmin is the minimum on z-axis
+           \param[in] zmax is the maximum on z-axis
+
+           \ingroup gfx_func_window
+        */
+        void setAxesLimits(const float xmin, const float xmax,
+                           const float ymin, const float ymax,
+                           const float zmin = -1.0, const float zmax = 1.0);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Setup the axes titles for a plot/surface/vector field
+
+           This function creates the axis titles for a chart.
+
+           \param[in] xtitle is the name of the x-axis
+           \param[in] ytitle is the name of the y-axis
+           \param[in] ztitle is the name of the z-axis
+
+           \ingroup gfx_func_window
+        */
+        void setAxesTitles(const char * const xtitle = "X-Axis",
+                           const char * const ytitle = "Y-Axis",
+                           const char * const ztitle = "Z-Axis");
+#endif
         /**
            Setup grid layout for multiview mode in a window
 
@@ -879,6 +944,73 @@ AFAPI af_err af_draw_vector_field_2d(
 */
 AFAPI af_err af_grid(const af_window wind, const int rows, const int cols);
 
+#if AF_API_VERSION >= 34
+/**
+   C Interface for setting axes limits for a histogram/plot/surface/vector field
+
+   This function computes the minimum and maximum for each dimension
+
+   \param[in] wind is the window handle
+   \param[in] x the data to compute the limits for x-axis.
+   \param[in] y the data to compute the limits for y-axis.
+   \param[in] z the data to compute the limits for z-axis.
+   \param[in] props is structure \ref af_cell that has the properties that
+              are used for the current rendering.
+
+   \note Set \p to NULL if the chart is 2D.
+
+   \ingroup gfx_func_window
+*/
+AFAPI af_err af_set_axes_limits_compute(const af_window wind,
+                                        const af_array x, const af_array y, const af_array z,
+                                        const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface for setting axes limits for a histogram/plot/surface/vector field
+
+   This function sets the axes limits to the ones provided by the user.
+
+   \param[in] wind is the window handle
+   \param[in] xmin is the minimum on x-axis
+   \param[in] xmax is the maximum on x-axis
+   \param[in] ymin is the minimum on y-axis
+   \param[in] ymax is the maximum on y-axis
+   \param[in] zmin is the minimum on z-axis
+   \param[in] zmax is the maximum on z-axis
+   \param[in] props is structure \ref af_cell that has the properties that
+              are used for the current rendering.
+
+   \ingroup gfx_func_window
+*/
+AFAPI af_err af_set_axes_limits(const af_window wind,
+                                const float xmin, const float xmax,
+                                const float ymin, const float ymax,
+                                const float zmin, const float zmax,
+                                const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface wrapper for setting axes titles for histogram/plot/surface/vector field
+
+   \param[in] wind is the window handle
+   \param[in] xtitle is the name of the x-axis
+   \param[in] ytitle is the name of the y-axis
+   \param[in] ztitle is the name of the z-axis
+   \param[in] props is structure \ref af_cell that has the properties that
+              are used for the current rendering.
+
+   \ingroup gfx_func_window
+*/
+AFAPI af_err af_set_axes_titles(const af_window wind,
+                                const char * const xtitles,
+                                const char * const ytitles,
+                                const char * const ztitles,
+                                const af_cell* const props);
+#endif
+
 /**
    C Interface wrapper for showing a window
 
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index b3f2d3f98e..6eccdaa54a 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -9,6 +9,7 @@
 
 
 #include <af/graphics.h>
+#include <af/algorithm.h>
 
 #include <graphics_common.hpp>
 #include <err_common.hpp>
@@ -136,6 +137,131 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
 #endif
 }
 
+af_err af_set_axes_limits_compute(const af_window wind,
+                                  const af_array x, const af_array y, const af_array z,
+                                  const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+
+        // Recreate a chart map
+        ForgeManager& fgMngr = ForgeManager::getInstance();
+
+        forge::Chart* chart = NULL;
+        // The ctype here below doesn't really matter as it is only fetching
+        // the chart. It will not set it.
+        // If this is actually being done, then it is extremely bad.
+        // But lets have a check anyway.
+        fg_chart_type ctype = (z == NULL || z == 0) ? FG_CHART_2D : FG_CHART_3D;
+
+        if (props->col > -1 && props->row > -1)
+            chart = fgMngr.getChart(window, props->row, props->col, ctype);
+        else
+            chart = fgMngr.getChart(window, 0, 0, ctype);
+
+        double xmin = -1, xmax = 1;
+        double ymin = -1, ymax = 1;
+        double zmin = -1, zmax = 1;
+        AF_CHECK(af_min_all(&xmin, NULL, x));
+        AF_CHECK(af_max_all(&xmax, NULL, x));
+        AF_CHECK(af_min_all(&ymin, NULL, y));
+        AF_CHECK(af_max_all(&ymax, NULL, y));
+
+        if(ctype == FG_CHART_3D) {
+            AF_CHECK(af_min_all(&zmin, NULL, z));
+            AF_CHECK(af_max_all(&zmax, NULL, z));
+        }
+        chart->setAxesLimits(xmin, xmax, ymin, ymax, zmin, zmax);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_axes_limits(const af_window wind,
+                          const float xmin, const float xmax,
+                          const float ymin, const float ymax,
+                          const float zmin, const float zmax,
+                          const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+
+        // Recreate a chart map
+        ForgeManager& fgMngr = ForgeManager::getInstance();
+
+        forge::Chart* chart = NULL;
+        // The ctype here below doesn't really matter as it is only fetching
+        // the chart. It will not set it.
+        // If this is actually being done, then it is extremely bad.
+        fg_chart_type ctype = FG_CHART_2D;
+
+        if (props->col > -1 && props->row > -1)
+            chart = fgMngr.getChart(window, props->row, props->col, ctype);
+        else
+            chart = fgMngr.getChart(window, 0, 0, ctype);
+
+        chart->setAxesLimits(xmin, xmax, ymin, ymax, zmin, zmax);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_axes_titles(const af_window wind,
+                          const char * const xtitle,
+                          const char * const ytitle,
+                          const char * const ztitle,
+                          const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+
+        // Recreate a chart map
+        ForgeManager& fgMngr = ForgeManager::getInstance();
+
+        forge::Chart* chart = NULL;
+        // The ctype here below doesn't really matter as it is only fetching
+        // the chart. It will not set it.
+        // If this is actually being done, then it is extremely bad.
+        fg_chart_type ctype = FG_CHART_2D;
+
+        if (props->col > -1 && props->row > -1)
+            chart = fgMngr.getChart(window, props->row, props->col, ctype);
+        else
+            chart = fgMngr.getChart(window, 0, 0, ctype);
+
+        chart->setAxesTitles(xtitle, ytitle, ztitle);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
 af_err af_show(const af_window wind)
 {
 #if defined(WITH_GRAPHICS)
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 4404a0a421..37fc246360 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -177,6 +177,34 @@ void Window::grid(const int rows, const int cols)
     AF_THROW(af_grid(get(), rows, cols));
 }
 
+void Window::setAxesLimits(const array& x, const array& y)
+{
+    af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), NULL, &temp));
+}
+
+void Window::setAxesLimits(const array& x, const array& y, const array &z)
+{
+    af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), z.get(), &temp));
+}
+
+void Window::setAxesLimits(const float xmin, const float xmax,
+                           const float ymin, const float ymax,
+                           const float zmin, const float zmax)
+{
+    af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_set_axes_limits(get(), xmin, xmax, ymin, ymax, zmin, zmax, &temp));
+}
+
+void Window::setAxesTitles(const char * const xtitle,
+                           const char * const ytitle,
+                           const char * const ztitle)
+{
+    af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_set_axes_titles(get(), xtitle, ytitle, ztitle, &temp));
+}
+
 void Window::show()
 {
     AF_THROW(af_show(get()));
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index f46bb11cb2..785d57b06c 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -148,6 +148,33 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
     return CALL(wind, rows, cols);
 }
 
+af_err af_set_axes_limits_compute(const af_window wind,
+                                  const af_array x, const af_array y, const af_array z,
+                                  const af_cell* const props)
+{
+    CHECK_ARRAYS(x, y);
+    if(z) CHECK_ARRAYS(z);
+    return CALL(wind, x, y, z, props);
+}
+
+af_err af_set_axes_limits(const af_window wind,
+                          const float xmin, const float xmax,
+                          const float ymin, const float ymax,
+                          const float zmin, const float zmax,
+                          const af_cell* const props)
+{
+    return CALL(wind, xmin, xmax, ymin, ymax, zmin, zmax, props);
+}
+
+af_err af_set_axes_titles(const af_window wind,
+                          const char * const xtitle,
+                          const char * const ytitle,
+                          const char * const ztitle,
+                          const af_cell* const props)
+{
+    return CALL(wind, xtitle, ytitle, ztitle, props);
+}
+
 af_err af_show(const af_window wind)
 {
     return CALL(wind);

From 3f0a7902e2a825da826ce294c40b0d98e7f8e6e2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 7 Sep 2016 15:09:57 -0400
Subject: [PATCH 0885/2677] Remove calls to setAxesTitles from plot, surface,
 vector field

---
 src/api/c/plot.cpp         | 2 --
 src/api/c/surface.cpp      | 2 --
 src/api/c/vector_field.cpp | 2 --
 3 files changed, 6 deletions(-)

diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 792f2f5383..37ffe4cbaa 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -63,8 +63,6 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
 
     plot->setColor(1.0, 0.0, 0.0, 1.0);
 
-    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
-
     copy_plot<T>(in, plot);
 
     return chart;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 450057b69c..d29dedfa31 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -81,8 +81,6 @@ forge::Chart* setup_surface(const forge::Window* const window,
 
     surface->setColor(0.0, 1.0, 0.0, 1.0);
 
-    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
-
     copy_surface<T>(Z, surface);
 
     return chart;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 7a092d6633..d0669269b4 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -60,8 +60,6 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
 
     vectorfield->setColor(1.0, 1.0, 0.0, 1.0);
 
-    chart->setAxesTitles("X Axis", "Y Axis", "Z Axis");
-
     copy_vector_field<T>(pIn, dIn, vectorfield);
 
     return chart;

From 79ed86528c8e54b8bc5cbd0d9abc7d673434fd39 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 7 Sep 2016 15:37:53 -0400
Subject: [PATCH 0886/2677] PERF: Improvements to bilateral filter for CUDA and
 OPENCL

---
 src/backend/cuda/kernel/bilateral.hpp  | 24 ++++++++++++------------
 src/backend/opencl/kernel/bilateral.cl | 24 +++++++++++++-----------
 2 files changed, 25 insertions(+), 23 deletions(-)

diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 93711a99f6..80e6a657cb 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -35,12 +35,6 @@ int clamp(int f, int a, int b)
     return max(a, min(f, b));
 }
 
-inline __device__
-float gaussian1d(float x, float variance)
-{
-    return __expf((x * x) / variance);
-}
-
 template<typename inType, typename outType>
 inline __device__
 void load2ShrdMem(outType * shrd, const inType * const in,
@@ -66,8 +60,10 @@ void bilateralKernel(Param<outType> out, CParam<inType> in,
     const int padding     = 2 * radius;
     const int window_size = padding + 1;
     const int shrdLen     = THREADS_X + padding;
-    const float variance_range = -2.f * sigma_color * sigma_color;
-    const float variance_space = -2.f * sigma_space * sigma_space;
+    const float variance_range = sigma_color * sigma_color;
+    const float variance_space = sigma_space * sigma_space;
+    const float variance_space_neg2 = -2.0 * variance_space;
+    const float inv_variance_range_neg2 = -0.5 / variance_range;
 
     // gfor batch offsets
     unsigned b2 = blockIdx.x / nBBS0;
@@ -85,7 +81,7 @@ void bilateralKernel(Param<outType> out, CParam<inType> in,
     if (lx<window_size && ly<window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = __expf(((x*x) + (y*y)) / variance_space);
+        gauss2d[ly*window_size+lx] = __expf(((x*x) + (y*y)) / variance_space_neg2);
     }
 
     // pull image to local memory
@@ -105,18 +101,22 @@ void bilateralKernel(Param<outType> out, CParam<inType> in,
         const outType center_color = localMem[ly*shrdLen+lx];
         outType res  = 0;
         outType norm = 0;
+        int joff = (ly - radius)*shrdLen + (lx - radius);
+        int goff = 0;
+
 #pragma unroll
         for(int wj=0; wj<window_size; ++wj) {
-            int joff = (ly+wj-radius)*shrdLen + (lx-radius);
-            int goff = wj*window_size;
 #pragma unroll
             for(int wi=0; wi<window_size; ++wi) {
                 const outType tmp_color   = localMem[joff+wi];
-                const outType gauss_range = gaussian1d(center_color - tmp_color, variance_range);
+                const outType c = center_color - tmp_color;
+                const outType gauss_range = __expf(c * c * inv_variance_range_neg2);
                 const outType weight      = gauss2d[goff+wi] * gauss_range;
                 norm += weight;
                 res  += tmp_color * weight;
             }
+            joff += shrdLen;
+            goff += window_size;
         }
         optr[gy*out.strides[1]+gx] = res / norm;
     }
diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index bf734b88e9..4b7baa8aa9 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -25,11 +25,6 @@ void load2LocalMem(__local outType *  shrd,
     shrd[ lIdx(lx, ly, shrdStride, 1) ] = (outType)in[ lIdx(gx_, gy_, inStride1, inStride0) ];
 }
 
-float gaussian1d(float x, float variance)
-{
-    return native_exp((x * x) / (-2.f * variance));
-}
-
 __kernel
 void bilateral(__global outType *        d_dst,
                KParam                    oInfo,
@@ -44,8 +39,10 @@ void bilateral(__global outType *        d_dst,
     const int padding     = 2 * radius;
     const int window_size = padding + 1;
     const int shrdLen     = get_local_size(0) + padding;
-    const float variance_range = -2.f * sigma_color * sigma_color;
-    const float variance_space = -2.f * sigma_space * sigma_space;
+    const float variance_range = sigma_color * sigma_color;
+    const float variance_space = sigma_space * sigma_space;
+    const float variance_space_neg2 = -2.0 * variance_space;
+    const float inv_variance_range_neg2 = -0.5 / (variance_range);
 
     // gfor batch offsets
     unsigned b2 = get_group_id(0) / nBBS0;
@@ -63,7 +60,7 @@ void bilateral(__global outType *        d_dst,
     if (lx<window_size && ly<window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = native_exp( ((x*x) + (y*y)) / variance_space);
+        gauss2d[ly*window_size+lx] = native_exp(((x*x) + (y*y)) / variance_space_neg2);
     }
 
     int s0 = iInfo.strides[0];
@@ -86,18 +83,23 @@ void bilateral(__global outType *        d_dst,
         outType center_color = localMem[ly*shrdLen+lx];
         outType res  = 0;
         outType norm = 0;
+
+        int joff = (ly - radius)*shrdLen + (lx-radius);
+        int goff = 0;
+
 #pragma unroll
         for(int wj=0; wj<window_size; ++wj) {
-            int joff = (ly+wj-radius)*shrdLen + (lx-radius);
-            int goff = wj*window_size;
 #pragma unroll
             for(int wi=0; wi<window_size; ++wi) {
                 outType tmp_color   = localMem[joff+wi];
-                outType gauss_range = gaussian1d(center_color - tmp_color, variance_range);
+                const outType c = center_color - tmp_color;
+                outType gauss_range = native_exp(c * c * inv_variance_range_neg2);
                 outType weight      = gauss2d[goff+wi] * gauss_range;
                 norm += weight;
                 res  += tmp_color * weight;
             }
+            joff += shrdLen;
+            goff += window_size;
         }
         out[gy*oInfo.strides[1] + gx] = res / norm;
     }

From d51c017c63ca88e36d41614ba1069abcb7d0b4eb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Wed, 7 Sep 2016 15:38:45 -0400
Subject: [PATCH 0887/2677] Cleaning up example function

- Removing unnecessary swith cases
- Moving core cpu code from exampleFunction to kernel folder
---
 src/backend/cpu/exampleFunction.cpp         | 43 ++----------------
 src/backend/cpu/kernel/exampleFunction.hpp  | 49 +++++++++++++++++++++
 src/backend/cuda/kernel/exampleFunction.hpp | 13 ++----
 src/backend/opencl/kernel/example.cl        | 13 ++----
 4 files changed, 61 insertions(+), 57 deletions(-)
 create mode 100644 src/backend/cpu/kernel/exampleFunction.hpp

diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index c953528e8e..04b361f55c 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -12,6 +12,7 @@
                                         // ArrayInfo base class
 
 #include <exampleFunction.hpp>          // cpu backend function header
+#include <kernel/exampleFunction.hpp>   // Function implementation header
 
 #include <err_cpu.hpp>                  // error check functions and Macros
                                         // specific to cpu backend
@@ -43,45 +44,9 @@ Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum
                                         // file to know what are the different types you
                                         // can create.
 
-    dim4 aDims    = a.dims();           // you can retrieve dimensions
-    dim4 bDims    = b.dims();
-    dim4 oDims    = out.dims();
-
-    dim_t aOffset = a.getOffset();      // you can retrieve the offset - used when given array
-                                        // is an sub-array pointing to some other array and
-                                        // doesn't have memory of its own
-    dim_t bOffset = b.getOffset();
-
-    dim4 aStrides = a.strides();        // you can retrieve strides
-    dim4 bStrides = b.strides();
-    dim4 oStrides = out.strides();
-
-    const T* src1 = a.get();            // cpu::Array<T>::get returns the pointer to the
-                                        // memory allocated for that Array
-    const T* src2 = b.get();            // cpu::Array<T>::get returns the pointer to the
-                                        // memory allocated for that Array
-    src1 += aOffset;
-    src2 += bOffset;
-
-    T* dst = out.get();
-
-    // Implement your algorithm and write results to dst
-    for(int j=0; j<aDims[1]; ++j) {
-        for (int i=0; i<aDims[0]; ++i) {
-
-            int src1Idx = i*aStrides[0] + j*aStrides[1];
-            int src2Idx = i*bStrides[0] + j*bStrides[1];
-            int dstIdx  = i*oStrides[0] + j*oStrides[1];
-
-            // kernel algorithm goes here
-            switch(method) {
-                case 1: dst[dstIdx] = src1[src1Idx] + src2[src2Idx]; break;
-                case 2: dst[dstIdx] = src1[src1Idx] - src2[src2Idx]; break;
-                case 3: dst[dstIdx] = src1[src1Idx] * src2[src2Idx]; break;
-                case 4: dst[dstIdx] = src1[src1Idx] / src2[src2Idx]; break;
-            }
-        }
-    }
+    // Enqueue the function call on the worker thread
+    // This code will be present in src/backend/cpu/kernel/exampleFunction.hpp
+    getQueue().enqueue(kernel::exampleFunction<T>, out, a, b, method);
 
     return out;                         // return the result
 }
diff --git a/src/backend/cpu/kernel/exampleFunction.hpp b/src/backend/cpu/kernel/exampleFunction.hpp
new file mode 100644
index 0000000000..6122579b27
--- /dev/null
+++ b/src/backend/cpu/kernel/exampleFunction.hpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <utility.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T>
+void exampleFunction(Array<T> out, Array<T> const a, Array<T> const b, const af_someenum_t method)
+{
+    dim4 oDims    = out.dims();
+
+    dim4 aStrides = a.strides();        // you can retrieve strides
+    dim4 bStrides = b.strides();
+    dim4 oStrides = out.strides();
+
+    const T* src1 = a.get();            // cpu::Array<T>::get returns the pointer to the
+                                        // memory allocated for that Array (with proper offsets)
+    const T* src2 = b.get();            // cpu::Array<T>::get returns the pointer to the
+                                        // memory allocated for that Array (with proper offsets)
+    T* dst = out.get();
+
+    // Implement your algorithm and write results to dst
+    for(int j=0; j<oDims[1]; ++j) {
+        for (int i=0; i<oDims[0]; ++i) {
+
+            int src1Idx = i + j*aStrides[1];
+            int src2Idx = i + j*bStrides[1];
+            int dstIdx  = i + j*oStrides[1];
+
+            // kernel algorithm goes here
+            dst[dstIdx] = src1[src1Idx] + src2[src2Idx];
+        }
+    }
+}
+
+}
+}
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index e1339b552f..4d1011b27f 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -43,21 +43,16 @@ void exampleFuncKernel(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t
 
     if ( i<a.dims[0] && j<a.dims[1] ) {
         // if needed use strides array to compute linear index of arrays
-        int src1Idx = i*a.strides[0] + j*a.strides[1];
-        int src2Idx = i*b.strides[0] + j*b.strides[1];
-        int dstIdx  = i*c.strides[0] + j*c.strides[1];
+        int src1Idx = i + j*a.strides[1];
+        int src2Idx = i + j*b.strides[1];
+        int dstIdx  = i + j*c.strides[1];
 
         T* dst = c.ptr;
         const T* src1 = a.ptr;
         const T* src2 = b.ptr;
 
         // kernel algorithm goes here
-        switch(p) {
-            case 1: dst[dstIdx] = src1[src1Idx] + src2[src2Idx]; break;
-            case 2: dst[dstIdx] = src1[src1Idx] - src2[src2Idx]; break;
-            case 3: dst[dstIdx] = src1[src1Idx] * src2[src2Idx]; break;
-            case 4: dst[dstIdx] = src1[src1Idx] / src2[src2Idx]; break;
-        }
+        dst[dstIdx] = src1[src1Idx] + src2[src2Idx];
     }
 }
 
diff --git a/src/backend/opencl/kernel/example.cl b/src/backend/opencl/kernel/example.cl
index 7ca84560e1..94edd711b3 100644
--- a/src/backend/opencl/kernel/example.cl
+++ b/src/backend/opencl/kernel/example.cl
@@ -22,16 +22,11 @@ void example(__global T *       d_dst,
 
     if ( i<iInfo1.dims[0] && j<iInfo1.dims[1] ) {
         // if needed use strides array to compute linear index of arrays
-        int src1Idx = i*iInfo1.strides[0] + j*iInfo1.strides[1];
-        int src2Idx = i*iInfo2.strides[0] + j*iInfo2.strides[1];
-        int dstIdx  = i* oInfo.strides[0] + j* oInfo.strides[1];
+        int src1Idx = i + j*iInfo1.strides[1];
+        int src2Idx = i + j*iInfo2.strides[1];
+        int dstIdx  = i + j* oInfo.strides[1];
 
         // kernel algorithm goes here
-        switch(method) {
-            case 1: d_dst[dstIdx] = d_src1[src1Idx] + d_src2[src2Idx]; break;
-            case 2: d_dst[dstIdx] = d_src1[src1Idx] - d_src2[src2Idx]; break;
-            case 3: d_dst[dstIdx] = d_src1[src1Idx] * d_src2[src2Idx]; break;
-            case 4: d_dst[dstIdx] = d_src1[src1Idx] / d_src2[src2Idx]; break;
-        }
+        d_dst[dstIdx] = d_src1[src1Idx] + d_src2[src2Idx];
     }
 }

From 5e79b5f3b565d31fa5e9b1a3ba5fd8611930ace2 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 7 Sep 2016 16:29:53 -0400
Subject: [PATCH 0888/2677] Removed homography dependency on old randu

---
 src/api/c/homography.cpp          | 7 ++++++-
 src/api/c/random_engine.cpp       | 1 -
 src/backend/cpu/homography.cpp    | 6 +++---
 src/backend/cpu/homography.hpp    | 1 +
 src/backend/cuda/homography.cu    | 6 +++---
 src/backend/cuda/homography.hpp   | 1 +
 src/backend/opencl/homography.cpp | 6 +++---
 src/backend/opencl/homography.hpp | 1 +
 8 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index c8fc9bd0ec..2857cd7218 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -10,6 +10,7 @@
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/vision.h>
+#include <af/random_engine.h>
 #include <err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
@@ -27,10 +28,14 @@ static inline void homography(af_array &H, int &inliers,
                               const unsigned iterations)
 {
     Array<T> bestH = createEmptyArray<T>(af::dim4(3, 3));
-
+    af_array initial;
+    unsigned d = (iterations + 256 - 1) / 256;
+    dim_t rdims[] = {4, d * 256};
+    af_randu(&initial, 2, rdims, f32);
     inliers = homography<T>(bestH,
                             getArray<float>(x_src), getArray<float>(y_src),
                             getArray<float>(x_dst), getArray<float>(y_dst),
+                            getArray<float>(initial),
                             htype, inlier_thr, iterations);
 
     H = getHandle<T>(bestH);
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index 35ab178cca..d2ca2735a7 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -17,7 +17,6 @@
 #include <err_common.hpp>
 #include <handle.hpp>
 #include <random_engine.hpp>
-#include <random.hpp>
 #include <MersenneTwister.hpp>
 #include <types.hpp>
 #include <memory>
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 16abd56ac0..d7ad07f9cb 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -13,7 +13,6 @@
 #include <handle.hpp>
 #include <homography.hpp>
 #include <arith.hpp>
-#include <random.hpp>
 #include <cstring>
 #include <cfloat>
 #include <platform.hpp>
@@ -348,6 +347,7 @@ int homography(Array<T> &bestH,
                const Array<float> &y_src,
                const Array<float> &x_dst,
                const Array<float> &y_dst,
+               const Array<float> &initial,
                const af_homography_type htype,
                const float inlier_thr,
                const unsigned iterations)
@@ -365,9 +365,8 @@ int homography(Array<T> &bestH,
         iter = std::min(iter, (unsigned)(log(1.f - LMEDSConfidence) / log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
 
     af::dim4 rdims(4, iter);
-    Array<float> frnd = randu<float>(rdims);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd = arithOp<float, af_mul_t>(frnd, fctr, rdims);
+    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
     rnd.eval();
     getQueue().sync();
 
@@ -378,6 +377,7 @@ int homography(Array<T> &bestH,
     template int homography<T>(Array<T> &bestH,                                         \
                                const Array<float> &x_src, const Array<float> &y_src,    \
                                const Array<float> &x_dst, const Array<float> &y_dst,    \
+                               const Array<float> &initial,                             \
                                const af_homography_type htype, const float inlier_thr,  \
                                const unsigned iterations);
 
diff --git a/src/backend/cpu/homography.hpp b/src/backend/cpu/homography.hpp
index 7b14d13a73..1d4db42b1b 100644
--- a/src/backend/cpu/homography.hpp
+++ b/src/backend/cpu/homography.hpp
@@ -16,6 +16,7 @@ template<typename T>
 int homography(Array<T> &H,
                const Array<float> &x_src, const Array<float> &y_src,
                const Array<float> &x_dst, const Array<float> &y_dst,
+               const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index a32f5cbfa9..bea911aacf 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -12,7 +12,6 @@
 #include <err_cuda.hpp>
 #include <handle.hpp>
 #include <arith.hpp>
-#include <random.hpp>
 #include <kernel/homography.hpp>
 #include <algorithm>
 
@@ -34,6 +33,7 @@ int homography(Array<T> &bestH,
                const Array<float> &y_src,
                const Array<float> &x_dst,
                const Array<float> &y_dst,
+               const Array<float> &initial,
                const af_homography_type htype,
                const float inlier_thr,
                const unsigned iterations)
@@ -49,9 +49,8 @@ int homography(Array<T> &bestH,
     }
 
     af::dim4 rdims(4, iter);
-    Array<float> frnd = randu<float>(rdims);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd = arithOp<float, af_mul_t>(frnd, fctr, rdims);
+    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter), (T)0);
 
@@ -66,6 +65,7 @@ int homography(Array<T> &bestH,
     template int homography<T>(Array<T> &H,                                             \
                                const Array<float> &x_src, const Array<float> &y_src,    \
                                const Array<float> &x_dst, const Array<float> &y_dst,    \
+                               const Array<float> &initial,                             \
                                const af_homography_type htype, const float inlier_thr,  \
                                const unsigned iterations);
 
diff --git a/src/backend/cuda/homography.hpp b/src/backend/cuda/homography.hpp
index 514040e296..564c0766b7 100644
--- a/src/backend/cuda/homography.hpp
+++ b/src/backend/cuda/homography.hpp
@@ -16,6 +16,7 @@ template<typename T>
 int homography(Array<T> &H,
                const Array<float> &x_src, const Array<float> &y_src,
                const Array<float> &x_dst, const Array<float> &y_dst,
+               const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 8d7b04dae6..cdeab9bffb 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -12,7 +12,6 @@
 #include <err_opencl.hpp>
 #include <handle.hpp>
 #include <arith.hpp>
-#include <random.hpp>
 #include <kernel/homography.hpp>
 #include <algorithm>
 
@@ -34,6 +33,7 @@ int homography(Array<T> &bestH,
                const Array<float> &y_src,
                const Array<float> &x_dst,
                const Array<float> &y_dst,
+               const Array<float> &initial,
                const af_homography_type htype,
                const float inlier_thr,
                const unsigned iterations)
@@ -55,9 +55,8 @@ int homography(Array<T> &bestH,
     const size_t iter_sz = divup(iter, 256) * 256;
 
     af::dim4 rdims(4, iter_sz);
-    Array<float> frnd = randu<float>(rdims);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd = arithOp<float, af_mul_t>(frnd, fctr, rdims);
+    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter_sz), (T)0);
 
@@ -83,6 +82,7 @@ int homography(Array<T> &bestH,
     template int homography(Array<T> &H,                                            \
                             const Array<float> &x_src, const Array<float> &y_src,   \
                             const Array<float> &x_dst, const Array<float> &y_dst,   \
+                            const Array<float> &initial,                            \
                             const af_homography_type htype, const float inlier_thr, \
                             const unsigned iterations);
 
diff --git a/src/backend/opencl/homography.hpp b/src/backend/opencl/homography.hpp
index 6c926e50f4..492d64d448 100644
--- a/src/backend/opencl/homography.hpp
+++ b/src/backend/opencl/homography.hpp
@@ -16,6 +16,7 @@ template<typename T>
 int homography(Array<T> &H,
                const Array<float> &x_src, const Array<float> &y_src,
                const Array<float> &x_dst, const Array<float> &y_dst,
+               const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 

From 790199ac0f326fafc99c4793167b04b276732591 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 7 Sep 2016 16:35:56 -0400
Subject: [PATCH 0889/2677] Added AF_CHECK to random call in homography

---
 src/api/c/homography.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index 2857cd7218..e8a025a6a1 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -31,7 +31,7 @@ static inline void homography(af_array &H, int &inliers,
     af_array initial;
     unsigned d = (iterations + 256 - 1) / 256;
     dim_t rdims[] = {4, d * 256};
-    af_randu(&initial, 2, rdims, f32);
+    AF_CHECK(af_randu(&initial, 2, rdims, f32));
     inliers = homography<T>(bestH,
                             getArray<float>(x_src), getArray<float>(y_src),
                             getArray<float>(x_dst), getArray<float>(y_dst),

From 752ef7aec0a5c54c091e72ae3d6a1dbff175f0ad Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 7 Sep 2016 18:14:44 -0400
Subject: [PATCH 0890/2677] FEAT: Add API to set axes limits for graphics

* setAxesTitles can be used to set titles
* setAxesLimits can take in arrays or floats
  * The exact argument can be used to use exact min/max values or to use
    buffered space
---
 include/af/graphics.h        |  84 ++++++++++++++++++---
 src/api/c/window.cpp         | 142 +++++++++++++++++++++++++++++++++--
 src/api/cpp/graphics.cpp     |  21 ++++--
 src/api/unified/graphics.cpp |  24 ++++--
 4 files changed, 241 insertions(+), 30 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 440fb16452..ca0673dfe1 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -396,10 +396,13 @@ class AFAPI Window {
 
            \param[in] x the data to compute the limits for x-axis.
            \param[in] y the data to compute the limits for y-axis.
+           \param[in] exact is for using the exact min/max values from \p x and \p y.
+                      If exact is false then the most significant digit is rounded up
+                      to next power of 2 and the magnitude remains the same.
 
            \ingroup gfx_func_window
         */
-        void setAxesLimits(const array &x, const array &y);
+        void setAxesLimits(const array &x, const array &y, const bool exact = false);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -411,10 +414,35 @@ class AFAPI Window {
            \param[in] x the data to compute the limits for x-axis.
            \param[in] y the data to compute the limits for y-axis.
            \param[in] z the data to compute the limits for z-axis.
+           \param[in] exact is for using the exact min/max values from \p x and \p y.
+                      If exact is false then the most significant digit is rounded up
+                      to next power of 2 and the magnitude remains the same.
 
            \ingroup gfx_func_window
         */
-        void setAxesLimits(const array &x, const array &y, const array &z);
+        void setAxesLimits(const array &x, const array &y, const array &z,
+                           const bool exact = false);
+#endif
+
+#if AF_API_VERSION >= 34
+        /**
+           Setup the axes limits for a histogram/plot/surface/vector field
+
+           This function sets the axes limits to the ones provided by the user.
+
+           \param[in] xmin is the minimum on x-axis
+           \param[in] xmax is the maximum on x-axis
+           \param[in] ymin is the minimum on y-axis
+           \param[in] ymax is the maximum on y-axis
+           \param[in] exact is for using the exact min/max values from \p x and \p y.
+                      If exact is false then the most significant digit is rounded up
+                      to next power of 2 and the magnitude remains the same.
+
+           \ingroup gfx_func_window
+        */
+        void setAxesLimits(const float xmin, const float xmax,
+                           const float ymin, const float ymax,
+                           const bool exact = false);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -429,12 +457,16 @@ class AFAPI Window {
            \param[in] ymax is the maximum on y-axis
            \param[in] zmin is the minimum on z-axis
            \param[in] zmax is the maximum on z-axis
+           \param[in] exact is for using the exact min/max values from \p x, \p y and \p z.
+                      If exact is false then the most significant digit is rounded up
+                      to next power of 2 and the magnitude remains the same.
 
            \ingroup gfx_func_window
         */
         void setAxesLimits(const float xmin, const float xmax,
                            const float ymin, const float ymax,
-                           const float zmin = -1.0, const float zmax = 1.0);
+                           const float zmin, const float zmax,
+                           const bool exact = false);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -954,6 +986,9 @@ AFAPI af_err af_grid(const af_window wind, const int rows, const int cols);
    \param[in] x the data to compute the limits for x-axis.
    \param[in] y the data to compute the limits for y-axis.
    \param[in] z the data to compute the limits for z-axis.
+   \param[in] exact is for using the exact min/max values from \p x, \p y and \p z.
+              If exact is false then the most significant digit is rounded up
+              to next power of 2 and the magnitude remains the same.
    \param[in] props is structure \ref af_cell that has the properties that
               are used for the current rendering.
 
@@ -963,12 +998,39 @@ AFAPI af_err af_grid(const af_window wind, const int rows, const int cols);
 */
 AFAPI af_err af_set_axes_limits_compute(const af_window wind,
                                         const af_array x, const af_array y, const af_array z,
+                                        const bool exact,
                                         const af_cell* const props);
 #endif
 
 #if AF_API_VERSION >= 34
 /**
-   C Interface for setting axes limits for a histogram/plot/surface/vector field
+   C Interface for setting axes limits for a 2D histogram/plot/vector field
+
+   This function sets the axes limits to the ones provided by the user.
+
+   \param[in] wind is the window handle
+   \param[in] xmin is the minimum on x-axis
+   \param[in] xmax is the maximum on x-axis
+   \param[in] ymin is the minimum on y-axis
+   \param[in] ymax is the maximum on y-axis
+   \param[in] exact is for using the exact min/max values from \p x, and \p y.
+              If exact is false then the most significant digit is rounded up
+              to next power of 2 and the magnitude remains the same.
+   \param[in] props is structure \ref af_cell that has the properties that
+              are used for the current rendering.
+
+   \ingroup gfx_func_window
+*/
+AFAPI af_err af_set_axes_limits_2d(const af_window wind,
+                                   const float xmin, const float xmax,
+                                   const float ymin, const float ymax,
+                                   const bool exact,
+                                   const af_cell* const props);
+#endif
+
+#if AF_API_VERSION >= 34
+/**
+   C Interface for setting axes limits for a 3D plot/surface/vector field
 
    This function sets the axes limits to the ones provided by the user.
 
@@ -979,16 +1041,20 @@ AFAPI af_err af_set_axes_limits_compute(const af_window wind,
    \param[in] ymax is the maximum on y-axis
    \param[in] zmin is the minimum on z-axis
    \param[in] zmax is the maximum on z-axis
+   \param[in] exact is for using the exact min/max values from \p x, \p y and \p z.
+              If exact is false then the most significant digit is rounded up
+              to next power of 2 and the magnitude remains the same.
    \param[in] props is structure \ref af_cell that has the properties that
               are used for the current rendering.
 
    \ingroup gfx_func_window
 */
-AFAPI af_err af_set_axes_limits(const af_window wind,
-                                const float xmin, const float xmax,
-                                const float ymin, const float ymax,
-                                const float zmin, const float zmax,
-                                const af_cell* const props);
+AFAPI af_err af_set_axes_limits_3d(const af_window wind,
+                                   const float xmin, const float xmax,
+                                   const float ymin, const float ymax,
+                                   const float zmin, const float zmax,
+                                   const bool exact,
+                                   const af_cell* const props);
 #endif
 
 #if AF_API_VERSION >= 34
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 6eccdaa54a..90aad38f44 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -137,9 +137,64 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
 #endif
 }
 
+// dir -> true = round up, false = round down
+double step_round(const double in, const bool dir)
+{
+    if(in == 0) return 0;
+
+    static const double __log2 = log10(2);
+    static const double __log4 = log10(4);
+    static const double __log6 = log10(6);
+    static const double __log8 = log10(8);
+
+    // log_in is of the form "s abc.xyz", where
+    // s is either + or -; + indicates abs(in) >= 1 and - indicates 0 < abs(in) < 1 (log10(1) is +0)
+    const double sign   = in < 0 ? -1 : 1;
+    const double log_in = std::log10(std::fabs(in));
+    const double mag    = std::pow(10, std::floor(log_in)) * sign;  // Number of digits either left or right of 0
+    const double dec    = std::log10(in / mag); // log of the fraction
+
+    // This means in is of the for 10^n
+    if(dec == 0) return in;
+
+    // For negative numbers, -ve round down = +ve round up and vice versa
+    bool op_dir = in > 0 ? dir : !dir;
+
+    double mult = 1;
+
+    // Round up
+    if(op_dir) {
+        if(dec <= __log2) {
+            mult = 2;
+        } else if(dec <= __log4) {
+            mult = 4;
+        } else if(dec <= __log6) {
+            mult = 6;
+        } else if(dec <= __log8) {
+            mult = 8;
+        } else {
+            mult = 10;
+        }
+    } else {    // Round down
+        if(dec < __log2) {
+            mult = 1;
+        } else if(dec < __log4) {
+            mult = 2;
+        } else if(dec < __log6) {
+            mult = 4;
+        } else if(dec < __log8) {
+            mult = 6;
+        } else {
+            mult = 8;
+        }
+    }
+
+    return mag * mult;
+}
+
 af_err af_set_axes_limits_compute(const af_window wind,
                                   const af_array x, const af_array y, const af_array z,
-                                  const af_cell* const props)
+                                  const bool exact, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
     if(wind==0) {
@@ -177,6 +232,16 @@ af_err af_set_axes_limits_compute(const af_window wind,
             AF_CHECK(af_min_all(&zmin, NULL, z));
             AF_CHECK(af_max_all(&zmax, NULL, z));
         }
+
+        if(!exact) {
+            xmin = step_round(xmin, false);
+            xmax = step_round(xmax, true );
+            ymin = step_round(ymin, false);
+            ymax = step_round(ymax, true );
+            zmin = step_round(zmin, false);
+            zmax = step_round(zmax, true );
+        }
+
         chart->setAxesLimits(xmin, xmax, ymin, ymax, zmin, zmax);
     }
     CATCHALL;
@@ -186,11 +251,10 @@ af_err af_set_axes_limits_compute(const af_window wind,
 #endif
 }
 
-af_err af_set_axes_limits(const af_window wind,
-                          const float xmin, const float xmax,
-                          const float ymin, const float ymax,
-                          const float zmin, const float zmax,
-                          const af_cell* const props)
+af_err af_set_axes_limits_2d(const af_window wind,
+                             const float xmin, const float xmax,
+                             const float ymin, const float ymax,
+                             const bool exact, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
     if(wind==0) {
@@ -215,7 +279,71 @@ af_err af_set_axes_limits(const af_window wind,
         else
             chart = fgMngr.getChart(window, 0, 0, ctype);
 
-        chart->setAxesLimits(xmin, xmax, ymin, ymax, zmin, zmax);
+        float _xmin = xmin;
+        float _xmax = xmax;
+        float _ymin = ymin;
+        float _ymax = ymax;
+        if(!exact) {
+            _xmin = step_round(_xmin, false);
+            _xmax = step_round(_xmax, true );
+            _ymin = step_round(_ymin, false);
+            _ymax = step_round(_ymax, true );
+        }
+
+        chart->setAxesLimits(_xmin, _xmax, _ymin, _ymax);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+#else
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+#endif
+}
+
+af_err af_set_axes_limits_3d(const af_window wind,
+                             const float xmin, const float xmax,
+                             const float ymin, const float ymax,
+                             const float zmin, const float zmax,
+                             const bool exact, const af_cell* const props)
+{
+#if defined(WITH_GRAPHICS)
+    if(wind==0) {
+        std::cerr<<"Not a valid window"<<std::endl;
+        return AF_SUCCESS;
+    }
+
+    try {
+        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+
+        // Recreate a chart map
+        ForgeManager& fgMngr = ForgeManager::getInstance();
+
+        forge::Chart* chart = NULL;
+        // The ctype here below doesn't really matter as it is only fetching
+        // the chart. It will not set it.
+        // If this is actually being done, then it is extremely bad.
+        fg_chart_type ctype = FG_CHART_3D;
+
+        if (props->col > -1 && props->row > -1)
+            chart = fgMngr.getChart(window, props->row, props->col, ctype);
+        else
+            chart = fgMngr.getChart(window, 0, 0, ctype);
+
+        float _xmin = xmin;
+        float _xmax = xmax;
+        float _ymin = ymin;
+        float _ymax = ymax;
+        float _zmin = zmin;
+        float _zmax = zmax;
+        if(!exact) {
+            _xmin = step_round(_xmin, false);
+            _xmax = step_round(_xmax, true );
+            _ymin = step_round(_ymin, false);
+            _ymax = step_round(_ymax, true );
+            _zmin = step_round(_zmin, false);
+            _zmax = step_round(_zmax, true );
+        }
+
+        chart->setAxesLimits(_xmin, _xmax, _ymin, _ymax, _zmin, _zmax);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 37fc246360..d26a134e7e 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -177,24 +177,33 @@ void Window::grid(const int rows, const int cols)
     AF_THROW(af_grid(get(), rows, cols));
 }
 
-void Window::setAxesLimits(const array& x, const array& y)
+void Window::setAxesLimits(const array& x, const array& y, const bool exact)
 {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), NULL, &temp));
+    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), NULL, exact, &temp));
 }
 
-void Window::setAxesLimits(const array& x, const array& y, const array &z)
+void Window::setAxesLimits(const array& x, const array& y, const array &z, const bool exact)
 {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), z.get(), &temp));
+    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), z.get(), exact, &temp));
 }
 
 void Window::setAxesLimits(const float xmin, const float xmax,
                            const float ymin, const float ymax,
-                           const float zmin, const float zmax)
+                           const bool exact)
 {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits(get(), xmin, xmax, ymin, ymax, zmin, zmax, &temp));
+    AF_THROW(af_set_axes_limits_2d(get(), xmin, xmax, ymin, ymax, exact, &temp));
+}
+
+void Window::setAxesLimits(const float xmin, const float xmax,
+                           const float ymin, const float ymax,
+                           const float zmin, const float zmax,
+                           const bool exact)
+{
+    af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_set_axes_limits_3d(get(), xmin, xmax, ymin, ymax, zmin, zmax, exact, &temp));
 }
 
 void Window::setAxesTitles(const char * const xtitle,
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 785d57b06c..08f8d81d58 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -150,20 +150,28 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
 
 af_err af_set_axes_limits_compute(const af_window wind,
                                   const af_array x, const af_array y, const af_array z,
-                                  const af_cell* const props)
+                                  const bool exact, const af_cell* const props)
 {
     CHECK_ARRAYS(x, y);
     if(z) CHECK_ARRAYS(z);
-    return CALL(wind, x, y, z, props);
+    return CALL(wind, x, y, z, exact, props);
 }
 
-af_err af_set_axes_limits(const af_window wind,
-                          const float xmin, const float xmax,
-                          const float ymin, const float ymax,
-                          const float zmin, const float zmax,
-                          const af_cell* const props)
+af_err af_set_axes_limits_2d(const af_window wind,
+                             const float xmin, const float xmax,
+                             const float ymin, const float ymax,
+                             const bool exact, const af_cell* const props)
+{
+    return CALL(wind, xmin, xmax, ymin, ymax, exact, props);
+}
+
+af_err af_set_axes_limits_3d(const af_window wind,
+                             const float xmin, const float xmax,
+                             const float ymin, const float ymax,
+                             const float zmin, const float zmax,
+                             const bool exact, const af_cell* const props)
 {
-    return CALL(wind, xmin, xmax, ymin, ymax, zmin, zmax, props);
+    return CALL(wind, xmin, xmax, ymin, ymax, zmin, zmax, exact, props);
 }
 
 af_err af_set_axes_titles(const af_window wind,

From ba94357b1094372b3c8e6e9d68a914317fbd001b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 11:15:37 -0400
Subject: [PATCH 0891/2677] Set axes limits in graphics examples

---
 examples/graphics/field.cpp                       |  3 +++
 examples/graphics/histogram.cpp                   |  5 +++++
 examples/graphics/plot2d.cpp                      | 11 +++++++----
 examples/graphics/plot3.cpp                       |  2 ++
 examples/graphics/surface.cpp                     |  1 +
 examples/image_processing/binary_thresholding.cpp | 14 ++++++++++++--
 examples/image_processing/edge.cpp                |  4 ++++
 examples/pde/swe.cpp                              | 13 +++++++++----
 8 files changed, 43 insertions(+), 10 deletions(-)

diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
index 7f50107b67..e1c1ee1e73 100644
--- a/examples/graphics/field.cpp
+++ b/examples/graphics/field.cpp
@@ -33,6 +33,9 @@ int main(int argc, char *argv[])
         array points = join(1, flat(range(dim4(10, 10), 1) * 2 + 1), flat(range(dim4(10, 10)) * 2 + 1));
         array directions = sin(2 * Pi * points / 10.0f);
 
+        myWindow.setAxesLimits(points.col(0), points.col(1));
+        myWindow.setAxesTitles();
+
         while(!myWindow.close()) {
             myWindow.scatter(divPoints, AF_MARKER_CIRCLE);
             myWindow.vectorField(points, directions);
diff --git a/examples/graphics/histogram.cpp b/examples/graphics/histogram.cpp
index ddf91b2f8b..31b3753f6d 100644
--- a/examples/graphics/histogram.cpp
+++ b/examples/graphics/histogram.cpp
@@ -24,6 +24,11 @@ int main(int argc, char *argv[])
         array img = loadImage(ASSETS_DIR"/examples/images/arrow.jpg", false);
         array hist_out = histogram(img, 256, 0, 255);
 
+        float freq_max = max<float>(hist_out);
+        myWindow.setAxesLimits(0, 255, 0, freq_max);
+        myWindow.setAxesTitles("Bins", "Frequency");
+        myWindow.setPos(480, 0);
+
         while (!myWindow.close() && !imgWnd.close()) {
             myWindow.hist(hist_out, 0, 255);
             imgWnd.image(img.as(u8));
diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index 9b311f55f9..77e6f36431 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -21,20 +21,23 @@ int main(int argc, char *argv[])
     try {
         // Initialize the kernel array just once
         af::info();
-        af::Window myWindow(1024, 512, "2D Plot example: ArrayFire");
+        af::Window myWindow(800, 800, "2D Plot example: ArrayFire");
 
         array Y;
         int sign = 1;
         array X = seq(-af::Pi, af::Pi, PRECISION);
         array noise = randn(X.dims(0))/5.f;
 
-        myWindow.grid(1, 2);
-        for (double val=-af::Pi; !myWindow.close(); ) {
+        myWindow.grid(2, 1);
+        myWindow(0, 0).setAxesLimits(-2 * af::Pi, 2 * af::Pi, -1, 1, true);
+        myWindow(1, 0).setAxesLimits(-2 * af::Pi, 2 * af::Pi, -1.25, 1.25, true);
+
+        for (double val=0; !myWindow.close(); ) {
 
             Y = sin(X);
 
             myWindow(0,0).plot(X, Y);
-            myWindow(0,1).scatter(X, Y + noise, AF_MARKER_POINT);
+            myWindow(1,0).scatter(X, Y + noise, AF_MARKER_POINT);
 
             myWindow.show();
 
diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index 4d7afd5af8..b58a6213c3 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -32,6 +32,8 @@ int main(int argc, char *argv[])
             X = max(min(X, 1.0), -1.0);
             Y = max(min(Y, 1.0), -1.0);
 
+            myWindow.setAxesLimits(X, Y, Z);
+
             //Pts can be passed in as a matrix in the form n x 3, 3 x n
             //or in the flattened xyz-triplet array with size 3n x 1
             myWindow.plot(X, Y, Z);
diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index b48cb8fa47..b39568bdcd 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -31,6 +31,7 @@ int main(int argc, char *argv[])
         while(!myWindow.close()) {
             t+=0.07;
             array z = 10*x*-abs(y) * cos(x*x*(y+t))+sin(y*(x+t))-1.5;
+            myWindow.setAxesLimits(x, y, z, true);
             myWindow.surface(x, y, z);
         }
 
diff --git a/examples/image_processing/binary_thresholding.cpp b/examples/image_processing/binary_thresholding.cpp
index 319e6eaa60..ba869f450f 100644
--- a/examples/image_processing/binary_thresholding.cpp
+++ b/examples/image_processing/binary_thresholding.cpp
@@ -76,10 +76,20 @@ int main(int argc, char **argv)
         array smooth = convolve(bimodal, gaussianKernel(5, 5));
         array smoothHist = histogram(smooth, 256, 0, 255);
 
-        af::Window wnd("Binary Thresholding Algorithms");
+        unsigned bimodHist_freq_max = max<unsigned>(bimodHist);
+        unsigned smoothHist_freq_max = max<unsigned>(smoothHist);
+
+        af::Window wnd(1536, 1024, "Binary Thresholding Algorithms");
         std::cout << "Press ESC while the window is in focus to proceed to exit" << std::endl;
+
+        wnd.grid(3, 3);
+        wnd(0, 1).setAxesLimits(0, 255, 0, bimodHist_freq_max, true);
+        wnd(0, 1).setAxesTitles("Bins", "Frequency");
+        wnd(1, 1).setAxesLimits(0, 255, 0, bimodHist_freq_max, true);
+        wnd(1, 1).setAxesTitles("Bins", "Frequency");
+        wnd(2, 1).setAxesLimits(0, 255, 0, smoothHist_freq_max, true);
+        wnd(2, 1).setAxesTitles("Bins", "Frequency");
         while (!wnd.close()) {
-            wnd.grid(3, 3);
             wnd(0, 0).image(bimodal / 255, "Input Image");
             wnd(1, 0).image(bimodal / 255, "Input Image");
             wnd(2, 0).image(smooth / 255, "Input Smoothed by Gaussian Filter");
diff --git a/examples/image_processing/edge.cpp b/examples/image_processing/edge.cpp
index bef7bc10e3..6830159394 100644
--- a/examples/image_processing/edge.cpp
+++ b/examples/image_processing/edge.cpp
@@ -80,6 +80,10 @@ void edge()
     array sobelFilter   = edge(in, 2);
     array hst = histogram(in, 256, 0, 255);
 
+    float freq_max = max<float>(hst);
+    myWindow2.setAxesLimits(0, 255, 0, freq_max);
+    myWindow2.setAxesTitles("Bins", "Frequency");
+
     while(!myWindow.close() && !myWindow2.close()) {
 
         /* show input, prewitt and sobel edge detectors in a grid */
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 129b5193ed..7dbb9a28cf 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -29,8 +29,8 @@ static void swe(bool console)
     unsigned io = (unsigned)floor(Lx  / 6.0f),
              jo = (unsigned)floor(Ly / 6.0f),
              k = 15;
-    array x = tile(moddims(seq(nx),nx,1), 1,ny);
-    array y = tile(moddims(seq(ny),1,ny), nx,1);
+    array x = tile(range(nx), 1, ny);
+    array y = tile(range(dim4(1, ny), 1), nx, 1);
 
     //initial condition
     array etam  = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
@@ -84,10 +84,15 @@ static void swe(bool console)
 
         m_eta = max<float>(etam);
         if (!console) {
-            (*win)(0,0).image(normalize(eta, m_eta));
             array hist_out = histogram(normalize(eta, m_eta), 15);
+
+            (*win)(0, 1).setAxesLimits(0, hist_out.elements(), 0, max<float>(hist_out));
+            (*win)(1, 0).setAxesLimits(range(up.dims(1)), vp.col(0));
+            (*win)(1, 1).setAxesLimits(eta.col(0), up.col(0), vp.col(0));
+
+            (*win)(0,0).image(normalize(eta, m_eta));
             (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
-            (*win)(1,0).plot(seq(up.dims(1)), vp.col(0), "Pressure at left boundary");
+            (*win)(1,0).plot(range(up.dims(1)), vp.col(0), "Pressure at left boundary");
             (*win)(1,1).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
             win->show();
         } else eval(eta, up, vp);

From 7697d4bc2872a013dc2f6c405e55b9fa9aa8fb0c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 12:03:30 -0400
Subject: [PATCH 0892/2677] BUGFIX: Create new render objects for different
 charts

---
 src/api/c/graphics_common.cpp | 52 +++++++++++++++++++----------------
 src/api/c/graphics_common.hpp | 13 +++++----
 2 files changed, 37 insertions(+), 28 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 106e8cbea8..85649dd276 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -237,13 +237,14 @@ forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, fo
     long long key = ((w & _16BIT) << 16) | (h & _16BIT);
     key = (((key << 16) | mode) << 16) | type;
 
-    ImgMapIter iter = mImgMap.find(key);
+    ChartKey_t keypair = std::make_pair(key, nullptr);
+    ImgMapIter iter = mImgMap.find(keypair);
     if (iter==mImgMap.end()) {
         forge::Image* temp = new forge::Image(w, h, mode, type);
-        mImgMap[key] = temp;
+        mImgMap[keypair] = temp;
     }
 
-    return mImgMap[key];
+    return mImgMap[keypair];
 }
 
 forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
@@ -259,17 +260,18 @@ forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
     long long key = ((w & _16BIT) << 16) | (h & _16BIT);
     key = (((key << 16) | mode) << 16) | type;
 
-    ImgMapIter iter = mImgMap.find(key);
+    ChartKey_t keypair = std::make_pair(key, chart);
+    ImgMapIter iter = mImgMap.find(keypair);
     if (iter==mImgMap.end()) {
         if(chart->getChartType() != FG_CHART_2D)
             AF_ERROR("Image can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
 
         forge::Image* temp = new forge::Image(w, h, mode, type);
-        mImgMap[key] = temp;
-        chart->add(*mImgMap[key]);
+        mImgMap[keypair] = temp;
+        chart->add(*mImgMap[keypair]);
     }
 
-    return mImgMap[key];
+    return mImgMap[keypair];
 }
 
 forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtype dtype,
@@ -284,14 +286,15 @@ forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtyp
     long long key = ((nPoints & _48BIT) << 48);
     key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
 
-    PltMapIter iter = mPltMap.find(key);
+    ChartKey_t keypair = std::make_pair(key, chart);
+    PltMapIter iter = mPltMap.find(keypair);
     if (iter==mPltMap.end()) {
         forge::Plot* temp = new forge::Plot(nPoints, dtype, chart->getChartType(), ptype, mtype);
-        mPltMap[key] = temp;
-        chart->add(*mPltMap[key]);
+        mPltMap[keypair] = temp;
+        chart->add(*mPltMap[keypair]);
     }
 
-    return mPltMap[key];
+    return mPltMap[keypair];
 }
 
 forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, forge::dtype type)
@@ -304,17 +307,18 @@ forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, for
     assert(nBins <= 2ll<<48);
     long long key = ((nBins & _48BIT) << 48) | (type & _16BIT);
 
-    HstMapIter iter = mHstMap.find(key);
+    ChartKey_t keypair = std::make_pair(key, chart);
+    HstMapIter iter = mHstMap.find(keypair);
     if (iter==mHstMap.end()) {
         if(chart->getChartType() != FG_CHART_2D)
             AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
 
         forge::Histogram* temp = new forge::Histogram(nBins, type);
-        mHstMap[key] = temp;
-        chart->add(*mHstMap[key]);
+        mHstMap[keypair] = temp;
+        chart->add(*mHstMap[keypair]);
     }
 
-    return mHstMap[key];
+    return mHstMap[keypair];
 }
 
 forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, forge::dtype type)
@@ -327,17 +331,18 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
     assert(nX * nY <= 2ll<<48);
     long long key = (((nX * nY) & _48BIT) << 48) | (type & _16BIT);
 
-    SfcMapIter iter = mSfcMap.find(key);
+    ChartKey_t keypair = std::make_pair(key, chart);
+    SfcMapIter iter = mSfcMap.find(keypair);
     if (iter==mSfcMap.end()) {
         if(chart->getChartType() != FG_CHART_3D)
             AF_ERROR("Surface can only be added to chart of type FG_CHART_3D", AF_ERR_TYPE);
 
         forge::Surface* temp = new forge::Surface(nX, nY, type);
-        mSfcMap[key] = temp;
-        chart->add(*mSfcMap[key]);
+        mSfcMap[keypair] = temp;
+        chart->add(*mSfcMap[keypair]);
     }
 
-    return mSfcMap[key];
+    return mSfcMap[keypair];
 }
 
 forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoints, forge::dtype type)
@@ -350,14 +355,15 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
     assert(nPoints <= 2ll<<48);
     long long key = (((nPoints) & _48BIT) << 48) | (type & _16BIT);
 
-    VcfMapIter iter = mVcfMap.find(key);
+    ChartKey_t keypair = std::make_pair(key, chart);
+    VcfMapIter iter = mVcfMap.find(keypair);
     if (iter==mVcfMap.end()) {
         forge::VectorField* temp = new forge::VectorField(nPoints, type, chart->getChartType());
-        mVcfMap[key] = temp;
-        chart->add(*mVcfMap[key]);
+        mVcfMap[keypair] = temp;
+        chart->add(*mVcfMap[keypair]);
     }
 
-    return mVcfMap[key];
+    return mVcfMap[keypair];
 }
 
 void ForgeManager::destroyResources()
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 361e3a4874..6459eaa49e 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -17,6 +17,7 @@
 #include <glbinding/Binding.h>
 #include <vector>
 #include <map>
+#include <utility>
 
 // default to f32(float) type
 template<typename T>
@@ -48,11 +49,13 @@ static const long long _16BIT = 0x000000000000FFFF;
 static const long long _32BIT = 0x00000000FFFFFFFF;
 static const long long _48BIT = 0x0000FFFFFFFFFFFF;
 
-typedef std::map<long long, forge::Image*> ImageMap_t;
-typedef std::map<long long, forge::Plot*> PlotMap_t;
-typedef std::map<long long, forge::Histogram*> HistogramMap_t;
-typedef std::map<long long, forge::Surface*> SurfaceMap_t;
-typedef std::map<long long, forge::VectorField*> VectorFieldMap_t;
+typedef std::pair<long long, forge::Chart*> ChartKey_t;
+
+typedef std::map<ChartKey_t, forge::Image*      > ImageMap_t;
+typedef std::map<ChartKey_t, forge::Plot*       > PlotMap_t;
+typedef std::map<ChartKey_t, forge::Histogram*  > HistogramMap_t;
+typedef std::map<ChartKey_t, forge::Surface*    > SurfaceMap_t;
+typedef std::map<ChartKey_t, forge::VectorField*> VectorFieldMap_t;
 
 typedef ImageMap_t::iterator ImgMapIter;
 typedef PlotMap_t::iterator PltMapIter;

From e0b02e029797f7e3d362a162936c35877825c32e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 8 Sep 2016 11:20:19 +0530
Subject: [PATCH 0893/2677] Add new location to search for cuda libdevice files

---
 src/backend/cuda/CMakeLists.txt | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ef5321f918..e8759496da 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -7,6 +7,7 @@ INCLUDE(CLKernelToH)
 INCLUDE(FindNVVM)
 
 OPTION(USE_LIBDEVICE "Use libdevice for CUDA JIT" ON)
+SET(CUDA_LIBDEVICE_DIR "${CUDA_NVVM_HOME}/libdevice" CACHE PATH "Path where libdevice compute files are located")
 
 MARK_AS_ADVANCED(
     CUDA_BUILD_CUBIN
@@ -332,20 +333,20 @@ CL_KERNEL_TO_H(
 
 SET(libdevice_bc "")
 IF (USE_LIBDEVICE)
-  SET(libdevice_computes "")
-  LIST(APPEND libdevice_computes "20" "30" "35" "50")
-  FOREACH(libdevice_compute ${libdevice_computes})
-    SET(_libdevice_bc_file "${CUDA_NVVM_HOME}/libdevice/libdevice.compute_${libdevice_compute}.10.bc")
-    SET(_libdevice_bc_copy "${PROJECT_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
-    IF (EXISTS ${_libdevice_bc_file})
-      ADD_CUSTOM_COMMAND(
-        OUTPUT "${_libdevice_bc_copy}"
-        DEPENDS "${_libdevice_bc_file}"
-        COMMAND ${CMAKE_COMMAND} -E copy "${_libdevice_bc_file}" "${_libdevice_bc_copy}")
-      LIST(APPEND libdevice_bc ${_libdevice_bc_copy})
-      ADD_DEFINITIONS(-D"__LIBDEVICE_COMPUTE_${libdevice_compute}")
-    ENDIF()
-  ENDFOREACH()
+    SET(libdevice_computes "")
+    LIST(APPEND libdevice_computes "20" "30" "35" "50")
+    FOREACH(libdevice_compute ${libdevice_computes})
+        SET(_libdevice_bc_file "${CUDA_LIBDEVICE_DIR}/libdevice.compute_${libdevice_compute}.10.bc")
+        SET(_libdevice_bc_copy "${PROJECT_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
+        IF (EXISTS ${_libdevice_bc_file})
+            ADD_CUSTOM_COMMAND(
+                OUTPUT "${_libdevice_bc_copy}"
+                DEPENDS "${_libdevice_bc_file}"
+                COMMAND ${CMAKE_COMMAND} -E copy "${_libdevice_bc_file}" "${_libdevice_bc_copy}")
+            LIST(APPEND libdevice_bc ${_libdevice_bc_copy})
+            ADD_DEFINITIONS(-D"__LIBDEVICE_COMPUTE_${libdevice_compute}")
+        ENDIF()
+    ENDFOREACH()
 ENDIF()
 
 LIST(LENGTH libdevice_bc libdevice_bc_len)

From 4993abe582cf9702724434e5b332eaba17014b98 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 14:25:16 -0400
Subject: [PATCH 0894/2677] TEMP Fix: Switch swe plot 3d to (0,0) grid space

---
 examples/pde/swe.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 7dbb9a28cf..cd7f727897 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -49,7 +49,6 @@ static void swe(bool console)
 
     if(!console) {
         win = new Window(1536, 768,"Shallow Water Equations");
-        win->setColorMap(AF_COLORMAP_BLUE);
         win->grid(2, 2);
     }
 
@@ -84,16 +83,17 @@ static void swe(bool console)
 
         m_eta = max<float>(etam);
         if (!console) {
+            (*win)(1, 1).setColorMap(AF_COLORMAP_BLUE);
             array hist_out = histogram(normalize(eta, m_eta), 15);
 
             (*win)(0, 1).setAxesLimits(0, hist_out.elements(), 0, max<float>(hist_out));
             (*win)(1, 0).setAxesLimits(range(up.dims(1)), vp.col(0));
-            (*win)(1, 1).setAxesLimits(eta.col(0), up.col(0), vp.col(0));
+            (*win)(0, 0).setAxesLimits(eta.col(0), up.col(0), vp.col(0));
 
-            (*win)(0,0).image(normalize(eta, m_eta));
+            (*win)(1,1).image(normalize(eta, m_eta));
             (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
             (*win)(1,0).plot(range(up.dims(1)), vp.col(0), "Pressure at left boundary");
-            (*win)(1,1).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
+            (*win)(0,0).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
             win->show();
         } else eval(eta, up, vp);
         iter++;

From 6ec572dde870250531d41625980c3422438d5eea Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 15:08:13 -0400
Subject: [PATCH 0895/2677] Move api c modDims header into handle.hpp

---
 src/api/c/assign.cpp | 5 -----
 src/api/c/handle.hpp | 4 ++++
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index a09fa65c9b..4dda94086b 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -25,11 +25,6 @@ using namespace detail;
 using std::vector;
 using std::swap;
 
-// From src/api/c/moddims.cpp TODO: move to header?
-template<typename T>
-Array<T> modDims(const Array<T>& in, const af::dim4 &newDims);
-
-
 template<typename Tout, typename Tin>
 static
 void assign(Array<Tout> &out, const unsigned &ndims, const af_seq *index, const Array<Tin> &in_)
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 3c7be92013..465b9252df 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -19,6 +19,10 @@
 
 const ArrayInfo& getInfo(const af_array arr, bool device_check = true, bool sparse_check = true);
 
+// Implemented in src/api/c/moddims.cpp
+template<typename T>
+detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims);
+
 template<typename T>
 static const detail::Array<T> &
 getArray(const af_array &arr)

From 18a4e5547c729ae9bbb45926a4d9d39fea166b38 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 15:08:45 -0400
Subject: [PATCH 0896/2677] Fix surface: Call api-c moddims, fix axes limits

---
 examples/graphics/surface.cpp | 2 +-
 src/api/c/surface.cpp         | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index b39568bdcd..eb661d4979 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -28,10 +28,10 @@ int main(int argc, char *argv[])
         const array y = iota(dim4(1, N), dim4(N, 1)) / M - 1;
 
         static float t=0;
+        myWindow.setAxesLimits(-1.0, 1.0, -1.0, 1.0, -10.0, 10.0);
         while(!myWindow.close()) {
             t+=0.07;
             array z = 10*x*-abs(y) * cos(x*x*(y+t))+sin(y*(x+t))-1.5;
-            myWindow.setAxesLimits(x, y, z, true);
             myWindow.surface(x, y, z);
         }
 
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index d29dedfa31..a342ab2f39 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -60,9 +60,9 @@ forge::Chart* setup_surface(const forge::Window* const window,
 
     // Flatten xIn, yIn and zIn into row vectors
     dim4 rowDims = dim4(1, zIn.elements());
-    xIn.modDims(rowDims);
-    yIn.modDims(rowDims);
-    zIn.modDims(rowDims);
+    xIn = modDims(xIn, rowDims);
+    yIn = modDims(yIn, rowDims);
+    zIn = modDims(zIn, rowDims);
 
     // Now join along first dimension, skip reorder
     std::vector<Array<T> > inputs{xIn, yIn, zIn};

From b8f3d286a61df3b59e3ceafce99f4414d687d2c9 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 15:37:31 -0400
Subject: [PATCH 0897/2677] CLEANUP: Remove handle.hpp headers from where they
 are not required

---
 src/backend/cpu/blas.cpp                 | 1 -
 src/backend/cpu/diagonal.cpp             | 1 -
 src/backend/cpu/fast.cpp                 | 1 -
 src/backend/cpu/harris.cpp               | 2 +-
 src/backend/cpu/homography.cpp           | 1 -
 src/backend/cpu/lu.cpp                   | 1 +
 src/backend/cpu/nearest_neighbour.cpp    | 3 ++-
 src/backend/cpu/orb.cpp                  | 1 -
 src/backend/cpu/qr.cpp                   | 1 +
 src/backend/cpu/sift.cpp                 | 1 -
 src/backend/cpu/solve.cpp                | 1 +
 src/backend/cpu/sort.cpp                 | 1 -
 src/backend/cpu/sparse_blas.cpp          | 1 -
 src/backend/cuda/fast.cu                 | 1 -
 src/backend/cuda/fast_pyramid.cu         | 1 -
 src/backend/cuda/harris.cu               | 1 -
 src/backend/cuda/homography.cu           | 1 -
 src/backend/cuda/inverse.cu              | 1 -
 src/backend/cuda/nearest_neighbour.cu    | 2 +-
 src/backend/cuda/orb.cu                  | 1 -
 src/backend/cuda/sift.cu                 | 1 -
 src/backend/cuda/solve.cu                | 1 -
 src/backend/opencl/fast.cpp              | 1 -
 src/backend/opencl/harris.cpp            | 1 -
 src/backend/opencl/homography.cpp        | 2 +-
 src/backend/opencl/nearest_neighbour.cpp | 2 +-
 src/backend/opencl/orb.cpp               | 2 +-
 src/backend/opencl/sift.cpp              | 2 +-
 src/backend/opencl/sparse_blas.cpp       | 1 -
 29 files changed, 11 insertions(+), 26 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 3ecb502ffa..3b14b3752b 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -9,7 +9,6 @@
 
 #include <blas.hpp>
 #include <af/dim4.hpp>
-#include <handle.hpp>
 #include <cassert>
 #include <err_common.hpp>
 #include <kernel/dot.hpp>
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 3b4b2a28c7..8096234ee5 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <handle.hpp>
 #include <Array.hpp>
 #include <diagonal.hpp>
 #include <math.hpp>
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 2e0c3d1168..3b760ae7d4 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -10,7 +10,6 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
-#include <handle.hpp>
 #include <fast.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 770bd11c20..48a844d090 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
 #include <harris.hpp>
 #include <convolve.hpp>
 #include <gradient.hpp>
@@ -17,6 +16,7 @@
 #include <cstring>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <math.hpp>
 #include <kernel/harris.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 16abd56ac0..d718a89469 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -10,7 +10,6 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
-#include <handle.hpp>
 #include <homography.hpp>
 #include <arith.hpp>
 #include <random.hpp>
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 24ca4acd78..f9f6389694 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -17,6 +17,7 @@
 #include <cassert>
 #include <range.hpp>
 #include <lapack_helper.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <kernel/lu.hpp>
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 257a541712..b4c2b4a087 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -9,9 +9,10 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <err_cpu.hpp>
 #include <kernel/nearest_neighbour.hpp>
 
 using af::dim4;
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 19ae60a0bf..5185c7f3d2 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -9,7 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
 #include <resize.hpp>
 #include <fast.hpp>
 #include <sort_index.hpp>
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index f8dbfa2013..fa450b92f9 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -17,6 +17,7 @@
 #include <err_cpu.hpp>
 #include <triangle.hpp>
 #include <lapack_helper.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 8c55f82047..0fe515c5db 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -10,7 +10,6 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cpu.hpp>
-#include <handle.hpp>
 #include <resize.hpp>
 #include <sort_index.hpp>
 #include <convolve.hpp>
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 367afa3884..339f8ee190 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -16,6 +16,7 @@
 #include <cassert>
 #include <err_cpu.hpp>
 #include <lapack_helper.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index de8a42b85a..8a7e91fe3c 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -17,7 +17,6 @@
 #include <queue.hpp>
 #include <range.hpp>
 #include <iota.hpp>
-#include <handle.hpp>
 #include <sort_by_key.hpp>
 #include <reorder.hpp>
 #include <kernel/sort.hpp>
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 1c74ff9960..dc18392a87 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -15,7 +15,6 @@
 
 #include <af/dim4.hpp>
 #include <complex.hpp>
-#include <handle.hpp>
 #include <err_common.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index 263445a7f0..e3de11f691 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -11,7 +11,6 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
 #include <kernel/fast.hpp>
 
 using af::dim4;
diff --git a/src/backend/cuda/fast_pyramid.cu b/src/backend/cuda/fast_pyramid.cu
index 3f29f761c5..64d9c67d21 100644
--- a/src/backend/cuda/fast_pyramid.cu
+++ b/src/backend/cuda/fast_pyramid.cu
@@ -11,7 +11,6 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
 #include <kernel/fast_pyramid.hpp>
 
 using af::dim4;
diff --git a/src/backend/cuda/harris.cu b/src/backend/cuda/harris.cu
index 2e05d5b843..d5b83c45ee 100644
--- a/src/backend/cuda/harris.cu
+++ b/src/backend/cuda/harris.cu
@@ -11,7 +11,6 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
 #include <kernel/harris.hpp>
 
 using af::dim4;
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index a32f5cbfa9..74053fea0d 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -10,7 +10,6 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
 #include <arith.hpp>
 #include <random.hpp>
 #include <kernel/homography.hpp>
diff --git a/src/backend/cuda/inverse.cu b/src/backend/cuda/inverse.cu
index 7b2ae3b17d..b82ec5ca9e 100644
--- a/src/backend/cuda/inverse.cu
+++ b/src/backend/cuda/inverse.cu
@@ -14,7 +14,6 @@
 
 #include <solve.hpp>
 #include <identity.hpp>
-#include <handle.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index 8c9b7c2280..ea316f82e8 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
+#include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
 #include <kernel/transpose.hpp>
 
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index 76903ee79e..f3683e3530 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -10,7 +10,6 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
 #include <fast_pyramid.hpp>
 #include <kernel/orb.hpp>
 #include <kernel/orb_patch.hpp>
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index 3734f7ba07..a7605bd396 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -11,7 +11,6 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <handle.hpp>
 
 #ifdef AF_BUILD_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 8008ba13f5..9db4889e2f 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -27,7 +27,6 @@
 #include <lu.hpp>
 #include <qr.hpp>
 
-#include <handle.hpp>
 #include <cstdio>
 
 namespace cuda
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index 72b44ccb4f..6b9c1ee7ef 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -11,7 +11,6 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
 #include <kernel/fast.hpp>
 
 using af::dim4;
diff --git a/src/backend/opencl/harris.cpp b/src/backend/opencl/harris.cpp
index d13cc26e4e..eef6074aaa 100644
--- a/src/backend/opencl/harris.cpp
+++ b/src/backend/opencl/harris.cpp
@@ -11,7 +11,6 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
 #include <kernel/harris.hpp>
 
 using af::dim4;
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 8d7b04dae6..775285c495 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -10,8 +10,8 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
 #include <arith.hpp>
+#include <math.hpp>
 #include <random.hpp>
 #include <kernel/homography.hpp>
 #include <algorithm>
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 7a994b03fc..cb711b5084 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
+#include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
 #include <kernel/transpose.hpp>
 
diff --git a/src/backend/opencl/orb.cpp b/src/backend/opencl/orb.cpp
index 241158b608..54e0ba6eb9 100644
--- a/src/backend/opencl/orb.cpp
+++ b/src/backend/opencl/orb.cpp
@@ -11,7 +11,7 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
+#include <math.hpp>
 #include <kernel/orb.hpp>
 
 using af::dim4;
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index d2f02375e0..508720978b 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -11,7 +11,7 @@
 #include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
+#include <math.hpp>
 
 #ifdef AF_BUILD_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 7ace15fe86..15e1c1eab4 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -20,7 +20,6 @@
 
 #include <af/dim4.hpp>
 #include <complex.hpp>
-#include <handle.hpp>
 #include <err_common.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>

From b2867d9e4af713ffba5c435db222010310abc24f Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 8 Sep 2016 17:39:44 -0400
Subject: [PATCH 0898/2677] update image moments documentation

---
 docs/details/image.dox | 29 +++++++++++++++++++++++++++++
 include/arrayfire.h    |  3 +++
 2 files changed, 32 insertions(+)

diff --git a/docs/details/image.dox b/docs/details/image.dox
index ef6d12a4f0..cc80ba71b0 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -829,5 +829,34 @@ unwrap(A, 3, 3, 3, 3, 2, 2) [9 9 1 1]
 Wrap takes an unwrapped image (see \ref unwrap()) and converts it back to an image.
 
 The inputs to this function should be the same as the inputs used to generate the unwrapped image.
+
+=======================================================================
+
+\defgroup image_func_moments moments
+\ingroup moments_mat
+
+The \ref moments() function allows for finding different
+properties of image regions. Currently, ArrayFire calculates all first order moments.
+The moments are defined within the \ref af_moment_type enum.
+
+As the enum details, each moment can be returned individually or all first-order
+moments can be calculated at once. This can be done as follows:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+af::array moments = af::moments(input_image, AF_MOMENT_FIRST_ORDER);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Here is an example of how the shorthand versions might be used to find the area(or gray level sum) and
+center of mass of an image:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+double m00, m01, m10;
+af::moments(&m00, input_image, AF_MOMENT_M00);
+af::moments(&m01, input_image, AF_MOMENT_M01);
+af::moments(&m10, input_image, AF_MOMENT_M10);
+
+double area = m00;
+double x_center = m10 / m00;
+double y_center = m01 / m00;
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 @}
 */
+
diff --git a/include/arrayfire.h b/include/arrayfire.h
index e9bb8cb6e1..daa9438644 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -169,6 +169,9 @@
      @defgroup hist_mat Histograms
      Image and data histograms
 
+     @defgroup moments_mat Image moments
+     Centroids, areas, etc.
+
      @defgroup transform_mat Image transformations
      rotate, skew, etc.
 

From 9267d8d01a7d7e8cb104272a37da702aa34cc196 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 7 Sep 2016 17:03:38 -0400
Subject: [PATCH 0899/2677] initial release notes

---
 docs/pages/release_notes.md | 58 +++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 2f3a447718..6d12829ec0 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,5 +1,63 @@
 Release Notes {#releasenotes}
 ==============
+v3.4.0
+==============
+The source code with submodules can be downloaded directly from the following link:
+http://arrayfire.com/arrayfire_source/arrayfire-full-3.4.0.tar.bz2
+
+Features
+----------
+* New [interpolation methods](https://github.com/arrayfire/arrayfire/issues/1562) for \ref af::resize(), \ref af::transform(), \ref af::approx1() and \ref af::approx2()
+* Support for complex mathematical functions
+* New Forge graphics [integration](https://github.com/arrayfire/arrayfire/pull/1555)!
+    * Vector Field plotting functionality
+    * API updates
+* \ref af::medfilt1(): [Median filter for 1-d signals](https://github.com/arrayfire/arrayfire/pull/1479)
+* af::RandomEngine(): New [random number generators](https://github.com/arrayfire/arrayfire/issues/868)//TODO
+    * Philox
+    * Threefry
+    * Mersenne Twister
+* \ref af::sparse(): [Sparse matrix support for all backends](https://github.com/arrayfire/arrayfire/issues/821)
+* \ref af::scan(): New [generalized scan](https://github.com/arrayfire/arrayfire/issues/388) functions
+* \ref af::moments(): New [image moments](https://github.com/arrayfire/arrayfire/pull/1453) functions
+
+Bug Fixes
+--------------
+
+* Fixes to edge-cases in [morphological operations.](https://github.com/arrayfire/arrayfire/issues/1564)
+* Makes JIT tree size [consistent between devices](https://github.com/arrayfire/arrayfire/issues/1457)
+* Delegate [higher-dimension convolutions](https://github.com/arrayfire/arrayfire/pull/1445) to correct dimension
+* Indexing fixes with [c++11x](https://github.com/arrayfire/arrayfire/pull/1426), [af_indexers](https://github.com/arrayfire/arrayfire/pull/1426), and [empty arrays](https://github.com/arrayfire/arrayfire/issues/799)
+* Single element [median bugfix](https://github.com/arrayfire/arrayfire/pull/1423)
+* Correct time from [timeit()](https://github.com/arrayfire/arrayfire/pull/1414) function
+* Fix floating point numbers in [af::seq and missing size_of() types](https://github.com/arrayfire/arrayfire/pull/1404)
+* Explicitly extantiate [af::device() for void * ](https://github.com/arrayfire/arrayfire/issues/1503)
+
+Improvements
+------------
+* Cuda 8 and compute 6.x support, current installer still on 7.5
+* Improved [JIT](https://github.com/arrayfire/arrayfire/issues/1472) evaluation heuristics for CUDA and OpenCL
+* User controlled FFT plan caching
+* Cuda [speedups](https://github.com/arrayfire/arrayfire/pull/1411) for \ref wrap, \ref unwrap and \ref approx.
+* Fallback for Cuda-OpenGL [interop](https://github.com/arrayfire/arrayfire/pull/1415)
+* Additional forms of batching with the \ref transform() function. [New behavior defined here.](https://github.com/arrayfire/arrayfire/pull/1412)
+* Update to [OpenCL2 headers](https://github.com/arrayfire/arrayfire/issues/1344) in backend
+* Support for interacting with [external OpenCL contexts](https://github.com/arrayfire/arrayfire/pull/1140)
+
+Build
+------
+* Compilation [speedups](https://github.com/arrayfire/arrayfire/pull/1526)
+* Build [fixes](https://github.com/arrayfire/arrayfire/pull/1526) with MKL.
+* Error message when [CUDA Compute Detection fails](https://github.com/arrayfire/arrayfire/issues/1535).
+* Several CMake build issues with Xcode generator fixed
+* Fix [multiple OpenCL definitions](https://github.com/arrayfire/arrayfire/issues/1429) at link-time
+* [Boost compute version update and lapacke detection fix](https://github.com/arrayfire/arrayfire/pull/1423)
+* Fix builds [with GCC 6.1.1](https://github.com/arrayfire/arrayfire/pull/1409) 
+
+Documentation
+-------------
+* Fixed grammar in license
+
 
 v3.3.2
 ==============

From 9e27f19c2d88a51bf1e4e489771fe006dec586ac Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 8 Sep 2016 22:27:27 -0400
Subject: [PATCH 0900/2677] OPENCL: Fixes to work on beignet

---
 src/backend/opencl/kernel/interp.cl    | 25 +++++++++++++++++++------
 src/backend/opencl/kernel/rotate.cl    |  5 ++++-
 src/backend/opencl/kernel/transform.cl |  5 ++++-
 3 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index 29f9145aab..713f44f86b 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -42,9 +42,22 @@ InterpValTy cubicInterpFunc(InterpValTy val[4], InterpPosTy xratio, bool spline)
 {
     InterpValTy a0, a1, a2, a3;
     if (spline) {
-        a0 = MULRC(-0.5, val[0]) + MULRC( 1.5, val[1]) + MULRC(-1.5, val[2]) + MULRC( 0.5, val[3]);
-        a1 = MULRC( 1.0, val[0]) + MULRC(-2.5, val[1]) + MULRC( 2.0, val[2]) + MULRC(-0.5, val[3]);
-        a2 = MULRC(-0.5, val[0]) + MULRC( 0.5, val[2]);
+        a0 =
+            MULRC((InterpPosTy)-0.5, val[0]) +
+            MULRC((InterpPosTy) 1.5, val[1]) +
+            MULRC((InterpPosTy)-1.5, val[2]) +
+            MULRC((InterpPosTy) 0.5, val[3]);
+
+        a1 =
+            MULRC((InterpPosTy) 1.0, val[0]) +
+            MULRC((InterpPosTy)-2.5, val[1]) +
+            MULRC((InterpPosTy) 2.0, val[2]) +
+            MULRC((InterpPosTy)-0.5, val[3]);
+
+        a2 =
+            MULRC((InterpPosTy)-0.5, val[0]) +
+            MULRC((InterpPosTy) 0.5, val[2]);
+
         a3 = val[1];
     } else {
         a0 = val[3] - val[2] - val[0] + val[1];
@@ -104,7 +117,7 @@ void interp1(
 
     InterpPosTy ratio = off_x;
     if (method == AF_INTERP_LINEAR_COSINE) {
-        ratio = (1 - cos(ratio * M_PI))/2;
+        ratio = (1 - cos(ratio * (InterpPosTy)M_PI))/2;
     }
 
     for (int n = 0; n < batch; n++) {
@@ -194,8 +207,8 @@ void interp2(
 
     InterpPosTy xratio = off_x, yratio = off_y;
     if (method == AF_INTERP_LINEAR_COSINE) {
-        xratio = (1 - cos(xratio * M_PI))/2;
-        yratio = (1 - cos(yratio * M_PI))/2;
+        xratio = (1 - cos(xratio * (InterpPosTy)M_PI))/2;
+        yratio = (1 - cos(yratio * (InterpPosTy)M_PI))/2;
     }
 
     InterpValTy zero = ZERO;
diff --git a/src/backend/opencl/kernel/rotate.cl b/src/backend/opencl/kernel/rotate.cl
index cc593198fa..e77c367ce3 100644
--- a/src/backend/opencl/kernel/rotate.cl
+++ b/src/backend/opencl/kernel/rotate.cl
@@ -50,7 +50,10 @@ void rotate_kernel(__global T *d_out, const KParam out,
         // Special conditions to deal with boundaries for bilinear and bicubic
         // FIXME: Ideally this condition should be removed or be present for all methods
         // But tests are expecting a different behavior for bilinear and nearest
-        if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi || in.dims[1] < yidi) {
+        if (xidi < (InterpPosTy)-0.0001 ||
+            yidi < (InterpPosTy)-0.0001 ||
+            in.dims[0] <= xidi ||
+            in.dims[1] <= yidi) {
             for(int i = 0; i < nimages; i++) {
                 d_out[loco + i * out.strides[2]] = zero;
             }
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 303b0d7539..3f50335ed7 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -157,7 +157,10 @@ void transform_kernel(__global T *d_out, const KParam out,
     bool clamp = INTERP_ORDER != 1;
 
     T zero = ZERO;
-    if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi || in.dims[1] <= yidi) {
+    if (xidi < (InterpPosTy)-0.0001 ||
+        yidi < (InterpPosTy)-0.0001 ||
+        in.dims[0] <= xidi ||
+        in.dims[1] <= yidi) {
         for(int n = 0; n < limages; n++) {
             d_out[loco + n * out.strides[2]] = zero;
         }

From d068c8a418240c6aa8438a2cb04423a268bda17b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Thu, 8 Sep 2016 23:38:32 -0400
Subject: [PATCH 0901/2677] OPENCL: Fixes for POCL

---
 src/backend/opencl/kernel/bilateral.cl  | 10 ++++++++--
 src/backend/opencl/kernel/bilateral.hpp | 24 ++++++++++++++----------
 2 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index 4b7baa8aa9..6ad8ea94d0 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -7,6 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if USE_NATIVE_EXP
+#define EXP native_exp
+#else
+#define EXP exp
+#endif
+
 int lIdx(int x, int y,
         int stride1, int stride0)
 {
@@ -60,7 +66,7 @@ void bilateral(__global outType *        d_dst,
     if (lx<window_size && ly<window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = native_exp(((x*x) + (y*y)) / variance_space_neg2);
+        gauss2d[ly*window_size+lx] = EXP(((x*x) + (y*y)) / variance_space_neg2);
     }
 
     int s0 = iInfo.strides[0];
@@ -93,7 +99,7 @@ void bilateral(__global outType *        d_dst,
             for(int wi=0; wi<window_size; ++wi) {
                 outType tmp_color   = localMem[joff+wi];
                 const outType c = center_color - tmp_color;
-                outType gauss_range = native_exp(c * c * inv_variance_range_neg2);
+                outType gauss_range = EXP(c * c * inv_variance_range_neg2);
                 outType weight      = gauss2d[goff+wi] * gauss_range;
                 norm += weight;
                 res  += tmp_color * weight;
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 0ee3058b6f..0493389645 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -18,6 +18,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <af/opencl.h>
 
 using cl::Buffer;
 using cl::Program;
@@ -48,20 +49,23 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D inType=" << dtype_traits<inType>::getName()
-                            << " -D outType=" << dtype_traits<outType>::getName();
-                    if (std::is_same<inType, double>::value ||
-                        std::is_same<inType, cdouble>::value) {
+                bool use_native_exp = getActivePlatform() != AFCL_PLATFORM_POCL;
+                printf("NATIVE_EXP: %d\n", use_native_exp);
+                std::ostringstream options;
+                options << " -D inType=" << dtype_traits<inType>::getName()
+                        << " -D outType=" << dtype_traits<outType>::getName();
+                if (std::is_same<inType, double>::value ||
+                    std::is_same<inType, cdouble>::value) {
                         options << " -D USE_DOUBLE";
-                    }
+                }
+                options << " -D USE_NATIVE_EXP=" << (int)use_native_exp;
 
-                    Program prog;
-                    buildProgram(prog, bilateral_cl, bilateral_cl_len, options.str());
-                    bilProgs[device] = new Program(prog);
+                Program prog;
+                buildProgram(prog, bilateral_cl, bilateral_cl_len, options.str());
+                bilProgs[device] = new Program(prog);
 
                     bilKernels[device] = new Kernel(*bilProgs[device], "bilateral");
-                });
+            });
 
         auto bilateralOp = KernelFunctor<Buffer, KParam,
                                        Buffer, KParam,

From 76b69f86a5a4186406cb367747fe2d20a135363c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 9 Sep 2016 12:02:06 -0400
Subject: [PATCH 0902/2677] Change image->pbo to image->pixels

---
 src/backend/cpu/image.cpp             |  6 +++---
 src/backend/cuda/image.cpp            | 10 +++++-----
 src/backend/cuda/interopManager.cpp   |  8 ++++----
 src/backend/opencl/image.cpp          |  2 +-
 src/backend/opencl/interopManager.cpp |  2 +-
 5 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 3c06b6521b..7678cae7f7 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -31,15 +31,15 @@ void copy_image(const Array<T> &in, const forge::Image* image)
     in.eval();
     getQueue().sync();
 
-    CheckGL("Before CopyArrayToPBO");
+    CheckGL("Before CopyArrayToImage");
     const T *d_X = in.get();
     size_t data_size = image->size();
 
-    glBindBuffer(gl::GL_PIXEL_UNPACK_BUFFER, image->pbo());
+    glBindBuffer(gl::GL_PIXEL_UNPACK_BUFFER, image->pixels());
     glBufferSubData(GL_PIXEL_UNPACK_BUFFER, 0, data_size, d_X);
     glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
 
-    CheckGL("In CopyArrayToPBO");
+    CheckGL("In CopyArrayToImage");
 }
 
 #define INSTANTIATE(T)  \
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 8aa247657a..2303184daf 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -33,12 +33,12 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
 
         const T *d_X = in.get();
-        // Map resource. Copy data to PBO. Unmap resource.
+        // Map resource. Copy data to pixels. Unmap resource.
         size_t num_bytes;
-        T* d_pbo = NULL;
+        T* d_pixels = NULL;
         cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsResourceGetMappedPointer((void **)&d_pbo, &num_bytes, resources[0]);
-        cudaMemcpyAsync(d_pbo, d_X, num_bytes, cudaMemcpyDeviceToDevice,
+        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels, &num_bytes, resources[0]);
+        cudaMemcpyAsync(d_pixels, d_X, num_bytes, cudaMemcpyDeviceToDevice,
                         cuda::getStream(cuda::getActiveDeviceId()));
         cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
 
@@ -46,7 +46,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         CheckGL("After cuda resource copy");
     } else {
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->pbo());
+        glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->pixels());
         glBufferData((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->size(), 0, (gl::GLenum)GL_STREAM_DRAW);
         gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
diff --git a/src/backend/cuda/interopManager.cpp b/src/backend/cuda/interopManager.cpp
index 981e87f785..077e4ac703 100644
--- a/src/backend/cuda/interopManager.cpp
+++ b/src/backend/cuda/interopManager.cpp
@@ -67,14 +67,14 @@ CGR_t* InteropManager::getBufferResource(const forge::Image* key)
     interop_t& i_map = getDeviceMap();
 
     if(i_map.find(key_value) == i_map.end()) {
-        CGR_t pboResource;
-        // Register PBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&pboResource, key->pbo(), cudaGraphicsMapFlagsWriteDiscard));
+        CGR_t pixelsResource;
+        // Register pixels with CUDA
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&pixelsResource, key->pixels(), cudaGraphicsMapFlagsWriteDiscard));
         // TODO:
         // A way to store multiple buffers and take PBO/CBO etc as
         // argument and return the appropriate buffer
         std::vector<CGR_t> vec(1);
-        vec[0] = pboResource;
+        vec[0] = pixelsResource;
         i_map[key_value] = vec;
     }
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 1b0e010b9c..8f403ea1b4 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -51,7 +51,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         CheckGL("End opencl resource copy");
     } else {
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pbo());
+        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pixels());
         glBufferData(GL_PIXEL_UNPACK_BUFFER, image->size(), 0, GL_STREAM_DRAW);
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
diff --git a/src/backend/opencl/interopManager.cpp b/src/backend/opencl/interopManager.cpp
index 7e222a9e8a..ff2da42efc 100644
--- a/src/backend/opencl/interopManager.cpp
+++ b/src/backend/opencl/interopManager.cpp
@@ -53,7 +53,7 @@ cl::Buffer** InteropManager::getBufferResource(const forge::Image* image)
 
     if (iter == i_map.end()) {
         std::vector<cl::Buffer *> vec(1);
-        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, image->pbo(), NULL);
+        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, image->pixels(), NULL);
         i_map[key] = vec;
     }
 

From 73dfe05a20e6c37342f9e8d3a89a70f9488cb822 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 9 Sep 2016 12:27:43 +0530
Subject: [PATCH 0903/2677] correct parameter order for grid calls to forge

Signed-off-by: Shehzan Mohammed <shehzan@arrayfire.com>
---
 src/api/c/hist.cpp    | 2 +-
 src/api/c/image.cpp   | 2 +-
 src/api/c/plot.cpp    | 6 +++---
 src/api/c/surface.cpp | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index f40acf5652..b560957111 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -85,7 +85,7 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
 
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
     }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 07cd2d7c04..da870c8cb6 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -108,7 +108,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
 
         window->setColorMap((forge::ColorMap)props->cmap);
         if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *image, props->title);
+            window->draw(props->row, props->col, *image, props->title);
         else
             window->draw(*image);
     }
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 37ffe4cbaa..0001cb6364 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -116,7 +116,7 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
 
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
     }
@@ -176,7 +176,7 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
 
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
 
@@ -230,7 +230,7 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
 
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
 
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index a342ab2f39..6ae60e2946 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -134,7 +134,7 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
         }
 
         if (props->col>-1 && props->row>-1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
     }

From eb3b8034ef0465a82ddb78f5b487aa3ca48a3b15 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 9 Sep 2016 12:28:07 +0530
Subject: [PATCH 0904/2677] revert temporary fix to shallow water equation
 example

Signed-off-by: Shehzan Mohammed <shehzan@arrayfire.com>
---
 examples/pde/swe.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index cd7f727897..966af4dbfc 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -83,17 +83,17 @@ static void swe(bool console)
 
         m_eta = max<float>(etam);
         if (!console) {
-            (*win)(1, 1).setColorMap(AF_COLORMAP_BLUE);
+            (*win)(0, 0).setColorMap(AF_COLORMAP_BLUE);
             array hist_out = histogram(normalize(eta, m_eta), 15);
 
             (*win)(0, 1).setAxesLimits(0, hist_out.elements(), 0, max<float>(hist_out));
             (*win)(1, 0).setAxesLimits(range(up.dims(1)), vp.col(0));
-            (*win)(0, 0).setAxesLimits(eta.col(0), up.col(0), vp.col(0));
+            (*win)(1, 1).setAxesLimits(eta.col(0), up.col(0), vp.col(0));
 
-            (*win)(1,1).image(normalize(eta, m_eta));
+            (*win)(0,0).image(normalize(eta, m_eta));
             (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
             (*win)(1,0).plot(range(up.dims(1)), vp.col(0), "Pressure at left boundary");
-            (*win)(0,0).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
+            (*win)(1,1).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
             win->show();
         } else eval(eta, up, vp);
         iter++;

From b4ac4f607d24b23338729900b6ec5c3ef2b7bb6c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Sep 2016 18:10:52 -0400
Subject: [PATCH 0905/2677] Add option to install Forge dev files with
 ArrayFire

---
 CMakeLists.txt | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 102cdb41a9..b204310fdd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -226,9 +226,21 @@ INSTALL(FILES
 )
 
 IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-    INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/lib/" DESTINATION "${AF_INSTALL_LIB_DIR}"
-        COMPONENT libraries
+    OPTION(INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
+    INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/lib/"
+            DESTINATION "${AF_INSTALL_LIB_DIR}"
+            COMPONENT libraries
     )
+    IF(${INSTALL_FORGE_DEV})
+        INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/include/"
+                DESTINATION "${AF_INSTALL_INC_DIR}"
+                COMPONENT headers
+        )
+        INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/share/Forge/"
+                DESTINATION "${AF_INSTALL_DATA_DIR}/../Forge"
+                COMPONENT share
+        )
+    ENDIF(${INSTALL_FORGE_DEV})
 ENDIF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
 
 ## configuration to be used from the binary directory directly

From 2ff3cd9bdabed63e04a4ed35c7358922611d9c16 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 9 Sep 2016 11:23:51 -0400
Subject: [PATCH 0906/2677] Build Forge Docs if building ArrayFire
 documentation

---
 CMakeModules/build_forge.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 0f6574a36e..61f5df39e3 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -58,6 +58,7 @@ ExternalProject_Add(
     -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
     -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
     -DBUILD_EXAMPLES:BOOL=OFF
+    -DBUILD_DOCUMENTATION:BOOL=${BUILD_DOCS}
     -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
     -Dglbinding_DIR:STRING=${glbinding_DIR}
     -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}

From 50ac00b206d6c17c0833e315e6f7ea993c8cce07 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 9 Sep 2016 12:19:17 -0400
Subject: [PATCH 0907/2677] Forge installation files setup for OSX

---
 CMakeModules/osx_install/OSXInstaller.cmake | 75 ++++++++++++++++++++-
 CMakeModules/osx_install/distribution.dist  | 31 +++++++++
 2 files changed, 104 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index f916536874..119d81e7c0 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -53,7 +53,9 @@ OSX_INSTALL_SETUP(Unified af)
 # Headers
 ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_INCLUDE
                   COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  ${CMAKE_INSTALL_PREFIX}/include "${OSX_TEMP}/include"
+                  ${CMAKE_INSTALL_PREFIX}/include/af "${OSX_TEMP}/include/af"
+                  COMMAND ${CMAKE_COMMAND} -E copy
+                  ${CMAKE_INSTALL_PREFIX}/include/arrayfire.h "${OSX_TEMP}/include/arrayfire.h"
                   WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                   COMMENT "Copying header files to temporary OSX Install Dir"
                   )
@@ -73,6 +75,41 @@ ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
                   WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                   COMMENT "Copying documentation files to temporary OSX Install Dir"
                   )
+
+# Forge Headers
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_INC
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/include/fg" "${OSX_TEMP}/Forge/include/fg"
+                  COMMAND ${CMAKE_COMMAND} -E copy
+                  "${CMAKE_INSTALL_PREFIX}/include/forge.h" "${OSX_TEMP}/Forge/include/forge.h"
+                  COMMAND ${CMAKE_COMMAND} -E copy
+                  "${CMAKE_INSTALL_PREFIX}/include/ComputeCopy.h" "${OSX_TEMP}/Forge/include/ComputeCopy.h"
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                  COMMENT "Copying examples files to temporary OSX Install Dir"
+                  )
+# Forge Examples
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_EXAMPLES
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/Forge/examples" "${OSX_TEMP}/Forge/examples"
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                  COMMENT "Copying examples files to temporary OSX Install Dir"
+                  )
+
+# Documentation
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_DOC
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/Forge/doc" "${OSX_TEMP}/Forge/doc"
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                  COMMENT "Copying documentation files to temporary OSX Install Dir"
+                  )
+
+# Forge CMake
+ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_CMAKE
+                  COMMAND ${CMAKE_COMMAND} -E copy_directory
+                  "${CMAKE_INSTALL_PREFIX}/share/Forge/cmake" "${OSX_TEMP}/Forge/cmake"
+                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                  COMMENT "Copying documentation files to temporary OSX Install Dir"
+                  )
 ################################################################################
 
 FUNCTION(PKG_BUILD)
@@ -202,5 +239,39 @@ PKG_BUILD(  PKG_NAME        ArrayFireDoc
             PATH_TO_FILES   ${OSX_TEMP}/doc
             FILTERS         cmake)
 
-PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package} ${common_package} ${header_package} ${examples_package} ${doc_package})
+PKG_BUILD(  PKG_NAME        ForgeHeaders
+            DEPENDS         OSX_INSTALL_SETUP_FORGE_INCLUDE
+            TARGETS         forge_header_package
+            INSTALL_LOCATION /usr/local/include
+            IDENTIFIER      com.arrayfire.pkg.forge.inc
+            PATH_TO_FILES   ${OSX_TEMP}/Forge/include)
+
+PKG_BUILD(  PKG_NAME        ForgeExamples
+            DEPENDS         OSX_INSTALL_SETUP_FORGE_EXAMPLES
+            TARGETS         forge_examples_package
+            INSTALL_LOCATION /usr/local/share/Forge/examples
+            IDENTIFIER      com.arrayfire.pkg.forge.examples
+            PATH_TO_FILES   ${OSX_TEMP}/Forge/examples
+            FILTERS         cmake)
+
+PKG_BUILD(  PKG_NAME        ForgeDoc
+            DEPENDS         OSX_INSTALL_SETUP_FORGE_DOC
+            TARGETS         forge_doc_package
+            INSTALL_LOCATION /usr/local/share/Forge/doc
+            IDENTIFIER      com.arrayfire.pkg.forge.doc
+            PATH_TO_FILES   ${OSX_TEMP}/share/Forge/doc
+            FILTERS         cmake)
+
+PKG_BUILD(  PKG_NAME        ForgeCMake
+            DEPENDS         OSX_INSTALL_SETUP_FORGE_CMAKE
+            TARGETS         forge_cmake_package
+            INSTALL_LOCATION /usr/local/share/Forge/cmake
+            IDENTIFIER      com.arrayfire.pkg.forge.cmake
+            PATH_TO_FILES   ${OSX_TEMP}/share/Forge/cmake
+            FILTERS         cmake)
+
+PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
+                      ${common_package} ${header_package} ${examples_package} ${doc_package}
+                      ${forge_header_package} ${forge_examples_package} ${forge_doc_package} ${forge_cmake_package}
+                      )
 
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
index 1494e7efde..496e8318c0 100644
--- a/CMakeModules/osx_install/distribution.dist
+++ b/CMakeModules/osx_install/distribution.dist
@@ -10,6 +10,9 @@
             choices.cuda_lib.selected ||
             choices.cpu_lib.selected;
       }
+      function CheckForgeSelected() {
+      return choices.forge_headers.selected;
+      }
     </script>
 
     <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"       version="${AF_VERSION}" onConclusion="none">ArrayFireCPU.pkg</pkg-ref>
@@ -20,6 +23,10 @@
     <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.forge.inc"               version="${AF_VERSION}" onConclusion="none">ForgeHeaders.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.forge.examples"          version="${AF_VERSION}" onConclusion="none">ForgeExamples.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.forge.doc"               version="${AF_VERSION}" onConclusion="none">ForgeDoc.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.forge.cmake"             version="${AF_VERSION}" onConclusion="none">ForgeCMake.pkg</pkg-ref>
     <options customize="always" require-scripts="false"/>
     <choices-outline>
         <line choice="libs">
@@ -32,6 +39,11 @@
         <line choice="com.arrayfire.arrayfire.inc"/>
         <line choice="com.arrayfire.arrayfire.examples"/>
         <line choice="com.arrayfire.arrayfire.doc"/>
+        <line choice="forge">
+            <line choice="forge_include"/>
+            <line choice="forge_doc"/>
+            <line choice="com.arrayfire.forge.cmake"/>
+        </line>
     </choices-outline>
     <choice id="libs" title="ArrayFire Libraries" visible="true" />
     <choice title="CPU Libraries"
@@ -74,4 +86,23 @@
     <choice title="Documentation" description="ArrayFire Documentation" id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.doc"/>
     </choice>
+
+    <choice id="forge" title="Forge Development Files" visible="true" />
+    <choice title="Forge Headers" description="Forge Headers" id="com.arrayfire.forge.inc" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.forge.inc"/>
+    </choice>
+    <choice title="Forge Examples" description="Forge ArrayFire Examples" id="com.arrayfire.forge.examples" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.forge.examples"/>
+    </choice>
+    <choice title="Forge Documentation" description="Forge Documentation" id="com.arrayfire.forge.doc" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.forge.doc"/>
+    </choice>
+    <choice title="Forge CMake"
+            description="Installs ForgeConfig.cmake files"
+            id="com.arrayfire.forge.cmake"
+            selected="CheckForgeSelected()"
+            visible="false"
+            enabled="CheckForgeSelected()">
+        <pkg-ref id="com.arrayfire.forge.cmake"/>
+    </choice>
 </installer-gui-script>

From e8201c55561ab218dea79701a8e1f210a282cec8 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 9 Sep 2016 12:12:07 -0400
Subject: [PATCH 0908/2677] add copyright notice for glbinding

---
 COPYRIGHT.md                | 28 ++++++++++++++--------------
 docs/pages/release_notes.md | 29 ++++++++++++++++++++---------
 2 files changed, 34 insertions(+), 23 deletions(-)

diff --git a/COPYRIGHT.md b/COPYRIGHT.md
index 2e7a5c18ed..9948438d88 100644
--- a/COPYRIGHT.md
+++ b/COPYRIGHT.md
@@ -10,6 +10,7 @@ Copyrights
 * [Boost Compute](#boost-compute)
 * [Thrust](#thrust)
 * [Magma](#magma)
+* [glbinding](#glbinding)
 
 ### Introduction
 ArrayFire uses software written by the following parties. Each software is listed with its copyright, license and home page.
@@ -68,7 +69,7 @@ Random123 is distributed under the BSD 3-Clause License. A copy of this license
 
 See Random123 home page https://www.deshawresearch.com/resources_random123.html for details and links to the source code.
 
-**How ArrayFire uses Random123:** ArrayFire uses a modified and stripped down version of Random123 in the OpenCL backend. Each of the source files using the modified version of Random123 contain the original copyright.
+**How ArrayFire uses Random123:** ArrayFire uses a modified and stripped down version of Random123 in all backends. Each of the source files using the modified version of Random123 contain the original copyright.
 
 ### Boost Compute
 Copyright (C) 2013-2015 Kyle Lutz
@@ -97,19 +98,6 @@ See clMagma home page http://icl.cs.utk.edu/magma/index.html for details and lin
 
 **How ArrayFire uses clMagma:** ArrayFire uses a modified and stripped down version of clMagma in the OpenCL backend. Each of the source files using the modified version of clMagma contain the original copyright.
 
-### GLEW
-The OpenGL Extension Wrangler Library
-Copyright (C) 2002-2007, Milan Ikits <milan ikits[]ieee org>
-Copyright (C) 2002-2007, Marcelo E. Magallon <mmagallo[]debian org>
-Copyright (C) 2002, Lev Povalahev
-
-GLEW is distributed under the BSD 3-Clause License, Mesa 3D License (MIT) and the Khronos License (MIT).
-A copy of these licenses is present in the LICENSES directory.
-
-See GLEW home page http://glew.sourceforge.net for details and links to the source code.
-
-**How ArrayFire uses GLEW:** The ArrayFire source code does not contain any source code from GLEW. GLEW can be optionally linked with during build time. The binary installers of ArrayFire may come packaged with GLEW.
-
 ### GLFW
 Copyright (C) 2002-2006 Marcus Geelnard
 Copyright (C) 2006-2011 Camilla Berglund
@@ -119,3 +107,15 @@ GLFW is distributed under the zlib/libpng License. A copy of this license is pre
 See GLFW home page http://www.glfw.org for details and links to the source code.
 
 **How ArrayFire uses GLFW:** The ArrayFire source code does not contain any source code from GLFW. GLFW can be optionally linked with or disabled during build time. The binary installers of ArrayFire may come packaged with GLFW.
+
+### glbinding
+
+Copyright (c) 2014-2015 Computer Graphics Systems Group at the Hasso-Plattner-Institute and CG Internals GmbH, Germany.
+
+glbinding is distributed under the MIT License. A copy of this license is present in the LICENSES directory.
+
+See glbinding home page http://www.glbinding.org for details and links to the source code.
+
+**How ArrayFire uses glbinding:** The ArrayFire source code does not contain any source code from glbinding. glbinding is statically linked during build time.
+
+
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 6d12829ec0..c0914dd915 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -2,28 +2,39 @@ Release Notes {#releasenotes}
 ==============
 v3.4.0
 ==============
-The source code with submodules can be downloaded directly from the following link:
-http://arrayfire.com/arrayfire_source/arrayfire-full-3.4.0.tar.bz2
 
 Features
 ----------
 * New [interpolation methods](https://github.com/arrayfire/arrayfire/issues/1562) for \ref af::resize(), \ref af::transform(), \ref af::approx1() and \ref af::approx2()
-* Support for complex mathematical functions
+* Support for [complex mathematical functions](\ref mathfunc_mat)
 * New Forge graphics [integration](https://github.com/arrayfire/arrayfire/pull/1555)!
     * Vector Field plotting functionality
     * API updates
+    * Removed GLEW and replaced with glbinding. (And links to both GLEW and glbinding)
+    * Multiple overlays on the same window are now possible.
+    * New API to set axes limits for graphs.
+        * Draw calls do not automatically compute the limits. This is now under user control.
+    * New API to set axes titles.
+    * New API for plot and scatter:
+        * \ref plot() and \ref scatter() now can handle 2D and 3D
+        * \ref af_draw_plot_nd
+        * \ref af_draw_plot_2d
+        * \ref af_draw_plot_3d
+        * \ref af_draw_scatter_nd
+        * \ref af_draw_scatter_2d
+        * \ref af_draw_scatter_3d
+
 * \ref af::medfilt1(): [Median filter for 1-d signals](https://github.com/arrayfire/arrayfire/pull/1479)
-* af::RandomEngine(): New [random number generators](https://github.com/arrayfire/arrayfire/issues/868)//TODO
+* af::RandomEngine(): New [random number generators](https://github.com/arrayfire/arrayfire/issues/868)
     * Philox
     * Threefry
     * Mersenne Twister
 * \ref af::sparse(): [Sparse matrix support for all backends](https://github.com/arrayfire/arrayfire/issues/821)
 * \ref af::scan(): New [generalized scan](https://github.com/arrayfire/arrayfire/issues/388) functions
-* \ref af::moments(): New [image moments](https://github.com/arrayfire/arrayfire/pull/1453) functions
+* \ref af::moments(): New [image moments](\ref moments_mat) functions
 
 Bug Fixes
 --------------
-
 * Fixes to edge-cases in [morphological operations.](https://github.com/arrayfire/arrayfire/issues/1564)
 * Makes JIT tree size [consistent between devices](https://github.com/arrayfire/arrayfire/issues/1457)
 * Delegate [higher-dimension convolutions](https://github.com/arrayfire/arrayfire/pull/1445) to correct dimension
@@ -35,11 +46,11 @@ Bug Fixes
 
 Improvements
 ------------
-* Cuda 8 and compute 6.x support, current installer still on 7.5
+* CUDA 8 and compute 6.x(Pascal) support, current installer still on 7.5
 * Improved [JIT](https://github.com/arrayfire/arrayfire/issues/1472) evaluation heuristics for CUDA and OpenCL
 * User controlled FFT plan caching
-* Cuda [speedups](https://github.com/arrayfire/arrayfire/pull/1411) for \ref wrap, \ref unwrap and \ref approx.
-* Fallback for Cuda-OpenGL [interop](https://github.com/arrayfire/arrayfire/pull/1415)
+* CUDA [speedups](https://github.com/arrayfire/arrayfire/pull/1411) for \ref wrap(), \ref unwrap() and [approx](\ref approx_mat).
+* Fallback for CUDA-OpenGL [interop](https://github.com/arrayfire/arrayfire/pull/1415)
 * Additional forms of batching with the \ref transform() function. [New behavior defined here.](https://github.com/arrayfire/arrayfire/pull/1412)
 * Update to [OpenCL2 headers](https://github.com/arrayfire/arrayfire/issues/1344) in backend
 * Support for interacting with [external OpenCL contexts](https://github.com/arrayfire/arrayfire/pull/1140)

From b99c59a2b2fd5c7a8ccdd32f62d2221e14c9cf6e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 14:53:45 -0400
Subject: [PATCH 0909/2677] Sanitizing the API and the header files

- Add missing version guards for v3.4
- Moved eval functions into the header for backwards compatibility
---
 include/af/array.h         | 81 ++++++++++++++++++++++++++++++++++----
 include/af/random_engine.h | 31 ++++++++++++++-
 src/api/cpp/array.cpp      | 30 --------------
 3 files changed, 103 insertions(+), 39 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index e53d920900..62c4191e10 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -122,7 +122,9 @@ namespace af
             bool isfloating() const;
             bool isinteger() const;
             bool isbool() const;
+#if AF_API_VERSION >= 34
             bool issparse() const;
+#endif
             void eval() const;
             array as(dtype type) const;
             array T() const;
@@ -650,10 +652,12 @@ namespace af
         */
         bool isbool() const;
 
+#if AF_API_VERSION >= 34
         /**
            \brief Returns true if the array is a sparse array
         */
         bool issparse() const;
+#endif
 
         /**
            \brief Evaluate any JIT expressions to generate data for the array
@@ -1249,20 +1253,76 @@ namespace af
        @{
     */
     inline array &eval(array &a) { a.eval(); return a; }
-    AFAPI void eval(array &a, array &b);
-    AFAPI void eval(array &a, array &b, array &c);
-    AFAPI void eval(array &a, array &b, array &c, array &d);
-    AFAPI void eval(array &a, array &b, array &c, array &d, array &e);
-    AFAPI void eval(array &a, array &b, array &c, array &d, array &e, array &f);
+
+#if AF_API_VERSION >= 34
+    ///
+    /// Evaluate multiple arrays simultaneously
+    ///
     AFAPI void eval(int num, array **arrays);
+#endif
+
+    inline void eval(array &a, array &b)
+    {
+#if AF_API_VERSION >= 34
+        array *arrays[] = {&a, &b};
+        return eval(2, arrays);
+#else
+        eval(a); b.eval();
+#endif
+    }
+
+    inline void eval(array &a, array &b, array &c)
+    {
+#if AF_API_VERSION >= 34
+        array *arrays[] = {&a, &b, &c};
+        return eval(3, arrays);
+#else
+        eval(a, b); c.eval();
+#endif
+    }
+
+    inline void eval(array &a, array &b, array &c, array &d)
+    {
+#if AF_API_VERSION >= 34
+        array *arrays[] = {&a, &b, &c, &d};
+        return eval(4, arrays);
+#else
+        eval(a, b, c); d.eval();
+#endif
+
+    }
 
+    inline void eval(array &a, array &b, array &c, array &d, array &e)
+    {
+#if AF_API_VERSION >= 34
+        array *arrays[] = {&a, &b, &c, &d, &e};
+        return eval(5, arrays);
+#else
+        eval(a, b, c, d); e.eval();
+#endif
+    }
+
+    inline void eval(array &a, array &b, array &c, array &d, array &e, array &f)
+    {
+#if AF_API_VERSION >= 34
+        array *arrays[] = {&a, &b, &c, &d, &e, &f};
+        return eval(6, arrays);
+#else
+        eval(a, b, c, d, e); f.eval();
+#endif
+    }
+
+#if AF_API_VERSION >= 34
     ///
     /// Turn the manual eval flag on or off
     ///
     AFAPI void setManualEvalFlag(bool flag);
+#endif
 
+#if AF_API_VERSION >= 34
     /// Get the manual eval flag
     AFAPI bool getManualEvalFlag();
+#endif
 
     /**
        @}
@@ -1362,6 +1422,7 @@ extern "C" {
     */
 
 
+#if AF_API_VERSION >= 34
     /**
        Evaluate multiple arrays together
     */
@@ -1369,7 +1430,9 @@ extern "C" {
     /**
       @}
     */
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        Turn the manual eval flag on or off
     */
@@ -1377,8 +1440,9 @@ extern "C" {
     /**
       @}
     */
+#endif
 
-
+#if AF_API_VERSION >= 34
     /**
        Get the manual eval flag
     */
@@ -1386,7 +1450,7 @@ extern "C" {
     /**
       @}
     */
-
+#endif
 
     /**
         \ingroup method_mat
@@ -1572,6 +1636,7 @@ extern "C" {
     */
     AFAPI af_err af_is_bool         (bool *result, const af_array arr);
 
+#if AF_API_VERSION >= 34
     /**
         \brief Check if an array is sparse
 
@@ -1584,7 +1649,7 @@ extern "C" {
     /**
         @}
     */
-
+#endif
 #ifdef __cplusplus
 }
 #endif
diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 21fc822c68..80314927cc 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -17,7 +17,7 @@ namespace af
 {
     class array;
     class dim4;
-
+#if AF_API_VERSION >= 34
     ///
     /// \brief A random number generator class
     ///
@@ -123,7 +123,9 @@ namespace af
                 @}
             */
     };
+#endif
 
+#if AF_API_VERSION >= 34
     /**
         \param[in] dims The dimensions of the array to be generated
         \param[in] ty The type of the array
@@ -134,7 +136,9 @@ namespace af
         \ingroup random_func_randu
     */
     AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
         \param[in] dims The dimensions of the array to be generated
         \param[in] ty The type of the array
@@ -145,6 +149,7 @@ namespace af
         \ingroup random_func_randn
     */
     AFAPI array randn(const dim4 &dims, const dtype ty, randomEngine &r);
+#endif
 
     /**
         \param[in] dims The dimensions of the array to be generated
@@ -264,12 +269,14 @@ namespace af
                       const dim_t d1, const dim_t d2,
                       const dim_t d3, const dtype ty=f32);
 
+#if AF_API_VERSION >= 34
     /**
         \param[in] rtype The type of the random number generator
 
         \ingroup random_func_set_type
     */
     AFAPI void setDefaultRandomEngine(randomEngineType rtype);
+#endif
 
     /**
         \param[in] seed A 64 bit unsigned integer
@@ -292,6 +299,7 @@ namespace af
 extern "C" {
 #endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for creating random engine
 
@@ -302,7 +310,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_engine_type rtype, uintl seed);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for retaining random engine
 
@@ -312,7 +322,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_retain_random_engine(af_random_engine *out, const af_random_engine engine);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for changing random engine type
 
@@ -322,7 +334,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for getting random engine type
 
@@ -332,7 +346,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for creating an array of uniform numbers using a random engine
 
@@ -345,7 +361,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for creating an array of normal numbers using a random engine
 
@@ -358,7 +376,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for setting the seed of a random engine
 
@@ -368,7 +388,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for getting the default random engine
 
@@ -377,7 +399,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_get_default_random_engine(af_random_engine *engine);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for setting the type of the default random engine
 
@@ -386,7 +410,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_set_default_random_engine(const af_random_engine_type rtype);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for getting the seed of a random engine
 
@@ -396,7 +422,9 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine);
+#endif
 
+#if AF_API_VERSION >= 34
     /**
        C Interface for releasing random engine
 
@@ -404,6 +432,7 @@ extern "C" {
        \returns \ref AF_SUCCESS if the execution completes properly
     */
     AFAPI af_err af_release_random_engine(af_random_engine engine);
+#endif
 
     //General rand calls
 
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index a96b4ebb07..b60935f077 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1056,36 +1056,6 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
         AF_THROW(af_unlock_array(get()));
     }
 
-    void eval(array &a, array &b)
-    {
-        af_array arrays[] = {a.get(), b.get()};
-        AF_THROW(af_eval_multiple(2, arrays));
-    }
-
-    void eval(array &a, array &b, array &c)
-    {
-        af_array arrays[] = {a.get(), b.get(), c.get()};
-        AF_THROW(af_eval_multiple(3, arrays));
-    }
-
-    void eval(array &a, array &b, array &c, array &d)
-    {
-        af_array arrays[] = {a.get(), b.get(), c.get(), d.get()};
-        AF_THROW(af_eval_multiple(4, arrays));
-    }
-
-    void eval(array &a, array &b, array &c, array &d, array &e)
-    {
-        af_array arrays[] = {a.get(), b.get(), c.get(), d.get(), e.get()};
-        AF_THROW(af_eval_multiple(5, arrays));
-    }
-
-    void eval(array &a, array &b, array &c, array &d, array &e, array &f)
-    {
-        af_array arrays[] = {a.get(), b.get(), c.get(), d.get(), e.get(), f.get()};
-        AF_THROW(af_eval_multiple(6, arrays));
-    }
-
     void eval(int num, array **arrays)
     {
         std::vector<af_array> outputs(num);

From 615f395a967b19944bb2f22ebb02dbf6f03b869e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 15:06:17 -0400
Subject: [PATCH 0910/2677] FEAT, API: Changes to get / set default random
 engine

- Added getDefaultRandomEngine() to C++ API
- Changed setDefaultRandomEngine() to setDefaultRandomEngineType()
---
 include/af/random_engine.h    | 22 ++++++++++++++++++++--
 src/api/c/random_engine.cpp   |  2 +-
 src/api/cpp/random_engine.cpp | 20 ++++++++++++++++----
 3 files changed, 37 insertions(+), 7 deletions(-)

diff --git a/include/af/random_engine.h b/include/af/random_engine.h
index 80314927cc..3ad5d7df7e 100644
--- a/include/af/random_engine.h
+++ b/include/af/random_engine.h
@@ -47,6 +47,15 @@ namespace af
                 @}
             */
 
+            /**
+               Creates a copy of the random engine object
+               \param in The input random engine object
+            */
+            randomEngine(af_random_engine engine);
+            /**
+               @}
+            */
+
             ~randomEngine();
 
             /**
@@ -275,7 +284,16 @@ namespace af
 
         \ingroup random_func_set_type
     */
-    AFAPI void setDefaultRandomEngine(randomEngineType rtype);
+    AFAPI void setDefaultRandomEngineType(randomEngineType rtype);
+#endif
+
+#if AF_API_VERSION >= 34
+    /**
+        \param[in] rtype The type of the random number generator
+
+        \ingroup random_func_set_type
+    */
+    AFAPI randomEngine getDefaultRandomEngine(void);
 #endif
 
     /**
@@ -409,7 +427,7 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
     */
-    AFAPI af_err af_set_default_random_engine(const af_random_engine_type rtype);
+    AFAPI af_err af_set_default_random_engine_type(const af_random_engine_type rtype);
 #endif
 
 #if AF_API_VERSION >= 34
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random_engine.cpp
index d2ca2735a7..7f32230998 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random_engine.cpp
@@ -217,7 +217,7 @@ af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_e
     return AF_SUCCESS;
 }
 
-af_err af_set_default_random_engine(const af_random_engine_type rtype)
+af_err af_set_default_random_engine_type(const af_random_engine_type rtype)
 {
     try {
         AF_CHECK(af_init());
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random_engine.cpp
index 28c47e7b70..2c60e59d87 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random_engine.cpp
@@ -15,18 +15,22 @@
 
 namespace af
 {
-    randomEngine::randomEngine(randomEngineType type, uintl seed)
+    randomEngine::randomEngine(randomEngineType type, uintl seed) : engine(0)
     {
         AF_THROW(af_create_random_engine(&engine, type, seed));
     }
 
-    randomEngine::randomEngine(const randomEngine& other)
+    randomEngine::randomEngine(const randomEngine& other) : engine(0)
     {
         if (this != &other) {
             AF_THROW(af_retain_random_engine(&engine, other.get()));
         }
     }
 
+    randomEngine::randomEngine(af_random_engine handle) : engine(handle)
+    {
+    }
+
     randomEngine::~randomEngine()
     {
         if (engine) {
@@ -148,9 +152,17 @@ namespace af
         return randn(dim4(d0, d1, d2, d3), ty);
     }
 
-    void setDefaultRandomEngine(randomEngineType rtype)
+    void setDefaultRandomEngineType(randomEngineType rtype)
+    {
+        AF_THROW(af_set_default_random_engine_type(rtype));
+    }
+
+    randomEngine getDefaultRandomEngine(void)
     {
-        AF_THROW(af_set_default_random_engine(rtype));
+        af_random_engine internal_handle, handle;
+        AF_THROW(af_get_default_random_engine(&internal_handle));
+        AF_THROW(af_retain_random_engine(&handle, internal_handle));
+        return randomEngine(handle);
     }
 
     void setSeed(const uintl seed)

From 440f383b43c5e8aadd4407c3e3f48260bccb6270 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 15:27:54 -0400
Subject: [PATCH 0911/2677] Renaming random_engine.* to random.*

---
 include/af/{random_engine.h => random.h}          | 0
 include/arrayfire.h                               | 2 +-
 src/api/c/homography.cpp                          | 2 +-
 src/api/c/{random_engine.cpp => random.cpp}       | 2 +-
 src/api/cpp/{random_engine.cpp => random.cpp}     | 2 +-
 src/api/unified/{random_engine.cpp => random.cpp} | 4 ++--
 6 files changed, 6 insertions(+), 6 deletions(-)
 rename include/af/{random_engine.h => random.h} (100%)
 rename src/api/c/{random_engine.cpp => random.cpp} (99%)
 rename src/api/cpp/{random_engine.cpp => random.cpp} (99%)
 rename src/api/unified/{random_engine.cpp => random.cpp} (95%)

diff --git a/include/af/random_engine.h b/include/af/random.h
similarity index 100%
rename from include/af/random_engine.h
rename to include/af/random.h
diff --git a/include/arrayfire.h b/include/arrayfire.h
index daa9438644..cfcd82221c 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -312,7 +312,7 @@
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
-#include "af/random_engine.h"
+#include "af/random.h"
 #include "af/seq.h"
 #include "af/signal.h"
 #include "af/sparse.h"
diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index e8a025a6a1..76b12fbf21 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -10,7 +10,7 @@
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/vision.h>
-#include <af/random_engine.h>
+#include <af/random.h>
 #include <err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
diff --git a/src/api/c/random_engine.cpp b/src/api/c/random.cpp
similarity index 99%
rename from src/api/c/random_engine.cpp
rename to src/api/c/random.cpp
index 7f32230998..0374f0380d 100644
--- a/src/api/c/random_engine.cpp
+++ b/src/api/c/random.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/random_engine.h>
+#include <af/random.h>
 
 #include <af/array.h>
 #include <af/data.h>
diff --git a/src/api/cpp/random_engine.cpp b/src/api/cpp/random.cpp
similarity index 99%
rename from src/api/cpp/random_engine.cpp
rename to src/api/cpp/random.cpp
index 2c60e59d87..ad1507dbb7 100644
--- a/src/api/cpp/random_engine.cpp
+++ b/src/api/cpp/random.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/random_engine.h>
+#include <af/random.h>
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/data.h>
diff --git a/src/api/unified/random_engine.cpp b/src/api/unified/random.cpp
similarity index 95%
rename from src/api/unified/random_engine.cpp
rename to src/api/unified/random.cpp
index 27ee7e7f49..dd8871efc3 100644
--- a/src/api/unified/random_engine.cpp
+++ b/src/api/unified/random.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/array.h>
-#include <af/random_engine.h>
+#include <af/random.h>
 #include "symbol_manager.hpp"
 
 af_err af_get_default_random_engine(af_random_engine *r)
@@ -36,7 +36,7 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engin
     return CALL(engine, rtype);
 }
 
-af_err af_set_default_random_engine(const af_random_engine_type rtype)
+af_err af_set_default_random_engine_type(const af_random_engine_type rtype)
 {
     return CALL(rtype);
 }

From 5011a9eeb9cb55b0220d4664ee44f962ce99a0cc Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 16:23:35 -0400
Subject: [PATCH 0912/2677] FEAT: Adding clamp across all backends

- Added necessary tests
- Updated examples to use builtin function
---
 examples/image_processing/filters.cpp |   5 --
 include/af/arith.h                    |  46 ++++++++++++
 src/api/c/clamp.cpp                   |  74 +++++++++++++++++++
 src/api/cpp/clamp.cpp                 |  41 +++++++++++
 src/api/unified/arith.cpp             |   7 ++
 test/clamp.cpp                        | 101 ++++++++++++++++++++++++++
 6 files changed, 269 insertions(+), 5 deletions(-)
 create mode 100644 src/api/c/clamp.cpp
 create mode 100644 src/api/cpp/clamp.cpp
 create mode 100644 test/clamp.cpp

diff --git a/examples/image_processing/filters.cpp b/examples/image_processing/filters.cpp
index ae1d7c155c..ec87b1f082 100644
--- a/examples/image_processing/filters.cpp
+++ b/examples/image_processing/filters.cpp
@@ -14,11 +14,6 @@
 
 using namespace af;
 
-array clamp(const array &in, float min = 0.0f, float max = 255.0f)
-{
-    return ((in<min)*0.0f + (in>max)*255.0f + (in >= min && in <= max)*in);
-}
-
 /**
 * randomization - controls % of total number of pixels in the image
 * that will be effected by random noise
diff --git a/include/af/arith.h b/include/af/arith.h
index 59f76776d5..8e8a282c66 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -47,6 +47,35 @@ namespace af
     AFAPI array max    (const double lhs, const array &rhs);
     /// @}
 
+#if AF_API_VERSION >= 34
+    /// \ingroup arith_func_clamp
+    /// @{
+    /// C++ Interface for clamping an array between two values
+    ///
+    /// \param[in] in Input array
+    /// \param[in] lo Value for lower limit
+    /// \param[in] hi  Value for upper limit
+    /// \return array containing values from \p in clamped between \p lo and \p hi
+    AFAPI array clamp(const array &in, const array &lo, const array &hi);
+#endif
+
+#if AF_API_VERSION >= 34
+    /// \copydoc clamp(const array&, const array&, const array&)
+    AFAPI array clamp(const array &in, const array &lo, const double hi);
+#endif
+
+#if AF_API_VERSION >= 34
+    /// \copydoc clamp(const array&, const array&, const array&)
+    AFAPI array clamp(const array &in, const double lo, const array &hi);
+    /// @}
+#endif
+
+#if AF_API_VERSION >= 34
+    /// \copydoc clamp(const array&, const array&, const array&)
+    AFAPI array clamp(const array &in, const double lo, const double hi);
+    /// @}
+#endif
+
     /// \ingroup arith_func_rem
     /// @{
     /// C++ Interface for remainder when array divides array,
@@ -806,6 +835,23 @@ extern "C" {
     */
     AFAPI af_err af_maxof (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
+#if AF_API_VERSION >= 34
+    /**
+       C Interface for max of two arrays
+
+       \param[out] out will contain the values from \p clamped between \p lo and \p hi
+       \param[in] in Input array
+       \param[in] lo Value for lower limit
+       \param[in] hi Value for upper limit
+       \param[in] batch specifies if operations need to be performed in batch mode
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup arith_func_max
+    */
+    AFAPI af_err af_clamp(af_array *out, const af_array in,
+                          const af_array lo, const af_array hi, const bool batch);
+#endif
+
     /**
        C Interface for remainder
 
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
new file mode 100644
index 0000000000..f4dd4f26e3
--- /dev/null
+++ b/src/api/c/clamp.cpp
@@ -0,0 +1,74 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/arith.h>
+#include <af/data.h>
+#include <ArrayInfo.hpp>
+#include <optypes.hpp>
+#include <implicit.hpp>
+#include <err_common.hpp>
+#include <handle.hpp>
+#include <backend.hpp>
+
+#include <arith.hpp>
+#include <logic.hpp>
+
+using namespace detail;
+using af::dim4;
+
+template<typename T>
+static inline af_array clampOp(const af_array in,
+                               const af_array lo,
+                               const af_array hi,
+                               const dim4 &odims)
+{
+    const Array<T> L = castArray<T>(lo);
+    const Array<T> H = castArray<T>(hi);
+    const Array<T> I = castArray<T>(in);
+    return getHandle(arithOp<T, af_min_t>(arithOp<T, af_max_t>(I, L, odims), H, odims));
+}
+
+af_err af_clamp(af_array *out, const af_array in,
+                const af_array lo, const af_array hi, const bool batch)
+{
+    try {
+        ArrayInfo linfo = getInfo(lo);
+        ArrayInfo hinfo = getInfo(hi);
+        ArrayInfo iinfo = getInfo(in);
+
+        DIM_ASSERT(2, linfo.dims() == hinfo.dims());
+        TYPE_ASSERT(linfo.getType() == hinfo.getType());
+
+        dim4 odims = getOutDims(iinfo.dims(), linfo.dims(), batch);
+        const af_dtype otype = implicit(iinfo.getType(), linfo.getType());
+
+        af_array res;
+        switch (otype) {
+        case f32: res = clampOp<float  >(in, lo, hi, odims); break;
+        case f64: res = clampOp<double >(in, lo, hi, odims); break;
+        case c32: res = clampOp<cfloat >(in, lo, hi, odims); break;
+        case c64: res = clampOp<cdouble>(in, lo, hi, odims); break;
+        case s32: res = clampOp<int    >(in, lo, hi, odims); break;
+        case u32: res = clampOp<uint   >(in, lo, hi, odims); break;
+        case u8 : res = clampOp<uchar  >(in, lo, hi, odims); break;
+        case b8 : res = clampOp<char   >(in, lo, hi, odims); break;
+        case s64: res = clampOp<intl   >(in, lo, hi, odims); break;
+        case u64: res = clampOp<uintl  >(in, lo, hi, odims); break;
+        case s16: res = clampOp<short  >(in, lo, hi, odims); break;
+        case u16: res = clampOp<ushort >(in, lo, hi, odims); break;
+        default: TYPE_ERROR(0, otype);
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/clamp.cpp b/src/api/cpp/clamp.cpp
new file mode 100644
index 0000000000..458f22ead0
--- /dev/null
+++ b/src/api/cpp/clamp.cpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/arith.h>
+#include <af/data.h>
+#include <af/gfor.h>
+#include "error.hpp"
+
+namespace af
+{
+    array clamp(const array &in, const array &lo, const array &hi)
+    {
+        af_array out;
+        AF_THROW(af_clamp(&out, in.get(), lo.get(), hi.get(), gforGet()));
+        return array(out);
+    }
+
+    array clamp(const array &in, const array &lo, const double hi)
+    {
+        return clamp(in, lo, constant(hi, lo.dims(), lo.type()));
+    }
+
+    array clamp(const array &in, const double lo, const array &hi)
+    {
+        return clamp(in, constant(lo, hi.dims(), hi.type()), hi);
+    }
+
+    array clamp(const array &in, const double lo, const double hi)
+    {
+        return clamp(in,
+                     constant(lo, in.dims(), in.type()),
+                     constant(hi, in.dims(), in.type()));
+    }
+}
diff --git a/src/api/unified/arith.cpp b/src/api/unified/arith.cpp
index c811500773..846cbf2a5e 100644
--- a/src/api/unified/arith.cpp
+++ b/src/api/unified/arith.cpp
@@ -100,3 +100,10 @@ UNARY_HAPI_DEF(af_iszero)
 UNARY_HAPI_DEF(af_isinf)
 UNARY_HAPI_DEF(af_isnan)
 UNARY_HAPI_DEF(af_not)
+
+af_err af_clamp(af_array *out, const af_array in,
+                const af_array lo, const af_array hi, const bool batch)
+{
+    CHECK_ARRAYS(in, lo, hi);
+    return CALL(out, in, lo, hi, batch);
+}
diff --git a/test/clamp.cpp b/test/clamp.cpp
new file mode 100644
index 0000000000..ea2ca04160
--- /dev/null
+++ b/test/clamp.cpp
@@ -0,0 +1,101 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <af/array.h>
+#include <af/arith.h>
+#include <af/data.h>
+#include <testHelpers.hpp>
+
+using namespace std;
+using std::abs;
+using namespace af;
+
+const int num = 10000;
+
+TEST(ClampTests, FloatArrayArray)
+{
+    array in = af::randu(num, f32);
+    array lo = af::randu(num, f32)/10;       // Ensure lo <= 0.1
+    array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
+    af::eval(lo, hi);
+
+
+    std::vector<float> hout(num), hin(num), hlo(num), hhi(num);
+    array out = clamp(in, lo, hi);
+    out.host(&hout[0]);
+    in.host(&hin[0]);
+    lo.host(&hlo[0]);
+    hi.host(&hhi[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_LE(hout[i], hhi[i]);
+        ASSERT_GE(hout[i], hlo[i]);
+        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == hlo[i] || hout[i] == hhi[i]);
+    }
+}
+
+TEST(ClampTests, FloatArrayScalar)
+{
+    array in = af::randu(num, f32);
+    array lo = af::randu(num, f32)/10; // Ensure lo <= 0.1
+    float hi = 0.9;
+
+    std::vector<float> hout(num), hin(num), hlo(num);
+    array out = clamp(in, lo, hi);
+
+    out.host(&hout[0]);
+    in.host(&hin[0]);
+    lo.host(&hlo[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_LE(hout[i], hi);
+        ASSERT_GE(hout[i], hlo[i]);
+        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == hlo[i] || hout[i] == hi);
+    }
+}
+
+TEST(ClampTests, FloatScalarArray)
+{
+    array in = af::randu(num, f32);
+    float lo = 0.1;
+    array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
+
+    std::vector<float> hout(num), hin(num), hhi(num);
+    array out = clamp(in, lo, hi);
+
+    out.host(&hout[0]);
+    in.host(&hin[0]);
+    hi.host(&hhi[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_LE(hout[i], hhi[i]);
+        ASSERT_GE(hout[i], lo);
+        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == lo || hout[i] == hhi[i]);
+    }
+}
+
+TEST(ClampTests, FloatScalarScalar)
+{
+    array in = af::randu(num, f32);
+    float lo = 0.1;
+    float hi = 0.9;
+
+    std::vector<float> hout(num), hin(num);
+    array out = clamp(in, lo, hi);
+
+    out.host(&hout[0]);
+    in.host(&hin[0]);
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_LE(hout[i], hi);
+        ASSERT_GE(hout[i], lo);
+        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == lo || hout[i] == hi);
+    }
+}

From f2759e9fb03e39676edb384966fbf193e7bfa881 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 17:18:20 -0400
Subject: [PATCH 0913/2677] FEAT: Adding support to query if an array has been
 locked

- Also fixes bugs in `lock` and `unlock` code
---
 include/af/array.h            | 15 ++++++++++++
 include/af/device.h           | 12 +++++++++
 src/api/c/memory.cpp          | 46 +++++++++++++++++++++++++++++++++--
 src/api/cpp/array.cpp         | 11 ++++++++-
 src/api/unified/device.cpp    |  6 +++++
 src/backend/MemoryManager.cpp | 12 +++++++++
 src/backend/MemoryManager.hpp |  2 ++
 src/backend/cpu/memory.cpp    |  5 ++++
 src/backend/cpu/memory.hpp    |  1 +
 src/backend/cuda/memory.cpp   |  5 ++++
 src/backend/cuda/memory.hpp   |  1 +
 src/backend/opencl/memory.cpp |  5 ++++
 src/backend/opencl/memory.hpp |  1 +
 13 files changed, 119 insertions(+), 3 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 62c4191e10..e1048dd114 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -136,6 +136,10 @@ namespace af
             void lock() const;
 #endif
 
+#if AF_API_VERSION >= 34
+            bool isLocked() const;
+#endif
+
                   array::array_proxy row(int index);
             const array::array_proxy row(int index) const;
 
@@ -976,6 +980,17 @@ namespace af
         /// While a buffer is locked, the memory manager doesn't free the memory until unlock() is invoked.
         void lock() const;
 
+
+#if AF_API_VERSION >= 34
+        ///
+        /// \brief Query if the array has been locked by the user.
+        ///
+        /// An array can be locked by the user by calling `arry.lock` or `arr.device`
+        /// or `getRawPtr` function.
+        bool isLocked() const;
+#endif
+
+
         ///
         /// \brief Unlocks the device buffer in the memory manager.
         ///
diff --git a/include/af/device.h b/include/af/device.h
index 62971025da..c6f2750374 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -423,6 +423,18 @@ extern "C" {
     AFAPI af_err af_unlock_array(const af_array arr);
 #endif
 
+#if AF_API_VERSION >= 34
+    /**
+       Query if the array has been locked by the user.
+
+       An array can be locked by the user by calling `af_lock_array`
+       or `af_get_device_ptr` or `af_get_raw_ptr` function.
+
+       \ingroup device_func_mem
+    */
+    AFAPI af_err af_is_locked_array(bool *res, const af_array arr);
+#endif
+
     /**
        Get the device pointer and lock the buffer in memory manager.
 
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 098665ba03..a2f3e2e041 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -91,7 +91,10 @@ af_err af_get_device_ptr(void **data, const af_array arr)
 template <typename T>
 inline void lockArray(const af_array arr)
 {
-    memLock((void *)getArray<T>(arr).get());
+    // Ideally we need to use .get(false), i.e. get ptr without offset
+    // This is however not supported in opencl
+    // Use getData().get() as alternative
+    memLock((void *)getArray<T>(arr).getData().get());
 }
 
 af_err af_lock_device_ptr(const af_array arr)
@@ -125,10 +128,49 @@ af_err af_lock_array(const af_array arr)
     return AF_SUCCESS;
 }
 
+
+template<typename T>
+inline bool checkUserLock(const af_array arr)
+{
+    // Ideally we need to use .get(false), i.e. get ptr without offset
+    // This is however not supported in opencl
+    // Use getData().get() as alternative
+    return isLocked((void *)getArray<T>(arr).getData().get());
+}
+
+af_err af_is_locked_array(bool *res, const af_array arr)
+{
+    try {
+        af_dtype type = getInfo(arr).getType();
+
+        switch (type) {
+        case f32: *res = checkUserLock<float  >(arr); break;
+        case f64: *res = checkUserLock<double >(arr); break;
+        case c32: *res = checkUserLock<cfloat >(arr); break;
+        case c64: *res = checkUserLock<cdouble>(arr); break;
+        case s32: *res = checkUserLock<int    >(arr); break;
+        case u32: *res = checkUserLock<uint   >(arr); break;
+        case s64: *res = checkUserLock<intl   >(arr); break;
+        case u64: *res = checkUserLock<uintl  >(arr); break;
+        case s16: *res = checkUserLock<short  >(arr); break;
+        case u16: *res = checkUserLock<ushort >(arr); break;
+        case u8 : *res = checkUserLock<uchar  >(arr); break;
+        case b8 : *res = checkUserLock<char   >(arr); break;
+        default: TYPE_ERROR(4, type);
+        }
+
+    } CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 template <typename T>
 inline void unlockArray(const af_array arr)
 {
-    memUnlock((void *)getArray<T>(arr).get());
+    // Ideally we need to use .get(false), i.e. get ptr without offset
+    // This is however not supported in opencl
+    // Use getData().get() as alternative
+    memUnlock((void *)getArray<T>(arr).getData().get());
 }
 
 af_err af_unlock_device_ptr(const af_array arr)
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index b60935f077..a4a7937523 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1041,8 +1041,10 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
 #undef INSTANTIATE
 #undef TEMPLATE_MEM_FUNC
 
-    //FIXME: This needs to be implemented at a later point
+    //FIXME: These functions need to be implemented properly at a later point
     void array::array_proxy::unlock() const {}
+    void array::array_proxy::lock() const {}
+    bool array::array_proxy::isLocked() const { return false; }
 
     int array::nonzeros() const { return count<int>(*this); }
 
@@ -1051,6 +1053,13 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
         AF_THROW(af_lock_array(get()));
     }
 
+    bool array::isLocked() const
+    {
+        bool res;
+        AF_THROW(af_is_locked_array(&res, get()));
+        return res;
+    }
+
     void array::unlock() const
     {
         AF_THROW(af_unlock_array(get()));
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 157d80e9b0..096c430eed 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -180,6 +180,12 @@ af_err af_unlock_array(const af_array arr)
     return CALL(arr);
 }
 
+af_err af_is_locked_array(bool *res, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(res, arr);
+}
+
 af_err af_get_device_ptr(void **ptr, const af_array arr)
 {
     CHECK_ARRAYS(arr);
diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
index 04bd46783f..3b0e81ddc5 100644
--- a/src/backend/MemoryManager.cpp
+++ b/src/backend/MemoryManager.cpp
@@ -221,6 +221,18 @@ void MemoryManager::userUnlock(const void *ptr)
     this->unlock(const_cast<void *>(ptr), true);
 }
 
+bool MemoryManager::isUserLocked(const void *ptr)
+{
+    memory_info& current = this->getCurrentMemoryInfo();
+    lock_guard_t lock(this->memory_mutex);
+    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    if (iter != current.locked_map.end()) {
+        return iter->second.user_lock;
+    } else {
+        return false;
+    }
+}
+
 size_t MemoryManager::getMemStepSize()
 {
     lock_guard_t lock(this->memory_mutex);
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
index cd991d3529..39ff8e1281 100644
--- a/src/backend/MemoryManager.hpp
+++ b/src/backend/MemoryManager.hpp
@@ -89,6 +89,8 @@ class MemoryManager
 
     void userUnlock(const void *ptr);
 
+    bool isUserLocked(const void *ptr);
+
     size_t getMemStepSize();
 
     size_t getMaxBytes();
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index b4b1b450d9..25df00f4e2 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -152,6 +152,11 @@ void memLock(const void *ptr)
     getMemoryManager().userLock((void *)ptr);
 }
 
+bool isLocked(const void *ptr)
+{
+    return getMemoryManager().isUserLocked((void *)ptr);
+}
+
 void memUnlock(const void *ptr)
 {
     getMemoryManager().userUnlock((void *)ptr);
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 91116fbcfc..b2105c5af4 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -23,6 +23,7 @@ namespace cpu
 
     void memLock(const void *ptr);
     void memUnlock(const void *ptr);
+    bool isLocked(const void *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 51eb507320..03ebc20d44 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -210,6 +210,11 @@ void memUnlock(const void *ptr)
     getMemoryManager().userUnlock((void *)ptr);
 }
 
+bool isLocked(const void *ptr)
+{
+    return getMemoryManager().isUserLocked((void *)ptr);
+}
+
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 25ce2a0203..ab895168d4 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -23,6 +23,7 @@ namespace cuda
 
     void memLock(const void *ptr);
     void memUnlock(const void *ptr);
+    bool isLocked(const void *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 5df64d6d86..9a2edd44a2 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -240,6 +240,11 @@ void memUnlock(const void *ptr)
     getMemoryManager().userUnlock((void *)ptr);
 }
 
+bool isLocked(const void *ptr)
+{
+    return getMemoryManager().isUserLocked((void *)ptr);
+}
+
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 72b259ad0b..549dfb3f08 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -27,6 +27,7 @@ namespace opencl
 
     void memLock(const void *ptr);
     void memUnlock(const void *ptr);
+    bool isLocked(const void *ptr);
 
     template<typename T> T* pinnedAlloc(const size_t &elements);
     template<typename T> void pinnedFree(T* ptr);

From 68d2c74cb39126f886fd317b9bf726b9e6eec0e3 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 17:20:30 -0400
Subject: [PATCH 0914/2677] Adding back the ability to cut down JIT tree by
 height

Removing this constraint was causing some programs to be extremely lazy
---
 .../configuring_arrayfire_environment.md      | 12 ++---
 src/backend/cpu/Array.cpp                     | 48 ++++++++++---------
 src/backend/cpu/TNJ/BinaryNode.hpp            |  1 +
 src/backend/cpu/TNJ/BufferNode.hpp            |  1 +
 src/backend/cpu/TNJ/Node.hpp                  |  5 ++
 src/backend/cpu/TNJ/ScalarNode.hpp            |  5 +-
 src/backend/cpu/TNJ/UnaryNode.hpp             |  1 +
 src/backend/cpu/platform.cpp                  |  2 +-
 src/backend/cuda/Array.cpp                    | 31 +++++++-----
 src/backend/cuda/JIT/BinaryNode.hpp           |  1 +
 src/backend/cuda/JIT/BufferNode.hpp           |  1 +
 src/backend/cuda/JIT/Node.hpp                 |  4 ++
 src/backend/cuda/JIT/ScalarNode.hpp           |  1 +
 src/backend/cuda/JIT/UnaryNode.hpp            |  2 +-
 src/backend/cuda/platform.cpp                 |  2 +-
 src/backend/opencl/Array.cpp                  | 36 +++++++-------
 src/backend/opencl/JIT/BinaryNode.hpp         |  1 +
 src/backend/opencl/JIT/BufferNode.hpp         |  1 +
 src/backend/opencl/JIT/Node.hpp               |  4 +-
 src/backend/opencl/JIT/ScalarNode.hpp         |  1 +
 src/backend/opencl/JIT/UnaryNode.hpp          |  1 +
 src/backend/opencl/platform.cpp               |  2 +-
 22 files changed, 97 insertions(+), 66 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 1b519ccbd6..33c5a39fe1 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -167,20 +167,14 @@ When not set, the default value is 1000.
 AF_OPENCL_MAX_JIT_LEN {#af_opencl_max_jit_len}
 -------------------------------------------------------------------------------
 
-This flag is no longer supported as of 3.4
-
-In older versions, When set, this environment variable specifies the maximum length of the OpenCL JIT tree after which evaluation is forced. The default value for this is 20.
+When set, this environment variable specifies the maximum height of the OpenCL JIT tree after which evaluation is forced. The default value for this is 100 as of v3.4 (20 for older versions).
 
 AF_CUDA_MAX_JIT_LEN {#af_cuda_max_jit_len}
 -------------------------------------------------------------------------------
 
-This flag is no longer supported as of 3.4
-
-In older versions, When set, this environment variable specifies the maximum length of the CUDA JIT tree after which evaluation is forced. The default value for this is 20.
+When set, this environment variable specifies the maximum height of the CUDA JIT tree after which evaluation is forced. The default value for this is 100 as of v3.4 (20 for older versions).
 
 AF_CPU_MAX_JIT_LEN {#af_cpu_max_jit_len}
 -------------------------------------------------------------------------------
 
-This flag is no longer supported as of 3.4
-
-In older versions, When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced. The default value for this is 20.
+When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced. The default value for this is 100 as of v3.4 (20 for older versions).
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 3634b22383..c3494e8d15 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -177,28 +177,32 @@ createNodeArray(const dim4 &dims, Node_ptr node)
     Array<T> out =  Array<T>(dims, node);
 
     if (evalFlag()) {
-        size_t alloc_bytes, alloc_buffers;
-        size_t lock_bytes, lock_buffers;
-
-        deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
-                         &lock_bytes, &lock_buffers);
-
-        // Check if approaching the memory limit
-        if (lock_bytes > getMaxBytes() ||
-            lock_buffers > getMaxBuffers()) {
-
-            // Calling sync to ensure the TNJ calls below
-            // don't overwrite the same nodes being evaluated
-            // FIXME: This should ideally be JIT specific mutex
-            getQueue().sync();
-
-            unsigned length =0, buf_count = 0, bytes = 0;
-            Node *n = node.get();
-            n->getInfo(length, buf_count, bytes);
-            n->reset();
-
-            if (2 * bytes > lock_bytes) {
-                out.eval();
+        if (node->getHeight() >= (int)getMaxJitSize()) {
+            out.eval();
+        } else {
+            size_t alloc_bytes, alloc_buffers;
+            size_t lock_bytes, lock_buffers;
+
+            deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
+                             &lock_bytes, &lock_buffers);
+
+            // Check if approaching the memory limit
+            if (lock_bytes > getMaxBytes() ||
+                lock_buffers > getMaxBuffers()) {
+
+                // Calling sync to ensure the TNJ calls below
+                // don't overwrite the same nodes being evaluated
+                // FIXME: This should ideally be JIT specific mutex
+                getQueue().sync();
+
+                unsigned length =0, buf_count = 0, bytes = 0;
+                Node *n = node.get();
+                n->getInfo(length, buf_count, bytes);
+                n->reset();
+
+                if (2 * bytes > lock_bytes) {
+                    out.eval();
+                }
             }
         }
     }
diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index c1247aaa8a..97e65890a3 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -45,6 +45,7 @@ namespace TNJ
             m_rhs(rhs),
             m_val(0)
         {
+            m_height = std::max(m_lhs->getHeight(), m_rhs->getHeight()) + 1;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index 3e97980176..9a5af60114 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -50,6 +50,7 @@ namespace TNJ
                 m_strides[i] = strs[i];
                 m_dims[i] = dms[i];
             }
+            m_height = 0;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index a6488991bb..1b7a402926 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -23,6 +23,7 @@ namespace TNJ
 
     protected:
 
+        int m_height;
         int x, y, z, w;
         bool m_is_eval;
         bool m_linear;
@@ -31,6 +32,7 @@ namespace TNJ
 
         void resetCommonFlags()
         {
+            m_height = 0;
             x = -1;
             y = -1;
             z = -1;
@@ -59,6 +61,7 @@ namespace TNJ
 
     public:
         Node() :
+            m_height(0),
             x(-1),
             y(-1),
             z(-1),
@@ -68,6 +71,8 @@ namespace TNJ
             m_set_is_linear(false)
         {}
 
+        int getHeight() { return m_height; }
+
         virtual void *calc(int x, int y, int z, int w)
         {
             m_is_eval = true;
diff --git a/src/backend/cpu/TNJ/ScalarNode.hpp b/src/backend/cpu/TNJ/ScalarNode.hpp
index 2498318736..fa6ec4b5e3 100644
--- a/src/backend/cpu/TNJ/ScalarNode.hpp
+++ b/src/backend/cpu/TNJ/ScalarNode.hpp
@@ -26,7 +26,10 @@ namespace TNJ
         T m_val;
 
     public:
-        ScalarNode(T val) : Node(), m_val(val) {}
+        ScalarNode(T val) : Node(), m_val(val)
+        {
+            m_height = 0;
+        }
 
         void *calc(int x, int y, int z, int w)
         {
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 98a40ff167..5601c01ed1 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -43,6 +43,7 @@ namespace TNJ
             m_child(in),
             m_val(0)
         {
+            m_height = m_child->getHeight() + 1;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 38fdfac990..5afd8316bf 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -188,7 +188,7 @@ namespace cpu
 
 unsigned getMaxJitSize()
 {
-    const int MAX_JIT_LEN = 20;
+    const int MAX_JIT_LEN = 100;
 
     static int length = 0;
     if (length == 0) {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 3da916d1af..8d07304d93 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -213,23 +213,28 @@ namespace cuda
         Array<T> out =  Array<T>(dims, node);
 
         if (evalFlag()) {
-            size_t alloc_bytes, alloc_buffers;
-            size_t lock_bytes, lock_buffers;
 
-            deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
-                             &lock_bytes, &lock_buffers);
+            if (node->getHeight() >= (int)getMaxJitSize()) {
+                out.eval();
+            } else {
+                size_t alloc_bytes, alloc_buffers;
+                size_t lock_bytes, lock_buffers;
 
-            // Check if approaching the memory limit
-            if (lock_bytes > getMaxBytes() ||
-                lock_buffers > getMaxBuffers()) {
+                deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
+                                 &lock_bytes, &lock_buffers);
 
-                unsigned length =0, buf_count = 0, bytes = 0;
-                Node *n = node.get();
-                n->getInfo(length, buf_count, bytes);
-                n->resetFlags();
+                // Check if approaching the memory limit
+                if (lock_bytes > getMaxBytes() ||
+                    lock_buffers > getMaxBuffers()) {
 
-                if (2 * bytes > lock_bytes) {
-                    out.eval();
+                    unsigned length =0, buf_count = 0, bytes = 0;
+                    Node *n = node.get();
+                    n->getInfo(length, buf_count, bytes);
+                    n->resetFlags();
+
+                    if (2 * bytes > lock_bytes) {
+                        out.eval();
+                    }
                 }
             }
         }
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index 73ed571ee5..5bdf2a88b5 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -36,6 +36,7 @@ namespace JIT
               m_op(op),
               m_call_type(call_type)
         {
+            m_height = std::max(m_lhs->getHeight(), m_rhs->getHeight()) + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 3f02214a11..1f1a25b485 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -41,6 +41,7 @@ namespace JIT
                    const char *name_str)
             : Node(type_str, name_str)
         {
+            m_height = 0;
         }
 
         void setData(Param<T> param, shared_ptr<T> data, const unsigned bytes, bool is_linear)
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 4ceac273b3..fa3b5191f4 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -29,6 +29,7 @@ namespace JIT
         std::string m_type_str;
         std::string m_name_str;
         int m_id;
+        int m_height;
         bool m_set_id;
         bool m_gen_func;
         bool m_gen_param;
@@ -42,6 +43,7 @@ namespace JIT
 
         void resetCommonFlags()
         {
+            m_height = 0;
             m_set_id = false;
             m_gen_func = false;
             m_gen_param = false;
@@ -59,6 +61,7 @@ namespace JIT
             : m_type_str(type_str),
               m_name_str(name_str),
               m_id(-1),
+              m_height(0),
               m_set_id(false),
               m_gen_func(false),
               m_gen_param(false),
@@ -101,6 +104,7 @@ namespace JIT
         bool isGenOffset() { return m_gen_offset; }
 
         int getId()  { return m_id; }
+        int getHeight()  { return m_height; }
         std::string getNameStr() { return m_name_str; }
 
         virtual ~Node() {}
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index 1765843bb6..0f9e88866d 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -29,6 +29,7 @@ namespace JIT
             : Node(irname<T>(), afShortName<T>(false)),
               m_val(val)
         {
+            m_height = 0;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 87679efa20..2ef02547ca 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -35,7 +35,7 @@ namespace JIT
               m_op(op),
               m_is_check(is_check)
         {
-
+            m_height = m_child->getHeight() + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index e8573d18a5..9578bce4d0 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -266,7 +266,7 @@ string getCUDARuntimeVersion()
 
 unsigned getMaxJitSize()
 {
-    const int MAX_JIT_LEN = 20;
+    const int MAX_JIT_LEN = 100;
 
     static int length = 0;
     if (length == 0) {
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 4cfee0d107..7cef377c88 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -220,22 +220,26 @@ namespace opencl
         Array<T> out =  Array<T>(dims, node);
 
         if (evalFlag()) {
-            size_t alloc_bytes, alloc_buffers;
-            size_t lock_bytes, lock_buffers;
-
-            deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
-                             &lock_bytes, &lock_buffers);
-
-            if (lock_bytes > getMaxBytes() ||
-                lock_buffers > getMaxBuffers()) {
-
-                unsigned length =0, buf_count = 0, bytes = 0;
-                Node *n = node.get();
-                n->getInfo(length, buf_count, bytes);
-                n->resetFlags();
-
-                if (2 * bytes > lock_bytes) {
-                    out.eval();
+            if (node->getHeight() >= (int)getMaxJitSize()) {
+                out.eval();
+            } else {
+                size_t alloc_bytes, alloc_buffers;
+                size_t lock_bytes, lock_buffers;
+
+                deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
+                                 &lock_bytes, &lock_buffers);
+
+                if (lock_bytes > getMaxBytes() ||
+                    lock_buffers > getMaxBuffers()) {
+
+                    unsigned length =0, buf_count = 0, bytes = 0;
+                    Node *n = node.get();
+                    n->getInfo(length, buf_count, bytes);
+                    n->resetFlags();
+
+                    if (2 * bytes > lock_bytes) {
+                        out.eval();
+                    }
                 }
             }
         }
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index 9239c74522..a812a7b297 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -34,6 +34,7 @@ namespace JIT
               m_rhs(rhs),
               m_op(op)
         {
+            m_height = std::max(m_lhs->getHeight(), m_rhs->getHeight()) + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 4fa8134ed0..a83d5267a8 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -32,6 +32,7 @@ namespace JIT
                    const char *name_str)
             : Node(type_str, name_str)
         {
+            m_height = 0;
         }
 
         bool isBuffer() { return true; }
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index 2bc0147120..b3caab22d3 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -27,7 +27,7 @@ namespace JIT
         std::string m_type_str;
         std::string m_name_str;
         int m_id;
-        int m_level;
+        int m_height;
         bool m_set_id;
         bool m_gen_func;
         bool m_gen_param;
@@ -40,6 +40,7 @@ namespace JIT
     protected:
         void resetCommonFlags()
         {
+            m_height = 0;
             m_set_id = false;
             m_gen_func = false;
             m_gen_param = false;
@@ -99,6 +100,7 @@ namespace JIT
         bool isGenOffset() { return m_gen_offset; }
 
         int getId()  { return m_id; }
+        int getHeight()  { return m_height; }
         std::string getNameStr() { return m_name_str; }
 
         virtual ~Node() {}
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index 0bc8664e54..fd8b3da94f 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -31,6 +31,7 @@ namespace JIT
             : Node(dtype_traits<T>::getName(), shortname<T>(false)),
               m_val(val)
         {
+            m_height = 0;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index 18a3441f81..cfb670cce8 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -33,6 +33,7 @@ namespace JIT
               m_child(child),
               m_op(op)
         {
+            m_height = m_child->getHeight() + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 9978aad472..b2d0af9f87 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -849,7 +849,7 @@ bool synchronize_calls() {
 
 unsigned getMaxJitSize()
 {
-    const int MAX_JIT_LEN = 20;
+    const int MAX_JIT_LEN = 100;
 
     static int length = 0;
     if (length == 0) {

From 5784b2b61f656da01434ac3d572cb662e7ff51e5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 18:05:10 -0400
Subject: [PATCH 0915/2677] BUGFIX: Fixed issue with array.device when array is
 nonlinear

- Added appropriate test
---
 src/backend/cpu/Array.cpp    | 10 ++++++++++
 src/backend/cpu/Array.hpp    |  9 +--------
 src/backend/cuda/Array.cpp   | 12 +++++++++++-
 src/backend/cuda/Array.hpp   |  8 +-------
 src/backend/opencl/Array.cpp | 10 ++++++++++
 src/backend/opencl/Array.hpp |  9 +--------
 test/memory.cpp              | 36 ++++++++++++++++++++++++++++++++++++
 7 files changed, 70 insertions(+), 24 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index c3494e8d15..343b70f5d1 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -105,6 +105,15 @@ void Array<T>::eval() const
     const_cast<Array<T> *>(this)->eval();
 }
 
+template<typename T>
+T* Array<T>::device()
+{
+    getQueue().sync();
+    if (!isOwner() || getOffset() || data.use_count() > 1) {
+        *this = copyArray<T>(*this);
+    }
+    return this->get();
+}
 
 template<typename T>
 void evalMultiple(std::vector<Array<T>*> arrays)
@@ -285,6 +294,7 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     template       Array<T>  createNodeArray<T>       (const dim4 &size, TNJ::Node_ptr node); \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
+    template       T*   Array<T>::device();                             \
     template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
                                    bool is_device, bool copy_device);   \
     template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 764374d2fc..8762e7e54e 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -188,14 +188,7 @@ namespace cpu
             data_dims = new_dims;
         }
 
-        T* device()
-        {
-            getQueue().sync();
-            if (!isOwner() || getOffset() || data.use_count() > 1) {
-                *this = Array<T>(dims(), get(), true, true);
-            }
-            return this->get();
-        }
+        T* device();
 
         T* device() const
         {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 8d07304d93..e95c6153f1 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -134,6 +134,15 @@ namespace cuda
         node = bufferNodePtr<T>();
     }
 
+    template<typename T>
+    T* Array<T>::device()
+    {
+        if (!isOwner() || getOffset() || data.use_count() > 1) {
+            *this = copyArray<T>(*this);
+        }
+        return this->get();
+    }
+
     template<typename T>
     void Array<T>::eval() const
     {
@@ -370,9 +379,10 @@ namespace cuda
     template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
                                    bool is_device, bool copy_device);   \
     template       Array<T>::~Array        ();                          \
-    template       Node_ptr Array<T>::getNode() const;             \
+    template       Node_ptr Array<T>::getNode() const;                  \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
+    template       T*   Array<T>::device();                             \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
     template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index e36a036e8d..6f8acc1ca6 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -184,13 +184,7 @@ namespace cuda
             data_dims = new_dims;
         }
 
-        T* device()
-        {
-            if (!isOwner() || getOffset() || data.use_count() > 1) {
-                *this = Array<T>(dims(), get(), true, true);
-            }
-            return this->get();
-        }
+        T* device();
 
         T* device() const
         {
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 7cef377c88..431de642cd 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -151,6 +151,15 @@ namespace opencl
         const_cast<Array<T> *>(this)->eval();
     }
 
+    template<typename T>
+    cl::Buffer* Array<T>::device()
+    {
+        if (!isOwner() || getOffset() || data.use_count() > 1) {
+            *this = copyArray<T>(*this);
+        }
+        return this->get();
+    }
+
     template<typename T>
     void evalMultiple(std::vector<Array<T>*> arrays)
     {
@@ -391,6 +400,7 @@ namespace opencl
     template       Node_ptr Array<T>::getNode() const;                  \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
+    template       cl::Buffer* Array<T>::device();                      \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
     template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 0726219814..9dc9b427f7 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -165,14 +165,7 @@ namespace opencl
         void eval();
         void eval() const;
 
-        cl::Buffer* device()
-        {
-            if (!isOwner() || getOffset() || data.use_count() > 1) {
-                *this = Array<T>(dims(), (*get())(), (size_t)getOffset(), true);
-            }
-            return this->get();
-        }
-
+        cl::Buffer* device();
         cl::Buffer* device() const
         {
             return const_cast<Array<T>*>(this)->device();
diff --git a/test/memory.cpp b/test/memory.cpp
index b13948303f..5342781b7f 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -15,6 +15,7 @@
 #include <iostream>
 #include <string>
 #include <testHelpers.hpp>
+#include <af/internal.h>
 
 using std::vector;
 using std::string;
@@ -661,3 +662,38 @@ TEST(Memory, unlock)
     ASSERT_EQ(alloc_bytes, 0u);
     ASSERT_EQ(lock_bytes, 0u);
 }
+
+TEST(Memory, IndexedDevice)
+{
+    // This test is checking to see if calling .device() will force copy to a new buffer
+    const int nx = 8;
+    const int ny = 8;
+
+    af::array in = af::randu(nx, ny);
+
+    std::vector<float> in1(in.elements());
+    in.host(&in1[0]);
+
+    int offx = nx / 4;
+    int offy = ny / 4;
+
+    in = in(af::seq(offx, offx + nx/2 - 1),
+            af::seq(offy, offy + ny/2- 1));
+
+    int nxo = (int)in.dims(0);
+    int nyo = (int)in.dims(1);
+
+    void *rawPtr = af::getRawPtr(in);
+    void *devPtr = in.device<float>();
+    ASSERT_NE(devPtr, rawPtr);
+    in.unlock();
+
+    std::vector<float> in2(in.elements());
+    in.host(&in2[0]);
+
+    for (int y = 0; y < nyo; y++) {
+        for (int x = 0; x < nxo; x++) {
+            ASSERT_EQ(in1[(offy + y) * nx + offx + x], in2[y * nxo + x]);
+        }
+    }
+}

From 8165f25fe9242e1bccab7df3f8c9e0b81cf980c4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Fri, 9 Sep 2016 18:34:47 -0400
Subject: [PATCH 0916/2677] OPENCL: Fixing af::info

- Last character was being removed from device name
---
 src/backend/opencl/platform.cpp | 13 -------------
 1 file changed, 13 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index b2d0af9f87..d04169ebf0 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -392,10 +392,6 @@ std::string getDeviceInfo()
         const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
         string dstr = device->getInfo<CL_DEVICE_NAME>();
-
-        // Remove null termination character from the strings
-        dstr.pop_back();
-
         bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
 
         string id =
@@ -410,8 +406,6 @@ std::string getDeviceInfo()
         info << " -- ";
         string devVersion = device->getInfo<CL_DEVICE_VERSION>();
         string driVersion = device->getInfo<CL_DRIVER_VERSION>();
-        devVersion.pop_back();
-        driVersion.pop_back();
         info << devVersion;
         info << " -- Device driver " << driVersion;
         info << " -- FP64 Support: "
@@ -431,13 +425,6 @@ std::string getPlatformName(const cl::Device &device)
 {
     const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
     std::string platStr = platform.getInfo<CL_PLATFORM_NAME>();
-
-    // BELOW NULL TERMINATION character removal was required with
-    // cl.hpp header, however with cl2.hpp this is not needed anymore.
-    //
-    // Remove null termination character from the strings
-    //platStr.pop_back();
-
     return platformMap(platStr);
 }
 

From ff186e10108a5a7b69622cfcc3e31ac9cd90e576 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 9 Sep 2016 18:52:36 -0400
Subject: [PATCH 0917/2677] CUDA Compute Auto Select: Enable Compute 6x only if
 CUDA 8 or greater

---
 src/backend/cuda/CMakeLists.txt | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e8759496da..8b890741ec 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -42,10 +42,15 @@ ENDIF()
 
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
 IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
-    MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50, 60")
     MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon separated list of CUDA Compute versions to enable the specified computes")
     MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
-    LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50" "60")
+    LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50")
+    IF(${CUDA_VERSION_MAJOR} GREATER 7) # Enable 60 only if CUDA 8 or greater
+        MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50, 60")
+        LIST(APPEND COMPUTES_DETECTED_LIST "60")
+    ELSE(${CUDA_VERSION_MAJOR} GREATER 7)
+        MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50")
+    ENDIF(${CUDA_VERSION_MAJOR} GREATER 7)
 ENDIF()
 
 LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)

From f3f3165df4334504f95a325b6478a6a7e1ec9c0c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 9 Sep 2016 18:53:37 -0400
Subject: [PATCH 0918/2677] Improvements to OSX Installer for Forge

* Now has options for Forge
* Forge lib is mandatory if AF libs are selected
---
 CMakeModules/osx_install/OSXInstaller.cmake   | 176 +++++++++++-------
 .../osx_install/cpu_scripts/postinstall       |  35 ----
 .../osx_install/distribution-no-gl.dist       |  78 ++++++++
 CMakeModules/osx_install/distribution.dist    |  64 +++++--
 .../postinstall                               |   4 +-
 .../osx_install/opencl_scripts/postinstall    |  34 ----
 6 files changed, 237 insertions(+), 154 deletions(-)
 delete mode 100755 CMakeModules/osx_install/cpu_scripts/postinstall
 create mode 100644 CMakeModules/osx_install/distribution-no-gl.dist
 rename CMakeModules/osx_install/{cuda_scripts => forge_scripts}/postinstall (89%)
 mode change 100755 => 100644
 delete mode 100755 CMakeModules/osx_install/opencl_scripts/postinstall

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 119d81e7c0..729f5a4996 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -13,8 +13,7 @@ SET(OSX_INSTALL_SOURCE ${PROJECT_SOURCE_DIR}/CMakeModules/osx_install)
 ################################################################################
 SET(OSX_TEMP "${PROJECT_BINARY_DIR}/osx_install_files")
 
-# Common files - libforge, ArrayFireConfig*.cmake
-FILE(GLOB COMMONLIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
+# Common files - ArrayFireConfig*.cmake
 FILE(GLOB COMMONCMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFireConfig*.cmake")
 
 ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_COMMON)
@@ -76,40 +75,57 @@ ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
                   COMMENT "Copying documentation files to temporary OSX Install Dir"
                   )
 
-# Forge Headers
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_INC
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  "${CMAKE_INSTALL_PREFIX}/include/fg" "${OSX_TEMP}/Forge/include/fg"
-                  COMMAND ${CMAKE_COMMAND} -E copy
-                  "${CMAKE_INSTALL_PREFIX}/include/forge.h" "${OSX_TEMP}/Forge/include/forge.h"
-                  COMMAND ${CMAKE_COMMAND} -E copy
-                  "${CMAKE_INSTALL_PREFIX}/include/ComputeCopy.h" "${OSX_TEMP}/Forge/include/ComputeCopy.h"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying examples files to temporary OSX Install Dir"
-                  )
-# Forge Examples
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_EXAMPLES
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  "${CMAKE_INSTALL_PREFIX}/share/Forge/examples" "${OSX_TEMP}/Forge/examples"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying examples files to temporary OSX Install Dir"
-                  )
+IF(BUILD_GRAPHICS)
+    MAKE_DIRECTORY("${OSX_TEMP}/Forge")
 
-# Documentation
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_DOC
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  "${CMAKE_INSTALL_PREFIX}/share/Forge/doc" "${OSX_TEMP}/Forge/doc"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying documentation files to temporary OSX Install Dir"
-                  )
+    # Forge Library
+    FILE(GLOB FORGE_LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_LIB)
+    FOREACH(SRC ${FORGE_LIB})
+        FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
+        ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
+                           COMMAND ${CMAKE_COMMAND} -E copy
+                           ${SRC} "${OSX_TEMP}/Forge/${SRC_REL}"
+                           WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                           COMMENT "Copying libforge files to temporary OSX Install Dir"
+        )
+    ENDFOREACH()
 
-# Forge CMake
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_CMAKE
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  "${CMAKE_INSTALL_PREFIX}/share/Forge/cmake" "${OSX_TEMP}/Forge/cmake"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying documentation files to temporary OSX Install Dir"
-                  )
+    # Forge Headers
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_INCLUDE
+                      COMMAND ${CMAKE_COMMAND} -E copy_directory
+                      "${CMAKE_INSTALL_PREFIX}/include/fg" "${OSX_TEMP}/Forge/include/fg"
+                      COMMAND ${CMAKE_COMMAND} -E copy
+                      "${CMAKE_INSTALL_PREFIX}/include/forge.h" "${OSX_TEMP}/Forge/include/forge.h"
+                      COMMAND ${CMAKE_COMMAND} -E copy
+                      "${CMAKE_INSTALL_PREFIX}/include/ComputeCopy.h" "${OSX_TEMP}/Forge/include/ComputeCopy.h"
+                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                      COMMENT "Copying examples files to temporary OSX Install Dir"
+                      )
+    # Forge Examples
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_EXAMPLES
+                      COMMAND ${CMAKE_COMMAND} -E copy_directory
+                      "${CMAKE_INSTALL_PREFIX}/share/Forge/examples" "${OSX_TEMP}/Forge/examples"
+                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                      COMMENT "Copying examples files to temporary OSX Install Dir"
+                      )
+
+    # Documentation
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_DOC
+                      COMMAND ${CMAKE_COMMAND} -E copy_directory
+                      "${CMAKE_INSTALL_PREFIX}/share/Forge/doc" "${OSX_TEMP}/Forge/doc"
+                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                      COMMENT "Copying documentation files to temporary OSX Install Dir"
+                      )
+
+    # Forge CMake
+    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_CMAKE
+                      COMMAND ${CMAKE_COMMAND} -E copy_directory
+                      "${CMAKE_INSTALL_PREFIX}/share/Forge/cmake" "${OSX_TEMP}/Forge/cmake"
+                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+                      COMMENT "Copying documentation files to temporary OSX Install Dir"
+                      )
+ENDIF(BUILD_GRAPHICS)
 ################################################################################
 
 FUNCTION(PKG_BUILD)
@@ -142,7 +158,12 @@ ENDFUNCTION(PKG_BUILD)
 
 FUNCTION(PRODUCT_BUILD)
     CMAKE_PARSE_ARGUMENTS(ARGS "" "" "DEPENDS" ${ARGN})
-    SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution.dist")
+    IF(BUILD_GRAPHICS)
+        SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution.dist")
+    ELSE(BUILD_GRAPHICS)
+        SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution-no-gl.dist")
+    ENDIF(BUILD_GRAPHICS)
+
     SET(DISTRIBUTION_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/distribution.dist.out")
 
     SET(WELCOME_FILE       "${OSX_INSTALL_SOURCE}/welcome.html")
@@ -177,7 +198,6 @@ PKG_BUILD(  PKG_NAME        ArrayFireCPU
             DEPENDS         OSX_INSTALL_SETUP_CPU
             TARGETS         cpu_package
             INSTALL_LOCATION /usr/local
-            SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/cpu_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cpu.lib
             PATH_TO_FILES   ${OSX_TEMP}/CPU
             FILTERS         opencl cuda unified)
@@ -186,7 +206,6 @@ PKG_BUILD(  PKG_NAME        ArrayFireCUDA
             DEPENDS         OSX_INSTALL_SETUP_CUDA
             TARGETS         cuda_package
             INSTALL_LOCATION /usr/local
-            SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/cuda_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.cuda.lib
             PATH_TO_FILES   ${OSX_TEMP}/CUDA
             FILTERS         cpu opencl unified)
@@ -195,7 +214,6 @@ PKG_BUILD(  PKG_NAME        ArrayFireOPENCL
             DEPENDS         OSX_INSTALL_SETUP_OpenCL
             TARGETS         opencl_package
             INSTALL_LOCATION /usr/local
-            SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/opencl_scripts
             IDENTIFIER      com.arrayfire.pkg.arrayfire.opencl.lib
             PATH_TO_FILES   ${OSX_TEMP}/OpenCL
             FILTERS         cpu cuda unified)
@@ -239,39 +257,55 @@ PKG_BUILD(  PKG_NAME        ArrayFireDoc
             PATH_TO_FILES   ${OSX_TEMP}/doc
             FILTERS         cmake)
 
-PKG_BUILD(  PKG_NAME        ForgeHeaders
-            DEPENDS         OSX_INSTALL_SETUP_FORGE_INCLUDE
-            TARGETS         forge_header_package
-            INSTALL_LOCATION /usr/local/include
-            IDENTIFIER      com.arrayfire.pkg.forge.inc
-            PATH_TO_FILES   ${OSX_TEMP}/Forge/include)
-
-PKG_BUILD(  PKG_NAME        ForgeExamples
-            DEPENDS         OSX_INSTALL_SETUP_FORGE_EXAMPLES
-            TARGETS         forge_examples_package
-            INSTALL_LOCATION /usr/local/share/Forge/examples
-            IDENTIFIER      com.arrayfire.pkg.forge.examples
-            PATH_TO_FILES   ${OSX_TEMP}/Forge/examples
-            FILTERS         cmake)
-
-PKG_BUILD(  PKG_NAME        ForgeDoc
-            DEPENDS         OSX_INSTALL_SETUP_FORGE_DOC
-            TARGETS         forge_doc_package
-            INSTALL_LOCATION /usr/local/share/Forge/doc
-            IDENTIFIER      com.arrayfire.pkg.forge.doc
-            PATH_TO_FILES   ${OSX_TEMP}/share/Forge/doc
-            FILTERS         cmake)
+IF(BUILD_GRAPHICS)
+    PKG_BUILD(  PKG_NAME        ForgeLibrary
+                DEPENDS         OSX_INSTALL_SETUP_FORGE_LIB
+                TARGETS         forge_lib_package
+                INSTALL_LOCATION /usr/local/
+                SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/forge_scripts
+                IDENTIFIER      com.arrayfire.pkg.forge.lib
+                PATH_TO_FILES   ${OSX_TEMP}/Forge)
+
+    PKG_BUILD(  PKG_NAME        ForgeHeaders
+                DEPENDS         OSX_INSTALL_SETUP_FORGE_INCLUDE
+                TARGETS         forge_header_package
+                INSTALL_LOCATION /usr/local/include
+                IDENTIFIER      com.arrayfire.pkg.forge.inc
+                PATH_TO_FILES   ${OSX_TEMP}/Forge/include)
+
+    PKG_BUILD(  PKG_NAME        ForgeExamples
+                DEPENDS         OSX_INSTALL_SETUP_FORGE_EXAMPLES
+                TARGETS         forge_examples_package
+                INSTALL_LOCATION /usr/local/share/Forge/examples
+                IDENTIFIER      com.arrayfire.pkg.forge.examples
+                PATH_TO_FILES   ${OSX_TEMP}/Forge/examples
+                )
 
-PKG_BUILD(  PKG_NAME        ForgeCMake
-            DEPENDS         OSX_INSTALL_SETUP_FORGE_CMAKE
-            TARGETS         forge_cmake_package
-            INSTALL_LOCATION /usr/local/share/Forge/cmake
-            IDENTIFIER      com.arrayfire.pkg.forge.cmake
-            PATH_TO_FILES   ${OSX_TEMP}/share/Forge/cmake
-            FILTERS         cmake)
+    PKG_BUILD(  PKG_NAME        ForgeDoc
+                DEPENDS         OSX_INSTALL_SETUP_FORGE_DOC
+                TARGETS         forge_doc_package
+                INSTALL_LOCATION /usr/local/share/Forge/doc
+                IDENTIFIER      com.arrayfire.pkg.forge.doc
+                PATH_TO_FILES   ${OSX_TEMP}/Forge/doc
+                )
 
-PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
-                      ${common_package} ${header_package} ${examples_package} ${doc_package}
-                      ${forge_header_package} ${forge_examples_package} ${forge_doc_package} ${forge_cmake_package}
-                      )
+    PKG_BUILD(  PKG_NAME        ForgeCMake
+                DEPENDS         OSX_INSTALL_SETUP_FORGE_CMAKE
+                TARGETS         forge_cmake_package
+                INSTALL_LOCATION /usr/local/share/Forge/cmake
+                IDENTIFIER      com.arrayfire.pkg.forge.cmake
+                PATH_TO_FILES   ${OSX_TEMP}/Forge/cmake
+                )
+ENDIF(BUILD_GRAPHICS)
+
+IF(BUILD_GRAPHICS)
+    PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
+                          ${common_package} ${header_package} ${examples_package} ${doc_package}
+                          ${forge_lib_package} ${forge_header_package} ${forge_examples_package} ${forge_doc_package} ${forge_cmake_package}
+                          )
+ELSE(BUILD_GRAPHICS)
+    PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
+                          ${common_package} ${header_package} ${examples_package} ${doc_package}
+                          )
+ENDIF(BUILD_GRAPHICS)
 
diff --git a/CMakeModules/osx_install/cpu_scripts/postinstall b/CMakeModules/osx_install/cpu_scripts/postinstall
deleted file mode 100755
index a9bce9de8e..0000000000
--- a/CMakeModules/osx_install/cpu_scripts/postinstall
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-
-set -e
-set -o pipefail
-
-err_file=/tmp/AFInstallerCPU.err
-brew=/usr/local/bin/brew
-
-echo $(date) > $err_file
-
-if [ ! -f $brew ]; then
-    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at http://brew.sh"'
-    open http://brew.sh
-    echo "Brew not found" >> $err_file
-    exit 1
-fi
-
-user=$(ps aux | grep console | grep -v 'grep\|root' | cut -d' ' -f1 | head -n1)
-
-if [ -z $user ]; then
-    echo "User not found" >> $err_file
-    exit 1
-fi
-
-function deps_err
-{
-    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
-    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
-    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
-    echo "Dependencies failed to install" >> $err_file
-    exit 1
-}
-
-su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
-su $user -c "$brew install fftw glfw3 fontconfig" >> $err_file 2>&1 || deps_err
diff --git a/CMakeModules/osx_install/distribution-no-gl.dist b/CMakeModules/osx_install/distribution-no-gl.dist
new file mode 100644
index 0000000000..81734358c7
--- /dev/null
+++ b/CMakeModules/osx_install/distribution-no-gl.dist
@@ -0,0 +1,78 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<installer-gui-script minSpecVersion="1">
+    <title>${AF_TITLE}</title>
+    <welcome    file="${WELCOME_FILE_OUT}" />
+    <readme     file="${README_FILE_OUT}" mime-type="test/html" />
+    <license    file="${PROJECT_SOURCE_DIR}/LICENSE" mime-type="test/plain" />
+    <script>
+      function CheckBackendSelected() {
+      return choices.opencl_lib.selected ||
+            choices.cuda_lib.selected ||
+            choices.cpu_lib.selected;
+      }
+    </script>
+
+    <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"       version="${AF_VERSION}" onConclusion="none">ArrayFireCPU.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"      version="${AF_VERSION}" onConclusion="none">ArrayFireCUDA.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"    version="${AF_VERSION}" onConclusion="none">ArrayFireOPENCL.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.unified.lib"   version="${AF_VERSION}" onConclusion="none">ArrayFireUNIFIED.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.inc"           version="${AF_VERSION}" onConclusion="none">ArrayFireHeaders.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
+    <options customize="always" require-scripts="false"/>
+    <choices-outline>
+        <line choice="libs">
+            <line choice="cpu_lib"/>
+            <line choice="cuda_lib"/>
+            <line choice="opencl_lib"/>
+            <line choice="com.arrayfire.arrayfire.unified.lib"/>
+            <line choice="com.arrayfire.arrayfire.libcommon"/>
+        </line>
+        <line choice="com.arrayfire.arrayfire.inc"/>
+        <line choice="com.arrayfire.arrayfire.examples"/>
+        <line choice="com.arrayfire.arrayfire.doc"/>
+    </choices-outline>
+    <choice id="libs" title="ArrayFire Libraries" visible="true" />
+    <choice title="CPU Libraries"
+            description="ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
+            id="cpu_lib" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"/>
+    </choice>
+    <choice title="CUDA Libraries"
+            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the corresponding CMake config files."
+            id="cuda_lib" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"/>
+    </choice>
+    <choice title="OpenCL Libraries"
+            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. Also installs the corresponding CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
+            id="opencl_lib" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"/>
+    </choice>
+    <choice title="Unified Library"
+            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. Also installs the corresponding CMake config files. NOTE: This option requires the other platforms to work properly"
+            id="com.arrayfire.arrayfire.unified.lib"
+            selected="CheckBackendSelected()"
+            visible="true"
+            enabled="CheckBackendSelected()">
+        <pkg-ref id="com.arrayfire.arrayfire.unified.lib"/>
+    </choice>
+    <choice title="Library Common"
+            description="Installs ArrayFireConfig.cmake files"
+            id="com.arrayfire.arrayfire.libcommon"
+            selected="CheckBackendSelected()"
+            visible="false"
+            enabled="CheckBackendSelected()">
+        <pkg-ref id="com.arrayfire.arrayfire.libcommon"/>
+    </choice>
+    <choice title="ArrayFire Headers" description="ArrayFire Headers" id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.inc"/>
+    </choice>
+    <choice title="Examples" description="ArrayFire Examples" id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.examples"/>
+    </choice>
+    <choice title="Documentation" description="ArrayFire Documentation" id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
+        <pkg-ref id="com.arrayfire.arrayfire.doc"/>
+    </choice>
+
+</installer-gui-script>
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
index 496e8318c0..9117d250db 100644
--- a/CMakeModules/osx_install/distribution.dist
+++ b/CMakeModules/osx_install/distribution.dist
@@ -10,8 +10,21 @@
             choices.cuda_lib.selected ||
             choices.cpu_lib.selected;
       }
+      function CheckBackendOrForgeSelected() {
+      return choices.opencl_lib.selected    ||
+            choices.cuda_lib.selected       ||
+            choices.cpu_lib.selected        ||
+            choices.forge_inc.selected      ||
+            choices.forge_examples.selected;
+      }
+      function EnableForgeLibOption() {
+      return !(choices.opencl_lib.selected     ||
+               choices.cuda_lib.selected       ||
+               choices.cpu_lib.selected        ||
+               choices.forge_inc.selected);
+      }
       function CheckForgeSelected() {
-      return choices.forge_headers.selected;
+      return choices.forge_lib.selected;
       }
     </script>
 
@@ -24,6 +37,7 @@
     <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.forge.inc"               version="${AF_VERSION}" onConclusion="none">ForgeHeaders.pkg</pkg-ref>
+    <pkg-ref id="com.arrayfire.forge.lib"               version="${AF_VERSION}" onConclusion="none">ForgeLibrary.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.forge.examples"          version="${AF_VERSION}" onConclusion="none">ForgeExamples.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.forge.doc"               version="${AF_VERSION}" onConclusion="none">ForgeDoc.pkg</pkg-ref>
     <pkg-ref id="com.arrayfire.forge.cmake"             version="${AF_VERSION}" onConclusion="none">ForgeCMake.pkg</pkg-ref>
@@ -40,12 +54,14 @@
         <line choice="com.arrayfire.arrayfire.examples"/>
         <line choice="com.arrayfire.arrayfire.doc"/>
         <line choice="forge">
-            <line choice="forge_include"/>
-            <line choice="forge_doc"/>
+            <line choice="forge_inc"/>
+            <line choice="forge_lib"/>
+            <line choice="forge_examples"/>
+            <line choice="com.arrayfire.forge.doc"/>
             <line choice="com.arrayfire.forge.cmake"/>
         </line>
     </choices-outline>
-    <choice id="libs" title="ArrayFire Libraries" visible="true" />
+    <choice id="libs" title="ArrayFire Libraries" visible="true" enabled="CheckForgeSelected()" />
     <choice title="CPU Libraries"
             description="ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
             id="cpu_lib" visible="true" enabled="true">
@@ -70,31 +86,55 @@
         <pkg-ref id="com.arrayfire.arrayfire.unified.lib"/>
     </choice>
     <choice title="Library Common"
-            description="Installs Forge and ArrayFireConfig.cmake files"
+            description="Installs ArrayFireConfig.cmake files"
             id="com.arrayfire.arrayfire.libcommon"
             selected="CheckBackendSelected()"
             visible="false"
             enabled="CheckBackendSelected()">
         <pkg-ref id="com.arrayfire.arrayfire.libcommon"/>
     </choice>
-    <choice title="ArrayFire Headers" description="ArrayFire Headers" id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
+    <choice title="ArrayFire Headers"
+        description="ArrayFire Headers. Installed to /usr/local/include."
+        id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.inc"/>
     </choice>
-    <choice title="Examples" description="ArrayFire Examples" id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
+    <choice title="ArrayFire Examples"
+        description="ArrayFire Examples. Installed to /usr/local/share/ArrayFire/examples."
+        id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.examples"/>
     </choice>
-    <choice title="Documentation" description="ArrayFire Documentation" id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
+    <choice title="ArrayFire Documentation"
+        description="ArrayFire Documentation. Installed to /usr/local/share/ArrayFire/doc."
+        id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.arrayfire.doc"/>
     </choice>
 
-    <choice id="forge" title="Forge Development Files" visible="true" />
-    <choice title="Forge Headers" description="Forge Headers" id="com.arrayfire.forge.inc" visible="true" enabled="true">
+    <choice id="forge" title="Forge Installation" visible="true" />
+    <choice title="Forge Headers"
+        description="Forge Headers Files. Installed to /usr/local/include."
+        id="forge_inc" visible="true" enabled="true">
         <pkg-ref id="com.arrayfire.forge.inc"/>
     </choice>
-    <choice title="Forge Examples" description="Forge ArrayFire Examples" id="com.arrayfire.forge.examples" visible="true" enabled="true">
+    <choice title="Forge Library"
+        description="Installs libforge. Installs to /usr/local/lib/libforge.dylib. This option is required for ArrayFire libs. To unset this, disable ArrayFire Libraries and Forge Headers."
+        id="forge_lib"
+        selected="CheckBackendOrForgeSelected()"
+        visible="true"
+        enabled="EnableForgeLibOption()">
+        <pkg-ref id="com.arrayfire.forge.lib"/>
+    </choice>
+    <choice title="Forge Examples"
+        description="Forge Examples. Installed to /usr/local/share/Forge/examples."
+        id="forge_examples"
+        visible="true"
+        enabled="true">
         <pkg-ref id="com.arrayfire.forge.examples"/>
     </choice>
-    <choice title="Forge Documentation" description="Forge Documentation" id="com.arrayfire.forge.doc" visible="true" enabled="true">
+    <choice title="Forge Documentation"
+        description="Forge Documentation. Installed to /usr/local/share/Forge/doc."
+        id="com.arrayfire.forge.doc"
+        visible="true"
+        enabled="true">
         <pkg-ref id="com.arrayfire.forge.doc"/>
     </choice>
     <choice title="Forge CMake"
diff --git a/CMakeModules/osx_install/cuda_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
old mode 100755
new mode 100644
similarity index 89%
rename from CMakeModules/osx_install/cuda_scripts/postinstall
rename to CMakeModules/osx_install/forge_scripts/postinstall
index 49f0fd2e2f..88ef20b231
--- a/CMakeModules/osx_install/cuda_scripts/postinstall
+++ b/CMakeModules/osx_install/forge_scripts/postinstall
@@ -3,7 +3,7 @@
 set -e
 set -o pipefail
 
-err_file=/tmp/AFInstallerCUDA.err
+err_file=/tmp/AFInstallerForge.err
 brew=/usr/local/bin/brew
 
 echo $(date) > $err_file
@@ -23,7 +23,7 @@ fi
 
 function deps_err
 {
-    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
+    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire/Forge dependencies using Brew."'
     osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
     open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
     echo "Dependencies failed to install" >> $err_file
diff --git a/CMakeModules/osx_install/opencl_scripts/postinstall b/CMakeModules/osx_install/opencl_scripts/postinstall
deleted file mode 100755
index 54ecb4df19..0000000000
--- a/CMakeModules/osx_install/opencl_scripts/postinstall
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-
-set -e
-set -o pipefail
-
-err_file=/tmp/AFInstallerOpenCL.err
-brew=/usr/local/bin/brew
-
-echo $(date) > $err_file
-
-if [ ! -f $brew ]; then
-    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at brew.sh"'
-    echo "Brew not found" >> $err_file
-    exit 1
-fi
-
-user=$(ps aux | grep console | grep -v 'grep\|root' | cut -d' ' -f1 | head -n1)
-
-if [ -z $user ]; then
-    echo "User not found" >> $err_file
-    exit 1
-fi
-
-function deps_err
-{
-    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire dependencies using Brew."'
-    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
-    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
-    echo "Dependencies failed to install" >> $err_file
-    exit 1
-}
-
-su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
-su $user -c "$brew install fftw glfw3 fontconfig" >> $err_file 2>&1 || deps_err

From a151ed8789aa517ccf01ed6957253700314ccd3a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 01:00:47 -0400
Subject: [PATCH 0919/2677] OPENCL: Fixed bugs in homography kernel

- Results consistent between runs
- CPU tests pass
---
 src/backend/opencl/kernel/homography.cl  | 258 +++++++++++------------
 src/backend/opencl/kernel/homography.hpp |  28 +--
 2 files changed, 131 insertions(+), 155 deletions(-)

diff --git a/src/backend/opencl/kernel/homography.cl b/src/backend/opencl/kernel/homography.cl
index 572cb23ab5..3ae68dadac 100644
--- a/src/backend/opencl/kernel/homography.cl
+++ b/src/backend/opencl/kernel/homography.cl
@@ -22,58 +22,57 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
     int tid_y = get_local_id(1);
     int gid_y = get_global_id(1);
 
-    // Copy first 80 elements
-    T t = l_S[tid_y*81 + tid_x];
-    l_acc1[tid_y*bsz_x + tid_x] = t*t;
-    for (int i = 1; i <= 4; i++) {
-        T t = l_S[tid_y*81 + tid_x+i*bsz_x];
-        l_acc1[tid_y*bsz_x + tid_x] += t*t;
-    }
-    if (tid_x < 8)
-        l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+8];
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid_x < 4)
-        l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+4];
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid_x < 2)
-        l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+2];
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid_x < 1) {
-        // Copy last element
-        T t = l_S[tid_y*bsz_x + tid_x+80];
-        l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+1] + t*t;
+    int doff = tid_y *  n;
+    int soff = tid_y * 81;
+
+    if (tid_x < n) {
+        T acc1 = 0;
+        for (int i = 0; i < m; i++) {
+            int stid = soff + tid_x * m + i;
+            T t = l_S[stid];
+            acc1 += t * t;
+            l_V[stid] = (tid_x == i) ? 1 : 0;
+        }
+        l_d[doff + tid_x] = acc1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (tid_x < n)
-        l_d[tid_y*9 + tid_x] = l_acc1[tid_y*bsz_x + tid_x];
-
-    // V is initialized as an identity matrix
-    for (int i = 0; i <= 4; i++) {
-        l_V[tid_y*81 + i*bsz_x + tid_x] = 0;
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid_x < m)
-        l_V[tid_y*81 + tid_x*m + tid_x] = 1;
-    barrier(CLK_LOCAL_MEM_FENCE);
+#if defined(IS_CPU)
+    // All threads do the same work
+    // FIXME: Figure out why code below doesnt work
+    int tst = 0, toff = 1, tcond = tid_x == 0;
+#define BARRIER //nothing
+#else
+    // Split work across subgroup
+    int tst = tid_x, toff = bsz_x, tcond = 1;
+#define BARRIER barrier(CLK_LOCAL_MEM_FENCE)
+#endif
 
-    for (int it = 0; it < iterations; it++) {
-        int converged = 0;
 
+    for (int it = 0; tcond && it < iterations; it++) {
         for (int i = 0; i < n-1; i++) {
             for (int j = i+1; j < n; j++) {
-                __local T* Si = l_S + tid_y*81 + i*m;
-                __local T* Sj = l_S + tid_y*81 + j*m;
+                __local T* Si = l_S + soff + i*m;
+                __local T* Sj = l_S + soff + j*m;
+
+                __local T* Vi = l_V + soff + i*n;
+                __local T* Vj = l_V + soff + j*n;
 
                 T p = (T)0;
                 for (int k = 0; k < m; k++)
                     p += Si[k]*Sj[k];
 
+                T di = l_d[doff + i];
+                T dj = l_d[doff + j];
+                BARRIER;
+
                 T c = 0, s = 0;
+                T t0 = 0, t1 = 0;
+                int cond = (fabs(p) > m*EPS*sqrt(di * dj));
+                T a = 0, b = 0;
 
-                int cond = (fabs(p) > EPS*sqrt(l_d[tid_y*9 + i]*l_d[tid_y*9 + j]));
                 if (cond) {
-                    T y = l_d[tid_y*9 + i] - l_d[tid_y*9 + j];
+                    T y = di - dj;
                     T r = hypot(p*2, y);
                     T r2 = r*2;
                     if (y >= 0) {
@@ -85,59 +84,42 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
                         c = p / (r2*s);
                     }
 
-                    if (tid_x < m) {
-                        T t0 = c*Si[tid_x] + s*Sj[tid_x];
-                        T t1 = c*Sj[tid_x] - s*Si[tid_x];
-                        Si[tid_x] = t0;
-                        Sj[tid_x] = t1;
-
-                        l_acc1[tid_y*16 + tid_x] = t0*t0;
-                        l_acc2[tid_y*16 + tid_x] = t1*t1;
+                    for (int k = tst; k < m; k+=toff) {
+                        t0 = c*Si[k] + s*Sj[k];
+                        t1 = c*Sj[k] - s*Si[k];
+                        Si[k] = t0;
+                        Sj[k] = t1;
+                        l_acc1[tid_y * bsz_x + k] = t0 * t0;
+                        l_acc2[tid_y * bsz_x + k] = t1 * t1;
                     }
                 }
-                barrier(CLK_LOCAL_MEM_FENCE);
+                BARRIER;
 
-                if (cond && tid_x < 4) {
-                    l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+4];
-                    l_acc2[tid_y*16 + tid_x] += l_acc2[tid_y*16 + tid_x+4];
-                }
-                barrier(CLK_LOCAL_MEM_FENCE);
-                if (cond && tid_x < 2) {
-                    l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+2];
-                    l_acc2[tid_y*16 + tid_x] += l_acc2[tid_y*16 + tid_x+2];
-                }
-                barrier(CLK_LOCAL_MEM_FENCE);
-                if (cond && tid_x < 1) {
-                    l_acc1[tid_y*16 + tid_x] += l_acc1[tid_y*16 + tid_x+1] + l_acc1[tid_y*16 + tid_x+8];
-                    l_acc2[tid_y*16 + tid_x] += l_acc2[tid_y*16 + tid_x+1] + l_acc2[tid_y*16 + tid_x+8];
-                }
-                barrier(CLK_LOCAL_MEM_FENCE);
-
-                if (cond && tid_x == 0) {
-                    l_d[tid_y*9 + i] = l_acc1[tid_y*16];
-                    l_d[tid_y*9 + j] = l_acc2[tid_y*16];
+                if (cond) {
+                    a = 0;
+                    b = 0;
+                    for (int k = 0; k < m; k++) {
+                        a += l_acc1[tid_y * bsz_x + k];
+                        b += l_acc2[tid_y * bsz_x + k];
+                    }
+                    l_d[doff + i] = a;
+                    l_d[doff + j] = b;
                 }
-                barrier(CLK_LOCAL_MEM_FENCE);
-
-                __local T* Vi = l_V + tid_y*81 + i*n;
-                __local T* Vj = l_V + tid_y*81 + j*n;
+                BARRIER;
 
-                if (cond && tid_x < n) {
-                    T t0 = Vi[tid_x] * c + Vj[tid_x] * s;
-                    T t1 = Vj[tid_x] * c - Vi[tid_x] * s;
+                if (cond) {
+                    for (int l = tst; l < n; l += toff) {
+                        T t0 = Vi[l] * c + Vj[l] * s;
+                        T t1 = Vj[l] * c - Vi[l] * s;
 
-                    Vi[tid_x] = t0;
-                    Vj[tid_x] = t1;
+                        Vi[l] = t0;
+                        Vj[l] = t1;
+                    }
                 }
-                barrier(CLK_LOCAL_MEM_FENCE);
-
-                converged = 1;
+                BARRIER;
             }
-            if (converged == 0)
-                break;
         }
     }
-    barrier(CLK_LOCAL_MEM_FENCE);
 }
 
 inline int compute_mean_scale(
@@ -194,7 +176,7 @@ inline int compute_mean_scale(
     *src_scale = sqrt(2.0f) / sqrt(src_var);
     *dst_scale = sqrt(2.0f) / sqrt(dst_var);
 
-    return !bad;
+    return bad;
 }
 
 #define LSPTR(Z, Y, X) (l_S[(Z) * 81 + (Y) * 9 + (X)])
@@ -212,19 +194,20 @@ __kernel void compute_homography(
 {
     unsigned i = get_global_id(1);
     unsigned tid_y = get_local_id(1);
+    unsigned tid_x = get_local_id(0);
 
     float x_src_mean, y_src_mean;
     float x_dst_mean, y_dst_mean;
     float src_scale, dst_scale;
     float src_pt_x[4], src_pt_y[4], dst_pt_x[4], dst_pt_y[4];
 
-    compute_mean_scale(&x_src_mean, &y_src_mean,
-                       &x_dst_mean, &y_dst_mean,
-                       &src_scale, &dst_scale,
-                       src_pt_x, src_pt_y,
-                       dst_pt_x, dst_pt_y,
-                       x_src, y_src, x_dst, y_dst,
-                       rnd, rInfo, i);
+    int bad = compute_mean_scale(&x_src_mean, &y_src_mean,
+                                 &x_dst_mean, &y_dst_mean,
+                                 &src_scale, &dst_scale,
+                                 src_pt_x, src_pt_y,
+                                 dst_pt_x, dst_pt_y,
+                                 x_src, y_src, x_dst, y_dst,
+                                 rnd, rInfo, i);
 
     __local T l_acc1[256];
     __local T l_acc2[256];
@@ -234,33 +217,33 @@ __kernel void compute_homography(
     __local T l_d[16*9];
 
     // Compute input matrix
-    for (unsigned j = get_local_id(0); j < 4; j+=get_local_size(0)) {
-        float srcx = (src_pt_x[j] - x_src_mean) * src_scale;
-        float srcy = (src_pt_y[j] - y_src_mean) * src_scale;
-        float dstx = (dst_pt_x[j] - x_dst_mean) * dst_scale;
-        float dsty = (dst_pt_y[j] - y_dst_mean) * dst_scale;
-
-        LSPTR(tid_y, 0, j*2) = 0.0f;
-        LSPTR(tid_y, 1, j*2) = 0.0f;
-        LSPTR(tid_y, 2, j*2) = 0.0f;
-        LSPTR(tid_y, 3, j*2) = -srcx;
-        LSPTR(tid_y, 4, j*2) = -srcy;
-        LSPTR(tid_y, 5, j*2) = -1.0f;
-        LSPTR(tid_y, 6, j*2) = dsty*srcx;
-        LSPTR(tid_y, 7, j*2) = dsty*srcy;
-        LSPTR(tid_y, 8, j*2) = dsty;
-
-        LSPTR(tid_y, 0, j*2+1) = srcx;
-        LSPTR(tid_y, 1, j*2+1) = srcy;
-        LSPTR(tid_y, 2, j*2+1) = 1.0f;
-        LSPTR(tid_y, 3, j*2+1) = 0.0f;
-        LSPTR(tid_y, 4, j*2+1) = 0.0f;
-        LSPTR(tid_y, 5, j*2+1) = 0.0f;
-        LSPTR(tid_y, 6, j*2+1) = -dstx*srcx;
-        LSPTR(tid_y, 7, j*2+1) = -dstx*srcy;
-        LSPTR(tid_y, 8, j*2+1) = -dstx;
-
-        if (j == 4) {
+    if (tid_x < 4) {
+        float srcx = (src_pt_x[tid_x] - x_src_mean) * src_scale;
+        float srcy = (src_pt_y[tid_x] - y_src_mean) * src_scale;
+        float dstx = (dst_pt_x[tid_x] - x_dst_mean) * dst_scale;
+        float dsty = (dst_pt_y[tid_x] - y_dst_mean) * dst_scale;
+
+        LSPTR(tid_y, 0, tid_x*2) = 0.0f;
+        LSPTR(tid_y, 1, tid_x*2) = 0.0f;
+        LSPTR(tid_y, 2, tid_x*2) = 0.0f;
+        LSPTR(tid_y, 3, tid_x*2) = -srcx;
+        LSPTR(tid_y, 4, tid_x*2) = -srcy;
+        LSPTR(tid_y, 5, tid_x*2) = -1.0f;
+        LSPTR(tid_y, 6, tid_x*2) = dsty*srcx;
+        LSPTR(tid_y, 7, tid_x*2) = dsty*srcy;
+        LSPTR(tid_y, 8, tid_x*2) = dsty;
+
+        LSPTR(tid_y, 0, tid_x*2+1) = srcx;
+        LSPTR(tid_y, 1, tid_x*2+1) = srcy;
+        LSPTR(tid_y, 2, tid_x*2+1) = 1.0f;
+        LSPTR(tid_y, 3, tid_x*2+1) = 0.0f;
+        LSPTR(tid_y, 4, tid_x*2+1) = 0.0f;
+        LSPTR(tid_y, 5, tid_x*2+1) = 0.0f;
+        LSPTR(tid_y, 6, tid_x*2+1) = -dstx*srcx;
+        LSPTR(tid_y, 7, tid_x*2+1) = -dstx*srcy;
+        LSPTR(tid_y, 8, tid_x*2+1) = -dstx;
+
+        if (tid_x == 4) {
             LSPTR(tid_y, 0, 8) = 0.0f;
             LSPTR(tid_y, 1, 8) = 0.0f;
             LSPTR(tid_y, 2, 8) = 0.0f;
@@ -276,28 +259,30 @@ __kernel void compute_homography(
 
     jacobi_svd(l_V, l_S, l_d, l_acc1, l_acc2, 9, 9);
 
-    T vH[9], H_tmp[9];
-    for (unsigned j = 0; j < 9; j++)
-        vH[j] = l_V[tid_y * 81 + 8 * 9 + j];
+    if (i < HInfo.dims[1] && tid_x == 0) {
+        T vH[9], H_tmp[9];
+        for (unsigned j = 0; j < 9; j++)
+            vH[j] = l_V[tid_y * 81 + 8 * 9 + j];
 
-    H_tmp[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
-    H_tmp[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
-    H_tmp[2] = x_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-                          (vH[2] - src_scale*y_src_mean*vH[1] - src_scale*x_src_mean*vH[0])/dst_scale;
+        H_tmp[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
+        H_tmp[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
+        H_tmp[2] = x_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
+            (vH[2] - src_scale*y_src_mean*vH[1] - src_scale*x_src_mean*vH[0])/dst_scale;
 
-    H_tmp[3] = src_scale*y_dst_mean*vH[6] + src_scale*vH[3]/dst_scale;
-    H_tmp[4] = src_scale*y_dst_mean*vH[7] + src_scale*vH[4]/dst_scale;
-    H_tmp[5] = y_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-                          (vH[5] - src_scale*y_src_mean*vH[4] - src_scale*x_src_mean*vH[3])/dst_scale;
+        H_tmp[3] = src_scale*y_dst_mean*vH[6] + src_scale*vH[3]/dst_scale;
+        H_tmp[4] = src_scale*y_dst_mean*vH[7] + src_scale*vH[4]/dst_scale;
+        H_tmp[5] = y_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
+            (vH[5] - src_scale*y_src_mean*vH[4] - src_scale*x_src_mean*vH[3])/dst_scale;
 
-    H_tmp[6] = src_scale*vH[6];
-    H_tmp[7] = src_scale*vH[7];
-    H_tmp[8] = vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6];
+        H_tmp[6] = src_scale*vH[6];
+        H_tmp[7] = src_scale*vH[7];
+        H_tmp[8] = vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6];
 
-    const unsigned Hidx = HInfo.dims[0] * i;
-    __global T* H_ptr = H + Hidx;
-    for (int h = 0; h < 9; h++)
-        H_ptr[h] = H_tmp[h];
+        const unsigned Hidx = HInfo.dims[0] * i;
+        __global T* H_ptr = H + Hidx;
+        for (int h = 0; h < 9; h++)
+            H_ptr[h] = bad ? 0 : H_tmp[h];
+    }
 }
 
 #undef APTR
@@ -364,6 +349,7 @@ __kernel void eval_homography(
         }
 #endif
     }
+    barrier(CLK_LOCAL_MEM_FENCE);
 
 #ifdef RANSAC
     unsigned bid_x = get_group_id(0);
@@ -379,8 +365,10 @@ __kernel void eval_homography(
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    inliers[bid_x] = l_inliers[0];
-    idx[bid_x]     = l_idx[0];
+    if (tid_x == 0) {
+        inliers[bid_x] = l_inliers[0];
+        idx[bid_x]     = l_idx[0];
+    }
 #endif
 }
 
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 1029d1b3f4..539b38ab4e 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -77,6 +77,10 @@ int computeH(
                 else if (htype == AF_HOMOGRAPHY_LMEDS)
                     options << " -D LMEDS";
 
+                if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
+                    options << " -D IS_CPU";
+                }
+
                 cl::Program prog;
                 buildProgram(prog, homography_cl, homography_cl_len, options.str());
                 hgProgs[device] = new Program(prog);
@@ -215,30 +219,14 @@ int computeH(
             getQueue().enqueueReadBuffer(*totalInliers.data, CL_TRUE, 0, sizeof(unsigned), &inliersH);
 
             bufferFree(totalInliers.data);
-        }
-        else if (htype == AF_HOMOGRAPHY_RANSAC) {
-            Param bestInliers, bestIdx;
-            bestInliers.info.offset = bestIdx.info.offset = 0;
-            for (int k = 0; k < 4; k++) {
-                bestInliers.info.dims[k] = bestIdx.info.dims[k] = 1;
-                bestInliers.info.strides[k] = bestIdx.info.strides[k] = 1;
-            }
-            bestInliers.data = bufferAlloc(sizeof(unsigned));
-            bestIdx.data = bufferAlloc(sizeof(unsigned));
-
-            kernel::ireduce<unsigned, af_max_t>(bestInliers, bestIdx.data, inliers, 0);
-
+        } else if (htype == AF_HOMOGRAPHY_RANSAC) {
             unsigned blockIdx;
-            getQueue().enqueueReadBuffer(*bestIdx.data, CL_TRUE, 0, sizeof(unsigned), &blockIdx);
+            inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
 
             // Copies back index and number of inliers of best homography estimation
-            getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, blockIdx*sizeof(unsigned), sizeof(unsigned), &idxH);
-            getQueue().enqueueReadBuffer(*bestInliers.data, CL_TRUE, 0, sizeof(unsigned), &inliersH);
-
+            getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, blockIdx*sizeof(unsigned),
+                                         sizeof(unsigned), &idxH);
             getQueue().enqueueCopyBuffer(*H.data, *bestH.data, idxH*9*sizeof(T), 0, 9*sizeof(T));
-
-            bufferFree(bestInliers.data);
-            bufferFree(bestIdx.data);
         }
 
         bufferFree(inliers.data);

From 0a8371a876b927931b5307baa8ff5aff0d66483e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 03:04:15 -0400
Subject: [PATCH 0920/2677] CUDA: Bugfixes and cleanup of homography kernel

---
 src/backend/cuda/kernel/homography.hpp | 149 +++++++++----------------
 1 file changed, 53 insertions(+), 96 deletions(-)

diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index d24261ea53..23eb043c57 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -65,64 +65,54 @@ __device__ void JacobiSVD(int m, int n)
     int tid_y = threadIdx.y;
     //int gid_y = blockIdx.y * blockDim.y + tid_y;
 
-    __shared__ T acc1[256];
-    __shared__ T acc2[256];
+    __shared__ T s_acc1[256];
+    __shared__ T s_acc2[256];
 
-    __shared__ T d[16*9];
+    __shared__ T s_d[16*9];
 
     T* s_V = (T*)sh;
     T* s_S = (T*)sh + 16*81;
 
-    // Copy first 80 elements
-    for (int i = 0; i <= 4; i++) {
-        T t = s_S[tid_y*81 + tid_x+i*bsz_x];
-        acc1[tid_y*bsz_x + tid_x] += t*t;
-    }
-    if (tid_x < 8)
-        acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+8];
-    __syncthreads();
-    if (tid_x < 4)
-        acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+4];
-    __syncthreads();
-    if (tid_x < 2)
-        acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+2];
-    __syncthreads();
-    if (tid_x < 1) {
-        // Copy last element
-        T t = s_S[tid_y*bsz_x + tid_x+80];
-        acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+1] + t*t;
-    }
-    __syncthreads();
 
-    if (tid_x < n)
-        d[tid_y*9 + tid_x] = acc1[tid_y*bsz_x + tid_x];
+    int doff = tid_y *  n;
+    int soff = tid_y * 81;
 
-    // V is initialized as an identity matrix
-    for (int i = 0; i <= 4; i++) {
-        s_V[tid_y*81 + i*bsz_x + tid_x] = 0;
+    if (tid_x < n) {
+        T acc1 = 0;
+        for (int i = 0; i < m; i++) {
+            int stid = soff + tid_x * m + i;
+            T t = s_S[stid];
+            acc1 += t * t;
+            s_V[stid] = (tid_x == i) ? 1 : 0;
+        }
+        s_d[doff + tid_x] = acc1;
     }
     __syncthreads();
-    if (tid_x < m)
-        s_V[tid_y*81 + tid_x*m + tid_x] = 1;
-    __syncthreads();
 
     for (int it = 0; it < iterations; it++) {
-        bool converged = false;
-
         for (int i = 0; i < n-1; i++) {
             for (int j = i+1; j < n; j++) {
-                T* Si = s_S + tid_y*81 + i*m;
-                T* Sj = s_S + tid_y*81 + j*m;
+                T* Si = s_S + soff + i*m;
+                T* Sj = s_S + soff + j*m;
+
+                T* Vi = s_V + soff + i*n;
+                T* Vj = s_V + soff + j*n;
 
                 T p = (T)0;
                 for (int k = 0; k < m; k++)
                     p += Si[k]*Sj[k];
 
+                T di = s_d[doff + i];
+                T dj = s_d[doff + j];
+                __syncthreads();
+
                 T c = 0, s = 0;
+                T t0 = 0, t1 = 0;
+                int cond = (fabs(p) > m*EPS<T>::eps()*sqrt(di * dj));
+                T a = 0, b = 0;
 
-                bool cond = (abs(p) > EPS<T>::eps()*sqrt(d[tid_y*9 + i]*d[tid_y*9 + j]));
                 if (cond) {
-                    T y = d[tid_y*9 + i] - d[tid_y*9 + j];
+                    T y = di - dj;
                     T r = hypot(p*2, y);
                     T r2 = r*2;
                     if (y >= 0) {
@@ -133,61 +123,43 @@ __device__ void JacobiSVD(int m, int n)
                         s = sqrt((r - y) / r2);
                         c = p / (r2*s);
                     }
-                }
-                __syncthreads();
-
-                if (cond && tid_x < m) {
-                    T t0 = c*Si[tid_x] + s*Sj[tid_x];
-                    T t1 = c*Sj[tid_x] - s*Si[tid_x];
-                    Si[tid_x] = t0;
-                    Sj[tid_x] = t1;
 
-                    acc1[tid_y*16 + tid_x] = t0*t0;
-                    acc2[tid_y*16 + tid_x] = t1*t1;
-                }
-                __syncthreads();
-
-                if (cond && tid_x < 4) {
-                    acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+4];
-                    acc2[tid_y*16 + tid_x] += acc2[tid_y*16 + tid_x+4];
-                }
-                __syncthreads();
-                if (cond && tid_x < 2) {
-                    acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+2];
-                    acc2[tid_y*16 + tid_x] += acc2[tid_y*16 + tid_x+2];
-                }
-                __syncthreads();
-                if (cond && tid_x < 1) {
-                    acc1[tid_y*16 + tid_x] += acc1[tid_y*16 + tid_x+1] + acc1[tid_y*16 + tid_x+8];
-                    acc2[tid_y*16 + tid_x] += acc2[tid_y*16 + tid_x+1] + acc2[tid_y*16 + tid_x+8];
+                    for (int k = tid_x; k < m; k+=bsz_x) {
+                        t0 = c*Si[k] + s*Sj[k];
+                        t1 = c*Sj[k] - s*Si[k];
+                        Si[k] = t0;
+                        Sj[k] = t1;
+                        s_acc1[tid_y * bsz_x + k] = t0 * t0;
+                        s_acc2[tid_y * bsz_x + k] = t1 * t1;
+                    }
                 }
                 __syncthreads();
 
-                if (cond && tid_x == 0) {
-                    d[tid_y*9 + i] = acc1[tid_y*16];
-                    d[tid_y*9 + j] = acc2[tid_y*16];
+                if (cond) {
+                    a = 0;
+                    b = 0;
+                    for (int k = 0; k < m; k++) {
+                        a += s_acc1[tid_y * bsz_x + k];
+                        b += s_acc2[tid_y * bsz_x + k];
+                    }
+                    s_d[doff + i] = a;
+                    s_d[doff + j] = b;
                 }
                 __syncthreads();
 
-                T* Vi = s_V + tid_y*81 + i*n;
-                T* Vj = s_V + tid_y*81 + j*n;
-
-                if (cond && tid_x < n) {
-                    T t0 = Vi[tid_x] * c + Vj[tid_x] * s;
-                    T t1 = Vj[tid_x] * c - Vi[tid_x] * s;
+                if (cond) {
+                    for (int l = tid_x; l < n; l += bsz_x) {
+                        T t0 = Vi[l] * c + Vj[l] * s;
+                        T t1 = Vj[l] * c - Vi[l] * s;
 
-                    Vi[tid_x] = t0;
-                    Vj[tid_x] = t1;
+                        Vi[l] = t0;
+                        Vj[l] = t1;
+                    }
                 }
                 __syncthreads();
-
-                converged = true;
             }
-            if (!converged)
-                break;
         }
     }
-    __syncthreads();
 }
 
 __device__ bool computeMeanScale(
@@ -411,6 +383,7 @@ __global__ void computeEvalHomography(
             }
         }
     }
+    __syncthreads();
 
     if (htype == AF_HOMOGRAPHY_RANSAC) {
         // Find sample with most inliers
@@ -680,30 +653,14 @@ int computeH(
         memFree(totalInliers.ptr);
         memFree(median.ptr);
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
-        Param<unsigned> bestInliers, bestIdx;
-        for (int k = 0; k < 4; k++) {
-            bestInliers.dims[k] = bestIdx.dims[k] = 1;
-            bestInliers.strides[k] = bestIdx.strides[k] = 1;
-        }
-        bestInliers.ptr = memAlloc<unsigned>(1);
-        bestIdx.ptr = memAlloc<unsigned>(1);
-
-        kernel::ireduce<unsigned, af_max_t>(bestInliers, bestIdx.ptr, inliers, 0);
-
         unsigned blockIdx;
-        CUDA_CHECK(cudaMemcpyAsync(&blockIdx, bestIdx.ptr, sizeof(unsigned),
-                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-
+        inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
         // Copies back index and number of inliers of best homography estimation
         CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.ptr+blockIdx, sizeof(unsigned),
                     cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaMemcpyAsync(&inliersH, bestInliers.ptr, sizeof(unsigned),
-                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T),
                     cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
 
-        memFree(bestInliers.ptr);
-        memFree(bestIdx.ptr);
     }
 
     memFree(inliers.ptr);

From c5f3faf4620c143232cc4005a6cab41be8610ad9 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 04:38:43 -0400
Subject: [PATCH 0921/2677] TEST: Removing global import of namespace std

---
 test/assign.cpp          |  4 ----
 test/basic.cpp           |  2 +-
 test/binary.cpp          |  1 +
 test/clamp.cpp           | 25 ++++++++++++-------------
 test/complex.cpp         |  1 -
 test/flat.cpp            |  1 -
 test/flip.cpp            |  1 -
 test/jit.cpp             |  1 -
 test/math.cpp            |  1 +
 test/ocl_ext_context.cpp |  3 +--
 test/stdev.cpp           |  3 ++-
 11 files changed, 18 insertions(+), 25 deletions(-)

diff --git a/test/assign.cpp b/test/assign.cpp
index af68acdfd1..0be85d26a4 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -115,7 +115,6 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv)
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
 
     vector<outType> currGoldBar = tests[0];
-    using namespace std;
     size_t nElems        = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
@@ -345,7 +344,6 @@ TYPED_TEST(ArrayAssign, Scalar4DCPP)
 TYPED_TEST(ArrayAssign, AssignRowCPP)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace std;
     using namespace af;
     int dimsize=10;
     vector<TypeParam> input(100, 1);
@@ -390,7 +388,6 @@ TYPED_TEST(ArrayAssign, AssignRowCPP)
 TYPED_TEST(ArrayAssign, AssignColumnCPP)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace std;
     using namespace af;
     int dimsize=10;
     vector<TypeParam> input(100, 1);
@@ -435,7 +432,6 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP)
 TYPED_TEST(ArrayAssign, AssignSliceCPP)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace std;
     using namespace af;
     int dimsize=10;
     vector<TypeParam> input(1000, 1);
diff --git a/test/basic.cpp b/test/basic.cpp
index d57c184be3..49eac81ee0 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -13,7 +13,7 @@
 #include <vector>
 #include <testHelpers.hpp>
 
-using namespace std;
+using std::vector;
 
 TEST(BasicTests, constant1000x1000)
 {
diff --git a/test/binary.cpp b/test/binary.cpp
index 64b8267835..d8dba143a0 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -13,6 +13,7 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
+// This makes the macros cleaner
 using namespace std;
 using std::abs;
 using namespace af;
diff --git a/test/clamp.cpp b/test/clamp.cpp
index ea2ca04160..9c5adb20e5 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -13,7 +13,6 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace std;
 using std::abs;
 using namespace af;
 
@@ -21,14 +20,14 @@ const int num = 10000;
 
 TEST(ClampTests, FloatArrayArray)
 {
-    array in = af::randu(num, f32);
-    array lo = af::randu(num, f32)/10;       // Ensure lo <= 0.1
-    array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
+    af::array in = af::randu(num, f32);
+    af::array lo = af::randu(num, f32)/10;       // Ensure lo <= 0.1
+    af::array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
     af::eval(lo, hi);
 
 
     std::vector<float> hout(num), hin(num), hlo(num), hhi(num);
-    array out = clamp(in, lo, hi);
+    af::array out = clamp(in, lo, hi);
     out.host(&hout[0]);
     in.host(&hin[0]);
     lo.host(&hlo[0]);
@@ -43,12 +42,12 @@ TEST(ClampTests, FloatArrayArray)
 
 TEST(ClampTests, FloatArrayScalar)
 {
-    array in = af::randu(num, f32);
-    array lo = af::randu(num, f32)/10; // Ensure lo <= 0.1
+    af::array in = af::randu(num, f32);
+    af::array lo = af::randu(num, f32)/10; // Ensure lo <= 0.1
     float hi = 0.9;
 
     std::vector<float> hout(num), hin(num), hlo(num);
-    array out = clamp(in, lo, hi);
+    af::array out = clamp(in, lo, hi);
 
     out.host(&hout[0]);
     in.host(&hin[0]);
@@ -63,12 +62,12 @@ TEST(ClampTests, FloatArrayScalar)
 
 TEST(ClampTests, FloatScalarArray)
 {
-    array in = af::randu(num, f32);
+    af::array in = af::randu(num, f32);
     float lo = 0.1;
-    array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
+    af::array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
 
     std::vector<float> hout(num), hin(num), hhi(num);
-    array out = clamp(in, lo, hi);
+    af::array out = clamp(in, lo, hi);
 
     out.host(&hout[0]);
     in.host(&hin[0]);
@@ -83,12 +82,12 @@ TEST(ClampTests, FloatScalarArray)
 
 TEST(ClampTests, FloatScalarScalar)
 {
-    array in = af::randu(num, f32);
+    af::array in = af::randu(num, f32);
     float lo = 0.1;
     float hi = 0.9;
 
     std::vector<float> hout(num), hin(num);
-    array out = clamp(in, lo, hi);
+    af::array out = clamp(in, lo, hi);
 
     out.host(&hout[0]);
     in.host(&hin[0]);
diff --git a/test/complex.cpp b/test/complex.cpp
index 6c60c4d8df..e13ec53e4d 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -13,7 +13,6 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace std;
 using namespace af;
 
 const int num = 10;
diff --git a/test/flat.cpp b/test/flat.cpp
index 2448788c93..a556c897d5 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -13,7 +13,6 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace std;
 using namespace af;
 
 TEST(FlatTests, Test_flat_1D)
diff --git a/test/flip.cpp b/test/flip.cpp
index 55d580c4af..5ec5206239 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -14,7 +14,6 @@
 #include <af/index.h>
 #include <testHelpers.hpp>
 
-using namespace std;
 using namespace af;
 
 TEST(FlipTests, Test_flip_1D)
diff --git a/test/jit.cpp b/test/jit.cpp
index 35e5ff61a4..6f83666a70 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -13,7 +13,6 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace std;
 using namespace af;
 
 TEST(JIT, CPP_JIT_HASH)
diff --git a/test/math.cpp b/test/math.cpp
index 0af39a48ef..66c8245594 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -12,6 +12,7 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
+// This makes the macros cleaner
 using namespace std;
 using namespace af;
 using std::abs;
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 6a6c0a1cb9..3dc46991c2 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -15,8 +15,7 @@
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
-using namespace std;
+using std::vector;
 
 inline void checkErr(cl_int err, const char * name) {
     if (err != CL_SUCCESS) {
diff --git a/test/stdev.cpp b/test/stdev.cpp
index f33d4e38fa..ff70948752 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -18,8 +18,9 @@
 #include <algorithm>
 #include <testHelpers.hpp>
 
-using namespace std;
 using namespace af;
+using std::string;
+using std::vector;
 
 template<typename T>
 class StandardDev : public ::testing::Test

From 85a026163c1b1117dba27a09f3a828bdf4cb9e17 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 12 Sep 2016 10:45:49 -0400
Subject: [PATCH 0922/2677] add known issues

---
 docs/pages/release_notes.md | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index c0914dd915..78622a23ce 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -69,6 +69,17 @@ Documentation
 -------------
 * Fixed grammar in license
 
+Known Issues
+-------------
+Certain CUDA functions are known to be broken on Tegra K1. The following ArrayFire tests are currently failing:
+* assign_cuda
+* harris_cuda
+* homography_cuda
+* median_cuda
+* orb_cudasort_cuda
+* sort_by_key_cuda
+* sort_index_cuda
+
 
 v3.3.2
 ==============

From cd5c07b8df09e39f7054d5e59701da76fdd971d6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 11:14:24 -0400
Subject: [PATCH 0923/2677] Fix documentation errors in headers files

---
 include/af/graphics.h | 6 +++---
 include/af/random.h   | 8 +++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index ca0673dfe1..1c44d0d360 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -1071,9 +1071,9 @@ AFAPI af_err af_set_axes_limits_3d(const af_window wind,
    \ingroup gfx_func_window
 */
 AFAPI af_err af_set_axes_titles(const af_window wind,
-                                const char * const xtitles,
-                                const char * const ytitles,
-                                const char * const ztitles,
+                                const char * const xtitle,
+                                const char * const ytitle,
+                                const char * const ztitle,
                                 const af_cell* const props);
 #endif
 
diff --git a/include/af/random.h b/include/af/random.h
index 3ad5d7df7e..1e2a2bdb98 100644
--- a/include/af/random.h
+++ b/include/af/random.h
@@ -49,7 +49,7 @@ namespace af
 
             /**
                Creates a copy of the random engine object
-               \param in The input random engine object
+               \param engine The input random engine object
             */
             randomEngine(af_random_engine engine);
             /**
@@ -289,9 +289,11 @@ namespace af
 
 #if AF_API_VERSION >= 34
     /**
-        \param[in] rtype The type of the random number generator
+        Returns the default random engine
 
-        \ingroup random_func_set_type
+        \returns the \ref randomEngine object for the default random engine
+
+        \ingroup random_func_get_type
     */
     AFAPI randomEngine getDefaultRandomEngine(void);
 #endif

From db244f61de08ac2e1aeb8d52a4b81261fa363d49 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 12 Sep 2016 21:29:19 +0530
Subject: [PATCH 0924/2677] BUGFIX: fix draw cell parameter order in vector
 field

---
 src/api/c/vector_field.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index d0669269b4..9e22047800 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -104,7 +104,7 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
 
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
     }
@@ -189,7 +189,7 @@ af_err vectorFieldWrapper(const af_window wind,
 
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
 
@@ -265,7 +265,7 @@ af_err vectorFieldWrapper(const af_window wind,
 
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->col, props->row, *chart, props->title);
+            window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
 

From e34b2cbd94d8cc6a2472ecf3d670eaf912099603 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 12 Sep 2016 21:29:55 +0530
Subject: [PATCH 0925/2677] Change default colors for vector field and
 plot/scatter

* Use ArrayFire LOGO Orange shade for plot/scatter
* Use ArrayFire LOGO Dark Blue shader for vector field
---
 src/api/c/plot.cpp         | 3 ++-
 src/api/c/vector_field.cpp | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 0001cb6364..bf11ae7642 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -61,7 +61,8 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
 
     forge::Plot* plot = fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
 
-    plot->setColor(1.0, 0.0, 0.0, 1.0);
+    // ArrayFire LOGO Orange shade
+    plot->setColor(0.929f, 0.529f, 0.212f, 1.0);
 
     copy_plot<T>(in, plot);
 
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 9e22047800..5ca9755248 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -58,7 +58,8 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
 
     forge::VectorField* vectorfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
 
-    vectorfield->setColor(1.0, 1.0, 0.0, 1.0);
+    // ArrayFire LOGO dark blue shade
+    vectorfield->setColor(0.130f, 0.173f, 0.263f, 1.0);
 
     copy_vector_field<T>(pIn, dIn, vectorfield);
 

From a35caa538df1e0248c926176e397f64fbf26edc2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 12 Sep 2016 22:17:39 +0530
Subject: [PATCH 0926/2677] update vector field to show animated hilly bowl
 field

---
 examples/graphics/field.cpp | 48 +++++++++++++++++++++++--------------
 1 file changed, 30 insertions(+), 18 deletions(-)

diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
index e1c1ee1e73..e9886c655d 100644
--- a/examples/graphics/field.cpp
+++ b/examples/graphics/field.cpp
@@ -13,34 +13,47 @@
 
 using namespace af;
 
-const float MINIMUM = 1.0f;
-const float MAXIMUM = 20.f;
-const float STEP    = 2.0f;
-const float NELEMS  = (MAXIMUM-MINIMUM+1)/STEP;
+const static float MINIMUM = -3.0f;
+const static float MAXIMUM =  3.0f;
+const static float STEP = 0.18f;
 
 int main(int argc, char *argv[])
 {
     try {
-        // Initialize the kernel array just once
         af::info();
         af::Window myWindow(1024, 1024, "2D Vector Field example: ArrayFire");
 
-        float h_divPoints[] = {5, 5, 15, 15,
-                               5, 15, 5, 15};
-        array divPoints(4, 2, h_divPoints);
+        myWindow.grid(1, 2);
 
-        //array points = join(1, flat(range(dim4(10, 10)) * 2 + 1), flat(range(dim4(10, 10), 1) * 2 + 1));
-        array points = join(1, flat(range(dim4(10, 10), 1) * 2 + 1), flat(range(dim4(10, 10)) * 2 + 1));
-        array directions = sin(2 * Pi * points / 10.0f);
+        myWindow(0, 0).setAxesLimits(MINIMUM, MAXIMUM, MINIMUM, MAXIMUM);
+        myWindow(0, 1).setAxesLimits(MINIMUM, MAXIMUM, MINIMUM, MAXIMUM);
 
-        myWindow.setAxesLimits(points.col(0), points.col(1));
-        myWindow.setAxesTitles();
+        array dataRange = seq(MINIMUM, MAXIMUM, STEP);
 
-        while(!myWindow.close()) {
-            myWindow.scatter(divPoints, AF_MARKER_CIRCLE);
-            myWindow.vectorField(points, directions);
+        array x = tile(dataRange, 1, dataRange.dims(0));
+        array y = tile(dataRange.T(), dataRange.dims(0), 1);
+        x.eval();
+        y.eval();
+
+        float scale = 2.0f;
+        do {
+            array points = join(1, flat(x), flat(y));
+
+            array saddle = join(1, flat(x), -1.0f*flat(y));
+
+            array bvals  = sin(scale*(x*x + y*y));
+            array hbowl  = join(1, constant(1, x.elements()), flat(bvals));
+            hbowl.eval();
+
+            myWindow(0, 0).vectorField(points, saddle, "Saddle point");
+            myWindow(0, 1).vectorField(points, hbowl, "hilly bowl (in a loop with varying amplitude)");
             myWindow.show();
-        }
+
+            scale -= 0.0010f;
+            if (scale < -0.01f) {
+                scale = 2.0f;
+            }
+        } while(!myWindow.close());
 
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
@@ -48,4 +61,3 @@ int main(int argc, char *argv[])
     }
     return 0;
 }
-

From 0b869681f63176bae220c34de4392dbe904d3000 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 14:24:29 -0400
Subject: [PATCH 0927/2677] DOCS: Fix random engine and other random function
 documentation

* Create a function group under data_mat called random_func
* The randomEngine class is a part of random_func
* random_fun will appear under arrayfire functions
---
 docs/details/data.dox   |  39 -----------
 docs/details/random.dox | 103 +++++++++++++++++++++++++++++
 docs/details/util.dox   |  11 ----
 include/af/random.h     | 143 ++++++++++++++++++++++++----------------
 4 files changed, 190 insertions(+), 106 deletions(-)
 create mode 100644 docs/details/random.dox

diff --git a/docs/details/data.dox b/docs/details/data.dox
index 453612f19b..a5edfb3e9d 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -13,45 +13,6 @@ The array created has the same value at all locations
 
 =======================================================================
 
-\defgroup data_func_randu randu
-
-\brief Create a random array sampled from uniform distribution
-
-The data is uniformly distributed between [0, 1]
-
-\ingroup data_mat
-\ingroup arrayfire_func
-
-=======================================================================
-
-\defgroup data_func_randn randn
-
-\brief Create a random array sampled from a normal distribution
-
-The distribution is centered around 0
-
-\ingroup data_mat
-\ingroup arrayfire_func
-
-
-\defgroup data_func_setseed setSeed
-
-\brief Set the seed for random number generator
-
-
-\ingroup data_mat
-\ingroup arrayfire_func
-
-
-\defgroup data_func_getseed getSeed
-
-\brief Get the seed for random number generator
-
-
-\ingroup data_mat
-\ingroup arrayfire_func
-
-
 \defgroup data_func_identity identity
 
 \brief Create an identity array with diagonal values 1
diff --git a/docs/details/random.dox b/docs/details/random.dox
new file mode 100644
index 0000000000..17bada453a
--- /dev/null
+++ b/docs/details/random.dox
@@ -0,0 +1,103 @@
+
+/**
+\addtogroup arrayfire_func
+@{
+
+\defgroup random_mat Random Number Generation
+
+\brief Random Number Generation Functions
+
+Functions to generate and manage random numbers and random number engines
+
+\ingroup data_mat
+
+===============================================================================
+
+\defgroup random_engine_class randomEngine
+
+\brief Random Number Engine Generation Class
+
+The \ref af::randomEngine class is used to set the type and seed of random
+number generation engine based on \ref af::randomEngineType.
+
+\ingroup random_mat
+
+===============================================================================
+
+\defgroup random_engine_func_constructor randomEngine Constructors
+
+\brief Create random number generator object
+
+A \ref af::randomEngine object can be used to generate psuedo random numbers
+using various types of random number generation algorithms defined by \ref
+af::randomEngineType.
+
+\ingroup random_engine_class
+
+===============================================================================
+
+\defgroup random_func_randu randu
+
+\brief Create a random array sampled from uniform distribution.
+
+The type of engine used is defined by \ref af::randomEngine.
+
+The data is uniformly distributed between [0, 1].
+
+\ingroup random_mat
+
+===============================================================================
+
+\defgroup random_func_randn randn
+
+\brief Create a random array sampled from normal distribution.
+
+The type of engine used is defined by \ref af::randomEngine.
+
+The data is centered around 0.
+
+\ingroup random_mat
+
+===============================================================================
+
+\defgroup random_func_set_type setDefaultRandomEngineType
+
+\brief Set the default random engine type.
+
+This random engine type is used when calling random number functions without
+an \ref af::randomEngine object as an argument.
+
+\ingroup random_mat
+
+===============================================================================
+
+\defgroup random_func_get_default_engine getDefaultRandomEngine
+
+\brief Returns the default random engine object.
+
+Returns the \ref af::randomEngine that is currently set as default.
+
+\ingroup random_mat
+
+===============================================================================
+
+\defgroup random_func_set_seed setSeed
+
+\brief Set the seed for random number generation
+
+Sets the seed for the current default random engine.
+
+\ingroup random_mat
+
+===============================================================================
+
+\defgroup random_func_get_seed getSeed
+
+\brief Returns the seed for random number generation
+
+Returns the seed for the current default random engine.
+
+\ingroup random_mat
+
+@}
+*/
diff --git a/docs/details/util.dox b/docs/details/util.dox
index a0a81008e6..ec8a01ee6e 100644
--- a/docs/details/util.dox
+++ b/docs/details/util.dox
@@ -132,17 +132,6 @@ tag is unique or not.
 \ingroup dataio_mat
 \ingroup arrayfire_func
 
-=======================================================================
-
-\defgroup data_func_randn randn
-
-\brief Create a random array sampled from a normal distribution
-
-The distribution is centered around 0
-
-\ingroup data_mat
-\ingroup arrayfire_func
-
 @}
 */
 
diff --git a/include/af/random.h b/include/af/random.h
index 1e2a2bdb98..f606cb3511 100644
--- a/include/af/random.h
+++ b/include/af/random.h
@@ -10,6 +10,11 @@
 #pragma once
 #include <af/defines.h>
 
+///
+/// \brief Handle for random engine
+///
+/// This handle is used to reference the internal random engine object.
+///
 typedef void * af_random_engine;
 
 #ifdef __cplusplus
@@ -20,117 +25,125 @@ namespace af
 #if AF_API_VERSION >= 34
     ///
     /// \brief A random number generator class
+    /// \ingroup random_mat
     ///
-    class AFAPI randomEngine {
+    class AFAPI randomEngine
+    {
         private:
+            ///
+            /// \brief Handle to the interal random engine object
+            ///
+            /// \ingroup random_engine_class
+            ///
             af_random_engine engine;
         public:
             /**
-                \ingroup construct_random
-                @{
-            */
-            /**
-                Create random number generator object
+                This function creates a \ref af::randomEngine object with a
+                \ref af::randomEngineType and a seed.
 
                 \code
                 randomEngine r(AF_RANDOM_ENGINE_DEFAULT, 1);   // creates random engine of default type with seed = 1
                 \endcode
+
+                \ingroup random_engine_func_constructor
             */
             explicit
             randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT, uintl seedIn = 0);
+
             /**
-                Creates a copy of the random engine object
+                Copy constructor for \ref af::randomEngine.
+
                 \param in The input random engine object
+
+                \ingroup random_engine_func_constructor
             */
             randomEngine(const randomEngine& in);
-            /**
-                @}
-            */
 
             /**
-               Creates a copy of the random engine object
-               \param engine The input random engine object
+                Creates a copy of the random engine object from a \ref af_random_engine handle.
+
+                \param engine The input random engine object
+
+                \ingroup random_engine_func_constructor
             */
             randomEngine(af_random_engine engine);
+
             /**
-               @}
-            */
+                \defgroup random_engine_destructor ~randomEngine
+
+                \brief Destructor for \ref af::randomEngine
 
+                \ingroup random_engine_class
+            */
             ~randomEngine();
 
             /**
-                \ingroup random_operator_eq
-                @{
+                \defgroup random_engine_operator_eq operator=
+
                 \brief Assigns the internal state of randome engine
 
                 \param[in] in The object to be assigned to the random engine
+
                 \returns the reference to this
 
+                \ingroup random_engine_class
             */
             randomEngine& operator= (const randomEngine& in);
-            /**
-                @}
-            */
 
             /**
-                \ingroup random_set_type
-                @{
+                \defgroup random_engine_set_type setType
+
                 \brief Sets the random type of the random engine
 
                 \param[in] type The type of the random number generator
+
+                \ingroup random_engine_class
             */
             void setType(const randomEngineType type);
-            /**
-                @}
-            */
 
             /**
-                \ingroup random_get_type
-                @{
+                \defgroup random_engine_get_type getType
+
                 \brief Return the random type of the random engine
 
-                \returns the random type enum associated with random engine
+                \returns the \ref af::randomEngineType associated with random engine
+
+                \ingroup random_engine_class
             */
             randomEngineType getType(void);
-            /**
-                @}
-            */
 
             /**
-                \ingroup random_set_seed
-                @{
+                \defgroup random_engine_set_seed setSeed
+
                 \brief Sets the seed of the random engine
 
                 \param[in] seed The initializing seed of the random number generator
+
+                \ingroup random_engine_class
             */
             void setSeed(const uintl seed);
-            /**
-                @}
-            */
 
             /**
-                \ingroup random_get_seed
-                @{
+                \defgroup random_engine_get_seed getSeed
+
                 \brief Returns the seed of the random engine
 
                 \returns the seed associated with random engine
+
+                \ingroup random_engine_class
             */
             uintl getSeed(void) const;
-            /**
-                @}
-            */
 
             /**
-                \ingroup random_get
-                @{
+                \defgroup random_engine_get_handle get
+
                 \brief Returns the internal state of the random engine
 
-                \returns the internal state associated with random engine
+                \returns the handle to the internal state associated with random engine
+
+                \ingroup random_engine_class
             */
             af_random_engine get(void) const;
-            /**
-                @}
-            */
     };
 #endif
 
@@ -289,11 +302,9 @@ namespace af
 
 #if AF_API_VERSION >= 34
     /**
-        Returns the default random engine
-
-        \returns the \ref randomEngine object for the default random engine
+        \returns the \ref af::randomEngine object for the default random engine
 
-        \ingroup random_func_get_type
+        \ingroup random_func_get_default_engine
     */
     AFAPI randomEngine getDefaultRandomEngine(void);
 #endif
@@ -301,14 +312,14 @@ namespace af
     /**
         \param[in] seed A 64 bit unsigned integer
 
-        \ingroup random_func_setseed
+        \ingroup random_func_set_seed
     */
     AFAPI void setSeed(const uintl seed);
 
     /**
         \returns seed A 64 bit unsigned integer
 
-        \ingroup random_func_getseed
+        \ingroup random_func_get_seed
     */
     AFAPI uintl getSeed();
 
@@ -328,6 +339,8 @@ extern "C" {
        \param[in]   seed The initializing seed of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_func_constructor
     */
     AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_engine_type rtype, uintl seed);
 #endif
@@ -340,6 +353,8 @@ extern "C" {
        \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_func_constructor
     */
     AFAPI af_err af_retain_random_engine(af_random_engine *out, const af_random_engine engine);
 #endif
@@ -352,6 +367,8 @@ extern "C" {
        \param[in]   rtype The type of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_set_type
     */
     AFAPI af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype);
 #endif
@@ -364,6 +381,8 @@ extern "C" {
        \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_get_type
     */
     AFAPI af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine);
 #endif
@@ -379,6 +398,8 @@ extern "C" {
        \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_func_randu
     */
     AFAPI af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
 #endif
@@ -394,6 +415,8 @@ extern "C" {
        \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_func_randn
     */
     AFAPI af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
 #endif
@@ -406,6 +429,8 @@ extern "C" {
        \param[in]   seed The initializing seed of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_set_seed
     */
     AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed);
 #endif
@@ -417,6 +442,8 @@ extern "C" {
        \param[out]  engine The pointer to returned default random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_func_get_default_engine
     */
     AFAPI af_err af_get_default_random_engine(af_random_engine *engine);
 #endif
@@ -428,6 +455,8 @@ extern "C" {
        \param[in]   rtype The type of the random number generator
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_func_set_type
     */
     AFAPI af_err af_set_default_random_engine_type(const af_random_engine_type rtype);
 #endif
@@ -440,6 +469,8 @@ extern "C" {
        \param[in]   engine The random engine object
 
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_get_type
     */
     AFAPI af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine);
 #endif
@@ -450,12 +481,12 @@ extern "C" {
 
        \param[in] engine The random engine object
        \returns \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup random_engine_destructor
     */
     AFAPI af_err af_release_random_engine(af_random_engine engine);
 #endif
 
-    //General rand calls
-
     /**
         \param[out] out The generated array
         \param[in] ndims Size of dimension array \p dims
@@ -479,14 +510,14 @@ extern "C" {
     /**
         \param[in] seed A 64 bit unsigned integer
 
-        \ingroup random_func_setseed
+        \ingroup random_func_set_seed
     */
     AFAPI af_err af_set_seed(const uintl seed);
 
     /**
         \param[out] seed A 64 bit unsigned integer
 
-        \ingroup random_func_getseed
+        \ingroup random_func_get_seed
     */
     AFAPI af_err af_get_seed(uintl *seed);
 

From 904a0b21b305ef0c2b69d94dfd16755cd3884fa8 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 15:41:21 -0400
Subject: [PATCH 0928/2677] BUGFIX: Fixing bug in CPU TNJ

---
 src/backend/cpu/TNJ/BinaryNode.hpp | 8 +++-----
 src/backend/cpu/TNJ/BufferNode.hpp | 4 +---
 src/backend/cpu/TNJ/UnaryNode.hpp  | 6 ++----
 3 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index 97e65890a3..1f6d704799 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -80,11 +80,9 @@ namespace TNJ
 
         void reset()
         {
-            if (m_is_eval) {
-                resetCommonFlags();
-                m_lhs->reset();
-                m_rhs->reset();
-            }
+            resetCommonFlags();
+            m_lhs->reset();
+            m_rhs->reset();
         }
 
         bool isLinear(const dim_t *dims)
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index 9a5af60114..3e5a67366c 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -87,9 +87,7 @@ namespace TNJ
 
         void reset()
         {
-            if (m_is_eval) {
-                resetCommonFlags();
-            }
+            resetCommonFlags();
         }
 
         bool isLinear(const dim_t *dims)
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 5601c01ed1..3edf399ec5 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -75,10 +75,8 @@ namespace TNJ
 
         void reset()
         {
-            if (m_is_eval) {
-                resetCommonFlags();
-                m_child->reset();
-            }
+            resetCommonFlags();
+            m_child->reset();
         }
 
         bool isLinear(const dim_t *dims)

From c36f51ac06096e9513c7188ff339c6133e433164 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 16:05:48 -0400
Subject: [PATCH 0929/2677] DOC: Added documentation structure for scan and
 scan by key

---
 docs/details/algorithm.dox | 28 ++++++++++++++++++++++++++++
 include/af/algorithm.h     |  4 ++--
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index a823572b59..27ef55ef48 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -134,6 +134,34 @@ The locations are provided by flattening the input into a linear array.
 
 
 
+\defgroup scan_func_scan scan
+
+\ingroup scan_mat
+
+Inclusive or exclusive scan of an array
+
+Perform inclusive or exclusive scan using a given binary operation along a
+given dimension.
+
+Binary operations can be [add](\ref AF_BINARY_ADD), [mul](\ref AF_BINARY_MUL),
+[min](\ref AF_BINARY_MIN), [max](\ref AF_BINARY_MAX) as defined by \ref af_binary_op.
+
+
+
+\defgroup scan_func_scanbykey scanByKey
+
+\ingroup scan_mat
+
+Inclusive or exclusive scan of an array by key
+
+Perform inclusive or exclusive scan using a given binary operation along a
+given dimension using a key.
+
+Binary operations can be [add](\ref AF_BINARY_ADD), [mul](\ref AF_BINARY_MUL),
+[min](\ref AF_BINARY_MIN), [max](\ref AF_BINARY_MAX) as defined by \ref af_binary_op.
+
+
+
 \defgroup calc_func_diff1 diff1
 
 \ingroup calc_mat
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 62afdd5b03..39d948fc20 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -340,7 +340,7 @@ namespace af
        \param[in] inclusive_scan is flag specifying whether scan is inclusive
        \return the output containing scan of the input
 
-       \ingroup scan_func_scan
+       \ingroup scan_func_scanbykey
     */
     AFAPI array scanByKey(const array &key, const array& in, const int dim = 0, binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
 #endif
@@ -798,7 +798,7 @@ extern "C" {
        \param[in] inclusive_scan is flag specifying whether scan is inclusive
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup scan_func_scan
+       \ingroup scan_func_scanbykey
     */
     AFAPI af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan);
 #endif

From 36e3731497c30298ff728e143e112155f8cfec99 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 17:18:12 -0400
Subject: [PATCH 0930/2677] PERF improvements and bugfixes for select and
 replace

- Performance improvements to CUDA backend
- Bugs fixed in OpenCL backend
---
 src/backend/cuda/kernel/select.hpp   | 63 ++++++++++++++++++----------
 src/backend/opencl/kernel/select.cl  | 56 ++++++++++++++++---------
 src/backend/opencl/kernel/select.hpp |  5 ++-
 3 files changed, 81 insertions(+), 43 deletions(-)

diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index ea242e45dd..44fb8d1d1b 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -20,15 +20,15 @@ namespace cuda
 
         static const uint DIMX = 32;
         static const uint DIMY =  8;
+        static const int REPEAT = 64;
 
         __device__ __host__
-        int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims)
+        int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4])
         {
             int off = 0;
-            off += (dims[3] == refdims[3]) * strides[3];
-            off += (dims[2] == refdims[2]) * strides[2];
-            off += (dims[1] == refdims[1]) * strides[1];
-            off += (dims[0] == refdims[0]);
+            off += ids[3] * (dims[3] == refdims[3]) * strides[3];
+            off += ids[2] * (dims[2] == refdims[2]) * strides[2];
+            off += ids[1] * (dims[1] == refdims[1]) * strides[1];
             return off;
         }
 
@@ -40,16 +40,24 @@ namespace cuda
             const int idz = blockIdx.x / blk_x;
             const int idw = blockIdx.y / blk_y;
 
+
             const int blockIdx_x = blockIdx.x - idz * blk_x;
             const int blockIdx_y = blockIdx.y - idw * blk_y;
 
-            const int idx = blockIdx_x * blockDim.x + threadIdx.x;
             const int idy = blockIdx_y * blockDim.y + threadIdx.y;
+            const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
 
-            const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1] + idx;
+            if (idw >= out.dims[3] ||
+                idz >= out.dims[2] ||
+                idy >= out.dims[1])  {
+                return;
+            }
 
+            const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
             T *optr = out.ptr + off;
 
+            int ids[] = {idx0, idy, idz, idw};
+
             const T *aptr = a.ptr;
             const T *bptr = b.ptr;
             const char *cptr = cond.ptr;
@@ -58,14 +66,19 @@ namespace cuda
                 aptr += off;
                 bptr += off;
                 cptr += off;
+                for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+                    optr[idx] = cptr[idx] ? aptr[idx] : bptr[idx];
+                }
             } else {
-                aptr += getOffset(a.dims, a.strides, out.dims);
-                bptr += getOffset(b.dims, b.strides, out.dims);
-                cptr += getOffset(cond.dims, cond.strides, out.dims);
-            }
-
-            if (idx < out.dims[0] && idy < out.dims[1] && idz < out.dims[2] && idw < out.dims[3]) {
-                *optr = (*cptr) ? *aptr : *bptr;
+                aptr += getOffset(a.dims, a.strides, out.dims, ids);
+                bptr += getOffset(b.dims, b.strides, out.dims, ids);
+                cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
+                bool csame = cond.dims[0] == out.dims[0];
+                bool asame = a.dims[0] == out.dims[0];
+                bool bsame = b.dims[0] == out.dims[0];
+                for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+                    optr[idx] = cptr[csame * idx] ? aptr[asame * idx] : bptr[bsame * idx];
+                }
             }
         }
 
@@ -84,7 +97,7 @@ namespace cuda
                 threads.y = 1;
             }
 
-            int blk_x = divup(out.dims[0], threads.x);
+            int blk_x = divup(out.dims[0], REPEAT * threads.x);
             int blk_y = divup(out.dims[1], threads.y);
 
 
@@ -112,10 +125,10 @@ namespace cuda
             const int blockIdx_x = blockIdx.x - idz * blk_x;
             const int blockIdx_y = blockIdx.y - idw * blk_y;
 
-            const int idx = blockIdx_x * blockDim.x + threadIdx.x;
+            const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
             const int idy = blockIdx_y * blockDim.y + threadIdx.y;
 
-            const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1] + idx;
+            const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
 
             T *optr = out.ptr + off;
 
@@ -125,8 +138,14 @@ namespace cuda
             aptr += off;
             cptr += off;
 
-            if (idx < out.dims[0] && idy < out.dims[1] && idz < out.dims[2] && idw < out.dims[3]) {
-                *optr = ((*cptr) ^ flip) ? *aptr : b;
+            if (idw >= out.dims[3] ||
+                idz >= out.dims[2] ||
+                idy >= out.dims[1]) {
+                return;
+            }
+
+            for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+                optr[idx] = ((cptr[idx]) ^ flip) ? aptr[idx] : b;
             }
         }
 
@@ -140,12 +159,12 @@ namespace cuda
                 threads.y = 1;
             }
 
-            int blk_x = divup(out.dims[0], threads.x);
+            int blk_x = divup(out.dims[0], REPEAT * threads.x);
             int blk_y = divup(out.dims[1], threads.y);
 
 
-            dim3 blocks(blk_x * threads.x,
-                        blk_y * threads.y);
+            dim3 blocks(blk_x * out.dims[2],
+                        blk_y * out.dims[3]);
 
             CUDA_LAUNCH((select_scalar_kernel<T, flip>), blocks, threads,
                         out, cond, a, scalar<T>(b), blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/select.cl b/src/backend/opencl/kernel/select.cl
index 03248be1b9..f6b92cb637 100644
--- a/src/backend/opencl/kernel/select.cl
+++ b/src/backend/opencl/kernel/select.cl
@@ -15,13 +15,12 @@
 #define is_same 0
 #endif
 
-int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims)
+int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4])
 {
     int off = 0;
-    off += (dims[3] == refdims[3]) * strides[3];
-    off += (dims[2] == refdims[2]) * strides[2];
-    off += (dims[1] == refdims[1]) * strides[1];
-    off += (dims[0] == refdims[0]);
+    off += ids[3] * (dims[3] == refdims[3]) * strides[3];
+    off += ids[2] * (dims[2] == refdims[2]) * strides[2];
+    off += ids[1] * (dims[1] == refdims[1]) * strides[1];
     return off;
 }
 
@@ -43,10 +42,18 @@ void select_kernel(__global T *optr, KParam oinfo,
     const int group_id_0 = get_group_id(0) - idz * groups_0;
     const int group_id_1 = get_group_id(1) - idw * groups_1;
 
-    const int idx = group_id_0 * get_local_size(0) + get_local_id(0);
+    const int idx0 = group_id_0 * get_local_size(0) + get_local_id(0);
     const int idy = group_id_1 * get_local_size(1) + get_local_id(1);
 
-    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1] + idx;
+    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1];
+
+    if (idw >= oinfo.dims[3] ||
+        idz >= oinfo.dims[2] ||
+        idy >= oinfo.dims[1])  {
+        return;
+    }
+
+    int ids[] = {idx0, idy, idz, idw};
 
     optr += off;
 
@@ -54,14 +61,19 @@ void select_kernel(__global T *optr, KParam oinfo,
         aptr += off;
         bptr += off;
         cptr += off;
+        for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
+            optr[idx] = (cptr[idx]) ? aptr[idx] : bptr[idx];
+        }
     } else {
-        aptr += getOffset(ainfo.dims, ainfo.strides, oinfo.dims);
-        bptr += getOffset(binfo.dims, binfo.strides, oinfo.dims);
-        cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims);
-    }
-
-    if (idx < oinfo.dims[0] && idy < oinfo.dims[1] && idz < oinfo.dims[2] && idw < oinfo.dims[3]) {
-        *optr = (*cptr) ? *aptr : *bptr;
+        aptr += getOffset(ainfo.dims, ainfo.strides, oinfo.dims, ids);
+        bptr += getOffset(binfo.dims, binfo.strides, oinfo.dims, ids);
+        cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims, ids);
+        bool csame = cinfo.dims[0] == oinfo.dims[0];
+        bool asame = ainfo.dims[0] == oinfo.dims[0];
+        bool bsame = binfo.dims[0] == oinfo.dims[0];
+        for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
+            optr[idx] = (cptr[csame * idx]) ? aptr[asame * idx] : bptr[bsame * idx];
+        }
     }
 }
 
@@ -82,16 +94,22 @@ void select_scalar_kernel(__global T *optr, KParam oinfo,
     const int group_id_0 = get_group_id(0) - idz * groups_0;
     const int group_id_1 = get_group_id(1) - idw * groups_1;
 
-    const int idx = group_id_0 * get_local_size(0) + get_local_id(0);
-    const int idy = group_id_1 * get_local_size(1) + get_local_id(1);
+    const int idx0 = group_id_0 * get_local_size(0) + get_local_id(0);
+    const int idy  = group_id_1 * get_local_size(1) + get_local_id(1);
 
-    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1] + idx;
+    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1];
 
     optr += off;
     aptr += off;
     cptr += off;
 
-    if (idx < oinfo.dims[0] && idy < oinfo.dims[1] && idz < oinfo.dims[2] && idw < oinfo.dims[3]) {
-        *optr = ((*cptr) ^ flip) ? *aptr : b;
+    if (idw >= oinfo.dims[3] ||
+        idz >= oinfo.dims[2] ||
+        idy >= oinfo.dims[1])  {
+        return;
+    }
+
+    for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
+        optr[idx] = (cptr[idx] ^ flip) ? aptr[idx] : b;
     }
 }
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index a0271712b5..3d474debd7 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -34,6 +34,7 @@ namespace opencl
     {
         static const uint DIMX = 32;
         static const uint DIMY =  8;
+        static const int REPEAT = 64;
 
         template<typename T, bool is_same>
         void select_launcher(Param out, Param cond, Param a, Param b, int ndims)
@@ -74,7 +75,7 @@ namespace opencl
                           threads[1]);
 
 
-            int groups_0 = divup(out.info.dims[0], local[0]);
+            int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
             int groups_1 = divup(out.info.dims[1], local[1]);
 
             NDRange global(groups_0 * out.info.dims[2] * local[0],
@@ -152,7 +153,7 @@ namespace opencl
             NDRange local(threads[0],
                           threads[1]);
 
-            int groups_0 = divup(out.info.dims[0], local[0]);
+            int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
             int groups_1 = divup(out.info.dims[1], local[1]);
 
             NDRange global(groups_0 * out.info.dims[2] * local[0],

From af8181304eaf8d5db32eb4ef0a1fa04eb4171921 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 17:23:44 -0400
Subject: [PATCH 0931/2677] TEST: Add additional test for JIT

The test code is stripped down version of field.cpp example
that showcased the bug in CPU backend
---
 test/jit.cpp | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/test/jit.cpp b/test/jit.cpp
index 35e5ff61a4..047326cb58 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -240,3 +240,31 @@ TEST(JIT, CPP_Multi_pre_eval)
         ASSERT_EQ((ha[i] - hb[i]), hy[i]);
     }
 }
+
+TEST(JIT, CPP_common_node)
+{
+    af::array r = seq(-3, 3, 0.5);
+
+    int n = r.dims(0);
+
+    af::array x = af::tile(r, 1, r.dims(0));
+    af::array y = af::tile(r.T(), r.dims(0), 1);
+    x.eval();
+    y.eval();
+
+
+    std::vector<float> hx(x.elements());
+    std::vector<float> hy(y.elements());
+    std::vector<float> hr(r.elements());
+
+    x.host(&hx[0]);
+    y.host(&hy[0]);
+    r.host(&hr[0]);
+
+    for (int j = 0; j < n; j++) {
+        for (int i = 0; i < n; i++) {
+            ASSERT_EQ(hx[j * n + i], hr[i]);
+            ASSERT_EQ(hy[j * n + i], hr[j]);
+        }
+    }
+}

From 698c6fd4ed214fbee2421b7bbbd1c7dd0d4530db Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 12 Sep 2016 17:31:10 -0400
Subject: [PATCH 0932/2677] CPU: Turn CPUID support on by default

---
 src/backend/cpu/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 9559099351..79c80cc26d 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -20,6 +20,7 @@ IF(NOT DEFINED BUILD_CPU_ASYNC)
 
     CMAKE_POLICY(POP)
 ENDIF(NOT DEFINED BUILD_CPU_ASYNC)
+SET(USE_CPUID ON CACHE BOOL "Build with CPUID integration")
 MARK_AS_ADVANCED(USE_CPUID)
 
 if (USE_CPUID)

From acc51fdaa242d0a21f290fb72efc9f9868342f18 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 21:43:50 -0400
Subject: [PATCH 0933/2677] Change build tag for clBLAS to arrayfire-release

---
 CMakeModules/build_clBLAS.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 76a65d1658..7a211aa32a 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG arrayfire-release-test
+    GIT_TAG arrayfire-release
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 3fee3cb97c1c1dc2cd479701ff1dc7d4c5d0ffeb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 21:43:59 -0400
Subject: [PATCH 0934/2677] Change build tag for clFFT to arrayfire-release

---
 CMakeModules/build_clFFT.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index e9b3ea979a..aa89452d86 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -14,7 +14,7 @@ ENDIF()
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG arrayfire-release-test
+    GIT_TAG arrayfire-release
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 75505467b04279e5151b8f09e021f0e9a4a588a5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 21:44:22 -0400
Subject: [PATCH 0935/2677] Change build tag for forge to v0.9.0

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 61f5df39e3..9e9b184425 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -46,7 +46,7 @@ ENDIF()
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG devel
+    GIT_TAG v0.9.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From e476e71be0ff7dce1837f7a9b58b7eb8a944db6f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 12 Sep 2016 21:41:58 -0400
Subject: [PATCH 0936/2677] Update release notes for v3.4.0

---
 docs/pages/release_notes.md | 220 +++++++++++++++++++++++++++---------
 1 file changed, 168 insertions(+), 52 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 78622a23ce..bdcd91158a 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,73 +1,189 @@
 Release Notes {#releasenotes}
 ==============
+
 v3.4.0
 ==============
 
+Major Updates
+-------------
+* [Sparse Matrix and BLAS](\ref sparse_func). <sup>[1](https://github.com/arrayfire/arrayfire/issues/821)
+  [2](https://github.com/arrayfire/arrayfire/pull/1319)</sup>
+* Faster JIT for CUDA and OpenCL. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1472)
+  [2](https://github.com/arrayfire/arrayfire/pull/1462)</sup>
+* Support for [random number generator engines](\ref af::randomEngine).
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/868)
+  [2](https://github.com/arrayfire/arrayfire/pull/1551)</sup>
+* Improvements to graphics. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1555)
+  [2](https://github.com/arrayfire/arrayfire/pull/1566)</sup>
+
 Features
 ----------
-* New [interpolation methods](https://github.com/arrayfire/arrayfire/issues/1562) for \ref af::resize(), \ref af::transform(), \ref af::approx1() and \ref af::approx2()
-* Support for [complex mathematical functions](\ref mathfunc_mat)
-* New Forge graphics [integration](https://github.com/arrayfire/arrayfire/pull/1555)!
-    * Vector Field plotting functionality
-    * API updates
-    * Removed GLEW and replaced with glbinding. (And links to both GLEW and glbinding)
-    * Multiple overlays on the same window are now possible.
-    * New API to set axes limits for graphs.
-        * Draw calls do not automatically compute the limits. This is now under user control.
-    * New API to set axes titles.
-    * New API for plot and scatter:
-        * \ref plot() and \ref scatter() now can handle 2D and 3D
-        * \ref af_draw_plot_nd
-        * \ref af_draw_plot_2d
-        * \ref af_draw_plot_3d
-        * \ref af_draw_scatter_nd
-        * \ref af_draw_scatter_2d
-        * \ref af_draw_scatter_3d
-
-* \ref af::medfilt1(): [Median filter for 1-d signals](https://github.com/arrayfire/arrayfire/pull/1479)
-* af::RandomEngine(): New [random number generators](https://github.com/arrayfire/arrayfire/issues/868)
-    * Philox
-    * Threefry
-    * Mersenne Twister
-* \ref af::sparse(): [Sparse matrix support for all backends](https://github.com/arrayfire/arrayfire/issues/821)
-* \ref af::scan(): New [generalized scan](https://github.com/arrayfire/arrayfire/issues/388) functions
-* \ref af::moments(): New [image moments](\ref moments_mat) functions
+* **[Sparse Matrix and BLAS](\ref sparse_func)** <sup>[1](https://github.com/arrayfire/arrayfire/issues/821)
+[2](https://github.com/arrayfire/arrayfire/pull/1319)</sup>
+  * Support for [CSR](\ref AF_STORAGE_CSR) and [COO](\ref AF_STORAGE_COO)
+    [storage types](\ref af_storage).
+  * Sparse-Dense Matrix Multiplication and Matrix-Vector Multiplication as a
+    part of af::matmul() using \ref AF_STORAGE_CSR format for sparse.
+  * Conversion to and from [dense](\ref AF_STORAGE_DENSE) matrix to [CSR](\ref AF_STORAGE_CSR)
+    and [COO](\ref AF_STORAGE_COO) [storage types](\ref af_storage).
+* **Faster JIT** <sup>[1](https://github.com/arrayfire/arrayfire/issues/1472)
+  [2](https://github.com/arrayfire/arrayfire/pull/1462)</sup>
+  * Performance improvements for CUDA and OpenCL JIT functions.
+  * Support for evaluating multiple outputs in a single kernel. See af::array::eval() for more.
+* **[Random Number Generation](\ref af::randomEngine)**
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/868)
+  [2](https://github.com/arrayfire/arrayfire/pull/1551)</sup>
+  * af::randomEngine(): A random engine class to handle setting the [type](af_random_type) and seed
+    for random number generator engines.
+  * Supported engine types are (\ref af_random_engine_type):
+    * [Philox](http://www.thesalmons.org/john/random123/)
+    * [Threefry](http://www.thesalmons.org/john/random123/)
+    * [Mersenne Twister](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MTGP/)
+* **Graphics** <sup>[1](https://github.com/arrayfire/arrayfire/pull/1555)
+  [2](https://github.com/arrayfire/arrayfire/pull/1566)</sup>
+  * Using [Forge v0.9.0](https://github.com/arrayfire/forge/releases/tag/v0.9.0)
+  * [Vector Field](\ref af::Window::vectorField) plotting functionality.
+    <sup>[1](https://github.com/arrayfire/arrayfire/pull/1566)</sup>
+  * Removed [GLEW](http://glew.sourceforge.net/) and replaced with [glbinding](https://github.com/cginternals/glbinding).
+    * Removed usage of GLEW after support for MX (multithreaded) was dropped in v2.0.
+      <sup>[1](https://github.com/arrayfire/arrayfire/issues/1540)</sup>
+  * Multiple overlays on the same window are now possible.
+    * Overlays support for same type of object (2D/3D)
+    * Supported by af::Window::plot, af::Window::hist, af::Window::surface,
+      af::Window::vectorField.
+  * New API to set axes limits for graphs.
+    * Draw calls do not automatically compute the limits. This is now under user control.
+    * af::Window::setAxesLimits can be used to set axes limits automatically or manually.
+    * af::Window::setAxesTitles can be used to set axes titles.
+  * New API for plot and scatter:
+    * af::Window::plot() and af::Window::scatter() now can handle 2D and 3D and determine appropriate order.
+    * af_draw_plot_nd()
+    * af_draw_plot_2d()
+    * af_draw_plot_3d()
+    * af_draw_scatter_nd()
+    * af_draw_scatter_2d()
+    * af_draw_scatter_3d()
+* **New [interpolation methods](\ref af_interp_type)**
+<sup>[1](https://github.com/arrayfire/arrayfire/issues/1562)</sup>
+  * Applies to
+    * \ref af::resize()
+    * \ref af::transform()
+    * \ref af::approx1()
+    * \ref af::approx2()
+* **Support for [complex mathematical functions](\ref mathfunc_mat)**
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1507)</sup>
+  * Add complex support for \ref trig_mat, \ref af::sqrt(), \ref af::log().
+* **af::medfilt1(): Median filter for 1-d signals** <sup>[1](https://github.com/arrayfire/arrayfire/pull/1479)</sup>
+* <b>Generalized scan functions: \ref scan_func_scan and \ref scan_func_scanbykey</b>
+  * Now supports inclusive or exclusive scans
+  * Supports binary operations defined by \ref af_binary_op.
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/388)</sup>
+* **[Image Moments](\ref moments_mat) functions**
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1453)</sup>
+* <b>Add af::getSizeOf() function for \ref af_dtype</b>
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1404)</sup>
+* <b>Explicitly extantiate \ref af::array::device() for `void *</b>
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1503)</sup>
 
 Bug Fixes
 --------------
-* Fixes to edge-cases in [morphological operations.](https://github.com/arrayfire/arrayfire/issues/1564)
-* Makes JIT tree size [consistent between devices](https://github.com/arrayfire/arrayfire/issues/1457)
-* Delegate [higher-dimension convolutions](https://github.com/arrayfire/arrayfire/pull/1445) to correct dimension
-* Indexing fixes with [c++11x](https://github.com/arrayfire/arrayfire/pull/1426), [af_indexers](https://github.com/arrayfire/arrayfire/pull/1426), and [empty arrays](https://github.com/arrayfire/arrayfire/issues/799)
-* Single element [median bugfix](https://github.com/arrayfire/arrayfire/pull/1423)
-* Correct time from [timeit()](https://github.com/arrayfire/arrayfire/pull/1414) function
-* Fix floating point numbers in [af::seq and missing size_of() types](https://github.com/arrayfire/arrayfire/pull/1404)
-* Explicitly extantiate [af::device() for void * ](https://github.com/arrayfire/arrayfire/issues/1503)
+* Fixes to edge-cases in \ref morph_mat. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1564)</sup>
+* Makes JIT tree size consistent between devices. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1457)</sup>
+* Delegate higher-dimension in \ref convolve_mat to correct dimensions. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1445)</sup>
+* Indexing fixes with C++11. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1426) [2](https://github.com/arrayfire/arrayfire/pull/1426)</sup>
+* Handle empty arrays as inputs in various functions. <sup>[1](https://github.com/arrayfire/arrayfire/issues/799)</sup>
+* Fix bug when single element input to af::median. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1423)</sup>
+* Fix bug in calculation of time from af::timeit(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1414)</sup>
+* Fix bug in floating point numbers in af::seq. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1404)</sup>
+* Fixes for OpenCL graphics interop on NVIDIA devices.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1408/commits/e1f16e6)</sup>
+* Fix bug when compiling large kernels for AMD devices.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1465)</sup>
+* Fix bug in af::bilateral when shared memory is over the limit.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1478)</sup>
+* Fix bug in kernel header compilation tool `bin2cpp`.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1544)</sup>
+* Fix inital values for \ref morph_mat functions.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1547)</sup>
+* Fix bugs in af::homography() CPU and OpenCL kernels.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1584)</sup>
+* Fix bug in CPU TNJ.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1587)</sup>
+
 
 Improvements
 ------------
-* CUDA 8 and compute 6.x(Pascal) support, current installer still on 7.5
-* Improved [JIT](https://github.com/arrayfire/arrayfire/issues/1472) evaluation heuristics for CUDA and OpenCL
-* User controlled FFT plan caching
-* CUDA [speedups](https://github.com/arrayfire/arrayfire/pull/1411) for \ref wrap(), \ref unwrap() and [approx](\ref approx_mat).
-* Fallback for CUDA-OpenGL [interop](https://github.com/arrayfire/arrayfire/pull/1415)
-* Additional forms of batching with the \ref transform() function. [New behavior defined here.](https://github.com/arrayfire/arrayfire/pull/1412)
-* Update to [OpenCL2 headers](https://github.com/arrayfire/arrayfire/issues/1344) in backend
-* Support for interacting with [external OpenCL contexts](https://github.com/arrayfire/arrayfire/pull/1140)
+* CUDA 8 and compute 6.x(Pascal) support, current installer ships with CUDA 7.5. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1432) [2](https://github.com/arrayfire/arrayfire/pull/1487) [3](https://github.com/arrayfire/arrayfire/pull/1539)</sup>
+* User controlled FFT plan caching. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1448)</sup>
+* CUDA performance improvements for \ref image_func_wrap, \ref image_func_unwrap and \ref approx_mat.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1411)</sup>
+* Fallback for CUDA-OpenGL interop when no devices does not support OpenGL.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1415)</sup>
+* Additional forms of batching with the \ref transform_func_transform functions.
+  [New behavior defined here](https://github.com/arrayfire/arrayfire/pull/1412).
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1412)</sup>
+* Update to OpenCL2 headers. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1344)</sup>
+* Support for integration with external OpenCL contexts. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1140)</sup>
+* Performance improvements to interal copy in CPU Backend.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1440)</sup>
+* Performance improvements to af::select and af::replace CUDA kernels.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1587)</sup>
+* Enable OpenCL-CPU offload by default for devices with Unified Host Memory.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1521)</sup>
+  * To disable, use the environment variable `AF_OPENCL_CPU_OFFLOAD=0`.
 
 Build
 ------
-* Compilation [speedups](https://github.com/arrayfire/arrayfire/pull/1526)
-* Build [fixes](https://github.com/arrayfire/arrayfire/pull/1526) with MKL.
-* Error message when [CUDA Compute Detection fails](https://github.com/arrayfire/arrayfire/issues/1535).
-* Several CMake build issues with Xcode generator fixed
-* Fix [multiple OpenCL definitions](https://github.com/arrayfire/arrayfire/issues/1429) at link-time
-* [Boost compute version update and lapacke detection fix](https://github.com/arrayfire/arrayfire/pull/1423)
-* Fix builds [with GCC 6.1.1](https://github.com/arrayfire/arrayfire/pull/1409) 
+* Compilation speedups. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1526)</sup>
+* Build fixes with MKL. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1526)</sup>
+* Error message when CMake CUDA Compute Detection fails. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1535)</sup>
+* Several CMake build issues with Xcode generator fixed.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1493) [2](https://github.com/arrayfire/arrayfire/pull/1499)</sup>
+* Fix multiple OpenCL definitions at link time. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1429)</sup>
+* Fix lapacke detection in CMake. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1423)</sup>
+* Update build tags of
+  * [clBLAS](https://github.com/clMathLibraries/clBLAS)
+  * [clFFT](https://github.com/clMathLibraries/clFFT)
+  * [Boost.Compute](https://github.com/boostorg/compute)
+  * [Forge](https://github.com/arrayfire/forge)
+  * [glbinding](https://github.com/cginternals/glbinding)
+* Fix builds with GCC 6.1.1 and GCC 5.3.0. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1409)</sup>
 
-Documentation
+Installers
+----------
+* All installers now ship with ArrayFire libraries build with MKL 2016.
+* All installers now ship with Forge development files and examples included.
+* CUDA Compute 2.0 has been removed from the installers. Please contact us
+  directly if you have a special need.
+
+Examples
 -------------
-* Fixed grammar in license
+* Added [example simulating gravity](\ref graphics/field.cpp) for
+  demonstration of vector field.
+* Improvements to \ref financial/black_scholes_options.cpp example.
+* Improvements to \ref graphics/gravity_sim.cpp example.
+* Fix graphics examples to use af::Window::setAxesLimits and
+  af::Window::setAxesTitles functions.
+
+Documentation & Licensing
+-------------------------
+* [ArrayFire copyright and trademark policy](http://arrayfire.com/trademark-policy)
+* Fixed grammar in license.
+* Add license information for glbinding.
+* Remove license infomation for GLEW.
+* Random123 now applies to all backends.
+* Random number functions are now under \ref random_mat.
+
+Deprecations
+------------
+The following functions have been deprecated and may be modified or removed
+permanently from future versions of ArrayFire.
+* \ref af::Window::plot3(): Use \ref af::Window::plot instead.
+* \ref af_draw_plot(): Use \ref af_draw_plot_nd or \ref af_draw_plot_2d instead.
+* \ref af_draw_plot3(): Use \ref af_draw_plot_nd or \ref af_draw_plot_3d instead.
+* \ref af::Window::scatter3(): Use \ref af::Window::scatter instead.
+* \ref af_draw_scatter(): Use \ref af_draw_scatter_nd or \ref af_draw_scatter_2d instead.
+* \ref af_draw_scatter3(): Use \ref af_draw_scatter_nd or \ref af_draw_scatter_3d instead.
 
 Known Issues
 -------------

From 75d7133a5dd2d0743c125f51c0fe2f9bbd0dbb45 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 13 Sep 2016 16:32:16 -0400
Subject: [PATCH 0937/2677] Force update CUDA_LIBDEVICE_DIR when CUDA
 directories are updated

---
 src/backend/cuda/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8b890741ec..a3a63958e7 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -7,7 +7,7 @@ INCLUDE(CLKernelToH)
 INCLUDE(FindNVVM)
 
 OPTION(USE_LIBDEVICE "Use libdevice for CUDA JIT" ON)
-SET(CUDA_LIBDEVICE_DIR "${CUDA_NVVM_HOME}/libdevice" CACHE PATH "Path where libdevice compute files are located")
+SET(CUDA_LIBDEVICE_DIR "${CUDA_NVVM_HOME}/libdevice" CACHE PATH "Path where libdevice compute files are located" FORCE)
 
 MARK_AS_ADVANCED(
     CUDA_BUILD_CUBIN

From 76ab83f56f2fe4a47260851951cec253551387d5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 15 Sep 2016 14:22:45 +0530
Subject: [PATCH 0938/2677] BUGFIX: Add missing unified call for
 set_fft_plan_cache_size

---
 src/api/unified/signal.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index d4cc372d4a..22dff492bf 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -23,6 +23,11 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
     return CALL(out, in, pos0, pos1, method, offGrid);
 }
 
+af_err af_set_fft_plan_cache_size(size_t cache_size)
+{
+    return CALL(cache_size);
+}
+
 #define FFT_HAPI_DEF(af_func)\
 af_err af_func(af_array in, const double norm_factor)\
 {\

From 11b7bd3a44c07b06670f9415cfd05604cb957619 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 15 Sep 2016 10:57:58 -0400
Subject: [PATCH 0939/2677] BUGFIX: AARCH64 (TX1 64-bit OS) does not define
 __arm__ - Requires __aarch64__

---
 src/backend/cuda/platform.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 9578bce4d0..8a13993a12 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -242,7 +242,7 @@ string getDriverVersion()
     int x = nvDriverVersion(driverVersion, sizeof(driverVersion));
     if (x != 1) {
         // Windows, OSX, Tegra Need a new way to fetch driver
-        #if !defined(OS_WIN) && !defined(OS_MAC) && !defined(__arm__)
+        #if !defined(OS_WIN) && !defined(OS_MAC) && !defined(__arm__) && !defined(__aarch64__)
         throw runtime_error("Invalid driver");
         #endif
         int driver = 0;

From 333a31ad03ebfbd7a67e0e439542f5d09c148f56 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 15 Sep 2016 11:45:56 -0400
Subject: [PATCH 0940/2677] DOCS: Move INSTALL.md to install.md

---
 docs/pages/{INSTALL.md => install.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/pages/{INSTALL.md => install.md} (100%)

diff --git a/docs/pages/INSTALL.md b/docs/pages/install.md
similarity index 100%
rename from docs/pages/INSTALL.md
rename to docs/pages/install.md

From 00a8a038a082483eafee795955cbc468834f810d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 15 Sep 2016 12:02:09 -0400
Subject: [PATCH 0941/2677] DOCS: Remove unused packages from installation
 documentation

---
 docs/pages/install.md | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index d31affaefe..c22606ecba 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -1,4 +1,4 @@
-ArrayFire binary installation instructions {#installing}
+ArrayFire Binary Installation Instructions {#installing}
 =====
 
 Installing ArrayFire couldn't be easier. We ship installers for Windows,
@@ -67,7 +67,7 @@ Finally, verify that the path addition worked correctly. You can do this by:
 First install the prerequisite packages:
 
     # Prerequisite packages:
-    apt-get install libfreeimage-dev libatlas3gf-base libfftw3-dev libglew-dev libglewmx-dev libglfw3-dev cmake
+    apt-get install libglfw3-dev cmake
 
     # Enable GPU support (OpenCL):
     apt-get install ocl-icd-libopencl1
@@ -86,7 +86,7 @@ file, run the installer.
 First install the prerequisite packages:
 
     # Install prerequiste packages
-    yum install freeimage atlas fftw libGLEW libGLEWmx glfw cmake
+    yum install glfw cmake
 
 On Centos and Redhat the `glfw` package is outdated and you will need to compile
 it from source. Please
@@ -106,7 +106,7 @@ file, run the installer.
 First install the prerequisite packages:
 
     # Prerequisite packages:
-    sudo apt-get install libfreeimage-dev libatlas3gf-base libfftw3-dev cmake
+    sudo apt-get install cmake
 
 Ubuntu 14.04 will not have the libglfw3-dev package in its repositories. You can either build the
 library from source (following the
@@ -131,6 +131,13 @@ with any drivers required for your hardware.
     # Enable GPU support (OpenCL):
     apt-get install ocl-icd-libopencl1
 
+### Special instructions for Tegra X1
+**The ArrayFire binary installer for Terga X1 requires JetPack 2.3 or L4T 24.2
+for Jetson TX1. This includes Ubuntu 16.04, CUDA 8.0 etc.**
+If you are using ArrayFire on the Tegra X1 also install these packages:
+
+    sudo apt-get install libopenblas-dev liblapacke-dev
+
 ### Special instructions for Tegra K1
 If you are using ArrayFire on the Tegra K1 also install these packages:
 

From b448612379f727a23aee358e906c3a825c927ae5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 18 Sep 2016 21:44:47 -0400
Subject: [PATCH 0942/2677] BUGFIX: Change the initial values for min and max
 operations

- Fixes issues with erode and dilate at corner cases
---
 src/api/c/ops.hpp                             | 57 +++++++------------
 src/backend/cpu/kernel/nearest_neighbour.hpp  |  2 +-
 src/backend/cpu/math.hpp                      | 11 ++--
 src/backend/cuda/kernel/nearest_neighbour.hpp |  2 +-
 src/backend/cuda/kernel/regions.hpp           |  6 +-
 src/backend/cuda/math.hpp                     | 42 +++++++-------
 .../opencl/kernel/nearest_neighbour.hpp       |  2 +-
 src/backend/opencl/kernel/regions.hpp         |  4 +-
 src/backend/opencl/math.hpp                   |  9 ++-
 9 files changed, 63 insertions(+), 72 deletions(-)

diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index b6e542207f..54e4d17fe0 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -111,7 +111,7 @@ struct Binary<T, af_min_t>
 {
     __DH__ T init()
     {
-        return detail::limit_max<T>();
+        return detail::maxval<T>();
     }
 
     __DH__ T operator() (T lhs, T rhs)
@@ -120,6 +120,21 @@ struct Binary<T, af_min_t>
     }
 };
 
+
+template<>
+struct Binary<char, af_min_t>
+{
+    __DH__ char init()
+    {
+        return 1;
+    }
+
+    __DH__ char operator() (char lhs, char rhs)
+    {
+        return detail::min(lhs > 0, rhs > 0);
+    }
+};
+
 #define SPECIALIZE_COMPLEX_MIN(T, Tr)           \
     template<>                                  \
     struct Binary<T, af_min_t>                  \
@@ -127,7 +142,7 @@ struct Binary<T, af_min_t>
         __DH__ T init()                         \
         {                                       \
             return detail::scalar<T>(           \
-                detail::limit_max<Tr>()         \
+                detail::maxval<Tr>()         \
                 );                              \
         }                                       \
                                                 \
@@ -147,7 +162,7 @@ struct Binary<T, af_max_t>
 {
     __DH__ T init()
     {
-        return detail::limit_min<T>();
+        return detail::minval<T>();
     }
 
     __DH__ T operator() (T lhs, T rhs)
@@ -170,40 +185,6 @@ struct Binary<char, af_max_t>
     }
 };
 
-template<>
-struct Binary<char, af_min_t>
-{
-    __DH__ char init()
-    {
-        return 1;
-    }
-
-    __DH__ char operator() (char lhs, char rhs)
-    {
-        return detail::min(lhs > 0, rhs > 0);
-    }
-};
-
-#define SPECIALIZE_FLOATING_MAX(T, Tr)          \
-    template<>                                  \
-    struct Binary<T, af_max_t>                  \
-    {                                           \
-        __DH__ T init()                         \
-        {                                       \
-            return detail::scalar<T>(           \
-                -detail::limit_max<Tr>()        \
-                );                              \
-        }                                       \
-                                                \
-        __DH__ T operator() (T lhs, T rhs)      \
-        {                                       \
-            return detail::max(lhs, rhs);       \
-        }                                       \
-    };                                          \
-
-SPECIALIZE_FLOATING_MAX(float, float)
-SPECIALIZE_FLOATING_MAX(double, double)
-
 #define SPECIALIZE_COMPLEX_MAX(T, Tr)           \
     template<>                                  \
     struct Binary<T, af_max_t>                  \
@@ -224,7 +205,7 @@ SPECIALIZE_FLOATING_MAX(double, double)
 SPECIALIZE_COMPLEX_MAX(cfloat, float)
 SPECIALIZE_COMPLEX_MAX(cdouble, double)
 
-#undef SPECIALIZE_FLOATING_MAX
+#undef SPECIALIZE_COMPLEX_MAX
 
 template<typename Ti, typename To, af_op_t op>
 struct Transform
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 14d841091c..1dbd3d9c45 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -106,7 +106,7 @@ void nearest_neighbour(Array<uint> idx, Array<To> dist,
     dist_op<T, To, dist_type> op;
 
     for (unsigned i = 0; i < nQuery; i++) {
-        To best_dist = limit_max<To>();
+        To best_dist = maxval<To>();
         unsigned best_idx  = 0;
 
         for (unsigned j = 0; j < nTrain; j++) {
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 5314f4162a..40e09fce37 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -43,11 +43,12 @@ namespace cpu
         return retVal;
     }
 
-    template <typename T> static inline T limit_max()
-    { return std::numeric_limits<T>::max(); }
-
-    template <typename T> static inline T limit_min()
-    { return std::numeric_limits<T>::min(); }
+    template <typename T> T maxval() { return std::numeric_limits<T>::max(); }
+    template <typename T> T minval() { return std::numeric_limits<T>::min(); }
+    template <> STATIC_ float maxval() { return std::numeric_limits<float>::infinity(); }
+    template <> STATIC_ double maxval() { return std::numeric_limits<double>::infinity(); }
+    template <> STATIC_ float minval() { return -std::numeric_limits<float>::infinity(); }
+    template <> STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
 
     template<typename T>
     static T scalar(double val)
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index e9f7e9553c..8972206221 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -424,7 +424,7 @@ void nearest_neighbour(Param<uint> idx,
                        const unsigned n_dist)
 {
     const unsigned feat_len = query.dims[dist_dim];
-    const To max_dist = limit_max<To>();
+    const To max_dist = maxval<To>();
 
     if (feat_len > THREADS) {
         CUDA_NOT_SUPPORTED();
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index f5d75c4934..7c583449b5 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -102,15 +102,15 @@ static void final_relabel(cuda::Param<T> equiv_map, cuda::CParam<char> bin, cons
 template<typename T>
 __device__ __inline__
 static T relabel(const T a, const T b) {
-    return min((a + (cuda::limit_max<T>() * (a == 0))),(b + (cuda::limit_max<T>() * (b == 0))));
+    return min((a + (cuda::maxval<T>() * (a == 0))),(b + (cuda::maxval<T>() * (b == 0))));
 }
 __device__ __inline__
 static double relabel(const double a, const double b) {
-    return fmin((a + (cuda::limit_max<double>() * (a == 0))),(b + (cuda::limit_max<double>() * (b == 0))));
+    return fmin((a + (cuda::maxval<double>() * (a == 0))),(b + (cuda::maxval<double>() * (b == 0))));
 }
 __device__ __inline__
 static float relabel(const float a, const float b) {
-    return fminf((a + (cuda::limit_max<float>() * (a == 0))),(b + (cuda::limit_max<float>() * (b == 0))));
+    return fminf((a + (cuda::maxval<float>() * (a == 0))),(b + (cuda::maxval<float>() * (b == 0))));
 }
 
 //Calculates the number of warps at compile time
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index ad7563f672..83ffc30cde 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -93,26 +93,30 @@ namespace cuda
     }
 
 #ifndef __CUDA_ARCH__
-    template <typename T> T limit_max() { return std::numeric_limits<T>::max(); }
-    template <typename T> T limit_min() { return std::numeric_limits<T>::min(); }
+    template <typename T> T maxval() { return std::numeric_limits<T>::max(); }
+    template <typename T> T minval() { return std::numeric_limits<T>::min(); }
+    template <> STATIC_ float maxval() { return std::numeric_limits<float>::infinity(); }
+    template <> STATIC_ double maxval() { return std::numeric_limits<double>::infinity(); }
+    template <> STATIC_ float minval() { return -std::numeric_limits<float>::infinity(); }
+    template <> STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
 #else
-    template <typename T> __device__ T limit_max() { return 1u << (8 * sizeof(T) - 1); }
-    template <typename T> __device__ T limit_min() { return scalar<T>(0); }
-
-    template<> __device__  int    limit_max<int>()    { return 0x7fffffff; }
-    template<> __device__  int    limit_min<int>()    { return 0x80000000; }
-    template<> __device__  intl   limit_max<intl>()   { return 0x7fffffffffffffff; }
-    template<> __device__  intl   limit_min<intl>()   { return 0x8000000000000000; }
-    template<> __device__  uintl  limit_max<uintl>()  { return 1ULL << (8 * sizeof(uintl) - 1); }
-    template<> __device__  char   limit_max<char>()   { return 0x7f; }
-    template<> __device__  char   limit_min<char>()   { return 0x80; }
-    template<> __device__  float  limit_max<float>()  { return  CUDART_INF_F; }
-    template<> __device__  float  limit_min<float>()  { return -CUDART_INF_F; }
-    template<> __device__  double limit_max<double>() { return  CUDART_INF; }
-    template<> __device__  double limit_min<double>() { return -CUDART_INF; }
-    template<> __device__  short  limit_max<short>()  { return 0x7fff; }
-    template<> __device__  short  limit_min<short>()  { return 0x8000; }
-    template<> __device__  ushort limit_max<ushort>() { return ((ushort)1) << (8 * sizeof(ushort) - 1); }
+    template <typename T> __device__ T maxval() { return 1u << (8 * sizeof(T) - 1); }
+    template <typename T> __device__ T minval() { return scalar<T>(0); }
+
+    template<> __device__  int    maxval<int>()    { return 0x7fffffff; }
+    template<> __device__  int    minval<int>()    { return 0x80000000; }
+    template<> __device__  intl   maxval<intl>()   { return 0x7fffffffffffffff; }
+    template<> __device__  intl   minval<intl>()   { return 0x8000000000000000; }
+    template<> __device__  uintl  maxval<uintl>()  { return 1ULL << (8 * sizeof(uintl) - 1); }
+    template<> __device__  char   maxval<char>()   { return 0x7f; }
+    template<> __device__  char   minval<char>()   { return 0x80; }
+    template<> __device__  float  maxval<float>()  { return  CUDART_INF_F; }
+    template<> __device__  float  minval<float>()  { return -CUDART_INF_F; }
+    template<> __device__  double maxval<double>() { return  CUDART_INF; }
+    template<> __device__  double minval<double>() { return -CUDART_INF; }
+    template<> __device__  short  maxval<short>()  { return 0x7fff; }
+    template<> __device__  short  minval<short>()  { return 0x8000; }
+    template<> __device__  ushort maxval<ushort>() { return ((ushort)1) << (8 * sizeof(ushort) - 1); }
 #endif
 
 #define upcast cuComplexFloatToDouble
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index fac9c9feff..438c74aca4 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -41,7 +41,7 @@ void nearest_neighbour(Param idx,
 {
     try {
         const unsigned feat_len = query.info.dims[dist_dim];
-        const To max_dist = limit_max<To>();
+        const To max_dist = maxval<To>();
 
         if (feat_len > THREADS) {
             OPENCL_NOT_SUPPORTED();
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 11f82d4535..15fe91d549 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -73,7 +73,7 @@ void regions(Param out, Param in)
                             << " -D BLOCK_DIM=" << block_dim
                             << " -D NUM_WARPS=" << num_warps
                             << " -D N_PER_THREAD=" << n_per_thread
-                            << " -D LIMIT_MAX=" << limit_max<T>()
+                            << " -D LIMIT_MAX=" << maxval<T>()
                             << " -D FULL_CONN";
                 }
                 else {
@@ -81,7 +81,7 @@ void regions(Param out, Param in)
                             << " -D BLOCK_DIM=" << block_dim
                             << " -D NUM_WARPS=" << num_warps
                             << " -D N_PER_THREAD=" << n_per_thread
-                            << " -D LIMIT_MAX=" << limit_max<T>();
+                            << " -D LIMIT_MAX=" << maxval<T>();
                 }
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index da052547d8..74e56b2cb7 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -97,8 +97,13 @@ namespace opencl
         return cval;
     }
 
-    template <typename T> T limit_max() { return std::numeric_limits<T>::max(); }
-    template <typename T> T limit_min() { return std::numeric_limits<T>::min(); }
+    template <typename T> T maxval() { return std::numeric_limits<T>::max(); }
+    template <typename T> T minval() { return std::numeric_limits<T>::min(); }
+    template <> STATIC_ float maxval() { return std::numeric_limits<float>::infinity(); }
+    template <> STATIC_ double maxval() { return std::numeric_limits<double>::infinity(); }
+    template <> STATIC_ float minval() { return -std::numeric_limits<float>::infinity(); }
+    template <> STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
+
 
     static inline double real(cdouble in)
     {

From dfbfca5fb77eb272af3ddcdebbaa704dbe519d9d Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 19 Sep 2016 08:01:51 -0400
Subject: [PATCH 0943/2677] BUGFIX: Fixing bug in nearest neighbour for CUDA
 backend

---
 src/backend/cuda/kernel/nearest_neighbour.hpp | 49 +++++++++++--------
 src/backend/cuda/nearest_neighbour.cu         | 16 ++----
 2 files changed, 31 insertions(+), 34 deletions(-)

diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 8972206221..b76c70ee2d 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -36,7 +36,16 @@ struct dist_op<T, To, AF_SAD>
 {
     __device__ To operator()(T v1, T v2)
     {
-        return abs((double)v1 - (double)v2);
+        return fabsf((float)v1 - (float)v2);
+    }
+};
+
+template<typename To>
+struct dist_op<double, To, AF_SAD>
+{
+    __device__ To operator()(double v1, double v2)
+    {
+        return fabs((double)v1 - (double)v2);
     }
 };
 
@@ -130,7 +139,7 @@ __global__ void nearest_neighbour_unroll(
 
         // Load one query feature that will be tested against all training
         // features in current block
-        if (tid < feat_len && valid_feat) {
+        if (tid < feat_len) {
             s_query[tid] = query.ptr[tid * nquery + j];
         }
         __syncthreads();
@@ -269,7 +278,7 @@ __global__ void nearest_neighbour(
 
         // Load one query feature that will be tested against all training
         // features in current block
-        if (tid < feat_len && valid_feat) {
+        if (tid < feat_len) {
             s_query[tid] = query.ptr[tid * nquery + j];
         }
         __syncthreads();
@@ -379,10 +388,8 @@ __global__ void select_matches(
     __shared__ To s_dist[THREADS];
     __shared__ unsigned s_idx[THREADS];
 
+    s_dist[sid] = max_dist;
     if (f < nfeat) {
-        s_dist[sid] = max_dist;
-        __syncthreads();
-
         for (unsigned i = threadIdx.y; i < nelem; i += blockDim.y) {
             To dist = in_dist[f * nelem + i];
 
@@ -392,26 +399,26 @@ __global__ void select_matches(
                 s_dist[sid] = dist;
                 s_idx[sid]  = in_idx[f * nelem + i];
             }
-            __syncthreads();
         }
+    }
+    __syncthreads();
 
-        // Reduce best matches and find the best of them all
-        for (unsigned i = blockDim.y / 2; i > 0; i >>= 1) {
-            if (threadIdx.y < i) {
-                To dist = s_dist[sid + i];
-                if (dist < s_dist[sid]) {
-                    s_dist[sid] = dist;
-                    s_idx[sid]  = s_idx[sid + i];
-                }
-                __syncthreads();
+    // Reduce best matches and find the best of them all
+    for (unsigned i = blockDim.y / 2; i > 0; i >>= 1) {
+        if (threadIdx.y < i) {
+            To dist = s_dist[sid + i];
+            if (dist < s_dist[sid]) {
+                s_dist[sid] = dist;
+                s_idx[sid]  = s_idx[sid + i];
             }
+            __syncthreads();
         }
+    }
 
-        // Store best matches and indexes to training dataset
-        if (threadIdx.y == 0) {
-            dist.ptr[f] = s_dist[threadIdx.x * blockDim.y];
-            idx.ptr[f]  = s_idx[threadIdx.x * blockDim.y];
-        }
+    // Store best matches and indexes to training dataset
+    if (threadIdx.y == 0 && f < nfeat) {
+        dist.ptr[f] = s_dist[threadIdx.x * blockDim.y];
+        idx.ptr[f]  = s_idx[threadIdx.x * blockDim.y];
     }
 }
 
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index ea316f82e8..eb1781fe4c 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -12,7 +12,7 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
-#include <kernel/transpose.hpp>
+#include <transpose.hpp>
 
 using af::dim4;
 
@@ -34,18 +34,8 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
     idx  = createEmptyArray<uint>(outDims);
     dist = createEmptyArray<To>(outDims);
 
-    Array<T> queryT = query;
-    Array<T> trainT = train;
-
-    if (dist_dim == 0) {
-        const dim4 queryTDims = dim4(qDims[1], qDims[0], qDims[2], qDims[3]);
-        const dim4 trainTDims = dim4(tDims[1], tDims[0], tDims[2], tDims[3]);
-        queryT = createEmptyArray<T>(queryTDims);
-        trainT = createEmptyArray<T>(trainTDims);
-
-        kernel::transpose<T, false>(queryT, query, query.ndims());
-        kernel::transpose<T, false>(trainT, train, train.ndims());
-    }
+    Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
+    Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
     switch(dist_type) {
         case AF_SAD: kernel::nearest_neighbour<T, To, AF_SAD>(idx, dist, queryT, trainT, 1, n_dist);

From f94aceb5f38888a3146d21dd4baf1fadb7437b74 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 19 Sep 2016 08:02:19 -0400
Subject: [PATCH 0944/2677] BUGFIX: fixing bug in nearest neighbour for opencl
 backend

---
 .../opencl/kernel/nearest_neighbour.cl        | 18 +++-----
 .../opencl/kernel/nearest_neighbour.hpp       | 13 +++---
 src/backend/opencl/nearest_neighbour.cpp      | 44 ++-----------------
 3 files changed, 17 insertions(+), 58 deletions(-)

diff --git a/src/backend/opencl/kernel/nearest_neighbour.cl b/src/backend/opencl/kernel/nearest_neighbour.cl
index 6247d1d3cd..dd9197c582 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.cl
+++ b/src/backend/opencl/kernel/nearest_neighbour.cl
@@ -88,7 +88,7 @@ void nearest_neighbour_unroll(
 
         // Load one query feature that will be tested against all training
         // features in current block
-        if (tid < FEAT_LEN && valid_feat) {
+        if (tid < FEAT_LEN) {
             l_query[tid] = query[tid * nquery + j + qInfo.offset];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -228,7 +228,7 @@ void nearest_neighbour(
 
         // Load one query feature that will be tested against all training
         // features in current block
-        if (tid < feat_len && valid_feat) {
+        if (tid < feat_len) {
             l_query[tid] = query[tid * nquery + j + qInfo.offset];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -342,15 +342,9 @@ void select_matches(
 
     bool valid_feat = (f < nfeat);
 
-    if (valid_feat)
-        l_dist[sid] = max_dist;
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    unsigned nelem_max = (nelem / lsz1) * lsz1;
-    nelem_max = (nelem % lsz1 == 0) ? nelem_max : nelem_max + lsz1;
-
-    for (unsigned i = get_local_id(1); i < nelem_max; i += get_local_size(1)) {
-        if (valid_feat && i < nelem) {
+    l_dist[sid] = max_dist;
+    if (valid_feat) {
+        for (unsigned i = get_local_id(1); i < nelem; i += get_local_size(1)) {
             To dist = in_dist[f * nelem + i];
 
             // Copy all best matches previously found in nearest_neighbour() to
@@ -360,8 +354,8 @@ void select_matches(
                 l_idx[sid]  = in_idx[f * nelem + i];
             }
         }
-        barrier(CLK_LOCAL_MEM_FENCE);
     }
+    barrier(CLK_LOCAL_MEM_FENCE);
 
     for (unsigned i = get_local_size(1) / 2; i > 0; i >>= 1) {
         if (get_local_id(1) < i) {
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 438c74aca4..3ad66dfa6c 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -35,17 +35,18 @@ void nearest_neighbour(Param idx,
                        Param query,
                        Param train,
                        const dim_t dist_dim,
-                       const unsigned n_dist,
-                       const size_t lmem_sz,
-                       bool use_lmem)
+                       const unsigned n_dist)
 {
     try {
         const unsigned feat_len = query.info.dims[dist_dim];
         const To max_dist = maxval<To>();
 
-        if (feat_len > THREADS) {
-            OPENCL_NOT_SUPPORTED();
-        }
+        // Determine maximum feat_len capable of using shared memory (faster)
+        cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
+        size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
+        size_t ltrain_sz = THREADS * feat_len * sizeof(T);
+        bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
+        size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
 
         unsigned unroll_len = nextpow2(feat_len);
         if (unroll_len != feat_len) unroll_len = 0;
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index cb711b5084..fa1ef53c0d 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -12,7 +12,7 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
-#include <kernel/transpose.hpp>
+#include <transpose.hpp>
 
 using af::dim4;
 using cl::Device;
@@ -29,51 +29,15 @@ void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
 {
     uint sample_dim = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
-    const dim4 tDims = train.dims();
-
     const dim4 outDims(n_dist, qDims[sample_dim]);
 
     idx  = createEmptyArray<uint>(outDims);
     dist = createEmptyArray<To>(outDims);
 
-    const unsigned feat_len = qDims[dist_dim];
-
-    // Determine maximum feat_len capable of using shared memory (faster)
-    cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
-    size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
-    size_t ltrain_sz = THREADS * feat_len * sizeof(T);
-    bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
-    size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
-
-    Array<T> queryT = query;
-    Array<T> trainT = train;
-
-    if (dist_dim == 0) {
-        const dim4 queryTDims = dim4(qDims[1], qDims[0], qDims[2], qDims[3]);
-        const dim4 trainTDims = dim4(tDims[1], tDims[0], tDims[2], tDims[3]);
-        queryT = createEmptyArray<T>(queryTDims);
-        trainT = createEmptyArray<T>(trainTDims);
-
-        bool queryIs32Multiple = false;
-        if (qDims[0] % 32 == 0 && qDims[1] % 32 == 0)
-            queryIs32Multiple = true;
-
-        bool trainIs32Multiple = false;
-        if (tDims[0] % 32 == 0 && tDims[1] % 32 == 0)
-        trainIs32Multiple = true;
-
-        if (queryIs32Multiple)
-            kernel::transpose<T, false, true >(queryT, query);
-        else
-            kernel::transpose<T, false, false>(queryT, query);
-
-        if (trainIs32Multiple)
-            kernel::transpose<T, false, true >(trainT, train);
-        else
-            kernel::transpose<T, false, false>(trainT, train);
-    }
+    Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
+    Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
-    kernel::nearest_neighbour<T, To, dist_type>(idx, dist, queryT, trainT, 1, n_dist, lmem_sz, use_lmem);
+    kernel::nearest_neighbour<T, To, dist_type>(idx, dist, queryT, trainT, 1, n_dist);
 
 }
 

From 7372ef2b341284ffb6d3ef5503d4e870397997de Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 19 Sep 2016 08:02:43 -0400
Subject: [PATCH 0945/2677] TEST: Adding additional test for nearest neighbour

---
 test/nearest_neighbour.cpp | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 2bca086f11..ea10735be1 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -228,3 +228,41 @@ TEST(NearestNeighbourSAD, CPP)
     delete[] outIdx;
     delete[] outDist;
 }
+
+TEST(NearestNeighbourSSD, small)
+{
+    const int ntrain = 1;
+    const int nquery = 5;
+    const int nfeat  = 2;
+    float train[ntrain * nfeat] = {
+        5, 5,
+    };
+
+    float query[5 * nfeat] = {
+        0, 0,
+        3.5, 4,
+        5, 5,
+        6, 5,
+        8, 6.5
+    };
+    af::array t(nfeat, ntrain, train);
+    af::array q(nfeat, nquery, query);
+    af::array indices;
+    af::array distances;
+    af::nearestNeighbour(indices, distances, q, t, 0, 1, AF_SSD);
+
+    float expectedDistances[nquery] = {
+        (5 - 0) * (5 - 0) + (5 - 0) * (5 - 0),
+        (5 - 3.5) * (5 - 3.5) + (5 - 4) * (5 - 4),
+        (5 - 5) * (5 - 5) + (5 - 5) * (5 - 5),
+        (5 - 6) * (5 - 6) + (5 - 5) * (5 - 5),
+        (5 - 8) * (5 - 8) + (5 - 6.5) * (5 - 6.5)
+    };
+
+    std::vector<float> actualDistances(nquery);
+    distances.host(&actualDistances[0]);
+    for (int i = 0; i < nquery; i++)
+    {
+        EXPECT_NEAR(expectedDistances[i], actualDistances[i], 1E-8);
+    }
+}

From e656f157f28c48e95f87d8232689432f806975c7 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 19 Sep 2016 11:03:02 -0400
Subject: [PATCH 0946/2677] OPENCL: add inf as an alias to INFINITY

Float values that are inf are outputted as inf to stringstream.
However inf isn't available in opencl, causing min and max to fail.

This alias is the easiest work around for now.
---
 src/backend/opencl/program.cpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 1362e87c44..e9900f9b19 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -22,7 +22,10 @@ using std::string;
 
 namespace opencl
 {
-    const static std::string USE_DBL_SRC_STR("\n\
+    const static std::string DEFAULT_MACROS_STR("\n\
+                                           #ifndef inf\n\
+                                           #define inf INFINITY\n\
+                                           #endif\n\
                                            #ifdef USE_DOUBLE\n\
                                            #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
                                            #endif\n                     \
@@ -41,7 +44,7 @@ namespace opencl
     {
         try {
             Program::Sources setSrc;
-            setSrc.emplace_back(USE_DBL_SRC_STR.c_str(), USE_DBL_SRC_STR.length());
+            setSrc.emplace_back(DEFAULT_MACROS_STR.c_str(), DEFAULT_MACROS_STR.length());
             setSrc.emplace_back(KParam_hpp, KParam_hpp_len);
 
             for (int i = 0; i < num_files; i++) {

From 61bc24c8bad3b3b8d6ccba50d47277443791a876 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 19 Sep 2016 12:43:15 -0400
Subject: [PATCH 0947/2677] BUGFIX: Fixing regions after bug caused by the
 change in maxval

---
 src/backend/cuda/kernel/regions.hpp  | 15 +++++----------
 src/backend/opencl/kernel/regions.cl |  9 +++++----
 2 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 7c583449b5..6c9abf3a02 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -101,16 +101,11 @@ static void final_relabel(cuda::Param<T> equiv_map, cuda::CParam<char> bin, cons
 // do not choose zero, which indicates invalid.
 template<typename T>
 __device__ __inline__
-static T relabel(const T a, const T b) {
-    return min((a + (cuda::maxval<T>() * (a == 0))),(b + (cuda::maxval<T>() * (b == 0))));
-}
-__device__ __inline__
-static double relabel(const double a, const double b) {
-    return fmin((a + (cuda::maxval<double>() * (a == 0))),(b + (cuda::maxval<double>() * (b == 0))));
-}
-__device__ __inline__
-static float relabel(const float a, const float b) {
-    return fminf((a + (cuda::maxval<float>() * (a == 0))),(b + (cuda::maxval<float>() * (b == 0))));
+static T relabel(const T a, const T b)
+{
+    T aa = (a == 0) ? cuda::maxval<T>() : a;
+    T bb = (b == 0) ? cuda::maxval<T>() : b;
+    return min(aa, bb);
 }
 
 //Calculates the number of warps at compile time
diff --git a/src/backend/opencl/kernel/regions.cl b/src/backend/opencl/kernel/regions.cl
index 51e4d9a1bb..b0ee96ca95 100644
--- a/src/backend/opencl/kernel/regions.cl
+++ b/src/backend/opencl/kernel/regions.cl
@@ -60,13 +60,14 @@ void final_relabel(global       T    * equiv_map,
     }
 }
 
-#define MIN(A,B) ((A < B) ? (A) : (B))
-
 // When two labels are equivalent, choose the lower label, but
 // do not choose zero, which indicates invalid.
 //#if T == double
-static inline T relabel(const T a, const T b) {
-    return MIN((a + (LIMIT_MAX * (a == 0))),(b + (LIMIT_MAX * (b == 0))));
+static inline T relabel(const T a, const T b)
+{
+    T aa = (a == 0) ? LIMIT_MAX : a;
+    T bb = (b == 0) ? LIMIT_MAX : b;
+    return min(aa, bb);
 }
 
 // The following kernel updates the equivalency map.  This kernel

From 663410ed1892f35645ee1be44871681d5f1c7706 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 20 Sep 2016 13:16:26 -0400
Subject: [PATCH 0948/2677] OPENCL BUGFIX: properly pass scalars as compile
 time options

---
 src/backend/opencl/kernel/approx.hpp          |  8 +-
 src/backend/opencl/kernel/gradient.hpp        |  4 +-
 src/backend/opencl/kernel/ireduce.hpp         |  8 +-
 src/backend/opencl/kernel/morph.hpp           |  8 +-
 src/backend/opencl/kernel/reduce.hpp          |  8 +-
 src/backend/opencl/kernel/regions.hpp         |  7 +-
 src/backend/opencl/kernel/rotate.hpp          |  4 +-
 src/backend/opencl/kernel/scan_dim.hpp        |  4 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  4 +-
 src/backend/opencl/kernel/scan_first.hpp      |  4 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |  4 +-
 src/backend/opencl/kernel/transform.hpp       |  4 +-
 src/backend/opencl/kernel/unwrap.hpp          |  4 +-
 src/backend/opencl/kernel/where.hpp           |  4 +-
 src/backend/opencl/kernel/wrap.hpp            |  4 +-
 src/backend/opencl/program.cpp                |  3 -
 src/backend/opencl/types.hpp                  | 76 +++++++++++++++++++
 17 files changed, 115 insertions(+), 43 deletions(-)

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 9445befc5e..8bd608a027 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -56,14 +56,14 @@ namespace opencl
                 int device = getActiveDeviceId();
 
                 std::call_once( compileFlags[device], [device] () {
-                    ToNum<Ty> toNum;
+                    ToNumStr<Ty> toNumStr;
                     std::ostringstream options;
                     options << " -D Ty="          << dtype_traits<Ty>::getName()
                             << " -D Tp="          << dtype_traits<Tp>::getName()
                             << " -D InterpInTy="  << dtype_traits<Ty>::getName()
                             << " -D InterpValTy=" << dtype_traits<Ty>::getName()
                             << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                            << " -D ZERO="        << toNum(scalar<Ty>(0));
+                            << " -D ZERO="        << toNumStr(scalar<Ty>(0));
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
                        (af_dtype) dtype_traits<Ty>::af_type == c64) {
@@ -128,14 +128,14 @@ namespace opencl
                 int device = getActiveDeviceId();
 
                 std::call_once( compileFlags[device], [device] () {
-                    ToNum<Ty> toNum;
+                    ToNumStr<Ty> toNumStr;
                     std::ostringstream options;
                     options << " -D Ty="          << dtype_traits<Ty>::getName()
                             << " -D Tp="          << dtype_traits<Tp>::getName()
                             << " -D InterpInTy="  << dtype_traits<Ty>::getName()
                             << " -D InterpValTy=" << dtype_traits<Ty>::getName()
                             << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                            << " -D ZERO="        << toNum(scalar<Ty>(0));
+                            << " -D ZERO="        << toNumStr(scalar<Ty>(0));
 
                     if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
                        (af_dtype) dtype_traits<Ty>::af_type == c64) {
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index bea33c9718..05e7cf58db 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -48,12 +48,12 @@ namespace opencl
                 int device = getActiveDeviceId();
 
                 std::call_once( compileFlags[device], [device] () {
-                    ToNum<T> toNum;
+                    ToNumStr<T> toNumStr;
                     std::ostringstream options;
                     options << " -D T=" << dtype_traits<T>::getName()
                             << " -D TX=" << TX
                             << " -D TY=" << TY
-                            << " -D ZERO=" << toNum(scalar<T>(0));
+                            << " -D ZERO=" << toNumStr(scalar<T>(0));
 
                     if((af_dtype) dtype_traits<T>::af_type == c32 ||
                        (af_dtype) dtype_traits<T>::af_type == c64) {
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index d752bacd34..aedbc863f4 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -68,14 +68,14 @@ namespace kernel
         if (idx == kernelCaches[device].end()) {
 
                 Binary<T, op> ireduce;
-                ToNum<T> toNum;
+                ToNumStr<T> toNumStr;
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D dim=" << dim
                         << " -D DIMY=" << threads_y
                         << " -D THREADS_X=" << THREADS_X
-                        << " -D init=" << toNum(ireduce.init())
+                        << " -D init=" << toNumStr(ireduce.init())
                         << " -D " << binOpName<op>()
                         << " -D CPLX=" << af::iscplx<T>()
                         << " -D IS_FIRST=" << is_first;
@@ -180,13 +180,13 @@ namespace kernel
         if (idx == kernelCaches[device].end()) {
 
             Binary<T, op> ireduce;
-            ToNum<T> toNum;
+            ToNumStr<T> toNumStr;
 
             std::ostringstream options;
             options << " -D T=" << dtype_traits<T>::getName()
                     << " -D DIMX=" << threads_x
                     << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                    << " -D init=" << toNum(ireduce.init())
+                    << " -D init=" << toNumStr(ireduce.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<T>()
                     << " -D IS_FIRST=" << is_first;
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index fa7b81618a..2fa6b23d5c 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -56,12 +56,12 @@ void morph(Param         out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                ToNum<T> toNum;
+                ToNumStr<T> toNumStr;
                 T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D isDilation="<< isDilation
-                        << " -D init=" << toNum(init)
+                        << " -D init=" << toNumStr(init)
                         << " -D windLen=" << windLen;
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
@@ -124,12 +124,12 @@ void morph3d(Param       out,
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                ToNum<T> toNum;
+                ToNumStr<T> toNumStr;
                 T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D isDilation="<< isDilation
-                        << " -D init=" << toNum(init)
+                        << " -D init=" << toNumStr(init)
                         << " -D windLen=" << windLen;
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index c48cea4072..cdbf562c74 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -66,7 +66,7 @@ namespace kernel
         kc_entry_t entry;
         if (idx == kernelCaches[device].end()) {
             Binary<To, op> reduce;
-            ToNum<To> toNum;
+            ToNumStr<To> toNumStr;
 
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
@@ -75,7 +75,7 @@ namespace kernel
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y
                     << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNum(reduce.init())
+                    << " -D init=" << toNumStr(reduce.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>();
             if (std::is_same<Ti, double>::value ||
@@ -185,7 +185,7 @@ namespace kernel
         if (idx == kernelCaches[device].end()) {
 
             Binary<To, op> reduce;
-            ToNum<To> toNum;
+            ToNumStr<To> toNumStr;
 
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
@@ -193,7 +193,7 @@ namespace kernel
                     << " -D T=To"
                     << " -D DIMX=" << threads_x
                     << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                    << " -D init=" << toNum(reduce.init())
+                    << " -D init=" << toNumStr(reduce.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>();
             if (std::is_same<Ti, double>::value ||
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 15fe91d549..a41c86fad3 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -61,19 +61,18 @@ void regions(Param out, Param in)
         static std::map<int, Kernel *>     ueKernel;
 
         int device = getActiveDeviceId();
-
         static const int block_dim = 16;
         static const int num_warps = 8;
 
         std::call_once( compileFlags[device], [device] () {
-
+                ToNumStr<T> toNumStr;
                 std::ostringstream options;
                 if (full_conn) {
                     options << " -D T=" << dtype_traits<T>::getName()
                             << " -D BLOCK_DIM=" << block_dim
                             << " -D NUM_WARPS=" << num_warps
                             << " -D N_PER_THREAD=" << n_per_thread
-                            << " -D LIMIT_MAX=" << maxval<T>()
+                            << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
                             << " -D FULL_CONN";
                 }
                 else {
@@ -81,7 +80,7 @@ void regions(Param out, Param in)
                             << " -D BLOCK_DIM=" << block_dim
                             << " -D NUM_WARPS=" << num_warps
                             << " -D N_PER_THREAD=" << n_per_thread
-                            << " -D LIMIT_MAX=" << maxval<T>();
+                            << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
                 }
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index be35de2ec7..01440ab027 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -66,10 +66,10 @@ namespace opencl
                 typedef typename dtype_traits<T>::base_type BT;
 
                 std::call_once( compileFlags[device], [device] () {
-                    ToNum<T> toNum;
+                    ToNumStr<T> toNumStr;
                     std::ostringstream options;
                     options << " -D T="        << dtype_traits<T>::getName();
-                    options << " -D ZERO="      << toNum(scalar<T>(0));
+                    options << " -D ZERO="      << toNumStr(scalar<T>(0));
                     options << " -D InterpInTy=" << dtype_traits<T>::getName();
                     options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
                     options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index af5a744946..27d4dbf521 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -61,7 +61,7 @@ namespace kernel
         if (idx == kernelCaches[device].end()) {
 
             Binary<To, op> scan;
-            ToNum<To> toNum;
+            ToNumStr<To> toNumStr;
 
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
@@ -70,7 +70,7 @@ namespace kernel
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y
                     << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNum(scan.init())
+                    << " -D init=" << toNumStr(scan.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D isFinalPass=" << (int)(isFinalPass)
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 83998516be..c3ef035996 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -63,7 +63,7 @@ namespace kernel
         if (idx == kernelCaches[device].end()) {
 
             Binary<To, op> scan;
-            ToNum<To> toNum;
+            ToNumStr<To> toNumStr;
 
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
@@ -73,7 +73,7 @@ namespace kernel
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y
                     << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNum(scan.init())
+                    << " -D init=" << toNumStr(scan.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D calculateFlags=" << calculateFlags
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index fd725acbd4..85f32ec2e9 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -65,7 +65,7 @@ namespace kernel
             const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
 
             Binary<To, op> scan;
-            ToNum<To> toNum;
+            ToNumStr<To> toNumStr;
 
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
@@ -74,7 +74,7 @@ namespace kernel
                     << " -D DIMX=" << threads_x
                     << " -D DIMY=" << threads_y
                     << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNum(scan.init())
+                    << " -D init=" << toNumStr(scan.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D isFinalPass=" << (int)(isFinalPass)
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 76964ca200..9417c1a777 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -67,7 +67,7 @@ namespace kernel
             const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
 
             Binary<To, op> scan;
-            ToNum<To> toNum;
+            ToNumStr<To> toNumStr;
 
             std::ostringstream options;
             options << " -D To=" << dtype_traits<To>::getName()
@@ -77,7 +77,7 @@ namespace kernel
                     << " -D DIMX=" << threads_x
                     << " -D DIMY=" << threads_y
                     << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNum(scan.init())
+                    << " -D init=" << toNumStr(scan.init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D calculateFlags=" << calculateFlags
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index b1dd6585aa..4120908acf 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -76,12 +76,12 @@ namespace opencl
                 kc_entry_t entry;
 
                 if (idx == kernelCaches[device].end()) {
-                    ToNum<T> toNum;
+                    ToNumStr<T> toNumStr;
                     std::ostringstream options;
                     options << " -D T="           << dtype_traits<T>::getName()
                             << " -D INVERSE="     << (isInverse ? 1 : 0)
                             << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
-                            << " -D ZERO="        << toNum(scalar<T>(0));
+                            << " -D ZERO="        << toNumStr(scalar<T>(0));
                     options << " -D InterpInTy=" << dtype_traits<T>::getName();
                     options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
                     options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 345cb0c108..6c364b19b0 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -54,10 +54,10 @@ namespace opencl
                 kc_entry_t entry;
                 if (idx == kernelCaches[device].end()) {
 
-                    ToNum<T> toNum;
+                    ToNumStr<T> toNumStr;
                     std::ostringstream options;
                     options << " -D is_column=" << is_column
-                            << " -D ZERO=" << toNum(scalar<T>(0))
+                            << " -D ZERO=" << toNumStr(scalar<T>(0))
                             << " -D T="    << dtype_traits<T>::getName();
 
                     if (std::is_same<T, double>::value ||
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index aadbb08efd..f46b7e8b1f 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -50,11 +50,11 @@ namespace kernel
 
         std::call_once(compileFlags[device], [device] () {
 
-                ToNum<T> toNum;
+                ToNumStr<T> toNumStr;
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D zero=" << toNum(scalar<T>(0))
+                        << " -D zero=" << toNumStr(scalar<T>(0))
                         << " -D CPLX=" << af::iscplx<T>();
                 if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index a04b5ad90e..bc3f1ca0e8 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -55,10 +55,10 @@ namespace opencl
                 kc_entry_t entry;
                 if (idx == kernelCaches[device].end()) {
 
-                    ToNum<T> toNum;
+                    ToNumStr<T> toNumStr;
                     std::ostringstream options;
                     options << " -D is_column=" << is_column
-                            << " -D ZERO=" << toNum(scalar<T>(0))
+                            << " -D ZERO=" << toNumStr(scalar<T>(0))
                             << " -D T="    << dtype_traits<T>::getName();
 
                     if (std::is_same<T, double>::value ||
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index e9900f9b19..5c0d9d3c75 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -23,9 +23,6 @@ using std::string;
 namespace opencl
 {
     const static std::string DEFAULT_MACROS_STR("\n\
-                                           #ifndef inf\n\
-                                           #define inf INFINITY\n\
-                                           #endif\n\
                                            #ifdef USE_DOUBLE\n\
                                            #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
                                            #endif\n                     \
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 44b7921473..5742c5d8c9 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -16,6 +16,10 @@
 #include <CL/cl.h>
 #endif
 #pragma GCC diagnostic pop
+#include <sstream>
+#include <string>
+#include <cmath>
+#include <type_util.hpp>
 
 namespace opencl
 {
@@ -32,4 +36,76 @@ namespace opencl
 
     template<typename T > const char *shortname(bool caps=false);
 
+    template<typename T>
+    struct ToNumStr
+    {
+        inline std::string operator()(T val)
+        {
+            ToNum<T> toNum;
+            return std::to_string(toNum(val));
+        }
+    };
+
+    template<>
+    struct ToNumStr<float>
+    {
+        inline std::string operator()(float val)
+        {
+            static const std::string PINF = "+INFINITY";
+            static const std::string NINF = "-INFINITY";
+            if (std::isinf(val)) {
+                return val < 0 ? NINF : PINF;
+            }
+            return std::to_string(val);
+        }
+    };
+
+    template<>
+    struct ToNumStr<double>
+    {
+        inline std::string operator()(double val)
+        {
+            static const std::string PINF = "+INFINITY";
+            static const std::string NINF = "-INFINITY";
+            if (std::isinf(val)) {
+                return val < 0 ? NINF : PINF;
+            }
+            return std::to_string(val);
+        }
+    };
+
+    template<>
+    struct ToNumStr<cfloat>
+    {
+        inline std::string operator()(cfloat val)
+        {
+            ToNumStr<float> realStr;
+            static const std::string INF = "INFINITY";
+            std::stringstream s;
+            s << "{";
+            s << realStr(val.s[0]);
+            s << ",";
+            s << realStr(val.s[1]);
+            s << "}";
+            return s.str();
+        }
+    };
+
+    template<>
+    struct ToNumStr<cdouble>
+    {
+        inline std::string operator()(cdouble val)
+        {
+            ToNumStr<double> realStr;
+            static const std::string INF = "INFINITY";
+            std::stringstream s;
+            s << "{";
+            s << realStr(val.s[0]);
+            s << ",";
+            s << realStr(val.s[1]);
+            s << "}";
+            return s.str();
+        }
+    };
+
 }

From d7f7eb0332c02981d26f7ce77546274b19e1628c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 20 Sep 2016 19:10:09 -0400
Subject: [PATCH 0949/2677] Fix variable names in getInfo declaration

The function definition and all calls to the getInfo functions are
correct.
---
 src/api/c/handle.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 465b9252df..6c6459ef88 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <af/dim4.hpp>
 
-const ArrayInfo& getInfo(const af_array arr, bool device_check = true, bool sparse_check = true);
+const ArrayInfo& getInfo(const af_array arr, bool sparse_check = true, bool device_check = true);
 
 // Implemented in src/api/c/moddims.cpp
 template<typename T>

From 7e1bb1ab2ddae338efd9d82a5a094305cf73c66e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 20 Sep 2016 19:20:42 -0400
Subject: [PATCH 0950/2677] Add support for sparse arrays to eval

---
 src/api/c/device.cpp | 51 ++++++++++++++++++++++++++++++--------------
 1 file changed, 35 insertions(+), 16 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 14dcd1c0ce..f9f7cc8dc1 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -15,6 +15,7 @@
 #include <platform.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
+#include <sparse_handle.hpp>
 #include "err_common.hpp"
 #include <cstring>
 
@@ -165,25 +166,43 @@ static inline void eval(af_array arr)
     return;
 }
 
+template<typename T>
+static inline void sparseEval(af_array arr)
+{
+    getSparseArray<T>(arr).eval();
+    return;
+}
+
 af_err af_eval(af_array arr)
 {
     try {
-        af_dtype type = getInfo(arr).getType();
-        switch (type) {
-        case f32: eval<float  >(arr); break;
-        case f64: eval<double >(arr); break;
-        case c32: eval<cfloat >(arr); break;
-        case c64: eval<cdouble>(arr); break;
-        case s32: eval<int    >(arr); break;
-        case u32: eval<uint   >(arr); break;
-        case u8 : eval<uchar  >(arr); break;
-        case b8 : eval<char   >(arr); break;
-        case s64: eval<intl   >(arr); break;
-        case u64: eval<uintl  >(arr); break;
-        case s16: eval<short  >(arr); break;
-        case u16: eval<ushort >(arr); break;
-        default:
-            TYPE_ERROR(0, type);
+        ArrayInfo info = getInfo(arr, false);
+        af_dtype type = info.getType();
+
+        if(info.isSparse()) {
+            switch(type) {
+                case f32: sparseEval<float  >(arr); break;
+                case f64: sparseEval<double >(arr); break;
+                case c32: sparseEval<cfloat >(arr); break;
+                case c64: sparseEval<cdouble>(arr); break;
+                default : TYPE_ERROR(0, type);
+            }
+        } else {
+            switch (type) {
+                case f32: eval<float  >(arr); break;
+                case f64: eval<double >(arr); break;
+                case c32: eval<cfloat >(arr); break;
+                case c64: eval<cdouble>(arr); break;
+                case s32: eval<int    >(arr); break;
+                case u32: eval<uint   >(arr); break;
+                case u8 : eval<uchar  >(arr); break;
+                case b8 : eval<char   >(arr); break;
+                case s64: eval<intl   >(arr); break;
+                case u64: eval<uintl  >(arr); break;
+                case s16: eval<short  >(arr); break;
+                case u16: eval<ushort >(arr); break;
+                default: TYPE_ERROR(0, type);
+            }
         }
     } CATCHALL;
 

From 6a4f57bfafe057777c00d4b99051ceff592a2f04 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Tue, 20 Sep 2016 19:03:20 -0400
Subject: [PATCH 0951/2677] Add Conjugate Gradient example to benchmarks -
 Includes sparse matrix

Compare the performance and memory usage of sparse vs dense using conjugate
gradient example
---
 examples/benchmarks/cg.cpp | 139 +++++++++++++++++++++++++++++++++++++
 1 file changed, 139 insertions(+)
 create mode 100644 examples/benchmarks/cg.cpp

diff --git a/examples/benchmarks/cg.cpp b/examples/benchmarks/cg.cpp
new file mode 100644
index 0000000000..57ee972661
--- /dev/null
+++ b/examples/benchmarks/cg.cpp
@@ -0,0 +1,139 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <arrayfire.h>
+#include <iostream>
+
+using namespace af;
+
+static size_t dimension = 4 * 1024;
+static const int maxIter = 10;
+static const int sparsityFactor = 7;
+
+static array A;
+static array spA;  // Sparse A
+static array x0;
+static array b;
+
+void setupInputs()
+{
+    // Generate a random input: A
+    array T = randu(dimension, dimension, f32);
+    // Create 0s in input.
+    // Anything that is no divisible by sparsityFactor will become 0.
+    A = floor(T * 1000);
+    A = A * ((A % sparsityFactor) == 0) / 1000;
+    // Make it positive definite
+    A = transpose(A) + A + A.dims(0)*identity(A.dims(0), A.dims(0), f32);
+
+    // Make A sparse as spA
+    spA = sparse(A);
+
+    // Generate x0: Random guess
+    x0 = randu(A.dims(0), f32);
+
+    //Generate b
+    b = matmul(A, x0);
+
+    std::cout << "Sparsity of A = "
+              << 100.f * (float)sparseGetNNZ(spA) / (float)spA.elements()
+              << "%" << std::endl;
+    std::cout << "Memory Usage of A = "
+              << A.bytes() / (1024.f * 1024.f)
+              << " MB" << std::endl;
+    std::cout << "Memory Usage of spA = "
+              <<(sparseGetValues(spA).bytes()
+               + sparseGetRowIdx(spA).bytes()
+               + sparseGetColIdx(spA).bytes()) / (1024.f * 1024.f)
+              << " MB" << std::endl;
+}
+
+void sparseConjugateGradient(void)
+{
+    array x = constant(0, b.dims(), f32);
+    array r = b - matmul(spA, x);
+    array p = r;
+
+    for (int i = 0; i < maxIter; ++i) {
+        array Ap = matmul(spA, p);
+        array alpha_num = dot(r, r);
+        array alpha_den = dot(p, Ap);
+        array alpha = alpha_num/alpha_den;
+        r -= tile(alpha, Ap.dims())*Ap;
+        x += tile(alpha, Ap.dims())*p;
+        array beta_num = dot(r, r);
+        array beta = beta_num/alpha_num;
+        p = r + tile(beta, p.dims()) * p;
+    }
+}
+
+void denseConjugateGradient(void)
+{
+    array x = constant(0, b.dims(), f32);
+    array r = b - matmul(A, x);
+    array p = r;
+
+    for (int i = 0; i < maxIter; ++i) {
+        array Ap = matmul(A, p);
+        array alpha_num = dot(r, r);
+        array alpha_den = dot(p, Ap);
+        array alpha = alpha_num/alpha_den;
+        r -= tile(alpha, Ap.dims())*Ap;
+        x += tile(alpha, Ap.dims())*p;
+        array beta_num = dot(r, r);
+        array beta = beta_num/alpha_num;
+        p = r + tile(beta, p.dims()) * p;
+    }
+}
+
+void checkConjugateGradient(const af::array in)
+{
+    array x = constant(0, b.dims(), f32);
+    array r = b - matmul(in, x);
+    array p = r;
+
+    for (int i = 0; i < maxIter; ++i) {
+        array Ap = matmul(in, p);
+        array alpha_num = dot(r, r);
+        array alpha_den = dot(p, Ap);
+        array alpha = alpha_num/alpha_den;
+        r -= tile(alpha, Ap.dims())*Ap;
+        x += tile(alpha, Ap.dims())*p;
+        array beta_num = dot(r, r);
+        array beta = beta_num/alpha_num;
+        p = r + tile(beta, p.dims()) * p;
+    }
+    array res = x0 - x;
+
+    std::cout<<"Final difference in solutions:\n";
+    af_print(dot(res, res));
+}
+
+int main(int argc, char *argv[])
+{
+    af::info();
+    setupInputs();
+
+    std::cout << "Verifying Dense Conjugate Gradient:" << std::endl;
+    checkConjugateGradient(A);
+
+    std::cout << "Verifying Sparse Conjugate Gradient:" << std::endl;
+    checkConjugateGradient(spA);
+
+    af::sync();
+
+    std::cout << "Dense Conjugate Gradient Time: "
+              << timeit(denseConjugateGradient) * 1000
+              << "ms" << std::endl;
+
+    std::cout << "Sparse Conjugate Gradient Time: "
+              << timeit(sparseConjugateGradient) * 1000
+              << "ms" << std::endl;
+
+    return 0;
+}

From 6526c5e4dc465005594dc251f84f338e3126d9dd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 21 Sep 2016 22:09:29 +0530
Subject: [PATCH 0952/2677] BUGFIX: Window::surface rendering function

Use function version of modDims function to properly
update metadata before JIT evaluation is carried out.
---
 src/api/c/surface.cpp    | 4 ++--
 src/api/cpp/graphics.cpp | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 6ae60e2946..c1458e239b 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -46,13 +46,13 @@ forge::Chart* setup_surface(const forge::Window* const window,
 
     if(Xinfo.isVector()){
         // Convert xIn is a column vector
-        xIn.modDims(xIn.elements());
+        xIn = modDims(xIn, xIn.elements());
         // Now tile along second dimension
         dim4 x_tdims(1, Y_dims[0], 1, 1);
         xIn = tile(xIn, x_tdims);
 
         // Convert yIn to a row vector
-        yIn.modDims(af::dim4(1, yIn.elements()));
+        yIn= modDims(yIn, af::dim4(1, yIn.elements()));
         // Now tile along first dimension
         dim4 y_tdims(X_dims[0], 1, 1, 1);
         yIn = tile(yIn, y_tdims);
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index d26a134e7e..4afd4a87dd 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/array.h>
+#include <af/data.h>
 #include <af/graphics.h>
 #include "error.hpp"
 
@@ -132,7 +133,8 @@ void Window::hist(const array& X, const double minval, const double maxval, cons
     AF_THROW(af_draw_hist(get(), X.get(), minval, maxval, &temp));
 }
 
-void Window::surface(const array& S, const char* const title){
+void Window::surface(const array& S, const char* const title)
+{
     af::array xVals = seq(0, S.dims(0)-1);
     af::array yVals = seq(0, S.dims(1)-1);
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};

From c17537f25c5d6b968c7cd2c3c0b175e0ef641142 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Sep 2016 20:56:56 +0530
Subject: [PATCH 0953/2677] More fixes related to modDims function

---
 src/api/c/histeq.cpp | 2 +-
 src/api/c/plot.cpp   | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 78c3f16e4a..9579df82b0 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -53,7 +53,7 @@ static af_array hist_equal(const af_array& in, const af_array& hist)
     Array<float> idxArr  = lookup<float, T>(normCdf, getArray<T>(vInput), 0);
 
     Array<T> result = cast<T>(idxArr);
-    result.modDims(input.dims());
+    result = modDims(result, input.dims());
 
     AF_CHECK(af_release_array(vInput));
 
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index bf11ae7642..2151c9197f 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -265,8 +265,8 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
 //
 //    // Force the vectors to be row vectors
 //    // This ensures we can use join(0,..) and skip reorder
-//    xIn.modDims(rowDims);
-//    yIn.modDims(rowDims);
+//    xIn = modDims(xIn, rowDims);
+//    yIn = modDims(yIn, rowDims);
 //
 //    // join along first dimension, skip reorder
 //    Array<T> P = join(0, xIn, yIn);

From 1b18226dfec811e4b7b7254f5cfc85a3116a3dc2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 23 Sep 2016 11:07:58 -0400
Subject: [PATCH 0954/2677] Fix double free corruption when release arrays in
 af_assign_seq

---
 src/api/c/assign.cpp | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 4dda94086b..f863214759 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -127,7 +127,10 @@ af_err af_assign_seq(af_array *out,
             AF_CHECK(af_assign_seq(&tmp_out, tmp_in, ndims, index, rhs));
             AF_CHECK(af_moddims(out, tmp_out, lInfo.ndims(), lInfo.dims().get()));
             AF_CHECK(af_release_array(tmp_in));
-            AF_CHECK(af_release_array(tmp_out));
+            // This can run into a double free issue if tmp_in == tmp_out
+            // The condition ensures release only if both are different
+            // Issue found on Tegra X1
+            if(tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
             return AF_SUCCESS;
         }
 
@@ -244,7 +247,10 @@ af_err af_assign_gen(af_array *out,
             AF_CHECK(af_assign_gen(&tmp_out, tmp_in, ndims, indexs, rhs_));
             AF_CHECK(af_moddims(out, tmp_out, lInfo.ndims(), lInfo.dims().get()));
             AF_CHECK(af_release_array(tmp_in));
-            AF_CHECK(af_release_array(tmp_out));
+            // This can run into a double free issue if tmp_in == tmp_out
+            // The condition ensures release only if both are different
+            // Issue found on Tegra X1
+            if(tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
             return AF_SUCCESS;
         }
 

From 0ed6cccc5fff587f05d7400bff6291f5b6aa886d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 21 Sep 2016 11:40:04 -0400
Subject: [PATCH 0955/2677] Fix syncthreads in cuda nearest neighbour

---
 src/backend/cuda/kernel/nearest_neighbour.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index b76c70ee2d..eb2bebed80 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -411,8 +411,8 @@ __global__ void select_matches(
                 s_dist[sid] = dist;
                 s_idx[sid]  = s_idx[sid + i];
             }
-            __syncthreads();
         }
+        __syncthreads();
     }
 
     // Store best matches and indexes to training dataset

From ab61f45e6b4574a64b3369be06ada049086c19f2 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 24 Sep 2016 11:01:55 -0400
Subject: [PATCH 0956/2677] Remove rogue printf from bilateral kernel

---
 src/backend/opencl/kernel/bilateral.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 0493389645..2dc2a5932d 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -50,7 +50,6 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
 
         std::call_once( compileFlags[device], [device] () {
                 bool use_native_exp = getActivePlatform() != AFCL_PLATFORM_POCL;
-                printf("NATIVE_EXP: %d\n", use_native_exp);
                 std::ostringstream options;
                 options << " -D inType=" << dtype_traits<inType>::getName()
                         << " -D outType=" << dtype_traits<outType>::getName();

From 38cba47f7ed919939f130b917d76c4651e243604 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 26 Sep 2016 10:31:49 -0400
Subject: [PATCH 0957/2677] Add key.eval to CPU scan by key

---
 src/backend/cpu/scan_by_key.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index d00db6cceb..3f0aad80a9 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -31,6 +31,8 @@ namespace cpu
         kernel::scan_dim_by_key<op, Ti, Tk, To, 4> func4(inclusive_scan);
 
         in.eval();
+        key.eval();
+
         switch (in.ndims()) {
         case 1:
             getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);

From 6100583ed99b9bd0b3546eb45794dd4d399c68f8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 28 Sep 2016 21:05:57 +0530
Subject: [PATCH 0958/2677] use range function in Window::surface method

---
 src/api/cpp/graphics.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 4afd4a87dd..669d1fde5d 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -135,8 +135,8 @@ void Window::hist(const array& X, const double minval, const double maxval, cons
 
 void Window::surface(const array& S, const char* const title)
 {
-    af::array xVals = seq(0, S.dims(0)-1);
-    af::array yVals = seq(0, S.dims(1)-1);
+    af::array xVals = range(S.dims(0));
+    af::array yVals = range(S.dims(1));
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_surface(get(), xVals.get(), yVals.get(), S.get(), &temp));
 }

From 67f9522c3342be6b089082450dbc3add7cc7a578 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 7 Oct 2016 12:40:59 -0400
Subject: [PATCH 0959/2677] BUILD: Default CUDA Computes for CUDA 8 are 30, 50,
 60

---
 src/backend/cuda/CMakeLists.txt | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a3a63958e7..f0b6512c6b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -44,12 +44,12 @@ LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
 IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
     MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon separated list of CUDA Compute versions to enable the specified computes")
     MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
-    LIST(APPEND COMPUTES_DETECTED_LIST "20" "30" "50")
+    LIST(APPEND COMPUTES_DETECTED_LIST "30" "50")
     IF(${CUDA_VERSION_MAJOR} GREATER 7) # Enable 60 only if CUDA 8 or greater
-        MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50, 60")
+        MESSAGE(STATUS "No computes detected. Fall back to 30, 50, 60")
         LIST(APPEND COMPUTES_DETECTED_LIST "60")
     ELSE(${CUDA_VERSION_MAJOR} GREATER 7)
-        MESSAGE(STATUS "No computes detected. Fall back to 20, 30, 50")
+        LIST(APPEND COMPUTES_DETECTED_LIST "20")
     ENDIF(${CUDA_VERSION_MAJOR} GREATER 7)
 ENDIF()
 
@@ -85,8 +85,8 @@ IF(${CUDA_VERSION_MAJOR} LESS 8)
       )
         MESSAGE(FATAL_ERROR
                 "CUDA Compute 6x was enabled.\
-                CUDA Compute 6x (Pascal) GPUs require CUDA 8 or greater.\
-                Your CUDA Version is ${CUDA_VERSION}."
+                 CUDA Compute 6x (Pascal) GPUs require CUDA 8 or greater.\
+                 Your CUDA Version is ${CUDA_VERSION}."
                 )
     ENDIF()
 ENDIF(${CUDA_VERSION_MAJOR} LESS 8)

From baac61f1c47af1a93444a612bf4ffddbddff69df Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 7 Oct 2016 12:41:37 -0400
Subject: [PATCH 0960/2677] BUILD: CUDA Use -arch=sm_30 when using CUDA 8 and
 not COMPUTE_20

---
 src/backend/cuda/CMakeLists.txt | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f0b6512c6b..9cfa8a5929 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -294,7 +294,12 @@ LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
 IF(${COMPUTE_COUNT} EQUAL 1)
     SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} ${CUDA_GENERATE_CODE}")
 ELSE()
-    SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch sm_20")
+    # Use -arch sm_30 if CUDA 8 or greater and compute_20 not defined
+    IF(CUDA_COMPUTE_20 OR ${CUDA_VERSION_MAJOR} LESS 8)
+        SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch sm_20")
+    ELSE()
+        SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch sm_30")
+    ENDIF()
 ENDIF()
 
 # PUSH/POP --keep-device-functions flag. Only available in CUDA 8 or newer

From 264d1f54eb7506ba06ed827a87f45e0a8f0129b8 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 7 Oct 2016 15:59:38 -0400
Subject: [PATCH 0961/2677] Fixed picking the right libdevice for device
 compute

---
 src/backend/cuda/jit.cpp | 39 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 37 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index e6979ec229..fc95014bbf 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -302,6 +302,35 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
                                                 \
     } while(0)
 
+void compute_to_libdevice_table(const char **buffer, size_t *bc_buffer_len, int compute)
+{
+    // Source: http://docs.nvidia.com/cuda/libdevice-users-guide/basic-usage.html#version-selection
+    if(compute >= 20 && compute < 30) {
+        *buffer        = compute_20_bc;
+        *bc_buffer_len = compute_20_bc_len;
+    } else if (compute == 30) {
+        *buffer        = compute_30_bc;
+        *bc_buffer_len = compute_30_bc_len;
+    } else if (compute >= 31 && compute <  35) {
+        *buffer        = compute_20_bc;
+        *bc_buffer_len = compute_20_bc_len;
+    } else if (compute >= 35 && compute <= 37) {
+        *buffer        = compute_35_bc;
+        *bc_buffer_len = compute_35_bc_len;
+    } else if (compute >  37 && compute <  50) {
+        *buffer        = compute_30_bc;
+        *bc_buffer_len = compute_30_bc_len;
+    } else if (compute >= 50 && compute <= 53) {
+        *buffer        = compute_50_bc;
+        *bc_buffer_len = compute_50_bc_len;
+    } else if (compute >  53) {
+        *buffer        = compute_30_bc;
+        *bc_buffer_len = compute_30_bc_len;
+    } else {
+        AF_ERROR("Invalid Compute for libdevice", AF_ERR_INTERNAL);
+    }
+}
+
 static char *irToPtx(string IR, size_t *ptx_size)
 {
     nvvmProgram prog;
@@ -309,8 +338,14 @@ static char *irToPtx(string IR, size_t *ptx_size)
     NVVM_CHECK(nvvmCreateProgram(&prog), "Failed to create program");
 
 #if defined(USE_LIBDEVICE)
-    //FIXME: Use proper compute
-    NVVM_CHECK(nvvmAddModuleToProgram(prog, compute_20_bc, compute_20_bc_len, "libdevice kernels"),
+    // Get compute version of device
+    cudaDeviceProp devProp = getDeviceProp(getActiveDeviceId());
+    int compute = devProp.major * 10 + devProp.minor;
+    const char *bc_buffer = NULL;
+    size_t bc_buffer_len = 0;
+    compute_to_libdevice_table(&bc_buffer, &bc_buffer_len, compute);
+
+    NVVM_CHECK(nvvmAddModuleToProgram(prog, bc_buffer, bc_buffer_len, "libdevice kernels"),
                "Failed to add libdevice");
 #endif
 

From 2c69075f7e7f6b748992a112c2fe769afa83213c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sun, 9 Oct 2016 12:35:07 -0400
Subject: [PATCH 0962/2677] Fix else case in compute to libdevice table

---
 src/backend/cuda/jit.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index fc95014bbf..70592176ec 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -327,7 +327,8 @@ void compute_to_libdevice_table(const char **buffer, size_t *bc_buffer_len, int
         *buffer        = compute_30_bc;
         *bc_buffer_len = compute_30_bc_len;
     } else {
-        AF_ERROR("Invalid Compute for libdevice", AF_ERR_INTERNAL);
+        *buffer        = compute_30_bc;
+        *bc_buffer_len = compute_30_bc_len;
     }
 }
 

From 64944480a47972230ada24040eed07ae43048590 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sun, 9 Oct 2016 14:39:24 -0400
Subject: [PATCH 0963/2677] FIX CUDA 6.5 or older does not have libdevice
 compute_50

---
 src/backend/cuda/jit.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 70592176ec..00ff7fa1bc 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -321,8 +321,13 @@ void compute_to_libdevice_table(const char **buffer, size_t *bc_buffer_len, int
         *buffer        = compute_30_bc;
         *bc_buffer_len = compute_30_bc_len;
     } else if (compute >= 50 && compute <= 53) {
+#if defined(__LIBDEVICE_COMPUTE_50)
         *buffer        = compute_50_bc;
         *bc_buffer_len = compute_50_bc_len;
+#else
+        *buffer        = compute_30_bc;
+        *bc_buffer_len = compute_30_bc_len;
+#endif
     } else if (compute >  53) {
         *buffer        = compute_30_bc;
         *bc_buffer_len = compute_30_bc_len;

From 8ec78f1633b4dd5fce696247eb52b332f805a6fa Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 10 Oct 2016 11:44:19 -0400
Subject: [PATCH 0964/2677] Properly check for libdevice header files and
 provide fallbacks

---
 src/backend/cuda/jit.cpp | 86 +++++++++++++++++++++++++++++-----------
 1 file changed, 62 insertions(+), 24 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 00ff7fa1bc..f79a6aa2ba 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -302,40 +302,75 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
                                                 \
     } while(0)
 
+#if defined(USE_LIBDEVICE)
 void compute_to_libdevice_table(const char **buffer, size_t *bc_buffer_len, int compute)
 {
+// These macros create a fallback compute if in case the specific libdevice
+// compute is not found
+// 50 -> 30 -> 20 -> Not Found
+// 35 -> 30 -> 20 -> Not Found
+// 30 -> 20 -> Not Found
+// 20 -> Not Found
+#if defined(__LIBDEVICE_COMPUTE_20)
+    #define COMPUTE_20_STR compute_20_bc
+    #define COMPUTE_20_LEN compute_20_bc_len
+#else
+    #define COMPUTE_20_STR NULL
+    #define COMPUTE_20_LEN 0
+#endif
+
+#if defined(__LIBDEVICE_COMPUTE_30)
+    #define COMPUTE_30_STR compute_30_bc
+    #define COMPUTE_30_LEN compute_30_bc_len
+#else   // Fallback
+    #define COMPUTE_30_STR COMPUTE_20_STR
+    #define COMPUTE_30_LEN COMPUTE_20_LEN
+#endif
+
+#if defined(__LIBDEVICE_COMPUTE_35)
+    #define COMPUTE_35_STR compute_35_bc
+    #define COMPUTE_35_LEN compute_35_bc_len
+#else   // Fallback
+    #define COMPUTE_35_STR COMPUTE_30_STR
+    #define COMPUTE_35_LEN COMPUTE_30_LEN
+#endif
+
+#if defined(__LIBDEVICE_COMPUTE_50)
+    #define COMPUTE_50_STR compute_50_bc
+    #define COMPUTE_50_LEN compute_50_bc_len
+#else   // Fallback
+    #define COMPUTE_50_STR COMPUTE_30_STR
+    #define COMPUTE_50_LEN COMPUTE_30_LEN
+#endif
+
     // Source: http://docs.nvidia.com/cuda/libdevice-users-guide/basic-usage.html#version-selection
     if(compute >= 20 && compute < 30) {
-        *buffer        = compute_20_bc;
-        *bc_buffer_len = compute_20_bc_len;
+        *buffer        = COMPUTE_20_STR;
+        *bc_buffer_len = COMPUTE_20_LEN;
     } else if (compute == 30) {
-        *buffer        = compute_30_bc;
-        *bc_buffer_len = compute_30_bc_len;
+        *buffer        = COMPUTE_30_STR;
+        *bc_buffer_len = COMPUTE_30_LEN;
     } else if (compute >= 31 && compute <  35) {
-        *buffer        = compute_20_bc;
-        *bc_buffer_len = compute_20_bc_len;
+        *buffer        = COMPUTE_20_STR;
+        *bc_buffer_len = COMPUTE_20_LEN;
     } else if (compute >= 35 && compute <= 37) {
-        *buffer        = compute_35_bc;
-        *bc_buffer_len = compute_35_bc_len;
+        *buffer        = COMPUTE_35_STR;
+        *bc_buffer_len = COMPUTE_35_LEN;
     } else if (compute >  37 && compute <  50) {
-        *buffer        = compute_30_bc;
-        *bc_buffer_len = compute_30_bc_len;
+        *buffer        = COMPUTE_30_STR;
+        *bc_buffer_len = COMPUTE_30_LEN;
     } else if (compute >= 50 && compute <= 53) {
-#if defined(__LIBDEVICE_COMPUTE_50)
-        *buffer        = compute_50_bc;
-        *bc_buffer_len = compute_50_bc_len;
-#else
-        *buffer        = compute_30_bc;
-        *bc_buffer_len = compute_30_bc_len;
-#endif
+        *buffer        = COMPUTE_50_STR;
+        *bc_buffer_len = COMPUTE_50_LEN;
     } else if (compute >  53) {
-        *buffer        = compute_30_bc;
-        *bc_buffer_len = compute_30_bc_len;
+        *buffer        = COMPUTE_30_STR;
+        *bc_buffer_len = COMPUTE_30_LEN;
     } else {
-        *buffer        = compute_30_bc;
-        *bc_buffer_len = compute_30_bc_len;
+        *buffer        = COMPUTE_30_STR;
+        *bc_buffer_len = COMPUTE_30_LEN;
     }
 }
+#endif
 
 static char *irToPtx(string IR, size_t *ptx_size)
 {
@@ -350,9 +385,12 @@ static char *irToPtx(string IR, size_t *ptx_size)
     const char *bc_buffer = NULL;
     size_t bc_buffer_len = 0;
     compute_to_libdevice_table(&bc_buffer, &bc_buffer_len, compute);
-
-    NVVM_CHECK(nvvmAddModuleToProgram(prog, bc_buffer, bc_buffer_len, "libdevice kernels"),
-               "Failed to add libdevice");
+    if(bc_buffer)
+        NVVM_CHECK(nvvmAddModuleToProgram(prog, bc_buffer, bc_buffer_len, "libdevice kernels"),
+                   "Failed to add libdevice");
+    else
+        NVVM_CHECK(nvvmAddModuleToProgram(prog, IR.c_str(), IR.size(), "generated kernel"),
+                   "Failed to add module");
 #endif
 
     NVVM_CHECK(nvvmAddModuleToProgram(prog, IR.c_str(), IR.size(), "generated kernel"),

From 449724168e5ef299ea2709ecc61baff466f2c92d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 11 Oct 2016 16:18:50 -0400
Subject: [PATCH 0965/2677] Increment version to 3.4.1

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index a4ea1b3560..bcc7409fd8 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -10,7 +10,7 @@ ENDIF()
 
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "4")
-SET(AF_VERSION_PATCH "0")
+SET(AF_VERSION_PATCH "1")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From 2889109fa254ce6323ea9875e14af80f7b813aa4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 12 Oct 2016 11:28:43 -0400
Subject: [PATCH 0966/2677] Enable OpenCL FFT prime factors 7, 11, 13

---
 src/backend/opencl/fft.cpp | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index 239fc74bff..7cd39f46af 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -228,10 +228,16 @@ inline bool isSupLen(dim_t length)
     {
         if( length % 2 == 0 )
             length /= 2;
-        else if( length % 3 == 0 )
+        else if( length % 3  == 0 )
             length /= 3;
-        else if( length % 5 == 0 )
+        else if( length % 5  == 0 )
             length /= 5;
+        else if( length % 7  == 0 )
+            length /= 7;
+        else if( length % 11 == 0 )
+            length /= 11;
+        else if( length % 13 == 0 )
+            length /= 13;
         else
             return false;
     }

From 9e24c1ca5cccc80963312ed9c675d5dbc5111ff4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 13 Oct 2016 11:37:45 -0400
Subject: [PATCH 0967/2677] Add tests for fft with multiples of 7, 11, 13

---
 test/data    |  2 +-
 test/fft.cpp | 15 +++++++++++++++
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/test/data b/test/data
index b69986ebbd..8493781ff0 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit b69986ebbd09a4308dec37287968ffa881c2beee
+Subproject commit 8493781ff0489626cb0f004ee046d7f187083e10
diff --git a/test/fft.cpp b/test/fft.cpp
index 19b0ae0950..370e2327c4 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -189,6 +189,21 @@ INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/sig
 INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
 INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
 
+// Factors 7, 11, 13
+INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+
+INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+
 // transforms on padded and truncated arrays
 INSTANTIATE_TEST(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
 INSTANTIATE_TEST(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);

From f942987724ed167bd2c7e23dde3e128f4bdeee5e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 13 Oct 2016 16:22:56 -0400
Subject: [PATCH 0968/2677] BUGFIX Fix the dimensions of values array in sparse
 COO

---
 src/backend/cpu/sparse.cpp    | 6 +++++-
 src/backend/cuda/sparse.cu    | 6 +++++-
 src/backend/opencl/sparse.cpp | 6 +++++-
 3 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 56fda0fac6..68d657a145 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -16,6 +16,7 @@
 #include <arith.hpp>
 #include <cast.hpp>
 #include <complex.hpp>
+#include <copy.hpp>
 #include <err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
@@ -138,7 +139,10 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 
     Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
     Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-    Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    Array<T> values = copyArray<T>(in);
+    values.modDims(dim4(values.elements()));
+    values = lookup<T, int>(values, nonZeroIdx, 0);
 
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
 }
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index bcdd8b0a5e..a393084899 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -17,6 +17,7 @@
 #include <arith.hpp>
 #include <cast.hpp>
 #include <complex.hpp>
+#include <copy.hpp>
 #include <err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
@@ -207,7 +208,10 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 
     Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
     Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-    Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    Array<T> values = copyArray<T>(in);
+    values.modDims(dim4(values.elements()));
+    values = lookup<T, int>(values, nonZeroIdx, 0);
 
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
 }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index e4aebbfaf8..07461f953e 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -16,6 +16,7 @@
 #include <arith.hpp>
 #include <cast.hpp>
 #include <complex.hpp>
+#include <copy.hpp>
 #include <err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
@@ -45,7 +46,10 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 
     Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
     Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-    Array<T>   values = lookup<T, int>(in, nonZeroIdx, 0);
+
+    Array<T> values = copyArray<T>(in);
+    values.modDims(dim4(values.elements()));
+    values = lookup<T, int>(values, nonZeroIdx, 0);
 
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
 }

From d0cb8d5ca12e14a307df15fa0d27ac64c0f138ef Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 13 Oct 2016 13:54:48 -0400
Subject: [PATCH 0969/2677] Remove Tegra K1 build status from README.md

---
 README.md | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index 07bf01a47c..c0d12b81fd 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,35 @@
 <a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="300"></a>
 
-ArrayFire is a general-purpose library that simplifies the process of developing 
-software that targets parallel and massively-parallel architectures including 
+ArrayFire is a general-purpose library that simplifies the process of developing
+software that targets parallel and massively-parallel architectures including
 CPUs, GPUs, and other hardware acceleration devices.
 
-To achieve this goal, ArrayFire provides software developers with a high-level 
-abstraction of data which resides on the accelerator, the `af::array` object 
+To achieve this goal, ArrayFire provides software developers with a high-level
+abstraction of data which resides on the accelerator, the `af::array` object
 (or C-style struct).
 Developers write code which performs operations on ArrayFire arrays which, in turn,
 are automatically translated into near-optimal kernels that execute on the computational
-device. 
-ArrayFire is successfully used on devices ranging from low-power mobile phones to 
-high-power GPU-enabled supercomputers including CPUs from all major vendors (Intel, AMD, Arm), 
-GPUs from the dominant manufacturers (NVIDIA, AMD, and Qualcomm), as well as a variety 
+device.
+ArrayFire is successfully used on devices ranging from low-power mobile phones to
+high-power GPU-enabled supercomputers including CPUs from all major vendors (Intel, AMD, Arm),
+GPUs from the dominant manufacturers (NVIDIA, AMD, and Qualcomm), as well as a variety
 of other accelerator devices on Windows, Mac, and Linux.
 
 Several of ArrayFire's benefits include:
 
-* [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable, 
+* [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable,
   [well-documented](http://arrayfire.org/docs) API.
-* Rigorously Tested for Performance and Accuracy 
+* Rigorously Tested for Performance and Accuracy
 * Commercially Friendly Open-Source Licensing
 * Commercial support from [ArrayFire](http://arrayfire.com)
 * [Read about more benefits on Arrayfire.com](http://arrayfire.com/the-arrayfire-library/)
- 
+
 ### Build and Test Status
 
-|         | Linux x86_64 | Linux armv7l | Linux aarch64 | Windows | OSX |
-|:-------:|:------------:|:------------:|:-------------:|:-------:|:---:|
-| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
-| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
+|         | Linux x86_64 | Linux aarch64 | Windows | OSX |
+|:-------:|:------------:|:-------------:|:-------:|:---:|
+| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
+| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
 
 ### Installation
 
@@ -143,7 +143,7 @@ details.
 
 ### Trademark Policy
 
-The literal mark “ArrayFire” and ArrayFire logos are trademarks of 
+The literal mark “ArrayFire” and ArrayFire logos are trademarks of
 AccelerEyes LLC DBA ArrayFire.
 If you wish to use either of these marks in your own project, please consult
 [ArrayFire's Trademark Policy](http://arrayfire.com/trademark-policy/)

From 9476a9e6932458d73011d8a5c5adfd01cd2efa68 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 13 Oct 2016 13:44:40 -0400
Subject: [PATCH 0970/2677] Updated release notes for v3.4.1

---
 docs/pages/release_notes.md | 84 +++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index bdcd91158a..b944a0c9c1 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,90 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.4.1
+==============
+
+Installers
+----------
+* Installers for Linux, OS X and Windows
+  * CUDA backend now uses [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit).
+  * Uses [Intel MKL 2017](https://software.intel.com/en-us/intel-mkl).
+  * CUDA Compute 2.x (Fermi) is no longer compiled into the library.
+* Installer for OS X
+  * The libraries shipping in the OS X Installer are now compiled with Apple
+    Clang v7.3.1 (previouly v6.1.0).
+  * The OS X version used is 10.11.6 (previously 10.10.5).
+* Installer for Jetson TX1 / Tegra X1
+  * Requires [JetPack for L4T 2.3](https://developer.nvidia.com/embedded/jetpack)
+    (containing Linux for Tegra r24.2 for TX1).
+  * CUDA backend now uses [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit) 64-bit.
+  * Using CUDA's cusolver instead of CPU fallback.
+  * Uses OpenBLAS for CPU BLAS.
+  * All ArrayFire libraries are now 64-bit.
+
+Improvements
+------------
+* Add [sparse array](\ref sparse_func) support to \ref af::eval().
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1598)</sup>
+* Add OpenCL-CPU fallback support for sparse \ref af::matmul() when running on
+  a unified memory device. Uses MKL Sparse BLAS.
+* When using CUDA libdevice, pick the correct compute version based on device.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1612)</sup>
+* OpenCL FFT now also supports prime factors 7, 11 and 13.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1383)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1619)</sup>
+
+Bug Fixes
+---------
+* Allow CUDA libdevice to be detected from custom directory.
+* Fix `aarch64` detection on Jetson TX1 64-bit OS.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1593)</sup>
+* Add missing definition of `af_set_fft_plan_cache_size` in unified backend.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1591)</sup>
+* Fix intial values for \ref af::min() and \ref af::max() operations.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1594)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1595)</sup>
+* Fix distance calculation in \ref af::nearestNeighbour for CUDA and OpenCL backend.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1596)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1595)</sup>
+* Fix OpenCL bug where scalars where are passed incorrectly to compile options.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1595)</sup>
+* Fix bug in \ref af::Window::surface() with respect to dimensions and ranges.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1604)</sup>
+* Fix possible double free corruption in \ref af_assign_seq().
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1605)</sup>
+* Add missing eval for key in \ref af::scanByKey in CPU backend.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1605)</sup>
+* Fixed creation of sparse values array using \ref AF_STORAGE_COO.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1620)</sup>
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1621)</sup>
+
+Examples
+--------
+* Add a [Conjugate Gradient solver example](\ref benchmarks/cg.cpp)
+  to demonstrate sparse and dense matrix operations.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1599)</sup>
+
+CUDA Backend
+------------
+* When using [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit),
+  compute 2.x are no longer in default compute list.
+  * This follows [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit)
+    deprecating computes 2.x.
+  * Default computes for CUDA 8.0 will be 30, 50, 60.
+* When using CUDA pre-8.0, the default selection remains 20, 30, 50.
+* CUDA backend now uses `-arch=sm_30` for PTX compilation as default.
+  * Unless compute 2.0 is enabled.
+
+Known Issues
+------------
+* \ref af::lu() on CPU is known to give incorrect results when built run on
+  OS X 10.11 or 10.12 and compiled with Accelerate Framework.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1617)</sup>
+  * Since the OS X Installer libraries uses MKL rather than Accelerate
+    Framework, this issue does not affect those libraries.
+
+
 v3.4.0
 ==============
 

From fb8305ad9f0d9aa9ed2bd1a7107ef114442b2803 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 13 Oct 2016 16:27:02 -0400
Subject: [PATCH 0971/2677] Add tests to verify dimensions and values of sparse
 arrays

---
 test/sparse.cpp | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index 826984ecdc..68e7350955 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -196,3 +196,33 @@ SPARSE_TESTS(cfloat, 1E-3)
 SPARSE_TESTS(cdouble, 1E-5)
 
 #undef SPARSE_TESTS
+
+// This test essentially verifies that the sparse structures have the correct
+// dimensions and indices using a very basic test
+template<af_storage stype>
+void createFunction()
+{
+    af::array in = af::sparse(af::identity(3, 3), stype);
+
+    af::array values = sparseGetValues(in);
+    af::array rowIdx = sparseGetRowIdx(in);
+    af::array colIdx = sparseGetColIdx(in);
+    dim_t     nNZ    = sparseGetNNZ(in);
+
+    ASSERT_EQ(nNZ, values.elements());
+
+    ASSERT_EQ(0, af::max<double>(values - af::constant(1, nNZ)));
+    ASSERT_EQ(0, af::max<int   >(rowIdx - af::range(af::dim4(rowIdx.elements()), 0, s32)));
+    ASSERT_EQ(0, af::max<int   >(colIdx - af::range(af::dim4(colIdx.elements()), 0, s32)));
+}
+
+#define CREATE_TESTS(STYPE)                                         \
+    TEST(SPARSE_CREATE, STYPE)                                      \
+    {                                                               \
+        createFunction<STYPE>();                                    \
+    }
+
+CREATE_TESTS(AF_STORAGE_CSR)
+CREATE_TESTS(AF_STORAGE_COO)
+
+#undef CREATE_TESTS

From 6bedacc818fbaadf1275cfe7fdbd7db074a60b94 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 10 Oct 2016 18:26:35 -0400
Subject: [PATCH 0972/2677] FEAT: Add CPU offload to Sparse matmul

* Available when using MKL and not
* Uses CPU fallback code when not using MKL
---
 src/backend/opencl/cpu/cpu_sparse_blas.cpp | 535 +++++++++++++++++++++
 src/backend/opencl/cpu/cpu_sparse_blas.hpp |  35 ++
 src/backend/opencl/sparse_blas.cpp         |  10 +
 3 files changed, 580 insertions(+)
 create mode 100644 src/backend/opencl/cpu/cpu_sparse_blas.cpp
 create mode 100644 src/backend/opencl/cpu/cpu_sparse_blas.hpp

diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
new file mode 100644
index 0000000000..b8ddb9df3f
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -0,0 +1,535 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#include <cpu/cpu_sparse_blas.hpp>
+
+#include <stdexcept>
+#include <string>
+#include <cassert>
+
+#include <af/dim4.hpp>
+#include <complex.hpp>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+
+namespace opencl
+{
+namespace cpu
+{
+
+using namespace common;
+
+using std::add_const;
+using std::add_pointer;
+using std::enable_if;
+using std::is_floating_point;
+using std::remove_const;
+using std::conditional;
+using std::is_same;
+
+template<typename T, class Enable = void>
+struct blas_base {
+    using type = T;
+};
+
+template<typename T>
+struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
+    using type = typename conditional<is_same<T, cdouble>::value,
+                                      sp_cdouble, sp_cfloat>
+                                     ::type;
+};
+
+template<typename T>
+using cptr_type     =   typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const T*>::type;
+template<typename T>
+using ptr_type     =    typename conditional<   is_complex<T>::value,
+                                                typename blas_base<T>::type *,
+                                                T*>::type;
+template<typename T>
+using scale_type   =    typename conditional<   is_complex<T>::value,
+                                                const typename blas_base<T>::type,
+                                                const T>::type;
+
+template<typename To, typename Ti>
+To getScaleValue(Ti val)
+{
+    return (To)(val);
+}
+
+#ifdef USE_MKL
+
+// MKL
+// sparse_status_t mkl_sparse_z_create_csr (
+//                 sparse_matrix_t *A,
+//                 sparse_index_base_t indexing,
+//                 MKL_INT rows, MKL_INT cols,
+//                 MKL_INT *rows_start, MKL_INT *rows_end,
+//                 MKL_INT *col_indx,
+//                 MKL_Complex16 *values);
+//
+// sparse_status_t mkl_sparse_z_mv (
+//                 sparse_operation_t operation,
+//                 MKL_Complex16 alpha,
+//                 const sparse_matrix_t A,
+//                 struct matrix_descr descr,
+//                 const MKL_Complex16 *x,
+//                 MKL_Complex16 beta,
+//                 MKL_Complex16 *y);
+//
+// sparse_status_t mkl_sparse_z_mm (
+//                 sparse_operation_t operation,
+//                 MKL_Complex16 alpha,
+//                 const sparse_matrix_t A,
+//                 struct matrix_descr descr,
+//                 sparse_layout_t layout,
+//                 const MKL_Complex16 *x,
+//                 MKL_INT columns, MKL_INT ldx,
+//                 MKL_Complex16 beta,
+//                 MKL_Complex16 *y,
+//                 MKL_INT ldy);
+
+template<typename T>
+using create_csr_func_def = sparse_status_t (*)
+                           (sparse_matrix_t *,
+                            sparse_index_base_t,
+                            int, int,
+                            int *, int *, int*,
+                            ptr_type<T>);
+
+template<typename T>
+using mv_func_def         = sparse_status_t (*)
+                           (sparse_operation_t,
+                            scale_type<T>,
+                            const sparse_matrix_t,
+                            struct matrix_descr,
+                            cptr_type<T>,
+                            scale_type<T>,
+                            ptr_type<T>);
+
+template<typename T>
+using mm_func_def         = sparse_status_t (*)
+                           (sparse_operation_t,
+                            scale_type<T>,
+                            const sparse_matrix_t,
+                            struct matrix_descr,
+                            sparse_layout_t,
+                            cptr_type<T>,
+                            int, int,
+                            scale_type<T>,
+                            ptr_type<T>, int);
+
+#define SPARSE_FUNC_DEF( FUNC )                         \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
+  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
+{ return &mkl_sparse_##PREFIX##_##FUNC; }
+
+SPARSE_FUNC_DEF( create_csr )
+SPARSE_FUNC(create_csr , float   , s)
+SPARSE_FUNC(create_csr , double  , d)
+SPARSE_FUNC(create_csr , cfloat  , c)
+SPARSE_FUNC(create_csr , cdouble , z)
+
+SPARSE_FUNC_DEF( mv )
+SPARSE_FUNC(mv , float   , s)
+SPARSE_FUNC(mv , double  , d)
+SPARSE_FUNC(mv , cfloat  , c)
+SPARSE_FUNC(mv , cdouble , z)
+
+SPARSE_FUNC_DEF( mm )
+SPARSE_FUNC(mm , float   , s)
+SPARSE_FUNC(mm , double  , d)
+SPARSE_FUNC(mm , cfloat  , c)
+SPARSE_FUNC(mm , cdouble , z)
+
+#undef SPARSE_FUNC
+#undef SPARSE_FUNC_DEF
+
+template<>
+const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
+{
+    sp_cfloat ret;
+    ret.real = val.s[0];
+    ret.imag = val.s[1];
+    return ret;
+}
+
+template<>
+const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
+{
+    sp_cdouble ret;
+    ret.real = val.s[0];
+    ret.imag = val.s[1];
+    return ret;
+}
+
+#else   // USE_MKL
+
+// From mkl_spblas.h
+typedef enum
+{
+    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
+    SPARSE_OPERATION_TRANSPOSE              = 11,
+    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
+} sparse_operation_t;
+
+#endif  // USE_MKL
+
+sparse_operation_t
+toSparseTranspose(af_mat_prop opt)
+{
+    sparse_operation_t out = SPARSE_OPERATION_NON_TRANSPOSE;
+    switch(opt) {
+        case AF_MAT_NONE        : out = SPARSE_OPERATION_NON_TRANSPOSE;         break;
+        case AF_MAT_TRANS       : out = SPARSE_OPERATION_TRANSPOSE;             break;
+        case AF_MAT_CTRANS      : out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE;   break;
+        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+    return out;
+}
+
+template<typename T, int value>
+scale_type<T> getScale()
+{
+    static T val = scalar<T>(value);
+    return getScaleValue<scale_type<T>, T>(val);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#ifdef USE_MKL // Implementation using MKL
+////////////////////////////////////////////////////////////////////////////////
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    // MKL: CSRMM Does not support optRhs
+
+    lhs.eval();
+    rhs.eval();
+
+    // Similar Operations to GEMM
+    sparse_operation_t lOpts = toSparseTranspose(optLhs);
+
+    int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
+    //int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+
+    //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
+    static const int rColDim = 1;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhs.dims();
+    int M = lDims[lRowDim];
+    int N = rDims[rColDim];
+    //int K = lDims[lColDim];
+
+    Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
+    out.eval();
+
+    auto alpha = getScale<T, 1>();
+    auto beta  = getScale<T, 0>();
+
+    int ldb = rhs.strides()[1];
+    int ldc = out.strides()[1];
+
+    // get host pointers from mapped memory
+    auto rhsPtr = rhs.getMappedPtr();
+    auto outPtr = out.getMappedPtr();
+
+    Array<T  > values = lhs.getValues();
+    Array<int> rowIdx = lhs.getRowIdx();
+    Array<int> colIdx = lhs.getColIdx();
+
+    auto vPtr = values.getMappedPtr();
+    auto rPtr = rowIdx.getMappedPtr();
+    auto cPtr = colIdx.getMappedPtr();
+    int* pB   = rPtr.get();
+    int* pE   = rPtr.get() + 1;
+
+    sparse_matrix_t csrLhs;
+    create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, lhs.dims()[0], lhs.dims()[1],
+                         pB, pE, cPtr.get(),
+                         reinterpret_cast<ptr_type<T>>(vPtr.get()));
+
+    struct matrix_descr descrLhs;
+    descrLhs.type = SPARSE_MATRIX_TYPE_GENERAL;
+
+    mkl_sparse_optimize(csrLhs);
+
+    if(rDims[rColDim] == 1) {
+        mkl_sparse_set_mv_hint(csrLhs, lOpts, descrLhs, 1);
+        mv_func<T>()(
+            lOpts, alpha,
+            csrLhs, descrLhs,
+            reinterpret_cast<cptr_type<T>>(rhsPtr.get()),
+            beta,
+            reinterpret_cast<ptr_type<T>>(outPtr.get()));
+    } else {
+        mkl_sparse_set_mm_hint(csrLhs, lOpts, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR, N, 1);
+        mm_func<T>()(
+            lOpts, alpha,
+            csrLhs, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR,
+            reinterpret_cast<cptr_type<T>>(rhsPtr.get()),
+            N, ldb, beta,
+            reinterpret_cast<ptr_type<T>>(outPtr.get()), ldc);
+    }
+    mkl_sparse_destroy(csrLhs);
+
+    return out;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#else // Implementation without using MKL
+////////////////////////////////////////////////////////////////////////////////
+
+template<typename T>
+T getConjugate(const T &in)
+{
+    // For non-complex types return same
+    return in;
+}
+
+template<>
+cfloat getConjugate(const cfloat &in)
+{
+    cfloat val;
+    val.s[0] =  in.s[0];
+    val.s[1] = -in.s[1];
+    return val;
+}
+
+template<>
+cdouble getConjugate(const cdouble &in)
+{
+    cdouble val;
+    val.s[0] =  in.s[0];
+    val.s[1] = -in.s[1];
+    return val;
+}
+
+template<typename T, bool conjugate>
+void mv(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M)
+{
+    auto oPtr   = output.getMappedPtr();
+    auto rhtPtr = right .getMappedPtr();
+    auto vPtr   = values.getMappedPtr();
+    auto rPtr   = rowIdx.getMappedPtr();
+    auto cPtr   = colIdx.getMappedPtr();
+
+    T   const * const valPtr = vPtr.get();
+    int const * const rowPtr = rPtr.get();
+    int const * const colPtr = cPtr.get();
+    T   const * const rhsPtr = rhtPtr.get();
+    T         * const outPtr = oPtr.get();
+
+    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        outPtr[i] = scalar<T>(0);
+        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+            //If stride[0] of right is not 1 then rhsPtr[colPtr[j]*stride]
+            if (conjugate) {
+                outPtr[i] = outPtr[i] + getConjugate(valPtr[j]) * rhsPtr[colPtr[j]];
+            } else {
+                outPtr[i] = outPtr[i] + valPtr[j] * rhsPtr[colPtr[j]];
+            }
+        }
+    }
+}
+
+template<typename T, bool conjugate>
+void mtv(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M)
+{
+    auto oPtr   = output.getMappedPtr();
+    auto rhtPtr = right .getMappedPtr();
+    auto vPtr   = values.getMappedPtr();
+    auto rPtr   = rowIdx.getMappedPtr();
+    auto cPtr   = colIdx.getMappedPtr();
+
+    T   const * const valPtr = vPtr.get();
+    int const * const rowPtr = rPtr.get();
+    int const * const colPtr = cPtr.get();
+    T   const * const rhsPtr = rhtPtr.get();
+    T         * const outPtr = oPtr.get();
+
+    for (int i = 0; i < M; ++i) {
+        outPtr[i] = scalar<T>(0);
+    }
+
+    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+            //If stride[0] of right is not 1 then rhsPtr[i*stride]
+            if (conjugate) {
+                outPtr[colPtr[j]] = outPtr[colPtr[j]] + getConjugate(valPtr[j]) * rhsPtr[i];
+            } else {
+                outPtr[colPtr[j]] = outPtr[colPtr[j]] + valPtr[j] * rhsPtr[i];
+            }
+        }
+    }
+}
+
+template<typename T, bool conjugate>
+void mm(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M, int N,
+        int ldb, int ldc)
+{
+    auto oPtr   = output.getMappedPtr();
+    auto rhtPtr = right .getMappedPtr();
+    auto vPtr   = values.getMappedPtr();
+    auto rPtr   = rowIdx.getMappedPtr();
+    auto cPtr   = colIdx.getMappedPtr();
+
+    T   const * const valPtr = vPtr.get();
+    int const * const rowPtr = rPtr.get();
+    int const * const colPtr = cPtr.get();
+    T   const *       rhsPtr = rhtPtr.get();
+    T         *       outPtr = oPtr.get();
+
+    for (int o = 0; o < N; ++o) {
+        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+            outPtr[i] = scalar<T>(0);
+            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+                //If stride[0] of right is not 1 then rhsPtr[colPtr[j]*stride]
+                if (conjugate) {
+                    outPtr[i] = outPtr[i] + getConjugate(valPtr[j]) * rhsPtr[colPtr[j]];
+                } else {
+                    outPtr[i] = outPtr[i] + valPtr[j] * rhsPtr[colPtr[j]];
+                }
+            }
+        }
+        rhsPtr += ldb;
+        outPtr += ldc;
+    }
+}
+
+template<typename T, bool conjugate>
+void mtm(Array<T> output,
+        const Array<T> values,
+        const Array<int> rowIdx,
+        const Array<int> colIdx,
+        const Array<T> right,
+        int M, int N,
+        int ldb, int ldc)
+{
+    auto oPtr   = output.getMappedPtr();
+    auto rhtPtr = right .getMappedPtr();
+    auto vPtr   = values.getMappedPtr();
+    auto rPtr   = rowIdx.getMappedPtr();
+    auto cPtr   = colIdx.getMappedPtr();
+
+    T   const * const valPtr = vPtr.get();
+    int const * const rowPtr = rPtr.get();
+    int const * const colPtr = cPtr.get();
+    T   const *       rhsPtr = rhtPtr.get();
+    T         *       outPtr = oPtr.get();
+
+    for (int o = 0; o < N; ++o) {
+        for (int i = 0; i < M; ++i) {
+            outPtr[i] = scalar<T>(0);
+        }
+
+        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
+                //If stride[0] of right is not 1 then rhsPtr[i*stride]
+                if (conjugate) {
+                    outPtr[colPtr[j]] = outPtr[colPtr[j]] + getConjugate(valPtr[j]) * rhsPtr[i];
+                } else {
+                    outPtr[colPtr[j]] = outPtr[colPtr[j]] + valPtr[j] * rhsPtr[i];
+                }
+            }
+        }
+        rhsPtr += ldb;
+        outPtr += ldc;
+    }
+}
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    lhs.eval();
+    rhs.eval();
+
+    // Similar Operations to GEMM
+    sparse_operation_t lOpts = toSparseTranspose(optLhs);
+
+    int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
+
+    static const int rColDim = 1;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhs.dims();
+    int M = lDims[lRowDim];
+    int N = rDims[rColDim];
+
+    Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
+    out.eval();
+
+    int ldb = rhs.strides()[1];
+    int ldc = out.strides()[1];
+
+    Array<T  > values = lhs.getValues();
+    Array<int> rowIdx = lhs.getRowIdx();
+    Array<int> colIdx = lhs.getColIdx();
+
+    if(rDims[rColDim] == 1) {
+        if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
+            mv<T, false>(out, values, rowIdx, colIdx, rhs, M);
+        } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
+            mtv<T, false>(out, values, rowIdx, colIdx, rhs, M);
+        } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
+            mtv<T, true>(out, values, rowIdx, colIdx, rhs, M);
+        }
+    } else {
+        if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
+            mm<T, false>(out, values, rowIdx, colIdx, rhs, M, N, ldb, ldc);
+        } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
+            mtm<T, false>(out, values, rowIdx, colIdx, rhs, M, N, ldb, ldc);
+        } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
+            mtm<T, true>(out, values, rowIdx, colIdx, rhs, M, N, ldb, ldc);
+        }
+    }
+
+    return out;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+#endif
+////////////////////////////////////////////////////////////////////////////////
+
+#define INSTANTIATE_SPARSE(T)                                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
+                                af_mat_prop optLhs, af_mat_prop optRhs);                \
+
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+#undef INSTANTIATE_SPARSE
+
+}
+}
+#endif
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.hpp b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
new file mode 100644
index 0000000000..e2475e0e07
--- /dev/null
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+
+#ifdef USE_MKL
+#include <mkl_spblas.h>
+#endif
+
+#ifdef USE_MKL
+typedef MKL_Complex8    sp_cfloat;
+typedef MKL_Complex16   sp_cdouble;
+#else
+typedef opencl::cfloat  sp_cfloat;
+typedef opencl::cdouble sp_cdouble;
+#endif
+
+namespace opencl
+{
+namespace cpu
+{
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs);
+
+}
+}
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 15e1c1eab4..8895a7ec02 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -26,6 +26,10 @@
 #include <platform.hpp>
 #include <transpose.hpp>
 
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#include <cpu/cpu_sparse_blas.hpp>
+#endif
+
 namespace opencl
 {
 
@@ -35,6 +39,12 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+    if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
+        return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
+    }
+#endif
+
     int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
     //int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
     static const int rColDim = 1; //Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;

From 2fa531145681c9b18bda72720bd9e6ac78963041 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 14 Oct 2016 15:45:51 -0400
Subject: [PATCH 0973/2677] Increment version to 3.5.0

---
 CMakeModules/Version.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index bcc7409fd8..832b5d2901 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -9,8 +9,8 @@ IF("${CMAKE_VERSION}" VERSION_GREATER "3.1" OR "${CMAKE_VERSION}" VERSION_EQUAL
 ENDIF()
 
 SET(AF_VERSION_MAJOR "3")
-SET(AF_VERSION_MINOR "4")
-SET(AF_VERSION_PATCH "1")
+SET(AF_VERSION_MINOR "5")
+SET(AF_VERSION_PATCH "0")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From 91c26a1c420595e19eff9468ce1eb80dc95dfb75 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 15 Oct 2016 15:59:15 -0400
Subject: [PATCH 0974/2677] Increment version to v3.4.2

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index bcc7409fd8..03ceebf1d5 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -10,7 +10,7 @@ ENDIF()
 
 SET(AF_VERSION_MAJOR "3")
 SET(AF_VERSION_MINOR "4")
-SET(AF_VERSION_PATCH "1")
+SET(AF_VERSION_PATCH "2")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
 SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})

From d190a8956a8446f8e2f066fac03e5695205dbb2a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 15 Oct 2016 15:59:39 -0400
Subject: [PATCH 0975/2677] Fix typo in v3.4.1 release notes

---
 docs/pages/release_notes.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index b944a0c9c1..c4ccb7a4fe 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -12,7 +12,7 @@ Installers
   * CUDA Compute 2.x (Fermi) is no longer compiled into the library.
 * Installer for OS X
   * The libraries shipping in the OS X Installer are now compiled with Apple
-    Clang v7.3.1 (previouly v6.1.0).
+    Clang v7.3.1 (previously v6.1.0).
   * The OS X version used is 10.11.6 (previously 10.10.5).
 * Installer for Jetson TX1 / Tegra X1
   * Requires [JetPack for L4T 2.3](https://developer.nvidia.com/embedded/jetpack)

From 3c7bd0fd309ca3a8fd444b2171f414fbc97ed80a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 11:22:09 -0400
Subject: [PATCH 0976/2677] Add T dot<T>() function to return scalar from dot
 operation

---
 include/af/blas.h        | 65 +++++++++++++++++++++++++++++++++++++++-
 src/api/c/blas.cpp       | 58 ++++++++++++++++++++++++++++++++---
 src/api/cpp/blas.cpp     | 31 +++++++++++++++++--
 src/api/unified/blas.cpp | 10 ++++++-
 4 files changed, 156 insertions(+), 8 deletions(-)

diff --git a/include/af/blas.h b/include/af/blas.h
index 605364d6be..a0ac2f81b3 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -166,6 +166,39 @@ namespace af
                        const matProp optLhs = AF_MAT_NONE,
                        const matProp optRhs = AF_MAT_NONE);
 
+#if AF_API_VERSION >= 35
+    /**
+        \brief Return the dot product of two vectors as a scalar
+
+        Scalar dot product between two vectors. Also referred to as the inner
+        product.
+
+        \code
+        // compute scalar dot product
+        array x = randu(100), y = randu(100);
+        float h_dot = dot<float>(x,y);
+        \endcode
+
+        \param[in] lhs The array object on the left hand side
+        \param[in] rhs The array object on the right hand side
+        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
+                   AF_MAT_CONJ are supported.
+        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
+        \return The result of the dot product of lhs, rhs as a host scalar
+
+        \note optLhs and optRhs can only be one of \ref AF_MAT_NONE or \ref AF_MAT_CONJ
+        \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run conjugate dot operation.
+        \note This function is not supported in GFOR
+
+        \returns out = dot(lhs, rhs)
+
+        \ingroup blas_func_dot
+    */
+    template<typename T> T dot(const array &lhs, const array &rhs,
+                               const matProp optLhs = AF_MAT_NONE,
+                               const matProp optRhs = AF_MAT_NONE);
+#endif
+
     /**
         \brief Transposes a matrix
 
@@ -235,11 +268,41 @@ extern "C" {
         print(dot<float>(x,y));
         \endcode
 
+        \param[out] out The array object with the result of the dot operation
+        \param[in] lhs The array object on the left hand side
+        \param[in] rhs The array object on the right hand side
+        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
+                   AF_MAT_CONJ are supported.
+        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
+        \return AF_SUCCESS if the process is successful.
+
         \ingroup blas_func_dot
     */
-    AFAPI af_err af_dot(    af_array *out,
+    AFAPI af_err af_dot(af_array *out,
+                        const af_array lhs, const af_array rhs,
+                        const af_mat_prop optLhs, const af_mat_prop optRhs);
+
+#if AF_API_VERSION >= 35
+    /**
+        Scalar dot product between two vectors. Also referred to as the inner
+        product. Returns the result as a host scalar.
+
+        \param[out] real is the real component of the result of dot operation
+        \param[out] imag is the imaginary component of the result of dot operation
+        \param[in] lhs The array object on the left hand side
+        \param[in] rhs The array object on the right hand side
+        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
+                   AF_MAT_CONJ are supported.
+        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
+
+        \return AF_SUCCESS if the process is successful.
+
+        \ingroup blas_func_dot
+    */
+    AFAPI af_err af_dot_all(double *real, double *imag,
                             const af_array lhs, const af_array rhs,
                             const af_mat_prop optLhs, const af_mat_prop optRhs);
+#endif
 
     /**
         \brief Transposes a matrix
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 50c240f174..a238f6b26b 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -151,9 +151,9 @@ af_err af_matmul(af_array *out,
     return AF_SUCCESS;
 }
 
-af_err af_dot(      af_array *out,
-                    const af_array lhs, const af_array rhs,
-                    const af_mat_prop optLhs, const af_mat_prop optRhs)
+af_err af_dot(af_array *out,
+              const af_array lhs, const af_array rhs,
+              const af_mat_prop optLhs, const af_mat_prop optRhs)
 {
     using namespace detail;
 
@@ -195,5 +195,55 @@ af_err af_dot(      af_array *out,
         std::swap(*out, output);
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
+}
+
+template<typename T>
+static inline
+T dotAll(af_array out)
+{
+    T res;
+    AF_CHECK(af_eval(out));
+    AF_CHECK(af_get_data_ptr((void *)&res, out));
+    return res;
+}
+
+af_err af_dot_all(double *rval, double *ival,
+                  const af_array lhs, const af_array rhs,
+                  const af_mat_prop optLhs, const af_mat_prop optRhs)
+{
+    using namespace detail;
+
+    try {
+                  *rval = 0;
+        if (ival) *ival = 0;
+
+        af_array out = 0;
+        AF_CHECK(af_dot(&out, lhs, rhs, optLhs, optRhs));
+
+        ArrayInfo lhsInfo = getInfo(lhs);
+        af_dtype lhs_type = lhsInfo.getType();
+
+        switch(lhs_type) {
+        case f32: *rval = dotAll<float >(out); break;
+        case f64: *rval = dotAll<double>(out); break;
+        case c32:
+        {
+            cfloat temp = dotAll<cfloat>(out);
+                      *rval = real(temp);
+            if (ival) *ival = imag(temp);
+        } break;
+        case c64:
+        {
+            cdouble temp = dotAll<cdouble>(out);
+                      *rval = real(temp);
+            if (ival) *ival = imag(temp);
+        } break;
+        default: TYPE_ERROR(1, lhs_type);
+        }
+
+        if(out != 0) AF_CHECK(af_release_array(out));
+    }
+    CATCHALL
+    return AF_SUCCESS;
 }
diff --git a/src/api/cpp/blas.cpp b/src/api/cpp/blas.cpp
index aac0cbabd7..4a9db3ddb1 100644
--- a/src/api/cpp/blas.cpp
+++ b/src/api/cpp/blas.cpp
@@ -69,11 +69,38 @@ namespace af
         }
     }
 
-    array dot   (const array &lhs, const array &rhs,
-                 const matProp optLhs, const matProp optRhs)
+    array dot(const array &lhs, const array &rhs,
+              const matProp optLhs, const matProp optRhs)
     {
         af_array out = 0;
         AF_THROW(af_dot(&out, lhs.get(), rhs.get(), optLhs, optRhs));
         return array(out);
     }
+
+#define INSTANTIATE_REAL(TYPE)                                                      \
+    template<> AFAPI                                                                \
+    TYPE dot(const array &lhs, const array &rhs,                                    \
+             const matProp optLhs, const matProp optRhs)                            \
+    {                                                                               \
+        double rval = 0, ival = 0;                                                  \
+        AF_THROW(af_dot_all(&rval, &ival, lhs.get(), rhs.get(), optLhs, optRhs));   \
+        return (TYPE)(rval);                                                        \
+    }
+
+#define INSTANTIATE_CPLX(TYPE, REAL)                                                \
+    template<> AFAPI                                                                \
+    TYPE dot(const array &lhs, const array &rhs,                                    \
+             const matProp optLhs, const matProp optRhs)                            \
+    {                                                                               \
+        double rval = 0, ival = 0;                                                  \
+        AF_THROW(af_dot_all(&rval, &ival, lhs.get(), rhs.get(), optLhs, optRhs));   \
+        TYPE out((REAL)rval, (REAL)ival);                                           \
+        return out;                                                                 \
+    }
+
+    INSTANTIATE_REAL(float)
+    INSTANTIATE_REAL(double)
+    INSTANTIATE_CPLX(cfloat, float)
+    INSTANTIATE_CPLX(cdouble, double)
+
 }
diff --git a/src/api/unified/blas.cpp b/src/api/unified/blas.cpp
index 547e3ac428..4bb5fced3d 100644
--- a/src/api/unified/blas.cpp
+++ b/src/api/unified/blas.cpp
@@ -19,7 +19,7 @@ af_err af_matmul( af_array *out ,
 }
 
 
-af_err af_dot(    af_array *out,
+af_err af_dot(af_array *out,
         const af_array lhs, const af_array rhs,
         const af_mat_prop optLhs, const af_mat_prop optRhs)
 {
@@ -27,6 +27,14 @@ af_err af_dot(    af_array *out,
     return CALL(out, lhs, rhs, optLhs, optRhs);
 }
 
+af_err af_dot_all(double *rval, double *ival,
+        const af_array lhs, const af_array rhs,
+        const af_mat_prop optLhs, const af_mat_prop optRhs)
+{
+    CHECK_ARRAYS(lhs, rhs);
+    return CALL(rval, ival, lhs, rhs, optLhs, optRhs);
+}
+
 af_err af_transpose(af_array *out, af_array in, const bool conjugate)
 {
     CHECK_ARRAYS(in);

From 9edeb1aa7f192698b0a174485ef2ae415e41e86e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 11:22:46 -0400
Subject: [PATCH 0977/2677] Add tests for T dot<T> / af_dot_all

---
 test/dot.cpp | 144 +++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 127 insertions(+), 17 deletions(-)

diff --git a/test/dot.cpp b/test/dot.cpp
index 58cfbb2ed6..36be490683 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -88,30 +88,92 @@ void dotTest(string pTestFile, const int resultIdx,
     ASSERT_EQ(AF_SUCCESS, af_release_array(out));
 }
 
+template<typename T>
+void compare(double rval, double ival, T gold)
+{
+    ASSERT_NEAR(gold, rval, 0.03);
+}
+
+template<>
+void compare<cfloat>(double rval, double ival, cfloat gold)
+{
+    ASSERT_NEAR(gold.real, rval, 0.03);
+    ASSERT_NEAR(gold.imag, ival, 0.03);
+}
+
+template<>
+void compare<cdouble>(double rval, double ival, cdouble gold)
+{
+    ASSERT_NEAR(gold.real, rval, 0.03);
+    ASSERT_NEAR(gold.imag, ival, 0.03);
+}
+
+template<typename T>
+void dotAllTest(string pTestFile, const int resultIdx,
+          const af_mat_prop optLhs = AF_MAT_NONE, const af_mat_prop optRhs = AF_MAT_NONE)
+{
+    if (noDoubleTests<T>()) return;
+
+    using af::dim4;
+
+    vector<dim4>        numDims;
+    vector<vector<T> >  in;
+    vector<vector<T> >  tests;
+
+    readTests<T, T, T>(pTestFile, numDims, in, tests);
+
+    dim4 aDims     = numDims[0];
+    dim4 bDims     = numDims[1];
+
+    af_array a = 0;
+    af_array b = 0;
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(in[0].front()),
+                aDims.ndims(), aDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&b, &(in[1].front()),
+                bDims.ndims(), bDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+
+    double rval = 0, ival = 0;
+    ASSERT_EQ(AF_SUCCESS, af_dot_all(&rval, &ival, a, b, optLhs, optRhs));
+
+    vector<T> goldData = tests[resultIdx];
+
+    compare<T>(rval, ival, goldData[0]);
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(b));
+}
+
+
 #define INSTANTIATEF(SIZE, FILENAME)                                                            \
 TYPED_TEST(DotF, DotF_##SIZE)                                                                   \
 {                                                                                               \
     dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0);                            \
+    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0);                         \
 }                                                                                               \
 
 
-#define INSTANTIATEC(SIZE, FILENAME)                                                            \
-TYPED_TEST(DotC, DotC_CC_##SIZE)                                                                \
-{                                                                                               \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0, AF_MAT_CONJ, AF_MAT_CONJ);  \
-}                                                                                               \
-TYPED_TEST(DotC, DotC_UU_##SIZE)                                                                \
-{                                                                                               \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 1, AF_MAT_NONE, AF_MAT_NONE);  \
-}                                                                                               \
-TYPED_TEST(DotC, DotC_CU_##SIZE)                                                                \
-{                                                                                               \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 2, AF_MAT_CONJ, AF_MAT_NONE);  \
-}                                                                                               \
-TYPED_TEST(DotC, DotC_UC_##SIZE)                                                                \
-{                                                                                               \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 3, AF_MAT_NONE, AF_MAT_CONJ);  \
-}                                                                                               \
+#define INSTANTIATEC(SIZE, FILENAME)                                                                \
+TYPED_TEST(DotC, DotC_CC_##SIZE)                                                                    \
+{                                                                                                   \
+    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0, AF_MAT_CONJ, AF_MAT_CONJ);      \
+    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0, AF_MAT_CONJ, AF_MAT_CONJ);   \
+}                                                                                                   \
+TYPED_TEST(DotC, DotC_UU_##SIZE)                                                                    \
+{                                                                                                   \
+    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 1, AF_MAT_NONE, AF_MAT_NONE);      \
+    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 1, AF_MAT_NONE, AF_MAT_NONE);   \
+}                                                                                                   \
+TYPED_TEST(DotC, DotC_CU_##SIZE)                                                                    \
+{                                                                                                   \
+    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 2, AF_MAT_CONJ, AF_MAT_NONE);      \
+    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 2, AF_MAT_CONJ, AF_MAT_NONE);   \
+}                                                                                                   \
+TYPED_TEST(DotC, DotC_UC_##SIZE)                                                                    \
+{                                                                                                   \
+    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 3, AF_MAT_NONE, AF_MAT_CONJ);      \
+    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 3, AF_MAT_NONE, AF_MAT_CONJ);   \
+}                                                                                                   \
 
 
 INSTANTIATEF(1000   , dot_f_1000);
@@ -186,3 +248,51 @@ TEST(DotCCU, CPP)
 
     delete[] outData;
 }
+
+TEST(DotAllF, CPP)
+{
+    using af::array;
+    using af::dim4;
+
+    vector<dim4>         numDims;
+    vector<vector<float> >  in;
+    vector<vector<float> >  tests;
+
+    readTests<float, float, float>(TEST_DIR"/blas/dot_f_1000.test", numDims, in, tests);
+
+    dim4 aDims     = numDims[0];
+    dim4 bDims     = numDims[1];
+
+    array a(aDims, &(in[0].front()));
+    array b(bDims, &(in[1].front()));
+
+    float out = af::dot<float>(a, b, AF_MAT_CONJ, AF_MAT_NONE);
+
+    vector<float> goldData = tests[0];
+
+    ASSERT_EQ(goldData[0], out);
+}
+
+TEST(DotAllCCU, CPP)
+{
+    using af::array;
+    using af::dim4;
+
+    vector<dim4>         numDims;
+    vector<vector<cfloat> >  in;
+    vector<vector<cfloat> >  tests;
+
+    readTests<cfloat, cfloat, cfloat>(TEST_DIR"/blas/dot_c_1000.test", numDims, in, tests);
+
+    dim4 aDims     = numDims[0];
+    dim4 bDims     = numDims[1];
+
+    array a(aDims, &(in[0].front()));
+    array b(bDims, &(in[1].front()));
+
+    cfloat out = af::dot<cfloat>(a, b, AF_MAT_CONJ, AF_MAT_NONE);
+
+    vector<cfloat> goldData = tests[2];
+
+    ASSERT_EQ(goldData[0], out);
+}

From 358f6dcc92750de39501671298b8485677c6aebd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 14:41:35 -0400
Subject: [PATCH 0978/2677] Fix Forge shared files being installer directly in
 /usr/local

---
 CMakeModules/osx_install/OSXInstaller.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 729f5a4996..1e4d4a640c 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -261,10 +261,10 @@ IF(BUILD_GRAPHICS)
     PKG_BUILD(  PKG_NAME        ForgeLibrary
                 DEPENDS         OSX_INSTALL_SETUP_FORGE_LIB
                 TARGETS         forge_lib_package
-                INSTALL_LOCATION /usr/local/
+                INSTALL_LOCATION /usr/local/lib
                 SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/forge_scripts
                 IDENTIFIER      com.arrayfire.pkg.forge.lib
-                PATH_TO_FILES   ${OSX_TEMP}/Forge)
+                PATH_TO_FILES   ${OSX_TEMP}/Forge/lib)
 
     PKG_BUILD(  PKG_NAME        ForgeHeaders
                 DEPENDS         OSX_INSTALL_SETUP_FORGE_INCLUDE

From 65bff6a87233386b8be92064f596c695b7fc5f33 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 14:42:08 -0400
Subject: [PATCH 0979/2677] OSX post install script needs to be executable

---
 CMakeModules/osx_install/forge_scripts/postinstall | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 CMakeModules/osx_install/forge_scripts/postinstall

diff --git a/CMakeModules/osx_install/forge_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
old mode 100644
new mode 100755

From 40840b35eefd128092cb1380fa02a5af9891c65a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 14:45:18 -0400
Subject: [PATCH 0980/2677] Fixes to postinstall script for OSX installer

* Fix how the user is obtained
(http://apple.stackexchange.com/questions/144159/how-can-i-determine-the-invoking-user-in-an-apple-installer-postinstall-script)
* Do brew install/tap commands only if the packages are not found
  * This was we avoid errors from double installation
---
 .../osx_install/forge_scripts/postinstall     | 28 +++++++++++++++++--
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/osx_install/forge_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
index 88ef20b231..316b16e4fd 100755
--- a/CMakeModules/osx_install/forge_scripts/postinstall
+++ b/CMakeModules/osx_install/forge_scripts/postinstall
@@ -14,7 +14,8 @@ if [ ! -f $brew ]; then
     exit 1
 fi
 
-user=$(ps aux | grep console | grep -v 'grep\|root' | cut -d' ' -f1 | head -n1)
+#user=$(ps aux | grep console | grep -v 'grep\|root' | cut -d' ' -f1 | head -n1)
+user=$(stat -f '%Su' $HOME)
 
 if [ -z $user ]; then
     echo "User not found" >> $err_file
@@ -30,5 +31,26 @@ function deps_err
     exit 1
 }
 
-su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
-su $user -c "$brew install glfw3 fontconfig" >> $err_file 2>&1 || deps_err
+BREW_VERSIONS=$(su $user -c "$brew tap" | grep "homebrew/versions")
+if [[ ${BREW_VERSIONS} != 0 ]]; then
+    su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
+else
+    echo "Homebrew/Versions already present in brew tap." >> $err_file
+fi
+
+GLFW_TEST=$(su $user -c "$brew ls --versions glfw3")
+GLFW_INSTALLED=$?
+if [[ ${GLFW_INSTALLED} != 0 ]]; then
+    su $user -c "$brew install glfw3" >> $err_file 2>&1 || deps_err
+else
+    echo "GLFW Version ${GLFW_TEST} is already installed." >> $err_file
+fi
+
+FTCG_TEST=$(su $user -c "$brew ls --versions fontconfig")
+FTCG_INSTALLED=$?
+echo ${FTCG_TEST} ${FTCG_INSTALLED}
+if [[ ${FTCG_INSTALLED} != 0 ]]; then
+    su $user -c "$brew install fontconfig" >> $err_file 2>&1 || deps_err
+else
+    echo "FontConfig Version ${FTCG_TEST} is already installed." >> $err_file
+fi

From e031dae907333b4249165bf76cfe4c5ab5fc68fd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 16:51:08 -0400
Subject: [PATCH 0981/2677] FEAT add allocated function to return physical
 allocated bytes

---
 include/af/array.h           |  7 +++++++
 include/af/internal.h        | 11 +++++++++++
 src/api/c/internal.cpp       | 29 +++++++++++++++++++++++++++++
 src/api/cpp/array.cpp        |  9 +++++++++
 src/api/unified/internal.cpp |  6 ++++++
 5 files changed, 62 insertions(+)

diff --git a/include/af/array.h b/include/af/array.h
index e1048dd114..ccf99ed0a4 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -108,6 +108,7 @@ namespace af
             dim_t dims(unsigned dim) const;
             unsigned numdims() const;
             size_t bytes() const;
+            size_t allocated() const;
             array copy() const;
             bool isempty() const;
             bool isscalar() const;
@@ -586,6 +587,12 @@ namespace af
         */
         size_t bytes() const;
 
+        /**
+           Get the size of the array in memory. This will return the parent's
+           bytes() if the array is indexed.
+        */
+        size_t allocated() const;
+
         /**
            Perform deep copy of the array
         */
diff --git a/include/af/internal.h b/include/af/internal.h
index 53002929c3..c441919107 100644
--- a/include/af/internal.h
+++ b/include/af/internal.h
@@ -176,6 +176,17 @@ extern "C"
     AFAPI af_err af_is_owner(bool *result, const af_array arr);
 #endif
 
+#if AF_API_VERSION >= 35
+    /**
+       \param[out] bytes the size of the physical allocated bytes. This will return the size
+       of the parent/owner if the \p arr is an indexed array.
+       \param[in] arr the input array.
+
+       \ingroup internal_func_allocatedbytes
+    */
+    AFAPI af_err af_get_allocated_bytes(size_t *bytes, const af_array arr);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 47c62c6478..d2a5f983b7 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -168,3 +168,32 @@ af_err af_is_owner(bool *result, const af_array arr)
     CATCHALL;
     return AF_SUCCESS;
 }
+
+af_err af_get_allocated_bytes(size_t *bytes, const af_array arr)
+{
+    try {
+        af_dtype ty = getInfo(arr).getType();
+
+        size_t res = 0;
+
+        switch (ty) {
+        case f32: res = getArray<float  >(arr).getDataDims().elements() * sizeof(float);            break;
+        case f64: res = getArray<double >(arr).getDataDims().elements() * sizeof(double);           break;
+        case c32: res = getArray<cfloat >(arr).getDataDims().elements() * sizeof(cfloat);           break;
+        case c64: res = getArray<cdouble>(arr).getDataDims().elements() * sizeof(cdouble);          break;
+        case u32: res = getArray<uint   >(arr).getDataDims().elements() * sizeof(unsigned int);     break;
+        case s32: res = getArray<int    >(arr).getDataDims().elements() * sizeof(int);              break;
+        case u64: res = getArray<uintl  >(arr).getDataDims().elements() * sizeof(uintl);            break;
+        case s64: res = getArray<intl   >(arr).getDataDims().elements() * sizeof(intl);             break;
+        case u16: res = getArray<ushort >(arr).getDataDims().elements() * sizeof(short);            break;
+        case s16: res = getArray<short  >(arr).getDataDims().elements() * sizeof(unsigned short);   break;
+        case b8 : res = getArray<char   >(arr).getDataDims().elements() * sizeof(char);             break;
+        case u8 : res = getArray<uchar  >(arr).getDataDims().elements() * sizeof(unsigned char);    break;
+        default: TYPE_ERROR(6, ty);
+        }
+
+        std::swap(*bytes, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index a4a7937523..8420582f33 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -17,6 +17,7 @@
 #include <af/device.h>
 #include <af/gfor.h>
 #include <af/algorithm.h>
+#include <af/internal.h>
 #include "error.hpp"
 
 namespace af
@@ -266,6 +267,13 @@ namespace af
         return nElements * getSizeOf(type());
     }
 
+    size_t array::allocated() const
+    {
+        size_t result = 0;
+        AF_THROW(af_get_allocated_bytes(&result, get()));
+        return result;
+    }
+
     array array::copy() const
     {
         af_array other = 0;
@@ -597,6 +605,7 @@ namespace af
     MEM_FUNC(dim4                   , dims)
     MEM_FUNC(unsigned               , numdims)
     MEM_FUNC(size_t                 , bytes)
+    MEM_FUNC(size_t                 , allocated)
     MEM_FUNC(array                  , copy)
     MEM_FUNC(bool                   , isempty)
     MEM_FUNC(bool                   , isscalar)
diff --git a/src/api/unified/internal.cpp b/src/api/unified/internal.cpp
index b9ac0ac277..3a2d51cca5 100644
--- a/src/api/unified/internal.cpp
+++ b/src/api/unified/internal.cpp
@@ -52,3 +52,9 @@ af_err af_is_owner(bool *result, const af_array arr)
     CHECK_ARRAYS(arr);
     return CALL(result, arr);
 }
+
+af_err af_get_allocated_bytes(size_t *bytes, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(bytes, arr);
+}

From 031bab0ec4c9a4e0aa3df7cc746789a9184bdd9f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 17 Oct 2016 17:00:37 -0400
Subject: [PATCH 0982/2677] Add test for allocated bytes

---
 test/internal.cpp | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/test/internal.cpp b/test/internal.cpp
index 75fa54fdb9..f60c570d99 100644
--- a/test/internal.cpp
+++ b/test/internal.cpp
@@ -122,3 +122,36 @@ TEST(Internal, Linear)
         ASSERT_EQ(isOwner(c), false);
     }
 }
+
+TEST(Internal, Allocated)
+{
+    af::array a = af::randu(10, 8);
+    const size_t aBytes = a.bytes();
+
+    // b is just pointing to same underlying data
+    // b is an owner;
+    af::array b = a;
+    ASSERT_EQ(b.allocated(), aBytes);
+    ASSERT_EQ(b.bytes(), aBytes);
+
+    // C is considered sub array
+    // C will not be an owner
+    af::array c = a(af::span);
+    ASSERT_EQ(c.allocated(), aBytes);
+    ASSERT_EQ(c.bytes(), aBytes);
+
+    af::array d = a.col(1);
+    ASSERT_EQ(d.allocated(), aBytes);
+    ASSERT_EQ(d.bytes(), (size_t)10 * 4);
+
+    a = af::randu(20);
+    b = af::randu(20);
+
+    // Even though a, b are reallocated and c, d are not owners
+    // the allocated and bytes should remain the same
+    ASSERT_EQ(c.allocated(), aBytes);
+    ASSERT_EQ(c.bytes(), aBytes);
+
+    ASSERT_EQ(d.allocated(), aBytes);
+    ASSERT_EQ(d.bytes(), (size_t)10 * 4);
+}

From b75962cc3e7e871c301c3df46ce71e5d38d53540 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 18 Oct 2016 14:07:05 -0400
Subject: [PATCH 0983/2677] Major OSX Installer Fix: Libraries were being deep
 copied and not symlinks

This issue probably originated around 3.3 where libaf*.dylib / libaf*.major.dylib
were not being treated as symlinks but were full versions of the library
on their own.
This was due to CMake copying symlinks as the files they point to rather
than symlinks.
---
 CMakeModules/osx_install/OSXInstaller.cmake | 43 +++++++++++++++++++--
 1 file changed, 39 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 1e4d4a640c..1540a275f4 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -29,7 +29,7 @@ ENDFOREACH()
 
 # Backends - CPU, CUDA, OpenCL, Unified
 MACRO(OSX_INSTALL_SETUP BACKEND LIB)
-    FILE(GLOB ${BACKEND}LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/lib${LIB}*.dylib")
+    FILE(GLOB ${BACKEND}LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/lib${LIB}.${AF_VERSION}.dylib")
     FILE(GLOB ${BACKEND}CMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFire${BACKEND}*.cmake")
 
     ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_${BACKEND})
@@ -39,9 +39,24 @@ MACRO(OSX_INSTALL_SETUP BACKEND LIB)
                            COMMAND ${CMAKE_COMMAND} -E copy
                            ${SRC} "${OSX_TEMP}/${BACKEND}/${SRC_REL}"
                            WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                           COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir"
+                           COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir - File: ${SRC_REL}"
                            )
     ENDFOREACH()
+    # Create symlinks separately. Copying them in above command will do a deep copy
+    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
+                       COMMAND ${CMAKE_COMMAND} -E create_symlink
+                       "lib${LIB}.${AF_VERSION}.dylib"
+                       "lib${LIB}.${AF_VERSION_MAJOR}.dylib"
+                       WORKING_DIRECTORY "${OSX_TEMP}/${BACKEND}/${AF_INSTALL_LIB_DIR}"
+                       COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir (Symlink)"
+                       )
+    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
+                       COMMAND ${CMAKE_COMMAND} -E create_symlink
+                       "lib${LIB}.${AF_VERSION_MAJOR}.dylib"
+                       "lib${LIB}.dylib"
+                       WORKING_DIRECTORY "${OSX_TEMP}/${BACKEND}/${AF_INSTALL_LIB_DIR}"
+                       COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir (Symlink)"
+                       )
 ENDMACRO(OSX_INSTALL_SETUP)
 
 OSX_INSTALL_SETUP(CPU afcpu)
@@ -79,7 +94,12 @@ IF(BUILD_GRAPHICS)
     MAKE_DIRECTORY("${OSX_TEMP}/Forge")
 
     # Forge Library
-    FILE(GLOB FORGE_LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge*.dylib")
+    FILE(GLOB FORGE_LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge.*.*.*.dylib")
+
+    GET_FILENAME_COMPONENT(LIBFORGE_NAME ${FORGE_LIB} NAME_WE) # Will return libforge
+    STRING(REGEX MATCH "([0-9]+)\\.([0-9]+)\\.([0-9]+)" FORGE_VERSION ${FORGE_LIB}) # Will return x.y.z
+    STRING(SUBSTRING ${FORGE_VERSION} 0 1 FORGE_VERSION_MAJOR) # Will return x
+
     ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_LIB)
     FOREACH(SRC ${FORGE_LIB})
         FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
@@ -87,9 +107,24 @@ IF(BUILD_GRAPHICS)
                            COMMAND ${CMAKE_COMMAND} -E copy
                            ${SRC} "${OSX_TEMP}/Forge/${SRC_REL}"
                            WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                           COMMENT "Copying libforge files to temporary OSX Install Dir"
+                           COMMENT "Copying libforge files to temporary OSX Install Dir - File: ${SRC_REL}"
         )
     ENDFOREACH()
+    # Create symlinks separately. Copying them in above command will do a deep copy
+    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
+                       COMMAND ${CMAKE_COMMAND} -E create_symlink
+                       "${LIBFORGE_NAME}.${FORGE_VERSION}.dylib"
+                       "${LIBFORGE_NAME}.${FORGE_VERSION_MAJOR}.dylib"
+                       WORKING_DIRECTORY "${OSX_TEMP}/Forge/${AF_INSTALL_LIB_DIR}"
+                       COMMENT "Copying libforge files to temporary OSX Install Dir (Symlink)"
+                       )
+    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
+                       COMMAND ${CMAKE_COMMAND} -E create_symlink
+                       "${LIBFORGE_NAME}.${FORGE_VERSION_MAJOR}.dylib"
+                       "${LIBFORGE_NAME}.dylib"
+                       WORKING_DIRECTORY "${OSX_TEMP}/Forge/${AF_INSTALL_LIB_DIR}"
+                       COMMENT "Copying libforge files to temporary OSX Install Dir (Symlink)"
+                       )
 
     # Forge Headers
     ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_INCLUDE

From 2ade7fc28084c0ff2e49831ef34e84b43c228e05 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 18 Oct 2016 16:07:44 -0400
Subject: [PATCH 0984/2677] More verbose post install script. Fix failures due
 to grep not found

---
 .../osx_install/forge_scripts/postinstall     | 32 ++++++++++++-------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/CMakeModules/osx_install/forge_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
index 316b16e4fd..209f35cdc0 100755
--- a/CMakeModules/osx_install/forge_scripts/postinstall
+++ b/CMakeModules/osx_install/forge_scripts/postinstall
@@ -22,6 +22,8 @@ if [ -z $user ]; then
     exit 1
 fi
 
+echo "User: ${user}" >> $err_file
+
 function deps_err
 {
     osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire/Forge dependencies using Brew."'
@@ -31,26 +33,34 @@ function deps_err
     exit 1
 }
 
-BREW_VERSIONS=$(su $user -c "$brew tap" | grep "homebrew/versions")
-if [[ ${BREW_VERSIONS} != 0 ]]; then
+echo "Output of brew tap:" >> $err_file
+echo "-------------------" >> $err_file
+su $user -c "$brew tap" >> $err_file
+echo "-------------------" >> $err_file
+
+BREW_VERSIONS=$(su $user -c "$brew tap" | grep "homebrew/versions") || true
+if [[ -z "${BREW_VERSIONS}" ]]; then
     su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
 else
     echo "Homebrew/Versions already present in brew tap." >> $err_file
 fi
 
-GLFW_TEST=$(su $user -c "$brew ls --versions glfw3")
-GLFW_INSTALLED=$?
-if [[ ${GLFW_INSTALLED} != 0 ]]; then
+GLFW_INSTALLED=$(su $user -c "$brew ls --versions glfw3" | grep "glfw3") || true
+if [[ -z "${GLFW_INSTALLED}" ]]; then
+    echo "Installing GLFW3" >> $err_file
+    echo "-------------------" >> $err_file
     su $user -c "$brew install glfw3" >> $err_file 2>&1 || deps_err
+    echo "-------------------" >> $err_file
 else
-    echo "GLFW Version ${GLFW_TEST} is already installed." >> $err_file
+    echo "GLFW Version ${GLFW_INSTALLED} is already installed." >> $err_file
 fi
 
-FTCG_TEST=$(su $user -c "$brew ls --versions fontconfig")
-FTCG_INSTALLED=$?
-echo ${FTCG_TEST} ${FTCG_INSTALLED}
-if [[ ${FTCG_INSTALLED} != 0 ]]; then
+FTCG_INSTALLED=$(su $user -c "$brew ls --versions fontconfig" | grep "fontconfig") || true
+if [[ -z "${FTCG_INSTALLED}" ]]; then
+    echo "Installing FontConfig" >> $err_file
+    echo "-------------------" >> $err_file
     su $user -c "$brew install fontconfig" >> $err_file 2>&1 || deps_err
+    echo "-------------------" >> $err_file
 else
-    echo "FontConfig Version ${FTCG_TEST} is already installed." >> $err_file
+    echo "FontConfig Version ${FTCG_INSTALLED} is already installed." >> $err_file
 fi

From e36a02897fae6e709c1d2db0c0989ff810e08923 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 20 Oct 2016 19:14:19 -0400
Subject: [PATCH 0985/2677] Compilation fix for VS2015: Add missing static
 definitions in opencl math.hpp

---
 src/backend/opencl/math.hpp | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 74e56b2cb7..65ce083bcd 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -97,13 +97,12 @@ namespace opencl
         return cval;
     }
 
-    template <typename T> T maxval() { return std::numeric_limits<T>::max(); }
-    template <typename T> T minval() { return std::numeric_limits<T>::min(); }
-    template <> STATIC_ float maxval() { return std::numeric_limits<float>::infinity(); }
-    template <> STATIC_ double maxval() { return std::numeric_limits<double>::infinity(); }
-    template <> STATIC_ float minval() { return -std::numeric_limits<float>::infinity(); }
-    template <> STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
-
+    template <typename T> STATIC_ T      maxval() { return  std::numeric_limits<T     >::max();      }
+    template <typename T> STATIC_ T      minval() { return  std::numeric_limits<T     >::min();      }
+    template <>           STATIC_ float  maxval() { return  std::numeric_limits<float >::infinity(); }
+    template <>           STATIC_ double maxval() { return  std::numeric_limits<double>::infinity(); }
+    template <>           STATIC_ float  minval() { return -std::numeric_limits<float >::infinity(); }
+    template <>           STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
 
     static inline double real(cdouble in)
     {

From aec7a038d45bebc176b114511429d1d71f60af2e Mon Sep 17 00:00:00 2001
From: Harald Lang <harald.lang@gmail.com>
Date: Sun, 23 Oct 2016 04:29:44 +0200
Subject: [PATCH 0986/2677] fix typos

---
 src/backend/opencl/platform.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d04169ebf0..f3f59b73a3 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -131,12 +131,12 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
         if (!is_l_curr_type &&  is_r_curr_type) return false;
     }
 
-    // For GPUs, this ensures discreet > integrated
-    auto is_l_integrared = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-    auto is_r_integrared = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+    // For GPUs, this ensures discrete > integrated
+    auto is_l_integrated = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+    auto is_r_integrated = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
 
-    if (!is_l_integrared &&  is_r_integrared) return true;
-    if ( is_l_integrared && !is_r_integrared) return false;
+    if (!is_l_integrated &&  is_r_integrated) return true;
+    if ( is_l_integrated && !is_r_integrated) return false;
 
     // At this point, the devices are of same type.
     // Sort based on emperical evidence of preferred platforms
@@ -190,7 +190,7 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
         if (rres) return false;
     }
 
-    // Default crietria, sort based on memory
+    // Default criteria, sort based on memory
     // Sort based on memory
     auto l_mem = ldev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
     auto r_mem = rdev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();

From b8029dcee134f5599cc67405f52f3bd6a659c8ca Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 25 Oct 2016 15:22:56 -0400
Subject: [PATCH 0987/2677] Set FORGE_VERSION in build_forge for CMake tag

---
 CMakeModules/build_forge.cmake | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 9e9b184425..3d69484c1f 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -42,11 +42,13 @@ ELSE()
     SET(byproducts BYPRODUCTS ${forge_location})
 ENDIF()
 
+SET(FORGE_VERSION 0.9.0)
+
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG v0.9.0
+    GIT_TAG v${FORGE_VERSION}
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 83649f0ccfc7a6cc3ac80d5a987fc88ae97771df Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 25 Oct 2016 15:23:04 -0400
Subject: [PATCH 0988/2677] OSX Installer: Get Forge version information from
 the tag in build_forge.cmake

This is because in the older version, forge version was being computed
from the forge library which is unavailable at CMake time and hence the
variable is empty.

Neither is it possible to fetch the Forge version from forge version
files as the clone happens at build time rather than at CMake time.

Happy to listen to any ideas about how to fix this better.
---
 CMakeModules/osx_install/OSXInstaller.cmake | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 1540a275f4..3d6b4a11f2 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -93,11 +93,7 @@ ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
 IF(BUILD_GRAPHICS)
     MAKE_DIRECTORY("${OSX_TEMP}/Forge")
 
-    # Forge Library
-    FILE(GLOB FORGE_LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/libforge.*.*.*.dylib")
-
-    GET_FILENAME_COMPONENT(LIBFORGE_NAME ${FORGE_LIB} NAME_WE) # Will return libforge
-    STRING(REGEX MATCH "([0-9]+)\\.([0-9]+)\\.([0-9]+)" FORGE_VERSION ${FORGE_LIB}) # Will return x.y.z
+    # Forge library versions for setting up symlinks
     STRING(SUBSTRING ${FORGE_VERSION} 0 1 FORGE_VERSION_MAJOR) # Will return x
 
     ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_LIB)

From 6acc93d209e08d4f3fcc75e1e9d84fe07ce0270f Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 26 Oct 2016 11:02:02 -0400
Subject: [PATCH 0989/2677] Move getAllocatedBytes into Array.hpp

---
 src/api/c/internal.cpp       | 24 ++++++++++++------------
 src/backend/cpu/Array.hpp    |  5 +++++
 src/backend/cuda/Array.hpp   |  5 +++++
 src/backend/opencl/Array.hpp |  5 +++++
 4 files changed, 27 insertions(+), 12 deletions(-)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index d2a5f983b7..5c9d6e3797 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -177,18 +177,18 @@ af_err af_get_allocated_bytes(size_t *bytes, const af_array arr)
         size_t res = 0;
 
         switch (ty) {
-        case f32: res = getArray<float  >(arr).getDataDims().elements() * sizeof(float);            break;
-        case f64: res = getArray<double >(arr).getDataDims().elements() * sizeof(double);           break;
-        case c32: res = getArray<cfloat >(arr).getDataDims().elements() * sizeof(cfloat);           break;
-        case c64: res = getArray<cdouble>(arr).getDataDims().elements() * sizeof(cdouble);          break;
-        case u32: res = getArray<uint   >(arr).getDataDims().elements() * sizeof(unsigned int);     break;
-        case s32: res = getArray<int    >(arr).getDataDims().elements() * sizeof(int);              break;
-        case u64: res = getArray<uintl  >(arr).getDataDims().elements() * sizeof(uintl);            break;
-        case s64: res = getArray<intl   >(arr).getDataDims().elements() * sizeof(intl);             break;
-        case u16: res = getArray<ushort >(arr).getDataDims().elements() * sizeof(short);            break;
-        case s16: res = getArray<short  >(arr).getDataDims().elements() * sizeof(unsigned short);   break;
-        case b8 : res = getArray<char   >(arr).getDataDims().elements() * sizeof(char);             break;
-        case u8 : res = getArray<uchar  >(arr).getDataDims().elements() * sizeof(unsigned char);    break;
+        case f32: res = getArray<float  >(arr).getAllocatedBytes(); break;
+        case f64: res = getArray<double >(arr).getAllocatedBytes(); break;
+        case c32: res = getArray<cfloat >(arr).getAllocatedBytes(); break;
+        case c64: res = getArray<cdouble>(arr).getAllocatedBytes(); break;
+        case u32: res = getArray<uint   >(arr).getAllocatedBytes(); break;
+        case s32: res = getArray<int    >(arr).getAllocatedBytes(); break;
+        case u64: res = getArray<uintl  >(arr).getAllocatedBytes(); break;
+        case s64: res = getArray<intl   >(arr).getAllocatedBytes(); break;
+        case u16: res = getArray<ushort >(arr).getAllocatedBytes(); break;
+        case s16: res = getArray<short  >(arr).getAllocatedBytes(); break;
+        case b8 : res = getArray<char   >(arr).getAllocatedBytes(); break;
+        case u8 : res = getArray<uchar  >(arr).getAllocatedBytes(); break;
         default: TYPE_ERROR(6, ty);
         }
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 8762e7e54e..65ef5f1434 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -188,6 +188,11 @@ namespace cpu
             data_dims = new_dims;
         }
 
+        size_t getAllocatedBytes() const
+        {
+            return data_dims.elements() * sizeof(T);
+        }
+
         T* device();
 
         T* device() const
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 6f8acc1ca6..127e0344e1 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -184,6 +184,11 @@ namespace cuda
             data_dims = new_dims;
         }
 
+        size_t getAllocatedBytes() const
+        {
+            return data_dims.elements() * sizeof(T);
+        }
+
         T* device();
 
         T* device() const
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 9dc9b427f7..b3a168efa2 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -211,6 +211,11 @@ namespace opencl
             data_dims = new_dims;
         }
 
+        size_t getAllocatedBytes() const
+        {
+            return data_dims.elements() * sizeof(T);
+        }
+
         operator Param() const
         {
             KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},

From 801f40f38f279398814c0e5266a2d46db2cc9b34 Mon Sep 17 00:00:00 2001
From: Vardan Akopian <vakopian@sustain.net>
Date: Mon, 24 Oct 2016 15:28:41 -0700
Subject: [PATCH 0990/2677] use scoped_array instead of scoped_ptr when
 managing array resources

---
 src/backend/cuda/jit.cpp            |  8 ++++----
 src/backend/cuda/kernel/ireduce.hpp | 10 +++++-----
 src/backend/cuda/kernel/orb.hpp     |  6 +++---
 src/backend/cuda/kernel/reduce.hpp  |  8 ++++----
 4 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index f79a6aa2ba..3d8cee69ad 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -45,10 +45,10 @@
 #include <vector>
 #include <nvvm.h>
 #include <boost/functional/hash.hpp>
-#include <boost/scoped_ptr.hpp>
+#include <boost/scoped_array.hpp>
 
 using std::vector;
-using boost::scoped_ptr;
+using boost::scoped_array;
 
 namespace cuda
 {
@@ -409,7 +409,7 @@ static char *irToPtx(string IR, size_t *ptx_size)
         size_t log_size = 0;
         nvvmGetProgramLogSize(prog, &log_size);
         printf("%ld, %zu\n", IR.size(), log_size);
-        scoped_ptr<char> log(new char[log_size]);
+        scoped_array<char> log(new char[log_size]);
         nvvmGetProgramLog(prog, log.get());
         printf("LOG:\n%s\n%s", log.get(), IR.c_str());
         NVVM_CHECK(comp_res, "Failed to compile program");
@@ -463,7 +463,7 @@ char linkError[size];
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
     size_t ptx_size;
-    scoped_ptr<const char> ptx(irToPtx(jit_ker, &ptx_size));
+    scoped_array<const char> ptx(irToPtx(jit_ker, &ptx_size));
 
     CUlinkState linkState;
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 4531d66440..9eec000e0f 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -16,9 +16,9 @@
 #include <debug_cuda.hpp>
 #include "config.hpp"
 #include <memory.hpp>
-#include <boost/scoped_ptr.hpp>
+#include <boost/scoped_array.hpp>
 
-using boost::scoped_ptr;
+using boost::scoped_array;
 
 namespace cuda
 {
@@ -486,8 +486,8 @@ namespace kernel
             tlptr = memAlloc<uint>(tmp_elements);
             ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x, blocks_y, threads_x);
 
-            scoped_ptr<T>       h_ptr(new T[tmp_elements]);
-            scoped_ptr<uint>    h_lptr(new uint[tmp_elements]);
+            scoped_array<T>       h_ptr(new T[tmp_elements]);
+            scoped_array<uint>    h_lptr(new uint[tmp_elements]);
             T*      h_ptr_raw = h_ptr.get();
             uint*   h_lptr_raw = h_lptr.get();
 
@@ -520,7 +520,7 @@ namespace kernel
             return Op.m_val;
         } else {
 
-            scoped_ptr<T> h_ptr(new T[in_elements]);
+            scoped_array<T> h_ptr(new T[in_elements]);
             T* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index be24ec2393..cbdb9cec3d 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -17,10 +17,10 @@
 #include "sort_by_key.hpp"
 #include "range.hpp"
 
-#include <boost/scoped_ptr.hpp>
+#include <boost/scoped_array.hpp>
 
 using std::vector;
-using boost::scoped_ptr;
+using boost::scoped_array;
 
 namespace cuda
 {
@@ -344,7 +344,7 @@ void orb(unsigned* out_feat,
     Param<convAccT> gauss_filter;
     if (blur_img) {
         unsigned gauss_len = 9;
-        scoped_ptr<convAccT> h_gauss(new convAccT[gauss_len]);
+        scoped_array<convAccT> h_gauss(new convAccT[gauss_len]);
         gaussian1D(h_gauss.get(), gauss_len, 2.f);
         gauss_filter.dims[0] = gauss_len;
         gauss_filter.strides[0] = 1;
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index e95bc4e2b0..6b74feec36 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -16,9 +16,9 @@
 #include <debug_cuda.hpp>
 #include "config.hpp"
 #include <memory.hpp>
-#include <boost/scoped_ptr.hpp>
+#include <boost/scoped_array.hpp>
 
-using boost::scoped_ptr;
+using boost::scoped_array;
 
 namespace cuda
 {
@@ -410,7 +410,7 @@ namespace kernel
             reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
                                               change_nan, nanval);
 
-            scoped_ptr<To> h_ptr(new To[tmp_elements]);
+            scoped_array<To> h_ptr(new To[tmp_elements]);
             To* h_ptr_raw = h_ptr.get();
 
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(To),
@@ -428,7 +428,7 @@ namespace kernel
 
         } else {
 
-            scoped_ptr<Ti> h_ptr(new Ti[in_elements]);
+            scoped_array<Ti> h_ptr(new Ti[in_elements]);
             Ti* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));

From 8078b3673704addba44146897172ad6356bd54b7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 31 Oct 2016 15:56:12 -0400
Subject: [PATCH 0991/2677] BUGFIX OpenCL bilateral - Fix native_exp
 compilation of OS X

---
 src/backend/opencl/kernel/bilateral.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 2dc2a5932d..7a949c4853 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -49,7 +49,8 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
         int device = getActiveDeviceId();
 
         std::call_once( compileFlags[device], [device] () {
-                bool use_native_exp = getActivePlatform() != AFCL_PLATFORM_POCL;
+                bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
+                                    && getActivePlatform() != AFCL_PLATFORM_APPLE);
                 std::ostringstream options;
                 options << " -D inType=" << dtype_traits<inType>::getName()
                         << " -D outType=" << dtype_traits<outType>::getName();

From a612cc8e93aa376673fc962a337fdb592f99975b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Nov 2016 11:08:32 -0400
Subject: [PATCH 0992/2677] Move graphics set axes step round computation into
 graphics_common

---
 src/api/c/graphics_common.cpp | 55 +++++++++++++++++++++++++++++++++++
 src/api/c/graphics_common.hpp |  2 ++
 src/api/c/window.cpp          | 55 -----------------------------------
 3 files changed, 57 insertions(+), 55 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 85649dd276..4b2932dffc 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -116,6 +116,61 @@ void makeContextCurrent(forge::Window *window)
     CheckGL("End makeContextCurrent");
 }
 
+// dir -> true = round up, false = round down
+double step_round(const double in, const bool dir)
+{
+    if(in == 0) return 0;
+
+    static const double __log2 = log10(2);
+    static const double __log4 = log10(4);
+    static const double __log6 = log10(6);
+    static const double __log8 = log10(8);
+
+    // log_in is of the form "s abc.xyz", where
+    // s is either + or -; + indicates abs(in) >= 1 and - indicates 0 < abs(in) < 1 (log10(1) is +0)
+    const double sign   = in < 0 ? -1 : 1;
+    const double log_in = std::log10(std::fabs(in));
+    const double mag    = std::pow(10, std::floor(log_in)) * sign;  // Number of digits either left or right of 0
+    const double dec    = std::log10(in / mag); // log of the fraction
+
+    // This means in is of the for 10^n
+    if(dec == 0) return in;
+
+    // For negative numbers, -ve round down = +ve round up and vice versa
+    bool op_dir = in > 0 ? dir : !dir;
+
+    double mult = 1;
+
+    // Round up
+    if(op_dir) {
+        if(dec <= __log2) {
+            mult = 2;
+        } else if(dec <= __log4) {
+            mult = 4;
+        } else if(dec <= __log6) {
+            mult = 6;
+        } else if(dec <= __log8) {
+            mult = 8;
+        } else {
+            mult = 10;
+        }
+    } else {    // Round down
+        if(dec < __log2) {
+            mult = 1;
+        } else if(dec < __log4) {
+            mult = 2;
+        } else if(dec < __log6) {
+            mult = 4;
+        } else if(dec < __log8) {
+            mult = 6;
+        } else {
+            mult = 8;
+        }
+    }
+
+    return mag * mult;
+}
+
 namespace graphics
 {
 
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 6459eaa49e..3b6a78f4df 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -37,6 +37,8 @@ forge::MarkerType getFGMarker(const af_marker_type af_marker);
 
 void makeContextCurrent(forge::Window *window);
 
+double step_round(const double in, const bool dir);
+
 namespace graphics
 {
 
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 90aad38f44..2cf866d4f9 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -137,61 +137,6 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
 #endif
 }
 
-// dir -> true = round up, false = round down
-double step_round(const double in, const bool dir)
-{
-    if(in == 0) return 0;
-
-    static const double __log2 = log10(2);
-    static const double __log4 = log10(4);
-    static const double __log6 = log10(6);
-    static const double __log8 = log10(8);
-
-    // log_in is of the form "s abc.xyz", where
-    // s is either + or -; + indicates abs(in) >= 1 and - indicates 0 < abs(in) < 1 (log10(1) is +0)
-    const double sign   = in < 0 ? -1 : 1;
-    const double log_in = std::log10(std::fabs(in));
-    const double mag    = std::pow(10, std::floor(log_in)) * sign;  // Number of digits either left or right of 0
-    const double dec    = std::log10(in / mag); // log of the fraction
-
-    // This means in is of the for 10^n
-    if(dec == 0) return in;
-
-    // For negative numbers, -ve round down = +ve round up and vice versa
-    bool op_dir = in > 0 ? dir : !dir;
-
-    double mult = 1;
-
-    // Round up
-    if(op_dir) {
-        if(dec <= __log2) {
-            mult = 2;
-        } else if(dec <= __log4) {
-            mult = 4;
-        } else if(dec <= __log6) {
-            mult = 6;
-        } else if(dec <= __log8) {
-            mult = 8;
-        } else {
-            mult = 10;
-        }
-    } else {    // Round down
-        if(dec < __log2) {
-            mult = 1;
-        } else if(dec < __log4) {
-            mult = 2;
-        } else if(dec < __log6) {
-            mult = 4;
-        } else if(dec < __log8) {
-            mult = 6;
-        } else {
-            mult = 8;
-        }
-    }
-
-    return mag * mult;
-}
-
 af_err af_set_axes_limits_compute(const af_window wind,
                                   const af_array x, const af_array y, const af_array z,
                                   const bool exact, const af_cell* const props)

From 7d24a399f690fe0d87d294931a4586c72313cd81 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Nov 2016 11:11:27 -0400
Subject: [PATCH 0993/2677] Update forge build tags

---
 CMakeModules/build_forge.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 3d69484c1f..efd593e601 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -42,13 +42,13 @@ ELSE()
     SET(byproducts BYPRODUCTS ${forge_location})
 ENDIF()
 
-SET(FORGE_VERSION 0.9.0)
+SET(FORGE_VERSION 0.9.1)
 
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG v${FORGE_VERSION}
+    GIT_TAG devel
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From caebfe052f29ad5c83eb92f257bed3e2398dadcf Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Nov 2016 11:09:59 -0400
Subject: [PATCH 0994/2677] Add data struct to track chart objects having user
 assigned limits

---
 src/api/c/graphics_common.cpp | 44 ++++++++++++++++++++++++++++-------
 src/api/c/graphics_common.hpp |  9 ++++++-
 2 files changed, 43 insertions(+), 10 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 4b2932dffc..1ad67c7127 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -233,15 +233,15 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
 {
     ChartMapIter iter = mChartMap.find(window);
 
-        if(iter != mChartMap.end()) {
-
-    }
-
     if(iter != mChartMap.end()) {
         // ChartVec found. Clear it.
         // TODO: Should we clear this even if r = old_r and c = old_c?
-        for(int i = 0; i < (int)(iter->second).size(); i++)
-            if((iter->second)[i] != NULL) delete (iter->second)[i];
+        for(int i = 0; i < (int)(iter->second).size(); i++) {
+            if((iter->second)[i] != NULL) {
+                delete (iter->second)[i];
+                mChartAxesOverrideMap.erase((iter->second)[i]);
+            }
+        }
         (iter->second).clear();
     }
 
@@ -271,6 +271,9 @@ forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, c
             // Chart has not been created
             chart = new forge::Chart(ctype);
             (iter->second)[c * gRows + r] = chart;
+
+            // Set Axes override to false
+            mChartAxesOverrideMap[chart] = false;
         }
     } else {
         // The chart map for this was never created
@@ -421,6 +424,24 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
     return mVcfMap[keypair];
 }
 
+bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
+{
+    ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
+    if (iter == mChartAxesOverrideMap.end()) {
+        AF_ERROR("Chart Not Found!", AF_ERR_ARG);
+    }
+    return mChartAxesOverrideMap[chart];
+}
+
+void ForgeManager::setChartAxesOverride(forge::Chart* chart, bool flag)
+{
+    ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
+    if (iter == mChartAxesOverrideMap.end()) {
+        AF_ERROR("Chart Not Found!", AF_ERR_ARG);
+    }
+    mChartAxesOverrideMap[chart] = flag;
+}
+
 void ForgeManager::destroyResources()
 {
     /* clear all OpenGL resource objects (images, plots, histograms etc) first
@@ -434,9 +455,14 @@ void ForgeManager::destroyResources()
     for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
         delete (iter->second);
 
-    for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++)
-        for(int i = 0; i < (int)(iter->second).size(); i++)
-            if((iter->second)[i] != NULL) delete (iter->second)[i];
+    for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++) {
+        for(int i = 0; i < (int)(iter->second).size(); i++) {
+            if((iter->second)[i] != NULL) {
+                delete (iter->second)[i];
+                mChartAxesOverrideMap.erase((iter->second)[i]);
+            }
+        }
+    }
 
     delete getFont(true);
     delete getMainWindow(true);
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 3b6a78f4df..06fb68d96b 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -70,6 +70,9 @@ typedef std::map<const forge::Window*, ChartVec_t> ChartMap_t;
 typedef ChartVec_t::iterator ChartVecIter;
 typedef ChartMap_t::iterator ChartMapIter;
 
+// Keeps track of which charts have manually assigned axes limits
+typedef std::map<forge::Chart*, bool> ChartAxesOverride_t;
+typedef ChartAxesOverride_t::iterator ChartAxesOverrideIter;
 
 /**
  * ForgeManager class follows a single pattern. Any user of this class, has
@@ -92,7 +95,8 @@ class ForgeManager
         SurfaceMap_t        mSfcMap;
         VectorFieldMap_t    mVcfMap;
 
-        ChartMap_t      mChartMap;
+        ChartMap_t          mChartMap;
+        ChartAxesOverride_t mChartAxesOverrideMap;
 
     public:
         static ForgeManager& getInstance();
@@ -117,6 +121,9 @@ class ForgeManager
         forge::Surface*     getSurface      (forge::Chart* chart, int nX, int nY, forge::dtype type);
         forge::VectorField* getVectorField  (forge::Chart* chart, int nPoints, forge::dtype type);
 
+        bool getChartAxesOverride(forge::Chart* chart);
+        void setChartAxesOverride(forge::Chart* chart, bool flag = true);
+
     protected:
         ForgeManager() {}
         ForgeManager(ForgeManager const&);

From c1e3a3ec3b36199af20250047a14c78f2a5e8942 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Nov 2016 11:12:48 -0400
Subject: [PATCH 0995/2677] Set chart axes override flag in setAxesLimits
 functions

---
 src/api/c/window.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 2cf866d4f9..ac8e884db8 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -187,6 +187,7 @@ af_err af_set_axes_limits_compute(const af_window wind,
             zmax = step_round(zmax, true );
         }
 
+        fgMngr.setChartAxesOverride(chart);
         chart->setAxesLimits(xmin, xmax, ymin, ymax, zmin, zmax);
     }
     CATCHALL;
@@ -235,6 +236,7 @@ af_err af_set_axes_limits_2d(const af_window wind,
             _ymax = step_round(_ymax, true );
         }
 
+        fgMngr.setChartAxesOverride(chart);
         chart->setAxesLimits(_xmin, _xmax, _ymin, _ymax);
     }
     CATCHALL;
@@ -288,6 +290,7 @@ af_err af_set_axes_limits_3d(const af_window wind,
             _zmax = step_round(_zmax, true );
         }
 
+        fgMngr.setChartAxesOverride(chart);
         chart->setAxesLimits(_xmin, _xmax, _ymin, _ymax, _zmin, _zmax);
     }
     CATCHALL;

From c739f20e92ed26cfa641ec72ebae9f7bffdf4b12 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Nov 2016 11:13:35 -0400
Subject: [PATCH 0996/2677] Compute and set axes limits in hist, plot, surface,
 vector feild

---
 src/api/c/hist.cpp         | 25 +++++++++++++++++++++++++
 src/api/c/plot.cpp         | 37 +++++++++++++++++++++++++++++++++++++
 src/api/c/surface.cpp      | 35 +++++++++++++++++++++++++++++++++++
 src/api/c/vector_field.cpp | 37 +++++++++++++++++++++++++++++++++++++
 4 files changed, 134 insertions(+)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index b560957111..b3e5f47f56 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -47,6 +47,31 @@ forge::Chart* setup_histogram(const forge::Window* const window,
     // Set histogram bar colors to orange
     hist->setColor(0.929f, 0.486f, 0.2745f, 1.0f);
 
+    // If chart axes limits do not have a manual override
+    // then compute and set axes limits
+    if(!fgMngr.getChartAxesOverride(chart)) {
+        float xMin, xMax, yMin, yMax;
+        chart->getAxesLimits(&xMin, &xMax, &yMin, &yMax);
+        T freqMax = detail::reduce_all<af_max_t, T, T>(histogramInput);
+
+        if(xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
+            // No previous limits. Set without checking
+            xMin = step_round(minval, false);
+            xMax = step_round(maxval, true);
+            yMax = step_round(freqMax, true);
+            // For histogram, always set yMin to 0.
+            yMin = 0;
+        } else {
+            if(xMin > minval)  xMin = step_round(minval, false);
+            if(xMax < maxval)  xMax = step_round(maxval, true);
+            if(yMax < freqMax) yMax = step_round(freqMax, true);
+            // For histogram, always set yMin to 0.
+            yMin = 0;
+        }
+
+        chart->setAxesLimits(xMin, xMax, yMin, yMax);
+    }
+
     copy_histogram<T>(histogramInput, hist);
 
     return chart;
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 2151c9197f..bd7d235a81 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -64,6 +64,43 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
     // ArrayFire LOGO Orange shade
     plot->setColor(0.929f, 0.529f, 0.212f, 1.0);
 
+    // If chart axes limits do not have a manual override
+    // then compute and set axes limits
+    if(!fgMngr.getChartAxesOverride(chart)) {
+        float cmin[3], cmax[3];
+        T     dmin[3], dmax[3];
+        chart->getAxesLimits(&cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2]);
+        copyData(dmin, reduce<af_min_t, T, T>(in, 1));
+        copyData(dmax, reduce<af_max_t, T, T>(in, 1));
+
+        if(cmin[0] == 0 && cmax[0] == 0
+        && cmin[1] == 0 && cmax[1] == 0
+        && cmin[2] == 0 && cmax[2] == 0) {
+            // No previous limits. Set without checking
+            cmin[0] = step_round(dmin[0], false);
+            cmax[0] = step_round(dmax[0], true);
+            cmin[1] = step_round(dmin[1], false);
+            cmax[1] = step_round(dmax[1], true);
+            if(order == 3) cmin[2] = step_round(dmin[2], false);
+            if(order == 3) cmax[2] = step_round(dmax[2], true);
+        } else {
+            if(cmin[0] > dmin[0])       cmin[0] = step_round(dmin[0], false);
+            if(cmax[0] < dmax[0])       cmax[0] = step_round(dmax[0], true);
+            if(cmin[1] > dmin[1])       cmin[1] = step_round(dmin[1], false);
+            if(cmax[1] < dmax[1])       cmax[1] = step_round(dmax[1], true);
+            if(order == 3) {
+                if(cmin[2] > dmin[2])   cmin[2] = step_round(dmin[2], false);
+                if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
+            }
+        }
+
+        if(order == 2) {
+            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1]);
+        } else if(order == 3) {
+            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1], cmin[2], cmax[2]);
+        }
+    }
+
     copy_plot<T>(in, plot);
 
     return chart;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index c1458e239b..c02989b926 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -81,6 +81,41 @@ forge::Chart* setup_surface(const forge::Window* const window,
 
     surface->setColor(0.0, 1.0, 0.0, 1.0);
 
+    // If chart axes limits do not have a manual override
+    // then compute and set axes limits
+    if(!fgMngr.getChartAxesOverride(chart)) {
+        float cmin[3], cmax[3];
+        T     dmin[3], dmax[3];
+        chart->getAxesLimits(&cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2]);
+        dmin[0] = reduce_all<af_min_t, T, T>(xIn);
+        dmax[0] = reduce_all<af_max_t, T, T>(xIn);
+        dmin[1] = reduce_all<af_min_t, T, T>(yIn);
+        dmax[1] = reduce_all<af_max_t, T, T>(yIn);
+        dmin[2] = reduce_all<af_min_t, T, T>(zIn);
+        dmax[2] = reduce_all<af_max_t, T, T>(zIn);
+
+        if(cmin[0] == 0 && cmax[0] == 0
+        && cmin[1] == 0 && cmax[1] == 0
+        && cmin[2] == 0 && cmax[2] == 0) {
+            // No previous limits. Set without checking
+            cmin[0] = step_round(dmin[0], false);
+            cmax[0] = step_round(dmax[0], true);
+            cmin[1] = step_round(dmin[1], false);
+            cmax[1] = step_round(dmax[1], true);
+            cmin[2] = step_round(dmin[2], false);
+            cmax[2] = step_round(dmax[2], true);
+        } else {
+            if(cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
+            if(cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
+            if(cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
+            if(cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
+            if(cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
+            if(cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
+        }
+
+        chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1], cmin[2], cmax[2]);
+    }
+
     copy_surface<T>(Z, surface);
 
     return chart;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 5ca9755248..ab77f1e4d5 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -61,6 +61,43 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
     // ArrayFire LOGO dark blue shade
     vectorfield->setColor(0.130f, 0.173f, 0.263f, 1.0);
 
+    // If chart axes limits do not have a manual override
+    // then compute and set axes limits
+    if(!fgMngr.getChartAxesOverride(chart)) {
+        float cmin[3], cmax[3];
+        T     dmin[3], dmax[3];
+        chart->getAxesLimits(&cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2]);
+        copyData(dmin, reduce<af_min_t, T, T>(pIn, 1));
+        copyData(dmax, reduce<af_max_t, T, T>(pIn, 1));
+
+        if(cmin[0] == 0 && cmax[0] == 0
+        && cmin[1] == 0 && cmax[1] == 0
+        && cmin[2] == 0 && cmax[2] == 0) {
+            // No previous limits. Set without checking
+            cmin[0] = step_round(dmin[0], false);
+            cmax[0] = step_round(dmax[0], true);
+            cmin[1] = step_round(dmin[1], false);
+            cmax[1] = step_round(dmax[1], true);
+            if(pIn.dims()[0] == 3) cmin[2] = step_round(dmin[2], false);
+            if(pIn.dims()[0] == 3) cmax[2] = step_round(dmax[2], true);
+        } else {
+            if(cmin[0] > dmin[0])       cmin[0] = step_round(dmin[0], false);
+            if(cmax[0] < dmax[0])       cmax[0] = step_round(dmax[0], true);
+            if(cmin[1] > dmin[1])       cmin[1] = step_round(dmin[1], false);
+            if(cmax[1] < dmax[1])       cmax[1] = step_round(dmax[1], true);
+            if(pIn.dims()[0] == 3) {
+                if(cmin[2] > dmin[2])   cmin[2] = step_round(dmin[2], false);
+                if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
+            }
+        }
+
+        if(pIn.dims()[0] == 2) {
+            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1]);
+        } else if(pIn.dims()[0] == 3) {
+            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1], cmin[2], cmax[2]);
+        }
+    }
+
     copy_vector_field<T>(pIn, dIn, vectorfield);
 
     return chart;

From cbc2b3bbd86930697f5464d5db1099a17b76fc33 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 2 Nov 2016 11:28:59 -0400
Subject: [PATCH 0997/2677] Remove setAxesLimit calls from examples

The setAxesLimit call in the swe example for histogram should remain
as it is needed for good visualization of the histogram
---
 examples/graphics/field.cpp                       | 3 ---
 examples/graphics/histogram.cpp                   | 2 --
 examples/graphics/plot2d.cpp                      | 2 --
 examples/graphics/plot3.cpp                       | 2 --
 examples/graphics/surface.cpp                     | 1 -
 examples/image_processing/binary_thresholding.cpp | 6 ------
 examples/image_processing/edge.cpp                | 2 --
 examples/pde/swe.cpp                              | 2 --
 8 files changed, 20 deletions(-)

diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
index e9886c655d..9c94a0a493 100644
--- a/examples/graphics/field.cpp
+++ b/examples/graphics/field.cpp
@@ -25,9 +25,6 @@ int main(int argc, char *argv[])
 
         myWindow.grid(1, 2);
 
-        myWindow(0, 0).setAxesLimits(MINIMUM, MAXIMUM, MINIMUM, MAXIMUM);
-        myWindow(0, 1).setAxesLimits(MINIMUM, MAXIMUM, MINIMUM, MAXIMUM);
-
         array dataRange = seq(MINIMUM, MAXIMUM, STEP);
 
         array x = tile(dataRange, 1, dataRange.dims(0));
diff --git a/examples/graphics/histogram.cpp b/examples/graphics/histogram.cpp
index 31b3753f6d..ded0d085a7 100644
--- a/examples/graphics/histogram.cpp
+++ b/examples/graphics/histogram.cpp
@@ -24,8 +24,6 @@ int main(int argc, char *argv[])
         array img = loadImage(ASSETS_DIR"/examples/images/arrow.jpg", false);
         array hist_out = histogram(img, 256, 0, 255);
 
-        float freq_max = max<float>(hist_out);
-        myWindow.setAxesLimits(0, 255, 0, freq_max);
         myWindow.setAxesTitles("Bins", "Frequency");
         myWindow.setPos(480, 0);
 
diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index 77e6f36431..a1e871d030 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -29,8 +29,6 @@ int main(int argc, char *argv[])
         array noise = randn(X.dims(0))/5.f;
 
         myWindow.grid(2, 1);
-        myWindow(0, 0).setAxesLimits(-2 * af::Pi, 2 * af::Pi, -1, 1, true);
-        myWindow(1, 0).setAxesLimits(-2 * af::Pi, 2 * af::Pi, -1.25, 1.25, true);
 
         for (double val=0; !myWindow.close(); ) {
 
diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index b58a6213c3..4d7afd5af8 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -32,8 +32,6 @@ int main(int argc, char *argv[])
             X = max(min(X, 1.0), -1.0);
             Y = max(min(Y, 1.0), -1.0);
 
-            myWindow.setAxesLimits(X, Y, Z);
-
             //Pts can be passed in as a matrix in the form n x 3, 3 x n
             //or in the flattened xyz-triplet array with size 3n x 1
             myWindow.plot(X, Y, Z);
diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index eb661d4979..b48cb8fa47 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -28,7 +28,6 @@ int main(int argc, char *argv[])
         const array y = iota(dim4(1, N), dim4(N, 1)) / M - 1;
 
         static float t=0;
-        myWindow.setAxesLimits(-1.0, 1.0, -1.0, 1.0, -10.0, 10.0);
         while(!myWindow.close()) {
             t+=0.07;
             array z = 10*x*-abs(y) * cos(x*x*(y+t))+sin(y*(x+t))-1.5;
diff --git a/examples/image_processing/binary_thresholding.cpp b/examples/image_processing/binary_thresholding.cpp
index ba869f450f..6a00bd2814 100644
--- a/examples/image_processing/binary_thresholding.cpp
+++ b/examples/image_processing/binary_thresholding.cpp
@@ -76,18 +76,12 @@ int main(int argc, char **argv)
         array smooth = convolve(bimodal, gaussianKernel(5, 5));
         array smoothHist = histogram(smooth, 256, 0, 255);
 
-        unsigned bimodHist_freq_max = max<unsigned>(bimodHist);
-        unsigned smoothHist_freq_max = max<unsigned>(smoothHist);
-
         af::Window wnd(1536, 1024, "Binary Thresholding Algorithms");
         std::cout << "Press ESC while the window is in focus to proceed to exit" << std::endl;
 
         wnd.grid(3, 3);
-        wnd(0, 1).setAxesLimits(0, 255, 0, bimodHist_freq_max, true);
         wnd(0, 1).setAxesTitles("Bins", "Frequency");
-        wnd(1, 1).setAxesLimits(0, 255, 0, bimodHist_freq_max, true);
         wnd(1, 1).setAxesTitles("Bins", "Frequency");
-        wnd(2, 1).setAxesLimits(0, 255, 0, smoothHist_freq_max, true);
         wnd(2, 1).setAxesTitles("Bins", "Frequency");
         while (!wnd.close()) {
             wnd(0, 0).image(bimodal / 255, "Input Image");
diff --git a/examples/image_processing/edge.cpp b/examples/image_processing/edge.cpp
index 6830159394..4fbc1a7670 100644
--- a/examples/image_processing/edge.cpp
+++ b/examples/image_processing/edge.cpp
@@ -80,8 +80,6 @@ void edge()
     array sobelFilter   = edge(in, 2);
     array hst = histogram(in, 256, 0, 255);
 
-    float freq_max = max<float>(hst);
-    myWindow2.setAxesLimits(0, 255, 0, freq_max);
     myWindow2.setAxesTitles("Bins", "Frequency");
 
     while(!myWindow.close() && !myWindow2.close()) {
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index 966af4dbfc..bee396a155 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -87,8 +87,6 @@ static void swe(bool console)
             array hist_out = histogram(normalize(eta, m_eta), 15);
 
             (*win)(0, 1).setAxesLimits(0, hist_out.elements(), 0, max<float>(hist_out));
-            (*win)(1, 0).setAxesLimits(range(up.dims(1)), vp.col(0));
-            (*win)(1, 1).setAxesLimits(eta.col(0), up.col(0), vp.col(0));
 
             (*win)(0,0).image(normalize(eta, m_eta));
             (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");

From 3d269e8f24337ff528457b206b31b66349edfcaa Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Nov 2016 15:53:54 -0400
Subject: [PATCH 0998/2677] Change AF_ERR_ARG to AF_ERR_INTERNAL in chart axes
 limits

---
 src/api/c/graphics_common.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 1ad67c7127..1f6ec12139 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -428,7 +428,7 @@ bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
 {
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
-        AF_ERROR("Chart Not Found!", AF_ERR_ARG);
+        AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
     return mChartAxesOverrideMap[chart];
 }
@@ -437,7 +437,7 @@ void ForgeManager::setChartAxesOverride(forge::Chart* chart, bool flag)
 {
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
-        AF_ERROR("Chart Not Found!", AF_ERR_ARG);
+        AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
     mChartAxesOverrideMap[chart] = flag;
 }

From cab9436b176ef4118d79dca49867c1ffc19ebcf4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 3 Nov 2016 16:02:26 -0400
Subject: [PATCH 0999/2677] Update the links for CI job build tags

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index c0d12b81fd..1025fa9bf8 100644
--- a/README.md
+++ b/README.md
@@ -26,10 +26,10 @@ Several of ArrayFire's benefits include:
 
 ### Build and Test Status
 
-|         | Linux x86_64 | Linux aarch64 | Windows | OSX |
-|:-------:|:------------:|:-------------:|:-------:|:---:|
-| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
-| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
+|         | Linux x86_64 | Linux aarch64 | OSX | Windows |
+|:-------:|:------------:|:-------------:|:---:|:-------:|
+| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build-mkl/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build-mkl/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/job/devel/) |
+| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test-mkl/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test-mkl/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/job/devel/) |
 
 ### Installation
 

From 892f622484b7f7adc3f8cc162c4dc3a664054ce3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 4 Nov 2016 16:05:27 -0400
Subject: [PATCH 1000/2677] TESTS: Split scan into scan and scan by key files

---
 test/scan.cpp        | 180 -------------------------------------
 test/scan_by_key.cpp | 207 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 207 insertions(+), 180 deletions(-)
 create mode 100644 test/scan_by_key.cpp

diff --git a/test/scan.cpp b/test/scan.cpp
index 3d0c885f2a..e69b6464c1 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -17,7 +17,6 @@
 #include <string>
 #include <testHelpers.hpp>
 #include <af/device.h>
-#include "binary_ops.hpp"
 #include <utility>
 
 using std::vector;
@@ -84,185 +83,6 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
 
-float randomInterval(float start, float end)
-{
-    return start + (end - start)*(std::rand()/float(RAND_MAX));
-}
-
-int randomInterval(int start, int end)
-{
-    return start + std::rand()%(end - start);
-}
-
-template <typename T>
-std::vector<T> createScanKey(af::dim4 dims, int scanDim,
-        const std::vector<int> &nodeLengths,
-        T keyStart, T keyEnd)
-{
-    std::srand(0);
-    int elemCount = dims.elements();
-    std::vector<T> key(elemCount);
-
-    int stride = 1;
-    for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
-
-    for (int start = 0; start < stride; ++start) {
-        T keyval = (T)(0);
-        for (int index = start, i = 0;
-                index < elemCount;
-                index += stride, i = (i+1)%dims[scanDim]) {
-            bool isNode = false;
-            for (unsigned n = 0; n < nodeLengths.size(); ++n) {
-                if (i % nodeLengths[n] == 0) {
-                    isNode = true;
-                }
-            }
-            if (isNode && (std::rand()%2)) {
-                keyval = randomInterval(keyStart, keyEnd);
-            }
-            key[index] = keyval;
-        }
-    }
-    return key;
-}
-
-template <typename T>
-std::vector<T> createScanData(af::dim4 dims, T dataStart, T dataEnd)
-{
-    int elemCount = dims.elements();
-    std::vector<T> in(elemCount);
-    for (int i = 0; i < elemCount; ++i) {
-        in[i] = randomInterval(dataStart, dataEnd);
-    }
-    return in;
-}
-
-template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive_scan>
-void verify(af::dim4 dims,
-        const std::vector<Ti> &in,
-        const std::vector<Tk> &key,
-        const std::vector<To> &out,
-        int scanDim, double eps)
-{
-    std::srand(1);
-    Binary<To, op> binOp;
-    int elemCount = dims.elements();
-
-    int stride = 1;
-    for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
-
-    for (int start = 0; start < stride; ++start) {
-        Tk keyval = key[start];
-        To gold = binOp.init();
-        for (int index = start + (!inclusive_scan)*stride, i = (!inclusive_scan);
-                index < elemCount;
-                index += stride, i = (i+1)%dims[scanDim]) {
-            if ((key[index] != keyval) || (i == 0)) {
-                keyval = key[index];
-                if (inclusive_scan) {
-                    gold = (To)in[index];
-                    ASSERT_NEAR(gold, out[index], eps);
-                } else {
-                    gold = binOp.init();
-                }
-            } else {
-                To dataval = (To)in[index - (!inclusive_scan)*stride];
-                gold = binOp(gold, dataval);
-                ASSERT_NEAR(gold, out[index], eps);
-            }
-        }
-    }
-}
-
-template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
-void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
-        int keyStart, int keyEnd, Ti dataStart, Ti dataEnd, double eps)
-{
-    std::vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
-    std::vector<Ti> in = createScanData<Ti>(dims, dataStart, dataEnd);
-
-    af::array afkey(dims, key.data());
-    af::array afin(dims, in.data());
-    af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
-    std::vector<To> out(afout.elements());
-    afout.host(out.data());
-
-    verify<Ti, int, To, op, inclusive_scan>(dims, in, key, out, scanDim, eps);
-}
-
-#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS)   \
-TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                    \
-{                                                                               \
-    af::dim4 dims(X, Y, Z, W);                                                  \
-    int scanDim = DIM;                                                          \
-    int nodel[] = {37, 256};                                                    \
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));       \
-    int keyStart = 0;                                                           \
-    int keyEnd = 15;                                                            \
-    int dataStart = DSTART;                                                     \
-    int dataEnd = DEND;                                                         \
-    scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths,                  \
-            keyStart, keyEnd, dataStart, dataEnd, EPS);                         \
-}
-
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
-
-TEST(ScanByKey,Test_Scan_By_key_Simple_0)
-{
-    af::dim4 dims(16, 8, 2, 1);
-    int scanDim = 0;
-    int nodel[] = {4, 8};
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
-    int keyStart = 0;
-    int keyEnd = 15;
-    int dataStart = 2;
-    int dataEnd = 4;
-    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
-            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
-}
-
-TEST(ScanByKey,Test_Scan_By_key_Simple_1)
-{
-    af::dim4 dims(8, 256+128, 1, 1);
-    int scanDim = 1;
-    int nodel[] = {4, 8};
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
-    int keyStart = 0;
-    int keyEnd = 15;
-    int dataStart = 2;
-    int dataEnd = 4;
-    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
-            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
-}
-
 #define SCAN_TESTS(FN, TAG, Ti, To)             \
     TEST(Scan,Test_##FN##_##TAG)                \
     {                                           \
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
new file mode 100644
index 0000000000..91149bdd99
--- /dev/null
+++ b/test/scan_by_key.cpp
@@ -0,0 +1,207 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <af/array.h>
+#include <vector>
+#include <iostream>
+#include <string>
+#include <testHelpers.hpp>
+#include <af/device.h>
+#include "binary_ops.hpp"
+#include <utility>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using af::cfloat;
+using af::cdouble;
+
+float randomInterval(float start, float end)
+{
+    return start + (end - start)*(std::rand()/float(RAND_MAX));
+}
+
+int randomInterval(int start, int end)
+{
+    return start + std::rand()%(end - start);
+}
+
+template <typename T>
+std::vector<T> createScanKey(af::dim4 dims, int scanDim,
+        const std::vector<int> &nodeLengths,
+        T keyStart, T keyEnd)
+{
+    std::srand(0);
+    int elemCount = dims.elements();
+    std::vector<T> key(elemCount);
+
+    int stride = 1;
+    for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
+
+    for (int start = 0; start < stride; ++start) {
+        T keyval = (T)(0);
+        for (int index = start, i = 0;
+                index < elemCount;
+                index += stride, i = (i+1)%dims[scanDim]) {
+            bool isNode = false;
+            for (unsigned n = 0; n < nodeLengths.size(); ++n) {
+                if (i % nodeLengths[n] == 0) {
+                    isNode = true;
+                }
+            }
+            if (isNode && (std::rand()%2)) {
+                keyval = randomInterval(keyStart, keyEnd);
+            }
+            key[index] = keyval;
+        }
+    }
+    return key;
+}
+
+template <typename T>
+std::vector<T> createScanData(af::dim4 dims, T dataStart, T dataEnd)
+{
+    int elemCount = dims.elements();
+    std::vector<T> in(elemCount);
+    for (int i = 0; i < elemCount; ++i) {
+        in[i] = randomInterval(dataStart, dataEnd);
+    }
+    return in;
+}
+
+template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive_scan>
+void verify(af::dim4 dims,
+        const std::vector<Ti> &in,
+        const std::vector<Tk> &key,
+        const std::vector<To> &out,
+        int scanDim, double eps)
+{
+    std::srand(1);
+    Binary<To, op> binOp;
+    int elemCount = dims.elements();
+
+    int stride = 1;
+    for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
+
+    for (int start = 0; start < stride; ++start) {
+        Tk keyval = key[start];
+        To gold = binOp.init();
+        for (int index = start + (!inclusive_scan)*stride, i = (!inclusive_scan);
+                index < elemCount;
+                index += stride, i = (i+1)%dims[scanDim]) {
+            if ((key[index] != keyval) || (i == 0)) {
+                keyval = key[index];
+                if (inclusive_scan) {
+                    gold = (To)in[index];
+                    ASSERT_NEAR(gold, out[index], eps);
+                } else {
+                    gold = binOp.init();
+                }
+            } else {
+                To dataval = (To)in[index - (!inclusive_scan)*stride];
+                gold = binOp(gold, dataval);
+                ASSERT_NEAR(gold, out[index], eps);
+            }
+        }
+    }
+}
+
+template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
+void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
+        int keyStart, int keyEnd, Ti dataStart, Ti dataEnd, double eps)
+{
+    std::vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
+    std::vector<Ti> in = createScanData<Ti>(dims, dataStart, dataEnd);
+
+    af::array afkey(dims, key.data());
+    af::array afin(dims, in.data());
+    af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
+    std::vector<To> out(afout.elements());
+    afout.host(out.data());
+
+    verify<Ti, int, To, op, inclusive_scan>(dims, in, key, out, scanDim, eps);
+}
+
+#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS)   \
+TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                    \
+{                                                                               \
+    af::dim4 dims(X, Y, Z, W);                                                  \
+    int scanDim = DIM;                                                          \
+    int nodel[] = {37, 256};                                                    \
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));       \
+    int keyStart = 0;                                                           \
+    int keyEnd = 15;                                                            \
+    int dataStart = DSTART;                                                     \
+    int dataEnd = DEND;                                                         \
+    scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths,                  \
+            keyStart, keyEnd, dataStart, dataEnd, EPS);                         \
+}
+
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
+
+TEST(ScanByKey,Test_Scan_By_key_Simple_0)
+{
+    af::dim4 dims(16, 8, 2, 1);
+    int scanDim = 0;
+    int nodel[] = {4, 8};
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+    int keyStart = 0;
+    int keyEnd = 15;
+    int dataStart = 2;
+    int dataEnd = 4;
+    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
+            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
+}
+
+TEST(ScanByKey,Test_Scan_By_key_Simple_1)
+{
+    af::dim4 dims(8, 256+128, 1, 1);
+    int scanDim = 1;
+    int nodel[] = {4, 8};
+    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+    int keyStart = 0;
+    int keyEnd = 15;
+    int dataStart = 2;
+    int dataEnd = 4;
+    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
+            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
+}

From b9d115ccdfad6d0121ebd8689ec2358f0b22c38d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 7 Nov 2016 13:55:32 -0500
Subject: [PATCH 1001/2677] Compilation fix for VS2015: Add missing static
 definitions in cpu math.hpp

Continues e36a028. Had missed checking CPU and CUDA compilation.
---
 src/backend/cpu/math.hpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 40e09fce37..3b20f0ede9 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -43,12 +43,12 @@ namespace cpu
         return retVal;
     }
 
-    template <typename T> T maxval() { return std::numeric_limits<T>::max(); }
-    template <typename T> T minval() { return std::numeric_limits<T>::min(); }
-    template <> STATIC_ float maxval() { return std::numeric_limits<float>::infinity(); }
-    template <> STATIC_ double maxval() { return std::numeric_limits<double>::infinity(); }
-    template <> STATIC_ float minval() { return -std::numeric_limits<float>::infinity(); }
-    template <> STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
+    template <typename T> STATIC_ T      maxval() { return  std::numeric_limits<T     >::max();      }
+    template <typename T> STATIC_ T      minval() { return  std::numeric_limits<T     >::min();      }
+    template <>           STATIC_ float  maxval() { return  std::numeric_limits<float >::infinity(); }
+    template <>           STATIC_ double maxval() { return  std::numeric_limits<double>::infinity(); }
+    template <>           STATIC_ float  minval() { return -std::numeric_limits<float >::infinity(); }
+    template <>           STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
 
     template<typename T>
     static T scalar(double val)

From 71875ef7003745ac1f444a9001bf15890d4d2655 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 7 Nov 2016 15:41:35 -0500
Subject: [PATCH 1002/2677] Compilation fix for VS2015: Add missing static
 definitions in cuda math.hpp

Continues e36a028. Had missed checking CPU and CUDA compilation.
---
 src/backend/cuda/math.hpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 83ffc30cde..26b1cef8f0 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -93,12 +93,12 @@ namespace cuda
     }
 
 #ifndef __CUDA_ARCH__
-    template <typename T> T maxval() { return std::numeric_limits<T>::max(); }
-    template <typename T> T minval() { return std::numeric_limits<T>::min(); }
-    template <> STATIC_ float maxval() { return std::numeric_limits<float>::infinity(); }
-    template <> STATIC_ double maxval() { return std::numeric_limits<double>::infinity(); }
-    template <> STATIC_ float minval() { return -std::numeric_limits<float>::infinity(); }
-    template <> STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
+    template <typename T> STATIC_ T      maxval() { return  std::numeric_limits<T     >::max();      }
+    template <typename T> STATIC_ T      minval() { return  std::numeric_limits<T     >::min();      }
+    template <>           STATIC_ float  maxval() { return  std::numeric_limits<float >::infinity(); }
+    template <>           STATIC_ double maxval() { return  std::numeric_limits<double>::infinity(); }
+    template <>           STATIC_ float  minval() { return -std::numeric_limits<float >::infinity(); }
+    template <>           STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
 #else
     template <typename T> __device__ T maxval() { return 1u << (8 * sizeof(T) - 1); }
     template <typename T> __device__ T minval() { return scalar<T>(0); }

From 12dfe4fe7d7ee2067f9c9168c1fc0112e73a9a1d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Nov 2016 14:50:33 -0500
Subject: [PATCH 1003/2677] BUGFIX: Fix row/col indices for creation of COO
 from Dense

---
 src/backend/cpu/sparse.cpp    | 8 ++++----
 src/backend/cuda/sparse.cu    | 6 +++---
 src/backend/opencl/sparse.cpp | 8 ++++----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 68d657a145..75b83d8322 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -134,11 +134,11 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 
     dim_t nNZ = nonZeroIdx.elements();
 
-    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
-    constNNZ.eval();
+    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
+    constDim.eval();
 
-    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
     Array<T> values = copyArray<T>(in);
     values.modDims(dim4(values.elements()));
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index a393084899..1bbe68d482 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -204,10 +204,10 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 
     dim_t nNZ = nonZeroIdx.elements();
 
-    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
+    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
 
-    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
     Array<T> values = copyArray<T>(in);
     values.modDims(dim4(values.elements()));
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 07461f953e..1c07b7b88a 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -41,11 +41,11 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
 
     dim_t nNZ = nonZeroIdx.elements();
 
-    Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
-    constNNZ.eval();
+    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
+    constDim.eval();
 
-    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
-    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
+    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
     Array<T> values = copyArray<T>(in);
     values.modDims(dim4(values.elements()));

From 91ec0efd233d93aa11471b7bbc61ec5d053834e5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Nov 2016 16:56:56 -0500
Subject: [PATCH 1004/2677] FEAT: Add CSR <-> COO Conversion to CPU Backend

---
 src/api/c/sparse.cpp              |   2 +-
 src/backend/cpu/kernel/sparse.hpp | 105 ++++++++++++++++++++++++++++++
 src/backend/cpu/sparse.cpp        |  48 ++++++++++++--
 3 files changed, 147 insertions(+), 8 deletions(-)

diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index a88c7a64f9..28ca49d75c 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -307,7 +307,7 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
 
         // Right now dest_storage can only be AF_STORAGE_DENSE
         // TODO: Add support for [CSR, CSC, COO] <-> [CSR, CSC, COO] in backends
-        ARG_ASSERT(1, destStorage == AF_STORAGE_DENSE);
+        ARG_ASSERT(1, destStorage != AF_STORAGE_CSC);
 
         if(base.getStorage() == destStorage) {
             // Return a reference
diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index 4a9362a15f..bf72af3aa4 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -11,6 +11,8 @@
 #include <Array.hpp>
 #include <utility.hpp>
 #include <math.hpp>
+#include <kernel/sort_helper.hpp>
+#include <algorithm>
 
 namespace cpu
 {
@@ -93,5 +95,108 @@ struct csr_dense
     }
 };
 
+// Modified code from sort helper
+template <typename T>
+using SpKeyIndexPair = std::tuple<int, T, int>; // sorting index, value, other index
+
+template <typename T>
+struct SpKIPCompareK
+{
+    bool operator()(const SpKeyIndexPair<T> &lhs, const SpKeyIndexPair<T> &rhs)
+    {
+        int lhsVal = std::get<0>(lhs);
+        int rhsVal = std::get<0>(rhs);
+        // Always returns ascending
+        return (lhsVal < rhsVal);
+    }
+};
+
+template<typename T>
+struct csr_coo
+{
+    void operator()(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
+                    Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
+    {
+        // First calculate the linear index
+        T         * const ovPtr = ovalues.get();
+        int       * const orPtr = orowIdx.get();
+        int       * const ocPtr = ocolIdx.get();
+
+        T   const * const ivPtr = ivalues.get();
+        int const * const irPtr = irowIdx.get();
+        int const * const icPtr = icolIdx.get();
+
+        // Create cordinate form of the row array
+        for(int i = 0; i < (int)irowIdx.elements() - 1; i++) {
+            std::fill_n(orPtr + irPtr[i], irPtr[i + 1] - irPtr[i], i);
+        }
+
+        // Sort the coordinate form using column index
+        // Uses code from sort_by_key kernels
+        typedef SpKeyIndexPair<T> CurrentPair;
+        int size = ovalues.dims()[0];
+        size_t bytes = size * sizeof(CurrentPair);
+        CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+
+        for(int x = 0; x < size; x++) {
+           pairKeyVal[x] = std::make_tuple(icPtr[x], ivPtr[x], orPtr[x]);
+        }
+
+        std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
+
+        for(int x = 0; x < (int)ovalues.elements(); x++) {
+            ocPtr[x] = std::get<0>(pairKeyVal[x]);
+            ovPtr[x] = std::get<1>(pairKeyVal[x]);
+            orPtr[x] = std::get<2>(pairKeyVal[x]);
+        }
+
+        memFree((char *)pairKeyVal);
+    }
+};
+
+template<typename T>
+struct coo_csr
+{
+    void operator()(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
+                    Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
+    {
+        T         * const ovPtr = ovalues.get();
+        int       * const orPtr = orowIdx.get();
+        int       * const ocPtr = ocolIdx.get();
+
+        T   const * const ivPtr = ivalues.get();
+        int const * const irPtr = irowIdx.get();
+        int const * const icPtr = icolIdx.get();
+
+        // Sort the colidx and values based on rowIdx
+        // Uses code from sort_by_key kernels
+        typedef SpKeyIndexPair<T> CurrentPair;
+        int size = ovalues.dims()[0];
+        size_t bytes = size * sizeof(CurrentPair);
+        CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+
+        for(int x = 0; x < size; x++) {
+           pairKeyVal[x] = std::make_tuple(irPtr[x], ivPtr[x], icPtr[x]);
+        }
+
+        std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
+
+        ovPtr[0] = 0;
+        for(int x = 0; x < (int)ovalues.elements(); x++) {
+            int row  = std::get<0>(pairKeyVal[x]);
+            ovPtr[x] = std::get<1>(pairKeyVal[x]);
+            ocPtr[x] = std::get<2>(pairKeyVal[x]);
+            orPtr[row + 1]++;
+        }
+
+        // Compress row storage
+        for(int x = 1; x < (int)orowIdx.elements(); x++) {
+            orPtr[x] += orPtr[x - 1];
+        }
+
+        memFree((char *)pairKeyVal);
+    }
+};
+
 }
 }
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 75b83d8322..a72a6b2495 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -339,18 +339,52 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 ////////////////////////////////////////////////////////////////////////////////
 // Common to MKL and Not MKL
 ////////////////////////////////////////////////////////////////////////////////
-template<typename T, af_storage src, af_storage dest>
+template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
-    // Dummy function
-    // TODO finish this function when support is required
-    AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
-
     in.eval();
 
-    SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    converted.eval();
 
-    return dense;
+    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+
+        auto func = [=] (SparseArray<T> out, const SparseArray<T> in_) {
+            Array<T  > ovalues = out.getValues();
+            Array<int> orowIdx = out.getRowIdx();
+            Array<int> ocolIdx = out.getColIdx();
+
+            Array<T  > ivalues = in_.getValues();
+            Array<int> irowIdx = in_.getRowIdx();
+            Array<int> icolIdx = in_.getColIdx();
+
+            kernel::csr_coo<T>()(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx);
+        };
+
+        getQueue().enqueue(func, converted, in);
+
+    } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+
+        auto func = [=] (SparseArray<T> out, const SparseArray<T> in_) {
+            Array<T  > ovalues = out.getValues();
+            Array<int> orowIdx = out.getRowIdx();
+            Array<int> ocolIdx = out.getColIdx();
+
+            Array<T  > ivalues = in_.getValues();
+            Array<int> irowIdx = in_.getRowIdx();
+            Array<int> icolIdx = in_.getColIdx();
+
+            kernel::coo_csr<T>()(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx);
+        };
+
+        getQueue().enqueue(func, converted, in);
+
+    } else {
+        // Should never come here
+        AF_ERROR("CPU Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return converted;
 }
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \

From df327d21b9af16c52c0acb11bd6e8ea5d187615c Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Nov 2016 16:57:28 -0500
Subject: [PATCH 1005/2677] TESTS: Add tests to check conversion between CSR
 and COO

---
 test/sparse.cpp | 67 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index 68e7350955..98ffa4c4cd 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -226,3 +226,70 @@ CREATE_TESTS(AF_STORAGE_CSR)
 CREATE_TESTS(AF_STORAGE_COO)
 
 #undef CREATE_TESTS
+
+template<typename T, af_storage src, af_storage dest>
+void sparseConvertTester(const int m, const int n, int factor)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    // Create Sparse Array of type src and dest From Dense
+    af::array sA = af::sparse(A, src);
+    af::array dA = af::sparse(A, dest);
+
+    //// Convert src to dest format and dest to src
+    af::array s2d = sparseConvertTo(sA, dest);
+    af::array d2s = sparseConvertTo(dA, src);
+
+    //// Get the individual arrays and verify equality
+    af::array dValues = sparseGetValues(dA);
+    af::array dRowIdx = sparseGetRowIdx(dA);
+    af::array dColIdx = sparseGetColIdx(dA);
+    dim_t     dNNZ    = sparseGetNNZ   (dA);
+
+    af::array s2dValues = sparseGetValues(s2d);
+    af::array s2dRowIdx = sparseGetRowIdx(s2d);
+    af::array s2dColIdx = sparseGetColIdx(s2d);
+    dim_t     s2dNNZ    = sparseGetNNZ   (s2d);
+
+    af::array sValues = sparseGetValues(sA);
+    af::array sRowIdx = sparseGetRowIdx(sA);
+    af::array sColIdx = sparseGetColIdx(sA);
+    dim_t     sNNZ    = sparseGetNNZ   (sA);
+
+    af::array d2sValues = sparseGetValues(d2s);
+    af::array d2sRowIdx = sparseGetRowIdx(d2s);
+    af::array d2sColIdx = sparseGetColIdx(d2s);
+    dim_t     d2sNNZ    = sparseGetNNZ   (d2s);
+
+    ASSERT_EQ(dNNZ, s2dNNZ);
+    ASSERT_EQ(0, af::max<double>(dValues - s2dValues));
+    ASSERT_EQ(0, af::max<int   >(dRowIdx - s2dRowIdx));
+    ASSERT_EQ(0, af::max<int   >(dColIdx - s2dColIdx));
+
+    ASSERT_EQ(sNNZ, d2sNNZ);
+    ASSERT_EQ(0, af::max<double>(sValues - d2sValues));
+    ASSERT_EQ(0, af::max<int   >(sRowIdx - d2sRowIdx));
+    ASSERT_EQ(0, af::max<int   >(sColIdx - d2sColIdx));
+}
+
+#define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE)                                 \
+    {                                                                           \
+        sparseConvertTester<T, STYPE, DTYPE>(10, 10, 5);                        \
+    }                                                                           \
+
+
+CONVERT_TESTS(float  , AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(double , AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(cfloat , AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(cdouble, AF_STORAGE_CSR, AF_STORAGE_COO)

From 84f5d92657982a61a88d647339c33d81017da654 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 9 Nov 2016 18:27:44 -0500
Subject: [PATCH 1006/2677] FEAT: Add CSR <-> COO Conversion to CUDA Backend
 (WIP)

---
 src/backend/cuda/sparse.cu | 131 +++++++++++++++++++++++++++++++++++--
 1 file changed, 126 insertions(+), 5 deletions(-)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 1bbe68d482..573b9fadf8 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -145,6 +145,21 @@ struct nnz_func_def_t
                                               int *, int *);
 };
 
+//cusparseStatus_t cusparseZgthr(cusparseHandle_t handle,
+//                               int nnz,
+//                               const cuDoubleComplex *y,
+//                               cuDoubleComplex *xVal, const int *xInd,
+//                               cusparseIndexBase_t idxBase)
+template<typename T>
+struct gthr_func_def_t
+{
+    typedef cusparseStatus_t (*gthr_func_def)(cusparseHandle_t,
+                                              int,
+                                              const T *,
+                                              T*, const int *,
+                                              cusparseIndexBase_t);
+};
+
 #define SPARSE_FUNC_DEF( FUNC )                     \
 template<typename T>                                \
 typename FUNC##_func_def_t<T>::FUNC##_func_def      \
@@ -191,6 +206,12 @@ SPARSE_FUNC(nnz, double, D)
 SPARSE_FUNC(nnz, cfloat, C)
 SPARSE_FUNC(nnz, cdouble,Z)
 
+SPARSE_FUNC_DEF(gthr)
+SPARSE_FUNC(gthr, float,  S)
+SPARSE_FUNC(gthr, double, D)
+SPARSE_FUNC(gthr, cfloat, C)
+SPARSE_FUNC(gthr, cdouble,Z)
+
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
 
@@ -340,14 +361,114 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     return dense;
 }
 
-template<typename T, af_storage src, af_storage dest>
+template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
-    // Dummy function
-    // TODO finish this function when support is required
-    SparseArray<T> dense = createEmptySparseArray<T>(in.dims(), in.getNNZ(), dest);
+    in.eval();
 
-    return dense;
+    int nNZ = in.getNNZ();
+    SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), nNZ, dest);
+
+    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+        // Copy colIdx as is
+        CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
+                                   in.getColIdx().elements() * sizeof(int),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+
+        // cusparse function to expand compressed row into coordinate
+        CUSPARSE_CHECK(cusparseXcsr2coo(
+                        getHandle(),
+                        in.getRowIdx().get(),
+                        nNZ, in.dims()[0],
+                        converted.getRowIdx().get(),
+                        CUSPARSE_INDEX_BASE_ZERO));
+
+        // Call sort
+        size_t pBufferSizeInBytes = 0;
+        CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
+                        getHandle(),
+                        in.dims()[0], in.dims()[1], nNZ,
+                        converted.getRowIdx().get(), converted.getColIdx().get(),
+                        &pBufferSizeInBytes));
+        char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
+
+        int *P = memAlloc<int>(nNZ);
+        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+
+        CUSPARSE_CHECK(cusparseXcoosortByColumn(
+                        getHandle(),
+                        in.dims()[0], in.dims()[1], nNZ,
+                        converted.getRowIdx().get(), converted.getColIdx().get(),
+                        P, (void*)pBuffer));
+
+        CUSPARSE_CHECK(gthr_func<T>()(
+                        getHandle(), nNZ,
+                        in.getValues().get(),
+                        converted.getValues().get(),
+                        P, CUSPARSE_INDEX_BASE_ZERO));
+
+        memFree(P);
+        memFree(pBuffer);
+
+    } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+        // Copy colIdx as is
+        CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
+                                   in.getColIdx().elements() * sizeof(int),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+
+        // cusparse function to compress row from coordinate
+        CUSPARSE_CHECK(cusparseXcoo2csr(
+                        getHandle(),
+                        in.getRowIdx().get(),
+                        nNZ, in.dims()[0],
+                        converted.getRowIdx().get(),
+                        CUSPARSE_INDEX_BASE_ZERO));
+
+        // Call sort
+        size_t pBufferSizeInBytes = 0;
+        CUSPARSE_CHECK(cusparseXcsrsort_bufferSizeExt(
+                        getHandle(),
+                        in.dims()[0], in.dims()[1], nNZ,
+                        converted.getRowIdx().get(), converted.getColIdx().get(),
+                        &pBufferSizeInBytes));
+        char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
+
+        int *P = memAlloc<int>(nNZ);
+        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+
+        // Create Sparse Matrix Descriptor
+        cusparseMatDescr_t descr = 0;
+        CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
+        cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+        cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+
+        CUSPARSE_CHECK(cusparseXcsrsort(
+                        getHandle(),
+                        in.dims()[0], in.dims()[1], nNZ,
+                        descr,
+                        converted.getRowIdx().get(), converted.getColIdx().get(),
+                        P, (void*)pBuffer));
+
+        CUSPARSE_CHECK(gthr_func<T>()(
+                        getHandle(), nNZ,
+                        in.getValues().get(),
+                        converted.getValues().get(),
+                        P, CUSPARSE_INDEX_BASE_ZERO));
+
+        // Destory Sparse Matrix Descriptor
+        CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+
+        memFree(P);
+        memFree(pBuffer);
+
+    } else {
+        // Should never come here
+        AF_ERROR("CUDA Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return converted;
 }
 
 

From b304128f5813e755067ae32b81f501adb8cdb57b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Nov 2016 11:15:37 -0500
Subject: [PATCH 1007/2677] Add copy option to createSparse functions

---
 src/backend/SparseArray.cpp    | 12 ++++++------
 src/backend/SparseArray.hpp    |  4 ++--
 src/backend/sparse_helpers.hpp |  4 ++--
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/backend/SparseArray.cpp b/src/backend/SparseArray.cpp
index eba69c411d..61e7b29a22 100644
--- a/src/backend/SparseArray.cpp
+++ b/src/backend/SparseArray.cpp
@@ -124,9 +124,9 @@ SparseArray<T> createDeviceDataSparseArray(
         const af::dim4 &_dims, const dim_t nNZ,
         const T * const _values,
         const int * const _rowIdx, const int * const _colIdx,
-        const af::storage _storage)
+        const af::storage _storage, const bool _copy)
 {
-    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, false);
+    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, _copy);
 }
 
 template<typename T>
@@ -134,9 +134,9 @@ SparseArray<T> createArrayDataSparseArray(
         const af::dim4 &_dims,
         const Array<T> &_values,
         const Array<int> &_rowIdx, const Array<int> &_colIdx,
-        const af::storage _storage)
+        const af::storage _storage, const bool _copy)
 {
-    return SparseArray<T>(_dims, _values, _rowIdx, _colIdx, _storage, false);
+    return SparseArray<T>(_dims, _values, _rowIdx, _colIdx, _storage, _copy);
 }
 
 template<typename T>
@@ -223,12 +223,12 @@ SparseArray<T>::~SparseArray()
             const af::dim4 &_dims, const dim_t _nNZ,                                                \
             const T * const _values,                                                                \
             const int * const _rowIdx, const int * const _colIdx,                                   \
-            const af::storage _storage);                                                            \
+            const af::storage _storage, const bool _copy);                                          \
     template SparseArray<T> createArrayDataSparseArray<T>(                                          \
             const af::dim4 &_dims,                                                                  \
             const Array<T> &_values,                                                                \
             const Array<int> &_rowIdx, const Array<int> &_colIdx,                                   \
-            const af::storage _storage);                                                            \
+            const af::storage _storage, const bool _copy);                                          \
     template SparseArray<T> *initSparseArray<T>();                                                  \
     template void destroySparseArray<T>(SparseArray<T> *sparse);                                    \
                                                                                                     \
diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
index 42667fedd9..70d5d0a4db 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/SparseArray.hpp
@@ -218,13 +218,13 @@ class SparseArray
             const af::dim4 &_dims, const dim_t nNZ,
             const T * const _values,
             const int * const _rowIdx, const int * const _colIdx,
-            const af::storage _storage);
+            const af::storage _storage, const bool _copy);
 
     friend SparseArray<T> createArrayDataSparseArray<T>(
             const af::dim4 &_dims,
             const Array<T> &_values,
             const Array<int> &_rowIdx, const Array<int> &_colIdx,
-            const af::storage _storage);
+            const af::storage _storage, const bool _copy);
 
     friend SparseArray<T> *initSparseArray<T>();
 
diff --git a/src/backend/sparse_helpers.hpp b/src/backend/sparse_helpers.hpp
index 31fced6e87..b57f881b5d 100644
--- a/src/backend/sparse_helpers.hpp
+++ b/src/backend/sparse_helpers.hpp
@@ -37,14 +37,14 @@ SparseArray<T> createDeviceDataSparseArray(
         const af::dim4 &_dims, const dim_t nNZ,
         const T * const _values,
         const int * const _rowIdx, const int * const _colIdx,
-        const af::storage _storage);
+        const af::storage _storage, const bool _copy = false);
 
 template<typename T>
 SparseArray<T> createArrayDataSparseArray(
         const af::dim4 &_dims,
         const Array<T> &_values,
         const Array<int> &_rowIdx, const Array<int> &_colIdx,
-        const af::storage _storage);
+        const af::storage _storage, const bool _copy = false);
 
 template<typename T>
 SparseArray<T> *initSparseArray();

From ec3c38700924f8ea51d6f159240dd3ed12a81b61 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Nov 2016 11:21:44 -0500
Subject: [PATCH 1008/2677] Working version of COO to CSR in CUDA backend

---
 src/backend/cuda/sparse.cu | 89 ++++++++++++++++++++------------------
 1 file changed, 48 insertions(+), 41 deletions(-)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 573b9fadf8..dc5fe3782a 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -412,56 +412,63 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         memFree(pBuffer);
 
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-        // Copy colIdx as is
-        CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
-                                   in.getColIdx().elements() * sizeof(int),
+        // The cusparse csr sort function is not behaving correctly.
+        // So the work around is to convert the COO into row major and then
+        // convert it to CSR
+
+        // Deep copy input into temporary COO Row Major
+        SparseArray<T> cooT = createArrayDataSparseArray<T>(in.dims(), in.getValues(),
+                                                            in.getRowIdx(), in.getColIdx(),
+                                                            in.getStorage(), true);
+
+        // Call sort to convert column major to row major
+        {
+            size_t pBufferSizeInBytes = 0;
+            CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
+                            getHandle(),
+                            cooT.dims()[0], cooT.dims()[1], nNZ,
+                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
+                            &pBufferSizeInBytes));
+            char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
+
+            int *P = memAlloc<int>(nNZ);
+            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+
+            CUSPARSE_CHECK(cusparseXcoosortByRow(
+                            getHandle(),
+                            cooT.dims()[0], cooT.dims()[1], nNZ,
+                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
+                            P, (void*)pBuffer));
+
+            CUSPARSE_CHECK(gthr_func<T>()(
+                            getHandle(), nNZ,
+                            cooT.getValues().get(),
+                            cooT.getValues().get(),
+                            P, CUSPARSE_INDEX_BASE_ZERO));
+
+            memFree(P);
+            memFree(pBuffer);
+        }
+
+        // Copy values and colIdx as is
+        CUDA_CHECK(cudaMemcpyAsync(converted.getValues().get(), cooT.getValues().get(),
+                                   cooT.getValues().elements() * sizeof(T),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), cooT.getColIdx().get(),
+                                   cooT.getColIdx().elements() * sizeof(int),
                                    cudaMemcpyDeviceToDevice,
                                    cuda::getStream(cuda::getActiveDeviceId())));
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(cusparseXcoo2csr(
                         getHandle(),
-                        in.getRowIdx().get(),
-                        nNZ, in.dims()[0],
+                        cooT.getRowIdx().get(),
+                        nNZ, cooT.dims()[0],
                         converted.getRowIdx().get(),
                         CUSPARSE_INDEX_BASE_ZERO));
 
-        // Call sort
-        size_t pBufferSizeInBytes = 0;
-        CUSPARSE_CHECK(cusparseXcsrsort_bufferSizeExt(
-                        getHandle(),
-                        in.dims()[0], in.dims()[1], nNZ,
-                        converted.getRowIdx().get(), converted.getColIdx().get(),
-                        &pBufferSizeInBytes));
-        char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
-
-        int *P = memAlloc<int>(nNZ);
-        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
-
-        // Create Sparse Matrix Descriptor
-        cusparseMatDescr_t descr = 0;
-        CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
-        cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
-        cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
-
-        CUSPARSE_CHECK(cusparseXcsrsort(
-                        getHandle(),
-                        in.dims()[0], in.dims()[1], nNZ,
-                        descr,
-                        converted.getRowIdx().get(), converted.getColIdx().get(),
-                        P, (void*)pBuffer));
-
-        CUSPARSE_CHECK(gthr_func<T>()(
-                        getHandle(), nNZ,
-                        in.getValues().get(),
-                        converted.getValues().get(),
-                        P, CUSPARSE_INDEX_BASE_ZERO));
-
-        // Destory Sparse Matrix Descriptor
-        CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
-
-        memFree(P);
-        memFree(pBuffer);
+        // No need to call CSRSORT
 
     } else {
         // Should never come here

From 9741548eb324bd6d56e714de740bd143c39724fd Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Nov 2016 15:45:26 -0500
Subject: [PATCH 1009/2677] FEAT: Add CSR <-> COO Conversion to OpenCL Backend

---
 src/backend/opencl/kernel/csr2coo.cl |  74 ++++++++++
 src/backend/opencl/kernel/sparse.hpp | 194 +++++++++++++++++++++++++++
 src/backend/opencl/sparse.cpp        |  48 +++++--
 3 files changed, 304 insertions(+), 12 deletions(-)
 create mode 100644 src/backend/opencl/kernel/csr2coo.cl

diff --git a/src/backend/opencl/kernel/csr2coo.cl b/src/backend/opencl/kernel/csr2coo.cl
new file mode 100644
index 0000000000..4322b606ba
--- /dev/null
+++ b/src/backend/opencl/kernel/csr2coo.cl
@@ -0,0 +1,74 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void csr2coo(__global       T   *ovalues,
+             __global       int *orowidx,
+             __global       int *ocolidx,
+             __global const T   *ivalues,
+             __global const int *irowidx,
+             __global const int *icolidx,
+             const int M)
+{
+    int lid = get_local_id(0);
+    for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
+        int colStart = irowidx[rowId];
+        int colEnd   = irowidx[rowId + 1];
+        for (int colId = colStart + lid;  colId < colEnd; colId += THREADS) {
+            //ovalues[colId] = ivalues[colId];
+            orowidx[colId] = rowId;
+            ocolidx[colId] = icolidx[colId];
+        }
+    }
+}
+
+__kernel
+void swapIndex_kernel(__global       T   *ovalues,
+                      __global       int *oindex,
+                      __global const T   *ivalues,
+                      __global const int *iindex,
+                      __global const int *swapIdx,
+                      const int nNZ)
+{
+    int id = get_global_id(0);
+    if(id >= nNZ) return;
+
+    int idx = swapIdx[id];
+
+    ovalues[id] = ivalues[idx];
+    oindex[id]  = iindex[idx];
+}
+
+__kernel
+void csrReduce_kernel(__global       int *orowIdx,
+                      __global const int *irowIdx,
+                      const int M, const int nNZ)
+{
+    int id = get_global_id(0);
+
+    if(id >= nNZ) return;
+
+    int iRId  = irowIdx[id];
+    int iRId1 = 0;
+    if(id > 0) iRId1 = irowIdx[id - 1];
+
+    if(id == 0) {
+        orowIdx[id] = 0;
+        orowIdx[M]  = nNZ;
+    } else if(iRId1 != iRId) {
+        for(int i = iRId1 + 1; i <= iRId; i++)
+            orowIdx[i] = id;
+    }
+
+    // The last X rows are corner cases if they dont have any values
+    if(id > irowIdx[nNZ - 1] && orowIdx[id] == 0) {
+        orowIdx[id] = nNZ;
+    }
+}
+
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 36c96a5f0e..aeb55d6e0a 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -11,6 +11,7 @@
 #include <kernel_headers/coo2dense.hpp>
 #include <kernel_headers/csr2dense.hpp>
 #include <kernel_headers/dense2csr.hpp>
+#include <kernel_headers/csr2coo.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -24,6 +25,7 @@
 #include "scan_dim.hpp"
 #include "reduce.hpp"
 #include "scan_first.hpp"
+#include "sort_by_key.hpp"
 #include "config.hpp"
 
 using cl::Buffer;
@@ -259,5 +261,197 @@ namespace opencl
                 CL_TO_AF_ERROR(err);
             }
         }
+
+        template<typename T>
+        void swapIndex(Param ovalues, Param oindex,
+                       const Param ivalues, const cl::Buffer *iindex,
+                       const Param swapIdx)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   swapIndexProgs;
+                static std::map<int, Kernel*>  swapIndexKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="        << dtype_traits<T>::getName()
+                            << " -D THREADS=256"; // This threads is a dummy for compilation
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
+                    swapIndexProgs[device]   = new Program(prog);
+                    swapIndexKernels[device] = new Kernel(*swapIndexProgs[device], "swapIndex_kernel");
+                });
+
+                auto swapIndexOp = KernelFunctor<Buffer, Buffer,
+                                                 const Buffer, const Buffer, const Buffer,
+                                                 const int> (*swapIndexKernels[device]);
+
+                static const int threads = 256;
+                NDRange local(threads, 1);
+                NDRange global(divup(ovalues.info.dims[0], threads) * threads, 1, 1);
+
+                swapIndexOp(EnqueueArgs(getQueue(), global, local),
+                            *ovalues.data, *oindex.data,
+                            *ivalues.data, *iindex,
+                            *swapIdx.data, ovalues.info.dims[0]);
+
+                CL_DEBUG_FINISH(getQueue());
+
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+                throw;
+            }
+        }
+
+        template<typename T>
+        void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx,
+                     const Param ivalues, const Param irowIdx, const Param icolIdx,
+                     Param index)
+        {
+            try {
+                const int MAX_GROUPS = 4096;
+                int M = irowIdx.info.dims[0] - 1;
+                //FIXME: This needs to be based non nonzeros per row
+                int threads = 64;
+
+                std::string ref_name =
+                    std::string("csr2coo_") +
+                    std::string(dtype_traits<T>::getName()) +
+                    std::string("_") +
+                    std::to_string(threads);
+
+                int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
+
+                    std::ostringstream options;
+                    options << " -D T=" << dtype_traits<T>::getName();
+                    options << " -D THREADS=" << threads;
+
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    const char *ker_strs[] = {csr2coo_cl};
+                    const int   ker_lens[] = {csr2coo_cl_len};
+
+                    Program prog;
+                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "csr2coo");
+                } else {
+                    entry = idx->second;
+                }
+
+                cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
+
+                NDRange local(threads, 1);
+                int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+                NDRange global(local[0] * groups_x, 1);
+                auto csr2coo_kernel = *entry.ker;
+                auto csr2coo_func = KernelFunctor<Buffer, Buffer, Buffer,
+                                                  const Buffer, const Buffer, const Buffer,
+                                                  int> (csr2coo_kernel);
+
+                csr2coo_func(EnqueueArgs(getQueue(), global, local),
+                               *ovalues.data, *scratch, *ocolIdx.data,
+                               *ivalues.data, *irowIdx.data, *icolIdx.data, M);
+
+                // Now we need to sort this into column major
+                kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
+
+                // Now use index to sort values and rows
+                kernel::swapIndex<T>(ovalues, orowIdx, ivalues, scratch, index);
+
+                CL_DEBUG_FINISH(getQueue());
+
+                bufferFree(scratch);
+
+            } catch(cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
+
+        template<typename T>
+        void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx,
+                     const Param ivalues, const Param irowIdx, const Param icolIdx,
+                     Param index, const int M)
+        {
+            try {
+                cl::Buffer *colCopy = bufferAlloc(icolIdx.info.dims[0] * sizeof(int));
+                getQueue().enqueueCopyBuffer(*icolIdx.data, *colCopy, 0, 0, icolIdx.info.dims[0] * sizeof(int));
+
+                cl::Buffer *rowCopy = bufferAlloc(irowIdx.info.dims[0] * sizeof(int));
+                getQueue().enqueueCopyBuffer(*irowIdx.data, *rowCopy, 0, 0, irowIdx.info.dims[0] * sizeof(int));
+
+                int dims[] = {(int)irowIdx.info.dims[0],
+                              (int)irowIdx.info.dims[1],
+                              (int)irowIdx.info.dims[2],
+                              (int)irowIdx.info.dims[3]
+                             };
+                int strides[] = {(int)irowIdx.info.strides[0],
+                                 (int)irowIdx.info.strides[1],
+                                 (int)irowIdx.info.strides[2],
+                                 (int)irowIdx.info.strides[3]
+                                };
+                Param scP = makeParam((*rowCopy)(), irowIdx.info.offset, dims, strides);
+
+                // Now we need to sort this into column major
+                kernel::sort0ByKeyIterative<int, int>(scP, index, true);
+
+                // Now use index to sort values and rows
+                kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, colCopy, index);
+
+                CL_DEBUG_FINISH(getQueue());
+
+                // Do row compression
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   csrReduceProgs;
+                static std::map<int, Kernel*>  csrReduceKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="        << dtype_traits<T>::getName()
+                            << " -D THREADS=256"; // This threads is a dummy for compilation
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+                    Program prog;
+                    buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
+                    csrReduceProgs[device]   = new Program(prog);
+                    csrReduceKernels[device] = new Kernel(*csrReduceProgs[device], "csrReduce_kernel");
+                });
+
+                auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int>
+                                   (*csrReduceKernels[device]);
+
+                static const int threads = 256;
+                NDRange local(threads, 1);
+                NDRange global(divup(irowIdx.info.dims[0], threads) * threads, 1, 1);
+
+                csrReduceOp(EnqueueArgs(getQueue(), global, local),
+                            *orowIdx.data, *rowCopy, M, ovalues.info.dims[0]);
+
+                CL_DEBUG_FINISH(getQueue());
+
+                bufferFree(colCopy);
+                bufferFree(rowCopy);
+
+            } catch(cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
     }
 }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 1c07b7b88a..e73a474baa 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -21,6 +21,7 @@
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <range.hpp>
 #include <reduce.hpp>
 #include <where.hpp>
 
@@ -115,25 +116,48 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     return dense_;
 }
 
-template<typename T, af_storage src, af_storage dest>
+template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
-    // TODO
-    // Convert CSR <-> CSC <-> COO <-> CSR
-    // Currently supports CSR <-> COO
+    in.eval();
 
-    AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+    SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    converted.eval();
 
-    // If src and dest are the same, simply return.
-    if(src == dest)
-        return in;
+    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
 
-    in.eval();
+        Array<int> index = range<int>(in.getNNZ(), 0);
+        index.eval();
+
+        Array<T>   &ovalues = converted.getValues();
+        Array<int> &orowIdx = converted.getRowIdx();
+        Array<int> &ocolIdx = converted.getColIdx();
+        const Array<T>   &ivalues = in.getValues();
+        const Array<int> &irowIdx = in.getRowIdx();
+        const Array<int> &icolIdx = in.getColIdx();
+
+        kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx, index);
+
+    } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+
+        Array<int> index = range<int>(in.getNNZ(), 0);
+        index.eval();
+
+        Array<T>   &ovalues = converted.getValues();
+        Array<int> &orowIdx = converted.getRowIdx();
+        Array<int> &ocolIdx = converted.getColIdx();
+        const Array<T>   &ivalues = in.getValues();
+        const Array<int> &irowIdx = in.getRowIdx();
+        const Array<int> &icolIdx = in.getColIdx();
+
+        kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx, index, in.dims()[0]);
 
-    SparseArray<T> out = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
-    out.eval();
+    } else {
+        // Should never come here
+        AF_ERROR("OpenCL Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
+    }
 
-    return out;
+    return converted;
 }
 
 

From 52158ac9fa82e75603b7e1f6c2b0ced4618ac932 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Nov 2016 16:16:28 -0500
Subject: [PATCH 1010/2677] Add more tests for sparse conversion

---
 test/sparse.cpp | 30 +++++++++++++++++++++---------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index 98ffa4c4cd..ae4bf07c17 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -246,11 +246,16 @@ void sparseConvertTester(const int m, const int n, int factor)
     af::array sA = af::sparse(A, src);
     af::array dA = af::sparse(A, dest);
 
-    //// Convert src to dest format and dest to src
+    // Convert src to dest format and dest to src
     af::array s2d = sparseConvertTo(sA, dest);
     af::array d2s = sparseConvertTo(dA, src);
 
-    //// Get the individual arrays and verify equality
+    // Get the individual arrays and verify equality
+    af::array sValues = sparseGetValues(sA);
+    af::array sRowIdx = sparseGetRowIdx(sA);
+    af::array sColIdx = sparseGetColIdx(sA);
+    dim_t     sNNZ    = sparseGetNNZ   (sA);
+
     af::array dValues = sparseGetValues(dA);
     af::array dRowIdx = sparseGetRowIdx(dA);
     af::array dColIdx = sparseGetColIdx(dA);
@@ -261,11 +266,6 @@ void sparseConvertTester(const int m, const int n, int factor)
     af::array s2dColIdx = sparseGetColIdx(s2d);
     dim_t     s2dNNZ    = sparseGetNNZ   (s2d);
 
-    af::array sValues = sparseGetValues(sA);
-    af::array sRowIdx = sparseGetRowIdx(sA);
-    af::array sColIdx = sparseGetColIdx(sA);
-    dim_t     sNNZ    = sparseGetNNZ   (sA);
-
     af::array d2sValues = sparseGetValues(d2s);
     af::array d2sRowIdx = sparseGetRowIdx(d2s);
     af::array d2sColIdx = sparseGetColIdx(d2s);
@@ -283,9 +283,21 @@ void sparseConvertTester(const int m, const int n, int factor)
 }
 
 #define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE)                                 \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_1)                             \
+    {                                                                           \
+        sparseConvertTester<T, STYPE, DTYPE>(1000, 1000, 5);                    \
+    }                                                                           \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_2)                             \
+    {                                                                           \
+        sparseConvertTester<T, STYPE, DTYPE>(512, 512, 1);                      \
+    }                                                                           \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_3)                             \
+    {                                                                           \
+        sparseConvertTester<T, STYPE, DTYPE>(512, 1024, 2);                     \
+    }                                                                           \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_4)                             \
     {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(10, 10, 5);                        \
+        sparseConvertTester<T, STYPE, DTYPE>(2048, 1024, 10);                   \
     }                                                                           \
 
 

From 3e8557bf5f8ea5fb81877d51547229b6bdd6f047 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Nov 2016 17:08:04 -0500
Subject: [PATCH 1011/2677] Fix race condition bug in COO->CSR in CUDA

---
 src/backend/cuda/sparse.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index dc5fe3782a..ddd17c2c8f 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -442,7 +442,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
             CUSPARSE_CHECK(gthr_func<T>()(
                             getHandle(), nNZ,
-                            cooT.getValues().get(),
+                            in.getValues().get(),
                             cooT.getValues().get(),
                             P, CUSPARSE_INDEX_BASE_ZERO));
 

From 490022a4ad9c6c708672193c819d62310d454017 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 10 Nov 2016 17:50:34 -0500
Subject: [PATCH 1012/2677] Clean up sparse conversion tests and split test
 based on direction

---
 test/sparse.cpp | 61 +++++++++++++++++++++++--------------------------
 1 file changed, 28 insertions(+), 33 deletions(-)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index ae4bf07c17..334dc22989 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -244,62 +244,57 @@ void sparseConvertTester(const int m, const int n, int factor)
 
     // Create Sparse Array of type src and dest From Dense
     af::array sA = af::sparse(A, src);
-    af::array dA = af::sparse(A, dest);
 
     // Convert src to dest format and dest to src
     af::array s2d = sparseConvertTo(sA, dest);
-    af::array d2s = sparseConvertTo(dA, src);
 
-    // Get the individual arrays and verify equality
-    af::array sValues = sparseGetValues(sA);
-    af::array sRowIdx = sparseGetRowIdx(sA);
-    af::array sColIdx = sparseGetColIdx(sA);
-    dim_t     sNNZ    = sparseGetNNZ   (sA);
+    // Create the dest type from dense - gold
+    af::array dA = af::sparse(A, dest);
 
+    // Verify nnZ
+    dim_t dNNZ   = sparseGetNNZ(dA);
+    dim_t s2dNNZ = sparseGetNNZ(s2d);
+
+    ASSERT_EQ(dNNZ, s2dNNZ);
+
+    // Verify Types
+    af_storage dType   = sparseGetStorage(dA);
+    af_storage s2dType = sparseGetStorage(s2d);
+
+    ASSERT_EQ(dType, s2dType);
+
+    // Get the individual arrays and verify equality
     af::array dValues = sparseGetValues(dA);
     af::array dRowIdx = sparseGetRowIdx(dA);
     af::array dColIdx = sparseGetColIdx(dA);
-    dim_t     dNNZ    = sparseGetNNZ   (dA);
 
     af::array s2dValues = sparseGetValues(s2d);
     af::array s2dRowIdx = sparseGetRowIdx(s2d);
     af::array s2dColIdx = sparseGetColIdx(s2d);
-    dim_t     s2dNNZ    = sparseGetNNZ   (s2d);
 
-    af::array d2sValues = sparseGetValues(d2s);
-    af::array d2sRowIdx = sparseGetRowIdx(d2s);
-    af::array d2sColIdx = sparseGetColIdx(d2s);
-    dim_t     d2sNNZ    = sparseGetNNZ   (d2s);
+    // Verify values
+    ASSERT_EQ(0, af::max<double>(af::abs(dValues - s2dValues)));
 
-    ASSERT_EQ(dNNZ, s2dNNZ);
-    ASSERT_EQ(0, af::max<double>(dValues - s2dValues));
+    // Verify row and col indices
     ASSERT_EQ(0, af::max<int   >(dRowIdx - s2dRowIdx));
     ASSERT_EQ(0, af::max<int   >(dColIdx - s2dColIdx));
-
-    ASSERT_EQ(sNNZ, d2sNNZ);
-    ASSERT_EQ(0, af::max<double>(sValues - d2sValues));
-    ASSERT_EQ(0, af::max<int   >(sRowIdx - d2sRowIdx));
-    ASSERT_EQ(0, af::max<int   >(sColIdx - d2sColIdx));
 }
 
-#define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_1)                             \
-    {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(1000, 1000, 5);                    \
-    }                                                                           \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_2)                             \
-    {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(512, 512, 1);                      \
-    }                                                                           \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_3)                             \
+#define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F)                   \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_##SUFFIX)                      \
     {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(512, 1024, 2);                     \
+        sparseConvertTester<T, STYPE, DTYPE>(M, N, F);                          \
     }                                                                           \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_4)                             \
+    TEST(SPARSE_CONVERT, T##_##DTYPE##_##STYPE##_##SUFFIX)                      \
     {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(2048, 1024, 10);                   \
+        sparseConvertTester<T, DTYPE, STYPE>(M, N, F);                          \
     }                                                                           \
 
+#define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 1, 1000, 1000,  5)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 2,  512,  512,  1)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 3,  512, 1024,  2)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 4, 2048, 1024, 10)                     \
 
 CONVERT_TESTS(float  , AF_STORAGE_CSR, AF_STORAGE_COO)
 CONVERT_TESTS(double , AF_STORAGE_CSR, AF_STORAGE_COO)

From 2d893574efb9268227a3f9f03b5be211aa188b9e Mon Sep 17 00:00:00 2001
From: Marius Brehler <marius.brehler@tu-dortmund.de>
Date: Tue, 29 Nov 2016 15:08:40 +0100
Subject: [PATCH 1013/2677] Minor fix in the documentation of the unified
 backend

---
 docs/pages/unified_backend.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/unified_backend.md b/docs/pages/unified_backend.md
index 67e340f2ab..bb6efb72ca 100644
--- a/docs/pages/unified_backend.md
+++ b/docs/pages/unified_backend.md
@@ -73,7 +73,7 @@ The af_backend enum stores the possible backends.
 To select a backend, call the af::setBackend function as shown below.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.c}
-af::setBackend(AF_BACKEND_OPENCL);    // Sets CUDA as current backend
+af::setBackend(AF_BACKEND_CUDA);    // Sets CUDA as current backend
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To get the count of the number of backends available (the number of `libaf*`

From 37216a9af659342cfa037e609744015d53af5aab Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 21 Nov 2016 18:29:32 -0500
Subject: [PATCH 1014/2677] FEAT add cast support to sparse arrays

---
 src/api/c/cast.cpp | 44 ++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 42 insertions(+), 2 deletions(-)

diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 343310d462..98c9dcb2af 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -10,6 +10,8 @@
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/arith.h>
+#include <sparse_handle.hpp>
+#include <sparse.hpp>
 #include <ArrayInfo.hpp>
 #include <optypes.hpp>
 
@@ -45,17 +47,55 @@ static af_array cast(const af_array in, const af_dtype type)
     }
 }
 
+template<typename T>
+static af_array castSparseValues(const af_array in, const af_dtype type)
+{
+    using namespace common;
+    const SparseArray<T> sparse = getSparseArray<T>(in);
+    Array<T> values = castArray<T>(getHandle(sparse.getValues()));
+    return getHandle(createArrayDataSparseArray(sparse.dims(), values,
+                                                sparse.getRowIdx(), sparse.getColIdx(),
+                                                sparse.getStorage()
+                                               )
+                    );
+}
+
+static af_array castSparse(const af_array in, const af_dtype type)
+{
+    using namespace common;
+
+    const SparseArrayBase info = getSparseArrayBase(in);
+
+    if (info.getType() == type) {
+        return retain(in);
+    }
+
+    switch (type) {
+    case f32: return castSparseValues<float  >(in, type);
+    case f64: return castSparseValues<double >(in, type);
+    case c32: return castSparseValues<cfloat >(in, type);
+    case c64: return castSparseValues<cdouble>(in, type);
+    default: TYPE_ERROR(2, type);
+    }
+}
+
 af_err af_cast(af_array *out, const af_array in, const af_dtype type)
 {
     try {
-        const ArrayInfo info = getInfo(in);
+        const ArrayInfo info = getInfo(in, false, true);
         dim4 idims = info.dims();
         if(idims.elements() == 0) {
             dim_t my_dims[] = {0, 0, 0, 0};
             return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
         }
 
-        af_array res = cast(in, type);
+        af_array res = 0;
+        if(info.isSparse()) {
+            res = castSparse(in, type);
+        } else {
+            res = cast(in, type);
+        }
+
         std::swap(*out, res);
     }
     CATCHALL;

From a296a08d15487f5ef687b320bec1205e927c44b3 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Nov 2016 14:38:39 -0500
Subject: [PATCH 1015/2677] FIX af_cast - disallow complex to real conversion

---
 src/api/c/cast.cpp | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 98c9dcb2af..e35f6631ac 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -83,6 +83,15 @@ af_err af_cast(af_array *out, const af_array in, const af_dtype type)
 {
     try {
         const ArrayInfo info = getInfo(in, false, true);
+
+        af_dtype inType = info.getType();
+        if((inType == c32 || inType == c64)
+            && (type == f32 || type == f64)) {
+            AF_ERROR("Casting is not allowed from complex (c32/c64) to real (f32/f64) types.\n"
+                     "Use abs, real, imag etc to convert complex to floating type.",
+                     AF_ERR_TYPE);
+        }
+
         dim4 idims = info.dims();
         if(idims.elements() == 0) {
             dim_t my_dims[] = {0, 0, 0, 0};

From 9895219645e80dcad883d3b791b999906ded0d12 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Nov 2016 15:31:47 -0500
Subject: [PATCH 1016/2677] Add tests for sparse casting

Conflicts:
	test/sparse.cpp
---
 test/sparse.cpp | 81 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index 68e7350955..9b88120244 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -226,3 +226,84 @@ CREATE_TESTS(AF_STORAGE_CSR)
 CREATE_TESTS(AF_STORAGE_COO)
 
 #undef CREATE_TESTS
+
+template<typename Ti, typename To>
+void sparseCastTester(const int m, const int n, int factor)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<Ti>()) return;
+    if (noDoubleTests<To>()) return;
+
+#if 1
+    af::array A = cpu_randu<Ti>(af::dim4(m, n));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<Ti>::af_type);
+#endif
+
+    A = makeSparse<Ti>(A, factor);
+
+    af::array sTi = af::sparse(A, AF_STORAGE_CSR);
+
+    // Cast
+    af::array sTo = sTi.as((af::dtype)af::dtype_traits<To>::af_type);
+
+    // Verify nnZ
+    dim_t iNNZ = sparseGetNNZ(sTi);
+    dim_t oNNZ = sparseGetNNZ(sTo);
+
+    ASSERT_EQ(iNNZ, oNNZ);
+
+    // Verify Types
+    dim_t iSType = sparseGetStorage(sTi);
+    dim_t oSType = sparseGetStorage(sTo);
+
+    ASSERT_EQ(iSType, oSType);
+
+    // Get the individual arrays and verify equality
+    af::array iValues = sparseGetValues(sTi);
+    af::array iRowIdx = sparseGetRowIdx(sTi);
+    af::array iColIdx = sparseGetColIdx(sTi);
+
+    af::array oValues = sparseGetValues(sTo);
+    af::array oRowIdx = sparseGetRowIdx(sTo);
+    af::array oColIdx = sparseGetColIdx(sTo);
+
+    // Verify values
+    ASSERT_EQ(0, af::max<int>(af::abs(iRowIdx - oRowIdx)));
+    ASSERT_EQ(0, af::max<int>(af::abs(iColIdx - oColIdx)));
+
+    if(iValues.iscomplex() && !oValues.iscomplex()) {
+        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)), 1e-6);
+    } else if(!iValues.iscomplex() && oValues.iscomplex()) {
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))), 1e-6);
+    } else {
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - oValues)), 1e-6);
+    }
+}
+
+#define CAST_TESTS_TYPES(Ti, To, SUFFIX, M, N, F)                               \
+    TEST(SPARSE_CAST, Ti##_##To##_##SUFFIX)                                     \
+    {                                                                           \
+        sparseCastTester<Ti, To>(M, N, F);                                      \
+    }                                                                           \
+
+#define CAST_TESTS(Ti, To)                                                      \
+    CAST_TESTS_TYPES(Ti, To, 1, 1000, 1000,  5)                                 \
+    CAST_TESTS_TYPES(Ti, To, 2,  512, 1024,  2)                                 \
+
+CAST_TESTS(float  , float   )
+CAST_TESTS(float  , double  )
+CAST_TESTS(float  , cfloat  )
+CAST_TESTS(float  , cdouble )
+
+CAST_TESTS(double , float   )
+CAST_TESTS(double , double  )
+CAST_TESTS(double , cfloat  )
+CAST_TESTS(double , cdouble )
+
+CAST_TESTS(cfloat , cfloat  )
+CAST_TESTS(cfloat , cdouble )
+
+CAST_TESTS(cdouble, cfloat  )
+CAST_TESTS(cdouble, cdouble )

From 5ac7fdd78e03e8a0a04b142f48b8b5fe059a6bf5 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 29 Nov 2016 17:03:41 -0500
Subject: [PATCH 1017/2677] Move sparse casting template function into
 sparse_handle.hpp

---
 src/api/c/cast.cpp          | 21 ++++-----------------
 src/api/c/sparse_handle.hpp | 13 +++++++++++++
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index e35f6631ac..e2c9807341 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -47,19 +47,6 @@ static af_array cast(const af_array in, const af_dtype type)
     }
 }
 
-template<typename T>
-static af_array castSparseValues(const af_array in, const af_dtype type)
-{
-    using namespace common;
-    const SparseArray<T> sparse = getSparseArray<T>(in);
-    Array<T> values = castArray<T>(getHandle(sparse.getValues()));
-    return getHandle(createArrayDataSparseArray(sparse.dims(), values,
-                                                sparse.getRowIdx(), sparse.getColIdx(),
-                                                sparse.getStorage()
-                                               )
-                    );
-}
-
 static af_array castSparse(const af_array in, const af_dtype type)
 {
     using namespace common;
@@ -71,10 +58,10 @@ static af_array castSparse(const af_array in, const af_dtype type)
     }
 
     switch (type) {
-    case f32: return castSparseValues<float  >(in, type);
-    case f64: return castSparseValues<double >(in, type);
-    case c32: return castSparseValues<cfloat >(in, type);
-    case c64: return castSparseValues<cdouble>(in, type);
+    case f32: return getHandle(castSparse<float  >(in));
+    case f64: return getHandle(castSparse<double >(in));
+    case c32: return getHandle(castSparse<cfloat >(in));
+    case c64: return getHandle(castSparse<cdouble>(in));
     default: TYPE_ERROR(2, type);
     }
 }
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 605042f787..03898da6aa 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -15,6 +15,7 @@
 #include <math.hpp>
 #include <copy.hpp>
 #include <cast.hpp>
+#include <handle.hpp>
 #include <af/dim4.hpp>
 
 #include <SparseArray.hpp>
@@ -61,3 +62,15 @@ af_array retainSparseHandle(const af_array in)
     *out = *sparse;
     return reinterpret_cast<af_array>(out);
 }
+
+// based on castArray in handle.hpp
+template<typename To>
+common::SparseArray<To> castSparse(const af_array &in)
+{
+    using namespace common;
+    const SparseArray<To> sparse = getSparseArray<To>(in);
+    Array<To> values = castArray<To>(getHandle(sparse.getValues()));
+    return createArrayDataSparseArray(sparse.dims(), values,
+                                      sparse.getRowIdx(), sparse.getColIdx(),
+                                      sparse.getStorage());
+}

From e94a775ed40b4f92c9aa9df503b0627dfa9c7a80 Mon Sep 17 00:00:00 2001
From: Marius Brehler <marius.brehler@tu-dortmund.de>
Date: Wed, 30 Nov 2016 14:07:21 +0100
Subject: [PATCH 1018/2677] FindCBLAS.cmake: Fix setting CBLAS_INCLUDE_DIR if
 PkgConfig is used

---
 CMakeModules/FindCBLAS.cmake | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index fbb646bc47..058b7d75ea 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -39,8 +39,14 @@ IF(PC_CBLAS_FOUND)
     LIST(APPEND CBLAS_LIBRARIES ${${PC_LIB}_LIBRARY})
   ENDFOREACH(PC_LIB)
 
-  FIND_PACKAGE_HANDLE_STANDARD_ARGS(CBLAS DEFAULT_MSG CBLAS_LIBRARIES)
-  MARK_AS_ADVANCED(CBLAS_LIBRARIES)
+  FIND_PATH(CBLAS_INCLUDE_DIRS NAMES cblas.h HINTS ${PC_CBLAS_INCLUDE_DIRS} )
+  IF (NOT CBLAS_INCLUDE_DIRS)
+    message(FATAL_ERROR "Something is wrong in your pkg-config file - cblas.h not found in ${PC_CBLAS_INCLUDE_DIRS}")
+  ENDIF (NOT CBLAS_INCLUDE_DIRS)
+  SET(CBLAS_INCLUDE_DIR ${CBLAS_INCLUDE_DIRS})
+
+  FIND_PACKAGE_HANDLE_STANDARD_ARGS(CBLAS DEFAULT_MSG CBLAS_LIBRARIES CBLAS_INCLUDE_DIR)
+  MARK_AS_ADVANCED(CBLAS_LIBRARIES CBLAS_INCLUDE_DIR)
 
 ELSE(PC_CBLAS_FOUND)
 

From 557e6b78241399cb1fc619be6a1c285896025b44 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Thu, 1 Dec 2016 14:29:54 -0500
Subject: [PATCH 1019/2677] Added tests for the cast function

Tests check for the following conversions:

* Real to real
* Real to complex
* Complex to real. Checking for failure here, as such conversion is not allowed.
---
 test/cast.cpp | 102 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 test/cast.cpp

diff --git a/test/cast.cpp b/test/cast.cpp
new file mode 100644
index 0000000000..eb824c156e
--- /dev/null
+++ b/test/cast.cpp
@@ -0,0 +1,102 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <af/array.h>
+#include <af/arith.h>
+#include <af/data.h>
+#include <testHelpers.hpp>
+
+using namespace af;
+using af::cfloat;
+using af::cdouble;
+
+const int num = 10;
+
+template <typename Ti, typename To>
+void cast_test()
+{
+    if (noDoubleTests<Ti>()) return;
+    if (noDoubleTests<To>()) return;
+
+    af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
+    af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
+    af::dim4 dims(num, 1, 1, 1);
+    af_array a, b;
+    af_randu(&a, dims.ndims(), dims.get(), ta);
+    af_err err = af_cast(&b, a, tb);
+    ASSERT_EQ(err, AF_SUCCESS);
+}
+
+#define REAL_TO_TESTS(Ti, To)                   \
+    TEST(CAST_TEST, Test_Real_##Ti##_##To)      \
+    {                                           \
+        cast_test<Ti, To>();                    \
+    }                                           \
+
+#define REAL_TEST_INVOKE(Ti)                    \
+    REAL_TO_TESTS(Ti, float);                   \
+    REAL_TO_TESTS(Ti, cfloat);                  \
+    REAL_TO_TESTS(Ti, double);                  \
+    REAL_TO_TESTS(Ti, cdouble);                 \
+    REAL_TO_TESTS(Ti, char);                    \
+    REAL_TO_TESTS(Ti, int);                     \
+    REAL_TO_TESTS(Ti, unsigned);                \
+    REAL_TO_TESTS(Ti, uchar);                   \
+    REAL_TO_TESTS(Ti, intl);                    \
+    REAL_TO_TESTS(Ti, uintl);                   \
+    REAL_TO_TESTS(Ti, short);                   \
+    REAL_TO_TESTS(Ti, ushort);                  \
+
+#define CPLX_TEST_INVOKE(Ti)                    \
+    REAL_TO_TESTS(Ti, cfloat);                  \
+    REAL_TO_TESTS(Ti, cdouble);                 \
+
+
+REAL_TEST_INVOKE(float)
+REAL_TEST_INVOKE(double)
+REAL_TEST_INVOKE(char)
+REAL_TEST_INVOKE(int)
+REAL_TEST_INVOKE(unsigned)
+REAL_TEST_INVOKE(uchar)
+REAL_TEST_INVOKE(intl)
+REAL_TEST_INVOKE(uintl)
+REAL_TEST_INVOKE(short)
+REAL_TEST_INVOKE(ushort)
+CPLX_TEST_INVOKE(cfloat)
+CPLX_TEST_INVOKE(cdouble)
+
+// Converting complex to real; expected to fail as this operation is
+// not allowed. Use functions abs, real, image, arg, etc to make the
+// conversion explicit.
+template <typename Ti, typename To>
+void cast_test_complex_real()
+{
+    if (noDoubleTests<Ti>()) return;
+    if (noDoubleTests<To>()) return;
+
+    af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
+    af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
+    af::dim4 dims(num, 1, 1, 1);
+    af_array a, b;
+    af_randu(&a, dims.ndims(), dims.get(), ta);
+    af_err err = af_cast(&b, a, tb);
+    ASSERT_EQ(err, AF_ERR_TYPE);
+}
+
+#define COMPLEX_REAL_TESTS(Ti, To)                      \
+    TEST(CAST_TEST, Test_Complex_To_Real_##Ti##_##To)   \
+    {                                                   \
+        cast_test_complex_real<Ti, To>();               \
+    }                                                   \
+
+COMPLEX_REAL_TESTS(cfloat, float)
+COMPLEX_REAL_TESTS(cfloat, double)
+COMPLEX_REAL_TESTS(cdouble, float)
+COMPLEX_REAL_TESTS(cdouble, double)

From 1b5331623ec944123242bd3b551752abdba0173a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 5 Dec 2016 16:34:13 -0500
Subject: [PATCH 1020/2677] Combine sparseCast and cast functions into one

---
 src/api/c/cast.cpp | 66 ++++++++++++++++++----------------------------
 1 file changed, 26 insertions(+), 40 deletions(-)

diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index e2c9807341..828b013fbc 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -24,45 +24,36 @@ using namespace detail;
 
 static af_array cast(const af_array in, const af_dtype type)
 {
-    const ArrayInfo info = getInfo(in);
+    const ArrayInfo info = getInfo(in, false, true);
 
     if (info.getType() == type) {
         return retain(in);
     }
 
-    switch (type) {
-    case f32: return getHandle(castArray<float   >(in));
-    case f64: return getHandle(castArray<double  >(in));
-    case c32: return getHandle(castArray<cfloat  >(in));
-    case c64: return getHandle(castArray<cdouble >(in));
-    case s32: return getHandle(castArray<int     >(in));
-    case u32: return getHandle(castArray<uint    >(in));
-    case u8 : return getHandle(castArray<uchar   >(in));
-    case b8 : return getHandle(castArray<char    >(in));
-    case s64: return getHandle(castArray<intl    >(in));
-    case u64: return getHandle(castArray<uintl   >(in));
-    case s16: return getHandle(castArray<short   >(in));
-    case u16: return getHandle(castArray<ushort  >(in));
-    default: TYPE_ERROR(2, type);
-    }
-}
-
-static af_array castSparse(const af_array in, const af_dtype type)
-{
-    using namespace common;
-
-    const SparseArrayBase info = getSparseArrayBase(in);
-
-    if (info.getType() == type) {
-        return retain(in);
-    }
-
-    switch (type) {
-    case f32: return getHandle(castSparse<float  >(in));
-    case f64: return getHandle(castSparse<double >(in));
-    case c32: return getHandle(castSparse<cfloat >(in));
-    case c64: return getHandle(castSparse<cdouble>(in));
-    default: TYPE_ERROR(2, type);
+    if(info.isSparse()) {
+        switch (type) {
+        case f32: return getHandle(castSparse<float  >(in));
+        case f64: return getHandle(castSparse<double >(in));
+        case c32: return getHandle(castSparse<cfloat >(in));
+        case c64: return getHandle(castSparse<cdouble>(in));
+        default: TYPE_ERROR(2, type);
+        }
+    } else {
+        switch (type) {
+        case f32: return getHandle(castArray<float   >(in));
+        case f64: return getHandle(castArray<double  >(in));
+        case c32: return getHandle(castArray<cfloat  >(in));
+        case c64: return getHandle(castArray<cdouble >(in));
+        case s32: return getHandle(castArray<int     >(in));
+        case u32: return getHandle(castArray<uint    >(in));
+        case u8 : return getHandle(castArray<uchar   >(in));
+        case b8 : return getHandle(castArray<char    >(in));
+        case s64: return getHandle(castArray<intl    >(in));
+        case u64: return getHandle(castArray<uintl   >(in));
+        case s16: return getHandle(castArray<short   >(in));
+        case u16: return getHandle(castArray<ushort  >(in));
+        default: TYPE_ERROR(2, type);
+        }
     }
 }
 
@@ -85,12 +76,7 @@ af_err af_cast(af_array *out, const af_array in, const af_dtype type)
             return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
         }
 
-        af_array res = 0;
-        if(info.isSparse()) {
-            res = castSparse(in, type);
-        } else {
-            res = cast(in, type);
-        }
+        af_array res = cast(in, type);
 
         std::swap(*out, res);
     }

From 68c8269fff662e7576eb23a15b7bd59962142cc0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 5 Dec 2016 16:34:30 -0500
Subject: [PATCH 1021/2677] Cast and sparse cast test cleanup

---
 test/cast.cpp   |  9 ++++-----
 test/sparse.cpp | 13 ++++---------
 2 files changed, 8 insertions(+), 14 deletions(-)

diff --git a/test/cast.cpp b/test/cast.cpp
index eb824c156e..4f7cf390ff 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -13,7 +13,6 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace af;
 using af::cfloat;
 using af::cdouble;
 
@@ -25,8 +24,8 @@ void cast_test()
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-    af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
-    af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
+    af_dtype ta = (af_dtype)af::dtype_traits<Ti>::af_type;
+    af_dtype tb = (af_dtype)af::dtype_traits<To>::af_type;
     af::dim4 dims(num, 1, 1, 1);
     af_array a, b;
     af_randu(&a, dims.ndims(), dims.get(), ta);
@@ -81,8 +80,8 @@ void cast_test_complex_real()
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-    af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
-    af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
+    af_dtype ta = (af_dtype)af::dtype_traits<Ti>::af_type;
+    af_dtype tb = (af_dtype)af::dtype_traits<To>::af_type;
     af::dim4 dims(num, 1, 1, 1);
     af_array a, b;
     af_randu(&a, dims.ndims(), dims.get(), ta);
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 9b88120244..6798919a37 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -230,16 +230,10 @@ CREATE_TESTS(AF_STORAGE_COO)
 template<typename Ti, typename To>
 void sparseCastTester(const int m, const int n, int factor)
 {
-    af::deviceGC();
-
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-#if 1
     af::array A = cpu_randu<Ti>(af::dim4(m, n));
-#else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<Ti>::af_type);
-#endif
 
     A = makeSparse<Ti>(A, factor);
 
@@ -273,12 +267,13 @@ void sparseCastTester(const int m, const int n, int factor)
     ASSERT_EQ(0, af::max<int>(af::abs(iRowIdx - oRowIdx)));
     ASSERT_EQ(0, af::max<int>(af::abs(iColIdx - oColIdx)));
 
+    static const double eps = 1e-6;
     if(iValues.iscomplex() && !oValues.iscomplex()) {
-        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)), 1e-6);
+        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)), eps);
     } else if(!iValues.iscomplex() && oValues.iscomplex()) {
-        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))), 1e-6);
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))), eps);
     } else {
-        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - oValues)), 1e-6);
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - oValues)), eps);
     }
 }
 

From 340e9bf4f53c182a14753436085efcbdf8c8a485 Mon Sep 17 00:00:00 2001
From: mlloreda <miguel@arrayfire.com>
Date: Mon, 5 Dec 2016 11:17:23 -0500
Subject: [PATCH 1022/2677] getting_started.md: `u8` is supported data type

---
 docs/pages/getting_started.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index 7ea9a75216..6a6dac6325 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -20,13 +20,14 @@ ArrayFire provides one generic container object, the [array](\ref af::array)
 on which functions and mathematical operations are performed. The `array`
 can represent one of many different [basic data types](\ref af::af_dtype):
 
-* [b8](\ref b8) 8-bit boolean values (`bool`)
 * [f32](\ref f32) real single-precision (`float`)
 * [c32](\ref c32) complex single-precision (`cfloat`)
-* [s32](\ref s32) 32-bit signed integer (`int`)
-* [u32](\ref u32) 32-bit unsigned integer (`unsigned`)
 * [f64](\ref f64) real double-precision (`double`)
 * [c64](\ref c64) complex double-precision (`cdouble`)
+* [b8](\ref b8) 8-bit boolean values (`bool`)
+* [s32](\ref s32) 32-bit signed integer (`int`)
+* [u32](\ref u32) 32-bit unsigned integer (`unsigned`)
+* [u8](\ref u8) 8-bit unsigned values (`unsigned char`)
 * [s64](\ref s64) 64-bit signed integer (`intl`)
 * [u64](\ref u64) 64-bit unsigned integer (`uintl`)
 * [s16](\ref s16) 16-bit signed integer (`short`)

From 222c6fc6793214a040953454a837d8321a51b729 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Dec 2016 12:01:08 -0500
Subject: [PATCH 1023/2677] Code cleanup for sparse conversions

---
 src/api/c/sparse.cpp                 |  13 ++-
 src/backend/cpu/kernel/sparse.hpp    | 136 +++++++++++++--------------
 src/backend/cpu/sparse.cpp           |  44 +++------
 src/backend/cuda/sparse.cu           |  61 +++++++-----
 src/backend/opencl/kernel/csr2coo.cl |   5 +-
 src/backend/opencl/kernel/sparse.hpp |  20 ++--
 6 files changed, 130 insertions(+), 149 deletions(-)

diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 28ca49d75c..0e9404bdfa 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -295,19 +295,18 @@ af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
 af_err af_sparse_convert_to(af_array *out, const af_array in,
                             const af_storage destStorage)
 {
-    // Right now dest_storage can only be AF_STORAGE_DENSE
     try {
         af_array output = 0;
 
         const SparseArrayBase base = getSparseArrayBase(in);
 
-        // Dense not allowed as input -> Should never happen
-        // To convert from dense to type, use the create* functions
-        ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE);
+        // Dense not allowed as input -> Should never happen with SparseArrayBase
+        // CSC is currently not supported
+        ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE
+                   && base.getStorage() != AF_STORAGE_CSC);
 
-        // Right now dest_storage can only be AF_STORAGE_DENSE
-        // TODO: Add support for [CSR, CSC, COO] <-> [CSR, CSC, COO] in backends
-        ARG_ASSERT(1, destStorage != AF_STORAGE_CSC);
+        // Conversion to and from CSC is not supported
+        ARG_ASSERT(2, destStorage != AF_STORAGE_CSC);
 
         if(base.getStorage() == destStorage) {
             // Return a reference
diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index bf72af3aa4..f87a4ced1f 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <kernel/sort_helper.hpp>
 #include <algorithm>
+#include <tuple>
 
 namespace cpu
 {
@@ -112,91 +113,82 @@ struct SpKIPCompareK
 };
 
 template<typename T>
-struct csr_coo
+void csr_coo(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
+             Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
 {
-    void operator()(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
-                    Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
-    {
-        // First calculate the linear index
-        T         * const ovPtr = ovalues.get();
-        int       * const orPtr = orowIdx.get();
-        int       * const ocPtr = ocolIdx.get();
-
-        T   const * const ivPtr = ivalues.get();
-        int const * const irPtr = irowIdx.get();
-        int const * const icPtr = icolIdx.get();
-
-        // Create cordinate form of the row array
-        for(int i = 0; i < (int)irowIdx.elements() - 1; i++) {
-            std::fill_n(orPtr + irPtr[i], irPtr[i + 1] - irPtr[i], i);
-        }
-
-        // Sort the coordinate form using column index
-        // Uses code from sort_by_key kernels
-        typedef SpKeyIndexPair<T> CurrentPair;
-        int size = ovalues.dims()[0];
-        size_t bytes = size * sizeof(CurrentPair);
-        CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+    // First calculate the linear index
+    T         * const ovPtr = ovalues.get();
+    int       * const orPtr = orowIdx.get();
+    int       * const ocPtr = ocolIdx.get();
+
+    T   const * const ivPtr = ivalues.get();
+    int const * const irPtr = irowIdx.get();
+    int const * const icPtr = icolIdx.get();
+
+    // Create cordinate form of the row array
+    for(int i = 0; i < (int)irowIdx.elements() - 1; i++) {
+        std::fill_n(orPtr + irPtr[i], irPtr[i + 1] - irPtr[i], i);
+    }
 
-        for(int x = 0; x < size; x++) {
-           pairKeyVal[x] = std::make_tuple(icPtr[x], ivPtr[x], orPtr[x]);
-        }
+    // Sort the coordinate form using column index
+    // Uses code from sort_by_key kernels
+    typedef SpKeyIndexPair<T> CurrentPair;
+    int size = ovalues.dims()[0];
+    size_t bytes = size * sizeof(CurrentPair);
+    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
 
-        std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
+    for(int x = 0; x < size; x++) {
+       pairKeyVal[x] = std::make_tuple(icPtr[x], ivPtr[x], orPtr[x]);
+    }
 
-        for(int x = 0; x < (int)ovalues.elements(); x++) {
-            ocPtr[x] = std::get<0>(pairKeyVal[x]);
-            ovPtr[x] = std::get<1>(pairKeyVal[x]);
-            orPtr[x] = std::get<2>(pairKeyVal[x]);
-        }
+    std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
 
-        memFree((char *)pairKeyVal);
+    for(int x = 0; x < (int)ovalues.elements(); x++) {
+        std::tie(ocPtr[x], ovPtr[x], orPtr[x]) = pairKeyVal[x];
     }
-};
+
+    memFree((char *)pairKeyVal);
+}
 
 template<typename T>
-struct coo_csr
+void coo_csr(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
+             Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
 {
-    void operator()(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
-                    Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
-    {
-        T         * const ovPtr = ovalues.get();
-        int       * const orPtr = orowIdx.get();
-        int       * const ocPtr = ocolIdx.get();
-
-        T   const * const ivPtr = ivalues.get();
-        int const * const irPtr = irowIdx.get();
-        int const * const icPtr = icolIdx.get();
-
-        // Sort the colidx and values based on rowIdx
-        // Uses code from sort_by_key kernels
-        typedef SpKeyIndexPair<T> CurrentPair;
-        int size = ovalues.dims()[0];
-        size_t bytes = size * sizeof(CurrentPair);
-        CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
-
-        for(int x = 0; x < size; x++) {
-           pairKeyVal[x] = std::make_tuple(irPtr[x], ivPtr[x], icPtr[x]);
-        }
-
-        std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
+    T         * const ovPtr = ovalues.get();
+    int       * const orPtr = orowIdx.get();
+    int       * const ocPtr = ocolIdx.get();
+
+    T   const * const ivPtr = ivalues.get();
+    int const * const irPtr = irowIdx.get();
+    int const * const icPtr = icolIdx.get();
+
+    // Sort the colidx and values based on rowIdx
+    // Uses code from sort_by_key kernels
+    typedef SpKeyIndexPair<T> CurrentPair;
+    int size = ovalues.dims()[0];
+    size_t bytes = size * sizeof(CurrentPair);
+    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+
+    for(int x = 0; x < size; x++) {
+       pairKeyVal[x] = std::make_tuple(irPtr[x], ivPtr[x], icPtr[x]);
+    }
 
-        ovPtr[0] = 0;
-        for(int x = 0; x < (int)ovalues.elements(); x++) {
-            int row  = std::get<0>(pairKeyVal[x]);
-            ovPtr[x] = std::get<1>(pairKeyVal[x]);
-            ocPtr[x] = std::get<2>(pairKeyVal[x]);
-            orPtr[row + 1]++;
-        }
+    std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
 
-        // Compress row storage
-        for(int x = 1; x < (int)orowIdx.elements(); x++) {
-            orPtr[x] += orPtr[x - 1];
-        }
+    ovPtr[0] = 0;
+    for(int x = 0; x < (int)ovalues.elements(); x++) {
+        int row = -2; // Some value that will make orPtr[row + 1] error out
+        std::tie(row, ovPtr[x], ocPtr[x]) = pairKeyVal[x];
+        orPtr[row + 1]++;
+    }
 
-        memFree((char *)pairKeyVal);
+    // Compress row storage
+    for(int x = 1; x < (int)orowIdx.elements(); x++) {
+        orPtr[x] += orPtr[x - 1];
     }
-};
+
+    memFree((char *)pairKeyVal);
+}
 
 }
 }
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index a72a6b2495..e57cc1ccba 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -119,7 +119,7 @@ SPARSE_FUNC(csrcsc, cdouble,z)
 #endif // USE_MKL
 
 ////////////////////////////////////////////////////////////////////////////////
-// Common to MKL and Not MKL
+// Common Funcs for MKL and Non-MKL Code Paths
 ////////////////////////////////////////////////////////////////////////////////
 
 // Partial template specialization of sparseConvertDenseToStorage for COO
@@ -337,7 +337,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
 ////////////////////////////////////////////////////////////////////////////////
 
 ////////////////////////////////////////////////////////////////////////////////
-// Common to MKL and Not MKL
+// Common Funcs for MKL and Non-MKL Code Paths
 ////////////////////////////////////////////////////////////////////////////////
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
@@ -347,43 +347,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
     SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
     converted.eval();
 
-    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
-
-        auto func = [=] (SparseArray<T> out, const SparseArray<T> in_) {
-            Array<T  > ovalues = out.getValues();
-            Array<int> orowIdx = out.getRowIdx();
-            Array<int> ocolIdx = out.getColIdx();
-
-            Array<T  > ivalues = in_.getValues();
-            Array<int> irowIdx = in_.getRowIdx();
-            Array<int> icolIdx = in_.getColIdx();
-
-            kernel::csr_coo<T>()(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx);
-        };
-
-        getQueue().enqueue(func, converted, in);
+    function<void (Array<T>, Array<int>, Array<int>,
+                   Array<T> const, Array<int> const, Array<int> const)
+            > converter;
 
+    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+        converter = kernel::csr_coo<T>;
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-
-        auto func = [=] (SparseArray<T> out, const SparseArray<T> in_) {
-            Array<T  > ovalues = out.getValues();
-            Array<int> orowIdx = out.getRowIdx();
-            Array<int> ocolIdx = out.getColIdx();
-
-            Array<T  > ivalues = in_.getValues();
-            Array<int> irowIdx = in_.getRowIdx();
-            Array<int> icolIdx = in_.getColIdx();
-
-            kernel::coo_csr<T>()(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx);
-        };
-
-        getQueue().enqueue(func, converted, in);
-
+        converter = kernel::coo_csr<T>;
     } else {
         // Should never come here
         AF_ERROR("CPU Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
     }
 
+    getQueue().enqueue(converter,
+                       converted.getValues(), converted.getRowIdx(), converted.getColIdx(),
+                       in.getValues(), in.getRowIdx(), in.getColIdx());
+
     return converted;
 }
 
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index ddd17c2c8f..ab2bde6ce1 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -361,6 +361,21 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     return dense;
 }
 
+#define CUSPARSE_CHECK_FREE(fn) do {                    \
+        cusparseStatus_t _error = fn;                   \
+        if (_error != CUSPARSE_STATUS_SUCCESS) {        \
+            memFree(P);                                 \
+            memFree(pBuffer);                           \
+            char _err_msg[1024];                        \
+            snprintf(_err_msg, sizeof(_err_msg),        \
+                     "CUSPARSE Error (%d): %s\n",       \
+                     (int)(_error),                     \
+                     cusparse::errorString( _error));   \
+                                                        \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);        \
+        }                                               \
+    } while(0)
+
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
@@ -394,19 +409,19 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
 
         int *P = memAlloc<int>(nNZ);
-        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+        CUSPARSE_CHECK_FREE(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
 
-        CUSPARSE_CHECK(cusparseXcoosortByColumn(
-                        getHandle(),
-                        in.dims()[0], in.dims()[1], nNZ,
-                        converted.getRowIdx().get(), converted.getColIdx().get(),
-                        P, (void*)pBuffer));
+        CUSPARSE_CHECK_FREE(cusparseXcoosortByColumn(
+                            getHandle(),
+                            in.dims()[0], in.dims()[1], nNZ,
+                            converted.getRowIdx().get(), converted.getColIdx().get(),
+                            P, (void*)pBuffer));
 
-        CUSPARSE_CHECK(gthr_func<T>()(
-                        getHandle(), nNZ,
-                        in.getValues().get(),
-                        converted.getValues().get(),
-                        P, CUSPARSE_INDEX_BASE_ZERO));
+        CUSPARSE_CHECK_FREE(gthr_func<T>()(
+                            getHandle(), nNZ,
+                            in.getValues().get(),
+                            converted.getValues().get(),
+                            P, CUSPARSE_INDEX_BASE_ZERO));
 
         memFree(P);
         memFree(pBuffer);
@@ -432,19 +447,19 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
             char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
 
             int *P = memAlloc<int>(nNZ);
-            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+            CUSPARSE_CHECK_FREE(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
 
-            CUSPARSE_CHECK(cusparseXcoosortByRow(
-                            getHandle(),
-                            cooT.dims()[0], cooT.dims()[1], nNZ,
-                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
-                            P, (void*)pBuffer));
+            CUSPARSE_CHECK_FREE(cusparseXcoosortByRow(
+                                getHandle(),
+                                cooT.dims()[0], cooT.dims()[1], nNZ,
+                                cooT.getRowIdx().get(), cooT.getColIdx().get(),
+                                P, (void*)pBuffer));
 
-            CUSPARSE_CHECK(gthr_func<T>()(
-                            getHandle(), nNZ,
-                            in.getValues().get(),
-                            cooT.getValues().get(),
-                            P, CUSPARSE_INDEX_BASE_ZERO));
+            CUSPARSE_CHECK_FREE(gthr_func<T>()(
+                                getHandle(), nNZ,
+                                in.getValues().get(),
+                                cooT.getValues().get(),
+                                P, CUSPARSE_INDEX_BASE_ZERO));
 
             memFree(P);
             memFree(pBuffer);
@@ -478,6 +493,8 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
     return converted;
 }
 
+#undef CUSPARSE_CHECK_FREE
+
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
diff --git a/src/backend/opencl/kernel/csr2coo.cl b/src/backend/opencl/kernel/csr2coo.cl
index 4322b606ba..350e90d1af 100644
--- a/src/backend/opencl/kernel/csr2coo.cl
+++ b/src/backend/opencl/kernel/csr2coo.cl
@@ -8,10 +8,8 @@
  ********************************************************/
 
 __kernel
-void csr2coo(__global       T   *ovalues,
-             __global       int *orowidx,
+void csr2coo(__global       int *orowidx,
              __global       int *ocolidx,
-             __global const T   *ivalues,
              __global const int *irowidx,
              __global const int *icolidx,
              const int M)
@@ -21,7 +19,6 @@ void csr2coo(__global       T   *ovalues,
         int colStart = irowidx[rowId];
         int colEnd   = irowidx[rowId + 1];
         for (int colId = colStart + lid;  colId < colEnd; colId += THREADS) {
-            //ovalues[colId] = ivalues[colId];
             orowidx[colId] = rowId;
             ocolidx[colId] = icolidx[colId];
         }
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index aeb55d6e0a..ae6da73264 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -292,11 +292,9 @@ namespace opencl
                                                  const Buffer, const Buffer, const Buffer,
                                                  const int> (*swapIndexKernels[device]);
 
-                static const int threads = 256;
-                NDRange local(threads, 1);
-                NDRange global(divup(ovalues.info.dims[0], threads) * threads, 1, 1);
+                NDRange global(ovalues.info.dims[0], 1, 1);
 
-                swapIndexOp(EnqueueArgs(getQueue(), global, local),
+                swapIndexOp(EnqueueArgs(getQueue(), global),
                             *ovalues.data, *oindex.data,
                             *ivalues.data, *iindex,
                             *swapIdx.data, ovalues.info.dims[0]);
@@ -358,13 +356,13 @@ namespace opencl
                 int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
                 NDRange global(local[0] * groups_x, 1);
                 auto csr2coo_kernel = *entry.ker;
-                auto csr2coo_func = KernelFunctor<Buffer, Buffer, Buffer,
-                                                  const Buffer, const Buffer, const Buffer,
+                auto csr2coo_func = KernelFunctor<Buffer, Buffer,
+                                                  const Buffer, const Buffer,
                                                   int> (csr2coo_kernel);
 
                 csr2coo_func(EnqueueArgs(getQueue(), global, local),
-                               *ovalues.data, *scratch, *ocolIdx.data,
-                               *ivalues.data, *irowIdx.data, *icolIdx.data, M);
+                               *scratch, *ocolIdx.data,
+                               *irowIdx.data, *icolIdx.data, M);
 
                 // Now we need to sort this into column major
                 kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
@@ -437,11 +435,9 @@ namespace opencl
                 auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int>
                                    (*csrReduceKernels[device]);
 
-                static const int threads = 256;
-                NDRange local(threads, 1);
-                NDRange global(divup(irowIdx.info.dims[0], threads) * threads, 1, 1);
+                NDRange global(irowIdx.info.dims[0], 1, 1);
 
-                csrReduceOp(EnqueueArgs(getQueue(), global, local),
+                csrReduceOp(EnqueueArgs(getQueue(), global),
                             *orowIdx.data, *rowCopy, M, ovalues.info.dims[0]);
 
                 CL_DEBUG_FINISH(getQueue());

From 0bb9f3dedbb229b8c82702856e1a74237bfef711 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Dec 2016 12:02:30 -0500
Subject: [PATCH 1024/2677] Remove CSC placeholders for conversions

---
 src/api/c/sparse.cpp | 25 ++-----------------------
 1 file changed, 2 insertions(+), 23 deletions(-)

diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 0e9404bdfa..fab891337d 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -183,10 +183,10 @@ af_array createSparseArrayFromDense(
     switch(stype) {
         case AF_STORAGE_CSR:
             return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(in));
-        case AF_STORAGE_CSC:
-            return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(in));
         case AF_STORAGE_COO:
             return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_COO>(in));
+        case AF_STORAGE_CSC:
+            //return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(in));
         default: AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
     }
 }
@@ -235,16 +235,11 @@ af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
 {
     const SparseArray<T> in = getSparseArray<T>(in_);
 
-    // Only destStorage == AF_STORAGE_DENSE is supported
-    // All the other calls are for future when conversions are supported in
-    // the backend
     if(destStorage == AF_STORAGE_DENSE) {
         // Returns a regular af_array, not sparse
         switch(in.getStorage()) {
             case AF_STORAGE_CSR:
                 return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_CSR>(in));
-            case AF_STORAGE_CSC:
-                return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_CSC>(in));
             case AF_STORAGE_COO:
                 return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_COO>(in));
             default:
@@ -255,32 +250,16 @@ af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
         switch(in.getStorage()) {
             case AF_STORAGE_CSR:
                 return retainSparseHandle<T>(in_);
-            case AF_STORAGE_CSC:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR, AF_STORAGE_CSC>(in));
             case AF_STORAGE_COO:
                 return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR, AF_STORAGE_COO>(in));
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
-    } else if(destStorage == AF_STORAGE_CSC) {
-        // Returns a sparse af_array
-        switch(in.getStorage()) {
-            case AF_STORAGE_CSR:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSC, AF_STORAGE_CSR>(in));
-            case AF_STORAGE_CSC:
-                return retainSparseHandle<T>(in_);
-            case AF_STORAGE_COO:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSC, AF_STORAGE_COO>(in));
-            default:
-                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
-        }
     } else if(destStorage == AF_STORAGE_COO) {
         // Returns a sparse af_array
         switch(in.getStorage()) {
             case AF_STORAGE_CSR:
                 return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO, AF_STORAGE_CSR>(in));
-            case AF_STORAGE_CSC:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO, AF_STORAGE_CSC>(in));
             case AF_STORAGE_COO:
                 return retainSparseHandle<T>(in_);
             default:

From 90ee62461276202825523ff68bd4d2f1230cf7e4 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Dec 2016 12:03:26 -0500
Subject: [PATCH 1025/2677] Allow dense in sparse convert to function (calls
 create)

---
 src/api/c/sparse.cpp                 | 6 ++++++
 src/backend/opencl/kernel/sparse.hpp | 1 -
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index fab891337d..87c03223d5 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -275,6 +275,12 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
                             const af_storage destStorage)
 {
     try {
+        // Handle dense case
+        const ArrayInfo& info = getInfo(in, false, true);
+        if(!info.isSparse()) {    // If input is dense
+            return af_create_sparse_array_from_dense(out, in, destStorage);
+        }
+
         af_array output = 0;
 
         const SparseArrayBase base = getSparseArrayBase(in);
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index ae6da73264..0a0af6c95e 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -303,7 +303,6 @@ namespace opencl
 
             } catch (cl::Error err) {
                 CL_TO_AF_ERROR(err);
-                throw;
             }
         }
 

From de48e257b90b3469a35bb21b7b6bcfa423b15c4b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 6 Dec 2016 12:16:03 -0500
Subject: [PATCH 1026/2677] Sparse Tests: split into 2 files, add tests for
 failing CSC

---
 test/sparse.cpp         |  74 ++-----------------
 test/sparse_convert.cpp | 158 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 163 insertions(+), 69 deletions(-)
 create mode 100644 test/sparse_convert.cpp

diff --git a/test/sparse.cpp b/test/sparse.cpp
index 334dc22989..2e76eb08f0 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -227,76 +227,12 @@ CREATE_TESTS(AF_STORAGE_COO)
 
 #undef CREATE_TESTS
 
-template<typename T, af_storage src, af_storage dest>
-void sparseConvertTester(const int m, const int n, int factor)
+TEST(SPARSE_CREATE, AF_STORAGE_CSC)
 {
-    af::deviceGC();
-
-    if (noDoubleTests<T>()) return;
-
-#if 1
-    af::array A = cpu_randu<T>(af::dim4(m, n));
-#else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-#endif
-
-    A = makeSparse<T>(A, factor);
+    af::array d = af::identity(3, 3);
 
-    // Create Sparse Array of type src and dest From Dense
-    af::array sA = af::sparse(A, src);
+    af_array out = 0;
+    ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array_from_dense(&out, d.get(), AF_STORAGE_CSC));
 
-    // Convert src to dest format and dest to src
-    af::array s2d = sparseConvertTo(sA, dest);
-
-    // Create the dest type from dense - gold
-    af::array dA = af::sparse(A, dest);
-
-    // Verify nnZ
-    dim_t dNNZ   = sparseGetNNZ(dA);
-    dim_t s2dNNZ = sparseGetNNZ(s2d);
-
-    ASSERT_EQ(dNNZ, s2dNNZ);
-
-    // Verify Types
-    af_storage dType   = sparseGetStorage(dA);
-    af_storage s2dType = sparseGetStorage(s2d);
-
-    ASSERT_EQ(dType, s2dType);
-
-    // Get the individual arrays and verify equality
-    af::array dValues = sparseGetValues(dA);
-    af::array dRowIdx = sparseGetRowIdx(dA);
-    af::array dColIdx = sparseGetColIdx(dA);
-
-    af::array s2dValues = sparseGetValues(s2d);
-    af::array s2dRowIdx = sparseGetRowIdx(s2d);
-    af::array s2dColIdx = sparseGetColIdx(s2d);
-
-    // Verify values
-    ASSERT_EQ(0, af::max<double>(af::abs(dValues - s2dValues)));
-
-    // Verify row and col indices
-    ASSERT_EQ(0, af::max<int   >(dRowIdx - s2dRowIdx));
-    ASSERT_EQ(0, af::max<int   >(dColIdx - s2dColIdx));
+    if(out != 0) af_release_array(out);
 }
-
-#define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F)                   \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_##SUFFIX)                      \
-    {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(M, N, F);                          \
-    }                                                                           \
-    TEST(SPARSE_CONVERT, T##_##DTYPE##_##STYPE##_##SUFFIX)                      \
-    {                                                                           \
-        sparseConvertTester<T, DTYPE, STYPE>(M, N, F);                          \
-    }                                                                           \
-
-#define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 1, 1000, 1000,  5)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 2,  512,  512,  1)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 3,  512, 1024,  2)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 4, 2048, 1024, 10)                     \
-
-CONVERT_TESTS(float  , AF_STORAGE_CSR, AF_STORAGE_COO)
-CONVERT_TESTS(double , AF_STORAGE_CSR, AF_STORAGE_COO)
-CONVERT_TESTS(cfloat , AF_STORAGE_CSR, AF_STORAGE_COO)
-CONVERT_TESTS(cdouble, AF_STORAGE_CSR, AF_STORAGE_COO)
diff --git a/test/sparse_convert.cpp b/test/sparse_convert.cpp
new file mode 100644
index 0000000000..6d52b71676
--- /dev/null
+++ b/test/sparse_convert.cpp
@@ -0,0 +1,158 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <complex>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using std::abs;
+using af::cfloat;
+using af::cdouble;
+
+///////////////////////////////// CPP ////////////////////////////////////
+//
+
+template<typename T>
+af::array makeSparse(af::array A, int factor)
+{
+    A = floor(A * 1000);
+    A = A * ((A % factor) == 0) / 1000;
+    return A;
+}
+
+template<>
+af::array makeSparse<cfloat>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = r / 2;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+template<>
+af::array makeSparse<cdouble>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = r / 2;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+template<typename T, af_storage src, af_storage dest>
+void sparseConvertTester(const int m, const int n, int factor)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+
+    A = makeSparse<T>(A, factor);
+
+    // Create Sparse Array of type src and dest From Dense
+    af::array sA = af::sparse(A, src);
+
+    // Convert src to dest format and dest to src
+    af::array s2d = sparseConvertTo(sA, dest);
+
+    // Create the dest type from dense - gold
+    af::array dA = af::sparse(A, dest);
+
+    // Verify nnZ
+    dim_t dNNZ   = sparseGetNNZ(dA);
+    dim_t s2dNNZ = sparseGetNNZ(s2d);
+
+    ASSERT_EQ(dNNZ, s2dNNZ);
+
+    // Verify Types
+    af_storage dType   = sparseGetStorage(dA);
+    af_storage s2dType = sparseGetStorage(s2d);
+
+    ASSERT_EQ(dType, s2dType);
+
+    // Get the individual arrays and verify equality
+    af::array dValues = sparseGetValues(dA);
+    af::array dRowIdx = sparseGetRowIdx(dA);
+    af::array dColIdx = sparseGetColIdx(dA);
+
+    af::array s2dValues = sparseGetValues(s2d);
+    af::array s2dRowIdx = sparseGetRowIdx(s2d);
+    af::array s2dColIdx = sparseGetColIdx(s2d);
+
+    // Verify values
+    ASSERT_EQ(0, af::max<double>(af::abs(dValues - s2dValues)));
+
+    // Verify row and col indices
+    ASSERT_EQ(0, af::max<int   >(dRowIdx - s2dRowIdx));
+    ASSERT_EQ(0, af::max<int   >(dColIdx - s2dColIdx));
+}
+
+#define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F)                   \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_##SUFFIX)                      \
+    {                                                                           \
+        sparseConvertTester<T, STYPE, DTYPE>(M, N, F);                          \
+    }                                                                           \
+    TEST(SPARSE_CONVERT, T##_##DTYPE##_##STYPE##_##SUFFIX)                      \
+    {                                                                           \
+        sparseConvertTester<T, DTYPE, STYPE>(M, N, F);                          \
+    }                                                                           \
+
+#define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 1, 1000, 1000,  5)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 2,  512,  512,  1)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 3,  512, 1024,  2)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 4, 2048, 1024, 10)                     \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 5,  237, 411,   5)                     \
+
+CONVERT_TESTS(float  , AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(double , AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(cfloat , AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(cdouble, AF_STORAGE_CSR, AF_STORAGE_COO)
+
+#undef CONVERT_TESTS
+#undef CONVERT_TESTS_TYPES
+
+// Test to check failure with CSC
+TEST(SPARSE_CONVERT, CSC_ARG_ERROR)
+{
+    const int m = 100, n = 28, factor = 5;
+
+    af::array A = cpu_randu<float>(af::dim4(m, n));
+
+    A = makeSparse<float>(A, factor);
+
+    // Create Sparse Array of type src and dest From Dense
+    af::array sA = af::sparse(A, AF_STORAGE_CSR);
+
+    // Convert src to dest format and dest to src
+    // Use C-API to catch error
+    af_array out = 0;
+    ASSERT_EQ(AF_ERR_ARG, af_sparse_convert_to(&out, sA.get(), AF_STORAGE_CSC));
+
+    if(out != 0) af_release_array(out);
+}

From 482eb97faf1e66fb892b35ffb6e88567169278e7 Mon Sep 17 00:00:00 2001
From: Vardan Akopian <vakopian@sustain.net>
Date: Tue, 6 Dec 2016 17:26:39 -0800
Subject: [PATCH 1027/2677] optimization: avoid unnecessarily copying ArrayInfo
 objects

---
 src/api/c/approx.cpp                | 10 ++++-----
 src/api/c/array.cpp                 | 14 ++++++-------
 src/api/c/assign.cpp                | 12 +++++------
 src/api/c/bilateral.cpp             |  2 +-
 src/api/c/binary.cpp                | 32 ++++++++++++++---------------
 src/api/c/blas.cpp                  | 10 ++++-----
 src/api/c/cast.cpp                  |  6 +++---
 src/api/c/cholesky.cpp              |  4 ++--
 src/api/c/clamp.cpp                 |  6 +++---
 src/api/c/complex.cpp               | 10 ++++-----
 src/api/c/convolve.cpp              | 14 ++++++-------
 src/api/c/corrcoef.cpp              |  4 ++--
 src/api/c/covariance.cpp            |  4 ++--
 src/api/c/data.cpp                  |  8 ++++----
 src/api/c/det.cpp                   |  2 +-
 src/api/c/device.cpp                | 10 ++++-----
 src/api/c/diff.cpp                  |  4 ++--
 src/api/c/dog.cpp                   |  2 +-
 src/api/c/exampleFunction.cpp       |  2 +-
 src/api/c/fast.cpp                  |  2 +-
 src/api/c/fft.cpp                   |  8 ++++----
 src/api/c/fftconvolve.cpp           |  4 ++--
 src/api/c/filters.cpp               |  8 ++++----
 src/api/c/flip.cpp                  |  2 +-
 src/api/c/gradient.cpp              |  2 +-
 src/api/c/handle.hpp                |  2 +-
 src/api/c/harris.cpp                |  2 +-
 src/api/c/hist.cpp                  |  2 +-
 src/api/c/histeq.cpp                |  4 ++--
 src/api/c/histogram.cpp             |  2 +-
 src/api/c/homography.cpp            |  8 ++++----
 src/api/c/hsv_rgb.cpp               |  2 +-
 src/api/c/iir.cpp                   |  6 +++---
 src/api/c/image.cpp                 |  2 +-
 src/api/c/imageio.cpp               | 16 +++++++--------
 src/api/c/imageio2.cpp              |  4 ++--
 src/api/c/implicit.cpp              |  4 ++--
 src/api/c/index.cpp                 | 10 ++++-----
 src/api/c/internal.cpp              |  2 +-
 src/api/c/inverse.cpp               |  2 +-
 src/api/c/join.cpp                  |  4 ++--
 src/api/c/lu.cpp                    |  4 ++--
 src/api/c/match_template.cpp        |  4 ++--
 src/api/c/mean.cpp                  | 12 +++++------
 src/api/c/meanshift.cpp             |  2 +-
 src/api/c/median.cpp                |  4 ++--
 src/api/c/moddims.cpp               |  4 ++--
 src/api/c/moments.cpp               |  6 +++---
 src/api/c/morph.cpp                 |  8 ++++----
 src/api/c/nearest_neighbour.cpp     |  4 ++--
 src/api/c/norm.cpp                  |  2 +-
 src/api/c/orb.cpp                   |  2 +-
 src/api/c/plot.cpp                  | 16 +++++++--------
 src/api/c/print.cpp                 | 10 ++++-----
 src/api/c/qr.cpp                    |  4 ++--
 src/api/c/rank.cpp                  |  2 +-
 src/api/c/reduce.cpp                | 16 +++++++--------
 src/api/c/regions.cpp               |  2 +-
 src/api/c/reorder.cpp               |  2 +-
 src/api/c/replace.cpp               | 10 ++++-----
 src/api/c/resize.cpp                |  2 +-
 src/api/c/rgb_gray.cpp              |  2 +-
 src/api/c/rotate.cpp                |  2 +-
 src/api/c/sat.cpp                   |  2 +-
 src/api/c/select.cpp                | 14 ++++++-------
 src/api/c/set.cpp                   | 10 ++++-----
 src/api/c/shift.cpp                 |  2 +-
 src/api/c/sift.cpp                  |  4 ++--
 src/api/c/sobel.cpp                 |  2 +-
 src/api/c/solve.cpp                 |  8 ++++----
 src/api/c/sort.cpp                  | 10 ++++-----
 src/api/c/sparse.cpp                |  8 ++++----
 src/api/c/stdev.cpp                 |  4 ++--
 src/api/c/stream.cpp                |  4 ++--
 src/api/c/surface.cpp               | 12 +++++------
 src/api/c/susan.cpp                 |  2 +-
 src/api/c/svd.cpp                   |  8 ++++----
 src/api/c/tile.cpp                  |  2 +-
 src/api/c/transform.cpp             |  6 +++---
 src/api/c/transform_coordinates.cpp |  2 +-
 src/api/c/transpose.cpp             |  4 ++--
 src/api/c/unary.cpp                 | 16 +++++++--------
 src/api/c/unwrap.cpp                |  2 +-
 src/api/c/var.cpp                   | 12 +++++------
 src/api/c/vector_field.cpp          | 24 +++++++++++-----------
 src/api/c/where.cpp                 |  2 +-
 src/api/c/wrap.cpp                  |  2 +-
 src/api/c/ycbcr_rgb.cpp             |  2 +-
 88 files changed, 269 insertions(+), 269 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 76f70a900c..db98f99566 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -38,8 +38,8 @@ af_err af_approx1(af_array *out, const af_array in, const af_array pos,
                   const af_interp_type method, const float offGrid)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
-        ArrayInfo p_info = getInfo(pos);
+        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo& p_info = getInfo(pos);
 
         dim4 idims = i_info.dims();
         dim4 pdims = p_info.dims();
@@ -85,9 +85,9 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
                   const af_interp_type method, const float offGrid)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
-        ArrayInfo p_info = getInfo(pos0);
-        ArrayInfo q_info = getInfo(pos1);
+        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo& p_info = getInfo(pos0);
+        const ArrayInfo& q_info = getInfo(pos1);
 
         dim4 idims = i_info.dims();
         dim4 pdims = p_info.dims();
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index cbf95543f5..c1a72aed1c 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -127,7 +127,7 @@ af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * co
 af_err af_copy_array(af_array *out, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         const af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
@@ -161,7 +161,7 @@ af_err af_copy_array(af_array *out, const af_array in)
 af_err af_get_data_ref_count(int *use_count, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in, false, false);
+        const ArrayInfo& info = getInfo(in, false, false);
         const af_dtype type = info.getType();
 
         int res;
@@ -191,7 +191,7 @@ af_err af_release_array(af_array arr)
     try {
         int dev = getActiveDeviceId();
 
-        ArrayInfo info = getInfo(arr, false, false);
+        const ArrayInfo& info = getInfo(arr, false, false);
         af_dtype type = info.getType();
 
         if(info.isSparse()) {
@@ -242,7 +242,7 @@ static af_array retainHandle(const af_array in)
 
 af_array retain(const af_array in)
 {
-    ArrayInfo info = getInfo(in, false, false);
+    const ArrayInfo& info = getInfo(in, false, false);
     af_dtype ty = info.getType();
 
     if(info.isSparse()) {
@@ -341,7 +341,7 @@ af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
 {
     try {
         // Do not check for device mismatch
-        ArrayInfo info = getInfo(in, false, false);
+        const ArrayInfo& info = getInfo(in, false, false);
         *d0 = info.dims()[0];
         *d1 = info.dims()[1];
         *d2 = info.dims()[2];
@@ -355,7 +355,7 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
 {
     try {
         // Do not check for device mismatch
-        ArrayInfo info = getInfo(in, false, false);
+        const ArrayInfo& info = getInfo(in, false, false);
         *nd = info.ndims();
     }
     CATCHALL
@@ -368,7 +368,7 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
     af_err fn1(bool *result, const af_array in)                 \
     {                                                           \
         try {                                                   \
-            ArrayInfo info = getInfo(in, false, false);         \
+            const ArrayInfo& info = getInfo(in, false, false);         \
             *result = info.fn2();                               \
         }                                                       \
         CATCHALL                                                \
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index f863214759..6151de6460 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -80,7 +80,7 @@ template<typename T>
 static
 void assign_helper(Array<T> &out, const unsigned &ndims, const af_seq *index, const af_array &in_)
 {
-    ArrayInfo iInfo = getInfo(in_);
+    const ArrayInfo& iInfo = getInfo(in_);
     af_dtype iType  = iInfo.getType();
 
     if(out.getType() == c64 || out.getType() == c32)
@@ -119,7 +119,7 @@ af_err af_assign_seq(af_array *out,
         ARG_ASSERT(1, (ndims>0));
         ARG_ASSERT(3, (rhs!=0));
 
-        ArrayInfo lInfo = getInfo(lhs);
+        const ArrayInfo& lInfo = getInfo(lhs);
 
         if (ndims == 1 && ndims != lInfo.ndims()) {
             af_array tmp_in, tmp_out;
@@ -155,7 +155,7 @@ af_err af_assign_seq(af_array *out,
         try {
 
             if (lhs != rhs) {
-                ArrayInfo oInfo = getInfo(lhs);
+                const ArrayInfo& oInfo = getInfo(lhs);
                 af_dtype oType  = oInfo.getType();
                 switch(oType) {
                 case c64: assign_helper<cdouble>(getWritableArray<cdouble>(res), ndims, index, rhs);  break;
@@ -223,8 +223,8 @@ af_err af_assign_gen(af_array *out,
         ARG_ASSERT(1, (lhs!=0));
         ARG_ASSERT(4, (rhs!=0));
 
-        ArrayInfo lInfo = getInfo(lhs);
-        ArrayInfo rInfo = getInfo(rhs);
+        const ArrayInfo& lInfo = getInfo(lhs);
+        const ArrayInfo& rInfo = getInfo(rhs);
         dim4 lhsDims    = lInfo.dims();
         dim4 rhsDims    = rInfo.dims();
         af_dtype lhsType= lInfo.getType();
@@ -319,7 +319,7 @@ af_err af_assign_gen(af_array *out,
             if (!indexs[i].isSeq) {
                 // check if all af_arrays have atleast one value
                 // to enable indexing along that dimension
-                ArrayInfo idxInfo = getInfo(indexs[i].idx.arr);
+                const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
                 af_dtype idxType  = idxInfo.getType();
 
                 ARG_ASSERT(3, (idxType!=c32));
diff --git a/src/api/c/bilateral.cpp b/src/api/c/bilateral.cpp
index 4f9281d782..8c2cfe2ca5 100644
--- a/src/api/c/bilateral.cpp
+++ b/src/api/c/bilateral.cpp
@@ -28,7 +28,7 @@ template<bool isColor>
 static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma, const float &c_sigma)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 39cb498e1e..5a0f4efdbf 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -36,8 +36,8 @@ template<af_op_t op>
 static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
@@ -70,8 +70,8 @@ static af_err af_arith_real(af_array *out, const af_array lhs, const af_array rh
 {
     try {
 
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
@@ -140,8 +140,8 @@ af_err af_mod(af_array *out, const af_array lhs, const af_array rhs, const bool
 af_err af_pow(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
         if (linfo.isComplex() || rinfo.isComplex()) {
             af_array log_lhs, log_res;
             af_array res;
@@ -159,8 +159,8 @@ af_err af_pow(af_array *out, const af_array lhs, const af_array rhs, const bool
 af_err af_root(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
     try {
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
         if (linfo.isComplex() || rinfo.isComplex()) {
             af_array log_lhs, log_res;
             af_array res;
@@ -198,8 +198,8 @@ af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs, const boo
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
@@ -227,8 +227,8 @@ af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs, const boo
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
@@ -258,8 +258,8 @@ static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs, co
     try {
         const af_dtype type = implicit(lhs, rhs);
 
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
@@ -339,8 +339,8 @@ static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs,
     try {
         const af_dtype type = implicit(lhs, rhs);
 
-        ArrayInfo linfo = getInfo(lhs);
-        ArrayInfo rinfo = getInfo(rhs);
+        const ArrayInfo& linfo = getInfo(lhs);
+        const ArrayInfo& rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 50c240f174..05931e275d 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -49,7 +49,7 @@ af_err af_sparse_matmul(af_array *out,
 
     try {
         common::SparseArrayBase lhsBase = getSparseArrayBase(lhs);
-        ArrayInfo rhsInfo = getInfo(rhs);
+        const ArrayInfo& rhsInfo = getInfo(rhs);
 
         ARG_ASSERT(2, lhsBase.isSparse() == true && rhsInfo.isSparse() == false);
 
@@ -103,8 +103,8 @@ af_err af_matmul(af_array *out,
     using namespace detail;
 
     try {
-        ArrayInfo lhsInfo = getInfo(lhs, false, true);
-        ArrayInfo rhsInfo = getInfo(rhs, true, true);
+        const ArrayInfo& lhsInfo = getInfo(lhs, false, true);
+        const ArrayInfo& rhsInfo = getInfo(rhs, true, true);
 
         if(lhsInfo.isSparse())
             return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
@@ -158,8 +158,8 @@ af_err af_dot(      af_array *out,
     using namespace detail;
 
     try {
-        ArrayInfo lhsInfo = getInfo(lhs);
-        ArrayInfo rhsInfo = getInfo(rhs);
+        const ArrayInfo& lhsInfo = getInfo(lhs);
+        const ArrayInfo& rhsInfo = getInfo(rhs);
 
         if (optLhs != AF_MAT_NONE && optLhs != AF_MAT_CONJ) {
             AF_ERROR("Using this property is not yet supported in dot", AF_ERR_NOT_SUPPORTED);
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 343310d462..be9edb8301 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -22,7 +22,7 @@ using namespace detail;
 
 static af_array cast(const af_array in, const af_dtype type)
 {
-    const ArrayInfo info = getInfo(in);
+    const ArrayInfo& info = getInfo(in);
 
     if (info.getType() == type) {
         return retain(in);
@@ -48,7 +48,7 @@ static af_array cast(const af_array in, const af_dtype type)
 af_err af_cast(af_array *out, const af_array in, const af_dtype type)
 {
     try {
-        const ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         dim4 idims = info.dims();
         if(idims.elements() == 0) {
             dim_t my_dims[] = {0, 0, 0, 0};
@@ -67,7 +67,7 @@ af_err af_cplx(af_array *out, const af_array in, const af_dtype type)
 {
     try {
         af_array res;
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (in_info.isDouble()) {
             res = cast(in, c64);
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index bd1a38e8ae..df073a4b10 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -34,7 +34,7 @@ static inline int cholesky_inplace(af_array in, const bool is_upper)
 af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_upper)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("cholesky can not be used in batch mode", AF_ERR_BATCH);
@@ -68,7 +68,7 @@ af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_up
 af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("cholesky can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index f4dd4f26e3..8235fc970a 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -40,9 +40,9 @@ af_err af_clamp(af_array *out, const af_array in,
                 const af_array lo, const af_array hi, const bool batch)
 {
     try {
-        ArrayInfo linfo = getInfo(lo);
-        ArrayInfo hinfo = getInfo(hi);
-        ArrayInfo iinfo = getInfo(in);
+        const ArrayInfo& linfo = getInfo(lo);
+        const ArrayInfo& hinfo = getInfo(hi);
+        const ArrayInfo& iinfo = getInfo(in);
 
         DIM_ASSERT(2, linfo.dims() == hinfo.dims());
         TYPE_ASSERT(linfo.getType() == hinfo.getType());
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index 38e2e2e8c8..ba377d6a9a 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -62,7 +62,7 @@ af_err af_cplx(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if (type == c32 || type == c64) {
@@ -96,7 +96,7 @@ af_err af_real(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if (type != c32 && type != c64) {
@@ -122,7 +122,7 @@ af_err af_imag(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if (type != c32 && type != c64) {
@@ -148,7 +148,7 @@ af_err af_conjg(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if (type != c32 && type != c64) {
@@ -174,7 +174,7 @@ af_err af_abs(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype in_type = in_info.getType();
         af_array res;
 
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index a6858ddfc1..66cbd22b5a 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -64,8 +64,8 @@ template<dim_t baseDim, bool expand>
 af_err convolve(af_array *out, const af_array signal, const af_array filter)
 {
     try {
-        ArrayInfo sInfo = getInfo(signal);
-        ArrayInfo fInfo = getInfo(filter);
+        const ArrayInfo& sInfo = getInfo(signal);
+        const ArrayInfo& fInfo = getInfo(filter);
 
         af_dtype stype  = sInfo.getType();
 
@@ -107,9 +107,9 @@ template<bool expand>
 af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter, const af_array signal)
 {
     try {
-        ArrayInfo sInfo = getInfo(signal);
-        ArrayInfo cfInfo= getInfo(col_filter);
-        ArrayInfo rfInfo= getInfo(row_filter);
+        const ArrayInfo& sInfo = getInfo(signal);
+        const ArrayInfo& cfInfo= getInfo(col_filter);
+        const ArrayInfo& rfInfo= getInfo(row_filter);
 
         af_dtype signalType  = sInfo.getType();
 
@@ -149,8 +149,8 @@ bool isFreqDomain(const af_array &signal, const af_array filter, af_conv_domain
     if (domain == AF_CONV_FREQ) return true;
     if (domain != AF_CONV_AUTO) return false;
 
-    ArrayInfo sInfo = getInfo(signal);
-    ArrayInfo fInfo = getInfo(filter);
+    const ArrayInfo& sInfo = getInfo(signal);
+    const ArrayInfo& fInfo = getInfo(filter);
 
     dim4 sdims = sInfo.dims();
     dim4 fdims = fInfo.dims();
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index 275fa80239..9f3292339c 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -51,8 +51,8 @@ static To corrcoef(const af_array& X, const af_array& Y)
 af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y)
 {
     try {
-        ArrayInfo xInfo = getInfo(X);
-        ArrayInfo yInfo = getInfo(Y);
+        const ArrayInfo& xInfo = getInfo(X);
+        const ArrayInfo& yInfo = getInfo(Y);
         dim4 xDims      = xInfo.dims();
         dim4 yDims      = yInfo.dims();
         af_dtype xType  = xInfo.getType();
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index f8bb9c4435..167017f123 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -52,8 +52,8 @@ static af_array cov(const af_array& X, const af_array& Y, const bool isbiased)
 af_err af_cov(af_array* out, const af_array X, const af_array Y, const bool isbiased)
 {
     try {
-        ArrayInfo xInfo = getInfo(X);
-        ArrayInfo yInfo = getInfo(Y);
+        const ArrayInfo& xInfo = getInfo(X);
+        const ArrayInfo& yInfo = getInfo(Y);
         dim4 xDims      = xInfo.dims();
         dim4 yDims      = yInfo.dims();
         af_dtype xType  = xInfo.getType();
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 99d00f6271..38f5476529 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -301,7 +301,7 @@ static inline af_array diagExtract(const af_array in, const int num)
 af_err af_diag_create(af_array *out, const af_array in, const int num)
 {
     try {
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         DIM_ASSERT(1, in_info.ndims() <= 2);
         af_dtype type = in_info.getType();
 
@@ -338,7 +338,7 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num)
 {
 
     try {
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
         if(in_info.ndims() == 0) {
@@ -384,7 +384,7 @@ af_array triangle(const af_array in, bool is_unit_diag)
 af_err af_lower(af_array *out, const af_array in, bool is_unit_diag)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
@@ -416,7 +416,7 @@ af_err af_lower(af_array *out, const af_array in, bool is_unit_diag)
 af_err af_upper(af_array *out, const af_array in, bool is_unit_diag)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index 7ef2e5296e..a8846cfae4 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -67,7 +67,7 @@ af_err af_det(double *real_val, double *imag_val, const af_array in)
 {
 
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index f9f7cc8dc1..9ca79a8de4 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -49,7 +49,7 @@ af_err af_get_backend_id(af_backend *result, const af_array in)
 {
     try {
         ARG_ASSERT(1, in != 0);
-        ArrayInfo info = getInfo(in, false, false);
+        const ArrayInfo& info = getInfo(in, false, false);
         *result = info.getBackendId();
     } CATCHALL;
     return AF_SUCCESS;
@@ -59,7 +59,7 @@ af_err af_get_device_id(int *device, const af_array in)
 {
     try {
         ARG_ASSERT(1, in != 0);
-        ArrayInfo info = getInfo(in, false, false);
+        const ArrayInfo& info = getInfo(in, false, false);
         *device = info.getDevId();
     } CATCHALL;
     return AF_SUCCESS;
@@ -176,7 +176,7 @@ static inline void sparseEval(af_array arr)
 af_err af_eval(af_array arr)
 {
     try {
-        ArrayInfo info = getInfo(arr, false);
+        const ArrayInfo& info = getInfo(arr, false);
         af_dtype type = info.getType();
 
         if(info.isSparse()) {
@@ -226,12 +226,12 @@ static inline void evalMultiple(int num, af_array *arrayPtrs)
 af_err af_eval_multiple(int num, af_array *arrays)
 {
     try {
-        ArrayInfo info = getInfo(arrays[0]);
+        const ArrayInfo& info = getInfo(arrays[0]);
         af_dtype type = info.getType();
         dim4 dims = info.dims();
 
         for (int i = 1; i < num; i++) {
-            ArrayInfo currInfo = getInfo(arrays[i]);
+            const ArrayInfo& currInfo = getInfo(arrays[i]);
 
             // FIXME: This needs to be removed when new functionality is added
             if (type != currInfo.getType()) {
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index 7f341f5d72..2d4f672964 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -36,7 +36,7 @@ af_err af_diff1(af_array *out, const af_array in, const int dim)
 
         ARG_ASSERT(2, ((dim >= 0) && (dim < 4)));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         af::dim4 in_dims = info.dims();
@@ -78,7 +78,7 @@ af_err af_diff2(af_array *out, const af_array in, const int dim)
 
         ARG_ASSERT(2, ((dim >= 0) && (dim < 4)));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         af::dim4 in_dims = info.dims();
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index 953db19a49..3e5fe6c264 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -46,7 +46,7 @@ static af_array dog(const af_array& in, const int radius1, const int radius2)
 af_err af_dog(af_array *out, const af_array in, const int radius1, const int radius2)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         dim4 inDims = info.dims();
         ARG_ASSERT(1, (inDims.ndims()>=2));
         ARG_ASSERT(1, (inDims.ndims()<=3));
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index 5234f0da8d..edc9ac77ac 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -52,7 +52,7 @@ af_err af_example_function(af_array* out, const af_array a, const af_someenum_t
 {
     try {
         af_array output = 0;
-        ArrayInfo info = getInfo(a);        // ArrayInfo is the base class which
+        const ArrayInfo& info = getInfo(a);        // ArrayInfo is the base class which
                                             // each backend specific Array inherits
                                             // This class stores the basic array meta-data
                                             // such as type of data, dimensions,
diff --git a/src/api/c/fast.cpp b/src/api/c/fast.cpp
index 9a403195f1..f72d9946e1 100644
--- a/src/api/c/fast.cpp
+++ b/src/api/c/fast.cpp
@@ -52,7 +52,7 @@ af_err af_fast(af_features *out, const af_array in, const float thr,
                const float feature_ratio, const unsigned edge)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         ARG_ASSERT(2, (dims[0] >= (dim_t)(2*edge+1) || dims[1] >= (dim_t)(2*edge+1)));
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 55df61001a..93e78ea042 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -29,7 +29,7 @@ template<int rank, bool direction>
 static af_err fft(af_array *out, const af_array in, const double norm_factor, const dim_t npad, const dim_t * const pad)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
@@ -104,7 +104,7 @@ template<int rank, bool direction>
 static af_err fft_inplace(af_array in, const double norm_factor)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
@@ -166,7 +166,7 @@ template<int rank>
 static af_err fft_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t npad, const dim_t * const pad)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
@@ -221,7 +221,7 @@ template<int rank>
 static af_err fft_c2r(af_array *out, const af_array in, const double norm_factor, const bool is_odd)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
         af::dim4 idims  = info.dims();
 
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index a7c17da929..cf7b9cc651 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -124,8 +124,8 @@ template<dim_t baseDim>
 af_err fft_convolve(af_array *out, const af_array signal, const af_array filter, const bool expand)
 {
     try {
-        ArrayInfo sInfo = getInfo(signal);
-        ArrayInfo fInfo = getInfo(filter);
+        const ArrayInfo& sInfo = getInfo(signal);
+        const ArrayInfo& fInfo = getInfo(filter);
 
         af_dtype stype  = sInfo.getType();
 
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index 0f4f6891ad..c4a29afc0e 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -41,7 +41,7 @@ af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, con
         ARG_ASSERT(2, (wind_width>0));
         ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         dim_t input_ndims = dims.ndims();
@@ -89,7 +89,7 @@ af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, co
         ARG_ASSERT(3, (wind_width>0));
         ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         if(info.isColumn()) {
@@ -132,7 +132,7 @@ af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
         ARG_ASSERT(3, (wind_width>0));
         ARG_ASSERT(4, (edge_pad==AF_PAD_ZERO));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         dim_t input_ndims = dims.ndims();
@@ -160,7 +160,7 @@ af_err af_maxfilt(af_array *out, const af_array in, const dim_t wind_length,
         ARG_ASSERT(3, (wind_width>0));
         ARG_ASSERT(4, (edge_pad==AF_PAD_ZERO));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         dim_t input_ndims = dims.ndims();
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 09cbaf75e4..4d5a4fa152 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -50,7 +50,7 @@ af_err af_flip(af_array *result, const af_array in, const unsigned dim)
 {
     af_array out;
     try {
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (in_info.ndims() <= dim) {
             *result = retain(in);
diff --git a/src/api/c/gradient.cpp b/src/api/c/gradient.cpp
index d6adfdff13..be801187d8 100644
--- a/src/api/c/gradient.cpp
+++ b/src/api/c/gradient.cpp
@@ -27,7 +27,7 @@ static inline void gradient(af_array *grad0, af_array *grad1, const af_array in)
 af_err af_gradient(af_array *grows, af_array *gcols, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         af::dim4 idims = info.dims();
 
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 6c6459ef88..b550c78b43 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -41,7 +41,7 @@ detail::Array<To> castArray(const af_array &in)
     using detail::uchar;
     using detail::ushort;
 
-    const ArrayInfo info = getInfo(in);
+    const ArrayInfo& info = getInfo(in);
     switch (info.getType()) {
     case f32: return detail::cast<To, float  >(getArray<float  >(in));
     case f64: return detail::cast<To, double >(getArray<double >(in));
diff --git a/src/api/c/harris.cpp b/src/api/c/harris.cpp
index 5781e192a4..578a3ed48b 100644
--- a/src/api/c/harris.cpp
+++ b/src/api/c/harris.cpp
@@ -52,7 +52,7 @@ af_err af_harris(af_features *out, const af_array in, const unsigned max_corners
                  const unsigned block_size, const float k_thr)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
         dim_t in_ndims = dims.ndims();
 
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index b560957111..830e387ae2 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -63,7 +63,7 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
     }
 
     try {
-        ArrayInfo Xinfo = getInfo(X);
+        const ArrayInfo& Xinfo = getInfo(X);
         af_dtype Xtype  = Xinfo.getType();
 
         ARG_ASSERT(0, Xinfo.isVector());
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 9579df82b0..95936eaca9 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -63,8 +63,8 @@ static af_array hist_equal(const af_array& in, const af_array& hist)
 af_err af_hist_equal(af_array *out, const af_array in, const af_array hist)
 {
     try {
-        ArrayInfo dataInfo = getInfo(in);
-        ArrayInfo histInfo = getInfo(hist);
+        const ArrayInfo& dataInfo = getInfo(in);
+        const ArrayInfo& histInfo = getInfo(hist);
 
         af_dtype dataType  = dataInfo.getType();
         af::dim4 histDims  = histInfo.dims();
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index 827c5b1d3e..688bebf165 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -32,7 +32,7 @@ af_err af_histogram(af_array *out, const af_array in,
                     const unsigned nbins, const double minval, const double maxval)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
 
         if(info.ndims() == 0) {
diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index 76b12fbf21..51b4b10f1c 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -48,10 +48,10 @@ af_err af_homography(af_array *H, int *inliers,
                      const unsigned iterations, const af_dtype otype)
 {
     try {
-        ArrayInfo xsinfo = getInfo(x_src);
-        ArrayInfo ysinfo = getInfo(y_src);
-        ArrayInfo xdinfo = getInfo(x_dst);
-        ArrayInfo ydinfo = getInfo(y_dst);
+        const ArrayInfo& xsinfo = getInfo(x_src);
+        const ArrayInfo& ysinfo = getInfo(y_src);
+        const ArrayInfo& xdinfo = getInfo(x_dst);
+        const ArrayInfo& ydinfo = getInfo(y_dst);
 
         af::dim4 xsdims  = xsinfo.dims();
         af::dim4 ysdims  = ysinfo.dims();
diff --git a/src/api/c/hsv_rgb.cpp b/src/api/c/hsv_rgb.cpp
index 585e6776c4..86eb38ccd4 100644
--- a/src/api/c/hsv_rgb.cpp
+++ b/src/api/c/hsv_rgb.cpp
@@ -34,7 +34,7 @@ template<bool isHSV2RGB>
 af_err convert(af_array* out, const af_array& in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype iType = info.getType();
         af::dim4 inputDims = info.dims();
 
diff --git a/src/api/c/iir.cpp b/src/api/c/iir.cpp
index a70207de59..afe91d7ba6 100644
--- a/src/api/c/iir.cpp
+++ b/src/api/c/iir.cpp
@@ -51,9 +51,9 @@ inline static af_array iir(const af_array b, const af_array a, const af_array x)
 af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
 {
     try {
-        ArrayInfo ainfo = getInfo(a);
-        ArrayInfo binfo = getInfo(b);
-        ArrayInfo xinfo = getInfo(x);
+        const ArrayInfo& ainfo = getInfo(a);
+        const ArrayInfo& binfo = getInfo(b);
+        const ArrayInfo& xinfo = getInfo(x);
 
         af_dtype xtype = xinfo.getType();
 
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index da870c8cb6..37ed268b87 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -84,7 +84,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
     }
 
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
 
         af::dim4 in_dims = info.dims();
         af_dtype type    = info.getType();
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index cef40ee99f..68c7d6f95d 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -297,7 +297,7 @@ af_err af_save_image(const char* filename, const af_array in_)
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
-        ArrayInfo info = getInfo(in_);
+        const ArrayInfo& info = getInfo(in_);
         // check image color type
         uint channels = info.dims()[2];
         DIM_ASSERT(1, channels <= 4);
@@ -359,7 +359,7 @@ af_err af_save_image(const char* filename, const af_array in_)
             AF_CHECK(af_transpose(&bbT, bb, false));
             AF_CHECK(af_transpose(&aaT, aa, false));
 
-            ArrayInfo cinfo = getInfo(rrT);
+            const ArrayInfo& cinfo = getInfo(rrT);
             float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
@@ -390,7 +390,7 @@ af_err af_save_image(const char* filename, const af_array in_)
             AF_CHECK(af_transpose(&ggT, gg, false));
             AF_CHECK(af_transpose(&bbT, bb, false));
 
-            ArrayInfo cinfo = getInfo(rrT);
+            const ArrayInfo& cinfo = getInfo(rrT);
             float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
@@ -414,7 +414,7 @@ af_err af_save_image(const char* filename, const af_array in_)
             pinnedFree(pSrc2);
         } else {
             AF_CHECK(af_transpose(&rrT, rr, false));
-            ArrayInfo cinfo = getInfo(rrT);
+            const ArrayInfo& cinfo = getInfo(rrT);
             float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
 
@@ -574,7 +574,7 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
-        ArrayInfo info = getInfo(in_);
+        const ArrayInfo& info = getInfo(in_);
         // check image color type
         uint channels = info.dims()[2];
         DIM_ASSERT(1, channels <= 4);
@@ -628,7 +628,7 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             AF_CHECK(af_transpose(&bbT, bb, false));
             AF_CHECK(af_transpose(&aaT, aa, false));
 
-            ArrayInfo cinfo = getInfo(rrT);
+            const ArrayInfo& cinfo = getInfo(rrT);
             float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
@@ -659,7 +659,7 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             AF_CHECK(af_transpose(&ggT, gg, false));
             AF_CHECK(af_transpose(&bbT, bb, false));
 
-            ArrayInfo cinfo = getInfo(rrT);
+            const ArrayInfo& cinfo = getInfo(rrT);
             float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
             float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
@@ -683,7 +683,7 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             pinnedFree(pSrc2);
         } else {
             AF_CHECK(af_transpose(&rrT, rr, false));
-            ArrayInfo cinfo = getInfo(rrT);
+            const ArrayInfo& cinfo = getInfo(rrT);
             float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
 
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 76c53f4ab4..0b8b340679 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -238,7 +238,7 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims, uint nDstPit
     if(channels >= 3) AF_CHECK(af_transpose(&bbT, bb, false));
     if(channels >= 4) AF_CHECK(af_transpose(&aaT, aa, false));
 
-    ArrayInfo cinfo = getInfo(rrT);
+    const ArrayInfo& cinfo = getInfo(rrT);
                       pSrc0 = pinnedAlloc<T>(cinfo.elements());
     if(channels >= 3) pSrc1 = pinnedAlloc<T>(cinfo.elements());
     if(channels >= 3) pSrc2 = pinnedAlloc<T>(cinfo.elements());
@@ -313,7 +313,7 @@ af_err af_save_image_native(const char* filename, const af_array in)
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         // check image color type
         FI_CHANNELS channels = (FI_CHANNELS)info.dims()[2];
         DIM_ASSERT(1, channels <= 4);
diff --git a/src/api/c/implicit.cpp b/src/api/c/implicit.cpp
index 372fb9654e..c1e02dd6c4 100644
--- a/src/api/c/implicit.cpp
+++ b/src/api/c/implicit.cpp
@@ -64,8 +64,8 @@ af_dtype implicit(const af_dtype lty, const af_dtype rty)
 
 af_dtype implicit(const af_array lhs, const af_array rhs)
 {
-    ArrayInfo lInfo = getInfo(lhs);
-    ArrayInfo rInfo = getInfo(rhs);
+    const ArrayInfo& lInfo = getInfo(lhs);
+    const ArrayInfo& rInfo = getInfo(rhs);
 
     return implicit(lInfo.getType(), rInfo.getType());
 }
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 23cd9a509b..04033b0ac2 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -41,7 +41,7 @@ af_err af_index(af_array *result, const af_array in, const unsigned ndims, const
     af_array out;
     try {
 
-        ArrayInfo iInfo = getInfo(in);
+        const ArrayInfo& iInfo = getInfo(in);
         if (ndims == 1 && ndims != iInfo.ndims()) {
             af_array tmp_in;
             AF_CHECK(af_flat(&tmp_in, in));
@@ -77,7 +77,7 @@ af_err af_index(af_array *result, const af_array in, const unsigned ndims, const
 template<typename idx_t>
 static af_array lookup(const af_array &in, const af_array &idx, const unsigned dim)
 {
-    ArrayInfo inInfo = getInfo(in);
+    const ArrayInfo& inInfo = getInfo(in);
 
     af_dtype inType  = inInfo.getType();
 
@@ -105,7 +105,7 @@ af_err af_lookup(af_array *out, const af_array in, const af_array indices, const
     try {
         ARG_ASSERT(3, (dim>=0 && dim<=3));
 
-        ArrayInfo idxInfo= getInfo(indices);
+        const ArrayInfo& idxInfo= getInfo(indices);
 
         if(idxInfo.ndims() == 0) {
             return af_retain_array(out, indices);
@@ -160,7 +160,7 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
         ARG_ASSERT(2, (ndims>0));
         ARG_ASSERT(3, (indexs!=NULL));
 
-        ArrayInfo iInfo = getInfo(in);
+        const ArrayInfo& iInfo = getInfo(in);
 
         dim4 iDims = iInfo.dims();
         af_dtype inType = getInfo(in).getType();
@@ -200,7 +200,7 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
             if (!indexs[i].isSeq) {
                 // check if all af_arrays have atleast one value
                 // to enable indexing along that dimension
-                ArrayInfo idxInfo = getInfo(indexs[i].idx.arr);
+                const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
                 af_dtype idxType  = idxInfo.getType();
 
                 ARG_ASSERT(3, (idxType!=c32));
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 47c62c6478..5c8723a738 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -78,7 +78,7 @@ af_err af_create_strided_array(af_array *arr,
 af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         *s0 = info.strides()[0];
         *s1 = info.strides()[1];
         *s2 = info.strides()[2];
diff --git a/src/api/c/inverse.cpp b/src/api/c/inverse.cpp
index e3dd68d0b4..653c26af2e 100644
--- a/src/api/c/inverse.cpp
+++ b/src/api/c/inverse.cpp
@@ -28,7 +28,7 @@ static inline af_array inverse(const af_array in)
 af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 8de18df12a..29527c1a8d 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -39,8 +39,8 @@ static inline af_array join_many(const int dim, const unsigned n_arrays, const a
 af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)
 {
     try {
-        ArrayInfo finfo = getInfo(first);
-        ArrayInfo sinfo = getInfo(second);
+        const ArrayInfo& finfo = getInfo(first);
+        const ArrayInfo& sinfo = getInfo(second);
         af::dim4  fdims = finfo.dims();
         af::dim4  sdims = sinfo.dims();
 
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index 59989bd932..1a625cf0e8 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -43,7 +43,7 @@ static inline af_array lu_inplace(af_array in, bool is_lapack_piv)
 af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array in)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("lu can not be used in batch mode", AF_ERR_BATCH);
@@ -78,7 +78,7 @@ af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv)
 {
     try {
 
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
         af_dtype type = i_info.getType();
 
         if (i_info.ndims() > 2) {
diff --git a/src/api/c/match_template.cpp b/src/api/c/match_template.cpp
index 0e618c2bc4..ddaf104f6b 100644
--- a/src/api/c/match_template.cpp
+++ b/src/api/c/match_template.cpp
@@ -40,8 +40,8 @@ af_err af_match_template(af_array *out, const af_array search_img, const af_arra
     try {
         ARG_ASSERT(3, (m_type>=AF_SAD && m_type<=AF_LSSD));
 
-        ArrayInfo sInfo = getInfo(search_img);
-        ArrayInfo tInfo = getInfo(template_img);
+        const ArrayInfo& sInfo = getInfo(search_img);
+        const ArrayInfo& tInfo = getInfo(template_img);
 
         dim4 const sDims = sInfo.dims();
         dim4 const tDims = tInfo.dims();
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 1cbee32ec6..f5fd38db2e 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -57,7 +57,7 @@ af_err af_mean(af_array *out, const af_array in, const dim_t dim)
         ARG_ASSERT(2, (dim>=0 && dim<=3));
 
         af_array output = 0;
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         switch(type) {
             case f64: output = mean<double  ,  double>(in, dim); break;
@@ -86,8 +86,8 @@ af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights
         ARG_ASSERT(2, (dim>=0 && dim<=3));
 
         af_array output = 0;
-        ArrayInfo iInfo = getInfo(in);
-        ArrayInfo wInfo = getInfo(weights);
+        const ArrayInfo& iInfo = getInfo(in);
+        const ArrayInfo& wInfo = getInfo(weights);
         af_dtype iType  = iInfo.getType();
         af_dtype wType  = wInfo.getType();
 
@@ -117,7 +117,7 @@ af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights
 af_err af_mean_all(double *realVal, double *imagVal, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         switch(type) {
             case f64: *realVal = mean<double  , double>(in); break;
@@ -150,8 +150,8 @@ af_err af_mean_all(double *realVal, double *imagVal, const af_array in)
 af_err af_mean_all_weighted(double *realVal, double *imagVal, const af_array in, const af_array weights)
 {
     try {
-        ArrayInfo iInfo = getInfo(in);
-        ArrayInfo wInfo = getInfo(weights);
+        const ArrayInfo& iInfo = getInfo(in);
+        const ArrayInfo& wInfo = getInfo(weights);
         af_dtype iType  = iInfo.getType();
         af_dtype wType  = wInfo.getType();
 
diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index eb4305a5d0..21a757fd3c 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -32,7 +32,7 @@ af_err mean_shift(af_array *out, const af_array in, const float s_sigma, const f
         ARG_ASSERT(3, (c_sigma>=0));
         ARG_ASSERT(4, (iter>0));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
         af::dim4 dims  = info.dims();
 
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 51399959c1..7ef6bf1afc 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -153,7 +153,7 @@ static af_array median(const af_array& in, const dim_t dim)
 af_err af_median_all(double *realVal, double *imagVal, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         ARG_ASSERT(2, info.ndims() > 0);
@@ -178,7 +178,7 @@ af_err af_median(af_array* out, const af_array in, const dim_t dim)
         ARG_ASSERT(2, (dim >= 0 && dim <= 4));
 
         af_array output = 0;
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
 
         ARG_ASSERT(1, info.ndims() > 0);
         af_dtype type = info.getType();
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 3fd4edb154..dc3158e0f5 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -61,7 +61,7 @@ af_err af_moddims(af_array *out, const af_array in,
 
         af_array output = 0;
         dim4 newDims(ndims, dims);
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         dim_t in_elements = info.elements();
         dim_t new_elements = newDims.elements();
 
@@ -96,7 +96,7 @@ af_err af_flat(af_array *out, const af_array in)
     af_array res;
     try {
 
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (in_info.ndims() == 1) {
             AF_CHECK(af_retain_array(&res, in));
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index b280600ff8..6572988a8e 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -42,7 +42,7 @@ static inline void moments(af_array *out, const af_array in, af_moment_type mome
 af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
 {
     try {
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
         switch(type) {
@@ -77,14 +77,14 @@ static inline void moment_copy(double* out, const af_array moments)
 af_err af_moments_all(double* out, const af_array in, const af_moment_type moment)
 {
     try {
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         dim4 idims = in_info.dims();
         DIM_ASSERT(1, idims[2] == 1 && idims[3] == 1);
 
         af_array moments_arr;
         af_moments(&moments_arr, in, moment);
 
-        const ArrayInfo m_info = getInfo(moments_arr);
+        const ArrayInfo& m_info = getInfo(moments_arr);
         af_dtype type = m_info.getType();
 
         switch(type) {
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index bd9c680b26..67d99bb672 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -40,8 +40,8 @@ template<bool isDilation>
 static af_err morph(af_array *out, const af_array &in, const af_array &mask)
 {
     try {
-        ArrayInfo info = getInfo(in);
-        ArrayInfo mInfo= getInfo(mask);
+        const ArrayInfo& info = getInfo(in);
+        const ArrayInfo& mInfo= getInfo(mask);
         af::dim4 dims  = info.dims();
         af::dim4 mdims = mInfo.dims();
         dim_t in_ndims = dims.ndims();
@@ -74,8 +74,8 @@ template<bool isDilation>
 static af_err morph3d(af_array *out, const af_array &in, const af_array &mask)
 {
     try {
-        ArrayInfo info = getInfo(in);
-        ArrayInfo mInfo= getInfo(mask);
+        const ArrayInfo& info = getInfo(in);
+        const ArrayInfo& mInfo= getInfo(mask);
         af::dim4 dims  = info.dims();
         af::dim4 mdims = mInfo.dims();
         dim_t in_ndims = dims.ndims();
diff --git a/src/api/c/nearest_neighbour.cpp b/src/api/c/nearest_neighbour.cpp
index 03064a4cb7..587502f4a4 100644
--- a/src/api/c/nearest_neighbour.cpp
+++ b/src/api/c/nearest_neighbour.cpp
@@ -40,8 +40,8 @@ af_err af_nearest_neighbour(af_array* idx, af_array* dist,
         const af_match_type dist_type)
 {
     try {
-        ArrayInfo qInfo = getInfo(query);
-        ArrayInfo tInfo = getInfo(train);
+        const ArrayInfo& qInfo = getInfo(query);
+        const ArrayInfo& tInfo = getInfo(train);
         af_dtype qType  = qInfo.getType();
         af_dtype tType  = tInfo.getType();
         af::dim4 qDims  = qInfo.dims();
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index dbae2f35e9..a2b99f8aaa 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -126,7 +126,7 @@ af_err af_norm(double *out, const af_array in,
 {
 
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/orb.cpp b/src/api/c/orb.cpp
index 98f5170027..05cc4560c9 100644
--- a/src/api/c/orb.cpp
+++ b/src/api/c/orb.cpp
@@ -55,7 +55,7 @@ af_err af_orb(af_features* feat, af_array* desc,
               const unsigned levels, const bool blur_img)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         ARG_ASSERT(2, (dims[0] >= 7 && dims[1] >= 7 && dims[2] == 1 && dims[3] == 1));
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 2151c9197f..b0ef9ccc57 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -93,7 +93,7 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
     }
 
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4  dims = info.dims();
         af_dtype  type = info.getType();
 
@@ -136,15 +136,15 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
     }
 
     try {
-        ArrayInfo xInfo = getInfo(X);
+        const ArrayInfo& xInfo = getInfo(X);
         af::dim4  xDims = xInfo.dims();
         af_dtype  xType = xInfo.getType();
 
-        ArrayInfo yInfo = getInfo(Y);
+        const ArrayInfo& yInfo = getInfo(Y);
         af::dim4  yDims = yInfo.dims();
         af_dtype  yType = yInfo.getType();
 
-        ArrayInfo zInfo = getInfo(Z);
+        const ArrayInfo& zInfo = getInfo(Z);
         af::dim4  zDims = zInfo.dims();
         af_dtype  zType = zInfo.getType();
 
@@ -197,11 +197,11 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
     }
 
     try {
-        ArrayInfo xInfo = getInfo(X);
+        const ArrayInfo& xInfo = getInfo(X);
         af::dim4  xDims = xInfo.dims();
         af_dtype  xType = xInfo.getType();
 
-        ArrayInfo yInfo = getInfo(Y);
+        const ArrayInfo& yInfo = getInfo(Y);
         af::dim4  yDims = yInfo.dims();
         af_dtype  yType = yInfo.getType();
 
@@ -344,7 +344,7 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
 {
 #if defined(WITH_GRAPHICS)
     try {
-        ArrayInfo info = getInfo(P);
+        const ArrayInfo& info = getInfo(P);
         af::dim4  dims = info.dims();
 
         if(dims.ndims() == 2 && dims[1] == 3) {
@@ -426,7 +426,7 @@ af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_
 #if defined(WITH_GRAPHICS)
     forge::MarkerType fg_marker = getFGMarker(af_marker);
     try {
-        ArrayInfo info = getInfo(P);
+        const ArrayInfo& info = getInfo(P);
         af::dim4  dims = info.dims();
 
         if(dims.ndims() == 2 && dims[1] == 3) {
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 81df34ced3..0b9c0323c8 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -66,7 +66,7 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
         os << exp << std::endl;
     }
 
-    const ArrayInfo info = getInfo(arr);
+    const ArrayInfo& info = getInfo(arr);
 
     std::ios_base::fmtflags backup = os.flags();
 
@@ -94,7 +94,7 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
 
     //FIXME: Use alternative function to avoid copies if possible
     AF_CHECK(af_get_data_ptr(&data.front(), arrT));
-    const ArrayInfo infoT = getInfo(arrT);
+    const ArrayInfo& infoT = getInfo(arrT);
 
     if(transpose) {
         AF_CHECK(af_release_array(arrT));
@@ -136,7 +136,7 @@ static void printSparse(const char *exp, af_array arr, const int precision,
 af_err af_print_array(af_array arr)
 {
     try {
-        ArrayInfo info = getInfo(arr, false);   // Don't assert sparse/dense
+        const ArrayInfo& info = getInfo(arr, false);   // Don't assert sparse/dense
         af_dtype type = info.getType();
 
         if(info.isSparse()) {
@@ -174,7 +174,7 @@ af_err af_print_array_gen(const char *exp, const af_array arr, const int precisi
 {
     try {
         ARG_ASSERT(0, exp != NULL);
-        ArrayInfo info = getInfo(arr, false);   // Don't assert sparse/dense
+        const ArrayInfo& info = getInfo(arr, false);   // Don't assert sparse/dense
         af_dtype type = info.getType();
 
         if(info.isSparse()) {
@@ -213,7 +213,7 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
 {
     try {
         ARG_ASSERT(0, exp != NULL);
-        ArrayInfo info = getInfo(arr, false);   // Don't assert sparse/dense
+        const ArrayInfo& info = getInfo(arr, false);   // Don't assert sparse/dense
         af_dtype type = info.getType();
         std::stringstream ss;
 
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index cd3f142455..d58c9c6b41 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -42,7 +42,7 @@ static inline af_array qr_inplace(af_array in)
 af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("qr can not be used in batch mode", AF_ERR_BATCH);
@@ -76,7 +76,7 @@ af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
 af_err af_qr_inplace(af_array *tau, af_array in)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("qr can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 208b967bc5..dfd122dabb 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -49,7 +49,7 @@ static inline uint rank(const af_array in, double tol)
 af_err af_rank(uint *out, const af_array in, const double tol)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index d1e0a74967..26dd2a42ef 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -37,7 +37,7 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim)
         ARG_ASSERT(2, dim >= 0);
         ARG_ASSERT(2, dim <  4);
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (dim >= (int)in_info.ndims()) {
             *out = retain(in);
@@ -78,7 +78,7 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim)
         ARG_ASSERT(2, dim >= 0);
         ARG_ASSERT(2, dim <  4);
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (dim >= (int)in_info.ndims()) {
             return af_retain_array(out, in);
@@ -119,7 +119,7 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
         ARG_ASSERT(2, dim >= 0);
         ARG_ASSERT(2, dim <  4);
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (dim >= (int)in_info.ndims()) {
             *out = retain(in);
@@ -208,7 +208,7 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in)
 {
     try {
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
         ARG_ASSERT(0, real != NULL);
@@ -242,7 +242,7 @@ static af_err reduce_all_common(double *real_val, double *imag_val, const af_arr
 {
     try {
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
         ARG_ASSERT(2, in_info.ndims() > 0);
@@ -294,7 +294,7 @@ static af_err reduce_all_promote(double *real_val, double *imag_val, const af_ar
 {
     try {
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
         ARG_ASSERT(0, real_val != NULL);
@@ -398,7 +398,7 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in, co
         ARG_ASSERT(2, dim >= 0);
         ARG_ASSERT(2, dim <  4);
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         ARG_ASSERT(2, in_info.ndims() > 0);
 
         if (dim >= (int)in_info.ndims()) {
@@ -455,7 +455,7 @@ static af_err ireduce_all_common(double *real_val, double *imag_val,
 {
     try {
 
-        const ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         af_dtype type = in_info.getType();
 
         ARG_ASSERT(3, in_info.ndims() > 0);
diff --git a/src/api/c/regions.cpp b/src/api/c/regions.cpp
index 49ddedf88c..0b5fd52425 100644
--- a/src/api/c/regions.cpp
+++ b/src/api/c/regions.cpp
@@ -29,7 +29,7 @@ af_err af_regions(af_array *out, const af_array in, const af_connectivity connec
     try {
         ARG_ASSERT(2, (connectivity==AF_CONNECTIVITY_4 || connectivity==AF_CONNECTIVITY_8));
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         dim_t in_ndims = dims.ndims();
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 23adad9834..b0d7f54137 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -27,7 +27,7 @@ static inline af_array reorder(const af_array in, const af::dim4 &rdims)
 af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.elements() == 0) {
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 0464e306cc..f4adf9b4c6 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -31,9 +31,9 @@ void replace(af_array a, const af_array cond, const af_array b)
 af_err af_replace(af_array a, const af_array cond, const af_array b)
 {
     try {
-        ArrayInfo ainfo = getInfo(a);
-        ArrayInfo binfo = getInfo(b);
-        ArrayInfo cinfo = getInfo(cond);
+        const ArrayInfo& ainfo = getInfo(a);
+        const ArrayInfo& binfo = getInfo(b);
+        const ArrayInfo& cinfo = getInfo(cond);
 
         if(cinfo.ndims() == 0) {
             return AF_SUCCESS;
@@ -83,8 +83,8 @@ void replace_scalar(af_array a, const af_array cond, const double b)
 af_err af_replace_scalar(af_array a, const af_array cond, const double b)
 {
     try {
-        ArrayInfo ainfo = getInfo(a);
-        ArrayInfo cinfo = getInfo(cond);
+        const ArrayInfo& ainfo = getInfo(a);
+        const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
         DIM_ASSERT(1, cinfo.ndims() == ainfo.ndims());
diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index 7471707dba..2f0c9f23e9 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -30,7 +30,7 @@ af_err af_resize(af_array *out, const af_array in, const dim_t odim0, const dim_
                  const af_interp_type method)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         ARG_ASSERT(4, method == AF_INTERP_NEAREST  ||
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 7e127440a3..b87803aa77 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -105,7 +105,7 @@ template<bool isRGB2GRAY>
 af_err convert(af_array* out, const af_array in, const float r, const float g, const float b)
 {
     try {
-        ArrayInfo info     = getInfo(in);
+        const ArrayInfo& info     = getInfo(in);
         af_dtype iType     = info.getType();
         af::dim4 inputDims = info.dims();
 
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index 92627b9383..3c57da6d8f 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -32,7 +32,7 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
     try {
         unsigned odims0 = 0, odims1 = 0;
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 idims = info.dims();
 
         if(!crop) {
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index fa6d0a4c23..05bac43f93 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -30,7 +30,7 @@ static af_array sat(const af_array& in)
 af_err af_sat(af_array* out, const af_array in)
 {
     try{
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         const dim4 dims = info.dims();
 
         ARG_ASSERT(1, (dims.ndims() >= 2));
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 162de25fdd..859eb0897c 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -32,9 +32,9 @@ af_array select(const af_array cond, const af_array a, const af_array b, const d
 af_err af_select(af_array *out, const af_array cond, const af_array a, const af_array b)
 {
     try {
-        ArrayInfo ainfo = getInfo(a);
-        ArrayInfo binfo = getInfo(b);
-        ArrayInfo cinfo = getInfo(cond);
+        const ArrayInfo& ainfo = getInfo(a);
+        const ArrayInfo& binfo = getInfo(b);
+        const ArrayInfo& cinfo = getInfo(cond);
 
         if(cinfo.ndims() == 0) {
             return af_retain_array(out, cond);
@@ -90,8 +90,8 @@ af_array select_scalar(const af_array cond, const af_array a, const double b, co
 af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a, const double b)
 {
     try {
-        ArrayInfo ainfo = getInfo(a);
-        ArrayInfo cinfo = getInfo(cond);
+        const ArrayInfo& ainfo = getInfo(a);
+        const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
         DIM_ASSERT(1, cinfo.ndims() == ainfo.ndims());
@@ -129,8 +129,8 @@ af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a,
 af_err af_select_scalar_l(af_array *out, const af_array cond, const double a, const af_array b)
 {
     try {
-        ArrayInfo binfo = getInfo(b);
-        ArrayInfo cinfo = getInfo(cond);
+        const ArrayInfo& binfo = getInfo(b);
+        const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
         DIM_ASSERT(1, cinfo.ndims() == binfo.ndims());
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index 097b608945..455e256c93 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -28,7 +28,7 @@ af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
 {
     try {
 
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         if(in_info.isEmpty()) {
             return af_retain_array(out, in);
         }
@@ -67,8 +67,8 @@ af_err af_set_union(af_array *out, const af_array first, const af_array second,
 {
     try {
 
-        ArrayInfo first_info = getInfo(first);
-        ArrayInfo second_info = getInfo(second);
+        const ArrayInfo& first_info = getInfo(first);
+        const ArrayInfo& second_info = getInfo(second);
 
         af_array res;
         if(first_info.isEmpty()) {
@@ -117,8 +117,8 @@ af_err af_set_intersect(af_array *out, const af_array first, const af_array seco
 {
     try {
 
-        ArrayInfo first_info = getInfo(first);
-        ArrayInfo second_info = getInfo(second);
+        const ArrayInfo& first_info = getInfo(first);
+        const ArrayInfo& second_info = getInfo(second);
 
         //TODO: fix for set intersect from union
         if(first_info.isEmpty()) {
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index e027ba5d44..c988b0d7fe 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -26,7 +26,7 @@ static inline af_array shift(const af_array in, const int sdims[4])
 af_err af_shift(af_array *out, const af_array in, const int sdims[4])
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index a14badc88d..c9fd065386 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -55,7 +55,7 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
 {
     try {
 #ifdef AF_BUILD_NONFREE_SIFT
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         ARG_ASSERT(2, (dims[0] >= 15 && dims[1] >= 15 && dims[2] == 1 && dims[3] == 1));
@@ -96,7 +96,7 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsig
 {
     try {
 #ifdef AF_BUILD_NONFREE_SIFT
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         ARG_ASSERT(2, (dims[0] >= 15 && dims[1] >= 15 && dims[2] == 1 && dims[3] == 1));
diff --git a/src/api/c/sobel.cpp b/src/api/c/sobel.cpp
index 6d28a6a95d..f9c0879260 100644
--- a/src/api/c/sobel.cpp
+++ b/src/api/c/sobel.cpp
@@ -36,7 +36,7 @@ af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img, const u
         //ARG_ASSERT(4, (ker_size==3 || ker_size==5 || ker_size==7));
         ARG_ASSERT(4, (ker_size==3));
 
-        ArrayInfo info = getInfo(img);
+        const ArrayInfo& info = getInfo(img);
         af::dim4 dims  = info.dims();
 
         DIM_ASSERT(3, (dims.ndims() >= 2));
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index 7035495af0..f31766b1ab 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -28,8 +28,8 @@ static inline af_array solve(const af_array a, const af_array b, const af_mat_pr
 af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_prop options)
 {
     try {
-        ArrayInfo a_info = getInfo(a);
-        ArrayInfo b_info = getInfo(b);
+        const ArrayInfo& a_info = getInfo(a);
+        const ArrayInfo& b_info = getInfo(b);
 
         if (a_info.ndims() > 2 ||
             b_info.ndims() > 2) {
@@ -98,8 +98,8 @@ af_err af_solve_lu(af_array *out, const af_array a,
                    const af_mat_prop options)
 {
     try {
-        ArrayInfo a_info = getInfo(a);
-        ArrayInfo b_info = getInfo(b);
+        const ArrayInfo& a_info = getInfo(a);
+        const ArrayInfo& b_info = getInfo(b);
 
         if (a_info.ndims() > 2 ||
             b_info.ndims() > 2) {
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 9172d7e828..f310a3769b 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -34,7 +34,7 @@ static inline af_array sort(const af_array in, const unsigned dim, const bool is
 af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.elements() == 0) {
@@ -82,7 +82,7 @@ static inline void sort_index(af_array *val, af_array *idx, const af_array in,
 af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const unsigned dim, const bool isAscending)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.elements() <= 0) {
@@ -136,7 +136,7 @@ template<typename Tk>
 void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey, const af_array ival,
                        const unsigned dim, const bool isAscending)
 {
-    ArrayInfo info = getInfo(ival);
+    const ArrayInfo& info = getInfo(ival);
     af_dtype vtype = info.getType();
 
     switch(vtype) {
@@ -163,10 +163,10 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                       const unsigned dim, const bool isAscending)
 {
     try {
-        ArrayInfo kinfo = getInfo(keys);
+        const ArrayInfo& kinfo = getInfo(keys);
         af_dtype ktype = kinfo.getType();
 
-        ArrayInfo vinfo = getInfo(values);
+        const ArrayInfo& vinfo = getInfo(values);
 
         DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
         if(kinfo.elements() == 0) {
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index a88c7a64f9..95356f329f 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -76,9 +76,9 @@ af_err af_create_sparse_array(
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
-        ArrayInfo vInfo = getInfo(values);
-        ArrayInfo rInfo = getInfo(rowIdx);
-        ArrayInfo cInfo = getInfo(colIdx);
+        const ArrayInfo& vInfo = getInfo(values);
+        const ArrayInfo& rInfo = getInfo(rowIdx);
+        const ArrayInfo& cInfo = getInfo(colIdx);
 
         TYPE_ASSERT(vInfo.isFloating());
         DIM_ASSERT(4, vInfo.isLinear());
@@ -199,7 +199,7 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
         // stype is within acceptable range
         // values is of floating point type
 
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
 
         if(!(stype == AF_STORAGE_CSR
           || stype == AF_STORAGE_CSC
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 59c9653bdf..9204250d6b 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -68,7 +68,7 @@ static af_array stdev(const af_array& in, int dim)
 af_err af_stdev_all(double *realVal, double *imagVal, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         switch(type) {
             case f64: *realVal = stdev<double, double>(in); break;
@@ -105,7 +105,7 @@ af_err af_stdev(af_array *out, const af_array in, const dim_t dim)
         ARG_ASSERT(2, (dim>=0 && dim<=3));
 
         af_array output = 0;
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         switch(type) {
             case f64: output = stdev<double,  double>(in, dim); break;
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index 17cc945520..2dfd0b72fb 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -43,7 +43,7 @@ static int save(const char *key, const af_array arr, const char *filename, const
     std::string k(key);
     int klen = k.size();
 
-    const ArrayInfo info = getInfo(arr);
+    const ArrayInfo& info = getInfo(arr);
     std::vector<T> data(info.elements());
 
     AF_CHECK(af_get_data_ptr(&data.front(), arr));
@@ -119,7 +119,7 @@ af_err af_save_array(int *index, const char *key, const af_array arr, const char
         ARG_ASSERT(0, key != NULL);
         ARG_ASSERT(2, filename != NULL);
 
-        ArrayInfo info = getInfo(arr);
+        const ArrayInfo& info = getInfo(arr);
         af_dtype type = info.getType();
         int id = -1;
         switch(type) {
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index c1458e239b..5d110725c6 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -36,9 +36,9 @@ forge::Chart* setup_surface(const forge::Window* const window,
     Array<T> yIn = getArray<T>(yVals);
     Array<T> zIn = getArray<T>(zVals);
 
-    ArrayInfo Xinfo = getInfo(xVals);
-    ArrayInfo Yinfo = getInfo(yVals);
-    ArrayInfo Zinfo = getInfo(zVals);
+    const ArrayInfo& Xinfo = getInfo(xVals);
+    const ArrayInfo& Yinfo = getInfo(yVals);
+    const ArrayInfo& Zinfo = getInfo(zVals);
 
     af::dim4 X_dims = Xinfo.dims();
     af::dim4 Y_dims = Yinfo.dims();
@@ -96,15 +96,15 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
     }
 
     try {
-        ArrayInfo Xinfo = getInfo(xVals);
+        const ArrayInfo& Xinfo = getInfo(xVals);
         af::dim4 X_dims = Xinfo.dims();
         af_dtype Xtype  = Xinfo.getType();
 
-        ArrayInfo Yinfo = getInfo(yVals);
+        const ArrayInfo& Yinfo = getInfo(yVals);
         af::dim4 Y_dims = Yinfo.dims();
         af_dtype Ytype  = Yinfo.getType();
 
-        ArrayInfo Sinfo = getInfo(S);
+        const ArrayInfo& Sinfo = getInfo(S);
         af::dim4 S_dims = Sinfo.dims();
         af_dtype Stype  = Sinfo.getType();
 
diff --git a/src/api/c/susan.cpp b/src/api/c/susan.cpp
index 75c295388e..fcac91227d 100644
--- a/src/api/c/susan.cpp
+++ b/src/api/c/susan.cpp
@@ -48,7 +48,7 @@ af_err af_susan(af_features* out, const af_array in,
                 const float feature_ratio, const unsigned edge)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
         ARG_ASSERT(1, dims.ndims()==2);
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index ad1e0265b3..1668674da3 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -23,7 +23,7 @@ using namespace detail;
 template <typename T>
 static inline void svd(af_array *s, af_array *u, af_array *vt, const af_array in)
 {
-    ArrayInfo info = getInfo(in);  // ArrayInfo is the base class which
+    const ArrayInfo& info = getInfo(in);  // ArrayInfo is the base class which
     af::dim4 dims = info.dims();
     int M = dims[0];
     int N = dims[1];
@@ -45,7 +45,7 @@ static inline void svd(af_array *s, af_array *u, af_array *vt, const af_array in
 template <typename T>
 static inline void svdInPlace(af_array *s, af_array *u, af_array *vt, af_array in)
 {
-    ArrayInfo info = getInfo(in);  // ArrayInfo is the base class which
+    const ArrayInfo& info = getInfo(in);  // ArrayInfo is the base class which
     af::dim4 dims = info.dims();
     int M = dims[0];
     int N = dims[1];
@@ -67,7 +67,7 @@ static inline void svdInPlace(af_array *s, af_array *u, af_array *vt, af_array i
 af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims = info.dims();
 
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
@@ -105,7 +105,7 @@ af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
 af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af::dim4 dims = info.dims();
 
         DIM_ASSERT(3, dims[0] <= dims[1]);
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 9b8e8b24b1..34c09710ae 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -52,7 +52,7 @@ static inline af_array tile(const af_array in, const af::dim4 &tileDims)
 af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index cbe1240fc3..cd5f8abc6f 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -57,8 +57,8 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
                     const af_interp_type method, const bool inverse)
 {
     try {
-        ArrayInfo t_info = getInfo(tf);
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& t_info = getInfo(tf);
+        const ArrayInfo& i_info = getInfo(in);
 
         af::dim4 idims = i_info.dims();
         af::dim4 tdims = t_info.dims();
@@ -181,7 +181,7 @@ af_err af_scale(af_array *out, const af_array in, const float scale0, const floa
                 const dim_t odim0, const dim_t odim1, const af_interp_type method)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
         af::dim4 idims = i_info.dims();
 
         dim_t _odim0 = odim0, _odim1 = odim1;
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index e1e1dfa15b..9623f58bc3 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -65,7 +65,7 @@ static af_array transform_coordinates(const af_array& tf, const float d0, const
 af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0, const float d1)
 {
     try {
-        ArrayInfo tfInfo = getInfo(tf);
+        const ArrayInfo& tfInfo = getInfo(tf);
         dim4 tfDims = tfInfo.dims();
         ARG_ASSERT(1, (tfDims[0]==3 && tfDims[1]==3 && tfDims.ndims()==2));
 
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index 6799651a19..f29b48d6a5 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -29,7 +29,7 @@ static inline af_array trs(const af_array in, const bool conjugate)
 af_err af_transpose(af_array *out, af_array in, const bool conjugate)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         af::dim4 dims = info.dims();
 
@@ -85,7 +85,7 @@ static inline void transpose_inplace(af_array in, const bool conjugate)
 af_err af_transpose_inplace(af_array in, const bool conjugate)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         af::dim4 dims = info.dims();
 
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 4eb58bfdcc..fa07354a0b 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -59,7 +59,7 @@ static af_err af_unary(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
         ARG_ASSERT(1, in_info.isReal());
 
         af_dtype in_type = in_info.getType();
@@ -85,7 +85,7 @@ template<af_op_t op>
 static af_err af_unary_complex(af_array *out, const af_array in)
 {
     try {
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         af_dtype in_type = in_info.getType();
         af_array res;
@@ -562,7 +562,7 @@ af_err af_not(af_array *out, const af_array in)
     try {
 
         af_array tmp;
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         AF_CHECK(af_constant(&tmp, 0,
                              in_info.ndims(),
@@ -580,7 +580,7 @@ af_err af_arg(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         if (!in_info.isComplex()) {
             return af_constant(out, 0,
@@ -608,7 +608,7 @@ af_err af_pow2(af_array *out, const af_array in)
     try {
 
         af_array two;
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         AF_CHECK(af_constant(&two, 2,
                              in_info.ndims(),
@@ -627,7 +627,7 @@ af_err af_factorial(af_array *out, const af_array in)
     try {
 
         af_array one;
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         AF_CHECK(af_constant(&one, 1,
                              in_info.ndims(),
@@ -679,7 +679,7 @@ static inline af_array checkOpCplx(const af_array in)
     Array<char> resR = checkOp<BT, op>(R);
     Array<char> resI = checkOp<BT, op>(I);
 
-    ArrayInfo in_info = getInfo(in);
+    const ArrayInfo& in_info = getInfo(in);
     dim4 dims = in_info.dims();
     cplxLogicOp<op> cplxLogic;
     af_array res = cplxLogic(resR, resI, dims);
@@ -692,7 +692,7 @@ static af_err af_check(af_array *out, const af_array in)
 {
     try {
 
-        ArrayInfo in_info = getInfo(in);
+        const ArrayInfo& in_info = getInfo(in);
 
         af_dtype in_type = in_info.getType();
         af_array res;
diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index 25b4a67bed..1e473a7ecd 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -30,7 +30,7 @@ af_err af_unwrap(af_array *out, const af_array in, const dim_t wx, const dim_t w
                  const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         af::dim4 idims = info.dims();
 
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 59a651b4af..8f476b57ad 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -122,7 +122,7 @@ af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t
         ARG_ASSERT(2, (dim>=0 && dim<=3));
 
         af_array output = 0;
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         switch(type) {
             case f64: output = var<double,  double>(in, isbiased, dim); break;
@@ -151,8 +151,8 @@ af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
         ARG_ASSERT(2, (dim>=0 && dim<=3));
 
         af_array output = 0;
-        ArrayInfo iInfo = getInfo(in);
-        ArrayInfo wInfo = getInfo(weights);
+        const ArrayInfo& iInfo = getInfo(in);
+        const ArrayInfo& wInfo = getInfo(weights);
         af_dtype iType  = iInfo.getType();
         af_dtype wType  = wInfo.getType();
 
@@ -182,7 +182,7 @@ af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
 af_err af_var_all(double *realVal, double *imagVal, const af_array in, const bool isbiased)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         switch(type) {
             case f64: *realVal = varAll<double, double>(in, isbiased); break;
@@ -215,8 +215,8 @@ af_err af_var_all(double *realVal, double *imagVal, const af_array in, const boo
 af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in, const af_array weights)
 {
     try {
-        ArrayInfo iInfo = getInfo(in);
-        ArrayInfo wInfo = getInfo(weights);
+        const ArrayInfo& iInfo = getInfo(in);
+        const ArrayInfo& wInfo = getInfo(weights);
         af_dtype iType  = iInfo.getType();
         af_dtype wType  = wInfo.getType();
 
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 5ca9755248..0ec11d60a6 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -74,11 +74,11 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
     }
 
     try {
-        ArrayInfo pInfo = getInfo(points);
+        const ArrayInfo& pInfo = getInfo(points);
         af::dim4 pDims  = pInfo.dims();
         af_dtype pType  = pInfo.getType();
 
-        ArrayInfo dInfo = getInfo(directions);
+        const ArrayInfo& dInfo = getInfo(directions);
         af::dim4 dDims  = dInfo.dims();
         af_dtype dType  = dInfo.getType();
 
@@ -123,9 +123,9 @@ af_err vectorFieldWrapper(const af_window wind,
     }
 
     try {
-        ArrayInfo xpInfo = getInfo(xPoints);
-        ArrayInfo ypInfo = getInfo(yPoints);
-        ArrayInfo zpInfo = getInfo(zPoints);
+        const ArrayInfo& xpInfo = getInfo(xPoints);
+        const ArrayInfo& ypInfo = getInfo(yPoints);
+        const ArrayInfo& zpInfo = getInfo(zPoints);
 
         af::dim4 xpDims  = xpInfo.dims();
         af::dim4 ypDims  = ypInfo.dims();
@@ -135,9 +135,9 @@ af_err vectorFieldWrapper(const af_window wind,
         af_dtype ypType  = ypInfo.getType();
         af_dtype zpType  = zpInfo.getType();
 
-        ArrayInfo xdInfo = getInfo(xDirs);
-        ArrayInfo ydInfo = getInfo(yDirs);
-        ArrayInfo zdInfo = getInfo(zDirs);
+        const ArrayInfo& xdInfo = getInfo(xDirs);
+        const ArrayInfo& ydInfo = getInfo(yDirs);
+        const ArrayInfo& zdInfo = getInfo(zDirs);
 
         af::dim4 xdDims  = xdInfo.dims();
         af::dim4 ydDims  = ydInfo.dims();
@@ -211,8 +211,8 @@ af_err vectorFieldWrapper(const af_window wind,
     }
 
     try {
-        ArrayInfo xpInfo = getInfo(xPoints);
-        ArrayInfo ypInfo = getInfo(yPoints);
+        const ArrayInfo& xpInfo = getInfo(xPoints);
+        const ArrayInfo& ypInfo = getInfo(yPoints);
 
         af::dim4 xpDims  = xpInfo.dims();
         af::dim4 ypDims  = ypInfo.dims();
@@ -220,8 +220,8 @@ af_err vectorFieldWrapper(const af_window wind,
         af_dtype xpType  = xpInfo.getType();
         af_dtype ypType  = ypInfo.getType();
 
-        ArrayInfo xdInfo = getInfo(xDirs);
-        ArrayInfo ydInfo = getInfo(yDirs);
+        const ArrayInfo& xdInfo = getInfo(xDirs);
+        const ArrayInfo& ydInfo = getInfo(yDirs);
 
         af::dim4 xdDims  = xdInfo.dims();
         af::dim4 ydDims  = ydInfo.dims();
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 30cb26ffbd..61bef67136 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -29,7 +29,7 @@ static inline af_array where(const af_array in)
 af_err af_where(af_array *idx, const af_array in)
 {
     try {
-        ArrayInfo i_info = getInfo(in);
+        const ArrayInfo& i_info = getInfo(in);
         af_dtype type = i_info.getType();
 
         if(i_info.ndims() == 0) {
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index 85386b2a6b..188196d3a3 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -37,7 +37,7 @@ af_err af_wrap(af_array *out, const af_array in,
                const bool is_column)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
         af::dim4 idims = info.dims();
 
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index b3dd5ea7ea..98efe6d1dd 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -131,7 +131,7 @@ template<bool isYCbCr2RGB>
 af_err convert(af_array* out, const af_array& in, const af_ycc_std standard)
 {
     try {
-        ArrayInfo info = getInfo(in);
+        const ArrayInfo& info = getInfo(in);
         af_dtype iType = info.getType();
         af::dim4 inputDims = info.dims();
 

From 170fc389eb49ec58587e5a3e9e1ccf097a6df1ec Mon Sep 17 00:00:00 2001
From: Vardan Akopian <vakopian@sustain.net>
Date: Tue, 6 Dec 2016 17:26:56 -0800
Subject: [PATCH 1028/2677] only release the arrT array after printer is done
 using its ArrayInfo reference

---
 src/api/c/print.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 0b9c0323c8..dc3ad834c5 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -96,12 +96,12 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
     AF_CHECK(af_get_data_ptr(&data.front(), arrT));
     const ArrayInfo& infoT = getInfo(arrT);
 
+    printer(os, &data.front(), infoT, infoT.ndims() - 1, precision);
+
     if(transpose) {
         AF_CHECK(af_release_array(arrT));
     }
 
-    printer(os, &data.front(), infoT, infoT.ndims() - 1, precision);
-
     os.flags(backup);
 }
 

From fb964a4cf3c95e3961c110795292ed22678ef3b3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 8 Dec 2016 11:03:03 +0530
Subject: [PATCH 1029/2677] Fix input valid checks for sets related functions

---
 src/api/c/set.cpp | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index 097b608945..7fba2acf0c 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -29,10 +29,13 @@ af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
     try {
 
         ArrayInfo in_info = getInfo(in);
-        if(in_info.isEmpty()) {
+
+        if (in_info.isEmpty() || in_info.isScalar()) {
             return af_retain_array(out, in);
         }
+
         ARG_ASSERT(1, in_info.isVector());
+
         af_dtype type = in_info.getType();
 
         af_array res;
@@ -79,8 +82,8 @@ af_err af_set_union(af_array *out, const af_array first, const af_array second,
             return af_retain_array(out, first);
         }
 
-        ARG_ASSERT(1, first_info.isVector());
-        ARG_ASSERT(1, second_info.isVector());
+        ARG_ASSERT(1, (first_info.isVector() || first_info.isScalar()));
+        ARG_ASSERT(1, (second_info.isVector() || second_info.isScalar()));
 
         af_dtype first_type = first_info.getType();
         af_dtype second_type = second_info.getType();
@@ -129,8 +132,8 @@ af_err af_set_intersect(af_array *out, const af_array first, const af_array seco
             return af_retain_array(out, second);
         }
 
-        ARG_ASSERT(1, first_info.isVector());
-        ARG_ASSERT(1, second_info.isVector());
+        ARG_ASSERT(1, (first_info.isVector() || first_info.isScalar()));
+        ARG_ASSERT(1, (second_info.isVector() || second_info.isScalar()));
 
         af_dtype first_type = first_info.getType();
         af_dtype second_type = second_info.getType();

From 3301e25ef8413258622669675a9c4642af0d11bc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 8 Dec 2016 11:04:21 +0530
Subject: [PATCH 1030/2677] correct indentation in src/api/c/set.cpp

---
 src/api/c/set.cpp | 66 +++++++++++++++++++++++------------------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index 7fba2acf0c..f8c4eab33b 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -40,17 +40,17 @@ af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
 
         af_array res;
         switch(type) {
-        case f32: res = setUnique<float  >(in, is_sorted); break;
-        case f64: res = setUnique<double >(in, is_sorted); break;
-        case s32: res = setUnique<int    >(in, is_sorted); break;
-        case u32: res = setUnique<uint   >(in, is_sorted); break;
-        case s16: res = setUnique<short  >(in, is_sorted); break;
-        case u16: res = setUnique<ushort >(in, is_sorted); break;
-        case s64: res = setUnique<intl   >(in, is_sorted); break;
-        case u64: res = setUnique<uintl  >(in, is_sorted); break;
-        case b8:  res = setUnique<char   >(in, is_sorted); break;
-        case u8:  res = setUnique<uchar  >(in, is_sorted); break;
-        default: TYPE_ERROR(1, type);
+            case f32: res = setUnique<float  >(in, is_sorted); break;
+            case f64: res = setUnique<double >(in, is_sorted); break;
+            case s32: res = setUnique<int    >(in, is_sorted); break;
+            case u32: res = setUnique<uint   >(in, is_sorted); break;
+            case s16: res = setUnique<short  >(in, is_sorted); break;
+            case u16: res = setUnique<ushort >(in, is_sorted); break;
+            case s64: res = setUnique<intl   >(in, is_sorted); break;
+            case u64: res = setUnique<uintl  >(in, is_sorted); break;
+            case b8:  res = setUnique<char   >(in, is_sorted); break;
+            case u8:  res = setUnique<uchar  >(in, is_sorted); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
@@ -91,17 +91,17 @@ af_err af_set_union(af_array *out, const af_array first, const af_array second,
         ARG_ASSERT(1, first_type == second_type);
 
         switch(first_type) {
-        case f32: res = setUnion<float  >(first, second, is_unique); break;
-        case f64: res = setUnion<double >(first, second, is_unique); break;
-        case s32: res = setUnion<int    >(first, second, is_unique); break;
-        case u32: res = setUnion<uint   >(first, second, is_unique); break;
-        case s16: res = setUnion<short  >(first, second, is_unique); break;
-        case u16: res = setUnion<ushort >(first, second, is_unique); break;
-        case s64: res = setUnion<intl   >(first, second, is_unique); break;
-        case u64: res = setUnion<uintl  >(first, second, is_unique); break;
-        case b8:  res = setUnion<char   >(first, second, is_unique); break;
-        case u8:  res = setUnion<uchar  >(first, second, is_unique); break;
-        default: TYPE_ERROR(1, first_type);
+            case f32: res = setUnion<float  >(first, second, is_unique); break;
+            case f64: res = setUnion<double >(first, second, is_unique); break;
+            case s32: res = setUnion<int    >(first, second, is_unique); break;
+            case u32: res = setUnion<uint   >(first, second, is_unique); break;
+            case s16: res = setUnion<short  >(first, second, is_unique); break;
+            case u16: res = setUnion<ushort >(first, second, is_unique); break;
+            case s64: res = setUnion<intl   >(first, second, is_unique); break;
+            case u64: res = setUnion<uintl  >(first, second, is_unique); break;
+            case b8:  res = setUnion<char   >(first, second, is_unique); break;
+            case u8:  res = setUnion<uchar  >(first, second, is_unique); break;
+            default: TYPE_ERROR(1, first_type);
         }
 
         std::swap(*out, res);
@@ -142,17 +142,17 @@ af_err af_set_intersect(af_array *out, const af_array first, const af_array seco
 
         af_array res;
         switch(first_type) {
-        case f32: res = setIntersect<float  >(first, second, is_unique); break;
-        case f64: res = setIntersect<double >(first, second, is_unique); break;
-        case s32: res = setIntersect<int    >(first, second, is_unique); break;
-        case u32: res = setIntersect<uint   >(first, second, is_unique); break;
-        case s16: res = setIntersect<short  >(first, second, is_unique); break;
-        case u16: res = setIntersect<ushort >(first, second, is_unique); break;
-        case s64: res = setIntersect<intl   >(first, second, is_unique); break;
-        case u64: res = setIntersect<uintl  >(first, second, is_unique); break;
-        case b8:  res = setIntersect<char   >(first, second, is_unique); break;
-        case u8:  res = setIntersect<uchar  >(first, second, is_unique); break;
-        default: TYPE_ERROR(1, first_type);
+            case f32: res = setIntersect<float  >(first, second, is_unique); break;
+            case f64: res = setIntersect<double >(first, second, is_unique); break;
+            case s32: res = setIntersect<int    >(first, second, is_unique); break;
+            case u32: res = setIntersect<uint   >(first, second, is_unique); break;
+            case s16: res = setIntersect<short  >(first, second, is_unique); break;
+            case u16: res = setIntersect<ushort >(first, second, is_unique); break;
+            case s64: res = setIntersect<intl   >(first, second, is_unique); break;
+            case u64: res = setIntersect<uintl  >(first, second, is_unique); break;
+            case b8:  res = setIntersect<char   >(first, second, is_unique); break;
+            case u8:  res = setIntersect<uchar  >(first, second, is_unique); break;
+            default: TYPE_ERROR(1, first_type);
         }
 
         std::swap(*out, res);

From 6edba840ef3ab0c6893299aed30b880c048b3087 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 8 Dec 2016 11:46:07 -0500
Subject: [PATCH 1031/2677] Use boost shared ptr for temp memory in cuda sparse
 convert

---
 src/backend/cuda/sparse.cu | 78 ++++++++++++++------------------------
 1 file changed, 29 insertions(+), 49 deletions(-)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index ab2bde6ce1..96e430285c 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -14,6 +14,8 @@
 #include <stdexcept>
 #include <string>
 
+#include <boost/shared_ptr.hpp>
+
 #include <arith.hpp>
 #include <cast.hpp>
 #include <complex.hpp>
@@ -361,24 +363,10 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     return dense;
 }
 
-#define CUSPARSE_CHECK_FREE(fn) do {                    \
-        cusparseStatus_t _error = fn;                   \
-        if (_error != CUSPARSE_STATUS_SUCCESS) {        \
-            memFree(P);                                 \
-            memFree(pBuffer);                           \
-            char _err_msg[1024];                        \
-            snprintf(_err_msg, sizeof(_err_msg),        \
-                     "CUSPARSE Error (%d): %s\n",       \
-                     (int)(_error),                     \
-                     cusparse::errorString( _error));   \
-                                                        \
-            AF_ERROR(_err_msg, AF_ERR_INTERNAL);        \
-        }                                               \
-    } while(0)
-
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
+    using boost::shared_ptr;
     in.eval();
 
     int nNZ = in.getNNZ();
@@ -406,25 +394,22 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
                         in.dims()[0], in.dims()[1], nNZ,
                         converted.getRowIdx().get(), converted.getColIdx().get(),
                         &pBufferSizeInBytes));
-        char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
-
-        int *P = memAlloc<int>(nNZ);
-        CUSPARSE_CHECK_FREE(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+        shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
-        CUSPARSE_CHECK_FREE(cusparseXcoosortByColumn(
-                            getHandle(),
-                            in.dims()[0], in.dims()[1], nNZ,
-                            converted.getRowIdx().get(), converted.getColIdx().get(),
-                            P, (void*)pBuffer));
+        shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
+        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P.get()));
 
-        CUSPARSE_CHECK_FREE(gthr_func<T>()(
-                            getHandle(), nNZ,
-                            in.getValues().get(),
-                            converted.getValues().get(),
-                            P, CUSPARSE_INDEX_BASE_ZERO));
+        CUSPARSE_CHECK(cusparseXcoosortByColumn(
+                       getHandle(),
+                       in.dims()[0], in.dims()[1], nNZ,
+                       converted.getRowIdx().get(), converted.getColIdx().get(),
+                       P.get(), (void*)pBuffer.get()));
 
-        memFree(P);
-        memFree(pBuffer);
+        CUSPARSE_CHECK(gthr_func<T>()(
+                       getHandle(), nNZ,
+                       in.getValues().get(),
+                       converted.getValues().get(),
+                       P.get(), CUSPARSE_INDEX_BASE_ZERO));
 
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
         // The cusparse csr sort function is not behaving correctly.
@@ -444,25 +429,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
                             cooT.dims()[0], cooT.dims()[1], nNZ,
                             cooT.getRowIdx().get(), cooT.getColIdx().get(),
                             &pBufferSizeInBytes));
-            char *pBuffer = memAlloc<char>(pBufferSizeInBytes);
+            shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
-            int *P = memAlloc<int>(nNZ);
-            CUSPARSE_CHECK_FREE(cusparseCreateIdentityPermutation(getHandle(), nNZ, P));
+            shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
+            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P.get()));
 
-            CUSPARSE_CHECK_FREE(cusparseXcoosortByRow(
-                                getHandle(),
-                                cooT.dims()[0], cooT.dims()[1], nNZ,
-                                cooT.getRowIdx().get(), cooT.getColIdx().get(),
-                                P, (void*)pBuffer));
+            CUSPARSE_CHECK(cusparseXcoosortByRow(
+                           getHandle(),
+                           cooT.dims()[0], cooT.dims()[1], nNZ,
+                           cooT.getRowIdx().get(), cooT.getColIdx().get(),
+                           P.get(), (void*)pBuffer.get()));
 
-            CUSPARSE_CHECK_FREE(gthr_func<T>()(
-                                getHandle(), nNZ,
-                                in.getValues().get(),
-                                cooT.getValues().get(),
-                                P, CUSPARSE_INDEX_BASE_ZERO));
+            CUSPARSE_CHECK(gthr_func<T>()(
+                           getHandle(), nNZ,
+                           in.getValues().get(),
+                           cooT.getValues().get(),
+                           P.get(), CUSPARSE_INDEX_BASE_ZERO));
 
-            memFree(P);
-            memFree(pBuffer);
         }
 
         // Copy values and colIdx as is
@@ -493,9 +476,6 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
     return converted;
 }
 
-#undef CUSPARSE_CHECK_FREE
-
-
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \

From cd673346c75cc4b291729049e0b031237b4284e3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 9 Dec 2016 12:06:12 +0530
Subject: [PATCH 1032/2677] fix initilization of chart type in set axes titles
 fn

---
 src/api/c/window.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 90aad38f44..313caa013e 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -371,10 +371,12 @@ af_err af_set_axes_titles(const af_window wind,
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
         forge::Chart* chart = NULL;
+
         // The ctype here below doesn't really matter as it is only fetching
         // the chart. It will not set it.
         // If this is actually being done, then it is extremely bad.
-        fg_chart_type ctype = FG_CHART_2D;
+        // But lets have a check anyway.
+        fg_chart_type ctype = (ztitle == NULL || ztitle == 0) ? FG_CHART_2D : FG_CHART_3D;
 
         if (props->col > -1 && props->row > -1)
             chart = fgMngr.getChart(window, props->row, props->col, ctype);

From 07fca508ec4db618f66586811ab9030276596b68 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Mon, 5 Dec 2016 23:27:59 -0800
Subject: [PATCH 1033/2677] BUGFIX: Do not reset JIT tree height after eval

---
 src/backend/cpu/TNJ/BinaryNode.hpp    |  3 +--
 src/backend/cpu/TNJ/BufferNode.hpp    |  3 +--
 src/backend/cpu/TNJ/Node.hpp          |  7 +++----
 src/backend/cpu/TNJ/ScalarNode.hpp    |  3 +--
 src/backend/cpu/TNJ/UnaryNode.hpp     |  7 +++----
 src/backend/cuda/JIT/BinaryNode.hpp   |  3 +--
 src/backend/cuda/JIT/BufferNode.hpp   |  3 +--
 src/backend/cuda/JIT/Node.hpp         | 11 +++++------
 src/backend/cuda/JIT/ScalarNode.hpp   |  3 +--
 src/backend/cuda/JIT/UnaryNode.hpp    |  3 +--
 src/backend/opencl/JIT/BinaryNode.hpp |  3 +--
 src/backend/opencl/JIT/BufferNode.hpp |  3 +--
 src/backend/opencl/JIT/Node.hpp       | 10 +++++-----
 src/backend/opencl/JIT/ScalarNode.hpp |  3 +--
 src/backend/opencl/JIT/UnaryNode.hpp  |  3 +--
 test/jit.cpp                          | 14 ++++++++++++++
 16 files changed, 41 insertions(+), 41 deletions(-)

diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index 1f6d704799..f06581a395 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -40,12 +40,11 @@ namespace TNJ
 
     public:
         BinaryNode(Node_ptr lhs, Node_ptr rhs) :
-            Node(),
+            Node(std::max(lhs->getHeight(), rhs->getHeight()) + 1),
             m_lhs(lhs),
             m_rhs(rhs),
             m_val(0)
         {
-            m_height = std::max(m_lhs->getHeight(), m_rhs->getHeight()) + 1;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index 3e5a67366c..d995314ae7 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -39,7 +39,7 @@ namespace TNJ
                    const dim_t *dms,
                    const dim_t *strs,
                    const bool is_linear) :
-            Node(),
+            Node(0),
             ptr(data),
             m_bytes(bytes),
             m_linear_buffer(is_linear),
@@ -50,7 +50,6 @@ namespace TNJ
                 m_strides[i] = strs[i];
                 m_dims[i] = dms[i];
             }
-            m_height = 0;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index 1b7a402926..f4ed00bd5c 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -23,7 +23,7 @@ namespace TNJ
 
     protected:
 
-        int m_height;
+        const int m_height;
         int x, y, z, w;
         bool m_is_eval;
         bool m_linear;
@@ -32,7 +32,6 @@ namespace TNJ
 
         void resetCommonFlags()
         {
-            m_height = 0;
             x = -1;
             y = -1;
             z = -1;
@@ -60,8 +59,8 @@ namespace TNJ
         }
 
     public:
-        Node() :
-            m_height(0),
+        Node(const int height) :
+            m_height(height),
             x(-1),
             y(-1),
             z(-1),
diff --git a/src/backend/cpu/TNJ/ScalarNode.hpp b/src/backend/cpu/TNJ/ScalarNode.hpp
index fa6ec4b5e3..bda529f8d4 100644
--- a/src/backend/cpu/TNJ/ScalarNode.hpp
+++ b/src/backend/cpu/TNJ/ScalarNode.hpp
@@ -26,9 +26,8 @@ namespace TNJ
         T m_val;
 
     public:
-        ScalarNode(T val) : Node(), m_val(val)
+        ScalarNode(T val) : Node(0), m_val(val)
         {
-            m_height = 0;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 3edf399ec5..047151f3f7 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -38,12 +38,11 @@ namespace TNJ
         To m_val;
 
     public:
-        UnaryNode(Node_ptr in) :
-            Node(),
-            m_child(in),
+        UnaryNode(Node_ptr child) :
+            Node(child->getHeight() + 1),
+            m_child(child),
             m_val(0)
         {
-            m_height = m_child->getHeight() + 1;
         }
 
         void *calc(int x, int y, int z, int w)
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index 5bdf2a88b5..a07d83d111 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -29,14 +29,13 @@ namespace JIT
         BinaryNode(const char *out_type_str, const char *name_str,
                    const std::string &op_str,
                    Node_ptr lhs, Node_ptr rhs, int op, int call_type)
-            : Node(out_type_str, name_str),
+            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1),
               m_op_str(op_str),
               m_lhs(lhs),
               m_rhs(rhs),
               m_op(op),
               m_call_type(call_type)
         {
-            m_height = std::max(m_lhs->getHeight(), m_rhs->getHeight()) + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 1f1a25b485..69d6407e5b 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -39,9 +39,8 @@ namespace JIT
 
         BufferNode(const char *type_str,
                    const char *name_str)
-            : Node(type_str, name_str)
+            : Node(type_str, name_str, 0)
         {
-            m_height = 0;
         }
 
         void setData(Param<T> param, shared_ptr<T> data, const unsigned bytes, bool is_linear)
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index fa3b5191f4..c82c4e1d5e 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -26,10 +26,10 @@ namespace JIT
     class Node
     {
     protected:
-        std::string m_type_str;
-        std::string m_name_str;
+        const std::string m_type_str;
+        const std::string m_name_str;
         int m_id;
-        int m_height;
+        const int m_height;
         bool m_set_id;
         bool m_gen_func;
         bool m_gen_param;
@@ -43,7 +43,6 @@ namespace JIT
 
         void resetCommonFlags()
         {
-            m_height = 0;
             m_set_id = false;
             m_gen_func = false;
             m_gen_param = false;
@@ -57,11 +56,11 @@ namespace JIT
 
     public:
 
-        Node(const char *type_str, const char *name_str)
+        Node(const char *type_str, const char *name_str, const int height)
             : m_type_str(type_str),
               m_name_str(name_str),
               m_id(-1),
-              m_height(0),
+              m_height(height),
               m_set_id(false),
               m_gen_func(false),
               m_gen_param(false),
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index 0f9e88866d..f7a1e33d99 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -26,10 +26,9 @@ namespace JIT
     public:
 
         ScalarNode(T val)
-            : Node(irname<T>(), afShortName<T>(false)),
+            : Node(irname<T>(), afShortName<T>(false), 0),
               m_val(val)
         {
-            m_height = 0;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 2ef02547ca..a85e05371c 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -29,13 +29,12 @@ namespace JIT
         UnaryNode(const char *out_type_str, const char *name_str,
                   const std::string &op_str,
                   Node_ptr child, int op, bool is_check=false)
-            : Node(out_type_str, name_str),
+            : Node(out_type_str, name_str, child->getHeight() + 1),
               m_op_str(op_str),
               m_child(child),
               m_op(op),
               m_is_check(is_check)
         {
-            m_height = m_child->getHeight() + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index a812a7b297..5aa98810bd 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -28,13 +28,12 @@ namespace JIT
         BinaryNode(const char *out_type_str, const char *name_str,
                    const char *op_str,
                    Node_ptr lhs, Node_ptr rhs, int op)
-            : Node(out_type_str, name_str),
+            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1),
               m_op_str(op_str),
               m_lhs(lhs),
               m_rhs(rhs),
               m_op(op)
         {
-            m_height = std::max(m_lhs->getHeight(), m_rhs->getHeight()) + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index a83d5267a8..280c796a79 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -30,9 +30,8 @@ namespace JIT
 
         BufferNode(const char *type_str,
                    const char *name_str)
-            : Node(type_str, name_str)
+            : Node(type_str, name_str, 0)
         {
-            m_height = 0;
         }
 
         bool isBuffer() { return true; }
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index b3caab22d3..abf7a2d908 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -24,10 +24,10 @@ namespace JIT
     class Node
     {
     protected:
-        std::string m_type_str;
-        std::string m_name_str;
+        const std::string m_type_str;
+        const std::string m_name_str;
         int m_id;
-        int m_height;
+        const int m_height;
         bool m_set_id;
         bool m_gen_func;
         bool m_gen_param;
@@ -40,7 +40,6 @@ namespace JIT
     protected:
         void resetCommonFlags()
         {
-            m_height = 0;
             m_set_id = false;
             m_gen_func = false;
             m_gen_param = false;
@@ -53,10 +52,11 @@ namespace JIT
 
     public:
 
-        Node(const char *type_str, const char *name_str)
+        Node(const char *type_str, const char *name_str, const int height)
             : m_type_str(type_str),
               m_name_str(name_str),
               m_id(-1),
+              m_height(height),
               m_set_id(false),
               m_gen_func(false),
               m_gen_param(false),
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index fd8b3da94f..b172b67680 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -28,10 +28,9 @@ namespace JIT
     public:
 
         ScalarNode(T val)
-            : Node(dtype_traits<T>::getName(), shortname<T>(false)),
+            : Node(dtype_traits<T>::getName(), shortname<T>(false), 0),
               m_val(val)
         {
-            m_height = 0;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index cfb670cce8..5b9bdbc481 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -28,12 +28,11 @@ namespace JIT
         UnaryNode(const char *out_type_str, const char *name_str,
                   const char *op_str,
                   Node_ptr child, int op)
-            : Node(out_type_str, name_str),
+            : Node(out_type_str, name_str, child->getHeight() + 1),
               m_op_str(op_str),
               m_child(child),
               m_op(op)
         {
-            m_height = m_child->getHeight() + 1;
         }
 
         bool isLinear(dim_t dims[4])
diff --git a/test/jit.cpp b/test/jit.cpp
index 1cc20bc114..4beebc63f4 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -267,3 +267,17 @@ TEST(JIT, CPP_common_node)
         }
     }
 }
+
+TEST(JIT, ISSUE_1646)
+{
+    af::array test1 = af::randn(10, 10);
+    af::array test2 = af::randn(10);
+    af::array test3 = af::randn(10);
+
+    for (int i = 0; i < 1000; i++) {
+        test3 += af::sum(test1, 1);
+        test2 += test3;
+    }
+    af::eval(test2);
+    af::eval(test3);
+}

From a83950e87ec26d7936f7e3d5f978ae7635713cc0 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 11 Dec 2016 15:02:39 +0100
Subject: [PATCH 1034/2677] Added CLBlast to the CMake files

---
 CMakeModules/build_CLBlast.cmake  | 36 +++++++++++++++++++++++++++++++
 src/backend/opencl/CMakeLists.txt | 11 ++++++++++
 2 files changed, 47 insertions(+)
 create mode 100644 CMakeModules/build_CLBlast.cmake

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
new file mode 100644
index 0000000000..4b54a23f61
--- /dev/null
+++ b/CMakeModules/build_CLBlast.cmake
@@ -0,0 +1,36 @@
+INCLUDE(ExternalProject)
+
+SET(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
+SET(CLBlast_location ${prefix}/${CMAKE_STATIC_LIBRARY_PREFIX}/libclblast${CMAKE_STATIC_LIBRARY_SUFFIX})
+SET(byproducts ${clBLAS_location})
+
+ExternalProject_Add(
+    CLBlast-ext
+    GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
+    GIT_TAG 0.10.0
+    PREFIX "${prefix}"
+    INSTALL_DIR "${prefix}"
+    UPDATE_COMMAND ""
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>/
+    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+    "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+    "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
+    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+    -DBUILD_SHARED_LIBS:BOOL=OFF
+    -DSAMPLES:BOOL=OFF
+    -DTUNERS:BOOL=OFF
+    -DCLIENTS:BOOL=OFF
+    -DTESTS:BOOL=OFF
+    -DNETLIB:BOOL=OFF
+    ${byproducts}
+    )
+
+ExternalProject_Get_Property(CLBlast-ext install_dir)
+ADD_LIBRARY(CLBlast IMPORTED STATIC)
+SET_TARGET_PROPERTIES(CLBlast PROPERTIES IMPORTED_LOCATION ${CLBlast_location})
+ADD_DEPENDENCIES(CLBlast CLBlast-ext)
+SET(CLBLAST_INCLUDE_DIRS ${install_dir}/include)
+SET(CLBLAST_LIBRARIES CLBlast)
+SET(CLBLAST_FOUND ON)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 37655cfd36..25401fac49 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -60,6 +60,15 @@ ENDIF()
 ADD_DEFINITIONS(-DAF_OPENCL
                 -D__CL_ENABLE_EXCEPTIONS)
 
+OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
+IF(USE_SYSTEM_CLBLAST)
+    FIND_PACKAGE(CLBlast REQUIRED)
+ELSE()
+    INCLUDE(build_CLBlast)
+ENDIF()
+INCLUDE_DIRECTORIES(${CLBLAST_INCLUDE_DIRS})
+LINK_DIRECTORIES(${CLBLAST_LIBRARY_DIR})
+
 OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
 IF(USE_SYSTEM_CLBLAS)
     FIND_PACKAGE(clBLAS REQUIRED)
@@ -100,6 +109,7 @@ INCLUDE_DIRECTORIES(
     ${OpenCL_INCLUDE_DIRS}
     ${CL2HPP_INCLUDE_DIRECTORY}
     "${CMAKE_CURRENT_BINARY_DIR}"
+    ${CLBLAST_INCLUDE_DIRS}
     ${CLBLAS_INCLUDE_DIRS}
     ${CLFFT_INCLUDE_DIRS}
     ${Boost_INCLUDE_DIR}
@@ -319,6 +329,7 @@ ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
 
 TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${OpenCL_LIBRARIES}
+                                PRIVATE ${CLBLAST_LIBRARIES}
                                 PRIVATE ${CLBLAS_LIBRARIES}
                                 PRIVATE ${CLFFT_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}

From a1552cd4bd5ac94f58ffcbce560ad288f34bdd27 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 11 Dec 2016 15:10:06 +0100
Subject: [PATCH 1035/2677] Added CLBlast support to the OpenCL BLAS back-end

---
 src/backend/opencl/blas.cpp        | 91 +++++++++++++++++++++++++++-
 src/backend/opencl/blas.hpp        | 14 ++++-
 src/backend/opencl/err_clblast.hpp | 97 ++++++++++++++++++++++++++++++
 3 files changed, 200 insertions(+), 2 deletions(-)
 create mode 100644 src/backend/opencl/err_clblast.hpp

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 4045bdee97..4350ebd846 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -15,13 +15,21 @@
 #include <stdexcept>
 #include <mutex>
 #include <err_common.hpp>
-#include <err_clblas.hpp>
 #include <math.hpp>
 #include <transpose.hpp>
 #include <arith.hpp>
 #include <reduce.hpp>
 #include <complex.hpp>
 
+#if defined(USE_CLBLAS)
+#include <err_clblas.hpp>
+#elif defined(USE_CLBLAST)
+#include <complex>
+#include <err_clblast.hpp>
+#else
+#error "Define either USE_CLBLAS or USE_CLBLAST"
+#endif
+
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_blas.hpp>
 #endif
@@ -36,6 +44,9 @@ using std::call_once;
 using std::runtime_error;
 using std::to_string;
 
+// clBLAS specific helper functions and macro's
+#if defined(USE_CLBLAS)
+
 clblasTranspose
 toClblasTranspose(af_mat_prop opt)
 {
@@ -115,6 +126,40 @@ BLAS_FUNC(dot, cdouble, false, Z, u)
 #undef BLAS_FUNC_DEF
 #undef BLAS_FUNC
 
+#endif // USE_CLBLAS
+
+// CLBlast specific helpers
+#if defined(USE_CLBLAST)
+
+clblast::Transpose
+toClblastTranspose(af_mat_prop opt)
+{
+    auto out = clblast::Transpose::kNo;
+    switch(opt) {
+        case AF_MAT_NONE    : out = clblast::Transpose::kNo; break;
+        case AF_MAT_TRANS   : out = clblast::Transpose::kYes; break;
+        case AF_MAT_CTRANS  : out = clblast::Transpose::kConjugate; break;
+        default             : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+    return out;
+}
+
+// Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
+template <typename T> struct CLBlastConstant { using Type = T; };
+template <> struct CLBlastConstant<cfloat> { using Type = std::complex<float>; };
+template <> struct CLBlastConstant<cdouble> { using Type = std::complex<double>; };
+
+// Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
+template <typename T> typename CLBlastConstant<T>::Type toCLBlastConstant(const T val);
+
+// Specializations of the above function
+template <> float toCLBlastConstant(const float val) { return val; }
+template <> double toCLBlastConstant(const double val) { return val; }
+template <> std::complex<float> toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
+template <> std::complex<double> toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
+
+#endif // USE_CLBLAST
+
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
@@ -126,12 +171,24 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 #endif
 
     initBlas();
+
+#if defined(USE_CLBLAS)
     clblasTranspose lOpts = toClblasTranspose(optLhs);
     clblasTranspose rOpts = toClblasTranspose(optRhs);
 
     int aRowDim = (lOpts == clblasNoTrans) ? 0 : 1;
     int aColDim = (lOpts == clblasNoTrans) ? 1 : 0;
     int bColDim = (rOpts == clblasNoTrans) ? 1 : 0;
+#endif // USE_CLBLAS
+
+#if defined(USE_CLBLAST)
+    auto lOpts = toClblastTranspose(optLhs);
+    auto rOpts = toClblastTranspose(optRhs);
+
+    int aRowDim = (lOpts == clblast::Transpose::kNo) ? 0 : 1;
+    int aColDim = (lOpts == clblast::Transpose::kNo) ? 1 : 0;
+    int bColDim = (rOpts == clblast::Transpose::kNo) ? 1 : 0;
+#endif // USE_CLBLAST
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
@@ -149,6 +206,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     cl::Event event;
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
+#if defined(USE_CLBLAS)
         gemv_func<T> gemv;
         CLBLAS_CHECK(
             gemv(
@@ -161,7 +219,23 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 (*out.get())(),   out.getOffset(),             1,
                 1, &getQueue()(), 0, nullptr, &event())
             );
+#endif // USE_CLBLAS
+#if defined(USE_CLBLAST)
+        auto alpha_clblast = toCLBlastConstant(alpha);
+        auto beta_clblast = toCLBlastConstant(beta);
+        CLBLAST_CHECK(
+            clblast::Gemv(clblast::Layout::kColMajor, lOpts,
+                          lDims[0], lDims[1],
+                          alpha_clblast,
+                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
+                          (*rhs.get())(), rhs.getOffset(), rStrides[0],
+                          beta_clblast,
+                          (*out.get())(), out.getOffset(), 1,
+                          &getQueue()(), &event())
+        );
+#endif // USE_CLBLAST
     } else {
+#if defined(USE_CLBLAS)
         gemm_func<T> gemm;
         CLBLAS_CHECK(
             gemm(
@@ -174,6 +248,21 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 (*out.get())(),   out.getOffset(),  out.dims()[0],
                 1, &getQueue()(), 0, nullptr, &event())
             );
+#endif // USE_CLBLAS
+#if defined(USE_CLBLAST)
+        auto alpha_clblast = toCLBlastConstant(alpha);
+        auto beta_clblast = toCLBlastConstant(beta);
+        CLBLAST_CHECK(
+            clblast::Gemm(clblast::Layout::kColMajor, lOpts, rOpts,
+                          M, N, K,
+                          alpha_clblast,
+                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
+                          (*rhs.get())(), rhs.getOffset(), rStrides[1],
+                          beta_clblast,
+                          (*out.get())(), out.getOffset(), out.dims()[0],
+                          &getQueue()(), &event())
+        );
+#endif // USE_CLBLAST
 
     }
 
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index f6676abeff..dd99813ed9 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -9,9 +9,19 @@
 
 #pragma once
 #include <Array.hpp>
-#include <clBLAS.h>
 #include <mutex>
 
+// TODO: Temporary choose between clBLAS and CLBlast here
+#define USE_CLBLAS // or USE_CLBLAST
+
+#if defined(USE_CLBLAS)
+#include <clBLAS.h>
+#elif defined(USE_CLBLAST)
+#include <clblast.h>
+#else
+#error "Define either USE_CLBLAS or USE_CLBLAST"
+#endif
+
 namespace opencl
 {
 
@@ -24,7 +34,9 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
 
 STATIC_ void
 initBlas() {
+#if defined(USE_CLBLAS)
     static std::once_flag clblasSetupFlag;
     call_once(clblasSetupFlag, clblasSetup);
+#endif // USE_CLBLAS
 }
 }
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
new file mode 100644
index 0000000000..fae1722251
--- /dev/null
+++ b/src/backend/opencl/err_clblast.hpp
@@ -0,0 +1,97 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <stdio.h>
+#include <err_common.hpp>
+#include <clblast.h>
+
+static const char * _clblastGetResultString(clblast::StatusCode st)
+{
+    switch (st)
+    {
+    // Status codes in common with the OpenCL standard
+    case clblast::StatusCode::kSuccess:                    return "CL_SUCCESS";
+    case clblast::StatusCode::kOpenCLCompilerNotAvailable: return "CL_COMPILER_NOT_AVAILABLE";
+    case clblast::StatusCode::kTempBufferAllocFailure:     return "CL_MEM_OBJECT_ALLOCATION_FAILURE";
+    case clblast::StatusCode::kOpenCLOutOfResources:       return "CL_OUT_OF_RESOURCES";
+    case clblast::StatusCode::kOpenCLOutOfHostMemory:      return "CL_OUT_OF_HOST_MEMORY";
+    case clblast::StatusCode::kOpenCLBuildProgramFailure:  return "CL_BUILD_PROGRAM_FAILURE: OpenCL compilation error";
+    case clblast::StatusCode::kInvalidValue:               return "CL_INVALID_VALUE";
+    case clblast::StatusCode::kInvalidCommandQueue:        return "CL_INVALID_COMMAND_QUEUE";
+    case clblast::StatusCode::kInvalidMemObject:           return "CL_INVALID_MEM_OBJECT";
+    case clblast::StatusCode::kInvalidBinary:              return "CL_INVALID_BINARY";
+    case clblast::StatusCode::kInvalidBuildOptions:        return "CL_INVALID_BUILD_OPTIONS";
+    case clblast::StatusCode::kInvalidProgram:             return "CL_INVALID_PROGRAM";
+    case clblast::StatusCode::kInvalidProgramExecutable:   return "CL_INVALID_PROGRAM_EXECUTABLE";
+    case clblast::StatusCode::kInvalidKernelName:          return "CL_INVALID_KERNEL_NAME";
+    case clblast::StatusCode::kInvalidKernelDefinition:    return "CL_INVALID_KERNEL_DEFINITION";
+    case clblast::StatusCode::kInvalidKernel:              return "CL_INVALID_KERNEL";
+    case clblast::StatusCode::kInvalidArgIndex:            return "CL_INVALID_ARG_INDEX";
+    case clblast::StatusCode::kInvalidArgValue:            return "CL_INVALID_ARG_VALUE";
+    case clblast::StatusCode::kInvalidArgSize:             return "CL_INVALID_ARG_SIZE";
+    case clblast::StatusCode::kInvalidKernelArgs:          return "CL_INVALID_KERNEL_ARGS";
+    case clblast::StatusCode::kInvalidLocalNumDimensions:  return "CL_INVALID_WORK_DIMENSION: Too many thread dimensions";
+    case clblast::StatusCode::kInvalidLocalThreadsTotal:   return "CL_INVALID_WORK_GROUP_SIZE: Too many threads in total";
+    case clblast::StatusCode::kInvalidLocalThreadsDim:     return "CL_INVALID_WORK_ITEM_SIZE: ... or for a specific dimension";
+    case clblast::StatusCode::kInvalidGlobalOffset:        return "CL_INVALID_GLOBAL_OFFSET";
+    case clblast::StatusCode::kInvalidEventWaitList:       return "CL_INVALID_EVENT_WAIT_LIST";
+    case clblast::StatusCode::kInvalidEvent:               return "CL_INVALID_EVENT";
+    case clblast::StatusCode::kInvalidOperation:           return "CL_INVALID_OPERATION";
+    case clblast::StatusCode::kInvalidBufferSize:          return "CL_INVALID_BUFFER_SIZE";
+    case clblast::StatusCode::kInvalidGlobalWorkSize:      return "CL_INVALID_GLOBAL_WORK_SIZE";
+
+    // Status codes in common with the clBLAS library
+    case clblast::StatusCode::kNotImplemented:             return "Routine or functionality not implemented yet";
+    case clblast::StatusCode::kInvalidMatrixA:             return "Matrix A is not a valid OpenCL buffer";
+    case clblast::StatusCode::kInvalidMatrixB:             return "Matrix B is not a valid OpenCL buffer";
+    case clblast::StatusCode::kInvalidMatrixC:             return "Matrix C is not a valid OpenCL buffer";
+    case clblast::StatusCode::kInvalidVectorX:             return "Vector X is not a valid OpenCL buffer";
+    case clblast::StatusCode::kInvalidVectorY:             return "Vector Y is not a valid OpenCL buffer";
+    case clblast::StatusCode::kInvalidDimension:           return "Dimensions M, N, and K have to be larger than zero";
+    case clblast::StatusCode::kInvalidLeadDimA:            return "LD of A is smaller than the matrix's first dimension";
+    case clblast::StatusCode::kInvalidLeadDimB:            return "LD of B is smaller than the matrix's first dimension";
+    case clblast::StatusCode::kInvalidLeadDimC:            return "LD of C is smaller than the matrix's first dimension";
+    case clblast::StatusCode::kInvalidIncrementX:          return "Increment of vector X cannot be zero";
+    case clblast::StatusCode::kInvalidIncrementY:          return "Increment of vector Y cannot be zero";
+    case clblast::StatusCode::kInsufficientMemoryA:        return "Matrix A's OpenCL buffer is too small";
+    case clblast::StatusCode::kInsufficientMemoryB:        return "Matrix B's OpenCL buffer is too small";
+    case clblast::StatusCode::kInsufficientMemoryC:        return "Matrix C's OpenCL buffer is too small";
+    case clblast::StatusCode::kInsufficientMemoryX:        return "Vector X's OpenCL buffer is too small";
+    case clblast::StatusCode::kInsufficientMemoryY:        return "Vector Y's OpenCL buffer is too small";
+
+    // Custom additional status codes for CLBlast
+    case clblast::StatusCode::kInvalidLocalMemUsage:       return "Not enough local memory available on this device";
+    case clblast::StatusCode::kNoHalfPrecision:            return "Half precision (16-bits) not supported by the device";
+    case clblast::StatusCode::kNoDoublePrecision:          return "Double precision (64-bits) not supported by the device";
+    case clblast::StatusCode::kInvalidVectorScalar:        return "The unit-sized vector is not a valid OpenCL buffer";
+    case clblast::StatusCode::kInsufficientMemoryScalar:   return "The unit-sized vector's OpenCL buffer is too small";
+    case clblast::StatusCode::kDatabaseError:              return "Entry for the device was not found in the database";
+    case clblast::StatusCode::kUnknownError:               return "A catch-all error code representing an unspecified error";
+    case clblast::StatusCode::kUnexpectedError:            return "A catch-all error code representing an unexpected exception";
+    }
+
+    return "Unknown error";
+}
+
+#define CLBLAST_CHECK(fn) do {                              \
+        clblast::StatusCode _clblast_st = fn;               \
+        if (_clblast_st != clblast::StatusCode::kSuccess) { \
+            char clblast_st_msg[1024];                      \
+            snprintf(clblast_st_msg,                        \
+                     sizeof(clblast_st_msg),                \
+                     "CLBlast Error (%d): %s\n",            \
+                     (int)(_clblast_st),                    \
+                     _clblastGetResultString(               \
+                         _clblast_st));                     \
+                                                            \
+            AF_ERROR(clblast_st_msg,                        \
+                     AF_ERR_INTERNAL);                      \
+        }                                                   \
+    } while(0)

From f8590d48f9f5591b52bfeb2047e54bab82a655f1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 7 Dec 2016 17:50:06 +0530
Subject: [PATCH 1036/2677] fix memory leak in regions cpu backend

---
 src/backend/cpu/kernel/regions.hpp | 86 +++++++++++++++---------------
 1 file changed, 43 insertions(+), 43 deletions(-)

diff --git a/src/backend/cpu/kernel/regions.hpp b/src/backend/cpu/kernel/regions.hpp
index 95484d422d..837d772442 100644
--- a/src/backend/cpu/kernel/regions.hpp
+++ b/src/backend/cpu/kernel/regions.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Array.hpp>
+#include <memory.hpp>
 
 namespace cpu
 {
@@ -98,55 +99,54 @@ static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
 template<typename T>
 void regions(Array<T> out, const Array<char> in, af_connectivity connectivity)
 {
-    const af::dim4 in_dims = in.dims();
-    const char *in_ptr  = in.get();
-    T    *out_ptr = out.get();
+    const af::dim4 inDims = in.dims();
+    const char *inPtr  = in.get();
+    T *outPtr = out.get();
 
     // Map labels
-    typedef typename std::map<T, LabelNode<T>* > label_map_t;
-    typedef typename label_map_t::iterator label_map_iterator_t;
+    typedef typename std::unique_ptr< LabelNode<T> > UnqLabelPtr;
+    typedef typename std::map<T, UnqLabelPtr > LabelMap;
+    typedef typename LabelMap::iterator LabelMapIterator;
 
-    label_map_t lmap;
+    LabelMap lmap;
 
     // Initial label
     T label = (T)1;
 
-    for (int j = 0; j < (int)in_dims[1]; j++) {
-        for (int i = 0; i < (int)in_dims[0]; i++) {
-            int idx = j * in_dims[0] + i;
-            if (in_ptr[idx] != 0) {
+    for (int j = 0; j < (int)inDims[1]; j++) {
+        for (int i = 0; i < (int)inDims[0]; i++) {
+            int idx = j * inDims[0] + i;
+            if (inPtr[idx] != 0) {
                 std::vector<T> l;
 
                 // Test neighbors
-                if (i > 0 && out_ptr[j * (int)in_dims[0] + i-1] > 0)
-                    l.push_back(out_ptr[j * in_dims[0] + i-1]);
-                if (j > 0 && out_ptr[(j-1) * (int)in_dims[0] + i] > 0)
-                    l.push_back(out_ptr[(j-1) * in_dims[0] + i]);
+                if (i > 0 && outPtr[j * (int)inDims[0] + i-1] > 0)
+                    l.push_back(outPtr[j * inDims[0] + i-1]);
+                if (j > 0 && outPtr[(j-1) * (int)inDims[0] + i] > 0)
+                    l.push_back(outPtr[(j-1) * inDims[0] + i]);
                 if (connectivity == AF_CONNECTIVITY_8 && i > 0 &&
-                        j > 0 && out_ptr[(j-1) * in_dims[0] + i-1] > 0)
-                    l.push_back(out_ptr[(j-1) * in_dims[0] + i-1]);
+                        j > 0 && outPtr[(j-1) * inDims[0] + i-1] > 0)
+                    l.push_back(outPtr[(j-1) * inDims[0] + i-1]);
                 if (connectivity == AF_CONNECTIVITY_8 &&
-                        i < (int)in_dims[0] - 1 && j > 0 && out_ptr[(j-1) * in_dims[0] + i+1] != 0)
-                    l.push_back(out_ptr[(j-1) * in_dims[0] + i+1]);
+                        i < (int)inDims[0] - 1 && j > 0 && outPtr[(j-1) * inDims[0] + i+1] != 0)
+                    l.push_back(outPtr[(j-1) * inDims[0] + i+1]);
 
                 if (!l.empty()) {
                     T minl = l[0];
                     for (size_t k = 0; k < l.size(); k++) {
                         minl = min(l[k], minl);
-                        label_map_iterator_t cur_map = lmap.find(l[k]);
-                        LabelNode<T> *node = cur_map->second;
+                        LabelMapIterator currentMap = lmap.find(l[k]);
+                        LabelNode<T> *node = currentMap->second.get();
                         // Group labels of the same region under a disjoint set
                         for (size_t m = k+1; m < l.size(); m++)
-                            setUnion(node, lmap.find(l[m])->second);
+                            setUnion(node, lmap.find(l[m])->second.get());
                     }
                     // Set label to smallest neighbor label
-                    out_ptr[idx] = minl;
-                }
-                else {
+                    outPtr[idx] = minl;
+                } else {
                     // Insert new label in map
-                    LabelNode<T> *node = new LabelNode<T>(label);
-                    lmap.insert(std::pair<T, LabelNode<T>* >(label, node));
-                    out_ptr[idx] = label++;
+                    lmap.insert(std::make_pair(label, UnqLabelPtr(new LabelNode<T>(label))));
+                    outPtr[idx] = label++;
                 }
             }
         }
@@ -154,22 +154,22 @@ void regions(Array<T> out, const Array<char> in, af_connectivity connectivity)
 
     std::set<T> removed;
 
-    for (int j = 0; j < (int)in_dims[1]; j++) {
-        for (int i = 0; i < (int)in_dims[0]; i++) {
-            int idx = j * (int)in_dims[0] + i;
-            if (in_ptr[idx] != 0) {
-                T l = out_ptr[idx];
-                label_map_iterator_t cur_map = lmap.find(l);
+    for (int j = 0; j < (int)inDims[1]; j++) {
+        for (int i = 0; i < (int)inDims[0]; i++) {
+            int idx = j * (int)inDims[0] + i;
+            if (inPtr[idx] != 0) {
+                T l = outPtr[idx];
+                LabelMapIterator currentMap = lmap.find(l);
 
-                if (cur_map != lmap.end()) {
-                    LabelNode<T>* node = cur_map->second;
+                if (currentMap != lmap.end()) {
+                    LabelNode<T>* node = currentMap->second.get();
 
-                    LabelNode<T>* node_root = find(node);
-                    out_ptr[idx] = node_root->getMinLabel();
+                    LabelNode<T>* nodeRoot = find(node);
+                    outPtr[idx] = nodeRoot->getMinLabel();
 
                     // Mark removed labels (those that are part of a region
                     // that contains a smaller label)
-                    if (node->getMinLabel() < l || node_root->getMinLabel() < l)
+                    if (node->getMinLabel() < l || nodeRoot->getMinLabel() < l)
                         removed.insert(l);
                     if (node->getLabel() > node->getMinLabel())
                         removed.insert(node->getLabel());
@@ -179,11 +179,11 @@ void regions(Array<T> out, const Array<char> in, af_connectivity connectivity)
     }
 
     // Calculate final neighbors (ensure final labels are sequential)
-    for (int j = 0; j < (int)in_dims[1]; j++) {
-        for (int i = 0; i < (int)in_dims[0]; i++) {
-            int idx = j * (int)in_dims[0] + i;
-            if (out_ptr[idx] > 0) {
-                out_ptr[idx] -= distance(removed.begin(), removed.lower_bound(out_ptr[idx]));
+    for (int j = 0; j < (int)inDims[1]; j++) {
+        for (int i = 0; i < (int)inDims[0]; i++) {
+            int idx = j * (int)inDims[0] + i;
+            if (outPtr[idx] > 0) {
+                outPtr[idx] -= distance(removed.begin(), removed.lower_bound(outPtr[idx]));
             }
         }
     }

From 8d53153e7ffd8ab46bc7079cebfeb2cd28f73c6b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 8 Dec 2016 12:21:47 +0530
Subject: [PATCH 1037/2677] Delegate separable convolve to multiplication for
 scalar filters

---
 src/api/c/convolve.cpp | 25 +++++++++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index a6858ddfc1..93b8a7d9c8 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -9,6 +9,8 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/signal.h>
+#include <af/arith.h>
+#include <af/data.h>
 #include <handle.hpp>
 #include <err_common.hpp>
 #include <backend.hpp>
@@ -113,9 +115,28 @@ af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter, co
 
         af_dtype signalType  = sInfo.getType();
 
-        dim4 signalDims = sInfo.dims();
+        dim4 sdims = sInfo.dims();
+
+        ARG_ASSERT(1, (sdims.ndims()>=2));
+
+        if (cfInfo.isScalar() && rfInfo.isScalar()) {
+            af_array colArray = 0;
+            af_array rowArray = 0;
+            af_array filter = 0;
+
+            AF_CHECK(af_tile(&colArray, col_filter, sdims[0], sdims[1], sdims[2], sdims[3]));
+            AF_CHECK(af_tile(&rowArray, row_filter, sdims[0], sdims[1], sdims[2], sdims[3]));
+            AF_CHECK(af_mul (&filter, colArray, rowArray, false));
+
+            AF_CHECK(af_mul(out, signal, filter, false));
+
+            if (colArray!=0) AF_CHECK(af_release_array(colArray));
+            if (rowArray!=0) AF_CHECK(af_release_array(rowArray));
+            if (filter!=0)   AF_CHECK(af_release_array(filter));
+
+            return AF_SUCCESS;
+        }
 
-        ARG_ASSERT(1, (signalDims.ndims()>=2));
         ARG_ASSERT(2, cfInfo.isVector());
         ARG_ASSERT(3, rfInfo.isVector());
 

From 6dccbf8dd9d6a48ce528b3d58a450de073ff1f62 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Tue, 13 Dec 2016 21:37:38 -0800
Subject: [PATCH 1038/2677] Fixing MAX JIT length for OSX

---
 docs/pages/configuring_arrayfire_environment.md | 12 +++++++++---
 src/backend/opencl/platform.cpp                 |  4 ++++
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 33c5a39fe1..a16e2ff14f 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -167,14 +167,20 @@ When not set, the default value is 1000.
 AF_OPENCL_MAX_JIT_LEN {#af_opencl_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum height of the OpenCL JIT tree after which evaluation is forced. The default value for this is 100 as of v3.4 (20 for older versions).
+When set, this environment variable specifies the maximum height of the OpenCL JIT tree after which evaluation is forced.
+
+The default value, as of v3.4, is 50 on OSX, 100 everywhere else. This value was 20 for older versions.
 
 AF_CUDA_MAX_JIT_LEN {#af_cuda_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum height of the CUDA JIT tree after which evaluation is forced. The default value for this is 100 as of v3.4 (20 for older versions).
+When set, this environment variable specifies the maximum height of the CUDA JIT tree after which evaluation is forced.
+
+The default value, as of v3.4, 100. This value was 20 for older versions.
 
 AF_CPU_MAX_JIT_LEN {#af_cpu_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced. The default value for this is 100 as of v3.4 (20 for older versions).
+When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced.
+
+The default value, as of v3.4, 100. This value was 20 for older versions.
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index f3f59b73a3..cb4bcd73bd 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -836,7 +836,11 @@ bool synchronize_calls() {
 
 unsigned getMaxJitSize()
 {
+#if defined(OS_MAC)
+    const int MAX_JIT_LEN = 50;
+#else
     const int MAX_JIT_LEN = 100;
+#endif
 
     static int length = 0;
     if (length == 0) {

From 1ce3d1f276645b11c73dc411a206cacd4b7493de Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 14 Dec 2016 19:40:22 +0530
Subject: [PATCH 1039/2677] fix in ForgeManager::getChart function

---
 examples/graphics/plot3.cpp   |  2 +-
 src/api/c/graphics_common.cpp | 14 ++++++++------
 src/api/c/window.cpp          | 13 +++----------
 3 files changed, 12 insertions(+), 17 deletions(-)

diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index b58a6213c3..97f7f60e12 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -26,7 +26,7 @@ int main(int argc, char *argv[])
         static float t=0.1;
         array Z = seq( 0.1f, 10.f, PRECISION);
 
-        do{
+        do {
             array Y = sin((Z*t) + t) / Z;
             array X = cos((Z*t) + t) / Z;
             X = max(min(X, 1.0), -1.0);
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 85649dd276..705a91ec1b 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -178,13 +178,10 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
 {
     ChartMapIter iter = mChartMap.find(window);
 
-        if(iter != mChartMap.end()) {
-
-    }
-
     if(iter != mChartMap.end()) {
         // ChartVec found. Clear it.
-        // TODO: Should we clear this even if r = old_r and c = old_c?
+        // This has to be cleared as there is no guarantee that existing
+        // chart types(2D/3D) match the future grid requirements
         for(int i = 0; i < (int)(iter->second).size(); i++)
             if((iter->second)[i] != NULL) delete (iter->second)[i];
         (iter->second).clear();
@@ -212,10 +209,15 @@ forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, c
 
         chart = (iter->second)[c * gRows + r];
 
-        if(chart == NULL) {
+        if (chart == NULL) {
             // Chart has not been created
             chart = new forge::Chart(ctype);
             (iter->second)[c * gRows + r] = chart;
+        } else if (chart->getChartType()!=ctype) {
+            // Existing chart is of incompatible type
+            delete (iter->second)[c * gRows + r];
+            chart = new forge::Chart(ctype);
+            (iter->second)[c * gRows + r] = chart;
         }
     } else {
         // The chart map for this was never created
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 313caa013e..cab6bc2077 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -209,11 +209,8 @@ af_err af_set_axes_limits_compute(const af_window wind,
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
         forge::Chart* chart = NULL;
-        // The ctype here below doesn't really matter as it is only fetching
-        // the chart. It will not set it.
-        // If this is actually being done, then it is extremely bad.
-        // But lets have a check anyway.
-        fg_chart_type ctype = (z == NULL || z == 0) ? FG_CHART_2D : FG_CHART_3D;
+
+        fg_chart_type ctype = (z ? FG_CHART_3D : FG_CHART_2D);
 
         if (props->col > -1 && props->row > -1)
             chart = fgMngr.getChart(window, props->row, props->col, ctype);
@@ -372,11 +369,7 @@ af_err af_set_axes_titles(const af_window wind,
 
         forge::Chart* chart = NULL;
 
-        // The ctype here below doesn't really matter as it is only fetching
-        // the chart. It will not set it.
-        // If this is actually being done, then it is extremely bad.
-        // But lets have a check anyway.
-        fg_chart_type ctype = (ztitle == NULL || ztitle == 0) ? FG_CHART_2D : FG_CHART_3D;
+        fg_chart_type ctype = (ztitle ? FG_CHART_3D : FG_CHART_2D);
 
         if (props->col > -1 && props->row > -1)
             chart = fgMngr.getChart(window, props->row, props->col, ctype);

From d1e61c2b133a9b5bfbdc2a80357095cd41b0523b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 14 Dec 2016 21:48:53 +0530
Subject: [PATCH 1040/2677] fix memory leak in rgb ycbcr conversion fns

---
 src/api/c/ycbcr_rgb.cpp | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index b3dd5ea7ea..b34bcebf4c 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -10,7 +10,6 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/image.h>
-#include <af/index.h>
 #include <handle.hpp>
 #include <err_common.hpp>
 #include <backend.hpp>
@@ -85,18 +84,18 @@ static af_array convert(const af_array& in, const af_ycc_std standard)
 
     // extract three channels as three slices
     // prepare sequence objects
-    af_seq slice1[4] = { af_span, af_span, {0, 0, 1}, af_span };
-    af_seq slice2[4] = { af_span, af_span, {1, 1, 1}, af_span };
-    af_seq slice3[4] = { af_span, af_span, {2, 2, 1}, af_span };
-    // index the array for channels
-    af_array ch1Temp=0, ch2Temp=0, ch3Temp=0;
-    AF_CHECK(af_index(&ch1Temp, in, 4, slice1));
-    AF_CHECK(af_index(&ch2Temp, in, 4, slice2));
-    AF_CHECK(af_index(&ch3Temp, in, 4, slice3));
     // get Array objects for corresponding channel views
-    Array<T> X = getArray<T>(ch1Temp);
-    Array<T> Y = getArray<T>(ch2Temp);
-    Array<T> Z = getArray<T>(ch3Temp);
+    const Array<T>& input = getArray<T>(in);
+    std::vector<af_seq> indices(4, af_span);
+
+    indices[2] = {0, 0, 1};
+    Array<T> X = createSubArray(input, indices, false);
+
+    indices[2] = {1, 1, 1};
+    Array<T> Y = createSubArray(input, indices, false);
+
+    indices[2] = {2, 2, 1};
+    Array<T> Z = createSubArray(input, indices, false);
 
     if (isYCbCr2RGB) {
         dim4 dims = X.dims();

From 93bbcb7198ab15c77039e57c50a4f6021271816a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 14 Dec 2016 16:25:53 -0500
Subject: [PATCH 1041/2677] Remove support for sparse conversions for CUDA 6.5
 and older

---
 src/backend/cuda/sparse.cu | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 96e430285c..1d13f94ed0 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -363,6 +363,8 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     return dense;
 }
 
+// Some of the API used here is available only in CUDA 7 or newer
+#if CUDA_VERSION >= 7000
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
@@ -475,6 +477,14 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
     return converted;
 }
+#else // CUDA 6.5 and older (older than 7)
+template<typename T, af_storage dest, af_storage src>
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
+{
+    AF_ERROR("Sparse storage format conversions are not supported for CUDA 6.5 or older",
+             AF_ERR_NOT_SUPPORTED);
+}
+#endif // CUDA_VERSION >= 7000
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \

From 3eb23bdf9814d9a979b5a3851fcfdd1a008a0935 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 15 Dec 2016 12:52:11 -0500
Subject: [PATCH 1042/2677] Fixes for sparse convert tests failing on AMD
 devices

---
 src/backend/opencl/kernel/csr2coo.cl |  2 +-
 src/backend/opencl/kernel/sparse.hpp | 13 +++++--------
 test/sparse_convert.cpp              |  5 ++---
 3 files changed, 8 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/kernel/csr2coo.cl b/src/backend/opencl/kernel/csr2coo.cl
index 350e90d1af..98eabfb432 100644
--- a/src/backend/opencl/kernel/csr2coo.cl
+++ b/src/backend/opencl/kernel/csr2coo.cl
@@ -18,7 +18,7 @@ void csr2coo(__global       int *orowidx,
     for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
         int colStart = irowidx[rowId];
         int colEnd   = irowidx[rowId + 1];
-        for (int colId = colStart + lid;  colId < colEnd; colId += THREADS) {
+        for (int colId = colStart + lid;  colId < colEnd; colId += get_local_size(0)) {
             orowidx[colId] = rowId;
             ocolidx[colId] = icolidx[colId];
         }
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 0a0af6c95e..20a03292e3 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -276,8 +276,8 @@ namespace opencl
 
                 std::call_once( compileFlags[device], [device] () {
                     std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName()
-                            << " -D THREADS=256"; // This threads is a dummy for compilation
+                    options << " -D T="        << dtype_traits<T>::getName();
+
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
                         options << " -D USE_DOUBLE";
@@ -319,9 +319,7 @@ namespace opencl
 
                 std::string ref_name =
                     std::string("csr2coo_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(threads);
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
                 auto idx = kernelCaches[device].find(ref_name);
@@ -331,7 +329,6 @@ namespace opencl
 
                     std::ostringstream options;
                     options << " -D T=" << dtype_traits<T>::getName();
-                    options << " -D THREADS=" << threads;
 
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
@@ -419,8 +416,8 @@ namespace opencl
 
                 std::call_once( compileFlags[device], [device] () {
                     std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName()
-                            << " -D THREADS=256"; // This threads is a dummy for compilation
+                    options << " -D T="        << dtype_traits<T>::getName();
+
                     if (std::is_same<T, double>::value ||
                         std::is_same<T, cdouble>::value) {
                         options << " -D USE_DOUBLE";
diff --git a/test/sparse_convert.cpp b/test/sparse_convert.cpp
index 6d52b71676..810a39b5a1 100644
--- a/test/sparse_convert.cpp
+++ b/test/sparse_convert.cpp
@@ -66,8 +66,6 @@ af::array makeSparse<cdouble>(af::array A, int factor)
 template<typename T, af_storage src, af_storage dest>
 void sparseConvertTester(const int m, const int n, int factor)
 {
-    af::deviceGC();
-
     if (noDoubleTests<T>()) return;
 
     af::array A = cpu_randu<T>(af::dim4(m, n));
@@ -105,7 +103,8 @@ void sparseConvertTester(const int m, const int n, int factor)
     af::array s2dColIdx = sparseGetColIdx(s2d);
 
     // Verify values
-    ASSERT_EQ(0, af::max<double>(af::abs(dValues - s2dValues)));
+    ASSERT_EQ(0, af::max<double>(af::real(dValues - s2dValues)));
+    ASSERT_EQ(0, af::max<double>(af::imag(dValues - s2dValues)));
 
     // Verify row and col indices
     ASSERT_EQ(0, af::max<int   >(dRowIdx - s2dRowIdx));

From 987d9761d33bde9ad45e519da2ce67e213953094 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 15 Dec 2016 15:02:53 -0500
Subject: [PATCH 1043/2677] Fix for COO->CSR OpenCL - Access out of bounds

---
 src/backend/opencl/kernel/csr2coo.cl | 13 +++++++++++--
 src/backend/opencl/kernel/sparse.hpp | 29 ++++------------------------
 src/backend/opencl/sparse.cpp        |  7 ++++++-
 3 files changed, 21 insertions(+), 28 deletions(-)

diff --git a/src/backend/opencl/kernel/csr2coo.cl b/src/backend/opencl/kernel/csr2coo.cl
index 98eabfb432..862b64d9bb 100644
--- a/src/backend/opencl/kernel/csr2coo.cl
+++ b/src/backend/opencl/kernel/csr2coo.cl
@@ -51,21 +51,30 @@ void csrReduce_kernel(__global       int *orowIdx,
 
     if(id >= nNZ) return;
 
+    // Read COO row indices
     int iRId  = irowIdx[id];
     int iRId1 = 0;
     if(id > 0) iRId1 = irowIdx[id - 1];
 
+    // If id is 0, then mark the edge cases of csrRow[0] and csrRow[M]
     if(id == 0) {
         orowIdx[id] = 0;
         orowIdx[M]  = nNZ;
     } else if(iRId1 != iRId) {
+        // If iRId1 and iRId are not same, that means the row has incremented
+        // For example, if iRId is 5 and iRId1 is 4, that means row 4 has
+        // ended and row 5 has begun at index id.
+        // We use the for-loop because there can be any number of empty rows
+        // between iRId1 and iRId, all of which should be marked by id
         for(int i = iRId1 + 1; i <= iRId; i++)
             orowIdx[i] = id;
     }
 
     // The last X rows are corner cases if they dont have any values
-    if(id > irowIdx[nNZ - 1] && orowIdx[id] == 0) {
-        orowIdx[id] = nNZ;
+    if(id < M) {
+        if(id > irowIdx[nNZ - 1] && orowIdx[id] == 0) {
+            orowIdx[id] = nNZ;
+        }
     }
 }
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 20a03292e3..7a6d3a7b5b 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -378,32 +378,14 @@ namespace opencl
         template<typename T>
         void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx,
                      const Param ivalues, const Param irowIdx, const Param icolIdx,
-                     Param index, const int M)
+                     Param index, Param rowCopy, const int M)
         {
             try {
-                cl::Buffer *colCopy = bufferAlloc(icolIdx.info.dims[0] * sizeof(int));
-                getQueue().enqueueCopyBuffer(*icolIdx.data, *colCopy, 0, 0, icolIdx.info.dims[0] * sizeof(int));
-
-                cl::Buffer *rowCopy = bufferAlloc(irowIdx.info.dims[0] * sizeof(int));
-                getQueue().enqueueCopyBuffer(*irowIdx.data, *rowCopy, 0, 0, irowIdx.info.dims[0] * sizeof(int));
-
-                int dims[] = {(int)irowIdx.info.dims[0],
-                              (int)irowIdx.info.dims[1],
-                              (int)irowIdx.info.dims[2],
-                              (int)irowIdx.info.dims[3]
-                             };
-                int strides[] = {(int)irowIdx.info.strides[0],
-                                 (int)irowIdx.info.strides[1],
-                                 (int)irowIdx.info.strides[2],
-                                 (int)irowIdx.info.strides[3]
-                                };
-                Param scP = makeParam((*rowCopy)(), irowIdx.info.offset, dims, strides);
-
                 // Now we need to sort this into column major
-                kernel::sort0ByKeyIterative<int, int>(scP, index, true);
+                kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
 
                 // Now use index to sort values and rows
-                kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, colCopy, index);
+                kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, icolIdx.data, index);
 
                 CL_DEBUG_FINISH(getQueue());
 
@@ -434,13 +416,10 @@ namespace opencl
                 NDRange global(irowIdx.info.dims[0], 1, 1);
 
                 csrReduceOp(EnqueueArgs(getQueue(), global),
-                            *orowIdx.data, *rowCopy, M, ovalues.info.dims[0]);
+                            *orowIdx.data, *rowCopy.data, M, ovalues.info.dims[0]);
 
                 CL_DEBUG_FINISH(getQueue());
 
-                bufferFree(colCopy);
-                bufferFree(rowCopy);
-
             } catch(cl::Error err) {
                 CL_TO_AF_ERROR(err);
             }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index e73a474baa..04d78b581e 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -150,7 +150,12 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         const Array<int> &irowIdx = in.getRowIdx();
         const Array<int> &icolIdx = in.getColIdx();
 
-        kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx, index, in.dims()[0]);
+        Array<int> rowCopy = copyArray<int>(irowIdx);
+        rowCopy.eval();
+
+        kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx,
+                           ivalues, irowIdx, icolIdx,
+                           index, rowCopy, in.dims()[0]);
 
     } else {
         // Should never come here

From 5b934294fa97bb91b9d90c6300c88ddb76324a52 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 15 Dec 2016 15:56:37 -0500
Subject: [PATCH 1044/2677] OpenCL: Use new caching method in sparse kernels

---
 src/backend/opencl/kernel/sparse.hpp | 44 ++++++++++++++++------------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 7a6d3a7b5b..d65b461be3 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -268,13 +268,15 @@ namespace opencl
                        const Param swapIdx)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   swapIndexProgs;
-                static std::map<int, Kernel*>  swapIndexKernels;
+                std::string ref_name =
+                    std::string("swapIndex_kernel_") +
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
 
-                std::call_once( compileFlags[device], [device] () {
+                if (idx == kernelCaches[device].end()) {
                     std::ostringstream options;
                     options << " -D T="        << dtype_traits<T>::getName();
 
@@ -282,15 +284,18 @@ namespace opencl
                         std::is_same<T, cdouble>::value) {
                         options << " -D USE_DOUBLE";
                     }
+
                     Program prog;
                     buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-                    swapIndexProgs[device]   = new Program(prog);
-                    swapIndexKernels[device] = new Kernel(*swapIndexProgs[device], "swapIndex_kernel");
-                });
+                    entry.prog   = new Program(prog);
+                    entry.ker = new Kernel(*entry.prog, "swapIndex_kernel");
+                } else {
+                    entry = idx->second;
+                };
 
                 auto swapIndexOp = KernelFunctor<Buffer, Buffer,
                                                  const Buffer, const Buffer, const Buffer,
-                                                 const int> (*swapIndexKernels[device]);
+                                                 const int> (*entry.ker);
 
                 NDRange global(ovalues.info.dims[0], 1, 1);
 
@@ -389,14 +394,15 @@ namespace opencl
 
                 CL_DEBUG_FINISH(getQueue());
 
-                // Do row compression
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   csrReduceProgs;
-                static std::map<int, Kernel*>  csrReduceKernels;
+                std::string ref_name =
+                    std::string("csrReduce_kernel_") +
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
 
-                std::call_once( compileFlags[device], [device] () {
+                if (idx == kernelCaches[device].end()) {
                     std::ostringstream options;
                     options << " -D T="        << dtype_traits<T>::getName();
 
@@ -404,14 +410,16 @@ namespace opencl
                         std::is_same<T, cdouble>::value) {
                         options << " -D USE_DOUBLE";
                     }
+
                     Program prog;
                     buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-                    csrReduceProgs[device]   = new Program(prog);
-                    csrReduceKernels[device] = new Kernel(*csrReduceProgs[device], "csrReduce_kernel");
-                });
+                    entry.prog   = new Program(prog);
+                    entry.ker = new Kernel(*entry.prog, "csrReduce_kernel");
+                } else {
+                    entry = idx->second;
+                };
 
-                auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int>
-                                   (*csrReduceKernels[device]);
+                auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int> (*entry.ker);
 
                 NDRange global(irowIdx.info.dims[0], 1, 1);
 

From eff3d459da7e4d36f31d4e0e99e695bb70dd5ad3 Mon Sep 17 00:00:00 2001
From: Vardan Akopian <vakopian@sustain.net>
Date: Thu, 15 Dec 2016 15:10:14 -0800
Subject: [PATCH 1045/2677] adjust line break char

---
 src/api/c/array.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index c1a72aed1c..1e3a15515b 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -368,7 +368,7 @@ af_err af_get_numdims(unsigned *nd, const af_array in)
     af_err fn1(bool *result, const af_array in)                 \
     {                                                           \
         try {                                                   \
-            const ArrayInfo& info = getInfo(in, false, false);         \
+            const ArrayInfo& info = getInfo(in, false, false);  \
             *result = info.fn2();                               \
         }                                                       \
         CATCHALL                                                \

From bff51dbbb126f9434600d130870af1f552d51b61 Mon Sep 17 00:00:00 2001
From: Joe Petviashvili <hub@gnom.us>
Date: Sun, 11 Dec 2016 00:14:09 -0800
Subject: [PATCH 1046/2677] add missing CholeskyInPlace example

Signed-off-by: Shehzan Mohammed <shehzan@arrayfire.com>
---
 examples/lin_algebra/cholesky.cpp | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/examples/lin_algebra/cholesky.cpp b/examples/lin_algebra/cholesky.cpp
index 3154c65a61..617ba1b74b 100644
--- a/examples/lin_algebra/cholesky.cpp
+++ b/examples/lin_algebra/cholesky.cpp
@@ -28,8 +28,14 @@ int main(int argc, char *argv[])
         af_print(in);
 
         printf("Running Cholesky InPlace\n");
-        array cin = in.copy();
-        af_print(cin);
+        array cin_upper = in.copy();
+        array cin_lower = in.copy();
+
+        choleskyInPlace(cin_upper, true);
+        choleskyInPlace(cin_lower, false);
+
+        af_print(cin_upper);
+        af_print(cin_lower);
 
         printf("Running Cholesky Out of place\n");
         array out_upper;

From 7f5981f6a3cab26096228f5a5f4f57fc6e3faa5f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 17 Dec 2016 11:21:13 +0530
Subject: [PATCH 1047/2677] fix potential memory leaks at src/api/c level

Replaced C-API usage at `src/api/c` level in convolve with usage of
internal(detail namespace) functions from backends for the following
API features

* separable convolution
* vector field functions
---
 src/api/c/convolve.cpp     | 52 ++++++++++++---------------
 src/api/c/vector_field.cpp | 73 ++++++++++++++++++++++++--------------
 2 files changed, 69 insertions(+), 56 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 5b19aa88fe..6fc99bd2a8 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -9,9 +9,11 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/signal.h>
-#include <af/arith.h>
 #include <af/data.h>
 #include <handle.hpp>
+#include <arith.hpp>
+#include <cast.hpp>
+#include <tile.hpp>
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <convolve.hpp>
@@ -31,9 +33,23 @@ inline static af_array convolve(const af_array &s, const af_array &f, AF_BATCH_K
 template<typename T, typename accT, bool expand>
 inline static af_array convolve2(const af_array &s, const af_array &c_f, const af_array &r_f)
 {
-    return getHandle(convolve2<T, accT, expand>(getArray<T>(s),
-                                                castArray<accT>(c_f),
-                                                castArray<accT>(r_f)));
+    const Array<accT> colFilter = castArray<accT>(c_f);
+    const Array<accT> rowFilter = castArray<accT>(r_f);
+    const Array<accT> signal    = castArray<accT>(s);
+
+    if (colFilter.isScalar() && rowFilter.isScalar()) {
+        Array<accT> colArray = detail::tile(colFilter, signal.dims());
+        Array<accT> rowArray = detail::tile(rowFilter, signal.dims());
+
+        Array<accT> filter = arithOp<accT, af_mul_t>(colArray, rowArray, signal.dims());
+
+        return getHandle(cast<T, accT>(arithOp<accT, af_mul_t>(signal, filter, signal.dims())));
+    }
+
+    ARG_ASSERT(2, colFilter.isVector());
+    ARG_ASSERT(3, rowFilter.isVector());
+
+    return getHandle(convolve2<T, accT, expand>(getArray<T>(s), colFilter, rowFilter));
 }
 
 template<dim_t baseDim>
@@ -110,37 +126,15 @@ af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter, co
 {
     try {
         const ArrayInfo& sInfo = getInfo(signal);
-        const ArrayInfo& cfInfo= getInfo(col_filter);
-        const ArrayInfo& rfInfo= getInfo(row_filter);
 
-        af_dtype signalType  = sInfo.getType();
+        const dim4& sdims = sInfo.dims();
 
-        dim4 sdims = sInfo.dims();
+        const af_dtype signalType  = sInfo.getType();
 
         ARG_ASSERT(1, (sdims.ndims()>=2));
 
-        if (cfInfo.isScalar() && rfInfo.isScalar()) {
-            af_array colArray = 0;
-            af_array rowArray = 0;
-            af_array filter = 0;
+        af_array output = 0;
 
-            AF_CHECK(af_tile(&colArray, col_filter, sdims[0], sdims[1], sdims[2], sdims[3]));
-            AF_CHECK(af_tile(&rowArray, row_filter, sdims[0], sdims[1], sdims[2], sdims[3]));
-            AF_CHECK(af_mul (&filter, colArray, rowArray, false));
-
-            AF_CHECK(af_mul(out, signal, filter, false));
-
-            if (colArray!=0) AF_CHECK(af_release_array(colArray));
-            if (rowArray!=0) AF_CHECK(af_release_array(rowArray));
-            if (filter!=0)   AF_CHECK(af_release_array(filter));
-
-            return AF_SUCCESS;
-        }
-
-        ARG_ASSERT(2, cfInfo.isVector());
-        ARG_ASSERT(3, rfInfo.isVector());
-
-        af_array output;
         switch(signalType) {
             case c32: output = convolve2<cfloat ,  cfloat, expand>(signal, col_filter, row_filter); break;
             case c64: output = convolve2<cdouble, cdouble, expand>(signal, col_filter, row_filter); break;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 23803c7ec4..47d32012fb 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -14,11 +14,15 @@
 #include <graphics_common.hpp>
 #include <err_common.hpp>
 #include <backend.hpp>
-#include <vector_field.hpp>
+#include <handle.hpp>
+#include <join.hpp>
 #include <reduce.hpp>
 #include <transpose.hpp>
-#include <handle.hpp>
+#include <vector_field.hpp>
+
+#include <vector>
 
+using std::vector;
 using af::dim4;
 using namespace detail;
 
@@ -27,11 +31,20 @@ using namespace graphics;
 
 template<typename T>
 forge::Chart* setup_vector_field(const forge::Window* const window,
-                                 const af_array points, const af_array directions,
+                                 const vector<af_array>& points, const vector<af_array>& directions,
                                  const af_cell* const props, const bool transpose_ = true)
 {
-    Array<T> pIn = getArray<T>(points);
-    Array<T> dIn = getArray<T>(directions);
+    vector< Array<T> > pnts;
+    vector< Array<T> > dirs;
+
+    for (unsigned i=0; i<points.size(); ++i) {
+        pnts.push_back(getArray<T>(points[i]));
+        dirs.push_back(getArray<T>(directions[i]));
+    }
+
+    // Join for set up vector
+    Array<T> pIn = detail::join(1, pnts);
+    Array<T> dIn = detail::join(1, dirs);
 
     // do transpose if required
     if(transpose_) {
@@ -130,13 +143,19 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
 
         forge::Chart* chart = NULL;
 
+        vector<af_array> pnts;
+        pnts.push_back(points);
+
+        vector<af_array> dirs;
+        dirs.push_back(directions);
+
         switch(pType) {
-            case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
-            case s32: chart = setup_vector_field<int    >(window, points, directions, props); break;
-            case u32: chart = setup_vector_field<uint   >(window, points, directions, props); break;
-            case s16: chart = setup_vector_field<short  >(window, points, directions, props); break;
-            case u16: chart = setup_vector_field<ushort >(window, points, directions, props); break;
-            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
+            case f32: chart = setup_vector_field<float  >(window, pnts, dirs, props); break;
+            case s32: chart = setup_vector_field<int    >(window, pnts, dirs, props); break;
+            case u32: chart = setup_vector_field<uint   >(window, pnts, dirs, props); break;
+            case s16: chart = setup_vector_field<short  >(window, pnts, dirs, props); break;
+            case u16: chart = setup_vector_field<ushort >(window, pnts, dirs, props); break;
+            case u8 : chart = setup_vector_field<uchar  >(window, pnts, dirs, props); break;
             default:  TYPE_ERROR(1, pType);
         }
 
@@ -208,12 +227,15 @@ af_err vectorFieldWrapper(const af_window wind,
 
         forge::Chart* chart = NULL;
 
-        // Join for set up vector
-        af_array points = 0, directions = 0;
-        af_array pIn[] = {xPoints, yPoints, zPoints};
-        af_array dIn[] = {xDirs, yDirs, zDirs};
-        AF_CHECK(af_join_many(&points, 1, 3, pIn));
-        AF_CHECK(af_join_many(&directions, 1, 3, dIn));
+        vector<af_array> points;
+        points.push_back(xPoints);
+        points.push_back(yPoints);
+        points.push_back(zPoints);
+
+        vector<af_array> directions;
+        directions.push_back(xDirs);
+        directions.push_back(yDirs);
+        directions.push_back(zDirs);
 
         switch(xpType) {
             case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
@@ -230,9 +252,6 @@ af_err vectorFieldWrapper(const af_window wind,
             window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
-
-        AF_CHECK(af_release_array(points));
-        AF_CHECK(af_release_array(directions));
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -286,10 +305,13 @@ af_err vectorFieldWrapper(const af_window wind,
 
         forge::Chart* chart = NULL;
 
-        // Join for set up vector
-        af_array points = 0, directions = 0;
-        AF_CHECK(af_join(&points, 1, xPoints, yPoints));
-        AF_CHECK(af_join(&directions, 1, xDirs, yDirs));
+        vector<af_array> points;
+        points.push_back(xPoints);
+        points.push_back(yPoints);
+
+        vector<af_array> directions;
+        directions.push_back(xDirs);
+        directions.push_back(yDirs);
 
         switch(xpType) {
             case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
@@ -306,9 +328,6 @@ af_err vectorFieldWrapper(const af_window wind,
             window->draw(props->row, props->col, *chart, props->title);
         else
             window->draw(*chart);
-
-        AF_CHECK(af_release_array(points));
-        AF_CHECK(af_release_array(directions));
     }
     CATCHALL;
     return AF_SUCCESS;

From 2f5a9a49541e284c36b1a802e023eff8174e6eca Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 19 Dec 2016 21:07:04 +0530
Subject: [PATCH 1048/2677] performance fixes in fftconvolve kernels

---
 src/backend/cpu/kernel/fftconvolve.hpp        | 21 ++++++-------------
 src/backend/cuda/kernel/fftconvolve.hpp       |  6 +++---
 .../opencl/kernel/fftconvolve_multiply.cl     | 21 ++++++-------------
 3 files changed, 15 insertions(+), 33 deletions(-)

diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index b8b3696fd5..f0600a9abc 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -114,11 +114,8 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         T c = in2_ptr[ridx];
                         T d = in2_ptr[iidx];
 
-                        T ac = a*c;
-                        T bd = b*d;
-
-                        out_ptr[ridx] = ac - bd;
-                        out_ptr[iidx] = (a+b) * (c+d) - ac - bd;
+                        out_ptr[ridx] = a*c - b*d;
+                        out_ptr[iidx] = a*d + b*c;
                     }
                     else if (kind == AF_BATCH_LHS) {
                         // Complex multiply all signals to filter
@@ -132,11 +129,8 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         T c = in2_ptr[ridx2];
                         T d = in2_ptr[iidx2];
 
-                        T ac = a*c;
-                        T bd = b*d;
-
-                        out_ptr[ridx1] = ac - bd;
-                        out_ptr[iidx1] = (a+b) * (c+d) - ac - bd;
+                        out_ptr[ridx1] = a*c - b*d;
+                        out_ptr[iidx1] = a*d + b*c;
                     }
                     else if (kind == AF_BATCH_RHS) {
                         // Complex multiply signal to all filters
@@ -150,11 +144,8 @@ void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         T c = in2_ptr[ridx2];
                         T d = in2_ptr[iidx2];
 
-                        T ac = a*c;
-                        T bd = b*d;
-
-                        out_ptr[ridx2] = ac - bd;
-                        out_ptr[iidx2] = (a+b) * (c+d) - ac - bd;
+                        out_ptr[ridx2] = a*c - b*d;
+                        out_ptr[iidx2] = a*d + b*c;
                     }
                 }
             }
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 5b926ba6da..d7412452a0 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -150,7 +150,7 @@ __global__ void complexMultiply(
         convT c2 = in2.ptr[ridx];
 
         out.ptr[ridx].x = c1.x*c2.x - c1.y*c2.y;
-        out.ptr[ridx].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
+        out.ptr[ridx].y = c1.x*c2.y + c1.y*c2.x;
     }
     else if (kind == AF_BATCH_LHS) {
         // Complex multiply all signals to filter
@@ -161,7 +161,7 @@ __global__ void complexMultiply(
         convT c2 = in2.ptr[ridx2];
 
         out.ptr[ridx1].x = c1.x*c2.x - c1.y*c2.y;
-        out.ptr[ridx1].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
+        out.ptr[ridx1].y = c1.x*c2.y + c1.y*c2.x;
     }
     else if (kind == AF_BATCH_RHS) {
         // Complex multiply signal to all filters
@@ -172,7 +172,7 @@ __global__ void complexMultiply(
         convT c2 = in2.ptr[ridx2];
 
         out.ptr[ridx2].x = c1.x*c2.x - c1.y*c2.y;
-        out.ptr[ridx2].y = (c1.x+c1.y) * (c2.x+c2.y) - c1.x*c2.x - c1.y*c2.y;
+        out.ptr[ridx2].y = c1.x*c2.y + c1.y*c2.x;
     }
 }
 
diff --git a/src/backend/opencl/kernel/fftconvolve_multiply.cl b/src/backend/opencl/kernel/fftconvolve_multiply.cl
index d0310ca0d2..6ff7a1162d 100644
--- a/src/backend/opencl/kernel/fftconvolve_multiply.cl
+++ b/src/backend/opencl/kernel/fftconvolve_multiply.cl
@@ -33,11 +33,8 @@ void complex_multiply(
         CONVT c = d_in2[i2Info.offset + ridx];
         CONVT d = d_in2[i2Info.offset + iidx];
 
-        CONVT ac = a*c;
-        CONVT bd = b*d;
-
-        d_out[oInfo.offset + ridx] = ac - bd;
-        d_out[oInfo.offset + iidx] = (a+b) * (c+d) - ac - bd;
+        d_out[oInfo.offset + ridx] = a*c - b*d;
+        d_out[oInfo.offset + iidx] = a*d + b*c;
     }
     else if (kind == AF_BATCH_LHS) {
         // Complex multiply all signals to filter
@@ -54,11 +51,8 @@ void complex_multiply(
         CONVT c = d_in2[i2Info.offset + ridx2];
         CONVT d = d_in2[i2Info.offset + iidx2];
 
-        CONVT ac = a*c;
-        CONVT bd = b*d;
-
-        d_out[oInfo.offset + ridx1] = ac - bd;
-        d_out[oInfo.offset + iidx1] = (a+b) * (c+d) - ac - bd;
+        d_out[oInfo.offset + ridx1] = a*c - b*d;
+        d_out[oInfo.offset + iidx1] = a*d + b*c;
     }
     else if (kind == AF_BATCH_RHS) {
         // Complex multiply signal to all filters
@@ -75,10 +69,7 @@ void complex_multiply(
         CONVT c = d_in2[i2Info.offset + ridx2];
         CONVT d = d_in2[i2Info.offset + iidx2];
 
-        CONVT ac = a*c;
-        CONVT bd = b*d;
-
-        d_out[oInfo.offset + ridx2] = ac - bd;
-        d_out[oInfo.offset + iidx2] = (a+b) * (c+d) - ac - bd;
+        d_out[oInfo.offset + ridx2] = a*c - b*d;
+        d_out[oInfo.offset + iidx2] = a*d + b*c;
     }
 }

From 5a561b794a9d7000bb8de0290243acd089063206 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 19 Dec 2016 14:05:30 -0500
Subject: [PATCH 1049/2677] Increment forge version/tag to 0.9.2

---
 CMakeModules/build_forge.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index efd593e601..3187d6c6ca 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -42,13 +42,13 @@ ELSE()
     SET(byproducts BYPRODUCTS ${forge_location})
 ENDIF()
 
-SET(FORGE_VERSION 0.9.1)
+SET(FORGE_VERSION 0.9.2)
 
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG devel
+    GIT_TAG v${FORGE_VERSION}
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From a05206c077561095bf35390154826fe30073d348 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 12 Dec 2016 18:00:47 +0530
Subject: [PATCH 1050/2677] Modify CPU backend device management to be thread
 safe

---
 src/backend/cpu/platform.cpp | 174 ++++++++++++-----------------------
 src/backend/cpu/platform.hpp | 130 +++++++++++++++++++++++---
 2 files changed, 175 insertions(+), 129 deletions(-)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 5afd8316bf..105346ea37 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -9,85 +9,15 @@
 
 #include <af/version.h>
 #include <platform.hpp>
-#include <sstream>
-#include <queue.hpp>
-#include <array>
-#include <algorithm>
-#include <iostream>
-#include <string>
 #include <defines.hpp>
 #include <version.hpp>
-#include <queue.hpp>
 #include <host_memory.hpp>
-#include <cctype>
-
-
-#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
-#define CPUID_CAPABLE USE_CPUID
-#else
-#define CPUID_CAPABLE 0
-#endif
 
-#ifdef _WIN32
-#include <limits.h>
-#include <intrin.h>
-typedef unsigned __int32  uint32_t;
-#else
-#include <stdint.h>
-#endif
+#include <cctype>
+#include <sstream>
 
 using namespace std;
 
-#if CPUID_CAPABLE
-
-#define MAX_INTEL_TOP_LVL 4
-
-class CPUID {
-    uint32_t regs[4];
-
-    public:
-    explicit CPUID(unsigned funcId, unsigned subFuncId) {
-#ifdef _WIN32
-        __cpuidex((int *)regs, (int)funcId, (int)subFuncId);
-
-#else
-        asm volatile
-            ("cpuid" : "=a" (regs[0]), "=b" (regs[1]), "=c" (regs[2]), "=d" (regs[3])
-             : "a" (funcId), "c" (subFuncId));
-#endif
-    }
-
-    inline const uint32_t &EAX() const { return regs[0]; }
-    inline const uint32_t &EBX() const { return regs[1]; }
-    inline const uint32_t &ECX() const { return regs[2]; }
-    inline const uint32_t &EDX() const { return regs[3]; }
-};
-
-#endif
-
-class CPUInfo {
-    public:
-        CPUInfo();
-        string  vendor()   const { return mVendorId;   }
-        string  model()    const { return mModelName;  }
-        int     threads()  const { return mNumLogCpus; }
-
-    private:
-        // Bit positions for data extractions
-        static const uint32_t LVL_NUM   = 0x000000FF;
-        static const uint32_t LVL_TYPE  = 0x0000FF00;
-        static const uint32_t LVL_CORES = 0x0000FFFF;
-        static const uint32_t HTT_POS   = 0x10000000;
-
-        // Attributes
-        string mVendorId;
-        string mModelName;
-        int    mNumSMT;
-        int    mNumCores;
-        int    mNumLogCpus;
-        bool   mIsHTT;
-};
-
 #if !CPUID_CAPABLE
 
 CPUInfo::CPUInfo()
@@ -116,7 +46,9 @@ CPUInfo::CPUInfo()
     mVendorId += string((const char *)&cpuID0.ECX(), 4);
 
     string upVId = mVendorId;
+
     for_each(upVId.begin(), upVId.end(), [](char& in) { in = ::toupper(in); });
+
     // Get num of cores
     if (upVId.find("INTEL") != std::string::npos) {
         mVendorId = "Intel";
@@ -166,8 +98,8 @@ CPUInfo::CPUInfo()
             mNumCores = mNumLogCpus = 1;
         }
     } else {
-        mVendorId = "Unkown, probably ARM";
-        cout<< "Unexpected vendor id" <<endl;
+        mVendorId = "Unkown";
+        std::cout<< "Unexpected vendor id" << std::endl;
     }
     // Get processor brand string
     // This seems to be working for both Intel & AMD vendors
@@ -186,27 +118,6 @@ CPUInfo::CPUInfo()
 namespace cpu
 {
 
-unsigned getMaxJitSize()
-{
-    const int MAX_JIT_LEN = 100;
-
-    static int length = 0;
-    if (length == 0) {
-        std::string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
-        if (!env_var.empty()) {
-            length = std::stoi(env_var);
-        } else {
-            length = MAX_JIT_LEN;
-        }
-    }
-    return length;
-}
-
-int getBackend()
-{
-    return AF_BACKEND_CPU;
-}
-
 static const std::string get_system(void)
 {
     std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
@@ -230,15 +141,24 @@ static inline std::string &ltrim(std::string &s)
     return s;
 }
 
+int getBackend()
+{
+    return AF_BACKEND_CPU;
+}
+
 std::string getDeviceInfo()
 {
+    const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
+
     std::ostringstream info;
-    static CPUInfo cinfo;
 
     info << "ArrayFire v" << AF_VERSION
          << " (CPU, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
+
     std::string model = cinfo.model();
+
     size_t memMB = getDeviceMemorySize(getActiveDeviceId()) / 1048576;
+
     info << string("[0] ") << cinfo.vendor() <<": " << ltrim(model);
 
     if(memMB) info << ", " << memMB << " MB, ";
@@ -249,17 +169,19 @@ std::string getDeviceInfo()
     info << AF_COMPILER_STR;
 #endif
     info << std::endl;
+
     return info.str();
 }
 
 bool isDoubleSupported(int device)
 {
-    return true;
+    return DeviceManager::IS_DOUBLE_SUPPORTED;
 }
 
 void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
 {
-    static CPUInfo cinfo;
+    const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
+
     snprintf(d_name, 64, "%s", cinfo.vendor().c_str());
     snprintf(d_platform, 10, "CPU");
     // report the compiler for toolkit
@@ -267,25 +189,30 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     snprintf(d_compute, 10, "%s", "0.0");
 }
 
-int getDeviceCount()
+unsigned getMaxJitSize()
 {
-    return 1;
-}
+    const int MAX_JIT_LEN = 100;
 
+    static int length = 0;
+    if (length == 0) {
+        std::string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
+        if (!env_var.empty()) {
+            length = std::stoi(env_var);
+        } else {
+            length = MAX_JIT_LEN;
+        }
+    }
+    return length;
+}
 
-int setDevice(int device)
+int getDeviceCount()
 {
-    static bool flag;
-    if(!flag && device != 0) {
-        printf("WARNING af_set_device(device): device can only be 0 for CPU\n");
-        flag = 1;
-    }
-    return 0;
+    return DeviceManager::NUM_DEVICES;
 }
 
 int getActiveDeviceId()
 {
-    return 0;
+    return DeviceManager::ACTIVE_DEVICE_ID;
 }
 
 size_t getDeviceMemorySize(int device)
@@ -298,17 +225,31 @@ size_t getHostMemorySize()
     return common::getHostMemorySize();
 }
 
-static const int MAX_QUEUES = 1;
+int setDevice(int device)
+{
+    static bool flag = false;
+    if (!flag && device != 0) {
+#ifndef NDEBUG
+        std::cerr << "WARNING af_set_device(device): device can only be 0 for CPU\n";
+#endif
+        flag = true;
+    }
+    return 0;
+}
 
+queue& getQueue(int device)
+{
+    return DeviceManager::getInstance().queues[device];
+}
 
-queue& getQueue(int idx) {
-    static std::array<queue, MAX_QUEUES> queues;
-    return queues[idx];
+CPUInfo DeviceManager::getCPUInfo() const
+{
+    return cinfo;
 }
 
 void sync(int device)
 {
-    getQueue().sync();
+    getQueue(device).sync();
 }
 
 bool& evalFlag()
@@ -317,5 +258,10 @@ bool& evalFlag()
     return flag;
 }
 
+DeviceManager& DeviceManager::getInstance()
+{
+    static DeviceManager my_instance;
+    return my_instance;
+}
 
 }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index edf7a072fc..066b711f88 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -9,34 +9,134 @@
 
 #pragma once
 
+#include <array>
+#include <algorithm>
+#include <iostream>
 #include <string>
 
-namespace cpu {
-    class queue;
+#include <queue.hpp>
 
-    int getBackend();
+#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
+#define CPUID_CAPABLE USE_CPUID
+#else
+#define CPUID_CAPABLE 0
+#endif
 
-    std::string getDeviceInfo();
+#ifdef _WIN32
+#include <limits.h>
+#include <intrin.h>
+typedef unsigned __int32  uint32_t;
+#else
+#include <stdint.h>
+#endif
 
-    bool isDoubleSupported(int device);
+#if CPUID_CAPABLE
 
-    void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+#define MAX_INTEL_TOP_LVL 4
 
-    int getDeviceCount();
+class CPUID {
+    uint32_t regs[4];
 
-    int setDevice(int device);
+    public:
+    explicit CPUID(unsigned funcId, unsigned subFuncId) {
+#ifdef _WIN32
+        __cpuidex((int *)regs, (int)funcId, (int)subFuncId);
 
-    int getActiveDeviceId();
+#else
+        asm volatile
+            ("cpuid" : "=a" (regs[0]), "=b" (regs[1]), "=c" (regs[2]), "=d" (regs[3])
+             : "a" (funcId), "c" (subFuncId));
+#endif
+    }
 
-    size_t getDeviceMemorySize(int device);
+    inline const uint32_t &EAX() const { return regs[0]; }
+    inline const uint32_t &EBX() const { return regs[1]; }
+    inline const uint32_t &ECX() const { return regs[2]; }
+    inline const uint32_t &EDX() const { return regs[3]; }
+};
 
-    size_t getHostMemorySize();
+#endif
 
-    void sync(int device);
+class CPUInfo {
+    public:
+        CPUInfo();
+        std::string  vendor() const { return mVendorId;   }
+        std::string  model()  const { return mModelName;  }
+        int threads() const { return mNumLogCpus; }
 
-    queue& getQueue(int idx = 0);
+    private:
+        // Bit positions for data extractions
+        static const uint32_t LVL_NUM   = 0x000000FF;
+        static const uint32_t LVL_TYPE  = 0x0000FF00;
+        static const uint32_t LVL_CORES = 0x0000FFFF;
+        static const uint32_t HTT_POS   = 0x10000000;
 
-    unsigned getMaxJitSize();
+        // Attributes
+        std::string mVendorId;
+        std::string mModelName;
+        int    mNumSMT;
+        int    mNumCores;
+        int    mNumLogCpus;
+        bool   mIsHTT;
+};
+
+namespace cpu
+{
+
+int getBackend();
+
+std::string getDeviceInfo();
+
+bool isDoubleSupported(int device);
+
+void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+
+unsigned getMaxJitSize();
+
+int getDeviceCount();
+
+int getActiveDeviceId();
+
+size_t getDeviceMemorySize(int device);
+
+size_t getHostMemorySize();
+
+int setDevice(int device);
+
+queue& getQueue(int device=0);
+
+void sync(int device);
+
+bool& evalFlag();
+
+class DeviceManager
+{
+    public:
+        static const int MAX_QUEUES = 1;
+        static const int NUM_DEVICES = 1;
+        static const int ACTIVE_DEVICE_ID = 0;
+        static const bool IS_DOUBLE_SUPPORTED = true;
+
+        static DeviceManager& getInstance();
+
+        friend queue& getQueue(int device);
+
+        CPUInfo getCPUInfo() const;
+
+    private:
+        DeviceManager() {
+        }
+
+        // Following two declarations are required to
+        // avoid copying accidental copy/assignment
+        // of instance returned by getInstance to other
+        // variables
+        DeviceManager(DeviceManager const&);
+        void operator=(DeviceManager const&);
+
+        // Attributes
+        const CPUInfo cinfo;
+        std::array<queue, MAX_QUEUES> queues;
+};
 
-    bool& evalFlag();
 }

From 6e990191be8372e34417c1ba23eee11bdac0a074 Mon Sep 17 00:00:00 2001
From: mlloreda <miguel@arrayfire.com>
Date: Tue, 20 Dec 2016 13:42:13 -0500
Subject: [PATCH 1051/2677] Release notes for v3.4.2

---
 docs/pages/release_notes.md | 109 ++++++++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index c4ccb7a4fe..85c187da09 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,115 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.4.2
+==============
+
+Deprecation Announcement
+------------------------
+
+This release supports CUDA 6.5 and higher. The next ArrayFire relase will
+support CUDA 7.0 and higher, dropping support for CUDA 6.5. Reasons for no
+longer supporting CUDA 6.5 include:
+
+* CUDA 7.0 NVCC supports the C++11 standard (whereas CUDA 6.5 does not), which
+  is used by ArrayFire's CPU and OpenCL backends.
+* Very few ArrayFire users still use CUDA 6.5.
+
+As a result, the older Jetson TK1 / Tegra K1 will no longer be supported in
+the next ArrayFire release. The newer Jetson TX1 / Tegra X1 will continue to
+have full capability with ArrayFire.
+
+Docker
+------
+* [ArrayFire has been Dockerized](https://github.com/arrayfire/arrayfire-docker).
+
+Improvements
+------------
+* Implemented sparse storage format conversions between \ref AF_STORAGE_CSR
+  and \ref AF_STORAGE_COO.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1642)</sup>
+  * Directly convert between \ref AF_STORAGE_COO <--> \ref AF_STORAGE_CSR
+    using the af::sparseConvertTo() function.
+  * af::sparseConvertTo() now also supports converting to dense.
+* Added cast support for [sparse arrays](\ref sparse_func).
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1653)</sup>
+  * Casting only changes the values array and the type. The row and column
+    index arrays are not changed.
+* Reintroduced automated computation of chart axes limits for graphics functions.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1639)</sup>
+  * The axes limits will always be the minimum/maximum of the current and new
+    limit.
+  * The user can still set limits from API calls. If the user sets a limit
+    from the API call, then the automatic limit setting will be disabled.
+* Using `boost::scoped_array` instead of `boost::scoped_ptr` when managing
+  array resources.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1637)</sup>
+* Internal performance improvements to getInfo() by using `const` references
+  to avoid unnecessary copying of `ArrayInfo` objects.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1665)</sup>
+* Added support for scalar af::array inputs for af::convolve() and
+  [set functions](\ref set_mat).
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1660)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/issues/1675)</sup>
+  <sup>[3](https://github.com/arrayfire/arrayfire/pull/1668)</sup>
+* Performance fixes in af::fftConvolve() kernels.
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1679)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1680)</sup>
+
+Build
+-----
+* Support for Visual Studio 2015 compilation.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1632)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1640)</sup>
+* Fixed `FindCBLAS.cmake` when PkgConfig is used.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1657)</sup>
+
+Bug fixes
+---------
+* Fixes to JIT when tree is large.
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1646)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1638)</sup>
+* Fixed indexing bug when converting dense to sparse af::array as \ref
+  AF_STORAGE_COO.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1642)</sup>
+* Fixed af::bilateral() OpenCL kernel compilation on OS X.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1638)</sup>
+* Fixed memory leak in af::regions() (CPU) and af::rgb2ycbcr().
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1664)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/issues/1664)</sup>
+  <sup>[3](https://github.com/arrayfire/arrayfire/pull/1666)</sup>
+
+Installers
+----------
+* Major OS X installer fixes.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1629)</sup>
+  * Fixed installation scripts.
+  * Fixed installation symlinks for libraries.
+* Windows installer now ships with more pre-built examples.
+
+Examples
+--------
+* Added af::choleskyInPlace() calls to `cholesky.cpp` example.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1671)</sup>
+
+Documentation
+-------------
+* Added `u8` as supported data type in `getting_started.md`.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1661)</sup>
+* Fixed typos.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1652)</sup>
+
+CUDA 8 on OSX
+-------------
+* [CUDA 8.0.55](https://developer.nvidia.com/cuda-toolkit) supports Xcode 8.
+  <sup>[1](https://github.com/arrayfire/arrayfire/issues/1664)</sup>
+
+Known Issues
+------------
+* Known failures with CUDA 6.5. These include all functions that use
+  sorting. As a result, sparse storage format conversion between \ref
+  AF_STORAGE_COO and \ref AF_STORAGE_CSR has been disabled for CUDA 6.5.
+
 v3.4.1
 ==============
 

From a684d4ae020270e1ecca1b667fd3b27d64f9a808 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 21 Dec 2016 15:39:41 -0500
Subject: [PATCH 1052/2677] Remove CUDA 6.5 code from CUDA CMakeLists

---
 src/backend/cuda/CMakeLists.txt | 44 ---------------------------------
 1 file changed, 44 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9cfa8a5929..b1f5f331b6 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -145,53 +145,9 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
     MARK_AS_ADVANCED(CUDA_cusolver_LIBRARY)
 ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
 
-IF(${CUDA_VERSION_MAJOR} LESS 7 AND CUDA_cusolver_LIBRARY)
-  UNSET(CUDA_cusolver_LIBRARY CACHE)  # Failsafe when going from higher version to lower version
-ENDIF()
-
 IF(CUDA_cusolver_LIBRARY)
     MESSAGE(STATUS "CUDA cusolver library available in CUDA Version ${CUDA_VERSION_STRING}")
     ADD_DEFINITIONS(-DWITH_CUDA_LINEAR_ALGEBRA)
-ELSE(CUDA_cusolver_LIBRARY)
-    # Use CPU Lapack as fallback?
-    OPTION(CUDA_LAPACK_CPU_FALLBACK "Use CPU LAPACK as fallback for CUDA LAPACK when cusolver is not available" OFF)
-    MARK_AS_ADVANCED(CUDA_LAPACK_CPU_FALLBACK)
-
-    IF(${CUDA_LAPACK_CPU_FALLBACK})
-        ## Try to use CPU side lapack
-        IF(APPLE)
-            FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
-            IF(NOT LAPACK_FOUND)
-                # UNSET THE VARIABLES FROM LAPACKE
-                UNSET(LAPACKE_LIB CACHE)
-                UNSET(LAPACK_LIB CACHE)
-                UNSET(LAPACKE_INCLUDES CACHE)
-                UNSET(LAPACKE_ROOT_DIR CACHE)
-                FIND_PACKAGE(LAPACK)
-            ENDIF()
-        ELSE(APPLE) # Linux and Windows
-            FIND_PACKAGE(LAPACKE)
-        ENDIF(APPLE)
-
-        IF(NOT LAPACK_FOUND)
-            MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. Linear Algebra will not be available.")
-        ELSE(NOT LAPACK_FOUND)
-            MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. But CPU LAPACK libraries are available. Will fallback to using host side code.")
-            ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
-            IF(USE_CUDA_MKL) # Manual MKL Setup
-                MESSAGE("CUDA LAPACK CPU Fallback Using MKL")
-                ADD_DEFINITIONS(-DUSE_MKL)
-            ELSE(USE_CUDA_MKL)
-                IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
-                    MESSAGE("CUDA LAPACK CPU Fallback Using MKL RT")
-                    ADD_DEFINITIONS(-DUSE_MKL)
-                ENDIF()
-            ENDIF()
-        ENDIF()
-    ELSE()
-        MESSAGE(STATUS "CUDA Version ${CUDA_VERSION_STRING} does not contain cusolver library. Linear Algebra will not be available.")
-    ENDIF()
-    UNSET(CUDA_cusolver_LIBRARY CACHE)  # Failsafe when going from higher version to lower version
 ENDIF(CUDA_cusolver_LIBRARY)
 
 INCLUDE_DIRECTORIES(

From a9b1b3978bd211f6d9e2eaa4808be9fa76ae212d Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 21 Dec 2016 15:40:03 -0500
Subject: [PATCH 1053/2677] Remove CUDA 6.5 fallback from sparse convert

---
 src/backend/cuda/sparse.cu | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 1d13f94ed0..96e430285c 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -363,8 +363,6 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
     return dense;
 }
 
-// Some of the API used here is available only in CUDA 7 or newer
-#if CUDA_VERSION >= 7000
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
@@ -477,14 +475,6 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
     return converted;
 }
-#else // CUDA 6.5 and older (older than 7)
-template<typename T, af_storage dest, af_storage src>
-SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
-{
-    AF_ERROR("Sparse storage format conversions are not supported for CUDA 6.5 or older",
-             AF_ERR_NOT_SUPPORTED);
-}
-#endif // CUDA_VERSION >= 7000
 
 #define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
     template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \

From 9b77c10ef0bc68e251ec24639a7db583d74f0af6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 21 Dec 2016 16:04:32 -0500
Subject: [PATCH 1054/2677] Removed lapack fallback for CUDA 6.5 in source code
 and CPU fallback

---
 src/backend/cuda/CMakeLists.txt               |  31 +--
 src/backend/cuda/cholesky.cu                  |  61 ------
 src/backend/cuda/cpu_lapack/cpu_cholesky.cpp  | 109 ---------
 src/backend/cuda/cpu_lapack/cpu_cholesky.hpp  |  22 --
 src/backend/cuda/cpu_lapack/cpu_inverse.cpp   |  92 --------
 src/backend/cuda/cpu_lapack/cpu_inverse.hpp   |  19 --
 src/backend/cuda/cpu_lapack/cpu_lu.cpp        | 197 -----------------
 src/backend/cuda/cpu_lapack/cpu_lu.hpp        |  22 --
 src/backend/cuda/cpu_lapack/cpu_qr.cpp        | 160 --------------
 src/backend/cuda/cpu_lapack/cpu_qr.hpp        |  22 --
 src/backend/cuda/cpu_lapack/cpu_solve.cpp     | 206 ------------------
 src/backend/cuda/cpu_lapack/cpu_solve.hpp     |  23 --
 src/backend/cuda/cpu_lapack/cpu_svd.cpp       | 153 -------------
 src/backend/cuda/cpu_lapack/cpu_svd.hpp       |  22 --
 src/backend/cuda/cpu_lapack/cpu_triangle.hpp  |  52 -----
 src/backend/cuda/cpu_lapack/lapack_helper.hpp |  35 ---
 src/backend/cuda/cusolverDnManager.cpp        |   4 -
 src/backend/cuda/cusolverDnManager.hpp        |   3 -
 src/backend/cuda/inverse.cu                   |  47 ----
 src/backend/cuda/lu.cu                        |  70 ------
 src/backend/cuda/qr.cu                        |  62 ------
 src/backend/cuda/solve.cu                     |  66 ------
 src/backend/cuda/svd.cu                       |  49 -----
 23 files changed, 2 insertions(+), 1525 deletions(-)
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_cholesky.cpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_cholesky.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_inverse.cpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_inverse.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_lu.cpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_lu.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_qr.cpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_qr.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_solve.cpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_solve.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_svd.cpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_svd.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/cpu_triangle.hpp
 delete mode 100644 src/backend/cuda/cpu_lapack/lapack_helper.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b1f5f331b6..6e89a8676b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -143,12 +143,8 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
         SET(CUDA_cusolver_DIR "${CUDA_TOOLKIT_ROOT_DIR}" CACHE INTERNAL "CUDA cusolver Root Directory")
     ENDIF()
     MARK_AS_ADVANCED(CUDA_cusolver_LIBRARY)
-ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
-
-IF(CUDA_cusolver_LIBRARY)
     MESSAGE(STATUS "CUDA cusolver library available in CUDA Version ${CUDA_VERSION_STRING}")
-    ADD_DEFINITIONS(-DWITH_CUDA_LINEAR_ALGEBRA)
-ENDIF(CUDA_cusolver_LIBRARY)
+ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
@@ -159,10 +155,6 @@ INCLUDE_DIRECTORIES(
     ${CUDA_nvvm_INCLUDE_DIR}
     )
 
-IF(CUDA_LAPACK_CPU_FALLBACK)
-  INCLUDE_DIRECTORIES(${LAPACK_INCLUDE_DIR})
-ENDIF()
-
 FILE(GLOB cuda_headers
      "*.hpp"
      "*.h")
@@ -192,18 +184,6 @@ SOURCE_GROUP(backend\\cuda\\Sources FILES ${cuda_sources})
 SOURCE_GROUP(backend\\cuda\\JIT FILES ${jit_sources})
 SOURCE_GROUP(backend\\cuda\\kernel\\Headers FILES ${kernel_headers})
 
-IF(CUDA_LAPACK_CPU_FALLBACK)
-    FILE(GLOB cpu_lapack_sources
-        "cpu_lapack/*.cpp")
-    FILE(GLOB cpu_lapack_headers
-        "cpu_lapack/*.hpp")
-
-    SOURCE_GROUP(backend\\cuda\\cpu_lapack\\Headers FILES ${cpu_lapack_headers})
-    SOURCE_GROUP(backend\\cuda\\cpu_lapack\\Sources FILES ${cpu_lapack_sources})
-    LIST(SORT cpu_lapack_headers)
-    LIST(SORT cpu_lapack_sources)
-ENDIF()
-
 FILE(GLOB backend_headers
     "../*.hpp"
     "../*.h"
@@ -431,8 +411,6 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cuda_sources}
                 ${jit_sources}
                 ${kernel_headers}
-                ${cpu_lapack_headers}
-                ${cpu_lapack_sources}
                 ${backend_headers}
                 ${backend_sources}
                 ${c_headers}
@@ -453,6 +431,7 @@ TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
                                 PRIVATE ${CUDA_cusparse_LIBRARY}
+                                PRIVATE ${CUDA_cusolver_LIBRARY}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
                                 PRIVATE ${CUDA_CUDA_LIBRARY})
 
@@ -465,12 +444,6 @@ IF(FORGE_FOUND)
     TARGET_LINK_LIBRARIES(afcuda PRIVATE ${GRAPHICS_LIBRARIES})
 ENDIF()
 
-IF(CUDA_cusolver_LIBRARY)
-    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${CUDA_cusolver_LIBRARY})
-ELSEIF(CUDA_LAPACK_CPU_FALLBACK)
-    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${LAPACK_LIBRARIES})
-ENDIF()
-
 SET_TARGET_PROPERTIES(afcuda PROPERTIES
     VERSION "${AF_VERSION}"
     SOVERSION "${AF_VERSION_MAJOR}")
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index c6869dc6a6..8a9ea550e5 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -10,8 +10,6 @@
 #include <err_common.hpp>
 #include <cholesky.hpp>
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <cusolverDnManager.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
@@ -147,62 +145,3 @@ INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 }
-
-#elif defined(WITH_CPU_LINEAR_ALGEBRA)
-#include <cpu_lapack/cpu_cholesky.hpp>
-namespace cuda
-{
-
-template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
-    return cpu::cholesky(info, in, is_upper);
-}
-
-template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
-    return cpu::cholesky_inplace(in, is_upper);
-}
-
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);
-
-INSTANTIATE_CH(float)
-INSTANTIATE_CH(cfloat)
-INSTANTIATE_CH(double)
-INSTANTIATE_CH(cdouble)
-
-}
-
-#else
-namespace cuda
-{
-
-template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);
-
-INSTANTIATE_CH(float)
-INSTANTIATE_CH(cfloat)
-INSTANTIATE_CH(double)
-INSTANTIATE_CH(cdouble)
-
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_cholesky.cpp b/src/backend/cuda/cpu_lapack/cpu_cholesky.cpp
deleted file mode 100644
index 29826dcecb..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_cholesky.cpp
+++ /dev/null
@@ -1,109 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
-
-#include <cpu_lapack/cpu_cholesky.hpp>
-#include <err_common.hpp>
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <copy.hpp>
-#include <iostream>
-#include <cassert>
-
-#include <cpu_lapack/cpu_triangle.hpp>
-#include "lapack_helper.hpp"
-
-namespace cuda
-{
-namespace cpu
-{
-
-template<typename T>
-using potrf_func_def = int (*)(ORDER_TYPE, char,
-                               int,
-                               T*, int);
-
-#define CH_FUNC_DEF( FUNC )                                     \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define CH_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-CH_FUNC_DEF( potrf )
-CH_FUNC(potrf , float  , s)
-CH_FUNC(potrf , double , d)
-CH_FUNC(potrf , cfloat , c)
-CH_FUNC(potrf , cdouble, z)
-
-template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
-    dim4 iDims = in.dims();
-    int N = iDims[0];
-
-    char uplo = 'L';
-    if(is_upper)
-        uplo = 'U';
-
-    T *inPtr = pinnedAlloc<T>(in.elements());
-    copyData(inPtr, in);
-
-    *info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo,
-                            N, inPtr, in.strides()[1]);
-
-    if (is_upper) triangle<T, true , false>(inPtr, inPtr, in.dims(), in.strides(), in.strides());
-    else          triangle<T, false, false>(inPtr, inPtr, in.dims(), in.strides(), in.strides());
-
-    Array<T> out = createHostDataArray<T>(in.dims(), inPtr);
-
-    pinnedFree(inPtr);
-
-    return out;
-}
-
-template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
-    dim4 iDims = in.dims();
-    int N = iDims[0];
-
-    char uplo = 'L';
-    if(is_upper)
-        uplo = 'U';
-
-    T *inPtr = pinnedAlloc<T>(in.elements());
-    copyData(inPtr, in);
-
-    int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo,
-                               N, inPtr, in.strides()[1]);
-
-    writeHostDataArray<T>(in, inPtr, in.elements() * sizeof(T));
-
-    pinnedFree(inPtr);
-
-    return info;
-}
-
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
-
-INSTANTIATE_CH(float)
-INSTANTIATE_CH(cfloat)
-INSTANTIATE_CH(double)
-INSTANTIATE_CH(cdouble)
-
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_cholesky.hpp b/src/backend/cuda/cpu_lapack/cpu_cholesky.hpp
deleted file mode 100644
index 03f9fa80d8..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_cholesky.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
-
-    template<typename T>
-    int cholesky_inplace(Array<T> &in, const bool is_upper);
-}
-}
diff --git a/src/backend/cuda/cpu_lapack/cpu_inverse.cpp b/src/backend/cuda/cpu_lapack/cpu_inverse.cpp
deleted file mode 100644
index a0ddf39335..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_inverse.cpp
+++ /dev/null
@@ -1,92 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
-
-#include <inverse.hpp>
-#include <err_common.hpp>
-
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <identity.hpp>
-#include <copy.hpp>
-#include <iostream>
-#include <cassert>
-
-#include "lapack_helper.hpp"
-#include <cpu_lapack/cpu_lu.hpp>
-#include <cpu_lapack/cpu_solve.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-
-template<typename T>
-using getri_func_def = int (*)(ORDER_TYPE, int,
-                               T *, int,
-                               const int *);
-
-#define INV_FUNC_DEF( FUNC )                                        \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define INV_FUNC( FUNC, TYPE, PREFIX )                              \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-INV_FUNC_DEF( getri )
-INV_FUNC(getri , float  , s)
-INV_FUNC(getri , double , d)
-INV_FUNC(getri , cfloat , c)
-INV_FUNC(getri , cdouble, z)
-
-template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
-    int M = in.dims()[0];
-    int N = in.dims()[1];
-
-    if (M != N) {
-        Array<T> I = identity<T>(in.dims());
-        return cpu::solve(in, I);
-    }
-
-    Array<T> A = copyArray<T>(in);
-
-    Array<int> pivot = lu_inplace<T>(A, false);
-
-    T *aPtr = pinnedAlloc<T>(A.elements());
-    int *pPtr = pinnedAlloc<int>(pivot.elements());
-    copyData(aPtr, A);
-    copyData(pPtr, pivot);
-
-    getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
-                    aPtr, A.strides()[1],
-                    pPtr);
-
-    writeHostDataArray<T>(A, aPtr, A.elements() * sizeof(T));
-
-    pinnedFree(aPtr);
-    pinnedFree(pPtr);
-
-    return A;
-}
-
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
-
-INSTANTIATE(float)
-INSTANTIATE(cfloat)
-INSTANTIATE(double)
-INSTANTIATE(cdouble)
-
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_inverse.hpp b/src/backend/cuda/cpu_lapack/cpu_inverse.hpp
deleted file mode 100644
index f45fdee990..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_inverse.hpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> inverse(const Array<T> &in);
-}
-}
diff --git a/src/backend/cuda/cpu_lapack/cpu_lu.cpp b/src/backend/cuda/cpu_lapack/cpu_lu.cpp
deleted file mode 100644
index ea8313206a..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_lu.cpp
+++ /dev/null
@@ -1,197 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
-
-#include <cpu_lapack/cpu_lu.hpp>
-#include <err_common.hpp>
-
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <iostream>
-#include <cassert>
-#include <err_cuda.hpp>
-
-#include "lapack_helper.hpp"
-
-namespace cuda
-{
-namespace cpu
-{
-
-template<typename T>
-using getrf_func_def = int (*)(ORDER_TYPE, int, int,
-                               T*, int,
-                               int*);
-
-#define LU_FUNC_DEF( FUNC )                                     \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define LU_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-LU_FUNC_DEF( getrf )
-LU_FUNC(getrf , float  , s)
-LU_FUNC(getrf , double , d)
-LU_FUNC(getrf , cfloat , c)
-LU_FUNC(getrf , cdouble, z)
-
-template<typename T>
-void lu_split(T *l, T *u, const T *i,
-        const dim4 ldm, const dim4 udm, const dim4 idm,
-        const dim4 lst, const dim4 ust, const dim4 ist)
-{
-    for(dim_t ow = 0; ow < idm[3]; ow++) {
-        const dim_t lW = ow * lst[3];
-        const dim_t uW = ow * ust[3];
-        const dim_t iW = ow * ist[3];
-
-        for(dim_t oz = 0; oz < idm[2]; oz++) {
-            const dim_t lZW = lW + oz * lst[2];
-            const dim_t uZW = uW + oz * ust[2];
-            const dim_t iZW = iW + oz * ist[2];
-
-            for(dim_t oy = 0; oy < idm[1]; oy++) {
-                const dim_t lYZW = lZW + oy * lst[1];
-                const dim_t uYZW = uZW + oy * ust[1];
-                const dim_t iYZW = iZW + oy * ist[1];
-
-                for(dim_t ox = 0; ox < idm[0]; ox++) {
-                    const dim_t lMem = lYZW + ox;
-                    const dim_t uMem = uYZW + ox;
-                    const dim_t iMem = iYZW + ox;
-                    if(ox > oy) {
-                        if(oy < ldm[1])
-                            l[lMem] = i[iMem];
-                        if(ox < udm[0])
-                            u[uMem] = scalar<T>(0);
-                    } else if (oy > ox) {
-                        if(oy < ldm[1])
-                            l[lMem] = scalar<T>(0);
-                        if(ox < udm[0])
-                            u[uMem] = i[iMem];
-                    } else if(ox == oy) {
-                        if(oy < ldm[1])
-                            l[lMem] = scalar<T>(1.0);
-                        if(ox < udm[0])
-                            u[uMem] = i[iMem];
-                    }
-                }
-            }
-        }
-    }
-}
-
-void convertPivot(int **pivot, int out_sz, dim_t d0)
-{
-    int* p = pinnedAlloc<int>(out_sz);
-    for(int i = 0; i < out_sz; i++)
-        p[i] = i;
-
-    for(int j = 0; j < (int)d0; j++) {
-        // 1 indexed in pivot
-        std::swap(p[j], p[(*pivot)[j] - 1]);
-    }
-
-    pinnedFree(*pivot);
-    *pivot = p;
-}
-
-template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
-    dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-
-    Array<T> in_copy = copyArray<T>(in);
-
-    //////////////////////////////////////////
-    // LU inplace
-    int *pivotPtr  = pinnedAlloc<int>(min(M, N));
-    T   *inPtr     = pinnedAlloc<T>  (in_copy.elements());
-    copyData(inPtr, in);
-
-    getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    inPtr, in_copy.strides()[1],
-                    pivotPtr);
-
-    convertPivot(&pivotPtr, M, min(M, N));
-
-    pivot = createHostDataArray<int>(af::dim4(M), pivotPtr);
-    //////////////////////////////////////////
-
-    // SPLIT into lower and upper
-    dim4 ldims(M, min(M, N));
-    dim4 udims(min(M, N), N);
-
-    T *lowerPtr = pinnedAlloc<T>(ldims.elements());
-    T *upperPtr = pinnedAlloc<T>(udims.elements());
-
-    dim4 lst(1, ldims[0], ldims[0] * ldims[1], ldims[0] * ldims[1] * ldims[2]);
-    dim4 ust(1, udims[0], udims[0] * udims[1], udims[0] * udims[1] * udims[2]);
-
-    lu_split<T>(lowerPtr, upperPtr, inPtr, ldims, udims, iDims,
-                lst, ust, in_copy.strides());
-
-    lower = createHostDataArray<T>(ldims, lowerPtr);
-    upper = createHostDataArray<T>(udims, upperPtr);
-
-    lower.eval();
-    upper.eval();
-
-    pinnedFree(lowerPtr);
-    pinnedFree(upperPtr);
-    pinnedFree(pivotPtr);
-    pinnedFree(inPtr);
-}
-
-template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
-    dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-
-    int *pivotPtr  = pinnedAlloc<int>(min(M, N));
-    T   *inPtr     = pinnedAlloc<T>  (in.elements());
-    copyData(inPtr, in);
-
-    getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    inPtr, in.strides()[1],
-                    pivotPtr);
-
-    if(convert_pivot) convertPivot(&pivotPtr, M, min(M, N));
-
-    writeHostDataArray<T>(in, inPtr, in.elements() * sizeof(T));
-    Array<int> pivot = createHostDataArray<int>(af::dim4(M), pivotPtr);
-
-    pivot.eval();
-
-    pinnedFree(inPtr);
-    pinnedFree(pivotPtr);
-
-    return pivot;
-}
-
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
-
-INSTANTIATE_LU(float)
-INSTANTIATE_LU(cfloat)
-INSTANTIATE_LU(double)
-INSTANTIATE_LU(cdouble)
-
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_lu.hpp b/src/backend/cuda/cpu_lapack/cpu_lu.hpp
deleted file mode 100644
index 39a638fbce..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_lu.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-    template<typename T>
-    void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
-
-    template<typename T>
-    Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
-}
-}
diff --git a/src/backend/cuda/cpu_lapack/cpu_qr.cpp b/src/backend/cuda/cpu_lapack/cpu_qr.cpp
deleted file mode 100644
index 853119ff16..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_qr.cpp
+++ /dev/null
@@ -1,160 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
-
-#include <cpu_lapack/cpu_qr.hpp>
-#include <err_common.hpp>
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <copy.hpp>
-#include <iostream>
-#include <cassert>
-
-#include <cpu_lapack/cpu_triangle.hpp>
-#include "lapack_helper.hpp"
-
-namespace cuda
-{
-namespace cpu
-{
-
-template<typename T>
-using geqrf_func_def = int (*)(ORDER_TYPE, int, int,
-                               T*, int,
-                               T*);
-
-template<typename T>
-using gqr_func_def = int (*)(ORDER_TYPE, int, int, int,
-                             T*, int,
-                             const T*);
-
-#define QR_FUNC_DEF( FUNC )                                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define QR_FUNC( FUNC, TYPE, PREFIX )                               \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-QR_FUNC_DEF( geqrf )
-QR_FUNC(geqrf , float  , s)
-QR_FUNC(geqrf , double , d)
-QR_FUNC(geqrf , cfloat , c)
-QR_FUNC(geqrf , cdouble, z)
-
-#define GQR_FUNC_DEF( FUNC )                                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define GQR_FUNC( FUNC, TYPE, PREFIX )                               \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()             \
-{ return & LAPACK_NAME(PREFIX); }
-
-GQR_FUNC_DEF( gqr )
-GQR_FUNC(gqr , float  , sorgqr)
-GQR_FUNC(gqr , double , dorgqr)
-GQR_FUNC(gqr , cfloat , cungqr)
-GQR_FUNC(gqr , cdouble, zungqr)
-
-template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
-    dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-
-    dim4 padDims(M, max(M, N));
-    q = padArray<T, T>(in, padDims, scalar<T>(0));
-    q.resetDims(iDims);
-
-    dim4 qdims = q.dims();
-
-    T *tPtr = NULL;
-    T *qPtr = NULL;
-    int nT = 0;
-    {
-        ///////////////////////////////////////////////
-        // QR Inplace on q
-        int M_ = qdims[0];
-        int N_ = qdims[1];
-        nT = min(M_, N_);
-
-        tPtr = pinnedAlloc<T>(nT);
-        qPtr = pinnedAlloc<T>(padDims.elements());
-        q.resetDims(padDims);
-        copyData(qPtr, q);
-        q.resetDims(iDims);
-
-        geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                        qPtr, M,
-                        tPtr);
-        ///////////////////////////////////////////////
-    }
-
-    // SPLIT into q and r
-    dim4 rdims(M, N);
-    T *rPtr = pinnedAlloc<T>(rdims.elements());
-
-    dim4 rst(1, rdims[0], rdims[0] * rdims[1], rdims[0] * rdims[1] * rdims[2]);
-
-    triangle<T, true, false>(rPtr, qPtr, rdims, rst, q.strides());
-
-    gqr_func<T>()(AF_LAPACK_COL_MAJOR,
-                  M, M, min(M, N),
-                  qPtr, q.strides()[1],
-                  tPtr);
-
-    q.resetDims(dim4(M, M));
-
-    t = createHostDataArray<T>(af::dim4(nT), tPtr);
-    r = createHostDataArray<T>(rdims, rPtr);
-    writeHostDataArray<T>(q, qPtr, q.elements() * sizeof(T));
-
-    pinnedFree(tPtr);
-    pinnedFree(rPtr);
-    pinnedFree(qPtr);
-}
-
-template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
-    dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-
-    T *tPtr  = pinnedAlloc<T>(min(M, N));
-    T *inPtr = pinnedAlloc<T>(in.elements());
-    copyData(inPtr, in);
-
-    geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    inPtr, in.strides()[1],
-                    tPtr);
-
-    writeHostDataArray<T>(in, inPtr, in.elements() * sizeof(T));
-    Array<T> t = createHostDataArray<T>(af::dim4(min(M, N)), tPtr);
-
-    pinnedFree(inPtr);
-    pinnedFree(tPtr);
-
-    return t;
-}
-
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
-
-INSTANTIATE_QR(float)
-INSTANTIATE_QR(cfloat)
-INSTANTIATE_QR(double)
-INSTANTIATE_QR(cdouble)
-
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_qr.hpp b/src/backend/cuda/cpu_lapack/cpu_qr.hpp
deleted file mode 100644
index a7a628466d..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_qr.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-    template<typename T>
-    void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
-
-    template<typename T>
-    Array<T> qr_inplace(Array<T> &in);
-}
-}
diff --git a/src/backend/cuda/cpu_lapack/cpu_solve.cpp b/src/backend/cuda/cpu_lapack/cpu_solve.cpp
deleted file mode 100644
index c9d080321b..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_solve.cpp
+++ /dev/null
@@ -1,206 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
-
-#include <cpu_lapack/cpu_solve.hpp>
-#include <err_common.hpp>
-
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <iostream>
-#include <cassert>
-#include <err_cuda.hpp>
-
-#include "lapack_helper.hpp"
-
-namespace cuda
-{
-namespace cpu
-{
-
-template<typename T>
-using gesv_func_def = int (*)(ORDER_TYPE, int, int,
-                              T *, int,
-                              int *,
-                              T *, int);
-
-template<typename T>
-using gels_func_def = int (*)(ORDER_TYPE, char,
-                              int, int, int,
-                              T *, int,
-                              T *, int);
-
-template<typename T>
-using getrs_func_def = int (*)(ORDER_TYPE, char,
-                               int, int,
-                               const T *, int,
-                               const int *,
-                               T *, int);
-
-template<typename T>
-using trtrs_func_def = int (*)(ORDER_TYPE,
-                               char, char, char,
-                               int, int,
-                               const T *, int,
-                               T *, int);
-
-
-#define SOLVE_FUNC_DEF( FUNC )                                      \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                            \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-SOLVE_FUNC_DEF( gesv )
-SOLVE_FUNC(gesv , float  , s)
-SOLVE_FUNC(gesv , double , d)
-SOLVE_FUNC(gesv , cfloat , c)
-SOLVE_FUNC(gesv , cdouble, z)
-
-SOLVE_FUNC_DEF( gels )
-SOLVE_FUNC(gels , float  , s)
-SOLVE_FUNC(gels , double , d)
-SOLVE_FUNC(gels , cfloat , c)
-SOLVE_FUNC(gels , cdouble, z)
-
-SOLVE_FUNC_DEF( getrs )
-SOLVE_FUNC(getrs , float  , s)
-SOLVE_FUNC(getrs , double , d)
-SOLVE_FUNC(getrs , cfloat , c)
-SOLVE_FUNC(getrs , cdouble, z)
-
-SOLVE_FUNC_DEF( trtrs )
-SOLVE_FUNC(trtrs , float  , s)
-SOLVE_FUNC(trtrs , double , d)
-SOLVE_FUNC(trtrs , cfloat , c)
-SOLVE_FUNC(trtrs , cdouble, z)
-
-template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
-    int N = A.dims()[0];
-    int NRHS = b.dims()[1];
-
-    T *aPtr = pinnedAlloc<T>(A.elements());
-    T *bPtr = pinnedAlloc<T>(b.elements());
-    int *pPtr = pinnedAlloc<int>(pivot.elements());
-
-    copyData(aPtr, A);
-    copyData(bPtr, b);
-    copyData(pPtr, pivot);
-
-    getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                    N, NRHS,
-                    aPtr, A.strides()[1],
-                    pPtr,
-                    bPtr, b.strides()[1]);
-
-    Array<T> B = createHostDataArray<T>(b.dims(), bPtr);
-
-    pinnedFree(aPtr);
-    pinnedFree(bPtr);
-    pinnedFree(pPtr);
-
-    return B;
-}
-
-template<typename T>
-Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
-{
-    int N = b.dims()[0];
-    int NRHS = b.dims()[1];
-
-    T *aPtr = pinnedAlloc<T>(A.elements());
-    T *bPtr = pinnedAlloc<T>(b.elements());
-    copyData(aPtr, A);
-    copyData(bPtr, b);
-
-    trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
-                    options & AF_MAT_UPPER ? 'U' : 'L',
-                    'N', // transpose flag
-                    options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
-                    N, NRHS,
-                    aPtr, A.strides()[1],
-                    bPtr, b.strides()[1]);
-
-    Array<T> B = createHostDataArray<T>(b.dims(), bPtr);
-
-    pinnedFree(aPtr);
-    pinnedFree(bPtr);
-
-    return B;
-}
-
-
-template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-
-    if (options & AF_MAT_UPPER ||
-        options & AF_MAT_LOWER) {
-        return triangleSolve<T>(a, b, options);
-    }
-
-    int M = a.dims()[0];
-    int N = a.dims()[1];
-    int K = b.dims()[1];
-
-    Array<T> B = padArray<T, T>(b, dim4(max(M, N), K), scalar<T>(0));
-
-    T *aPtr = pinnedAlloc<T>(a.elements());
-    T *bPtr = pinnedAlloc<T>(B.elements());
-    copyData(aPtr, a);
-    copyData(bPtr, B);
-
-    if(M == N) {
-        int *pivotPtr  = pinnedAlloc<int>(N);
-        gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K,
-                       aPtr, a.strides()[1],
-                       pivotPtr,
-                       bPtr, B.strides()[1]);
-        pinnedFree(pivotPtr);
-
-        writeHostDataArray<T>(B, bPtr, B.elements() * sizeof(T));
-    } else {
-        int sM = a.strides()[1];
-        int sN = a.strides()[2] / sM;
-
-        gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                       M, N, K,
-                       aPtr, a.strides()[1],
-                       bPtr, max(sM, sN));
-        writeHostDataArray<T>(B, bPtr, B.elements() * sizeof(T));
-        B.resetDims(dim4(N, K));
-    }
-
-    pinnedFree(aPtr);
-    pinnedFree(bPtr);
-
-    return B;
-}
-
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
-    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
-
-INSTANTIATE_SOLVE(float)
-INSTANTIATE_SOLVE(cfloat)
-INSTANTIATE_SOLVE(double)
-INSTANTIATE_SOLVE(cdouble)
-
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_solve.hpp b/src/backend/cuda/cpu_lapack/cpu_solve.hpp
deleted file mode 100644
index 64a1ef3d44..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_solve.hpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
-
-    template<typename T>
-    Array<T> solveLU(const Array<T> &a, const Array<int> &pivot,
-                     const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
-}
-}
diff --git a/src/backend/cuda/cpu_lapack/cpu_svd.cpp b/src/backend/cuda/cpu_lapack/cpu_svd.cpp
deleted file mode 100644
index eb71606ee4..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_svd.cpp
+++ /dev/null
@@ -1,153 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
-#include <cpu_lapack/cpu_svd.hpp>
-
-#include <Array.hpp>
-#include <svd.hpp>
-#include <err_common.hpp>
-#include <copy.hpp>
-
-#include "lapack_helper.hpp"
-
-namespace cuda
-{
-namespace cpu
-{
-
-#define SVD_FUNC_DEF( FUNC )                                            \
-    template<typename T,typename Tr> svd_func_def<T, Tr> svd_func();
-
-#define SVD_FUNC( FUNC, T, Tr, PREFIX )                     \
-    template<> svd_func_def<T, Tr>     svd_func<T, Tr>()    \
-    { return & LAPACK_NAME(PREFIX##FUNC); }
-
-#if defined(USE_MKL) || defined(__APPLE__)
-
-    template<typename T, typename Tr>
-    using svd_func_def = int (*)(ORDER_TYPE,
-                                 char jobz,
-                                 int m, int n,
-                                 T* in, int ldin,
-                                 Tr* s,
-                                 T* u, int ldu,
-                                 T* vt, int ldvt);
-
-    SVD_FUNC_DEF( gesdd )
-    SVD_FUNC(gesdd, float  , float , s)
-    SVD_FUNC(gesdd, double , double, d)
-    SVD_FUNC(gesdd, cfloat , float , c)
-    SVD_FUNC(gesdd, cdouble, double, z)
-
-#else   // Atlas causes memory freeing issues with using gesdd
-
-    template<typename T, typename Tr>
-    using svd_func_def = int (*)(ORDER_TYPE,
-                                 char jobu, char jobvt,
-                                 int m, int n,
-                                 T* in, int ldin,
-                                 Tr* s,
-                                 T* u, int ldu,
-                                 T* vt, int ldvt,
-                                 Tr *superb);
-
-    SVD_FUNC_DEF( gesvd )
-    SVD_FUNC(gesvd, float  , float , s)
-    SVD_FUNC(gesvd, double , double, d)
-    SVD_FUNC(gesvd, cfloat , float , c)
-    SVD_FUNC(gesvd, cdouble, double, z)
-
-#endif
-
-    template <typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-    {
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-
-        // S, U, Vt are empty. Simply write to them
-        Tr *sPtr = pinnedAlloc<Tr>(s.elements());
-        T  *uPtr = pinnedAlloc<T >(u.elements());
-        T  *vPtr = pinnedAlloc<T >(vt.elements());
-        T  *iPtr = pinnedAlloc<T >(in.elements());
-
-        copyData(sPtr, s);
-        copyData(uPtr, u);
-        copyData(vPtr, vt);
-        copyData(iPtr, in);
-
-#if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, iPtr, in.strides()[1],
-                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1]);
-#else
-        std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, iPtr, in.strides()[1],
-                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1], &superb[0]);
-#endif
-        writeHostDataArray(s , sPtr, s.elements()  * sizeof(Tr));
-        writeHostDataArray(u , uPtr, u.elements()  * sizeof(T ));
-        writeHostDataArray(vt, vPtr, vt.elements() * sizeof(T ));
-        writeHostDataArray(in, iPtr, in.elements() * sizeof(T ));
-
-        pinnedFree(sPtr);
-        pinnedFree(uPtr);
-        pinnedFree(vPtr);
-        pinnedFree(iPtr);
-    }
-
-    template <typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-    {
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-
-        // S, U, Vt are empty. Simply write to them
-        Tr *sPtr = pinnedAlloc<Tr>(s.elements());
-        T  *uPtr = pinnedAlloc<T >(u.elements());
-        T  *vPtr = pinnedAlloc<T >(vt.elements());
-        T  *iPtr = pinnedAlloc<T >(in.elements());
-
-        copyData(sPtr, s);
-        copyData(uPtr, u);
-        copyData(vPtr, vt);
-        copyData(iPtr, in);
-
-#if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, iPtr, in.strides()[1],
-                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1]);
-#else
-        std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, iPtr, in.strides()[1],
-                          sPtr, uPtr, u.strides()[1], vPtr, vt.strides()[1], &superb[0]);
-#endif
-        writeHostDataArray(s , sPtr, s.elements()  * sizeof(Tr));
-        writeHostDataArray(u , uPtr, u.elements()  * sizeof(T ));
-        writeHostDataArray(vt, vPtr, vt.elements() * sizeof(T ));
-
-        pinnedFree(sPtr);
-        pinnedFree(uPtr);
-        pinnedFree(vPtr);
-        pinnedFree(iPtr);
-    }
-
-#define INSTANTIATE_SVD(T, Tr)                                          \
-    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, const Array<T> &in); \
-    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, Array<T> &in);
-
-    INSTANTIATE_SVD(float  , float )
-    INSTANTIATE_SVD(double , double)
-    INSTANTIATE_SVD(cfloat , float )
-    INSTANTIATE_SVD(cdouble, double)
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/cpu_svd.hpp b/src/backend/cuda/cpu_lapack/cpu_svd.hpp
deleted file mode 100644
index f5fc1a8e9c..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_svd.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-
-namespace cuda
-{
-namespace cpu
-{
-    template<typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
-
-    template<typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
-}
-}
diff --git a/src/backend/cuda/cpu_lapack/cpu_triangle.hpp b/src/backend/cuda/cpu_lapack/cpu_triangle.hpp
deleted file mode 100644
index fb8fea1fae..0000000000
--- a/src/backend/cuda/cpu_lapack/cpu_triangle.hpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#ifndef CPU_LAPACK_TRIANGLE
-#define CPU_LAPACK_TRIANGLE
-namespace cuda
-{
-namespace cpu
-{
-
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(T *o, const T *i, const dim4 odm, const dim4 ost, const dim4 ist)
-{
-    for(dim_t ow = 0; ow < odm[3]; ow++) {
-        const dim_t oW = ow * ost[3];
-        const dim_t iW = ow * ist[3];
-
-        for(dim_t oz = 0; oz < odm[2]; oz++) {
-            const dim_t oZW = oW + oz * ost[2];
-            const dim_t iZW = iW + oz * ist[2];
-
-            for(dim_t oy = 0; oy < odm[1]; oy++) {
-                const dim_t oYZW = oZW + oy * ost[1];
-                const dim_t iYZW = iZW + oy * ist[1];
-
-                for(dim_t ox = 0; ox < odm[0]; ox++) {
-                    const dim_t oMem = oYZW + ox;
-                    const dim_t iMem = iYZW + ox;
-
-                    bool cond = is_upper ? (oy >= ox) : (oy <= ox);
-                    bool do_unit_diag = (is_unit_diag && ox == oy);
-                    if(cond) {
-                        o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
-                    } else {
-                        o[oMem] = scalar<T>(0);
-                    }
-                }
-            }
-        }
-    }
-}
-
-}
-}
-
-#endif
diff --git a/src/backend/cuda/cpu_lapack/lapack_helper.hpp b/src/backend/cuda/cpu_lapack/lapack_helper.hpp
deleted file mode 100644
index b85a80b10c..0000000000
--- a/src/backend/cuda/cpu_lapack/lapack_helper.hpp
+++ /dev/null
@@ -1,35 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#ifndef AFCPU_LAPACK
-#define AFCPU_LAPACK
-
-#include <types.hpp>
-
-#define lapack_complex_float cuda::cfloat
-#define lapack_complex_double cuda::cdouble
-#define LAPACK_PREFIX LAPACKE_
-#define ORDER_TYPE int
-#define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
-#define LAPACK_NAME(fn) LAPACKE_##fn
-
-#ifdef USE_MKL
-    #include<mkl_lapacke.h>
-#else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-        #include <lapacke.hpp>
-        #undef AF_LAPACK_COL_MAJOR
-        #define AF_LAPACK_COL_MAJOR 0
-    #else // NETLIB LAPACKE
-        #include<lapacke.h>
-    #endif
-#endif
-
-#endif
diff --git a/src/backend/cuda/cusolverDnManager.cpp b/src/backend/cuda/cusolverDnManager.cpp
index 3fa1f9ce11..10e8b5b4f4 100644
--- a/src/backend/cuda/cusolverDnManager.cpp
+++ b/src/backend/cuda/cusolverDnManager.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <cusolverDnManager.hpp>
 #include <platform.hpp>
 #include <debug_cuda.hpp>
@@ -93,5 +91,3 @@ namespace cusolver {
     }
 
 }
-
-#endif
diff --git a/src/backend/cuda/cusolverDnManager.hpp b/src/backend/cuda/cusolverDnManager.hpp
index dbaa694d58..49687996aa 100644
--- a/src/backend/cuda/cusolverDnManager.hpp
+++ b/src/backend/cuda/cusolverDnManager.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
 #pragma once
 
 #include <cuda_runtime.h>
@@ -38,5 +37,3 @@ namespace cusolver
                      AF_ERR_INTERNAL);              \
         }                                           \
     } while(0)
-
-#endif
diff --git a/src/backend/cuda/inverse.cu b/src/backend/cuda/inverse.cu
index b82ec5ca9e..69a3e8f354 100644
--- a/src/backend/cuda/inverse.cu
+++ b/src/backend/cuda/inverse.cu
@@ -10,8 +10,6 @@
 #include <inverse.hpp>
 #include <err_common.hpp>
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <solve.hpp>
 #include <identity.hpp>
 
@@ -34,48 +32,3 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }
-
-#elif defined(WITH_CPU_LINEAR_ALGEBRA)
-#include <cpu_lapack/cpu_inverse.hpp>
-
-namespace cuda
-{
-
-template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
-    return cpu::inverse(in);
-}
-
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
-
-INSTANTIATE(float)
-INSTANTIATE(cfloat)
-INSTANTIATE(double)
-INSTANTIATE(cdouble)
-
-}
-
-#else
-namespace cuda
-{
-
-template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-              AF_ERR_NOT_CONFIGURED);
-}
-
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
-
-INSTANTIATE(float)
-INSTANTIATE(cfloat)
-INSTANTIATE(double)
-INSTANTIATE(cdouble)
-
-}
-
-#endif
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index ce0b545a84..0b92b9593a 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -10,8 +10,6 @@
 #include <lu.hpp>
 #include <err_common.hpp>
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <cusolverDnManager.hpp>
 #include <memory.hpp>
 #include <copy.hpp>
@@ -170,71 +168,3 @@ INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 }
-
-#elif defined(WITH_CPU_LINEAR_ALGEBRA)
-////////////////////////////////////////////////////////////////////////////////
-// For versions earlier than CUDA 7, use CPU fallback
-////////////////////////////////////////////////////////////////////////////////
-#include <cpu_lapack/cpu_lu.hpp>
-
-namespace cuda
-{
-template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
-    return cpu::lu(lower, upper, pivot, in);
-}
-
-template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
-    return cpu::lu_inplace(in, convert_pivot);
-}
-
-bool isLAPACKAvailable()
-{
-    return true;
-}
-
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
-
-INSTANTIATE_LU(float)
-INSTANTIATE_LU(cfloat)
-INSTANTIATE_LU(double)
-INSTANTIATE_LU(cdouble)
-}
-
-#else
-namespace cuda
-{
-template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-bool isLAPACKAvailable()
-{
-    return false;
-}
-
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
-
-INSTANTIATE_LU(float)
-INSTANTIATE_LU(cfloat)
-INSTANTIATE_LU(double)
-INSTANTIATE_LU(cdouble)
-}
-#endif
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index 41ad1c2600..9da6405006 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -10,8 +10,6 @@
 #include <qr.hpp>
 #include <err_common.hpp>
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <cusolverDnManager.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
@@ -218,63 +216,3 @@ INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 }
-
-#elif defined(WITH_CPU_LINEAR_ALGEBRA)
-#include <cpu_lapack/cpu_qr.hpp>
-
-namespace cuda
-{
-
-template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
-    return cpu::qr(q, r, t, in);
-}
-
-template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
-    return cpu::qr_inplace(in);
-}
-
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
-
-INSTANTIATE_QR(float)
-INSTANTIATE_QR(cfloat)
-INSTANTIATE_QR(double)
-INSTANTIATE_QR(cdouble)
-
-}
-
-#else
-namespace cuda
-{
-
-template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
-
-INSTANTIATE_QR(float)
-INSTANTIATE_QR(cfloat)
-INSTANTIATE_QR(double)
-INSTANTIATE_QR(cdouble)
-
-}
-
-#endif
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 9db4889e2f..68e2dcbc9c 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -10,8 +10,6 @@
 #include <err_common.hpp>
 #include <solve.hpp>
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <cusolverDnManager.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
@@ -382,67 +380,3 @@ INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
 }
-
-#elif defined(WITH_CPU_LINEAR_ALGEBRA)
-#include<cpu_lapack/cpu_solve.hpp>
-
-namespace cuda
-{
-
-template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
-    return cpu::solveLU(A, pivot, b, options);
-}
-
-template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-    return cpu::solve(a, b, options);
-}
-
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
-    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
-
-INSTANTIATE_SOLVE(float)
-INSTANTIATE_SOLVE(cfloat)
-INSTANTIATE_SOLVE(double)
-INSTANTIATE_SOLVE(cdouble)
-}
-
-#else
-namespace cuda
-{
-
-template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
-    AF_ERROR("Linear Algebra is diabled on CUDA",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-    AF_ERROR("Linear Algebra is diabled on CUDA",
-              AF_ERR_NOT_CONFIGURED);
-}
-
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
-    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
-
-INSTANTIATE_SOLVE(float)
-INSTANTIATE_SOLVE(cfloat)
-INSTANTIATE_SOLVE(double)
-INSTANTIATE_SOLVE(cdouble)
-}
-
-#endif
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index e07c1f0564..2cea6b9277 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -17,8 +17,6 @@
 #include <math.hpp>
 #include <err_common.hpp>
 
-#if defined(WITH_CUDA_LINEAR_ALGEBRA)
-
 #include <cusolverDnManager.hpp>
 
 namespace cuda
@@ -123,53 +121,6 @@ SVD_SPECIALIZE(cdouble, double, Z);
             transpose_inplace(u, true);
         }
     }
-}
-#elif defined(WITH_CPU_LINEAR_ALGEBRA)
-
-#include <cpu_lapack/cpu_svd.hpp>
-
-namespace cuda
-{
-
-template<typename T, typename Tr>
-void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-{
-    return cpu::svd<T, Tr>(s, u, vt, in);
-}
-
-template<typename T, typename Tr>
-void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-{
-    return cpu::svdInPlace<T, Tr>(s, u, vt, in);
-}
-
-}
-
-#else
-
-namespace cuda
-{
-
-template<typename T, typename Tr>
-void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-template<typename T, typename Tr>
-void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-{
-    AF_ERROR("CUDA cusolver not available. Linear Algebra is disabled",
-             AF_ERR_NOT_CONFIGURED);
-}
-
-}
-
-#endif
-
-namespace cuda
-{
 
 #define INSTANTIATE(T, Tr)                                              \
     template void svd<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in); \

From 48230c286aa50541134e3fc3eb56662e09c0f648 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 21 Dec 2016 16:05:54 -0500
Subject: [PATCH 1055/2677] Fix for the OSX installer

---
 CMakeModules/osx_install/OSXInstaller.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 3d6b4a11f2..555bd3e245 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -97,6 +97,7 @@ IF(BUILD_GRAPHICS)
     STRING(SUBSTRING ${FORGE_VERSION} 0 1 FORGE_VERSION_MAJOR) # Will return x
 
     ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_LIB)
+    SET(FORGE_LIB "${CMAKE_INSTALL_PREFIX}/lib/libforge.${FORGE_VERSION}.dylib")
     FOREACH(SRC ${FORGE_LIB})
         FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
         ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD

From acc23bafb543c48f52bc8cfe90cbe2283d70c0fb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Wed, 21 Dec 2016 16:11:25 -0500
Subject: [PATCH 1056/2677] CUDA: Assert CUDA 7.0 or newer requirement

Also remove boost inline for older CUDA versions
---
 src/backend/cuda/Array.hpp | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 127e0344e1..35e7586f14 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -8,19 +8,13 @@
  ********************************************************/
 #pragma once
 
-// Workaround for BOOST_NOINLINE not being defined with nvcc / CUDA < 6.5
-#if CUDA_VERSION < 6050
-#ifndef BOOST_NOINLINE
-#define BOOST_NOINLINE __attribute__ ((noinline))
-#endif
-#endif
-
 #include <af/dim4.hpp>
 #include <ArrayInfo.hpp>
 #include "traits.hpp"
 #include <backend.hpp>
 #include <types.hpp>
 #include <traits.hpp>
+#include <cuda.h>
 #include <cuda_runtime_api.h>
 #include <Param.hpp>
 #include <JIT/Node.hpp>
@@ -28,6 +22,10 @@
 #include <vector>
 #include <memory.hpp>
 
+#if CUDA_VERSION < 7000
+    #error "ArrayFire CUDA requires CUDA Toolkit Version 7.0 or newer."
+#endif
+
 namespace cuda
 {
 

From ddf6fde42ec42141f7e32e89b26acbfadb352f07 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Dec 2016 00:11:37 +0530
Subject: [PATCH 1057/2677] Merge cuda::InteropManager into cuda::DeviceManager

Access to InteropManager has to go through device manager now.
---
 src/backend/cuda/hist_graphics.cpp  |  6 ++---
 src/backend/cuda/image.cpp          |  4 ++--
 src/backend/cuda/interopManager.cpp | 28 ----------------------
 src/backend/cuda/interopManager.hpp |  5 +---
 src/backend/cuda/platform.cpp       | 36 ++++++++++++++++++++++++++++-
 src/backend/cuda/platform.hpp       | 12 ++++++++++
 src/backend/cuda/plot.cpp           |  6 ++---
 src/backend/cuda/surface.cpp        |  6 ++---
 src/backend/cuda/vector_field.cpp   |  6 ++---
 9 files changed, 62 insertions(+), 47 deletions(-)

diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 4e9017da88..d207961f59 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -9,11 +9,11 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
 #include <hist_graphics.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <interopManager.hpp>
 
 namespace cuda
 {
@@ -22,10 +22,10 @@ using namespace gl;
 template<typename T>
 void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 {
-    if(InteropManager::checkGraphicsInteropCapability()) {
+    if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 2303184daf..ca87d3129f 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -27,8 +27,8 @@ using namespace gl;
 template<typename T>
 void copy_image(const Array<T> &in, const forge::Image* image)
 {
-    if(InteropManager::checkGraphicsInteropCapability()) {
-        InteropManager& intrpMngr = InteropManager::getInstance();
+    if(DeviceManager::checkGraphicsInteropCapability()) {
+        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
 
diff --git a/src/backend/cuda/interopManager.cpp b/src/backend/cuda/interopManager.cpp
index 077e4ac703..a8339a52f4 100644
--- a/src/backend/cuda/interopManager.cpp
+++ b/src/backend/cuda/interopManager.cpp
@@ -50,12 +50,6 @@ InteropManager::~InteropManager()
     }
 }
 
-InteropManager& InteropManager::getInstance()
-{
-    static InteropManager my_instance;
-    return my_instance;
-}
-
 interop_t& InteropManager::getDeviceMap(int device)
 {
     return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
@@ -172,28 +166,6 @@ CGR_t* InteropManager::getBufferResource(const forge::VectorField* key)
     return &i_map[key_value].front();
 }
 
-bool InteropManager::checkGraphicsInteropCapability()
-{
-    static bool run_once = true;
-    static bool capable  = true;
-
-    if(run_once) {
-        unsigned int pCudaEnabledDeviceCount = 0;
-        int pCudaGraphicsEnabledDeviceIds = 0;
-        cudaGetLastError(); // Reset Errors
-        cudaError_t err = cudaGLGetDevices(&pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds, getDeviceCount(), cudaGLDeviceListAll);
-        if(err == 63) { // OS Support Failure - Happens when devices are only Tesla
-            capable = false;
-            printf("Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL Interop will use CPU fallback.\n");
-            printf("Corresponding CUDA Error (%d): %s.\n", err, cudaGetErrorString(err));
-            printf("This may happen if all CUDA Devices are in TCC Mode and/or not connected to a display.\n");
-        }
-        cudaGetLastError(); // Reset Errors
-        run_once = false;
-    }
-    return capable;
-}
-
 }
 
 #endif
diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
index deb4f126e9..38ecee2400 100644
--- a/src/backend/cuda/interopManager.hpp
+++ b/src/backend/cuda/interopManager.hpp
@@ -52,9 +52,7 @@ class InteropManager
         interop_t interop_maps[DeviceManager::MAX_DEVICES];
 
     public:
-        static InteropManager& getInstance();
-        static bool checkGraphicsInteropCapability();
-
+        InteropManager() {}
         ~InteropManager();
         CGR_t* getBufferResource(const forge::Image       *handle);
         CGR_t* getBufferResource(const forge::Plot        *handle);
@@ -63,7 +61,6 @@ class InteropManager
         CGR_t* getBufferResource(const forge::VectorField *handle);
 
     protected:
-        InteropManager() {}
         InteropManager(InteropManager const&);
         void operator=(InteropManager const&);
         interop_t& getDeviceMap(int device = -1); // default will return current device
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 8a13993a12..752ea71c96 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -24,6 +24,7 @@
 #include <err_cuda.hpp>
 #include <util.hpp>
 #include <host_memory.hpp>
+#include <interopManager.hpp>
 
 using namespace std;
 
@@ -350,14 +351,47 @@ cudaDeviceProp getDeviceProp(int device)
 ///////////////////////////////////////////////////////////////////////////
 // DeviceManager Class Functions
 ///////////////////////////////////////////////////////////////////////////
+bool DeviceManager::checkGraphicsInteropCapability()
+{
+    static bool run_once = true;
+    static bool capable  = true;
+
+    if(run_once) {
+        unsigned int pCudaEnabledDeviceCount = 0;
+        int pCudaGraphicsEnabledDeviceIds = 0;
+        cudaGetLastError(); // Reset Errors
+        cudaError_t err = cudaGLGetDevices(&pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds, getDeviceCount(), cudaGLDeviceListAll);
+        if(err == 63) { // OS Support Failure - Happens when devices are only Tesla
+            capable = false;
+            printf("Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL Interop will use CPU fallback.\n");
+            printf("Corresponding CUDA Error (%d): %s.\n", err, cudaGetErrorString(err));
+            printf("This may happen if all CUDA Devices are in TCC Mode and/or not connected to a display.\n");
+        }
+        cudaGetLastError(); // Reset Errors
+        run_once = false;
+    }
+
+    return capable;
+}
+
 DeviceManager& DeviceManager::getInstance()
 {
     static DeviceManager my_instance;
     return my_instance;
 }
 
+DeviceManager::~DeviceManager()
+{
+    if (gfxManager) delete gfxManager;
+}
+
+InteropManager& DeviceManager::getGfxInteropManager()
+{
+    return *gfxManager;
+}
+
 DeviceManager::DeviceManager()
-    : cuDevices(0), activeDev(0), nDevices(0)
+    : cuDevices(0), activeDev(0), nDevices(0), gfxManager(new InteropManager())
 {
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
     if (nDevices == 0)
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index fa73ccf5e0..ca7ba418bd 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -66,13 +66,21 @@ struct cudaDevice_t {
 
 bool& evalFlag();
 
+class InteropManager;
+
 class DeviceManager
 {
     public:
         static const unsigned MAX_DEVICES = 16;
 
+        static bool checkGraphicsInteropCapability();
+
         static DeviceManager& getInstance();
 
+        ~DeviceManager();
+
+        InteropManager& getGfxInteropManager();
+
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -119,6 +127,10 @@ class DeviceManager
         int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
+        //FIXME: Once C++11 has been enabled in CUDA backend
+        //shift to use of smart pointer. In this, unique_ptr
+        //should be good
+        InteropManager* gfxManager;
 };
 
 }
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 6c1aad70af..40ae7b4574 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -9,7 +9,6 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
 #include <plot.hpp>
 #include <err_cuda.hpp>
@@ -17,6 +16,7 @@
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
+#include <interopManager.hpp>
 
 using af::dim4;
 
@@ -27,10 +27,10 @@ using namespace gl;
 template<typename T>
 void copy_plot(const Array<T> &P, forge::Plot* plot)
 {
-    if(InteropManager::checkGraphicsInteropCapability()) {
+    if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 6d84d714b6..18385decd6 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -9,7 +9,6 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
 #include <surface.hpp>
 #include <err_cuda.hpp>
@@ -17,6 +16,7 @@
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
+#include <interopManager.hpp>
 
 using af::dim4;
 
@@ -27,10 +27,10 @@ using namespace gl;
 template<typename T>
 void copy_surface(const Array<T> &P, forge::Surface* surface)
 {
-    if(InteropManager::checkGraphicsInteropCapability()) {
+    if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 7ce587f199..6fa4dd9fee 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -9,11 +9,11 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
 #include <vector_field.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <interopManager.hpp>
 
 using af::dim4;
 
@@ -25,8 +25,8 @@ template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        forge::VectorField* vector_field)
 {
-    if(InteropManager::checkGraphicsInteropCapability()) {
-        InteropManager& intrpMngr = InteropManager::getInstance();
+    if(DeviceManager::checkGraphicsInteropCapability()) {
+        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(vector_field);
 

From 6ff8d910914581369a3ede508465716cd5fd3fbe Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Dec 2016 17:31:12 +0530
Subject: [PATCH 1058/2677] Merge cufft plan cacher into cuda::DeviceManager

Access to fft plan cacher singleton has to go through
device manager now.
---
 src/backend/cuda/cufftManager.cpp       | 149 ++++++++++++++++++++++
 src/backend/cuda/cufftManager.hpp       | 121 ++++++++++++++++++
 src/backend/cuda/err_cufft.hpp          |  92 --------------
 src/backend/cuda/fft.cpp                | 159 +-----------------------
 src/backend/cuda/kernel/fftconvolve.hpp |   2 -
 src/backend/cuda/platform.cpp           |   5 +
 src/backend/cuda/platform.hpp           |   7 ++
 7 files changed, 287 insertions(+), 248 deletions(-)
 create mode 100644 src/backend/cuda/cufftManager.cpp
 create mode 100644 src/backend/cuda/cufftManager.hpp
 delete mode 100644 src/backend/cuda/err_cufft.hpp

diff --git a/src/backend/cuda/cufftManager.cpp b/src/backend/cuda/cufftManager.cpp
new file mode 100644
index 0000000000..f3aea5f45a
--- /dev/null
+++ b/src/backend/cuda/cufftManager.cpp
@@ -0,0 +1,149 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cuda.h> // Need this for CUDA_VERSION
+#include <platform.hpp>
+#include <memory.hpp>
+
+namespace cufft
+{
+
+const char * _cufftGetResultString(cufftResult res)
+{
+    switch (res)
+    {
+        case CUFFT_SUCCESS:
+            return "cuFFT: success";
+
+        case CUFFT_INVALID_PLAN:
+            return "cuFFT: invalid plan handle passed";
+
+        case CUFFT_ALLOC_FAILED:
+            return "cuFFT: resources allocation failed";
+
+        case CUFFT_INVALID_TYPE:
+            return "cuFFT: invalid type (deprecated)";
+
+        case CUFFT_INVALID_VALUE:
+            return "cuFFT: invalid parameters passed to cuFFT API";
+
+        case CUFFT_INTERNAL_ERROR:
+            return "cuFFT: internal error detected using cuFFT";
+
+        case CUFFT_EXEC_FAILED:
+            return "cuFFT: FFT execution failed";
+
+        case CUFFT_SETUP_FAILED:
+            return "cuFFT: library initialization failed";
+
+        case CUFFT_INVALID_SIZE:
+            return "cuFFT: invalid size parameters passed";
+
+        case CUFFT_UNALIGNED_DATA:
+            return "cuFFT: unaligned data (deprecated)";
+
+        case CUFFT_INCOMPLETE_PARAMETER_LIST:
+            return "cuFFT: call is missing parameters";
+
+        case CUFFT_INVALID_DEVICE:
+            return "cuFFT: plan execution different than plan creation";
+
+        case CUFFT_PARSE_ERROR:
+            return "cuFFT: plan parse error";
+
+        case CUFFT_NO_WORKSPACE:
+            return "cuFFT: no workspace provided";
+
+#if CUDA_VERSION >= 6050
+        case CUFFT_NOT_IMPLEMENTED:
+            return "cuFFT: not implemented";
+
+        case CUFFT_LICENSE_ERROR:
+            return "cuFFT: license error";
+#endif
+#if CUDA_VERSION >= 8000
+        case CUFFT_NOT_SUPPORTED:
+            return "cuFFT: not supported";
+#endif
+    }
+
+    return "cuFFT: unknown error";
+}
+
+void findPlan(cufftHandle &plan, int rank, int *n,
+              int *inembed, int istride, int idist,
+              int *onembed, int ostride, int odist,
+              cufftType type, int batch)
+{
+    // create the key string
+    char key_str_temp[64];
+    sprintf(key_str_temp, "%d:", rank);
+
+    std::string key_string(key_str_temp);
+
+    for(int r=0; r<rank; ++r) {
+        sprintf(key_str_temp, "%d:", n[r]);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    if (inembed!=NULL) {
+        for(int r=0; r<rank; ++r) {
+            sprintf(key_str_temp, "%d:", inembed[r]);
+            key_string.append(std::string(key_str_temp));
+        }
+        sprintf(key_str_temp, "%d:%d:", istride, idist);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    if (onembed!=NULL) {
+        for(int r=0; r<rank; ++r) {
+            sprintf(key_str_temp, "%d:", onembed[r]);
+            key_string.append(std::string(key_str_temp));
+        }
+        sprintf(key_str_temp, "%d:%d:", ostride, odist);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    sprintf(key_str_temp, "%d:%d", (int)type, batch);
+    key_string.append(std::string(key_str_temp));
+
+    // find the matching plan_index in the array cuFFTPlanner::mKeys
+    cuFFTPlanner &planner = cuda::DeviceManager::getInstance().getcufftPlanManager();
+
+    int planIndex = planner.findIfPlanExists(key_string);
+
+    // if found a valid plan, return it
+    if (planIndex!=-1) {
+        plan = planner.getPlan(planIndex);
+        return;
+    }
+
+    cufftHandle temp;
+    cufftResult res = cufftPlanMany(&temp, rank, n,
+                                    inembed, istride, idist,
+                                    onembed, ostride, odist,
+                                    type, batch);
+
+    // If plan creation fails, clean up the memory we hold on to and try again
+    if (res != CUFFT_SUCCESS) {
+        cuda::garbageCollect();
+        CUFFT_CHECK(cufftPlanMany(&temp, rank, n,
+                                  inembed, istride, idist,
+                                  onembed, ostride, odist,
+                                  type, batch));
+    }
+
+    plan = temp;
+    cufftSetStream(plan, cuda::getStream(cuda::getActiveDeviceId()));
+
+    // push the plan into plan cache
+    planner.pushPlan(key_string, plan);
+}
+
+}
diff --git a/src/backend/cuda/cufftManager.hpp b/src/backend/cuda/cufftManager.hpp
new file mode 100644
index 0000000000..2d78f891c2
--- /dev/null
+++ b/src/backend/cuda/cufftManager.hpp
@@ -0,0 +1,121 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <err_common.hpp>
+#include <cufft.h>
+
+#include <cstdio>
+#include <deque>
+#include <string>
+#include <utility>
+
+namespace cuda
+{
+class DeviceManager;
+}
+
+namespace cufft
+{
+
+typedef std::pair<std::string, cufftHandle> FFTPlanPair;
+typedef std::deque<FFTPlanPair> FFTPlanCache;
+
+const char * _cufftGetResultString(cufftResult res);
+
+void findPlan(cufftHandle &plan, int rank, int *n,
+              int *inembed, int istride, int idist,
+              int *onembed, int ostride, int odist,
+              cufftType type, int batch);
+
+// cuFFTPlanner caches fft plans
+//
+// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
+//          |
+//          |--> ELSE just push the plan
+// existing plan -> reuse a plan
+class cuFFTPlanner
+{
+    friend class cuda::DeviceManager;
+
+    friend void findPlan(cufftHandle &plan, int rank, int *n,
+                         int *inembed, int istride, int idist,
+                         int *onembed, int ostride, int odist,
+                         cufftType type, int batch);
+
+    public:
+        inline void setMaxCacheSize(size_t size) {
+            mCache.resize(size, FFTPlanPair(std::string(""), 0));
+        }
+
+        inline size_t getMaxCacheSize() const {
+            return mMaxCacheSize;
+        }
+
+        inline cufftHandle getPlan(int index) const {
+            return mCache[index].second;
+        }
+
+        // iterates through plan cache from front to back
+        // of the cache(queue)
+        int findIfPlanExists(std::string keyString) const {
+            int retVal = -1;
+            for(uint i=0; i<mCache.size(); ++i) {
+                if (keyString == mCache[i].first) {
+                    retVal = i;
+                }
+            }
+            return retVal;
+        }
+
+        // pops plan from the back of cache(queue)
+        void popPlan() {
+            if (!mCache.empty()) {
+                // destroy the cufft plan associated with the
+                // least recently used plan
+                cufftDestroy(mCache.back().second);
+                // now pop the entry from cache
+                mCache.pop_back();
+            }
+        }
+
+        // pushes plan to the front of cache(queue)
+        void pushPlan(std::string keyString, cufftHandle plan) {
+            if (mCache.size()>mMaxCacheSize) {
+                popPlan();
+            }
+            mCache.push_front(FFTPlanPair(keyString, plan));
+        }
+
+    private:
+        cuFFTPlanner() : mMaxCacheSize(5) {}
+        cuFFTPlanner(cuFFTPlanner const&);
+        void operator=(cuFFTPlanner const&);
+
+        size_t       mMaxCacheSize;
+        FFTPlanCache mCache;
+};
+
+}
+
+#define CUFFT_CHECK(fn) do {                        \
+        cufftResult _cufft_res = fn;                \
+        if (_cufft_res != CUFFT_SUCCESS) {          \
+            char cufft_res_msg[1024];               \
+            snprintf(cufft_res_msg,                 \
+                     sizeof(cufft_res_msg),         \
+                     "cuFFT Error (%d): %s\n",      \
+                     (int)(_cufft_res),             \
+                     cufft::_cufftGetResultString(  \
+                         _cufft_res));              \
+                                                    \
+            AF_ERROR(cufft_res_msg,                 \
+                     AF_ERR_INTERNAL);              \
+        }                                           \
+    } while(0)
diff --git a/src/backend/cuda/err_cufft.hpp b/src/backend/cuda/err_cufft.hpp
deleted file mode 100644
index 99fcd97cee..0000000000
--- a/src/backend/cuda/err_cufft.hpp
+++ /dev/null
@@ -1,92 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <stdio.h>
-#include <err_common.hpp>
-#include <cuda.h> // Need this for CUDA_VERSION
-#include <cufft.h>
-
-static const char * _cufftGetResultString(cufftResult res)
-{
-    switch (res)
-    {
-        case CUFFT_SUCCESS:
-            return "cuFFT: success";
-
-        case CUFFT_INVALID_PLAN:
-            return "cuFFT: invalid plan handle passed";
-
-        case CUFFT_ALLOC_FAILED:
-            return "cuFFT: resources allocation failed";
-
-        case CUFFT_INVALID_TYPE:
-            return "cuFFT: invalid type (deprecated)";
-
-        case CUFFT_INVALID_VALUE:
-            return "cuFFT: invalid parameters passed to cuFFT API";
-
-        case CUFFT_INTERNAL_ERROR:
-            return "cuFFT: internal error detected using cuFFT";
-
-        case CUFFT_EXEC_FAILED:
-            return "cuFFT: FFT execution failed";
-
-        case CUFFT_SETUP_FAILED:
-            return "cuFFT: library initialization failed";
-
-        case CUFFT_INVALID_SIZE:
-            return "cuFFT: invalid size parameters passed";
-
-        case CUFFT_UNALIGNED_DATA:
-            return "cuFFT: unaligned data (deprecated)";
-
-        case CUFFT_INCOMPLETE_PARAMETER_LIST:
-            return "cuFFT: call is missing parameters";
-
-        case CUFFT_INVALID_DEVICE:
-            return "cuFFT: plan execution different than plan creation";
-
-        case CUFFT_PARSE_ERROR:
-            return "cuFFT: plan parse error";
-
-        case CUFFT_NO_WORKSPACE:
-            return "cuFFT: no workspace provided";
-
-#if CUDA_VERSION >= 6050
-        case CUFFT_NOT_IMPLEMENTED:
-            return "cuFFT: not implemented";
-
-        case CUFFT_LICENSE_ERROR:
-            return "cuFFT: license error";
-#endif
-#if CUDA_VERSION >= 8000
-        case CUFFT_NOT_SUPPORTED:
-            return "cuFFT: not supported";
-#endif
-    }
-
-    return "cuFFT: unknown error";
-}
-
-#define CUFFT_CHECK(fn) do {                    \
-        cufftResult _cufft_res = fn;            \
-        if (_cufft_res != CUFFT_SUCCESS) {      \
-            char cufft_res_msg[1024];           \
-            snprintf(cufft_res_msg,             \
-                     sizeof(cufft_res_msg),     \
-                     "cuFFT Error (%d): %s\n",  \
-                     (int)(_cufft_res),         \
-                     _cufftGetResultString(     \
-                         _cufft_res));          \
-                                                \
-            AF_ERROR(cufft_res_msg,             \
-                     AF_ERR_INTERNAL);          \
-        }                                       \
-    } while(0)
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 30e61084ce..b2b36c90e1 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -12,13 +12,8 @@
 #include <copy.hpp>
 #include <fft.hpp>
 #include <debug_cuda.hpp>
-#include <err_cufft.hpp>
-#include <cufft.h>
+#include <cufftManager.hpp>
 #include <math.hpp>
-#include <string>
-#include <deque>
-#include <utility>
-#include <cstdio>
 #include <memory.hpp>
 
 using af::dim4;
@@ -27,153 +22,9 @@ using std::string;
 namespace cuda
 {
 
-typedef std::pair<string, cufftHandle> FFTPlanPair;
-typedef std::deque<FFTPlanPair> FFTPlanCache;
-
-// cuFFTPlanner caches fft plans
-//
-// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
-//          |
-//          |--> ELSE just push the plan
-// existing plan -> reuse a plan
-class cuFFTPlanner
-{
-    friend void find_cufft_plan(cufftHandle &plan, int rank, int *n,
-                                int *inembed, int istride, int idist,
-                                int *onembed, int ostride, int odist,
-                                cufftType type, int batch);
-
-    public:
-        static cuFFTPlanner& getInstance() {
-            static cuFFTPlanner instances[cuda::DeviceManager::MAX_DEVICES];
-            return instances[cuda::getActiveDeviceId()];
-        }
-
-        inline void setMaxCacheSize(size_t size) {
-            mCache.resize(size, FFTPlanPair(std::string(""), 0));
-        }
-
-        inline size_t getMaxCacheSize() const {
-            return mMaxCacheSize;
-        }
-
-        inline cufftHandle getPlan(int index) const {
-            return mCache[index].second;
-        }
-
-        // iterates through plan cache from front to back
-        // of the cache(queue)
-        int findIfPlanExists(std::string keyString) const {
-            int retVal = -1;
-            for(uint i=0; i<mCache.size(); ++i) {
-                if (keyString == mCache[i].first) {
-                    retVal = i;
-                }
-            }
-            return retVal;
-        }
-
-        // pops plan from the back of cache(queue)
-        void popPlan() {
-            if (!mCache.empty()) {
-                // destroy the cufft plan associated with the
-                // least recently used plan
-                cufftDestroy(mCache.back().second);
-                // now pop the entry from cache
-                mCache.pop_back();
-            }
-        }
-
-        // pushes plan to the front of cache(queue)
-        void pushPlan(std::string keyString, cufftHandle plan) {
-            if (mCache.size()>mMaxCacheSize) {
-                popPlan();
-            }
-            mCache.push_front(FFTPlanPair(keyString, plan));
-        }
-
-    private:
-        cuFFTPlanner() : mMaxCacheSize(5) {}
-        cuFFTPlanner(cuFFTPlanner const&);
-        void operator=(cuFFTPlanner const&);
-
-        size_t       mMaxCacheSize;
-        FFTPlanCache mCache;
-};
-
-void find_cufft_plan(cufftHandle &plan, int rank, int *n,
-                     int *inembed, int istride, int idist,
-                     int *onembed, int ostride, int odist,
-                     cufftType type, int batch)
-{
-    // create the key string
-    char key_str_temp[64];
-    sprintf(key_str_temp, "%d:", rank);
-
-    string key_string(key_str_temp);
-
-    for(int r=0; r<rank; ++r) {
-        sprintf(key_str_temp, "%d:", n[r]);
-        key_string.append(std::string(key_str_temp));
-    }
-
-    if (inembed!=NULL) {
-        for(int r=0; r<rank; ++r) {
-            sprintf(key_str_temp, "%d:", inembed[r]);
-            key_string.append(std::string(key_str_temp));
-        }
-        sprintf(key_str_temp, "%d:%d:", istride, idist);
-        key_string.append(std::string(key_str_temp));
-    }
-
-    if (onembed!=NULL) {
-        for(int r=0; r<rank; ++r) {
-            sprintf(key_str_temp, "%d:", onembed[r]);
-            key_string.append(std::string(key_str_temp));
-        }
-        sprintf(key_str_temp, "%d:%d:", ostride, odist);
-        key_string.append(std::string(key_str_temp));
-    }
-
-    sprintf(key_str_temp, "%d:%d", (int)type, batch);
-    key_string.append(std::string(key_str_temp));
-
-    // find the matching plan_index in the array cuFFTPlanner::mKeys
-    cuFFTPlanner &planner = cuFFTPlanner::getInstance();
-
-    int planIndex = planner.findIfPlanExists(key_string);
-
-    // if found a valid plan, return it
-    if (planIndex!=-1) {
-        plan = planner.getPlan(planIndex);
-        return;
-    }
-
-    cufftHandle temp;
-    cufftResult res = cufftPlanMany(&temp, rank, n,
-                                    inembed, istride, idist,
-                                    onembed, ostride, odist,
-                                    type, batch);
-
-    // If plan creation fails, clean up the memory we hold on to and try again
-    if (res != CUFFT_SUCCESS) {
-        garbageCollect();
-        CUFFT_CHECK(cufftPlanMany(&temp, rank, n,
-                                  inembed, istride, idist,
-                                  onembed, ostride, odist,
-                                  type, batch));
-    }
-
-    plan = temp;
-    cufftSetStream(plan, cuda::getStream(cuda::getActiveDeviceId()));
-
-    // push the plan into plan cache
-    planner.pushPlan(key_string, plan);
-}
-
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    cuFFTPlanner::getInstance().setMaxCacheSize(numPlans);
+    DeviceManager::getInstance().getcufftPlanManager().setMaxCacheSize(numPlans);
 }
 
 template<typename T>
@@ -239,7 +90,7 @@ void fft_inplace(Array<T> &in)
     }
 
     cufftHandle plan;
-    find_cufft_plan(plan, rank, t_dims,
+    cufft::findPlan(plan, rank, t_dims,
                     in_embed , istrides[0], istrides[rank],
                     in_embed , istrides[0], istrides[rank],
                     (cufftType)cufft_transform<T>::type, batch);
@@ -274,7 +125,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     dim4 ostrides = out.strides();
 
     cufftHandle plan;
-    find_cufft_plan(plan, rank, t_dims,
+    cufft::findPlan(plan, rank, t_dims,
                     in_embed  , istrides[0], istrides[rank],
                     out_embed , ostrides[0], ostrides[rank],
                     (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
@@ -307,7 +158,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     cufft_real_transform<Tr, Tc> transform;
 
     cufftHandle plan;
-    find_cufft_plan(plan, rank, t_dims,
+    cufft::findPlan(plan, rank, t_dims,
                     in_embed  , istrides[0], istrides[rank],
                     out_embed , ostrides[0], ostrides[rank],
                     (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index d7412452a0..399e3f389e 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -10,11 +10,9 @@
 #include <backend.hpp>
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
-#include <err_cufft.hpp>
 #include <debug_cuda.hpp>
 #include <Param.hpp>
 #include <memory.hpp>
-#include <cufft.h>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 752ea71c96..c696854c14 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -390,6 +390,11 @@ InteropManager& DeviceManager::getGfxInteropManager()
     return *gfxManager;
 }
 
+cufft::cuFFTPlanner& DeviceManager::getcufftPlanManager()
+{
+    return cufftManagers[cuda::getActiveDeviceId()];
+}
+
 DeviceManager::DeviceManager()
     : cuDevices(0), activeDev(0), nDevices(0), gfxManager(new InteropManager())
 {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ca7ba418bd..42e16aa969 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -17,6 +17,8 @@
 #include <fg/window.h>
 #endif
 
+#include <cufftManager.hpp>
+
 namespace cuda
 {
 
@@ -81,6 +83,8 @@ class DeviceManager
 
         InteropManager& getGfxInteropManager();
 
+        cufft::cuFFTPlanner& getcufftPlanManager();
+
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -127,10 +131,13 @@ class DeviceManager
         int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
+
         //FIXME: Once C++11 has been enabled in CUDA backend
         //shift to use of smart pointer. In this, unique_ptr
         //should be good
         InteropManager* gfxManager;
+
+        cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];
 };
 
 }

From b789dcf0107102d638db0918526e5012b73dcaf7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Dec 2016 19:39:01 +0530
Subject: [PATCH 1059/2677] Merge cublas handle manager into
 cuda::DeviceManager

---
 src/backend/cuda/blas.cpp          |  8 ++-
 src/backend/cuda/cublasManager.cpp | 79 ++++++++++++------------------
 src/backend/cuda/cublasManager.hpp | 30 ++++++++++--
 src/backend/cuda/platform.cpp      | 11 +++++
 src/backend/cuda/platform.hpp      |  7 +++
 5 files changed, 78 insertions(+), 57 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 9d3b9ca7b7..d5c475de6e 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -25,8 +25,6 @@
 namespace cuda
 {
 
-using cublas::getHandle;
-
 cublasOperation_t
 toCblasTranspose(af_mat_prop opt)
 {
@@ -173,7 +171,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
         CUBLAS_CHECK(gemv_func<T>()(
-                         getHandle(),
+                         DeviceManager::getInstance().getcublasHandle(),
                          lOpts,
                          lDims[0],
                          lDims[1],
@@ -184,7 +182,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                          out.get(), 1));
     } else {
         CUBLAS_CHECK(gemm_func<T>()(
-                         getHandle(),
+                         DeviceManager::getInstance().getcublasHandle(),
                          lOpts,
                          rOpts,
                          M, N, K,
@@ -226,7 +224,7 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans,
     dim4 rStrides = rhs.strides();
 
     CUBLAS_CHECK(trsm_func<T>()(
-                     getHandle(),
+                     DeviceManager::getInstance().getcublasHandle(),
                      is_left  ? CUBLAS_SIDE_LEFT : CUBLAS_SIDE_RIGHT,
                      is_upper ? CUBLAS_FILL_MODE_UPPER : CUBLAS_FILL_MODE_LOWER,
                      toCblasTranspose(trans),
diff --git a/src/backend/cuda/cublasManager.cpp b/src/backend/cuda/cublasManager.cpp
index ca6cfbb2e0..86e167bbef 100644
--- a/src/backend/cuda/cublasManager.cpp
+++ b/src/backend/cuda/cublasManager.cpp
@@ -14,62 +14,43 @@
 #include <boost/scoped_ptr.hpp>
 #include <platform.hpp>
 
-namespace cublas {
+namespace cublas
+{
 
-    const char *errorString(cublasStatus_t err)
-    {
+const char *errorString(cublasStatus_t err)
+{
 
-        switch(err)
-        {
-        case    CUBLAS_STATUS_SUCCESS:              return "CUBLAS_STATUS_SUCCESS";
-        case    CUBLAS_STATUS_NOT_INITIALIZED:      return "CUBLAS_STATUS_NOT_INITIALIZED";
-        case    CUBLAS_STATUS_ALLOC_FAILED:         return "CUBLAS_STATUS_ALLOC_FAILED";
-        case    CUBLAS_STATUS_INVALID_VALUE:        return "CUBLAS_STATUS_INVALID_VALUE";
-        case    CUBLAS_STATUS_ARCH_MISMATCH:        return "CUBLAS_STATUS_ARCH_MISMATCH";
-        case    CUBLAS_STATUS_MAPPING_ERROR:        return "CUBLAS_STATUS_MAPPING_ERROR";
-        case    CUBLAS_STATUS_EXECUTION_FAILED:     return "CUBLAS_STATUS_EXECUTION_FAILED";
-        case    CUBLAS_STATUS_INTERNAL_ERROR:       return "CUBLAS_STATUS_INTERNAL_ERROR";
+    switch(err)
+    {
+    case    CUBLAS_STATUS_SUCCESS:              return "CUBLAS_STATUS_SUCCESS";
+    case    CUBLAS_STATUS_NOT_INITIALIZED:      return "CUBLAS_STATUS_NOT_INITIALIZED";
+    case    CUBLAS_STATUS_ALLOC_FAILED:         return "CUBLAS_STATUS_ALLOC_FAILED";
+    case    CUBLAS_STATUS_INVALID_VALUE:        return "CUBLAS_STATUS_INVALID_VALUE";
+    case    CUBLAS_STATUS_ARCH_MISMATCH:        return "CUBLAS_STATUS_ARCH_MISMATCH";
+    case    CUBLAS_STATUS_MAPPING_ERROR:        return "CUBLAS_STATUS_MAPPING_ERROR";
+    case    CUBLAS_STATUS_EXECUTION_FAILED:     return "CUBLAS_STATUS_EXECUTION_FAILED";
+    case    CUBLAS_STATUS_INTERNAL_ERROR:       return "CUBLAS_STATUS_INTERNAL_ERROR";
 #if CUDA_VERSION > 5050
-        case    CUBLAS_STATUS_NOT_SUPPORTED:        return "CUBLAS_STATUS_NOT_SUPPORTED";
+    case    CUBLAS_STATUS_NOT_SUPPORTED:        return "CUBLAS_STATUS_NOT_SUPPORTED";
 #endif
-        default:                                    return "UNKNOWN";
-        }
+    default:                                    return "UNKNOWN";
     }
+}
 
-    //RAII class around the cublas Handle
-    class cublasHandle
-    {
-        cublasHandle_t handle;
-    public:
-
-        cublasHandle()  : handle(0)
-        {
-            CUBLAS_CHECK(cublasCreate(&handle));
-            CUBLAS_CHECK(cublasSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
-        }
-
-        ~cublasHandle()
-        {
-            cublasDestroy(handle);
-        }
-
-        cublasHandle_t get() const
-        {
-            return handle;
-        }
-    };
-
-    cublasHandle_t getHandle()
-    {
-        using boost::scoped_ptr;
-        static scoped_ptr<cublasHandle> handle[cuda::DeviceManager::MAX_DEVICES];
+cublasHandle::cublasHandle()  : handle(0)
+{
+    CUBLAS_CHECK(cublasCreate(&handle));
+    CUBLAS_CHECK(cublasSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
+}
 
-        int id = cuda::getActiveDeviceId();
+cublasHandle::~cublasHandle()
+{
+    cublasDestroy(handle);
+}
 
-        if(!handle[id]) {
-            handle[id].reset(new cublasHandle());
-        }
+cublasHandle_t cublasHandle::get() const
+{
+    return handle;
+}
 
-        return handle[id]->get();
-    }
 }
diff --git a/src/backend/cuda/cublasManager.hpp b/src/backend/cuda/cublasManager.hpp
index 52a2793342..41fb567e04 100644
--- a/src/backend/cuda/cublasManager.hpp
+++ b/src/backend/cuda/cublasManager.hpp
@@ -13,11 +13,35 @@
 #include <defines.hpp>
 #include <cublas_v2.h>
 
+namespace cuda
+{
 
-namespace cublas {
+class DeviceManager;
+
+}
+
+namespace cublas
+{
+
+const char * errorString(cublasStatus_t err);
+
+//RAII class around the cublas Handle
+class cublasHandle
+{
+    friend class cuda::DeviceManager;
+
+    public:
+        ~cublasHandle();
+        cublasHandle_t get() const;
+
+    private:
+        cublasHandle();
+        cublasHandle(cublasHandle const&);
+        void operator=(cublasHandle const&);
+
+        cublasHandle_t handle;
+};
 
-    const char * errorString(cublasStatus_t err);
-    cublasHandle_t getHandle();
 }
 
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index c696854c14..8a571f2df8 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -395,6 +395,17 @@ cufft::cuFFTPlanner& DeviceManager::getcufftPlanManager()
     return cufftManagers[cuda::getActiveDeviceId()];
 }
 
+cublasHandle_t DeviceManager::getcublasHandle()
+{
+    int id = cuda::getActiveDeviceId();
+
+    if(!cublasHandles[id]) {
+        cublasHandles[id].reset(new cublas::cublasHandle());
+    }
+
+    return cublasHandles[id]->get();
+}
+
 DeviceManager::DeviceManager()
     : cuDevices(0), activeDev(0), nDevices(0), gfxManager(new InteropManager())
 {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 42e16aa969..c3b1cac8d1 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -17,7 +17,10 @@
 #include <fg/window.h>
 #endif
 
+#include <boost/scoped_ptr.hpp>
+
 #include <cufftManager.hpp>
+#include <cublasManager.hpp>
 
 namespace cuda
 {
@@ -85,6 +88,8 @@ class DeviceManager
 
         cufft::cuFFTPlanner& getcufftPlanManager();
 
+        cublasHandle_t getcublasHandle();
+
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -138,6 +143,8 @@ class DeviceManager
         InteropManager* gfxManager;
 
         cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];
+
+        boost::scoped_ptr<cublas::cublasHandle> cublasHandles[MAX_DEVICES];
 };
 
 }

From 5e4d401d548eeec83ab67684365c531c910031b0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Dec 2016 20:21:53 +0530
Subject: [PATCH 1060/2677] Make cuda Manager objects access less hierarchical

---
 src/backend/cuda/blas.cpp          |  6 +++---
 src/backend/cuda/cufftManager.cpp  |  2 +-
 src/backend/cuda/fft.cpp           |  2 +-
 src/backend/cuda/hist_graphics.cpp |  2 +-
 src/backend/cuda/image.cpp         |  2 +-
 src/backend/cuda/platform.cpp      | 24 +++++++++++++++---------
 src/backend/cuda/platform.hpp      | 18 +++++++++++++++---
 src/backend/cuda/plot.cpp          |  2 +-
 src/backend/cuda/surface.cpp       |  2 +-
 src/backend/cuda/vector_field.cpp  |  2 +-
 10 files changed, 40 insertions(+), 22 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index d5c475de6e..3d9d3aba78 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -171,7 +171,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
         CUBLAS_CHECK(gemv_func<T>()(
-                         DeviceManager::getInstance().getcublasHandle(),
+                         getcublasHandle(),
                          lOpts,
                          lDims[0],
                          lDims[1],
@@ -182,7 +182,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                          out.get(), 1));
     } else {
         CUBLAS_CHECK(gemm_func<T>()(
-                         DeviceManager::getInstance().getcublasHandle(),
+                         getcublasHandle(),
                          lOpts,
                          rOpts,
                          M, N, K,
@@ -224,7 +224,7 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans,
     dim4 rStrides = rhs.strides();
 
     CUBLAS_CHECK(trsm_func<T>()(
-                     DeviceManager::getInstance().getcublasHandle(),
+                     getcublasHandle(),
                      is_left  ? CUBLAS_SIDE_LEFT : CUBLAS_SIDE_RIGHT,
                      is_upper ? CUBLAS_FILL_MODE_UPPER : CUBLAS_FILL_MODE_LOWER,
                      toCblasTranspose(trans),
diff --git a/src/backend/cuda/cufftManager.cpp b/src/backend/cuda/cufftManager.cpp
index f3aea5f45a..28c994b464 100644
--- a/src/backend/cuda/cufftManager.cpp
+++ b/src/backend/cuda/cufftManager.cpp
@@ -114,7 +114,7 @@ void findPlan(cufftHandle &plan, int rank, int *n,
     key_string.append(std::string(key_str_temp));
 
     // find the matching plan_index in the array cuFFTPlanner::mKeys
-    cuFFTPlanner &planner = cuda::DeviceManager::getInstance().getcufftPlanManager();
+    cuFFTPlanner &planner = cuda::getcufftPlanManager();
 
     int planIndex = planner.findIfPlanExists(key_string);
 
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index b2b36c90e1..189033e8ae 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -24,7 +24,7 @@ namespace cuda
 
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    DeviceManager::getInstance().getcufftPlanManager().setMaxCacheSize(numPlans);
+    cuda::getcufftPlanManager().setMaxCacheSize(numPlans);
 }
 
 template<typename T>
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index d207961f59..750dafae3e 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -25,7 +25,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
-        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index ca87d3129f..0492c9c6d4 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -28,7 +28,7 @@ template<typename T>
 void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 8a571f2df8..79ec7e021f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -385,25 +385,26 @@ DeviceManager::~DeviceManager()
     if (gfxManager) delete gfxManager;
 }
 
-InteropManager& DeviceManager::getGfxInteropManager()
+InteropManager& getGfxInteropManager()
 {
-    return *gfxManager;
+    return *(DeviceManager::getInstance().gfxManager);
 }
 
-cufft::cuFFTPlanner& DeviceManager::getcufftPlanManager()
+cufft::cuFFTPlanner& getcufftPlanManager()
 {
-    return cufftManagers[cuda::getActiveDeviceId()];
+    return DeviceManager::getInstance().cufftManagers[cuda::getActiveDeviceId()];
 }
 
-cublasHandle_t DeviceManager::getcublasHandle()
+cublasHandle_t getcublasHandle()
 {
+    DeviceManager& instance = DeviceManager::getInstance();
+
     int id = cuda::getActiveDeviceId();
 
-    if(!cublasHandles[id]) {
-        cublasHandles[id].reset(new cublas::cublasHandle());
-    }
+    if (!(instance.cublasHandles[id]))
+        instance.resetcublasHandle(id);
 
-    return cublasHandles[id]->get();
+    return instance.cublasHandles[id]->get();
 }
 
 DeviceManager::DeviceManager()
@@ -528,6 +529,11 @@ int DeviceManager::setActiveDevice(int device, int nId)
     return old;
 }
 
+void DeviceManager::resetcublasHandle(int device)
+{
+    cublasHandles[device].reset(new cublas::cublasHandle());
+}
+
 void sync(int device)
 {
     int currDevice = getActiveDeviceId();
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index c3b1cac8d1..163df71f03 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -73,6 +73,16 @@ bool& evalFlag();
 
 class InteropManager;
 
+///////////////////////// BEGIN Sub-Managers ///////////////////
+//
+InteropManager& getGfxInteropManager();
+
+cufft::cuFFTPlanner& getcufftPlanManager();
+
+cublasHandle_t getcublasHandle();
+//
+///////////////////////// END Sub-Managers /////////////////////
+
 class DeviceManager
 {
     public:
@@ -84,11 +94,11 @@ class DeviceManager
 
         ~DeviceManager();
 
-        InteropManager& getGfxInteropManager();
+        friend InteropManager& getGfxInteropManager();
 
-        cufft::cuFFTPlanner& getcufftPlanManager();
+        friend cufft::cuFFTPlanner& getcufftPlanManager();
 
-        cublasHandle_t getcublasHandle();
+        friend cublasHandle_t getcublasHandle();
 
         friend std::string getDeviceInfo(int device);
 
@@ -133,6 +143,8 @@ class DeviceManager
 
         int setActiveDevice(int device, int native = -1);
 
+        void resetcublasHandle(int device);
+
         int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 40ae7b4574..437c276226 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -30,7 +30,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 18385decd6..749ff859d6 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -30,7 +30,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 6fa4dd9fee..aab3f13036 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -26,7 +26,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        forge::VectorField* vector_field)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        InteropManager& intrpMngr = DeviceManager::getInstance().getGfxInteropManager();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(vector_field);
 

From b75419476ffb31199e84d732b5c5ce226d074181 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Dec 2016 20:36:36 +0530
Subject: [PATCH 1061/2677] Wrap interopManager pointer with boost::scoped_ptr

---
 src/backend/cuda/platform.cpp | 7 +------
 src/backend/cuda/platform.hpp | 7 +------
 2 files changed, 2 insertions(+), 12 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 79ec7e021f..0b1123df2b 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -380,14 +380,9 @@ DeviceManager& DeviceManager::getInstance()
     return my_instance;
 }
 
-DeviceManager::~DeviceManager()
-{
-    if (gfxManager) delete gfxManager;
-}
-
 InteropManager& getGfxInteropManager()
 {
-    return *(DeviceManager::getInstance().gfxManager);
+    return *(DeviceManager::getInstance().gfxManager.get());
 }
 
 cufft::cuFFTPlanner& getcufftPlanManager()
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 163df71f03..9b15457fda 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -92,8 +92,6 @@ class DeviceManager
 
         static DeviceManager& getInstance();
 
-        ~DeviceManager();
-
         friend InteropManager& getGfxInteropManager();
 
         friend cufft::cuFFTPlanner& getcufftPlanManager();
@@ -149,10 +147,7 @@ class DeviceManager
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
 
-        //FIXME: Once C++11 has been enabled in CUDA backend
-        //shift to use of smart pointer. In this, unique_ptr
-        //should be good
-        InteropManager* gfxManager;
+        boost::scoped_ptr<InteropManager> gfxManager;
 
         cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];
 

From de3f9f747934beebe0a7ddabc6a2b952ff018b04 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 22 Dec 2016 21:13:07 +0530
Subject: [PATCH 1062/2677] Merge cusolver handle manager into
 cuda::DeviceManager

---
 src/backend/cuda/cholesky.cu           |   8 +-
 src/backend/cuda/cusolverDnManager.cpp | 101 ++++++++-----------------
 src/backend/cuda/cusolverDnManager.hpp |  56 ++++++++++----
 src/backend/cuda/lu.cu                 |   8 +-
 src/backend/cuda/platform.cpp          |  32 ++++++++
 src/backend/cuda/platform.hpp          |   9 +++
 src/backend/cuda/qr.cu                 |  14 ++--
 src/backend/cuda/solve.cu              |  20 +++--
 src/backend/cuda/svd.cu                |   8 +-
 9 files changed, 138 insertions(+), 118 deletions(-)

diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index c6869dc6a6..db90dba238 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -12,7 +12,7 @@
 
 #if defined(WITH_CUDA_LINEAR_ALGEBRA)
 
-#include <cusolverDnManager.hpp>
+#include <platform.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
 #include <iostream>
@@ -26,8 +26,6 @@
 namespace cuda
 {
 
-using cusolver::getDnHandle;
-
 //cusolverStatus_t cusolverDn<>potrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        cublasFillMode_t uplo,
@@ -114,7 +112,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     if(is_upper)
         uplo = CUBLAS_FILL_MODE_UPPER;
 
-    CUSOLVER_CHECK(potrf_buf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(potrf_buf_func<T>()(getcusolverDnHandle(),
                                        uplo,
                                        N,
                                        in.get(), in.strides()[1],
@@ -123,7 +121,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     T *workspace = memAlloc<T>(lwork);
     int *d_info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(potrf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(potrf_func<T>()(getcusolverDnHandle(),
                                    uplo,
                                    N,
                                    in.get(), in.strides()[1],
diff --git a/src/backend/cuda/cusolverDnManager.cpp b/src/backend/cuda/cusolverDnManager.cpp
index 3fa1f9ce11..3dc9aa0b2d 100644
--- a/src/backend/cuda/cusolverDnManager.cpp
+++ b/src/backend/cuda/cusolverDnManager.cpp
@@ -18,79 +18,44 @@
 #include <iostream>
 #include <boost/scoped_ptr.hpp>
 
-namespace cusolver {
-
-    const char *errorString(cusolverStatus_t err)
-    {
-        switch(err) {
-        case    CUSOLVER_STATUS_SUCCESS                     :   return "CUSOLVER_STATUS_SUCCESS"                    ;
-        case    CUSOLVER_STATUS_NOT_INITIALIZED             :   return "CUSOLVER_STATUS_NOT_INITIALIZED"            ;
-        case    CUSOLVER_STATUS_ALLOC_FAILED                :   return "CUSOLVER_STATUS_ALLOC_FAILED"               ;
-        case    CUSOLVER_STATUS_INVALID_VALUE               :   return "CUSOLVER_STATUS_INVALID_VALUE"              ;
-        case    CUSOLVER_STATUS_ARCH_MISMATCH               :   return "CUSOLVER_STATUS_ARCH_MISMATCH"              ;
-        case    CUSOLVER_STATUS_MAPPING_ERROR               :   return "CUSOLVER_STATUS_MAPPING_ERROR"              ;
-        case    CUSOLVER_STATUS_EXECUTION_FAILED            :   return "CUSOLVER_STATUS_EXECUTION_FAILED"           ;
-        case    CUSOLVER_STATUS_INTERNAL_ERROR              :   return "CUSOLVER_STATUS_INTERNAL_ERROR"             ;
-        case    CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
-        case    CUSOLVER_STATUS_NOT_SUPPORTED               :   return "CUSOLVER_STATUS_NOT_SUPPORTED"              ;
-        case    CUSOLVER_STATUS_ZERO_PIVOT                  :   return "CUSOLVER_STATUS_ZERO_PIVOT"                 ;
-        case    CUSOLVER_STATUS_INVALID_LICENSE             :   return "CUSOLVER_STATUS_INVALID_LICENSE"            ;
-        default                                             :   return "UNKNOWN";
-        }
+namespace cusolver
+{
+
+const char *errorString(cusolverStatus_t err)
+{
+    switch(err) {
+    case    CUSOLVER_STATUS_SUCCESS                     :   return "CUSOLVER_STATUS_SUCCESS"                    ;
+    case    CUSOLVER_STATUS_NOT_INITIALIZED             :   return "CUSOLVER_STATUS_NOT_INITIALIZED"            ;
+    case    CUSOLVER_STATUS_ALLOC_FAILED                :   return "CUSOLVER_STATUS_ALLOC_FAILED"               ;
+    case    CUSOLVER_STATUS_INVALID_VALUE               :   return "CUSOLVER_STATUS_INVALID_VALUE"              ;
+    case    CUSOLVER_STATUS_ARCH_MISMATCH               :   return "CUSOLVER_STATUS_ARCH_MISMATCH"              ;
+    case    CUSOLVER_STATUS_MAPPING_ERROR               :   return "CUSOLVER_STATUS_MAPPING_ERROR"              ;
+    case    CUSOLVER_STATUS_EXECUTION_FAILED            :   return "CUSOLVER_STATUS_EXECUTION_FAILED"           ;
+    case    CUSOLVER_STATUS_INTERNAL_ERROR              :   return "CUSOLVER_STATUS_INTERNAL_ERROR"             ;
+    case    CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
+    case    CUSOLVER_STATUS_NOT_SUPPORTED               :   return "CUSOLVER_STATUS_NOT_SUPPORTED"              ;
+    case    CUSOLVER_STATUS_ZERO_PIVOT                  :   return "CUSOLVER_STATUS_ZERO_PIVOT"                 ;
+    case    CUSOLVER_STATUS_INVALID_LICENSE             :   return "CUSOLVER_STATUS_INVALID_LICENSE"            ;
+    default                                             :   return "UNKNOWN";
     }
+}
 
 
-//RAII class around the cusolver Handle
-    class cusolverDnHandle
-    {
-        cusolverDnHandle_t handle;
-    public:
-
-        cusolverDnHandle()
-            : handle(0)
-        {
-            CUSOLVER_CHECK(cusolverDnCreate(&handle));
-        }
-
-        ~cusolverDnHandle()
-        {
-            cusolverDnDestroy(handle);
-        }
-
-        cusolverDnHandle_t get() const
-        {
-            return handle;
-        }
-    };
-
-    cusolverDnHandle_t getDnHandle()
-    {
-        using boost::scoped_ptr;
-        static scoped_ptr<cusolverDnHandle> handle[cuda::DeviceManager::MAX_DEVICES];
-
-        int id = cuda::getActiveDeviceId();
-
-        if(!handle[id]) {
-            handle[id].reset(new cusolverDnHandle());
-        }
+cusolverDnHandle::cusolverDnHandle()
+    : handle(0)
+{
+    CUSOLVER_CHECK(cusolverDnCreate(&handle));
+}
 
-        // FIXME
-        // This is not an ideal case. It's just a hack.
-        // The correct way to do is to use
-        // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
-        // in the class constructor.
-        // However, this is causing a lot of the cusolver functions to fail.
-        // The only way to fix them is to use cudaDeviceSynchronize() and cudaStreamSynchronize()
-        // all over the place, but even then some calls like getrs in solve_lu
-        // continue to fail on any stream other than 0.
-        //
-        // cuSolver Streams patch:
-        // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
-        //
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
+cusolverDnHandle::~cusolverDnHandle()
+{
+    cusolverDnDestroy(handle);
+}
 
-        return handle[id]->get();
-    }
+cusolverDnHandle_t cusolverDnHandle::get() const
+{
+    return handle;
+}
 
 }
 
diff --git a/src/backend/cuda/cusolverDnManager.hpp b/src/backend/cuda/cusolverDnManager.hpp
index dbaa694d58..0793297efb 100644
--- a/src/backend/cuda/cusolverDnManager.hpp
+++ b/src/backend/cuda/cusolverDnManager.hpp
@@ -16,27 +16,51 @@
 #include <defines.hpp>
 #include <err_common.hpp>
 
+namespace cuda
+{
+
+class DeviceManager;
+
+}
+
 namespace cusolver
 {
 
-    const char * errorString(cusolverStatus_t err);
-    cusolverDnHandle_t getDnHandle();
+const char * errorString(cusolverStatus_t err);
+
+//RAII class around the cusolver Handle
+class cusolverDnHandle
+{
+    friend class cuda::DeviceManager;
+
+    public:
+        ~cusolverDnHandle();
+        cusolverDnHandle_t get() const;
+
+    private:
+        cusolverDnHandle();
+        cusolverDnHandle(cusolverDnHandle const&);
+        void operator=(cusolverDnHandle const&);
+
+        cusolverDnHandle_t handle;
+};
+
 }
 
 #define CUSOLVER_CHECK(fn) do {                     \
-        cusolverStatus_t _error = fn;               \
-        if (_error != CUSOLVER_STATUS_SUCCESS) {    \
-            char _err_msg[1024];                    \
-            snprintf(_err_msg,                      \
-                     sizeof(_err_msg),              \
-                     "CUBLAS Error (%d): %s\n",     \
-                     (int)(_error),                 \
-                     cusolver::errorString(         \
-                         _error));                  \
-                                                    \
-            AF_ERROR(_err_msg,                      \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
+    cusolverStatus_t _error = fn;               \
+    if (_error != CUSOLVER_STATUS_SUCCESS) {    \
+        char _err_msg[1024];                    \
+        snprintf(_err_msg,                      \
+                sizeof(_err_msg),              \
+                "CUBLAS Error (%d): %s\n",     \
+                (int)(_error),                 \
+                cusolver::errorString(         \
+                    _error));                  \
+        \
+        AF_ERROR(_err_msg,                      \
+                AF_ERR_INTERNAL);              \
+    }                                           \
+} while(0)
 
 #endif
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index ce0b545a84..b4a7b946b2 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -12,7 +12,7 @@
 
 #if defined(WITH_CUDA_LINEAR_ALGEBRA)
 
-#include <cusolverDnManager.hpp>
+#include <platform.hpp>
 #include <memory.hpp>
 #include <copy.hpp>
 #include <math.hpp>
@@ -23,8 +23,6 @@
 namespace cuda
 {
 
-using cusolver::getDnHandle;
-
 //cusolverStatus_t CUDENSEAPI cusolverDn<>getrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
@@ -133,7 +131,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(getrf_buf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(getrf_buf_func<T>()(getcusolverDnHandle(),
                                        M, N,
                                        in.get(), in.strides()[1],
                                        &lwork));
@@ -141,7 +139,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     T *workspace = memAlloc<T>(lwork);
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(getrf_func<T>()(getcusolverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    workspace,
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 0b1123df2b..4e6d581e12 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -402,6 +402,33 @@ cublasHandle_t getcublasHandle()
     return instance.cublasHandles[id]->get();
 }
 
+cusolverDnHandle_t getcusolverDnHandle()
+{
+    DeviceManager& instance = DeviceManager::getInstance();
+
+    int id = cuda::getActiveDeviceId();
+
+    if (!(instance.cusolverHandles[id]))
+        instance.resetcusolverHandle(id);
+
+    // FIXME
+    // This is not an ideal case. It's just a hack.
+    // The correct way to do is to use
+    // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
+    // in the class constructor.
+    // However, this is causing a lot of the cusolver functions to fail.
+    // The only way to fix them is to use cudaDeviceSynchronize() and cudaStreamSynchronize()
+    // all over the place, but even then some calls like getrs in solve_lu
+    // continue to fail on any stream other than 0.
+    //
+    // cuSolver Streams patch:
+    // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
+    //
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
+
+    return instance.cusolverHandles[id]->get();
+}
+
 DeviceManager::DeviceManager()
     : cuDevices(0), activeDev(0), nDevices(0), gfxManager(new InteropManager())
 {
@@ -529,6 +556,11 @@ void DeviceManager::resetcublasHandle(int device)
     cublasHandles[device].reset(new cublas::cublasHandle());
 }
 
+void DeviceManager::resetcusolverHandle(int device)
+{
+    cusolverHandles[device].reset(new cusolver::cusolverDnHandle());
+}
+
 void sync(int device)
 {
     int currDevice = getActiveDeviceId();
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 9b15457fda..e87a843f0b 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -21,6 +21,7 @@
 
 #include <cufftManager.hpp>
 #include <cublasManager.hpp>
+#include <cusolverDnManager.hpp>
 
 namespace cuda
 {
@@ -80,6 +81,8 @@ InteropManager& getGfxInteropManager();
 cufft::cuFFTPlanner& getcufftPlanManager();
 
 cublasHandle_t getcublasHandle();
+
+cusolverDnHandle_t getcusolverDnHandle();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -98,6 +101,8 @@ class DeviceManager
 
         friend cublasHandle_t getcublasHandle();
 
+        friend cusolverDnHandle_t getcusolverDnHandle();
+
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -143,6 +148,8 @@ class DeviceManager
 
         void resetcublasHandle(int device);
 
+        void resetcusolverHandle(int device);
+
         int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
@@ -152,6 +159,8 @@ class DeviceManager
         cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];
 
         boost::scoped_ptr<cublas::cublasHandle> cublasHandles[MAX_DEVICES];
+
+        boost::scoped_ptr<cusolver::cusolverDnHandle> cusolverHandles[MAX_DEVICES];
 };
 
 }
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index 41ad1c2600..d3dc20374f 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -12,7 +12,7 @@
 
 #if defined(WITH_CUDA_LINEAR_ALGEBRA)
 
-#include <cusolverDnManager.hpp>
+#include <platform.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
 #include <memory.hpp>
@@ -26,8 +26,6 @@
 namespace cuda
 {
 
-using cusolver::getDnHandle;
-
 //cusolverStatus_t cusolverDn<>geqrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
@@ -134,7 +132,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(getcusolverDnHandle(),
                                        M, N,
                                        in_copy.get(), in_copy.strides()[1],
                                        &lwork));
@@ -144,7 +142,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(geqrf_func<T>()(getcusolverDnHandle(),
                                    M, N,
                                    in_copy.get(), in_copy.strides()[1],
                                    t.get(),
@@ -161,7 +159,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     dim4 qdims(M, mn);
     q = identity<T>(qdims);
 
-    CUSOLVER_CHECK(mqr_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(mqr_func<T>()(getcusolverDnHandle(),
                                  CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
                                  q.dims()[0],
                                  q.dims()[1],
@@ -189,7 +187,7 @@ Array<T> qr_inplace(Array<T> &in)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(getcusolverDnHandle(),
                                        M, N,
                                        in.get(), in.strides()[1],
                                        &lwork));
@@ -197,7 +195,7 @@ Array<T> qr_inplace(Array<T> &in)
     T *workspace = memAlloc<T>(lwork);
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(geqrf_func<T>()(getcusolverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    t.get(),
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 9db4889e2f..8d42a53227 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -12,7 +12,7 @@
 
 #if defined(WITH_CUDA_LINEAR_ALGEBRA)
 
-#include <cusolverDnManager.hpp>
+#include <platform.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
 #include <iostream>
@@ -32,8 +32,6 @@
 namespace cuda
 {
 
-using cusolver::getDnHandle;
-
 //cusolverStatus_t cusolverDn<>getrs(
 //    cusolverDnHandle_t handle,
 //    cublasOperation_t trans,
@@ -178,7 +176,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(getrs_func<T>()(getcusolverDnHandle(),
                                    CUBLAS_OP_N,
                                    N, NRHS,
                                    A.get(), A.strides()[1],
@@ -203,7 +201,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
 
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(getDnHandle(),
+    CUSOLVER_CHECK(getrs_func<T>()(getcusolverDnHandle(),
                                    CUBLAS_OP_N,
                                    N, K,
                                    A.get(), A.strides()[1],
@@ -246,7 +244,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(getDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(getcusolverDnHandle(),
                                                  A.dims()[0], A.dims()[1],
                                                  A.get(), A.strides()[1],
                                                  &lwork));
@@ -256,7 +254,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int *info = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(getDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(getcusolverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
@@ -274,7 +272,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(getDnHandle(),
+        CUSOLVER_CHECK(mqr_solve_func<T>()(getcusolverDnHandle(),
                                            CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
                                            B.dims()[0],
                                            B.dims()[1],
@@ -304,7 +302,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(getDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(getcusolverDnHandle(),
                                                  A.dims()[0], A.dims()[1],
                                                  A.get(), A.strides()[1],
                                                  &lwork));
@@ -314,7 +312,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int *info = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(getDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(getcusolverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
@@ -322,7 +320,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                              info));
 
         // matmul(Q1, B)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(getDnHandle(),
+        CUSOLVER_CHECK(mqr_solve_func<T>()(getcusolverDnHandle(),
                                            CUBLAS_SIDE_LEFT,
                                            trans<T>(),
                                            M, K, N,
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index e07c1f0564..5c9d58f74e 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -10,7 +10,7 @@
 #include <svd.hpp>
 #include <err_common.hpp>
 
-#include <cusolverDnManager.hpp>
+#include <platform.hpp>
 #include "transpose.hpp"
 #include <memory.hpp>
 #include <copy.hpp>
@@ -23,8 +23,6 @@
 
 namespace cuda
 {
-    using cusolver::getDnHandle;
-
     template<typename T>
     cusolverStatus_t gesvd_buf_func(cusolverDnHandle_t handle, int m, int n, int *Lwork)
     {
@@ -90,14 +88,14 @@ SVD_SPECIALIZE(cdouble, double, Z);
 
         int lwork = 0;
 
-        CUSOLVER_CHECK(gesvd_buf_func<T>(getDnHandle(), M, N, &lwork));
+        CUSOLVER_CHECK(gesvd_buf_func<T>(getcusolverDnHandle(), M, N, &lwork));
 
         T  *lWorkspace = memAlloc<T >(lwork);
         Tr *rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
 
         int *info = memAlloc<int>(1);
 
-        gesvd_func<T, Tr>(getDnHandle(), 'A', 'A', M, N, in.get(),
+        gesvd_func<T, Tr>(getcusolverDnHandle(), 'A', 'A', M, N, in.get(),
                           M, s.get(), u.get(), M, vt.get(), N,
                           lWorkspace, lwork, rWorkspace, info);
 

From 13c484e10537b52bcd012e6850520659769cece6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 22 Dec 2016 18:10:54 -0500
Subject: [PATCH 1063/2677] Build fix for CUDA Compile PTX generated names by
 CMake 3.7

---
 src/backend/cuda/CMakeLists.txt | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9cfa8a5929..2f2045dc45 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -315,8 +315,16 @@ foreach(ptx_src_file ${ptx_sources})
 
       get_filename_component(_name "${ptx_src_file}" NAME_WE)
 
+      # CUDA_COMPILE_PTX from CMake 3.7 has new features that require this change
+      # TODO Fix this with a more complete solution
+      IF(CMAKE_VERSION VERSION_LESS 3.7) # Before 3.7
+        SET(NAME_APPEND "")
+      ELSE(CMAKE_VERSION VERSION_LESS 3.7)  # 3.7 and newer
+        SET(NAME_APPEND "_1")
+      ENDIF(CMAKE_VERSION VERSION_LESS 3.7)
+
       set(_gen_file_name
-        "${PROJECT_BINARY_DIR}/src/backend/cuda/cuda_compile_ptx_generated_${_name}.cu.ptx")
+        "${PROJECT_BINARY_DIR}/src/backend/cuda/cuda_compile_ptx${NAME_APPEND}_generated_${_name}.cu.ptx")
       set(_out_file_name
         "${PROJECT_BINARY_DIR}/src/backend/cuda/${_name}.ptx")
 

From 5689b849197c64f355184ec3afa35050714fb84e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 23 Dec 2016 06:20:00 +0530
Subject: [PATCH 1064/2677] Merge cusparse handle manager into
 cuda::DeviceManager

---
 src/backend/cuda/cusparseManager.cpp | 85 +++++++++++-----------------
 src/backend/cuda/cusparseManager.hpp | 31 +++++++++-
 src/backend/cuda/platform.cpp        | 17 ++++++
 src/backend/cuda/platform.hpp        |  9 +++
 src/backend/cuda/sparse.cu           | 32 +++++------
 src/backend/cuda/sparse_blas.cpp     |  6 +-
 6 files changed, 103 insertions(+), 77 deletions(-)

diff --git a/src/backend/cuda/cusparseManager.cpp b/src/backend/cuda/cusparseManager.cpp
index 24f0c40045..52fc40e4bc 100644
--- a/src/backend/cuda/cusparseManager.cpp
+++ b/src/backend/cuda/cusparseManager.cpp
@@ -13,64 +13,43 @@
 #include <stdexcept>
 #include <string>
 #include <iostream>
-#include <boost/scoped_ptr.hpp>
 
-namespace cusparse {
-
-    const char *errorString(cusparseStatus_t err)
-    {
-        switch(err) {
-        case    CUSPARSE_STATUS_SUCCESS                     :   return "CUSPARSE_STATUS_SUCCESS"                    ;
-        case    CUSPARSE_STATUS_NOT_INITIALIZED             :   return "CUSPARSE_STATUS_NOT_INITIALIZED"            ;
-        case    CUSPARSE_STATUS_ALLOC_FAILED                :   return "CUSPARSE_STATUS_ALLOC_FAILED"               ;
-        case    CUSPARSE_STATUS_INVALID_VALUE               :   return "CUSPARSE_STATUS_INVALID_VALUE"              ;
-        case    CUSPARSE_STATUS_ARCH_MISMATCH               :   return "CUSPARSE_STATUS_ARCH_MISMATCH"              ;
-        case    CUSPARSE_STATUS_MAPPING_ERROR               :   return "CUSPARSE_STATUS_MAPPING_ERROR"              ;
-        case    CUSPARSE_STATUS_EXECUTION_FAILED            :   return "CUSPARSE_STATUS_EXECUTION_FAILED"           ;
-        case    CUSPARSE_STATUS_INTERNAL_ERROR              :   return "CUSPARSE_STATUS_INTERNAL_ERROR"             ;
-        case    CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
-        case    CUSPARSE_STATUS_ZERO_PIVOT                  :   return "CUSPARSE_STATUS_ZERO_PIVOT"                 ;
-        default                                             :   return "UNKNOWN";
-        }
+namespace cusparse
+{
+
+const char *errorString(cusparseStatus_t err)
+{
+    switch(err) {
+    case    CUSPARSE_STATUS_SUCCESS                     :   return "CUSPARSE_STATUS_SUCCESS"                    ;
+    case    CUSPARSE_STATUS_NOT_INITIALIZED             :   return "CUSPARSE_STATUS_NOT_INITIALIZED"            ;
+    case    CUSPARSE_STATUS_ALLOC_FAILED                :   return "CUSPARSE_STATUS_ALLOC_FAILED"               ;
+    case    CUSPARSE_STATUS_INVALID_VALUE               :   return "CUSPARSE_STATUS_INVALID_VALUE"              ;
+    case    CUSPARSE_STATUS_ARCH_MISMATCH               :   return "CUSPARSE_STATUS_ARCH_MISMATCH"              ;
+    case    CUSPARSE_STATUS_MAPPING_ERROR               :   return "CUSPARSE_STATUS_MAPPING_ERROR"              ;
+    case    CUSPARSE_STATUS_EXECUTION_FAILED            :   return "CUSPARSE_STATUS_EXECUTION_FAILED"           ;
+    case    CUSPARSE_STATUS_INTERNAL_ERROR              :   return "CUSPARSE_STATUS_INTERNAL_ERROR"             ;
+    case    CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
+    case    CUSPARSE_STATUS_ZERO_PIVOT                  :   return "CUSPARSE_STATUS_ZERO_PIVOT"                 ;
+    default                                             :   return "UNKNOWN";
     }
+}
 
 
-//RAII class around the cusparse Handle
-    class cusparseHandle
-    {
-        cusparseHandle_t handle;
-    public:
-
-        cusparseHandle()
-            : handle(0)
-        {
-            CUSPARSE_CHECK(cusparseCreate(&handle));
-            CUSPARSE_CHECK(cusparseSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
-        }
-
-        ~cusparseHandle()
-        {
-            cusparseDestroy(handle);
-        }
-
-        cusparseHandle_t get() const
-        {
-            return handle;
-        }
-    };
-
-    cusparseHandle_t getHandle()
-    {
-        using boost::scoped_ptr;
-        static scoped_ptr<cusparseHandle> handle[cuda::DeviceManager::MAX_DEVICES];
-
-        int id = cuda::getActiveDeviceId();
+cusparseHandle::cusparseHandle()
+    : handle(0)
+{
+    CUSPARSE_CHECK(cusparseCreate(&handle));
+    CUSPARSE_CHECK(cusparseSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
+}
 
-        if(!handle[id]) {
-            handle[id].reset(new cusparseHandle());
-        }
+cusparseHandle::~cusparseHandle()
+{
+    cusparseDestroy(handle);
+}
 
-        return handle[id]->get();
-    }
+cusparseHandle_t cusparseHandle::get() const
+{
+    return handle;
+}
 
 }
diff --git a/src/backend/cuda/cusparseManager.hpp b/src/backend/cuda/cusparseManager.hpp
index 23fcbf12c9..b5b3625f18 100644
--- a/src/backend/cuda/cusparseManager.hpp
+++ b/src/backend/cuda/cusparseManager.hpp
@@ -13,10 +13,35 @@
 #include <defines.hpp>
 #include <cusparse_v2.h>
 
-namespace cusparse {
+namespace cuda
+{
+
+class DeviceManager;
+
+}
+
+namespace cusparse
+{
+
+const char * errorString(cusparseStatus_t err);
+
+//RAII class around the cusparse Handle
+class cusparseHandle
+{
+    friend class cuda::DeviceManager;
+
+    public:
+    ~cusparseHandle();
+    cusparseHandle_t get() const;
+
+    private:
+    cusparseHandle();
+    cusparseHandle(cusparseHandle const&);
+    void operator=(cusparseHandle const&);
+
+    cusparseHandle_t handle;
+};
 
-    const char * errorString(cusparseStatus_t err);
-    cusparseHandle_t getHandle();
 }
 
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 4e6d581e12..58def6f604 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -429,6 +429,18 @@ cusolverDnHandle_t getcusolverDnHandle()
     return instance.cusolverHandles[id]->get();
 }
 
+cusparseHandle_t getcusparseHandle()
+{
+    DeviceManager& instance = DeviceManager::getInstance();
+
+    int id = cuda::getActiveDeviceId();
+
+    if (!(instance.cusparseHandles[id]))
+        instance.resetcusparseHandle(id);
+
+    return instance.cusparseHandles[id]->get();
+}
+
 DeviceManager::DeviceManager()
     : cuDevices(0), activeDev(0), nDevices(0), gfxManager(new InteropManager())
 {
@@ -561,6 +573,11 @@ void DeviceManager::resetcusolverHandle(int device)
     cusolverHandles[device].reset(new cusolver::cusolverDnHandle());
 }
 
+void DeviceManager::resetcusparseHandle(int device)
+{
+    cusparseHandles[device].reset(new cusparse::cusparseHandle());
+}
+
 void sync(int device)
 {
     int currDevice = getActiveDeviceId();
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index e87a843f0b..5c861e7744 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -22,6 +22,7 @@
 #include <cufftManager.hpp>
 #include <cublasManager.hpp>
 #include <cusolverDnManager.hpp>
+#include <cusparseManager.hpp>
 
 namespace cuda
 {
@@ -83,6 +84,8 @@ cufft::cuFFTPlanner& getcufftPlanManager();
 cublasHandle_t getcublasHandle();
 
 cusolverDnHandle_t getcusolverDnHandle();
+
+cusparseHandle_t getcusparseHandle();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -103,6 +106,8 @@ class DeviceManager
 
         friend cusolverDnHandle_t getcusolverDnHandle();
 
+        friend cusparseHandle_t getcusparseHandle();
+
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -150,6 +155,8 @@ class DeviceManager
 
         void resetcusolverHandle(int device);
 
+        void resetcusparseHandle(int device);
+
         int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
@@ -161,6 +168,8 @@ class DeviceManager
         boost::scoped_ptr<cublas::cublasHandle> cublasHandles[MAX_DEVICES];
 
         boost::scoped_ptr<cusolver::cusolverDnHandle> cusolverHandles[MAX_DEVICES];
+
+        boost::scoped_ptr<cusparse::cusparseHandle> cusparseHandles[MAX_DEVICES];
 };
 
 }
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 1d13f94ed0..6a2402e33e 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <sparse.hpp>
-#include <cusparseManager.hpp>
 #include <kernel/sparse.hpp>
 
 #include <stdexcept>
@@ -29,7 +28,6 @@
 namespace cuda
 {
 
-using cusparse::getHandle;
 using namespace common;
 using namespace std;
 
@@ -265,7 +263,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 
     int nNZ = -1;
     CUSPARSE_CHECK(nnz_func<T>()(
-                        getHandle(),
+                        getcusparseHandle(),
                         dir,
                         M, N,
                         descr,
@@ -286,7 +284,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 
     if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(dense2csr_func<T>()(
-                        getHandle(),
+                        getcusparseHandle(),
                         M, N,
                         descr,
                         in.get(), in.strides()[1],
@@ -294,7 +292,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
                         values.get(), rowIdx.get(), colIdx.get()));
     else
         CUSPARSE_CHECK(dense2csc_func<T>()(
-                        getHandle(),
+                        getcusparseHandle(),
                         M, N,
                         descr,
                         in.get(), in.strides()[1],
@@ -340,7 +338,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 
     if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(csr2dense_func<T>()(
-                        getHandle(),
+                        getcusparseHandle(),
                         M, N,
                         descr,
                         in.getValues().get(),
@@ -349,7 +347,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
                         dense.get(), d_strides1));
     else
         CUSPARSE_CHECK(csc2dense_func<T>()(
-                        getHandle(),
+                        getcusparseHandle(),
                         M, N,
                         descr,
                         in.getValues().get(),
@@ -383,7 +381,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
         // cusparse function to expand compressed row into coordinate
         CUSPARSE_CHECK(cusparseXcsr2coo(
-                        getHandle(),
+                        getcusparseHandle(),
                         in.getRowIdx().get(),
                         nNZ, in.dims()[0],
                         converted.getRowIdx().get(),
@@ -392,23 +390,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         // Call sort
         size_t pBufferSizeInBytes = 0;
         CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                        getHandle(),
+                        getcusparseHandle(),
                         in.dims()[0], in.dims()[1], nNZ,
                         converted.getRowIdx().get(), converted.getColIdx().get(),
                         &pBufferSizeInBytes));
         shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
         shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
-        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P.get()));
+        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getcusparseHandle(), nNZ, P.get()));
 
         CUSPARSE_CHECK(cusparseXcoosortByColumn(
-                       getHandle(),
+                       getcusparseHandle(),
                        in.dims()[0], in.dims()[1], nNZ,
                        converted.getRowIdx().get(), converted.getColIdx().get(),
                        P.get(), (void*)pBuffer.get()));
 
         CUSPARSE_CHECK(gthr_func<T>()(
-                       getHandle(), nNZ,
+                       getcusparseHandle(), nNZ,
                        in.getValues().get(),
                        converted.getValues().get(),
                        P.get(), CUSPARSE_INDEX_BASE_ZERO));
@@ -427,23 +425,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         {
             size_t pBufferSizeInBytes = 0;
             CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                            getHandle(),
+                            getcusparseHandle(),
                             cooT.dims()[0], cooT.dims()[1], nNZ,
                             cooT.getRowIdx().get(), cooT.getColIdx().get(),
                             &pBufferSizeInBytes));
             shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
             shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
-            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getHandle(), nNZ, P.get()));
+            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getcusparseHandle(), nNZ, P.get()));
 
             CUSPARSE_CHECK(cusparseXcoosortByRow(
-                           getHandle(),
+                           getcusparseHandle(),
                            cooT.dims()[0], cooT.dims()[1], nNZ,
                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
                            P.get(), (void*)pBuffer.get()));
 
             CUSPARSE_CHECK(gthr_func<T>()(
-                           getHandle(), nNZ,
+                           getcusparseHandle(), nNZ,
                            in.getValues().get(),
                            cooT.getValues().get(),
                            P.get(), CUSPARSE_INDEX_BASE_ZERO));
@@ -462,7 +460,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(cusparseXcoo2csr(
-                        getHandle(),
+                        getcusparseHandle(),
                         cooT.getRowIdx().get(),
                         nNZ, cooT.dims()[0],
                         converted.getRowIdx().get(),
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index a8394abdcb..1d6dc27775 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <sparse_blas.hpp>
-#include <cusparseManager.hpp>
 #include <cuda_runtime.h>
 #include <platform.hpp>
 
@@ -21,7 +20,6 @@
 namespace cuda
 {
 
-using cusparse::getHandle;
 using namespace std;
 
 cusparseOperation_t
@@ -157,7 +155,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     // and not OP(A) (gemm wants row/col of OP(A)).
     if(rDims[rColDim] == 1) {
         CUSPARSE_CHECK(csrmv_func<T>()(
-                       getHandle(),
+                       getcusparseHandle(),
                        lOpts,
                        lDims[0], lDims[1], lhs.getNNZ(),
                        &alpha,
@@ -168,7 +166,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                        out.get()));
     } else {
         CUSPARSE_CHECK(csrmm_func<T>()(
-                       getHandle(),
+                       getcusparseHandle(),
                        lOpts,
                        lDims[0], rDims[rColDim], lDims[1], lhs.getNNZ(),
                        &alpha,

From 8de2a5911bc3d4d2d03a1fd6644718d82075d6e3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 23 Dec 2016 07:15:16 +0530
Subject: [PATCH 1065/2677] Merge cuda memory manager into cuda::DeviceManager

---
 src/backend/cuda/memory.cpp        | 135 +----------------------------
 src/backend/cuda/memoryManager.cpp |  86 ++++++++++++++++++
 src/backend/cuda/memoryManager.hpp |  63 ++++++++++++++
 src/backend/cuda/platform.cpp      |  11 +++
 src/backend/cuda/platform.hpp      |  14 +++
 5 files changed, 175 insertions(+), 134 deletions(-)
 create mode 100644 src/backend/cuda/memoryManager.cpp
 create mode 100644 src/backend/cuda/memoryManager.hpp

diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 03ebc20d44..9b2eb07c44 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -8,147 +8,14 @@
  ********************************************************/
 
 #include <memory.hpp>
-#include <cuda.h>
-#include <cuda_runtime_api.h>
-#include <cuda_runtime.h>
-#include <err_cuda.hpp>
 #include <util.hpp>
 #include <types.hpp>
-#include <iostream>
-#include <iomanip>
-#include <string>
-#include <map>
 #include <dispatch.hpp>
-#include <platform.hpp>
-#include <MemoryManager.hpp>
-
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_CUDA_MEM_DEBUG
-#define AF_CUDA_MEM_DEBUG 0
-#endif
+#include <memoryManager.hpp>
 
 namespace cuda
 {
 
-class MemoryManager  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManager();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManager()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            try {
-                cuda::setDevice(n);
-                this->garbageCollect();
-            } catch(AfError err) {
-                continue; // Do not throw any errors while shutting down
-            }
-        }
-    }
-};
-
-// CUDA Pinned Memory does not depend on device
-// So we pass 1 as numDevices to the constructor so that it creates 1 vector
-// of memory_info
-// When allocating and freeing, it doesn't really matter which device is active
-class MemoryManagerPinned  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManagerPinned();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManagerPinned()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
-    }
-};
-
-int MemoryManager::getActiveDeviceId()
-{
-    return cuda::getActiveDeviceId();
-}
-
-size_t MemoryManager::getMaxMemorySize(int id)
-{
-    return cuda::getDeviceMemorySize(id);
-}
-
-MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
-    void *ptr = NULL;
-    CUDA_CHECK(cudaMalloc(&ptr, bytes));
-    return ptr;
-}
-
-void MemoryManager::nativeFree(void *ptr)
-{
-    cudaError_t err = cudaFree(ptr);
-    if (err != cudaErrorCudartUnloading) {
-        CUDA_CHECK(err);
-    }
-}
-
-static MemoryManager &getMemoryManager()
-{
-    static MemoryManager instance;
-    return instance;
-}
-
-int MemoryManagerPinned::getActiveDeviceId()
-{
-    return 0; // pinned uses a single vector
-}
-
-size_t MemoryManagerPinned::getMaxMemorySize(int id)
-{
-    return cuda::getHostMemorySize();
-}
-
-MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
-{
-    void *ptr;
-    CUDA_CHECK(cudaMallocHost(&ptr, bytes));
-    return ptr;
-}
-
-void MemoryManagerPinned::nativeFree(void *ptr)
-{
-    cudaError_t err = cudaFreeHost(ptr);
-    if (err != cudaErrorCudartUnloading) {
-        CUDA_CHECK(err);
-    }
-}
-
-static MemoryManagerPinned &getMemoryManagerPinned()
-{
-    static MemoryManagerPinned instance;
-    return instance;
-}
-
 void setMemStepSize(size_t step_bytes)
 {
     getMemoryManager().setMemStepSize(step_bytes);
diff --git a/src/backend/cuda/memoryManager.cpp b/src/backend/cuda/memoryManager.cpp
new file mode 100644
index 0000000000..d160471814
--- /dev/null
+++ b/src/backend/cuda/memoryManager.cpp
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <err_cuda.hpp>
+#include <memoryManager.hpp>
+
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
+
+#ifndef AF_CUDA_MEM_DEBUG
+#define AF_CUDA_MEM_DEBUG 0
+#endif
+
+namespace cuda
+{
+
+int MemoryManager::getActiveDeviceId()
+{
+    return cuda::getActiveDeviceId();
+}
+
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return cuda::getDeviceMemorySize(id);
+}
+
+MemoryManager::MemoryManager() :
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    void *ptr = NULL;
+    CUDA_CHECK(cudaMalloc(&ptr, bytes));
+    return ptr;
+}
+
+void MemoryManager::nativeFree(void *ptr)
+{
+    cudaError_t err = cudaFree(ptr);
+    if (err != cudaErrorCudartUnloading) {
+        CUDA_CHECK(err);
+    }
+}
+
+int MemoryManagerPinned::getActiveDeviceId()
+{
+    return 0; // pinned uses a single vector
+}
+
+size_t MemoryManagerPinned::getMaxMemorySize(int id)
+{
+    return cuda::getHostMemorySize();
+}
+
+MemoryManagerPinned::MemoryManagerPinned() :
+    common::MemoryManager(1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
+{
+    void *ptr;
+    CUDA_CHECK(cudaMallocHost(&ptr, bytes));
+    return ptr;
+}
+
+void MemoryManagerPinned::nativeFree(void *ptr)
+{
+    cudaError_t err = cudaFreeHost(ptr);
+    if (err != cudaErrorCudartUnloading) {
+        CUDA_CHECK(err);
+    }
+}
+
+}
diff --git a/src/backend/cuda/memoryManager.hpp b/src/backend/cuda/memoryManager.hpp
new file mode 100644
index 0000000000..b22bd02c1f
--- /dev/null
+++ b/src/backend/cuda/memoryManager.hpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <cuda_runtime.h>
+
+#include <platform.hpp>
+#include <MemoryManager.hpp>
+
+namespace cuda
+{
+
+class MemoryManager  : public common::MemoryManager
+{
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+public:
+    MemoryManager();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManager()
+    {
+        common::lock_guard_t lock(this->memory_mutex);
+        for (int n = 0; n < getDeviceCount(); n++) {
+            try {
+                cuda::setDevice(n);
+                this->garbageCollect();
+            } catch(AfError err) {
+                continue; // Do not throw any errors while shutting down
+            }
+        }
+    }
+};
+
+// CUDA Pinned Memory does not depend on device
+// So we pass 1 as numDevices to the constructor so that it creates 1 vector
+// of memory_info
+// When allocating and freeing, it doesn't really matter which device is active
+class MemoryManagerPinned  : public common::MemoryManager
+{
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+public:
+    MemoryManagerPinned();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManagerPinned()
+    {
+        common::lock_guard_t lock(this->memory_mutex);
+        this->garbageCollect();
+    }
+};
+
+}
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 58def6f604..9e50865025 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -24,6 +24,7 @@
 #include <err_cuda.hpp>
 #include <util.hpp>
 #include <host_memory.hpp>
+#include <memoryManager.hpp>
 #include <interopManager.hpp>
 
 using namespace std;
@@ -380,6 +381,16 @@ DeviceManager& DeviceManager::getInstance()
     return my_instance;
 }
 
+MemoryManager &getMemoryManager()
+{
+    return *(DeviceManager::getInstance().memManager.get());
+}
+
+MemoryManagerPinned &getMemoryManagerPinned()
+{
+    return *(DeviceManager::getInstance().pinnedMemManager.get());
+}
+
 InteropManager& getGfxInteropManager()
 {
     return *(DeviceManager::getInstance().gfxManager.get());
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 5c861e7744..0e82653991 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -73,10 +73,16 @@ struct cudaDevice_t {
 
 bool& evalFlag();
 
+class MemoryManager;
+class MemoryManagerPinned;
 class InteropManager;
 
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
+MemoryManager& getMemoryManager();
+
+MemoryManagerPinned& getMemoryManagerPinned();
+
 InteropManager& getGfxInteropManager();
 
 cufft::cuFFTPlanner& getcufftPlanManager();
@@ -98,6 +104,10 @@ class DeviceManager
 
         static DeviceManager& getInstance();
 
+        friend MemoryManager& getMemoryManager();
+
+        friend MemoryManagerPinned& getMemoryManagerPinned();
+
         friend InteropManager& getGfxInteropManager();
 
         friend cufft::cuFFTPlanner& getcufftPlanManager();
@@ -161,6 +171,10 @@ class DeviceManager
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
 
+        boost::scoped_ptr<MemoryManager> memManager;
+
+        boost::scoped_ptr<MemoryManagerPinned> pinnedMemManager;
+
         boost::scoped_ptr<InteropManager> gfxManager;
 
         cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];

From b204faf04688203088367e4fc2117eadd015d111 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 23 Dec 2016 15:06:25 +0530
Subject: [PATCH 1066/2677] fix cuda backend sub-managers initilization order

---
 src/backend/cuda/platform.cpp | 23 +++++++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 9e50865025..bce94b4945 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -383,17 +383,32 @@ DeviceManager& DeviceManager::getInstance()
 
 MemoryManager &getMemoryManager()
 {
-    return *(DeviceManager::getInstance().memManager.get());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    if (!inst.memManager.get())
+        inst.memManager.reset(new cuda::MemoryManager());
+
+    return *(inst.memManager.get());
 }
 
 MemoryManagerPinned &getMemoryManagerPinned()
 {
-    return *(DeviceManager::getInstance().pinnedMemManager.get());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    if (!inst.pinnedMemManager.get())
+        inst.pinnedMemManager.reset(new cuda::MemoryManagerPinned());
+
+    return *(inst.pinnedMemManager.get());
 }
 
 InteropManager& getGfxInteropManager()
 {
-    return *(DeviceManager::getInstance().gfxManager.get());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    if (!inst.gfxManager.get())
+        inst.gfxManager.reset(new cuda::InteropManager());
+
+    return *(inst.gfxManager.get());
 }
 
 cufft::cuFFTPlanner& getcufftPlanManager()
@@ -453,7 +468,7 @@ cusparseHandle_t getcusparseHandle()
 }
 
 DeviceManager::DeviceManager()
-    : cuDevices(0), activeDev(0), nDevices(0), gfxManager(new InteropManager())
+    : cuDevices(0), activeDev(0), nDevices(0)
 {
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
     if (nDevices == 0)

From bc9bd5d982bdcaaa5cf5e7fae52854d44493d7eb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 26 Dec 2016 14:04:29 -0500
Subject: [PATCH 1067/2677] Assert minimum CUDA version 7.0 in CMake

---
 src/backend/cuda/Array.hpp      | 4 ----
 src/backend/cuda/CMakeLists.txt | 2 +-
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 35e7586f14..9e7bed8e29 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -22,10 +22,6 @@
 #include <vector>
 #include <memory.hpp>
 
-#if CUDA_VERSION < 7000
-    #error "ArrayFire CUDA requires CUDA Toolkit Version 7.0 or newer."
-#endif
-
 namespace cuda
 {
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6e89a8676b..eb138c6c98 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -1,6 +1,6 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
-FIND_PACKAGE(CUDA REQUIRED)
+FIND_PACKAGE(CUDA 7.0 REQUIRED) # Assert Minimum CUDA Version 7.0
 FIND_PACKAGE(Boost REQUIRED)
 
 INCLUDE(CLKernelToH)

From 211757166a97d6c6847c286ee17686c0ec5bdbab Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 23 Dec 2016 17:53:42 +0530
Subject: [PATCH 1068/2677] Added locks for memory manager initialization

---
 src/backend/cuda/CMakeLists.txt |  5 +++--
 src/backend/cuda/platform.cpp   | 34 +++++++++++++++++++++++++++++----
 src/backend/cuda/platform.hpp   |  3 +++
 3 files changed, 36 insertions(+), 6 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9cfa8a5929..424bda9a0e 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -1,7 +1,7 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
 FIND_PACKAGE(CUDA REQUIRED)
-FIND_PACKAGE(Boost REQUIRED)
+FIND_PACKAGE(Boost REQUIRED COMPONENTS "system" "thread")
 
 INCLUDE(CLKernelToH)
 INCLUDE(FindNVVM)
@@ -498,7 +498,8 @@ TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
                                 PRIVATE ${CUDA_cusparse_LIBRARY}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
-                                PRIVATE ${CUDA_CUDA_LIBRARY})
+                                PRIVATE ${CUDA_CUDA_LIBRARY}
+                                PRIVATE ${Boost_LIBRARIES})
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
 IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index bce94b4945..afdbe3eaea 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -385,8 +385,21 @@ MemoryManager &getMemoryManager()
 {
     DeviceManager& inst = DeviceManager::getInstance();
 
-    if (!inst.memManager.get())
-        inst.memManager.reset(new cuda::MemoryManager());
+    boost::upgrade_lock<boost::shared_mutex> lock(inst.memManagerMutex);
+
+    // multiple threads should be able to reach the
+    // conditional statement without contention
+    if (!inst.memManager.get()) {
+        // upgrade the shared ownership we acquired earlier to
+        // exclusive owner ship to initialize the memory manager
+        boost::upgrade_to_unique_lock<boost::shared_mutex> unqLock(lock);
+
+        // if multiple threads above statment, allow the
+        // initialization of memory manager to happen once
+        // by checking if pinned memory manager pointer is already set
+        if (!inst.memManager.get())
+            inst.memManager.reset(new cuda::MemoryManager());
+    }
 
     return *(inst.memManager.get());
 }
@@ -395,8 +408,21 @@ MemoryManagerPinned &getMemoryManagerPinned()
 {
     DeviceManager& inst = DeviceManager::getInstance();
 
-    if (!inst.pinnedMemManager.get())
-        inst.pinnedMemManager.reset(new cuda::MemoryManagerPinned());
+    boost::upgrade_lock<boost::shared_mutex> lock(inst.memManagerMutex);
+
+    // multiple threads should be able to reach the
+    // conditional statement without contention
+    if (!inst.pinnedMemManager.get()) {
+        // upgrade the shared ownership we acquired earlier to
+        // exclusive owner ship to initialize the memory manager
+        boost::upgrade_to_unique_lock<boost::shared_mutex> unqLock(lock);
+
+        // if multiple threads above statment, allow the
+        // initialization of memory manager to happen once
+        // by checking if pinned memory manager pointer is already set
+        if (!inst.pinnedMemManager.get())
+            inst.pinnedMemManager.reset(new cuda::MemoryManagerPinned());
+    }
 
     return *(inst.pinnedMemManager.get());
 }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 0e82653991..7264c5aa16 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -18,6 +18,7 @@
 #endif
 
 #include <boost/scoped_ptr.hpp>
+#include <boost/thread/shared_mutex.hpp>
 
 #include <cufftManager.hpp>
 #include <cublasManager.hpp>
@@ -171,8 +172,10 @@ class DeviceManager
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
 
+        boost::shared_mutex memManagerMutex;
         boost::scoped_ptr<MemoryManager> memManager;
 
+        boost::shared_mutex pinnedMemManagerMutex;
         boost::scoped_ptr<MemoryManagerPinned> pinnedMemManager;
 
         boost::scoped_ptr<InteropManager> gfxManager;

From 76e982aa9b84eae5b5dc25572ab4d4ac0481b915 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Thu, 29 Dec 2016 17:53:42 -0500
Subject: [PATCH 1069/2677] Get rid of dead preprocessor directives.

---
 include/af/dim4.hpp | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/include/af/dim4.hpp b/include/af/dim4.hpp
index 44016abb53..1e0a60c969 100644
--- a/include/af/dim4.hpp
+++ b/include/af/dim4.hpp
@@ -14,9 +14,6 @@
 #include <ostream>
 #include <istream>
 #include <vector>
-#if __cplusplus > 199711L // Necessary for NVCC
-//#include <initializer_list>
-#endif
 #include <af/defines.h>
 #include <af/seq.h>
 
@@ -29,9 +26,6 @@ class AFAPI dim4
     dim_t dims[4]; //FIXME: Make this C compatiable
     dim4(); //deleted
 public:
-#if __cplusplus > 199711L
-    //dim4(std::initializer_list<dim_t> dim_vals);
-#endif
     dim4(   dim_t first,
             dim_t second = 1,
             dim_t third = 1,

From 2cad4611a594c816240f0976ba14bc582d81b68b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 22 Dec 2016 23:58:46 -0500
Subject: [PATCH 1070/2677] Remove CL_TO_AF_ERROR macro

---
 src/api/c/err_common.cpp                      |  18 +
 src/api/c/err_common.hpp                      |   1 -
 src/backend/opencl/Array.hpp                  |  27 +-
 src/backend/opencl/blas.cpp                   |   2 +-
 src/backend/opencl/cholesky.cpp               |  58 +-
 src/backend/opencl/cpu/cpu_helper.hpp         |   1 -
 src/backend/opencl/cpu/cpu_sparse_blas.cpp    |   2 +-
 src/backend/opencl/err_clblas.hpp             |   2 +-
 src/backend/opencl/err_clfft.hpp              |   2 +-
 src/backend/opencl/err_opencl.hpp             |  15 -
 src/backend/opencl/iir.cpp                    |  43 +-
 src/backend/opencl/inverse.cpp                |   2 +-
 src/backend/opencl/jit.cpp                    | 155 ++--
 src/backend/opencl/kernel/approx.hpp          | 254 +++----
 src/backend/opencl/kernel/assign.hpp          |  59 +-
 src/backend/opencl/kernel/bilateral.hpp       | 119 ++-
 .../opencl/kernel/convolve/conv2_impl.hpp     | 108 ++-
 .../opencl/kernel/convolve/conv_common.hpp    |  72 +-
 .../opencl/kernel/convolve_separable.cpp      | 118 ++-
 src/backend/opencl/kernel/cscmm.hpp           | 152 ++--
 src/backend/opencl/kernel/cscmv.hpp           | 128 ++--
 src/backend/opencl/kernel/csrmm.hpp           | 158 ++--
 src/backend/opencl/kernel/csrmv.hpp           | 170 +++--
 src/backend/opencl/kernel/diagonal.hpp        | 147 ++--
 src/backend/opencl/kernel/diff.hpp            |  77 +-
 src/backend/opencl/kernel/exampleFunction.hpp | 127 ++--
 src/backend/opencl/kernel/fast.hpp            | 259 ++++---
 src/backend/opencl/kernel/fftconvolve.hpp     | 365 +++++----
 src/backend/opencl/kernel/gradient.hpp        |  83 +--
 src/backend/opencl/kernel/harris.hpp          | 437 ++++++-----
 src/backend/opencl/kernel/histogram.hpp       |  88 +--
 src/backend/opencl/kernel/homography.hpp      | 331 ++++-----
 src/backend/opencl/kernel/hsv_rgb.hpp         |  57 +-
 src/backend/opencl/kernel/identity.hpp        |  60 +-
 src/backend/opencl/kernel/index.hpp           |  59 +-
 src/backend/opencl/kernel/iota.hpp            |  65 +-
 src/backend/opencl/kernel/ireduce.hpp         | 146 ++--
 src/backend/opencl/kernel/join.hpp            |  71 +-
 src/backend/opencl/kernel/lookup.hpp          |  87 +--
 src/backend/opencl/kernel/lu_split.hpp        |  73 +-
 src/backend/opencl/kernel/match_template.hpp  |  99 ++-
 src/backend/opencl/kernel/meanshift.hpp       | 107 ++-
 src/backend/opencl/kernel/medfilt.hpp         | 196 +++--
 src/backend/opencl/kernel/memcopy.hpp         | 205 +++--
 src/backend/opencl/kernel/moments.hpp         |  81 +-
 src/backend/opencl/kernel/morph.hpp           | 236 +++---
 .../opencl/kernel/nearest_neighbour.hpp       | 255 ++++---
 src/backend/opencl/kernel/orb.hpp             | 697 +++++++++--------
 src/backend/opencl/kernel/random_engine.hpp   |  82 +-
 src/backend/opencl/kernel/range.hpp           |  59 +-
 src/backend/opencl/kernel/reduce.hpp          | 120 ++-
 src/backend/opencl/kernel/regions.hpp         | 283 ++++---
 src/backend/opencl/kernel/reorder.hpp         |  65 +-
 src/backend/opencl/kernel/resize.hpp          | 129 ++--
 src/backend/opencl/kernel/rotate.hpp          | 179 +++--
 src/backend/opencl/kernel/scan_dim.hpp        | 169 ++---
 .../opencl/kernel/scan_dim_by_key_impl.hpp    | 240 +++---
 src/backend/opencl/kernel/select.hpp          |  22 +-
 src/backend/opencl/kernel/shift.hpp           |  81 +-
 src/backend/opencl/kernel/sift_nonfree.hpp    | 699 +++++++++---------
 src/backend/opencl/kernel/sobel.hpp           |  89 ++-
 src/backend/opencl/kernel/sort.hpp            | 186 +++--
 .../opencl/kernel/sort_by_key_impl.hpp        | 240 +++---
 src/backend/opencl/kernel/sparse.hpp          | 595 +++++++--------
 src/backend/opencl/kernel/susan.hpp           | 168 ++---
 src/backend/opencl/kernel/tile.hpp            |  61 +-
 src/backend/opencl/kernel/transform.hpp       | 179 +++--
 src/backend/opencl/kernel/transpose.hpp       |  69 +-
 .../opencl/kernel/transpose_inplace.hpp       |  65 +-
 src/backend/opencl/kernel/triangle.hpp        |  67 +-
 src/backend/opencl/kernel/unwrap.hpp          | 125 ++--
 src/backend/opencl/kernel/where.hpp           | 112 ++-
 src/backend/opencl/kernel/wrap.hpp            | 103 ++-
 src/backend/opencl/lu.cpp                     |  84 +--
 src/backend/opencl/magma/magma_cpu_blas.h     |   2 +-
 src/backend/opencl/magma/magma_cpu_lapack.h   |   2 +-
 src/backend/opencl/memory.cpp                 |  37 +-
 src/backend/opencl/platform.cpp               | 303 ++++----
 src/backend/opencl/qr.cpp                     | 144 ++--
 src/backend/opencl/scan.cpp                   |  30 +-
 src/backend/opencl/scan_by_key.cpp            |  33 +-
 src/backend/opencl/solve.cpp                  |  30 +-
 src/backend/opencl/sparse.cpp                 |   2 +-
 83 files changed, 5205 insertions(+), 5723 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 6d56b65c1a..c997621f62 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -22,6 +22,11 @@
 #include <graphics_common.hpp>
 #endif
 
+#ifdef AF_OPENCL
+#include <platform.hpp>
+#include <errorcodes.hpp>
+#endif
+
 using std::string;
 using std::stringstream;
 
@@ -210,6 +215,19 @@ af_err processException()
         ss << ex << "\n";
         print_error(ss.str());
         err = AF_ERR_INTERNAL;
+#endif
+#ifdef AF_OPENCL
+    } catch(const cl::Error &ex) {
+      char opencl_err_msg[1024];
+      snprintf(opencl_err_msg, sizeof(opencl_err_msg),
+               "OpenCL Error (%d): %s when calling %s", ex.err(),
+               getErrorMessage(ex.err()).c_str(), ex.what());
+      print_error(opencl_err_msg);
+      if (ex.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {
+        err = AF_ERR_NO_MEM;
+      } else {
+        err = AF_ERR_INTERNAL;
+      }
 #endif
     } catch (...) {
         print_error(ss.str());
diff --git a/src/api/c/err_common.hpp b/src/api/c/err_common.hpp
index 60ef64276b..a0da16c3d5 100644
--- a/src/api/c/err_common.hpp
+++ b/src/api/c/err_common.hpp
@@ -204,6 +204,5 @@ void print_error(const std::string &msg);
                       "\n", __err);                         \
     } while(0)
 
-
 static const int MAX_ERR_SIZE = 1024;
 std::string& get_global_error_string();
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index b3a168efa2..64ac5c742f 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -19,7 +19,6 @@
 #include <JIT/Node.hpp>
 #include <memory.hpp>
 #include <memory>
-#include <err_common.hpp>
 #include <err_opencl.hpp>
 
 namespace opencl
@@ -242,27 +241,19 @@ namespace opencl
         std::shared_ptr<T> getMappedPtr() const
         {
             auto func = [=] (void* ptr) {
-                try {
-                    if(ptr != nullptr) {
-                        getQueue().enqueueUnmapMemObject(*data, ptr);
-                        ptr = nullptr;
-                    }
-                } catch(cl::Error err) {
-                    CL_TO_AF_ERROR(err);
+                if(ptr != nullptr) {
+                    getQueue().enqueueUnmapMemObject(*data, ptr);
+                    ptr = nullptr;
                 }
             };
 
             T *ptr = nullptr;
-            try {
-                if(ptr == nullptr) {
-                    ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()),
-                                                          true, CL_MAP_READ|CL_MAP_WRITE,
-                                                          getOffset() * sizeof(T),
-                                                          (getDataDims().elements() - getOffset())
-                                                          * sizeof(T));
-                }
-            } catch(cl::Error err) {
-                CL_TO_AF_ERROR(err);
+            if(ptr == nullptr) {
+                ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()),
+                                                      true, CL_MAP_READ|CL_MAP_WRITE,
+                                                      getOffset() * sizeof(T),
+                                                      (getDataDims().elements() - getOffset())
+                                                      * sizeof(T));
             }
 
             return std::shared_ptr<T>(ptr, func);
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 4045bdee97..113a6a6e66 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -14,7 +14,7 @@
 #include <functional>
 #include <stdexcept>
 #include <mutex>
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <err_clblas.hpp>
 #include <math.hpp>
 #include <transpose.hpp>
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index a2034a331a..f949b85a01 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <cholesky.hpp>
-#include <err_common.hpp>
 #include <err_opencl.hpp>
 #include <blas.hpp>
 #include <copy.hpp>
@@ -25,48 +24,39 @@ namespace opencl
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::cholesky_inplace(in, is_upper);
-        }
-
-        initBlas();
-
-        dim4 iDims = in.dims();
-        int N = iDims[0];
-
-        magma_uplo_t uplo = is_upper ? MagmaUpper : MagmaLower;
-
-        int info = 0;
-        cl::Buffer *in_buf = in.get();
-        magma_potrf_gpu<T>(uplo, N,
-                           (*in_buf)(), in.getOffset(),  in.strides()[1],
-                           getQueue()(), &info);
-        return info;
-    } catch (cl::Error &err) {
-        CL_TO_AF_ERROR(err);
+    if(OpenCLCPUOffload()) {
+        return cpu::cholesky_inplace(in, is_upper);
     }
+
+    initBlas();
+
+    dim4 iDims = in.dims();
+    int N = iDims[0];
+
+    magma_uplo_t uplo = is_upper ? MagmaUpper : MagmaLower;
+
+    int info = 0;
+    cl::Buffer *in_buf = in.get();
+    magma_potrf_gpu<T>(uplo, N,
+                        (*in_buf)(), in.getOffset(),  in.strides()[1],
+                        getQueue()(), &info);
+    return info;
 }
 
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::cholesky(info, in, is_upper);
-        }
-
-        Array<T> out = copyArray<T>(in);
-        *info = cholesky_inplace(out, is_upper);
+    if(OpenCLCPUOffload()) {
+        return cpu::cholesky(info, in, is_upper);
+    }
 
-        if (is_upper) triangle<T, true , false>(out, out);
-        else          triangle<T, false, false>(out, out);
+    Array<T> out = copyArray<T>(in);
+    *info = cholesky_inplace(out, is_upper);
 
-        return out;
+    if (is_upper) triangle<T, true , false>(out, out);
+    else          triangle<T, false, false>(out, out);
 
-    } catch (cl::Error &err) {
-        CL_TO_AF_ERROR(err);
-    }
+    return out;
 }
 
 #define INSTANTIATE_CH(T)                                                                   \
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index d4862f983b..6da565e1c6 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -13,7 +13,6 @@
 #include <Array.hpp>
 #include <memory.hpp>
 #include <types.hpp>
-#include <err_common.hpp>
 #include <platform.hpp>
 
 //********************************************************/
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index b8ddb9df3f..ba6ba4920d 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -16,7 +16,7 @@
 
 #include <af/dim4.hpp>
 #include <complex.hpp>
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 
diff --git a/src/backend/opencl/err_clblas.hpp b/src/backend/opencl/err_clblas.hpp
index b5a9733622..4d28f94960 100644
--- a/src/backend/opencl/err_clblas.hpp
+++ b/src/backend/opencl/err_clblas.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <stdio.h>
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <clBLAS.h>
 
 static const char * _clblasGetResultString(clblasStatus st)
diff --git a/src/backend/opencl/err_clfft.hpp b/src/backend/opencl/err_clfft.hpp
index 8d74bb9f02..94ca210c46 100644
--- a/src/backend/opencl/err_clfft.hpp
+++ b/src/backend/opencl/err_clfft.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <stdio.h>
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <clFFT.h>
 
 static const char * _clfftGetResultString(clfftStatus st)
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 955275203a..5d12ab2db0 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -19,21 +19,6 @@
                 __AF_FILENAME__, __LINE__, "OpenCL");   \
     } while(0)
 
-#define CL_TO_AF_ERROR(ERR) do {                                \
-        char opencl_err_msg[1024];                              \
-        snprintf(opencl_err_msg,                                \
-                 sizeof(opencl_err_msg),                        \
-                 "OpenCL Error (%d): %s when calling %s",       \
-                 ERR.err(), getErrorMessage(ERR.err()).c_str(), \
-                 ERR.what());                                   \
-        if (ERR.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {    \
-            AF_ERROR(opencl_err_msg, AF_ERR_NO_MEM);            \
-        } else {                                                \
-            AF_ERROR(opencl_err_msg,                            \
-                     AF_ERR_INTERNAL);                          \
-        }                                                       \
-    } while(0)
-
 namespace opencl
 {
     template <typename T>
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 1ee7398204..098d44f5ba 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -23,36 +23,31 @@ namespace opencl
     template<typename T>
     Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
     {
-        try {
-
-            AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
-            if (x.ndims() != b.ndims()) {
-                type = (x.ndims() < b.ndims()) ?  AF_BATCH_RHS  : AF_BATCH_LHS;
-            }
-
-            // Extract the first N elements
-            Array<T> c = convolve<T, T, 1, true>(x, b, type);
-            dim4 cdims = c.dims();
-            cdims[0] = x.dims()[0];
-            c.resetDims(cdims);
+        AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
+        if (x.ndims() != b.ndims()) {
+            type = (x.ndims() < b.ndims()) ?  AF_BATCH_RHS  : AF_BATCH_LHS;
+        }
 
-            int num_a = a.dims()[0];
+        // Extract the first N elements
+        Array<T> c = convolve<T, T, 1, true>(x, b, type);
+        dim4 cdims = c.dims();
+        cdims[0] = x.dims()[0];
+        c.resetDims(cdims);
 
-            if (num_a == 1) return c;
+        int num_a = a.dims()[0];
 
-            dim4 ydims = c.dims();
-            Array<T> y = createEmptyArray<T>(ydims);
+        if (num_a == 1) return c;
 
-            if (a.ndims() > 1) {
-                kernel::iir<T,  true>(y, c, a);
-            } else {
-                kernel::iir<T, false>(y, c, a);
-            }
+        dim4 ydims = c.dims();
+        Array<T> y = createEmptyArray<T>(ydims);
 
-            return y;
-        } catch (cl::Error &err) {
-            CL_TO_AF_ERROR(err);
+        if (a.ndims() > 1) {
+            kernel::iir<T,  true>(y, c, a);
+        } else {
+            kernel::iir<T, false>(y, c, a);
         }
+
+        return y;
     }
 
 #define INSTANTIATE(T)                          \
diff --git a/src/backend/opencl/inverse.cpp b/src/backend/opencl/inverse.cpp
index df955547ba..d468249921 100644
--- a/src/backend/opencl/inverse.cpp
+++ b/src/backend/opencl/inverse.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <solve.hpp>
 #include <identity.hpp>
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 258f57fa50..83aab34186 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -184,89 +184,82 @@ static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
 
 void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
 {
-    try {
-
-        if (outputs.size() == 0) return;
-
-        // Assume all ouputs are of same size
-        //FIXME: Add assert to check if all outputs are same size?
-        KParam out_info = outputs[0].info;
-
-        // Verify if all ASTs hold Linear Arrays
-        bool is_linear = true;
-        for (auto node : nodes) {
-            is_linear &= node->isLinear(out_info.dims);
-        }
-
-        Kernel ker = getKernel(nodes, is_linear);
-
-        uint local_0 = 1;
-        uint local_1 = 1;
-        uint global_0 = 1;
-        uint global_1 = 1;
-        uint groups_0 = 1;
-        uint groups_1 = 1;
-        uint num_odims = 4;
-
-        // CPUs seem to perform better with work group size 1024
-        const int work_group_size = (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
-
-        while (num_odims >= 1) {
-            if (out_info.dims[num_odims - 1] == 1) num_odims--;
-            else break;
-        }
-
-        if (is_linear) {
-            local_0 = work_group_size;
-            uint out_elements = out_info.dims[3] * out_info.strides[3];
-            uint groups = divup(out_elements, local_0);
-
-            global_1 = divup(groups,     1000) * local_1;
-            global_0 = divup(groups, global_1) * local_0;
-
-        } else {
-            local_1 =  4;
-            local_0 = work_group_size / local_1;
-
-            groups_0 = divup(out_info.dims[0], local_0);
-            groups_1 = divup(out_info.dims[1], local_1);
-
-            global_0 = groups_0 * local_0 * out_info.dims[2];
-            global_1 = groups_1 * local_1 * out_info.dims[3];
-        }
-
-        NDRange local(local_0, local_1);
-        NDRange global(global_0, global_1);
-
-        int args = 0;
-        for (auto node : nodes) {
-            args = node->setArgs(ker, args, is_linear);
-        }
-
-        // Set output parameters
-        for (auto output : outputs) {
-            ker.setArg(args, *(output.data));
-            ++args;
-        }
-
-        // Set dimensions
-        // All outputs are asserted to be of same size
-        // Just use the size from the first output
-        ker.setArg(args + 0,  out_info);
-        ker.setArg(args + 1,  groups_0);
-        ker.setArg(args + 2,  groups_1);
-        ker.setArg(args + 3,  num_odims);
-
-        getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
-
-        for (auto node : nodes) {
-            node->resetFlags();
-        }
-
-    } catch (const cl::Error &ex) {
-        CL_TO_AF_ERROR(ex);
+    if (outputs.size() == 0) return;
+
+    // Assume all ouputs are of same size
+    //FIXME: Add assert to check if all outputs are same size?
+    KParam out_info = outputs[0].info;
+
+    // Verify if all ASTs hold Linear Arrays
+    bool is_linear = true;
+    for (auto node : nodes) {
+        is_linear &= node->isLinear(out_info.dims);
+    }
+
+    Kernel ker = getKernel(nodes, is_linear);
+
+    uint local_0 = 1;
+    uint local_1 = 1;
+    uint global_0 = 1;
+    uint global_1 = 1;
+    uint groups_0 = 1;
+    uint groups_1 = 1;
+    uint num_odims = 4;
+
+    // CPUs seem to perform better with work group size 1024
+    const int work_group_size = (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
+
+    while (num_odims >= 1) {
+        if (out_info.dims[num_odims - 1] == 1) num_odims--;
+        else break;
+    }
+
+    if (is_linear) {
+        local_0 = work_group_size;
+        uint out_elements = out_info.dims[3] * out_info.strides[3];
+        uint groups = divup(out_elements, local_0);
+
+        global_1 = divup(groups,     1000) * local_1;
+        global_0 = divup(groups, global_1) * local_0;
+
+    } else {
+        local_1 =  4;
+        local_0 = work_group_size / local_1;
+
+        groups_0 = divup(out_info.dims[0], local_0);
+        groups_1 = divup(out_info.dims[1], local_1);
+
+        global_0 = groups_0 * local_0 * out_info.dims[2];
+        global_1 = groups_1 * local_1 * out_info.dims[3];
+    }
+
+    NDRange local(local_0, local_1);
+    NDRange global(global_0, global_1);
+
+    int args = 0;
+    for (auto node : nodes) {
+        args = node->setArgs(ker, args, is_linear);
     }
 
+    // Set output parameters
+    for (auto output : outputs) {
+        ker.setArg(args, *(output.data));
+        ++args;
+    }
+
+    // Set dimensions
+    // All outputs are asserted to be of same size
+    // Just use the size from the first output
+    ker.setArg(args + 0,  out_info);
+    ker.setArg(args + 1,  groups_0);
+    ker.setArg(args + 2,  groups_1);
+    ker.setArg(args + 3,  num_odims);
+
+    getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
+
+    for (auto node : nodes) {
+        node->resetFlags();
+    }
 }
 
 void evalNodes(Param &out, Node *node)
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 8bd608a027..85d3ba579c 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -48,144 +48,134 @@ namespace opencl
         void approx1(Param out, const Param in, const Param xpos, const float offGrid,
                      af_interp_type method)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  approxProgs;
-                static std::map<int, Kernel*> approxKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                    ToNumStr<Ty> toNumStr;
-                    std::ostringstream options;
-                    options << " -D Ty="          << dtype_traits<Ty>::getName()
-                            << " -D Tp="          << dtype_traits<Tp>::getName()
-                            << " -D InterpInTy="  << dtype_traits<Ty>::getName()
-                            << " -D InterpValTy=" << dtype_traits<Ty>::getName()
-                            << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                            << " -D ZERO="        << toNumStr(scalar<Ty>(0));
-
-                    if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
-                       (af_dtype) dtype_traits<Ty>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<Ty, double>::value ||
-                        std::is_same<Ty, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    options << " -D INTERP_ORDER=" << order;
-                    addInterpEnumOptions(options);
-
-                    Program prog;
-                    const char *ker_strs[] = {interp_cl, approx1_cl};
-                    const int   ker_lens[] = {interp_cl_len, approx1_cl_len};
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    approxProgs[device] = new Program(prog);
-
-                    approxKernels[device] = new Kernel(*approxProgs[device], "approx1_kernel");
-                });
-
-
-                auto approx1Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                               const Buffer, const KParam, const Ty,
-                                               const int, const int, const int>
-                                      (*approxKernels[device]);
-
-                NDRange local(THREADS, 1, 1);
-                dim_t blocksPerMat = divup(out.info.dims[0], local[0]);
-                NDRange global(blocksPerMat * local[0] * out.info.dims[1],
-                               out.info.dims[2] * out.info.dims[3] * local[0],
-                               1);
-
-                // Passing bools to opencl kernels is not allowed
-                bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 &&
-                                xpos.info.dims[3] == 1);
-
-                approx1Op(EnqueueArgs(getQueue(), global, local),
-                          *out.data, out.info, *in.data, in.info,
-                          *xpos.data, xpos.info, scalar<Ty>(offGrid),
-                          blocksPerMat, (int)batch, (int)method);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>  approxProgs;
+            static std::map<int, Kernel*> approxKernels;
+
+            int device = getActiveDeviceId();
+
+            std::call_once( compileFlags[device], [device] () {
+                ToNumStr<Ty> toNumStr;
+                std::ostringstream options;
+                options << " -D Ty="          << dtype_traits<Ty>::getName()
+                        << " -D Tp="          << dtype_traits<Tp>::getName()
+                        << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+                        << " -D InterpValTy=" << dtype_traits<Ty>::getName()
+                        << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
+                        << " -D ZERO="        << toNumStr(scalar<Ty>(0));
+
+                if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
+                    (af_dtype) dtype_traits<Ty>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
+                } else {
+                    options << " -D IS_CPLX=0";
+                }
+                if (std::is_same<Ty, double>::value ||
+                    std::is_same<Ty, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                options << " -D INTERP_ORDER=" << order;
+                addInterpEnumOptions(options);
+
+                Program prog;
+                const char *ker_strs[] = {interp_cl, approx1_cl};
+                const int   ker_lens[] = {interp_cl_len, approx1_cl_len};
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                approxProgs[device] = new Program(prog);
+
+                approxKernels[device] = new Kernel(*approxProgs[device], "approx1_kernel");
+            });
+
+
+            auto approx1Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                                            const Buffer, const KParam, const Ty,
+                                            const int, const int, const int>
+                                  (*approxKernels[device]);
+
+            NDRange local(THREADS, 1, 1);
+            dim_t blocksPerMat = divup(out.info.dims[0], local[0]);
+            NDRange global(blocksPerMat * local[0] * out.info.dims[1],
+                            out.info.dims[2] * out.info.dims[3] * local[0],
+                            1);
+
+            // Passing bools to opencl kernels is not allowed
+            bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 &&
+                            xpos.info.dims[3] == 1);
+
+            approx1Op(EnqueueArgs(getQueue(), global, local),
+                      *out.data, out.info, *in.data, in.info,
+                      *xpos.data, xpos.info, scalar<Ty>(offGrid),
+                      blocksPerMat, (int)batch, (int)method);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template <typename Ty, typename Tp, int order>
         void approx2(Param out, const Param in, const Param xpos, const Param ypos,
                      const float offGrid, af_interp_type method)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>       approxProgs;
-                static std::map<int, Kernel*>      approxKernels;
-
-                int device = getActiveDeviceId();
-
-                std::call_once( compileFlags[device], [device] () {
-                    ToNumStr<Ty> toNumStr;
-                    std::ostringstream options;
-                    options << " -D Ty="          << dtype_traits<Ty>::getName()
-                            << " -D Tp="          << dtype_traits<Tp>::getName()
-                            << " -D InterpInTy="  << dtype_traits<Ty>::getName()
-                            << " -D InterpValTy=" << dtype_traits<Ty>::getName()
-                            << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                            << " -D ZERO="        << toNumStr(scalar<Ty>(0));
-
-                    if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
-                       (af_dtype) dtype_traits<Ty>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<Ty, double>::value ||
-                        std::is_same<Ty, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    options << " -D INTERP_ORDER=" << order;
-                    addInterpEnumOptions(options);
-
-                    Program prog;
-                    const char *ker_strs[] = {interp_cl, approx2_cl};
-                    const int   ker_lens[] = {interp_cl_len, approx2_cl_len};
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    approxProgs[device] = new Program(prog);
-
-                    approxKernels[device] = new Kernel(*approxProgs[device], "approx2_kernel");
-                });
-
-                auto approx2Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                       const Buffer, const KParam, const Buffer, const KParam,
-                                               const Ty, const int, const int, const int, const int>
-                                       (*approxKernels[device]);
-
-                NDRange local(TX, TY, 1);
-                dim_t blocksPerMatX = divup(out.info.dims[0], local[0]);
-                dim_t blocksPerMatY = divup(out.info.dims[1], local[1]);
-                NDRange global(blocksPerMatX * local[0] * out.info.dims[2],
-                               blocksPerMatY * local[1] * out.info.dims[3],
-                               1);
-
-                // Passing bools to opencl kernels is not allowed
-                bool batch = !(xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
-
-                approx2Op(EnqueueArgs(getQueue(), global, local),
-                          *out.data, out.info,
-                          *in.data, in.info,
-                          *xpos.data, xpos.info,
-                          *ypos.data, ypos.info,
-                          scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>       approxProgs;
+            static std::map<int, Kernel*>      approxKernels;
+
+            int device = getActiveDeviceId();
+
+            std::call_once( compileFlags[device], [device] () {
+                ToNumStr<Ty> toNumStr;
+                std::ostringstream options;
+                options << " -D Ty="          << dtype_traits<Ty>::getName()
+                        << " -D Tp="          << dtype_traits<Tp>::getName()
+                        << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+                        << " -D InterpValTy=" << dtype_traits<Ty>::getName()
+                        << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
+                        << " -D ZERO="        << toNumStr(scalar<Ty>(0));
+
+                if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
+                    (af_dtype) dtype_traits<Ty>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
+                } else {
+                    options << " -D IS_CPLX=0";
+                }
+                if (std::is_same<Ty, double>::value ||
+                    std::is_same<Ty, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                options << " -D INTERP_ORDER=" << order;
+                addInterpEnumOptions(options);
+
+                Program prog;
+                const char *ker_strs[] = {interp_cl, approx2_cl};
+                const int   ker_lens[] = {interp_cl_len, approx2_cl_len};
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                approxProgs[device] = new Program(prog);
+
+                approxKernels[device] = new Kernel(*approxProgs[device], "approx2_kernel");
+            });
+
+            auto approx2Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                                    const Buffer, const KParam, const Buffer, const KParam,
+                                            const Ty, const int, const int, const int, const int>
+                                    (*approxKernels[device]);
+
+            NDRange local(TX, TY, 1);
+            dim_t blocksPerMatX = divup(out.info.dims[0], local[0]);
+            dim_t blocksPerMatY = divup(out.info.dims[1], local[1]);
+            NDRange global(blocksPerMatX * local[0] * out.info.dims[2],
+                            blocksPerMatY * local[1] * out.info.dims[3],
+                            1);
+
+            // Passing bools to opencl kernels is not allowed
+            bool batch = !(xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
+
+            approx2Op(EnqueueArgs(getQueue(), global, local),
+                      *out.data, out.info,
+                      *in.data, in.info,
+                      *xpos.data, xpos.info,
+                      *ypos.data, ypos.info,
+                      scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index d46049ab75..079768c4a9 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -44,48 +44,43 @@ typedef struct {
 template<typename T>
 void assign(Param out, const Param in, const AssignKernelParam_t& p, Buffer *bPtr[4])
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  agnProgs;
-        static std::map<int, Kernel*> agnKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  agnProgs;
+    static std::map<int, Kernel*> agnKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
+    std::call_once( compileFlags[device], [device] () {
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-                }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+            }
 
-                Program prog;
-                buildProgram(prog, assign_cl, assign_cl_len, options.str());
-                agnProgs[device]   = new Program(prog);
-                agnKernels[device] = new Kernel(*agnProgs[device], "assignKernel");
-                });
+            Program prog;
+            buildProgram(prog, assign_cl, assign_cl_len, options.str());
+            agnProgs[device]   = new Program(prog);
+            agnKernels[device] = new Kernel(*agnProgs[device], "assignKernel");
+            });
 
-        NDRange local(THREADS_X, THREADS_Y);
+    NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-        NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+            blk_y * in.info.dims[3] * THREADS_Y);
 
-        auto assignOp = KernelFunctor<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
-             Buffer, Buffer, Buffer, Buffer, int, int>(*agnKernels[device]);
+    auto assignOp = KernelFunctor<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
+          Buffer, Buffer, Buffer, Buffer, int, int>(*agnKernels[device]);
 
-        assignOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, p,
-                *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
+    assignOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *in.data, in.info, p,
+            *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 7a949c4853..46eabab6c6 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -41,69 +41,64 @@ static const int THREADS_Y = 16;
 template<typename inType, typename outType, bool isColor>
 void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  bilProgs;
-        static std::map<int, Kernel*> bilKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
-                                    && getActivePlatform() != AFCL_PLATFORM_APPLE);
-                std::ostringstream options;
-                options << " -D inType=" << dtype_traits<inType>::getName()
-                        << " -D outType=" << dtype_traits<outType>::getName();
-                if (std::is_same<inType, double>::value ||
-                    std::is_same<inType, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                }
-                options << " -D USE_NATIVE_EXP=" << (int)use_native_exp;
-
-                Program prog;
-                buildProgram(prog, bilateral_cl, bilateral_cl_len, options.str());
-                bilProgs[device] = new Program(prog);
-
-                    bilKernels[device] = new Kernel(*bilProgs[device], "bilateral");
-            });
-
-        auto bilateralOp = KernelFunctor<Buffer, KParam,
-                                       Buffer, KParam,
-                                       LocalSpaceArg,
-                                       LocalSpaceArg,
-                                       float, float,
-                                       int, int, int
-                                      >(*bilKernels[device]);
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
-
-        NDRange global(blk_x*in.info.dims[2]*THREADS_X,
-                       blk_y*in.info.dims[3]*THREADS_Y);
-
-        // calculate local memory size
-        int radius = (int)std::max(s_sigma * 1.5f, 1.f);
-        int num_shrd_elems    = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
-        int num_gauss_elems   = (2*radius+1)*(2*radius+1);
-        size_t localMemSize   = (num_shrd_elems + num_gauss_elems)*sizeof(outType);
-        size_t MaxLocalSize   = getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
-        if (localMemSize>MaxLocalSize) {
-            OPENCL_NOT_SUPPORTED();
-        }
-
-        bilateralOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info,
-                    cl::Local(num_shrd_elems*sizeof(outType)),
-                    cl::Local(num_gauss_elems*sizeof(outType)),
-                    s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  bilProgs;
+    static std::map<int, Kernel*> bilKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+            bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
+                                && getActivePlatform() != AFCL_PLATFORM_APPLE);
+            std::ostringstream options;
+            options << " -D inType=" << dtype_traits<inType>::getName()
+                    << " -D outType=" << dtype_traits<outType>::getName();
+            if (std::is_same<inType, double>::value ||
+                std::is_same<inType, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+            }
+            options << " -D USE_NATIVE_EXP=" << (int)use_native_exp;
+
+            Program prog;
+            buildProgram(prog, bilateral_cl, bilateral_cl_len, options.str());
+            bilProgs[device] = new Program(prog);
+
+                bilKernels[device] = new Kernel(*bilProgs[device], "bilateral");
+        });
+
+    auto bilateralOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    LocalSpaceArg,
+                                    LocalSpaceArg,
+                                    float, float,
+                                    int, int, int
+                                  >(*bilKernels[device]);
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    NDRange global(blk_x*in.info.dims[2]*THREADS_X,
+                    blk_y*in.info.dims[3]*THREADS_Y);
+
+    // calculate local memory size
+    int radius = (int)std::max(s_sigma * 1.5f, 1.f);
+    int num_shrd_elems    = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
+    int num_gauss_elems   = (2*radius+1)*(2*radius+1);
+    size_t localMemSize   = (num_shrd_elems + num_gauss_elems)*sizeof(outType);
+    size_t MaxLocalSize   = getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
+    if (localMemSize>MaxLocalSize) {
+        OPENCL_NOT_SUPPORTED();
     }
+
+    bilateralOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info,
+                cl::Local(num_shrd_elems*sizeof(outType)),
+                cl::Local(num_gauss_elems*sizeof(outType)),
+                s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 7f2693ce41..04fae02d72 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -19,66 +19,60 @@ namespace kernel
 template<typename T, typename aT, bool expand>
 void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, const Param filter)
 {
-    try {
-        int f0 = filter.info.dims[0];
-        int f1 = filter.info.dims[1];
-
-        std::string ref_name =
-            std::string("conv2_") +
-            std::string(dtype_traits<T>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<aT>::getName()) +
-            std::string("_") +
-            std::to_string(expand) +
-            std::string("_") +
-            std::to_string(f0) +
-            std::string("_") +
-            std::to_string(f1);
-
-        int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
-            size_t LOC_SIZE = (THREADS_X+2*(f0-1))*(THREADS_Y+2*(f1-1));
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D accType="<< dtype_traits<aT>::getName()
-                    << " -D BASE_DIM="<< 2 /* hard constant specific to this convolution type */
-                    << " -D FLEN0=" << f0
-                    << " -D FLEN1=" << f1
-                    << " -D EXPAND="<< expand
-                    << " -D C_SIZE="<< LOC_SIZE;
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
-            entry.prog   = new Program(prog);
-            entry.ker = new Kernel(*entry.prog, "convolve");
-
-            kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = idx->second;
+    int f0 = filter.info.dims[0];
+    int f1 = filter.info.dims[1];
+
+    std::string ref_name =
+        std::string("conv2_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::string("_") +
+        std::string(dtype_traits<aT>::getName()) +
+        std::string("_") +
+        std::to_string(expand) +
+        std::string("_") +
+        std::to_string(f0) +
+        std::string("_") +
+        std::to_string(f1);
+
+    int device = getActiveDeviceId();
+    kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+    kc_entry_t entry;
+    if (idx == kernelCaches[device].end()) {
+        size_t LOC_SIZE = (THREADS_X+2*(f0-1))*(THREADS_Y+2*(f1-1));
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D accType="<< dtype_traits<aT>::getName()
+                << " -D BASE_DIM="<< 2 /* hard constant specific to this convolution type */
+                << " -D FLEN0=" << f0
+                << " -D FLEN1=" << f1
+                << " -D EXPAND="<< expand
+                << " -D C_SIZE="<< LOC_SIZE;
+        if (std::is_same<T, double>::value ||
+            std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+        Program prog;
+        buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
+        entry.prog   = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "convolve");
+
+        kernelCaches[device][ref_name] = entry;
+    } else {
+        entry = idx->second;
+    }
 
-        auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                  Buffer, KParam, int, int,
-                                  int, int,
-                                  int, int
-                                 >(*entry.ker);
-
-        convOp(EnqueueArgs(getQueue(), param.global, param.local),
-                *out.data, out.info, *signal.data, signal.info,
-                *param.impulse, filter.info, param.nBBS0, param.nBBS1,
-                param.o[1], param.o[2], param.s[1], param.s[2]);
+    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
+                              Buffer, KParam, int, int,
+                              int, int,
+                              int, int
+                              >(*entry.ker);
 
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    convOp(EnqueueArgs(getQueue(), param.global, param.local),
+            *out.data, out.info, *signal.data, signal.info,
+            *param.impulse, filter.info, param.nBBS0, param.nBBS1,
+            param.o[1], param.o[2], param.s[1], param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index e5d8b233ba..9ea8e23e70 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -97,45 +97,39 @@ void prepareKernelArgs(conv_kparam_t& param, dim_t *oDims,
 template<typename T, typename aT, int bDim, bool expand>
 void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, const Param& filter)
 {
-    try {
-        static std::once_flag  compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> convProgs;
-        static std::map<int, Kernel*>  convKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D accType="<< dtype_traits<aT>::getName()
-                            << " -D BASE_DIM="<< bDim
-                            << " -D EXPAND=" << expand;
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
-                    convProgs[device]   = new Program(prog);
-                    convKernels[device] = new Kernel(*convProgs[device], "convolve");
-                });
-
-        auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                        cl::LocalSpaceArg, Buffer, KParam,
-                                        int, int,
-                                        int, int, int,
-                                        int, int, int
-                                       >(*convKernels[device]);
-
-        convOp(EnqueueArgs(getQueue(), param.global, param.local),
-                *out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
-                *param.impulse, filter.info, param.nBBS0, param.nBBS1,
-                param.o[0], param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
-
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag  compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> convProgs;
+    static std::map<int, Kernel*>  convKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D accType="<< dtype_traits<aT>::getName()
+                        << " -D BASE_DIM="<< bDim
+                        << " -D EXPAND=" << expand;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
+                convProgs[device]   = new Program(prog);
+                convKernels[device] = new Kernel(*convProgs[device], "convolve");
+            });
+
+    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                    cl::LocalSpaceArg, Buffer, KParam,
+                                    int, int,
+                                    int, int, int,
+                                    int, int, int
+                                    >(*convKernels[device]);
+
+    convOp(EnqueueArgs(getQueue(), param.global, param.local),
+            *out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
+            *param.impulse, filter.info, param.nBBS0, param.nBBS1,
+            param.o[0], param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index e0b0877498..b212cbe452 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -39,76 +39,70 @@ static const int THREADS_Y = 16;
 template<typename T, typename accType, int conv_dim, bool expand>
 void convSep(Param out, const Param signal, const Param filter)
 {
-    try {
-
-        const int fLen = filter.info.dims[0] * filter.info.dims[1];
-
-        std::string ref_name =
-            std::string("convsep_") +
-            std::to_string(conv_dim) +
-            std::string("_") +
-            std::string(dtype_traits<T>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<accType>::getName()) +
-            std::string("_") +
-            std::to_string(expand) +
-            std::string("_") +
-            std::to_string(fLen);
-
-        int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
-            const size_t C0_SIZE  = (THREADS_X+2*(fLen-1))* THREADS_Y;
-            const size_t C1_SIZE  = (THREADS_Y+2*(fLen-1))* THREADS_X;
-
-            size_t locSize = (conv_dim==0 ? C0_SIZE : C1_SIZE);
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D accType="<< dtype_traits<accType>::getName()
-                    << " -D CONV_DIM="<< conv_dim
-                    << " -D EXPAND="<< expand
-                    << " -D FLEN="<< fLen
-                    << " -D LOCAL_MEM_SIZE="<<locSize;
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, convolve_separable_cl, convolve_separable_cl_len, options.str());
-
-            entry.prog   = new Program(prog);
-            entry.ker  = new Kernel(*entry.prog, "convolve");
-            kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = idx->second;
+    const int fLen = filter.info.dims[0] * filter.info.dims[1];
+
+    std::string ref_name =
+        std::string("convsep_") +
+        std::to_string(conv_dim) +
+        std::string("_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::string("_") +
+        std::string(dtype_traits<accType>::getName()) +
+        std::string("_") +
+        std::to_string(expand) +
+        std::string("_") +
+        std::to_string(fLen);
+
+    int device = getActiveDeviceId();
+    kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+    kc_entry_t entry;
+    if (idx == kernelCaches[device].end()) {
+        const size_t C0_SIZE  = (THREADS_X+2*(fLen-1))* THREADS_Y;
+        const size_t C1_SIZE  = (THREADS_Y+2*(fLen-1))* THREADS_X;
+
+        size_t locSize = (conv_dim==0 ? C0_SIZE : C1_SIZE);
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D accType="<< dtype_traits<accType>::getName()
+                << " -D CONV_DIM="<< conv_dim
+                << " -D EXPAND="<< expand
+                << " -D FLEN="<< fLen
+                << " -D LOCAL_MEM_SIZE="<<locSize;
+        if (std::is_same<T, double>::value ||
+            std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+        Program prog;
+        buildProgram(prog, convolve_separable_cl, convolve_separable_cl_len, options.str());
+
+        entry.prog   = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "convolve");
+        kernelCaches[device][ref_name] = entry;
+    } else {
+        entry = idx->second;
+    }
 
-        auto convOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                  int, int>(*entry.ker);
+    auto convOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                              int, int>(*entry.ker);
 
-        NDRange local(THREADS_X, THREADS_Y);
+    NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(out.info.dims[0], THREADS_X);
-        int blk_y = divup(out.info.dims[1], THREADS_Y);
+    int blk_x = divup(out.info.dims[0], THREADS_X);
+    int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-        NDRange global(blk_x*signal.info.dims[2]*THREADS_X,
-                       blk_y*signal.info.dims[3]*THREADS_Y);
+    NDRange global(blk_x*signal.info.dims[2]*THREADS_X,
+                    blk_y*signal.info.dims[3]*THREADS_Y);
 
-        cl::Buffer *mBuff = bufferAlloc(fLen*sizeof(accType));
-        // FIX ME: if the filter array is strided, direct might cause issues
-        getQueue().enqueueCopyBuffer(*filter.data, *mBuff, 0, 0, fLen*sizeof(accType));
+    cl::Buffer *mBuff = bufferAlloc(fLen*sizeof(accType));
+    // FIX ME: if the filter array is strided, direct might cause issues
+    getQueue().enqueueCopyBuffer(*filter.data, *mBuff, 0, 0, fLen*sizeof(accType));
 
-        convOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info, *signal.data, signal.info, *mBuff, blk_x, blk_y);
+    convOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *signal.data, signal.info, *mBuff, blk_x, blk_y);
 
-        bufferFree(mBuff);
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    bufferFree(mBuff);
 }
 
 #define INSTANTIATE(T, accT)  \
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index f480c65fc6..3b6bc13827 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -43,91 +43,87 @@ namespace opencl
                       const Param &values, const Param &colIdx, const Param &rowIdx,
                       const Param &rhs, const T alpha, const T beta, bool is_conj)
         {
-            try {
-                bool use_alpha = (alpha != scalar<T>(1.0));
-                bool use_beta = (beta != scalar<T>(0.0));
-
-                int threads = 256;
-                // TODO: Find a better way to tune these parameters
-                int rows_per_group = 8;
-                int cols_per_group = 8;
-
-                std::string ref_name =
-                    std::string("cscmm_nn_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(use_alpha) +
-                    std::string("_") +
-                    std::to_string(use_beta) +
-                    std::string("_") +
-                    std::to_string(is_conj) +
-                    std::string("_") +
-                    std::to_string(rows_per_group) +
-                    std::string("_") +
-                    std::to_string(cols_per_group) +
-                    std::string("_") +
-                    std::to_string(threads);
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    options << " -D USE_ALPHA=" << use_alpha;
-                    options << " -D USE_BETA=" << use_beta;
-                    options << " -D IS_CONJ=" << is_conj;
-                    options << " -D THREADS=" << threads;
-                    options << " -D ROWS_PER_GROUP=" << rows_per_group;
-                    options << " -D COLS_PER_GROUP=" << cols_per_group;
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    if (std::is_same<T, cfloat>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-
-                    const char *ker_strs[] = {cscmm_cl};
-                    const int   ker_lens[] = {cscmm_cl_len};
-
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
+            bool use_alpha = (alpha != scalar<T>(1.0));
+            bool use_beta = (beta != scalar<T>(0.0));
+
+            int threads = 256;
+            // TODO: Find a better way to tune these parameters
+            int rows_per_group = 8;
+            int cols_per_group = 8;
+
+            std::string ref_name =
+                std::string("cscmm_nn_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(use_alpha) +
+                std::string("_") +
+                std::to_string(use_beta) +
+                std::string("_") +
+                std::to_string(is_conj) +
+                std::string("_") +
+                std::to_string(rows_per_group) +
+                std::string("_") +
+                std::to_string(cols_per_group) +
+                std::string("_") +
+                std::to_string(threads);
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+
+            if (idx == kernelCaches[device].end()) {
+
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                options << " -D USE_ALPHA=" << use_alpha;
+                options << " -D USE_BETA=" << use_beta;
+                options << " -D IS_CONJ=" << is_conj;
+                options << " -D THREADS=" << threads;
+                options << " -D ROWS_PER_GROUP=" << rows_per_group;
+                options << " -D COLS_PER_GROUP=" << cols_per_group;
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                if (std::is_same<T, cfloat>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
                 }
 
-                auto cscmm_kernel = *entry.ker;
-                auto cscmm_func = KernelFunctor<Buffer,
-                                                Buffer, Buffer, Buffer,
-                                                int, int, int,
-                                                Buffer, KParam, T, T>(cscmm_kernel);
+                const char *ker_strs[] = {cscmm_cl};
+                const int   ker_lens[] = {cscmm_cl_len};
 
-                NDRange local(threads, 1);
-                int M = out.info.dims[0];
-                int N = out.info.dims[1];
-                int K = colIdx.info.dims[0] - 1;
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
+            } else {
+                entry = idx->second;
+            }
 
-                int groups_x = divup(M, rows_per_group);
-                int groups_y = divup(N, cols_per_group);
-                NDRange global(local[0] * groups_x, local[1] * groups_y);
+            auto cscmm_kernel = *entry.ker;
+            auto cscmm_func = KernelFunctor<Buffer,
+                                            Buffer, Buffer, Buffer,
+                                            int, int, int,
+                                            Buffer, KParam, T, T>(cscmm_kernel);
 
-                cscmm_func(EnqueueArgs(getQueue(), global, local),
-                           *out.data, *values.data, *colIdx.data, *rowIdx.data,
-                           M, K, N, *rhs.data, rhs.info, alpha, beta);
+            NDRange local(threads, 1);
+            int M = out.info.dims[0];
+            int N = out.info.dims[1];
+            int K = colIdx.info.dims[0] - 1;
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error &ex) {
-                CL_TO_AF_ERROR(ex);
-            }
+            int groups_x = divup(M, rows_per_group);
+            int groups_y = divup(N, cols_per_group);
+            NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+            cscmm_func(EnqueueArgs(getQueue(), global, local),
+                        *out.data, *values.data, *colIdx.data, *rowIdx.data,
+                        M, K, N, *rhs.data, rhs.info, alpha, beta);
+
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index e121b387fb..6aabe2cfd3 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -43,84 +43,80 @@ namespace opencl
                    const Param &values, const Param &colIdx, const Param &rowIdx,
                    const Param &rhs, const T alpha, const T beta, bool is_conj)
         {
-            try {
-                bool use_alpha = (alpha != scalar<T>(1.0));
-                bool use_beta = (beta != scalar<T>(0.0));
+            bool use_alpha = (alpha != scalar<T>(1.0));
+            bool use_beta = (beta != scalar<T>(0.0));
 
-                int threads = 256;
-                //TODO: rows_per_group limited by register pressure. Find better way to handle this.
-                int rows_per_group = 64;
+            int threads = 256;
+            //TODO: rows_per_group limited by register pressure. Find better way to handle this.
+            int rows_per_group = 64;
 
-                std::string ref_name =
-                    std::string("cscmv_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(use_alpha) +
-                    std::string("_") +
-                    std::to_string(use_beta) +
-                    std::string("_") +
-                    std::to_string(is_conj) +
-                    std::string("_") +
-                    std::to_string(rows_per_group) +
-                    std::string("_") +
-                    std::to_string(threads);
+            std::string ref_name =
+                std::string("cscmv_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(use_alpha) +
+                std::string("_") +
+                std::to_string(use_beta) +
+                std::string("_") +
+                std::to_string(is_conj) +
+                std::string("_") +
+                std::to_string(rows_per_group) +
+                std::string("_") +
+                std::to_string(threads);
 
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                if (idx == kernelCaches[device].end()) {
+            if (idx == kernelCaches[device].end()) {
 
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    options << " -D USE_ALPHA=" << use_alpha;
-                    options << " -D USE_BETA=" << use_beta;
-                    options << " -D IS_CONJ=" << is_conj;
-                    options << " -D THREADS=" << threads;
-                    options << " -D ROWS_PER_GROUP=" << rows_per_group;
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                options << " -D USE_ALPHA=" << use_alpha;
+                options << " -D USE_BETA=" << use_beta;
+                options << " -D IS_CONJ=" << is_conj;
+                options << " -D THREADS=" << threads;
+                options << " -D ROWS_PER_GROUP=" << rows_per_group;
 
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    if (std::is_same<T, cfloat>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-
-                    const char *ker_strs[] = {cscmv_cl};
-                    const int   ker_lens[] = {cscmv_cl_len};
-
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "cscmv_block");
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                if (std::is_same<T, cfloat>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
                 }
 
-                auto cscmv_kernel = *entry.ker;
-                auto cscmv_func = KernelFunctor<Buffer,
-                                                Buffer, Buffer, Buffer,
-                                                int, int,
-                                                Buffer, KParam, T, T>(cscmv_kernel);
+                const char *ker_strs[] = {cscmv_cl};
+                const int   ker_lens[] = {cscmv_cl_len};
 
-                NDRange local(threads);
-                int K = colIdx.info.dims[0] - 1;
-                int M = out.info.dims[0];
-                int groups_x = divup(M, rows_per_group);
-                NDRange global(local[0] * groups_x, 1);
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "cscmv_block");
+            } else {
+                entry = idx->second;
+            }
 
-                cscmv_func(EnqueueArgs(getQueue(), global, local),
-                           *out.data, *values.data, *colIdx.data, *rowIdx.data,
-                           M, K, *rhs.data, rhs.info, alpha, beta);
+            auto cscmv_kernel = *entry.ker;
+            auto cscmv_func = KernelFunctor<Buffer,
+                                            Buffer, Buffer, Buffer,
+                                            int, int,
+                                            Buffer, KParam, T, T>(cscmv_kernel);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error &ex) {
-                CL_TO_AF_ERROR(ex);
-            }
+            NDRange local(threads);
+            int K = colIdx.info.dims[0] - 1;
+            int M = out.info.dims[0];
+            int groups_x = divup(M, rows_per_group);
+            NDRange global(local[0] * groups_x, 1);
+
+            cscmv_func(EnqueueArgs(getQueue(), global, local),
+                        *out.data, *values.data, *colIdx.data, *rowIdx.data,
+                        M, K, *rhs.data, rhs.info, alpha, beta);
+
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 73c37d3845..0afab2973c 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -43,91 +43,87 @@ namespace opencl
                       const Param &values, const Param &rowIdx, const Param &colIdx,
                       const Param &rhs, const T alpha, const T beta)
         {
-            try {
-                bool use_alpha = (alpha != scalar<T>(1.0));
-                bool use_beta = (beta != scalar<T>(0.0));
-
-                // Using greedy indexing is causing performance issues on many platforms
-                // FIXME: Figure out why
-                bool use_greedy = false;
-
-                std::string ref_name =
-                    std::string("csrmm_nt_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(use_alpha) +
-                    std::string("_") +
-                    std::to_string(use_beta) +
-                    std::string("_") +
-                    std::to_string(use_greedy);
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    options << " -D USE_ALPHA=" << use_alpha;
-                    options << " -D USE_BETA=" << use_beta;
-                    options << " -D USE_GREEDY=" << use_greedy;
-                    options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    if (std::is_same<T, cfloat>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-
-                    const char *ker_strs[] = {csrmm_cl};
-                    const int   ker_lens[] = {csrmm_cl_len};
-
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel[2];
-                    entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
-                    // FIXME: Change this after adding another kernel
-                    entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
+            bool use_alpha = (alpha != scalar<T>(1.0));
+            bool use_beta = (beta != scalar<T>(0.0));
+
+            // Using greedy indexing is causing performance issues on many platforms
+            // FIXME: Figure out why
+            bool use_greedy = false;
+
+            std::string ref_name =
+                std::string("csrmm_nt_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(use_alpha) +
+                std::string("_") +
+                std::to_string(use_beta) +
+                std::string("_") +
+                std::to_string(use_greedy);
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+
+            if (idx == kernelCaches[device].end()) {
+
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                options << " -D USE_ALPHA=" << use_alpha;
+                options << " -D USE_BETA=" << use_beta;
+                options << " -D USE_GREEDY=" << use_greedy;
+                options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                if (std::is_same<T, cfloat>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
                 }
 
-                auto csrmm_nt_kernel = entry.ker[0];
-                auto csrmm_nt_func = KernelFunctor<Buffer,
-                                                   Buffer, Buffer, Buffer,
-                                                   int, int,
-                                                   Buffer, KParam, T, T, Buffer>(csrmm_nt_kernel);
-                NDRange local(THREADS_PER_GROUP, 1);
-                int M = rowIdx.info.dims[0] - 1;
-                int N = rhs.info.dims[0];
-
-                int groups_x = divup(N, local[0]);
-                int groups_y = divup(M, REPEAT);
-                groups_y = std::min(groups_y, MAX_CSRMM_GROUPS);
-                NDRange global(local[0] * groups_x, local[1] * groups_y);
-
-                std::vector<int> count(groups_x);
-                cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
-                getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
-                                              0,
-                                              count.size() * sizeof(int),
-                                              (void *)count.data());
-
-                csrmm_nt_func(EnqueueArgs(getQueue(), global, local),
-                              *out.data, *values.data, *rowIdx.data, *colIdx.data,
-                              M, N, *rhs.data, rhs.info, alpha, beta, *counter);
-
-                bufferFree(counter);
-            } catch (cl::Error &ex) {
-                CL_TO_AF_ERROR(ex);
+                const char *ker_strs[] = {csrmm_cl};
+                const int   ker_lens[] = {csrmm_cl_len};
+
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel[2];
+                entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
+                // FIXME: Change this after adding another kernel
+                entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
+            } else {
+                entry = idx->second;
             }
+
+            auto csrmm_nt_kernel = entry.ker[0];
+            auto csrmm_nt_func = KernelFunctor<Buffer,
+                                                Buffer, Buffer, Buffer,
+                                                int, int,
+                                                Buffer, KParam, T, T, Buffer>(csrmm_nt_kernel);
+            NDRange local(THREADS_PER_GROUP, 1);
+            int M = rowIdx.info.dims[0] - 1;
+            int N = rhs.info.dims[0];
+
+            int groups_x = divup(N, local[0]);
+            int groups_y = divup(M, REPEAT);
+            groups_y = std::min(groups_y, MAX_CSRMM_GROUPS);
+            NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+            std::vector<int> count(groups_x);
+            cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
+            getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
+                                          0,
+                                          count.size() * sizeof(int),
+                                          (void *)count.data());
+
+            csrmm_nt_func(EnqueueArgs(getQueue(), global, local),
+                          *out.data, *values.data, *rowIdx.data, *colIdx.data,
+                          M, N, *rhs.data, rhs.info, alpha, beta, *counter);
+
+            bufferFree(counter);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 2415c8e62b..faffb828e8 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -44,97 +44,93 @@ namespace opencl
                    const Param &values, const Param &rowIdx, const Param &colIdx,
                    const Param &rhs, const T alpha, const T beta)
         {
-            try {
-                bool use_alpha = (alpha != scalar<T>(1.0));
-                bool use_beta = (beta != scalar<T>(0.0));
-
-                // Using greedy indexing is causing performance issues on many platforms
-                // FIXME: Figure out why
-                bool use_greedy = false;
-
-                // FIXME: Find a better number based on average non zeros per row
-                int threads = 64;
-
-                std::string ref_name =
-                    std::string("csrmv_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(use_alpha) +
-                    std::string("_") +
-                    std::to_string(use_beta) +
-                    std::string("_") +
-                    std::to_string(use_greedy) +
-                    std::string("_") +
-                    std::to_string(threads);
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    options << " -D USE_ALPHA=" << use_alpha;
-                    options << " -D USE_BETA=" << use_beta;
-                    options << " -D USE_GREEDY=" << use_greedy;
-                    options << " -D THREADS=" << threads;
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    if (std::is_same<T, cfloat>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-
-                    const char *ker_strs[] = {csrmv_cl};
-                    const int   ker_lens[] = {csrmv_cl_len};
-
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel[2];
-                    entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
-                    entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
+            bool use_alpha = (alpha != scalar<T>(1.0));
+            bool use_beta = (beta != scalar<T>(0.0));
+
+            // Using greedy indexing is causing performance issues on many platforms
+            // FIXME: Figure out why
+            bool use_greedy = false;
+
+            // FIXME: Find a better number based on average non zeros per row
+            int threads = 64;
+
+            std::string ref_name =
+                std::string("csrmv_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(use_alpha) +
+                std::string("_") +
+                std::to_string(use_beta) +
+                std::string("_") +
+                std::to_string(use_greedy) +
+                std::string("_") +
+                std::to_string(threads);
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+
+            if (idx == kernelCaches[device].end()) {
+
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                options << " -D USE_ALPHA=" << use_alpha;
+                options << " -D USE_BETA=" << use_beta;
+                options << " -D USE_GREEDY=" << use_greedy;
+                options << " -D THREADS=" << threads;
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                if (std::is_same<T, cfloat>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
                 }
 
-                int count = 0;
-                cl::Buffer *counter = bufferAlloc(sizeof(int));
-                getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
-                                              0,
-                                              sizeof(int),
-                                              (void *)&count);
-
-                // TODO: Figure out the proper way to choose either csrmv_thread or csrmv_block
-                bool is_csrmv_block = true;
-                auto csrmv_kernel = is_csrmv_block ? entry.ker[1] : entry.ker[0];
-                auto csrmv_func = KernelFunctor<Buffer,
-                                                Buffer, Buffer, Buffer,
-                                                int,
-                                                Buffer, KParam, T, T, Buffer>(csrmv_kernel);
-
-                NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
-                int M = rowIdx.info.dims[0] - 1;
-
-                int groups_x = is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
-                groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
-                NDRange global(local[0] * groups_x, 1);
-
-                csrmv_func(EnqueueArgs(getQueue(), global, local),
-                           *out.data, *values.data, *rowIdx.data, *colIdx.data,
-                           M, *rhs.data, rhs.info, alpha, beta, *counter);
-
-                CL_DEBUG_FINISH(getQueue());
-                bufferFree(counter);
-            } catch (cl::Error &ex) {
-                CL_TO_AF_ERROR(ex);
+                const char *ker_strs[] = {csrmv_cl};
+                const int   ker_lens[] = {csrmv_cl_len};
+
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel[2];
+                entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
+                entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
+            } else {
+                entry = idx->second;
             }
+
+            int count = 0;
+            cl::Buffer *counter = bufferAlloc(sizeof(int));
+            getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
+                                          0,
+                                          sizeof(int),
+                                          (void *)&count);
+
+            // TODO: Figure out the proper way to choose either csrmv_thread or csrmv_block
+            bool is_csrmv_block = true;
+            auto csrmv_kernel = is_csrmv_block ? entry.ker[1] : entry.ker[0];
+            auto csrmv_func = KernelFunctor<Buffer,
+                                            Buffer, Buffer, Buffer,
+                                            int,
+                                            Buffer, KParam, T, T, Buffer>(csrmv_kernel);
+
+            NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
+            int M = rowIdx.info.dims[0] - 1;
+
+            int groups_x = is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
+            groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
+            NDRange global(local[0] * groups_x, 1);
+
+            csrmv_func(EnqueueArgs(getQueue(), global, local),
+                        *out.data, *values.data, *rowIdx.data, *colIdx.data,
+                        M, *rhs.data, rhs.info, alpha, beta, *counter);
+
+            CL_DEBUG_FINISH(getQueue());
+            bufferFree(counter);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 581658c0d6..bf6acce9af 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -37,89 +37,80 @@ namespace kernel
     template<typename T>
     static void diagCreate(Param out, Param in, int num)
     {
-        try {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   diagCreateProgs;
-            static std::map<int, Kernel*>  diagCreateKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T="    << dtype_traits<T>::getName()
-                            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, diag_create_cl, diag_create_cl_len, options.str());
-                    diagCreateProgs[device]   = new Program(prog);
-                    diagCreateKernels[device] = new Kernel(*diagCreateProgs[device],
-                                                           "diagCreateKernel");
-                });
-
-            NDRange local(32, 8);
-            int groups_x = divup(out.info.dims[0], local[0]);
-            int groups_y = divup(out.info.dims[1], local[1]);
-            NDRange global(groups_x * local[0] * out.info.dims[2],
-                           groups_y * local[1]);
-
-            auto diagCreateOp = KernelFunctor<Buffer, const KParam,
-                                            Buffer, const KParam,
-                                            int, int> (*diagCreateKernels[device]);
-
-            diagCreateOp(EnqueueArgs(getQueue(), global, local),
-                         *(out.data), out.info, *(in.data), in.info, num, groups_x);
-            CL_DEBUG_FINISH(getQueue());
-
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-        }
+        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+        static std::map<int, Program*>   diagCreateProgs;
+        static std::map<int, Kernel*>  diagCreateKernels;
+
+        int device = getActiveDeviceId();
+
+        std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T="    << dtype_traits<T>::getName()
+                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, diag_create_cl, diag_create_cl_len, options.str());
+                diagCreateProgs[device]   = new Program(prog);
+                diagCreateKernels[device] = new Kernel(*diagCreateProgs[device],
+                                                        "diagCreateKernel");
+            });
+
+        NDRange local(32, 8);
+        int groups_x = divup(out.info.dims[0], local[0]);
+        int groups_y = divup(out.info.dims[1], local[1]);
+        NDRange global(groups_x * local[0] * out.info.dims[2],
+                        groups_y * local[1]);
+
+        auto diagCreateOp = KernelFunctor<Buffer, const KParam,
+                                        Buffer, const KParam,
+                                        int, int> (*diagCreateKernels[device]);
+
+        diagCreateOp(EnqueueArgs(getQueue(), global, local),
+                      *(out.data), out.info, *(in.data), in.info, num, groups_x);
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename T>
     static void diagExtract(Param out, Param in, int num)
     {
-        try {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   diagExtractProgs;
-            static std::map<int, Kernel*>  diagExtractKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T="    << dtype_traits<T>::getName()
-                            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, diag_extract_cl, diag_extract_cl_len, options.str());
-                    diagExtractProgs[device]   = new Program(prog);
-                    diagExtractKernels[device] = new Kernel(*diagExtractProgs[device],
-                                                           "diagExtractKernel");
-                });
-
-            NDRange local(256, 1);
-            int groups_x = divup(out.info.dims[0], local[0]);
-            int groups_z = out.info.dims[2];
-            NDRange global(groups_x * local[0],
-                           groups_z * local[1] * out.info.dims[3]);
-
-            auto diagExtractOp = KernelFunctor<Buffer, const KParam,
-                                             Buffer, const KParam,
-                                             int, int> (*diagExtractKernels[device]);
-
-            diagExtractOp(EnqueueArgs(getQueue(), global, local),
-                          *(out.data), out.info, *(in.data), in.info, num, groups_z);
-            CL_DEBUG_FINISH(getQueue());
-
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-        }
+        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+        static std::map<int, Program*>   diagExtractProgs;
+        static std::map<int, Kernel*>  diagExtractKernels;
+
+        int device = getActiveDeviceId();
+
+        std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T="    << dtype_traits<T>::getName()
+                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, diag_extract_cl, diag_extract_cl_len, options.str());
+                diagExtractProgs[device]   = new Program(prog);
+                diagExtractKernels[device] = new Kernel(*diagExtractProgs[device],
+                                                        "diagExtractKernel");
+            });
+
+        NDRange local(256, 1);
+        int groups_x = divup(out.info.dims[0], local[0]);
+        int groups_z = out.info.dims[2];
+        NDRange global(groups_x * local[0],
+                        groups_z * local[1] * out.info.dims[3]);
+
+        auto diagExtractOp = KernelFunctor<Buffer, const KParam,
+                                          Buffer, const KParam,
+                                          int, int> (*diagExtractKernels[device]);
+
+        diagExtractOp(EnqueueArgs(getQueue(), global, local),
+                      *(out.data), out.info, *(in.data), in.info, num, groups_z);
+        CL_DEBUG_FINISH(getQueue());
+
     }
 
 }
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index f8eaa7fc36..1445829c19 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -36,55 +36,50 @@ namespace opencl
         template<typename T, unsigned dim, bool isDiff2>
         void diff(Param out, const Param in, const unsigned indims)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   diffProgs;
-                static std::map<int, Kernel*>  diffKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   diffProgs;
+            static std::map<int, Kernel*>  diffKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName()
-                            << " -D DIM="      << dim
-                            << " -D isDiff2=" << isDiff2;
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, diff_cl, diff_cl_len, options.str());
-                    diffProgs[device]   = new Program(prog);
-                    diffKernels[device] = new Kernel(*diffProgs[device], "diff_kernel");
-                });
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName()
+                        << " -D DIM="      << dim
+                        << " -D isDiff2=" << isDiff2;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, diff_cl, diff_cl_len, options.str());
+                diffProgs[device]   = new Program(prog);
+                diffKernels[device] = new Kernel(*diffProgs[device], "diff_kernel");
+            });
 
-                auto diffOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                          const int, const int, const int>
-                                          (*diffKernels[device]);
+            auto diffOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
+                                      const int, const int, const int>
+                                      (*diffKernels[device]);
 
-                NDRange local(TX, TY, 1);
-                if(dim == 0 && indims == 1) {
-                    local = NDRange(TX * TY, 1, 1);
-                }
+            NDRange local(TX, TY, 1);
+            if(dim == 0 && indims == 1) {
+                local = NDRange(TX * TY, 1, 1);
+            }
 
-                int blocksPerMatX = divup(out.info.dims[0], local[0]);
-                int blocksPerMatY = divup(out.info.dims[1], local[1]);
-                NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                               local[1] * blocksPerMatY * out.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(out.info.dims[0], local[0]);
+            int blocksPerMatY = divup(out.info.dims[1], local[1]);
+            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                            local[1] * blocksPerMatY * out.info.dims[3],
+                            1);
 
-                const int oElem = out.info.dims[0] * out.info.dims[1]
-                                     * out.info.dims[2] * out.info.dims[3];
+            const int oElem = out.info.dims[0] * out.info.dims[1]
+                                  * out.info.dims[2] * out.info.dims[3];
 
-                diffOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, *in.data, out.info, in.info,
-                       oElem, blocksPerMatX, blocksPerMatY);
+            diffOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, *in.data, out.info, in.info,
+                    oElem, blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index c044346c0f..2af050e33b 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -53,72 +53,67 @@ static const int THREADS_Y = 16;
 template<typename T>
 void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  egProgs;
-        static std::map<int, Kernel*> egKernels;
-
-        int device = getActiveDeviceId();
-
-        // std::call_once is used to ensure OpenCL kernels
-        // are compiled only once for any given device and combination
-        // of template parameters to this kernel wrapper function 'exampleFunc<T>'
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                // You can pass any template parameters as compile options
-                // to kernel the compilation step. This is equivalent of
-                // having templated kernels in CUDA
-
-                // The following option is passed to kernel compilation
-                // if template parameter T is double or complex double
-                // to enable FP64 extension
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                // below helper function 'buildProgram' uses the option string
-                // we just created and compiles the kernel string
-                // 'example_cl' which was created by our opencl kernel code obfuscation
-                // stage
-                buildProgram(prog, example_cl, example_cl_len, options.str());
-
-                // create a cl::Program object on heap
-                egProgs[device]   = new Program(prog);
-
-                // create a cl::Kernel object on heap
-                egKernels[device] = new Kernel(*egProgs[device], "example");
-            });
-
-        // configure work group parameters
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(c.info.dims[0], THREADS_X);
-        int blk_y = divup(c.info.dims[1], THREADS_Y);
-
-        // configure global launch parameters
-        NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
-
-        // create a kernel functor from the cl::Kernel object
-        // corresponding to the device on which current execution
-        // is happending.
-        auto exampleFuncOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                           Buffer, KParam, int>(*egKernels[device]);
-
-        // launch the kernel
-        exampleFuncOp(EnqueueArgs(getQueue(), global, local),
-                    *c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
-
-        // Below Macro activates validations ONLY in DEBUG
-        // mode as its name indicates
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) { // Catch all cl::Errors and convert them
-                              // to appropriate ArrayFire error codes
-        CL_TO_AF_ERROR(err);
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  egProgs;
+    static std::map<int, Kernel*> egKernels;
+
+    int device = getActiveDeviceId();
+
+    // std::call_once is used to ensure OpenCL kernels
+    // are compiled only once for any given device and combination
+    // of template parameters to this kernel wrapper function 'exampleFunc<T>'
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
+            // You can pass any template parameters as compile options
+            // to kernel the compilation step. This is equivalent of
+            // having templated kernels in CUDA
+
+            // The following option is passed to kernel compilation
+            // if template parameter T is double or complex double
+            // to enable FP64 extension
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            Program prog;
+            // below helper function 'buildProgram' uses the option string
+            // we just created and compiles the kernel string
+            // 'example_cl' which was created by our opencl kernel code obfuscation
+            // stage
+            buildProgram(prog, example_cl, example_cl_len, options.str());
+
+            // create a cl::Program object on heap
+            egProgs[device]   = new Program(prog);
+
+            // create a cl::Kernel object on heap
+            egKernels[device] = new Kernel(*egProgs[device], "example");
+        });
+
+    // configure work group parameters
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(c.info.dims[0], THREADS_X);
+    int blk_y = divup(c.info.dims[1], THREADS_Y);
+
+    // configure global launch parameters
+    NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
+
+    // create a kernel functor from the cl::Kernel object
+    // corresponding to the device on which current execution
+    // is happending.
+    auto exampleFuncOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                        Buffer, KParam, int>(*egKernels[device]);
+
+    // launch the kernel
+    exampleFuncOp(EnqueueArgs(getQueue(), global, local),
+                *c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
+
+    // Below Macro activates validations ONLY in DEBUG
+    // mode as its name indicates
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 3143bf4407..ec66867b93 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -47,145 +47,140 @@ void fast(const unsigned arc_length,
           const float feature_ratio,
           const unsigned edge)
 {
-    try {
-        std::string ref_name =
-            std::string("fast_") +
-            std::to_string(arc_length) +
-            std::string("_") +
-            std::to_string(nonmax) +
-            std::string("_") +
-            std::string(dtype_traits<T>::getName());
-
-        int device = getActiveDeviceId();
-        kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
-
-        kc_entry_t entry;
-        if (cache_idx == kernelCaches[device].end()) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D ARC_LENGTH=" << arc_length
-                        << " -D NONMAX=" << static_cast<unsigned>(nonmax);
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, fast_cl, fast_cl_len, options.str());
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel[3];
-
-                entry.ker[0] = Kernel(*entry.prog, "locate_features");
-                entry.ker[1] = Kernel(*entry.prog, "non_max_counts");
-                entry.ker[2] = Kernel(*entry.prog, "get_features");
-
-                kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = cache_idx -> second;
-        }
-
-        const unsigned max_feat = ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
-
-        // Matrix containing scores for detected features, scores are stored in the
-        // same coordinates as features, dimensions should be equal to in.
-        cl::Buffer *d_score = bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
-        std::vector<float> score_init(in.info.dims[0] * in.info.dims[1], (float)0);
-        getQueue().enqueueWriteBuffer(*d_score, CL_TRUE, 0, in.info.dims[0] * in.info.dims[1] * sizeof(float), &score_init[0]);
-
-        cl::Buffer *d_flags = d_score;
-        if (nonmax) {
-            d_flags = bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
-        }
-
-        const int blk_x = divup(in.info.dims[0]-edge*2, FAST_THREADS_X);
-        const int blk_y = divup(in.info.dims[1]-edge*2, FAST_THREADS_Y);
-
-        // Locate features kernel sizes
-        const NDRange local(FAST_THREADS_X, FAST_THREADS_Y);
-        const NDRange global(blk_x * FAST_THREADS_X, blk_y * FAST_THREADS_Y);
-
-        auto lfOp = KernelFunctor<Buffer, KParam,
-                                Buffer, const float, const unsigned,
-                                LocalSpaceArg> (entry.ker[0]);
-
-        lfOp(EnqueueArgs(getQueue(), global, local),
-             *in.data, in.info, *d_score, thr, edge,
-             cl::Local((FAST_THREADS_X + 6) * (FAST_THREADS_Y + 6) * sizeof(T)));
-        CL_DEBUG_FINISH(getQueue());
-
-        const int blk_nonmax_x = divup(in.info.dims[0], 64);
-        const int blk_nonmax_y = divup(in.info.dims[1], 64);
+    std::string ref_name =
+        std::string("fast_") +
+        std::to_string(arc_length) +
+        std::string("_") +
+        std::to_string(nonmax) +
+        std::string("_") +
+        std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
+
+    kc_entry_t entry;
+    if (cache_idx == kernelCaches[device].end()) {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D ARC_LENGTH=" << arc_length
+                    << " -D NONMAX=" << static_cast<unsigned>(nonmax);
+
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            cl::Program prog;
+            buildProgram(prog, fast_cl, fast_cl_len, options.str());
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel[3];
+
+            entry.ker[0] = Kernel(*entry.prog, "locate_features");
+            entry.ker[1] = Kernel(*entry.prog, "non_max_counts");
+            entry.ker[2] = Kernel(*entry.prog, "get_features");
+
+            kernelCaches[device][ref_name] = entry;
+    } else {
+        entry = cache_idx -> second;
+    }
 
-        // Nonmax kernel sizes
-        const NDRange local_nonmax(FAST_THREADS_NONMAX_X, FAST_THREADS_NONMAX_Y);
-        const NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X, blk_nonmax_y * FAST_THREADS_NONMAX_Y);
+    const unsigned max_feat = ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
 
-        unsigned count_init = 0;
-        cl::Buffer *d_total = bufferAlloc(sizeof(unsigned));
-        getQueue().enqueueWriteBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned), &count_init);
+    // Matrix containing scores for detected features, scores are stored in the
+    // same coordinates as features, dimensions should be equal to in.
+    cl::Buffer *d_score = bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
+    std::vector<float> score_init(in.info.dims[0] * in.info.dims[1], (float)0);
+    getQueue().enqueueWriteBuffer(*d_score, CL_TRUE, 0, in.info.dims[0] * in.info.dims[1] * sizeof(float), &score_init[0]);
 
-        //size_t *global_nonmax_dims = global_nonmax();
-        size_t blocks_sz = blk_nonmax_x * FAST_THREADS_NONMAX_X * blk_nonmax_y * FAST_THREADS_NONMAX_Y * sizeof(unsigned);
-        cl::Buffer *d_counts  = bufferAlloc(blocks_sz);
-        cl::Buffer *d_offsets = bufferAlloc(blocks_sz);
+    cl::Buffer *d_flags = d_score;
+    if (nonmax) {
+        d_flags = bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
+    }
 
-        auto nmOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                Buffer, Buffer,
-                                KParam, const unsigned> (entry.ker[1]);
-        nmOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
-                         *d_counts, *d_offsets, *d_total, *d_flags, *d_score, in.info, edge);
+    const int blk_x = divup(in.info.dims[0]-edge*2, FAST_THREADS_X);
+    const int blk_y = divup(in.info.dims[1]-edge*2, FAST_THREADS_Y);
+
+    // Locate features kernel sizes
+    const NDRange local(FAST_THREADS_X, FAST_THREADS_Y);
+    const NDRange global(blk_x * FAST_THREADS_X, blk_y * FAST_THREADS_Y);
+
+    auto lfOp = KernelFunctor<Buffer, KParam,
+                            Buffer, const float, const unsigned,
+                            LocalSpaceArg> (entry.ker[0]);
+
+    lfOp(EnqueueArgs(getQueue(), global, local),
+          *in.data, in.info, *d_score, thr, edge,
+          cl::Local((FAST_THREADS_X + 6) * (FAST_THREADS_Y + 6) * sizeof(T)));
+    CL_DEBUG_FINISH(getQueue());
+
+    const int blk_nonmax_x = divup(in.info.dims[0], 64);
+    const int blk_nonmax_y = divup(in.info.dims[1], 64);
+
+    // Nonmax kernel sizes
+    const NDRange local_nonmax(FAST_THREADS_NONMAX_X, FAST_THREADS_NONMAX_Y);
+    const NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X, blk_nonmax_y * FAST_THREADS_NONMAX_Y);
+
+    unsigned count_init = 0;
+    cl::Buffer *d_total = bufferAlloc(sizeof(unsigned));
+    getQueue().enqueueWriteBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned), &count_init);
+
+    //size_t *global_nonmax_dims = global_nonmax();
+    size_t blocks_sz = blk_nonmax_x * FAST_THREADS_NONMAX_X * blk_nonmax_y * FAST_THREADS_NONMAX_Y * sizeof(unsigned);
+    cl::Buffer *d_counts  = bufferAlloc(blocks_sz);
+    cl::Buffer *d_offsets = bufferAlloc(blocks_sz);
+
+    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer,
+                            Buffer, Buffer,
+                            KParam, const unsigned> (entry.ker[1]);
+    nmOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
+                      *d_counts, *d_offsets, *d_total, *d_flags, *d_score, in.info, edge);
+    CL_DEBUG_FINISH(getQueue());
+
+    unsigned total;
+    getQueue().enqueueReadBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned), &total);
+    total = total < max_feat ? total : max_feat;
+
+    if (total > 0) {
+        size_t out_sz = total * sizeof(float);
+        x_out.data = bufferAlloc(out_sz);
+        y_out.data = bufferAlloc(out_sz);
+        score_out.data = bufferAlloc(out_sz);
+
+        auto gfOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer,
+                                KParam, const unsigned,
+                                const unsigned> (entry.ker[2]);
+        gfOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
+                          *x_out.data, *y_out.data, *score_out.data,
+                          *d_flags, *d_counts, *d_offsets,
+                          in.info, total, edge);
         CL_DEBUG_FINISH(getQueue());
+    }
 
-        unsigned total;
-        getQueue().enqueueReadBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned), &total);
-        total = total < max_feat ? total : max_feat;
-
-        if (total > 0) {
-            size_t out_sz = total * sizeof(float);
-            x_out.data = bufferAlloc(out_sz);
-            y_out.data = bufferAlloc(out_sz);
-            score_out.data = bufferAlloc(out_sz);
-
-            auto gfOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer,
-                                    KParam, const unsigned,
-                                    const unsigned> (entry.ker[2]);
-            gfOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
-                             *x_out.data, *y_out.data, *score_out.data,
-                             *d_flags, *d_counts, *d_offsets,
-                             in.info, total, edge);
-            CL_DEBUG_FINISH(getQueue());
-        }
-
-        *out_feat = total;
-
-        x_out.info.dims[0] = total;
-        x_out.info.strides[0] = 1;
-        y_out.info.dims[0] = total;
-        y_out.info.strides[0] = 1;
-        score_out.info.dims[0] = total;
-        score_out.info.strides[0] = 1;
-
-        for (int k = 1; k < 4; k++) {
-            x_out.info.dims[k] = 1;
-            x_out.info.strides[k] = total;
-            y_out.info.dims[k] = 1;
-            y_out.info.strides[k] = total;
-            score_out.info.dims[k] = 1;
-            score_out.info.strides[k] = total;
-        }
-
-        bufferFree(d_score);
-        if (nonmax) bufferFree(d_flags);
-        bufferFree(d_total);
-        bufferFree(d_counts);
-        bufferFree(d_offsets);
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+    *out_feat = total;
+
+    x_out.info.dims[0] = total;
+    x_out.info.strides[0] = 1;
+    y_out.info.dims[0] = total;
+    y_out.info.strides[0] = 1;
+    score_out.info.dims[0] = total;
+    score_out.info.strides[0] = 1;
+
+    for (int k = 1; k < 4; k++) {
+        x_out.info.dims[k] = 1;
+        x_out.info.strides[k] = total;
+        y_out.info.dims[k] = 1;
+        y_out.info.strides[k] = total;
+        score_out.info.dims[k] = 1;
+        score_out.info.strides[k] = total;
     }
+
+    bufferFree(d_score);
+    if (nonmax) bufferFree(d_flags);
+    bufferFree(d_total);
+    bufferFree(d_counts);
+    bufferFree(d_offsets);
 }
 
 template<typename T>
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 7cc39c6002..aa1449d1fc 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -81,77 +81,72 @@ void packDataHelper(Param packed,
                     const int baseDim,
                     AF_BATCH_KIND kind)
 {
-    try {
-        static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  fftconvolveProgs;
-        static std::map<int, Kernel*>   pdKernel;
-        static std::map<int, Kernel*>   paKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-
-                if ((af_dtype) dtype_traits<convT>::af_type == c32) {
-                    options << " -D CONVT=float";
-                }
-                else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
-                    options << " -D CONVT=double"
-                            << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, fftconvolve_pack_cl, fftconvolve_pack_cl_len, options.str());
-                fftconvolveProgs[device] = new Program(prog);
-
-                pdKernel[device] = new Kernel(*fftconvolveProgs[device], "pack_data");
-                paKernel[device] = new Kernel(*fftconvolveProgs[device], "pad_array");
-            });
-
-        Param sig_tmp, filter_tmp;
-        calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
-
-        int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
-        int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
-
-        // Number of packed complex elements in dimension 0
-        int sig_half_d0 = divup(sig.info.dims[0], 2);
-        int sig_half_d0_odd = sig.info.dims[0] % 2;
-
-        int blocks = divup(sig_packed_elem, THREADS);
-
-        // Locate features kernel sizes
-        NDRange local(THREADS);
-        NDRange global(blocks * THREADS);
-
-        // Pack signal in a complex matrix where first dimension is half the input
-        // (allows faster FFT computation) and pad array to a power of 2 with 0s
-        auto pdOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                const int, const int> (*pdKernel[device]);
-
-        pdOp(EnqueueArgs(getQueue(), global, local),
-             *sig_tmp.data, sig_tmp.info, *sig.data, sig.info,
-             sig_half_d0, sig_half_d0_odd);
-        CL_DEBUG_FINISH(getQueue());
-
-        blocks = divup(filter_packed_elem, THREADS);
-        global = NDRange(blocks * THREADS);
-
-        // Pad filter array with 0s
-        auto paOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam> (*paKernel[device]);
-
-        paOp(EnqueueArgs(getQueue(), global, local),
-             *filter_tmp.data, filter_tmp.info,
-             *filter.data, filter.info);
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  fftconvolveProgs;
+    static std::map<int, Kernel*>   pdKernel;
+    static std::map<int, Kernel*>   paKernel;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
+
+            if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+                options << " -D CONVT=float";
+            }
+            else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+                options << " -D CONVT=double"
+                        << " -D USE_DOUBLE";
+            }
+
+            cl::Program prog;
+            buildProgram(prog, fftconvolve_pack_cl, fftconvolve_pack_cl_len, options.str());
+            fftconvolveProgs[device] = new Program(prog);
+
+            pdKernel[device] = new Kernel(*fftconvolveProgs[device], "pack_data");
+            paKernel[device] = new Kernel(*fftconvolveProgs[device], "pad_array");
+        });
+
+    Param sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
+
+    int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
+    int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+
+    // Number of packed complex elements in dimension 0
+    int sig_half_d0 = divup(sig.info.dims[0], 2);
+    int sig_half_d0_odd = sig.info.dims[0] % 2;
+
+    int blocks = divup(sig_packed_elem, THREADS);
+
+    // Locate features kernel sizes
+    NDRange local(THREADS);
+    NDRange global(blocks * THREADS);
+
+    // Pack signal in a complex matrix where first dimension is half the input
+    // (allows faster FFT computation) and pad array to a power of 2 with 0s
+    auto pdOp = KernelFunctor<Buffer, KParam,
+                            Buffer, KParam,
+                            const int, const int> (*pdKernel[device]);
+
+    pdOp(EnqueueArgs(getQueue(), global, local),
+          *sig_tmp.data, sig_tmp.info, *sig.data, sig.info,
+          sig_half_d0, sig_half_d0_odd);
+    CL_DEBUG_FINISH(getQueue());
+
+    blocks = divup(filter_packed_elem, THREADS);
+    global = NDRange(blocks * THREADS);
+
+    // Pad filter array with 0s
+    auto paOp = KernelFunctor<Buffer, KParam,
+                            Buffer, KParam> (*paKernel[device]);
+
+    paOp(EnqueueArgs(getQueue(), global, local),
+          *filter_tmp.data, filter_tmp.info,
+          *filter.data, filter.info);
+    CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename convT, typename T, bool isDouble, typename printT>
@@ -161,69 +156,64 @@ void complexMultiplyHelper(Param packed,
                            const int baseDim,
                            AF_BATCH_KIND kind)
 {
-    try {
-        static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> fftconvolveProgs;
-        static std::map<int, Kernel*>  cmKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
-                        << " -D AF_BATCH_LHS="  << (int)AF_BATCH_LHS
-                        << " -D AF_BATCH_RHS="  << (int)AF_BATCH_RHS
-                        << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
-
-                if ((af_dtype) dtype_traits<convT>::af_type == c32) {
-                    options << " -D CONVT=float";
-                }
-                else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
-                    options << " -D CONVT=double"
-                            << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog,
-                             fftconvolve_multiply_cl,
-                             fftconvolve_multiply_cl_len,
-                             options.str());
-                fftconvolveProgs[device] = new Program(prog);
-
-                cmKernel[device] = new Kernel(*fftconvolveProgs[device], "complex_multiply");
-            });
-
-        Param sig_tmp, filter_tmp;
-        calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
-
-        int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
-        int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
-        int mul_elem = (sig_packed_elem < filter_packed_elem) ?
-                            filter_packed_elem : sig_packed_elem;
-
-        int blocks = divup(mul_elem, THREADS);
-
-        NDRange local(THREADS);
-        NDRange global(blocks * THREADS);
-
-        // Multiply filter and signal FFT arrays
-        auto cmOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer, KParam,
-                                const int, const int> (*cmKernel[device]);
-
-        cmOp(EnqueueArgs(getQueue(), global, local),
-             *packed.data, packed.info,
-             *sig_tmp.data, sig_tmp.info,
-             *filter_tmp.data, filter_tmp.info,
-             mul_elem, (int)kind);
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> fftconvolveProgs;
+    static std::map<int, Kernel*>  cmKernel;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
+                    << " -D AF_BATCH_LHS="  << (int)AF_BATCH_LHS
+                    << " -D AF_BATCH_RHS="  << (int)AF_BATCH_RHS
+                    << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
+
+            if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+                options << " -D CONVT=float";
+            }
+            else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+                options << " -D CONVT=double"
+                        << " -D USE_DOUBLE";
+            }
+
+            cl::Program prog;
+            buildProgram(prog,
+                          fftconvolve_multiply_cl,
+                          fftconvolve_multiply_cl_len,
+                          options.str());
+            fftconvolveProgs[device] = new Program(prog);
+
+            cmKernel[device] = new Kernel(*fftconvolveProgs[device], "complex_multiply");
+        });
+
+    Param sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
+
+    int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
+    int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+    int mul_elem = (sig_packed_elem < filter_packed_elem) ?
+                        filter_packed_elem : sig_packed_elem;
+
+    int blocks = divup(mul_elem, THREADS);
+
+    NDRange local(THREADS);
+    NDRange global(blocks * THREADS);
+
+    // Multiply filter and signal FFT arrays
+    auto cmOp = KernelFunctor<Buffer, KParam,
+                            Buffer, KParam,
+                            Buffer, KParam,
+                            const int, const int> (*cmKernel[device]);
+
+    cmOp(EnqueueArgs(getQueue(), global, local),
+          *packed.data, packed.info,
+          *sig_tmp.data, sig_tmp.info,
+          *filter_tmp.data, filter_tmp.info,
+          mul_elem, (int)kind);
+    CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, typename convT, bool isDouble, bool roundOut, bool expand, typename printT>
@@ -234,77 +224,72 @@ void reorderOutputHelper(Param out,
                          const int baseDim,
                          AF_BATCH_KIND kind)
 {
-    try {
-        static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> fftconvolveProgs;
-        static std::map<int, Kernel*>  roKernel;
+    static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> fftconvolveProgs;
+    static std::map<int, Kernel*>  roKernel;
 
-        int fftScale = 1;
+    int fftScale = 1;
 
-        // Calculate the scale by which to divide clFFT results
-        for (int k = 0; k < baseDim; k++)
-            fftScale *= packed.info.dims[k];
+    // Calculate the scale by which to divide clFFT results
+    for (int k = 0; k < baseDim; k++)
+        fftScale *= packed.info.dims[k];
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once( compileFlags[device], [device] () {
+    std::call_once( compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D ROUND_OUT=" << (int)roundOut
-                        << " -D EXPAND=" << (int)expand;
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D ROUND_OUT=" << (int)roundOut
+                    << " -D EXPAND=" << (int)expand;
 
-                if ((af_dtype) dtype_traits<convT>::af_type == c32) {
-                    options << " -D CONVT=float";
-                }
-                else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
-                    options << " -D CONVT=double"
-                            << " -D USE_DOUBLE";
-                }
+            if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+                options << " -D CONVT=float";
+            }
+            else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+                options << " -D CONVT=double"
+                        << " -D USE_DOUBLE";
+            }
 
-                cl::Program prog;
-                buildProgram(prog,
-                             fftconvolve_reorder_cl,
-                             fftconvolve_reorder_cl_len,
-                             options.str());
-                fftconvolveProgs[device] = new Program(prog);
+            cl::Program prog;
+            buildProgram(prog,
+                          fftconvolve_reorder_cl,
+                          fftconvolve_reorder_cl_len,
+                          options.str());
+            fftconvolveProgs[device] = new Program(prog);
 
-                roKernel[device] = new Kernel(*fftconvolveProgs[device], "reorder_output");
-            });
+            roKernel[device] = new Kernel(*fftconvolveProgs[device], "reorder_output");
+        });
 
-        Param sig_tmp, filter_tmp;
-        calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
+    Param sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
 
-        // Number of packed complex elements in dimension 0
-        int sig_half_d0 = divup(sig.info.dims[0], 2);
+    // Number of packed complex elements in dimension 0
+    int sig_half_d0 = divup(sig.info.dims[0], 2);
 
-        int blocks = divup(out.info.strides[3] * out.info.dims[3], THREADS);
+    int blocks = divup(out.info.strides[3] * out.info.dims[3], THREADS);
 
-        NDRange local(THREADS);
-        NDRange global(blocks * THREADS);
+    NDRange local(THREADS);
+    NDRange global(blocks * THREADS);
 
-        auto roOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                KParam, const int,
-                                const int, const int> (*roKernel[device]);
+    auto roOp = KernelFunctor<Buffer, KParam,
+                            Buffer, KParam,
+                            KParam, const int,
+                            const int, const int> (*roKernel[device]);
 
-        if (kind == AF_BATCH_RHS) {
-            roOp(EnqueueArgs(getQueue(), global, local),
-                 *out.data, out.info,
-                 *filter_tmp.data, filter_tmp.info,
-                 filter.info, sig_half_d0, baseDim, fftScale);
-        }
-        else {
-            roOp(EnqueueArgs(getQueue(), global, local),
-                 *out.data, out.info,
-                 *sig_tmp.data, sig_tmp.info,
-                 filter.info, sig_half_d0, baseDim, fftScale);
-        }
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+    if (kind == AF_BATCH_RHS) {
+        roOp(EnqueueArgs(getQueue(), global, local),
+              *out.data, out.info,
+              *filter_tmp.data, filter_tmp.info,
+              filter.info, sig_half_d0, baseDim, fftScale);
+    }
+    else {
+        roOp(EnqueueArgs(getQueue(), global, local),
+              *out.data, out.info,
+              *sig_tmp.data, sig_tmp.info,
+              filter.info, sig_half_d0, baseDim, fftScale);
     }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 } // namespace kernel
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 05e7cf58db..4dcca9bc94 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -40,58 +40,53 @@ namespace opencl
         template<typename T>
         void gradient(Param grad0, Param grad1, const Param in)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  gradProgs;
-                static std::map<int, Kernel*> gradKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>  gradProgs;
+            static std::map<int, Kernel*> gradKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    ToNumStr<T> toNumStr;
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D TX=" << TX
-                            << " -D TY=" << TY
-                            << " -D ZERO=" << toNumStr(scalar<T>(0));
+            std::call_once( compileFlags[device], [device] () {
+                ToNumStr<T> toNumStr;
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D TX=" << TX
+                        << " -D TY=" << TY
+                        << " -D ZERO=" << toNumStr(scalar<T>(0));
 
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D CPLX=1";
-                    } else {
-                        options << " -D CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, gradient_cl, gradient_cl_len, options.str());
-                    gradProgs[device]   = new Program(prog);
-                    gradKernels[device] = new Kernel(*gradProgs[device], "gradient_kernel");
-                });
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                    (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D CPLX=1";
+                } else {
+                    options << " -D CPLX=0";
+                }
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, gradient_cl, gradient_cl_len, options.str());
+                gradProgs[device]   = new Program(prog);
+                gradKernels[device] = new Kernel(*gradProgs[device], "gradient_kernel");
+            });
 
-                auto gradOp = KernelFunctor<Buffer, const KParam, Buffer, const KParam,
-                                    const Buffer, const KParam, const int, const int>
-                                        (*gradKernels[device]);
+            auto gradOp = KernelFunctor<Buffer, const KParam, Buffer, const KParam,
+                                const Buffer, const KParam, const int, const int>
+                                    (*gradKernels[device]);
 
-                NDRange local(TX, TY, 1);
+            NDRange local(TX, TY, 1);
 
-                int blocksPerMatX = divup(in.info.dims[0], TX);
-                int blocksPerMatY = divup(in.info.dims[1], TY);
-                NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                               local[1] * blocksPerMatY * in.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(in.info.dims[0], TX);
+            int blocksPerMatY = divup(in.info.dims[1], TY);
+            NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                            local[1] * blocksPerMatY * in.info.dims[3],
+                            1);
 
-                gradOp(EnqueueArgs(getQueue(), global, local),
-                        *grad0.data, grad0.info, *grad1.data, grad1.info,
-                        *in.data, in.info, blocksPerMatX, blocksPerMatY);
+            gradOp(EnqueueArgs(getQueue(), global, local),
+                    *grad0.data, grad0.info, *grad1.data, grad1.info,
+                    *in.data, in.info, blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index fe7126c0f1..37785db588 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -97,246 +97,241 @@ void harris(unsigned* corners_out,
             const unsigned filter_len,
             const float k_thr)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> harrisProgs;
-        static std::map<int, Kernel*>  soKernel;
-        static std::map<int, Kernel*>  kcKernel;
-        static std::map<int, Kernel*>  hrKernel;
-        static std::map<int, Kernel*>  nmKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, harris_cl, harris_cl_len, options.str());
-                harrisProgs[device] = new Program(prog);
-
-                soKernel[device] = new Kernel(*harrisProgs[device], "second_order_deriv");
-                kcKernel[device] = new Kernel(*harrisProgs[device], "keep_corners");
-                hrKernel[device] = new Kernel(*harrisProgs[device], "harris_responses");
-                nmKernel[device] = new Kernel(*harrisProgs[device], "non_maximal");
-            });
-
-        // Window filter
-        convAccT* h_filter = new convAccT[filter_len];
-        // Decide between rectangular or circular filter
-        if (sigma < 0.5f) {
-            for (unsigned i = 0; i < filter_len; i++)
-                h_filter[i] = (T)1.f / (filter_len);
-        }
-        else {
-            gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
-        }
-
-        const unsigned border_len = filter_len / 2 + 1;
-
-        // Copy filter to device object
-        Param filter;
-        filter.info.dims[0] = filter_len;
-        filter.info.strides[0] = 1;
-        filter.info.offset = 0;
-
-        for (int k = 1; k < 4; k++) {
-            filter.info.dims[k] = 1;
-            filter.info.strides[k] = filter.info.dims[k - 1] * filter.info.strides[k - 1];
-        }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> harrisProgs;
+    static std::map<int, Kernel*>  soKernel;
+    static std::map<int, Kernel*>  kcKernel;
+    static std::map<int, Kernel*>  hrKernel;
+    static std::map<int, Kernel*>  nmKernel;
 
-        int filter_elem = filter.info.strides[3] * filter.info.dims[3];
-        filter.data = bufferAlloc(filter_elem * sizeof(convAccT));
-        getQueue().enqueueWriteBuffer(*filter.data, CL_TRUE, 0, filter_elem * sizeof(convAccT), h_filter);
+    int device = getActiveDeviceId();
 
-        Param ix, iy;
-        ix.info.offset = iy.info.offset = 0;
-        for (dim_t i = 0; i < 4; i++) {
-            ix.info.dims[i] = iy.info.dims[i] = in.info.dims[i];
-            ix.info.strides[i] = iy.info.strides[i] = in.info.strides[i];
-        }
-        ix.data = bufferAlloc(ix.info.dims[3] * ix.info.strides[3] * sizeof(T));
-        iy.data = bufferAlloc(iy.info.dims[3] * iy.info.strides[3] * sizeof(T));
+    std::call_once( compileFlags[device], [device] () {
 
-        // Compute first-order derivatives as gradients
-        gradient<T>(iy, ix, in);
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
 
-        Param ixx, ixy, iyy;
-        ixx.info.offset = ixy.info.offset = iyy.info.offset = 0;
-        for (dim_t i = 0; i < 4; i++) {
-            ixx.info.dims[i] = ixy.info.dims[i] = iyy.info.dims[i] = in.info.dims[i];
-            ixx.info.strides[i] = ixy.info.strides[i] = iyy.info.strides[i] = in.info.strides[i];
-        }
-        ixx.data = bufferAlloc(ixx.info.dims[3] * ixx.info.strides[3] * sizeof(T));
-        ixy.data = bufferAlloc(ixy.info.dims[3] * ixy.info.strides[3] * sizeof(T));
-        iyy.data = bufferAlloc(iyy.info.dims[3] * iyy.info.strides[3] * sizeof(T));
-
-        // Second order-derivatives kernel sizes
-        const unsigned blk_x_so = divup(in.info.dims[3] * in.info.strides[3], HARRIS_THREADS_PER_GROUP);
-        const NDRange local_so(HARRIS_THREADS_PER_GROUP, 1);
-        const NDRange global_so(blk_x_so * HARRIS_THREADS_PER_GROUP, 1);
-
-        auto soOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                unsigned, Buffer, Buffer> (*soKernel[device]);
-
-        // Compute second-order derivatives
-        soOp(EnqueueArgs(getQueue(), global_so, local_so),
-             *ixx.data, *ixy.data, *iyy.data,
-             in.info.dims[3] * in.info.strides[3], *ix.data, *iy.data);
-        CL_DEBUG_FINISH(getQueue());
-
-        bufferFree(ix.data);
-        bufferFree(iy.data);
-
-        // Convolve second order derivatives with proper window filter
-        conv_helper<T, convAccT>(ixx, ixy, iyy, filter);
-        bufferFree(filter.data);
-
-        cl::Buffer *d_responses = bufferAlloc(in.info.dims[3] * in.info.strides[3] * sizeof(T));
-
-        // Harris responses kernel sizes
-        unsigned blk_x_hr = divup(in.info.dims[0] - border_len*2, HARRIS_THREADS_X);
-        unsigned blk_y_hr = divup(in.info.dims[1] - border_len*2, HARRIS_THREADS_Y);
-        const NDRange local_hr(HARRIS_THREADS_X, HARRIS_THREADS_Y);
-        const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X, blk_y_hr * HARRIS_THREADS_Y);
-
-        auto hrOp = KernelFunctor<Buffer, unsigned, unsigned,
-                                Buffer, Buffer, Buffer,
-                                float, unsigned> (*hrKernel[device]);
-
-        // Calculate Harris responses for all pixels
-        hrOp(EnqueueArgs(getQueue(), global_hr, local_hr),
-             *d_responses, in.info.dims[0], in.info.dims[1],
-             *ixx.data, *ixy.data, *iyy.data, k_thr, border_len);
-        CL_DEBUG_FINISH(getQueue());
-
-        bufferFree(ixx.data);
-        bufferFree(ixy.data);
-        bufferFree(iyy.data);
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-        // Number of corners is not known a priori, limit maximum number of corners
-        // according to image dimensions
-        unsigned corner_lim = in.info.dims[3] * in.info.strides[3] * 0.2f;
+            cl::Program prog;
+            buildProgram(prog, harris_cl, harris_cl_len, options.str());
+            harrisProgs[device] = new Program(prog);
+
+            soKernel[device] = new Kernel(*harrisProgs[device], "second_order_deriv");
+            kcKernel[device] = new Kernel(*harrisProgs[device], "keep_corners");
+            hrKernel[device] = new Kernel(*harrisProgs[device], "harris_responses");
+            nmKernel[device] = new Kernel(*harrisProgs[device], "non_maximal");
+        });
+
+    // Window filter
+    convAccT* h_filter = new convAccT[filter_len];
+    // Decide between rectangular or circular filter
+    if (sigma < 0.5f) {
+        for (unsigned i = 0; i < filter_len; i++)
+            h_filter[i] = (T)1.f / (filter_len);
+    }
+    else {
+        gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
+    }
 
-        unsigned corners_found = 0;
-        cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
-        getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    const unsigned border_len = filter_len / 2 + 1;
 
-        cl::Buffer *d_x_corners = bufferAlloc(corner_lim * sizeof(float));
-        cl::Buffer *d_y_corners = bufferAlloc(corner_lim * sizeof(float));
-        cl::Buffer *d_resp_corners = bufferAlloc(corner_lim * sizeof(float));
+    // Copy filter to device object
+    Param filter;
+    filter.info.dims[0] = filter_len;
+    filter.info.strides[0] = 1;
+    filter.info.offset = 0;
 
-        const float min_r = (max_corners > 0) ? 0.f : min_response;
+    for (int k = 1; k < 4; k++) {
+        filter.info.dims[k] = 1;
+        filter.info.strides[k] = filter.info.dims[k - 1] * filter.info.strides[k - 1];
+    }
 
-        auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                                Buffer, unsigned, unsigned,
-                                float, unsigned, unsigned> (*nmKernel[device]);
+    int filter_elem = filter.info.strides[3] * filter.info.dims[3];
+    filter.data = bufferAlloc(filter_elem * sizeof(convAccT));
+    getQueue().enqueueWriteBuffer(*filter.data, CL_TRUE, 0, filter_elem * sizeof(convAccT), h_filter);
 
-        // Perform non-maximal suppression
-        nmOp(EnqueueArgs(getQueue(), global_hr, local_hr),
-             *d_x_corners, *d_y_corners, *d_resp_corners, *d_corners_found,
-             *d_responses, in.info.dims[0], in.info.dims[1],
-             min_r, border_len, corner_lim);
-        CL_DEBUG_FINISH(getQueue());
+    Param ix, iy;
+    ix.info.offset = iy.info.offset = 0;
+    for (dim_t i = 0; i < 4; i++) {
+        ix.info.dims[i] = iy.info.dims[i] = in.info.dims[i];
+        ix.info.strides[i] = iy.info.strides[i] = in.info.strides[i];
+    }
+    ix.data = bufferAlloc(ix.info.dims[3] * ix.info.strides[3] * sizeof(T));
+    iy.data = bufferAlloc(iy.info.dims[3] * iy.info.strides[3] * sizeof(T));
 
-        getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    // Compute first-order derivatives as gradients
+    gradient<T>(iy, ix, in);
 
-        bufferFree(d_responses);
-        bufferFree(d_corners_found);
+    Param ixx, ixy, iyy;
+    ixx.info.offset = ixy.info.offset = iyy.info.offset = 0;
+    for (dim_t i = 0; i < 4; i++) {
+        ixx.info.dims[i] = ixy.info.dims[i] = iyy.info.dims[i] = in.info.dims[i];
+        ixx.info.strides[i] = ixy.info.strides[i] = iyy.info.strides[i] = in.info.strides[i];
+    }
+    ixx.data = bufferAlloc(ixx.info.dims[3] * ixx.info.strides[3] * sizeof(T));
+    ixy.data = bufferAlloc(ixy.info.dims[3] * ixy.info.strides[3] * sizeof(T));
+    iyy.data = bufferAlloc(iyy.info.dims[3] * iyy.info.strides[3] * sizeof(T));
+
+    // Second order-derivatives kernel sizes
+    const unsigned blk_x_so = divup(in.info.dims[3] * in.info.strides[3], HARRIS_THREADS_PER_GROUP);
+    const NDRange local_so(HARRIS_THREADS_PER_GROUP, 1);
+    const NDRange global_so(blk_x_so * HARRIS_THREADS_PER_GROUP, 1);
+
+    auto soOp = KernelFunctor<Buffer, Buffer, Buffer,
+                            unsigned, Buffer, Buffer> (*soKernel[device]);
+
+    // Compute second-order derivatives
+    soOp(EnqueueArgs(getQueue(), global_so, local_so),
+          *ixx.data, *ixy.data, *iyy.data,
+          in.info.dims[3] * in.info.strides[3], *ix.data, *iy.data);
+    CL_DEBUG_FINISH(getQueue());
+
+    bufferFree(ix.data);
+    bufferFree(iy.data);
+
+    // Convolve second order derivatives with proper window filter
+    conv_helper<T, convAccT>(ixx, ixy, iyy, filter);
+    bufferFree(filter.data);
+
+    cl::Buffer *d_responses = bufferAlloc(in.info.dims[3] * in.info.strides[3] * sizeof(T));
+
+    // Harris responses kernel sizes
+    unsigned blk_x_hr = divup(in.info.dims[0] - border_len*2, HARRIS_THREADS_X);
+    unsigned blk_y_hr = divup(in.info.dims[1] - border_len*2, HARRIS_THREADS_Y);
+    const NDRange local_hr(HARRIS_THREADS_X, HARRIS_THREADS_Y);
+    const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X, blk_y_hr * HARRIS_THREADS_Y);
+
+    auto hrOp = KernelFunctor<Buffer, unsigned, unsigned,
+                            Buffer, Buffer, Buffer,
+                            float, unsigned> (*hrKernel[device]);
+
+    // Calculate Harris responses for all pixels
+    hrOp(EnqueueArgs(getQueue(), global_hr, local_hr),
+          *d_responses, in.info.dims[0], in.info.dims[1],
+          *ixx.data, *ixy.data, *iyy.data, k_thr, border_len);
+    CL_DEBUG_FINISH(getQueue());
+
+    bufferFree(ixx.data);
+    bufferFree(ixy.data);
+    bufferFree(iyy.data);
+
+    // Number of corners is not known a priori, limit maximum number of corners
+    // according to image dimensions
+    unsigned corner_lim = in.info.dims[3] * in.info.strides[3] * 0.2f;
+
+    unsigned corners_found = 0;
+    cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
+    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+
+    cl::Buffer *d_x_corners = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *d_y_corners = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *d_resp_corners = bufferAlloc(corner_lim * sizeof(float));
+
+    const float min_r = (max_corners > 0) ? 0.f : min_response;
+
+    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
+                            Buffer, unsigned, unsigned,
+                            float, unsigned, unsigned> (*nmKernel[device]);
+
+    // Perform non-maximal suppression
+    nmOp(EnqueueArgs(getQueue(), global_hr, local_hr),
+          *d_x_corners, *d_y_corners, *d_resp_corners, *d_corners_found,
+          *d_responses, in.info.dims[0], in.info.dims[1],
+          min_r, border_len, corner_lim);
+    CL_DEBUG_FINISH(getQueue());
+
+    getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+
+    bufferFree(d_responses);
+    bufferFree(d_corners_found);
+
+    *corners_out = (max_corners > 0) ?
+                    min(corners_found, max_corners) :
+                    min(corners_found, corner_lim);
+
+    if (*corners_out == 0)
+        return;
+
+    // Set output Param info
+    x_out.info.dims[0] = y_out.info.dims[0] = resp_out.info.dims[0] = *corners_out;
+    x_out.info.strides[0] = y_out.info.strides[0] = resp_out.info.strides[0] = 1;
+    x_out.info.offset = y_out.info.offset = resp_out.info.offset = 0;
+    for (int k = 1; k < 4; k++) {
+        x_out.info.dims[k] = y_out.info.dims[k] = resp_out.info.dims[k] =  1;
+        x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
+        y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
+        resp_out.info.strides[k] = resp_out.info.dims[k - 1] * resp_out.info.strides[k - 1];
+    }
 
-        *corners_out = (max_corners > 0) ?
-                       min(corners_found, max_corners) :
-                       min(corners_found, corner_lim);
+    if (max_corners > 0 && corners_found > *corners_out) {
+        Param harris_resp;
+        Param harris_idx;
 
-        if (*corners_out == 0)
-            return;
+        harris_resp.info.dims[0] = harris_idx.info.dims[0] = corners_found;
+        harris_resp.info.strides[0] = harris_idx.info.strides[0] = 1;
 
-        // Set output Param info
-        x_out.info.dims[0] = y_out.info.dims[0] = resp_out.info.dims[0] = *corners_out;
-        x_out.info.strides[0] = y_out.info.strides[0] = resp_out.info.strides[0] = 1;
-        x_out.info.offset = y_out.info.offset = resp_out.info.offset = 0;
         for (int k = 1; k < 4; k++) {
-            x_out.info.dims[k] = y_out.info.dims[k] = resp_out.info.dims[k] =  1;
-            x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
-            y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
-            resp_out.info.strides[k] = resp_out.info.dims[k - 1] * resp_out.info.strides[k - 1];
+            harris_resp.info.dims[k] = 1;
+            harris_resp.info.strides[k] = harris_resp.info.dims[k - 1] * harris_resp.info.strides[k - 1];
+            harris_idx.info.dims[k] = 1;
+            harris_idx.info.strides[k] = harris_idx.info.dims[k - 1] * harris_idx.info.strides[k - 1];
         }
 
-        if (max_corners > 0 && corners_found > *corners_out) {
-            Param harris_resp;
-            Param harris_idx;
-
-            harris_resp.info.dims[0] = harris_idx.info.dims[0] = corners_found;
-            harris_resp.info.strides[0] = harris_idx.info.strides[0] = 1;
-
-            for (int k = 1; k < 4; k++) {
-                harris_resp.info.dims[k] = 1;
-                harris_resp.info.strides[k] = harris_resp.info.dims[k - 1] * harris_resp.info.strides[k - 1];
-                harris_idx.info.dims[k] = 1;
-                harris_idx.info.strides[k] = harris_idx.info.dims[k - 1] * harris_idx.info.strides[k - 1];
-            }
+        int sort_elem = harris_resp.info.strides[3] * harris_resp.info.dims[3];
+        harris_resp.data = d_resp_corners;
+        // Create indices using range
+        harris_idx.data = bufferAlloc(sort_elem * sizeof(unsigned));
+        kernel::range<uint>(harris_idx, 0);
+
+        // Sort Harris responses
+        kernel::sort0ByKey<float, uint>(harris_resp, harris_idx, false);
+
+        x_out.data = bufferAlloc(*corners_out * sizeof(float));
+        y_out.data = bufferAlloc(*corners_out * sizeof(float));
+        resp_out.data = bufferAlloc(*corners_out * sizeof(float));
+
+        // Keep corners kernel sizes
+        const unsigned blk_x_kc = divup(*corners_out, HARRIS_THREADS_PER_GROUP);
+        const NDRange local_kc(HARRIS_THREADS_PER_GROUP, 1);
+        const NDRange global_kc(blk_x_kc * HARRIS_THREADS_PER_GROUP, 1);
+
+        auto kcOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer, Buffer,
+                                unsigned> (*kcKernel[device]);
+
+        // Keep only the first corners_to_keep corners with higher Harris
+        // responses
+        kcOp(EnqueueArgs(getQueue(), global_kc, local_kc),
+              *x_out.data, *y_out.data, *resp_out.data,
+              *d_x_corners, *d_y_corners, *harris_resp.data, *harris_idx.data,
+              *corners_out);
+        CL_DEBUG_FINISH(getQueue());
 
-            int sort_elem = harris_resp.info.strides[3] * harris_resp.info.dims[3];
-            harris_resp.data = d_resp_corners;
-            // Create indices using range
-            harris_idx.data = bufferAlloc(sort_elem * sizeof(unsigned));
-            kernel::range<uint>(harris_idx, 0);
-
-            // Sort Harris responses
-            kernel::sort0ByKey<float, uint>(harris_resp, harris_idx, false);
-
-            x_out.data = bufferAlloc(*corners_out * sizeof(float));
-            y_out.data = bufferAlloc(*corners_out * sizeof(float));
-            resp_out.data = bufferAlloc(*corners_out * sizeof(float));
-
-            // Keep corners kernel sizes
-            const unsigned blk_x_kc = divup(*corners_out, HARRIS_THREADS_PER_GROUP);
-            const NDRange local_kc(HARRIS_THREADS_PER_GROUP, 1);
-            const NDRange global_kc(blk_x_kc * HARRIS_THREADS_PER_GROUP, 1);
-
-            auto kcOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer, Buffer,
-                                    unsigned> (*kcKernel[device]);
-
-            // Keep only the first corners_to_keep corners with higher Harris
-            // responses
-            kcOp(EnqueueArgs(getQueue(), global_kc, local_kc),
-                 *x_out.data, *y_out.data, *resp_out.data,
-                 *d_x_corners, *d_y_corners, *harris_resp.data, *harris_idx.data,
-                 *corners_out);
-            CL_DEBUG_FINISH(getQueue());
-
-            bufferFree(d_x_corners);
-            bufferFree(d_y_corners);
-            bufferFree(harris_resp.data);
-            bufferFree(harris_idx.data);
-        }
-        else if (max_corners == 0 && corners_found < corner_lim) {
-            x_out.data = bufferAlloc(*corners_out * sizeof(float));
-            y_out.data = bufferAlloc(*corners_out * sizeof(float));
-            resp_out.data = bufferAlloc(*corners_out * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_x_corners, *x_out.data, 0, 0, *corners_out * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_y_corners, *y_out.data, 0, 0, *corners_out * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_resp_corners, *resp_out.data, 0, 0, *corners_out * sizeof(float));
-
-            bufferFree(d_x_corners);
-            bufferFree(d_y_corners);
-            bufferFree(d_resp_corners);
-        }
-        else {
-            x_out.data = d_x_corners;
-            y_out.data = d_y_corners;
-            resp_out.data = d_resp_corners;
-        }
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+        bufferFree(d_x_corners);
+        bufferFree(d_y_corners);
+        bufferFree(harris_resp.data);
+        bufferFree(harris_idx.data);
+    }
+    else if (max_corners == 0 && corners_found < corner_lim) {
+        x_out.data = bufferAlloc(*corners_out * sizeof(float));
+        y_out.data = bufferAlloc(*corners_out * sizeof(float));
+        resp_out.data = bufferAlloc(*corners_out * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_x_corners, *x_out.data, 0, 0, *corners_out * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_y_corners, *y_out.data, 0, 0, *corners_out * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_resp_corners, *resp_out.data, 0, 0, *corners_out * sizeof(float));
+
+        bufferFree(d_x_corners);
+        bufferFree(d_y_corners);
+        bufferFree(d_resp_corners);
+    }
+    else {
+        x_out.data = d_x_corners;
+        y_out.data = d_y_corners;
+        resp_out.data = d_resp_corners;
     }
 }
 
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index fa0321bd8b..28b0e1ad3d 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -34,54 +34,48 @@ static const int THRD_LOAD =   16;
 template<typename inType, typename outType, bool isLinear>
 void histogram(Param out, const Param in, int nbins, float minval, float maxval)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> histProgs;
-        static std::map<int, Kernel *> histKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D inType=" << dtype_traits<inType>::getName()
-                            << " -D outType=" << dtype_traits<outType>::getName()
-                            << " -D THRD_LOAD=" << THRD_LOAD;
-                    if (isLinear)
-                        options << " -D IS_LINEAR";
-                    if (std::is_same<inType, double>::value ||
-                        std::is_same<inType, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, histogram_cl, histogram_cl_len, options.str());
-                    histProgs[device]   = new Program(prog);
-                    histKernels[device] = new Kernel(*histProgs[device], "histogram");
-                });
-
-        auto histogramOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                       cl::LocalSpaceArg,
-                                       int, int, float, float, int
-                                      >(*histKernels[device]);
-
-        int nElems = in.info.dims[0]*in.info.dims[1];
-        int blk_x  = divup(nElems, THRD_LOAD*THREADS_X);
-        int locSize = nbins * sizeof(outType);
-
-        NDRange local(THREADS_X, 1);
-        NDRange global(blk_x*in.info.dims[2]*THREADS_X, in.info.dims[3]);
-
-        histogramOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info,
-                cl::Local(locSize), nElems, nbins, minval, maxval, blk_x);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> histProgs;
+    static std::map<int, Kernel *> histKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D inType=" << dtype_traits<inType>::getName()
+                        << " -D outType=" << dtype_traits<outType>::getName()
+                        << " -D THRD_LOAD=" << THRD_LOAD;
+                if (isLinear)
+                    options << " -D IS_LINEAR";
+                if (std::is_same<inType, double>::value ||
+                    std::is_same<inType, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                Program prog;
+                buildProgram(prog, histogram_cl, histogram_cl_len, options.str());
+                histProgs[device]   = new Program(prog);
+                histKernels[device] = new Kernel(*histProgs[device], "histogram");
+            });
+
+    auto histogramOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                    cl::LocalSpaceArg,
+                                    int, int, float, float, int
+                                  >(*histKernels[device]);
+
+    int nElems = in.info.dims[0]*in.info.dims[1];
+    int blk_x  = divup(nElems, THRD_LOAD*THREADS_X);
+    int locSize = nbins * sizeof(outType);
+
+    NDRange local(THREADS_X, 1);
+    NDRange global(blk_x*in.info.dims[2]*THREADS_X, in.info.dims[3]);
+
+    histogramOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *in.data, in.info,
+            cl::Local(locSize), nElems, nbins, minval, maxval, blk_x);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
-
 }
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 539b38ab4e..a5f6faf096 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -50,194 +50,189 @@ int computeH(
     const unsigned nsamples,
     const float inlier_thr)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> hgProgs;
-        static std::map<int, Kernel*>  chKernel;
-        static std::map<int, Kernel*>  ehKernel;
-        static std::map<int, Kernel*>  cmKernel;
-        static std::map<int, Kernel*>  fmKernel;
-        static std::map<int, Kernel*>  clKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-
-                if (std::is_same<T, double>::value) {
-                    options << " -D USE_DOUBLE";
-                    options << " -D EPS=" << DBL_EPSILON;
-                } else
-                    options << " -D EPS=" << FLT_EPSILON;
-
-                if (htype == AF_HOMOGRAPHY_RANSAC)
-                    options << " -D RANSAC";
-                else if (htype == AF_HOMOGRAPHY_LMEDS)
-                    options << " -D LMEDS";
-
-                if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
-                    options << " -D IS_CPU";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, homography_cl, homography_cl_len, options.str());
-                hgProgs[device] = new Program(prog);
-
-                chKernel[device] = new Kernel(*hgProgs[device], "compute_homography");
-                ehKernel[device] = new Kernel(*hgProgs[device], "eval_homography");
-                cmKernel[device] = new Kernel(*hgProgs[device], "compute_median");
-                fmKernel[device] = new Kernel(*hgProgs[device], "find_min_median");
-                clKernel[device] = new Kernel(*hgProgs[device], "compute_lmeds_inliers");
-            });
-
-        const int blk_x_ch = 1;
-        const int blk_y_ch = divup(iterations, HG_THREADS_Y);
-        const NDRange local_ch(HG_THREADS_X, HG_THREADS_Y);
-        const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
-
-        // Build linear system and solve SVD
-        auto chOp = KernelFunctor<Buffer, KParam,
-                                Buffer, Buffer, Buffer, Buffer,
-                                Buffer, KParam, unsigned>(*chKernel[device]);
-
-        chOp(EnqueueArgs(getQueue(), global_ch, local_ch),
-             *H.data, H.info,
-             *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-             *rnd.data, rnd.info, iterations);
-        CL_DEBUG_FINISH(getQueue());
-
-        const int blk_x_eh = divup(iterations, HG_THREADS);
-        const NDRange local_eh(HG_THREADS);
-        const NDRange global_eh(blk_x_eh * HG_THREADS);
-
-        // Allocate some temporary buffers
-        Param inliers, idx, median;
-        inliers.info.offset = idx.info.offset = median.info.offset = 0;
-        inliers.info.dims[0] = (htype == AF_HOMOGRAPHY_RANSAC) ? blk_x_eh : divup(nsamples, HG_THREADS);
-        inliers.info.strides[0] = 1;
-        idx.info.dims[0] = median.info.dims[0] = blk_x_eh;
-        idx.info.strides[0] = median.info.strides[0] = 1;
-        for (int k = 1; k < 4; k++) {
-            inliers.info.dims[k] = 1;
-            inliers.info.strides[k] = inliers.info.dims[k-1] * inliers.info.strides[k-1];
-            idx.info.dims[k] = median.info.dims[k] = 1;
-            idx.info.strides[k] = median.info.strides[k] = idx.info.dims[k-1] * idx.info.strides[k-1];
-        }
-        idx.data = bufferAlloc(idx.info.dims[3] * idx.info.strides[3] * sizeof(unsigned));
-        inliers.data = bufferAlloc(inliers.info.dims[3] * inliers.info.strides[3] * sizeof(unsigned));
-        if (htype == AF_HOMOGRAPHY_LMEDS)
-            median.data = bufferAlloc(median.info.dims[3] * median.info.strides[3] * sizeof(float));
-        else
-            median.data = bufferAlloc(sizeof(float));
-
-        // Compute (and for RANSAC, evaluate) homographies
-        auto ehOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer, Buffer, Buffer, Buffer,
-                                Buffer, unsigned, unsigned, float>(*ehKernel[device]);
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> hgProgs;
+    static std::map<int, Kernel*>  chKernel;
+    static std::map<int, Kernel*>  ehKernel;
+    static std::map<int, Kernel*>  cmKernel;
+    static std::map<int, Kernel*>  fmKernel;
+    static std::map<int, Kernel*>  clKernel;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
+
+            if (std::is_same<T, double>::value) {
+                options << " -D USE_DOUBLE";
+                options << " -D EPS=" << DBL_EPSILON;
+            } else
+                options << " -D EPS=" << FLT_EPSILON;
+
+            if (htype == AF_HOMOGRAPHY_RANSAC)
+                options << " -D RANSAC";
+            else if (htype == AF_HOMOGRAPHY_LMEDS)
+                options << " -D LMEDS";
+
+            if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
+                options << " -D IS_CPU";
+            }
 
-        ehOp(EnqueueArgs(getQueue(), global_eh, local_eh),
-             *inliers.data, *idx.data, *H.data, H.info,
-             *err.data, err.info,
-             *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-             *rnd.data, iterations, nsamples, inlier_thr);
+            cl::Program prog;
+            buildProgram(prog, homography_cl, homography_cl_len, options.str());
+            hgProgs[device] = new Program(prog);
+
+            chKernel[device] = new Kernel(*hgProgs[device], "compute_homography");
+            ehKernel[device] = new Kernel(*hgProgs[device], "eval_homography");
+            cmKernel[device] = new Kernel(*hgProgs[device], "compute_median");
+            fmKernel[device] = new Kernel(*hgProgs[device], "find_min_median");
+            clKernel[device] = new Kernel(*hgProgs[device], "compute_lmeds_inliers");
+        });
+
+    const int blk_x_ch = 1;
+    const int blk_y_ch = divup(iterations, HG_THREADS_Y);
+    const NDRange local_ch(HG_THREADS_X, HG_THREADS_Y);
+    const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
+
+    // Build linear system and solve SVD
+    auto chOp = KernelFunctor<Buffer, KParam,
+                            Buffer, Buffer, Buffer, Buffer,
+                            Buffer, KParam, unsigned>(*chKernel[device]);
+
+    chOp(EnqueueArgs(getQueue(), global_ch, local_ch),
+          *H.data, H.info,
+          *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
+          *rnd.data, rnd.info, iterations);
+    CL_DEBUG_FINISH(getQueue());
+
+    const int blk_x_eh = divup(iterations, HG_THREADS);
+    const NDRange local_eh(HG_THREADS);
+    const NDRange global_eh(blk_x_eh * HG_THREADS);
+
+    // Allocate some temporary buffers
+    Param inliers, idx, median;
+    inliers.info.offset = idx.info.offset = median.info.offset = 0;
+    inliers.info.dims[0] = (htype == AF_HOMOGRAPHY_RANSAC) ? blk_x_eh : divup(nsamples, HG_THREADS);
+    inliers.info.strides[0] = 1;
+    idx.info.dims[0] = median.info.dims[0] = blk_x_eh;
+    idx.info.strides[0] = median.info.strides[0] = 1;
+    for (int k = 1; k < 4; k++) {
+        inliers.info.dims[k] = 1;
+        inliers.info.strides[k] = inliers.info.dims[k-1] * inliers.info.strides[k-1];
+        idx.info.dims[k] = median.info.dims[k] = 1;
+        idx.info.strides[k] = median.info.strides[k] = idx.info.dims[k-1] * idx.info.strides[k-1];
+    }
+    idx.data = bufferAlloc(idx.info.dims[3] * idx.info.strides[3] * sizeof(unsigned));
+    inliers.data = bufferAlloc(inliers.info.dims[3] * inliers.info.strides[3] * sizeof(unsigned));
+    if (htype == AF_HOMOGRAPHY_LMEDS)
+        median.data = bufferAlloc(median.info.dims[3] * median.info.strides[3] * sizeof(float));
+    else
+        median.data = bufferAlloc(sizeof(float));
+
+    // Compute (and for RANSAC, evaluate) homographies
+    auto ehOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
+                            Buffer, KParam,
+                            Buffer, Buffer, Buffer, Buffer,
+                            Buffer, unsigned, unsigned, float>(*ehKernel[device]);
+
+    ehOp(EnqueueArgs(getQueue(), global_eh, local_eh),
+          *inliers.data, *idx.data, *H.data, H.info,
+          *err.data, err.info,
+          *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
+          *rnd.data, iterations, nsamples, inlier_thr);
+    CL_DEBUG_FINISH(getQueue());
+
+    unsigned inliersH, idxH;
+    if (htype == AF_HOMOGRAPHY_LMEDS) {
+        // TODO: Improve this sorting, if the number of iterations is
+        // sufficiently large, this can be *very* slow
+        kernel::sort0<float>(err, true);
+
+        unsigned minIdx;
+        float minMedian;
+
+        // Compute median of every iteration
+        auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
+                                unsigned>(*cmKernel[device]);
+
+        cmOp(EnqueueArgs(getQueue(), global_eh, local_eh),
+              *median.data, *idx.data, *err.data, err.info,
+              iterations);
         CL_DEBUG_FINISH(getQueue());
 
-        unsigned inliersH, idxH;
-        if (htype == AF_HOMOGRAPHY_LMEDS) {
-            // TODO: Improve this sorting, if the number of iterations is
-            // sufficiently large, this can be *very* slow
-            kernel::sort0<float>(err, true);
+        // Reduce medians, only in case iterations > 256
+        if (blk_x_eh > 1) {
+            const NDRange local_fm(HG_THREADS);
+            const NDRange global_fm(HG_THREADS);
 
-            unsigned minIdx;
-            float minMedian;
+            cl::Buffer* finalMedian = bufferAlloc(sizeof(float));
+            cl::Buffer* finalIdx = bufferAlloc(sizeof(unsigned));
 
-            // Compute median of every iteration
-            auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
-                                    unsigned>(*cmKernel[device]);
+            auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
+                                    Buffer>(*fmKernel[device]);
 
-            cmOp(EnqueueArgs(getQueue(), global_eh, local_eh),
-                 *median.data, *idx.data, *err.data, err.info,
-                 iterations);
+            fmOp(EnqueueArgs(getQueue(), global_fm, local_fm),
+                  *finalMedian, *finalIdx, *median.data, median.info,
+                  *idx.data);
             CL_DEBUG_FINISH(getQueue());
 
-            // Reduce medians, only in case iterations > 256
-            if (blk_x_eh > 1) {
-                const NDRange local_fm(HG_THREADS);
-                const NDRange global_fm(HG_THREADS);
-
-                cl::Buffer* finalMedian = bufferAlloc(sizeof(float));
-                cl::Buffer* finalIdx = bufferAlloc(sizeof(unsigned));
-
-                auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
-                                        Buffer>(*fmKernel[device]);
-
-                fmOp(EnqueueArgs(getQueue(), global_fm, local_fm),
-                     *finalMedian, *finalIdx, *median.data, median.info,
-                     *idx.data);
-                CL_DEBUG_FINISH(getQueue());
+            getQueue().enqueueReadBuffer(*finalMedian, CL_TRUE, 0, sizeof(float), &minMedian);
+            getQueue().enqueueReadBuffer(*finalIdx, CL_TRUE, 0, sizeof(unsigned), &minIdx);
 
-                getQueue().enqueueReadBuffer(*finalMedian, CL_TRUE, 0, sizeof(float), &minMedian);
-                getQueue().enqueueReadBuffer(*finalIdx, CL_TRUE, 0, sizeof(unsigned), &minIdx);
-
-                bufferFree(finalMedian);
-                bufferFree(finalIdx);
-            }
-            else {
-                getQueue().enqueueReadBuffer(*median.data, CL_TRUE, 0, sizeof(float), &minMedian);
-                getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, 0, sizeof(unsigned), &minIdx);
-            }
+            bufferFree(finalMedian);
+            bufferFree(finalIdx);
+        }
+        else {
+            getQueue().enqueueReadBuffer(*median.data, CL_TRUE, 0, sizeof(float), &minMedian);
+            getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, 0, sizeof(unsigned), &minIdx);
+        }
 
-            // Copy best homography to output
-            getQueue().enqueueCopyBuffer(*H.data, *bestH.data, minIdx*9*sizeof(T), 0, 9*sizeof(T));
+        // Copy best homography to output
+        getQueue().enqueueCopyBuffer(*H.data, *bestH.data, minIdx*9*sizeof(T), 0, 9*sizeof(T));
 
-            const int blk_x_cl = divup(nsamples, HG_THREADS);
-            const NDRange local_cl(HG_THREADS);
-            const NDRange global_cl(blk_x_cl * HG_THREADS);
+        const int blk_x_cl = divup(nsamples, HG_THREADS);
+        const NDRange local_cl(HG_THREADS);
+        const NDRange global_cl(blk_x_cl * HG_THREADS);
 
-            auto clOp = KernelFunctor<Buffer, Buffer,
-                                    Buffer, Buffer, Buffer, Buffer,
-                                    float, unsigned>(*clKernel[device]);
+        auto clOp = KernelFunctor<Buffer, Buffer,
+                                Buffer, Buffer, Buffer, Buffer,
+                                float, unsigned>(*clKernel[device]);
 
-            clOp(EnqueueArgs(getQueue(), global_cl, local_cl),
-                 *inliers.data, *bestH.data,
-                 *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-                 minMedian, nsamples);
-            CL_DEBUG_FINISH(getQueue());
+        clOp(EnqueueArgs(getQueue(), global_cl, local_cl),
+              *inliers.data, *bestH.data,
+              *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
+              minMedian, nsamples);
+        CL_DEBUG_FINISH(getQueue());
 
-            // Adds up the total number of inliers
-            Param totalInliers;
-            totalInliers.info.offset = 0;
-            for (int k = 0; k < 4; k++)
-                totalInliers.info.dims[k] = totalInliers.info.strides[k] = 1;
-            totalInliers.data = bufferAlloc(sizeof(unsigned));
+        // Adds up the total number of inliers
+        Param totalInliers;
+        totalInliers.info.offset = 0;
+        for (int k = 0; k < 4; k++)
+            totalInliers.info.dims[k] = totalInliers.info.strides[k] = 1;
+        totalInliers.data = bufferAlloc(sizeof(unsigned));
 
-            kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
+        kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
 
-            getQueue().enqueueReadBuffer(*totalInliers.data, CL_TRUE, 0, sizeof(unsigned), &inliersH);
+        getQueue().enqueueReadBuffer(*totalInliers.data, CL_TRUE, 0, sizeof(unsigned), &inliersH);
 
-            bufferFree(totalInliers.data);
-        } else if (htype == AF_HOMOGRAPHY_RANSAC) {
-            unsigned blockIdx;
-            inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
+        bufferFree(totalInliers.data);
+    } else if (htype == AF_HOMOGRAPHY_RANSAC) {
+        unsigned blockIdx;
+        inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
 
-            // Copies back index and number of inliers of best homography estimation
-            getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, blockIdx*sizeof(unsigned),
-                                         sizeof(unsigned), &idxH);
-            getQueue().enqueueCopyBuffer(*H.data, *bestH.data, idxH*9*sizeof(T), 0, 9*sizeof(T));
-        }
+        // Copies back index and number of inliers of best homography estimation
+        getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, blockIdx*sizeof(unsigned),
+                                      sizeof(unsigned), &idxH);
+        getQueue().enqueueCopyBuffer(*H.data, *bestH.data, idxH*9*sizeof(T), 0, 9*sizeof(T));
+    }
 
-        bufferFree(inliers.data);
-        bufferFree(idx.data);
-        bufferFree(median.data);
+    bufferFree(inliers.data);
+    bufferFree(idx.data);
+    bufferFree(median.data);
 
-        return (int)inliersH;
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    return (int)inliersH;
 }
 
 } // namespace kernel
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 62460311b9..569912c836 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -38,48 +38,43 @@ static const int THREADS_Y = 16;
 template<typename T, bool isHSV2RGB>
 void hsv2rgb_convert(Param out, const Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  hrProgs;
-        static std::map<int, Kernel*> hrKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  hrProgs;
+    static std::map<int, Kernel*> hrKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once( compileFlags[device], [device] () {
+    std::call_once( compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
 
-                if(isHSV2RGB) options << " -D isHSV2RGB";
+            if(isHSV2RGB) options << " -D isHSV2RGB";
 
-                if (std::is_same<T, double>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, hsv_rgb_cl, hsv_rgb_cl_len, options.str());
-                hrProgs[device]   = new Program(prog);
-                hrKernels[device] = new Kernel(*hrProgs[device], "convert");
-            });
+            if (std::is_same<T, double>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, hsv_rgb_cl, hsv_rgb_cl_len, options.str());
+            hrProgs[device]   = new Program(prog);
+            hrKernels[device] = new Kernel(*hrProgs[device], "convert");
+        });
 
-        NDRange local(THREADS_X, THREADS_Y);
+    NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-        // all images are three channels, so batch
-        // parameter would be along 4th dimension
-        NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
+    // all images are three channels, so batch
+    // parameter would be along 4th dimension
+    NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
 
-        auto hsvrgbOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int> (*hrKernels[device]);
+    auto hsvrgbOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int> (*hrKernels[device]);
 
-        hsvrgbOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, blk_x);
+    hsvrgbOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info, blk_x);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index cb1a677eeb..3882de037c 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -37,44 +37,40 @@ namespace kernel
     template<typename T>
     static void identity(Param out)
     {
-        try {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   identityProgs;
-            static std::map<int, Kernel*>  identityKernels;
+        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+        static std::map<int, Program*>   identityProgs;
+        static std::map<int, Kernel*>  identityKernels;
 
-            int device = getActiveDeviceId();
+        int device = getActiveDeviceId();
 
-            std::call_once( compileFlags[device], [device] () {
-                    ostringstream options;
-                    options << " -D T="    << dtype_traits<T>::getName()
-                            << " -D ONE=(T)("  << scalar_to_option(scalar<T>(1)) << ")"
-                            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, identity_cl, identity_cl_len, options.str());
-                    identityProgs[device]   = new Program(prog);
-                    identityKernels[device] = new Kernel(*identityProgs[device], "identity_kernel");
-                });
+        std::call_once( compileFlags[device], [device] () {
+                ostringstream options;
+                options << " -D T="    << dtype_traits<T>::getName()
+                        << " -D ONE=(T)("  << scalar_to_option(scalar<T>(1)) << ")"
+                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, identity_cl, identity_cl_len, options.str());
+                identityProgs[device]   = new Program(prog);
+                identityKernels[device] = new Kernel(*identityProgs[device], "identity_kernel");
+            });
 
-            NDRange local(32, 8);
-            int groups_x = divup(out.info.dims[0], local[0]);
-            int groups_y = divup(out.info.dims[1], local[1]);
-            NDRange global(groups_x * out.info.dims[2] * local[0],
-                           groups_y * out.info.dims[3] * local[1]);
+        NDRange local(32, 8);
+        int groups_x = divup(out.info.dims[0], local[0]);
+        int groups_y = divup(out.info.dims[1], local[1]);
+        NDRange global(groups_x * out.info.dims[2] * local[0],
+                        groups_y * out.info.dims[3] * local[1]);
 
-            auto identityOp = KernelFunctor<Buffer, const KParam,
-                                          int, int> (*identityKernels[device]);
+        auto identityOp = KernelFunctor<Buffer, const KParam,
+                                      int, int> (*identityKernels[device]);
 
-            identityOp(EnqueueArgs(getQueue(), global, local),
-                       *(out.data), out.info, groups_x, groups_y);
-            CL_DEBUG_FINISH(getQueue());
+        identityOp(EnqueueArgs(getQueue(), global, local),
+                    *(out.data), out.info, groups_x, groups_y);
+        CL_DEBUG_FINISH(getQueue());
 
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-        }
     }
 
 }
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 6266a251cd..ae39e1f64a 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -44,48 +44,43 @@ typedef struct {
 template<typename T>
 void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[4])
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  idxProgs;
-        static std::map<int, Kernel*> idxKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  idxProgs;
+    static std::map<int, Kernel*> idxKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
+    std::call_once( compileFlags[device], [device] () {
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-                }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+            }
 
-                Program prog;
-                buildProgram(prog, index_cl, index_cl_len, options.str());
-                idxProgs[device]   = new Program(prog);
-                idxKernels[device] = new Kernel(*idxProgs[device], "indexKernel");
-                });
+            Program prog;
+            buildProgram(prog, index_cl, index_cl_len, options.str());
+            idxProgs[device]   = new Program(prog);
+            idxKernels[device] = new Kernel(*idxProgs[device], "indexKernel");
+            });
 
-        NDRange local(THREADS_X, THREADS_Y);
+    NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(out.info.dims[0], THREADS_X);
-        int blk_y = divup(out.info.dims[1], THREADS_Y);
+    int blk_x = divup(out.info.dims[0], THREADS_X);
+    int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-        NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-                blk_y * out.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
+            blk_y * out.info.dims[3] * THREADS_Y);
 
-        auto indexOp = KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
-             Buffer, Buffer, Buffer, Buffer, int, int>(*idxKernels[device]);
+    auto indexOp = KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
+          Buffer, Buffer, Buffer, Buffer, int, int>(*idxKernels[device]);
 
-        indexOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, p,
-                *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
+    indexOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *in.data, in.info, p,
+            *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 54d79c3dc7..d018357ab2 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -40,48 +40,43 @@ namespace opencl
         template<typename T>
         void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  iotaProgs;
-                static std::map<int, Kernel*> iotaKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>  iotaProgs;
+            static std::map<int, Kernel*> iotaKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, iota_cl, iota_cl_len, options.str());
-                    iotaProgs[device]   = new Program(prog);
-                    iotaKernels[device] = new Kernel(*iotaProgs[device], "iota_kernel");
-                });
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, iota_cl, iota_cl_len, options.str());
+                iotaProgs[device]   = new Program(prog);
+                iotaKernels[device] = new Kernel(*iotaProgs[device], "iota_kernel");
+            });
 
-                auto iotaOp = KernelFunctor<Buffer, const KParam,
-                                          const int, const int, const int, const int,
-                                          const int, const int, const int, const int,
-                                          const int, const int> (*iotaKernels[device]);
+            auto iotaOp = KernelFunctor<Buffer, const KParam,
+                                      const int, const int, const int, const int,
+                                      const int, const int, const int, const int,
+                                      const int, const int> (*iotaKernels[device]);
 
-                NDRange local(IOTA_TX, IOTA_TY, 1);
+            NDRange local(IOTA_TX, IOTA_TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], TILEY);
-                NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                               local[1] * blocksPerMatY * out.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(out.info.dims[0], TILEX);
+            int blocksPerMatY = divup(out.info.dims[1], TILEY);
+            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                            local[1] * blocksPerMatY * out.info.dims[3],
+                            1);
 
-                iotaOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, out.info, sdims[0], sdims[1], sdims[2], sdims[3],
-                       tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
+            iotaOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, sdims[0], sdims[1], sdims[2], sdims[3],
+                    tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index aedbc863f4..2c76f2720f 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -271,14 +271,10 @@ namespace kernel
     template<typename T, af_op_t op>
     void ireduce(Param out, cl::Buffer *oidx, Param in, int dim)
     {
-        try {
-            if (dim == 0)
-                return ireduce_first<T, op>(out, oidx, in);
-            else
-                return ireduce_dim  <T, op>(out, oidx, in, dim);
-        } catch(cl::Error ex) {
-            CL_TO_AF_ERROR(ex);
-        }
+        if (dim == 0)
+            return ireduce_first<T, op>(out, oidx, in);
+        else
+            return ireduce_dim  <T, op>(out, oidx, in, dim);
     }
 
 #if defined(__GNUC__) || defined(__GNUG__)
@@ -345,102 +341,96 @@ namespace kernel
     template<typename T, af_op_t op>
     T ireduce_all(uint *loc, Param in)
     {
-        try {
-            int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+        int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
-            // FIXME: Use better heuristics to get to the optimum number
-            if (in_elements > 4096) {
+        // FIXME: Use better heuristics to get to the optimum number
+        if (in_elements > 4096) {
 
-                bool is_linear = (in.info.strides[0] == 1);
-                for (int k = 1; k < 4; k++) {
-                    is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
-                }
+            bool is_linear = (in.info.strides[0] == 1);
+            for (int k = 1; k < 4; k++) {
+                is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
+            }
 
-                if (is_linear) {
-                    in.info.dims[0] = in_elements;
-                    for (int k = 1; k < 4; k++) {
-                        in.info.dims[k] = 1;
-                        in.info.strides[k] = in_elements;
-                    }
+            if (is_linear) {
+                in.info.dims[0] = in_elements;
+                for (int k = 1; k < 4; k++) {
+                    in.info.dims[k] = 1;
+                    in.info.strides[k] = in_elements;
                 }
+            }
 
-                uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-                threads_x = std::min(threads_x, THREADS_PER_GROUP);
-                uint threads_y = THREADS_PER_GROUP / threads_x;
-
-                Param tmp;
-                uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-                uint groups_y = divup(in.info.dims[1], threads_y);
+            uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+            threads_x = std::min(threads_x, THREADS_PER_GROUP);
+            uint threads_y = THREADS_PER_GROUP / threads_x;
 
-                tmp.info.offset = 0;
-                tmp.info.dims[0] = groups_x;
-                tmp.info.strides[0] = 1;
+            Param tmp;
+            uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+            uint groups_y = divup(in.info.dims[1], threads_y);
 
-                for (int k = 1; k < 4; k++) {
-                    tmp.info.dims[k] = in.info.dims[k];
-                    tmp.info.strides[k] = tmp.info.dims[k - 1] * tmp.info.strides[k - 1];
-                }
+            tmp.info.offset = 0;
+            tmp.info.dims[0] = groups_x;
+            tmp.info.strides[0] = 1;
 
-                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-                tmp.data = bufferAlloc(tmp_elements * sizeof(T));
-                cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
+            for (int k = 1; k < 4; k++) {
+                tmp.info.dims[k] = in.info.dims[k];
+                tmp.info.strides[k] = tmp.info.dims[k - 1] * tmp.info.strides[k - 1];
+            }
 
-                ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x, groups_y);
+            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+            tmp.data = bufferAlloc(tmp_elements * sizeof(T));
+            cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
 
-                unique_ptr<T> h_ptr(new T[tmp_elements]);
-                unique_ptr<uint> h_iptr(new uint[tmp_elements]);
+            ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x, groups_y);
 
-                getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
-                getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0, sizeof(uint) * tmp_elements, h_iptr.get());
+            unique_ptr<T> h_ptr(new T[tmp_elements]);
+            unique_ptr<uint> h_iptr(new uint[tmp_elements]);
 
-                T* h_ptr_raw = h_ptr.get();
-                uint* h_iptr_raw = h_iptr.get();
+            getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
+            getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0, sizeof(uint) * tmp_elements, h_iptr.get());
 
-                if (!is_linear) {
-                    // Converting n-d index into a linear index
-                    // in is of size   [   dims0, dims1, dims2, dims3]
-                    // tidx is of size [groups_x, dims1, dims2, dims3]
-                    // i / groups_x gives you the batch number "N"
-                    // "N * dims0 + i" gives the linear index
-                    for (int i = 0; i < tmp_elements; i++) {
-                        h_iptr_raw[i] += (i / groups_x) * in.info.dims[0];
-                    }
-                }
+            T* h_ptr_raw = h_ptr.get();
+            uint* h_iptr_raw = h_iptr.get();
 
-                MinMaxOp<op, T> Op(h_ptr_raw[0], h_iptr_raw[0]);
-                for (int i = 1; i < (int)tmp_elements; i++) {
-                    Op(h_ptr_raw[i], h_iptr_raw[i]);
+            if (!is_linear) {
+                // Converting n-d index into a linear index
+                // in is of size   [   dims0, dims1, dims2, dims3]
+                // tidx is of size [groups_x, dims1, dims2, dims3]
+                // i / groups_x gives you the batch number "N"
+                // "N * dims0 + i" gives the linear index
+                for (int i = 0; i < tmp_elements; i++) {
+                    h_iptr_raw[i] += (i / groups_x) * in.info.dims[0];
                 }
+            }
 
-                bufferFree(tmp.data);
-                bufferFree(tidx);
+            MinMaxOp<op, T> Op(h_ptr_raw[0], h_iptr_raw[0]);
+            for (int i = 1; i < (int)tmp_elements; i++) {
+                Op(h_ptr_raw[i], h_iptr_raw[i]);
+            }
 
-                *loc = Op.m_idx;
-                return Op.m_val;
+            bufferFree(tmp.data);
+            bufferFree(tidx);
 
-            } else {
+            *loc = Op.m_idx;
+            return Op.m_val;
 
-                unique_ptr<T> h_ptr(new T[in_elements]);
-                T* h_ptr_raw = h_ptr.get();
+        } else {
 
-                getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
-                                             sizeof(T) * in_elements, h_ptr_raw);
+            unique_ptr<T> h_ptr(new T[in_elements]);
+            T* h_ptr_raw = h_ptr.get();
 
+            getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
+                                          sizeof(T) * in_elements, h_ptr_raw);
 
-                MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
-                for (int i = 1; i < (int)in_elements; i++) {
-                    Op(h_ptr_raw[i], i);
-                }
 
-                *loc = Op.m_idx;
-                return Op.m_val;
+            MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
+            for (int i = 1; i < (int)in_elements; i++) {
+                Op(h_ptr_raw[i], i);
             }
-        } catch(cl::Error ex) {
-            CL_TO_AF_ERROR(ex);
+
+            *loc = Op.m_idx;
+            return Op.m_val;
         }
     }
-
-
 }
 
 }
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 878d95b7cd..52cfcaeb98 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -39,53 +39,48 @@ namespace opencl
         template<typename To, typename Ti, int dim>
         void join(Param out, const Param in, const af::dim4 offset)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   joinProgs;
-                static std::map<int, Kernel *> joinKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   joinProgs;
+            static std::map<int, Kernel *> joinKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D To=" << dtype_traits<To>::getName()
-                            << " -D Ti=" << dtype_traits<Ti>::getName()
-                            << " -D dim=" << dim;
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D To=" << dtype_traits<To>::getName()
+                        << " -D Ti=" << dtype_traits<Ti>::getName()
+                        << " -D dim=" << dim;
 
-                    if (std::is_same<To, double>::value ||
-                        std::is_same<To, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    } else if (std::is_same<Ti, double>::value ||
-                               std::is_same<Ti, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+                if (std::is_same<To, double>::value ||
+                    std::is_same<To, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                } else if (std::is_same<Ti, double>::value ||
+                            std::is_same<Ti, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 
-                    Program prog;
-                    buildProgram(prog, join_cl, join_cl_len, options.str());
-                    joinProgs[device] = new Program(prog);
-                    joinKernels[device] = new Kernel(*joinProgs[device], "join_kernel");
-                });
+                Program prog;
+                buildProgram(prog, join_cl, join_cl_len, options.str());
+                joinProgs[device] = new Program(prog);
+                joinKernels[device] = new Kernel(*joinProgs[device], "join_kernel");
+            });
 
-                auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                              const int, const int, const int, const int,
-                              const int, const int> (*joinKernels[device]);
+            auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                          const int, const int, const int, const int,
+                          const int, const int> (*joinKernels[device]);
 
-                NDRange local(TX, TY, 1);
+            NDRange local(TX, TY, 1);
 
-                int blocksPerMatX = divup(in.info.dims[0], TILEX);
-                int blocksPerMatY = divup(in.info.dims[1], TILEY);
-                NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                               local[1] * blocksPerMatY * in.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(in.info.dims[0], TILEX);
+            int blocksPerMatY = divup(in.info.dims[1], TILEY);
+            NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                            local[1] * blocksPerMatY * in.info.dims[3],
+                            1);
 
-                joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info,
-                        offset[0], offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
+            joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info,
+                    offset[0], offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 9ee6d9cfb7..995dffbadf 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -38,52 +38,47 @@ static const int THREADS_Y = 8;
 template<typename in_t, typename idx_t, unsigned dim>
 void lookup(Param out, const Param in, const Param indices, int nDims)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  aiProgs;
-        static std::map<int, Kernel*> aiKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D in_t=" << dtype_traits<in_t>::getName()
-                            << " -D idx_t=" << dtype_traits<idx_t>::getName()
-                            << " -D DIM=" <<dim;
-
-                    if (std::is_same<in_t, double>::value ||
-                        std::is_same<in_t, cdouble>::value ||
-                        std::is_same<idx_t, double>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, lookup_cl, lookup_cl_len, options.str());
-                    aiProgs[device]   = new Program(prog);
-                    aiKernels[device] = new Kernel(*aiProgs[device], "lookupND");
-                });
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(out.info.dims[0], THREADS_X);
-        int blk_y = divup(out.info.dims[1], THREADS_Y);
-
-        NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-                       blk_y * out.info.dims[3] * THREADS_Y);
-
-        auto arrIdxOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    int, int>(*aiKernels[device]);
-
-        arrIdxOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  aiProgs;
+    static std::map<int, Kernel*> aiKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D in_t=" << dtype_traits<in_t>::getName()
+                        << " -D idx_t=" << dtype_traits<idx_t>::getName()
+                        << " -D DIM=" <<dim;
+
+                if (std::is_same<in_t, double>::value ||
+                    std::is_same<in_t, cdouble>::value ||
+                    std::is_same<idx_t, double>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                Program prog;
+                buildProgram(prog, lookup_cl, lookup_cl_len, options.str());
+                aiProgs[device]   = new Program(prog);
+                aiKernels[device] = new Kernel(*aiProgs[device], "lookupND");
+            });
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(out.info.dims[0], THREADS_X);
+    int blk_y = divup(out.info.dims[1], THREADS_Y);
+
+    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
+                    blk_y * out.info.dims[3] * THREADS_Y);
+
+    auto arrIdxOp = KernelFunctor<Buffer, KParam,
+                                Buffer, KParam,
+                                Buffer, KParam,
+                                int, int>(*aiKernels[device]);
+
+    arrIdxOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index db6fc58c7f..6784039614 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -44,58 +44,53 @@ static const unsigned TILEY = 32;
 template<typename T, bool same_dims>
 void lu_split_launcher(Param lower, Param upper, const Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  splitProgs;
-        static std::map<int, Kernel*> splitKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  splitProgs;
+    static std::map<int, Kernel*> splitKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once(compileFlags[device], [device] () {
+    std::call_once(compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D same_dims=" << same_dims
-                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-                        << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D same_dims=" << same_dims
+                    << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+                    << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-                cl::Program prog;
-                buildProgram(prog, lu_split_cl, lu_split_cl_len, options.str());
-                splitProgs[device] = new Program(prog);
+            cl::Program prog;
+            buildProgram(prog, lu_split_cl, lu_split_cl_len, options.str());
+            splitProgs[device] = new Program(prog);
 
-                splitKernels[device] = new Kernel(*splitProgs[device], "lu_split_kernel");
-            });
+            splitKernels[device] = new Kernel(*splitProgs[device], "lu_split_kernel");
+        });
 
 
-        NDRange local(TX, TY);
+    NDRange local(TX, TY);
 
-        int groups_x = divup(in.info.dims[0], TILEX);
-        int groups_y = divup(in.info.dims[1], TILEY);
+    int groups_x = divup(in.info.dims[0], TILEX);
+    int groups_y = divup(in.info.dims[1], TILEY);
 
-        NDRange global(groups_x * local[0] * in.info.dims[2],
-                       groups_y * local[1] * in.info.dims[3]);
+    NDRange global(groups_x * local[0] * in.info.dims[2],
+                    groups_y * local[1] * in.info.dims[3]);
 
-        auto lu_split_op = KernelFunctor<Buffer, const KParam,
-                                       Buffer, const KParam,
-                                       const Buffer, const KParam,
-                                       const int, const int> (*splitKernels[device]);
+    auto lu_split_op = KernelFunctor<Buffer, const KParam,
+                                    Buffer, const KParam,
+                                    const Buffer, const KParam,
+                                    const int, const int> (*splitKernels[device]);
 
-        lu_split_op(EnqueueArgs(getQueue(), global, local),
-                    *lower.data, lower.info,
-                    *upper.data, upper.info,
-                    *in.data, in.info,
-                    groups_x, groups_y);
+    lu_split_op(EnqueueArgs(getQueue(), global, local),
+                *lower.data, lower.info,
+                *upper.data, upper.info,
+                *in.data, in.info,
+                groups_x, groups_y);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index b1bdd49236..4922abb784 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -38,58 +38,53 @@ static const int THREADS_Y = 16;
 template<typename inType, typename outType, af_match_type mType, bool needMean>
 void matchTemplate(Param out, const Param srch, const Param tmplt)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  mtProgs;
-        static std::map<int, Kernel*> mtKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D inType="  << dtype_traits<inType>::getName()
-                        << " -D outType=" << dtype_traits<outType>::getName()
-                        << " -D MATCH_T=" << mType
-                        << " -D NEEDMEAN="<< needMean
-                        << " -D AF_SAD="  << AF_SAD
-                        << " -D AF_ZSAD=" << AF_ZSAD
-                        << " -D AF_LSAD=" << AF_LSAD
-                        << " -D AF_SSD="  << AF_SSD
-                        << " -D AF_ZSSD=" << AF_ZSSD
-                        << " -D AF_LSSD=" << AF_LSSD
-                        << " -D AF_NCC="  << AF_NCC
-                        << " -D AF_ZNCC=" << AF_ZNCC
-                        << " -D AF_SHD="  << AF_SHD;
-                if (std::is_same<outType, double>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, matchTemplate_cl, matchTemplate_cl_len, options.str());
-                mtProgs[device]   = new Program(prog);
-                mtKernels[device] = new Kernel(*mtProgs[device], "matchTemplate");
-            });
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(srch.info.dims[0], THREADS_X);
-        int blk_y = divup(srch.info.dims[1], THREADS_Y);
-
-        NDRange global(blk_x * srch.info.dims[2] * THREADS_X, blk_y * srch.info.dims[3] * THREADS_Y);
-
-        auto matchImgOp = KernelFunctor<Buffer, KParam,
-                                       Buffer, KParam,
-                                       Buffer, KParam,
-                                       int, int> (*mtKernels[device]);
-
-        matchImgOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  mtProgs;
+    static std::map<int, Kernel*> mtKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D inType="  << dtype_traits<inType>::getName()
+                    << " -D outType=" << dtype_traits<outType>::getName()
+                    << " -D MATCH_T=" << mType
+                    << " -D NEEDMEAN="<< needMean
+                    << " -D AF_SAD="  << AF_SAD
+                    << " -D AF_ZSAD=" << AF_ZSAD
+                    << " -D AF_LSAD=" << AF_LSAD
+                    << " -D AF_SSD="  << AF_SSD
+                    << " -D AF_ZSSD=" << AF_ZSSD
+                    << " -D AF_LSSD=" << AF_LSSD
+                    << " -D AF_NCC="  << AF_NCC
+                    << " -D AF_ZNCC=" << AF_ZNCC
+                    << " -D AF_SHD="  << AF_SHD;
+            if (std::is_same<outType, double>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, matchTemplate_cl, matchTemplate_cl_len, options.str());
+            mtProgs[device]   = new Program(prog);
+            mtKernels[device] = new Kernel(*mtProgs[device], "matchTemplate");
+        });
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(srch.info.dims[0], THREADS_X);
+    int blk_y = divup(srch.info.dims[1], THREADS_Y);
+
+    NDRange global(blk_x * srch.info.dims[2] * THREADS_X, blk_y * srch.info.dims[3] * THREADS_Y);
+
+    auto matchImgOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    int, int> (*mtKernels[device]);
+
+    matchImgOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index 0df2cc2abf..ddab5ed330 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -40,62 +40,57 @@ static const int THREADS_Y = 16;
 template<typename T, bool is_color>
 void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint iter)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> msProgs;
-        static std::map<int, Kernel*> msKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, meanshift_cl, meanshift_cl_len, options.str());
-                    msProgs[device]   = new Program(prog);
-                    msKernels[device] = new Kernel(*msProgs[device], "meanshift");
-                });
-
-        auto meanshiftOp = KernelFunctor<Buffer, KParam,
-                                       Buffer, KParam,
-                                       LocalSpaceArg,
-                                       int, float,
-                                       int, float,
-                                       unsigned, int, int
-                                      >(*msKernels[device]);
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
-
-        const int bCount   = (is_color ? 1 : in.info.dims[2]);
-        const int channels = (is_color ? in.info.dims[2] : 1);
-
-        NDRange global(bCount*blk_x*THREADS_X, in.info.dims[3]*blk_y*THREADS_Y);
-
-        // clamp spatical and chromatic sigma's
-        float space_ = std::min(11.5f, s_sigma);
-        int radius   = std::max((int)(space_ * 1.5f), 1);
-        int padding  = 2*radius+1;
-        const float cvar = c_sigma*c_sigma;
-        size_t loc_size  = channels*(local[0]+padding)*(local[1]+padding)*sizeof(T);
-
-        meanshiftOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info,
-                    cl::Local(loc_size), channels,
-                    space_, radius, cvar, iter, blk_x, blk_y);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> msProgs;
+    static std::map<int, Kernel*> msKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, meanshift_cl, meanshift_cl_len, options.str());
+                msProgs[device]   = new Program(prog);
+                msKernels[device] = new Kernel(*msProgs[device], "meanshift");
+            });
+
+    auto meanshiftOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    LocalSpaceArg,
+                                    int, float,
+                                    int, float,
+                                    unsigned, int, int
+                                  >(*msKernels[device]);
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    const int bCount   = (is_color ? 1 : in.info.dims[2]);
+    const int channels = (is_color ? in.info.dims[2] : 1);
+
+    NDRange global(bCount*blk_x*THREADS_X, in.info.dims[3]*blk_y*THREADS_Y);
+
+    // clamp spatical and chromatic sigma's
+    float space_ = std::min(11.5f, s_sigma);
+    int radius   = std::max((int)(space_ * 1.5f), 1);
+    int padding  = 2*radius+1;
+    const float cvar = c_sigma*c_sigma;
+    size_t loc_size  = channels*(local[0]+padding)*(local[1]+padding)*sizeof(T);
+
+    meanshiftOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info,
+                cl::Local(loc_size), channels,
+                space_, radius, cvar, iter, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 0e631f8f0a..493071d7e9 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -42,114 +42,104 @@ static const int THREADS_Y = 16;
 template<typename T, af_border_type pad>
 void medfilt1(Param out, const Param in, unsigned w_wid)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  mfProgs;
-        static std::map<int, Kernel*> mfKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device, w_wid] () {
-
-                const int ARR_SIZE = (w_wid-w_wid/2) + 1;
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D pad="<< pad
-                        << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
-                        << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                        << " -D ARR_SIZE="<< ARR_SIZE
-                        << " -D w_wid=" << w_wid;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, medfilt1_cl, medfilt1_cl_len, options.str());
-                mfProgs[device]   = new Program(prog);
-                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt1");
-            });
-
-        NDRange local(THREADS_X, 1, 1);
-
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-
-        NDRange global(blk_x * in.info.dims[1] * THREADS_X,
-                                           in.info.dims[2],
-                                           in.info.dims[3]);
-
-        auto medfiltOp = KernelFunctor<Buffer, KParam,
-                                       Buffer, KParam,
-                                       cl::LocalSpaceArg,
-                                       int> (*mfKernels[device]);
-
-        size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
-
-        medfiltOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  mfProgs;
+    static std::map<int, Kernel*> mfKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device, w_wid] () {
+
+            const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D pad="<< pad
+                    << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
+                    << " -D AF_PAD_SYM="<< AF_PAD_SYM
+                    << " -D ARR_SIZE="<< ARR_SIZE
+                    << " -D w_wid=" << w_wid;
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, medfilt1_cl, medfilt1_cl_len, options.str());
+            mfProgs[device]   = new Program(prog);
+            mfKernels[device] = new Kernel(*mfProgs[device], "medfilt1");
+        });
+
+    NDRange local(THREADS_X, 1, 1);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+
+    NDRange global(blk_x * in.info.dims[1] * THREADS_X,
+                                        in.info.dims[2],
+                                        in.info.dims[3]);
+
+    auto medfiltOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    cl::LocalSpaceArg,
+                                    int> (*mfKernels[device]);
+
+    size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
+
+    medfiltOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
 void medfilt2(Param out, const Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  mfProgs;
-        static std::map<int, Kernel*> mfKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                const int ARR_SIZE = w_len * (w_wid-w_wid/2);
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D pad="<< pad
-                        << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
-                        << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                        << " -D ARR_SIZE="<< ARR_SIZE
-                        << " -D w_len="<< w_len
-                        << " -D w_wid=" << w_wid;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, medfilt2_cl, medfilt2_cl_len, options.str());
-                mfProgs[device]   = new Program(prog);
-                mfKernels[device] = new Kernel(*mfProgs[device], "medfilt2");
-            });
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
-
-        NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                       blk_y * in.info.dims[3] * THREADS_Y);
-
-        auto medfiltOp = KernelFunctor<Buffer, KParam,
-                                     Buffer, KParam,
-                                     cl::LocalSpaceArg,
-                                     int, int> (*mfKernels[device]);
-
-        size_t loc_size = (THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)*sizeof(T);
-
-        medfiltOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  mfProgs;
+    static std::map<int, Kernel*> mfKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            const int ARR_SIZE = w_len * (w_wid-w_wid/2);
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D pad="<< pad
+                    << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
+                    << " -D AF_PAD_SYM="<< AF_PAD_SYM
+                    << " -D ARR_SIZE="<< ARR_SIZE
+                    << " -D w_len="<< w_len
+                    << " -D w_wid=" << w_wid;
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, medfilt2_cl, medfilt2_cl_len, options.str());
+            mfProgs[device]   = new Program(prog);
+            mfKernels[device] = new Kernel(*mfProgs[device], "medfilt2");
+        });
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                    blk_y * in.info.dims[3] * THREADS_Y);
+
+    auto medfiltOp = KernelFunctor<Buffer, KParam,
+                                  Buffer, KParam,
+                                  cl::LocalSpaceArg,
+                                  int, int> (*mfKernels[device]);
+
+    size_t loc_size = (THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)*sizeof(T);
+
+    medfiltOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index d44bc20cb0..7b413b0a10 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -46,127 +46,116 @@ namespace kernel
                  const cl::Buffer in, const dim_t *idims,
                  const dim_t *istrides, int offset, uint ndims)
     {
-        try {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>    cpyProgs;
-            static std::map<int, Kernel*>   cpyKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once(compileFlags[device], [&]() {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, memcopy_cl, memcopy_cl_len, options.str());
-                cpyProgs[device]   = new Program(prog);
-                cpyKernels[device] = new Kernel(*cpyProgs[device], "memcopy_kernel");
-            });
-
-            dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
-            dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
-            dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
-
-            size_t local_size[2] = {DIM0, DIM1};
-            if (ndims == 1) {
-                local_size[0] *= local_size[1];
-                local_size[1]  = 1;
+        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+        static std::map<int, Program*>    cpyProgs;
+        static std::map<int, Kernel*>   cpyKernels;
+
+        int device = getActiveDeviceId();
+
+        std::call_once(compileFlags[device], [&]() {
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
             }
+            Program prog;
+            buildProgram(prog, memcopy_cl, memcopy_cl_len, options.str());
+            cpyProgs[device]   = new Program(prog);
+            cpyKernels[device] = new Kernel(*cpyProgs[device], "memcopy_kernel");
+        });
+
+        dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
+        dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
+        dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
+
+        size_t local_size[2] = {DIM0, DIM1};
+        if (ndims == 1) {
+            local_size[0] *= local_size[1];
+            local_size[1]  = 1;
+        }
 
-            int groups_0 = divup(idims[0], local_size[0]);
-            int groups_1 = divup(idims[1], local_size[1]);
+        int groups_0 = divup(idims[0], local_size[0]);
+        int groups_1 = divup(idims[1], local_size[1]);
 
-            NDRange local(local_size[0], local_size[1]);
-            NDRange global(groups_0 * idims[2] * local_size[0],
-                           groups_1 * idims[3] * local_size[1]);
+        NDRange local(local_size[0], local_size[1]);
+        NDRange global(groups_0 * idims[2] * local_size[0],
+                        groups_1 * idims[3] * local_size[1]);
 
-            auto memcopy_kernel = KernelFunctor< Buffer, dims_t,
-                                               Buffer, dims_t,
-                                               dims_t, int,
-                                               int, int >(*cpyKernels[device]);
+        auto memcopy_kernel = KernelFunctor< Buffer, dims_t,
+                                            Buffer, dims_t,
+                                            dims_t, int,
+                                            int, int >(*cpyKernels[device]);
 
-            memcopy_kernel(EnqueueArgs(getQueue(), global, local),
-                out, _ostrides, in, _idims, _istrides, offset, groups_0, groups_1);
-            CL_DEBUG_FINISH(getQueue());
-        }
-        catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        memcopy_kernel(EnqueueArgs(getQueue(), global, local),
+            out, _ostrides, in, _idims, _istrides, offset, groups_0, groups_1);
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename inType, typename outType, bool same_dims>
     void copy(Param dst, const Param src, int ndims, outType default_value, double factor)
     {
-        try {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>    cpyProgs;
-            static std::map<int, Kernel*>   cpyKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once(compileFlags[device], [&]() {
-
-                        std::ostringstream options;
-                        options << " -D inType=" << dtype_traits<inType>::getName()
-                            << " -D outType=" << dtype_traits<outType>::getName()
-                            << " -D inType_" << dtype_traits<inType>::getName()
-                            << " -D outType_" << dtype_traits<outType>::getName()
-                            << " -D SAME_DIMS=" << same_dims;
-                        if (std::is_same<inType, double>::value  ||
-                            std::is_same<inType, cdouble>::value ||
-                            std::is_same<outType, double>::value ||
-                            std::is_same<outType, cdouble>::value) {
-                            options << " -D USE_DOUBLE";
-                        }
-
-                        Program prog;
-                        buildProgram(prog, copy_cl, copy_cl_len, options.str());
-                        cpyProgs[device]   = new Program(prog);
-                        cpyKernels[device] = new Kernel(*cpyProgs[device], "copy");
-                    });
-
-            NDRange local(DIM0, DIM1);
-            size_t local_size[] = {DIM0, DIM1};
+        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+        static std::map<int, Program*>    cpyProgs;
+        static std::map<int, Kernel*>   cpyKernels;
+
+        int device = getActiveDeviceId();
+
+        std::call_once(compileFlags[device], [&]() {
+
+                    std::ostringstream options;
+                    options << " -D inType=" << dtype_traits<inType>::getName()
+                        << " -D outType=" << dtype_traits<outType>::getName()
+                        << " -D inType_" << dtype_traits<inType>::getName()
+                        << " -D outType_" << dtype_traits<outType>::getName()
+                        << " -D SAME_DIMS=" << same_dims;
+                    if (std::is_same<inType, double>::value  ||
+                        std::is_same<inType, cdouble>::value ||
+                        std::is_same<outType, double>::value ||
+                        std::is_same<outType, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    Program prog;
+                    buildProgram(prog, copy_cl, copy_cl_len, options.str());
+                    cpyProgs[device]   = new Program(prog);
+                    cpyKernels[device] = new Kernel(*cpyProgs[device], "copy");
+                });
+
+        NDRange local(DIM0, DIM1);
+        size_t local_size[] = {DIM0, DIM1};
+
+        local_size[0] *= local_size[1];
+        if (ndims == 1) {
+            local_size[1] = 1;
+        }
 
-            local_size[0] *= local_size[1];
-            if (ndims == 1) {
-                local_size[1] = 1;
-            }
+        int blk_x = divup(dst.info.dims[0], local_size[0]);
+        int blk_y = divup(dst.info.dims[1], local_size[1]);
+
+        NDRange global(blk_x * dst.info.dims[2] * DIM0,
+                blk_y * dst.info.dims[3] * DIM1);
+
+        dims_t trgt_dims;
+        if (same_dims) {
+            trgt_dims= {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2], dst.info.dims[3]}};
+        } else {
+            dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
+            dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
+            dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
+            dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
+            trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
+        }
 
-            int blk_x = divup(dst.info.dims[0], local_size[0]);
-            int blk_y = divup(dst.info.dims[1], local_size[1]);
-
-            NDRange global(blk_x * dst.info.dims[2] * DIM0,
-                    blk_y * dst.info.dims[3] * DIM1);
-
-            dims_t trgt_dims;
-            if (same_dims) {
-                trgt_dims= {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2], dst.info.dims[3]}};
-            } else {
-                dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
-                dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
-                dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
-                dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
-                trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
-            }
+        auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                  outType, float, dims_t,
+                                  int, int
+                                  >(*cpyKernels[device]);
 
-            auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                      outType, float, dims_t,
-                                      int, int
-                                     >(*cpyKernels[device]);
-
-            copyOp(EnqueueArgs(getQueue(), global, local),
-                   *dst.data, dst.info, *src.data, src.info,
-                   default_value, (float)factor, trgt_dims, blk_x, blk_y);
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        copyOp(EnqueueArgs(getQueue(), global, local),
+                *dst.data, dst.info, *src.data, src.info,
+                default_value, (float)factor, trgt_dims, blk_x, blk_y);
+        CL_DEBUG_FINISH(getQueue());
     }
 
 }
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 7ed24da55b..10200e2aa7 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -42,59 +42,54 @@ namespace opencl
         template <typename T>
         void moments(Param out, const Param in, af_moment_type moment)
         {
-            try {
-                std::string ref_name =
-                std::string("moments_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(out.info.dims[0]);
-
-                int device = getActiveDeviceId();
-                kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-                kc_entry_t entry;
-                if (idx == kernelCaches[device].end()) {
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName();
-                    options << " -D MOMENTS_SZ=" << out.info.dims[0];
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+            std::string ref_name =
+            std::string("moments_") +
+            std::string(dtype_traits<T>::getName()) +
+            std::string("_") +
+            std::to_string(out.info.dims[0]);
+
+            int device = getActiveDeviceId();
+            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+            kc_entry_t entry;
+            if (idx == kernelCaches[device].end()) {
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName();
+                options << " -D MOMENTS_SZ=" << out.info.dims[0];
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 
 
-                    Program prog;
-                    buildProgram(prog, moments_cl, moments_cl_len, options.str());
+                Program prog;
+                buildProgram(prog, moments_cl, moments_cl_len, options.str());
 
-                    entry.prog = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "moments_kernel");
+                entry.prog = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "moments_kernel");
 
-                    kernelCaches[device][ref_name] = entry;
-                } else {
-                    entry = idx->second;
-                }
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
+            }
 
 
-                auto momentsp = KernelFunctor<Buffer, const KParam,
-                                              const Buffer, const KParam,
-                                              const int, const int>(*entry.ker);
+            auto momentsp = KernelFunctor<Buffer, const KParam,
+                                          const Buffer, const KParam,
+                                          const int, const int>(*entry.ker);
 
-                NDRange local(THREADS, 1, 1);
-                NDRange global(in.info.dims[1] * local[0] ,
-                               in.info.dims[2] * in.info.dims[3] * local[1] );
+            NDRange local(THREADS, 1, 1);
+            NDRange global(in.info.dims[1] * local[0] ,
+                            in.info.dims[2] * in.info.dims[3] * local[1] );
 
-                bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
+            bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
 
-                momentsp(EnqueueArgs(getQueue(), global, local),
-                          *out.data, out.info, *in.data, in.info,
-                          (int)moment, (int)pBatch);
+            momentsp(EnqueueArgs(getQueue(), global, local),
+                      *out.data, out.info, *in.data, in.info,
+                      (int)moment, (int)pBatch);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 2fa6b23d5c..3f129ffa9e 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -48,67 +48,62 @@ void morph(Param         out,
         const Param      in,
         const Param      mask)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> morProgs;
-        static std::map<int, Kernel*> morKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                ToNumStr<T> toNumStr;
-                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D isDilation="<< isDilation
-                        << " -D init=" << toNumStr(init)
-                        << " -D windLen=" << windLen;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                morProgs[device]   = new Program(prog);
-                morKernels[device] = new Kernel(*morProgs[device], "morph");
-            });
-
-        auto morphOp = KernelFunctor<Buffer, KParam,
-                                   Buffer, KParam,
-                                   Buffer, cl::LocalSpaceArg,
-                                   int, int
-                                  >(*morKernels[device]);
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
-        // launch batch * blk_x blocks along x dimension
-        NDRange global(blk_x * THREADS_X * in.info.dims[2],
-                       blk_y * THREADS_Y * in.info.dims[3]);
-
-        // copy mask/filter to constant memory
-        cl_int se_size   = sizeof(T)*windLen*windLen;
-        cl::Buffer *mBuff = bufferAlloc(se_size);
-        getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
-
-        // calculate shared memory size
-        const int halo    = windLen/2;
-        const int padding = 2*halo;
-        const int locLen  = THREADS_X + padding + 1;
-        const int locSize = locLen * (THREADS_Y+padding);
-
-        morphOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, *mBuff,
-                cl::Local(locSize*sizeof(T)), blk_x, blk_y);
-
-        bufferFree(mBuff);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> morProgs;
+    static std::map<int, Kernel*> morKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+            ToNumStr<T> toNumStr;
+            T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D isDilation="<< isDilation
+                    << " -D init=" << toNumStr(init)
+                    << " -D windLen=" << windLen;
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, morph_cl, morph_cl_len, options.str());
+            morProgs[device]   = new Program(prog);
+            morKernels[device] = new Kernel(*morProgs[device], "morph");
+        });
+
+    auto morphOp = KernelFunctor<Buffer, KParam,
+                                Buffer, KParam,
+                                Buffer, cl::LocalSpaceArg,
+                                int, int
+                              >(*morKernels[device]);
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * THREADS_X * in.info.dims[2],
+                    blk_y * THREADS_Y * in.info.dims[3]);
+
+    // copy mask/filter to constant memory
+    cl_int se_size   = sizeof(T)*windLen*windLen;
+    cl::Buffer *mBuff = bufferAlloc(se_size);
+    getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
+
+    // calculate shared memory size
+    const int halo    = windLen/2;
+    const int padding = 2*halo;
+    const int locLen  = THREADS_X + padding + 1;
+    const int locSize = locLen * (THREADS_Y+padding);
+
+    morphOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *in.data, in.info, *mBuff,
+            cl::Local(locSize*sizeof(T)), blk_x, blk_y);
+
+    bufferFree(mBuff);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, bool isDilation, int windLen>
@@ -116,68 +111,63 @@ void morph3d(Param       out,
         const Param      in,
         const Param      mask)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  morProgs;
-        static std::map<int, Kernel*> morKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                ToNumStr<T> toNumStr;
-                T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D isDilation="<< isDilation
-                        << " -D init=" << toNumStr(init)
-                        << " -D windLen=" << windLen;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, morph_cl, morph_cl_len, options.str());
-                morProgs[device]   = new Program(prog);
-                morKernels[device] = new Kernel(*morProgs[device], "morph3d");
-            });
-
-        auto morphOp = KernelFunctor<Buffer, KParam,
-                                   Buffer, KParam,
-                                   Buffer, cl::LocalSpaceArg, int
-                                  >(*morKernels[device]);
-
-        NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
-
-        int blk_x = divup(in.info.dims[0], CUBE_X);
-        int blk_y = divup(in.info.dims[1], CUBE_Y);
-        int blk_z = divup(in.info.dims[2], CUBE_Z);
-        // launch batch * blk_x blocks along x dimension
-        NDRange global(blk_x * CUBE_X * in.info.dims[3],
-                       blk_y * CUBE_Y,
-                       blk_z * CUBE_Z);
-
-        // copy mask/filter to constant memory
-        cl_int se_size   = sizeof(T)*windLen*windLen*windLen;
-        cl::Buffer *mBuff = bufferAlloc(se_size);
-        getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
-
-        // calculate shared memory size
-        const int halo    = windLen/2;
-        const int padding = 2*halo;
-        const int locLen  = CUBE_X+padding+1;
-        const int locArea = locLen *(CUBE_Y+padding);
-        const int locSize = locArea*(CUBE_Z+padding);
-
-        morphOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info,
-                *mBuff, cl::Local(locSize*sizeof(T)), blk_x);
-
-        bufferFree(mBuff);
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  morProgs;
+    static std::map<int, Kernel*> morKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+            ToNumStr<T> toNumStr;
+            T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D isDilation="<< isDilation
+                    << " -D init=" << toNumStr(init)
+                    << " -D windLen=" << windLen;
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, morph_cl, morph_cl_len, options.str());
+            morProgs[device]   = new Program(prog);
+            morKernels[device] = new Kernel(*morProgs[device], "morph3d");
+        });
+
+    auto morphOp = KernelFunctor<Buffer, KParam,
+                                Buffer, KParam,
+                                Buffer, cl::LocalSpaceArg, int
+                              >(*morKernels[device]);
+
+    NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
+
+    int blk_x = divup(in.info.dims[0], CUBE_X);
+    int blk_y = divup(in.info.dims[1], CUBE_Y);
+    int blk_z = divup(in.info.dims[2], CUBE_Z);
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * CUBE_X * in.info.dims[3],
+                    blk_y * CUBE_Y,
+                    blk_z * CUBE_Z);
+
+    // copy mask/filter to constant memory
+    cl_int se_size   = sizeof(T)*windLen*windLen*windLen;
+    cl::Buffer *mBuff = bufferAlloc(se_size);
+    getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
+
+    // calculate shared memory size
+    const int halo    = windLen/2;
+    const int padding = 2*halo;
+    const int locLen  = CUBE_X+padding+1;
+    const int locArea = locLen *(CUBE_Y+padding);
+    const int locSize = locArea*(CUBE_Z+padding);
+
+    morphOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, *in.data, in.info,
+            *mBuff, cl::Local(locSize*sizeof(T)), blk_x);
+
+    bufferFree(mBuff);
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 3ad66dfa6c..328708cfe9 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -37,137 +37,132 @@ void nearest_neighbour(Param idx,
                        const dim_t dist_dim,
                        const unsigned n_dist)
 {
-    try {
-        const unsigned feat_len = query.info.dims[dist_dim];
-        const To max_dist = maxval<To>();
-
-        // Determine maximum feat_len capable of using shared memory (faster)
-        cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
-        size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
-        size_t ltrain_sz = THREADS * feat_len * sizeof(T);
-        bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
-        size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
-
-        unsigned unroll_len = nextpow2(feat_len);
-        if (unroll_len != feat_len) unroll_len = 0;
-
-        std::string ref_name =
-            std::string("knn_") +
-            std::to_string(dist_type) +
-            std::string("_") +
-            std::to_string(use_lmem) +
-            std::string("_") +
-            std::string(dtype_traits<T>::getName()) +
-            std::string("_") +
-            std::to_string(unroll_len);
-
-        int device = getActiveDeviceId();
-        kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
-
-        kc_entry_t entry;
-        if (cache_idx == kernelCaches[device].end()) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D To=" << dtype_traits<To>::getName()
-                        << " -D THREADS=" << THREADS
-                        << " -D FEAT_LEN=" << unroll_len;
-
-                switch(dist_type) {
-                    case AF_SAD: options <<" -D DISTOP=_sad_"; break;
-                    case AF_SSD: options <<" -D DISTOP=_ssd_"; break;
-                    case AF_SHD: options <<" -D DISTOP=_shd_ -D __SHD__";
-                                 break;
-                    default: break;
-                }
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                if (use_lmem)
-                    options << " -D USE_LOCAL_MEM";
-
-                cl::Program prog;
-                buildProgram(prog,
-                             nearest_neighbour_cl,
-                             nearest_neighbour_cl_len,
-                             options.str());
-
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel[3];
-
-                entry.ker[0] = Kernel(*entry.prog, "nearest_neighbour_unroll");
-                entry.ker[1] = Kernel(*entry.prog, "nearest_neighbour");
-                entry.ker[2] = Kernel(*entry.prog, "select_matches");
-
-                kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = cache_idx->second;
-        }
-
-        const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
-
-        const unsigned nquery = query.info.dims[sample_dim];
-        const unsigned ntrain = train.info.dims[sample_dim];
-
-        unsigned nblk = divup(ntrain, THREADS);
-        const NDRange local(THREADS, 1);
-        const NDRange global(nblk * THREADS, 1);
-
-        cl::Buffer *d_blk_idx  = bufferAlloc(nblk * nquery * sizeof(unsigned));
-        cl::Buffer *d_blk_dist = bufferAlloc(nblk * nquery * sizeof(To));
-
-        // For each query vector, find training vector with smallest Hamming
-        // distance per CUDA block
-        if (unroll_len > 0) {
-            auto huOp = KernelFunctor<Buffer, Buffer,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    const To,
-                                    LocalSpaceArg> (entry.ker[0]);
-
-            huOp(EnqueueArgs(getQueue(), global, local),
-                 *d_blk_idx, *d_blk_dist,
-                 *query.data, query.info, *train.data, train.info,
-                 max_dist, cl::Local(lmem_sz));
-        }
-        else {
-            auto hmOp = KernelFunctor<Buffer, Buffer,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    const To, const unsigned,
-                                    LocalSpaceArg> (entry.ker[1]);
-
-            hmOp(EnqueueArgs(getQueue(), global, local),
-                 *d_blk_idx, *d_blk_dist,
-                 *query.data, query.info, *train.data, train.info,
-                 max_dist, feat_len, cl::Local(lmem_sz));
-        }
-        CL_DEBUG_FINISH(getQueue());
-
-        const NDRange local_sm(32, 8);
-        const NDRange global_sm(divup(nquery, 32) * 32, 8);
-
-        // Reduce all smallest Hamming distances from each block and store final
-        // best match
-        auto smOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                                const unsigned, const unsigned,
-                                const To> (entry.ker[2]);
-
-        smOp(EnqueueArgs(getQueue(), global_sm, local_sm),
-             *idx.data, *dist.data,
-             *d_blk_idx, *d_blk_dist,
-             nquery, nblk, max_dist);
-        CL_DEBUG_FINISH(getQueue());
-
-        bufferFree(d_blk_idx);
-        bufferFree(d_blk_dist);
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+    const unsigned feat_len = query.info.dims[dist_dim];
+    const To max_dist = maxval<To>();
+
+    // Determine maximum feat_len capable of using shared memory (faster)
+    cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
+    size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
+    size_t ltrain_sz = THREADS * feat_len * sizeof(T);
+    bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
+    size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
+
+    unsigned unroll_len = nextpow2(feat_len);
+    if (unroll_len != feat_len) unroll_len = 0;
+
+    std::string ref_name =
+        std::string("knn_") +
+        std::to_string(dist_type) +
+        std::string("_") +
+        std::to_string(use_lmem) +
+        std::string("_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::string("_") +
+        std::to_string(unroll_len);
+
+    int device = getActiveDeviceId();
+    kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
+
+    kc_entry_t entry;
+    if (cache_idx == kernelCaches[device].end()) {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D To=" << dtype_traits<To>::getName()
+                    << " -D THREADS=" << THREADS
+                    << " -D FEAT_LEN=" << unroll_len;
+
+            switch(dist_type) {
+                case AF_SAD: options <<" -D DISTOP=_sad_"; break;
+                case AF_SSD: options <<" -D DISTOP=_ssd_"; break;
+                case AF_SHD: options <<" -D DISTOP=_shd_ -D __SHD__";
+                              break;
+                default: break;
+            }
+
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            if (use_lmem)
+                options << " -D USE_LOCAL_MEM";
+
+            cl::Program prog;
+            buildProgram(prog,
+                          nearest_neighbour_cl,
+                          nearest_neighbour_cl_len,
+                          options.str());
+
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel[3];
+
+            entry.ker[0] = Kernel(*entry.prog, "nearest_neighbour_unroll");
+            entry.ker[1] = Kernel(*entry.prog, "nearest_neighbour");
+            entry.ker[2] = Kernel(*entry.prog, "select_matches");
+
+            kernelCaches[device][ref_name] = entry;
+    } else {
+        entry = cache_idx->second;
     }
+
+    const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
+
+    const unsigned nquery = query.info.dims[sample_dim];
+    const unsigned ntrain = train.info.dims[sample_dim];
+
+    unsigned nblk = divup(ntrain, THREADS);
+    const NDRange local(THREADS, 1);
+    const NDRange global(nblk * THREADS, 1);
+
+    cl::Buffer *d_blk_idx  = bufferAlloc(nblk * nquery * sizeof(unsigned));
+    cl::Buffer *d_blk_dist = bufferAlloc(nblk * nquery * sizeof(To));
+
+    // For each query vector, find training vector with smallest Hamming
+    // distance per CUDA block
+    if (unroll_len > 0) {
+        auto huOp = KernelFunctor<Buffer, Buffer,
+                                Buffer, KParam,
+                                Buffer, KParam,
+                                const To,
+                                LocalSpaceArg> (entry.ker[0]);
+
+        huOp(EnqueueArgs(getQueue(), global, local),
+              *d_blk_idx, *d_blk_dist,
+              *query.data, query.info, *train.data, train.info,
+              max_dist, cl::Local(lmem_sz));
+    }
+    else {
+        auto hmOp = KernelFunctor<Buffer, Buffer,
+                                Buffer, KParam,
+                                Buffer, KParam,
+                                const To, const unsigned,
+                                LocalSpaceArg> (entry.ker[1]);
+
+        hmOp(EnqueueArgs(getQueue(), global, local),
+              *d_blk_idx, *d_blk_dist,
+              *query.data, query.info, *train.data, train.info,
+              max_dist, feat_len, cl::Local(lmem_sz));
+    }
+    CL_DEBUG_FINISH(getQueue());
+
+    const NDRange local_sm(32, 8);
+    const NDRange global_sm(divup(nquery, 32) * 32, 8);
+
+    // Reduce all smallest Hamming distances from each block and store final
+    // best match
+    auto smOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
+                            const unsigned, const unsigned,
+                            const To> (entry.ker[2]);
+
+    smOp(EnqueueArgs(getQueue(), global_sm, local_sm),
+          *idx.data, *dist.data,
+          *d_blk_idx, *d_blk_dist,
+          nquery, nblk, max_dist);
+    CL_DEBUG_FINISH(getQueue());
+
+    bufferFree(d_blk_idx);
+    bufferFree(d_blk_dist);
 }
 
 } // namespace kernel
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index b7350821ae..5c312db5e1 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -101,423 +101,418 @@ void orb(unsigned* out_feat,
          const unsigned levels,
          const bool blur_img)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> orbProgs;
-        static std::map<int, Kernel*>  hrKernel;
-        static std::map<int, Kernel*>  kfKernel;
-        static std::map<int, Kernel*>  caKernel;
-        static std::map<int, Kernel*>  eoKernel;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> orbProgs;
+    static std::map<int, Kernel*>  hrKernel;
+    static std::map<int, Kernel*>  kfKernel;
+    static std::map<int, Kernel*>  caKernel;
+    static std::map<int, Kernel*>  eoKernel;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once( compileFlags[device], [device] () {
+    std::call_once( compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D BLOCK_SIZE=" << ORB_THREADS_X;
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D BLOCK_SIZE=" << ORB_THREADS_X;
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, orb_cl, orb_cl_len, options.str());
-                orbProgs[device] = new Program(prog);
-
-                hrKernel[device] = new Kernel(*orbProgs[device], "harris_response");
-                kfKernel[device] = new Kernel(*orbProgs[device], "keep_features");
-                caKernel[device] = new Kernel(*orbProgs[device], "centroid_angle");
-                eoKernel[device] = new Kernel(*orbProgs[device], "extract_orb");
-            });
-
-        unsigned patch_size = REF_PAT_SIZE;
-
-        unsigned min_side = std::min(image.info.dims[0], image.info.dims[1]);
-        unsigned max_levels = 0;
-        float scl_sum = 0.f;
-        for (unsigned i = 0; i < levels; i++) {
-            min_side /= scl_fctr;
-
-            // Minimum image side for a descriptor to be computed
-            if (min_side < patch_size || max_levels == levels) break;
-
-            max_levels++;
-            scl_sum += 1.f / (float)pow(scl_fctr,(float)i);
-        }
-
-        vector<cl::Buffer*> d_x_pyr(max_levels);
-        vector<cl::Buffer*> d_y_pyr(max_levels);
-        vector<cl::Buffer*> d_score_pyr(max_levels);
-        vector<cl::Buffer*> d_ori_pyr(max_levels);
-        vector<cl::Buffer*> d_size_pyr(max_levels);
-        vector<cl::Buffer*> d_desc_pyr(max_levels);
-
-        vector<unsigned> feat_pyr(max_levels);
-        unsigned total_feat = 0;
-
-        // Compute number of features to keep for each level
-        vector<unsigned> lvl_best(max_levels);
-        unsigned feat_sum = 0;
-        for (unsigned i = 0; i < max_levels-1; i++) {
-            float lvl_scl = (float)pow(scl_fctr,(float)i);
-            lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl);
-            feat_sum += lvl_best[i];
-        }
-        lvl_best[max_levels-1] = max_feat - feat_sum;
-
-        // Maintain a reference to previous level image
-        Param prev_img;
-        Param lvl_img;
-
-        const unsigned gauss_len = 9;
-        T* h_gauss = nullptr;
-        Param gauss_filter;
-        gauss_filter.data = nullptr;
-
-        for (unsigned i = 0; i < max_levels; i++) {
-            const float lvl_scl = (float)pow(scl_fctr,(float)i);
-
-            if (i == 0) {
-                // First level is used in its original size
-                lvl_img = image;
-
-                prev_img = image;
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
             }
-            else if (i > 0) {
-                // Resize previous level image to current level dimensions
-                lvl_img.info.dims[0] = round(image.info.dims[0] / lvl_scl);
-                lvl_img.info.dims[1] = round(image.info.dims[1] / lvl_scl);
 
-                lvl_img.info.strides[0] = 1;
-                lvl_img.info.strides[1] = lvl_img.info.dims[0];
+            cl::Program prog;
+            buildProgram(prog, orb_cl, orb_cl_len, options.str());
+            orbProgs[device] = new Program(prog);
 
-                for (int k = 2; k < 4; k++) {
-                    lvl_img.info.dims[k] = 1;
-                    lvl_img.info.strides[k] = lvl_img.info.dims[k - 1] * lvl_img.info.strides[k - 1];
-                }
+            hrKernel[device] = new Kernel(*orbProgs[device], "harris_response");
+            kfKernel[device] = new Kernel(*orbProgs[device], "keep_features");
+            caKernel[device] = new Kernel(*orbProgs[device], "centroid_angle");
+            eoKernel[device] = new Kernel(*orbProgs[device], "extract_orb");
+        });
 
-                lvl_img.info.offset = 0;
-                lvl_img.data = bufferAlloc(lvl_img.info.dims[3] * lvl_img.info.strides[3] * sizeof(T));
+    unsigned patch_size = REF_PAT_SIZE;
 
-                resize<T, AF_INTERP_BILINEAR>(lvl_img, prev_img);
+    unsigned min_side = std::min(image.info.dims[0], image.info.dims[1]);
+    unsigned max_levels = 0;
+    float scl_sum = 0.f;
+    for (unsigned i = 0; i < levels; i++) {
+        min_side /= scl_fctr;
 
-                if (i > 1)
-                   bufferFree(prev_img.data);
-                prev_img = lvl_img;
-            }
+        // Minimum image side for a descriptor to be computed
+        if (min_side < patch_size || max_levels == levels) break;
 
-            unsigned lvl_feat = 0;
-            Param d_x_feat, d_y_feat, d_score_feat;
-
-            // Round feature size to nearest odd integer
-            float size = 2.f * floor(patch_size / 2.f) + 1.f;
+        max_levels++;
+        scl_sum += 1.f / (float)pow(scl_fctr,(float)i);
+    }
 
-            // Avoid keeping features that might be too wide and might not fit on
-            // the image, sqrt(2.f) is the radius when angle is 45 degrees and
-            // represents widest case possible
-            unsigned edge = ceil(size * sqrt(2.f) / 2.f);
+    vector<cl::Buffer*> d_x_pyr(max_levels);
+    vector<cl::Buffer*> d_y_pyr(max_levels);
+    vector<cl::Buffer*> d_score_pyr(max_levels);
+    vector<cl::Buffer*> d_ori_pyr(max_levels);
+    vector<cl::Buffer*> d_size_pyr(max_levels);
+    vector<cl::Buffer*> d_desc_pyr(max_levels);
+
+    vector<unsigned> feat_pyr(max_levels);
+    unsigned total_feat = 0;
+
+    // Compute number of features to keep for each level
+    vector<unsigned> lvl_best(max_levels);
+    unsigned feat_sum = 0;
+    for (unsigned i = 0; i < max_levels-1; i++) {
+        float lvl_scl = (float)pow(scl_fctr,(float)i);
+        lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl);
+        feat_sum += lvl_best[i];
+    }
+    lvl_best[max_levels-1] = max_feat - feat_sum;
 
-            // Detect FAST features
-            fast<T, true>(9, &lvl_feat, d_x_feat, d_y_feat, d_score_feat,
-                          lvl_img, fast_thr, 0.15f, edge);
+    // Maintain a reference to previous level image
+    Param prev_img;
+    Param lvl_img;
 
-            if (lvl_feat == 0) {
-                feat_pyr[i] = 0;
+    const unsigned gauss_len = 9;
+    T* h_gauss = nullptr;
+    Param gauss_filter;
+    gauss_filter.data = nullptr;
 
-                if (i > 0 && i == max_levels-1)
-                    bufferFree(lvl_img.data);
+    for (unsigned i = 0; i < max_levels; i++) {
+        const float lvl_scl = (float)pow(scl_fctr,(float)i);
 
-                continue;
-            }
+        if (i == 0) {
+            // First level is used in its original size
+            lvl_img = image;
 
-            bufferFree(d_score_feat.data);
+            prev_img = image;
+        }
+        else if (i > 0) {
+            // Resize previous level image to current level dimensions
+            lvl_img.info.dims[0] = round(image.info.dims[0] / lvl_scl);
+            lvl_img.info.dims[1] = round(image.info.dims[1] / lvl_scl);
 
-            unsigned usable_feat = 0;
-            cl::Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
-            getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
+            lvl_img.info.strides[0] = 1;
+            lvl_img.info.strides[1] = lvl_img.info.dims[0];
 
-            cl::Buffer* d_x_harris = bufferAlloc(lvl_feat * sizeof(float));
-            cl::Buffer* d_y_harris = bufferAlloc(lvl_feat * sizeof(float));
-            cl::Buffer* d_score_harris = bufferAlloc(lvl_feat * sizeof(float));
+            for (int k = 2; k < 4; k++) {
+                lvl_img.info.dims[k] = 1;
+                lvl_img.info.strides[k] = lvl_img.info.dims[k - 1] * lvl_img.info.strides[k - 1];
+            }
 
-            // Calculate Harris responses
-            // Good block_size >= 7 (must be an odd number)
-            const int blk_x = divup(lvl_feat, ORB_THREADS_X);
-            const NDRange local(ORB_THREADS_X, ORB_THREADS_Y);
-            const NDRange global(blk_x * ORB_THREADS_X, ORB_THREADS_Y);
+            lvl_img.info.offset = 0;
+            lvl_img.data = bufferAlloc(lvl_img.info.dims[3] * lvl_img.info.strides[3] * sizeof(T));
 
-            unsigned block_size = 7;
-            float k_thr = 0.04f;
+            resize<T, AF_INTERP_BILINEAR>(lvl_img, prev_img);
 
-            auto hrOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, const unsigned,
-                                    Buffer, Buffer, KParam,
-                                    const unsigned, const float, const unsigned> (*hrKernel[device]);
+            if (i > 1)
+                bufferFree(prev_img.data);
+            prev_img = lvl_img;
+        }
 
-            hrOp(EnqueueArgs(getQueue(), global, local),
-                 *d_x_harris, *d_y_harris, *d_score_harris,
-                 *d_x_feat.data, *d_y_feat.data, lvl_feat,
-                 *d_usable_feat, *lvl_img.data, lvl_img.info,
-                 block_size, k_thr, patch_size);
-            CL_DEBUG_FINISH(getQueue());
+        unsigned lvl_feat = 0;
+        Param d_x_feat, d_y_feat, d_score_feat;
 
-            getQueue().enqueueReadBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
+        // Round feature size to nearest odd integer
+        float size = 2.f * floor(patch_size / 2.f) + 1.f;
 
-            if (lvl_feat > 0) { //This is just to supress warnings
-                bufferFree(d_x_feat.data);
-                bufferFree(d_y_feat.data);
-                bufferFree(d_usable_feat);
-            }
+        // Avoid keeping features that might be too wide and might not fit on
+        // the image, sqrt(2.f) is the radius when angle is 45 degrees and
+        // represents widest case possible
+        unsigned edge = ceil(size * sqrt(2.f) / 2.f);
 
-            if (usable_feat == 0) {
-                feat_pyr[i] = 0;
+        // Detect FAST features
+        fast<T, true>(9, &lvl_feat, d_x_feat, d_y_feat, d_score_feat,
+                      lvl_img, fast_thr, 0.15f, edge);
 
-                bufferFree(d_x_harris);
-                bufferFree(d_y_harris);
-                bufferFree(d_score_harris);
+        if (lvl_feat == 0) {
+            feat_pyr[i] = 0;
 
-                if (i > 0 && i == max_levels-1)
-                    bufferFree(lvl_img.data);
+            if (i > 0 && i == max_levels-1)
+                bufferFree(lvl_img.data);
 
-                continue;
-            }
+            continue;
+        }
 
-            // Sort features according to Harris responses
-            Param d_harris_sorted;
-            Param d_harris_idx;
+        bufferFree(d_score_feat.data);
 
-            d_harris_sorted.info.dims[0] = usable_feat;
-            d_harris_idx.info.dims[0] = usable_feat;
-            d_harris_sorted.info.strides[0] = 1;
-            d_harris_idx.info.strides[0] = 1;
+        unsigned usable_feat = 0;
+        cl::Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
+        getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
 
-            for (int k = 1; k < 4; k++) {
-                d_harris_sorted.info.dims[k] = 1;
-                d_harris_idx.info.dims[k] = 1;
-                d_harris_sorted.info.strides[k] = d_harris_sorted.info.dims[k - 1] * d_harris_sorted.info.strides[k - 1];
-                d_harris_idx.info.strides[k] = d_harris_idx.info.dims[k - 1] * d_harris_idx.info.strides[k - 1];
-            }
+        cl::Buffer* d_x_harris = bufferAlloc(lvl_feat * sizeof(float));
+        cl::Buffer* d_y_harris = bufferAlloc(lvl_feat * sizeof(float));
+        cl::Buffer* d_score_harris = bufferAlloc(lvl_feat * sizeof(float));
 
-            d_harris_sorted.info.offset = 0;
-            d_harris_idx.info.offset = 0;
-            d_harris_sorted.data = d_score_harris;
-            // Create indices using range
-            d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
-            kernel::range<uint>(d_harris_idx, 0);
+        // Calculate Harris responses
+        // Good block_size >= 7 (must be an odd number)
+        const int blk_x = divup(lvl_feat, ORB_THREADS_X);
+        const NDRange local(ORB_THREADS_X, ORB_THREADS_Y);
+        const NDRange global(blk_x * ORB_THREADS_X, ORB_THREADS_Y);
 
-            kernel::sort0ByKey<float, uint>(d_harris_sorted, d_harris_idx, false);
+        unsigned block_size = 7;
+        float k_thr = 0.04f;
 
-            cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
-            cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
-            cl::Buffer* d_score_lvl = bufferAlloc(usable_feat * sizeof(float));
+        auto hrOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                Buffer, Buffer, const unsigned,
+                                Buffer, Buffer, KParam,
+                                const unsigned, const float, const unsigned> (*hrKernel[device]);
 
-            usable_feat = min(usable_feat, lvl_best[i]);
+        hrOp(EnqueueArgs(getQueue(), global, local),
+              *d_x_harris, *d_y_harris, *d_score_harris,
+              *d_x_feat.data, *d_y_feat.data, lvl_feat,
+              *d_usable_feat, *lvl_img.data, lvl_img.info,
+              block_size, k_thr, patch_size);
+        CL_DEBUG_FINISH(getQueue());
 
-            // Keep only features with higher Harris responses
-            const int keep_blk = divup(usable_feat, ORB_THREADS);
-            const NDRange local_keep(ORB_THREADS, 1);
-            const NDRange global_keep(keep_blk * ORB_THREADS, 1);
+        getQueue().enqueueReadBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
 
-            auto kfOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer, Buffer,
-                                    const unsigned> (*kfKernel[device]);
+        if (lvl_feat > 0) { //This is just to supress warnings
+            bufferFree(d_x_feat.data);
+            bufferFree(d_y_feat.data);
+            bufferFree(d_usable_feat);
+        }
 
-            kfOp(EnqueueArgs(getQueue(), global_keep, local_keep),
-                 *d_x_lvl, *d_y_lvl, *d_score_lvl,
-                 *d_x_harris, *d_y_harris, *d_harris_sorted.data, *d_harris_idx.data,
-                 usable_feat);
-            CL_DEBUG_FINISH(getQueue());
+        if (usable_feat == 0) {
+            feat_pyr[i] = 0;
 
             bufferFree(d_x_harris);
             bufferFree(d_y_harris);
-            bufferFree(d_harris_sorted.data);
-            bufferFree(d_harris_idx.data);
+            bufferFree(d_score_harris);
 
-            cl::Buffer* d_ori_lvl = bufferAlloc(usable_feat * sizeof(float));
-            cl::Buffer* d_size_lvl = bufferAlloc(usable_feat * sizeof(float));
-
-            // Compute orientation of features
-            const int centroid_blk_x = divup(usable_feat, ORB_THREADS_X);
-            const NDRange local_centroid(ORB_THREADS_X, ORB_THREADS_Y);
-            const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X, ORB_THREADS_Y);
-
-            auto caOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                    const unsigned, Buffer, KParam,
-                                    const unsigned> (*caKernel[device]);
-
-            caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
-                 *d_x_lvl, *d_y_lvl, *d_ori_lvl,
-                 usable_feat, *lvl_img.data, lvl_img.info,
-                 patch_size);
-            CL_DEBUG_FINISH(getQueue());
-
-            Param lvl_filt;
-            Param lvl_tmp;
+            if (i > 0 && i == max_levels-1)
+                bufferFree(lvl_img.data);
 
-            if (blur_img) {
-                lvl_filt = lvl_img;
-                lvl_tmp = lvl_img;
+            continue;
+        }
 
-                lvl_filt.data = bufferAlloc(lvl_filt.info.dims[0] * lvl_filt.info.dims[1] * sizeof(T));
-                lvl_tmp.data = bufferAlloc(lvl_tmp.info.dims[0] * lvl_tmp.info.dims[1] * sizeof(T));
+        // Sort features according to Harris responses
+        Param d_harris_sorted;
+        Param d_harris_idx;
 
-                // Calculate a separable Gaussian kernel
-                if (h_gauss == nullptr) {
-                    h_gauss = new T[gauss_len];
-                    gaussian1D(h_gauss, gauss_len, 2.f);
-                    gauss_filter.info.dims[0] = gauss_len;
-                    gauss_filter.info.strides[0] = 1;
+        d_harris_sorted.info.dims[0] = usable_feat;
+        d_harris_idx.info.dims[0] = usable_feat;
+        d_harris_sorted.info.strides[0] = 1;
+        d_harris_idx.info.strides[0] = 1;
 
-                    for (int k = 1; k < 4; k++) {
-                        gauss_filter.info.dims[k] = 1;
-                        gauss_filter.info.strides[k] = gauss_filter.info.dims[k - 1] * gauss_filter.info.strides[k - 1];
-                    }
+        for (int k = 1; k < 4; k++) {
+            d_harris_sorted.info.dims[k] = 1;
+            d_harris_idx.info.dims[k] = 1;
+            d_harris_sorted.info.strides[k] = d_harris_sorted.info.dims[k - 1] * d_harris_sorted.info.strides[k - 1];
+            d_harris_idx.info.strides[k] = d_harris_idx.info.dims[k - 1] * d_harris_idx.info.strides[k - 1];
+        }
 
-                    int gauss_elem = gauss_filter.info.strides[3] * gauss_filter.info.dims[3];
-                    gauss_filter.data = bufferAlloc(gauss_elem * sizeof(T));
-                    getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0, gauss_elem * sizeof(T), h_gauss);
+        d_harris_sorted.info.offset = 0;
+        d_harris_idx.info.offset = 0;
+        d_harris_sorted.data = d_score_harris;
+        // Create indices using range
+        d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
+        kernel::range<uint>(d_harris_idx, 0);
+
+        kernel::sort0ByKey<float, uint>(d_harris_sorted, d_harris_idx, false);
+
+        cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
+        cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
+        cl::Buffer* d_score_lvl = bufferAlloc(usable_feat * sizeof(float));
+
+        usable_feat = min(usable_feat, lvl_best[i]);
+
+        // Keep only features with higher Harris responses
+        const int keep_blk = divup(usable_feat, ORB_THREADS);
+        const NDRange local_keep(ORB_THREADS, 1);
+        const NDRange global_keep(keep_blk * ORB_THREADS, 1);
+
+        auto kfOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer, Buffer,
+                                const unsigned> (*kfKernel[device]);
+
+        kfOp(EnqueueArgs(getQueue(), global_keep, local_keep),
+              *d_x_lvl, *d_y_lvl, *d_score_lvl,
+              *d_x_harris, *d_y_harris, *d_harris_sorted.data, *d_harris_idx.data,
+              usable_feat);
+        CL_DEBUG_FINISH(getQueue());
+
+        bufferFree(d_x_harris);
+        bufferFree(d_y_harris);
+        bufferFree(d_harris_sorted.data);
+        bufferFree(d_harris_idx.data);
+
+        cl::Buffer* d_ori_lvl = bufferAlloc(usable_feat * sizeof(float));
+        cl::Buffer* d_size_lvl = bufferAlloc(usable_feat * sizeof(float));
+
+        // Compute orientation of features
+        const int centroid_blk_x = divup(usable_feat, ORB_THREADS_X);
+        const NDRange local_centroid(ORB_THREADS_X, ORB_THREADS_Y);
+        const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X, ORB_THREADS_Y);
+
+        auto caOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                const unsigned, Buffer, KParam,
+                                const unsigned> (*caKernel[device]);
+
+        caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
+              *d_x_lvl, *d_y_lvl, *d_ori_lvl,
+              usable_feat, *lvl_img.data, lvl_img.info,
+              patch_size);
+        CL_DEBUG_FINISH(getQueue());
+
+        Param lvl_filt;
+        Param lvl_tmp;
+
+        if (blur_img) {
+            lvl_filt = lvl_img;
+            lvl_tmp = lvl_img;
+
+            lvl_filt.data = bufferAlloc(lvl_filt.info.dims[0] * lvl_filt.info.dims[1] * sizeof(T));
+            lvl_tmp.data = bufferAlloc(lvl_tmp.info.dims[0] * lvl_tmp.info.dims[1] * sizeof(T));
+
+            // Calculate a separable Gaussian kernel
+            if (h_gauss == nullptr) {
+                h_gauss = new T[gauss_len];
+                gaussian1D(h_gauss, gauss_len, 2.f);
+                gauss_filter.info.dims[0] = gauss_len;
+                gauss_filter.info.strides[0] = 1;
+
+                for (int k = 1; k < 4; k++) {
+                    gauss_filter.info.dims[k] = 1;
+                    gauss_filter.info.strides[k] = gauss_filter.info.dims[k - 1] * gauss_filter.info.strides[k - 1];
                 }
 
-                // Filter level image with Gaussian kernel to reduce noise sensitivity
-                convSep<T, convAccT, 0, false>(lvl_tmp, lvl_img, gauss_filter);
-                convSep<T, convAccT, 1, false>(lvl_filt, lvl_tmp, gauss_filter);
-
-                bufferFree(lvl_tmp.data);
-            }
-
-            // Compute ORB descriptors
-            cl::Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
-            {
-                vector<unsigned> h_desc_lvl(usable_feat * 8);
-                getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0, usable_feat * 8 * sizeof(unsigned), h_desc_lvl.data());
+                int gauss_elem = gauss_filter.info.strides[3] * gauss_filter.info.dims[3];
+                gauss_filter.data = bufferAlloc(gauss_elem * sizeof(T));
+                getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0, gauss_elem * sizeof(T), h_gauss);
             }
 
-            auto eoOp = KernelFunctor<Buffer, const unsigned,
-                                    Buffer, Buffer, Buffer, Buffer,
-                                    Buffer, KParam,
-                                    const float, const unsigned> (*eoKernel[device]);
-
-            if (blur_img) {
-                eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
-                     *d_desc_lvl, usable_feat,
-                     *d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
-                     *lvl_filt.data, lvl_filt.info,
-                     lvl_scl, patch_size);
-                CL_DEBUG_FINISH(getQueue());
-
-                bufferFree(lvl_filt.data);
-            }
-            else {
-                eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
-                     *d_desc_lvl, usable_feat,
-                     *d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
-                     *lvl_img.data, lvl_img.info,
-                     lvl_scl, patch_size);
-                CL_DEBUG_FINISH(getQueue());
-            }
+            // Filter level image with Gaussian kernel to reduce noise sensitivity
+            convSep<T, convAccT, 0, false>(lvl_tmp, lvl_img, gauss_filter);
+            convSep<T, convAccT, 1, false>(lvl_filt, lvl_tmp, gauss_filter);
 
-            // Store results to pyramids
-            total_feat += usable_feat;
-            feat_pyr[i] = usable_feat;
-            d_x_pyr[i] = d_x_lvl;
-            d_y_pyr[i] = d_y_lvl;
-            d_score_pyr[i] = d_score_lvl;
-            d_ori_pyr[i] = d_ori_lvl;
-            d_size_pyr[i] = d_size_lvl;
-            d_desc_pyr[i] = d_desc_lvl;
+            bufferFree(lvl_tmp.data);
+        }
 
-            if (i > 0 && i == max_levels-1)
-                bufferFree(lvl_img.data);
+        // Compute ORB descriptors
+        cl::Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
+        {
+            vector<unsigned> h_desc_lvl(usable_feat * 8);
+            getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0, usable_feat * 8 * sizeof(unsigned), h_desc_lvl.data());
         }
 
-        if (gauss_filter.data != nullptr)
-            bufferFree(gauss_filter.data);
-        if (h_gauss != nullptr)
-            delete[] h_gauss;
+        auto eoOp = KernelFunctor<Buffer, const unsigned,
+                                Buffer, Buffer, Buffer, Buffer,
+                                Buffer, KParam,
+                                const float, const unsigned> (*eoKernel[device]);
+
+        if (blur_img) {
+            eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
+                  *d_desc_lvl, usable_feat,
+                  *d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
+                  *lvl_filt.data, lvl_filt.info,
+                  lvl_scl, patch_size);
+            CL_DEBUG_FINISH(getQueue());
 
-        // If no features are found, set found features to 0 and return
-        if (total_feat == 0) {
-            *out_feat = 0;
-            return;
+            bufferFree(lvl_filt.data);
+        }
+        else {
+            eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
+                  *d_desc_lvl, usable_feat,
+                  *d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
+                  *lvl_img.data, lvl_img.info,
+                  lvl_scl, patch_size);
+            CL_DEBUG_FINISH(getQueue());
         }
 
-        // Allocate output memory
-        x_out.info.dims[0] = total_feat;
-        x_out.info.strides[0] = 1;
-        y_out.info.dims[0] = total_feat;
-        y_out.info.strides[0] = 1;
-        score_out.info.dims[0] = total_feat;
-        score_out.info.strides[0] = 1;
-        ori_out.info.dims[0] = total_feat;
-        ori_out.info.strides[0] = 1;
-        size_out.info.dims[0] = total_feat;
-        size_out.info.strides[0] = 1;
-
-        desc_out.info.dims[0] = 8;
-        desc_out.info.strides[0] = 1;
-        desc_out.info.dims[1] = total_feat;
-        desc_out.info.strides[1] = desc_out.info.dims[0];
+        // Store results to pyramids
+        total_feat += usable_feat;
+        feat_pyr[i] = usable_feat;
+        d_x_pyr[i] = d_x_lvl;
+        d_y_pyr[i] = d_y_lvl;
+        d_score_pyr[i] = d_score_lvl;
+        d_ori_pyr[i] = d_ori_lvl;
+        d_size_pyr[i] = d_size_lvl;
+        d_desc_pyr[i] = d_desc_lvl;
+
+        if (i > 0 && i == max_levels-1)
+            bufferFree(lvl_img.data);
+    }
 
-        for (int k = 1; k < 4; k++) {
-            x_out.info.dims[k] = 1;
-            x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
-            y_out.info.dims[k] = 1;
-            y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
-            score_out.info.dims[k] = 1;
-            score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
-            ori_out.info.dims[k] = 1;
-            ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
-            size_out.info.dims[k] = 1;
-            size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
-            if (k > 1) {
-                desc_out.info.dims[k] = 1;
-                desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
-            }
-        }
+    if (gauss_filter.data != nullptr)
+        bufferFree(gauss_filter.data);
+    if (h_gauss != nullptr)
+        delete[] h_gauss;
 
-        if (total_feat > 0) {
-            size_t out_sz  = total_feat * sizeof(float);
-            x_out.data     = bufferAlloc(out_sz);
-            y_out.data     = bufferAlloc(out_sz);
-            score_out.data = bufferAlloc(out_sz);
-            ori_out.data   = bufferAlloc(out_sz);
-            size_out.data  = bufferAlloc(out_sz);
+    // If no features are found, set found features to 0 and return
+    if (total_feat == 0) {
+        *out_feat = 0;
+        return;
+    }
 
-            size_t desc_sz = total_feat * 8 * sizeof(unsigned);
-            desc_out.data  = bufferAlloc(desc_sz);
+    // Allocate output memory
+    x_out.info.dims[0] = total_feat;
+    x_out.info.strides[0] = 1;
+    y_out.info.dims[0] = total_feat;
+    y_out.info.strides[0] = 1;
+    score_out.info.dims[0] = total_feat;
+    score_out.info.strides[0] = 1;
+    ori_out.info.dims[0] = total_feat;
+    ori_out.info.strides[0] = 1;
+    size_out.info.dims[0] = total_feat;
+    size_out.info.strides[0] = 1;
+
+    desc_out.info.dims[0] = 8;
+    desc_out.info.strides[0] = 1;
+    desc_out.info.dims[1] = total_feat;
+    desc_out.info.strides[1] = desc_out.info.dims[0];
+
+    for (int k = 1; k < 4; k++) {
+        x_out.info.dims[k] = 1;
+        x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
+        y_out.info.dims[k] = 1;
+        y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
+        score_out.info.dims[k] = 1;
+        score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
+        ori_out.info.dims[k] = 1;
+        ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
+        size_out.info.dims[k] = 1;
+        size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
+        if (k > 1) {
+            desc_out.info.dims[k] = 1;
+            desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
         }
+    }
 
-        unsigned offset = 0;
-        for (unsigned i = 0; i < max_levels; i++) {
-            if (feat_pyr[i] == 0)
-                continue;
-
-            if (i > 0)
-                offset += feat_pyr[i-1];
-
-            getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_score_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*8*sizeof(unsigned), feat_pyr[i] * 8 * sizeof(unsigned));
-
-            bufferFree(d_x_pyr[i]);
-            bufferFree(d_y_pyr[i]);
-            bufferFree(d_score_pyr[i]);
-            bufferFree(d_ori_pyr[i]);
-            bufferFree(d_size_pyr[i]);
-            bufferFree(d_desc_pyr[i]);
-        }
+    if (total_feat > 0) {
+        size_t out_sz  = total_feat * sizeof(float);
+        x_out.data     = bufferAlloc(out_sz);
+        y_out.data     = bufferAlloc(out_sz);
+        score_out.data = bufferAlloc(out_sz);
+        ori_out.data   = bufferAlloc(out_sz);
+        size_out.data  = bufferAlloc(out_sz);
+
+        size_t desc_sz = total_feat * 8 * sizeof(unsigned);
+        desc_out.data  = bufferAlloc(desc_sz);
+    }
 
-        // Sets number of output features
-        *out_feat = total_feat;
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+    unsigned offset = 0;
+    for (unsigned i = 0; i < max_levels; i++) {
+        if (feat_pyr[i] == 0)
+            continue;
+
+        if (i > 0)
+            offset += feat_pyr[i-1];
+
+        getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_score_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*8*sizeof(unsigned), feat_pyr[i] * 8 * sizeof(unsigned));
+
+        bufferFree(d_x_pyr[i]);
+        bufferFree(d_y_pyr[i]);
+        bufferFree(d_score_pyr[i]);
+        bufferFree(d_ori_pyr[i]);
+        bufferFree(d_size_pyr[i]);
+        bufferFree(d_desc_pyr[i]);
     }
+
+    // Sets number of output features
+    *out_feat = total_feat;
 }
 
 } //namespace kernel
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 4294b3b211..8829b15a68 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -141,28 +141,24 @@ namespace opencl
         static void randomDistribution(cl::Buffer out, const size_t elements,
                 const af_random_engine_type type, const uintl &seed, uintl &counter, int kerIdx)
         {
-            try {
-                uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
-                uint groups = divup(elements, elementsPerBlock);
+            uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
+            uint groups = divup(elements, elementsPerBlock);
 
-                uint hi = seed>>32;
-                uint lo = seed;
+            uint hi = seed>>32;
+            uint lo = seed;
 
-                NDRange local(THREADS, 1);
-                NDRange global(THREADS * groups, 1);
+            NDRange local(THREADS, 1);
+            NDRange global(THREADS * groups, 1);
 
-                if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) || (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
-                    Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
-                    auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
-                    randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                            out, elements, counter, hi, lo);
-                }
-
-                counter += elements;
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
+            if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) || (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
+                Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
+                auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
+                randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                        out, elements, counter, hi, lo);
             }
+
+            counter += elements;
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template <typename T>
@@ -171,24 +167,20 @@ namespace opencl
                 const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table,
                 int kerIdx)
         {
-            try {
-                int threads = THREADS;
-                int min_elements_per_block = 32*THREADS*4*sizeof(uint)/sizeof(T);
-                int blocks = divup(elements, min_elements_per_block);
-                blocks = (blocks > MAX_BLOCKS)? MAX_BLOCKS : blocks;
-                int elementsPerBlock = divup(elements, blocks);
-
-                NDRange local(threads, 1);
-                NDRange global(threads * blocks, 1);
-                Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213, kerIdx, elementsPerBlock);
-                auto randomEngineOp = KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
-                     uint, cl::Buffer, cl::Buffer, uint, uint>(ker);
-                randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                        out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            int threads = THREADS;
+            int min_elements_per_block = 32*THREADS*4*sizeof(uint)/sizeof(T);
+            int blocks = divup(elements, min_elements_per_block);
+            blocks = (blocks > MAX_BLOCKS)? MAX_BLOCKS : blocks;
+            int elementsPerBlock = divup(elements, blocks);
+
+            NDRange local(threads, 1);
+            NDRange global(threads * blocks, 1);
+            Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213, kerIdx, elementsPerBlock);
+            auto randomEngineOp = KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
+                  uint, cl::Buffer, cl::Buffer, uint, uint>(ker);
+            randomEngineOp(EnqueueArgs(getQueue(), global, local),
+                    out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template <typename T>
@@ -223,17 +215,13 @@ namespace opencl
 
         void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed)
         {
-            try{
-                NDRange local(THREADS_PER_GROUP, 1);
-                NDRange global(local[0] * MAX_BLOCKS, 1);
-
-                Kernel ker = get_mersenne_init_kernel();
-                auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
-                initOp(EnqueueArgs(getQueue(), global, local), state, table, seed);
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            NDRange local(THREADS_PER_GROUP, 1);
+            NDRange global(local[0] * MAX_BLOCKS, 1);
+
+            Kernel ker = get_mersenne_init_kernel();
+            auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
+            initOp(EnqueueArgs(getQueue(), global, local), state, table, seed);
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index b57e01ab4b..0ea609f558 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -39,45 +39,40 @@ namespace opencl
         template<typename T>
         void range(Param out, const int dim)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>  rangeProgs;
-                static std::map<int, Kernel*> rangeKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>  rangeProgs;
+            static std::map<int, Kernel*> rangeKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, range_cl, range_cl_len, options.str());
-                    rangeProgs[device]   = new Program(prog);
-                    rangeKernels[device] = new Kernel(*rangeProgs[device], "range_kernel");
-                });
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, range_cl, range_cl_len, options.str());
+                rangeProgs[device]   = new Program(prog);
+                rangeKernels[device] = new Kernel(*rangeProgs[device], "range_kernel");
+            });
 
-                auto rangeOp = KernelFunctor<Buffer, const KParam, const int,
-                                           const int, const int> (*rangeKernels[device]);
+            auto rangeOp = KernelFunctor<Buffer, const KParam, const int,
+                                        const int, const int> (*rangeKernels[device]);
 
-                NDRange local(RANGE_TX, RANGE_TY, 1);
+            NDRange local(RANGE_TX, RANGE_TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
-                NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                               local[1] * blocksPerMatY * out.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
+            int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
+            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                            local[1] * blocksPerMatY * out.info.dims[3],
+                            1);
 
-                rangeOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, out.info, dim, blocksPerMatX, blocksPerMatY);
+            rangeOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, dim, blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index cdbf562c74..31038a3080 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -273,93 +273,85 @@ namespace kernel
     template<typename Ti, typename To, af_op_t op>
     void reduce(Param out, Param in, int dim, int change_nan, double nanval)
     {
-        try {
-            if (dim == 0)
-                return reduce_first<Ti, To, op>(out, in, change_nan, nanval);
-            else
-                return reduce_dim  <Ti, To, op>(out, in, change_nan, nanval, dim);
-        } catch(cl::Error ex) {
-            CL_TO_AF_ERROR(ex);
-        }
+        if (dim == 0)
+            return reduce_first<Ti, To, op>(out, in, change_nan, nanval);
+        else
+            return reduce_dim  <Ti, To, op>(out, in, change_nan, nanval, dim);
     }
 
     template<typename Ti, typename To, af_op_t op>
     To reduce_all(Param in, int change_nan, double nanval)
     {
-        try {
-            int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+        int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
-            bool is_linear = (in.info.strides[0] == 1);
-            for (int k = 1; k < 4; k++) {
-                is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
-            }
+        bool is_linear = (in.info.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
+        }
 
-            // FIXME: Use better heuristics to get to the optimum number
-            if (in_elements > 4096 || !is_linear) {
+        // FIXME: Use better heuristics to get to the optimum number
+        if (in_elements > 4096 || !is_linear) {
 
-                if (is_linear) {
-                    in.info.dims[0] = in_elements;
-                    for (int k = 1; k < 4; k++) {
-                        in.info.dims[k] = 1;
-                        in.info.strides[k] = in_elements;
-                    }
+            if (is_linear) {
+                in.info.dims[0] = in_elements;
+                for (int k = 1; k < 4; k++) {
+                    in.info.dims[k] = 1;
+                    in.info.strides[k] = in_elements;
                 }
+            }
 
-                uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-                threads_x = std::min(threads_x, THREADS_PER_GROUP);
-                uint threads_y = THREADS_PER_GROUP / threads_x;
-
-                Param tmp;
-                uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-                uint groups_y = divup(in.info.dims[1], threads_y);
+            uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+            threads_x = std::min(threads_x, THREADS_PER_GROUP);
+            uint threads_y = THREADS_PER_GROUP / threads_x;
 
-                tmp.info.offset = 0;
-                tmp.info.dims[0] = groups_x;
-                tmp.info.strides[0] = 1;
+            Param tmp;
+            uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+            uint groups_y = divup(in.info.dims[1], threads_y);
 
-                for (int k = 1; k < 4; k++) {
-                    tmp.info.dims[k] = in.info.dims[k];
-                    tmp.info.strides[k] = tmp.info.dims[k - 1] * tmp.info.strides[k - 1];
-                }
+            tmp.info.offset = 0;
+            tmp.info.dims[0] = groups_x;
+            tmp.info.strides[0] = 1;
 
-                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-                tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+            for (int k = 1; k < 4; k++) {
+                tmp.info.dims[k] = in.info.dims[k];
+                tmp.info.strides[k] = tmp.info.dims[k - 1] * tmp.info.strides[k - 1];
+            }
 
-                reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x, change_nan, nanval);
+            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
 
-                unique_ptr<To> h_ptr(new To[tmp_elements]);
-                getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.get());
+            reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x, change_nan, nanval);
 
-                Binary<To, op> reduce;
-                To out = reduce.init();
-                for (int i = 0; i < (int)tmp_elements; i++) {
-                    out = reduce(out, h_ptr.get()[i]);
-                }
+            unique_ptr<To> h_ptr(new To[tmp_elements]);
+            getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.get());
 
-                bufferFree(tmp.data);
-                return out;
+            Binary<To, op> reduce;
+            To out = reduce.init();
+            for (int i = 0; i < (int)tmp_elements; i++) {
+                out = reduce(out, h_ptr.get()[i]);
+            }
 
-            } else {
+            bufferFree(tmp.data);
+            return out;
 
-                unique_ptr<Ti> h_ptr(new Ti[in_elements]);
-                getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
-                                             sizeof(Ti) * in_elements, h_ptr.get());
+        } else {
 
-                Transform<Ti, To, op> transform;
-                Binary<To, op> reduce;
-                To out = reduce.init();
-                To nanval_to = scalar<To>(nanval);
+            unique_ptr<Ti> h_ptr(new Ti[in_elements]);
+            getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
+                                          sizeof(Ti) * in_elements, h_ptr.get());
 
-                for (int i = 0; i < (int)in_elements; i++) {
-                    To in_val = transform(h_ptr.get()[i]);
-                    if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
-                    out = reduce(out, in_val);
-                }
+            Transform<Ti, To, op> transform;
+            Binary<To, op> reduce;
+            To out = reduce.init();
+            To nanval_to = scalar<To>(nanval);
 
-                return out;
+            for (int i = 0; i < (int)in_elements; i++) {
+                To in_val = transform(h_ptr.get()[i]);
+                if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
+                out = reduce(out, in_val);
             }
-        } catch(cl::Error ex) {
-            CL_TO_AF_ERROR(ex);
+
+            return out;
         }
     }
 
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index a41c86fad3..e381399342 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -53,169 +53,164 @@ static const int THREADS_Y = 16;
 template<typename T, bool full_conn, int n_per_thread>
 void regions(Param out, Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> regionsProgs;
-        static std::map<int, Kernel *>     ilKernel;
-        static std::map<int, Kernel *>     frKernel;
-        static std::map<int, Kernel *>     ueKernel;
-
-        int device = getActiveDeviceId();
-        static const int block_dim = 16;
-        static const int num_warps = 8;
-
-        std::call_once( compileFlags[device], [device] () {
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                if (full_conn) {
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D BLOCK_DIM=" << block_dim
-                            << " -D NUM_WARPS=" << num_warps
-                            << " -D N_PER_THREAD=" << n_per_thread
-                            << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
-                            << " -D FULL_CONN";
-                }
-                else {
-                    options << " -D T=" << dtype_traits<T>::getName()
-                            << " -D BLOCK_DIM=" << block_dim
-                            << " -D NUM_WARPS=" << num_warps
-                            << " -D N_PER_THREAD=" << n_per_thread
-                            << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
-                }
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                buildProgram(prog, regions_cl, regions_cl_len, options.str());
-                regionsProgs[device] = new Program(prog);
-
-                ilKernel[device] = new Kernel(*regionsProgs[device], "initial_label");
-                frKernel[device] = new Kernel(*regionsProgs[device], "final_relabel");
-                ueKernel[device] = new Kernel(*regionsProgs[device], "update_equiv");
-            });
-
-        const NDRange local(THREADS_X, THREADS_Y);
-
-        const int blk_x = divup(in.info.dims[0], THREADS_X*2);
-        const int blk_y = divup(in.info.dims[1], THREADS_Y*2);
-
-        const NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
-
-        auto ilOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam> (*ilKernel[device]);
-
-        ilOp(EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *in.data, in.info);
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> regionsProgs;
+    static std::map<int, Kernel *>     ilKernel;
+    static std::map<int, Kernel *>     frKernel;
+    static std::map<int, Kernel *>     ueKernel;
+
+    int device = getActiveDeviceId();
+    static const int block_dim = 16;
+    static const int num_warps = 8;
+
+    std::call_once( compileFlags[device], [device] () {
+            ToNumStr<T> toNumStr;
+            std::ostringstream options;
+            if (full_conn) {
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D BLOCK_DIM=" << block_dim
+                        << " -D NUM_WARPS=" << num_warps
+                        << " -D N_PER_THREAD=" << n_per_thread
+                        << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
+                        << " -D FULL_CONN";
+            }
+            else {
+                options << " -D T=" << dtype_traits<T>::getName()
+                        << " -D BLOCK_DIM=" << block_dim
+                        << " -D NUM_WARPS=" << num_warps
+                        << " -D N_PER_THREAD=" << n_per_thread
+                        << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
+            }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-        CL_DEBUG_FINISH(getQueue());
+            Program prog;
+            buildProgram(prog, regions_cl, regions_cl_len, options.str());
+            regionsProgs[device] = new Program(prog);
 
-        int h_continue = 1;
-        cl::Buffer *d_continue = bufferAlloc(sizeof(int));
+            ilKernel[device] = new Kernel(*regionsProgs[device], "initial_label");
+            frKernel[device] = new Kernel(*regionsProgs[device], "final_relabel");
+            ueKernel[device] = new Kernel(*regionsProgs[device], "update_equiv");
+        });
 
-        while (h_continue) {
-            h_continue = 0;
-            getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
+    const NDRange local(THREADS_X, THREADS_Y);
 
-            auto ueOp = KernelFunctor<Buffer, KParam,
-                                    Buffer> (*ueKernel[device]);
+    const int blk_x = divup(in.info.dims[0], THREADS_X*2);
+    const int blk_y = divup(in.info.dims[1], THREADS_Y*2);
 
-            ueOp(EnqueueArgs(getQueue(), global, local),
-                 *out.data, out.info, *d_continue);
-            CL_DEBUG_FINISH(getQueue());
+    const NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
 
-            getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
-        }
+    auto ilOp = KernelFunctor<Buffer, KParam,
+                            Buffer, KParam> (*ilKernel[device]);
 
-        bufferFree(d_continue);
+    ilOp(EnqueueArgs(getQueue(), global, local),
+          *out.data, out.info, *in.data, in.info);
 
-        // Now, perform the final relabeling.  This converts the equivalency
-        // map from having unique labels based on the lowest pixel in the
-        // component to being sequentially numbered components starting at
-        // 1.
-        int size = in.info.dims[0] * in.info.dims[1];
+    CL_DEBUG_FINISH(getQueue());
 
-        compute::command_queue c_queue(getQueue()());
+    int h_continue = 1;
+    cl::Buffer *d_continue = bufferAlloc(sizeof(int));
 
-        // Wrap raw device ptr
-        compute::context context(getContext()());
-        compute::vector<T> tmp(size, context);
-        clEnqueueCopyBuffer(getQueue()(), (*out.data)(), tmp.get_buffer().get(), 0, 0, size * sizeof(T), 0, NULL, NULL);
+    while (h_continue) {
+        h_continue = 0;
+        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
 
-        // Sort the copy
-        compute::sort(tmp.begin(), tmp.end(), c_queue);
+        auto ueOp = KernelFunctor<Buffer, KParam,
+                                Buffer> (*ueKernel[device]);
 
-        // Take the max element, this is the number of label assignments to
-        // compute.
-        //int num_bins = tmp[size - 1] + 1;
-        T last_label;
-        clEnqueueReadBuffer(getQueue()(), tmp.get_buffer().get(), CL_TRUE, (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL, NULL);
-        int num_bins = (int)last_label + 1;
+        ueOp(EnqueueArgs(getQueue(), global, local),
+              *out.data, out.info, *d_continue);
+        CL_DEBUG_FINISH(getQueue());
 
-        Buffer labels(getContext(), CL_MEM_READ_WRITE, num_bins * sizeof(T));
-        compute::buffer c_labels(labels());
-        compute::buffer_iterator<T> labels_begin = compute::make_buffer_iterator<T>(c_labels, 0);
-        compute::buffer_iterator<T> labels_end   = compute::make_buffer_iterator<T>(c_labels, num_bins);
+        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
+    }
 
-        // Find the end of each section of values
-        compute::counting_iterator<T> search_begin(0);
+    bufferFree(d_continue);
 
-        int tmp_size = size;
-        BOOST_COMPUTE_CLOSURE(int, upper_bound_closure, (int v), (tmp, tmp_size),
-        {
-            int start = 0, n = tmp_size, i;
-            while(start < n)
-            {
-                i = (start + n) / 2;
-                if(v < tmp[i])
-                {
-                    n = i;
-                }
-                else
-                {
-                    start = i + 1;
-                }
-            }
+    // Now, perform the final relabeling.  This converts the equivalency
+    // map from having unique labels based on the lowest pixel in the
+    // component to being sequentially numbered components starting at
+    // 1.
+    int size = in.info.dims[0] * in.info.dims[1];
 
-            return start;
-        });
+    compute::command_queue c_queue(getQueue()());
+
+    // Wrap raw device ptr
+    compute::context context(getContext()());
+    compute::vector<T> tmp(size, context);
+    clEnqueueCopyBuffer(getQueue()(), (*out.data)(), tmp.get_buffer().get(), 0, 0, size * sizeof(T), 0, NULL, NULL);
 
-        BOOST_COMPUTE_FUNCTION(int, clamp_to_one, (int i),
+    // Sort the copy
+    compute::sort(tmp.begin(), tmp.end(), c_queue);
+
+    // Take the max element, this is the number of label assignments to
+    // compute.
+    //int num_bins = tmp[size - 1] + 1;
+    T last_label;
+    clEnqueueReadBuffer(getQueue()(), tmp.get_buffer().get(), CL_TRUE, (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL, NULL);
+    int num_bins = (int)last_label + 1;
+
+    Buffer labels(getContext(), CL_MEM_READ_WRITE, num_bins * sizeof(T));
+    compute::buffer c_labels(labels());
+    compute::buffer_iterator<T> labels_begin = compute::make_buffer_iterator<T>(c_labels, 0);
+    compute::buffer_iterator<T> labels_end   = compute::make_buffer_iterator<T>(c_labels, num_bins);
+
+    // Find the end of each section of values
+    compute::counting_iterator<T> search_begin(0);
+
+    int tmp_size = size;
+    BOOST_COMPUTE_CLOSURE(int, upper_bound_closure, (int v), (tmp, tmp_size),
+    {
+        int start = 0, n = tmp_size, i;
+        while(start < n)
         {
-            return (i >= 1) ? 1 : i;
-        });
+            i = (start + n) / 2;
+            if(v < tmp[i])
+            {
+                n = i;
+            }
+            else
+            {
+                start = i + 1;
+            }
+        }
 
-        compute::transform(search_begin, search_begin + num_bins,
-                           labels_begin,
-                           upper_bound_closure,
-                           c_queue);
-        compute::adjacent_difference(labels_begin, labels_end, labels_begin, c_queue);
-
-        // Perform the scan -- this can computes the correct labels for each
-        // component
-        compute::transform(labels_begin, labels_end,
-                           labels_begin,
-                           clamp_to_one,
-                           c_queue);
-        compute::exclusive_scan(labels_begin,
-                                labels_end,
-                                labels_begin,
-                                c_queue);
-
-        // Apply the correct labels to the equivalency map
-        auto frOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer> (*frKernel[device]);
-
-        //Buffer labels_buf(tmp.get_buffer().get());
-        frOp(EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *in.data, in.info, labels);
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+        return start;
+    });
+
+    BOOST_COMPUTE_FUNCTION(int, clamp_to_one, (int i),
+    {
+        return (i >= 1) ? 1 : i;
+    });
+
+    compute::transform(search_begin, search_begin + num_bins,
+                        labels_begin,
+                        upper_bound_closure,
+                        c_queue);
+    compute::adjacent_difference(labels_begin, labels_end, labels_begin, c_queue);
+
+    // Perform the scan -- this can computes the correct labels for each
+    // component
+    compute::transform(labels_begin, labels_end,
+                        labels_begin,
+                        clamp_to_one,
+                        c_queue);
+    compute::exclusive_scan(labels_begin,
+                            labels_end,
+                            labels_begin,
+                            c_queue);
+
+    // Apply the correct labels to the equivalency map
+    auto frOp = KernelFunctor<Buffer, KParam,
+                            Buffer, KParam,
+                            Buffer> (*frKernel[device]);
+
+    //Buffer labels_buf(tmp.get_buffer().get());
+    frOp(EnqueueArgs(getQueue(), global, local),
+          *out.data, out.info, *in.data, in.info, labels);
+    CL_DEBUG_FINISH(getQueue());
 }
 
 } //namespace kernel
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 1aa576350a..5bd7690e6d 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -39,48 +39,43 @@ namespace opencl
         template<typename T>
         void reorder(Param out, const Param in, const dim_t *rdims)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   reorderProgs;
-                static std::map<int, Kernel *> reorderKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   reorderProgs;
+            static std::map<int, Kernel *> reorderKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, reorder_cl, reorder_cl_len, options.str());
-                    reorderProgs[device] = new Program(prog);
-                    reorderKernels[device] = new Kernel(*reorderProgs[device], "reorder_kernel");
-                });
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, reorder_cl, reorder_cl_len, options.str());
+                reorderProgs[device] = new Program(prog);
+                reorderKernels[device] = new Kernel(*reorderProgs[device], "reorder_kernel");
+            });
 
-                auto reorderOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                          const int, const int, const int, const int,
-                                          const int, const int> (*reorderKernels[device]);
+            auto reorderOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
+                                      const int, const int, const int, const int,
+                                      const int, const int> (*reorderKernels[device]);
 
-                NDRange local(TX, TY, 1);
+            NDRange local(TX, TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], TILEY);
-                NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                               local[1] * blocksPerMatY * out.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(out.info.dims[0], TILEX);
+            int blocksPerMatY = divup(out.info.dims[1], TILEY);
+            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                            local[1] * blocksPerMatY * out.info.dims[3],
+                            1);
 
-                reorderOp(EnqueueArgs(getQueue(), global, local),
-                          *out.data, *in.data, out.info, in.info,
-                          rdims[0], rdims[1], rdims[2], rdims[3],
-                          blocksPerMatX, blocksPerMatY);
+            reorderOp(EnqueueArgs(getQueue(), global, local),
+                      *out.data, *in.data, out.info, in.info,
+                      rdims[0], rdims[1], rdims[2], rdims[3],
+                      blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 0f659f1761..bb4216c6a9 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -46,73 +46,68 @@ namespace opencl
         template<typename T, af_interp_type method>
         void resize(Param out, const Param in)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   resizeProgs;
-                static std::map<int, Kernel *> resizeKernels;
-
-                int device = getActiveDeviceId();
-
-                typedef typename dtype_traits<T>::base_type BT;
-
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName();
-                    options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
-                    options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
-
-                    switch(method) {
-                        case AF_INTERP_NEAREST:  options <<" -D INTERP=NEAREST" ; break;
-                        case AF_INTERP_BILINEAR: options <<" -D INTERP=BILINEAR"; break;
-                        case AF_INTERP_LOWER:    options <<" -D INTERP=LOWER"   ; break;
-                        default: break;
-                    }
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D CPLX=1";
-                        options << " -D TB=" << dtype_traits<BT>::getName();
-                    } else {
-                        options << " -D CPLX=0";
-                    }
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, resize_cl, resize_cl_len, options.str());
-                    resizeProgs[device] = new Program(prog);
-                    resizeKernels[device] = new Kernel(*resizeProgs[device], "resize_kernel");
-                });
-
-                auto resizeOp = KernelFunctor<Buffer, const KParam,
-                                      const Buffer, const KParam,
-                                      const int, const int, const float, const float>
-                                      (*resizeKernels[device]);
-
-                NDRange local(RESIZE_TX, RESIZE_TY, 1);
-
-                int blocksPerMatX = divup(out.info.dims[0], local[0]);
-                int blocksPerMatY = divup(out.info.dims[1], local[1]);
-                NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                               local[1] * blocksPerMatY * in.info.dims[3],
-                               1);
-
-                double xd = (double)in.info.dims[0] / (double)out.info.dims[0];
-                double yd = (double)in.info.dims[1] / (double)out.info.dims[1];
-
-                float xf = (float)xd, yf = (float)yd;
-
-                resizeOp(EnqueueArgs(getQueue(), global, local),
-                         *out.data, out.info, *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   resizeProgs;
+            static std::map<int, Kernel *> resizeKernels;
+
+            int device = getActiveDeviceId();
+
+            typedef typename dtype_traits<T>::base_type BT;
+
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName();
+                options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
+                options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
+
+                switch(method) {
+                    case AF_INTERP_NEAREST:  options <<" -D INTERP=NEAREST" ; break;
+                    case AF_INTERP_BILINEAR: options <<" -D INTERP=BILINEAR"; break;
+                    case AF_INTERP_LOWER:    options <<" -D INTERP=LOWER"   ; break;
+                    default: break;
+                }
+
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                    (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D CPLX=1";
+                    options << " -D TB=" << dtype_traits<BT>::getName();
+                } else {
+                    options << " -D CPLX=0";
+                }
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                Program prog;
+                buildProgram(prog, resize_cl, resize_cl_len, options.str());
+                resizeProgs[device] = new Program(prog);
+                resizeKernels[device] = new Kernel(*resizeProgs[device], "resize_kernel");
+            });
+
+            auto resizeOp = KernelFunctor<Buffer, const KParam,
+                                  const Buffer, const KParam,
+                                  const int, const int, const float, const float>
+                                  (*resizeKernels[device]);
+
+            NDRange local(RESIZE_TX, RESIZE_TY, 1);
+
+            int blocksPerMatX = divup(out.info.dims[0], local[0]);
+            int blocksPerMatY = divup(out.info.dims[1], local[1]);
+            NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                            local[1] * blocksPerMatY * in.info.dims[3],
+                            1);
+
+            double xd = (double)in.info.dims[0] / (double)out.info.dims[0];
+            double yd = (double)in.info.dims[1] / (double)out.info.dims[1];
+
+            float xf = (float)xd, yf = (float)yd;
+
+            resizeOp(EnqueueArgs(getQueue(), global, local),
+                      *out.data, out.info, *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
+
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 01440ab027..521383524d 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -57,103 +57,98 @@ namespace opencl
         template<typename T, int order>
         void rotate(Param out, const Param in, const float theta, af_interp_type method)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   rotateProgs;
-                static std::map<int, Kernel *> rotateKernels;
-
-                int device = getActiveDeviceId();
-                typedef typename dtype_traits<T>::base_type BT;
-
-                std::call_once( compileFlags[device], [device] () {
-                    ToNumStr<T> toNumStr;
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName();
-                    options << " -D ZERO="      << toNumStr(scalar<T>(0));
-                    options << " -D InterpInTy=" << dtype_traits<T>::getName();
-                    options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
-                    options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                        options << " -D TB=" << dtype_traits<BT>::getName();
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    options << " -D INTERP_ORDER=" << order;
-                    addInterpEnumOptions(options);
-
-                    const char *ker_strs[] = {interp_cl, rotate_cl};
-                    const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
-                    Program prog;
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    rotateProgs[device] = new Program(prog);
-                    rotateKernels[device] = new Kernel(*rotateProgs[device], "rotate_kernel");
-                });
-
-                auto rotateOp = KernelFunctor<Buffer, const KParam,
-                                              const Buffer, const KParam,
-                                              const tmat_t,
-                                              const int, const int,
-                                              const int, const int,
-                                              const int>(*rotateKernels[device]);
-
-                const float c = cos(-theta), s = sin(-theta);
-                float tx, ty;
-                {
-                    const float nx = 0.5 * (in.info.dims[0] - 1);
-                    const float ny = 0.5 * (in.info.dims[1] - 1);
-                    const float mx = 0.5 * (out.info.dims[0] - 1);
-                    const float my = 0.5 * (out.info.dims[1] - 1);
-                    const float sx = (mx * c + my *-s);
-                    const float sy = (mx * s + my * c);
-                    tx = -(sx - nx);
-                    ty = -(sy - ny);
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   rotateProgs;
+            static std::map<int, Kernel *> rotateKernels;
+
+            int device = getActiveDeviceId();
+            typedef typename dtype_traits<T>::base_type BT;
+
+            std::call_once( compileFlags[device], [device] () {
+                ToNumStr<T> toNumStr;
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName();
+                options << " -D ZERO="      << toNumStr(scalar<T>(0));
+                options << " -D InterpInTy=" << dtype_traits<T>::getName();
+                options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+                options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
+
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                    (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
+                    options << " -D TB=" << dtype_traits<BT>::getName();
+                } else {
+                    options << " -D IS_CPLX=0";
                 }
-
-                // Rounding error. Anything more than 3 decimal points wont make a diff
-                tmat_t t;
-                t.tmat[0] = round( c * 1000) / 1000.0f;
-                t.tmat[1] = round(-s * 1000) / 1000.0f;
-                t.tmat[2] = round(tx * 1000) / 1000.0f;
-                t.tmat[3] = round( s * 1000) / 1000.0f;
-                t.tmat[4] = round( c * 1000) / 1000.0f;
-                t.tmat[5] = round(ty * 1000) / 1000.0f;
-
-
-                NDRange local(TX, TY, 1);
-
-                int nimages  = in.info.dims[2];
-                int nbatches = in.info.dims[3];
-                int global_x = local[0] * divup(out.info.dims[0], local[0]);
-                int global_y = local[1] * divup(out.info.dims[1], local[1]);
-                const int blocksXPerImage = global_x / local[0];
-                const int blocksYPerImage = global_y / local[1];
-
-                if(nimages > TI) {
-                    int tile_images = divup(nimages, TI);
-                    nimages = TI;
-                    global_x = global_x * tile_images;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
-                global_y *= nbatches;
-
-                NDRange global(global_x, global_y, 1);
 
-                rotateOp(EnqueueArgs(getQueue(), global, local),
-                         *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
-                         blocksXPerImage, blocksYPerImage, (int)method);
+                options << " -D INTERP_ORDER=" << order;
+                addInterpEnumOptions(options);
+
+                const char *ker_strs[] = {interp_cl, rotate_cl};
+                const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
+                Program prog;
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                rotateProgs[device] = new Program(prog);
+                rotateKernels[device] = new Kernel(*rotateProgs[device], "rotate_kernel");
+            });
+
+            auto rotateOp = KernelFunctor<Buffer, const KParam,
+                                          const Buffer, const KParam,
+                                          const tmat_t,
+                                          const int, const int,
+                                          const int, const int,
+                                          const int>(*rotateKernels[device]);
+
+            const float c = cos(-theta), s = sin(-theta);
+            float tx, ty;
+            {
+                const float nx = 0.5 * (in.info.dims[0] - 1);
+                const float ny = 0.5 * (in.info.dims[1] - 1);
+                const float mx = 0.5 * (out.info.dims[0] - 1);
+                const float my = 0.5 * (out.info.dims[1] - 1);
+                const float sx = (mx * c + my *-s);
+                const float sy = (mx * s + my * c);
+                tx = -(sx - nx);
+                ty = -(sy - ny);
+            }
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
+            // Rounding error. Anything more than 3 decimal points wont make a diff
+            tmat_t t;
+            t.tmat[0] = round( c * 1000) / 1000.0f;
+            t.tmat[1] = round(-s * 1000) / 1000.0f;
+            t.tmat[2] = round(tx * 1000) / 1000.0f;
+            t.tmat[3] = round( s * 1000) / 1000.0f;
+            t.tmat[4] = round( c * 1000) / 1000.0f;
+            t.tmat[5] = round(ty * 1000) / 1000.0f;
+
+
+            NDRange local(TX, TY, 1);
+
+            int nimages  = in.info.dims[2];
+            int nbatches = in.info.dims[3];
+            int global_x = local[0] * divup(out.info.dims[0], local[0]);
+            int global_y = local[1] * divup(out.info.dims[1], local[1]);
+            const int blocksXPerImage = global_x / local[0];
+            const int blocksYPerImage = global_y / local[1];
+
+            if(nimages > TI) {
+                int tile_images = divup(nimages, TI);
+                nimages = TI;
+                global_x = global_x * tile_images;
             }
+            global_y *= nbatches;
+
+            NDRange global(global_x, global_y, 1);
+
+            rotateOp(EnqueueArgs(getQueue(), global, local),
+                      *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
+                      blocksXPerImage, blocksYPerImage, (int)method);
+
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 27d4dbf521..46ad2d1673 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -107,31 +107,26 @@ namespace kernel
                                   int dim, bool isFinalPass, uint threads_y,
                                   const uint groups_all[4])
     {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(0, dim, isFinalPass, threads_y);
+        Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(0, dim, isFinalPass, threads_y);
 
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
+        NDRange local(THREADS_X, threads_y);
+        NDRange global(groups_all[0] * groups_all[2] * local[0],
+                        groups_all[1] * groups_all[3] * local[1]);
 
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto scanOp = KernelFunctor<Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      uint, uint,
-                                      uint, uint>(ker);
+        auto scanOp = KernelFunctor<Buffer, KParam,
+                                  Buffer, KParam,
+                                  Buffer, KParam,
+                                  uint, uint,
+                                  uint, uint>(ker);
 
 
-            scanOp(EnqueueArgs(getQueue(), global, local),
-                   *out.data, out.info, *tmp.data, tmp.info, *in.data, in.info,
-                   groups_all[0], groups_all[1], groups_all[dim], lim);
+        scanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *tmp.data, tmp.info, *in.data, in.info,
+                groups_all[0], groups_all[1], groups_all[dim], lim);
 
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
@@ -140,95 +135,85 @@ namespace kernel
                                    int dim, bool isFinalPass, uint threads_y,
                                    const uint groups_all[4])
     {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(1, dim, isFinalPass, threads_y);
+        Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(1, dim, isFinalPass, threads_y);
 
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
+        NDRange local(THREADS_X, threads_y);
+        NDRange global(groups_all[0] * groups_all[2] * local[0],
+                        groups_all[1] * groups_all[3] * local[1]);
 
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto bcastOp = KernelFunctor<Buffer, KParam,
-                                       Buffer, KParam,
-                                       uint, uint,
-                                       uint, uint>(ker);
+        auto bcastOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    uint, uint,
+                                    uint, uint>(ker);
 
-            bcastOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *tmp.data, tmp.info,
-                    groups_all[0], groups_all[1], groups_all[dim], lim);
+        bcastOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *tmp.data, tmp.info,
+                groups_all[0], groups_all[1], groups_all[dim], lim);
 
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
     static void scan_dim(Param &out, const Param &in, int dim)
     {
-        try {
-            uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
-            uint threads_x = THREADS_X;
+        uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+        uint threads_x = THREADS_X;
 
-            uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
-                                 (uint)out.info.dims[1],
-                                 (uint)out.info.dims[2],
-                                 (uint)out.info.dims[3]};
+        uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
+                              (uint)out.info.dims[1],
+                              (uint)out.info.dims[2],
+                              (uint)out.info.dims[3]};
 
-            groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+        groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
 
-            if (groups_all[dim] == 1) {
+        if (groups_all[dim] == 1) {
 
-                scan_dim_launcher<Ti, To, op, inclusive_scan>(out, out, in,
-                                              dim, true,
-                                              threads_y,
-                                              groups_all);
-            } else {
+            scan_dim_launcher<Ti, To, op, inclusive_scan>(out, out, in,
+                                          dim, true,
+                                          threads_y,
+                                          groups_all);
+        } else {
+
+            Param tmp = out;
+
+            tmp.info.dims[dim] = groups_all[dim];
+            tmp.info.strides[0] = 1;
+            for (int k = 1; k < 4; k++) {
+                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+            }
+
+            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+            // FIXME: Do I need to free this ?
+            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+
+            scan_dim_launcher<Ti, To, op, inclusive_scan>(out, tmp, in,
+                                          dim, false,
+                                          threads_y,
+                                          groups_all);
 
-                Param tmp = out;
-
-                tmp.info.dims[dim] = groups_all[dim];
-                tmp.info.strides[0] = 1;
-                for (int k = 1; k < 4; k++) {
-                    tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
-                }
-
-                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-                // FIXME: Do I need to free this ?
-                tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-
-                scan_dim_launcher<Ti, To, op, inclusive_scan>(out, tmp, in,
-                                              dim, false,
-                                              threads_y,
-                                              groups_all);
-
-                int gdim = groups_all[dim];
-                groups_all[dim] = 1;
-
-                if (op == af_notzero_t) {
-                    scan_dim_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
-                                                        dim, true,
-                                                        threads_y,
-                                                        groups_all);
-                } else {
-                    scan_dim_launcher<To, To,       op, true>(tmp, tmp, tmp,
-                                                        dim, true,
-                                                        threads_y,
-                                                        groups_all);
-                }
-
-                groups_all[dim] = gdim;
-                bcast_dim_launcher<To, To, op, inclusive_scan>(out, tmp,
-                                               dim, true,
-                                               threads_y,
-                                               groups_all);
-                bufferFree(tmp.data);
+            int gdim = groups_all[dim];
+            groups_all[dim] = 1;
+
+            if (op == af_notzero_t) {
+                scan_dim_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
+                                                    dim, true,
+                                                    threads_y,
+                                                    groups_all);
+            } else {
+                scan_dim_launcher<To, To,       op, true>(tmp, tmp, tmp,
+                                                    dim, true,
+                                                    threads_y,
+                                                    groups_all);
             }
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
+
+            groups_all[dim] = gdim;
+            bcast_dim_launcher<To, To, op, inclusive_scan>(out, tmp,
+                                            dim, true,
+                                            threads_y,
+                                            groups_all);
+            bufferFree(tmp.data);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index c3ef035996..a8adbff7d3 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -114,37 +114,32 @@ namespace kernel
                                   int dim, uint threads_y,
                                   const uint groups_all[4])
     {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(1, dim, false, threads_y);
-
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
-
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
-
-            auto scanOp = KernelFunctor<Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        uint, uint,
-                                        uint, uint>(ker);
-
-            scanOp(EnqueueArgs(getQueue(), global, local),
-                   *out.data, out.info,
-                   *tmp.data, tmp.info,
-                   *tmpflg.data, tmpflg.info,
-                   *tmpid.data, tmpid.info,
-                   *in.data, in.info, *key.data, key.info,
-                   groups_all[0], groups_all[1], groups_all[dim], lim);
-
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(1, dim, false, threads_y);
+
+        NDRange local(THREADS_X, threads_y);
+        NDRange global(groups_all[0] * groups_all[2] * local[0],
+                        groups_all[1] * groups_all[3] * local[1]);
+
+        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+
+        auto scanOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    uint, uint,
+                                    uint, uint>(ker);
+
+        scanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *tmp.data, tmp.info,
+                *tmpflg.data, tmpflg.info,
+                *tmpid.data, tmpid.info,
+                *in.data, in.info, *key.data, key.info,
+                groups_all[0], groups_all[1], groups_all[dim], lim);
+
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
@@ -154,30 +149,25 @@ namespace kernel
                                   int dim, const bool calculateFlags, uint threads_y,
                                   const uint groups_all[4])
     {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(0, dim, calculateFlags, threads_y);
+        Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(0, dim, calculateFlags, threads_y);
 
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
+        NDRange local(THREADS_X, threads_y);
+        NDRange global(groups_all[0] * groups_all[2] * local[0],
+                        groups_all[1] * groups_all[3] * local[1]);
 
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto scanOp = KernelFunctor<Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        uint, uint,
-                                        uint, uint>(ker);
+        auto scanOp = KernelFunctor<Buffer, KParam,
+                                    Buffer, KParam,
+                                    Buffer, KParam,
+                                    uint, uint,
+                                    uint, uint>(ker);
 
-            scanOp(EnqueueArgs(getQueue(), global, local),
-                   *out.data, out.info, *in.data, in.info, *key.data, key.info,
-                   groups_all[0], groups_all[1], groups_all[dim], lim);
+        scanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info, *key.data, key.info,
+                groups_all[0], groups_all[1], groups_all[dim], lim);
 
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
@@ -187,102 +177,92 @@ namespace kernel
                                    int dim, uint threads_y,
                                    const uint groups_all[4])
     {
-        try {
-            Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(2, dim, false, threads_y);
+        Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(2, dim, false, threads_y);
 
-            NDRange local(THREADS_X, threads_y);
-            NDRange global(groups_all[0] * groups_all[2] * local[0],
-                           groups_all[1] * groups_all[3] * local[1]);
+        NDRange local(THREADS_X, threads_y);
+        NDRange global(groups_all[0] * groups_all[2] * local[0],
+                        groups_all[1] * groups_all[3] * local[1]);
 
-            uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            auto bcastOp = KernelFunctor<Buffer, KParam,
-                                         Buffer, KParam,
-                                         Buffer, KParam,
-                                         uint, uint,
-                                         uint, uint>(ker);
+        auto bcastOp = KernelFunctor<Buffer, KParam,
+                                      Buffer, KParam,
+                                      Buffer, KParam,
+                                      uint, uint,
+                                      uint, uint>(ker);
 
-            bcastOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
-                    groups_all[0], groups_all[1], groups_all[dim], lim);
+        bcastOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
+                groups_all[0], groups_all[1], groups_all[dim], lim);
 
-            CL_DEBUG_FINISH(getQueue());
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
-        }
+        CL_DEBUG_FINISH(getQueue());
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
     void scan_dim(Param &out, const Param &in, const Param &key, int dim)
     {
-        try {
-            uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
-            uint threads_x = THREADS_X;
+        uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+        uint threads_x = THREADS_X;
 
-            uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
-                                 (uint)out.info.dims[1],
-                                 (uint)out.info.dims[2],
-                                 (uint)out.info.dims[3]};
+        uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
+                              (uint)out.info.dims[1],
+                              (uint)out.info.dims[2],
+                              (uint)out.info.dims[3]};
 
-            groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+        groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
 
-            if (groups_all[dim] == 1) {
+        if (groups_all[dim] == 1) {
 
-                scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
-                                              dim, true,
-                                              threads_y,
-                                              groups_all);
-            } else {
+            scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
+                                          dim, true,
+                                          threads_y,
+                                          groups_all);
+        } else {
 
-                Param tmp = out;
-
-                tmp.info.dims[dim] = groups_all[dim];
-                tmp.info.strides[0] = 1;
-                for (int k = 1; k < 4; k++) {
-                    tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
-                }
-                Param tmpflg = tmp;
-                Param tmpid = tmp;
-
-                int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-                // FIXME: Do I need to free this ?
-                tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-                tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
-                tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
-
-                scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
-                                              dim,
-                                              threads_y,
-                                              groups_all);
-
-                int gdim = groups_all[dim];
-                groups_all[dim] = 1;
-
-                if (op == af_notzero_t) {
-                    scan_dim_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
-                                                        dim, false,
-                                                        threads_y,
-                                                        groups_all);
-                } else {
-                    scan_dim_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
-                                                        dim, false,
-                                                        threads_y,
-                                                        groups_all);
-                }
-
-                groups_all[dim] = gdim;
-                bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
-                                               dim,
-                                               threads_y,
-                                               groups_all);
-                bufferFree(tmp.data);
-                bufferFree(tmpflg.data);
-                bufferFree(tmpid.data);
+            Param tmp = out;
+
+            tmp.info.dims[dim] = groups_all[dim];
+            tmp.info.strides[0] = 1;
+            for (int k = 1; k < 4; k++) {
+                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+            }
+            Param tmpflg = tmp;
+            Param tmpid = tmp;
+
+            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+            // FIXME: Do I need to free this ?
+            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+            tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+            tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
+
+            scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
+                                          dim,
+                                          threads_y,
+                                          groups_all);
+
+            int gdim = groups_all[dim];
+            groups_all[dim] = 1;
+
+            if (op == af_notzero_t) {
+                scan_dim_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
+                                                    dim, false,
+                                                    threads_y,
+                                                    groups_all);
+            } else {
+                scan_dim_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
+                                                    dim, false,
+                                                    threads_y,
+                                                    groups_all);
             }
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
-            throw;
+
+            groups_all[dim] = gdim;
+            bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
+                                            dim,
+                                            threads_y,
+                                            groups_all);
+            bufferFree(tmp.data);
+            bufferFree(tmpflg.data);
+            bufferFree(tmpid.data);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 3d474debd7..12d55fa60a 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -99,19 +99,15 @@ namespace opencl
         template<typename T>
         void select(Param out, Param cond, Param a, Param b, int ndims)
         {
-            try {
-                bool is_same = true;
-                for (int i = 0; i < 4; i++) {
-                    is_same &= (a.info.dims[i] == b.info.dims[i]);
-                }
-
-                if (is_same) {
-                    select_launcher<T, true >(out, cond, a, b, ndims);
-                } else {
-                    select_launcher<T, false>(out, cond, a, b, ndims);
-                }
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
+            bool is_same = true;
+            for (int i = 0; i < 4; i++) {
+                is_same &= (a.info.dims[i] == b.info.dims[i]);
+            }
+
+            if (is_same) {
+                select_launcher<T, true >(out, cond, a, b, ndims);
+            } else {
+                select_launcher<T, false>(out, cond, a, b, ndims);
             }
         }
 
diff --git a/src/backend/opencl/kernel/shift.hpp b/src/backend/opencl/kernel/shift.hpp
index ed40038d6a..1bbfbe9fdc 100644
--- a/src/backend/opencl/kernel/shift.hpp
+++ b/src/backend/opencl/kernel/shift.hpp
@@ -40,57 +40,52 @@ namespace opencl
         template<typename T>
         void shift(Param out, const Param in, const int *sdims)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   shiftProgs;
-                static std::map<int, Kernel *> shiftKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   shiftProgs;
+            static std::map<int, Kernel *> shiftKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, shift_cl, shift_cl_len, options.str());
-                    shiftProgs[device] = new Program(prog);
-                    shiftKernels[device] = new Kernel(*shiftProgs[device], "shift_kernel");
-                });
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, shift_cl, shift_cl_len, options.str());
+                shiftProgs[device] = new Program(prog);
+                shiftKernels[device] = new Kernel(*shiftProgs[device], "shift_kernel");
+            });
 
-                auto shiftOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                          const int, const int, const int, const int,
-                                          const int, const int> (*shiftKernels[device]);
+            auto shiftOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
+                                      const int, const int, const int, const int,
+                                      const int, const int> (*shiftKernels[device]);
 
-                NDRange local(TX, TY, 1);
+            NDRange local(TX, TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], TILEY);
-                NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                               local[1] * blocksPerMatY * out.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(out.info.dims[0], TILEX);
+            int blocksPerMatY = divup(out.info.dims[1], TILEY);
+            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                            local[1] * blocksPerMatY * out.info.dims[3],
+                            1);
 
-                int sdims_[4];
-                // Need to do this because we are mapping output to input in the kernel
-                for(int i = 0; i < 4; i++) {
-                    // sdims_[i] will always be positive and always [0, oDims[i]].
-                    // Negative shifts are converted to position by going the other way round
-                    sdims_[i] = -(sdims[i] % (int)out.info.dims[i]) + out.info.dims[i] * (sdims[i] > 0);
-                    assert(sdims_[i] >= 0 && sdims_[i] <= out.info.dims[i]);
-                }
+            int sdims_[4];
+            // Need to do this because we are mapping output to input in the kernel
+            for(int i = 0; i < 4; i++) {
+                // sdims_[i] will always be positive and always [0, oDims[i]].
+                // Negative shifts are converted to position by going the other way round
+                sdims_[i] = -(sdims[i] % (int)out.info.dims[i]) + out.info.dims[i] * (sdims[i] > 0);
+                assert(sdims_[i] >= 0 && sdims_[i] <= out.info.dims[i]);
+            }
 
-                shiftOp(EnqueueArgs(getQueue(), global, local),
-                        *out.data, *in.data, out.info, in.info,
-                        sdims_[0], sdims_[1], sdims_[2], sdims_[3],
-                        blocksPerMatX, blocksPerMatY);
+            shiftOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, *in.data, out.info, in.info,
+                    sdims_[0], sdims_[1], sdims_[2], sdims_[3],
+                    blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index a76d46882c..af01b60d45 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -415,411 +415,406 @@ void sift(unsigned* out_feat,
           const float feature_ratio,
           const bool compute_GLOH)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> siftProgs;
-        static std::map<int, Kernel*>  suKernel;
-        static std::map<int, Kernel*>  deKernel;
-        static std::map<int, Kernel*>  ieKernel;
-        static std::map<int, Kernel*>  coKernel;
-        static std::map<int, Kernel*>  rdKernel;
-        static std::map<int, Kernel*>  cdKernel;
-        static std::map<int, Kernel*>  cgKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
-                siftProgs[device] = new Program(prog);
-
-                suKernel[device] = new Kernel(*siftProgs[device], "sub");
-                deKernel[device] = new Kernel(*siftProgs[device], "detectExtrema");
-                ieKernel[device] = new Kernel(*siftProgs[device], "interpolateExtrema");
-                coKernel[device] = new Kernel(*siftProgs[device], "calcOrientation");
-                rdKernel[device] = new Kernel(*siftProgs[device], "removeDuplicates");
-                cdKernel[device] = new Kernel(*siftProgs[device], "computeDescriptor");
-                cgKernel[device] = new Kernel(*siftProgs[device], "computeGLOHDescriptor");
-            });
-
-        const unsigned min_dim = (double_input) ? min(img.info.dims[0]*2, img.info.dims[1]*2)
-                                                : min(img.info.dims[0], img.info.dims[1]);
-        const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> siftProgs;
+    static std::map<int, Kernel*>  suKernel;
+    static std::map<int, Kernel*>  deKernel;
+    static std::map<int, Kernel*>  ieKernel;
+    static std::map<int, Kernel*>  coKernel;
+    static std::map<int, Kernel*>  rdKernel;
+    static std::map<int, Kernel*>  cdKernel;
+    static std::map<int, Kernel*>  cgKernel;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName();
+
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-        Param init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
+            cl::Program prog;
+            buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
+            siftProgs[device] = new Program(prog);
 
-        std::vector<Param> gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+            suKernel[device] = new Kernel(*siftProgs[device], "sub");
+            deKernel[device] = new Kernel(*siftProgs[device], "detectExtrema");
+            ieKernel[device] = new Kernel(*siftProgs[device], "interpolateExtrema");
+            coKernel[device] = new Kernel(*siftProgs[device], "calcOrientation");
+            rdKernel[device] = new Kernel(*siftProgs[device], "removeDuplicates");
+            cdKernel[device] = new Kernel(*siftProgs[device], "computeDescriptor");
+            cgKernel[device] = new Kernel(*siftProgs[device], "computeGLOHDescriptor");
+        });
 
-        std::vector<Param> dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, suKernel[device]);
+    const unsigned min_dim = (double_input) ? min(img.info.dims[0]*2, img.info.dims[1]*2)
+                                            : min(img.info.dims[0], img.info.dims[1]);
+    const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
-        std::vector<cl::Buffer*> d_x_pyr(n_octaves, NULL);
-        std::vector<cl::Buffer*> d_y_pyr(n_octaves, NULL);
-        std::vector<cl::Buffer*> d_response_pyr(n_octaves, NULL);
-        std::vector<cl::Buffer*> d_size_pyr(n_octaves, NULL);
-        std::vector<cl::Buffer*> d_ori_pyr(n_octaves, NULL);
-        std::vector<cl::Buffer*> d_desc_pyr(n_octaves, NULL);
-        std::vector<unsigned> feat_pyr(n_octaves, 0);
-        unsigned total_feat = 0;
+    Param init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
 
-        const unsigned d = DescrWidth;
-        const unsigned n = DescrHistBins;
-        const unsigned rb = GLOHRadialBins;
-        const unsigned ab = GLOHAngularBins;
-        const unsigned hb = GLOHHistBins;
-        const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
+    std::vector<Param> gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-        cl::Buffer* d_count = bufferAlloc(sizeof(unsigned));
+    std::vector<Param> dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, suKernel[device]);
 
-        for (unsigned o = 0; o < n_octaves; o++) {
-            if (dog_pyr[o].info.dims[0]-2*ImgBorder < 1 ||
-                dog_pyr[o].info.dims[1]-2*ImgBorder < 1)
-                continue;
+    std::vector<cl::Buffer*> d_x_pyr(n_octaves, NULL);
+    std::vector<cl::Buffer*> d_y_pyr(n_octaves, NULL);
+    std::vector<cl::Buffer*> d_response_pyr(n_octaves, NULL);
+    std::vector<cl::Buffer*> d_size_pyr(n_octaves, NULL);
+    std::vector<cl::Buffer*> d_ori_pyr(n_octaves, NULL);
+    std::vector<cl::Buffer*> d_desc_pyr(n_octaves, NULL);
+    std::vector<unsigned> feat_pyr(n_octaves, 0);
+    unsigned total_feat = 0;
 
-            const unsigned imel = dog_pyr[o].info.dims[0] * dog_pyr[o].info.dims[1];
-            const unsigned max_feat = ceil(imel * feature_ratio);
+    const unsigned d = DescrWidth;
+    const unsigned n = DescrHistBins;
+    const unsigned rb = GLOHRadialBins;
+    const unsigned ab = GLOHAngularBins;
+    const unsigned hb = GLOHHistBins;
+    const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
 
-            cl::Buffer* d_extrema_x     = bufferAlloc(max_feat * sizeof(float));
-            cl::Buffer* d_extrema_y     = bufferAlloc(max_feat * sizeof(float));
-            cl::Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
+    cl::Buffer* d_count = bufferAlloc(sizeof(unsigned));
 
-            unsigned extrema_feat = 0;
-            getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &extrema_feat);
+    for (unsigned o = 0; o < n_octaves; o++) {
+        if (dog_pyr[o].info.dims[0]-2*ImgBorder < 1 ||
+            dog_pyr[o].info.dims[1]-2*ImgBorder < 1)
+            continue;
 
-            int dim0 = dog_pyr[o].info.dims[0];
-            int dim1 = dog_pyr[o].info.dims[1];
+        const unsigned imel = dog_pyr[o].info.dims[0] * dog_pyr[o].info.dims[1];
+        const unsigned max_feat = ceil(imel * feature_ratio);
 
-            const int blk_x = divup(dim0-2*ImgBorder, SIFT_THREADS_X);
-            const int blk_y = divup(dim1-2*ImgBorder, SIFT_THREADS_Y);
-            const NDRange local(SIFT_THREADS_X, SIFT_THREADS_Y);
-            const NDRange global(blk_x * SIFT_THREADS_X, blk_y * SIFT_THREADS_Y);
+        cl::Buffer* d_extrema_x     = bufferAlloc(max_feat * sizeof(float));
+        cl::Buffer* d_extrema_y     = bufferAlloc(max_feat * sizeof(float));
+        cl::Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
 
-            float extrema_thr = 0.5f * contrast_thr / n_layers;
+        unsigned extrema_feat = 0;
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &extrema_feat);
 
-            auto deOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                                    Buffer, KParam, unsigned, float,
-                                    LocalSpaceArg> (*deKernel[device]);
+        int dim0 = dog_pyr[o].info.dims[0];
+        int dim1 = dog_pyr[o].info.dims[1];
 
-            deOp(EnqueueArgs(getQueue(), global, local),
-                 *d_extrema_x, *d_extrema_y, *d_extrema_layer, *d_count,
-                 *dog_pyr[o].data, dog_pyr[o].info, max_feat, extrema_thr,
-                 cl::Local((SIFT_THREADS_X+2) * (SIFT_THREADS_Y+2) * 3 * sizeof(float)));
-            CL_DEBUG_FINISH(getQueue());
+        const int blk_x = divup(dim0-2*ImgBorder, SIFT_THREADS_X);
+        const int blk_y = divup(dim1-2*ImgBorder, SIFT_THREADS_Y);
+        const NDRange local(SIFT_THREADS_X, SIFT_THREADS_Y);
+        const NDRange global(blk_x * SIFT_THREADS_X, blk_y * SIFT_THREADS_Y);
 
-            getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &extrema_feat);
-            extrema_feat = min(extrema_feat, max_feat);
+        float extrema_thr = 0.5f * contrast_thr / n_layers;
 
-            if (extrema_feat == 0) {
-                bufferFree(d_extrema_x);
-                bufferFree(d_extrema_y);
-                bufferFree(d_extrema_layer);
+        auto deOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
+                                Buffer, KParam, unsigned, float,
+                                LocalSpaceArg> (*deKernel[device]);
 
-                continue;
-            }
+        deOp(EnqueueArgs(getQueue(), global, local),
+              *d_extrema_x, *d_extrema_y, *d_extrema_layer, *d_count,
+              *dog_pyr[o].data, dog_pyr[o].info, max_feat, extrema_thr,
+              cl::Local((SIFT_THREADS_X+2) * (SIFT_THREADS_Y+2) * 3 * sizeof(float)));
+        CL_DEBUG_FINISH(getQueue());
 
-            unsigned interp_feat = 0;
-            getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &interp_feat);
-
-            cl::Buffer* d_interp_x = bufferAlloc(extrema_feat * sizeof(float));
-            cl::Buffer* d_interp_y = bufferAlloc(extrema_feat * sizeof(float));
-            cl::Buffer* d_interp_layer = bufferAlloc(extrema_feat * sizeof(unsigned));
-            cl::Buffer* d_interp_response = bufferAlloc(extrema_feat * sizeof(float));
-            cl::Buffer* d_interp_size = bufferAlloc(extrema_feat * sizeof(float));
-
-            const int blk_x_interp = divup(extrema_feat, SIFT_THREADS);
-            const NDRange local_interp(SIFT_THREADS, 1);
-            const NDRange global_interp(blk_x_interp * SIFT_THREADS, 1);
-
-            auto ieOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer, unsigned,
-                                    Buffer, KParam, unsigned, unsigned, unsigned,
-                                    float, float, float, float> (*ieKernel[device]);
-
-            ieOp(EnqueueArgs(getQueue(), global_interp, local_interp),
-                 *d_interp_x, *d_interp_y, *d_interp_layer,
-                 *d_interp_response, *d_interp_size, *d_count,
-                 *d_extrema_x, *d_extrema_y, *d_extrema_layer, extrema_feat,
-                 *dog_pyr[o].data, dog_pyr[o].info, extrema_feat, o, n_layers,
-                 contrast_thr, edge_thr, init_sigma, img_scale);
-            CL_DEBUG_FINISH(getQueue());
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &extrema_feat);
+        extrema_feat = min(extrema_feat, max_feat);
 
+        if (extrema_feat == 0) {
             bufferFree(d_extrema_x);
             bufferFree(d_extrema_y);
             bufferFree(d_extrema_layer);
 
-            getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &interp_feat);
-            interp_feat = min(interp_feat, extrema_feat);
+            continue;
+        }
 
-            if (interp_feat == 0) {
-                bufferFree(d_interp_x);
-                bufferFree(d_interp_y);
-                bufferFree(d_interp_layer);
-                bufferFree(d_interp_response);
-                bufferFree(d_interp_size);
+        unsigned interp_feat = 0;
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &interp_feat);
+
+        cl::Buffer* d_interp_x = bufferAlloc(extrema_feat * sizeof(float));
+        cl::Buffer* d_interp_y = bufferAlloc(extrema_feat * sizeof(float));
+        cl::Buffer* d_interp_layer = bufferAlloc(extrema_feat * sizeof(unsigned));
+        cl::Buffer* d_interp_response = bufferAlloc(extrema_feat * sizeof(float));
+        cl::Buffer* d_interp_size = bufferAlloc(extrema_feat * sizeof(float));
+
+        const int blk_x_interp = divup(extrema_feat, SIFT_THREADS);
+        const NDRange local_interp(SIFT_THREADS, 1);
+        const NDRange global_interp(blk_x_interp * SIFT_THREADS, 1);
+
+        auto ieOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer, unsigned,
+                                Buffer, KParam, unsigned, unsigned, unsigned,
+                                float, float, float, float> (*ieKernel[device]);
+
+        ieOp(EnqueueArgs(getQueue(), global_interp, local_interp),
+              *d_interp_x, *d_interp_y, *d_interp_layer,
+              *d_interp_response, *d_interp_size, *d_count,
+              *d_extrema_x, *d_extrema_y, *d_extrema_layer, extrema_feat,
+              *dog_pyr[o].data, dog_pyr[o].info, extrema_feat, o, n_layers,
+              contrast_thr, edge_thr, init_sigma, img_scale);
+        CL_DEBUG_FINISH(getQueue());
 
-                continue;
-            }
+        bufferFree(d_extrema_x);
+        bufferFree(d_extrema_y);
+        bufferFree(d_extrema_layer);
 
-            compute::command_queue queue(getQueue()());
-            compute::context context(getContext()());
-
-            compute::buffer buf_interp_x((*d_interp_x)(), true);
-            compute::buffer buf_interp_y((*d_interp_y)(), true);
-            compute::buffer buf_interp_layer((*d_interp_layer)(), true);
-            compute::buffer buf_interp_response((*d_interp_response)(), true);
-            compute::buffer buf_interp_size((*d_interp_size)(), true);
-
-            compute::buffer_iterator<float> interp_x_begin = compute::make_buffer_iterator<float>(buf_interp_x, 0);
-            compute::buffer_iterator<float> interp_y_begin = compute::make_buffer_iterator<float>(buf_interp_y, 0);
-            compute::buffer_iterator<unsigned> interp_layer_begin = compute::make_buffer_iterator<unsigned>(buf_interp_layer, 0);
-            compute::buffer_iterator<float> interp_response_begin = compute::make_buffer_iterator<float>(buf_interp_response, 0);
-            compute::buffer_iterator<float> interp_size_begin = compute::make_buffer_iterator<float>(buf_interp_size, 0);
-
-            compute::vector<int> permutation(interp_feat, context);
-            compute::iota(permutation.begin(), permutation.end(), 0, queue);
-
-            update_permutation<float>(interp_x_begin, permutation, queue);
-            update_permutation<float>(interp_y_begin, permutation, queue);
-            update_permutation<unsigned>(interp_layer_begin, permutation, queue);
-            update_permutation<float>(interp_response_begin, permutation, queue);
-            update_permutation<float>(interp_size_begin, permutation, queue);
-
-            apply_permutation<float>(interp_x_begin, permutation, queue);
-            apply_permutation<float>(interp_y_begin, permutation, queue);
-            apply_permutation<unsigned>(interp_layer_begin, permutation, queue);
-            apply_permutation<float>(interp_response_begin, permutation, queue);
-            apply_permutation<float>(interp_size_begin, permutation, queue);
-
-            unsigned nodup_feat = 0;
-            getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &nodup_feat);
-
-            cl::Buffer* d_nodup_x = bufferAlloc(interp_feat * sizeof(float));
-            cl::Buffer* d_nodup_y = bufferAlloc(interp_feat * sizeof(float));
-            cl::Buffer* d_nodup_layer = bufferAlloc(interp_feat * sizeof(unsigned));
-            cl::Buffer* d_nodup_response = bufferAlloc(interp_feat * sizeof(float));
-            cl::Buffer* d_nodup_size = bufferAlloc(interp_feat * sizeof(float));
-
-            const int blk_x_nodup = divup(extrema_feat, SIFT_THREADS);
-            const NDRange local_nodup(SIFT_THREADS, 1);
-            const NDRange global_nodup(blk_x_nodup * SIFT_THREADS, 1);
-
-            auto rdOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer, Buffer, Buffer,
-                                    unsigned> (*rdKernel[device]);
-
-            rdOp(EnqueueArgs(getQueue(), global_nodup, local_nodup),
-                 *d_nodup_x, *d_nodup_y, *d_nodup_layer,
-                 *d_nodup_response, *d_nodup_size, *d_count,
-                 *d_interp_x, *d_interp_y, *d_interp_layer,
-                 *d_interp_response, *d_interp_size, interp_feat);
-            CL_DEBUG_FINISH(getQueue());
-
-            getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &nodup_feat);
-            nodup_feat = min(nodup_feat, interp_feat);
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &interp_feat);
+        interp_feat = min(interp_feat, extrema_feat);
 
+        if (interp_feat == 0) {
             bufferFree(d_interp_x);
             bufferFree(d_interp_y);
             bufferFree(d_interp_layer);
             bufferFree(d_interp_response);
             bufferFree(d_interp_size);
 
-            unsigned oriented_feat = 0;
-            getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &oriented_feat);
-            const unsigned max_oriented_feat = nodup_feat * 3;
-
-            cl::Buffer* d_oriented_x        = bufferAlloc(max_oriented_feat * sizeof(float));
-            cl::Buffer* d_oriented_y        = bufferAlloc(max_oriented_feat * sizeof(float));
-            cl::Buffer* d_oriented_layer    = bufferAlloc(max_oriented_feat * sizeof(unsigned));
-            cl::Buffer* d_oriented_response = bufferAlloc(max_oriented_feat * sizeof(float));
-            cl::Buffer* d_oriented_size     = bufferAlloc(max_oriented_feat * sizeof(float));
-            cl::Buffer* d_oriented_ori      = bufferAlloc(max_oriented_feat * sizeof(float));
-
-            const int blk_x_ori = divup(nodup_feat, SIFT_THREADS_Y);
-            const NDRange local_ori(SIFT_THREADS_X, SIFT_THREADS_Y);
-            const NDRange global_ori(SIFT_THREADS_X, blk_x_ori * SIFT_THREADS_Y);
-
-            auto coOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                                    Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                                    Buffer, KParam, unsigned, unsigned, int,
-                                    LocalSpaceArg> (*coKernel[device]);
-
-            coOp(EnqueueArgs(getQueue(), global_ori, local_ori),
-                 *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-                 *d_oriented_response, *d_oriented_size, *d_oriented_ori, *d_count,
-                 *d_nodup_x, *d_nodup_y, *d_nodup_layer,
-                 *d_nodup_response, *d_nodup_size, nodup_feat,
-                 *gauss_pyr[o].data, gauss_pyr[o].info, max_oriented_feat, o, (int)double_input,
-                 cl::Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
-            CL_DEBUG_FINISH(getQueue());
-
-            bufferFree(d_nodup_x);
-            bufferFree(d_nodup_y);
-            bufferFree(d_nodup_layer);
-            bufferFree(d_nodup_response);
-            bufferFree(d_nodup_size);
-
-            getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &oriented_feat);
-            oriented_feat = min(oriented_feat, max_oriented_feat);
-
-            if (oriented_feat == 0) {
-                bufferFree(d_oriented_x);
-                bufferFree(d_oriented_y);
-                bufferFree(d_oriented_layer);
-                bufferFree(d_oriented_response);
-                bufferFree(d_oriented_size);
-
-                continue;
-            }
+            continue;
+        }
 
-            cl::Buffer* d_desc = bufferAlloc(oriented_feat * desc_len * sizeof(float));
+        compute::command_queue queue(getQueue()());
+        compute::context context(getContext()());
+
+        compute::buffer buf_interp_x((*d_interp_x)(), true);
+        compute::buffer buf_interp_y((*d_interp_y)(), true);
+        compute::buffer buf_interp_layer((*d_interp_layer)(), true);
+        compute::buffer buf_interp_response((*d_interp_response)(), true);
+        compute::buffer buf_interp_size((*d_interp_size)(), true);
+
+        compute::buffer_iterator<float> interp_x_begin = compute::make_buffer_iterator<float>(buf_interp_x, 0);
+        compute::buffer_iterator<float> interp_y_begin = compute::make_buffer_iterator<float>(buf_interp_y, 0);
+        compute::buffer_iterator<unsigned> interp_layer_begin = compute::make_buffer_iterator<unsigned>(buf_interp_layer, 0);
+        compute::buffer_iterator<float> interp_response_begin = compute::make_buffer_iterator<float>(buf_interp_response, 0);
+        compute::buffer_iterator<float> interp_size_begin = compute::make_buffer_iterator<float>(buf_interp_size, 0);
+
+        compute::vector<int> permutation(interp_feat, context);
+        compute::iota(permutation.begin(), permutation.end(), 0, queue);
+
+        update_permutation<float>(interp_x_begin, permutation, queue);
+        update_permutation<float>(interp_y_begin, permutation, queue);
+        update_permutation<unsigned>(interp_layer_begin, permutation, queue);
+        update_permutation<float>(interp_response_begin, permutation, queue);
+        update_permutation<float>(interp_size_begin, permutation, queue);
+
+        apply_permutation<float>(interp_x_begin, permutation, queue);
+        apply_permutation<float>(interp_y_begin, permutation, queue);
+        apply_permutation<unsigned>(interp_layer_begin, permutation, queue);
+        apply_permutation<float>(interp_response_begin, permutation, queue);
+        apply_permutation<float>(interp_size_begin, permutation, queue);
+
+        unsigned nodup_feat = 0;
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &nodup_feat);
+
+        cl::Buffer* d_nodup_x = bufferAlloc(interp_feat * sizeof(float));
+        cl::Buffer* d_nodup_y = bufferAlloc(interp_feat * sizeof(float));
+        cl::Buffer* d_nodup_layer = bufferAlloc(interp_feat * sizeof(unsigned));
+        cl::Buffer* d_nodup_response = bufferAlloc(interp_feat * sizeof(float));
+        cl::Buffer* d_nodup_size = bufferAlloc(interp_feat * sizeof(float));
+
+        const int blk_x_nodup = divup(extrema_feat, SIFT_THREADS);
+        const NDRange local_nodup(SIFT_THREADS, 1);
+        const NDRange global_nodup(blk_x_nodup * SIFT_THREADS, 1);
+
+        auto rdOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer, Buffer, Buffer,
+                                unsigned> (*rdKernel[device]);
+
+        rdOp(EnqueueArgs(getQueue(), global_nodup, local_nodup),
+              *d_nodup_x, *d_nodup_y, *d_nodup_layer,
+              *d_nodup_response, *d_nodup_size, *d_count,
+              *d_interp_x, *d_interp_y, *d_interp_layer,
+              *d_interp_response, *d_interp_size, interp_feat);
+        CL_DEBUG_FINISH(getQueue());
 
-            float scale = 1.f/(1 << o);
-            if (double_input) scale *= 2.f;
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &nodup_feat);
+        nodup_feat = min(nodup_feat, interp_feat);
+
+        bufferFree(d_interp_x);
+        bufferFree(d_interp_y);
+        bufferFree(d_interp_layer);
+        bufferFree(d_interp_response);
+        bufferFree(d_interp_size);
+
+        unsigned oriented_feat = 0;
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &oriented_feat);
+        const unsigned max_oriented_feat = nodup_feat * 3;
+
+        cl::Buffer* d_oriented_x        = bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_y        = bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_layer    = bufferAlloc(max_oriented_feat * sizeof(unsigned));
+        cl::Buffer* d_oriented_response = bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_size     = bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_ori      = bufferAlloc(max_oriented_feat * sizeof(float));
+
+        const int blk_x_ori = divup(nodup_feat, SIFT_THREADS_Y);
+        const NDRange local_ori(SIFT_THREADS_X, SIFT_THREADS_Y);
+        const NDRange global_ori(SIFT_THREADS_X, blk_x_ori * SIFT_THREADS_Y);
+
+        auto coOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                                Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
+                                Buffer, KParam, unsigned, unsigned, int,
+                                LocalSpaceArg> (*coKernel[device]);
+
+        coOp(EnqueueArgs(getQueue(), global_ori, local_ori),
+              *d_oriented_x, *d_oriented_y, *d_oriented_layer,
+              *d_oriented_response, *d_oriented_size, *d_oriented_ori, *d_count,
+              *d_nodup_x, *d_nodup_y, *d_nodup_layer,
+              *d_nodup_response, *d_nodup_size, nodup_feat,
+              *gauss_pyr[o].data, gauss_pyr[o].info, max_oriented_feat, o, (int)double_input,
+              cl::Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
+        CL_DEBUG_FINISH(getQueue());
 
-            const int blk_x_desc = divup(oriented_feat, 1);
-            const NDRange local_desc(SIFT_THREADS, 1);
-            const NDRange global_desc(SIFT_THREADS, blk_x_desc);
+        bufferFree(d_nodup_x);
+        bufferFree(d_nodup_y);
+        bufferFree(d_nodup_layer);
+        bufferFree(d_nodup_response);
+        bufferFree(d_nodup_size);
 
-            const unsigned histsz = 8;
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &oriented_feat);
+        oriented_feat = min(oriented_feat, max_oriented_feat);
 
-            if (compute_GLOH) {
-                auto cgOp = KernelFunctor<Buffer, unsigned, unsigned,
-                                        Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                                        Buffer, KParam, int, unsigned, unsigned, unsigned, float, int,
-                                        LocalSpaceArg> (*cgKernel[device]);
+        if (oriented_feat == 0) {
+            bufferFree(d_oriented_x);
+            bufferFree(d_oriented_y);
+            bufferFree(d_oriented_layer);
+            bufferFree(d_oriented_response);
+            bufferFree(d_oriented_size);
 
-                cgOp(EnqueueArgs(getQueue(), global_desc, local_desc),
-                     *d_desc, desc_len, histsz,
-                     *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-                     *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
-                     *gauss_pyr[o].data, gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
-                     cl::Local(desc_len * (histsz+1) * sizeof(float)));
-            }
-            else {
-                auto cdOp = KernelFunctor<Buffer, unsigned, unsigned,
-                                        Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                                        Buffer, KParam, int, int, float, int,
-                                        LocalSpaceArg> (*cdKernel[device]);
-
-                cdOp(EnqueueArgs(getQueue(), global_desc, local_desc),
-                     *d_desc, desc_len, histsz,
-                     *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-                     *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
-                     *gauss_pyr[o].data, gauss_pyr[o].info, d, n, scale, n_layers,
-                     cl::Local(desc_len * (histsz+1) * sizeof(float)));
-            }
-            CL_DEBUG_FINISH(getQueue());
-
-            total_feat += oriented_feat;
-            feat_pyr[o] = oriented_feat;
-
-            if (oriented_feat > 0) {
-                d_x_pyr[o] = d_oriented_x;
-                d_y_pyr[o] = d_oriented_y;
-                d_response_pyr[o] = d_oriented_response;
-                d_ori_pyr[o] = d_oriented_ori;
-                d_size_pyr[o] = d_oriented_size;
-                d_desc_pyr[o] = d_desc;
-            }
+            continue;
         }
 
-        bufferFree(d_count);
+        cl::Buffer* d_desc = bufferAlloc(oriented_feat * desc_len * sizeof(float));
 
-        for (size_t i = 0; i < gauss_pyr.size(); i++)
-            bufferFree(gauss_pyr[i].data);
-        for (size_t i = 0; i < dog_pyr.size(); i++)
-            bufferFree(dog_pyr[i].data);
+        float scale = 1.f/(1 << o);
+        if (double_input) scale *= 2.f;
 
-        // If no features are found, set found features to 0 and return
-        if (total_feat == 0) {
-            *out_feat = 0;
-            return;
-        }
+        const int blk_x_desc = divup(oriented_feat, 1);
+        const NDRange local_desc(SIFT_THREADS, 1);
+        const NDRange global_desc(SIFT_THREADS, blk_x_desc);
 
-        // Allocate output memory
-        x_out.info.dims[0] = total_feat;
-        x_out.info.strides[0] = 1;
-        y_out.info.dims[0] = total_feat;
-        y_out.info.strides[0] = 1;
-        score_out.info.dims[0] = total_feat;
-        score_out.info.strides[0] = 1;
-        ori_out.info.dims[0] = total_feat;
-        ori_out.info.strides[0] = 1;
-        size_out.info.dims[0] = total_feat;
-        size_out.info.strides[0] = 1;
-
-        desc_out.info.dims[0] = desc_len;
-        desc_out.info.strides[0] = 1;
-        desc_out.info.dims[1] = total_feat;
-        desc_out.info.strides[1] = desc_out.info.dims[0];
-
-        for (int k = 1; k < 4; k++) {
-            x_out.info.dims[k] = 1;
-            x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
-            y_out.info.dims[k] = 1;
-            y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
-            score_out.info.dims[k] = 1;
-            score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
-            ori_out.info.dims[k] = 1;
-            ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
-            size_out.info.dims[k] = 1;
-            size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
-            if (k > 1) {
-                desc_out.info.dims[k] = 1;
-                desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
-            }
+        const unsigned histsz = 8;
+
+        if (compute_GLOH) {
+            auto cgOp = KernelFunctor<Buffer, unsigned, unsigned,
+                                    Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
+                                    Buffer, KParam, int, unsigned, unsigned, unsigned, float, int,
+                                    LocalSpaceArg> (*cgKernel[device]);
+
+            cgOp(EnqueueArgs(getQueue(), global_desc, local_desc),
+                  *d_desc, desc_len, histsz,
+                  *d_oriented_x, *d_oriented_y, *d_oriented_layer,
+                  *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
+                  *gauss_pyr[o].data, gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
+                  cl::Local(desc_len * (histsz+1) * sizeof(float)));
+        }
+        else {
+            auto cdOp = KernelFunctor<Buffer, unsigned, unsigned,
+                                    Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
+                                    Buffer, KParam, int, int, float, int,
+                                    LocalSpaceArg> (*cdKernel[device]);
+
+            cdOp(EnqueueArgs(getQueue(), global_desc, local_desc),
+                  *d_desc, desc_len, histsz,
+                  *d_oriented_x, *d_oriented_y, *d_oriented_layer,
+                  *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
+                  *gauss_pyr[o].data, gauss_pyr[o].info, d, n, scale, n_layers,
+                  cl::Local(desc_len * (histsz+1) * sizeof(float)));
         }
+        CL_DEBUG_FINISH(getQueue());
 
-        if (total_feat > 0) {
-            size_t out_sz  = total_feat * sizeof(float);
-            x_out.data     = bufferAlloc(out_sz);
-            y_out.data     = bufferAlloc(out_sz);
-            score_out.data = bufferAlloc(out_sz);
-            ori_out.data   = bufferAlloc(out_sz);
-            size_out.data  = bufferAlloc(out_sz);
+        total_feat += oriented_feat;
+        feat_pyr[o] = oriented_feat;
 
-            size_t desc_sz = total_feat * desc_len * sizeof(unsigned);
-            desc_out.data  = bufferAlloc(desc_sz);
+        if (oriented_feat > 0) {
+            d_x_pyr[o] = d_oriented_x;
+            d_y_pyr[o] = d_oriented_y;
+            d_response_pyr[o] = d_oriented_response;
+            d_ori_pyr[o] = d_oriented_ori;
+            d_size_pyr[o] = d_oriented_size;
+            d_desc_pyr[o] = d_desc;
         }
+    }
+
+    bufferFree(d_count);
+
+    for (size_t i = 0; i < gauss_pyr.size(); i++)
+        bufferFree(gauss_pyr[i].data);
+    for (size_t i = 0; i < dog_pyr.size(); i++)
+        bufferFree(dog_pyr[i].data);
 
-        unsigned offset = 0;
-        for (unsigned i = 0; i < n_octaves; i++) {
-            if (feat_pyr[i] == 0)
-                continue;
-
-            getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_response_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-            getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*desc_len*sizeof(unsigned), feat_pyr[i] * desc_len * sizeof(unsigned));
-
-            bufferFree(d_x_pyr[i]);
-            bufferFree(d_y_pyr[i]);
-            bufferFree(d_response_pyr[i]);
-            bufferFree(d_ori_pyr[i]);
-            bufferFree(d_size_pyr[i]);
-            bufferFree(d_desc_pyr[i]);
-
-            offset += feat_pyr[i];
+    // If no features are found, set found features to 0 and return
+    if (total_feat == 0) {
+        *out_feat = 0;
+        return;
+    }
+
+    // Allocate output memory
+    x_out.info.dims[0] = total_feat;
+    x_out.info.strides[0] = 1;
+    y_out.info.dims[0] = total_feat;
+    y_out.info.strides[0] = 1;
+    score_out.info.dims[0] = total_feat;
+    score_out.info.strides[0] = 1;
+    ori_out.info.dims[0] = total_feat;
+    ori_out.info.strides[0] = 1;
+    size_out.info.dims[0] = total_feat;
+    size_out.info.strides[0] = 1;
+
+    desc_out.info.dims[0] = desc_len;
+    desc_out.info.strides[0] = 1;
+    desc_out.info.dims[1] = total_feat;
+    desc_out.info.strides[1] = desc_out.info.dims[0];
+
+    for (int k = 1; k < 4; k++) {
+        x_out.info.dims[k] = 1;
+        x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
+        y_out.info.dims[k] = 1;
+        y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
+        score_out.info.dims[k] = 1;
+        score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
+        ori_out.info.dims[k] = 1;
+        ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
+        size_out.info.dims[k] = 1;
+        size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
+        if (k > 1) {
+            desc_out.info.dims[k] = 1;
+            desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
         }
+    }
 
-        // Sets number of output features and descriptor length
-        *out_feat = total_feat;
-        *out_dlen = desc_len;
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
+    if (total_feat > 0) {
+        size_t out_sz  = total_feat * sizeof(float);
+        x_out.data     = bufferAlloc(out_sz);
+        y_out.data     = bufferAlloc(out_sz);
+        score_out.data = bufferAlloc(out_sz);
+        ori_out.data   = bufferAlloc(out_sz);
+        size_out.data  = bufferAlloc(out_sz);
+
+        size_t desc_sz = total_feat * desc_len * sizeof(unsigned);
+        desc_out.data  = bufferAlloc(desc_sz);
+    }
+
+    unsigned offset = 0;
+    for (unsigned i = 0; i < n_octaves; i++) {
+        if (feat_pyr[i] == 0)
+            continue;
+
+        getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_response_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*desc_len*sizeof(unsigned), feat_pyr[i] * desc_len * sizeof(unsigned));
+
+        bufferFree(d_x_pyr[i]);
+        bufferFree(d_y_pyr[i]);
+        bufferFree(d_response_pyr[i]);
+        bufferFree(d_ori_pyr[i]);
+        bufferFree(d_size_pyr[i]);
+        bufferFree(d_desc_pyr[i]);
+
+        offset += feat_pyr[i];
     }
+
+    // Sets number of output features and descriptor length
+    *out_feat = total_feat;
+    *out_dlen = desc_len;
 }
 
 } //namespace kernel
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 12c3ba7894..2bec2e085f 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -38,53 +38,48 @@ static const int THREADS_Y = 16;
 template<typename Ti, typename To, unsigned ker_size>
 void sobel(Param dx, Param dy, const Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  sobProgs;
-        static std::map<int, Kernel*> sobKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D Ti=" << dtype_traits<Ti>::getName()
-                        << " -D To=" << dtype_traits<To>::getName()
-                        << " -D KER_SIZE="<< ker_size;
-                if (std::is_same<Ti, double>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, sobel_cl, sobel_cl_len, options.str());
-                sobProgs[device]   = new Program(prog);
-                sobKernels[device] = new Kernel(*sobProgs[device], "sobel3x3");
-            });
-
-        NDRange local(THREADS_X, THREADS_Y);
-
-        int blk_x = divup(in.info.dims[0], THREADS_X);
-        int blk_y = divup(in.info.dims[1], THREADS_Y);
-
-        NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                       blk_y * in.info.dims[3] * THREADS_Y);
-
-        auto sobelOp = KernelFunctor<Buffer, KParam,
-                                   Buffer, KParam,
-                                   Buffer, KParam,
-                                   cl::LocalSpaceArg,
-                                   int, int> (*sobKernels[device]);
-
-        size_t loc_size = (THREADS_X+ker_size-1)*(THREADS_Y+ker_size-1)*sizeof(Ti);
-
-        sobelOp(EnqueueArgs(getQueue(), global, local),
-                    *dx.data, dx.info, *dy.data, dy.info,
-                    *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
-
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  sobProgs;
+    static std::map<int, Kernel*> sobKernels;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D Ti=" << dtype_traits<Ti>::getName()
+                    << " -D To=" << dtype_traits<To>::getName()
+                    << " -D KER_SIZE="<< ker_size;
+            if (std::is_same<Ti, double>::value) {
+                options << " -D USE_DOUBLE";
+            }
+            Program prog;
+            buildProgram(prog, sobel_cl, sobel_cl_len, options.str());
+            sobProgs[device]   = new Program(prog);
+            sobKernels[device] = new Kernel(*sobProgs[device], "sobel3x3");
+        });
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                    blk_y * in.info.dims[3] * THREADS_Y);
+
+    auto sobelOp = KernelFunctor<Buffer, KParam,
+                                Buffer, KParam,
+                                Buffer, KParam,
+                                cl::LocalSpaceArg,
+                                int, int> (*sobKernels[device]);
+
+    size_t loc_size = (THREADS_X+ker_size-1)*(THREADS_Y+ker_size-1)*sizeof(Ti);
+
+    sobelOp(EnqueueArgs(getQueue(), global, local),
+                *dx.data, dx.info, *dy.data, dy.info,
+                *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index de9d77786b..357b67d51c 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -44,117 +44,107 @@ namespace opencl
         template<typename T>
         void sort0Iterative(Param val, bool isAscending)
         {
-            try {
-                compute::command_queue c_queue(getQueue()());
-
-                compute::buffer val_buf((*val.data)());
-
-                for(int w = 0; w < val.info.dims[3]; w++) {
-                    int valW = w * val.info.strides[3];
-                    for(int z = 0; z < val.info.dims[2]; z++) {
-                        int valWZ = valW + z * val.info.strides[2];
-                        for(int y = 0; y < val.info.dims[1]; y++) {
-
-                            int valOffset = valWZ + y * val.info.strides[1];
-
-                            if(isAscending) {
-                                compute::sort(
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        compute::less< type_t<T> >(), c_queue);
-                            } else {
-                                compute::sort(
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                        compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                        compute::greater< type_t<T> >(), c_queue);
-                            }
+            compute::command_queue c_queue(getQueue()());
+
+            compute::buffer val_buf((*val.data)());
+
+            for(int w = 0; w < val.info.dims[3]; w++) {
+                int valW = w * val.info.strides[3];
+                for(int z = 0; z < val.info.dims[2]; z++) {
+                    int valWZ = valW + z * val.info.strides[2];
+                    for(int y = 0; y < val.info.dims[1]; y++) {
+
+                        int valOffset = valWZ + y * val.info.strides[1];
+
+                        if(isAscending) {
+                            compute::sort(
+                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
+                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
+                                    compute::less< type_t<T> >(), c_queue);
+                        } else {
+                            compute::sort(
+                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
+                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
+                                    compute::greater< type_t<T> >(), c_queue);
                         }
                     }
                 }
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
             }
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T, int dim>
         void sortBatched(Param pVal, bool isAscending)
         {
-            try{
-                af::dim4 inDims;
-                for(int i = 0; i < 4; i++)
-                    inDims[i] = pVal.info.dims[i];
-
-                // Sort dimension
-                // tileDims * seqDims = inDims
-                af::dim4 tileDims(1);
-                af::dim4 seqDims = inDims;
-                tileDims[dim] = inDims[dim];
-                seqDims[dim] = 1;
-
-                // Create/call iota
-                // Array<uint> key = iota<uint>(seqDims, tileDims);
-                dim4 keydims = inDims;
-                cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(uint));
-                Param pKey;
-                pKey.data = key;
-                pKey.info.offset = 0;
-                pKey.info.dims[0] = keydims[0];
-                pKey.info.strides[0] = 1;
-                for(int i = 1; i < 4; i++) {
-                    pKey.info.dims[i] = keydims[i];
-                    pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
-                }
-                kernel::iota<uint>(pKey, seqDims, tileDims);
-
-                // Flat
-                //val.modDims(inDims.elements());
-                //key.modDims(inDims.elements());
-                pKey.info.dims[0] = inDims.elements();
-                pKey.info.strides[0] = 1;
-                pVal.info.dims[0] = inDims.elements();
-                pVal.info.strides[0] = 1;
-                for(int i = 1; i < 4; i++) {
-                    pKey.info.dims[i] = 1;
-                    pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
-                    pVal.info.dims[i] = 1;
-                    pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
-                }
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pVal.info.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<uint> key = iota<uint>(seqDims, tileDims);
+            dim4 keydims = inDims;
+            cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(uint));
+            Param pKey;
+            pKey.data = key;
+            pKey.info.offset = 0;
+            pKey.info.dims[0] = keydims[0];
+            pKey.info.strides[0] = 1;
+            for(int i = 1; i < 4; i++) {
+                pKey.info.dims[i] = keydims[i];
+                pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
+            }
+            kernel::iota<uint>(pKey, seqDims, tileDims);
+
+            // Flat
+            //val.modDims(inDims.elements());
+            //key.modDims(inDims.elements());
+            pKey.info.dims[0] = inDims.elements();
+            pKey.info.strides[0] = 1;
+            pVal.info.dims[0] = inDims.elements();
+            pVal.info.strides[0] = 1;
+            for(int i = 1; i < 4; i++) {
+                pKey.info.dims[i] = 1;
+                pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
+                pVal.info.dims[i] = 1;
+                pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
+            }
 
-                // Sort indices
-                // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-                //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
-                compute::command_queue c_queue(getQueue()());
-
-                compute::buffer pKey_buf((*pKey.data)());
-                compute::buffer pVal_buf((*pVal.data)());
-
-                compute::buffer_iterator<type_t<T> > val0 = compute::make_buffer_iterator<type_t<T> >(pVal_buf, 0);
-                compute::buffer_iterator<type_t<T> > valN = compute::make_buffer_iterator<type_t<T> >(pVal_buf,+ pVal.info.dims[0]);
-                compute::buffer_iterator<uint      > key0 = compute::make_buffer_iterator<uint>(pKey_buf, 0);
-                compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.info.dims[0]);
-                if(isAscending) {
-                    compute::sort_by_key(val0, valN, key0, c_queue);
-                } else {
-                    compute::sort_by_key(val0, valN, key0, compute::greater< type_t<T> >(), c_queue);
-                }
+            // Sort indices
+            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+            //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+            compute::command_queue c_queue(getQueue()());
+
+            compute::buffer pKey_buf((*pKey.data)());
+            compute::buffer pVal_buf((*pVal.data)());
+
+            compute::buffer_iterator<type_t<T> > val0 = compute::make_buffer_iterator<type_t<T> >(pVal_buf, 0);
+            compute::buffer_iterator<type_t<T> > valN = compute::make_buffer_iterator<type_t<T> >(pVal_buf,+ pVal.info.dims[0]);
+            compute::buffer_iterator<uint      > key0 = compute::make_buffer_iterator<uint>(pKey_buf, 0);
+            compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.info.dims[0]);
+            if(isAscending) {
+                compute::sort_by_key(val0, valN, key0, c_queue);
+            } else {
+                compute::sort_by_key(val0, valN, key0, compute::greater< type_t<T> >(), c_queue);
+            }
 
-                // Needs to be ascending (true) in order to maintain the indices properly
-                //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
-                compute::sort_by_key(key0, keyN, val0, c_queue);
+            // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+            compute::sort_by_key(key0, keyN, val0, c_queue);
 
-                // No need of doing moddims here because the original Array<T>
-                // dimensions have not been changed
-                //val.modDims(inDims);
+            // No need of doing moddims here because the original Array<T>
+            // dimensions have not been changed
+            //val.modDims(inDims);
 
-                CL_DEBUG_FINISH(getQueue());
-                bufferFree(key);
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
+            bufferFree(key);
         }
 
         template<typename T>
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 575f676ca2..922f74fd0c 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -110,41 +110,36 @@ namespace opencl
         template<typename Tk, typename Tv>
         void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending)
         {
-            try {
-                compute::command_queue c_queue(getQueue()());
-
-                compute::buffer pKey_buf((*pKey.data)());
-                compute::buffer pVal_buf((*pVal.data)());
-
-                for(int w = 0; w < pKey.info.dims[3]; w++) {
-                    int pKeyW = w * pKey.info.strides[3];
-                    int pValW = w * pVal.info.strides[3];
-                    for(int z = 0; z < pKey.info.dims[2]; z++) {
-                        int pKeyWZ = pKeyW + z * pKey.info.strides[2];
-                        int pValWZ = pValW + z * pVal.info.strides[2];
-                        for(int y = 0; y < pKey.info.dims[1]; y++) {
-
-                            int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
-                            int pValOffset = pValWZ + y * pVal.info.strides[1];
-
-                            compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
-                            compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
-                            compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
-                            if(isAscending) {
-                                compute::sort_by_key(start, end, vals, c_queue);
-                            } else {
-                                compute::sort_by_key(start, end, vals,
-                                                     compute::greater< type_t<Tk> >(), c_queue);
-                            }
+            compute::command_queue c_queue(getQueue()());
+
+            compute::buffer pKey_buf((*pKey.data)());
+            compute::buffer pVal_buf((*pVal.data)());
+
+            for(int w = 0; w < pKey.info.dims[3]; w++) {
+                int pKeyW = w * pKey.info.strides[3];
+                int pValW = w * pVal.info.strides[3];
+                for(int z = 0; z < pKey.info.dims[2]; z++) {
+                    int pKeyWZ = pKeyW + z * pKey.info.strides[2];
+                    int pValWZ = pValW + z * pVal.info.strides[2];
+                    for(int y = 0; y < pKey.info.dims[1]; y++) {
+
+                        int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
+                        int pValOffset = pValWZ + y * pVal.info.strides[1];
+
+                        compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
+                        compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
+                        compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
+                        if(isAscending) {
+                            compute::sort_by_key(start, end, vals, c_queue);
+                        } else {
+                            compute::sort_by_key(start, end, vals,
+                                                  compute::greater< type_t<Tk> >(), c_queue);
                         }
                     }
                 }
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
             }
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename Tk_, typename Tv_>
@@ -153,101 +148,96 @@ namespace opencl
             typedef type_t<Tk_> Tk;
             typedef type_t<Tv_> Tv;
 
-            try {
-                af::dim4 inDims;
-                for(int i = 0; i < 4; i++)
-                    inDims[i] = pKey.info.dims[i];
-
-                // Sort dimension
-                // tileDims * seqDims = inDims
-                af::dim4 tileDims(1);
-                af::dim4 seqDims = inDims;
-                tileDims[dim] = inDims[dim];
-                seqDims[dim] = 1;
-
-                // Create/call iota
-                // Array<Tv> key = iota<Tv>(seqDims, tileDims);
-                cl::Buffer* Seq = bufferAlloc(inDims.elements() * sizeof(unsigned));
-                Param pSeq;
-                pSeq.data = Seq;
-                pSeq.info.offset = 0;
-                pSeq.info.dims[0] = inDims[0];
-                pSeq.info.strides[0] = 1;
-                for(int i = 1; i < 4; i++) {
-                    pSeq.info.dims[i] = inDims[i];
-                    pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
-                }
-                kernel::iota<unsigned>(pSeq, seqDims, tileDims);
-
-                int elements = inDims.elements();
-
-                // Flat - Not required since inplace and both are continuous
-                //val.modDims(inDims.elements());
-                //key.modDims(inDims.elements());
-
-                // Sort indices
-                // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
-                //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
-                compute::command_queue c_queue(getQueue()());
-                compute::context c_context(getContext()());
-
-                // Create buffer iterators for seq
-                compute::buffer pSeq_buf((*pSeq.data)());
-                compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
-                compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
-                // Create buffer iterators for key and val
-                compute::buffer pKey_buf((*pKey.data)());
-                compute::buffer pVal_buf((*pVal.data)());
-                compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
-                compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
-                compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
-                compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
-
-                // Sort By Key for descending is stable in the reverse
-                // (greater) order. Sorting in ascending with negated values
-                // will give the right result
-                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
-
-                // Create a copy of the pKey buffer
-                cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
-                compute::buffer cKey_buf((*cKey)());
-                compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
-                compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
-                compute::copy(key0, keyN, cKey0, c_queue);
-
-                // FIRST SORT
-                compute::sort_by_key(key0, keyN, seq0, c_queue);
-                compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
-
-                // Create a copy of the seq buffer after first sort
-                cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
-                compute::buffer cSeq_buf((*cSeq)());
-                compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
-                compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
-                compute::copy(seq0, seqN, cSeq0, c_queue);
-
-                // SECOND SORT
-                // First call will sort key, second sort will sort val
-                // Needs to be ascending (true) in order to maintain the indices properly
-                //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
-                compute::sort_by_key(seq0, seqN, key0, c_queue);
-                compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
-
-                // If descending, flip it back
-                if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
-
-                //// No need of doing moddims here because the original Array<T>
-                //// dimensions have not been changed
-                ////val.modDims(inDims);
-
-                CL_DEBUG_FINISH(getQueue());
-                bufferFree(Seq);
-                bufferFree(cSeq);
-                bufferFree(cKey);
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
+            af::dim4 inDims;
+            for(int i = 0; i < 4; i++)
+                inDims[i] = pKey.info.dims[i];
+
+            // Sort dimension
+            // tileDims * seqDims = inDims
+            af::dim4 tileDims(1);
+            af::dim4 seqDims = inDims;
+            tileDims[dim] = inDims[dim];
+            seqDims[dim] = 1;
+
+            // Create/call iota
+            // Array<Tv> key = iota<Tv>(seqDims, tileDims);
+            cl::Buffer* Seq = bufferAlloc(inDims.elements() * sizeof(unsigned));
+            Param pSeq;
+            pSeq.data = Seq;
+            pSeq.info.offset = 0;
+            pSeq.info.dims[0] = inDims[0];
+            pSeq.info.strides[0] = 1;
+            for(int i = 1; i < 4; i++) {
+                pSeq.info.dims[i] = inDims[i];
+                pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
             }
+            kernel::iota<unsigned>(pSeq, seqDims, tileDims);
+
+            int elements = inDims.elements();
+
+            // Flat - Not required since inplace and both are continuous
+            //val.modDims(inDims.elements());
+            //key.modDims(inDims.elements());
+
+            // Sort indices
+            // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
+            //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
+            compute::command_queue c_queue(getQueue()());
+            compute::context c_context(getContext()());
+
+            // Create buffer iterators for seq
+            compute::buffer pSeq_buf((*pSeq.data)());
+            compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
+            compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
+            // Create buffer iterators for key and val
+            compute::buffer pKey_buf((*pKey.data)());
+            compute::buffer pVal_buf((*pVal.data)());
+            compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
+            compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
+            compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
+            compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
+
+            // Sort By Key for descending is stable in the reverse
+            // (greater) order. Sorting in ascending with negated values
+            // will give the right result
+            if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+            // Create a copy of the pKey buffer
+            cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
+            compute::buffer cKey_buf((*cKey)());
+            compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
+            compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
+            compute::copy(key0, keyN, cKey0, c_queue);
+
+            // FIRST SORT
+            compute::sort_by_key(key0, keyN, seq0, c_queue);
+            compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
+
+            // Create a copy of the seq buffer after first sort
+            cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
+            compute::buffer cSeq_buf((*cSeq)());
+            compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
+            compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
+            compute::copy(seq0, seqN, cSeq0, c_queue);
+
+            // SECOND SORT
+            // First call will sort key, second sort will sort val
+            // Needs to be ascending (true) in order to maintain the indices properly
+            //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
+            compute::sort_by_key(seq0, seqN, key0, c_queue);
+            compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
+
+            // If descending, flip it back
+            if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+            //// No need of doing moddims here because the original Array<T>
+            //// dimensions have not been changed
+            ////val.modDims(inDims);
+
+            CL_DEBUG_FINISH(getQueue());
+            bufferFree(Seq);
+            bufferFree(cSeq);
+            bufferFree(cKey);
         }
 
         template<typename Tk, typename Tv>
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index d65b461be3..df56d5fedb 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -43,223 +43,209 @@ namespace opencl
         template<typename T>
         void coo2dense(Param out, const Param values, const Param rowIdx, const Param colIdx)
         {
-            try {
-
-                std::string ref_name =
-                    std::string("coo2dense_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(REPEAT);
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName()
-                            << " -D reps="     << REPEAT
-                            ;
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
-                    entry.prog   = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "coo2dense_kernel");
-                } else {
-                    entry = idx->second;
-                };
+            std::string ref_name =
+                std::string("coo2dense_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(REPEAT);
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+
+            if (idx == kernelCaches[device].end()) {
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName()
+                        << " -D reps="     << REPEAT
+                        ;
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 
-                auto coo2denseOp = KernelFunctor<Buffer, const KParam,
-                                           const Buffer, const KParam,
-                                           const Buffer, const KParam,
-                                           const Buffer, const KParam>
-                                          (*entry.ker);
+                Program prog;
+                buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
+                entry.prog   = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "coo2dense_kernel");
+            } else {
+                entry = idx->second;
+            };
 
-                NDRange local(THREADS_PER_GROUP, 1, 1);
+            auto coo2denseOp = KernelFunctor<Buffer, const KParam,
+                                        const Buffer, const KParam,
+                                        const Buffer, const KParam,
+                                        const Buffer, const KParam>
+                                      (*entry.ker);
 
-                NDRange global(divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
+            NDRange local(THREADS_PER_GROUP, 1, 1);
 
-                coo2denseOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, out.info,
-                       *values.data, values.info,
-                       *rowIdx.data, rowIdx.info,
-                       *colIdx.data, colIdx.info);
+            NDRange global(divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            coo2denseOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info,
+                    *values.data, values.info,
+                    *rowIdx.data, rowIdx.info,
+                    *colIdx.data, colIdx.info);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T>
         void csr2dense(Param output, const Param values, const Param rowIdx, const Param colIdx)
         {
-            try {
-                const int MAX_GROUPS = 4096;
-                int M = rowIdx.info.dims[0] - 1;
-                //FIXME: This needs to be based non nonzeros per row
-                int threads = 64;
-
-                std::string ref_name =
-                    std::string("csr2dense_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(threads);
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    options << " -D THREADS=" << threads;
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    const char *ker_strs[] = {csr2dense_cl};
-                    const int   ker_lens[] = {csr2dense_cl_len};
-
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "csr2dense");
-                } else {
-                    entry = idx->second;
+            const int MAX_GROUPS = 4096;
+            int M = rowIdx.info.dims[0] - 1;
+            //FIXME: This needs to be based non nonzeros per row
+            int threads = 64;
+
+            std::string ref_name =
+                std::string("csr2dense_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(threads);
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+
+            if (idx == kernelCaches[device].end()) {
+
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                options << " -D THREADS=" << threads;
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
-                NDRange local(threads, 1);
-                int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
-                NDRange global(local[0] * groups_x, 1);
-                auto csr2dense_kernel = *entry.ker;
-                auto csr2dense_func = KernelFunctor<Buffer,
-                                                    Buffer, Buffer, Buffer,
-                                                    int> (csr2dense_kernel);
+                const char *ker_strs[] = {csr2dense_cl};
+                const int   ker_lens[] = {csr2dense_cl_len};
 
-                csr2dense_func(EnqueueArgs(getQueue(), global, local),
-                               *output.data, *values.data, *rowIdx.data, *colIdx.data, M);
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "csr2dense");
+            } else {
+                entry = idx->second;
+            }
 
-                CL_DEBUG_FINISH(getQueue());
+            NDRange local(threads, 1);
+            int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+            NDRange global(local[0] * groups_x, 1);
+            auto csr2dense_kernel = *entry.ker;
+            auto csr2dense_func = KernelFunctor<Buffer,
+                                                Buffer, Buffer, Buffer,
+                                                int> (csr2dense_kernel);
 
-            } catch(cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            csr2dense_func(EnqueueArgs(getQueue(), global, local),
+                            *output.data, *values.data, *rowIdx.data, *colIdx.data, M);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T>
         void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense)
         {
-            try {
-                int num_rows = dense.info.dims[0];
-                int num_cols = dense.info.dims[1];
-                int dense_elements = num_rows * num_cols;
-                Param sd1, rd1, sd0;
-                // sd1 contains output of scan along dim 1 of dense
-                sd1.data = bufferAlloc(dense_elements * sizeof(int));
-                // rd1 contains output of nonzero count along dim 1 along dense
-                rd1.data = bufferAlloc(num_rows * sizeof(int));
-                // sd0 contains output of exclusive scan rd1
-                sd0 = rowIdx;
-
-                sd1.info.offset = 0;
-                rd1.info.offset = 0;
-
-                sd1.info.dims[0] = num_rows;
-                rd1.info.dims[0] = num_rows;
-
-                sd1.info.dims[1] = num_cols;
-                rd1.info.dims[1] = 1;
-
-                sd1.info.dims[2] = 1;
-                rd1.info.dims[2] = 1;
-
-                sd1.info.dims[3] = 1;
-                rd1.info.dims[3] = 1;
-
-                sd1.info.strides[0] = 1;
-                rd1.info.strides[0] = 1;
-                for (int i = 1; i < 4; i++) {
-                    sd1.info.strides[i] = sd1.info.dims[i - 1] * sd1.info.strides[i - 1];
-                    rd1.info.strides[i] = rd1.info.dims[i - 1] * rd1.info.strides[i - 1];
-                }
+            int num_rows = dense.info.dims[0];
+            int num_cols = dense.info.dims[1];
+            int dense_elements = num_rows * num_cols;
+            Param sd1, rd1, sd0;
+            // sd1 contains output of scan along dim 1 of dense
+            sd1.data = bufferAlloc(dense_elements * sizeof(int));
+            // rd1 contains output of nonzero count along dim 1 along dense
+            rd1.data = bufferAlloc(num_rows * sizeof(int));
+            // sd0 contains output of exclusive scan rd1
+            sd0 = rowIdx;
+
+            sd1.info.offset = 0;
+            rd1.info.offset = 0;
+
+            sd1.info.dims[0] = num_rows;
+            rd1.info.dims[0] = num_rows;
+
+            sd1.info.dims[1] = num_cols;
+            rd1.info.dims[1] = 1;
+
+            sd1.info.dims[2] = 1;
+            rd1.info.dims[2] = 1;
+
+            sd1.info.dims[3] = 1;
+            rd1.info.dims[3] = 1;
+
+            sd1.info.strides[0] = 1;
+            rd1.info.strides[0] = 1;
+            for (int i = 1; i < 4; i++) {
+                sd1.info.strides[i] = sd1.info.dims[i - 1] * sd1.info.strides[i - 1];
+                rd1.info.strides[i] = rd1.info.dims[i - 1] * rd1.info.strides[i - 1];
+            }
+
+            scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
+            reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
+            scan_first<int, int, af_add_t, false>(sd0, rd1);
+
+            int nnz = values.info.dims[0];
+            getQueue().enqueueWriteBuffer(*sd0.data, CL_TRUE,
+                                          sd0.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
+                                          sizeof(int),
+                                          (void *)&nnz);
+
+            std::string ref_name =
+                std::string("dense2csr_") +
+                std::string(dtype_traits<T>::getName());
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
-                reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
-                scan_first<int, int, af_add_t, false>(sd0, rd1);
-
-                int nnz = values.info.dims[0];
-                getQueue().enqueueWriteBuffer(*sd0.data, CL_TRUE,
-                                              sd0.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
-                                              sizeof(int),
-                                              (void *)&nnz);
-
-                std::string ref_name =
-                    std::string("dense2csr_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    if (std::is_same<T, cfloat>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-
-                    const char *ker_strs[] = {dense2csr_cl};
-                    const int   ker_lens[] = {dense2csr_cl_len};
-
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
-
-                    kernelCaches[device][ref_name] = entry;
+            if (idx == kernelCaches[device].end()) {
+
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                if (std::is_same<T, cfloat>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
                 }
 
-                NDRange local(THREADS_X, THREADS_Y);
-                int groups_x = divup(dense.info.dims[0], local[0]);
-                int groups_y = divup(dense.info.dims[1], local[1]);
-                NDRange global(groups_x * local[0], groups_y * local[1]);
-                auto dense2csr_split = KernelFunctor<Buffer, Buffer,
-                                                     Buffer, KParam,
-                                                     Buffer, KParam,
-                                                     Buffer>(*entry.ker);
-
-                dense2csr_split(EnqueueArgs(getQueue(), global, local),
-                                *values.data, *colIdx.data,
-                                *dense.data, dense.info,
-                                *sd1.data, sd1.info,
-                                *sd0.data);
-
-                CL_DEBUG_FINISH(getQueue());
-
-                bufferFree(rd1.data);
-                bufferFree(sd1.data);
-            } catch (cl::Error &err) {
-                CL_TO_AF_ERROR(err);
+                const char *ker_strs[] = {dense2csr_cl};
+                const int   ker_lens[] = {dense2csr_cl_len};
+
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
+
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
             }
+
+            NDRange local(THREADS_X, THREADS_Y);
+            int groups_x = divup(dense.info.dims[0], local[0]);
+            int groups_y = divup(dense.info.dims[1], local[1]);
+            NDRange global(groups_x * local[0], groups_y * local[1]);
+            auto dense2csr_split = KernelFunctor<Buffer, Buffer,
+                                                  Buffer, KParam,
+                                                  Buffer, KParam,
+                                                  Buffer>(*entry.ker);
+
+            dense2csr_split(EnqueueArgs(getQueue(), global, local),
+                            *values.data, *colIdx.data,
+                            *dense.data, dense.info,
+                            *sd1.data, sd1.info,
+                            *sd0.data);
+
+            CL_DEBUG_FINISH(getQueue());
+
+            bufferFree(rd1.data);
+            bufferFree(sd1.data);
         }
 
         template<typename T>
@@ -267,48 +253,43 @@ namespace opencl
                        const Param ivalues, const cl::Buffer *iindex,
                        const Param swapIdx)
         {
-            try {
-                std::string ref_name =
-                    std::string("swapIndex_kernel_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName();
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-                    entry.prog   = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "swapIndex_kernel");
-                } else {
-                    entry = idx->second;
-                };
+            std::string ref_name =
+                std::string("swapIndex_kernel_") +
+                std::string(dtype_traits<T>::getName());
 
-                auto swapIndexOp = KernelFunctor<Buffer, Buffer,
-                                                 const Buffer, const Buffer, const Buffer,
-                                                 const int> (*entry.ker);
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                NDRange global(ovalues.info.dims[0], 1, 1);
+            if (idx == kernelCaches[device].end()) {
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName();
 
-                swapIndexOp(EnqueueArgs(getQueue(), global),
-                            *ovalues.data, *oindex.data,
-                            *ivalues.data, *iindex,
-                            *swapIdx.data, ovalues.info.dims[0]);
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 
-                CL_DEBUG_FINISH(getQueue());
+                Program prog;
+                buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
+                entry.prog   = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "swapIndex_kernel");
+            } else {
+                entry = idx->second;
+            };
 
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            auto swapIndexOp = KernelFunctor<Buffer, Buffer,
+                                              const Buffer, const Buffer, const Buffer,
+                                              const int> (*entry.ker);
+
+            NDRange global(ovalues.info.dims[0], 1, 1);
+
+            swapIndexOp(EnqueueArgs(getQueue(), global),
+                        *ovalues.data, *oindex.data,
+                        *ivalues.data, *iindex,
+                        *swapIdx.data, ovalues.info.dims[0]);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T>
@@ -316,68 +297,63 @@ namespace opencl
                      const Param ivalues, const Param irowIdx, const Param icolIdx,
                      Param index)
         {
-            try {
-                const int MAX_GROUPS = 4096;
-                int M = irowIdx.info.dims[0] - 1;
-                //FIXME: This needs to be based non nonzeros per row
-                int threads = 64;
-
-                std::string ref_name =
-                    std::string("csr2coo_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
+            const int MAX_GROUPS = 4096;
+            int M = irowIdx.info.dims[0] - 1;
+            //FIXME: This needs to be based non nonzeros per row
+            int threads = 64;
 
-                if (idx == kernelCaches[device].end()) {
+            std::string ref_name =
+                std::string("csr2coo_") +
+                std::string(dtype_traits<T>::getName());
 
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+            if (idx == kernelCaches[device].end()) {
 
-                    const char *ker_strs[] = {csr2coo_cl};
-                    const int   ker_lens[] = {csr2coo_cl_len};
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
 
-                    Program prog;
-                    buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "csr2coo");
-                } else {
-                    entry = idx->second;
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
-                cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
+                const char *ker_strs[] = {csr2coo_cl};
+                const int   ker_lens[] = {csr2coo_cl_len};
+
+                Program prog;
+                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "csr2coo");
+            } else {
+                entry = idx->second;
+            }
 
-                NDRange local(threads, 1);
-                int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
-                NDRange global(local[0] * groups_x, 1);
-                auto csr2coo_kernel = *entry.ker;
-                auto csr2coo_func = KernelFunctor<Buffer, Buffer,
-                                                  const Buffer, const Buffer,
-                                                  int> (csr2coo_kernel);
+            cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
 
-                csr2coo_func(EnqueueArgs(getQueue(), global, local),
-                               *scratch, *ocolIdx.data,
-                               *irowIdx.data, *icolIdx.data, M);
+            NDRange local(threads, 1);
+            int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+            NDRange global(local[0] * groups_x, 1);
+            auto csr2coo_kernel = *entry.ker;
+            auto csr2coo_func = KernelFunctor<Buffer, Buffer,
+                                              const Buffer, const Buffer,
+                                              int> (csr2coo_kernel);
 
-                // Now we need to sort this into column major
-                kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
+            csr2coo_func(EnqueueArgs(getQueue(), global, local),
+                            *scratch, *ocolIdx.data,
+                            *irowIdx.data, *icolIdx.data, M);
 
-                // Now use index to sort values and rows
-                kernel::swapIndex<T>(ovalues, orowIdx, ivalues, scratch, index);
+            // Now we need to sort this into column major
+            kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
 
-                CL_DEBUG_FINISH(getQueue());
+            // Now use index to sort values and rows
+            kernel::swapIndex<T>(ovalues, orowIdx, ivalues, scratch, index);
 
-                bufferFree(scratch);
+            CL_DEBUG_FINISH(getQueue());
 
-            } catch(cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            bufferFree(scratch);
         }
 
         template<typename T>
@@ -385,52 +361,47 @@ namespace opencl
                      const Param ivalues, const Param irowIdx, const Param icolIdx,
                      Param index, Param rowCopy, const int M)
         {
-            try {
-                // Now we need to sort this into column major
-                kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
+            // Now we need to sort this into column major
+            kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
 
-                // Now use index to sort values and rows
-                kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, icolIdx.data, index);
+            // Now use index to sort values and rows
+            kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, icolIdx.data, index);
 
-                CL_DEBUG_FINISH(getQueue());
+            CL_DEBUG_FINISH(getQueue());
 
-                std::string ref_name =
-                    std::string("csrReduce_kernel_") +
-                    std::string(dtype_traits<T>::getName());
+            std::string ref_name =
+                std::string("csrReduce_kernel_") +
+                std::string(dtype_traits<T>::getName());
 
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                if (idx == kernelCaches[device].end()) {
-                    std::ostringstream options;
-                    options << " -D T="        << dtype_traits<T>::getName();
+            if (idx == kernelCaches[device].end()) {
+                std::ostringstream options;
+                options << " -D T="        << dtype_traits<T>::getName();
 
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-                    entry.prog   = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "csrReduce_kernel");
-                } else {
-                    entry = idx->second;
-                };
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 
-                auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int> (*entry.ker);
+                Program prog;
+                buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
+                entry.prog   = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "csrReduce_kernel");
+            } else {
+                entry = idx->second;
+            };
 
-                NDRange global(irowIdx.info.dims[0], 1, 1);
+            auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int> (*entry.ker);
 
-                csrReduceOp(EnqueueArgs(getQueue(), global),
-                            *orowIdx.data, *rowCopy.data, M, ovalues.info.dims[0]);
+            NDRange global(irowIdx.info.dims[0], 1, 1);
 
-                CL_DEBUG_FINISH(getQueue());
+            csrReduceOp(EnqueueArgs(getQueue(), global),
+                        *orowIdx.data, *rowCopy.data, M, ovalues.info.dims[0]);
 
-            } catch(cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 3e638023fc..c4d321e01f 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -41,50 +41,45 @@ void susan(cl::Buffer* out, const cl::Buffer* in,
            const unsigned idim0, const unsigned idim1,
            const float t, const float g, const unsigned edge)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> suProg;
-        static std::map<int, Kernel*>  suKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                const size_t LOCAL_MEM_SIZE = (SUSAN_THREADS_X+2*radius)*(SUSAN_THREADS_Y+2*radius);
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D LOCAL_MEM_SIZE=" << LOCAL_MEM_SIZE
-                        << " -D BLOCK_X="<< SUSAN_THREADS_X
-                        << " -D BLOCK_Y="<< SUSAN_THREADS_Y
-                        << " -D RADIUS="<< radius
-                        << " -D RESPONSE";
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, susan_cl, susan_cl_len, options.str());
-                suProg[device]   = new Program(prog);
-                suKernel[device] = new Kernel(*suProg[device], "susan_responses");
-            });
-
-        auto susanOp = KernelFunctor<Buffer, Buffer,
-                                   unsigned,
-                                   unsigned, unsigned,
-                                   float, float, unsigned>(*suKernel[device]);
-
-        NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-        NDRange global(divup(idim0-2*edge, local[0])*local[0],
-                       divup(idim1-2*edge, local[1])*local[1]);
-
-        susanOp(EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0, idim1, t, g, edge);
-
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> suProg;
+    static std::map<int, Kernel*>  suKernel;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            const size_t LOCAL_MEM_SIZE = (SUSAN_THREADS_X+2*radius)*(SUSAN_THREADS_Y+2*radius);
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D LOCAL_MEM_SIZE=" << LOCAL_MEM_SIZE
+                    << " -D BLOCK_X="<< SUSAN_THREADS_X
+                    << " -D BLOCK_Y="<< SUSAN_THREADS_Y
+                    << " -D RADIUS="<< radius
+                    << " -D RESPONSE";
+
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            cl::Program prog;
+            buildProgram(prog, susan_cl, susan_cl_len, options.str());
+            suProg[device]   = new Program(prog);
+            suKernel[device] = new Kernel(*suProg[device], "susan_responses");
+        });
+
+    auto susanOp = KernelFunctor<Buffer, Buffer,
+                                unsigned,
+                                unsigned, unsigned,
+                                float, float, unsigned>(*suKernel[device]);
+
+    NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
+    NDRange global(divup(idim0-2*edge, local[0])*local[0],
+                    divup(idim1-2*edge, local[1])*local[1]);
+
+    susanOp(EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0, idim1, t, g, edge);
+
 }
 
 template<typename T>
@@ -93,51 +88,46 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
                     const unsigned edge, const unsigned max_corners)
 {
     unsigned corners_found = 0;
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> nmProg;
-        static std::map<int, Kernel*>  nmKernel;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D NONMAX";
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                cl::Program prog;
-                buildProgram(prog, susan_cl, susan_cl_len, options.str());
-                nmProg[device]   = new Program(prog);
-                nmKernel[device] = new Kernel(*nmProg[device], "non_maximal");
-            });
-
-        cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
-        getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
-
-        auto nonMaximalOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                                        unsigned, unsigned, Buffer,
-                                        unsigned, unsigned>(*nmKernel[device]);
-
-        NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-        NDRange global(divup(idim0-2*edge, local[0])*local[0],
-                       divup(idim1-2*edge, local[1])*local[1]);
-
-        nonMaximalOp(EnqueueArgs(getQueue(), global, local),
-                     *x_out, *y_out, *resp_out, *d_corners_found,
-                     idim0, idim1, *resp_in, edge, max_corners);
-
-        getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
-        bufferFree(d_corners_found);
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*> nmProg;
+    static std::map<int, Kernel*>  nmKernel;
+
+    int device = getActiveDeviceId();
+
+    std::call_once( compileFlags[device], [device] () {
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D NONMAX";
+
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
+
+            cl::Program prog;
+            buildProgram(prog, susan_cl, susan_cl_len, options.str());
+            nmProg[device]   = new Program(prog);
+            nmKernel[device] = new Kernel(*nmProg[device], "non_maximal");
+        });
+
+    cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
+    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+
+    auto nonMaximalOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
+                                    unsigned, unsigned, Buffer,
+                                    unsigned, unsigned>(*nmKernel[device]);
+
+    NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
+    NDRange global(divup(idim0-2*edge, local[0])*local[0],
+                    divup(idim1-2*edge, local[1])*local[1]);
+
+    nonMaximalOp(EnqueueArgs(getQueue(), global, local),
+                  *x_out, *y_out, *resp_out, *d_corners_found,
+                  idim0, idim1, *resp_in, edge, max_corners);
+
+    getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    bufferFree(d_corners_found);
     return corners_found;
 }
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index f96765850b..3ad71c21f5 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -39,46 +39,41 @@ namespace opencl
         template<typename T>
         void tile(Param out, const Param in)
         {
-            try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   tileProgs;
-                static std::map<int, Kernel *> tileKernels;
+            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+            static std::map<int, Program*>   tileProgs;
+            static std::map<int, Kernel *> tileKernels;
 
-                int device = getActiveDeviceId();
+            int device = getActiveDeviceId();
 
-                std::call_once( compileFlags[device], [device] () {
-                    std::ostringstream options;
-                    options << " -D T=" << dtype_traits<T>::getName();
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-                    Program prog;
-                    buildProgram(prog, tile_cl, tile_cl_len, options.str());
-                    tileProgs[device] = new Program(prog);
-                    tileKernels[device] = new Kernel(*tileProgs[device], "tile_kernel");
-                });
+            std::call_once( compileFlags[device], [device] () {
+                std::ostringstream options;
+                options << " -D T=" << dtype_traits<T>::getName();
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+                Program prog;
+                buildProgram(prog, tile_cl, tile_cl_len, options.str());
+                tileProgs[device] = new Program(prog);
+                tileKernels[device] = new Kernel(*tileProgs[device], "tile_kernel");
+            });
 
-                auto tileOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                          const int, const int> (*tileKernels[device]);
+            auto tileOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
+                                      const int, const int> (*tileKernels[device]);
 
-                NDRange local(TX, TY, 1);
+            NDRange local(TX, TY, 1);
 
-                int blocksPerMatX = divup(out.info.dims[0], TILEX);
-                int blocksPerMatY = divup(out.info.dims[1], TILEY);
-                NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                               local[1] * blocksPerMatY * out.info.dims[3],
-                               1);
+            int blocksPerMatX = divup(out.info.dims[0], TILEX);
+            int blocksPerMatY = divup(out.info.dims[1], TILEY);
+            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                            local[1] * blocksPerMatY * out.info.dims[3],
+                            1);
 
-                tileOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, *in.data, out.info, in.info,
-                       blocksPerMatX, blocksPerMatY);
+            tileOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, *in.data, out.info, in.info,
+                    blocksPerMatX, blocksPerMatY);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 4120908acf..dc38696d8d 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -57,102 +57,97 @@ namespace opencl
                        const Param tf, bool isInverse,
                        bool isPerspective, af_interp_type method)
         {
-            try {
-
-                typedef typename dtype_traits<T>::base_type BT;
-
-                std::string ref_name =
-                    std::string("transform_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(isInverse) +
-                    std::string("_") +
-                    std::to_string(isPerspective) +
-                    std::string("_") +
-                    std::to_string(order);
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-                    ToNumStr<T> toNumStr;
-                    std::ostringstream options;
-                    options << " -D T="           << dtype_traits<T>::getName()
-                            << " -D INVERSE="     << (isInverse ? 1 : 0)
-                            << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
-                            << " -D ZERO="        << toNumStr(scalar<T>(0));
-                    options << " -D InterpInTy=" << dtype_traits<T>::getName();
-                    options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
-                    options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                        options << " -D TB=" << dtype_traits<BT>::getName();
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    options << " -D INTERP_ORDER=" << order;
-                    addInterpEnumOptions(options);
-
-                    const char *ker_strs[] = {interp_cl, transform_cl};
-                    const int   ker_lens[] = {interp_cl_len, transform_cl_len};
-                    Program prog;
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "transform_kernel");
+
+            typedef typename dtype_traits<T>::base_type BT;
+
+            std::string ref_name =
+                std::string("transform_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(isInverse) +
+                std::string("_") +
+                std::to_string(isPerspective) +
+                std::string("_") +
+                std::to_string(order);
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
+
+            if (idx == kernelCaches[device].end()) {
+                ToNumStr<T> toNumStr;
+                std::ostringstream options;
+                options << " -D T="           << dtype_traits<T>::getName()
+                        << " -D INVERSE="     << (isInverse ? 1 : 0)
+                        << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
+                        << " -D ZERO="        << toNumStr(scalar<T>(0));
+                options << " -D InterpInTy=" << dtype_traits<T>::getName();
+                options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+                options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
+
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                    (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
+                    options << " -D TB=" << dtype_traits<BT>::getName();
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
+                }
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
-                auto transformOp = KernelFunctor<Buffer, const KParam,
-                                                 const Buffer, const KParam,
-                                                 const Buffer, const KParam,
-                                                 const int, const int, const int, const int,
-                                                 const int, const int, const int, const int>(*entry.ker);
-
-                const int nImg2 = in.info.dims[2];
-                const int nImg3 = in.info.dims[3];
-                const int nTfs2 = tf.info.dims[2];
-                const int nTfs3 = tf.info.dims[3];
-
-                NDRange local(TX, TY, 1);
-
-                int batchImg2 = 1;
-                if(nImg2 != nTfs2)
-                    batchImg2 = min(nImg2, TI);
-
-                const int blocksXPerImage = divup(out.info.dims[0], local[0]);
-                const int blocksYPerImage = divup(out.info.dims[1], local[1]);
-
-                int global_x = local[0]
-                             * blocksXPerImage
-                             * (nImg2 / batchImg2);
-                int global_y = local[1]
-                             * blocksYPerImage
-                             * nImg3;
-                int global_z = local[2]
-                             * max((nTfs2 / nImg2), 1)
-                             * max((nTfs3 / nImg3), 1);
-
-                NDRange global(global_x, global_y, global_z);
-
-                transformOp(EnqueueArgs(getQueue(), global, local),
-                            *out.data, out.info, *in.data, in.info, *tf.data, tf.info,
-                            nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                            blocksXPerImage, blocksYPerImage, (int)method);
-
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
+                options << " -D INTERP_ORDER=" << order;
+                addInterpEnumOptions(options);
+
+                const char *ker_strs[] = {interp_cl, transform_cl};
+                const int   ker_lens[] = {interp_cl_len, transform_cl_len};
+                Program prog;
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "transform_kernel");
+            } else {
+                entry = idx->second;
             }
+
+            auto transformOp = KernelFunctor<Buffer, const KParam,
+                                              const Buffer, const KParam,
+                                              const Buffer, const KParam,
+                                              const int, const int, const int, const int,
+                                              const int, const int, const int, const int>(*entry.ker);
+
+            const int nImg2 = in.info.dims[2];
+            const int nImg3 = in.info.dims[3];
+            const int nTfs2 = tf.info.dims[2];
+            const int nTfs3 = tf.info.dims[3];
+
+            NDRange local(TX, TY, 1);
+
+            int batchImg2 = 1;
+            if(nImg2 != nTfs2)
+                batchImg2 = min(nImg2, TI);
+
+            const int blocksXPerImage = divup(out.info.dims[0], local[0]);
+            const int blocksYPerImage = divup(out.info.dims[1], local[1]);
+
+            int global_x = local[0]
+                          * blocksXPerImage
+                          * (nImg2 / batchImg2);
+            int global_y = local[1]
+                          * blocksYPerImage
+                          * nImg3;
+            int global_z = local[2]
+                          * max((nTfs2 / nImg2), 1)
+                          * max((nTfs3 / nImg3), 1);
+
+            NDRange global(global_x, global_y, global_z);
+
+            transformOp(EnqueueArgs(getQueue(), global, local),
+                        *out.data, out.info, *in.data, in.info, *tf.data, tf.info,
+                        nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+                        blocksXPerImage, blocksYPerImage, (int)method);
+
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 4643aba7a5..bd4ccfe34f 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -40,56 +40,51 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T, bool conjugate, bool IS32MULTIPLE>
 void transpose(Param out, const Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  trsProgs;
-        static std::map<int, Kernel*> trsKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  trsProgs;
+    static std::map<int, Kernel*> trsKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once(compileFlags[device], [device] () {
+    std::call_once(compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D TILE_DIM=" << TILE_DIM
-                        << " -D THREADS_Y=" << THREADS_Y
-                        << " -D IS32MULTIPLE=" << IS32MULTIPLE
-                        << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
-                        << " -D T=" << dtype_traits<T>::getName();
+            std::ostringstream options;
+            options << " -D TILE_DIM=" << TILE_DIM
+                    << " -D THREADS_Y=" << THREADS_Y
+                    << " -D IS32MULTIPLE=" << IS32MULTIPLE
+                    << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
+                    << " -D T=" << dtype_traits<T>::getName();
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-                cl::Program prog;
-                buildProgram(prog, transpose_cl, transpose_cl_len, options.str());
-                trsProgs[device] = new Program(prog);
+            cl::Program prog;
+            buildProgram(prog, transpose_cl, transpose_cl_len, options.str());
+            trsProgs[device] = new Program(prog);
 
-                trsKernels[device] = new Kernel(*trsProgs[device], "transpose");
-            });
+            trsKernels[device] = new Kernel(*trsProgs[device], "transpose");
+        });
 
 
-        NDRange local(THREADS_X, THREADS_Y);
+    NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(in.info.dims[0], TILE_DIM);
-        int blk_y = divup(in.info.dims[1], TILE_DIM);
+    int blk_x = divup(in.info.dims[0], TILE_DIM);
+    int blk_y = divup(in.info.dims[1], TILE_DIM);
 
-        // launch batch * blk_x blocks along x dimension
-        NDRange global(blk_x * local[0] * in.info.dims[2],
-                       blk_y * local[1] * in.info.dims[3]);
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * local[0] * in.info.dims[2],
+                    blk_y * local[1] * in.info.dims[3]);
 
-        auto transposeOp = KernelFunctor<Buffer, const KParam,
-                                       const Buffer, const KParam,
-                                       const int, const int> (*trsKernels[device]);
+    auto transposeOp = KernelFunctor<Buffer, const KParam,
+                                    const Buffer, const KParam,
+                                    const int, const int> (*trsKernels[device]);
 
-        transposeOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, blk_x, blk_y);
+    transposeOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info, blk_x, blk_y);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 239a909512..6784eca86f 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -40,54 +40,49 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T, bool conjugate, bool IS32MULTIPLE>
 void transpose_inplace(Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  transposeProgs;
-        static std::map<int, Kernel*> transposeKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  transposeProgs;
+    static std::map<int, Kernel*> transposeKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once(compileFlags[device], [device] () {
+    std::call_once(compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D TILE_DIM=" << TILE_DIM
-                        << " -D THREADS_Y=" << THREADS_Y
-                        << " -D IS32MULTIPLE=" << IS32MULTIPLE
-                        << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
-                        << " -D T=" << dtype_traits<T>::getName();
+            std::ostringstream options;
+            options << " -D TILE_DIM=" << TILE_DIM
+                    << " -D THREADS_Y=" << THREADS_Y
+                    << " -D IS32MULTIPLE=" << IS32MULTIPLE
+                    << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
+                    << " -D T=" << dtype_traits<T>::getName();
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-                cl::Program prog;
-                buildProgram(prog, transpose_inplace_cl, transpose_inplace_cl_len, options.str());
-                transposeProgs[device] = new Program(prog);
+            cl::Program prog;
+            buildProgram(prog, transpose_inplace_cl, transpose_inplace_cl_len, options.str());
+            transposeProgs[device] = new Program(prog);
 
-                transposeKernels[device] = new Kernel(*transposeProgs[device], "transpose_inplace");
-            });
+            transposeKernels[device] = new Kernel(*transposeProgs[device], "transpose_inplace");
+        });
 
 
-        NDRange local(THREADS_X, THREADS_Y);
+    NDRange local(THREADS_X, THREADS_Y);
 
-        int blk_x = divup(in.info.dims[0], TILE_DIM);
-        int blk_y = divup(in.info.dims[1], TILE_DIM);
+    int blk_x = divup(in.info.dims[0], TILE_DIM);
+    int blk_y = divup(in.info.dims[1], TILE_DIM);
 
-        // launch batch * blk_x blocks along x dimension
-        NDRange global(blk_x * local[0] * in.info.dims[2],
-                       blk_y * local[1] * in.info.dims[3]);
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * local[0] * in.info.dims[2],
+                    blk_y * local[1] * in.info.dims[3]);
 
-        auto transposeOp = KernelFunctor<Buffer, const KParam,
-                                       const int, const int> (*transposeKernels[device]);
+    auto transposeOp = KernelFunctor<Buffer, const KParam,
+                                    const int, const int> (*transposeKernels[device]);
 
-        transposeOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, blk_x, blk_y);
+    transposeOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, blk_x, blk_y);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 7fa6240aee..acfc4424dd 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -44,54 +44,49 @@ static const unsigned TILEY = 32;
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Param out, const Param in)
 {
-    try {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>  trgProgs;
-        static std::map<int, Kernel*> trgKernels;
+    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+    static std::map<int, Program*>  trgProgs;
+    static std::map<int, Kernel*> trgKernels;
 
-        int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
 
-        std::call_once(compileFlags[device], [device] () {
+    std::call_once(compileFlags[device], [device] () {
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D is_upper=" << is_upper
-                        << " -D is_unit_diag=" << is_unit_diag
-                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-                        << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D is_upper=" << is_upper
+                    << " -D is_unit_diag=" << is_unit_diag
+                    << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+                    << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+            if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+                options << " -D USE_DOUBLE";
+            }
 
-                cl::Program prog;
-                buildProgram(prog, triangle_cl, triangle_cl_len, options.str());
-                trgProgs[device] = new Program(prog);
+            cl::Program prog;
+            buildProgram(prog, triangle_cl, triangle_cl_len, options.str());
+            trgProgs[device] = new Program(prog);
 
-                trgKernels[device] = new Kernel(*trgProgs[device], "triangle_kernel");
-            });
+            trgKernels[device] = new Kernel(*trgProgs[device], "triangle_kernel");
+        });
 
-        NDRange local(TX, TY);
+    NDRange local(TX, TY);
 
-        int groups_x = divup(out.info.dims[0], TILEX);
-        int groups_y = divup(out.info.dims[1], TILEY);
+    int groups_x = divup(out.info.dims[0], TILEX);
+    int groups_y = divup(out.info.dims[1], TILEY);
 
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                    groups_y * out.info.dims[3] * local[1]);
 
-        auto triangleOp = KernelFunctor<Buffer, KParam,
-                                      const Buffer, KParam,
-                                      const int, const int> (*trgKernels[device]);
+    auto triangleOp = KernelFunctor<Buffer, KParam,
+                                  const Buffer, KParam,
+                                  const int, const int> (*trgKernels[device]);
 
-        triangleOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, groups_x, groups_y);
+    triangleOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, *in.data, in.info, groups_x, groups_y);
 
-        CL_DEBUG_FINISH(getQueue());
-    } catch (cl::Error err) {
-        CL_TO_AF_ERROR(err);
-        throw;
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
 
 }
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 6c364b19b0..0ae3b7002a 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -41,77 +41,72 @@ namespace opencl
                     const dim_t px, const dim_t py,
                     const dim_t nx, const bool is_column)
         {
-            try {
-                std::string ref_name =
-                    std::string("unwrap_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(is_column);
-
-                int device = getActiveDeviceId();
-                kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-                kc_entry_t entry;
-                if (idx == kernelCaches[device].end()) {
-
-                    ToNumStr<T> toNumStr;
-                    std::ostringstream options;
-                    options << " -D is_column=" << is_column
-                            << " -D ZERO=" << toNumStr(scalar<T>(0))
-                            << " -D T="    << dtype_traits<T>::getName();
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, unwrap_cl, unwrap_cl_len, options.str());
-
-                    entry.prog = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "unwrap_kernel");
-
-                    kernelCaches[device][ref_name] = entry;
-                } else {
-                    entry = idx->second;
+            std::string ref_name =
+                std::string("unwrap_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(is_column);
+
+            int device = getActiveDeviceId();
+            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+            kc_entry_t entry;
+            if (idx == kernelCaches[device].end()) {
+
+                ToNumStr<T> toNumStr;
+                std::ostringstream options;
+                options << " -D is_column=" << is_column
+                        << " -D ZERO=" << toNumStr(scalar<T>(0))
+                        << " -D T="    << dtype_traits<T>::getName();
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
-                dim_t TX = 1, TY = 1;
-                dim_t BX = 1;
-                const dim_t BY = out.info.dims[2] * out.info.dims[3];
-                dim_t reps = 1;
-
-                if (is_column) {
-                    TX = std::min(THREADS_PER_GROUP, nextpow2(out.info.dims[0]));
-                    TY = THREADS_PER_GROUP / TX;
-                    BX = divup(out.info.dims[1], TY);
-                    reps = divup((wx * wy), TX);
-                } else {
-                    TX = THREADS_X;
-                    TY = THREADS_Y;
-                    BX = divup(out.info.dims[0], TX);
-                    reps = divup((wx * wy), TY);
-                }
-
-                NDRange local(TX, TY);
-                NDRange global(local[0] * BX,
-                               local[1] * BY);
+                Program prog;
+                buildProgram(prog, unwrap_cl, unwrap_cl_len, options.str());
 
-                auto unwrapOp = KernelFunctor<Buffer, const KParam,
-                                            const Buffer, const KParam,
-                                            const int, const int,
-                                            const int, const int,
-                                            const int, const int,
-                                            const int, const int> (*entry.ker);
+                entry.prog = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "unwrap_kernel");
 
-                unwrapOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, out.info, *in.data, in.info, wx, wy, sx, sy, px, py, nx, reps);
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
+            }
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
+            dim_t TX = 1, TY = 1;
+            dim_t BX = 1;
+            const dim_t BY = out.info.dims[2] * out.info.dims[3];
+            dim_t reps = 1;
+
+            if (is_column) {
+                TX = std::min(THREADS_PER_GROUP, nextpow2(out.info.dims[0]));
+                TY = THREADS_PER_GROUP / TX;
+                BX = divup(out.info.dims[1], TY);
+                reps = divup((wx * wy), TX);
+            } else {
+                TX = THREADS_X;
+                TY = THREADS_Y;
+                BX = divup(out.info.dims[0], TX);
+                reps = divup((wx * wy), TY);
             }
+
+            NDRange local(TX, TY);
+            NDRange global(local[0] * BX,
+                            local[1] * BY);
+
+            auto unwrapOp = KernelFunctor<Buffer, const KParam,
+                                        const Buffer, const KParam,
+                                        const int, const int,
+                                        const int, const int,
+                                        const int, const int,
+                                        const int, const int> (*entry.ker);
+
+            unwrapOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, *in.data, in.info, wx, wy, sx, sy, px, py, nx, reps);
+
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index f46b7e8b1f..26f1bb1ae3 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -92,82 +92,78 @@ namespace kernel
     template<typename T>
     static void where(Param &out, Param &in)
     {
-        try {
-            uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_GROUP);
-            uint threads_y = THREADS_PER_GROUP / threads_x;
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
 
-            uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-            uint groups_y = divup(in.info.dims[1], threads_y);
+        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(in.info.dims[1], threads_y);
 
-            Param rtmp;
-            Param otmp;
+        Param rtmp;
+        Param otmp;
 
-            rtmp.info.dims[0] = groups_x;
-            otmp.info.dims[0] = in.info.dims[0];
+        rtmp.info.dims[0] = groups_x;
+        otmp.info.dims[0] = in.info.dims[0];
 
-            rtmp.info.strides[0] = 1;
-            otmp.info.strides[0] = 1;
+        rtmp.info.strides[0] = 1;
+        otmp.info.strides[0] = 1;
 
-            rtmp.info.offset = 0;
-            otmp.info.offset = 0;
+        rtmp.info.offset = 0;
+        otmp.info.offset = 0;
 
-            for (int k = 1; k < 4; k++) {
-                rtmp.info.dims[k] = in.info.dims[k];
-                rtmp.info.strides[k] = rtmp.info.strides[k - 1] * rtmp.info.dims[k - 1];
+        for (int k = 1; k < 4; k++) {
+            rtmp.info.dims[k] = in.info.dims[k];
+            rtmp.info.strides[k] = rtmp.info.strides[k - 1] * rtmp.info.dims[k - 1];
 
-                otmp.info.dims[k] = in.info.dims[k];
-                otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
-            }
-
-            int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
-            rtmp.data = bufferAlloc(rtmp_elements * sizeof(uint));
+            otmp.info.dims[k] = in.info.dims[k];
+            otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
+        }
 
-            int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
-            otmp.data = bufferAlloc(otmp_elements * sizeof(uint));
+        int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
+        rtmp.data = bufferAlloc(rtmp_elements * sizeof(uint));
 
-            scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in,
-                                                       false,
-                                                       groups_x, groups_y,
-                                                       threads_x);
+        int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
+        otmp.data = bufferAlloc(otmp_elements * sizeof(uint));
 
-            // Linearize the dimensions and perform scan
-            Param ltmp = rtmp;
-            ltmp.info.offset = 0;
-            ltmp.info.dims[0] = rtmp_elements;
-            for (int k = 1; k < 4; k++) {
-                ltmp.info.dims[k] = 1;
-                ltmp.info.strides[k] = rtmp_elements;
-            }
+        scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in,
+                                                    false,
+                                                    groups_x, groups_y,
+                                                    threads_x);
 
-            scan_first<uint, uint, af_add_t>(ltmp, ltmp);
+        // Linearize the dimensions and perform scan
+        Param ltmp = rtmp;
+        ltmp.info.offset = 0;
+        ltmp.info.dims[0] = rtmp_elements;
+        for (int k = 1; k < 4; k++) {
+            ltmp.info.dims[k] = 1;
+            ltmp.info.strides[k] = rtmp_elements;
+        }
 
-            // Get output size and allocate output
-            uint total;
-            getQueue().enqueueReadBuffer(*rtmp.data, CL_TRUE,
-                                         sizeof(uint) * (rtmp_elements - 1),
-                                         sizeof(uint),
-                                         &total);
+        scan_first<uint, uint, af_add_t>(ltmp, ltmp);
 
+        // Get output size and allocate output
+        uint total;
+        getQueue().enqueueReadBuffer(*rtmp.data, CL_TRUE,
+                                      sizeof(uint) * (rtmp_elements - 1),
+                                      sizeof(uint),
+                                      &total);
 
-            out.data = bufferAlloc(total * sizeof(uint));
 
-            out.info.dims[0] = total;
-            out.info.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                out.info.dims[k] = 1;
-                out.info.strides[k] = total;
-            }
+        out.data = bufferAlloc(total * sizeof(uint));
 
-            if (total > 0) {
-                get_out_idx<T>(out.data, otmp, rtmp, in, threads_x, groups_x, groups_y);
-            }
+        out.info.dims[0] = total;
+        out.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++) {
+            out.info.dims[k] = 1;
+            out.info.strides[k] = total;
+        }
 
-            bufferFree(rtmp.data);
-            bufferFree(otmp.data);
-        } catch (cl::Error err) {
-            CL_TO_AF_ERROR(err);
+        if (total > 0) {
+            get_out_idx<T>(out.data, otmp, rtmp, in, threads_x, groups_x, groups_y);
         }
+
+        bufferFree(rtmp.data);
+        bufferFree(otmp.data);
     }
 }
 }
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index bc3f1ca0e8..3e35a2fbea 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -41,72 +41,65 @@ namespace opencl
                   const dim_t px, const dim_t py,
                   const bool is_column)
         {
-            try {
-
-                std::string ref_name =
-                    std::string("wrap_") +
-                    std::string(dtype_traits<T>::getName()) +
-                    std::string("_") +
-                    std::to_string(is_column);
-
-                int device = getActiveDeviceId();
-                kc_t::iterator idx = kernelCaches[device].find(ref_name);
-
-                kc_entry_t entry;
-                if (idx == kernelCaches[device].end()) {
-
-                    ToNumStr<T> toNumStr;
-                    std::ostringstream options;
-                    options << " -D is_column=" << is_column
-                            << " -D ZERO=" << toNumStr(scalar<T>(0))
-                            << " -D T="    << dtype_traits<T>::getName();
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
+            std::string ref_name =
+                std::string("wrap_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::string("_") +
+                std::to_string(is_column);
+
+            int device = getActiveDeviceId();
+            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+
+            kc_entry_t entry;
+            if (idx == kernelCaches[device].end()) {
+
+                ToNumStr<T> toNumStr;
+                std::ostringstream options;
+                options << " -D is_column=" << is_column
+                        << " -D ZERO=" << toNumStr(scalar<T>(0))
+                        << " -D T="    << dtype_traits<T>::getName();
+
+                if (std::is_same<T, double>::value ||
+                    std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
 
-                    entry.prog = new Program(prog);
-                    entry.ker = new Kernel(*entry.prog, "wrap_kernel");
+                Program prog;
+                buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
 
-                    kernelCaches[device][ref_name] = entry;
-                } else {
-                    entry = idx->second;
-                }
+                entry.prog = new Program(prog);
+                entry.ker = new Kernel(*entry.prog, "wrap_kernel");
 
-                dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
-                dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
+            }
 
-                NDRange local(THREADS_X, THREADS_Y);
+            dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
+            dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
 
-                dim_t groups_x = divup(out.info.dims[0], local[0]);
-                dim_t groups_y = divup(out.info.dims[1], local[1]);
+            NDRange local(THREADS_X, THREADS_Y);
 
-                NDRange global(local[0] * groups_x * out.info.dims[2],
-                               local[1] * groups_y * out.info.dims[3]);
+            dim_t groups_x = divup(out.info.dims[0], local[0]);
+            dim_t groups_y = divup(out.info.dims[1], local[1]);
 
+            NDRange global(local[0] * groups_x * out.info.dims[2],
+                            local[1] * groups_y * out.info.dims[3]);
 
-                auto wrapOp = KernelFunctor<Buffer, const KParam,
-                                          const Buffer, const KParam,
-                                          const int, const int,
-                                          const int, const int,
-                                          const int, const int,
-                                          const int, const int,
-                                          const int, const int> (*entry.ker);
 
-                wrapOp(EnqueueArgs(getQueue(), global, local),
-                       *out.data, out.info, *in.data, in.info,
-                       wx, wy, sx, sy, px, py, nx, ny, groups_x, groups_y);
+            auto wrapOp = KernelFunctor<Buffer, const KParam,
+                                      const Buffer, const KParam,
+                                      const int, const int,
+                                      const int, const int,
+                                      const int, const int,
+                                      const int, const int,
+                                      const int, const int> (*entry.ker);
 
-                CL_DEBUG_FINISH(getQueue());
+            wrapOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info, *in.data, in.info,
+                    wx, wy, sx, sy, px, py, nx, ny, groups_x, groups_y);
 
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-                throw;
-            }
+            CL_DEBUG_FINISH(getQueue());
         }
     }
 }
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index 0bc6bd5283..70b6d97f41 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <lu.hpp>
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <kernel/lu_split.hpp>
@@ -43,58 +43,50 @@ Array<int> convertPivot(int *ipiv, int in_sz, int out_sz)
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::lu(lower, upper, pivot, in);
-        }
-
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-        int MN = std::min(M, N);
-
-        Array<T> in_copy = copyArray<T>(in);
-        pivot = lu_inplace(in_copy);
-
-        // SPLIT into lower and upper
-        dim4 ldims(M, MN);
-        dim4 udims(MN, N);
-        lower = createEmptyArray<T>(ldims);
-        upper = createEmptyArray<T>(udims);
-        kernel::lu_split<T>(lower, upper, in_copy);
-
-    } catch (cl::Error &err) {
-        CL_TO_AF_ERROR(err);
+    if(OpenCLCPUOffload()) {
+        return cpu::lu(lower, upper, pivot, in);
     }
+
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+    int MN = std::min(M, N);
+
+    Array<T> in_copy = copyArray<T>(in);
+    pivot = lu_inplace(in_copy);
+
+    // SPLIT into lower and upper
+    dim4 ldims(M, MN);
+    dim4 udims(MN, N);
+    lower = createEmptyArray<T>(ldims);
+    upper = createEmptyArray<T>(udims);
+    kernel::lu_split<T>(lower, upper, in_copy);
+
 }
 
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::lu_inplace(in, convert_pivot);
-        }
-
-        initBlas();
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-        int MN = std::min(M, N);
-        std::vector<int> ipiv(MN);
-
-        cl::Buffer *in_buf = in.get();
-        int info = 0;
-        magma_getrf_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
-                           &ipiv[0], getQueue()(), &info);
-
-        if (!convert_pivot) return createHostDataArray<int>(dim4(MN), &ipiv[0]);
-
-        Array<int> pivot = convertPivot(&ipiv[0], MN, M);
-        return pivot;
-    } catch(cl::Error &err) {
-        CL_TO_AF_ERROR(err);
+    if(OpenCLCPUOffload()) {
+        return cpu::lu_inplace(in, convert_pivot);
     }
+
+    initBlas();
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+    int MN = std::min(M, N);
+    std::vector<int> ipiv(MN);
+
+    cl::Buffer *in_buf = in.get();
+    int info = 0;
+    magma_getrf_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
+                        &ipiv[0], getQueue()(), &info);
+
+    if (!convert_pivot) return createHostDataArray<int>(dim4(MN), &ipiv[0]);
+
+    Array<int> pivot = convertPivot(&ipiv[0], MN, M);
+    return pivot;
 }
 
 bool isLAPACKAvailable()
diff --git a/src/backend/opencl/magma/magma_cpu_blas.h b/src/backend/opencl/magma/magma_cpu_blas.h
index 6661aad657..6d06b2caae 100644
--- a/src/backend/opencl/magma/magma_cpu_blas.h
+++ b/src/backend/opencl/magma/magma_cpu_blas.h
@@ -9,7 +9,7 @@
 
 #ifndef MAGMA_CPU_BLAS
 #define MAGMA_CPU_BLAS
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <defines.hpp>
 #include "magma_types.h"
 
diff --git a/src/backend/opencl/magma/magma_cpu_lapack.h b/src/backend/opencl/magma/magma_cpu_lapack.h
index 54c26ae0e9..df17496e6b 100644
--- a/src/backend/opencl/magma/magma_cpu_lapack.h
+++ b/src/backend/opencl/magma/magma_cpu_lapack.h
@@ -10,7 +10,7 @@
 #ifndef MAGMA_CPU_LAPACK
 #define MAGMA_CPU_LAPACK
 
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <defines.hpp>
 #include "magma_types.h"
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 9a2edd44a2..59f8c31154 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -95,20 +95,12 @@ MemoryManager::MemoryManager() :
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
-    try {
-        return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
-    } catch(cl::Error err) {
-        CL_TO_AF_ERROR(err);
-    }
+    return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
 }
 
 void MemoryManager::nativeFree(void *ptr)
 {
-    try {
-        delete (cl::Buffer *)ptr;
-    } catch(cl::Error err) {
-        CL_TO_AF_ERROR(err);
-    }
+    delete (cl::Buffer *)ptr;
 }
 
 static MemoryManager &getMemoryManager()
@@ -137,29 +129,20 @@ MemoryManagerPinned::MemoryManagerPinned() :
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {
     void *ptr = NULL;
-    try {
-        cl::Buffer buf= cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
-        ptr = getQueue().enqueueMapBuffer(buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
-        pinned_maps[opencl::getActiveDeviceId()][ptr] = buf;
-    } catch(cl::Error err) {
-        CL_TO_AF_ERROR(err);
-    }
+    cl::Buffer buf= cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
+    ptr = getQueue().enqueueMapBuffer(buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
+    pinned_maps[opencl::getActiveDeviceId()][ptr] = buf;
     return ptr;
 }
 
 void MemoryManagerPinned::nativeFree(void *ptr)
 {
-    try {
-        int n = opencl::getActiveDeviceId();
-        auto iter = pinned_maps[n].find(ptr);
-
-        if (iter != pinned_maps[n].end()) {
-            getQueue().enqueueUnmapMemObject(pinned_maps[n][ptr], ptr);
-            pinned_maps[n].erase(iter);
-        }
+    int n = opencl::getActiveDeviceId();
+    auto iter = pinned_maps[n].find(ptr);
 
-    } catch(cl::Error err) {
-        CL_TO_AF_ERROR(err);
+    if (iter != pinned_maps[n].end()) {
+        getQueue().enqueueUnmapMemObject(pinned_maps[n][ptr], ptr);
+        pinned_maps[n].erase(iter);
     }
 }
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index cb4bcd73bd..c3d5cf7f16 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -219,115 +219,109 @@ static afcl::platform getPlatformEnum(cl::Device dev)
 DeviceManager::DeviceManager()
     : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
 {
-    try {
-        std::vector<cl::Platform>   platforms;
-        Platform::get(&platforms);
+    std::vector<cl::Platform>   platforms;
+    Platform::get(&platforms);
 
-        // This is all we need because the sort takes care of the order of devices
+    // This is all we need because the sort takes care of the order of devices
 #ifdef OS_MAC
-        cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
 #else
-        cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
+    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
 #endif
 
-        std::string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
+    std::string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
 
-        if (deviceENV.compare("GPU") == 0) {
-            DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
-        } else if (deviceENV.compare("CPU") == 0) {
-            DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
-        } else if (deviceENV.compare("ACC") >= 0) {
-            DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
-        }
+    if (deviceENV.compare("GPU") == 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+    } else if (deviceENV.compare("CPU") == 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
+    } else if (deviceENV.compare("ACC") >= 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
+    }
 
 
 
-        // Iterate through platforms, get all available devices and store them
-        for (auto &platform : platforms) {
-            std::vector<Device> current_devices;
+    // Iterate through platforms, get all available devices and store them
+    for (auto &platform : platforms) {
+        std::vector<Device> current_devices;
 
-            try {
-                platform.getDevices(DEVICE_TYPES, &current_devices);
-            } catch(const cl::Error &err) {
-                if (err.err() != CL_DEVICE_NOT_FOUND) {
-                    throw;
-                }
+        try {
+            platform.getDevices(DEVICE_TYPES, &current_devices);
+        } catch(const cl::Error &err) {
+            if (err.err() != CL_DEVICE_NOT_FOUND) {
+                throw;
             }
+        }
 
-            for (auto dev : current_devices) {
-                mDevices.push_back(new Device(dev));
-            }
+        for (auto dev : current_devices) {
+            mDevices.push_back(new Device(dev));
         }
+    }
 
-        int nDevices = mDevices.size();
+    int nDevices = mDevices.size();
 
-        if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
+    if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
 
-        // Sort OpenCL devices based on default criteria
-        std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
+    // Sort OpenCL devices based on default criteria
+    std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
 
-        // Create contexts and queues once the sort is done
-        for (int i = 0; i < nDevices; i++) {
-            cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
-            cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
-                                            (cl_context_properties)(device_platform),
-                                            0};
-
-            Context *ctx = new Context(*mDevices[i], cps);
-            CommandQueue *cq = new CommandQueue(*ctx, *mDevices[i]);
-            mContexts.push_back(ctx);
-            mQueues.push_back(cq);
-            mIsGLSharingOn.push_back(false);
-            mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
-            mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
+    // Create contexts and queues once the sort is done
+    for (int i = 0; i < nDevices; i++) {
+        cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
+        cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
+                                        (cl_context_properties)(device_platform),
+                                        0};
+
+        Context *ctx = new Context(*mDevices[i], cps);
+        CommandQueue *cq = new CommandQueue(*ctx, *mDevices[i]);
+        mContexts.push_back(ctx);
+        mQueues.push_back(cq);
+        mIsGLSharingOn.push_back(false);
+        mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
+        mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
+    }
+
+    bool default_device_set = false;
+    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
+    if(!deviceENV.empty()) {
+        std::stringstream s(deviceENV);
+        int def_device = -1;
+        s >> def_device;
+        if(def_device < 0 || def_device >= (int)nDevices) {
+            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
+            printf("Setting default device as 0\n");
+        } else {
+            setContext(def_device);
+            default_device_set = true;
+        }
+    }
+
+    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
+    if (!default_device_set && !deviceENV.empty())
+    {
+        cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
+        if (deviceENV.compare("CPU") == 0) {
+            default_device_type = CL_DEVICE_TYPE_CPU;
+        } else if (deviceENV.compare("ACC") >= 0) {
+            default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
         }
 
         bool default_device_set = false;
-        deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
-        if(!deviceENV.empty()) {
-            std::stringstream s(deviceENV);
-            int def_device = -1;
-            s >> def_device;
-            if(def_device < 0 || def_device >= (int)nDevices) {
-                printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
-                printf("Setting default device as 0\n");
-            } else {
-                setContext(def_device);
+        for (int i = 0; i < nDevices; i++) {
+            if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
                 default_device_set = true;
+                setContext(i);
+                break;
             }
         }
 
-        deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
-        if (!default_device_set && !deviceENV.empty())
-        {
-            cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
-            if (deviceENV.compare("CPU") == 0) {
-                default_device_type = CL_DEVICE_TYPE_CPU;
-            } else if (deviceENV.compare("ACC") >= 0) {
-                default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
-            }
-
-            bool default_device_set = false;
-            for (int i = 0; i < nDevices; i++) {
-                if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
-                    default_device_set = true;
-                    setContext(i);
-                    break;
-                }
-            }
-
-            if (!default_device_set) {
-                printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
-                       deviceENV.c_str());
-                printf("Using default device as 0\n");
-            }
+        if (!default_device_set) {
+            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
+                    deviceENV.c_str());
+            printf("Using default device as 0\n");
         }
-
-    } catch (const cl::Error &error) {
-            CL_TO_AF_ERROR(error);
     }
 
-
 #if defined(WITH_GRAPHICS)
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
@@ -605,14 +599,10 @@ int setDevice(int device)
 
 void sync(int device)
 {
-    try {
-        int currDevice = getActiveDeviceId();
-        setDevice(device);
-        getQueue().finish();
-        setDevice(currDevice);
-    } catch (const cl::Error &ex) {
-        CL_TO_AF_ERROR(ex);
-    }
+    int currDevice = getActiveDeviceId();
+    setDevice(device);
+    getQueue().finish();
+    setDevice(currDevice);
 }
 
 bool checkExtnAvailability(const Device &pDevice, std::string pName)
@@ -736,95 +726,82 @@ void DeviceManager::markDeviceForInterop(const int device, const forge::Window*
 
 void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
-    try {
-
-        clRetainDevice(dev);
-        clRetainContext(ctx);
-        clRetainCommandQueue(que);
-
-        DeviceManager& devMngr   = DeviceManager::getInstance();
-        cl::Device* tDevice      = new cl::Device(dev);
-        cl::Context* tContext    = new cl::Context(ctx);
-        cl::CommandQueue* tQueue = (que==NULL ?
-                new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
-        devMngr.mDevices.push_back(tDevice);
-        devMngr.mContexts.push_back(tContext);
-        devMngr.mQueues.push_back(tQueue);
-        devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
-        // FIXME: add OpenGL Interop for user provided contexts later
-        devMngr.mIsGLSharingOn.push_back(false);
-    } catch (const cl::Error &ex) {
-        CL_TO_AF_ERROR(ex);
-    }
+    clRetainDevice(dev);
+    clRetainContext(ctx);
+    clRetainCommandQueue(que);
+
+    DeviceManager& devMngr   = DeviceManager::getInstance();
+    cl::Device* tDevice      = new cl::Device(dev);
+    cl::Context* tContext    = new cl::Context(ctx);
+    cl::CommandQueue* tQueue = (que==NULL ?
+            new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
+    devMngr.mDevices.push_back(tDevice);
+    devMngr.mContexts.push_back(tContext);
+    devMngr.mQueues.push_back(tQueue);
+    devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
+    // FIXME: add OpenGL Interop for user provided contexts later
+    devMngr.mIsGLSharingOn.push_back(false);
 }
 
 void setDeviceContext(cl_device_id dev, cl_context ctx)
 {
     // FIXME: add OpenGL Interop for user provided contexts later
-    try {
-        DeviceManager& devMngr = DeviceManager::getInstance();
-        const int dCount = devMngr.mDevices.size();
-        for (int i=0; i<dCount; ++i) {
-            if(devMngr.mDevices[i]->operator()()==dev &&
-                    devMngr.mContexts[i]->operator()()==ctx) {
-                setDevice(i);
-                return;
-            }
+    DeviceManager& devMngr = DeviceManager::getInstance();
+    const int dCount = devMngr.mDevices.size();
+    for (int i=0; i<dCount; ++i) {
+        if(devMngr.mDevices[i]->operator()()==dev &&
+                devMngr.mContexts[i]->operator()()==ctx) {
+            setDevice(i);
+            return;
         }
-    } catch (const cl::Error &ex) {
-        CL_TO_AF_ERROR(ex);
     }
     AF_ERROR("No matching device found", AF_ERR_ARG);
 }
 
 void removeDeviceContext(cl_device_id dev, cl_context ctx)
 {
-    try {
-        if (getDevice()() == dev && getContext()()==ctx) {
-            AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
-        }
+    if (getDevice()() == dev && getContext()()==ctx) {
+        AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
+    }
 
-        DeviceManager& devMngr = DeviceManager::getInstance();
-        const int dCount = devMngr.mDevices.size();
-        int deleteIdx = -1;
-        for (int i = 0; i<dCount; ++i) {
-            if(devMngr.mDevices[i]->operator()()==dev &&
-                    devMngr.mContexts[i]->operator()()==ctx) {
-                deleteIdx = i;
-                break;
-            }
+    DeviceManager& devMngr = DeviceManager::getInstance();
+    const int dCount = devMngr.mDevices.size();
+    int deleteIdx = -1;
+    for (int i = 0; i<dCount; ++i) {
+        if(devMngr.mDevices[i]->operator()()==dev &&
+                devMngr.mContexts[i]->operator()()==ctx) {
+            deleteIdx = i;
+            break;
         }
-        if (deleteIdx < (int)devMngr.mUserDeviceOffset) {
-            AF_ERROR("Cannot pop ArrayFire internal devices", AF_ERR_ARG);
-        } else if (deleteIdx == -1) {
-            AF_ERROR("No matching device found", AF_ERR_ARG);
-        } else {
+    }
+    if (deleteIdx < (int)devMngr.mUserDeviceOffset) {
+        AF_ERROR("Cannot pop ArrayFire internal devices", AF_ERR_ARG);
+    } else if (deleteIdx == -1) {
+        AF_ERROR("No matching device found", AF_ERR_ARG);
+    } else {
 
-            clReleaseDevice((*devMngr.mDevices[deleteIdx])());
-            clReleaseContext((*devMngr.mContexts[deleteIdx])());
-            clReleaseCommandQueue((*devMngr.mQueues[deleteIdx])());
-
-            // FIXME: this case can potentially cause issues due to the
-            // modification of the device pool stl containers.
-
-            // IF the current active device is enumerated at a position
-            // that lies ahead of the device that has been requested
-            // to be removed. We just pop the entries from pool since it
-            // has no side effects.
-            devMngr.mDevices.erase(devMngr.mDevices.begin()+deleteIdx);
-            devMngr.mContexts.erase(devMngr.mContexts.begin()+deleteIdx);
-            devMngr.mQueues.erase(devMngr.mQueues.begin()+deleteIdx);
-            devMngr.mPlatforms.erase(devMngr.mPlatforms.begin()+deleteIdx);
-            // FIXME: add OpenGL Interop for user provided contexts later
-            devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin()+deleteIdx);
-            // OTHERWISE, update(decrement) the `mActive*Id` variables
-            if (deleteIdx < (int)devMngr.mActiveCtxId) {
-                --devMngr.mActiveCtxId;
-                --devMngr.mActiveQId;
-            }
+        clReleaseDevice((*devMngr.mDevices[deleteIdx])());
+        clReleaseContext((*devMngr.mContexts[deleteIdx])());
+        clReleaseCommandQueue((*devMngr.mQueues[deleteIdx])());
+
+        // FIXME: this case can potentially cause issues due to the
+        // modification of the device pool stl containers.
+
+        // IF the current active device is enumerated at a position
+        // that lies ahead of the device that has been requested
+        // to be removed. We just pop the entries from pool since it
+        // has no side effects.
+        devMngr.mDevices.erase(devMngr.mDevices.begin()+deleteIdx);
+        devMngr.mContexts.erase(devMngr.mContexts.begin()+deleteIdx);
+        devMngr.mQueues.erase(devMngr.mQueues.begin()+deleteIdx);
+        devMngr.mPlatforms.erase(devMngr.mPlatforms.begin()+deleteIdx);
+        // FIXME: add OpenGL Interop for user provided contexts later
+        devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin()+deleteIdx);
+        // OTHERWISE, update(decrement) the `mActive*Id` variables
+        if (deleteIdx < (int)devMngr.mActiveCtxId) {
+            --devMngr.mActiveCtxId;
+            --devMngr.mActiveQId;
         }
-    } catch (const cl::Error &ex) {
-        CL_TO_AF_ERROR(ex);
     }
 }
 
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 56101a8b97..cb730122db 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <qr.hpp>
-#include <err_common.hpp>
 #include <err_opencl.hpp>
 #include <blas.hpp>
 #include <copy.hpp>
@@ -29,94 +28,85 @@ namespace opencl
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::qr(q, r, t, orig);
-        }
-
-        initBlas();
-        dim4 iDims = orig.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-
-        dim4 pDims(M, std::max(M, N));
-        Array<T> in = padArray<T, T>(orig, pDims, scalar<T>(0));  //copyArray<T>(orig);
-        in.resetDims(iDims);
-
-        int MN = std::min(M, N);
-        int NB = magma_get_geqrf_nb<T>(M);
-
-        int NUM = (2*MN + ((N+31)/32)*32)*NB;
-        Array<T> tmp = createEmptyArray<T>(dim4(NUM));
-
-        std::vector<T> h_tau(MN);
-
-        int info = 0;
-        cl::Buffer *in_buf = in.get();
-        cl::Buffer *dT = tmp.get();
-
-        magma_geqrf3_gpu<T>(M, N,
-                           (*in_buf)(), in.getOffset(), in.strides()[1],
-                           &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
-
-        r = createEmptyArray<T>(in.dims());
-        kernel::triangle<T, true, false>(r, in);
-
-        cl::Buffer *r_buf = r.get();
-        magmablas_swapdblk<T>(MN - 1, NB,
-                              ( *r_buf)(), r.getOffset(),
-                              r.strides()[1], 1,
-                              (*dT)(), tmp.getOffset() + MN * NB,
-                              NB, 0, getQueue()());
-
-        q = in; // No need to copy
-        q.resetDims(dim4(M, M));
-        cl::Buffer *q_buf = q.get();
-
-        magma_ungqr_gpu<T>(q.dims()[0], q.dims()[1], std::min(M, N),
-                           (*q_buf)(), q.getOffset(), q.strides()[1],
-                           &h_tau[0],
-                           (*dT)(), tmp.getOffset(), NB, getQueue()(), &info);
-
-        t = createHostDataArray(dim4(MN), &h_tau[0]);
-    } catch(cl::Error &err) {
-        CL_TO_AF_ERROR(err);
+    if(OpenCLCPUOffload()) {
+        return cpu::qr(q, r, t, orig);
     }
+
+    initBlas();
+    dim4 iDims = orig.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+
+    dim4 pDims(M, std::max(M, N));
+    Array<T> in = padArray<T, T>(orig, pDims, scalar<T>(0));  //copyArray<T>(orig);
+    in.resetDims(iDims);
+
+    int MN = std::min(M, N);
+    int NB = magma_get_geqrf_nb<T>(M);
+
+    int NUM = (2*MN + ((N+31)/32)*32)*NB;
+    Array<T> tmp = createEmptyArray<T>(dim4(NUM));
+
+    std::vector<T> h_tau(MN);
+
+    int info = 0;
+    cl::Buffer *in_buf = in.get();
+    cl::Buffer *dT = tmp.get();
+
+    magma_geqrf3_gpu<T>(M, N,
+                        (*in_buf)(), in.getOffset(), in.strides()[1],
+                        &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
+
+    r = createEmptyArray<T>(in.dims());
+    kernel::triangle<T, true, false>(r, in);
+
+    cl::Buffer *r_buf = r.get();
+    magmablas_swapdblk<T>(MN - 1, NB,
+                          ( *r_buf)(), r.getOffset(),
+                          r.strides()[1], 1,
+                          (*dT)(), tmp.getOffset() + MN * NB,
+                          NB, 0, getQueue()());
+
+    q = in; // No need to copy
+    q.resetDims(dim4(M, M));
+    cl::Buffer *q_buf = q.get();
+
+    magma_ungqr_gpu<T>(q.dims()[0], q.dims()[1], std::min(M, N),
+                        (*q_buf)(), q.getOffset(), q.strides()[1],
+                        &h_tau[0],
+                        (*dT)(), tmp.getOffset(), NB, getQueue()(), &info);
+
+    t = createHostDataArray(dim4(MN), &h_tau[0]);
 }
 
 template<typename T>
 Array<T> qr_inplace(Array<T> &in)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::qr_inplace(in);
-        }
-
-        initBlas();
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-        int MN = std::min(M, N);
+    if(OpenCLCPUOffload()) {
+        return cpu::qr_inplace(in);
+    }
 
-        getQueue().finish(); // FIXME: Does this need to be here?
-        cl::CommandQueue Queue2(getContext(), getDevice());
-        cl_command_queue queues[] = {getQueue()(), Queue2()};
+    initBlas();
+    dim4 iDims = in.dims();
+    int M = iDims[0];
+    int N = iDims[1];
+    int MN = std::min(M, N);
 
+    getQueue().finish(); // FIXME: Does this need to be here?
+    cl::CommandQueue Queue2(getContext(), getDevice());
+    cl_command_queue queues[] = {getQueue()(), Queue2()};
 
-        std::vector<T> h_tau(MN);
-        cl::Buffer *in_buf = in.get();
 
-        int info = 0;
-        magma_geqrf2_gpu<T>(M, N, (*in_buf)(),
-                            in.getOffset(), in.strides()[1],
-                            &h_tau[0], queues, &info);
+    std::vector<T> h_tau(MN);
+    cl::Buffer *in_buf = in.get();
 
-        Array<T> t = createHostDataArray(dim4(MN), &h_tau[0]);
-        return t;
+    int info = 0;
+    magma_geqrf2_gpu<T>(M, N, (*in_buf)(),
+                        in.getOffset(), in.strides()[1],
+                        &h_tau[0], queues, &info);
 
-    } catch(cl::Error &err) {
-        CL_TO_AF_ERROR(err);
-    }
+    Array<T> t = createHostDataArray(dim4(MN), &h_tau[0]);
+    return t;
 }
 
 #define INSTANTIATE_QR(T)                                                                           \
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 29c256894a..0bc82e0a2d 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -23,25 +23,19 @@ namespace opencl
     {
         Array<To> out = createEmptyArray<To>(in.dims());
 
-        try {
-            Param Out = out;
-            Param In  =   in;
+        Param Out = out;
+        Param In  =   in;
 
-            if (inclusive_scan) {
-                if (dim == 0)
-                    kernel::scan_first<Ti, To, op, true>(Out, In);
-                else
-                    kernel::scan_dim  <Ti, To, op, true>(Out, In, dim);
-            } else {
-                if (dim == 0)
-                    kernel::scan_first<Ti, To, op, false>(Out, In);
-                else
-                    kernel::scan_dim  <Ti, To, op, false>(Out, In, dim);
-            }
-
-        } catch (cl::Error &ex) {
-
-            CL_TO_AF_ERROR(ex);
+        if (inclusive_scan) {
+            if (dim == 0)
+                kernel::scan_first<Ti, To, op, true>(Out, In);
+            else
+                kernel::scan_dim  <Ti, To, op, true>(Out, In, dim);
+        } else {
+            if (dim == 0)
+                kernel::scan_first<Ti, To, op, false>(Out, In);
+            else
+                kernel::scan_dim  <Ti, To, op, false>(Out, In, dim);
         }
 
         return out;
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index 56fab0bfd4..0556caa073 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -23,28 +23,21 @@ namespace opencl
     {
         Array<To> out = createEmptyArray<To>(in.dims());
 
-        try {
-            Param Out = out;
-            Param Key = key;
-            Param In  =   in;
+        Param Out = out;
+        Param Key = key;
+        Param In  =   in;
 
-            if (inclusive_scan) {
-                if (dim == 0)
-                    kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
-                else
-                    kernel::scan_dim  <Ti, Tk, To, op, true>(Out, In, Key, dim);
-            } else {
-                if (dim == 0)
-                    kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
-                else
-                    kernel::scan_dim  <Ti, Tk, To, op, false>(Out, In, Key, dim);
-            }
-
-        } catch (cl::Error &ex) {
-
-            CL_TO_AF_ERROR(ex);
+        if (inclusive_scan) {
+            if (dim == 0)
+                kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
+            else
+                kernel::scan_dim  <Ti, Tk, To, op, true>(Out, In, Key, dim);
+        } else {
+            if (dim == 0)
+                kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
+            else
+                kernel::scan_dim  <Ti, Tk, To, op, false>(Out, In, Key, dim);
         }
-
         return out;
     }
 
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 93176752b5..61de3dc692 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <solve.hpp>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
@@ -300,25 +300,21 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 {
-    try {
-        if(OpenCLCPUOffload()) {
-            return cpu::solve(a, b, options);
-        }
+    if(OpenCLCPUOffload()) {
+        return cpu::solve(a, b, options);
+    }
 
-        initBlas();
+    initBlas();
 
-        if (options & AF_MAT_UPPER ||
-            options & AF_MAT_LOWER) {
-            return triangleSolve<T>(a, b, options);
-        }
+    if (options & AF_MAT_UPPER ||
+        options & AF_MAT_LOWER) {
+        return triangleSolve<T>(a, b, options);
+    }
 
-        if(a.dims()[0] == a.dims()[1]) {
-            return generalSolve<T>(a, b);
-        } else {
-            return leastSquares<T>(a, b);
-        }
-    } catch(cl::Error &err) {
-        CL_TO_AF_ERROR(err);
+    if(a.dims()[0] == a.dims()[1]) {
+        return generalSolve<T>(a, b);
+    } else {
+        return leastSquares<T>(a, b);
     }
 }
 
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 04d78b581e..0372d81f83 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <err_opencl.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>

From 8151aec4e6624e741a89e05c68d4611cd50cc1a6 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 31 Dec 2016 11:54:10 +0100
Subject: [PATCH 1071/2677] Separated the clBLAS and CLBlast backend into
 different files

---
 src/backend/opencl/CMakeLists.txt             |  46 ++++--
 src/backend/opencl/blas.hpp                   |  24 +--
 .../opencl/{blas.cpp => blas_clblas.cpp}      |  97 ++---------
 src/backend/opencl/blas_clblast.cpp           | 151 ++++++++++++++++++
 4 files changed, 196 insertions(+), 122 deletions(-)
 rename src/backend/opencl/{blas.cpp => blas_clblas.cpp} (68%)
 create mode 100644 src/backend/opencl/blas_clblast.cpp

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 25401fac49..f032717ce6 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -60,23 +60,35 @@ ENDIF()
 ADD_DEFINITIONS(-DAF_OPENCL
                 -D__CL_ENABLE_EXCEPTIONS)
 
-OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
-IF(USE_SYSTEM_CLBLAST)
-    FIND_PACKAGE(CLBlast REQUIRED)
-ELSE()
-    INCLUDE(build_CLBlast)
-ENDIF()
-INCLUDE_DIRECTORIES(${CLBLAST_INCLUDE_DIRS})
-LINK_DIRECTORIES(${CLBLAST_LIBRARY_DIR})
-
-OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
-IF(USE_SYSTEM_CLBLAS)
-    FIND_PACKAGE(clBLAS REQUIRED)
-ELSE()
-    INCLUDE(build_clBLAS)
-ENDIF()
-INCLUDE_DIRECTORIES(${CLBLAS_INCLUDE_DIRS})
-LINK_DIRECTORIES(${CLBLAS_LIBRARY_DIR})
+# IF(USE_CLBLAST AND USE_CLBLAS)
+#     MESSAGE(ERROR "Cannot use both CLBlast and clBLAS, please select only one of them")
+# ENDIF()
+
+# IF(USE_CLBLAST)
+    OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
+    IF(USE_SYSTEM_CLBLAST)
+        FIND_PACKAGE(CLBlast REQUIRED)
+    ELSE()
+        INCLUDE(build_CLBlast)
+    ENDIF()
+    INCLUDE_DIRECTORIES(${CLBLAST_INCLUDE_DIRS})
+    LINK_DIRECTORIES(${CLBLAST_LIBRARY_DIR})
+    ADD_DEFINITIONS(-DUSE_CLBLAST)
+    MESSAGE(STATUS "Building with CLBlast as an OpenCL BLAS back-end")
+# ENDIF()
+
+# IF(USE_CLBLAS)
+    OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
+    IF(USE_SYSTEM_CLBLAS)
+        FIND_PACKAGE(clBLAS REQUIRED)
+    ELSE()
+        INCLUDE(build_clBLAS)
+    ENDIF()
+    INCLUDE_DIRECTORIES(${CLBLAS_INCLUDE_DIRS})
+    LINK_DIRECTORIES(${CLBLAS_LIBRARY_DIR})
+    # ADD_DEFINITIONS(-DUSE_CLBLAS)
+    MESSAGE(STATUS "Building with clBLAS as an OpenCL BLAS back-end")
+# ENDIF()
 
 OPTION(USE_SYSTEM_CLFFT "Use system clFFT" OFF)
 IF(USE_SYSTEM_CLFFT)
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index dd99813ed9..a40801cea8 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -9,18 +9,10 @@
 
 #pragma once
 #include <Array.hpp>
-#include <mutex>
 
-// TODO: Temporary choose between clBLAS and CLBlast here
-#define USE_CLBLAS // or USE_CLBLAST
-
-#if defined(USE_CLBLAS)
-#include <clBLAS.h>
-#elif defined(USE_CLBLAST)
-#include <clblast.h>
-#else
-#error "Define either USE_CLBLAS or USE_CLBLAST"
-#endif
+// This file contains the common interface for OpenCL BLAS
+// functions. They can be implemented in different back-ends,
+// such as CLBlast or clBLAS.
 
 namespace opencl
 {
@@ -28,15 +20,11 @@ namespace opencl
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
+
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs);
 
-STATIC_ void
-initBlas() {
-#if defined(USE_CLBLAS)
-    static std::once_flag clblasSetupFlag;
-    call_once(clblasSetupFlag, clblasSetup);
-#endif // USE_CLBLAS
-}
+void initBlas();
+
 }
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas_clblas.cpp
similarity index 68%
rename from src/backend/opencl/blas.cpp
rename to src/backend/opencl/blas_clblas.cpp
index 4350ebd846..c24b958189 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas_clblas.cpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(USE_CLBLAS)
+
 #include <blas.hpp>
 #include <Array.hpp>
 #include <cassert>
@@ -21,14 +23,7 @@
 #include <reduce.hpp>
 #include <complex.hpp>
 
-#if defined(USE_CLBLAS)
 #include <err_clblas.hpp>
-#elif defined(USE_CLBLAST)
-#include <complex>
-#include <err_clblast.hpp>
-#else
-#error "Define either USE_CLBLAS or USE_CLBLAST"
-#endif
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_blas.hpp>
@@ -44,8 +39,11 @@ using std::call_once;
 using std::runtime_error;
 using std::to_string;
 
-// clBLAS specific helper functions and macro's
-#if defined(USE_CLBLAS)
+void
+initBlas() {
+    static std::once_flag clblasSetupFlag;
+    call_once(clblasSetupFlag, clblasSetup);
+}
 
 clblasTranspose
 toClblasTranspose(af_mat_prop opt)
@@ -60,6 +58,7 @@ toClblasTranspose(af_mat_prop opt)
     return out;
 }
 
+
 #define BLAS_FUNC_DEF(NAME)                                             \
 template<typename T>                                                    \
 struct NAME##_func;
@@ -126,40 +125,6 @@ BLAS_FUNC(dot, cdouble, false, Z, u)
 #undef BLAS_FUNC_DEF
 #undef BLAS_FUNC
 
-#endif // USE_CLBLAS
-
-// CLBlast specific helpers
-#if defined(USE_CLBLAST)
-
-clblast::Transpose
-toClblastTranspose(af_mat_prop opt)
-{
-    auto out = clblast::Transpose::kNo;
-    switch(opt) {
-        case AF_MAT_NONE    : out = clblast::Transpose::kNo; break;
-        case AF_MAT_TRANS   : out = clblast::Transpose::kYes; break;
-        case AF_MAT_CTRANS  : out = clblast::Transpose::kConjugate; break;
-        default             : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
-    }
-    return out;
-}
-
-// Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
-template <typename T> struct CLBlastConstant { using Type = T; };
-template <> struct CLBlastConstant<cfloat> { using Type = std::complex<float>; };
-template <> struct CLBlastConstant<cdouble> { using Type = std::complex<double>; };
-
-// Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
-template <typename T> typename CLBlastConstant<T>::Type toCLBlastConstant(const T val);
-
-// Specializations of the above function
-template <> float toCLBlastConstant(const float val) { return val; }
-template <> double toCLBlastConstant(const double val) { return val; }
-template <> std::complex<float> toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
-template <> std::complex<double> toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
-
-#endif // USE_CLBLAST
-
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
@@ -172,23 +137,12 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
     initBlas();
 
-#if defined(USE_CLBLAS)
     clblasTranspose lOpts = toClblasTranspose(optLhs);
     clblasTranspose rOpts = toClblasTranspose(optRhs);
 
     int aRowDim = (lOpts == clblasNoTrans) ? 0 : 1;
     int aColDim = (lOpts == clblasNoTrans) ? 1 : 0;
     int bColDim = (rOpts == clblasNoTrans) ? 1 : 0;
-#endif // USE_CLBLAS
-
-#if defined(USE_CLBLAST)
-    auto lOpts = toClblastTranspose(optLhs);
-    auto rOpts = toClblastTranspose(optRhs);
-
-    int aRowDim = (lOpts == clblast::Transpose::kNo) ? 0 : 1;
-    int aColDim = (lOpts == clblast::Transpose::kNo) ? 1 : 0;
-    int bColDim = (rOpts == clblast::Transpose::kNo) ? 1 : 0;
-#endif // USE_CLBLAST
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
@@ -206,7 +160,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     cl::Event event;
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
-#if defined(USE_CLBLAS)
         gemv_func<T> gemv;
         CLBLAS_CHECK(
             gemv(
@@ -219,23 +172,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 (*out.get())(),   out.getOffset(),             1,
                 1, &getQueue()(), 0, nullptr, &event())
             );
-#endif // USE_CLBLAS
-#if defined(USE_CLBLAST)
-        auto alpha_clblast = toCLBlastConstant(alpha);
-        auto beta_clblast = toCLBlastConstant(beta);
-        CLBLAST_CHECK(
-            clblast::Gemv(clblast::Layout::kColMajor, lOpts,
-                          lDims[0], lDims[1],
-                          alpha_clblast,
-                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                          (*rhs.get())(), rhs.getOffset(), rStrides[0],
-                          beta_clblast,
-                          (*out.get())(), out.getOffset(), 1,
-                          &getQueue()(), &event())
-        );
-#endif // USE_CLBLAST
     } else {
-#if defined(USE_CLBLAS)
         gemm_func<T> gemm;
         CLBLAS_CHECK(
             gemm(
@@ -248,22 +185,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 (*out.get())(),   out.getOffset(),  out.dims()[0],
                 1, &getQueue()(), 0, nullptr, &event())
             );
-#endif // USE_CLBLAS
-#if defined(USE_CLBLAST)
-        auto alpha_clblast = toCLBlastConstant(alpha);
-        auto beta_clblast = toCLBlastConstant(beta);
-        CLBLAST_CHECK(
-            clblast::Gemm(clblast::Layout::kColMajor, lOpts, rOpts,
-                          M, N, K,
-                          alpha_clblast,
-                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                          (*rhs.get())(), rhs.getOffset(), rStrides[1],
-                          beta_clblast,
-                          (*out.get())(), out.getOffset(), out.dims()[0],
-                          &getQueue()(), &event())
-        );
-#endif // USE_CLBLAST
-
     }
 
     return out;
@@ -298,3 +219,5 @@ INSTANTIATE_DOT(double)
 INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
 }
+
+#endif // USE_CLBLAS
diff --git a/src/backend/opencl/blas_clblast.cpp b/src/backend/opencl/blas_clblast.cpp
new file mode 100644
index 0000000000..3a84b39657
--- /dev/null
+++ b/src/backend/opencl/blas_clblast.cpp
@@ -0,0 +1,151 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(USE_CLBLAST)
+
+#include <complex>
+
+#include <blas.hpp>
+#include <Array.hpp>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <transpose.hpp>
+#include <arith.hpp>
+#include <reduce.hpp>
+#include <complex.hpp>
+
+#include <err_clblast.hpp>
+
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#include <cpu/cpu_blas.hpp>
+#endif
+
+namespace opencl
+{
+
+void
+initBlas() {
+  // Nothing to do here for CLBlast
+}
+
+clblast::Transpose
+toClblastTranspose(af_mat_prop opt)
+{
+    switch(opt) {
+        case AF_MAT_NONE    : return clblast::Transpose::kNo;
+        case AF_MAT_TRANS   : return clblast::Transpose::kYes;
+        case AF_MAT_CTRANS  : return clblast::Transpose::kConjugate;
+        default             : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+}
+
+// Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
+template <typename T> struct CLBlastConstant { using Type = T; };
+template <> struct CLBlastConstant<cfloat> { using Type = std::complex<float>; };
+template <> struct CLBlastConstant<cdouble> { using Type = std::complex<double>; };
+
+// Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
+template <typename T> typename CLBlastConstant<T>::Type toCLBlastConstant(const T val);
+
+// Specializations of the above function
+template <> float toCLBlastConstant(const float val) { return val; }
+template <> double toCLBlastConstant(const double val) { return val; }
+template <> std::complex<float> toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
+template <> std::complex<double> toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
+
+template<typename T>
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs)
+{
+#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+    if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
+        return cpu::matmul(lhs, rhs, optLhs, optRhs);
+    }
+#endif
+
+    const auto lOpts = toClblastTranspose(optLhs);
+    const auto rOpts = toClblastTranspose(optRhs);
+
+    const auto aRowDim = (lOpts == clblast::Transpose::kNo) ? 0 : 1;
+    const auto aColDim = (lOpts == clblast::Transpose::kNo) ? 1 : 0;
+    const auto bColDim = (rOpts == clblast::Transpose::kNo) ? 1 : 0;
+
+    const dim4 lDims = lhs.dims();
+    const dim4 rDims = rhs.dims();
+    const int M = lDims[aRowDim];
+    const int N = rDims[bColDim];
+    const int K = lDims[aColDim];
+
+    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    const auto alpha = scalar<T>(1);
+    const auto beta  = scalar<T>(0);
+    const auto alpha_clblast = toCLBlastConstant(alpha);
+    const auto beta_clblast = toCLBlastConstant(beta);
+
+    const dim4 lStrides = lhs.strides();
+    const dim4 rStrides = rhs.strides();
+    if(rDims[bColDim] == 1) {
+        CLBLAST_CHECK(
+            clblast::Gemv(clblast::Layout::kColMajor, lOpts,
+                          lDims[0], lDims[1],
+                          alpha_clblast,
+                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
+                          (*rhs.get())(), rhs.getOffset(), rStrides[0],
+                          beta_clblast,
+                          (*out.get())(), out.getOffset(), 1,
+                          &getQueue()())
+        );
+    } else {
+        CLBLAST_CHECK(
+            clblast::Gemm(clblast::Layout::kColMajor, lOpts, rOpts,
+                          M, N, K,
+                          alpha_clblast,
+                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
+                          (*rhs.get())(), rhs.getOffset(), rStrides[1],
+                          beta_clblast,
+                          (*out.get())(), out.getOffset(), out.dims()[0],
+                          &getQueue()())
+        );
+    }
+
+    return out;
+}
+
+template<typename T>
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
+             af_mat_prop optLhs, af_mat_prop optRhs)
+{
+    const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
+    const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
+
+    const Array<T> temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
+    return reduce<af_add_t, T, T>(temp, 0, false, 0);
+}
+
+#define INSTANTIATE_BLAS(TYPE)                                                          \
+    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
+                    af_mat_prop optLhs, af_mat_prop optRhs);
+
+INSTANTIATE_BLAS(float)
+INSTANTIATE_BLAS(cfloat)
+INSTANTIATE_BLAS(double)
+INSTANTIATE_BLAS(cdouble)
+
+#define INSTANTIATE_DOT(TYPE)                                                       \
+    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,  \
+                                   af_mat_prop optLhs, af_mat_prop optRhs);
+
+INSTANTIATE_DOT(float)
+INSTANTIATE_DOT(double)
+INSTANTIATE_DOT(cfloat)
+INSTANTIATE_DOT(cdouble)
+
+}
+
+#endif // USE_CLBLAST

From b811cce1c47ff09869937a6b86d2919e4796b578 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 1 Jan 2017 12:40:39 +0100
Subject: [PATCH 1072/2677] Separated out all clBLAS specific code for Magma
 into a single file

---
 src/backend/opencl/blas_clblas.cpp           |   3 +-
 src/backend/opencl/blas_clblast.cpp          |   3 +-
 src/backend/opencl/magma/gebrd.cpp           |  26 ++--
 src/backend/opencl/magma/getrf.cpp           |  98 +++++++--------
 src/backend/opencl/magma/getrs.cpp           |  24 ++--
 src/backend/opencl/magma/labrd.cpp           |  46 +++----
 src/backend/opencl/magma/larfb.cpp           | 125 +++++++++----------
 src/backend/opencl/magma/magma_blas.h        |  46 ++-----
 src/backend/opencl/magma/magma_blas_clblas.h |  74 +++++++++++
 src/backend/opencl/magma/magma_common.h      |   3 -
 src/backend/opencl/magma/magma_data.h        |   4 +-
 src/backend/opencl/magma/magma_types.h       |  13 --
 src/backend/opencl/magma/potrf.cpp           |  92 +++++++-------
 src/backend/opencl/platform.cpp              |   2 +-
 14 files changed, 289 insertions(+), 270 deletions(-)
 create mode 100644 src/backend/opencl/magma/magma_blas_clblas.h

diff --git a/src/backend/opencl/blas_clblas.cpp b/src/backend/opencl/blas_clblas.cpp
index c24b958189..73f7a756ac 100644
--- a/src/backend/opencl/blas_clblas.cpp
+++ b/src/backend/opencl/blas_clblas.cpp
@@ -40,7 +40,8 @@ using std::runtime_error;
 using std::to_string;
 
 void
-initBlas() {
+initBlas()
+{
     static std::once_flag clblasSetupFlag;
     call_once(clblasSetupFlag, clblasSetup);
 }
diff --git a/src/backend/opencl/blas_clblast.cpp b/src/backend/opencl/blas_clblast.cpp
index 3a84b39657..163e381995 100644
--- a/src/backend/opencl/blas_clblast.cpp
+++ b/src/backend/opencl/blas_clblast.cpp
@@ -30,7 +30,8 @@ namespace opencl
 {
 
 void
-initBlas() {
+initBlas()
+{
   // Nothing to do here for CLBlast
 }
 
diff --git a/src/backend/opencl/magma/gebrd.cpp b/src/backend/opencl/magma/gebrd.cpp
index c83efb4ca2..b287b70a5d 100644
--- a/src/backend/opencl/magma/gebrd.cpp
+++ b/src/backend/opencl/magma/gebrd.cpp
@@ -302,19 +302,19 @@ magma_gebrd_hybrid(
                             work  +               (ldwrkx+1)*nb, ldwrky,
                             dwork, dwork_offset + (ldwrkx+1)*nb, ldwrky, queue);
 
-        CLBLAS_CHECK(gpu_blas_gemm(clblasNoTrans, clblasConjTrans,
-                                   nrow, ncol, nb,
-                                   c_neg_one, dA(i+nb, i  ),      ldda,
-                                   dwork, dwork_offset+(ldwrkx+1)*nb, ldwrky,
-                                   c_one,     dA(i+nb, i+nb), ldda,
-                                   1, &queue, 0, nullptr, &event));
-
-        CLBLAS_CHECK(gpu_blas_gemm(clblasNoTrans, clblasNoTrans,
-                                   nrow, ncol, nb,
-                                   c_neg_one, dwork, dwork_offset+nb, ldwrkx,
-                                   dA(i,    i+nb), ldda,
-                                   c_one,     dA(i+nb, i+nb), ldda,
-                                   1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_CONJ_TRANS,
+                                        nrow, ncol, nb,
+                                        c_neg_one, dA(i+nb, i  ),      ldda,
+                                        dwork, dwork_offset+(ldwrkx+1)*nb, ldwrky,
+                                        c_one,     dA(i+nb, i+nb), ldda,
+                                        1, &queue, 0, nullptr, &event));
+
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
+                                        nrow, ncol, nb,
+                                        c_neg_one, dwork, dwork_offset+nb, ldwrkx,
+                                        dA(i,    i+nb), ldda,
+                                        c_one,     dA(i+nb, i+nb), ldda,
+                                        1, &queue, 0, nullptr, &event));
 
         /* Copy diagonal and off-diagonal elements of B back into A */
         if (m >= n) {
diff --git a/src/backend/opencl/magma/getrf.cpp b/src/backend/opencl/magma/getrf.cpp
index bd9c9d2a55..3cf0f18a32 100644
--- a/src/backend/opencl/magma/getrf.cpp
+++ b/src/backend/opencl/magma/getrf.cpp
@@ -219,23 +219,22 @@ magma_int_t magma_getrf_gpu(
             magma_getmatrix<Ty>(m-j*nb, nb, dAP(0,0), maxm, work(0), ldwork, queue);
 
             if (j > 0 && n > (j + 1) * nb) {
-                CLBLAS_CHECK(gpu_blas_trsm(
-                                 clblasRight, clblasUpper, clblasNoTrans, clblasUnit,
-                                 n - (j+1)*nb, nb,
-                                 c_one,
-                                 dAT(j-1,j-1), lddat,
-                                 dAT(j-1,j+1), lddat,
-                                 1, &queue, 0, nullptr, &event));
-
-                if (m > j * nb)  {
-                    CLBLAS_CHECK(gpu_blas_gemm( clblasNoTrans, clblasNoTrans,
-                                                n-(j+1)*nb, m-j*nb, nb,
-                                                c_neg_one,
-                                                dAT(j-1,j+1), lddat,
-                                                dAT(j,  j-1), lddat,
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                                                n - (j+1)*nb, nb,
                                                 c_one,
-                                                dAT(j,  j+1), lddat,
+                                                dAT(j-1,j-1), lddat,
+                                                dAT(j-1,j+1), lddat,
                                                 1, &queue, 0, nullptr, &event));
+
+                if (m > j * nb)  {
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
+                                                    n-(j+1)*nb, m-j*nb, nb,
+                                                    c_neg_one,
+                                                    dAT(j-1,j+1), lddat,
+                                                    dAT(j,  j-1), lddat,
+                                                    c_one,
+                                                    dAT(j,  j+1), lddat,
+                                                    1, &queue, 0, nullptr, &event));
                 }
             }
 
@@ -257,44 +256,42 @@ magma_int_t magma_getrf_gpu(
 
             // do the small non-parallel computations (next panel update)
             if (s > (j+1)) {
-                CLBLAS_CHECK(gpu_blas_trsm(
-                                 clblasRight, clblasUpper, clblasNoTrans, clblasUnit,
-                                 nb, nb,
-                                 c_one,
-                                 dAT(j, j  ), lddat,
-                                 dAT(j, j+1), lddat,
-                                 1, &queue, 0, nullptr, &event));
-
-
-                CLBLAS_CHECK(gpu_blas_gemm( clblasNoTrans, clblasNoTrans,
-                                            nb, m-(j+1)*nb, nb,
-                                            c_neg_one,
-                                            dAT(j,   j+1), lddat,
-                                            dAT(j+1, j  ), lddat,
-                                            c_one,
-                                            dAT(j+1, j+1), lddat,
-                                            1, &queue, 0, nullptr, &event));
-            }
-            else {
-                if (n > s * nb) {
-                    CLBLAS_CHECK(gpu_blas_trsm(
-                                     clblasRight, clblasUpper, clblasNoTrans, clblasUnit,
-                                     n-s*nb, nb,
-                                     c_one,
-                                     dAT(j, j  ), lddat,
-                                     dAT(j, j+1), lddat,
-                                     1, &queue, 0, nullptr, &event));
-                }
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                                                nb, nb,
+                                                c_one,
+                                                dAT(j, j  ), lddat,
+                                                dAT(j, j+1), lddat,
+                                                1, &queue, 0, nullptr, &event));
 
-                if ((n > (j+1) * nb) && (m > (j+1) * nb)) {
-                    CLBLAS_CHECK(gpu_blas_gemm( clblasNoTrans, clblasNoTrans,
-                                                n-(j+1)*nb, m-(j+1)*nb, nb,
+
+                OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
+                                                nb, m-(j+1)*nb, nb,
                                                 c_neg_one,
                                                 dAT(j,   j+1), lddat,
                                                 dAT(j+1, j  ), lddat,
                                                 c_one,
                                                 dAT(j+1, j+1), lddat,
                                                 1, &queue, 0, nullptr, &event));
+            }
+            else {
+                if (n > s * nb) {
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                                                    n-s*nb, nb,
+                                                    c_one,
+                                                    dAT(j, j  ), lddat,
+                                                    dAT(j, j+1), lddat,
+                                                    1, &queue, 0, nullptr, &event));
+                }
+
+                if ((n > (j+1) * nb) && (m > (j+1) * nb)) {
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
+                                                    n-(j+1)*nb, m-(j+1)*nb, nb,
+                                                    c_neg_one,
+                                                    dAT(j,   j+1), lddat,
+                                                    dAT(j+1, j  ), lddat,
+                                                    c_one,
+                                                    dAT(j+1, j+1), lddat,
+                                                    1, &queue, 0, nullptr, &event));
                 }
             }
         }
@@ -322,11 +319,10 @@ magma_int_t magma_getrf_gpu(
             magmablas_transpose<Ty>(rows, nb0, dAP(0,0), maxm, dAT(s,s), lddat, queue);
 
             if (n > s * nb + nb0) {
-                CLBLAS_CHECK(gpu_blas_trsm(
-                                 clblasRight, clblasUpper, clblasNoTrans, clblasUnit,
-                                 n-s*nb-nb0, nb0,
-                                 c_one, dAT(s,s),     lddat,
-                                 dAT(s,s)+nb0, lddat, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                                                n-s*nb-nb0, nb0,
+                                                c_one, dAT(s,s),     lddat,
+                                                dAT(s,s)+nb0, lddat, 1, &queue, 0, nullptr, &event));
             }
         }
 
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index eb28a5175a..608cfc835c 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -166,8 +166,8 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
 
     cl_event event = NULL;
 
-    clblasTranspose cltrans =(trans == MagmaNoTrans) ? clblasNoTrans :
-        (trans == MagmaTrans ? clblasTrans : clblasConjTrans);
+    OPENCL_BLAS_TRANS_TYPE cltrans =(trans == MagmaNoTrans) ? OPENCL_BLAS_NO_TRANS :
+        (trans == MagmaTrans ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS);
 
     bool cond = opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
     cl_mem dAT = 0;
@@ -183,15 +183,15 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
         LAPACKE_CHECK(cpu_lapack_laswp( nrhs, work, n, i1, i2, ipiv, inc));
         magma_setmatrix<Ty>( n, nrhs, work, n, dB, dB_offset, lddb, queue );
         if ( nrhs == 1) {
-            CLBLAS_CHECK(gpu_blas_trsv( clblasLower, clblasNoTrans, clblasUnit, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
-            CLBLAS_CHECK(gpu_blas_trsv( clblasUpper, clblasNoTrans, clblasNonUnit, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
         } else {
-            CLBLAS_CHECK(gpu_blas_trsm( clblasLeft, clblasLower, clblasNoTrans, clblasUnit, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
 
             if(cond) {
-                CLBLAS_CHECK(gpu_blas_trsm( clblasLeft, clblasLower, clblasTrans, clblasNonUnit, n, nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
             } else {
-                CLBLAS_CHECK(gpu_blas_trsm( clblasLeft, clblasUpper, clblasNoTrans, clblasNonUnit, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
             }
         }
     } else {
@@ -199,15 +199,15 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
 
         /* Solve A' * X = B. */
         if ( nrhs == 1) {
-            CLBLAS_CHECK(gpu_blas_trsv( clblasUpper, cltrans, clblasNonUnit, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
-            CLBLAS_CHECK(gpu_blas_trsv( clblasLower, cltrans, clblasUnit, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_UPPER, cltrans, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_LOWER, cltrans, OPENCL_BLAS_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
         } else {
             if(cond) {
-                CLBLAS_CHECK(gpu_blas_trsm( clblasLeft, clblasLower, clblasNoTrans, clblasNonUnit, n, nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
             } else {
-                CLBLAS_CHECK(gpu_blas_trsm( clblasLeft, clblasUpper, cltrans, clblasNonUnit, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER, cltrans, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
             }
-            CLBLAS_CHECK(gpu_blas_trsm( clblasLeft, clblasLower, cltrans, clblasUnit, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, cltrans, OPENCL_BLAS_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
         }
         magma_getmatrix<Ty>( n, nrhs, dB, dB_offset, lddb, work, n, queue );
         LAPACKE_CHECK(cpu_lapack_laswp( nrhs, work, n, i1, i2, ipiv, inc));
diff --git a/src/backend/opencl/magma/labrd.cpp b/src/backend/opencl/magma/labrd.cpp
index 115b48d2cd..bde9fdb4eb 100644
--- a/src/backend/opencl/magma/labrd.cpp
+++ b/src/backend/opencl/magma/labrd.cpp
@@ -303,11 +303,11 @@ magma_labrd_gpu(
                                     da, da_offset + (i__-1)+(i__-1)* (ldda), 1,
                                     queue);
                 // 2. Multiply ---------------------------------------------
-                CLBLAS_CHECK(gpu_blas_gemv(clblasConjTrans, i__2, i__3, c_one,
-                                           da, da_offset + (i__-1) + ((i__-1) + 1) * (ldda), ldda,
-                                           da, da_offset + (i__-1) + (i__-1) * (ldda), c__1, c_zero,
-                                           dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1,
-                                           1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_CONJ_TRANS, i__2, i__3, c_one,
+                                                da, da_offset + (i__-1) + ((i__-1) + 1) * (ldda), ldda,
+                                                da, da_offset + (i__-1) + (i__-1) * (ldda), c__1, c_zero,
+                                                dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1,
+                                                1, &queue, 0, nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
                 magma_getmatrix_async<Ty>(i__3, 1,
@@ -395,12 +395,12 @@ magma_labrd_gpu(
                 // 2. Multiply ---------------------------------------------
                 //magma_zcopy(i__3, da+(i__-1)+((i__-1)+1)*(ldda), ldda,
                 //            dy + 1 + lddy, 1);
-                CLBLAS_CHECK(gpu_blas_gemv(clblasNoTrans, i__2, i__3, c_one,
-                                           da, da_offset + (i__-1)+1+ ((i__-1)+1) * (ldda), ldda,
-                                           da, da_offset + (i__-1) +  ((i__-1)+1) * (ldda), ldda,
-                                           //dy + 1 + lddy, 1,
-                                           c_zero, dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1,
-                                           1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_NO_TRANS, i__2, i__3, c_one,
+                                                da, da_offset + (i__-1)+1+ ((i__-1)+1) * (ldda), ldda,
+                                                da, da_offset + (i__-1) +  ((i__-1)+1) * (ldda), ldda,
+                                                //dy + 1 + lddy, 1,
+                                                c_zero, dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1,
+                                                1, &queue, 0, nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
                 magma_getmatrix_async<Ty>(i__2, 1,
@@ -500,13 +500,13 @@ magma_labrd_gpu(
                 // 2. Multiply ---------------------------------------------
                 //magma_zcopy(i__3, da+(i__-1)+(i__-1)*(ldda), ldda,
                 //            dy + 1 + lddy, 1);
-                CLBLAS_CHECK(gpu_blas_gemv(clblasNoTrans, i__2, i__3, c_one,
-                                           da, da_offset + (i__-1)+1 + (i__-1) * ldda, ldda,
-                                           da, da_offset + (i__-1)   + (i__-1) * ldda, ldda,
-                                           // dy + 1 + lddy, 1,
-                                           c_zero,
-                                           dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1,
-                                           1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_NO_TRANS, i__2, i__3, c_one,
+                                                da, da_offset + (i__-1)+1 + (i__-1) * ldda, ldda,
+                                                da, da_offset + (i__-1)   + (i__-1) * ldda, ldda,
+                                                // dy + 1 + lddy, 1,
+                                                c_zero,
+                                                dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1,
+                                                1, &queue, 0, nullptr, &event));
 
 
                 // 3. Put the result back ----------------------------------
@@ -595,11 +595,11 @@ magma_labrd_gpu(
                                     da, da_offset + (i__-1)+1+ (i__-1)*(ldda),  1,
                                     queue);
                 // 2. Multiply ---------------------------------------------
-                CLBLAS_CHECK(gpu_blas_gemv(clblasConjTrans, i__2, i__3, c_one,
-                                           da, da_offset + (i__-1)+1+ ((i__-1)+1) * ldda, ldda,
-                                           da, da_offset + (i__-1)+1+  (i__-1)    * ldda, c__1,
-                                           c_zero, dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1,
-                                           1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_CONJ_TRANS, i__2, i__3, c_one,
+                                                da, da_offset + (i__-1)+1+ ((i__-1)+1) * ldda, ldda,
+                                                da, da_offset + (i__-1)+1+  (i__-1)    * ldda, c__1,
+                                                c_zero, dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1,
+                                                1, &queue, 0, nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
                 magma_getmatrix_async<Ty>(i__3, 1,
diff --git a/src/backend/opencl/magma/larfb.cpp b/src/backend/opencl/magma/larfb.cpp
index 20d2902d44..eebc6ec9ec 100644
--- a/src/backend/opencl/magma/larfb.cpp
+++ b/src/backend/opencl/magma/larfb.cpp
@@ -192,7 +192,7 @@ magma_larfb_gpu(
     static const Ty c_zero    = magma_zero<Ty>();
     static const Ty c_one     = magma_one<Ty>();
     static const Ty c_neg_one = magma_neg_one<Ty>();
-    static const clblasTranspose transType = magma_is_real<Ty>() ? clblasTrans : clblasConjTrans;
+    static const OPENCL_BLAS_TRANS_TYPE transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
 
     /* Check input arguments */
     magma_int_t info = 0;
@@ -225,33 +225,33 @@ magma_larfb_gpu(
     }
 
     // opposite of trans
-    clblasTranspose transt;
-    clblasTranspose cltrans;
+    OPENCL_BLAS_TRANS_TYPE transt;
+    OPENCL_BLAS_TRANS_TYPE cltrans;
     if (trans == MagmaNoTrans) {
         transt = transType;
-        cltrans = clblasNoTrans;
+        cltrans = OPENCL_BLAS_NO_TRANS;
     }
     else {
-        transt = clblasNoTrans;
+        transt = OPENCL_BLAS_NO_TRANS;
         cltrans = transType;
     }
 
     // whether T is upper or lower triangular
-    clblasUplo uplo;
+    OPENCL_BLAS_TRIANGLE_TYPE uplo;
     if (direct == MagmaForward)
-        uplo = clblasUpper;
+        uplo = OPENCL_BLAS_TRIANGLE_UPPER;
     else
-        uplo = clblasLower;
+        uplo = OPENCL_BLAS_TRIANGLE_LOWER;
 
     // whether V is stored transposed or not
-    clblasTranspose notransV, transV;
+    OPENCL_BLAS_TRANS_TYPE notransV, transV;
     if (storev == MagmaColumnwise) {
-        notransV = clblasNoTrans;
+        notransV = OPENCL_BLAS_NO_TRANS;
         transV   = transType;
     }
     else {
         notransV = transType;
-        transV   = clblasNoTrans;
+        transV   = OPENCL_BLAS_NO_TRANS;
     }
 
     gpu_blas_gemm_func<Ty> gpu_blas_gemm;
@@ -264,73 +264,66 @@ magma_larfb_gpu(
         // Comments assume H C. When forming H^H C, T gets transposed via transt.
 
         // W = C^H V
-        CLBLAS_CHECK(gpu_blas_gemm(
-                         transType, notransV,
-                         n, k, m,
-                         c_one,
-                         dC(0,0),  lddc,
-                         dV(0,0),  lddv,
-                         c_zero,
-                         dwork(0), ldwork,
-                         1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(transType, notransV,
+                                        n, k, m,
+                                        c_one,
+                                        dC(0,0),  lddc,
+                                        dV(0,0),  lddv,
+                                        c_zero,
+                                        dwork(0), ldwork,
+                                        1, &queue, 0, nullptr, &event));
 
         // W = W T^H = C^H V T^H
-        CLBLAS_CHECK(gpu_blas_trmm(
-                         clblasRight,
-                         uplo, transt, clblasNonUnit,
-                         n, k,
-                         c_one,
-                         dT(0,0) , lddt,
-                         dwork(0), ldwork,
-                         1, &queue, 0, nullptr, &event));
-
-        // C = C - V W^H = C - V T V^H C = (I - V T V^H) C = H C
-        CLBLAS_CHECK(gpu_blas_gemm(
-                         notransV, transType,
-                         m, n, k,
-                         c_neg_one,
-                         dV(0,0),  lddv,
-                         dwork(0), ldwork,
-                         c_one,
-                         dC(0,0),  lddc,
-                         1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_trmm(OPENCL_BLAS_SIDE_RIGHT,
+                                        uplo, transt, OPENCL_BLAS_NON_UNIT_DIAGONAL,
+                                        n, k,
+                                        c_one,
+                                        dT(0,0) , lddt,
+                                        dwork(0), ldwork,
+                                        1, &queue, 0, nullptr, &event));
+                        // C = C - V W^H = C - V T V^H C = (I - V T V^H) C = H C
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(notransV, transType,
+                                        m, n, k,
+                                        c_neg_one,
+                                        dV(0,0),  lddv,
+                                        dwork(0), ldwork,
+                                        c_one,
+                                        dC(0,0),  lddc,
+                                        1, &queue, 0, nullptr, &event));
     }
     else {
         // Form C H or C H^H
         // Comments assume C H. When forming C H^H, T gets transposed via trans.
 
         // W = C V
-        CLBLAS_CHECK(gpu_blas_gemm(
-                         clblasNoTrans, notransV,
-                         m, k, n,
-                         c_one,
-                         dC(0,0),  lddc,
-                         dV(0,0),  lddv,
-                         c_zero,
-                         dwork(0), ldwork,
-                         1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, notransV,
+                                        m, k, n,
+                                        c_one,
+                                        dC(0,0),  lddc,
+                                        dV(0,0),  lddv,
+                                        c_zero,
+                                        dwork(0), ldwork,
+                                        1, &queue, 0, nullptr, &event));
 
         // W = W T = C V T
-        CLBLAS_CHECK(gpu_blas_trmm(
-                         clblasRight, uplo,
-                         cltrans,
-                         clblasNonUnit,
-                         m, k,
-                         c_one,
-                         dT(0,0),  lddt,
-                         dwork(0), ldwork,
-                         1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_trmm(OPENCL_BLAS_SIDE_RIGHT, uplo,
+                                        cltrans,
+                                        OPENCL_BLAS_NON_UNIT_DIAGONAL,
+                                        m, k,
+                                        c_one,
+                                        dT(0,0),  lddt,
+                                        dwork(0), ldwork,
+                                        1, &queue, 0, nullptr, &event));
 
         // C = C - W V^H = C - C V T V^H = C (I - V T V^H) = C H
-        CLBLAS_CHECK(gpu_blas_gemm(
-                         clblasNoTrans, transV,
-                         m, n, k,
-                         c_neg_one,
-                         dwork(0), ldwork,
-                         dV(0,0),  lddv,
-                         c_one,
-                         dC(0,0),  lddc,
-                         1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, transV,
+                                        m, n, k,
+                                        c_neg_one,
+                                        dwork(0), ldwork,
+                                        dV(0,0),  lddv,
+                                        c_one,
+                                        dC(0,0),  lddc,
+                                        1, &queue, 0, nullptr, &event));
     }
 
     return info;
diff --git a/src/backend/opencl/magma/magma_blas.h b/src/backend/opencl/magma/magma_blas.h
index 8e2565b3ae..6be8f9f220 100644
--- a/src/backend/opencl/magma/magma_blas.h
+++ b/src/backend/opencl/magma/magma_blas.h
@@ -10,47 +10,23 @@
 #ifndef __MAGMA_BLAS_H
 #define __MAGMA_BLAS_H
 
+// This file contains the common interface for Magma OpenCL BLAS
+// functions. They can be implemented in different back-ends,
+// such as CLBlast or clBLAS.
+
 #include "magma_common.h"
-#include <defines.hpp>
 #include <types.hpp>
-#include <clBLAS.h>
-#include <err_clblas.hpp>
 
 using opencl::cfloat;
 using opencl::cdouble;
 
-#define clblasSherk(...) clblasSsyrk(__VA_ARGS__)
-#define clblasDherk(...) clblasDsyrk(__VA_ARGS__)
-
-#define BLAS_FUNC_DEF(NAME)                     \
-    template<typename T>                        \
-    struct gpu_blas_##NAME##_func;
-
-#define BLAS_FUNC(NAME, TYPE, PREFIX)                       \
-    template<>                                              \
-    struct gpu_blas_##NAME##_func<TYPE>                     \
-    {                                                       \
-        template<typename... Args>                          \
-            clblasStatus                                    \
-            operator() (Args... args)                       \
-        {                                                   \
-            return clblas##PREFIX##NAME(clblasColumnMajor,  \
-                                        args...);           \
-        }                                                   \
-    };
-
-#define BLAS_FUNC_DECL(NAME)                    \
-    BLAS_FUNC_DEF(NAME)                         \
-    BLAS_FUNC(NAME, float,      S)              \
-    BLAS_FUNC(NAME, double,     D)              \
-    BLAS_FUNC(NAME, cfloat,     C)              \
-    BLAS_FUNC(NAME, cdouble,    Z)              \
+template<typename T> struct gpu_blas_gemm_func;
+template<typename T> struct gpu_blas_gemv_func;
+template<typename T> struct gpu_blas_trmm_func;
+template<typename T> struct gpu_blas_trsm_func;
+template<typename T> struct gpu_blas_trsv_func;
+template<typename T> struct gpu_blas_herk_func;
 
-BLAS_FUNC_DECL(gemm)
-BLAS_FUNC_DECL(gemv)
-BLAS_FUNC_DECL(trmm)
-BLAS_FUNC_DECL(trsm)
-BLAS_FUNC_DECL(trsv)
-BLAS_FUNC_DECL(herk)
+#include "magma_blas_clblas.h"
 
 #endif
diff --git a/src/backend/opencl/magma/magma_blas_clblas.h b/src/backend/opencl/magma/magma_blas_clblas.h
new file mode 100644
index 0000000000..02e2a10059
--- /dev/null
+++ b/src/backend/opencl/magma/magma_blas_clblas.h
@@ -0,0 +1,74 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <defines.hpp>
+
+#include <clBLAS.h>
+#include <err_clblas.hpp>
+
+// Convert MAGMA constants to clBLAS constants
+clblasOrder          clblas_order_const( magma_order_t order );
+clblasTranspose      clblas_trans_const( magma_trans_t trans );
+clblasUplo           clblas_uplo_const ( magma_uplo_t  uplo  );
+clblasDiag           clblas_diag_const ( magma_diag_t  diag  );
+clblasSide           clblas_side_const ( magma_side_t  side  );
+
+// Error checking
+#define OPENCL_BLAS_CHECK CLBLAS_CHECK
+
+// Transposing
+#define OPENCL_BLAS_TRANS_TYPE clblasTranspose // the type
+#define OPENCL_BLAS_NO_TRANS clblasNoTrans
+#define OPENCL_BLAS_TRANS clblasTrans
+#define OPENCL_BLAS_CONJ_TRANS clblasConjTrans
+
+// Triangles
+#define OPENCL_BLAS_TRIANGLE_TYPE clblasUplo // the type
+#define OPENCL_BLAS_TRIANGLE_UPPER clblasUpper
+#define OPENCL_BLAS_TRIANGLE_LOWER clblasLower
+
+// Sides
+#define OPENCL_BLAS_SIDE_RIGHT clblasRight
+#define OPENCL_BLAS_SIDE_LEFT clblasLeft
+
+// Unit or non-unit diagonal
+#define OPENCL_BLAS_UNIT_DIAGONAL clblasUnit
+#define OPENCL_BLAS_NON_UNIT_DIAGONAL clblasNonUnit
+
+
+#define clblasSherk(...) clblasSsyrk(__VA_ARGS__)
+#define clblasDherk(...) clblasDsyrk(__VA_ARGS__)
+
+#define BLAS_FUNC(NAME, TYPE, PREFIX)                       \
+    template<>                                              \
+    struct gpu_blas_##NAME##_func<TYPE>                     \
+    {                                                       \
+        template<typename... Args>                          \
+            clblasStatus                                    \
+            operator() (Args... args)                       \
+        {                                                   \
+            return clblas##PREFIX##NAME(clblasColumnMajor,  \
+                                        args...);           \
+        }                                                   \
+    };
+
+#define BLAS_FUNC_DECL(NAME)                    \
+    BLAS_FUNC(NAME, float,      S)              \
+    BLAS_FUNC(NAME, double,     D)              \
+    BLAS_FUNC(NAME, cfloat,     C)              \
+    BLAS_FUNC(NAME, cdouble,    Z)              \
+
+BLAS_FUNC_DECL(gemm)
+BLAS_FUNC_DECL(gemv)
+BLAS_FUNC_DECL(trmm)
+BLAS_FUNC_DECL(trsm)
+BLAS_FUNC_DECL(trsv)
+BLAS_FUNC_DECL(herk)
diff --git a/src/backend/opencl/magma/magma_common.h b/src/backend/opencl/magma/magma_common.h
index 0a84147db1..83d3001e54 100644
--- a/src/backend/opencl/magma/magma_common.h
+++ b/src/backend/opencl/magma/magma_common.h
@@ -16,9 +16,6 @@
 #include <CL/cl.h>
 #endif
 
-#define HAVE_clBLAS
-#include <clBLAS.h>
-
 #include "magma_types.h"
 
 #define magma_s magmaFloat_ptr
diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 34b0a5397f..740f2d322f 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -74,7 +74,7 @@ magma_malloc( magma_ptr* ptrPtr, int num)
         size = sizeof(T);
     cl_int err;
     *ptrPtr = clCreateBuffer(opencl::getContext()(), CL_MEM_READ_WRITE, size, NULL, &err );
-    if ( err != clblasSuccess ) {
+    if ( err != CL_SUCCESS ) {
         return MAGMA_ERR_DEVICE_ALLOC;
     }
     return MAGMA_SUCCESS;
@@ -86,7 +86,7 @@ static inline magma_int_t
 magma_free(cl_mem ptr)
 {
     cl_int err = clReleaseMemObject( ptr );
-    if ( err != clblasSuccess ) {
+    if ( err != CL_SUCCESS ) {
         return MAGMA_ERR_INVALID_PTR;
     }
     return MAGMA_SUCCESS;
diff --git a/src/backend/opencl/magma/magma_types.h b/src/backend/opencl/magma/magma_types.h
index 33e6e667af..b8e0bcca4d 100644
--- a/src/backend/opencl/magma/magma_types.h
+++ b/src/backend/opencl/magma/magma_types.h
@@ -60,8 +60,6 @@ typedef int magma_index_t;
 // Define new type that the precision generator will not change (matches PLASMA)
 typedef double real_Double_t;
 
-#include <clBLAS.h>
-
 typedef cl_command_queue  magma_queue_t;
 typedef cl_event          magma_event_t;
 typedef cl_device_id      magma_device_t;
@@ -515,17 +513,6 @@ static inline char lapacke_direct_const( magma_direct_t magma_const ) { return *
 static inline char lapacke_storev_const( magma_storev_t magma_const ) { return *lapack_storev_const( magma_const ); }
 
 
-// --------------------
-// Convert MAGMA constants to clBLAS constants.
-#if defined(HAVE_clBLAS)
-clblasOrder          clblas_order_const( magma_order_t order );
-clblasTranspose      clblas_trans_const( magma_trans_t trans );
-clblasUplo           clblas_uplo_const ( magma_uplo_t  uplo  );
-clblasDiag           clblas_diag_const ( magma_diag_t  diag  );
-clblasSide           clblas_side_const ( magma_side_t  side  );
-#endif
-
-
 // --------------------
 // Convert MAGMA constants to CUBLAS constants.
 #if defined(CUBLAS_V2_H_)
diff --git a/src/backend/opencl/magma/potrf.cpp b/src/backend/opencl/magma/potrf.cpp
index 4f9984f325..7f1d1ccaaf 100644
--- a/src/backend/opencl/magma/potrf.cpp
+++ b/src/backend/opencl/magma/potrf.cpp
@@ -131,7 +131,7 @@ magma_int_t magma_potrf_gpu(
     static const double    one =  1.0;
     static const double  m_one = -1.0;
 
-    static const clblasTranspose transType = magma_is_real<Ty>() ? clblasTrans : clblasConjTrans;
+    static const OPENCL_BLAS_TRANS_TYPE transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
 
     Ty* work;
     magma_int_t err;
@@ -185,14 +185,13 @@ magma_int_t magma_potrf_gpu(
                 // apply all previous updates to diagonal block
                 jb = std::min(nb, n-j);
                 if (j > 0) {
-                    CLBLAS_CHECK(gpu_blas_herk(
-                                     clblasUpper, transType,
-                                     jb, j,
-                                     m_one,
-                                     dA(0,j), ldda,
-                                     one,
-                                     dA(j,j), ldda,
-                                     1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_herk(OPENCL_BLAS_TRIANGLE_UPPER, transType,
+                                                    jb, j,
+                                                    m_one,
+                                                    dA(0,j), ldda,
+                                                    one,
+                                                    dA(j,j), ldda,
+                                                    1, &queue, 0, nullptr, &blas_event));
                 }
 
                 // start asynchronous data transfer
@@ -200,15 +199,14 @@ magma_int_t magma_potrf_gpu(
 
                 // apply all previous updates to block row right of diagonal block
                 if (j+jb < n && j > 0) {
-                    CLBLAS_CHECK(gpu_blas_gemm(
-                                     transType, clblasNoTrans,
-                                     jb, n-j-jb, j,
-                                     mz_one,
-                                     dA(0, j   ), ldda,
-                                     dA(0, j+jb), ldda,
-                                     z_one,
-                                     dA(j, j+jb), ldda,
-                                     1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(transType, OPENCL_BLAS_NO_TRANS,
+                                                    jb, n-j-jb, j,
+                                                    mz_one,
+                                                    dA(0, j   ), ldda,
+                                                    dA(0, j+jb), ldda,
+                                                    z_one,
+                                                    dA(j, j+jb), ldda,
+                                                    1, &queue, 0, nullptr, &blas_event));
                 }
 
                 // simultaneous with above zgemm, transfer data, factor
@@ -227,14 +225,13 @@ magma_int_t magma_potrf_gpu(
                 // apply diagonal block to block row right of diagonal block
                 if (j+jb < n) {
                     magma_event_sync(event);
-                    CLBLAS_CHECK(gpu_blas_trsm(
-                                     clblasLeft, clblasUpper,
-                                     transType, clblasNonUnit,
-                                     jb, n-j-jb,
-                                     z_one,
-                                     dA(j, j   ), ldda,
-                                     dA(j, j+jb), ldda,
-                                     1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                                                    transType, OPENCL_BLAS_NON_UNIT_DIAGONAL,
+                                                    jb, n-j-jb,
+                                                    z_one,
+                                                    dA(j, j   ), ldda,
+                                                    dA(j, j+jb), ldda,
+                                                    1, &queue, 0, nullptr, &blas_event));
                 }
             }
         }
@@ -246,13 +243,12 @@ magma_int_t magma_potrf_gpu(
                 // apply all previous updates to diagonal block
                 jb = std::min(nb, n-j);
                 if (j>0) {
-                    CLBLAS_CHECK(gpu_blas_herk(
-                                     clblasLower, clblasNoTrans, jb, j,
-                                     m_one,
-                                     dA(j, 0), ldda,
-                                     one,
-                                     dA(j, j), ldda,
-                                     1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_herk(OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, jb, j,
+                                                    m_one,
+                                                    dA(j, 0), ldda,
+                                                    one,
+                                                    dA(j, j), ldda,
+                                                    1, &queue, 0, nullptr, &blas_event));
                 }
 
                 // start asynchronous data transfer
@@ -260,15 +256,14 @@ magma_int_t magma_potrf_gpu(
 
                 // apply all previous updates to block column below diagonal block
                 if (j+jb < n && j > 0) {
-                    CLBLAS_CHECK(gpu_blas_gemm(
-                                     clblasNoTrans, transType,
-                                     n-j-jb, jb, j,
-                                     mz_one,
-                                     dA(j+jb, 0), ldda,
-                                     dA(j,    0), ldda,
-                                     z_one,
-                                     dA(j+jb, j), ldda,
-                                     1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, transType,
+                                                    n-j-jb, jb, j,
+                                                    mz_one,
+                                                    dA(j+jb, 0), ldda,
+                                                    dA(j,    0), ldda,
+                                                    z_one,
+                                                    dA(j+jb, j), ldda,
+                                                    1, &queue, 0, nullptr, &blas_event));
                 }
 
                 // simultaneous with above zgemm, transfer data, factor
@@ -286,13 +281,12 @@ magma_int_t magma_potrf_gpu(
                 // apply diagonal block to block column below diagonal
                 if (j+jb < n) {
                     magma_event_sync(event);
-                    CLBLAS_CHECK(gpu_blas_trsm(
-                                     clblasRight, clblasLower, transType, clblasNonUnit,
-                                     n-j-jb, jb,
-                                     z_one,
-                                     dA(j   , j), ldda,
-                                     dA(j+jb, j), ldda,
-                                     1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_LOWER, transType, OPENCL_BLAS_NON_UNIT_DIAGONAL,
+                                                    n-j-jb, jb,
+                                                    z_one,
+                                                    dA(j   , j), ldda,
+                                                    dA(j+jb, j), ldda,
+                                                    1, &queue, 0, nullptr, &blas_event));
                 }
             }
         }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d04169ebf0..8dc8a4bfe8 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -524,7 +524,7 @@ bool OpenCLCPUOffload(bool forceOffloadOSX)
     // Force condition
     offload = osx_offload && (offload || forceOffloadOSX);
 #endif
-    return offload;
+    return false;//offload;
 }
 
 bool isGLSharingSupported()

From 376688681e969bf77bbfb4dbac9da3d50d3d0f2b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Dec 2016 01:48:00 -0500
Subject: [PATCH 1073/2677] Enable c++11 in the cuda backend. Remove boost
 dependency

---
 src/backend/cuda/Array.cpp                    |  5 +-
 src/backend/cuda/Array.hpp                    |  7 +-
 src/backend/cuda/CMakeLists.txt               | 10 +--
 src/backend/cuda/JIT/BufferNode.hpp           |  5 +-
 src/backend/cuda/JIT/Node.hpp                 |  8 +--
 src/backend/cuda/cublasManager.cpp            |  5 +-
 src/backend/cuda/cusolverDnManager.cpp        |  7 +-
 src/backend/cuda/cusparseManager.cpp          |  7 +-
 src/backend/cuda/jit.cpp                      | 66 ++++++++++---------
 src/backend/cuda/kernel/ireduce.hpp           | 15 ++---
 src/backend/cuda/kernel/memcopy.hpp           |  6 +-
 src/backend/cuda/kernel/orb.hpp               |  6 +-
 src/backend/cuda/kernel/reduce.hpp            | 11 ++--
 src/backend/cuda/kernel/scan_dim.hpp          |  6 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |  8 +--
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  8 +--
 src/backend/cuda/sparse.cu                    |  4 +-
 17 files changed, 89 insertions(+), 95 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index e95c6153f1..90b9f8d8d1 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -15,10 +15,13 @@
 #include <scalar.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
-#include <cstddef>
 #include <MemoryManager.hpp>
 
+#include <cstddef>
+#include <memory>
+
 using af::dim4;
+using std::shared_ptr;
 
 namespace cuda
 {
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 9e7bed8e29..b7092ad867 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -18,15 +18,12 @@
 #include <cuda_runtime_api.h>
 #include <Param.hpp>
 #include <JIT/Node.hpp>
-#include <boost/shared_ptr.hpp>
 #include <vector>
 #include <memory.hpp>
 
 namespace cuda
 {
-
     using af::dim4;
-    using boost::shared_ptr;
 
     template<typename T> class Array;
 
@@ -99,7 +96,7 @@ namespace cuda
     class Array
     {
         ArrayInfo       info; // This must be the first element of Array<T>
-        shared_ptr<T> data;
+        std::shared_ptr<T> data;
         af::dim4 data_dims;
 
         JIT::Node_ptr node;
@@ -165,7 +162,7 @@ namespace cuda
         void eval() const;
 
         dim_t getOffset() const { return info.getOffset(); }
-        shared_ptr<T> getData() const { return data; }
+        std::shared_ptr<T> getData() const { return data; }
 
         dim4 getDataDims() const
         {
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 560260269a..c2e0ba25aa 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -1,7 +1,6 @@
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
-FIND_PACKAGE(CUDA 7.0 REQUIRED) # Assert Minimum CUDA Version 7.0
-FIND_PACKAGE(Boost REQUIRED)
+FIND_PACKAGE(CUDA 7.0 REQUIRED)
 
 INCLUDE(CLKernelToH)
 INCLUDE(FindNVVM)
@@ -104,7 +103,7 @@ IF(UNIX)
         # Enabling c++11 with nvcc 7.5 + gcc 6.x doesn't seem to work
         # Only solution for now is to force use c++03 for gcc 6.x
         IF(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
-          SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -Xcompiler -std=c++98")
+          message( FATAL_ERROR "NVCC does not support GCC version 6.0 or greater." )
         ENDIF()
     ENDIF()
 
@@ -148,7 +147,6 @@ ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
 
 INCLUDE_DIRECTORIES(
     ${CMAKE_INCLUDE_PATH}
-    ${Boost_INCLUDE_DIR}
     ${CUDA_INCLUDE_DIRS}
     "${PROJECT_SOURCE_DIR}/src/backend/cuda"
     "${CMAKE_CURRENT_BINARY_DIR}"
@@ -426,7 +424,9 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cpp_sources}
                 ${thrust_sort_by_key_sources}
                 ${scan_by_key_sources}
-                OPTIONS ${CUDA_GENERATE_CODE})
+                OPTIONS ${CUDA_GENERATE_CODE}
+                #These flags enable C++11 and disable invalid offsetof warning
+                -std=c++11 -Xcudafe "--diag_suppress=1427")
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
 
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 69d6407e5b..bdc692b447 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include "Node.hpp"
 #include <iomanip>
+#include <memory>
 
 namespace cuda
 {
@@ -30,7 +31,7 @@ namespace JIT
     {
     private:
         // Keep the shared pointer for reference counting
-        shared_ptr<T> m_data;
+        std::shared_ptr<T> m_data;
         Param<T> m_param;
         unsigned m_bytes;
 
@@ -43,7 +44,7 @@ namespace JIT
         {
         }
 
-        void setData(Param<T> param, shared_ptr<T> data, const unsigned bytes, bool is_linear)
+        void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
         {
             m_param = param;
             m_data = data;
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index c82c4e1d5e..bd4307dd1e 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -9,10 +9,11 @@
 
 #pragma once
 #include <optypes.hpp>
+
+#include <map>
+#include <memory>
 #include <string>
 #include <vector>
-#include <map>
-#include <boost/shared_ptr.hpp>
 
 namespace cuda
 {
@@ -21,7 +22,6 @@ namespace JIT
 {
     typedef std::map<std::string, bool> str_map_t;
     typedef str_map_t::iterator str_map_iter;
-    using boost::shared_ptr;
 
     class Node
     {
@@ -109,7 +109,7 @@ namespace JIT
         virtual ~Node() {}
     };
 
-    typedef shared_ptr<Node> Node_ptr;
+    typedef std::shared_ptr<Node> Node_ptr;
 
 }
 
diff --git a/src/backend/cuda/cublasManager.cpp b/src/backend/cuda/cublasManager.cpp
index ca6cfbb2e0..f525e42a30 100644
--- a/src/backend/cuda/cublasManager.cpp
+++ b/src/backend/cuda/cublasManager.cpp
@@ -11,8 +11,8 @@
 #include <platform.hpp>
 #include <err_common.hpp>
 #include <cublasManager.hpp>
-#include <boost/scoped_ptr.hpp>
 #include <platform.hpp>
+#include <memory>
 
 namespace cublas {
 
@@ -61,8 +61,7 @@ namespace cublas {
 
     cublasHandle_t getHandle()
     {
-        using boost::scoped_ptr;
-        static scoped_ptr<cublasHandle> handle[cuda::DeviceManager::MAX_DEVICES];
+        static std::unique_ptr<cublasHandle> handle[cuda::DeviceManager::MAX_DEVICES];
 
         int id = cuda::getActiveDeviceId();
 
diff --git a/src/backend/cuda/cusolverDnManager.cpp b/src/backend/cuda/cusolverDnManager.cpp
index 10e8b5b4f4..4b8b19defd 100644
--- a/src/backend/cuda/cusolverDnManager.cpp
+++ b/src/backend/cuda/cusolverDnManager.cpp
@@ -11,10 +11,10 @@
 #include <platform.hpp>
 #include <debug_cuda.hpp>
 
+#include <iostream>
+#include <memory>
 #include <stdexcept>
 #include <string>
-#include <iostream>
-#include <boost/scoped_ptr.hpp>
 
 namespace cusolver {
 
@@ -63,8 +63,7 @@ namespace cusolver {
 
     cusolverDnHandle_t getDnHandle()
     {
-        using boost::scoped_ptr;
-        static scoped_ptr<cusolverDnHandle> handle[cuda::DeviceManager::MAX_DEVICES];
+        static std::unique_ptr<cusolverDnHandle> handle[cuda::DeviceManager::MAX_DEVICES];
 
         int id = cuda::getActiveDeviceId();
 
diff --git a/src/backend/cuda/cusparseManager.cpp b/src/backend/cuda/cusparseManager.cpp
index 24f0c40045..3bfe5934e6 100644
--- a/src/backend/cuda/cusparseManager.cpp
+++ b/src/backend/cuda/cusparseManager.cpp
@@ -10,10 +10,10 @@
 #include <cusparseManager.hpp>
 #include <platform.hpp>
 
+#include <iostream>
+#include <memory>
 #include <stdexcept>
 #include <string>
-#include <iostream>
-#include <boost/scoped_ptr.hpp>
 
 namespace cusparse {
 
@@ -61,8 +61,7 @@ namespace cusparse {
 
     cusparseHandle_t getHandle()
     {
-        using boost::scoped_ptr;
-        static scoped_ptr<cusparseHandle> handle[cuda::DeviceManager::MAX_DEVICES];
+        static std::unique_ptr<cusparseHandle> handle[cuda::DeviceManager::MAX_DEVICES];
 
         int id = cuda::getActiveDeviceId();
 
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 3d8cee69ad..44fa9e1616 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -9,8 +9,6 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <map>
-#include <stdexcept>
 #include <copy.hpp>
 #include <JIT/Node.hpp>
 
@@ -42,22 +40,26 @@
 #include <dispatch.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
-#include <vector>
 #include <nvvm.h>
-#include <boost/functional/hash.hpp>
-#include <boost/scoped_array.hpp>
 
-using std::vector;
-using boost::scoped_array;
+#include <functional>
+#include <map>
+#include <memory>
+#include <stdexcept>
+#include <vector>
 
 namespace cuda
 {
 
 using JIT::Node;
+using JIT::str_map_iter;
+using JIT::str_map_t;
+using std::hash;
+using std::map;
 using std::string;
 using std::stringstream;
-using JIT::str_map_t;
-using JIT::str_map_iter;
+using std::unique_ptr;
+using std::vector;
 
 const char *layout64 = "target datalayout = \"e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64\"\n\n\n";
 const char *layout32 = "target datalayout = \"e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64\"\n\n\n";
@@ -65,7 +67,7 @@ const char *layout32 = "target datalayout = \"e-p:32:32:32-i1:8:8-i8:8:8-i16:16:
 const char *triple64 = "target triple = \"nvptx64-unknown-cuda\"\n\n";
 const char *triple32 = "target triple = \"nvptx-unknown-cuda\"\n\n";
 
-static string getFuncName(std::vector<Node *> nodes, bool is_linear)
+static string getFuncName(vector<Node *> nodes, bool is_linear)
 {
     stringstream funcName;
     stringstream hashName;
@@ -83,14 +85,14 @@ static string getFuncName(std::vector<Node *> nodes, bool is_linear)
         funcName << "]";
     }
 
-    boost::hash<std::string> hash_fn;
+    hash<string> hash_fn;
 
     hashName << "@KER";
     hashName << hash_fn(funcName.str());
     return hashName.str();
 }
 
-static string getKernelString(string funcName, std::vector<Node *> nodes, bool is_linear)
+static string getKernelString(string funcName, vector<Node *> nodes, bool is_linear)
 {
     static const char *defineVoid = "define void ";
     static const char *generalDimParams = "\n"
@@ -217,7 +219,7 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
     str_map_t declStrs;
 
     for (int i = 0; i < (int)nodes.size(); i++) {
-        std::string outTypeStr = nodes[i]->getTypeStr();
+        string outTypeStr = nodes[i]->getTypeStr();
         int id = nodes[i]->getId();
 
         nodes[i]->genParams(inParamStream, inAnnStream, is_linear);
@@ -409,7 +411,7 @@ static char *irToPtx(string IR, size_t *ptx_size)
         size_t log_size = 0;
         nvvmGetProgramLogSize(prog, &log_size);
         printf("%ld, %zu\n", IR.size(), log_size);
-        scoped_array<char> log(new char[log_size]);
+        unique_ptr<char> log(new char[log_size]);
         nvvmGetProgramLog(prog, log.get());
         printf("LOG:\n%s\n%s", log.get(), IR.c_str());
         NVVM_CHECK(comp_res, "Failed to compile program");
@@ -463,7 +465,7 @@ char linkError[size];
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
     size_t ptx_size;
-    scoped_array<const char> ptx(irToPtx(jit_ker, &ptx_size));
+    unique_ptr<const char> ptx(irToPtx(jit_ker, &ptx_size));
 
     CUlinkState linkState;
 
@@ -526,12 +528,12 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
     return entry;
 }
 
-static CUfunction getKernel(std::vector<Node *> nodes, bool is_linear)
+static CUfunction getKernel(vector<Node *> nodes, bool is_linear)
 {
 
     string funcName = getFuncName(nodes, is_linear);
 
-    typedef std::map<string, kc_entry_t> kc_t;
+    typedef map<string, kc_entry_t> kc_t;
     static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
     int device = getActiveDeviceId();
 
@@ -550,7 +552,7 @@ static CUfunction getKernel(std::vector<Node *> nodes, bool is_linear)
 }
 
 template<typename T>
-void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
+void evalNodes(vector<Param<T> >&outputs, vector<Node *> nodes)
 {
     int num_outputs = (int)outputs.size();
 
@@ -665,8 +667,8 @@ void evalNodes(std::vector<Param<T> >&outputs, std::vector<Node *> nodes)
 template<typename T>
 void evalNodes(Param<T> &out, Node *node)
 {
-    std::vector<Param<T> > outputs;
-    std::vector<Node *> nodes;
+    vector<Param<T>> outputs;
+    vector<Node *> nodes;
 
     outputs.push_back(out);
     nodes.push_back(node);
@@ -687,18 +689,18 @@ template void evalNodes<uintl  >(Param<uintl  > &out, Node *node);
 template void evalNodes<short  >(Param<short  > &out, Node *node);
 template void evalNodes<ushort >(Param<ushort > &out, Node *node);
 
-template void evalNodes<float  >(std::vector<Param<float  > > &out, std::vector<Node *> node);
-template void evalNodes<double >(std::vector<Param<double > > &out, std::vector<Node *> node);
-template void evalNodes<cfloat >(std::vector<Param<cfloat > > &out, std::vector<Node *> node);
-template void evalNodes<cdouble>(std::vector<Param<cdouble> > &out, std::vector<Node *> node);
-template void evalNodes<int    >(std::vector<Param<int    > > &out, std::vector<Node *> node);
-template void evalNodes<uint   >(std::vector<Param<uint   > > &out, std::vector<Node *> node);
-template void evalNodes<char   >(std::vector<Param<char   > > &out, std::vector<Node *> node);
-template void evalNodes<uchar  >(std::vector<Param<uchar  > > &out, std::vector<Node *> node);
-template void evalNodes<intl   >(std::vector<Param<intl   > > &out, std::vector<Node *> node);
-template void evalNodes<uintl  >(std::vector<Param<uintl  > > &out, std::vector<Node *> node);
-template void evalNodes<short  >(std::vector<Param<short  > > &out, std::vector<Node *> node);
-template void evalNodes<ushort >(std::vector<Param<ushort > > &out, std::vector<Node *> node);
+template void evalNodes<float  >(vector<Param<float  > > &out, vector<Node *> node);
+template void evalNodes<double >(vector<Param<double > > &out, vector<Node *> node);
+template void evalNodes<cfloat >(vector<Param<cfloat > > &out, vector<Node *> node);
+template void evalNodes<cdouble>(vector<Param<cdouble> > &out, vector<Node *> node);
+template void evalNodes<int    >(vector<Param<int    > > &out, vector<Node *> node);
+template void evalNodes<uint   >(vector<Param<uint   > > &out, vector<Node *> node);
+template void evalNodes<char   >(vector<Param<char   > > &out, vector<Node *> node);
+template void evalNodes<uchar  >(vector<Param<uchar  > > &out, vector<Node *> node);
+template void evalNodes<intl   >(vector<Param<intl   > > &out, vector<Node *> node);
+template void evalNodes<uintl  >(vector<Param<uintl  > > &out, vector<Node *> node);
+template void evalNodes<short  >(vector<Param<short  > > &out, vector<Node *> node);
+template void evalNodes<ushort >(vector<Param<ushort > > &out, vector<Node *> node);
 
 
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 9eec000e0f..12d4a8956f 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -16,9 +16,7 @@
 #include <debug_cuda.hpp>
 #include "config.hpp"
 #include <memory.hpp>
-#include <boost/scoped_array.hpp>
-
-using boost::scoped_array;
+#include <memory>
 
 namespace cuda
 {
@@ -189,7 +187,7 @@ namespace kernel
     template<typename T, af_op_t op, int dim, bool is_first>
     void ireduce_dim_launcher(Param<T> out, uint *olptr,
                              CParam<T> in, const uint *ilptr,
-                             const uint threads_y, const uint blocks_dim[4])
+                             const uint threads_y, const dim_t blocks_dim[4])
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -220,7 +218,7 @@ namespace kernel
         uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
         uint threads_x = THREADS_X;
 
-        uint blocks_dim[] = {divup(in.dims[0], threads_x),
+        dim_t blocks_dim[] = {divup(in.dims[0], threads_x),
                              in.dims[1], in.dims[2], in.dims[3]};
 
         blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
@@ -443,6 +441,7 @@ namespace kernel
     template<typename T, af_op_t op>
     T ireduce_all(uint *idx, CParam<T> in)
     {
+        using std::unique_ptr;
         int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
 
         // FIXME: Use better heuristics to get to the optimum number
@@ -486,8 +485,8 @@ namespace kernel
             tlptr = memAlloc<uint>(tmp_elements);
             ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x, blocks_y, threads_x);
 
-            scoped_array<T>       h_ptr(new T[tmp_elements]);
-            scoped_array<uint>    h_lptr(new uint[tmp_elements]);
+            unique_ptr<T>       h_ptr(new T[tmp_elements]);
+            unique_ptr<uint>    h_lptr(new uint[tmp_elements]);
             T*      h_ptr_raw = h_ptr.get();
             uint*   h_lptr_raw = h_lptr.get();
 
@@ -520,7 +519,7 @@ namespace kernel
             return Op.m_val;
         } else {
 
-            scoped_array<T> h_ptr(new T[in_elements]);
+            unique_ptr<T> h_ptr(new T[in_elements]);
             T* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 2dda550e58..f1b4bb3c28 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -75,9 +75,9 @@ namespace kernel
         dim3 blocks(blocks_x * idims[2],
                     blocks_y * idims[3]);
 
-        dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
-        dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
-        dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
+        dims_t _ostrides = {{(int)ostrides[0], (int)ostrides[1], (int)ostrides[2], (int)ostrides[3]}};
+        dims_t _istrides = {{(int)istrides[0], (int)istrides[1], (int)istrides[2], (int)istrides[3]}};
+        dims_t _idims = {{(int)idims[0], (int)idims[1], (int)idims[2], (int)idims[3]}};
 
         CUDA_LAUNCH((memcopy_kernel<T>), blocks, threads,
                 out, _ostrides, in, _idims, _istrides, blocks_x, blocks_y);
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index cbdb9cec3d..d0cd2e7d3b 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -17,10 +17,8 @@
 #include "sort_by_key.hpp"
 #include "range.hpp"
 
-#include <boost/scoped_array.hpp>
-
 using std::vector;
-using boost::scoped_array;
+using std::unique_ptr;
 
 namespace cuda
 {
@@ -344,7 +342,7 @@ void orb(unsigned* out_feat,
     Param<convAccT> gauss_filter;
     if (blur_img) {
         unsigned gauss_len = 9;
-        scoped_array<convAccT> h_gauss(new convAccT[gauss_len]);
+        unique_ptr<convAccT> h_gauss(new convAccT[gauss_len]);
         gaussian1D(h_gauss.get(), gauss_len, 2.f);
         gauss_filter.dims[0] = gauss_len;
         gauss_filter.strides[0] = 1;
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 6b74feec36..3491fa69f1 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -16,9 +16,8 @@
 #include <debug_cuda.hpp>
 #include "config.hpp"
 #include <memory.hpp>
-#include <boost/scoped_array.hpp>
 
-using boost::scoped_array;
+using std::unique_ptr;
 
 namespace cuda
 {
@@ -107,7 +106,7 @@ namespace kernel
 
     template<typename Ti, typename To, af_op_t op, int dim>
     void reduce_dim_launcher(Param<To> out, CParam<Ti> in,
-                             const uint threads_y, const uint blocks_dim[4],
+                             const uint threads_y, const dim_t blocks_dim[4],
                              bool change_nan, double nanval)
     {
         dim3 threads(THREADS_X, threads_y);
@@ -143,7 +142,7 @@ namespace kernel
         uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
         uint threads_x = THREADS_X;
 
-        uint blocks_dim[] = {divup(in.dims[0], threads_x),
+        dim_t blocks_dim[] = {divup(in.dims[0], threads_x),
                              in.dims[1], in.dims[2], in.dims[3]};
 
         blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
@@ -410,7 +409,7 @@ namespace kernel
             reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
                                               change_nan, nanval);
 
-            scoped_array<To> h_ptr(new To[tmp_elements]);
+            unique_ptr<To> h_ptr(new To[tmp_elements]);
             To* h_ptr_raw = h_ptr.get();
 
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(To),
@@ -428,7 +427,7 @@ namespace kernel
 
         } else {
 
-            scoped_array<Ti> h_ptr(new Ti[in_elements]);
+            unique_ptr<Ti> h_ptr(new Ti[in_elements]);
             Ti* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 9be57ee39e..2cb27a7227 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -191,7 +191,7 @@ namespace kernel
                            Param<To> tmp,
                            CParam<Ti> in,
                            const uint threads_y,
-                           const uint blocks_all[4])
+                           const dim_t blocks_all[4])
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -224,7 +224,7 @@ namespace kernel
     static void bcast_dim_launcher(Param<To> out,
                                    CParam<To> tmp,
                                    const uint threads_y,
-                                   const uint blocks_all[4])
+                                   const dim_t blocks_all[4])
     {
 
         dim3 threads(THREADS_X, threads_y);
@@ -246,7 +246,7 @@ namespace kernel
         uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
         uint threads_x = THREADS_X;
 
-        uint blocks_all[] = {divup(out.dims[0], threads_x),
+        dim_t blocks_all[] = {divup(out.dims[0], threads_x),
                              out.dims[1], out.dims[2], out.dims[3]};
 
         blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 7badd3066b..322caa632a 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -401,7 +401,7 @@ namespace kernel
                                         CParam<Tk> key,
                                         const int dim,
                                         const uint threads_y,
-                                        const uint blocks_all[4],
+                                        const dim_t blocks_all[4],
                                         bool calculateFlags,
                                         bool inclusive_scan)
     {
@@ -439,7 +439,7 @@ namespace kernel
                                            CParam<Tk> key,
                                            const int dim,
                                            const uint threads_y,
-                                           const uint blocks_all[4],
+                                           const dim_t blocks_all[4],
                                            bool inclusive_scan)
     {
         dim3 threads(THREADS_X, threads_y);
@@ -473,7 +473,7 @@ namespace kernel
                                    Param<int> tlid,
                                    const int dim,
                                    const uint threads_y,
-                                   const uint blocks_all[4])
+                                   const dim_t blocks_all[4])
     {
 
         dim3 threads(THREADS_X, threads_y);
@@ -495,7 +495,7 @@ namespace kernel
         uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
         uint threads_x = THREADS_X;
 
-        uint blocks_all[] = {divup(out.dims[0], threads_x),
+        dim_t blocks_all[] = {divup(out.dims[0], threads_x),
                              out.dims[1], out.dims[2], out.dims[3]};
 
         blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index aecd8b957c..49cf8db267 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -411,8 +411,8 @@ namespace kernel
     static void bcast_first_launcher(Param<To> out,
                                      Param<To> tmp,
                                      Param<int> tlid,
-                                     const uint blocks_x,
-                                     const uint blocks_y,
+                                     const dim_t blocks_x,
+                                     const dim_t blocks_y,
                                      const uint threads_x)
     {
 
@@ -432,8 +432,8 @@ namespace kernel
         threads_x = std::min(threads_x, THREADS_PER_BLOCK);
         uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-        uint blocks_x = divup(out.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(out.dims[1], threads_y);
+        dim_t blocks_x = divup(out.dims[0], threads_x * REPEAT);
+        dim_t blocks_y = divup(out.dims[1], threads_y);
 
         if (blocks_x == 1) {
             scan_final_launcher<Ti, Tk, To, op>(
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 96e430285c..dbdf008a98 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -14,8 +14,6 @@
 #include <stdexcept>
 #include <string>
 
-#include <boost/shared_ptr.hpp>
-
 #include <arith.hpp>
 #include <cast.hpp>
 #include <complex.hpp>
@@ -366,7 +364,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
-    using boost::shared_ptr;
+    using std::shared_ptr;
     in.eval();
 
     int nNZ = in.getNNZ();

From 66180bbb0a964b69c81f12c8ef6808e8251f7460 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 3 Jan 2017 14:07:03 -0500
Subject: [PATCH 1074/2677] Use the default standard library on OSX

---
 src/backend/cuda/CMakeLists.txt | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index c2e0ba25aa..8ae37999fb 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -326,18 +326,6 @@ ENDIF()
 
 IF("${APPLE}")
     ADD_DEFINITIONS(-D__STRICT_ANSI__)
-    IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-        IF(${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            SET(STD_LIB_BINDING "-stdlib=libstdc++")
-        ELSE(${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            SET(STD_LIB_BINDING "-stdlib=libc++")
-        ENDIF()
-
-        ADD_DEFINITIONS("${STD_LIB_BINDING}")
-        SET(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${STD_LIB_BINDING}")
-        SET(CMAKE_STATIC_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${STD_LIB_BINDING}")
-        SET(CUDA_HOST_COMPILER "/usr/bin/clang++")
-    ENDIF()
 ELSE()
   IF(UNIX)
     IF(${CUDA_VERSION_MAJOR} GREATER 7)

From 59cdddd0acbc89711dd72a5b47a9a183c35c0126 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 7 Jan 2017 13:25:35 +0100
Subject: [PATCH 1075/2677] Separated out clBLAS specific code from the solver

---
 src/backend/opencl/solve.cpp | 36 +++++++++++++++++++-----------------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 93176752b5..0a6f6157f8 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -145,9 +145,9 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                               (*dA)(), A.getOffset(), A.strides()[1], 1,
                               (*dT)(), tmp.getOffset() + MN * NB, NB, 0, queue);
 
-        CLBLAS_CHECK(gpu_blas_trsm(
-                         clblasLeft, clblasUpper,
-                         clblasConjTrans, clblasNonUnit,
+        OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                         OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                         OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
                          B.dims()[0], B.dims()[1],
                          scalar<T>(1),
                          (*dA)(), A.getOffset(), A.strides()[1],
@@ -231,15 +231,17 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         {
             Array<T> AT = transpose<T>(A, true);
             cl::Buffer* AT_buf = AT.get();
-            CLBLAS_CHECK(gpu_blas_trsm(
-                             clblasLeft, clblasLower, clblasConjTrans, clblasNonUnit,
+            OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                             OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+                             OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
                              N, NRHS, scalar<T>(1),
                              (*AT_buf)(), AT.getOffset(), AT.strides()[1],
                              (*B_buf)(), B.getOffset(), B.strides()[1],
                              1, &queue, 0, nullptr, &event));
         } else {
-            CLBLAS_CHECK(gpu_blas_trsm(
-                             clblasLeft, clblasUpper, clblasNoTrans, clblasNonUnit,
+            OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                             OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                             OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
                              N, NRHS, scalar<T>(1),
                              (*A_buf)(), A.getOffset(), A.strides()[1],
                              (*B_buf)(), B.getOffset(), B.strides()[1],
@@ -272,21 +274,21 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
         Array<T> AT = transpose<T>(A, true);
 
         cl::Buffer* AT_buf = AT.get();
-        CLBLAS_CHECK(gpu_blas_trsm(
-                         clblasLeft,
-                         clblasLower,
-                         clblasConjTrans,
-                         options & AF_MAT_DIAG_UNIT ? clblasUnit : clblasNonUnit,
+        OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                         OPENCL_BLAS_SIDE_LEFT,
+                         OPENCL_BLAS_TRIANGLE_LOWER,
+                         OPENCL_BLAS_CONJ_TRANS,
+                         options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL : OPENCL_BLAS_NON_UNIT_DIAGONAL,
                          N, NRHS, scalar<T>(1),
                          (*AT_buf)(), AT.getOffset(), AT.strides()[1],
                          (*B_buf)(), B.getOffset(), B.strides()[1],
                          1, &queue, 0, nullptr, &event));
     } else {
-        CLBLAS_CHECK(gpu_blas_trsm(
-                         clblasLeft,
-                         options & AF_MAT_LOWER ? clblasLower : clblasUpper,
-                         clblasNoTrans,
-                         options & AF_MAT_DIAG_UNIT ? clblasUnit : clblasNonUnit,
+        OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                         OPENCL_BLAS_SIDE_LEFT,
+                         options & AF_MAT_LOWER ? OPENCL_BLAS_TRIANGLE_LOWER : OPENCL_BLAS_TRIANGLE_UPPER,
+                         OPENCL_BLAS_NO_TRANS,
+                         options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL : OPENCL_BLAS_NON_UNIT_DIAGONAL,
                          N, NRHS, scalar<T>(1),
                          (*A_buf)(), A.getOffset(), A.strides()[1],
                          (*B_buf)(), B.getOffset(), B.strides()[1],

From 443f554c4361bc112953cad900f3882f4cb4e047 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 7 Jan 2017 13:26:19 +0100
Subject: [PATCH 1076/2677] Added CLBlast back-end for the Magma routines

---
 src/backend/opencl/blas_clblas.cpp            |   1 +
 src/backend/opencl/blas_clblast.cpp           |   9 +-
 src/backend/opencl/magma/magma_blas.h         |   8 +-
 src/backend/opencl/magma/magma_blas_clblast.h | 200 ++++++++++++++++++
 4 files changed, 213 insertions(+), 5 deletions(-)
 create mode 100644 src/backend/opencl/magma/magma_blas_clblast.h

diff --git a/src/backend/opencl/blas_clblas.cpp b/src/backend/opencl/blas_clblas.cpp
index 73f7a756ac..343a14c119 100644
--- a/src/backend/opencl/blas_clblas.cpp
+++ b/src/backend/opencl/blas_clblas.cpp
@@ -23,6 +23,7 @@
 #include <reduce.hpp>
 #include <complex.hpp>
 
+#include <clBLAS.h>
 #include <err_clblas.hpp>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
diff --git a/src/backend/opencl/blas_clblast.cpp b/src/backend/opencl/blas_clblast.cpp
index 163e381995..59ddd60908 100644
--- a/src/backend/opencl/blas_clblast.cpp
+++ b/src/backend/opencl/blas_clblast.cpp
@@ -20,6 +20,7 @@
 #include <reduce.hpp>
 #include <complex.hpp>
 
+#include <clblast.h>
 #include <err_clblast.hpp>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
@@ -47,12 +48,12 @@ toClblastTranspose(af_mat_prop opt)
 }
 
 // Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
-template <typename T> struct CLBlastConstant { using Type = T; };
-template <> struct CLBlastConstant<cfloat> { using Type = std::complex<float>; };
-template <> struct CLBlastConstant<cdouble> { using Type = std::complex<double>; };
+template <typename T> struct CLBlastType { using Type = T; };
+template <> struct CLBlastType<cfloat> { using Type = std::complex<float>; };
+template <> struct CLBlastType<cdouble> { using Type = std::complex<double>; };
 
 // Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
-template <typename T> typename CLBlastConstant<T>::Type toCLBlastConstant(const T val);
+template <typename T> typename CLBlastType<T>::Type toCLBlastConstant(const T val);
 
 // Specializations of the above function
 template <> float toCLBlastConstant(const float val) { return val; }
diff --git a/src/backend/opencl/magma/magma_blas.h b/src/backend/opencl/magma/magma_blas.h
index 6be8f9f220..c937c0612c 100644
--- a/src/backend/opencl/magma/magma_blas.h
+++ b/src/backend/opencl/magma/magma_blas.h
@@ -27,6 +27,12 @@ template<typename T> struct gpu_blas_trsm_func;
 template<typename T> struct gpu_blas_trsv_func;
 template<typename T> struct gpu_blas_herk_func;
 
-#include "magma_blas_clblas.h"
+#if defined(USE_CLBLAST)
+#include "magma_blas_clblast.h"
+#endif
 
+#if defined(USE_CLBLAS)
+#include "magma_blas_clblas.h"
 #endif
+
+#endif // __MAGMA_BLAS_H
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
new file mode 100644
index 0000000000..22bb640371
--- /dev/null
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -0,0 +1,200 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <complex>
+
+#include <defines.hpp>
+
+#include <clblast.h>
+#include <err_clblast.hpp>
+
+// Convert MAGMA constants to CLBlast constants
+clblast::Layout         clblast_order_const( magma_order_t order );
+clblast::Transpose      clblast_trans_const( magma_trans_t trans );
+clblast::Triangle       clblast_uplo_const ( magma_uplo_t  uplo  );
+clblast::Diagonal       clblast_diag_const ( magma_diag_t  diag  );
+clblast::Side           clblast_side_const ( magma_side_t  side  );
+
+// Error checking
+#define OPENCL_BLAS_CHECK CLBLAST_CHECK
+
+// Transposing
+#define OPENCL_BLAS_TRANS_TYPE clblast::Transpose // the type
+#define OPENCL_BLAS_NO_TRANS clblast::Transpose::kNo
+#define OPENCL_BLAS_TRANS clblast::Transpose::kYes
+#define OPENCL_BLAS_CONJ_TRANS clblast::Transpose::kConjugate
+
+// Triangles
+#define OPENCL_BLAS_TRIANGLE_TYPE clblast::Triangle // the type
+#define OPENCL_BLAS_TRIANGLE_UPPER clblast::Triangle::kUpper
+#define OPENCL_BLAS_TRIANGLE_LOWER clblast::Triangle::kLower
+
+// Sides
+#define OPENCL_BLAS_SIDE_RIGHT clblast::Side::kRight
+#define OPENCL_BLAS_SIDE_LEFT clblast::Side::kLeft
+
+// Unit or non-unit diagonal
+#define OPENCL_BLAS_UNIT_DIAGONAL clblast::Diagonal::kUnit
+#define OPENCL_BLAS_NON_UNIT_DIAGONAL clblast::Diagonal::kNonUnit
+
+// Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
+template <typename T> struct CLBlastType { using Type = T; };
+template <> struct CLBlastType<cfloat> { using Type = std::complex<float>; };
+template <> struct CLBlastType<cdouble> { using Type = std::complex<double>; };
+
+// Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
+template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(const T val);
+
+// Specializations of the above function
+template <> float inline toCLBlastConstant(const float val) { return val; }
+template <> double inline toCLBlastConstant(const double val) { return val; }
+template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
+template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
+
+template <typename T>
+struct gpu_blas_gemm_func
+{
+    clblast::StatusCode operator() (
+        const clblast::Transpose a_transpose, const clblast::Transpose b_transpose,
+        const size_t m, const size_t n, const size_t k, const T alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        const cl_mem b_buffer, const size_t b_offset, const size_t b_ld, const T beta,
+        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        const auto beta_clblast = toCLBlastConstant(beta);
+        return clblast::Gemm(clblast::Layout::kColMajor, a_transpose, b_transpose, m, n, k, alpha_clblast,
+                             a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld, beta_clblast, c_buffer, c_offset, c_ld,
+                             queues, events);
+    }
+};
+
+template <typename T>
+struct gpu_blas_gemv_func
+{
+    clblast::StatusCode operator() (
+        const clblast::Transpose a_transpose,
+        const size_t m, const size_t n, const T alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        const cl_mem x_buffer, const size_t x_offset, const size_t x_inc, const T beta,
+        cl_mem y_buffer, const size_t y_offset, const size_t y_inc,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        const auto beta_clblast = toCLBlastConstant(beta);
+        return clblast::Gemv(clblast::Layout::kColMajor, a_transpose, m, n, alpha_clblast,
+                             a_buffer, a_offset, a_ld, x_buffer, x_offset, x_inc, beta_clblast, y_buffer, y_offset, y_inc,
+                             queues, events);
+    }
+};
+
+template <typename T>
+struct gpu_blas_trmm_func
+{
+    clblast::StatusCode operator() (
+        const clblast::Side side, const clblast::Triangle triangle, const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
+        const size_t m, const size_t n, const T alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        return clblast::Trmm(clblast::Layout::kColMajor, side, triangle, a_transpose, diagonal, m, n, alpha_clblast,
+                             a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld,
+                             queues, events);
+    }
+};
+
+template <typename T>
+struct gpu_blas_trsm_func
+{
+    clblast::StatusCode operator() (
+        const clblast::Side side, const clblast::Triangle triangle, const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
+        const size_t m, const size_t n, const T alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        return clblast::Trsm(clblast::Layout::kColMajor, side, triangle, a_transpose, diagonal, m, n, alpha_clblast,
+                             a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld,
+                             queues, events);
+    }
+};
+
+template <typename T>
+struct gpu_blas_trsv_func
+{
+    clblast::StatusCode operator() (
+        const clblast::Triangle triangle, const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
+        const size_t n,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        cl_mem x_buffer, const size_t x_offset, const size_t x_inc,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        return clblast::Trsv<typename CLBlastType<T>::Type>(
+            clblast::Layout::kColMajor, triangle, a_transpose, diagonal, n,
+            a_buffer, a_offset, a_ld, x_buffer, x_offset, x_inc,
+            queues, events);
+    }
+};
+
+template <typename T>
+struct gpu_blas_herk_func
+{
+    template <typename U>
+    clblast::StatusCode operator() (
+        const clblast::Triangle triangle, const clblast::Transpose a_transpose,
+        const size_t n, const size_t k, const U alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const U beta,
+        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        const auto beta_clblast = toCLBlastConstant(beta);
+        return clblast::Herk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
+                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
+                             queues, events);
+    }
+};
+
+template <typename T>
+struct gpu_blas_syrk_func
+{
+    clblast::StatusCode operator() (
+        const clblast::Triangle triangle, const clblast::Transpose a_transpose,
+        const size_t n, const size_t k, const T alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const T beta,
+        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        const auto beta_clblast = toCLBlastConstant(beta);
+        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
+                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
+                             queues, events);
+    }
+};

From 533eb4f78492624648cdcc929b6fb985574df485 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 7 Jan 2017 13:26:50 +0100
Subject: [PATCH 1077/2677] Added CLBlast versus clBLAS selection options in
 CMake

---
 CMakeLists.txt                    |  2 ++
 src/backend/opencl/CMakeLists.txt | 20 ++++++++++++--------
 2 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b204310fdd..e71de084c9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -23,6 +23,8 @@ IF(${OpenCL_FOUND})
     SET(BUILD_OPENCL ON CACHE BOOL "")
 ENDIF(${OpenCL_FOUND})
 OPTION(BUILD_OPENCL "Build ArrayFire with a OpenCL backend" OFF)
+OPTION(USE_CLBLAST "Build ArrayFire with the CLBlast BLAS library for the OpenCL backend" OFF)
+OPTION(USE_CLBLAS "Build ArrayFire with the clBLAS BLAS library for the OpenCL backend" OFF)
 
 OPTION(BUILD_GRAPHICS "Build ArrayFire with Forge Graphics" ON)
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f032717ce6..f669a22e68 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -60,11 +60,15 @@ ENDIF()
 ADD_DEFINITIONS(-DAF_OPENCL
                 -D__CL_ENABLE_EXCEPTIONS)
 
-# IF(USE_CLBLAST AND USE_CLBLAS)
-#     MESSAGE(ERROR "Cannot use both CLBlast and clBLAS, please select only one of them")
-# ENDIF()
+IF(USE_CLBLAST AND USE_CLBLAS)
+    MESSAGE(SEND_ERROR "Cannot use both CLBlast and clBLAS, please select only one of them using USE_CLBLAST=OFF or USE_CLBLAS=OFF")
+ENDIF()
+
+IF(NOT USE_CLBLAST AND NOT USE_CLBLAS)
+    MESSAGE(SEND_ERROR "The OpenCL backend requires either CLBlast or clBLAS, please select one of them using USE_CLBLAST=ON or USE_CLBLAS=ON")
+ENDIF()
 
-# IF(USE_CLBLAST)
+IF(USE_CLBLAST)
     OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
     IF(USE_SYSTEM_CLBLAST)
         FIND_PACKAGE(CLBlast REQUIRED)
@@ -75,9 +79,9 @@ ADD_DEFINITIONS(-DAF_OPENCL
     LINK_DIRECTORIES(${CLBLAST_LIBRARY_DIR})
     ADD_DEFINITIONS(-DUSE_CLBLAST)
     MESSAGE(STATUS "Building with CLBlast as an OpenCL BLAS back-end")
-# ENDIF()
+ENDIF()
 
-# IF(USE_CLBLAS)
+IF(USE_CLBLAS)
     OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
     IF(USE_SYSTEM_CLBLAS)
         FIND_PACKAGE(clBLAS REQUIRED)
@@ -86,9 +90,9 @@ ADD_DEFINITIONS(-DAF_OPENCL
     ENDIF()
     INCLUDE_DIRECTORIES(${CLBLAS_INCLUDE_DIRS})
     LINK_DIRECTORIES(${CLBLAS_LIBRARY_DIR})
-    # ADD_DEFINITIONS(-DUSE_CLBLAS)
+    ADD_DEFINITIONS(-DUSE_CLBLAS)
     MESSAGE(STATUS "Building with clBLAS as an OpenCL BLAS back-end")
-# ENDIF()
+ENDIF()
 
 OPTION(USE_SYSTEM_CLFFT "Use system clFFT" OFF)
 IF(USE_SYSTEM_CLFFT)

From 15cc0183396dbaf0a7a284ce3747eddf37533e33 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 9 Jan 2017 12:58:52 +0530
Subject: [PATCH 1078/2677] Fix for CUDA usage with gcc > 5.x

---
 src/backend/cuda/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9e4b5090b9..4da7cec1be 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -338,6 +338,10 @@ ELSE()
   ENDIF()
 ENDIF()
 
+IF("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_MWAITXINTRIN_H_INCLUDED -D_FORCE_INLINES")
+ENDIF()
+
 ## Copied from FindCUDA.cmake
 ## The target_link_library needs to link with the cuda libraries using
 ## PRIVATE

From aaca5f9ff5eb1030c15cf5d931fe79f438cf147d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 9 Jan 2017 18:02:59 +0530
Subject: [PATCH 1079/2677] Move clfft intialization into opencl::DeviceManager

---
 .../{err_clfft.hpp => clfftManager.cpp}       | 154 +++++++++++---
 src/backend/opencl/clfftManager.hpp           | 127 ++++++++++++
 src/backend/opencl/fft.cpp                    | 193 +-----------------
 src/backend/opencl/memory.hpp                 |   7 +-
 src/backend/opencl/platform.cpp               |   5 +
 src/backend/opencl/platform.hpp               |  13 ++
 6 files changed, 285 insertions(+), 214 deletions(-)
 rename src/backend/opencl/{err_clfft.hpp => clfftManager.cpp} (52%)
 create mode 100644 src/backend/opencl/clfftManager.hpp

diff --git a/src/backend/opencl/err_clfft.hpp b/src/backend/opencl/clfftManager.cpp
similarity index 52%
rename from src/backend/opencl/err_clfft.hpp
rename to src/backend/opencl/clfftManager.cpp
index 94ca210c46..ef08442474 100644
--- a/src/backend/opencl/err_clfft.hpp
+++ b/src/backend/opencl/clfftManager.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2016, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,12 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#pragma once
-#include <stdio.h>
-#include <err_opencl.hpp>
-#include <clFFT.h>
+#include <af/defines.h>
 
-static const char * _clfftGetResultString(clfftStatus st)
+#include <err_common.hpp>
+#include <clfftManager.hpp>
+#include <platform.hpp>
+
+#include <string>
+
+using std::string;
+
+namespace clfft
+{
+
+const char * _clfftGetResultString(clfftStatus st)
 {
     switch (st)
     {
@@ -78,23 +86,119 @@ static const char * _clfftGetResultString(clfftStatus st)
     return "Unknown error";
 }
 
-#define CLFFT_CHECK(fn) do {                    \
-        clfftStatus _clfft_st = fn;             \
-        if (_clfft_st != CLFFT_SUCCESS) {       \
-            garbageCollect();                   \
-            _clfft_st = (fn);                   \
-        }                                       \
-        if (_clfft_st != CLFFT_SUCCESS) {       \
-            char clfft_st_msg[1024];            \
-            snprintf(clfft_st_msg,              \
-                     sizeof(clfft_st_msg),      \
-                     "clFFT Error (%d): %s\n",  \
-                     (int)(_clfft_st),          \
-                     _clfftGetResultString(     \
-                         _clfft_st));           \
-                                                \
-            AF_ERROR(clfft_st_msg,              \
-                     AF_ERR_INTERNAL);          \
-        }                                       \
-    } while(0)
+void findPlan(clfftPlanHandle &plan,
+              clfftLayout iLayout, clfftLayout oLayout,
+              clfftDim rank, size_t *clLengths,
+              size_t *istrides, size_t idist,
+              size_t *ostrides, size_t odist,
+              clfftPrecision precision, size_t batch)
+{
+    // create the key string
+    char key_str_temp[64];
+    sprintf(key_str_temp, "%d:%d:%d:", iLayout, oLayout, rank);
+
+    string key_string(key_str_temp);
+
+    /* WARNING: DO NOT CHANGE sprintf format specifier */
+    for(int r=0; r<rank; ++r) {
+        sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", clLengths[r]);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    if(istrides!=NULL) {
+        for(int r=0; r<rank; ++r) {
+            sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", istrides[r]);
+            key_string.append(std::string(key_str_temp));
+        }
+        sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", idist);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    if (ostrides!=NULL) {
+        for(int r=0; r<rank; ++r) {
+            sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", ostrides[r]);
+            key_string.append(std::string(key_str_temp));
+        }
+        sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", odist);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    sprintf(key_str_temp, "%d:" SIZE_T_FRMT_SPECIFIER, (int)precision, batch);
+    key_string.append(std::string(key_str_temp));
+
+    // find the matching plan_index in the array clFFTPlanner::mKeys
+    clFFTPlanner &planner = opencl::getclfftPlanManager();
+
+    int planIndex = planner.findIfPlanExists(key_string);
+
+    // if found a valid plan, return it
+    if (planIndex!=-1) {
+        plan = planner.getPlan(planIndex);
+        return;
+    }
+
+    clfftPlanHandle temp;
+
+    // getContext() returns object of type Context
+    // Context() returns the actual cl_context handle
+    CLFFT_CHECK(clfftCreateDefaultPlan(&temp, opencl::getContext()(), rank, clLengths));
 
+    // complex to complex
+    if (iLayout == oLayout) {
+        CLFFT_CHECK(clfftSetResultLocation(temp, CLFFT_INPLACE));
+    } else {
+        CLFFT_CHECK(clfftSetResultLocation(temp, CLFFT_OUTOFPLACE));
+    }
+
+    CLFFT_CHECK(clfftSetLayout(temp, iLayout, oLayout));
+    CLFFT_CHECK(clfftSetPlanBatchSize(temp, batch));
+    CLFFT_CHECK(clfftSetPlanDistance(temp, idist, odist));
+    CLFFT_CHECK(clfftSetPlanInStride(temp, rank, istrides));
+    CLFFT_CHECK(clfftSetPlanOutStride(temp, rank, ostrides));
+    CLFFT_CHECK(clfftSetPlanPrecision(temp, precision));
+    CLFFT_CHECK(clfftSetPlanScale(temp, CLFFT_BACKWARD, 1.0));
+
+    // getQueue() returns object of type CommandQueue
+    // CommandQueue() returns the actual cl_command_queue handle
+    CLFFT_CHECK(clfftBakePlan(temp, 1, &(opencl::getQueue()()), NULL, NULL));
+
+    plan = temp;
+
+    // push the plan into plan cache
+    planner.pushPlan(key_string, plan);
+}
+
+clFFTPlanner::clFFTPlanner()
+    : mMaxCacheSize(5)
+{
+    CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
+    CLFFT_CHECK(clfftSetup(&mFFTSetup));
+}
+
+clFFTPlanner::~clFFTPlanner()
+{
+    //TODO: FIXME:
+    // clfftTeardown() causes a "Pure Virtual Function Called" crash on
+    // Windows for Intel devices. This causes tests to fail.
+    #ifndef OS_WIN
+    static bool flag = true;
+    if(flag) {
+        // THOU SHALL NOT THROW IN DESTRUCTORS
+        clfftTeardown();
+        flag = false;
+    }
+    #endif
+}
+
+void clFFTPlanner::popPlan()
+{
+    if (!mCache.empty()) {
+        // destroy the clfft plan associated with the
+        // least recently used plan
+        CLFFT_CHECK(clfftDestroyPlan(&mCache.back().second));
+        // now pop the entry from cache
+        mCache.pop_back();
+    }
+}
+
+}
diff --git a/src/backend/opencl/clfftManager.hpp b/src/backend/opencl/clfftManager.hpp
new file mode 100644
index 0000000000..37ffd0fda0
--- /dev/null
+++ b/src/backend/opencl/clfftManager.hpp
@@ -0,0 +1,127 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <clFFT.h>
+
+#include <memory.hpp>
+
+#include <cstdio>
+#include <deque>
+#include <string>
+#include <utility>
+
+namespace opencl
+{
+class DeviceManager;
+}
+
+namespace clfft
+{
+
+typedef std::pair<std::string, clfftPlanHandle> FFTPlanPair;
+typedef std::deque<FFTPlanPair> FFTPlanCache;
+
+const char * _clfftGetResultString(clfftStatus st);
+
+void findPlan(clfftPlanHandle &plan,
+              clfftLayout iLayout, clfftLayout oLayout,
+              clfftDim rank, size_t *clLengths,
+              size_t *istrides, size_t idist,
+              size_t *ostrides, size_t odist,
+              clfftPrecision precision, size_t batch);
+
+// clFFTPlanner caches fft plans
+//
+// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
+//          |
+//          |--> ELSE just push the plan
+// existing plan -> reuse a plan
+class clFFTPlanner
+{
+    friend class opencl::DeviceManager;
+
+    friend void findPlan(clfftPlanHandle &plan,
+                         clfftLayout iLayout, clfftLayout oLayout,
+                         clfftDim rank, size_t *clLengths,
+                         size_t *istrides, size_t idist,
+                         size_t *ostrides, size_t odist,
+                         clfftPrecision precision, size_t batch);
+
+    public:
+        clFFTPlanner();
+        ~clFFTPlanner();
+
+        inline void setMaxCacheSize(size_t size) {
+            mCache.resize(size, FFTPlanPair(std::string(""), 0));
+        }
+
+        inline size_t getMaxCacheSize() const {
+            return mMaxCacheSize;
+        }
+
+        inline clfftPlanHandle getPlan(int index) const {
+            return mCache[index].second;
+        }
+
+        // iterates through plan cache from front to back
+        // of the cache(queue)
+        int findIfPlanExists(std::string keyString) const {
+            int retVal = -1;
+            for(uint i=0; i<mCache.size(); ++i) {
+                if (keyString == mCache[i].first) {
+                    retVal = i;
+                }
+            }
+            return retVal;
+        }
+
+        // pops plan from the back of cache(queue)
+        void popPlan();
+
+        // pushes plan to the front of cache(queue)
+        void pushPlan(std::string keyString, clfftPlanHandle plan) {
+            if (mCache.size()>mMaxCacheSize) {
+                popPlan();
+            }
+            mCache.push_front(FFTPlanPair(keyString, plan));
+        }
+
+    private:
+        clFFTPlanner(clFFTPlanner const&);
+        void operator=(clFFTPlanner const&);
+
+        clfftSetupData  mFFTSetup;
+
+        size_t       mMaxCacheSize;
+        FFTPlanCache mCache;
+};
+
+}
+
+#define CLFFT_CHECK(fn) do {                        \
+        clfftStatus _clfft_st = fn;                 \
+        if (_clfft_st != CLFFT_SUCCESS) {           \
+            opencl::garbageCollect();               \
+            _clfft_st = (fn);                       \
+        }                                           \
+        if (_clfft_st != CLFFT_SUCCESS) {           \
+            char clfft_st_msg[1024];                \
+            snprintf(clfft_st_msg,                  \
+                     sizeof(clfft_st_msg),          \
+                     "clFFT Error (%d): %s\n",      \
+                     (int)(_clfft_st),              \
+                     clfft::_clfftGetResultString(  \
+                         _clfft_st));               \
+                                                    \
+            AF_ERROR(clfft_st_msg,                  \
+                     AF_ERR_INTERNAL);              \
+        }                                           \
+    } while(0)
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index 7cd39f46af..c5a550304d 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -12,14 +12,9 @@
 #include <copy.hpp>
 #include <fft.hpp>
 #include <err_opencl.hpp>
-#include <err_clfft.hpp>
-#include <clFFT.h>
 #include <math.hpp>
-#include <string>
-#include <deque>
-#include <utility>
-#include <cstdio>
 #include <memory.hpp>
+#include <clfftManager.hpp>
 
 using af::dim4;
 using std::string;
@@ -27,187 +22,9 @@ using std::string;
 namespace opencl
 {
 
-typedef std::pair<string, clfftPlanHandle> FFTPlanPair;
-typedef std::deque<FFTPlanPair> FFTPlanCache;
-
-// clFFTPlanner caches fft plans
-//
-// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
-//          |
-//          |--> ELSE just push the plan
-// existing plan -> reuse a plan
-class clFFTPlanner
-{
-    friend void find_clfft_plan(clfftPlanHandle &plan,
-                                clfftLayout iLayout, clfftLayout oLayout,
-                                clfftDim rank, size_t *clLengths,
-                                size_t *istrides, size_t idist,
-                                size_t *ostrides, size_t odist,
-                                clfftPrecision precision, size_t batch);
-
-    public:
-        static clFFTPlanner& getInstance() {
-            static clFFTPlanner instances[opencl::DeviceManager::MAX_DEVICES];
-            return instances[opencl::getActiveDeviceId()];
-        }
-
-        ~clFFTPlanner() {
-            //TODO: FIXME:
-            // clfftTeardown() cause a "Pure Virtual Function Called" crash on
-            // Window only when Intel devices are called. This causes tests to
-            // fail.
-            #ifndef OS_WIN
-            static bool flag = true;
-            if(flag) {
-                // THOU SHALL NOT THROW IN DESTRUCTORS
-                clfftTeardown();
-                flag = false;
-            }
-            #endif
-        }
-
-        inline void setMaxCacheSize(size_t size) {
-            mCache.resize(size, FFTPlanPair(std::string(""), 0));
-        }
-
-        inline size_t getMaxCacheSize() const {
-            return mMaxCacheSize;
-        }
-
-        inline clfftPlanHandle getPlan(int index) const {
-            return mCache[index].second;
-        }
-
-        // iterates through plan cache from front to back
-        // of the cache(queue)
-        int findIfPlanExists(std::string keyString) const {
-            int retVal = -1;
-            for(uint i=0; i<mCache.size(); ++i) {
-                if (keyString == mCache[i].first) {
-                    retVal = i;
-                }
-            }
-            return retVal;
-        }
-
-        // pops plan from the back of cache(queue)
-        void popPlan() {
-            if (!mCache.empty()) {
-                // destroy the clfft plan associated with the
-                // least recently used plan
-                CLFFT_CHECK(clfftDestroyPlan(&mCache.back().second));
-                // now pop the entry from cache
-                mCache.pop_back();
-            }
-        }
-
-        // pushes plan to the front of cache(queue)
-        void pushPlan(std::string keyString, clfftPlanHandle plan) {
-            if (mCache.size()>mMaxCacheSize) {
-                popPlan();
-            }
-            mCache.push_front(FFTPlanPair(keyString, plan));
-        }
-
-    private:
-        clFFTPlanner() : mMaxCacheSize(5) {
-            CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
-            CLFFT_CHECK(clfftSetup(&mFFTSetup));
-        }
-        clFFTPlanner(clFFTPlanner const&);
-        void operator=(clFFTPlanner const&);
-
-        clfftSetupData  mFFTSetup;
-
-        size_t       mMaxCacheSize;
-        FFTPlanCache mCache;
-};
-
-void find_clfft_plan(clfftPlanHandle &plan,
-                     clfftLayout iLayout, clfftLayout oLayout,
-                     clfftDim rank, size_t *clLengths,
-                     size_t *istrides, size_t idist,
-                     size_t *ostrides, size_t odist,
-                     clfftPrecision precision, size_t batch)
-{
-    // create the key string
-    char key_str_temp[64];
-    sprintf(key_str_temp, "%d:%d:%d:", iLayout, oLayout, rank);
-
-    string key_string(key_str_temp);
-
-    /* WARNING: DO NOT CHANGE sprintf format specifier */
-    for(int r=0; r<rank; ++r) {
-        sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", clLengths[r]);
-        key_string.append(std::string(key_str_temp));
-    }
-
-    if(istrides!=NULL) {
-        for(int r=0; r<rank; ++r) {
-            sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", istrides[r]);
-            key_string.append(std::string(key_str_temp));
-        }
-        sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", idist);
-        key_string.append(std::string(key_str_temp));
-    }
-
-    if (ostrides!=NULL) {
-        for(int r=0; r<rank; ++r) {
-            sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", ostrides[r]);
-            key_string.append(std::string(key_str_temp));
-        }
-        sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", odist);
-        key_string.append(std::string(key_str_temp));
-    }
-
-    sprintf(key_str_temp, "%d:" SIZE_T_FRMT_SPECIFIER, (int)precision, batch);
-    key_string.append(std::string(key_str_temp));
-
-    // find the matching plan_index in the array clFFTPlanner::mKeys
-    clFFTPlanner &planner = clFFTPlanner::getInstance();
-
-    int planIndex = planner.findIfPlanExists(key_string);
-
-    // if found a valid plan, return it
-    if (planIndex!=-1) {
-        plan = planner.getPlan(planIndex);
-        return;
-    }
-
-    clfftPlanHandle temp;
-
-    // getContext() returns object of type Context
-    // Context() returns the actual cl_context handle
-    CLFFT_CHECK(clfftCreateDefaultPlan(&temp, getContext()(), rank, clLengths));
-
-    // complex to complex
-    if (iLayout == oLayout) {
-        CLFFT_CHECK(clfftSetResultLocation(temp, CLFFT_INPLACE));
-    } else {
-        CLFFT_CHECK(clfftSetResultLocation(temp, CLFFT_OUTOFPLACE));
-    }
-
-    CLFFT_CHECK(clfftSetLayout(temp, iLayout, oLayout));
-    CLFFT_CHECK(clfftSetPlanBatchSize(temp, batch));
-    CLFFT_CHECK(clfftSetPlanDistance(temp, idist, odist));
-    CLFFT_CHECK(clfftSetPlanInStride(temp, rank, istrides));
-    CLFFT_CHECK(clfftSetPlanOutStride(temp, rank, ostrides));
-    CLFFT_CHECK(clfftSetPlanPrecision(temp, precision));
-    CLFFT_CHECK(clfftSetPlanScale(temp, CLFFT_BACKWARD, 1.0));
-
-    // getQueue() returns object of type CommandQueue
-    // CommandQueue() returns the actual cl_command_queue handle
-    CLFFT_CHECK(clfftBakePlan(temp, 1, &(getQueue()()), NULL, NULL));
-
-    plan = temp;
-
-    // push the plan into plan cache
-    planner.pushPlan(key_string, plan);
-}
-
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    clFFTPlanner::getInstance().setMaxCacheSize(numPlans);
+    getclfftPlanManager().setMaxCacheSize(numPlans);
 }
 
 template<typename T> struct Precision;
@@ -268,7 +85,7 @@ void fft_inplace(Array<T> &in)
         batch *= tdims[i];
     }
 
-    find_clfft_plan(plan,
+    clfft::findPlan(plan,
                     CLFFT_COMPLEX_INTERLEAVED,
                     CLFFT_COMPLEX_INTERLEAVED,
                     (clfftDim)rank, tdims,
@@ -309,7 +126,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
         batch *= tdims[i];
     }
 
-    find_clfft_plan(plan,
+    clfft::findPlan(plan,
                     CLFFT_REAL,
                     CLFFT_HERMITIAN_INTERLEAVED,
                     (clfftDim)rank, tdims,
@@ -349,7 +166,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
         batch *= tdims[i];
     }
 
-    find_clfft_plan(plan,
+    clfft::findPlan(plan,
                     CLFFT_HERMITIAN_INTERLEAVED,
                     CLFFT_REAL,
                     (clfftDim)rank, tdims,
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 549dfb3f08..823fd4581f 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -8,7 +8,12 @@
  ********************************************************/
 #pragma once
 
-#include <platform.hpp>
+#include <cstdlib>
+
+namespace cl
+{
+class Buffer;
+}
 
 namespace opencl
 {
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index c3d5cf7f16..9dcffc89cd 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -837,6 +837,11 @@ bool& evalFlag()
     return flag;
 }
 
+clfft::clFFTPlanner& getclfftPlanManager()
+{
+    return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
+}
+
 }
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index f1ed903974..da6905c42a 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -22,14 +22,25 @@
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
 
+#include <memory>
 #include <vector>
 #include <string>
 
+#include <clfftManager.hpp>
+
 namespace opencl
 {
 
+///////////////////////// BEGIN Sub-Managers ///////////////////
+//
+clfft::clFFTPlanner& getclfftPlanManager();
+//
+///////////////////////// END Sub-Managers /////////////////////
+
 class DeviceManager
 {
+    friend clfft::clFFTPlanner& getclfftPlanManager();
+
     friend std::string getDeviceInfo();
 
     friend int getDeviceCount();
@@ -97,6 +108,8 @@ class DeviceManager
 
         unsigned mActiveCtxId;
         unsigned mActiveQId;
+
+        clfft::clFFTPlanner clfftManagers[MAX_DEVICES];
 };
 
 int getBackend();

From d2cc3ba98d61d1b8afef6666e0a14e4b79f69c7b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 10 Jan 2017 10:35:39 +0530
Subject: [PATCH 1080/2677] Memory-managers(opencl) initialize from
 DeviceManager

---
 src/backend/opencl/err_opencl.hpp    |   2 +-
 src/backend/opencl/memory.cpp        | 142 +--------------------------
 src/backend/opencl/memoryManager.cpp |  86 ++++++++++++++++
 src/backend/opencl/memoryManager.hpp |  68 +++++++++++++
 src/backend/opencl/platform.cpp      |  45 +++++++--
 src/backend/opencl/platform.hpp      |  15 +++
 6 files changed, 209 insertions(+), 149 deletions(-)
 create mode 100644 src/backend/opencl/memoryManager.cpp
 create mode 100644 src/backend/opencl/memoryManager.hpp

diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 5d12ab2db0..841670e97e 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <stdio.h>
+#include <cstdio>
 #include <errorcodes.hpp>
 #include <err_common.hpp>
 #include <platform.hpp>
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 59f8c31154..3542e3451a 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -8,150 +8,14 @@
  ********************************************************/
 
 #include <memory.hpp>
-#include <dispatch.hpp>
-#include <map>
-#include <iostream>
-#include <iomanip>
-#include <string>
-#include <types.hpp>
-#include <err_opencl.hpp>
-
-#include <MemoryManager.hpp>
 
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_OPENCL_MEM_DEBUG
-#define AF_OPENCL_MEM_DEBUG 0
-#endif
+#include <memoryManager.hpp>
+#include <platform.hpp>
+#include <types.hpp>
 
 namespace opencl
 {
 
-class MemoryManager  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManager();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManager()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            opencl::setDevice(n);
-            this->garbageCollect();
-        }
-    }
-};
-
-class MemoryManagerPinned  : public common::MemoryManager
-{
-    std::vector<
-        std::map<void *, cl::Buffer>
-        > pinned_maps;
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-
-public:
-
-    MemoryManagerPinned();
-
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-
-    ~MemoryManagerPinned()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            opencl::setDevice(n);
-            this->garbageCollect();
-            auto pinned_curr_iter = pinned_maps[n].begin();
-            auto pinned_end_iter  = pinned_maps[n].end();
-            while (pinned_curr_iter != pinned_end_iter) {
-                pinned_maps[n].erase(pinned_curr_iter++);
-            }
-        }
-    }
-};
-
-int MemoryManager::getActiveDeviceId()
-{
-    return opencl::getActiveDeviceId();
-}
-
-size_t MemoryManager::getMaxMemorySize(int id)
-{
-    return opencl::getDeviceMemorySize(id);
-}
-
-MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
-    return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
-}
-
-void MemoryManager::nativeFree(void *ptr)
-{
-    delete (cl::Buffer *)ptr;
-}
-
-static MemoryManager &getMemoryManager()
-{
-    static MemoryManager instance;
-    return instance;
-}
-
-int MemoryManagerPinned::getActiveDeviceId()
-{
-    return opencl::getActiveDeviceId();
-}
-
-size_t MemoryManagerPinned::getMaxMemorySize(int id)
-{
-    return opencl::getDeviceMemorySize(id);
-}
-
-MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
-    pinned_maps(getDeviceCount())
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
-{
-    void *ptr = NULL;
-    cl::Buffer buf= cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
-    ptr = getQueue().enqueueMapBuffer(buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
-    pinned_maps[opencl::getActiveDeviceId()][ptr] = buf;
-    return ptr;
-}
-
-void MemoryManagerPinned::nativeFree(void *ptr)
-{
-    int n = opencl::getActiveDeviceId();
-    auto iter = pinned_maps[n].find(ptr);
-
-    if (iter != pinned_maps[n].end()) {
-        getQueue().enqueueUnmapMemObject(pinned_maps[n][ptr], ptr);
-        pinned_maps[n].erase(iter);
-    }
-}
-
-static MemoryManagerPinned &getMemoryManagerPinned()
-{
-    static MemoryManagerPinned instance;
-    return instance;
-}
-
 void setMemStepSize(size_t step_bytes)
 {
     getMemoryManager().setMemStepSize(step_bytes);
diff --git a/src/backend/opencl/memoryManager.cpp b/src/backend/opencl/memoryManager.cpp
new file mode 100644
index 0000000000..c329faef5c
--- /dev/null
+++ b/src/backend/opencl/memoryManager.cpp
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <memoryManager.hpp>
+
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
+
+#ifndef AF_OPENCL_MEM_DEBUG
+#define AF_OPENCL_MEM_DEBUG 0
+#endif
+
+namespace opencl
+{
+
+int MemoryManager::getActiveDeviceId()
+{
+    return opencl::getActiveDeviceId();
+}
+
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return opencl::getDeviceMemorySize(id);
+}
+
+MemoryManager::MemoryManager() :
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
+}
+
+void MemoryManager::nativeFree(void *ptr)
+{
+    delete (cl::Buffer *)ptr;
+}
+
+int MemoryManagerPinned::getActiveDeviceId()
+{
+    return opencl::getActiveDeviceId();
+}
+
+size_t MemoryManagerPinned::getMaxMemorySize(int id)
+{
+    return opencl::getDeviceMemorySize(id);
+}
+
+MemoryManagerPinned::MemoryManagerPinned() :
+    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
+    pinned_maps(getDeviceCount())
+{
+    this->setMaxMemorySize();
+}
+
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
+{
+    void *ptr = NULL;
+    cl::Buffer buf= cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
+    ptr = getQueue().enqueueMapBuffer(buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
+    pinned_maps[opencl::getActiveDeviceId()][ptr] = buf;
+    return ptr;
+}
+
+void MemoryManagerPinned::nativeFree(void *ptr)
+{
+    int n = opencl::getActiveDeviceId();
+    auto iter = pinned_maps[n].find(ptr);
+
+    if (iter != pinned_maps[n].end()) {
+        getQueue().enqueueUnmapMemObject(pinned_maps[n][ptr], ptr);
+        pinned_maps[n].erase(iter);
+    }
+}
+
+}
diff --git a/src/backend/opencl/memoryManager.hpp b/src/backend/opencl/memoryManager.hpp
new file mode 100644
index 0000000000..eab26ed235
--- /dev/null
+++ b/src/backend/opencl/memoryManager.hpp
@@ -0,0 +1,68 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <platform.hpp>
+#include <MemoryManager.hpp>
+
+#include <map>
+
+namespace opencl
+{
+
+class MemoryManager  : public common::MemoryManager
+{
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+public:
+    MemoryManager();
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+    ~MemoryManager()
+    {
+        common::lock_guard_t lock(this->memory_mutex);
+        for (int n = 0; n < getDeviceCount(); n++) {
+            opencl::setDevice(n);
+            this->garbageCollect();
+        }
+    }
+};
+
+class MemoryManagerPinned  : public common::MemoryManager
+{
+    std::vector<
+        std::map<void *, cl::Buffer>
+        > pinned_maps;
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+
+public:
+
+    MemoryManagerPinned();
+
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+
+    ~MemoryManagerPinned()
+    {
+        common::lock_guard_t lock(this->memory_mutex);
+        for (int n = 0; n < getDeviceCount(); n++) {
+            opencl::setDevice(n);
+            this->garbageCollect();
+            auto pinned_curr_iter = pinned_maps[n].begin();
+            auto pinned_end_iter  = pinned_maps[n].end();
+            while (pinned_curr_iter != pinned_end_iter) {
+                pinned_maps[n].erase(pinned_curr_iter++);
+            }
+        }
+    }
+};
+
+}
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 9dcffc89cd..5b5ac9e985 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -25,23 +25,24 @@
 #include <af/version.h>
 #include <af/opencl.h>
 #include <defines.hpp>
-#include <version.hpp>
+#include <errorcodes.hpp>
+#include <err_opencl.hpp>
+#include <host_memory.hpp>
+#include <memoryManager.hpp>
 #include <platform.hpp>
 #include <util.hpp>
-#include <functional>
+#include <version.hpp>
+
 #include <algorithm>
 #include <cctype>
-#include <vector>
+#include <functional>
+#include <map>
+#include <mutex>
 #include <string>
 #include <sstream>
 #include <stdexcept>
 #include <cstring>
-#include <algorithm>
-#include <map>
-#include <errorcodes.hpp>
-#include <err_opencl.hpp>
-#include <util.hpp>
-#include <host_memory.hpp>
+#include <vector>
 
 using std::string;
 using std::vector;
@@ -837,6 +838,32 @@ bool& evalFlag()
     return flag;
 }
 
+MemoryManager& getMemoryManager()
+{
+    static std::once_flag myFlag;
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(myFlag, [&]() {
+                inst.memManager.reset(new MemoryManager());
+            });
+
+    return *(inst.memManager.get());
+}
+
+MemoryManagerPinned& getMemoryManagerPinned()
+{
+    static std::once_flag myFlag;
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(myFlag, [&]() {
+                inst.pinnedMemManager.reset(new MemoryManagerPinned());
+            });
+
+    return *(inst.pinnedMemManager.get());
+}
+
 clfft::clFFTPlanner& getclfftPlanManager()
 {
     return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index da6905c42a..c921ca69b1 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -31,14 +31,26 @@
 namespace opencl
 {
 
+// Forward Declarations
+class MemoryManager;
+class MemoryManagerPinned;
+
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
+MemoryManager &getMemoryManager();
+
+MemoryManagerPinned& getMemoryManagerPinned();
+
 clfft::clFFTPlanner& getclfftPlanManager();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
 class DeviceManager
 {
+    friend MemoryManager &getMemoryManager();
+
+    friend MemoryManagerPinned& getMemoryManagerPinned();
+
     friend clfft::clFFTPlanner& getclfftPlanManager();
 
     friend std::string getDeviceInfo();
@@ -109,6 +121,9 @@ class DeviceManager
         unsigned mActiveCtxId;
         unsigned mActiveQId;
 
+        std::unique_ptr<MemoryManager> memManager;
+        std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+
         clfft::clFFTPlanner clfftManagers[MAX_DEVICES];
 };
 

From a7dd2e804179c57eef62dbc1f0972884a4bd6dfa Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 10 Jan 2017 11:01:55 +0530
Subject: [PATCH 1081/2677] Init opencl gfx manager from DeviceManager

---
 src/backend/opencl/hist_graphics.cpp  |  8 ++++----
 src/backend/opencl/image.cpp          |  9 +++++----
 src/backend/opencl/interopManager.cpp |  8 ++------
 src/backend/opencl/interopManager.hpp |  9 +++++++--
 src/backend/opencl/platform.cpp       | 22 ++++++++++++++++++----
 src/backend/opencl/platform.hpp       |  6 ++++++
 src/backend/opencl/plot.cpp           |  8 ++++----
 src/backend/opencl/surface.cpp        |  8 ++++----
 src/backend/opencl/vector_field.cpp   |  8 ++++----
 9 files changed, 54 insertions(+), 32 deletions(-)

diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 50a78be927..675110e8b7 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -9,11 +9,11 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
-#include <hist_graphics.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <hist_graphics.hpp>
+#include <interopManager.hpp>
 
 namespace opencl
 {
@@ -27,7 +27,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         const cl::Buffer *d_P = data.get();
         size_t bytes = hist->verticesSize();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(hist);
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 8f403ea1b4..27258694cd 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -9,11 +9,12 @@
 
 #if defined(WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
-#include <image.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <image.hpp>
+#include <interopManager.hpp>
+
 #include <stdexcept>
 #include <vector>
 
@@ -26,7 +27,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(image);
         const cl::Buffer *d_X = in.get();
diff --git a/src/backend/opencl/interopManager.cpp b/src/backend/opencl/interopManager.cpp
index ff2da42efc..d8ed8641bb 100644
--- a/src/backend/opencl/interopManager.cpp
+++ b/src/backend/opencl/interopManager.cpp
@@ -11,6 +11,8 @@
 
 #include <interopManager.hpp>
 
+#include <platform.hpp>
+
 namespace opencl
 {
 
@@ -34,12 +36,6 @@ InteropManager::~InteropManager()
     }
 }
 
-InteropManager& InteropManager::getInstance()
-{
-    static InteropManager my_instance;
-    return my_instance;
-}
-
 interop_t& InteropManager::getDeviceMap(int device)
 {
     return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
diff --git a/src/backend/opencl/interopManager.hpp b/src/backend/opencl/interopManager.hpp
index 6858f51c4b..d495794e36 100644
--- a/src/backend/opencl/interopManager.hpp
+++ b/src/backend/opencl/interopManager.hpp
@@ -14,9 +14,15 @@
 
 #include <graphics_common.hpp>
 #include <platform.hpp>
+
 #include <map>
 #include <vector>
 
+namespace cl
+{
+class Buffer;
+}
+
 namespace opencl
 {
 
@@ -29,7 +35,7 @@ class InteropManager
         interop_t interop_maps[DeviceManager::MAX_DEVICES];
 
     public:
-        static InteropManager& getInstance();
+        InteropManager() {}
         ~InteropManager();
         cl::Buffer** getBufferResource(const forge::Image        *handle);
         cl::Buffer** getBufferResource(const forge::Plot         *handle);
@@ -38,7 +44,6 @@ class InteropManager
         cl::Buffer** getBufferResource(const forge::VectorField  *handle);
 
     protected:
-        InteropManager() {}
         InteropManager(InteropManager const&);
         void operator=(InteropManager const&);
         interop_t& getDeviceMap(int device = -1); // default will return current device
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 5b5ac9e985..9519e0099e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -28,6 +28,7 @@
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
 #include <host_memory.hpp>
+#include <interopManager.hpp>
 #include <memoryManager.hpp>
 #include <platform.hpp>
 #include <util.hpp>
@@ -840,11 +841,11 @@ bool& evalFlag()
 
 MemoryManager& getMemoryManager()
 {
-    static std::once_flag myFlag;
+    static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(myFlag, [&]() {
+    std::call_once(flag, [&]() {
                 inst.memManager.reset(new MemoryManager());
             });
 
@@ -853,17 +854,30 @@ MemoryManager& getMemoryManager()
 
 MemoryManagerPinned& getMemoryManagerPinned()
 {
-    static std::once_flag myFlag;
+    static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(myFlag, [&]() {
+    std::call_once(flag, [&]() {
                 inst.pinnedMemManager.reset(new MemoryManagerPinned());
             });
 
     return *(inst.pinnedMemManager.get());
 }
 
+InteropManager& getGfxInteropManager()
+{
+    static std::once_flag flag;
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(flag, [&]() {
+                inst.gfxManager.reset(new InteropManager());
+            });
+
+    return *(inst.gfxManager.get());
+}
+
 clfft::clFFTPlanner& getclfftPlanManager()
 {
     return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index c921ca69b1..c0803a8deb 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -34,6 +34,7 @@ namespace opencl
 // Forward Declarations
 class MemoryManager;
 class MemoryManagerPinned;
+class InteropManager;
 
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
@@ -41,6 +42,8 @@ MemoryManager &getMemoryManager();
 
 MemoryManagerPinned& getMemoryManagerPinned();
 
+InteropManager& getGfxInteropManager();
+
 clfft::clFFTPlanner& getclfftPlanManager();
 //
 ///////////////////////// END Sub-Managers /////////////////////
@@ -51,6 +54,8 @@ class DeviceManager
 
     friend MemoryManagerPinned& getMemoryManagerPinned();
 
+    friend InteropManager& getGfxInteropManager();
+
     friend clfft::clFFTPlanner& getclfftPlanManager();
 
     friend std::string getDeviceInfo();
@@ -123,6 +128,7 @@ class DeviceManager
 
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+        std::unique_ptr<InteropManager> gfxManager;
 
         clfft::clFFTPlanner clfftManagers[MAX_DEVICES];
 };
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index e597dff270..40240fbd10 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -9,12 +9,12 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
-#include <plot.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <interopManager.hpp>
 #include <join.hpp>
+#include <plot.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
 
@@ -32,7 +32,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         const cl::Buffer *d_P = P.get();
         size_t bytes = plot->verticesSize();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(plot);
 
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 8a54a8e3f6..0ed38a3e90 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -9,14 +9,14 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
-#include <surface.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <interopManager.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
+#include <surface.hpp>
 
 using af::dim4;
 
@@ -32,7 +32,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         const cl::Buffer *d_P = P.get();
         size_t bytes = surface->verticesSize();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(surface);
 
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 5f35437e95..3fd4e32456 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -9,11 +9,11 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <interopManager.hpp>
 #include <Array.hpp>
-#include <vector_field.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <interopManager.hpp>
+#include <vector_field.hpp>
 
 using af::dim4;
 
@@ -32,7 +32,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         size_t pBytes = vector_field->verticesSize();
         size_t dBytes = vector_field->directionsSize();
 
-        InteropManager& intrpMngr = InteropManager::getInstance();
+        InteropManager& intrpMngr = getGfxInteropManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(vector_field);
 

From 4360a34775c83548c291737e6b3f09859ba30074 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 10 Jan 2017 11:06:42 +0530
Subject: [PATCH 1082/2677] Fix gfx manager initialization to be thread safe

---
 src/backend/cuda/platform.cpp | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index ad606b6d14..6f1f048f38 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -383,11 +383,11 @@ DeviceManager& DeviceManager::getInstance()
 
 MemoryManager &getMemoryManager()
 {
-    static std::once_flag myFlag;
+    static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(myFlag, [&]() {
+    std::call_once(flag, [&]() {
                 inst.memManager.reset(new cuda::MemoryManager());
             });
 
@@ -396,11 +396,11 @@ MemoryManager &getMemoryManager()
 
 MemoryManagerPinned &getMemoryManagerPinned()
 {
-    static std::once_flag myFlag;
+    static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(myFlag, [&]() {
+    std::call_once(flag, [&]() {
                 inst.pinnedMemManager.reset(new cuda::MemoryManagerPinned());
             });
 
@@ -409,10 +409,13 @@ MemoryManagerPinned &getMemoryManagerPinned()
 
 InteropManager& getGfxInteropManager()
 {
+    static std::once_flag flag;
+
     DeviceManager& inst = DeviceManager::getInstance();
 
-    if (!inst.gfxManager)
-        inst.gfxManager.reset(new cuda::InteropManager());
+    std::call_once(flag, [&]() {
+                inst.gfxManager.reset(new cuda::InteropManager());
+            });
 
     return *(inst.gfxManager.get());
 }

From 264b541e8b108ec358521af652f6e18b2748566e Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Tue, 10 Jan 2017 21:22:59 -0500
Subject: [PATCH 1083/2677] Fix broken forge symlinks in OSX Installer

---
 CMakeModules/osx_install/OSXInstaller.cmake | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 555bd3e245..eae93202c0 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -110,15 +110,15 @@ IF(BUILD_GRAPHICS)
     # Create symlinks separately. Copying them in above command will do a deep copy
     ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
                        COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "${LIBFORGE_NAME}.${FORGE_VERSION}.dylib"
-                       "${LIBFORGE_NAME}.${FORGE_VERSION_MAJOR}.dylib"
+                       "libforge.${FORGE_VERSION}.dylib"
+                       "libforge.${FORGE_VERSION_MAJOR}.dylib"
                        WORKING_DIRECTORY "${OSX_TEMP}/Forge/${AF_INSTALL_LIB_DIR}"
                        COMMENT "Copying libforge files to temporary OSX Install Dir (Symlink)"
                        )
     ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
                        COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "${LIBFORGE_NAME}.${FORGE_VERSION_MAJOR}.dylib"
-                       "${LIBFORGE_NAME}.dylib"
+                       "libforge.${FORGE_VERSION_MAJOR}.dylib"
+                       "libforge.dylib"
                        WORKING_DIRECTORY "${OSX_TEMP}/Forge/${AF_INSTALL_LIB_DIR}"
                        COMMENT "Copying libforge files to temporary OSX Install Dir (Symlink)"
                        )

From ab74af83e5d936fdf6cfeedcb49e4c5a53e81f7c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 11 Jan 2017 12:18:11 +0530
Subject: [PATCH 1084/2677] Remove unused link libraries from link command

---
 src/backend/cuda/CMakeLists.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4da7cec1be..f5ff45092a 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -433,8 +433,7 @@ TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_cusparse_LIBRARY}
                                 PRIVATE ${CUDA_cusolver_LIBRARY}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
-                                PRIVATE ${CUDA_CUDA_LIBRARY}
-                                PRIVATE ${Boost_LIBRARIES})
+                                PRIVATE ${CUDA_CUDA_LIBRARY})
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
 IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)

From 09da66a62bfe810f6646214759ae4efa0e9ccf29 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 11 Jan 2017 13:09:44 +0530
Subject: [PATCH 1085/2677] Remove unused directory from src/backend

---
 src/backend/template/.gitignore | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 src/backend/template/.gitignore

diff --git a/src/backend/template/.gitignore b/src/backend/template/.gitignore
deleted file mode 100644
index e69de29bb2..0000000000

From bf103c05bea41902cf9186224a6fa9435cb4e849 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 9 Jan 2017 12:58:52 +0530
Subject: [PATCH 1086/2677] Fix for CUDA usage with gcc > 5.x

---
 src/backend/cuda/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8ae37999fb..f5ff45092a 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -338,6 +338,10 @@ ELSE()
   ENDIF()
 ENDIF()
 
+IF("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_MWAITXINTRIN_H_INCLUDED -D_FORCE_INLINES")
+ENDIF()
+
 ## Copied from FindCUDA.cmake
 ## The target_link_library needs to link with the cuda libraries using
 ## PRIVATE

From db3b747b692e2b8ccbb970226807751c5d751462 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 12 Jan 2017 20:56:00 +0530
Subject: [PATCH 1087/2677] Abstract InteropManager(cuda/opencl) logic using
 CRTP

---
 src/api/c/graphics_common.cpp                 |  10 +-
 src/api/c/graphics_common.hpp                 |   2 +
 src/backend/common/InteropManager.hpp         | 123 ++++++++++++++++++
 src/backend/cuda/GraphicsResourceManager.hpp  |  57 ++++++++
 src/backend/cuda/hist_graphics.cpp            |   4 +-
 src/backend/cuda/image.cpp                    |   4 +-
 src/backend/cuda/platform.cpp                 |  15 +--
 src/backend/cuda/platform.hpp                 |  12 +-
 src/backend/cuda/plot.cpp                     |   4 +-
 src/backend/cuda/surface.cpp                  |   4 +-
 src/backend/cuda/vector_field.cpp             |   4 +-
 .../opencl/GraphicsResourceManager.cpp        |  33 +++++
 .../opencl/GraphicsResourceManager.hpp        |  35 +++++
 src/backend/opencl/hist_graphics.cpp          |   4 +-
 src/backend/opencl/image.cpp                  |   4 +-
 src/backend/opencl/interopManager.cpp         | 123 ------------------
 src/backend/opencl/interopManager.hpp         |  55 --------
 src/backend/opencl/platform.cpp               |  15 +--
 src/backend/opencl/platform.hpp               | 111 ++++++++--------
 src/backend/opencl/plot.cpp                   |   4 +-
 src/backend/opencl/surface.cpp                |   4 +-
 src/backend/opencl/vector_field.cpp           |   4 +-
 22 files changed, 348 insertions(+), 283 deletions(-)
 create mode 100644 src/backend/common/InteropManager.hpp
 create mode 100644 src/backend/cuda/GraphicsResourceManager.hpp
 create mode 100644 src/backend/opencl/GraphicsResourceManager.cpp
 create mode 100644 src/backend/opencl/GraphicsResourceManager.hpp
 delete mode 100644 src/backend/opencl/interopManager.cpp
 delete mode 100644 src/backend/opencl/interopManager.hpp

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 725aac19a8..b35d15b879 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -10,14 +10,12 @@
 #if defined(WITH_GRAPHICS)
 
 #include <graphics_common.hpp>
+#include <glbinding/Meta.h>
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <platform.hpp>
 #include <util.hpp>
 
-#include <glbinding/gl/gl.h>
-#include <glbinding/Meta.h>
-
 using namespace std;
 using namespace gl;
 
@@ -54,7 +52,7 @@ INSTANTIATE_GET_FG_TYPE(short           , forge::s16);
 gl::GLenum glErrorSkip(const char *msg, const char* file, int line)
 {
 #ifndef NDEBUG
-    gl::GLenum x = glGetError();
+    gl::GLenum x = gl::glGetError();
     if (x != GL_NO_ERROR) {
         char buf[1024];
         sprintf(buf, "GL Error Skipped at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
@@ -70,7 +68,7 @@ gl::GLenum glErrorCheck(const char *msg, const char* file, int line)
 {
 // Skipped in release mode
 #ifndef NDEBUG
-    gl::GLenum x = glGetError();
+    gl::GLenum x = gl::glGetError();
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
@@ -85,7 +83,7 @@ gl::GLenum glErrorCheck(const char *msg, const char* file, int line)
 
 gl::GLenum glForceErrorCheck(const char *msg, const char* file, int line)
 {
-    gl::GLenum x = glGetError();
+    gl::GLenum x = gl::glGetError();
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index 06fb68d96b..ac5ebe7709 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -12,9 +12,11 @@
 #if defined(WITH_GRAPHICS)
 
 #include <af/graphics.h>
+
 #include <forge.h>
 #include <glbinding/gl/gl.h>
 #include <glbinding/Binding.h>
+
 #include <vector>
 #include <map>
 #include <utility>
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
new file mode 100644
index 0000000000..f32a952e27
--- /dev/null
+++ b/src/backend/common/InteropManager.hpp
@@ -0,0 +1,123 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#if defined(WITH_GRAPHICS)
+#include <forge.h>
+#include <err_common.hpp>
+#include <util.hpp>
+#include <cstdio>
+#include <map>
+#include <vector>
+
+namespace common
+{
+template<typename T, typename R>
+class InteropManager
+{
+    public:
+        InteropManager() {}
+
+        ~InteropManager() {
+            try {
+                destroyResources();
+            } catch (AfError &ex) {
+
+                std::string perr = getEnvVar("AF_PRINT_ERRORS");
+                if(!perr.empty()) {
+                    if(perr != "0") fprintf(stderr, "%s\n", ex.what());
+                }
+            }
+        }
+
+        R* getBufferResource(const forge::Image* image) {
+            void * key = (void*)image;
+
+            if (interopMap.find(key) == interopMap.end()) {
+                std::vector<uint32_t> handles;
+                handles.push_back(image->pixels());
+                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
+                interopMap[key] = output;
+            }
+
+            return &interopMap[key].front();
+        }
+
+        R* getBufferResource(const forge::Plot* plot) {
+            void * key = (void*)plot;
+
+            if (interopMap.find(key) == interopMap.end()) {
+                std::vector<uint32_t> handles;
+                handles.push_back(plot->vertices());
+                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
+                interopMap[key] = output;
+            }
+
+            return &interopMap[key].front();
+        }
+
+        R* getBufferResource(const forge::Histogram* histogram) {
+            void * key = (void*)histogram;
+
+            if (interopMap.find(key) == interopMap.end()) {
+                std::vector<uint32_t> handles;
+                handles.push_back(histogram->vertices());
+                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
+                interopMap[key] = output;
+            }
+
+            return &interopMap[key].front();
+        }
+
+        R* getBufferResource(const forge::Surface* surface) {
+            void * key = (void*)surface;
+
+            if (interopMap.find(key) == interopMap.end()) {
+                std::vector<uint32_t> handles;
+                handles.push_back(surface->vertices());
+                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
+                interopMap[key] = output;
+            }
+
+            return &interopMap[key].front();
+        }
+
+        R* getBufferResource(const forge::VectorField* field) {
+            void * key = (void*)field;
+
+            if (interopMap.find(key) == interopMap.end()) {
+                std::vector<uint32_t> handles;
+                handles.push_back(field->vertices());
+                handles.push_back(field->directions());
+                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
+                interopMap[key] = output;
+            }
+
+            return &interopMap[key].front();
+        }
+
+    protected:
+        InteropManager(InteropManager const&);
+        void operator=(InteropManager const&);
+
+        void destroyResources() {
+            for(auto iter : interopMap) {
+                for(auto ct : iter.second) {
+                    static_cast<T*>(this)->unregisterResource(ct);
+                }
+                iter.second.clear();
+            }
+        }
+
+    private:
+        std::map<void *, std::vector<R> > interopMap;
+};
+}
+#endif
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
new file mode 100644
index 0000000000..b897d500c6
--- /dev/null
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -0,0 +1,57 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(WITH_GRAPHICS)
+#if defined(OS_WIN)
+#include <windows.h>
+#endif
+
+#include <platform.hpp>
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+#include <graphics_common.hpp>
+using namespace gl;
+#define GL_VERSION gl::GL_VERSION
+#define __gl_h_ //Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#include <cuda_gl_interop.h>
+#include <common/InteropManager.hpp>
+#include <err_cuda.hpp>
+#include <map>
+#include <vector>
+
+namespace cuda
+{
+typedef cudaGraphicsResource_t CGR_t;
+
+class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, CGR_t>
+{
+    public:
+        GraphicsResourceManager() {}
+
+        std::vector<CGR_t> registerResources(std::vector<uint32_t> resources) {
+            std::vector<CGR_t> output;
+
+            for (auto id: resources) {
+                CGR_t r;
+                CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&r, id, cudaGraphicsMapFlagsWriteDiscard));
+                output.push_back(r);
+            }
+
+            return output;
+        }
+
+        void unregisterResource(CGR_t handle) {
+            CUDA_CHECK(cudaGraphicsUnregisterResource(handle));
+        }
+
+    protected:
+        GraphicsResourceManager(GraphicsResourceManager const&);
+        void operator=(GraphicsResourceManager const&);
+};
+}
+#endif
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 750dafae3e..52c959b4d4 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -13,7 +13,7 @@
 #include <hist_graphics.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 namespace cuda
 {
@@ -25,7 +25,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 0492c9c6d4..816cc2b8cd 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -16,7 +16,7 @@
 #include <image.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 using af::dim4;
 
@@ -28,7 +28,7 @@ template<typename T>
 void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 6f1f048f38..447a3d03c9 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -25,7 +25,7 @@
 #include <util.hpp>
 #include <host_memory.hpp>
 #include <memoryManager.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 using namespace std;
 
@@ -407,17 +407,16 @@ MemoryManagerPinned &getMemoryManagerPinned()
     return *(inst.pinnedMemManager.get());
 }
 
-InteropManager& getGfxInteropManager()
+GraphicsManager& interopManager()
 {
-    static std::once_flag flag;
-
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
-                inst.gfxManager.reset(new cuda::InteropManager());
-            });
+    int id = cuda::getActiveDeviceId();
+
+    if (! inst.gfxManagers[id] )
+        inst.gfxManagers[id].reset(new GraphicsManager());
 
-    return *(inst.gfxManager.get());
+    return *(inst.gfxManagers[id].get());
 }
 
 cufft::cuFFTPlanner& getcufftPlanManager()
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 7e256bc8eb..03ab23f5b1 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -14,10 +14,8 @@
 #include <memory>
 #include <vector>
 #include <string>
-#if defined(WITH_GRAPHICS)
-#include <fg/window.h>
-#endif
 
+#include <GraphicsResourceManager.hpp>
 #include <cufftManager.hpp>
 #include <cublasManager.hpp>
 #include <cusolverDnManager.hpp>
@@ -74,14 +72,14 @@ bool& evalFlag();
 
 class MemoryManager;
 class MemoryManagerPinned;
-class InteropManager;
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
 MemoryManager& getMemoryManager();
 
 MemoryManagerPinned& getMemoryManagerPinned();
 
-InteropManager& getGfxInteropManager();
+typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
+GraphicsManager& interopManager();
 
 cufft::cuFFTPlanner& getcufftPlanManager();
 
@@ -106,7 +104,7 @@ class DeviceManager
 
         friend MemoryManagerPinned& getMemoryManagerPinned();
 
-        friend InteropManager& getGfxInteropManager();
+        friend GraphicsManager& interopManager();
 
         friend cufft::cuFFTPlanner& getcufftPlanManager();
 
@@ -173,7 +171,7 @@ class DeviceManager
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
-        std::unique_ptr<InteropManager> gfxManager;
+        std::unique_ptr<GraphicsManager> gfxManagers[MAX_DEVICES];
 
         cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];
 
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 437c276226..f2ca1f19cf 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -16,7 +16,7 @@
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 using af::dim4;
 
@@ -30,7 +30,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 749ff859d6..0752a950eb 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -16,7 +16,7 @@
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 using af::dim4;
 
@@ -30,7 +30,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index aab3f13036..36021aa1c3 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -13,7 +13,7 @@
 #include <vector_field.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 using af::dim4;
 
@@ -26,7 +26,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        forge::VectorField* vector_field)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(vector_field);
 
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
new file mode 100644
index 0000000000..8e043313ed
--- /dev/null
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -0,0 +1,33 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(WITH_GRAPHICS)
+#include <platform.hpp>
+#include <GraphicsResourceManager.hpp>
+
+namespace opencl
+{
+std::vector<CGR_t> GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
+{
+    std::vector<CGR_t> output;
+
+    for (auto id: resources) {
+        CGR_t r = new cl::BufferGL(opencl::getContext(), CL_MEM_WRITE_ONLY, id, NULL);
+        output.push_back(r);
+    }
+
+    return output;
+}
+
+void GraphicsResourceManager::unregisterResource(CGR_t handle)
+{
+    delete handle;
+}
+}
+#endif
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
new file mode 100644
index 0000000000..774cb0f124
--- /dev/null
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#if defined(WITH_GRAPHICS)
+#include <platform.hpp>
+#include <common/InteropManager.hpp>
+#include <map>
+#include <vector>
+
+namespace opencl
+{
+typedef cl::Buffer* CGR_t;
+
+class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, CGR_t>
+{
+    public:
+        GraphicsResourceManager() {}
+
+        std::vector<CGR_t> registerResources(std::vector<uint32_t> resources);
+        void unregisterResource(CGR_t handle);
+
+    protected:
+        GraphicsResourceManager(GraphicsResourceManager const&);
+        void operator=(GraphicsResourceManager const&);
+};
+}
+#endif
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 675110e8b7..41d7e2734c 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -13,7 +13,7 @@
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <hist_graphics.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 namespace opencl
 {
@@ -27,7 +27,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         const cl::Buffer *d_P = data.get();
         size_t bytes = hist->verticesSize();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(hist);
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 27258694cd..8429e1c71d 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -13,7 +13,7 @@
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <image.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 
 #include <stdexcept>
 #include <vector>
@@ -27,7 +27,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(image);
         const cl::Buffer *d_X = in.get();
diff --git a/src/backend/opencl/interopManager.cpp b/src/backend/opencl/interopManager.cpp
deleted file mode 100644
index d8ed8641bb..0000000000
--- a/src/backend/opencl/interopManager.cpp
+++ /dev/null
@@ -1,123 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_GRAPHICS)
-
-#include <interopManager.hpp>
-
-#include <platform.hpp>
-
-namespace opencl
-{
-
-void InteropManager::destroyResources()
-{
-    typedef std::vector<cl::Buffer *>::iterator buffer_t;
-    int n = getActiveDeviceId();
-    for(iter_t iter = interop_maps[n].begin(); iter != interop_maps[n].end(); iter++) {
-        for(buffer_t bt = (iter->second).begin(); bt != (iter->second).end(); bt++) {
-            delete *bt;
-        }
-        (iter->second).clear();
-    }
-}
-
-InteropManager::~InteropManager()
-{
-    for(int i = 0; i < getDeviceCount(); i++) {
-        setDevice(i);
-        destroyResources();
-    }
-}
-
-interop_t& InteropManager::getDeviceMap(int device)
-{
-    return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
-}
-
-cl::Buffer** InteropManager::getBufferResource(const forge::Image* image)
-{
-    void * key = (void*)image;
-    interop_t& i_map = getDeviceMap();
-    iter_t iter = i_map.find(key);
-
-    if (iter == i_map.end()) {
-        std::vector<cl::Buffer *> vec(1);
-        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, image->pixels(), NULL);
-        i_map[key] = vec;
-    }
-
-    return &i_map[key].front();
-}
-
-cl::Buffer** InteropManager::getBufferResource(const forge::Plot* plot)
-{
-    void * key = (void*)plot;
-    interop_t& i_map = getDeviceMap();
-    iter_t iter = i_map.find(key);
-
-    if (iter == i_map.end()) {
-        std::vector<cl::Buffer *> vec(1);
-        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, plot->vertices(), NULL);
-        i_map[key] = vec;
-    }
-
-    return &i_map[key].front();
-}
-
-cl::Buffer** InteropManager::getBufferResource(const forge::Histogram* hist)
-{
-    void * key = (void*)hist;
-    interop_t& i_map = getDeviceMap();
-    iter_t iter = i_map.find(key);
-
-    if (iter == i_map.end()) {
-        std::vector<cl::Buffer *> vec(1);
-        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, hist->vertices(), NULL);
-        i_map[key] = vec;
-    }
-
-    return &i_map[key].front();
-}
-
-cl::Buffer** InteropManager::getBufferResource(const forge::Surface* surface)
-{
-    void * key = (void*)surface;
-    interop_t& i_map = getDeviceMap();
-    iter_t iter = i_map.find(key);
-
-    if (iter == i_map.end()) {
-        std::vector<cl::Buffer *> vec(1);
-        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, surface->vertices(), NULL);
-        i_map[key] = vec;
-    }
-
-    return &i_map[key].front();
-}
-
-cl::Buffer** InteropManager::getBufferResource(const forge::VectorField* vector_field)
-{
-    void * key = (void*)vector_field;
-    interop_t& i_map = getDeviceMap();
-    iter_t iter = i_map.find(key);
-
-    if (iter == i_map.end()) {
-        std::vector<cl::Buffer *> vec(2);
-        vec[0] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, vector_field->vertices(), NULL);
-        vec[1] = new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, vector_field->directions(), NULL);
-        i_map[key] = vec;
-    }
-
-    return &i_map[key].front();
-}
-
-}
-
-#endif
-
diff --git a/src/backend/opencl/interopManager.hpp b/src/backend/opencl/interopManager.hpp
deleted file mode 100644
index d495794e36..0000000000
--- a/src/backend/opencl/interopManager.hpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_GRAPHICS)
-
-#include <glbinding/gl/gl.h>
-#include <glbinding/Binding.h>
-
-#include <graphics_common.hpp>
-#include <platform.hpp>
-
-#include <map>
-#include <vector>
-
-namespace cl
-{
-class Buffer;
-}
-
-namespace opencl
-{
-
-typedef std::map<void *, std::vector<cl::Buffer*> > interop_t;
-typedef interop_t::iterator iter_t;
-
-class InteropManager
-{
-    private:
-        interop_t interop_maps[DeviceManager::MAX_DEVICES];
-
-    public:
-        InteropManager() {}
-        ~InteropManager();
-        cl::Buffer** getBufferResource(const forge::Image        *handle);
-        cl::Buffer** getBufferResource(const forge::Plot         *handle);
-        cl::Buffer** getBufferResource(const forge::Histogram    *handle);
-        cl::Buffer** getBufferResource(const forge::Surface      *handle);
-        cl::Buffer** getBufferResource(const forge::VectorField  *handle);
-
-    protected:
-        InteropManager(InteropManager const&);
-        void operator=(InteropManager const&);
-        interop_t& getDeviceMap(int device = -1); // default will return current device
-        void destroyResources();
-};
-
-}
-
-#endif
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 9519e0099e..4c263e57ad 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -28,7 +28,7 @@
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
 #include <host_memory.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 #include <memoryManager.hpp>
 #include <platform.hpp>
 #include <util.hpp>
@@ -865,17 +865,16 @@ MemoryManagerPinned& getMemoryManagerPinned()
     return *(inst.pinnedMemManager.get());
 }
 
-InteropManager& getGfxInteropManager()
+GraphicsManager& interopManager()
 {
-    static std::once_flag flag;
-
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
-                inst.gfxManager.reset(new InteropManager());
-            });
+    int id = getActiveDeviceId();
+
+    if (! inst.gfxManagers[id] )
+        inst.gfxManagers[id].reset(new GraphicsManager());
 
-    return *(inst.gfxManager.get());
+    return *(inst.gfxManagers[id].get());
 }
 
 clfft::clFFTPlanner& getclfftPlanManager()
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index c0803a8deb..9badbe8a58 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -26,15 +26,65 @@
 #include <vector>
 #include <string>
 
+#include <GraphicsResourceManager.hpp>
 #include <clfftManager.hpp>
 
 namespace opencl
 {
+int getBackend();
+
+std::string getDeviceInfo();
+
+int getDeviceCount();
+
+int getActiveDeviceId();
+
+unsigned getMaxJitSize();
+
+const cl::Context& getContext();
+
+cl::CommandQueue& getQueue();
+
+const cl::Device& getDevice(int id = -1);
+
+size_t getDeviceMemorySize(int device);
+
+size_t getHostMemorySize();
+
+cl_device_type getDeviceType();
+
+bool isHostUnifiedMemory(const cl::Device &device);
+
+bool OpenCLCPUOffload(bool forceOffloadOSX = true);
+
+bool isGLSharingSupported();
+
+bool isDoubleSupported(int device);
+
+void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+
+std::string getPlatformName(const cl::Device &device);
+
+int setDevice(int device);
+
+void addDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+
+void setDeviceContext(cl_device_id dev, cl_context cxt);
+
+void removeDeviceContext(cl_device_id dev, cl_context ctx);
+
+void sync(int device);
+
+bool synchronize_calls();
+
+int getActiveDeviceType();
+int getActivePlatform();
+
+bool& evalFlag();
 
 // Forward Declarations
 class MemoryManager;
 class MemoryManagerPinned;
-class InteropManager;
 
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
@@ -42,7 +92,8 @@ MemoryManager &getMemoryManager();
 
 MemoryManagerPinned& getMemoryManagerPinned();
 
-InteropManager& getGfxInteropManager();
+typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
+GraphicsManager& interopManager();
 
 clfft::clFFTPlanner& getclfftPlanManager();
 //
@@ -54,7 +105,7 @@ class DeviceManager
 
     friend MemoryManagerPinned& getMemoryManagerPinned();
 
-    friend InteropManager& getGfxInteropManager();
+    friend GraphicsManager& interopManager();
 
     friend clfft::clFFTPlanner& getclfftPlanManager();
 
@@ -128,60 +179,8 @@ class DeviceManager
 
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
-        std::unique_ptr<InteropManager> gfxManager;
 
+        std::unique_ptr<GraphicsManager> gfxManagers[MAX_DEVICES];
         clfft::clFFTPlanner clfftManagers[MAX_DEVICES];
 };
-
-int getBackend();
-
-std::string getDeviceInfo();
-
-int getDeviceCount();
-
-int getActiveDeviceId();
-
-unsigned getMaxJitSize();
-
-const cl::Context& getContext();
-
-cl::CommandQueue& getQueue();
-
-const cl::Device& getDevice(int id = -1);
-
-size_t getDeviceMemorySize(int device);
-
-size_t getHostMemorySize();
-
-cl_device_type getDeviceType();
-
-bool isHostUnifiedMemory(const cl::Device &device);
-
-bool OpenCLCPUOffload(bool forceOffloadOSX = true);
-
-bool isGLSharingSupported();
-
-bool isDoubleSupported(int device);
-
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
-
-std::string getPlatformName(const cl::Device &device);
-
-int setDevice(int device);
-
-void addDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
-
-void setDeviceContext(cl_device_id dev, cl_context cxt);
-
-void removeDeviceContext(cl_device_id dev, cl_context ctx);
-
-void sync(int device);
-
-bool synchronize_calls();
-
-int getActiveDeviceType();
-int getActivePlatform();
-
-bool& evalFlag();
-
 }
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 40240fbd10..a8eb7694e5 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 #include <join.hpp>
 #include <plot.hpp>
 #include <reduce.hpp>
@@ -32,7 +32,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         const cl::Buffer *d_P = P.get();
         size_t bytes = plot->verticesSize();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(plot);
 
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 0ed38a3e90..29158de24e 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
@@ -32,7 +32,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         const cl::Buffer *d_P = P.get();
         size_t bytes = surface->verticesSize();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(surface);
 
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 3fd4e32456..9debfc9d6a 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <interopManager.hpp>
+#include <common/InteropManager.hpp>
 #include <vector_field.hpp>
 
 using af::dim4;
@@ -32,7 +32,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         size_t pBytes = vector_field->verticesSize();
         size_t dBytes = vector_field->directionsSize();
 
-        InteropManager& intrpMngr = getGfxInteropManager();
+        GraphicsManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(vector_field);
 

From c05c29b5143d8d8131beacc5ce4aa502a0462f23 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 14 Jan 2017 09:34:58 +0530
Subject: [PATCH 1088/2677] Abstract cuda/opencl fft plan caching logic using
 CRTP

---
 src/backend/common/FFTPlanCache.hpp           |  91 +++++++++++++
 .../cuda/{cufftManager.cpp => cufft.cpp}      |  39 +++---
 src/backend/cuda/cufft.hpp                    |  59 ++++++++
 src/backend/cuda/cufftManager.hpp             | 121 -----------------
 src/backend/cuda/fft.cpp                      |  32 ++---
 src/backend/cuda/platform.cpp                 |   4 +-
 src/backend/cuda/platform.hpp                 |   9 +-
 .../opencl/{clfftManager.cpp => clfft.cpp}    |  72 ++++------
 src/backend/opencl/clfft.hpp                  |  67 +++++++++
 src/backend/opencl/clfftManager.hpp           | 127 ------------------
 src/backend/opencl/fft.cpp                    |  46 ++-----
 src/backend/opencl/platform.cpp               |   2 +-
 src/backend/opencl/platform.hpp               |   9 +-
 13 files changed, 305 insertions(+), 373 deletions(-)
 create mode 100644 src/backend/common/FFTPlanCache.hpp
 rename src/backend/cuda/{cufftManager.cpp => cufft.cpp} (77%)
 create mode 100644 src/backend/cuda/cufft.hpp
 delete mode 100644 src/backend/cuda/cufftManager.hpp
 rename src/backend/opencl/{clfftManager.cpp => clfft.cpp} (79%)
 create mode 100644 src/backend/opencl/clfft.hpp
 delete mode 100644 src/backend/opencl/clfftManager.hpp

diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
new file mode 100644
index 0000000000..bb0fa73803
--- /dev/null
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -0,0 +1,91 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <deque>
+#include <string>
+#include <utility>
+
+namespace common
+{
+// FFTPlanCache caches backend specific fft plans
+//
+// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
+//          |
+//          |--> ELSE just push the plan
+// existing plan -> reuse a plan
+template<typename T, typename P>
+class FFTPlanCache
+{
+    public:
+        FFTPlanCache() : mMaxCacheSize(5) {
+            static_cast<T*>(this)->initLibrary();
+        }
+
+        ~FFTPlanCache() {
+            static_cast<T*>(this)->deInitLibrary();
+        }
+
+        inline void maxCacheSize(size_t size) {
+            mCache.resize(size, std::make_pair<std::string, P>(std::string(""), 0));
+        }
+
+        inline size_t maxCacheSize() const {
+            return mMaxCacheSize;
+        }
+
+        inline P get(int index) const {
+            return mCache[index].second;
+        }
+
+        // iterates through plan cache from front to back
+        // of the cache(queue)
+        //
+        // A valid index of the plan in the cache is returned
+        // otherwise -1 is returned
+        int find(std::string key) const {
+            int retVal = -1;
+            for(uint i=0; i<mCache.size(); ++i) {
+                if (key == mCache[i].first) {
+                    retVal = i;
+                }
+            }
+            return retVal;
+        }
+
+        // pops plan from the back of cache(queue)
+        void pop() {
+            if (!mCache.empty()) {
+                // destroy the cufft plan associated with the
+                // least recently used plan
+                static_cast<T*>(this)->removePlan(mCache.back().second);
+                // now pop the entry from cache
+                mCache.pop_back();
+            }
+        }
+
+        // pushes plan to the front of cache(queue)
+        void push(std::string key, P plan) {
+            if (mCache.size()>mMaxCacheSize) {
+                pop();
+            }
+            mCache.push_front(std::pair<std::string, P>(key, plan));
+        }
+
+    private:
+        FFTPlanCache(FFTPlanCache const&);
+        void operator=(FFTPlanCache const&);
+
+        size_t       mMaxCacheSize;
+
+        std::deque< std::pair<std::string, P> > mCache;
+
+};
+}
diff --git a/src/backend/cuda/cufftManager.cpp b/src/backend/cuda/cufft.cpp
similarity index 77%
rename from src/backend/cuda/cufftManager.cpp
rename to src/backend/cuda/cufft.cpp
index 28c994b464..f8d36e7ee8 100644
--- a/src/backend/cuda/cufftManager.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cuda.h> // Need this for CUDA_VERSION
 #include <platform.hpp>
+#include <cufft.hpp>
 #include <memory.hpp>
 
-namespace cufft
+namespace cuda
 {
 
 const char * _cufftGetResultString(cufftResult res)
@@ -76,10 +76,10 @@ const char * _cufftGetResultString(cufftResult res)
     return "cuFFT: unknown error";
 }
 
-void findPlan(cufftHandle &plan, int rank, int *n,
-              int *inembed, int istride, int idist,
-              int *onembed, int ostride, int odist,
-              cufftType type, int batch)
+PlanType findPlan(int rank, int *n,
+                  int *inembed, int istride, int idist,
+                  int *onembed, int ostride, int odist,
+                  cufftType type, int batch)
 {
     // create the key string
     char key_str_temp[64];
@@ -113,37 +113,34 @@ void findPlan(cufftHandle &plan, int rank, int *n,
     sprintf(key_str_temp, "%d:%d", (int)type, batch);
     key_string.append(std::string(key_str_temp));
 
-    // find the matching plan_index in the array cuFFTPlanner::mKeys
-    cuFFTPlanner &planner = cuda::getcufftPlanManager();
+    FFTManager &planner = cuda::cufftManager();
 
-    int planIndex = planner.findIfPlanExists(key_string);
+    int planIndex = planner.find(key_string);
 
     // if found a valid plan, return it
     if (planIndex!=-1) {
-        plan = planner.getPlan(planIndex);
-        return;
+        return planner.get(planIndex);
     }
 
-    cufftHandle temp;
-    cufftResult res = cufftPlanMany(&temp, rank, n,
-                                    inembed, istride, idist,
-                                    onembed, ostride, odist,
+    PlanType retVal;
+    cufftResult res = cufftPlanMany(&retVal, rank, n,
+                                    inembed, istride, idist, onembed, ostride, odist,
                                     type, batch);
 
     // If plan creation fails, clean up the memory we hold on to and try again
     if (res != CUFFT_SUCCESS) {
         cuda::garbageCollect();
-        CUFFT_CHECK(cufftPlanMany(&temp, rank, n,
-                                  inembed, istride, idist,
-                                  onembed, ostride, odist,
+        CUFFT_CHECK(cufftPlanMany(&retVal, rank, n,
+                                  inembed, istride, idist, onembed, ostride, odist,
                                   type, batch));
     }
 
-    plan = temp;
-    cufftSetStream(plan, cuda::getStream(cuda::getActiveDeviceId()));
+    cufftSetStream(retVal, cuda::getStream(cuda::getActiveDeviceId()));
 
     // push the plan into plan cache
-    planner.pushPlan(key_string, plan);
+    planner.push(key_string, retVal);
+
+    return retVal;
 }
 
 }
diff --git a/src/backend/cuda/cufft.hpp b/src/backend/cuda/cufft.hpp
new file mode 100644
index 0000000000..6142004151
--- /dev/null
+++ b/src/backend/cuda/cufft.hpp
@@ -0,0 +1,59 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <err_common.hpp>
+#include <cufft.h>
+#include <common/FFTPlanCache.hpp>
+#include <cstdio>
+
+namespace cuda
+{
+typedef cufftHandle PlanType; //used in platform.hpp
+
+const char * _cufftGetResultString(cufftResult res);
+
+PlanType findPlan(int rank, int *n,
+                  int *inembed, int istride, int idist,
+                  int *onembed, int ostride, int odist,
+                  cufftType type, int batch);
+
+class PlanCache : public common::FFTPlanCache<PlanCache, PlanType>
+{
+    friend PlanType findPlan(int rank, int *n,
+                             int *inembed, int istride, int idist,
+                             int *onembed, int ostride, int odist,
+                             cufftType type, int batch);
+
+    public:
+        PlanCache() {}
+        void initLibrary() {}
+        void deInitLibrary() {}
+
+        void removePlan(PlanType plan) {
+            cufftDestroy(plan);
+        }
+};
+}
+
+#define CUFFT_CHECK(fn) do {                        \
+        cufftResult _cufft_res = fn;                \
+        if (_cufft_res != CUFFT_SUCCESS) {          \
+            char cufft_res_msg[1024];               \
+            snprintf(cufft_res_msg,                 \
+                     sizeof(cufft_res_msg),         \
+                     "cuFFT Error (%d): %s\n",      \
+                     (int)(_cufft_res),             \
+                     cuda::_cufftGetResultString(   \
+                         _cufft_res));              \
+                                                    \
+            AF_ERROR(cufft_res_msg,                 \
+                     AF_ERR_INTERNAL);              \
+        }                                           \
+    } while(0)
diff --git a/src/backend/cuda/cufftManager.hpp b/src/backend/cuda/cufftManager.hpp
deleted file mode 100644
index 2d78f891c2..0000000000
--- a/src/backend/cuda/cufftManager.hpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <err_common.hpp>
-#include <cufft.h>
-
-#include <cstdio>
-#include <deque>
-#include <string>
-#include <utility>
-
-namespace cuda
-{
-class DeviceManager;
-}
-
-namespace cufft
-{
-
-typedef std::pair<std::string, cufftHandle> FFTPlanPair;
-typedef std::deque<FFTPlanPair> FFTPlanCache;
-
-const char * _cufftGetResultString(cufftResult res);
-
-void findPlan(cufftHandle &plan, int rank, int *n,
-              int *inembed, int istride, int idist,
-              int *onembed, int ostride, int odist,
-              cufftType type, int batch);
-
-// cuFFTPlanner caches fft plans
-//
-// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
-//          |
-//          |--> ELSE just push the plan
-// existing plan -> reuse a plan
-class cuFFTPlanner
-{
-    friend class cuda::DeviceManager;
-
-    friend void findPlan(cufftHandle &plan, int rank, int *n,
-                         int *inembed, int istride, int idist,
-                         int *onembed, int ostride, int odist,
-                         cufftType type, int batch);
-
-    public:
-        inline void setMaxCacheSize(size_t size) {
-            mCache.resize(size, FFTPlanPair(std::string(""), 0));
-        }
-
-        inline size_t getMaxCacheSize() const {
-            return mMaxCacheSize;
-        }
-
-        inline cufftHandle getPlan(int index) const {
-            return mCache[index].second;
-        }
-
-        // iterates through plan cache from front to back
-        // of the cache(queue)
-        int findIfPlanExists(std::string keyString) const {
-            int retVal = -1;
-            for(uint i=0; i<mCache.size(); ++i) {
-                if (keyString == mCache[i].first) {
-                    retVal = i;
-                }
-            }
-            return retVal;
-        }
-
-        // pops plan from the back of cache(queue)
-        void popPlan() {
-            if (!mCache.empty()) {
-                // destroy the cufft plan associated with the
-                // least recently used plan
-                cufftDestroy(mCache.back().second);
-                // now pop the entry from cache
-                mCache.pop_back();
-            }
-        }
-
-        // pushes plan to the front of cache(queue)
-        void pushPlan(std::string keyString, cufftHandle plan) {
-            if (mCache.size()>mMaxCacheSize) {
-                popPlan();
-            }
-            mCache.push_front(FFTPlanPair(keyString, plan));
-        }
-
-    private:
-        cuFFTPlanner() : mMaxCacheSize(5) {}
-        cuFFTPlanner(cuFFTPlanner const&);
-        void operator=(cuFFTPlanner const&);
-
-        size_t       mMaxCacheSize;
-        FFTPlanCache mCache;
-};
-
-}
-
-#define CUFFT_CHECK(fn) do {                        \
-        cufftResult _cufft_res = fn;                \
-        if (_cufft_res != CUFFT_SUCCESS) {          \
-            char cufft_res_msg[1024];               \
-            snprintf(cufft_res_msg,                 \
-                     sizeof(cufft_res_msg),         \
-                     "cuFFT Error (%d): %s\n",      \
-                     (int)(_cufft_res),             \
-                     cufft::_cufftGetResultString(  \
-                         _cufft_res));              \
-                                                    \
-            AF_ERROR(cufft_res_msg,                 \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 189033e8ae..19a753421a 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -12,7 +12,7 @@
 #include <copy.hpp>
 #include <fft.hpp>
 #include <debug_cuda.hpp>
-#include <cufftManager.hpp>
+#include <cufft.hpp>
 #include <math.hpp>
 #include <memory.hpp>
 
@@ -21,10 +21,9 @@ using std::string;
 
 namespace cuda
 {
-
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    cuda::getcufftPlanManager().setMaxCacheSize(numPlans);
+    cufftManager().maxCacheSize(numPlans);
 }
 
 template<typename T>
@@ -89,11 +88,10 @@ void fft_inplace(Array<T> &in)
         batch *= idims[i];
     }
 
-    cufftHandle plan;
-    cufft::findPlan(plan, rank, t_dims,
-                    in_embed , istrides[0], istrides[rank],
-                    in_embed , istrides[0], istrides[rank],
-                    (cufftType)cufft_transform<T>::type, batch);
+    cufftHandle plan = findPlan(rank, t_dims,
+                                in_embed , istrides[0], istrides[rank],
+                                in_embed , istrides[0], istrides[rank],
+                                (cufftType)cufft_transform<T>::type, batch);
 
     cufft_transform<T> transform;
     CUFFT_CHECK(transform(plan, (T *)in.get(), in.get(), direction ? CUFFT_FORWARD : CUFFT_INVERSE));
@@ -124,11 +122,10 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     dim4 istrides = in.strides();
     dim4 ostrides = out.strides();
 
-    cufftHandle plan;
-    cufft::findPlan(plan, rank, t_dims,
-                    in_embed  , istrides[0], istrides[rank],
-                    out_embed , ostrides[0], ostrides[rank],
-                    (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
+    cufftHandle plan = findPlan(rank, t_dims,
+                                in_embed  , istrides[0], istrides[rank],
+                                out_embed , ostrides[0], ostrides[rank],
+                                (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
 
     cufft_real_transform<Tc, Tr> transform;
     CUFFT_CHECK(transform(plan, (Tr *)in.get(), out.get()));
@@ -157,11 +154,10 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
 
     cufft_real_transform<Tr, Tc> transform;
 
-    cufftHandle plan;
-    cufft::findPlan(plan, rank, t_dims,
-                    in_embed  , istrides[0], istrides[rank],
-                    out_embed , ostrides[0], ostrides[rank],
-                    (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
+    cufftHandle plan = findPlan(rank, t_dims,
+                                in_embed  , istrides[0], istrides[rank],
+                                out_embed , ostrides[0], ostrides[rank],
+                                (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
 
     CUFFT_CHECK(transform(plan, (Tc *)in.get(), out.get()));
     return out;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 447a3d03c9..ab4949f154 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -419,9 +419,9 @@ GraphicsManager& interopManager()
     return *(inst.gfxManagers[id].get());
 }
 
-cufft::cuFFTPlanner& getcufftPlanManager()
+FFTManager& cufftManager()
 {
-    return DeviceManager::getInstance().cufftManagers[cuda::getActiveDeviceId()];
+    return DeviceManager::getInstance().cufftManagers[getActiveDeviceId()];
 }
 
 cublasHandle_t getcublasHandle()
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 03ab23f5b1..a18e43b215 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -16,7 +16,7 @@
 #include <string>
 
 #include <GraphicsResourceManager.hpp>
-#include <cufftManager.hpp>
+#include <cufft.hpp>
 #include <cublasManager.hpp>
 #include <cusolverDnManager.hpp>
 #include <cusparseManager.hpp>
@@ -81,7 +81,8 @@ MemoryManagerPinned& getMemoryManagerPinned();
 typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
 GraphicsManager& interopManager();
 
-cufft::cuFFTPlanner& getcufftPlanManager();
+typedef common::FFTPlanCache<PlanCache, PlanType> FFTManager;
+FFTManager& cufftManager();
 
 cublasHandle_t getcublasHandle();
 
@@ -106,7 +107,7 @@ class DeviceManager
 
         friend GraphicsManager& interopManager();
 
-        friend cufft::cuFFTPlanner& getcufftPlanManager();
+        friend FFTManager& cufftManager();
 
         friend cublasHandle_t getcublasHandle();
 
@@ -173,7 +174,7 @@ class DeviceManager
 
         std::unique_ptr<GraphicsManager> gfxManagers[MAX_DEVICES];
 
-        cufft::cuFFTPlanner cufftManagers[MAX_DEVICES];
+        FFTManager cufftManagers[MAX_DEVICES];
 
         std::unique_ptr<cublas::cublasHandle> cublasHandles[MAX_DEVICES];
 
diff --git a/src/backend/opencl/clfftManager.cpp b/src/backend/opencl/clfft.cpp
similarity index 79%
rename from src/backend/opencl/clfftManager.cpp
rename to src/backend/opencl/clfft.cpp
index ef08442474..dae1558fbe 100644
--- a/src/backend/opencl/clfftManager.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -8,18 +8,15 @@
  ********************************************************/
 
 #include <af/defines.h>
-
 #include <err_common.hpp>
-#include <clfftManager.hpp>
+#include <clfft.hpp>
 #include <platform.hpp>
-
 #include <string>
 
 using std::string;
 
-namespace clfft
+namespace opencl
 {
-
 const char * _clfftGetResultString(clfftStatus st)
 {
     switch (st)
@@ -86,12 +83,11 @@ const char * _clfftGetResultString(clfftStatus st)
     return "Unknown error";
 }
 
-void findPlan(clfftPlanHandle &plan,
-              clfftLayout iLayout, clfftLayout oLayout,
-              clfftDim rank, size_t *clLengths,
-              size_t *istrides, size_t idist,
-              size_t *ostrides, size_t odist,
-              clfftPrecision precision, size_t batch)
+PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
+                  clfftDim rank, size_t *clLengths,
+                  size_t *istrides, size_t idist,
+                  size_t *ostrides, size_t odist,
+                  clfftPrecision precision, size_t batch)
 {
     // create the key string
     char key_str_temp[64];
@@ -126,56 +122,53 @@ void findPlan(clfftPlanHandle &plan,
     sprintf(key_str_temp, "%d:" SIZE_T_FRMT_SPECIFIER, (int)precision, batch);
     key_string.append(std::string(key_str_temp));
 
-    // find the matching plan_index in the array clFFTPlanner::mKeys
-    clFFTPlanner &planner = opencl::getclfftPlanManager();
+    FFTManager &planner = opencl::clfftManager();
 
-    int planIndex = planner.findIfPlanExists(key_string);
+    int planIndex = planner.find(key_string);
 
     // if found a valid plan, return it
     if (planIndex!=-1) {
-        plan = planner.getPlan(planIndex);
-        return;
+        return planner.get(planIndex);
     }
 
-    clfftPlanHandle temp;
+    PlanType retVal;
 
     // getContext() returns object of type Context
     // Context() returns the actual cl_context handle
-    CLFFT_CHECK(clfftCreateDefaultPlan(&temp, opencl::getContext()(), rank, clLengths));
+    CLFFT_CHECK(clfftCreateDefaultPlan(&retVal, opencl::getContext()(), rank, clLengths));
 
     // complex to complex
     if (iLayout == oLayout) {
-        CLFFT_CHECK(clfftSetResultLocation(temp, CLFFT_INPLACE));
+        CLFFT_CHECK(clfftSetResultLocation(retVal, CLFFT_INPLACE));
     } else {
-        CLFFT_CHECK(clfftSetResultLocation(temp, CLFFT_OUTOFPLACE));
+        CLFFT_CHECK(clfftSetResultLocation(retVal, CLFFT_OUTOFPLACE));
     }
 
-    CLFFT_CHECK(clfftSetLayout(temp, iLayout, oLayout));
-    CLFFT_CHECK(clfftSetPlanBatchSize(temp, batch));
-    CLFFT_CHECK(clfftSetPlanDistance(temp, idist, odist));
-    CLFFT_CHECK(clfftSetPlanInStride(temp, rank, istrides));
-    CLFFT_CHECK(clfftSetPlanOutStride(temp, rank, ostrides));
-    CLFFT_CHECK(clfftSetPlanPrecision(temp, precision));
-    CLFFT_CHECK(clfftSetPlanScale(temp, CLFFT_BACKWARD, 1.0));
+    CLFFT_CHECK(clfftSetLayout(retVal, iLayout, oLayout));
+    CLFFT_CHECK(clfftSetPlanBatchSize(retVal, batch));
+    CLFFT_CHECK(clfftSetPlanDistance(retVal, idist, odist));
+    CLFFT_CHECK(clfftSetPlanInStride(retVal, rank, istrides));
+    CLFFT_CHECK(clfftSetPlanOutStride(retVal, rank, ostrides));
+    CLFFT_CHECK(clfftSetPlanPrecision(retVal, precision));
+    CLFFT_CHECK(clfftSetPlanScale(retVal, CLFFT_BACKWARD, 1.0));
 
     // getQueue() returns object of type CommandQueue
     // CommandQueue() returns the actual cl_command_queue handle
-    CLFFT_CHECK(clfftBakePlan(temp, 1, &(opencl::getQueue()()), NULL, NULL));
-
-    plan = temp;
+    CLFFT_CHECK(clfftBakePlan(retVal, 1, &(opencl::getQueue()()), NULL, NULL));
 
     // push the plan into plan cache
-    planner.pushPlan(key_string, plan);
+    planner.push(key_string, retVal);
+
+    return retVal;
 }
 
-clFFTPlanner::clFFTPlanner()
-    : mMaxCacheSize(5)
+void PlanCache::initLibrary()
 {
     CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
     CLFFT_CHECK(clfftSetup(&mFFTSetup));
 }
 
-clFFTPlanner::~clFFTPlanner()
+void PlanCache::deInitLibrary()
 {
     //TODO: FIXME:
     // clfftTeardown() causes a "Pure Virtual Function Called" crash on
@@ -190,15 +183,8 @@ clFFTPlanner::~clFFTPlanner()
     #endif
 }
 
-void clFFTPlanner::popPlan()
+void PlanCache::removePlan(PlanType plan)
 {
-    if (!mCache.empty()) {
-        // destroy the clfft plan associated with the
-        // least recently used plan
-        CLFFT_CHECK(clfftDestroyPlan(&mCache.back().second));
-        // now pop the entry from cache
-        mCache.pop_back();
-    }
+     CLFFT_CHECK(clfftDestroyPlan(&plan));
 }
-
 }
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
new file mode 100644
index 0000000000..92267e27c7
--- /dev/null
+++ b/src/backend/opencl/clfft.hpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <clFFT.h>
+#include <common/FFTPlanCache.hpp>
+#include <memory.hpp>
+#include <cstdio>
+
+namespace opencl
+{
+typedef clfftPlanHandle PlanType;
+
+const char * _clfftGetResultString(clfftStatus st);
+
+PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
+                  clfftDim rank, size_t *clLengths,
+                  size_t *istrides, size_t idist,
+                  size_t *ostrides, size_t odist,
+                  clfftPrecision precision, size_t batch);
+
+class PlanCache : public common::FFTPlanCache<PlanCache, PlanType>
+{
+    friend PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
+                             clfftDim rank, size_t *clLengths,
+                             size_t *istrides, size_t idist,
+                             size_t *ostrides, size_t odist,
+                             clfftPrecision precision, size_t batch);
+
+    public:
+        PlanCache() {}
+        void initLibrary();
+        void deInitLibrary();
+
+        void removePlan(PlanType plan);
+
+    private:
+        clfftSetupData  mFFTSetup;
+};
+}
+
+#define CLFFT_CHECK(fn) do {                        \
+        clfftStatus _clfft_st = fn;                 \
+        if (_clfft_st != CLFFT_SUCCESS) {           \
+            opencl::garbageCollect();               \
+            _clfft_st = (fn);                       \
+        }                                           \
+        if (_clfft_st != CLFFT_SUCCESS) {           \
+            char clfft_st_msg[1024];                \
+            snprintf(clfft_st_msg,                  \
+                     sizeof(clfft_st_msg),          \
+                     "clFFT Error (%d): %s\n",      \
+                     (int)(_clfft_st),              \
+                     opencl::_clfftGetResultString( \
+                         _clfft_st));               \
+                                                    \
+            AF_ERROR(clfft_st_msg,                  \
+                     AF_ERR_INTERNAL);              \
+        }                                           \
+    } while(0)
diff --git a/src/backend/opencl/clfftManager.hpp b/src/backend/opencl/clfftManager.hpp
deleted file mode 100644
index 37ffd0fda0..0000000000
--- a/src/backend/opencl/clfftManager.hpp
+++ /dev/null
@@ -1,127 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <clFFT.h>
-
-#include <memory.hpp>
-
-#include <cstdio>
-#include <deque>
-#include <string>
-#include <utility>
-
-namespace opencl
-{
-class DeviceManager;
-}
-
-namespace clfft
-{
-
-typedef std::pair<std::string, clfftPlanHandle> FFTPlanPair;
-typedef std::deque<FFTPlanPair> FFTPlanCache;
-
-const char * _clfftGetResultString(clfftStatus st);
-
-void findPlan(clfftPlanHandle &plan,
-              clfftLayout iLayout, clfftLayout oLayout,
-              clfftDim rank, size_t *clLengths,
-              size_t *istrides, size_t idist,
-              size_t *ostrides, size_t odist,
-              clfftPrecision precision, size_t batch);
-
-// clFFTPlanner caches fft plans
-//
-// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
-//          |
-//          |--> ELSE just push the plan
-// existing plan -> reuse a plan
-class clFFTPlanner
-{
-    friend class opencl::DeviceManager;
-
-    friend void findPlan(clfftPlanHandle &plan,
-                         clfftLayout iLayout, clfftLayout oLayout,
-                         clfftDim rank, size_t *clLengths,
-                         size_t *istrides, size_t idist,
-                         size_t *ostrides, size_t odist,
-                         clfftPrecision precision, size_t batch);
-
-    public:
-        clFFTPlanner();
-        ~clFFTPlanner();
-
-        inline void setMaxCacheSize(size_t size) {
-            mCache.resize(size, FFTPlanPair(std::string(""), 0));
-        }
-
-        inline size_t getMaxCacheSize() const {
-            return mMaxCacheSize;
-        }
-
-        inline clfftPlanHandle getPlan(int index) const {
-            return mCache[index].second;
-        }
-
-        // iterates through plan cache from front to back
-        // of the cache(queue)
-        int findIfPlanExists(std::string keyString) const {
-            int retVal = -1;
-            for(uint i=0; i<mCache.size(); ++i) {
-                if (keyString == mCache[i].first) {
-                    retVal = i;
-                }
-            }
-            return retVal;
-        }
-
-        // pops plan from the back of cache(queue)
-        void popPlan();
-
-        // pushes plan to the front of cache(queue)
-        void pushPlan(std::string keyString, clfftPlanHandle plan) {
-            if (mCache.size()>mMaxCacheSize) {
-                popPlan();
-            }
-            mCache.push_front(FFTPlanPair(keyString, plan));
-        }
-
-    private:
-        clFFTPlanner(clFFTPlanner const&);
-        void operator=(clFFTPlanner const&);
-
-        clfftSetupData  mFFTSetup;
-
-        size_t       mMaxCacheSize;
-        FFTPlanCache mCache;
-};
-
-}
-
-#define CLFFT_CHECK(fn) do {                        \
-        clfftStatus _clfft_st = fn;                 \
-        if (_clfft_st != CLFFT_SUCCESS) {           \
-            opencl::garbageCollect();               \
-            _clfft_st = (fn);                       \
-        }                                           \
-        if (_clfft_st != CLFFT_SUCCESS) {           \
-            char clfft_st_msg[1024];                \
-            snprintf(clfft_st_msg,                  \
-                     sizeof(clfft_st_msg),          \
-                     "clFFT Error (%d): %s\n",      \
-                     (int)(_clfft_st),              \
-                     clfft::_clfftGetResultString(  \
-                         _clfft_st));               \
-                                                    \
-            AF_ERROR(clfft_st_msg,                  \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index c5a550304d..f3423ec6c0 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -14,7 +14,7 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <clfftManager.hpp>
+#include <clfft.hpp>
 
 using af::dim4;
 using std::string;
@@ -24,7 +24,7 @@ namespace opencl
 
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    getclfftPlanManager().setMaxCacheSize(numPlans);
+    clfftManager().maxCacheSize(numPlans);
 }
 
 template<typename T> struct Precision;
@@ -78,21 +78,15 @@ void fft_inplace(Array<T> &in)
     computeDims(tdims   , in.dims());
     computeDims(istrides, in.strides());
 
-    clfftPlanHandle plan;
-
     int batch = 1;
     for (int i = rank; i < 4; i++) {
         batch *= tdims[i];
     }
 
-    clfft::findPlan(plan,
-                    CLFFT_COMPLEX_INTERLEAVED,
-                    CLFFT_COMPLEX_INTERLEAVED,
-                    (clfftDim)rank, tdims,
-                    istrides, istrides[rank],
-                    istrides, istrides[rank],
-                    (clfftPrecision)Precision<T>::type,
-                    batch);
+    PlanType plan = findPlan(CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
+                             (clfftDim)rank, tdims,
+                             istrides, istrides[rank], istrides, istrides[rank],
+                             (clfftPrecision)Precision<T>::type, batch);
 
     cl_mem imem = (*in.get())();
     cl_command_queue queue = getQueue()();
@@ -119,21 +113,15 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     computeDims(istrides,  in.strides());
     computeDims(ostrides, out.strides());
 
-    clfftPlanHandle plan;
-
     int batch = 1;
     for (int i = rank; i < 4; i++) {
         batch *= tdims[i];
     }
 
-    clfft::findPlan(plan,
-                    CLFFT_REAL,
-                    CLFFT_HERMITIAN_INTERLEAVED,
-                    (clfftDim)rank, tdims,
-                    istrides, istrides[rank],
-                    ostrides, ostrides[rank],
-                    (clfftPrecision)Precision<Tc>::type,
-                    batch);
+    PlanType plan = findPlan(CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED,
+                             (clfftDim)rank, tdims,
+                             istrides, istrides[rank], ostrides, ostrides[rank],
+                             (clfftPrecision)Precision<Tc>::type, batch);
 
     cl_mem imem = (*in.get())();
     cl_mem omem = (*out.get())();
@@ -159,21 +147,15 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     computeDims(istrides,  in.strides());
     computeDims(ostrides, out.strides());
 
-    clfftPlanHandle plan;
-
     int batch = 1;
     for (int i = rank; i < 4; i++) {
         batch *= tdims[i];
     }
 
-    clfft::findPlan(plan,
-                    CLFFT_HERMITIAN_INTERLEAVED,
-                    CLFFT_REAL,
-                    (clfftDim)rank, tdims,
-                    istrides, istrides[rank],
-                    ostrides, ostrides[rank],
-                    (clfftPrecision)Precision<Tc>::type,
-                    batch);
+    PlanType plan = findPlan(CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL,
+                             (clfftDim)rank, tdims,
+                             istrides, istrides[rank], ostrides, ostrides[rank],
+                             (clfftPrecision)Precision<Tc>::type, batch);
 
     cl_mem imem = (*in.get())();
     cl_mem omem = (*out.get())();
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 4c263e57ad..31b3abafa3 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -877,7 +877,7 @@ GraphicsManager& interopManager()
     return *(inst.gfxManagers[id].get());
 }
 
-clfft::clFFTPlanner& getclfftPlanManager()
+FFTManager& clfftManager()
 {
     return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 9badbe8a58..d94d60ae43 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -27,7 +27,7 @@
 #include <string>
 
 #include <GraphicsResourceManager.hpp>
-#include <clfftManager.hpp>
+#include <clfft.hpp>
 
 namespace opencl
 {
@@ -95,7 +95,8 @@ MemoryManagerPinned& getMemoryManagerPinned();
 typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
 GraphicsManager& interopManager();
 
-clfft::clFFTPlanner& getclfftPlanManager();
+typedef common::FFTPlanCache<PlanCache, PlanType> FFTManager;
+FFTManager& clfftManager();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -107,7 +108,7 @@ class DeviceManager
 
     friend GraphicsManager& interopManager();
 
-    friend clfft::clFFTPlanner& getclfftPlanManager();
+    friend FFTManager& clfftManager();
 
     friend std::string getDeviceInfo();
 
@@ -181,6 +182,6 @@ class DeviceManager
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
         std::unique_ptr<GraphicsManager> gfxManagers[MAX_DEVICES];
-        clfft::clFFTPlanner clfftManagers[MAX_DEVICES];
+        FFTManager clfftManagers[MAX_DEVICES];
 };
 }

From be345da187afbb0cbba05ae117400db3702935e0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 14 Jan 2017 17:08:15 +0530
Subject: [PATCH 1089/2677] Abstract cu{sparse|blas|solve} handle RAII wrapper

---
 src/backend/common/MatrixAlgebraHandle.hpp    | 39 +++++++++++++
 src/backend/cuda/blas.cpp                     |  8 +--
 src/backend/cuda/cholesky.cu                  |  4 +-
 src/backend/cuda/cublas.cpp                   | 40 +++++++++++++
 .../cuda/{cublasManager.hpp => cublas.hpp}    | 48 ++++++----------
 src/backend/cuda/cublasManager.cpp            | 53 ------------------
 src/backend/cuda/cusolverDn.cpp               | 36 ++++++++++++
 .../{cusolverDnManager.hpp => cusolverDn.hpp} | 48 ++++++----------
 src/backend/cuda/cusolverDnManager.cpp        | 56 -------------------
 src/backend/cuda/cusparse.cpp                 | 39 +++++++++++++
 .../{cusparseManager.hpp => cusparse.hpp}     | 48 ++++++----------
 src/backend/cuda/cusparseManager.cpp          | 54 ------------------
 src/backend/cuda/lu.cu                        |  4 +-
 src/backend/cuda/platform.cpp                 | 33 +++--------
 src/backend/cuda/platform.hpp                 | 30 ++++------
 src/backend/cuda/qr.cu                        | 10 ++--
 src/backend/cuda/solve.cu                     | 16 +++---
 src/backend/cuda/sparse.cu                    | 30 +++++-----
 src/backend/cuda/sparse_blas.cpp              |  4 +-
 src/backend/cuda/svd.cu                       |  6 +-
 20 files changed, 267 insertions(+), 339 deletions(-)
 create mode 100644 src/backend/common/MatrixAlgebraHandle.hpp
 create mode 100644 src/backend/cuda/cublas.cpp
 rename src/backend/cuda/{cublasManager.hpp => cublas.hpp} (68%)
 delete mode 100644 src/backend/cuda/cublasManager.cpp
 create mode 100644 src/backend/cuda/cusolverDn.cpp
 rename src/backend/cuda/{cusolverDnManager.hpp => cusolverDn.hpp} (68%)
 delete mode 100644 src/backend/cuda/cusolverDnManager.cpp
 create mode 100644 src/backend/cuda/cusparse.cpp
 rename src/backend/cuda/{cusparseManager.hpp => cusparse.hpp} (70%)
 delete mode 100644 src/backend/cuda/cusparseManager.cpp

diff --git a/src/backend/common/MatrixAlgebraHandle.hpp b/src/backend/common/MatrixAlgebraHandle.hpp
new file mode 100644
index 0000000000..bf4f6c1110
--- /dev/null
+++ b/src/backend/common/MatrixAlgebraHandle.hpp
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <err_common.hpp>
+#include <cstdio>
+
+namespace common
+{
+template<typename T, typename H>
+class MatrixAlgebraHandle
+{
+    public:
+        MatrixAlgebraHandle() {
+            static_cast<T*>(this)->createHandle(&handle);
+        }
+
+        ~MatrixAlgebraHandle() {
+            static_cast<T*>(this)->destroyHandle(handle);
+        }
+
+        H get() const {
+            return handle;
+        }
+
+    private:
+        MatrixAlgebraHandle(MatrixAlgebraHandle const&);
+        void operator=(MatrixAlgebraHandle const&);
+
+        H handle;
+};
+}
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 3d9d3aba78..46e4bfa8fb 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -17,7 +17,7 @@
 #include <cassert>
 #include <math.hpp>
 #include <err_common.hpp>
-#include <cublasManager.hpp>
+#include <cublas.hpp>
 #include <arith.hpp>
 #include <reduce.hpp>
 #include <complex.hpp>
@@ -171,7 +171,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
         CUBLAS_CHECK(gemv_func<T>()(
-                         getcublasHandle(),
+                         cublasHandle(),
                          lOpts,
                          lDims[0],
                          lDims[1],
@@ -182,7 +182,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                          out.get(), 1));
     } else {
         CUBLAS_CHECK(gemm_func<T>()(
-                         getcublasHandle(),
+                         cublasHandle(),
                          lOpts,
                          rOpts,
                          M, N, K,
@@ -224,7 +224,7 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans,
     dim4 rStrides = rhs.strides();
 
     CUBLAS_CHECK(trsm_func<T>()(
-                     getcublasHandle(),
+                     cublasHandle(),
                      is_left  ? CUBLAS_SIDE_LEFT : CUBLAS_SIDE_RIGHT,
                      is_upper ? CUBLAS_FILL_MODE_UPPER : CUBLAS_FILL_MODE_LOWER,
                      toCblasTranspose(trans),
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index 1c9b3c23ea..53df19b67f 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -110,7 +110,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     if(is_upper)
         uplo = CUBLAS_FILL_MODE_UPPER;
 
-    CUSOLVER_CHECK(potrf_buf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(potrf_buf_func<T>()(cusolverDnHandle(),
                                        uplo,
                                        N,
                                        in.get(), in.strides()[1],
@@ -119,7 +119,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     T *workspace = memAlloc<T>(lwork);
     int *d_info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(potrf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(potrf_func<T>()(cusolverDnHandle(),
                                    uplo,
                                    N,
                                    in.get(), in.strides()[1],
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
new file mode 100644
index 0000000000..5e40781c0a
--- /dev/null
+++ b/src/backend/cuda/cublas.cpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <platform.hpp>
+#include <err_common.hpp>
+#include <cublas.hpp>
+
+namespace cuda
+{
+const char *errorString(cublasStatus_t err)
+{
+    switch(err)
+    {
+    case CUBLAS_STATUS_SUCCESS         : return "CUBLAS_STATUS_SUCCESS"         ;
+    case CUBLAS_STATUS_NOT_INITIALIZED : return "CUBLAS_STATUS_NOT_INITIALIZED" ;
+    case CUBLAS_STATUS_ALLOC_FAILED    : return "CUBLAS_STATUS_ALLOC_FAILED"    ;
+    case CUBLAS_STATUS_INVALID_VALUE   : return "CUBLAS_STATUS_INVALID_VALUE"   ;
+    case CUBLAS_STATUS_ARCH_MISMATCH   : return "CUBLAS_STATUS_ARCH_MISMATCH"   ;
+    case CUBLAS_STATUS_MAPPING_ERROR   : return "CUBLAS_STATUS_MAPPING_ERROR"   ;
+    case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
+    case CUBLAS_STATUS_INTERNAL_ERROR  : return "CUBLAS_STATUS_INTERNAL_ERROR"  ;
+#if CUDA_VERSION > 5050
+    case CUBLAS_STATUS_NOT_SUPPORTED   : return "CUBLAS_STATUS_NOT_SUPPORTED"   ;
+#endif
+    default: return "UNKNOWN";
+    }
+}
+
+void cublasHandle::createHandle(BlasHandle* handle)
+{
+    CUBLAS_CHECK(cublasCreate(handle));
+    CUBLAS_CHECK(cublasSetStream(*handle, cuda::getStream(cuda::getActiveDeviceId())));
+}
+}
diff --git a/src/backend/cuda/cublasManager.hpp b/src/backend/cuda/cublas.hpp
similarity index 68%
rename from src/backend/cuda/cublasManager.hpp
rename to src/backend/cuda/cublas.hpp
index 41fb567e04..5e5dd40922 100644
--- a/src/backend/cuda/cublasManager.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -8,43 +8,16 @@
  ********************************************************/
 
 #pragma once
-#include <stdio.h>
-#include <err_common.hpp>
-#include <defines.hpp>
 #include <cublas_v2.h>
+#include <defines.hpp>
+#include <common/MatrixAlgebraHandle.hpp>
 
 namespace cuda
 {
-
-class DeviceManager;
-
-}
-
-namespace cublas
-{
+typedef cublasHandle_t BlasHandle;
 
 const char * errorString(cublasStatus_t err);
 
-//RAII class around the cublas Handle
-class cublasHandle
-{
-    friend class cuda::DeviceManager;
-
-    public:
-        ~cublasHandle();
-        cublasHandle_t get() const;
-
-    private:
-        cublasHandle();
-        cublasHandle(cublasHandle const&);
-        void operator=(cublasHandle const&);
-
-        cublasHandle_t handle;
-};
-
-}
-
-
 #define CUBLAS_CHECK(fn) do {                   \
         cublasStatus_t _error = fn;             \
         if (_error != CUBLAS_STATUS_SUCCESS) {  \
@@ -53,10 +26,21 @@ class cublasHandle
                      sizeof(_err_msg),          \
                      "CUBLAS Error (%d): %s\n", \
                      (int)(_error),             \
-                     cublas::errorString(       \
-                         _error));              \
+                     errorString(_error));      \
                                                 \
             AF_ERROR(_err_msg,                  \
                      AF_ERR_INTERNAL);          \
         }                                       \
     } while(0)
+
+class cublasHandle : public common::MatrixAlgebraHandle<cublasHandle, BlasHandle>
+{
+    public:
+        void createHandle(BlasHandle* handle);
+        void destroyHandle(BlasHandle handle) {
+            cublasDestroy(handle);
+        }
+};
+
+typedef common::MatrixAlgebraHandle<cublasHandle, BlasHandle> BlasHandleWrapper;
+}
diff --git a/src/backend/cuda/cublasManager.cpp b/src/backend/cuda/cublasManager.cpp
deleted file mode 100644
index e00beffad1..0000000000
--- a/src/backend/cuda/cublasManager.cpp
+++ /dev/null
@@ -1,53 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <platform.hpp>
-#include <err_common.hpp>
-#include <cublasManager.hpp>
-
-namespace cublas
-{
-
-const char *errorString(cublasStatus_t err)
-{
-
-    switch(err)
-    {
-    case    CUBLAS_STATUS_SUCCESS:              return "CUBLAS_STATUS_SUCCESS";
-    case    CUBLAS_STATUS_NOT_INITIALIZED:      return "CUBLAS_STATUS_NOT_INITIALIZED";
-    case    CUBLAS_STATUS_ALLOC_FAILED:         return "CUBLAS_STATUS_ALLOC_FAILED";
-    case    CUBLAS_STATUS_INVALID_VALUE:        return "CUBLAS_STATUS_INVALID_VALUE";
-    case    CUBLAS_STATUS_ARCH_MISMATCH:        return "CUBLAS_STATUS_ARCH_MISMATCH";
-    case    CUBLAS_STATUS_MAPPING_ERROR:        return "CUBLAS_STATUS_MAPPING_ERROR";
-    case    CUBLAS_STATUS_EXECUTION_FAILED:     return "CUBLAS_STATUS_EXECUTION_FAILED";
-    case    CUBLAS_STATUS_INTERNAL_ERROR:       return "CUBLAS_STATUS_INTERNAL_ERROR";
-#if CUDA_VERSION > 5050
-    case    CUBLAS_STATUS_NOT_SUPPORTED:        return "CUBLAS_STATUS_NOT_SUPPORTED";
-#endif
-    default:                                    return "UNKNOWN";
-    }
-}
-
-cublasHandle::cublasHandle()  : handle(0)
-{
-    CUBLAS_CHECK(cublasCreate(&handle));
-    CUBLAS_CHECK(cublasSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
-}
-
-cublasHandle::~cublasHandle()
-{
-    cublasDestroy(handle);
-}
-
-cublasHandle_t cublasHandle::get() const
-{
-    return handle;
-}
-
-}
diff --git a/src/backend/cuda/cusolverDn.cpp b/src/backend/cuda/cusolverDn.cpp
new file mode 100644
index 0000000000..e02f50cae2
--- /dev/null
+++ b/src/backend/cuda/cusolverDn.cpp
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cusolverDn.hpp>
+#include <platform.hpp>
+#include <debug_cuda.hpp>
+#include <stdexcept>
+#include <string>
+
+namespace cuda
+{
+const char *errorString(cusolverStatus_t err)
+{
+    switch(err) {
+    case CUSOLVER_STATUS_SUCCESS                  : return "CUSOLVER_STATUS_SUCCESS"                  ;
+    case CUSOLVER_STATUS_NOT_INITIALIZED          : return "CUSOLVER_STATUS_NOT_INITIALIZED"          ;
+    case CUSOLVER_STATUS_ALLOC_FAILED             : return "CUSOLVER_STATUS_ALLOC_FAILED"             ;
+    case CUSOLVER_STATUS_INVALID_VALUE            : return "CUSOLVER_STATUS_INVALID_VALUE"            ;
+    case CUSOLVER_STATUS_ARCH_MISMATCH            : return "CUSOLVER_STATUS_ARCH_MISMATCH"            ;
+    case CUSOLVER_STATUS_MAPPING_ERROR            : return "CUSOLVER_STATUS_MAPPING_ERROR"            ;
+    case CUSOLVER_STATUS_EXECUTION_FAILED         : return "CUSOLVER_STATUS_EXECUTION_FAILED"         ;
+    case CUSOLVER_STATUS_INTERNAL_ERROR           : return "CUSOLVER_STATUS_INTERNAL_ERROR"           ;
+    case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED: return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
+    case CUSOLVER_STATUS_NOT_SUPPORTED            : return "CUSOLVER_STATUS_NOT_SUPPORTED"            ;
+    case CUSOLVER_STATUS_ZERO_PIVOT               : return "CUSOLVER_STATUS_ZERO_PIVOT"               ;
+    case CUSOLVER_STATUS_INVALID_LICENSE          : return "CUSOLVER_STATUS_INVALID_LICENSE"          ;
+    default: return "UNKNOWN";
+    }
+}
+}
diff --git a/src/backend/cuda/cusolverDnManager.hpp b/src/backend/cuda/cusolverDn.hpp
similarity index 68%
rename from src/backend/cuda/cusolverDnManager.hpp
rename to src/backend/cuda/cusolverDn.hpp
index c12cea178e..67592e67f0 100644
--- a/src/backend/cuda/cusolverDnManager.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -11,41 +11,15 @@
 
 #include <cuda_runtime.h>
 #include <cusolverDn.h>
-#include <stdio.h>
 #include <defines.hpp>
-#include <err_common.hpp>
+#include <common/MatrixAlgebraHandle.hpp>
 
 namespace cuda
 {
-
-class DeviceManager;
-
-}
-
-namespace cusolver
-{
+typedef cusolverDnHandle_t SolveHandle;
 
 const char * errorString(cusolverStatus_t err);
 
-//RAII class around the cusolver Handle
-class cusolverDnHandle
-{
-    friend class cuda::DeviceManager;
-
-    public:
-        ~cusolverDnHandle();
-        cusolverDnHandle_t get() const;
-
-    private:
-        cusolverDnHandle();
-        cusolverDnHandle(cusolverDnHandle const&);
-        void operator=(cusolverDnHandle const&);
-
-        cusolverDnHandle_t handle;
-};
-
-}
-
 #define CUSOLVER_CHECK(fn) do {                     \
         cusolverStatus_t _error = fn;               \
         if (_error != CUSOLVER_STATUS_SUCCESS) {    \
@@ -54,10 +28,24 @@ class cusolverDnHandle
                      sizeof(_err_msg),              \
                      "CUBLAS Error (%d): %s\n",     \
                      (int)(_error),                 \
-                     cusolver::errorString(         \
-                         _error));                  \
+                     errorString(_error));          \
                                                     \
             AF_ERROR(_err_msg,                      \
                      AF_ERR_INTERNAL);              \
         }                                           \
     } while(0)
+
+class cusolverDnHandle : public common::MatrixAlgebraHandle<cusolverDnHandle, SolveHandle>
+{
+    public:
+        void createHandle(SolveHandle* handle) {
+            CUSOLVER_CHECK(cusolverDnCreate(handle));
+        }
+
+        void destroyHandle(SolveHandle handle) {
+            cusolverDnDestroy(handle);
+        }
+};
+
+typedef common::MatrixAlgebraHandle<cusolverDnHandle, SolveHandle> SolveHandleWrapper;
+}
diff --git a/src/backend/cuda/cusolverDnManager.cpp b/src/backend/cuda/cusolverDnManager.cpp
deleted file mode 100644
index 5940e929df..0000000000
--- a/src/backend/cuda/cusolverDnManager.cpp
+++ /dev/null
@@ -1,56 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <cusolverDnManager.hpp>
-#include <platform.hpp>
-#include <debug_cuda.hpp>
-
-#include <stdexcept>
-#include <string>
-
-namespace cusolver
-{
-
-const char *errorString(cusolverStatus_t err)
-{
-    switch(err) {
-    case    CUSOLVER_STATUS_SUCCESS                     :   return "CUSOLVER_STATUS_SUCCESS"                    ;
-    case    CUSOLVER_STATUS_NOT_INITIALIZED             :   return "CUSOLVER_STATUS_NOT_INITIALIZED"            ;
-    case    CUSOLVER_STATUS_ALLOC_FAILED                :   return "CUSOLVER_STATUS_ALLOC_FAILED"               ;
-    case    CUSOLVER_STATUS_INVALID_VALUE               :   return "CUSOLVER_STATUS_INVALID_VALUE"              ;
-    case    CUSOLVER_STATUS_ARCH_MISMATCH               :   return "CUSOLVER_STATUS_ARCH_MISMATCH"              ;
-    case    CUSOLVER_STATUS_MAPPING_ERROR               :   return "CUSOLVER_STATUS_MAPPING_ERROR"              ;
-    case    CUSOLVER_STATUS_EXECUTION_FAILED            :   return "CUSOLVER_STATUS_EXECUTION_FAILED"           ;
-    case    CUSOLVER_STATUS_INTERNAL_ERROR              :   return "CUSOLVER_STATUS_INTERNAL_ERROR"             ;
-    case    CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
-    case    CUSOLVER_STATUS_NOT_SUPPORTED               :   return "CUSOLVER_STATUS_NOT_SUPPORTED"              ;
-    case    CUSOLVER_STATUS_ZERO_PIVOT                  :   return "CUSOLVER_STATUS_ZERO_PIVOT"                 ;
-    case    CUSOLVER_STATUS_INVALID_LICENSE             :   return "CUSOLVER_STATUS_INVALID_LICENSE"            ;
-    default                                             :   return "UNKNOWN";
-    }
-}
-
-
-cusolverDnHandle::cusolverDnHandle()
-    : handle(0)
-{
-    CUSOLVER_CHECK(cusolverDnCreate(&handle));
-}
-
-cusolverDnHandle::~cusolverDnHandle()
-{
-    cusolverDnDestroy(handle);
-}
-
-cusolverDnHandle_t cusolverDnHandle::get() const
-{
-    return handle;
-}
-
-}
diff --git a/src/backend/cuda/cusparse.cpp b/src/backend/cuda/cusparse.cpp
new file mode 100644
index 0000000000..08204f51b7
--- /dev/null
+++ b/src/backend/cuda/cusparse.cpp
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <platform.hpp>
+#include <cusparse.hpp>
+#include <stdexcept>
+#include <string>
+
+namespace cuda
+{
+const char *errorString(cusparseStatus_t err)
+{
+    switch(err) {
+    case CUSPARSE_STATUS_SUCCESS                  : return "CUSPARSE_STATUS_SUCCESS"                  ;
+    case CUSPARSE_STATUS_NOT_INITIALIZED          : return "CUSPARSE_STATUS_NOT_INITIALIZED"          ;
+    case CUSPARSE_STATUS_ALLOC_FAILED             : return "CUSPARSE_STATUS_ALLOC_FAILED"             ;
+    case CUSPARSE_STATUS_INVALID_VALUE            : return "CUSPARSE_STATUS_INVALID_VALUE"            ;
+    case CUSPARSE_STATUS_ARCH_MISMATCH            : return "CUSPARSE_STATUS_ARCH_MISMATCH"            ;
+    case CUSPARSE_STATUS_MAPPING_ERROR            : return "CUSPARSE_STATUS_MAPPING_ERROR"            ;
+    case CUSPARSE_STATUS_EXECUTION_FAILED         : return "CUSPARSE_STATUS_EXECUTION_FAILED"         ;
+    case CUSPARSE_STATUS_INTERNAL_ERROR           : return "CUSPARSE_STATUS_INTERNAL_ERROR"           ;
+    case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED: return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
+    case CUSPARSE_STATUS_ZERO_PIVOT               : return "CUSPARSE_STATUS_ZERO_PIVOT"               ;
+    default: return "UNKNOWN";
+    }
+}
+
+void cusparseHandle::createHandle(SparseHandle* handle)
+{
+    CUSPARSE_CHECK(cusparseCreate(handle));
+    CUSPARSE_CHECK(cusparseSetStream(*handle, cuda::getStream(cuda::getActiveDeviceId())));
+}
+}
diff --git a/src/backend/cuda/cusparseManager.hpp b/src/backend/cuda/cusparse.hpp
similarity index 70%
rename from src/backend/cuda/cusparseManager.hpp
rename to src/backend/cuda/cusparse.hpp
index b5b3625f18..3bbdec81d1 100644
--- a/src/backend/cuda/cusparseManager.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -8,43 +8,16 @@
  ********************************************************/
 
 #pragma once
-#include <stdio.h>
-#include <err_common.hpp>
-#include <defines.hpp>
 #include <cusparse_v2.h>
+#include <defines.hpp>
+#include <common/MatrixAlgebraHandle.hpp>
 
 namespace cuda
 {
-
-class DeviceManager;
-
-}
-
-namespace cusparse
-{
+typedef cusparseHandle_t SparseHandle;
 
 const char * errorString(cusparseStatus_t err);
 
-//RAII class around the cusparse Handle
-class cusparseHandle
-{
-    friend class cuda::DeviceManager;
-
-    public:
-    ~cusparseHandle();
-    cusparseHandle_t get() const;
-
-    private:
-    cusparseHandle();
-    cusparseHandle(cusparseHandle const&);
-    void operator=(cusparseHandle const&);
-
-    cusparseHandle_t handle;
-};
-
-}
-
-
 #define CUSPARSE_CHECK(fn) do {                         \
         cusparseStatus_t _error = fn;                   \
         if (_error != CUSPARSE_STATUS_SUCCESS) {        \
@@ -52,8 +25,21 @@ class cusparseHandle
             snprintf(_err_msg, sizeof(_err_msg),        \
                      "CUSPARSE Error (%d): %s\n",       \
                      (int)(_error),                     \
-                     cusparse::errorString( _error));   \
+                     errorString( _error));             \
                                                         \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);        \
         }                                               \
     } while(0)
+
+class cusparseHandle : public common::MatrixAlgebraHandle<cusparseHandle, SparseHandle>
+{
+    public:
+        void createHandle(SparseHandle* handle);
+        void destroyHandle(SparseHandle handle) {
+            cusparseDestroy(handle);
+        }
+};
+
+typedef common::MatrixAlgebraHandle<cusparseHandle, SparseHandle> SparseHandleWrapper;
+
+}
diff --git a/src/backend/cuda/cusparseManager.cpp b/src/backend/cuda/cusparseManager.cpp
deleted file mode 100644
index bab949c8a7..0000000000
--- a/src/backend/cuda/cusparseManager.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <cusparseManager.hpp>
-#include <platform.hpp>
-
-#include <stdexcept>
-#include <string>
-
-namespace cusparse
-{
-
-const char *errorString(cusparseStatus_t err)
-{
-    switch(err) {
-    case    CUSPARSE_STATUS_SUCCESS                     :   return "CUSPARSE_STATUS_SUCCESS"                    ;
-    case    CUSPARSE_STATUS_NOT_INITIALIZED             :   return "CUSPARSE_STATUS_NOT_INITIALIZED"            ;
-    case    CUSPARSE_STATUS_ALLOC_FAILED                :   return "CUSPARSE_STATUS_ALLOC_FAILED"               ;
-    case    CUSPARSE_STATUS_INVALID_VALUE               :   return "CUSPARSE_STATUS_INVALID_VALUE"              ;
-    case    CUSPARSE_STATUS_ARCH_MISMATCH               :   return "CUSPARSE_STATUS_ARCH_MISMATCH"              ;
-    case    CUSPARSE_STATUS_MAPPING_ERROR               :   return "CUSPARSE_STATUS_MAPPING_ERROR"              ;
-    case    CUSPARSE_STATUS_EXECUTION_FAILED            :   return "CUSPARSE_STATUS_EXECUTION_FAILED"           ;
-    case    CUSPARSE_STATUS_INTERNAL_ERROR              :   return "CUSPARSE_STATUS_INTERNAL_ERROR"             ;
-    case    CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED   :   return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED"  ;
-    case    CUSPARSE_STATUS_ZERO_PIVOT                  :   return "CUSPARSE_STATUS_ZERO_PIVOT"                 ;
-    default                                             :   return "UNKNOWN";
-    }
-}
-
-
-cusparseHandle::cusparseHandle()
-    : handle(0)
-{
-    CUSPARSE_CHECK(cusparseCreate(&handle));
-    CUSPARSE_CHECK(cusparseSetStream(handle, cuda::getStream(cuda::getActiveDeviceId())));
-}
-
-cusparseHandle::~cusparseHandle()
-{
-    cusparseDestroy(handle);
-}
-
-cusparseHandle_t cusparseHandle::get() const
-{
-    return handle;
-}
-
-}
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index 3051e0dade..79bbd5029e 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -129,7 +129,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(getrf_buf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(getrf_buf_func<T>()(cusolverDnHandle(),
                                        M, N,
                                        in.get(), in.strides()[1],
                                        &lwork));
@@ -137,7 +137,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     T *workspace = memAlloc<T>(lwork);
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(getrf_func<T>()(cusolverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    workspace,
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index ab4949f154..3679d66ed6 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -424,26 +424,26 @@ FFTManager& cufftManager()
     return DeviceManager::getInstance().cufftManagers[getActiveDeviceId()];
 }
 
-cublasHandle_t getcublasHandle()
+BlasHandle cublasHandle()
 {
     DeviceManager& instance = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! instance.cublasHandles[id] )
-        instance.resetcublasHandle(id);
+        instance.cublasHandles[id].reset(new BlasHandleWrapper());
 
-    return instance.cublasHandles[id]->get();
+    return instance.cublasHandles[id].get()->get();
 }
 
-cusolverDnHandle_t getcusolverDnHandle()
+SolveHandle cusolverDnHandle()
 {
     DeviceManager& instance = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! instance.cusolverHandles[id] )
-        instance.resetcusolverHandle(id);
+        instance.cusolverHandles[id].reset(new SolveHandleWrapper());
 
     // FIXME
     // This is not an ideal case. It's just a hack.
@@ -460,19 +460,19 @@ cusolverDnHandle_t getcusolverDnHandle()
     //
     CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
 
-    return instance.cusolverHandles[id]->get();
+    return instance.cusolverHandles[id].get()->get();
 }
 
-cusparseHandle_t getcusparseHandle()
+SparseHandle cusparseHandle()
 {
     DeviceManager& instance = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! instance.cusparseHandles[id] )
-        instance.resetcusparseHandle(id);
+        instance.cusparseHandles[id].reset(new SparseHandleWrapper());
 
-    return instance.cusparseHandles[id]->get();
+    return instance.cusparseHandles[id].get()->get();
 }
 
 DeviceManager::DeviceManager()
@@ -597,21 +597,6 @@ int DeviceManager::setActiveDevice(int device, int nId)
     return old;
 }
 
-void DeviceManager::resetcublasHandle(int device)
-{
-    cublasHandles[device].reset(new cublas::cublasHandle());
-}
-
-void DeviceManager::resetcusolverHandle(int device)
-{
-    cusolverHandles[device].reset(new cusolver::cusolverDnHandle());
-}
-
-void DeviceManager::resetcusparseHandle(int device)
-{
-    cusparseHandles[device].reset(new cusparse::cusparseHandle());
-}
-
 void sync(int device)
 {
     int currDevice = getActiveDeviceId();
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index a18e43b215..d665e9539b 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -17,9 +17,9 @@
 
 #include <GraphicsResourceManager.hpp>
 #include <cufft.hpp>
-#include <cublasManager.hpp>
-#include <cusolverDnManager.hpp>
-#include <cusparseManager.hpp>
+#include <cublas.hpp>
+#include <cusolverDn.hpp>
+#include <cusparse.hpp>
 
 namespace cuda
 {
@@ -84,11 +84,11 @@ GraphicsManager& interopManager();
 typedef common::FFTPlanCache<PlanCache, PlanType> FFTManager;
 FFTManager& cufftManager();
 
-cublasHandle_t getcublasHandle();
+BlasHandle cublasHandle();
 
-cusolverDnHandle_t getcusolverDnHandle();
+SolveHandle cusolverDnHandle();
 
-cusparseHandle_t getcusparseHandle();
+SparseHandle cusparseHandle();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -109,11 +109,11 @@ class DeviceManager
 
         friend FFTManager& cufftManager();
 
-        friend cublasHandle_t getcublasHandle();
+        friend BlasHandle cublasHandle();
 
-        friend cusolverDnHandle_t getcusolverDnHandle();
+        friend SolveHandle cusolverDnHandle();
 
-        friend cusparseHandle_t getcusparseHandle();
+        friend SparseHandle cusparseHandle();
 
         friend std::string getDeviceInfo(int device);
 
@@ -158,12 +158,6 @@ class DeviceManager
 
         int setActiveDevice(int device, int native = -1);
 
-        void resetcublasHandle(int device);
-
-        void resetcusolverHandle(int device);
-
-        void resetcusparseHandle(int device);
-
         int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
@@ -176,11 +170,11 @@ class DeviceManager
 
         FFTManager cufftManagers[MAX_DEVICES];
 
-        std::unique_ptr<cublas::cublasHandle> cublasHandles[MAX_DEVICES];
+        std::unique_ptr<BlasHandleWrapper> cublasHandles[MAX_DEVICES];
 
-        std::unique_ptr<cusolver::cusolverDnHandle> cusolverHandles[MAX_DEVICES];
+        std::unique_ptr<SolveHandleWrapper> cusolverHandles[MAX_DEVICES];
 
-        std::unique_ptr<cusparse::cusparseHandle> cusparseHandles[MAX_DEVICES];
+        std::unique_ptr<SparseHandleWrapper> cusparseHandles[MAX_DEVICES];
 };
 
 }
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index 1a46fd95f8..7a73512ea9 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -130,7 +130,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(cusolverDnHandle(),
                                        M, N,
                                        in_copy.get(), in_copy.strides()[1],
                                        &lwork));
@@ -140,7 +140,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_func<T>()(cusolverDnHandle(),
                                    M, N,
                                    in_copy.get(), in_copy.strides()[1],
                                    t.get(),
@@ -157,7 +157,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     dim4 qdims(M, mn);
     q = identity<T>(qdims);
 
-    CUSOLVER_CHECK(mqr_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(mqr_func<T>()(cusolverDnHandle(),
                                  CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
                                  q.dims()[0],
                                  q.dims()[1],
@@ -185,7 +185,7 @@ Array<T> qr_inplace(Array<T> &in)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(cusolverDnHandle(),
                                        M, N,
                                        in.get(), in.strides()[1],
                                        &lwork));
@@ -193,7 +193,7 @@ Array<T> qr_inplace(Array<T> &in)
     T *workspace = memAlloc<T>(lwork);
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_func<T>()(cusolverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    t.get(),
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index e8758fc2bd..c4984c4868 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -174,7 +174,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(getrs_func<T>()(cusolverDnHandle(),
                                    CUBLAS_OP_N,
                                    N, NRHS,
                                    A.get(), A.strides()[1],
@@ -199,7 +199,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
 
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(getcusolverDnHandle(),
+    CUSOLVER_CHECK(getrs_func<T>()(cusolverDnHandle(),
                                    CUBLAS_OP_N,
                                    N, K,
                                    A.get(), A.strides()[1],
@@ -242,7 +242,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(getcusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(cusolverDnHandle(),
                                                  A.dims()[0], A.dims()[1],
                                                  A.get(), A.strides()[1],
                                                  &lwork));
@@ -252,7 +252,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int *info = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(getcusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(cusolverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
@@ -270,7 +270,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(getcusolverDnHandle(),
+        CUSOLVER_CHECK(mqr_solve_func<T>()(cusolverDnHandle(),
                                            CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
                                            B.dims()[0],
                                            B.dims()[1],
@@ -300,7 +300,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(getcusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(cusolverDnHandle(),
                                                  A.dims()[0], A.dims()[1],
                                                  A.get(), A.strides()[1],
                                                  &lwork));
@@ -310,7 +310,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int *info = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(getcusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(cusolverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
@@ -318,7 +318,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                              info));
 
         // matmul(Q1, B)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(getcusolverDnHandle(),
+        CUSOLVER_CHECK(mqr_solve_func<T>()(cusolverDnHandle(),
                                            CUBLAS_SIDE_LEFT,
                                            trans<T>(),
                                            M, K, N,
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 7c16226a50..6ce7c9226b 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -261,7 +261,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 
     int nNZ = -1;
     CUSPARSE_CHECK(nnz_func<T>()(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         dir,
                         M, N,
                         descr,
@@ -282,7 +282,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 
     if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(dense2csr_func<T>()(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         M, N,
                         descr,
                         in.get(), in.strides()[1],
@@ -290,7 +290,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
                         values.get(), rowIdx.get(), colIdx.get()));
     else
         CUSPARSE_CHECK(dense2csc_func<T>()(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         M, N,
                         descr,
                         in.get(), in.strides()[1],
@@ -336,7 +336,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 
     if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(csr2dense_func<T>()(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         M, N,
                         descr,
                         in.getValues().get(),
@@ -345,7 +345,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
                         dense.get(), d_strides1));
     else
         CUSPARSE_CHECK(csc2dense_func<T>()(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         M, N,
                         descr,
                         in.getValues().get(),
@@ -377,7 +377,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
         // cusparse function to expand compressed row into coordinate
         CUSPARSE_CHECK(cusparseXcsr2coo(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         in.getRowIdx().get(),
                         nNZ, in.dims()[0],
                         converted.getRowIdx().get(),
@@ -386,23 +386,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         // Call sort
         size_t pBufferSizeInBytes = 0;
         CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         in.dims()[0], in.dims()[1], nNZ,
                         converted.getRowIdx().get(), converted.getColIdx().get(),
                         &pBufferSizeInBytes));
         shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
         shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
-        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getcusparseHandle(), nNZ, P.get()));
+        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(cusparseHandle(), nNZ, P.get()));
 
         CUSPARSE_CHECK(cusparseXcoosortByColumn(
-                       getcusparseHandle(),
+                       cusparseHandle(),
                        in.dims()[0], in.dims()[1], nNZ,
                        converted.getRowIdx().get(), converted.getColIdx().get(),
                        P.get(), (void*)pBuffer.get()));
 
         CUSPARSE_CHECK(gthr_func<T>()(
-                       getcusparseHandle(), nNZ,
+                       cusparseHandle(), nNZ,
                        in.getValues().get(),
                        converted.getValues().get(),
                        P.get(), CUSPARSE_INDEX_BASE_ZERO));
@@ -421,23 +421,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         {
             size_t pBufferSizeInBytes = 0;
             CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                            getcusparseHandle(),
+                            cusparseHandle(),
                             cooT.dims()[0], cooT.dims()[1], nNZ,
                             cooT.getRowIdx().get(), cooT.getColIdx().get(),
                             &pBufferSizeInBytes));
             shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
             shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
-            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(getcusparseHandle(), nNZ, P.get()));
+            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(cusparseHandle(), nNZ, P.get()));
 
             CUSPARSE_CHECK(cusparseXcoosortByRow(
-                           getcusparseHandle(),
+                           cusparseHandle(),
                            cooT.dims()[0], cooT.dims()[1], nNZ,
                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
                            P.get(), (void*)pBuffer.get()));
 
             CUSPARSE_CHECK(gthr_func<T>()(
-                           getcusparseHandle(), nNZ,
+                           cusparseHandle(), nNZ,
                            in.getValues().get(),
                            cooT.getValues().get(),
                            P.get(), CUSPARSE_INDEX_BASE_ZERO));
@@ -456,7 +456,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(cusparseXcoo2csr(
-                        getcusparseHandle(),
+                        cusparseHandle(),
                         cooT.getRowIdx().get(),
                         nNZ, cooT.dims()[0],
                         converted.getRowIdx().get(),
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 1d6dc27775..be768d054d 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -155,7 +155,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     // and not OP(A) (gemm wants row/col of OP(A)).
     if(rDims[rColDim] == 1) {
         CUSPARSE_CHECK(csrmv_func<T>()(
-                       getcusparseHandle(),
+                       cusparseHandle(),
                        lOpts,
                        lDims[0], lDims[1], lhs.getNNZ(),
                        &alpha,
@@ -166,7 +166,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                        out.get()));
     } else {
         CUSPARSE_CHECK(csrmm_func<T>()(
-                       getcusparseHandle(),
+                       cusparseHandle(),
                        lOpts,
                        lDims[0], rDims[rColDim], lDims[1], lhs.getNNZ(),
                        &alpha,
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index 62a016b019..9645aecf66 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -17,7 +17,7 @@
 #include <math.hpp>
 #include <err_common.hpp>
 
-#include <cusolverDnManager.hpp>
+#include <cusolverDn.hpp>
 
 namespace cuda
 {
@@ -86,14 +86,14 @@ SVD_SPECIALIZE(cdouble, double, Z);
 
         int lwork = 0;
 
-        CUSOLVER_CHECK(gesvd_buf_func<T>(getcusolverDnHandle(), M, N, &lwork));
+        CUSOLVER_CHECK(gesvd_buf_func<T>(cusolverDnHandle(), M, N, &lwork));
 
         T  *lWorkspace = memAlloc<T >(lwork);
         Tr *rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
 
         int *info = memAlloc<int>(1);
 
-        gesvd_func<T, Tr>(getcusolverDnHandle(), 'A', 'A', M, N, in.get(),
+        gesvd_func<T, Tr>(cusolverDnHandle(), 'A', 'A', M, N, in.get(),
                           M, s.get(), u.get(), M, vt.get(), N,
                           lWorkspace, lwork, rWorkspace, info);
 

From c27933d4aec838aecdd43989a6335a2c53aef145 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 14 Jan 2017 17:13:46 +0530
Subject: [PATCH 1090/2677] Remove obsolete files

Forgot to remove these files in earlier commit
---
 src/backend/cuda/interopManager.cpp | 171 ----------------------------
 src/backend/cuda/interopManager.hpp |  71 ------------
 2 files changed, 242 deletions(-)
 delete mode 100644 src/backend/cuda/interopManager.cpp
 delete mode 100644 src/backend/cuda/interopManager.hpp

diff --git a/src/backend/cuda/interopManager.cpp b/src/backend/cuda/interopManager.cpp
deleted file mode 100644
index a8339a52f4..0000000000
--- a/src/backend/cuda/interopManager.cpp
+++ /dev/null
@@ -1,171 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-// Parts of this code sourced from SnopyDogy
-// https://gist.github.com/SnopyDogy/a9a22497a893ec86aa3e
-
-#if defined(WITH_GRAPHICS)
-
-#include <interopManager.hpp>
-#include <err_cuda.hpp>
-#include <util.hpp>
-#include <cstdio>
-
-namespace cuda
-{
-
-void InteropManager::destroyResources()
-{
-    typedef std::vector<CGR_t>::iterator CGRIter_t;
-
-    int n = getActiveDeviceId();
-    for(iter_t iter = interop_maps[n].begin(); iter != interop_maps[n].end(); iter++) {
-        for(CGRIter_t ct = (iter->second).begin(); ct != (iter->second).end(); ct++) {
-            CUDA_CHECK(cudaGraphicsUnregisterResource(*ct));
-        }
-        (iter->second).clear();
-    }
-}
-
-InteropManager::~InteropManager()
-{
-    try {
-        for(int i = 0; i < getDeviceCount(); i++) {
-            setDevice(i);
-            destroyResources();
-        }
-    } catch (AfError &ex) {
-
-        std::string perr = getEnvVar("AF_PRINT_ERRORS");
-        if(!perr.empty()) {
-            if(perr != "0")
-                fprintf(stderr, "%s\n", ex.what());
-        }
-    }
-}
-
-interop_t& InteropManager::getDeviceMap(int device)
-{
-    return (device == -1) ? interop_maps[getActiveDeviceId()] : interop_maps[device];
-}
-
-CGR_t* InteropManager::getBufferResource(const forge::Image* key)
-{
-    void* key_value = (void*)key;
-    interop_t& i_map = getDeviceMap();
-
-    if(i_map.find(key_value) == i_map.end()) {
-        CGR_t pixelsResource;
-        // Register pixels with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&pixelsResource, key->pixels(), cudaGraphicsMapFlagsWriteDiscard));
-        // TODO:
-        // A way to store multiple buffers and take PBO/CBO etc as
-        // argument and return the appropriate buffer
-        std::vector<CGR_t> vec(1);
-        vec[0] = pixelsResource;
-        i_map[key_value] = vec;
-    }
-
-    return &i_map[key_value].front();
-}
-
-CGR_t* InteropManager::getBufferResource(const forge::Plot* key)
-{
-    void* key_value = (void*)key;
-    interop_t& i_map = getDeviceMap();
-
-    iter_t iter = i_map.find(key_value);
-
-    if(iter == i_map.end()) {
-        CGR_t vboResource;
-        // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&vboResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        // TODO:
-        // A way to store multiple buffers and take PBO/CBO etc as
-        // argument and return the appropriate buffer
-        std::vector<CGR_t> vec(1);
-        vec[0] = vboResource;
-        i_map[key_value] = vec;
-    }
-
-    return &i_map[key_value].front();
-}
-
-CGR_t* InteropManager::getBufferResource(const forge::Histogram* key)
-{
-    void* key_value = (void*)key;
-    interop_t& i_map = getDeviceMap();
-
-    iter_t iter = i_map.find(key_value);
-
-    if(iter == i_map.end()) {
-        CGR_t vboResource;
-        // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&vboResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        // TODO:
-        // A way to store multiple buffers and take PBO/CBO etc as
-        // argument and return the appropriate buffer
-        std::vector<CGR_t> vec(1);
-        vec[0] = vboResource;
-        i_map[key_value] = vec;
-    }
-
-    return &i_map[key_value].front();
-}
-
-CGR_t* InteropManager::getBufferResource(const forge::Surface* key)
-{
-    void* key_value = (void*)key;
-    interop_t& i_map = getDeviceMap();
-
-    iter_t iter = i_map.find(key_value);
-
-    if(iter == i_map.end()) {
-        CGR_t  vboResource;
-        // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&vboResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        // TODO:
-        // A way to store multiple buffers and take PBO/CBO etc as
-        // argument and return the appropriate buffer
-        std::vector<CGR_t> vec(1);
-        vec[0] = vboResource;
-        i_map[key_value] = vec;
-    }
-
-    return &i_map[key_value].front();
-}
-
-CGR_t* InteropManager::getBufferResource(const forge::VectorField* key)
-{
-    void* key_value = (void*)key;
-    interop_t& i_map = getDeviceMap();
-
-    iter_t iter = i_map.find(key_value);
-
-    if(iter == i_map.end()) {
-        CGR_t   pResource;
-        CGR_t   dResource;
-        // Register VBO with CUDA
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&pResource, key->vertices(), cudaGraphicsMapFlagsWriteDiscard));
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&dResource, key->directions(), cudaGraphicsMapFlagsWriteDiscard));
-        // TODO:
-        // A way to store multiple buffers and take PBO/CBO etc as
-        // argument and return the appropriate buffer
-        std::vector<CGR_t> vec(2);
-        vec[0] = pResource;
-        vec[1] = dResource;
-        i_map[key_value] = vec;
-    }
-
-    return &i_map[key_value].front();
-}
-
-}
-
-#endif
diff --git a/src/backend/cuda/interopManager.hpp b/src/backend/cuda/interopManager.hpp
deleted file mode 100644
index 6864079ee9..0000000000
--- a/src/backend/cuda/interopManager.hpp
+++ /dev/null
@@ -1,71 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-// Parts of this code sourced from SnopyDogy
-// https://gist.github.com/SnopyDogy/a9a22497a893ec86aa3e
-
-#if defined(WITH_GRAPHICS)
-
-#if defined(OS_WIN)
-#include <windows.h>
-#endif
-
-// cuda_gl_interop.h does not include OpenGL headers for ARM
-#include <glbinding/gl/gl.h>
-#include <glbinding/Binding.h>
-
-#if defined(__arm__) || defined(__aarch64__)
-using namespace gl;
-#define GL_VERSION gl::GL_VERSION
-#endif
-
-#include <platform.hpp>
-#include <graphics_common.hpp>
-
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cuda_gl_interop.h>
-
-#include <map>
-#include <vector>
-
-using af::dim4;
-
-namespace cuda
-{
-
-typedef std::map<void *, std::vector<cudaGraphicsResource_t> > interop_t;
-typedef interop_t::iterator iter_t;
-typedef cudaGraphicsResource_t CGR_t;
-
-// Manager Class for cudaPBOResource: calls garbage collection at the end of the program
-class InteropManager
-{
-    private:
-        interop_t interop_maps[DeviceManager::MAX_DEVICES];
-
-    public:
-        InteropManager() {}
-        ~InteropManager();
-        CGR_t* getBufferResource(const forge::Image       *handle);
-        CGR_t* getBufferResource(const forge::Plot        *handle);
-        CGR_t* getBufferResource(const forge::Histogram   *handle);
-        CGR_t* getBufferResource(const forge::Surface     *handle);
-        CGR_t* getBufferResource(const forge::VectorField *handle);
-
-    protected:
-        InteropManager(InteropManager const&);
-        void operator=(InteropManager const&);
-        interop_t& getDeviceMap(int device = -1); // default will return current device
-        void destroyResources();
-};
-
-}
-
-#endif

From 8387dad5028454088d226186404a027512d3b253 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 14 Jan 2017 17:24:18 +0530
Subject: [PATCH 1091/2677] Remove unnecessary std::cout

---
 src/backend/cpu/platform.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 105346ea37..c8103bb93f 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -99,7 +99,6 @@ CPUInfo::CPUInfo()
         }
     } else {
         mVendorId = "Unkown";
-        std::cout<< "Unexpected vendor id" << std::endl;
     }
     // Get processor brand string
     // This seems to be working for both Intel & AMD vendors

From c7b07a0639a6911507bcab8841ea2a3c736b58d3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 14 Jan 2017 17:28:46 +0530
Subject: [PATCH 1092/2677] Remove checks for CUDA versions less than 7.0

Minimum required CUDA version currently is 7.0, hence the checks
are not needed any more.
---
 src/backend/cuda/cublas.cpp | 2 --
 src/backend/cuda/cufft.cpp  | 3 +--
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 5e40781c0a..685c7d26f6 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -25,9 +25,7 @@ const char *errorString(cublasStatus_t err)
     case CUBLAS_STATUS_MAPPING_ERROR   : return "CUBLAS_STATUS_MAPPING_ERROR"   ;
     case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
     case CUBLAS_STATUS_INTERNAL_ERROR  : return "CUBLAS_STATUS_INTERNAL_ERROR"  ;
-#if CUDA_VERSION > 5050
     case CUBLAS_STATUS_NOT_SUPPORTED   : return "CUBLAS_STATUS_NOT_SUPPORTED"   ;
-#endif
     default: return "UNKNOWN";
     }
 }
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index f8d36e7ee8..dcb1cee5bd 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -60,13 +60,12 @@ const char * _cufftGetResultString(cufftResult res)
         case CUFFT_NO_WORKSPACE:
             return "cuFFT: no workspace provided";
 
-#if CUDA_VERSION >= 6050
         case CUFFT_NOT_IMPLEMENTED:
             return "cuFFT: not implemented";
 
         case CUFFT_LICENSE_ERROR:
             return "cuFFT: license error";
-#endif
+
 #if CUDA_VERSION >= 8000
         case CUFFT_NOT_SUPPORTED:
             return "cuFFT: not supported";

From 25b7d90f4313fce8c52d1d47053256023c321356 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 14 Jan 2017 20:03:06 +0530
Subject: [PATCH 1093/2677] Add new getActiveStream fn in cuda backend

This abbrievates the lengthy fn call getStream(getActiveDeviceId())
---
 src/backend/cuda/Array.cpp                    | 19 ++++-----
 src/backend/cuda/copy.cu                      |  4 +-
 src/backend/cuda/cublas.cpp                   |  2 +-
 src/backend/cuda/cufft.cpp                    |  2 +-
 src/backend/cuda/cusparse.cpp                 |  2 +-
 src/backend/cuda/debug_cuda.hpp               | 12 +++---
 src/backend/cuda/hist_graphics.cpp            |  6 +--
 src/backend/cuda/image.cpp                    |  6 +--
 src/backend/cuda/jit.cpp                      |  2 +-
 src/backend/cuda/kernel/convolve.cu           |  6 +--
 src/backend/cuda/kernel/convolve_separable.cu |  2 +-
 src/backend/cuda/kernel/fast.hpp              |  6 +--
 src/backend/cuda/kernel/harris.hpp            | 14 +++----
 src/backend/cuda/kernel/homography.hpp        | 20 ++++-----
 src/backend/cuda/kernel/ireduce.hpp           | 10 ++---
 src/backend/cuda/kernel/orb.hpp               | 20 ++++-----
 src/backend/cuda/kernel/reduce.hpp            |  8 ++--
 src/backend/cuda/kernel/regions.hpp           |  8 ++--
 src/backend/cuda/kernel/sift_nonfree.hpp      | 42 +++++++++----------
 src/backend/cuda/kernel/sort.hpp              |  2 +-
 src/backend/cuda/kernel/sort_by_key.hpp       |  4 +-
 src/backend/cuda/kernel/susan.hpp             |  6 +--
 src/backend/cuda/kernel/transform.hpp         |  2 +-
 src/backend/cuda/kernel/where.hpp             |  4 +-
 src/backend/cuda/morph3d_impl.hpp             |  2 +-
 src/backend/cuda/morph_impl.hpp               |  2 +-
 src/backend/cuda/platform.cpp                 |  7 +++-
 src/backend/cuda/platform.hpp                 |  2 +
 src/backend/cuda/plot.cpp                     |  6 +--
 src/backend/cuda/sparse.cu                    |  6 +--
 src/backend/cuda/surface.cpp                  |  6 +--
 src/backend/cuda/vector_field.cpp             |  8 ++--
 32 files changed, 126 insertions(+), 122 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 90b9f8d8d1..e94bcb787a 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -58,12 +58,12 @@ namespace cuda
 #endif
         if (!is_device) {
             CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                                       cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                                       cudaMemcpyHostToDevice, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         } else if (copy_device) {
             CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                                       cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                                       cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         }
     }
 
@@ -107,7 +107,7 @@ namespace cuda
         owner(true)
     {
         if (!is_device) {
-            cudaStream_t stream = getStream(getActiveDeviceId());
+            cudaStream_t stream = getActiveStream();
             CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, info.total() * sizeof(T),
                                        cudaMemcpyHostToDevice, stream));
             CUDA_CHECK(cudaStreamSynchronize(stream));
@@ -340,9 +340,8 @@ namespace cuda
 
         T *ptr = arr.get();
 
-        CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice,
-                    cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
         return;
     }
@@ -357,9 +356,7 @@ namespace cuda
 
         T *ptr = arr.get();
 
-        CUDA_CHECK(cudaMemcpyAsync(ptr, data,
-                                   bytes, cudaMemcpyDeviceToDevice,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         return;
     }
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 7164d63635..6a92051490 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -54,7 +54,7 @@ namespace cuda
             CUDA_CHECK(cudaMemcpyAsync(out.get(), A.get(),
                                        A.elements() * sizeof(T),
                                        cudaMemcpyDeviceToDevice,
-                                       cuda::getStream(cuda::getActiveDeviceId())));
+                                       cuda::getActiveStream()));
         } else {
             // FIXME: Seems to fail when using Param<T>
             kernel::memcopy(out.get(), out.strides().get(), A.get(), A.dims().get(),
@@ -97,7 +97,7 @@ namespace cuda
                 CUDA_CHECK(cudaMemcpyAsync(out.get(), in.get(),
                                            in.elements() * sizeof(T),
                                            cudaMemcpyDeviceToDevice,
-                                           cuda::getStream(cuda::getActiveDeviceId())));
+                                           cuda::getActiveStream()));
             } else {
                 kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1);
             }
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 685c7d26f6..515a6e4bd0 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -33,6 +33,6 @@ const char *errorString(cublasStatus_t err)
 void cublasHandle::createHandle(BlasHandle* handle)
 {
     CUBLAS_CHECK(cublasCreate(handle));
-    CUBLAS_CHECK(cublasSetStream(*handle, cuda::getStream(cuda::getActiveDeviceId())));
+    CUBLAS_CHECK(cublasSetStream(*handle, cuda::getActiveStream()));
 }
 }
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index dcb1cee5bd..ac743f47f8 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -134,7 +134,7 @@ PlanType findPlan(int rank, int *n,
                                   type, batch));
     }
 
-    cufftSetStream(retVal, cuda::getStream(cuda::getActiveDeviceId()));
+    cufftSetStream(retVal, cuda::getActiveStream());
 
     // push the plan into plan cache
     planner.push(key_string, retVal);
diff --git a/src/backend/cuda/cusparse.cpp b/src/backend/cuda/cusparse.cpp
index 08204f51b7..526c1c50a3 100644
--- a/src/backend/cuda/cusparse.cpp
+++ b/src/backend/cuda/cusparse.cpp
@@ -34,6 +34,6 @@ const char *errorString(cusparseStatus_t err)
 void cusparseHandle::createHandle(SparseHandle* handle)
 {
     CUSPARSE_CHECK(cusparseCreate(handle));
-    CUSPARSE_CHECK(cusparseSetStream(*handle, cuda::getStream(cuda::getActiveDeviceId())));
+    CUSPARSE_CHECK(cusparseSetStream(*handle, cuda::getActiveStream()));
 }
 }
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index f5424950dc..f7c60d37b2 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -13,7 +13,7 @@
 #include <thrust/version.h>
 #include <thrust/system/cuda/detail/par.h>
 
-#define THRUST_STREAM thrust::cuda::par.on(cuda::getStream(cuda::getActiveDeviceId()))
+#define THRUST_STREAM thrust::cuda::par.on(cuda::getActiveStream())
 
 #if THRUST_MAJOR_VERSION>=1 && THRUST_MINOR_VERSION>=8
 
@@ -24,20 +24,20 @@
 
 #define THRUST_SELECT(fn, ...) \
     do {                          \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId()))); \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
         fn(__VA_ARGS__);       \
     } while(0)
 
 #define THRUST_SELECT_OUT(res, fn, ...) \
     do {                          \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId()))); \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
         res = fn(__VA_ARGS__);       \
     } while(0)
 
 #endif
 
 #define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...) \
-	fn<<<blks, thrds, smem_size, cuda::getStream(cuda::getActiveDeviceId())>>>(__VA_ARGS__)
+	fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__)
 
 #define CUDA_LAUNCH(fn, blks, thrds, ...) \
 	CUDA_LAUNCH_SMEM(fn, blks, thrds, 0, __VA_ARGS__)
@@ -46,14 +46,14 @@
 #ifndef NDEBUG
 
 #define POST_LAUNCH_CHECK() do {                        \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId()))); \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
     } while(0)                                          \
 
 #else
 
 #define POST_LAUNCH_CHECK() do {                                        \
     if(cuda::synchronize_calls()) {                                     \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId()))); \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
     } else {                                                            \
         CUDA_CHECK(cudaPeekAtLastError());                              \
     }                                                                   \
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 52c959b4d4..f74e03c23a 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -31,11 +31,11 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = hist->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                        cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+                        cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 816cc2b8cd..de9d0efa9c 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -36,11 +36,11 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         // Map resource. Copy data to pixels. Unmap resource.
         size_t num_bytes;
         T* d_pixels = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
         cudaGraphicsResourceGetMappedPointer((void **)&d_pixels, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_pixels, d_X, num_bytes, cudaMemcpyDeviceToDevice,
-                        cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+                        cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
 
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 44fa9e1616..7d4111874a 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -655,7 +655,7 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> nodes)
                             threads_y,
                             1,
                             0,
-                            getStream(getActiveDeviceId()),
+                            getActiveStream(),
                             &args.front(),
                             NULL));
 
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 0a7f5425cf..d237fd8ca1 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -372,7 +372,7 @@ void convolve_1d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
                                               filt.ptr+(f1Off+f2Off+f3Off),
                                               filterLen*sizeof(aT),
                                               0, cudaMemcpyDeviceToDevice,
-                                              cuda::getStream(cuda::getActiveDeviceId())));
+                                              cuda::getActiveStream()));
 
                 p.o[0] = (p.outHasNoOffset ? 0 : b1);
                 p.o[1] = (p.outHasNoOffset ? 0 : b2);
@@ -410,7 +410,7 @@ void convolve_2d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
                                           filt.ptr+(f2Off+f3Off),
                                           filterLen*sizeof(aT),
                                           0, cudaMemcpyDeviceToDevice,
-                                          cuda::getStream(cuda::getActiveDeviceId())));
+                                          cuda::getActiveStream()));
 
             p.o[1] = (p.outHasNoOffset ? 0 : b2);
             p.o[2] = (p.outHasNoOffset ? 0 : b3);
@@ -438,7 +438,7 @@ void convolve_3d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
                     filt.ptr+f3Off,
                     filterLen*sizeof(aT),
                     0, cudaMemcpyDeviceToDevice,
-                    cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
 
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
         p.s[2] = (p.inHasNoOffset ? 0 : b3);
diff --git a/src/backend/cuda/kernel/convolve_separable.cu b/src/backend/cuda/kernel/convolve_separable.cu
index 1b34e64043..65d7901a2c 100644
--- a/src/backend/cuda/kernel/convolve_separable.cu
+++ b/src/backend/cuda/kernel/convolve_separable.cu
@@ -133,7 +133,7 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter)
    // FIX ME: if the filter array is strided, direct copy of symbols
    // might cause issues
    CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::sFilter, filter.ptr, fLen*sizeof(accType), 0,
-               cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+               cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
     switch(fLen) {
         case  2: conv2Helper<T, accType, conv_dim, expand,  2>(blocks, threads, out, signal, blk_x, blk_y); break;
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index c50a3a1a3c..491ffe5f01 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -447,7 +447,7 @@ void fast(unsigned* out_feat,
     blocks.y = divup(in.dims[1], 64);
 
     unsigned *d_total = (unsigned *)(d_score + in.dims[0] * in.dims[1]);
-    CUDA_CHECK(cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getStream(cuda::getActiveDeviceId())));
+    CUDA_CHECK(cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getActiveStream()));
     unsigned *d_counts  = memAlloc<unsigned>(blocks.x * blocks.y);
     unsigned *d_offsets = memAlloc<unsigned>(blocks.x * blocks.y);
 
@@ -465,8 +465,8 @@ void fast(unsigned* out_feat,
     // Dimensions of output array
     unsigned total;
     CUDA_CHECK(cudaMemcpyAsync(&total, d_total, sizeof(unsigned), cudaMemcpyDeviceToHost,
-                cuda::getStream(cuda::getActiveDeviceId())));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     total = total < max_feat ? total : max_feat;
 
     if (total > 0) {
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 11e6003158..cae2ae935e 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -216,7 +216,7 @@ void harris(unsigned* corners_out,
     int filter_elem = filter.strides[3] * filter.dims[3];
     filter.ptr = memAlloc<convAccT>(filter_elem);
     CUDA_CHECK(cudaMemcpyAsync(filter.ptr, h_filter, filter_elem * sizeof(convAccT),
-                cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                cudaMemcpyHostToDevice, cuda::getActiveStream()));
 
     delete[] h_filter;
 
@@ -277,7 +277,7 @@ void harris(unsigned* corners_out,
 
     unsigned* d_corners_found = memAlloc<unsigned>(1);
     CUDA_CHECK(cudaMemsetAsync(d_corners_found, 0, sizeof(unsigned),
-                cuda::getStream(cuda::getActiveDeviceId())));
+                cuda::getActiveStream()));
 
     float* d_x_corners = memAlloc<float>(corner_lim);
     float* d_y_corners = memAlloc<float>(corner_lim);
@@ -306,8 +306,8 @@ void harris(unsigned* corners_out,
 
     unsigned corners_found = 0;
     CUDA_CHECK(cudaMemcpyAsync(&corners_found, d_corners_found, sizeof(unsigned),
-                cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     memFree(d_responses);
     memFree(d_corners_found);
@@ -364,11 +364,11 @@ void harris(unsigned* corners_out,
         *y_out = memAlloc<float>(*corners_out);
         *resp_out = memAlloc<float>(*corners_out);
         CUDA_CHECK(cudaMemcpyAsync(*x_out, d_x_corners, *corners_out * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*y_out, d_y_corners, *corners_out * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*resp_out, d_resp_corners, *corners_out * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         memFree(d_x_corners);
         memFree(d_y_corners);
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 23eb043c57..051fdb875e 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -612,27 +612,27 @@ int computeH(
             POST_LAUNCH_CHECK();
 
             CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian, sizeof(float),
-                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx, sizeof(unsigned),
-                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
 
             memFree(finalMedian);
             memFree(finalIdx);
         } else {
             CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.ptr, sizeof(float),
-                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(&minIdx, idx.ptr, sizeof(unsigned),
-                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         }
 
         // Copy best homography to output
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + minIdx * 9, 9*sizeof(T),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         blocks = dim3(divup(nsamples, threads.x));
         // sync stream for the device to host copies to be visible for
         // the subsequent kernel launch
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
         CUDA_LAUNCH((computeLMedSInliers<T>), blocks, threads,
                     inliers, bestH, x_src, y_src, x_dst, y_dst,
@@ -648,7 +648,7 @@ int computeH(
         kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
 
         CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.ptr, sizeof(unsigned),
-                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToHost, cuda::getActiveStream()));
 
         memFree(totalInliers.ptr);
         memFree(median.ptr);
@@ -657,9 +657,9 @@ int computeH(
         inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
         // Copies back index and number of inliers of best homography estimation
         CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.ptr+blockIdx, sizeof(unsigned),
-                    cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
     }
 
@@ -667,7 +667,7 @@ int computeH(
     memFree(idx.ptr);
     // sync stream for the device to host copies to be visible for
     // the subsequent kernel launch
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     return (int)inliersH;
 }
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 12d4a8956f..e87360eb76 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -491,10 +491,10 @@ namespace kernel
             uint*   h_lptr_raw = h_lptr.get();
 
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(T),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(h_lptr_raw, tlptr, tmp_elements * sizeof(uint),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
             memFree(tmp.ptr);
             memFree(tlptr);
 
@@ -522,8 +522,8 @@ namespace kernel
             unique_ptr<T> h_ptr(new T[in_elements]);
             T* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
             MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
             for (int i = 1; i < in_elements; i++) {
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index d0cd2e7d3b..9b6c67bfd8 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -328,7 +328,7 @@ void orb(unsigned* out_feat,
     // In future implementations, the user will be capable of passing his
     // distribution instead of using the reference one
     //CUDA_CHECK(cudaMemcpyToSymbolAsync(d_ref_pat, h_ref_pat, 256 * 4 * sizeof(int), 0,
-    // cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+    // cudaMemcpyHostToDevice, cuda::getActiveStream()));
 
     vector<float*> d_score_pyr(max_levels);
     vector<float*> d_ori_pyr(max_levels);
@@ -355,8 +355,8 @@ void orb(unsigned* out_feat,
         int gauss_elem = gauss_filter.strides[3] * gauss_filter.dims[3];
         gauss_filter.ptr = memAlloc<convAccT>(gauss_elem);
         CUDA_CHECK(cudaMemcpyAsync(gauss_filter.ptr, h_gauss.get(), gauss_elem * sizeof(convAccT),
-                    cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyHostToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 
     for (int i = 0; i < (int)max_levels; i++) {
@@ -460,7 +460,7 @@ void orb(unsigned* out_feat,
 
         unsigned* d_desc_lvl = memAlloc<unsigned>(feat_pyr[i] * 8);
         CUDA_CHECK(cudaMemsetAsync(d_desc_lvl, 0, feat_pyr[i] * 8 * sizeof(unsigned),
-                    cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
 
         // Compute ORB descriptors
         threads = dim3(THREADS_X, THREADS_Y);
@@ -508,17 +508,17 @@ void orb(unsigned* out_feat,
             offset += feat_pyr[i-1];
 
         CUDA_CHECK(cudaMemcpyAsync(*d_x+offset, d_x_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_y+offset, d_y_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_score+offset, d_score_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_ori+offset, d_ori_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_size+offset, d_size_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_desc+(offset*8), d_desc_pyr[i], feat_pyr[i] * 8 * sizeof(unsigned),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         memFree(d_x_pyr[i]);
         memFree(d_y_pyr[i]);
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 3491fa69f1..30cbd05ff3 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -413,8 +413,8 @@ namespace kernel
             To* h_ptr_raw = h_ptr.get();
 
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(To),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
             memFree(tmp.ptr);
 
             Binary<To, op> reduce;
@@ -430,8 +430,8 @@ namespace kernel
             unique_ptr<Ti> h_ptr(new Ti[in_elements]);
             Ti* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
             Transform<Ti, To, op> transform;
             Binary<To, op> reduce;
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 6c9abf3a02..d9ef4080a8 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -415,7 +415,7 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
         h_continue = 0;
         CUDA_CHECK(cudaMemcpyToSymbolAsync(continue_flag, &h_continue, sizeof(int),
                     0, cudaMemcpyHostToDevice,
-                    cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
 
         CUDA_LAUNCH((update_equiv<T, 16, n_per_thread, full_conn>), blocks, threads, out, tex);
 
@@ -423,8 +423,8 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
 
         CUDA_CHECK(cudaMemcpyFromSymbolAsync(&h_continue, continue_flag, sizeof(int),
                     0, cudaMemcpyDeviceToHost,
-                    cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 
     // Now, perform the final relabeling.  This converts the equivalency
@@ -435,7 +435,7 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     T* tmp = cuda::memAlloc<T>(size);
     CUDA_CHECK(cudaMemcpyAsync(tmp, out.ptr, size * sizeof(T),
                           cudaMemcpyDeviceToDevice,
-                          cuda::getStream(cuda::getActiveDeviceId())));
+                          cuda::getActiveStream()));
 
     // Wrap raw device ptr
     thrust::device_ptr<T> wrapped_tmp = thrust::device_pointer_cast(tmp);
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 54b2a715db..5fa051b82d 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -190,8 +190,8 @@ Param<T> gauss_filter(float sigma)
     dim_t gauss_elem = gauss_filter.strides[3] * gauss_filter.dims[3];
     gauss_filter.ptr = memAlloc<T>(gauss_elem);
     CUDA_CHECK(cudaMemcpyAsync(gauss_filter.ptr, h_gauss, gauss_elem * sizeof(T),
-                cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                cudaMemcpyHostToDevice, cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     delete[] h_gauss;
 
@@ -1239,7 +1239,7 @@ std::vector< Param<T> > buildGaussPyr(
 
             CUDA_CHECK(cudaMemcpyAsync(gauss_pyr[o].ptr + offset, tmp_pyr[idx].ptr,
                         imel * sizeof(T), cudaMemcpyDeviceToDevice,
-                        cuda::getStream(cuda::getActiveDeviceId())));
+                        cuda::getActiveStream()));
         }
     }
 
@@ -1360,7 +1360,7 @@ void sift(unsigned* out_feat,
         const unsigned max_feat = ceil(imel * feature_ratio);
 
         CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
 
         float* d_extrema_x = memAlloc<float>(max_feat);
         float* d_extrema_y = memAlloc<float>(max_feat);
@@ -1381,8 +1381,8 @@ void sift(unsigned* out_feat,
 
         unsigned extrema_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&extrema_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         extrema_feat = min(extrema_feat, max_feat);
 
         if (extrema_feat == 0) {
@@ -1394,7 +1394,7 @@ void sift(unsigned* out_feat,
         }
 
         CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
 
         unsigned interp_feat = 0;
 
@@ -1420,12 +1420,12 @@ void sift(unsigned* out_feat,
         memFree(d_extrema_layer);
 
         CUDA_CHECK(cudaMemcpyAsync(&interp_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         interp_feat = min(interp_feat, max_feat);
 
         CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
 
         if (interp_feat == 0) {
             memFree(d_interp_x);
@@ -1482,10 +1482,10 @@ void sift(unsigned* out_feat,
 
         unsigned nodup_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&nodup_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
 
         const unsigned max_oriented_feat = nodup_feat * 3;
 
@@ -1516,8 +1516,8 @@ void sift(unsigned* out_feat,
 
         unsigned oriented_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&oriented_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         oriented_feat = min(oriented_feat, max_oriented_feat);
 
         if (oriented_feat == 0) {
@@ -1591,19 +1591,19 @@ void sift(unsigned* out_feat,
             continue;
 
         CUDA_CHECK(cudaMemcpyAsync(*d_x+offset, d_x_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_y+offset, d_y_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_score+offset, d_response_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_ori+offset, d_ori_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_size+offset, d_size_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         CUDA_CHECK(cudaMemcpyAsync(*d_desc+(offset*desc_len), d_desc_pyr[i],
                     feat_pyr[i] * desc_len * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         memFree(d_x_pyr[i]);
         memFree(d_y_pyr[i]);
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index 45dc023206..ac92ec8387 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -104,7 +104,7 @@ namespace cuda
             //val.modDims(inDims);
 
             // Not really necessary
-            // CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+            // CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
             memFree(key);
         }
 
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index a84e41c53d..8095da2d16 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -82,7 +82,7 @@ namespace cuda
             Tk *cKey = memAlloc<Tk>(elements);
             CUDA_CHECK(cudaMemcpyAsync(cKey, Key, elements * sizeof(Tk),
                                        cudaMemcpyDeviceToDevice,
-                                       getStream(cuda::getActiveDeviceId())));
+                                       getActiveStream()));
 
             Tv *Val = pVal.ptr;
             thrustSortByKey(Key, Val, elements, isAscending);
@@ -91,7 +91,7 @@ namespace cuda
             uint *cSeq = memAlloc<uint>(elements);
             CUDA_CHECK(cudaMemcpyAsync(cSeq, Seq, elements * sizeof(uint),
                                        cudaMemcpyDeviceToDevice,
-                                       getStream(cuda::getActiveDeviceId())));
+                                       getActiveStream()));
 
             // This always needs to be ascending
             thrustSortByKey(Seq, Val, elements, true);
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 5c11e35367..c0ecc7f000 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -163,7 +163,7 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out,
 
     unsigned* d_corners_found = memAlloc<unsigned>(1);
     CUDA_CHECK(cudaMemsetAsync(d_corners_found, 0, sizeof(unsigned),
-                cuda::getStream(cuda::getActiveDeviceId())));
+                cuda::getActiveStream()));
 
     CUDA_LAUNCH((nonMaxKernel<T>), blocks, threads,
             x_out, y_out, resp_out, d_corners_found, idim0, idim1, resp_in, edge, max_corners);
@@ -171,8 +171,8 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out,
     POST_LAUNCH_CHECK();
 
     CUDA_CHECK(cudaMemcpyAsync(count, d_corners_found, sizeof(unsigned),
-                cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     memFree(d_corners_found);
 }
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 735eaa5408..f3f551a3c6 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -199,7 +199,7 @@ namespace cuda
             CUDA_CHECK(cudaMemcpyToSymbolAsync(c_tmat, tf.ptr,
                                                nTfs2 * nTfs3 * tf_len * sizeof(float),
                                                0, cudaMemcpyDeviceToDevice,
-                                               cuda::getStream(cuda::getActiveDeviceId())));
+                                               cuda::getActiveStream()));
 
             dim3 threads(TX, TY, 1);
             dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 8df9b506de..c6c5894367 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -118,8 +118,8 @@ namespace kernel
         uint total;
         CUDA_CHECK(cudaMemcpyAsync(&total, rtmp.ptr + rtmp_elements - 1,
                               sizeof(uint), cudaMemcpyDeviceToHost,
-                              cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+                              cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
         out.ptr = memAlloc<uint>(total);
 
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index d5a19f7128..e6233c989e 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -34,7 +34,7 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
     CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter, mask.get(),
                                   mdims[0] * mdims[1] *mdims[2] * sizeof(T),
                                   0, cudaMemcpyDeviceToDevice,
-                                  cuda::getStream(cuda::getActiveDeviceId())));
+                                  cuda::getActiveStream()));
 
     if (isDilation)
         kernel::morph3d<T, true >(out, in, mdims[0]);
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index 16437985bd..994e792a4b 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -33,7 +33,7 @@ Array<T>  morph(const Array<T> &in, const Array<T> &mask)
     CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter, mask.get(),
                                   mdims[0] * mdims[1] * sizeof(T),
                                   0, cudaMemcpyDeviceToDevice,
-                                  cuda::getStream(cuda::getActiveDeviceId())));
+                                  cuda::getActiveStream()));
 
     if (isDilation)
         kernel::morph<T, true >(out, in, mdims[0]);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 3679d66ed6..ca00ccf06c 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -327,6 +327,11 @@ cudaStream_t getStream(int device)
     return str;
 }
 
+cudaStream_t getActiveStream()
+{
+    return getStream(getActiveDeviceId());
+}
+
 size_t getDeviceMemorySize(int device)
 {
     return getDeviceProp(device).totalGlobalMem;
@@ -601,7 +606,7 @@ void sync(int device)
 {
     int currDevice = getActiveDeviceId();
     setDevice(device);
-    CUDA_CHECK(cudaStreamSynchronize(getStream(getActiveDeviceId())));
+    CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
     setDevice(currDevice);
 }
 
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index d665e9539b..5a025621d3 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -49,6 +49,8 @@ int getDeviceNativeId(int device);
 
 cudaStream_t getStream(int device);
 
+cudaStream_t getActiveStream();
+
 size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index f2ca1f19cf..3c67e7d36b 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -36,11 +36,11 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = plot->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+                cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 6ce7c9226b..49eb442d99 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -373,7 +373,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
                                    in.getColIdx().elements() * sizeof(int),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
 
         // cusparse function to expand compressed row into coordinate
         CUSPARSE_CHECK(cusparseXcsr2coo(
@@ -448,11 +448,11 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         CUDA_CHECK(cudaMemcpyAsync(converted.getValues().get(), cooT.getValues().get(),
                                    cooT.getValues().elements() * sizeof(T),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), cooT.getColIdx().get(),
                                    cooT.getColIdx().elements() * sizeof(int),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
+                                   cuda::getActiveStream()));
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(cusparseXcoo2csr(
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 0752a950eb..95ff72645f 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -36,11 +36,11 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = surface->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
         cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
         cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                cuda::getStream(cuda::getActiveDeviceId()));
-        cudaGraphicsUnmapResources(1, resources, cuda::getStream(cuda::getActiveDeviceId()));
+                cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 36021aa1c3..d6eb919422 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -32,7 +32,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Map resource. Copy data to VBO. Unmap resource.
         // Map all resources at once.
-        cudaGraphicsMapResources(2, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsMapResources(2, resources, cuda::getActiveStream());
 
         // Points
         {
@@ -41,7 +41,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
             T* d_vbo = NULL;
             cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
             cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
-                            cuda::getStream(cuda::getActiveDeviceId()));
+                            cuda::getActiveStream());
         }
         // Directions
         {
@@ -50,9 +50,9 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
             T* d_vbo = NULL;
             cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[1]);
             cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
-                            cuda::getStream(cuda::getActiveDeviceId()));
+                            cuda::getActiveStream());
         }
-        cudaGraphicsUnmapResources(2, resources, cuda::getStream(cuda::getActiveDeviceId()));
+        cudaGraphicsUnmapResources(2, resources, cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 

From 528ef9ae316800168bf1ea9f18fdf15559130878 Mon Sep 17 00:00:00 2001
From: Jason Newton <nevion@gmail.com>
Date: Sun, 15 Jan 2017 18:45:14 -0500
Subject: [PATCH 1094/2677] lets call regular exp on amd too, native_exp is
 broken for doubles/other types

---
 src/backend/opencl/kernel/bilateral.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 46eabab6c6..9aafae09c3 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -49,7 +49,8 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
 
     std::call_once( compileFlags[device], [device] () {
             bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
-                                && getActivePlatform() != AFCL_PLATFORM_APPLE);
+                                && getActivePlatform() != AFCL_PLATFORM_APPLE
+                                && getActivePlatform() != AFCL_PLATFORM_AMD);
             std::ostringstream options;
             options << " -D inType=" << dtype_traits<inType>::getName()
                     << " -D outType=" << dtype_traits<outType>::getName();

From ab3bc878dee55cd730ea6b50f0f35292d1ad0258 Mon Sep 17 00:00:00 2001
From: Jason Newton <nevion@gmail.com>
Date: Sun, 15 Jan 2017 18:45:47 -0500
Subject: [PATCH 1095/2677] disable inlineing - broken on ROCm

a smart compiler should do this already anyway
---
 src/backend/opencl/kernel/random_engine_philox.cl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index f5720e3421..d70232c4a7 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -54,19 +54,19 @@
 #define w32_0 0x9E3779B9
 #define w32_1 0xBB67AE85
 
-inline void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
+void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
 {
     *hi = mul_hi(a, b);
     *lo = a*b;
 }
 
-inline void philoxBump(uint k[2])
+void philoxBump(uint k[2])
 {
     k[0] += w32_0;
     k[1] += w32_1;
 }
 
-inline void philoxRound(const uint k[2], uint c[4])
+void philoxRound(const uint k[2], uint c[4])
 {
     uint hi0, lo0, hi1, lo1;
     mulhilo(m4x32_0, c[0], &hi0, &lo0);
@@ -77,7 +77,7 @@ inline void philoxRound(const uint k[2], uint c[4])
     c[3] = lo0;
 }
 
-inline void philox(uint key[2], uint ctr[4])
+void philox(uint key[2], uint ctr[4])
 {
     //10 Rounds
                        philoxRound(key, ctr);

From 8d249415973c7c7f7a525dc1a1f3838ab8b67502 Mon Sep 17 00:00:00 2001
From: Jason Newton <nevion@gmail.com>
Date: Sun, 15 Jan 2017 18:47:31 -0500
Subject: [PATCH 1096/2677] hand off build OpenCL header/library paths

---
 CMakeModules/build_clFFT.cmake | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index aa89452d86..e5304befe8 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -31,6 +31,8 @@ ExternalProject_Add(
     -DBUILD_TEST:BOOL=OFF
     -DSUFFIX_LIB:STRING=
     -DUSE_SYSTEM_GTEST:BOOL=ON
+    -DOpenCL_INCLUDE_DIR:FILEPATH=${OpenCL_INCLUDE_DIR}
+    -DOpenCL_LIBRARY:FILEPATH=${OpenCL_LIBRARY}
     ${byproducts}
     )
 

From b2e54ff67c4687bc3f4368bf670ff1254fbe8808 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Jan 2017 17:57:13 +0530
Subject: [PATCH 1097/2677] Change memory manager classes to use CRTP

---
 src/backend/MemoryManager.cpp        | 331 ----------------------
 src/backend/MemoryManager.hpp        | 123 --------
 src/backend/common/MemoryManager.hpp | 402 +++++++++++++++++++++++++++
 src/backend/cpu/Array.cpp            |   1 -
 src/backend/cpu/memory.cpp           | 135 ++++-----
 src/backend/cpu/memory.hpp           |  56 ++--
 src/backend/cpu/platform.cpp         |  11 +
 src/backend/cpu/platform.hpp         |  11 +-
 src/backend/cuda/Array.cpp           |   1 -
 src/backend/cuda/memory.cpp          | 130 +++++++--
 src/backend/cuda/memory.hpp          |  71 +++--
 src/backend/cuda/memoryManager.cpp   |  86 ------
 src/backend/cuda/memoryManager.hpp   |  63 -----
 src/backend/cuda/platform.cpp        |  13 +-
 src/backend/cuda/platform.hpp        |  12 +-
 src/backend/opencl/Array.cpp         |   1 -
 src/backend/opencl/memory.cpp        | 146 ++++++++--
 src/backend/opencl/memory.hpp        |  76 +++--
 src/backend/opencl/memoryManager.cpp |  86 ------
 src/backend/opencl/memoryManager.hpp |  68 -----
 src/backend/opencl/platform.cpp      |  13 +-
 src/backend/opencl/platform.hpp      |  13 +-
 22 files changed, 868 insertions(+), 981 deletions(-)
 delete mode 100644 src/backend/MemoryManager.cpp
 delete mode 100644 src/backend/MemoryManager.hpp
 create mode 100644 src/backend/common/MemoryManager.hpp
 delete mode 100644 src/backend/cuda/memoryManager.cpp
 delete mode 100644 src/backend/cuda/memoryManager.hpp
 delete mode 100644 src/backend/opencl/memoryManager.cpp
 delete mode 100644 src/backend/opencl/memoryManager.hpp

diff --git a/src/backend/MemoryManager.cpp b/src/backend/MemoryManager.cpp
deleted file mode 100644
index 3b0e81ddc5..0000000000
--- a/src/backend/MemoryManager.cpp
+++ /dev/null
@@ -1,331 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <iostream>
-#include <iomanip>
-#include <string>
-#include <algorithm>
-#include "MemoryManager.hpp"
-#include "dispatch.hpp"
-#include "err_common.hpp"
-#include "util.hpp"
-
-namespace common
-{
-
-MemoryManager::MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug):
-    mem_step_size(1024),
-    max_buffers(MAX_BUFFERS),
-    memory(num_devices),
-    debug_mode(debug)
-{
-    lock_guard_t lock(this->memory_mutex);
-
-    for (int n = 0; n < num_devices; n++) {
-        // Calling getMaxMemorySize() here calls the virtual function that returns 0
-        // Call it from outside the constructor.
-        memory[n].max_bytes     = ONE_GB;
-        memory[n].total_bytes   = 0;
-        memory[n].total_buffers = 0;
-        memory[n].lock_bytes    = 0;
-        memory[n].lock_buffers  = 0;
-    }
-
-    // Check for environment variables
-
-    std::string env_var;
-
-    // Debug mode
-    env_var = getEnvVar("AF_MEM_DEBUG");
-    if (!env_var.empty()) {
-        this->debug_mode = env_var[0] != '0';
-    }
-    if (this->debug_mode) mem_step_size = 1;
-
-    // Max Buffer count
-    env_var = getEnvVar("AF_MAX_BUFFERS");
-    if (!env_var.empty()) {
-        this->max_buffers = std::max(1, std::stoi(env_var));
-    }
-}
-
-void MemoryManager::setMaxMemorySize()
-{
-    for (unsigned n = 0; n < memory.size(); n++) {
-        // Calls garbage collection when:
-        // total_bytes > memsize * 0.75 when memsize <  4GB
-        // total_bytes > memsize - 1 GB when memsize >= 4GB
-        // If memsize returned 0, then use 1GB
-        size_t memsize = this->getMaxMemorySize(n);
-        memory[n].max_bytes = memsize == 0 ? ONE_GB : std::max(memsize * 0.75, (double)(memsize - ONE_GB));
-    }
-}
-
-void MemoryManager::garbageCollect()
-{
-    if (this->debug_mode) return;
-
-    lock_guard_t lock(this->memory_mutex);
-    memory_info& current = this->getCurrentMemoryInfo();
-
-    // Return if all buffers are locked
-    if (current.total_buffers == current.lock_buffers) return;
-
-    for (auto &kv : current.free_map) {
-        size_t num_ptrs = kv.second.size();
-        //Free memory by popping the last element
-        for (int n = num_ptrs-1; n >= 0; n--) {
-            this->nativeFree(kv.second[n]);
-            current.total_bytes -= kv.first;
-            current.total_buffers--;
-            kv.second.pop_back();
-        }
-    }
-    current.free_map.clear();
-}
-
-void MemoryManager::unlock(void *ptr, bool user_unlock)
-{
-    // Shortcut for empty arrays
-    if (!ptr) return;
-
-    lock_guard_t lock(this->memory_mutex);
-    memory_info& current = this->getCurrentMemoryInfo();
-
-    locked_iter iter = current.locked_map.find((void *)ptr);
-
-    // Pointer not found in locked map
-    if (iter == current.locked_map.end()) {
-        // Probably came from user, just free it
-        this->nativeFree(ptr);
-        return;
-    }
-
-    if (user_unlock) {
-        (iter->second).user_lock = false;
-    } else {
-        (iter->second).manager_lock = false;
-    }
-
-    // Return early if either one is locked
-    if ((iter->second).user_lock || (iter->second).manager_lock) return;
-
-    size_t bytes = iter->second.bytes;
-    current.lock_bytes -= iter->second.bytes;
-    current.lock_buffers--;
-
-    current.locked_map.erase(iter);
-
-    if (this->debug_mode) {
-        // Just free memory in debug mode
-        if ((iter->second).bytes > 0) {
-            this->nativeFree(iter->first);
-            current.total_buffers--;
-            current.total_bytes -= iter->second.bytes;
-        }
-    } else {
-        // In regular mode, move buffer to free map
-        free_iter fiter = current.free_map.find(bytes);
-        if (fiter != current.free_map.end()) {
-            // If found, push back
-            fiter->second.push_back(ptr);
-        } else {
-            // If not found, create new vector for this size
-            std::vector<void *> ptrs;
-            ptrs.push_back(ptr);
-            current.free_map[bytes] = ptrs;
-        }
-    }
-}
-
-void *MemoryManager::alloc(const size_t bytes, bool user_lock)
-{
-    lock_guard_t lock(this->memory_mutex);
-
-    void *ptr = NULL;
-    size_t alloc_bytes = this->debug_mode ? bytes : (divup(bytes, mem_step_size) * mem_step_size);
-
-    if (bytes > 0) {
-        memory_info& current = this->getCurrentMemoryInfo();
-
-        // There is no memory cache in debug mode
-        if (!this->debug_mode) {
-
-            // FIXME: Add better checks for garbage collection
-            // Perhaps look at total memory available as a metric
-            if (this->checkMemoryLimit()) {
-                this->garbageCollect();
-            }
-
-            free_iter iter = current.free_map.find(alloc_bytes);
-
-            if (iter != current.free_map.end() && !iter->second.empty()) {
-                ptr = iter->second.back();
-                iter->second.pop_back();
-            }
-
-        }
-
-        // Only comes here if buffer size not found or in debug mode
-        if (ptr == NULL) {
-            // Perform garbage collection if memory can not be allocated
-            try {
-                ptr = this->nativeAlloc(alloc_bytes);
-            } catch (AfError &ex) {
-                // If out of memory, run garbage collect and try again
-                if (ex.getError() != AF_ERR_NO_MEM) throw;
-                this->garbageCollect();
-                ptr = this->nativeAlloc(alloc_bytes);
-            }
-            // Increment these two only when it succeeds to come here.
-            current.total_bytes += alloc_bytes;
-            current.total_buffers += 1;
-        }
-
-
-        locked_info info = {!user_lock, user_lock, alloc_bytes};
-        current.locked_map[ptr] = info;
-        current.lock_bytes += alloc_bytes;
-        current.lock_buffers++;
-    }
-    return ptr;
-}
-
-void MemoryManager::userLock(const void *ptr)
-{
-    memory_info& current = this->getCurrentMemoryInfo();
-
-    lock_guard_t lock(this->memory_mutex);
-
-    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-
-    if (iter != current.locked_map.end()) {
-        iter->second.user_lock = true;
-    } else {
-        locked_info info = {false,
-                            true,
-                            100}; //This number is not relevant
-
-        current.locked_map[(void *)ptr] = info;
-    }
-}
-
-void MemoryManager::userUnlock(const void *ptr)
-{
-    this->unlock(const_cast<void *>(ptr), true);
-}
-
-bool MemoryManager::isUserLocked(const void *ptr)
-{
-    memory_info& current = this->getCurrentMemoryInfo();
-    lock_guard_t lock(this->memory_mutex);
-    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-    if (iter != current.locked_map.end()) {
-        return iter->second.user_lock;
-    } else {
-        return false;
-    }
-}
-
-size_t MemoryManager::getMemStepSize()
-{
-    lock_guard_t lock(this->memory_mutex);
-    return this->mem_step_size;
-}
-
-void MemoryManager::setMemStepSize(size_t new_step_size)
-{
-    lock_guard_t lock(this->memory_mutex);
-    this->mem_step_size = new_step_size;
-}
-
-size_t MemoryManager::getMaxBytes()
-{
-    lock_guard_t lock(this->memory_mutex);
-    return this->getCurrentMemoryInfo().max_bytes;
-}
-
-void MemoryManager::printInfo(const char *msg, const int device)
-{
-    lock_guard_t lock(this->memory_mutex);
-    const memory_info& current = this->getCurrentMemoryInfo();
-
-    std::cout << msg << std::endl;
-
-    static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
-    static const std::string line(head.size(), '-');
-    std::cout << line << std::endl << head << std::endl << line << std::endl;
-
-    for(auto& kv : current.locked_map) {
-        std::string status_mngr("Yes");
-        std::string status_user("Unknown");
-        if(kv.second.user_lock)     status_user = "Yes";
-        else                        status_user = " No";
-
-        std::string unit = "KB";
-        double size = (double)(kv.second.bytes) / 1024;
-        if(size >= 1024) {
-            size = size / 1024;
-            unit = "MB";
-        }
-
-        std::cout << "|  " << std::right << std::setw(14) << kv.first << " "
-                  << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                  << " | " << std::setw(9) << status_mngr
-                  << " | " << std::setw(9) << status_user
-                  << " |"  << std::endl;
-    }
-
-    for(auto &kv : current.free_map) {
-
-        std::string status_mngr("No");
-        std::string status_user("No");
-
-        std::string unit = "KB";
-        double size = (double)(kv.first) / 1024;
-        if(size >= 1024) {
-            size = size / 1024;
-            unit = "MB";
-        }
-
-        for (auto &ptr : kv.second) {
-            std::cout << "|  " << std::right << std::setw(14) << ptr << " "
-                      << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                      << " | " << std::setw(9) << status_mngr
-                      << " | " << std::setw(9) << status_user
-                      << " |"  << std::endl;
-        }
-    }
-
-    std::cout << line << std::endl;
-}
-
-void MemoryManager::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                               size_t *lock_bytes,  size_t *lock_buffers)
-{
-    lock_guard_t lock(this->memory_mutex);
-    const memory_info& current = this->getCurrentMemoryInfo();
-    if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
-    if (alloc_buffers ) *alloc_buffers = current.total_buffers;
-    if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
-    if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
-}
-
-unsigned MemoryManager::getMaxBuffers()
-{
-    return this->max_buffers;
-}
-
-bool MemoryManager::checkMemoryLimit()
-{
-    const memory_info& current = this->getCurrentMemoryInfo();
-    return current.lock_bytes >= current.max_bytes || current.total_buffers >= this->max_buffers;
-}
-
-}
diff --git a/src/backend/MemoryManager.hpp b/src/backend/MemoryManager.hpp
deleted file mode 100644
index 39ff8e1281..0000000000
--- a/src/backend/MemoryManager.hpp
+++ /dev/null
@@ -1,123 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <vector>
-#include <mutex>
-#include <unordered_map>
-
-namespace common
-{
-
-typedef std::recursive_mutex mutex_t;
-typedef std::lock_guard<mutex_t> lock_guard_t;
-
-const unsigned MAX_BUFFERS   = 1000;
-const size_t ONE_GB = 1 << 30;
-
-class MemoryManager
-{
-    typedef struct
-    {
-        bool manager_lock;
-        bool user_lock;
-        size_t bytes;
-    } locked_info;
-
-    typedef std::unordered_map<void *, locked_info> locked_t;
-    typedef locked_t::iterator locked_iter;
-
-    typedef std::unordered_map<size_t, std::vector<void *> >free_t;
-    typedef free_t::iterator free_iter;
-
-    typedef struct
-    {
-        locked_t locked_map;
-        free_t   free_map;
-
-        size_t lock_bytes;
-        size_t lock_buffers;
-        size_t total_bytes;
-        size_t total_buffers;
-        size_t max_bytes;
-    } memory_info;
-
-    size_t mem_step_size;
-    unsigned max_buffers;
-    std::vector<memory_info> memory;
-    bool debug_mode;
-
-    memory_info& getCurrentMemoryInfo()
-    {
-        return memory[this->getActiveDeviceId()];
-    }
-
-    virtual int getActiveDeviceId()
-    {
-        return 0;
-    }
-
-    virtual size_t getMaxMemorySize(int id)
-    {
-        return 0;
-    }
-
-public:
-    MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug);
-
-    void setMaxMemorySize();
-
-    void *alloc(const size_t bytes, bool user_lock);
-
-    void unlock(void *ptr, bool user_unlock);
-
-    void garbageCollect();
-
-    void printInfo(const char *msg, const int device);
-
-    void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                    size_t *lock_bytes,  size_t *lock_buffers);
-
-    void userLock(const void *ptr);
-
-    void userUnlock(const void *ptr);
-
-    bool isUserLocked(const void *ptr);
-
-    size_t getMemStepSize();
-
-    size_t getMaxBytes();
-
-    unsigned getMaxBuffers();
-
-    void setMemStepSize(size_t new_step_size);
-
-    virtual void *nativeAlloc(const size_t bytes)
-    {
-        return malloc(bytes);
-    }
-
-    virtual void nativeFree(void *ptr)
-    {
-        free((void *)ptr);
-    }
-
-    virtual ~MemoryManager()
-    {
-    }
-
-    bool checkMemoryLimit();
-
-protected:
-    mutex_t memory_mutex;
-
-};
-
-}
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
new file mode 100644
index 0000000000..892df3f50c
--- /dev/null
+++ b/src/backend/common/MemoryManager.hpp
@@ -0,0 +1,402 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <vector>
+#include <mutex>
+#include <unordered_map>
+#include <iostream>
+#include <iomanip>
+#include <err_common.hpp>
+#include <dispatch.hpp>
+#include <util.hpp>
+
+namespace common
+{
+typedef std::recursive_mutex mutex_t;
+typedef std::lock_guard<mutex_t> lock_guard_t;
+
+const unsigned MAX_BUFFERS   = 1000;
+const size_t ONE_GB = 1 << 30;
+
+template<typename T>
+class MemoryManager
+{
+    typedef struct
+    {
+        bool manager_lock;
+        bool user_lock;
+        size_t bytes;
+    } locked_info;
+
+    using locked_t = typename std::unordered_map<void *, locked_info>;
+    using locked_iter = typename locked_t::iterator;
+
+    typedef std::unordered_map<size_t, std::vector<void *> >free_t;
+    typedef free_t::iterator free_iter;
+
+    typedef struct
+    {
+        locked_t locked_map;
+        free_t   free_map;
+
+        size_t lock_bytes;
+        size_t lock_buffers;
+        size_t total_bytes;
+        size_t total_buffers;
+        size_t max_bytes;
+    } memory_info;
+
+    size_t mem_step_size;
+    unsigned max_buffers;
+    std::vector<memory_info> memory;
+    bool debug_mode;
+
+    memory_info& getCurrentMemoryInfo()
+    {
+        return memory[this->getActiveDeviceId()];
+    }
+
+    inline int getActiveDeviceId()
+    {
+        return static_cast<T*>(this)->getActiveDeviceId();
+    }
+
+    inline size_t getMaxMemorySize(int id)
+    {
+        return static_cast<T*>(this)->getMaxMemorySize(id);
+    }
+
+    public:
+    MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug)
+        : mem_step_size(1024), max_buffers(MAX_BUFFERS), memory(num_devices), debug_mode(debug)
+    {
+        lock_guard_t lock(this->memory_mutex);
+
+        for (int n = 0; n < num_devices; n++) {
+            // Calling getMaxMemorySize() here calls the virtual function that returns 0
+            // Call it from outside the constructor.
+            memory[n].max_bytes     = ONE_GB;
+            memory[n].total_bytes   = 0;
+            memory[n].total_buffers = 0;
+            memory[n].lock_bytes    = 0;
+            memory[n].lock_buffers  = 0;
+        }
+
+        // Check for environment variables
+
+        std::string env_var;
+
+        // Debug mode
+        env_var = getEnvVar("AF_MEM_DEBUG");
+        if (!env_var.empty()) {
+            this->debug_mode = env_var[0] != '0';
+        }
+        if (this->debug_mode) mem_step_size = 1;
+
+        // Max Buffer count
+        env_var = getEnvVar("AF_MAX_BUFFERS");
+        if (!env_var.empty()) {
+            this->max_buffers = std::max(1, std::stoi(env_var));
+        }
+    }
+
+    void setMaxMemorySize()
+    {
+        for (unsigned n = 0; n < memory.size(); n++) {
+            // Calls garbage collection when:
+            // total_bytes > memsize * 0.75 when memsize <  4GB
+            // total_bytes > memsize - 1 GB when memsize >= 4GB
+            // If memsize returned 0, then use 1GB
+            size_t memsize = this->getMaxMemorySize(n);
+            memory[n].max_bytes = memsize == 0 ? ONE_GB :
+                std::max(memsize * 0.75, (double)(memsize - ONE_GB));
+        }
+    }
+
+    void *alloc(const size_t bytes, bool user_lock)
+    {
+        lock_guard_t lock(this->memory_mutex);
+
+        void *ptr = NULL;
+        size_t alloc_bytes = this->debug_mode ? bytes : (divup(bytes, mem_step_size) * mem_step_size);
+
+        if (bytes > 0) {
+            memory_info& current = this->getCurrentMemoryInfo();
+
+            // There is no memory cache in debug mode
+            if (!this->debug_mode) {
+
+                // FIXME: Add better checks for garbage collection
+                // Perhaps look at total memory available as a metric
+                if (this->checkMemoryLimit()) {
+                    this->garbageCollect();
+                }
+
+                free_iter iter = current.free_map.find(alloc_bytes);
+
+                if (iter != current.free_map.end() && !iter->second.empty()) {
+                    ptr = iter->second.back();
+                    iter->second.pop_back();
+                }
+
+            }
+
+            // Only comes here if buffer size not found or in debug mode
+            if (ptr == NULL) {
+                // Perform garbage collection if memory can not be allocated
+                try {
+                    ptr = this->nativeAlloc(alloc_bytes);
+                } catch (AfError &ex) {
+                    // If out of memory, run garbage collect and try again
+                    if (ex.getError() != AF_ERR_NO_MEM) throw;
+                    this->garbageCollect();
+                    ptr = this->nativeAlloc(alloc_bytes);
+                }
+                // Increment these two only when it succeeds to come here.
+                current.total_bytes += alloc_bytes;
+                current.total_buffers += 1;
+            }
+
+
+            locked_info info = {!user_lock, user_lock, alloc_bytes};
+            current.locked_map[ptr] = info;
+            current.lock_bytes += alloc_bytes;
+            current.lock_buffers++;
+        }
+        return ptr;
+    }
+
+    void unlock(void *ptr, bool user_unlock)
+    {
+        // Shortcut for empty arrays
+        if (!ptr) return;
+
+        lock_guard_t lock(this->memory_mutex);
+        memory_info& current = this->getCurrentMemoryInfo();
+
+        locked_iter iter = current.locked_map.find((void *)ptr);
+
+        // Pointer not found in locked map
+        if (iter == current.locked_map.end()) {
+            // Probably came from user, just free it
+            this->nativeFree(ptr);
+            return;
+        }
+
+        if (user_unlock) {
+            (iter->second).user_lock = false;
+        } else {
+            (iter->second).manager_lock = false;
+        }
+
+        // Return early if either one is locked
+        if ((iter->second).user_lock || (iter->second).manager_lock) return;
+
+        size_t bytes = iter->second.bytes;
+        current.lock_bytes -= iter->second.bytes;
+        current.lock_buffers--;
+
+        current.locked_map.erase(iter);
+
+        if (this->debug_mode) {
+            // Just free memory in debug mode
+            if ((iter->second).bytes > 0) {
+                this->nativeFree(iter->first);
+                current.total_buffers--;
+                current.total_bytes -= iter->second.bytes;
+            }
+        } else {
+            // In regular mode, move buffer to free map
+            free_iter fiter = current.free_map.find(bytes);
+            if (fiter != current.free_map.end()) {
+                // If found, push back
+                fiter->second.push_back(ptr);
+            } else {
+                // If not found, create new vector for this size
+                std::vector<void *> ptrs;
+                ptrs.push_back(ptr);
+                current.free_map[bytes] = ptrs;
+            }
+        }
+    }
+
+    void garbageCollect()
+    {
+        if (this->debug_mode) return;
+
+        lock_guard_t lock(this->memory_mutex);
+        memory_info& current = this->getCurrentMemoryInfo();
+
+        // Return if all buffers are locked
+        if (current.total_buffers == current.lock_buffers) return;
+
+        for (auto &kv : current.free_map) {
+            size_t num_ptrs = kv.second.size();
+            //Free memory by popping the last element
+            for (int n = num_ptrs-1; n >= 0; n--) {
+                this->nativeFree(kv.second[n]);
+                current.total_bytes -= kv.first;
+                current.total_buffers--;
+                kv.second.pop_back();
+            }
+        }
+        current.free_map.clear();
+    }
+
+
+    void printInfo(const char *msg, const int device)
+    {
+        lock_guard_t lock(this->memory_mutex);
+        const memory_info& current = this->getCurrentMemoryInfo();
+
+        std::cout << msg << std::endl;
+
+        static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
+        static const std::string line(head.size(), '-');
+        std::cout << line << std::endl << head << std::endl << line << std::endl;
+
+        for(auto& kv : current.locked_map) {
+            std::string status_mngr("Yes");
+            std::string status_user("Unknown");
+            if(kv.second.user_lock)     status_user = "Yes";
+            else                        status_user = " No";
+
+            std::string unit = "KB";
+            double size = (double)(kv.second.bytes) / 1024;
+            if(size >= 1024) {
+                size = size / 1024;
+                unit = "MB";
+            }
+
+            std::cout << "|  " << std::right << std::setw(14) << kv.first << " "
+                << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                << " | " << std::setw(9) << status_mngr
+                << " | " << std::setw(9) << status_user
+                << " |"  << std::endl;
+        }
+
+        for(auto &kv : current.free_map) {
+
+            std::string status_mngr("No");
+            std::string status_user("No");
+
+            std::string unit = "KB";
+            double size = (double)(kv.first) / 1024;
+            if(size >= 1024) {
+                size = size / 1024;
+                unit = "MB";
+            }
+
+            for (auto &ptr : kv.second) {
+                std::cout << "|  " << std::right << std::setw(14) << ptr << " "
+                    << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
+                    << " | " << std::setw(9) << status_mngr
+                    << " | " << std::setw(9) << status_user
+                    << " |"  << std::endl;
+            }
+        }
+
+        std::cout << line << std::endl;
+    }
+
+    void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                    size_t *lock_bytes,  size_t *lock_buffers)
+    {
+        lock_guard_t lock(this->memory_mutex);
+        const memory_info& current = this->getCurrentMemoryInfo();
+        if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
+        if (alloc_buffers ) *alloc_buffers = current.total_buffers;
+        if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
+        if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
+    }
+
+    void userLock(const void *ptr)
+    {
+        memory_info& current = this->getCurrentMemoryInfo();
+
+        lock_guard_t lock(this->memory_mutex);
+
+        locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+
+        if (iter != current.locked_map.end()) {
+            iter->second.user_lock = true;
+        } else {
+            locked_info info = {false,
+                true,
+                100}; //This number is not relevant
+
+            current.locked_map[(void *)ptr] = info;
+        }
+    }
+
+    void userUnlock(const void *ptr)
+    {
+        this->unlock(const_cast<void *>(ptr), true);
+    }
+
+    bool isUserLocked(const void *ptr)
+    {
+        memory_info& current = this->getCurrentMemoryInfo();
+        lock_guard_t lock(this->memory_mutex);
+        locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+        if (iter != current.locked_map.end()) {
+            return iter->second.user_lock;
+        } else {
+            return false;
+        }
+    }
+
+    size_t getMemStepSize()
+    {
+        lock_guard_t lock(this->memory_mutex);
+        return this->mem_step_size;
+    }
+
+    size_t getMaxBytes()
+    {
+        lock_guard_t lock(this->memory_mutex);
+        return this->getCurrentMemoryInfo().max_bytes;
+    }
+
+    unsigned getMaxBuffers()
+    {
+        return this->max_buffers;
+    }
+
+    void setMemStepSize(size_t new_step_size)
+    {
+        lock_guard_t lock(this->memory_mutex);
+        this->mem_step_size = new_step_size;
+    }
+
+    inline void *nativeAlloc(const size_t bytes)
+    {
+        return static_cast<T*>(this)->nativeAlloc(bytes);
+    }
+
+    inline void nativeFree(void *ptr)
+    {
+        static_cast<T*>(this)->nativeFree(ptr);
+    }
+
+    virtual ~MemoryManager() {}
+
+    bool checkMemoryLimit()
+    {
+        const memory_info& current = this->getCurrentMemoryInfo();
+        return current.lock_bytes >= current.max_bytes || current.total_buffers >= this->max_buffers;
+    }
+
+    protected:
+    mutex_t memory_mutex;
+};
+
+}
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 343b70f5d1..96d0345f6d 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -19,7 +19,6 @@
 #include <queue.hpp>
 #include <cstring>
 #include <cstddef>
-#include <MemoryManager.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 25df00f4e2..8c246289a0 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -12,8 +12,6 @@
 #include <types.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <memory>
-#include <MemoryManager.hpp>
 
 #ifndef AF_MEM_DEBUG
 #define AF_MEM_DEBUG 0
@@ -25,88 +23,34 @@
 
 namespace cpu
 {
-
-class MemoryManager  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManager();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManager()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            cpu::setDevice(n);
-            this->garbageCollect();
-        }
-    }
-};
-
-int MemoryManager::getActiveDeviceId()
-{
-    return cpu::getActiveDeviceId();
-}
-
-size_t MemoryManager::getMaxMemorySize(int id)
-{
-    return cpu::getDeviceMemorySize(id);
-}
-
-MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
-    void *ptr = malloc(bytes);
-    if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
-    return ptr;
-}
-
-void MemoryManager::nativeFree(void *ptr)
-{
-    return free((void *)ptr);
-}
-
-static MemoryManager &getMemoryManager()
-{
-    static MemoryManager instance;
-    return instance;
-}
-
 void setMemStepSize(size_t step_bytes)
 {
-    getMemoryManager().setMemStepSize(step_bytes);
+    memoryManager().setMemStepSize(step_bytes);
 }
 
 size_t getMemStepSize(void)
 {
-    return getMemoryManager().getMemStepSize();
+    return memoryManager().getMemStepSize();
 }
 
 size_t getMaxBytes()
 {
-    return getMemoryManager().getMaxBytes();
+    return memoryManager().getMaxBytes();
 }
 
 unsigned getMaxBuffers()
 {
-    return getMemoryManager().getMaxBuffers();
+    return memoryManager().getMaxBuffers();
 }
 
 void garbageCollect()
 {
-    getMemoryManager().garbageCollect();
+    memoryManager().garbageCollect();
 }
 
 void printMemInfo(const char *msg, const int device)
 {
-    getMemoryManager().printInfo(msg, device);
+    memoryManager().printInfo(msg, device);
 }
 
 template<typename T>
@@ -115,10 +59,10 @@ T* memAlloc(const size_t &elements)
     T *ptr = nullptr;
 
     try {
-        ptr = (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+        ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
     } catch(...) {
         getQueue().sync();
-        ptr = (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+        ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
     }
     return ptr;
 }
@@ -128,10 +72,10 @@ void* memAllocUser(const size_t &bytes)
     void *ptr = nullptr;
 
     try {
-        ptr = getMemoryManager().alloc(bytes, true);
+        ptr = memoryManager().alloc(bytes, true);
     } catch(...) {
         getQueue().sync();
-        ptr = getMemoryManager().alloc(bytes, true);
+        ptr = memoryManager().alloc(bytes, true);
     }
     return ptr;
 }
@@ -139,52 +83,52 @@ void* memAllocUser(const size_t &bytes)
 template<typename T>
 void memFree(T *ptr)
 {
-    return getMemoryManager().unlock((void *)ptr, false);
+    return memoryManager().unlock((void *)ptr, false);
 }
 
 void memFreeUser(void *ptr)
 {
-    getMemoryManager().unlock((void *)ptr, true);
+    memoryManager().unlock((void *)ptr, true);
 }
 
 void memLock(const void *ptr)
 {
-    getMemoryManager().userLock((void *)ptr);
+    memoryManager().userLock((void *)ptr);
 }
 
 bool isLocked(const void *ptr)
 {
-    return getMemoryManager().isUserLocked((void *)ptr);
+    return memoryManager().isUserLocked((void *)ptr);
 }
 
 void memUnlock(const void *ptr)
 {
-    getMemoryManager().userUnlock((void *)ptr);
+    memoryManager().userUnlock((void *)ptr);
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
     getQueue().sync();
-    getMemoryManager().bufferInfo(alloc_bytes, alloc_buffers,
+    memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
                                   lock_bytes,  lock_buffers);
 }
 
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+    return (T *)memoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
 void pinnedFree(T* ptr)
 {
-    return getMemoryManager().unlock((void *)ptr, false);
+    return memoryManager().unlock((void *)ptr, false);
 }
 
 bool checkMemoryLimit()
 {
-    return getMemoryManager().checkMemoryLimit();
+    return memoryManager().checkMemoryLimit();
 }
 
 #define INSTANTIATE(T)                                      \
@@ -206,4 +150,45 @@ INSTANTIATE(uintl)
 INSTANTIATE(ushort)
 INSTANTIATE(short )
 
+MemoryManager::MemoryManager()
+    : common::MemoryManager<cpu::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
+                                                AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+MemoryManager::~MemoryManager()
+{
+    common::lock_guard_t lock(this->memory_mutex);
+    for (int n = 0; n < cpu::getDeviceCount(); n++) {
+        try {
+            cpu::setDevice(n);
+            garbageCollect();
+        } catch(AfError err) {
+            continue; // Do not throw any errors while shutting down
+        }
+    }
+}
+
+int MemoryManager::getActiveDeviceId()
+{
+    return cpu::getActiveDeviceId();
+}
+
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return cpu::getDeviceMemorySize(id);
+}
+
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    void *ptr = malloc(bytes);
+    if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
+    return ptr;
+}
+
+void MemoryManager::nativeFree(void *ptr)
+{
+    return free((void *)ptr);
+}
 }
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index b2105c5af4..e85c89fc94 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -9,36 +9,48 @@
 #pragma once
 
 #include <af/defines.h>
+#include <common/MemoryManager.hpp>
 
 namespace cpu
 {
-    template<typename T> T* memAlloc(const size_t &elements);
-    void *memAllocUser(const size_t &bytes);
+template<typename T> T* memAlloc(const size_t &elements);
+void *memAllocUser(const size_t &bytes);
 
-    // Need these as 2 separate function and not a default argument
-    // This is because it is used as the deleter in shared pointer
-    // which cannot support default arguments
-    template<typename T> void memFree(T* ptr);
-    void memFreeUser(void* ptr);
+// Need these as 2 separate function and not a default argument
+// This is because it is used as the deleter in shared pointer
+// which cannot support default arguments
+template<typename T> void memFree(T* ptr);
+void memFreeUser(void* ptr);
 
-    void memLock(const void *ptr);
-    void memUnlock(const void *ptr);
-    bool isLocked(const void *ptr);
+void memLock(const void *ptr);
+void memUnlock(const void *ptr);
+bool isLocked(const void *ptr);
 
-    template<typename T> T* pinnedAlloc(const size_t &elements);
-    template<typename T> void pinnedFree(T* ptr);
+template<typename T> T* pinnedAlloc(const size_t &elements);
+template<typename T> void pinnedFree(T* ptr);
 
-    size_t getMaxBytes();
-    unsigned getMaxBuffers();
+size_t getMaxBytes();
+unsigned getMaxBuffers();
 
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers);
-    void garbageCollect();
-    void pinnedGarbageCollect();
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes,  size_t *lock_buffers);
+void garbageCollect();
+void pinnedGarbageCollect();
 
-    void printMemInfo(const char *msg, const int device);
+void printMemInfo(const char *msg, const int device);
 
-    void setMemStepSize(size_t step_bytes);
-    size_t getMemStepSize(void);
-    bool checkMemoryLimit();
+void setMemStepSize(size_t step_bytes);
+size_t getMemStepSize(void);
+bool checkMemoryLimit();
+
+class MemoryManager : public common::MemoryManager<cpu::MemoryManager>
+{
+    public:
+        MemoryManager();
+        ~MemoryManager();
+        int getActiveDeviceId();
+        size_t getMaxMemorySize(int id);
+        void *nativeAlloc(const size_t bytes);
+        void nativeFree(void *ptr);
+};
 }
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index c8103bb93f..af694f90e1 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -257,6 +257,17 @@ bool& evalFlag()
     return flag;
 }
 
+MemoryManager& memoryManager()
+{
+    static std::once_flag flag;
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
+
+    return *(inst.memManager.get());
+}
+
 DeviceManager& DeviceManager::getInstance()
 {
     static DeviceManager my_instance;
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 066b711f88..eeefbbec88 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -12,8 +12,9 @@
 #include <array>
 #include <algorithm>
 #include <iostream>
+#include <memory>
 #include <string>
-
+#include <memory.hpp>
 #include <queue.hpp>
 
 #if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
@@ -82,7 +83,6 @@ class CPUInfo {
 
 namespace cpu
 {
-
 int getBackend();
 
 std::string getDeviceInfo();
@@ -109,6 +109,8 @@ void sync(int device);
 
 bool& evalFlag();
 
+MemoryManager& memoryManager();
+
 class DeviceManager
 {
     public:
@@ -121,6 +123,8 @@ class DeviceManager
 
         friend queue& getQueue(int device);
 
+        friend MemoryManager& memoryManager();
+
         CPUInfo getCPUInfo() const;
 
     private:
@@ -137,6 +141,7 @@ class DeviceManager
         // Attributes
         const CPUInfo cinfo;
         std::array<queue, MAX_QUEUES> queues;
-};
 
+        std::unique_ptr<MemoryManager> memManager;
+};
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index e94bcb787a..d34abd1d42 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -15,7 +15,6 @@
 #include <scalar.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
-#include <MemoryManager.hpp>
 
 #include <cstddef>
 #include <memory>
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 9b2eb07c44..f6261dc80a 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -7,103 +7,114 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <cuda_runtime.h>
+#include <platform.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
 #include <util.hpp>
 #include <types.hpp>
 #include <dispatch.hpp>
-#include <memoryManager.hpp>
+
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
+
+#ifndef AF_CUDA_MEM_DEBUG
+#define AF_CUDA_MEM_DEBUG 0
+#endif
 
 namespace cuda
 {
-
 void setMemStepSize(size_t step_bytes)
 {
-    getMemoryManager().setMemStepSize(step_bytes);
+    memoryManager().setMemStepSize(step_bytes);
 }
 
 size_t getMemStepSize(void)
 {
-    return getMemoryManager().getMemStepSize();
+    return memoryManager().getMemStepSize();
 }
 
 size_t getMaxBytes()
 {
-    return getMemoryManager().getMaxBytes();
+    return memoryManager().getMaxBytes();
 }
 
 unsigned getMaxBuffers()
 {
-    return getMemoryManager().getMaxBuffers();
+    return memoryManager().getMaxBuffers();
 }
 
 void garbageCollect()
 {
-    getMemoryManager().garbageCollect();
+    memoryManager().garbageCollect();
 }
 
 void printMemInfo(const char *msg, const int device)
 {
-    getMemoryManager().printInfo(msg, device);
+    memoryManager().printInfo(msg, device);
 }
 
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+    return (T *)memoryManager().alloc(elements * sizeof(T), false);
 }
 
 void* memAllocUser(const size_t &bytes)
 {
-    return getMemoryManager().alloc(bytes, true);
+    return memoryManager().alloc(bytes, true);
 }
 template<typename T>
 void memFree(T *ptr)
 {
-    return getMemoryManager().unlock((void *)ptr, false);
+    return memoryManager().unlock((void *)ptr, false);
 }
 
 void memFreeUser(void *ptr)
 {
-    getMemoryManager().unlock((void *)ptr, true);
+    memoryManager().unlock((void *)ptr, true);
 }
 
 void memLock(const void *ptr)
 {
-    getMemoryManager().userLock((void *)ptr);
+    memoryManager().userLock((void *)ptr);
 }
 
 void memUnlock(const void *ptr)
 {
-    getMemoryManager().userUnlock((void *)ptr);
+    memoryManager().userUnlock((void *)ptr);
 }
 
 bool isLocked(const void *ptr)
 {
-    return getMemoryManager().isUserLocked((void *)ptr);
+    return memoryManager().isUserLocked((void *)ptr);
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
-    getMemoryManager().bufferInfo(alloc_bytes, alloc_buffers,
+    memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
                                   lock_bytes,  lock_buffers);
 }
 
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T), false);
+    return (T *)pinnedMemoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
 void pinnedFree(T* ptr)
 {
-    return getMemoryManagerPinned().unlock((void *)ptr, false);
+    return pinnedMemoryManager().unlock((void *)ptr, false);
 }
 
 bool checkMemoryLimit()
 {
-    return getMemoryManager().checkMemoryLimit();
+    return memoryManager().checkMemoryLimit();
 }
 
 #define INSTANTIATE(T)                                      \
@@ -125,4 +136,85 @@ bool checkMemoryLimit()
     INSTANTIATE(short)
     INSTANTIATE(ushort)
 
+MemoryManager::MemoryManager()
+    : common::MemoryManager<cuda::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
+                                                 AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+MemoryManager::~MemoryManager()
+{
+    common::lock_guard_t lock(this->memory_mutex);
+    for (int n = 0; n < cuda::getDeviceCount(); n++) {
+        try {
+            cuda::setDevice(n);
+            garbageCollect();
+        } catch(AfError err) {
+            continue; // Do not throw any errors while shutting down
+        }
+    }
+}
+
+int MemoryManager::getActiveDeviceId()
+{
+    return cuda::getActiveDeviceId();
+}
+
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return cuda::getDeviceMemorySize(id);
+}
+
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    void *ptr = NULL;
+    CUDA_CHECK(cudaMalloc(&ptr, bytes));
+    return ptr;
+}
+
+void MemoryManager::nativeFree(void *ptr)
+{
+    cudaError_t err = cudaFree(ptr);
+    if (err != cudaErrorCudartUnloading) {
+        CUDA_CHECK(err);
+    }
+}
+
+MemoryManagerPinned::MemoryManagerPinned()
+    : common::MemoryManager<MemoryManagerPinned>(1, common::MAX_BUFFERS,
+                                                 AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+MemoryManagerPinned::~MemoryManagerPinned()
+{
+    garbageCollect();
+}
+
+int MemoryManagerPinned::getActiveDeviceId()
+{
+    return 0; // pinned uses a single vector
+}
+
+size_t MemoryManagerPinned::getMaxMemorySize(int id)
+{
+    return cuda::getHostMemorySize();
+}
+
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
+{
+    void *ptr;
+    CUDA_CHECK(cudaMallocHost(&ptr, bytes));
+    return ptr;
+}
+
+void MemoryManagerPinned::nativeFree(void *ptr)
+{
+    cudaError_t err = cudaFreeHost(ptr);
+    if (err != cudaErrorCudartUnloading) {
+        CUDA_CHECK(err);
+    }
+}
 }
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index ab895168d4..9c0ff38503 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -9,37 +9,64 @@
 #pragma once
 
 #include <cstdlib>
+#include <common/MemoryManager.hpp>
 
 namespace cuda
 {
-    template<typename T> T* memAlloc(const size_t &elements);
-    void *memAllocUser(const size_t &bytes);
+template<typename T> T* memAlloc(const size_t &elements);
+void *memAllocUser(const size_t &bytes);
 
-    // Need these as 2 separate function and not a default argument
-    // This is because it is used as the deleter in shared pointer
-    // which cannot support default arguments
-    template<typename T> void memFree(T* ptr);
-    void memFreeUser(void* ptr);
+// Need these as 2 separate function and not a default argument
+// This is because it is used as the deleter in shared pointer
+// which cannot support default arguments
+template<typename T> void memFree(T* ptr);
+void memFreeUser(void* ptr);
 
-    void memLock(const void *ptr);
-    void memUnlock(const void *ptr);
-    bool isLocked(const void *ptr);
+void memLock(const void *ptr);
+void memUnlock(const void *ptr);
+bool isLocked(const void *ptr);
 
-    template<typename T> T* pinnedAlloc(const size_t &elements);
-    template<typename T> void pinnedFree(T* ptr);
+template<typename T> T* pinnedAlloc(const size_t &elements);
+template<typename T> void pinnedFree(T* ptr);
 
-    size_t getMaxBytes();
-    unsigned getMaxBuffers();
+size_t getMaxBytes();
+unsigned getMaxBuffers();
 
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers);
-    void garbageCollect();
-    void pinnedGarbageCollect();
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes,  size_t *lock_buffers);
+void garbageCollect();
+void pinnedGarbageCollect();
 
-    void printMemInfo(const char *msg, const int device);
+void printMemInfo(const char *msg, const int device);
 
-    void setMemStepSize(size_t step_bytes);
-    size_t getMemStepSize(void);
+void setMemStepSize(size_t step_bytes);
+size_t getMemStepSize(void);
 
-    bool checkMemoryLimit();
+bool checkMemoryLimit();
+
+class MemoryManager : public common::MemoryManager<cuda::MemoryManager>
+{
+    public:
+        MemoryManager();
+        ~MemoryManager();
+        int getActiveDeviceId();
+        size_t getMaxMemorySize(int id);
+        void *nativeAlloc(const size_t bytes);
+        void nativeFree(void *ptr);
+};
+
+// CUDA Pinned Memory does not depend on device
+// So we pass 1 as numDevices to the constructor so that it creates 1 vector
+// of memory_info
+// When allocating and freeing, it doesn't really matter which device is active
+class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned>
+{
+    public:
+        MemoryManagerPinned();
+        ~MemoryManagerPinned();
+        int getActiveDeviceId();
+        size_t getMaxMemorySize(int id);
+        void *nativeAlloc(const size_t bytes);
+        void nativeFree(void *ptr);
+};
 }
diff --git a/src/backend/cuda/memoryManager.cpp b/src/backend/cuda/memoryManager.cpp
deleted file mode 100644
index d160471814..0000000000
--- a/src/backend/cuda/memoryManager.cpp
+++ /dev/null
@@ -1,86 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <err_cuda.hpp>
-#include <memoryManager.hpp>
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_CUDA_MEM_DEBUG
-#define AF_CUDA_MEM_DEBUG 0
-#endif
-
-namespace cuda
-{
-
-int MemoryManager::getActiveDeviceId()
-{
-    return cuda::getActiveDeviceId();
-}
-
-size_t MemoryManager::getMaxMemorySize(int id)
-{
-    return cuda::getDeviceMemorySize(id);
-}
-
-MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
-    void *ptr = NULL;
-    CUDA_CHECK(cudaMalloc(&ptr, bytes));
-    return ptr;
-}
-
-void MemoryManager::nativeFree(void *ptr)
-{
-    cudaError_t err = cudaFree(ptr);
-    if (err != cudaErrorCudartUnloading) {
-        CUDA_CHECK(err);
-    }
-}
-
-int MemoryManagerPinned::getActiveDeviceId()
-{
-    return 0; // pinned uses a single vector
-}
-
-size_t MemoryManagerPinned::getMaxMemorySize(int id)
-{
-    return cuda::getHostMemorySize();
-}
-
-MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
-{
-    void *ptr;
-    CUDA_CHECK(cudaMallocHost(&ptr, bytes));
-    return ptr;
-}
-
-void MemoryManagerPinned::nativeFree(void *ptr)
-{
-    cudaError_t err = cudaFreeHost(ptr);
-    if (err != cudaErrorCudartUnloading) {
-        CUDA_CHECK(err);
-    }
-}
-
-}
diff --git a/src/backend/cuda/memoryManager.hpp b/src/backend/cuda/memoryManager.hpp
deleted file mode 100644
index b22bd02c1f..0000000000
--- a/src/backend/cuda/memoryManager.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <cuda.h>
-#include <cuda_runtime_api.h>
-#include <cuda_runtime.h>
-
-#include <platform.hpp>
-#include <MemoryManager.hpp>
-
-namespace cuda
-{
-
-class MemoryManager  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManager();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManager()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            try {
-                cuda::setDevice(n);
-                this->garbageCollect();
-            } catch(AfError err) {
-                continue; // Do not throw any errors while shutting down
-            }
-        }
-    }
-};
-
-// CUDA Pinned Memory does not depend on device
-// So we pass 1 as numDevices to the constructor so that it creates 1 vector
-// of memory_info
-// When allocating and freeing, it doesn't really matter which device is active
-class MemoryManagerPinned  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManagerPinned();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManagerPinned()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        this->garbageCollect();
-    }
-};
-
-}
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index ca00ccf06c..fbfee69d34 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -24,7 +24,6 @@
 #include <err_cuda.hpp>
 #include <util.hpp>
 #include <host_memory.hpp>
-#include <memoryManager.hpp>
 #include <common/InteropManager.hpp>
 
 using namespace std;
@@ -386,28 +385,24 @@ DeviceManager& DeviceManager::getInstance()
     return my_instance;
 }
 
-MemoryManager &getMemoryManager()
+MemoryManager& memoryManager()
 {
     static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
-                inst.memManager.reset(new cuda::MemoryManager());
-            });
+    std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
 
     return *(inst.memManager.get());
 }
 
-MemoryManagerPinned &getMemoryManagerPinned()
+MemoryManagerPinned& pinnedMemoryManager()
 {
     static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
-                inst.pinnedMemManager.reset(new cuda::MemoryManagerPinned());
-            });
+    std::call_once(flag, [&]() { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
 
     return *(inst.pinnedMemManager.get());
 }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 5a025621d3..8440d58b81 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -14,7 +14,7 @@
 #include <memory>
 #include <vector>
 #include <string>
-
+#include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <cufft.hpp>
 #include <cublas.hpp>
@@ -72,13 +72,11 @@ struct cudaDevice_t {
 
 bool& evalFlag();
 
-class MemoryManager;
-class MemoryManagerPinned;
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
-MemoryManager& getMemoryManager();
+MemoryManager& memoryManager();
 
-MemoryManagerPinned& getMemoryManagerPinned();
+MemoryManagerPinned& pinnedMemoryManager();
 
 typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
 GraphicsManager& interopManager();
@@ -103,9 +101,9 @@ class DeviceManager
 
         static DeviceManager& getInstance();
 
-        friend MemoryManager& getMemoryManager();
+        friend MemoryManager& memoryManager();
 
-        friend MemoryManagerPinned& getMemoryManagerPinned();
+        friend MemoryManagerPinned& pinnedMemoryManager();
 
         friend GraphicsManager& interopManager();
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 431de642cd..88355ee3dd 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -18,7 +18,6 @@
 #include <cstddef>
 #include <af/opencl.h>
 #include <util.hpp>
-#include <MemoryManager.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 3542e3451a..e032f7bc6e 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -8,112 +8,118 @@
  ********************************************************/
 
 #include <memory.hpp>
-
-#include <memoryManager.hpp>
 #include <platform.hpp>
 #include <types.hpp>
+#include <err_opencl.hpp>
+
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
+
+#ifndef AF_OPENCL_MEM_DEBUG
+#define AF_OPENCL_MEM_DEBUG 0
+#endif
 
 namespace opencl
 {
-
 void setMemStepSize(size_t step_bytes)
 {
-    getMemoryManager().setMemStepSize(step_bytes);
+    memoryManager().setMemStepSize(step_bytes);
 }
 
 size_t getMemStepSize(void)
 {
-    return getMemoryManager().getMemStepSize();
+    return memoryManager().getMemStepSize();
 }
 
 size_t getMaxBytes()
 {
-    return getMemoryManager().getMaxBytes();
+    return memoryManager().getMaxBytes();
 }
 
 unsigned getMaxBuffers()
 {
-    return getMemoryManager().getMaxBuffers();
+    return memoryManager().getMaxBuffers();
 }
 
 void garbageCollect()
 {
-    getMemoryManager().garbageCollect();
+    memoryManager().garbageCollect();
 }
 
 void printMemInfo(const char *msg, const int device)
 {
-    getMemoryManager().printInfo(msg, device);
+    memoryManager().printInfo(msg, device);
 }
 
 template<typename T>
 T* memAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManager().alloc(elements * sizeof(T), false);
+    return (T *)memoryManager().alloc(elements * sizeof(T), false);
 }
 
 void* memAllocUser(const size_t &bytes)
 {
-    return getMemoryManager().alloc(bytes, true);
+    return memoryManager().alloc(bytes, true);
 }
 template<typename T>
 void memFree(T *ptr)
 {
-    return getMemoryManager().unlock((void *)ptr, false);
+    return memoryManager().unlock((void *)ptr, false);
 }
 
 void memFreeUser(void *ptr)
 {
-    getMemoryManager().unlock((void *)ptr, true);
+    memoryManager().unlock((void *)ptr, true);
 }
 
 cl::Buffer *bufferAlloc(const size_t &bytes)
 {
-    return (cl::Buffer *)getMemoryManager().alloc(bytes, false);
+    return (cl::Buffer *)memoryManager().alloc(bytes, false);
 }
 
 void bufferFree(cl::Buffer *buf)
 {
-    return getMemoryManager().unlock((void *)buf, false);
+    return memoryManager().unlock((void *)buf, false);
 }
 
 void memLock(const void *ptr)
 {
-    getMemoryManager().userLock((void *)ptr);
+    memoryManager().userLock((void *)ptr);
 }
 
 void memUnlock(const void *ptr)
 {
-    getMemoryManager().userUnlock((void *)ptr);
+    memoryManager().userUnlock((void *)ptr);
 }
 
 bool isLocked(const void *ptr)
 {
-    return getMemoryManager().isUserLocked((void *)ptr);
+    return memoryManager().isUserLocked((void *)ptr);
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
-    getMemoryManager().bufferInfo(alloc_bytes, alloc_buffers,
+    memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
                                   lock_bytes,  lock_buffers);
 }
 
 template<typename T>
 T* pinnedAlloc(const size_t &elements)
 {
-    return (T *)getMemoryManagerPinned().alloc(elements * sizeof(T), false);
+    return (T *)pinnedMemoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
 void pinnedFree(T* ptr)
 {
-    return getMemoryManagerPinned().unlock((void *)ptr, false);
+    return pinnedMemoryManager().unlock((void *)ptr, false);
 }
 
 bool checkMemoryLimit()
 {
-    return getMemoryManager().checkMemoryLimit();
+    return memoryManager().checkMemoryLimit();
 }
 
 #define INSTANTIATE(T)                                      \
@@ -134,4 +140,100 @@ bool checkMemoryLimit()
     INSTANTIATE(uintl)
     INSTANTIATE(short)
     INSTANTIATE(ushort)
+
+MemoryManager::MemoryManager()
+    : common::MemoryManager<opencl::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
+                                                   AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
+{
+    this->setMaxMemorySize();
+}
+
+MemoryManager::~MemoryManager()
+{
+    common::lock_guard_t lock(this->memory_mutex);
+    for (int n = 0; n < opencl::getDeviceCount(); n++) {
+        try {
+            opencl::setDevice(n);
+            garbageCollect();
+        } catch(AfError err) {
+            continue; // Do not throw any errors while shutting down
+        }
+    }
+}
+
+int MemoryManager::getActiveDeviceId()
+{
+    return opencl::getActiveDeviceId();
+}
+
+size_t MemoryManager::getMaxMemorySize(int id)
+{
+    return opencl::getDeviceMemorySize(id);
+}
+
+void *MemoryManager::nativeAlloc(const size_t bytes)
+{
+    return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
+}
+
+void MemoryManager::nativeFree(void *ptr)
+{
+    delete (cl::Buffer *)ptr;
+}
+
+MemoryManagerPinned::MemoryManagerPinned()
+    : common::MemoryManager<MemoryManagerPinned>(getDeviceCount(), common::MAX_BUFFERS,
+                                                 AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
+    pinnedMaps(getDeviceCount())
+{
+    this->setMaxMemorySize();
+}
+
+MemoryManagerPinned::~MemoryManagerPinned()
+{
+    common::lock_guard_t lock(this->memory_mutex);
+    for (int n = 0; n < getDeviceCount(); n++) {
+        opencl::setDevice(n);
+        garbageCollect();
+        auto currIterator = pinnedMaps[n].begin();
+        auto endIterator  = pinnedMaps[n].end();
+        while (currIterator != endIterator) {
+            delete currIterator->second;
+            pinnedMaps[n].erase(currIterator++);
+        }
+    }
+}
+
+int MemoryManagerPinned::getActiveDeviceId()
+{
+    return opencl::getActiveDeviceId();
+}
+
+size_t MemoryManagerPinned::getMaxMemorySize(int id)
+{
+    return opencl::getDeviceMemorySize(id);
+}
+
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
+{
+    void *ptr = NULL;
+    cl::Buffer* buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
+    ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
+    pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, buf);
+    return ptr;
+}
+
+void MemoryManagerPinned::nativeFree(void *ptr)
+{
+    int n = opencl::getActiveDeviceId();
+    auto map = pinnedMaps[n];
+    auto iter = map.find(ptr);
+
+    if (iter != map.end()) {
+        cl::Buffer* buf = map[ptr];
+        getQueue().enqueueUnmapMemObject(*buf, ptr);
+        delete buf;
+        map.erase(iter);
+    }
+}
 }
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 823fd4581f..c6af6dfa1d 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -9,45 +9,71 @@
 #pragma once
 
 #include <cstdlib>
+#include <map>
+#include <vector>
+#include <common/MemoryManager.hpp>
 
 namespace cl
 {
-class Buffer;
+class Buffer; //Forward declaration of cl::Buffer from CL/cl2.hpp
 }
 
 namespace opencl
 {
+cl::Buffer *bufferAlloc(const size_t &bytes);
+void bufferFree(cl::Buffer *buf);
 
-    cl::Buffer *bufferAlloc(const size_t &bytes);
-    void bufferFree(cl::Buffer *buf);
+template<typename T> T* memAlloc(const size_t &elements);
+void *memAllocUser(const size_t &bytes);
 
-    template<typename T> T* memAlloc(const size_t &elements);
-    void *memAllocUser(const size_t &bytes);
+// Need these as 2 separate function and not a default argument
+// This is because it is used as the deleter in shared pointer
+// which cannot support default arguments
+template<typename T> void memFree(T* ptr);
+void memFreeUser(void* ptr);
 
-    // Need these as 2 separate function and not a default argument
-    // This is because it is used as the deleter in shared pointer
-    // which cannot support default arguments
-    template<typename T> void memFree(T* ptr);
-    void memFreeUser(void* ptr);
+void memLock(const void *ptr);
+void memUnlock(const void *ptr);
+bool isLocked(const void *ptr);
 
-    void memLock(const void *ptr);
-    void memUnlock(const void *ptr);
-    bool isLocked(const void *ptr);
+template<typename T> T* pinnedAlloc(const size_t &elements);
+template<typename T> void pinnedFree(T* ptr);
 
-    template<typename T> T* pinnedAlloc(const size_t &elements);
-    template<typename T> void pinnedFree(T* ptr);
+size_t getMaxBytes();
+unsigned getMaxBuffers();
 
-    size_t getMaxBytes();
-    unsigned getMaxBuffers();
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes,  size_t *lock_buffers);
+void garbageCollect();
+void pinnedGarbageCollect();
 
-    void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers);
-    void garbageCollect();
-    void pinnedGarbageCollect();
+void printMemInfo(const char *msg, const int device);
 
-    void printMemInfo(const char *msg, const int device);
+void setMemStepSize(size_t step_bytes);
+size_t getMemStepSize(void);
+bool checkMemoryLimit();
 
-    void setMemStepSize(size_t step_bytes);
-    size_t getMemStepSize(void);
-    bool checkMemoryLimit();
+class MemoryManager : public common::MemoryManager<opencl::MemoryManager>
+{
+    public:
+        MemoryManager();
+        ~MemoryManager();
+        int getActiveDeviceId();
+        size_t getMaxMemorySize(int id);
+        void *nativeAlloc(const size_t bytes);
+        void nativeFree(void *ptr);
+};
+
+class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned>
+{
+    public:
+        MemoryManagerPinned();
+        ~MemoryManagerPinned();
+        int getActiveDeviceId();
+        size_t getMaxMemorySize(int id);
+        void *nativeAlloc(const size_t bytes);
+        void nativeFree(void *ptr);
+    private:
+        std::vector< std::map<void *, cl::Buffer*> > pinnedMaps;
+};
 }
diff --git a/src/backend/opencl/memoryManager.cpp b/src/backend/opencl/memoryManager.cpp
deleted file mode 100644
index c329faef5c..0000000000
--- a/src/backend/opencl/memoryManager.cpp
+++ /dev/null
@@ -1,86 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <memoryManager.hpp>
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_OPENCL_MEM_DEBUG
-#define AF_OPENCL_MEM_DEBUG 0
-#endif
-
-namespace opencl
-{
-
-int MemoryManager::getActiveDeviceId()
-{
-    return opencl::getActiveDeviceId();
-}
-
-size_t MemoryManager::getMaxMemorySize(int id)
-{
-    return opencl::getDeviceMemorySize(id);
-}
-
-MemoryManager::MemoryManager() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
-    return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
-}
-
-void MemoryManager::nativeFree(void *ptr)
-{
-    delete (cl::Buffer *)ptr;
-}
-
-int MemoryManagerPinned::getActiveDeviceId()
-{
-    return opencl::getActiveDeviceId();
-}
-
-size_t MemoryManagerPinned::getMaxMemorySize(int id)
-{
-    return opencl::getDeviceMemorySize(id);
-}
-
-MemoryManagerPinned::MemoryManagerPinned() :
-    common::MemoryManager(getDeviceCount(), common::MAX_BUFFERS, AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
-    pinned_maps(getDeviceCount())
-{
-    this->setMaxMemorySize();
-}
-
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
-{
-    void *ptr = NULL;
-    cl::Buffer buf= cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
-    ptr = getQueue().enqueueMapBuffer(buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
-    pinned_maps[opencl::getActiveDeviceId()][ptr] = buf;
-    return ptr;
-}
-
-void MemoryManagerPinned::nativeFree(void *ptr)
-{
-    int n = opencl::getActiveDeviceId();
-    auto iter = pinned_maps[n].find(ptr);
-
-    if (iter != pinned_maps[n].end()) {
-        getQueue().enqueueUnmapMemObject(pinned_maps[n][ptr], ptr);
-        pinned_maps[n].erase(iter);
-    }
-}
-
-}
diff --git a/src/backend/opencl/memoryManager.hpp b/src/backend/opencl/memoryManager.hpp
deleted file mode 100644
index eab26ed235..0000000000
--- a/src/backend/opencl/memoryManager.hpp
+++ /dev/null
@@ -1,68 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <platform.hpp>
-#include <MemoryManager.hpp>
-
-#include <map>
-
-namespace opencl
-{
-
-class MemoryManager  : public common::MemoryManager
-{
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-public:
-    MemoryManager();
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-    ~MemoryManager()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            opencl::setDevice(n);
-            this->garbageCollect();
-        }
-    }
-};
-
-class MemoryManagerPinned  : public common::MemoryManager
-{
-    std::vector<
-        std::map<void *, cl::Buffer>
-        > pinned_maps;
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-
-public:
-
-    MemoryManagerPinned();
-
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
-
-    ~MemoryManagerPinned()
-    {
-        common::lock_guard_t lock(this->memory_mutex);
-        for (int n = 0; n < getDeviceCount(); n++) {
-            opencl::setDevice(n);
-            this->garbageCollect();
-            auto pinned_curr_iter = pinned_maps[n].begin();
-            auto pinned_end_iter  = pinned_maps[n].end();
-            while (pinned_curr_iter != pinned_end_iter) {
-                pinned_maps[n].erase(pinned_curr_iter++);
-            }
-        }
-    }
-};
-
-}
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 31b3abafa3..295fcccad5 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -29,7 +29,6 @@
 #include <err_opencl.hpp>
 #include <host_memory.hpp>
 #include <common/InteropManager.hpp>
-#include <memoryManager.hpp>
 #include <platform.hpp>
 #include <util.hpp>
 #include <version.hpp>
@@ -839,28 +838,24 @@ bool& evalFlag()
     return flag;
 }
 
-MemoryManager& getMemoryManager()
+MemoryManager& memoryManager()
 {
     static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
-                inst.memManager.reset(new MemoryManager());
-            });
+    std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
 
     return *(inst.memManager.get());
 }
 
-MemoryManagerPinned& getMemoryManagerPinned()
+MemoryManagerPinned& pinnedMemoryManager()
 {
     static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
-                inst.pinnedMemManager.reset(new MemoryManagerPinned());
-            });
+    std::call_once(flag, [&]() { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
 
     return *(inst.pinnedMemManager.get());
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index d94d60ae43..71c2ae0c56 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -26,6 +26,7 @@
 #include <vector>
 #include <string>
 
+#include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <clfft.hpp>
 
@@ -82,15 +83,11 @@ int getActivePlatform();
 
 bool& evalFlag();
 
-// Forward Declarations
-class MemoryManager;
-class MemoryManagerPinned;
-
 ///////////////////////// BEGIN Sub-Managers ///////////////////
 //
-MemoryManager &getMemoryManager();
+MemoryManager& memoryManager();
 
-MemoryManagerPinned& getMemoryManagerPinned();
+MemoryManagerPinned& pinnedMemoryManager();
 
 typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
 GraphicsManager& interopManager();
@@ -102,9 +99,9 @@ FFTManager& clfftManager();
 
 class DeviceManager
 {
-    friend MemoryManager &getMemoryManager();
+    friend MemoryManager& memoryManager();
 
-    friend MemoryManagerPinned& getMemoryManagerPinned();
+    friend MemoryManagerPinned& pinnedMemoryManager();
 
     friend GraphicsManager& interopManager();
 

From 391d0e341f40b9762211bed8775d4e4c1b08c204 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Jan 2017 19:40:23 +0530
Subject: [PATCH 1098/2677] Fix CRTP classes of graphics, fft, blas, solve,
 sparse

---
 src/backend/cuda/GraphicsResourceManager.hpp |  2 ++
 src/backend/cuda/blas.cpp                    |  6 ++--
 src/backend/cuda/cholesky.cu                 |  4 +--
 src/backend/cuda/cublas.hpp                  |  2 --
 src/backend/cuda/cufft.cpp                   |  4 +--
 src/backend/cuda/cusolverDn.hpp              |  2 --
 src/backend/cuda/cusparse.hpp                |  3 --
 src/backend/cuda/fft.cpp                     |  2 +-
 src/backend/cuda/hist_graphics.cpp           |  4 +--
 src/backend/cuda/image.cpp                   |  4 +--
 src/backend/cuda/lu.cu                       |  4 +--
 src/backend/cuda/platform.cpp                | 19 ++++++------
 src/backend/cuda/platform.hpp                | 32 +++++++++-----------
 src/backend/cuda/plot.cpp                    |  4 +--
 src/backend/cuda/qr.cu                       | 10 +++---
 src/backend/cuda/solve.cu                    | 16 +++++-----
 src/backend/cuda/sparse.cu                   | 30 +++++++++---------
 src/backend/cuda/sparse_blas.cpp             |  4 +--
 src/backend/cuda/surface.cpp                 |  4 +--
 src/backend/cuda/svd.cu                      |  4 +--
 src/backend/cuda/vector_field.cpp            |  4 +--
 src/backend/opencl/clfft.cpp                 |  2 +-
 src/backend/opencl/fft.cpp                   |  2 +-
 src/backend/opencl/hist_graphics.cpp         |  4 +--
 src/backend/opencl/image.cpp                 |  4 +--
 src/backend/opencl/platform.cpp              |  6 ++--
 src/backend/opencl/platform.hpp              | 14 ++++-----
 src/backend/opencl/plot.cpp                  |  4 +--
 src/backend/opencl/surface.cpp               |  4 +--
 src/backend/opencl/vector_field.cpp          |  4 +--
 30 files changed, 98 insertions(+), 110 deletions(-)

diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index b897d500c6..6f8b39fd8e 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #if defined(WITH_GRAPHICS)
 #if defined(OS_WIN)
 #include <windows.h>
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 46e4bfa8fb..921fe685ea 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -171,7 +171,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
         CUBLAS_CHECK(gemv_func<T>()(
-                         cublasHandle(),
+                         blasHandle(),
                          lOpts,
                          lDims[0],
                          lDims[1],
@@ -182,7 +182,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                          out.get(), 1));
     } else {
         CUBLAS_CHECK(gemm_func<T>()(
-                         cublasHandle(),
+                         blasHandle(),
                          lOpts,
                          rOpts,
                          M, N, K,
@@ -224,7 +224,7 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans,
     dim4 rStrides = rhs.strides();
 
     CUBLAS_CHECK(trsm_func<T>()(
-                     cublasHandle(),
+                     blasHandle(),
                      is_left  ? CUBLAS_SIDE_LEFT : CUBLAS_SIDE_RIGHT,
                      is_upper ? CUBLAS_FILL_MODE_UPPER : CUBLAS_FILL_MODE_LOWER,
                      toCblasTranspose(trans),
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index 53df19b67f..dc7a239f8b 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -110,7 +110,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     if(is_upper)
         uplo = CUBLAS_FILL_MODE_UPPER;
 
-    CUSOLVER_CHECK(potrf_buf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(potrf_buf_func<T>()(solverDnHandle(),
                                        uplo,
                                        N,
                                        in.get(), in.strides()[1],
@@ -119,7 +119,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     T *workspace = memAlloc<T>(lwork);
     int *d_info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(potrf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(potrf_func<T>()(solverDnHandle(),
                                    uplo,
                                    N,
                                    in.get(), in.strides()[1],
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index 5e5dd40922..994cb92023 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -41,6 +41,4 @@ class cublasHandle : public common::MatrixAlgebraHandle<cublasHandle, BlasHandle
             cublasDestroy(handle);
         }
 };
-
-typedef common::MatrixAlgebraHandle<cublasHandle, BlasHandle> BlasHandleWrapper;
 }
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index ac743f47f8..b185486158 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -13,7 +13,6 @@
 
 namespace cuda
 {
-
 const char * _cufftGetResultString(cufftResult res)
 {
     switch (res)
@@ -112,7 +111,7 @@ PlanType findPlan(int rank, int *n,
     sprintf(key_str_temp, "%d:%d", (int)type, batch);
     key_string.append(std::string(key_str_temp));
 
-    FFTManager &planner = cuda::cufftManager();
+    PlanCache &planner = cuda::fftManager();
 
     int planIndex = planner.find(key_string);
 
@@ -141,5 +140,4 @@ PlanType findPlan(int rank, int *n,
 
     return retVal;
 }
-
 }
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index 67592e67f0..dd42094a0c 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -46,6 +46,4 @@ class cusolverDnHandle : public common::MatrixAlgebraHandle<cusolverDnHandle, So
             cusolverDnDestroy(handle);
         }
 };
-
-typedef common::MatrixAlgebraHandle<cusolverDnHandle, SolveHandle> SolveHandleWrapper;
 }
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 3bbdec81d1..bb4237fb6a 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -39,7 +39,4 @@ class cusparseHandle : public common::MatrixAlgebraHandle<cusparseHandle, Sparse
             cusparseDestroy(handle);
         }
 };
-
-typedef common::MatrixAlgebraHandle<cusparseHandle, SparseHandle> SparseHandleWrapper;
-
 }
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 19a753421a..ca41896e29 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -23,7 +23,7 @@ namespace cuda
 {
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    cufftManager().maxCacheSize(numPlans);
+    fftManager().maxCacheSize(numPlans);
 }
 
 template<typename T>
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index f74e03c23a..6367208c5c 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -13,7 +13,7 @@
 #include <hist_graphics.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 namespace cuda
 {
@@ -25,7 +25,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index de9d0efa9c..17d0f12b0f 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -16,7 +16,7 @@
 #include <image.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 using af::dim4;
 
@@ -28,7 +28,7 @@ template<typename T>
 void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
 
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index 79bbd5029e..104bc9025f 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -129,7 +129,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(getrf_buf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(getrf_buf_func<T>()(solverDnHandle(),
                                        M, N,
                                        in.get(), in.strides()[1],
                                        &lwork));
@@ -137,7 +137,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     T *workspace = memAlloc<T>(lwork);
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(getrf_func<T>()(solverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    workspace,
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index fbfee69d34..48408ef105 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -24,7 +24,6 @@
 #include <err_cuda.hpp>
 #include <util.hpp>
 #include <host_memory.hpp>
-#include <common/InteropManager.hpp>
 
 using namespace std;
 
@@ -407,43 +406,43 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
-GraphicsManager& interopManager()
+GraphicsResourceManager& interopManager()
 {
     DeviceManager& inst = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! inst.gfxManagers[id] )
-        inst.gfxManagers[id].reset(new GraphicsManager());
+        inst.gfxManagers[id].reset(new GraphicsResourceManager());
 
     return *(inst.gfxManagers[id].get());
 }
 
-FFTManager& cufftManager()
+PlanCache& fftManager()
 {
     return DeviceManager::getInstance().cufftManagers[getActiveDeviceId()];
 }
 
-BlasHandle cublasHandle()
+BlasHandle blasHandle()
 {
     DeviceManager& instance = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! instance.cublasHandles[id] )
-        instance.cublasHandles[id].reset(new BlasHandleWrapper());
+        instance.cublasHandles[id].reset(new cublasHandle());
 
     return instance.cublasHandles[id].get()->get();
 }
 
-SolveHandle cusolverDnHandle()
+SolveHandle solverDnHandle()
 {
     DeviceManager& instance = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! instance.cusolverHandles[id] )
-        instance.cusolverHandles[id].reset(new SolveHandleWrapper());
+        instance.cusolverHandles[id].reset(new cusolverDnHandle());
 
     // FIXME
     // This is not an ideal case. It's just a hack.
@@ -463,14 +462,14 @@ SolveHandle cusolverDnHandle()
     return instance.cusolverHandles[id].get()->get();
 }
 
-SparseHandle cusparseHandle()
+SparseHandle sparseHandle()
 {
     DeviceManager& instance = DeviceManager::getInstance();
 
     int id = cuda::getActiveDeviceId();
 
     if (! instance.cusparseHandles[id] )
-        instance.cusparseHandles[id].reset(new SparseHandleWrapper());
+        instance.cusparseHandles[id].reset(new cusparseHandle());
 
     return instance.cusparseHandles[id].get()->get();
 }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 8440d58b81..ba660cda62 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -78,17 +78,15 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
-typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
-GraphicsManager& interopManager();
+GraphicsResourceManager& interopManager();
 
-typedef common::FFTPlanCache<PlanCache, PlanType> FFTManager;
-FFTManager& cufftManager();
+PlanCache& fftManager();
 
-BlasHandle cublasHandle();
+BlasHandle blasHandle();
 
-SolveHandle cusolverDnHandle();
+SolveHandle solverDnHandle();
 
-SparseHandle cusparseHandle();
+SparseHandle sparseHandle();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -105,15 +103,15 @@ class DeviceManager
 
         friend MemoryManagerPinned& pinnedMemoryManager();
 
-        friend GraphicsManager& interopManager();
+        friend GraphicsResourceManager& interopManager();
 
-        friend FFTManager& cufftManager();
+        friend PlanCache& fftManager();
 
-        friend BlasHandle cublasHandle();
+        friend BlasHandle blasHandle();
 
-        friend SolveHandle cusolverDnHandle();
+        friend SolveHandle solverDnHandle();
 
-        friend SparseHandle cusparseHandle();
+        friend SparseHandle sparseHandle();
 
         friend std::string getDeviceInfo(int device);
 
@@ -166,15 +164,15 @@ class DeviceManager
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
-        std::unique_ptr<GraphicsManager> gfxManagers[MAX_DEVICES];
+        std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 
-        FFTManager cufftManagers[MAX_DEVICES];
+        PlanCache cufftManagers[MAX_DEVICES];
 
-        std::unique_ptr<BlasHandleWrapper> cublasHandles[MAX_DEVICES];
+        std::unique_ptr<cublasHandle> cublasHandles[MAX_DEVICES];
 
-        std::unique_ptr<SolveHandleWrapper> cusolverHandles[MAX_DEVICES];
+        std::unique_ptr<cusolverDnHandle> cusolverHandles[MAX_DEVICES];
 
-        std::unique_ptr<SparseHandleWrapper> cusparseHandles[MAX_DEVICES];
+        std::unique_ptr<cusparseHandle> cusparseHandles[MAX_DEVICES];
 };
 
 }
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 3c67e7d36b..e7a31537ff 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -16,7 +16,7 @@
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 using af::dim4;
 
@@ -30,7 +30,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index 7a73512ea9..b058453310 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -130,7 +130,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(solverDnHandle(),
                                        M, N,
                                        in_copy.get(), in_copy.strides()[1],
                                        &lwork));
@@ -140,7 +140,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(),
                                    M, N,
                                    in_copy.get(), in_copy.strides()[1],
                                    t.get(),
@@ -157,7 +157,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     dim4 qdims(M, mn);
     q = identity<T>(qdims);
 
-    CUSOLVER_CHECK(mqr_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(mqr_func<T>()(solverDnHandle(),
                                  CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
                                  q.dims()[0],
                                  q.dims()[1],
@@ -185,7 +185,7 @@ Array<T> qr_inplace(Array<T> &in)
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(solverDnHandle(),
                                        M, N,
                                        in.get(), in.strides()[1],
                                        &lwork));
@@ -193,7 +193,7 @@ Array<T> qr_inplace(Array<T> &in)
     T *workspace = memAlloc<T>(lwork);
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    t.get(),
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index c4984c4868..7f3e7901ac 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -174,7 +174,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(),
                                    CUBLAS_OP_N,
                                    N, NRHS,
                                    A.get(), A.strides()[1],
@@ -199,7 +199,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
 
     int *info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(cusolverDnHandle(),
+    CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(),
                                    CUBLAS_OP_N,
                                    N, K,
                                    A.get(), A.strides()[1],
@@ -242,7 +242,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(cusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(solverDnHandle(),
                                                  A.dims()[0], A.dims()[1],
                                                  A.get(), A.strides()[1],
                                                  &lwork));
@@ -252,7 +252,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int *info = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(cusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(solverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
@@ -270,7 +270,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(cusolverDnHandle(),
+        CUSOLVER_CHECK(mqr_solve_func<T>()(solverDnHandle(),
                                            CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
                                            B.dims()[0],
                                            B.dims()[1],
@@ -300,7 +300,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(cusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(solverDnHandle(),
                                                  A.dims()[0], A.dims()[1],
                                                  A.get(), A.strides()[1],
                                                  &lwork));
@@ -310,7 +310,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         int *info = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(cusolverDnHandle(),
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(solverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
@@ -318,7 +318,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                              info));
 
         // matmul(Q1, B)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(cusolverDnHandle(),
+        CUSOLVER_CHECK(mqr_solve_func<T>()(solverDnHandle(),
                                            CUBLAS_SIDE_LEFT,
                                            trans<T>(),
                                            M, K, N,
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 49eb442d99..0fe53c438d 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -261,7 +261,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 
     int nNZ = -1;
     CUSPARSE_CHECK(nnz_func<T>()(
-                        cusparseHandle(),
+                        sparseHandle(),
                         dir,
                         M, N,
                         descr,
@@ -282,7 +282,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 
     if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(dense2csr_func<T>()(
-                        cusparseHandle(),
+                        sparseHandle(),
                         M, N,
                         descr,
                         in.get(), in.strides()[1],
@@ -290,7 +290,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
                         values.get(), rowIdx.get(), colIdx.get()));
     else
         CUSPARSE_CHECK(dense2csc_func<T>()(
-                        cusparseHandle(),
+                        sparseHandle(),
                         M, N,
                         descr,
                         in.get(), in.strides()[1],
@@ -336,7 +336,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 
     if(stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(csr2dense_func<T>()(
-                        cusparseHandle(),
+                        sparseHandle(),
                         M, N,
                         descr,
                         in.getValues().get(),
@@ -345,7 +345,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
                         dense.get(), d_strides1));
     else
         CUSPARSE_CHECK(csc2dense_func<T>()(
-                        cusparseHandle(),
+                        sparseHandle(),
                         M, N,
                         descr,
                         in.getValues().get(),
@@ -377,7 +377,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
         // cusparse function to expand compressed row into coordinate
         CUSPARSE_CHECK(cusparseXcsr2coo(
-                        cusparseHandle(),
+                        sparseHandle(),
                         in.getRowIdx().get(),
                         nNZ, in.dims()[0],
                         converted.getRowIdx().get(),
@@ -386,23 +386,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         // Call sort
         size_t pBufferSizeInBytes = 0;
         CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                        cusparseHandle(),
+                        sparseHandle(),
                         in.dims()[0], in.dims()[1], nNZ,
                         converted.getRowIdx().get(), converted.getColIdx().get(),
                         &pBufferSizeInBytes));
         shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
         shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
-        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(cusparseHandle(), nNZ, P.get()));
+        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
         CUSPARSE_CHECK(cusparseXcoosortByColumn(
-                       cusparseHandle(),
+                       sparseHandle(),
                        in.dims()[0], in.dims()[1], nNZ,
                        converted.getRowIdx().get(), converted.getColIdx().get(),
                        P.get(), (void*)pBuffer.get()));
 
         CUSPARSE_CHECK(gthr_func<T>()(
-                       cusparseHandle(), nNZ,
+                       sparseHandle(), nNZ,
                        in.getValues().get(),
                        converted.getValues().get(),
                        P.get(), CUSPARSE_INDEX_BASE_ZERO));
@@ -421,23 +421,23 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         {
             size_t pBufferSizeInBytes = 0;
             CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                            cusparseHandle(),
+                            sparseHandle(),
                             cooT.dims()[0], cooT.dims()[1], nNZ,
                             cooT.getRowIdx().get(), cooT.getColIdx().get(),
                             &pBufferSizeInBytes));
             shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
 
             shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
-            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(cusparseHandle(), nNZ, P.get()));
+            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
             CUSPARSE_CHECK(cusparseXcoosortByRow(
-                           cusparseHandle(),
+                           sparseHandle(),
                            cooT.dims()[0], cooT.dims()[1], nNZ,
                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
                            P.get(), (void*)pBuffer.get()));
 
             CUSPARSE_CHECK(gthr_func<T>()(
-                           cusparseHandle(), nNZ,
+                           sparseHandle(), nNZ,
                            in.getValues().get(),
                            cooT.getValues().get(),
                            P.get(), CUSPARSE_INDEX_BASE_ZERO));
@@ -456,7 +456,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(cusparseXcoo2csr(
-                        cusparseHandle(),
+                        sparseHandle(),
                         cooT.getRowIdx().get(),
                         nNZ, cooT.dims()[0],
                         converted.getRowIdx().get(),
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index be768d054d..df57c9c667 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -155,7 +155,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     // and not OP(A) (gemm wants row/col of OP(A)).
     if(rDims[rColDim] == 1) {
         CUSPARSE_CHECK(csrmv_func<T>()(
-                       cusparseHandle(),
+                       sparseHandle(),
                        lOpts,
                        lDims[0], lDims[1], lhs.getNNZ(),
                        &alpha,
@@ -166,7 +166,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                        out.get()));
     } else {
         CUSPARSE_CHECK(csrmm_func<T>()(
-                       cusparseHandle(),
+                       sparseHandle(),
                        lOpts,
                        lDims[0], rDims[rColDim], lDims[1], lhs.getNNZ(),
                        &alpha,
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 95ff72645f..650968a61f 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -16,7 +16,7 @@
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 using af::dim4;
 
@@ -30,7 +30,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index 9645aecf66..c323ab62ea 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -86,14 +86,14 @@ SVD_SPECIALIZE(cdouble, double, Z);
 
         int lwork = 0;
 
-        CUSOLVER_CHECK(gesvd_buf_func<T>(cusolverDnHandle(), M, N, &lwork));
+        CUSOLVER_CHECK(gesvd_buf_func<T>(solverDnHandle(), M, N, &lwork));
 
         T  *lWorkspace = memAlloc<T >(lwork);
         Tr *rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
 
         int *info = memAlloc<int>(1);
 
-        gesvd_func<T, Tr>(cusolverDnHandle(), 'A', 'A', M, N, in.get(),
+        gesvd_func<T, Tr>(solverDnHandle(), 'A', 'A', M, N, in.get(),
                           M, s.get(), u.get(), M, vt.get(), N,
                           lWorkspace, lwork, rWorkspace, info);
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index d6eb919422..c875130a8b 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -13,7 +13,7 @@
 #include <vector_field.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 using af::dim4;
 
@@ -26,7 +26,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        forge::VectorField* vector_field)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(vector_field);
 
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index dae1558fbe..483d88cfd6 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -122,7 +122,7 @@ PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
     sprintf(key_str_temp, "%d:" SIZE_T_FRMT_SPECIFIER, (int)precision, batch);
     key_string.append(std::string(key_str_temp));
 
-    FFTManager &planner = opencl::clfftManager();
+    PlanCache &planner = opencl::fftManager();
 
     int planIndex = planner.find(key_string);
 
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index f3423ec6c0..56a1927d84 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -24,7 +24,7 @@ namespace opencl
 
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    clfftManager().maxCacheSize(numPlans);
+    fftManager().maxCacheSize(numPlans);
 }
 
 template<typename T> struct Precision;
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 41d7e2734c..701c8c337a 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -13,7 +13,7 @@
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <hist_graphics.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 namespace opencl
 {
@@ -27,7 +27,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         const cl::Buffer *d_P = data.get();
         size_t bytes = hist->verticesSize();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(hist);
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 8429e1c71d..4df7f5cde5 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -13,7 +13,7 @@
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <image.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 
 #include <stdexcept>
 #include <vector>
@@ -27,7 +27,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(image);
         const cl::Buffer *d_X = in.get();
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 295fcccad5..eb72bf5fc8 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -860,19 +860,19 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
-GraphicsManager& interopManager()
+GraphicsResourceManager& interopManager()
 {
     DeviceManager& inst = DeviceManager::getInstance();
 
     int id = getActiveDeviceId();
 
     if (! inst.gfxManagers[id] )
-        inst.gfxManagers[id].reset(new GraphicsManager());
+        inst.gfxManagers[id].reset(new GraphicsResourceManager());
 
     return *(inst.gfxManagers[id].get());
 }
 
-FFTManager& clfftManager()
+PlanCache& fftManager()
 {
     return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 71c2ae0c56..55aff48aac 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -89,11 +89,9 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
-typedef common::InteropManager<GraphicsResourceManager, CGR_t> GraphicsManager;
-GraphicsManager& interopManager();
+GraphicsResourceManager& interopManager();
 
-typedef common::FFTPlanCache<PlanCache, PlanType> FFTManager;
-FFTManager& clfftManager();
+PlanCache& fftManager();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -103,9 +101,9 @@ class DeviceManager
 
     friend MemoryManagerPinned& pinnedMemoryManager();
 
-    friend GraphicsManager& interopManager();
+    friend GraphicsResourceManager& interopManager();
 
-    friend FFTManager& clfftManager();
+    friend PlanCache& fftManager();
 
     friend std::string getDeviceInfo();
 
@@ -178,7 +176,7 @@ class DeviceManager
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
-        std::unique_ptr<GraphicsManager> gfxManagers[MAX_DEVICES];
-        FFTManager clfftManagers[MAX_DEVICES];
+        std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+        PlanCache clfftManagers[MAX_DEVICES];
 };
 }
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index a8eb7694e5..cd8134fe4f 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <join.hpp>
 #include <plot.hpp>
 #include <reduce.hpp>
@@ -32,7 +32,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         const cl::Buffer *d_P = P.get();
         size_t bytes = plot->verticesSize();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(plot);
 
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 29158de24e..ad296dbb54 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
@@ -32,7 +32,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         const cl::Buffer *d_P = P.get();
         size_t bytes = surface->verticesSize();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(surface);
 
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 9debfc9d6a..53ac16d88a 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <common/InteropManager.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <vector_field.hpp>
 
 using af::dim4;
@@ -32,7 +32,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         size_t pBytes = vector_field->verticesSize();
         size_t dBytes = vector_field->directionsSize();
 
-        GraphicsManager& intrpMngr = interopManager();
+        GraphicsResourceManager& intrpMngr = interopManager();
 
         cl::Buffer **resources = intrpMngr.getBufferResource(vector_field);
 

From dc3e84e8871a14d5c7c7ed6079400c2c4d3e7d35 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Jan 2017 14:44:55 +0530
Subject: [PATCH 1099/2677] fix memory manager clean up from device manager

---
 src/backend/opencl/memory.cpp   | 3 +--
 src/backend/opencl/platform.cpp | 5 +++++
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index e032f7bc6e..097bf1aadd 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -194,11 +194,10 @@ MemoryManagerPinned::~MemoryManagerPinned()
     common::lock_guard_t lock(this->memory_mutex);
     for (int n = 0; n < getDeviceCount(); n++) {
         opencl::setDevice(n);
-        garbageCollect();
+        this->garbageCollect();
         auto currIterator = pinnedMaps[n].begin();
         auto endIterator  = pinnedMaps[n].end();
         while (currIterator != endIterator) {
-            delete currIterator->second;
             pinnedMaps[n].erase(currIterator++);
         }
     }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index eb72bf5fc8..3352f9092a 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -90,6 +90,11 @@ DeviceManager& DeviceManager::getInstance()
 
 DeviceManager::~DeviceManager()
 {
+    for (int i=0; i<getDeviceCount(); ++i)
+        delete gfxManagers[i].release();
+    delete memManager.release();
+    delete pinnedMemManager.release();
+
     //TODO: FIXME:
     // OpenCL libs on Windows platforms
     // are crashing the application at program exit

From e51255c34421e4d7f0927d44f970aff1d82c7884 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Jan 2017 16:42:51 +0530
Subject: [PATCH 1100/2677] Delegate thrust malloc/free calls to ArrayFire
 memory manager

---
 src/backend/cuda/ThrustAllocator.cuh     | 44 ++++++++++++++++++++++++
 src/backend/cuda/debug_cuda.hpp          |  7 ++++
 src/backend/cuda/kernel/regions.hpp      |  3 +-
 src/backend/cuda/kernel/sift_nonfree.hpp | 10 +++---
 4 files changed, 57 insertions(+), 7 deletions(-)
 create mode 100644 src/backend/cuda/ThrustAllocator.cuh

diff --git a/src/backend/cuda/ThrustAllocator.cuh b/src/backend/cuda/ThrustAllocator.cuh
new file mode 100644
index 0000000000..40cb899e01
--- /dev/null
+++ b/src/backend/cuda/ThrustAllocator.cuh
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <thrust/device_malloc_allocator.h>
+#include <thrust/device_vector.h>
+#include <memory.hpp>
+
+//Below Class definition is found at the following URL
+//http://stackoverflow.com/questions/9007343/mix-custom-memory-managment-and-thrust-in-cuda
+
+namespace cuda
+{
+template<typename T>
+struct ThrustAllocator : thrust::device_malloc_allocator<T>
+{
+    // shorthand for the name of the base class
+    typedef thrust::device_malloc_allocator<T> super_t;
+
+    // get access to some of the base class's typedefs
+    // note that because we inherited from device_malloc_allocator,
+    // pointer is actually thrust::device_ptr<T>
+    typedef typename super_t::pointer   pointer;
+
+    typedef typename super_t::size_type size_type;
+
+    pointer allocate(size_type elements)
+    {
+        return thrust::device_ptr<T>(memAlloc<T>(elements));// delegate to ArrayFire allocator
+    }
+
+    void deallocate(pointer p, size_type n)
+    {
+        memFree<T>(p.get());// delegate to ArrayFire allocator
+    }
+};
+}
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index f7c60d37b2..5cf036b503 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -12,6 +12,13 @@
 #include <err_cuda.hpp>
 #include <thrust/version.h>
 #include <thrust/system/cuda/detail/par.h>
+#include <ThrustAllocator.cuh>
+
+namespace cuda
+{
+template<typename T>
+using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T> >;
+}
 
 #define THRUST_STREAM thrust::cuda::par.on(cuda::getActiveStream())
 
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index d9ef4080a8..6014e971eb 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -13,7 +13,6 @@
 #include <debug_cuda.hpp>
 #include <stdio.h>
 #include <memory.hpp>
-
 #include <thrust/system/cuda/detail/par.h>
 #include <thrust/adjacent_difference.h>
 #include <thrust/binary_search.h>
@@ -447,7 +446,7 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     // compute.
     int num_bins = wrapped_tmp[size - 1] + 1;
 
-    thrust::device_vector<T> labels(num_bins);
+    cuda::ThrustVector<T> labels(num_bins);
 
     // Find the end of each section of values
     thrust::counting_iterator<T> search_begin(0);
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 5fa051b82d..bdf233da77 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -1283,10 +1283,10 @@ std::vector< Param<T> > buildDoGPyr(
 }
 
 template <typename T>
-void update_permutation(thrust::device_ptr<T>& keys, thrust::device_vector<int>& permutation)
+void update_permutation(thrust::device_ptr<T>& keys, cuda::ThrustVector<int>& permutation)
 {
     // temporary storage for keys
-    thrust::device_vector<T> temp(permutation.size());
+    cuda::ThrustVector<T> temp(permutation.size());
 
     // permute the keys with the current reordering
     THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(), keys, temp.begin());
@@ -1296,10 +1296,10 @@ void update_permutation(thrust::device_ptr<T>& keys, thrust::device_vector<int>&
 }
 
 template <typename T>
-void apply_permutation(thrust::device_ptr<T>& keys, thrust::device_vector<int>& permutation)
+void apply_permutation(thrust::device_ptr<T>& keys, cuda::ThrustVector<int>& permutation)
 {
     // copy keys to temporary vector
-    thrust::device_vector<T> temp(keys, keys+permutation.size());
+    cuda::ThrustVector<T> temp(keys, keys+permutation.size());
 
     // permute the keys
     THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(), temp.begin(), keys);
@@ -1443,7 +1443,7 @@ void sift(unsigned* out_feat,
         thrust::device_ptr<float> interp_response_ptr = thrust::device_pointer_cast(d_interp_response);
         thrust::device_ptr<float> interp_size_ptr = thrust::device_pointer_cast(d_interp_size);
 
-        thrust::device_vector<int> permutation(interp_feat);
+        cuda::ThrustVector<int> permutation(interp_feat);
         thrust::sequence(permutation.begin(), permutation.end());
 
         update_permutation<float>(interp_size_ptr, permutation);

From 19953ffd894590e0fefa536651717ec849442a5f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Jan 2017 01:47:46 +0530
Subject: [PATCH 1101/2677] Make resource managers(cuda/opencl) access thread
 safe

---
 src/backend/common/FFTPlanCache.hpp   |   6 +-
 src/backend/common/InteropManager.hpp |   9 +-
 src/backend/common/types.hpp          |  17 +
 src/backend/cpu/types.hpp             |  17 +-
 src/backend/cuda/platform.cpp         |  37 +-
 src/backend/cuda/platform.hpp         |   4 +-
 src/backend/cuda/types.hpp            |  22 +-
 src/backend/opencl/platform.cpp       | 605 +++++++++++++-------------
 src/backend/opencl/platform.hpp       |   3 +
 src/backend/opencl/types.hpp          | 140 +++---
 10 files changed, 451 insertions(+), 409 deletions(-)
 create mode 100644 src/backend/common/types.hpp

diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
index bb0fa73803..6d0699c5c1 100644
--- a/src/backend/common/FFTPlanCache.hpp
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -12,6 +12,7 @@
 #include <deque>
 #include <string>
 #include <utility>
+#include <common/types.hpp>
 
 namespace common
 {
@@ -34,6 +35,7 @@ class FFTPlanCache
         }
 
         inline void maxCacheSize(size_t size) {
+            lock_guard_t lock(mutex);
             mCache.resize(size, std::make_pair<std::string, P>(std::string(""), 0));
         }
 
@@ -63,6 +65,7 @@ class FFTPlanCache
         // pops plan from the back of cache(queue)
         void pop() {
             if (!mCache.empty()) {
+                lock_guard_t lock(mutex);
                 // destroy the cufft plan associated with the
                 // least recently used plan
                 static_cast<T*>(this)->removePlan(mCache.back().second);
@@ -73,6 +76,7 @@ class FFTPlanCache
 
         // pushes plan to the front of cache(queue)
         void push(std::string key, P plan) {
+            lock_guard_t lock(mutex);
             if (mCache.size()>mMaxCacheSize) {
                 pop();
             }
@@ -86,6 +90,6 @@ class FFTPlanCache
         size_t       mMaxCacheSize;
 
         std::deque< std::pair<std::string, P> > mCache;
-
+        mutex_t mutex;
 };
 }
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index f32a952e27..6d0d6f85aa 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -16,6 +16,7 @@
 #include <cstdio>
 #include <map>
 #include <vector>
+#include <common/types.hpp>
 
 namespace common
 {
@@ -38,6 +39,7 @@ class InteropManager
         }
 
         R* getBufferResource(const forge::Image* image) {
+            lock_guard_t lock(mutex);
             void * key = (void*)image;
 
             if (interopMap.find(key) == interopMap.end()) {
@@ -51,6 +53,7 @@ class InteropManager
         }
 
         R* getBufferResource(const forge::Plot* plot) {
+            lock_guard_t lock(mutex);
             void * key = (void*)plot;
 
             if (interopMap.find(key) == interopMap.end()) {
@@ -64,6 +67,7 @@ class InteropManager
         }
 
         R* getBufferResource(const forge::Histogram* histogram) {
+            lock_guard_t lock(mutex);
             void * key = (void*)histogram;
 
             if (interopMap.find(key) == interopMap.end()) {
@@ -77,6 +81,7 @@ class InteropManager
         }
 
         R* getBufferResource(const forge::Surface* surface) {
+            lock_guard_t lock(mutex);
             void * key = (void*)surface;
 
             if (interopMap.find(key) == interopMap.end()) {
@@ -90,6 +95,7 @@ class InteropManager
         }
 
         R* getBufferResource(const forge::VectorField* field) {
+            lock_guard_t lock(mutex);
             void * key = (void*)field;
 
             if (interopMap.find(key) == interopMap.end()) {
@@ -116,8 +122,9 @@ class InteropManager
             }
         }
 
-    private:
+        //Attributes
         std::map<void *, std::vector<R> > interopMap;
+        mutex_t mutex;
 };
 }
 #endif
diff --git a/src/backend/common/types.hpp b/src/backend/common/types.hpp
new file mode 100644
index 0000000000..9d08b04d48
--- /dev/null
+++ b/src/backend/common/types.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <mutex>
+
+namespace common
+{
+typedef std::recursive_mutex mutex_t;
+typedef std::lock_guard<mutex_t> lock_guard_t;
+}
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 0776df783c..84e62f8286 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -12,14 +12,13 @@
 
 namespace cpu
 {
-    typedef std::complex<float>     cfloat;
-    typedef std::complex<double>    cdouble;
-    typedef unsigned int            uint;
-    typedef unsigned char           uchar;
-    typedef unsigned short          ushort;
-
-    template<typename T> struct is_complex          { static const bool value = false;  };
-    template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-    template<> struct           is_complex<cdouble> { static const bool value = true;   };
+typedef std::complex<float>     cfloat;
+typedef std::complex<double>    cdouble;
+typedef unsigned int   uint;
+typedef unsigned char  uchar;
+typedef unsigned short ushort;
 
+template<typename T> struct is_complex          { static const bool value = false;  };
+template<> struct           is_complex<cfloat>  { static const bool value = true;   };
+template<> struct           is_complex<cdouble> { static const bool value = true;   };
 }
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 48408ef105..0c19208311 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -408,12 +408,13 @@ MemoryManagerPinned& pinnedMemoryManager()
 
 GraphicsResourceManager& interopManager()
 {
-    DeviceManager& inst = DeviceManager::getInstance();
+    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
-    int id = cuda::getActiveDeviceId();
+    int id = getActiveDeviceId();
+
+    DeviceManager& inst = DeviceManager::getInstance();
 
-    if (! inst.gfxManagers[id] )
-        inst.gfxManagers[id].reset(new GraphicsResourceManager());
+    std::call_once(initFlags[id], [&]{ inst.gfxManagers[id].reset(new GraphicsResourceManager()); });
 
     return *(inst.gfxManagers[id].get());
 }
@@ -425,24 +426,26 @@ PlanCache& fftManager()
 
 BlasHandle blasHandle()
 {
-    DeviceManager& instance = DeviceManager::getInstance();
+    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    if (! instance.cublasHandles[id] )
-        instance.cublasHandles[id].reset(new cublasHandle());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(initFlags[id], [&]{ inst.cublasHandles[id].reset(new cublasHandle()); });
 
-    return instance.cublasHandles[id].get()->get();
+    return inst.cublasHandles[id].get()->get();
 }
 
 SolveHandle solverDnHandle()
 {
-    DeviceManager& instance = DeviceManager::getInstance();
+    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    if (! instance.cusolverHandles[id] )
-        instance.cusolverHandles[id].reset(new cusolverDnHandle());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(initFlags[id], [&]{ inst.cusolverHandles[id].reset(new cusolverDnHandle()); });
 
     // FIXME
     // This is not an ideal case. It's just a hack.
@@ -459,17 +462,18 @@ SolveHandle solverDnHandle()
     //
     CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
 
-    return instance.cusolverHandles[id].get()->get();
+    return inst.cusolverHandles[id].get()->get();
 }
 
 SparseHandle sparseHandle()
 {
-    DeviceManager& instance = DeviceManager::getInstance();
+    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    if (! instance.cusparseHandles[id] )
-        instance.cusparseHandles[id].reset(new cusparseHandle());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(initFlags[id], [&]{ inst.cusparseHandles[id].reset(new cusparseHandle()); });
 
     return instance.cusparseHandles[id].get()->get();
 }
@@ -517,6 +521,7 @@ DeviceManager::DeviceManager()
 
 void DeviceManager::sortDevices(sort_mode mode)
 {
+    lock_guard_t lock(deviceMutex);
     switch(mode) {
         case memory :
             std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_mem);
@@ -537,6 +542,8 @@ int DeviceManager::setActiveDevice(int device, int nId)
 {
     static bool first = true;
 
+    lock_guard_t lock(deviceMutex);
+
     int numDevices = cuDevices.size();
 
     if(device > numDevices) return -1;
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ba660cda62..ff2a9f8a79 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -20,10 +20,10 @@
 #include <cublas.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
+#include <common/types.hpp>
 
 namespace cuda
 {
-
 int getBackend();
 
 std::string getDeviceInfo();
@@ -148,6 +148,7 @@ class DeviceManager
         void operator=(DeviceManager const&);
 
         // Attributes
+        common::mutex_t deviceMutex;
         std::vector<cudaDevice_t> cuDevices;
 
         enum sort_mode {flops = 0, memory = 1, compute = 2, none = 3};
@@ -174,5 +175,4 @@ class DeviceManager
 
         std::unique_ptr<cusparseHandle> cusparseHandles[MAX_DEVICES];
 };
-
 }
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 26d0bb658d..08aab5f374 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -13,17 +13,17 @@
 
 namespace cuda
 {
-    typedef cuFloatComplex   cfloat;
-    typedef cuDoubleComplex cdouble;
-    typedef unsigned int   uint;
-    typedef unsigned char  uchar;
-    typedef unsigned short ushort;
+typedef cuFloatComplex   cfloat;
+typedef cuDoubleComplex cdouble;
+typedef unsigned int   uint;
+typedef unsigned char  uchar;
+typedef unsigned short ushort;
 
-    template<typename T> struct is_complex          { static const bool value = false;  };
-    template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-    template<> struct           is_complex<cdouble> { static const bool value = true;   };
+template<typename T> struct is_complex          { static const bool value = false;  };
+template<> struct           is_complex<cfloat>  { static const bool value = true;   };
+template<> struct           is_complex<cdouble> { static const bool value = true;   };
 
-    template<typename T, bool binary> const std::string cuMangledName(const char *fn);
-    template<typename T > const char *afShortName(bool caps = true);
-    template<typename T > const char *irname();
+template<typename T, bool binary> const std::string cuMangledName(const char *fn);
+template<typename T > const char *afShortName(bool caps = true);
+template<typename T > const char *irname();
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 3352f9092a..910d500a58 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -56,7 +56,6 @@ using cl::Device;
 
 namespace opencl
 {
-
 #if defined (OS_MAC)
 static const std::string CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
@@ -82,39 +81,6 @@ int getBackend()
     return AF_BACKEND_OPENCL;
 }
 
-DeviceManager& DeviceManager::getInstance()
-{
-    static DeviceManager my_instance;
-    return my_instance;
-}
-
-DeviceManager::~DeviceManager()
-{
-    for (int i=0; i<getDeviceCount(); ++i)
-        delete gfxManagers[i].release();
-    delete memManager.release();
-    delete pinnedMemManager.release();
-
-    //TODO: FIXME:
-    // OpenCL libs on Windows platforms
-    // are crashing the application at program exit
-    // most probably a reference counting issue based
-    // on the investigation done so far. This problem
-    // doesn't seem to happen on Linux or MacOSX.
-    // So, clean up OpenCL resources on non-Windows platforms
-#ifndef OS_WIN
-    for (auto q: mQueues) delete q;
-    for (auto d : mDevices) delete d;
-    for (auto c : mContexts) delete c;
-#endif
-}
-
-void DeviceManager::setContext(int device)
-{
-    mActiveQId = device;
-    mActiveCtxId = device;
-}
-
 static inline bool verify_present(std::string pname, const char *ref)
 {
     return pname.find(ref) != std::string::npos;
@@ -208,7 +174,6 @@ static afcl::deviceType getDeviceTypeEnum(cl::Device dev)
     return (afcl::deviceType)dev.getInfo<CL_DEVICE_TYPE>();
 }
 
-
 static afcl::platform getPlatformEnum(cl::Device dev)
 {
     std::string pname = getPlatformName(dev);
@@ -221,132 +186,6 @@ static afcl::platform getPlatformEnum(cl::Device dev)
     return AFCL_PLATFORM_UNKNOWN;
 }
 
-
-DeviceManager::DeviceManager()
-    : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
-{
-    std::vector<cl::Platform>   platforms;
-    Platform::get(&platforms);
-
-    // This is all we need because the sort takes care of the order of devices
-#ifdef OS_MAC
-    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
-#else
-    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
-#endif
-
-    std::string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
-
-    if (deviceENV.compare("GPU") == 0) {
-        DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
-    } else if (deviceENV.compare("CPU") == 0) {
-        DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
-    } else if (deviceENV.compare("ACC") >= 0) {
-        DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
-    }
-
-
-
-    // Iterate through platforms, get all available devices and store them
-    for (auto &platform : platforms) {
-        std::vector<Device> current_devices;
-
-        try {
-            platform.getDevices(DEVICE_TYPES, &current_devices);
-        } catch(const cl::Error &err) {
-            if (err.err() != CL_DEVICE_NOT_FOUND) {
-                throw;
-            }
-        }
-
-        for (auto dev : current_devices) {
-            mDevices.push_back(new Device(dev));
-        }
-    }
-
-    int nDevices = mDevices.size();
-
-    if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
-
-    // Sort OpenCL devices based on default criteria
-    std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
-
-    // Create contexts and queues once the sort is done
-    for (int i = 0; i < nDevices; i++) {
-        cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
-        cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
-                                        (cl_context_properties)(device_platform),
-                                        0};
-
-        Context *ctx = new Context(*mDevices[i], cps);
-        CommandQueue *cq = new CommandQueue(*ctx, *mDevices[i]);
-        mContexts.push_back(ctx);
-        mQueues.push_back(cq);
-        mIsGLSharingOn.push_back(false);
-        mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
-        mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
-    }
-
-    bool default_device_set = false;
-    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
-    if(!deviceENV.empty()) {
-        std::stringstream s(deviceENV);
-        int def_device = -1;
-        s >> def_device;
-        if(def_device < 0 || def_device >= (int)nDevices) {
-            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
-            printf("Setting default device as 0\n");
-        } else {
-            setContext(def_device);
-            default_device_set = true;
-        }
-    }
-
-    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
-    if (!default_device_set && !deviceENV.empty())
-    {
-        cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
-        if (deviceENV.compare("CPU") == 0) {
-            default_device_type = CL_DEVICE_TYPE_CPU;
-        } else if (deviceENV.compare("ACC") >= 0) {
-            default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
-        }
-
-        bool default_device_set = false;
-        for (int i = 0; i < nDevices; i++) {
-            if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
-                default_device_set = true;
-                setContext(i);
-                break;
-            }
-        }
-
-        if (!default_device_set) {
-            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
-                    deviceENV.c_str());
-            printf("Using default device as 0\n");
-        }
-    }
-
-#if defined(WITH_GRAPHICS)
-    // Define AF_DISABLE_GRAPHICS with any value to disable initialization
-    std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
-    if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
-        try {
-            /* loop over devices and replace contexts with
-             * OpenGL shared contexts whereever applicable */
-            int devCount = mDevices.size();
-            forge::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
-            for(int i=0; i<devCount; ++i)
-                markDeviceForInterop(i, wHandle);
-        } catch (...) {
-        }
-    }
-#endif
-    mUserDeviceOffset = mDevices.size();
-}
-
-
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
 static inline std::string &ltrim(std::string &s)
@@ -627,150 +466,56 @@ bool checkExtnAvailability(const Device &pDevice, std::string pName)
     return ret_val;
 }
 
-#if defined(WITH_GRAPHICS)
-void DeviceManager::markDeviceForInterop(const int device, const forge::Window* wHandle)
+void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 {
-    try {
-        if (device >= (int)mQueues.size() ||
-                device>= (int)DeviceManager::MAX_DEVICES) {
-            throw cl::Error(CL_INVALID_DEVICE, "Invalid device passed for CL-GL Interop");
-        } else {
-            mQueues[device]->finish();
+    clRetainDevice(dev);
+    clRetainContext(ctx);
+    clRetainCommandQueue(que);
 
-            // check if the device has CL_GL sharing extension enabled
-            bool temp = checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
-            if (!temp) {
-                /* return silently if given device has not OpenGL sharing extension
-                 * enabled so that regular queue is used for it */
-                return;
-            }
+    DeviceManager& devMngr   = DeviceManager::getInstance();
 
-            // call forge to get OpenGL sharing context and details
-            cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
+    common::lock_guard_t lock(devMngr.deviceMutex);
 
-#ifdef OS_MAC
-            CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
-            CGLShareGroupObj cgl_share_group = CGLGetShareGroup(cgl_current_ctx);
+    cl::Device* tDevice      = new cl::Device(dev);
+    cl::Context* tContext    = new cl::Context(ctx);
+    cl::CommandQueue* tQueue = (que==NULL ?
+            new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
+    devMngr.mDevices.push_back(tDevice);
+    devMngr.mContexts.push_back(tContext);
+    devMngr.mQueues.push_back(tQueue);
+    devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
+    // FIXME: add OpenGL Interop for user provided contexts later
+    devMngr.mIsGLSharingOn.push_back(false);
+}
 
-            cl_context_properties cps[] = {
-                CL_CONTEXT_PROPERTY_USE_CGL_SHAREGROUP_APPLE, (cl_context_properties)cgl_share_group,
-                0
-            };
-#else
-            cl_context_properties cps[] = {
-                CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
-#if defined(_WIN32) || defined(_MSC_VER)
-                CL_WGL_HDC_KHR, (cl_context_properties)wHandle->display(),
-#else
-                CL_GLX_DISPLAY_KHR, (cl_context_properties)wHandle->display(),
-#endif
-                CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
-                0
-            };
+void setDeviceContext(cl_device_id dev, cl_context ctx)
+{
+    // FIXME: add OpenGL Interop for user provided contexts later
+    DeviceManager& devMngr = DeviceManager::getInstance();
 
-            // Check if current OpenCL device is belongs to the OpenGL context
-            {
-                cl_context_properties test_cps[] = {
-                    CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
-                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
-                    0
-                };
+    common::lock_guard_t lock(devMngr.deviceMutex);
 
-                // Load the extension
-                // If cl_khr_gl_sharing is available, this function should be present
-                // This has been checked earlier, it comes to this point only if it is found
-                auto func = (clGetGLContextInfoKHR_fn)
-                    clGetExtensionFunctionAddressForPlatform(plat(), "clGetGLContextInfoKHR");
+    const int dCount = devMngr.mDevices.size();
+    for (int i=0; i<dCount; ++i) {
+        if(devMngr.mDevices[i]->operator()()==dev &&
+                devMngr.mContexts[i]->operator()()==ctx) {
+            setDevice(i);
+            return;
+        }
+    }
+    AF_ERROR("No matching device found", AF_ERR_ARG);
+}
 
-                // If the function doesn't load, bail early
-                if (!func) return;
+void removeDeviceContext(cl_device_id dev, cl_context ctx)
+{
+    if (getDevice()() == dev && getContext()()==ctx) {
+        AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
+    }
 
-                // Get all devices associated with opengl context
-                std::vector<cl_device_id> devices(16);
-                size_t ret = 0;
-                cl_int err = func(test_cps,
-                                  CL_DEVICES_FOR_GL_CONTEXT_KHR,
-                                  devices.size() * sizeof(cl_device_id),
-                                  &devices[0],
-                                  &ret);
-                if (err != CL_SUCCESS) return;
-                int num = ret / sizeof(cl_device_id);
-                devices.resize(num);
-
-                // Check if current device is present in the associated devices
-                cl_device_id current_device = (*mDevices[device])();
-                auto res = std::find(std::begin(devices),
-                                     std::end(devices),
-                                     current_device);
-
-                if (res == std::end(devices)) return;
-            }
-#endif
-
-            // Change current device to use GL sharing
-            Context * ctx = new Context(*mDevices[device], cps);
-            CommandQueue * cq = new CommandQueue(*ctx, *mDevices[device]);
-
-            // May be fixes the AMD GL issues we see on windows?
-#if !defined(_WIN32) && !defined(_MSC_VER)
-            delete mContexts[device];
-            delete mQueues[device];
-#endif
-
-            mContexts[device] = ctx;
-            mQueues[device] = cq;
-            mIsGLSharingOn[device] = true;
-        }
-    } catch (const cl::Error &ex) {
-        /* If replacing the original context with GL shared context
-         * failes, don't throw an error and instead fall back to
-         * original context and use copy via host to support graphics
-         * on that particular OpenCL device. So mark it as no GL sharing */
-    }
-}
-#endif
-
-void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
-{
-    clRetainDevice(dev);
-    clRetainContext(ctx);
-    clRetainCommandQueue(que);
-
-    DeviceManager& devMngr   = DeviceManager::getInstance();
-    cl::Device* tDevice      = new cl::Device(dev);
-    cl::Context* tContext    = new cl::Context(ctx);
-    cl::CommandQueue* tQueue = (que==NULL ?
-            new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
-    devMngr.mDevices.push_back(tDevice);
-    devMngr.mContexts.push_back(tContext);
-    devMngr.mQueues.push_back(tQueue);
-    devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
-    // FIXME: add OpenGL Interop for user provided contexts later
-    devMngr.mIsGLSharingOn.push_back(false);
-}
-
-void setDeviceContext(cl_device_id dev, cl_context ctx)
-{
-    // FIXME: add OpenGL Interop for user provided contexts later
     DeviceManager& devMngr = DeviceManager::getInstance();
-    const int dCount = devMngr.mDevices.size();
-    for (int i=0; i<dCount; ++i) {
-        if(devMngr.mDevices[i]->operator()()==dev &&
-                devMngr.mContexts[i]->operator()()==ctx) {
-            setDevice(i);
-            return;
-        }
-    }
-    AF_ERROR("No matching device found", AF_ERR_ARG);
-}
 
-void removeDeviceContext(cl_device_id dev, cl_context ctx)
-{
-    if (getDevice()() == dev && getContext()()==ctx) {
-        AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
-    }
+    common::lock_guard_t lock(devMngr.deviceMutex);
 
-    DeviceManager& devMngr = DeviceManager::getInstance();
     const int dCount = devMngr.mDevices.size();
     int deleteIdx = -1;
     for (int i = 0; i<dCount; ++i) {
@@ -816,7 +561,6 @@ bool synchronize_calls() {
     return sync;
 }
 
-
 unsigned getMaxJitSize()
 {
 #if defined(OS_MAC)
@@ -849,7 +593,7 @@ MemoryManager& memoryManager()
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
+    std::call_once(flag, [&]{ inst.memManager.reset(new MemoryManager()); });
 
     return *(inst.memManager.get());
 }
@@ -860,19 +604,20 @@ MemoryManagerPinned& pinnedMemoryManager()
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
+    std::call_once(flag, [&]{ inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
 
     return *(inst.pinnedMemManager.get());
 }
 
 GraphicsResourceManager& interopManager()
 {
-    DeviceManager& inst = DeviceManager::getInstance();
+    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = getActiveDeviceId();
 
-    if (! inst.gfxManagers[id] )
-        inst.gfxManagers[id].reset(new GraphicsResourceManager());
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(initFlags[id], [&]{ inst.gfxManagers[id].reset(new GraphicsResourceManager()); });
 
     return *(inst.gfxManagers[id].get());
 }
@@ -882,6 +627,268 @@ PlanCache& fftManager()
     return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
 }
 
+DeviceManager& DeviceManager::getInstance()
+{
+    static DeviceManager my_instance;
+    return my_instance;
+}
+
+DeviceManager::~DeviceManager()
+{
+    for (int i=0; i<getDeviceCount(); ++i)
+        delete gfxManagers[i].release();
+    delete memManager.release();
+    delete pinnedMemManager.release();
+
+    //TODO: FIXME:
+    // OpenCL libs on Windows platforms
+    // are crashing the application at program exit
+    // most probably a reference counting issue based
+    // on the investigation done so far. This problem
+    // doesn't seem to happen on Linux or MacOSX.
+    // So, clean up OpenCL resources on non-Windows platforms
+#ifndef OS_WIN
+    for (auto q: mQueues) delete q;
+    for (auto d : mDevices) delete d;
+    for (auto c : mContexts) delete c;
+#endif
+}
+
+void DeviceManager::setContext(int device)
+{
+    common::lock_guard_t lock(deviceMutex);
+
+    mActiveQId = device;
+    mActiveCtxId = device;
+}
+
+DeviceManager::DeviceManager()
+    : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
+{
+    std::vector<cl::Platform>   platforms;
+    Platform::get(&platforms);
+
+    // This is all we need because the sort takes care of the order of devices
+#ifdef OS_MAC
+    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+#else
+    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
+#endif
+
+    std::string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
+
+    if (deviceENV.compare("GPU") == 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+    } else if (deviceENV.compare("CPU") == 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
+    } else if (deviceENV.compare("ACC") >= 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
+    }
+
+
+
+    // Iterate through platforms, get all available devices and store them
+    for (auto &platform : platforms) {
+        std::vector<Device> current_devices;
+
+        try {
+            platform.getDevices(DEVICE_TYPES, &current_devices);
+        } catch(const cl::Error &err) {
+            if (err.err() != CL_DEVICE_NOT_FOUND) {
+                throw;
+            }
+        }
+
+        for (auto dev : current_devices) {
+            mDevices.push_back(new Device(dev));
+        }
+    }
+
+    int nDevices = mDevices.size();
+
+    if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
+
+    // Sort OpenCL devices based on default criteria
+    std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
+
+    // Create contexts and queues once the sort is done
+    for (int i = 0; i < nDevices; i++) {
+        cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
+        cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
+                                        (cl_context_properties)(device_platform),
+                                        0};
+
+        Context *ctx = new Context(*mDevices[i], cps);
+        CommandQueue *cq = new CommandQueue(*ctx, *mDevices[i]);
+        mContexts.push_back(ctx);
+        mQueues.push_back(cq);
+        mIsGLSharingOn.push_back(false);
+        mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
+        mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
+    }
+
+    bool default_device_set = false;
+    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
+    if(!deviceENV.empty()) {
+        std::stringstream s(deviceENV);
+        int def_device = -1;
+        s >> def_device;
+        if(def_device < 0 || def_device >= (int)nDevices) {
+            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
+            printf("Setting default device as 0\n");
+        } else {
+            setContext(def_device);
+            default_device_set = true;
+        }
+    }
+
+    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
+    if (!default_device_set && !deviceENV.empty())
+    {
+        cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
+        if (deviceENV.compare("CPU") == 0) {
+            default_device_type = CL_DEVICE_TYPE_CPU;
+        } else if (deviceENV.compare("ACC") >= 0) {
+            default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
+        }
+
+        bool default_device_set = false;
+        for (int i = 0; i < nDevices; i++) {
+            if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
+                default_device_set = true;
+                setContext(i);
+                break;
+            }
+        }
+
+        if (!default_device_set) {
+            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
+                    deviceENV.c_str());
+            printf("Using default device as 0\n");
+        }
+    }
+
+#if defined(WITH_GRAPHICS)
+    // Define AF_DISABLE_GRAPHICS with any value to disable initialization
+    std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
+    if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
+        try {
+            /* loop over devices and replace contexts with
+             * OpenGL shared contexts whereever applicable */
+            int devCount = mDevices.size();
+            forge::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
+            for(int i=0; i<devCount; ++i)
+                markDeviceForInterop(i, wHandle);
+        } catch (...) {
+        }
+    }
+#endif
+    mUserDeviceOffset = mDevices.size();
+}
+
+#if defined(WITH_GRAPHICS)
+void DeviceManager::markDeviceForInterop(const int device, const forge::Window* wHandle)
+{
+    common::lock_guard_t lock(deviceMutex);
+    try {
+        if (device >= (int)mQueues.size() ||
+                device>= (int)DeviceManager::MAX_DEVICES) {
+            throw cl::Error(CL_INVALID_DEVICE, "Invalid device passed for CL-GL Interop");
+        } else {
+            mQueues[device]->finish();
+
+            // check if the device has CL_GL sharing extension enabled
+            bool temp = checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
+            if (!temp) {
+                /* return silently if given device has not OpenGL sharing extension
+                 * enabled so that regular queue is used for it */
+                return;
+            }
+
+            // call forge to get OpenGL sharing context and details
+            cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
+
+#ifdef OS_MAC
+            CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
+            CGLShareGroupObj cgl_share_group = CGLGetShareGroup(cgl_current_ctx);
+
+            cl_context_properties cps[] = {
+                CL_CONTEXT_PROPERTY_USE_CGL_SHAREGROUP_APPLE, (cl_context_properties)cgl_share_group,
+                0
+            };
+#else
+            cl_context_properties cps[] = {
+                CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
+#if defined(_WIN32) || defined(_MSC_VER)
+                CL_WGL_HDC_KHR, (cl_context_properties)wHandle->display(),
+#else
+                CL_GLX_DISPLAY_KHR, (cl_context_properties)wHandle->display(),
+#endif
+                CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
+                0
+            };
+
+            // Check if current OpenCL device is belongs to the OpenGL context
+            {
+                cl_context_properties test_cps[] = {
+                    CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
+                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
+                    0
+                };
+
+                // Load the extension
+                // If cl_khr_gl_sharing is available, this function should be present
+                // This has been checked earlier, it comes to this point only if it is found
+                auto func = (clGetGLContextInfoKHR_fn)
+                    clGetExtensionFunctionAddressForPlatform(plat(), "clGetGLContextInfoKHR");
+
+                // If the function doesn't load, bail early
+                if (!func) return;
+
+                // Get all devices associated with opengl context
+                std::vector<cl_device_id> devices(16);
+                size_t ret = 0;
+                cl_int err = func(test_cps,
+                                  CL_DEVICES_FOR_GL_CONTEXT_KHR,
+                                  devices.size() * sizeof(cl_device_id),
+                                  &devices[0],
+                                  &ret);
+                if (err != CL_SUCCESS) return;
+                int num = ret / sizeof(cl_device_id);
+                devices.resize(num);
+
+                // Check if current device is present in the associated devices
+                cl_device_id current_device = (*mDevices[device])();
+                auto res = std::find(std::begin(devices),
+                                     std::end(devices),
+                                     current_device);
+
+                if (res == std::end(devices)) return;
+            }
+#endif
+
+            // Change current device to use GL sharing
+            Context * ctx = new Context(*mDevices[device], cps);
+            CommandQueue * cq = new CommandQueue(*ctx, *mDevices[device]);
+
+            // May be fixes the AMD GL issues we see on windows?
+#if !defined(_WIN32) && !defined(_MSC_VER)
+            delete mContexts[device];
+            delete mQueues[device];
+#endif
+
+            mContexts[device] = ctx;
+            mQueues[device] = cq;
+            mIsGLSharingOn[device] = true;
+        }
+    } catch (const cl::Error &ex) {
+        /* If replacing the original context with GL shared context
+         * failes, don't throw an error and instead fall back to
+         * original context and use copy via host to support graphics
+         * on that particular OpenCL device. So mark it as no GL sharing */
+    }
+}
+#endif
 }
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 55aff48aac..846b839ee5 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -29,6 +29,7 @@
 #include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <clfft.hpp>
+#include <common/types.hpp>
 
 namespace opencl
 {
@@ -136,6 +137,7 @@ class DeviceManager
     friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
     friend int getActiveDeviceType();
+
     friend int getActivePlatform();
 
     public:
@@ -162,6 +164,7 @@ class DeviceManager
 
     private:
         // Attributes
+        common::mutex_t deviceMutex;
         std::vector<cl::Device*>       mDevices;
         std::vector<cl::Context*>     mContexts;
         std::vector<cl::CommandQueue*>  mQueues;
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 5742c5d8c9..4490f2bb36 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -23,89 +23,87 @@
 
 namespace opencl
 {
+typedef cl_float2   cfloat;
+typedef cl_double2 cdouble;
+typedef cl_uchar     uchar;
+typedef cl_uint       uint;
+typedef cl_ushort   ushort;
 
-    typedef cl_float2   cfloat;
-    typedef cl_double2 cdouble;
-    typedef cl_uchar     uchar;
-    typedef cl_uint       uint;
-    typedef cl_ushort   ushort;
+template<typename T> struct is_complex          { static const bool value = false;  };
+template<> struct           is_complex<cfloat>  { static const bool value = true;   };
+template<> struct           is_complex<cdouble> { static const bool value = true;   };
 
-    template<typename T> struct is_complex          { static const bool value = false;  };
-    template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-    template<> struct           is_complex<cdouble> { static const bool value = true;   };
+template<typename T > const char *shortname(bool caps=false);
 
-    template<typename T > const char *shortname(bool caps=false);
-
-    template<typename T>
-    struct ToNumStr
+template<typename T>
+struct ToNumStr
+{
+    inline std::string operator()(T val)
     {
-        inline std::string operator()(T val)
-        {
-            ToNum<T> toNum;
-            return std::to_string(toNum(val));
-        }
-    };
+        ToNum<T> toNum;
+        return std::to_string(toNum(val));
+    }
+};
 
-    template<>
-    struct ToNumStr<float>
+template<>
+struct ToNumStr<float>
+{
+    inline std::string operator()(float val)
     {
-        inline std::string operator()(float val)
-        {
-            static const std::string PINF = "+INFINITY";
-            static const std::string NINF = "-INFINITY";
-            if (std::isinf(val)) {
-                return val < 0 ? NINF : PINF;
-            }
-            return std::to_string(val);
+        static const std::string PINF = "+INFINITY";
+        static const std::string NINF = "-INFINITY";
+        if (std::isinf(val)) {
+            return val < 0 ? NINF : PINF;
         }
-    };
+        return std::to_string(val);
+    }
+};
 
-    template<>
-    struct ToNumStr<double>
+template<>
+struct ToNumStr<double>
+{
+    inline std::string operator()(double val)
     {
-        inline std::string operator()(double val)
-        {
-            static const std::string PINF = "+INFINITY";
-            static const std::string NINF = "-INFINITY";
-            if (std::isinf(val)) {
-                return val < 0 ? NINF : PINF;
-            }
-            return std::to_string(val);
+        static const std::string PINF = "+INFINITY";
+        static const std::string NINF = "-INFINITY";
+        if (std::isinf(val)) {
+            return val < 0 ? NINF : PINF;
         }
-    };
+        return std::to_string(val);
+    }
+};
 
-    template<>
-    struct ToNumStr<cfloat>
+template<>
+struct ToNumStr<cfloat>
+{
+    inline std::string operator()(cfloat val)
     {
-        inline std::string operator()(cfloat val)
-        {
-            ToNumStr<float> realStr;
-            static const std::string INF = "INFINITY";
-            std::stringstream s;
-            s << "{";
-            s << realStr(val.s[0]);
-            s << ",";
-            s << realStr(val.s[1]);
-            s << "}";
-            return s.str();
-        }
-    };
+        ToNumStr<float> realStr;
+        static const std::string INF = "INFINITY";
+        std::stringstream s;
+        s << "{";
+        s << realStr(val.s[0]);
+        s << ",";
+        s << realStr(val.s[1]);
+        s << "}";
+        return s.str();
+    }
+};
 
-    template<>
-    struct ToNumStr<cdouble>
+template<>
+struct ToNumStr<cdouble>
+{
+    inline std::string operator()(cdouble val)
     {
-        inline std::string operator()(cdouble val)
-        {
-            ToNumStr<double> realStr;
-            static const std::string INF = "INFINITY";
-            std::stringstream s;
-            s << "{";
-            s << realStr(val.s[0]);
-            s << ",";
-            s << realStr(val.s[1]);
-            s << "}";
-            return s.str();
-        }
-    };
-
+        ToNumStr<double> realStr;
+        static const std::string INF = "INFINITY";
+        std::stringstream s;
+        s << "{";
+        s << realStr(val.s[0]);
+        s << ",";
+        s << realStr(val.s[1]);
+        s << "}";
+        return s.str();
+    }
+};
 }

From 38f92ecf0aa3a16054daf19d35b49e04541e9096 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Mon, 23 Jan 2017 23:28:49 -0500
Subject: [PATCH 1102/2677] Corrected af_random_engine_set_type

- Correct usage of getRandomEngine
- Remove redundant release of Mersenne arrays
- Added test for setDefaultRandomEngine
---
 src/api/c/random.cpp | 39 ++++++++++++++++++---------------------
 test/random.cpp      |  9 +++++++++
 2 files changed, 27 insertions(+), 21 deletions(-)

diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 0374f0380d..682934798b 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -179,29 +179,26 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engin
     try {
         AF_CHECK(af_init());
         validateRandomType(rtype);
-        RandomEngine e = *(getRandomEngine(engine));
-        if (rtype != e.type) {
+        RandomEngine *e = getRandomEngine(*engine);
+        if (rtype != e->type) {
             if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-                bool empty;
-                AF_CHECK(af_is_empty(&empty, e.state));
-                if (empty) {
-                    AF_CHECK(af_release_array(e.pos));
-                    AF_CHECK(af_release_array(e.sh1));
-                    AF_CHECK(af_release_array(e.sh2));
-                    AF_CHECK(af_release_array(e.recursion_table));
-                    AF_CHECK(af_release_array(e.temper_table));
-                    AF_CHECK(af_release_array(e.state));
-                    AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
-                    AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
-                    AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
-                    e.mask = mask;
-                    AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
-                    AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
-                    AF_CHECK(af_create_handle(&e.state, 1, &MtStateLength, u32));
-                    initMersenneState(getWritableArray<uint>(e.state), *e.seed, getArray<uint>(e.recursion_table));
-                }
+                AF_CHECK(af_create_array(&e->pos, pos, 1, &MaxBlocks, u32));
+                AF_CHECK(af_create_array(&e->sh1, sh1, 1, &MaxBlocks, u32));
+                AF_CHECK(af_create_array(&e->sh2, sh2, 1, &MaxBlocks, u32));
+                e->mask = mask;
+                AF_CHECK(af_create_array(&e->recursion_table, recursion_tbl, 1, &TableLength, u32));
+                AF_CHECK(af_create_array(&e->temper_table, temper_tbl, 1, &TableLength, u32));
+                AF_CHECK(af_create_handle(&e->state, 1, &MtStateLength, u32));
+                initMersenneState(getWritableArray<uint>(e->state), *(e->seed), getArray<uint>(e->recursion_table));
+            } else if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
+                AF_CHECK(af_release_array(e->pos));
+                AF_CHECK(af_release_array(e->sh1));
+                AF_CHECK(af_release_array(e->sh2));
+                AF_CHECK(af_release_array(e->recursion_table));
+                AF_CHECK(af_release_array(e->temper_table));
+                AF_CHECK(af_release_array(e->state));
             }
-            e.type = rtype;
+            e->type = rtype;
         }
     } CATCHALL;
     return AF_SUCCESS;
diff --git a/test/random.cpp b/test/random.cpp
index 03109f9974..71010b03fa 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -188,6 +188,15 @@ TEST(Random, CPP)
     af::dim4 dims(1, 2, 3, 1);
     af::array out1 = af::randu(dims);
     af::array out2 = af::randn(dims);
+    af::setDefaultRandomEngineType(AF_RANDOM_ENGINE_PHILOX);
+    af::array out3 = af::randu(dims);
+    af::array out4 = af::randn(dims);
+    af::setDefaultRandomEngineType(AF_RANDOM_ENGINE_THREEFRY);
+    af::array out5 = af::randu(dims);
+    af::array out6 = af::randn(dims);
+    af::setDefaultRandomEngineType(AF_RANDOM_ENGINE_MERSENNE);
+    af::array out7 = af::randu(dims);
+    af::array out8 = af::randn(dims);
     af::sync();
 }
 

From 11116269d68cd00433fb326eacd9b4cd00e152e3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Jan 2017 22:40:26 +0530
Subject: [PATCH 1103/2677] Make common::{InteropManager,FFTPlanCache}
 interfaces thread safe

---
 CMakeLists.txt                                |   5 +
 src/backend/common/FFTPlanCache.cpp           |  69 ++++++++
 src/backend/common/FFTPlanCache.hpp           |  72 ++------
 src/backend/common/InteropManager.cpp         | 154 ++++++++++++++++++
 src/backend/common/InteropManager.hpp         | 112 ++-----------
 src/backend/cpu/CMakeLists.txt                |   2 +
 src/backend/cuda/CMakeLists.txt               |   8 +-
 src/backend/cuda/GraphicsResourceManager.cpp  |  34 ++++
 src/backend/cuda/GraphicsResourceManager.hpp  |  25 +--
 src/backend/cuda/cufft.cpp                    |  29 ++--
 src/backend/cuda/cufft.hpp                    |  28 ++--
 src/backend/cuda/fft.cpp                      |  32 ++--
 src/backend/cuda/hist_graphics.cpp            |  12 +-
 src/backend/cuda/image.cpp                    |  13 +-
 src/backend/cuda/platform.cpp                 |   6 +-
 src/backend/cuda/plot.cpp                     |  12 +-
 src/backend/cuda/surface.cpp                  |  12 +-
 src/backend/cuda/vector_field.cpp             |  11 +-
 src/backend/opencl/CMakeLists.txt             |   7 +-
 .../opencl/GraphicsResourceManager.cpp        |  15 +-
 .../opencl/GraphicsResourceManager.hpp        |  16 +-
 src/backend/opencl/clfft.cpp                  |  73 +++------
 src/backend/opencl/clfft.hpp                  |  31 ++--
 src/backend/opencl/fft.cpp                    |  32 ++--
 src/backend/opencl/hist_graphics.cpp          |   8 +-
 src/backend/opencl/image.cpp                  |   8 +-
 src/backend/opencl/platform.cpp               |  27 ++-
 src/backend/opencl/platform.hpp               |   3 +-
 src/backend/opencl/plot.cpp                   |   8 +-
 src/backend/opencl/surface.cpp                |   8 +-
 src/backend/opencl/vector_field.cpp           |  12 +-
 31 files changed, 483 insertions(+), 401 deletions(-)
 create mode 100644 src/backend/common/FFTPlanCache.cpp
 create mode 100644 src/backend/common/InteropManager.cpp
 create mode 100644 src/backend/cuda/GraphicsResourceManager.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b204310fdd..5d159c8ea5 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -53,6 +53,10 @@ ELSE(FREEIMAGE_FOUND)
     MESSAGE(WARNING, "FreeImage not found!")
 ENDIF(FREEIMAGE_FOUND)
 
+ADD_DEFINITIONS(-DBOOST_ALL_NO_LIB)
+SET(Boost_USE_STATIC_LIBS OFF)
+FIND_PACKAGE(Boost REQUIRED COMPONENTS "system" "thread")
+
 OPTION(USE_SYSTEM_CL2HPP "Use cl2.hpp installed on system" OFF)
 
 IF(BUILD_GRAPHICS)
@@ -128,6 +132,7 @@ INCLUDE_DIRECTORIES(BEFORE
     "${CMAKE_CURRENT_SOURCE_DIR}/include"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/backend"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/api/c"
+    "${Boost_INCLUDE_DIR}"
     )
 
 IF(${UNIX})
diff --git a/src/backend/common/FFTPlanCache.cpp b/src/backend/common/FFTPlanCache.cpp
new file mode 100644
index 0000000000..0360cb943c
--- /dev/null
+++ b/src/backend/common/FFTPlanCache.cpp
@@ -0,0 +1,69 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+//FIXME CPU backend doesn't required the following class implementation
+//FFTPlanCache.hpp is not used while building CPU backend.
+#ifndef AF_CPU
+#include <boost/thread.hpp>
+#include <common/FFTPlanCache.hpp>
+#include <backend.hpp>
+#include <platform.hpp>
+
+typedef boost::shared_mutex smutex_t;
+typedef boost::shared_lock<smutex_t> rlock_t;
+typedef boost::unique_lock<smutex_t> wlock_t;
+
+namespace common
+{
+static smutex_t gFFTMutexes[detail::DeviceManager::MAX_DEVICES];
+
+template<class T, typename P>
+void FFTPlanCache<T, P>::setMaxCacheSize(size_t size)
+{
+    wlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
+    mMaxCacheSize = size;
+}
+
+template<class T, typename P>
+size_t FFTPlanCache<T, P>::getMaxCacheSize() const
+{
+    rlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
+    return mMaxCacheSize;
+}
+
+template<class T, typename P>
+std::shared_ptr<P> FFTPlanCache<T, P>::find(const std::string& key) const
+{
+    std::shared_ptr<P> res;
+
+    rlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
+    for(uint i=0; i<mCache.size(); ++i) {
+        if (key == mCache[i].first) {
+            res = mCache[i].second;
+            break;
+        }
+    }
+
+    return res;
+}
+
+template<class T, typename P>
+void FFTPlanCache<T, P>::push(const std::string key, std::shared_ptr<P> plan)
+{
+    wlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
+
+    if (mCache.size()>=mMaxCacheSize)
+        mCache.pop_back();
+
+    mCache.push_front(plan_pair_t(key, plan));
+}
+
+template class FFTPlanCache<detail::PlanCache, detail::PlanType>;
+}
+#endif
diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
index 6d0699c5c1..d10baabd29 100644
--- a/src/backend/common/FFTPlanCache.hpp
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -10,86 +10,46 @@
 #pragma once
 
 #include <deque>
+#include <memory>
 #include <string>
 #include <utility>
-#include <common/types.hpp>
 
 namespace common
 {
-// FFTPlanCache caches backend specific fft plans
+// FFTPlanCache caches backend specific fft plans in FIFO order
 //
-// new plan |--> IF number of plans cached is at limit, pop the least used entry and push new plan.
+// new plan |--> IF number of plans cached is at limit, pop the oldest entry and push new plan.
 //          |
 //          |--> ELSE just push the plan
 // existing plan -> reuse a plan
 template<typename T, typename P>
 class FFTPlanCache
 {
-    public:
-        FFTPlanCache() : mMaxCacheSize(5) {
-            static_cast<T*>(this)->initLibrary();
-        }
-
-        ~FFTPlanCache() {
-            static_cast<T*>(this)->deInitLibrary();
-        }
-
-        inline void maxCacheSize(size_t size) {
-            lock_guard_t lock(mutex);
-            mCache.resize(size, std::make_pair<std::string, P>(std::string(""), 0));
-        }
+    using plan_t = typename std::shared_ptr<P>;
+    using plan_pair_t = typename std::pair<std::string, plan_t>;
+    using plan_cache_t = typename std::deque<plan_pair_t>;
 
-        inline size_t maxCacheSize() const {
-            return mMaxCacheSize;
-        }
+    public:
+        FFTPlanCache() : mMaxCacheSize(5) {}
 
-        inline P get(int index) const {
-            return mCache[index].second;
-        }
+        void setMaxCacheSize(size_t size);
+        size_t getMaxCacheSize() const;
 
         // iterates through plan cache from front to back
         // of the cache(queue)
-        //
-        // A valid index of the plan in the cache is returned
-        // otherwise -1 is returned
-        int find(std::string key) const {
-            int retVal = -1;
-            for(uint i=0; i<mCache.size(); ++i) {
-                if (key == mCache[i].first) {
-                    retVal = i;
-                }
-            }
-            return retVal;
-        }
-
-        // pops plan from the back of cache(queue)
-        void pop() {
-            if (!mCache.empty()) {
-                lock_guard_t lock(mutex);
-                // destroy the cufft plan associated with the
-                // least recently used plan
-                static_cast<T*>(this)->removePlan(mCache.back().second);
-                // now pop the entry from cache
-                mCache.pop_back();
-            }
-        }
+        // A valid shared_ptr of the plan in the cache is returned
+        // if found, and empty share_ptr otherwise.
+        plan_t find(const std::string& key) const;
 
         // pushes plan to the front of cache(queue)
-        void push(std::string key, P plan) {
-            lock_guard_t lock(mutex);
-            if (mCache.size()>mMaxCacheSize) {
-                pop();
-            }
-            mCache.push_front(std::pair<std::string, P>(key, plan));
-        }
+        void push(const std::string key, plan_t plan);
 
-    private:
+    protected:
         FFTPlanCache(FFTPlanCache const&);
         void operator=(FFTPlanCache const&);
 
         size_t       mMaxCacheSize;
 
-        std::deque< std::pair<std::string, P> > mCache;
-        mutex_t mutex;
+        plan_cache_t mCache;
 };
 }
diff --git a/src/backend/common/InteropManager.cpp b/src/backend/common/InteropManager.cpp
new file mode 100644
index 0000000000..9f970e4324
--- /dev/null
+++ b/src/backend/common/InteropManager.cpp
@@ -0,0 +1,154 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(WITH_GRAPHICS)
+//FIXME CPU backend doesn't required the following class implementation
+//InteropManager.hpp is not used while building CPU backend.
+#ifndef AF_CPU
+#include <boost/thread.hpp>
+#include <common/InteropManager.hpp>
+#include <common/types.hpp>
+#include <cstdio>
+#include <err_common.hpp>
+#include <util.hpp>
+#include <backend.hpp>
+#include <GraphicsResourceManager.hpp>
+#include <platform.hpp>
+
+typedef boost::shared_mutex smutex_t;
+typedef boost::shared_lock<smutex_t> rlock_t;
+typedef boost::unique_lock<smutex_t> wlock_t;
+typedef boost::upgrade_lock<smutex_t> ulock_t;
+typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
+
+template<typename R>
+using RVector = std::vector<std::shared_ptr<R>>;
+
+namespace common
+{
+static smutex_t gInteropMutexes[detail::DeviceManager::MAX_DEVICES];
+
+template<class T, typename R>
+InteropManager<T, R>::~InteropManager()
+{
+    try {
+        destroyResources();
+    } catch (AfError &ex) {
+
+        std::string perr = getEnvVar("AF_PRINT_ERRORS");
+        if(!perr.empty()) {
+            if(perr != "0") fprintf(stderr, "%s\n", ex.what());
+        }
+    }
+}
+
+template<class T, typename R>
+RVector<R> InteropManager<T, R>::getBufferResource(const forge::Image* image)
+{
+    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
+    void * key = (void*)image;
+
+    if (mInteropMap.find(key) == mInteropMap.end()) {
+        std::vector<uint32_t> handles;
+        handles.push_back(image->pixels());
+        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
+
+        u2ulock_t wlock(lock);
+        mInteropMap[key] = output;
+    }
+
+    return mInteropMap[key];
+}
+
+template<class T, typename R>
+RVector<R> InteropManager<T, R>::getBufferResource(const forge::Plot* plot)
+{
+    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
+    void * key = (void*)plot;
+
+    if (mInteropMap.find(key) == mInteropMap.end()) {
+        std::vector<uint32_t> handles;
+        handles.push_back(plot->vertices());
+        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
+
+        u2ulock_t wlock(lock);
+        mInteropMap[key] = output;
+    }
+
+    return mInteropMap[key];
+}
+
+template<class T, typename R>
+RVector<R> InteropManager<T, R>::getBufferResource(const forge::Histogram* histogram)
+{
+    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
+    void * key = (void*)histogram;
+
+    if (mInteropMap.find(key) == mInteropMap.end()) {
+        std::vector<uint32_t> handles;
+        handles.push_back(histogram->vertices());
+        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
+
+        u2ulock_t wlock(lock);
+        mInteropMap[key] = output;
+    }
+
+    return mInteropMap[key];
+}
+
+template<class T, typename R>
+RVector<R> InteropManager<T, R>::getBufferResource(const forge::Surface* surface)
+{
+    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
+    void * key = (void*)surface;
+
+    if (mInteropMap.find(key) == mInteropMap.end()) {
+        std::vector<uint32_t> handles;
+        handles.push_back(surface->vertices());
+        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
+
+        u2ulock_t wlock(lock);
+        mInteropMap[key] = output;
+    }
+
+    return mInteropMap[key];
+}
+
+template<class T, typename R>
+RVector<R> InteropManager<T, R>::getBufferResource(const forge::VectorField* field)
+{
+    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
+    void * key = (void*)field;
+
+    if (mInteropMap.find(key) == mInteropMap.end()) {
+        std::vector<uint32_t> handles;
+        handles.push_back(field->vertices());
+        handles.push_back(field->directions());
+        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
+
+        u2ulock_t wlock(lock);
+        mInteropMap[key] = output;
+    }
+
+    return mInteropMap[key];
+}
+
+template<class T, typename R>
+void InteropManager<T, R>::destroyResources()
+{
+    wlock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
+    for(auto iter : mInteropMap) {
+        iter.second.clear();
+    }
+}
+
+template class InteropManager<detail::GraphicsResourceManager, detail::CGR_t>;
+}
+#endif
+#endif
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index 6d0d6f85aa..5cfc3736e1 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -11,120 +11,34 @@
 
 #if defined(WITH_GRAPHICS)
 #include <forge.h>
-#include <err_common.hpp>
-#include <util.hpp>
-#include <cstdio>
 #include <map>
 #include <vector>
-#include <common/types.hpp>
 
 namespace common
 {
-template<typename T, typename R>
+template<class T, typename R>
 class InteropManager
 {
+    using resource_t = typename std::shared_ptr<R>;
+    using res_vec_t = typename std::vector<resource_t>;
+    using res_map_t = typename std::map<void*, res_vec_t>;
+
     public:
         InteropManager() {}
+        ~InteropManager();
 
-        ~InteropManager() {
-            try {
-                destroyResources();
-            } catch (AfError &ex) {
-
-                std::string perr = getEnvVar("AF_PRINT_ERRORS");
-                if(!perr.empty()) {
-                    if(perr != "0") fprintf(stderr, "%s\n", ex.what());
-                }
-            }
-        }
-
-        R* getBufferResource(const forge::Image* image) {
-            lock_guard_t lock(mutex);
-            void * key = (void*)image;
-
-            if (interopMap.find(key) == interopMap.end()) {
-                std::vector<uint32_t> handles;
-                handles.push_back(image->pixels());
-                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
-                interopMap[key] = output;
-            }
-
-            return &interopMap[key].front();
-        }
-
-        R* getBufferResource(const forge::Plot* plot) {
-            lock_guard_t lock(mutex);
-            void * key = (void*)plot;
-
-            if (interopMap.find(key) == interopMap.end()) {
-                std::vector<uint32_t> handles;
-                handles.push_back(plot->vertices());
-                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
-                interopMap[key] = output;
-            }
-
-            return &interopMap[key].front();
-        }
-
-        R* getBufferResource(const forge::Histogram* histogram) {
-            lock_guard_t lock(mutex);
-            void * key = (void*)histogram;
-
-            if (interopMap.find(key) == interopMap.end()) {
-                std::vector<uint32_t> handles;
-                handles.push_back(histogram->vertices());
-                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
-                interopMap[key] = output;
-            }
-
-            return &interopMap[key].front();
-        }
-
-        R* getBufferResource(const forge::Surface* surface) {
-            lock_guard_t lock(mutex);
-            void * key = (void*)surface;
-
-            if (interopMap.find(key) == interopMap.end()) {
-                std::vector<uint32_t> handles;
-                handles.push_back(surface->vertices());
-                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
-                interopMap[key] = output;
-            }
-
-            return &interopMap[key].front();
-        }
-
-        R* getBufferResource(const forge::VectorField* field) {
-            lock_guard_t lock(mutex);
-            void * key = (void*)field;
-
-            if (interopMap.find(key) == interopMap.end()) {
-                std::vector<uint32_t> handles;
-                handles.push_back(field->vertices());
-                handles.push_back(field->directions());
-                std::vector<R> output = static_cast<T*>(this)->registerResources(handles);
-                interopMap[key] = output;
-            }
-
-            return &interopMap[key].front();
-        }
+        res_vec_t getBufferResource(const forge::Image* image);
+        res_vec_t getBufferResource(const forge::Plot* plot);
+        res_vec_t getBufferResource(const forge::Histogram* histogram);
+        res_vec_t getBufferResource(const forge::Surface* surface);
+        res_vec_t getBufferResource(const forge::VectorField* field);
 
     protected:
         InteropManager(InteropManager const&);
         void operator=(InteropManager const&);
+        void destroyResources();
 
-        void destroyResources() {
-            for(auto iter : interopMap) {
-                for(auto ct : iter.second) {
-                    static_cast<T*>(this)->unregisterResource(ct);
-                }
-                iter.second.clear();
-            }
-        }
-
-        //Attributes
-        std::map<void *, std::vector<R> > interopMap;
-        mutex_t mutex;
+        res_map_t mInteropMap;
 };
 }
 #endif
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 79c80cc26d..ab4659697f 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -126,6 +126,7 @@ FILE(GLOB backend_headers
     )
 
 FILE(GLOB backend_sources
+    "../common/*.cpp"
     "../*.cpp"
     )
 
@@ -213,6 +214,7 @@ TARGET_LINK_LIBRARIES(afcpu
                             PRIVATE ${CBLAS_LIBRARIES}
                             PRIVATE ${FFTW_LIBRARIES}
                             PRIVATE ${FreeImage_LIBS}
+                            PRIVATE ${Boost_LIBRARIES}
                      )
 
 IF(LAPACK_FOUND)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f5ff45092a..9b3538e576 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -188,6 +188,7 @@ FILE(GLOB backend_headers
     )
 
 FILE(GLOB backend_sources
+    "../common/*.cpp"
     "../*.cpp"
     )
 
@@ -426,14 +427,17 @@ IF (${libdevice_bc_len} GREATER 0)
   ADD_DEPENDENCIES(afcuda ${libdevice_targets})
 ENDIF()
 
-TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
+TARGET_LINK_LIBRARIES(afcuda
+                                PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
                                 PRIVATE ${CUDA_cusparse_LIBRARY}
                                 PRIVATE ${CUDA_cusolver_LIBRARY}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
-                                PRIVATE ${CUDA_CUDA_LIBRARY})
+                                PRIVATE ${CUDA_CUDA_LIBRARY}
+                                PRIVATE ${Boost_LIBRARIES}
+                     )
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
 IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
new file mode 100644
index 0000000000..b27da053ad
--- /dev/null
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(WITH_GRAPHICS)
+#include <err_cuda.hpp>
+#include <GraphicsResourceManager.hpp>
+
+namespace cuda
+{
+ShrdResVector GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
+{
+    ShrdResVector output;
+
+    auto deleter = [](CGR_t* handle) {
+        CUDA_CHECK(cudaGraphicsUnregisterResource(*handle));
+        delete handle;
+    };
+
+    for (auto id: resources) {
+        CGR_t r;
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&r, id, cudaGraphicsMapFlagsWriteDiscard));
+        output.emplace_back(new CGR_t(r), deleter);
+    }
+
+    return output;
+}
+}
+#endif
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index 6f8b39fd8e..fb0700d441 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -14,42 +14,29 @@
 #include <windows.h>
 #endif
 
-#include <platform.hpp>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <graphics_common.hpp>
 using namespace gl;
 #define GL_VERSION gl::GL_VERSION
-#define __gl_h_ //Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#include <cuda.h>
+#include <cuda_runtime.h>
 #include <cuda_gl_interop.h>
 #include <common/InteropManager.hpp>
-#include <err_cuda.hpp>
 #include <map>
 #include <vector>
 
 namespace cuda
 {
 typedef cudaGraphicsResource_t CGR_t;
+typedef std::shared_ptr<CGR_t> SharedResource;
+typedef std::vector<SharedResource> ShrdResVector;
 
 class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, CGR_t>
 {
     public:
         GraphicsResourceManager() {}
-
-        std::vector<CGR_t> registerResources(std::vector<uint32_t> resources) {
-            std::vector<CGR_t> output;
-
-            for (auto id: resources) {
-                CGR_t r;
-                CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&r, id, cudaGraphicsMapFlagsWriteDiscard));
-                output.push_back(r);
-            }
-
-            return output;
-        }
-
-        void unregisterResource(CGR_t handle) {
-            CUDA_CHECK(cudaGraphicsUnregisterResource(handle));
-        }
+        ShrdResVector registerResources(std::vector<uint32_t> resources);
 
     protected:
         GraphicsResourceManager(GraphicsResourceManager const&);
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index b185486158..727c693f2a 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -74,10 +74,10 @@ const char * _cufftGetResultString(cufftResult res)
     return "cuFFT: unknown error";
 }
 
-PlanType findPlan(int rank, int *n,
-                  int *inembed, int istride, int idist,
-                  int *onembed, int ostride, int odist,
-                  cufftType type, int batch)
+SharedPlan findPlan(int rank, int *n,
+                    int *inembed, int istride, int idist,
+                    int *onembed, int ostride, int odist,
+                    cufftType type, int batch)
 {
     // create the key string
     char key_str_temp[64];
@@ -112,29 +112,30 @@ PlanType findPlan(int rank, int *n,
     key_string.append(std::string(key_str_temp));
 
     PlanCache &planner = cuda::fftManager();
+    SharedPlan retVal  = planner.find(key_string);
 
-    int planIndex = planner.find(key_string);
+    if (retVal)
+        return retVal;
 
-    // if found a valid plan, return it
-    if (planIndex!=-1) {
-        return planner.get(planIndex);
-    }
-
-    PlanType retVal;
-    cufftResult res = cufftPlanMany(&retVal, rank, n,
+    PlanType* temp = (PlanType*)malloc(sizeof(PlanType));
+    cufftResult res = cufftPlanMany(temp, rank, n,
                                     inembed, istride, idist, onembed, ostride, odist,
                                     type, batch);
 
     // If plan creation fails, clean up the memory we hold on to and try again
     if (res != CUFFT_SUCCESS) {
         cuda::garbageCollect();
-        CUFFT_CHECK(cufftPlanMany(&retVal, rank, n,
+        CUFFT_CHECK(cufftPlanMany(temp, rank, n,
                                   inembed, istride, idist, onembed, ostride, odist,
                                   type, batch));
     }
 
-    cufftSetStream(retVal, cuda::getActiveStream());
+    cufftSetStream(*temp, cuda::getActiveStream());
 
+    retVal.reset(temp, [](PlanType* p) {
+        cufftDestroy(*p);
+        delete p;
+    });
     // push the plan into plan cache
     planner.push(key_string, retVal);
 
diff --git a/src/backend/cuda/cufft.hpp b/src/backend/cuda/cufft.hpp
index 6142004151..e4f4326000 100644
--- a/src/backend/cuda/cufft.hpp
+++ b/src/backend/cuda/cufft.hpp
@@ -15,30 +15,22 @@
 
 namespace cuda
 {
-typedef cufftHandle PlanType; //used in platform.hpp
+typedef cufftHandle PlanType;
+typedef std::shared_ptr<PlanType> SharedPlan;
 
 const char * _cufftGetResultString(cufftResult res);
 
-PlanType findPlan(int rank, int *n,
-                  int *inembed, int istride, int idist,
-                  int *onembed, int ostride, int odist,
-                  cufftType type, int batch);
+SharedPlan findPlan(int rank, int *n,
+                    int *inembed, int istride, int idist,
+                    int *onembed, int ostride, int odist,
+                    cufftType type, int batch);
 
 class PlanCache : public common::FFTPlanCache<PlanCache, PlanType>
 {
-    friend PlanType findPlan(int rank, int *n,
-                             int *inembed, int istride, int idist,
-                             int *onembed, int ostride, int odist,
-                             cufftType type, int batch);
-
-    public:
-        PlanCache() {}
-        void initLibrary() {}
-        void deInitLibrary() {}
-
-        void removePlan(PlanType plan) {
-            cufftDestroy(plan);
-        }
+    friend SharedPlan findPlan(int rank, int *n,
+                               int *inembed, int istride, int idist,
+                               int *onembed, int ostride, int odist,
+                               cufftType type, int batch);
 };
 }
 
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index ca41896e29..280704012a 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -23,7 +23,7 @@ namespace cuda
 {
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    fftManager().maxCacheSize(numPlans);
+    fftManager().setMaxCacheSize(numPlans);
 }
 
 template<typename T>
@@ -88,13 +88,13 @@ void fft_inplace(Array<T> &in)
         batch *= idims[i];
     }
 
-    cufftHandle plan = findPlan(rank, t_dims,
-                                in_embed , istrides[0], istrides[rank],
-                                in_embed , istrides[0], istrides[rank],
-                                (cufftType)cufft_transform<T>::type, batch);
+    SharedPlan plan = findPlan(rank, t_dims,
+                               in_embed , istrides[0], istrides[rank],
+                               in_embed , istrides[0], istrides[rank],
+                               (cufftType)cufft_transform<T>::type, batch);
 
     cufft_transform<T> transform;
-    CUFFT_CHECK(transform(plan, (T *)in.get(), in.get(), direction ? CUFFT_FORWARD : CUFFT_INVERSE));
+    CUFFT_CHECK(transform(*plan.get(), (T *)in.get(), in.get(), direction ? CUFFT_FORWARD : CUFFT_INVERSE));
 }
 
 template<typename Tc, typename Tr, int rank>
@@ -122,13 +122,13 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     dim4 istrides = in.strides();
     dim4 ostrides = out.strides();
 
-    cufftHandle plan = findPlan(rank, t_dims,
-                                in_embed  , istrides[0], istrides[rank],
-                                out_embed , ostrides[0], ostrides[rank],
-                                (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
+    SharedPlan plan = findPlan(rank, t_dims,
+                               in_embed  , istrides[0], istrides[rank],
+                               out_embed , ostrides[0], ostrides[rank],
+                               (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
 
     cufft_real_transform<Tc, Tr> transform;
-    CUFFT_CHECK(transform(plan, (Tr *)in.get(), out.get()));
+    CUFFT_CHECK(transform(*plan.get(), (Tr *)in.get(), out.get()));
     return out;
 }
 
@@ -154,12 +154,12 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
 
     cufft_real_transform<Tr, Tc> transform;
 
-    cufftHandle plan = findPlan(rank, t_dims,
-                                in_embed  , istrides[0], istrides[rank],
-                                out_embed , ostrides[0], ostrides[rank],
-                                (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
+    SharedPlan plan = findPlan(rank, t_dims,
+                               in_embed  , istrides[0], istrides[rank],
+                               out_embed , ostrides[0], ostrides[rank],
+                               (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
 
-    CUFFT_CHECK(transform(plan, (Tc *)in.get(), out.get()));
+    CUFFT_CHECK(transform(*plan.get(), (Tc *)in.get(), out.get()));
     return out;
 }
 
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 6367208c5c..d43355a123 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -25,17 +25,15 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
+        ShrdResVector res = interopManager().getBufferResource(hist);
 
-        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(hist);
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = hist->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                        cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 17d0f12b0f..ef177dc28f 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -28,19 +28,16 @@ template<typename T>
 void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        GraphicsResourceManager& intrpMngr = interopManager();
-
-        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(image);
+        ShrdResVector res = interopManager().getBufferResource(image);
 
         const T *d_X = in.get();
         // Map resource. Copy data to pixels. Unmap resource.
         size_t num_bytes;
         T* d_pixels = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels, &num_bytes, resources[0]);
-        cudaMemcpyAsync(d_pixels, d_X, num_bytes, cudaMemcpyDeviceToDevice,
-                        cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels, &num_bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_pixels, d_X, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
 
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 0c19208311..ce55bca8eb 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -475,7 +475,7 @@ SparseHandle sparseHandle()
 
     std::call_once(initFlags[id], [&]{ inst.cusparseHandles[id].reset(new cusparseHandle()); });
 
-    return instance.cusparseHandles[id].get()->get();
+    return inst.cusparseHandles[id].get()->get();
 }
 
 DeviceManager::DeviceManager()
@@ -521,7 +521,7 @@ DeviceManager::DeviceManager()
 
 void DeviceManager::sortDevices(sort_mode mode)
 {
-    lock_guard_t lock(deviceMutex);
+    common::lock_guard_t lock(deviceMutex);
     switch(mode) {
         case memory :
             std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_mem);
@@ -542,7 +542,7 @@ int DeviceManager::setActiveDevice(int device, int nId)
 {
     static bool first = true;
 
-    lock_guard_t lock(deviceMutex);
+    common::lock_guard_t lock(deviceMutex);
 
     int numDevices = cuDevices.size();
 
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index e7a31537ff..e9d7bea738 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -30,17 +30,15 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
+        ShrdResVector res = interopManager().getBufferResource(plot);
 
-        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(plot);
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = plot->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 650968a61f..cc7ac29e73 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -30,17 +30,15 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
+        ShrdResVector res = interopManager().getBufferResource(surface);
 
-        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(surface);
         // Map resource. Copy data to VBO. Unmap resource.
         size_t num_bytes = surface->verticesSize();
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, resources, cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice,
-                cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, resources, cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
+        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
 
         CheckGL("After cuda resource copy");
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index c875130a8b..043d0ba591 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -26,9 +26,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        forge::VectorField* vector_field)
 {
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        GraphicsResourceManager& intrpMngr = interopManager();
-
-        cudaGraphicsResource_t *resources = intrpMngr.getBufferResource(vector_field);
+        ShrdResVector res = interopManager().getBufferResource(vector_field);
+        CGR_t resources[2] = {*res[0].get(), *res[1].get()};
 
         // Map resource. Copy data to VBO. Unmap resource.
         // Map all resources at once.
@@ -40,8 +39,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
             size_t num_bytes = vector_field->verticesSize();
             T* d_vbo = NULL;
             cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
-            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
-                            cuda::getActiveStream());
+            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
         }
         // Directions
         {
@@ -49,8 +47,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
             size_t num_bytes = vector_field->directionsSize();
             T* d_vbo = NULL;
             cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[1]);
-            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice,
-                            cuda::getActiveStream());
+            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
         }
         cudaGraphicsUnmapResources(2, resources, cuda::getActiveStream());
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 37655cfd36..de2b9ec588 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -78,10 +78,6 @@ ENDIF()
 INCLUDE_DIRECTORIES(${CLFFT_INCLUDE_DIRS})
 LINK_DIRECTORIES(${CLFFT_LIBRARY_DIR})
 
-ADD_DEFINITIONS( -DBOOST_ALL_NO_LIB )
-SET(Boost_USE_STATIC_LIBS OFF)
-FIND_PACKAGE(Boost 1.48 REQUIRED)
-
 OPTION(USE_SYSTEM_BOOST_COMPUTE "Use system BoostCompute" OFF)
 IF(USE_SYSTEM_BOOST_COMPUTE)
     IF(Boost_VERSION VERSION_LESS "1.61")
@@ -102,7 +98,6 @@ INCLUDE_DIRECTORIES(
     "${CMAKE_CURRENT_BINARY_DIR}"
     ${CLBLAS_INCLUDE_DIRS}
     ${CLFFT_INCLUDE_DIRS}
-    ${Boost_INCLUDE_DIR}
     ${BoostCompute_INCLUDE_DIRS}
     ${CBLAS_INCLUDE_DIR}
     )
@@ -186,6 +181,7 @@ FILE(GLOB backend_headers
     )
 
 FILE(GLOB backend_sources
+    "../common/*.cpp"
     "../*.cpp"
     )
 
@@ -323,6 +319,7 @@ TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${CLFFT_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
                                 PRIVATE ${FreeImage_LIBS}
+                                PRIVATE ${Boost_LIBRARIES}
                      )
 
 IF(LAPACK_FOUND)
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
index 8e043313ed..77d20ee9b9 100644
--- a/src/backend/opencl/GraphicsResourceManager.cpp
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -13,21 +13,14 @@
 
 namespace opencl
 {
-std::vector<CGR_t> GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
+ShrdResVector GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
 {
-    std::vector<CGR_t> output;
+    ShrdResVector output;
 
-    for (auto id: resources) {
-        CGR_t r = new cl::BufferGL(opencl::getContext(), CL_MEM_WRITE_ONLY, id, NULL);
-        output.push_back(r);
-    }
+    for (auto id: resources)
+        output.emplace_back(new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, id, NULL));
 
     return output;
 }
-
-void GraphicsResourceManager::unregisterResource(CGR_t handle)
-{
-    delete handle;
-}
 }
 #endif
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index 774cb0f124..d498dab894 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -10,22 +10,26 @@
 #pragma once
 
 #if defined(WITH_GRAPHICS)
-#include <platform.hpp>
 #include <common/InteropManager.hpp>
 #include <map>
 #include <vector>
 
+namespace cl
+{
+class Buffer;
+}
+
 namespace opencl
 {
-typedef cl::Buffer* CGR_t;
+typedef cl::Buffer CGR_t;
+typedef std::shared_ptr<CGR_t> SharedResource;
+typedef std::vector<SharedResource> ShrdResVector;
 
-class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, CGR_t>
+class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, cl::Buffer>
 {
     public:
         GraphicsResourceManager() {}
-
-        std::vector<CGR_t> registerResources(std::vector<uint32_t> resources);
-        void unregisterResource(CGR_t handle);
+        ShrdResVector registerResources(std::vector<uint32_t> resources);
 
     protected:
         GraphicsResourceManager(GraphicsResourceManager const&);
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index 483d88cfd6..aa7d26e63d 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -83,11 +83,11 @@ const char * _clfftGetResultString(clfftStatus st)
     return "Unknown error";
 }
 
-PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
-                  clfftDim rank, size_t *clLengths,
-                  size_t *istrides, size_t idist,
-                  size_t *ostrides, size_t odist,
-                  clfftPrecision precision, size_t batch)
+SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
+                    clfftDim rank, size_t *clLengths,
+                    size_t *istrides, size_t idist,
+                    size_t *ostrides, size_t odist,
+                    clfftPrecision precision, size_t batch)
 {
     // create the key string
     char key_str_temp[64];
@@ -123,68 +123,43 @@ PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
     key_string.append(std::string(key_str_temp));
 
     PlanCache &planner = opencl::fftManager();
+    SharedPlan retVal = planner.find(key_string);
 
-    int planIndex = planner.find(key_string);
+    if (retVal)
+        return retVal;
 
-    // if found a valid plan, return it
-    if (planIndex!=-1) {
-        return planner.get(planIndex);
-    }
-
-    PlanType retVal;
+    PlanType* temp = (PlanType*)malloc(sizeof(PlanType));
 
     // getContext() returns object of type Context
     // Context() returns the actual cl_context handle
-    CLFFT_CHECK(clfftCreateDefaultPlan(&retVal, opencl::getContext()(), rank, clLengths));
+    CLFFT_CHECK(clfftCreateDefaultPlan(temp, opencl::getContext()(), rank, clLengths));
 
     // complex to complex
     if (iLayout == oLayout) {
-        CLFFT_CHECK(clfftSetResultLocation(retVal, CLFFT_INPLACE));
+        CLFFT_CHECK(clfftSetResultLocation(*temp, CLFFT_INPLACE));
     } else {
-        CLFFT_CHECK(clfftSetResultLocation(retVal, CLFFT_OUTOFPLACE));
+        CLFFT_CHECK(clfftSetResultLocation(*temp, CLFFT_OUTOFPLACE));
     }
 
-    CLFFT_CHECK(clfftSetLayout(retVal, iLayout, oLayout));
-    CLFFT_CHECK(clfftSetPlanBatchSize(retVal, batch));
-    CLFFT_CHECK(clfftSetPlanDistance(retVal, idist, odist));
-    CLFFT_CHECK(clfftSetPlanInStride(retVal, rank, istrides));
-    CLFFT_CHECK(clfftSetPlanOutStride(retVal, rank, ostrides));
-    CLFFT_CHECK(clfftSetPlanPrecision(retVal, precision));
-    CLFFT_CHECK(clfftSetPlanScale(retVal, CLFFT_BACKWARD, 1.0));
+    CLFFT_CHECK(clfftSetLayout(*temp, iLayout, oLayout));
+    CLFFT_CHECK(clfftSetPlanBatchSize(*temp, batch));
+    CLFFT_CHECK(clfftSetPlanDistance(*temp, idist, odist));
+    CLFFT_CHECK(clfftSetPlanInStride(*temp, rank, istrides));
+    CLFFT_CHECK(clfftSetPlanOutStride(*temp, rank, ostrides));
+    CLFFT_CHECK(clfftSetPlanPrecision(*temp, precision));
+    CLFFT_CHECK(clfftSetPlanScale(*temp, CLFFT_BACKWARD, 1.0));
 
     // getQueue() returns object of type CommandQueue
     // CommandQueue() returns the actual cl_command_queue handle
-    CLFFT_CHECK(clfftBakePlan(retVal, 1, &(opencl::getQueue()()), NULL, NULL));
+    CLFFT_CHECK(clfftBakePlan(*temp, 1, &(opencl::getQueue()()), NULL, NULL));
 
+    retVal.reset(temp, [](PlanType* p) {
+        CLFFT_CHECK(clfftDestroyPlan(p));
+        free(p);
+    });
     // push the plan into plan cache
     planner.push(key_string, retVal);
 
     return retVal;
 }
-
-void PlanCache::initLibrary()
-{
-    CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
-    CLFFT_CHECK(clfftSetup(&mFFTSetup));
-}
-
-void PlanCache::deInitLibrary()
-{
-    //TODO: FIXME:
-    // clfftTeardown() causes a "Pure Virtual Function Called" crash on
-    // Windows for Intel devices. This causes tests to fail.
-    #ifndef OS_WIN
-    static bool flag = true;
-    if(flag) {
-        // THOU SHALL NOT THROW IN DESTRUCTORS
-        clfftTeardown();
-        flag = false;
-    }
-    #endif
-}
-
-void PlanCache::removePlan(PlanType plan)
-{
-     CLFFT_CHECK(clfftDestroyPlan(&plan));
-}
 }
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
index 92267e27c7..f249735712 100644
--- a/src/backend/opencl/clfft.hpp
+++ b/src/backend/opencl/clfft.hpp
@@ -17,32 +17,23 @@
 namespace opencl
 {
 typedef clfftPlanHandle PlanType;
+typedef std::shared_ptr<PlanType> SharedPlan;
 
 const char * _clfftGetResultString(clfftStatus st);
 
-PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
-                  clfftDim rank, size_t *clLengths,
-                  size_t *istrides, size_t idist,
-                  size_t *ostrides, size_t odist,
-                  clfftPrecision precision, size_t batch);
+SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
+                    clfftDim rank, size_t *clLengths,
+                    size_t *istrides, size_t idist,
+                    size_t *ostrides, size_t odist,
+                    clfftPrecision precision, size_t batch);
 
 class PlanCache : public common::FFTPlanCache<PlanCache, PlanType>
 {
-    friend PlanType findPlan(clfftLayout iLayout, clfftLayout oLayout,
-                             clfftDim rank, size_t *clLengths,
-                             size_t *istrides, size_t idist,
-                             size_t *ostrides, size_t odist,
-                             clfftPrecision precision, size_t batch);
-
-    public:
-        PlanCache() {}
-        void initLibrary();
-        void deInitLibrary();
-
-        void removePlan(PlanType plan);
-
-    private:
-        clfftSetupData  mFFTSetup;
+    friend SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
+                               clfftDim rank, size_t *clLengths,
+                               size_t *istrides, size_t idist,
+                               size_t *ostrides, size_t odist,
+                               clfftPrecision precision, size_t batch);
 };
 }
 
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index 56a1927d84..b882b21cdf 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -24,7 +24,7 @@ namespace opencl
 
 void setFFTPlanCacheSize(size_t numPlans)
 {
-    fftManager().maxCacheSize(numPlans);
+    fftManager().setMaxCacheSize(numPlans);
 }
 
 template<typename T> struct Precision;
@@ -83,15 +83,15 @@ void fft_inplace(Array<T> &in)
         batch *= tdims[i];
     }
 
-    PlanType plan = findPlan(CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
-                             (clfftDim)rank, tdims,
-                             istrides, istrides[rank], istrides, istrides[rank],
-                             (clfftPrecision)Precision<T>::type, batch);
+    SharedPlan plan = findPlan(CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
+                               (clfftDim)rank, tdims,
+                               istrides, istrides[rank], istrides, istrides[rank],
+                               (clfftPrecision)Precision<T>::type, batch);
 
     cl_mem imem = (*in.get())();
     cl_command_queue queue = getQueue()();
 
-    CLFFT_CHECK(clfftEnqueueTransform(plan,
+    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(),
                                       direction ? CLFFT_FORWARD : CLFFT_BACKWARD,
                                       1, &queue, 0, NULL, NULL,
                                       &imem, &imem, NULL));
@@ -118,16 +118,16 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
         batch *= tdims[i];
     }
 
-    PlanType plan = findPlan(CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED,
-                             (clfftDim)rank, tdims,
-                             istrides, istrides[rank], ostrides, ostrides[rank],
-                             (clfftPrecision)Precision<Tc>::type, batch);
+    SharedPlan plan = findPlan(CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED,
+                               (clfftDim)rank, tdims,
+                               istrides, istrides[rank], ostrides, ostrides[rank],
+                               (clfftPrecision)Precision<Tc>::type, batch);
 
     cl_mem imem = (*in.get())();
     cl_mem omem = (*out.get())();
     cl_command_queue queue = getQueue()();
 
-    CLFFT_CHECK(clfftEnqueueTransform(plan,
+    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(),
                                       CLFFT_FORWARD,
                                       1, &queue, 0, NULL, NULL,
                                       &imem, &omem, NULL));
@@ -152,16 +152,16 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
         batch *= tdims[i];
     }
 
-    PlanType plan = findPlan(CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL,
-                             (clfftDim)rank, tdims,
-                             istrides, istrides[rank], ostrides, ostrides[rank],
-                             (clfftPrecision)Precision<Tc>::type, batch);
+    SharedPlan plan = findPlan(CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL,
+                               (clfftDim)rank, tdims,
+                               istrides, istrides[rank], ostrides, ostrides[rank],
+                               (clfftPrecision)Precision<Tc>::type, batch);
 
     cl_mem imem = (*in.get())();
     cl_mem omem = (*out.get())();
     cl_command_queue queue = getQueue()();
 
-    CLFFT_CHECK(clfftEnqueueTransform(plan,
+    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(),
                                       CLFFT_BACKWARD,
                                       1, &queue, 0, NULL, NULL,
                                       &imem, &omem, NULL));
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 701c8c337a..798363769e 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -27,12 +27,10 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         const cl::Buffer *d_P = data.get();
         size_t bytes = hist->verticesSize();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
-
-        cl::Buffer **resources = intrpMngr.getBufferResource(hist);
+        ShrdResVector res = interopManager().getBufferResource(hist);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*resources[0]);
+        shared_objects.push_back(*(res[0].get()));
 
         glFinish();
 
@@ -42,7 +40,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *resources[0], 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 4df7f5cde5..7acf5a66c9 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -27,14 +27,14 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 {
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
-        GraphicsResourceManager& intrpMngr = interopManager();
 
-        cl::Buffer **resources = intrpMngr.getBufferResource(image);
+        ShrdResVector res = interopManager().getBufferResource(image);
+
         const cl::Buffer *d_X = in.get();
         size_t num_bytes = image->size();
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*resources[0]);
+        shared_objects.push_back(*(res[0].get()));
 
         glFinish();
 
@@ -44,7 +44,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_X, *resources[0], 0, 0, num_bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_X, *(res[0].get()), 0, 0, num_bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 910d500a58..1f11a54937 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -624,7 +624,15 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    return DeviceManager::getInstance().clfftManagers[getActiveDeviceId()];
+    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+
+    int id = getActiveDeviceId();
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    std::call_once(initFlags[id], [&]{ inst.clfftManagers[id].reset(new PlanCache()); });
+
+    return *(inst.clfftManagers[id].get());
 }
 
 DeviceManager& DeviceManager::getInstance()
@@ -635,8 +643,16 @@ DeviceManager& DeviceManager::getInstance()
 
 DeviceManager::~DeviceManager()
 {
-    for (int i=0; i<getDeviceCount(); ++i)
+    for (int i=0; i<getDeviceCount(); ++i) {
         delete gfxManagers[i].release();
+        delete clfftManagers[i].release();
+    }
+#ifndef OS_WIN
+    //TODO: FIXME:
+    // clfftTeardown() causes a "Pure Virtual Function Called" crash on
+    // Windows for Intel devices. This causes tests to fail.
+    clfftTeardown();
+#endif
     delete memManager.release();
     delete pinnedMemManager.release();
 
@@ -649,8 +665,8 @@ DeviceManager::~DeviceManager()
     // So, clean up OpenCL resources on non-Windows platforms
 #ifndef OS_WIN
     for (auto q: mQueues) delete q;
-    for (auto d : mDevices) delete d;
-    for (auto c : mContexts) delete c;
+    for (auto c: mContexts) delete c;
+    for (auto d: mDevices) delete d;
 #endif
 }
 
@@ -784,6 +800,9 @@ DeviceManager::DeviceManager()
     }
 #endif
     mUserDeviceOffset = mDevices.size();
+    //Initialize FFT setup data structure
+    CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
+    CLFFT_CHECK(clfftSetup(&mFFTSetup));
 }
 
 #if defined(WITH_GRAPHICS)
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 846b839ee5..fe9ded084f 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -180,6 +180,7 @@ class DeviceManager
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
-        PlanCache clfftManagers[MAX_DEVICES];
+        clfftSetupData mFFTSetup;
+        std::unique_ptr<PlanCache> clfftManagers[MAX_DEVICES];
 };
 }
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index cd8134fe4f..4651d60737 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -32,12 +32,10 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         const cl::Buffer *d_P = P.get();
         size_t bytes = plot->verticesSize();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
-
-        cl::Buffer **resources = intrpMngr.getBufferResource(plot);
+        ShrdResVector res = interopManager().getBufferResource(plot);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*resources[0]);
+        shared_objects.push_back(*(res[0].get()));
 
         glFinish();
 
@@ -47,7 +45,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *resources[0], 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index ad296dbb54..ef7782c561 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -32,12 +32,10 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         const cl::Buffer *d_P = P.get();
         size_t bytes = surface->verticesSize();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
-
-        cl::Buffer **resources = intrpMngr.getBufferResource(surface);
+        ShrdResVector res = interopManager().getBufferResource(surface);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*resources[0]);
+        shared_objects.push_back(*(res[0].get()));
 
         glFinish();
 
@@ -47,7 +45,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *resources[0], 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 53ac16d88a..c53279aa0c 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -32,13 +32,11 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         size_t pBytes = vector_field->verticesSize();
         size_t dBytes = vector_field->directionsSize();
 
-        GraphicsResourceManager& intrpMngr = interopManager();
-
-        cl::Buffer **resources = intrpMngr.getBufferResource(vector_field);
+        ShrdResVector res = interopManager().getBufferResource(vector_field);
 
         std::vector<cl::Memory> shared_objects;
-        shared_objects.push_back(*resources[0]);
-        shared_objects.push_back(*resources[1]);
+        shared_objects.push_back(*(res[0].get()));
+        shared_objects.push_back(*(res[1].get()));
 
         glFinish();
 
@@ -48,8 +46,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_points    , *resources[0], 0, 0, pBytes, NULL, &event);
-        getQueue().enqueueCopyBuffer(*d_directions, *resources[1], 0, 0, dBytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_points    , *(res[0].get()), 0, 0, pBytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_directions, *(res[1].get()), 0, 0, dBytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 

From 73ebc03a38866d1a8a60380aeda90e530aba3bad Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 30 Jan 2017 21:18:01 +0530
Subject: [PATCH 1104/2677] Fix cmake statement in glbinding build script

---
 CMakeModules/build_glbinding.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
index c929778d92..3f4f493dc7 100644
--- a/CMakeModules/build_glbinding.cmake
+++ b/CMakeModules/build_glbinding.cmake
@@ -3,7 +3,7 @@ INCLUDE(ExternalProject)
 SET(prefix ${PROJECT_BINARY_DIR}/third_party/glb)
 
 SET(LIB_POSTFIX "")
-IF (${CMAKE_BUILD_TYPE} STREQUAL "Debug")
+IF (${CMAKE_BUILD_TYPE} MATCHES DEBUG)
     SET(LIB_POSTFIX "d")
 ENDIF()
 

From bcd03ef0743d6191a2b386c540a6c3adb965407b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 30 Jan 2017 21:18:43 +0530
Subject: [PATCH 1105/2677] tyepdef typo fix in common::FFTPlanCache class

---
 src/backend/common/FFTPlanCache.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/common/FFTPlanCache.cpp b/src/backend/common/FFTPlanCache.cpp
index 0360cb943c..be5f4d9582 100644
--- a/src/backend/common/FFTPlanCache.cpp
+++ b/src/backend/common/FFTPlanCache.cpp
@@ -43,7 +43,7 @@ std::shared_ptr<P> FFTPlanCache<T, P>::find(const std::string& key) const
     std::shared_ptr<P> res;
 
     rlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
-    for(uint i=0; i<mCache.size(); ++i) {
+    for(unsigned i=0; i<mCache.size(); ++i) {
         if (key == mCache[i].first) {
             res = mCache[i].second;
             break;

From 2aaf25775abd9fdbfd1e9b46dc6f19c8e32639f8 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Mon, 23 Jan 2017 08:19:40 -0500
Subject: [PATCH 1106/2677] Updated README.md to mention Julia and Nim
 wrappers.

Also included message recognizing contributors that have helped tremendously
with the NodeJS and Julia wrappers for ArrayFire.
---
 README.md | 26 ++++++++++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 1025fa9bf8..9863680db3 100644
--- a/README.md
+++ b/README.md
@@ -111,14 +111,30 @@ Quick links:
 
 ### Language wrappers
 
+ArrayFire has several official and third-party language wrappers.
+
+__Official wrappers__
+
 We currently support the following language wrappers for ArrayFire:
 
 * [`arrayfire-python`](https://github.com/arrayfire/arrayfire-python)
 * [`arrayfire-rust`](https://github.com/arrayfire/arrayfire-rust)
 
-Wrappers for other languages are a work in progress:
+Wrappers for other languages are a work-in-progress:
+  [.NET](https://github.com/arrayfire/arrayfire-dotnet),
+  [Fortran](https://github.com/arrayfire/arrayfire-fortran),
+  [Go](https://github.com/arrayfire/arrayfire-go),
+  [Java](https://github.com/arrayfire/arrayfire-java),
+  [Lua](https://github.com/arrayfire/arrayfire-lua),
+  [NodeJS](https://github.com/arrayfire/arrayfire-js),
+  [R](https://github.com/arrayfire/arrayfire-r)
+
+__Third-party wrappers__
 
-[`arrayfire-dotnet`](https://github.com/arrayfire/arrayfire-dotnet), [`arrayfire-fortran`](https://github.com/arrayfire/arrayfire-fortran), [`arrayfire-go`](https://github.com/arrayfire/arrayfire-go), [`arrayfire-java`](https://github.com/arrayfire/arrayfire-java), [`arrayfire-lua`](https://github.com/arrayfire/arrayfire-lua), [`arrayfire-nodejs`](https://github.com/arrayfire/arrayfire-js), [`arrayfire-r`](https://github.com/arrayfire/arrayfire-r)
+The following wrappers are being maintained and supported by third parties:
+
+* [`ArrayFire.jl`](https://github.com/JuliaComputing/ArrayFire.jl)
+* [`ArrayFire-Nim`](https://github.com/bitstormGER/ArrayFire-Nim)
 
 ### Contributing
 
@@ -136,6 +152,12 @@ ArrayFire development is funded by ArrayFire LLC and several third parties,
 please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for further
 details.
 
+We would like to thank the [JuliaComputing](https://github.com/JuliaComputing)
+guys as well as [Gabor Mezo](https://github.com/unbornchikken) for their
+diligent work on the [Julia](https://github.com/JuliaComputing/ArrayFire.jl)
+and [NodeJS](https://github.com/arrayfire/arrayfire-js) wrappers,
+respectively.
+
 ### Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)

From dcbaf8339779c44f4b476eb42a129e49e104f281 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Tue, 31 Jan 2017 21:10:38 -0500
Subject: [PATCH 1107/2677] osx_install/forge_scripts/postinstall: Grabbing
 correct glfw package

The brew package manager renamed the GLFW packge from `glfw3` to `glfw`. This
pull request reflects these changes in our `postinstall` script.
---
 CMakeModules/osx_install/forge_scripts/postinstall | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/osx_install/forge_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
index 209f35cdc0..1dd306c848 100755
--- a/CMakeModules/osx_install/forge_scripts/postinstall
+++ b/CMakeModules/osx_install/forge_scripts/postinstall
@@ -49,7 +49,7 @@ GLFW_INSTALLED=$(su $user -c "$brew ls --versions glfw3" | grep "glfw3") || true
 if [[ -z "${GLFW_INSTALLED}" ]]; then
     echo "Installing GLFW3" >> $err_file
     echo "-------------------" >> $err_file
-    su $user -c "$brew install glfw3" >> $err_file 2>&1 || deps_err
+    su $user -c "$brew install glfw" >> $err_file 2>&1 || deps_err
     echo "-------------------" >> $err_file
 else
     echo "GLFW Version ${GLFW_INSTALLED} is already installed." >> $err_file

From 76cde992bcdcd52cf9a49e2d8c0186d7e5cdf93e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 1 Feb 2017 23:24:51 +0530
Subject: [PATCH 1108/2677] Revert "Fix cmake statement in glbinding build
 script"

This reverts commit 73ebc03a38866d1a8a60380aeda90e530aba3bad.

Reverted change fixed build configuration generation error on Windows
platform but the condition is not being evaluated correctly on non-single
configuration generators such as make and ninja.
---
 CMakeModules/build_glbinding.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
index 3f4f493dc7..c929778d92 100644
--- a/CMakeModules/build_glbinding.cmake
+++ b/CMakeModules/build_glbinding.cmake
@@ -3,7 +3,7 @@ INCLUDE(ExternalProject)
 SET(prefix ${PROJECT_BINARY_DIR}/third_party/glb)
 
 SET(LIB_POSTFIX "")
-IF (${CMAKE_BUILD_TYPE} MATCHES DEBUG)
+IF (${CMAKE_BUILD_TYPE} STREQUAL "Debug")
     SET(LIB_POSTFIX "d")
 ENDIF()
 

From bb4e2823274dab7fbc56a5b9b8e2c8d87cbf5590 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Feb 2017 09:47:14 +0530
Subject: [PATCH 1109/2677] fix build type check in glbinding cmake build
 script

---
 CMakeModules/build_glbinding.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
index c929778d92..05ad586e76 100644
--- a/CMakeModules/build_glbinding.cmake
+++ b/CMakeModules/build_glbinding.cmake
@@ -3,7 +3,7 @@ INCLUDE(ExternalProject)
 SET(prefix ${PROJECT_BINARY_DIR}/third_party/glb)
 
 SET(LIB_POSTFIX "")
-IF (${CMAKE_BUILD_TYPE} STREQUAL "Debug")
+IF (CMAKE_BUILD_TYPE MATCHES Debug)
     SET(LIB_POSTFIX "d")
 ENDIF()
 

From afda285603ca1da2a354c90205404cf5b545f279 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Feb 2017 15:26:27 +0530
Subject: [PATCH 1110/2677] change fft manager cache managers to be thread
 local

Though the upstream libraries(clFFT/cuFFT) can guarantee thread safety,
they can do so only if same fft plan is not being used from two different
threads. Hence, it is safe to cache fft plans on per thread basis.
---
 src/backend/cuda/platform.cpp   |  4 +++-
 src/backend/cuda/platform.hpp   |  2 --
 src/backend/opencl/platform.cpp | 11 ++---------
 src/backend/opencl/platform.hpp |  1 -
 4 files changed, 5 insertions(+), 13 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index ce55bca8eb..847be47c3c 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -421,7 +421,9 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    return DeviceManager::getInstance().cufftManagers[getActiveDeviceId()];
+    thread_local static PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
+
+    return cufftManagers[getActiveDeviceId()];
 }
 
 BlasHandle blasHandle()
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ff2a9f8a79..f91e9da641 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -167,8 +167,6 @@ class DeviceManager
 
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 
-        PlanCache cufftManagers[MAX_DEVICES];
-
         std::unique_ptr<cublasHandle> cublasHandles[MAX_DEVICES];
 
         std::unique_ptr<cusolverDnHandle> cusolverHandles[MAX_DEVICES];
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 1f11a54937..67dd594e08 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -624,15 +624,9 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
-
-    int id = getActiveDeviceId();
-
-    DeviceManager& inst = DeviceManager::getInstance();
-
-    std::call_once(initFlags[id], [&]{ inst.clfftManagers[id].reset(new PlanCache()); });
+    thread_local static PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
 
-    return *(inst.clfftManagers[id].get());
+    return clfftManagers[getActiveDeviceId()];
 }
 
 DeviceManager& DeviceManager::getInstance()
@@ -645,7 +639,6 @@ DeviceManager::~DeviceManager()
 {
     for (int i=0; i<getDeviceCount(); ++i) {
         delete gfxManagers[i].release();
-        delete clfftManagers[i].release();
     }
 #ifndef OS_WIN
     //TODO: FIXME:
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index fe9ded084f..66a1acb53a 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -181,6 +181,5 @@ class DeviceManager
 
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
         clfftSetupData mFFTSetup;
-        std::unique_ptr<PlanCache> clfftManagers[MAX_DEVICES];
 };
 }

From 9663aee9831ff1b00788d8943323f9afb877ffe0 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sun, 5 Feb 2017 15:29:17 -0500
Subject: [PATCH 1111/2677] Fixes for Windows for CUDA after C++11 introduction

---
 src/backend/cuda/CMakeLists.txt     | 10 +++++++---
 src/backend/cuda/kernel/memcopy.hpp |  2 ++
 src/backend/cuda/set.cu             |  2 ++
 3 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f5ff45092a..4d134afdae 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -404,6 +404,12 @@ IF(NOT CUDA_CUDA_LIBRARY)
     MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
 ENDIF(NOT CUDA_CUDA_LIBRARY)
 
+SET(CUDA_ADD_LIBRARY_OPTIONS "")
+IF(UNIX)
+    # These flags enable C++11 and disable invalid offsetof warning
+    SET(CUDA_ADD_LIBRARY_OPTIONS "-std=c++11 -Xcudafe \"--diag_suppress=1427\"")
+ENDIF(UNIX)
+
 MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cuda_headers}
                 ${cuda_sources}
@@ -416,9 +422,7 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cpp_sources}
                 ${thrust_sort_by_key_sources}
                 ${scan_by_key_sources}
-                OPTIONS ${CUDA_GENERATE_CODE}
-                #These flags enable C++11 and disable invalid offsetof warning
-                -std=c++11 -Xcudafe "--diag_suppress=1427")
+                OPTIONS ${CUDA_GENERATE_CODE} ${CUDA_ADD_LIBRARY_OPTIONS})
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
 
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index f1b4bb3c28..2a650a1b97 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -13,6 +13,8 @@
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 
+#include <algorithm>
+
 namespace cuda
 {
 namespace kernel
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index 133537ffa3..eede44d403 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -14,6 +14,8 @@
 #include <sort.hpp>
 #include <debug_cuda.hpp>
 
+#include <algorithm>
+
 #include <thrust/device_ptr.h>
 #include <thrust/sort.h>
 #include <thrust/unique.h>

From 912bb19965d8082eea3447fdd4c5afbe50cdd790 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Fri, 10 Feb 2017 21:45:10 +0100
Subject: [PATCH 1112/2677] Set clBLAS to be the default backend

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e71de084c9..fa9e7b74ea 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,7 +24,7 @@ IF(${OpenCL_FOUND})
 ENDIF(${OpenCL_FOUND})
 OPTION(BUILD_OPENCL "Build ArrayFire with a OpenCL backend" OFF)
 OPTION(USE_CLBLAST "Build ArrayFire with the CLBlast BLAS library for the OpenCL backend" OFF)
-OPTION(USE_CLBLAS "Build ArrayFire with the clBLAS BLAS library for the OpenCL backend" OFF)
+OPTION(USE_CLBLAS "Build ArrayFire with the clBLAS BLAS library for the OpenCL backend" ON)
 
 OPTION(BUILD_GRAPHICS "Build ArrayFire with Forge Graphics" ON)
 

From c42a3bc597affa3b8957b66f71496ce65fce1a58 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 11 Feb 2017 11:20:59 +0100
Subject: [PATCH 1113/2677] Reverted an accidental change

---
 src/backend/opencl/platform.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index c56e8ee2d8..c3d5cf7f16 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -518,7 +518,7 @@ bool OpenCLCPUOffload(bool forceOffloadOSX)
     // Force condition
     offload = osx_offload && (offload || forceOffloadOSX);
 #endif
-    return false;//offload;
+    return offload;
 }
 
 bool isGLSharingSupported()

From 5f3d9f37f20f16d9c1872c0393d3aff1d1183954 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 12 Feb 2017 20:48:00 +0100
Subject: [PATCH 1114/2677] Moved CMake options related to clBLAS and CLBlast
 to the root of the OpenCL CMakeLists.txt

---
 CMakeLists.txt                    |  2 --
 src/backend/opencl/CMakeLists.txt | 13 +++++++++++--
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index fa9e7b74ea..b204310fdd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -23,8 +23,6 @@ IF(${OpenCL_FOUND})
     SET(BUILD_OPENCL ON CACHE BOOL "")
 ENDIF(${OpenCL_FOUND})
 OPTION(BUILD_OPENCL "Build ArrayFire with a OpenCL backend" OFF)
-OPTION(USE_CLBLAST "Build ArrayFire with the CLBlast BLAS library for the OpenCL backend" OFF)
-OPTION(USE_CLBLAS "Build ArrayFire with the clBLAS BLAS library for the OpenCL backend" ON)
 
 OPTION(BUILD_GRAPHICS "Build ArrayFire with Forge Graphics" ON)
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f669a22e68..61ad060e95 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -1,5 +1,8 @@
 FIND_PACKAGE(OpenCL REQUIRED)
 
+OPTION(USE_CLBLAST "Build ArrayFire with the CLBlast BLAS library for the OpenCL backend" OFF)
+OPTION(USE_CLBLAS "Build ArrayFire with the clBLAS BLAS library for the OpenCL backend" ON)
+
 ADD_DEFINITIONS(-DCL_USE_DEPRECATED_OPENCL_1_2_APIS)
 
 IF(NOT USE_SYSTEM_CL2HPP)
@@ -68,8 +71,11 @@ IF(NOT USE_CLBLAST AND NOT USE_CLBLAS)
     MESSAGE(SEND_ERROR "The OpenCL backend requires either CLBlast or clBLAS, please select one of them using USE_CLBLAST=ON or USE_CLBLAS=ON")
 ENDIF()
 
+OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
+IF(USE_SYSTEM_CLBLAST AND NOT USE_CLBLAST)
+    MESSAGE(SEND_ERROR "Using the system CLBlast (USE_SYSTEM_CLBLAST=ON) only makes sense if USE_CLBLAST=ON is set")
+ENDIF()
 IF(USE_CLBLAST)
-    OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
     IF(USE_SYSTEM_CLBLAST)
         FIND_PACKAGE(CLBlast REQUIRED)
     ELSE()
@@ -81,8 +87,11 @@ IF(USE_CLBLAST)
     MESSAGE(STATUS "Building with CLBlast as an OpenCL BLAS back-end")
 ENDIF()
 
+OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
+IF(USE_SYSTEM_CLBLAS AND NOT USE_CLBLAS)
+    MESSAGE(SEND_ERROR "Using the system clBLAS (USE_SYSTEM_CLBLAS=ON) only makes sense if USE_CLBLAS=ON is set")
+ENDIF()
 IF(USE_CLBLAS)
-    OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
     IF(USE_SYSTEM_CLBLAS)
         FIND_PACKAGE(clBLAS REQUIRED)
     ELSE()

From ee2b26f87e5a33d7e4da1478a7cee52b1282fe6e Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Tue, 7 Feb 2017 14:47:10 -0500
Subject: [PATCH 1115/2677] Updated README.md

Improved readability and formatting.
---
 README.md | 49 +++++++++++++++++++++++++------------------------
 1 file changed, 25 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 9863680db3..8f02810f7a 100644
--- a/README.md
+++ b/README.md
@@ -4,34 +4,35 @@ ArrayFire is a general-purpose library that simplifies the process of developing
 software that targets parallel and massively-parallel architectures including
 CPUs, GPUs, and other hardware acceleration devices.
 
-To achieve this goal, ArrayFire provides software developers with a high-level
-abstraction of data which resides on the accelerator, the `af::array` object
-(or C-style struct).
-Developers write code which performs operations on ArrayFire arrays which, in turn,
-are automatically translated into near-optimal kernels that execute on the computational
-device.
-ArrayFire is successfully used on devices ranging from low-power mobile phones to
-high-power GPU-enabled supercomputers including CPUs from all major vendors (Intel, AMD, Arm),
-GPUs from the dominant manufacturers (NVIDIA, AMD, and Qualcomm), as well as a variety
-of other accelerator devices on Windows, Mac, and Linux.
-
 Several of ArrayFire's benefits include:
 
 * [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable,
-  [well-documented](http://arrayfire.org/docs) API.
-* Rigorously Tested for Performance and Accuracy
-* Commercially Friendly Open-Source Licensing
+  [well-documented](http://arrayfire.org/docs) API
+* Rigorously tested for performance and accuracy
+* Commercially friendly open-source licensing
 * Commercial support from [ArrayFire](http://arrayfire.com)
-* [Read about more benefits on Arrayfire.com](http://arrayfire.com/the-arrayfire-library/)
+* [Read about more benefits on arrayfire.com](http://arrayfire.com/the-arrayfire-library/)
+
+ArrayFire provides software developers with a high-level
+abstraction of data which resides on the accelerator, the `af::array` object.
+Developers write code which performs operations on ArrayFire arrays which, in turn,
+are automatically translated into near-optimal kernels that execute on the computational
+device.
+
+ArrayFire is successfully used on devices ranging from low-power mobile phones
+to high-power GPU-enabled supercomputers. ArrayFire runs on CPUs from all
+major vendors (Intel, AMD, ARM), GPUs from the prominent manufacturers
+(NVIDIA, AMD, and Qualcomm), as well as a variety of other accelerator devices
+on Windows, Mac, and Linux.
 
-### Build and Test Status
+## Build and Test Status
 
 |         | Linux x86_64 | Linux aarch64 | OSX | Windows |
 |:-------:|:------------:|:-------------:|:---:|:-------:|
 | Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build-mkl/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build-mkl/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/job/devel/) |
 | Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test-mkl/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test-mkl/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/job/devel/) |
 
-### Installation
+## Installation
 
 You can install the ArrayFire library from one of the following ways:
 
@@ -45,7 +46,7 @@ for Linux, OSX, and Windows platforms.
 Build from source by following instructions on our
 [wiki](https://github.com/arrayfire/arrayfire/wiki).
 
-### Examples
+## Examples
 
 The following examples are simplified versions of
 [`helloworld.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/helloworld/helloworld.cpp)
@@ -98,7 +99,7 @@ while(!myWindow.close()) {
 <img src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif" alt="Conway's Game of Life" height="256" width="256">
 </p>
 
-### Documentation
+## Documentation
 
 You can find our complete documentation [here](http://www.arrayfire.com/docs/index.htm).
 
@@ -109,7 +110,7 @@ Quick links:
 * [Examples](http://www.arrayfire.org/docs/examples.htm)
 * [Blog](http://arrayfire.com/blog/)
 
-### Language wrappers
+## Language wrappers
 
 ArrayFire has several official and third-party language wrappers.
 
@@ -136,13 +137,13 @@ The following wrappers are being maintained and supported by third parties:
 * [`ArrayFire.jl`](https://github.com/JuliaComputing/ArrayFire.jl)
 * [`ArrayFire-Nim`](https://github.com/bitstormGER/ArrayFire-Nim)
 
-### Contributing
+## Contributing
 
 Contributions of any kind are welcome! Please refer to
 [CONTRIBUTING.md](https://github.com/arrayfire/arrayfire/blob/master/CONTRIBUTING.md)
 to learn more about how you can get involved with ArrayFire.
 
-### Citations and Acknowledgements
+## Citations and Acknowledgements
 
 If you redistribute ArrayFire, please follow the terms established in
 [the license](LICENSE). If you wish to cite ArrayFire in an academic
@@ -158,12 +159,12 @@ diligent work on the [Julia](https://github.com/JuliaComputing/ArrayFire.jl)
 and [NodeJS](https://github.com/arrayfire/arrayfire-js) wrappers,
 respectively.
 
-### Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+## Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
 * ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
 
-### Trademark Policy
+## Trademark Policy
 
 The literal mark “ArrayFire” and ArrayFire logos are trademarks of
 AccelerEyes LLC DBA ArrayFire.

From fd6dd9b5cbc0f1a1938372ca61fc63bc48870c4e Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 18 Feb 2017 14:12:14 +0100
Subject: [PATCH 1116/2677] Split too long lines for getrf into two smaller
 lines

---
 src/backend/opencl/magma/getrf.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/magma/getrf.cpp b/src/backend/opencl/magma/getrf.cpp
index 3cf0f18a32..c57ea32893 100644
--- a/src/backend/opencl/magma/getrf.cpp
+++ b/src/backend/opencl/magma/getrf.cpp
@@ -219,7 +219,8 @@ magma_int_t magma_getrf_gpu(
             magma_getmatrix<Ty>(m-j*nb, nb, dAP(0,0), maxm, work(0), ldwork, queue);
 
             if (j > 0 && n > (j + 1) * nb) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                                                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
                                                 n - (j+1)*nb, nb,
                                                 c_one,
                                                 dAT(j-1,j-1), lddat,
@@ -256,7 +257,8 @@ magma_int_t magma_getrf_gpu(
 
             // do the small non-parallel computations (next panel update)
             if (s > (j+1)) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                                                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
                                                 nb, nb,
                                                 c_one,
                                                 dAT(j, j  ), lddat,
@@ -275,7 +277,8 @@ magma_int_t magma_getrf_gpu(
             }
             else {
                 if (n > s * nb) {
-                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                                                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
                                                     n-s*nb, nb,
                                                     c_one,
                                                     dAT(j, j  ), lddat,
@@ -319,7 +322,8 @@ magma_int_t magma_getrf_gpu(
             magmablas_transpose<Ty>(rows, nb0, dAP(0,0), maxm, dAT(s,s), lddat, queue);
 
             if (n > s * nb + nb0) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                                                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
                                                 n-s*nb-nb0, nb0,
                                                 c_one, dAT(s,s),     lddat,
                                                 dAT(s,s)+nb0, lddat, 1, &queue, 0, nullptr, &event));

From 4d276cb1c783562b2945eb99a2a8ea15974d56df Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 19 Feb 2017 10:40:09 +0100
Subject: [PATCH 1117/2677] Merged the blas_clblast.cpp and blas_clblas.cpp
 files into a single blas.cpp by taking advantage of the magma_blas header
 files

---
 .../opencl/{blas_clblast.cpp => blas.cpp}     |  93 +++----
 src/backend/opencl/blas_clblas.cpp            | 226 ------------------
 src/backend/opencl/magma/magma_blas_clblas.h  |   7 +
 src/backend/opencl/magma/magma_blas_clblast.h |   6 +
 4 files changed, 51 insertions(+), 281 deletions(-)
 rename src/backend/opencl/{blas_clblast.cpp => blas.cpp} (51%)
 delete mode 100644 src/backend/opencl/blas_clblas.cpp

diff --git a/src/backend/opencl/blas_clblast.cpp b/src/backend/opencl/blas.cpp
similarity index 51%
rename from src/backend/opencl/blas_clblast.cpp
rename to src/backend/opencl/blas.cpp
index 0ad47b1967..e797284f21 100644
--- a/src/backend/opencl/blas_clblast.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(USE_CLBLAST)
-
 #include <complex>
 
 #include <blas.hpp>
@@ -20,8 +18,8 @@
 #include <reduce.hpp>
 #include <complex.hpp>
 
-#include <clblast.h>
-#include <err_clblast.hpp>
+// Includes one of the supported OpenCL BLAS back-ends (e.g. clBLAS, CLBlast)
+#include <magma/magma_blas.h>
 
 #if defined(WITH_OPENCL_LINEAR_ALGEBRA)
 #include <cpu/cpu_blas.hpp>
@@ -30,36 +28,24 @@
 namespace opencl
 {
 
-void
-initBlas()
-{
-  // Nothing to do here for CLBlast
-}
-
-clblast::Transpose
-toClblastTranspose(af_mat_prop opt)
+// Converts an af_mat_prop options to a transpose type for one of the OpenCL BLAS back-ends
+OPENCL_BLAS_TRANS_TYPE
+toBlasTranspose(af_mat_prop opt)
 {
     switch(opt) {
-        case AF_MAT_NONE    : return clblast::Transpose::kNo;
-        case AF_MAT_TRANS   : return clblast::Transpose::kYes;
-        case AF_MAT_CTRANS  : return clblast::Transpose::kConjugate;
+        case AF_MAT_NONE    : return OPENCL_BLAS_NO_TRANS;
+        case AF_MAT_TRANS   : return OPENCL_BLAS_TRANS;
+        case AF_MAT_CTRANS  : return OPENCL_BLAS_CONJ_TRANS;
         default             : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
 }
 
-// Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
-template <typename T> struct CLBlastType { using Type = T; };
-template <> struct CLBlastType<cfloat> { using Type = std::complex<float>; };
-template <> struct CLBlastType<cdouble> { using Type = std::complex<double>; };
-
-// Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
-template <typename T> typename CLBlastType<T>::Type toCLBlastConstant(const T val);
-
-// Specializations of the above function
-template <> float toCLBlastConstant(const float val) { return val; }
-template <> double toCLBlastConstant(const double val) { return val; }
-template <> std::complex<float> toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
-template <> std::complex<double> toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
+// Initialization of the OpenCL BLAS library
+void
+initBlas()
+{
+    gpu_blas_init();
+}
 
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
@@ -71,12 +57,12 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     }
 #endif
 
-    const auto lOpts = toClblastTranspose(optLhs);
-    const auto rOpts = toClblastTranspose(optRhs);
+    const auto lOpts = toBlasTranspose(optLhs);
+    const auto rOpts = toBlasTranspose(optRhs);
 
-    const auto aRowDim = (lOpts == clblast::Transpose::kNo) ? 0 : 1;
-    const auto aColDim = (lOpts == clblast::Transpose::kNo) ? 1 : 0;
-    const auto bColDim = (rOpts == clblast::Transpose::kNo) ? 1 : 0;
+    const auto aRowDim = (lOpts == OPENCL_BLAS_NO_TRANS) ? 0 : 1;
+    const auto aColDim = (lOpts == OPENCL_BLAS_NO_TRANS) ? 1 : 0;
+    const auto bColDim = (rOpts == OPENCL_BLAS_NO_TRANS) ? 1 : 0;
 
     const dim4 lDims = lhs.dims();
     const dim4 rDims = rhs.dims();
@@ -87,32 +73,31 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
     const auto alpha = scalar<T>(1);
     const auto beta  = scalar<T>(0);
-    const auto alpha_clblast = toCLBlastConstant(alpha);
-    const auto beta_clblast = toCLBlastConstant(beta);
 
     const dim4 lStrides = lhs.strides();
     const dim4 rStrides = rhs.strides();
+    cl::Event event;
     if(rDims[bColDim] == 1) {
-        CLBLAST_CHECK(
-            clblast::Gemv(clblast::Layout::kColMajor, lOpts,
-                          lDims[0], lDims[1],
-                          alpha_clblast,
-                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                          (*rhs.get())(), rhs.getOffset(), rStrides[0],
-                          beta_clblast,
-                          (*out.get())(), out.getOffset(), 1,
-                          &getQueue()())
+        gpu_blas_gemv_func<T> gemv;
+        OPENCL_BLAS_CHECK(
+            gemv(lOpts, lDims[0], lDims[1],
+                 alpha,
+                 (*lhs.get())(), lhs.getOffset(), lStrides[1],
+                 (*rhs.get())(), rhs.getOffset(), rStrides[0],
+                 beta,
+                 (*out.get())(), out.getOffset(), 1,
+                 1, &getQueue()(), 0, nullptr, &event())
         );
     } else {
-        CLBLAST_CHECK(
-            clblast::Gemm(clblast::Layout::kColMajor, lOpts, rOpts,
-                          M, N, K,
-                          alpha_clblast,
-                          (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                          (*rhs.get())(), rhs.getOffset(), rStrides[1],
-                          beta_clblast,
-                          (*out.get())(), out.getOffset(), out.dims()[0],
-                          &getQueue()())
+        gpu_blas_gemm_func<T> gemm;
+        OPENCL_BLAS_CHECK(
+            gemm(lOpts, rOpts, M, N, K,
+                 alpha,
+                 (*lhs.get())(), lhs.getOffset(), lStrides[1],
+                 (*rhs.get())(), rhs.getOffset(), rStrides[1],
+                 beta,
+                 (*out.get())(), out.getOffset(), out.dims()[0],
+                 1, &getQueue()(), 0, nullptr, &event())
         );
     }
 
@@ -149,5 +134,3 @@ INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
 
 }
-
-#endif // USE_CLBLAST
diff --git a/src/backend/opencl/blas_clblas.cpp b/src/backend/opencl/blas_clblas.cpp
deleted file mode 100644
index f2517ea470..0000000000
--- a/src/backend/opencl/blas_clblas.cpp
+++ /dev/null
@@ -1,226 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(USE_CLBLAS)
-
-#include <blas.hpp>
-#include <Array.hpp>
-#include <cassert>
-#include <string>
-#include <functional>
-#include <stdexcept>
-#include <mutex>
-#include <err_opencl.hpp>
-#include <err_clblas.hpp>
-#include <math.hpp>
-#include <transpose.hpp>
-#include <arith.hpp>
-#include <reduce.hpp>
-#include <complex.hpp>
-
-#include <clBLAS.h>
-#include <err_clblas.hpp>
-
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
-#include <cpu/cpu_blas.hpp>
-#endif
-
-namespace opencl
-{
-
-using std::is_floating_point;
-using std::enable_if;
-using std::once_flag;
-using std::call_once;
-using std::runtime_error;
-using std::to_string;
-
-void
-initBlas()
-{
-    static std::once_flag clblasSetupFlag;
-    call_once(clblasSetupFlag, clblasSetup);
-}
-
-clblasTranspose
-toClblasTranspose(af_mat_prop opt)
-{
-    clblasTranspose out = clblasNoTrans;
-    switch(opt) {
-        case AF_MAT_NONE    : out = clblasNoTrans;   break;
-        case AF_MAT_TRANS   : out = clblasTrans;     break;
-        case AF_MAT_CTRANS  : out = clblasConjTrans; break;
-        default             : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
-    }
-    return out;
-}
-
-
-#define BLAS_FUNC_DEF(NAME)                                             \
-template<typename T>                                                    \
-struct NAME##_func;
-
-#define BLAS_FUNC(NAME, TYPE, PREFIX)                                   \
-template<>                                                              \
-struct NAME##_func<TYPE>                                                \
-{                                                                       \
-    template<typename... Args>                                          \
-    clblasStatus                                                        \
-    operator() (Args... args) { return clblas##PREFIX##NAME(args...); } \
-};
-
-BLAS_FUNC_DEF(gemm)
-BLAS_FUNC(gemm, float,      S)
-BLAS_FUNC(gemm, double,     D)
-BLAS_FUNC(gemm, cfloat,     C)
-BLAS_FUNC(gemm, cdouble,    Z)
-
-BLAS_FUNC_DEF(gemv)
-BLAS_FUNC(gemv, float,      S)
-BLAS_FUNC(gemv, double,     D)
-BLAS_FUNC(gemv, cfloat,     C)
-BLAS_FUNC(gemv, cdouble,    Z)
-
-#undef BLAS_FUNC_DEF
-#undef BLAS_FUNC
-
-#define BLAS_FUNC_DEF(NAME)                                             \
-template<typename T, bool conjugate>                                    \
-struct NAME##_func;
-
-#define BLAS_FUNC(NAME, TYPE, CONJUGATE, PREFIX)                        \
-template<>                                                              \
-struct NAME##_func<TYPE, CONJUGATE>                                     \
-{                                                                       \
-    template<typename... Args>                                          \
-    clblasStatus                                                        \
-    operator() (Args... args) { return clblas##PREFIX##NAME(args...); } \
-};
-
-BLAS_FUNC_DEF( dot )
-BLAS_FUNC(dot, float,  false, S)
-BLAS_FUNC(dot, double, false, D)
-BLAS_FUNC(dot, float,  true , S)
-BLAS_FUNC(dot, double, true , D)
-
-#undef BLAS_FUNC
-
-#define BLAS_FUNC(NAME, TYPE, CONJUGATE, PREFIX, SUFFIX)                \
-template<>                                                              \
-struct NAME##_func<TYPE, CONJUGATE>                                     \
-{                                                                       \
-    template<typename... Args>                                          \
-    clblasStatus                                                        \
-    operator() (Args... args) { return clblas##PREFIX##NAME##SUFFIX(args...); } \
-};
-
-BLAS_FUNC(dot, cfloat,  true , C, c)
-BLAS_FUNC(dot, cdouble, true , Z, c)
-BLAS_FUNC(dot, cfloat,  false, C, u)
-BLAS_FUNC(dot, cdouble, false, Z, u)
-
-#undef BLAS_FUNC_DEF
-#undef BLAS_FUNC
-
-template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
-    if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
-        return cpu::matmul(lhs, rhs, optLhs, optRhs);
-    }
-#endif
-
-    initBlas();
-
-    clblasTranspose lOpts = toClblasTranspose(optLhs);
-    clblasTranspose rOpts = toClblasTranspose(optRhs);
-
-    int aRowDim = (lOpts == clblasNoTrans) ? 0 : 1;
-    int aColDim = (lOpts == clblasNoTrans) ? 1 : 0;
-    int bColDim = (rOpts == clblasNoTrans) ? 1 : 0;
-
-    dim4 lDims = lhs.dims();
-    dim4 rDims = rhs.dims();
-    int M = lDims[aRowDim];
-    int N = rDims[bColDim];
-    int K = lDims[aColDim];
-
-    //FIXME: Leaks on errors.
-    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
-    auto alpha = scalar<T>(1);
-    auto beta  = scalar<T>(0);
-
-    dim4 lStrides = lhs.strides();
-    dim4 rStrides = rhs.strides();
-    cl::Event event;
-    if(rDims[bColDim] == 1) {
-        N = lDims[aColDim];
-        gemv_func<T> gemv;
-        CLBLAS_CHECK(
-            gemv(
-                clblasColumnMajor, lOpts,
-                lDims[0], lDims[1],
-                alpha,
-                (*lhs.get())(),    lhs.getOffset(),   lStrides[1],
-                (*rhs.get())(),    rhs.getOffset(),   rStrides[0],
-                beta ,
-                (*out.get())(),   out.getOffset(),             1,
-                1, &getQueue()(), 0, nullptr, &event())
-            );
-    } else {
-        gemm_func<T> gemm;
-        CLBLAS_CHECK(
-            gemm(
-                clblasColumnMajor, lOpts, rOpts,
-                M, N, K,
-                alpha,
-                (*lhs.get())(),    lhs.getOffset(),   lStrides[1],
-                (*rhs.get())(),    rhs.getOffset(),   rStrides[1],
-                beta,
-                (*out.get())(),   out.getOffset(),  out.dims()[0],
-                1, &getQueue()(), 0, nullptr, &event())
-            );
-    }
-
-    return out;
-}
-
-template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
-    const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
-
-    const Array<T> temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
-    return reduce<af_add_t, T, T>(temp, 0, false, 0);
-}
-
-#define INSTANTIATE_BLAS(TYPE)                                                          \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
-                    af_mat_prop optLhs, af_mat_prop optRhs);
-
-INSTANTIATE_BLAS(float)
-INSTANTIATE_BLAS(cfloat)
-INSTANTIATE_BLAS(double)
-INSTANTIATE_BLAS(cdouble)
-
-#define INSTANTIATE_DOT(TYPE)                                                       \
-    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,  \
-                                   af_mat_prop optLhs, af_mat_prop optRhs);
-
-INSTANTIATE_DOT(float)
-INSTANTIATE_DOT(double)
-INSTANTIATE_DOT(cfloat)
-INSTANTIATE_DOT(cdouble)
-}
-
-#endif // USE_CLBLAS
diff --git a/src/backend/opencl/magma/magma_blas_clblas.h b/src/backend/opencl/magma/magma_blas_clblas.h
index 02e2a10059..bdc7cd24f1 100644
--- a/src/backend/opencl/magma/magma_blas_clblas.h
+++ b/src/backend/opencl/magma/magma_blas_clblas.h
@@ -13,6 +13,7 @@
 
 #include <clBLAS.h>
 #include <err_clblas.hpp>
+#include <mutex>  // for std::once_flag
 
 // Convert MAGMA constants to clBLAS constants
 clblasOrder          clblas_order_const( magma_order_t order );
@@ -43,6 +44,12 @@ clblasSide           clblas_side_const ( magma_side_t  side  );
 #define OPENCL_BLAS_UNIT_DIAGONAL clblasUnit
 #define OPENCL_BLAS_NON_UNIT_DIAGONAL clblasNonUnit
 
+// Initialization of the OpenCL BLAS library
+inline void gpu_blas_init()
+{
+    static std::once_flag clblasSetupFlag;
+    call_once(clblasSetupFlag, clblasSetup);
+}
 
 #define clblasSherk(...) clblasSsyrk(__VA_ARGS__)
 #define clblasDherk(...) clblasDsyrk(__VA_ARGS__)
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 22bb640371..04b5ea8480 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -59,6 +59,12 @@ template <> double inline toCLBlastConstant(const double val) { return val; }
 template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
 template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
 
+// Initialization of the OpenCL BLAS library
+inline void gpu_blas_init()
+{
+  // Nothing to do here for CLBlast
+}
+
 template <typename T>
 struct gpu_blas_gemm_func
 {

From 55702af11791134872d0366409ab68ac7005e2f2 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 19 Feb 2017 10:48:11 +0100
Subject: [PATCH 1118/2677] Renamed the layout/transpose types from *_TYPE to
 *_T

---
 src/backend/opencl/blas.cpp                   |  2 +-
 src/backend/opencl/magma/getrs.cpp            |  2 +-
 src/backend/opencl/magma/larfb.cpp            | 10 +++++-----
 src/backend/opencl/magma/magma_blas_clblas.h  |  4 ++--
 src/backend/opencl/magma/magma_blas_clblast.h |  4 ++--
 src/backend/opencl/magma/potrf.cpp            |  2 +-
 6 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index e797284f21..bf95a61153 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -29,7 +29,7 @@ namespace opencl
 {
 
 // Converts an af_mat_prop options to a transpose type for one of the OpenCL BLAS back-ends
-OPENCL_BLAS_TRANS_TYPE
+OPENCL_BLAS_TRANS_T
 toBlasTranspose(af_mat_prop opt)
 {
     switch(opt) {
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index 608cfc835c..096eddadba 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -166,7 +166,7 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
 
     cl_event event = NULL;
 
-    OPENCL_BLAS_TRANS_TYPE cltrans =(trans == MagmaNoTrans) ? OPENCL_BLAS_NO_TRANS :
+    OPENCL_BLAS_TRANS_T cltrans =(trans == MagmaNoTrans) ? OPENCL_BLAS_NO_TRANS :
         (trans == MagmaTrans ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS);
 
     bool cond = opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
diff --git a/src/backend/opencl/magma/larfb.cpp b/src/backend/opencl/magma/larfb.cpp
index eebc6ec9ec..e4800e1580 100644
--- a/src/backend/opencl/magma/larfb.cpp
+++ b/src/backend/opencl/magma/larfb.cpp
@@ -192,7 +192,7 @@ magma_larfb_gpu(
     static const Ty c_zero    = magma_zero<Ty>();
     static const Ty c_one     = magma_one<Ty>();
     static const Ty c_neg_one = magma_neg_one<Ty>();
-    static const OPENCL_BLAS_TRANS_TYPE transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
+    static const OPENCL_BLAS_TRANS_T transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
 
     /* Check input arguments */
     magma_int_t info = 0;
@@ -225,8 +225,8 @@ magma_larfb_gpu(
     }
 
     // opposite of trans
-    OPENCL_BLAS_TRANS_TYPE transt;
-    OPENCL_BLAS_TRANS_TYPE cltrans;
+    OPENCL_BLAS_TRANS_T transt;
+    OPENCL_BLAS_TRANS_T cltrans;
     if (trans == MagmaNoTrans) {
         transt = transType;
         cltrans = OPENCL_BLAS_NO_TRANS;
@@ -237,14 +237,14 @@ magma_larfb_gpu(
     }
 
     // whether T is upper or lower triangular
-    OPENCL_BLAS_TRIANGLE_TYPE uplo;
+    OPENCL_BLAS_TRIANGLE_T uplo;
     if (direct == MagmaForward)
         uplo = OPENCL_BLAS_TRIANGLE_UPPER;
     else
         uplo = OPENCL_BLAS_TRIANGLE_LOWER;
 
     // whether V is stored transposed or not
-    OPENCL_BLAS_TRANS_TYPE notransV, transV;
+    OPENCL_BLAS_TRANS_T notransV, transV;
     if (storev == MagmaColumnwise) {
         notransV = OPENCL_BLAS_NO_TRANS;
         transV   = transType;
diff --git a/src/backend/opencl/magma/magma_blas_clblas.h b/src/backend/opencl/magma/magma_blas_clblas.h
index bdc7cd24f1..f9cc8d5748 100644
--- a/src/backend/opencl/magma/magma_blas_clblas.h
+++ b/src/backend/opencl/magma/magma_blas_clblas.h
@@ -26,13 +26,13 @@ clblasSide           clblas_side_const ( magma_side_t  side  );
 #define OPENCL_BLAS_CHECK CLBLAS_CHECK
 
 // Transposing
-#define OPENCL_BLAS_TRANS_TYPE clblasTranspose // the type
+#define OPENCL_BLAS_TRANS_T clblasTranspose // the type
 #define OPENCL_BLAS_NO_TRANS clblasNoTrans
 #define OPENCL_BLAS_TRANS clblasTrans
 #define OPENCL_BLAS_CONJ_TRANS clblasConjTrans
 
 // Triangles
-#define OPENCL_BLAS_TRIANGLE_TYPE clblasUplo // the type
+#define OPENCL_BLAS_TRIANGLE_T clblasUplo // the type
 #define OPENCL_BLAS_TRIANGLE_UPPER clblasUpper
 #define OPENCL_BLAS_TRIANGLE_LOWER clblasLower
 
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 04b5ea8480..7584aeee77 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -27,13 +27,13 @@ clblast::Side           clblast_side_const ( magma_side_t  side  );
 #define OPENCL_BLAS_CHECK CLBLAST_CHECK
 
 // Transposing
-#define OPENCL_BLAS_TRANS_TYPE clblast::Transpose // the type
+#define OPENCL_BLAS_TRANS_T clblast::Transpose // the type
 #define OPENCL_BLAS_NO_TRANS clblast::Transpose::kNo
 #define OPENCL_BLAS_TRANS clblast::Transpose::kYes
 #define OPENCL_BLAS_CONJ_TRANS clblast::Transpose::kConjugate
 
 // Triangles
-#define OPENCL_BLAS_TRIANGLE_TYPE clblast::Triangle // the type
+#define OPENCL_BLAS_TRIANGLE_T clblast::Triangle // the type
 #define OPENCL_BLAS_TRIANGLE_UPPER clblast::Triangle::kUpper
 #define OPENCL_BLAS_TRIANGLE_LOWER clblast::Triangle::kLower
 
diff --git a/src/backend/opencl/magma/potrf.cpp b/src/backend/opencl/magma/potrf.cpp
index 7f1d1ccaaf..e457f0187b 100644
--- a/src/backend/opencl/magma/potrf.cpp
+++ b/src/backend/opencl/magma/potrf.cpp
@@ -131,7 +131,7 @@ magma_int_t magma_potrf_gpu(
     static const double    one =  1.0;
     static const double  m_one = -1.0;
 
-    static const OPENCL_BLAS_TRANS_TYPE transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
+    static const OPENCL_BLAS_TRANS_T transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
 
     Ty* work;
     magma_int_t err;

From 15fb5e5b49f281eade75df881bd79b0e59de57b2 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 19 Feb 2017 11:17:18 +0100
Subject: [PATCH 1119/2677] Changed clBLAS and CLBlast CMake options into an
 OpenCL BLAS library selection cache string

---
 src/backend/opencl/CMakeLists.txt | 27 +++++++++++----------------
 1 file changed, 11 insertions(+), 16 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 61ad060e95..f0e06d9a20 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -1,7 +1,10 @@
 FIND_PACKAGE(OpenCL REQUIRED)
 
-OPTION(USE_CLBLAST "Build ArrayFire with the CLBlast BLAS library for the OpenCL backend" OFF)
-OPTION(USE_CLBLAS "Build ArrayFire with the clBLAS BLAS library for the OpenCL backend" ON)
+# Selects the OpenCL BLAS back-end to use (clBLAS or CLBlast)
+if(NOT OPENCL_BLAS_LIBRARY)
+    set(OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end" FORCE)
+    set_property(CACHE OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
+endif()
 
 ADD_DEFINITIONS(-DCL_USE_DEPRECATED_OPENCL_1_2_APIS)
 
@@ -63,19 +66,11 @@ ENDIF()
 ADD_DEFINITIONS(-DAF_OPENCL
                 -D__CL_ENABLE_EXCEPTIONS)
 
-IF(USE_CLBLAST AND USE_CLBLAS)
-    MESSAGE(SEND_ERROR "Cannot use both CLBlast and clBLAS, please select only one of them using USE_CLBLAST=OFF or USE_CLBLAS=OFF")
-ENDIF()
-
-IF(NOT USE_CLBLAST AND NOT USE_CLBLAS)
-    MESSAGE(SEND_ERROR "The OpenCL backend requires either CLBlast or clBLAS, please select one of them using USE_CLBLAST=ON or USE_CLBLAS=ON")
-ENDIF()
-
 OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
-IF(USE_SYSTEM_CLBLAST AND NOT USE_CLBLAST)
-    MESSAGE(SEND_ERROR "Using the system CLBlast (USE_SYSTEM_CLBLAST=ON) only makes sense if USE_CLBLAST=ON is set")
+IF(USE_SYSTEM_CLBLAST AND NOT OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
+    MESSAGE(SEND_ERROR "Using the system CLBlast (USE_SYSTEM_CLBLAST=ON) only makes sense if OPENCL_BLAS_LIBRARY=CLBlast is set")
 ENDIF()
-IF(USE_CLBLAST)
+IF(OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
     IF(USE_SYSTEM_CLBLAST)
         FIND_PACKAGE(CLBlast REQUIRED)
     ELSE()
@@ -88,10 +83,10 @@ IF(USE_CLBLAST)
 ENDIF()
 
 OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
-IF(USE_SYSTEM_CLBLAS AND NOT USE_CLBLAS)
-    MESSAGE(SEND_ERROR "Using the system clBLAS (USE_SYSTEM_CLBLAS=ON) only makes sense if USE_CLBLAS=ON is set")
+IF(USE_SYSTEM_CLBLAS AND NOT OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
+    MESSAGE(SEND_ERROR "Using the system clBLAS (USE_SYSTEM_CLBLAS=ON) only makes sense if OPENCL_BLAS_LIBRARY=clBLAS is set")
 ENDIF()
-IF(USE_CLBLAS)
+IF(OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
     IF(USE_SYSTEM_CLBLAS)
         FIND_PACKAGE(clBLAS REQUIRED)
     ELSE()

From 60df0089fc6ef7c03cb5b880930c62e698dd9540 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 20 Feb 2017 22:17:31 +0530
Subject: [PATCH 1120/2677] Make opencl kernel cache management thread safe

---
 src/backend/opencl/cache.hpp                  | 10 +--
 src/backend/opencl/jit.cpp                    | 13 ++--
 .../opencl/kernel/convolve/conv2_impl.hpp     | 10 ++-
 .../opencl/kernel/convolve_separable.cpp      | 11 ++-
 src/backend/opencl/kernel/cscmm.hpp           | 10 +--
 src/backend/opencl/kernel/cscmv.hpp           | 10 +--
 src/backend/opencl/kernel/csrmm.hpp           | 10 +--
 src/backend/opencl/kernel/csrmv.hpp           | 10 +--
 src/backend/opencl/kernel/fast.hpp            | 38 +++++-----
 src/backend/opencl/kernel/ireduce.hpp         | 60 ++++++++--------
 src/backend/opencl/kernel/moments.hpp         |  9 +--
 .../opencl/kernel/nearest_neighbour.hpp       | 72 +++++++++----------
 src/backend/opencl/kernel/random_engine.hpp   | 24 +++----
 src/backend/opencl/kernel/reduce.hpp          | 19 ++---
 src/backend/opencl/kernel/scan_dim.hpp        |  9 +--
 .../opencl/kernel/scan_dim_by_key_impl.hpp    | 11 ++-
 src/backend/opencl/kernel/scan_first.hpp      | 10 +--
 .../opencl/kernel/scan_first_by_key_impl.hpp  | 10 +--
 src/backend/opencl/kernel/sparse.hpp          | 54 ++++++--------
 src/backend/opencl/kernel/transform.hpp       |  9 ++-
 src/backend/opencl/kernel/unwrap.hpp          |  9 +--
 src/backend/opencl/kernel/wrap.hpp            |  9 +--
 src/backend/opencl/platform.cpp               | 36 ++++++++++
 src/backend/opencl/platform.hpp               | 15 ++++
 24 files changed, 237 insertions(+), 241 deletions(-)

diff --git a/src/backend/opencl/cache.hpp b/src/backend/opencl/cache.hpp
index 937d3d8eac..0dab9d9e24 100644
--- a/src/backend/opencl/cache.hpp
+++ b/src/backend/opencl/cache.hpp
@@ -14,14 +14,10 @@
 
 namespace opencl
 {
-    using cl::Kernel;
-    using cl::Program;
-
     typedef struct {
-        Program* prog;
-        Kernel* ker;
+        cl::Program* prog;
+        cl::Kernel* ker;
     } kc_entry_t;
 
-    typedef std::map<string, kc_entry_t> kc_t;
-    static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
+    typedef std::map<std::string, kc_entry_t> kc_t;
 }
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 83aab34186..360028d967 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -16,7 +16,6 @@
 #include <JIT/Node.hpp>
 #include <kernel_headers/jit.hpp>
 #include <program.hpp>
-#include <cache.hpp>
 #include <dispatch.hpp>
 #include <err_opencl.hpp>
 #include <functional>
@@ -156,27 +155,25 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
 
 static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
 {
-
     bool is_dbl = false;
     string funcName = getFuncName(nodes, is_linear, &is_dbl);
     int device = getActiveDeviceId();
 
-    kc_t::iterator idx = kernelCaches[device].find(funcName);
-    kc_entry_t entry;
+    kc_entry_t entry = kernelCache(device, funcName);
 
-    if (idx == kernelCaches[device].end()) {
+    if (entry.prog==0 && entry.ker==0) {
         string jit_ker = getKernelString(funcName, nodes, is_linear);
 
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
+
         cl::Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, is_dbl ? string(" -D USE_DOUBLE") :  string(""));
+
         entry.prog = new cl::Program(prog);
         entry.ker = new Kernel(*entry.prog, funcName.c_str());
 
-        kernelCaches[device][funcName] = entry;
-    } else {
-        entry = idx->second;
+        addKernelToCache(device, funcName, entry);
     }
 
     return *entry.ker;
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 04fae02d72..ef8df00d3b 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -35,10 +35,10 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, cons
         std::to_string(f1);
 
     int device = getActiveDeviceId();
-    kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-    kc_entry_t entry;
-    if (idx == kernelCaches[device].end()) {
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog==0 && entry.ker==0) {
         size_t LOC_SIZE = (THREADS_X+2*(f0-1))*(THREADS_Y+2*(f1-1));
 
         std::ostringstream options;
@@ -58,9 +58,7 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, cons
         entry.prog   = new Program(prog);
         entry.ker = new Kernel(*entry.prog, "convolve");
 
-        kernelCaches[device][ref_name] = entry;
-    } else {
-        entry = idx->second;
+        addKernelToCache(device, ref_name, entry);
     }
 
     auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index b212cbe452..aafc4253ef 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -54,10 +54,10 @@ void convSep(Param out, const Param signal, const Param filter)
         std::to_string(fLen);
 
     int device = getActiveDeviceId();
-    kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-    kc_entry_t entry;
-    if (idx == kernelCaches[device].end()) {
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog==0 && entry.ker==0) {
         const size_t C0_SIZE  = (THREADS_X+2*(fLen-1))* THREADS_Y;
         const size_t C1_SIZE  = (THREADS_Y+2*(fLen-1))* THREADS_X;
 
@@ -79,9 +79,8 @@ void convSep(Param out, const Param signal, const Param filter)
 
         entry.prog   = new Program(prog);
         entry.ker  = new Kernel(*entry.prog, "convolve");
-        kernelCaches[device][ref_name] = entry;
-    } else {
-        entry = idx->second;
+
+        addKernelToCache(device, ref_name, entry);
     }
 
     auto convOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 3b6bc13827..a72a150501 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -68,10 +68,10 @@ namespace opencl
                 std::to_string(threads);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
 
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
+
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -100,8 +100,8 @@ namespace opencl
                 buildProgram(prog, 1, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             auto cscmm_kernel = *entry.ker;
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 6aabe2cfd3..2a2b676c44 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -65,10 +65,10 @@ namespace opencl
                 std::to_string(threads);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
 
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
+
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -96,8 +96,8 @@ namespace opencl
                 buildProgram(prog, 1, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "cscmv_block");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             auto cscmv_kernel = *entry.ker;
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 0afab2973c..40e2bd30e6 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -61,10 +61,10 @@ namespace opencl
                 std::to_string(use_greedy);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
 
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
+
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -94,8 +94,8 @@ namespace opencl
                 entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
                 // FIXME: Change this after adding another kernel
                 entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             auto csrmm_nt_kernel = entry.ker[0];
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index faffb828e8..2ebb0b964e 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -67,10 +67,10 @@ namespace opencl
                 std::to_string(threads);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
 
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
+
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -99,8 +99,8 @@ namespace opencl
                 entry.ker  = new Kernel[2];
                 entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
                 entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             int count = 0;
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index ec66867b93..bf719e810d 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -56,33 +56,31 @@ void fast(const unsigned arc_length,
         std::string(dtype_traits<T>::getName());
 
     int device = getActiveDeviceId();
-    kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
 
-    kc_entry_t entry;
-    if (cache_idx == kernelCaches[device].end()) {
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D ARC_LENGTH=" << arc_length
-                    << " -D NONMAX=" << static_cast<unsigned>(nonmax);
+    if (entry.prog==0 && entry.ker==0) {
 
-            if (std::is_same<T, double>::value ||
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+            << " -D ARC_LENGTH=" << arc_length
+            << " -D NONMAX=" << static_cast<unsigned>(nonmax);
+
+        if (std::is_same<T, double>::value ||
                 std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+            options << " -D USE_DOUBLE";
+        }
 
-            cl::Program prog;
-            buildProgram(prog, fast_cl, fast_cl_len, options.str());
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[3];
+        cl::Program prog;
+        buildProgram(prog, fast_cl, fast_cl_len, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel[3];
 
-            entry.ker[0] = Kernel(*entry.prog, "locate_features");
-            entry.ker[1] = Kernel(*entry.prog, "non_max_counts");
-            entry.ker[2] = Kernel(*entry.prog, "get_features");
+        entry.ker[0] = Kernel(*entry.prog, "locate_features");
+        entry.ker[1] = Kernel(*entry.prog, "non_max_counts");
+        entry.ker[2] = Kernel(*entry.prog, "get_features");
 
-            kernelCaches[device][ref_name] = entry;
-    } else {
-        entry = cache_idx -> second;
+        addKernelToCache(device, ref_name, entry);
     }
 
     const unsigned max_feat = ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 2c76f2720f..ab71beb6df 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -62,39 +62,37 @@ namespace kernel
             std::to_string(threads_y);
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        kc_entry_t entry = kernelCache(device, ref_name);
 
-                Binary<T, op> ireduce;
-                ToNumStr<T> toNumStr;
+        if (entry.prog==0 && entry.ker==0) {
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D dim=" << dim
-                        << " -D DIMY=" << threads_y
-                        << " -D THREADS_X=" << THREADS_X
-                        << " -D init=" << toNumStr(ireduce.init())
-                        << " -D " << binOpName<op>()
-                        << " -D CPLX=" << af::iscplx<T>()
-                        << " -D IS_FIRST=" << is_first;
+            Binary<T, op> ireduce;
+            ToNumStr<T> toNumStr;
+
+            std::ostringstream options;
+            options << " -D T=" << dtype_traits<T>::getName()
+                << " -D dim=" << dim
+                << " -D DIMY=" << threads_y
+                << " -D THREADS_X=" << THREADS_X
+                << " -D init=" << toNumStr(ireduce.init())
+                << " -D " << binOpName<op>()
+                << " -D CPLX=" << af::iscplx<T>()
+                << " -D IS_FIRST=" << is_first;
 
-                if (std::is_same<T, double>::value ||
+            if (std::is_same<T, double>::value ||
                     std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+                options << " -D USE_DOUBLE";
+            }
 
-                const char *ker_strs[] = {iops_cl, ireduce_dim_cl};
-                const int   ker_lens[] = {iops_cl_len, ireduce_dim_cl_len};
-                Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "ireduce_dim_kernel");
+            const char *ker_strs[] = {iops_cl, ireduce_dim_cl};
+            const int   ker_lens[] = {iops_cl_len, ireduce_dim_cl_len};
+            Program prog;
+            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+            entry.prog = new Program(prog);
+            entry.ker = new Kernel(*entry.prog, "ireduce_dim_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         NDRange local(THREADS_X, threads_y);
@@ -174,10 +172,10 @@ namespace kernel
             std::to_string(threads_x);
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        kc_entry_t entry = kernelCache(device, ref_name);
+
+        if (entry.prog==0 && entry.ker==0) {
 
             Binary<T, op> ireduce;
             ToNumStr<T> toNumStr;
@@ -203,9 +201,7 @@ namespace kernel
             entry.prog = new Program(prog);
             entry.ker = new Kernel(*entry.prog, "ireduce_first_kernel");
 
-            kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 10200e2aa7..a4fafe2b5e 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -49,10 +49,9 @@ namespace opencl
             std::to_string(out.info.dims[0]);
 
             int device = getActiveDeviceId();
-            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            kc_entry_t entry;
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T="        << dtype_traits<T>::getName();
                 options << " -D MOMENTS_SZ=" << out.info.dims[0];
@@ -69,9 +68,7 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "moments_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 328708cfe9..7280508a63 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -61,49 +61,47 @@ void nearest_neighbour(Param idx,
         std::to_string(unroll_len);
 
     int device = getActiveDeviceId();
-    kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
-
-    kc_entry_t entry;
-    if (cache_idx == kernelCaches[device].end()) {
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D To=" << dtype_traits<To>::getName()
-                    << " -D THREADS=" << THREADS
-                    << " -D FEAT_LEN=" << unroll_len;
-
-            switch(dist_type) {
-                case AF_SAD: options <<" -D DISTOP=_sad_"; break;
-                case AF_SSD: options <<" -D DISTOP=_ssd_"; break;
-                case AF_SHD: options <<" -D DISTOP=_shd_ -D __SHD__";
-                              break;
-                default: break;
-            }
-
-            if (std::is_same<T, double>::value ||
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog==0 && entry.ker==0) {
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+            << " -D To=" << dtype_traits<To>::getName()
+            << " -D THREADS=" << THREADS
+            << " -D FEAT_LEN=" << unroll_len;
+
+        switch(dist_type) {
+            case AF_SAD: options <<" -D DISTOP=_sad_"; break;
+            case AF_SSD: options <<" -D DISTOP=_ssd_"; break;
+            case AF_SHD: options <<" -D DISTOP=_shd_ -D __SHD__";
+                         break;
+            default: break;
+        }
+
+        if (std::is_same<T, double>::value ||
                 std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+            options << " -D USE_DOUBLE";
+        }
 
-            if (use_lmem)
-                options << " -D USE_LOCAL_MEM";
+        if (use_lmem)
+            options << " -D USE_LOCAL_MEM";
 
-            cl::Program prog;
-            buildProgram(prog,
-                          nearest_neighbour_cl,
-                          nearest_neighbour_cl_len,
-                          options.str());
+        cl::Program prog;
+        buildProgram(prog,
+                nearest_neighbour_cl,
+                nearest_neighbour_cl_len,
+                options.str());
 
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[3];
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel[3];
 
-            entry.ker[0] = Kernel(*entry.prog, "nearest_neighbour_unroll");
-            entry.ker[1] = Kernel(*entry.prog, "nearest_neighbour");
-            entry.ker[2] = Kernel(*entry.prog, "select_matches");
+        entry.ker[0] = Kernel(*entry.prog, "nearest_neighbour_unroll");
+        entry.ker[1] = Kernel(*entry.prog, "nearest_neighbour");
+        entry.ker[2] = Kernel(*entry.prog, "select_matches");
 
-            kernelCaches[device][ref_name] = entry;
-    } else {
-        entry = cache_idx->second;
+        addKernelToCache(device, ref_name, entry);
     }
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 8829b15a68..827f56e552 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -83,9 +83,10 @@ namespace opencl
                 "_" + string(dtype_traits<T>::getName()) +
                 "_" + to_string(kerIdx);
             int device = getActiveDeviceId();
-            kc_t::iterator idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
-            if (idx == kernelCaches[device].end()) {
+
+            kc_entry_t entry = kernelCache(device, ref_name);
+
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName()
                         << " -D THREADS=" << THREADS
@@ -104,9 +105,8 @@ namespace opencl
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "generate");
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             return *entry.ker;
@@ -121,17 +121,17 @@ namespace opencl
             int ker_len = random_engine_mersenne_init_cl_len;
             string ref_name = "mersenne_init";
             int device = getActiveDeviceId();
-            kc_t::iterator idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
-            if (idx == kernelCaches[device].end()) {
+
+            kc_entry_t entry = kernelCache(device, ref_name);
+
+            if (entry.prog==0 && entry.ker==0) {
                 std::string emptyOptionString;
                 cl::Program prog;
                 buildProgram(prog, 1, &ker_str, &ker_len, emptyOptionString);
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "initState");
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             return *entry.ker;
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 31038a3080..d9c67c90fe 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -61,10 +61,9 @@ namespace kernel
             std::to_string(threads_y);
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
+        kc_entry_t entry = kernelCache(device, ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        if (entry.prog==0 && entry.ker==0) {
             Binary<To, op> reduce;
             ToNumStr<To> toNumStr;
 
@@ -92,9 +91,7 @@ namespace kernel
             entry.prog = new Program(prog);
             entry.ker = new Kernel(*entry.prog, "reduce_dim_kernel");
 
-            kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         NDRange local(THREADS_X, threads_y);
@@ -179,10 +176,10 @@ namespace kernel
             std::to_string(threads_x);
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        kc_entry_t entry = kernelCache(device, ref_name);
+
+        if (entry.prog==0 && entry.ker==0) {
 
             Binary<To, op> reduce;
             ToNumStr<To> toNumStr;
@@ -209,9 +206,7 @@ namespace kernel
             entry.prog = new Program(prog);
             entry.ker = new Kernel(*entry.prog, "reduce_first_kernel");
 
-            kernelCaches[device][ref_name] = entry;
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 46ad2d1673..a48d271a5d 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -55,11 +55,10 @@ namespace kernel
             std::to_string(int(inclusive_scan));
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        kc_entry_t entry = kernelCache(device, ref_name);
 
+        if (entry.prog==0 && entry.ker==0) {
             Binary<To, op> scan;
             ToNumStr<To> toNumStr;
 
@@ -91,10 +90,8 @@ namespace kernel
             entry.ker[0] = Kernel(*entry.prog, "scan_dim_kernel");
             entry.ker[1] = Kernel(*entry.prog, "bcast_dim_kernel");
 
-            kernelCaches[device][ref_name] = entry;
 
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         return entry.ker[kerIdx];
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index a8adbff7d3..217153d18c 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -57,10 +57,10 @@ namespace kernel
             std::to_string(int(inclusive_scan));
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        kc_entry_t entry = kernelCache(device, ref_name);
+
+        if (entry.prog==0 && entry.ker==0) {
 
             Binary<To, op> scan;
             ToNumStr<To> toNumStr;
@@ -95,10 +95,7 @@ namespace kernel
             entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
             entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
 
-            kernelCaches[device][ref_name] = entry;
-
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         return entry.ker[kerIdx];
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index 85f32ec2e9..58587c1ba9 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -56,11 +56,10 @@ namespace kernel
             std::to_string(int(inclusive_scan));
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        kc_entry_t entry = kernelCache(device, ref_name);
 
+        if (entry.prog==0 && entry.ker==0) {
             const uint threads_y = THREADS_PER_GROUP / threads_x;
             const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
 
@@ -95,10 +94,7 @@ namespace kernel
             entry.ker[0] = Kernel(*entry.prog, "scan_first_kernel");
             entry.ker[1] = Kernel(*entry.prog, "bcast_first_kernel");
 
-            kernelCaches[device][ref_name] = entry;
-
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         return entry.ker[kerIdx];
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 9417c1a777..771dfca8f4 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -58,10 +58,9 @@ namespace kernel
             std::to_string(int(inclusive_scan));
 
         int device = getActiveDeviceId();
-        kc_t::iterator idx = kernelCaches[device].find(ref_name);
+        kc_entry_t entry = kernelCache(device, ref_name);
 
-        kc_entry_t entry;
-        if (idx == kernelCaches[device].end()) {
+        if (entry.prog==0 && entry.ker==0) {
 
             const uint threads_y = THREADS_PER_GROUP / threads_x;
             const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
@@ -99,10 +98,7 @@ namespace kernel
             entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
             entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
 
-            kernelCaches[device][ref_name] = entry;
-
-        } else {
-            entry = idx->second;
+            addKernelToCache(device, ref_name, entry);
         }
 
         return entry.ker[kerIdx];
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index df56d5fedb..67c2650bcb 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -50,10 +50,9 @@ namespace opencl
                 std::to_string(REPEAT);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T="        << dtype_traits<T>::getName()
                         << " -D reps="     << REPEAT
@@ -68,8 +67,8 @@ namespace opencl
                 buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
                 entry.prog   = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "coo2dense_kernel");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             };
 
             auto coo2denseOp = KernelFunctor<Buffer, const KParam,
@@ -106,10 +105,9 @@ namespace opencl
                 std::to_string(threads);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -127,8 +125,8 @@ namespace opencl
                 buildProgram(prog, 1, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "csr2dense");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             NDRange local(threads, 1);
@@ -196,10 +194,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -222,9 +219,7 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
             NDRange local(THREADS_X, THREADS_Y);
@@ -258,10 +253,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T="        << dtype_traits<T>::getName();
 
@@ -274,8 +268,8 @@ namespace opencl
                 buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
                 entry.prog   = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "swapIndex_kernel");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             };
 
             auto swapIndexOp = KernelFunctor<Buffer, Buffer,
@@ -307,10 +301,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T=" << dtype_traits<T>::getName();
@@ -327,8 +320,8 @@ namespace opencl
                 buildProgram(prog, 1, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "csr2coo");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
@@ -374,10 +367,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T="        << dtype_traits<T>::getName();
 
@@ -390,8 +382,8 @@ namespace opencl
                 buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
                 entry.prog   = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "csrReduce_kernel");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             };
 
             auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int> (*entry.ker);
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index dc38696d8d..b27445e603 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -71,10 +71,9 @@ namespace opencl
                 std::to_string(order);
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
                 ToNumStr<T> toNumStr;
                 std::ostringstream options;
                 options << " -D T="           << dtype_traits<T>::getName()
@@ -106,8 +105,8 @@ namespace opencl
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "transform_kernel");
-            } else {
-                entry = idx->second;
+
+                addKernelToCache(device, ref_name, entry);
             }
 
             auto transformOp = KernelFunctor<Buffer, const KParam,
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 0ae3b7002a..100bd9ca66 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -48,11 +48,10 @@ namespace opencl
                 std::to_string(is_column);
 
             int device = getActiveDeviceId();
-            kc_t::iterator idx = kernelCaches[device].find(ref_name);
 
-            kc_entry_t entry;
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
 
+            if (entry.prog==0 && entry.ker==0) {
                 ToNumStr<T> toNumStr;
                 std::ostringstream options;
                 options << " -D is_column=" << is_column
@@ -70,9 +69,7 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "unwrap_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
             dim_t TX = 1, TY = 1;
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 3e35a2fbea..5ad2efe38c 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -48,10 +48,9 @@ namespace opencl
                 std::to_string(is_column);
 
             int device = getActiveDeviceId();
-            kc_t::iterator idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            kc_entry_t entry;
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
 
                 ToNumStr<T> toNumStr;
                 std::ostringstream options;
@@ -70,9 +69,7 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker = new Kernel(*entry.prog, "wrap_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
             dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 67dd594e08..7acd2baeeb 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -34,6 +34,7 @@
 #include <version.hpp>
 
 #include <algorithm>
+#include <boost/thread.hpp>
 #include <cctype>
 #include <functional>
 #include <map>
@@ -54,8 +55,16 @@ using cl::Context;
 using cl::CommandQueue;
 using cl::Device;
 
+typedef boost::shared_mutex smutex_t;
+typedef boost::shared_lock<smutex_t> rlock_t;
+typedef boost::unique_lock<smutex_t> wlock_t;
+typedef boost::upgrade_lock<smutex_t> ulock_t;
+typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
+
 namespace opencl
 {
+static smutex_t kernelCacheMutexes[DeviceManager::MAX_DEVICES];
+
 #if defined (OS_MAC)
 static const std::string CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
@@ -629,6 +638,33 @@ PlanCache& fftManager()
     return clfftManagers[getActiveDeviceId()];
 }
 
+void addKernelToCache(int device, const std::string& key, const kc_entry_t entry)
+{
+    wlock_t lock(kernelCacheMutexes[device]);
+
+    DeviceManager::getInstance().kernelCaches[device].emplace(key, entry);
+}
+
+void removeKernelFromCache(int device, const std::string& key)
+{
+    wlock_t lock(kernelCacheMutexes[device]);
+
+    DeviceManager::getInstance().kernelCaches[device].erase(key);
+}
+
+kc_entry_t kernelCache(int device, const std::string& key)
+{
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    rlock_t lock(kernelCacheMutexes[device]);
+
+    kc_t::iterator iter = inst.kernelCaches[device].find(key);
+    if (iter == inst.kernelCaches[device].end()) {
+        return kc_entry_t{0, 0};
+    } else
+        return iter->second;
+}
+
 DeviceManager& DeviceManager::getInstance()
 {
     static DeviceManager my_instance;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 66a1acb53a..de55c38947 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -26,6 +26,7 @@
 #include <vector>
 #include <string>
 
+#include <cache.hpp>
 #include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <clfft.hpp>
@@ -93,6 +94,12 @@ MemoryManagerPinned& pinnedMemoryManager();
 GraphicsResourceManager& interopManager();
 
 PlanCache& fftManager();
+
+void addKernelToCache(int device, const std::string& key, const kc_entry_t entry);
+
+void removeKernelFromCache(int device, const std::string& key);
+
+kc_entry_t kernelCache(int device, const std::string& key);
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
@@ -106,6 +113,12 @@ class DeviceManager
 
     friend PlanCache& fftManager();
 
+    friend void addKernelToCache(int device, const std::string& key, const kc_entry_t entry);
+
+    friend void removeKernelFromCache(int device, const std::string& key);
+
+    friend kc_entry_t kernelCache(int device, const std::string& key);
+
     friend std::string getDeviceInfo();
 
     friend int getDeviceCount();
@@ -181,5 +194,7 @@ class DeviceManager
 
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
         clfftSetupData mFFTSetup;
+
+        kc_t kernelCaches[MAX_DEVICES];
 };
 }

From c9f53fc264a1a2f824798c94a6104443820562cd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 20 Feb 2017 22:53:34 +0530
Subject: [PATCH 1121/2677] Add thread safety to CUDA JIT kernel cache

---
 src/backend/cuda/jit.cpp | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 7d4111874a..1ec2e12b62 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -42,12 +42,17 @@
 #include <math.hpp>
 #include <nvvm.h>
 
+#include <boost/thread.hpp>
 #include <functional>
 #include <map>
 #include <memory>
 #include <stdexcept>
 #include <vector>
 
+typedef boost::shared_mutex smutex_t;
+typedef boost::upgrade_lock<smutex_t> ulock_t;
+typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
+
 namespace cuda
 {
 
@@ -530,17 +535,21 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 
 static CUfunction getKernel(vector<Node *> nodes, bool is_linear)
 {
+    typedef std::map<std::string, kc_entry_t> kc_t;
+
+    static smutex_t mutexes[DeviceManager::MAX_DEVICES];
+    static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
     string funcName = getFuncName(nodes, is_linear);
+    int device      = getActiveDeviceId();
 
-    typedef map<string, kc_entry_t> kc_t;
-    static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
-    int device = getActiveDeviceId();
+    ulock_t lock(mutexes[device]);
 
     kc_t::iterator idx = kernelCaches[device].find(funcName);
     kc_entry_t entry = {NULL, NULL};
 
     if (idx == kernelCaches[device].end()) {
+        u2ulock_t unqLock(lock);
         string jit_ker = getKernelString(funcName, nodes, is_linear);
         entry = compileKernel(funcName.c_str(), jit_ker);
         kernelCaches[device][funcName] = entry;

From 49793ed87d017a298707c99e152598db569a11b6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Feb 2017 16:51:54 +0530
Subject: [PATCH 1122/2677] Make ForgeManager(gfx upstream) interface thread
 safe

---
 src/api/c/graphics_common.cpp | 172 +++++++++++++++++++++++-----------
 src/api/c/graphics_common.hpp |   9 +-
 2 files changed, 121 insertions(+), 60 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index b35d15b879..ec16030541 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -15,10 +15,18 @@
 #include <backend.hpp>
 #include <platform.hpp>
 #include <util.hpp>
+#include <mutex>
+#include <boost/thread.hpp>
 
 using namespace std;
 using namespace gl;
 
+typedef boost::shared_mutex smutex_t;
+typedef boost::shared_lock<smutex_t> rlock_t;
+typedef boost::unique_lock<smutex_t> wlock_t;
+typedef boost::upgrade_lock<smutex_t> ulock_t;
+typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
+
 template<typename T>
 gl::GLenum getGLType() { return GL_FLOAT; }
 
@@ -171,6 +179,12 @@ double step_round(const double in, const bool dir)
 
 namespace graphics
 {
+static smutex_t  gImgMapMutex;
+static smutex_t  gPltMapMutex;
+static smutex_t  gHstMapMutex;
+static smutex_t  gSfcMapMutex;
+static smutex_t  gVcfMapMutex;
+static smutex_t  gChartMutex;
 
 ForgeManager& ForgeManager::getInstance()
 {
@@ -180,55 +194,75 @@ ForgeManager& ForgeManager::getInstance()
 
 ForgeManager::~ForgeManager()
 {
-    destroyResources();
+    /* clear all OpenGL resource objects (images, plots, histograms etc) first
+     * and then delete the windows */
+    for(ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
+        delete (iter->second);
+
+    for(PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
+        delete (iter->second);
+
+    for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
+        delete (iter->second);
+
+    for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++) {
+        for(int i = 0; i < (int)(iter->second).size(); i++) {
+            if((iter->second)[i] != NULL) {
+                delete (iter->second)[i];
+                mChartAxesOverrideMap.erase((iter->second)[i]);
+            }
+        }
+    }
 }
 
-forge::Font* ForgeManager::getFont(const bool dontCreate)
+forge::Font* ForgeManager::getFont()
 {
-    static bool flag = true;
-    static forge::Font* fnt = NULL;
+    static std::once_flag flag;
+    static std::unique_ptr<forge::Font> fnt;
 
     CheckGL("Begin ForgeManager::getFont");
-
-    if (flag && !dontCreate) {
-        fnt = new forge::Font();
+    std::call_once(flag,
+            [] {
+                fnt.reset(new forge::Font());
 #if defined(_WIN32) || defined(_MSC_VER)
-        fnt->loadSystemFont("Arial");
+                fnt->loadSystemFont("Arial");
 #else
-        fnt->loadSystemFont("Vera");
+                fnt->loadSystemFont("Vera");
 #endif
-        CheckGL("End ForgeManager::getFont");
-        flag = false;
-    };
+            });
+    CheckGL("End ForgeManager::getFont");
 
-    return fnt;
+    return fnt.get();
 }
 
-forge::Window* ForgeManager::getMainWindow(const bool dontCreate)
+forge::Window* ForgeManager::getMainWindow()
 {
-    static bool flag = true;
-    static forge::Window* wnd = NULL;
+    static std::once_flag flag;
+    static std::unique_ptr<forge::Window> wnd;
 
+    CheckGL("Begin ForgeManager::getMainWindow");
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
-    if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
-        if (flag && !dontCreate) {
-            wnd = new forge::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true);
-            makeContextCurrent(wnd);
 
-            ForgeManager& fgMngr = ForgeManager::getInstance();
-            fgMngr.setWindowChartGrid(wnd, 1, 1);
+    if (noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
+        std::call_once(flag,
+                [] {
+                    wnd.reset(new forge::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true));
+                    makeContextCurrent(wnd.get());
 
-            CheckGL("End ForgeManager::getMainWindow");
-            flag = false;
-        };
+                    ForgeManager::getInstance().setWindowChartGrid(wnd.get(), 1, 1);
+                });
     }
-    return wnd;
+    CheckGL("End ForgeManager::getMainWindow");
+
+    return wnd.get();
 }
 
 void ForgeManager::setWindowChartGrid(const forge::Window* window,
                                       const int r, const int c)
 {
+    wlock_t lock(gChartMutex);
+
     ChartMapIter iter = mChartMap.find(window);
 
     if(iter != mChartMap.end()) {
@@ -254,16 +288,22 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
 forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, const int c,
                                      const forge::ChartType ctype)
 {
+    ulock_t lock(gChartMutex);
+
     forge::Chart* chart = NULL;
     ChartMapIter iter = mChartMap.find(window);
 
-    if(iter != mChartMap.end()) {
+    if (iter != mChartMap.end()) {
+
         int gRows = window->gridRows();
         int gCols = window->gridCols();
 
         if(c >= gCols || r >= gRows)
             AF_ERROR("Grid points are out of bounds", AF_ERR_TYPE);
 
+        // upgrade to exclusive access to make changes
+        u2ulock_t unqLock(lock);
+
         chart = (iter->second)[c * gRows + r];
 
         if (chart == NULL) {
@@ -301,9 +341,16 @@ forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, fo
     key = (((key << 16) | mode) << 16) | type;
 
     ChartKey_t keypair = std::make_pair(key, nullptr);
+
+    ulock_t lock(gImgMapMutex);
+
     ImgMapIter iter = mImgMap.find(keypair);
+
     if (iter==mImgMap.end()) {
         forge::Image* temp = new forge::Image(w, h, mode, type);
+
+        u2ulock_t unqLock(lock);
+
         mImgMap[keypair] = temp;
     }
 
@@ -324,13 +371,21 @@ forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
     key = (((key << 16) | mode) << 16) | type;
 
     ChartKey_t keypair = std::make_pair(key, chart);
+
+    ulock_t lock(gImgMapMutex);
+
     ImgMapIter iter = mImgMap.find(keypair);
+
     if (iter==mImgMap.end()) {
         if(chart->getChartType() != FG_CHART_2D)
             AF_ERROR("Image can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
 
         forge::Image* temp = new forge::Image(w, h, mode, type);
+
+        u2ulock_t unqLock(lock);
+
         mImgMap[keypair] = temp;
+
         chart->add(*mImgMap[keypair]);
     }
 
@@ -350,10 +405,18 @@ forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtyp
     key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
 
     ChartKey_t keypair = std::make_pair(key, chart);
+
+    ulock_t lock(gPltMapMutex);
+
     PltMapIter iter = mPltMap.find(keypair);
+
     if (iter==mPltMap.end()) {
         forge::Plot* temp = new forge::Plot(nPoints, dtype, chart->getChartType(), ptype, mtype);
+
+        u2ulock_t unqLock(lock);
+
         mPltMap[keypair] = temp;
+
         chart->add(*mPltMap[keypair]);
     }
 
@@ -371,13 +434,21 @@ forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, for
     long long key = ((nBins & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
+
+    ulock_t lock(gHstMapMutex);
+
     HstMapIter iter = mHstMap.find(keypair);
+
     if (iter==mHstMap.end()) {
         if(chart->getChartType() != FG_CHART_2D)
             AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
 
         forge::Histogram* temp = new forge::Histogram(nBins, type);
+
+        u2ulock_t unqLock(lock);
+
         mHstMap[keypair] = temp;
+
         chart->add(*mHstMap[keypair]);
     }
 
@@ -395,13 +466,21 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
     long long key = (((nX * nY) & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
+
+    ulock_t lock(gSfcMapMutex);
+
     SfcMapIter iter = mSfcMap.find(keypair);
+
     if (iter==mSfcMap.end()) {
         if(chart->getChartType() != FG_CHART_3D)
             AF_ERROR("Surface can only be added to chart of type FG_CHART_3D", AF_ERR_TYPE);
 
         forge::Surface* temp = new forge::Surface(nX, nY, type);
+
+        u2ulock_t unqLock(lock);
+
         mSfcMap[keypair] = temp;
+
         chart->add(*mSfcMap[keypair]);
     }
 
@@ -419,10 +498,18 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
     long long key = (((nPoints) & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
+
+    ulock_t lock(gVcfMapMutex);
+
     VcfMapIter iter = mVcfMap.find(keypair);
+
     if (iter==mVcfMap.end()) {
         forge::VectorField* temp = new forge::VectorField(nPoints, type, chart->getChartType());
+
+        u2ulock_t unqLock(lock);
+
         mVcfMap[keypair] = temp;
+
         chart->add(*mVcfMap[keypair]);
     }
 
@@ -431,6 +518,8 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
 
 bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
 {
+    rlock_t lock(gChartMutex);
+
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
@@ -440,39 +529,14 @@ bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
 
 void ForgeManager::setChartAxesOverride(forge::Chart* chart, bool flag)
 {
+    rlock_t lock(gChartMutex);
+
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
     mChartAxesOverrideMap[chart] = flag;
 }
-
-void ForgeManager::destroyResources()
-{
-    /* clear all OpenGL resource objects (images, plots, histograms etc) first
-     * and then delete the windows */
-    for(ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
-        delete (iter->second);
-
-    for(PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
-        delete (iter->second);
-
-    for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
-        delete (iter->second);
-
-    for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++) {
-        for(int i = 0; i < (int)(iter->second).size(); i++) {
-            if((iter->second)[i] != NULL) {
-                delete (iter->second)[i];
-                mChartAxesOverrideMap.erase((iter->second)[i]);
-            }
-        }
-    }
-
-    delete getFont(true);
-    delete getMainWindow(true);
-}
-
 }
 
 #endif
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index ac5ebe7709..b8f29a343a 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -43,7 +43,6 @@ double step_round(const double in, const bool dir);
 
 namespace graphics
 {
-
 enum Defaults {
     WIDTH = 1280,
     HEIGHT= 720
@@ -104,8 +103,8 @@ class ForgeManager
         static ForgeManager& getInstance();
         ~ForgeManager();
 
-        forge::Font*    getFont(const bool dontCreate=false);
-        forge::Window*  getMainWindow(const bool dontCreate=false);
+        forge::Font*    getFont();
+        forge::Window*  getMainWindow();
 
         void            setWindowChartGrid(const forge::Window* window,
                                            const int r, const int c);
@@ -130,11 +129,9 @@ class ForgeManager
         ForgeManager() {}
         ForgeManager(ForgeManager const&);
         void operator=(ForgeManager const&);
-        void destroyResources();
 };
-
 }
 
-#define MAIN_WINDOW graphics::ForgeManager::getInstance().getMainWindow(true)
+#define MAIN_WINDOW graphics::ForgeManager::getInstance().getMainWindow()
 
 #endif

From d45c4c86de5942c15e6f15a3e4edaa9767476958 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Feb 2017 21:00:39 +0530
Subject: [PATCH 1123/2677] Omit thread_local qualifier for fft cache objects
 on OSX

XCode's Clang doesn't support thread_local qualifier yet.
---
 src/backend/cuda/platform.cpp   | 8 ++++++++
 src/backend/opencl/platform.cpp | 8 ++++++++
 2 files changed, 16 insertions(+)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 847be47c3c..68d0cb502b 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -421,7 +421,15 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
+    //FIXME Change to better check later, may be Clang version based check
+#if defined(OS_MAC)
+    // XCode Clang doesn't support thread_local qualifier
+    // Hence, making the cache manager per device
+    static PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
+#else
+    //Otherwise, cache manager is per thread per devicea, less congestion
     thread_local static PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
+#endif
 
     return cufftManagers[getActiveDeviceId()];
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 7acd2baeeb..4b43a6865a 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -633,7 +633,15 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
+    //FIXME Change to better check later, may be Clang version based check
+#if defined(OS_MAC)
+    // XCode Clang doesn't support thread_local qualifier
+    // Hence, making the cache manager per device
+    static PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
+#else
+    //Otherwise, cache manager is per thread per devicea, less congestion
     thread_local static PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
+#endif
 
     return clfftManagers[getActiveDeviceId()];
 }

From 60a325906385f80c7f256f58a576a69d204ae414 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 22 Feb 2017 18:16:57 +0530
Subject: [PATCH 1124/2677] Fix memory management issue for user added opencl
 devices

Earlier, for user added devices amid the program execution, the
memory management wasn't being properly initialised, thereby causing
segmentation faults in such cases. This has been taken care of now.
---
 src/api/c/graphics_common.cpp        |   2 -
 src/backend/common/MemoryManager.hpp | 108 +++++++++++++++++----------
 src/backend/opencl/platform.cpp      |   5 ++
 test/ocl_ext_context.cpp             |  40 +++++-----
 4 files changed, 96 insertions(+), 59 deletions(-)

diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index ec16030541..88a8f8e230 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -240,7 +240,6 @@ forge::Window* ForgeManager::getMainWindow()
     static std::once_flag flag;
     static std::unique_ptr<forge::Window> wnd;
 
-    CheckGL("Begin ForgeManager::getMainWindow");
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
 
@@ -253,7 +252,6 @@ forge::Window* ForgeManager::getMainWindow()
                     ForgeManager::getInstance().setWindowChartGrid(wnd.get(), 1, 1);
                 });
     }
-    CheckGL("End ForgeManager::getMainWindow");
 
     return wnd.get();
 }
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 892df3f50c..219385ffd2 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -42,7 +42,7 @@ class MemoryManager
     typedef std::unordered_map<size_t, std::vector<void *> >free_t;
     typedef free_t::iterator free_iter;
 
-    typedef struct
+    typedef struct memory_info
     {
         locked_t locked_map;
         free_t   free_map;
@@ -52,6 +52,17 @@ class MemoryManager
         size_t total_bytes;
         size_t total_buffers;
         size_t max_bytes;
+
+        memory_info()
+        {
+            // Calling getMaxMemorySize() here calls the virtual function that returns 0
+            // Call it from outside the constructor.
+            max_bytes     = ONE_GB;
+            total_bytes   = 0;
+            total_buffers = 0;
+            lock_bytes    = 0;
+            lock_buffers  = 0;
+        }
     } memory_info;
 
     size_t mem_step_size;
@@ -74,38 +85,75 @@ class MemoryManager
         return static_cast<T*>(this)->getMaxMemorySize(id);
     }
 
+    void cleanDeviceMemoryManager(int device)
+    {
+        if (this->debug_mode) return;
+
+        lock_guard_t lock(this->memory_mutex);
+        memory_info& current = memory[device];
+
+        // Return if all buffers are locked
+        if (current.total_buffers == current.lock_buffers) return;
+
+        for (auto &kv : current.free_map) {
+            size_t num_ptrs = kv.second.size();
+            //Free memory by popping the last element
+            for (int n = num_ptrs-1; n >= 0; n--) {
+                this->nativeFree(kv.second[n]);
+                current.total_bytes -= kv.first;
+                current.total_buffers--;
+                kv.second.pop_back();
+            }
+        }
+        current.free_map.clear();
+    }
+
     public:
     MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug)
         : mem_step_size(1024), max_buffers(MAX_BUFFERS), memory(num_devices), debug_mode(debug)
     {
         lock_guard_t lock(this->memory_mutex);
 
-        for (int n = 0; n < num_devices; n++) {
-            // Calling getMaxMemorySize() here calls the virtual function that returns 0
-            // Call it from outside the constructor.
-            memory[n].max_bytes     = ONE_GB;
-            memory[n].total_bytes   = 0;
-            memory[n].total_buffers = 0;
-            memory[n].lock_bytes    = 0;
-            memory[n].lock_buffers  = 0;
-        }
-
         // Check for environment variables
 
-        std::string env_var;
-
         // Debug mode
-        env_var = getEnvVar("AF_MEM_DEBUG");
-        if (!env_var.empty()) {
-            this->debug_mode = env_var[0] != '0';
-        }
+        std::string env_var = getEnvVar("AF_MEM_DEBUG");
+        if (!env_var.empty()) this->debug_mode = env_var[0] != '0';
         if (this->debug_mode) mem_step_size = 1;
 
         // Max Buffer count
         env_var = getEnvVar("AF_MAX_BUFFERS");
-        if (!env_var.empty()) {
-            this->max_buffers = std::max(1, std::stoi(env_var));
-        }
+        if (!env_var.empty()) this->max_buffers = std::max(1, std::stoi(env_var));
+    }
+
+    // Intended to be used with OpenCL backend, where
+    // users are allowed to add external devices(context, device pair)
+    // to the list of devices automatically detected by the library
+    void addMemoryManagement(int device)
+    {
+        // If there is a memory manager allocated for
+        // this device id, we might as well use it and the
+        // buffers allocated for it
+        if ((size_t)device < memory.size())
+            return;
+
+        // Assuming, device need not be always the next device
+        // Lets resize to current_size + device + 1
+        // +1 is to account for device being 0-based index of devices
+        memory.resize(memory.size()+device+1);
+    }
+
+    // Intended to be used with OpenCL backend, where
+    // users are allowed to add external devices(context, device pair)
+    // to the list of devices automatically detected by the library
+    void removeMemoryManagement(int device)
+    {
+        if ((size_t)device>=memory.size())
+            AF_ERROR("No matching device found", AF_ERR_ARG);
+
+        // Do garbage collection for the device and leave
+        // the memory_info struct from the memory vector intact
+        cleanDeviceMemoryManager(device);
     }
 
     void setMaxMemorySize()
@@ -230,25 +278,7 @@ class MemoryManager
 
     void garbageCollect()
     {
-        if (this->debug_mode) return;
-
-        lock_guard_t lock(this->memory_mutex);
-        memory_info& current = this->getCurrentMemoryInfo();
-
-        // Return if all buffers are locked
-        if (current.total_buffers == current.lock_buffers) return;
-
-        for (auto &kv : current.free_map) {
-            size_t num_ptrs = kv.second.size();
-            //Free memory by popping the last element
-            for (int n = num_ptrs-1; n >= 0; n--) {
-                this->nativeFree(kv.second[n]);
-                current.total_bytes -= kv.first;
-                current.total_buffers--;
-                kv.second.pop_back();
-            }
-        }
-        current.free_map.clear();
+        cleanDeviceMemoryManager(this->getActiveDeviceId());
     }
 
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 4b43a6865a..fe6d774654 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -495,6 +495,9 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
     devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
     // FIXME: add OpenGL Interop for user provided contexts later
     devMngr.mIsGLSharingOn.push_back(false);
+
+    // Last/newly added device needs memory management
+    memoryManager().addMemoryManagement(devMngr.mDevices.size()-1);
 }
 
 void setDeviceContext(cl_device_id dev, cl_context ctx)
@@ -539,6 +542,8 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
     } else if (deleteIdx == -1) {
         AF_ERROR("No matching device found", AF_ERR_ARG);
     } else {
+        //remove memory management for device added by user
+        memoryManager().removeMemoryManagement(deleteIdx);
 
         clReleaseDevice((*devMngr.mDevices[deleteIdx])());
         clReleaseContext((*devMngr.mContexts[deleteIdx])());
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 3dc46991c2..13951413ee 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -55,7 +55,7 @@ void getExternals(cl_device_id &deviceId, cl_context &context, cl_command_queue
     queue    = qId;
 }
 
-TEST(OCLExtContext, push)
+TEST(OCLExtContext, PushAndPop)
 {
     cl_device_id deviceId = NULL;
     cl_context context = NULL;
@@ -63,11 +63,16 @@ TEST(OCLExtContext, push)
 
     getExternals(deviceId, context, queue);
     int dCount = af::getDeviceCount();
-    printf("%d devices before afcl::addDevice\n", dCount);
+    printf("\n%d devices before afcl::addDevice\n\n", dCount);
     af::info();
+
     afcl::addDevice(deviceId, context, queue);
     ASSERT_EQ(true, dCount+1==af::getDeviceCount());
-    printf("%d devices after afcl::addDevice\n", af::getDeviceCount());
+    printf("\n%d devices after afcl::addDevice\n", af::getDeviceCount());
+
+    afcl::deleteDevice(deviceId, context);
+    ASSERT_EQ(true, dCount==af::getDeviceCount());
+    printf("\n%d devices after afcl::deleteDevice\n\n", af::getDeviceCount());
     af::info();
 }
 
@@ -77,8 +82,19 @@ TEST(OCLExtContext, set)
     cl_context context = NULL;
     cl_command_queue queue = NULL;
 
+    int dCount = af::getDeviceCount(); //Before user device addition
+    af::setDevice(0);
+    af::info();
+    af::array t = af::randu(5,5);
+    af_print(t);
+
     getExternals(deviceId, context, queue);
-    afcl::setDevice(deviceId, context);
+    afcl::addDevice(deviceId, context, queue);
+    printf("\nBefore setting device to newly added one\n\n");
+    af::info();
+
+    printf("\n\nBefore setting device to newly added one\n\n");
+    af::setDevice(dCount); //In 0-based index, dCount is index of newly added device
     af::info();
 
     const int x = 5;
@@ -89,23 +105,11 @@ TEST(OCLExtContext, set)
     a.host((void*)host.data());
     for (int i=0; i<s; ++i)
         ASSERT_EQ(host[i], 1.0f);
-}
 
-TEST(OCLExtContext, pop)
-{
-    cl_device_id deviceId = NULL;
-    cl_context context = NULL;
-    cl_command_queue queue = NULL;
-
-    getExternals(deviceId, context, queue);
-    int dCount = af::getDeviceCount();
-    printf("%d devices before afcl::deleteDevice\n", dCount);
+    printf("\n\nAfter reset to default set of devices\n\n");
     af::setDevice(0);
     af::info();
-    afcl::deleteDevice(deviceId, context);
-    ASSERT_EQ(true, dCount-1==af::getDeviceCount());
-    printf("%d devices after afcl::deleteDevice\n", af::getDeviceCount());
-    af::info();
+    af_print(t);
 }
 
 TEST(OCLCheck, DeviceType)

From 38ef85b9283d88adecd8bbf0397440adc2c9f7df Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 24 Feb 2017 12:03:22 +0530
Subject: [PATCH 1125/2677] Remove shared_mutex in favour of thread_local
 storage

This removes the usage of boost::shared_mutex from graphics,
fft cache management and jit.

Graphcis shall be stated as thread unsafe and hence to be used
from the main thread. Rest of the caches shall use thread_local
in the subsequent commits once an alternative to thread_local
has been implemented for OSX platform.
---
 CMakeLists.txt                        |  2 +-
 src/api/c/graphics_common.cpp         | 48 -------------------
 src/backend/common/FFTPlanCache.cpp   | 69 ---------------------------
 src/backend/common/FFTPlanCache.hpp   | 27 +++++++++--
 src/backend/common/InteropManager.cpp | 20 --------
 src/backend/common/InteropManager.hpp |  1 +
 src/backend/cpu/CMakeLists.txt        |  1 -
 src/backend/cuda/CMakeLists.txt       |  1 -
 src/backend/cuda/jit.cpp              |  9 ----
 src/backend/opencl/CMakeLists.txt     |  1 -
 src/backend/opencl/platform.cpp       | 15 ------
 11 files changed, 24 insertions(+), 170 deletions(-)
 delete mode 100644 src/backend/common/FFTPlanCache.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5d159c8ea5..6972b7d628 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -55,7 +55,7 @@ ENDIF(FREEIMAGE_FOUND)
 
 ADD_DEFINITIONS(-DBOOST_ALL_NO_LIB)
 SET(Boost_USE_STATIC_LIBS OFF)
-FIND_PACKAGE(Boost REQUIRED COMPONENTS "system" "thread")
+FIND_PACKAGE(Boost REQUIRED)
 
 OPTION(USE_SYSTEM_CL2HPP "Use cl2.hpp installed on system" OFF)
 
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 88a8f8e230..249a325586 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -16,17 +16,10 @@
 #include <platform.hpp>
 #include <util.hpp>
 #include <mutex>
-#include <boost/thread.hpp>
 
 using namespace std;
 using namespace gl;
 
-typedef boost::shared_mutex smutex_t;
-typedef boost::shared_lock<smutex_t> rlock_t;
-typedef boost::unique_lock<smutex_t> wlock_t;
-typedef boost::upgrade_lock<smutex_t> ulock_t;
-typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
-
 template<typename T>
 gl::GLenum getGLType() { return GL_FLOAT; }
 
@@ -179,13 +172,6 @@ double step_round(const double in, const bool dir)
 
 namespace graphics
 {
-static smutex_t  gImgMapMutex;
-static smutex_t  gPltMapMutex;
-static smutex_t  gHstMapMutex;
-static smutex_t  gSfcMapMutex;
-static smutex_t  gVcfMapMutex;
-static smutex_t  gChartMutex;
-
 ForgeManager& ForgeManager::getInstance()
 {
     static ForgeManager my_instance;
@@ -259,8 +245,6 @@ forge::Window* ForgeManager::getMainWindow()
 void ForgeManager::setWindowChartGrid(const forge::Window* window,
                                       const int r, const int c)
 {
-    wlock_t lock(gChartMutex);
-
     ChartMapIter iter = mChartMap.find(window);
 
     if(iter != mChartMap.end()) {
@@ -286,8 +270,6 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
 forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, const int c,
                                      const forge::ChartType ctype)
 {
-    ulock_t lock(gChartMutex);
-
     forge::Chart* chart = NULL;
     ChartMapIter iter = mChartMap.find(window);
 
@@ -300,8 +282,6 @@ forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, c
             AF_ERROR("Grid points are out of bounds", AF_ERR_TYPE);
 
         // upgrade to exclusive access to make changes
-        u2ulock_t unqLock(lock);
-
         chart = (iter->second)[c * gRows + r];
 
         if (chart == NULL) {
@@ -340,15 +320,11 @@ forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, fo
 
     ChartKey_t keypair = std::make_pair(key, nullptr);
 
-    ulock_t lock(gImgMapMutex);
-
     ImgMapIter iter = mImgMap.find(keypair);
 
     if (iter==mImgMap.end()) {
         forge::Image* temp = new forge::Image(w, h, mode, type);
 
-        u2ulock_t unqLock(lock);
-
         mImgMap[keypair] = temp;
     }
 
@@ -370,8 +346,6 @@ forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
-    ulock_t lock(gImgMapMutex);
-
     ImgMapIter iter = mImgMap.find(keypair);
 
     if (iter==mImgMap.end()) {
@@ -380,8 +354,6 @@ forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
 
         forge::Image* temp = new forge::Image(w, h, mode, type);
 
-        u2ulock_t unqLock(lock);
-
         mImgMap[keypair] = temp;
 
         chart->add(*mImgMap[keypair]);
@@ -404,15 +376,11 @@ forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtyp
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
-    ulock_t lock(gPltMapMutex);
-
     PltMapIter iter = mPltMap.find(keypair);
 
     if (iter==mPltMap.end()) {
         forge::Plot* temp = new forge::Plot(nPoints, dtype, chart->getChartType(), ptype, mtype);
 
-        u2ulock_t unqLock(lock);
-
         mPltMap[keypair] = temp;
 
         chart->add(*mPltMap[keypair]);
@@ -433,8 +401,6 @@ forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, for
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
-    ulock_t lock(gHstMapMutex);
-
     HstMapIter iter = mHstMap.find(keypair);
 
     if (iter==mHstMap.end()) {
@@ -443,8 +409,6 @@ forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, for
 
         forge::Histogram* temp = new forge::Histogram(nBins, type);
 
-        u2ulock_t unqLock(lock);
-
         mHstMap[keypair] = temp;
 
         chart->add(*mHstMap[keypair]);
@@ -465,8 +429,6 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
-    ulock_t lock(gSfcMapMutex);
-
     SfcMapIter iter = mSfcMap.find(keypair);
 
     if (iter==mSfcMap.end()) {
@@ -475,8 +437,6 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
 
         forge::Surface* temp = new forge::Surface(nX, nY, type);
 
-        u2ulock_t unqLock(lock);
-
         mSfcMap[keypair] = temp;
 
         chart->add(*mSfcMap[keypair]);
@@ -497,15 +457,11 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
-    ulock_t lock(gVcfMapMutex);
-
     VcfMapIter iter = mVcfMap.find(keypair);
 
     if (iter==mVcfMap.end()) {
         forge::VectorField* temp = new forge::VectorField(nPoints, type, chart->getChartType());
 
-        u2ulock_t unqLock(lock);
-
         mVcfMap[keypair] = temp;
 
         chart->add(*mVcfMap[keypair]);
@@ -516,8 +472,6 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
 
 bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
 {
-    rlock_t lock(gChartMutex);
-
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
@@ -527,8 +481,6 @@ bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
 
 void ForgeManager::setChartAxesOverride(forge::Chart* chart, bool flag)
 {
-    rlock_t lock(gChartMutex);
-
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
diff --git a/src/backend/common/FFTPlanCache.cpp b/src/backend/common/FFTPlanCache.cpp
deleted file mode 100644
index be5f4d9582..0000000000
--- a/src/backend/common/FFTPlanCache.cpp
+++ /dev/null
@@ -1,69 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-//FIXME CPU backend doesn't required the following class implementation
-//FFTPlanCache.hpp is not used while building CPU backend.
-#ifndef AF_CPU
-#include <boost/thread.hpp>
-#include <common/FFTPlanCache.hpp>
-#include <backend.hpp>
-#include <platform.hpp>
-
-typedef boost::shared_mutex smutex_t;
-typedef boost::shared_lock<smutex_t> rlock_t;
-typedef boost::unique_lock<smutex_t> wlock_t;
-
-namespace common
-{
-static smutex_t gFFTMutexes[detail::DeviceManager::MAX_DEVICES];
-
-template<class T, typename P>
-void FFTPlanCache<T, P>::setMaxCacheSize(size_t size)
-{
-    wlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
-    mMaxCacheSize = size;
-}
-
-template<class T, typename P>
-size_t FFTPlanCache<T, P>::getMaxCacheSize() const
-{
-    rlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
-    return mMaxCacheSize;
-}
-
-template<class T, typename P>
-std::shared_ptr<P> FFTPlanCache<T, P>::find(const std::string& key) const
-{
-    std::shared_ptr<P> res;
-
-    rlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
-    for(unsigned i=0; i<mCache.size(); ++i) {
-        if (key == mCache[i].first) {
-            res = mCache[i].second;
-            break;
-        }
-    }
-
-    return res;
-}
-
-template<class T, typename P>
-void FFTPlanCache<T, P>::push(const std::string key, std::shared_ptr<P> plan)
-{
-    wlock_t lock(gFFTMutexes[detail::getActiveDeviceId()]);
-
-    if (mCache.size()>=mMaxCacheSize)
-        mCache.pop_back();
-
-    mCache.push_front(plan_pair_t(key, plan));
-}
-
-template class FFTPlanCache<detail::PlanCache, detail::PlanType>;
-}
-#endif
diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
index d10baabd29..3d18f31117 100644
--- a/src/backend/common/FFTPlanCache.hpp
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-
 #include <deque>
 #include <memory>
 #include <string>
@@ -32,17 +31,35 @@ class FFTPlanCache
     public:
         FFTPlanCache() : mMaxCacheSize(5) {}
 
-        void setMaxCacheSize(size_t size);
-        size_t getMaxCacheSize() const;
+        void setMaxCacheSize(size_t size) { mMaxCacheSize = size; }
+        size_t getMaxCacheSize() const    { return mMaxCacheSize; }
 
         // iterates through plan cache from front to back
         // of the cache(queue)
         // A valid shared_ptr of the plan in the cache is returned
         // if found, and empty share_ptr otherwise.
-        plan_t find(const std::string& key) const;
+        plan_t find(const std::string& key) const
+        {
+            std::shared_ptr<P> res;
+
+            for(unsigned i=0; i<mCache.size(); ++i) {
+                if (key == mCache[i].first) {
+                    res = mCache[i].second;
+                    break;
+                }
+            }
+
+            return res;
+        }
 
         // pushes plan to the front of cache(queue)
-        void push(const std::string key, plan_t plan);
+        void push(const std::string key, plan_t plan)
+        {
+            if (mCache.size()>=mMaxCacheSize)
+                mCache.pop_back();
+
+            mCache.push_front(plan_pair_t(key, plan));
+        }
 
     protected:
         FFTPlanCache(FFTPlanCache const&);
diff --git a/src/backend/common/InteropManager.cpp b/src/backend/common/InteropManager.cpp
index 9f970e4324..7c1ae3bc07 100644
--- a/src/backend/common/InteropManager.cpp
+++ b/src/backend/common/InteropManager.cpp
@@ -11,7 +11,6 @@
 //FIXME CPU backend doesn't required the following class implementation
 //InteropManager.hpp is not used while building CPU backend.
 #ifndef AF_CPU
-#include <boost/thread.hpp>
 #include <common/InteropManager.hpp>
 #include <common/types.hpp>
 #include <cstdio>
@@ -21,19 +20,11 @@
 #include <GraphicsResourceManager.hpp>
 #include <platform.hpp>
 
-typedef boost::shared_mutex smutex_t;
-typedef boost::shared_lock<smutex_t> rlock_t;
-typedef boost::unique_lock<smutex_t> wlock_t;
-typedef boost::upgrade_lock<smutex_t> ulock_t;
-typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
-
 template<typename R>
 using RVector = std::vector<std::shared_ptr<R>>;
 
 namespace common
 {
-static smutex_t gInteropMutexes[detail::DeviceManager::MAX_DEVICES];
-
 template<class T, typename R>
 InteropManager<T, R>::~InteropManager()
 {
@@ -51,7 +42,6 @@ InteropManager<T, R>::~InteropManager()
 template<class T, typename R>
 RVector<R> InteropManager<T, R>::getBufferResource(const forge::Image* image)
 {
-    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
     void * key = (void*)image;
 
     if (mInteropMap.find(key) == mInteropMap.end()) {
@@ -59,7 +49,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Image* image)
         handles.push_back(image->pixels());
         std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
 
-        u2ulock_t wlock(lock);
         mInteropMap[key] = output;
     }
 
@@ -69,7 +58,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Image* image)
 template<class T, typename R>
 RVector<R> InteropManager<T, R>::getBufferResource(const forge::Plot* plot)
 {
-    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
     void * key = (void*)plot;
 
     if (mInteropMap.find(key) == mInteropMap.end()) {
@@ -77,7 +65,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Plot* plot)
         handles.push_back(plot->vertices());
         std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
 
-        u2ulock_t wlock(lock);
         mInteropMap[key] = output;
     }
 
@@ -87,7 +74,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Plot* plot)
 template<class T, typename R>
 RVector<R> InteropManager<T, R>::getBufferResource(const forge::Histogram* histogram)
 {
-    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
     void * key = (void*)histogram;
 
     if (mInteropMap.find(key) == mInteropMap.end()) {
@@ -95,7 +81,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Histogram* histo
         handles.push_back(histogram->vertices());
         std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
 
-        u2ulock_t wlock(lock);
         mInteropMap[key] = output;
     }
 
@@ -105,7 +90,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Histogram* histo
 template<class T, typename R>
 RVector<R> InteropManager<T, R>::getBufferResource(const forge::Surface* surface)
 {
-    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
     void * key = (void*)surface;
 
     if (mInteropMap.find(key) == mInteropMap.end()) {
@@ -113,7 +97,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Surface* surface
         handles.push_back(surface->vertices());
         std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
 
-        u2ulock_t wlock(lock);
         mInteropMap[key] = output;
     }
 
@@ -123,7 +106,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::Surface* surface
 template<class T, typename R>
 RVector<R> InteropManager<T, R>::getBufferResource(const forge::VectorField* field)
 {
-    ulock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
     void * key = (void*)field;
 
     if (mInteropMap.find(key) == mInteropMap.end()) {
@@ -132,7 +114,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::VectorField* fie
         handles.push_back(field->directions());
         std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
 
-        u2ulock_t wlock(lock);
         mInteropMap[key] = output;
     }
 
@@ -142,7 +123,6 @@ RVector<R> InteropManager<T, R>::getBufferResource(const forge::VectorField* fie
 template<class T, typename R>
 void InteropManager<T, R>::destroyResources()
 {
-    wlock_t lock(gInteropMutexes[detail::getActiveDeviceId()]);
     for(auto iter : mInteropMap) {
         iter.second.clear();
     }
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index 5cfc3736e1..a026340afd 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -13,6 +13,7 @@
 #include <forge.h>
 #include <map>
 #include <vector>
+#include <memory>
 
 namespace common
 {
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index ab4659697f..ac94f897ea 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -214,7 +214,6 @@ TARGET_LINK_LIBRARIES(afcpu
                             PRIVATE ${CBLAS_LIBRARIES}
                             PRIVATE ${FFTW_LIBRARIES}
                             PRIVATE ${FreeImage_LIBS}
-                            PRIVATE ${Boost_LIBRARIES}
                      )
 
 IF(LAPACK_FOUND)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9b3538e576..46f86dc566 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -436,7 +436,6 @@ TARGET_LINK_LIBRARIES(afcuda
                                 PRIVATE ${CUDA_cusolver_LIBRARY}
                                 PRIVATE ${CUDA_nvvm_LIBRARY}
                                 PRIVATE ${CUDA_CUDA_LIBRARY}
-                                PRIVATE ${Boost_LIBRARIES}
                      )
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 1ec2e12b62..5fab3f8f5f 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -42,17 +42,12 @@
 #include <math.hpp>
 #include <nvvm.h>
 
-#include <boost/thread.hpp>
 #include <functional>
 #include <map>
 #include <memory>
 #include <stdexcept>
 #include <vector>
 
-typedef boost::shared_mutex smutex_t;
-typedef boost::upgrade_lock<smutex_t> ulock_t;
-typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
-
 namespace cuda
 {
 
@@ -537,19 +532,15 @@ static CUfunction getKernel(vector<Node *> nodes, bool is_linear)
 {
     typedef std::map<std::string, kc_entry_t> kc_t;
 
-    static smutex_t mutexes[DeviceManager::MAX_DEVICES];
     static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
     string funcName = getFuncName(nodes, is_linear);
     int device      = getActiveDeviceId();
 
-    ulock_t lock(mutexes[device]);
-
     kc_t::iterator idx = kernelCaches[device].find(funcName);
     kc_entry_t entry = {NULL, NULL};
 
     if (idx == kernelCaches[device].end()) {
-        u2ulock_t unqLock(lock);
         string jit_ker = getKernelString(funcName, nodes, is_linear);
         entry = compileKernel(funcName.c_str(), jit_ker);
         kernelCaches[device][funcName] = entry;
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index de2b9ec588..5a31c89405 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -319,7 +319,6 @@ TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${CLFFT_LIBRARIES}
                                 PRIVATE ${CMAKE_DL_LIBS}
                                 PRIVATE ${FreeImage_LIBS}
-                                PRIVATE ${Boost_LIBRARIES}
                      )
 
 IF(LAPACK_FOUND)
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index fe6d774654..bb0a41d82d 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -34,7 +34,6 @@
 #include <version.hpp>
 
 #include <algorithm>
-#include <boost/thread.hpp>
 #include <cctype>
 #include <functional>
 #include <map>
@@ -55,16 +54,8 @@ using cl::Context;
 using cl::CommandQueue;
 using cl::Device;
 
-typedef boost::shared_mutex smutex_t;
-typedef boost::shared_lock<smutex_t> rlock_t;
-typedef boost::unique_lock<smutex_t> wlock_t;
-typedef boost::upgrade_lock<smutex_t> ulock_t;
-typedef boost::upgrade_to_unique_lock<smutex_t> u2ulock_t;
-
 namespace opencl
 {
-static smutex_t kernelCacheMutexes[DeviceManager::MAX_DEVICES];
-
 #if defined (OS_MAC)
 static const std::string CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
@@ -653,15 +644,11 @@ PlanCache& fftManager()
 
 void addKernelToCache(int device, const std::string& key, const kc_entry_t entry)
 {
-    wlock_t lock(kernelCacheMutexes[device]);
-
     DeviceManager::getInstance().kernelCaches[device].emplace(key, entry);
 }
 
 void removeKernelFromCache(int device, const std::string& key)
 {
-    wlock_t lock(kernelCacheMutexes[device]);
-
     DeviceManager::getInstance().kernelCaches[device].erase(key);
 }
 
@@ -669,8 +656,6 @@ kc_entry_t kernelCache(int device, const std::string& key)
 {
     DeviceManager& inst = DeviceManager::getInstance();
 
-    rlock_t lock(kernelCacheMutexes[device]);
-
     kc_t::iterator iter = inst.kernelCaches[device].find(key);
     if (iter == inst.kernelCaches[device].end()) {
         return kc_entry_t{0, 0};

From e355be316e4fef6e468786b09b6deba7251c7554 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sat, 25 Feb 2017 10:32:03 -0800
Subject: [PATCH 1126/2677] Bugfix to select in CUDA and OpenCL

---
 src/backend/cuda/kernel/select.hpp  | 18 ++++----
 src/backend/opencl/kernel/select.cl | 14 +++----
 test/select.cpp                     | 65 +++++++++++++++++++++++++++++
 3 files changed, 79 insertions(+), 18 deletions(-)

diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 44fb8d1d1b..4787ad048a 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -56,23 +56,20 @@ namespace cuda
             const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
             T *optr = out.ptr + off;
 
-            int ids[] = {idx0, idy, idz, idw};
-
             const T *aptr = a.ptr;
             const T *bptr = b.ptr;
             const char *cptr = cond.ptr;
 
+            int ids[] = {idx0, idy, idz, idw};
+            aptr += getOffset(a.dims, a.strides, out.dims, ids);
+            bptr += getOffset(b.dims, b.strides, out.dims, ids);
+            cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
+
             if (is_same) {
-                aptr += off;
-                bptr += off;
-                cptr += off;
                 for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
                     optr[idx] = cptr[idx] ? aptr[idx] : bptr[idx];
                 }
             } else {
-                aptr += getOffset(a.dims, a.strides, out.dims, ids);
-                bptr += getOffset(b.dims, b.strides, out.dims, ids);
-                cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
                 bool csame = cond.dims[0] == out.dims[0];
                 bool asame = a.dims[0] == out.dims[0];
                 bool bsame = b.dims[0] == out.dims[0];
@@ -135,8 +132,9 @@ namespace cuda
             const T *aptr = a.ptr;
             const char *cptr = cond.ptr;
 
-            aptr += off;
-            cptr += off;
+            int ids[] = {idx0, idy, idz, idw};
+            aptr += getOffset(a.dims, a.strides, out.dims, ids);
+            cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
 
             if (idw >= out.dims[3] ||
                 idz >= out.dims[2] ||
diff --git a/src/backend/opencl/kernel/select.cl b/src/backend/opencl/kernel/select.cl
index f6b92cb637..a16a7b4ee3 100644
--- a/src/backend/opencl/kernel/select.cl
+++ b/src/backend/opencl/kernel/select.cl
@@ -56,18 +56,15 @@ void select_kernel(__global T *optr, KParam oinfo,
     int ids[] = {idx0, idy, idz, idw};
 
     optr += off;
+    aptr += getOffset(ainfo.dims, ainfo.strides, oinfo.dims, ids);
+    bptr += getOffset(binfo.dims, binfo.strides, oinfo.dims, ids);
+    cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims, ids);
 
     if (is_same) {
-        aptr += off;
-        bptr += off;
-        cptr += off;
         for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
             optr[idx] = (cptr[idx]) ? aptr[idx] : bptr[idx];
         }
     } else {
-        aptr += getOffset(ainfo.dims, ainfo.strides, oinfo.dims, ids);
-        bptr += getOffset(binfo.dims, binfo.strides, oinfo.dims, ids);
-        cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims, ids);
         bool csame = cinfo.dims[0] == oinfo.dims[0];
         bool asame = ainfo.dims[0] == oinfo.dims[0];
         bool bsame = binfo.dims[0] == oinfo.dims[0];
@@ -99,9 +96,10 @@ void select_scalar_kernel(__global T *optr, KParam oinfo,
 
     const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1];
 
+    int ids[] = {idx0, idy, idz, idw};
     optr += off;
-    aptr += off;
-    cptr += off;
+    aptr += getOffset(ainfo.dims, ainfo.strides, oinfo.dims, ids);
+    cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims, ids);
 
     if (idw >= oinfo.dims[3] ||
         idz >= oinfo.dims[2] ||
diff --git a/test/select.cpp b/test/select.cpp
index 6e772ac7c4..411836e8cb 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -176,3 +176,68 @@ TEST(Select, 4D)
         ASSERT_EQ(hc[i], hb[i]) << "at " << i;
     }
 }
+
+TEST(Select, Issue_1730)
+{
+    const int n = 1000;
+    const int m = 200;
+    af::array a = af::randu(n, m) - 0.5;
+    af::eval(a);
+
+    std::vector<float> ha1(a.elements());
+    a.host(&ha1[0]);
+
+    const int n1 = n / 2;
+    const int n2 = n1 + n / 4;
+
+    a(af::seq(n1, n2), af::span) =
+        af::select(a(af::seq(n1, n2), af::span) >= 0,
+                   a(af::seq(n1, n2), af::span),
+                   a(af::seq(n1, n2), af::span) * -1);
+
+    std::vector<float> ha2(a.elements());
+    a.host(&ha2[0]);
+
+    for (int j = 0; j < m; j++) {
+        for (int i = 0; i < n; i++) {
+            if (i < n1 || i > n2) {
+                ASSERT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
+            } else {
+                ASSERT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : -ha1[i]))  << "at (" << i << ", " << j << ")";
+            }
+        }
+    }
+}
+
+TEST(Select, Issue_1730_scalar)
+{
+    const int n = 1000;
+    const int m = 200;
+    af::array a = af::randu(n, m) - 0.5;
+    af::eval(a);
+
+    std::vector<float> ha1(a.elements());
+    a.host(&ha1[0]);
+
+    const int n1 = n / 2;
+    const int n2 = n1 + n / 4;
+
+    float val = 0;
+    a(af::seq(n1, n2), af::span) =
+        af::select(a(af::seq(n1, n2), af::span) >= 0,
+                   a(af::seq(n1, n2), af::span),
+                   val);
+
+    std::vector<float> ha2(a.elements());
+    a.host(&ha2[0]);
+
+    for (int j = 0; j < m; j++) {
+        for (int i = 0; i < n; i++) {
+            if (i < n1 || i > n2) {
+                ASSERT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
+            } else {
+                ASSERT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : val))  << "at (" << i << ", " << j << ")";
+            }
+        }
+    }
+}

From f80e94c9501613665a8600f8d8005a0a68d5186b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 27 Feb 2017 23:39:03 +0530
Subject: [PATCH 1127/2677] Add thread_local qualifier to fft & kernel caches

---
 src/backend/cuda/jit.cpp        |  2 +-
 src/backend/cuda/platform.cpp   |  8 --------
 src/backend/opencl/platform.cpp | 29 +++++++++++++----------------
 src/backend/opencl/platform.hpp |  2 --
 4 files changed, 14 insertions(+), 27 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 5fab3f8f5f..f5e64551df 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -532,7 +532,7 @@ static CUfunction getKernel(vector<Node *> nodes, bool is_linear)
 {
     typedef std::map<std::string, kc_entry_t> kc_t;
 
-    static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
+    thread_local static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
     string funcName = getFuncName(nodes, is_linear);
     int device      = getActiveDeviceId();
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 68d0cb502b..847be47c3c 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -421,15 +421,7 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    //FIXME Change to better check later, may be Clang version based check
-#if defined(OS_MAC)
-    // XCode Clang doesn't support thread_local qualifier
-    // Hence, making the cache manager per device
-    static PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
-#else
-    //Otherwise, cache manager is per thread per devicea, less congestion
     thread_local static PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
-#endif
 
     return cufftManagers[getActiveDeviceId()];
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index bb0a41d82d..93690547ff 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -629,38 +629,35 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    //FIXME Change to better check later, may be Clang version based check
-#if defined(OS_MAC)
-    // XCode Clang doesn't support thread_local qualifier
-    // Hence, making the cache manager per device
-    static PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
-#else
-    //Otherwise, cache manager is per thread per devicea, less congestion
     thread_local static PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
-#endif
 
     return clfftManagers[getActiveDeviceId()];
 }
 
+kc_t& getKernelCache(int device)
+{
+    thread_local static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
+
+    return kernelCaches[device];
+}
+
 void addKernelToCache(int device, const std::string& key, const kc_entry_t entry)
 {
-    DeviceManager::getInstance().kernelCaches[device].emplace(key, entry);
+    getKernelCache(device).emplace(key, entry);
 }
 
 void removeKernelFromCache(int device, const std::string& key)
 {
-    DeviceManager::getInstance().kernelCaches[device].erase(key);
+    getKernelCache(device).erase(key);
 }
 
 kc_entry_t kernelCache(int device, const std::string& key)
 {
-    DeviceManager& inst = DeviceManager::getInstance();
+    kc_t& cache = getKernelCache(device);
+
+    kc_t::iterator iter = cache.find(key);
 
-    kc_t::iterator iter = inst.kernelCaches[device].find(key);
-    if (iter == inst.kernelCaches[device].end()) {
-        return kc_entry_t{0, 0};
-    } else
-        return iter->second;
+    return (iter==cache.end() ? kc_entry_t{0, 0} : iter->second);
 }
 
 DeviceManager& DeviceManager::getInstance()
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index de55c38947..bf490d282a 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -194,7 +194,5 @@ class DeviceManager
 
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
         clfftSetupData mFFTSetup;
-
-        kc_t kernelCaches[MAX_DEVICES];
 };
 }

From 8086e296cb62428af948569e4345d973a4ee5840 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 28 Feb 2017 01:25:44 +0530
Subject: [PATCH 1128/2677] Fix WITH_GRAPHICS build checks in CUDA/OpenCL
 backends

---
 src/backend/cuda/platform.cpp   | 4 ++++
 src/backend/cuda/platform.hpp   | 8 ++++++++
 src/backend/opencl/platform.cpp | 4 ++++
 src/backend/opencl/platform.hpp | 6 ++++++
 4 files changed, 22 insertions(+)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 847be47c3c..47000273df 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -355,6 +355,7 @@ cudaDeviceProp getDeviceProp(int device)
 ///////////////////////////////////////////////////////////////////////////
 // DeviceManager Class Functions
 ///////////////////////////////////////////////////////////////////////////
+#if defined(WITH_GRAPHICS)
 bool DeviceManager::checkGraphicsInteropCapability()
 {
     static bool run_once = true;
@@ -377,6 +378,7 @@ bool DeviceManager::checkGraphicsInteropCapability()
 
     return capable;
 }
+#endif
 
 DeviceManager& DeviceManager::getInstance()
 {
@@ -406,6 +408,7 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
+#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager()
 {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -418,6 +421,7 @@ GraphicsResourceManager& interopManager()
 
     return *(inst.gfxManagers[id].get());
 }
+#endif
 
 PlanCache& fftManager()
 {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index f91e9da641..f3521aad21 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -78,7 +78,9 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
+#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager();
+#endif
 
 PlanCache& fftManager();
 
@@ -95,7 +97,9 @@ class DeviceManager
     public:
         static const unsigned MAX_DEVICES = 16;
 
+#if defined(WITH_GRAPHICS)
         static bool checkGraphicsInteropCapability();
+#endif
 
         static DeviceManager& getInstance();
 
@@ -103,7 +107,9 @@ class DeviceManager
 
         friend MemoryManagerPinned& pinnedMemoryManager();
 
+#if defined(WITH_GRAPHICS)
         friend GraphicsResourceManager& interopManager();
+#endif
 
         friend PlanCache& fftManager();
 
@@ -165,7 +171,9 @@ class DeviceManager
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
+#if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+#endif
 
         std::unique_ptr<cublasHandle> cublasHandles[MAX_DEVICES];
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 93690547ff..fef7afd853 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -614,6 +614,7 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
+#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager()
 {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -626,6 +627,7 @@ GraphicsResourceManager& interopManager()
 
     return *(inst.gfxManagers[id].get());
 }
+#endif
 
 PlanCache& fftManager()
 {
@@ -668,9 +670,11 @@ DeviceManager& DeviceManager::getInstance()
 
 DeviceManager::~DeviceManager()
 {
+#if defined(WITH_GRAPHICS)
     for (int i=0; i<getDeviceCount(); ++i) {
         delete gfxManagers[i].release();
     }
+#endif
 #ifndef OS_WIN
     //TODO: FIXME:
     // clfftTeardown() causes a "Pure Virtual Function Called" crash on
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index bf490d282a..43953ec527 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -91,7 +91,9 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
+#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager();
+#endif
 
 PlanCache& fftManager();
 
@@ -109,7 +111,9 @@ class DeviceManager
 
     friend MemoryManagerPinned& pinnedMemoryManager();
 
+#if defined(WITH_GRAPHICS)
     friend GraphicsResourceManager& interopManager();
+#endif
 
     friend PlanCache& fftManager();
 
@@ -192,7 +196,9 @@ class DeviceManager
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
+#if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+#endif
         clfftSetupData mFFTSetup;
 };
 }

From 124c96b0114aba1057c466c95be29864f21d5bed Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 1 Mar 2017 00:13:48 +0530
Subject: [PATCH 1129/2677] Support for maintaining per thread active devie Ids

This change effects CUDA/OpenCL backend.
---
 src/backend/cuda/platform.cpp   |  81 +++++++++----------
 src/backend/cuda/platform.hpp   |   4 -
 src/backend/opencl/platform.cpp | 137 ++++++++++++++++++++++++--------
 src/backend/opencl/platform.hpp |   7 --
 4 files changed, 145 insertions(+), 84 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 47000273df..078c1983d4 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -29,7 +29,6 @@ using namespace std;
 
 namespace cuda
 {
-
 ///////////////////////////////////////////////////////////////////////////
 // HELPERS
 ///////////////////////////////////////////////////////////////////////////
@@ -281,6 +280,13 @@ unsigned getMaxJitSize()
     return length;
 }
 
+int& tlocalActiveDeviceId()
+{
+    thread_local static int activeDeviceId = 0;
+
+    return activeDeviceId;
+}
+
 int getDeviceCount()
 {
     return DeviceManager::getInstance().nDevices;
@@ -288,7 +294,7 @@ int getDeviceCount()
 
 int getActiveDeviceId()
 {
-    return DeviceManager::getInstance().activeDev;
+    return tlocalActiveDeviceId();
 }
 
 int getDeviceNativeId(int device)
@@ -312,17 +318,15 @@ int getDeviceIdFromNativeId(int nativeId)
 
 cudaStream_t getStream(int device)
 {
-    cudaStream_t str = DeviceManager::getInstance().streams[device];
-    // if the stream has not yet been initialized, ie. the device has not been
-    // set to active at least once (cuz that's where the stream is created)
-    // then set the device, get the stream, reset the device to current
-    if(!str) {
-        int active_dev = DeviceManager::getInstance().activeDev;
-        setDevice(device);
-        str = DeviceManager::getInstance().streams[device];
-        setDevice(active_dev);
-    }
-    return str;
+    static std::once_flag streamInitFlags[DeviceManager::MAX_DEVICES];
+
+    std::call_once(streamInitFlags[device],
+            [device]() {
+                DeviceManager& inst = DeviceManager::getInstance();
+                CUDA_CHECK(cudaStreamCreate( & (inst.streams[device]) ));
+            });
+
+    return DeviceManager::getInstance().streams[device];
 }
 
 cudaStream_t getActiveStream()
@@ -485,7 +489,7 @@ SparseHandle sparseHandle()
 }
 
 DeviceManager::DeviceManager()
-    : cuDevices(0), activeDev(0), nDevices(0)
+    : cuDevices(0), nDevices(0)
 {
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
     if (nDevices == 0)
@@ -527,7 +531,6 @@ DeviceManager::DeviceManager()
 
 void DeviceManager::sortDevices(sort_mode mode)
 {
-    common::lock_guard_t lock(deviceMutex);
     switch(mode) {
         case memory :
             std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_mem);
@@ -546,50 +549,49 @@ void DeviceManager::sortDevices(sort_mode mode)
 
 int DeviceManager::setActiveDevice(int device, int nId)
 {
-    static bool first = true;
-
-    common::lock_guard_t lock(deviceMutex);
+    thread_local static bool retryFlag = true;
 
     int numDevices = cuDevices.size();
 
-    if(device > numDevices) return -1;
+    if (device > numDevices)
+        return -1;
 
-    int old = activeDev;
-    if(nId == -1) nId = getDeviceNativeId(device);
-    CUDA_CHECK(cudaSetDevice(nId));
+    int old = getActiveDeviceId();
 
-    cudaError_t err = cudaSuccess;
-    if(!streams[device])
-        err = cudaStreamCreate(&streams[device]);
+    if(nId == -1)
+        nId = getDeviceNativeId(device);
 
-    activeDev = device;
+    cudaError_t err = cudaSetDevice(nId);
 
-    if (err == cudaSuccess) return old;
+    if (err == cudaSuccess) {
+        tlocalActiveDeviceId() = device;
+        return old;
+    }
 
-    // Comes when user sets device
-    // If success, return. Else throw error
-    if (!first) {
+    // For the first time a thread calls setDevice,
+    // if the requested device is unavailable, try checking
+    // for other available devices - while loop below
+    if (!retryFlag) {
         CUDA_CHECK(err);
         return old;
     }
 
-    // Comes only when first is true. Set it to false
-    first = false;
+    // Comes only when retryFlag is true. Set it to false
+    retryFlag = false;
 
     while(true) {
         // Check for errors other than DevicesUnavailable
         // If success, return. Else throw error
         // If DevicesUnavailable, try other devices (while loop below)
-        if (err != cudaErrorDevicesUnavailable) {
+        if (err != cudaErrorDeviceAlreadyInUse) {
             CUDA_CHECK(err);
-            activeDev = device;
+            tlocalActiveDeviceId() = device;
             return old;
         }
         cudaGetLastError(); // Reset error stack
 #ifndef NDEBUG
         printf("Warning: Device %d is unavailable. Incrementing to next device \n", device);
 #endif
-
         // Comes here is the device is in exclusive mode or
         // otherwise fails streamCreate with this error.
         // All other errors will error out
@@ -599,11 +601,10 @@ int DeviceManager::setActiveDevice(int device, int nId)
         // Can't call getNativeId here as it will cause an infinite loop with the constructor
         nId = cuDevices[device].nativeId;
 
-        CUDA_CHECK(cudaSetDevice(nId));
-        err = cudaStreamCreate(&streams[device]);
+        err = cudaSetDevice(nId);
     }
 
-    // If all devices fail with DevicesUnavailable, then throw this error
+    // If all devices fail with DeviceAlreadyInUse, then throw this error
     CUDA_CHECK(err);
 
     return old;
@@ -617,7 +618,8 @@ void sync(int device)
     setDevice(currDevice);
 }
 
-bool synchronize_calls() {
+bool synchronize_calls()
+{
     static bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
 }
@@ -627,7 +629,6 @@ bool& evalFlag()
     static bool flag = true;
     return flag;
 }
-
 }
 
 af_err afcu_get_stream(cudaStream_t* stream, int id)
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index f3521aad21..152dfc6a14 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -131,8 +131,6 @@ class DeviceManager
 
         friend int getDeviceCount();
 
-        friend int getActiveDeviceId();
-
         friend int getDeviceNativeId(int device);
 
         friend int getDeviceIdFromNativeId(int nativeId);
@@ -154,7 +152,6 @@ class DeviceManager
         void operator=(DeviceManager const&);
 
         // Attributes
-        common::mutex_t deviceMutex;
         std::vector<cudaDevice_t> cuDevices;
 
         enum sort_mode {flops = 0, memory = 1, compute = 2, none = 3};
@@ -163,7 +160,6 @@ class DeviceManager
 
         int setActiveDevice(int device, int native = -1);
 
-        int activeDev;
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index fef7afd853..3ea48f4ac6 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -42,6 +42,7 @@
 #include <sstream>
 #include <stdexcept>
 #include <cstring>
+#include <utility>
 #include <vector>
 
 using std::string;
@@ -222,12 +223,16 @@ static std::string platformMap(std::string &platStr)
 
 std::string getDeviceInfo()
 {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
     ostringstream info;
     info << "ArrayFire v" << AF_VERSION
          << " (OpenCL, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
 
     unsigned nDevices = 0;
-    for(auto &device: DeviceManager::getInstance().mDevices) {
+    for(auto &device: devMngr.mDevices) {
         const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
         string dstr = device->getInfo<CL_DEVICE_NAME>();
@@ -267,60 +272,117 @@ std::string getPlatformName(const cl::Device &device)
     return platformMap(platStr);
 }
 
+typedef std::pair<unsigned, unsigned> device_id_t;
+
+std::pair<unsigned, unsigned>& tlocalActiveDeviceId()
+{
+    // First element is active context id
+    // Second element is active queue id
+    thread_local static device_id_t activeDeviceId(0, 0);
+
+    return activeDeviceId;
+}
+
+void setActiveContext(int device)
+{
+    tlocalActiveDeviceId() = std::make_pair(device, device);
+}
+
 int getDeviceCount()
 {
-    return DeviceManager::getInstance().mQueues.size();
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mQueues.size();
 }
 
 int getActiveDeviceId()
 {
-    return DeviceManager::getInstance().mActiveQId;
+    // Second element is the queue id, which is
+    // what we mean by active device id in opencl backend
+    return std::get<1>(tlocalActiveDeviceId());
 }
 
 int getDeviceIdFromNativeId(cl_device_id id)
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
     int nDevices = devMngr.mDevices.size();
     int devId = 0;
     for (devId=0; devId<nDevices; ++devId) {
         if (id == devMngr.mDevices[devId]->operator()())
             break;
     }
+
     return devId;
 }
 
 int getActiveDeviceType()
 {
-    DeviceManager &instance = DeviceManager::getInstance();
-    return instance.mDeviceTypes[instance.mActiveQId];
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mDeviceTypes[std::get<1>(devId)];
 }
 
 int getActivePlatform()
 {
-    DeviceManager &instance = DeviceManager::getInstance();
-    return instance.mPlatforms[instance.mActiveQId];
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mPlatforms[std::get<1>(devId)];
 }
 const Context& getContext()
 {
+    device_id_t& devId = tlocalActiveDeviceId();
+
     DeviceManager& devMngr = DeviceManager::getInstance();
-    return *(devMngr.mContexts[devMngr.mActiveCtxId]);
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return *(devMngr.mContexts[std::get<0>(devId)]);
 }
 
 CommandQueue& getQueue()
 {
+    device_id_t& devId = tlocalActiveDeviceId();
+
     DeviceManager& devMngr = DeviceManager::getInstance();
-    return *(devMngr.mQueues[devMngr.mActiveQId]);
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return *(devMngr.mQueues[std::get<1>(devId)]);
 }
 
 const cl::Device& getDevice(int id)
 {
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    if (id == -1)
+        id = std::get<1>(devId);
+
     DeviceManager& devMngr = DeviceManager::getInstance();
-    if(id == -1) id = devMngr.mActiveQId;
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
     return *(devMngr.mDevices[id]);
 }
 
 size_t getDeviceMemorySize(int device)
 {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
     const cl::Device& dev = getDevice(device);
     size_t msize = dev.getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
     return msize;
@@ -368,13 +430,21 @@ bool OpenCLCPUOffload(bool forceOffloadOSX)
 
 bool isGLSharingSupported()
 {
+    device_id_t& devId = tlocalActiveDeviceId();
+
     DeviceManager& devMngr = DeviceManager::getInstance();
-    return devMngr.mIsGLSharingOn[devMngr.mActiveQId];
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mIsGLSharingOn[std::get<1>(devId)];
 }
 
 bool isDoubleSupported(int device)
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
     return (devMngr.mDevices[device]->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0);
 }
 
@@ -384,7 +454,11 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     unsigned currActiveDevId = (unsigned)getActiveDeviceId();
     bool devset = false;
 
-    for (auto context : DeviceManager::getInstance().mContexts) {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    for (auto context : devMngr.mContexts) {
         vector<Device> devices = context->getInfo<CL_CONTEXT_DEVICES>();
 
         for (auto &device : devices) {
@@ -430,14 +504,15 @@ int setDevice(int device)
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
     if (device >= (int)devMngr.mQueues.size() ||
             device>= (int)DeviceManager::MAX_DEVICES) {
         //throw runtime_error("@setDevice: invalid device index");
         return -1;
-    }
-    else {
-        int old = devMngr.mActiveQId;
-        devMngr.setContext(device);
+    } else {
+        int old = getActiveDeviceId();
+        setActiveContext(device);
         return old;
     }
 }
@@ -551,17 +626,22 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
         devMngr.mContexts.erase(devMngr.mContexts.begin()+deleteIdx);
         devMngr.mQueues.erase(devMngr.mQueues.begin()+deleteIdx);
         devMngr.mPlatforms.erase(devMngr.mPlatforms.begin()+deleteIdx);
+
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin()+deleteIdx);
-        // OTHERWISE, update(decrement) the `mActive*Id` variables
-        if (deleteIdx < (int)devMngr.mActiveCtxId) {
-            --devMngr.mActiveCtxId;
-            --devMngr.mActiveQId;
+
+        // OTHERWISE, update(decrement) the thread local active device ids
+        device_id_t& devId = tlocalActiveDeviceId();
+
+        if (deleteIdx < (int)devId.first) {
+            device_id_t newVals = std::make_pair(devId.first-1, devId.second-1);
+            devId = newVals;
         }
     }
 }
 
-bool synchronize_calls() {
+bool synchronize_calls()
+{
     static bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
 }
@@ -698,16 +778,8 @@ DeviceManager::~DeviceManager()
 #endif
 }
 
-void DeviceManager::setContext(int device)
-{
-    common::lock_guard_t lock(deviceMutex);
-
-    mActiveQId = device;
-    mActiveCtxId = device;
-}
-
 DeviceManager::DeviceManager()
-    : mUserDeviceOffset(0), mActiveCtxId(0), mActiveQId(0)
+    : mUserDeviceOffset(0)
 {
     std::vector<cl::Platform>   platforms;
     Platform::get(&platforms);
@@ -781,7 +853,7 @@ DeviceManager::DeviceManager()
             printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
             printf("Setting default device as 0\n");
         } else {
-            setContext(def_device);
+            setActiveContext(def_device);
             default_device_set = true;
         }
     }
@@ -800,7 +872,7 @@ DeviceManager::DeviceManager()
         for (int i = 0; i < nDevices; i++) {
             if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
                 default_device_set = true;
-                setContext(i);
+                setActiveContext(i);
                 break;
             }
         }
@@ -836,7 +908,6 @@ DeviceManager::DeviceManager()
 #if defined(WITH_GRAPHICS)
 void DeviceManager::markDeviceForInterop(const int device, const forge::Window* wHandle)
 {
-    common::lock_guard_t lock(deviceMutex);
     try {
         if (device >= (int)mQueues.size() ||
                 device>= (int)DeviceManager::MAX_DEVICES) {
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 43953ec527..1d132ca820 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -127,8 +127,6 @@ class DeviceManager
 
     friend int getDeviceCount();
 
-    friend int getActiveDeviceId();
-
     friend int getDeviceIdFromNativeId(cl_device_id id);
 
     friend const cl::Context& getContext();
@@ -165,8 +163,6 @@ class DeviceManager
         ~DeviceManager();
 
     protected:
-        void setContext(int device);
-
         DeviceManager();
 
         // Following two declarations are required to
@@ -190,9 +186,6 @@ class DeviceManager
         std::vector<int>         mPlatforms;
         unsigned mUserDeviceOffset;
 
-        unsigned mActiveCtxId;
-        unsigned mActiveQId;
-
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 

From 86b715c9e74473a364491fa36b16f63637abb23b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 3 Mar 2017 09:48:36 +0530
Subject: [PATCH 1130/2677] Add basic test that validates morph function
 results from 2 threads

---
 test/CMakeLists.txt |   2 -
 test/threading.cpp  | 141 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 141 insertions(+), 2 deletions(-)
 create mode 100644 test/threading.cpp

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 65b91855e4..b1023d1e14 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -52,8 +52,6 @@ ELSE()
     ENABLE_TESTING()
 ENDIF()
 
-REMOVE_DEFINITIONS(-std=c++11)
-
 MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
     STRING(TOUPPER ${BACKEND} DEF_NAME)
 
diff --git a/test/threading.cpp b/test/threading.cpp
new file mode 100644
index 0000000000..e64337c731
--- /dev/null
+++ b/test/threading.cpp
@@ -0,0 +1,141 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cstddef>
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <testHelpers.hpp>
+#include <thread>
+#include <chrono>
+
+using namespace af;
+
+using std::vector;
+using std::string;
+
+#if defined(AF_CPU)
+static const unsigned ITERATION_COUNT = 10;
+#else
+static const unsigned ITERATION_COUNT = 1000;
+#endif
+
+void morphTest(const array input, const array mask, const bool isDilation,
+               const array gold, int targetDevice)
+{
+    auto start = std::chrono::high_resolution_clock::now();
+
+    af::setDevice(targetDevice);
+
+    vector<float> goldData(gold.elements());
+    vector<float> outData(gold.elements());
+
+    gold.host((void*)goldData.data());
+
+    af::array out;
+
+    for (unsigned i=0; i<ITERATION_COUNT; ++i)
+        out = isDilation ? dilate(input, mask) : erode(input, mask);
+
+    out.host((void*)outData.data());
+
+    ASSERT_EQ(true, compareArraysRMSD(gold.elements(), goldData.data(), outData.data(), 0.018f));
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    std::chrono::duration<double> diff = end - start;
+
+    std::cout << "Thread(" << std::this_thread::get_id()
+              << "): time taken for "
+              << ITERATION_COUNT
+              <<" is "
+              << diff.count() << " s\n";
+}
+
+TEST(Threading, SimultaneousRead)
+{
+    if (noImageIOTests()) return;
+
+    vector<bool> isDilationFlags;
+    vector<bool> isColorFlags;
+    vector<string> files;
+
+    files.push_back( string(TEST_DIR "/morph/gray.test") );
+    isDilationFlags.push_back(true);
+    isColorFlags.push_back(false);
+
+    files.push_back( string(TEST_DIR "/morph/color.test") );
+    isDilationFlags.push_back(false);
+    isColorFlags.push_back(true);
+
+    vector<std::thread> tests;
+    unsigned totalTestCount = 0;
+
+    auto start = std::chrono::high_resolution_clock::now();
+
+    for(size_t pos = 0; pos<files.size(); ++pos)
+    {
+        const bool isDilation = isDilationFlags[pos];
+        const bool isColor    = isColorFlags[pos];
+
+        vector<dim4>    inDims;
+        vector<string>  inFiles;
+        vector<dim_t>   outSizes;
+        vector<string>  outFiles;
+
+        readImageTests(files[pos], inDims, inFiles, outSizes, outFiles);
+
+        const unsigned testCount = inDims.size();
+
+        const dim4 maskdims(3,3,1,1);
+
+        for (size_t testId=0; testId<testCount; ++testId)
+        {
+            int trgtDeviceId = totalTestCount % af::getDeviceCount();
+
+            //prefix full path to image file names
+            inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+            outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+
+            af::setDevice(trgtDeviceId);
+
+            const array mask = constant(1.0, maskdims);
+
+            array input= loadImage(inFiles[testId].c_str(), isColor);
+            array gold = loadImage(outFiles[testId].c_str(), isColor);
+
+            //Push the new test as a new thread of execution
+            tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
+
+            std::cout<<"morph test launched with the following params on device ("
+                     <<trgtDeviceId<<"):"<<std::endl;
+            std::cout<<"\t Input image dims: "<<input.dims()<<std::endl;
+            std::cout<<"\t Mask dims: "<<mask.dims()<<std::endl;
+            std::cout<<"\t IsDilation : "<< (isDilation ? "True" : "False") <<std::endl;
+
+            totalTestCount++;
+        }
+    }
+    std::cout<< std::endl << "Waiting for results ..." << std::endl << std::endl;
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+    {
+        if (tests[testId].joinable()) {
+            std::cout<<"Attempting join for test ..."<<testId<<std::endl;
+            tests[testId].join();
+            std::cout<<"test "<< testId <<" completed." << std::endl;
+        }
+        std::cout<<std::endl;
+    }
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    std::chrono::duration<double> diff = end - start;
+
+    std::cout << "Total time taken for test : " << diff.count() << " s\n";
+}

From b5aea95552ee0363d474df1e53e5787b75349ded Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 4 Mar 2017 09:20:12 +0530
Subject: [PATCH 1131/2677] Threading test with 32 threads calling arithmetic
 JIT operations

---
 test/threading.cpp | 192 +++++++++++++++++++++++++++++----------------
 1 file changed, 123 insertions(+), 69 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index e64337c731..a1e084f227 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -57,85 +57,139 @@ void morphTest(const array input, const array mask, const bool isDilation,
               << diff.count() << " s\n";
 }
 
-TEST(Threading, SimultaneousRead)
+//TEST(Threading, SetPerThreadActiveDevice)
+//{
+//    if (noImageIOTests()) return;
+//
+//    vector<bool> isDilationFlags;
+//    vector<bool> isColorFlags;
+//    vector<string> files;
+//
+//    files.push_back( string(TEST_DIR "/morph/gray.test") );
+//    isDilationFlags.push_back(true);
+//    isColorFlags.push_back(false);
+//
+//    files.push_back( string(TEST_DIR "/morph/color.test") );
+//    isDilationFlags.push_back(false);
+//    isColorFlags.push_back(true);
+//
+//    vector<std::thread> tests;
+//    unsigned totalTestCount = 0;
+//
+//    auto start = std::chrono::high_resolution_clock::now();
+//
+//    for(size_t pos = 0; pos<files.size(); ++pos)
+//    {
+//        const bool isDilation = isDilationFlags[pos];
+//        const bool isColor    = isColorFlags[pos];
+//
+//        vector<dim4>    inDims;
+//        vector<string>  inFiles;
+//        vector<dim_t>   outSizes;
+//        vector<string>  outFiles;
+//
+//        readImageTests(files[pos], inDims, inFiles, outSizes, outFiles);
+//
+//        const unsigned testCount = inDims.size();
+//
+//        const dim4 maskdims(3,3,1,1);
+//
+//        for (size_t testId=0; testId<testCount; ++testId)
+//        {
+//            int trgtDeviceId = totalTestCount % af::getDeviceCount();
+//
+//            //prefix full path to image file names
+//            inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+//            outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+//
+//            af::setDevice(trgtDeviceId);
+//
+//            const array mask = constant(1.0, maskdims);
+//
+//            array input= loadImage(inFiles[testId].c_str(), isColor);
+//            array gold = loadImage(outFiles[testId].c_str(), isColor);
+//
+//            //Push the new test as a new thread of execution
+//            tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
+//
+//            std::cout<<"morph test launched with the following params on device ("
+//                     <<trgtDeviceId<<"):"<<std::endl;
+//            std::cout<<"\t Input image dims: "<<input.dims()<<std::endl;
+//            std::cout<<"\t Mask dims: "<<mask.dims()<<std::endl;
+//            std::cout<<"\t IsDilation : "<< (isDilation ? "True" : "False") <<std::endl;
+//
+//            totalTestCount++;
+//        }
+//    }
+//    std::cout<< std::endl << "Waiting for results ..." << std::endl << std::endl;
+//
+//    for (size_t testId=0; testId<tests.size(); ++testId)
+//    {
+//        if (tests[testId].joinable()) {
+//            std::cout<<"Attempting join for test ..."<<testId<<std::endl;
+//            tests[testId].join();
+//            std::cout<<"test "<< testId <<" completed." << std::endl;
+//        }
+//        std::cout<<std::endl;
+//    }
+//
+//    auto end = std::chrono::high_resolution_clock::now();
+//
+//    std::chrono::duration<double> diff = end - start;
+//
+//    std::cout << "Total time taken for test : " << diff.count() << " s\n";
+//}
+
+enum ArithOp
 {
-    if (noImageIOTests()) return;
-
-    vector<bool> isDilationFlags;
-    vector<bool> isColorFlags;
-    vector<string> files;
-
-    files.push_back( string(TEST_DIR "/morph/gray.test") );
-    isDilationFlags.push_back(true);
-    isColorFlags.push_back(false);
-
-    files.push_back( string(TEST_DIR "/morph/color.test") );
-    isDilationFlags.push_back(false);
-    isColorFlags.push_back(true);
-
-    vector<std::thread> tests;
-    unsigned totalTestCount = 0;
-
-    auto start = std::chrono::high_resolution_clock::now();
+    ADD, SUB, DIV, MUL
+};
 
-    for(size_t pos = 0; pos<files.size(); ++pos)
+void calc(ArithOp opcode, array op1, array op2, float outValue)
+{
+    af::setDevice(0);
+    array res;
+    for (unsigned i=0; i<ITERATION_COUNT; ++i)
     {
-        const bool isDilation = isDilationFlags[pos];
-        const bool isColor    = isColorFlags[pos];
-
-        vector<dim4>    inDims;
-        vector<string>  inFiles;
-        vector<dim_t>   outSizes;
-        vector<string>  outFiles;
-
-        readImageTests(files[pos], inDims, inFiles, outSizes, outFiles);
-
-        const unsigned testCount = inDims.size();
-
-        const dim4 maskdims(3,3,1,1);
-
-        for (size_t testId=0; testId<testCount; ++testId)
-        {
-            int trgtDeviceId = totalTestCount % af::getDeviceCount();
-
-            //prefix full path to image file names
-            inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
-            outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
-
-            af::setDevice(trgtDeviceId);
-
-            const array mask = constant(1.0, maskdims);
+        switch(opcode) {
+            case ADD: res = op1 + op2; break;
+            case SUB: res = op1 - op2; break;
+            case DIV: res = op1 / op2; break;
+            case MUL: res = op1 * op2; break;
+        }
+    }
 
-            array input= loadImage(inFiles[testId].c_str(), isColor);
-            array gold = loadImage(outFiles[testId].c_str(), isColor);
+    std::vector<float> out(res.elements());
+    res.host((void*)out.data());
 
-            //Push the new test as a new thread of execution
-            tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
+    for (unsigned i=0; i <out.size(); ++i)
+        ASSERT_EQ(out[i], outValue);
+}
 
-            std::cout<<"morph test launched with the following params on device ("
-                     <<trgtDeviceId<<"):"<<std::endl;
-            std::cout<<"\t Input image dims: "<<input.dims()<<std::endl;
-            std::cout<<"\t Mask dims: "<<mask.dims()<<std::endl;
-            std::cout<<"\t IsDilation : "<< (isDilation ? "True" : "False") <<std::endl;
+TEST(Threading, SimultaneousRead)
+{
+    af::setDevice(0);
+    af::array A = af::constant(1.0, 100, 100);
+    af::array B = af::constant(1.0, 100, 100);
 
-            totalTestCount++;
-        }
-    }
-    std::cout<< std::endl << "Waiting for results ..." << std::endl << std::endl;
+    vector<std::thread> tests;
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
+    for (int t=0; t<32; ++t)
     {
-        if (tests[testId].joinable()) {
-            std::cout<<"Attempting join for test ..."<<testId<<std::endl;
-            tests[testId].join();
-            std::cout<<"test "<< testId <<" completed." << std::endl;
+        ArithOp op;
+        float outValue;
+
+        switch(t%4) {
+            case 0: op = ADD; outValue = 2.0f; break;
+            case 1: op = SUB; outValue = 0.0f; break;
+            case 2: op = DIV; outValue = 1.0f; break;
+            case 3: op = MUL; outValue = 1.0f; break;
         }
-        std::cout<<std::endl;
-    }
 
-    auto end = std::chrono::high_resolution_clock::now();
-
-    std::chrono::duration<double> diff = end - start;
+        tests.emplace_back(calc, op, A, B, outValue);
+    }
 
-    std::cout << "Total time taken for test : " << diff.count() << " s\n";
+    for (int t=0; t<32; ++t)
+        if (tests[t].joinable())
+            tests[t].join();
 }

From 2ce12436c2ae734da0a447308ceef204ee332419 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Mon, 13 Mar 2017 23:41:24 -0700
Subject: [PATCH 1132/2677] Changes required to make JIT in CUDA backend thread
 safe

---
 src/backend/cuda/Array.cpp          |  28 +++++-
 src/backend/cuda/Array.hpp          |  17 +---
 src/backend/cuda/JIT/BinaryNode.hpp | 132 +++++--------------------
 src/backend/cuda/JIT/BufferNode.hpp | 147 +++++++++++-----------------
 src/backend/cuda/JIT/Node.hpp       |  98 ++++++++-----------
 src/backend/cuda/JIT/ScalarNode.hpp |  54 +---------
 src/backend/cuda/JIT/UnaryNode.hpp  | 117 ++++------------------
 src/backend/cuda/Param.hpp          |  16 ++-
 src/backend/cuda/jit.cpp            | 103 ++++++++++++-------
 9 files changed, 261 insertions(+), 451 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index d34abd1d42..495f35807e 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -240,8 +240,13 @@ namespace cuda
 
                     unsigned length =0, buf_count = 0, bytes = 0;
                     Node *n = node.get();
-                    n->getInfo(length, buf_count, bytes);
-                    n->resetFlags();
+                    JIT::Node_map_t nodes_map;
+                    n->getNodesMap(nodes_map);
+
+                    for(auto &entry : nodes_map) {
+                        Node *node = entry.first;
+                        node->getInfo(length, buf_count, bytes);
+                    }
 
                     if (2 * bytes > lock_bytes) {
                         out.eval();
@@ -360,6 +365,18 @@ namespace cuda
         return;
     }
 
+    template<typename T>
+    void
+    Array<T>::setDataDims(const dim4 &new_dims)
+    {
+        modDims(new_dims);
+        data_dims = new_dims;
+        if (node->isBuffer()) {
+            node = bufferNodePtr<T>();
+        }
+    }
+
+
 #define INSTANTIATE(T)                                                  \
     template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
@@ -382,9 +399,12 @@ namespace cuda
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       T*   Array<T>::device();                             \
-    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
-    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
+    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, \
+                                                       const size_t bytes); \
+    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, \
+                                                       const size_t bytes); \
     template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
+    template       void Array<T>::setDataDims(const dim4 &new_dims);    \
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index b7092ad867..1136cdaea4 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -169,11 +169,7 @@ namespace cuda
             return data_dims;
         }
 
-        void setDataDims(const dim4 &new_dims)
-        {
-            modDims(new_dims);
-            data_dims = new_dims;
-        }
+        void setDataDims(const dim4 &new_dims);
 
         size_t getAllocatedBytes() const
         {
@@ -208,19 +204,12 @@ namespace cuda
 
         operator Param<T>()
         {
-            Param<T> out;
-            out.ptr = this->get();
-            for (int  i = 0; i < 4; i++) {
-                out.dims[i] = dims()[i];
-                out.strides[i] = strides()[i];
-            }
-            return out;
+            return Param<T>(this->get(), this->dims().get(), this->strides().get());
         }
 
         operator CParam<T>() const
         {
-            CParam<T> out(this->get(), this->dims().get(), this->strides().get());
-            return out;
+            return CParam<T>(this->get(), this->dims().get(), this->strides().get());
         }
 
         JIT::Node_ptr getNode();
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index a07d83d111..115c892119 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -20,148 +20,68 @@ namespace JIT
     class BinaryNode : public Node
     {
     private:
-        std::string m_op_str;
-        Node_ptr m_lhs, m_rhs;
-        int m_op;
-        int m_call_type;
+        const std::string m_op_str;
+        const int m_op;
+        const int m_call_type;
 
     public:
         BinaryNode(const char *out_type_str, const char *name_str,
                    const std::string &op_str,
                    Node_ptr lhs, Node_ptr rhs, int op, int call_type)
-            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1),
+            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
               m_op_str(op_str),
-              m_lhs(lhs),
-              m_rhs(rhs),
               m_op(op),
               m_call_type(call_type)
         {
         }
 
-        bool isLinear(dim_t dims[4])
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            if (!m_set_is_linear) {
-                m_linear = m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
-                m_set_is_linear = true;
-            }
-            return m_linear;
-        }
-
-        void genParams(std::stringstream &kerStream,
-                       std::stringstream &annStream, bool is_linear)
-        {
-            if (m_gen_param) return;
-            if (!(m_lhs->isGenParam())) m_lhs->genParams(kerStream, annStream, is_linear);
-            if (!(m_rhs->isGenParam())) m_rhs->genParams(kerStream, annStream, is_linear);
-            m_gen_param = true;
-        }
-
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_offset) return;
-            if (!(m_lhs->isGenOffset())) m_lhs->genOffsets(kerStream, is_linear);
-            if (!(m_rhs->isGenOffset())) m_rhs->genOffsets(kerStream, is_linear);
-            m_gen_offset = true;
-        }
-
-        void genKerName(std::stringstream &kerStream)
-        {
-            if (m_gen_name) return;
-            m_lhs->genKerName(kerStream);
-            m_rhs->genKerName(kerStream);
-
             // Make the hex representation of enum part of the Kernel name
             kerStream << "_" << std::setw(2) << std::setfill('0') << std::hex << m_op;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_lhs->getId();
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_rhs->getId();
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.child_ids[0];
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.child_ids[1];
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, Node_ids ids, bool is_linear)
         {
-            if (m_gen_func) return;
-
-            if (!(m_lhs->isGenFunc())) m_lhs->genFuncs(kerStream, declStrs, is_linear);
-            if (!(m_rhs->isGenFunc())) m_rhs->genFuncs(kerStream, declStrs, is_linear);
-
             if (m_call_type == 0) {
                 std::stringstream declStream;
                 declStream << "declare " << m_type_str << " " << m_op_str
-                           << "(" << m_lhs->getTypeStr() << " , " << m_rhs->getTypeStr() << ")\n";
-
-                str_map_iter loc = declStrs.find(declStream.str());
-                if (loc == declStrs.end()) {
-                    declStrs[declStream.str()] = true;
-                }
+                           << "(" << m_children[0]->getTypeStr() << " , "
+                           << m_children[1]->getTypeStr() << ")\n";
+                declStrs[declStream.str()] = true;
 
-                kerStream << "%val" << m_id << " = call "
+                kerStream << "%val" << ids.id << " = call "
                           << m_type_str << " "
                           << m_op_str << "("
-                          << m_lhs->getTypeStr() << " "
-                          << "%val" << m_lhs->getId() << ", "
-                          << m_rhs->getTypeStr() << " "
-                          << "%val" << m_rhs->getId() << ")\n";
+                          << m_children[0]->getTypeStr() << " "
+                          << "%val" << ids.child_ids[0] << ", "
+                          << m_children[1]->getTypeStr() << " "
+                          << "%val" << ids.child_ids[1] << ")\n";
 
             } else {
                 if (m_call_type == 1) {
                     // arithmetic operations
-                    kerStream << "%val" << m_id << " = "
+                    kerStream << "%val" << ids.id << " = "
                               << m_op_str << " "
                               << m_type_str << " "
-                              << "%val" << m_lhs->getId() << ", "
-                              << "%val" << m_rhs->getId() << "\n";
+                              << "%val" << ids.child_ids[0] << ", "
+                              << "%val" << ids.child_ids[1] << "\n";
                 } else {
                     // logical operators
-                    kerStream << "%tmp" << m_id << " = "
+                    kerStream << "%tmp" << ids.id << " = "
                               << m_op_str << " "
-                              << m_lhs->getTypeStr() << " "
-                              << "%val" << m_lhs->getId() << ", "
-                              << "%val" << m_rhs->getId() << "\n";
+                              << m_children[0]->getTypeStr() << " "
+                              << "%val" << ids.child_ids[0] << ", "
+                              << "%val" << ids.child_ids[1] << "\n";
 
-                    kerStream << "%val" << m_id << " = "
-                              << "zext i1 %tmp" << m_id << " to i8\n";
+                    kerStream << "%val" << ids.id << " = "
+                              << "zext i1 %tmp" << ids.id << " to i8\n";
 
                 }
             }
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-            id = m_lhs->setId(id);
-            id = m_rhs->setId(id);
-            m_id = id;
-            m_set_id = true;
-            return m_id + 1;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_set_id) return;
-            m_lhs->getInfo(len, buf_count, bytes);
-            m_rhs->getInfo(len, buf_count, bytes);
-            len++;
-            m_set_id = true;
-            return;
-        }
-
-        void resetFlags()
-        {
-            if (m_set_id) {
-                resetCommonFlags();
-                m_lhs->resetFlags();
-                m_rhs->resetFlags();
-            }
-        }
-
-        void setArgs(std::vector<void *> &args, bool is_linear)
-        {
-            if (m_set_arg) return;
-            m_lhs->setArgs(args, is_linear);
-            m_rhs->setArgs(args, is_linear);
-            m_set_arg = true;
         }
     };
 
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index bdc692b447..c6a03d2899 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -11,6 +11,7 @@
 #include "Node.hpp"
 #include <iomanip>
 #include <memory>
+#include <mutex>
 
 namespace cuda
 {
@@ -34,163 +35,132 @@ namespace JIT
         std::shared_ptr<T> m_data;
         Param<T> m_param;
         unsigned m_bytes;
+        std::once_flag m_set_data_flag;
 
         bool m_linear_buffer;
     public:
 
         BufferNode(const char *type_str,
                    const char *name_str)
-            : Node(type_str, name_str, 0)
+            : Node(type_str, name_str, 0, {})
         {
         }
 
         void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
         {
-            m_param = param;
-            m_data = data;
-            m_bytes = bytes;
-            m_linear_buffer = is_linear;
+            std::call_once(m_set_data_flag, [this, param, data, bytes, is_linear]() {
+                    m_param = param;
+                    m_data = data;
+                    m_bytes = bytes;
+                    m_linear_buffer = is_linear;
+                });
         }
 
         bool isLinear(dim_t dims[4])
         {
-            if (!m_set_is_linear) {
-                bool same_dims = true;
-                for (int i = 0; same_dims && i < 4; i++) {
-                    same_dims &= (dims[i] == m_param.dims[i]);
-                }
-                m_linear = m_linear_buffer && same_dims;
-                m_set_is_linear = true;
+            bool same_dims = true;
+            for (int i = 0; same_dims && i < 4; i++) {
+                same_dims &= (dims[i] == m_param.dims[i]);
             }
-            return m_linear;
+            return m_linear_buffer && same_dims;
         }
 
         bool isBuffer() { return true; }
 
-        void genKerName(std::stringstream &kerStream)
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_gen_name) return;
-
             kerStream << "_" << m_name_str;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
         }
 
         void genParams(std::stringstream &kerStream,
-                       std::stringstream &annStream, bool is_linear)
+                       std::stringstream &annStream,
+                       int id,
+                       bool is_linear)
         {
-            if (m_gen_param) return;
-            kerStream << m_type_str << "* %in" << m_id << ",\n";
+            kerStream << m_type_str << "* %in" << id << ",\n";
             annStream << m_type_str << "*,\n";
 
             if (!is_linear) {
-                kerStream << "i32 %dim0"     << m_id << ","
-                          << "i32 %dim1"     << m_id << ","
-                          << "i32 %dim2"     << m_id << ","
-                          << "i32 %dim3"     << m_id << ","
+                kerStream << "i32 %dim0"     << id << ","
+                          << "i32 %dim1"     << id << ","
+                          << "i32 %dim2"     << id << ","
+                          << "i32 %dim3"     << id << ","
                           << "\n"
-                          << "i32 %str1"     << m_id << ","
-                          << "i32 %str2"     << m_id << ","
-                          << "i32 %str3"     << m_id << ","
+                          << "i32 %str1"     << id << ","
+                          << "i32 %str2"     << id << ","
+                          << "i32 %str3"     << id << ","
                           << "\n";
 
                 annStream << "i32, i32, i32, i32,\n"
                           << "i32, i32, i32,\n";
             }
-
-            m_gen_param = true;
         }
 
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
         {
-            if (m_gen_offset) return;
-
             if (!is_linear) {
-                kerStream << "%b3" << m_id << " = icmp slt i32 %id3, %dim3" << m_id << "\n";
-                kerStream << "%b2" << m_id << " = icmp slt i32 %id2, %dim2" << m_id << "\n";
-                kerStream << "%b1" << m_id << " = icmp slt i32 %id1, %dim1" << m_id << "\n";
-                kerStream << "%b0" << m_id << " = icmp slt i32 %id0, %dim0" << m_id << "\n";
+                kerStream << "%b3" << id << " = icmp slt i32 %id3, %dim3" << id << "\n";
+                kerStream << "%b2" << id << " = icmp slt i32 %id2, %dim2" << id << "\n";
+                kerStream << "%b1" << id << " = icmp slt i32 %id1, %dim1" << id << "\n";
+                kerStream << "%b0" << id << " = icmp slt i32 %id0, %dim0" << id << "\n";
 
-                kerStream << "%c3" << m_id << " = zext i1 %b3" << m_id << " to i32\n";
-                kerStream << "%c2" << m_id << " = zext i1 %b2" << m_id << " to i32\n";
-                kerStream << "%c1" << m_id << " = zext i1 %b1" << m_id << " to i32\n";
-                kerStream << "%c0" << m_id << " = zext i1 %b0" << m_id << " to i32\n";
+                kerStream << "%c3" << id << " = zext i1 %b3" << id << " to i32\n";
+                kerStream << "%c2" << id << " = zext i1 %b2" << id << " to i32\n";
+                kerStream << "%c1" << id << " = zext i1 %b1" << id << " to i32\n";
+                kerStream << "%c0" << id << " = zext i1 %b0" << id << " to i32\n";
 
-                kerStream << "%d3" << m_id << " = mul i32 %c3" << m_id << ", %id3\n";
-                kerStream << "%d2" << m_id << " = mul i32 %c2" << m_id << ", %id2\n";
-                kerStream << "%d1" << m_id << " = mul i32 %c1" << m_id << ", %id1\n";
-                kerStream << "%d0" << m_id << " = mul i32 %c0" << m_id << ", %id0\n";
+                kerStream << "%d3" << id << " = mul i32 %c3" << id << ", %id3\n";
+                kerStream << "%d2" << id << " = mul i32 %c2" << id << ", %id2\n";
+                kerStream << "%d1" << id << " = mul i32 %c1" << id << ", %id1\n";
+                kerStream << "%d0" << id << " = mul i32 %c0" << id << ", %id0\n";
 
-                kerStream << "%off3i" << m_id << " = mul i32 %d3" << m_id
-                          << ", %str3" << m_id << "\n";
+                kerStream << "%off3i" << id << " = mul i32 %d3" << id
+                          << ", %str3" << id << "\n";
 
-                kerStream << "%off2i" << m_id << " = mul i32 %d2" << m_id
-                          << ", %str2" << m_id << "\n";
+                kerStream << "%off2i" << id << " = mul i32 %d2" << id
+                          << ", %str2" << id << "\n";
 
-                kerStream << "%off1i" << m_id << " = mul i32 %d1" << m_id
-                          << ", %str1" << m_id << "\n";
+                kerStream << "%off1i" << id << " = mul i32 %d1" << id
+                          << ", %str1" << id << "\n";
 
-                kerStream << "%off23i" << m_id << " = add i32 %off2i"
-                          << m_id << ", %off3i" << m_id << "\n";
+                kerStream << "%off23i" << id << " = add i32 %off2i"
+                          << id << ", %off3i" << id << "\n";
 
-                kerStream << "%off123i" << m_id << " = add i32 %off23i"
-                          << m_id << ", %off1i" << m_id << "\n";
+                kerStream << "%off123i" << id << " = add i32 %off23i"
+                          << id << ", %off1i" << id << "\n";
 
-                kerStream << "%idxa" << m_id << " = add i32 %off123i"
-                          << m_id << ", %d0" << m_id << "\n";
+                kerStream << "%idxa" << id << " = add i32 %off123i"
+                          << id << ", %d0" << id << "\n";
 
-                kerStream << "%idx" << m_id << " = sext i32 %idxa" << m_id <<" to i64\n\n";
+                kerStream << "%idx" << id << " = sext i32 %idxa" << id <<" to i64\n\n";
             }
-
-            m_gen_offset = true;
         }
 
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, Node_ids ids, bool is_linear)
         {
-            if (m_gen_func) return;
-
-            kerStream << "%inIdx" << m_id << " = "
-                      << "getelementptr inbounds " << m_type_str << "* %in" << m_id
+            kerStream << "%inIdx" << ids.id << " = "
+                      << "getelementptr inbounds " << m_type_str << "* %in" << ids.id
                       << ", i64 %idx";
 
-            if (!is_linear) kerStream << m_id;
+            if (!is_linear) kerStream << ids.id;
             kerStream << "\n";
 
-            kerStream << "%val" << m_id << " = " << "load "
-                      << m_type_str << "* %inIdx" << m_id << "\n\n";
-
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-
-            m_id = id;
-            m_set_id = true;
+            kerStream << "%val" << ids.id << " = " << "load "
+                      << m_type_str << "* %inIdx" << ids.id << "\n\n";
 
-            return m_id + 1;
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
-            if (m_set_id) return;
-
             len++;
             buf_count++;
             bytes += m_bytes;
-            m_set_id = true;
             return;
         }
 
-        void resetFlags()
-        {
-            if (m_set_id) resetCommonFlags();
-        }
-
         void setArgs(std::vector<void *> &args, bool is_linear)
         {
-            if (m_set_arg) return;
             args.push_back((void *)&(m_param.ptr));
 
             if (!is_linear) {
@@ -202,7 +172,6 @@ namespace JIT
                 args.push_back((void *)&m_param.strides[2]);
                 args.push_back((void *)&m_param.strides[3]);
             }
-            m_set_arg = true;
         }
     };
 
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index bd4307dd1e..035692550d 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <optypes.hpp>
 
-#include <map>
+#include <unordered_map>
 #include <memory>
 #include <string>
 #include <vector>
@@ -20,97 +20,77 @@ namespace cuda
 
 namespace JIT
 {
-    typedef std::map<std::string, bool> str_map_t;
+    class Node;
+
+    typedef struct
+    {
+        int id;
+        std::vector<int> child_ids;
+    } Node_ids;
+
+    typedef std::unordered_map<std::string, bool> str_map_t;
     typedef str_map_t::iterator str_map_iter;
+    typedef std::shared_ptr<Node> Node_ptr;
+    typedef std::unordered_map<Node *, Node_ids> Node_map_t;
+    typedef Node_map_t::iterator Node_map_iter;
 
     class Node
     {
     protected:
         const std::string m_type_str;
         const std::string m_name_str;
-        int m_id;
         const int m_height;
-        bool m_set_id;
-        bool m_gen_func;
-        bool m_gen_param;
-        bool m_gen_offset;
-        bool m_set_arg;
-        bool m_gen_name;
-        bool m_linear;
-        bool m_set_is_linear;
-
-    protected:
-
-        void resetCommonFlags()
-        {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_set_arg = false;
-            m_gen_name = false;
-            m_linear = false;
-            m_set_is_linear = false;
-        }
-
+        const std::vector<Node_ptr> m_children;
 
     public:
 
-        Node(const char *type_str, const char *name_str, const int height)
+        Node(const char *type_str, const char *name_str, const int height,
+             const std::vector<Node_ptr> children)
             : m_type_str(type_str),
               m_name_str(name_str),
-              m_id(-1),
               m_height(height),
-              m_set_id(false),
-              m_gen_func(false),
-              m_gen_param(false),
-              m_gen_offset(false),
-              m_set_arg(false),
-              m_gen_name(false),
-              m_linear(false),
-              m_set_is_linear(false)
+              m_children(children)
         {}
 
-        virtual void genKerName(std::stringstream &kerStream) {}
+        void getNodesMap(Node_map_t &node_map)
+        {
+            if (node_map.find(this) == node_map.end()) {
+                Node_ids ids;
+                for (const auto &child : m_children) {
+                    child->getNodesMap(node_map);
+                    ids.child_ids.push_back(node_map[child.get()].id);
+                }
+                ids.id = node_map.size();
+                node_map[this] = ids;
+            }
+        }
+
+        virtual void genKerName(std::stringstream &kerStream, Node_ids ids) {}
         virtual void genParams  (std::stringstream &kerStream,
-                                 std::stringstream &annStream, bool is_linear) {}
-        virtual void genOffsets (std::stringstream &kerStream, bool is_linear) {}
-        virtual void genFuncs   (std::stringstream &kerStream, str_map_t &declStrs, bool is_linear)
-        { m_gen_func = true;}
+                                 std::stringstream &annStream,
+                                 int id, bool is_linear) {}
+        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) {}
+        virtual void genFuncs   (std::stringstream &kerStream, str_map_t &declStrs,
+                                 Node_ids id, bool is_linear)
+        {}
 
-        virtual int setId(int id) { m_set_id = true; return id; }
-        virtual void setArgs(std::vector<void *> &args, bool is_linear) { m_set_arg = true; }
+        virtual void setArgs(std::vector<void *> &args, bool is_linear) {}
         virtual bool isLinear(dim_t dims[4]) { return true; }
 
-        virtual void resetFlags()
-        {
-            resetCommonFlags();
-        }
-
         virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
-            len = 0;
-            buf_count = 0;
-            bytes = 0;
+            len++;
         }
 
         virtual bool isBuffer() { return false; }
 
         std::string getTypeStr() { return m_type_str; }
 
-        bool isGenFunc() { return m_gen_func; }
-        bool isGenParam() { return m_gen_param; }
-        bool isGenOffset() { return m_gen_offset; }
-
-        int getId()  { return m_id; }
         int getHeight()  { return m_height; }
         std::string getNameStr() { return m_name_str; }
 
         virtual ~Node() {}
     };
-
-    typedef std::shared_ptr<Node> Node_ptr;
-
 }
 
 }
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index f7a1e33d99..264b7be61e 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -26,73 +26,29 @@ namespace JIT
     public:
 
         ScalarNode(T val)
-            : Node(irname<T>(), afShortName<T>(false), 0),
+            : Node(irname<T>(), afShortName<T>(false), 0, {}),
               m_val(val)
         {
         }
 
-        bool isLinear(dim_t dims[4])
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            return true;
-        }
-
-        void genKerName(std::stringstream &kerStream)
-        {
-            if (m_gen_name) return;
-
             kerStream << "_" << m_name_str;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
         }
 
         void genParams(std::stringstream &kerStream,
                        std::stringstream &annStream,
+                       int id,
                        bool is_linear)
         {
-            if (m_gen_param) return;
-            kerStream << m_type_str << " %val" << m_id << ", " << std::endl;
+            kerStream << m_type_str << " %val" << id << ", " << std::endl;
             annStream << m_type_str << ",\n";
-            m_gen_param = true;
-        }
-
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_offset) return;
-            m_gen_offset = true;
-        }
-
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, bool is_linear)
-        {
-            if (m_gen_func) return;
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-            m_id = id;
-            m_set_id = true;
-            return m_id + 1;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_set_id) return;
-            len++;
-            m_set_id = true;
-            return;
-        }
-
-        void resetFlags()
-        {
-            if (m_set_id) resetCommonFlags();
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
         {
-            if (m_set_arg) return;
             args.push_back((void *)&m_val);
-            m_set_arg = true;
         }
     };
 }
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index a85e05371c..ac334c14b8 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -20,139 +20,64 @@ namespace JIT
     class UnaryNode : public Node
     {
     private:
-        std::string m_op_str;
-        Node_ptr m_child;
-        int m_op;
-        bool m_is_check;
+        const std::string m_op_str;
+        const int m_op;
+        const bool m_is_check;
 
     public:
         UnaryNode(const char *out_type_str, const char *name_str,
                   const std::string &op_str,
                   Node_ptr child, int op, bool is_check=false)
-            : Node(out_type_str, name_str, child->getHeight() + 1),
+            : Node(out_type_str, name_str, child->getHeight() + 1, {child}),
               m_op_str(op_str),
-              m_child(child),
               m_op(op),
               m_is_check(is_check)
         {
         }
 
-        bool isLinear(dim_t dims[4])
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            if (!m_set_is_linear) {
-                m_linear = m_child->isLinear(dims);
-                m_set_is_linear = true;
-            }
-            return m_linear;
-        }
-
-        void genParams(std::stringstream &kerStream,
-                       std::stringstream &annStream, bool is_linear)
-        {
-            if (m_gen_param) return;
-            if (!(m_child->isGenParam())) m_child->genParams(kerStream, annStream, is_linear);
-            m_gen_param = true;
-        }
-
-
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_offset) return;
-            if (!(m_child->isGenOffset())) m_child->genOffsets(kerStream, is_linear);
-            m_gen_offset = true;
-        }
-
-        void genKerName(std::stringstream &kerStream)
-        {
-            if (m_gen_name) return;
-
-            m_child->genKerName(kerStream);
-
             // Make the hex representation of enum part of the Kernel name
             kerStream << "_" << std::setw(2) << std::setfill('0') << std::hex << m_op;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_child->getId();
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.child_ids[0];
+            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, Node_ids ids, bool is_linear)
         {
-            if (m_gen_func) return;
-
-            if (!(m_child->isGenFunc())) m_child->genFuncs(kerStream, declStrs, is_linear);
-
             std::stringstream declStream;
 
             if (m_is_check) {
                 declStream << "declare " << "i32 " << m_op_str
-                           << "(" << m_child->getTypeStr() << ")\n";
+                           << "(" << m_children[0]->getTypeStr() << ")\n";
             } else {
                 declStream << "declare " << m_type_str << " " << m_op_str
-                           << "(" << m_child->getTypeStr() << ")\n";
+                           << "(" << m_children[0]->getTypeStr() << ")\n";
             }
 
-            str_map_iter loc = declStrs.find(declStream.str());
-            if (loc == declStrs.end()) {
-                declStrs[declStream.str()] = true;
-            }
+            declStrs[declStream.str()] = true;
 
             if (m_is_check) {
-                kerStream << "%tmp" << m_id << " = call i32 "
+                kerStream << "%tmp" << ids.id << " = call i32 "
                           << m_op_str << "("
-                          << m_child->getTypeStr() << " "
-                          << "%val" << m_child->getId() << ")\n";
+                          << m_children[0]->getTypeStr() << " "
+                          << "%val" << ids.child_ids[0] << ")\n";
 
                 if (m_type_str[0] == 'i') {
-                    kerStream << "%val" << m_id << " = "
-                              << "trunc i32 %tmp" << m_id << " to " << m_type_str << "\n";
+                    kerStream << "%val" << ids.id << " = "
+                              << "trunc i32 %tmp" << ids.id << " to " << m_type_str << "\n";
                 } else {
-                    kerStream << "%val" << m_id << " = "
-                              << "sitofp i32 %tmp" << m_id << " to " << m_type_str << "\n";
+                    kerStream << "%val" << ids.id << " = "
+                              << "sitofp i32 %tmp" << ids.id << " to " << m_type_str << "\n";
                 }
 
             } else {
-                kerStream << "%val" << m_id << " = call "
+                kerStream << "%val" << ids.id << " = call "
                           << m_type_str << " "
                           << m_op_str << "("
-                          << m_child->getTypeStr() << " "
-                          << "%val" << m_child->getId() << ")\n";
+                          << m_children[0]->getTypeStr() << " "
+                          << "%val" << ids.child_ids[0] << ")\n";
             }
-
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-            id = m_child->setId(id);
-            m_id = id;
-            m_set_id = true;
-            return m_id + 1;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_set_id) return;
-            m_child->getInfo(len, buf_count, bytes);
-            len++;
-            m_set_id = true;
-            return;
-        }
-
-        void resetFlags()
-        {
-            if (m_set_id) {
-                m_is_check = false;
-                resetCommonFlags();
-                m_child->resetFlags();
-            }
-        }
-
-        void setArgs(std::vector<void *> &args, bool is_linear)
-        {
-            if (m_set_arg) return;
-            m_child->setArgs(args, is_linear);
-            m_set_arg = true;
         }
     };
 
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index c07aaa72b5..6bee8a5106 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -15,11 +15,25 @@ namespace cuda
 {
 
 template<typename T>
-struct Param
+class Param
 {
+public:
     T *ptr;
     dim_t dims[4];
     dim_t strides[4];
+
+    __DH__ Param() : ptr(nullptr)
+    {
+    }
+
+    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides) :
+        ptr(iptr)
+    {
+        for (int i = 0; i < 4; i++) {
+            dims[i] = idims[i];
+            strides[i] = istrides[i];
+        }
+    }
 };
 
 template<typename T>
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index f5e64551df..776c5c8f28 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -54,6 +54,8 @@ namespace cuda
 using JIT::Node;
 using JIT::str_map_iter;
 using JIT::str_map_t;
+using JIT::Node_ids;
+using JIT::Node_map_t;
 using std::hash;
 using std::map;
 using std::string;
@@ -67,7 +69,10 @@ const char *layout32 = "target datalayout = \"e-p:32:32:32-i1:8:8-i8:8:8-i16:16:
 const char *triple64 = "target triple = \"nvptx64-unknown-cuda\"\n\n";
 const char *triple32 = "target triple = \"nvptx-unknown-cuda\"\n\n";
 
-static string getFuncName(vector<Node *> nodes, bool is_linear)
+static string getFuncName(const vector<Node *> &output_nodes,
+                          const vector<Node *> &full_nodes,
+                          const vector<Node_ids> &full_ids,
+                          bool is_linear)
 {
     stringstream funcName;
     stringstream hashName;
@@ -75,14 +80,12 @@ static string getFuncName(vector<Node *> nodes, bool is_linear)
     if (is_linear) funcName << "L_"; //Kernel Linear
     else           funcName << "G_"; //Kernel General
 
-    int id = 0;
+    for (const auto &node : output_nodes) {
+        funcName << node->getNameStr() << "_";
+    }
 
-    for (int i = 0; i < (int)nodes.size(); i++) {
-        funcName << "[";
-        id = nodes[i]->setId(id);
-        funcName << nodes[i]->getNameStr();
-        nodes[i]->genKerName(funcName);
-        funcName << "]";
+    for (int i = 0; i < (int)full_nodes.size(); i++) {
+        full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
     hash<string> hash_fn;
@@ -92,7 +95,11 @@ static string getFuncName(vector<Node *> nodes, bool is_linear)
     return hashName.str();
 }
 
-static string getKernelString(string funcName, vector<Node *> nodes, bool is_linear)
+static string getKernelString(const string funcName,
+                              const vector<Node *> &full_nodes,
+                              const vector<Node_ids> &full_ids,
+                              const vector<int> &output_ids,
+                              bool is_linear)
 {
     static const char *defineVoid = "define void ";
     static const char *generalDimParams = "\n"
@@ -218,15 +225,30 @@ static string getKernelString(string funcName, vector<Node *> nodes, bool is_lin
     stringstream outWriteStream;
     str_map_t declStrs;
 
-    for (int i = 0; i < (int)nodes.size(); i++) {
-        string outTypeStr = nodes[i]->getTypeStr();
-        int id = nodes[i]->getId();
+    vector<string> types_output(output_ids.size());
+    for (int i = 0; i < (int)output_ids.size(); i++) {
+        types_output[i] = full_nodes[output_ids[i]]->getTypeStr();
+    }
+
+    for (int i = 0; i < (int)full_nodes.size(); i++) {
+        const auto &node = full_nodes[i];
+        const auto &ids_curr = full_ids[i];
+        // Generate input parameters, needs only current id
+        node->genParams(inParamStream, inAnnStream, ids_curr.id, is_linear);
+        // Generate input offsets, needs only current id
+        node->genOffsets(offsetsStream, ids_curr.id, is_linear);
+        // Generate the core function body, needs children id as well
+        node->genFuncs(funcBodyStream, declStrs, ids_curr, is_linear);
+    }
 
-        nodes[i]->genParams(inParamStream, inAnnStream, is_linear);
+    for (int i = 0; i < (int)output_ids.size(); i++) {
+        int id = output_ids[i];
+        string outTypeStr = types_output[i];
+
+        // Generate output parameters
         outParamStream << outTypeStr << "* %out" << id << ",\n";
-        nodes[i]->genOffsets(offsetsStream, is_linear);
-        nodes[i]->genFuncs(funcBodyStream, declStrs, is_linear);
 
+        // Generate instruction to write output
         outWriteStream << "%outIdx" << id
                        << "= getelementptr inbounds "
                        << outTypeStr
@@ -237,6 +259,8 @@ static string getKernelString(string funcName, vector<Node *> nodes, bool is_lin
                        << " %val" << id << ", "
                        << outTypeStr
                        << "* %outIdx" << id << "\n";
+
+        // Generate output annotation string
         outAnnStream << outTypeStr << "*,\n";
     }
 
@@ -267,7 +291,8 @@ static string getKernelString(string funcName, vector<Node *> nodes, bool is_lin
               << outWriteStream.str()
               << blockEnd;
 
-    for(str_map_iter iterator = declStrs.begin(); iterator != declStrs.end(); iterator++) {
+    for(str_map_iter iterator = declStrs.begin();
+        iterator != declStrs.end(); iterator++) {
         kerStream << iterator->first << "\n";
     }
     kerStream << functionLoad;
@@ -528,20 +553,24 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
     return entry;
 }
 
-static CUfunction getKernel(vector<Node *> nodes, bool is_linear)
+static CUfunction getKernel(const vector<Node *> &output_nodes,
+                            const vector<int> &output_ids,
+                            const vector<Node *> &full_nodes,
+                            const vector<Node_ids> &full_ids,
+                            const bool is_linear)
 {
     typedef std::map<std::string, kc_entry_t> kc_t;
 
     thread_local static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
-    string funcName = getFuncName(nodes, is_linear);
+    string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear);
     int device      = getActiveDeviceId();
 
     kc_t::iterator idx = kernelCaches[device].find(funcName);
     kc_entry_t entry = {NULL, NULL};
 
     if (idx == kernelCaches[device].end()) {
-        string jit_ker = getKernelString(funcName, nodes, is_linear);
+        string jit_ker = getKernelString(funcName, full_nodes, full_ids, output_ids, is_linear);
         entry = compileKernel(funcName.c_str(), jit_ker);
         kernelCaches[device][funcName] = entry;
     } else {
@@ -552,19 +581,31 @@ static CUfunction getKernel(vector<Node *> nodes, bool is_linear)
 }
 
 template<typename T>
-void evalNodes(vector<Param<T> >&outputs, vector<Node *> nodes)
+void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
 {
     int num_outputs = (int)outputs.size();
 
     if (num_outputs == 0) return;
 
-    bool is_linear = true;
+    Node_map_t nodes;
+    vector<int> output_ids;
+    for (auto &node : output_nodes) {
+        node->getNodesMap(nodes);
+        output_ids.push_back(nodes[node].id);
+    }
 
-    for (int i = 0; i < num_outputs; i++) {
-        is_linear &= nodes[i]->isLinear(outputs[0].dims);
+    vector<Node *> full_nodes(nodes.size());
+    vector<Node_ids> full_ids(nodes.size());
+    bool is_linear = true;
+    for (auto &map_entry : nodes) {
+        full_nodes[map_entry.second.id] = map_entry.first;
+        full_ids[map_entry.second.id] = map_entry.second;
+        is_linear &= map_entry.first->isLinear(outputs[0].dims);
     }
 
-    CUfunction ker = getKernel(nodes, is_linear);
+    CUfunction ker = getKernel(output_nodes, output_ids,
+                               full_nodes, full_ids,
+                               is_linear);
 
     int threads_x = 1, threads_y = 1;
     int blocks_x_ = 1, blocks_y_ = 1;
@@ -607,8 +648,8 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> nodes)
 
     vector<void *> args;
 
-    for (int i = 0; i < num_outputs; i++) {
-        nodes[i]->setArgs(args, is_linear);
+    for (const auto &node : full_nodes) {
+        node->setArgs(args, is_linear);
     }
 
     for (int i = 0; i < num_outputs; i++) {
@@ -658,21 +699,17 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> nodes)
                             getActiveStream(),
                             &args.front(),
                             NULL));
-
-    for (int i = 0; i < num_outputs; i++) {
-        nodes[i]->resetFlags();
-    }
 }
 
 template<typename T>
 void evalNodes(Param<T> &out, Node *node)
 {
     vector<Param<T>> outputs;
-    vector<Node *> nodes;
+    vector<Node *> output_nodes;
 
     outputs.push_back(out);
-    nodes.push_back(node);
-    evalNodes(outputs, nodes);
+    output_nodes.push_back(node);
+    evalNodes(outputs, output_nodes);
     return;
 }
 

From 9ffd295aadb2f02960bb679068c3418c2ca2f44e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 14 Mar 2017 23:43:45 -0700
Subject: [PATCH 1133/2677] Changes required to make JIT in OpenCL backend
 thread safe

---
 src/backend/opencl/Array.cpp          |  27 ++++++-
 src/backend/opencl/Array.hpp          |   6 +-
 src/backend/opencl/JIT/BinaryNode.hpp | 102 +++---------------------
 src/backend/opencl/JIT/BufferNode.hpp |  90 +++++++--------------
 src/backend/opencl/JIT/Node.hpp       |  93 +++++++++-------------
 src/backend/opencl/JIT/ScalarNode.hpp |  59 ++------------
 src/backend/opencl/JIT/UnaryNode.hpp  |  88 +++------------------
 src/backend/opencl/jit.cpp            | 108 ++++++++++++++++----------
 8 files changed, 179 insertions(+), 394 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 88355ee3dd..1ab28a8e31 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -242,8 +242,13 @@ namespace opencl
 
                     unsigned length =0, buf_count = 0, bytes = 0;
                     Node *n = node.get();
-                    n->getInfo(length, buf_count, bytes);
-                    n->resetFlags();
+                    JIT::Node_map_t nodes_map;
+                    n->getNodesMap(nodes_map);
+
+                    for(auto &entry : nodes_map) {
+                        Node *node = entry.first;
+                        node->getInfo(length, buf_count, bytes);
+                    }
 
                     if (2 * bytes > lock_bytes) {
                         out.eval();
@@ -379,6 +384,17 @@ namespace opencl
         return;
     }
 
+    template<typename T>
+    void
+    Array<T>::setDataDims(const dim4 &new_dims)
+    {
+        modDims(new_dims);
+        data_dims = new_dims;
+        if (node->isBuffer()) {
+            node = bufferNodePtr<T>();
+        }
+    }
+
 #define INSTANTIATE(T)                                                  \
     template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
@@ -400,9 +416,12 @@ namespace opencl
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       cl::Buffer* Array<T>::device();                      \
-    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
-    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
+    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, \
+                                                       const size_t bytes); \
+    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, \
+                                                       const size_t bytes); \
     template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
+    template       void Array<T>::setDataDims(const dim4 &new_dims);    \
 
     INSTANTIATE(float)
     INSTANTIATE(double)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 64ac5c742f..f7c36b697b 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -204,11 +204,7 @@ namespace opencl
             return data_dims;
         }
 
-        void setDataDims(const dim4 &new_dims)
-        {
-            modDims(new_dims);
-            data_dims = new_dims;
-        }
+        void setDataDims(const dim4 &new_dims);
 
         size_t getAllocatedBytes() const
         {
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index 5aa98810bd..c2f605f6e2 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -21,117 +21,33 @@ namespace JIT
     {
     private:
         std::string m_op_str;
-        Node_ptr m_lhs, m_rhs;
         int m_op;
 
     public:
         BinaryNode(const char *out_type_str, const char *name_str,
                    const char *op_str,
                    Node_ptr lhs, Node_ptr rhs, int op)
-            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1),
+            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
               m_op_str(op_str),
-              m_lhs(lhs),
-              m_rhs(rhs),
               m_op(op)
         {
         }
 
-        bool isLinear(dim_t dims[4])
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            if (!m_set_is_linear) {
-                m_linear = m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
-                m_set_is_linear = true;
-            }
-            return m_linear;
-        }
-
-        void genParams(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_param) return;
-            if (!(m_lhs->isGenParam())) m_lhs->genParams(kerStream, is_linear);
-            if (!(m_rhs->isGenParam())) m_rhs->genParams(kerStream, is_linear);
-            m_gen_param = true;
-        }
-
-        int setArgs(cl::Kernel &ker, int id, bool is_linear)
-        {
-            if (m_set_arg) return id;
-            m_set_arg = true;
-
-            id = m_lhs->setArgs(ker, id, is_linear);
-            id = m_rhs->setArgs(ker, id, is_linear);
-            return id;
-        }
-
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_offset) return;
-            if (!(m_lhs->isGenOffset())) m_lhs->genOffsets(kerStream, is_linear);
-            if (!(m_rhs->isGenOffset())) m_rhs->genOffsets(kerStream, is_linear);
-            m_gen_offset = true;
-        }
-
-        void genKerName(std::stringstream &kerStream)
-        {
-            if (m_gen_name) return;
-            m_lhs->genKerName(kerStream);
-            m_rhs->genKerName(kerStream);
-
             // Make the dec representation of enum part of the Kernel name
             kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_lhs->getId();
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_rhs->getId();
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[1];
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_gen_func) return;
-
-            if (!(m_lhs->isGenFunc())) m_lhs->genFuncs(kerStream);
-            if (!(m_rhs->isGenFunc())) m_rhs->genFuncs(kerStream);
-
-            kerStream << m_type_str << " val" << m_id << " = "
-                      << m_op_str << "(val" << m_lhs->getId()
-                      << ", val" << m_rhs->getId() << ");"
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << m_op_str << "(val" << ids.child_ids[0]
+                      << ", val" << ids.child_ids[1] << ");"
                       << "\n";
-
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-
-            id = m_lhs->setId(id);
-            id = m_rhs->setId(id);
-
-            m_id = id;
-            m_set_id = true;
-
-            return m_id + 1;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_set_id) return;
-
-            m_lhs->getInfo(len, buf_count, bytes);
-            m_rhs->getInfo(len, buf_count, bytes);
-            len++;
-
-            m_set_id = true;
-            return;
-        }
-
-        void resetFlags()
-        {
-            if (m_set_id) {
-                resetCommonFlags();
-                m_lhs->resetFlags();
-                m_rhs->resetFlags();
-            }
         }
     };
 
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 280c796a79..92a2c32697 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include "Node.hpp"
 #include <iomanip>
+#include <mutex>
 
 namespace opencl
 {
@@ -25,12 +26,13 @@ namespace JIT
         KParam m_info;
         unsigned m_bytes;
         bool m_linear_buffer;
+        std::once_flag m_set_data_flag;
 
     public:
 
         BufferNode(const char *type_str,
                    const char *name_str)
-            : Node(type_str, name_str, 0)
+            : Node(type_str, name_str, 0, {})
         {
         }
 
@@ -42,70 +44,55 @@ namespace JIT
 
         void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
         {
-            m_info = info;
-            m_data = data;
-            m_bytes = bytes;
-            m_linear_buffer = is_linear;
+            std::call_once(m_set_data_flag, [this, info, data, bytes, is_linear]() {
+                    m_info = info;
+                    m_data = data;
+                    m_bytes = bytes;
+                    m_linear_buffer = is_linear;
+                });
         }
 
         bool isLinear(dim_t dims[4])
         {
-            if (!m_set_is_linear) {
-                bool same_dims = true;
-                for (int i = 0; same_dims && i < 4; i++) {
-                    same_dims &= (dims[i] == m_info.dims[i]);
-                }
-                m_set_is_linear = true;
-                m_linear = m_linear_buffer && same_dims;
+            bool same_dims = true;
+            for (int i = 0; same_dims && i < 4; i++) {
+                same_dims &= (dims[i] == m_info.dims[i]);
             }
-            return m_linear;
+            return m_linear_buffer && same_dims;
         }
 
-        void genKerName(std::stringstream &kerStream)
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_gen_name) return;
-
             kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear)
         {
-            if (m_gen_param) return;
-
             if (!is_linear) {
-                kerStream << "__global " << m_type_str << " *in" << m_id
-                          << ", KParam iInfo" << m_id << ", " << "\n";
+                kerStream << "__global " << m_type_str << " *in" << id
+                          << ", KParam iInfo" << id << ", " << "\n";
             } else {
-                kerStream << "__global " << m_type_str << " *in" << m_id
-                          << ", dim_t iInfo" << m_id << "_offset, " << "\n";
+                kerStream << "__global " << m_type_str << " *in" << id
+                          << ", dim_t iInfo" << id << "_offset, " << "\n";
             }
-            m_gen_param = true;
         }
 
         int setArgs(cl::Kernel &ker, int id, bool is_linear)
         {
-            if (m_set_arg) return id;
-
             ker.setArg(id + 0, *m_data);
-
             if (!is_linear) {
                 ker.setArg(id + 1,  m_info);
             } else {
                 ker.setArg(id + 1, m_info.offset);
             }
-
-            m_set_arg = true;
             return id + 2;
         }
 
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
         {
-            if (m_gen_offset) return;
-
-            std::string idx_str = std::string("int idx") + std::to_string(m_id);
-            std::string info_str = std::string("iInfo") + std::to_string(m_id);;
+            std::string idx_str = std::string("int idx") + std::to_string(id);
+            std::string info_str = std::string("iInfo") + std::to_string(id);;
 
             if (!is_linear) {
                 kerStream << idx_str << " = "
@@ -121,47 +108,22 @@ namespace JIT
             } else {
                 kerStream << idx_str << " = idx + " << info_str << "_offset;" << "\n";
             }
-
-            m_gen_offset = true;
         }
 
-        void genFuncs(std::stringstream &kerStream)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_gen_func) return;
-
-            kerStream << m_type_str << " val" << m_id << " = "
-                      << "in" << m_id << "[idx" << m_id << "];"
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << "in" << ids.id << "[idx" << ids.id << "];"
                       << "\n";
-
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-
-            m_id = id;
-            m_set_id = true;
-
-            return m_id + 1;
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
-            if (m_set_id) return;
-
             len++;
             buf_count++;
             bytes += m_bytes;
-            m_set_id = true;
             return;
         }
-
-
-        void resetFlags()
-        {
-            if (m_set_id) resetCommonFlags();
-        }
     };
 
 }
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index abf7a2d908..c4e0176664 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -13,101 +13,78 @@
 #include <string>
 #include <vector>
 #include <memory>
+#include <unordered_map>
 
 namespace opencl
 {
 
 namespace JIT
 {
+
+    class Node;
     using std::shared_ptr;
+    typedef shared_ptr<Node> Node_ptr;
+
+    typedef struct
+    {
+        int id;
+        std::vector<int> child_ids;
+    } Node_ids;
+
+    typedef std::unordered_map<Node *, Node_ids> Node_map_t;
+    typedef Node_map_t::iterator Node_map_iter;
 
     class Node
     {
     protected:
         const std::string m_type_str;
         const std::string m_name_str;
-        int m_id;
         const int m_height;
-        bool m_set_id;
-        bool m_gen_func;
-        bool m_gen_param;
-        bool m_gen_offset;
-        bool m_set_arg;
-        bool m_gen_name;
-        bool m_linear;
-        bool m_set_is_linear;
-
-    protected:
-        void resetCommonFlags()
-        {
-            m_set_id = false;
-            m_gen_func = false;
-            m_gen_param = false;
-            m_gen_offset = false;
-            m_set_arg = false;
-            m_gen_name = false;
-            m_linear = false;
-            m_set_is_linear = false;
-        }
+        const std::vector<Node_ptr> m_children;
 
     public:
 
-        Node(const char *type_str, const char *name_str, const int height)
+        Node(const char *type_str, const char *name_str, const int height,
+             const std::vector<Node_ptr> children)
             : m_type_str(type_str),
               m_name_str(name_str),
-              m_id(-1),
               m_height(height),
-              m_set_id(false),
-              m_gen_func(false),
-              m_gen_param(false),
-              m_gen_offset(false),
-              m_set_arg(false),
-              m_gen_name(false),
-              m_linear(false),
-              m_set_is_linear(false)
+              m_children(children)
         {}
 
-        virtual void genKerName(std::stringstream &kerStream) {}
-        virtual void genParams  (std::stringstream &kerStream, bool is_linear) {}
-        virtual void genOffsets (std::stringstream &kerStream, bool is_linear) {}
-        virtual void genFuncs   (std::stringstream &kerStream) { m_gen_func = true;}
-
-        virtual int setArgs (cl::Kernel &ker, int id, bool is_linear) { return id; }
-
-        virtual int setId(int id) { m_set_id = true; return id; }
-
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void getNodesMap(Node_map_t &node_map)
         {
-            len = 0;
-            buf_count = 0;
-            bytes = 0;
+            if (node_map.find(this) == node_map.end()) {
+                Node_ids ids;
+                for (const auto &child : m_children) {
+                    child->getNodesMap(node_map);
+                    ids.child_ids.push_back(node_map[child.get()].id);
+                }
+                ids.id = node_map.size();
+                node_map[this] = ids;
+            }
         }
 
-        virtual bool isBuffer() { return false; }
+        virtual void genKerName(std::stringstream &kerStream, Node_ids ids) {}
+        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) {}
+        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) {}
+        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) {}
 
+        virtual int setArgs (cl::Kernel &ker, int id, bool is_linear) { return id; }
 
-        virtual void resetFlags()
+        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
-            resetCommonFlags();
+            len++;
         }
 
+        virtual bool isBuffer() { return false; }
         virtual bool isLinear(dim_t dims[4]) { return true; }
-
         std::string getTypeStr() { return m_type_str; }
-
-        bool isGenFunc() { return m_gen_func; }
-        bool isGenParam() { return m_gen_param; }
-        bool isGenOffset() { return m_gen_offset; }
-
-        int getId()  { return m_id; }
         int getHeight()  { return m_height; }
         std::string getNameStr() { return m_name_str; }
 
         virtual ~Node() {}
     };
-
-    typedef shared_ptr<Node> Node_ptr;
-
 }
 
 }
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index b172b67680..e3e269e1fd 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -28,76 +28,33 @@ namespace JIT
     public:
 
         ScalarNode(T val)
-            : Node(dtype_traits<T>::getName(), shortname<T>(false), 0),
+            : Node(dtype_traits<T>::getName(), shortname<T>(false), 0, {}),
               m_val(val)
         {
         }
 
-        bool isLinear(dim_t dims[4])
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            return true;
-        }
-
-        void genKerName(std::stringstream &kerStream)
-        {
-            if (m_gen_name) return;
-
             kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear)
         {
-            if (m_gen_param) return;
-            kerStream << m_type_str << " scalar" << m_id << ", " << "\n";
-            m_gen_param = true;
+            kerStream << m_type_str << " scalar" << id << ", " << "\n";
         }
 
         int setArgs(cl::Kernel &ker, int id, bool is_linear)
         {
-            if (m_set_arg) return id;
             ker.setArg(id, m_val);
-            m_set_arg = true;
             return id + 1;
         }
 
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_gen_offset) return;
-            m_gen_offset = true;
-        }
-
-        void genFuncs(std::stringstream &kerStream)
-        {
-            if (m_gen_func) return;
-
-            kerStream << m_type_str << " val" << m_id << " = "
-                      << "scalar" << m_id << ";"
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << "scalar" << ids.id << ";"
                       << "\n";
-
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-            m_id = id;
-            m_set_id = true;
-            return m_id + 1;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_set_id) return;
-            len++;
-            m_set_id = true;
-            return;
-        }
-
-        void resetFlags()
-        {
-            if (m_set_id) resetCommonFlags();
         }
     };
 
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index 5b9bdbc481..6b178615ab 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -20,102 +20,32 @@ namespace JIT
     class UnaryNode : public Node
     {
     private:
-        std::string m_op_str;
-        Node_ptr m_child;
-        int m_op;
+        const std::string m_op_str;
+        const int m_op;
 
     public:
         UnaryNode(const char *out_type_str, const char *name_str,
                   const char *op_str,
                   Node_ptr child, int op)
-            : Node(out_type_str, name_str, child->getHeight() + 1),
+            : Node(out_type_str, name_str, child->getHeight() + 1, {child}),
               m_op_str(op_str),
-              m_child(child),
               m_op(op)
         {
         }
 
-        bool isLinear(dim_t dims[4])
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            if (!m_set_is_linear) {
-                m_linear = m_child->isLinear(dims);
-                m_set_is_linear = true;
-            }
-            return m_linear;
-        }
-
-        void genParams(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_param) return;
-            if (!(m_child->isGenParam())) m_child->genParams(kerStream, is_linear);
-            m_gen_param = true;
-        }
-
-        int setArgs(cl::Kernel &ker, int id, bool is_linear)
-        {
-            if (m_set_arg) return id;
-            m_set_arg = true;
-            return m_child->setArgs(ker, id, is_linear);
-        }
-
-        void genOffsets(std::stringstream &kerStream, bool is_linear)
-        {
-            if (m_gen_offset) return;
-            if (!(m_child->isGenOffset())) m_child->genOffsets(kerStream, is_linear);
-            m_gen_offset = true;
-        }
-
-        void genKerName(std::stringstream &kerStream)
-        {
-            if (m_gen_name) return;
-            m_child->genKerName(kerStream);
-
             // Make the dec representation of enum part of the Kernel name
             kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_child->getId();
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << m_id << std::dec;
-            m_gen_name = true;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_gen_func) return;
-
-            if (!(m_child->isGenFunc())) m_child->genFuncs(kerStream);
-
-            kerStream << m_type_str << " val" << m_id << " = "
-                      << m_op_str << "(val" << m_child->getId() << ");"
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << m_op_str << "(val" << ids.child_ids[0] << ");"
                       << "\n";
-
-            m_gen_func = true;
-        }
-
-        int setId(int id)
-        {
-            if (m_set_id) return id;
-            id = m_child->setId(id);
-            m_id = id;
-            m_set_id = true;
-            return m_id + 1;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_set_id) return;
-
-            m_child->getInfo(len, buf_count, bytes);
-            len++;
-
-            m_set_id = true;
-            return;
-        }
-
-        void resetFlags()
-        {
-            if (m_set_id) {
-                resetCommonFlags();
-                m_child->resetFlags();
-            }
         }
     };
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 360028d967..5442eba7f4 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -25,6 +25,8 @@ namespace opencl
 {
 
 using JIT::Node;
+using JIT::Node_ids;
+using JIT::Node_map_t;
 
 using cl::Buffer;
 using cl::Program;
@@ -34,8 +36,12 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 using std::stringstream;
+using std::vector;
 
-static string getFuncName(std::vector<Node *> nodes, bool is_linear, bool *is_double)
+static string getFuncName(const vector<Node *> &output_nodes,
+                          const vector<Node *> &full_nodes,
+                          const vector<Node_ids> &full_ids,
+                          bool is_linear, bool *is_double)
 {
     stringstream hashName;
     stringstream funcName;
@@ -46,13 +52,12 @@ static string getFuncName(std::vector<Node *> nodes, bool is_linear, bool *is_do
         funcName << "G_";
     }
 
-    int id = 0;
-    for (auto node :  nodes) {
-        funcName << "[";
-        id = node->setId(id);
-        funcName << node->getNameStr();
-        node->genKerName(funcName);
-        funcName << "]";
+    for (auto node :  output_nodes) {
+        funcName << node->getNameStr() << "_";
+    }
+
+    for (int i = 0; i < (int)full_nodes.size(); i++) {
+        full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
     string nameStr = funcName.str();
@@ -65,7 +70,11 @@ static string getFuncName(std::vector<Node *> nodes, bool is_linear, bool *is_do
     return hashName.str();
 }
 
-static string getKernelString(string funcName, std::vector<Node *> nodes, bool is_linear)
+static string getKernelString(const string funcName,
+                              const vector<Node *> &full_nodes,
+                              const vector<Node_ids> &full_ids,
+                              const vector<int> &output_ids,
+                              bool is_linear)
 {
 
     // Common OpenCL code
@@ -118,16 +127,23 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
     stringstream offsetsStream;
     stringstream opsStream;
 
-    int count  = 0;
+    for (int i = 0; i < (int)full_nodes.size(); i++) {
+        const auto &node = full_nodes[i];
+        const auto &ids_curr = full_ids[i];
+        // Generate input parameters, only needs current id
+        node->genParams(inParamStream, ids_curr.id, is_linear);
+        // Generate input offsets, only needs current id
+        node->genOffsets(offsetsStream, ids_curr.id, is_linear);
+        // Generate the core function body, needs children ids as well
+        node->genFuncs(opsStream, ids_curr);
+    }
 
-    for (auto node : nodes) {
-        int id = node->getId();
-        node->genParams(inParamStream, is_linear);
-        outParamStream << "__global " << node->getTypeStr() << " *out" << id << ", \n";
+    for (int i = 0; i < (int)output_ids.size(); i++) {
+        int id = output_ids[i];
+        // Generate output parameters
+        outParamStream << "__global " << full_nodes[id]->getTypeStr() << " *out" << id << ", \n";
+        // Generate code to write the output
         outWriteStream << "out" << id << "[idx] = " << "val" << id << ";\n";
-        node->genOffsets(offsetsStream, is_linear);
-        node->genFuncs(opsStream);
-        opsStream << "//" << ++count << std::endl << std::endl;
     }
 
     // Put various blocks into a single stream
@@ -153,16 +169,20 @@ static string getKernelString(string funcName, std::vector<Node *> nodes, bool i
     return kerStream.str();
 }
 
-static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
+static Kernel getKernel(const vector<Node *> &output_nodes,
+                        const vector<int> &output_ids,
+                        const vector<Node *> &full_nodes,
+                        const vector<Node_ids> &full_ids,
+                        const bool is_linear)
 {
     bool is_dbl = false;
-    string funcName = getFuncName(nodes, is_linear, &is_dbl);
+    string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear, &is_dbl);
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, funcName);
 
     if (entry.prog==0 && entry.ker==0) {
-        string jit_ker = getKernelString(funcName, nodes, is_linear);
+        string jit_ker = getKernelString(funcName, full_nodes, full_ids, output_ids, is_linear);
 
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
@@ -179,7 +199,7 @@ static Kernel getKernel(std::vector<Node *> nodes, bool is_linear)
     return *entry.ker;
 }
 
-void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
+void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
 {
     if (outputs.size() == 0) return;
 
@@ -187,13 +207,25 @@ void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
     //FIXME: Add assert to check if all outputs are same size?
     KParam out_info = outputs[0].info;
 
-    // Verify if all ASTs hold Linear Arrays
+    Node_map_t nodes;
+    vector<int> output_ids;
+    for (auto &node : output_nodes) {
+        node->getNodesMap(nodes);
+        output_ids.push_back(nodes[node].id);
+    }
+
+    vector<Node *> full_nodes(nodes.size());
+    vector<Node_ids> full_ids(nodes.size());
     bool is_linear = true;
-    for (auto node : nodes) {
-        is_linear &= node->isLinear(out_info.dims);
+    for (auto &map_entry : nodes) {
+        full_nodes[map_entry.second.id] = map_entry.first;
+        full_ids[map_entry.second.id] = map_entry.second;
+        is_linear &= map_entry.first->isLinear(out_info.dims);
     }
 
-    Kernel ker = getKernel(nodes, is_linear);
+    Kernel ker = getKernel(output_nodes, output_ids,
+                           full_nodes, full_ids,
+                           is_linear);
 
     uint local_0 = 1;
     uint local_1 = 1;
@@ -233,36 +265,32 @@ void evalNodes(std::vector<Param> &outputs, std::vector<Node *> nodes)
     NDRange local(local_0, local_1);
     NDRange global(global_0, global_1);
 
-    int args = 0;
-    for (auto node : nodes) {
-        args = node->setArgs(ker, args, is_linear);
+    int nargs = 0;
+    for (const auto &node : full_nodes) {
+        nargs = node->setArgs(ker, nargs, is_linear);
     }
 
     // Set output parameters
     for (auto output : outputs) {
-        ker.setArg(args, *(output.data));
-        ++args;
+        ker.setArg(nargs, *(output.data));
+        ++nargs;
     }
 
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
-    ker.setArg(args + 0,  out_info);
-    ker.setArg(args + 1,  groups_0);
-    ker.setArg(args + 2,  groups_1);
-    ker.setArg(args + 3,  num_odims);
+    ker.setArg(nargs + 0,  out_info);
+    ker.setArg(nargs + 1,  groups_0);
+    ker.setArg(nargs + 2,  groups_1);
+    ker.setArg(nargs + 3,  num_odims);
 
     getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
-
-    for (auto node : nodes) {
-        node->resetFlags();
-    }
 }
 
 void evalNodes(Param &out, Node *node)
 {
-    std::vector<Param>  outputs{out};
-    std::vector<Node *> nodes{node};
+    vector<Param>  outputs{out};
+    vector<Node *> nodes{node};
     return evalNodes(outputs, nodes);
 }
 

From baf27d4bfae63425ee91c05ba0c6d45799fb94de Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Thu, 16 Mar 2017 02:44:15 -0700
Subject: [PATCH 1134/2677] Cleaning up TNJ in CPU backend and making it thread
 safe

---
 src/backend/cpu/Array.cpp          | 74 +++++++++++++---------
 src/backend/cpu/Array.hpp          |  5 ++
 src/backend/cpu/TNJ/BinaryNode.hpp | 57 +++--------------
 src/backend/cpu/TNJ/BufferNode.hpp | 97 ++++++++++++++---------------
 src/backend/cpu/TNJ/Node.hpp       | 95 +++++++++++++----------------
 src/backend/cpu/TNJ/ScalarNode.hpp | 29 +--------
 src/backend/cpu/TNJ/UnaryNode.hpp  | 48 +++------------
 src/backend/cpu/kernel/Array.hpp   | 98 ++++++++++++++++++++++++++----
 8 files changed, 242 insertions(+), 261 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 96d0345f6d..781521bf41 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -30,18 +30,25 @@ using TNJ::Node_ptr;
 
 using af::dim4;
 
+
+template<typename T>
+Node_ptr bufferNodePtr()
+{
+    return Node_ptr(reinterpret_cast<Node *>(new BufferNode<T>()));
+}
+
 template<typename T>
 Array<T>::Array(dim4 dims):
     info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-    node(), ready(true), owner(true)
+    node(bufferNodePtr<T>()), ready(true), owner(true)
 { }
 
 template<typename T>
 Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
     info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
-    node(), ready(true), owner(true)
+    node(bufferNodePtr<T>()), ready(true), owner(true)
 {
     static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
     static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
@@ -62,7 +69,7 @@ template<typename T>
 Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &strides) :
     info(parent.getDevId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
     data(parent.getData()), data_dims(parent.getDataDims()),
-    node(),
+    node(bufferNodePtr<T>()),
     ready(true), owner(false)
 { }
 
@@ -72,7 +79,7 @@ Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
     info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
     data(is_device ? (T*)in_data : memAlloc<T>(info.total()), memFree<T>),
     data_dims(dims),
-    node(),
+    node(bufferNodePtr<T>()),
     ready(true),
     owner(true)
 {
@@ -93,7 +100,7 @@ void Array<T>::eval()
 
     getQueue().enqueue(kernel::evalArray<T>, *this);
     // Reset shared_ptr
-    this->node.reset();
+    this->node = bufferNodePtr<T>();
     ready = true;
 }
 
@@ -115,12 +122,25 @@ T* Array<T>::device()
 }
 
 template<typename T>
-void evalMultiple(std::vector<Array<T>*> arrays)
+void evalMultiple(std::vector<Array<T>*> array_ptrs)
 {
-    //FIXME: implement this correctly
-    //Using fallback for now
-    for (auto array : arrays) {
-        array->eval();
+    std::vector<Array<T>> arrays;
+    bool isWorker = getQueue().is_worker();
+    for (auto &array : array_ptrs) {
+        if (array->ready) continue;
+        if (isWorker) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+        array->setId(getActiveDeviceId());
+        array->data = std::shared_ptr<T>(memAlloc<T>(array->elements()), memFree<T>);
+        arrays.push_back(*array);
+    }
+
+    if (arrays.size() > 0) {
+        getQueue().enqueue(kernel::evalMultiple<T>, arrays);
+        for (auto &array : array_ptrs) {
+            if (array->ready) continue;
+            array->ready = true;
+            array->node = bufferNodePtr<T>();
+        }
     }
     return;
 }
@@ -128,20 +148,16 @@ void evalMultiple(std::vector<Array<T>*> arrays)
 template<typename T>
 Node_ptr Array<T>::getNode() const
 {
-    if (!node) {
-
+    if (node->isBuffer()) {
+        BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
         unsigned bytes = this->getDataDims().elements() * sizeof(T);
-
-        BufferNode<T> *buf_node = new BufferNode<T>(data,
-                                                    bytes,
-                                                    getOffset(),
-                                                    dims().get(),
-                                                    strides().get(),
-                                                    isLinear());
-
-        const_cast<Array<T> *>(this)->node = Node_ptr(reinterpret_cast<Node *>(buf_node));
+        bufNode->setData(data,
+                         bytes,
+                         getOffset(),
+                         dims().get(),
+                         strides().get(),
+                         isLinear());
     }
-
     return node;
 }
 
@@ -198,15 +214,15 @@ createNodeArray(const dim4 &dims, Node_ptr node)
             if (lock_bytes > getMaxBytes() ||
                 lock_buffers > getMaxBuffers()) {
 
-                // Calling sync to ensure the TNJ calls below
-                // don't overwrite the same nodes being evaluated
-                // FIXME: This should ideally be JIT specific mutex
-                getQueue().sync();
+                Node *n = node.get();
 
+                TNJ::Node_map_t nodes_map;
+                n->getNodesMap(nodes_map);
                 unsigned length =0, buf_count = 0, bytes = 0;
-                Node *n = node.get();
-                n->getInfo(length, buf_count, bytes);
-                n->reset();
+                for(auto &entry : nodes_map) {
+                    Node *node = entry.first;
+                    node->getInfo(length, buf_count, bytes);
+                }
 
                 if (2 * bytes > lock_bytes) {
                     out.eval();
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 65ef5f1434..c3a1286708 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -30,6 +30,10 @@ template<typename T> class Array;
 namespace kernel
 {
 template<typename T> void evalArray(cpu::Array<T> in);
+
+template<typename T>
+void evalMultiple(std::vector<Array<T>> arrays);
+
 }
 }
 
@@ -234,6 +238,7 @@ namespace cpu
                                           bool copy);
 
         friend void kernel::evalArray<T>(Array<T> in);
+        friend void kernel::evalMultiple<T>(std::vector<Array<T>> arrays);
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index f06581a395..78c7c6df66 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -29,68 +29,29 @@ namespace TNJ
 {
 
     template<typename To, typename Ti, af_op_t op>
-    class BinaryNode  : public Node
+    class BinaryNode  : public TNode<To>
     {
 
     protected:
-        Node_ptr m_lhs;
-        Node_ptr m_rhs;
         BinOp<To, Ti, op> m_op;
-        To m_val;
+        TNode<Ti> *m_lhs, *m_rhs;
 
     public:
         BinaryNode(Node_ptr lhs, Node_ptr rhs) :
-            Node(std::max(lhs->getHeight(), rhs->getHeight()) + 1),
-            m_lhs(lhs),
-            m_rhs(rhs),
-            m_val(0)
+            TNode<To>(0, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
+            m_lhs(reinterpret_cast<TNode<Ti> *>(lhs.get())),
+            m_rhs(reinterpret_cast<TNode<Ti> *>(rhs.get()))
         {
         }
 
-        void *calc(int x, int y, int z, int w)
+        void calc(int x, int y, int z, int w)
         {
-            if (calcCurrent(x, y, z, w)) {
-                m_val = m_op.eval(*(Ti *)m_lhs->calc(x, y, z, w),
-                                  *(Ti *)m_rhs->calc(x, y, z, w));
-            }
-            return  (void *)&m_val;
+            this->m_val = m_op.eval(m_lhs->m_val, m_rhs->m_val);
         }
 
-        void *calc(int idx)
+        void calc(int idx)
         {
-            if (calcCurrent(idx)) {
-                m_val = m_op.eval(*(Ti *)m_lhs->calc(idx),
-                                  *(Ti *)m_rhs->calc(idx));
-            }
-            return (void *)&m_val;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
-            if (m_is_eval) return;
-
-            m_lhs->getInfo(len, buf_count, bytes);
-            m_rhs->getInfo(len, buf_count, bytes);
-            len++;
-
-            m_is_eval = true;
-            return;
-        }
-
-        void reset()
-        {
-            resetCommonFlags();
-            m_lhs->reset();
-            m_rhs->reset();
-        }
-
-        bool isLinear(const dim_t *dims)
-        {
-            if (!m_set_is_linear) {
-                m_linear = m_lhs->isLinear(dims) && m_rhs->isLinear(dims);
-                m_set_is_linear = true;
-            }
-            return m_linear;
+            this->m_val = m_op.eval(m_lhs->m_val, m_rhs->m_val);
         }
     };
 
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index d995314ae7..bdd7c7e700 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -11,7 +11,7 @@
 #include <optypes.hpp>
 #include <vector>
 #include "Node.hpp"
-
+#include <mutex>
 namespace cpu
 {
 
@@ -20,87 +20,78 @@ namespace TNJ
 
     using std::shared_ptr;
     template<typename T>
-    class BufferNode : public Node
+    class BufferNode : public TNode<T>
     {
 
     protected:
-        shared_ptr<T> ptr;
+        shared_ptr<T> m_sptr;
+        T *m_ptr;
         unsigned m_bytes;
         bool m_linear_buffer;
-        dim_t m_off;
         dim_t m_strides[4];
         dim_t m_dims[4];
-        T m_val;
+        std::once_flag m_set_data_flag;
     public:
 
-        BufferNode(shared_ptr<T> data,
-                   unsigned bytes,
-                   dim_t data_off,
-                   const dim_t *dms,
-                   const dim_t *strs,
-                   const bool is_linear) :
-            Node(0),
-            ptr(data),
-            m_bytes(bytes),
-            m_linear_buffer(is_linear),
-            m_off(data_off),
-            m_val(0)
+        BufferNode() : TNode<T>(0, 0, {})
+        {}
+
+        void setData(shared_ptr<T> data,
+                     unsigned bytes,
+                     dim_t data_off,
+                     const dim_t *dims,
+                     const dim_t *strides,
+                     const bool is_linear)
         {
-            for (int i = 0; i < 4; i++) {
-                m_strides[i] = strs[i];
-                m_dims[i] = dms[i];
-            }
+            std::call_once(m_set_data_flag,
+                           [this, data, bytes,
+                            data_off, dims, strides, is_linear]()
+                           {
+                               m_sptr = data;
+                               m_ptr = data.get() + data_off;
+                               m_bytes = bytes;
+                               m_linear_buffer = is_linear;
+                               for (int i = 0; i < 4; i++) {
+                                   m_strides[i] = strides[i];
+                                   m_dims[i] = dims[i];
+                               }
+                           });
         }
 
-        void *calc(int x, int y, int z, int w)
+        void calc(int x, int y, int z, int w)
         {
-            if (calcCurrent(x, y, z, w)) {
-                dim_t l_off = 0;
-                l_off += (w < (int)m_dims[3]) * w * m_strides[3];
-                l_off += (z < (int)m_dims[2]) * z * m_strides[2];
-                l_off += (y < (int)m_dims[1]) * y * m_strides[1];
-                l_off += (x < (int)m_dims[0]) * x;
-                m_val = *(ptr.get() + m_off + l_off);
-            }
-            return (void *)&m_val;
+            dim_t l_off = 0;
+            l_off += (w < (int)m_dims[3]) * w * m_strides[3];
+            l_off += (z < (int)m_dims[2]) * z * m_strides[2];
+            l_off += (y < (int)m_dims[1]) * y * m_strides[1];
+            l_off += (x < (int)m_dims[0]) * x;
+            this->m_val = m_ptr[l_off];
         }
 
-        void *calc(int idx)
+        void calc(int idx)
         {
-            if (calcCurrent(idx)) {
-                m_val = *(ptr.get() + idx + m_off);
-            }
-            return (void *)&m_val;
+            this->m_val = m_ptr[idx];
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
-            if (m_is_eval) return;
-
             len++;
             buf_count++;
             bytes += m_bytes;
-            m_is_eval = true;
             return;
         }
 
-        void reset()
-        {
-            resetCommonFlags();
-        }
-
         bool isLinear(const dim_t *dims)
         {
-            if (!m_set_is_linear) {
-                m_linear = m_linear_buffer &&
-                    dims[0] == m_dims[0] &&
-                    dims[1] == m_dims[1] &&
-                    dims[2] == m_dims[2] &&
-                    dims[3] == m_dims[3];
-                m_set_is_linear = true;
-            }
-            return m_linear;
+            return m_linear_buffer &&
+                dims[0] == m_dims[0] &&
+                dims[1] == m_dims[1] &&
+                dims[2] == m_dims[2] &&
+                dims[3] == m_dims[3];
         }
+
+        bool isBuffer() { return true; }
+
     };
 
 }
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index f4ed00bd5c..0d3e6c9896 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -11,6 +11,7 @@
 #include <optypes.hpp>
 #include <vector>
 #include <memory>
+#include <unordered_map>
 
 namespace cpu
 {
@@ -18,86 +19,74 @@ namespace cpu
 namespace TNJ
 {
 
+    class Node;
+    using std::shared_ptr;
+    typedef shared_ptr<Node> Node_ptr;
+
+    typedef std::unordered_map<Node *, int> Node_map_t;
+    typedef Node_map_t::iterator Node_map_iter;
+
     class Node
     {
 
     protected:
 
         const int m_height;
-        int x, y, z, w;
-        bool m_is_eval;
-        bool m_linear;
-        bool m_set_is_linear;
-
-
-        void resetCommonFlags()
-        {
-            x = -1;
-            y = -1;
-            z = -1;
-            w = -1;
-            m_is_eval = false;
-            m_linear = false;
-            m_set_is_linear = false;
-        }
-
-        bool calcCurrent(int xc)
-        {
-            bool res = (x == xc);
-            x = xc;
-            return !res;
-        }
-
-        bool calcCurrent(int xc, int yc, int zc, int wc)
-        {
-            bool res = (xc == x) && (yc == y) && (zc == z) && (wc == w);
-            x = xc;
-            y = yc;
-            z = zc;
-            w = wc;
-            return !res;
-        }
+        const std::vector<Node_ptr> m_children;
 
     public:
-        Node(const int height) :
+        Node(const int height, const std::vector<Node_ptr> children) :
             m_height(height),
-            x(-1),
-            y(-1),
-            z(-1),
-            w(-1),
-            m_is_eval(false),
-            m_linear(false),
-            m_set_is_linear(false)
+            m_children(children)
         {}
 
+        void getNodesMap(Node_map_t &node_map)
+        {
+            if (node_map.find(this) == node_map.end()) {
+                for (const auto &child : m_children) {
+                    child->getNodesMap(node_map);
+                }
+                int id = node_map.size();
+                node_map[this] = id;
+            }
+        }
+
         int getHeight() { return m_height; }
 
-        virtual void *calc(int x, int y, int z, int w)
+        virtual void calc(int x, int y, int z, int w)
         {
-            m_is_eval = true;
-            return NULL;
         }
 
-        virtual void *calc(int idx)
+        virtual void calc(int idx)
         {
-            m_is_eval = true;
-            return NULL;
         }
 
         virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
-            len = 0;
-            buf_count = 0;
-            bytes = 0;
+            len++;
         }
 
         virtual bool isLinear(const dim_t *dims) { return true; }
-        virtual void reset() { resetCommonFlags(); }
-
+        virtual bool isBuffer() { return false; }
         virtual ~Node() {}
+
+    };
+
+    template<typename T>
+    class TNode : public Node
+    {
+    public:
+        T m_val;
+    public:
+        TNode(T val, const int height, const std::vector<Node_ptr> children) :
+            Node(height, children),
+            m_val(val)
+            {
+            }
     };
 
-    typedef std::shared_ptr<Node> Node_ptr;
+    template<typename T>
+    using TNode_ptr = std::shared_ptr<TNode<T>>;
 }
 
 }
diff --git a/src/backend/cpu/TNJ/ScalarNode.hpp b/src/backend/cpu/TNJ/ScalarNode.hpp
index bda529f8d4..716c5964a9 100644
--- a/src/backend/cpu/TNJ/ScalarNode.hpp
+++ b/src/backend/cpu/TNJ/ScalarNode.hpp
@@ -19,38 +19,13 @@ namespace TNJ
 {
 
     template<typename T>
-    class ScalarNode : public Node
+    class ScalarNode : public TNode<T>
     {
 
-    protected:
-        T m_val;
-
     public:
-        ScalarNode(T val) : Node(0), m_val(val)
-        {
-        }
-
-        void *calc(int x, int y, int z, int w)
-        {
-            return (void *)(&m_val);
-        }
-
-        void *calc(int idx)
-        {
-            return (void *)&m_val;
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        ScalarNode(T val) : TNode<T>(val, 0, {})
         {
-            if (m_is_eval) return;
-            len++;
-            m_is_eval = true;
-            return;
         }
-
-        void reset() { resetCommonFlags(); }
-
-        bool isLinear(const dim_t *dims) { return true; }
     };
 }
 
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 047151f3f7..2f9d121c20 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -29,63 +29,31 @@ namespace TNJ
 {
 
     template<typename To, typename Ti, af_op_t op>
-    class UnaryNode  : public Node
+    class UnaryNode  : public TNode<To>
     {
 
     protected:
-        Node_ptr m_child;
         UnOp <To, Ti, op> m_op;
-        To m_val;
+        TNode<Ti> *m_child;
 
     public:
         UnaryNode(Node_ptr child) :
-            Node(child->getHeight() + 1),
-            m_child(child),
-            m_val(0)
+            TNode<To>(0, child->getHeight() + 1, {child}),
+            m_child(reinterpret_cast<TNode<Ti> *>(child.get()))
         {
         }
 
-        void *calc(int x, int y, int z, int w)
-        {
-            if (calcCurrent(x, y, z, w)) {
-                m_val = m_op.eval(*(Ti *)m_child->calc(x, y, z, w));
-            }
-            return (void *)(&m_val);
-        }
 
-        void *calc(int idx)
+        void calc(int x, int y, int z, int w)
         {
-            if (calcCurrent(idx)) {
-                m_val = m_op.eval(*(Ti *)m_child->calc(idx));
-            }
-            return (void *)&m_val;
+            this->m_val = m_op.eval(m_child->m_val);
         }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void calc(int idx)
         {
-            if (m_is_eval) return;
-
-            m_child->getInfo(len, buf_count, bytes);
-            len++;
-
-            m_is_eval = true;
-            return;
+            this->m_val = m_op.eval(m_child->m_val);
         }
 
-        void reset()
-        {
-            resetCommonFlags();
-            m_child->reset();
-        }
-
-        bool isLinear(const dim_t *dims)
-        {
-            if (!m_set_is_linear) {
-                m_linear = m_child->isLinear(dims);
-                m_set_is_linear = true;
-            }
-            return m_linear;
-        }
     };
 
 }
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 3c4a736298..7b7504064c 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -10,6 +10,8 @@
 #pragma once
 #include <Array.hpp>
 #include <platform.hpp>
+#include <TNJ/Node.hpp>
+#include <vector>
 
 namespace cpu
 {
@@ -17,20 +19,39 @@ namespace kernel
 {
 
 template<typename T>
-void evalArray(Array<T> in)
+void evalMultiple(std::vector<Array<T>> arrays)
 {
-    in.setId(cpu::getActiveDeviceId());
-    T *ptr = in.data.get();
+    af::dim4 odims = arrays[0].dims();
+    af::dim4 ostrs = arrays[0].strides();
 
-    af::dim4 odims = in.dims();
-    af::dim4 ostrs = in.strides();
+    int devId = cpu::getActiveDeviceId();
+    TNJ::Node_map_t nodes;
+    std::vector<T *> ptrs;
+    std::vector<TNJ::TNode<T> *> output_nodes;
 
-    bool is_linear = in.node->isLinear(odims.get());
+    for (auto &arr : arrays) {
+        arr.setId(devId);
+        ptrs.push_back(arr.data.get());
+        output_nodes.push_back(reinterpret_cast<TNJ::TNode<T> *>(arr.node.get()));
+        arr.node->getNodesMap(nodes);
+    }
+
+    bool is_linear = true;
+    std::vector<TNJ::Node *> full_nodes(nodes.size());
+    for(const auto &map_entry : nodes) {
+        full_nodes[map_entry.second] = map_entry.first;
+        is_linear &= map_entry.first->isLinear(odims.get());
+    }
 
     if (is_linear) {
-        int num = in.elements();
+        int num = arrays[0].elements();
         for (int i = 0; i < num; i++) {
-            ptr[i] = *(T *)in.node->calc(i);
+            for (int n = 0; n < (int)full_nodes.size(); n++) {
+                full_nodes[n]->calc(i);
+            }
+            for (int n = 0; n < (int)output_nodes.size(); n++) {
+                ptrs[n][i] = output_nodes[n]->m_val;
+            }
         }
     } else {
         for (int w = 0; w < (int)odims[3]; w++) {
@@ -45,15 +66,70 @@ void evalArray(Array<T> in)
                     for (int x = 0; x < (int)odims[0]; x++) {
                         dim_t id = x + offy;
 
-                        ptr[id] = *(T *)in.node->calc(x, y, z, w);
+                        for (int n = 0; n < (int)full_nodes.size(); n++) {
+                            full_nodes[n]->calc(x, y, z, w);
+                        }
+                        for (int n = 0; n < (int)output_nodes.size(); n++) {
+                            ptrs[n][id] = output_nodes[n]->m_val;
+                        }
                     }
                 }
             }
         }
     }
+}
+
+template<typename T>
+void evalArray(Array<T> arr)
+{
+    arr.setId(cpu::getActiveDeviceId());
+    T *ptr = arr.data.get();
+
+    af::dim4 odims = arr.dims();
+    af::dim4 ostrs = arr.strides();
+
+    TNJ::Node_map_t nodes;
+    arr.node->getNodesMap(nodes);
+
+    bool is_linear = true;
+    std::vector<TNJ::Node *> full_nodes(nodes.size());
+
+    for(const auto &map_entry : nodes) {
+        full_nodes[map_entry.second] = map_entry.first;
+        is_linear &= map_entry.first->isLinear(odims.get());
+    }
+
+    TNJ::TNode<T> *output_node = reinterpret_cast<TNJ::TNode<T> *>(full_nodes.back());
+    if (is_linear) {
+        int num = arr.elements();
+        for (int i = 0; i < num; i++) {
+            for (int n = 0; n < (int)full_nodes.size(); n++) {
+                full_nodes[n]->calc(i);
+            }
+            ptr[i] = output_node->m_val;
+        }
+    } else {
+        for (int w = 0; w < (int)odims[3]; w++) {
+            dim_t offw = w * ostrs[3];
+
+            for (int z = 0; z < (int)odims[2]; z++) {
+                dim_t offz = z * ostrs[2] + offw;
+
+                for (int y = 0; y < (int)odims[1]; y++) {
+                    dim_t offy = y * ostrs[1] + offz;
 
-    // Reset TNJ flags
-    in.node->reset();
+                    for (int x = 0; x < (int)odims[0]; x++) {
+                        dim_t id = x + offy;
+
+                        for (int n = 0; n < (int)full_nodes.size(); n++) {
+                            full_nodes[n]->calc(x, y, z, w);
+                        }
+                        ptr[id] = output_node->m_val;
+                    }
+                }
+            }
+        }
+    }
 }
 
 }

From efbb836d72c452ff9de7bcc4e5cfeb26d4d12869 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Thu, 16 Mar 2017 13:42:17 -0400
Subject: [PATCH 1135/2677] Improvements to `docs/pages/install.md`

* Instructions on how to install GLFW on Ubuntu 14.04 and 16.04
* Improved wording
* Other minor fixes
---
 docs/pages/install.md | 109 ++++++++++++++++++++++++------------------
 1 file changed, 62 insertions(+), 47 deletions(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index c22606ecba..4057f2a922 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -21,10 +21,10 @@ In general, the installation process for ArrayFire looks like this:
 4. Test the installation
 5. [Where to go for help?](#GettingHelp)
 
-Below you will find instructions for
+Below you will find instructions for:
 
 * [Windows](#Windows)
-* Linux including
+* Linux
     * [Debian 8](#Debian)
     * [Ubuntu 14.04 and later](#Ubuntu)
     * [RedHat, Fedora, and CentOS](#RPM-distros)
@@ -35,8 +35,9 @@ Below you will find instructions for
 If you wish to use CUDA or OpenCL please ensure that you have also installed
 support for these technologies from your video card vendor's website.
 
-Next [download](http://arrayfire.com/download/) and run the ArrayFire installer.
-After it has completed, you need to add ArrayFire to the path for all users.
+Next, [download](http://arrayfire.com/download/) and run the ArrayFire
+installer. After installation, you'll need to add ArrayFire to the path for
+all users:
 
 1. Open Advanced System Settings:
     * Windows 8: Move the Mouse pointer to the bottom right corner of the
@@ -46,83 +47,94 @@ After it has completed, you need to add ArrayFire to the path for all users.
 2. In Advanced System Settings window, click on Advanced tab
 3. Click on Environment Variables, then under System Variables, find PATH, and
    click on it.
-4. In edit mode, append %AF_PATH%/lib. NOTE: Ensure that there is a semi-colon
-   separating %AF_PATH%/lib from any existing content (e.g.
-   EXISTING_PATHS;%AF_PATH%/lib;) otherwise other software may not function
-   correctly.
+4. In edit mode, append `%AF_PATH%/lib`. Make sure to separate `%AF_PATH%/lib`
+   from any existing content using a semicolon (e.g.
+   `EXISTING_PATHS;%AF_PATH%/lib;`). Other software may function incorrectly
+   if this is not the case.
 
 Finally, verify that the path addition worked correctly. You can do this by:
 
-1. Open Visual Studio 2013. Open the HelloWorld solution which is located at
+1. Open Visual Studio 2013. Open the `HelloWorld` solution which is located at
    `%AF_PATH%/examples/helloworld/helloworld.exe`.
-2. Build and run the helloworld example. Be sure to, select the
-   platform/configuration of your choice using the platform drop-down (the
-   options are CPU, CUDA, and OpenCL) and Solution Configuration drop down
-   (options of Release and Debug) menus. Run the helloworld example
+2. Build and run the `helloworld` example. Use the "Solution Platform"
+   drop-down to select from the CPU, CUDA, or OpenCL backends ArrayFire
+   provides.
 
 # Linux
 
 ## <a name="Debian"></a> Debian 8
 
-First install the prerequisite packages:
+First, install the prerequisite packages:
 
-    # Prerequisite packages:
+    # Install prerequisite packages:
     apt-get install libglfw3-dev cmake
 
     # Enable GPU support (OpenCL):
     apt-get install ocl-icd-libopencl1
 
-If you wish to use CUDA, please
-[download the latest version of CUDA](https://developer.nvidia.com/cuda-zone)
-and install it on your system.
+If you wish to use CUDA,
+[download](https://developer.nvidia.com/cuda-downloads) and install the latest
+version.
 
-Next [download](http://arrayfire.com/download/) ArrayFire. After you have the
-file, run the installer.
+Next, [download](http://arrayfire.com/download/) the ArrayFire installer for
+your system. After you have the file, run the installer:
 
     ./arrayfire_*_Linux_x86_64.sh --exclude-subdir --prefix=/usr/local
 
 ## <a name="RPM-distros"></a> RedHat, Fedora, and CentOS
 
-First install the prerequisite packages:
+First, install the prerequisite packages:
 
     # Install prerequiste packages
     yum install glfw cmake
 
-On Centos and Redhat the `glfw` package is outdated and you will need to compile
-it from source. Please
-[these instructions](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire).
+NOTE: On CentOS and Redhat, the `glfw` package is outdated and you will need
+to compile it from source. Follow these
+[instructions](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire)
+for more information on how to build and install GFLW.
 
-If you wish to use CUDA, please
-[download the latest version of CUDA](https://developer.nvidia.com/cuda-downloads)
-and install it on your system.
+If you wish to use CUDA,
+[download](https://developer.nvidia.com/cuda-downloads) and install the latest
+version.
 
-Next [download](http://arrayfire.com/download/) ArrayFire. After you have the
-file, run the installer.
+Next, [download](http://arrayfire.com/download/) the ArrayFire installer for
+your system. After you have the file, run the installer:
 
     ./arrayfire_*_Linux_x86_64.sh --exclude-subdir --prefix=/usr/local
 
 ## <a name="Ubuntu"></a> Ubuntu 14.04 and later
 
-First install the prerequisite packages:
+First, install the prerequisite packages:
 
-    # Prerequisite packages:
+### Ubuntu 16.04
+
+    # Install prerequisite packages:
+    sudo apt-get install libglfw3-dev cmake
+
+### Ubuntu 14.04
+
+    # Install prerequisite packages:
     sudo apt-get install cmake
 
-Ubuntu 14.04 will not have the libglfw3-dev package in its repositories. You can either build the
-library from source (following the
-[instructions listed here](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire)) or
-install the library from a PPA as follows:
+Ubuntu 14.04 does not include the `libglfw3-dev` package in its
+repositories. In order to install, you can either:
+
+1. Build the library from source by following these
+   [instructions](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire),
+   or
+2. Install the library from a PPA as follows:
 
     sudo apt-add-repository ppa:keithw/glfw3
     sudo apt-get update
     sudo apt-get install glfw3
 
-After this point, the installation should proceed identically to Ubuntu 14.10 or newer.
+At this point, the installation should proceed identically for Ubuntu 14.04
+and newer.
 
 If your system has a CUDA GPU, we suggest downloading the latest drivers
 from NVIDIA in the form of a Debian package and installing using the
 package manager. At present, CUDA downloads can be found on the
-[NVIDIA CUDA download page](https://developer.nvidia.com/cuda-downloads)
+[NVIDIA CUDA download page](https://developer.nvidia.com/cuda-downloads).
 Follow NVIDIA's instructions for getting CUDA set up.
 
 If you wish to use OpenCL, simply install the OpenCL ICD loader along
@@ -132,22 +144,24 @@ with any drivers required for your hardware.
     apt-get install ocl-icd-libopencl1
 
 ### Special instructions for Tegra X1
-**The ArrayFire binary installer for Terga X1 requires JetPack 2.3 or L4T 24.2
-for Jetson TX1. This includes Ubuntu 16.04, CUDA 8.0 etc.**
-If you are using ArrayFire on the Tegra X1 also install these packages:
+
+**The ArrayFire binary installer for Tegra X1 requires at least JetPack 2.3 or
+L4T 24.2 for Jetson TX1. This includes Ubuntu 16.04, CUDA 8.0 etc.**
+
+You will also want to install the following packages when using ArrayFire on
+the Tegra X1:
 
     sudo apt-get install libopenblas-dev liblapacke-dev
 
 ### Special instructions for Tegra K1
-If you are using ArrayFire on the Tegra K1 also install these packages:
 
-    sudo apt-get install libatlas3gf-base libatlas-dev libfftw3-dev liblapacke-dev
+You will also want to install the following packages when using ArrayFire on
+the Tegra K1:
 
-In addition to these packages, you will need to compile GLFW3 from source
-using the instructions above.
+    sudo apt-get install libatlas3gf-base libatlas-dev libfftw3-dev liblapacke-dev
 
-Finally, [download](http://arrayfire.com/download/) ArrayFire. After you have
-the file, run the installer using:
+Finally, [download](http://arrayfire.com/download/) ArrayFire for your
+system. After you have the file, run the installer using:
 
     ./arrayfire_*_Linux_x86_64.sh --exclude-subdir --prefix=/usr/local
 
@@ -168,7 +182,8 @@ not include MKL acceleration of linear algebra functions.
 
 ## Testing installation
 
-After ArrayFire is installed, you can build the example programs as follows:
+Test ArrayFire after the installation process by building the example programs
+as follows:
 
     cp -r /usr/local/share/ArrayFire/examples .
     cd examples

From 96e9ff1a98470873d801bc33866c9b89440ab726 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 20 Mar 2017 11:57:28 +0530
Subject: [PATCH 1136/2677] Re-enable per thread device unit test

---
 test/threading.cpp | 164 ++++++++++++++++++++++-----------------------
 1 file changed, 82 insertions(+), 82 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index a1e084f227..52ce7f6cff 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -57,88 +57,88 @@ void morphTest(const array input, const array mask, const bool isDilation,
               << diff.count() << " s\n";
 }
 
-//TEST(Threading, SetPerThreadActiveDevice)
-//{
-//    if (noImageIOTests()) return;
-//
-//    vector<bool> isDilationFlags;
-//    vector<bool> isColorFlags;
-//    vector<string> files;
-//
-//    files.push_back( string(TEST_DIR "/morph/gray.test") );
-//    isDilationFlags.push_back(true);
-//    isColorFlags.push_back(false);
-//
-//    files.push_back( string(TEST_DIR "/morph/color.test") );
-//    isDilationFlags.push_back(false);
-//    isColorFlags.push_back(true);
-//
-//    vector<std::thread> tests;
-//    unsigned totalTestCount = 0;
-//
-//    auto start = std::chrono::high_resolution_clock::now();
-//
-//    for(size_t pos = 0; pos<files.size(); ++pos)
-//    {
-//        const bool isDilation = isDilationFlags[pos];
-//        const bool isColor    = isColorFlags[pos];
-//
-//        vector<dim4>    inDims;
-//        vector<string>  inFiles;
-//        vector<dim_t>   outSizes;
-//        vector<string>  outFiles;
-//
-//        readImageTests(files[pos], inDims, inFiles, outSizes, outFiles);
-//
-//        const unsigned testCount = inDims.size();
-//
-//        const dim4 maskdims(3,3,1,1);
-//
-//        for (size_t testId=0; testId<testCount; ++testId)
-//        {
-//            int trgtDeviceId = totalTestCount % af::getDeviceCount();
-//
-//            //prefix full path to image file names
-//            inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
-//            outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
-//
-//            af::setDevice(trgtDeviceId);
-//
-//            const array mask = constant(1.0, maskdims);
-//
-//            array input= loadImage(inFiles[testId].c_str(), isColor);
-//            array gold = loadImage(outFiles[testId].c_str(), isColor);
-//
-//            //Push the new test as a new thread of execution
-//            tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
-//
-//            std::cout<<"morph test launched with the following params on device ("
-//                     <<trgtDeviceId<<"):"<<std::endl;
-//            std::cout<<"\t Input image dims: "<<input.dims()<<std::endl;
-//            std::cout<<"\t Mask dims: "<<mask.dims()<<std::endl;
-//            std::cout<<"\t IsDilation : "<< (isDilation ? "True" : "False") <<std::endl;
-//
-//            totalTestCount++;
-//        }
-//    }
-//    std::cout<< std::endl << "Waiting for results ..." << std::endl << std::endl;
-//
-//    for (size_t testId=0; testId<tests.size(); ++testId)
-//    {
-//        if (tests[testId].joinable()) {
-//            std::cout<<"Attempting join for test ..."<<testId<<std::endl;
-//            tests[testId].join();
-//            std::cout<<"test "<< testId <<" completed." << std::endl;
-//        }
-//        std::cout<<std::endl;
-//    }
-//
-//    auto end = std::chrono::high_resolution_clock::now();
-//
-//    std::chrono::duration<double> diff = end - start;
-//
-//    std::cout << "Total time taken for test : " << diff.count() << " s\n";
-//}
+TEST(Threading, SetPerThreadActiveDevice)
+{
+    if (noImageIOTests()) return;
+
+    vector<bool> isDilationFlags;
+    vector<bool> isColorFlags;
+    vector<string> files;
+
+    files.push_back( string(TEST_DIR "/morph/gray.test") );
+    isDilationFlags.push_back(true);
+    isColorFlags.push_back(false);
+
+    files.push_back( string(TEST_DIR "/morph/color.test") );
+    isDilationFlags.push_back(false);
+    isColorFlags.push_back(true);
+
+    vector<std::thread> tests;
+    unsigned totalTestCount = 0;
+
+    auto start = std::chrono::high_resolution_clock::now();
+
+    for(size_t pos = 0; pos<files.size(); ++pos)
+    {
+        const bool isDilation = isDilationFlags[pos];
+        const bool isColor    = isColorFlags[pos];
+
+        vector<dim4>    inDims;
+        vector<string>  inFiles;
+        vector<dim_t>   outSizes;
+        vector<string>  outFiles;
+
+        readImageTests(files[pos], inDims, inFiles, outSizes, outFiles);
+
+        const unsigned testCount = inDims.size();
+
+        const dim4 maskdims(3,3,1,1);
+
+        for (size_t testId=0; testId<testCount; ++testId)
+        {
+            int trgtDeviceId = totalTestCount % af::getDeviceCount();
+
+            //prefix full path to image file names
+            inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+            outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+
+            af::setDevice(trgtDeviceId);
+
+            const array mask = constant(1.0, maskdims);
+
+            array input= loadImage(inFiles[testId].c_str(), isColor);
+            array gold = loadImage(outFiles[testId].c_str(), isColor);
+
+            //Push the new test as a new thread of execution
+            tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
+
+            std::cout<<"morph test launched with the following params on device ("
+                     <<trgtDeviceId<<"):"<<std::endl;
+            std::cout<<"\t Input image dims: "<<input.dims()<<std::endl;
+            std::cout<<"\t Mask dims: "<<mask.dims()<<std::endl;
+            std::cout<<"\t IsDilation : "<< (isDilation ? "True" : "False") <<std::endl;
+
+            totalTestCount++;
+        }
+    }
+    std::cout<< std::endl << "Waiting for results ..." << std::endl << std::endl;
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+    {
+        if (tests[testId].joinable()) {
+            std::cout<<"Attempting join for test ..."<<testId<<std::endl;
+            tests[testId].join();
+            std::cout<<"test "<< testId <<" completed." << std::endl;
+        }
+        std::cout<<std::endl;
+    }
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    std::chrono::duration<double> diff = end - start;
+
+    std::cout << "Total time taken for test : " << diff.count() << " s\n";
+}
 
 enum ArithOp
 {

From ee6591eb3e2e5a9c5a541724f93076c9d56adb3f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 20 Mar 2017 11:58:30 +0530
Subject: [PATCH 1137/2677] Remove c++11 flag for basic_c test

---
 CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6972b7d628..a575b68f97 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -136,7 +136,9 @@ INCLUDE_DIRECTORIES(BEFORE
     )
 
 IF(${UNIX})
-    ADD_DEFINITIONS(-Wall -std=c++11 -fvisibility=hidden)
+    ADD_DEFINITIONS(-Wall -fvisibility=hidden)
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+
     IF(${WITH_COVERAGE})
         SET(CMAKE_CXX_FLAGS             "-fprofile-arcs -ftest-coverage")
         SET(CMAKE_EXE_LINKER_FLAGS      "-fprofile-arcs -ftest-coverage")

From 1e9030a138c149b9176eaa71e1730b9a7fde67f9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Mar 2017 18:55:19 +0530
Subject: [PATCH 1138/2677] Add c++11 flag for threading test alone

Earlier commit broke CUDA builds on build server though the local
build on Ubuntu 16.04 compiled fine.
---
 CMakeLists.txt                  | 3 +--
 src/backend/cuda/CMakeLists.txt | 2 +-
 test/CMakeLists.txt             | 6 ++++++
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a575b68f97..3ffe04c032 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -136,8 +136,7 @@ INCLUDE_DIRECTORIES(BEFORE
     )
 
 IF(${UNIX})
-    ADD_DEFINITIONS(-Wall -fvisibility=hidden)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+    ADD_DEFINITIONS(-std=c++11 -Wall -fvisibility=hidden)
 
     IF(${WITH_COVERAGE})
         SET(CMAKE_CXX_FLAGS             "-fprofile-arcs -ftest-coverage")
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 46f86dc566..e45558deed 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -418,7 +418,7 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${thrust_sort_by_key_sources}
                 ${scan_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE}
-                #These flags enable C++11 and disable invalid offsetof warning
+                #disable invalid offsetof warning
                 -std=c++11 -Xcudafe "--diag_suppress=1427")
 
 ADD_DEPENDENCIES(afcuda ${ptx_targets})
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b1023d1e14..031af068a3 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -8,6 +8,8 @@ FIND_PACKAGE(OpenCL QUIET)
 
 OPTION(BUILD_SINGLE_TEST_FILE "Build tests in a single file" OFF)
 
+REMOVE_DEFINITIONS(-std=c++11)
+
 # If the tests are not being built at the same time as ArrayFire,
 # we need to first find the ArrayFire library
 IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
@@ -118,6 +120,10 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
           COMPILE_FLAGS -DAF_${DEF_NAME}
           FOLDER "Tests/${BACKEND}")
 
+        IF (${FNAME} STREQUAL "threading")
+            SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_OPTIONS -std=c++11)
+        ENDIF()
+
         IF(TEST_LINK_FLAGS)
           SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS ${TEST_LINK_FLAGS})
         ENDIF(TEST_LINK_FLAGS)

From 47747c681d751ae960866d54b8e464ed643b1b67 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Fri, 24 Mar 2017 14:18:44 -0400
Subject: [PATCH 1139/2677] Added argument checks to af_create_sparse_array

- Added checks to verify that rowIdx and colIdx are of s32 type
- Fixed index argument to existing ARG_ASSERT and DIM_ASSERT checks
- Added check which causes failure if template and array types do not match in getArray()
- Fixed a few misspellings in comments across different header files
- Added corresponding test to test/sparse.cpp
- Fixed bug with src/api/c/rotate.cpp

Addresses issue #1745.
---
 include/af/array.h   |  4 ++--
 include/af/dim4.hpp  |  2 +-
 src/api/c/handle.hpp |  2 ++
 src/api/c/rotate.cpp |  4 ++--
 src/api/c/sparse.cpp |  8 +++++---
 test/sparse.cpp      | 16 ++++++++++++++++
 6 files changed, 28 insertions(+), 8 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index ccf99ed0a4..327347abe3 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -709,7 +709,7 @@ namespace af
         /**
             \brief This operator returns a reference of the original array at a given coordinate.
 
-            You can pass \ref af::seq, \ref af::array, or an int as it's parameters.
+            You can pass \ref af::seq, \ref af::array, or an int as its parameters.
             These references can be used for assignment or returning references
             to \ref af::array objects.
 
@@ -1499,7 +1499,7 @@ extern "C" {
     AFAPI af_err af_get_type(af_dtype *type, const af_array arr);
 
     /**
-        \brief Gets the dimseions of an array.
+        \brief Gets the dimensions of an array.
 
         \param[out] d0 is the output that contains the size of first dimension of \p arr
         \param[out] d1 is the output that contains the size of second dimension of \p arr
diff --git a/include/af/dim4.hpp b/include/af/dim4.hpp
index 1e0a60c969..4ed4c56603 100644
--- a/include/af/dim4.hpp
+++ b/include/af/dim4.hpp
@@ -23,7 +23,7 @@ namespace af
 class AFAPI dim4
 {
     public:
-    dim_t dims[4]; //FIXME: Make this C compatiable
+    dim_t dims[4]; //FIXME: Make this C compatible
     dim4(); //deleted
 public:
     dim4(   dim_t first,
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index b550c78b43..3085ce31a9 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -28,6 +28,8 @@ static const detail::Array<T> &
 getArray(const af_array &arr)
 {
     detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
+    if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
+        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
     ARG_ASSERT(0, A->isSparse() == false);
     return *A;
 }
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index 3c57da6d8f..72af356d6b 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -21,7 +21,7 @@ template<typename T>
 static inline af_array rotate(const af_array in, const float theta, const af::dim4 &odims,
                               const af_interp_type method)
 {
-    return getHandle(rotate<T>(getArray<T>(in), theta, odims, method));
+    return getHandle(rotate<T>(castArray<T>(in), theta, odims, method));
 }
 
 
@@ -45,7 +45,7 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
 
         af_dtype itype = info.getType();
 
-        ARG_ASSERT(3, method == AF_INTERP_NEAREST  ||
+        ARG_ASSERT(4, method == AF_INTERP_NEAREST  ||
                       method == AF_INTERP_BILINEAR ||
                       method == AF_INTERP_BILINEAR_COSINE ||
                       method == AF_INTERP_BICUBIC ||
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 88166208f0..50951038a8 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -81,9 +81,11 @@ af_err af_create_sparse_array(
         const ArrayInfo& cInfo = getInfo(colIdx);
 
         TYPE_ASSERT(vInfo.isFloating());
-        DIM_ASSERT(4, vInfo.isLinear());
-        DIM_ASSERT(5, rInfo.isLinear());
-        DIM_ASSERT(6, cInfo.isLinear());
+        DIM_ASSERT(3, vInfo.isLinear());
+        ARG_ASSERT(4, rInfo.getType() == s32);
+        DIM_ASSERT(4, rInfo.isLinear());
+        ARG_ASSERT(5, cInfo.getType() == s32);
+        DIM_ASSERT(5, cInfo.isLinear());
 
         af_array output = 0;
 
diff --git a/test/sparse.cpp b/test/sparse.cpp
index fc87b95a4d..cac314c76e 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -312,3 +312,19 @@ CAST_TESTS(cfloat , cdouble )
 
 CAST_TESTS(cdouble, cfloat  )
 CAST_TESTS(cdouble, cdouble )
+
+
+TEST(Sparse, ISSUE_1745)
+{
+  af::array A = af::randu(4, 4);
+  A(1, af::span) = 0;
+  A(2, af::span) = 0;
+
+  af::array idx = where(A);
+  af::array data = A(idx);
+  af::array row_idx = (idx / A.dims()[0]).as(s64);
+  af::array col_idx = (idx % A.dims()[0]).as(s64);
+
+  af_array A_sparse;
+  ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array(&A_sparse, A.dims(0), A.dims(1), data.get(), row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
+}

From 8084be352f11aa9abf62d3ad3207529cebe660c5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 30 Mar 2017 17:14:37 +0530
Subject: [PATCH 1140/2677] Unit test for memory management in multi-threaded
 scenario

---
 test/threading.cpp | 145 ++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 143 insertions(+), 2 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 52ce7f6cff..7978cb960c 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -19,6 +19,8 @@ using namespace af;
 using std::vector;
 using std::string;
 
+static const int THREAD_COUNT = 32;
+
 #if defined(AF_CPU)
 static const unsigned ITERATION_COUNT = 10;
 #else
@@ -174,7 +176,7 @@ TEST(Threading, SimultaneousRead)
 
     vector<std::thread> tests;
 
-    for (int t=0; t<32; ++t)
+    for (int t=0; t<THREAD_COUNT; ++t)
     {
         ArithOp op;
         float outValue;
@@ -189,7 +191,146 @@ TEST(Threading, SimultaneousRead)
         tests.emplace_back(calc, op, A, B, outValue);
     }
 
-    for (int t=0; t<32; ++t)
+    for (int t=0; t<THREAD_COUNT; ++t)
         if (tests[t].joinable())
             tests[t].join();
 }
+
+int nextTargetDeviceId()
+{
+    static int nextId = 0;
+    return nextId++;
+}
+
+static void cleanSlate()
+{
+    const size_t step_bytes = 1024;
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceGC();
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(alloc_buffers, 0u);
+    ASSERT_EQ(lock_buffers, 0u);
+    ASSERT_EQ(alloc_bytes, 0u);
+    ASSERT_EQ(lock_bytes, 0u);
+
+    af::setMemStepSize(step_bytes);
+
+    ASSERT_EQ(af::getMemStepSize(), step_bytes);
+}
+
+void doubleAllocationTest()
+{
+    af::setDevice(0);
+
+    af::array a = randu(5, 5);
+
+    for (int i = 0; i < 100; ++i)
+    {
+        a = randu(5, 5);
+        std::this_thread::sleep_for(std::chrono::milliseconds(10));
+    }
+}
+
+TEST(Threading, MemoryManagement_Double_Alloc)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    for (int t=0; t<THREAD_COUNT; ++t)
+        tests.emplace_back(doubleAllocationTest);
+
+    for (int t=0; t<THREAD_COUNT; ++t)
+        if (tests[t].joinable())
+            tests[t].join();
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+            &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ( lock_buffers,     0u);
+    ASSERT_EQ(  lock_bytes,      0u);
+    ASSERT_LE(alloc_buffers,    64u);
+    ASSERT_GT(alloc_buffers,    32u);
+    ASSERT_LE(  alloc_bytes, 65536u);
+    ASSERT_GT(  alloc_bytes, 32768u);
+}
+
+void singleAllocationTest()
+{
+    af::setDevice(0);
+
+    for (int i = 0; i < 100; ++i)
+    {
+        af::array a = af::randu(5, 5);
+        std::this_thread::sleep_for(std::chrono::milliseconds(10));
+    }
+}
+
+TEST(Threading, MemoryManagement_Single_Alloc)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    for (int t=0; t<THREAD_COUNT; ++t)
+        tests.emplace_back(singleAllocationTest);
+
+    for (int t=0; t<THREAD_COUNT; ++t)
+        if (tests[t].joinable())
+            tests[t].join();
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+            &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ( lock_buffers,     0u);
+    ASSERT_EQ(  lock_bytes,      0u);
+    ASSERT_LE(alloc_buffers,    32u);
+    ASSERT_GT(alloc_buffers,     0u);
+    ASSERT_LE(  alloc_bytes, 32768u);
+    ASSERT_GT(  alloc_bytes,     0u);
+}
+
+void jitAllocationTest()
+{
+    af::setDevice(0);
+
+    for (int i = 0; i < 100; ++i)
+        af::array a = af::constant(1, 5, 5);
+}
+
+TEST(Threading, MemoryManagement_JIT_Node)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    for (int t=0; t<THREAD_COUNT; ++t)
+        tests.emplace_back(jitAllocationTest);
+
+    for (int t=0; t<THREAD_COUNT; ++t)
+        if (tests[t].joinable())
+            tests[t].join();
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+            &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(alloc_buffers,     0u);
+    ASSERT_EQ( lock_buffers,     0u);
+    ASSERT_EQ(  alloc_bytes,     0u);
+    ASSERT_EQ(  lock_bytes,      0u);
+}

From 89340b63c3700584a7d99d9c129d47f8620cb16c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 31 Mar 2017 15:32:11 +0530
Subject: [PATCH 1141/2677] FFT multi-threaded unit tests

---
 test/threading.cpp | 145 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 145 insertions(+)

diff --git a/test/threading.cpp b/test/threading.cpp
index 7978cb960c..5f8ce9bee0 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -334,3 +334,148 @@ TEST(Threading, MemoryManagement_JIT_Node)
     ASSERT_EQ(  alloc_bytes,     0u);
     ASSERT_EQ(  lock_bytes,      0u);
 }
+
+template<typename inType, typename outType, bool isInverse>
+void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
+{
+    if (noDoubleTests<inType>()) return;
+    if (noDoubleTests<outType>()) return;
+
+    vector<af::dim4>        numDims;
+    vector<vector<inType> >       in;
+    vector<vector<outType> >   tests;
+
+    readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
+
+    af::dim4 dims       = numDims[0];
+    af_array outArray   = 0;
+    af_array inArray    = 0;
+
+    ASSERT_EQ(AF_SUCCESS, af_set_device(targetDevice));
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+
+    if (isInverse){
+        switch (dims.ndims()) {
+            case 1 : ASSERT_EQ(AF_SUCCESS, af_ifft (&outArray, inArray, 1.0, pad0));              break;
+            case 2 : ASSERT_EQ(AF_SUCCESS, af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
+            case 3 : ASSERT_EQ(AF_SUCCESS, af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
+            default: throw std::runtime_error("This error shouldn't happen, pls check");
+        }
+    } else {
+        switch(dims.ndims()) {
+            case 1 : ASSERT_EQ(AF_SUCCESS, af_fft (&outArray, inArray, 1.0, pad0));               break;
+            case 2 : ASSERT_EQ(AF_SUCCESS, af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
+            case 3 : ASSERT_EQ(AF_SUCCESS, af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
+            default: throw std::runtime_error("This error shouldn't happen, pls check");
+        }
+    }
+
+    size_t out_size = tests[0].size();
+    outType *outData= new outType[out_size];
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+    vector<outType> goldBar(tests[0].begin(), tests[0].end());
+
+    size_t test_size = 0;
+    switch(dims.ndims()) {
+        case 1  : test_size = dims[0]/2+1;                       break;
+        case 2  : test_size = dims[1] * (dims[0]/2+1);           break;
+        case 3  : test_size = dims[2] * dims[1] * (dims[0]/2+1); break;
+        default : test_size = dims[0]/2+1;                       break;
+    }
+    outType output_scale = (outType)(isInverse ? test_size : 1);
+    for (size_t elIter=0; elIter<test_size; ++elIter) {
+        bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
+        ASSERT_EQ(true, isUnderTolerance)<<
+            "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
+            (output_scale*outData[elIter]) << " at: " << elIter <<
+            " from thread: "<< std::this_thread::get_id() << std::endl;
+    }
+
+    // cleanup
+    delete[] outData;
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+}
+
+#define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, file)                         \
+    {                                                                                       \
+        int targetDevice = nextTargetDeviceId() % numDevices;                               \
+        tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, file, 0, 0, 0);  \
+    }
+
+#define INSTANTIATE_TEST_TP(func, name, is_inverse, in_t, out_t, file, p0, p1)              \
+    {                                                                                       \
+        int targetDevice = nextTargetDeviceId() % numDevices;                               \
+        tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, file, p0, p1, 0);\
+    }
+
+#if !defined(AF_OPENCL)
+/// OpenCL backend tests seem to be failing even when
+/// each thead has it's own plan cache(thread_local).
+/// The issue seems to present itself randomly in the form of crashes,
+/// garbage values.
+TEST(Threading, FFT)
+{
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    // Real to complex transforms
+    INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
+    INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
+    INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
+    INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
+    INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
+    INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
+
+    // complex to complex transforms
+    INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
+    INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
+    INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
+    INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
+
+    // Factors 7, 11, 13
+    INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+
+    INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+
+    // transforms on padded and truncated arrays
+    INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+
+    INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+
+    // inverse transforms
+    // complex to complex transforms
+    INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
+    INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
+    INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
+    INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
+    INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
+    INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+    {
+        if (tests[testId].joinable()) {
+            tests[testId].join();
+        }
+    }
+}
+#endif

From b34b8038a6c36287a9268ebb0fb11e8611ff5b3d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 2 Apr 2017 07:10:05 +0530
Subject: [PATCH 1142/2677] blas multi-threaded unit tests

---
 test/threading.cpp | 95 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)

diff --git a/test/threading.cpp b/test/threading.cpp
index 5f8ce9bee0..16b0314ef4 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -13,6 +13,7 @@
 #include <testHelpers.hpp>
 #include <thread>
 #include <chrono>
+#include <iterator>
 
 using namespace af;
 
@@ -479,3 +480,97 @@ TEST(Threading, FFT)
     }
 }
 #endif
+
+template<typename T, bool isBVector>
+void cppMatMulCheck(int targetDevice, string TestFile)
+{
+    if (noDoubleTests<T>()) return;
+
+    using std::vector;
+    vector<af::dim4> numDims;
+
+    vector<vector<T> > hData;
+    vector<vector<T> > tests;
+    readTests<T,T,int>(TestFile, numDims, hData, tests);
+
+    af::setDevice(targetDevice);
+
+    af::array a(numDims[0], &hData[0].front());
+    af::array b(numDims[1], &hData[1].front());
+
+    af::dim4 atdims = numDims[0];
+    {
+        dim_t f  =    atdims[0];
+        atdims[0]   =    atdims[1];
+        atdims[1]   =    f;
+    }
+    af::dim4 btdims = numDims[1];
+    {
+        dim_t f = btdims[0];
+        btdims[0] = btdims[1];
+        btdims[1] = f;
+    }
+
+    af::array aT = moddims(a, atdims.ndims(), atdims.get());
+    af::array bT = moddims(b, btdims.ndims(), btdims.get());
+
+    vector<af::array> out(tests.size());
+    if(isBVector) {
+        out[0] = af::matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
+        out[1] = af::matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
+        out[2] = af::matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
+        out[3] = af::matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
+        out[4] = af::matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
+    }
+    else {
+        out[0] = af::matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
+        out[1] = af::matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
+        out[2] = af::matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
+        out[3] = af::matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
+    }
+
+    for(size_t i = 0; i < tests.size(); i++) {
+        dim_t elems = out[i].elements();
+        vector<T> h_out(elems);
+        out[i].host((void*)&h_out.front());
+
+        if (false == equal(h_out.begin(), h_out.end(), tests[i].begin())) {
+
+            std::cout << "Failed test " << i << "\nCalculated: " << std::endl;
+            std::copy(h_out.begin(), h_out.end(), std::ostream_iterator<T>(std::cout, ", "));
+            std::cout << "Expected: " << std::endl;
+            std::copy(tests[i].begin(), tests[i].end(), std::ostream_iterator<T>(std::cout, ", "));
+            FAIL();
+        }
+    }
+}
+
+#define TEST_FOR_TYPE(TypeName)                                   \
+    tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
+            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/Basic.test");          \
+    tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
+            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/NonSquare.test");      \
+    tests.emplace_back(cppMatMulCheck<TypeName, true>,                              \
+            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/SquareVector.test");   \
+    tests.emplace_back(cppMatMulCheck<TypeName, true>,                              \
+            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/RectangleVector.test");
+
+TEST(Threading, BLAS)
+{
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    TEST_FOR_TYPE(      float);
+    TEST_FOR_TYPE( af::cfloat);
+    TEST_FOR_TYPE(     double);
+    TEST_FOR_TYPE(af::cdouble);
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+    {
+        if (tests[testId].joinable()) {
+            tests[testId].join();
+        }
+    }
+}

From 8c56c58eba7bb939d9ead3485607fe1673533c18 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 3 Apr 2017 17:55:14 +0530
Subject: [PATCH 1143/2677] Add thread_local qualifier for
 cu{blas,solver,sparse} handles

---
 src/backend/cuda/platform.cpp | 42 +++++++++++++----------------------
 src/backend/cuda/platform.hpp | 14 ------------
 2 files changed, 15 insertions(+), 41 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 078c1983d4..baaa1ee3eb 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -436,56 +436,44 @@ PlanCache& fftManager()
 
 BlasHandle blasHandle()
 {
-    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local static std::unique_ptr<cublasHandle> cublasHandles[DeviceManager::MAX_DEVICES];
+    thread_local static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    DeviceManager& inst = DeviceManager::getInstance();
+    std::call_once(initFlags[id], [&]{ cublasHandles[id].reset(new cublasHandle()); });
 
-    std::call_once(initFlags[id], [&]{ inst.cublasHandles[id].reset(new cublasHandle()); });
+    CUBLAS_CHECK(cublasSetStream(cublasHandles[id].get()->get(), cuda::getStream(id)));
 
-    return inst.cublasHandles[id].get()->get();
+    return cublasHandles[id].get()->get();
 }
 
 SolveHandle solverDnHandle()
 {
-    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local static std::unique_ptr<cusolverDnHandle> cusolverHandles[DeviceManager::MAX_DEVICES];
+    thread_local static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    DeviceManager& inst = DeviceManager::getInstance();
+    std::call_once(initFlags[id], [&]{ cusolverHandles[id].reset(new cusolverDnHandle()); });
 
-    std::call_once(initFlags[id], [&]{ inst.cusolverHandles[id].reset(new cusolverDnHandle()); });
+    CUSOLVER_CHECK(cusolverDnSetStream(cusolverHandles[id].get()->get(), cuda::getStream(id)));
 
-    // FIXME
-    // This is not an ideal case. It's just a hack.
-    // The correct way to do is to use
-    // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
-    // in the class constructor.
-    // However, this is causing a lot of the cusolver functions to fail.
-    // The only way to fix them is to use cudaDeviceSynchronize() and cudaStreamSynchronize()
-    // all over the place, but even then some calls like getrs in solve_lu
-    // continue to fail on any stream other than 0.
-    //
-    // cuSolver Streams patch:
-    // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
-    //
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
-
-    return inst.cusolverHandles[id].get()->get();
+    return cusolverHandles[id].get()->get();
 }
 
 SparseHandle sparseHandle()
 {
-    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local static std::unique_ptr<cusparseHandle> cusparseHandles[DeviceManager::MAX_DEVICES];
+    thread_local static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    DeviceManager& inst = DeviceManager::getInstance();
+    std::call_once(initFlags[id], [&]{ cusparseHandles[id].reset(new cusparseHandle()); });
 
-    std::call_once(initFlags[id], [&]{ inst.cusparseHandles[id].reset(new cusparseHandle()); });
+    CUSPARSE_CHECK(cusparseSetStream(cusparseHandles[id].get()->get(), cuda::getStream(id)));
 
-    return inst.cusparseHandles[id].get()->get();
+    return cusparseHandles[id].get()->get();
 }
 
 DeviceManager::DeviceManager()
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 152dfc6a14..b9ac3df401 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -111,14 +111,6 @@ class DeviceManager
         friend GraphicsResourceManager& interopManager();
 #endif
 
-        friend PlanCache& fftManager();
-
-        friend BlasHandle blasHandle();
-
-        friend SolveHandle solverDnHandle();
-
-        friend SparseHandle sparseHandle();
-
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -170,11 +162,5 @@ class DeviceManager
 #if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 #endif
-
-        std::unique_ptr<cublasHandle> cublasHandles[MAX_DEVICES];
-
-        std::unique_ptr<cusolverDnHandle> cusolverHandles[MAX_DEVICES];
-
-        std::unique_ptr<cusparseHandle> cusparseHandles[MAX_DEVICES];
 };
 }

From 895267f260e7dae05cd307be5883e65a993f5eac Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 3 Apr 2017 17:56:48 +0530
Subject: [PATCH 1144/2677] Add Solve Unit tests for multi-threaded scenarios

* Moved common solver unit test code to test/solve_common.hpp
  which is used by both solve_dense.cpp and threading.cpp files.
* Disabled blas, solve and fft tests in threading.cpp file
  temporarily for OpenCL backend. They will be re-enabled
  once the issues are resolved.
---
 src/backend/cuda/cublas.cpp   |   1 -
 src/backend/cuda/cusparse.cpp |   1 -
 src/backend/cuda/platform.cpp |  15 +++-
 src/backend/opencl/blas.cpp   |   1 -
 test/solve_common.hpp         | 142 ++++++++++++++++++++++++++++++++++
 test/solve_dense.cpp          | 122 +----------------------------
 test/threading.cpp            |  65 +++++++++++++---
 7 files changed, 213 insertions(+), 134 deletions(-)
 create mode 100644 test/solve_common.hpp

diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 515a6e4bd0..26d6211c39 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -33,6 +33,5 @@ const char *errorString(cublasStatus_t err)
 void cublasHandle::createHandle(BlasHandle* handle)
 {
     CUBLAS_CHECK(cublasCreate(handle));
-    CUBLAS_CHECK(cublasSetStream(*handle, cuda::getActiveStream()));
 }
 }
diff --git a/src/backend/cuda/cusparse.cpp b/src/backend/cuda/cusparse.cpp
index 526c1c50a3..1e6776fff1 100644
--- a/src/backend/cuda/cusparse.cpp
+++ b/src/backend/cuda/cusparse.cpp
@@ -34,6 +34,5 @@ const char *errorString(cusparseStatus_t err)
 void cusparseHandle::createHandle(SparseHandle* handle)
 {
     CUSPARSE_CHECK(cusparseCreate(handle));
-    CUSPARSE_CHECK(cusparseSetStream(*handle, cuda::getActiveStream()));
 }
 }
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index baaa1ee3eb..89cc827758 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -457,7 +457,20 @@ SolveHandle solverDnHandle()
 
     std::call_once(initFlags[id], [&]{ cusolverHandles[id].reset(new cusolverDnHandle()); });
 
-    CUSOLVER_CHECK(cusolverDnSetStream(cusolverHandles[id].get()->get(), cuda::getStream(id)));
+    //FIXME
+    // This is not an ideal case. It's just a hack.
+    // The correct way to do is to use
+    // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
+    // in the class constructor.
+    // However, this is causing a lot of the cusolver functions to fail.
+    // The only way to fix them is to use cudaDeviceSynchronize() and
+    //     cudaStreamSynchronize()
+    // all over the place, but even then some calls like getrs in solve_lu
+    // continue to fail on any stream other than 0.
+    //
+    // cuSolver Streams patch:
+    // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
 
     return cusolverHandles[id].get()->get();
 }
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 113a6a6e66..420ee1f332 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -174,7 +174,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 (*out.get())(),   out.getOffset(),  out.dims()[0],
                 1, &getQueue()(), 0, nullptr, &event())
             );
-
     }
 
     return out;
diff --git a/test/solve_common.hpp b/test/solve_common.hpp
new file mode 100644
index 0000000000..e5f5520f88
--- /dev/null
+++ b/test/solve_common.hpp
@@ -0,0 +1,142 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <complex>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using std::abs;
+using af::cfloat;
+using af::cdouble;
+
+///////////////////////////////// CPP ////////////////////////////////////
+//
+
+template<typename T>
+void solveTester(const int m, const int n, const int k, double eps, int targetDevice=-1)
+{
+    if (targetDevice>=0)
+        af::setDevice(targetDevice);
+
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
+#if 1
+    af::array A  = cpu_randu<T>(af::dim4(m, n));
+    af::array X0 = cpu_randu<T>(af::dim4(n, k));
+#else
+    af::array A  = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array X0 = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+    af::array B0 = af::matmul(A, X0);
+
+    //! [ex_solve]
+    af::array X1 = af::solve(A, B0);
+    //! [ex_solve]
+
+    //! [ex_solve_recon]
+    af::array B1 = af::matmul(A, X1);
+    //! [ex_solve_recon]
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
+}
+
+template<typename T>
+void solveLUTester(const int n, const int k, double eps, int targetDevice=-1)
+{
+    if (targetDevice>=0)
+        af::setDevice(targetDevice);
+
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
+#if 1
+    af::array A  = cpu_randu<T>(af::dim4(n, n));
+    af::array X0 = cpu_randu<T>(af::dim4(n, k));
+#else
+    af::array A  = af::randu(n, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array X0 = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+    af::array B0 = af::matmul(A, X0);
+
+    //! [ex_solve_lu]
+    af::array A_lu, pivot;
+    af::lu(A_lu, pivot, A);
+    af::array X1 = af::solveLU(A_lu, pivot, B0);
+    //! [ex_solve_lu]
+
+    af::array B1 = af::matmul(A, X1);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
+}
+
+template<typename T>
+void solveTriangleTester(const int n, const int k, bool is_upper, double eps, int targetDevice=-1)
+{
+    if (targetDevice>=0)
+        af::setDevice(targetDevice);
+
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
+#if 1
+    af::array A  = cpu_randu<T>(af::dim4(n, n));
+    af::array X0 = cpu_randu<T>(af::dim4(n, k));
+#else
+    af::array A  = af::randu(n, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array X0 = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    af::array L, U, pivot;
+    af::lu(L, U, pivot, A);
+
+    af::array AT = is_upper ? U : L;
+    af::array B0 = af::matmul(AT, X0);
+    af::array X1;
+
+    if (is_upper) {
+        //! [ex_solve_upper]
+        af::array X = af::solve(AT, B0, AF_MAT_UPPER);
+        //! [ex_solve_upper]
+
+        X1 = X;
+    } else {
+        //! [ex_solve_lower]
+        af::array X = af::solve(AT, B0, AF_MAT_LOWER);
+        //! [ex_solve_lower]
+
+        X1 = X;
+    }
+
+    af::array B1 = af::matmul(AT, X1);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
+}
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 171551e8cf..641da961ee 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -8,127 +8,7 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <arrayfire.h>
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/traits.hpp>
-#include <vector>
-#include <iostream>
-#include <complex>
-#include <string>
-#include <testHelpers.hpp>
-
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
-using af::cfloat;
-using af::cdouble;
-
-///////////////////////////////// CPP ////////////////////////////////////
-//
-
-template<typename T>
-void solveTester(const int m, const int n, const int k, double eps)
-{
-    af::deviceGC();
-
-    if (noDoubleTests<T>()) return;
-    if (noLAPACKTests()) return;
-
-#if 1
-    af::array A  = cpu_randu<T>(af::dim4(m, n));
-    af::array X0 = cpu_randu<T>(af::dim4(n, k));
-#else
-    af::array A  = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array X0 = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
-#endif
-    af::array B0 = af::matmul(A, X0);
-
-    //! [ex_solve]
-    af::array X1 = af::solve(A, B0);
-    //! [ex_solve]
-
-    //! [ex_solve_recon]
-    af::array B1 = af::matmul(A, X1);
-    //! [ex_solve_recon]
-
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
-}
-
-template<typename T>
-void solveLUTester(const int n, const int k, double eps)
-{
-    af::deviceGC();
-
-    if (noDoubleTests<T>()) return;
-    if (noLAPACKTests()) return;
-
-#if 1
-    af::array A  = cpu_randu<T>(af::dim4(n, n));
-    af::array X0 = cpu_randu<T>(af::dim4(n, k));
-#else
-    af::array A  = af::randu(n, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array X0 = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
-#endif
-    af::array B0 = af::matmul(A, X0);
-
-    //! [ex_solve_lu]
-    af::array A_lu, pivot;
-    af::lu(A_lu, pivot, A);
-    af::array X1 = af::solveLU(A_lu, pivot, B0);
-    //! [ex_solve_lu]
-
-    af::array B1 = af::matmul(A, X1);
-
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
-}
-
-template<typename T>
-void solveTriangleTester(const int n, const int k, bool is_upper, double eps)
-{
-    af::deviceGC();
-
-    if (noDoubleTests<T>()) return;
-    if (noLAPACKTests()) return;
-
-#if 1
-    af::array A  = cpu_randu<T>(af::dim4(n, n));
-    af::array X0 = cpu_randu<T>(af::dim4(n, k));
-#else
-    af::array A  = af::randu(n, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array X0 = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
-#endif
-
-    af::array L, U, pivot;
-    af::lu(L, U, pivot, A);
-
-    af::array AT = is_upper ? U : L;
-    af::array B0 = af::matmul(AT, X0);
-    af::array X1;
-
-    if (is_upper) {
-        //! [ex_solve_upper]
-        af::array X = af::solve(AT, B0, AF_MAT_UPPER);
-        //! [ex_solve_upper]
-
-        X1 = X;
-    } else {
-        //! [ex_solve_lower]
-        af::array X = af::solve(AT, B0, AF_MAT_LOWER);
-        //! [ex_solve_lower]
-
-        X1 = X;
-    }
-
-    af::array B1 = af::matmul(AT, X1);
-
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
-}
+#include "solve_common.hpp"
 
 #define SOLVE_LU_TESTS(T, eps)                          \
     TEST(SOLVE_LU, T##Reg)                              \
diff --git a/test/threading.cpp b/test/threading.cpp
index 16b0314ef4..5ee0396d36 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -10,10 +10,13 @@
 #include <cstddef>
 #include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <testHelpers.hpp>
+#include <af/traits.hpp>
 #include <thread>
+#include <complex>
 #include <chrono>
+#include <thread>
 #include <iterator>
+#include <solve_common.hpp>
 
 using namespace af;
 
@@ -28,6 +31,12 @@ static const unsigned ITERATION_COUNT = 10;
 static const unsigned ITERATION_COUNT = 1000;
 #endif
 
+int nextTargetDeviceId()
+{
+    static int nextId = 0;
+    return nextId++;
+}
+
 void morphTest(const array input, const array mask, const bool isDilation,
                const array gold, int targetDevice)
 {
@@ -197,12 +206,6 @@ TEST(Threading, SimultaneousRead)
             tests[t].join();
 }
 
-int nextTargetDeviceId()
-{
-    static int nextId = 0;
-    return nextId++;
-}
-
 static void cleanSlate()
 {
     const size_t step_bytes = 1024;
@@ -336,6 +339,7 @@ TEST(Threading, MemoryManagement_JIT_Node)
     ASSERT_EQ(  lock_bytes,      0u);
 }
 
+#if !defined(AF_OPENCL)
 template<typename inType, typename outType, bool isInverse>
 void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
 {
@@ -413,7 +417,6 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
         tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, file, p0, p1, 0);\
     }
 
-#if !defined(AF_OPENCL)
 /// OpenCL backend tests seem to be failing even when
 /// each thead has it's own plan cache(thread_local).
 /// The issue seems to present itself randomly in the form of crashes,
@@ -479,7 +482,6 @@ TEST(Threading, FFT)
         }
     }
 }
-#endif
 
 template<typename T, bool isBVector>
 void cppMatMulCheck(int targetDevice, string TestFile)
@@ -574,3 +576,48 @@ TEST(Threading, BLAS)
         }
     }
 }
+
+#define SOLVE_LU_TESTS(T, eps)                                                                          \
+    tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
+    tests.emplace_back(solveLUTester<T>, 2048, 512, eps, nextTargetDeviceId()%numDevices);              \
+    std::this_thread::sleep_for(std::chrono::seconds(2));   \
+    tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps, nextTargetDeviceId()%numDevices);  \
+    tests.emplace_back(solveTriangleTester<T>, 2048, 512, true, eps, nextTargetDeviceId()%numDevices);  \
+    std::this_thread::sleep_for(std::chrono::seconds(2));   \
+    tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, nextTargetDeviceId()%numDevices); \
+    tests.emplace_back(solveTriangleTester<T>, 2048, 512, false, eps, nextTargetDeviceId()%numDevices); \
+    std::this_thread::sleep_for(std::chrono::seconds(2));   \
+    tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps, nextTargetDeviceId()%numDevices);          \
+    tests.emplace_back(solveTester<T>, 2048, 2048, 512, eps, nextTargetDeviceId()%numDevices);          \
+    std::this_thread::sleep_for(std::chrono::seconds(2));   \
+    tests.emplace_back(solveTester<T>, 800, 1000, 200, eps, nextTargetDeviceId()%numDevices);           \
+    tests.emplace_back(solveTester<T>, 1536, 2048, 400, eps, nextTargetDeviceId()%numDevices);          \
+    std::this_thread::sleep_for(std::chrono::seconds(2));   \
+    tests.emplace_back(solveTester<T>, 800, 600, 64, eps, nextTargetDeviceId()%numDevices);             \
+    tests.emplace_back(solveTester<T>, 1536, 1024, 1, eps, nextTargetDeviceId()%numDevices);
+
+// Added 2s sleep for every two test threads to make sure
+// we are not running out of memory.
+TEST(Threading, SolveDense)
+{
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    SOLVE_LU_TESTS(float, 0.01);
+    SOLVE_LU_TESTS(double, 1E-5);
+    SOLVE_LU_TESTS(cfloat, 0.01);
+    SOLVE_LU_TESTS(cdouble, 1E-5);
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+    {
+        if (tests[testId].joinable()) {
+            tests[testId].join();
+        }
+    }
+}
+
+#undef SOLVE_LU_TESTS
+
+#endif

From 38662260ff45ee9aa4eaa49adb86f59c0c691ebb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 8 Apr 2017 20:32:34 +0530
Subject: [PATCH 1145/2677] Sparse multi-threaded tests

* OpenCL Backend disabled
---
 test/solve_common.hpp  |   3 -
 test/solve_dense.cpp   |   1 +
 test/sparse.cpp        | 212 +--------------------------------------
 test/sparse_common.hpp | 221 +++++++++++++++++++++++++++++++++++++++++
 test/threading.cpp     |  36 +++++++
 5 files changed, 259 insertions(+), 214 deletions(-)
 create mode 100644 test/sparse_common.hpp

diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index e5f5520f88..3149a7b3f3 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -8,8 +8,6 @@
  ********************************************************/
 
 #pragma once
-
-#include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <af/dim4.hpp>
 #include <af/defines.h>
@@ -18,7 +16,6 @@
 #include <iostream>
 #include <complex>
 #include <string>
-#include <testHelpers.hpp>
 
 using std::vector;
 using std::string;
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 641da961ee..641b05c7e3 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include "solve_common.hpp"
 
 #define SOLVE_LU_TESTS(T, eps)                          \
diff --git a/test/sparse.cpp b/test/sparse.cpp
index fc87b95a4d..12bb58be4e 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -8,149 +8,8 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <arrayfire.h>
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/traits.hpp>
-#include <vector>
-#include <iostream>
-#include <complex>
-#include <string>
 #include <testHelpers.hpp>
-
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
-using af::cfloat;
-using af::cdouble;
-
-///////////////////////////////// CPP ////////////////////////////////////
-//
-
-template<typename T>
-af::array makeSparse(af::array A, int factor)
-{
-    A = floor(A * 1000);
-    A = A * ((A % factor) == 0) / 1000;
-    return A;
-}
-
-template<>
-af::array makeSparse<cfloat>(af::array A, int factor)
-{
-    af::array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
-
-    af::array i = r / 2;
-
-    A = af::complex(r, i);
-    return A;
-}
-
-template<>
-af::array makeSparse<cdouble>(af::array A, int factor)
-{
-    af::array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
-
-    af::array i = r / 2;
-
-    A = af::complex(r, i);
-    return A;
-}
-
-double calc_norm(af::array lhs, af::array rhs)
-{
-    return af::max<double>(af::abs(lhs - rhs) / (af::abs(lhs) + af::abs(rhs) + 1E-5));
-}
-
-template<typename T>
-void sparseTester(const int m, const int n, const int k, int factor, double eps)
-{
-    af::deviceGC();
-
-    if (noDoubleTests<T>()) return;
-
-#if 1
-    af::array A = cpu_randu<T>(af::dim4(m, n));
-    af::array B = cpu_randu<T>(af::dim4(n, k));
-#else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array B = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
-#endif
-
-    A = makeSparse<T>(A, factor);
-
-    // Result of GEMM
-    af::array dRes1 = matmul(A, B);
-
-    // Create Sparse Array From Dense
-    af::array sA = af::sparse(A, AF_STORAGE_CSR);
-
-    // Sparse Matmul
-    af::array sRes1 = matmul(sA, B);
-
-    // Verify Results
-    ASSERT_NEAR(0, calc_norm(real(dRes1), real(sRes1)), eps);
-    ASSERT_NEAR(0, calc_norm(imag(dRes1), imag(sRes1)), eps);
-}
-
-template<typename T>
-void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps)
-{
-    af::deviceGC();
-
-    if (noDoubleTests<T>()) return;
-
-#if 1
-    af::array A = cpu_randu<T>(af::dim4(m, n));
-    af::array B = cpu_randu<T>(af::dim4(m, k));
-#else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array B = af::randu(m, k, (af::dtype)af::dtype_traits<T>::af_type);
-#endif
-
-    A = makeSparse<T>(A, factor);
-
-    // Result of GEMM
-    af::array dRes2 = matmul(A, B, AF_MAT_TRANS, AF_MAT_NONE);
-    af::array dRes3 = matmul(A, B, AF_MAT_CTRANS, AF_MAT_NONE);
-
-    // Create Sparse Array From Dense
-    af::array sA = af::sparse(A, AF_STORAGE_CSR);
-
-    // Sparse Matmul
-    af::array sRes2 = matmul(sA, B, AF_MAT_TRANS, AF_MAT_NONE);
-    af::array sRes3 = matmul(sA, B, AF_MAT_CTRANS, AF_MAT_NONE);
-
-    // Verify Results
-    ASSERT_NEAR(0, calc_norm(real(dRes2), real(sRes2)), eps);
-    ASSERT_NEAR(0, calc_norm(imag(dRes2), imag(sRes2)), eps);
-
-    ASSERT_NEAR(0, calc_norm(real(dRes3), real(sRes3)), eps);
-    ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
-}
-
-template<typename T>
-void convertCSR(const int M, const int N, const float ratio)
-{
-    if (noDoubleTests<T>()) return;
-#if 1
-    af::array a = cpu_randu<T>(af::dim4(M, N));
-#else
-    af::array a = af::randu(M, N);
-#endif
-    a = a * (a > ratio);
-
-    af::array s = af::sparse(a, AF_STORAGE_CSR);
-    af::array aa = af::dense(s);
-
-    ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));
-}
+#include <sparse_common.hpp>
 
 #define SPARSE_TESTS(T, eps)                                \
     TEST(SPARSE, T##Square)                                 \
@@ -197,25 +56,6 @@ SPARSE_TESTS(cdouble, 1E-5)
 
 #undef SPARSE_TESTS
 
-// This test essentially verifies that the sparse structures have the correct
-// dimensions and indices using a very basic test
-template<af_storage stype>
-void createFunction()
-{
-    af::array in = af::sparse(af::identity(3, 3), stype);
-
-    af::array values = sparseGetValues(in);
-    af::array rowIdx = sparseGetRowIdx(in);
-    af::array colIdx = sparseGetColIdx(in);
-    dim_t     nNZ    = sparseGetNNZ(in);
-
-    ASSERT_EQ(nNZ, values.elements());
-
-    ASSERT_EQ(0, af::max<double>(values - af::constant(1, nNZ)));
-    ASSERT_EQ(0, af::max<int   >(rowIdx - af::range(af::dim4(rowIdx.elements()), 0, s32)));
-    ASSERT_EQ(0, af::max<int   >(colIdx - af::range(af::dim4(colIdx.elements()), 0, s32)));
-}
-
 #define CREATE_TESTS(STYPE)                                         \
     TEST(SPARSE_CREATE, STYPE)                                      \
     {                                                               \
@@ -237,56 +77,6 @@ TEST(SPARSE_CREATE, AF_STORAGE_CSC)
     if(out != 0) af_release_array(out);
 }
 
-template<typename Ti, typename To>
-void sparseCastTester(const int m, const int n, int factor)
-{
-    if (noDoubleTests<Ti>()) return;
-    if (noDoubleTests<To>()) return;
-
-    af::array A = cpu_randu<Ti>(af::dim4(m, n));
-
-    A = makeSparse<Ti>(A, factor);
-
-    af::array sTi = af::sparse(A, AF_STORAGE_CSR);
-
-    // Cast
-    af::array sTo = sTi.as((af::dtype)af::dtype_traits<To>::af_type);
-
-    // Verify nnZ
-    dim_t iNNZ = sparseGetNNZ(sTi);
-    dim_t oNNZ = sparseGetNNZ(sTo);
-
-    ASSERT_EQ(iNNZ, oNNZ);
-
-    // Verify Types
-    dim_t iSType = sparseGetStorage(sTi);
-    dim_t oSType = sparseGetStorage(sTo);
-
-    ASSERT_EQ(iSType, oSType);
-
-    // Get the individual arrays and verify equality
-    af::array iValues = sparseGetValues(sTi);
-    af::array iRowIdx = sparseGetRowIdx(sTi);
-    af::array iColIdx = sparseGetColIdx(sTi);
-
-    af::array oValues = sparseGetValues(sTo);
-    af::array oRowIdx = sparseGetRowIdx(sTo);
-    af::array oColIdx = sparseGetColIdx(sTo);
-
-    // Verify values
-    ASSERT_EQ(0, af::max<int>(af::abs(iRowIdx - oRowIdx)));
-    ASSERT_EQ(0, af::max<int>(af::abs(iColIdx - oColIdx)));
-
-    static const double eps = 1e-6;
-    if(iValues.iscomplex() && !oValues.iscomplex()) {
-        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)), eps);
-    } else if(!iValues.iscomplex() && oValues.iscomplex()) {
-        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))), eps);
-    } else {
-        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - oValues)), eps);
-    }
-}
-
 #define CAST_TESTS_TYPES(Ti, To, SUFFIX, M, N, F)                               \
     TEST(SPARSE_CAST, Ti##_##To##_##SUFFIX)                                     \
     {                                                                           \
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
new file mode 100644
index 0000000000..57705d3ca0
--- /dev/null
+++ b/test/sparse_common.hpp
@@ -0,0 +1,221 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <complex>
+#include <string>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using std::abs;
+using af::cfloat;
+using af::cdouble;
+
+///////////////////////////////// CPP ////////////////////////////////////
+//
+
+template<typename T>
+af::array makeSparse(af::array A, int factor)
+{
+    A = floor(A * 1000);
+    A = A * ((A % factor) == 0) / 1000;
+    return A;
+}
+
+template<>
+af::array makeSparse<cfloat>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = r / 2;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+template<>
+af::array makeSparse<cdouble>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = r / 2;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+double calc_norm(af::array lhs, af::array rhs)
+{
+    return af::max<double>(af::abs(lhs - rhs) / (af::abs(lhs) + af::abs(rhs) + 1E-5));
+}
+
+template<typename T>
+void sparseTester(const int m, const int n, const int k, int factor, double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(n, k));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(n, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    // Result of GEMM
+    af::array dRes1 = matmul(A, B);
+
+    // Create Sparse Array From Dense
+    af::array sA = af::sparse(A, AF_STORAGE_CSR);
+
+    // Sparse Matmul
+    af::array sRes1 = matmul(sA, B);
+
+    // Verify Results
+    ASSERT_NEAR(0, calc_norm(real(dRes1), real(sRes1)), eps);
+    ASSERT_NEAR(0, calc_norm(imag(dRes1), imag(sRes1)), eps);
+}
+
+template<typename T>
+void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(m, k));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(m, k, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    // Result of GEMM
+    af::array dRes2 = matmul(A, B, AF_MAT_TRANS, AF_MAT_NONE);
+    af::array dRes3 = matmul(A, B, AF_MAT_CTRANS, AF_MAT_NONE);
+
+    // Create Sparse Array From Dense
+    af::array sA = af::sparse(A, AF_STORAGE_CSR);
+
+    // Sparse Matmul
+    af::array sRes2 = matmul(sA, B, AF_MAT_TRANS, AF_MAT_NONE);
+    af::array sRes3 = matmul(sA, B, AF_MAT_CTRANS, AF_MAT_NONE);
+
+    // Verify Results
+    ASSERT_NEAR(0, calc_norm(real(dRes2), real(sRes2)), eps);
+    ASSERT_NEAR(0, calc_norm(imag(dRes2), imag(sRes2)), eps);
+
+    ASSERT_NEAR(0, calc_norm(real(dRes3), real(sRes3)), eps);
+    ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
+}
+
+template<typename T>
+void convertCSR(const int M, const int N, const float ratio)
+{
+    if (noDoubleTests<T>()) return;
+#if 1
+    af::array a = cpu_randu<T>(af::dim4(M, N));
+#else
+    af::array a = af::randu(M, N);
+#endif
+    a = a * (a > ratio);
+
+    af::array s = af::sparse(a, AF_STORAGE_CSR);
+    af::array aa = af::dense(s);
+
+    ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));
+}
+
+// This test essentially verifies that the sparse structures have the correct
+// dimensions and indices using a very basic test
+template<af_storage stype>
+void createFunction()
+{
+    af::array in = af::sparse(af::identity(3, 3), stype);
+
+    af::array values = sparseGetValues(in);
+    af::array rowIdx = sparseGetRowIdx(in);
+    af::array colIdx = sparseGetColIdx(in);
+    dim_t     nNZ    = sparseGetNNZ(in);
+
+    ASSERT_EQ(nNZ, values.elements());
+
+    ASSERT_EQ(0, af::max<double>(values - af::constant(1, nNZ)));
+    ASSERT_EQ(0, af::max<int   >(rowIdx - af::range(af::dim4(rowIdx.elements()), 0, s32)));
+    ASSERT_EQ(0, af::max<int   >(colIdx - af::range(af::dim4(colIdx.elements()), 0, s32)));
+}
+
+template<typename Ti, typename To>
+void sparseCastTester(const int m, const int n, int factor)
+{
+    if (noDoubleTests<Ti>()) return;
+    if (noDoubleTests<To>()) return;
+
+    af::array A = cpu_randu<Ti>(af::dim4(m, n));
+
+    A = makeSparse<Ti>(A, factor);
+
+    af::array sTi = af::sparse(A, AF_STORAGE_CSR);
+
+    // Cast
+    af::array sTo = sTi.as((af::dtype)af::dtype_traits<To>::af_type);
+
+    // Verify nnZ
+    dim_t iNNZ = sparseGetNNZ(sTi);
+    dim_t oNNZ = sparseGetNNZ(sTo);
+
+    ASSERT_EQ(iNNZ, oNNZ);
+
+    // Verify Types
+    dim_t iSType = sparseGetStorage(sTi);
+    dim_t oSType = sparseGetStorage(sTo);
+
+    ASSERT_EQ(iSType, oSType);
+
+    // Get the individual arrays and verify equality
+    af::array iValues = sparseGetValues(sTi);
+    af::array iRowIdx = sparseGetRowIdx(sTi);
+    af::array iColIdx = sparseGetColIdx(sTi);
+
+    af::array oValues = sparseGetValues(sTo);
+    af::array oRowIdx = sparseGetRowIdx(sTo);
+    af::array oColIdx = sparseGetColIdx(sTo);
+
+    // Verify values
+    ASSERT_EQ(0, af::max<int>(af::abs(iRowIdx - oRowIdx)));
+    ASSERT_EQ(0, af::max<int>(af::abs(iColIdx - oColIdx)));
+
+    static const double eps = 1e-6;
+    if(iValues.iscomplex() && !oValues.iscomplex()) {
+        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)), eps);
+    } else if(!iValues.iscomplex() && oValues.iscomplex()) {
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))), eps);
+    } else {
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - oValues)), eps);
+    }
+}
diff --git a/test/threading.cpp b/test/threading.cpp
index 5ee0396d36..d5df9cf0c1 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -16,7 +16,9 @@
 #include <chrono>
 #include <thread>
 #include <iterator>
+#include <testHelpers.hpp>
 #include <solve_common.hpp>
+#include <sparse_common.hpp>
 
 using namespace af;
 
@@ -620,4 +622,38 @@ TEST(Threading, SolveDense)
 
 #undef SOLVE_LU_TESTS
 
+#define SPARSE_TESTS(T, eps)                                                    \
+        tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps);           \
+        tests.emplace_back(sparseTester<T>, 2048, 1024, 512, 3, eps);           \
+        tests.emplace_back(sparseTester<T>, 500, 1000, 250, 1, eps);            \
+        tests.emplace_back(sparseTester<T>, 625, 1331, 1, 2, eps);              \
+        tests.emplace_back(sparseTransposeTester<T>, 625, 1331, 1, 2, eps);     \
+        tests.emplace_back(sparseTransposeTester<T>, 1000, 1000, 100, 5, eps);  \
+        tests.emplace_back(sparseTransposeTester<T>, 2048, 1024, 512, 3, eps);  \
+        tests.emplace_back(sparseTransposeTester<T>, 453, 751, 397, 1, eps);    \
+        tests.emplace_back(convertCSR<T>, 2345, 5678, 0.5);                     \
+        std::this_thread::sleep_for(std::chrono::seconds(5));
+
+// Added 2s sleep for every two test threads to make sure
+// we are not running out of memory.
+TEST(Threading, Sparse)
+{
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    SPARSE_TESTS(  float, 1E-3);
+    SPARSE_TESTS( double, 1E-5);
+    SPARSE_TESTS( cfloat, 1E-3);
+    SPARSE_TESTS(cdouble, 1E-5);
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+    {
+        if (tests[testId].joinable()) {
+            tests[testId].join();
+        }
+    }
+}
+
 #endif

From 9809394483aa56c3d4941a0fcc4fc1d6925f5dbb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 10 Apr 2017 22:45:55 +0530
Subject: [PATCH 1146/2677] fix bug in fft cache size modification

---
 src/backend/common/FFTPlanCache.hpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
index 3d18f31117..c2d9d7c9bd 100644
--- a/src/backend/common/FFTPlanCache.hpp
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -31,7 +31,13 @@ class FFTPlanCache
     public:
         FFTPlanCache() : mMaxCacheSize(5) {}
 
-        void setMaxCacheSize(size_t size) { mMaxCacheSize = size; }
+        void setMaxCacheSize(size_t size)
+        {
+            mMaxCacheSize = size;
+            while (mCache.size()>mMaxCacheSize)
+                mCache.pop_back();
+        }
+
         size_t getMaxCacheSize() const    { return mMaxCacheSize; }
 
         // iterates through plan cache from front to back

From 8c10b3faa31aaea723d153990888905623709db0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 10 Apr 2017 22:46:30 +0530
Subject: [PATCH 1147/2677] C++ interface for fft cache size modifier fn

---
 include/af/signal.h | 14 ++++++++++++++
 src/api/cpp/fft.cpp |  6 ++++--
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index ffa0c108af..fa04bc8dbb 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -654,6 +654,18 @@ AFAPI array medfilt1(const array& in, const dim_t wind_width = 3, const borderTy
 AFAPI array medfilt2(const array& in, const dim_t wind_length = 3, const dim_t wind_width = 3, const borderType edge_pad = AF_PAD_ZERO);
 #endif
 
+#if AF_API_VERSION >= 35
+/**
+   C++ Interface for setting plan cache size
+
+   This function doesn't do anything if called when CPU backend is active. The plans associated with
+   the most recently used array sizes are cached.
+
+   \param[in] cacheSize is the number of plans that shall be cached
+*/
+AFAPI void setFFTPlanCacheSize(size_t cacheSize);
+#endif
+
 }
 #endif
 
@@ -1190,6 +1202,8 @@ AFAPI af_err af_iir(af_array *y, const af_array b, const af_array a, const af_ar
    the most recently used array sizes are cached.
 
    \param[in] cache_size is the number of plans that shall be cached
+
+   \ingroup signal_func_fft
 */
 AFAPI af_err af_set_fft_plan_cache_size(size_t cache_size);
 #endif
diff --git a/src/api/cpp/fft.cpp b/src/api/cpp/fft.cpp
index 1fe4a0921c..e7e96ca195 100644
--- a/src/api/cpp/fft.cpp
+++ b/src/api/cpp/fft.cpp
@@ -14,8 +14,6 @@
 
 namespace af
 {
-
-
 array fftNorm(const array& in, const double norm_factor, const dim_t odim0)
 {
     af_array out = 0;
@@ -281,4 +279,8 @@ FFT_REAL(1)
 FFT_REAL(2)
 FFT_REAL(3)
 
+void setFFTPlanCacheSize(size_t cacheSize)
+{
+    AF_THROW(af_set_fft_plan_cache_size(cacheSize));
+}
 }

From c239b86b1ceed3837e2e03c7940f2e20d2c9da24 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 12 Apr 2017 00:53:58 +0530
Subject: [PATCH 1148/2677] Use global kernel cache for all opencl kernels

Earlier to this change, all the kernel wrapper functions used to
maintain a set of static objects that are compiled only once.
---
 src/backend/opencl/kernel/approx.hpp          | 275 +++++++++---------
 src/backend/opencl/kernel/assign.hpp          |  49 ++--
 src/backend/opencl/kernel/bilateral.hpp       |  66 ++---
 .../opencl/kernel/convolve/conv_common.hpp    |  61 ++--
 src/backend/opencl/kernel/diagonal.hpp        | 153 +++++-----
 src/backend/opencl/kernel/diff.hpp            |  96 +++---
 src/backend/opencl/kernel/exampleFunction.hpp |  83 +++---
 src/backend/opencl/kernel/fftconvolve.hpp     | 251 +++++++++-------
 src/backend/opencl/kernel/gradient.hpp        | 103 +++----
 src/backend/opencl/kernel/harris.hpp          | 127 ++++----
 src/backend/opencl/kernel/histogram.hpp       |  60 ++--
 src/backend/opencl/kernel/homography.hpp      | 171 ++++++-----
 src/backend/opencl/kernel/hsv_rgb.hpp         |  43 ++-
 src/backend/opencl/kernel/identity.hpp        |  68 ++---
 src/backend/opencl/kernel/iir.hpp             |  99 +++----
 src/backend/opencl/kernel/index.hpp           |  41 ++-
 src/backend/opencl/kernel/iota.hpp            |  89 +++---
 src/backend/opencl/kernel/join.hpp            |  93 +++---
 src/backend/opencl/kernel/laset.hpp           |  55 ++--
 src/backend/opencl/kernel/laset_band.hpp      |  49 ++--
 src/backend/opencl/kernel/laswp.hpp           |  56 ++--
 src/backend/opencl/kernel/lookup.hpp          |  65 ++---
 src/backend/opencl/kernel/lu_split.hpp        |  70 ++---
 src/backend/opencl/kernel/match_template.hpp  |  76 +++--
 src/backend/opencl/kernel/meanshift.hpp       |  53 ++--
 src/backend/opencl/kernel/medfilt.hpp         | 132 ++++-----
 src/backend/opencl/kernel/memcopy.hpp         | 242 +++++++--------
 src/backend/opencl/kernel/morph.hpp           | 122 ++++----
 src/backend/opencl/kernel/orb.hpp             |  99 ++++---
 src/backend/opencl/kernel/range.hpp           |  81 +++---
 src/backend/opencl/kernel/regions.hpp         | 147 +++++-----
 src/backend/opencl/kernel/reorder.hpp         |  88 +++---
 src/backend/opencl/kernel/resize.hpp          | 161 +++++-----
 src/backend/opencl/kernel/rotate.hpp          | 232 ++++++++-------
 src/backend/opencl/kernel/select.hpp          | 222 +++++++-------
 src/backend/opencl/kernel/shift.hpp           | 102 +++----
 src/backend/opencl/kernel/sift_nonfree.hpp    | 126 ++++----
 src/backend/opencl/kernel/sobel.hpp           |  60 ++--
 src/backend/opencl/kernel/susan.hpp           | 114 ++++----
 src/backend/opencl/kernel/swapdblk.hpp        |  49 ++--
 src/backend/opencl/kernel/tile.hpp            |  83 +++---
 src/backend/opencl/kernel/transform.hpp       |   6 +-
 src/backend/opencl/kernel/transpose.hpp       |  53 ++--
 .../opencl/kernel/transpose_inplace.hpp       |  54 ++--
 src/backend/opencl/kernel/triangle.hpp        |  64 ++--
 src/backend/opencl/kernel/where.hpp           | 206 ++++++-------
 46 files changed, 2327 insertions(+), 2468 deletions(-)

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 85d3ba579c..21a0274384 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -14,8 +14,6 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -34,148 +32,135 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        static const int TX = 16;
-        static const int TY = 16;
-
-        static const int THREADS = 256;
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename Ty, typename Tp, int order>
-        void approx1(Param out, const Param in, const Param xpos, const float offGrid,
-                     af_interp_type method)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  approxProgs;
-            static std::map<int, Kernel*> approxKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once( compileFlags[device], [device] () {
-                ToNumStr<Ty> toNumStr;
-                std::ostringstream options;
-                options << " -D Ty="          << dtype_traits<Ty>::getName()
-                        << " -D Tp="          << dtype_traits<Tp>::getName()
-                        << " -D InterpInTy="  << dtype_traits<Ty>::getName()
-                        << " -D InterpValTy=" << dtype_traits<Ty>::getName()
-                        << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                        << " -D ZERO="        << toNumStr(scalar<Ty>(0));
-
-                if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
-                    (af_dtype) dtype_traits<Ty>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<Ty, double>::value ||
-                    std::is_same<Ty, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                options << " -D INTERP_ORDER=" << order;
-                addInterpEnumOptions(options);
-
-                Program prog;
-                const char *ker_strs[] = {interp_cl, approx1_cl};
-                const int   ker_lens[] = {interp_cl_len, approx1_cl_len};
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                approxProgs[device] = new Program(prog);
-
-                approxKernels[device] = new Kernel(*approxProgs[device], "approx1_kernel");
-            });
-
-
-            auto approx1Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                            const Buffer, const KParam, const Ty,
-                                            const int, const int, const int>
-                                  (*approxKernels[device]);
-
-            NDRange local(THREADS, 1, 1);
-            dim_t blocksPerMat = divup(out.info.dims[0], local[0]);
-            NDRange global(blocksPerMat * local[0] * out.info.dims[1],
-                            out.info.dims[2] * out.info.dims[3] * local[0],
-                            1);
-
-            // Passing bools to opencl kernels is not allowed
-            bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 &&
-                            xpos.info.dims[3] == 1);
-
-            approx1Op(EnqueueArgs(getQueue(), global, local),
-                      *out.data, out.info, *in.data, in.info,
-                      *xpos.data, xpos.info, scalar<Ty>(offGrid),
-                      blocksPerMat, (int)batch, (int)method);
-
-            CL_DEBUG_FINISH(getQueue());
-        }
-
-        template <typename Ty, typename Tp, int order>
-        void approx2(Param out, const Param in, const Param xpos, const Param ypos,
-                     const float offGrid, af_interp_type method)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>       approxProgs;
-            static std::map<int, Kernel*>      approxKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once( compileFlags[device], [device] () {
-                ToNumStr<Ty> toNumStr;
-                std::ostringstream options;
-                options << " -D Ty="          << dtype_traits<Ty>::getName()
-                        << " -D Tp="          << dtype_traits<Tp>::getName()
-                        << " -D InterpInTy="  << dtype_traits<Ty>::getName()
-                        << " -D InterpValTy=" << dtype_traits<Ty>::getName()
-                        << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-                        << " -D ZERO="        << toNumStr(scalar<Ty>(0));
-
-                if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
-                    (af_dtype) dtype_traits<Ty>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<Ty, double>::value ||
-                    std::is_same<Ty, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                options << " -D INTERP_ORDER=" << order;
-                addInterpEnumOptions(options);
-
-                Program prog;
-                const char *ker_strs[] = {interp_cl, approx2_cl};
-                const int   ker_lens[] = {interp_cl_len, approx2_cl_len};
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                approxProgs[device] = new Program(prog);
-
-                approxKernels[device] = new Kernel(*approxProgs[device], "approx2_kernel");
-            });
-
-            auto approx2Op = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                    const Buffer, const KParam, const Buffer, const KParam,
-                                            const Ty, const int, const int, const int, const int>
-                                    (*approxKernels[device]);
-
-            NDRange local(TX, TY, 1);
-            dim_t blocksPerMatX = divup(out.info.dims[0], local[0]);
-            dim_t blocksPerMatY = divup(out.info.dims[1], local[1]);
-            NDRange global(blocksPerMatX * local[0] * out.info.dims[2],
-                            blocksPerMatY * local[1] * out.info.dims[3],
-                            1);
-
-            // Passing bools to opencl kernels is not allowed
-            bool batch = !(xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
-
-            approx2Op(EnqueueArgs(getQueue(), global, local),
-                      *out.data, out.info,
-                      *in.data, in.info,
-                      *xpos.data, xpos.info,
-                      *ypos.data, ypos.info,
-                      scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
-            CL_DEBUG_FINISH(getQueue());
-        }
+namespace kernel
+{
+static const int TX = 16;
+static const int TY = 16;
+
+static const int THREADS = 256;
+
+template<typename Ty, typename Tp, int order>
+std::string generateOptionsString()
+{
+    ToNumStr<Ty> toNumStr;
+
+    std::ostringstream options;
+    options << " -D Ty="          << dtype_traits<Ty>::getName()
+        << " -D Tp="          << dtype_traits<Tp>::getName()
+        << " -D InterpInTy="  << dtype_traits<Ty>::getName()
+        << " -D InterpValTy=" << dtype_traits<Ty>::getName()
+        << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
+        << " -D ZERO="        << toNumStr(scalar<Ty>(0));
+
+    if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
+            (af_dtype) dtype_traits<Ty>::af_type == c64) {
+        options << " -D IS_CPLX=1";
+    } else {
+        options << " -D IS_CPLX=0";
     }
+    if (std::is_same<Ty, double>::value ||
+            std::is_same<Ty, cdouble>::value) {
+        options << " -D USE_DOUBLE";
+    }
+
+    options << " -D INTERP_ORDER=" << order;
+    addInterpEnumOptions(options);
+
+    return options.str();
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template <typename Ty, typename Tp, int order>
+void approx1(Param out, const Param in, const Param xpos, const float offGrid,
+             af_interp_type method)
+{
+    std::string refName = std::string("approx1_kernel_") +
+        std::string(dtype_traits<Ty>::getName()) +
+        std::string(dtype_traits<Tp>::getName()) +
+        std::to_string(order);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::string options = generateOptionsString<Ty, Tp, order>();
+
+        const char *ker_strs[] = {interp_cl, approx1_cl};
+        const int   ker_lens[] = {interp_cl_len, approx1_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "approx1_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto approx1Op = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
+                                    const Buffer, const KParam, const Ty,
+                                    const int, const int, const int >(*entry.ker);
+
+    NDRange local(THREADS, 1, 1);
+    dim_t blocksPerMat = divup(out.info.dims[0], local[0]);
+    NDRange global(blocksPerMat * local[0] * out.info.dims[1],
+                   out.info.dims[2] * out.info.dims[3] * local[0], 1);
+
+    // Passing bools to opencl kernels is not allowed
+    bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
+
+    approx1Op(EnqueueArgs(getQueue(), global, local),
+              *out.data, out.info, *in.data, in.info,
+              *xpos.data, xpos.info, scalar<Ty>(offGrid),
+              blocksPerMat, (int)batch, (int)method);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Ty, typename Tp, int order>
+void approx2(Param out, const Param in, const Param xpos, const Param ypos,
+             const float offGrid, af_interp_type method)
+{
+    std::string refName = std::string("approx2_kernel_") +
+        std::string(dtype_traits<Ty>::getName()) +
+        std::string(dtype_traits<Tp>::getName()) +
+        std::to_string(order);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::string options = generateOptionsString<Ty, Tp, order>();
+
+        const char *ker_strs[] = {interp_cl, approx2_cl};
+        const int   ker_lens[] = {interp_cl_len, approx2_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "approx2_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto approx2Op = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
+                                    const Buffer, const KParam, const Buffer, const KParam,
+                                    const Ty, const int, const int, const int, const int >(*entry.ker);
+
+    NDRange local(TX, TY, 1);
+    dim_t blocksPerMatX = divup(out.info.dims[0], local[0]);
+    dim_t blocksPerMatY = divup(out.info.dims[1], local[1]);
+    NDRange global(blocksPerMatX * local[0] * out.info.dims[2],
+                   blocksPerMatY * local[1] * out.info.dims[3], 1);
+
+    // Passing bools to opencl kernels is not allowed
+    bool batch = !(xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
+
+    approx2Op(EnqueueArgs(getQueue(), global, local),
+              *out.data, out.info, *in.data, in.info,
+              *xpos.data, xpos.info, *ypos.data, ypos.info,
+              scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 079768c4a9..580e7ab582 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,10 +27,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 32;
 static const int THREADS_Y =  8;
 
@@ -44,46 +41,42 @@ typedef struct {
 template<typename T>
 void assign(Param out, const Param in, const AssignKernelParam_t& p, Buffer *bPtr[4])
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  agnProgs;
-    static std::map<int, Kernel*> agnKernels;
+    std::string refName = std::string("assignKernel_") + std::string(dtype_traits<T>::getName());
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once( compileFlags[device], [device] () {
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
-
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
-            }
 
-            Program prog;
-            buildProgram(prog, assign_cl, assign_cl_len, options.str());
-            agnProgs[device]   = new Program(prog);
-            agnKernels[device] = new Kernel(*agnProgs[device], "assignKernel");
-            });
+        const char* ker_strs[] = {assign_cl};
+        const int   ker_lens[] = {assign_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "assignKernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-            blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X, blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto assignOp = KernelFunctor<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
-          Buffer, Buffer, Buffer, Buffer, int, int>(*agnKernels[device]);
+    auto assignOp = KernelFunctor< Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
+                                   Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
 
     assignOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *in.data, in.info, p,
-            *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
+             *out.data, out.info, *in.data, in.info, p,
+             *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
-
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 46eabab6c6..1d93de83ba 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -12,9 +12,8 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
 #include <algorithm>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -31,56 +30,53 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename inType, typename outType, bool isColor>
 void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  bilProgs;
-    static std::map<int, Kernel*> bilKernels;
+    std::string refName = std::string("bilateral_") +
+        std::string(dtype_traits<inType>::getName()) +
+        std::string(dtype_traits<outType>::getName()) +
+        std::to_string(isColor);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-            bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
-                                && getActivePlatform() != AFCL_PLATFORM_APPLE);
-            std::ostringstream options;
-            options << " -D inType=" << dtype_traits<inType>::getName()
-                    << " -D outType=" << dtype_traits<outType>::getName();
-            if (std::is_same<inType, double>::value ||
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
+                && getActivePlatform() != AFCL_PLATFORM_APPLE);
+        std::ostringstream options;
+        options << " -D inType=" << dtype_traits<inType>::getName()
+            << " -D outType=" << dtype_traits<outType>::getName();
+        if (std::is_same<inType, double>::value ||
                 std::is_same<inType, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-            }
-            options << " -D USE_NATIVE_EXP=" << (int)use_native_exp;
-
-            Program prog;
-            buildProgram(prog, bilateral_cl, bilateral_cl_len, options.str());
-            bilProgs[device] = new Program(prog);
-
-                bilKernels[device] = new Kernel(*bilProgs[device], "bilateral");
-        });
+            options << " -D USE_DOUBLE";
+        }
+        options << " -D USE_NATIVE_EXP=" << (int)use_native_exp;
+
+        const char* ker_strs[] = {bilateral_cl};
+        const int   ker_lens[] = {bilateral_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "bilateral");
+
+        addKernelToCache(device, refName, entry);
+    }
 
-    auto bilateralOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    LocalSpaceArg,
-                                    LocalSpaceArg,
-                                    float, float,
-                                    int, int, int
-                                  >(*bilKernels[device]);
+    auto bilateralOp = KernelFunctor< Buffer, KParam, Buffer, KParam, LocalSpaceArg, LocalSpaceArg,
+                                      float, float, int, int, int >(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x*in.info.dims[2]*THREADS_X,
-                    blk_y*in.info.dims[3]*THREADS_Y);
+    NDRange global(blk_x*in.info.dims[2]*THREADS_X, blk_y*in.info.dims[3]*THREADS_Y);
 
     // calculate local memory size
     int radius = (int)std::max(s_sigma * 1.5f, 1.f);
@@ -100,7 +96,5 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 9ea8e23e70..f74a22e50c 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -12,8 +12,6 @@
 
 #include <kernel_headers/convolve.hpp>
 
-#include <map>
-#include <mutex>
 #include <string>
 #include <Param.hpp>
 #include <types.hpp>
@@ -23,6 +21,7 @@
 #include <dispatch.hpp>
 #include <platform.hpp>
 #include <debug_opencl.hpp>
+#include <cache.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -33,10 +32,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS   = 256;
 
 static const int THREADS_X = 16;
@@ -97,39 +94,37 @@ void prepareKernelArgs(conv_kparam_t& param, dim_t *oDims,
 template<typename T, typename aT, int bDim, bool expand>
 void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, const Param& filter)
 {
-    static std::once_flag  compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> convProgs;
-    static std::map<int, Kernel*>  convKernels;
+    std::string ref_name = std::string("convolveND_") +
+        std::string(dtype_traits<T>::getName()) + std::string(dtype_traits<aT>::getName()) +
+        std::to_string(bDim) + std::to_string(expand);
 
     int device = getActiveDeviceId();
 
-    std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D accType="<< dtype_traits<aT>::getName()
-                        << " -D BASE_DIM="<< bDim
-                        << " -D EXPAND=" << expand;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
-                convProgs[device]   = new Program(prog);
-                convKernels[device] = new Kernel(*convProgs[device], "convolve");
-            });
-
-    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                    cl::LocalSpaceArg, Buffer, KParam,
-                                    int, int,
-                                    int, int, int,
-                                    int, int, int
-                                    >(*convKernels[device]);
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T="         << dtype_traits<T>::getName()
+                << " -D accType="   << dtype_traits<aT>::getName()
+                << " -D BASE_DIM="  << bDim
+                << " -D EXPAND="    << expand;
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+        Program prog;
+        buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
+        entry.prog   = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "convolve");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, Buffer, KParam,
+                                    int, int, int, int, int, int, int, int >(*entry.ker);
 
     convOp(EnqueueArgs(getQueue(), param.global, param.local),
-            *out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
-            *param.impulse, filter.info, param.nBBS0, param.nBBS1,
-            param.o[0], param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
+           *out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
+           *param.impulse, filter.info, param.nBBS0, param.nBBS1,
+           param.o[0], param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
@@ -140,7 +135,5 @@ void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
 
 template<typename T, typename aT, bool expand>
 void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index bf6acce9af..70659e580a 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -14,8 +14,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <map>
-#include <mutex>
+#include <cache.hpp>
 #include <math.hpp>
 #include "config.hpp"
 
@@ -30,89 +29,87 @@ using af::scalar_to_option;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
-    template<typename T>
-    static void diagCreate(Param out, Param in, int num)
-    {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>   diagCreateProgs;
-        static std::map<int, Kernel*>  diagCreateKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T="    << dtype_traits<T>::getName()
-                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, diag_create_cl, diag_create_cl_len, options.str());
-                diagCreateProgs[device]   = new Program(prog);
-                diagCreateKernels[device] = new Kernel(*diagCreateProgs[device],
-                                                        "diagCreateKernel");
-            });
-
-        NDRange local(32, 8);
-        int groups_x = divup(out.info.dims[0], local[0]);
-        int groups_y = divup(out.info.dims[1], local[1]);
-        NDRange global(groups_x * local[0] * out.info.dims[2],
-                        groups_y * local[1]);
-
-        auto diagCreateOp = KernelFunctor<Buffer, const KParam,
-                                        Buffer, const KParam,
-                                        int, int> (*diagCreateKernels[device]);
-
-        diagCreateOp(EnqueueArgs(getQueue(), global, local),
-                      *(out.data), out.info, *(in.data), in.info, num, groups_x);
-        CL_DEBUG_FINISH(getQueue());
+template<typename T>
+std::string generateOptionsString()
+{
+    std::ostringstream options;
+    options << " -D T="    << dtype_traits<T>::getName()
+            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+    if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+        options << " -D USE_DOUBLE";
     }
+    return options.str();
+}
+
+template<typename T>
+static void diagCreate(Param out, Param in, int num)
+{
+    std::string refName = std::string("diagCreateKernel_") + std::string(dtype_traits<T>::getName());
 
-    template<typename T>
-    static void diagExtract(Param out, Param in, int num)
-    {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>   diagExtractProgs;
-        static std::map<int, Kernel*>  diagExtractKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T="    << dtype_traits<T>::getName()
-                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, diag_extract_cl, diag_extract_cl_len, options.str());
-                diagExtractProgs[device]   = new Program(prog);
-                diagExtractKernels[device] = new Kernel(*diagExtractProgs[device],
-                                                        "diagExtractKernel");
-            });
-
-        NDRange local(256, 1);
-        int groups_x = divup(out.info.dims[0], local[0]);
-        int groups_z = out.info.dims[2];
-        NDRange global(groups_x * local[0],
-                        groups_z * local[1] * out.info.dims[3]);
-
-        auto diagExtractOp = KernelFunctor<Buffer, const KParam,
-                                          Buffer, const KParam,
-                                          int, int> (*diagExtractKernels[device]);
-
-        diagExtractOp(EnqueueArgs(getQueue(), global, local),
-                      *(out.data), out.info, *(in.data), in.info, num, groups_z);
-        CL_DEBUG_FINISH(getQueue());
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
+    if (entry.prog==0 && entry.ker==0) {
+        std::string options = generateOptionsString<T>();
+        const char* ker_strs[] = {diag_create_cl};
+        const int   ker_lens[] = {diag_create_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "diagCreateKernel");
+
+        addKernelToCache(device, refName, entry);
     }
 
+    NDRange local(32, 8);
+    int groups_x = divup(out.info.dims[0], local[0]);
+    int groups_y = divup(out.info.dims[1], local[1]);
+    NDRange global(groups_x * local[0] * out.info.dims[2], groups_y * local[1]);
+
+    auto diagCreateOp = KernelFunctor< Buffer, const KParam, Buffer, const KParam,
+                                       int, int > (*entry.ker);
+
+    diagCreateOp(EnqueueArgs(getQueue(), global, local),
+                 *(out.data), out.info, *(in.data), in.info, num, groups_x);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
+template<typename T>
+static void diagExtract(Param out, Param in, int num)
+{
+    std::string refName = std::string("diagExtractKernel_") + std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::string options = generateOptionsString<T>();
+        const char* ker_strs[] = {diag_extract_cl};
+        const int   ker_lens[] = {diag_extract_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "diagExtractKernel");
+
+        addKernelToCache(device, refName, entry);
+    }
+
+    NDRange local(256, 1);
+    int groups_x = divup(out.info.dims[0], local[0]);
+    int groups_z = out.info.dims[2];
+    NDRange global(groups_x * local[0], groups_z * local[1] * out.info.dims[3]);
+
+    auto diagExtractOp = KernelFunctor< Buffer, const KParam, Buffer, const KParam,
+                                        int, int > (*entry.ker);
+
+    diagExtractOp(EnqueueArgs(getQueue(), global, local),
+                  *(out.data), out.info, *(in.data), in.info, num, groups_z);
+
+    CL_DEBUG_FINISH(getQueue());
+
+}
+}
 }
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 1445829c19..438ad13354 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,58 +27,61 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        static const int TX = 16;
-        static const int TY = 16;
+namespace kernel
+{
+static const int TX = 16;
+static const int TY = 16;
 
-        template<typename T, unsigned dim, bool isDiff2>
-        void diff(Param out, const Param in, const unsigned indims)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   diffProgs;
-            static std::map<int, Kernel*>  diffKernels;
+template<typename T, unsigned dim, bool isDiff2>
+void diff(Param out, const Param in, const unsigned indims)
+{
+    std::string refName = std::string("diff_kernel_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(dim) +
+        std::to_string(isDiff2);
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName()
-                        << " -D DIM="      << dim
-                        << " -D isDiff2=" << isDiff2;
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, diff_cl, diff_cl_len, options.str());
-                diffProgs[device]   = new Program(prog);
-                diffKernels[device] = new Kernel(*diffProgs[device], "diff_kernel");
-            });
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T="        << dtype_traits<T>::getName()
+                << " -D DIM="      << dim
+                << " -D isDiff2=" << isDiff2;
+        if (std::is_same<T, double>::value ||
+            std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-            auto diffOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                      const int, const int, const int>
-                                      (*diffKernels[device]);
+        const char* ker_strs[] = {diff_cl};
+        const int   ker_lens[] = {diff_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "diff_kernel");
 
-            NDRange local(TX, TY, 1);
-            if(dim == 0 && indims == 1) {
-                local = NDRange(TX * TY, 1, 1);
-            }
+        addKernelToCache(device, refName, entry);
+    }
 
-            int blocksPerMatX = divup(out.info.dims[0], local[0]);
-            int blocksPerMatY = divup(out.info.dims[1], local[1]);
-            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                            local[1] * blocksPerMatY * out.info.dims[3],
-                            1);
+    auto diffOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
+                                 const int, const int, const int> (*entry.ker);
 
-            const int oElem = out.info.dims[0] * out.info.dims[1]
-                                  * out.info.dims[2] * out.info.dims[3];
+    NDRange local(TX, TY, 1);
+    if(dim == 0 && indims == 1) {
+        local = NDRange(TX * TY, 1, 1);
+    }
 
-            diffOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, *in.data, out.info, in.info,
-                    oElem, blocksPerMatX, blocksPerMatY);
+    int blocksPerMatX = divup(out.info.dims[0], local[0]);
+    int blocksPerMatY = divup(out.info.dims[1], local[1]);
+    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                   local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    const int oElem = out.info.dims[0] * out.info.dims[1] * out.info.dims[2] * out.info.dims[3];
+
+    diffOp(EnqueueArgs(getQueue(), global, local),
+           *out.data, *in.data, out.info, in.info, oElem, blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 2af050e33b..acad35b831 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -18,8 +18,12 @@
 // Following c++ standard library headers are needed to maintain
 // OpenCL cl::Kernel & cl::Program objects
 #include <string>
-#include <mutex>
-#include <map>
+
+#include <cache.hpp>                    // Has the definitions of functions such as the following
+                                        // used in caching and fetching kernels.
+                                        // * kernelCache - used to fetch existing kernel from cache
+                                        // if any
+                                        // * addKernelToCache - push new kernels into cache
 
 #include <dispatch.hpp>                 // common utility header for CUDA & OpenCL backends
                                         // has the divup macro
@@ -43,54 +47,51 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T>
 void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  egProgs;
-    static std::map<int, Kernel*> egKernels;
+    std::string refName =
+        std::string("example_") + //<kernel_function_name>_
+        std::string(dtype_traits<T>::getName());
+        // std::string("encode template parameters one after one");
+        // If you have numericals, you can use std::to_string to convert
+        // them into std::strings
 
     int device = getActiveDeviceId();
-
-    // std::call_once is used to ensure OpenCL kernels
-    // are compiled only once for any given device and combination
-    // of template parameters to this kernel wrapper function 'exampleFunc<T>'
-    std::call_once( compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
-            // You can pass any template parameters as compile options
-            // to kernel the compilation step. This is equivalent of
-            // having templated kernels in CUDA
-
-            // The following option is passed to kernel compilation
-            // if template parameter T is double or complex double
-            // to enable FP64 extension
-            if (std::is_same<T, double>::value ||
+    kc_entry_t entry = kernelCache(device, refName);
+
+    // Make sure OpenCL kernel isn't already available before
+    // compiling for given device and combination of template
+    // parameters to this kernel wrapper function 'exampleFunc<T>'
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        // You can pass any template parameters as compile options
+        // to kernel the compilation step. This is equivalent of
+        // having templated kernels in CUDA
+
+        // The following option is passed to kernel compilation
+        // if template parameter T is double or complex double
+        // to enable FP64 extension
+        if (std::is_same<T, double>::value ||
                 std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+            options << " -D USE_DOUBLE";
+        }
 
-            Program prog;
-            // below helper function 'buildProgram' uses the option string
-            // we just created and compiles the kernel string
-            // 'example_cl' which was created by our opencl kernel code obfuscation
-            // stage
-            buildProgram(prog, example_cl, example_cl_len, options.str());
+        const char *ker_strs[] = {example_cl};
+        const int   ker_lens[] = {example_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "example");
 
-            // create a cl::Program object on heap
-            egProgs[device]   = new Program(prog);
-
-            // create a cl::Kernel object on heap
-            egKernels[device] = new Kernel(*egProgs[device], "example");
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     // configure work group parameters
     NDRange local(THREADS_X, THREADS_Y);
@@ -104,18 +105,16 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
     // create a kernel functor from the cl::Kernel object
     // corresponding to the device on which current execution
     // is happending.
-    auto exampleFuncOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                        Buffer, KParam, int>(*egKernels[device]);
+    auto exampleFuncOp = KernelFunctor< Buffer, KParam, Buffer, KParam,
+                                        Buffer, KParam, int>(*entry.ker);
 
     // launch the kernel
     exampleFuncOp(EnqueueArgs(getQueue(), global, local),
-                *c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
+                  *c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
 
     // Below Macro activates validations ONLY in DEBUG
     // mode as its name indicates
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index aa1449d1fc..1204047fe5 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -11,13 +11,13 @@
 #include <backend.hpp>
 #include <program.hpp>
 #include <dispatch.hpp>
+#include <cache.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/fftconvolve_pack.hpp>
 #include <kernel_headers/fftconvolve_multiply.hpp>
 #include <kernel_headers/fftconvolve_reorder.hpp>
 #include <memory.hpp>
-#include <map>
 
 using cl::Buffer;
 using cl::Program;
@@ -29,10 +29,8 @@ using cl::NDRange;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS = 256;
 
 void calcParamSizes(Param& sig_tmp,
@@ -81,33 +79,37 @@ void packDataHelper(Param packed,
                     const int baseDim,
                     AF_BATCH_KIND kind)
 {
-    static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  fftconvolveProgs;
-    static std::map<int, Kernel*>   pdKernel;
-    static std::map<int, Kernel*>   paKernel;
+    std::string refName =
+        std::string("pack_data_") +
+        std::string(dtype_traits<convT>::getName()) +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(isDouble);
 
     int device = getActiveDeviceId();
+    kc_entry_t pdkEntry = kernelCache(device, refName);
 
-    std::call_once( compileFlags[device], [device] () {
+    if (pdkEntry.prog==0 && pdkEntry.ker==0) {
+        std::ostringstream options;
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D T=" << dtype_traits<T>::getName();
 
-            if ((af_dtype) dtype_traits<convT>::af_type == c32) {
-                options << " -D CONVT=float";
-            }
-            else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
-                options << " -D CONVT=double"
-                        << " -D USE_DOUBLE";
-            }
+        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+            options << " -D CONVT=float";
+        }
+        else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+            options << " -D CONVT=double"
+                << " -D USE_DOUBLE";
+        }
 
-            cl::Program prog;
-            buildProgram(prog, fftconvolve_pack_cl, fftconvolve_pack_cl_len, options.str());
-            fftconvolveProgs[device] = new Program(prog);
+        const char* ker_strs[] = {fftconvolve_pack_cl};
+        const int   ker_lens[] = {fftconvolve_pack_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        pdkEntry.prog = new Program(prog);
+        pdkEntry.ker  = new Kernel(*pdkEntry.prog, "pack_data");
 
-            pdKernel[device] = new Kernel(*fftconvolveProgs[device], "pack_data");
-            paKernel[device] = new Kernel(*fftconvolveProgs[device], "pad_array");
-        });
+        addKernelToCache(device, refName, pdkEntry);
+    }
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -127,25 +129,53 @@ void packDataHelper(Param packed,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    auto pdOp = KernelFunctor<Buffer, KParam,
-                            Buffer, KParam,
-                            const int, const int> (*pdKernel[device]);
+    auto pdOp = KernelFunctor< Buffer, KParam, Buffer, KParam, const int, const int > (*pdkEntry.ker);
 
     pdOp(EnqueueArgs(getQueue(), global, local),
-          *sig_tmp.data, sig_tmp.info, *sig.data, sig.info,
-          sig_half_d0, sig_half_d0_odd);
+         *sig_tmp.data, sig_tmp.info, *sig.data, sig.info, sig_half_d0, sig_half_d0_odd);
+
     CL_DEBUG_FINISH(getQueue());
 
+    refName =
+        std::string("pack_array_") +
+        std::string(dtype_traits<convT>::getName()) +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(isDouble);
+
+    kc_entry_t pakEntry = kernelCache(device, refName);
+
+    if (pakEntry.prog==0 && pakEntry.ker==0) {
+        std::ostringstream options;
+
+        options << " -D T=" << dtype_traits<T>::getName();
+
+        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+            options << " -D CONVT=float";
+        }
+        else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+            options << " -D CONVT=double"
+                << " -D USE_DOUBLE";
+        }
+
+        const char* ker_strs[] = {fftconvolve_pack_cl};
+        const int   ker_lens[] = {fftconvolve_pack_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        pakEntry.prog = new Program(prog);
+        pakEntry.ker  = new Kernel(*pakEntry.prog, "pad_array");
+
+        addKernelToCache(device, refName, pakEntry);
+    }
+
     blocks = divup(filter_packed_elem, THREADS);
     global = NDRange(blocks * THREADS);
 
     // Pad filter array with 0s
-    auto paOp = KernelFunctor<Buffer, KParam,
-                            Buffer, KParam> (*paKernel[device]);
+    auto paOp = KernelFunctor< Buffer, KParam, Buffer, KParam > (*pakEntry.ker);
 
     paOp(EnqueueArgs(getQueue(), global, local),
-          *filter_tmp.data, filter_tmp.info,
-          *filter.data, filter.info);
+         *filter_tmp.data, filter_tmp.info, *filter.data, filter.info);
+
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -156,38 +186,40 @@ void complexMultiplyHelper(Param packed,
                            const int baseDim,
                            AF_BATCH_KIND kind)
 {
-    static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> fftconvolveProgs;
-    static std::map<int, Kernel*>  cmKernel;
+    std::string refName =
+        std::string("complex_multiply_") +
+        std::string(dtype_traits<convT>::getName()) +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(isDouble);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+
+        options << " -D T=" << dtype_traits<T>::getName()
+            << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
+            << " -D AF_BATCH_LHS="  << (int)AF_BATCH_LHS
+            << " -D AF_BATCH_RHS="  << (int)AF_BATCH_RHS
+            << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
+
+        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+            options << " -D CONVT=float";
+        } else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+            options << " -D CONVT=double"
+                    << " -D USE_DOUBLE";
+        }
+
+        const char* ker_strs[] = {fftconvolve_multiply_cl};
+        const int   ker_lens[] = {fftconvolve_multiply_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "complex_multiply");
 
-    std::call_once( compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
-                    << " -D AF_BATCH_LHS="  << (int)AF_BATCH_LHS
-                    << " -D AF_BATCH_RHS="  << (int)AF_BATCH_RHS
-                    << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
-
-            if ((af_dtype) dtype_traits<convT>::af_type == c32) {
-                options << " -D CONVT=float";
-            }
-            else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
-                options << " -D CONVT=double"
-                        << " -D USE_DOUBLE";
-            }
-
-            cl::Program prog;
-            buildProgram(prog,
-                          fftconvolve_multiply_cl,
-                          fftconvolve_multiply_cl_len,
-                          options.str());
-            fftconvolveProgs[device] = new Program(prog);
-
-            cmKernel[device] = new Kernel(*fftconvolveProgs[device], "complex_multiply");
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -203,16 +235,13 @@ void complexMultiplyHelper(Param packed,
     NDRange global(blocks * THREADS);
 
     // Multiply filter and signal FFT arrays
-    auto cmOp = KernelFunctor<Buffer, KParam,
-                            Buffer, KParam,
-                            Buffer, KParam,
-                            const int, const int> (*cmKernel[device]);
+    auto cmOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                               const int, const int > (*entry.ker);
 
     cmOp(EnqueueArgs(getQueue(), global, local),
-          *packed.data, packed.info,
-          *sig_tmp.data, sig_tmp.info,
-          *filter_tmp.data, filter_tmp.info,
-          mul_elem, (int)kind);
+         *packed.data, packed.info, *sig_tmp.data, sig_tmp.info,
+         *filter_tmp.data, filter_tmp.info, mul_elem, (int)kind);
+
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -224,42 +253,46 @@ void reorderOutputHelper(Param out,
                          const int baseDim,
                          AF_BATCH_KIND kind)
 {
-    static std::once_flag     compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> fftconvolveProgs;
-    static std::map<int, Kernel*>  roKernel;
+    std::string refName =
+        std::string("reorder_output_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::string(dtype_traits<convT>::getName()) +
+        std::to_string(isDouble) +
+        std::to_string(roundOut) +
+        std::to_string(expand);
 
-    int fftScale = 1;
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    // Calculate the scale by which to divide clFFT results
-    for (int k = 0; k < baseDim; k++)
-        fftScale *= packed.info.dims[k];
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
 
-    int device = getActiveDeviceId();
+        options << " -D T=" << dtype_traits<T>::getName()
+            << " -D ROUND_OUT=" << (int)roundOut
+            << " -D EXPAND=" << (int)expand;
 
-    std::call_once( compileFlags[device], [device] () {
+        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+            options << " -D CONVT=float";
+        } else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+            options << " -D CONVT=double"
+                << " -D USE_DOUBLE";
+        }
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D ROUND_OUT=" << (int)roundOut
-                    << " -D EXPAND=" << (int)expand;
+        const char* ker_strs[] = {fftconvolve_reorder_cl};
+        const int   ker_lens[] = {fftconvolve_reorder_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "reorder_output");
 
-            if ((af_dtype) dtype_traits<convT>::af_type == c32) {
-                options << " -D CONVT=float";
-            }
-            else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
-                options << " -D CONVT=double"
-                        << " -D USE_DOUBLE";
-            }
+        addKernelToCache(device, refName, entry);
+    }
 
-            cl::Program prog;
-            buildProgram(prog,
-                          fftconvolve_reorder_cl,
-                          fftconvolve_reorder_cl_len,
-                          options.str());
-            fftconvolveProgs[device] = new Program(prog);
+    int fftScale = 1;
 
-            roKernel[device] = new Kernel(*fftconvolveProgs[device], "reorder_output");
-        });
+    // Calculate the scale by which to divide clFFT results
+    for (int k = 0; k < baseDim; k++)
+        fftScale *= packed.info.dims[k];
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -272,26 +305,18 @@ void reorderOutputHelper(Param out,
     NDRange local(THREADS);
     NDRange global(blocks * THREADS);
 
-    auto roOp = KernelFunctor<Buffer, KParam,
-                            Buffer, KParam,
-                            KParam, const int,
-                            const int, const int> (*roKernel[device]);
+    auto roOp = KernelFunctor< Buffer, KParam, Buffer, KParam, KParam, const int,
+                               const int, const int > (*entry.ker);
 
     if (kind == AF_BATCH_RHS) {
-        roOp(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info,
-              *filter_tmp.data, filter_tmp.info,
-              filter.info, sig_half_d0, baseDim, fftScale);
-    }
-    else {
-        roOp(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info,
-              *sig_tmp.data, sig_tmp.info,
-              filter.info, sig_half_d0, baseDim, fftScale);
+        roOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *filter_tmp.data, filter_tmp.info, filter.info, sig_half_d0, baseDim, fftScale);
+    } else {
+        roOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, baseDim, fftScale);
     }
+
     CL_DEBUG_FINISH(getQueue());
 }
-
 } // namespace kernel
-
 } // namespace opencl
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 4dcca9bc94..393fa16743 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -31,62 +30,64 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int TX = 32;
+static const int TY = 8;
 
-        template<typename T>
-        void gradient(Param grad0, Param grad1, const Param in)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  gradProgs;
-            static std::map<int, Kernel*> gradKernels;
+template<typename T>
+void gradient(Param grad0, Param grad1, const Param in)
+{
+    std::string refName = std::string("gradient_kernel_") + std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D TX=" << TX
-                        << " -D TY=" << TY
-                        << " -D ZERO=" << toNumStr(scalar<T>(0));
+    if (entry.prog==0 && entry.ker==0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D TX=" << TX
+                << " -D TY=" << TY
+                << " -D ZERO=" << toNumStr(scalar<T>(0));
 
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                    (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D CPLX=1";
-                } else {
-                    options << " -D CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, gradient_cl, gradient_cl_len, options.str());
-                gradProgs[device]   = new Program(prog);
-                gradKernels[device] = new Kernel(*gradProgs[device], "gradient_kernel");
-            });
+        if((af_dtype) dtype_traits<T>::af_type == c32 ||
+            (af_dtype) dtype_traits<T>::af_type == c64) {
+            options << " -D CPLX=1";
+        } else {
+            options << " -D CPLX=0";
+        }
+        if (std::is_same<T, double>::value ||
+            std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-            auto gradOp = KernelFunctor<Buffer, const KParam, Buffer, const KParam,
-                                const Buffer, const KParam, const int, const int>
-                                    (*gradKernels[device]);
+        const char* ker_strs[] = {gradient_cl};
+        const int   ker_lens[] = {gradient_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "gradient_kernel");
 
-            NDRange local(TX, TY, 1);
+        addKernelToCache(device, refName, entry);
+    }
 
-            int blocksPerMatX = divup(in.info.dims[0], TX);
-            int blocksPerMatY = divup(in.info.dims[1], TY);
-            NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                            local[1] * blocksPerMatY * in.info.dims[3],
-                            1);
+    auto gradOp = KernelFunctor< Buffer, const KParam, Buffer, const KParam,
+                                 const Buffer, const KParam, const int, const int >(*entry.ker);
 
-            gradOp(EnqueueArgs(getQueue(), global, local),
-                    *grad0.data, grad0.info, *grad1.data, grad1.info,
-                    *in.data, in.info, blocksPerMatX, blocksPerMatY);
+    NDRange local(TX, TY, 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    int blocksPerMatX = divup(in.info.dims[0], TX);
+    int blocksPerMatY = divup(in.info.dims[1], TY);
+    NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                   local[1] * blocksPerMatY * in.info.dims[3], 1);
+
+    gradOp(EnqueueArgs(getQueue(), global, local),
+           *grad0.data, grad0.info, *grad1.data, grad1.info,
+           *in.data, in.info, blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 37785db588..b4cf77decf 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -19,7 +19,8 @@
 #include <kernel/range.hpp>
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
-#include <map>
+#include <cache.hpp>
+#include <tuple>
 
 using cl::Buffer;
 using cl::Program;
@@ -30,10 +31,8 @@ using cl::NDRange;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const unsigned HARRIS_THREADS_PER_GROUP = 256;
 static const unsigned HARRIS_THREADS_X = 16;
 static const unsigned HARRIS_THREADS_Y = HARRIS_THREADS_PER_GROUP / HARRIS_THREADS_X;
@@ -85,6 +84,55 @@ void conv_helper(Param &ixx, Param &ixy, Param &iyy, Param &filter)
     bufferFree(iyy_tmp.data);
 }
 
+template<typename T>
+std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
+getHarrisKernels()
+{
+    static const std::string kernelNames[4] =
+        {"second_order_deriv", "keep_corners", "harris_responses", "non_maximal"};
+
+    kc_entry_t entries[4];
+
+    int device = getActiveDeviceId();
+
+    std::string checkName = kernelNames[0] + std::string("_") + std::string(dtype_traits<T>::getName());
+
+    entries[0] = kernelCache(device, checkName);
+
+    if (entries[0].prog==0 && entries[0].ker==0)
+    {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {harris_cl};
+        const int   ker_lens[] = {harris_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+
+        for (int i=0; i<4; ++i)
+        {
+            entries[i].prog = new Program(prog);
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+
+            std::string name = kernelNames[i] +
+                std::string("_") + std::string(dtype_traits<T>::getName());
+
+            addKernelToCache(device, name, entries[i]);
+        }
+    } else {
+        for (int i=1; i<4; ++i) {
+            std::string name = kernelNames[i] +
+                std::string("_") + std::string(dtype_traits<T>::getName());
+
+            entries[i] = kernelCache(device, name);
+        }
+    }
+
+    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker, entries[3].ker);
+}
+
 template<typename T, typename convAccT>
 void harris(unsigned* corners_out,
             Param &x_out,
@@ -97,34 +145,7 @@ void harris(unsigned* corners_out,
             const unsigned filter_len,
             const float k_thr)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> harrisProgs;
-    static std::map<int, Kernel*>  soKernel;
-    static std::map<int, Kernel*>  kcKernel;
-    static std::map<int, Kernel*>  hrKernel;
-    static std::map<int, Kernel*>  nmKernel;
-
-    int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
-
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            cl::Program prog;
-            buildProgram(prog, harris_cl, harris_cl_len, options.str());
-            harrisProgs[device] = new Program(prog);
-
-            soKernel[device] = new Kernel(*harrisProgs[device], "second_order_deriv");
-            kcKernel[device] = new Kernel(*harrisProgs[device], "keep_corners");
-            hrKernel[device] = new Kernel(*harrisProgs[device], "harris_responses");
-            nmKernel[device] = new Kernel(*harrisProgs[device], "non_maximal");
-        });
+    auto kernels = getHarrisKernels<T>();
 
     // Window filter
     convAccT* h_filter = new convAccT[filter_len];
@@ -132,8 +153,7 @@ void harris(unsigned* corners_out,
     if (sigma < 0.5f) {
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
-    }
-    else {
+    } else {
         gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
     }
 
@@ -181,13 +201,13 @@ void harris(unsigned* corners_out,
     const NDRange local_so(HARRIS_THREADS_PER_GROUP, 1);
     const NDRange global_so(blk_x_so * HARRIS_THREADS_PER_GROUP, 1);
 
-    auto soOp = KernelFunctor<Buffer, Buffer, Buffer,
-                            unsigned, Buffer, Buffer> (*soKernel[device]);
+    auto soOp = KernelFunctor< Buffer, Buffer, Buffer,
+                               unsigned, Buffer, Buffer > (*std::get<0>(kernels));
 
     // Compute second-order derivatives
     soOp(EnqueueArgs(getQueue(), global_so, local_so),
-          *ixx.data, *ixy.data, *iyy.data,
-          in.info.dims[3] * in.info.strides[3], *ix.data, *iy.data);
+         *ixx.data, *ixy.data, *iyy.data,
+         in.info.dims[3] * in.info.strides[3], *ix.data, *iy.data);
     CL_DEBUG_FINISH(getQueue());
 
     bufferFree(ix.data);
@@ -205,14 +225,13 @@ void harris(unsigned* corners_out,
     const NDRange local_hr(HARRIS_THREADS_X, HARRIS_THREADS_Y);
     const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X, blk_y_hr * HARRIS_THREADS_Y);
 
-    auto hrOp = KernelFunctor<Buffer, unsigned, unsigned,
-                            Buffer, Buffer, Buffer,
-                            float, unsigned> (*hrKernel[device]);
+    auto hrOp = KernelFunctor< Buffer, unsigned, unsigned, Buffer, Buffer, Buffer,
+                               float, unsigned> (*std::get<2>(kernels));
 
     // Calculate Harris responses for all pixels
     hrOp(EnqueueArgs(getQueue(), global_hr, local_hr),
-          *d_responses, in.info.dims[0], in.info.dims[1],
-          *ixx.data, *ixy.data, *iyy.data, k_thr, border_len);
+         *d_responses, in.info.dims[0], in.info.dims[1],
+         *ixx.data, *ixy.data, *iyy.data, k_thr, border_len);
     CL_DEBUG_FINISH(getQueue());
 
     bufferFree(ixx.data);
@@ -233,15 +252,14 @@ void harris(unsigned* corners_out,
 
     const float min_r = (max_corners > 0) ? 0.f : min_response;
 
-    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                            Buffer, unsigned, unsigned,
-                            float, unsigned, unsigned> (*nmKernel[device]);
+    auto nmOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, Buffer, unsigned, unsigned,
+                            float, unsigned, unsigned> (*std::get<3>(kernels));
 
     // Perform non-maximal suppression
     nmOp(EnqueueArgs(getQueue(), global_hr, local_hr),
-          *d_x_corners, *d_y_corners, *d_resp_corners, *d_corners_found,
-          *d_responses, in.info.dims[0], in.info.dims[1],
-          min_r, border_len, corner_lim);
+         *d_x_corners, *d_y_corners, *d_resp_corners, *d_corners_found,
+         *d_responses, in.info.dims[0], in.info.dims[1],
+         min_r, border_len, corner_lim);
     CL_DEBUG_FINISH(getQueue());
 
     getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
@@ -299,16 +317,15 @@ void harris(unsigned* corners_out,
         const NDRange local_kc(HARRIS_THREADS_PER_GROUP, 1);
         const NDRange global_kc(blk_x_kc * HARRIS_THREADS_PER_GROUP, 1);
 
-        auto kcOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer, Buffer,
-                                unsigned> (*kcKernel[device]);
+        auto kcOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                                   unsigned> (*std::get<1>(kernels));
 
         // Keep only the first corners_to_keep corners with higher Harris
         // responses
         kcOp(EnqueueArgs(getQueue(), global_kc, local_kc),
-              *x_out.data, *y_out.data, *resp_out.data,
-              *d_x_corners, *d_y_corners, *harris_resp.data, *harris_idx.data,
-              *corners_out);
+             *x_out.data, *y_out.data, *resp_out.data,
+             *d_x_corners, *d_y_corners, *harris_resp.data, *harris_idx.data,
+             *corners_out);
         CL_DEBUG_FINISH(getQueue());
 
         bufferFree(d_x_corners);
@@ -334,7 +351,5 @@ void harris(unsigned* corners_out,
         resp_out.data = d_resp_corners;
     }
 }
-
 } //namespace kernel
-
 } //namespace opencl
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 28b0e1ad3d..6a998b6621 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -23,10 +22,8 @@ using cl::KernelFunctor;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const unsigned MAX_BINS  = 4000;
 static const int THREADS_X =  256;
 static const int THRD_LOAD =   16;
@@ -34,34 +31,38 @@ static const int THRD_LOAD =   16;
 template<typename inType, typename outType, bool isLinear>
 void histogram(Param out, const Param in, int nbins, float minval, float maxval)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> histProgs;
-    static std::map<int, Kernel *> histKernels;
+    std::string refName = std::string("histogram_") +
+        std::string(dtype_traits<inType>::getName()) +
+        std::string(dtype_traits<inType>::getName()) +
+        std::to_string(isLinear);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D inType=" << dtype_traits<inType>::getName()
-                        << " -D outType=" << dtype_traits<outType>::getName()
-                        << " -D THRD_LOAD=" << THRD_LOAD;
-                if (isLinear)
-                    options << " -D IS_LINEAR";
-                if (std::is_same<inType, double>::value ||
-                    std::is_same<inType, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D inType=" << dtype_traits<inType>::getName()
+            << " -D outType=" << dtype_traits<outType>::getName()
+            << " -D THRD_LOAD=" << THRD_LOAD;
+        if (isLinear)
+            options << " -D IS_LINEAR";
+        if (std::is_same<inType, double>::value ||
+                std::is_same<inType, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-                Program prog;
-                buildProgram(prog, histogram_cl, histogram_cl_len, options.str());
-                histProgs[device]   = new Program(prog);
-                histKernels[device] = new Kernel(*histProgs[device], "histogram");
-            });
+        const char* ker_strs[] = {histogram_cl};
+        const int   ker_lens[] = {histogram_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "histogram");
 
-    auto histogramOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                    cl::LocalSpaceArg,
-                                    int, int, float, float, int
-                                  >(*histKernels[device]);
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto histogramOp = KernelFunctor< Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg,
+                                      int, int, float, float, int >(*entry.ker);
 
     int nElems = in.info.dims[0]*in.info.dims[1];
     int blk_x  = divup(nElems, THRD_LOAD*THREADS_X);
@@ -71,11 +72,10 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval)
     NDRange global(blk_x*in.info.dims[2]*THREADS_X, in.info.dims[3]);
 
     histogramOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *in.data, in.info,
-            cl::Local(locSize), nElems, nbins, minval, maxval, blk_x);
+                *out.data, out.info, *in.data, in.info,
+                cl::Local(locSize), nElems, nbins, minval, maxval, blk_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
 }
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index a5f6faf096..572c21bdca 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -17,6 +17,7 @@
 #include <kernel/reduce.hpp>
 #include <kernel/sort.hpp>
 #include <cfloat>
+#include <cache.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -28,68 +29,88 @@ using std::vector;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 const int HG_THREADS_X = 16;
 const int HG_THREADS_Y = 16;
 const int HG_THREADS   = 256;
 
 template<typename T, af_homography_type htype>
-int computeH(
-    Param bestH,
-    Param H,
-    Param err,
-    Param x_src,
-    Param y_src,
-    Param x_dst,
-    Param y_dst,
-    Param rnd,
-    const unsigned iterations,
-    const unsigned nsamples,
-    const float inlier_thr)
+std::array<cl::Kernel*, 5> getHomographyKernels()
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> hgProgs;
-    static std::map<int, Kernel*>  chKernel;
-    static std::map<int, Kernel*>  ehKernel;
-    static std::map<int, Kernel*>  cmKernel;
-    static std::map<int, Kernel*>  fmKernel;
-    static std::map<int, Kernel*>  clKernel;
+    static const unsigned NUM_KERNELS = 5;
+    static const std::string kernelNames[NUM_KERNELS] =
+        {"compute_homography", "eval_homography", "compute_median",
+         "find_min_median", "compute_lmeds_inliers"};
+
+    kc_entry_t entries[NUM_KERNELS];
 
     int device = getActiveDeviceId();
 
-    std::call_once( compileFlags[device], [device] () {
+    std::string checkName = kernelNames[0] + std::string("_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(htype);
+
+    entries[0] = kernelCache(device, checkName);
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
+    if (entries[0].prog==0 && entries[0].ker==0)
+    {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
 
-            if (std::is_same<T, double>::value) {
-                options << " -D USE_DOUBLE";
-                options << " -D EPS=" << DBL_EPSILON;
-            } else
-                options << " -D EPS=" << FLT_EPSILON;
+        if (std::is_same<T, double>::value) {
+            options << " -D USE_DOUBLE";
+            options << " -D EPS=" << DBL_EPSILON;
+        } else
+            options << " -D EPS=" << FLT_EPSILON;
+
+        if (htype == AF_HOMOGRAPHY_RANSAC)
+            options << " -D RANSAC";
+        else if (htype == AF_HOMOGRAPHY_LMEDS)
+            options << " -D LMEDS";
+
+        if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
+            options << " -D IS_CPU";
+        }
 
-            if (htype == AF_HOMOGRAPHY_RANSAC)
-                options << " -D RANSAC";
-            else if (htype == AF_HOMOGRAPHY_LMEDS)
-                options << " -D LMEDS";
+        cl::Program prog;
+        buildProgram(prog, homography_cl, homography_cl_len, options.str());
+
+        for (unsigned i=0; i<NUM_KERNELS; ++i)
+        {
+            entries[i].prog = new Program(prog);
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+
+            std::string name = kernelNames[i] + std::string("_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::to_string(htype);
+
+            addKernelToCache(device, name, entries[i]);
+        }
+    } else {
+        for (unsigned i=1; i<NUM_KERNELS; ++i) {
+            std::string name = kernelNames[i] + std::string("_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::to_string(htype);
+
+            entries[i] = kernelCache(device, name);
+        }
+    }
 
-            if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
-                options << " -D IS_CPU";
-            }
+    std::array<cl::Kernel*, NUM_KERNELS> retVal;
+    for (unsigned i=0; i<NUM_KERNELS; ++i)
+        retVal[i] = entries[i].ker;
 
-            cl::Program prog;
-            buildProgram(prog, homography_cl, homography_cl_len, options.str());
-            hgProgs[device] = new Program(prog);
+    return retVal;
+}
 
-            chKernel[device] = new Kernel(*hgProgs[device], "compute_homography");
-            ehKernel[device] = new Kernel(*hgProgs[device], "eval_homography");
-            cmKernel[device] = new Kernel(*hgProgs[device], "compute_median");
-            fmKernel[device] = new Kernel(*hgProgs[device], "find_min_median");
-            clKernel[device] = new Kernel(*hgProgs[device], "compute_lmeds_inliers");
-        });
+template<typename T, af_homography_type htype>
+int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
+             Param x_dst, Param y_dst, Param rnd, const unsigned iterations,
+             const unsigned nsamples,
+             const float inlier_thr)
+{
+    auto kernels = getHomographyKernels<T, htype>();
 
     const int blk_x_ch = 1;
     const int blk_y_ch = divup(iterations, HG_THREADS_Y);
@@ -97,14 +118,12 @@ int computeH(
     const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
 
     // Build linear system and solve SVD
-    auto chOp = KernelFunctor<Buffer, KParam,
-                            Buffer, Buffer, Buffer, Buffer,
-                            Buffer, KParam, unsigned>(*chKernel[device]);
-
-    chOp(EnqueueArgs(getQueue(), global_ch, local_ch),
-          *H.data, H.info,
-          *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-          *rnd.data, rnd.info, iterations);
+    auto chOp = KernelFunctor< Buffer, KParam, Buffer, Buffer, Buffer, Buffer,
+                               Buffer, KParam, unsigned>(*kernels[0]);
+
+    chOp(EnqueueArgs(getQueue(), global_ch, local_ch), *H.data, H.info,
+         *x_src.data, *y_src.data, *x_dst.data, *y_dst.data, *rnd.data, rnd.info, iterations);
+
     CL_DEBUG_FINISH(getQueue());
 
     const int blk_x_eh = divup(iterations, HG_THREADS);
@@ -132,16 +151,14 @@ int computeH(
         median.data = bufferAlloc(sizeof(float));
 
     // Compute (and for RANSAC, evaluate) homographies
-    auto ehOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
-                            Buffer, KParam,
-                            Buffer, Buffer, Buffer, Buffer,
-                            Buffer, unsigned, unsigned, float>(*ehKernel[device]);
-
-    ehOp(EnqueueArgs(getQueue(), global_eh, local_eh),
-          *inliers.data, *idx.data, *H.data, H.info,
-          *err.data, err.info,
-          *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-          *rnd.data, iterations, nsamples, inlier_thr);
+    auto ehOp = KernelFunctor< Buffer, Buffer, Buffer, KParam, Buffer, KParam,
+                               Buffer, Buffer, Buffer, Buffer,
+                               Buffer, unsigned, unsigned, float>(*kernels[1]);
+
+    ehOp(EnqueueArgs(getQueue(), global_eh, local_eh), *inliers.data, *idx.data, *H.data, H.info,
+         *err.data, err.info, *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
+         *rnd.data, iterations, nsamples, inlier_thr);
+
     CL_DEBUG_FINISH(getQueue());
 
     unsigned inliersH, idxH;
@@ -154,12 +171,11 @@ int computeH(
         float minMedian;
 
         // Compute median of every iteration
-        auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
-                                unsigned>(*cmKernel[device]);
+        auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, unsigned>(*kernels[2]);
 
         cmOp(EnqueueArgs(getQueue(), global_eh, local_eh),
-              *median.data, *idx.data, *err.data, err.info,
-              iterations);
+             *median.data, *idx.data, *err.data, err.info, iterations);
+
         CL_DEBUG_FINISH(getQueue());
 
         // Reduce medians, only in case iterations > 256
@@ -170,12 +186,11 @@ int computeH(
             cl::Buffer* finalMedian = bufferAlloc(sizeof(float));
             cl::Buffer* finalIdx = bufferAlloc(sizeof(unsigned));
 
-            auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam,
-                                    Buffer>(*fmKernel[device]);
+            auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer>(*kernels[3]);
 
             fmOp(EnqueueArgs(getQueue(), global_fm, local_fm),
-                  *finalMedian, *finalIdx, *median.data, median.info,
-                  *idx.data);
+                 *finalMedian, *finalIdx, *median.data, median.info, *idx.data);
+
             CL_DEBUG_FINISH(getQueue());
 
             getQueue().enqueueReadBuffer(*finalMedian, CL_TRUE, 0, sizeof(float), &minMedian);
@@ -196,14 +211,12 @@ int computeH(
         const NDRange local_cl(HG_THREADS);
         const NDRange global_cl(blk_x_cl * HG_THREADS);
 
-        auto clOp = KernelFunctor<Buffer, Buffer,
-                                Buffer, Buffer, Buffer, Buffer,
-                                float, unsigned>(*clKernel[device]);
+        auto clOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                                   float, unsigned >(*kernels[4]);
+
+        clOp(EnqueueArgs(getQueue(), global_cl, local_cl), *inliers.data, *bestH.data,
+             *x_src.data, *y_src.data, *x_dst.data, *y_dst.data, minMedian, nsamples);
 
-        clOp(EnqueueArgs(getQueue(), global_cl, local_cl),
-              *inliers.data, *bestH.data,
-              *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-              minMedian, nsamples);
         CL_DEBUG_FINISH(getQueue());
 
         // Adds up the total number of inliers
@@ -234,7 +247,5 @@ int computeH(
 
     return (int)inliersH;
 }
-
 } // namespace kernel
-
 } // namespace cuda
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 569912c836..0165512173 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,37 +27,36 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool isHSV2RGB>
 void hsv2rgb_convert(Param out, const Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  hrProgs;
-    static std::map<int, Kernel*> hrKernels;
+    std::string refName = std::string("hsvrgb_convert_") +
+        std::string(dtype_traits<T>::getName()) + std::to_string(isHSV2RGB);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once( compileFlags[device], [device] () {
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
+        if(isHSV2RGB) options << " -D isHSV2RGB";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
-            if(isHSV2RGB) options << " -D isHSV2RGB";
+        const char* ker_strs[] = {hsv_rgb_cl};
+        const int   ker_lens[] = {hsv_rgb_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "convert");
 
-            if (std::is_same<T, double>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, hsv_rgb_cl, hsv_rgb_cl_len, options.str());
-            hrProgs[device]   = new Program(prog);
-            hrKernels[device] = new Kernel(*hrProgs[device], "convert");
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -69,14 +67,11 @@ void hsv2rgb_convert(Param out, const Param in)
     // parameter would be along 4th dimension
     NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
 
-    auto hsvrgbOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int> (*hrKernels[device]);
+    auto hsvrgbOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int> (*entry.ker);
 
-    hsvrgbOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, blk_x);
+    hsvrgbOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info, blk_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 3882de037c..49197ba2d3 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -13,8 +13,7 @@
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <map>
-#include <mutex>
+#include <cache.hpp>
 #include <math.hpp>
 #include "config.hpp"
 
@@ -30,49 +29,46 @@ using af::scalar_to_option;
 
 namespace opencl
 {
-
 namespace kernel
 {
+template<typename T>
+static void identity(Param out)
+{
+    std::string refName = std::string("identity_kernel") + std::string(dtype_traits<T>::getName());
 
-    template<typename T>
-    static void identity(Param out)
-    {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>   identityProgs;
-        static std::map<int, Kernel*>  identityKernels;
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-        int device = getActiveDeviceId();
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T="    << dtype_traits<T>::getName()
+            << " -D ONE=(T)("  << scalar_to_option(scalar<T>(1)) << ")"
+            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+        if (std::is_same<T, double>::value ||
+                std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-        std::call_once( compileFlags[device], [device] () {
-                ostringstream options;
-                options << " -D T="    << dtype_traits<T>::getName()
-                        << " -D ONE=(T)("  << scalar_to_option(scalar<T>(1)) << ")"
-                        << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, identity_cl, identity_cl_len, options.str());
-                identityProgs[device]   = new Program(prog);
-                identityKernels[device] = new Kernel(*identityProgs[device], "identity_kernel");
-            });
+        const char* ker_strs[] = {identity_cl};
+        const int   ker_lens[] = {identity_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "identity_kernel");
 
-        NDRange local(32, 8);
-        int groups_x = divup(out.info.dims[0], local[0]);
-        int groups_y = divup(out.info.dims[1], local[1]);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                        groups_y * out.info.dims[3] * local[1]);
+        addKernelToCache(device, refName, entry);
+    }
 
-        auto identityOp = KernelFunctor<Buffer, const KParam,
-                                      int, int> (*identityKernels[device]);
+    NDRange local(32, 8);
+    int groups_x = divup(out.info.dims[0], local[0]);
+    int groups_y = divup(out.info.dims[1], local[1]);
+    NDRange global(groups_x * out.info.dims[2] * local[0], groups_y * out.info.dims[3] * local[1]);
 
-        identityOp(EnqueueArgs(getQueue(), global, local),
-                    *(out.data), out.info, groups_x, groups_y);
-        CL_DEBUG_FINISH(getQueue());
+    auto identityOp = KernelFunctor<Buffer, const KParam, int, int> (*entry.ker);
 
-    }
+    identityOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info, groups_x, groups_y);
 
+    CL_DEBUG_FINISH(getQueue());
+}
 }
-
 }
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index d9b220195b..ef3025e7f6 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -30,69 +29,59 @@ using af::scalar_to_option;
 
 namespace opencl
 {
+namespace kernel
+{
+template<typename T, bool batch_a>
+void iir(Param y, Param c, Param a)
+{
+    //FIXME: This is a temporary fix. Ideally the local memory should be allocted outside
+    static const int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
 
-    namespace kernel
-    {
-        template<typename T, bool batch_a>
-        void iir(Param y, Param c, Param a)
-        {
-
-            //FIXME: This is a temporary fix. Ideally the local memory should be allocted outside
-            static const int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
-
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  iirProgs;
-            static std::map<int, Kernel*> iirKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once(compileFlags[device], [device] () {
-
-                    std::ostringstream options;
-                    options << " -D MAX_A_SIZE=" << MAX_A_SIZE
-                            << " -D BATCH_A=" << batch_a
-                            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-                            << " -D T=" << dtype_traits<T>::getName();
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    cl::Program prog;
-                    buildProgram(prog, iir_cl, iir_cl_len, options.str());
-                    iirProgs[device] = new Program(prog);
+    std::string refName = std::string("iir_kernel_") +
+        std::string(dtype_traits<T>::getName()) + std::to_string(batch_a);
 
-                    iirKernels[device] = new Kernel(*iirProgs[device], "iir_kernel");
-                });
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D MAX_A_SIZE=" << MAX_A_SIZE
+            << " -D BATCH_A=" << batch_a
+            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+            << " -D T=" << dtype_traits<T>::getName();
 
-            const int groups_y = y.info.dims[1];
-            const int groups_x = y.info.dims[2];
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            int threads = 256;
-            while (threads > (int)y.info.dims[0] && threads > 32) threads /= 2;
+        const char* ker_strs[] = {iir_cl};
+        const int   ker_lens[] = {iir_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "iir_kernel");
 
+        addKernelToCache(device, refName, entry);
+    }
 
-            NDRange local(threads, 1);
-            NDRange global(groups_x * local[0],
-                           groups_y * y.info.dims[3] * local[1]);
+    const int groups_y = y.info.dims[1];
+    const int groups_x = y.info.dims[2];
 
-            auto iirOp = KernelFunctor<Buffer, KParam,
-                                     Buffer, KParam,
-                                     Buffer, KParam,
-                                     int>(*iirKernels[device]);
+    int threads = 256;
+    while (threads > (int)y.info.dims[0] && threads > 32) threads /= 2;
 
-            try {
-                iirOp(EnqueueArgs(getQueue(), global, local),
-                      *y.data, y.info, *c.data, c.info, *a.data, a.info, groups_y);
-            } catch(cl::Error &clerr) {
-                AF_ERROR("Size of a too big for this datatype",
-                         AF_ERR_SIZE);
-            }
+    NDRange local(threads, 1);
+    NDRange global(groups_x * local[0], groups_y * y.info.dims[3] * local[1]);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
+    auto iirOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int>(*entry.ker);
 
+    try {
+        iirOp(EnqueueArgs(getQueue(), global, local),
+              *y.data, y.info, *c.data, c.info, *a.data, a.info, groups_y);
+    } catch(cl::Error &clerr) {
+        AF_ERROR("Size of a too big for this datatype", AF_ERR_SIZE);
     }
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index ae39e1f64a..4b41c82172 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,10 +27,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 32;
 static const int THREADS_Y =  8;
 
@@ -44,37 +41,37 @@ typedef struct {
 template<typename T>
 void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[4])
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  idxProgs;
-    static std::map<int, Kernel*> idxKernels;
+    std::string refName = std::string("indexKernel_") + std::string(dtype_traits<T>::getName());
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once( compileFlags[device], [device] () {
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
-            }
 
-            Program prog;
-            buildProgram(prog, index_cl, index_cl_len, options.str());
-            idxProgs[device]   = new Program(prog);
-            idxKernels[device] = new Kernel(*idxProgs[device], "indexKernel");
-            });
+        const char* ker_strs[] = {index_cl};
+        const int   ker_lens[] = {index_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "indexKernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-            blk_y * out.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * out.info.dims[2] * THREADS_X, blk_y * out.info.dims[3] * THREADS_Y);
 
     auto indexOp = KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
-          Buffer, Buffer, Buffer, Buffer, int, int>(*idxKernels[device]);
+                                 Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
 
     indexOp(EnqueueArgs(getQueue(), global, local),
             *out.data, out.info, *in.data, in.info, p,
@@ -82,7 +79,5 @@ void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index d018357ab2..8e0d85e41e 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -13,8 +13,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -29,54 +28,56 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int IOTA_TX = 32;
-        static const int IOTA_TY = 8;
-        static const int TILEX = 512;
-        static const int TILEY = 32;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int IOTA_TX = 32;
+static const int IOTA_TY = 8;
+static const int TILEX = 512;
+static const int TILEY = 32;
 
-        template<typename T>
-        void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  iotaProgs;
-            static std::map<int, Kernel*> iotaKernels;
+template<typename T>
+void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
+{
+    std::string refName = std::string("iota_kernel_") + std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, iota_cl, iota_cl_len, options.str());
-                iotaProgs[device]   = new Program(prog);
-                iotaKernels[device] = new Kernel(*iotaProgs[device], "iota_kernel");
-            });
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
 
-            auto iotaOp = KernelFunctor<Buffer, const KParam,
-                                      const int, const int, const int, const int,
-                                      const int, const int, const int, const int,
-                                      const int, const int> (*iotaKernels[device]);
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            NDRange local(IOTA_TX, IOTA_TY, 1);
+        const char* ker_strs[] = {iota_cl};
+        const int   ker_lens[] = {iota_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "iota_kernel");
 
-            int blocksPerMatX = divup(out.info.dims[0], TILEX);
-            int blocksPerMatY = divup(out.info.dims[1], TILEY);
-            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                            local[1] * blocksPerMatY * out.info.dims[3],
-                            1);
+        addKernelToCache(device, refName, entry);
+    }
 
-            iotaOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, sdims[0], sdims[1], sdims[2], sdims[3],
-                    tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
+    auto iotaOp = KernelFunctor<Buffer, const KParam,
+                                const int, const int, const int, const int,
+                                const int, const int, const int, const int,
+                                const int, const int> (*entry.ker);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    NDRange local(IOTA_TX, IOTA_TY, 1);
+
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                   local[1] * blocksPerMatY * out.info.dims[3], 1);
+
+    iotaOp(EnqueueArgs(getQueue(), global, local),
+           *out.data, out.info, sdims[0], sdims[1], sdims[2], sdims[3],
+           tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 52cfcaeb98..a450f22fcf 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -28,59 +28,62 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
-        static const int TILEX = 256;
-        static const int TILEY = 32;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int TX = 32;
+static const int TY = 8;
+static const int TILEX = 256;
+static const int TILEY = 32;
 
-        template<typename To, typename Ti, int dim>
-        void join(Param out, const Param in, const af::dim4 offset)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   joinProgs;
-            static std::map<int, Kernel *> joinKernels;
+template<typename To, typename Ti, int dim>
+void join(Param out, const Param in, const af::dim4 offset)
+{
+    std::string refName = std::string("join_kernel_") +
+        std::string(dtype_traits<To>::getName()) +
+        std::string(dtype_traits<Ti>::getName()) +
+        std::to_string(dim);
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D To=" << dtype_traits<To>::getName()
-                        << " -D Ti=" << dtype_traits<Ti>::getName()
-                        << " -D dim=" << dim;
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName()
+                << " -D dim=" << dim;
 
-                if (std::is_same<To, double>::value ||
-                    std::is_same<To, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                } else if (std::is_same<Ti, double>::value ||
-                            std::is_same<Ti, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+        if (std::is_same<To, double>::value || std::is_same<To, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        } else if (std::is_same<Ti, double>::value || std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-                Program prog;
-                buildProgram(prog, join_cl, join_cl_len, options.str());
-                joinProgs[device] = new Program(prog);
-                joinKernels[device] = new Kernel(*joinProgs[device], "join_kernel");
-            });
+        const char* ker_strs[] = {join_cl};
+        const int   ker_lens[] = {join_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "join_kernel");
 
-            auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                          const int, const int, const int, const int,
-                          const int, const int> (*joinKernels[device]);
+        addKernelToCache(device, refName, entry);
+    }
 
-            NDRange local(TX, TY, 1);
+    auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                                const int, const int, const int, const int,
+                                const int, const int> (*entry.ker);
 
-            int blocksPerMatX = divup(in.info.dims[0], TILEX);
-            int blocksPerMatY = divup(in.info.dims[1], TILEY);
-            NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                            local[1] * blocksPerMatY * in.info.dims[3],
-                            1);
+    NDRange local(TX, TY, 1);
 
-            joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info,
-                    offset[0], offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
+    int blocksPerMatX = divup(in.info.dims[0], TILEX);
+    int blocksPerMatY = divup(in.info.dims[1], TILEY);
+    NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                   local[1] * blocksPerMatY * in.info.dims[3], 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info,
+           offset[0], offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 8f5fc1f432..612df24383 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,13 +27,10 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 
-
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int BLK_X = 64;
 static const int BLK_Y = 32;
 
@@ -49,46 +45,45 @@ void laset(int m, int  n,
            T offdiag, T diag,
            cl_mem dA, size_t dA_offset, magma_int_t ldda)
 {
-
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  setProgs;
-    static std::map<int, Kernel*> setKernels;
+    std::string refName = laset_name<uplo>() + std::string("_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(uplo);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once(compileFlags[device], [device] () {
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D BLK_X=" << BLK_X
+                << " -D BLK_Y=" << BLK_Y
+                << " -D IS_CPLX=" << af::iscplx<T>();
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D BLK_X=" << BLK_X
-                    << " -D BLK_Y=" << BLK_Y
-                    << " -D IS_CPLX=" << af::iscplx<T>();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        const char* ker_strs[] = {laset_cl};
+        const int   ker_lens[] = {laset_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, laset_name<uplo>());
 
-            cl::Program prog;
-            buildProgram(prog, laset_cl, laset_cl_len, options.str());
-            setProgs[device] = new Program(prog);
-            setKernels[device] = new Kernel(*setProgs[device], laset_name<uplo>());
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     int groups_x = (m - 1) / BLK_X + 1;
     int groups_y = (n - 1) / BLK_Y + 1;
 
     NDRange local(BLK_X, 1);
-    NDRange global(groups_x * local[0],
-                   groups_y * local[1]);
+    NDRange global(groups_x * local[0], groups_y * local[1]);
 
     // retain the cl_mem object during cl::Buffer creation
     cl::Buffer dAObj(dA, true);
 
-    auto lasetOp = KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(*setKernels[device]);
-    lasetOp(EnqueueArgs(getQueue(), global, local),
-            m, n, offdiag, diag, dAObj, dA_offset, ldda);
-}
+    auto lasetOp = KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(*entry.ker);
 
+    lasetOp(EnqueueArgs(getQueue(), global, local), m, n, offdiag, diag, dAObj, dA_offset, ldda);
+}
 }
 }
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 915be6f560..645622c279 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,13 +27,10 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 
-
 namespace opencl
 {
-
 namespace kernel
 {
-
 #if 0 // Needs to be enabled when unmqr2 is enabled
 static const int NB = 64;
 template<int num>
@@ -47,30 +43,31 @@ void laset_band(int m, int  n, int k,
                 T offdiag, T diag,
                 cl_mem dA, size_t dA_offset, magma_int_t ldda)
 {
-
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  setProgs;
-    static std::map<int, Kernel*> setKernels;
+    std::string refName = laset_band_name<uplo>() + std::string("_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(uplo);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once(compileFlags[device], [device] () {
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+            << " -D NB=" << NB
+            << " -D IS_CPLX=" << af::iscplx<T>();
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D NB=" << NB
-                    << " -D IS_CPLX=" << af::iscplx<T>();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        const char* ker_strs[] = {laset_band_cl};
+        const int   ker_lens[] = {laset_band_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, laset_band_name<uplo>());
 
-            cl::Program prog;
-            buildProgram(prog, laset_band_cl, laset_band_cl_len, options.str());
-            setProgs[device] = new Program(prog);
-            setKernels[device] = new Kernel(*setProgs[device], laset_band_name<uplo>());
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     int threads = 1;
     int groups = 1;
@@ -86,12 +83,10 @@ void laset_band(int m, int  n, int k,
     NDRange local(threads, 1);
     NDRange global(threads * groups, 1);
 
-    auto lasetBandOp = KernelFunctor<int, int, T, T, cl_mem, unsigned long long, int>(*setKernels[device]);
+    auto lasetBandOp = KernelFunctor<int, int, T, T, cl_mem, unsigned long long, int>(*entry.ker);
 
-    lasetBandOp(EnqueueArgs(getQueue(), global, local),
-                m, n, offdiag, diag, dA, dA_offset, ldda);
+    lasetBandOp(EnqueueArgs(getQueue(), global, local), m, n, offdiag, diag, dA, dA_offset, ldda);
 }
 #endif
-
 }
 }
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 99eb4096ed..9d6b486c3b 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -27,13 +26,10 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 
-
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int NTHREADS = 256;
 static const int MAX_PIVOTS =  32;
 
@@ -42,35 +38,31 @@ typedef struct {
     int ipiv[MAX_PIVOTS];
 } zlaswp_params_t;
 
-
 template<typename T>
-void laswp(int n, cl_mem in, size_t offset, int ldda,
-           int k1, int k2, const int *ipiv, int inci)
+void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int *ipiv, int inci)
 {
-
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  swpProgs;
-    static std::map<int, Kernel*> swpKernels;
+    std::string refName = std::string("laswp_") + std::string(dtype_traits<T>::getName());
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once(compileFlags[device], [device] () {
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D MAX_PIVOTS=" << MAX_PIVOTS;
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D MAX_PIVOTS=" << MAX_PIVOTS;
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        const char* ker_strs[] = {laswp_cl};
+        const int   ker_lens[] = {laswp_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "laswp");
 
-            cl::Program prog;
-            buildProgram(prog, laswp_cl, laswp_cl_len, options.str());
-            swpProgs[device] = new Program(prog);
-
-            swpKernels[device] = new Kernel(*swpProgs[device], "laswp");
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     int groups = divup(n, NTHREADS);
     NDRange local(NTHREADS);
@@ -80,26 +72,20 @@ void laswp(int n, cl_mem in, size_t offset, int ldda,
     //retain the cl_mem object during cl::Buffer creation
     cl::Buffer inObj(in, true);
 
-    auto laswpOp = KernelFunctor<int, Buffer, unsigned long long,
-                               int, zlaswp_params_t>(*swpKernels[device]);
+    auto laswpOp = KernelFunctor<int, Buffer, unsigned long long, int, zlaswp_params_t>(*entry.ker);
 
     for( int k = k1-1; k < k2; k += MAX_PIVOTS ) {
-
         int pivots_left = k2-k;
 
         params.npivots = pivots_left > MAX_PIVOTS ? MAX_PIVOTS : pivots_left;
 
-        for( int j = 0; j < params.npivots; ++j ) {
+        for( int j = 0; j < params.npivots; ++j )
             params.ipiv[j] = ipiv[(k+j)*inci] - k - 1;
-        }
 
         unsigned long long k_offset = offset + k*ldda;
 
-        laswpOp(EnqueueArgs(getQueue(), global, local),
-                n, inObj, k_offset, ldda, params);
+        laswpOp(EnqueueArgs(getQueue(), global, local), n, inObj, k_offset, ldda, params);
     }
-
 }
-
 }
 }
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 995dffbadf..9528680366 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,59 +27,57 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
 
 template<typename in_t, typename idx_t, unsigned dim>
 void lookup(Param out, const Param in, const Param indices, int nDims)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  aiProgs;
-    static std::map<int, Kernel*> aiKernels;
+    std::string refName = std::string("lookupND_") +
+        std::string(dtype_traits<in_t>::getName()) +
+        std::string(dtype_traits<idx_t>::getName()) +
+        std::to_string(dim);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D in_t=" << dtype_traits<in_t>::getName()
-                        << " -D idx_t=" << dtype_traits<idx_t>::getName()
-                        << " -D DIM=" <<dim;
-
-                if (std::is_same<in_t, double>::value ||
-                    std::is_same<in_t, cdouble>::value ||
-                    std::is_same<idx_t, double>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                buildProgram(prog, lookup_cl, lookup_cl_len, options.str());
-                aiProgs[device]   = new Program(prog);
-                aiKernels[device] = new Kernel(*aiProgs[device], "lookupND");
-            });
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D in_t=" << dtype_traits<in_t>::getName()
+                << " -D idx_t=" << dtype_traits<idx_t>::getName()
+                << " -D DIM=" <<dim;
+
+        if (std::is_same<in_t, double>::value ||
+                std::is_same<in_t, cdouble>::value ||
+                std::is_same<idx_t, double>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char* ker_strs[] = {lookup_cl};
+        const int   ker_lens[] = {lookup_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "lookupND");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-                    blk_y * out.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * out.info.dims[2] * THREADS_X, blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer, KParam,
-                                int, int>(*aiKernels[device]);
+    auto arrIdxOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int, int>(*entry.ker);
 
     arrIdxOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
+             *out.data, out.info, *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 6784039614..858b931660 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -31,10 +30,8 @@ using af::scalar_to_option;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 // Kernel Launch Config Values
 static const unsigned TX = 32;
 static const unsigned TY = 8;
@@ -44,51 +41,46 @@ static const unsigned TILEY = 32;
 template<typename T, bool same_dims>
 void lu_split_launcher(Param lower, Param upper, const Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  splitProgs;
-    static std::map<int, Kernel*> splitKernels;
+    std::string refName = std::string("lu_split_kernel_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(same_dims);
 
     int device = getActiveDeviceId();
-
-    std::call_once(compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D same_dims=" << same_dims
-                    << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-                    << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
-
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            cl::Program prog;
-            buildProgram(prog, lu_split_cl, lu_split_cl_len, options.str());
-            splitProgs[device] = new Program(prog);
-
-            splitKernels[device] = new Kernel(*splitProgs[device], "lu_split_kernel");
-        });
-
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+            << " -D same_dims=" << same_dims
+            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+            << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {lu_split_cl};
+        const int   ker_lens[] = {lu_split_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "lu_split_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(TX, TY);
 
     int groups_x = divup(in.info.dims[0], TILEX);
     int groups_y = divup(in.info.dims[1], TILEY);
 
-    NDRange global(groups_x * local[0] * in.info.dims[2],
-                    groups_y * local[1] * in.info.dims[3]);
+    NDRange global(groups_x * local[0] * in.info.dims[2], groups_y * local[1] * in.info.dims[3]);
 
-    auto lu_split_op = KernelFunctor<Buffer, const KParam,
-                                    Buffer, const KParam,
-                                    const Buffer, const KParam,
-                                    const int, const int> (*splitKernels[device]);
+    auto lu_split_op = KernelFunctor<Buffer, const KParam, Buffer, const KParam,
+                                    const Buffer, const KParam, const int, const int> (*entry.ker);
 
     lu_split_op(EnqueueArgs(getQueue(), global, local),
-                *lower.data, lower.info,
-                *upper.data, upper.info,
-                *in.data, in.info,
-                groups_x, groups_y);
+                *lower.data, lower.info, *upper.data, upper.info,
+                *in.data, in.info, groups_x, groups_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
@@ -106,7 +98,5 @@ void lu_split(Param lower, Param upper, const Param in)
         lu_split_launcher<T, false>(lower, upper, in);
     }
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index 4922abb784..4cd5965371 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,46 +27,49 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename inType, typename outType, af_match_type mType, bool needMean>
 void matchTemplate(Param out, const Param srch, const Param tmplt)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  mtProgs;
-    static std::map<int, Kernel*> mtKernels;
+    std::string refName = std::string("matchTemplate_") +
+        std::string(dtype_traits<inType>::getName()) +
+        std::string(dtype_traits<outType>::getName()) +
+        std::to_string(mType) + std::to_string(needMean);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D inType="  << dtype_traits<inType>::getName()
-                    << " -D outType=" << dtype_traits<outType>::getName()
-                    << " -D MATCH_T=" << mType
-                    << " -D NEEDMEAN="<< needMean
-                    << " -D AF_SAD="  << AF_SAD
-                    << " -D AF_ZSAD=" << AF_ZSAD
-                    << " -D AF_LSAD=" << AF_LSAD
-                    << " -D AF_SSD="  << AF_SSD
-                    << " -D AF_ZSSD=" << AF_ZSSD
-                    << " -D AF_LSSD=" << AF_LSSD
-                    << " -D AF_NCC="  << AF_NCC
-                    << " -D AF_ZNCC=" << AF_ZNCC
-                    << " -D AF_SHD="  << AF_SHD;
-            if (std::is_same<outType, double>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, matchTemplate_cl, matchTemplate_cl_len, options.str());
-            mtProgs[device]   = new Program(prog);
-            mtKernels[device] = new Kernel(*mtProgs[device], "matchTemplate");
-        });
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D inType="  << dtype_traits<inType>::getName()
+                << " -D outType=" << dtype_traits<outType>::getName()
+                << " -D MATCH_T=" << mType
+                << " -D NEEDMEAN="<< needMean
+                << " -D AF_SAD="  << AF_SAD
+                << " -D AF_ZSAD=" << AF_ZSAD
+                << " -D AF_LSAD=" << AF_LSAD
+                << " -D AF_SSD="  << AF_SSD
+                << " -D AF_ZSSD=" << AF_ZSSD
+                << " -D AF_LSSD=" << AF_LSSD
+                << " -D AF_NCC="  << AF_NCC
+                << " -D AF_ZNCC=" << AF_ZNCC
+                << " -D AF_SHD="  << AF_SHD;
+        if (std::is_same<outType, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {matchTemplate_cl};
+        const int   ker_lens[] = {matchTemplate_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "matchTemplate");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -76,17 +78,13 @@ void matchTemplate(Param out, const Param srch, const Param tmplt)
 
     NDRange global(blk_x * srch.info.dims[2] * THREADS_X, blk_y * srch.info.dims[3] * THREADS_Y);
 
-    auto matchImgOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    int, int> (*mtKernels[device]);
+    auto matchImgOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                    int, int> (*entry.ker);
 
     matchImgOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
+               *out.data, out.info, *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index ddab5ed330..cb82021c3d 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <algorithm>
 #include <dispatch.hpp>
 #include <Param.hpp>
@@ -30,43 +29,39 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool is_color>
 void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint iter)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> msProgs;
-    static std::map<int, Kernel*> msKernels;
+    std::string refName = std::string("meanshift_") +
+        std::string(dtype_traits<T>::getName()) + std::to_string(is_color);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {meanshift_cl};
+        const int   ker_lens[] = {meanshift_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "meanshift");
 
-    std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, meanshift_cl, meanshift_cl_len, options.str());
-                msProgs[device]   = new Program(prog);
-                msKernels[device] = new Kernel(*msProgs[device], "meanshift");
-            });
-
-    auto meanshiftOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    LocalSpaceArg,
-                                    int, float,
-                                    int, float,
-                                    unsigned, int, int
-                                  >(*msKernels[device]);
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto meanshiftOp = KernelFunctor<Buffer, KParam, Buffer, KParam, LocalSpaceArg,
+                                    int, float, int, float, unsigned, int, int >(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -92,7 +87,5 @@ void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint ite
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 493071d7e9..3a7a471825 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -13,8 +13,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -29,10 +28,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int MAX_MEDFILTER2_LEN = 15;
 static const int MAX_MEDFILTER1_LEN = 121;
 
@@ -42,50 +39,47 @@ static const int THREADS_Y = 16;
 template<typename T, af_border_type pad>
 void medfilt1(Param out, const Param in, unsigned w_wid)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  mfProgs;
-    static std::map<int, Kernel*> mfKernels;
+    std::string refName = std::string("medfilt1_") +
+        std::string(dtype_traits<T>::getName()) + std::to_string(pad);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device, w_wid] () {
-
-            const int ARR_SIZE = (w_wid-w_wid/2) + 1;
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D pad="<< pad
-                    << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
-                    << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                    << " -D ARR_SIZE="<< ARR_SIZE
-                    << " -D w_wid=" << w_wid;
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, medfilt1_cl, medfilt1_cl_len, options.str());
-            mfProgs[device]   = new Program(prog);
-            mfKernels[device] = new Kernel(*mfProgs[device], "medfilt1");
-        });
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D pad="<< pad
+                << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
+                << " -D AF_PAD_SYM="<< AF_PAD_SYM
+                << " -D ARR_SIZE="<< ARR_SIZE
+                << " -D w_wid=" << w_wid;
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {medfilt1_cl};
+        const int   ker_lens[] = {medfilt1_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "medfilt1");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, 1, 1);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
 
-    NDRange global(blk_x * in.info.dims[1] * THREADS_X,
-                                        in.info.dims[2],
-                                        in.info.dims[3]);
+    NDRange global(blk_x * in.info.dims[1] * THREADS_X, in.info.dims[2], in.info.dims[3]);
 
-    auto medfiltOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    cl::LocalSpaceArg,
-                                    int> (*mfKernels[device]);
+    auto medfiltOp = KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, int> (*entry.ker);
 
     size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
 
     medfiltOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
+              *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
@@ -93,55 +87,53 @@ void medfilt1(Param out, const Param in, unsigned w_wid)
 template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
 void medfilt2(Param out, const Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  mfProgs;
-    static std::map<int, Kernel*> mfKernels;
+    std::string refName = std::string("medfilt2_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(pad) + std::to_string(w_len) + std::to_string(w_wid);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-
-            const int ARR_SIZE = w_len * (w_wid-w_wid/2);
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D pad="<< pad
-                    << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
-                    << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                    << " -D ARR_SIZE="<< ARR_SIZE
-                    << " -D w_len="<< w_len
-                    << " -D w_wid=" << w_wid;
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, medfilt2_cl, medfilt2_cl_len, options.str());
-            mfProgs[device]   = new Program(prog);
-            mfKernels[device] = new Kernel(*mfProgs[device], "medfilt2");
-        });
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        const int ARR_SIZE = w_len * (w_wid-w_wid/2);
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D pad="<< pad
+                << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
+                << " -D AF_PAD_SYM="<< AF_PAD_SYM
+                << " -D ARR_SIZE="<< ARR_SIZE
+                << " -D w_len="<< w_len
+                << " -D w_wid=" << w_wid;
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {medfilt2_cl};
+        const int   ker_lens[] = {medfilt2_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "medfilt2");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                    blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X, blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto medfiltOp = KernelFunctor<Buffer, KParam,
-                                  Buffer, KParam,
-                                  cl::LocalSpaceArg,
-                                  int, int> (*mfKernels[device]);
+    auto medfiltOp = KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg,
+                                   int, int> (*entry.ker);
 
     size_t loc_size = (THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)*sizeof(T);
 
     medfiltOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+              *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 7b413b0a10..6ee1733ecb 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -14,8 +14,8 @@
 #include <traits.hpp>
 #include <sstream>
 #include <string>
-#include <map>
 #include <algorithm>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -29,135 +29,135 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
+typedef struct
+{
+    dim_t dim[4];
+} dims_t;
+
+static const uint DIM0 = 32;
+static const uint DIM1 =  8;
+
+template<typename T>
+void memcopy(cl::Buffer out, const dim_t *ostrides,
+             const cl::Buffer in, const dim_t *idims,
+             const dim_t *istrides, int offset, uint ndims)
+{
+    std::string refName = std::string("memcopy_") + std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
 
-    typedef struct
-    {
-        dim_t dim[4];
-    } dims_t;
-
-    static const uint DIM0 = 32;
-    static const uint DIM1 =  8;
-
-    template<typename T>
-    void memcopy(cl::Buffer out, const dim_t *ostrides,
-                 const cl::Buffer in, const dim_t *idims,
-                 const dim_t *istrides, int offset, uint ndims)
-    {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>    cpyProgs;
-        static std::map<int, Kernel*>   cpyKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once(compileFlags[device], [&]() {
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, memcopy_cl, memcopy_cl_len, options.str());
-            cpyProgs[device]   = new Program(prog);
-            cpyKernels[device] = new Kernel(*cpyProgs[device], "memcopy_kernel");
-        });
-
-        dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
-        dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
-        dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
-
-        size_t local_size[2] = {DIM0, DIM1};
-        if (ndims == 1) {
-            local_size[0] *= local_size[1];
-            local_size[1]  = 1;
-        }
-
-        int groups_0 = divup(idims[0], local_size[0]);
-        int groups_1 = divup(idims[1], local_size[1]);
-
-        NDRange local(local_size[0], local_size[1]);
-        NDRange global(groups_0 * idims[2] * local_size[0],
-                        groups_1 * idims[3] * local_size[1]);
-
-        auto memcopy_kernel = KernelFunctor< Buffer, dims_t,
-                                            Buffer, dims_t,
-                                            dims_t, int,
-                                            int, int >(*cpyKernels[device]);
-
-        memcopy_kernel(EnqueueArgs(getQueue(), global, local),
-            out, _ostrides, in, _idims, _istrides, offset, groups_0, groups_1);
-        CL_DEBUG_FINISH(getQueue());
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {memcopy_cl};
+        const int   ker_lens[] = {memcopy_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "memcopy_kernel");
+
+        addKernelToCache(device, refName, entry);
     }
 
-    template<typename inType, typename outType, bool same_dims>
-    void copy(Param dst, const Param src, int ndims, outType default_value, double factor)
-    {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*>    cpyProgs;
-        static std::map<int, Kernel*>   cpyKernels;
-
-        int device = getActiveDeviceId();
-
-        std::call_once(compileFlags[device], [&]() {
-
-                    std::ostringstream options;
-                    options << " -D inType=" << dtype_traits<inType>::getName()
-                        << " -D outType=" << dtype_traits<outType>::getName()
-                        << " -D inType_" << dtype_traits<inType>::getName()
-                        << " -D outType_" << dtype_traits<outType>::getName()
-                        << " -D SAME_DIMS=" << same_dims;
-                    if (std::is_same<inType, double>::value  ||
-                        std::is_same<inType, cdouble>::value ||
-                        std::is_same<outType, double>::value ||
-                        std::is_same<outType, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    Program prog;
-                    buildProgram(prog, copy_cl, copy_cl_len, options.str());
-                    cpyProgs[device]   = new Program(prog);
-                    cpyKernels[device] = new Kernel(*cpyProgs[device], "copy");
-                });
-
-        NDRange local(DIM0, DIM1);
-        size_t local_size[] = {DIM0, DIM1};
+    dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
+    dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
+    dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
 
+    size_t local_size[2] = {DIM0, DIM1};
+    if (ndims == 1) {
         local_size[0] *= local_size[1];
-        if (ndims == 1) {
-            local_size[1] = 1;
-        }
-
-        int blk_x = divup(dst.info.dims[0], local_size[0]);
-        int blk_y = divup(dst.info.dims[1], local_size[1]);
-
-        NDRange global(blk_x * dst.info.dims[2] * DIM0,
-                blk_y * dst.info.dims[3] * DIM1);
-
-        dims_t trgt_dims;
-        if (same_dims) {
-            trgt_dims= {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2], dst.info.dims[3]}};
-        } else {
-            dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
-            dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
-            dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
-            dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
-            trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
-        }
-
-        auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                  outType, float, dims_t,
-                                  int, int
-                                  >(*cpyKernels[device]);
-
-        copyOp(EnqueueArgs(getQueue(), global, local),
-                *dst.data, dst.info, *src.data, src.info,
-                default_value, (float)factor, trgt_dims, blk_x, blk_y);
-        CL_DEBUG_FINISH(getQueue());
+        local_size[1]  = 1;
     }
 
+    int groups_0 = divup(idims[0], local_size[0]);
+    int groups_1 = divup(idims[1], local_size[1]);
+
+    NDRange local(local_size[0], local_size[1]);
+    NDRange global(groups_0 * idims[2] * local_size[0], groups_1 * idims[3] * local_size[1]);
+
+    auto memCpyOp = KernelFunctor< Buffer, dims_t, Buffer, dims_t,
+                                   dims_t, int, int, int >(*entry.ker);
+
+    memCpyOp(EnqueueArgs(getQueue(), global, local),
+             out, _ostrides, in, _idims, _istrides, offset, groups_0, groups_1);
+
+    CL_DEBUG_FINISH(getQueue());
 }
 
+template<typename inType, typename outType, bool same_dims>
+void copy(Param dst, const Param src, int ndims, outType default_value, double factor)
+{
+    std::string refName =
+        std::string("copy_") +
+        std::string(dtype_traits<inType>::getName()) +
+        std::string(dtype_traits<outType>::getName()) +
+        std::to_string(same_dims);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+
+        options << " -D inType="    << dtype_traits<inType>::getName()
+                << " -D outType="   << dtype_traits<outType>::getName()
+                << " -D inType_"    << dtype_traits<inType>::getName()
+                << " -D outType_"   << dtype_traits<outType>::getName()
+                << " -D SAME_DIMS=" << same_dims;
+
+        if (std::is_same<inType, double>::value  || std::is_same<inType, cdouble>::value ||
+            std::is_same<outType, double>::value || std::is_same<outType, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {copy_cl};
+        const int   ker_lens[] = {copy_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "copy");
+
+        addKernelToCache(device, refName, entry);
+    }
+
+    NDRange local(DIM0, DIM1);
+    size_t local_size[] = {DIM0, DIM1};
+
+    local_size[0] *= local_size[1];
+    if (ndims == 1) {
+        local_size[1] = 1;
+    }
+
+    int blk_x = divup(dst.info.dims[0], local_size[0]);
+    int blk_y = divup(dst.info.dims[1], local_size[1]);
+
+    NDRange global(blk_x * dst.info.dims[2] * DIM0, blk_y * dst.info.dims[3] * DIM1);
+
+    dims_t trgt_dims;
+    if (same_dims) {
+        trgt_dims= {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2], dst.info.dims[3]}};
+    } else {
+        dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
+        dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
+        dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
+        dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
+        trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
+    }
+
+    auto copyOp = KernelFunctor< Buffer, KParam, Buffer, KParam,
+                                 outType, float, dims_t, int, int >(*entry.ker);
+
+    copyOp(EnqueueArgs(getQueue(), global, local),
+           *dst.data, dst.info, *src.data, src.info,
+           default_value, (float)factor, trgt_dims, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 3f129ffa9e..8c84033b56 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -32,10 +31,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
@@ -44,47 +41,50 @@ static const int CUBE_Y    =  8;
 static const int CUBE_Z    =  4;
 
 template<typename T, bool isDilation, int windLen>
-void morph(Param         out,
-        const Param      in,
-        const Param      mask)
+std::string generateOptionsString()
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> morProgs;
-    static std::map<int, Kernel*> morKernels;
+    ToNumStr<T> toNumStr;
+    T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    std::ostringstream options;
+    options << " -D T=" << dtype_traits<T>::getName()
+        << " -D isDilation="<< isDilation
+        << " -D init=" << toNumStr(init)
+        << " -D windLen=" << windLen;
+    if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+        options << " -D USE_DOUBLE";
+    return options.str();
+}
 
-    int device = getActiveDeviceId();
+template<typename T, bool isDilation, int windLen>
+void morph(Param out, const Param in, const Param mask)
+{
+    std::string refName = std::string("morph_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(isDilation) + std::to_string(windLen);
 
-    std::call_once( compileFlags[device], [device] () {
-            ToNumStr<T> toNumStr;
-            T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D isDilation="<< isDilation
-                    << " -D init=" << toNumStr(init)
-                    << " -D windLen=" << windLen;
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, morph_cl, morph_cl_len, options.str());
-            morProgs[device]   = new Program(prog);
-            morKernels[device] = new Kernel(*morProgs[device], "morph");
-        });
-
-    auto morphOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer, cl::LocalSpaceArg,
-                                int, int
-                              >(*morKernels[device]);
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::string options = generateOptionsString<T, isDilation, windLen>();
+        const char* ker_strs[] = {morph_cl};
+        const int   ker_lens[] = {morph_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "morph");
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto morphOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, cl::LocalSpaceArg,
+                                  int, int >(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
     // launch batch * blk_x blocks along x dimension
-    NDRange global(blk_x * THREADS_X * in.info.dims[2],
-                    blk_y * THREADS_Y * in.info.dims[3]);
+    NDRange global(blk_x * THREADS_X * in.info.dims[2], blk_y * THREADS_Y * in.info.dims[3]);
 
     // copy mask/filter to constant memory
     cl_int se_size   = sizeof(T)*windLen*windLen;
@@ -111,34 +111,26 @@ void morph3d(Param       out,
         const Param      in,
         const Param      mask)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  morProgs;
-    static std::map<int, Kernel*> morKernels;
+    std::string refName = std::string("morph3d_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(isDilation) + std::to_string(windLen);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-            ToNumStr<T> toNumStr;
-            T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D isDilation="<< isDilation
-                    << " -D init=" << toNumStr(init)
-                    << " -D windLen=" << windLen;
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, morph_cl, morph_cl_len, options.str());
-            morProgs[device]   = new Program(prog);
-            morKernels[device] = new Kernel(*morProgs[device], "morph3d");
-        });
-
-    auto morphOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer, cl::LocalSpaceArg, int
-                              >(*morKernels[device]);
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::string options = generateOptionsString<T, isDilation, windLen>();
+        const char* ker_strs[] = {morph_cl};
+        const int   ker_lens[] = {morph_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "morph3d");
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto morphOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer,
+                                  cl::LocalSpaceArg, int >(*entry.ker);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
@@ -146,9 +138,7 @@ void morph3d(Param       out,
     int blk_y = divup(in.info.dims[1], CUBE_Y);
     int blk_z = divup(in.info.dims[2], CUBE_Z);
     // launch batch * blk_x blocks along x dimension
-    NDRange global(blk_x * CUBE_X * in.info.dims[3],
-                    blk_y * CUBE_Y,
-                    blk_z * CUBE_Z);
+    NDRange global(blk_x * CUBE_X * in.info.dims[3], blk_y * CUBE_Y, blk_z * CUBE_Z);
 
     // copy mask/filter to constant memory
     cl_int se_size   = sizeof(T)*windLen*windLen*windLen;
@@ -169,7 +159,5 @@ void morph3d(Param       out,
     bufferFree(mBuff);
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 5c312db5e1..cd3da4ed60 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -20,6 +20,7 @@
 #include <kernel_headers/orb.hpp>
 #include <memory.hpp>
 #include <vector>
+#include <cache.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -50,10 +51,8 @@ using std::vector;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int ORB_THREADS   = 256;
 static const int ORB_THREADS_X = 16;
 static const int ORB_THREADS_Y = 16;
@@ -86,50 +85,64 @@ void gaussian1D(T* out, const int dim, double sigma=0.0)
         out[k] /= sum;
 }
 
-template<typename T, typename convAccT>
-void orb(unsigned* out_feat,
-         Param& x_out,
-         Param& y_out,
-         Param& score_out,
-         Param& ori_out,
-         Param& size_out,
-         Param& desc_out,
-         Param image,
-         const float fast_thr,
-         const unsigned max_feat,
-         const float scl_fctr,
-         const unsigned levels,
-         const bool blur_img)
+template<typename T>
+std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
+getOrbKernels()
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> orbProgs;
-    static std::map<int, Kernel*>  hrKernel;
-    static std::map<int, Kernel*>  kfKernel;
-    static std::map<int, Kernel*>  caKernel;
-    static std::map<int, Kernel*>  eoKernel;
+    static const std::string kernelNames[4] =
+        {"harris_response", "keep_features", "centroid_angle", "extract_orb"};
+
+    kc_entry_t entries[4];
 
     int device = getActiveDeviceId();
 
-    std::call_once( compileFlags[device], [device] () {
+    std::string checkName = kernelNames[0] + std::string("_") + std::string(dtype_traits<T>::getName());
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D BLOCK_SIZE=" << ORB_THREADS_X;
+    entries[0] = kernelCache(device, checkName);
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+    if (entries[0].prog==0 && entries[0].ker==0)
+    {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D BLOCK_SIZE=" << ORB_THREADS_X;
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {orb_cl};
+        const int   ker_lens[] = {orb_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+
+        for (int i=0; i<4; ++i)
+        {
+            entries[i].prog = new Program(prog);
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
 
-            cl::Program prog;
-            buildProgram(prog, orb_cl, orb_cl_len, options.str());
-            orbProgs[device] = new Program(prog);
+            std::string name = kernelNames[i] +
+                std::string("_") + std::string(dtype_traits<T>::getName());
 
-            hrKernel[device] = new Kernel(*orbProgs[device], "harris_response");
-            kfKernel[device] = new Kernel(*orbProgs[device], "keep_features");
-            caKernel[device] = new Kernel(*orbProgs[device], "centroid_angle");
-            eoKernel[device] = new Kernel(*orbProgs[device], "extract_orb");
-        });
+            addKernelToCache(device, name, entries[i]);
+        }
+    } else {
+        for (int i=1; i<4; ++i) {
+            std::string name = kernelNames[i] +
+                std::string("_") + std::string(dtype_traits<T>::getName());
+
+            entries[i] = kernelCache(device, name);
+        }
+    }
+
+    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker, entries[3].ker);
+}
+
+template<typename T, typename convAccT>
+void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
+         Param& ori_out, Param& size_out, Param& desc_out, Param image,
+         const float fast_thr, const unsigned max_feat, const float scl_fctr,
+         const unsigned levels, const bool blur_img)
+{
+    auto kernels = getOrbKernels<T>();
 
     unsigned patch_size = REF_PAT_SIZE;
 
@@ -253,7 +266,7 @@ void orb(unsigned* out_feat,
         auto hrOp = KernelFunctor<Buffer, Buffer, Buffer,
                                 Buffer, Buffer, const unsigned,
                                 Buffer, Buffer, KParam,
-                                const unsigned, const float, const unsigned> (*hrKernel[device]);
+                                const unsigned, const float, const unsigned> (*std::get<0>(kernels));
 
         hrOp(EnqueueArgs(getQueue(), global, local),
               *d_x_harris, *d_y_harris, *d_score_harris,
@@ -321,7 +334,7 @@ void orb(unsigned* out_feat,
 
         auto kfOp = KernelFunctor<Buffer, Buffer, Buffer,
                                 Buffer, Buffer, Buffer, Buffer,
-                                const unsigned> (*kfKernel[device]);
+                                const unsigned> (*std::get<1>(kernels));
 
         kfOp(EnqueueArgs(getQueue(), global_keep, local_keep),
               *d_x_lvl, *d_y_lvl, *d_score_lvl,
@@ -344,7 +357,7 @@ void orb(unsigned* out_feat,
 
         auto caOp = KernelFunctor<Buffer, Buffer, Buffer,
                                 const unsigned, Buffer, KParam,
-                                const unsigned> (*caKernel[device]);
+                                const unsigned> (*std::get<2>(kernels));
 
         caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
               *d_x_lvl, *d_y_lvl, *d_ori_lvl,
@@ -396,7 +409,7 @@ void orb(unsigned* out_feat,
         auto eoOp = KernelFunctor<Buffer, const unsigned,
                                 Buffer, Buffer, Buffer, Buffer,
                                 Buffer, KParam,
-                                const float, const unsigned> (*eoKernel[device]);
+                                const float, const unsigned> (*std::get<3>(kernels));
 
         if (blur_img) {
             eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
@@ -514,9 +527,7 @@ void orb(unsigned* out_feat,
     // Sets number of output features
     *out_feat = total_feat;
 }
-
 } //namespace kernel
-
 } //namespace opencl
 
 #if defined(__clang__)
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 0ea609f558..a1bb664b92 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,51 +27,51 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int RANGE_TX = 32;
-        static const int RANGE_TY = 8;
-        static const int RANGE_TILEX = 512;
-        static const int RANGE_TILEY = 32;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int RANGE_TX = 32;
+static const int RANGE_TY = 8;
+static const int RANGE_TILEX = 512;
+static const int RANGE_TILEY = 32;
 
-        template<typename T>
-        void range(Param out, const int dim)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  rangeProgs;
-            static std::map<int, Kernel*> rangeKernels;
+template<typename T>
+void range(Param out, const int dim)
+{
+    std::string refName = std::string("range_kernel_") + std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, range_cl, range_cl_len, options.str());
-                rangeProgs[device]   = new Program(prog);
-                rangeKernels[device] = new Kernel(*rangeProgs[device], "range_kernel");
-            });
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            auto rangeOp = KernelFunctor<Buffer, const KParam, const int,
-                                        const int, const int> (*rangeKernels[device]);
+        const char* ker_strs[] = {range_cl};
+        const int   ker_lens[] = {range_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "range_kernel");
 
-            NDRange local(RANGE_TX, RANGE_TY, 1);
+        addKernelToCache(device, refName, entry);
+    }
 
-            int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
-            int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
-            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                            local[1] * blocksPerMatY * out.info.dims[3],
-                            1);
+    auto rangeOp = KernelFunctor< Buffer, const KParam, const int, const int, const int > (*entry.ker);
 
-            rangeOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, dim, blocksPerMatX, blocksPerMatY);
+    NDRange local(RANGE_TX, RANGE_TY, 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
+    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                   local[1] * blocksPerMatY * out.info.dims[3], 1);
+
+    rangeOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, out.info, dim, blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index e381399342..35dd4bec52 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -16,7 +16,7 @@
 #include <debug_opencl.hpp>
 #include <math.hpp>
 #include <stdio.h>
-#include <map>
+#include <cache.hpp>
 #include <memory.hpp>
 
 #pragma GCC diagnostic push
@@ -43,57 +43,86 @@ namespace compute = boost::compute;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool full_conn, int n_per_thread>
-void regions(Param out, Param in)
+std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*>
+getRegionsKernels()
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> regionsProgs;
-    static std::map<int, Kernel *>     ilKernel;
-    static std::map<int, Kernel *>     frKernel;
-    static std::map<int, Kernel *>     ueKernel;
-
-    int device = getActiveDeviceId();
     static const int block_dim = 16;
     static const int num_warps = 8;
+    static const unsigned NUM_KERNELS = 3;
+    static const std::string kernelNames[NUM_KERNELS] =
+        {"initial_label", "final_relabel", "update_equiv"};
 
-    std::call_once( compileFlags[device], [device] () {
-            ToNumStr<T> toNumStr;
-            std::ostringstream options;
-            if (full_conn) {
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D BLOCK_DIM=" << block_dim
-                        << " -D NUM_WARPS=" << num_warps
-                        << " -D N_PER_THREAD=" << n_per_thread
-                        << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
-                        << " -D FULL_CONN";
-            }
-            else {
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D BLOCK_DIM=" << block_dim
-                        << " -D NUM_WARPS=" << num_warps
-                        << " -D N_PER_THREAD=" << n_per_thread
-                        << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
-            }
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+    kc_entry_t entries[NUM_KERNELS];
+
+    int device = getActiveDeviceId();
+
+    std::string checkName = kernelNames[0] + std::string("_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(full_conn) + std::to_string(n_per_thread);
+
+    entries[0] = kernelCache(device, checkName);
+
+    if (entries[0].prog==0 && entries[0].ker==0)
+    {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        if (full_conn) {
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D BLOCK_DIM=" << block_dim
+                    << " -D NUM_WARPS=" << num_warps
+                    << " -D N_PER_THREAD=" << n_per_thread
+                    << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
+                    << " -D FULL_CONN";
+        }
+        else {
+            options << " -D T=" << dtype_traits<T>::getName()
+                    << " -D BLOCK_DIM=" << block_dim
+                    << " -D NUM_WARPS=" << num_warps
+                    << " -D N_PER_THREAD=" << n_per_thread
+                    << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
+        }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {regions_cl};
+        const int   ker_lens[] = {regions_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
 
-            Program prog;
-            buildProgram(prog, regions_cl, regions_cl_len, options.str());
-            regionsProgs[device] = new Program(prog);
+        for (unsigned i=0; i<NUM_KERNELS; ++i)
+        {
+            entries[i].prog = new Program(prog);
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+
+            std::string name = kernelNames[i] + std::string("_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::to_string(full_conn) + std::to_string(n_per_thread);
 
-            ilKernel[device] = new Kernel(*regionsProgs[device], "initial_label");
-            frKernel[device] = new Kernel(*regionsProgs[device], "final_relabel");
-            ueKernel[device] = new Kernel(*regionsProgs[device], "update_equiv");
-        });
+            addKernelToCache(device, name, entries[i]);
+        }
+    } else {
+        for (unsigned i=1; i<NUM_KERNELS; ++i) {
+            std::string name = kernelNames[i] + std::string("_") +
+                std::string(dtype_traits<T>::getName()) +
+                std::to_string(full_conn) + std::to_string(n_per_thread);
+
+            entries[i] = kernelCache(device, name);
+        }
+    }
+
+    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker);
+}
+
+template<typename T, bool full_conn, int n_per_thread>
+void regions(Param out, Param in)
+{
+    auto kernels = getRegionsKernels<T, full_conn, n_per_thread>();
 
     const NDRange local(THREADS_X, THREADS_Y);
 
@@ -102,11 +131,9 @@ void regions(Param out, Param in)
 
     const NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
 
-    auto ilOp = KernelFunctor<Buffer, KParam,
-                            Buffer, KParam> (*ilKernel[device]);
+    auto ilOp = KernelFunctor<Buffer, KParam, Buffer, KParam> (*std::get<0>(kernels));
 
-    ilOp(EnqueueArgs(getQueue(), global, local),
-          *out.data, out.info, *in.data, in.info);
+    ilOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info);
 
     CL_DEBUG_FINISH(getQueue());
 
@@ -117,8 +144,7 @@ void regions(Param out, Param in)
         h_continue = 0;
         getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
 
-        auto ueOp = KernelFunctor<Buffer, KParam,
-                                Buffer> (*ueKernel[device]);
+        auto ueOp = KernelFunctor<Buffer, KParam, Buffer> (*std::get<2>(kernels));
 
         ueOp(EnqueueArgs(getQueue(), global, local),
               *out.data, out.info, *d_continue);
@@ -149,7 +175,8 @@ void regions(Param out, Param in)
     // compute.
     //int num_bins = tmp[size - 1] + 1;
     T last_label;
-    clEnqueueReadBuffer(getQueue()(), tmp.get_buffer().get(), CL_TRUE, (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL, NULL);
+    clEnqueueReadBuffer(getQueue()(), tmp.get_buffer().get(), CL_TRUE,
+                        (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL, NULL);
     int num_bins = (int)last_label + 1;
 
     Buffer labels(getContext(), CL_MEM_READ_WRITE, num_bins * sizeof(T));
@@ -176,7 +203,6 @@ void regions(Param out, Param in)
                 start = i + 1;
             }
         }
-
         return start;
     });
 
@@ -186,33 +212,22 @@ void regions(Param out, Param in)
     });
 
     compute::transform(search_begin, search_begin + num_bins,
-                        labels_begin,
-                        upper_bound_closure,
-                        c_queue);
+                       labels_begin, upper_bound_closure, c_queue);
     compute::adjacent_difference(labels_begin, labels_end, labels_begin, c_queue);
 
     // Perform the scan -- this can computes the correct labels for each
     // component
-    compute::transform(labels_begin, labels_end,
-                        labels_begin,
-                        clamp_to_one,
-                        c_queue);
-    compute::exclusive_scan(labels_begin,
-                            labels_end,
-                            labels_begin,
-                            c_queue);
+    compute::transform(labels_begin, labels_end, labels_begin, clamp_to_one, c_queue);
+
+    compute::exclusive_scan(labels_begin, labels_end, labels_begin, c_queue);
 
     // Apply the correct labels to the equivalency map
-    auto frOp = KernelFunctor<Buffer, KParam,
-                            Buffer, KParam,
-                            Buffer> (*frKernel[device]);
+    auto frOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer> (*std::get<1>(kernels));
 
     //Buffer labels_buf(tmp.get_buffer().get());
-    frOp(EnqueueArgs(getQueue(), global, local),
-          *out.data, out.info, *in.data, in.info, labels);
+    frOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info, labels);
+
     CL_DEBUG_FINISH(getQueue());
 }
-
 } //namespace kernel
-
 } //namespace opencl
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 5bd7690e6d..87a9e725f3 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,54 +27,55 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
-        static const int TILEX = 512;
-        static const int TILEY = 32;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int TX = 32;
+static const int TY = 8;
+static const int TILEX = 512;
+static const int TILEY = 32;
 
-        template<typename T>
-        void reorder(Param out, const Param in, const dim_t *rdims)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   reorderProgs;
-            static std::map<int, Kernel *> reorderKernels;
+template<typename T>
+void reorder(Param out, const Param in, const dim_t *rdims)
+{
+    std::string refName = std::string("reorder_kernel_") + std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, reorder_cl, reorder_cl_len, options.str());
-                reorderProgs[device] = new Program(prog);
-                reorderKernels[device] = new Kernel(*reorderProgs[device], "reorder_kernel");
-            });
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            auto reorderOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                      const int, const int, const int, const int,
-                                      const int, const int> (*reorderKernels[device]);
+        const char* ker_strs[] = {reorder_cl};
+        const int   ker_lens[] = {reorder_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "reorder_kernel");
 
-            NDRange local(TX, TY, 1);
+        addKernelToCache(device, refName, entry);
+    }
 
-            int blocksPerMatX = divup(out.info.dims[0], TILEX);
-            int blocksPerMatY = divup(out.info.dims[1], TILEY);
-            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                            local[1] * blocksPerMatY * out.info.dims[3],
-                            1);
+    auto reorderOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
+                                    const int, const int, const int, const int,
+                                    const int, const int >(*entry.ker);
 
-            reorderOp(EnqueueArgs(getQueue(), global, local),
-                      *out.data, *in.data, out.info, in.info,
-                      rdims[0], rdims[1], rdims[2], rdims[3],
-                      blocksPerMatX, blocksPerMatY);
+    NDRange local(TX, TY, 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                   local[1] * blocksPerMatY * out.info.dims[3], 1);
+
+    reorderOp(EnqueueArgs(getQueue(), global, local),
+              *out.data, *in.data, out.info, in.info,
+              rdims[0], rdims[1], rdims[2], rdims[3],
+              blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index bb4216c6a9..c58f343e5f 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <map>
-#include <mutex>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,86 +27,84 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        static const int RESIZE_TX = 16;
-        static const int RESIZE_TY = 16;
-
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
-
-        template<typename T>
-        using vtype_t = typename conditional<is_complex<T>::value,
-                                             T, wtype_t<T>
-                                            >::type;
-
-        template<typename T, af_interp_type method>
-        void resize(Param out, const Param in)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   resizeProgs;
-            static std::map<int, Kernel *> resizeKernels;
-
-            int device = getActiveDeviceId();
-
-            typedef typename dtype_traits<T>::base_type BT;
-
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName();
-                options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
-                options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
-
-                switch(method) {
-                    case AF_INTERP_NEAREST:  options <<" -D INTERP=NEAREST" ; break;
-                    case AF_INTERP_BILINEAR: options <<" -D INTERP=BILINEAR"; break;
-                    case AF_INTERP_LOWER:    options <<" -D INTERP=LOWER"   ; break;
-                    default: break;
-                }
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                    (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D CPLX=1";
-                    options << " -D TB=" << dtype_traits<BT>::getName();
-                } else {
-                    options << " -D CPLX=0";
-                }
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                buildProgram(prog, resize_cl, resize_cl_len, options.str());
-                resizeProgs[device] = new Program(prog);
-                resizeKernels[device] = new Kernel(*resizeProgs[device], "resize_kernel");
-            });
-
-            auto resizeOp = KernelFunctor<Buffer, const KParam,
-                                  const Buffer, const KParam,
-                                  const int, const int, const float, const float>
-                                  (*resizeKernels[device]);
-
-            NDRange local(RESIZE_TX, RESIZE_TY, 1);
-
-            int blocksPerMatX = divup(out.info.dims[0], local[0]);
-            int blocksPerMatY = divup(out.info.dims[1], local[1]);
-            NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                            local[1] * blocksPerMatY * in.info.dims[3],
-                            1);
-
-            double xd = (double)in.info.dims[0] / (double)out.info.dims[0];
-            double yd = (double)in.info.dims[1] / (double)out.info.dims[1];
-
-            float xf = (float)xd, yf = (float)yd;
-
-            resizeOp(EnqueueArgs(getQueue(), global, local),
-                      *out.data, out.info, *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace kernel
+{
+static const int RESIZE_TX = 16;
+static const int RESIZE_TY = 16;
+
+using std::conditional;
+using std::is_same;
+template<typename T>
+using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+
+template<typename T>
+using vtype_t = typename conditional< is_complex<T>::value, T, wtype_t<T> >::type;
+
+template<typename T, af_interp_type method>
+void resize(Param out, const Param in)
+{
+    typedef typename dtype_traits<T>::base_type BT;
+
+    std::string refName = std::string("reorder_kernel_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(method);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T="        << dtype_traits<T>::getName();
+        options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
+        options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
+
+        switch(method) {
+            case AF_INTERP_NEAREST:  options <<" -D INTERP=NEAREST" ; break;
+            case AF_INTERP_BILINEAR: options <<" -D INTERP=BILINEAR"; break;
+            case AF_INTERP_LOWER:    options <<" -D INTERP=LOWER"   ; break;
+            default: break;
         }
+
+        if((af_dtype) dtype_traits<T>::af_type == c32 ||
+            (af_dtype) dtype_traits<T>::af_type == c64) {
+            options << " -D CPLX=1";
+            options << " -D TB=" << dtype_traits<BT>::getName();
+        } else {
+            options << " -D CPLX=0";
+        }
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {resize_cl};
+        const int   ker_lens[] = {resize_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "resize_kernel");
+
+        addKernelToCache(device, refName, entry);
     }
+
+    auto resizeOp = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
+                                   const int, const int, const float, const float > (*entry.ker);
+
+    NDRange local(RESIZE_TX, RESIZE_TY, 1);
+
+    int blocksPerMatX = divup(out.info.dims[0], local[0]);
+    int blocksPerMatY = divup(out.info.dims[1], local[1]);
+    NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                   local[1] * blocksPerMatY * in.info.dims[3], 1);
+
+    double xd = (double)in.info.dims[0] / (double)out.info.dims[0];
+    double yd = (double)in.info.dims[1] / (double)out.info.dims[1];
+
+    float xf = (float)xd, yf = (float)yd;
+
+    resizeOp(EnqueueArgs(getQueue(), global, local),
+             *out.data, out.info, *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 521383524d..4f595b5ec4 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -13,8 +13,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -33,122 +32,119 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        static const int TX = 16;
-        static const int TY = 16;
-        // Used for batching images
-        static const int TI = 4;
-
-        typedef struct {
-            float tmat[6];
-        } tmat_t;
-
-        using std::conditional;
-        using std::is_same;
-        template<typename T>
-        using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
-
-        template<typename T>
-        using vtype_t = typename conditional<is_complex<T>::value,
-                                             T, wtype_t<T>
-                                            >::type;
-
-        template<typename T, int order>
-        void rotate(Param out, const Param in, const float theta, af_interp_type method)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   rotateProgs;
-            static std::map<int, Kernel *> rotateKernels;
-
-            int device = getActiveDeviceId();
-            typedef typename dtype_traits<T>::base_type BT;
-
-            std::call_once( compileFlags[device], [device] () {
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName();
-                options << " -D ZERO="      << toNumStr(scalar<T>(0));
-                options << " -D InterpInTy=" << dtype_traits<T>::getName();
-                options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
-                options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                    (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                    options << " -D TB=" << dtype_traits<BT>::getName();
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                options << " -D INTERP_ORDER=" << order;
-                addInterpEnumOptions(options);
-
-                const char *ker_strs[] = {interp_cl, rotate_cl};
-                const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
-                Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                rotateProgs[device] = new Program(prog);
-                rotateKernels[device] = new Kernel(*rotateProgs[device], "rotate_kernel");
-            });
-
-            auto rotateOp = KernelFunctor<Buffer, const KParam,
-                                          const Buffer, const KParam,
-                                          const tmat_t,
-                                          const int, const int,
-                                          const int, const int,
-                                          const int>(*rotateKernels[device]);
-
-            const float c = cos(-theta), s = sin(-theta);
-            float tx, ty;
-            {
-                const float nx = 0.5 * (in.info.dims[0] - 1);
-                const float ny = 0.5 * (in.info.dims[1] - 1);
-                const float mx = 0.5 * (out.info.dims[0] - 1);
-                const float my = 0.5 * (out.info.dims[1] - 1);
-                const float sx = (mx * c + my *-s);
-                const float sy = (mx * s + my * c);
-                tx = -(sx - nx);
-                ty = -(sy - ny);
-            }
-
-            // Rounding error. Anything more than 3 decimal points wont make a diff
-            tmat_t t;
-            t.tmat[0] = round( c * 1000) / 1000.0f;
-            t.tmat[1] = round(-s * 1000) / 1000.0f;
-            t.tmat[2] = round(tx * 1000) / 1000.0f;
-            t.tmat[3] = round( s * 1000) / 1000.0f;
-            t.tmat[4] = round( c * 1000) / 1000.0f;
-            t.tmat[5] = round(ty * 1000) / 1000.0f;
-
-
-            NDRange local(TX, TY, 1);
-
-            int nimages  = in.info.dims[2];
-            int nbatches = in.info.dims[3];
-            int global_x = local[0] * divup(out.info.dims[0], local[0]);
-            int global_y = local[1] * divup(out.info.dims[1], local[1]);
-            const int blocksXPerImage = global_x / local[0];
-            const int blocksYPerImage = global_y / local[1];
-
-            if(nimages > TI) {
-                int tile_images = divup(nimages, TI);
-                nimages = TI;
-                global_x = global_x * tile_images;
-            }
-            global_y *= nbatches;
-
-            NDRange global(global_x, global_y, 1);
-
-            rotateOp(EnqueueArgs(getQueue(), global, local),
-                      *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
-                      blocksXPerImage, blocksYPerImage, (int)method);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace kernel
+{
+static const int TX = 16;
+static const int TY = 16;
+// Used for batching images
+static const int TI = 4;
+
+typedef struct {
+    float tmat[6];
+} tmat_t;
+
+using std::conditional;
+using std::is_same;
+template<typename T>
+using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+
+template<typename T>
+using vtype_t = typename conditional< is_complex<T>::value, T, wtype_t<T> >::type;
+
+template<typename T, int order>
+void rotate(Param out, const Param in, const float theta, af_interp_type method)
+{
+    typedef typename dtype_traits<T>::base_type BT;
+
+    std::string refName = std::string("rotate_kernel_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(order);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D T="        << dtype_traits<T>::getName();
+        options << " -D ZERO="      << toNumStr(scalar<T>(0));
+        options << " -D InterpInTy=" << dtype_traits<T>::getName();
+        options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+        options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
+
+        if((af_dtype) dtype_traits<T>::af_type == c32 ||
+            (af_dtype) dtype_traits<T>::af_type == c64) {
+            options << " -D IS_CPLX=1";
+            options << " -D TB=" << dtype_traits<BT>::getName();
+        } else {
+            options << " -D IS_CPLX=0";
         }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        options << " -D INTERP_ORDER=" << order;
+        addInterpEnumOptions(options);
+
+        const char *ker_strs[] = {interp_cl, rotate_cl};
+        const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "rotate_kernel");
+
+        addKernelToCache(device, refName, entry);
     }
+
+    auto rotateOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                                  const tmat_t, const int, const int,
+                                  const int, const int, const int>(*entry.ker);
+
+    const float c = cos(-theta), s = sin(-theta);
+    float tx, ty;
+    {
+        const float nx = 0.5 * (in.info.dims[0] - 1);
+        const float ny = 0.5 * (in.info.dims[1] - 1);
+        const float mx = 0.5 * (out.info.dims[0] - 1);
+        const float my = 0.5 * (out.info.dims[1] - 1);
+        const float sx = (mx * c + my *-s);
+        const float sy = (mx * s + my * c);
+        tx = -(sx - nx);
+        ty = -(sy - ny);
+    }
+
+    // Rounding error. Anything more than 3 decimal points wont make a diff
+    tmat_t t;
+    t.tmat[0] = round( c * 1000) / 1000.0f;
+    t.tmat[1] = round(-s * 1000) / 1000.0f;
+    t.tmat[2] = round(tx * 1000) / 1000.0f;
+    t.tmat[3] = round( s * 1000) / 1000.0f;
+    t.tmat[4] = round( c * 1000) / 1000.0f;
+    t.tmat[5] = round(ty * 1000) / 1000.0f;
+
+
+    NDRange local(TX, TY, 1);
+
+    int nimages  = in.info.dims[2];
+    int nbatches = in.info.dims[3];
+    int global_x = local[0] * divup(out.info.dims[0], local[0]);
+    int global_y = local[1] * divup(out.info.dims[1], local[1]);
+    const int blocksXPerImage = global_x / local[0];
+    const int blocksYPerImage = global_y / local[1];
+
+    if(nimages > TI) {
+        int tile_images = divup(nimages, TI);
+        nimages = TI;
+        global_x = global_x * tile_images;
+    }
+    global_y *= nbatches;
+
+    NDRange global(global_x, global_y, 1);
+
+    rotateOp(EnqueueArgs(getQueue(), global, local),
+             *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
+             blocksXPerImage, blocksYPerImage, (int)method);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 12d55fa60a..ed8fe02640 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -30,143 +29,120 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        static const uint DIMX = 32;
-        static const uint DIMY =  8;
-        static const int REPEAT = 64;
-
-        template<typename T, bool is_same>
-        void select_launcher(Param out, Param cond, Param a, Param b, int ndims)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  selProgs;
-            static std::map<int, Kernel*> selKernels;
-
-            int device = getActiveDeviceId();
-
-            std::call_once(compileFlags[device], [device] () {
-
-                    std::ostringstream options;
-                    options << " -D is_same=" << is_same
-                            << " -D T=" << dtype_traits<T>::getName();
-
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
-
-                    cl::Program prog;
-                    buildProgram(prog, select_cl, select_cl_len, options.str());
-                    selProgs[device] = new Program(prog);
-
-                    selKernels[device] = new Kernel(*selProgs[device], "select_kernel");
-                });
-
-
-            int threads[] = {DIMX, DIMY};
-
-            if (ndims == 1) {
-                threads[0] *= threads[1];
-                threads[1] = 1;
-            }
-
-            NDRange local(threads[0],
-                          threads[1]);
-
-
-            int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
-            int groups_1 = divup(out.info.dims[1], local[1]);
-
-            NDRange global(groups_0 * out.info.dims[2] * local[0],
-                           groups_1 * out.info.dims[3] * local[1]);
-
-            auto selectOp = KernelFunctor<Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        int, int>(*selKernels[device]);
-
-            selectOp(EnqueueArgs(getQueue(), global, local),
-                     *out.data, out.info,
-                     *cond.data, cond.info,
-                     *a.data, a.info,
-                     *b.data, b.info,
-                     groups_0, groups_1);
+namespace kernel
+{
+static const uint DIMX = 32;
+static const uint DIMY =  8;
+static const int REPEAT = 64;
 
-        }
+template<typename T, bool is_same>
+void select_launcher(Param out, Param cond, Param a, Param b, int ndims)
+{
+    std::string refName = std::string("select_kernel_") +
+        std::string(dtype_traits<T>::getName()) + std::to_string(is_same);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D is_same=" << is_same << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {select_cl};
+        const int   ker_lens[] = {select_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "select_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
-        template<typename T>
-        void select(Param out, Param cond, Param a, Param b, int ndims)
-        {
-            bool is_same = true;
-            for (int i = 0; i < 4; i++) {
-                is_same &= (a.info.dims[i] == b.info.dims[i]);
-            }
+    int threads[] = {DIMX, DIMY};
 
-            if (is_same) {
-                select_launcher<T, true >(out, cond, a, b, ndims);
-            } else {
-                select_launcher<T, false>(out, cond, a, b, ndims);
-            }
-        }
+    if (ndims == 1) {
+        threads[0] *= threads[1];
+        threads[1] = 1;
+    }
 
-        template<typename T, bool flip>
-        void select_scalar(Param out, Param cond, Param a, const double b, int ndims)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>  selProgs;
-            static std::map<int, Kernel*> selKernels;
+    NDRange local(threads[0], threads[1]);
 
-            int device = getActiveDeviceId();
+    int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
+    int groups_1 = divup(out.info.dims[1], local[1]);
 
-            std::call_once(compileFlags[device], [device] () {
+    NDRange global(groups_0 * out.info.dims[2] * local[0], groups_1 * out.info.dims[3] * local[1]);
 
-                    std::ostringstream options;
-                    options << " -D flip=" << flip
-                            << " -D T=" << dtype_traits<T>::getName();
+    auto selectOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                   Buffer, KParam, int, int>(*entry.ker);
 
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+    selectOp(EnqueueArgs(getQueue(), global, local),
+             *out.data, out.info, *cond.data, cond.info, *a.data, a.info,
+             *b.data, b.info, groups_0, groups_1);
+}
 
-                    cl::Program prog;
-                    buildProgram(prog, select_cl, select_cl_len, options.str());
-                    selProgs[device] = new Program(prog);
+template<typename T>
+void select(Param out, Param cond, Param a, Param b, int ndims)
+{
+    bool is_same = true;
+    for (int i = 0; i < 4; i++) {
+        is_same &= (a.info.dims[i] == b.info.dims[i]);
+    }
 
-                    selKernels[device] = new Kernel(*selProgs[device], "select_scalar_kernel");
-                });
+    if (is_same) {
+        select_launcher<T, true >(out, cond, a, b, ndims);
+    } else {
+        select_launcher<T, false>(out, cond, a, b, ndims);
+    }
+}
 
+template<typename T, bool flip>
+void select_scalar(Param out, Param cond, Param a, const double b, int ndims)
+{
+    std::string refName = std::string("select_scalar_kernel_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(flip);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D flip=" << flip << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {select_cl};
+        const int   ker_lens[] = {select_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "select_scalar_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
-            int threads[] = {DIMX, DIMY};
+    int threads[] = {DIMX, DIMY};
 
-            if (ndims == 1) {
-                threads[0] *= threads[1];
-                threads[1] = 1;
-            }
+    if (ndims == 1) {
+        threads[0] *= threads[1];
+        threads[1] = 1;
+    }
 
-            NDRange local(threads[0],
-                          threads[1]);
+    NDRange local(threads[0], threads[1]);
 
-            int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
-            int groups_1 = divup(out.info.dims[1], local[1]);
+    int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
+    int groups_1 = divup(out.info.dims[1], local[1]);
 
-            NDRange global(groups_0 * out.info.dims[2] * local[0],
-                           groups_1 * out.info.dims[3] * local[1]);
+    NDRange global(groups_0 * out.info.dims[2] * local[0], groups_1 * out.info.dims[3] * local[1]);
 
-            auto selectOp = KernelFunctor<Buffer, KParam,
-                                        Buffer, KParam,
-                                        Buffer, KParam,
-                                        T,
-                                        int, int>(*selKernels[device]);
+    auto selectOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                   T, int, int>(*entry.ker);
 
-            selectOp(EnqueueArgs(getQueue(), global, local),
-                     *out.data, out.info,
-                     *cond.data, cond.info,
-                     *a.data, a.info,
-                     scalar<T>(b),
-                     groups_0, groups_1);
-        }
-    }
+    selectOp(EnqueueArgs(getQueue(), global, local),
+             *out.data, out.info, *cond.data, cond.info,
+             *a.data, a.info, scalar<T>(b), groups_0, groups_1);
+}
+}
 }
diff --git a/src/backend/opencl/kernel/shift.hpp b/src/backend/opencl/kernel/shift.hpp
index 1bbfbe9fdc..7a6fb81650 100644
--- a/src/backend/opencl/kernel/shift.hpp
+++ b/src/backend/opencl/kernel/shift.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -29,63 +28,64 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
-        static const int TILEX = 128;
-        static const int TILEY = 32;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int TX = 32;
+static const int TY = 8;
+static const int TILEX = 128;
+static const int TILEY = 32;
 
-        template<typename T>
-        void shift(Param out, const Param in, const int *sdims)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   shiftProgs;
-            static std::map<int, Kernel *> shiftKernels;
+template<typename T>
+void shift(Param out, const Param in, const int *sdims)
+{
+    std::string refName = std::string("shift_kernel_") + std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, shift_cl, shift_cl_len, options.str());
-                shiftProgs[device] = new Program(prog);
-                shiftKernels[device] = new Kernel(*shiftProgs[device], "shift_kernel");
-            });
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            auto shiftOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                      const int, const int, const int, const int,
-                                      const int, const int> (*shiftKernels[device]);
+        const char* ker_strs[] = {shift_cl};
+        const int   ker_lens[] = {shift_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "shift_kernel");
 
-            NDRange local(TX, TY, 1);
+        addKernelToCache(device, refName, entry);
+    }
 
-            int blocksPerMatX = divup(out.info.dims[0], TILEX);
-            int blocksPerMatY = divup(out.info.dims[1], TILEY);
-            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                            local[1] * blocksPerMatY * out.info.dims[3],
-                            1);
+    auto shiftOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
+                                  const int, const int, const int, const int,
+                                  const int, const int> (*entry.ker);
 
-            int sdims_[4];
-            // Need to do this because we are mapping output to input in the kernel
-            for(int i = 0; i < 4; i++) {
-                // sdims_[i] will always be positive and always [0, oDims[i]].
-                // Negative shifts are converted to position by going the other way round
-                sdims_[i] = -(sdims[i] % (int)out.info.dims[i]) + out.info.dims[i] * (sdims[i] > 0);
-                assert(sdims_[i] >= 0 && sdims_[i] <= out.info.dims[i]);
-            }
+    NDRange local(TX, TY, 1);
 
-            shiftOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, *in.data, out.info, in.info,
-                    sdims_[0], sdims_[1], sdims_[2], sdims_[3],
-                    blocksPerMatX, blocksPerMatY);
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                   local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
+    int sdims_[4];
+    // Need to do this because we are mapping output to input in the kernel
+    for(int i = 0; i < 4; i++) {
+        // sdims_[i] will always be positive and always [0, oDims[i]].
+        // Negative shifts are converted to position by going the other way round
+        sdims_[i] = -(sdims[i] % (int)out.info.dims[i]) + out.info.dims[i] * (sdims[i] > 0);
+        assert(sdims_[i] >= 0 && sdims_[i] <= out.info.dims[i]);
     }
+
+    shiftOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, *in.data, out.info, in.info,
+            sdims_[0], sdims_[1], sdims_[2], sdims_[3],
+            blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index af01b60d45..229ba64560 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -93,6 +93,7 @@
 #include <kernel/resize.hpp>
 #include <kernel_headers/sift_nonfree.hpp>
 #include <memory.hpp>
+#include <cache.hpp>
 #include <vector>
 
 namespace compute = boost::compute;
@@ -107,10 +108,8 @@ using std::vector;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int SIFT_THREADS   = 256;
 static const int SIFT_THREADS_X = 32;
 static const int SIFT_THREADS_Y = 8;
@@ -396,59 +395,66 @@ void apply_permutation(compute::buffer_iterator<T>& keys, compute::vector<int>&
     compute::gather(permutation.begin(), permutation.end(), temp.begin(), keys, queue);
 }
 
-template<typename T, typename convAccT>
-void sift(unsigned* out_feat,
-          unsigned* out_dlen,
-          Param& x_out,
-          Param& y_out,
-          Param& score_out,
-          Param& ori_out,
-          Param& size_out,
-          Param& desc_out,
-          Param img,
-          const unsigned n_layers,
-          const float contrast_thr,
-          const float edge_thr,
-          const float init_sigma,
-          const bool double_input,
-          const float img_scale,
-          const float feature_ratio,
-          const bool compute_GLOH)
+template<typename T>
+std::array<cl::Kernel*, 7> getSiftKernels()
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> siftProgs;
-    static std::map<int, Kernel*>  suKernel;
-    static std::map<int, Kernel*>  deKernel;
-    static std::map<int, Kernel*>  ieKernel;
-    static std::map<int, Kernel*>  coKernel;
-    static std::map<int, Kernel*>  rdKernel;
-    static std::map<int, Kernel*>  cdKernel;
-    static std::map<int, Kernel*>  cgKernel;
+    static const unsigned NUM_KERNELS = 7;
+    static const std::string kernelNames[NUM_KERNELS] =
+        {"sub", "detectExtrema", "interpolateExtrema", "calcOrientation", "removeDuplicates",
+         "computeDescriptor", "computeGLOHDescriptor"};
+
+    kc_entry_t entries[NUM_KERNELS];
 
     int device = getActiveDeviceId();
 
-    std::call_once( compileFlags[device], [device] () {
+    std::string checkName = kernelNames[0] + std::string("_") + std::string(dtype_traits<T>::getName());
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
+    entries[0] = kernelCache(device, checkName);
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+    if (entries[0].prog==0 && entries[0].ker==0)
+    {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            cl::Program prog;
-            buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
-            siftProgs[device] = new Program(prog);
+        cl::Program prog;
+        buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
 
-            suKernel[device] = new Kernel(*siftProgs[device], "sub");
-            deKernel[device] = new Kernel(*siftProgs[device], "detectExtrema");
-            ieKernel[device] = new Kernel(*siftProgs[device], "interpolateExtrema");
-            coKernel[device] = new Kernel(*siftProgs[device], "calcOrientation");
-            rdKernel[device] = new Kernel(*siftProgs[device], "removeDuplicates");
-            cdKernel[device] = new Kernel(*siftProgs[device], "computeDescriptor");
-            cgKernel[device] = new Kernel(*siftProgs[device], "computeGLOHDescriptor");
-        });
+        for (unsigned i=0; i<NUM_KERNELS; ++i)
+        {
+            entries[i].prog = new Program(prog);
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+
+            std::string name = kernelNames[i] + std::string("_") +
+                std::string(dtype_traits<T>::getName());
+
+            addKernelToCache(device, name, entries[i]);
+        }
+    } else {
+        for (unsigned i=1; i<NUM_KERNELS; ++i) {
+            std::string name = kernelNames[i] + std::string("_") +
+                std::string(dtype_traits<T>::getName());
+
+            entries[i] = kernelCache(device, name);
+        }
+    }
+
+    std::array<cl::Kernel*, NUM_KERNELS> retVal;
+    for (unsigned i=0; i<NUM_KERNELS; ++i)
+        retVal[i] = entries[i].ker;
+
+    return retVal;
+}
+
+template<typename T, typename convAccT>
+void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
+          Param& score_out, Param& ori_out, Param& size_out, Param& desc_out,
+          Param img, const unsigned n_layers, const float contrast_thr, const float edge_thr,
+          const float init_sigma, const bool double_input, const float img_scale,
+          const float feature_ratio, const bool compute_GLOH)
+{
+    auto kernels = getSiftKernels<T>();
 
     const unsigned min_dim = (double_input) ? min(img.info.dims[0]*2, img.info.dims[1]*2)
                                             : min(img.info.dims[0], img.info.dims[1]);
@@ -458,7 +464,7 @@ void sift(unsigned* out_feat,
 
     std::vector<Param> gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-    std::vector<Param> dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, suKernel[device]);
+    std::vector<Param> dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, kernels[0]);
 
     std::vector<cl::Buffer*> d_x_pyr(n_octaves, NULL);
     std::vector<cl::Buffer*> d_y_pyr(n_octaves, NULL);
@@ -505,7 +511,7 @@ void sift(unsigned* out_feat,
 
         auto deOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
                                 Buffer, KParam, unsigned, float,
-                                LocalSpaceArg> (*deKernel[device]);
+                                LocalSpaceArg> (*kernels[1]);
 
         deOp(EnqueueArgs(getQueue(), global, local),
               *d_extrema_x, *d_extrema_y, *d_extrema_layer, *d_count,
@@ -541,7 +547,7 @@ void sift(unsigned* out_feat,
                                 Buffer, Buffer, Buffer,
                                 Buffer, Buffer, Buffer, unsigned,
                                 Buffer, KParam, unsigned, unsigned, unsigned,
-                                float, float, float, float> (*ieKernel[device]);
+                                float, float, float, float> (*kernels[2]);
 
         ieOp(EnqueueArgs(getQueue(), global_interp, local_interp),
               *d_interp_x, *d_interp_y, *d_interp_layer,
@@ -613,7 +619,7 @@ void sift(unsigned* out_feat,
 
         auto rdOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
                                 Buffer, Buffer, Buffer, Buffer, Buffer,
-                                unsigned> (*rdKernel[device]);
+                                unsigned> (*kernels[4]);
 
         rdOp(EnqueueArgs(getQueue(), global_nodup, local_nodup),
               *d_nodup_x, *d_nodup_y, *d_nodup_layer,
@@ -649,7 +655,7 @@ void sift(unsigned* out_feat,
         auto coOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
                                 Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
                                 Buffer, KParam, unsigned, unsigned, int,
-                                LocalSpaceArg> (*coKernel[device]);
+                                LocalSpaceArg> (*kernels[3]);
 
         coOp(EnqueueArgs(getQueue(), global_ori, local_ori),
               *d_oriented_x, *d_oriented_y, *d_oriented_layer,
@@ -694,20 +700,20 @@ void sift(unsigned* out_feat,
             auto cgOp = KernelFunctor<Buffer, unsigned, unsigned,
                                     Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
                                     Buffer, KParam, int, unsigned, unsigned, unsigned, float, int,
-                                    LocalSpaceArg> (*cgKernel[device]);
+                                    LocalSpaceArg> (*kernels[6]);
 
             cgOp(EnqueueArgs(getQueue(), global_desc, local_desc),
-                  *d_desc, desc_len, histsz,
-                  *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-                  *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
-                  *gauss_pyr[o].data, gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
-                  cl::Local(desc_len * (histsz+1) * sizeof(float)));
+                 *d_desc, desc_len, histsz,
+                 *d_oriented_x, *d_oriented_y, *d_oriented_layer,
+                 *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
+                 *gauss_pyr[o].data, gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
+                 cl::Local(desc_len * (histsz+1) * sizeof(float)));
         }
         else {
             auto cdOp = KernelFunctor<Buffer, unsigned, unsigned,
                                     Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
                                     Buffer, KParam, int, int, float, int,
-                                    LocalSpaceArg> (*cdKernel[device]);
+                                    LocalSpaceArg> (*kernels[5]);
 
             cdOp(EnqueueArgs(getQueue(), global_desc, local_desc),
                   *d_desc, desc_len, histsz,
@@ -816,7 +822,5 @@ void sift(unsigned* out_feat,
     *out_feat = total_feat;
     *out_dlen = desc_len;
 }
-
 } //namespace kernel
-
 } //namespace opencl
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 2bec2e085f..f77e3ea911 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,60 +27,55 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename Ti, typename To, unsigned ker_size>
 void sobel(Param dx, Param dy, const Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  sobProgs;
-    static std::map<int, Kernel*> sobKernels;
+    std::string refName = std::string("sobel3x3_") + std::string(dtype_traits<Ti>::getName()) +
+        std::string(dtype_traits<To>::getName()) + std::to_string(ker_size);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D To=" << dtype_traits<To>::getName()
-                    << " -D KER_SIZE="<< ker_size;
-            if (std::is_same<Ti, double>::value) {
-                options << " -D USE_DOUBLE";
-            }
-            Program prog;
-            buildProgram(prog, sobel_cl, sobel_cl_len, options.str());
-            sobProgs[device]   = new Program(prog);
-            sobKernels[device] = new Kernel(*sobProgs[device], "sobel3x3");
-        });
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D Ti=" << dtype_traits<Ti>::getName()
+                << " -D To=" << dtype_traits<To>::getName()
+                << " -D KER_SIZE="<< ker_size;
+        if (std::is_same<Ti, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {sobel_cl};
+        const int   ker_lens[] = {sobel_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "sobel3x3");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                    blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X, blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto sobelOp = KernelFunctor<Buffer, KParam,
-                                Buffer, KParam,
-                                Buffer, KParam,
-                                cl::LocalSpaceArg,
-                                int, int> (*sobKernels[device]);
+    auto sobelOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                  cl::LocalSpaceArg, int, int> (*entry.ker);
 
     size_t loc_size = (THREADS_X+ker_size-1)*(THREADS_Y+ker_size-1)*sizeof(Ti);
 
     sobelOp(EnqueueArgs(getQueue(), global, local),
-                *dx.data, dx.info, *dy.data, dy.info,
-                *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+            *dx.data, dx.info, *dy.data, dy.info,
+            *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index c4d321e01f..bb008bed21 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -14,7 +14,7 @@
 #include <debug_opencl.hpp>
 #include <kernel_headers/susan.hpp>
 #include <memory.hpp>
-#include <map>
+#include <cache.hpp>
 #include "config.hpp"
 
 using cl::Buffer;
@@ -27,10 +27,8 @@ using cl::NDRange;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const unsigned THREADS_PER_BLOCK = 256;
 static const unsigned SUSAN_THREADS_X = 16;
 static const unsigned SUSAN_THREADS_Y = 16;
@@ -41,45 +39,41 @@ void susan(cl::Buffer* out, const cl::Buffer* in,
            const unsigned idim0, const unsigned idim1,
            const float t, const float g, const unsigned edge)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> suProg;
-    static std::map<int, Kernel*>  suKernel;
+    std::string refName = std::string("susan_responses_") +
+        std::string(dtype_traits<T>::getName()) + std::to_string(radius);
 
     int device = getActiveDeviceId();
-
-    std::call_once( compileFlags[device], [device] () {
-
-            const size_t LOCAL_MEM_SIZE = (SUSAN_THREADS_X+2*radius)*(SUSAN_THREADS_Y+2*radius);
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D LOCAL_MEM_SIZE=" << LOCAL_MEM_SIZE
-                    << " -D BLOCK_X="<< SUSAN_THREADS_X
-                    << " -D BLOCK_Y="<< SUSAN_THREADS_Y
-                    << " -D RADIUS="<< radius
-                    << " -D RESPONSE";
-
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            cl::Program prog;
-            buildProgram(prog, susan_cl, susan_cl_len, options.str());
-            suProg[device]   = new Program(prog);
-            suKernel[device] = new Kernel(*suProg[device], "susan_responses");
-        });
-
-    auto susanOp = KernelFunctor<Buffer, Buffer,
-                                unsigned,
-                                unsigned, unsigned,
-                                float, float, unsigned>(*suKernel[device]);
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        const size_t LOCAL_MEM_SIZE = (SUSAN_THREADS_X+2*radius)*(SUSAN_THREADS_Y+2*radius);
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D LOCAL_MEM_SIZE=" << LOCAL_MEM_SIZE
+                << " -D BLOCK_X="<< SUSAN_THREADS_X
+                << " -D BLOCK_Y="<< SUSAN_THREADS_Y
+                << " -D RADIUS="<< radius
+                << " -D RESPONSE";
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {susan_cl};
+        const int   ker_lens[] = {susan_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "susan_responses");
+
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto susanOp = KernelFunctor< Buffer, Buffer, unsigned, unsigned, unsigned,
+                                  float, float, unsigned >(*entry.ker);
 
     NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-    NDRange global(divup(idim0-2*edge, local[0])*local[0],
-                    divup(idim1-2*edge, local[1])*local[1]);
+    NDRange global(divup(idim0-2*edge, local[0])*local[0], divup(idim1-2*edge, local[1])*local[1]);
 
     susanOp(EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0, idim1, t, g, edge);
-
 }
 
 template<typename T>
@@ -88,49 +82,45 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
                     const unsigned edge, const unsigned max_corners)
 {
     unsigned corners_found = 0;
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*> nmProg;
-    static std::map<int, Kernel*>  nmKernel;
 
-    int device = getActiveDeviceId();
+    std::string refName = std::string("non_maximal_") + std::string(dtype_traits<T>::getName());
 
-    std::call_once( compileFlags[device], [device] () {
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D NONMAX";
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName() << " -D NONMAX";
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        const char* ker_strs[] = {susan_cl};
+        const int   ker_lens[] = {susan_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "non_maximal");
 
-            cl::Program prog;
-            buildProgram(prog, susan_cl, susan_cl_len, options.str());
-            nmProg[device]   = new Program(prog);
-            nmKernel[device] = new Kernel(*nmProg[device], "non_maximal");
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
     getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
 
-    auto nonMaximalOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                                    unsigned, unsigned, Buffer,
-                                    unsigned, unsigned>(*nmKernel[device]);
+    auto nonMaximalOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, unsigned, unsigned, Buffer,
+                                       unsigned, unsigned >(*entry.ker);
 
     NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-    NDRange global(divup(idim0-2*edge, local[0])*local[0],
-                    divup(idim1-2*edge, local[1])*local[1]);
+    NDRange global(divup(idim0-2*edge, local[0])*local[0], divup(idim1-2*edge, local[1])*local[1]);
 
     nonMaximalOp(EnqueueArgs(getQueue(), global, local),
-                  *x_out, *y_out, *resp_out, *d_corners_found,
-                  idim0, idim1, *resp_in, edge, max_corners);
+                 *x_out, *y_out, *resp_out, *d_corners_found,
+                 idim0, idim1, *resp_in, edge, max_corners);
 
     getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
     bufferFree(d_corners_found);
+
     return corners_found;
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 0e0f4b16c5..e7c4ab9ed3 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -27,41 +26,36 @@ using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
 
-
 namespace opencl
 {
-
 namespace kernel
 {
-
 template<typename T>
 void swapdblk(int n, int nb,
               cl_mem dA, size_t dA_offset, int ldda, int inca,
               cl_mem dB, size_t dB_offset, int lddb, int incb)
 {
-
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  swpProgs;
-    static std::map<int, Kernel*> swpKernels;
+    std::string refName = std::string("swapdblk_") + std::string(dtype_traits<T>::getName());
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once(compileFlags[device], [device] () {
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
 
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        const char* ker_strs[] = {swapdblk_cl};
+        const int   ker_lens[] = {swapdblk_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "swapdblk");
 
-            cl::Program prog;
-            buildProgram(prog, swapdblk_cl, swapdblk_cl_len, options.str());
-            swpProgs[device] = new Program(prog);
-
-            swpKernels[device] = new Kernel(*swpProgs[device], "swapdblk");
-        });
+        addKernelToCache(device, refName, entry);
+    }
 
     int nblocks = n / nb;
 
@@ -94,16 +88,11 @@ void swapdblk(int n, int nb,
     cl::Buffer dAObj(dA, true);
     cl::Buffer dBObj(dB, true);
 
-    auto swapdOp = KernelFunctor<int,
-                               Buffer, unsigned long long, int, int,
-                               Buffer, unsigned long long, int, int>(*swpKernels[device]);
+    auto swapdOp = KernelFunctor<int, Buffer, unsigned long long, int, int,
+                               Buffer, unsigned long long, int, int>(*entry.ker);
 
     swapdOp(EnqueueArgs(getQueue(), global, local),
-            nb,
-            dAObj, dA_offset, ldda, inca,
-            dBObj, dB_offset, lddb, incb);
-
+            nb, dAObj, dA_offset, ldda, inca, dBObj, dB_offset, lddb, incb);
 }
-
 }
 }
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index 3ad71c21f5..7d15384f86 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -28,52 +27,52 @@ using std::string;
 
 namespace opencl
 {
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int TX = 32;
-        static const int TY = 8;
-        static const int TILEX = 512;
-        static const int TILEY = 32;
+namespace kernel
+{
+// Kernel Launch Config Values
+static const int TX = 32;
+static const int TY = 8;
+static const int TILEX = 512;
+static const int TILEY = 32;
 
-        template<typename T>
-        void tile(Param out, const Param in)
-        {
-            static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-            static std::map<int, Program*>   tileProgs;
-            static std::map<int, Kernel *> tileKernels;
+template<typename T>
+void tile(Param out, const Param in)
+{
+    std::string refName = std::string("tile_kernel_") + std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-            std::call_once( compileFlags[device], [device] () {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, tile_cl, tile_cl_len, options.str());
-                tileProgs[device] = new Program(prog);
-                tileKernels[device] = new Kernel(*tileProgs[device], "tile_kernel");
-            });
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            auto tileOp = KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                                      const int, const int> (*tileKernels[device]);
+        const char* ker_strs[] = {tile_cl};
+        const int   ker_lens[] = {tile_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "tile_kernel");
 
-            NDRange local(TX, TY, 1);
+        addKernelToCache(device, refName, entry);
+    }
 
-            int blocksPerMatX = divup(out.info.dims[0], TILEX);
-            int blocksPerMatY = divup(out.info.dims[1], TILEY);
-            NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                            local[1] * blocksPerMatY * out.info.dims[3],
-                            1);
+    auto tileOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
+                                 const int, const int> (*entry.ker);
 
-            tileOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, *in.data, out.info, in.info,
-                    blocksPerMatX, blocksPerMatY);
+    NDRange local(TX, TY, 1);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                   local[1] * blocksPerMatY * out.info.dims[3], 1);
+
+    tileOp(EnqueueArgs(getQueue(), global, local),
+            *out.data, *in.data, out.info, in.info, blocksPerMatX, blocksPerMatY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
 }
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index b27445e603..3aa37d97e2 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -13,8 +13,6 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
@@ -80,8 +78,8 @@ namespace opencl
                         << " -D INVERSE="     << (isInverse ? 1 : 0)
                         << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
                         << " -D ZERO="        << toNumStr(scalar<T>(0));
-                options << " -D InterpInTy=" << dtype_traits<T>::getName();
-                options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+                options << " -D InterpInTy="  << dtype_traits<T>::getName();
+                options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
                 options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
 
                 if((af_dtype) dtype_traits<T>::af_type == c32 ||
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index bd4ccfe34f..3fba8c12c8 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -29,10 +28,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int TILE_DIM  = 32;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
@@ -40,33 +37,32 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T, bool conjugate, bool IS32MULTIPLE>
 void transpose(Param out, const Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  trsProgs;
-    static std::map<int, Kernel*> trsKernels;
+    std::string refName = std::string("transpose_") + std::string(dtype_traits<T>::getName()) +
+        std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once(compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D TILE_DIM=" << TILE_DIM
-                    << " -D THREADS_Y=" << THREADS_Y
-                    << " -D IS32MULTIPLE=" << IS32MULTIPLE
-                    << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
-                    << " -D T=" << dtype_traits<T>::getName();
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D TILE_DIM=" << TILE_DIM
+                << " -D THREADS_Y=" << THREADS_Y
+                << " -D IS32MULTIPLE=" << IS32MULTIPLE
+                << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
+                << " -D T=" << dtype_traits<T>::getName();
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            cl::Program prog;
-            buildProgram(prog, transpose_cl, transpose_cl_len, options.str());
-            trsProgs[device] = new Program(prog);
-
-            trsKernels[device] = new Kernel(*trsProgs[device], "transpose");
-        });
+        const char* ker_strs[] = {transpose_cl};
+        const int   ker_lens[] = {transpose_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "transpose");
 
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -77,16 +73,13 @@ void transpose(Param out, const Param in)
     NDRange global(blk_x * local[0] * in.info.dims[2],
                     blk_y * local[1] * in.info.dims[3]);
 
-    auto transposeOp = KernelFunctor<Buffer, const KParam,
-                                    const Buffer, const KParam,
-                                    const int, const int> (*trsKernels[device]);
+    auto transposeOp = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
+                                      const int, const int> (*entry.ker);
 
     transposeOp(EnqueueArgs(getQueue(), global, local),
                 *out.data, out.info, *in.data, in.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 6784eca86f..b9f7b12a01 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -29,10 +28,8 @@ using std::string;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 static const int TILE_DIM  = 16;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
@@ -40,33 +37,32 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T, bool conjugate, bool IS32MULTIPLE>
 void transpose_inplace(Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  transposeProgs;
-    static std::map<int, Kernel*> transposeKernels;
+    std::string refName = std::string("transpose_inplace_") + std::string(dtype_traits<T>::getName()) +
+        std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
 
     int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
 
-    std::call_once(compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D TILE_DIM=" << TILE_DIM
-                    << " -D THREADS_Y=" << THREADS_Y
-                    << " -D IS32MULTIPLE=" << IS32MULTIPLE
-                    << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
-                    << " -D T=" << dtype_traits<T>::getName();
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D TILE_DIM=" << TILE_DIM
+                << " -D THREADS_Y=" << THREADS_Y
+                << " -D IS32MULTIPLE=" << IS32MULTIPLE
+                << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
+                << " -D T=" << dtype_traits<T>::getName();
 
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
 
-            cl::Program prog;
-            buildProgram(prog, transpose_inplace_cl, transpose_inplace_cl_len, options.str());
-            transposeProgs[device] = new Program(prog);
-
-            transposeKernels[device] = new Kernel(*transposeProgs[device], "transpose_inplace");
-        });
+        const char* ker_strs[] = {transpose_inplace_cl};
+        const int   ker_lens[] = {transpose_inplace_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "transpose_inplace");
 
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -74,17 +70,13 @@ void transpose_inplace(Param in)
     int blk_y = divup(in.info.dims[1], TILE_DIM);
 
     // launch batch * blk_x blocks along x dimension
-    NDRange global(blk_x * local[0] * in.info.dims[2],
-                    blk_y * local[1] * in.info.dims[3]);
+    NDRange global(blk_x * local[0] * in.info.dims[2], blk_y * local[1] * in.info.dims[3]);
 
-    auto transposeOp = KernelFunctor<Buffer, const KParam,
-                                    const int, const int> (*transposeKernels[device]);
+    auto transposeOp = KernelFunctor<Buffer, const KParam, const int, const int> (*entry.ker);
 
     transposeOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index acfc4424dd..32925d1cbb 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -12,8 +12,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
@@ -31,10 +30,8 @@ using af::scalar_to_option;
 
 namespace opencl
 {
-
 namespace kernel
 {
-
 // Kernel Launch Config Values
 static const unsigned TX = 32;
 static const unsigned TY = 8;
@@ -44,51 +41,46 @@ static const unsigned TILEY = 32;
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Param out, const Param in)
 {
-    static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-    static std::map<int, Program*>  trgProgs;
-    static std::map<int, Kernel*> trgKernels;
+    std::string refName = std::string("triangle_kernel_") + std::string(dtype_traits<T>::getName()) +
+        std::to_string(is_upper) + std::to_string(is_unit_diag);
 
     int device = getActiveDeviceId();
-
-    std::call_once(compileFlags[device], [device] () {
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D is_upper=" << is_upper
-                    << " -D is_unit_diag=" << is_unit_diag
-                    << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-                    << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
-
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            cl::Program prog;
-            buildProgram(prog, triangle_cl, triangle_cl_len, options.str());
-            trgProgs[device] = new Program(prog);
-
-            trgKernels[device] = new Kernel(*trgProgs[device], "triangle_kernel");
-        });
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D is_upper=" << is_upper
+                << " -D is_unit_diag=" << is_unit_diag
+                << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+                << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {triangle_cl};
+        const int   ker_lens[] = {triangle_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "triangle_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
     NDRange local(TX, TY);
 
     int groups_x = divup(out.info.dims[0], TILEX);
     int groups_y = divup(out.info.dims[1], TILEY);
 
-    NDRange global(groups_x * out.info.dims[2] * local[0],
-                    groups_y * out.info.dims[3] * local[1]);
+    NDRange global(groups_x * out.info.dims[2] * local[0], groups_y * out.info.dims[3] * local[1]);
 
-    auto triangleOp = KernelFunctor<Buffer, KParam,
-                                  const Buffer, KParam,
-                                  const int, const int> (*trgKernels[device]);
+    auto triangleOp = KernelFunctor< Buffer, KParam, const Buffer, KParam,
+                                     const int, const int >(*entry.ker);
 
     triangleOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, groups_x, groups_y);
+               *out.data, out.info, *in.data, in.info, groups_x, groups_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-
 }
-
 }
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 26f1bb1ae3..e76943e3c2 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -9,8 +9,7 @@
 
 #pragma once
 #include <string>
-#include <mutex>
-#include <map>
+#include <cache.hpp>
 #include <kernel_headers/where.hpp>
 #include <program.hpp>
 #include <traits.hpp>
@@ -35,135 +34,122 @@ namespace opencl
 {
 namespace kernel
 {
+template<typename T>
+static void get_out_idx(Buffer *out_data,
+                        Param &otmp, Param &rtmp,
+                        Param &in, uint threads_x,
+                        uint groups_x, uint groups_y)
+{
+    std::string refName = std::string("get_out_idx_kernel_") + std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D zero=" << toNumStr(scalar<T>(0))
+                << " -D CPLX=" << af::iscplx<T>();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {where_cl};
+        const int   ker_lens[] = {where_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "get_out_idx_kernel");
+
+        addKernelToCache(device, refName, entry);
+    }
 
-    template<typename T>
-    static void get_out_idx(Buffer *out_data,
-                            Param &otmp, Param &rtmp,
-                            Param &in, uint threads_x,
-                            uint groups_x, uint groups_y)
-    {
-        static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-        static std::map<int, Program*> whereProgs;
-        static std::map<int, Kernel *> whereKerns;
-
-        int device= getActiveDeviceId();
-
-        std::call_once(compileFlags[device], [device] () {
-
-                ToNumStr<T> toNumStr;
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D zero=" << toNumStr(scalar<T>(0))
-                        << " -D CPLX=" << af::iscplx<T>();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                Program prog;
-                buildProgram(prog, where_cl, where_cl_len, options.str());
-                whereProgs[device] = new Program(prog);
-                whereKerns[device] = new Kernel(*whereProgs[device], "get_out_idx_kernel");
-            });
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(local[0] * groups_x * in.info.dims[2],
-                       local[1] * groups_y * in.info.dims[3]);
-
-        uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
-
-        auto whereOp = KernelFunctor<Buffer,
-                                   Buffer, KParam,
-                                   Buffer, KParam,
-                                   Buffer, KParam,
-                                   uint, uint, uint>(*whereKerns[device]);
-
-        whereOp(EnqueueArgs(getQueue(), global, local),
-                *out_data,
-                *otmp.data, otmp.info,
-                *rtmp.data, rtmp.info,
-                *in.data, in.info,
-                groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(local[0] * groups_x * in.info.dims[2], local[1] * groups_y * in.info.dims[3]);
 
-    }
+    uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
 
-    template<typename T>
-    static void where(Param &out, Param &in)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
+    auto whereOp = KernelFunctor< Buffer, Buffer, KParam, Buffer, KParam,
+                                  Buffer, KParam, uint, uint, uint>(*entry.ker);
 
-        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(in.info.dims[1], threads_y);
+    whereOp(EnqueueArgs(getQueue(), global, local),
+            *out_data, *otmp.data, otmp.info,
+            *rtmp.data, rtmp.info, *in.data, in.info,
+            groups_x, groups_y, lim);
 
-        Param rtmp;
-        Param otmp;
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        rtmp.info.dims[0] = groups_x;
-        otmp.info.dims[0] = in.info.dims[0];
+template<typename T>
+static void where(Param &out, Param &in)
+{
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
 
-        rtmp.info.strides[0] = 1;
-        otmp.info.strides[0] = 1;
+    uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(in.info.dims[1], threads_y);
 
-        rtmp.info.offset = 0;
-        otmp.info.offset = 0;
+    Param rtmp;
+    Param otmp;
 
-        for (int k = 1; k < 4; k++) {
-            rtmp.info.dims[k] = in.info.dims[k];
-            rtmp.info.strides[k] = rtmp.info.strides[k - 1] * rtmp.info.dims[k - 1];
+    rtmp.info.dims[0] = groups_x;
+    otmp.info.dims[0] = in.info.dims[0];
 
-            otmp.info.dims[k] = in.info.dims[k];
-            otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
-        }
+    rtmp.info.strides[0] = 1;
+    otmp.info.strides[0] = 1;
 
-        int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
-        rtmp.data = bufferAlloc(rtmp_elements * sizeof(uint));
+    rtmp.info.offset = 0;
+    otmp.info.offset = 0;
 
-        int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
-        otmp.data = bufferAlloc(otmp_elements * sizeof(uint));
+    for (int k = 1; k < 4; k++) {
+        rtmp.info.dims[k] = in.info.dims[k];
+        rtmp.info.strides[k] = rtmp.info.strides[k - 1] * rtmp.info.dims[k - 1];
 
-        scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in,
-                                                    false,
-                                                    groups_x, groups_y,
-                                                    threads_x);
+        otmp.info.dims[k] = in.info.dims[k];
+        otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
+    }
 
-        // Linearize the dimensions and perform scan
-        Param ltmp = rtmp;
-        ltmp.info.offset = 0;
-        ltmp.info.dims[0] = rtmp_elements;
-        for (int k = 1; k < 4; k++) {
-            ltmp.info.dims[k] = 1;
-            ltmp.info.strides[k] = rtmp_elements;
-        }
+    int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
+    rtmp.data = bufferAlloc(rtmp_elements * sizeof(uint));
 
-        scan_first<uint, uint, af_add_t>(ltmp, ltmp);
+    int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
+    otmp.data = bufferAlloc(otmp_elements * sizeof(uint));
 
-        // Get output size and allocate output
-        uint total;
-        getQueue().enqueueReadBuffer(*rtmp.data, CL_TRUE,
-                                      sizeof(uint) * (rtmp_elements - 1),
-                                      sizeof(uint),
-                                      &total);
+    scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in, false, groups_x, groups_y, threads_x);
 
+    // Linearize the dimensions and perform scan
+    Param ltmp = rtmp;
+    ltmp.info.offset = 0;
+    ltmp.info.dims[0] = rtmp_elements;
+    for (int k = 1; k < 4; k++) {
+        ltmp.info.dims[k] = 1;
+        ltmp.info.strides[k] = rtmp_elements;
+    }
 
-        out.data = bufferAlloc(total * sizeof(uint));
+    scan_first<uint, uint, af_add_t>(ltmp, ltmp);
 
-        out.info.dims[0] = total;
-        out.info.strides[0] = 1;
-        for (int k = 1; k < 4; k++) {
-            out.info.dims[k] = 1;
-            out.info.strides[k] = total;
-        }
+    // Get output size and allocate output
+    uint total;
+    getQueue().enqueueReadBuffer(*rtmp.data, CL_TRUE,
+                                  sizeof(uint) * (rtmp_elements - 1),
+                                  sizeof(uint),
+                                  &total);
 
-        if (total > 0) {
-            get_out_idx<T>(out.data, otmp, rtmp, in, threads_x, groups_x, groups_y);
-        }
+    out.data = bufferAlloc(total * sizeof(uint));
 
-        bufferFree(rtmp.data);
-        bufferFree(otmp.data);
+    out.info.dims[0] = total;
+    out.info.strides[0] = 1;
+    for (int k = 1; k < 4; k++) {
+        out.info.dims[k] = 1;
+        out.info.strides[k] = total;
     }
+
+    if (total > 0)
+        get_out_idx<T>(out.data, otmp, rtmp, in, threads_x, groups_x, groups_y);
+
+    bufferFree(rtmp.data);
+    bufferFree(otmp.data);
+}
 }
 }

From f3f083e0e0eea6f04726de5c6bc52e40b7e0d0cc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 13 Apr 2017 13:39:31 +0530
Subject: [PATCH 1149/2677] Move clBlas library initilisation to DeviceManager
 constructor

* Re-enabled FFT, BLAS multi-threaded tests for OpenCL backend.
* clblasSetup() call is not thread safe. Having it initialsed from the
  DeviceManager makes more sense instead of using call_once mechanism.
* Split FFT tests into R2C and C2C categories.
---
 src/backend/opencl/blas.cpp     |   5 --
 src/backend/opencl/blas.hpp     |   8 ---
 src/backend/opencl/cholesky.cpp |   2 -
 src/backend/opencl/lu.cpp       |   1 -
 src/backend/opencl/platform.cpp |   6 ++
 src/backend/opencl/qr.cpp       |   2 -
 src/backend/opencl/solve.cpp    |   2 -
 src/backend/opencl/svd.cpp      |   1 -
 test/threading.cpp              | 109 ++++++++++++--------------------
 9 files changed, 48 insertions(+), 88 deletions(-)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 420ee1f332..986e2c226d 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -13,7 +13,6 @@
 #include <string>
 #include <functional>
 #include <stdexcept>
-#include <mutex>
 #include <err_opencl.hpp>
 #include <err_clblas.hpp>
 #include <math.hpp>
@@ -31,8 +30,6 @@ namespace opencl
 
 using std::is_floating_point;
 using std::enable_if;
-using std::once_flag;
-using std::call_once;
 using std::runtime_error;
 using std::to_string;
 
@@ -124,8 +121,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
         return cpu::matmul(lhs, rhs, optLhs, optRhs);
     }
 #endif
-
-    initBlas();
     clblasTranspose lOpts = toClblasTranspose(optLhs);
     clblasTranspose rOpts = toClblasTranspose(optRhs);
 
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index f6676abeff..e9408c4b77 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -10,21 +10,13 @@
 #pragma once
 #include <Array.hpp>
 #include <clBLAS.h>
-#include <mutex>
 
 namespace opencl
 {
-
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
              af_mat_prop optLhs, af_mat_prop optRhs);
-
-STATIC_ void
-initBlas() {
-    static std::once_flag clblasSetupFlag;
-    call_once(clblasSetupFlag, clblasSetup);
-}
 }
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index f949b85a01..47df889539 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -28,8 +28,6 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
         return cpu::cholesky_inplace(in, is_upper);
     }
 
-    initBlas();
-
     dim4 iDims = in.dims();
     int N = iDims[0];
 
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index 70b6d97f41..aa8cfeef3d 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -71,7 +71,6 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
         return cpu::lu_inplace(in, convert_pivot);
     }
 
-    initBlas();
     dim4 iDims = in.dims();
     int M = iDims[0];
     int N = iDims[1];
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 3ea48f4ac6..24bdefc2ea 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -27,6 +27,7 @@
 #include <defines.hpp>
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
+#include <err_clblas.hpp>
 #include <host_memory.hpp>
 #include <common/InteropManager.hpp>
 #include <platform.hpp>
@@ -903,6 +904,11 @@ DeviceManager::DeviceManager()
     //Initialize FFT setup data structure
     CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
     CLFFT_CHECK(clfftSetup(&mFFTSetup));
+
+    //Initialize clBlas library, clblasSetup is not thread safe
+    //Since DeviceManager class is singleton, it is okay to call
+    //without any synchronization mechanisms
+    CLBLAS_CHECK(clblasSetup());
 }
 
 #if defined(WITH_GRAPHICS)
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index cb730122db..2f3df19dee 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -32,7 +32,6 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig)
         return cpu::qr(q, r, t, orig);
     }
 
-    initBlas();
     dim4 iDims = orig.dims();
     int M = iDims[0];
     int N = iDims[1];
@@ -86,7 +85,6 @@ Array<T> qr_inplace(Array<T> &in)
         return cpu::qr_inplace(in);
     }
 
-    initBlas();
     dim4 iDims = in.dims();
     int M = iDims[0];
     int N = iDims[1];
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 61de3dc692..068321f36c 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -304,8 +304,6 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
         return cpu::solve(a, b, options);
     }
 
-    initBlas();
-
     if (options & AF_MAT_UPPER ||
         options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 61da27bdcd..bc0043ee10 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -202,7 +202,6 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
         return cpu::svdInPlace(s, u, vt, in);
     }
 
-    initBlas();
     svd<T, Tr>(u, s, vt, in, true);
 }
 
diff --git a/test/threading.cpp b/test/threading.cpp
index d5df9cf0c1..f4c415e64d 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -42,8 +42,6 @@ int nextTargetDeviceId()
 void morphTest(const array input, const array mask, const bool isDilation,
                const array gold, int targetDevice)
 {
-    auto start = std::chrono::high_resolution_clock::now();
-
     af::setDevice(targetDevice);
 
     vector<float> goldData(gold.elements());
@@ -59,16 +57,6 @@ void morphTest(const array input, const array mask, const bool isDilation,
     out.host((void*)outData.data());
 
     ASSERT_EQ(true, compareArraysRMSD(gold.elements(), goldData.data(), outData.data(), 0.018f));
-
-    auto end = std::chrono::high_resolution_clock::now();
-
-    std::chrono::duration<double> diff = end - start;
-
-    std::cout << "Thread(" << std::this_thread::get_id()
-              << "): time taken for "
-              << ITERATION_COUNT
-              <<" is "
-              << diff.count() << " s\n";
 }
 
 TEST(Threading, SetPerThreadActiveDevice)
@@ -90,8 +78,6 @@ TEST(Threading, SetPerThreadActiveDevice)
     vector<std::thread> tests;
     unsigned totalTestCount = 0;
 
-    auto start = std::chrono::high_resolution_clock::now();
-
     for(size_t pos = 0; pos<files.size(); ++pos)
     {
         const bool isDilation = isDilationFlags[pos];
@@ -126,32 +112,13 @@ TEST(Threading, SetPerThreadActiveDevice)
             //Push the new test as a new thread of execution
             tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
 
-            std::cout<<"morph test launched with the following params on device ("
-                     <<trgtDeviceId<<"):"<<std::endl;
-            std::cout<<"\t Input image dims: "<<input.dims()<<std::endl;
-            std::cout<<"\t Mask dims: "<<mask.dims()<<std::endl;
-            std::cout<<"\t IsDilation : "<< (isDilation ? "True" : "False") <<std::endl;
-
             totalTestCount++;
         }
     }
-    std::cout<< std::endl << "Waiting for results ..." << std::endl << std::endl;
 
     for (size_t testId=0; testId<tests.size(); ++testId)
-    {
-        if (tests[testId].joinable()) {
-            std::cout<<"Attempting join for test ..."<<testId<<std::endl;
+        if (tests[testId].joinable())
             tests[testId].join();
-            std::cout<<"test "<< testId <<" completed." << std::endl;
-        }
-        std::cout<<std::endl;
-    }
-
-    auto end = std::chrono::high_resolution_clock::now();
-
-    std::chrono::duration<double> diff = end - start;
-
-    std::cout << "Total time taken for test : " << diff.count() << " s\n";
 }
 
 enum ArithOp
@@ -341,7 +308,6 @@ TEST(Threading, MemoryManagement_JIT_Node)
     ASSERT_EQ(  lock_bytes,      0u);
 }
 
-#if !defined(AF_OPENCL)
 template<typename inType, typename outType, bool isInverse>
 void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
 {
@@ -419,12 +385,10 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
         tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, file, p0, p1, 0);\
     }
 
-/// OpenCL backend tests seem to be failing even when
-/// each thead has it's own plan cache(thread_local).
-/// The issue seems to present itself randomly in the form of crashes,
-/// garbage values.
-TEST(Threading, FFT)
+TEST(Threading, FFT_R2C)
 {
+    cleanSlate(); // Clean up everything done so far
+
     vector<std::thread> tests;
 
     int numDevices = 1;
@@ -438,14 +402,6 @@ TEST(Threading, FFT)
     INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
     INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
 
-    // complex to complex transforms
-    INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
-    INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
-    INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
-    INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
-    INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
-    INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
-
     // Factors 7, 11, 13
     INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
     INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
@@ -454,6 +410,32 @@ TEST(Threading, FFT)
     INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
     INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
 
+    // transforms on padded and truncated arrays
+    INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+        if (tests[testId].joinable())
+            tests[testId].join();
+}
+
+TEST(Threading, FFT_C2C)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    // complex to complex transforms
+    INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
+    INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
+    INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
+    INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
+
     INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
     INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
     INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
@@ -462,9 +444,6 @@ TEST(Threading, FFT)
     INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
 
     // transforms on padded and truncated arrays
-    INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-    INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-
     INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
     INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
 
@@ -478,11 +457,8 @@ TEST(Threading, FFT)
     INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
 
     for (size_t testId=0; testId<tests.size(); ++testId)
-    {
-        if (tests[testId].joinable()) {
+        if (tests[testId].joinable())
             tests[testId].join();
-        }
-    }
 }
 
 template<typename T, bool isBVector>
@@ -561,6 +537,8 @@ void cppMatMulCheck(int targetDevice, string TestFile)
 
 TEST(Threading, BLAS)
 {
+    cleanSlate(); // Clean up everything done so far
+
     vector<std::thread> tests;
 
     int numDevices = 1;
@@ -572,13 +550,12 @@ TEST(Threading, BLAS)
     TEST_FOR_TYPE(af::cdouble);
 
     for (size_t testId=0; testId<tests.size(); ++testId)
-    {
-        if (tests[testId].joinable()) {
+        if (tests[testId].joinable())
             tests[testId].join();
-        }
-    }
 }
 
+#if !defined(AF_OPENCL)
+
 #define SOLVE_LU_TESTS(T, eps)                                                                          \
     tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
     tests.emplace_back(solveLUTester<T>, 2048, 512, eps, nextTargetDeviceId()%numDevices);              \
@@ -602,6 +579,8 @@ TEST(Threading, BLAS)
 // we are not running out of memory.
 TEST(Threading, SolveDense)
 {
+    cleanSlate(); // Clean up everything done so far
+
     vector<std::thread> tests;
 
     int numDevices = 1;
@@ -613,11 +592,8 @@ TEST(Threading, SolveDense)
     SOLVE_LU_TESTS(cdouble, 1E-5);
 
     for (size_t testId=0; testId<tests.size(); ++testId)
-    {
-        if (tests[testId].joinable()) {
+        if (tests[testId].joinable())
             tests[testId].join();
-        }
-    }
 }
 
 #undef SOLVE_LU_TESTS
@@ -638,6 +614,8 @@ TEST(Threading, SolveDense)
 // we are not running out of memory.
 TEST(Threading, Sparse)
 {
+    cleanSlate(); // Clean up everything done so far
+
     vector<std::thread> tests;
 
     int numDevices = 1;
@@ -649,11 +627,8 @@ TEST(Threading, Sparse)
     SPARSE_TESTS(cdouble, 1E-5);
 
     for (size_t testId=0; testId<tests.size(); ++testId)
-    {
-        if (tests[testId].joinable()) {
+        if (tests[testId].joinable())
             tests[testId].join();
-        }
-    }
 }
 
 #endif

From 11177611aacd33463b9bf77d86e336aaa070a128 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 13 Apr 2017 12:59:33 -0400
Subject: [PATCH 1150/2677] Simple Genetic Algorithm Example

---
 .../machine_learning/geneticalgorithm.cpp     | 184 ++++++++++++++++++
 1 file changed, 184 insertions(+)
 create mode 100644 examples/machine_learning/geneticalgorithm.cpp

diff --git a/examples/machine_learning/geneticalgorithm.cpp b/examples/machine_learning/geneticalgorithm.cpp
new file mode 100644
index 0000000000..74d71c6c22
--- /dev/null
+++ b/examples/machine_learning/geneticalgorithm.cpp
@@ -0,0 +1,184 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <climits>
+#include <cstring>
+#include <cstdio>
+
+using namespace af;
+static const float DefaultTopFittest = 0.5;
+
+array update(const array& searchSpace, const array& sampleX, const array& sampleY, const int n)
+{
+    return searchSpace(sampleY*n + sampleX);
+}
+
+array selectFittest(const array& sampleZ, const int nSamples,
+        const float topFit = DefaultTopFittest)
+{
+    //pick top fittest
+    array indices, values;
+    sort(values, indices, sampleZ);
+    int topFitElem = topFit*nSamples;
+    int n = indices.elements();
+    return (n > topFitElem) ? indices(seq(n - topFitElem, n-1)) : indices;
+}
+
+void reproduce(array& searchSpace, array& sampleX, array& sampleY, array& sampleZ, const int nSamples, const int n)
+{
+    //Get fittest parents
+    array selection = selectFittest(sampleZ, nSamples);
+    array parentsX = sampleX(selection);
+    array parentsY = sampleY(selection);
+    int bits = (int)log2(n);
+
+    //Divide selection in two
+    array parentsX1 = parentsX.rows(0, parentsX.elements() / 2 - 1);
+    array parentsX2 = parentsX.rows(parentsX.elements() / 2, parentsX.elements() - 1);
+    array parentsY1 = parentsY.rows(0, parentsY.elements() / 2 - 1);
+    array parentsY2 = parentsY.rows(parentsY.elements() / 2, parentsY.elements() - 1);
+
+    //Get crossover points (at which bit to crossover) and construct bit masks from them
+    array crossover = randu(nSamples / 4, u32) % bits;
+    array lowermask = (1 << crossover) - 1;
+    array uppermask = INT_MAX - lowermask;
+
+    //Create children as the cross between two parents
+    array childrenX1 = (parentsX1 & uppermask) + (parentsX2 & lowermask);
+    array childrenY1 = (parentsY1 & uppermask) + (parentsY2 & lowermask);
+
+    array childrenX2 = (parentsX2 & uppermask) + (parentsX1 & lowermask);
+    array childrenY2 = (parentsY2 & uppermask) + (parentsY1 & lowermask);
+
+    //Join two new sets
+    sampleX = join(0, childrenX1, childrenX2);
+    sampleY = join(0, childrenY1, childrenY2);
+
+    //Create mutant children
+    array mutantX = sampleX;
+    array mutantY = sampleY;
+
+    //Flip a random bit to vary the gene pool
+    mutantX = mutantX ^ (1 << (randu(nSamples / 2, u32) % bits));
+    mutantY = mutantY ^ (1 << (randu(nSamples / 2, u32) % bits));
+
+    sampleX = join(0, sampleX, mutantX);
+    sampleY = join(0, sampleY, mutantY);
+
+    //Update the value of each sample with the new coordinates
+    sampleZ = update(searchSpace, sampleX, sampleY, n);
+}
+
+void initSamples(array& searchSpace, array& sampleX, array& sampleY, array& sampleZ, const int nSamples, const int n)
+{
+    setSeed(time(NULL));
+    sampleX = randu(nSamples, u32) % n;
+    sampleY = randu(nSamples, u32) % n;
+    sampleZ = update(searchSpace, sampleX, sampleY, n);
+}
+
+void init(array& searchSpace, array& searchSpaceXDisplay, array& searchSpaceYDisplay, array& sampleX, array& sampleY, array& sampleZ, const int nSamples, const int n)
+{
+    //initialize space
+    searchSpace = range(dim4(n/2, n/2), 0) + range(dim4(n/2, n/2), 1);
+    searchSpace = join(0, searchSpace, flip(searchSpace, 0));
+    searchSpace = join(1, searchSpace, flip(searchSpace, 1));
+
+    //initialize display data
+    searchSpaceXDisplay = iota(dim4(n, 1), dim4(1, n));
+    searchSpaceYDisplay = iota(dim4(1, n), dim4(n, 1));
+
+    //initalize searchers
+    initSamples(searchSpace, sampleX, sampleY, sampleZ, nSamples, n);
+}
+
+void reproducePrint(float& currentMax,
+        array& searchSpace, array& sampleX, array& sampleY, array& sampleZ,
+        const float trueMax, const int nSamples, const int n)
+{
+    if (currentMax < trueMax * 0.99) {
+        float maximum = max<float>(sampleZ);
+        array whereM = where(sampleZ == maximum);
+        if (maximum < trueMax * 0.99) {
+            printf("Current max at ");
+        } else {
+            printf("\nMax found at ");
+        }
+        printf("(%d,%d): %f (trueMax %f)\n",
+                sampleX(whereM).scalar<unsigned int>(),
+                sampleY(whereM).scalar<unsigned int>(), maximum, trueMax);
+        currentMax = maximum;
+        reproduce(searchSpace, sampleX, sampleY, sampleZ, nSamples, n);
+    }
+}
+
+void geneticSearch(bool console, const int nSamples, const int n)
+{
+    array searchSpaceXDisplay = 0;
+    array searchSpaceYDisplay = 0;
+    array searchSpace;
+    array sampleX;
+    array sampleY;
+    array sampleZ;
+
+    init(searchSpace, searchSpaceXDisplay, searchSpaceYDisplay,
+            sampleX, sampleY, sampleZ, nSamples, n);
+    float trueMax = max<float>(searchSpace);
+    float maximum = -trueMax;
+
+    if (!console) {
+        af::Window win(1600, 800, "Arrayfire Genetic Algorithm Search Demo");
+        win.grid(1, 2);
+        do {
+            reproducePrint(maximum, searchSpace, sampleX, sampleY, sampleZ,
+                    trueMax, nSamples, n);
+            win(0,0).setAxesTitles("IdX", "IdY", "Search Space");
+            win(0,1).setAxesTitles("IdX", "IdY", "Search Space");
+            win(0,0).surface(searchSpaceXDisplay, searchSpaceYDisplay, searchSpace);
+            win(0,1).scatter(sampleX.as(f32), sampleY.as(f32), sampleZ.as(f32), AF_MARKER_CIRCLE);
+            win.show();
+        } while (!win.close());
+    } else {
+        do {
+            reproducePrint(maximum, searchSpace, sampleX, sampleY, sampleZ,
+                    trueMax, nSamples, n);
+        } while (maximum < trueMax * 0.99);
+    }
+}
+
+int main(int argc, char** argv)
+{
+    bool console = false;
+    const int n = 32;
+    const int nSamples = 16;
+    if (argc > 2 || (argc == 2 && strcmp(argv[1], "-"))) {
+        printf("usage: %s [-]\n", argv[0]);
+        return -1;
+    } else if (argc == 2 && argv[1][0] == '-') {
+        console = true;
+    }
+
+    try {
+        af::info();
+        printf("** ArrayFire Genetic Algorithm Search Demo **\n\n");
+        printf("Search for trueMax in a search space where the objective function is defined as :\n\n");
+        printf("SS(x ,y) = min(x, n - (x + 1)) + min(y, n - (y + 1))\n\n");
+        printf("(x, y) belongs to RxR; R = [0, n); n = %d\n\n", n);
+        if (!console) {
+            printf("The left figure shows the objective function.\n");
+            printf("The figure on the right shows current generation's parameters and function values.\n\n");
+        }
+        geneticSearch(console, nSamples, n);
+    } catch (af::exception& e) {
+        fprintf(stderr, "%s\n", e.what());
+    }
+
+    return 0;
+}

From 25279dad1d2cfbd85edff25b47113d21e0aaff7e Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Fri, 14 Apr 2017 14:57:30 -0400
Subject: [PATCH 1151/2677] Added ctime header file

---
 examples/machine_learning/geneticalgorithm.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/machine_learning/geneticalgorithm.cpp b/examples/machine_learning/geneticalgorithm.cpp
index 74d71c6c22..db43bf0e67 100644
--- a/examples/machine_learning/geneticalgorithm.cpp
+++ b/examples/machine_learning/geneticalgorithm.cpp
@@ -10,6 +10,7 @@
 #include <arrayfire.h>
 #include <climits>
 #include <cstring>
+#include <ctime>
 #include <cstdio>
 
 using namespace af;

From b1627dd38e5682508919c39ec8962321aa6388d0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 16 Apr 2017 12:05:37 +0530
Subject: [PATCH 1152/2677] Add FFT multi-threaded test for all cases

---
 test/threading.cpp | 62 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/test/threading.cpp b/test/threading.cpp
index f4c415e64d..181b1270b3 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -461,6 +461,68 @@ TEST(Threading, FFT_C2C)
             tests[testId].join();
 }
 
+TEST(Threading, FFT_ALL)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    // Real to complex transforms
+    INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
+    INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
+    INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
+    INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
+    INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
+    INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
+
+    // Factors 7, 11, 13
+    INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+
+    // transforms on padded and truncated arrays
+    INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+
+    // complex to complex transforms
+    INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
+    INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
+    INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
+    INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
+
+    INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+
+    // transforms on padded and truncated arrays
+    INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+
+    // inverse transforms
+    // complex to complex transforms
+    INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
+    INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
+    INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
+    INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
+    INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
+    INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+        if (tests[testId].joinable())
+            tests[testId].join();
+}
+
 template<typename T, bool isBVector>
 void cppMatMulCheck(int targetDevice, string TestFile)
 {

From 41a3e056ccf3e1a747c75b65d89d3fee6d6c6e2e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 16 Apr 2017 16:34:36 +0530
Subject: [PATCH 1153/2677] Add global mutex to serialize clblas calls

---
 src/backend/opencl/err_clblas.hpp | 7 ++++++-
 src/backend/opencl/platform.cpp   | 6 ++++++
 test/threading.cpp                | 4 ----
 3 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/err_clblas.hpp b/src/backend/opencl/err_clblas.hpp
index 4d28f94960..768ead7856 100644
--- a/src/backend/opencl/err_clblas.hpp
+++ b/src/backend/opencl/err_clblas.hpp
@@ -11,6 +11,7 @@
 #include <stdio.h>
 #include <err_opencl.hpp>
 #include <clBLAS.h>
+#include <mutex>
 
 static const char * _clblasGetResultString(clblasStatus st)
 {
@@ -51,9 +52,12 @@ static const char * _clblasGetResultString(clblasStatus st)
     return "Unknown error";
 }
 
+static std::recursive_mutex gCLBlasMutex;
+
 #define CLBLAS_CHECK(fn) do {                   \
+        gCLBlasMutex.lock();                    \
         clblasStatus _clblas_st = fn;           \
-        if (_clblas_st != clblasSuccess) {     \
+        if (_clblas_st != clblasSuccess) {      \
             char clblas_st_msg[1024];           \
             snprintf(clblas_st_msg,             \
                      sizeof(clblas_st_msg),     \
@@ -65,4 +69,5 @@ static const char * _clblasGetResultString(clblasStatus st)
             AF_ERROR(clblas_st_msg,             \
                      AF_ERR_INTERNAL);          \
         }                                       \
+        gCLBlasMutex.unlock();                  \
     } while(0)
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 24bdefc2ea..a1646eaa76 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -761,6 +761,12 @@ DeviceManager::~DeviceManager()
     // clfftTeardown() causes a "Pure Virtual Function Called" crash on
     // Windows for Intel devices. This causes tests to fail.
     clfftTeardown();
+#endif
+#ifndef OS_WIN
+    //TODO: FIXME:
+    // clblasTeardown() causes a "Pure Virtual Function Called" crash on
+    // Windows for Intel devices. This causes tests to fail.
+    clblasTeardown();
 #endif
     delete memManager.release();
     delete pinnedMemManager.release();
diff --git a/test/threading.cpp b/test/threading.cpp
index 181b1270b3..9c044ae792 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -616,8 +616,6 @@ TEST(Threading, BLAS)
             tests[testId].join();
 }
 
-#if !defined(AF_OPENCL)
-
 #define SOLVE_LU_TESTS(T, eps)                                                                          \
     tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
     tests.emplace_back(solveLUTester<T>, 2048, 512, eps, nextTargetDeviceId()%numDevices);              \
@@ -692,5 +690,3 @@ TEST(Threading, Sparse)
         if (tests[testId].joinable())
             tests[testId].join();
 }
-
-#endif

From ede103bc08c6cf48698d1550fd33764409f9b819 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Mon, 17 Apr 2017 16:49:56 +0200
Subject: [PATCH 1154/2677] Fixed missing initBlas call

---
 src/backend/opencl/blas.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index bf95a61153..b1ee8b63d3 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -56,6 +56,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
         return cpu::matmul(lhs, rhs, optLhs, optRhs);
     }
 #endif
+    initBlas();
 
     const auto lOpts = toBlasTranspose(optLhs);
     const auto rOpts = toBlasTranspose(optRhs);

From fa7900022a74e525a231a047c9c7b142a2b7a446 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Fri, 21 Apr 2017 07:45:57 -0700
Subject: [PATCH 1155/2677] Fixing bug in indexing for cpu backend

---
 src/backend/cpu/kernel/copy.hpp |  2 +-
 test/assign.cpp                 | 21 +++++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index 3b9e4abae8..a4bce91421 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -114,8 +114,8 @@ struct CopyImpl<T, T>
         while (linear_end < 4
                 && count == src_strides[linear_end]
                 && count == dst_strides[linear_end]) {
-            ++linear_end;
             count *= src_dims[linear_end];
+            ++linear_end;
         }
 
         // traverse through the array using strides only until neccessary
diff --git a/test/assign.cpp b/test/assign.cpp
index 0be85d26a4..14a3f8c4b0 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -981,3 +981,24 @@ TEST(Asssign, LinearAssignGenArr)
             ASSERT_EQ(hout[i], val) << "at " << i;
     }
 }
+
+TEST(Assign, ISSUE_1764)
+{
+    using af::array;
+    int x = 2;
+    int y = 2;
+    int z = 2;
+    af::array a = af::randu(x,y,z);
+    std::vector<float> ha0(a.elements());
+    a.host(&ha0[0]);
+    a(0, af::span, af::span) = a(1, af::span, af::span);
+    std::vector<float> ha1(a.elements());
+    a.host(&ha1[0]);
+    for (int k = 0; k < z; k++) {
+        for (int j = 0; j < y; j++) {
+            int offset = (j + k * y) * x;
+            ASSERT_EQ(ha0[offset + 1], ha1[offset + 0]);
+            ASSERT_EQ(ha0[offset + 1], ha1[offset + 1]);
+        }
+    }
+}

From 61a6ae82dc279f40ac43744d423f85ec7d649cd4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Fri, 21 Apr 2017 08:28:41 -0700
Subject: [PATCH 1156/2677] Fixing correctness of af_pow for complex numbers

---
 src/api/c/binary.cpp   | 26 +++++++++++++++++++++++++-
 src/api/cpp/binary.cpp | 24 ++++++++++++++++--------
 test/binary.cpp        | 12 ++++++++++++
 3 files changed, 53 insertions(+), 9 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 5a0f4efdbf..bea84b8536 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -142,12 +142,36 @@ af_err af_pow(af_array *out, const af_array lhs, const af_array rhs, const bool
     try {
         const ArrayInfo& linfo = getInfo(lhs);
         const ArrayInfo& rinfo = getInfo(rhs);
-        if (linfo.isComplex() || rinfo.isComplex()) {
+        if (rinfo.isComplex()) {
             af_array log_lhs, log_res;
             af_array res;
             AF_CHECK(af_log(&log_lhs, lhs));
             AF_CHECK(af_mul(&log_res, log_lhs, rhs, batchMode));
             AF_CHECK(af_exp(&res, log_res));
+            AF_CHECK(af_release_array(log_lhs));
+            AF_CHECK(af_release_array(log_res));
+            std::swap(*out, res);
+            return AF_SUCCESS;
+        } else if (linfo.isComplex()) {
+            af_array mag, angle;
+            af_array mag_res, angle_res;
+            af_array real_res, imag_res, cplx_res;
+            af_array res;
+            AF_CHECK(af_abs(&mag, lhs));
+            AF_CHECK(af_arg(&angle, lhs));
+            AF_CHECK(af_pow(&mag_res, mag, rhs, batchMode));
+            AF_CHECK(af_mul(&angle_res, angle, rhs, batchMode));
+            AF_CHECK(af_cos(&real_res, angle_res));
+            AF_CHECK(af_sin(&imag_res, angle_res));
+            AF_CHECK(af_cplx2(&cplx_res, real_res, imag_res, batchMode));
+            AF_CHECK(af_mul(&res, mag_res, cplx_res, batchMode));
+            AF_CHECK(af_release_array(mag));
+            AF_CHECK(af_release_array(angle));
+            AF_CHECK(af_release_array(mag_res));
+            AF_CHECK(af_release_array(angle_res));
+            AF_CHECK(af_release_array(real_res));
+            AF_CHECK(af_release_array(imag_res));
+            AF_CHECK(af_release_array(cplx_res));
             std::swap(*out, res);
             return AF_SUCCESS;
         }
diff --git a/src/api/cpp/binary.cpp b/src/api/cpp/binary.cpp
index 966397e8f8..11ebbc45c6 100644
--- a/src/api/cpp/binary.cpp
+++ b/src/api/cpp/binary.cpp
@@ -35,14 +35,22 @@ namespace af
     INSTANTIATE(atan2, af_atan2)
     INSTANTIATE(hypot, af_hypot)
 
-#define WRAPPER(func)                                                   \
-    array func(const array &lhs, const double rhs)                      \
-    {                                                                   \
-        return func(lhs, constant(rhs, lhs.dims(), lhs.type()));        \
-    }                                                                   \
-    array func(const double lhs, const array &rhs)                      \
-    {                                                                   \
-        return func(constant(lhs, rhs.dims(), rhs.type()), rhs);        \
+#define WRAPPER(func)                                       \
+    array func(const array &lhs, const double rhs)          \
+    {                                                       \
+        af::dtype ty = lhs.type();                          \
+        if (lhs.iscomplex()) {                              \
+            ty = lhs.issingle() ? f32 : f64;                \
+        }                                                   \
+        return func(lhs, constant(rhs, lhs.dims(), ty));    \
+    }                                                       \
+    array func(const double lhs, const array &rhs)          \
+    {                                                       \
+        af::dtype ty = rhs.type();                          \
+        if (rhs.iscomplex()) {                              \
+            ty = rhs.issingle() ? f32 : f64;                \
+        }                                                   \
+        return func(constant(lhs, rhs.dims(), ty), rhs);    \
     }
 
     WRAPPER(min)
diff --git a/test/binary.cpp b/test/binary.cpp
index d8dba143a0..e7bffd129e 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -336,3 +336,15 @@ TEST(BinaryTests, Test_pow_cdouble_cdouble)
     delete[] h_b;
     delete[] h_c;
 }
+
+TEST(BinaryTests, ISSUE_1762)
+{
+    af::array zero = af::constant(0, 5, f32);
+    af::array result = af::pow(zero, 2);
+    std::vector<complex_float> hres(result.elements());
+    result.host(&hres[0]);
+    for (int i = 0; i < 5; i++) {
+        ASSERT_EQ(real(hres[i]), 0);
+        ASSERT_EQ(imag(hres[i]), 0);
+    }
+}

From 23c6190773ceff968525fe455e2c870c43c7cefb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Fri, 21 Apr 2017 08:30:39 -0700
Subject: [PATCH 1157/2677] Fixing memory leak in af_fir

---
 src/api/c/iir.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/c/iir.cpp b/src/api/c/iir.cpp
index afe91d7ba6..95bf3249dc 100644
--- a/src/api/c/iir.cpp
+++ b/src/api/c/iir.cpp
@@ -34,6 +34,7 @@ af_err af_fir(af_array *y, const af_array b, const af_array x)
         seqs[0].step = 1;
         af_array res;
         AF_CHECK(af_index(&res, out, 4, seqs));
+        AF_CHECK(af_release_array(out));
         std::swap(*y, res);
 
     } CATCHALL;

From f6369e29bc854eead55f8f51830a23197f96bcd3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 22 Apr 2017 21:52:11 +0530
Subject: [PATCH 1158/2677] canny edge detector (#1743)

* FEAT: canny edge detector

* CPU backend implementation
* CUDA backend implementation
* OpenCL backend implementation
* Unit tests for Array of less than block size and equals block size.
* Added canny edge to exiting edge detection filters demo example

* Remove use of hard coded values from canny kernels

Also:
* added missing stl header
* fixed couple of typos

* FIX in canny opencl backend for OSX platform

For a weird reason, having all kernels in single file is causing
parse error for opencl backend on OSX platform. Splitting the
non-maximum suppression kernel into a separate file seems to fix
the issue.

* Change canny API to facilitate auto-thresholding

The user can opt for one of the following thresholding mechanisms:
* AF_MANUAL_THRESHOLD - User has to provide two threshold ratios.
* AF_AUTO_OTSU_THRESHOLD - High threshold is chosen using otsu algorithm
  and lower threshold is computed based on lowThresholdRatio and high threshold
  value calculated using Otsu's algorithm.

* Remove debug print statements from canny

* Unit tests for auto thresholding in canny function

* Replace index with createSubArray

detail::index is not required as the indexing is based on only
sequences.

* Style fixes

* Change threshold parameters order in canny API
---
 docs/details/image.dox                        |  11 +
 examples/image_processing/edge.cpp            |   7 +-
 include/af/defines.h                          |  10 +
 include/af/image.h                            |  41 ++
 src/api/c/canny.cpp                           | 230 +++++++++++
 src/api/cpp/canny.cpp                         |  23 ++
 src/api/unified/image.cpp                     |   7 +
 src/backend/cpu/canny.cpp                     |  47 +++
 src/backend/cpu/canny.hpp                     |  18 +
 src/backend/cpu/kernel/canny.hpp              | 190 +++++++++
 src/backend/cuda/canny.cu                     |  37 ++
 src/backend/cuda/canny.hpp                    |  18 +
 src/backend/cuda/kernel/canny.hpp             | 383 ++++++++++++++++++
 src/backend/opencl/canny.cpp                  |  37 ++
 src/backend/opencl/canny.hpp                  |  18 +
 src/backend/opencl/kernel/canny.hpp           | 253 ++++++++++++
 .../opencl/kernel/nonmax_suppression.cl       | 127 ++++++
 src/backend/opencl/kernel/trace_edge.cl       | 213 ++++++++++
 test/canny.cpp                                | 181 +++++++++
 test/data                                     |   2 +-
 20 files changed, 1850 insertions(+), 3 deletions(-)
 create mode 100644 src/api/c/canny.cpp
 create mode 100644 src/api/cpp/canny.cpp
 create mode 100644 src/backend/cpu/canny.cpp
 create mode 100644 src/backend/cpu/canny.hpp
 create mode 100644 src/backend/cpu/kernel/canny.hpp
 create mode 100644 src/backend/cuda/canny.cu
 create mode 100644 src/backend/cuda/canny.hpp
 create mode 100644 src/backend/cuda/kernel/canny.hpp
 create mode 100644 src/backend/opencl/canny.cpp
 create mode 100644 src/backend/opencl/canny.hpp
 create mode 100644 src/backend/opencl/kernel/canny.hpp
 create mode 100644 src/backend/opencl/kernel/nonmax_suppression.cl
 create mode 100644 src/backend/opencl/kernel/trace_edge.cl
 create mode 100644 test/canny.cpp

diff --git a/docs/details/image.dox b/docs/details/image.dox
index cc80ba71b0..ca0fe179ae 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -857,6 +857,17 @@ double area = m00;
 double x_center = m10 / m00;
 double y_center = m01 / m00;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+=======================================================================
+
+\defgroup image_func_canny canny
+\ingroup imageflt_mat
+
+\brief Canny Edge Detector
+
+The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a
+wide range of edges in images. A more in depth discussion on it can be found [here](https://en.wikipedia.org/wiki/Canny_edge_detector).
+
 @}
 */
 
diff --git a/examples/image_processing/edge.cpp b/examples/image_processing/edge.cpp
index 4fbc1a7670..17deeebe75 100644
--- a/examples/image_processing/edge.cpp
+++ b/examples/image_processing/edge.cpp
@@ -61,8 +61,9 @@ array edge(const array &in, int method = 0)
     array mag, dir;
 
     switch(method) {
-        case  1: prewitt(mag, dir, smooth); break;
-        case  2: sobelFilter(mag, dir, smooth);   break;
+        case  1: prewitt(mag, dir, smooth);     break;
+        case  2: sobelFilter(mag, dir, smooth); break;
+        case  3: mag = canny(in, AF_AUTO_OTSU_THRESHOLD, 0.18, 0.54).as(f32);   break;
         default: throw af::exception("Unsupported type");
     }
 
@@ -79,6 +80,7 @@ void edge()
     array prewitt = edge(in, 1);
     array sobelFilter   = edge(in, 2);
     array hst = histogram(in, 256, 0, 255);
+    array cny = edge(in, 3);
 
     myWindow2.setAxesTitles("Bins", "Frequency");
 
@@ -90,6 +92,7 @@ void edge()
         myWindow(0,0).image(in/255     , "Input Image");
         myWindow(0,1).image(prewitt    , "Prewitt"    );
         myWindow(1,0).image(sobelFilter, "Sobel"      );
+        myWindow(1,1).image(cny        , "Canny"      );
 
         myWindow.show();
 
diff --git a/include/af/defines.h b/include/af/defines.h
index 2b9d3f9c53..1d7bb91165 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -446,6 +446,13 @@ typedef enum {
 #endif
 ////////////////////////////////////////////////////////////////////////////////
 
+#if AF_API_VERSION >= 35
+typedef enum {
+    AF_MANUAL_THRESHOLD    = 0, ///< User has to define canny thresholds manually
+    AF_AUTO_OTSU_THRESHOLD = 1, ///< Determine canny algorithm thresholds using Otsu algorithm
+} af_canny_threshold;
+#endif
+
 #if AF_API_VERSION >= 34
 typedef enum {
     AF_STORAGE_DENSE     = 0,   ///< Storage type is dense
@@ -496,6 +503,9 @@ namespace af
 #if AF_API_VERSION >= 34
     typedef af_random_engine_type randomEngineType;
 #endif
+#if AF_API_VERSION >= 35
+    typedef af_canny_threshold cannyThreshold;
+#endif
 }
 
 #endif
diff --git a/include/af/image.h b/include/af/image.h
index af7b0af304..98a181f5da 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -688,6 +688,24 @@ AFAPI void moments(double* out, const array& in, const momentType moment=AF_MOME
 AFAPI array moments(const array& in, const momentType moment=AF_MOMENT_FIRST_ORDER);
 #endif
 
+#if AF_API_VERSION >= 35
+/**
+   C++ Interface for canny edge detector
+
+   \param[in] in is the input image
+   \param[in] thresholdType determines if user set high threshold is to be used or not. It can take values defined by the enum \ref af_canny_threshold
+   \param[in] lowThresholdRatio is the lower threshold % of maximum or auto-derived high threshold
+   \param[in] highThresholdRatio is the higher threshold % of maximum value in gradient image used in hysteresis procedure. This value is ignored if \ref AF_AUTO_OTSU_THRESHOLD is chosen as \ref af_canny_threshold
+   \param[in] sobelWindow is the window size of sobel kernel for computing gradient direction and magnitude
+   \param[in] isFast indicates if L<SUB>1</SUB> norm(faster but less accurate) is used to compute image gradient magnitude instead of L<SUB>2</SUB> norm.
+   \return binary array containing edges
+
+   \ingroup image_func_canny
+*/
+AFAPI array canny(const array& in, const cannyThreshold thresholdType,
+                  const float lowThresholdRatio, const float highThresholdRatio,
+                  const unsigned sobelWindow = 3, const bool isFast = false);
+#endif
 }
 #endif
 
@@ -1375,6 +1393,29 @@ extern "C" {
     AFAPI af_err af_moments_all(double* out, const af_array in, const af_moment_type moment);
 #endif
 
+#if AF_API_VERSION >= 35
+    /**
+       C Interface for canny edge detector
+
+       \param[out] out is an binary array containing edges
+       \param[in] in is the input image
+       \param[in] threshold_type determines if user set high threshold is to be used or not. It can take values defined by the enum \ref af_canny_threshold
+       \param[in] low_threshold_ratio is the lower threshold % of the maximum or auto-derived high threshold
+       \param[in] high_threshold_ratio is the higher threshold % of maximum value in gradient image used in hysteresis procedure. This value is ignored if \ref AF_AUTO_OTSU_THRESHOLD is chosen as \ref af_canny_threshold
+       \param[in] sobel_window is the window size of sobel kernel for computing gradient direction and magnitude
+       \param[in] is_fast indicates if L<SUB>1</SUB> norm(faster but less accurate) is used to compute image gradient magnitude instead of L<SUB>2</SUB> norm.
+       \return    \ref AF_SUCCESS if the moment calculation is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_canny
+    */
+    AFAPI af_err af_canny(af_array* out, const af_array in,
+                          const af_canny_threshold threshold_type,
+                          const float low_threshold_ratio,
+                          const float high_threshold_ratio,
+                          const unsigned sobel_window, const bool is_fast);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
new file mode 100644
index 0000000000..b32dfee6a1
--- /dev/null
+++ b/src/api/c/canny.cpp
@@ -0,0 +1,230 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/image.h>
+#include <af/seq.h>
+#include <err_common.hpp>
+#include <backend.hpp>
+#include <Array.hpp>
+#include <handle.hpp>
+#include <arith.hpp>
+#include <canny.hpp>
+#include <complex.hpp>
+#include <convolve.hpp>
+#include <copy.hpp>
+#include <histogram.hpp>
+#include <logic.hpp>
+#include <sobel.hpp>
+#include <transpose.hpp>
+#include <unary.hpp>
+#include <reduce.hpp>
+#include <ireduce.hpp>
+#include <tile.hpp>
+#include <iota.hpp>
+#include <utility>
+#include <iostream>
+
+using af::dim4;
+using namespace detail;
+
+Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy, const bool& isf)
+{
+    if (isf) {
+        Array<float> gx2 = detail::abs<float, float>(gx);
+        Array<float> gy2 = detail::abs<float, float>(gy);
+        return detail::arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
+    } else {
+        Array<float> gx2 = detail::arithOp<float, af_mul_t>(gx, gx, gx.dims());
+        Array<float> gy2 = detail::arithOp<float, af_mul_t>(gy, gy, gy.dims());
+        Array<float> sg  = detail::arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
+        return detail::unaryOp<float, af_sqrt_t>(sg);
+    }
+}
+
+Array<float> otsuThreshold(const Array<float>& supEdges,
+                           const unsigned NUM_BINS, const float maxVal)
+{
+    Array<uint> hist = detail::histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
+
+    const af::dim4 hDims = hist.dims();
+
+    // reduce along histogram dimension i.e. 0th dimension
+    auto totals  = reduce<af_add_t, uint, float>(hist, 0);
+
+    // tile histogram total along 0th dimension
+    auto ttotals = tile(totals, af::dim4(hDims[0]));
+
+    // pixel frequency probabilities
+    auto probability = arithOp<float, af_div_t>(cast<float, uint>(hist), ttotals, hDims);
+
+    std::vector<af_seq> seqBegin(4, af_span);
+    std::vector<af_seq> seqRest(4, af_span);
+
+    seqBegin[0] = af_make_seq(0, hDims[0]-1, 1);
+    seqRest[0] = af_make_seq(0, hDims[0]-1, 1);
+
+    const af::dim4& iDims = supEdges.dims();
+
+    Array<float> sigmas = detail::createEmptyArray<float>(hDims);
+
+    for (unsigned b=0; b<(NUM_BINS-1); ++b)
+    {
+        seqBegin[0].end  = (double)b;
+        seqRest[0].begin = (double)(b+1);
+
+        auto frontPartition = createSubArray(probability, seqBegin, false);
+        auto endPartition   = createSubArray(probability, seqRest, false);
+
+        auto qL = reduce<af_add_t, float, float>(frontPartition, 0);
+        auto qH = reduce<af_add_t, float, float>(endPartition, 0);
+
+        const dim4 fdims(b+1, hDims[1], hDims[2], hDims[3]);
+        const dim4 edims(NUM_BINS-1-b, hDims[1], hDims[2], hDims[3]);
+
+        const dim4 tdims(1, hDims[1], hDims[2], hDims[3]);
+        auto frontWeights   = iota<float>(dim4(b+1), tdims);
+        auto endWeights     = iota<float>(dim4(NUM_BINS-1-b), tdims);
+        auto offsetValues   = createValueArray<float>(edims, b+1);
+
+        endWeights = arithOp<float, af_add_t>(endWeights, offsetValues, edims);
+        auto __muL = arithOp<float, af_mul_t>(frontPartition, frontWeights, fdims);
+        auto __muH = arithOp<float, af_mul_t>(endPartition, endWeights, edims);
+        auto _muL  = reduce<af_add_t, float, float>(__muL, 0);
+        auto _muH  = reduce<af_add_t, float, float>(__muH, 0);
+        auto muL   = arithOp<float, af_div_t>(_muL, qL, tdims);
+        auto muH   = arithOp<float, af_div_t>(_muH, qH, tdims);
+        auto TWOS  = createValueArray<float>(tdims, 2.0f);
+        auto diff  = arithOp<float, af_sub_t>(muL, muH, tdims);
+        auto sqrd  = arithOp<float, af_pow_t>(diff, TWOS, tdims);
+        auto op2   = arithOp<float, af_mul_t>(qL, qH, tdims);
+        auto sigma = arithOp<float, af_mul_t>(sqrd, op2, tdims);
+
+        std::vector<af_seq> sliceIndex(4, af_span);
+        sliceIndex[0] = {double(b), double(b), 1};
+
+        auto binRes = createSubArray<float>(sigmas, sliceIndex, false);
+
+        copyArray<float>(binRes, sigma);
+    }
+
+    dim4 odims = sigmas.dims();
+    odims[0] = 1;
+    Array<float> thresh = createEmptyArray<float>(odims);
+    Array<uint> locs    = createEmptyArray<uint>(odims);
+
+    ireduce<af_max_t, float>(thresh, locs, sigmas, 0);
+
+    return cast<float, uint>(tile(locs, dim4(iDims[0], iDims[1], 1, 1)));
+}
+
+Array<float> normalize(const Array<float>& supEdges, const float minVal, const float maxVal)
+{
+    auto minArray = createValueArray<float>(supEdges.dims(), minVal);
+    auto diff     = arithOp<float, af_sub_t>(supEdges, minArray, supEdges.dims());
+    auto denom    = createValueArray<float>(supEdges.dims(), (maxVal-minVal));
+    return arithOp<float, af_div_t>(diff, denom, supEdges.dims());
+}
+
+std::pair< Array<char>, Array<char> >
+computeCandidates(const Array<float>& supEdges, const float t1,
+                  const af_canny_threshold ct, const float t2)
+{
+    float maxVal = detail::reduce_all<af_max_t, float, float>(supEdges);
+    const unsigned NUM_BINS = static_cast<unsigned>(maxVal);
+
+    auto lowRatio = createValueArray<float>(supEdges.dims(), t1);
+
+    switch(ct)
+    {
+        case AF_AUTO_OTSU_THRESHOLD:
+            {
+                auto T2            = otsuThreshold(supEdges, NUM_BINS, maxVal);
+                auto T1            = arithOp<float, af_mul_t>(T2, lowRatio, T2.dims());
+                Array<char> weak1  = logicOp<float, af_ge_t >(supEdges,    T1, supEdges.dims());
+                Array<char> weak2  = logicOp<float, af_lt_t >(supEdges,    T2, supEdges.dims());
+                Array<char> weak   = logicOp<char , af_and_t>(   weak1, weak2,    weak1.dims());
+                Array<char> strong = logicOp<float, af_ge_t >(supEdges,    T2, supEdges.dims());
+                return std::make_pair(strong, weak);
+            };
+        default:
+            {
+                float minVal       = detail::reduce_all<af_min_t, float, float>(supEdges);
+                auto normG         = normalize(supEdges, minVal, maxVal);
+                auto T2            = createValueArray<float>(supEdges.dims(), t2);
+                auto T1            = createValueArray<float>(supEdges.dims(), t1);
+                Array<char> weak1  = logicOp<float, af_ge_t >(normG,    T1, normG.dims());
+                Array<char> weak2  = logicOp<float, af_lt_t >(normG,    T2, normG.dims());
+                Array<char> weak   = logicOp<char , af_and_t>(weak1, weak2, weak1.dims());
+                Array<char> strong = logicOp<float, af_ge_t >(normG,    T2, normG.dims());
+                return std::make_pair(strong, weak);
+            };
+    }
+}
+
+template<typename T>
+af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold ct,
+                     const float t2, const unsigned sw, const bool isf)
+{
+    static const std::vector<float> v = {-0.11021, -0.23691, -0.30576, -0.23691, -0.11021};
+    Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v.data());
+    Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v.data());
+
+    // Run separable convolution to smooth the input image
+    Array<float> smt = detail::convolve2<float, float, false>(cast<float, T>(in), cFilter, rFilter);
+
+    auto g  = detail::sobelDerivatives<float, float>(smt, sw);
+    Array<float> gx = g.first;
+    Array<float> gy = g.second;
+
+    Array<float> gmag = gradientMagnitude(gx, gy, isf);
+
+    Array<float> supEdges = detail::nonMaximumSuppression(gmag, gx, gy);
+
+    auto swpair = computeCandidates(supEdges, t1, ct, t2);
+
+    return getHandle(detail::edgeTrackingByHysteresis(swpair.first, swpair.second));
+}
+
+af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
+                const float t1, const float t2, const unsigned sw, const bool isf)
+{
+    try {
+        const ArrayInfo& info = getInfo(in);
+        af::dim4 dims  = info.dims();
+
+        DIM_ASSERT(2, (dims.ndims() >= 2));
+        // Input should be a minimum of 5x5 image
+        // since the gaussian filter used for smoothing
+        // the input is of 5x5 size. It's not mandatory but
+        // it is essentially of no use if image is less than 5x5
+        DIM_ASSERT(2, (dims[0]>=5 && dims[1]>=5));
+        ARG_ASSERT(5, (sw==3));
+
+        af_array output;
+
+        af_dtype type  = info.getType();
+        switch(type) {
+            case f32: output = cannyHelper<float >(getArray<float >(in), t1, ct, t2, sw, isf); break;
+            case f64: output = cannyHelper<double>(getArray<double>(in), t1, ct, t2, sw, isf); break;
+            case s32: output = cannyHelper<int   >(getArray<int   >(in), t1, ct, t2, sw, isf); break;
+            case u32: output = cannyHelper<uint  >(getArray<uint  >(in), t1, ct, t2, sw, isf); break;
+            case s16: output = cannyHelper<short >(getArray<short >(in), t1, ct, t2, sw, isf); break;
+            case u16: output = cannyHelper<ushort>(getArray<ushort>(in), t1, ct, t2, sw, isf); break;
+            case u8:  output = cannyHelper<uchar >(getArray<uchar >(in), t1, ct, t2, sw, isf); break;
+            default : TYPE_ERROR(1, type);
+        }
+        // output array is binary array
+        std::swap(output, *out);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/canny.cpp b/src/api/cpp/canny.cpp
new file mode 100644
index 0000000000..be8b14bd78
--- /dev/null
+++ b/src/api/cpp/canny.cpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/image.h>
+#include <af/array.h>
+#include "error.hpp"
+
+namespace af
+{
+array canny(const array& in, const cannyThreshold ctType,
+            const float ltr, const float htr, const unsigned sW, const bool isFast)
+{
+    af_array temp = 0;
+    AF_THROW(af_canny(&temp, in.get(), ctType, ltr, htr, sW, isFast));
+    return array(temp);
+}
+}
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index a01b8ff600..66ad70be92 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -256,3 +256,10 @@ af_err af_rgb2ycbcr(af_array* out, const af_array in, const af_ycc_std standard)
     CHECK_ARRAYS(in);
     return CALL(out, in, standard);
 }
+
+af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
+                const float t1, const float t2, const unsigned sw, const bool isf)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, ct, t1, t2, sw, isf);
+}
diff --git a/src/backend/cpu/canny.cpp b/src/backend/cpu/canny.cpp
new file mode 100644
index 0000000000..6a66151e4d
--- /dev/null
+++ b/src/backend/cpu/canny.cpp
@@ -0,0 +1,47 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <canny.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+#include <kernel/canny.hpp>
+
+using af::dim4;
+
+namespace cpu
+{
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx, const Array<float>& gy)
+{
+    mag.eval();
+    gx.eval();
+    gy.eval();
+
+    Array<float> out = createValueArray<float>(mag.dims(), 0);
+    out.eval();
+
+    getQueue().enqueue(kernel::nonMaxSuppression<float>, out, mag, gx, gy);
+
+    return out;
+}
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak)
+{
+    strong.eval();
+    weak.eval();
+
+    Array<char> out = createValueArray<char>(strong.dims(), 0);
+    out.eval();
+
+    getQueue().enqueue(kernel::edgeTrackingHysteresis<char>, out, strong, weak);
+
+    return out;
+}
+}
diff --git a/src/backend/cpu/canny.hpp b/src/backend/cpu/canny.hpp
new file mode 100644
index 0000000000..0d2ce29e0d
--- /dev/null
+++ b/src/backend/cpu/canny.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx, const Array<float>& gy);
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak);
+}
diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
new file mode 100644
index 0000000000..cc34005f30
--- /dev/null
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -0,0 +1,190 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <cassert>
+#include <list>
+#include <array>
+
+namespace cpu
+{
+namespace kernel
+{
+template<typename T>
+void nonMaxSuppression(Array<T> output, const Array<T> magnitude,
+                       const Array<T> dxArray, const Array<T> dyArray)
+{
+    const af::dim4 dims    = magnitude.dims();
+    const af::dim4 strides = magnitude.strides();
+
+          T* out = output.get();
+    const T* mag = magnitude.get();
+    const T* dX  = dxArray.get();
+    const T* dY  = dyArray.get();
+
+    for(dim_t b3=0; b3<dims[3]; ++b3) {
+        for(dim_t b2=0; b2<dims[2]; ++b2) {
+
+            dim_t offset;
+
+            offset = dims[0]+1;
+
+            for(dim_t j=1; j<dims[1]-1; ++j, offset+=2) {
+
+                for(dim_t i=1; i<dims[0]-1; ++i, ++offset) {
+                    if (mag[offset]==0)
+                        out[offset] = (T)0;
+                    else {
+                        const float se = mag[offset+dims[0]+1];
+                        const float nw = mag[offset-dims[0]-1];
+                        const float ea = mag[offset+1];
+                        const float we = mag[offset-1];
+                        const float ne = mag[offset-dims[0]+1];
+                        const float sw = mag[offset+dims[0]-1];
+                        const float no = mag[offset-dims[0]];
+                        const float so = mag[offset+dims[0]];
+                        const float dx = dX[offset];
+                        const float dy = dY[offset];
+
+                        float a1, a2, b1, b2, alpha;
+
+                        if (dx>=0) {
+                            if (dy>=0) {
+                                const bool isTrue = (dx-dy)>=0;
+
+                                a1    = isTrue ? ea : so;
+                                a2    = isTrue ? we : no;
+                                b1    = se;
+                                b2    = nw;
+                                alpha = isTrue ? dy/dx : dx/dy;
+                            } else {
+                                const bool isTrue = (dx+dy)>=0;
+
+                                a1    = isTrue ? ea : no;
+                                a2    = isTrue ? we : so;
+                                b1    = ne;
+                                b2    = sw;
+                                alpha = isTrue ? -dy/dx : dx/-dy;
+                            }
+                        } else {
+                            if (dy>=0) {
+                                const bool isTrue = (dx+dy)>=0;
+
+                                a1    = isTrue ? so : we;
+                                a2    = isTrue ? no : ea;
+                                b1    = sw;
+                                b2    = ne;
+                                alpha = isTrue ? -dx/dy : dy/-dx;
+                            } else {
+                                const bool isTrue = (-dx+dy)>=0;
+
+                                a1    = isTrue ? we : no;
+                                a2    = isTrue ? ea : so;
+                                b1    = nw;
+                                b2    = se;
+                                alpha = isTrue ? -dy/dx : dx/-dy;
+                            }
+                        }
+
+                        float mag1 = (1-alpha)*a1 + alpha*b1;
+                        float mag2 = (1-alpha)*a2 + alpha*b2;
+
+                        if (mag[offset]>mag1 && mag[offset]>mag2) {
+                            out[offset] = mag[offset];
+                        } else {
+                            out[offset] = (T)0;
+                        }
+                    }
+                }
+            }
+
+            out += strides[2];
+            mag += strides[2];
+            dX  += strides[2];
+            dY  += strides[2];
+        }
+        out += strides[3];
+        mag += strides[3];
+        dX  += strides[3];
+        dY  += strides[3];
+    }
+}
+
+template<typename T>
+void traceEdge(T* out, const T* strong, const T* weak, int t, int width)
+{
+    if (!out || !strong || !weak)
+        return;
+
+    const T EDGE = 1;
+
+    std::list<dim_t> edges; // list of edges to be checked
+    edges.push_back(t);
+
+    do {
+        t = edges.front();
+        edges.pop_front(); // remove the last after read
+
+        // get indices of 8 neighbours
+        std::array<dim_t, 8> potentials;
+
+        potentials[0] = t - width - 1;              // north-west
+        potentials[1] = potentials[0] + 1;          // north
+        potentials[2] = potentials[1] + 1;          // north-east
+        potentials[3] = t - 1;                      // west
+        potentials[4] = t + 1;                      // east
+        potentials[5] = t + width - 1;              // south-west
+        potentials[6] = potentials[5] + 1;          // south
+        potentials[7] = potentials[6] + 1;          // south-east
+
+        // test 8 neighbours and add them into edge
+        // list only if they are also edges
+        for (auto it: potentials)
+        {
+            if (weak[it] > 0 && out[it] != EDGE)
+            {
+                out[it] = EDGE;
+                edges.emplace_back(it);
+            }
+        }
+    } while(!edges.empty());
+}
+
+
+template<typename T>
+void edgeTrackingHysteresis(Array<T> out, const Array<T> strong, const Array<T> weak)
+{
+    const af::dim4 dims = strong.dims();
+
+    dim_t t    = dims[0] + 1;   // skip the first coloumn and first element of second coloumn
+    dim_t jMax = dims[1] - 1;   // max Y value to traverse, ignore right coloumn
+    dim_t iMax = dims[0] - 1;   // max X value to traverse, ignore bottom border
+
+          T* optr = out.get();
+    const T* sptr = strong.get();
+    const T* wptr = weak.get();
+
+    for (dim_t j = 1; j <= jMax; ++j)
+    {
+        for (dim_t i = 1; i <= iMax; ++i, ++t)
+        {
+            // if current pixel(sptr) is part of a edge
+            // and output doesn't have it marked already,
+            // mark it and trace the pixels from here.
+            if (sptr[t] > 0 && optr[t]!=1)
+            {
+                optr[t] = 1;
+                traceEdge(optr, sptr, wptr, t, dims[0]);
+            }
+        }
+    }
+}
+}
+}
diff --git a/src/backend/cuda/canny.cu b/src/backend/cuda/canny.cu
new file mode 100644
index 0000000000..c8c2ab9a76
--- /dev/null
+++ b/src/backend/cuda/canny.cu
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <canny.hpp>
+#include <err_cuda.hpp>
+#include <kernel/canny.hpp>
+
+using af::dim4;
+
+namespace cuda
+{
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx, const Array<float>& gy)
+{
+    Array<float> out = createValueArray<float>(mag.dims(), 0);
+
+    kernel::nonMaxSuppression<float>(out, mag, gx, gy);
+
+    return out;
+}
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak)
+{
+    Array<char> out = createValueArray<char>(strong.dims(), 0);
+
+    kernel::edgeTrackingHysteresis<char>(out, strong, weak);
+
+    return out;
+}
+}
diff --git a/src/backend/cuda/canny.hpp b/src/backend/cuda/canny.hpp
new file mode 100644
index 0000000000..8c9a286b15
--- /dev/null
+++ b/src/backend/cuda/canny.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cuda
+{
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx, const Array<float>& gy);
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak);
+}
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
new file mode 100644
index 0000000000..5759602d85
--- /dev/null
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -0,0 +1,383 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <backend.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_cuda.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+
+namespace cuda
+{
+namespace kernel
+{
+static const int STRONG = 1;
+static const int WEAK   = 2;
+static const int NOEDGE = 0;
+
+static const int THREADS_X = 16;
+static const int THREADS_Y = 16;
+
+__forceinline__ __device__
+int lIdx(int x, int y, int stride0, int stride1)
+{
+    return (x*stride0 + y*stride1);
+}
+
+template<typename T>
+static __global__
+void nonMaxSuppressionKernel(Param<float> output, CParam<T> in, CParam<T> dx, CParam<T> dy,
+                             unsigned nBBS0, unsigned nBBS1)
+{
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2; //Coloumns
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2; //Rows
+
+    // Declared shared memory with 1 pixel border
+    __shared__ T shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    // local thread indices
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    const T* mag = (const T *)in.ptr +
+                      (b2 * in.strides[2] + b3 * in.strides[3]) + in.strides[1] + 1;
+    const T* dX  = (const T *)dx.ptr  +
+                      (b2 * dx.strides[2]  + b3 * dx.strides[3] ) + dx.strides[1] + 1;
+    const T* dY  = (const T *)dy.ptr  +
+                      (b2 * dy.strides[2]  + b3 * dy.strides[3] ) + dy.strides[1] + 1;
+          T* out = (float *  )output.ptr +
+                      (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+
+    // pull image to shared memory
+#pragma unroll
+    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=blockDim.y, gy2+=blockDim.y)
+#pragma unroll
+        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=blockDim.x, gx2+=blockDim.x)
+            shrdMem[b][a] = mag[ lIdx(gx2-1, gy2-1, in.strides[0], in.strides[1]) ];
+
+    int i = lx + 1;
+    int j = ly + 1;
+
+    __syncthreads();
+
+    if (gx<in.dims[0]-2 && gy<in.dims[1]-2)
+    {
+        int idx = lIdx(gx, gy, in.strides[0], in.strides[1]);
+
+        const float cmag = shrdMem[j][i];
+
+        if (cmag == 0.0f)
+            out[idx] = (T)0;
+        else {
+            const float dx = dX[idx];
+            const float dy = dY[idx];
+            const float se = shrdMem[j+1][i+1];
+            const float nw = shrdMem[j-1][i-1];
+            const float ea = shrdMem[j  ][i+1];
+            const float we = shrdMem[j  ][i-1];
+            const float ne = shrdMem[j-1][i+1];
+            const float sw = shrdMem[j+1][i-1];
+            const float no = shrdMem[j-1][i  ];
+            const float so = shrdMem[j+1][i  ];
+
+            float a1, a2, b1, b2, alpha;
+
+            if (dx>=0) {
+                if (dy>=0) {
+                    const bool isTrue = (dx-dy)>=0;
+
+                    a1    = isTrue ? ea : so;
+                    a2    = isTrue ? we : no;
+                    b1    = se;
+                    b2    = nw;
+                    alpha = isTrue ? dy/dx : dx/dy;
+                } else {
+                    const bool isTrue = (dx+dy)>=0;
+
+                    a1    = isTrue ? ea : no;
+                    a2    = isTrue ? we : so;
+                    b1    = ne;
+                    b2    = sw;
+                    alpha = isTrue ? -dy/dx : dx/-dy;
+                }
+            } else {
+                if (dy>=0) {
+                    const bool isTrue = (dx+dy)>=0;
+
+                    a1    = isTrue ? so : we;
+                    a2    = isTrue ? no : ea;
+                    b1    = sw;
+                    b2    = ne;
+                    alpha = isTrue ? -dx/dy : dy/-dx;
+                } else {
+                    const bool isTrue = (-dx+dy)>=0;
+
+                    a1    = isTrue ? we : no;
+                    a2    = isTrue ? ea : so;
+                    b1    = nw;
+                    b2    = se;
+                    alpha = isTrue ? -dy/dx : dx/-dy;
+                }
+            }
+
+            float mag1 = (1-alpha)*a1 + alpha*b1;
+            float mag2 = (1-alpha)*a2 + alpha*b2;
+
+            if (cmag>mag1 && cmag>mag2) {
+                out[idx] = cmag;
+            } else {
+                out[idx] = (T)0;
+            }
+        }
+    }
+}
+
+template<typename T>
+void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx, CParam<T> dy)
+{
+    dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
+
+    // Launch only threads to process non-border pixels
+    int blk_x = divup(magnitude.dims[0]-2, threads.x);
+    int blk_y = divup(magnitude.dims[1]-2, threads.y);
+
+    // launch batch * blk_x blocks along x dimension
+    dim3 blocks(blk_x * magnitude.dims[2], blk_y * magnitude.dims[3]);
+
+    CUDA_LAUNCH(nonMaxSuppressionKernel<T>, blocks, threads, output, magnitude, dx, dy, blk_x, blk_y);
+
+    POST_LAUNCH_CHECK();
+}
+
+template<typename T>
+static __global__
+void initEdgeOutKernel(Param<T> output, CParam<T> strong, CParam<T> weak,
+                       unsigned nBBS0, unsigned nBBS1)
+{
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + threadIdx.y;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    const T* wPtr = weak.ptr +
+                         (b2 * weak.strides[2] + b3 * weak.strides[3]) + weak.strides[1] + 1;
+    const T* sPtr = strong.ptr +
+                         (b2 * strong.strides[2] + b3 * strong.strides[3]) + strong.strides[1] + 1;
+          T* oPtr = output.ptr +
+                         (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+
+    if (gx<(output.dims[0]-2) && gy<(output.dims[1]-2))
+    {
+        int idx   = lIdx(gx, gy, output.strides[0], output.strides[1]);
+        oPtr[idx] = (sPtr[idx] > 0 ? STRONG : (wPtr[idx] > 0 ? WEAK : NOEDGE));
+    }
+}
+
+// hasChanged is a variable in kernel space
+// used to track the convergence of
+// the breath first search algorithm
+__device__ int hasChanged = 0;
+
+#define VALID_BLOCK_IDX(j, i) ( (j)>0 && (j)<(SHRD_MEM_HEIGHT-1) && (i)>0 && (i)<(SHRD_MEM_WIDTH-1) )
+
+template<typename T>
+static __global__
+void edgeTrackKernel(Param<T> output, unsigned nBBS0, unsigned nBBS1)
+{
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2; // Cols
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2; // Rows
+
+    // shared memory with 1 pixel border
+    // strong and weak images are binary(char) images thus,
+    // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
+    __shared__ int outMem  [ SHRD_MEM_HEIGHT ] [ SHRD_MEM_WIDTH ];
+
+    // local thread indices
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+
+    // pull image to shared memory
+#pragma unroll
+    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=blockDim.y, gy2+=blockDim.y)
+    {
+#pragma unroll
+        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=blockDim.x, gx2+=blockDim.x)
+        {
+            int x = gx2-1;
+            int y = gy2-1;
+            if (x>=0 && x<output.dims[0] && y>=0 && y<output.dims[1])
+                outMem[b][a]  = oPtr[ lIdx(x, y, output.strides[0], output.strides[1]) ];
+            else
+                outMem[b][a]  = NOEDGE;
+        }
+    }
+
+    int i = lx + 1;
+    int j = ly + 1;
+
+    __syncthreads();
+
+    int continueIter = 1;
+
+    while (continueIter)
+    {
+        int cu = outMem[j][i];
+        int nw = outMem[j-1][i-1];
+        int no = outMem[j-1][i  ];
+        int ne = outMem[j-1][i+1];
+        int ea = outMem[j  ][i+1];
+        int se = outMem[j+1][i+1];
+        int so = outMem[j+1][i  ];
+        int sw = outMem[j+1][i-1];
+        int we = outMem[j  ][i-1];
+
+        bool hasStrongNeighbour = nw==STRONG || no==STRONG || ne==STRONG || ea==STRONG ||
+                                  se==STRONG || so==STRONG || sw==STRONG || we==STRONG;
+
+        if (cu==WEAK && hasStrongNeighbour)
+            outMem[j][i] = STRONG;
+
+        __syncthreads();
+
+        //Check if there are any STRONG pixels with weak neighbours.
+        //This search however ignores 1-pixel border encompassing the
+        //shared memory tile region.
+
+        cu = outMem[j][i];
+
+        bool _nw = outMem[j-1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i-1);
+        bool _no = outMem[j-1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j-1, i  );
+        bool _ne = outMem[j-1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i+1);
+        bool _ea = outMem[j  ][i+1] == WEAK  &&  VALID_BLOCK_IDX(j  , i+1);
+        bool _se = outMem[j+1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i+1);
+        bool _so = outMem[j+1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j+1, i  );
+        bool _sw = outMem[j+1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i-1);
+        bool _we = outMem[j  ][i-1] == WEAK  &&  VALID_BLOCK_IDX(j  , i-1);
+
+        bool hasWeakNeighbour = _nw || _no || _ne || _ea || _se || _so || _sw || _we;
+
+        continueIter = __syncthreads_or(cu==STRONG && hasWeakNeighbour);
+    };
+
+    // Check if any 1-pixel border ring
+    // has weak pixels with strong candidates
+    // within the main region, then increment hasChanged.
+    int cu = outMem[j][i];
+    int nw = outMem[j-1][i-1];
+    int no = outMem[j-1][i  ];
+    int ne = outMem[j-1][i+1];
+    int ea = outMem[j  ][i+1];
+    int se = outMem[j+1][i+1];
+    int so = outMem[j+1][i  ];
+    int sw = outMem[j+1][i-1];
+    int we = outMem[j  ][i-1];
+
+    bool hasWeakNeighbour = nw==WEAK || no==WEAK || ne==WEAK || ea==WEAK ||
+                            se==WEAK || so==WEAK || sw==WEAK || we==WEAK;
+
+    if (__syncthreads_or(cu==STRONG && hasWeakNeighbour) && lx==0 && ly==0)
+        atomicAdd(&hasChanged, 1);
+
+    // Update output with shared memory result
+    if (gx<(output.dims[0]-2) && gy<(output.dims[1]-2))
+        oPtr[ lIdx(gx, gy, output.strides[0], output.strides[1]) ] = outMem[j][i];
+}
+
+template<typename T>
+static __global__
+void suppressLeftOverKernel(Param<T> output, unsigned nBBS0, unsigned nBBS1)
+{
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + threadIdx.y;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+
+    if (gx<(output.dims[0]-2) && gy<(output.dims[1]-2))
+    {
+        int idx = lIdx(gx, gy, output.strides[0], output.strides[1]);
+        T val   = oPtr[idx];
+        if (val==WEAK)
+            oPtr[idx] = NOEDGE;
+    }
+}
+
+template<typename T>
+void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak)
+{
+    dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
+
+    // Launch only threads to process non-border pixels
+    int blk_x = divup(weak.dims[0]-2, threads.x);
+    int blk_y = divup(weak.dims[1]-2, threads.y);
+
+    // launch batch * blk_x blocks along x dimension
+    dim3 blocks(blk_x * weak.dims[2], blk_y * weak.dims[3]);
+
+    CUDA_LAUNCH(initEdgeOutKernel<T>, blocks, threads, output, strong, weak, blk_x, blk_y);
+
+    POST_LAUNCH_CHECK();
+
+    int notFinished = 1;
+
+    while(notFinished) {
+        notFinished = 0;
+        CUDA_CHECK(cudaMemcpyToSymbolAsync(hasChanged, &notFinished, sizeof(int),
+                    0, cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+
+        CUDA_LAUNCH(edgeTrackKernel<T>, blocks, threads, output, blk_x, blk_y);
+
+        POST_LAUNCH_CHECK();
+
+        CUDA_CHECK(cudaMemcpyFromSymbolAsync(&notFinished, hasChanged, sizeof(int),
+                    0, cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+    }
+
+    CUDA_LAUNCH(suppressLeftOverKernel<T>, blocks, threads, output, blk_x, blk_y);
+
+    POST_LAUNCH_CHECK();
+}
+}
+}
diff --git a/src/backend/opencl/canny.cpp b/src/backend/opencl/canny.cpp
new file mode 100644
index 0000000000..601422703f
--- /dev/null
+++ b/src/backend/opencl/canny.cpp
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <canny.hpp>
+#include <err_opencl.hpp>
+#include <kernel/canny.hpp>
+
+using af::dim4;
+
+namespace opencl
+{
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx, const Array<float>& gy)
+{
+    Array<float> out = createValueArray<float>(mag.dims(), 0);
+
+    kernel::nonMaxSuppression<float>(out, mag, gx, gy);
+
+    return out;
+}
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak)
+{
+    Array<char> out = createValueArray<char>(strong.dims(), 0);
+
+    kernel::edgeTrackingHysteresis<char>(out, strong, weak);
+
+    return out;
+}
+}
diff --git a/src/backend/opencl/canny.hpp b/src/backend/opencl/canny.hpp
new file mode 100644
index 0000000000..d24919ce2f
--- /dev/null
+++ b/src/backend/opencl/canny.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx, const Array<float>& gy);
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak);
+}
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
new file mode 100644
index 0000000000..1fe52425b1
--- /dev/null
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -0,0 +1,253 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/trace_edge.hpp>
+#include <kernel_headers/nonmax_suppression.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <memory.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+namespace kernel
+{
+static const int THREADS_X = 16;
+static const int THREADS_Y = 16;
+
+template<typename T>
+void nonMaxSuppression(Param output, const Param magnitude, const Param dx, const Param dy)
+{
+    std::string ref_name =
+        std::string("non_max_suppression_") +
+        std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    auto idx = kernelCaches[device].find(ref_name);
+    kc_entry_t entry;
+
+    if (idx == kernelCaches[device].find(ref_name)) {
+        std::ostringstream options;
+        options << " -D T="           << dtype_traits<T>::getName()
+                << " -D SHRD_MEM_HEIGHT="       << (THREADS_X+2)
+                << " -D SHRD_MEM_WIDTH="        << (THREADS_Y+2)
+                << " -D NON_MAX_SUPPRESSION";
+        if (std::is_same<T, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {nonmax_suppression_cl};
+        const int   ker_lens[] = {nonmax_suppression_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "nonMaxSuppressionKernel");
+    } else {
+        entry = idx->second;
+    }
+
+    auto nonMaxOp = KernelFunctor<Buffer, const KParam,
+                                  const Buffer, const KParam,
+                                  const Buffer, const KParam,
+                                  const Buffer, const KParam,
+                                  const unsigned, const unsigned>(*entry.ker);
+
+    NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
+
+    // Launch only threads to process non-border pixels
+    int blk_x = divup(magnitude.info.dims[0]-2, threads[0]);
+    int blk_y = divup(magnitude.info.dims[1]-2, threads[1]);
+
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * magnitude.info.dims[2] * threads[0],
+                   blk_y * magnitude.info.dims[3] * threads[1], 1);
+
+    nonMaxOp(EnqueueArgs(getQueue(), global, threads),
+             *output.data, output.info, *magnitude.data, magnitude.info,
+             *dx.data, dx.info, *dy.data, dy.info, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void initEdgeOut(Param output, const Param strong, const Param weak)
+{
+    std::string ref_name =
+        std::string("init_edge_out_") +
+        std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    auto idx = kernelCaches[device].find(ref_name);
+    kc_entry_t entry;
+
+    if (idx == kernelCaches[device].find(ref_name)) {
+        std::ostringstream options;
+        options << " -D T="           << dtype_traits<T>::getName()
+                << " -D INIT_EDGE_OUT";
+        if (std::is_same<T, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {trace_edge_cl};
+        const int   ker_lens[] = {trace_edge_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "initEdgeOutKernel");
+    } else {
+        entry = idx->second;
+    }
+
+    auto initOp = KernelFunctor<Buffer, const KParam,
+                                const Buffer, const KParam,
+                                const Buffer, const KParam,
+                                const unsigned, const unsigned>(*entry.ker);
+
+    NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
+
+    // Launch only threads to process non-border pixels
+    int blk_x = divup(strong.info.dims[0]-2, threads[0]);
+    int blk_y = divup(strong.info.dims[1]-2, threads[1]);
+
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * strong.info.dims[2] * threads[0],
+                   blk_y * strong.info.dims[3] * threads[1], 1);
+
+    initOp(EnqueueArgs(getQueue(), global, threads),
+           *output.data, output.info, *strong.data, strong.info, *weak.data, weak.info, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void suppressLeftOver(Param output)
+{
+    std::string ref_name =
+        std::string("suppress_left_over_") +
+        std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    auto idx = kernelCaches[device].find(ref_name);
+    kc_entry_t entry;
+
+    if (idx == kernelCaches[device].find(ref_name)) {
+        std::ostringstream options;
+        options << " -D T="           << dtype_traits<T>::getName()
+                << " -D SUPPRESS_LEFT_OVER";
+        if (std::is_same<T, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {trace_edge_cl};
+        const int   ker_lens[] = {trace_edge_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "suppressLeftOverKernel");
+    } else {
+        entry = idx->second;
+    }
+
+    auto finalOp = KernelFunctor<Buffer, const KParam, const unsigned, const unsigned>(*entry.ker);
+
+    NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
+
+    // Launch only threads to process non-border pixels
+    int blk_x = divup(output.info.dims[0]-2, threads[0]);
+    int blk_y = divup(output.info.dims[1]-2, threads[1]);
+
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * output.info.dims[2] * threads[0],
+                   blk_y * output.info.dims[3] * threads[1], 1);
+
+    finalOp(EnqueueArgs(getQueue(), global, threads), *output.data, output.info, blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void edgeTrackingHysteresis(Param output, const Param strong, const Param weak)
+{
+    std::string ref_name =
+        std::string("edge_track_") +
+        std::string(dtype_traits<T>::getName());
+
+    int device = getActiveDeviceId();
+    auto idx = kernelCaches[device].find(ref_name);
+    kc_entry_t entry;
+
+    if (idx == kernelCaches[device].find(ref_name)) {
+        std::ostringstream options;
+        options << " -D T="           << dtype_traits<T>::getName()
+                << " -D SHRD_MEM_HEIGHT="       << (THREADS_X+2)
+                << " -D SHRD_MEM_WIDTH="        << (THREADS_Y+2)
+                << " -D TOTAL_NUM_THREADS="     << (THREADS_X*THREADS_Y)
+                << " -D EDGE_TRACER";
+        if (std::is_same<T, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {trace_edge_cl};
+        const int   ker_lens[] = {trace_edge_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "edgeTrackKernel");
+    } else {
+        entry = idx->second;
+    }
+
+    NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
+
+    // Launch only threads to process non-border pixels
+    int blk_x = divup(weak.info.dims[0]-2, threads[0]);
+    int blk_y = divup(weak.info.dims[1]-2, threads[1]);
+
+    // launch batch * blk_x blocks along x dimension
+    NDRange global(blk_x * weak.info.dims[2] * threads[0],
+                   blk_y * weak.info.dims[3] * threads[1], 1);
+
+    auto edgeTraceOp = KernelFunctor<Buffer, const KParam,
+                                     const unsigned, const unsigned, Buffer>(*entry.ker);
+
+    initEdgeOut<T>(output, strong, weak);
+
+    int notFinished = 1;
+    cl::Buffer *d_continue = bufferAlloc(sizeof(int));
+
+    while(notFinished) {
+        notFinished = 0;
+        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &notFinished);
+
+        edgeTraceOp(EnqueueArgs(getQueue(), global, threads),
+                    *output.data, output.info, blk_x, blk_y, *d_continue);
+        CL_DEBUG_FINISH(getQueue());
+
+        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &notFinished);
+    }
+
+    bufferFree(d_continue);
+
+    suppressLeftOver<T>(output);
+}
+}
+}
diff --git a/src/backend/opencl/kernel/nonmax_suppression.cl b/src/backend/opencl/kernel/nonmax_suppression.cl
new file mode 100644
index 0000000000..02b599542f
--- /dev/null
+++ b/src/backend/opencl/kernel/nonmax_suppression.cl
@@ -0,0 +1,127 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void nonMaxSuppressionKernel(__global     T* output, KParam   oInfo,
+                             __global const T*   in, KParam  inInfo,
+                             __global const T*   dx, KParam  dxInfo,
+                             __global const T*   dy, KParam  dyInfo,
+                             unsigned nBBS0, unsigned nBBS1)
+{
+    // local thread indices
+    const int lx = get_local_id(0);
+    const int ly = get_local_id(1);
+
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = get_group_id(0) / nBBS0;
+    const unsigned b3 = get_group_id(1) / nBBS1;
+
+    // global indices
+    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+
+    __local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    __global const T* mag = in +
+        (b2 * inInfo.strides[2] + b3 * inInfo.strides[3] + inInfo.offset) +
+        inInfo.strides[1] + 1;
+    __global const T* dX  = dx +
+        (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3] + dxInfo.offset) +
+        dxInfo.strides[1] + 1;
+    __global const T* dY  = dy +
+        (b2 * dyInfo.strides[2] + b3 * dyInfo.strides[3] + dyInfo.offset) +
+        dyInfo.strides[1] + 1;
+    __global     T*   out = output +
+        (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
+        oInfo.strides[1] + 1;
+
+#pragma unroll
+    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=get_local_size(1), gy2+=get_local_size(1))
+    {
+#pragma unroll
+        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=get_local_size(0), gx2+=get_local_size(0))
+        {
+            localMem[b][a] = mag[ (gx2-1)*inInfo.strides[0] + (gy2-1)*inInfo.strides[1] ];
+        }
+    }
+    int i = lx + 1;
+    int j = ly + 1;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (gx<inInfo.dims[0]-2 && gy<inInfo.dims[1]-2)
+    {
+        int idx = gx*inInfo.strides[0]+gy*inInfo.strides[1];
+
+        const float cmag = localMem[j][i];
+
+        if (cmag == 0.0f)
+            out[idx] = (T)0;
+        else {
+            const float dx = dX[idx];
+            const float dy = dY[idx];
+            const float se = localMem[j+1][i+1];
+            const float nw = localMem[j-1][i-1];
+            const float ea = localMem[j  ][i+1];
+            const float we = localMem[j  ][i-1];
+            const float ne = localMem[j-1][i+1];
+            const float sw = localMem[j+1][i-1];
+            const float no = localMem[j-1][i  ];
+            const float so = localMem[j+1][i  ];
+
+            float a1, a2, b1, b2, alpha;
+
+            if (dx>=0) {
+                if (dy>=0) {
+                    const bool isTrue = (dx-dy)>=0;
+
+                    a1    = isTrue ? ea : so;
+                    a2    = isTrue ? we : no;
+                    b1    = se;
+                    b2    = nw;
+                    alpha = isTrue ? dy/dx : dx/dy;
+                } else {
+                    const bool isTrue = (dx+dy)>=0;
+
+                    a1    = isTrue ? ea : no;
+                    a2    = isTrue ? we : so;
+                    b1    = ne;
+                    b2    = sw;
+                    alpha = isTrue ? -dy/dx : dx/-dy;
+                }
+            } else {
+                if (dy>=0) {
+                    const bool isTrue = (dx+dy)>=0;
+
+                    a1    = isTrue ? so : we;
+                    a2    = isTrue ? no : ea;
+                    b1    = sw;
+                    b2    = ne;
+                    alpha = isTrue ? -dx/dy : dy/-dx;
+                } else {
+                    const bool isTrue = (-dx+dy)>=0;
+
+                    a1    = isTrue ? we : no;
+                    a2    = isTrue ? ea : so;
+                    b1    = nw;
+                    b2    = se;
+                    alpha = isTrue ? -dy/dx : dx/-dy;
+                }
+            }
+
+            float mag1 = (1-alpha)*a1 + alpha*b1;
+            float mag2 = (1-alpha)*a2 + alpha*b2;
+
+            if (cmag>mag1 && cmag>mag2) {
+                out[idx] = cmag;
+            } else {
+                out[idx] = (T)0;
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
new file mode 100644
index 0000000000..4cec56d7d5
--- /dev/null
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -0,0 +1,213 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__constant int STRONG = 1;
+__constant int WEAK   = 2;
+__constant int NOEDGE = 0;
+
+#if defined(INIT_EDGE_OUT)
+__kernel
+void initEdgeOutKernel(__global T*       output, KParam oInfo,
+                       __global const T* strong, KParam sInfo,
+                       __global const T*   weak, KParam wInfo,
+                       unsigned nBBS0, unsigned nBBS1)
+{
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = get_group_id(0) / nBBS0;
+    const unsigned b3 = get_group_id(1) / nBBS1;
+
+    // global indices
+    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + get_local_id(0);
+    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + get_local_id(1);
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    __global const T* wPtr = weak   +
+        (b2 * wInfo.strides[2] + b3 * wInfo.strides[3] + wInfo.offset) + wInfo.strides[1] + 1;
+
+    __global const T* sPtr = strong +
+        (b2 * sInfo.strides[2] + b3 * sInfo.strides[3] + sInfo.offset) + sInfo.strides[1] + 1;
+
+    __global       T* oPtr = output +
+        (b2 * oInfo.strides[2] + b3 * oInfo.strides[3] + oInfo.offset) + oInfo.strides[1] + 1;
+
+    if (gx<(oInfo.dims[0]-2) && gy<(oInfo.dims[1]-2))
+    {
+        int idx   = gx*oInfo.strides[0] + gy*oInfo.strides[1];
+        oPtr[idx] = (sPtr[idx] > 0 ? STRONG : (wPtr[idx] > 0 ? WEAK : NOEDGE));
+    }
+}
+#endif
+
+#define VALID_BLOCK_IDX(j, i) ( (j)>0 && (j)<(SHRD_MEM_HEIGHT-1) && (i)>0 && (i)<(SHRD_MEM_WIDTH-1) )
+
+#if defined(EDGE_TRACER)
+__kernel
+void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned nBBS1,
+                    __global volatile int* hasChanged)
+{
+    // shared memory with 1 pixel border
+    // strong and weak images are binary(char) images thus,
+    // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
+    __local int outMem  [ SHRD_MEM_HEIGHT ] [ SHRD_MEM_WIDTH ];
+    __local int predicates[TOTAL_NUM_THREADS];
+
+    // local thread indices
+    const int lx = get_local_id(0);
+    const int ly = get_local_id(1);
+
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = get_group_id(0) / nBBS0;
+    const unsigned b3 = get_group_id(1) / nBBS1;
+
+    // global indices
+    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    __global T* oPtr = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) + oInfo.strides[1] + 1;
+
+    // pull image to local memory
+#pragma unroll
+    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=get_local_size(1), gy2+=get_local_size(1))
+    {
+#pragma unroll
+        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=get_local_size(0), gx2+=get_local_size(0))
+        {
+            int x = gx2-1;
+            int y = gy2-1;
+            if (x>=0 && x<oInfo.dims[0] && y>=0 && y<oInfo.dims[1])
+                outMem[b][a] = oPtr[ x*oInfo.strides[0] + y*oInfo.strides[1] ];
+            else
+                outMem[b][a] = NOEDGE;
+        }
+    }
+
+    int i = lx + 1;
+    int j = ly + 1;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    int tid = get_local_id(0) + get_local_size(0) * get_local_id(1);
+
+    int continueIter = 1;
+
+    while (continueIter)
+    {
+        int cu = outMem[j][i];
+        int nw = outMem[j-1][i-1];
+        int no = outMem[j-1][i  ];
+        int ne = outMem[j-1][i+1];
+        int ea = outMem[j  ][i+1];
+        int se = outMem[j+1][i+1];
+        int so = outMem[j+1][i  ];
+        int sw = outMem[j+1][i-1];
+        int we = outMem[j  ][i-1];
+
+        bool hasStrongNeighbour = nw==STRONG || no==STRONG || ne==STRONG || ea==STRONG ||
+                                  se==STRONG || so==STRONG || sw==STRONG || we==STRONG;
+
+        if (cu==WEAK && hasStrongNeighbour)
+            outMem[j][i] = STRONG;
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        cu = outMem[j][i];
+
+        bool _nw = outMem[j-1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i-1);
+        bool _no = outMem[j-1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j-1, i  );
+        bool _ne = outMem[j-1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i+1);
+        bool _ea = outMem[j  ][i+1] == WEAK  &&  VALID_BLOCK_IDX(j  , i+1);
+        bool _se = outMem[j+1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i+1);
+        bool _so = outMem[j+1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j+1, i  );
+        bool _sw = outMem[j+1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i-1);
+        bool _we = outMem[j  ][i-1] == WEAK  &&  VALID_BLOCK_IDX(j  , i-1);
+
+        bool hasWeakNeighbour = _nw || _no || _ne || _ea || _se || _so || _sw || _we;
+
+        // Following Block is equivalent of __syncthreads_or in CUDA
+        predicates[tid] = cu==STRONG && hasWeakNeighbour;
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        for (int nt = TOTAL_NUM_THREADS/2;  nt>0; nt>>=1)
+        {
+            if (tid < nt)
+                predicates[tid] = predicates[tid] || predicates[tid+nt];
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        continueIter = predicates[0];
+    };
+
+    // Check if any 1-pixel border ring
+    // has weak pixels with strong candidates
+    // within the main region, then increment hasChanged.
+    int cu = outMem[j][i];
+    int nw = outMem[j-1][i-1];
+    int no = outMem[j-1][i  ];
+    int ne = outMem[j-1][i+1];
+    int ea = outMem[j  ][i+1];
+    int se = outMem[j+1][i+1];
+    int so = outMem[j+1][i  ];
+    int sw = outMem[j+1][i-1];
+    int we = outMem[j  ][i-1];
+
+    bool hasWeakNeighbour = nw==WEAK || no==WEAK || ne==WEAK || ea==WEAK ||
+                            se==WEAK || so==WEAK || sw==WEAK || we==WEAK;
+
+    // Following Block is equivalent of __syncthreads_or in CUDA
+    predicates[tid] = cu==STRONG && hasWeakNeighbour;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int nt = TOTAL_NUM_THREADS/2;  nt>0; nt>>=1)
+    {
+        if (tid < nt)
+            predicates[tid] = predicates[tid] || predicates[tid+nt];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    continueIter = predicates[0];
+
+    if (continueIter>0 && lx==0 && ly==0)
+        atomic_add(hasChanged, 1);
+
+    // Update output with shared memory result
+    if (gx<(oInfo.dims[0]-2) && gy<(oInfo.dims[1]-2))
+        oPtr[ gx*oInfo.strides[0] + gy*oInfo.strides[1] ] = outMem[j][i];
+}
+#endif
+
+#if defined(SUPPRESS_LEFT_OVER)
+__kernel
+void suppressLeftOverKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned nBBS1)
+{
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = get_group_id(0) / nBBS0;
+    const unsigned b3 = get_group_id(1) / nBBS1;
+
+    // global indices
+    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + get_local_id(0);
+    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + get_local_id(1);
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    __global T* oPtr = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) + oInfo.strides[1] + 1;
+
+    if (gx<(oInfo.dims[0]-2) && gy<(oInfo.dims[1]-2))
+    {
+        int idx = gx*oInfo.strides[0]+gy*oInfo.strides[1];
+        T val   = oPtr[idx];
+        if (val==WEAK)
+            oPtr[idx] = NOEDGE;
+    }
+}
+#endif
diff --git a/test/canny.cpp b/test/canny.cpp
new file mode 100644
index 0000000000..16dd5d5171
--- /dev/null
+++ b/test/canny.cpp
@@ -0,0 +1,181 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+using std::string;
+using std::vector;
+
+template<typename T>
+class CannyEdgeDetector : public ::testing::Test
+{
+    public:
+        virtual void SetUp() {}
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, int, uint, short, ushort, uchar, double> TestTypes;
+
+// register the type list
+TYPED_TEST_CASE(CannyEdgeDetector, TestTypes);
+
+template<typename T>
+void cannyTest(string pTestFile)
+{
+    if (noDoubleTests<T>()) return;
+
+    vector<af::dim4>  numDims;
+    vector<vector<T> >      in;
+    vector<vector<char> >   tests;
+
+    readTests<T, char, int>(pTestFile, numDims, in, tests);
+
+    af::dim4 sDims    = numDims[0];
+    af_array outArray = 0;
+    af_array sArray   = 0;
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&sArray, &(in[0].front()),
+                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+
+    ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, sArray, AF_MANUAL_THRESHOLD, 0.4147f, 0.8454f, 3, true));
+
+    char *outData = new char[sDims.elements()];
+
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+    vector<char> currGoldBar = tests[0];
+    size_t nElems        = currGoldBar.size();
+    for (size_t elIter=0; elIter<nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+    }
+
+    // cleanup
+    delete[] outData;
+    ASSERT_EQ(AF_SUCCESS, af_release_array(sArray));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+}
+
+TYPED_TEST(CannyEdgeDetector, ArraySizeLessThanBlockSize10x10)
+{
+    cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast10x10.test"));
+}
+
+TYPED_TEST(CannyEdgeDetector, ArraySizeEqualBlockSize16x16)
+{
+    cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast16x16.test"));
+}
+
+template<typename T>
+void cannyImageOtsuTest(string pTestFile, bool isColor)
+{
+    if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
+
+    using af::dim4;
+
+    vector<dim4>       inDims;
+    vector<string>    inFiles;
+    vector<dim_t>    outSizes;
+    vector<string>   outFiles;
+
+    readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
+
+    size_t testCount = inDims.size();
+
+    for (size_t testId=0; testId<testCount; ++testId) {
+
+        af_array inArray  = 0;
+        af_array outArray = 0;
+        af_array goldArray= 0;
+        dim_t nElems   = 0;
+
+        inFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
+        outFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, af_load_image_native(&goldArray, outFiles[testId].c_str()));
+        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, inArray, AF_AUTO_OTSU_THRESHOLD, 0.08, 0.32, 3, false));
+
+        char * outData = new char[nElems];
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+
+        char * goldData= new char[nElems];
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 1.0e-3));
+
+        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+    }
+}
+
+TEST(CannyEdgeDetector, OtsuThreshold)
+{
+    cannyImageOtsuTest<float>(string(TEST_DIR "/CannyEdgeDetector/gray.test"), false);
+}
+
+TEST(CannyEdgeDetector, InvalidSizeArray)
+{
+    af_array inArray   = 0;
+    af_array outArray  = 0;
+
+    vector<float>   in(100, 1);
+
+    af::dim4 sDims(100, 1, 1, 1);
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+
+    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_MANUAL_THRESHOLD, 0.24, 0.72, 3, true));
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+}
+
+TEST(CannyEdgeDetector, Array4x4_Invalid)
+{
+    af_array inArray   = 0;
+    af_array outArray  = 0;
+
+    vector<float>   in(16, 1);
+
+    af::dim4 sDims(4, 4, 1, 1);
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+
+    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_MANUAL_THRESHOLD, 0.24, 0.72, 3, true));
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+}
+
+TEST(CannyEdgeDetector, Sobel5x5_Invalid)
+{
+    af_array inArray   = 0;
+    af_array outArray  = 0;
+
+    vector<float>   in(25, 1);
+
+    af::dim4 sDims(5, 5, 1, 1);
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+
+    ASSERT_EQ(AF_ERR_ARG, af_canny(&outArray, inArray, AF_MANUAL_THRESHOLD, 0.24, 0.72, 5, true));
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+}
diff --git a/test/data b/test/data
index 8493781ff0..21d4c0671c 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 8493781ff0489626cb0f004ee046d7f187083e10
+Subproject commit 21d4c0671c9da50ca2c921a6cd73a46172feb68c

From ed5bc29e29beb976b494ad828337b3a3dee4c69e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 22 Apr 2017 23:52:33 +0530
Subject: [PATCH 1159/2677] Serialize clblast(blas) calls

Blas in OpenCL backend was failing in multiple threads scenario.
Upon discussion with pavan, blas calls are serialized using a
global mutex. This is a temporary fix.
---
 src/backend/opencl/err_clblas.hpp  | 2 +-
 src/backend/opencl/err_clblast.hpp | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/err_clblas.hpp b/src/backend/opencl/err_clblas.hpp
index 768ead7856..1440582e2c 100644
--- a/src/backend/opencl/err_clblas.hpp
+++ b/src/backend/opencl/err_clblas.hpp
@@ -57,6 +57,7 @@ static std::recursive_mutex gCLBlasMutex;
 #define CLBLAS_CHECK(fn) do {                   \
         gCLBlasMutex.lock();                    \
         clblasStatus _clblas_st = fn;           \
+        gCLBlasMutex.unlock();                  \
         if (_clblas_st != clblasSuccess) {      \
             char clblas_st_msg[1024];           \
             snprintf(clblas_st_msg,             \
@@ -69,5 +70,4 @@ static std::recursive_mutex gCLBlasMutex;
             AF_ERROR(clblas_st_msg,             \
                      AF_ERR_INTERNAL);          \
         }                                       \
-        gCLBlasMutex.unlock();                  \
     } while(0)
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
index fae1722251..c02d600d49 100644
--- a/src/backend/opencl/err_clblast.hpp
+++ b/src/backend/opencl/err_clblast.hpp
@@ -80,8 +80,12 @@ static const char * _clblastGetResultString(clblast::StatusCode st)
     return "Unknown error";
 }
 
+static std::recursive_mutex gCLBlastMutex;
+
 #define CLBLAST_CHECK(fn) do {                              \
+        gCLBlastMutex.lock();                               \
         clblast::StatusCode _clblast_st = fn;               \
+        gCLBlastMutex.unlock();                             \
         if (_clblast_st != clblast::StatusCode::kSuccess) { \
             char clblast_st_msg[1024];                      \
             snprintf(clblast_st_msg,                        \

From 6727ab654a9b869bec567a4c3b6b48c3e658cbf9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 22 Apr 2017 23:56:47 +0530
Subject: [PATCH 1160/2677] Reduce memory footprint of solve/sparse
 multithreaded tests

Reduced the memory usage of multi-threaded tests of sparse and
solve to avoid out of memory exception on low memory GPUs.
---
 test/sparse_common.hpp | 17 ++++++++++++++---
 test/threading.cpp     | 33 +++++++--------------------------
 2 files changed, 21 insertions(+), 29 deletions(-)

diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index 57705d3ca0..e8a8efc32e 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -68,8 +68,12 @@ double calc_norm(af::array lhs, af::array rhs)
 }
 
 template<typename T>
-void sparseTester(const int m, const int n, const int k, int factor, double eps)
+void sparseTester(const int m, const int n, const int k, int factor, double eps,
+                  int targetDevice=-1)
 {
+    if (targetDevice>=0)
+        af::setDevice(targetDevice);
+
     af::deviceGC();
 
     if (noDoubleTests<T>()) return;
@@ -99,8 +103,12 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps)
 }
 
 template<typename T>
-void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps)
+void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps,
+                           int targetDevice=-1)
 {
+    if (targetDevice>=0)
+        af::setDevice(targetDevice);
+
     af::deviceGC();
 
     if (noDoubleTests<T>()) return;
@@ -135,8 +143,11 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
 }
 
 template<typename T>
-void convertCSR(const int M, const int N, const float ratio)
+void convertCSR(const int M, const int N, const float ratio, int targetDevice=-1)
 {
+    if (targetDevice>=0)
+        af::setDevice(targetDevice);
+
     if (noDoubleTests<T>()) return;
 #if 1
     af::array a = cpu_randu<T>(af::dim4(M, N));
diff --git a/test/threading.cpp b/test/threading.cpp
index 9c044ae792..aa427f72b6 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -618,25 +618,12 @@ TEST(Threading, BLAS)
 
 #define SOLVE_LU_TESTS(T, eps)                                                                          \
     tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
-    tests.emplace_back(solveLUTester<T>, 2048, 512, eps, nextTargetDeviceId()%numDevices);              \
-    std::this_thread::sleep_for(std::chrono::seconds(2));   \
     tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps, nextTargetDeviceId()%numDevices);  \
-    tests.emplace_back(solveTriangleTester<T>, 2048, 512, true, eps, nextTargetDeviceId()%numDevices);  \
-    std::this_thread::sleep_for(std::chrono::seconds(2));   \
     tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, nextTargetDeviceId()%numDevices); \
-    tests.emplace_back(solveTriangleTester<T>, 2048, 512, false, eps, nextTargetDeviceId()%numDevices); \
-    std::this_thread::sleep_for(std::chrono::seconds(2));   \
     tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps, nextTargetDeviceId()%numDevices);          \
-    tests.emplace_back(solveTester<T>, 2048, 2048, 512, eps, nextTargetDeviceId()%numDevices);          \
-    std::this_thread::sleep_for(std::chrono::seconds(2));   \
     tests.emplace_back(solveTester<T>, 800, 1000, 200, eps, nextTargetDeviceId()%numDevices);           \
-    tests.emplace_back(solveTester<T>, 1536, 2048, 400, eps, nextTargetDeviceId()%numDevices);          \
-    std::this_thread::sleep_for(std::chrono::seconds(2));   \
     tests.emplace_back(solveTester<T>, 800, 600, 64, eps, nextTargetDeviceId()%numDevices);             \
-    tests.emplace_back(solveTester<T>, 1536, 1024, 1, eps, nextTargetDeviceId()%numDevices);
 
-// Added 2s sleep for every two test threads to make sure
-// we are not running out of memory.
 TEST(Threading, SolveDense)
 {
     cleanSlate(); // Clean up everything done so far
@@ -659,19 +646,13 @@ TEST(Threading, SolveDense)
 #undef SOLVE_LU_TESTS
 
 #define SPARSE_TESTS(T, eps)                                                    \
-        tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps);           \
-        tests.emplace_back(sparseTester<T>, 2048, 1024, 512, 3, eps);           \
-        tests.emplace_back(sparseTester<T>, 500, 1000, 250, 1, eps);            \
-        tests.emplace_back(sparseTester<T>, 625, 1331, 1, 2, eps);              \
-        tests.emplace_back(sparseTransposeTester<T>, 625, 1331, 1, 2, eps);     \
-        tests.emplace_back(sparseTransposeTester<T>, 1000, 1000, 100, 5, eps);  \
-        tests.emplace_back(sparseTransposeTester<T>, 2048, 1024, 512, 3, eps);  \
-        tests.emplace_back(sparseTransposeTester<T>, 453, 751, 397, 1, eps);    \
-        tests.emplace_back(convertCSR<T>, 2345, 5678, 0.5);                     \
-        std::this_thread::sleep_for(std::chrono::seconds(5));
-
-// Added 2s sleep for every two test threads to make sure
-// we are not running out of memory.
+  tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps, nextTargetDeviceId()%numDevices);  \
+  tests.emplace_back(sparseTester<T>, 500, 1000, 250, 1, eps, nextTargetDeviceId()%numDevices);   \
+  tests.emplace_back(sparseTester<T>, 625, 1331, 1, 2, eps, nextTargetDeviceId()%numDevices);     \
+  tests.emplace_back(sparseTransposeTester<T>, 625, 1331, 1, 2, eps, nextTargetDeviceId()%numDevices); \
+  tests.emplace_back(sparseTransposeTester<T>, 453, 751, 397, 1, eps, nextTargetDeviceId()%numDevices);\
+  tests.emplace_back(convertCSR<T>, 2345, 5678, 0.5, nextTargetDeviceId()%numDevices);
+
 TEST(Threading, Sparse)
 {
     cleanSlate(); // Clean up everything done so far

From 53e1fd8cdb2bfe2ec37503cc42bc85ea30c8c029 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 23 Apr 2017 00:06:47 +0530
Subject: [PATCH 1161/2677] Disable Solve/Sparse multi-threaded tests for
 OpenCL

---
 test/threading.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/test/threading.cpp b/test/threading.cpp
index aa427f72b6..5301567187 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -616,6 +616,8 @@ TEST(Threading, BLAS)
             tests[testId].join();
 }
 
+#if !defined(AF_OPENCL)
+
 #define SOLVE_LU_TESTS(T, eps)                                                                          \
     tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
     tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps, nextTargetDeviceId()%numDevices);  \
@@ -671,3 +673,5 @@ TEST(Threading, Sparse)
         if (tests[testId].joinable())
             tests[testId].join();
 }
+
+#endif

From bdb3d6ef3b2af5d0703b2f01dc93aae6040d5da0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 24 Apr 2017 16:37:37 -0400
Subject: [PATCH 1162/2677] Disable double tests for devices that do not
 support doubles

---
 test/threading.cpp | 112 ++++++++++++++++++++++++++++-----------------
 1 file changed, 69 insertions(+), 43 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 5301567187..5b1cdc14b0 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -396,23 +396,33 @@ TEST(Threading, FFT_R2C)
 
     // Real to complex transforms
     INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
-    INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
     INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
-    INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
     INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
-    INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
 
     // Factors 7, 11, 13
     INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
     INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
     INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
 
     // transforms on padded and truncated arrays
     INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-    INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+
+
+    if (noDoubleTests<double>()) {
+        // Real to complex transforms
+        INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
+        INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
+        INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
+
+        // Factors 7, 11, 13
+        INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+
+        // transforms on padded and truncated arrays
+        INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+    }
+
 
     for (size_t testId=0; testId<tests.size(); ++testId)
         if (tests[testId].joinable())
@@ -430,31 +440,38 @@ TEST(Threading, FFT_C2C)
 
     // complex to complex transforms
     INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
-    INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
     INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
-    INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
     INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
-    INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
 
     INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
     INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
     INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
 
     // transforms on padded and truncated arrays
     INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
-    INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
 
     // inverse transforms
     // complex to complex transforms
     INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
-    INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
     INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
-    INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
     INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
-    INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+
+
+    if (noDoubleTests<double>()) {
+        INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
+        INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
+        INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
+
+        INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+
+        INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+
+        INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
+        INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
+        INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+    }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
         if (tests[testId].joinable())
@@ -472,51 +489,54 @@ TEST(Threading, FFT_ALL)
 
     // Real to complex transforms
     INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
-    INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
     INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
-    INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
     INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
-    INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
 
     // Factors 7, 11, 13
     INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
     INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
     INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
 
     // transforms on padded and truncated arrays
     INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-    INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
 
     // complex to complex transforms
     INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
-    INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
     INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
-    INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
     INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
-    INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
 
     INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
     INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
     INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
 
     // transforms on padded and truncated arrays
     INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
-    INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
 
     // inverse transforms
     // complex to complex transforms
     INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
-    INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
     INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
-    INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
     INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
-    INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+
+    if (noDoubleTests<double>()) {
+        INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
+        INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
+        INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
+        INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+        INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+        INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
+        INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
+        INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
+        INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+        INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+        INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
+        INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
+        INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+    }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
         if (tests[testId].joinable())
@@ -587,7 +607,7 @@ void cppMatMulCheck(int targetDevice, string TestFile)
     }
 }
 
-#define TEST_FOR_TYPE(TypeName)                                   \
+#define TEST_FOR_TYPE(TypeName)                                         \
     tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
             nextTargetDeviceId()%numDevices, TEST_DIR "/blas/Basic.test");          \
     tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
@@ -608,8 +628,11 @@ TEST(Threading, BLAS)
 
     TEST_FOR_TYPE(      float);
     TEST_FOR_TYPE( af::cfloat);
-    TEST_FOR_TYPE(     double);
-    TEST_FOR_TYPE(af::cdouble);
+
+    if (noDoubleTests<double>()) {
+        TEST_FOR_TYPE(     double);
+        TEST_FOR_TYPE(af::cdouble);
+    }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
         if (tests[testId].joinable())
@@ -636,9 +659,11 @@ TEST(Threading, SolveDense)
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
 
     SOLVE_LU_TESTS(float, 0.01);
-    SOLVE_LU_TESTS(double, 1E-5);
     SOLVE_LU_TESTS(cfloat, 0.01);
-    SOLVE_LU_TESTS(cdouble, 1E-5);
+    if (noDoubleTests<double>()) {
+        SOLVE_LU_TESTS(double, 1E-5);
+        SOLVE_LU_TESTS(cdouble, 1E-5);
+    }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
         if (tests[testId].joinable())
@@ -646,6 +671,7 @@ TEST(Threading, SolveDense)
 }
 
 #undef SOLVE_LU_TESTS
+#endif
 
 #define SPARSE_TESTS(T, eps)                                                    \
   tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps, nextTargetDeviceId()%numDevices);  \
@@ -665,13 +691,13 @@ TEST(Threading, Sparse)
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
 
     SPARSE_TESTS(  float, 1E-3);
-    SPARSE_TESTS( double, 1E-5);
     SPARSE_TESTS( cfloat, 1E-3);
-    SPARSE_TESTS(cdouble, 1E-5);
+    if (noDoubleTests<double>()) {
+        SPARSE_TESTS( double, 1E-5);
+        SPARSE_TESTS(cdouble, 1E-5);
+    }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
         if (tests[testId].joinable())
             tests[testId].join();
 }
-
-#endif

From da684f07d185a153193ca5c858d7c9f9e253bbd7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 25 Apr 2017 21:40:04 +0530
Subject: [PATCH 1163/2677] Use a pool of cuda streams in round robin fashion
 for threads

Each thread will have a separate stream, although if too many threads
are launched i.e. greater than the pool size, then existing set of
streams are just used up in round robin fashion.
---
 src/backend/cuda/platform.cpp | 36 +++++++++++++++++++++++------------
 src/backend/cuda/platform.hpp |  9 ++++++++-
 2 files changed, 32 insertions(+), 13 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 89cc827758..3fbad720a4 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -316,17 +316,23 @@ int getDeviceIdFromNativeId(int nativeId)
     return devId;
 }
 
-cudaStream_t getStream(int device)
+cudaStream_t DeviceManager::nextAvailableStream(int device)
 {
-    static std::once_flag streamInitFlags[DeviceManager::MAX_DEVICES];
+    common::lock_guard_t lock(poolCounterMutexes[device]);
+
+    unsigned oldPoolId = nextPoolCounter[device];
 
-    std::call_once(streamInitFlags[device],
-            [device]() {
-                DeviceManager& inst = DeviceManager::getInstance();
-                CUDA_CHECK(cudaStreamCreate( & (inst.streams[device]) ));
-            });
+    nextPoolCounter[device] = (oldPoolId+1) % streamPoolCluster[device].size();
 
-    return DeviceManager::getInstance().streams[device];
+    return streamPoolCluster[device][oldPoolId];
+}
+
+cudaStream_t getStream(int device)
+{
+    static thread_local cudaStream_t myDefaultStream =
+        DeviceManager::getInstance().nextAvailableStream(device);
+
+    return myDefaultStream;
 }
 
 cudaStream_t getActiveStream()
@@ -508,10 +514,16 @@ DeviceManager::DeviceManager()
 
     sortDevices();
 
-    // Initialize all streams to 0.
-    // Streams will be created in setActiveDevice()
-    for(int i = 0; i < (int)MAX_DEVICES; i++)
-        streams[i] = (cudaStream_t)0;
+    // Initialize stream pools for all devices.
+    for (unsigned c=0; c < streamPoolCluster.size(); ++c) {
+        streamPoolCluster[c].resize(MAX_SIZE_STREAM_POOL, static_cast<cudaStream_t>(0));
+        for (unsigned p=0; p<MAX_SIZE_STREAM_POOL; ++p) {
+            cudaStream_t temp = 0;
+            CUDA_CHECK(cudaStreamCreate(&temp));
+            streamPoolCluster[c][p] = temp;
+        }
+    }
+    nextPoolCounter.fill(0);
 
     std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
     if(deviceENV.empty()) {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index b9ac3df401..f93b8c6b94 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -96,6 +96,8 @@ class DeviceManager
 {
     public:
         static const unsigned MAX_DEVICES = 16;
+        static const unsigned MAX_SIZE_STREAM_POOL = 16;
+        typedef std::vector<cudaStream_t> StreamPool;
 
 #if defined(WITH_GRAPHICS)
         static bool checkGraphicsInteropCapability();
@@ -152,8 +154,13 @@ class DeviceManager
 
         int setActiveDevice(int device, int native = -1);
 
+        cudaStream_t nextAvailableStream(int device);
+
         int nDevices;
-        cudaStream_t streams[MAX_DEVICES];
+
+        std::array<     StreamPool, MAX_DEVICES> streamPoolCluster;
+        std::array<       unsigned, MAX_DEVICES> nextPoolCounter;
+        std::array<common::mutex_t, MAX_DEVICES> poolCounterMutexes;
 
         std::unique_ptr<MemoryManager> memManager;
 

From 3c765754fc26d5dc47176d0ea407551ec3ea7504 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 25 Apr 2017 22:05:31 +0530
Subject: [PATCH 1164/2677] Typo fix in accum/af_accum fn documentation

---
 docs/details/algorithm.dox | 2 +-
 include/af/algorithm.h     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index 27ef55ef48..6e6f3b8cde 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -107,7 +107,7 @@ Return type is u32 for all input types
 
 \ingroup scan_mat
 
-Perform exclusive sum along specified dimension
+Perform inclusive sum along specified dimension
 
 This table defines the return value types for the corresponding input types
 
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 39d948fc20..737ef1a3d3 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -306,7 +306,7 @@ namespace af
     template<typename T> void max(T *val, unsigned *idx, const array &in);
 
     /**
-       C++ Interface exclusive sum (cumulative sum) of an array
+       C++ Interface inclusive sum (cumulative sum) of an array
 
        \param[in] in is the input array
        \param[in] dim The dimension along which exclusive sum is performed
@@ -761,7 +761,7 @@ extern "C" {
     AFAPI af_err af_imax_all(double *real, double *imag, unsigned *idx, const af_array in);
 
     /**
-       C Interface exclusive sum (cumulative sum) of an array
+       C Interface inclusive sum (cumulative sum) of an array
 
        \param[out] out will contain exclusive sums of the input
        \param[in] in is the input array

From 4b6a93195470fd2933a0b44f30c85295bb890f9e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 25 Apr 2017 18:39:19 -0400
Subject: [PATCH 1165/2677] Add a memory manager stress test.

---
 test/threading.cpp | 35 ++++++++++++++++++++++++++++++++++-
 1 file changed, 34 insertions(+), 1 deletion(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 5b1cdc14b0..60d088e79d 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -16,6 +16,7 @@
 #include <chrono>
 #include <thread>
 #include <iterator>
+#include <vector>
 #include <testHelpers.hpp>
 #include <solve_common.hpp>
 #include <sparse_common.hpp>
@@ -697,7 +698,39 @@ TEST(Threading, Sparse)
         SPARSE_TESTS(cdouble, 1E-5);
     }
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
+    for (size_t testId=0; testId < tests.size(); ++testId)
         if (tests[testId].joinable())
             tests[testId].join();
 }
+
+TEST(Threading, MemoryManagerStressTest) {
+  vector<std::thread> threads;
+  for (int i = 0; i < THREAD_COUNT; i++) {
+    threads.emplace_back([] {
+        vector<af::array> arrg;
+        int size = 100;
+        int ex_count = 0;
+
+        // Continue until the memory runs out multiple times
+        while (true) {
+          try {
+            // constantly change size of the array allocated
+            size+=10;
+            arrg.push_back(af::randu(size));
+
+            // delete some values intermittently
+            if (!(size%200)) {
+              arrg.erase(std::begin(arrg), std::begin(arrg)+5);
+            }
+          } catch( const af::exception &ex ) {
+            if (ex_count++ > 3) {
+              break;
+            }
+          }
+        }
+      });
+  }
+  for (auto& t : threads) {
+    t.join();
+  }
+}

From fdd2ba032b06ecbae7ca606684cafe874f86a37d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 26 Apr 2017 09:56:12 +0530
Subject: [PATCH 1166/2677] Revert "Use a pool of cuda streams in round robin
 fashion for threads"

This reverts commit da684f07d185a153193ca5c858d7c9f9e253bbd7.
---
 src/backend/cuda/platform.cpp | 36 ++++++++++++-----------------------
 src/backend/cuda/platform.hpp |  9 +--------
 2 files changed, 13 insertions(+), 32 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 3fbad720a4..89cc827758 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -316,23 +316,17 @@ int getDeviceIdFromNativeId(int nativeId)
     return devId;
 }
 
-cudaStream_t DeviceManager::nextAvailableStream(int device)
-{
-    common::lock_guard_t lock(poolCounterMutexes[device]);
-
-    unsigned oldPoolId = nextPoolCounter[device];
-
-    nextPoolCounter[device] = (oldPoolId+1) % streamPoolCluster[device].size();
-
-    return streamPoolCluster[device][oldPoolId];
-}
-
 cudaStream_t getStream(int device)
 {
-    static thread_local cudaStream_t myDefaultStream =
-        DeviceManager::getInstance().nextAvailableStream(device);
+    static std::once_flag streamInitFlags[DeviceManager::MAX_DEVICES];
+
+    std::call_once(streamInitFlags[device],
+            [device]() {
+                DeviceManager& inst = DeviceManager::getInstance();
+                CUDA_CHECK(cudaStreamCreate( & (inst.streams[device]) ));
+            });
 
-    return myDefaultStream;
+    return DeviceManager::getInstance().streams[device];
 }
 
 cudaStream_t getActiveStream()
@@ -514,16 +508,10 @@ DeviceManager::DeviceManager()
 
     sortDevices();
 
-    // Initialize stream pools for all devices.
-    for (unsigned c=0; c < streamPoolCluster.size(); ++c) {
-        streamPoolCluster[c].resize(MAX_SIZE_STREAM_POOL, static_cast<cudaStream_t>(0));
-        for (unsigned p=0; p<MAX_SIZE_STREAM_POOL; ++p) {
-            cudaStream_t temp = 0;
-            CUDA_CHECK(cudaStreamCreate(&temp));
-            streamPoolCluster[c][p] = temp;
-        }
-    }
-    nextPoolCounter.fill(0);
+    // Initialize all streams to 0.
+    // Streams will be created in setActiveDevice()
+    for(int i = 0; i < (int)MAX_DEVICES; i++)
+        streams[i] = (cudaStream_t)0;
 
     std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
     if(deviceENV.empty()) {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index f93b8c6b94..b9ac3df401 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -96,8 +96,6 @@ class DeviceManager
 {
     public:
         static const unsigned MAX_DEVICES = 16;
-        static const unsigned MAX_SIZE_STREAM_POOL = 16;
-        typedef std::vector<cudaStream_t> StreamPool;
 
 #if defined(WITH_GRAPHICS)
         static bool checkGraphicsInteropCapability();
@@ -154,13 +152,8 @@ class DeviceManager
 
         int setActiveDevice(int device, int native = -1);
 
-        cudaStream_t nextAvailableStream(int device);
-
         int nDevices;
-
-        std::array<     StreamPool, MAX_DEVICES> streamPoolCluster;
-        std::array<       unsigned, MAX_DEVICES> nextPoolCounter;
-        std::array<common::mutex_t, MAX_DEVICES> poolCounterMutexes;
+        cudaStream_t streams[MAX_DEVICES];
 
         std::unique_ptr<MemoryManager> memManager;
 

From 005cc1bb9eed6156fd54edd4fbd1024f47d4ce84 Mon Sep 17 00:00:00 2001
From: Ilya Ivanov <ivanov@viva64.com>
Date: Wed, 26 Apr 2017 15:16:27 +0300
Subject: [PATCH 1167/2677] Fixed PVS-Studio issues We have found and fixed
 some bugs using PVS-Studio tool. PVS-Studio is a static code analyzer for C,
 C++ and C#: https://www.viva64.com/en/pvs-studio/ We suggests having a look
 at the emails, sent from @pvs-studio.com. V501 There are identical
 sub-expressions to the left and to the right of the '==' operator: yddims[0]
 == yddims[0] afcpu homography.cpp 74 V522 Dereferencing of the null pointer
 'imag' might take place. afcpu reduce.cpp 216 V522 Dereferencing of the null
 pointer 'imag_val' might take place. afcpu reduce.cpp 464 V523 The 'then'
 statement is equivalent to the 'else' statement. afcpu median.cpp 47 V656
 Variables 'adims', 'bdims' are initialized through the call to the same
 function. It's probably an error or un-optimized code. Consider inspecting
 the 'a_info.dims()' expression. Check lines: 42, 43. afcpu solve.cpp 43 V656
 Variables 'adims', 'bdims' are initialized through the call to the same
 function. It's probably an error or un-optimized code. Consider inspecting
 the 'a_info.dims()' expression. Check lines: 112, 113. afcpu solve.cpp 113

---
 src/api/c/homography.cpp | 2 +-
 src/api/c/median.cpp     | 2 +-
 src/api/c/reduce.cpp     | 4 ++--
 src/api/c/solve.cpp      | 4 ++--
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index 51b4b10f1c..5992909f4a 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -71,7 +71,7 @@ af_err af_homography(af_array *H, int *inliers,
         ARG_ASSERT(1, (xsdims[0] > 0));
         ARG_ASSERT(2, (ysdims[0] == xsdims[0]));
         ARG_ASSERT(3, (xddims[0] > 0));
-        ARG_ASSERT(4, (yddims[0] == yddims[0]));
+        ARG_ASSERT(4, (yddims[0] == xddims[0]));
 
         ARG_ASSERT(5, (inlier_thr >= 0.1f));
         ARG_ASSERT(6, (iterations > 0));
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 7ef6bf1afc..97adcf6d47 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -45,7 +45,7 @@ static double median(const af_array& in)
         if (input.isFloating()) {
             return division(result[0] + result[1], 2.0);
         } else {
-            return division(result[0] + result[1], 2.0);
+            return division((float)result[0] + (float)result[1], 2.0);
         }
     }
 
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 26dd2a42ef..6cfe3d2a66 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -213,7 +213,7 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in)
 
         ARG_ASSERT(0, real != NULL);
         *real = 0;
-        if (!imag) *imag = 0;
+        if (imag) *imag = 0;
 
         switch(type) {
         case f32:  *real = (double)reduce_all<op, float  , To>(in); break;
@@ -461,7 +461,7 @@ static af_err ireduce_all_common(double *real_val, double *imag_val,
         ARG_ASSERT(3, in_info.ndims() > 0);
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
-        if (!imag_val) *imag_val = 0;
+        if (imag_val) *imag_val = 0;
 
         cfloat  cfval;
         cdouble cdval;
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index f31766b1ab..73ba745886 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -40,7 +40,7 @@ af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_
         af_dtype b_type = b_info.getType();
 
         dim4 adims = a_info.dims();
-        dim4 bdims = a_info.dims();
+        dim4 bdims = b_info.dims();
 
         ARG_ASSERT(1, a_info.isFloating());                       // Only floating and complex types
         ARG_ASSERT(2, b_info.isFloating());                       // Only floating and complex types
@@ -110,7 +110,7 @@ af_err af_solve_lu(af_array *out, const af_array a,
         af_dtype b_type = b_info.getType();
 
         dim4 adims = a_info.dims();
-        dim4 bdims = a_info.dims();
+        dim4 bdims = b_info.dims();
 
         ARG_ASSERT(1, a_info.isFloating());                       // Only floating and complex types
         ARG_ASSERT(2, b_info.isFloating());                       // Only floating and complex types

From 48482d443ab604bb3cba3a00783262e8aa663196 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 26 Apr 2017 17:52:07 +0530
Subject: [PATCH 1168/2677] Replace new/deleta with std::vector in image
 processing tests

Also, fixes memory leaks in medfilt and morph unit tests
---
 test/bilateral.cpp      | 27 +++++++++++----------------
 test/canny.cpp          | 15 +++++++--------
 test/covariance.cpp     |  7 ++-----
 test/gen_assign.cpp     | 19 ++++++++++---------
 test/gen_index.cpp      | 10 ++++------
 test/histogram.cpp      | 14 +++++---------
 test/hsv_rgb.cpp        | 14 ++++----------
 test/match_template.cpp |  5 ++---
 test/mean.cpp           | 14 ++++----------
 test/meanshift.cpp      | 23 ++++++++++-------------
 test/medfilt.cpp        | 35 ++++++++++++++---------------------
 test/morph.cpp          | 33 ++++++++++++++-------------------
 test/sobel.cpp          | 10 ++++------
 test/stdev.cpp          | 14 ++++----------
 test/susan.cpp          | 29 ++++++++++++-----------------
 test/ycbcr_rgb.cpp      | 14 ++++----------
 16 files changed, 111 insertions(+), 172 deletions(-)

diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index cde330dca4..2362e1c134 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -52,13 +52,13 @@ void bilateralTest(string pTestFile)
 
         ASSERT_EQ(AF_SUCCESS, af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
 
-        T * outData = new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        std::vector<T> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        T * goldData= new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+        std::vector<T> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 0.02f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -103,25 +103,23 @@ void bilateralDataTest(string pTestFile)
     af::dim4 dims      = numDims[0];
     af_array outArray  = 0;
     af_array inArray   = 0;
-    outType *outData;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_bilateral(&outArray, inArray, 2.25f, 25.56f, false));
 
-    outData = new outType[dims.elements()];
+    std::vector<outType> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<outType> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
-        ASSERT_EQ(true, compareArraysRMSD(nElems, &currGoldBar.front(), outData, 0.02f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, &currGoldBar.front(), outData.data(), 0.02f));
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
@@ -171,17 +169,14 @@ TEST(Bilateral, CPP)
     array a(dims, &(in[0].front()));
     array b = af::bilateral(a, 2.25f, 25.56f, false);
 
-    float *outData = new float[dims.elements()];
-    b.host(outData);
+    std::vector<float> outData(dims.elements());
+    b.host(outData.data());
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
-        ASSERT_EQ(true, compareArraysRMSD(nElems, &currGoldBar.front(), outData, 0.02f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, currGoldBar.data(), outData.data(), 0.02f));
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 
diff --git a/test/canny.cpp b/test/canny.cpp
index 16dd5d5171..19e159edaa 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -51,9 +51,9 @@ void cannyTest(string pTestFile)
 
     ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, sArray, AF_MANUAL_THRESHOLD, 0.4147f, 0.8454f, 3, true));
 
-    char *outData = new char[sDims.elements()];
+    std::vector<char> outData(sDims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<char> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -62,7 +62,6 @@ void cannyTest(string pTestFile)
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(sArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
@@ -110,13 +109,13 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
 
         ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, inArray, AF_AUTO_OTSU_THRESHOLD, 0.08, 0.32, 3, false));
 
-        char * outData = new char[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        std::vector<char> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        char * goldData= new char[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+        std::vector<char> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 1.0e-3));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 1.0e-3));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
diff --git a/test/covariance.cpp b/test/covariance.cpp
index 933f617612..224003dfb7 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -96,17 +96,14 @@ void covTest(string pFileName, bool isbiased=false)
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
     size_t nElems    = currGoldBar.size();
-    outType *outData = new outType[nElems];
+    std::vector<outType> outData(nElems);
 
-    c.host((void*)outData);
+    c.host((void*)outData.data());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
         ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 TYPED_TEST(Covariance, Vector)
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 3e950c3d46..70cecf7ae3 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -59,15 +59,14 @@ void testGeneralAssignOneArray(string pTestFile, const dim_t ndims, af_index_t*
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
+    std::vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray));
@@ -124,15 +123,14 @@ TEST(GeneralAssign, SSSS)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
+    std::vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -192,18 +190,21 @@ TEST(GeneralAssign, AAAA)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
+    std::vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray0));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray1));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray2));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray3));
 }
 
 
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index 7fcd3fd46e..7b4638e25b 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -54,15 +54,14 @@ void testGeneralIndexOneArray(string pTestFile, const dim_t ndims, af_index_t* i
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
+    std::vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -141,15 +140,14 @@ TEST(GeneralIndex, AASS)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
+    std::vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray0));
     ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray1));
diff --git a/test/histogram.cpp b/test/histogram.cpp
index c83ba0464f..992e998e29 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -48,14 +48,14 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
 
     af_array outArray   = 0;
     af_array inArray    = 0;
-    outType *outData;
+
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<inType>::af_type));
 
     ASSERT_EQ(AF_SUCCESS,af_histogram(&outArray,inArray,nbins,minval,maxval));
 
-    outData = new outType[dims.elements()];
+    std::vector<outType> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<outType> currGoldBar = tests[testIter];
@@ -66,7 +66,6 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
@@ -118,8 +117,8 @@ TEST(Histogram, CPP)
     af::array output = histogram(input, nbins, minval, maxval);
 //! [hist_nominmax]
 
-    uint *outData = new uint[output.elements()];
-    output.host((void*)outData);
+    std::vector<uint> outData(output.elements());
+    output.host((void*)outData.data());
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<uint> currGoldBar = tests[testIter];
@@ -128,9 +127,6 @@ TEST(Histogram, CPP)
             ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< std::endl;
         }
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 /////////////////////////////////// Documentation Snippets //////////////////////////////////
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index 5e221c2e6d..cd057b0152 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -45,17 +45,14 @@ TEST(hsv2rgb, CPP)
     af::array input(dims, &(in[0].front()));
     af::array output = af::hsv2rgb(input);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
+    std::vector<float> outData(dims.elements());
+    output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 TEST(rgb2hsv, CPP)
@@ -70,15 +67,12 @@ TEST(rgb2hsv, CPP)
     af::array input(dims, &(in[0].front()));
     af::array output = af::rgb2hsv(input);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
+    std::vector<float> outData(dims.elements());
+    output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
diff --git a/test/match_template.cpp b/test/match_template.cpp
index adebea4ac1..cfa23e7847 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -57,9 +57,9 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType)
 
     ASSERT_EQ(AF_SUCCESS, af_match_template(&outArray, sArray, tArray, pMatchType));
 
-    outType *outData = new outType[sDims.elements()];
+    std::vector<outType> outData(sDims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<outType> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -68,7 +68,6 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType)
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(sArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(tArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
diff --git a/test/mean.cpp b/test/mean.cpp
index e3f7031747..dd370f4e93 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -92,9 +92,9 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
 
         af::array outArray = af::mean(inArray, dim);
 
-        outType *outData = new outType[dims.elements()];
+        std::vector<outType> outData(dims.elements());
 
-        outArray.host((void*)outData);
+        outArray.host((void*)outData.data());
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
@@ -102,9 +102,6 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
             ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
             ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
         }
-
-        // cleanup
-        delete[] outData;
     } else {
         af::dim4 dims  = numDims[0];
         af::dim4 wdims = numDims[1];
@@ -116,9 +113,9 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
 
         af::array outArray = af::mean(inArray, wtsArray, dim);
 
-        outType *outData = new outType[dims.elements()];
+        std::vector<outType> outData(dims.elements());
 
-        outArray.host((void*)outData);
+        outArray.host((void*)outData.data());
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
@@ -126,9 +123,6 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
             ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
             ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
         }
-
-        // cleanup
-        delete[] outData;
     }
 }
 
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index a35ca288d9..c9fe41af29 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -84,13 +84,13 @@ void meanshiftTest(string pTestFile)
 
         ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, 2.25f, 25.56f, 5, isColor));
 
-        T * outData = new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        std::vector<T> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        T * goldData= new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+        std::vector<T> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 0.07f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.07f));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
@@ -145,16 +145,13 @@ TEST(Meanshift, Color_CPP)
         dim_t nElems = gold.elements();
         af::array output= af::meanShift(img, 2.25f, 25.56f, 5, true);
 
-        float * outData = new float[nElems];
-        output.host((void*)outData);
+        std::vector<float> outData(nElems);
+        output.host((void*)outData.data());
 
-        float * goldData= new float[nElems];
-        gold.host((void*)goldData);
+        std::vector<float> goldData(nElems);
+        gold.host((void*)goldData.data());
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 0.07f));
-        // cleanup
-        delete[] outData;
-        delete[] goldData;
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.07f));
     }
 }
 
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 1296e22b0c..e41e7a45cb 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -60,9 +60,9 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
 
     ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, pad));
 
-    T *outData = new T[dims.elements()];
+    std::vector<T> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -71,7 +71,6 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
@@ -117,9 +116,9 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
 
     ASSERT_EQ(AF_SUCCESS, af_medfilt1(&outArray, inArray, w_wid, pad));
 
-    T *outData = new T[dims.elements()];
+    std::vector<T> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -128,7 +127,6 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
@@ -186,13 +184,13 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
 
         ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
 
-        T * outData = new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        std::vector<T> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        T * goldData= new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+        std::vector<T> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 0.018f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -224,6 +222,7 @@ void medfiltInputTest(void)
     ASSERT_EQ(true, medfilt1);
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
 
 TYPED_TEST(MedianFilter, InvalidArray)
@@ -360,17 +359,14 @@ TEST(MedianFilter, CPP)
     af::array input(dims, &(in[0].front()));
     af::array output = af::medfilt(input, w_len, w_wid, AF_PAD_SYM);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
+    std::vector<float> outData(dims.elements());
+    output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 TEST(MedianFilter1d, CPP)
@@ -390,17 +386,14 @@ TEST(MedianFilter1d, CPP)
     af::array input(dims, &(in[0].front()));
     af::array output = af::medfilt1(input, w_wid, AF_PAD_SYM);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
+    std::vector<float> outData(dims.elements());
+    output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 
diff --git a/test/morph.cpp b/test/morph.cpp
index 8aa0885cc4..11499b6ca5 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -49,7 +49,6 @@ void morphTest(string pTestFile)
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array maskArray = 0;
-    inType *outData;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
@@ -69,9 +68,9 @@ void morphTest(string pTestFile)
             ASSERT_EQ(AF_SUCCESS, af_erode(&outArray, inArray, maskArray));
     }
 
-    outData = new inType[dims.elements()];
+    std::vector<inType> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<inType> currGoldBar = tests[testIter];
@@ -82,7 +81,6 @@ void morphTest(string pTestFile)
     }
 
     // cleanup
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -159,13 +157,13 @@ void morphImageTest(string pTestFile)
         else
             ASSERT_EQ(AF_SUCCESS, af_erode(&outArray, inArray, maskArray));
 
-        T * outData = new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        std::vector<T> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        T * goldData= new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+        std::vector<T> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 0.018f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
@@ -371,16 +369,13 @@ void cppMorphImageTest(string pTestFile)
         else
             output = erode(img, mask);
 
-        T * outData = new T[nElems];
-        output.host((void*)outData);
+        std::vector<T> outData(nElems);
+        output.host((void*)outData.data());
 
-        T * goldData= new T[nElems];
-        gold.host((void*)goldData);
+        std::vector<T> goldData(nElems);
+        gold.host((void*)goldData.data());
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData, outData, 0.018f));
-        //cleanup
-        delete[] outData;
-        delete[] goldData;
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
     }
 }
 
@@ -452,8 +447,8 @@ TEST(Morph, EdgeIssue1564)
     array dilated = dilate(input.as(b8), mask.as(b8));
 
     size_t nElems = dilated.elements();
-    char * outData = new char[nElems];
-    dilated.host((void*)outData);
+    std::vector<char> outData(nElems);
+    dilated.host((void*)outData.data());
 
     for (size_t i=0; i<nElems; ++i) {
         ASSERT_EQ((int)outData[i], goldData[i]);
diff --git a/test/sobel.cpp b/test/sobel.cpp
index d3f4528af0..69a6250a8c 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -61,11 +61,11 @@ void testSobelDerivatives(string pTestFile)
 
     ASSERT_EQ(AF_SUCCESS, af_sobel_operator(&dxArray, &dyArray, inArray, 3));
 
-    To *dxData = new To[dims.elements()];
-    To *dyData = new To[dims.elements()];
+    std::vector<To> dxData(dims.elements());
+    std::vector<To> dyData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dxData, dxArray));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dyData, dyArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dxData.data(), dxArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dyData.data(), dyArray));
 
     vector<To> currDXGoldBar = tests[0];
     vector<To> currDYGoldBar = tests[1];
@@ -79,8 +79,6 @@ void testSobelDerivatives(string pTestFile)
     }
 
     // cleanup
-    delete[] dxData;
-    delete[] dyData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(dxArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(dyArray));
diff --git a/test/stdev.cpp b/test/stdev.cpp
index ff70948752..c8f61e4364 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -93,17 +93,14 @@ void stdevDimTest(string pFileName, dim_t dim=-1)
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
     size_t nElems    = currGoldBar.size();
-    outType *outData = new outType[nElems];
+    std::vector<outType> outData(nElems);
 
-    b.host((void*)outData);
+    b.host((void*)outData.data());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
         ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 TYPED_TEST(StandardDev, Dim0)
@@ -160,17 +157,14 @@ void stdevDimIndexTest(string pFileName, dim_t dim=-1)
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
     size_t nElems    = currGoldBar.size();
-    outType *outData = new outType[nElems];
+    std::vector<outType> outData(nElems);
 
-    c.host((void*)outData);
+    c.host((void*)outData.data());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
         ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 TYPED_TEST(StandardDev, IndexedArrayDim0)
diff --git a/test/susan.cpp b/test/susan.cpp
index 259a319ce7..ee846d1c4d 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -81,19 +81,20 @@ void susanTest(string pTestFile, float t, float g)
 
         af::features out = af::susan(in, 3, t, g, 0.05f, 3);
 
-        float * outX           = new float[gold[0].size()];
-        float * outY           = new float[gold[1].size()];
-        float * outScore       = new float[gold[2].size()];
-        float * outOrientation = new float[gold[3].size()];
-        float * outSize        = new float[gold[4].size()];
-        out.getX().host(outX);
-        out.getY().host(outY);
-        out.getScore().host(outScore);
-        out.getOrientation().host(outOrientation);
-        out.getSize().host(outSize);
+        std::vector<float> outX          (gold[0].size());
+        std::vector<float> outY          (gold[1].size());
+        std::vector<float> outScore      (gold[2].size());
+        std::vector<float> outOrientation(gold[3].size());
+        std::vector<float> outSize       (gold[4].size());
+        out.getX().host(outX.data());
+        out.getY().host(outY.data());
+        out.getScore().host(outScore.data());
+        out.getOrientation().host(outOrientation.data());
+        out.getSize().host(outSize.data());
 
         vector<feat_t> out_feat;
-        array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize, out.getNumFeatures());
+        array_to_feat(out_feat, outX.data(), outY.data(), outScore.data(),
+                      outOrientation.data(), outSize.data(), out.getNumFeatures());
 
         vector<feat_t> gold_feat;
         array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(), &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
@@ -108,12 +109,6 @@ void susanTest(string pTestFile, float t, float g)
             ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
             ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
         }
-
-        delete [] outX;
-        delete [] outY;
-        delete [] outScore;
-        delete [] outOrientation;
-        delete [] outSize;
     }
 }
 
diff --git a/test/ycbcr_rgb.cpp b/test/ycbcr_rgb.cpp
index f2c7aacf79..4f4d7cec73 100644
--- a/test/ycbcr_rgb.cpp
+++ b/test/ycbcr_rgb.cpp
@@ -45,17 +45,14 @@ TEST(ycbcr2rgb, CPP)
     af::array input(dims, &(in[0].front()));
     af::array output = af::ycbcr2rgb(input);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
+    std::vector<float> outData(dims.elements());
+    output.host(outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }
 
 TEST(rgb2ycbcr, CPP)
@@ -70,15 +67,12 @@ TEST(rgb2ycbcr, CPP)
     af::array input(dims, &(in[0].front()));
     af::array output = af::rgb2ycbcr(input);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
+    std::vector<float> outData(dims.elements());
+    output.host(outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }

From 657c1eae63ae3ead35bb8440113fb8e76bfd0002 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 26 Apr 2017 18:58:32 +0530
Subject: [PATCH 1169/2677] Fix memory scope tests in threading unit tests

Disable memory management stress test for CPU backend.
---
 test/threading.cpp | 77 +++++++++++++++++-----------------------------
 1 file changed, 28 insertions(+), 49 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 60d088e79d..40f82414eb 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -14,6 +14,7 @@
 #include <thread>
 #include <complex>
 #include <chrono>
+#include <condition_variable>
 #include <thread>
 #include <iterator>
 #include <vector>
@@ -198,20 +199,34 @@ static void cleanSlate()
     ASSERT_EQ(af::getMemStepSize(), step_bytes);
 }
 
+std::condition_variable cv;
+std::mutex cvMutex;
+size_t counter = THREAD_COUNT;
+
 void doubleAllocationTest()
 {
     af::setDevice(0);
 
+    //Block until all threads are launched and the
+    //counter variable hits zero
+    std::unique_lock<std::mutex> lock(cvMutex);
+    //Check for current thread launch counter value
+    //if reached zero, notify others to continue
+    //otherwise block current thread
+    if (--counter==0)
+        cv.notify_all();
+    else
+        cv.wait(lock, [] {return counter==0;});
+    lock.unlock();
+
     af::array a = randu(5, 5);
 
-    for (int i = 0; i < 100; ++i)
-    {
-        a = randu(5, 5);
-        std::this_thread::sleep_for(std::chrono::milliseconds(10));
-    }
+    // Wait for for other threads to hit randu call
+    // while this thread's variable a is still in scope.
+    std::this_thread::sleep_for(std::chrono::seconds(2));
 }
 
-TEST(Threading, MemoryManagement_Double_Alloc)
+TEST(Threading, MemoryManagementScope)
 {
     cleanSlate(); // Clean up everything done so far
 
@@ -232,48 +247,8 @@ TEST(Threading, MemoryManagement_Double_Alloc)
 
     ASSERT_EQ( lock_buffers,     0u);
     ASSERT_EQ(  lock_bytes,      0u);
-    ASSERT_LE(alloc_buffers,    64u);
-    ASSERT_GT(alloc_buffers,    32u);
-    ASSERT_LE(  alloc_bytes, 65536u);
-    ASSERT_GT(  alloc_bytes, 32768u);
-}
-
-void singleAllocationTest()
-{
-    af::setDevice(0);
-
-    for (int i = 0; i < 100; ++i)
-    {
-        af::array a = af::randu(5, 5);
-        std::this_thread::sleep_for(std::chrono::milliseconds(10));
-    }
-}
-
-TEST(Threading, MemoryManagement_Single_Alloc)
-{
-    cleanSlate(); // Clean up everything done so far
-
-    vector<std::thread> tests;
-
-    for (int t=0; t<THREAD_COUNT; ++t)
-        tests.emplace_back(singleAllocationTest);
-
-    for (int t=0; t<THREAD_COUNT; ++t)
-        if (tests[t].joinable())
-            tests[t].join();
-
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-            &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ( lock_buffers,     0u);
-    ASSERT_EQ(  lock_bytes,      0u);
-    ASSERT_LE(alloc_buffers,    32u);
-    ASSERT_GT(alloc_buffers,     0u);
-    ASSERT_LE(  alloc_bytes, 32768u);
-    ASSERT_GT(  alloc_bytes,     0u);
+    ASSERT_EQ(alloc_buffers,    32u);
+    ASSERT_EQ(  alloc_bytes, 32768u);
 }
 
 void jitAllocationTest()
@@ -703,7 +678,10 @@ TEST(Threading, Sparse)
             tests[testId].join();
 }
 
-TEST(Threading, MemoryManagerStressTest) {
+//FIXME Disable for CPU backend
+#if !defined(AF_CPU)
+TEST(Threading, MemoryManagerStressTest)
+{
   vector<std::thread> threads;
   for (int i = 0; i < THREAD_COUNT; i++) {
     threads.emplace_back([] {
@@ -734,3 +712,4 @@ TEST(Threading, MemoryManagerStressTest) {
     t.join();
   }
 }
+#endif

From 24a44da9b3e7f8cdb9c5d4730b57ea431053317e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sun, 23 Apr 2017 16:33:25 -0700
Subject: [PATCH 1170/2677] Enabling a fast pass option for reorder

---
 src/api/c/reorder.cpp | 45 +++++++++++++++++++++++++++++++++----------
 1 file changed, 35 insertions(+), 10 deletions(-)

diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index b0d7f54137..b2f2a127f9 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -14,14 +14,47 @@
 #include <backend.hpp>
 #include <ArrayInfo.hpp>
 #include <reorder.hpp>
+#include <transpose.hpp>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array reorder(const af_array in, const af::dim4 &rdims)
+static inline af_array reorder(const af_array in, const af::dim4 &rdims0)
 {
-    return getHandle(reorder<T>(getArray<T>(in), rdims));
+    Array<T> In = getArray<T>(in);
+    dim4 rdims = rdims0;
+
+    if (rdims[0] == 1 && rdims[1] == 0) {
+        In = transpose(In, false);
+        std::swap(rdims[0], rdims[1]);
+    }
+    const dim4 idims = In.dims();
+    const dim4 istrides = In.strides();
+
+    af_array out;
+    if (rdims[0] == 0 &&
+        rdims[1] == 1 &&
+        rdims[2] == 2 &&
+        rdims[3] == 3) {
+        Array<T> Out = In;
+        out = getHandle(Out);
+    } else if (rdims[0] == 0) {
+        dim4 odims = dim4(1,1,1,1);
+        dim4 ostrides = dim4(1,1,1,1);
+        for(int i = 0; i < 4; i++) {
+            odims[i] = idims[rdims[i]];
+            ostrides[i] = istrides[rdims[i]];
+        }
+        Array<T> Out = In;
+        Out.modDims(odims);
+        Out.modStrides(ostrides);
+        out = getHandle(Out);
+    } else {
+        Array<T> Out = reorder<T>(In, rdims);
+        out = getHandle(Out);
+    }
+    return out;
 }
 
 af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims)
@@ -54,14 +87,6 @@ af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims)
             allDims[rdims[i]] = -1;
         }
 
-        // If reorder is a (batched) transpose, then call transpose
-        if(info.dims()[3] == 1) {
-            if(rdims[0] == 1 && rdims[1] == 0 &&
-               rdims[2] == 2 && rdims[3] == 3) {
-                return af_transpose(out, in, false);
-            }
-        }
-
         af_array output;
 
         switch(type) {

From a812ccee3cb139613142daf1757b0abd05f1e03e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Thu, 27 Apr 2017 15:45:17 -0700
Subject: [PATCH 1171/2677] Fixing issues with strides along last dimension for
 CPU backend

---
 src/backend/cpu/kernel/bilateral.hpp |  7 ++-
 src/backend/cpu/kernel/histogram.hpp |  4 +-
 src/backend/cpu/kernel/meanshift.hpp |  5 +-
 src/backend/cpu/kernel/medfilt.hpp   |  8 ++-
 src/backend/cpu/kernel/morph.hpp     |  6 +-
 src/backend/cpu/kernel/sobel.hpp     | 83 ++++++++++++++--------------
 6 files changed, 59 insertions(+), 54 deletions(-)

diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index 0b7b2d56af..9fa0902625 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -24,9 +24,6 @@ void bilateral(Array<OutT> out, Array<InT> const in, float const s_sigma, float
     af::dim4 const istrides = in.strides();
     af::dim4 const ostrides = out.strides();
 
-          OutT *outData = out.get();
-    InT const * inData  = in.get();
-
     // clamp spatical and chromatic sigma's
     float space_       = std::min(11.5f, std::max(s_sigma, 0.f));
     float color_       = std::max(c_sigma, 0.f);
@@ -35,6 +32,10 @@ void bilateral(Array<OutT> out, Array<InT> const in, float const s_sigma, float
     float const cvar   = color_*color_;
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
+
+        OutT *outData = out.get() + b3 * ostrides[3];
+        InT const * inData  = in.get() + b3 * istrides[3];
+
         // b3 for loop handles following batch configurations
         //  - gfor
         //  - input based batch
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 64ee72658a..668ada6458 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -26,10 +26,10 @@ void histogram(Array<OutT> out, Array<InT> const in,
     dim4 const oStrides  = out.strides();
     dim_t const nElems   = inDims[0]*inDims[1];
 
-    OutT *outData    = out.get();
-    const InT* inData= in.get();
 
     for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
+        OutT *outData    = out.get() + b3 * oStrides[3];
+        const InT* inData= in.get() + b3 * iStrides[3];
         for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
             for(dim_t i=0; i<nElems; i++) {
                 int idx = IsLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 7008995a32..4036bf61e7 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -37,10 +37,11 @@ void meanShift(Array<T> out, const Array<T> in, const float s_sigma,
     std::vector<float> centers(channels);
     std::vector<float> tmpclrs(channels);
 
-    T *outData       = out.get();
-    const T * inData = in.get();
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
+        T *outData       = out.get() + b3 * ostrides[3];
+        const T * inData = in.get() + b3 * istrides[3];
+
         for(dim_t b2=0; b2<bCount; ++b2) {
 
             for(dim_t j=0; j<dims[1]; ++j) {
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index a11e1f0b6c..e8f34fb173 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -27,11 +27,11 @@ void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
     std::vector<T> wind_vals;
     wind_vals.reserve(w_wid);
 
-    T const * in_ptr = in.get();
-    T * out_ptr = out.get();
-
     for(int b3=0; b3<(int)dims[3]; b3++) {
 
+        T const * in_ptr = in.get() + b3 * istrides[3];
+        T * out_ptr = out.get() + b3 * ostrides[3];
+
         for(int b2=0; b2<(int)dims[2]; b2++) {
 
             for(int col=0; col<(int)dims[1]; col++) {
@@ -100,6 +100,8 @@ void medfilt2(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
     T * out_ptr = out.get();
 
     for(int b3=0; b3<(int)dims[3]; b3++) {
+        T const * in_ptr = in.get() + b3 * istrides[3];
+        T * out_ptr = out.get() + b3 * ostrides[3];
 
         for(int b2=0; b2<(int)dims[2]; b2++) {
 
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 4cec3b363a..0789928aaf 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -26,8 +26,6 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
     const af::dim4 fstrides = mask.strides();
     const af::dim4 dims     = in.dims();
     const af::dim4 window   = mask.dims();
-    T* outData          = out.get();
-    const T*   inData   = in.get();
     const T*   filter   = mask.get();
     const dim_t R0      = window[0]/2;
     const dim_t R1      = window[1]/2;
@@ -35,6 +33,10 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
     T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
+
+        T* outData          = out.get() + b3 * ostrides[3];
+        const T*   inData   = in.get() + b3 * istrides[3];
+
         for(dim_t b2=0; b2<dims[2]; ++b2) {
             // either channels or batch is handled by outer most loop
             for(dim_t j=0; j<dims[1]; ++j) {
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index e59f742c85..44e38033a4 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -20,64 +20,63 @@ template<typename Ti, typename To, bool isDX>
 void derivative(Array<To> output, const Array<Ti> input)
 {
     const af::dim4 dims    = input.dims();
-    const af::dim4 strides = input.strides();
-          To* optr     = output.get();
-    const Ti* iptr     = input.get();
+    const af::dim4 istrides = input.strides();
+    const af::dim4 ostrides = output.strides();
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
-    for(dim_t b2=0; b2<dims[2]; ++b2) {
+        To* optr     = output.get() + b3 * ostrides[3];
+        const Ti* iptr     = input.get() + b3 * istrides[3];
+        for(dim_t b2=0; b2<dims[2]; ++b2) {
 
-        for(dim_t j=0; j<dims[1]; ++j) {
+            for(dim_t j=0; j<dims[1]; ++j) {
 
-            int joff  = j;
-            int _joff = j-1;
-            int joff_ = j+1;
-            int joffset = j*strides[1];
+                int joff  = j;
+                int _joff = j-1;
+                int joff_ = j+1;
+                int joffset = j*ostrides[1];
 
-            for(dim_t i=0; i<dims[0]; ++i) {
+                for(dim_t i=0; i<dims[0]; ++i) {
 
-                To accum = To(0);
+                    To accum = To(0);
 
-                int  ioff = i;
-                int _ioff = i-1;
-                int ioff_ = i+1;
+                    int  ioff = i;
+                    int _ioff = i-1;
+                    int ioff_ = i+1;
 
-                To NW = (_ioff>=0 && _joff>=0) ?
-                        iptr[_joff*strides[1]+_ioff*strides[0]] : 0;
-                To SW = (ioff_<(int)dims[0] && _joff>=0) ?
-                        iptr[_joff*strides[1]+ioff_*strides[0]] : 0;
-                To NE = (_ioff>=0 && joff_<(int)dims[1]) ?
-                        iptr[joff_*strides[1]+_ioff*strides[0]] : 0;
-                To SE = (ioff_<(int)dims[0] && joff_<(int)dims[1]) ?
-                        iptr[joff_*strides[1]+ioff_*strides[0]] : 0;
+                    To NW = (_ioff>=0 && _joff>=0) ?
+                        iptr[_joff*istrides[1]+_ioff*istrides[0]] : 0;
+                    To SW = (ioff_<(int)dims[0] && _joff>=0) ?
+                        iptr[_joff*istrides[1]+ioff_*istrides[0]] : 0;
+                    To NE = (_ioff>=0 && joff_<(int)dims[1]) ?
+                        iptr[joff_*istrides[1]+_ioff*istrides[0]] : 0;
+                    To SE = (ioff_<(int)dims[0] && joff_<(int)dims[1]) ?
+                                   iptr[joff_*istrides[1]+ioff_*istrides[0]] : 0;
 
-                if (isDX) {
-                    To W  = _joff>=0 ?
-                            iptr[_joff*strides[1]+ioff*strides[0]] : 0;
+                    if (isDX) {
+                        To W  = _joff>=0 ?
+                                   iptr[_joff*istrides[1]+ioff*istrides[0]] : 0;
 
-                    To E  = joff_<(int)dims[1] ?
-                            iptr[joff_*strides[1]+ioff*strides[0]] : 0;
+                        To E  = joff_<(int)dims[1] ?
+                                      iptr[joff_*istrides[1]+ioff*istrides[0]] : 0;
 
-                    accum = NW+SW - (NE+SE) + 2*(W-E);
-                } else {
-                    To N  = _ioff>=0 ?
-                            iptr[joff*strides[1]+_ioff*strides[0]] : 0;
+                        accum = NW+SW - (NE+SE) + 2*(W-E);
+                    } else {
+                        To N  = _ioff>=0 ?
+                                   iptr[joff*istrides[1]+_ioff*istrides[0]] : 0;
 
-                    To S  = ioff_<(int)dims[0] ?
-                            iptr[joff*strides[1]+ioff_*strides[0]] : 0;
+                        To S  = ioff_<(int)dims[0] ?
+                                      iptr[joff*istrides[1]+ioff_*istrides[0]] : 0;
 
-                    accum = NW+NE - (SW+SE) + 2*(N-S);
-                }
+                        accum = NW+NE - (SW+SE) + 2*(N-S);
+                    }
 
-                optr[joffset+i*strides[0]] = accum;
+                    optr[joffset+i*ostrides[0]] = accum;
+                }
             }
-        }
 
-        optr += strides[2];
-        iptr += strides[2];
-    }
-    optr += strides[3];
-    iptr += strides[3];
+            optr += ostrides[2];
+            iptr += istrides[2];
+        }
     }
 }
 

From 9154174bc2861c17e49f9f50092ff72d7277f3b5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 29 Apr 2017 17:36:55 -0400
Subject: [PATCH 1172/2677] Make sparse_common function static. c++11 flag for
 single file test

---
 test/CMakeLists.txt    |  1 +
 test/sparse_common.hpp | 14 +++++++-------
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 031af068a3..38d5cc7f3d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -75,6 +75,7 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
       SET(TEST_NAME_BASIC test_basic_${BACKEND})
       ADD_EXECUTABLE(${TEST_NAME} ${CPP_FILES})
       ADD_EXECUTABLE(${TEST_NAME_BASIC} basic_c.c)
+      SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_OPTIONS -std=c++11)
 
       TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE ${AFLIBNAME}
         PRIVATE ${THREAD_LIB_FLAG}
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index e8a8efc32e..34cfd12ae3 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -28,7 +28,7 @@ using af::cdouble;
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 
-template<typename T>
+template<typename T> static
 af::array makeSparse(af::array A, int factor)
 {
     A = floor(A * 1000);
@@ -62,12 +62,12 @@ af::array makeSparse<cdouble>(af::array A, int factor)
     return A;
 }
 
-double calc_norm(af::array lhs, af::array rhs)
+static double calc_norm(af::array lhs, af::array rhs)
 {
     return af::max<double>(af::abs(lhs - rhs) / (af::abs(lhs) + af::abs(rhs) + 1E-5));
 }
 
-template<typename T>
+template<typename T> static
 void sparseTester(const int m, const int n, const int k, int factor, double eps,
                   int targetDevice=-1)
 {
@@ -102,7 +102,7 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps,
     ASSERT_NEAR(0, calc_norm(imag(dRes1), imag(sRes1)), eps);
 }
 
-template<typename T>
+template<typename T> static
 void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps,
                            int targetDevice=-1)
 {
@@ -142,7 +142,7 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
     ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
 }
 
-template<typename T>
+template<typename T> static
 void convertCSR(const int M, const int N, const float ratio, int targetDevice=-1)
 {
     if (targetDevice>=0)
@@ -164,7 +164,7 @@ void convertCSR(const int M, const int N, const float ratio, int targetDevice=-1
 
 // This test essentially verifies that the sparse structures have the correct
 // dimensions and indices using a very basic test
-template<af_storage stype>
+template<af_storage stype> static
 void createFunction()
 {
     af::array in = af::sparse(af::identity(3, 3), stype);
@@ -181,7 +181,7 @@ void createFunction()
     ASSERT_EQ(0, af::max<int   >(colIdx - af::range(af::dim4(colIdx.elements()), 0, s32)));
 }
 
-template<typename Ti, typename To>
+template<typename Ti, typename To> static
 void sparseCastTester(const int m, const int n, int factor)
 {
     if (noDoubleTests<Ti>()) return;

From b86e5dff00f9039762078413ed293fe13b0e459d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 29 Apr 2017 20:01:46 -0400
Subject: [PATCH 1173/2677] Disable memory stress test

---
 test/threading.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 40f82414eb..cf82960f3c 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -678,9 +678,7 @@ TEST(Threading, Sparse)
             tests[testId].join();
 }
 
-//FIXME Disable for CPU backend
-#if !defined(AF_CPU)
-TEST(Threading, MemoryManagerStressTest)
+TEST(Threading, DISABLED_MemoryManagerStressTest)
 {
   vector<std::thread> threads;
   for (int i = 0; i < THREAD_COUNT; i++) {
@@ -712,4 +710,3 @@ TEST(Threading, MemoryManagerStressTest)
     t.join();
   }
 }
-#endif

From 5f53f6bd82e198f9bd92e1814a980e0b1bb69cee Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 29 Apr 2017 22:59:17 -0400
Subject: [PATCH 1174/2677] Added missing headers

---
 src/backend/common/MemoryManager.hpp | 12 +++++++-----
 src/backend/opencl/platform.cpp      |  5 +++--
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 219385ffd2..b2138da488 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -9,15 +9,17 @@
 
 #pragma once
 
-#include <vector>
-#include <mutex>
-#include <unordered_map>
-#include <iostream>
-#include <iomanip>
 #include <err_common.hpp>
 #include <dispatch.hpp>
 #include <util.hpp>
 
+#include <algorithm>
+#include <iomanip>
+#include <iostream>
+#include <mutex>
+#include <unordered_map>
+#include <vector>
+
 namespace common
 {
 typedef std::recursive_mutex mutex_t;
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 652abc2c93..b88c100927 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -36,13 +36,14 @@
 
 #include <algorithm>
 #include <cctype>
+#include <cstring>
 #include <functional>
 #include <map>
 #include <mutex>
-#include <string>
 #include <sstream>
 #include <stdexcept>
-#include <cstring>
+#include <string>
+#include <thread>
 #include <utility>
 #include <vector>
 

From 9ba6a9657d1bf064b08dea570401bc500f4bf6a9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 30 Apr 2017 12:04:22 -0400
Subject: [PATCH 1175/2677] Fix narrowing error/warning in canny

---
 src/api/c/canny.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index b32dfee6a1..b6b4dbe93c 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -173,7 +173,7 @@ template<typename T>
 af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold ct,
                      const float t2, const unsigned sw, const bool isf)
 {
-    static const std::vector<float> v = {-0.11021, -0.23691, -0.30576, -0.23691, -0.11021};
+    static const std::vector<float> v = {-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
     Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v.data());
     Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v.data());
 

From 3e8187420fd87f7195ce592478fd2bc3f0479881 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 30 Apr 2017 12:04:22 -0400
Subject: [PATCH 1176/2677] Fix narrowing error/warning in canny

---
 src/api/c/canny.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index b32dfee6a1..b6b4dbe93c 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -173,7 +173,7 @@ template<typename T>
 af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold ct,
                      const float t2, const unsigned sw, const bool isf)
 {
-    static const std::vector<float> v = {-0.11021, -0.23691, -0.30576, -0.23691, -0.11021};
+    static const std::vector<float> v = {-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
     Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v.data());
     Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v.data());
 

From 77edd69dd88718030f667e81498f2dcc08562269 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 1 May 2017 13:12:21 -0400
Subject: [PATCH 1177/2677] Fix headers in the CUDA backend

---
 src/backend/cuda/jit.cpp      |  1 +
 src/backend/cuda/platform.cpp | 17 ++++++++++-------
 2 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 776c5c8f28..8341731289 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -46,6 +46,7 @@
 #include <map>
 #include <memory>
 #include <stdexcept>
+#include <thread>
 #include <vector>
 
 namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 89cc827758..48a32bf96e 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -14,17 +14,20 @@
 #include <util.hpp>
 #include <version.hpp>
 #include <driver.h>
-#include <vector>
-#include <string>
-#include <algorithm>
-#include <sstream>
-#include <stdexcept>
-#include <cstdio>
-#include <cstring>
 #include <err_cuda.hpp>
 #include <util.hpp>
 #include <host_memory.hpp>
 
+#include <algorithm>
+#include <cstdio>
+#include <cstring>
+#include <mutex>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <thread>
+#include <vector>
+
 using namespace std;
 
 namespace cuda

From 913e1abdf385c3afb950898c38ee4286fc21343f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 1 May 2017 15:31:24 -0400
Subject: [PATCH 1178/2677] Remove static storage qualifier from thread_local
 variables

---
 src/backend/cuda/jit.cpp        |  2 +-
 src/backend/cuda/platform.cpp   | 18 +++++++++---------
 src/backend/opencl/platform.cpp |  6 +++---
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 8341731289..097bc120cf 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -562,7 +562,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
 {
     typedef std::map<std::string, kc_entry_t> kc_t;
 
-    thread_local static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
+    thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
     string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear);
     int device      = getActiveDeviceId();
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 48a32bf96e..b80d288f91 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -285,7 +285,7 @@ unsigned getMaxJitSize()
 
 int& tlocalActiveDeviceId()
 {
-    thread_local static int activeDeviceId = 0;
+    thread_local int activeDeviceId = 0;
 
     return activeDeviceId;
 }
@@ -432,15 +432,15 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    thread_local static PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
+    thread_local PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
 
     return cufftManagers[getActiveDeviceId()];
 }
 
 BlasHandle blasHandle()
 {
-    thread_local static std::unique_ptr<cublasHandle> cublasHandles[DeviceManager::MAX_DEVICES];
-    thread_local static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local std::unique_ptr<cublasHandle> cublasHandles[DeviceManager::MAX_DEVICES];
+    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
@@ -453,8 +453,8 @@ BlasHandle blasHandle()
 
 SolveHandle solverDnHandle()
 {
-    thread_local static std::unique_ptr<cusolverDnHandle> cusolverHandles[DeviceManager::MAX_DEVICES];
-    thread_local static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local std::unique_ptr<cusolverDnHandle> cusolverHandles[DeviceManager::MAX_DEVICES];
+    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
@@ -480,8 +480,8 @@ SolveHandle solverDnHandle()
 
 SparseHandle sparseHandle()
 {
-    thread_local static std::unique_ptr<cusparseHandle> cusparseHandles[DeviceManager::MAX_DEVICES];
-    thread_local static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local std::unique_ptr<cusparseHandle> cusparseHandles[DeviceManager::MAX_DEVICES];
+    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
@@ -553,7 +553,7 @@ void DeviceManager::sortDevices(sort_mode mode)
 
 int DeviceManager::setActiveDevice(int device, int nId)
 {
-    thread_local static bool retryFlag = true;
+    thread_local bool retryFlag = true;
 
     int numDevices = cuDevices.size();
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index b88c100927..dc7e16b7b4 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -280,7 +280,7 @@ std::pair<unsigned, unsigned>& tlocalActiveDeviceId()
 {
     // First element is active context id
     // Second element is active queue id
-    thread_local static device_id_t activeDeviceId(0, 0);
+    thread_local device_id_t activeDeviceId(0, 0);
 
     return activeDeviceId;
 }
@@ -713,14 +713,14 @@ GraphicsResourceManager& interopManager()
 
 PlanCache& fftManager()
 {
-    thread_local static PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
+    thread_local PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
 
     return clfftManagers[getActiveDeviceId()];
 }
 
 kc_t& getKernelCache(int device)
 {
-    thread_local static kc_t kernelCaches[DeviceManager::MAX_DEVICES];
+    thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
     return kernelCaches[device];
 }

From f129a08918c91333f70a1dd63f686cbda91be9f7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 6 May 2017 20:12:42 -0400
Subject: [PATCH 1179/2677] Workaround for ternary operator bug in vs2015

* Workaround for ternary operator bug in vs2015
* Additional ternary fixes for vs2015
* ternary changes for CPU backend.

* TODO: CPU SIFT and GLOH still failing with optimizations on VS2015
---
 src/backend/cpu/harris.cpp                 | 5 ++---
 src/backend/cpu/kernel/sift_nonfree.hpp    | 5 +++--
 src/backend/cuda/kernel/harris.hpp         | 4 +---
 src/backend/cuda/kernel/sift_nonfree.hpp   | 5 +++--
 src/backend/opencl/kernel/harris.hpp       | 8 ++------
 src/backend/opencl/kernel/sift_nonfree.hpp | 5 +++--
 6 files changed, 14 insertions(+), 18 deletions(-)

diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 48a844d090..8c74a3981d 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -83,9 +83,8 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     kernel::non_maximal<T>(xCorners, yCorners, respCorners, &corners_found,
                    idims[0], idims[1], responses, min_r, border_len, corner_lim);
 
-    const unsigned corners_out = (max_corners > 0) ?
-                                 min(corners_found, max_corners) :
-                                 min(corners_found, corner_lim);
+    const unsigned corners_out = min(corners_found,
+                                    (max_corners > 0) ? max_corners : corner_lim);
     if (corners_out == 0)
         return 0;
 
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index e7ca19175c..bf1f8d7fc6 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -972,8 +972,9 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
     getQueue().sync();
     af::dim4 idims = in.dims();
 
-    const unsigned min_dim = (double_input) ? min(idims[0]*2, idims[1]*2)
-        : min(idims[0], idims[1]);
+    unsigned min_dim = min(idims[0], idims[1]);
+    if (double_input) min_dim *= 2;
+
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
     Array<T> init_img = createInitialImage<T, convAccT>(in, init_sigma, double_input);
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 11e6003158..c2391c666f 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -312,9 +312,7 @@ void harris(unsigned* corners_out,
     memFree(d_responses);
     memFree(d_corners_found);
 
-    *corners_out = (max_corners > 0) ?
-                   min(corners_found, max_corners) :
-                   min(corners_found, corner_lim);
+    *corners_out = min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
 
     if (*corners_out == 0)
         return;
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 54b2a715db..0aa5749b1b 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -1324,8 +1324,9 @@ void sift(unsigned* out_feat,
           const float feature_ratio,
           const bool compute_GLOH)
 {
-    const unsigned min_dim = (double_input) ? min(img.dims[0]*2, img.dims[1]*2)
-                                            : min(img.dims[0], img.dims[1]);
+    unsigned min_dim = min(img.dims[0], img.dims[1]);
+    if (double_input) min_dim *= 2;
+
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
     Param<T> init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 37785db588..bfa12a0eab 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -249,12 +249,8 @@ void harris(unsigned* corners_out,
     bufferFree(d_responses);
     bufferFree(d_corners_found);
 
-    *corners_out = (max_corners > 0) ?
-                    min(corners_found, max_corners) :
-                    min(corners_found, corner_lim);
-
-    if (*corners_out == 0)
-        return;
+    *corners_out = min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
+    if (*corners_out == 0) return;
 
     // Set output Param info
     x_out.info.dims[0] = y_out.info.dims[0] = resp_out.info.dims[0] = *corners_out;
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index af01b60d45..95385f2802 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -450,8 +450,9 @@ void sift(unsigned* out_feat,
             cgKernel[device] = new Kernel(*siftProgs[device], "computeGLOHDescriptor");
         });
 
-    const unsigned min_dim = (double_input) ? min(img.info.dims[0]*2, img.info.dims[1]*2)
-                                            : min(img.info.dims[0], img.info.dims[1]);
+    unsigned min_dim = min(img.info.dims[0], img.info.dims[1]);
+    if (double_input) min_dim *= 2;
+
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
     Param init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);

From 030cc11d67fb642b60e851c6c9c8a63566c84fa5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 8 May 2017 21:25:12 +0530
Subject: [PATCH 1180/2677] fix inconsistent behaviour of replace &
 replace_scalar

replace(output, cond, input) where input is af::array works as
expected(output has input where cond is false). However, if the
input is a scalar parameter, then the output has inverse behaviour.
This change fixes this inconsistency.

Afer this change, output will have values from input when cond is
false irrespective of whether input is af::array or scalar.
---
 src/api/c/replace.cpp | 2 +-
 test/replace.cpp      | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index f4adf9b4c6..720cae4ad0 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -77,7 +77,7 @@ af_err af_replace(af_array a, const af_array cond, const af_array b)
 template<typename T>
 void replace_scalar(af_array a, const af_array cond, const double b)
 {
-    select_scalar<T, true>(getWritableArray<T>(a), getArray<char>(cond), getArray<T>(a), b);
+    select_scalar<T, false>(getWritableArray<T>(a), getArray<char>(cond), getArray<T>(a), b);
 }
 
 af_err af_replace_scalar(af_array a, const af_array cond, const double b)
diff --git a/test/replace.cpp b/test/replace.cpp
index faa5636eb8..c4b8793232 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -93,7 +93,7 @@ void replaceScalarTest(const dim4 &dims)
     cond.host(&hcond[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hcond[i] ? T(b) : ha[i]);
+        ASSERT_EQ(hc[i], hcond[i] ? ha[i] : T(b));
     }
 }
 
@@ -116,7 +116,7 @@ TEST(Replace, NaN)
     a(seq(a.dims(0) / 2), span, span, span) = af::NaN;
     array c = a.copy();
     float b = 0;
-    replace(c, isNaN(c), b);
+    replace(c, !isNaN(c), b);
 
     int num = (int)a.elements();
 
@@ -127,7 +127,7 @@ TEST(Replace, NaN)
     c.host(&hc[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], std::isnan(ha[i]) ? b : ha[i]);
+        ASSERT_EQ(hc[i], ( std::isnan(ha[i]) ? b : ha[i]) );
     }
 }
 

From d7552e34a3ea123d950553466f6d153b997254e0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 14 May 2017 00:37:50 -0400
Subject: [PATCH 1181/2677] Fix GLFW3 library name during install on OSX

The glfw library has been renamed on OSX from glfw3 to glfw.
---
 CMakeModules/osx_install/InstallTool.cmake         | 3 +--
 CMakeModules/osx_install/forge_scripts/postinstall | 4 ++--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/osx_install/InstallTool.cmake b/CMakeModules/osx_install/InstallTool.cmake
index 535e5b0038..dbb1e45c2a 100755
--- a/CMakeModules/osx_install/InstallTool.cmake
+++ b/CMakeModules/osx_install/InstallTool.cmake
@@ -2,8 +2,7 @@
 EXECUTE_PROCESS( COMMAND otool -L ${CMAKE_CURRENT_BINARY_DIR}/package/lib/libforge.dylib
                  COMMAND grep glfw
                  COMMAND cut -d\  -f1
-                 COMMAND xargs -Jglfwlib install_name_tool -change glfwlib /usr/local/lib/libglfw3.dylib ${CMAKE_CURRENT_BINARY_DIR}/package/lib/libforge.dylib
+                 COMMAND xargs -Jglfwlib install_name_tool -change glfwlib /usr/local/lib/libglfw.dylib ${CMAKE_CURRENT_BINARY_DIR}/package/lib/libforge.dylib
                  OUTPUT_FILE /tmp/af.out
                  ERROR_FILE /tmp/af.err
 )
-
diff --git a/CMakeModules/osx_install/forge_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
index 1dd306c848..6ff54687b2 100755
--- a/CMakeModules/osx_install/forge_scripts/postinstall
+++ b/CMakeModules/osx_install/forge_scripts/postinstall
@@ -45,9 +45,9 @@ else
     echo "Homebrew/Versions already present in brew tap." >> $err_file
 fi
 
-GLFW_INSTALLED=$(su $user -c "$brew ls --versions glfw3" | grep "glfw3") || true
+GLFW_INSTALLED=$(su $user -c "$brew ls --versions glfw" | grep "glfw") || true
 if [[ -z "${GLFW_INSTALLED}" ]]; then
-    echo "Installing GLFW3" >> $err_file
+    echo "Installing GLFW" >> $err_file
     echo "-------------------" >> $err_file
     su $user -c "$brew install glfw" >> $err_file 2>&1 || deps_err
     echo "-------------------" >> $err_file

From 2e11fd2cb29dcb4b482df69c36ace078bb1549be Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 14 May 2017 23:23:28 -0400
Subject: [PATCH 1182/2677] Fix approx tests for single precision. Reduce
 memory requirements

---
 test/approx1.cpp | 133 ++++++++++++++++++++++++++---------------------
 test/approx2.cpp | 131 +++++++++++++++++++++++-----------------------
 2 files changed, 142 insertions(+), 122 deletions(-)

diff --git a/test/approx1.cpp b/test/approx1.cpp
index ae4df4f0f7..252ef006a5 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -7,26 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <af/array.h>
 #include <af/complex.h>
-#include <af/signal.h>
+#include <af/dim4.hpp>
 #include <af/index.h>
+#include <af/signal.h>
 #include <af/traits.hpp>
-#include <af/dim4.hpp>
-#include <vector>
-#include <iostream>
+
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
+using af::abs;
+using af::approx1;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::dtype_traits;
+using af::randu;
+using af::span;
+using af::seq;
+using af::sum;
+
+using std::abs;
 using std::cout;
 using std::endl;
-using std::abs;
-using af::cfloat;
-using af::cdouble;
+using std::string;
+using std::vector;
 
 template<typename T>
 class Approx1 : public ::testing::Test
@@ -277,22 +289,21 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 //
 TEST(Approx1, CPP)
 {
-    if (noDoubleTests<float>()) return;
     const unsigned resultIdx = 1;
     const af_interp_type method = AF_INTERP_LINEAR;
-#define BT af::dtype_traits<float>::base_type
-    vector<af::dim4> numDims;
+#define BT dtype_traits<float>::base_type
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<float> > tests;
     readTests<BT, float, float>(string(TEST_DIR"/approx/approx1.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
 
-    af::array input(idims, &(in[0].front()));
-    af::array pos(pdims, &(in[1].front()));
+    array input(idims, &(in[0].front()));
+    array pos(pdims, &(in[1].front()));
 
-    af::array output = approx1(input, pos, method, 0);
+    array output = approx1(input, pos, method, 0);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -314,69 +325,75 @@ TEST(Approx1, CPP)
 
 TEST(Approx1, CPPNearestBatch)
 {
-    if (noDoubleTests<float>()) return;
+    array input = randu(600, 10);
+    array pos   = input.dims(0) * randu(100, 10);
 
-    af::array input = af::randu(600, 10);
-    af::array pos   = input.dims(0) * af::randu(100, 10);
+    array outBatch = approx1(input, pos, AF_INTERP_NEAREST);
 
-    af::array outBatch = af::approx1(input, pos, AF_INTERP_NEAREST);
-
-    af::array outSerial(pos.dims());
-    for(int i = 0; i < pos.dims(1); i++) {
-        outSerial(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_NEAREST);
+    array outSerial(pos.dims());
+    for (int i = 0; i < pos.dims(1); i++) {
+        outSerial(span, i) = approx1(input(span, i),
+                                         pos(span, i),
+                                         AF_INTERP_NEAREST);
     }
 
-    af::array outGFOR(pos.dims());
-    gfor(af::seq i, pos.dims(1)) {
-        outGFOR(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_NEAREST);
+    array outGFOR(pos.dims());
+    gfor(seq i, pos.dims(1)) {
+        outGFOR(span, i) = approx1(input(span, i),
+                                       pos(span, i),
+                                       AF_INTERP_NEAREST);
     }
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
 TEST(Approx1, CPPLinearBatch)
 {
-    if (noDoubleTests<float>()) return;
-
-    af::array input = af::iota(af::dim4(10000, 20), c32);
-    af::array pos   = input.dims(0) * af::randu(50000, 20);
+    array input = iota(dim4(10000, 20), c32);
+    array pos   = input.dims(0) * randu(10000, 20);
 
-    af::array outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+    array outBatch = approx1(input, pos, AF_INTERP_LINEAR);
 
-    af::array outSerial(pos.dims());
-    for(int i = 0; i < pos.dims(1); i++) {
-        outSerial(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_LINEAR);
+    array outSerial(pos.dims());
+    for (int i = 0; i < pos.dims(1); i++) {
+        outSerial(span, i) = approx1(input(span, i),
+                                     pos(span, i),
+                                     AF_INTERP_LINEAR);
     }
 
-    af::array outGFOR(pos.dims());
-    gfor(af::seq i, pos.dims(1)) {
-        outGFOR(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_LINEAR);
+    array outGFOR(pos.dims());
+    gfor(seq i, pos.dims(1)) {
+        outGFOR(span, i) = approx1(input(span, i),
+                                   pos(span, i),
+                                   AF_INTERP_LINEAR);
     }
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
 TEST(Approx1, CPPCubicBatch)
 {
-    if (noDoubleTests<float>()) return;
-
-    af::array input = af::iota(af::dim4(10000, 20), c32);
-    af::array pos   = input.dims(0) * af::randu(50000, 20);
+    array input = iota(dim4(10000, 20), c32);
+    array pos   = input.dims(0) * randu(10000, 20);
 
-    af::array outBatch = af::approx1(input, pos, AF_INTERP_CUBIC_SPLINE);
+    array outBatch = approx1(input, pos, AF_INTERP_CUBIC_SPLINE);
 
-    af::array outSerial(pos.dims());
-    for(int i = 0; i < pos.dims(1); i++) {
-        outSerial(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC_SPLINE);
+    array outSerial(pos.dims());
+    for (int i = 0; i < pos.dims(1); i++) {
+        outSerial(span, i) = approx1(input(span, i),
+                                     pos(span, i),
+                                     AF_INTERP_CUBIC_SPLINE);
     }
 
-    af::array outGFOR(pos.dims());
-    gfor(af::seq i, pos.dims(1)) {
-        outGFOR(af::span, i) = af::approx1(input(af::span, i), pos(af::span, i), AF_INTERP_CUBIC_SPLINE);
+    array outGFOR(pos.dims());
+    gfor(seq i, pos.dims(1)) {
+        outGFOR(span, i) = approx1(input(span, i),
+                                           pos(span, i),
+                                           AF_INTERP_CUBIC_SPLINE);
     }
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index b150840bb3..dd4da6955c 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -7,24 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
-#include <af/signal.h>
-#include <arrayfire.h>
-#include <af/dim4.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/signal.h>
 #include <af/traits.hpp>
-#include <vector>
+
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
-using af::cfloat;
+using af::abs;
+using af::approx2;
+using af::array;
 using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::dtype_traits;
+using af::randu;
+using af::seq;
+using af::span;
+using af::sum;
+
+using std::abs;
+using std::string;
+using std::vector;
 
 template<typename T>
 class Approx2 : public ::testing::Test
@@ -213,22 +222,21 @@ void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 //
 TEST(Approx2, CPP)
 {
-    if (noDoubleTests<float>()) return;
     const unsigned resultIdx = 1;
-#define BT af::dtype_traits<float>::base_type
-    vector<af::dim4> numDims;
+#define BT dtype_traits<float>::base_type
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<float> > tests;
     readTests<BT, float, float>(string(TEST_DIR"/approx/approx2.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
-    af::dim4 qdims = numDims[2];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
+    dim4 qdims = numDims[2];
 
-    af::array input(idims,&(in[0].front()));
-    af::array pos0(pdims,&(in[1].front()));
-    af::array pos1(qdims,&(in[2].front()));
-    af::array output = af::approx2(input, pos0, pos1, AF_INTERP_LINEAR, 0);
+    array input(idims,&(in[0].front()));
+    array pos0(pdims,&(in[1].front()));
+    array pos1(qdims,&(in[2].front()));
+    array output = approx2(input, pos0, pos1, AF_INTERP_LINEAR, 0);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -250,25 +258,24 @@ TEST(Approx2, CPP)
 
 TEST(Approx2Cubic, CPP)
 {
-    if (noDoubleTests<float>()) return;
     const unsigned resultIdx = 0;
-#define BT af::dtype_traits<float>::base_type
-    vector<af::dim4> numDims;
+#define BT dtype_traits<float>::base_type
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<float> > tests;
     readTests<BT, float, float>(string(TEST_DIR"/approx/approx2_cubic.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
-    af::dim4 qdims = numDims[2];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
+    dim4 qdims = numDims[2];
 
-    af::array input(idims,&(in[0].front()));
+    array input(idims,&(in[0].front()));
     input = input.T();
-    af::array pos0(pdims,&(in[1].front()));
-    af::array pos1(qdims,&(in[2].front()));
+    array pos0(pdims,&(in[1].front()));
+    array pos1(qdims,&(in[2].front()));
     pos0 = tile(pos0, 1, pos0.dims(0));
     pos1 = tile(pos1.T(), pos1.dims(0));
-    af::array output = af::approx2(input, pos0, pos1, AF_INTERP_BICUBIC_SPLINE, 0).T();
+    array output = approx2(input, pos0, pos1, AF_INTERP_BICUBIC_SPLINE, 0).T();
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -299,52 +306,48 @@ TEST(Approx2Cubic, CPP)
 
 TEST(Approx2, CPPNearestBatch)
 {
-    if (noDoubleTests<float>()) return;
-
-    af::array input = af::randu(200, 100, 10);
-    af::array pos   = input.dims(0) * af::randu(100, 100, 10);
-    af::array qos   = input.dims(1) * af::randu(100, 100, 10);
+    array input = randu(200, 100, 10);
+    array pos   = input.dims(0) * randu(100, 100, 10);
+    array qos   = input.dims(1) * randu(100, 100, 10);
 
-    af::array outBatch = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+    array outBatch = approx2(input, pos, qos, AF_INTERP_NEAREST);
 
-    af::array outSerial(pos.dims());
-    for(int i = 0; i < pos.dims(2); i++) {
-        outSerial(af::span, af::span, i) = af::approx2(input(af::span, af::span, i),
-            pos(af::span, af::span, i), qos(af::span, af::span, i), AF_INTERP_NEAREST);
+    array outSerial(pos.dims());
+    for (int i = 0; i < pos.dims(2); i++) {
+        outSerial(span, span, i) = approx2(input(span, span, i),
+            pos(span, span, i), qos(span, span, i), AF_INTERP_NEAREST);
     }
 
-    af::array outGFOR(pos.dims());
-    gfor(af::seq i, pos.dims(2)) {
-        outGFOR(af::span, af::span, i) = af::approx2(input(af::span, af::span, i),
-            pos(af::span, af::span, i), qos(af::span, af::span, i), AF_INTERP_NEAREST);
+    array outGFOR(pos.dims());
+    gfor(seq i, pos.dims(2)) {
+        outGFOR(span, span, i) = approx2(input(span, span, i),
+            pos(span, span, i), qos(span, span, i), AF_INTERP_NEAREST);
     }
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
 TEST(Approx2, CPPLinearBatch)
 {
-    if (noDoubleTests<float>()) return;
-
-    af::array input = af::randu(200, 100, 10);
-    af::array pos   = input.dims(0) * af::randu(100, 100, 10);
-    af::array qos   = input.dims(1) * af::randu(100, 100, 10);
+    array input = randu(200, 100, 10);
+    array pos   = input.dims(0) * randu(100, 100, 10);
+    array qos   = input.dims(1) * randu(100, 100, 10);
 
-    af::array outBatch = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+    array outBatch = approx2(input, pos, qos, AF_INTERP_LINEAR);
 
-    af::array outSerial(pos.dims());
-    for(int i = 0; i < pos.dims(2); i++) {
-        outSerial(af::span, af::span, i) = af::approx2(input(af::span, af::span, i),
-            pos(af::span, af::span, i), qos(af::span, af::span, i), AF_INTERP_LINEAR);
+    array outSerial(pos.dims());
+    for (int i = 0; i < pos.dims(2); i++) {
+        outSerial(span, span, i) = approx2(input(span, span, i),
+            pos(span, span, i), qos(span, span, i), AF_INTERP_LINEAR);
     }
 
-    af::array outGFOR(pos.dims());
-    gfor(af::seq i, pos.dims(2)) {
-        outGFOR(af::span, af::span, i) = af::approx2(input(af::span, af::span, i),
-            pos(af::span, af::span, i), qos(af::span, af::span, i), AF_INTERP_LINEAR);
+    array outGFOR(pos.dims());
+    gfor(seq i, pos.dims(2)) {
+        outGFOR(span, span, i) = approx2(input(span, span, i),
+            pos(span, span, i), qos(span, span, i), AF_INTERP_LINEAR);
     }
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outSerial)), 1e-3);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(outBatch - outGFOR)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
+    ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }

From d2907650f42f76d665cc31fffce1de34c52eeed5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 17 May 2017 16:30:51 -0400
Subject: [PATCH 1183/2677] Move solve threading tests to solve_dense.cpp

---
 test/CMakeLists.txt         |  2 +-
 test/manual_memory_test.cpp | 16 -----------
 test/memory.cpp             | 20 -------------
 test/memory_lock.cpp        | 20 -------------
 test/solve_common.hpp       | 27 ++++++++++++++----
 test/solve_dense.cpp        | 40 ++++++++++++++++++++++++++
 test/testHelpers.hpp        | 23 +++++++++++++++
 test/threading.cpp          | 57 -------------------------------------
 8 files changed, 85 insertions(+), 120 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 38d5cc7f3d..ed123c5781 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -121,7 +121,7 @@ MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
           COMPILE_FLAGS -DAF_${DEF_NAME}
           FOLDER "Tests/${BACKEND}")
 
-        IF (${FNAME} STREQUAL "threading")
+        IF (${FNAME} MATCHES "threading|solve")
             SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_OPTIONS -std=c++11)
         ENDIF()
 
diff --git a/test/manual_memory_test.cpp b/test/manual_memory_test.cpp
index 95011ac8f1..3f6fa1ac47 100644
--- a/test/manual_memory_test.cpp
+++ b/test/manual_memory_test.cpp
@@ -21,22 +21,6 @@ using std::string;
 using std::cout;
 using std::endl;
 
-static void cleanSlate()
-{
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceGC();
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ(alloc_buffers, 0u);
-    ASSERT_EQ(lock_buffers, 0u);
-    ASSERT_EQ(alloc_bytes, 0u);
-    ASSERT_EQ(lock_bytes, 0u);
-}
-
 TEST(Memory, recover)
 {
     cleanSlate(); // Clean up everything done so far
diff --git a/test/memory.cpp b/test/memory.cpp
index 5342781b7f..d5d15dd37b 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -26,26 +26,6 @@ using af::cdouble;
 
 const size_t step_bytes = 1024;
 
-static void cleanSlate()
-{
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceGC();
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ(alloc_buffers, 0u);
-    ASSERT_EQ(lock_buffers, 0u);
-    ASSERT_EQ(alloc_bytes, 0u);
-    ASSERT_EQ(lock_bytes, 0u);
-
-    af::setMemStepSize(step_bytes);
-
-    ASSERT_EQ(af::getMemStepSize(), step_bytes);
-}
-
 TEST(Memory, Scope)
 {
     size_t alloc_bytes, alloc_buffers;
diff --git a/test/memory_lock.cpp b/test/memory_lock.cpp
index 6a4fe8665a..63331f0d03 100644
--- a/test/memory_lock.cpp
+++ b/test/memory_lock.cpp
@@ -23,26 +23,6 @@ using std::endl;
 
 const size_t step_bytes = 1024;
 
-static void cleanSlate()
-{
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceGC();
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ(alloc_buffers, 0u);
-    ASSERT_EQ(lock_buffers, 0u);
-    ASSERT_EQ(alloc_bytes, 0u);
-    ASSERT_EQ(lock_bytes, 0u);
-
-    af::setMemStepSize(step_bytes);
-
-    ASSERT_EQ(af::getMemStepSize(), step_bytes);
-}
-
 // This test should be by itself as it leaks memory intentionally
 TEST(Memory, lock)
 {
diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index 3149a7b3f3..c532cad42c 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -56,8 +56,13 @@ void solveTester(const int m, const int n, const int k, double eps, int targetDe
     af::array B1 = af::matmul(A, X1);
     //! [ex_solve_recon]
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
+    if(noDoubleTests<T>()) {
+        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
+        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
+    } else {
+        ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (m * k), eps);
+        ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (m * k), eps);
+    }
 }
 
 template<typename T>
@@ -88,8 +93,13 @@ void solveLUTester(const int n, const int k, double eps, int targetDevice=-1)
 
     af::array B1 = af::matmul(A, X1);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    if(noDoubleTests<T>()) {
+        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
+        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    } else {
+        ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (n * k), eps);
+        ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    }
 }
 
 template<typename T>
@@ -134,6 +144,11 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps, in
 
     af::array B1 = af::matmul(AT, X1);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    if(noDoubleTests<T>()) {
+        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
+        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    } else {
+        ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (n * k), eps);
+        ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    }
 }
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 641b05c7e3..3caa656336 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -10,6 +10,7 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include "solve_common.hpp"
+#include <thread>
 
 #define SOLVE_LU_TESTS(T, eps)                          \
     TEST(SOLVE_LU, T##Reg)                              \
@@ -80,4 +81,43 @@ SOLVE_TESTS(double, 1E-5)
 SOLVE_TESTS(cfloat, 0.01)
 SOLVE_TESTS(cdouble, 1E-5)
 
+
+#if !defined(AF_OPENCL)
+int nextTargetDeviceId()
+{
+  static int nextId = 0;
+  return nextId++;
+}
+
+#define SOLVE_LU_TESTS_THREADING(T, eps)                                                                          \
+    tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
+    tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps, nextTargetDeviceId()%numDevices);  \
+    tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, nextTargetDeviceId()%numDevices); \
+    tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps, nextTargetDeviceId()%numDevices);          \
+    tests.emplace_back(solveTester<T>, 800, 1000, 200, eps, nextTargetDeviceId()%numDevices);           \
+    tests.emplace_back(solveTester<T>, 800, 600, 64, eps, nextTargetDeviceId()%numDevices);             \
+
+TEST(SOLVE, Threading)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    int numDevices = 1;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+
+    SOLVE_LU_TESTS_THREADING(float, 0.01);
+    SOLVE_LU_TESTS_THREADING(cfloat, 0.01);
+    if (noDoubleTests<double>()) {
+        SOLVE_LU_TESTS_THREADING(double, 1E-5);
+        SOLVE_LU_TESTS_THREADING(cdouble, 1E-5);
+    }
+
+    for (size_t testId=0; testId<tests.size(); ++testId)
+        if (tests[testId].joinable())
+            tests[testId].join();
+}
+
+#undef SOLVE_LU_TESTS_THREADING
+#endif
 #undef SOLVE_TESTS
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 999a71b97d..58b820eabd 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 
@@ -453,4 +454,26 @@ af::array cpu_randu(const af::dim4 dims)
     return af::array(dims, (T *)&out[0]);
 }
 
+void cleanSlate()
+{
+  const size_t step_bytes = 1024;
+
+  size_t alloc_bytes, alloc_buffers;
+  size_t lock_bytes, lock_buffers;
+
+  af::deviceGC();
+
+  af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                    &lock_bytes, &lock_buffers);
+
+  ASSERT_EQ(alloc_buffers, 0u);
+  ASSERT_EQ(lock_buffers, 0u);
+  ASSERT_EQ(alloc_bytes, 0u);
+  ASSERT_EQ(lock_bytes, 0u);
+
+  af::setMemStepSize(step_bytes);
+
+  ASSERT_EQ(af::getMemStepSize(), step_bytes);
+}
+
 #pragma GCC diagnostic pop
diff --git a/test/threading.cpp b/test/threading.cpp
index cf82960f3c..01a130fb99 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -19,7 +19,6 @@
 #include <iterator>
 #include <vector>
 #include <testHelpers.hpp>
-#include <solve_common.hpp>
 #include <sparse_common.hpp>
 
 using namespace af;
@@ -177,28 +176,6 @@ TEST(Threading, SimultaneousRead)
             tests[t].join();
 }
 
-static void cleanSlate()
-{
-    const size_t step_bytes = 1024;
-
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceGC();
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ(alloc_buffers, 0u);
-    ASSERT_EQ(lock_buffers, 0u);
-    ASSERT_EQ(alloc_bytes, 0u);
-    ASSERT_EQ(lock_bytes, 0u);
-
-    af::setMemStepSize(step_bytes);
-
-    ASSERT_EQ(af::getMemStepSize(), step_bytes);
-}
-
 std::condition_variable cv;
 std::mutex cvMutex;
 size_t counter = THREAD_COUNT;
@@ -615,40 +592,6 @@ TEST(Threading, BLAS)
             tests[testId].join();
 }
 
-#if !defined(AF_OPENCL)
-
-#define SOLVE_LU_TESTS(T, eps)                                                                          \
-    tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
-    tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps, nextTargetDeviceId()%numDevices);  \
-    tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, nextTargetDeviceId()%numDevices); \
-    tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps, nextTargetDeviceId()%numDevices);          \
-    tests.emplace_back(solveTester<T>, 800, 1000, 200, eps, nextTargetDeviceId()%numDevices);           \
-    tests.emplace_back(solveTester<T>, 800, 600, 64, eps, nextTargetDeviceId()%numDevices);             \
-
-TEST(Threading, SolveDense)
-{
-    cleanSlate(); // Clean up everything done so far
-
-    vector<std::thread> tests;
-
-    int numDevices = 1;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
-
-    SOLVE_LU_TESTS(float, 0.01);
-    SOLVE_LU_TESTS(cfloat, 0.01);
-    if (noDoubleTests<double>()) {
-        SOLVE_LU_TESTS(double, 1E-5);
-        SOLVE_LU_TESTS(cdouble, 1E-5);
-    }
-
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
-}
-
-#undef SOLVE_LU_TESTS
-#endif
-
 #define SPARSE_TESTS(T, eps)                                                    \
   tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps, nextTargetDeviceId()%numDevices);  \
   tests.emplace_back(sparseTester<T>, 500, 1000, 250, 1, eps, nextTargetDeviceId()%numDevices);   \

From 24c0b31a11ee5a386272b45fd0d891239354e8e5 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 16 May 2017 23:37:42 -0700
Subject: [PATCH 1184/2677] Fix for reorder after JIT

---
 src/api/c/reorder.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index b2f2a127f9..5f97c27185 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -32,6 +32,9 @@ static inline af_array reorder(const af_array in, const af::dim4 &rdims0)
     const dim4 idims = In.dims();
     const dim4 istrides = In.strides();
 
+    // Ensure all JIT nodes are evaled
+    In.eval();
+
     af_array out;
     if (rdims[0] == 0 &&
         rdims[1] == 1 &&

From 056a02a012613c4579e1f25851dc50022cc530c4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 16 May 2017 23:58:59 -0700
Subject: [PATCH 1185/2677] Adding tests for reorder after tile

---
 test/reorder.cpp | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/test/reorder.cpp b/test/reorder.cpp
index 4b57170c42..7f1156fe0e 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -134,8 +134,6 @@ void reorderTest(string pTestFile, const unsigned resultIdx,
 //
 TEST(Reorder, CPP)
 {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 0;
     const unsigned x = 0;
     const unsigned y = 1;
@@ -166,3 +164,28 @@ TEST(Reorder, CPP)
     delete[] outData;
 }
 
+TEST(Reorder, ISSUE_1777)
+{
+    const int m = 5;
+    const int n = 4;
+    const int k = 3;
+    vector<float> h_input(m * n);
+
+    for (int i = 0; i < m * n; i++) {
+        h_input[i] = (float)(i);
+    }
+
+    af::array a(m, n, &h_input[0]);
+    af::array a_t = af::tile(a, 1, 1, 3);
+    af::array a_r = af::reorder(a_t, 0, 2, 1);
+
+    vector<float> h_output(m * n * k);
+    a_r.host((void *)&h_output[0]);
+    for (int z = 0; z < n; z++) {
+        for (int y = 0; y < k; y++) {
+            for (int x = 0; x < m; x++) {
+                ASSERT_EQ(h_output[z * k * m + y * m + x], h_input[z * m + x]);
+            }
+        }
+    }
+}

From 9cb4e0b79305b6d6bd39e6906cf02aac72ef499e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 2 May 2017 15:19:11 -0400
Subject: [PATCH 1186/2677] Cache backend symbols in the unified backend

---
 src/api/unified/symbol_manager.hpp | 25 +++++++++++++++++++++----
 1 file changed, 21 insertions(+), 4 deletions(-)

diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 658ac74b64..e1b7419c7f 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -22,6 +22,9 @@ typedef HMODULE LibHandle;
 typedef void* LibHandle;
 #endif
 
+#include <array>
+#include <unordered_map>
+
 namespace unified
 {
 
@@ -34,6 +37,14 @@ const int NUM_ENV_VARS = 2;
                     "for instructions to set up environment for Unified backend.", \
                     AF_ERR_LOAD_LIB)
 
+static int backend_index(af::Backend be) {
+    switch (be) {
+        case AF_BACKEND_CPU: return 0;
+        case AF_BACKEND_CUDA: return 1;
+        case AF_BACKEND_OPENCL: return 2;
+        default: return -1;
+    }
+}
 
 class AFSymbolManager {
     public:
@@ -51,16 +62,22 @@ class AFSymbolManager {
 
         template<typename... CalleeArgs>
         af_err call(const char* symbolName, CalleeArgs... args) {
+            typedef af_err(*af_func)(CalleeArgs...);
             if (!activeHandle) {
                 UNIFIED_ERROR_LOAD_LIB();
             }
-            typedef af_err(*af_func)(CalleeArgs...);
-            af_func funcHandle;
+            static std::array<std::unordered_map<const char*, af_func>, NUM_BACKENDS> funcHandles;
+
+            int index = backend_index(getActiveBackend());
+            af_func& funcHandle = funcHandles[index][symbolName];
+
+            if (!funcHandle) {
 #if defined(OS_WIN)
-            funcHandle = (af_func)GetProcAddress(activeHandle, symbolName);
+                funcHandle = (af_func)GetProcAddress(activeHandle, symbolName);
 #else
-            funcHandle = (af_func)dlsym(activeHandle, symbolName);
+                funcHandle = (af_func)dlsym(activeHandle, symbolName);
 #endif
+            }
             if (!funcHandle) {
                 std::string str = "Failed to load symbol: ";
                 str += symbolName;

From 6422505e1805655079d53230eab8aef1b5ae2d3d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 May 2017 16:53:55 -0400
Subject: [PATCH 1187/2677] Fix cpu errors caused by order of deletion in the
 device manager

---
 src/backend/cpu/platform.hpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index eeefbbec88..ecf7cf71fd 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -128,8 +128,7 @@ class DeviceManager
         CPUInfo getCPUInfo() const;
 
     private:
-        DeviceManager() {
-        }
+        DeviceManager() {}
 
         // Following two declarations are required to
         // avoid copying accidental copy/assignment
@@ -140,8 +139,7 @@ class DeviceManager
 
         // Attributes
         const CPUInfo cinfo;
-        std::array<queue, MAX_QUEUES> queues;
-
         std::unique_ptr<MemoryManager> memManager;
+        std::array<queue, MAX_QUEUES> queues;
 };
 }

From ee4898e0dbac90fd152e096f44d50c1ef9493382 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 May 2017 17:50:51 -0400
Subject: [PATCH 1188/2677] Fix minor warnings in medfilt

---
 src/backend/cpu/kernel/medfilt.hpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index e8f34fb173..b6a531eda0 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -28,7 +28,6 @@ void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
     wind_vals.reserve(w_wid);
 
     for(int b3=0; b3<(int)dims[3]; b3++) {
-
         T const * in_ptr = in.get() + b3 * istrides[3];
         T * out_ptr = out.get() + b3 * ostrides[3];
 
@@ -96,9 +95,6 @@ void medfilt2(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
     std::vector<T> wind_vals;
     wind_vals.reserve(w_len*w_wid);
 
-    T const * in_ptr = in.get();
-    T * out_ptr = out.get();
-
     for(int b3=0; b3<(int)dims[3]; b3++) {
         T const * in_ptr = in.get() + b3 * istrides[3];
         T * out_ptr = out.get() + b3 * ostrides[3];

From fcb7a0bf4c785a1f4356d7a971c1d4174841a843 Mon Sep 17 00:00:00 2001
From: Yang Li <idealhack@gmail.com>
Date: Sat, 29 Apr 2017 23:20:22 +0800
Subject: [PATCH 1189/2677] update googletest submodule url

---
 .gitmodules | 2 +-
 test/gtest  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.gitmodules b/.gitmodules
index c91b7f1585..f2cc14295f 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -6,7 +6,7 @@
 	url = https://github.com/arrayfire/assets
 [submodule "test/gtest"]
 	path = test/gtest
-	url = https://chromium.googlesource.com/external/googletest
+	url = https://github.com/google/googletest.git
 [submodule "src/backend/cpu/threads"]
 	path = src/backend/cpu/threads
 	url = https://github.com/alltheflops/threads.git
diff --git a/test/gtest b/test/gtest
index 23574bf233..1197daf357 160000
--- a/test/gtest
+++ b/test/gtest
@@ -1 +1 @@
-Subproject commit 23574bf2333f834ff665f894c97bef8a5b33a0a9
+Subproject commit 1197daf3571161590dce2bc4879512ef7bc1ba67

From 8f7163d5487dab84a668e64d0a850d4f971f9ef7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 19 May 2017 13:41:25 -0400
Subject: [PATCH 1190/2677] Fix the double checks for solve tests

---
 test/solve_common.hpp | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index c532cad42c..c6a97f7cc7 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -57,11 +57,11 @@ void solveTester(const int m, const int n, const int k, double eps, int targetDe
     //! [ex_solve_recon]
 
     if(noDoubleTests<T>()) {
-        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
-        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
-    } else {
         ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (m * k), eps);
         ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (m * k), eps);
+    } else {
+        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
+        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
     }
 }
 
@@ -94,11 +94,11 @@ void solveLUTester(const int n, const int k, double eps, int targetDevice=-1)
     af::array B1 = af::matmul(A, X1);
 
     if(noDoubleTests<T>()) {
-        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
-    } else {
         ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (n * k), eps);
         ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    } else {
+        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
+        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
     }
 }
 
@@ -145,10 +145,10 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps, in
     af::array B1 = af::matmul(AT, X1);
 
     if(noDoubleTests<T>()) {
-        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
-    } else {
         ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (n * k), eps);
         ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    } else {
+        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
+        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
     }
 }

From dabd0dfe74e8ec4b615addfa02dd5c1021106ed9 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Thu, 18 May 2017 23:17:13 -0700
Subject: [PATCH 1191/2677] Converting a few static variables to thread_local

---
 src/api/c/device.cpp                       |  9 +++--
 src/api/c/err_common.cpp                   |  2 +-
 src/api/c/imageio.cpp                      |  2 --
 src/api/c/imageio_helper.h                 |  2 --
 src/api/c/random.cpp                       |  2 +-
 src/api/c/transform.cpp                    | 18 +++++-----
 src/api/cpp/gfor.cpp                       |  2 +-
 src/api/cpp/timing.cpp                     |  4 +--
 src/api/unified/symbol_manager.cpp         |  4 +--
 src/api/unified/symbol_manager.hpp         |  2 +-
 src/backend/cpu/platform.cpp               |  6 ++--
 src/backend/cuda/platform.cpp              | 39 +++++++++++-----------
 src/backend/opencl/cpu/cpu_blas.cpp        |  2 +-
 src/backend/opencl/cpu/cpu_sparse_blas.cpp |  2 +-
 src/backend/opencl/platform.cpp            | 27 +++++++--------
 src/backend/opencl/program.cpp             |  2 +-
 16 files changed, 58 insertions(+), 67 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 9ca79a8de4..f1145e634a 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -74,11 +74,10 @@ af_err af_get_active_backend(af_backend *result)
 af_err af_init()
 {
     try {
-        static bool first = true;
-        if(first) {
-            getDeviceInfo();
-            first = false;
-        }
+        static std::once_flag flag;
+        std::call_once(flag, []() {
+                getDeviceInfo();
+            });
     } CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index c997621f62..7a7307783f 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -239,7 +239,7 @@ af_err processException()
 
 std::string& get_global_error_string()
 {
-    static std::string global_error_string = std::string("");
+    thread_local std::string global_error_string = std::string("");
     return global_error_string;
 }
 
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 68c7d6f95d..c953de5713 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -34,8 +34,6 @@
 using af::dim4;
 using namespace detail;
 
-bool FI_Manager::initialized = false;
-
 template<typename T, FI_CHANNELS fi_color, FI_CHANNELS fo_color>
 static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcPitch,
                         const uint fi_w, const uint fi_h)
diff --git a/src/api/c/imageio_helper.h b/src/api/c/imageio_helper.h
index a37973f006..eee6a06899 100644
--- a/src/api/c/imageio_helper.h
+++ b/src/api/c/imageio_helper.h
@@ -20,13 +20,11 @@
 class FI_Manager
 {
     public:
-    static bool initialized;
     FI_Manager()
     {
 #ifdef FREEIMAGE_LIB
         FreeImage_Initialise();
 #endif
-        initialized = true;
     }
 
     ~FI_Manager()
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 682934798b..0c392abbd0 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -108,7 +108,7 @@ static void validateRandomType(const af_random_engine_type type)
 
 af_err af_get_default_random_engine(af_random_engine *r)
 {
-    static RandomEngine re;
+    thread_local RandomEngine re;
     *r = static_cast<af_random_engine> (&re);
     return AF_SUCCESS;
 }
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index cd5f8abc6f..2dd7fbc59c 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -160,12 +160,12 @@ af_err af_translate(af_array *out, const af_array in, const float trans0, const
 {
 
     try {
-        static float trans_mat[6] = {1, 0, 0,
-                                     0, 1, 0};
+        float trans_mat[6] = {1, 0, 0,
+                              0, 1, 0};
         trans_mat[2] = trans0;
         trans_mat[5] = trans1;
 
-        static af::dim4 tdims(3, 2, 1, 1);
+        const af::dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
 
         AF_CHECK(af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
@@ -209,12 +209,12 @@ af_err af_scale(af_array *out, const af_array in, const float scale0, const floa
             sx = 1.f / scale0, sy = 1.f / scale1;
         }
 
-        static float trans_mat[6] = {1, 0, 0,
-                                     0, 1, 0};
+        float trans_mat[6] = {1, 0, 0,
+                              0, 1, 0};
         trans_mat[0] = sx;
         trans_mat[4] = sy;
 
-        static af::dim4 tdims(3, 2, 1, 1);
+        const af::dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
         AF_CHECK(af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
         AF_CHECK(af_transform(out, in, t, _odim0, _odim1, method, true));
@@ -232,8 +232,8 @@ af_err af_skew(af_array *out, const af_array in, const float skew0, const float
         float tx = std::tan(skew0);
         float ty = std::tan(skew1);
 
-        static float trans_mat[6] = {1, 0, 0,
-                                     0, 1, 0};
+        float trans_mat[6] = {1, 0, 0,
+                              0, 1, 0};
         trans_mat[1] = ty;
         trans_mat[3] = tx;
 
@@ -251,7 +251,7 @@ af_err af_skew(af_array *out, const af_array in, const float skew0, const float
                 trans_mat[4] = d;
             }
         }
-        static af::dim4 tdims(3, 2, 1, 1);
+        const af::dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
         AF_CHECK(af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
         AF_CHECK(af_transform(out, in, t, odim0, odim1, method, true));
diff --git a/src/api/cpp/gfor.cpp b/src/api/cpp/gfor.cpp
index b442164e24..acc312ef56 100644
--- a/src/api/cpp/gfor.cpp
+++ b/src/api/cpp/gfor.cpp
@@ -17,7 +17,7 @@
 namespace af
 {
 
-    static bool gforStatus;
+    thread_local bool gforStatus;
 
     bool gforGet() { return gforStatus; }
     void gforSet(bool val) { gforStatus = val; }
diff --git a/src/api/cpp/timing.cpp b/src/api/cpp/timing.cpp
index caf77fff60..9b26f94236 100644
--- a/src/api/cpp/timing.cpp
+++ b/src/api/cpp/timing.cpp
@@ -49,7 +49,7 @@ static inline double time_seconds(timer start, timer end)
         end   = temp;
     }
     // calculate platform timing epoch
-    static mach_timebase_info_data_t info;
+    thread_local mach_timebase_info_data_t info;
     mach_timebase_info(&info);
     double nano = (double)info.numer / (double)info.denom;
     return (end.val - start.val) * nano * 1e-9;
@@ -66,7 +66,7 @@ static inline double time_seconds(timer start, timer end)
 
 namespace af {
 
-static timer _timer_;
+thread_local timer _timer_;
 
 timer timer::start()
 {
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index ef92cd3902..febf8c5375 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -113,7 +113,7 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
          * /usr/local/arrayfire-3/lib
         */
         if (retVal == NULL) {
-            static
+            static const
             std::vector<std::string> extraLibPaths {"/opt/arrayfire-3/lib/",
                                                     "/opt/arrayfire/lib/",
                                                     "/usr/local/lib/",
@@ -151,7 +151,7 @@ void closeDynLibrary(LibHandle handle)
 
 AFSymbolManager& AFSymbolManager::getInstance()
 {
-    static AFSymbolManager symbolManager;
+    thread_local AFSymbolManager symbolManager;
     return symbolManager;
 }
 
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index e1b7419c7f..ef807db37c 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -66,7 +66,7 @@ class AFSymbolManager {
             if (!activeHandle) {
                 UNIFIED_ERROR_LOAD_LIB();
             }
-            static std::array<std::unordered_map<const char*, af_func>, NUM_BACKENDS> funcHandles;
+            thread_local std::array<std::unordered_map<const char*, af_func>, NUM_BACKENDS> funcHandles;
 
             int index = backend_index(getActiveBackend());
             af_func& funcHandle = funcHandles[index][symbolName];
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index af694f90e1..a5b4342621 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -192,7 +192,7 @@ unsigned getMaxJitSize()
 {
     const int MAX_JIT_LEN = 100;
 
-    static int length = 0;
+    thread_local int length = 0;
     if (length == 0) {
         std::string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
         if (!env_var.empty()) {
@@ -226,7 +226,7 @@ size_t getHostMemorySize()
 
 int setDevice(int device)
 {
-    static bool flag = false;
+    thread_local bool flag = false;
     if (!flag && device != 0) {
 #ifndef NDEBUG
         std::cerr << "WARNING af_set_device(device): device can only be 0 for CPU\n";
@@ -253,7 +253,7 @@ void sync(int device)
 
 bool& evalFlag()
 {
-    static bool flag = true;
+    thread_local bool flag = true;
     return flag;
 }
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index b80d288f91..fd267b6427 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -270,7 +270,7 @@ unsigned getMaxJitSize()
 {
     const int MAX_JIT_LEN = 100;
 
-    static int length = 0;
+    thread_local int length = 0;
     if (length == 0) {
         std::string env_var = getEnvVar("AF_CUDA_MAX_JIT_LEN");
         if (!env_var.empty()) {
@@ -365,23 +365,22 @@ cudaDeviceProp getDeviceProp(int device)
 #if defined(WITH_GRAPHICS)
 bool DeviceManager::checkGraphicsInteropCapability()
 {
-    static bool run_once = true;
-    static bool capable  = true;
-
-    if(run_once) {
-        unsigned int pCudaEnabledDeviceCount = 0;
-        int pCudaGraphicsEnabledDeviceIds = 0;
-        cudaGetLastError(); // Reset Errors
-        cudaError_t err = cudaGLGetDevices(&pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds, getDeviceCount(), cudaGLDeviceListAll);
-        if(err == 63) { // OS Support Failure - Happens when devices are only Tesla
-            capable = false;
-            printf("Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL Interop will use CPU fallback.\n");
-            printf("Corresponding CUDA Error (%d): %s.\n", err, cudaGetErrorString(err));
-            printf("This may happen if all CUDA Devices are in TCC Mode and/or not connected to a display.\n");
-        }
-        cudaGetLastError(); // Reset Errors
-        run_once = false;
-    }
+    static std::once_flag checkInteropFlag;
+    thread_local bool capable  = true;
+
+    std::call_once(checkInteropFlag, [](){
+            unsigned int pCudaEnabledDeviceCount = 0;
+            int pCudaGraphicsEnabledDeviceIds = 0;
+            cudaGetLastError(); // Reset Errors
+            cudaError_t err = cudaGLGetDevices(&pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds, getDeviceCount(), cudaGLDeviceListAll);
+            if(err == 63) { // OS Support Failure - Happens when devices are only Tesla
+                capable = false;
+                printf("Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL Interop will use CPU fallback.\n");
+                printf("Corresponding CUDA Error (%d): %s.\n", err, cudaGetErrorString(err));
+                printf("This may happen if all CUDA Devices are in TCC Mode and/or not connected to a display.\n");
+            }
+            cudaGetLastError(); // Reset Errors
+        });
 
     return capable;
 }
@@ -624,13 +623,13 @@ void sync(int device)
 
 bool synchronize_calls()
 {
-    static bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
+    static const bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
 }
 
 bool& evalFlag()
 {
-    static bool flag = true;
+    thread_local bool flag = true;
     return flag;
 }
 }
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 724c6bb1e9..5cc3028893 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -124,7 +124,7 @@ template<typename T, int value>
 typename enable_if<is_complex<T>::value, scale_type<T>>::type
 getScale()
 {
-    static T val = scalar<T>(value);
+    thread_local T val = scalar<T>(value);
     return (const typename blas_base<T>::type *)&val;
 }
 
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index ba6ba4920d..5339042150 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -202,7 +202,7 @@ toSparseTranspose(af_mat_prop opt)
 template<typename T, int value>
 scale_type<T> getScale()
 {
-    static T val = scalar<T>(value);
+    thread_local T val = scalar<T>(value);
     return getScaleValue<scale_type<T>, T>(val);
 }
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index dc7e16b7b4..3ad0e34904 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -200,21 +200,18 @@ static inline std::string &ltrim(std::string &s)
 
 static std::string platformMap(std::string &platStr)
 {
-    static bool isFirst = true;
 
     typedef std::map<std::string, std::string> strmap_t;
-    static strmap_t platMap;
-    if (isFirst) {
-        platMap["NVIDIA CUDA"]                         = "NVIDIA  ";
-        platMap["Intel(R) OpenCL"]                     = "INTEL   ";
-        platMap["AMD Accelerated Parallel Processing"] = "AMD     ";
-        platMap["Intel Gen OCL Driver"]                = "BEIGNET ";
-        platMap["Apple"]                               = "APPLE   ";
-        platMap["Portable Computing Language"]         = "POCL    ";
-        isFirst = false;
-    }
+    static const strmap_t platMap = {
+        std::make_pair("NVIDIA CUDA", "NVIDIA"),
+        std::make_pair("Intel(R) OpenCL", "INTEL"),
+        std::make_pair("AMD Accelerated Parallel Processing", "AMD"),
+        std::make_pair("Intel Gen OCL Driver", "BEIGNET"),
+        std::make_pair("Apple", "APPLE"),
+        std::make_pair("Portable Computing Language", "POCL"),
+    };
 
-    strmap_t::iterator idx = platMap.find(platStr);
+    auto idx = platMap.find(platStr);
 
     if (idx == platMap.end()) {
         return platStr;
@@ -644,7 +641,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
 
 bool synchronize_calls()
 {
-    static bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
+    static const bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
 }
 
@@ -656,7 +653,7 @@ unsigned getMaxJitSize()
     const int MAX_JIT_LEN = 100;
 #endif
 
-    static int length = 0;
+    thread_local int length = 0;
     if (length == 0) {
         std::string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
         if (!env_var.empty()) {
@@ -670,7 +667,7 @@ unsigned getMaxJitSize()
 
 bool& evalFlag()
 {
-    static bool flag = true;
+    thread_local bool flag = true;
     return flag;
 }
 
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 5c0d9d3c75..31e467d4b0 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -48,7 +48,7 @@ namespace opencl
                 setSrc.emplace_back(ker_strs[i], ker_lens[i]);
             }
 
-            static std::string defaults =
+            const std::string defaults =
                 std::string(" -D dim_t=") +
                 std::string(dtype_traits<dim_t>::getName());
 

From 1b3c60cf3ba70cd4dc85c62407ed3010d3498b7a Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 1 Dec 2016 11:38:46 -0500
Subject: [PATCH 1192/2677] Add sparse-dense arithmetic to CPU backend

---
 src/api/c/binary.cpp                    | 117 +++++++++++++++++++++++-
 src/backend/cpu/kernel/sparse_arith.hpp | 108 ++++++++++++++++++++++
 src/backend/cpu/sparse_arith.cpp        | 107 ++++++++++++++++++++++
 src/backend/cpu/sparse_arith.hpp        |  22 +++++
 4 files changed, 350 insertions(+), 4 deletions(-)
 create mode 100644 src/backend/cpu/kernel/sparse_arith.hpp
 create mode 100644 src/backend/cpu/sparse_arith.cpp
 create mode 100644 src/backend/cpu/sparse_arith.hpp

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index bea84b8536..93861f9fc6 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -17,9 +17,12 @@
 #include <err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
+#include <sparse_handle.hpp>
+#include <sparse.hpp>
 
 #include <arith.hpp>
 #include <logic.hpp>
+#include <sparse_arith.hpp>
 
 using namespace detail;
 using af::dim4;
@@ -32,6 +35,14 @@ static inline af_array arithOp(const af_array lhs, const af_array rhs,
     return res;
 }
 
+template<typename T, af_op_t op>
+static inline af_array arithSparseDenseOp(const af_array lhs, const af_array rhs,
+                                          const bool reverse)
+{
+    af_array res = getHandle(arithOp<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
+    return res;
+}
+
 template<af_op_t op>
 static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
@@ -97,24 +108,122 @@ static af_err af_arith_real(af_array *out, const af_array lhs, const af_array rh
     return AF_SUCCESS;
 }
 
+//template<af_op_t op>
+//static af_err af_arith_sparse(af_array *out, const af_array lhs, const af_array rhs)
+//{
+//    try {
+//        SparseArrayBase linfo = getSparseArrayBase(lhs);
+//        SparseArrayBase rinfo = getSparseArrayBase(rhs);
+//
+//        dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
+//
+//        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
+//        af_array res;
+//        switch (otype) {
+//        case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
+//        case f64: res = arithOp<double , op>(lhs, rhs, odims); break;
+//        case c32: res = arithOp<cfloat , op>(lhs, rhs, odims); break;
+//        case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
+//        default: TYPE_ERROR(0, otype);
+//        }
+//
+//        std::swap(*out, res);
+//    }
+//    CATCHALL;
+//    return AF_SUCCESS;
+//}
+
+template<af_op_t op>
+static af_err af_arith_sparse_dense(af_array *out, const af_array lhs, const af_array rhs,
+                                    const bool reverse = false)
+{
+    using namespace common;
+    try {
+        SparseArrayBase linfo = getSparseArrayBase(lhs);
+        ArrayInfo       rinfo = getInfo(rhs);
+
+        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
+        af_array res;
+        switch (otype) {
+        case f32: res = arithSparseDenseOp<float  , op>(lhs, rhs, reverse); break;
+        case f64: res = arithSparseDenseOp<double , op>(lhs, rhs, reverse); break;
+        case c32: res = arithSparseDenseOp<cfloat , op>(lhs, rhs, reverse); break;
+        case c64: res = arithSparseDenseOp<cdouble, op>(lhs, rhs, reverse); break;
+        default: TYPE_ERROR(0, otype);
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_add(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
-    return af_arith<af_add_t>(out, lhs, rhs, batchMode);
+    // Check if inputs are sparse
+    ArrayInfo linfo = getInfo(lhs, false, true);
+    ArrayInfo rinfo = getInfo(rhs, false, true);
+
+    if(linfo.isSparse() && rinfo.isSparse()) {
+        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_add_t>(out, lhs, rhs);
+    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_add_t>(out, lhs, rhs);
+    } else if(!linfo.isSparse() && rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true); // dense should be rhs
+    } else {
+        return af_arith<af_add_t>(out, lhs, rhs, batchMode);
+    }
 }
 
 af_err af_mul(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
-    return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
+    // Check if inputs are sparse
+    ArrayInfo linfo = getInfo(lhs, false, true);
+    ArrayInfo rinfo = getInfo(rhs, false, true);
+
+    if(linfo.isSparse() && rinfo.isSparse()) {
+        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_mul_t>(out, lhs, rhs);
+    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_mul_t>(out, lhs, rhs);
+    } else if(!linfo.isSparse() && rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_mul_t>(out, rhs, lhs, true); // dense should be rhs
+    } else {
+        return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
+    }
 }
 
 af_err af_sub(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
-    return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
+    // Check if inputs are sparse
+    ArrayInfo linfo = getInfo(lhs, false, true);
+    ArrayInfo rinfo = getInfo(rhs, false, true);
+
+    if(linfo.isSparse() && rinfo.isSparse()) {
+        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_sub_t>(out, lhs, rhs);
+    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_sub_t>(out, lhs, rhs);
+    } else if(!linfo.isSparse() && rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_sub_t>(out, rhs, lhs, true); // dense should be rhs
+    } else {
+        return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
+    }
 }
 
 af_err af_div(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
 {
-    return af_arith<af_div_t>(out, lhs, rhs, batchMode);
+    // Check if inputs are sparse
+    ArrayInfo linfo = getInfo(lhs, false, true);
+    ArrayInfo rinfo = getInfo(rhs, false, true);
+
+    if(linfo.isSparse() && rinfo.isSparse()) {
+        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_div_t>(out, lhs, rhs);
+    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
+    } else if(!linfo.isSparse() && rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense should be rhs
+    } else {
+        return af_arith<af_div_t>(out, lhs, rhs, batchMode);
+    }
 }
 
 af_err af_maxof(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
new file mode 100644
index 0000000000..d7bc24e9cf
--- /dev/null
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -0,0 +1,108 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <math.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename T, af_op_t op>
+struct arith_op
+{
+    T operator()(T v1, T v2)
+    {
+        return scalar<T>(0);
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_add_t>
+{
+    T operator()(T v1, T v2)
+    {
+        return v1 + v2;
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_sub_t>
+{
+    T operator()(T v1, T v2)
+    {
+        return v1 - v2;
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_mul_t>
+{
+    T operator()(T v1, T v2)
+    {
+        return v1 * v2;
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_div_t>
+{
+    T operator()(T v1, T v2)
+    {
+        return v1 / v2;
+    }
+};
+
+template<typename T, af_op_t op, af_storage type>
+void sparseArithOp(Array<T> output,
+                   const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
+                   const Array<T> rhs, const bool reverse = false)
+{
+    T * oPtr = output.get();
+    const T   * hPtr = rhs.get();
+
+    const T   * vPtr = values.get();
+    const int * rPtr = rowIdx.get();
+    const int * cPtr = colIdx.get();
+
+    dim4 odims    = output.dims();
+    dim4 ostrides = output.strides();
+    dim4 hstrides = rhs.strides();
+
+    std::vector<int> temp;
+    if(type == AF_STORAGE_CSR) {
+        temp.resize(values.elements());
+        for(int i = 0; i < rowIdx.dims()[0] - 1; i++) {
+            for(int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) {
+                temp[ii] = i;
+            }
+        }
+    //} else if(type == AF_STORAGE_CSC) {   // For future
+    }
+
+    const int *xx = (type == AF_STORAGE_CSR) ? temp.data() : rPtr;
+    const int *yy = (type == AF_STORAGE_CSC) ? temp.data() : cPtr;
+
+    for(int i = 0; i < (int)values.elements(); i++) {
+        // Bad index data
+        if(xx[i] >= odims[0] || yy [i]>= odims[1]) continue;
+
+        int offset = xx[i] + yy[i] * ostrides[1];
+        int hoff   = xx[i] + yy[i] * hstrides[1];
+
+        if(reverse) oPtr[offset] = arith_op<T, op>()(hPtr[hoff], vPtr[i]);
+        else        oPtr[offset] = arith_op<T, op>()(vPtr[i], hPtr[hoff]);
+    }
+}
+
+}
+}
+
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
new file mode 100644
index 0000000000..64129a5430
--- /dev/null
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -0,0 +1,107 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse_arith.hpp>
+#include <SparseArray.hpp>
+#include <optypes.hpp>
+#include <sparse.hpp>
+
+#include <kernel/sparse_arith.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <af/dim4.hpp>
+#include <arith.hpp>
+#include <complex.hpp>
+#include <copy.hpp>
+#include <err_common.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+
+namespace cpu
+{
+
+using namespace common;
+
+template<typename T>
+T getInf()
+{
+    return scalar<T>(std::numeric_limits<T>::infinity());
+}
+
+template<>
+cfloat getInf()
+{
+    return scalar<cfloat, float>(
+            std::numeric_limits<float>::infinity(),
+            std::numeric_limits<float>::infinity()
+            );
+}
+
+template<>
+cdouble getInf()
+{
+    return scalar<cdouble, double>(
+            std::numeric_limits<double>::infinity(),
+            std::numeric_limits<double>::infinity()
+            );
+}
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+{
+    lhs.eval();
+    rhs.eval();
+
+    Array<T> out = createEmptyArray<T>(dim4(0));
+    Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
+    switch(op) {
+        case af_add_t: out = copyArray<T>(rhs); break;
+        case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
+        case af_mul_t: out = zero; break;
+        case af_div_t: out = reverse ? createValueArray(rhs.dims(), getInf<T>()) : zero; break;
+        default      : out = copyArray<T>(rhs);
+    }
+    out.eval();
+    switch(lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            getQueue().enqueue(kernel::sparseArithOp<T, op, AF_STORAGE_CSR>,
+                               out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                               rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            getQueue().enqueue(kernel::sparseArithOp<T, op, AF_STORAGE_COO>,
+                               out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                               rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                          \
+    template Array<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+
+INSTANTIATE(float  )
+INSTANTIATE(double )
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
+
+}
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
new file mode 100644
index 0000000000..a258177332
--- /dev/null
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+#include <sparse.hpp>
+#include <optypes.hpp>
+
+namespace cpu
+{
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                 const bool reverse = false);
+
+}

From 4434c41a11d16dee75abf09fcacbbf62fe9499e6 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 5 Dec 2016 13:18:12 -0500
Subject: [PATCH 1193/2677] Add sparse-dense arithmetic to CUDA backend

---
 src/backend/cuda/kernel/sparse_arith.hpp | 165 +++++++++++++++++++++++
 src/backend/cuda/sparse_arith.cu         | 100 ++++++++++++++
 src/backend/cuda/sparse_arith.hpp        |  23 ++++
 3 files changed, 288 insertions(+)
 create mode 100644 src/backend/cuda/kernel/sparse_arith.hpp
 create mode 100644 src/backend/cuda/sparse_arith.cu
 create mode 100644 src/backend/cuda/sparse_arith.hpp

diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
new file mode 100644
index 0000000000..23ea54b625
--- /dev/null
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -0,0 +1,165 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <dispatch.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include <backend.hpp>
+
+namespace cuda
+{
+
+namespace kernel
+{
+
+static const unsigned TX = 32;
+static const unsigned TY = 8;
+static const unsigned THREADS = TX * TY;
+
+template<typename T, af_op_t op>
+struct arith_op
+{
+    __DH__ T operator()(T v1, T v2)
+    {
+        return T(0);
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_add_t>
+{
+    __device__ T operator()(T v1, T v2)
+    {
+        return v1 + v2;
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_sub_t>
+{
+    __device__ T operator()(T v1, T v2)
+    {
+        return v1 - v2;
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_mul_t>
+{
+    __device__ T operator()(T v1, T v2)
+    {
+        return v1 * v2;
+    }
+};
+
+template<typename T>
+struct arith_op<T, af_div_t>
+{
+    __device__ T operator()(T v1, T v2)
+    {
+        return v1 / v2;
+    }
+};
+
+template<typename T, af_op_t op>
+__global__
+void sparseArithCSRKernel(Param<T> out,
+                          CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
+                          CParam<T> rhs,
+                          const bool reverse)
+{
+    const int row         = blockIdx.x * TY + threadIdx.y;
+
+    if(row >= out.dims[0]) return;
+
+    const int rowStartIdx = rowIdx.ptr[row  ];
+    const int rowEndIdx   = rowIdx.ptr[row+1];
+
+    // Repeat loop until all values in the row are computed
+    for(int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
+        const int col = colIdx.ptr[idx];
+
+        if(row >= out.dims[0] || col >= out.dims[1]) continue;    // Bad indices
+
+        // Get Values
+        const T val  = values.ptr[idx];
+        const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+        const int offset = col * out.strides[1] + row;
+        if(reverse) out.ptr[offset] = arith_op<T, op>()(rval, val);
+        else        out.ptr[offset] = arith_op<T, op>()(val, rval);
+    }
+}
+
+template<typename T, af_op_t op>
+__global__
+void sparseArithCOOKernel(Param<T> out,
+                          CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
+                          CParam<T> rhs,
+                          const bool reverse)
+{
+    const int idx = blockIdx.x * THREADS + threadIdx.x;
+
+    if(idx >= values.dims[0]) return;
+
+    const int row = rowIdx.ptr[idx];
+    const int col = colIdx.ptr[idx];
+
+    if(row >= out.dims[0] || col >= out.dims[1]) return;    // Bad indices
+
+    // Get Values
+    const T val  = values.ptr[idx];
+    const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+    const int offset = col * out.strides[1] + row;
+    if(reverse) out.ptr[offset] = arith_op<T, op>()(rval, val);
+    else        out.ptr[offset] = arith_op<T, op>()(val, rval);
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCSR(Param<T> out,
+                      CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
+                      CParam<T> rhs,
+                      const bool reverse)
+{
+    // Each Y for threads does one row
+    dim3 threads(TX, TY, 1);
+
+    // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
+    dim3 blocks(divup(out.dims[0], TY), 1, 1);
+
+    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads,
+                 out, values, rowIdx, colIdx, rhs, reverse);
+
+    POST_LAUNCH_CHECK();
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCOO(Param<T> out,
+                      CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
+                      CParam<T> rhs,
+                      const bool reverse)
+{
+    // Linear indexing with one elements per thread
+    dim3 threads(THREADS, 1, 1);
+
+    // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
+    dim3 blocks(divup(values.dims[0], THREADS), 1, 1);
+
+    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads,
+                 out, values, rowIdx, colIdx, rhs, reverse);
+
+    POST_LAUNCH_CHECK();
+}
+
+} // namespace kernel
+
+} // namespace cuda
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
new file mode 100644
index 0000000000..84ab7a7efd
--- /dev/null
+++ b/src/backend/cuda/sparse_arith.cu
@@ -0,0 +1,100 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse.hpp>
+#include <cusparseManager.hpp>
+#include <kernel/sparse_arith.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <complex.hpp>
+#include <copy.hpp>
+#include <err_common.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <where.hpp>
+
+namespace cuda
+{
+
+using cusparse::getHandle;
+using namespace common;
+using namespace std;
+
+template<typename T>
+T getInf()
+{
+    return scalar<T>(std::numeric_limits<T>::infinity());
+}
+
+template<>
+cfloat getInf()
+{
+    return scalar<cfloat, float>(NAN, NAN); // Matches behavior of complex division by 0 in CUDA
+}
+
+template<>
+cdouble getInf()
+{
+    return scalar<cdouble, double>(NAN, NAN); // Matches behavior of complex division by 0 in CUDA
+}
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+{
+    lhs.eval();
+    rhs.eval();
+
+    Array<T> out = createEmptyArray<T>(dim4(0));
+    Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
+    switch(op) {
+        case af_add_t: out = copyArray<T>(rhs); break;
+        case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
+        case af_mul_t: out = zero; break;
+        case af_div_t: out = reverse ? createValueArray(rhs.dims(), getInf<T>()) : zero; break;
+        default      : out = copyArray<T>(rhs);
+    }
+    out.eval();
+    switch(lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                                            rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                                            rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                          \
+    template Array<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+
+INSTANTIATE(float  )
+INSTANTIATE(double )
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
+
+}
+
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
new file mode 100644
index 0000000000..bf3ac04e91
--- /dev/null
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+#include <sparse.hpp>
+#include <optypes.hpp>
+
+namespace cuda
+{
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                 const bool reverse = false);
+
+}
+

From ed7ebb82ff561f96d82ba654d0efaa5b25acfa53 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Mon, 5 Dec 2016 15:29:30 -0500
Subject: [PATCH 1194/2677] Add sparse-dense arithmetic to OpenCL backend

---
 src/backend/opencl/kernel/sparse_arith.hpp    | 165 ++++++++++++++++++
 .../opencl/kernel/sparse_arith_common.cl      |  52 ++++++
 src/backend/opencl/kernel/sparse_arith_coo.cl |  37 ++++
 src/backend/opencl/kernel/sparse_arith_csr.cl |  42 +++++
 src/backend/opencl/sparse_arith.cpp           |  98 +++++++++++
 src/backend/opencl/sparse_arith.hpp           |  24 +++
 6 files changed, 418 insertions(+)
 create mode 100644 src/backend/opencl/kernel/sparse_arith.hpp
 create mode 100644 src/backend/opencl/kernel/sparse_arith_common.cl
 create mode 100644 src/backend/opencl/kernel/sparse_arith_coo.cl
 create mode 100644 src/backend/opencl/kernel/sparse_arith_csr.cl
 create mode 100644 src/backend/opencl/sparse_arith.cpp
 create mode 100644 src/backend/opencl/sparse_arith.hpp

diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
new file mode 100644
index 0000000000..dac572720c
--- /dev/null
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -0,0 +1,165 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/sparse_arith_csr.hpp>
+#include <kernel_headers/sparse_arith_coo.hpp>
+#include <kernel_headers/sparse_arith_common.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <mutex>
+#include <map>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+#include <cache.hpp>
+#include <type_util.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+    namespace kernel
+    {
+        static const unsigned TX = 32;
+        static const unsigned TY = 8;
+        static const unsigned THREADS = TX * TY;
+
+        template<af_op_t op>
+        std::string getOpString()
+        {
+            switch(op) {
+                case af_add_t : return "ADD";
+                case af_sub_t : return "SUB";
+                case af_mul_t : return "MUL";
+                case af_div_t : return "DIV";
+                default       : return ""; // kernel will fail to compile
+            }
+            return "";
+        }
+
+        template<typename T, af_op_t op>
+        void sparseArithOpCSR(Param out, const Param values, const Param rowIdx, const Param colIdx,
+                        const Param rhs, const bool reverse)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sparseArithCSRProgs;
+                static std::map<int, Kernel *> sparseArithCSRKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="  << dtype_traits<T>::getName();
+                    options << " -D OP=" << getOpString<op>();
+
+                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                       (af_dtype) dtype_traits<T>::af_type == c64) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
+                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
+                    Program prog;
+                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                    sparseArithCSRProgs[device] = new Program(prog);
+                    sparseArithCSRKernels[device] = new Kernel(*sparseArithCSRProgs[device], "sparse_arith_csr_kernel");
+                });
+
+                auto sparseArithCSROp = KernelFunctor<Buffer, const KParam,
+                                              const Buffer, const Buffer, const Buffer,
+                                              const int,
+                                              const Buffer, const KParam,
+                                              const int>(*sparseArithCSRKernels[device]);
+
+                NDRange local(TX, TY, 1);
+                NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
+
+                sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
+                         *out.data, out.info,
+                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                         *rhs.data, rhs.info, reverse);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
+
+        template<typename T, af_op_t op>
+        void sparseArithOpCOO(Param out, const Param values, const Param rowIdx, const Param colIdx,
+                        const Param rhs, const bool reverse)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sparseArithCOOProgs;
+                static std::map<int, Kernel *> sparseArithCOOKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="  << dtype_traits<T>::getName();
+                    options << " -D OP=" << getOpString<op>();
+
+                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                       (af_dtype) dtype_traits<T>::af_type == c64) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
+                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
+                    Program prog;
+                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                    sparseArithCOOProgs[device] = new Program(prog);
+                    sparseArithCOOKernels[device] = new Kernel(*sparseArithCOOProgs[device], "sparse_arith_coo_kernel");
+                });
+
+                auto sparseArithCOOOp = KernelFunctor<Buffer, const KParam,
+                                              const Buffer, const Buffer, const Buffer,
+                                              const int,
+                                              const Buffer, const KParam,
+                                              const int>(*sparseArithCOOKernels[device]);
+
+                NDRange local(THREADS, 1, 1);
+                NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+
+                sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
+                         *out.data, out.info,
+                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                         *rhs.data, rhs.info, reverse);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
+
+    }
+}
diff --git a/src/backend/opencl/kernel/sparse_arith_common.cl b/src/backend/opencl/kernel/sparse_arith_common.cl
new file mode 100644
index 0000000000..0a6058e86f
--- /dev/null
+++ b/src/backend/opencl/kernel/sparse_arith_common.cl
@@ -0,0 +1,52 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+T _add_(T v1, T v2)
+{
+    return v1 + v2;
+}
+
+T _sub_(T v1, T v2)
+{
+    return v1 - v2;
+}
+
+#if IS_CPLX
+T _mul_(T v1, T v2)
+{
+    T out;
+    out.x = v1.x * v2.x - v1.y * v2.y;
+    out.y = v1.x * v2.y + v1.y * v2.x;
+    return out;
+}
+
+T _div_(T v1, T v2)
+{
+    T out;
+    out.x = (v1.x * v2.x + v1.y * v2.y) / (v2.x * v2.x + v2.y * v2.y);
+    out.y = (v1.y * v2.x - v1.x * v2.y) / (v2.x * v2.x + v2.y * v2.y);
+    return out;
+}
+#else
+T _mul_(T v1, T v2)
+{
+    return v1 * v2;
+}
+
+T _div_(T v1, T v2)
+{
+    return v1 / v2;
+}
+#endif
+
+
+#define ADD _add_
+#define SUB _sub_
+#define MUL _mul_
+#define DIV _div_
diff --git a/src/backend/opencl/kernel/sparse_arith_coo.cl b/src/backend/opencl/kernel/sparse_arith_coo.cl
new file mode 100644
index 0000000000..57e7355ac3
--- /dev/null
+++ b/src/backend/opencl/kernel/sparse_arith_coo.cl
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void sparse_arith_coo_kernel(__global T *oPtr,
+                             const KParam out,
+                             __global const T *values,
+                             __global const int *rowIdx,
+                             __global const int *colIdx,
+                             const int nNZ,
+                             __global const T *rPtr,
+                             const KParam rhs,
+                             const int reverse)
+{
+    const int idx = get_global_id(0);
+
+    if(idx >= nNZ) return;
+
+    const int row = rowIdx[idx];
+    const int col = colIdx[idx];
+
+    if(row >= out.dims[0] || col >= out.dims[1]) return;    // Bad indices
+
+    // Get Values
+    const T val  = values[idx];
+    const T rval = rPtr[col * rhs.strides[1] + row];
+
+    const int offset = col * out.strides[1] + row;
+    if(reverse) oPtr[offset] = OP(rval, val);
+    else        oPtr[offset] = OP(val, rval);
+}
diff --git a/src/backend/opencl/kernel/sparse_arith_csr.cl b/src/backend/opencl/kernel/sparse_arith_csr.cl
new file mode 100644
index 0000000000..b3a7aab449
--- /dev/null
+++ b/src/backend/opencl/kernel/sparse_arith_csr.cl
@@ -0,0 +1,42 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void sparse_arith_csr_kernel(__global T *oPtr,
+                             const KParam out,
+                             __global const T *values,
+                             __global const int *rowIdx,
+                             __global const int *colIdx,
+                             const int nNZ,
+                             __global const T *rPtr,
+                             const KParam rhs,
+                             const int reverse)
+{
+    const int row = get_group_id(0) * get_local_size(1) + get_local_id(1);
+
+    if(row >= out.dims[0]) return;
+
+    const int rowStartIdx = rowIdx[row  ];
+    const int rowEndIdx   = rowIdx[row+1];
+
+    // Repeat loop until all values in the row are computed
+    for(int idx = rowStartIdx + get_local_id(0); idx < rowEndIdx; idx += get_local_size(0)) {
+        const int col = colIdx[idx];
+
+        if(row >= out.dims[0] || col >= out.dims[1]) continue;    // Bad indices
+
+        // Get Values
+        const T val  = values[idx];
+        const T rval = rPtr[col * rhs.strides[1] + row];
+
+        const int offset = col * out.strides[1] + row;
+        if(reverse) oPtr[offset] = OP(rval, val);
+        else        oPtr[offset] = OP(val, rval);
+    }
+}
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
new file mode 100644
index 0000000000..da56d7cea5
--- /dev/null
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -0,0 +1,98 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse.hpp>
+#include <kernel/sparse_arith.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <complex.hpp>
+#include <copy.hpp>
+#include <err_common.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <where.hpp>
+
+namespace opencl
+{
+
+using namespace common;
+using namespace std;
+
+template<typename T>
+T getInf()
+{
+    return scalar<T>(std::numeric_limits<T>::infinity());
+}
+
+template<>
+cfloat getInf()
+{
+    return scalar<cfloat, float>(NAN, NAN); // Matches behavior of complex division by 0 in OpenCL
+}
+
+template<>
+cdouble getInf()
+{
+    return scalar<cdouble, double>(NAN, NAN); // Matches behavior of complex division by 0 in OpenCL
+}
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+{
+    lhs.eval();
+    rhs.eval();
+
+    Array<T> out = createEmptyArray<T>(dim4(0));
+    Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
+    switch(op) {
+        case af_add_t: out = copyArray<T>(rhs); break;
+        case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
+        case af_mul_t: out = zero; break;
+        case af_div_t: out = reverse ? createValueArray(rhs.dims(), getInf<T>()) : zero; break;
+        default      : out = copyArray<T>(rhs);
+    }
+    out.eval();
+    switch(lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                                            rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                                            rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                          \
+    template Array<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+    template Array<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
+                                           const bool reverse);                                 \
+
+INSTANTIATE(float  )
+INSTANTIATE(double )
+INSTANTIATE(cfloat )
+INSTANTIATE(cdouble)
+
+}
+
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
new file mode 100644
index 0000000000..2ec0d523a3
--- /dev/null
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <SparseArray.hpp>
+#include <sparse.hpp>
+#include <optypes.hpp>
+
+namespace opencl
+{
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                 const bool reverse = false);
+
+}
+
+

From cf4cd1807bf6db9789e8bf1ff6ba4b08090ceabb Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 29 Dec 2016 14:38:49 -0500
Subject: [PATCH 1195/2677] Add test for sparse arithmetic

---
 test/sparse_arith.cpp | 249 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 249 insertions(+)
 create mode 100644 test/sparse_arith.cpp

diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
new file mode 100644
index 0000000000..73228574b1
--- /dev/null
+++ b/test/sparse_arith.cpp
@@ -0,0 +1,249 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/traits.hpp>
+#include <vector>
+#include <iostream>
+#include <complex>
+#include <string>
+#include <testHelpers.hpp>
+
+using std::vector;
+using std::string;
+using std::cout;
+using std::endl;
+using std::abs;
+using af::cfloat;
+using af::cdouble;
+
+template<typename T>
+af::array makeSparse(af::array A, int factor)
+{
+    A = floor(A * 1000);
+    A = A * ((A % factor) == 0) / 1000;
+    return A;
+}
+
+template<>
+af::array makeSparse<cfloat>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = r / 2;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+template<>
+af::array makeSparse<cdouble>(af::array A, int factor)
+{
+    af::array r = real(A);
+    r = floor(r * 1000);
+    r = r * ((r % factor) == 0) / 1000;
+
+    af::array i = r / 2;
+
+    A = af::complex(r, i);
+    return A;
+}
+
+typedef enum {
+    af_add_t,
+    af_sub_t,
+    af_mul_t,
+    af_div_t,
+} af_op_t;
+
+template<af_op_t op>
+struct arith_op
+{
+    af::array operator()(af::array v1, af::array v2)
+    {
+        return v1;
+    }
+};
+
+template<>
+struct arith_op<af_add_t>
+{
+    af::array operator()(af::array v1, af::array v2)
+    {
+        return v1 + v2;
+    }
+};
+
+template<>
+struct arith_op<af_sub_t>
+{
+    af::array operator()(af::array v1, af::array v2)
+    {
+        return v1 - v2;
+    }
+};
+
+template<>
+struct arith_op<af_mul_t>
+{
+    af::array operator()(af::array v1, af::array v2)
+    {
+        return v1 * v2;
+    }
+};
+
+template<>
+struct arith_op<af_div_t>
+{
+    af::array operator()(af::array v1, af::array v2)
+    {
+        return v1 / v2;
+    }
+};
+
+template<typename T, af_op_t op>
+void sparseArithTester(const int m, const int n, int factor, const double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(m, n));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    af::array SA = af::sparse(A, AF_STORAGE_CSR);
+    af::array OA = af::sparse(A, AF_STORAGE_COO);
+
+    // Arith Op
+    af::array resS = arith_op<op>()(SA, B);
+    af::array resO = arith_op<op>()(OA, B);
+    af::array resD = arith_op<op>()( A, B);
+
+    af::array revS = arith_op<op>()(B, SA);
+    af::array revO = arith_op<op>()(B, OA);
+    af::array revD = arith_op<op>()(B,  A);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(resS - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resS - resD))) / (m * n), eps);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(resO - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resO - resD))) / (m * n), eps);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(revS - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revS - revD))) / (m * n), eps);
+
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(revO - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revO - revD))) / (m * n), eps);
+}
+
+template<typename T>
+void sparseArithTesterDiv(const int m, const int n, int factor, const double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(m, n));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    af::array SA = af::sparse(A, AF_STORAGE_CSR);
+    af::array OA = af::sparse(A, AF_STORAGE_COO);
+
+    // Arith Op
+    af::array resS = arith_op<af_div_t>()(SA, B);
+    af::array resO = arith_op<af_div_t>()(OA, B);
+    af::array resD = arith_op<af_div_t>()( A, B);
+
+    af::array revS = arith_op<af_div_t>()(B, SA);
+    af::array revO = arith_op<af_div_t>()(B, OA);
+    af::array revD = arith_op<af_div_t>()(B,  A);
+
+    T *hResS = resS.host<T>();
+    T *hResO = resO.host<T>();
+    T *hResD = resD.host<T>();
+    T *hRevS = revS.host<T>();
+    T *hRevO = revO.host<T>();
+    T *hRevD = revD.host<T>();
+
+// This macro is used to check if either value is finite and then call assert
+// If neither value is finite, then they can be assumed to be equal to either inf or nan
+#define ASSERT_FINITE_EQ(V1, V2)                                                            \
+    if(std::isfinite(V1) || std::isfinite(V2)) ASSERT_NEAR(V1, V2, eps) << "at : " << i;    \
+
+    for(int i = 0; i < B.elements(); i++) {
+        ASSERT_FINITE_EQ(real(hResS[i]), real(hResD[i]));
+        ASSERT_FINITE_EQ(real(hResO[i]), real(hResD[i]));
+        ASSERT_FINITE_EQ(real(hRevS[i]), real(hRevD[i]));
+        ASSERT_FINITE_EQ(real(hRevO[i]), real(hRevD[i]));
+
+        if(A.iscomplex()) {
+            ASSERT_FINITE_EQ(imag(hResS[i]), imag(hResD[i]));
+            ASSERT_FINITE_EQ(imag(hResO[i]), imag(hResD[i]));
+            ASSERT_FINITE_EQ(imag(hRevS[i]), imag(hRevD[i]));
+            ASSERT_FINITE_EQ(imag(hRevO[i]), imag(hRevD[i]));
+        }
+    }
+#undef ASSERT_FINITE_EQ
+
+    af::freeHost(hResS);
+    af::freeHost(hResO);
+    af::freeHost(hResD);
+    af::freeHost(hRevS);
+    af::freeHost(hRevO);
+    af::freeHost(hRevD);
+}
+
+#define ARITH_TESTS_OPS(T, M, N, F, EPS)                                    \
+    TEST(SPARSE_ARITH, T##_ADD_##M##_##N)                                   \
+    {                                                                       \
+        sparseArithTester<T, af_add_t>(M, N, F, EPS);                       \
+    }                                                                       \
+    TEST(SPARSE_ARITH, T##_SUB_##M##_##N)                                   \
+    {                                                                       \
+        sparseArithTester<T, af_sub_t>(M, N, F, EPS);                       \
+    }                                                                       \
+    TEST(SPARSE_ARITH, T##_MUL_##M##_##N)                                   \
+    {                                                                       \
+        sparseArithTester<T, af_mul_t>(M, N, F, EPS);                       \
+    }                                                                       \
+    TEST(SPARSE_ARITH, T##_DIV_##M##_##N)                                   \
+    {                                                                       \
+        sparseArithTesterDiv<T>(M, N, F, EPS);                              \
+    }                                                                       \
+
+#define ARITH_TESTS(T, eps)                                                 \
+    ARITH_TESTS_OPS(T, 10  , 10  , 5, eps)                                  \
+    ARITH_TESTS_OPS(T, 1024, 1024, 5, eps)                                  \
+    ARITH_TESTS_OPS(T, 100 , 100 , 1, eps)                                  \
+    ARITH_TESTS_OPS(T, 2048, 1000, 6, eps)                                  \
+    ARITH_TESTS_OPS(T, 123 , 278 , 5, eps)                                  \
+
+ARITH_TESTS(float  , 1e-6)
+ARITH_TESTS(double , 1e-6)
+ARITH_TESTS(cfloat , 1e-4) // This is mostly for complex division in OpenCL
+ARITH_TESTS(cdouble, 1e-6)

From 45e333be355d7ddefa8e89a6d8a740589bbcbf2b Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 29 Dec 2016 14:44:34 -0500
Subject: [PATCH 1196/2677] Disable division by sparse array

---
 src/api/c/binary.cpp  |  5 ++++-
 test/sparse_arith.cpp | 18 +++++-------------
 2 files changed, 9 insertions(+), 14 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 93861f9fc6..631a16edc5 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -220,7 +220,10 @@ af_err af_div(af_array *out, const af_array lhs, const af_array rhs, const bool
     } else if(linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
     } else if(!linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense should be rhs
+        // Division by sparse is currently not allowed - for convinence of
+        // dealing with division by 0
+        // return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense should be rhs
+        return AF_ERR_NOT_SUPPORTED;
     } else {
         return af_arith<af_div_t>(out, lhs, rhs, batchMode);
     }
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 73228574b1..aaafb2c558 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -179,16 +179,15 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     af::array resO = arith_op<af_div_t>()(OA, B);
     af::array resD = arith_op<af_div_t>()( A, B);
 
-    af::array revS = arith_op<af_div_t>()(B, SA);
-    af::array revO = arith_op<af_div_t>()(B, OA);
-    af::array revD = arith_op<af_div_t>()(B,  A);
+    // Assert division by sparse is not allowed
+    af_array out_temp = 0;
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), SA.get(), false));
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), OA.get(), false));
+    if(out_temp != 0) af_release_array(out_temp);
 
     T *hResS = resS.host<T>();
     T *hResO = resO.host<T>();
     T *hResD = resD.host<T>();
-    T *hRevS = revS.host<T>();
-    T *hRevO = revO.host<T>();
-    T *hRevD = revD.host<T>();
 
 // This macro is used to check if either value is finite and then call assert
 // If neither value is finite, then they can be assumed to be equal to either inf or nan
@@ -198,14 +197,10 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     for(int i = 0; i < B.elements(); i++) {
         ASSERT_FINITE_EQ(real(hResS[i]), real(hResD[i]));
         ASSERT_FINITE_EQ(real(hResO[i]), real(hResD[i]));
-        ASSERT_FINITE_EQ(real(hRevS[i]), real(hRevD[i]));
-        ASSERT_FINITE_EQ(real(hRevO[i]), real(hRevD[i]));
 
         if(A.iscomplex()) {
             ASSERT_FINITE_EQ(imag(hResS[i]), imag(hResD[i]));
             ASSERT_FINITE_EQ(imag(hResO[i]), imag(hResD[i]));
-            ASSERT_FINITE_EQ(imag(hRevS[i]), imag(hRevD[i]));
-            ASSERT_FINITE_EQ(imag(hRevO[i]), imag(hRevD[i]));
         }
     }
 #undef ASSERT_FINITE_EQ
@@ -213,9 +208,6 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     af::freeHost(hResS);
     af::freeHost(hResO);
     af::freeHost(hResD);
-    af::freeHost(hRevS);
-    af::freeHost(hRevO);
-    af::freeHost(hRevD);
 }
 
 #define ARITH_TESTS_OPS(T, M, N, F, EPS)                                    \

From ba5c3844e89a89feee56872b7a4b8cffacacae68 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Thu, 29 Dec 2016 15:01:31 -0500
Subject: [PATCH 1197/2677] Style: Use R instead of S for CSR (following O for
 COO)

---
 test/sparse_arith.cpp | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index aaafb2c558..93db3dc28b 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -129,26 +129,26 @@ void sparseArithTester(const int m, const int n, int factor, const double eps)
 
     A = makeSparse<T>(A, factor);
 
-    af::array SA = af::sparse(A, AF_STORAGE_CSR);
+    af::array RA = af::sparse(A, AF_STORAGE_CSR);
     af::array OA = af::sparse(A, AF_STORAGE_COO);
 
     // Arith Op
-    af::array resS = arith_op<op>()(SA, B);
+    af::array resR = arith_op<op>()(RA, B);
     af::array resO = arith_op<op>()(OA, B);
     af::array resD = arith_op<op>()( A, B);
 
-    af::array revS = arith_op<op>()(B, SA);
+    af::array revR = arith_op<op>()(B, RA);
     af::array revO = arith_op<op>()(B, OA);
     af::array revD = arith_op<op>()(B,  A);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(resS - resD))) / (m * n), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resS - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(resR - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resR - resD))) / (m * n), eps);
 
     ASSERT_NEAR(0, af::sum<double>(af::abs(real(resO - resD))) / (m * n), eps);
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resO - resD))) / (m * n), eps);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(revS - revD))) / (m * n), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revS - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(real(revR - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revR - revD))) / (m * n), eps);
 
     ASSERT_NEAR(0, af::sum<double>(af::abs(real(revO - revD))) / (m * n), eps);
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revO - revD))) / (m * n), eps);
@@ -171,21 +171,21 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
 
     A = makeSparse<T>(A, factor);
 
-    af::array SA = af::sparse(A, AF_STORAGE_CSR);
+    af::array RA = af::sparse(A, AF_STORAGE_CSR);
     af::array OA = af::sparse(A, AF_STORAGE_COO);
 
     // Arith Op
-    af::array resS = arith_op<af_div_t>()(SA, B);
+    af::array resR = arith_op<af_div_t>()(RA, B);
     af::array resO = arith_op<af_div_t>()(OA, B);
     af::array resD = arith_op<af_div_t>()( A, B);
 
     // Assert division by sparse is not allowed
     af_array out_temp = 0;
-    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), SA.get(), false));
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), RA.get(), false));
     ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), OA.get(), false));
     if(out_temp != 0) af_release_array(out_temp);
 
-    T *hResS = resS.host<T>();
+    T *hResR = resR.host<T>();
     T *hResO = resO.host<T>();
     T *hResD = resD.host<T>();
 
@@ -195,17 +195,17 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     if(std::isfinite(V1) || std::isfinite(V2)) ASSERT_NEAR(V1, V2, eps) << "at : " << i;    \
 
     for(int i = 0; i < B.elements(); i++) {
-        ASSERT_FINITE_EQ(real(hResS[i]), real(hResD[i]));
+        ASSERT_FINITE_EQ(real(hResR[i]), real(hResD[i]));
         ASSERT_FINITE_EQ(real(hResO[i]), real(hResD[i]));
 
         if(A.iscomplex()) {
-            ASSERT_FINITE_EQ(imag(hResS[i]), imag(hResD[i]));
+            ASSERT_FINITE_EQ(imag(hResR[i]), imag(hResD[i]));
             ASSERT_FINITE_EQ(imag(hResO[i]), imag(hResD[i]));
         }
     }
 #undef ASSERT_FINITE_EQ
 
-    af::freeHost(hResS);
+    af::freeHost(hResR);
     af::freeHost(hResO);
     af::freeHost(hResD);
 }

From 4e281179038e0634c5fb5fa9d48b94d6db1c4900 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 30 Dec 2016 12:22:20 -0500
Subject: [PATCH 1198/2677] Sparse Arith: Mul and Div return sparse arrays (may
 include 0s)

---
 src/api/c/binary.cpp                          |   7 +-
 src/backend/cpu/kernel/sparse_arith.hpp       |  44 +++++++-
 src/backend/cpu/sparse_arith.cpp              |  54 ++++++---
 src/backend/cpu/sparse_arith.hpp              |   8 +-
 src/backend/cuda/kernel/sparse_arith.hpp      |  82 ++++++++++++++
 src/backend/cuda/sparse_arith.cu              |  48 ++++++--
 src/backend/cuda/sparse_arith.hpp             |   8 +-
 src/backend/opencl/kernel/sparse_arith.hpp    | 106 ++++++++++++++++++
 src/backend/opencl/kernel/sparse_arith_coo.cl |  26 +++++
 src/backend/opencl/kernel/sparse_arith_csr.cl |  31 +++++
 src/backend/opencl/sparse_arith.cpp           |  48 ++++++--
 src/backend/opencl/sparse_arith.hpp           |   8 +-
 12 files changed, 424 insertions(+), 46 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 631a16edc5..bfa349ea79 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -39,8 +39,11 @@ template<typename T, af_op_t op>
 static inline af_array arithSparseDenseOp(const af_array lhs, const af_array rhs,
                                           const bool reverse)
 {
-    af_array res = getHandle(arithOp<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
-    return res;
+    if(op == af_add_t || op == af_sub_t)
+        return getHandle(arithOpD<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
+    else if(op == af_mul_t || op == af_div_t)
+        return getHandle(arithOpS<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
+
 }
 
 template<af_op_t op>
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index d7bc24e9cf..fd7d579259 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -62,9 +62,9 @@ struct arith_op<T, af_div_t>
 };
 
 template<typename T, af_op_t op, af_storage type>
-void sparseArithOp(Array<T> output,
-                   const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
-                   const Array<T> rhs, const bool reverse = false)
+void sparseArithOpD(Array<T> output,
+                    const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
+                    const Array<T> rhs, const bool reverse = false)
 {
     T * oPtr = output.get();
     const T   * hPtr = rhs.get();
@@ -103,6 +103,44 @@ void sparseArithOp(Array<T> output,
     }
 }
 
+template<typename T, af_op_t op, af_storage type>
+void sparseArithOpS(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
+                    const Array<T> rhs, const bool reverse = false)
+{
+          T   * vPtr = values.get();
+    const int * rPtr = rowIdx.get();
+    const int * cPtr = colIdx.get();
+
+    const T   * hPtr = rhs.get();
+
+    dim4 dims     = rhs.dims();
+    dim4 hstrides = rhs.strides();
+
+    std::vector<int> temp;
+    if(type == AF_STORAGE_CSR) {
+        temp.resize(values.elements());
+        for(int i = 0; i < rowIdx.dims()[0] - 1; i++) {
+            for(int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) {
+                temp[ii] = i;
+            }
+        }
+    //} else if(type == AF_STORAGE_CSC) {   // For future
+    }
+
+    const int *xx = (type == AF_STORAGE_CSR) ? temp.data() : rPtr;
+    const int *yy = (type == AF_STORAGE_CSC) ? temp.data() : cPtr;
+
+    for(int i = 0; i < (int)values.elements(); i++) {
+        // Bad index data
+        if(xx[i] >= dims[0] || yy [i]>= dims[1]) continue;
+
+        int hoff = xx[i] + yy[i] * hstrides[1];
+
+        if(reverse) vPtr[i] = arith_op<T, op>()(hPtr[hoff], vPtr[i]);
+        else        vPtr[i] = arith_op<T, op>()(vPtr[i], hPtr[hoff]);
+    }
+}
+
 }
 }
 
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index 64129a5430..b165d6ea25 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -56,7 +56,7 @@ cdouble getInf()
 }
 
 template<typename T, af_op_t op>
-Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
 {
     lhs.eval();
     rhs.eval();
@@ -66,19 +66,17 @@ Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reve
     switch(op) {
         case af_add_t: out = copyArray<T>(rhs); break;
         case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
-        case af_mul_t: out = zero; break;
-        case af_div_t: out = reverse ? createValueArray(rhs.dims(), getInf<T>()) : zero; break;
         default      : out = copyArray<T>(rhs);
     }
     out.eval();
     switch(lhs.getStorage()) {
         case AF_STORAGE_CSR:
-            getQueue().enqueue(kernel::sparseArithOp<T, op, AF_STORAGE_CSR>,
+            getQueue().enqueue(kernel::sparseArithOpD<T, op, AF_STORAGE_CSR>,
                                out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
                                rhs, reverse);
             break;
         case AF_STORAGE_COO:
-            getQueue().enqueue(kernel::sparseArithOp<T, op, AF_STORAGE_COO>,
+            getQueue().enqueue(kernel::sparseArithOpD<T, op, AF_STORAGE_COO>,
                                out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
                                rhs, reverse);
             break;
@@ -89,15 +87,43 @@ Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reve
     return out;
 }
 
-#define INSTANTIATE(T)                                                                          \
-    template Array<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
+template<typename T, af_op_t op>
+SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+{
+    lhs.eval();
+    rhs.eval();
+
+    SparseArray<T> out = createArrayDataSparseArray<T>(lhs.dims(), lhs.getValues(),
+                                                       lhs.getRowIdx(), lhs.getColIdx(),
+                                                       lhs.getStorage(), true);
+    out.eval();
+    switch(out.getStorage()) {
+        case AF_STORAGE_CSR:
+            getQueue().enqueue(kernel::sparseArithOpS<T, op, AF_STORAGE_CSR>,
+                               out.getValues(), out.getRowIdx(), out.getColIdx(),
+                               rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            getQueue().enqueue(kernel::sparseArithOpS<T, op, AF_STORAGE_COO>,
+                               out.getValues(), out.getRowIdx(), out.getColIdx(),
+                               rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                              \
+    template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                           const bool reverse);                                     \
+    template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
+    template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                 const bool reverse);                               \
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
index a258177332..ba01feeeb1 100644
--- a/src/backend/cpu/sparse_arith.hpp
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -15,8 +15,14 @@
 namespace cpu
 {
 
+// These two functions cannot be overloaded by return type.
+// So have to give them separate names.
 template<typename T, af_op_t op>
-Array<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                  const bool reverse = false);
 
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                                const bool reverse = false);
+
 }
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 23ea54b625..1e8dac4ec9 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -160,6 +160,88 @@ void sparseArithOpCOO(Param<T> out,
     POST_LAUNCH_CHECK();
 }
 
+template<typename T, af_op_t op>
+__global__
+void sparseArithCSRKernel(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                          CParam<T> rhs, const bool reverse)
+{
+    const int row         = blockIdx.x * TY + threadIdx.y;
+
+    if(row >= rhs.dims[0]) return;
+
+    const int rowStartIdx = rowIdx.ptr[row  ];
+    const int rowEndIdx   = rowIdx.ptr[row+1];
+
+    // Repeat loop until all values in the row are computed
+    for(int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
+        const int col = colIdx.ptr[idx];
+
+        if(row >= rhs.dims[0] || col >= rhs.dims[1]) continue;    // Bad indices
+
+        // Get Values
+        const T val  = values.ptr[idx];
+        const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+        if(reverse) values.ptr[idx] = arith_op<T, op>()(rval, val);
+        else        values.ptr[idx] = arith_op<T, op>()(val, rval);
+    }
+}
+
+template<typename T, af_op_t op>
+__global__
+void sparseArithCOOKernel(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                          CParam<T> rhs, const bool reverse)
+{
+    const int idx = blockIdx.x * THREADS + threadIdx.x;
+
+    if(idx >= values.dims[0]) return;
+
+    const int row = rowIdx.ptr[idx];
+    const int col = colIdx.ptr[idx];
+
+    if(row >= rhs.dims[0] || col >= rhs.dims[1]) return;    // Bad indices
+
+    // Get Values
+    const T val  = values.ptr[idx];
+    const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+    if(reverse) values.ptr[idx] = arith_op<T, op>()(rval, val);
+    else        values.ptr[idx] = arith_op<T, op>()(val, rval);
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                      CParam<T> rhs, const bool reverse)
+{
+    // Each Y for threads does one row
+    dim3 threads(TX, TY, 1);
+
+    // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
+    dim3 blocks(divup(rhs.dims[0], TY), 1, 1);
+
+    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads,
+                 values, rowIdx, colIdx, rhs, reverse);
+
+    POST_LAUNCH_CHECK();
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                      CParam<T> rhs,
+                      const bool reverse)
+{
+    // Linear indexing with one elements per thread
+    dim3 threads(THREADS, 1, 1);
+
+    // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
+    dim3 blocks(divup(values.dims[0], THREADS), 1, 1);
+
+    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads,
+                 values, rowIdx, colIdx, rhs, reverse);
+
+    POST_LAUNCH_CHECK();
+}
+
 } // namespace kernel
 
 } // namespace cuda
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 84ab7a7efd..adeb5e96c6 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -50,7 +50,7 @@ cdouble getInf()
 }
 
 template<typename T, af_op_t op>
-Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
 {
     lhs.eval();
     rhs.eval();
@@ -60,8 +60,6 @@ Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reve
     switch(op) {
         case af_add_t: out = copyArray<T>(rhs); break;
         case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
-        case af_mul_t: out = zero; break;
-        case af_div_t: out = reverse ? createValueArray(rhs.dims(), getInf<T>()) : zero; break;
         default      : out = copyArray<T>(rhs);
     }
     out.eval();
@@ -81,15 +79,41 @@ Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reve
     return out;
 }
 
-#define INSTANTIATE(T)                                                                          \
-    template Array<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
+template<typename T, af_op_t op>
+SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+{
+    lhs.eval();
+    rhs.eval();
+
+    SparseArray<T> out = createArrayDataSparseArray<T>(lhs.dims(), lhs.getValues(),
+                                                       lhs.getRowIdx(), lhs.getColIdx(),
+                                                       lhs.getStorage(), true);
+    out.eval();
+    switch(lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
+                                            rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
+                                            rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                              \
+    template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
+    template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
index bf3ac04e91..50c40d2258 100644
--- a/src/backend/cuda/sparse_arith.hpp
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -15,9 +15,15 @@
 namespace cuda
 {
 
+// These two functions cannot be overloaded by return type.
+// So have to give them separate names.
 template<typename T, af_op_t op>
-Array<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                  const bool reverse = false);
 
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                                const bool reverse = false);
+
 }
 
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index dac572720c..98c88b3407 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -161,5 +161,111 @@ namespace opencl
             }
         }
 
+        template<typename T, af_op_t op>
+        void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx,
+                        const Param rhs, const bool reverse)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sparseArithCSRProgs;
+                static std::map<int, Kernel *> sparseArithCSRKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="  << dtype_traits<T>::getName();
+                    options << " -D OP=" << getOpString<op>();
+
+                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                       (af_dtype) dtype_traits<T>::af_type == c64) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
+                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
+                    Program prog;
+                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                    sparseArithCSRProgs[device] = new Program(prog);
+                    sparseArithCSRKernels[device] = new Kernel(*sparseArithCSRProgs[device], "sparse_arith_csr_kernel_S");
+                });
+
+                auto sparseArithCSROp = KernelFunctor<const Buffer, const Buffer, const Buffer,
+                                                      const int,
+                                                      const Buffer, const KParam,
+                                                      const int>(*sparseArithCSRKernels[device]);
+
+                NDRange local(TX, TY, 1);
+                NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
+
+                sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
+                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                         *rhs.data, rhs.info, reverse);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
+
+        template<typename T, af_op_t op>
+        void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx,
+                        const Param rhs, const bool reverse)
+        {
+            try {
+                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
+                static std::map<int, Program*>   sparseArithCOOProgs;
+                static std::map<int, Kernel *> sparseArithCOOKernels;
+
+                int device = getActiveDeviceId();
+
+                std::call_once( compileFlags[device], [device] () {
+                    std::ostringstream options;
+                    options << " -D T="  << dtype_traits<T>::getName();
+                    options << " -D OP=" << getOpString<op>();
+
+                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                       (af_dtype) dtype_traits<T>::af_type == c64) {
+                        options << " -D IS_CPLX=1";
+                    } else {
+                        options << " -D IS_CPLX=0";
+                    }
+                    if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                        options << " -D USE_DOUBLE";
+                    }
+
+                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
+                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
+                    Program prog;
+                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                    sparseArithCOOProgs[device] = new Program(prog);
+                    sparseArithCOOKernels[device] = new Kernel(*sparseArithCOOProgs[device], "sparse_arith_coo_kernel_S");
+                });
+
+                auto sparseArithCOOOp = KernelFunctor<Buffer, Buffer, Buffer,
+                                                      const int,
+                                                      const Buffer, const KParam,
+                                                      const int>(*sparseArithCOOKernels[device]);
+
+                NDRange local(THREADS, 1, 1);
+                NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+
+                sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
+                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                         *rhs.data, rhs.info, reverse);
+
+                CL_DEBUG_FINISH(getQueue());
+            } catch (cl::Error err) {
+                CL_TO_AF_ERROR(err);
+            }
+        }
+
     }
 }
diff --git a/src/backend/opencl/kernel/sparse_arith_coo.cl b/src/backend/opencl/kernel/sparse_arith_coo.cl
index 57e7355ac3..1dc18422dc 100644
--- a/src/backend/opencl/kernel/sparse_arith_coo.cl
+++ b/src/backend/opencl/kernel/sparse_arith_coo.cl
@@ -35,3 +35,29 @@ void sparse_arith_coo_kernel(__global T *oPtr,
     if(reverse) oPtr[offset] = OP(rval, val);
     else        oPtr[offset] = OP(val, rval);
 }
+
+__kernel
+void sparse_arith_coo_kernel_S(__global T *values,
+                               __global int *rowIdx,
+                               __global int *colIdx,
+                               const int nNZ,
+                               __global const T *rPtr,
+                               const KParam rhs,
+                               const int reverse)
+{
+    const int idx = get_global_id(0);
+
+    if(idx >= nNZ) return;
+
+    const int row = rowIdx[idx];
+    const int col = colIdx[idx];
+
+    if(row >= rhs.dims[0] || col >= rhs.dims[1]) return;    // Bad indices
+
+    // Get Values
+    const T val  = values[idx];
+    const T rval = rPtr[col * rhs.strides[1] + row];
+
+    if(reverse) values[idx] = OP(rval, val);
+    else        values[idx] = OP(val, rval);
+}
diff --git a/src/backend/opencl/kernel/sparse_arith_csr.cl b/src/backend/opencl/kernel/sparse_arith_csr.cl
index b3a7aab449..1bea95b5e6 100644
--- a/src/backend/opencl/kernel/sparse_arith_csr.cl
+++ b/src/backend/opencl/kernel/sparse_arith_csr.cl
@@ -40,3 +40,34 @@ void sparse_arith_csr_kernel(__global T *oPtr,
         else        oPtr[offset] = OP(val, rval);
     }
 }
+
+__kernel
+void sparse_arith_csr_kernel_S(__global T *values,
+                               __global int *rowIdx,
+                               __global int *colIdx,
+                               const int nNZ,
+                               __global const T *rPtr,
+                               const KParam rhs,
+                               const int reverse)
+{
+    const int row = get_group_id(0) * get_local_size(1) + get_local_id(1);
+
+    if(row >= rhs.dims[0]) return;
+
+    const int rowStartIdx = rowIdx[row  ];
+    const int rowEndIdx   = rowIdx[row+1];
+
+    // Repeat loop until all values in the row are computed
+    for(int idx = rowStartIdx + get_local_id(0); idx < rowEndIdx; idx += get_local_size(0)) {
+        const int col = colIdx[idx];
+
+        if(row >= rhs.dims[0] || col >= rhs.dims[1]) continue;    // Bad indices
+
+        // Get Values
+        const T val  = values[idx];
+        const T rval = rPtr[col * rhs.strides[1] + row];
+
+        if(reverse) values[idx] = OP(rval, val);
+        else        values[idx] = OP(val, rval);
+    }
+}
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index da56d7cea5..d62d42eb5d 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -48,7 +48,7 @@ cdouble getInf()
 }
 
 template<typename T, af_op_t op>
-Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
 {
     lhs.eval();
     rhs.eval();
@@ -58,8 +58,6 @@ Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reve
     switch(op) {
         case af_add_t: out = copyArray<T>(rhs); break;
         case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
-        case af_mul_t: out = zero; break;
-        case af_div_t: out = reverse ? createValueArray(rhs.dims(), getInf<T>()) : zero; break;
         default      : out = copyArray<T>(rhs);
     }
     out.eval();
@@ -79,15 +77,41 @@ Array<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reve
     return out;
 }
 
-#define INSTANTIATE(T)                                                                          \
-    template Array<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
-    template Array<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,      \
-                                           const bool reverse);                                 \
+template<typename T, af_op_t op>
+SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+{
+    lhs.eval();
+    rhs.eval();
+
+    SparseArray<T> out = createArrayDataSparseArray<T>(lhs.dims(), lhs.getValues(),
+                                                       lhs.getRowIdx(), lhs.getColIdx(),
+                                                       lhs.getStorage(), true);
+    out.eval();
+    switch(lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
+                                            rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
+                                            rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                                              \
+    template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
+    template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
index 2ec0d523a3..3de623c02e 100644
--- a/src/backend/opencl/sparse_arith.hpp
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -15,10 +15,16 @@
 namespace opencl
 {
 
+// These two functions cannot be overloaded by return type.
+// So have to give them separate names.
 template<typename T, af_op_t op>
-Array<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                  const bool reverse = false);
 
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                                const bool reverse = false);
+
 }
 
 

From a4a3871129f0b477b7d8bdd1d28542561c9c1837 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 30 Dec 2016 12:23:06 -0500
Subject: [PATCH 1199/2677] Update sparse arith tests for sparse arrays
 returned from mul and div

---
 test/sparse_arith.cpp | 129 ++++++++++++++++++++++++++++++++++--------
 1 file changed, 106 insertions(+), 23 deletions(-)

diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 93db3dc28b..241cde6c6b 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -112,6 +112,41 @@ struct arith_op<af_div_t>
     }
 };
 
+template<typename T>
+void sparseCompare(af::array A, af::array B, const double eps)
+{
+// This macro is used to check if either value is finite and then call assert
+// If neither value is finite, then they can be assumed to be equal to either inf or nan
+#define ASSERT_FINITE_EQ(V1, V2)                                                            \
+    if(std::isfinite(V1) || std::isfinite(V2)) ASSERT_NEAR(V1, V2, eps) << "at : " << i;    \
+
+    af::array AValues = sparseGetValues(A);
+    af::array ARowIdx = sparseGetRowIdx(A);
+    af::array AColIdx = sparseGetColIdx(A);
+
+    af::array BValues = sparseGetValues(B);
+    af::array BRowIdx = sparseGetRowIdx(B);
+    af::array BColIdx = sparseGetColIdx(B);
+
+    // Verify row and col indices
+    ASSERT_EQ(0, af::max<int>(ARowIdx - BRowIdx));
+    ASSERT_EQ(0, af::max<int>(AColIdx - BColIdx));
+
+    T *ptrA = AValues.host<T>();
+    T *ptrB = BValues.host<T>();
+    for(int i = 0; i < AValues.elements(); i++) {
+        ASSERT_FINITE_EQ(real(ptrA[i]), real(ptrB[i]));
+
+        if(A.iscomplex()) {
+            ASSERT_FINITE_EQ(imag(ptrA[i]), imag(ptrB[i]));
+        }
+    }
+    af::freeHost(ptrA);
+    af::freeHost(ptrB);
+
+#undef ASSERT_FINITE_EQ
+}
+
 template<typename T, af_op_t op>
 void sparseArithTester(const int m, const int n, int factor, const double eps)
 {
@@ -154,6 +189,67 @@ void sparseArithTester(const int m, const int n, int factor, const double eps)
     ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revO - revD))) / (m * n), eps);
 }
 
+// Mul
+template<typename T>
+void sparseArithTesterMul(const int m, const int n, int factor, const double eps)
+{
+    af::deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    af::array A = cpu_randu<T>(af::dim4(m, n));
+    af::array B = cpu_randu<T>(af::dim4(m, n));
+#else
+    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    af::array B = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+
+    af::array RA = af::sparse(A, AF_STORAGE_CSR);
+    af::array OA = af::sparse(A, AF_STORAGE_COO);
+
+    // Forward
+    {
+        // Arith Op
+        af::array resR = arith_op<af_mul_t>()(RA, B);
+        af::array resO = arith_op<af_mul_t>()(OA, B);
+
+        // We will test this by converting the COO to CSR and CSR to COO and
+        // comparing them. In essense, we are comparing the resR and resO
+        // TODO: Make a better comparison using dense
+
+        // Check resR against conR
+        af::array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
+        sparseCompare<T>(resR, conR, eps);
+
+        // Check resO against conO
+        af::array conO = sparseConvertTo(resR, AF_STORAGE_COO);
+        sparseCompare<T>(resO, conO, eps);
+    }
+
+    // Reverse
+    {
+        // Arith Op
+        af::array resR = arith_op<af_mul_t>()(B, RA);
+        af::array resO = arith_op<af_mul_t>()(B, OA);
+
+        // We will test this by converting the COO to CSR and CSR to COO and
+        // comparing them. In essense, we are comparing the resR and resO
+        // TODO: Make a better comparison using dense
+
+        // Check resR against conR
+        af::array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
+        sparseCompare<T>(resR, conR, eps);
+
+        // Check resO against conO
+        af::array conO = sparseConvertTo(resR, AF_STORAGE_COO);
+        sparseCompare<T>(resO, conO, eps);
+    }
+}
+
+// Div
 template<typename T>
 void sparseArithTesterDiv(const int m, const int n, int factor, const double eps)
 {
@@ -177,7 +273,6 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     // Arith Op
     af::array resR = arith_op<af_div_t>()(RA, B);
     af::array resO = arith_op<af_div_t>()(OA, B);
-    af::array resD = arith_op<af_div_t>()( A, B);
 
     // Assert division by sparse is not allowed
     af_array out_temp = 0;
@@ -185,29 +280,17 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), OA.get(), false));
     if(out_temp != 0) af_release_array(out_temp);
 
-    T *hResR = resR.host<T>();
-    T *hResO = resO.host<T>();
-    T *hResD = resD.host<T>();
+    // We will test this by converting the COO to CSR and CSR to COO and
+    // comparing them. In essense, we are comparing the resR and resO
+    // TODO: Make a better comparison using dense
 
-// This macro is used to check if either value is finite and then call assert
-// If neither value is finite, then they can be assumed to be equal to either inf or nan
-#define ASSERT_FINITE_EQ(V1, V2)                                                            \
-    if(std::isfinite(V1) || std::isfinite(V2)) ASSERT_NEAR(V1, V2, eps) << "at : " << i;    \
-
-    for(int i = 0; i < B.elements(); i++) {
-        ASSERT_FINITE_EQ(real(hResR[i]), real(hResD[i]));
-        ASSERT_FINITE_EQ(real(hResO[i]), real(hResD[i]));
-
-        if(A.iscomplex()) {
-            ASSERT_FINITE_EQ(imag(hResR[i]), imag(hResD[i]));
-            ASSERT_FINITE_EQ(imag(hResO[i]), imag(hResD[i]));
-        }
-    }
-#undef ASSERT_FINITE_EQ
+    // Check resR against conR
+    af::array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
+    sparseCompare<T>(resR, conR, eps);
 
-    af::freeHost(hResR);
-    af::freeHost(hResO);
-    af::freeHost(hResD);
+    // Check resO against conO
+    af::array conO = sparseConvertTo(resR, AF_STORAGE_COO);
+    sparseCompare<T>(resO, conO, eps);
 }
 
 #define ARITH_TESTS_OPS(T, M, N, F, EPS)                                    \
@@ -221,7 +304,7 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     }                                                                       \
     TEST(SPARSE_ARITH, T##_MUL_##M##_##N)                                   \
     {                                                                       \
-        sparseArithTester<T, af_mul_t>(M, N, F, EPS);                       \
+        sparseArithTesterMul<T>(M, N, F, EPS);                              \
     }                                                                       \
     TEST(SPARSE_ARITH, T##_DIV_##M##_##N)                                   \
     {                                                                       \

From 2b8dbced79a6caacddd36c5991fd66f5d606f969 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Fri, 30 Dec 2016 13:10:16 -0500
Subject: [PATCH 1200/2677] Sparse Arith OpenCL - Use new kernel caching method

---
 src/backend/opencl/kernel/sparse_arith.hpp | 101 ++++++++++++++-------
 1 file changed, 69 insertions(+), 32 deletions(-)

diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 98c88b3407..7079f57f90 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -56,13 +56,17 @@ namespace opencl
                         const Param rhs, const bool reverse)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sparseArithCSRProgs;
-                static std::map<int, Kernel *> sparseArithCSRKernels;
+                std::string ref_name =
+                    std::string("sparseArithOpCSR_") +
+                    getOpString<op>() + std::string("_") +
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
 
-                std::call_once( compileFlags[device], [device] () {
                     std::ostringstream options;
                     options << " -D T="  << dtype_traits<T>::getName();
                     options << " -D OP=" << getOpString<op>();
@@ -80,17 +84,22 @@ namespace opencl
 
                     const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
                     const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
+
                     Program prog;
                     buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    sparseArithCSRProgs[device] = new Program(prog);
-                    sparseArithCSRKernels[device] = new Kernel(*sparseArithCSRProgs[device], "sparse_arith_csr_kernel");
-                });
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel");
+
+                    kernelCaches[device][ref_name] = entry;
+                } else {
+                    entry = idx->second;
+                }
 
                 auto sparseArithCSROp = KernelFunctor<Buffer, const KParam,
                                               const Buffer, const Buffer, const Buffer,
                                               const int,
                                               const Buffer, const KParam,
-                                              const int>(*sparseArithCSRKernels[device]);
+                                              const int>(*entry.ker);
 
                 NDRange local(TX, TY, 1);
                 NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
@@ -111,13 +120,17 @@ namespace opencl
                         const Param rhs, const bool reverse)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sparseArithCOOProgs;
-                static std::map<int, Kernel *> sparseArithCOOKernels;
+                std::string ref_name =
+                    std::string("sparseArithOpCOO_") +
+                    getOpString<op>() + std::string("_") +
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
 
-                std::call_once( compileFlags[device], [device] () {
                     std::ostringstream options;
                     options << " -D T="  << dtype_traits<T>::getName();
                     options << " -D OP=" << getOpString<op>();
@@ -135,17 +148,22 @@ namespace opencl
 
                     const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
                     const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
+
                     Program prog;
                     buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    sparseArithCOOProgs[device] = new Program(prog);
-                    sparseArithCOOKernels[device] = new Kernel(*sparseArithCOOProgs[device], "sparse_arith_coo_kernel");
-                });
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel");
+
+                    kernelCaches[device][ref_name] = entry;
+                } else {
+                    entry = idx->second;
+                }
 
                 auto sparseArithCOOOp = KernelFunctor<Buffer, const KParam,
                                               const Buffer, const Buffer, const Buffer,
                                               const int,
                                               const Buffer, const KParam,
-                                              const int>(*sparseArithCOOKernels[device]);
+                                              const int>(*entry.ker);
 
                 NDRange local(THREADS, 1, 1);
                 NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
@@ -166,13 +184,17 @@ namespace opencl
                         const Param rhs, const bool reverse)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sparseArithCSRProgs;
-                static std::map<int, Kernel *> sparseArithCSRKernels;
+                std::string ref_name =
+                    std::string("sparseArithOpSCSR_") +
+                    getOpString<op>() + std::string("_") +
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
 
-                std::call_once( compileFlags[device], [device] () {
                     std::ostringstream options;
                     options << " -D T="  << dtype_traits<T>::getName();
                     options << " -D OP=" << getOpString<op>();
@@ -190,16 +212,21 @@ namespace opencl
 
                     const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
                     const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
+
                     Program prog;
                     buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    sparseArithCSRProgs[device] = new Program(prog);
-                    sparseArithCSRKernels[device] = new Kernel(*sparseArithCSRProgs[device], "sparse_arith_csr_kernel_S");
-                });
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
+
+                    kernelCaches[device][ref_name] = entry;
+                } else {
 
+                    entry = idx->second;
+                }
                 auto sparseArithCSROp = KernelFunctor<const Buffer, const Buffer, const Buffer,
                                                       const int,
                                                       const Buffer, const KParam,
-                                                      const int>(*sparseArithCSRKernels[device]);
+                                                      const int>(*entry.ker);
 
                 NDRange local(TX, TY, 1);
                 NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
@@ -219,13 +246,17 @@ namespace opencl
                         const Param rhs, const bool reverse)
         {
             try {
-                static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
-                static std::map<int, Program*>   sparseArithCOOProgs;
-                static std::map<int, Kernel *> sparseArithCOOKernels;
+                std::string ref_name =
+                    std::string("sparseArithOpSCOO_") +
+                    getOpString<op>() + std::string("_") +
+                    std::string(dtype_traits<T>::getName());
 
                 int device = getActiveDeviceId();
+                auto idx = kernelCaches[device].find(ref_name);
+                kc_entry_t entry;
+
+                if (idx == kernelCaches[device].end()) {
 
-                std::call_once( compileFlags[device], [device] () {
                     std::ostringstream options;
                     options << " -D T="  << dtype_traits<T>::getName();
                     options << " -D OP=" << getOpString<op>();
@@ -243,16 +274,22 @@ namespace opencl
 
                     const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
                     const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
+
                     Program prog;
                     buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    sparseArithCOOProgs[device] = new Program(prog);
-                    sparseArithCOOKernels[device] = new Kernel(*sparseArithCOOProgs[device], "sparse_arith_coo_kernel_S");
-                });
+                    entry.prog = new Program(prog);
+                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
+
+                    kernelCaches[device][ref_name] = entry;
+                } else {
+                    entry = idx->second;
+                }
+
 
                 auto sparseArithCOOOp = KernelFunctor<Buffer, Buffer, Buffer,
                                                       const int,
                                                       const Buffer, const KParam,
-                                                      const int>(*sparseArithCOOKernels[device]);
+                                                      const int>(*entry.ker);
 
                 NDRange local(THREADS, 1, 1);
                 NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);

From e7c309177def129f68c98fe0eb8e084eec4fbbc7 Mon Sep 17 00:00:00 2001
From: Shehzan Mohammed <shehzan@arrayfire.com>
Date: Sat, 15 Apr 2017 12:01:48 -0400
Subject: [PATCH 1201/2677] Remove CL_TO_AF_ERROR from OpenCL sparse_arith
 kernel header

---
 src/backend/opencl/kernel/sparse_arith.hpp | 377 ++++++++++-----------
 1 file changed, 180 insertions(+), 197 deletions(-)

diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 7079f57f90..70ccaa18ac 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -53,256 +53,239 @@ namespace opencl
 
         template<typename T, af_op_t op>
         void sparseArithOpCSR(Param out, const Param values, const Param rowIdx, const Param colIdx,
-                        const Param rhs, const bool reverse)
+                const Param rhs, const bool reverse)
         {
-            try {
-                std::string ref_name =
-                    std::string("sparseArithOpCSR_") +
-                    getOpString<op>() + std::string("_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T="  << dtype_traits<T>::getName();
-                    options << " -D OP=" << getOpString<op>();
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+            std::string ref_name =
+                std::string("sparseArithOpCSR_") +
+                getOpString<op>() + std::string("_") +
+                std::string(dtype_traits<T>::getName());
+
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
-                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
+            if (idx == kernelCaches[device].end()) {
 
-                    Program prog;
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel");
+                std::ostringstream options;
+                options << " -D T="  << dtype_traits<T>::getName();
+                options << " -D OP=" << getOpString<op>();
 
-                    kernelCaches[device][ref_name] = entry;
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                        (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
+                }
+                if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
-                auto sparseArithCSROp = KernelFunctor<Buffer, const KParam,
-                                              const Buffer, const Buffer, const Buffer,
-                                              const int,
-                                              const Buffer, const KParam,
-                                              const int>(*entry.ker);
-
-                NDRange local(TX, TY, 1);
-                NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
+                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
+                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
 
-                sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
-                         *out.data, out.info,
-                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                         *rhs.data, rhs.info, reverse);
+                Program prog;
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel");
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
             }
+
+            auto sparseArithCSROp = KernelFunctor<Buffer, const KParam,
+                 const Buffer, const Buffer, const Buffer,
+                 const int,
+                 const Buffer, const KParam,
+                 const int>(*entry.ker);
+
+            NDRange local(TX, TY, 1);
+            NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
+
+            sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info,
+                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                    *rhs.data, rhs.info, reverse);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T, af_op_t op>
         void sparseArithOpCOO(Param out, const Param values, const Param rowIdx, const Param colIdx,
-                        const Param rhs, const bool reverse)
+                const Param rhs, const bool reverse)
         {
-            try {
-                std::string ref_name =
-                    std::string("sparseArithOpCOO_") +
-                    getOpString<op>() + std::string("_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T="  << dtype_traits<T>::getName();
-                    options << " -D OP=" << getOpString<op>();
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+            std::string ref_name =
+                std::string("sparseArithOpCOO_") +
+                getOpString<op>() + std::string("_") +
+                std::string(dtype_traits<T>::getName());
 
-                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
-                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                    Program prog;
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel");
+            if (idx == kernelCaches[device].end()) {
 
-                    kernelCaches[device][ref_name] = entry;
+                std::ostringstream options;
+                options << " -D T="  << dtype_traits<T>::getName();
+                options << " -D OP=" << getOpString<op>();
+
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                        (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
+                }
+                if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
-                auto sparseArithCOOOp = KernelFunctor<Buffer, const KParam,
-                                              const Buffer, const Buffer, const Buffer,
-                                              const int,
-                                              const Buffer, const KParam,
-                                              const int>(*entry.ker);
-
-                NDRange local(THREADS, 1, 1);
-                NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
+                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
 
-                sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
-                         *out.data, out.info,
-                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                         *rhs.data, rhs.info, reverse);
+                Program prog;
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel");
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
             }
+
+            auto sparseArithCOOOp = KernelFunctor<Buffer, const KParam,
+                 const Buffer, const Buffer, const Buffer,
+                 const int,
+                 const Buffer, const KParam,
+                 const int>(*entry.ker);
+
+            NDRange local(THREADS, 1, 1);
+            NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+
+            sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
+                    *out.data, out.info,
+                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                    *rhs.data, rhs.info, reverse);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T, af_op_t op>
         void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx,
-                        const Param rhs, const bool reverse)
+                const Param rhs, const bool reverse)
         {
-            try {
-                std::string ref_name =
-                    std::string("sparseArithOpSCSR_") +
-                    getOpString<op>() + std::string("_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T="  << dtype_traits<T>::getName();
-                    options << " -D OP=" << getOpString<op>();
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+            std::string ref_name =
+                std::string("sparseArithOpSCSR_") +
+                getOpString<op>() + std::string("_") +
+                std::string(dtype_traits<T>::getName());
 
-                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
-                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                    Program prog;
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
+            if (idx == kernelCaches[device].end()) {
 
-                    kernelCaches[device][ref_name] = entry;
-                } else {
+                std::ostringstream options;
+                options << " -D T="  << dtype_traits<T>::getName();
+                options << " -D OP=" << getOpString<op>();
 
-                    entry = idx->second;
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                        (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
+                } else {
+                    options << " -D IS_CPLX=0";
                 }
-                auto sparseArithCSROp = KernelFunctor<const Buffer, const Buffer, const Buffer,
-                                                      const int,
-                                                      const Buffer, const KParam,
-                                                      const int>(*entry.ker);
+                if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
+                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
 
-                NDRange local(TX, TY, 1);
-                NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
+                Program prog;
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
 
-                sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
-                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                         *rhs.data, rhs.info, reverse);
+                kernelCaches[device][ref_name] = entry;
+            } else {
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
+                entry = idx->second;
             }
+            auto sparseArithCSROp = KernelFunctor<const Buffer, const Buffer, const Buffer,
+                 const int,
+                 const Buffer, const KParam,
+                 const int>(*entry.ker);
+
+            NDRange local(TX, TY, 1);
+            NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
+
+            sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
+                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                    *rhs.data, rhs.info, reverse);
+
+            CL_DEBUG_FINISH(getQueue());
         }
 
         template<typename T, af_op_t op>
         void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx,
-                        const Param rhs, const bool reverse)
+                const Param rhs, const bool reverse)
         {
-            try {
-                std::string ref_name =
-                    std::string("sparseArithOpSCOO_") +
-                    getOpString<op>() + std::string("_") +
-                    std::string(dtype_traits<T>::getName());
-
-                int device = getActiveDeviceId();
-                auto idx = kernelCaches[device].find(ref_name);
-                kc_entry_t entry;
-
-                if (idx == kernelCaches[device].end()) {
-
-                    std::ostringstream options;
-                    options << " -D T="  << dtype_traits<T>::getName();
-                    options << " -D OP=" << getOpString<op>();
-
-                    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                       (af_dtype) dtype_traits<T>::af_type == c64) {
-                        options << " -D IS_CPLX=1";
-                    } else {
-                        options << " -D IS_CPLX=0";
-                    }
-                    if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                        options << " -D USE_DOUBLE";
-                    }
+            std::string ref_name =
+                std::string("sparseArithOpSCOO_") +
+                getOpString<op>() + std::string("_") +
+                std::string(dtype_traits<T>::getName());
 
-                    const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
-                    const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
+            int device = getActiveDeviceId();
+            auto idx = kernelCaches[device].find(ref_name);
+            kc_entry_t entry;
 
-                    Program prog;
-                    buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                    entry.prog = new Program(prog);
-                    entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
+            if (idx == kernelCaches[device].end()) {
 
-                    kernelCaches[device][ref_name] = entry;
+                std::ostringstream options;
+                options << " -D T="  << dtype_traits<T>::getName();
+                options << " -D OP=" << getOpString<op>();
+
+                if((af_dtype) dtype_traits<T>::af_type == c32 ||
+                        (af_dtype) dtype_traits<T>::af_type == c64) {
+                    options << " -D IS_CPLX=1";
                 } else {
-                    entry = idx->second;
+                    options << " -D IS_CPLX=0";
+                }
+                if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
                 }
 
+                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
+                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
 
-                auto sparseArithCOOOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                                      const int,
-                                                      const Buffer, const KParam,
-                                                      const int>(*entry.ker);
+                Program prog;
+                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+                entry.prog = new Program(prog);
+                entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
 
-                NDRange local(THREADS, 1, 1);
-                NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+                kernelCaches[device][ref_name] = entry;
+            } else {
+                entry = idx->second;
+            }
 
-                sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
-                         *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                         *rhs.data, rhs.info, reverse);
 
-                CL_DEBUG_FINISH(getQueue());
-            } catch (cl::Error err) {
-                CL_TO_AF_ERROR(err);
-            }
-        }
+            auto sparseArithCOOOp = KernelFunctor<Buffer, Buffer, Buffer,
+                 const int,
+                 const Buffer, const KParam,
+                 const int>(*entry.ker);
+
+            NDRange local(THREADS, 1, 1);
+            NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
 
+            sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
+                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
+                    *rhs.data, rhs.info, reverse);
+
+            CL_DEBUG_FINISH(getQueue());
+        }
     }
 }

From cd5b287bf1f1a604498dc6f2ade974b4aa059d07 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 20 May 2017 01:07:13 -0400
Subject: [PATCH 1202/2677] Fix sparse compile errors

---
 src/api/unified/symbol_manager.hpp         |  2 +-
 src/backend/cuda/sparse_arith.cu           |  2 --
 src/backend/opencl/kernel/sparse_arith.hpp | 41 +++++++---------------
 test/math.cpp                              |  2 --
 4 files changed, 14 insertions(+), 33 deletions(-)

diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index ef807db37c..8a599d6ab4 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -37,7 +37,7 @@ const int NUM_ENV_VARS = 2;
                     "for instructions to set up environment for Unified backend.", \
                     AF_ERR_LOAD_LIB)
 
-static int backend_index(af::Backend be) {
+static inline int backend_index(af::Backend be) {
     switch (be) {
         case AF_BACKEND_CPU: return 0;
         case AF_BACKEND_CUDA: return 1;
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index adeb5e96c6..f4a29d60e2 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <sparse.hpp>
-#include <cusparseManager.hpp>
 #include <kernel/sparse_arith.hpp>
 
 #include <stdexcept>
@@ -27,7 +26,6 @@
 namespace cuda
 {
 
-using cusparse::getHandle;
 using namespace common;
 using namespace std;
 
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 70ccaa18ac..7015aa0818 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -61,10 +61,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T="  << dtype_traits<T>::getName();
@@ -89,9 +88,7 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
             auto sparseArithCSROp = KernelFunctor<Buffer, const KParam,
@@ -121,11 +118,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
-
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
 
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T="  << dtype_traits<T>::getName();
                 options << " -D OP=" << getOpString<op>();
@@ -149,9 +144,7 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
             auto sparseArithCOOOp = KernelFunctor<Buffer, const KParam,
@@ -181,10 +174,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
+            kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (idx == kernelCaches[device].end()) {
+            if (entry.prog==0 && entry.ker==0) {
 
                 std::ostringstream options;
                 options << " -D T="  << dtype_traits<T>::getName();
@@ -209,11 +201,9 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
+
             auto sparseArithCSROp = KernelFunctor<const Buffer, const Buffer, const Buffer,
                  const int,
                  const Buffer, const KParam,
@@ -239,11 +229,9 @@ namespace opencl
                 std::string(dtype_traits<T>::getName());
 
             int device = getActiveDeviceId();
-            auto idx = kernelCaches[device].find(ref_name);
-            kc_entry_t entry;
-
-            if (idx == kernelCaches[device].end()) {
+            kc_entry_t entry = kernelCache(device, ref_name);
 
+            if (entry.prog==0 && entry.ker==0) {
                 std::ostringstream options;
                 options << " -D T="  << dtype_traits<T>::getName();
                 options << " -D OP=" << getOpString<op>();
@@ -267,12 +255,9 @@ namespace opencl
                 entry.prog = new Program(prog);
                 entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
 
-                kernelCaches[device][ref_name] = entry;
-            } else {
-                entry = idx->second;
+                addKernelToCache(device, ref_name, entry);
             }
 
-
             auto sparseArithCOOOp = KernelFunctor<Buffer, Buffer, Buffer,
                  const int,
                  const Buffer, const KParam,
diff --git a/test/math.cpp b/test/math.cpp
index 66c8245594..80f9df336d 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -20,8 +20,6 @@ using std::abs;
 const int num = 10000;
 const float flt_err = 1e-3;
 const double dbl_err = 1e-10;
-const float cflt_err = 1e-3;
-const double cdbl_err = 1e-8;
 
 typedef std::complex<float> complex_float;
 typedef std::complex<double> complex_double;

From 77daa2ca1df9b8737e209365ac9691ae986e5f57 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Mon, 22 May 2017 08:43:54 -0400
Subject: [PATCH 1203/2677] Increase `svd_dense` test thresholds on macOS.

---
 test/svd_dense.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index 7ce31e2ee5..9673feada5 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -81,7 +81,11 @@ void svdTest(const int M, const int N)
     AA.host(&hAA[0]);
 
     for (int i = 0; i < M * N; i++) {
+#if defined(OS_MAC)
+        ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 2E-3);
+#else
         ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 1E-3);
+#endif
     }
 }
 

From 5afd2ce0b2612a5bb508277de29c27d98858ba3a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 22 May 2017 08:16:50 -0400
Subject: [PATCH 1204/2677] Remove superfluous macros from approx tests

---
 test/approx1.cpp | 70 +++++++++++++++++++++++++++---------------------
 test/approx2.cpp | 44 +++++++++++++++++++-----------
 2 files changed, 67 insertions(+), 47 deletions(-)

diff --git a/test/approx1.cpp b/test/approx1.cpp
index 252ef006a5..67ca048ac0 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -112,14 +112,16 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define APPROX1_INIT(desc, file, resultIdx, method)                                         \
-    TYPED_TEST(Approx1, desc)                                                               \
-    {                                                                                       \
-        approx1Test<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method);  \
-    }
+TYPED_TEST(Approx1, Approx1Nearest)
+{
+    approx1Test<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 0, AF_INTERP_NEAREST);
+}
+
+TYPED_TEST(Approx1, Approx1Linear)
+{
+    approx1Test<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 1, AF_INTERP_LINEAR);
+}
 
-    APPROX1_INIT(Approx1Nearest, approx1, 0, AF_INTERP_NEAREST);
-    APPROX1_INIT(Approx1Linear,  approx1, 1, AF_INTERP_LINEAR);
 
 template<typename T>
 void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
@@ -190,13 +192,10 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define APPROX1_INIT_CUBIC_SPLINE(desc, file, resultIdx, method)                                       \
-    TYPED_TEST(Approx1, desc)                                                                   \
-    {                                                                                           \
-        approx1CubicTest<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method); \
-    }
-
-APPROX1_INIT_CUBIC_SPLINE(Approx1Cubic, approx1_cubic, 0, AF_INTERP_CUBIC_SPLINE);
+TYPED_TEST(Approx1, Approx1Cubic)
+{
+    approx1CubicTest<TypeParam>(string(TEST_DIR"/approx/approx1_cubic.test"), 0, AF_INTERP_CUBIC_SPLINE);
+}
 
 ///////////////////////////////////////////////////////////////////////////////
 // Test Argument Failure Cases
@@ -231,15 +230,18 @@ void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
     if(outArray  != 0) af_release_array(outArray);
 }
 
-#define APPROX1_ARGS(desc, file, resultIdx, method, err)                                            \
-    TYPED_TEST(Approx1, desc)                                                                       \
-    {                                                                                               \
-        approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method, err); \
-    }
-
-    APPROX1_ARGS(Approx1NearestArgsPos2D, approx1_pos2d, 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
-    APPROX1_ARGS(Approx1LinearArgsPos2D, approx1_pos2d, 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
-    APPROX1_ARGS(Approx1ArgsInterpBilinear, approx1, 0, AF_INTERP_BILINEAR, AF_ERR_ARG);
+TYPED_TEST(Approx1, Approx1NearestArgsPos2D)
+{
+    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+}
+TYPED_TEST(Approx1, Approx1LinearArgsPos2D)
+{
+    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
+}
+TYPED_TEST(Approx1, Approx1ArgsInterpBilinear)
+{
+    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 0, AF_INTERP_BILINEAR, AF_ERR_ARG);
+}
 
 template<typename T>
 void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
@@ -275,15 +277,21 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
     if(outArray  != 0) af_release_array(outArray);
 }
 
-#define APPROX1_ARGSP(desc, file, resultIdx, method)                                                    \
-    TYPED_TEST(Approx1, desc)                                                                           \
-    {                                                                                                   \
-        approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method); \
-    }
+TYPED_TEST(Approx1, Approx1NearestArgsPrecision)
+{
+    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 0, AF_INTERP_NEAREST);
+}
+
+TYPED_TEST(Approx1, Approx1LinearArgsPrecision)
+{
+    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 1, AF_INTERP_LINEAR);
+}
+
+TYPED_TEST(Approx1, Approx1CubicArgsPrecision)
+{
+    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/approx1_cubic.test"), 2, AF_INTERP_CUBIC_SPLINE);
+}
 
-    APPROX1_ARGSP(Approx1NearestArgsPrecision, approx1, 0, AF_INTERP_NEAREST);
-    APPROX1_ARGSP(Approx1LinearArgsPrecision, approx1, 1, AF_INTERP_LINEAR);
-    APPROX1_ARGSP(Approx1CubicArgsPrecision, approx1_cubic, 2, AF_INTERP_CUBIC_SPLINE);
 
 //////////////////////////////////////// CPP //////////////////////////////////
 //
diff --git a/test/approx2.cpp b/test/approx2.cpp
index dd4da6955c..48150fe6f3 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -110,16 +110,23 @@ void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define APPROX2_INIT(desc, file, resultIdx, method)                               \
-    TYPED_TEST(Approx2, desc)                                                                    \
-    {                                                                                           \
-        approx2Test<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method);\
-    }
+TYPED_TEST(Approx2, Approx2Nearest)
+{
+    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2.test"), 0, AF_INTERP_NEAREST);
+}
 
-    APPROX2_INIT(Approx2Nearest, approx2, 0, AF_INTERP_NEAREST);
-    APPROX2_INIT(Approx2Linear, approx2, 1, AF_INTERP_LINEAR);
-    APPROX2_INIT(Approx2NearestBatch, approx2_batch, 0, AF_INTERP_NEAREST);
-    APPROX2_INIT(Approx2LinearBatch, approx2_batch, 1, AF_INTERP_LINEAR);
+TYPED_TEST(Approx2, Approx2Linear)
+{
+    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2.test"), 1, AF_INTERP_LINEAR);
+}
+TYPED_TEST(Approx2, NearestBatch)
+{
+    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2_batch.test"), 0, AF_INTERP_NEAREST);
+}
+TYPED_TEST(Approx2, LinearBatch)
+{
+    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2_batch.test"), 1, AF_INTERP_LINEAR);
+}
 
 ///////////////////////////////////////////////////////////////////////////////
 // Test Argument Failure Cases
@@ -158,15 +165,20 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
     if(outArray  != 0) af_release_array(outArray);
 }
 
-#define APPROX2_ARGS(desc, file, resultIdx, method, err)                                            \
-    TYPED_TEST(Approx2, desc)                                                                       \
-    {                                                                                               \
-        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/"#file".test"), resultIdx, method, err); \
+    TYPED_TEST(Approx2, Approx2NearestArgsPos3D)
+    {
+        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
     }
 
-    APPROX2_ARGS(Approx2NearestArgsPos3D,      approx2_pos3d,   0, AF_INTERP_NEAREST,  AF_ERR_SIZE);
-    APPROX2_ARGS(Approx2LinearArgsPos3D,       approx2_pos3d,   1, AF_INTERP_LINEAR,   AF_ERR_SIZE);
-    APPROX2_ARGS(Approx2NearestArgsPosUnequal, approx2_unequal, 0, AF_INTERP_NEAREST,  AF_ERR_SIZE);
+    TYPED_TEST(Approx2, Approx2LinearArgsPos3D)
+    {
+        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
+    }
+
+    TYPED_TEST(Approx2, Approx2NearestArgsPosUnequal)
+    {
+        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_unequal.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+    }
 
 template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)

From 0e52af03d8070251f2810f9a903ede57b940eb15 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 22 May 2017 14:45:08 +0530
Subject: [PATCH 1205/2677] fix potential memory leak

Earlier to this change, memory allocated by malloc call was
being freed by delete call.
---
 src/backend/cuda/cufft.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index 727c693f2a..ea58180744 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -134,7 +134,7 @@ SharedPlan findPlan(int rank, int *n,
 
     retVal.reset(temp, [](PlanType* p) {
         cufftDestroy(*p);
-        delete p;
+        free(p);
     });
     // push the plan into plan cache
     planner.push(key_string, retVal);

From 1b7963a9072b83de4606769eb76c41a5f2b2bf17 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 22 May 2017 14:46:17 +0530
Subject: [PATCH 1206/2677] Set current thread's active stream using
 cufftSetStream

---
 src/backend/cuda/cufft.cpp | 2 --
 src/backend/cuda/fft.cpp   | 3 +++
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index ea58180744..2dcf2eaa97 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -130,8 +130,6 @@ SharedPlan findPlan(int rank, int *n,
                                   type, batch));
     }
 
-    cufftSetStream(*temp, cuda::getActiveStream());
-
     retVal.reset(temp, [](PlanType* p) {
         cufftDestroy(*p);
         free(p);
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 280704012a..9af0f25ccc 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -94,6 +94,7 @@ void fft_inplace(Array<T> &in)
                                (cufftType)cufft_transform<T>::type, batch);
 
     cufft_transform<T> transform;
+    CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (T *)in.get(), in.get(), direction ? CUFFT_FORWARD : CUFFT_INVERSE));
 }
 
@@ -128,6 +129,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
                                (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
 
     cufft_real_transform<Tc, Tr> transform;
+    CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (Tr *)in.get(), out.get()));
     return out;
 }
@@ -159,6 +161,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
                                out_embed , ostrides[0], ostrides[rank],
                                (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
 
+    CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (Tc *)in.get(), out.get()));
     return out;
 }

From b38e4497996f66bf9d9d806a9ea2c160250f7bf8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 21 May 2017 20:42:55 +0530
Subject: [PATCH 1207/2677] fix build target dependencies in opencl backend

---
 src/backend/opencl/CMakeLists.txt | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index b0d1a1e537..106cbf7504 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -311,7 +311,6 @@ IF(DEFINED BLAS_SYM_FILE)
       TARGET_LINK_LIBRARIES(afopencl PUBLIC ${PROJECT_BINARY_DIR}/afopencl_static.renamed)
     ENDIF(APPLE)
 
-
 ELSE(DEFINED BLAS_SYM_FILE)
 
     ADD_LIBRARY(afopencl SHARED
@@ -337,12 +336,6 @@ ELSE(DEFINED BLAS_SYM_FILE)
 
 ENDIF()
 
-IF(NOT USE_SYSTEM_CL2HPP)
-    ADD_DEPENDENCIES(afopencl cl2hpp)
-ENDIF(NOT USE_SYSTEM_CL2HPP)
-
-ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
-
 TARGET_LINK_LIBRARIES(afopencl
                                 PRIVATE ${OpenCL_LIBRARIES}
                                 PRIVATE ${CLBLAST_LIBRARIES}
@@ -359,9 +352,24 @@ IF(LAPACK_FOUND)
 ENDIF(LAPACK_FOUND)
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
+
+ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
+
 IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
     ADD_DEPENDENCIES(afopencl ${GRAPHICS_DEPENDENCIES})
 ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
+IF(NOT USE_SYSTEM_CL2HPP)
+    ADD_DEPENDENCIES(afopencl cl2hpp)
+ENDIF(NOT USE_SYSTEM_CL2HPP)
+IF(CLFFT_FOUND AND NOT USE_SYSTEM_CLFFT)
+    ADD_DEPENDENCIES(afopencl clFFT)
+ENDIF()
+IF(CLBLAST_FOUND AND NOT USE_SYSTEM_CLBLAST)
+    ADD_DEPENDENCIES(afopencl CLBlast)
+ENDIF()
+IF(CLBLAS_FOUND AND NOT USE_SYSTEM_CLBLAS)
+    ADD_DEPENDENCIES(afopencl clBLAS)
+ENDIF()
 
 IF(FORGE_FOUND)
     TARGET_LINK_LIBRARIES(afopencl

From eec4e709cef98ee687ec0dbacd75886a760ab6f1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 19 May 2017 01:14:16 -0400
Subject: [PATCH 1208/2677] CMake changes for Ninja

---
 CMakeLists.txt                     |  3 +++
 CMakeModules/build_cl2hpp.cmake    | 10 ++++++++++
 CMakeModules/build_clBLAS.cmake    |  4 ++--
 CMakeModules/build_clFFT.cmake     |  2 +-
 CMakeModules/build_forge.cmake     | 10 +++++++---
 CMakeModules/build_glbinding.cmake |  2 +-
 src/backend/cpu/CMakeLists.txt     |  1 +
 7 files changed, 25 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3ffe04c032..fed528960c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -2,6 +2,9 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8.12)
 PROJECT(ARRAYFIRE)
 
 SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
+if(POLICY CMP0058)
+  CMAKE_POLICY(SET CMP0058 NEW)
+endif(POLICY CMP0058)
 
 SET(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 INCLUDE(UploadCoveralls)
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 9c950af3fc..98b8b3a4b3 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -2,9 +2,19 @@ INCLUDE(ExternalProject)
 
 SET(prefix ${PROJECT_BINARY_DIR}/third_party/cl2hpp)
 
+IF(CMAKE_VERSION VERSION_LESS 3.2)
+    IF(CMAKE_GENERATOR MATCHES "Ninja")
+        MESSAGE(WARNING "Building forge with Ninja has known issues with CMake older than 3.2")
+    endif()
+    SET(byproducts)
+ELSE()
+    SET(byproducts BUILD_BYPRODUCTS "${prefix}/package/CL/cl2.hpp")
+ENDIF()
+
 ExternalProject_Add(
     cl2hpp-ext
     GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
+    ${byproducts}
     GIT_TAG 75bb7d0d8b2ffc6aac0a3dcaa22f6622cab81f7c
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}/package"
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 7a211aa32a..79a507de24 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -8,13 +8,14 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
     endif()
     SET(byproducts)
 ELSE()
-    SET(byproducts BYPRODUCTS ${clBLAS_location})
+    SET(byproducts BUILD_BYPRODUCTS ${clBLAS_location})
 ENDIF()
 
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
     GIT_TAG arrayfire-release
+    ${byproducts}
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
@@ -30,7 +31,6 @@ ExternalProject_Add(
     -DBUILD_TEST:BOOL=OFF
     -DBUILD_KTEST:BOOL=OFF
     -DSUFFIX_LIB:STRING=
-    ${byproducts}
     )
 
 ExternalProject_Get_Property(clBLAS-ext install_dir)
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index e5304befe8..ad7dae65a0 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -8,7 +8,7 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
     endif()
     SET(byproducts)
 ELSE()
-    SET(byproducts BYPRODUCTS ${clFFT_location})
+    SET(byproducts BUILD_BYPRODUCTS ${clFFT_location})
 ENDIF()
 
 ExternalProject_Add(
diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 3187d6c6ca..6b9fb4ffe5 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -6,7 +6,7 @@ ELSE(USE_SYSTEM_GLBINDING)
     SET(GLBINDING_TARGET glbinding)
 ENDIF(USE_SYSTEM_GLBINDING)
 
-SET(prefix ${PROJECT_BINARY_DIR}/third_party/forge)
+SET(prefix ${CMAKE_BINARY_DIR}/third_party/forge)
 
 # FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
 #        does not yet support it for the OUTPUT argument, see also:
@@ -39,7 +39,11 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
     endif()
     SET(byproducts)
 ELSE()
-    SET(byproducts BYPRODUCTS ${forge_location})
+    IF   (WIN32)
+        SET(byproducts BUILD_BYPRODUCTS third_party/forge/lib/forge${CMAKE_STATIC_LIBRARY_SUFFIX})
+    ELSE (WIN32)
+        SET(byproducts BUILD_BYPRODUCTS ${forge_location})
+    ENDIF(WIN32)
 ENDIF()
 
 SET(FORGE_VERSION 0.9.2)
@@ -49,6 +53,7 @@ ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
     GIT_TAG v${FORGE_VERSION}
+    ${byproducts}
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
@@ -69,7 +74,6 @@ ExternalProject_Add(
     -DFREEIMAGE_STATIC_LIBRARY:PATH=${FREEIMAGE_STATIC_LIBRARY}
     -DUSE_FREEIMAGE_STATIC:BOOL=${USE_FREEIMAGE_STATIC}
     BUILD_COMMAND ${CMAKE_COMMAND} --build . --config ${forge_lib_config}
-    ${byproducts}
     )
 
 ExternalProject_Get_Property(forge-ext binary_dir)
diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
index 05ad586e76..bfc7f0ddaf 100644
--- a/CMakeModules/build_glbinding.cmake
+++ b/CMakeModules/build_glbinding.cmake
@@ -15,7 +15,7 @@ IF(CMAKE_VERSION VERSION_LESS 3.2)
     endif()
     SET(byproducts)
 ELSE()
-    SET(byproducts BYPRODUCTS ${glbinding_location})
+    SET(byproducts BUILD_BYPRODUCTS ${glbinding_location})
 ENDIF()
 
 IF(UNIX)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index ac94f897ea..aec7aab535 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -191,6 +191,7 @@ IF(DEFINED BLAS_SYM_FILE)
     TARGET_LINK_LIBRARIES(afcpu PUBLIC $<TARGET_FILE:afcpu_static>)
   ELSE(APPLE)
     add_custom_command(OUTPUT ${PROJECT_BINARY_DIR}/afcpu_static.renamed
+      BYPRODUCTS ${PROJECT_BINARY_DIR}/afcpu_static.renamed
       COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afcpu_static> ${PROJECT_BINARY_DIR}/afcpu_static.renamed
       DEPENDS $<TARGET_FILE:afcpu_static>)
       TARGET_LINK_LIBRARIES(afcpu PUBLIC ${PROJECT_BINARY_DIR}/afcpu_static.renamed)

From e59eb93c16b08ff790a68b6be85ba1b51773ad01 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 18 May 2017 10:59:32 -0400
Subject: [PATCH 1209/2677] Fixed randu and randn ranges for floating types

Randu now generates random values in range [0, 1) for floating types.
Randn avoids Infs by making sure the Box-Muller transform is not given
0 as an input.
---
 src/backend/cpu/kernel/random_engine.hpp      | 21 ++++--
 src/backend/cuda/kernel/random_engine.hpp     | 65 +++++++++++--------
 .../opencl/kernel/random_engine_write.cl      | 43 +++++++-----
 3 files changed, 79 insertions(+), 50 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index bc8e998af6..3c191e8ac9 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -20,10 +20,17 @@ namespace cpu
 namespace kernel
 {
     //Utils
-    static const float UINTMAXFLOAT = 4294967296.0f;
-    static const float UINTLMAXDOUBLE = (4294967296.0*4294967296.0);
     static const double PI_VAL = 3.1415926535897932384626433832795028841971693993751058209749445923078164;
 
+    //Conversion to floats adapted from Random123
+    #define UINTMAX 0xffffffff
+    #define FLT_FACTOR ((1.0f)/(UINTMAX + (1.0f)))
+    #define HALF_FLT_FACTOR ((0.5f)*FLT_FACTOR)
+
+    #define UINTLMAX 0xffffffffffffffff
+    #define DBL_FACTOR ((1.0)/(UINTLMAX + (1.0)))
+    #define HALF_DBL_FACTOR ((0.5)*DBL_FACTOR)
+
     template <typename T>
     T transform(uint *val, int index)
     {
@@ -76,15 +83,17 @@ namespace kernel
         return transform<uintl>(val, index);
     }
 
+    //Generates rationals in [0, 1)
     template <> float transform<float>(uint *val, int index)
     {
-        return (float)val[index]/UINTMAXFLOAT;
+        return 1.f - (val[index]*FLT_FACTOR + HALF_FLT_FACTOR);
     }
 
+    //Generates rationals in [0, 1)
     template <> double transform<double>(uint *val, int index)
     {
         uintl v = transform<uintl>(val, index);
-        return (double)v/UINTLMAXDOUBLE;
+        return 1.0 - (v*DBL_FACTOR + HALF_DBL_FACTOR);
     }
 
     template <typename T>
@@ -131,8 +140,8 @@ namespace kernel
         /*
          * The log of a real value x where 0 < x < 1 is negative.
          */
-        T r = sqrt((T)(-2.0) * log(r1));
-        T theta = 2 * (T)PI_VAL * (r2);
+        T r = sqrt((T)(-2.0) * log((T)(1.0) - r1));
+        T theta = 2 * (T)PI_VAL * ((T)(1.0) - r2);
         *out1 = r*sin(theta);
         *out2 = r*cos(theta);
     }
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 56b03b872c..abf1814246 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -25,19 +25,28 @@ namespace kernel
     //Utils
 
     static const int THREADS = 256;
-    #define UINTMAXFLOAT 4294967296.0f
-    #define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
     #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
 
+    //Conversion to floats adapted from Random123
+    #define UINTMAX 0xffffffff
+    #define FLT_FACTOR ((1.0f)/(UINTMAX + (1.0f)))
+    #define HALF_FLT_FACTOR ((0.5f)*FLT_FACTOR)
+
+    #define UINTLMAX 0xffffffffffffffff
+    #define DBL_FACTOR ((1.0)/(UINTLMAX + (1.0)))
+    #define HALF_DBL_FACTOR ((0.5)*DBL_FACTOR)
+
+    //Generates rationals in (0, 1]
     __device__ static float getFloat(const uint &num)
     {
-        return float(num)/UINTMAXFLOAT;
+        return (num*FLT_FACTOR + HALF_FLT_FACTOR);
     }
 
+    //Generates rationals in (0, 1]
     __device__ static double getDouble(const uint &num1, const uint &num2)
     {
         uintl num = (((uintl)num1)<<32) | ((uintl)num2);
-        return double(num)/UINTLMAXDOUBLE;
+        return (num*DBL_FACTOR + HALF_DBL_FACTOR);
     }
 
     template <typename T>
@@ -150,33 +159,33 @@ namespace kernel
     __device__ static void writeOut128Bytes(float *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        out[index]                = getFloat(r1);
-        out[index +   blockDim.x] = getFloat(r2);
-        out[index + 2*blockDim.x] = getFloat(r3);
-        out[index + 3*blockDim.x] = getFloat(r4);
+        out[index]                = 1.f - getFloat(r1);
+        out[index +   blockDim.x] = 1.f - getFloat(r2);
+        out[index + 2*blockDim.x] = 1.f - getFloat(r3);
+        out[index + 3*blockDim.x] = 1.f - getFloat(r4);
     }
 
     __device__ static void writeOut128Bytes(cfloat *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        out[index].x              = getFloat(r1);
-        out[index].y              = getFloat(r2);
-        out[index + blockDim.x].x = getFloat(r3);
-        out[index + blockDim.x].y = getFloat(r4);
+        out[index].x              = 1.f - getFloat(r1);
+        out[index].y              = 1.f - getFloat(r2);
+        out[index + blockDim.x].x = 1.f - getFloat(r3);
+        out[index + blockDim.x].y = 1.f - getFloat(r4);
     }
 
     __device__ static void writeOut128Bytes(double *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        out[index]              = getDouble(r1, r2);
-        out[index + blockDim.x] = getDouble(r3, r4);
+        out[index]              = 1.0 - getDouble(r1, r2);
+        out[index + blockDim.x] = 1.0 - getDouble(r3, r4);
     }
 
     __device__ static void writeOut128Bytes(cdouble *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4)
     {
-        out[index].x = getDouble(r1, r2);
-        out[index].y = getDouble(r3, r4);
+        out[index].x = 1.0 - getDouble(r1, r2);
+        out[index].y = 1.0 - getDouble(r3, r4);
     }
 
     //Normalized writes without boundary checking
@@ -305,38 +314,38 @@ namespace kernel
     __device__ static void partialWriteOut128Bytes(float *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
-        if (index                < elements) {out[index]                = getFloat(r1);}
-        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = getFloat(r2);}
-        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = getFloat(r3);}
-        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = getFloat(r4);}
+        if (index                < elements) {out[index]                = 1.f - getFloat(r1);}
+        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = 1.f - getFloat(r2);}
+        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = 1.f - getFloat(r3);}
+        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = 1.f - getFloat(r4);}
     }
 
     __device__ static void partialWriteOut128Bytes(cfloat *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index              < elements) {
-            out[index].x              = getFloat(r1);
-            out[index].y              = getFloat(r2);
+            out[index].x              = 1.f - getFloat(r1);
+            out[index].y              = 1.f - getFloat(r2);
         }
         if (index + blockDim.x < elements) {
-            out[index + blockDim.x].x = getFloat(r3);
-            out[index + blockDim.x].y = getFloat(r4);
+            out[index + blockDim.x].x = 1.f - getFloat(r3);
+            out[index + blockDim.x].y = 1.f - getFloat(r4);
         }
     }
 
     __device__ static void partialWriteOut128Bytes(double *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
-        if (index              < elements) {out[index]              = getDouble(r1, r2);}
-        if (index + blockDim.x < elements) {out[index + blockDim.x] = getDouble(r3, r4);}
+        if (index              < elements) {out[index]              = 1.0 - getDouble(r1, r2);}
+        if (index + blockDim.x < elements) {out[index + blockDim.x] = 1.0 - getDouble(r3, r4);}
     }
 
     __device__ static void partialWriteOut128Bytes(cdouble *out, const uint &index,
             const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
     {
         if (index < elements) {
-            out[index].x = getDouble(r1, r2);
-            out[index].y = getDouble(r3, r4);
+            out[index].x = 1.0 - getDouble(r1, r2);
+            out[index].y = 1.0 - getDouble(r3, r4);
         }
     }
 
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 6e76862b8d..dd07e61946 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -7,13 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define UINTMAXFLOAT 4294967296.0f
-#define UINTLMAXDOUBLE (4294967296.0*4294967296.0)
 #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
 
+//Conversion to floats adapted from Random123
+#define UINTMAX 0xffffffff
+#define FLT_FACTOR ((1.0f)/(UINTMAX + (1.0f)))
+#define HALF_FLT_FACTOR ((0.5f)*FLT_FACTOR)
+
+//Generates rationals in (0, 1]
 float getFloat(const uint * const num)
 {
-    return ((float)(*num))/UINTMAXFLOAT;
+    return ((*num)*FLT_FACTOR + HALF_FLT_FACTOR);
 }
 
 //Writes without boundary checking
@@ -129,10 +133,10 @@ void writeOut128Bytes_ulong(__global ulong *out, const uint * const index,
 void writeOut128Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
-    out[*index]             = getFloat(r1);
-    out[*index +   THREADS] = getFloat(r2);
-    out[*index + 2*THREADS] = getFloat(r3);
-    out[*index + 3*THREADS] = getFloat(r4);
+    out[*index]             = 1.f - getFloat(r1);
+    out[*index +   THREADS] = 1.f - getFloat(r2);
+    out[*index + 2*THREADS] = 1.f - getFloat(r3);
+    out[*index + 3*THREADS] = 1.f - getFloat(r4);
 }
 
 
@@ -252,10 +256,10 @@ void partialWriteOut128Bytes_ulong(__global ulong *out, const uint * const index
 void partialWriteOut128Bytes_float(__global float *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
-    if (*index             < *elements) {out[*index]             = getFloat(r1);}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = getFloat(r2);}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = getFloat(r3);}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = getFloat(r4);}
+    if (*index             < *elements) {out[*index]             = 1.f - getFloat(r1);}
+    if (*index +   THREADS < *elements) {out[*index +   THREADS] = 1.f - getFloat(r2);}
+    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = 1.f - getFloat(r3);}
+    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = 1.f - getFloat(r4);}
 }
 
 #if RAND_DIST == 1
@@ -302,24 +306,31 @@ void partialBoxMullerWriteOut128Bytes_float(__global float *out, const uint * co
 #endif
 
 #ifdef USE_DOUBLE
+
+//Conversion to floats adapted from Random123
+#define UINTLMAX 0xffffffffffffffff
+#define DBL_FACTOR ((1.0)/(UINTLMAX + (1.0)))
+#define HALF_DBL_FACTOR ((0.5)*DBL_FACTOR)
+
+//Generates rationals in (0, 1]
 double getDouble(const uint * const num1, const uint * const num2)
 {
     ulong num = (((ulong)*num1)<<32) | ((ulong)*num2);
-    return ((double)num)/UINTLMAXDOUBLE;
+    return (num*DBL_FACTOR + HALF_DBL_FACTOR);
 }
 
 void writeOut128Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
 {
-    out[*index]           = getDouble(r1, r2);
-    out[*index + THREADS] = getDouble(r3, r4);
+    out[*index]           = 1.0 - getDouble(r1, r2);
+    out[*index + THREADS] = 1.0 - getDouble(r3, r4);
 }
 
 void partialWriteOut128Bytes_double(__global double *out, const uint * const index,
         const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
 {
-    if (*index           < *elements) {out[*index]           = getDouble(r1, r2);}
-    if (*index + THREADS < *elements) {out[*index + THREADS] = getDouble(r3, r4);}
+    if (*index           < *elements) {out[*index]           = 1.0 - getDouble(r1, r2);}
+    if (*index + THREADS < *elements) {out[*index + THREADS] = 1.0 - getDouble(r3, r4);}
 }
 
 #if RAND_DIST == 1

From 9ab3802b45d39e01d8170e5618627153692ca7e1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 24 May 2017 12:23:42 +0530
Subject: [PATCH 1210/2677] fix mismatched new/delete in cuda jit

---
 src/backend/cuda/jit.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 097bc120cf..4845ec34da 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -491,7 +491,7 @@ char linkError[size];
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
     size_t ptx_size;
-    unique_ptr<const char> ptx(irToPtx(jit_ker, &ptx_size));
+    unique_ptr<char[]> ptx(irToPtx(jit_ker, &ptx_size));
 
     CUlinkState linkState;
 

From c86432ffbbea5e25578adc96e43a526a6ef2d351 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 24 May 2017 12:24:07 +0530
Subject: [PATCH 1211/2677] fix mismatched new/delete in opencl reduce_all

---
 src/backend/opencl/kernel/reduce.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index d9c67c90fe..28cd01fb03 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -317,7 +317,7 @@ namespace kernel
 
             reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x, change_nan, nanval);
 
-            unique_ptr<To> h_ptr(new To[tmp_elements]);
+            unique_ptr<To[]> h_ptr(new To[tmp_elements]);
             getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.get());
 
             Binary<To, op> reduce;

From 12d574bddb82f39b1eeee4451acfd536cb7ab6b0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 27 May 2017 21:36:57 +0530
Subject: [PATCH 1212/2677] Remove locks from MemoryManager
 constructor/destructor

Constructor doesn't need a mutex lock and since the MemoryManager
class's desctructor is not modifying any global state, it also
doesn't need to lock the mutex.
---
 src/backend/common/MemoryManager.hpp | 2 --
 src/backend/opencl/memory.cpp        | 6 ++----
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index b2138da488..6a1788b274 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -114,8 +114,6 @@ class MemoryManager
     MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug)
         : mem_step_size(1024), max_buffers(MAX_BUFFERS), memory(num_devices), debug_mode(debug)
     {
-        lock_guard_t lock(this->memory_mutex);
-
         // Check for environment variables
 
         // Debug mode
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 097bf1aadd..5867ea5269 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -150,11 +150,10 @@ MemoryManager::MemoryManager()
 
 MemoryManager::~MemoryManager()
 {
-    common::lock_guard_t lock(this->memory_mutex);
     for (int n = 0; n < opencl::getDeviceCount(); n++) {
         try {
             opencl::setDevice(n);
-            garbageCollect();
+            this->garbageCollect();
         } catch(AfError err) {
             continue; // Do not throw any errors while shutting down
         }
@@ -191,8 +190,7 @@ MemoryManagerPinned::MemoryManagerPinned()
 
 MemoryManagerPinned::~MemoryManagerPinned()
 {
-    common::lock_guard_t lock(this->memory_mutex);
-    for (int n = 0; n < getDeviceCount(); n++) {
+    for (int n = 0; n < opencl::getDeviceCount(); n++) {
         opencl::setDevice(n);
         this->garbageCollect();
         auto currIterator = pinnedMaps[n].begin();

From cb342db0dee825c0c0c3f61b86b27ac5a154f48b Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Mon, 22 May 2017 15:25:28 -0400
Subject: [PATCH 1213/2677] Increase `svd_dense` test thresholds on macOS.

---
 test/svd_dense.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index 9673feada5..5d94ab2625 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -82,7 +82,7 @@ void svdTest(const int M, const int N)
 
     for (int i = 0; i < M * N; i++) {
 #if defined(OS_MAC)
-        ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 2E-3);
+        ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 3E-3);
 #else
         ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 1E-3);
 #endif

From ed859550cff7f6d414f94089a2971fb547822671 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Fri, 26 May 2017 11:54:22 -0400
Subject: [PATCH 1214/2677] Removed unnecessary clfft.hpp dependency from
 opencl/platform.hpp

---
 src/backend/opencl/platform.cpp |  8 +++++---
 src/backend/opencl/platform.hpp | 10 ++++++++--
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 3ad0e34904..d44d82f47e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -46,6 +46,7 @@
 #include <thread>
 #include <utility>
 #include <vector>
+#include <clfft.hpp>
 
 using std::string;
 using std::vector;
@@ -781,7 +782,8 @@ DeviceManager::~DeviceManager()
 }
 
 DeviceManager::DeviceManager()
-    : mUserDeviceOffset(0)
+    : mUserDeviceOffset(0),
+      mFFTSetup(new clfftSetupData)
 {
     std::vector<cl::Platform>   platforms;
     Platform::get(&platforms);
@@ -903,8 +905,8 @@ DeviceManager::DeviceManager()
 #endif
     mUserDeviceOffset = mDevices.size();
     //Initialize FFT setup data structure
-    CLFFT_CHECK(clfftInitSetupData(&mFFTSetup));
-    CLFFT_CHECK(clfftSetup(&mFFTSetup));
+    CLFFT_CHECK(clfftInitSetupData(mFFTSetup.get()));
+    CLFFT_CHECK(clfftSetup(mFFTSetup.get()));
 
     //Initialize clBlas library
     initBlas();
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 1d132ca820..35003dbf23 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -29,11 +29,17 @@
 #include <cache.hpp>
 #include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
-#include <clfft.hpp>
 #include <common/types.hpp>
 
+// Forward declaration from clFFT.h
+struct clfftSetupData_;
+typedef clfftSetupData_ clfftSetupData;
+
 namespace opencl
 {
+// Forward declaration from clfft.hpp
+class PlanCache;
+
 int getBackend();
 
 std::string getDeviceInfo();
@@ -192,6 +198,6 @@ class DeviceManager
 #if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 #endif
-        clfftSetupData mFFTSetup;
+        std::unique_ptr<clfftSetupData> mFFTSetup;
 };
 }

From 911c9a257e65a3944f9e11d274345b434323e2d6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 May 2017 23:06:05 +0530
Subject: [PATCH 1215/2677] add missing template specialisations for sparse
 arith ops

* Add af_mul_t, af_div_t op specialisation for opencl::arithOpD
* Add af_add_t, af_sub_t op specialisation for opencl::arithOpS
---
 src/backend/cpu/sparse_arith.cpp    | 8 ++++++++
 src/backend/cuda/sparse_arith.cu    | 8 ++++++++
 src/backend/opencl/sparse_arith.cpp | 8 ++++++++
 3 files changed, 24 insertions(+)

diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index b165d6ea25..d076d57d49 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -120,6 +120,14 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
                                             const bool reverse);                                    \
     template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                            const bool reverse);                                     \
+    template Array<T> arithOpD<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template SparseArray<T> arithOpS<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
+    template SparseArray<T> arithOpS<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
                                                   const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index f4a29d60e2..3dfa5308b6 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -108,6 +108,14 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
                                             const bool reverse);                                    \
     template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template SparseArray<T> arithOpS<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
+    template SparseArray<T> arithOpS<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
                                                   const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index d62d42eb5d..5af3c76e4f 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -108,6 +108,14 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
                                             const bool reverse);                                    \
     template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
+                                            const bool reverse);                                    \
+    template SparseArray<T> arithOpS<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
+    template SparseArray<T> arithOpS<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+                                                  const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
                                                   const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \

From bdb658105cde73a7dd574b2c0a93164947e1638c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 23 May 2017 11:56:33 +0530
Subject: [PATCH 1216/2677] Increment forge tag v1.0.1 stable release

Change all necessary arrayfire internal forge calls
to use forge v1.0 API.
---
 CMakeModules/build_forge.cmake |  2 +-
 src/api/c/graphics_common.cpp  | 21 +++++++++++++++++++--
 src/api/c/graphics_common.hpp  |  5 +++++
 src/api/c/hist.cpp             |  4 +++-
 src/api/c/image.cpp            |  4 +++-
 src/api/c/plot.cpp             | 12 +++++++++---
 src/api/c/surface.cpp          |  4 +++-
 src/api/c/vector_field.cpp     | 12 +++++++++---
 src/api/c/window.cpp           |  1 -
 9 files changed, 52 insertions(+), 13 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 6b9fb4ffe5..1f5089a31f 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -46,7 +46,7 @@ ELSE()
     ENDIF(WIN32)
 ENDIF()
 
-SET(FORGE_VERSION 0.9.2)
+SET(FORGE_VERSION 1.0.1)
 
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
diff --git a/src/api/c/graphics_common.cpp b/src/api/c/graphics_common.cpp
index 249a325586..fe17094c51 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/api/c/graphics_common.cpp
@@ -16,6 +16,7 @@
 #include <platform.hpp>
 #include <util.hpp>
 #include <mutex>
+#include <utility>
 
 using namespace std;
 using namespace gl;
@@ -246,6 +247,7 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
                                       const int r, const int c)
 {
     ChartMapIter iter = mChartMap.find(window);
+    GridMapIter gIter = mWndGridMap.find(window);
 
     if(iter != mChartMap.end()) {
         // ChartVec found. Clear it.
@@ -258,25 +260,40 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
             }
         }
         (iter->second).clear();
+        gIter->second = std::make_pair<int, int>(1, 1);
     }
 
     if(r == 0 || c == 0) {
         mChartMap.erase(window);
+        mWndGridMap.erase(window);
     } else {
         mChartMap[window] = std::vector<forge::Chart*>(r * c);
+        mWndGridMap[window] = std::make_pair(r, c);
     }
 }
 
+WindGridDims_t ForgeManager::getWindowGrid(const forge::Window* window)
+{
+    GridMapIter gIter = mWndGridMap.find(window);
+
+    if (gIter == mWndGridMap.end()) {
+        mWndGridMap[window] = std::make_pair(1, 1);
+    }
+
+    return mWndGridMap[window];
+}
+
 forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, const int c,
                                      const forge::ChartType ctype)
 {
     forge::Chart* chart = NULL;
     ChartMapIter iter = mChartMap.find(window);
+    GridMapIter gIter = mWndGridMap.find(window);
 
     if (iter != mChartMap.end()) {
 
-        int gRows = window->gridRows();
-        int gCols = window->gridCols();
+        int gRows = std::get<0>(gIter->second);
+        int gCols = std::get<1>(gIter->second);
 
         if(c >= gCols || r >= gRows)
             AF_ERROR("Grid points are out of bounds", AF_ERR_TYPE);
diff --git a/src/api/c/graphics_common.hpp b/src/api/c/graphics_common.hpp
index b8f29a343a..e895f5c90f 100644
--- a/src/api/c/graphics_common.hpp
+++ b/src/api/c/graphics_common.hpp
@@ -68,8 +68,11 @@ typedef VectorFieldMap_t::iterator VcfMapIter;
 
 typedef std::vector<forge::Chart*> ChartVec_t;
 typedef std::map<const forge::Window*, ChartVec_t> ChartMap_t;
+typedef std::pair<int, int> WindGridDims_t;
+typedef std::map<const forge::Window*, WindGridDims_t> WindGridMap_t;
 typedef ChartVec_t::iterator ChartVecIter;
 typedef ChartMap_t::iterator ChartMapIter;
+typedef WindGridMap_t::iterator GridMapIter;
 
 // Keeps track of which charts have manually assigned axes limits
 typedef std::map<forge::Chart*, bool> ChartAxesOverride_t;
@@ -97,6 +100,7 @@ class ForgeManager
         VectorFieldMap_t    mVcfMap;
 
         ChartMap_t          mChartMap;
+        WindGridMap_t       mWndGridMap;
         ChartAxesOverride_t mChartAxesOverrideMap;
 
     public:
@@ -109,6 +113,7 @@ class ForgeManager
         void            setWindowChartGrid(const forge::Window* window,
                                            const int r, const int c);
 
+        WindGridDims_t getWindowGrid(const forge::Window* window);
         forge::Chart*   getChart(const forge::Window* window, const int r, const int c,
                                  const forge::ChartType ctype);
 
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 053bdc422d..0b028daf63 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -108,9 +108,11 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
             default:  TYPE_ERROR(1, Xtype);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
     }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 37ed268b87..ccf619affb 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -106,9 +106,11 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
             default:  TYPE_ERROR(1, type);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         window->setColorMap((forge::ColorMap)props->cmap);
         if (props->col>-1 && props->row>-1)
-            window->draw(props->row, props->col, *image, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *image, props->title);
         else
             window->draw(*image);
     }
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 9b1d17a70e..35b25b307a 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -152,9 +152,11 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
             default:  TYPE_ERROR(1, type);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
     }
@@ -212,9 +214,11 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
             default:  TYPE_ERROR(1, xType);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
 
@@ -266,9 +270,11 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
             default:  TYPE_ERROR(1, xType);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
 
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index b7b2377593..bbbfd80a38 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -168,8 +168,10 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
             default:  TYPE_ERROR(1, Xtype);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         if (props->col>-1 && props->row>-1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
     }
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 47d32012fb..d3c303c770 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -159,9 +159,11 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
             default:  TYPE_ERROR(1, pType);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
     }
@@ -247,9 +249,11 @@ af_err vectorFieldWrapper(const af_window wind,
             default:  TYPE_ERROR(1, xpType);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
     }
@@ -323,9 +327,11 @@ af_err vectorFieldWrapper(const af_window wind,
             default:  TYPE_ERROR(1, xpType);
         }
 
+        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(props->row, props->col, *chart, props->title);
+            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+                         *chart, props->title);
         else
             window->draw(*chart);
     }
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index e7681529d5..a8cc4316f3 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -124,7 +124,6 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
 
     try {
         forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
-        wnd->grid(rows, cols);
 
         // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();

From 8ce550d50c154beccf20ab889d61f00ae4751a1b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 24 May 2017 17:49:44 +0530
Subject: [PATCH 1217/2677] Work around for gfx interop resource cleanup in
 cuda backend

---
 src/backend/cuda/GraphicsResourceManager.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index b27da053ad..8f89f706ea 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -18,7 +18,13 @@ ShrdResVector GraphicsResourceManager::registerResources(std::vector<uint32_t> r
     ShrdResVector output;
 
     auto deleter = [](CGR_t* handle) {
-        CUDA_CHECK(cudaGraphicsUnregisterResource(*handle));
+        //FIXME Having a CUDA_CHECK around unregister
+        //call is causing invalid GL context.
+        //Moving ForgeManager class singleton as data
+        //member of DeviceManager with proper ordering
+        //of member destruction doesn't help either.
+        //Calling makeContextCurrent also doesn't help.
+        cudaGraphicsUnregisterResource(*handle);
         delete handle;
     };
 

From 5176e1f9fcea8afe1344b40c59e635b4a822ce2e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 1 Jun 2017 21:31:22 +0530
Subject: [PATCH 1218/2677] Set boost path in forge external project cmake
 build command

---
 CMakeModules/build_forge.cmake | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 1f5089a31f..9989f45277 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -69,6 +69,8 @@ ExternalProject_Add(
     -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
     -Dglbinding_DIR:STRING=${glbinding_DIR}
     -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
+    -DBOOST_ROOT:PATH=${BOOST_ROOT}
+    -DBOOST_INCLUDEDIR:PATH=${BOOST_INCLUDEDIR}
     -DFREEIMAGE_INCLUDE_PATH:PATH=${FREEIMAGE_INCLUDE_PATH}
     -DFREEIMAGE_DYNAMIC_LIBRARY:PATH=${FREEIMAGE_DYNAMIC_LIBRARY}
     -DFREEIMAGE_STATIC_LIBRARY:PATH=${FREEIMAGE_STATIC_LIBRARY}

From 2ad3f93727a1fbce97fbd3934daf02dff7ae57dd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 6 Jun 2017 18:02:04 -0400
Subject: [PATCH 1219/2677] Add locks around cuda driver api and sparse calls

The driver API in CUDA is not thread safe(or we are using it incorrectly). We
need to protect these calls with mutexes. The cuSparse library also needs
to be protected with these locks.
---
 src/api/c/device.cpp             |  2 +-
 src/backend/cuda/jit.cpp         |  5 ++---
 src/backend/cuda/platform.cpp    |  4 ++++
 src/backend/cuda/platform.hpp    | 13 ++++++++++---
 src/backend/cuda/sparse_blas.cpp |  5 +++++
 5 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index f1145e634a..996846a0a4 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -74,7 +74,7 @@ af_err af_get_active_backend(af_backend *result)
 af_err af_init()
 {
     try {
-        static std::once_flag flag;
+        thread_local std::once_flag flag;
         std::call_once(flag, []() {
                 getDeviceInfo();
             });
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 4845ec34da..136ab149e0 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -514,6 +514,7 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
         reinterpret_cast<void*>(1)
     };
 
+    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
     CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.get(),
                            ptx_size, ker_name, 0, NULL, NULL));
@@ -689,6 +690,7 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
         args.push_back((void *)&num_odims);
     }
 
+    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLaunchKernel(ker,
                             blocks_x,
                             blocks_y,
@@ -739,7 +741,4 @@ template void evalNodes<intl   >(vector<Param<intl   > > &out, vector<Node *> no
 template void evalNodes<uintl  >(vector<Param<uintl  > > &out, vector<Node *> node);
 template void evalNodes<short  >(vector<Param<short  > > &out, vector<Node *> node);
 template void evalNodes<ushort >(vector<Param<ushort > > &out, vector<Node *> node);
-
-
-
 }
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index fd267b6427..8fba0f88f6 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -283,6 +283,10 @@ unsigned getMaxJitSize()
     return length;
 }
 
+std::mutex& getDriverApiMutex(int device) {
+  return DeviceManager::getInstance().driver_api_mutex[device];
+}
+
 int& tlocalActiveDeviceId()
 {
     thread_local int activeDeviceId = 0;
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index b9ac3df401..d955df4f63 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -11,9 +11,6 @@
 
 #include <cuda.h>
 #include <cuda_runtime.h>
-#include <memory>
-#include <vector>
-#include <string>
 #include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <cufft.hpp>
@@ -22,6 +19,11 @@
 #include <cusparse.hpp>
 #include <common/types.hpp>
 
+#include <memory>
+#include <mutex>
+#include <string>
+#include <vector>
+
 namespace cuda
 {
 int getBackend();
@@ -41,6 +43,8 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
 
+std::mutex& getDriverApiMutex(int device);
+
 int getDeviceCount();
 
 int getActiveDeviceId();
@@ -111,6 +115,8 @@ class DeviceManager
         friend GraphicsResourceManager& interopManager();
 #endif
 
+        friend std::mutex& getDriverApiMutex(int device);
+
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -159,6 +165,7 @@ class DeviceManager
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
+        std::mutex driver_api_mutex[MAX_DEVICES];
 #if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 #endif
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index df57c9c667..43911b858a 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -142,6 +142,11 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     dim4 rStrides = rhs.strides();
 
+    // NOTE: The cuSparse library seems to be using the driver API in the
+    // implementation. This is causing issues with our JIT kernel generation.
+    // This may be a bug in the cuSparse library.
+    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));

From 269bb5b31248d6855867966fa670c9e55e5326f8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 12 Jun 2017 15:01:09 -0400
Subject: [PATCH 1220/2677] Lock the entire compileKernel function

---
 src/backend/cuda/jit.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 136ab149e0..946dfd42a7 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -490,6 +490,8 @@ char linkError[size];
 
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
+    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+
     size_t ptx_size;
     unique_ptr<char[]> ptx(irToPtx(jit_ker, &ptx_size));
 
@@ -514,7 +516,6 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
         reinterpret_cast<void*>(1)
     };
 
-    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
     CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.get(),
                            ptx_size, ker_name, 0, NULL, NULL));

From 783c1dd83bee5584c859e198d3446e1f7bb80e82 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Thu, 15 Jun 2017 00:24:09 -0700
Subject: [PATCH 1221/2677] Fixing memory leaks in af_cast for sparse arrays

---
 src/api/c/sparse_handle.hpp | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 03898da6aa..c59de22778 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -67,10 +67,23 @@ af_array retainSparseHandle(const af_array in)
 template<typename To>
 common::SparseArray<To> castSparse(const af_array &in)
 {
+    const ArrayInfo& info = getInfo(in, false, true);
     using namespace common;
-    const SparseArray<To> sparse = getSparseArray<To>(in);
-    Array<To> values = castArray<To>(getHandle(sparse.getValues()));
-    return createArrayDataSparseArray(sparse.dims(), values,
-                                      sparse.getRowIdx(), sparse.getColIdx(),
-                                      sparse.getStorage());
+
+#define CAST_SPARSE(Ti) do {                                            \
+        const SparseArray<Ti> sparse = getSparseArray<Ti>(in);          \
+        Array<To> values = detail::cast<To, Ti>(sparse.getValues());    \
+        return createArrayDataSparseArray(sparse.dims(), values,        \
+                                          sparse.getRowIdx(),           \
+                                          sparse.getColIdx(),           \
+                                          sparse.getStorage());         \
+    } while(0)
+
+    switch(info.getType()) {
+    case f32: CAST_SPARSE(float);
+    case f64: CAST_SPARSE(double);
+    case c32: CAST_SPARSE(cfloat);
+    case c64: CAST_SPARSE(cdouble);
+    default: TYPE_ERROR(1, info.getType());
+    }
 }

From 35e81b9113cb1dc4a9c1ca468f1fa4ece78de0ef Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 14 Jun 2017 23:57:40 -0400
Subject: [PATCH 1222/2677] Add deep copy for sparse arrays. Unit tests

---
 src/api/c/array.cpp            | 41 ++++++++++++++++----------
 src/api/c/sparse_handle.hpp    |  7 +++++
 src/backend/SparseArray.cpp    | 37 ++++++++++++------------
 src/backend/SparseArray.hpp    |  6 ++++
 src/backend/sparse_helpers.hpp |  3 ++
 test/sparse.cpp                | 53 ++++++++++++++++++++++++++++++++++
 test/testHelpers.hpp           |  8 ++---
 7 files changed, 118 insertions(+), 37 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 1e3a15515b..589d8f51a2 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -127,7 +127,7 @@ af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * co
 af_err af_copy_array(af_array *out, const af_array in)
 {
     try {
-        const ArrayInfo& info = getInfo(in);
+        const ArrayInfo& info = getInfo(in, false);
         const af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
@@ -136,20 +136,31 @@ af_err af_copy_array(af_array *out, const af_array in)
         }
 
         af_array res;
-        switch(type) {
-        case f32:   res = copyArray<float   >(in); break;
-        case c32:   res = copyArray<cfloat  >(in); break;
-        case f64:   res = copyArray<double  >(in); break;
-        case c64:   res = copyArray<cdouble >(in); break;
-        case b8:    res = copyArray<char    >(in); break;
-        case s32:   res = copyArray<int     >(in); break;
-        case u32:   res = copyArray<uint    >(in); break;
-        case u8:    res = copyArray<uchar   >(in); break;
-        case s64:   res = copyArray<intl    >(in); break;
-        case u64:   res = copyArray<uintl   >(in); break;
-        case s16:   res = copyArray<short   >(in); break;
-        case u16:   res = copyArray<ushort  >(in); break;
-        default:    TYPE_ERROR(1, type);
+
+        if(info.isSparse()) {
+            switch(type) {
+            case f32: res = copySparseArray<float  >(in); break;
+            case f64: res = copySparseArray<double >(in); break;
+            case c32: res = copySparseArray<cfloat >(in); break;
+            case c64: res = copySparseArray<cdouble>(in); break;
+            default : TYPE_ERROR(0, type);
+            }
+        } else {
+            switch(type) {
+            case f32:   res = copyArray<float   >(in); break;
+            case c32:   res = copyArray<cfloat  >(in); break;
+            case f64:   res = copyArray<double  >(in); break;
+            case c64:   res = copyArray<cdouble >(in); break;
+            case b8:    res = copyArray<char    >(in); break;
+            case s32:   res = copyArray<int     >(in); break;
+            case u32:   res = copyArray<uint    >(in); break;
+            case u8:    res = copyArray<uchar   >(in); break;
+            case s64:   res = copyArray<intl    >(in); break;
+            case u64:   res = copyArray<uintl   >(in); break;
+            case s16:   res = copyArray<short   >(in); break;
+            case u16:   res = copyArray<ushort  >(in); break;
+            default:    TYPE_ERROR(1, type);
+          }
         }
         std::swap(*out, res);
     }
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index c59de22778..6ca348454a 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -87,3 +87,10 @@ common::SparseArray<To> castSparse(const af_array &in)
     default: TYPE_ERROR(1, info.getType());
     }
 }
+
+template<typename T>
+static af_array copySparseArray(const af_array in)
+{
+  const common::SparseArray<T> &inArray = getSparseArray<T>(in);
+  return getHandle<T>(common::copySparseArray<T>(inArray));
+}
diff --git a/src/backend/SparseArray.cpp b/src/backend/SparseArray.cpp
index 61e7b29a22..bf647a96ed 100644
--- a/src/backend/SparseArray.cpp
+++ b/src/backend/SparseArray.cpp
@@ -81,6 +81,12 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims,
 #endif
 }
 
+SparseArrayBase::SparseArrayBase(const SparseArrayBase &base, bool copy):
+    info(base.info),
+    stype(base.stype),
+    rowIdx(copy ? copyArray<int>(base.rowIdx): base.rowIdx),
+    colIdx(copy ? copyArray<int>(base.colIdx): base.colIdx) {}
+
 SparseArrayBase::~SparseArrayBase()
 {
 }
@@ -139,6 +145,11 @@ SparseArray<T> createArrayDataSparseArray(
     return SparseArray<T>(_dims, _values, _rowIdx, _colIdx, _storage, _copy);
 }
 
+template<typename T>
+SparseArray<T> copySparseArray(const SparseArray<T>& other) {
+    return SparseArray<T>(other, true);
+}
+
 template<typename T>
 SparseArray<T> *initSparseArray()
 {
@@ -179,12 +190,6 @@ SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,
                          : createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
         : createHostDataArray<T>(dim4(_nNZ), _values))
 {
-#if __cplusplus > 199711L
-        static_assert(std::is_standard_layout<SparseArray<T>>::value,
-                      "SparseArray<T> must be a standard layout type");
-        static_assert(offsetof(SparseArray<T>, base) == 0,
-                      "SparseArray<T>::base must be the first member variable of SparseArray<T>");
-#endif
     if(_is_device && _copy_device) {
         writeDeviceDataArray<T>(values, _values, _nNZ * sizeof(T));
     }
@@ -196,20 +201,15 @@ SparseArray<T>::SparseArray(af::dim4 _dims,
             const Array<int> &_rowIdx, const Array<int> &_colIdx,
             const af::storage _storage, bool _copy):
     base(_dims, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _copy),
-    values(_copy ? copyArray<T>(_values): _values)
-{
-#if __cplusplus > 199711L
-        static_assert(std::is_standard_layout<SparseArray<T>>::value,
-                      "SparseArray<T> must be a standard layout type");
-        static_assert(offsetof(SparseArray<T>, base) == 0,
-                      "SparseArray<T>::base must be the first member variable of SparseArray<T>");
-#endif
-}
+    values(_copy ? copyArray<T>(_values): _values) {}
 
 template<typename T>
-SparseArray<T>::~SparseArray()
-{
-}
+SparseArray<T>::SparseArray(const SparseArray<T> &other, bool copy):
+    base(other.base, copy),
+    values(copy ? copyArray<T>(other.values): other.values) {}
+
+template<typename T>
+SparseArray<T>::~SparseArray() {}
 
 #define INSTANTIATE(T)                                                                              \
     template SparseArray<T> createEmptySparseArray<T>(                                              \
@@ -230,6 +230,7 @@ SparseArray<T>::~SparseArray()
             const Array<int> &_rowIdx, const Array<int> &_colIdx,                                   \
             const af::storage _storage, const bool _copy);                                          \
     template SparseArray<T> *initSparseArray<T>();                                                  \
+    template SparseArray<T> copySparseArray<T>(const SparseArray<T>& other);                        \
     template void destroySparseArray<T>(SparseArray<T> *sparse);                                    \
                                                                                                     \
     template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage _storage);         \
diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
index 70d5d0a4db..e3b0fc0dbd 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/SparseArray.hpp
@@ -55,6 +55,8 @@ class SparseArrayBase
                     const af::storage _storage, af_dtype _type,
                     bool _copy = false);
 
+    SparseArrayBase(const SparseArrayBase &in, bool deep_copy = false);
+
     ~SparseArrayBase();
 
     ////////////////////////////////////////////////////////////////////////////
@@ -139,6 +141,8 @@ class SparseArray
                 const Array<int> &_rowIdx, const Array<int> &_colIdx,
                 const af::storage _storage, bool _copy = false);
 
+    SparseArray(const SparseArray<T> &in, bool deep_copy = false);
+
 public:
 
     ~SparseArray();
@@ -228,6 +232,8 @@ class SparseArray
 
     friend SparseArray<T> *initSparseArray<T>();
 
+    friend SparseArray<T> copySparseArray<T>(const SparseArray<T>& in);
+
     friend void destroySparseArray<T>(SparseArray<T> *sparse);
 
 };
diff --git a/src/backend/sparse_helpers.hpp b/src/backend/sparse_helpers.hpp
index b57f881b5d..cc703bb486 100644
--- a/src/backend/sparse_helpers.hpp
+++ b/src/backend/sparse_helpers.hpp
@@ -52,4 +52,7 @@ SparseArray<T> *initSparseArray();
 template<typename T>
 void destroySparseArray(SparseArray<T> *sparse);
 
+template<typename T>
+SparseArray<T> copySparseArray(const SparseArray<T>& input);
+
 } // namespace common
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 836fa6c5ef..79eabe068d 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -118,3 +118,56 @@ TEST(Sparse, ISSUE_1745)
   af_array A_sparse;
   ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array(&A_sparse, A.dims(0), A.dims(1), data.get(), row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
 }
+
+template<typename T>
+class Sparse : public ::testing::Test {};
+
+typedef ::testing::Types<float, af::cfloat, double, af::cdouble> SparseTypes;
+TYPED_TEST_CASE(Sparse, SparseTypes);
+
+TYPED_TEST(Sparse, DeepCopy) {
+    if (noDoubleTests<TypeParam>()) return;
+    using namespace af;
+    cleanSlate();
+
+    array s;
+    {
+        // Create a sparse array from a dense array. Make sure that the dense arrays
+        // are removed
+        array dense = randu(10, 10);
+        array d = makeSparse<TypeParam>(dense, 5);
+        s = sparse(d);
+    }
+
+    // At this point only the sparse array will be allocated in memory. Determine
+    // how much memory is allocated by one sparse array
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+    size_t size_of_alloc = lock_bytes;
+    size_t buffers_per_sparse = lock_buffers;
+
+    {
+        array s2 = s.copy();
+        s2.eval();
+
+        // Make sure that the deep copy allocated additional memory
+        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                          &lock_bytes, &lock_buffers);
+
+        EXPECT_NE(s.get(), s2.get()) << "The sparse arrays point to the same "
+                                        "af_array object.";
+        EXPECT_EQ(size_of_alloc * 2,
+                  lock_bytes) << "The number of bytes allocated by the deep copy do "
+                                "not match the original array";
+
+        EXPECT_EQ(buffers_per_sparse * 2,
+                  lock_buffers) << "The number of buffers allocated by the deep "
+                                  "copy do not match the original array";
+        array d = dense(s);
+        array d2 = dense(s2);
+        ASSERT_TRUE(allTrue<bool>(d == d2));
+    }
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 58b820eabd..eb5023df82 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -466,10 +466,10 @@ void cleanSlate()
   af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
                     &lock_bytes, &lock_buffers);
 
-  ASSERT_EQ(alloc_buffers, 0u);
-  ASSERT_EQ(lock_buffers, 0u);
-  ASSERT_EQ(alloc_bytes, 0u);
-  ASSERT_EQ(lock_bytes, 0u);
+  ASSERT_EQ(0u, alloc_buffers);
+  ASSERT_EQ(0u, lock_buffers);
+  ASSERT_EQ(0u, alloc_bytes);
+  ASSERT_EQ(0u, lock_bytes);
 
   af::setMemStepSize(step_bytes);
 

From 5c6fc12a9f32d02c4f517701742d5a8a7104c770 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 15 Jun 2017 13:30:22 -0400
Subject: [PATCH 1223/2677] Document some of the SparseArray Functions. Minor
 cleanup.

---
 src/backend/SparseArray.hpp    | 76 ++++++++++++++++++----------------
 src/backend/sparse_helpers.hpp |  4 ++
 2 files changed, 44 insertions(+), 36 deletions(-)

diff --git a/src/backend/SparseArray.hpp b/src/backend/SparseArray.hpp
index e3b0fc0dbd..ade77cc22d 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/SparseArray.hpp
@@ -18,29 +18,25 @@
 namespace common
 {
 
-// SparseArray Arrayementation Info class
-// This class is the base class to all SparseArray objects. The purpose of this class
-// was to have a way to retrieve basic information of an Array object without
-// specifying what type the object is at compile time.
-//
-// Early declaration
-
 using namespace detail;
 
 template<typename T> class SparseArray;
 
-////////////////////////////////////////////////////////////////////////////
-// Sparse Array Base Class
-// No templates
-// Contains all data except values array
-////////////////////////////////////////////////////////////////////////////
+/// SparseArray Array Info class
+///
+/// This class is the base class to all SparseArray objects. The purpose of this
+/// class was to have a way to retrieve basic information of an Array object
+/// without specifying what type the object is at compile time.
+///
+/// NOTE: This is not a template class to allow the frontend to determine the
+/// af_array type at runtime
 class SparseArrayBase
 {
 private:
-    ArrayInfo  info;        // This must be the first element of SparseArray<T>.
-    af::storage stype;      // Storage format: CSR, CSC, COO
-    Array<int> rowIdx;      // Linear array containing row indices
-    Array<int> colIdx;      // Linear array containing col indices
+    ArrayInfo  info;        ///< NOTE: This must be the first element of SparseArray<T>.
+    af::storage stype;      ///< Storage format: CSR, CSC, COO
+    Array<int> rowIdx;      ///< Linear array containing row indices
+    Array<int> colIdx;      ///< Linear array containing col indices
 
 public:
     SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage, af_dtype _type);
@@ -55,6 +51,13 @@ class SparseArrayBase
                     const af::storage _storage, af_dtype _type,
                     bool _copy = false);
 
+    /// A copy constructor for SparseArray
+    ///
+    /// This constructor copies the \p in SparseArray and creates a new object
+    /// from it. It can also perform a deep copy if the second argument is true.
+    ///
+    /// \param[in] in         The array that will be copied
+    /// \param[in] deep_copy  If true a deep copy is performed
     SparseArrayBase(const SparseArrayBase &in, bool deep_copy = false);
 
     ~SparseArrayBase();
@@ -98,18 +101,19 @@ class SparseArrayBase
         colIdx.setId(id);
     }
 
-    ////////////////////////////////////////////////////////////////////////////
-    // Specialized functions for SparseArray
-    ////////////////////////////////////////////////////////////////////////////
-    // Get the internal arrays
-    Array<int>& getRowIdx()                 { return rowIdx;            }
-    Array<int>& getColIdx()                 { return colIdx;            }
-
+    /// Returns the row indices for the corresponding values in the SparseArray
+          Array<int>& getRowIdx()           { return rowIdx;            }
     const Array<int>& getRowIdx()     const { return rowIdx;            }
+
+    /// Returns the column indices for the corresponding values in the
+    /// SparseArray
+          Array<int>& getColIdx()           { return colIdx;            }
     const Array<int>& getColIdx()     const { return colIdx;            }
 
-    // Dims, types etc
+    /// Returns the number of non-zero elements in the array.
     dim_t getNNZ()                    const;
+
+    /// Returns the storage format of the SparseArray
     af::storage getStorage()          const { return stype;             }
 };
 #if __cplusplus > 199711L
@@ -124,8 +128,8 @@ template<typename T>
 class SparseArray
 {
 private:
-    SparseArrayBase  base;    // This must be the first element of SparseArray<T>.
-    Array<T>         values;  // Linear array containing actual values
+    SparseArrayBase  base;    ///< This must be the first element of SparseArray<T>.
+    Array<T>         values;  ///< Linear array containing actual values
 
     SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage stype);
 
@@ -141,16 +145,20 @@ class SparseArray
                 const Array<int> &_rowIdx, const Array<int> &_colIdx,
                 const af::storage _storage, bool _copy = false);
 
-    SparseArray(const SparseArray<T> &in, bool deep_copy = false);
+    /// A copy constructor for SparseArray
+    ///
+    /// This constructor copies the \p in SparseArray and creates a new object
+    /// from it. It can also perform a deep copy if the second argument is true.
+    ///
+    /// \param[in] in         The array that will be copied
+    /// \param[in] deep_copy  If true a deep copy is performed
+    SparseArray(const SparseArray<T> &in, bool deep_copy);
 
 public:
 
     ~SparseArray();
 
-    ////////////////////////////////////////////////////////////////////////////
-    // Functions that call ArrayInfo object's functions
-    ////////////////////////////////////////////////////////////////////////////
-
+// Functions that call ArrayInfo object's functions
 #define INSTANTIATE_INFO(return_type, func)         \
     return_type func() const { return base.func();  }
 
@@ -205,10 +213,7 @@ class SparseArray
         getColIdx().eval();
     }
 
-    ////////////////////////////////////////////////////////////////////////////
     // Friend functions for Sparse Array Creation
-    ////////////////////////////////////////////////////////////////////////////
-
     friend SparseArray<T> createEmptySparseArray<T>(
             const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);
 
@@ -232,10 +237,9 @@ class SparseArray
 
     friend SparseArray<T> *initSparseArray<T>();
 
-    friend SparseArray<T> copySparseArray<T>(const SparseArray<T>& in);
+    friend SparseArray<T> copySparseArray<T>(const SparseArray<T>& input);
 
     friend void destroySparseArray<T>(SparseArray<T> *sparse);
-
 };
 
 } // namespace common
diff --git a/src/backend/sparse_helpers.hpp b/src/backend/sparse_helpers.hpp
index cc703bb486..5e50efacb7 100644
--- a/src/backend/sparse_helpers.hpp
+++ b/src/backend/sparse_helpers.hpp
@@ -52,6 +52,10 @@ SparseArray<T> *initSparseArray();
 template<typename T>
 void destroySparseArray(SparseArray<T> *sparse);
 
+/// Performs a deep copy of the \p input array.
+///
+/// \param[in] input    The sparse array that is to be copied
+/// \returns A deep copy of the input sparse array
 template<typename T>
 SparseArray<T> copySparseArray(const SparseArray<T>& input);
 

From 9bee0fcc43b04d99a8b07321db24f7d1eb0e1a78 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 16 Jun 2017 14:08:52 -0400
Subject: [PATCH 1224/2677] Allow af_create_handle to accept nullptr for dims

---
 src/api/c/approx.cpp   |  6 ++----
 src/api/c/array.cpp    |  9 ++++-----
 src/api/c/assign.cpp   |  3 +--
 src/api/c/binary.cpp   |  3 +--
 src/api/c/cast.cpp     |  3 +--
 src/api/c/cholesky.cpp | 15 ++++++---------
 src/api/c/data.cpp     | 29 ++++++++++-------------------
 src/api/c/det.cpp      |  3 ++-
 src/api/c/diff.cpp     |  6 ++----
 src/api/c/hsv_rgb.cpp  |  3 +--
 src/api/c/index.cpp    |  3 +--
 src/api/c/lu.cpp       | 10 ++++------
 src/api/c/qr.cpp       | 10 ++++------
 src/api/c/rgb_gray.cpp |  3 +--
 src/api/c/solve.cpp    | 11 ++++-------
 src/api/c/sort.cpp     | 10 ++++------
 src/api/c/svd.cpp      | 14 ++++++--------
 src/api/c/where.cpp    |  3 +--
 18 files changed, 55 insertions(+), 89 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index db98f99566..e77458d59c 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -61,8 +61,7 @@ af_err af_approx1(af_array *out, const af_array in, const af_array pos,
                        method == AF_INTERP_LOWER));
 
         if(idims.ndims() == 0 || pdims.ndims() ==  0) {
-            dim_t my_dims[] = { 0, 0, 0, 0 };
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, itype);
+            return af_create_handle(out, 0, nullptr, itype);
         }
 
         af_array output;
@@ -108,8 +107,7 @@ af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const a
                       (pdims[2] == idims[2] && pdims[3] == idims[3]));
 
         if(idims.ndims() == 0 || pdims.ndims() ==  0 || qdims.ndims() == 0) {
-            dim_t my_dims[] = { 0, 0, 0, 0 };
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, itype);
+            return af_create_handle(out, 0, nullptr, itype);
         }
 
         af_array output;
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 589d8f51a2..6fb25413c8 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -94,11 +94,11 @@ af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * co
                         const af_dtype type)
 {
     try {
-        af_array out;
+        af_array out = 0;
         AF_CHECK(af_init());
 
-        dim4 d((size_t)dims[0]);
-        for(unsigned i = 1; i < ndims; i++) {
+        dim4 d(0, 0, 0, 0);
+        for(unsigned i = 0; i < ndims; i++) {
             d[i] = dims[i];
         }
 
@@ -131,8 +131,7 @@ af_err af_copy_array(af_array *out, const af_array in)
         const af_dtype type = info.getType();
 
         if(info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         af_array res;
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 6151de6460..26220f197c 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -235,8 +235,7 @@ af_err af_assign_gen(af_array *out,
         }
 
         if(lhsDims.ndims() == 0) {
-            dim_t my_dims[] = { 0, 0, 0, 0 };
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, lhsType);
+            return af_create_handle(out, 0, nullptr, lhsType);
         }
 
         ARG_ASSERT(2, (ndims == 1) || (ndims == (dim_t)lInfo.ndims()));
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index bfa349ea79..930141b092 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -484,8 +484,7 @@ static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs,
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
         if(odims.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         af_array res;
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 89602115f2..f15783dcaa 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -72,8 +72,7 @@ af_err af_cast(af_array *out, const af_array in, const af_dtype type)
 
         dim4 idims = info.dims();
         if(idims.elements() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         af_array res = cast(in, type);
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index df073a4b10..88a4bceb00 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -42,13 +42,11 @@ af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_up
 
         af_dtype type = i_info.getType();
 
-        ARG_ASSERT(2, i_info.isFloating());                  // Only floating and complex types
-        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
-
         if(i_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
+        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
+        ARG_ASSERT(2, i_info.isFloating());                  // Only floating and complex types
 
         af_array output;
         switch(type) {
@@ -75,13 +73,12 @@ af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper)
         }
 
         af_dtype type = i_info.getType();
-
-        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
-        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
-
         if(i_info.ndims() == 0) {
             return AF_SUCCESS;
         }
+        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
+        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
+
 
         int out;
 
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 38f5476529..980a18836f 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -54,8 +54,7 @@ af_err af_constant(af_array *result, const double value,
 
         dim4 d(1, 1, 1, 1);
         if(ndims <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(result, 0, nullptr, type);
         } else {
             d = verifyDims(ndims, dims);
         }
@@ -99,8 +98,7 @@ af_err af_constant_complex(af_array *result, const double real, const double ima
 
         dim4 d(1, 1, 1, 1);
         if(ndims <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(result, 0, nullptr, type);
         } else {
             d = verifyDims(ndims, dims);
         }
@@ -126,8 +124,7 @@ af_err af_constant_long(af_array *result, const intl val,
 
         dim4 d(1, 1, 1, 1);
         if(ndims <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(result, AF_MAX_DIMS, my_dims, s64);
+            return af_create_handle(result, 0, nullptr, s64);
         } else {
             d = verifyDims(ndims, dims);
         }
@@ -149,8 +146,7 @@ af_err af_constant_ulong(af_array *result, const uintl val,
 
         dim4 d(1, 1, 1, 1);
         if(ndims <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(result, AF_MAX_DIMS, my_dims, u64);
+            return af_create_handle(result, 0, nullptr, u64);
         } else {
             d = verifyDims(ndims, dims);
         }
@@ -175,8 +171,7 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
         AF_CHECK(af_init());
 
         if(ndims == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         dim4 d = verifyDims(ndims, dims);
@@ -217,10 +212,9 @@ af_err af_range(af_array *result, const unsigned ndims, const dim_t * const dims
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d(1, 1, 1, 1);
+        dim4 d(0);
         if(ndims <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(result, 0, nullptr, type);
         } else {
             d = verifyDims(ndims, dims);
         }
@@ -258,8 +252,7 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
         AF_CHECK(af_init());
 
         if(ndims == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(result, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(result, 0, nullptr, type);
         }
 
         DIM_ASSERT(1, ndims > 0 && ndims <= 4);
@@ -308,8 +301,7 @@ af_err af_diag_create(af_array *out, const af_array in, const int num)
         af_array result;
 
         if(in_info.dims()[0] == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         switch(type) {
@@ -342,8 +334,7 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num)
         af_dtype type = in_info.getType();
 
         if(in_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         DIM_ASSERT(1, in_info.ndims() >= 2);
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index a8846cfae4..92721b8588 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -75,7 +75,8 @@ af_err af_det(double *real_val, double *imag_val, const af_array in)
 
         af_dtype type = i_info.getType();
 
-        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]);      // Only square matrices
+        if(i_info.dims()[0])
+          DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]);      // Only square matrices
         ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
 
         *real_val = 0;
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index 2d4f672964..bd6996a6c4 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -41,8 +41,7 @@ af_err af_diff1(af_array *out, const af_array in, const int dim)
 
         af::dim4 in_dims = info.dims();
         if(in_dims[dim] < 2) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
 
         DIM_ASSERT(1, in_dims[dim] >= 2);
@@ -83,8 +82,7 @@ af_err af_diff2(af_array *out, const af_array in, const int dim)
 
         af::dim4 in_dims = info.dims();
         if(in_dims[dim] < 3) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(out, 0, nullptr, type);
         }
         DIM_ASSERT(1, in_dims[dim] >= 3);
 
diff --git a/src/api/c/hsv_rgb.cpp b/src/api/c/hsv_rgb.cpp
index 86eb38ccd4..7662dd7c83 100644
--- a/src/api/c/hsv_rgb.cpp
+++ b/src/api/c/hsv_rgb.cpp
@@ -39,8 +39,7 @@ af_err convert(af_array* out, const af_array& in)
         af::dim4 inputDims = info.dims();
 
         if(info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, iType);
+            return af_create_handle(out, 0, nullptr, iType);
         }
 
         ARG_ASSERT(1, (inputDims.ndims() >= 3));
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 04033b0ac2..07559b1f32 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -166,8 +166,7 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
         af_dtype inType = getInfo(in).getType();
 
         if(iDims.ndims() <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, inType);
+            return af_create_handle(out, 0, nullptr, inType);
         }
 
         if (ndims == 1 && ndims != (dim_t)iInfo.ndims()) {
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index 1a625cf0e8..2928a57cfc 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -54,10 +54,9 @@ af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array i
         ARG_ASSERT(3, i_info.isFloating());                       // Only floating and complex types
 
         if(i_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            AF_CHECK(af_create_handle(lower, AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(upper, AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(pivot, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(lower, 0, nullptr, type));
+            AF_CHECK(af_create_handle(upper, 0, nullptr, type));
+            AF_CHECK(af_create_handle(pivot, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
@@ -88,8 +87,7 @@ af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv)
         ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
 
         if(i_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(pivot, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(pivot, 0, nullptr, type);
         }
 
         af_array out;
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index d58c9c6b41..546b37d92d 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -51,10 +51,9 @@ af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
         af_dtype type = i_info.getType();
 
         if(i_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            AF_CHECK(af_create_handle(q,   AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(r,   AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(tau, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(q,   0, nullptr, type));
+            AF_CHECK(af_create_handle(r,   0, nullptr, type));
+            AF_CHECK(af_create_handle(tau, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
@@ -87,8 +86,7 @@ af_err af_qr_inplace(af_array *tau, af_array in)
         ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
 
         if(i_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(tau, AF_MAX_DIMS, my_dims, type);
+            return af_create_handle(tau, 0, nullptr, type);
         }
 
         af_array out;
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index b87803aa77..c7255a896e 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -111,8 +111,7 @@ af_err convert(af_array* out, const af_array in, const float r, const float g, c
 
         // 2D is not required.
         if(info.elements() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, iType);
+            return af_create_handle(out, 0, nullptr, iType);
         }
 
         // If RGB is input, then assert 3 channels
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index 73ba745886..b42f3f4f1e 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -52,8 +52,7 @@ af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_
         DIM_ASSERT(1, bdims[3] == adims[3]);
 
         if(a_info.ndims() == 0 || b_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, a_type);
+            return af_create_handle(out, 0, nullptr, a_type);
         }
 
         bool is_triangle_solve = (options & AF_MAT_LOWER) || (options & AF_MAT_UPPER);
@@ -111,6 +110,9 @@ af_err af_solve_lu(af_array *out, const af_array a,
 
         dim4 adims = a_info.dims();
         dim4 bdims = b_info.dims();
+        if(a_info.ndims() == 0 || b_info.ndims() == 0) {
+          return af_create_handle(out, 0, nullptr, a_type);
+        }
 
         ARG_ASSERT(1, a_info.isFloating());                       // Only floating and complex types
         ARG_ASSERT(2, b_info.isFloating());                       // Only floating and complex types
@@ -122,11 +124,6 @@ af_err af_solve_lu(af_array *out, const af_array a,
         DIM_ASSERT(1, bdims[2] == adims[2]);
         DIM_ASSERT(1, bdims[3] == adims[3]);
 
-        if(a_info.ndims() == 0 || b_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(out, AF_MAX_DIMS, my_dims, a_type);
-        }
-
         if (options != AF_MAT_NONE) {
             AF_ERROR("Using this property is not yet supported in solveLU", AF_ERR_NOT_SUPPORTED);
         }
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index f310a3769b..46395bfec9 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -86,9 +86,8 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const
         af_dtype type = info.getType();
 
         if(info.elements() <= 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            AF_CHECK(af_create_handle(out,     AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(indices, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(out,     0, nullptr, type));
+            AF_CHECK(af_create_handle(indices, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
@@ -170,9 +169,8 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
 
         DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
         if(kinfo.elements() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            AF_CHECK(af_create_handle(out_keys,   AF_MAX_DIMS, my_dims, ktype));
-            AF_CHECK(af_create_handle(out_values, AF_MAX_DIMS, my_dims, ktype));
+            AF_CHECK(af_create_handle(out_keys,   0, nullptr, ktype));
+            AF_CHECK(af_create_handle(out_values, 0, nullptr, ktype));
             return AF_SUCCESS;
         }
 
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index 1668674da3..ec543c6386 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -74,10 +74,9 @@ af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
         af_dtype type = info.getType();
 
         if(dims.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            AF_CHECK(af_create_handle(u, AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(s, AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(vt, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(u, 0, nullptr, type));
+            AF_CHECK(af_create_handle(s, 0, nullptr, type));
+            AF_CHECK(af_create_handle(vt, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
@@ -113,10 +112,9 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
         af_dtype type = info.getType();
 
         if(dims.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            AF_CHECK(af_create_handle(u, AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(s, AF_MAX_DIMS, my_dims, type));
-            AF_CHECK(af_create_handle(vt, AF_MAX_DIMS, my_dims, type));
+            AF_CHECK(af_create_handle(u, 0, nullptr, type));
+            AF_CHECK(af_create_handle(s, 0, nullptr, type));
+            AF_CHECK(af_create_handle(vt, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 61bef67136..45ad458ab7 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -33,8 +33,7 @@ af_err af_where(af_array *idx, const af_array in)
         af_dtype type = i_info.getType();
 
         if(i_info.ndims() == 0) {
-            dim_t my_dims[] = {0, 0, 0, 0};
-            return af_create_handle(idx, AF_MAX_DIMS, my_dims, u32);
+            return af_create_handle(idx, 0, nullptr, u32);
         }
 
         af_array res;

From 2e8db09370d3f9398cc2dfa8aa9f23735c2a210a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 16 Jun 2017 23:02:38 -0400
Subject: [PATCH 1225/2677] Empty arrays are 0, 1, 1, 1. Update isVector and
 tests.

---
 src/api/c/array.cpp       | 5 ++++-
 src/api/cpp/array.cpp     | 2 +-
 src/backend/ArrayInfo.cpp | 4 +++-
 test/array.cpp            | 8 +++++---
 4 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 6fb25413c8..7124cd1e31 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -97,7 +97,10 @@ af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * co
         af_array out = 0;
         AF_CHECK(af_init());
 
-        dim4 d(0, 0, 0, 0);
+        if (ndims > 0) {
+            ARG_ASSERT(2, ndims > 0 && dims != NULL);
+        }
+        dim4 d(0);
         for(unsigned i = 0; i < ndims; i++) {
             d[i] = dims[i];
         }
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 8420582f33..a300f09e8b 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -127,7 +127,7 @@ namespace af
 
     array::array() : arr(0)
     {
-        initEmptyArray(&arr, f32, 0, 0, 0, 0);
+        initEmptyArray(&arr, f32, 0, 1, 1, 1);
     }
 
     array::array(const dim4 &dims, af::dtype ty) : arr(0)
diff --git a/src/backend/ArrayInfo.cpp b/src/backend/ArrayInfo.cpp
index a95754346a..d2430c20eb 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/ArrayInfo.cpp
@@ -99,10 +99,12 @@ bool ArrayInfo::isColumn() const
 bool ArrayInfo::isVector() const
 {
     int singular_dims = 0;
+    int non_singular_dims = 0;
     for(int i = 0; i < AF_MAX_DIMS; i++) {
+        non_singular_dims += (dims()[i] != 0 && dims()[i] != 1);
         singular_dims += (dims()[i] == 1);
     }
-    return singular_dims == AF_MAX_DIMS - 1;
+    return singular_dims == AF_MAX_DIMS - 1 && non_singular_dims == 1;
 }
 
 bool ArrayInfo::isComplex() const
diff --git a/test/array.cpp b/test/array.cpp
index b712df302e..03f5965b00 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -29,9 +29,6 @@ TEST(Array, ConstructorDefault)
     array a;
     EXPECT_EQ(0u,    a.numdims());
     EXPECT_EQ(dim_t(0),    a.dims(0));
-    EXPECT_EQ(dim_t(0),    a.dims(1));
-    EXPECT_EQ(dim_t(0),    a.dims(2));
-    EXPECT_EQ(dim_t(0),    a.dims(3));
     EXPECT_EQ(dim_t(0),    a.elements());
     EXPECT_EQ(f32,  a.type());
     EXPECT_EQ(0u,    a.bytes());
@@ -411,6 +408,11 @@ TEST(Array, ISSUE_951)
     af::array b = a.cols(0, 20).rows(10, 20);
 }
 
+TEST(Array, CreateHandleInvalidNullDimsPointer) {
+    af_array out = 0;
+    EXPECT_EQ(AF_ERR_ARG, af_create_handle(&out, 1, NULL, f32));
+}
+
 
 TEST(Device, simple)
 {

From a4c36c28eaf819a602c88627b1f25845489196df Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 13 Jun 2017 14:48:06 +0530
Subject: [PATCH 1226/2677] add af_get_scalar fn to retrieve first element from
 array

Implemented in all currently available backends.

af::array::scalar<T>() is changed to use af_get_scalar instead
of fetching entire array and the copying over only first element.
---
 include/af/array.h          | 15 ++++++++++++++-
 src/api/c/array.cpp         | 36 ++++++++++++++++++++++++++++++++++++
 src/api/cpp/array.cpp       |  7 +++----
 src/api/unified/array.cpp   |  6 ++++++
 src/backend/cpu/copy.cpp    | 23 +++++++++++++++++++++++
 src/backend/cpu/copy.hpp    |  3 +++
 src/backend/cuda/copy.cu    | 26 ++++++++++++++++++++++++++
 src/backend/cuda/copy.hpp   |  3 +++
 src/backend/opencl/copy.cpp | 23 +++++++++++++++++++++++
 src/backend/opencl/copy.hpp |  3 +++
 test/array.cpp              | 15 +++++++++++++++
 11 files changed, 155 insertions(+), 5 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 327347abe3..07f6c5fe15 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1668,10 +1668,23 @@ extern "C" {
         \returns error codes
     */
     AFAPI af_err af_is_sparse       (bool *result, const af_array arr);
+#endif
+
+#if AF_API_VERSION >= 35
     /**
-        @}
+        \brief Get first element from an array
+
+        \param[out] output_value is the element requested
+        \param[in] arr is the input array
+        \return \ref AF_SUCCESS if the execution completes properly
     */
+    AFAPI af_err af_get_scalar(void* output_value, const af_array arr);
 #endif
+
+    /**
+        @}
+    */
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 7124cd1e31..507cf36a35 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -11,6 +11,7 @@
 #include <platform.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
+#include <copy.hpp>
 #include <sparse_handle.hpp>
 
 using namespace detail;
@@ -404,3 +405,38 @@ INSTANTIATE(af_is_bool        , isBool        )
 INSTANTIATE(af_is_sparse      , isSparse      )
 
 #undef INSTANTIATE
+
+template<typename T>
+inline void getScalar(T* out, const af_array& arr)
+{
+    out[0] = getScalar<T>(getArray<T>(arr));
+}
+
+af_err af_get_scalar(void* output_value, const af_array arr)
+{
+    try {
+        ARG_ASSERT(0, (output_value!=NULL));
+
+        const ArrayInfo& info   = getInfo(arr);
+        const af_dtype type     = info.getType();
+
+        switch(type) {
+        case f32: getScalar<float  >(reinterpret_cast<float*  >(output_value), arr); break;
+        case f64: getScalar<double >(reinterpret_cast<double* >(output_value), arr); break;
+        case  b8: getScalar<char   >(reinterpret_cast<char*   >(output_value), arr); break;
+        case s32: getScalar<int    >(reinterpret_cast<int*    >(output_value), arr); break;
+        case u32: getScalar<uint   >(reinterpret_cast<uint*   >(output_value), arr); break;
+        case  u8: getScalar<uchar  >(reinterpret_cast<uchar*  >(output_value), arr); break;
+        case s64: getScalar<intl   >(reinterpret_cast<intl*   >(output_value), arr); break;
+        case u64: getScalar<uintl  >(reinterpret_cast<uintl*  >(output_value), arr); break;
+        case s16: getScalar<short  >(reinterpret_cast<short*  >(output_value), arr); break;
+        case u16: getScalar<ushort >(reinterpret_cast<ushort* >(output_value), arr); break;
+        case c32: getScalar<cfloat >(reinterpret_cast<cfloat* >(output_value), arr); break;
+        case c64: getScalar<cdouble>(reinterpret_cast<cdouble*>(output_value), arr); break;
+        default:    TYPE_ERROR(4, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index a300f09e8b..777b7ec4ae 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -972,10 +972,9 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     }                                                               \
     template<> AFAPI T array::scalar() const                        \
     {                                                               \
-        T *h_ptr = host<T>();                                       \
-        T scalar = h_ptr[0];                                        \
-        delete[] h_ptr;                                             \
-        return scalar;                                              \
+        T val;                                                      \
+        AF_THROW(af_get_scalar(&val, get()));                       \
+        return val;                                                 \
     }                                                               \
     template<> AFAPI T* array::device() const                       \
     {                                                               \
diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index 37caceb3fb..81a0324228 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -116,3 +116,9 @@ ARRAY_HAPI_DEF(af_is_floating)
 ARRAY_HAPI_DEF(af_is_integer)
 ARRAY_HAPI_DEF(af_is_bool)
 ARRAY_HAPI_DEF(af_is_sparse)
+
+af_err af_get_scalar(void* output_value, const af_array arr)
+{
+    CHECK_ARRAYS(arr);
+    return CALL(output_value, arr);
+}
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 97f4514eb9..daa445acda 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -131,4 +131,27 @@ SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
 SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
 SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
 
+template<typename T>
+T getScalar(const Array<T> &in)
+{
+    in.eval();
+    getQueue().sync();
+    return in.get()[0];
+}
+
+#define INSTANTIATE_GETSCALAR(T) \
+    template T getScalar(const Array<T> &in);
+
+INSTANTIATE_GETSCALAR(float  )
+INSTANTIATE_GETSCALAR(double )
+INSTANTIATE_GETSCALAR(cfloat )
+INSTANTIATE_GETSCALAR(cdouble)
+INSTANTIATE_GETSCALAR(int    )
+INSTANTIATE_GETSCALAR(uint   )
+INSTANTIATE_GETSCALAR(uchar  )
+INSTANTIATE_GETSCALAR(char   )
+INSTANTIATE_GETSCALAR(intl   )
+INSTANTIATE_GETSCALAR(uintl  )
+INSTANTIATE_GETSCALAR(short  )
+INSTANTIATE_GETSCALAR(ushort )
 }
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 8e02e6cd98..864d42067c 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -28,4 +28,7 @@ namespace cpu
 
     template<typename T>
     void multiply_inplace(Array<T> &in, double val);
+
+    template<typename T>
+    T getScalar(const Array<T> &in);
 }
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 6a92051490..32128d00df 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -202,4 +202,30 @@ namespace cuda
     SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
     SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
     SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
+
+    template<typename T>
+    T getScalar(const Array<T> &in)
+    {
+        T retVal;
+        CUDA_CHECK(cudaMemcpyAsync(&retVal, in.get(), sizeof(T),
+                                   cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+        return retVal;
+    }
+
+#define INSTANTIATE_GETSCALAR(T) \
+    template T getScalar(const Array<T> &in);
+
+    INSTANTIATE_GETSCALAR(float  )
+    INSTANTIATE_GETSCALAR(double )
+    INSTANTIATE_GETSCALAR(cfloat )
+    INSTANTIATE_GETSCALAR(cdouble)
+    INSTANTIATE_GETSCALAR(int    )
+    INSTANTIATE_GETSCALAR(uint   )
+    INSTANTIATE_GETSCALAR(uchar  )
+    INSTANTIATE_GETSCALAR(char   )
+    INSTANTIATE_GETSCALAR(intl   )
+    INSTANTIATE_GETSCALAR(uintl  )
+    INSTANTIATE_GETSCALAR(short  )
+    INSTANTIATE_GETSCALAR(ushort )
 }
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index ff72af7fee..c25f084876 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -28,4 +28,7 @@ namespace cuda
 
     template<typename T>
     void multiply_inplace(Array<T> &in, double val);
+
+    template<typename T>
+    T getScalar(const Array<T> &in);
 }
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 7221a1f015..6feb948c6b 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -215,4 +215,27 @@ namespace opencl
     SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
     SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
 
+    template<typename T>
+    T getScalar(const Array<T> &in)
+    {
+        T retVal;
+        getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, sizeof(T) * in.getOffset(), sizeof(T), &retVal);
+        return retVal;
+    }
+
+#define INSTANTIATE_GETSCALAR(T) \
+    template T getScalar(const Array<T> &in);
+
+    INSTANTIATE_GETSCALAR(float  )
+    INSTANTIATE_GETSCALAR(double )
+    INSTANTIATE_GETSCALAR(cfloat )
+    INSTANTIATE_GETSCALAR(cdouble)
+    INSTANTIATE_GETSCALAR(int    )
+    INSTANTIATE_GETSCALAR(uint   )
+    INSTANTIATE_GETSCALAR(uchar  )
+    INSTANTIATE_GETSCALAR(char   )
+    INSTANTIATE_GETSCALAR(intl   )
+    INSTANTIATE_GETSCALAR(uintl  )
+    INSTANTIATE_GETSCALAR(short  )
+    INSTANTIATE_GETSCALAR(ushort )
 }
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index ea26df45c6..c1f5d9d670 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -28,4 +28,7 @@ namespace opencl
 
     template<typename T>
     void multiply_inplace(Array<T> &in, double val);
+
+    template<typename T>
+    T getScalar(const Array<T> &in);
 }
diff --git a/test/array.cpp b/test/array.cpp
index 03f5965b00..9457379bfb 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -22,6 +22,7 @@ class Array : public ::testing::Test
 };
 
 typedef ::testing::Types<float, double, af::cfloat, af::cdouble, char, unsigned char, int, uint, intl, uintl, short, ushort> TestTypes;
+
 TYPED_TEST_CASE(Array, TestTypes);
 
 TEST(Array, ConstructorDefault)
@@ -505,3 +506,17 @@ TEST(Device, JIT)
     array a = constant(1, 5, 5);
     ASSERT_EQ(a.device<float>() != NULL, 1);
 }
+
+TYPED_TEST(Array, Scalar)
+{
+    if (noDoubleTests<TypeParam>()) return;
+
+    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    array a = randu(dim4(1), type);
+
+    std::vector<TypeParam> gold(a.elements());
+
+    a.host((void*)gold.data());
+
+    EXPECT_EQ(true, gold[0]==a.scalar<TypeParam>());
+}

From 8eaceb80e4dc09cc73ff32f496d8f119b3341c2f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 18 Jun 2017 13:31:49 -0400
Subject: [PATCH 1227/2677] Fix doxygen snippets in lapack docs

---
 docs/details/lapack.dox | 10 +++++-----
 docs/doxygen.mk         |  1 +
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/docs/details/lapack.dox b/docs/details/lapack.dox
index 522dbe544f..13232e5209 100644
--- a/docs/details/lapack.dox
+++ b/docs/details/lapack.dox
@@ -157,11 +157,11 @@ This function takes a co-efficient matrix **A** and an output matrix **B**  as i
 
 This operation can be done in ArrayFire using the following code snippet.
 
-\snippet test/solve_dense.cpp ex_solve
+\snippet test/solve_common.hpp ex_solve
 
 The results can be verified by reconstructing the output matrix using \ref af::matmul in the following manner.
 
-\snippet test/solve_dense.cpp ex_solve_recon
+\snippet test/solve_common.hpp ex_solve_recon
 
 The sample output can be seen below
 
@@ -191,11 +191,11 @@ If the coefficient matrix is known to be a triangular matrix, \ref AF_MAT_LOWER
 
 The sample code snippets for solving a lower triangular matrix can be seen below.
 
-\snippet test/solve_dense.cpp ex_solve_lower
+\snippet test/solve_common.hpp ex_solve_lower
 
 Similarily, the code snippet for solving an upper triangular matrix can be seen below.
 
-\snippet test/solve_dense.cpp ex_solve_upper
+\snippet test/solve_common.hpp ex_solve_upper
 
 See also: \ref af::solveLU
 
@@ -213,7 +213,7 @@ This function takes a co-efficient matrix **A** and an output matrix **B**  as i
 
 This operation can be done in ArrayFire using the following code snippet.
 
-\snippet test/solve_dense.cpp ex_solve_lu
+\snippet test/solve_common.hpp ex_solve_lu
 
 This function along with \ref af::lu split up the task af::solve performs for square matrices.
 
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 46a1dc0861..76fd59d3f4 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -849,6 +849,7 @@ EXAMPLE_PATH           = ${EXAMPLES_DIR}/ \
 # files are included.
 
 EXAMPLE_PATTERNS       = *.cpp \
+                         *.hpp \
                          *.cu
 
 # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be

From f225a4f4f55d653e6820dd121c194fcb87d6d9d1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 18 Jun 2017 04:20:54 -0400
Subject: [PATCH 1228/2677] Allow creation of empty sparse arrays. Allow sparse
 deep copies

---
 src/api/c/array.cpp  | 64 +++++++++++++++++++++++++-------------------
 src/api/c/sparse.cpp | 14 +++++-----
 test/sparse.cpp      | 55 ++++++++++++++++++++++++++++---------
 3 files changed, 88 insertions(+), 45 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 507cf36a35..661714ca04 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -12,9 +12,12 @@
 #include <handle.hpp>
 #include <backend.hpp>
 #include <copy.hpp>
+#include <sparse.hpp>
 #include <sparse_handle.hpp>
+#include <af/sparse.h>
 
 using namespace detail;
+using common::SparseArrayBase;
 
 const ArrayInfo&
 getInfo(const af_array arr, bool sparse_check, bool device_check)
@@ -134,36 +137,43 @@ af_err af_copy_array(af_array *out, const af_array in)
         const ArrayInfo& info = getInfo(in, false);
         const af_dtype type = info.getType();
 
-        if(info.ndims() == 0) {
-            return af_create_handle(out, 0, nullptr, type);
-        }
-
-        af_array res;
-
+        af_array res = 0;
         if(info.isSparse()) {
-            switch(type) {
-            case f32: res = copySparseArray<float  >(in); break;
-            case f64: res = copySparseArray<double >(in); break;
-            case c32: res = copySparseArray<cfloat >(in); break;
-            case c64: res = copySparseArray<cdouble>(in); break;
-            default : TYPE_ERROR(0, type);
+            SparseArrayBase sbase = getSparseArrayBase(in);
+            if(info.ndims() == 0) {
+                return af_create_sparse_array_from_ptr(out,
+                                               info.dims()[0], info.dims()[1],
+                                                0, nullptr, nullptr, nullptr,
+                                                type, sbase.getStorage(), afDevice);
+            } else {
+                switch(type) {
+                case f32: res = copySparseArray<float  >(in); break;
+                case f64: res = copySparseArray<double >(in); break;
+                case c32: res = copySparseArray<cfloat >(in); break;
+                case c64: res = copySparseArray<cdouble>(in); break;
+                default : TYPE_ERROR(0, type);
+                }
             }
         } else {
-            switch(type) {
-            case f32:   res = copyArray<float   >(in); break;
-            case c32:   res = copyArray<cfloat  >(in); break;
-            case f64:   res = copyArray<double  >(in); break;
-            case c64:   res = copyArray<cdouble >(in); break;
-            case b8:    res = copyArray<char    >(in); break;
-            case s32:   res = copyArray<int     >(in); break;
-            case u32:   res = copyArray<uint    >(in); break;
-            case u8:    res = copyArray<uchar   >(in); break;
-            case s64:   res = copyArray<intl    >(in); break;
-            case u64:   res = copyArray<uintl   >(in); break;
-            case s16:   res = copyArray<short   >(in); break;
-            case u16:   res = copyArray<ushort  >(in); break;
-            default:    TYPE_ERROR(1, type);
-          }
+            if(info.ndims() == 0) {
+                return af_create_handle(out, 0, nullptr, type);
+            } else {
+                  switch(type) {
+                  case f32:   res = copyArray<float   >(in); break;
+                  case c32:   res = copyArray<cfloat  >(in); break;
+                  case f64:   res = copyArray<double  >(in); break;
+                  case c64:   res = copyArray<cdouble >(in); break;
+                  case b8:    res = copyArray<char    >(in); break;
+                  case s32:   res = copyArray<int     >(in); break;
+                  case u32:   res = copyArray<uint    >(in); break;
+                  case u8:    res = copyArray<uchar   >(in); break;
+                  case s64:   res = copyArray<intl    >(in); break;
+                  case u64:   res = copyArray<uintl   >(in); break;
+                  case s16:   res = copyArray<short   >(in); break;
+                  case u16:   res = copyArray<ushort  >(in); break;
+                  default:    TYPE_ERROR(1, type);
+                }
+            }
         }
         std::swap(*out, res);
     }
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 50951038a8..bef869bdd9 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -113,12 +113,14 @@ af_array createSparseArrayFromPtr(
 {
     SparseArray<T> sparse = createEmptySparseArray<T>(dims, nNZ, stype);
 
-    if(source == afHost)
-        sparse = common::createHostDataSparseArray(
-                         dims, nNZ, values, rowIdx, colIdx, stype);
-    else if (source == afDevice)
-        sparse = common::createDeviceDataSparseArray(
-                         dims, nNZ, values, rowIdx, colIdx, stype);
+    if(nNZ) {
+        if(source == afHost)
+            sparse = common::createHostDataSparseArray(
+                            dims, nNZ, values, rowIdx, colIdx, stype);
+        else if (source == afDevice)
+            sparse = common::createDeviceDataSparseArray(
+                            dims, nNZ, values, rowIdx, colIdx, stype);
+    }
 
     return getHandle(sparse);
 }
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 79eabe068d..052a6b95a1 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -12,39 +12,39 @@
 #include <sparse_common.hpp>
 
 #define SPARSE_TESTS(T, eps)                                \
-    TEST(SPARSE, T##Square)                                 \
+    TEST(Sparse, T##Square)                                 \
     {                                                       \
         sparseTester<T>(1000, 1000, 100, 5, eps);           \
     }                                                       \
-    TEST(SPARSE, T##RectMultiple)                           \
+    TEST(Sparse, T##RectMultiple)                           \
     {                                                       \
         sparseTester<T>(2048, 1024, 512, 3, eps);           \
     }                                                       \
-    TEST(SPARSE, T##RectDense)                              \
+    TEST(Sparse, T##RectDense)                              \
     {                                                       \
         sparseTester<T>(500, 1000, 250, 1, eps);            \
     }                                                       \
-    TEST(SPARSE, T##MatVec)                                 \
+    TEST(Sparse, T##MatVec)                                 \
     {                                                       \
         sparseTester<T>(625, 1331, 1, 2, eps);              \
     }                                                       \
-    TEST(SPARSE_TRANSPOSE, T##MatVec)                       \
+    TEST(Sparse, Transpose_##T##MatVec)                     \
     {                                                       \
         sparseTransposeTester<T>(625, 1331, 1, 2, eps);     \
     }                                                       \
-    TEST(SPARSE_TRANSPOSE, T##Square)                       \
+    TEST(Sparse, Transpose_##T##Square)                     \
     {                                                       \
         sparseTransposeTester<T>(1000, 1000, 100, 5, eps);  \
     }                                                       \
-    TEST(SPARSE_TRANSPOSE, T##RectMultiple)                 \
+    TEST(Sparse, Transpose_##T##RectMultiple)               \
     {                                                       \
         sparseTransposeTester<T>(2048, 1024, 512, 3, eps);  \
     }                                                       \
-    TEST(SPARSE_TRANSPOSE, T##RectDense)                    \
+    TEST(Sparse, Transpose_##T##RectDense)                  \
     {                                                       \
         sparseTransposeTester<T>(453, 751, 397, 1, eps);    \
     }                                                       \
-    TEST(SPARSE, T##ConvertCSR)                             \
+    TEST(Sparse, T##ConvertCSR)                             \
     {                                                       \
         convertCSR<T>(2345, 5678, 0.5);                     \
     }                                                       \
@@ -57,7 +57,7 @@ SPARSE_TESTS(cdouble, 1E-5)
 #undef SPARSE_TESTS
 
 #define CREATE_TESTS(STYPE)                                         \
-    TEST(SPARSE_CREATE, STYPE)                                      \
+    TEST(Sparse, Create_##STYPE)                                    \
     {                                                               \
         createFunction<STYPE>();                                    \
     }
@@ -67,7 +67,7 @@ CREATE_TESTS(AF_STORAGE_COO)
 
 #undef CREATE_TESTS
 
-TEST(SPARSE_CREATE, AF_STORAGE_CSC)
+TEST(Sparse, Create_AF_STORAGE_CSC)
 {
     af::array d = af::identity(3, 3);
 
@@ -78,7 +78,7 @@ TEST(SPARSE_CREATE, AF_STORAGE_CSC)
 }
 
 #define CAST_TESTS_TYPES(Ti, To, SUFFIX, M, N, F)                               \
-    TEST(SPARSE_CAST, Ti##_##To##_##SUFFIX)                                     \
+    TEST(Sparse, Cast_##Ti##_##To##_##SUFFIX)                                   \
     {                                                                           \
         sparseCastTester<Ti, To>(M, N, F);                                      \
     }                                                                           \
@@ -171,3 +171,34 @@ TYPED_TEST(Sparse, DeepCopy) {
         ASSERT_TRUE(allTrue<bool>(d == d2));
     }
 }
+
+TYPED_TEST(Sparse, Empty) {
+    if (noDoubleTests<TypeParam>()) return;
+    using namespace af;
+    af_array ret = 0;
+    dim_t rows = 0, cols = 0, nnz = 0;
+    EXPECT_EQ(AF_SUCCESS,
+              af_create_sparse_array_from_ptr(
+                  &ret,
+                  rows, cols,
+                  nnz, NULL, NULL, NULL,
+                  (af_dtype)dtype_traits<TypeParam>::af_type,
+                  AF_STORAGE_CSR, afHost));
+    bool sparse = false;
+    EXPECT_EQ(AF_SUCCESS, af_is_sparse(&sparse, ret));
+    EXPECT_EQ(true, sparse);
+}
+
+TYPED_TEST(Sparse, EmptyDeepCopy) {
+    if (noDoubleTests<TypeParam>()) return;
+    using namespace af;
+    array a = sparse(0, 0,
+                     array(0, (af_dtype)af::dtype_traits<TypeParam>::af_type),
+                     array(0, s32), array(0, s32));
+    EXPECT_TRUE(a.issparse());
+    EXPECT_EQ(0, sparseGetNNZ(a));
+
+    array b = a.copy();
+    EXPECT_TRUE(b.issparse());
+    EXPECT_EQ(0, sparseGetNNZ(b));
+}

From 20403a6aa04f2e25da6d5b44df19377f7032915a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 13 Jun 2017 08:05:49 +0530
Subject: [PATCH 1229/2677] Patch forge build to use arrayfire fork of
 freetype2

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 9989f45277..cc6c880633 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -46,7 +46,7 @@ ELSE()
     ENDIF(WIN32)
 ENDIF()
 
-SET(FORGE_VERSION 1.0.1)
+SET(FORGE_VERSION 1.0.2-ft)
 
 # FIXME Tag forge correctly during release
 ExternalProject_Add(

From 42124bb4c9913c6ad374558a51f144d6681aee2f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 7 Jun 2017 17:30:50 +0530
Subject: [PATCH 1230/2677] Fix c32, c64 multiplication in convolution kernels

---
 src/backend/opencl/kernel/convolve.cl         | 11 ++++--
 .../opencl/kernel/convolve/conv2_impl.hpp     | 34 +++++++++++++------
 .../opencl/kernel/convolve/conv_common.hpp    | 20 +++++++++--
 .../opencl/kernel/convolve_separable.cl       |  4 ++-
 .../opencl/kernel/convolve_separable.cpp      | 33 +++++++++++++-----
 5 files changed, 76 insertions(+), 26 deletions(-)

diff --git a/src/backend/opencl/kernel/convolve.cl b/src/backend/opencl/kernel/convolve.cl
index 7839e2ad29..0d13a0eee2 100644
--- a/src/backend/opencl/kernel/convolve.cl
+++ b/src/backend/opencl/kernel/convolve.cl
@@ -54,7 +54,8 @@ void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
         int lx   = get_local_id(0) + padding + (EXPAND ? 0 : fLen>>1);
         accType accum = (accType)(0);
         for(int f=0; f<fLen; ++f) {
-            accum = accum + ((accType)localMem[lx-f] * (accType)impulse[f]);
+            //binOp will do MUL_OP for convolution operation
+            accum = accum + binOp((accType)localMem[lx-f], (accType)impulse[f]);
         }
         dst[gx] = (T)accum;
     }
@@ -122,7 +123,9 @@ void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
             for(int fi=0; fi<FLEN0; ++fi) {
                 accType f_val = impulse[fj*FLEN0+fi];
                 T s_val = localMem[(cj-fj)*shrdLen0+(ci-fi)];
-                accum   = accum + ((accType)s_val*(accType)f_val);
+
+                //binOp will do MUL_OP for convolution operation
+                accum = accum + binOp((accType)s_val, (accType)f_val);
             }
         }
         dst[gy*oInfo.strides[1]+gx] = (T)accum;
@@ -202,7 +205,9 @@ void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
                 for(int fi=0; fi<fLen0; ++fi) {
                     accType f_val = impulse[index(fi, fj, fk, fLen0, fStride)];
                     T s_val = localMem[index(ci-fi, cj-fj, ck-fk, shrdLen0, skStride)];
-                    accum   = accum + ((accType)s_val*(accType)f_val);
+
+                    //binOp will do MUL_OP for convolution operation
+                    accum = accum + binOp((accType)s_val, (accType)f_val);
                 }
             }
         }
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index ef8df00d3b..e61a1c46c4 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -42,19 +42,31 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, cons
         size_t LOC_SIZE = (THREADS_X+2*(f0-1))*(THREADS_Y+2*(f1-1));
 
         std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D accType="<< dtype_traits<aT>::getName()
-                << " -D BASE_DIM="<< 2 /* hard constant specific to this convolution type */
-                << " -D FLEN0=" << f0
-                << " -D FLEN1=" << f1
-                << " -D EXPAND="<< expand
-                << " -D C_SIZE="<< LOC_SIZE;
-        if (std::is_same<T, double>::value ||
-            std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
+        options << " -D T="         << dtype_traits<T>::getName()
+                << " -D Ti="        << dtype_traits<T>::getName()
+                << " -D To="        << dtype_traits<aT>::getName()
+                << " -D accType="   << dtype_traits<aT>::getName()
+                << " -D BASE_DIM="  << 2 /* hard constant specific to this convolution type */
+                << " -D FLEN0="     << f0
+                << " -D FLEN1="     << f1
+                << " -D EXPAND="    << expand
+                << " -D C_SIZE="    << LOC_SIZE
+                << " -D "           << binOpName<af_mul_t>();
+
+        if((af_dtype) dtype_traits<T>::af_type == c32 ||
+            (af_dtype) dtype_traits<T>::af_type == c64) {
+            options << " -D CPLX=1";
+        } else {
+            options << " -D CPLX=0";
         }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {ops_cl, convolve_cl};
+        const int   ker_lens[] = {ops_cl_len, convolve_cl_len};
         Program prog;
-        buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
         entry.prog   = new Program(prog);
         entry.ker = new Kernel(*entry.prog, "convolve");
 
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index f74a22e50c..95c761776b 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <af/defines.h>
 
+#include <kernel_headers/ops.hpp>
 #include <kernel_headers/convolve.hpp>
 
 #include <string>
@@ -22,6 +23,7 @@
 #include <platform.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
+#include <kernel/names.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -105,13 +107,27 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, co
     if (entry.prog==0 && entry.ker==0) {
         std::ostringstream options;
         options << " -D T="         << dtype_traits<T>::getName()
+                << " -D Ti="        << dtype_traits<T>::getName()
+                << " -D To="        << dtype_traits<aT>::getName()
                 << " -D accType="   << dtype_traits<aT>::getName()
                 << " -D BASE_DIM="  << bDim
-                << " -D EXPAND="    << expand;
+                << " -D EXPAND="    << expand
+                << " -D "           << binOpName<af_mul_t>();
+
+        if((af_dtype) dtype_traits<T>::af_type == c32 ||
+            (af_dtype) dtype_traits<T>::af_type == c64) {
+            options << " -D CPLX=1";
+        } else {
+            options << " -D CPLX=0";
+        }
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {ops_cl, convolve_cl};
+        const int   ker_lens[] = {ops_cl_len, convolve_cl_len};
         Program prog;
-        buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
         entry.prog   = new Program(prog);
         entry.ker = new Kernel(*entry.prog, "convolve");
 
diff --git a/src/backend/opencl/kernel/convolve_separable.cl b/src/backend/opencl/kernel/convolve_separable.cl
index 763fed1701..02aadb14c5 100644
--- a/src/backend/opencl/kernel/convolve_separable.cl
+++ b/src/backend/opencl/kernel/convolve_separable.cl
@@ -71,7 +71,9 @@ void convolve(global T *out, KParam oInfo, global T const *signal,
             // below conditional statement is based on MACRO value passed while kernel compilation
             int s_idx = (CONV_DIM==0 ? (ly*shrdLen+(i-f)) : ((i-f)*shrdLen+lx));
             T s_val = localMem[s_idx];
-            accum   = accum + ((accType)s_val*(accType)f_val);
+
+            //binOp will do MUL_OP for convolution operation
+            accum = accum + binOp((accType)s_val, (accType)f_val);
         }
         dst[oy*oInfo.strides[1]+ox] = (T)accum;
     }
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index aafc4253ef..c3aacfc525 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -7,7 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <kernel_headers/ops.hpp>
 #include <kernel_headers/convolve_separable.hpp>
+
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
@@ -18,6 +20,7 @@
 #include <debug_opencl.hpp>
 #include <memory.hpp>
 #include <cache.hpp>
+#include <kernel/names.hpp>
 
 using cl::Buffer;
 using cl::Program;
@@ -64,18 +67,30 @@ void convSep(Param out, const Param signal, const Param filter)
         size_t locSize = (conv_dim==0 ? C0_SIZE : C1_SIZE);
 
         std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D accType="<< dtype_traits<accType>::getName()
-                << " -D CONV_DIM="<< conv_dim
-                << " -D EXPAND="<< expand
-                << " -D FLEN="<< fLen
-                << " -D LOCAL_MEM_SIZE="<<locSize;
-        if (std::is_same<T, double>::value ||
-            std::is_same<T, cdouble>::value) {
+        options << " -D T="             << dtype_traits<T>::getName()
+                << " -D Ti="            << dtype_traits<T>::getName()
+                << " -D To="            << dtype_traits<accType>::getName()
+                << " -D accType="       << dtype_traits<accType>::getName()
+                << " -D CONV_DIM="      << conv_dim
+                << " -D EXPAND="        << expand
+                << " -D FLEN="          << fLen
+                << " -D LOCAL_MEM_SIZE="<<locSize
+                << " -D "               << binOpName<af_mul_t>();
+
+        if((af_dtype) dtype_traits<T>::af_type == c32 ||
+            (af_dtype) dtype_traits<T>::af_type == c64) {
+            options << " -D CPLX=1";
+        } else {
+            options << " -D CPLX=0";
+        }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
+
+        const char *ker_strs[] = {ops_cl, convolve_separable_cl};
+        const int   ker_lens[] = {ops_cl_len, convolve_separable_cl_len};
         Program prog;
-        buildProgram(prog, convolve_separable_cl, convolve_separable_cl_len, options.str());
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
         entry.prog   = new Program(prog);
         entry.ker  = new Kernel(*entry.prog, "convolve");

From 18278ea246e3145cbf6bf859b89018fb6ea205f8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 7 Jun 2017 23:38:33 +0530
Subject: [PATCH 1231/2677] convolve tests for complex types

---
 test/convolve.cpp | 91 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 91 insertions(+)

diff --git a/test/convolve.cpp b/test/convolve.cpp
index fff5ebffea..7344317c5c 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -13,6 +13,7 @@
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
+#include <cmath>
 #include <testHelpers.hpp>
 
 using std::vector;
@@ -668,3 +669,93 @@ TEST(GFOR, convolve2_MM)
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
+
+TEST(Convolve, 1D_C32)
+{
+    array A = randu(10, c32);
+    array B = randu( 3, c32);
+
+    array out = convolve1(A, B);
+    array gld = fftConvolve1(A, B);
+
+    cfloat acc = sum<cfloat>(out-gld);
+
+    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+}
+
+TEST(Convolve, 2D_C32)
+{
+    array A = randu(10, 10, c32);
+    array B = randu( 3,  3, c32);
+
+    array out = convolve2(A, B);
+    array gld = fftConvolve2(A, B);
+
+    cfloat acc = sum<cfloat>(out-gld);
+
+    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+}
+
+TEST(Convolve, 3D_C32)
+{
+    array A = randu(10, 10, 3, c32);
+    array B = randu( 3,  3, 3, c32);
+
+    array out = convolve3(A, B);
+    array gld = fftConvolve3(A, B);
+
+    cfloat acc = sum<cfloat>(out-gld);
+
+    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+}
+
+TEST(Convolve, 1D_C64)
+{
+    if (noDoubleTests<double>()) return;
+
+    array A = randu(10, c64);
+    array B = randu( 3, c64);
+
+    array out = convolve1(A, B);
+    array gld = fftConvolve1(A, B);
+
+    cdouble acc = sum<cdouble>(out-gld);
+
+    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+}
+
+TEST(Convolve, 2D_C64)
+{
+    if (noDoubleTests<double>()) return;
+
+    array A = randu(10, 10, c64);
+    array B = randu( 3,  3, c64);
+
+    array out = convolve2(A, B);
+    array gld = fftConvolve2(A, B);
+
+    cdouble acc = sum<cdouble>(out-gld);
+
+    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+}
+
+TEST(Convolve, 3D_C64)
+{
+    if (noDoubleTests<double>()) return;
+
+    array A = randu(10, 10, 3, c64);
+    array B = randu( 3,  3, 3, c64);
+
+    array out = convolve3(A, B);
+    array gld = fftConvolve3(A, B);
+
+    cdouble acc = sum<cdouble>(out-gld);
+
+    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+}

From 0c8010c4c98dc0abb032a8182aba5f965baad0bf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 18 Jun 2017 13:27:59 -0400
Subject: [PATCH 1232/2677] Update uptr<T> to uptr<T[]> to use the proper
 delete function

---
 src/backend/cuda/jit.cpp              | 33 +++++++++++++++------------
 src/backend/cuda/kernel/ireduce.hpp   |  6 ++---
 src/backend/cuda/kernel/orb.hpp       |  2 +-
 src/backend/cuda/kernel/reduce.hpp    |  4 ++--
 src/backend/opencl/kernel/ireduce.hpp |  6 ++---
 src/backend/opencl/kernel/reduce.hpp  |  2 +-
 6 files changed, 28 insertions(+), 25 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 946dfd42a7..a7870aa7bd 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -13,12 +13,12 @@
 #include <JIT/Node.hpp>
 
 #include <ptx_headers/arith.hpp>
-#include <ptx_headers/logic.hpp>
+#include <ptx_headers/cast.hpp>
 #include <ptx_headers/exp.hpp>
+#include <ptx_headers/hyper.hpp>
+#include <ptx_headers/logic.hpp>
 #include <ptx_headers/numeric.hpp>
 #include <ptx_headers/trig.hpp>
-#include <ptx_headers/hyper.hpp>
-#include <ptx_headers/cast.hpp>
 
 #if defined(__LIBDEVICE_COMPUTE_20)
 #include <libdevice_headers/compute_20.hpp>
@@ -53,12 +53,15 @@ namespace cuda
 {
 
 using JIT::Node;
-using JIT::str_map_iter;
-using JIT::str_map_t;
 using JIT::Node_ids;
 using JIT::Node_map_t;
+using JIT::str_map_iter;
+using JIT::str_map_t;
+
 using std::hash;
+using std::lock_guard;
 using std::map;
+using std::mutex;
 using std::string;
 using std::stringstream;
 using std::unique_ptr;
@@ -299,7 +302,7 @@ static string getKernelString(const string funcName,
     kerStream << functionLoad;
 
     kerStream << "!nvvm.annotations = !{!1}\n"
-              << "!1 = metadata !{void (\n"
+                 "!1 = metadata !{void (\n"
               << inAnnStream.str()
               << outAnnStream.str();
 
@@ -307,8 +310,8 @@ static string getKernelString(const string funcName,
         kerStream << "i32, i32, i32\n";
     } else {
         kerStream  << "i32, i32, i32, i32,\n"
-                   << "i32, i32, i32, i32,\n"
-                   << "i32, i32, i32\n";
+                      "i32, i32, i32, i32,\n"
+                      "i32, i32, i32\n";
     }
 
     kerStream << ")* " << funcName << ",\n "
@@ -400,7 +403,7 @@ void compute_to_libdevice_table(const char **buffer, size_t *bc_buffer_len, int
 }
 #endif
 
-static char *irToPtx(string IR, size_t *ptx_size)
+static unique_ptr<char[]> irToPtx(string IR, size_t *ptx_size)
 {
     nvvmProgram prog;
 
@@ -437,7 +440,7 @@ static char *irToPtx(string IR, size_t *ptx_size)
         size_t log_size = 0;
         nvvmGetProgramLogSize(prog, &log_size);
         printf("%ld, %zu\n", IR.size(), log_size);
-        unique_ptr<char> log(new char[log_size]);
+        unique_ptr<char[]> log(new char[log_size]);
         nvvmGetProgramLog(prog, log.get());
         printf("LOG:\n%s\n%s", log.get(), IR.c_str());
         NVVM_CHECK(comp_res, "Failed to compile program");
@@ -446,8 +449,8 @@ static char *irToPtx(string IR, size_t *ptx_size)
 
     NVVM_CHECK(nvvmGetCompiledResultSize(prog, ptx_size), "Can not get ptx size");
 
-    char *ptx = new char[*ptx_size];
-    NVVM_CHECK(nvvmGetCompiledResult(prog, ptx), "Can not get ptx from NVVM IR");
+    unique_ptr<char[]> ptx{new char[*ptx_size]};
+    NVVM_CHECK(nvvmGetCompiledResult(prog, ptx.get()), "Can not get ptx from NVVM IR");
     NVVM_CHECK(nvvmDestroyProgram(&prog), "Failed to destroy program");
     return ptx;
 }
@@ -490,7 +493,7 @@ char linkError[size];
 
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
-    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
 
     size_t ptx_size;
     unique_ptr<char[]> ptx(irToPtx(jit_ker, &ptx_size));
@@ -562,7 +565,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear)
 {
-    typedef std::map<std::string, kc_entry_t> kc_t;
+    typedef map<string, kc_entry_t> kc_t;
 
     thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
@@ -691,7 +694,7 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
         args.push_back((void *)&num_odims);
     }
 
-    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLaunchKernel(ker,
                             blocks_x,
                             blocks_y,
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index e87360eb76..b959500139 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -485,8 +485,8 @@ namespace kernel
             tlptr = memAlloc<uint>(tmp_elements);
             ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x, blocks_y, threads_x);
 
-            unique_ptr<T>       h_ptr(new T[tmp_elements]);
-            unique_ptr<uint>    h_lptr(new uint[tmp_elements]);
+            unique_ptr<T[]>       h_ptr(new T[tmp_elements]);
+            unique_ptr<uint[]>    h_lptr(new uint[tmp_elements]);
             T*      h_ptr_raw = h_ptr.get();
             uint*   h_lptr_raw = h_lptr.get();
 
@@ -519,7 +519,7 @@ namespace kernel
             return Op.m_val;
         } else {
 
-            unique_ptr<T> h_ptr(new T[in_elements]);
+            unique_ptr<T[]> h_ptr(new T[in_elements]);
             T* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 9b6c67bfd8..91ce9a7580 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -342,7 +342,7 @@ void orb(unsigned* out_feat,
     Param<convAccT> gauss_filter;
     if (blur_img) {
         unsigned gauss_len = 9;
-        unique_ptr<convAccT> h_gauss(new convAccT[gauss_len]);
+        unique_ptr<convAccT[]> h_gauss(new convAccT[gauss_len]);
         gaussian1D(h_gauss.get(), gauss_len, 2.f);
         gauss_filter.dims[0] = gauss_len;
         gauss_filter.strides[0] = 1;
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 30cbd05ff3..eb13239c30 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -409,7 +409,7 @@ namespace kernel
             reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
                                               change_nan, nanval);
 
-            unique_ptr<To> h_ptr(new To[tmp_elements]);
+            unique_ptr<To[]> h_ptr(new To[tmp_elements]);
             To* h_ptr_raw = h_ptr.get();
 
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(To),
@@ -427,7 +427,7 @@ namespace kernel
 
         } else {
 
-            unique_ptr<Ti> h_ptr(new Ti[in_elements]);
+            unique_ptr<Ti[]> h_ptr(new Ti[in_elements]);
             Ti* h_ptr_raw = h_ptr.get();
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index ab71beb6df..d29e46b7d5 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -378,8 +378,8 @@ namespace kernel
 
             ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x, groups_y);
 
-            unique_ptr<T> h_ptr(new T[tmp_elements]);
-            unique_ptr<uint> h_iptr(new uint[tmp_elements]);
+            unique_ptr<T[]> h_ptr(new T[tmp_elements]);
+            unique_ptr<uint[]> h_iptr(new uint[tmp_elements]);
 
             getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
             getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0, sizeof(uint) * tmp_elements, h_iptr.get());
@@ -411,7 +411,7 @@ namespace kernel
 
         } else {
 
-            unique_ptr<T> h_ptr(new T[in_elements]);
+            unique_ptr<T[]> h_ptr(new T[in_elements]);
             T* h_ptr_raw = h_ptr.get();
 
             getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 28cd01fb03..ebb3f1f071 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -331,7 +331,7 @@ namespace kernel
 
         } else {
 
-            unique_ptr<Ti> h_ptr(new Ti[in_elements]);
+            unique_ptr<Ti[]> h_ptr(new Ti[in_elements]);
             getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
                                           sizeof(Ti) * in_elements, h_ptr.get());
 

From 7cf4f9afcaf1c6e6ce742b867661be49000f895c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 17 Jun 2017 01:03:23 -0400
Subject: [PATCH 1233/2677] Add matching af_free_host and use vector instead of
 new in tests

---
 src/api/c/memory.cpp           | 12 +++----
 src/api/cpp/array.cpp          |  8 ++---
 test/assign.cpp                | 50 +++++++++++++-------------
 test/binary.cpp                | 52 +++++++++++++--------------
 test/convolve.cpp              | 32 +++++++----------
 test/dot.cpp                   | 19 ++++------
 test/fft_large.cpp             |  8 ++---
 test/fftconvolve.cpp           | 34 +++++++-----------
 test/harris.cpp                | 66 ++++++++++++++++------------------
 test/iota.cpp                  | 26 +++++---------
 test/ireduce.cpp               | 14 ++++----
 test/math.cpp                  |  4 +--
 test/median.cpp                |  2 +-
 test/set.cpp                   | 18 +++-------
 test/transform.cpp             | 29 ++++++---------
 test/transform_coordinates.cpp | 14 +++-----
 test/transpose_inplace.cpp     | 21 +++++------
 17 files changed, 169 insertions(+), 240 deletions(-)

diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index a2f3e2e041..48aeb2647d 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -241,17 +241,15 @@ af_err af_free_pinned(void *ptr)
 
 af_err af_alloc_host(void **ptr, const dim_t bytes)
 {
-    try {
-        *ptr = malloc(bytes);
-    } CATCHALL;
-    return AF_SUCCESS;
+    if((*ptr = malloc(bytes))) {
+      return AF_SUCCESS;
+    }
+    return AF_ERR_NO_MEM;
 }
 
 af_err af_free_host(void *ptr)
 {
-    try {
-        free(ptr);
-    } CATCHALL;
+    free(ptr);
     return AF_SUCCESS;
 }
 
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 777b7ec4ae..bdd5df19b9 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -964,11 +964,11 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
             AF_THROW_ERR("Requested type doesn't match with array", \
                          AF_ERR_TYPE);                              \
         }                                                           \
+        void *res;                                                  \
+        AF_THROW(af_alloc_host(&res, bytes()));                     \
+        AF_THROW(af_get_data_ptr(res, get()));                      \
                                                                     \
-        T *res = new T[elements()];                                 \
-        AF_THROW(af_get_data_ptr((void *)res, get()));              \
-                                                                    \
-        return res;                                                 \
+        return (T*)res;                                             \
     }                                                               \
     template<> AFAPI T array::scalar() const                        \
     {                                                               \
diff --git a/test/assign.cpp b/test/assign.cpp
index 14a3f8c4b0..8eb81584df 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -547,8 +547,8 @@ TEST(ArrayAssign, CPP_END)
     }
 
 
-    delete[] hA;
-    delete[] hB;
+    af_free_host(hA);
+    af_free_host(hB);
 }
 
 TEST(ArrayAssign, CPP_END_SEQ)
@@ -571,8 +571,8 @@ TEST(ArrayAssign, CPP_END_SEQ)
         ASSERT_EQ(hA[i + end_begin - 1], hB[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
+    af_free_host(hA);
+    af_free_host(hB);
 }
 
 TEST(ArrayAssign, CPP_COPY_ON_WRITE)
@@ -608,10 +608,10 @@ TEST(ArrayAssign, CPP_COPY_ON_WRITE)
         ASSERT_EQ(hAO[i], hAC[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hAC;
-    delete[] hAO;
+    af_free_host(hA);
+    af_free_host(hB);
+    af_free_host(hAC);
+    af_free_host(hAO);
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_BINOP)
@@ -647,10 +647,10 @@ TEST(ArrayAssign, CPP_ASSIGN_BINOP)
         ASSERT_EQ(hAO[i], hAC[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hAC;
-    delete[] hAO;
+    af_free_host(hA);
+    af_free_host(hB);
+    af_free_host(hAC);
+    af_free_host(hAO);
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR)
@@ -678,8 +678,8 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR)
         ASSERT_EQ(h_a[i], h_b[i]) << "at " << i;
     }
 
-    delete[] h_a;
-    delete[] h_b;
+    af_free_host(h_a);
+    af_free_host(h_b);
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ)
@@ -716,9 +716,9 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ)
         }
     }
 
-    delete[] h_a0;
-    delete[] h_a;
-    delete[] h_b;
+    af_free_host(h_a0);
+    af_free_host(h_a);
+    af_free_host(h_b);
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D)
@@ -748,8 +748,8 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D)
         ASSERT_EQ(h_a[i], h_b[i]) << "at " << i;
     }
 
-    delete[] h_a;
-    delete[] h_b;
+    af_free_host(h_a);
+    af_free_host(h_b);
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
@@ -786,9 +786,9 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
         }
     }
 
-    delete[] h_a0;
-    delete[] h_a;
-    delete[] h_b;
+    af_free_host(h_a0);
+    af_free_host(h_a);
+    af_free_host(h_b);
 }
 
 TEST(Assign, Copy)
@@ -822,9 +822,9 @@ TEST(Assign, Copy)
         }
     }
 
-    delete[] h_a0;
-    delete[] h_a;
-    delete[] h_b;
+    af_free_host(h_a0);
+    af_free_host(h_a);
+    af_free_host(h_b);
 }
 
 TEST(Asssign, LinearCPP)
diff --git a/test/binary.cpp b/test/binary.cpp
index e7bffd129e..5071a024c0 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -59,10 +59,10 @@ af::array randgen(const int num, af::dtype ty)
         Tc *h_c = c.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], func(h_a[i], h_b[i])) <<                  \
-                "for values: " << h_a[i]  << "," << h_b[i] << std::endl; \
-        delete[] h_a;                                                   \
-        delete[] h_b;                                                   \
-        delete[] h_c;                                                   \
+              "for values: " << h_a[i]  << "," << h_b[i] << std::endl;  \
+        af_free_host(h_a);                                              \
+        af_free_host(h_b);                                              \
+        af_free_host(h_c);                                              \
     }                                                                   \
                                                                         \
     TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_left)                 \
@@ -79,8 +79,8 @@ af::array randgen(const int num, af::dtype ty)
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], func(h_a[i], h_b)) <<                     \
                 "for values: " << h_a[i]  << "," << h_b << std::endl;   \
-        delete[] h_a;                                                   \
-        delete[] h_c;                                                   \
+        af_free_host(h_a);                                              \
+        af_free_host(h_c);                                              \
     }                                                                   \
                                                                         \
     TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_right)                \
@@ -96,9 +96,9 @@ af::array randgen(const int num, af::dtype ty)
         Tb *h_c = c.host<Tb>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], func(h_a, h_b[i])) <<                     \
-                "for values: " << h_a  << "," << h_b[i] << std::endl;   \
-        delete[] h_b;                                                   \
-        delete[] h_c;                                                   \
+              "for values: " << h_a  << "," << h_b[i] << std::endl;     \
+        af_free_host(h_b);                                              \
+        af_free_host(h_c);                                              \
     }                                                                   \
 
 
@@ -120,9 +120,9 @@ af::array randgen(const int num, af::dtype ty)
         for (int i = 0; i < num; i++)                                   \
             MY_ASSERT_NEAR(h_c[i], func(h_a[i], h_b[i]), (err)) <<      \
                 "for values: " << h_a[i]  << "," << h_b[i] << std::endl; \
-        delete[] h_a;                                                   \
-        delete[] h_b;                                                   \
-        delete[] h_c;                                                   \
+        af_free_host(h_a);                                              \
+        af_free_host(h_b);                                              \
+        af_free_host(h_c);                                              \
     }                                                                   \
                                                                         \
     TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_left)         \
@@ -139,8 +139,8 @@ af::array randgen(const int num, af::dtype ty)
         for (int i = 0; i < num; i++)                                   \
             MY_ASSERT_NEAR(h_d[i], func(h_a[i], h_b), err) <<           \
                 "for values: " << h_a[i]  << "," << h_b << std::endl;   \
-        delete[] h_a;                                                   \
-        delete[] h_d;                                                   \
+        af_free_host(h_a);                                              \
+        af_free_host(h_d);                                              \
     }                                                                   \
                                                                         \
     TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_right)        \
@@ -157,9 +157,9 @@ af::array randgen(const int num, af::dtype ty)
         Te *h_e = c.host<Te>();                                         \
         for (int i = 0; i < num; i++)                                   \
             MY_ASSERT_NEAR(h_e[i], func(h_a, h_b[i]), err) <<           \
-                "for values: " << h_a  << "," << h_b[i] << std::endl;   \
-        delete[] h_b;                                                   \
-        delete[] h_e;                                                   \
+              "for values: " << h_a  << "," << h_b[i] << std::endl;     \
+        af_free_host(h_b);                                              \
+        af_free_host(h_e);                                              \
     }                                                                   \
 
 #define BINARY_TESTS_NEAR(Ta, Tb, Tc, func, err) BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Ta, Tc, func, err)
@@ -267,9 +267,9 @@ BINARY_TESTS_NEAR_GENERAL(cfloat, double, cdouble, cfloat, cdouble, div, 1e-5)
             ASSERT_EQ(h_c[i], valc) <<                      \
                 "for values: " << h_a[i]  <<                \
                 "," << h_b[i] << std::endl;                 \
-        delete[] h_a;                                       \
-        delete[] h_b;                                       \
-        delete[] h_c;                                       \
+        af_free_host(h_a);                                  \
+        af_free_host(h_b);                                  \
+        af_free_host(h_c);                                  \
     }                                                       \
 
 BITOP(bitor, int, |)
@@ -310,9 +310,9 @@ TEST(BinaryTests, Test_pow_cfloat_float)
             << "for imag values of: " << h_a[i]  << "," << h_b[i] << std::endl;
 
     }
-    delete[] h_a;
-    delete[] h_b;
-    delete[] h_c;
+    af_free_host(h_a);
+    af_free_host(h_b);
+    af_free_host(h_c);
 }
 
 TEST(BinaryTests, Test_pow_cdouble_cdouble)
@@ -332,9 +332,9 @@ TEST(BinaryTests, Test_pow_cdouble_cdouble)
             << "for imag values of: " << h_a[i]  << "," << h_b[i] << std::endl;
 
     }
-    delete[] h_a;
-    delete[] h_b;
-    delete[] h_c;
+    af_free_host(h_a);
+    af_free_host(h_b);
+    af_free_host(h_c);
 }
 
 TEST(BinaryTests, ISSUE_1762)
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 7344317c5c..c6893b54ef 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -68,15 +68,14 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
 
     vector<T> currGoldBar = tests[0];
     size_t nElems         = currGoldBar.size();
-    T *outData            = new T[nElems];
+    vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
     ASSERT_EQ(AF_SUCCESS, af_release_array(filter));
@@ -235,15 +234,14 @@ void sepConvolveTest(string pTestFile, bool expand)
 
     vector<T> currGoldBar = tests[0];
     size_t nElems         = currGoldBar.size();
-    T *outData            = new T[nElems];
+    vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
     ASSERT_EQ(AF_SUCCESS, af_release_array(c_filter));
@@ -380,14 +378,13 @@ TEST(Convolve1, CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
-    output.host(outData);
+    vector<float> outData(nElems);
+    output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
 }
 
 TEST(Convolve2, CPP)
@@ -420,14 +417,13 @@ TEST(Convolve2, CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
-    output.host(outData);
+    vector<float> outData(nElems);
+    output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
 }
 
 TEST(Convolve3, CPP)
@@ -459,14 +455,12 @@ TEST(Convolve3, CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
-    output.host(outData);
+    vector<float> outData(nElems);
+    output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(Convolve, separable_CPP)
@@ -502,15 +496,13 @@ TEST(Convolve, separable_CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
+    vector<float> outData(nElems);
 
-    output.host((void*)outData);
+    output.host((void*)&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(Convolve, Docs_Unified_Wrapper)
diff --git a/test/dot.cpp b/test/dot.cpp
index 36be490683..2474e8d6fa 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -74,15 +74,14 @@ void dotTest(string pTestFile, const int resultIdx,
 
     vector<T> goldData = tests[resultIdx];
     size_t nElems      = goldData.size();
-    T *outData      = new T[nElems];
+    vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, out));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), out));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(a));
     ASSERT_EQ(AF_SUCCESS, af_release_array(b));
     ASSERT_EQ(AF_SUCCESS, af_release_array(out));
@@ -205,16 +204,14 @@ TEST(DotF, CPP)
     array out = dot(a, b, AF_MAT_CONJ, AF_MAT_NONE);
 
     vector<float> goldData = tests[0];
-    size_t nElems         = goldData.size();
-    float *outData       = new float[nElems];
+    size_t nElems = goldData.size();
+    vector<float> outData(nElems);
 
-    out.host(outData);
+    out.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(DotCCU, CPP)
@@ -238,15 +235,13 @@ TEST(DotCCU, CPP)
 
     vector<cfloat> goldData = tests[2];
     size_t nElems         = goldData.size();
-    cfloat *outData       = new cfloat[nElems];
+    vector<cfloat> outData(nElems);
 
-    out.host(outData);
+    out.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(DotAllF, CPP)
diff --git a/test/fft_large.cpp b/test/fft_large.cpp
index a3c331ce5a..71c2c74a98 100644
--- a/test/fft_large.cpp
+++ b/test/fft_large.cpp
@@ -34,8 +34,8 @@ TEST(fft2, CPP_4D)
         ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_B;
+    af_free_host(h_b);
+    af_free_host(h_B);
 }
 
 TEST(ifft2, CPP_4D)
@@ -53,6 +53,6 @@ TEST(ifft2, CPP_4D)
         ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_B;
+    af_free_host(h_b);
+    af_free_host(h_B);
 }
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index ec6a3f3279..e5ae4ed084 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -82,9 +82,9 @@ void fftconvolveTest(string pTestFile, bool expand)
     ASSERT_EQ(AF_SUCCESS, af_get_elements(&out_elems, outArray));
     ASSERT_EQ(nElems, (size_t)out_elems);
 
-    T *outData            = new T[nElems];
+    vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(
@@ -93,7 +93,6 @@ void fftconvolveTest(string pTestFile, bool expand)
             , 1e-2)<< "at: " << elIter<< std::endl;
     }
 
-    delete[] outData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
     ASSERT_EQ(AF_SUCCESS, af_release_array(filter));
@@ -177,18 +176,15 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
 
     ASSERT_EQ(goldElems, outElems);
 
-    T *goldData = new T[goldElems];
-    gold.host(goldData);
+    vector<T> goldData(goldElems);
+    gold.host(&goldData.front());
 
-    T *outData = new T[outElems];
-    out.host(outData);
+    vector<T> outData(outElems);
+    out.host(&outData.front());
 
     for (size_t elIter=0; elIter<outElems; ++elIter) {
         ASSERT_NEAR(goldData[elIter], outData[elIter], 5e-2) << "at: " << elIter << std::endl;
     }
-
-    delete[] goldData;
-    delete[] outData;
 }
 
 TYPED_TEST(FFTConvolveLarge, VectorLargeSignalSmallFilter)
@@ -397,14 +393,12 @@ TEST(FFTConvolve1, CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
-    output.host(outData);
+    vector<float> outData(nElems);
+    output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(FFTConvolve2, CPP)
@@ -437,14 +431,12 @@ TEST(FFTConvolve2, CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
-    output.host(outData);
+    vector<float> outData(nElems);
+    output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(FFTConvolve3, CPP)
@@ -476,14 +468,12 @@ TEST(FFTConvolve3, CPP)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems  = output.elements();
-    float *outData = new float[nElems];
-    output.host(outData);
+    vector<float> outData(nElems);
+    output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;
     }
-
-    delete[] outData;
 }
 
 TEST(FFTConvolve, Docs_Unified_Wrapper)
diff --git a/test/harris.cpp b/test/harris.cpp
index 0adde6f95d..90c5cf16de 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -101,22 +101,24 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
 
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, x));
 
-        float * outX           = new float[gold[0].size()];
-        float * outY           = new float[gold[1].size()];
-        float * outScore       = new float[gold[2].size()];
-        float * outOrientation = new float[gold[3].size()];
-        float * outSize        = new float[gold[4].size()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outX, x));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outY, y));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outScore, score));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outOrientation, orientation));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outSize, size));
+        vector<float> outX           (gold[0].size());
+        vector<float> outY           (gold[1].size());
+        vector<float> outScore       (gold[2].size());
+        vector<float> outOrientation (gold[3].size());
+        vector<float> outSize        (gold[4].size());
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outX.front(), x));
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outY.front(), y));
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outScore.front(), score));
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outOrientation.front(), orientation));
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outSize.front(), size));
 
         vector<feat_t> out_feat;
-        array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize, n);
+        array_to_feat(out_feat, &outX.front(), &outY.front(),
+                      &outScore.front(), &outOrientation.front(), &outSize.front(), n);
 
         vector<feat_t> gold_feat;
-        array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(), &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
+        array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(),
+                      &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
 
         std::sort(out_feat.begin(), out_feat.end(), feat_cmp);
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
@@ -137,12 +139,6 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
         ASSERT_EQ(AF_SUCCESS, af_release_array(score));
         ASSERT_EQ(AF_SUCCESS, af_release_array(orientation));
         ASSERT_EQ(AF_SUCCESS, af_release_array(size));
-
-        delete [] outX;
-        delete [] outY;
-        delete [] outScore;
-        delete [] outOrientation;
-        delete [] outSize;
     }
 }
 
@@ -179,22 +175,26 @@ TEST(FloatHarris, CPP)
 
     af::features out = harris(in, 500, 1e5f, 0.0f, 3, 0.04f);
 
-    float * outX           = new float[gold[0].size()];
-    float * outY           = new float[gold[1].size()];
-    float * outScore       = new float[gold[2].size()];
-    float * outOrientation = new float[gold[3].size()];
-    float * outSize        = new float[gold[4].size()];
-    out.getX().host(outX);
-    out.getY().host(outY);
-    out.getScore().host(outScore);
-    out.getOrientation().host(outOrientation);
-    out.getSize().host(outSize);
+    vector<float> outX           (gold[0].size());
+    vector<float> outY           (gold[1].size());
+    vector<float> outScore       (gold[2].size());
+    vector<float> outOrientation (gold[3].size());
+    vector<float> outSize        (gold[4].size());
+    out.getX().host(&outX.front());
+    out.getY().host(&outY.front());
+    out.getScore().host(&outScore.front());
+    out.getOrientation().host(&outOrientation.front());
+    out.getSize().host(&outSize.front());
 
     vector<feat_t> out_feat;
-    array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize, out.getNumFeatures());
+    array_to_feat(out_feat, &outX.front(), &outY.front(),
+                  &outScore.front(), &outOrientation.front(), &outSize.front(),
+                  out.getNumFeatures());
 
     vector<feat_t> gold_feat;
-    array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(), &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
+    array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(),
+                  &gold[2].front(), &gold[3].front(), &gold[4].front(),
+                  gold[0].size());
 
     std::sort(out_feat.begin(), out_feat.end(), feat_cmp);
     std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
@@ -206,10 +206,4 @@ TEST(FloatHarris, CPP)
         ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
         ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
     }
-
-    delete[] outX;
-    delete[] outY;
-    delete[] outScore;
-    delete[] outOrientation;
-    delete[] outSize;
 }
diff --git a/test/iota.cpp b/test/iota.cpp
index e91741d199..bacf29d34b 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -64,20 +64,16 @@ void iotaTest(const af::dim4 idims, const af::dim4 tdims)
     ASSERT_EQ(AF_SUCCESS, af_tile(&temp0, temp1, tdims[0], tdims[1], tdims[2], tdims[3]));
 
     // Get result
-    T* outData = new T[fulldims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    vector<T> outData(fulldims.elements());
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
-    T* tileData = new T[fulldims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)tileData, temp0));
+    vector<T> tileData(fulldims.elements());
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&tileData.front(), temp0));
 
     // Compare result
     for(int i = 0; i < (int) fulldims.elements(); i++)
         ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << std::endl;
 
-    // Delete
-    delete[] outData;
-    delete[] tileData;
-
     if(outArray  != 0) af_release_array(outArray);
     if(temp0     != 0) af_release_array(temp0);
     if(temp1     != 0) af_release_array(temp1);
@@ -121,19 +117,13 @@ TEST(Iota, CPP)
     af::array tileArray = af::tile(af::moddims(af::range(af::dim4(idims.elements()), 0), idims), tdims);
 
     // Get result
-    float* outData = new float[fulldims.elements()];
-    output.host((void*)outData);
-
-    float* tileData = new float[fulldims.elements()];
-    tileArray.host((void*)tileData);
+    vector<float> outData (fulldims.elements());
+    output.host((void*)&outData.front());
 
-    // Compare result
+    vector<float> tileData (fulldims.elements());
+    tileArray.host((void*)&tileData.front());
 
     // Compare result
     for(int i = 0; i < (int)fulldims.elements(); i++)
         ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << std::endl;
-
-    // Delete
-    delete[] outData;
-    delete[] tileData;
 }
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index c0536be267..26731d6654 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -40,9 +40,9 @@ using namespace af;
                 << "for index" << i;                    \
             h_in += nx;                                 \
         }                                               \
-        delete[] h_in_st;                               \
-        delete[] h_val;                                 \
-        delete[] h_idx;                                 \
+        af_free_host(h_in_st);                          \
+        af_free_host(h_val);                            \
+        af_free_host(h_idx);                            \
     }                                                   \
     TEST(IndexedMinMaxTests, Test_##fn##_##ty##_1)      \
     {                                                   \
@@ -65,9 +65,9 @@ using namespace af;
             }                                           \
             ASSERT_EQ(val, h_in[h_idx[i] * nx + i]);    \
         }                                               \
-        delete[] h_in;                                  \
-        delete[] h_val;                                 \
-        delete[] h_idx;                                 \
+        af_free_host(h_in);                             \
+        af_free_host(h_val);                            \
+        af_free_host(h_idx);                            \
     }                                                   \
     TEST(IndexedMinMaxTests, Test_##fn##_##ty##_all)    \
     {                                                   \
@@ -82,7 +82,7 @@ using namespace af;
         ty tmp = *std::fn##_element(h_in, h_in + num);  \
         ASSERT_EQ(tmp, val);                            \
         ASSERT_EQ(tmp, h_in[idx]);                      \
-        delete[] h_in;                                  \
+        af_free_host(h_in);                             \
     }                                                   \
 
 MINMAXOP(min, float)
diff --git a/test/math.cpp b/test/math.cpp
index 80f9df336d..3a8bab5703 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -158,6 +158,6 @@ TEST(MathTests, Not)
         ASSERT_EQ(ha[i] ^ hb[i], true);
     }
 
-    delete[] ha;
-    delete[] hb;
+    af_free_host(ha);
+    af_free_host(hb);
 }
diff --git a/test/median.cpp b/test/median.cpp
index 5b26a44a97..29a91d9732 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -61,7 +61,7 @@ void median_flat(int nx, int ny=1, int nz=1, int nw=1)
 
     ASSERT_EQ(verify, val);
 
-    delete[] h_sa;
+    af_free_host(h_sa);
 }
 
 template<typename To, typename Ti, int dim>
diff --git a/test/set.cpp b/test/set.cpp
index a6d04ed45e..003b6e0dc7 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -56,9 +56,8 @@ void uniqueTest(string pTestFile)
         ASSERT_EQ(AF_SUCCESS, af_set_unique(&outArray, inArray, d == 0 ? false : true));
 
         // Get result
-        T *outData;
-        outData = new T[currGoldBar.size()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        vector<T >outData (currGoldBar.size());
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
@@ -66,9 +65,6 @@ void uniqueTest(string pTestFile)
                                                             << " for test: " << d << std::endl;
         }
 
-        // Delete
-        delete[] outData;
-
         if(inArray   != 0) af_release_array(inArray);
         if(outArray  != 0) af_release_array(outArray);
     }
@@ -123,17 +119,14 @@ void setTest(string pTestFile)
 
         ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray1, &in1.front(), dims1.ndims(),
                                               dims1.get(), (af_dtype) af::dtype_traits<T>::af_type));
-
-
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
         ASSERT_EQ(AF_SUCCESS, af_set_func(&outArray, inArray0, inArray1, d == 0 ? false : true));
 
         // Get result
-        T *outData;
-        outData = new T[currGoldBar.size()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        vector<T> outData(currGoldBar.size());
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
@@ -141,9 +134,6 @@ void setTest(string pTestFile)
                                                             << " for test: " << d << std::endl;
         }
 
-        // Delete
-        delete[] outData;
-
         if(inArray0   != 0) af_release_array(inArray0);
         if(inArray1   != 0) af_release_array(inArray1);
         if(outArray  != 0) af_release_array(outArray);
diff --git a/test/transform.cpp b/test/transform.cpp
index 80504d76e7..81129c020a 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -90,14 +90,14 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     // Get gold data
     dim_t goldEl = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_elements(&goldEl, goldArray));
-    T* goldData = new T[goldEl];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData, goldArray));
+    vector<T> goldData(goldEl);
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&goldData.front(), goldArray));
 
     // Get result
     dim_t outEl = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_elements(&outEl, outArray));
-    T* outData = new T[outEl];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    vector<T> outData(outEl);
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
     const float thr = 1.1f;
 
@@ -113,9 +113,6 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
             ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
     }
 
-    delete[] goldData;
-    delete[] outData;
-
     if(sceneArray_f32 != 0) af_release_array(sceneArray_f32);
     if(goldArray_f32  != 0) af_release_array(goldArray_f32);
     if(outArray_f32   != 0) af_release_array(outArray_f32);
@@ -245,13 +242,12 @@ TEST(Transform, CPP)
     af::dim4 outDims = out_img.dims();
     af::dim4 goldDims = gold_img.dims();
 
-    float* h_out_img = new float[outDims[0] * outDims[1]];
-    out_img.host(h_out_img);
-    float* h_gold_img = new float[goldDims[0] * goldDims[1]];
-    gold_img.host(h_gold_img);
+    vector<float> h_out_img(outDims[0] * outDims[1]);
+    out_img.host(&h_out_img.front());
+    vector<float> h_gold_img(goldDims[0] * goldDims[1]);
+    gold_img.host(&h_gold_img.front());
 
     const dim_t n = gold_img.elements();
-
     const float thr = 1.0f;
 
     // Maximum number of wrong pixels must be <= 0.01% of number of elements,
@@ -265,9 +261,6 @@ TEST(Transform, CPP)
         if (err > maxErr)
             ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
     }
-
-    delete[] h_gold_img;
-    delete[] h_out_img;
 }
 
 // This tests batching of different forms
@@ -334,14 +327,12 @@ TEST(TransformBatching, CPP)
 
     for(int i = 0; i < (int)gold.size(); i++) {
         // Get result
-        float *outData = new float[out[i].elements()];
-        out[i].host((void*)outData);
+        vector<float> outData(out[i].elements());
+        out[i].host((void*)&outData.front());
 
         for(int iter = 0; iter < (int)gold[i].size(); iter++) {
             ASSERT_EQ(gold[i][iter], outData[iter]) << "at: " << iter << std::endl
                     << "for " << i << "-th operation"<< std::endl;
         }
-
-        delete[] outData;
     }
 }
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index dc8598121e..3bb531a2ab 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -58,16 +58,14 @@ void transformCoordinatesTest(string pTestFile)
         // Get result
         dim_t outEl = 0;
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&outEl, outArray));
-        T* outData = new T[outEl];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        vector<T> outData(outEl);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
         const float thr = 1.f;
 
         for (dim_t elIter = 0; elIter < outEl; elIter++) {
             ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << std::endl;
         }
-
-        delete[] outData;
     }
 
     if(tfArray  != 0) af_release_array(tfArray);
@@ -100,19 +98,15 @@ TEST(TransformCoordinates, CPP)
     float d1 = in[1][1];
 
     af::array out = af::transformCoordinates(tf, d0, d1);
-
     af::dim4 outDims = out.dims();
 
-    float* h_out = new float[outDims[0] * outDims[1]];
-    out.host(h_out);
+    vector<float> h_out(outDims[0] * outDims[1]);
+    out.host(&h_out.front());
 
     const size_t n = gold[0].size();
-
     const float thr = 1.f;
 
     for (size_t elIter = 0; elIter < n; elIter++) {
         ASSERT_LE(fabs(h_out[elIter] - gold[0][elIter]), thr) << "at: " << elIter << std::endl;
     }
-
-    delete[] h_out;
 }
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index a54ff75d34..14b4af8964 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -48,11 +48,11 @@ void transposeip_test(af::dim4 dims)
     ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray, inArray, false));
     ASSERT_EQ(AF_SUCCESS, af_transpose_inplace(inArray, false));
 
-    T *outData = new T[dims.elements()];
-    T *trsData = new T[dims.elements()];
+    vector<T> outData(dims.elements());
+    vector<T> trsData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)trsData, inArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&trsData.front(), inArray));
 
     dim_t nElems = dims.elements();
     for (int elIter = 0; elIter < (int)nElems; ++elIter) {
@@ -60,8 +60,6 @@ void transposeip_test(af::dim4 dims)
     }
 
     // cleanup
-    delete[] outData;
-    delete[] trsData;
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
@@ -91,17 +89,14 @@ void transposeInPlaceCPPTest()
     af::array output = af::transpose(input);
     transposeInPlace(input);
 
-    float *outData = new float[dims.elements()];
-    float *trsData = new float[dims.elements()];
+    vector<float> outData(dims.elements());
+    vector<float> trsData(dims.elements());
 
-    output.host((void*)outData);
-    input.host((void*)trsData);
+    output.host((void*)&outData.front());
+    input.host((void*)&trsData.front());
 
     dim_t nElems = dims.elements();
     for (int elIter = 0; elIter < (int)nElems; ++elIter) {
         ASSERT_EQ(trsData[elIter], outData[elIter])<< "at: " << elIter << std::endl;
     }
-
-    // cleanup
-    delete[] outData;
 }

From 90e3dab8ba6f8e4ce5eacdfcadb5d4b3b283b381 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 18 Jun 2017 12:29:02 -0400
Subject: [PATCH 1234/2677] Update af_canny_threshold enum to follow enum
 naming conventions

---
 examples/image_processing/edge.cpp |  2 +-
 include/af/defines.h               |  4 ++--
 include/af/image.h                 | 35 ++++++++++++++++++++----------
 src/api/c/canny.cpp                |  2 +-
 test/canny.cpp                     | 10 ++++-----
 5 files changed, 33 insertions(+), 20 deletions(-)

diff --git a/examples/image_processing/edge.cpp b/examples/image_processing/edge.cpp
index 17deeebe75..a145e83058 100644
--- a/examples/image_processing/edge.cpp
+++ b/examples/image_processing/edge.cpp
@@ -63,7 +63,7 @@ array edge(const array &in, int method = 0)
     switch(method) {
         case  1: prewitt(mag, dir, smooth);     break;
         case  2: sobelFilter(mag, dir, smooth); break;
-        case  3: mag = canny(in, AF_AUTO_OTSU_THRESHOLD, 0.18, 0.54).as(f32);   break;
+        case  3: mag = canny(in, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.18, 0.54).as(f32);   break;
         default: throw af::exception("Unsupported type");
     }
 
diff --git a/include/af/defines.h b/include/af/defines.h
index 1d7bb91165..124ce85542 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -448,8 +448,8 @@ typedef enum {
 
 #if AF_API_VERSION >= 35
 typedef enum {
-    AF_MANUAL_THRESHOLD    = 0, ///< User has to define canny thresholds manually
-    AF_AUTO_OTSU_THRESHOLD = 1, ///< Determine canny algorithm thresholds using Otsu algorithm
+    AF_CANNY_THRESHOLD_MANUAL    = 0, ///< User has to define canny thresholds manually
+    AF_CANNY_THRESHOLD_AUTO_OTSU = 1, ///< Determine canny algorithm thresholds using Otsu algorithm
 } af_canny_threshold;
 #endif
 
diff --git a/include/af/image.h b/include/af/image.h
index 98a181f5da..5343596cb8 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -692,12 +692,18 @@ AFAPI array moments(const array& in, const momentType moment=AF_MOMENT_FIRST_ORD
 /**
    C++ Interface for canny edge detector
 
-   \param[in] in is the input image
-   \param[in] thresholdType determines if user set high threshold is to be used or not. It can take values defined by the enum \ref af_canny_threshold
-   \param[in] lowThresholdRatio is the lower threshold % of maximum or auto-derived high threshold
-   \param[in] highThresholdRatio is the higher threshold % of maximum value in gradient image used in hysteresis procedure. This value is ignored if \ref AF_AUTO_OTSU_THRESHOLD is chosen as \ref af_canny_threshold
-   \param[in] sobelWindow is the window size of sobel kernel for computing gradient direction and magnitude
-   \param[in] isFast indicates if L<SUB>1</SUB> norm(faster but less accurate) is used to compute image gradient magnitude instead of L<SUB>2</SUB> norm.
+   \param[in] in                  is the input image
+   \param[in] thresholdType       determines if user set high threshold is to be used or not. It
+                                  can take values defined by the enum \ref af_canny_threshold
+   \param[in] lowThresholdRatio   is the lower threshold % of maximum or auto-derived high threshold
+   \param[in] highThresholdRatio  is the higher threshold % of maximum value in gradient image used
+                                  in hysteresis procedure. This value is ignored if
+                                  \ref AF_CANNY_THRESHOLD_AUTO_OTSU is chosen as
+                                  \ref af_canny_threshold
+   \param[in] sobelWindow     is the window size of sobel kernel for computing gradient direction and
+                              magnitude
+   \param[in] isFast     indicates if L<SUB>1</SUB> norm(faster but less accurate) is used to compute
+                         image gradient magnitude instead of L<SUB>2</SUB> norm.
    \return binary array containing edges
 
    \ingroup image_func_canny
@@ -1399,11 +1405,18 @@ extern "C" {
 
        \param[out] out is an binary array containing edges
        \param[in] in is the input image
-       \param[in] threshold_type determines if user set high threshold is to be used or not. It can take values defined by the enum \ref af_canny_threshold
-       \param[in] low_threshold_ratio is the lower threshold % of the maximum or auto-derived high threshold
-       \param[in] high_threshold_ratio is the higher threshold % of maximum value in gradient image used in hysteresis procedure. This value is ignored if \ref AF_AUTO_OTSU_THRESHOLD is chosen as \ref af_canny_threshold
-       \param[in] sobel_window is the window size of sobel kernel for computing gradient direction and magnitude
-       \param[in] is_fast indicates if L<SUB>1</SUB> norm(faster but less accurate) is used to compute image gradient magnitude instead of L<SUB>2</SUB> norm.
+       \param[in] threshold_type     determines if user set high threshold is to be used or not. It
+                                     can take values defined by the enum \ref af_canny_threshold
+       \param[in] low_threshold_ratio   is the lower threshold % of the maximum or auto-derived high
+                                        threshold
+       \param[in] high_threshold_ratio  is the higher threshold % of maximum value in gradient image
+                                        used in hysteresis procedure. This value is ignored if
+                                        \ref AF_CANNY_THRESHOLD_AUTO_OTSU is chosen as
+                                        \ref af_canny_threshold
+       \param[in] sobel_window      is the window size of sobel kernel for computing gradient direction
+                                    and magnitude
+       \param[in] is_fast indicates   if L<SUB>1</SUB> norm(faster but less accurate) is used to
+                                      compute image gradient magnitude instead of L<SUB>2</SUB> norm.
        \return    \ref AF_SUCCESS if the moment calculation is successful,
        otherwise an appropriate error code is returned.
 
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index b6b4dbe93c..3e7b0b0982 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -144,7 +144,7 @@ computeCandidates(const Array<float>& supEdges, const float t1,
 
     switch(ct)
     {
-        case AF_AUTO_OTSU_THRESHOLD:
+        case AF_CANNY_THRESHOLD_AUTO_OTSU:
             {
                 auto T2            = otsuThreshold(supEdges, NUM_BINS, maxVal);
                 auto T1            = arithOp<float, af_mul_t>(T2, lowRatio, T2.dims());
diff --git a/test/canny.cpp b/test/canny.cpp
index 19e159edaa..de6a495020 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -49,7 +49,7 @@ void cannyTest(string pTestFile)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&sArray, &(in[0].front()),
                 sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, sArray, AF_MANUAL_THRESHOLD, 0.4147f, 0.8454f, 3, true));
+    ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL, 0.4147f, 0.8454f, 3, true));
 
     std::vector<char> outData(sDims.elements());
 
@@ -107,7 +107,7 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
         ASSERT_EQ(AF_SUCCESS, af_load_image_native(&goldArray, outFiles[testId].c_str()));
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, inArray, AF_AUTO_OTSU_THRESHOLD, 0.08, 0.32, 3, false));
+        ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false));
 
         std::vector<char> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
@@ -140,7 +140,7 @@ TEST(CannyEdgeDetector, InvalidSizeArray)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_MANUAL_THRESHOLD, 0.24, 0.72, 3, true));
+    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -157,7 +157,7 @@ TEST(CannyEdgeDetector, Array4x4_Invalid)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_MANUAL_THRESHOLD, 0.24, 0.72, 3, true));
+    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -174,7 +174,7 @@ TEST(CannyEdgeDetector, Sobel5x5_Invalid)
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_canny(&outArray, inArray, AF_MANUAL_THRESHOLD, 0.24, 0.72, 5, true));
+    ASSERT_EQ(AF_ERR_ARG, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 5, true));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }

From a8709ee722de06e6536a95431843a445ccf900f7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 19 Jun 2017 23:52:08 -0400
Subject: [PATCH 1235/2677] Fix variable name(IN) that conflicts with a macro
 defined in Windows

The IN name is defined in the minwindef.h header. Transform also defines
an IN variable which conflicts with this macro and causing compilation
errors.
---
 src/api/c/transform_coordinates.cpp | 38 ++++++++++++++---------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index 9623f58bc3..b5b6ee736c 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -29,40 +29,40 @@ Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs, std::vector<a
 }
 
 template<typename T>
-static af_array transform_coordinates(const af_array& tf, const float d0, const float d1)
+static af_array transform_coordinates(const af_array& tf_, const float d0_, const float d1_)
 {
     af::dim4 h_dims(4, 3);
-    T h_in[4*3] = { (T)0, (T)0,  (T)d1, (T)d1,
-                    (T)0, (T)d0, (T)d0, (T)0,
+    T h_in[4*3] = { (T)0, (T)0,  (T)d1_, (T)d1_,
+                    (T)0, (T)d0_, (T)d0_, (T)0,
                     (T)1, (T)1,  (T)1,  (T)1 };
 
-    const Array<T> TF = getArray<T>(tf);
-    Array<T> IN = createHostDataArray<T>(h_dims, h_in);
+    const Array<T> tf = getArray<T>(tf_);
+    Array<T> in = createHostDataArray<T>(h_dims, h_in);
 
     std::vector<af_seq> idx(2);
     idx[0] = af_make_seq(0, 2, 1);
 
-    // w = 1.0 / matmul(TF, IN(span, 2));
-    // iw = matmul(TF, IN(span, 2));
+    // w = 1.0 / matmul(tf, in(span, 2));
+    // iw = matmul(tf, in(span, 2));
     idx[1] = af_make_seq(2, 2, 1);
-    Array<T> IW = multiplyIndexed(IN, TF, idx);
+    Array<T> iw = multiplyIndexed(in, tf, idx);
 
-    // xt = w * matmul(TF, IN(span, 0));
-    // xt = matmul(TF, IN(span, 0)) / iw;
+    // xt = w * matmul(tf, in(span, 0));
+    // xt = matmul(tf, in(span, 0)) / iw;
     idx[1] = af_make_seq(0, 0, 1);
-    Array<T> XT = arithOp<T, af_div_t>(multiplyIndexed(IN, TF, idx), IW, IW.dims());
+    Array<T> xt = arithOp<T, af_div_t>(multiplyIndexed(in, tf, idx), iw, iw.dims());
 
-    // yt = w * matmul(TF, IN(span, 1));
-    // yt = matmul(TF, IN(span, 1)) / iw;
+    // yt = w * matmul(tf, in(span, 1));
+    // yt = matmul(tf, in(span, 1)) / iw;
     idx[1] = af_make_seq(1, 1, 1);
-    Array<T> YT = arithOp<T, af_div_t>(multiplyIndexed(IN, TF, idx), IW, IW.dims());
+    Array<T> yw = arithOp<T, af_div_t>(multiplyIndexed(in, tf, idx), iw, iw.dims());
 
     // return join(1, xt, yt)
-    Array<T> R = join(1, XT, YT);
-    return getHandle(R);
+    Array<T> r = join(1, xt, yw);
+    return getHandle(r);
 }
 
-af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0, const float d1)
+af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0_, const float d1_)
 {
     try {
         const ArrayInfo& tfInfo = getInfo(tf);
@@ -72,8 +72,8 @@ af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0
         af_array output;
         af_dtype type  = tfInfo.getType();
         switch(type) {
-            case f32: output = transform_coordinates<float >(tf, d0, d1); break;
-            case f64: output = transform_coordinates<double>(tf, d0, d1); break;
+            case f32: output = transform_coordinates<float >(tf, d0_, d1_); break;
+            case f64: output = transform_coordinates<double>(tf, d0_, d1_); break;
             default : TYPE_ERROR(1, type);
         }
         std::swap(*out, output);

From 1ac1e5b215ed97b6190f4a89e417fdf56387d45c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 20 Jun 2017 00:40:51 -0400
Subject: [PATCH 1236/2677] Add driver locks around cudaMalloc and cudaFree

---
 src/backend/cuda/memory.cpp   | 9 +++++++++
 src/backend/cuda/platform.hpp | 4 ++--
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index f6261dc80a..60b70940c7 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -17,6 +17,8 @@
 #include <types.hpp>
 #include <dispatch.hpp>
 
+#include <mutex>
+
 #ifndef AF_MEM_DEBUG
 #define AF_MEM_DEBUG 0
 #endif
@@ -25,6 +27,9 @@
 #define AF_CUDA_MEM_DEBUG 0
 #endif
 
+using std::lock_guard;
+using std::mutex;
+
 namespace cuda
 {
 void setMemStepSize(size_t step_bytes)
@@ -168,6 +173,7 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
+    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     void *ptr = NULL;
     CUDA_CHECK(cudaMalloc(&ptr, bytes));
     return ptr;
@@ -175,6 +181,7 @@ void *MemoryManager::nativeAlloc(const size_t bytes)
 
 void MemoryManager::nativeFree(void *ptr)
 {
+    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     cudaError_t err = cudaFree(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
@@ -205,6 +212,7 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {
+    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     void *ptr;
     CUDA_CHECK(cudaMallocHost(&ptr, bytes));
     return ptr;
@@ -212,6 +220,7 @@ void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 
 void MemoryManagerPinned::nativeFree(void *ptr)
 {
+    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     cudaError_t err = cudaFreeHost(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index d955df4f63..ff6fb1485e 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -149,6 +149,8 @@ class DeviceManager
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
 
+        std::mutex driver_api_mutex[MAX_DEVICES];
+
         // Attributes
         std::vector<cudaDevice_t> cuDevices;
 
@@ -164,8 +166,6 @@ class DeviceManager
         std::unique_ptr<MemoryManager> memManager;
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
-
-        std::mutex driver_api_mutex[MAX_DEVICES];
 #if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 #endif

From b19c02a2ca85d66a8ce45426ee7927a23321ce41 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 20 Jun 2017 15:49:05 -0700
Subject: [PATCH 1237/2677] Convert CUDA JIT to use nvrtc instead of nvvm
 (#1836)

* Convert CUDA JIT to use nvrtc instead of nvvm

* Fixing underflow/overflow issues with complex math in opencl jit

* Style changes for JIT in CUDA and OpenCL backends

* Use std::string instead of char * for storing text of the jit.cuh file
---
 src/backend/cuda/Array.cpp            |   4 +-
 src/backend/cuda/CMakeLists.txt       | 128 ++----
 src/backend/cuda/JIT/BinaryNode.hpp   |  64 +--
 src/backend/cuda/JIT/BufferNode.hpp   | 132 ++----
 src/backend/cuda/JIT/Node.hpp         |  28 +-
 src/backend/cuda/JIT/ScalarNode.hpp   |  28 +-
 src/backend/cuda/JIT/UnaryNode.hpp    |  54 +--
 src/backend/cuda/JIT/arith.cu         |  44 --
 src/backend/cuda/JIT/cast.cu          | 104 -----
 src/backend/cuda/JIT/exp.cu           | 100 -----
 src/backend/cuda/JIT/hyper.cu         |  41 --
 src/backend/cuda/JIT/logic.cu         | 109 -----
 src/backend/cuda/JIT/numeric.cu       | 191 --------
 src/backend/cuda/JIT/trig.cu          |  62 ---
 src/backend/cuda/arith.hpp            |   1 -
 src/backend/cuda/binary.hpp           | 353 ++++++---------
 src/backend/cuda/cast.hpp             |  84 +++-
 src/backend/cuda/complex.hpp          |  80 ++--
 src/backend/cuda/jit.cpp              | 610 +++++++-------------------
 src/backend/cuda/kernel/jit.cuh       | 205 +++++++++
 src/backend/cuda/logic.hpp            |   3 +-
 src/backend/cuda/scalar.hpp           |   3 +-
 src/backend/cuda/types.cpp            |  99 +----
 src/backend/cuda/types.hpp            |   6 +-
 src/backend/cuda/unary.hpp            | 244 ++++-------
 src/backend/opencl/Array.cpp          |   4 +-
 src/backend/opencl/JIT/BufferNode.hpp |   9 +-
 src/backend/opencl/binary.hpp         |   3 +-
 src/backend/opencl/cast.hpp           |   6 +-
 src/backend/opencl/complex.hpp        |   8 +-
 src/backend/opencl/jit.cpp            |  75 ++--
 src/backend/opencl/kernel/jit.cl      |  70 +--
 src/backend/opencl/scalar.hpp         |   3 +-
 src/backend/opencl/types.cpp          |   2 +-
 src/backend/opencl/types.hpp          |   2 +-
 src/backend/opencl/unary.hpp          |   4 +-
 36 files changed, 934 insertions(+), 2029 deletions(-)
 delete mode 100644 src/backend/cuda/JIT/arith.cu
 delete mode 100644 src/backend/cuda/JIT/cast.cu
 delete mode 100644 src/backend/cuda/JIT/exp.cu
 delete mode 100644 src/backend/cuda/JIT/hyper.cu
 delete mode 100644 src/backend/cuda/JIT/logic.cu
 delete mode 100644 src/backend/cuda/JIT/numeric.cu
 delete mode 100644 src/backend/cuda/JIT/trig.cu
 create mode 100644 src/backend/cuda/kernel/jit.cuh

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 495f35807e..cec91d5c04 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -33,8 +33,8 @@ namespace cuda
     template<typename T>
     Node_ptr bufferNodePtr()
     {
-        Node_ptr node(reinterpret_cast<Node *>(new BufferNode<T>(irname<T>(), afShortName<T>())));
-        return node;
+        return Node_ptr(new BufferNode<T>(getFullName<T>(),
+                                          shortname<T>(true)));
     }
 
     template<typename T>
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index eb4b27b44b..e48dda4446 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -3,10 +3,6 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 FIND_PACKAGE(CUDA 7.0 REQUIRED)
 
 INCLUDE(CLKernelToH)
-INCLUDE(FindNVVM)
-
-OPTION(USE_LIBDEVICE "Use libdevice for CUDA JIT" ON)
-SET(CUDA_LIBDEVICE_DIR "${CUDA_NVVM_HOME}/libdevice" CACHE PATH "Path where libdevice compute files are located" FORCE)
 
 MARK_AS_ADVANCED(
     CUDA_BUILD_CUBIN
@@ -168,14 +164,10 @@ FILE(GLOB jit_sources
 FILE(GLOB kernel_headers
     "kernel/*.hpp")
 
-FILE(GLOB ptx_sources
-    "JIT/*.cu")
-
 LIST(SORT cuda_headers)
 LIST(SORT cuda_sources)
 LIST(SORT jit_sources)
 LIST(SORT kernel_headers)
-LIST(SORT ptx_sources)
 
 SOURCE_GROUP(backend\\cuda\\Headers FILES ${cuda_headers})
 SOURCE_GROUP(backend\\cuda\\Sources FILES ${cuda_sources})
@@ -219,10 +211,23 @@ FILE(GLOB cpp_sources
 
 LIST(SORT cpp_sources)
 
+SET(jit_kernel_headers
+    "kernel_headers")
+
+FILE(GLOB jit_src "kernel/jit.cuh")
+CL_KERNEL_TO_H(
+    SOURCES ${jit_src}
+    VARNAME jit_files
+    EXTENSION "hpp"
+    OUTPUT_DIR ${jit_kernel_headers}
+    TARGETS jit_kernel_targets
+    NAMESPACE "cuda"
+    )
+
+
 SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
 
 INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/CMakeLists.txt")
-
 INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/CMakeLists.txt")
 
 LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
@@ -242,89 +247,8 @@ SET(OLD_CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS})
 IF(${CUDA_VERSION_MAJOR} GREATER 7) # CUDA 8 or newer
     SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} --keep-device-functions")
 ENDIF()
-CUDA_COMPILE_PTX(ptx_files ${ptx_sources})
 SET(CUDA_NVCC_FLAGS ${OLD_CUDA_NVCC_FLAGS})
 
-set(cuda_ptx "")
-foreach(ptx_src_file ${ptx_sources})
-
-      get_filename_component(_name "${ptx_src_file}" NAME_WE)
-
-      # CUDA_COMPILE_PTX from CMake 3.7 has new features that require this change
-      # TODO Fix this with a more complete solution
-      IF(CMAKE_VERSION VERSION_LESS 3.7) # Before 3.7
-        SET(NAME_APPEND "")
-      ELSE(CMAKE_VERSION VERSION_LESS 3.7)  # 3.7 and newer
-        SET(NAME_APPEND "_1")
-      ENDIF(CMAKE_VERSION VERSION_LESS 3.7)
-
-      set(_gen_file_name
-        "${PROJECT_BINARY_DIR}/src/backend/cuda/cuda_compile_ptx${NAME_APPEND}_generated_${_name}.cu.ptx")
-      set(_out_file_name
-        "${PROJECT_BINARY_DIR}/src/backend/cuda/${_name}.ptx")
-
-      ADD_CUSTOM_COMMAND(
-        OUTPUT "${_out_file_name}"
-        DEPENDS "${_gen_file_name}"
-        COMMAND ${CMAKE_COMMAND} -E copy "${_gen_file_name}" "${_out_file_name}")
-
-      list(APPEND cuda_ptx "${_out_file_name}")
-endforeach()
-
-SET( ptx_headers
-    "ptx_headers")
-
-CL_KERNEL_TO_H(
-    SOURCES ${cuda_ptx}
-    VARNAME kernel_files
-    EXTENSION "hpp"
-    OUTPUT_DIR ${ptx_headers}
-    TARGETS ptx_targets
-    NAMESPACE "cuda"
-    NULLTERM TRUE
-    )
-
-SET(libdevice_bc "")
-IF (USE_LIBDEVICE)
-    SET(libdevice_computes "")
-    LIST(APPEND libdevice_computes "20" "30" "35" "50")
-    FOREACH(libdevice_compute ${libdevice_computes})
-        SET(_libdevice_bc_file "${CUDA_LIBDEVICE_DIR}/libdevice.compute_${libdevice_compute}.10.bc")
-        SET(_libdevice_bc_copy "${PROJECT_BINARY_DIR}/src/backend/cuda/compute_${libdevice_compute}.bc")
-        IF (EXISTS ${_libdevice_bc_file})
-            ADD_CUSTOM_COMMAND(
-                OUTPUT "${_libdevice_bc_copy}"
-                DEPENDS "${_libdevice_bc_file}"
-                COMMAND ${CMAKE_COMMAND} -E copy "${_libdevice_bc_file}" "${_libdevice_bc_copy}")
-            LIST(APPEND libdevice_bc ${_libdevice_bc_copy})
-            ADD_DEFINITIONS(-D"__LIBDEVICE_COMPUTE_${libdevice_compute}")
-        ENDIF()
-    ENDFOREACH()
-ENDIF()
-
-LIST(LENGTH libdevice_bc libdevice_bc_len)
-
-IF (${libdevice_bc_len} GREATER 0)
-
-  SET(libdevice_headers
-    "libdevice_headers")
-
-  CL_KERNEL_TO_H(
-    SOURCES ${libdevice_bc}
-    VARNAME libdevice_files
-    EXTENSION "hpp"
-    OUTPUT_DIR ${libdevice_headers}
-    TARGETS libdevice_targets
-    NAMESPACE "cuda"
-    BINARY TRUE
-    )
-
-  MESSAGE(STATUS "LIBDEVICE found.")
-  ADD_DEFINITIONS(-DUSE_LIBDEVICE)
-ELSE()
-  MESSAGE(STATUS "LIBDEVICE not found on system. CUDA JIT may be slower")
-ENDIF()
-
 IF("${APPLE}")
     ADD_DEFINITIONS(-D__STRICT_ANSI__)
 ELSE()
@@ -407,10 +331,11 @@ ENDIF(NOT CUDA_CUDA_LIBRARY)
 
 SET(CUDA_ADD_LIBRARY_OPTIONS "")
 IF(UNIX)
-    # These flags enable C++11 and disable invalid offsetof warning
-    SET(CUDA_ADD_LIBRARY_OPTIONS "-std=c++11 -Xcudafe \"--diag_suppress=1427\"")
+  # These flags enable C++11 and disable invalid offsetof warning
+  SET(CUDA_ADD_LIBRARY_OPTIONS "-std=c++11 -Xcudafe \"--diag_suppress=1427\"")
 ENDIF(UNIX)
 
+
 MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${cuda_headers}
                 ${cuda_sources}
@@ -425,23 +350,26 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${scan_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE} ${CUDA_ADD_LIBRARY_OPTIONS})
 
-ADD_DEPENDENCIES(afcuda ${ptx_targets})
-
-IF (${libdevice_bc_len} GREATER 0)
-  ADD_DEPENDENCIES(afcuda ${libdevice_targets})
-ENDIF()
+FIND_LIBRARY (
+  CUDA_nvrtc_LIBRARY
+  NAMES "nvrtc"
+  PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+  PATH_SUFFIXES "lib64" "lib/x64" "lib"
+  DOC "CUDA NVRTC Library"
+  NO_DEFAULT_PATH
+  )
 
-TARGET_LINK_LIBRARIES(afcuda
-                                PRIVATE ${CUDA_CUBLAS_LIBRARIES}
+TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}
                                 PRIVATE ${CUDA_CUFFT_LIBRARIES}
                                 PRIVATE ${CUDA_cusparse_LIBRARY}
                                 PRIVATE ${CUDA_cusolver_LIBRARY}
-                                PRIVATE ${CUDA_nvvm_LIBRARY}
+                                PRIVATE ${CUDA_nvrtc_LIBRARY}
                                 PRIVATE ${CUDA_CUDA_LIBRARY}
                      )
 
+ADD_DEPENDENCIES(afcuda ${jit_kernel_targets})
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
 IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
     ADD_DEPENDENCIES(afcuda ${GRAPHICS_DEPENDENCIES})
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index 115c892119..8edb88098d 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -20,68 +20,34 @@ namespace JIT
     class BinaryNode : public Node
     {
     private:
-        const std::string m_op_str;
-        const int m_op;
-        const int m_call_type;
+        std::string m_op_str;
+        int m_op;
 
     public:
         BinaryNode(const char *out_type_str, const char *name_str,
-                   const std::string &op_str,
-                   Node_ptr lhs, Node_ptr rhs, int op, int call_type)
+                   const char *op_str,
+                   Node_ptr lhs, Node_ptr rhs, int op)
             : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
               m_op_str(op_str),
-              m_op(op),
-              m_call_type(call_type)
+              m_op(op)
         {
         }
 
         void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            // Make the hex representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(2) << std::setfill('0') << std::hex << m_op;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.child_ids[0];
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.child_ids[1];
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
+            // Make the dec representation of enum part of the Kernel name
+            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[1];
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, Node_ids ids, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            if (m_call_type == 0) {
-                std::stringstream declStream;
-                declStream << "declare " << m_type_str << " " << m_op_str
-                           << "(" << m_children[0]->getTypeStr() << " , "
-                           << m_children[1]->getTypeStr() << ")\n";
-                declStrs[declStream.str()] = true;
-
-                kerStream << "%val" << ids.id << " = call "
-                          << m_type_str << " "
-                          << m_op_str << "("
-                          << m_children[0]->getTypeStr() << " "
-                          << "%val" << ids.child_ids[0] << ", "
-                          << m_children[1]->getTypeStr() << " "
-                          << "%val" << ids.child_ids[1] << ")\n";
-
-            } else {
-                if (m_call_type == 1) {
-                    // arithmetic operations
-                    kerStream << "%val" << ids.id << " = "
-                              << m_op_str << " "
-                              << m_type_str << " "
-                              << "%val" << ids.child_ids[0] << ", "
-                              << "%val" << ids.child_ids[1] << "\n";
-                } else {
-                    // logical operators
-                    kerStream << "%tmp" << ids.id << " = "
-                              << m_op_str << " "
-                              << m_children[0]->getTypeStr() << " "
-                              << "%val" << ids.child_ids[0] << ", "
-                              << "%val" << ids.child_ids[1] << "\n";
-
-                    kerStream << "%val" << ids.id << " = "
-                              << "zext i1 %tmp" << ids.id << " to i8\n";
-
-                }
-            }
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << m_op_str << "(val" << ids.child_ids[0]
+                      << ", val" << ids.child_ids[1] << ");"
+                      << "\n";
         }
     };
 
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index c6a03d2899..4c14f6c2d5 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -10,7 +10,6 @@
 #pragma once
 #include "Node.hpp"
 #include <iomanip>
-#include <memory>
 #include <mutex>
 
 namespace cuda
@@ -19,25 +18,17 @@ namespace cuda
 namespace JIT
 {
 
-    template <typename T>
-    static inline std::string toString(T val)
-    {
-        std::stringstream s;
-        s << val;
-        return s.str();
-    }
 
     template<typename T>
     class BufferNode : public Node
     {
     private:
-        // Keep the shared pointer for reference counting
         std::shared_ptr<T> m_data;
         Param<T> m_param;
         unsigned m_bytes;
         std::once_flag m_set_data_flag;
-
         bool m_linear_buffer;
+
     public:
 
         BufferNode(const char *type_str,
@@ -46,6 +37,8 @@ namespace JIT
         {
         }
 
+        bool isBuffer() { return true; }
+
         void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
         {
             std::call_once(m_set_data_flag, [this, param, data, bytes, is_linear]() {
@@ -65,90 +58,58 @@ namespace JIT
             return m_linear_buffer && same_dims;
         }
 
-        bool isBuffer() { return true; }
-
         void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
             kerStream << "_" << m_name_str;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream,
-                       std::stringstream &annStream,
-                       int id,
-                       bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear)
         {
-            kerStream << m_type_str << "* %in" << id << ",\n";
-            annStream << m_type_str << "*,\n";
-
-            if (!is_linear) {
-                kerStream << "i32 %dim0"     << id << ","
-                          << "i32 %dim1"     << id << ","
-                          << "i32 %dim2"     << id << ","
-                          << "i32 %dim3"     << id << ","
-                          << "\n"
-                          << "i32 %str1"     << id << ","
-                          << "i32 %str2"     << id << ","
-                          << "i32 %str3"     << id << ","
-                          << "\n";
+            if (is_linear) {
+                kerStream << m_type_str << " *in" << id << "_ptr,\n";
+            } else {
+                kerStream << "Param<" << m_type_str << "> in" << id
+                          << ",\n";
+            }
+        }
 
-                annStream << "i32, i32, i32, i32,\n"
-                          << "i32, i32, i32,\n";
+        void setArgs(std::vector<void *> &args, bool is_linear)
+        {
+            if (is_linear) {
+                args.push_back((void *)&m_param.ptr);
+            } else {
+                args.push_back((void *)&m_param);
             }
         }
 
         void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
         {
-            if (!is_linear) {
-                kerStream << "%b3" << id << " = icmp slt i32 %id3, %dim3" << id << "\n";
-                kerStream << "%b2" << id << " = icmp slt i32 %id2, %dim2" << id << "\n";
-                kerStream << "%b1" << id << " = icmp slt i32 %id1, %dim1" << id << "\n";
-                kerStream << "%b0" << id << " = icmp slt i32 %id0, %dim0" << id << "\n";
-
-                kerStream << "%c3" << id << " = zext i1 %b3" << id << " to i32\n";
-                kerStream << "%c2" << id << " = zext i1 %b2" << id << " to i32\n";
-                kerStream << "%c1" << id << " = zext i1 %b1" << id << " to i32\n";
-                kerStream << "%c0" << id << " = zext i1 %b0" << id << " to i32\n";
-
-                kerStream << "%d3" << id << " = mul i32 %c3" << id << ", %id3\n";
-                kerStream << "%d2" << id << " = mul i32 %c2" << id << ", %id2\n";
-                kerStream << "%d1" << id << " = mul i32 %c1" << id << ", %id1\n";
-                kerStream << "%d0" << id << " = mul i32 %c0" << id << ", %id0\n";
-
-                kerStream << "%off3i" << id << " = mul i32 %d3" << id
-                          << ", %str3" << id << "\n";
-
-                kerStream << "%off2i" << id << " = mul i32 %d2" << id
-                          << ", %str2" << id << "\n";
-
-                kerStream << "%off1i" << id << " = mul i32 %d1" << id
-                          << ", %str1" << id << "\n";
-
-                kerStream << "%off23i" << id << " = add i32 %off2i"
-                          << id << ", %off3i" << id << "\n";
-
-                kerStream << "%off123i" << id << " = add i32 %off23i"
-                          << id << ", %off1i" << id << "\n";
-
-                kerStream << "%idxa" << id << " = add i32 %off123i"
-                          << id << ", %d0" << id << "\n";
-
-                kerStream << "%idx" << id << " = sext i32 %idxa" << id <<" to i64\n\n";
+            std::string idx_str = std::string("int idx") + std::to_string(id);
+
+            if (is_linear) {
+                kerStream << idx_str << " = idx;\n";
+            } else {
+                std::string info_str = std::string("in") + std::to_string(id);
+                kerStream << idx_str << " = "
+                          << "(id3 < " << info_str << ".dims[3]) * "
+                          << info_str << ".strides[3] * id3 + "
+                          << "(id2 < " << info_str << ".dims[2]) * "
+                          << info_str << ".strides[2] * id2 + "
+                          << "(id1 < " << info_str << ".dims[1]) * "
+                          << info_str << ".strides[1] * id1 + "
+                          << "(id0 < " << info_str << ".dims[0]) * "
+                          << "id0;"
+                          << "\n";
+                kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
             }
         }
 
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, Node_ids ids, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            kerStream << "%inIdx" << ids.id << " = "
-                      << "getelementptr inbounds " << m_type_str << "* %in" << ids.id
-                      << ", i64 %idx";
-
-            if (!is_linear) kerStream << ids.id;
-            kerStream << "\n";
-
-            kerStream << "%val" << ids.id << " = " << "load "
-                      << m_type_str << "* %inIdx" << ids.id << "\n\n";
-
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << "in" << ids.id << "_ptr[idx" << ids.id << "];"
+                      << "\n";
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
@@ -158,21 +119,6 @@ namespace JIT
             bytes += m_bytes;
             return;
         }
-
-        void setArgs(std::vector<void *> &args, bool is_linear)
-        {
-            args.push_back((void *)&(m_param.ptr));
-
-            if (!is_linear) {
-                args.push_back((void *)&m_param.dims[0]);
-                args.push_back((void *)&m_param.dims[1]);
-                args.push_back((void *)&m_param.dims[2]);
-                args.push_back((void *)&m_param.dims[3]);
-                args.push_back((void *)&m_param.strides[1]);
-                args.push_back((void *)&m_param.strides[2]);
-                args.push_back((void *)&m_param.strides[3]);
-            }
-        }
     };
 
 }
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 035692550d..995d4c7b1d 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -8,19 +8,22 @@
  ********************************************************/
 
 #pragma once
+#include <platform.hpp>
 #include <optypes.hpp>
-
-#include <unordered_map>
-#include <memory>
 #include <string>
 #include <vector>
+#include <memory>
+#include <unordered_map>
 
 namespace cuda
 {
 
 namespace JIT
 {
+
     class Node;
+    using std::shared_ptr;
+    typedef shared_ptr<Node> Node_ptr;
 
     typedef struct
     {
@@ -28,9 +31,6 @@ namespace JIT
         std::vector<int> child_ids;
     } Node_ids;
 
-    typedef std::unordered_map<std::string, bool> str_map_t;
-    typedef str_map_t::iterator str_map_iter;
-    typedef std::shared_ptr<Node> Node_ptr;
     typedef std::unordered_map<Node *, Node_ids> Node_map_t;
     typedef Node_map_t::iterator Node_map_iter;
 
@@ -65,17 +65,12 @@ namespace JIT
             }
         }
 
-        virtual void genKerName(std::stringstream &kerStream, Node_ids ids) {}
-        virtual void genParams  (std::stringstream &kerStream,
-                                 std::stringstream &annStream,
-                                 int id, bool is_linear) {}
+        virtual void genKerName (std::stringstream &kerStream, Node_ids ids) {}
+        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) {}
         virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) {}
-        virtual void genFuncs   (std::stringstream &kerStream, str_map_t &declStrs,
-                                 Node_ids id, bool is_linear)
-        {}
+        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) {}
 
-        virtual void setArgs(std::vector<void *> &args, bool is_linear) {}
-        virtual bool isLinear(dim_t dims[4]) { return true; }
+        virtual void setArgs (std::vector<void *> &args, bool is_linear) { }
 
         virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
         {
@@ -83,9 +78,8 @@ namespace JIT
         }
 
         virtual bool isBuffer() { return false; }
-
+        virtual bool isLinear(dim_t dims[4]) { return true; }
         std::string getTypeStr() { return m_type_str; }
-
         int getHeight()  { return m_height; }
         std::string getNameStr() { return m_name_str; }
 
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index 264b7be61e..aae0496ec3 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #pragma once
-#include <types.hpp>
 #include "Node.hpp"
 #include <math.hpp>
+#include <types.hpp>
 #include <iomanip>
 
 namespace cuda
@@ -18,15 +18,17 @@ namespace cuda
 
 namespace JIT
 {
-    template<typename T>
+
+    template <typename T>
     class ScalarNode : public Node
     {
     private:
-        T m_val;
+        const T m_val;
+
     public:
 
         ScalarNode(T val)
-            : Node(irname<T>(), afShortName<T>(false), 0, {}),
+            : Node(getFullName<T>(), shortname<T>(false), 0, {}),
               m_val(val)
         {
         }
@@ -34,23 +36,27 @@ namespace JIT
         void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
             kerStream << "_" << m_name_str;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream,
-                       std::stringstream &annStream,
-                       int id,
-                       bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear)
         {
-            kerStream << m_type_str << " %val" << id << ", " << std::endl;
-            annStream << m_type_str << ",\n";
+            kerStream << m_type_str << " scalar" << id << ", " << "\n";
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear)
         {
             args.push_back((void *)&m_val);
         }
+
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << "scalar" << ids.id << ";"
+                      << "\n";
+        }
     };
+
 }
 
 }
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index ac334c14b8..72e148289f 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -22,62 +22,30 @@ namespace JIT
     private:
         const std::string m_op_str;
         const int m_op;
-        const bool m_is_check;
 
     public:
         UnaryNode(const char *out_type_str, const char *name_str,
-                  const std::string &op_str,
-                  Node_ptr child, int op, bool is_check=false)
+                  const char *op_str,
+                  Node_ptr child, int op)
             : Node(out_type_str, name_str, child->getHeight() + 1, {child}),
               m_op_str(op_str),
-              m_op(op),
-              m_is_check(is_check)
+              m_op(op)
         {
         }
 
         void genKerName(std::stringstream &kerStream, Node_ids ids)
         {
-            // Make the hex representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(2) << std::setfill('0') << std::hex << m_op;
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.child_ids[0];
-            kerStream << std::setw(2) << std::setfill('0') << std::hex << ids.id << std::dec;
+            // Make the dec representation of enum part of the Kernel name
+            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, str_map_t &declStrs, Node_ids ids, bool is_linear)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
         {
-            std::stringstream declStream;
-
-            if (m_is_check) {
-                declStream << "declare " << "i32 " << m_op_str
-                           << "(" << m_children[0]->getTypeStr() << ")\n";
-            } else {
-                declStream << "declare " << m_type_str << " " << m_op_str
-                           << "(" << m_children[0]->getTypeStr() << ")\n";
-            }
-
-            declStrs[declStream.str()] = true;
-
-            if (m_is_check) {
-                kerStream << "%tmp" << ids.id << " = call i32 "
-                          << m_op_str << "("
-                          << m_children[0]->getTypeStr() << " "
-                          << "%val" << ids.child_ids[0] << ")\n";
-
-                if (m_type_str[0] == 'i') {
-                    kerStream << "%val" << ids.id << " = "
-                              << "trunc i32 %tmp" << ids.id << " to " << m_type_str << "\n";
-                } else {
-                    kerStream << "%val" << ids.id << " = "
-                              << "sitofp i32 %tmp" << ids.id << " to " << m_type_str << "\n";
-                }
-
-            } else {
-                kerStream << "%val" << ids.id << " = call "
-                          << m_type_str << " "
-                          << m_op_str << "("
-                          << m_children[0]->getTypeStr() << " "
-                          << "%val" << ids.child_ids[0] << ")\n";
-            }
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << m_op_str << "(val" << ids.child_ids[0] << ");"
+                      << "\n";
         }
     };
 
diff --git a/src/backend/cuda/JIT/arith.cu b/src/backend/cuda/JIT/arith.cu
deleted file mode 100644
index adfa9e9068..0000000000
--- a/src/backend/cuda/JIT/arith.cu
+++ /dev/null
@@ -1,44 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-#define ARITH_BASIC(fn, op, T)                  \
-    __device__ T ___##fn(T a, T b)              \
-    {                                           \
-        return a op b;                          \
-    }                                           \
-
-
-#define ARITH(fn, op)                                   \
-    ARITH_BASIC(fn, op, float)                          \
-    ARITH_BASIC(fn, op, double)                         \
-    ARITH_BASIC(fn, op, int)                            \
-    ARITH_BASIC(fn, op, uint)                           \
-    ARITH_BASIC(fn, op, char)                           \
-    ARITH_BASIC(fn, op, uchar)                          \
-    ARITH_BASIC(fn, op, intl)                           \
-    ARITH_BASIC(fn, op, uintl)                          \
-    ARITH_BASIC(fn, op, short)                          \
-    ARITH_BASIC(fn, op, ushort)                         \
-                                                        \
-    __device__ cfloat ___##fn(cfloat a, cfloat b)       \
-    {                                                   \
-        return cuC##fn##f(a, b);                        \
-    }                                                   \
-                                                        \
-    __device__ cdouble ___##fn(cdouble a, cdouble b)    \
-    {                                                   \
-        return cuC##fn(a, b);                           \
-    }                                                   \
-
-ARITH(add, +)
-ARITH(sub, -)
-ARITH(mul, *)
-ARITH(div, /)
diff --git a/src/backend/cuda/JIT/cast.cu b/src/backend/cuda/JIT/cast.cu
deleted file mode 100644
index 8905955145..0000000000
--- a/src/backend/cuda/JIT/cast.cu
+++ /dev/null
@@ -1,104 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-#define CAST_BASIC(FN, To, Ti) __device__ To FN(Ti in) { return (To) in; }
-
-#define CAST_BASIC_BOOL(FN, To, Ti) __device__ To FN(Ti in) { return (To)(in != 0); }
-
-#define CAST(T, X)                              \
-    CAST_BASIC(___mk##X, T, float)              \
-    CAST_BASIC(___mk##X, T, double)             \
-    CAST_BASIC(___mk##X, T, int)                \
-    CAST_BASIC(___mk##X, T, uint)               \
-    CAST_BASIC(___mk##X, T, char)               \
-    CAST_BASIC(___mk##X, T, uchar)              \
-    CAST_BASIC(___mk##X, T, intl)               \
-    CAST_BASIC(___mk##X, T, uintl)              \
-    CAST_BASIC(___mk##X, T, short)              \
-    CAST_BASIC(___mk##X, T, ushort)             \
-
-CAST(float , S)
-CAST(double, D)
-CAST(int   , I)
-CAST(intl  , X)
-CAST(short , P)
-CAST(uint  , U)
-CAST(uchar , V)
-CAST(uintl , Y)
-CAST(ushort, Q)
-
-CAST_BASIC_BOOL(___mkJ, char, float)
-CAST_BASIC_BOOL(___mkJ, char, double)
-CAST_BASIC_BOOL(___mkJ, char, int)
-CAST_BASIC_BOOL(___mkJ, char, uint)
-CAST_BASIC_BOOL(___mkJ, char, char)
-CAST_BASIC_BOOL(___mkJ, char, uchar)
-CAST_BASIC_BOOL(___mkJ, char, intl)
-CAST_BASIC_BOOL(___mkJ, char, uintl)
-CAST_BASIC_BOOL(___mkJ, char, short)
-CAST_BASIC_BOOL(___mkJ, char, ushort)
-
-#define CPLX_BASIC(FN, To, Tr, Ti)              \
-    __device__ To FN(Ti in)                     \
-    {                                           \
-        To out = {(Tr)in, 0};                   \
-        return out;                             \
-    }                                           \
-
-#define CPLX_CAST(T, Tr, X)                     \
-    CPLX_BASIC(___mk##X, T, Tr, float)          \
-    CPLX_BASIC(___mk##X, T, Tr, double)         \
-    CPLX_BASIC(___mk##X, T, Tr, int)            \
-    CPLX_BASIC(___mk##X, T, Tr, uint)           \
-    CPLX_BASIC(___mk##X, T, Tr, char)           \
-    CPLX_BASIC(___mk##X, T, Tr, uchar)          \
-    CPLX_BASIC(___mk##X, T, Tr, uintl)          \
-    CPLX_BASIC(___mk##X, T, Tr, intl)           \
-    CPLX_BASIC(___mk##X, T, Tr, ushort)         \
-    CPLX_BASIC(___mk##X, T, Tr, short)          \
-
-CPLX_CAST(cfloat, float, C)
-CPLX_CAST(cdouble, double, Z)
-
-__device__ cfloat ___mkC(cfloat C)
-{
-    return C;
-}
-
-__device__ cfloat ___mkC(cdouble C)
-{
-    cfloat res = {C.x, C.y};
-    return res;
-}
-
-__device__ cdouble ___mkZ(cdouble C)
-{
-    return C;
-}
-
-__device__ cdouble ___mkZ(cfloat C)
-{
-    cdouble res = {C.x, C.y};
-    return res;
-}
-
-__device__ float ___real(cfloat in) { return in.x; }
-__device__ double ___real(cdouble in) { return in.x; }
-
-
-__device__ float ___imag(cfloat in) { return in.y; }
-__device__ double ___imag(cdouble in) { return in.y; }
-
-__device__ cfloat ___cplx(float l, float r) { cfloat out = {l, r}; return out; }
-__device__ cdouble ___cplx(double l, double r) { cdouble out = {l, r}; return out; }
-
-__device__ cfloat  ___conj(cfloat  in) { return cuConjf(in); }
-__device__ cdouble ___conj(cdouble in) { return cuConj (in); }
diff --git a/src/backend/cuda/JIT/exp.cu b/src/backend/cuda/JIT/exp.cu
deleted file mode 100644
index 3f110b4328..0000000000
--- a/src/backend/cuda/JIT/exp.cu
+++ /dev/null
@@ -1,100 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-__device__ double sigmoid(double in)
-{
-    return (1.0) / (1 + exp(-in));
-}
-
-__device__ float sigmoidf(float in)
-{
-    return (1.0) / (1 + expf(-in));
-}
-
-#define MATH_BASIC(fn, T)                       \
-    __device__ T ___##fn(T a)                   \
-    {                                           \
-        return fn##f((float)a);                 \
-    }                                           \
-
-
-#define MATH(fn)                                \
-    MATH_BASIC(fn, float)                       \
-    MATH_BASIC(fn, int)                         \
-    MATH_BASIC(fn, uint)                        \
-    MATH_BASIC(fn, char)                        \
-    MATH_BASIC(fn, uchar)                       \
-    MATH_BASIC(fn, uintl)                       \
-    MATH_BASIC(fn, intl)                        \
-    MATH_BASIC(fn, ushort)                      \
-    MATH_BASIC(fn, short)                       \
-    __device__ double ___##fn(double a)         \
-    {                                           \
-        return fn(a);                           \
-    }                                           \
-
-
-MATH(exp)
-MATH(expm1)
-MATH(erf)
-MATH(erfc)
-MATH(sigmoid)
-
-MATH(log)
-MATH(log10)
-MATH(log1p)
-MATH(log2)
-
-MATH(sqrt)
-MATH(cbrt)
-
-#define MATH2_BASIC(fn, T)                      \
-    __device__ T ___##fn(T a, T b)              \
-    {                                           \
-        return fn##f((float)a, (float)b);       \
-    }                                           \
-
-#define MATH2(fn)                                   \
-    MATH2_BASIC(fn, float)                          \
-    MATH2_BASIC(fn, int)                            \
-    MATH2_BASIC(fn, uint)                           \
-    MATH2_BASIC(fn, char)                           \
-    MATH2_BASIC(fn, uchar)                          \
-    MATH2_BASIC(fn, uintl)                          \
-    MATH2_BASIC(fn, intl)                           \
-    MATH2_BASIC(fn, ushort)                         \
-    MATH2_BASIC(fn, short)                          \
-    __device__ double ___##fn(double a, double b)   \
-    {                                               \
-        return fn(a, b);                            \
-    }                                               \
-
-MATH2(pow)
-
-__device__ cfloat ___pow(cfloat a, float b)
-{
-    float R = cuCabsf(a);
-    float Theta = atan2(a.y, a.x);
-    float R_b = powf(R, b);
-    float Theta_b = Theta * b;
-    cfloat res = {R_b * cosf(Theta_b), R_b * sinf(Theta_b)};
-    return res;
-}
-
-__device__ cdouble ___pow(cdouble a, float b)
-{
-    float R = cuCabs(a);
-    float Theta = atan2(a.y, a.x);
-    float R_b = pow(R, b);
-    float Theta_b = Theta * b;
-    cdouble res = {R_b * cos(Theta_b), R_b * sin(Theta_b)};
-    return res;
-}
diff --git a/src/backend/cuda/JIT/hyper.cu b/src/backend/cuda/JIT/hyper.cu
deleted file mode 100644
index 6673fb1f14..0000000000
--- a/src/backend/cuda/JIT/hyper.cu
+++ /dev/null
@@ -1,41 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-#define MATH_BASIC(fn, T)                       \
-    __device__ T ___##fn(T a)                   \
-    {                                           \
-        return fn##f((float)a);                 \
-    }                                           \
-
-
-#define MATH(fn)                                \
-    MATH_BASIC(fn, float)                       \
-    MATH_BASIC(fn, int)                         \
-    MATH_BASIC(fn, uint)                        \
-    MATH_BASIC(fn, char)                        \
-    MATH_BASIC(fn, uchar)                       \
-    MATH_BASIC(fn, uintl)                       \
-    MATH_BASIC(fn, intl)                        \
-    MATH_BASIC(fn, ushort)                      \
-    MATH_BASIC(fn, short)                       \
-    __device__ double ___##fn(double a)         \
-    {                                           \
-        return fn(a);                           \
-    }                                           \
-
-
-MATH(sinh)
-MATH(cosh)
-MATH(tanh)
-
-MATH(asinh)
-MATH(acosh)
-MATH(atanh)
diff --git a/src/backend/cuda/JIT/logic.cu b/src/backend/cuda/JIT/logic.cu
deleted file mode 100644
index 6072c3c447..0000000000
--- a/src/backend/cuda/JIT/logic.cu
+++ /dev/null
@@ -1,109 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-#define LOGIC_BASIC(fn, op, T)                  \
-    __device__ bool ___##fn(T a, T b)           \
-    {                                           \
-        return a op b;                          \
-    }                                           \
-
-
-#define LOGIC(fn, op)                               \
-    LOGIC_BASIC(fn, op, float)                      \
-    LOGIC_BASIC(fn, op, double)                     \
-    LOGIC_BASIC(fn, op, int)                        \
-    LOGIC_BASIC(fn, op, uint)                       \
-    LOGIC_BASIC(fn, op, char)                       \
-    LOGIC_BASIC(fn, op, uchar)                      \
-    LOGIC_BASIC(fn, op, intl)                       \
-    LOGIC_BASIC(fn, op, uintl)                      \
-    LOGIC_BASIC(fn, op, short)                      \
-    LOGIC_BASIC(fn, op, ushort)                     \
-                                                    \
-    __device__ bool ___##fn(cfloat a, cfloat b)     \
-    {                                               \
-        return cabs2(a) op cabs2(b);                \
-    }                                               \
-                                                    \
-    __device__ bool ___##fn(cdouble a, cdouble b)   \
-    {                                               \
-        return cabs2(a) op cabs2(b);                \
-    }                                               \
-
-LOGIC(lt, <)
-LOGIC(gt, >)
-LOGIC(le, <=)
-LOGIC(ge, >=)
-LOGIC(and, &&)
-LOGIC(or, ||)
-
-#define LOGIC_EQ(fn, op, op2)                       \
-    LOGIC_BASIC(fn, op, float)                      \
-    LOGIC_BASIC(fn, op, double)                     \
-    LOGIC_BASIC(fn, op, int)                        \
-    LOGIC_BASIC(fn, op, uint)                       \
-    LOGIC_BASIC(fn, op, char)                       \
-    LOGIC_BASIC(fn, op, uchar)                      \
-    LOGIC_BASIC(fn, op, intl)                       \
-    LOGIC_BASIC(fn, op, uintl)                      \
-    LOGIC_BASIC(fn, op, short)                      \
-    LOGIC_BASIC(fn, op, ushort)                     \
-                                                    \
-    __device__ bool ___##fn(cfloat a, cfloat b)     \
-    {                                               \
-        return (a.x op b.x) op2 (a.y op b.y);       \
-    }                                               \
-                                                    \
-    __device__ bool ___##fn(cdouble a, cdouble b)   \
-    {                                               \
-        return (a.x op b.x) op2 (a.y op b.y);       \
-    }                                               \
-
-LOGIC_EQ(eq, ==, &&)
-LOGIC_EQ(neq, !=, ||)
-
-#define NOT_FN(T)                                   \
-    __device__ bool ___not(T in) { return !in; }    \
-
-NOT_FN(float)
-NOT_FN(double)
-NOT_FN(int)
-NOT_FN(uint)
-NOT_FN(char)
-NOT_FN(uchar)
-NOT_FN(intl)
-NOT_FN(uintl)
-NOT_FN(short)
-NOT_FN(ushort)
-
-#define BIT_FN(T)                                                   \
-    __device__ T ___bitand   (T lhs, T rhs) { return lhs &  rhs; }  \
-    __device__ T ___bitor    (T lhs, T rhs) { return lhs |  rhs; }  \
-    __device__ T ___bitxor   (T lhs, T rhs) { return lhs ^  rhs; }  \
-    __device__ T ___bitshiftl(T lhs, T rhs) { return lhs << rhs; }  \
-    __device__ T ___bitshiftr(T lhs, T rhs) { return lhs >> rhs; }  \
-
-BIT_FN(int)
-BIT_FN(char)
-BIT_FN(intl)
-BIT_FN(uchar)
-BIT_FN(uint)
-BIT_FN(uintl)
-BIT_FN(short)
-BIT_FN(ushort)
-
-__device__ char ___isNaN(float in) { return isnan(in); }
-__device__ char ___isINF(float in) { return isinf(in); }
-__device__ char ___iszero(float in) { return (in == 0); }
-
-__device__ char ___isNaN(double in) { return isnan(in); }
-__device__ char ___isINF(double in) { return isinf(in); }
-__device__ char ___iszero(double in) { return (in == 0); }
diff --git a/src/backend/cuda/JIT/numeric.cu b/src/backend/cuda/JIT/numeric.cu
deleted file mode 100644
index 2bcb15a112..0000000000
--- a/src/backend/cuda/JIT/numeric.cu
+++ /dev/null
@@ -1,191 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-template<typename T> __device__ T sign(T a) { return signbit(a); }
-
-#define MATH_BASIC(fn, T)                       \
-    __device__ T ___##fn(T a)                   \
-    {                                           \
-        return fn(a);                           \
-    }                                           \
-
-
-#define MATH_NOOP(fn, T)                        \
-    __device__ T ___##fn(T a)                   \
-    {                                           \
-        return a;                               \
-    }                                           \
-
-
-#define MATH_CAST(fn, T, Tc)                    \
-    __device__ T ___##fn(T a)                   \
-    {                                           \
-        return (T)fn((Tc)a);                    \
-    }                                           \
-
-MATH_BASIC(floor, float)
-MATH_BASIC(floor, double)
-MATH_NOOP(floor, int)
-MATH_NOOP(floor, uint)
-MATH_NOOP(floor, char)
-MATH_NOOP(floor, uchar)
-MATH_NOOP(floor, uintl)
-MATH_NOOP(floor, intl)
-MATH_NOOP(floor, ushort)
-MATH_NOOP(floor, short)
-
-MATH_BASIC(ceil, float)
-MATH_BASIC(ceil, double)
-MATH_NOOP(ceil, int)
-MATH_NOOP(ceil, uint)
-MATH_NOOP(ceil, char)
-MATH_NOOP(ceil, uchar)
-MATH_NOOP(ceil, uintl)
-MATH_NOOP(ceil, intl)
-MATH_NOOP(ceil, ushort)
-MATH_NOOP(ceil, short)
-
-MATH_BASIC(round, float)
-MATH_BASIC(round, double)
-MATH_NOOP(round, int)
-MATH_NOOP(round, uint)
-MATH_NOOP(round, char)
-MATH_NOOP(round, uchar)
-MATH_NOOP(round, uintl)
-MATH_NOOP(round, intl)
-MATH_NOOP(round, ushort)
-MATH_NOOP(round, short)
-
-MATH_BASIC(trunc, float)
-MATH_BASIC(trunc, double)
-MATH_NOOP(trunc, int)
-MATH_NOOP(trunc, uint)
-MATH_NOOP(trunc, char)
-MATH_NOOP(trunc, uchar)
-MATH_NOOP(trunc, uintl)
-MATH_NOOP(trunc, intl)
-MATH_NOOP(trunc, ushort)
-MATH_NOOP(trunc, short)
-
-MATH_BASIC(sign, float)
-MATH_BASIC(sign, double)
-MATH_NOOP(sign, int)
-MATH_NOOP(sign, uint)
-MATH_NOOP(sign, char)
-MATH_NOOP(sign, uchar)
-MATH_NOOP(sign, uintl)
-MATH_NOOP(sign, intl)
-MATH_NOOP(sign, ushort)
-MATH_NOOP(sign, short)
-
-MATH_BASIC(abs, float)
-MATH_BASIC(abs, double)
-MATH_BASIC(abs, int)
-MATH_CAST(abs, char, int)
-MATH_NOOP(abs, uint)
-MATH_NOOP(abs, uchar)
-MATH_NOOP(abs, uintl)
-MATH_NOOP(abs, intl)
-MATH_NOOP(abs, ushort)
-MATH_NOOP(abs, short)
-
-MATH_BASIC(tgamma, float)
-MATH_BASIC(tgamma, double)
-MATH_CAST(tgamma, int   , float)
-MATH_CAST(tgamma, uint  , float)
-MATH_CAST(tgamma, char  , float)
-MATH_CAST(tgamma, uchar , float)
-MATH_CAST(tgamma, uintl , float)
-MATH_CAST(tgamma, intl  , float)
-MATH_CAST(tgamma, ushort, float)
-MATH_CAST(tgamma, short , float)
-
-MATH_BASIC(lgamma, float)
-MATH_BASIC(lgamma, double)
-MATH_CAST(lgamma, int   , float)
-MATH_CAST(lgamma, uint  , float)
-MATH_CAST(lgamma, char  , float)
-MATH_CAST(lgamma, uchar , float)
-MATH_CAST(lgamma, uintl , float)
-MATH_CAST(lgamma, intl  , float)
-MATH_CAST(lgamma, ushort, float)
-MATH_CAST(lgamma, short , float)
-
-MATH_NOOP(noop, float)
-MATH_NOOP(noop, double)
-MATH_NOOP(noop, cfloat)
-MATH_NOOP(noop, cdouble)
-MATH_NOOP(noop, int)
-MATH_NOOP(noop, uint)
-MATH_NOOP(noop, char)
-MATH_NOOP(noop, uchar)
-MATH_NOOP(noop, uintl)
-MATH_NOOP(noop, intl)
-MATH_NOOP(noop, ushort)
-MATH_NOOP(noop, short)
-
-__device__ float ___abs(cfloat a) { return cuCabsf(a); }
-__device__ double ___abs(cdouble a) { return cuCabs(a); }
-
-template<typename T> __device__ T rem(T a, T b) { return a % b; }
-__device__ float rem(float a, float b) { return remainderf(a, b); }
-__device__ double rem(double a, double b) { return remainder(a, b); }
-
-template<typename T> __device__ T mod(T a, T b) { return a % b; }
-__device__ float mod(float a, float b) { return fmodf(a, b); }
-__device__ double mod(double a, double b) { return fmod(a, b); }
-
-#define MATH2_BASIC(fn, T)                      \
-    __device__ T ___##fn(T a, T b)              \
-    {                                           \
-        return fn(a, b);                        \
-    }                                           \
-
-#define MATH2(fn)                                   \
-    MATH2_BASIC(fn, float)                          \
-    MATH2_BASIC(fn, int)                            \
-    MATH2_BASIC(fn, uint)                           \
-    MATH2_BASIC(fn, intl)                           \
-    MATH2_BASIC(fn, uintl)                          \
-    MATH2_BASIC(fn, char)                           \
-    MATH2_BASIC(fn, uchar)                          \
-    MATH2_BASIC(fn, short)                          \
-    MATH2_BASIC(fn, ushort)                         \
-    __device__ double ___##fn(double a, double b)   \
-    {                                               \
-        return fn(a, b);                            \
-    }                                               \
-
-MATH2(min)
-MATH2(max)
-MATH2(mod)
-MATH2(rem)
-
-__device__ float ___hypot(float a, float b)
-{
-    return hypot(a, b);
-}
-
-__device__ double ___hypot(double a, double b)
-{
-    return hypot(a, b);
-}
-
-#define COMPARE_CPLX(fn, op, T)                 \
-    __device__ T ___##fn(T a, T b)              \
-    {                                           \
-        return cabs2(a) op cabs2(b) ? a : b;    \
-    }                                           \
-
-COMPARE_CPLX(min, <, cfloat)
-COMPARE_CPLX(min, <, cdouble)
-COMPARE_CPLX(max, >, cfloat)
-COMPARE_CPLX(max, >, cdouble)
diff --git a/src/backend/cuda/JIT/trig.cu b/src/backend/cuda/JIT/trig.cu
deleted file mode 100644
index 372bd4d026..0000000000
--- a/src/backend/cuda/JIT/trig.cu
+++ /dev/null
@@ -1,62 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "types.h"
-
-#define MATH_BASIC(fn, T)                       \
-    __device__ T ___##fn(T a)                   \
-    {                                           \
-        return fn##f((float)a);                 \
-    }                                           \
-
-
-#define MATH(fn)                                \
-    MATH_BASIC(fn, float)                       \
-    MATH_BASIC(fn, int)                         \
-    MATH_BASIC(fn, uint)                        \
-    MATH_BASIC(fn, char)                        \
-    MATH_BASIC(fn, uchar)                       \
-    MATH_BASIC(fn, uintl)                       \
-    MATH_BASIC(fn, intl)                        \
-    MATH_BASIC(fn, ushort)                      \
-    MATH_BASIC(fn, short)                       \
-    __device__ double ___##fn(double a)         \
-    {                                           \
-        return fn(a);                           \
-    }                                           \
-
-
-MATH(sin)
-MATH(cos)
-MATH(tan)
-
-MATH(asin)
-MATH(acos)
-MATH(atan)
-
-#define ATAN2(T)                                \
-    __device__ T ___atan2(T x, T y)             \
-    {                                           \
-        return atan2((float)x, (float)y);       \
-    }                                           \
-
-ATAN2(float)
-ATAN2(int)
-ATAN2(uint)
-ATAN2(char)
-ATAN2(uchar)
-ATAN2(uintl)
-ATAN2(intl)
-ATAN2(ushort)
-ATAN2(short)
-
-__device__ double ___atan2(double x, double y)
-{
-    return atan2(x, y);
-}
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index 5a39fcdf1c..87117e90bb 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -10,7 +10,6 @@
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
-#include <err_cuda.hpp>
 #include <binary.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index bb81c19ffa..58d6254453 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -17,220 +17,156 @@
 namespace cuda
 {
 
-
-template<typename To, typename Ti, af_op_t op>
-struct BinOp
-{
-    std::string name;
-    int call_type;
-    BinOp() :
-        name("noop"),
-        call_type(0)
-    {}
-};
-
-#define BINARY(fn)                                      \
-    template<typename To, typename Ti>                  \
-    struct BinOp<To, Ti, af_##fn##_t>                   \
-    {                                                   \
-        std::string name;                               \
-        int call_type;                                  \
-        BinOp() :                                       \
-            name(cuMangledName<Ti, true>("___"#fn)),    \
-            call_type(0)                                \
-            {}                                          \
+    template<typename To, typename Ti, af_op_t op>
+    struct BinOp
+    {
+        const char *name()
+        {
+            return "__invalid";
+        }
     };
 
-#if defined(USE_LIBDEVICE)
-#define NVVM_ARITH_OP(T, fn, fname)             \
-    template<>                                  \
-    struct BinOp<T, T, af_##fn##_t>             \
+#define BINARY_TYPE_1(fn)                      \
+    template<typename To, typename Ti>          \
+    struct BinOp<To, Ti, af_##fn##_t>           \
     {                                           \
-        std::string name;                       \
-        int call_type;                          \
-        BinOp() :                               \
-            name(fname),                        \
-            call_type(1)                        \
-            {}                                  \
+        const char *name()                      \
+        {                                       \
+            return "__"#fn;                     \
+        }                                       \
     };                                          \
-
-#define NVVM_COMPARE_OP(T, fn, fname)           \
-    template<>                                  \
-    struct BinOp<char, T, af_##fn##_t>          \
+                                                \
+    template<typename To>                       \
+    struct BinOp<To, cfloat, af_##fn##_t>       \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "__c"#fn"f";                 \
+        }                                       \
+    };                                          \
+                                                \
+    template<typename To>                       \
+    struct BinOp<To, cdouble, af_##fn##_t>      \
     {                                           \
-        std::string name;                       \
-        int call_type;                          \
-        BinOp() :                               \
-            name(fname),                        \
-            call_type(2)                        \
-            {}                                  \
+        const char *name()                      \
+        {                                       \
+            return "__c"#fn;                    \
+        }                                       \
     };                                          \
 
-#define NVVM_BINARY_FUNC(T, fn, fname)          \
-    template<>                                  \
-    struct BinOp<T, T, af_##fn##_t>             \
+
+BINARY_TYPE_1(eq)
+BINARY_TYPE_1(neq)
+BINARY_TYPE_1(lt)
+BINARY_TYPE_1(le)
+BINARY_TYPE_1(gt)
+BINARY_TYPE_1(ge)
+BINARY_TYPE_1(add)
+BINARY_TYPE_1(sub)
+BINARY_TYPE_1(mul)
+BINARY_TYPE_1(div)
+BINARY_TYPE_1(and)
+BINARY_TYPE_1(or)
+BINARY_TYPE_1(bitand)
+BINARY_TYPE_1(bitor)
+BINARY_TYPE_1(bitxor)
+BINARY_TYPE_1(bitshiftl)
+BINARY_TYPE_1(bitshiftr)
+
+#undef BINARY_TYPE_1
+
+#define BINARY_TYPE_2(fn)                       \
+    template<typename To, typename Ti>          \
+    struct BinOp<To, Ti, af_##fn##_t>           \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "__"#fn;                     \
+        }                                       \
+    };                                          \
+    template<typename To>                       \
+    struct BinOp<To, float, af_##fn##_t>        \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "f"#fn;                      \
+        }                                       \
+    };                                          \
+    template<typename To>                       \
+    struct BinOp<To, double, af_##fn##_t>       \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "f"#fn;                      \
+        }                                       \
+    };                                          \
+    template<typename To>                       \
+    struct BinOp<To, cfloat, af_##fn##_t>       \
     {                                           \
-        std::string name;                       \
-        int call_type;                          \
-        BinOp() :                               \
-            name("@__nv_"#fname),               \
-            call_type(0)                        \
-            {}                                  \
+        const char *name()                      \
+        {                                       \
+            return "__c"#fn"f";                 \
+        }                                       \
     };                                          \
+    template<typename To>                       \
+    struct BinOp<To, cdouble, af_##fn##_t>      \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "__c"#fn;                    \
+        }                                       \
+    };                                          \
+
+BINARY_TYPE_2(min)
+BINARY_TYPE_2(max)
+BINARY_TYPE_2(pow)
+BINARY_TYPE_2(rem)
+BINARY_TYPE_2(mod)
 
-#else
+template<typename Ti>
+struct BinOp<cfloat, Ti, af_cplx2_t>
+{
+    const char *name()
+    {
+        return "__cplx2f";
+    }
+};
 
-#define NVVM_ARITH_OP(T, fn, fname)    // No specialization
-#define NVVM_COMPARE_OP(T, fn, fname)  // No specialization
-#define NVVM_BINARY_FUNC(T, fn, fname) // No specialization
+template<typename Ti>
+struct BinOp<cdouble, Ti, af_cplx2_t>
+{
+    const char *name()
+    {
+        return "__cplx2";
+    }
+};
 
-#endif
-
-#define NVVM_ARITH_OP_INT(fn, fname)         \
-    NVVM_ARITH_OP(int, fn, fname)            \
-    NVVM_ARITH_OP(short, fn, fname)          \
-    NVVM_ARITH_OP(intl, fn, fname)           \
-
-#define NVVM_ARITH_OP_UINT(fn, fname)        \
-    NVVM_ARITH_OP(uint, fn, fname)           \
-    NVVM_ARITH_OP(ushort, fn, fname)         \
-    NVVM_ARITH_OP(uintl, fn, fname)          \
-
-#define NVVM_ARITH_OP_FLOAT(fn, fname)       \
-    NVVM_ARITH_OP(float, fn, fname)          \
-    NVVM_ARITH_OP(double, fn, fname)         \
-
-#define NVVM_ARITH_OP_CPLX(fn, fname)        \
-    NVVM_ARITH_OP(cfloat, fn, fname)         \
-    NVVM_ARITH_OP(cdouble, fn, fname)        \
-
-#define NVVM_COMPARE_OP_INT(fn, fname)       \
-    NVVM_COMPARE_OP(int, fn, fname)          \
-    NVVM_COMPARE_OP(short, fn, fname)        \
-    NVVM_COMPARE_OP(intl, fn, fname)         \
-
-#define NVVM_COMPARE_OP_UINT(fn, fname)      \
-    NVVM_COMPARE_OP(uint, fn, fname)         \
-    NVVM_COMPARE_OP(ushort, fn, fname)       \
-    NVVM_COMPARE_OP(uintl, fn, fname)        \
-
-#define NVVM_COMPARE_OP_FLOAT(fn, fname)     \
-    NVVM_COMPARE_OP(float, fn, fname)        \
-    NVVM_COMPARE_OP(double, fn, fname)       \
-
-BINARY(add)
-NVVM_ARITH_OP_INT(add, "add")
-NVVM_ARITH_OP_UINT(add, "add")
-NVVM_ARITH_OP_FLOAT(add, "fadd")
-NVVM_ARITH_OP_CPLX(add, "fadd")
-
-BINARY(sub)
-NVVM_ARITH_OP_INT(sub, "sub")
-NVVM_ARITH_OP_UINT(sub, "sub")
-NVVM_ARITH_OP_FLOAT(sub, "fsub")
-NVVM_ARITH_OP_CPLX(sub, "fsub")
-
-BINARY(mul)
-NVVM_ARITH_OP_INT(mul, "mul")
-NVVM_ARITH_OP_UINT(mul, "mul")
-NVVM_ARITH_OP_FLOAT(mul, "fmul")
-
-BINARY(div)
-NVVM_ARITH_OP_INT(div, "sdiv")
-NVVM_ARITH_OP_UINT(div, "udiv")
-NVVM_ARITH_OP_FLOAT(div, "fdiv")
-
-BINARY(bitand)
-NVVM_ARITH_OP_INT(bitand, "and")
-NVVM_ARITH_OP_UINT(bitand, "and")
-
-BINARY(bitor)
-NVVM_ARITH_OP_INT(bitor, "or")
-NVVM_ARITH_OP_UINT(bitor, "or")
-
-BINARY(bitxor)
-NVVM_ARITH_OP_INT(bitxor, "xor")
-NVVM_ARITH_OP_UINT(bitxor, "xor")
-
-BINARY(bitshiftl)
-NVVM_ARITH_OP_INT(bitshiftl, "shl")
-NVVM_ARITH_OP_UINT(bitshiftl, "shl")
-
-BINARY(bitshiftr)
-NVVM_ARITH_OP_INT(bitshiftr, "lshr")
-NVVM_ARITH_OP_UINT(bitshiftr, "lshr")
-
-
-BINARY(and)
-BINARY(or)
-
-BINARY(lt)
-NVVM_COMPARE_OP_INT(lt, "icmp slt")
-NVVM_COMPARE_OP_UINT(lt, "icmp ult")
-NVVM_COMPARE_OP_FLOAT(lt, "fcmp olt")
-
-BINARY(gt)
-NVVM_COMPARE_OP_INT(gt, "icmp sgt")
-NVVM_COMPARE_OP_UINT(gt, "icmp ugt")
-NVVM_COMPARE_OP_FLOAT(gt, "fcmp ogt")
-
-BINARY(le)
-NVVM_COMPARE_OP_INT(le, "icmp sle")
-NVVM_COMPARE_OP_UINT(le, "icmp ule")
-NVVM_COMPARE_OP_FLOAT(le, "fcmp ole")
-
-BINARY(ge)
-NVVM_COMPARE_OP_INT(ge, "icmp sge")
-NVVM_COMPARE_OP_UINT(ge, "icmp uge")
-NVVM_COMPARE_OP_FLOAT(ge, "fcmp oge")
-
-BINARY(eq)
-NVVM_COMPARE_OP_INT(eq, "icmp eq")
-NVVM_COMPARE_OP_UINT(eq, "icmp eq")
-NVVM_COMPARE_OP_FLOAT(eq, "fcmp oeq")
-
-BINARY(neq)
-NVVM_COMPARE_OP_INT(neq, "icmp ne")
-NVVM_COMPARE_OP_UINT(neq, "icmp ne")
-NVVM_COMPARE_OP_FLOAT(neq, "fcmp one")
-
-BINARY(max)
-NVVM_BINARY_FUNC(float, max, fmaxf)
-NVVM_BINARY_FUNC(double, max, fmax)
-NVVM_BINARY_FUNC(int, max, max)
-NVVM_BINARY_FUNC(uint, max, umax)
-NVVM_BINARY_FUNC(intl, max, llmax)
-NVVM_BINARY_FUNC(uintl, max, ullmax)
-
-BINARY(min)
-NVVM_BINARY_FUNC(float, min, fminf)
-NVVM_BINARY_FUNC(double, min, fmin)
-NVVM_BINARY_FUNC(int, min, min)
-NVVM_BINARY_FUNC(uint, min, umin)
-NVVM_BINARY_FUNC(intl, min, llmin)
-NVVM_BINARY_FUNC(uintl, min, ullmin)
-
-BINARY(pow)
-NVVM_BINARY_FUNC(float, pow, powf)
-NVVM_BINARY_FUNC(double, pow, pow)
-
-BINARY(mod)
-NVVM_BINARY_FUNC(float, mod, fmodf)
-NVVM_BINARY_FUNC(double, mod, fmod)
-
-BINARY(rem)
-NVVM_BINARY_FUNC(float, rem, remainderf)
-NVVM_BINARY_FUNC(double, rem, remainder)
-
-BINARY(atan2)
-NVVM_BINARY_FUNC(float, atan2, atan2f)
-NVVM_BINARY_FUNC(double, atan2, atan2)
-
-BINARY(hypot)
-NVVM_BINARY_FUNC(float, hypot, hypotf)
-NVVM_BINARY_FUNC(double, hypot, hypot)
-
-#undef BINARY
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_cplx2_t>
+{
+    const char *name()
+    {
+        return "noop";
+    }
+};
+
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_atan2_t>
+{
+    const char *name()
+    {
+        return "atan2";
+    }
+};
+
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_hypot_t>
+{
+    const char *name()
+    {
+        return "hypot";
+    }
+};
 
 template<typename To, typename Ti, af_op_t op>
 Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
@@ -239,16 +175,13 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af:
 
     JIT::Node_ptr lhs_node = lhs.getNode();
     JIT::Node_ptr rhs_node = rhs.getNode();
-
-    JIT::BinaryNode *node = new JIT::BinaryNode(irname<To>(),
-                                                afShortName<To>(),
-                                                bop.name,
+    JIT::BinaryNode *node = new JIT::BinaryNode(getFullName<To>(),
+                                                shortname<To>(true),
+                                                bop.name(),
                                                 lhs_node,
-                                                rhs_node,
-                                                (int)(op),
-                                                bop.call_type);
+                                                rhs_node, (int)(op));
 
-    return createNodeArray<To>(odims, JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<To>(odims, JIT::Node_ptr(node));
 }
 
 }
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 906620036e..5b1ffe3a96 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -13,8 +13,8 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
-#include <types.hpp>
 #include <JIT/UnaryNode.hpp>
+#include <types.hpp>
 #include <Array.hpp>
 
 namespace cuda
@@ -23,18 +23,82 @@ namespace cuda
 template<typename To, typename Ti>
 struct CastOp
 {
-    std::string func;
-    CastOp() {
-        std::string tmp = std::string("___mk") + afShortName<To>();
-        func = cuMangledName<Ti, false>(tmp.c_str());
+    const char *name()
+    {
+        return "";
+    }
+};
+
+#define CAST_FN(TYPE)                           \
+    template<typename Ti>                       \
+        struct CastOp<TYPE, Ti>                 \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "("#TYPE")";                 \
+        }                                       \
+    };
+
+CAST_FN(int)
+CAST_FN(unsigned int)
+CAST_FN(unsigned char)
+CAST_FN(unsigned short)
+CAST_FN(short)
+CAST_FN(float)
+CAST_FN(double)
+
+#define CAST_CFN(TYPE)                          \
+    template<typename Ti>                       \
+    struct CastOp<TYPE, Ti>                     \
+    {                                           \
+        const char *name()                      \
+        {                                       \
+            return "__convert_"#TYPE;           \
+        }                                       \
+    };
+
+CAST_CFN(cfloat)
+CAST_CFN(cdouble)
+CAST_CFN(char)
+
+template<>
+struct CastOp<cfloat, cdouble>
+{
+    const char *name()
+    {
+        return "__convert_z2c";
+    }
+};
+
+template<>
+struct CastOp<cdouble, cfloat>
+{
+    const char *name()
+    {
+        return "__convert_c2z";
+    }
+};
+
+template<>
+struct CastOp<cfloat, cfloat>
+{
+    const char *name()
+    {
+        return "__convert_c2c";
     }
+};
 
-    const std::string name()
+template<>
+struct CastOp<cdouble, cdouble>
+{
+    const char *name()
     {
-        return func;
+        return "__convert_z2z";
     }
 };
 
+#undef CAST_FN
+#undef CAST_CFN
 
 template<typename To, typename Ti>
 struct CastWrapper
@@ -43,11 +107,11 @@ struct CastWrapper
     {
         CastOp<To, Ti> cop;
         JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<To>(),
-                                                  afShortName<To>(),
+        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
+                                                  shortname<To>(true),
                                                   cop.name(),
                                                   in_node, af_cast_t);
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 };
 
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 6082a5e194..578d982087 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -6,104 +6,74 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
-#include <err_cuda.hpp>
-#include <JIT/BinaryNode.hpp>
+#include <binary.hpp>
 #include <JIT/UnaryNode.hpp>
 
 namespace cuda
 {
-    template<typename T> static const std::string cplx_name() { return cuMangledName<T, false>("___noop");       }
-    template<> STATIC_ const std::string cplx_name<cfloat >() { return cuMangledName<float , true>("___cplx");   }
-    template<> STATIC_ const std::string cplx_name<cdouble>() { return cuMangledName<double, true>("___cplx");   }
-
-    template<typename T> static const std::string real_name() { return cuMangledName<T, false>("___noop");       }
-    template<> STATIC_ const std::string real_name<cfloat >() { return cuMangledName<cfloat , false>("___real"); }
-    template<> STATIC_ const std::string real_name<cdouble>() { return cuMangledName<cdouble, false>("___real"); }
-
-    template<typename T> static const std::string imag_name() { return cuMangledName<T, false>("___noop");       }
-    template<> STATIC_ const std::string imag_name<cfloat >() { return cuMangledName<cfloat , false>("___imag"); }
-    template<> STATIC_ const std::string imag_name<cdouble>() { return cuMangledName<cdouble, false>("___imag"); }
-
-    template<typename T> static const std::string abs_name()  { return cuMangledName<T, false>("___noop");       }
-#if defined(USE_LIBDEVICE)
-    template<> STATIC_ const std::string abs_name<float  >()  { return "@__nv_fabsf";  }
-    template<> STATIC_ const std::string abs_name<double >()  { return "@__nv_fabs" ;  }
-#else
-    template<> STATIC_ const std::string abs_name<float >()  { return cuMangledName<float , false>("___abs");  }
-    template<> STATIC_ const std::string abs_name<double>()  { return cuMangledName<double, false>("___abs");  }
-#endif
-    template<> STATIC_ const std::string abs_name<cfloat >()  { return cuMangledName<cfloat , false>("___abs");  }
-    template<> STATIC_ const std::string abs_name<cdouble>()  { return cuMangledName<cdouble, false>("___abs");  }
-
-    template<typename T> static const std::string conj_name() { return cuMangledName<T, false>("___noop");       }
-    template<> STATIC_ const std::string conj_name<cfloat >() { return cuMangledName<cfloat , false>("___conj"); }
-    template<> STATIC_ const std::string conj_name<cdouble>() { return cuMangledName<cdouble, false>("___conj"); }
-
     template<typename To, typename Ti>
     Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
     {
-        JIT::Node_ptr lhs_node = lhs.getNode();
-        JIT::Node_ptr rhs_node = rhs.getNode();
-
-        JIT::BinaryNode *node = new JIT::BinaryNode(irname<To>(),
-                                                    afShortName<To>(),
-                                                    cplx_name<To>(),
-                                                    lhs_node,
-                                                    rhs_node,
-                                                    (int)(af_cplx2_t),
-                                                    0);
-
-        return createNodeArray<To>(odims, JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
     }
 
     template<typename To, typename Ti>
     Array<To> real(const Array<Ti> &in)
     {
         JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<To>(),
-                                                  afShortName<To>(),
-                                                  real_name<Ti>(),
+        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
+                                                  shortname<To>(true),
+                                                  "__creal",
                                                   in_node, af_real_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 
     template<typename To, typename Ti>
     Array<To> imag(const Array<Ti> &in)
     {
         JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<To>(),
-                                                  afShortName<To>(),
-                                                  imag_name<Ti>(),
+        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
+                                                  shortname<To>(true),
+                                                  "__cimag",
                                                   in_node, af_imag_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 
+    template<typename T> static const char *abs_name() { return "fabs"; }
+    template<> STATIC_ const char *abs_name<cfloat>() { return "__cabsf"; }
+    template<> STATIC_ const char *abs_name<cdouble>() { return "__cabs"; }
+
     template<typename To, typename Ti>
     Array<To> abs(const Array<Ti> &in)
     {
         JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<To>(),
-                                                  afShortName<To>(),
+        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
+                                                  shortname<To>(true),
                                                   abs_name<Ti>(),
                                                   in_node, af_abs_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 
+    template<typename T> static const char *conj_name() { return "__noop"; }
+    template<> STATIC_ const char *conj_name<cfloat>() { return "__cconjf"; }
+    template<> STATIC_ const char *conj_name<cdouble>() { return "__cconj"; }
+
     template<typename T>
     Array<T> conj(const Array<T> &in)
     {
         JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<T>(),
-                                                  afShortName<T>(),
+        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<T>(),
+                                                  shortname<T>(true),
                                                   conj_name<T>(),
                                                   in_node, af_conj_t);
 
-        return createNodeArray<T>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
     }
 }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index a7870aa7bd..e8d960e02c 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -7,40 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
+#include <kernel_headers/jit.hpp>
+#include <dispatch.hpp>
+#include <err_cuda.hpp>
+
 #include <Array.hpp>
 #include <copy.hpp>
 #include <JIT/Node.hpp>
-
-#include <ptx_headers/arith.hpp>
-#include <ptx_headers/cast.hpp>
-#include <ptx_headers/exp.hpp>
-#include <ptx_headers/hyper.hpp>
-#include <ptx_headers/logic.hpp>
-#include <ptx_headers/numeric.hpp>
-#include <ptx_headers/trig.hpp>
-
-#if defined(__LIBDEVICE_COMPUTE_20)
-#include <libdevice_headers/compute_20.hpp>
-#endif
-
-#if defined(__LIBDEVICE_COMPUTE_30)
-#include <libdevice_headers/compute_30.hpp>
-#endif
-
-#if defined(__LIBDEVICE_COMPUTE_35)
-#include <libdevice_headers/compute_35.hpp>
-#endif
-
-#if defined(__LIBDEVICE_COMPUTE_50)
-#include <libdevice_headers/compute_50.hpp>
-#endif
-
 #include <platform.hpp>
-#include <dispatch.hpp>
-#include <err_cuda.hpp>
 #include <math.hpp>
-#include <nvvm.h>
+
+#include <af/dim4.hpp>
+#include <nvrtc.h>
 
 #include <functional>
 #include <map>
@@ -55,8 +33,6 @@ namespace cuda
 using JIT::Node;
 using JIT::Node_ids;
 using JIT::Node_map_t;
-using JIT::str_map_iter;
-using JIT::str_map_t;
 
 using std::hash;
 using std::lock_guard;
@@ -67,12 +43,6 @@ using std::stringstream;
 using std::unique_ptr;
 using std::vector;
 
-const char *layout64 = "target datalayout = \"e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64\"\n\n\n";
-const char *layout32 = "target datalayout = \"e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64\"\n\n\n";
-
-const char *triple64 = "target triple = \"nvptx64-unknown-cuda\"\n\n";
-const char *triple32 = "target triple = \"nvptx-unknown-cuda\"\n\n";
-
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<Node *> &full_nodes,
                           const vector<Node_ids> &full_ids,
@@ -94,7 +64,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
 
     hash<string> hash_fn;
 
-    hashName << "@KER";
+    hashName << "KER";
     hashName << hash_fn(funcName.str());
     return hashName.str();
 }
@@ -105,404 +75,210 @@ static string getKernelString(const string funcName,
                               const vector<int> &output_ids,
                               bool is_linear)
 {
-    static const char *defineVoid = "define void ";
-    static const char *generalDimParams = "\n"
-        "i32 %ostr0, i32 %ostr1, i32 %ostr2, i32 %ostr3,\n"
-        "i32 %odim0, i32 %odim1, i32 %odim2, i32 %odim3,\n"
-        "i32 %blkx, i32 %blky, i32 %ndims";
-
-    static const char *linearDimParams = "\n"
-        "i32 %nelem, i32 %blkx, i32 %blky";
-
-    const char *dimParams = is_linear ? linearDimParams : generalDimParams;
-
-    static const char *blockStart = "\n{\n\n"
-        "entry:\n\n";
-    static const char *blockEnd = "\n\n"
-        "ret void\n"
-        "\n\n}\n";
-
-    static const char *idAlias = "\n"
-        "%tidx = call i32 @llvm.nvvm.read.ptx.sreg.tid.x()\n"
-        "%bdmx = call i32 @llvm.nvvm.read.ptx.sreg.ntid.x()\n"
-        "%bidx = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()\n"
-        "%bidy = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.y()\n"
-        "%gdmx = call i32 @llvm.nvvm.read.ptx.sreg.nctaid.x()\n"
-        "\n\n";
-    static const char *earlyExit = "\n"
-        "end:\n\n"
-        "ret void\n";
-    static const char *core = "\n"
-        "core:\n\n";
-
-    static const char *generalIndex = "\n"
-        "%tidy = call i32 @llvm.nvvm.read.ptx.sreg.tid.y()\n"
-        "%bdmy = call i32 @llvm.nvvm.read.ptx.sreg.ntid.y()\n"
-        "%blk_x = alloca i32, align 4\n"
-        "%blk_y = alloca i32, align 4\n"
-        "%id_3 = alloca i32, align 4\n"
-        "%id_2 = alloca i32, align 4\n"
-        "store i32 %bidx, i32* %blk_x, align 4\n"
-        "store i32 %bidy, i32* %blk_y, align 4\n"
-        "store i32 0, i32* %id_2, align 4\n"
-        "store i32 0, i32* %id_3, align 4\n"
-        "%two = alloca i32, align 4\n"
-        "store i32 2, i32* %two, align 4\n"
-        "%twoval = load i32* %two, align 4\n"
-        "%is34 = icmp sgt i32 %ndims, %twoval\n"
-        "br i1 %is34, label %do34, label %do2\n"
-        "\ndo34:\n"
-        "%id2t = sdiv i32 %bidx, %blkx\n"
-        "store i32 %id2t, i32* %id_2, align 4\n"
-        "%id2m = mul i32 %id2t, %blkx\n"
-        "%blk_xx = sub i32 %bidx, %id2m\n"
-        "store i32 %blk_xx, i32* %blk_x, align 4\n"
-        "%three = alloca i32, align 4\n"
-        "store i32 3, i32* %three, align 4\n"
-        "%threeval = load i32* %three, align 4\n"
-        "%is4 = icmp sgt i32 %ndims, %threeval\n"
-        "br i1 %is4, label %do4, label %do2\n"
-        "\ndo4:\n"
-        "%id3t = sdiv i32 %bidy, %blky\n"
-        "store i32 %id3t, i32* %id_3, align 4\n"
-        "%id3m = mul i32 %id3t, %blky\n"
-        "%blk_yy = sub i32 %bidy, %id3m\n"
-        "store i32 %blk_yy, i32* %blk_y, align 4\n"
-        "br label %do2\n"
-        "\ndo2:\n"
-        "%id2 = load i32* %id_2, align 4\n"
-        "%id3 = load i32* %id_3, align 4\n"
-        "%tmp_x = load i32* %blk_x, align 4\n"
-        "%id0m = mul i32 %tmp_x, %bdmx\n"
-        "%id0 = add i32 %tidx, %id0m\n"
-        "%tmp_y = load i32* %blk_y, align 4\n"
-        "%id1m = mul i32 %tmp_y, %bdmy\n"
-        "%id1 = add i32 %tidy, %id1m\n"
-        "\n\n"
-        "%off3o = mul i32 %id3, %ostr3\n"
-        "%off2o = mul i32 %id2, %ostr2\n"
-        "%off1o = mul i32 %id1, %ostr1\n"
-        "%off23o = add i32 %off3o, %off2o\n"
-        "%off123o = add i32 %off23o, %off1o\n"
-        "%idxa = add i32 %off123o, %id0\n"
-        "%idx = sext i32 %idxa to i64\n"
-        "\n\n"
-        "%cmp3 = icmp slt i32 %id3, %odim3\n"
-        "%cmp2 = icmp slt i32 %id2, %odim2\n"
-        "%cmp1 = icmp slt i32 %id1, %odim1\n"
-        "%cmp0 = icmp slt i32 %id0, %odim0\n"
-        "br i1 %cmp3, label %check2, label %end\n"
-        "\ncheck2:\n"
-        "br i1 %cmp2, label %check1, label %end\n"
-        "\ncheck1:\n"
-        "br i1 %cmp1, label %check0, label %end\n"
-        "\ncheck0:\n"
-        "br i1 %cmp0, label %core, label %end\n";
-
-    static const char *linearIndex = "\n"
-        "%boff = mul i32 %bidy, %gdmx\n"
-        "%bid  = add i32 %boff, %bidx\n"
-        "%goff = mul i32 %bid , %bdmx\n"
-        "%gid  = add i32 %goff ,%tidx\n"
-        "%idx  = sext i32 %gid to i64\n"
-        "%cmp0 = icmp slt i32 %gid, %nelem\n"
-        "br i1 %cmp0, label %core, label %end\n";
-
-    static const char *functionLoad = "\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() nounwind readnone\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.tid.y() nounwind readnone\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x() nounwind readnone\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.ntid.y() nounwind readnone\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x() nounwind readnone\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.y() nounwind readnone\n"
-        "declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.x() nounwind readnone\n"
-        "\n";
 
+    const std::string includeFileStr(jit_cuh, jit_cuh_len);
+
+    const std::string paramTStr = R"JIT(
+        template<typename T>
+        struct Param
+        {
+          T *ptr;
+          dim_t dims[4];
+          dim_t strides[4];
+        };)JIT";
+
+    std::string typedefStr = "typedef unsigned int uint;\n";
+    typedefStr += "typedef ";
+    typedefStr += getFullName<dim_t>();
+    typedefStr += " dim_t;\n";
+
+    // Common CUDA code
+    // This part of the code does not change with the kernel.
+
+    static const char *kernelVoid = "extern \"C\" __global__ void\n";
+    static const char *dimParams = "uint blocks_x, uint blocks_y, uint num_odims";
+    static const char *blockStart = "{\n\n";
+    static const char *blockEnd = "\n\n}";
+
+    static const char *linearIndex = R"JIT(
+        uint blockId  = blockIdx.y * gridDim.x + blockIdx.x;
+        uint threadId = threadIdx.x;
+        int idx = blockId * blockDim.x * blockDim.y + threadId;
+        if (idx >= outref.dims[3] * outref.strides[3]) return;
+        )JIT";
+
+    static const char *generalIndex = R"JIT(
+        uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        if (num_odims > 2) {
+            id2 = blockIdx.x / blocks_x;
+            id0 = blockIdx.x - id2 * blocks_x;
+            id0 = threadIdx.x + id0 * blockDim.x;
+            if (num_odims > 3) {
+                id3 = blockIdx.y / blocks_y;
+                id1 = blockIdx.y - id3 * blocks_y;
+                id1 = threadIdx.y + id1 * blockDim.y;
+            } else {
+                id1 = threadIdx.y + blockDim.y * blockIdx.y;
+            }
+        } else {
+            id3 = 0;
+            id2 = 0;
+            id1 = threadIdx.y + blockDim.y * blockIdx.y;
+            id0 = threadIdx.x + blockDim.x * blockIdx.x;
+        }
+
+        bool cond = id0 < outref.dims[0] &&
+                    id1 < outref.dims[1] &&
+                    id2 < outref.dims[2] &&
+                    id3 < outref.dims[3];
+        if (!cond) return;
+
+        int idx = outref.strides[3] * id3 +
+                  outref.strides[2] * id2 +
+                  outref.strides[1] * id1 + id0;
+        )JIT";
 
-    stringstream kerStream;
-    stringstream inAnnStream;
-    stringstream outAnnStream;
     stringstream inParamStream;
     stringstream outParamStream;
-    stringstream funcBodyStream;
-    stringstream offsetsStream;
     stringstream outWriteStream;
-    str_map_t declStrs;
-
-    vector<string> types_output(output_ids.size());
-    for (int i = 0; i < (int)output_ids.size(); i++) {
-        types_output[i] = full_nodes[output_ids[i]]->getTypeStr();
-    }
+    stringstream offsetsStream;
+    stringstream opsStream;
+    stringstream outrefstream;
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
         const auto &node = full_nodes[i];
         const auto &ids_curr = full_ids[i];
-        // Generate input parameters, needs only current id
-        node->genParams(inParamStream, inAnnStream, ids_curr.id, is_linear);
-        // Generate input offsets, needs only current id
+        // Generate input parameters, only needs current id
+        node->genParams(inParamStream, ids_curr.id, is_linear);
+        // Generate input offsets, only needs current id
         node->genOffsets(offsetsStream, ids_curr.id, is_linear);
-        // Generate the core function body, needs children id as well
-        node->genFuncs(funcBodyStream, declStrs, ids_curr, is_linear);
+        // Generate the core function body, needs children ids as well
+        node->genFuncs(opsStream, ids_curr);
     }
 
+    outrefstream << "Param<" << full_nodes[output_ids[0]]->getTypeStr()
+                 << "> outref = out" << output_ids[0] << ";\n";
+
     for (int i = 0; i < (int)output_ids.size(); i++) {
         int id = output_ids[i];
-        string outTypeStr = types_output[i];
-
         // Generate output parameters
-        outParamStream << outTypeStr << "* %out" << id << ",\n";
-
-        // Generate instruction to write output
-        outWriteStream << "%outIdx" << id
-                       << "= getelementptr inbounds "
-                       << outTypeStr
-                       << "* %out" << id
-                       << ", i64 %idx\n";
-        outWriteStream << "store "
-                       << outTypeStr
-                       << " %val" << id << ", "
-                       << outTypeStr
-                       << "* %outIdx" << id << "\n";
-
-        // Generate output annotation string
-        outAnnStream << outTypeStr << "*,\n";
+        outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out" << id << ", \n";
+        // Generate code to write the output
+        outWriteStream << "out" << id << ".ptr[idx] = val" << id << ";\n";
     }
 
-    if (sizeof(void *) == 8) {
-        kerStream << layout64;
-        kerStream << triple64;
-    } else {
-        kerStream << layout32;
-        kerStream << triple32;
-    }
-
-    const char *index = is_linear ? linearIndex : generalIndex;
-
-    kerStream << defineVoid
-              << funcName
-              << " (\n"
-              << inParamStream.str()
-              << outParamStream.str()
-              << dimParams
-              << " )\n"
-              << blockStart
-              << idAlias
-              << index
-              << earlyExit
-              << core
-              << offsetsStream.str()
-              << funcBodyStream.str()
-              << outWriteStream.str()
-              << blockEnd;
-
-    for(str_map_iter iterator = declStrs.begin();
-        iterator != declStrs.end(); iterator++) {
-        kerStream << iterator->first << "\n";
-    }
-    kerStream << functionLoad;
-
-    kerStream << "!nvvm.annotations = !{!1}\n"
-                 "!1 = metadata !{void (\n"
-              << inAnnStream.str()
-              << outAnnStream.str();
-
+    // Put various blocks into a single stream
+    stringstream kerStream;
+    kerStream << typedefStr;
+    kerStream << paramTStr;
+    kerStream << includeFileStr << "\n\n";
+    kerStream << kernelVoid;
+    kerStream << funcName;
+    kerStream << "(\n";
+    kerStream << inParamStream.str();
+    kerStream << outParamStream.str();
+    kerStream << dimParams;
+    kerStream << ")\n";
+    kerStream << blockStart;
+    kerStream << outrefstream.str();
     if (is_linear) {
-        kerStream << "i32, i32, i32\n";
+        kerStream << linearIndex;
     } else {
-        kerStream  << "i32, i32, i32, i32,\n"
-                      "i32, i32, i32, i32,\n"
-                      "i32, i32, i32\n";
+        kerStream << generalIndex;
     }
-
-    kerStream << ")* " << funcName << ",\n "
-              << "metadata !\"kernel\", i32 1}\n";
+    kerStream << offsetsStream.str();
+    kerStream << opsStream.str();
+    kerStream << outWriteStream.str();
+    kerStream << blockEnd;
 
     return kerStream.str();
 }
 
-#define NVVM_CHECK(fn, msg) do {                \
-        nvvmResult res = fn;                    \
-        if (res == NVVM_SUCCESS) break;         \
-        char nvvm_err_msg[1024];                \
-        snprintf(nvvm_err_msg,                  \
-                 sizeof(nvvm_err_msg),          \
-                 "NVVM Error (%d): %s\n",       \
-                 (int)(res), msg);              \
-        AF_ERROR(nvvm_err_msg,                  \
-                 AF_ERR_INTERNAL);              \
-                                                \
-    } while(0)
-
-#if defined(USE_LIBDEVICE)
-void compute_to_libdevice_table(const char **buffer, size_t *bc_buffer_len, int compute)
-{
-// These macros create a fallback compute if in case the specific libdevice
-// compute is not found
-// 50 -> 30 -> 20 -> Not Found
-// 35 -> 30 -> 20 -> Not Found
-// 30 -> 20 -> Not Found
-// 20 -> Not Found
-#if defined(__LIBDEVICE_COMPUTE_20)
-    #define COMPUTE_20_STR compute_20_bc
-    #define COMPUTE_20_LEN compute_20_bc_len
-#else
-    #define COMPUTE_20_STR NULL
-    #define COMPUTE_20_LEN 0
-#endif
-
-#if defined(__LIBDEVICE_COMPUTE_30)
-    #define COMPUTE_30_STR compute_30_bc
-    #define COMPUTE_30_LEN compute_30_bc_len
-#else   // Fallback
-    #define COMPUTE_30_STR COMPUTE_20_STR
-    #define COMPUTE_30_LEN COMPUTE_20_LEN
-#endif
-
-#if defined(__LIBDEVICE_COMPUTE_35)
-    #define COMPUTE_35_STR compute_35_bc
-    #define COMPUTE_35_LEN compute_35_bc_len
-#else   // Fallback
-    #define COMPUTE_35_STR COMPUTE_30_STR
-    #define COMPUTE_35_LEN COMPUTE_30_LEN
-#endif
-
-#if defined(__LIBDEVICE_COMPUTE_50)
-    #define COMPUTE_50_STR compute_50_bc
-    #define COMPUTE_50_LEN compute_50_bc_len
-#else   // Fallback
-    #define COMPUTE_50_STR COMPUTE_30_STR
-    #define COMPUTE_50_LEN COMPUTE_30_LEN
-#endif
-
-    // Source: http://docs.nvidia.com/cuda/libdevice-users-guide/basic-usage.html#version-selection
-    if(compute >= 20 && compute < 30) {
-        *buffer        = COMPUTE_20_STR;
-        *bc_buffer_len = COMPUTE_20_LEN;
-    } else if (compute == 30) {
-        *buffer        = COMPUTE_30_STR;
-        *bc_buffer_len = COMPUTE_30_LEN;
-    } else if (compute >= 31 && compute <  35) {
-        *buffer        = COMPUTE_20_STR;
-        *bc_buffer_len = COMPUTE_20_LEN;
-    } else if (compute >= 35 && compute <= 37) {
-        *buffer        = COMPUTE_35_STR;
-        *bc_buffer_len = COMPUTE_35_LEN;
-    } else if (compute >  37 && compute <  50) {
-        *buffer        = COMPUTE_30_STR;
-        *bc_buffer_len = COMPUTE_30_LEN;
-    } else if (compute >= 50 && compute <= 53) {
-        *buffer        = COMPUTE_50_STR;
-        *bc_buffer_len = COMPUTE_50_LEN;
-    } else if (compute >  53) {
-        *buffer        = COMPUTE_30_STR;
-        *bc_buffer_len = COMPUTE_30_LEN;
-    } else {
-        *buffer        = COMPUTE_30_STR;
-        *bc_buffer_len = COMPUTE_30_LEN;
-    }
-}
-#endif
-
-static unique_ptr<char[]> irToPtx(string IR, size_t *ptx_size)
-{
-    nvvmProgram prog;
-
-    NVVM_CHECK(nvvmCreateProgram(&prog), "Failed to create program");
-
-#if defined(USE_LIBDEVICE)
-    // Get compute version of device
-    cudaDeviceProp devProp = getDeviceProp(getActiveDeviceId());
-    int compute = devProp.major * 10 + devProp.minor;
-    const char *bc_buffer = NULL;
-    size_t bc_buffer_len = 0;
-    compute_to_libdevice_table(&bc_buffer, &bc_buffer_len, compute);
-    if(bc_buffer)
-        NVVM_CHECK(nvvmAddModuleToProgram(prog, bc_buffer, bc_buffer_len, "libdevice kernels"),
-                   "Failed to add libdevice");
-    else
-        NVVM_CHECK(nvvmAddModuleToProgram(prog, IR.c_str(), IR.size(), "generated kernel"),
-                   "Failed to add module");
-#endif
-
-    NVVM_CHECK(nvvmAddModuleToProgram(prog, IR.c_str(), IR.size(), "generated kernel"),
-               "Failed to add module");
-
-    //FIXME: Use proper compute
-    const char *options = NULL;
-    const int noptions = 0;
-
-//#ifdef NDEBUG
-#if 0
-    NVVM_CHECK(nvvmCompileProgram(prog, noptions, &options), "Failed to compile program");
-#else
-    nvvmResult comp_res = nvvmCompileProgram(prog, noptions, &options);
-    if (comp_res != NVVM_SUCCESS) {
-        size_t log_size = 0;
-        nvvmGetProgramLogSize(prog, &log_size);
-        printf("%ld, %zu\n", IR.size(), log_size);
-        unique_ptr<char[]> log(new char[log_size]);
-        nvvmGetProgramLog(prog, log.get());
-        printf("LOG:\n%s\n%s", log.get(), IR.c_str());
-        NVVM_CHECK(comp_res, "Failed to compile program");
-    }
-#endif
-
-    NVVM_CHECK(nvvmGetCompiledResultSize(prog, ptx_size), "Can not get ptx size");
-
-    unique_ptr<char[]> ptx{new char[*ptx_size]};
-    NVVM_CHECK(nvvmGetCompiledResult(prog, ptx.get()), "Can not get ptx from NVVM IR");
-    NVVM_CHECK(nvvmDestroyProgram(&prog), "Failed to destroy program");
-    return ptx;
-}
-
 typedef struct {
     CUmodule prog;
     CUfunction ker;
 } kc_entry_t;
 
-
-const size_t size = 1024;
-char linkInfo[size];
-char linkError[size];
-
-#ifndef NDEBUG
 #define CU_CHECK(fn) do {                       \
         CUresult res = fn;                      \
         if (res == CUDA_SUCCESS) break;         \
         char cu_err_msg[1024];                  \
         snprintf(cu_err_msg,                    \
                  sizeof(cu_err_msg),            \
-                 "CU Error (%d)\n%s\n",         \
-                 (int)(res), linkError);        \
+                 "CU Error (%d)\n",             \
+                 (int)(res));                   \
         AF_ERROR(cu_err_msg,                    \
                  AF_ERR_INTERNAL);              \
     } while(0)
-#else
-#define CU_CHECK(fn) do {                       \
+
+#ifndef NDEBUG
+#define CU_LINK_CHECK(fn) do {                  \
         CUresult res = fn;                      \
         if (res == CUDA_SUCCESS) break;         \
         char cu_err_msg[1024];                  \
         snprintf(cu_err_msg,                    \
                  sizeof(cu_err_msg),            \
-                 "CU Error (%d)\n",             \
-                 (int)(res));                   \
+                 "CU Error (%d)\n%s\n",         \
+                 (int)(res), linkError);        \
         AF_ERROR(cu_err_msg,                    \
                  AF_ERR_INTERNAL);              \
     } while(0)
+#else
+#define CU_LINK_CHECK(fn) CU_CHECK(fn)
+#endif
+
+#ifndef NDEBUG
+#define NVRTC_CHECK(fn) do {                            \
+        nvrtcResult res = fn;                           \
+        if (res == NVRTC_SUCCESS) break;                \
+        size_t logSize;                                 \
+        nvrtcGetProgramLogSize(prog, &logSize);         \
+        unique_ptr<char []> log(new char[logSize +1]);  \
+        char *logptr = log.get();                       \
+        nvrtcGetProgramLog(prog, logptr);               \
+        logptr[logSize] = '\x0';                        \
+        printf("%s\n", logptr);                         \
+        AF_ERROR("NVRTC ERROR",                         \
+                 AF_ERR_INTERNAL);                      \
+    } while(0)
+#else
+#define NVRTC_CHECK(fn) do {                        \
+        nvrtcResult res = fn;                       \
+        if (res == NVRTC_SUCCESS) break;            \
+        char nvrtc_err_msg[1024];                   \
+        snprintf(nvrtc_err_msg,                     \
+                 sizeof(nvrtc_err_msg),             \
+                 "NVRTC Error(%d): %s\n",           \
+                 res, nvrtcGetErrorString(res));    \
+        AF_ERROR(nvrtc_err_msg,                     \
+                 AF_ERR_INTERNAL);                  \
+    } while(0)
 #endif
 
+std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
+{
+    nvrtcProgram prog;
+    size_t ptx_size;
+    std::vector<char> ptx;
+    NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 0, NULL, NULL));
+    NVRTC_CHECK(nvrtcCompileProgram(prog, 0, NULL));
+    NVRTC_CHECK(nvrtcGetPTXSize(prog, &ptx_size));
+    ptx.resize(ptx_size);
+    NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));
+    return ptx;
+}
+
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
     lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
 
-    size_t ptx_size;
-    unique_ptr<char[]> ptx(irToPtx(jit_ker, &ptx_size));
+    const size_t linkLogSize = 1024;
+    char linkInfo[linkLogSize] = {0};
+    char linkError[linkLogSize] = {0};
 
-    CUlinkState linkState;
-
-    linkInfo[0] = 0;
-    linkError[0] = 0;
+    auto ptx = compileToPTX(ker_name, jit_ker);
 
+    CUlinkState linkState;
     CUjit_option linkOptions[] = {
         CU_JIT_INFO_LOG_BUFFER,
         CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
@@ -513,49 +289,25 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 
     void *linkOptionValues[] = {
         linkInfo,
-        reinterpret_cast<void*>(1024),
+        reinterpret_cast<void*>(linkLogSize),
         linkError,
-        reinterpret_cast<void*>(1024),
+        reinterpret_cast<void*>(linkLogSize),
         reinterpret_cast<void*>(1)
     };
 
-    CU_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.get(),
-                           ptx_size, ker_name, 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)arith_ptx,
-                           arith_ptx_len, "arith", 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)cast_ptx,
-                           cast_ptx_len, "cast", 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)exp_ptx,
-                           exp_ptx_len, "exp", 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)hyper_ptx,
-                           hyper_ptx_len, "hyper", 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)logic_ptx,
-                           logic_ptx_len, "logic", 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)numeric_ptx,
-                           numeric_ptx_len, "numeric", 0, NULL, NULL));
-
-    CU_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)trig_ptx,
-                           trig_ptx_len, "trig", 0, NULL, NULL));
+    CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
+    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.data(),
+                                ptx.size(), ker_name, 0, NULL, NULL));
 
     void *cubin;
     size_t cubinSize;
 
     CUmodule module;
     CUfunction kernel;
-
-    CU_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
+    CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
     CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
-    CU_CHECK(cuModuleGetFunction(&kernel, module, ker_name + 1));
-
+    CU_CHECK(cuModuleGetFunction(&kernel, module, ker_name));
     kc_entry_t entry = {module, kernel};
-
     return entry;
 }
 
@@ -659,40 +411,12 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
     }
 
     for (int i = 0; i < num_outputs; i++) {
-        args.push_back(&outputs[i].ptr);
-    }
-
-    // DO NOT PUT THESE IN A SCOPE.
-    // The pointers are used later.
-    // Scoping them results in undefined behavior.
-
-    int strides[] = {(int)outputs[0].strides[0],
-                     (int)outputs[0].strides[1],
-                     (int)outputs[0].strides[2],
-                     (int)outputs[0].strides[3]};
-
-    int dims[] = {(int)outputs[0].dims[0],
-                  (int)outputs[0].dims[1],
-                  (int)outputs[0].dims[2],
-                  (int)outputs[0].dims[3]};
-
-    if (is_linear) {
-        int nelem = 1;
-        for (int i = 0; i < 4; i++) {
-            nelem *= outputs[0].dims[i];
-        }
-        args.push_back((void *)&nelem);
-    } else {
-        for (int i = 0; i < 4; i++) args.push_back((void *)(strides + i));
-        for (int i = 0; i < 4; i++) args.push_back((void *)(dims + i));
+        args.push_back((void *)&outputs[i]);
     }
 
     args.push_back((void *)&blocks_x_);
     args.push_back((void *)&blocks_y_);
-
-    if (!is_linear) {
-        args.push_back((void *)&num_odims);
-    }
+    args.push_back((void *)&num_odims);
 
     lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLaunchKernel(ker,
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
new file mode 100644
index 0000000000..830a9e58c4
--- /dev/null
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -0,0 +1,205 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+typedef float2 cuFloatComplex;
+typedef cuFloatComplex cfloat;
+
+typedef double2 cuDoubleComplex;
+typedef cuDoubleComplex cdouble;
+
+// ----------------------------------------------
+// REAL NUMBER OPERATIONS
+// ----------------------------------------------
+#define sign(in) signbit((in))
+#define __noop(a) (a)
+#define __add(lhs, rhs) (lhs) + (rhs)
+#define __sub(lhs, rhs) (lhs) - (rhs)
+#define __mul(lhs, rhs) (lhs) * (rhs)
+#define __div(lhs, rhs) (lhs) / (rhs)
+#define __and(lhs, rhs) (lhs) && (rhs)
+#define __or(lhs, rhs) (lhs) || (rhs)
+
+#define __lt(lhs, rhs) (lhs) < (rhs)
+#define __gt(lhs, rhs) (lhs) > (rhs)
+#define __le(lhs, rhs) (lhs) <= (rhs)
+#define __ge(lhs, rhs) (lhs) >= (rhs)
+#define __eq(lhs, rhs) (lhs) == (rhs)
+#define __neq(lhs, rhs) (lhs) != (rhs)
+
+#define __conj(in) (in)
+#define __real(in) (in)
+#define __imag(in) (0)
+#define __abs(in) abs(in)
+#define __sigmoid(in) (1.0/(1 + exp(-(in))))
+
+#define __bitor(lhs, rhs) ((lhs) | (rhs))
+#define __bitand(lhs, rhs) ((lhs) & (rhs))
+#define __bitxor(lhs, rhs) ((lhs) ^ (rhs))
+#define __bitshiftl(lhs, rhs) ((lhs) << (rhs))
+#define __bitshiftr(lhs, rhs) ((lhs) >> (rhs))
+
+#define __min(lhs, rhs) ((lhs) < (rhs)) ? (lhs) : (rhs)
+#define __max(lhs, rhs) ((lhs) > (rhs)) ? (lhs) : (rhs)
+#define __rem(lhs, rhs) ((lhs) % (rhs))
+#define __mod(lhs, rhs) ((lhs) % (rhs))
+#define __pow(lhs, rhs) fpow((float)lhs, (float)rhs)
+
+#define __convert_char(val) (char)((val) != 0)
+#define fpow(lhs, rhs) pow((lhs), (rhs))
+#define frem(lhs, rhs) remainder((lhs), (rhs))
+#define iszero(a) ((a) == 0)
+
+// ----------------------------------------------
+// COMPLEX FLOAT OPERATIONS
+// ----------------------------------------------
+
+#define __crealf(in) ((in).x)
+#define __cimagf(in) ((in).y)
+#define __cabsf(in) hypotf(in.x, in.y)
+
+__device__ cfloat __cplx2f(float x, float y)
+{
+    cfloat res = {x, y};
+    return res;
+}
+
+__device__ cfloat __cconjf(cfloat in)
+{
+    cfloat res = {in.x, -in.y};
+    return res;
+}
+
+__device__ cfloat __caddf(cfloat lhs, cfloat rhs)
+{
+    cfloat res = {lhs.x + rhs.x, lhs.y + rhs.y};
+    return res;
+}
+
+__device__ cfloat __csubf(cfloat lhs, cfloat rhs)
+{
+    cfloat res = {lhs.x - rhs.x, lhs.y - rhs.y};
+    return res;
+}
+
+__device__ cfloat __cmulf(cfloat lhs, cfloat rhs)
+{
+    cfloat out;
+    out.x = lhs.x * rhs.x - lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y + lhs.y * rhs.x;
+    return out;
+}
+
+__device__ cfloat __cdivf(cfloat lhs, cfloat rhs)
+{
+    // Normalize by absolute value and multiply
+    float rhs_abs = __cabsf(rhs);
+    float inv_rhs_abs = 1.0f / rhs_abs;
+    float rhs_x = inv_rhs_abs * rhs.x;
+    float rhs_y = inv_rhs_abs * rhs.y;
+    cfloat out = {lhs.x * rhs_x + lhs.y * rhs_y,
+                  lhs.y * rhs_x - lhs.x * rhs_y};
+    out.x *= inv_rhs_abs;
+    out.y *= inv_rhs_abs;
+    return out;
+}
+
+__device__ cfloat __cminf(cfloat lhs, cfloat rhs)
+{
+    return __cabsf(lhs) < __cabsf(rhs) ? lhs : rhs;
+}
+
+__device__ cfloat __cmaxf(cfloat lhs, cfloat rhs)
+{
+    return __cabsf(lhs) > __cabsf(rhs) ? lhs : rhs;
+}
+#define __candf(lhs, rhs) __cabsf(lhs) && __cabsf(rhs)
+#define __corf(lhs, rhs) __cabsf(lhs) || __cabsf(rhs)
+#define __ceqf(lhs, rhs) (((lhs).x == (rhs).x) && ((lhs).y == (rhs).y))
+#define __cneqf(lhs, rhs) !__ceqf((lhs), (rhs))
+#define __cltf(lhs, rhs) (__cabsf(lhs) < __cabsf(rhs))
+#define __clef(lhs, rhs) (__cabsf(lhs) <= __cabsf(rhs))
+#define __cgtf(lhs, rhs) (__cabsf(lhs) > __cabsf(rhs))
+#define __cgef(lhs, rhs) (__cabsf(lhs) >= __cabsf(rhs))
+#define __convert_cfloat(real) __cplx2f(real, 0)
+#define __convert_c2c(in) (in)
+#define __convert_z2c(in) __cplx2f((float)in.x, (float)in.y)
+
+// ----------------------------------------------
+// COMPLEX DOUBLE OPERATIONS
+// ----------------------------------------------
+#define __creal(in) ((in).x)
+#define __cimag(in) ((in).y)
+#define __cabs(in) hypot(in.x, in.y)
+
+__device__ cdouble __cplx2(double x, double y)
+{
+    cdouble res = {x, y};
+    return res;
+}
+
+__device__ cdouble __cconj(cdouble in)
+{
+    cdouble res = {in.x, -in.y};
+    return res;
+}
+
+__device__ cdouble __cadd(cdouble lhs, cdouble rhs)
+{
+    cdouble res = {lhs.x + rhs.x, lhs.y + rhs.y};
+    return res;
+}
+
+__device__ cdouble __csub(cdouble lhs, cdouble rhs)
+{
+    cdouble res = {lhs.x - rhs.x, lhs.y - rhs.y};
+    return res;
+}
+
+__device__ cdouble __cmul(cdouble lhs, cdouble rhs)
+{
+    cdouble out;
+    out.x = lhs.x * rhs.x - lhs.y * rhs.y;
+    out.y = lhs.x * rhs.y + lhs.y * rhs.x;
+    return out;
+}
+
+__device__ cdouble __cdiv(cdouble lhs, cdouble rhs)
+{
+    // Normalize by absolute value and multiply
+    double rhs_abs = __cabs(rhs);
+    double inv_rhs_abs = 1.0 / rhs_abs;
+    double rhs_x = inv_rhs_abs * rhs.x;
+    double rhs_y = inv_rhs_abs * rhs.y;
+    cdouble out = {lhs.x * rhs_x + lhs.y * rhs_y,
+                   lhs.y * rhs_x - lhs.x * rhs_y};
+    out.x *= inv_rhs_abs;
+    out.y *= inv_rhs_abs;
+    return out;
+}
+
+__device__ cdouble __cmin(cdouble lhs, cdouble rhs)
+{
+    return __cabs(lhs) < __cabs(rhs) ? lhs : rhs;
+}
+
+__device__ cdouble __cmax(cdouble lhs, cdouble rhs)
+{
+    return __cabs(lhs) > __cabs(rhs) ? lhs : rhs;
+}
+#define __cand(lhs, rhs) __cabs(lhs) && __cabs(rhs)
+#define __cor(lhs, rhs) __cabs(lhs) || __cabs(rhs)
+#define __ceq(lhs, rhs) (((lhs).x == (rhs).x) && ((lhs).y == (rhs).y))
+#define __cneq(lhs, rhs) !__ceq((lhs), (rhs))
+#define __clt(lhs, rhs) (__cabs(lhs) < __cabs(rhs))
+#define __cle(lhs, rhs) (__cabs(lhs) <= __cabs(rhs))
+#define __cgt(lhs, rhs) (__cabs(lhs) > __cabs(rhs))
+#define __cge(lhs, rhs) (__cabs(lhs) >= __cabs(rhs))
+#define __convert_cdouble(real) __cplx2(real, 0)
+#define __convert_z2z(in) (in)
+#define __convert_c2z(in) __cplx2((double)in.x, (double)in.y)
diff --git a/src/backend/cuda/logic.hpp b/src/backend/cuda/logic.hpp
index 8261a02d73..2c047ba8f8 100644
--- a/src/backend/cuda/logic.hpp
+++ b/src/backend/cuda/logic.hpp
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <Array.hpp>
 #include <optypes.hpp>
-#include <err_cuda.hpp>
 #include <binary.hpp>
+#include <err_cuda.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/scalar.hpp b/src/backend/cuda/scalar.hpp
index b2bd1606cc..46fca748a8 100644
--- a/src/backend/cuda/scalar.hpp
+++ b/src/backend/cuda/scalar.hpp
@@ -18,8 +18,7 @@ namespace cuda
 template<typename T>
 Array<T> createScalarNode(const dim4 &size, const T val)
 {
-    JIT::ScalarNode<T> *node = new JIT::ScalarNode<T>(val);
-    return createNodeArray<T>(size, JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<T>(size, JIT::Node_ptr(new JIT::ScalarNode<T>(val)));
 }
 
 }
diff --git a/src/backend/cuda/types.cpp b/src/backend/cuda/types.cpp
index 8c29c00b45..9d85037ba6 100644
--- a/src/backend/cuda/types.cpp
+++ b/src/backend/cuda/types.cpp
@@ -10,91 +10,38 @@
 #include <af/defines.h>
 #include "types.hpp"
 #include <sstream>
+#include <af/traits.hpp>
 
 namespace cuda
 {
-    template<typename T > const char *cuShortName() { return "q"; }
-    template<> const char *cuShortName<float   >() { return "f"; }
-    template<> const char *cuShortName<double  >() { return "d"; }
-    template<> const char *cuShortName<cfloat  >() { return "6float2"; }
-    template<> const char *cuShortName<cdouble >() { return "7double2"; }
-    template<> const char *cuShortName<int     >() { return "i"; }
-    template<> const char *cuShortName<uint    >() { return "j"; }
-    template<> const char *cuShortName<char    >() { return "c"; }
-    template<> const char *cuShortName<uchar   >() { return "h"; }
-    template<> const char *cuShortName<intl    >() { return "x"; }
-    template<> const char *cuShortName<uintl   >() { return "y"; }
-    template<> const char *cuShortName<short   >() { return "s"; }
-    template<> const char *cuShortName<ushort  >() { return "t"; }
 
-    template<typename T > const char *afShortName(bool caps) { return caps ?  "Q" : "q"; }
-    template<> const char *afShortName<float   >(bool caps) { return caps ?  "S" : "s"; }
-    template<> const char *afShortName<double  >(bool caps) { return caps ?  "D" : "d"; }
-    template<> const char *afShortName<cfloat  >(bool caps) { return caps ?  "C" : "c"; }
-    template<> const char *afShortName<cdouble >(bool caps) { return caps ?  "Z" : "z"; }
-    template<> const char *afShortName<int     >(bool caps) { return caps ?  "I" : "i"; }
-    template<> const char *afShortName<uint    >(bool caps) { return caps ?  "U" : "u"; }
-    template<> const char *afShortName<char    >(bool caps) { return caps ?  "J" : "j"; }
-    template<> const char *afShortName<uchar   >(bool caps) { return caps ?  "V" : "v"; }
-    template<> const char *afShortName<intl    >(bool caps) { return caps ?  "X" : "x"; }
-    template<> const char *afShortName<uintl   >(bool caps) { return caps ?  "Y" : "y"; }
-    template<> const char *afShortName<short   >(bool caps) { return caps ?  "P" : "P"; }
-    template<> const char *afShortName<ushort  >(bool caps) { return caps ?  "Q" : "Q"; }
-
-    template<typename T > const char *irname() { return  "i32"; }
-    template<> const char *irname<float   >() { return  "float"; }
-    template<> const char *irname<double  >() { return  "double"; }
-    template<> const char *irname<cfloat  >() { return  "<2 x float>"; }
-    template<> const char *irname<cdouble >() { return  "<2 x double>"; }
-    template<> const char *irname<int     >() { return  "i32"; }
-    template<> const char *irname<uint    >() { return  "i32"; }
-    template<> const char *irname<intl    >() { return  "i64"; }
-    template<> const char *irname<uintl   >() { return  "i64"; }
-    template<> const char *irname<char    >() { return  "i8"; }
-    template<> const char *irname<uchar   >() { return  "i8"; }
-    template<> const char *irname<short   >() { return  "i16"; }
-    template<> const char *irname<ushort  >() { return  "i16"; }
-
-    template <typename T>
-    static inline std::string toString(T val)
-    {
-        std::stringstream s;
-        s << val;
-        return s.str();
-    }
-
-    template<typename T, bool binary>
-    const std::string cuMangledName(const char *fn)
-    {
-        std::string cname(cuShortName<T>());
-        std::string fname(fn);
-        size_t flen = fname.size();
-
-        std::string res = std::string("@_Z") + toString(flen) + fname + cname;
-        if (binary) {
-            if (cname.size() > 1) {
-                res = res + "S_";
-            } else {
-                res = res + cname;
-            }
-        }
-        return res;
-    }
-
-#define INSTANTIATE(T)                                                  \
-    template const std::string cuMangledName<T, false>(const char *fn); \
-    template const std::string cuMangledName<T, true>(const char *fn);  \
+    template<typename T> const char *shortname(bool caps) { return caps ?  "Q" : "q"; }
+    template<> const char *shortname<float   >(bool caps) { return caps ?  "S" : "s"; }
+    template<> const char *shortname<double  >(bool caps) { return caps ?  "D" : "d"; }
+    template<> const char *shortname<cfloat  >(bool caps) { return caps ?  "C" : "c"; }
+    template<> const char *shortname<cdouble >(bool caps) { return caps ?  "Z" : "z"; }
+    template<> const char *shortname<int     >(bool caps) { return caps ?  "I" : "i"; }
+    template<> const char *shortname<uint    >(bool caps) { return caps ?  "U" : "u"; }
+    template<> const char *shortname<char    >(bool caps) { return caps ?  "J" : "j"; }
+    template<> const char *shortname<uchar   >(bool caps) { return caps ?  "V" : "v"; }
+    template<> const char *shortname<intl    >(bool caps) { return caps ?  "X" : "x"; }
+    template<> const char *shortname<uintl   >(bool caps) { return caps ?  "Y" : "y"; }
+    template<> const char *shortname<short   >(bool caps) { return caps ?  "P" : "P"; }
+    template<> const char *shortname<ushort  >(bool caps) { return caps ?  "Q" : "Q"; }
+
+#define INSTANTIATE(T)                                      \
+    template<> const char *getFullName<T>() { return #T; }  \
 
     INSTANTIATE(float)
     INSTANTIATE(double)
     INSTANTIATE(cfloat)
     INSTANTIATE(cdouble)
     INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+    INSTANTIATE(unsigned char)
     INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    INSTANTIATE(unsigned short)
+    INSTANTIATE(int)
+    INSTANTIATE(unsigned int)
+    INSTANTIATE(unsigned long long)
+    INSTANTIATE(long long)
 }
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 08aab5f374..3376e84bb5 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <cuComplex.h>
 #include <string>
+#include <af/traits.hpp>
 
 namespace cuda
 {
@@ -23,7 +24,6 @@ template<typename T> struct is_complex          { static const bool value = fals
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
 template<> struct           is_complex<cdouble> { static const bool value = true;   };
 
-template<typename T, bool binary> const std::string cuMangledName(const char *fn);
-template<typename T > const char *afShortName(bool caps = true);
-template<typename T > const char *irname();
+template<typename T> const char *shortname(bool caps = true);
+template<typename T> const char *getFullName();
 }
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 291f805193..ed3d81944a 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -10,176 +10,90 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <err_cuda.hpp>
 #include <JIT/UnaryNode.hpp>
 
 namespace cuda
 {
 
+template<af_op_t op>
+static const char *unaryName() { return "__noop"; }
+
+#define UNARY_DECL(OP, FNAME)                   \
+    template<> STATIC_                          \
+    const char *unaryName<af_##OP##_t>()        \
+    {                                           \
+        return FNAME;                           \
+    }                                           \
+
+#define UNARY_FN(OP) UNARY_DECL(OP, #OP)
+
+UNARY_FN(sin)
+UNARY_FN(cos)
+UNARY_FN(tan)
+
+UNARY_FN(asin)
+UNARY_FN(acos)
+UNARY_FN(atan)
+
+UNARY_FN(sinh)
+UNARY_FN(cosh)
+UNARY_FN(tanh)
+
+UNARY_FN(asinh)
+UNARY_FN(acosh)
+UNARY_FN(atanh)
+
+UNARY_FN(exp)
+UNARY_DECL(sigmoid, "__sigmoid")
+UNARY_FN(expm1)
+UNARY_FN(erf)
+UNARY_FN(erfc)
+
+UNARY_FN(tgamma)
+UNARY_FN(lgamma)
+
+UNARY_FN(log)
+UNARY_FN(log1p)
+UNARY_FN(log10)
+UNARY_FN(log2)
+
+UNARY_FN(sqrt)
+UNARY_FN(cbrt)
+
+UNARY_FN(trunc)
+UNARY_FN(round)
+UNARY_FN(sign)
+UNARY_FN(ceil)
+UNARY_FN(floor)
+
+UNARY_FN(isinf)
+UNARY_FN(isnan)
+UNARY_FN(iszero)
+
 template<typename T, af_op_t op>
-struct UnOp
+Array<T> unaryOp(const Array<T> &in)
 {
-    const char *name()
-    {
-        return "noop";
-    }
-};
-
-#define UNARY_FN(fn)                                \
-    template<typename T>                            \
-    struct UnOp<T, af_##fn##_t>                     \
-    {                                               \
-        std::string res;                            \
-        bool is_check;                              \
-        UnOp() :                                    \
-            res(cuMangledName<T, false>("___"#fn)), \
-            is_check(false)                         \
-        {                                           \
-        }                                           \
-        const std::string name()                    \
-        {                                           \
-            return res;                             \
-        }                                           \
-    };                                              \
-
-#define UNARY_FN_NAME(op, fn)                       \
-    template<typename T>                            \
-    struct UnOp<T, af_##op##_t>                     \
-    {                                               \
-        std::string res;                            \
-        bool is_check;                              \
-        UnOp() :                                    \
-            res(cuMangledName<T, false>("___"#fn)), \
-            is_check(false)                         \
-        {                                           \
-        }                                           \
-        const std::string name()                    \
-        {                                           \
-            return res;                             \
-        }                                           \
-    };                                              \
-
-#if defined(USE_LIBDEVICE)
-#define NVVM_SPECIALIZE_TYPE(T, fn, fname)      \
-    template<>                                  \
-    struct UnOp<T, af_##fn##_t>                 \
-    {                                           \
-        std::string res;                        \
-        bool is_check;                          \
-        UnOp() :                                \
-            res("@__nv_"#fname),                \
-            is_check(false)                     \
-        {                                       \
-        }                                       \
-        const std::string name()                \
-        {                                       \
-            return res;                         \
-        }                                       \
-    };                                          \
-
-#define NVVM_SPECIALIZE_CHECK(T, fn, fname)     \
-    template<>                                  \
-    struct UnOp<T, af_##fn##_t>                 \
-    {                                           \
-        std::string res;                        \
-        bool is_check;                          \
-        UnOp() :                                \
-            res("@__nv_"#fname),                \
-            is_check(true)                      \
-        {                                       \
-        }                                       \
-        const std::string name()                \
-        {                                       \
-            return res;                         \
-        }                                       \
-    };                                          \
-
-#else
-#define NVVM_SPECIALIZE_TYPE(T, fn, fname)  // no specialization
-#define NVVM_SPECIALIZE_CHECK(T, fn, fname)  // no specialization
-#endif
-
-#define NVVM_SPECIALIZE_FLOATING_NAME(fn, fname)    \
-    UNARY_FN(fn)                                    \
-    NVVM_SPECIALIZE_TYPE(float, fn, fname##f)       \
-    NVVM_SPECIALIZE_TYPE(double, fn, fname)         \
-
-
-#define NVVM_SPECIALIZE_FLOATING(fn)            \
-    NVVM_SPECIALIZE_FLOATING_NAME(fn, fn)
-
-NVVM_SPECIALIZE_FLOATING(sin)
-NVVM_SPECIALIZE_FLOATING(cos)
-NVVM_SPECIALIZE_FLOATING(tan)
-NVVM_SPECIALIZE_FLOATING(asin)
-NVVM_SPECIALIZE_FLOATING(acos)
-NVVM_SPECIALIZE_FLOATING(atan)
-NVVM_SPECIALIZE_FLOATING(sinh)
-NVVM_SPECIALIZE_FLOATING(cosh)
-NVVM_SPECIALIZE_FLOATING(tanh)
-NVVM_SPECIALIZE_FLOATING(asinh)
-NVVM_SPECIALIZE_FLOATING(acosh)
-NVVM_SPECIALIZE_FLOATING(atanh)
-NVVM_SPECIALIZE_FLOATING(exp)
-NVVM_SPECIALIZE_FLOATING(expm1)
-NVVM_SPECIALIZE_FLOATING(erf)
-NVVM_SPECIALIZE_FLOATING(erfc)
-NVVM_SPECIALIZE_FLOATING(tgamma)
-NVVM_SPECIALIZE_FLOATING(lgamma)
-NVVM_SPECIALIZE_FLOATING(log)
-NVVM_SPECIALIZE_FLOATING(log1p)
-NVVM_SPECIALIZE_FLOATING(log10)
-NVVM_SPECIALIZE_FLOATING(log2)
-NVVM_SPECIALIZE_FLOATING(sqrt)
-NVVM_SPECIALIZE_FLOATING(cbrt)
-NVVM_SPECIALIZE_FLOATING(round)
-NVVM_SPECIALIZE_FLOATING(trunc)
-NVVM_SPECIALIZE_FLOATING(ceil)
-NVVM_SPECIALIZE_FLOATING(floor)
-
-UNARY_FN(sign )
-NVVM_SPECIALIZE_CHECK(float , sign, signbitf)
-NVVM_SPECIALIZE_CHECK(double, sign, signbitd)
-
-UNARY_FN_NAME(isnan, isNaN)
-NVVM_SPECIALIZE_CHECK(float , isnan, isnanf)
-NVVM_SPECIALIZE_CHECK(double, isnan, isnand)
-
-UNARY_FN_NAME(isinf, isINF)
-NVVM_SPECIALIZE_CHECK(float , isinf, isinff)
-NVVM_SPECIALIZE_CHECK(double, isinf, isinfd)
-
-UNARY_FN_NAME(iszero, iszero)
-UNARY_FN(sigmoid)
-
-#undef UNARY_FN
-
-    template<typename T, af_op_t op>
-    Array<T> unaryOp(const Array<T> &in)
-    {
-
-        UnOp<T, op> uop;
-
-        JIT::Node_ptr in_node = in.getNode();
-
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<T>(),
-                                                  afShortName<T>(),
-                                                  uop.name(),
-                                                  in_node, op, uop.is_check);
-
-        return createNodeArray<T>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
-    }
-
-    template<typename T, af_op_t op>
-    Array<char> checkOp(const Array<T> &in)
-    {
-        UnOp<T, op> uop;
-
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(irname<char>(),
-                                                  afShortName<char>(),
-                                                  uop.name(),
-                                                  in_node, op, uop.is_check);
-        return createNodeArray<char>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
-    }
+    JIT::Node_ptr in_node = in.getNode();
+
+    JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<T>(),
+                                              shortname<T>(true),
+                                              unaryName<op>(),
+                                              in_node, op);
+
+    return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
+}
+
+template<typename T, af_op_t op>
+Array<char> checkOp(const Array<T> &in)
+{
+    JIT::Node_ptr in_node = in.getNode();
+
+    JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<char>(),
+                                              shortname<char>(true),
+                                              unaryName<op>(),
+                                              in_node, op);
+
+    return createNodeArray<char>(in.dims(), JIT::Node_ptr(node));
+}
+
 }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 1ab28a8e31..ea061e21e3 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -30,8 +30,8 @@ namespace opencl
     template<typename T>
     Node_ptr bufferNodePtr()
     {
-        return Node_ptr(reinterpret_cast<Node *>(new BufferNode(dtype_traits<T>::getName(),
-                                                                shortname<T>(true))));
+        return Node_ptr(new BufferNode(dtype_traits<T>::getName(),
+                                       shortname<T>(true)));
     }
 
     template<typename T>
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 92a2c32697..c55206b472 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -25,8 +25,8 @@ namespace JIT
         std::shared_ptr<cl::Buffer> m_data;
         KParam m_info;
         unsigned m_bytes;
-        bool m_linear_buffer;
         std::once_flag m_set_data_flag;
+        bool m_linear_buffer;
 
     public:
 
@@ -38,10 +38,6 @@ namespace JIT
 
         bool isBuffer() { return true; }
 
-        ~BufferNode()
-        {
-        }
-
         void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
         {
             std::call_once(m_set_data_flag, [this, info, data, bytes, is_linear]() {
@@ -92,7 +88,7 @@ namespace JIT
         void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
         {
             std::string idx_str = std::string("int idx") + std::to_string(id);
-            std::string info_str = std::string("iInfo") + std::to_string(id);;
+            std::string info_str = std::string("iInfo") + std::to_string(id);
 
             if (!is_linear) {
                 kerStream << idx_str << " = "
@@ -122,7 +118,6 @@ namespace JIT
             len++;
             buf_count++;
             bytes += m_bytes;
-            return;
         }
     };
 
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 11493a5966..9a653a8302 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -183,8 +183,7 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af:
                                                 lhs_node,
                                                 rhs_node, (int)(op));
 
-    return createNodeArray<To>(odims, JIT::Node_ptr(
-                                   reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<To>(odims, JIT::Node_ptr(node));
 }
 
 }
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index bddbd5ad34..3df062053f 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -55,12 +55,10 @@ CAST_FN(double)
         }                                       \
     };
 
-
 CAST_CFN(cfloat)
 CAST_CFN(cdouble)
 CAST_CFN(char)
 
-
 template<>
 struct CastOp<cfloat, cdouble>
 {
@@ -70,7 +68,6 @@ struct CastOp<cfloat, cdouble>
     }
 };
 
-
 template<>
 struct CastOp<cdouble, cfloat>
 {
@@ -89,7 +86,6 @@ struct CastOp<cfloat, cfloat>
     }
 };
 
-
 template<>
 struct CastOp<cdouble, cdouble>
 {
@@ -113,7 +109,7 @@ struct CastWrapper
                                                   shortname<To>(true),
                                                   cop.name(),
                                                   in_node, af_cast_t);
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 };
 
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index 0838370c3c..e72850675d 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -30,7 +30,7 @@ namespace opencl
                                                   "__creal",
                                                   in_node, af_real_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 
     template<typename To, typename Ti>
@@ -42,7 +42,7 @@ namespace opencl
                                                   "__cimag",
                                                   in_node, af_imag_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 
     template<typename T> static const char *abs_name() { return "fabs"; }
@@ -58,7 +58,7 @@ namespace opencl
                                                   abs_name<Ti>(),
                                                   in_node, af_abs_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
     }
 
     template<typename T> static const char *conj_name() { return "__noop"; }
@@ -74,6 +74,6 @@ namespace opencl
                                                   conj_name<T>(),
                                                   in_node, af_conj_t);
 
-        return createNodeArray<T>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
     }
 }
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 5442eba7f4..7f2c1f4644 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -80,46 +80,47 @@ static string getKernelString(const string funcName,
     // Common OpenCL code
     // This part of the code does not change with the kernel.
 
-    static const char *kernelVoid =  "__kernel void\n";
+    static const char *kernelVoid = "__kernel void\n";
     static const char *dimParams = "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
     static const char *blockStart = "{\n\n";
     static const char *blockEnd = "\n\n}";
 
-    static const char *linearIndex = "\n"
-        "uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);\n"
-        "uint threadId = get_local_id(0);\n"
-        "int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;\n"
-        "if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;\n";
-
-    static const char *generalIndex = "\n"
-        "uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;\n"
-        "if (num_odims > 2) {\n"
-        "id2 = get_group_id(0) / groups_0;\n"
-        "id0 = get_group_id(0) - id2 * groups_0;\n"
-        "id0 = get_local_id(0) + id0 * get_local_size(0);\n"
-        "if (num_odims > 3) {\n"
-        "id3 = get_group_id(1) / groups_1;\n"
-        "id1 = get_group_id(1) - id3 * groups_1;\n"
-        "id1 = get_local_id(1) + id1 * get_local_size(1);\n"
-        "} else {\n"
-        "id1 = get_global_id(1);\n"
-        "}\n"
-        " } else {\n"
-        "id3 = 0;\n"
-        "id2 = 0;\n"
-        "id1 = get_global_id(1);\n"
-        "id0 = get_global_id(0);\n"
-        "}\n"
-        "bool cond = \n"
-        "id0 < oInfo.dims[0] && \n"
-        "id1 < oInfo.dims[1] && \n"
-        "id2 < oInfo.dims[2] && \n"
-        "id3 < oInfo.dims[3];\n\n"
-        "if (!cond) return;\n\n"
-        "int idx = "
-        "oInfo.strides[3] * id3 + oInfo.strides[2] * id2 + "
-        "oInfo.strides[1] * id1 + id0 + oInfo.offset;\n\n";
-
+    static const char *linearIndex = R"JIT(
+        uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
+        uint threadId = get_local_id(0);
+        int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;
+        if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;
+        )JIT";
+
+    static const char *generalIndex = R"JIT(
+        uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        if (num_odims > 2) {
+            id2 = get_group_id(0) / groups_0;
+            id0 = get_group_id(0) - id2 * groups_0;
+            id0 = get_local_id(0) + id0 * get_local_size(0);
+            if (num_odims > 3) {
+                id3 = get_group_id(1) / groups_1;
+                id1 = get_group_id(1) - id3 * groups_1;
+                id1 = get_local_id(1) + id1 * get_local_size(1);
+            } else {
+                id1 = get_global_id(1);
+            }
+        } else {
+            id3 = 0;
+            id2 = 0;
+            id1 = get_global_id(1);
+            id0 = get_global_id(0);
+        }
+        bool cond = id0 < oInfo.dims[0] &&
+                    id1 < oInfo.dims[1] &&
+                    id2 < oInfo.dims[2] &&
+                    id3 < oInfo.dims[3];
+        if (!cond) return;
+        int idx = oInfo.strides[3] * id3 +
+                  oInfo.strides[2] * id2 +
+                  oInfo.strides[1] * id1 +
+                  id0 + oInfo.offset;
+        )JIT";
 
     stringstream inParamStream;
     stringstream outParamStream;
@@ -143,7 +144,7 @@ static string getKernelString(const string funcName,
         // Generate output parameters
         outParamStream << "__global " << full_nodes[id]->getTypeStr() << " *out" << id << ", \n";
         // Generate code to write the output
-        outWriteStream << "out" << id << "[idx] = " << "val" << id << ";\n";
+        outWriteStream << "out" << id << "[idx] = val" << id << ";\n";
     }
 
     // Put various blocks into a single stream
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index 3092449418..3e797ac050 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -27,17 +27,14 @@
 #define __real(in) (in)
 #define __imag(in) (0)
 #define __abs(in) abs(in)
-#define __abs2(in) (in) * (in)
 
 #define __crealf(in) ((in).x)
 #define __cimagf(in) ((in).y)
-#define __cabsf2(in) ((in).x * (in).x + (in).y * (in).y)
-#define __cabsf(in) sqrt(__cabsf2(in))
+#define __cabsf(in) hypot((in).x, (in).y)
 
 #define __creal(in) ((in).x)
 #define __cimag(in) ((in).y)
-#define __cabs2(in) ((in).x * (in).x + (in).y * (in).y)
-#define __cabs(in) sqrt(__cabs2(in))
+#define __cabs(in) hypot((in).x, (in).y)
 #define __sigmoid(in) (1.0/(1 + exp(-(in))))
 
 float2 __cconjf(float2 in)
@@ -69,35 +66,37 @@ float2 __cmulf(float2 lhs, float2 rhs)
 // FIXME: overflow / underflow issues
 float2 __cdivf(float2 lhs, float2 rhs)
 {
-    float2 out;
-    float den = (rhs.x * rhs.x + rhs.y * rhs.y);
-    float2 num = __cmulf(lhs, __cconjf(rhs));
-
-    out.x = num.x / den;
-    out.y = num.y / den;
-
+    // Normalize by absolute value and multiply
+    float rhs_abs = __cabsf(rhs);
+    float inv_rhs_abs = 1.0f / rhs_abs;
+    float rhs_x = inv_rhs_abs * rhs.x;
+    float rhs_y = inv_rhs_abs * rhs.y;
+    float2 out = {lhs.x * rhs_x + lhs.y * rhs_y,
+                  lhs.y * rhs_x - lhs.x * rhs_y};
+    out.x *= inv_rhs_abs;
+    out.y *= inv_rhs_abs;
     return out;
 }
 
-#define __candf(lhs, rhs) __cabsf2(lhs) && __cabsf2(rhs)
-#define __cand(lhs, rhs) __cabs2(lhs) && __cabs2(rhs)
+#define __candf(lhs, rhs) __cabsf(lhs) && __cabsf(rhs)
+#define __cand(lhs, rhs) __cabs(lhs) && __cabs(rhs)
 
-#define __corf(lhs, rhs) __cabsf2(lhs) || __cabsf2(rhs)
-#define __cor(lhs, rhs) __cabs2(lhs) || __cabs2(rhs)
+#define __corf(lhs, rhs) __cabsf(lhs) || __cabsf(rhs)
+#define __cor(lhs, rhs) __cabs(lhs) || __cabs(rhs)
 
 #define __ceqf(lhs, rhs) (((lhs).x == (rhs).x) && ((lhs).y == (rhs).y))
 #define __cneqf(lhs, rhs) !__ceqf((lhs), (rhs))
-#define __cltf(lhs, rhs) (__cabsf2(lhs) < __cabsf2(rhs))
-#define __clef(lhs, rhs) (__cabsf2(lhs) <= __cabsf2(rhs))
-#define __cgtf(lhs, rhs) (__cabsf2(lhs) > __cabsf2(rhs))
-#define __cgef(lhs, rhs) (__cabsf2(lhs) >= __cabsf2(rhs))
+#define __cltf(lhs, rhs) (__cabsf(lhs) < __cabsf(rhs))
+#define __clef(lhs, rhs) (__cabsf(lhs) <= __cabsf(rhs))
+#define __cgtf(lhs, rhs) (__cabsf(lhs) > __cabsf(rhs))
+#define __cgef(lhs, rhs) (__cabsf(lhs) >= __cabsf(rhs))
 
 #define __ceq(lhs, rhs) (((lhs).x == (rhs).x) && ((lhs).y == (rhs).y))
 #define __cneq(lhs, rhs) !__ceq((lhs), (rhs))
-#define __clt(lhs, rhs) (__cabs2(lhs) < __cabs2(rhs))
-#define __cle(lhs, rhs) (__cabs2(lhs) <= __cabs2(rhs))
-#define __cgt(lhs, rhs) (__cabs2(lhs) > __cabs2(rhs))
-#define __cge(lhs, rhs) (__cabs2(lhs) >= __cabs2(rhs))
+#define __clt(lhs, rhs) (__cabs(lhs) < __cabs(rhs))
+#define __cle(lhs, rhs) (__cabs(lhs) <= __cabs(rhs))
+#define __cgt(lhs, rhs) (__cabs(lhs) > __cabs(rhs))
+#define __cge(lhs, rhs) (__cabs(lhs) >= __cabs(rhs))
 
 #define __bitor(lhs, rhs) ((lhs) | (rhs))
 #define __bitand(lhs, rhs) ((lhs) & (rhs))
@@ -113,12 +112,12 @@ float2 __cdivf(float2 lhs, float2 rhs)
 
 float2 __cminf(float2 lhs, float2 rhs)
 {
-    return __abs2(lhs) < __abs2(rhs) ? lhs : rhs;
+    return __cabsf(lhs) < __cabsf(rhs) ? lhs : rhs;
 }
 
 float2 __cmaxf(float2 lhs, float2 rhs)
 {
-    return __abs2(lhs) > __abs2(rhs) ? lhs : rhs;
+    return __cabsf(lhs) > __cabsf(rhs) ? lhs : rhs;
 }
 
 float2 __cplx2f(float lhs, float rhs)
@@ -175,23 +174,26 @@ double2 __cmul(double2 lhs, double2 rhs)
 
 double2 __cdiv(double2 lhs, double2 rhs)
 {
-    double2 out;
-    double den = (rhs.x * rhs.x + rhs.y * rhs.y);
-    double2 num = __cmul(lhs, __cconj(rhs));
-
-    out.x = num.x / den;
-    out.y = num.y / den;
+    // Normalize by absolute value and multiply
+    double rhs_abs = __cabs(rhs);
+    double inv_rhs_abs = 1.0 / rhs_abs;
+    double rhs_x = inv_rhs_abs * rhs.x;
+    double rhs_y = inv_rhs_abs * rhs.y;
+    double2 out = {lhs.x * rhs_x + lhs.y * rhs_y,
+                   lhs.y * rhs_x - lhs.x * rhs_y};
+    out.x *= inv_rhs_abs;
+    out.y *= inv_rhs_abs;
     return out;
 }
 
 double2 __cmin(double2 lhs, double2 rhs)
 {
-    return __abs2(lhs) < __abs2(rhs) ? lhs : rhs;
+    return __cabs(lhs) < __cabs(rhs) ? lhs : rhs;
 }
 
 double2 __cmax(double2 lhs, double2 rhs)
 {
-    return __abs2(lhs) > __abs2(rhs) ? lhs : rhs;
+    return __cabs(lhs) > __cabs(rhs) ? lhs : rhs;
 }
 
 double2 __cplx2(double lhs, double rhs)
diff --git a/src/backend/opencl/scalar.hpp b/src/backend/opencl/scalar.hpp
index b6abf47ff9..fbd96b3ecc 100644
--- a/src/backend/opencl/scalar.hpp
+++ b/src/backend/opencl/scalar.hpp
@@ -18,8 +18,7 @@ namespace opencl
 template<typename T>
 Array<T> createScalarNode(const dim4 &size, const T val)
 {
-    JIT::ScalarNode<T> *node = new JIT::ScalarNode<T>(val);
-    return createNodeArray<T>(size, JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<T>(size, JIT::Node_ptr(new JIT::ScalarNode<T>(val)));
 }
 
 }
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index 6581b047db..13744e444b 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -13,7 +13,7 @@
 namespace opencl
 {
 
-    template<typename T > const char *shortname(bool caps) { return caps ? "X" : "x"; }
+    template<typename T> const char *shortname(bool caps) { return caps ? "X" : "x"; }
 
     template<> const char *shortname<float   >(bool caps) { return caps ? "S" : "s"; }
     template<> const char *shortname<double  >(bool caps) { return caps ? "D" : "d"; }
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 4490f2bb36..277ba2c07e 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -33,7 +33,7 @@ template<typename T> struct is_complex          { static const bool value = fals
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
 template<> struct           is_complex<cdouble> { static const bool value = true;   };
 
-template<typename T > const char *shortname(bool caps=false);
+template<typename T> const char *shortname(bool caps=false);
 
 template<typename T>
 struct ToNumStr
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 1e363d7dcb..66f775da5d 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -80,7 +80,7 @@ Array<T> unaryOp(const Array<T> &in)
                                               unaryName<op>(),
                                               in_node, op);
 
-    return createNodeArray<T>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
@@ -93,7 +93,7 @@ Array<char> checkOp(const Array<T> &in)
                                               unaryName<op>(),
                                               in_node, op);
 
-    return createNodeArray<char>(in.dims(), JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<char>(in.dims(), JIT::Node_ptr(node));
 }
 
 }

From 88583319850eb30632bf963b47a26f5896fab0d7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 21 Jun 2017 20:49:28 +0530
Subject: [PATCH 1238/2677] Add FindNVRTC cmake script to locate nvrtc libs
 correctly (#1843)

nvrtc libs are not being found correctly on Ubuntu 16.04. Repurposed
the old FindNVVM.cmake file to locate nvrtc libs.
---
 CMakeModules/FindNVRTC.cmake    | 41 ++++++++++++++++++++++++++++++++
 CMakeModules/FindNVVM.cmake     | 42 ---------------------------------
 src/backend/cuda/CMakeLists.txt | 12 ++--------
 3 files changed, 43 insertions(+), 52 deletions(-)
 create mode 100644 CMakeModules/FindNVRTC.cmake
 delete mode 100644 CMakeModules/FindNVVM.cmake

diff --git a/CMakeModules/FindNVRTC.cmake b/CMakeModules/FindNVRTC.cmake
new file mode 100644
index 0000000000..0ecd7cdee0
--- /dev/null
+++ b/CMakeModules/FindNVRTC.cmake
@@ -0,0 +1,41 @@
+# - Find the NVRTC include directory and libraries
+# Modified version of the file found here:
+# https://raw.githubusercontent.com/nvidia-compiler-sdk/nvvmir-samples/master/CMakeLists.txt
+# CUDA_NVRTC_FOUND
+# CUDA_NVRTC_INCLUDE_DIR
+# CUDA_NVRTC_LIBRARY
+
+# libNVRTC
+IF(NOT DEFINED ENV{CUDA_NVRTC_HOME})
+    # If the toolkit path was changed then refind the library
+    IF(NOT "${CUDA_NVRTC_HOME}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR}/nvrtc")
+        UNSET(CUDA_NVRTC_HOME CACHE)
+        UNSET(CUDA_nvrtc_INCLUDE_DIR CACHE)
+        UNSET(CUDA_nvrtc_LIBRARY CACHE)
+        SET(CUDA_NVRTC_HOME "${CUDA_TOOLKIT_ROOT_DIR}/nvrtc" CACHE INTERNAL "CUDA NVRTC Directory")
+    ENDIF()
+ELSE()
+    SET(CUDA_NVRTC_HOME "$ENV{CUDA_NVRTC_HOME}" CACHE INTERNAL "CUDA NVRTC Directory")
+    MESSAGE(STATUS "Using CUDA_NVRTC_HOME: ${CUDA_NVRTC_HOME}")
+ENDIF()
+
+FIND_LIBRARY(CUDA_nvrtc_LIBRARY
+             NAMES "nvrtc"
+             PATHS ${CUDA_NVRTC_HOME} ${CUDA_TOOLKIT_ROOT_DIR}
+             PATH_SUFFIXES "lib64" "lib" "lib/x64" "lib/Win32"
+             DOC "CUDA NVRTC Library"
+            )
+
+FIND_PATH(CUDA_nvrtc_INCLUDE_DIR
+          NAMES nvrtc.h
+          PATHS ${CUDA_NVRTC_HOME} ${CUDA_TOOLKIT_ROOT_DIR}
+          PATH_SUFFIXES "include"
+          DOC "CUDA NVRTC Include Directory"
+         )
+
+MARK_AS_ADVANCED(
+    CUDA_nvrtc_INCLUDE_DIR
+    CUDA_nvrtc_LIBRARY)
+
+INCLUDE(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(NVRTC DEFAULT_MSG CUDA_nvrtc_INCLUDE_DIR CUDA_nvrtc_LIBRARY)
diff --git a/CMakeModules/FindNVVM.cmake b/CMakeModules/FindNVVM.cmake
deleted file mode 100644
index 2504eae99d..0000000000
--- a/CMakeModules/FindNVVM.cmake
+++ /dev/null
@@ -1,42 +0,0 @@
-# - Find the NVVM include directory and libraries
-# Modified version of the file found here:
-# https://raw.githubusercontent.com/nvidia-compiler-sdk/nvvmir-samples/master/CMakeLists.txt
-# CUDA_NVVM_FOUND
-# CUDA_NVVM_INCLUDE_DIR
-# CUDA_NVVM_LIBRARY
-
-# libNVVM
-IF(NOT DEFINED ENV{CUDA_NVVM_HOME})
-    # If the toolkit path was changed then refind the library
-    IF(NOT "${CUDA_NVVM_HOME}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR}/nvvm")
-        UNSET(CUDA_NVVM_HOME CACHE)
-        UNSET(CUDA_nvvm_INCLUDE_DIR CACHE)
-        UNSET(CUDA_nvvm_LIBRARY CACHE)
-        SET(CUDA_NVVM_HOME "${CUDA_TOOLKIT_ROOT_DIR}/nvvm" CACHE INTERNAL "CUDA NVVM Directory")
-    ENDIF()
-ELSE()
-  SET(CUDA_NVVM_HOME "$ENV{CUDA_NVVM_HOME}" CACHE INTERNAL "CUDA NVVM Directory")
-  MESSAGE(STATUS "Using CUDA_NVVM_HOME: ${CUDA_NVVM_HOME}")
-ENDIF()
-
-FIND_LIBRARY(CUDA_nvvm_LIBRARY
-             NAMES "nvvm"
-             PATHS ${CUDA_NVVM_HOME}
-             PATH_SUFFIXES "lib64" "lib" "lib/x64" "lib/Win32"
-             DOC "CUDA NVVM Library"
-            )
-
-FIND_PATH(CUDA_nvvm_INCLUDE_DIR
-          NAMES nvvm.h
-          PATHS ${CUDA_NVVM_HOME}
-          PATH_SUFFIXES "include"
-          DOC "CUDA NVVM Include Directory"
-         )
-
-MARK_AS_ADVANCED(
-    CUDA_nvvm_INCLUDE_DIR
-    CUDA_nvvm_LIBRARY)
-
-INCLUDE(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(NVVM DEFAULT_MSG
-    CUDA_nvvm_INCLUDE_DIR CUDA_nvvm_LIBRARY)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e48dda4446..efd9a834b3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -3,6 +3,7 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 FIND_PACKAGE(CUDA 7.0 REQUIRED)
 
 INCLUDE(CLKernelToH)
+INCLUDE(FindNVRTC)
 
 MARK_AS_ADVANCED(
     CUDA_BUILD_CUBIN
@@ -146,7 +147,7 @@ INCLUDE_DIRECTORIES(
     ${CUDA_INCLUDE_DIRS}
     "${PROJECT_SOURCE_DIR}/src/backend/cuda"
     "${CMAKE_CURRENT_BINARY_DIR}"
-    ${CUDA_nvvm_INCLUDE_DIR}
+    ${CUDA_nvrtc_INCLUDE_DIR}
     )
 
 FILE(GLOB cuda_headers
@@ -350,15 +351,6 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${scan_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE} ${CUDA_ADD_LIBRARY_OPTIONS})
 
-FIND_LIBRARY (
-  CUDA_nvrtc_LIBRARY
-  NAMES "nvrtc"
-  PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-  PATH_SUFFIXES "lib64" "lib/x64" "lib"
-  DOC "CUDA NVRTC Library"
-  NO_DEFAULT_PATH
-  )
-
 TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
                                 PRIVATE ${CUDA_LIBRARIES}
                                 PRIVATE ${FreeImage_LIBS}

From 8b01a86d5a709288f09de9e778973c4fe4d88ac6 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Mon, 22 May 2017 14:13:50 -0400
Subject: [PATCH 1239/2677] Release notes for v3.5.0

---
 docs/pages/release_notes.md | 132 ++++++++++++++++++++++++++++++++++++
 1 file changed, 132 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 85c187da09..3b37bb0e71 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,138 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.5.0
+==============
+
+Major Updates
+-------------
+
+* ArrayFire now supports threaded applications.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1706)</sup>
+* Added Canny edge detector.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1743)</sup>
+* Added Sparse-Dense arithmetic operations.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1696)</sup>
+
+Features
+--------
+
+* ArrayFire Threading
+  * \ref af::array can be read by multiple threads
+  * All ArrayFire functions can be executed concurrently by multiple threads
+  * Threads can operate on different devices to simplify Muli-device workloads
+* New Canny edge detector function, \ref af::canny().
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1743)</sup>
+  * Can automatically calculate high threshold with `AF_CANNY_THRESHOLD_AUTO_OTSU`
+  * Supports both L1 and L2 Norms to calculate gradients
+* New tuned OpenCL BLAS backend,
+  [CLBlast](https://github.com/arrayfire/arrayfire/pull/1727).
+
+Improvements
+------------
+
+* Converted CUDA JIT to use
+  [NVRTC](http://docs.nvidia.com/cuda/nvrtc/index.html) instead of
+  [NVVM](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html).
+* Performance improvements in \ref af::reorder().
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1766)</sup>
+* Performance improvements in \ref array::scalar<T>().
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1809)</sup>
+* Improved unified backend performance.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1770)</sup>
+* ArrayFire now depends on Forge
+  v1.0. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1800)</sup>
+* Can now specify the FFT plan cache size using the
+  \ref af::setFFTPlanCacheSize() function.
+* Get the number of physical bytes allocated by the memory manager
+  \ref `af_get_allocated_bytes()`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
+* \ref af::dot() can now return a scalar value to the
+  host. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1628)</sup>
+
+Bug Fixes
+---------
+
+* Fixed improper release of default Mersenne random
+  engine. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1716)</sup>
+* Fixed \ref af::randu() and \ref af::randn() ranges for floating point
+  types. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1784)</sup>
+* Fixed assignment bug in CPU
+  backend. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1765)</sup>
+* Fixed complex (`c32`,`c64`) multiplication in OpenCL convolution
+  kernels. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1816)</sup>
+* Fixed inconsistent behavior with \ref af::replace() and \ref
+  replace_scalar(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
+* Fixed memory leak in \ref
+  af_fir(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1765)</sup>
+* Fixed memory leaks in \ref af_cast for sparse arrays.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1826)</sup>
+* Fixing correctness of \ref af_pow for complex numbers by using Cartesian
+  form. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1765)</sup>
+* Corrected \ref af::select() with indexing in CUDA and OpenCL
+  backends. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1731)</sup>
+* Workaround for VS2015 compiler ternary
+  bug. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1771)</sup>
+* Fixed memory corruption in
+  `cuda::findPlan()`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1793)</sup>
+* Argument checks in \ref af_create_sparse_array avoids inputs of type
+  int64. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1747)</sup>
+
+Build fixes
+-----------
+
+* On OSX, utilize new GLFW package from the brew package
+  manager. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1720)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1775)</sup>
+* Fixed CUDA PTX names generated by CMake
+  v3.7. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1689)</sup>
+* Support `gcc` > 5.x for
+  CUDA. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1708)</sup>
+
+Examples
+--------
+
+* New genetic algorithm example.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1695)</sup>
+
+Documentation
+-------------
+
+* Updated `README.md` to improve readability and
+  formatting. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1726)</sup>
+* Updated `README.md` to mention Julia and Nim
+  wrappers. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1714)</sup>
+* Improved installation instructions -
+  `docs/pages/install.md`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1740)</sup>
+
+Miscellaneous
+-------------
+
+* A few improvements for ROCm
+  support. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1710)</sup>
+* Removed CUDA 6.5 support.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1687)</sup>
+
+Known issues
+------------
+
+* Windows
+  * The Windows NVIDIA driver version `37x.xx` contains a bug which causes
+    `fftconvolve_opencl` to fail. Upgrade or downgrade to a different version of
+    the driver to avoid this failure.
+  * The following tests fail on Windows with NVIDIA hardware:
+    `threading_cuda`,`qr_dense_opencl`, `solve_dense_opencl`.
+* macOS
+  * The Accelerate framework, used by the CPU backend on macOS, leverages Intel
+    graphics cards (Iris) when there are no discrete GPUs available. This OpenCL
+    implementation is known to give incorrect results on the following tests:
+    `lu_dense_{cpu,opencl}`, `solve_dense_{cpu,opencl}`,
+    `inverse_dense_{cpu,opencl}`.
+  * Certain tests intermittently fail on macOS with NVIDIA GPUs apparently due
+    to inconsistent driver behavior: `fft_large_cuda` and `svd_dense_cuda`.
+  * The following tests are currently failing on macOS with AMD GPUs:
+    `cholesky_dense_opencl` and `scan_by_key_opencl`.
+
+
 v3.4.2
 ==============
 

From a4854b5756bd33becfb02aff02cb042a66dcd9ec Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Wed, 21 Jun 2017 15:35:48 -0700
Subject: [PATCH 1240/2677] Quick Fix for issues arising with step size in
 sequences > 1

- Added relevant tests
- Updated release notes
---
 docs/pages/release_notes.md  |  1 +
 src/backend/cpu/Array.cpp    | 10 ++++++++--
 src/backend/cuda/Array.cpp   | 10 ++++++++--
 src/backend/opencl/Array.cpp |  9 ++++++++-
 test/index.cpp               | 19 +++++++++++++++++++
 5 files changed, 44 insertions(+), 5 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 3b37bb0e71..91a72b9cba 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -76,6 +76,7 @@ Bug Fixes
   `cuda::findPlan()`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1793)</sup>
 * Argument checks in \ref af_create_sparse_array avoids inputs of type
   int64. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1747)</sup>
+* Fixed issue with indexing an array with a step size != 1. <sup>[1](https://github.com/arrayfire/arrayfire/issues/1846)</sup>
 
 Build fixes
 -----------
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 781521bf41..80e9084273 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -242,14 +242,20 @@ Array<T> createSubArray(const Array<T>& parent,
     parent.eval();
 
     dim4 dDims = parent.getDataDims();
-    dim4 pDims = parent.dims();
+    dim4 dStrides = calcStrides(dDims);
+    dim4 parent_strides = parent.strides();
+
+    if (dStrides != parent_strides) {
+        const Array<T> parentCopy = copyArray(parent);
+        return createSubArray(parentCopy, index, copy);
+    }
 
+    dim4 pDims = parent.dims();
     dim4 dims    = toDims  (index, pDims);
     dim4 strides = toStride (index, dDims);
 
     // Find total offsets after indexing
     dim4 offsets = toOffset(index, pDims);
-    dim4 parent_strides = parent.strides();
     dim_t offset = parent.getOffset();
     for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index cec91d5c04..18539cef26 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -296,14 +296,20 @@ namespace cuda
         parent.eval();
 
         dim4 dDims = parent.getDataDims();
-        dim4 pDims = parent.dims();
+        dim4 dStrides = calcStrides(dDims);
+        dim4 parent_strides = parent.strides();
+
+        if (dStrides != parent_strides) {
+            const Array<T> parentCopy = copyArray(parent);
+            return createSubArray(parentCopy, index, copy);
+        }
 
+        dim4 pDims = parent.dims();
         dim4 dims    = toDims  (index, pDims);
         dim4 strides = toStride (index, dDims);
 
         // Find total offsets after indexing
         dim4 offsets = toOffset(index, pDims);
-        dim4 parent_strides = parent.strides();
         dim_t offset = parent.getOffset();
         for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index ea061e21e3..516695bca6 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -268,6 +268,14 @@ namespace opencl
         parent.eval();
 
         dim4 dDims = parent.getDataDims();
+        dim4 dStrides = calcStrides(dDims);
+        dim4 parent_strides = parent.strides();
+
+        if (dStrides != parent_strides) {
+            const Array<T> parentCopy = copyArray(parent);
+            return createSubArray(parentCopy, index, copy);
+        }
+
         dim4 pDims = parent.dims();
 
         dim4 dims    = toDims  (index, pDims);
@@ -275,7 +283,6 @@ namespace opencl
 
         // Find total offsets after indexing
         dim4 offsets = toOffset(index, pDims);
-        dim4 parent_strides = parent.strides();
         dim_t offset = parent.getOffset();
         for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
 
diff --git a/test/index.cpp b/test/index.cpp
index 3ab2037d6d..90c1780feb 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1536,3 +1536,22 @@ TEST(Index, ISSUE_1101_MODDIMS)
         ASSERT_EQ(ha[i + st], hc[i]);
     }
 }
+
+TEST(Index, ISSUE_1846_Index_Step_Cascade)
+{
+    using namespace af;
+    array a = randu(3, 12);
+    array b = a(span, seq(0, af::end, 2));
+    array c = b(span, seq(0, af::end, 3));
+    array d = a(span, seq(0, af::end, 6));
+    EXPECT_EQ(allTrue<bool>(c == d), true);
+}
+
+TEST(Index, ISSUE_1845_Index_Step_reorder)
+{
+    using namespace af;
+    array a = randu(1,8,1);
+    array b = reorder(a,0,2,1);
+    array d = reorder(b(0,0,span),2,1,0);
+    EXPECT_EQ(allTrue<bool>(a.T() == d), true);
+}

From daa477f743efb10c9a63483483bb9be7262fac00 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Thu, 22 Jun 2017 17:07:03 -0400
Subject: [PATCH 1241/2677] Fixed issue with `build_forge.cmake`

Differentiate between forge version and tag due to issues with OSX installer
scripts.
---
 CMakeModules/build_forge.cmake | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index cc6c880633..b13220223f 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -46,13 +46,14 @@ ELSE()
     ENDIF(WIN32)
 ENDIF()
 
-SET(FORGE_VERSION 1.0.2-ft)
+SET(FORGE_VERSION 1.0.2)
+SET(FORGE_TAG ${FORGE_VERSION}-ft)
 
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG v${FORGE_VERSION}
+    GIT_TAG v${FORGE_TAG}
     ${byproducts}
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"

From a0dd4f927559044ae6d0ea49fc5ff5dfa66e5d81 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Fri, 23 Jun 2017 12:54:09 -0400
Subject: [PATCH 1242/2677] Updated ACKNOWLEDGEMENTS.md and README.md.

Included NIH as sponsor.
---
 ACKNOWLEDGEMENTS.md | 6 ++++++
 README.md           | 6 ------
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/ACKNOWLEDGEMENTS.md b/ACKNOWLEDGEMENTS.md
index 283909c6b2..0574ee5fa3 100644
--- a/ACKNOWLEDGEMENTS.md
+++ b/ACKNOWLEDGEMENTS.md
@@ -20,3 +20,9 @@ under Contract Numbers W31P4Q-14-C-0012 and W31P4Q-15-C-0008.
 Any opinions, findings and conclusions or recommendations expressed in this
 material are those of the author(s) and do not necessarily reflect the views of
 the DARPA SBIR Program Office.
+
+Research reported in this publication is supported by the National Library of
+Medicine of the National Institutes of Health under award number
+R43LM012359. The content is solely the responsibility of the author(s) and
+does not necessarily represent the official views of the National Institutes
+of Health.
diff --git a/README.md b/README.md
index 8f02810f7a..c25966f6cb 100644
--- a/README.md
+++ b/README.md
@@ -153,12 +153,6 @@ ArrayFire development is funded by ArrayFire LLC and several third parties,
 please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for further
 details.
 
-We would like to thank the [JuliaComputing](https://github.com/JuliaComputing)
-guys as well as [Gabor Mezo](https://github.com/unbornchikken) for their
-diligent work on the [Julia](https://github.com/JuliaComputing/ArrayFire.jl)
-and [NodeJS](https://github.com/arrayfire/arrayfire-js) wrappers,
-respectively.
-
 ## Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)

From 4a605715c70f7417ab3900abea406b9878d6ae9b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sat, 24 Jun 2017 21:19:08 -0700
Subject: [PATCH 1243/2677] Updating ArrayFire version to 3.6.0

---
 CMakeModules/Version.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 832b5d2901..79b0b186a9 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -9,7 +9,7 @@ IF("${CMAKE_VERSION}" VERSION_GREATER "3.1" OR "${CMAKE_VERSION}" VERSION_EQUAL
 ENDIF()
 
 SET(AF_VERSION_MAJOR "3")
-SET(AF_VERSION_MINOR "5")
+SET(AF_VERSION_MINOR "6")
 SET(AF_VERSION_PATCH "0")
 
 SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")

From d407f03f348dfc811c978523678ab5ac5ddd2589 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Wed, 19 Jul 2017 14:31:00 -0700
Subject: [PATCH 1244/2677] Change Array<T> variables to Param<T> on the queue
 for CPU backend (#1871)

* Change Array<T> variables to Param<T> on the queue for CPU backend

This change allows us to efficiently reuse memory
allocated on the same queue for cpu backend
---
 src/backend/cpu/Array.cpp                    |  13 +-
 src/backend/cpu/Array.hpp                    |  26 +++-
 src/backend/cpu/Param.hpp                    | 140 +++++++++++++++++
 src/backend/cpu/assign.cpp                   |   5 +-
 src/backend/cpu/blas.cpp                     |   4 +-
 src/backend/cpu/cholesky.cpp                 |   7 +-
 src/backend/cpu/convolve.cpp                 |   3 +-
 src/backend/cpu/copy.cpp                     |   1 +
 src/backend/cpu/fast.cpp                     |   4 +-
 src/backend/cpu/fft.cpp                      |   9 +-
 src/backend/cpu/fftconvolve.cpp              |   4 +-
 src/backend/cpu/harris.cpp                   |   8 +-
 src/backend/cpu/index.cpp                    |   5 +-
 src/backend/cpu/inverse.cpp                  |   6 +-
 src/backend/cpu/ireduce.cpp                  |   4 +-
 src/backend/cpu/join.cpp                     |  21 +--
 src/backend/cpu/kernel/Array.hpp             |  25 ++-
 src/backend/cpu/kernel/approx.hpp            |  10 +-
 src/backend/cpu/kernel/assign.hpp            |   8 +-
 src/backend/cpu/kernel/bilateral.hpp         |   4 +-
 src/backend/cpu/kernel/canny.hpp             |  12 +-
 src/backend/cpu/kernel/convolve.hpp          |  24 ++-
 src/backend/cpu/kernel/copy.hpp              |  10 +-
 src/backend/cpu/kernel/diagonal.hpp          |  24 +--
 src/backend/cpu/kernel/diff.hpp              |   6 +-
 src/backend/cpu/kernel/dot.hpp               |   6 +-
 src/backend/cpu/kernel/exampleFunction.hpp   |  12 +-
 src/backend/cpu/kernel/fast.hpp              |  12 +-
 src/backend/cpu/kernel/fft.hpp               |  20 +--
 src/backend/cpu/kernel/fftconvolve.hpp       |  14 +-
 src/backend/cpu/kernel/gradient.hpp          |   4 +-
 src/backend/cpu/kernel/harris.hpp            |  20 +--
 src/backend/cpu/kernel/histogram.hpp         |   4 +-
 src/backend/cpu/kernel/hsv_rgb.hpp           |   8 +-
 src/backend/cpu/kernel/identity.hpp          |   4 +-
 src/backend/cpu/kernel/iir.hpp               |  26 ++--
 src/backend/cpu/kernel/index.hpp             |   7 +-
 src/backend/cpu/kernel/interp.hpp            |  26 ++--
 src/backend/cpu/kernel/iota.hpp              |   4 +-
 src/backend/cpu/kernel/ireduce.hpp           |  14 +-
 src/backend/cpu/kernel/join.hpp              |  23 ++-
 src/backend/cpu/kernel/lookup.hpp            |   6 +-
 src/backend/cpu/kernel/lu.hpp                |   8 +-
 src/backend/cpu/kernel/match_template.hpp    |   6 +-
 src/backend/cpu/kernel/meanshift.hpp         |   4 +-
 src/backend/cpu/kernel/medfilt.hpp           |   6 +-
 src/backend/cpu/kernel/moments.hpp           |   4 +-
 src/backend/cpu/kernel/morph.hpp             |   6 +-
 src/backend/cpu/kernel/nearest_neighbour.hpp |   6 +-
 src/backend/cpu/kernel/orb.hpp               |  10 +-
 src/backend/cpu/kernel/random_engine.hpp     |   2 +-
 src/backend/cpu/kernel/range.hpp             |   4 +-
 src/backend/cpu/kernel/reduce.hpp            |  10 +-
 src/backend/cpu/kernel/regions.hpp           |   4 +-
 src/backend/cpu/kernel/reorder.hpp           |   4 +-
 src/backend/cpu/kernel/resize.hpp            |   4 +-
 src/backend/cpu/kernel/rotate.hpp            |   4 +-
 src/backend/cpu/kernel/scan.hpp              |  10 +-
 src/backend/cpu/kernel/scan_by_key.hpp       |  14 +-
 src/backend/cpu/kernel/select.hpp            |   6 +-
 src/backend/cpu/kernel/shift.hpp             |   4 +-
 src/backend/cpu/kernel/sobel.hpp             |   4 +-
 src/backend/cpu/kernel/sort.hpp              |  18 +--
 src/backend/cpu/kernel/sort_by_key.hpp       |   8 +-
 src/backend/cpu/kernel/sort_by_key_impl.hpp  |  66 ++++----
 src/backend/cpu/kernel/sort_helper.hpp       |   1 +
 src/backend/cpu/kernel/sparse.hpp            | 147 +++++++++---------
 src/backend/cpu/kernel/sparse_arith.hpp      |  29 ++--
 src/backend/cpu/kernel/susan.hpp             |   8 +-
 src/backend/cpu/kernel/tile.hpp              |   4 +-
 src/backend/cpu/kernel/transform.hpp         |   6 +-
 src/backend/cpu/kernel/transpose.hpp         |  10 +-
 src/backend/cpu/kernel/triangle.hpp          |   4 +-
 src/backend/cpu/kernel/unwrap.hpp            |   4 +-
 src/backend/cpu/kernel/wrap.hpp              |   4 +-
 src/backend/cpu/lu.cpp                       |   4 +-
 src/backend/cpu/memory.cpp                   |  19 +--
 src/backend/cpu/platform.hpp                 |  12 +-
 src/backend/cpu/qr.cpp                       |   8 +-
 src/backend/cpu/queue.hpp                    |   5 +-
 src/backend/cpu/reduce.cpp                   |   4 +-
 src/backend/cpu/solve.cpp                    |  26 ++--
 src/backend/cpu/sparse.cpp                   |  78 +++++-----
 src/backend/cpu/sparse_blas.cpp              | 152 ++++++++++---------
 src/backend/cpu/svd.cpp                      |  10 +-
 85 files changed, 748 insertions(+), 592 deletions(-)
 create mode 100644 src/backend/cpu/Param.hpp

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 80e9084273..fd0d55eace 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -53,6 +53,8 @@ Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_de
     static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
     static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
     if (!is_device || copy_device) {
+        // Ensure the memory being written to isnt used anywhere else.
+        getQueue().sync();
         std::copy(in_data, in_data + dims.elements(), data.get());
     }
 }
@@ -84,6 +86,8 @@ Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
     owner(true)
 {
     if (!is_device) {
+        // Ensure the memory being written to isnt used anywhere else.
+        getQueue().sync();
         std::copy(in_data, in_data + info.total(), data.get());
     }
 }
@@ -98,7 +102,7 @@ void Array<T>::eval()
 
     data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
 
-    getQueue().enqueue(kernel::evalArray<T>, *this);
+    getQueue().enqueue(kernel::evalArray<T>, *this, this->node);
     // Reset shared_ptr
     this->node = bufferNodePtr<T>();
     ready = true;
@@ -125,6 +129,7 @@ template<typename T>
 void evalMultiple(std::vector<Array<T>*> array_ptrs)
 {
     std::vector<Array<T>> arrays;
+    std::vector<TNJ::Node_ptr> nodes;
     bool isWorker = getQueue().is_worker();
     for (auto &array : array_ptrs) {
         if (array->ready) continue;
@@ -132,10 +137,12 @@ void evalMultiple(std::vector<Array<T>*> array_ptrs)
         array->setId(getActiveDeviceId());
         array->data = std::shared_ptr<T>(memAlloc<T>(array->elements()), memFree<T>);
         arrays.push_back(*array);
+        nodes.push_back(array->node);
     }
 
+    std::vector<Param<T>> params(arrays.begin(), arrays.end());
     if (arrays.size() > 0) {
-        getQueue().enqueue(kernel::evalMultiple<T>, arrays);
+        getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
         for (auto &array : array_ptrs) {
             if (array->ready) continue;
             array->ready = true;
@@ -289,6 +296,8 @@ writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
         arr = copyArray<T>(arr);
     }
     arr.eval();
+    // Ensure the memory being written to isnt used anywhere else.
+    getQueue().sync();
     memcpy(arr.get(), data, bytes);
 }
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index c3a1286708..569f970d15 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -15,6 +15,7 @@
 #include <types.hpp>
 #include <traits.hpp>
 #include <TNJ/Node.hpp>
+#include <Param.hpp>
 #include <memory.hpp>
 #include <memory>
 #include <algorithm>
@@ -29,10 +30,10 @@ template<typename T> class Array;
 // kernel::evalArray fn forward declaration
 namespace kernel
 {
-template<typename T> void evalArray(cpu::Array<T> in);
+    template<typename T> void evalArray(Param<T> in, TNJ::Node_ptr node);
 
-template<typename T>
-void evalMultiple(std::vector<Array<T>> arrays);
+    template<typename T>
+    void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> nodes);
 
 }
 }
@@ -211,16 +212,26 @@ namespace cpu
 
         const T* get(bool withOffset = true) const
         {
-            if (!isReady()) eval();
+            if (!data.get()) eval();
             return data.get() + (withOffset ? getOffset() : 0);
         }
 
         int useCount() const
         {
-            if (!isReady()) eval();
+            if (!data.get()) eval();
             return data.use_count();
         }
 
+        operator Param<T>()
+        {
+            return Param<T>(this->get(), this->dims(), this->strides());
+        }
+
+        operator CParam<T>() const
+        {
+            return CParam<T>(this->get(), this->dims(), this->strides());
+        }
+
         TNJ::Node_ptr getNode() const;
 
         friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
@@ -237,8 +248,9 @@ namespace cpu
                                           const std::vector<af_seq> &index,
                                           bool copy);
 
-        friend void kernel::evalArray<T>(Array<T> in);
-        friend void kernel::evalMultiple<T>(std::vector<Array<T>> arrays);
+        friend void kernel::evalArray<T>(Param<T> in, TNJ::Node_ptr node);
+        friend void kernel::evalMultiple<T>(std::vector<Param<T>> arrays,
+                                            std::vector<TNJ::Node_ptr> nodes);
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
new file mode 100644
index 0000000000..11b3a843f9
--- /dev/null
+++ b/src/backend/cpu/Param.hpp
@@ -0,0 +1,140 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <backend.hpp>
+
+namespace cpu
+{
+
+using af::dim4;
+
+template<typename T>
+class CParam
+{
+private:
+    const T *m_ptr;
+    dim4 m_dims;
+    dim4 m_strides;
+
+public:
+    CParam(const T *iptr, const dim4 &idims, const dim4 &istrides) :
+        m_ptr(iptr)
+    {
+        for (int i = 0; i < 4; i++) {
+            m_dims[i] = idims[i];
+            m_strides[i] = istrides[i];
+        }
+    }
+
+    const T *get() const
+    {
+        return m_ptr;
+    }
+
+    dim4 dims() const
+    {
+        return m_dims;
+    }
+
+    dim4 strides() const
+    {
+        return m_strides;
+    }
+
+    int dims(int i) const
+    {
+        return m_dims[i];
+    }
+
+    int strides(int i) const
+    {
+        return m_strides[i];
+    }
+};
+
+template<typename T>
+class Param
+{
+private:
+    T *m_ptr;
+    dim4 m_dims;
+    dim4 m_strides;
+
+public:
+    Param() : m_ptr(nullptr)
+    {
+    }
+
+    Param(T *iptr, const dim4 &idims, const dim4 &istrides) :
+        m_ptr(iptr)
+    {
+        for (int i = 0; i < 4; i++) {
+            m_dims[i] = idims[i];
+            m_strides[i] = istrides[i];
+        }
+    }
+
+    T *get()
+    {
+        return m_ptr;
+    }
+
+    operator CParam<T>() const
+    {
+        return CParam<T>(const_cast<T *>(m_ptr), m_dims, m_strides);
+    }
+
+    dim4 dims() const
+    {
+        return m_dims;
+    }
+
+    dim4 strides() const
+    {
+        return m_strides;
+    }
+
+    int dims(int i) const
+    {
+        return m_dims[i];
+    }
+
+    int strides(int i) const
+    {
+        return m_strides[i];
+    }
+};
+
+template<typename T> class Array;
+
+// These functions are needed to convert Array<T> to Param<T> when queueing up functions.
+// This is necessary because the memory used by Array<T> can be put back into the queue faster.
+// This is fine becacuse we only have 1 compute queue. This ensures there's no race conditions.
+template<typename T>
+T toParam(const T &val)
+{
+    return val;
+}
+
+template<typename T>
+Param<T> toParam(Array<T> &val)
+{
+    return (Param<T>)(val);
+}
+
+template<typename T>
+CParam<T> toParam(const Array<T> &val)
+{
+    return (CParam<T>)(val);
+}
+
+}
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index d3a44e19df..627b5973ce 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -46,8 +46,9 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
         }
     }
 
-    getQueue().enqueue(kernel::assign<T>, out, rhs, std::move(isSeq),
-            std::move(seqs), std::move(idxArrs));
+    vector<CParam<uint>> idxParams(idxArrs.begin(), idxArrs.end());
+    getQueue().enqueue(kernel::assign<T>, out, out.getDataDims(), rhs, std::move(isSeq),
+            std::move(seqs), std::move(idxParams));
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 3b14b3752b..50525a9bb1 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -166,7 +166,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     using CBT = const typename blas_base<T>::type;
 
     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
-    auto func = [=] (Array<T> output, const Array<T> left, const Array<T> right) {
+    auto func = [=] (Param<T> output, CParam<T> left, CParam<T> right) {
         auto alpha = getScale<T, 1>();
         auto beta  = getScale<T, 0>();
 
@@ -190,7 +190,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 reinterpret_cast<CBT*>(left.get()), lStrides[1],
                 reinterpret_cast<CBT*>(right.get()), rStrides[1],
                 beta,
-                reinterpret_cast<BT*>(output.get()), output.dims()[0]);
+                reinterpret_cast<BT*>(output.get()), output.dims(0));
         }
     };
     getQueue().enqueue(func, out, lhs, rhs);
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 5e393f0082..777fa46b04 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -71,11 +71,12 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
         uplo = 'U';
 
     int info = 0;
-    auto func = [&] (int& info, Array<T>& in) {
-        info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(), in.strides()[1]);
+    auto func = [&] (int *info, Param<T> in) {
+        *info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(), in.strides(1));
     };
 
-    getQueue().enqueue(func, info, in);
+    getQueue().enqueue(func, &info, in);
+    // Ensure the value of info has been written into info.
     getQueue().sync();
 
     return info;
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 7bc77e1a29..b4ce8643a8 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -78,8 +78,9 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
     }
 
     Array<T> out  = createEmptyArray<T>(oDims);
+    Array<T> temp = createEmptyArray<T>(tDims);
 
-    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, tDims);
+    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, temp);
 
     return out;
 }
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index daa445acda..8ca26b3e12 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -28,6 +28,7 @@ template<typename T>
 void copyData(T *to, const Array<T> &from)
 {
     from.eval();
+    // Ensure all operations on 'from' are complete before copying data to host.
     getQueue().sync();
     if(from.isLinear()) {
         // FIXME: Check for errors / exceptions
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 3b760ae7d4..a65cfc85a0 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -70,8 +70,8 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
 
         count = 0;
         kernel::non_maximal(V, x, y,
-                    x_total, y_total, score_total,
-                    &count, feat_found, edge);
+                            x_total, y_total, score_total,
+                            &count, feat_found, edge);
 
         feat_found = std::min(max_feat, count);
     } else {
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 0c94280bd8..d09ed44409 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -29,7 +29,7 @@ template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in)
 {
     in.eval();
-    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in);
+    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in, in.getDataDims());
 }
 
 template<typename Tc, typename Tr, int rank>
@@ -41,7 +41,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     odims[0] = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, in);
+    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, out.getDataDims(), in, in.getDataDims());
 
     return out;
 }
@@ -52,7 +52,10 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     in.eval();
 
     Array<Tr> out = createEmptyArray<Tr>(odims);
-    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, in, odims);
+    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>,
+                       out, out.getDataDims(),
+                       in, in.getDataDims(),
+                       odims);
 
     return out;
 }
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index bd86a53062..0902147d96 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -92,7 +92,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     for (int i=0; i<baseDim; ++i)
         fftDims[i] = fft_dims[i];
 
-    auto upstream_dft = [=] (Array<convT> packed, const dim4 fftDims) {
+    auto upstream_dft = [=] (Param<convT> packed, const dim4 fftDims) {
         int fft_dims[baseDim];
         for (int i=0; i<baseDim; ++i)
             fft_dims[i] = fftDims[i];
@@ -143,7 +143,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                        filter_tmp_dims, filter_tmp_strides,
                        kind, offset);
 
-    auto upstream_idft = [=] (Array<convT> packed, const dim4 fftDims) {
+    auto upstream_idft = [=] (Param<convT> packed, const dim4 fftDims) {
         int fft_dims[baseDim];
         for (int i=0; i<baseDim; ++i)
             fft_dims[i] = fftDims[i];
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 8c74a3981d..0c87007de2 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -50,7 +50,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     Array<T> iy = createEmptyArray<T>(idims);
 
     // Compute first order derivatives
-    getQueue().enqueue(gradient<T>, iy, ix, in);
+    gradient<T>(iy, ix, in);
 
     Array<T> ixx = createEmptyArray<T>(idims);
     Array<T> ixy = createEmptyArray<T>(idims);
@@ -108,9 +108,9 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         y_out = createEmptyArray<float>(dim4(corners_out));
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
-        auto copyFunc = [=](Array<float> x_out, Array<float> y_out,
-                            Array<float> outResponses, const Array<float> x_crnrs,
-                            const Array<float> y_crnrs, const Array<float> inResponses,
+        auto copyFunc = [=](Param<float> x_out, Param<float> y_out,
+                            Param<float> outResponses, CParam<float> x_crnrs,
+                            CParam<float> y_crnrs, CParam<float> inResponses,
                             const unsigned corners_out) {
             memcpy(x_out.get(), x_crnrs.get(), corners_out * sizeof(float));
             memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index f70e961299..320f562b71 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -54,9 +54,10 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
+    std::vector<CParam<uint>> idxParams(idxArrs.begin(), idxArrs.end());
 
-
-    getQueue().enqueue(kernel::index<T>, out, in, std::move(isSeq), std::move(seqs), std::move(idxArrs));
+    getQueue().enqueue(kernel::index<T>, out, in, in.getDataDims(),
+                       std::move(isSeq), std::move(seqs), std::move(idxParams));
 
     return out;
 }
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index ea7d7ee828..bff2a47399 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -63,10 +63,10 @@ Array<T> inverse(const Array<T> &in)
     Array<T> A = copyArray<T>(in);
     Array<int> pivot = lu_inplace<T>(A, false);
 
-    auto func = [=] (Array<T> A, Array<int> pivot, int M) {
+    auto func = [=] (Param<T> A, Param<int> pivot, int M) {
         getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
-                A.get(), A.strides()[1],
-                pivot.get());
+                        A.get(), A.strides(1),
+                        pivot.get());
     };
     getQueue().enqueue(func, A, pivot, M);
 
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 58259a382f..9cab39c502 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -21,8 +21,8 @@ namespace cpu
 {
 
 template<af_op_t op, typename T>
-using ireduce_dim_func = std::function<void(Array<T>, Array<uint>, const dim_t,
-                                            const Array<T>, const dim_t, const int)>;
+using ireduce_dim_func = std::function<void(Param<T>, Param<uint>, const dim_t,
+                                            CParam<T>, const dim_t, const int)>;
 
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 0a5b99cd13..a7af895b06 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -68,38 +68,39 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
         }
     }
 
+    std::vector<CParam<T>> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
     switch(n_arrays) {
         case 1:
-            getQueue().enqueue(kernel::join<T, 1>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 1>, dim, out, inputParams);
             break;
         case 2:
-            getQueue().enqueue(kernel::join<T, 2>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 2>, dim, out, inputParams);
             break;
         case 3:
-            getQueue().enqueue(kernel::join<T, 3>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 3>, dim, out, inputParams);
             break;
         case 4:
-            getQueue().enqueue(kernel::join<T, 4>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 4>, dim, out, inputParams);
             break;
         case 5:
-            getQueue().enqueue(kernel::join<T, 5>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 5>, dim, out, inputParams);
             break;
         case 6:
-            getQueue().enqueue(kernel::join<T, 6>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 6>, dim, out, inputParams);
             break;
         case 7:
-            getQueue().enqueue(kernel::join<T, 7>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 7>, dim, out, inputParams);
             break;
         case 8:
-            getQueue().enqueue(kernel::join<T, 8>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 8>, dim, out, inputParams);
             break;
         case 9:
-            getQueue().enqueue(kernel::join<T, 9>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T, 9>, dim, out, inputParams);
             break;
         case 10:
-            getQueue().enqueue(kernel::join<T,10>, dim, out, inputs);
+            getQueue().enqueue(kernel::join<T,10>, dim, out, inputParams);
             break;
     }
 
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 7b7504064c..8f363a97f6 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <platform.hpp>
 #include <TNJ/Node.hpp>
 #include <vector>
@@ -19,21 +19,19 @@ namespace kernel
 {
 
 template<typename T>
-void evalMultiple(std::vector<Array<T>> arrays)
+void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> output_nodes_)
 {
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
-    int devId = cpu::getActiveDeviceId();
     TNJ::Node_map_t nodes;
     std::vector<T *> ptrs;
     std::vector<TNJ::TNode<T> *> output_nodes;
 
-    for (auto &arr : arrays) {
-        arr.setId(devId);
-        ptrs.push_back(arr.data.get());
-        output_nodes.push_back(reinterpret_cast<TNJ::TNode<T> *>(arr.node.get()));
-        arr.node->getNodesMap(nodes);
+    for (int i = 0; i < (int)arrays.size(); i++) {
+        ptrs.push_back(arrays[i].get());
+        output_nodes.push_back(reinterpret_cast<TNJ::TNode<T> *>(output_nodes_[i].get()));
+        output_nodes_[i]->getNodesMap(nodes);
     }
 
     bool is_linear = true;
@@ -44,7 +42,7 @@ void evalMultiple(std::vector<Array<T>> arrays)
     }
 
     if (is_linear) {
-        int num = arrays[0].elements();
+        int num = arrays[0].dims().elements();
         for (int i = 0; i < num; i++) {
             for (int n = 0; n < (int)full_nodes.size(); n++) {
                 full_nodes[n]->calc(i);
@@ -80,16 +78,15 @@ void evalMultiple(std::vector<Array<T>> arrays)
 }
 
 template<typename T>
-void evalArray(Array<T> arr)
+void evalArray(Param<T> arr, TNJ::Node_ptr node)
 {
-    arr.setId(cpu::getActiveDeviceId());
-    T *ptr = arr.data.get();
+    T *ptr = arr.get();
 
     af::dim4 odims = arr.dims();
     af::dim4 ostrs = arr.strides();
 
     TNJ::Node_map_t nodes;
-    arr.node->getNodesMap(nodes);
+    node->getNodesMap(nodes);
 
     bool is_linear = true;
     std::vector<TNJ::Node *> full_nodes(nodes.size());
@@ -101,7 +98,7 @@ void evalArray(Array<T> arr)
 
     TNJ::TNode<T> *output_node = reinterpret_cast<TNJ::TNode<T> *>(full_nodes.back());
     if (is_linear) {
-        int num = arr.elements();
+        int num = arr.dims().elements();
         for (int i = 0; i < num; i++) {
             for (int n = 0; n < (int)full_nodes.size(); n++) {
                 full_nodes[n]->calc(i);
diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index 6977b0dbc7..c8d1137476 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 #include "interp.hpp"
 
@@ -18,8 +18,8 @@ namespace kernel
 {
 
 template<typename InT, typename LocT, int order>
-void approx1(Array<InT> output, const Array<InT> input,
-             const Array<LocT> xposition, const float offGrid, af_interp_type method)
+void approx1(Param<InT> output, CParam<InT> input,
+             CParam<LocT> xposition, const float offGrid, af_interp_type method)
 {
     InT * out = output.get();
     const LocT *xpos = xposition.get();
@@ -68,8 +68,8 @@ void approx1(Array<InT> output, const Array<InT> input,
 }
 
 template<typename InT, typename LocT, int order>
-void approx2(Array<InT> output, const Array<InT> input,
-             const Array<LocT> xposition, const Array<LocT> yposition,
+void approx2(Param<InT> output, CParam<InT> input,
+             CParam<LocT> xposition, CParam<LocT> yposition,
              float const offGrid, af_interp_type method)
 {
     InT * out = output.get();
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 470979fb2f..faa46b399b 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <vector>
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -18,10 +18,10 @@ namespace kernel
 {
 
 template<typename T>
-void assign(Array<T> out, Array<T> const rhs, std::vector<bool> const isSeq,
-            std::vector<af_seq> const seqs, std::vector< Array<uint> > const idxArrs)
+void assign(Param<T> out, af::dim4 dDims,
+            CParam<T> rhs, std::vector<bool> const isSeq,
+            std::vector<af_seq> const seqs, std::vector< CParam<uint> > idxArrs)
 {
-    af::dim4 dDims = out.getDataDims();
     af::dim4 pDims = out.dims();
     // retrieve dimensions & strides for array to which rhs is being copied to
     af::dim4 dst_offsets = toOffset(seqs, dDims);
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index 9fa0902625..e45d45c7dd 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 #include <cmath>
 
@@ -18,7 +18,7 @@ namespace kernel
 {
 
 template<typename OutT, typename InT, bool IsColor>
-void bilateral(Array<OutT> out, Array<InT> const in, float const s_sigma, float const c_sigma)
+void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma, float const c_sigma)
 {
     af::dim4 const dims     = in.dims();
     af::dim4 const istrides = in.strides();
diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
index cc34005f30..4fa156c502 100644
--- a/src/backend/cpu/kernel/canny.hpp
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <cassert>
 #include <list>
 #include <array>
@@ -18,16 +18,16 @@ namespace cpu
 namespace kernel
 {
 template<typename T>
-void nonMaxSuppression(Array<T> output, const Array<T> magnitude,
-                       const Array<T> dxArray, const Array<T> dyArray)
+void nonMaxSuppression(Param<T> output, CParam<T> magnitude,
+                       CParam<T> dxParam, CParam<T> dyParam)
 {
     const af::dim4 dims    = magnitude.dims();
     const af::dim4 strides = magnitude.strides();
 
           T* out = output.get();
     const T* mag = magnitude.get();
-    const T* dX  = dxArray.get();
-    const T* dY  = dyArray.get();
+    const T* dX  = dxParam.get();
+    const T* dY  = dyParam.get();
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
         for(dim_t b2=0; b2<dims[2]; ++b2) {
@@ -159,7 +159,7 @@ void traceEdge(T* out, const T* strong, const T* weak, int t, int width)
 
 
 template<typename T>
-void edgeTrackingHysteresis(Array<T> out, const Array<T> strong, const Array<T> weak)
+void edgeTrackingHysteresis(Param<T> out, CParam<T> strong, CParam<T> weak)
 {
     const af::dim4 dims = strong.dims();
 
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 4855f94b4f..2bb8f945d4 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -122,7 +122,7 @@ void one2one_3d(InT *optr, InT const * const iptr, AccT const * const fptr, af::
 }
 
 template<typename InT, typename AccT, dim_t baseDim, bool Expand>
-void convolve_nd(Array<InT> out, Array<InT> const signal, Array<AccT> const filter, AF_BATCH_KIND kind)
+void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter, AF_BATCH_KIND kind)
 {
     InT * optr = out.get();
     InT const * const iptr = signal.get();
@@ -223,14 +223,12 @@ void convolve2_separable(InT *optr, InT const * const iptr, AccT const * const f
 }
 
 template<typename InT, typename AccT, bool Expand>
-void convolve2(Array<InT> out, Array<InT> const signal,
-               Array<AccT> const c_filter, Array<AccT> const r_filter,
-               af::dim4 const tDims)
+void convolve2(Param<InT> out, CParam<InT> signal,
+               CParam<AccT> c_filter, CParam<AccT> r_filter,
+               Param<InT> temp)
 {
-    Array<InT> temp = createEmptyArray<InT>(tDims);
-
-    dim_t cflen = (dim_t)c_filter.elements();
-    dim_t rflen = (dim_t)r_filter.elements();
+    dim_t cflen = (dim_t)c_filter.dims().elements();
+    dim_t rflen = (dim_t)r_filter.dims().elements();
 
     auto oDims = out.dims();
     auto sDims = signal.dims();
@@ -252,12 +250,12 @@ void convolve2(Array<InT> out, Array<InT> const signal,
             InT *optr = out.get()  + b2*oStrides[2] + o_b3Off;
 
             convolve2_separable<InT, AccT, 0, Expand>(tptr, iptr, c_filter.get(),
-                    tDims, sDims, sDims, cflen,
-                    tStrides, sStrides, c_filter.strides()[0]);
+                                                      temp.dims(), sDims, sDims, cflen,
+                                                      tStrides, sStrides, c_filter.strides(0));
 
             convolve2_separable<InT, AccT, 1, Expand>(optr, tptr, r_filter.get(),
-                    oDims, tDims, sDims, rflen,
-                    oStrides, tStrides, r_filter.strides()[0]);
+                                                      oDims, temp.dims(), sDims, rflen,
+                                                      oStrides, tStrides, r_filter.strides(0));
         }
     }
 }
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index a4bce91421..da5fa561d8 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 
 namespace cpu
@@ -39,7 +39,7 @@ void stridedCopy(T* dst, af::dim4 const & ostrides, T const * src,
 }
 
 template<typename OutT, typename InT>
-void copyElemwise(Array<OutT> dst, Array<InT> const src, OutT default_value, double factor)
+void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value, double factor)
 {
     af::dim4 src_dims       = src.dims();
     af::dim4 dst_dims       = dst.dims();
@@ -89,7 +89,7 @@ void copyElemwise(Array<OutT> dst, Array<InT> const src, OutT default_value, dou
 template<typename OutT, typename InT>
 struct CopyImpl
 {
-    static void copy(Array<OutT> dst, Array<InT> const src)
+    static void copy(Param<OutT> dst, CParam<InT> src)
     {
         copyElemwise(dst, src, scalar<OutT>(0), 1.0);
     }
@@ -98,7 +98,7 @@ struct CopyImpl
 template<typename T>
 struct CopyImpl<T, T>
 {
-    static void copy(Array<T> dst, Array<T> const src)
+    static void copy(Param<T> dst, CParam<T> src)
     {
         af::dim4 src_dims       = src.dims();
         af::dim4 dst_dims       = dst.dims();
@@ -153,7 +153,7 @@ struct CopyImpl<T, T>
 };
 
 template<typename OutT, typename InT>
-void copy(Array<OutT> dst, Array<InT> const src)
+void copy(Param<OutT> dst, CParam<InT> src)
 {
     CopyImpl<OutT, InT>::copy(dst, src);
 }
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
index f887f7fc9a..6a16562d69 100644
--- a/src/backend/cpu/kernel/diagonal.hpp
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,10 +16,10 @@ namespace kernel
 {
 
 template<typename T>
-void diagCreate(Array<T> out, Array<T> const in, int const num)
+void diagCreate(Param<T> out, CParam<T> in, int const num)
 {
-    int batch = in.dims()[1];
-    int size  = out.dims()[0];
+    int batch = in.dims(1);
+    int size  = out.dims(0);
 
     T const * iptr = in.get();
     T * optr = out.get();
@@ -31,31 +31,31 @@ void diagCreate(Array<T> out, Array<T> const in, int const num)
                 if (i == j - num) {
                     val = (num > 0) ? iptr[i] : iptr[j];
                 }
-                optr[i + j * out.strides()[1]] = val;
+                optr[i + j * out.strides(1)] = val;
             }
         }
-        optr += out.strides()[2];
-        iptr += in.strides()[1];
+        optr += out.strides(2);
+        iptr += in.strides(1);
     }
 }
 
 template<typename T>
-void diagExtract(Array<T> out, Array<T> const in, int const num)
+void diagExtract(Param<T> out, CParam<T> in, int const num)
 {
     dim4 const odims = out.dims();
     dim4 const idims = in.dims();
 
-    int const i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
+    int const i_off = (num > 0) ? (num * in.strides(1)) : (-num);
 
     for (int l = 0; l < (int)odims[3]; l++) {
 
         for (int k = 0; k < (int)odims[2]; k++) {
-            const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
-            T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
+            const T *iptr = in.get() + l * in.strides(3) + k * in.strides(2) + i_off;
+            T *optr = out.get() + l * out.strides(3) + k * out.strides(2);
 
             for (int i = 0; i < (int)odims[0]; i++) {
                 T val = scalar<T>(0);
-                if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides()[1] + i];
+                if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides(1) + i];
                 optr[i] = val;
             }
         }
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
index 937748316d..db86532230 100644
--- a/src/backend/cpu/kernel/diff.hpp
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -17,7 +17,7 @@ namespace kernel
 {
 
 template<typename T>
-void diff1(Array<T> out, Array<T> const in, int const dim)
+void diff1(Param<T> out, CParam<T> in, int const dim)
 {
     af::dim4 dims = out.dims();
     // Bool for dimension
@@ -48,7 +48,7 @@ void diff1(Array<T> out, Array<T> const in, int const dim)
 }
 
 template<typename T>
-void diff2(Array<T> out, Array<T> const in, int const dim)
+void diff2(Param<T> out, CParam<T> in, int const dim)
 {
     af::dim4 dims = out.dims();
     // Bool for dimension
diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
index 6b31d8d07f..2d9a85be7e 100644
--- a/src/backend/cpu/kernel/dot.hpp
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <complex>
 
 namespace cpu
@@ -23,10 +23,10 @@ template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
 template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
 
 template<typename T, bool conjugate, bool both_conjugate>
-void dot(Array<T> output, const Array<T> lhs, const Array<T> rhs,
+void dot(Param<T> output, CParam<T> lhs, CParam<T> rhs,
          af_mat_prop optLhs, af_mat_prop optRhs)
 {
-    int N = lhs.dims()[0];
+    int N = lhs.dims(0);
 
     T out = 0;
     const T *pL = lhs.get();
diff --git a/src/backend/cpu/kernel/exampleFunction.hpp b/src/backend/cpu/kernel/exampleFunction.hpp
index 6122579b27..6bf58cef4b 100644
--- a/src/backend/cpu/kernel/exampleFunction.hpp
+++ b/src/backend/cpu/kernel/exampleFunction.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -17,7 +17,7 @@ namespace kernel
 {
 
 template<typename T>
-void exampleFunction(Array<T> out, Array<T> const a, Array<T> const b, const af_someenum_t method)
+void exampleFunction(Param<T> out, CParam<T> a, CParam<T> b, const af_someenum_t method)
 {
     dim4 oDims    = out.dims();
 
@@ -25,10 +25,10 @@ void exampleFunction(Array<T> out, Array<T> const a, Array<T> const b, const af_
     dim4 bStrides = b.strides();
     dim4 oStrides = out.strides();
 
-    const T* src1 = a.get();            // cpu::Array<T>::get returns the pointer to the
-                                        // memory allocated for that Array (with proper offsets)
-    const T* src2 = b.get();            // cpu::Array<T>::get returns the pointer to the
-                                        // memory allocated for that Array (with proper offsets)
+    const T* src1 = a.get();            // cpu::Param<T>::get returns the pointer to the
+                                        // memory allocated for that Param (with proper offsets)
+    const T* src2 = b.get();            // cpu::Param<T>::get returns the pointer to the
+                                        // memory allocated for that Param (with proper offsets)
     T* dst = out.get();
 
     // Implement your algorithm and write results to dst
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index 7054ddb8db..7f552cfca6 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -81,9 +81,9 @@ inline double abs_diff(double x, double y)
 }
 
 template<typename T>
-void locate_features(Array<T> const & in, Array<float> & score,
-                     Array<float> & x_out, Array<float> & y_out,
-                     Array<float> & score_out, unsigned* count, float const thr,
+void locate_features(CParam<T> in, Param<float> score,
+                     Param<float> x_out, Param<float> y_out,
+                     Param<float> score_out, unsigned* count, float const thr,
                      unsigned const arc_length, unsigned const nonmax,
                      unsigned const max_feat, unsigned const edge)
 {
@@ -174,8 +174,8 @@ void locate_features(Array<T> const & in, Array<float> & score,
     }
 }
 
-void non_maximal(Array<float> const & score, const Array<float> & x_in, const Array<float> & y_in,
-                 Array<float> & x_out, Array<float> & y_out, Array<float> & score_out,
+void non_maximal(CParam<float> score, CParam<float> x_in, CParam<float> y_in,
+                 Param<float> x_out, Param<float> y_out, Param<float> score_out,
                  unsigned* count, unsigned const total_feat, unsigned const edge)
 {
     float const * score_ptr = score.get();
diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
index 906c8ef5f5..42bb3c3db4 100644
--- a/src/backend/cpu/kernel/fft.hpp
+++ b/src/backend/cpu/kernel/fft.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <fftw3.h>
 
 namespace cpu
@@ -70,7 +70,7 @@ TRANSFORM_REAL(fftw , double, cdouble, c2r)
 
 
 template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> in)
+void fft_inplace(Param<T> in, const af::dim4 iDataDims)
 {
     int t_dims[rank];
     int in_embed[rank];
@@ -78,7 +78,7 @@ void fft_inplace(Array<T> in)
     const af::dim4 idims = in.dims();
 
     computeDims<rank>(t_dims  , idims);
-    computeDims<rank>(in_embed , in.getDataDims());
+    computeDims<rank>(in_embed , iDataDims);
 
     const af::dim4 istrides = in.strides();
 
@@ -109,7 +109,7 @@ void fft_inplace(Array<T> in)
 }
 
 template<typename Tc, typename Tr, int rank>
-void fft_r2c(Array<Tc> out, const Array<Tr> in)
+void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in, const af::dim4 iDataDims)
 {
     af::dim4 idims = in.dims();
 
@@ -118,8 +118,8 @@ void fft_r2c(Array<Tc> out, const Array<Tr> in)
     int out_embed[rank];
 
     computeDims<rank>(t_dims  , idims);
-    computeDims<rank>(in_embed , in.getDataDims());
-    computeDims<rank>(out_embed , out.getDataDims());
+    computeDims<rank>(in_embed , iDataDims);
+    computeDims<rank>(out_embed , oDataDims);
 
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -150,15 +150,17 @@ void fft_r2c(Array<Tc> out, const Array<Tr> in)
 }
 
 template<typename Tr, typename Tc, int rank>
-void fft_c2r(Array<Tr> out, const Array<Tc> in, const af::dim4 odims)
+void fft_c2r(Param<Tr> out, const af::dim4 oDataDims,
+             CParam<Tc> in, const af::dim4 iDataDims,
+             const af::dim4 odims)
 {
     int t_dims[rank];
     int in_embed[rank];
     int out_embed[rank];
 
     computeDims<rank>(t_dims  , odims);
-    computeDims<rank>(in_embed , in.getDataDims());
-    computeDims<rank>(out_embed , out.getDataDims());
+    computeDims<rank>(in_embed , iDataDims);
+    computeDims<rank>(out_embed , oDataDims);
 
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index f0600a9abc..5825f25ca3 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename To, typename Ti>
-void packData(Array<To> out, const af::dim4 od, const af::dim4 os, Array<Ti> const in)
+void packData(Param<To> out, const af::dim4 od, const af::dim4 os, CParam<Ti> in)
 {
     To* out_ptr = out.get();
 
@@ -53,8 +53,8 @@ void packData(Array<To> out, const af::dim4 od, const af::dim4 os, Array<Ti> con
 }
 
 template<typename To, typename Ti>
-void padArray(Array<To> out, const af::dim4 od, const af::dim4 os,
-              Array<Ti> const in, const dim_t offset)
+void padArray(Param<To> out, const af::dim4 od, const af::dim4 os,
+              CParam<Ti> in, const dim_t offset)
 {
     To* out_ptr = out.get() + offset;
     const af::dim4 id = in.dims();
@@ -85,7 +85,7 @@ void padArray(Array<To> out, const af::dim4 od, const af::dim4 os,
 }
 
 template<typename T>
-void complexMultiply(Array<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
+void complexMultiply(Param<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
                      const af::dim4 fit_dims, const af::dim4 fit_strides,
                      AF_BATCH_KIND kind, const dim_t offset)
 {
@@ -213,8 +213,8 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
 }
 
 template<typename T, typename convT, bool roundOut, int baseDim>
-void reorder(Array<T> out, Array<convT> packed,
-             const Array<T> filter, const dim_t sig_half_d0, const dim_t fftScale,
+void reorder(Param<T> out, Param<convT> packed,
+             CParam<T> filter, const dim_t sig_half_d0, const dim_t fftScale,
              const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
              const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
              bool expand, AF_BATCH_KIND kind)
diff --git a/src/backend/cpu/kernel/gradient.hpp b/src/backend/cpu/kernel/gradient.hpp
index 178d581c65..33deb9d125 100644
--- a/src/backend/cpu/kernel/gradient.hpp
+++ b/src/backend/cpu/kernel/gradient.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void gradient(Array<T> grad0, Array<T> grad1, Array<T> const in)
+void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in)
 {
     const af::dim4 dims = in.dims();
 
diff --git a/src/backend/cpu/kernel/harris.hpp b/src/backend/cpu/kernel/harris.hpp
index 00df1a608e..8e871d0713 100644
--- a/src/backend/cpu/kernel/harris.hpp
+++ b/src/backend/cpu/kernel/harris.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -17,8 +17,8 @@ namespace kernel
 {
 
 template<typename T>
-void second_order_deriv(Array<T> ixx, Array<T> ixy, Array<T> iyy,
-                        const unsigned in_len, const Array<T> ix, const Array<T> iy)
+void second_order_deriv(Param<T> ixx, Param<T> ixy, Param<T> iyy,
+                        const unsigned in_len, CParam<T> ix, CParam<T> iy)
 {
     T* ixx_out     = ixx.get();
     T* ixy_out     = ixy.get();
@@ -33,8 +33,8 @@ void second_order_deriv(Array<T> ixx, Array<T> ixy, Array<T> iyy,
 }
 
 template<typename T>
-void harris_responses(Array<T> resp, const unsigned idim0, const unsigned idim1,
-                      const Array<T> ixx, const Array<T> ixy, const Array<T> iyy,
+void harris_responses(Param<T> resp, const unsigned idim0, const unsigned idim1,
+                      CParam<T> ixx, CParam<T> ixy, CParam<T> iyy,
                       const float k_thr, const unsigned border_len)
 {
     T* resp_out      = resp.get();
@@ -58,8 +58,8 @@ void harris_responses(Array<T> resp, const unsigned idim0, const unsigned idim1,
 }
 
 template<typename T>
-void non_maximal(Array<float> xOut, Array<float> yOut, Array<float> respOut, unsigned* count,
-                 const unsigned idim0, const unsigned idim1, const Array<T> respIn,
+void non_maximal(Param<float> xOut, Param<float> yOut, Param<float> respOut, unsigned* count,
+                 const unsigned idim0, const unsigned idim1, CParam<T> respIn,
                  const float min_resp, const unsigned border_len, const unsigned max_corners)
 {
     float* x_out = xOut.get();
@@ -98,9 +98,9 @@ void non_maximal(Array<float> xOut, Array<float> yOut, Array<float> respOut, uns
     }
 }
 
-static void keep_corners(Array<float> xOut, Array<float> yOut, Array<float> respOut,
-                         const Array<float> xIn, const Array<float> yIn,
-                         const Array<float> respIn, const Array<unsigned> respIdx,
+static void keep_corners(Param<float> xOut, Param<float> yOut, Param<float> respOut,
+                         CParam<float> xIn, CParam<float> yIn,
+                         CParam<float> respIn, CParam<unsigned> respIdx,
                          const unsigned n_corners)
 {
     float* x_out = xOut.get();
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 668ada6458..639130dc45 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename OutT, typename InT, bool IsLinear>
-void histogram(Array<OutT> out, Array<InT> const in,
+void histogram(Param<OutT> out, CParam<InT> in,
                unsigned const nbins, double const minval, double const maxval)
 {
     dim4 const outDims   = out.dims();
diff --git a/src/backend/cpu/kernel/hsv_rgb.hpp b/src/backend/cpu/kernel/hsv_rgb.hpp
index b2fbf8ac7d..828b4deda4 100644
--- a/src/backend/cpu/kernel/hsv_rgb.hpp
+++ b/src/backend/cpu/kernel/hsv_rgb.hpp
@@ -8,7 +8,7 @@
 ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <cmath>
 
 namespace cpu
@@ -17,11 +17,11 @@ namespace kernel
 {
 
 template<typename T>
-void hsv2rgb(Array<T> out, Array<T> const in)
+void hsv2rgb(Param<T> out, CParam<T> in)
 {
     const af::dim4 dims    = in.dims();
     const af::dim4 strides = in.strides();
-    dim_t obStride  = out.strides()[3];
+    dim_t obStride  = out.strides(3);
     dim_t coff      = strides[2];
     dim_t bCount    = dims[3];
 
@@ -69,7 +69,7 @@ void hsv2rgb(Array<T> out, Array<T> const in)
 }
 
 template<typename T>
-void rgb2hsv(Array<T> out, Array<T> const in)
+void rgb2hsv(Param<T> out, CParam<T> in)
 {
     const af::dim4 dims    = in.dims();
     const af::dim4 strides = in.strides();
diff --git a/src/backend/cpu/kernel/identity.hpp b/src/backend/cpu/kernel/identity.hpp
index 4b950b0a9b..6ba4d0bd90 100644
--- a/src/backend/cpu/kernel/identity.hpp
+++ b/src/backend/cpu/kernel/identity.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 
 namespace cpu
@@ -17,7 +17,7 @@ namespace kernel
 {
 
 template<typename T>
-void identity(Array<T> out)
+void identity(Param<T> out)
 {
     T *ptr = out.get();
     const af::dim4 out_dims  = out.dims();
diff --git a/src/backend/cpu/kernel/iir.hpp b/src/backend/cpu/kernel/iir.hpp
index b7f243b41a..1b31e1523c 100644
--- a/src/backend/cpu/kernel/iir.hpp
+++ b/src/backend/cpu/kernel/iir.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,31 +16,31 @@ namespace kernel
 {
 
 template<typename T>
-void iir(Array<T> y, Array<T> c, Array<T> const a)
+void iir(Param<T> y, Param<T> c, CParam<T> a)
 {
     dim4 ydims = c.dims();
-    int num_a = a.dims()[0];
+    int num_a = a.dims(0);
 
     for (int l = 0; l < (int)ydims[3]; l++) {
-        dim_t yidx3 = l * y.strides()[3];
-        dim_t cidx3 = l * c.strides()[3];
-        dim_t aidx3 = l * a.strides()[3];
+        dim_t yidx3 = l * y.strides(3);
+        dim_t cidx3 = l * c.strides(3);
+        dim_t aidx3 = l * a.strides(3);
 
         for (int k = 0; k < (int)ydims[2]; k++) {
 
-            dim_t yidx2 = k * y.strides()[2] + yidx3;
-            dim_t cidx2 = k * c.strides()[2] + cidx3;
-            dim_t aidx2 = k * a.strides()[2] + aidx3;
+            dim_t yidx2 = k * y.strides(2) + yidx3;
+            dim_t cidx2 = k * c.strides(2) + cidx3;
+            dim_t aidx2 = k * a.strides(2) + aidx3;
 
             for (int j = 0; j < (int)ydims[1]; j++) {
 
-                dim_t yidx1 = j * y.strides()[1] + yidx2;
-                dim_t cidx1 = j * c.strides()[1] + cidx2;
-                dim_t aidx1 = j * a.strides()[1] + aidx2;
+                dim_t yidx1 = j * y.strides(1) + yidx2;
+                dim_t cidx1 = j * c.strides(1) + cidx2;
+                dim_t aidx1 = j * a.strides(1) + aidx2;
 
                 std::vector<T> h_z(num_a);
 
-                const T *h_a = a.get() + (a.ndims() > 1 ? aidx1 : 0);
+                const T *h_a = a.get() + (a.dims().ndims() > 1 ? aidx1 : 0);
                 T *h_c = c.get() + cidx1;
                 T *h_y = y.get() + yidx1;
 
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index f52e5db3ff..065bc310ab 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <vector>
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -18,12 +18,11 @@ namespace kernel
 {
 
 template<typename T>
-void index(Array<T> out, Array<T> const in,
+void index(Param<T> out, CParam<T> in, const af::dim4 dDims,
            std::vector<bool> const isSeq, std::vector<af_seq> const seqs,
-           std::vector< Array<uint> > const idxArrs)
+           std::vector<CParam<uint>> idxArrs)
 {
     const af::dim4 iDims    = in.dims();
-    const af::dim4 dDims    = in.getDataDims();
     const af::dim4 iOffs    = toOffset(seqs, dDims);
     const af::dim4 iStrds   = toStride(seqs, dDims);
     const af::dim4 oDims    = out.dims();
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index 008913c407..a4fffa5802 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 #include <af/constants.h>
 #include <type_traits>
@@ -91,8 +91,8 @@ struct Interp1
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 1>
 {
-    void operator()(Array<InT> &out, int ooff,
-                    const Array<InT> &in, int ioff, LocT x,
+    void operator()(Param<InT> &out, int ooff,
+                    CParam<InT> &in, int ioff, LocT x,
                     af_interp_type  method, int batch, bool clamp)
     {
         const InT *inptr = in.get();
@@ -116,8 +116,8 @@ struct Interp1<InT, LocT, 1>
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 2>
 {
-    void operator()(Array<InT> &out, int ooff,
-                    const Array<InT> &in, int ioff, LocT x,
+    void operator()(Param<InT> &out, int ooff,
+                    CParam<InT> &in, int ioff, LocT x,
                     af_interp_type  method, int batch, bool clamp)
     {
         typedef vtype_t<InT> VT;
@@ -155,8 +155,8 @@ struct Interp1<InT, LocT, 2>
 template<typename InT, typename LocT>
 struct Interp1<InT, LocT, 3>
 {
-    void operator()(Array<InT> &out, int ooff,
-                    const Array<InT> &in, int ioff, LocT x,
+    void operator()(Param<InT> &out, int ooff,
+                    CParam<InT> &in, int ioff, LocT x,
                     af_interp_type  method, int batch, bool clamp)
     {
         typedef vtype_t<InT> VT;
@@ -194,8 +194,8 @@ struct Interp2
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 1>
 {
-    void operator()(Array<InT> &out, int ooff,
-                    const Array<InT> &in, int ioff, LocT x, LocT y,
+    void operator()(Param<InT> &out, int ooff,
+                    CParam<InT> &in, int ioff, LocT x, LocT y,
                     af_interp_type  method, int nimages, bool clamp)
     {
         const InT *inptr = in.get();
@@ -228,8 +228,8 @@ struct Interp2<InT, LocT, 1>
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 2>
 {
-    void operator()(Array<InT> &out, int ooff,
-                    const Array<InT> &in, int ioff, LocT x, LocT y,
+    void operator()(Param<InT> &out, int ooff,
+                    CParam<InT> &in, int ioff, LocT x, LocT y,
                     af_interp_type  method, int nimages, bool clamp)
     {
         typedef vtype_t<InT> VT;
@@ -283,8 +283,8 @@ struct Interp2<InT, LocT, 2>
 template<typename InT, typename LocT>
 struct Interp2<InT, LocT, 3>
 {
-    void operator()(Array<InT> &out, int ooff,
-                    const Array<InT> &in, int ioff, LocT x, LocT y,
+    void operator()(Param<InT> &out, int ooff,
+                    CParam<InT> &in, int ioff, LocT x, LocT y,
                     af_interp_type  method, int nimages, bool clamp)
     {
         typedef vtype_t<InT> VT;
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index d867914523..873ab56036 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void iota(Array<T> output, const af::dim4 &sdims, const af::dim4 &tdims)
+void iota(Param<T> output, const af::dim4 &sdims, const af::dim4 &tdims)
 {
     const af::dim4 dims    = output.dims();
     T* out             = output.get();
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index d860425112..a62e278d4f 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -65,15 +65,15 @@ struct MinMaxOp<af_max_t, T>
 template<af_op_t op, typename T, int D>
 struct ireduce_dim
 {
-    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
-                    const Array<T> input, const dim_t inOffset, const int dim)
+    void operator()(Param<T> output, Param<uint> locParam, const dim_t outOffset,
+                    CParam<T> input, const dim_t inOffset, const int dim)
     {
         const af::dim4 odims    = output.dims();
         const af::dim4 ostrides = output.strides();
         const af::dim4 istrides = input.strides();
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
-            ireduce_dim<op, T, D1>()(output, locArray, outOffset + i * ostrides[D1],
+            ireduce_dim<op, T, D1>()(output, locParam, outOffset + i * ostrides[D1],
                                      input, inOffset + i * istrides[D1], dim);
         }
     }
@@ -82,15 +82,15 @@ struct ireduce_dim
 template<af_op_t op, typename T>
 struct ireduce_dim<op, T, 0>
 {
-    void operator()(Array<T> output, Array<uint> locArray, const dim_t outOffset,
-                    const Array<T> input, const dim_t inOffset, const int dim)
+    void operator()(Param<T> output, Param<uint> locParam, const dim_t outOffset,
+                    CParam<T> input, const dim_t inOffset, const int dim)
     {
         const af::dim4 idims = input.dims();
         const af::dim4 istrides = input.strides();
 
         T const * const in = input.get();
         T * out = output.get();
-        uint * loc = locArray.get();
+        uint * loc = locParam.get();
 
         dim_t stride = istrides[dim];
         MinMaxOp<op, T> Op(in[inOffset], 0);
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index de044d66b3..13830799a9 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -54,7 +54,7 @@ void join_append(To *out, const Tx *X, const af::dim4 &offset,
 }
 
 template<typename Tx, typename Ty>
-void join(Array<Tx> out, const int dim, const Array<Tx> first, const Array<Ty> second)
+void join(Param<Tx> out, const int dim, CParam<Tx> first, CParam<Ty> second)
 {
     Tx* outPtr = out.get();
     const Tx* fptr = first.get();
@@ -68,33 +68,33 @@ void join(Array<Tx> out, const int dim, const Array<Tx> first, const Array<Ty> s
     switch(dim) {
         case 0:
             join_append<Tx, Tx, 0>(outPtr, fptr, zero,
-                    odims, fdims, out.strides(), first.strides());
+                                   odims, fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims),
-                    odims, sdims, out.strides(), second.strides());
+                                   odims, sdims, out.strides(), second.strides());
             break;
         case 1:
             join_append<Tx, Tx, 1>(outPtr, fptr, zero,
-                    odims, fdims, out.strides(), first.strides());
+                                   odims, fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims),
-                    odims, sdims, out.strides(), second.strides());
+                                   odims, sdims, out.strides(), second.strides());
             break;
         case 2:
             join_append<Tx, Tx, 2>(outPtr, fptr, zero,
-                    odims, fdims, out.strides(), first.strides());
+                                   odims, fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims),
-                    odims, sdims, out.strides(), second.strides());
+                                   odims, sdims, out.strides(), second.strides());
             break;
         case 3:
             join_append<Tx, Tx, 3>(outPtr, fptr, zero,
-                    odims, fdims, out.strides(), first.strides());
+                                   odims, fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims),
-                    odims, sdims, out.strides(), second.strides());
+                                   odims, sdims, out.strides(), second.strides());
             break;
     }
 }
 
 template<typename T, int n_arrays>
-void join(const int dim, Array<T> out, const std::vector<Array<T>> inputs)
+void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs)
 {
     af::dim4 zero(0,0,0,0);
     af::dim4 d = zero;
@@ -140,4 +140,3 @@ void join(const int dim, Array<T> out, const std::vector<Array<T>> inputs)
 
 }
 }
-
diff --git a/src/backend/cpu/kernel/lookup.hpp b/src/backend/cpu/kernel/lookup.hpp
index 3886474d05..a9ec855c3d 100644
--- a/src/backend/cpu/kernel/lookup.hpp
+++ b/src/backend/cpu/kernel/lookup.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <vector>
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -18,8 +18,8 @@ namespace kernel
 {
 
 template<typename InT, typename IndexT>
-void lookup(Array<InT> out, Array<InT> const input,
-            Array<IndexT> const indices, unsigned const dim)
+void lookup(Param<InT> out, CParam<InT> input,
+            CParam<IndexT> indices, unsigned const dim)
 {
     const af::dim4 iDims    = input.dims();
     const af::dim4 oDims    = out.dims();
diff --git a/src/backend/cpu/kernel/lu.hpp b/src/backend/cpu/kernel/lu.hpp
index d69d6ee3a8..0717cd223f 100644
--- a/src/backend/cpu/kernel/lu.hpp
+++ b/src/backend/cpu/kernel/lu.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void lu_split(Array<T> lower, Array<T> upper, const Array<T> in)
+void lu_split(Param<T> lower, Param<T> upper, CParam<T> in)
 {
     T *l = lower.get();
     T *u = upper.get();
@@ -64,11 +64,11 @@ void lu_split(Array<T> lower, Array<T> upper, const Array<T> in)
     }
 }
 
-void convertPivot(Array<int> p, Array<int> pivot)
+void convertPivot(Param<int> p, Param<int> pivot)
 {
     int *d_pi = pivot.get();
     int *d_po = p.get();
-    dim_t d0  = pivot.dims()[0];
+    dim_t d0  = pivot.dims(0);
     for(int j = 0; j < (int)d0; j++) {
         // 1 indexed in pivot
         std::swap(d_po[j], d_po[d_pi[j] - 1]);
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
index afbef67a7e..9a0402ca80 100644
--- a/src/backend/cpu/kernel/match_template.hpp
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename OutT, typename InT, af_match_type MatchT>
-void matchTemplate(Array<OutT> out, const Array<InT> sImg, const Array<InT> tImg)
+void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg)
 {
     const af::dim4 sDims = sImg.dims();
     const af::dim4 tDims = tImg.dims();
@@ -31,7 +31,7 @@ void matchTemplate(Array<OutT> out, const Array<InT> sImg, const Array<InT> tImg
     const af::dim4 oStrides = out.strides();
 
     OutT tImgMean = OutT(0);
-    dim_t winNumElements = tImg.elements();
+    dim_t winNumElements = tImg.dims().elements();
     bool needMean = MatchT==AF_ZSAD || MatchT==AF_LSAD ||
         MatchT==AF_ZSSD || MatchT==AF_LSSD ||
         MatchT==AF_ZNCC;
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 4036bf61e7..2569fefb23 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <vector>
 #include <utility.hpp>
 
@@ -18,7 +18,7 @@ namespace kernel
 {
 
 template<typename T, bool IsColor>
-void meanShift(Array<T> out, const Array<T> in, const float s_sigma,
+void meanShift(Param<T> out, CParam<T> in, const float s_sigma,
                const float c_sigma, const unsigned iter)
 {
     const af::dim4 dims     = in.dims();
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index b6a531eda0..af17a7a081 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <vector>
 #include <algorithm>
 
@@ -18,7 +18,7 @@ namespace kernel
 {
 
 template<typename T, af_border_type Pad>
-void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
+void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid)
 {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
@@ -86,7 +86,7 @@ void medfilt1(Array<T> out, const Array<T> in, dim_t w_wid)
 
 
 template<typename T, af_border_type Pad>
-void medfilt2(Array<T> out, const Array<T> in, dim_t w_len, dim_t w_wid)
+void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid)
 {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index bf1302a64f..fd99884cd7 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <af/defines.h>
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 #include <math.hpp>
 
@@ -20,7 +20,7 @@ namespace kernel
 
 
 template<typename T>
-void moments(Array<float> &output, Array<T> const &input, af_moment_type moment)
+void moments(Param<float> output, CParam<T> input, af_moment_type moment)
 {
     T const * const in       = input.get();
     af::dim4  const idims    = input.dims();
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 0789928aaf..e75e0d1393 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <limits>
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 #include <ops.hpp>
 
@@ -19,7 +19,7 @@ namespace kernel
 {
 
 template<typename T, bool IsDilation>
-void morph(Array<T> out, Array<T> const in, Array<T> const mask)
+void morph(Param<T> out, CParam<T> in, CParam<T> mask)
 {
     const af::dim4 ostrides = out.strides();
     const af::dim4 istrides = in.strides();
@@ -79,7 +79,7 @@ void morph(Array<T> out, Array<T> const in, Array<T> const mask)
 }
 
 template<typename T, bool IsDilation>
-void morph3d(Array<T> out, Array<T> const in, Array<T> const mask)
+void morph3d(Param<T> out, CParam<T> in, CParam<T> mask)
 {
     const af::dim4 dims     = in.dims();
     const af::dim4 window   = mask.dims();
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 1dbd3d9c45..7f515966b0 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -86,8 +86,8 @@ struct dist_op<ushort, To, AF_SHD>
 };
 
 template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour(Array<uint> idx, Array<To> dist,
-                       const Array<T> query, const Array<T> train,
+void nearest_neighbour(Param<uint> idx, Param<To> dist,
+                       CParam<T> query, CParam<T> train,
                        const uint dist_dim, const uint n_dist)
 {
     uint sample_dim = (dist_dim == 0) ? 1 : 0;
diff --git a/src/backend/cpu/kernel/orb.hpp b/src/backend/cpu/kernel/orb.hpp
index 12cd5eb4ef..a1c7362d90 100644
--- a/src/backend/cpu/kernel/orb.hpp
+++ b/src/backend/cpu/kernel/orb.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 
 namespace cpu
@@ -319,7 +319,7 @@ void harris_response(
     const float* scl_in,
     const unsigned total_feat,
     unsigned* usable_feat,
-    const Array<T>& image,
+    CParam<T> image,
     const unsigned block_size,
     const float k_thr,
     const unsigned patch_size)
@@ -395,7 +395,7 @@ void centroid_angle(
     const float* y_in,
     float* orientation_out,
     const unsigned total_feat,
-    const Array<T>& image,
+    CParam<T> image,
     const unsigned patch_size)
 {
     const af::dim4 idims = image.dims();
@@ -433,7 +433,7 @@ inline T get_pixel(
     const unsigned size,
     const int dist_x,
     const int dist_y,
-    const Array<T>& image,
+    CParam<T> image,
     const unsigned patch_size)
 {
     const af::dim4 idims = image.dims();
@@ -457,7 +457,7 @@ void extract_orb(
     float* y_in_out,
     const float* ori_in,
     float* size_out,
-    const Array<T>& image,
+    CParam<T> image,
     const float scl,
     const unsigned patch_size)
 {
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 3c191e8ac9..304eb8e24e 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <Array.hpp>
+#include <Param.hpp>
 #include <err_cpu.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
diff --git a/src/backend/cpu/kernel/range.hpp b/src/backend/cpu/kernel/range.hpp
index 0732d30e0a..982cba91b3 100644
--- a/src/backend/cpu/kernel/range.hpp
+++ b/src/backend/cpu/kernel/range.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T, int dim>
-void range(Array<T> output)
+void range(Param<T> output)
 {
     T* out = output.get();
 
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index 9479fa62f6..bc0a284086 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -18,8 +18,8 @@ namespace kernel
 template<af_op_t op, typename Ti, typename To, int D>
 struct reduce_dim
 {
-    void operator()(Array<To> out, const dim_t outOffset,
-                    const Array<Ti> in, const dim_t inOffset,
+    void operator()(Param<To> out, const dim_t outOffset,
+                    CParam<Ti> in, const dim_t inOffset,
                     const int dim, bool change_nan, double nanval)
     {
         static const int D1 = D - 1;
@@ -43,8 +43,8 @@ struct reduce_dim<op, Ti, To, 0>
 
     Transform<Ti, To, op> transform;
     Binary<To, op> reduce;
-    void operator()(Array<To> out, const dim_t outOffset,
-                    const Array<Ti> in, const dim_t inOffset,
+    void operator()(Param<To> out, const dim_t outOffset,
+                    CParam<Ti> in, const dim_t inOffset,
                     const int dim, bool change_nan, double nanval)
     {
         const af::dim4 istrides = in.strides();
diff --git a/src/backend/cpu/kernel/regions.hpp b/src/backend/cpu/kernel/regions.hpp
index 837d772442..9d10e333ec 100644
--- a/src/backend/cpu/kernel/regions.hpp
+++ b/src/backend/cpu/kernel/regions.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <memory.hpp>
 
 namespace cpu
@@ -97,7 +97,7 @@ static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
 }
 
 template<typename T>
-void regions(Array<T> out, const Array<char> in, af_connectivity connectivity)
+void regions(Param<T> out, CParam<char> in, af_connectivity connectivity)
 {
     const af::dim4 inDims = in.dims();
     const char *inPtr  = in.get();
diff --git a/src/backend/cpu/kernel/reorder.hpp b/src/backend/cpu/kernel/reorder.hpp
index dcd894c0f9..60cf0748a4 100644
--- a/src/backend/cpu/kernel/reorder.hpp
+++ b/src/backend/cpu/kernel/reorder.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void reorder(Array<T> out, const Array<T> in, const af::dim4 oDims, const af::dim4 rdims)
+void reorder(Param<T> out, CParam<T> in, const af::dim4 oDims, const af::dim4 rdims)
 {
     T* outPtr = out.get();
     const T* inPtr = in.get();
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index df8fc702a5..09594a2250 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -155,7 +155,7 @@ struct resize_op<T, AF_INTERP_LOWER>
 };
 
 template<typename T, af_interp_type method>
-void resize(Array<T> out, const Array<T> in)
+void resize(Param<T> out, CParam<T> in)
 {
     af::dim4 idims    = in.dims();
     af::dim4 odims    = out.dims();
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index 7776ff414d..77f20c75a4 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 #include <err_cpu.hpp>
 #include "interp.hpp"
@@ -19,7 +19,7 @@ namespace kernel
 {
 
 template<typename T, int order>
-void rotate(Array<T> output, const Array<T> input,
+void rotate(Param<T> output, CParam<T> input,
             const float theta, af_interp_type method)
 {
     typedef typename dtype_traits<T>::base_type BT;
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index e8db08ac25..0550393448 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -18,8 +18,8 @@ namespace kernel
 template<af_op_t op, typename Ti, typename To, int D, bool inclusive_scan>
 struct scan_dim
 {
-    void operator()(Array<To> out, dim_t outOffset,
-                    const Array<Ti> in, dim_t inOffset,
+    void operator()(Param<To> out, dim_t outOffset,
+                    CParam<Ti> in, dim_t inOffset,
                     const int dim) const
     {
         const dim4 odims    = out.dims();
@@ -40,8 +40,8 @@ struct scan_dim
 template<af_op_t op, typename Ti, typename To, bool inclusive_scan>
 struct scan_dim<op, Ti, To, 0, inclusive_scan>
 {
-    void operator()(Array<To> output, dim_t outOffset,
-                    const Array<Ti> input,  dim_t inOffset,
+    void operator()(Param<To> output, dim_t outOffset,
+                    CParam<Ti> input,  dim_t inOffset,
                     const int dim) const
     {
         const Ti* in = input.get() + inOffset;
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index 8e5c332567..4fef32e05e 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -21,9 +21,9 @@ struct scan_dim_by_key
     bool inclusive_scan;
     scan_dim_by_key(bool inclusiveSanKey) : inclusive_scan(inclusiveSanKey) {}
 
-    void operator()(Array<To> out, dim_t outOffset,
-                    const Array<Tk> key, dim_t keyOffset,
-                    const Array<Ti> in, dim_t inOffset,
+    void operator()(Param<To> out, dim_t outOffset,
+                    CParam<Tk> key, dim_t keyOffset,
+                    CParam<Ti> in, dim_t inOffset,
                     const int dim) const
     {
         const dim4 odims    = out.dims();
@@ -49,9 +49,9 @@ struct scan_dim_by_key<op, Ti, Tk, To, 0>
     bool inclusive_scan;
     scan_dim_by_key(bool inclusiveSanKey) : inclusive_scan(inclusiveSanKey) {}
 
-    void operator()(Array<To> output, dim_t outOffset,
-                    const Array<Tk> keyinput, dim_t keyOffset,
-                    const Array<Ti> input, dim_t inOffset,
+    void operator()(Param<To> output, dim_t outOffset,
+                    CParam<Tk> keyinput, dim_t keyOffset,
+                    CParam<Ti> input, dim_t inOffset,
                     const int dim) const
     {
         const Ti* in  = input.get()    + inOffset;
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index c3fb47be69..d88bae4fea 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void select(Array<T> out, const Array<char> cond, const Array<T> a, const Array<T> b)
+void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b)
 {
     af::dim4 adims = a.dims();
     af::dim4 astrides = a.strides();
@@ -78,7 +78,7 @@ void select(Array<T> out, const Array<char> cond, const Array<T> a, const Array<
 }
 
 template<typename T, bool flip>
-void select_scalar(Array<T> out, const Array<char> cond, const Array<T> a, const double b)
+void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b)
 {
     af::dim4 astrides = a.strides();
     af::dim4 cstrides = cond.strides();
diff --git a/src/backend/cpu/kernel/shift.hpp b/src/backend/cpu/kernel/shift.hpp
index bef796ecb7..02a58fdac3 100644
--- a/src/backend/cpu/kernel/shift.hpp
+++ b/src/backend/cpu/kernel/shift.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <cassert>
 
 namespace cpu
@@ -22,7 +22,7 @@ static inline dim_t simple_mod(const dim_t i, const dim_t dim)
 }
 
 template<typename T>
-void shift(Array<T> out, const Array<T> in, const af::dim4 sdims)
+void shift(Param<T> out, CParam<T> in, const af::dim4 sdims)
 {
     T* outPtr = out.get();
     const T* inPtr = in.get();
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index 44e38033a4..0f629a3d13 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <cassert>
 
 namespace cpu
@@ -17,7 +17,7 @@ namespace kernel
 {
 
 template<typename Ti, typename To, bool isDX>
-void derivative(Array<To> output, const Array<Ti> input)
+void derivative(Param<To> output, CParam<Ti> input)
 {
     const af::dim4 dims    = input.dims();
     const af::dim4 istrides = input.strides();
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index afc5b5adaa..dd842dceb1 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 #include <algorithm>
 #include <numeric>
@@ -22,7 +22,7 @@ namespace kernel
 
 // Based off of http://stackoverflow.com/a/12399290
 template<typename T>
-void sort0Iterative(Array<T> val, bool isAscending)
+void sort0Iterative(Param<T> val, bool isAscending)
 {
     // initialize original index locations
     T *val_ptr = val.get();
@@ -31,16 +31,16 @@ void sort0Iterative(Array<T> val, bool isAscending)
     if(isAscending) { op = std::less<T>(); }
 
     T *comp_ptr = nullptr;
-    for(dim_t w = 0; w < val.dims()[3]; w++) {
-        dim_t valW = w * val.strides()[3];
-        for(dim_t z = 0; z < val.dims()[2]; z++) {
-            dim_t valWZ = valW + z * val.strides()[2];
-            for(dim_t y = 0; y < val.dims()[1]; y++) {
+    for(dim_t w = 0; w < val.dims(3); w++) {
+        dim_t valW = w * val.strides(3);
+        for(dim_t z = 0; z < val.dims(2); z++) {
+            dim_t valWZ = valW + z * val.strides(2);
+            for(dim_t y = 0; y < val.dims(1); y++) {
 
-                dim_t valOffset = valWZ + y * val.strides()[1];
+                dim_t valOffset = valWZ + y * val.strides(1);
 
                 comp_ptr = val_ptr + valOffset;
-                std::sort(comp_ptr, comp_ptr + val.dims()[0], op);
+                std::sort(comp_ptr, comp_ptr + val.dims(0), op);
             }
         }
     }
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 0ff8881a8b..1b2bede9ac 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <err_cpu.hpp>
 
 namespace cpu
@@ -17,13 +17,13 @@ namespace kernel
 {
 
 template<typename Tk, typename Tv>
-void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending);
+void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
 template<typename Tk, typename Tv>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim, bool isAscending);
+void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim, bool isAscending);
 
 template<typename Tk, typename Tv>
-void sort0ByKey(Array<Tk> okey, Array<Tv> oval, bool isAscending);
+void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
 }
 }
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index eea05e8198..f8fae4fc6c 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -10,13 +10,15 @@
 #pragma once
 #include <kernel/sort_by_key.hpp>
 #include <kernel/sort_helper.hpp>
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 #include <algorithm>
 #include <numeric>
 #include <queue>
 #include <err_cpu.hpp>
 #include <functional>
+#include <tuple>
+#include <utility>
 
 namespace cpu
 {
@@ -24,7 +26,7 @@ namespace kernel
 {
 
 template<typename Tk, typename Tv>
-void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending)
+void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
 {
     // Get pointers and initialize original index locations
     Tk *okey_ptr = okey.get();
@@ -32,22 +34,21 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending)
 
     typedef IndexPair<Tk, Tv> CurrentPair;
 
-    dim_t size = okey.dims()[0];
-    size_t bytes = size * sizeof(CurrentPair);
-    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+    dim_t size = okey.dims(0);
+    std::vector<CurrentPair> pairKeyVal(size);
 
-    for(dim_t w = 0; w < okey.dims()[3]; w++) {
-        dim_t okeyW = w * okey.strides()[3];
-        dim_t ovalW = w * oval.strides()[3];
+    for(dim_t w = 0; w < okey.dims(3); w++) {
+        dim_t okeyW = w * okey.strides(3);
+        dim_t ovalW = w * oval.strides(3);
 
-        for(dim_t z = 0; z < okey.dims()[2]; z++) {
-            dim_t okeyWZ = okeyW + z * okey.strides()[2];
-            dim_t ovalWZ = ovalW + z * oval.strides()[2];
+        for(dim_t z = 0; z < okey.dims(2); z++) {
+            dim_t okeyWZ = okeyW + z * okey.strides(2);
+            dim_t ovalWZ = ovalW + z * oval.strides(2);
 
-            for(dim_t y = 0; y < okey.dims()[1]; y++) {
+            for(dim_t y = 0; y < okey.dims(1); y++) {
 
-                dim_t okeyOffset = okeyWZ + y * okey.strides()[1];
-                dim_t ovalOffset = ovalWZ + y * oval.strides()[1];
+                dim_t okeyOffset = okeyWZ + y * okey.strides(1);
+                dim_t ovalOffset = ovalWZ + y * oval.strides(1);
 
                 Tk *okey_col_ptr = okey_ptr + okeyOffset;
                 Tv *oval_col_ptr = oval_ptr + ovalOffset;
@@ -57,9 +58,9 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending)
                 }
 
                 if(isAscending) {
-                    std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, true>());
+                    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), IPCompare<Tk, Tv, true>());
                 } else {
-                    std::stable_sort(pairKeyVal, pairKeyVal + size, IPCompare<Tk, Tv, false>());
+                    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), IPCompare<Tk, Tv, false>());
                 }
 
                 for(unsigned x = 0; x < size; x++) {
@@ -70,12 +71,11 @@ void sort0ByKeyIterative(Array<Tk> okey, Array<Tv> oval, bool isAscending)
         }
     }
 
-    memFree((char *)pairKeyVal);
     return;
 }
 
 template<typename Tk, typename Tv>
-void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim, bool isAscending)
+void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim, bool isAscending)
 {
     af::dim4 inDims = okey.dims();
 
@@ -84,11 +84,11 @@ void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim, bool isAsce
     tileDims[dim] = inDims[dim];
     seqDims[dim] = 1;
 
-    uint* key = memAlloc<uint>(inDims.elements());
+    std::vector<uint> key(inDims.elements());
     // IOTA
     {
         af::dim4 dims    = inDims;
-        uint* out        = key;
+        uint* out        = key.data();
         af::dim4 strides(1);
         for(int i = 1; i < 4; i++)
             strides[i] = strides[i-1] * dims[i-1];
@@ -116,38 +116,34 @@ void sortByKeyBatched(Array<Tk> okey, Array<Tv> oval, const int dim, bool isAsce
     Tv *oval_ptr = oval.get();
 
     typedef KeyIndexPair<Tk, Tv> CurrentTuple;
-    size_t size = okey.elements();
-    size_t bytes = okey.elements() * sizeof(CurrentTuple);
-    CurrentTuple *tupleKeyValIdx = (CurrentTuple *)memAlloc<char>(bytes);
+    size_t size = okey.dims().elements();
+    std::vector<CurrentTuple> tupleKeyValIdx(size);
 
     for(unsigned i = 0; i < size; i++) {
         tupleKeyValIdx[i] = std::make_tuple(okey_ptr[i], oval_ptr[i], key[i]);
     }
 
-    memFree(key); // key is no longer required
 
     if(isAscending) {
-      std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, true>());
+        std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(), KIPCompareV<Tk, Tv, true>());
     }
     else {
-      std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareV<Tk, Tv, false>());
+        std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(), KIPCompareV<Tk, Tv, false>());
     }
 
-    std::stable_sort(tupleKeyValIdx, tupleKeyValIdx + size, KIPCompareK<Tk, Tv, true>());
+    std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(), KIPCompareK<Tk, Tv, true>());
 
-    for(unsigned x = 0; x < okey.elements(); x++) {
+    for(unsigned x = 0; x < okey.dims().elements(); x++) {
         okey_ptr[x] = std::get<0>(tupleKeyValIdx[x]);
         oval_ptr[x] = std::get<1>(tupleKeyValIdx[x]);
     }
 
-    memFree((char *)tupleKeyValIdx);
-    return;
 }
 
 template<typename Tk, typename Tv>
-void sort0ByKey(Array<Tk> okey, Array<Tv> oval, bool isAscending)
+void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
 {
-    int higherDims =  okey.dims()[1] * okey.dims()[2] * okey.dims()[3];
+    int higherDims =  okey.dims(1) * okey.dims(2) * okey.dims(3);
     // TODO Make a better heurisitic
     if(higherDims > 4)
         kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
@@ -156,10 +152,10 @@ void sort0ByKey(Array<Tk> okey, Array<Tv> oval, bool isAscending)
 }
 
 #define INSTANTIATE(Tk, Tv)                                                             \
-    template void sort0ByKey<Tk, Tv>(Array<Tk> okey, Array<Tv> oval, bool isAscending); \
-    template void sort0ByKeyIterative<Tk, Tv>(Array<Tk> okey, Array<Tv> oval,           \
+    template void sort0ByKey<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, bool isAscending); \
+    template void sort0ByKeyIterative<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,           \
                                               bool isAscending);                        \
-    template void sortByKeyBatched<Tk, Tv>(Array<Tk> okey, Array<Tv> oval,              \
+    template void sortByKeyBatched<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,              \
                                            const int dim, bool isAscending);
 
 #define INSTANTIATE1(Tk) \
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
index 99479fddc3..47dfd8b8ca 100644
--- a/src/backend/cpu/kernel/sort_helper.hpp
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <err_cpu.hpp>
+#include <tuple>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index f87a4ced1f..36992b97b5 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 #include <math.hpp>
 #include <kernel/sort_helper.hpp>
@@ -21,18 +21,18 @@ namespace kernel
 {
 
 template<typename T>
-void coo2dense(Array<T> output,
-               Array<T> const values, Array<int> const rowIdx, Array<int> const colIdx)
+void coo2dense(Param<T> output,
+               CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
 {
-    T   const * const vPtr = values.get();
-    int const * const rPtr = rowIdx.get();
-    int const * const cPtr = colIdx.get();
+    const T *vPtr = values.get();
+    const int * rPtr = rowIdx.get();
+    const int * cPtr = colIdx.get();
 
     T * outPtr = output.get();
 
     af::dim4 ostrides = output.strides();
 
-    int nNZ = values.dims()[0];
+    int nNZ = values.dims(0);
     for(int i = 0; i < nNZ; i++) {
         T   v = vPtr[i];
         int r = rPtr[i];
@@ -45,56 +45,50 @@ void coo2dense(Array<T> output,
 }
 
 template<typename T>
-struct dense_csr
+void dense_csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+               CParam<T> in)
 {
-    void operator()(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
-                    Array<T> const in)
-    {
-        T const * const iPtr = in.get();
-        T       * const vPtr = values.get();
-        int     * const rPtr = rowIdx.get();
-        int     * const cPtr = colIdx.get();
-
-        int stride = in.strides()[1];
-        af::dim4 dims = in.dims();
-
-        int offset = 0;
-        for (int i = 0; i < dims[0]; ++i) {
-            rPtr[i] = offset;
-            for (int j = 0; j < dims[1]; ++j) {
-                if (iPtr[j*stride + i] != scalar<T>(0)) {
-                    vPtr[offset] = iPtr[j*stride + i];
-                    cPtr[offset++] = j;
-                }
+    const T * iPtr = in.get();
+    T       * vPtr = values.get();
+    int     * rPtr = rowIdx.get();
+    int     * cPtr = colIdx.get();
+
+    int stride = in.strides(1);
+    af::dim4 dims = in.dims();
+
+    int offset = 0;
+    for (int i = 0; i < dims[0]; ++i) {
+        rPtr[i] = offset;
+        for (int j = 0; j < dims[1]; ++j) {
+            if (iPtr[j*stride + i] != scalar<T>(0)) {
+                vPtr[offset] = iPtr[j*stride + i];
+                cPtr[offset++] = j;
             }
         }
-        rPtr[dims[0]] = offset;
     }
-};
+    rPtr[dims[0]] = offset;
+}
 
 template<typename T>
-struct csr_dense
+void csr_dense(Param<T> out,
+                CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
 {
-    void operator()(Array<T> out,
-                    Array<T> const values, Array<int> const rowIdx, Array<int> const colIdx)
-    {
-        T         * const oPtr = out.get();
-        T   const * const vPtr = values.get();
-        int const * const rPtr = rowIdx.get();
-        int const * const cPtr = colIdx.get();
-
-        int stride = out.strides()[1];
-
-        int r = rowIdx.dims()[0];
-        for (int i = 0; i < r - 1; i++) {
-            for (int ii = rPtr[i]; ii < rPtr[i+1]; ++ii) {
-                int j = cPtr[ii];
-                T v = vPtr[ii];
-                oPtr[j*stride + i] = v;
-            }
+    T   *oPtr = out.get();
+    const T   *vPtr = values.get();
+    const int *rPtr = rowIdx.get();
+    const int *cPtr = colIdx.get();
+
+    int stride = out.strides(1);
+
+    int r = rowIdx.dims(0);
+    for (int i = 0; i < r - 1; i++) {
+        for (int ii = rPtr[i]; ii < rPtr[i+1]; ++ii) {
+            int j = cPtr[ii];
+            T v = vPtr[ii];
+            oPtr[j*stride + i] = v;
         }
     }
-};
+}
 
 // Modified code from sort helper
 template <typename T>
@@ -113,81 +107,76 @@ struct SpKIPCompareK
 };
 
 template<typename T>
-void csr_coo(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
-             Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
+void csr_coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
+             CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx)
 {
     // First calculate the linear index
-    T         * const ovPtr = ovalues.get();
-    int       * const orPtr = orowIdx.get();
-    int       * const ocPtr = ocolIdx.get();
+    T         * ovPtr = ovalues.get();
+    int       * orPtr = orowIdx.get();
+    int       * ocPtr = ocolIdx.get();
 
-    T   const * const ivPtr = ivalues.get();
-    int const * const irPtr = irowIdx.get();
-    int const * const icPtr = icolIdx.get();
+    const T   *ivPtr = ivalues.get();
+    const int *irPtr = irowIdx.get();
+    const int *icPtr = icolIdx.get();
 
     // Create cordinate form of the row array
-    for(int i = 0; i < (int)irowIdx.elements() - 1; i++) {
+    for(int i = 0; i < (int)irowIdx.dims().elements() - 1; i++) {
         std::fill_n(orPtr + irPtr[i], irPtr[i + 1] - irPtr[i], i);
     }
 
     // Sort the coordinate form using column index
     // Uses code from sort_by_key kernels
     typedef SpKeyIndexPair<T> CurrentPair;
-    int size = ovalues.dims()[0];
-    size_t bytes = size * sizeof(CurrentPair);
-    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+    int size = ovalues.dims(0);
+    std::vector<CurrentPair> pairKeyVal(size);
 
     for(int x = 0; x < size; x++) {
        pairKeyVal[x] = std::make_tuple(icPtr[x], ivPtr[x], orPtr[x]);
     }
 
-    std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
+    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), SpKIPCompareK<T>());
 
-    for(int x = 0; x < (int)ovalues.elements(); x++) {
+    for(int x = 0; x < (int)ovalues.dims().elements(); x++) {
         std::tie(ocPtr[x], ovPtr[x], orPtr[x]) = pairKeyVal[x];
     }
 
-    memFree((char *)pairKeyVal);
 }
 
 template<typename T>
-void coo_csr(Array<T> ovalues, Array<int> orowIdx, Array<int> ocolIdx,
-             Array<T> const ivalues, Array<int> const irowIdx, Array<int> const icolIdx)
+void coo_csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
+             CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx)
 {
-    T         * const ovPtr = ovalues.get();
-    int       * const orPtr = orowIdx.get();
-    int       * const ocPtr = ocolIdx.get();
+    T   * ovPtr = ovalues.get();
+    int *orPtr = orowIdx.get();
+    int *ocPtr = ocolIdx.get();
 
-    T   const * const ivPtr = ivalues.get();
-    int const * const irPtr = irowIdx.get();
-    int const * const icPtr = icolIdx.get();
+    const T   *ivPtr = ivalues.get();
+    const int *irPtr = irowIdx.get();
+    const int *icPtr = icolIdx.get();
 
     // Sort the colidx and values based on rowIdx
     // Uses code from sort_by_key kernels
     typedef SpKeyIndexPair<T> CurrentPair;
-    int size = ovalues.dims()[0];
-    size_t bytes = size * sizeof(CurrentPair);
-    CurrentPair *pairKeyVal = (CurrentPair *)memAlloc<char>(bytes);
+    int size = ovalues.dims(0);
+    std::vector<CurrentPair>pairKeyVal(size);
 
     for(int x = 0; x < size; x++) {
        pairKeyVal[x] = std::make_tuple(irPtr[x], ivPtr[x], icPtr[x]);
     }
 
-    std::stable_sort(pairKeyVal, pairKeyVal + size, SpKIPCompareK<T>());
+    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), SpKIPCompareK<T>());
 
     ovPtr[0] = 0;
-    for(int x = 0; x < (int)ovalues.elements(); x++) {
+    for(int x = 0; x < (int)ovalues.dims().elements(); x++) {
         int row = -2; // Some value that will make orPtr[row + 1] error out
         std::tie(row, ovPtr[x], ocPtr[x]) = pairKeyVal[x];
         orPtr[row + 1]++;
     }
 
     // Compress row storage
-    for(int x = 1; x < (int)orowIdx.elements(); x++) {
+    for(int x = 1; x < (int)orowIdx.dims().elements(); x++) {
         orPtr[x] += orPtr[x - 1];
     }
-
-    memFree((char *)pairKeyVal);
 }
 
 }
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index fd7d579259..9b5a7cc4f1 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <math.hpp>
 
 namespace cpu
@@ -62,9 +62,9 @@ struct arith_op<T, af_div_t>
 };
 
 template<typename T, af_op_t op, af_storage type>
-void sparseArithOpD(Array<T> output,
-                    const Array<T> values, const Array<int> rowIdx, const Array<int> colIdx,
-                    const Array<T> rhs, const bool reverse = false)
+void sparseArithOpD(Param<T> output,
+                    CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
+                    CParam<T> rhs, const bool reverse = false)
 {
     T * oPtr = output.get();
     const T   * hPtr = rhs.get();
@@ -74,13 +74,13 @@ void sparseArithOpD(Array<T> output,
     const int * cPtr = colIdx.get();
 
     dim4 odims    = output.dims();
-    dim4 ostrides = output.strides();
-    dim4 hstrides = rhs.strides();
+    dim4 ostrides = output.strides();;
+    dim4 hstrides = rhs.strides();;
 
     std::vector<int> temp;
     if(type == AF_STORAGE_CSR) {
-        temp.resize(values.elements());
-        for(int i = 0; i < rowIdx.dims()[0] - 1; i++) {
+        temp.resize(values.dims().elements());
+        for(int i = 0; i < rowIdx.dims(0) - 1; i++) {
             for(int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) {
                 temp[ii] = i;
             }
@@ -91,7 +91,7 @@ void sparseArithOpD(Array<T> output,
     const int *xx = (type == AF_STORAGE_CSR) ? temp.data() : rPtr;
     const int *yy = (type == AF_STORAGE_CSC) ? temp.data() : cPtr;
 
-    for(int i = 0; i < (int)values.elements(); i++) {
+    for(int i = 0; i < (int)values.dims().elements(); i++) {
         // Bad index data
         if(xx[i] >= odims[0] || yy [i]>= odims[1]) continue;
 
@@ -104,8 +104,8 @@ void sparseArithOpD(Array<T> output,
 }
 
 template<typename T, af_op_t op, af_storage type>
-void sparseArithOpS(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
-                    const Array<T> rhs, const bool reverse = false)
+void sparseArithOpS(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                    CParam<T> rhs, const bool reverse = false)
 {
           T   * vPtr = values.get();
     const int * rPtr = rowIdx.get();
@@ -118,8 +118,8 @@ void sparseArithOpS(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
 
     std::vector<int> temp;
     if(type == AF_STORAGE_CSR) {
-        temp.resize(values.elements());
-        for(int i = 0; i < rowIdx.dims()[0] - 1; i++) {
+        temp.resize(values.dims().elements());
+        for(int i = 0; i < rowIdx.dims(0) - 1; i++) {
             for(int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) {
                 temp[ii] = i;
             }
@@ -130,7 +130,7 @@ void sparseArithOpS(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
     const int *xx = (type == AF_STORAGE_CSR) ? temp.data() : rPtr;
     const int *yy = (type == AF_STORAGE_CSC) ? temp.data() : cPtr;
 
-    for(int i = 0; i < (int)values.elements(); i++) {
+    for(int i = 0; i < (int)values.dims().elements(); i++) {
         // Bad index data
         if(xx[i] >= dims[0] || yy [i]>= dims[1]) continue;
 
@@ -143,4 +143,3 @@ void sparseArithOpS(Array<T> values, Array<int> rowIdx, Array<int> colIdx,
 
 }
 }
-
diff --git a/src/backend/cpu/kernel/susan.hpp b/src/backend/cpu/kernel/susan.hpp
index 2fb72d4ba4..3d9c098c9e 100644
--- a/src/backend/cpu/kernel/susan.hpp
+++ b/src/backend/cpu/kernel/susan.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void susan_responses(Array<T> output, const Array<T> input,
+void susan_responses(Param<T> output, CParam<T> input,
                      const unsigned idim0, const unsigned idim1,
                      const int radius, const float t, const float g,
                      const unsigned border_len)
@@ -52,9 +52,9 @@ void susan_responses(Array<T> output, const Array<T> input,
 }
 
 template<typename T>
-void non_maximal(Array<float> xcoords, Array<float> ycoords, Array<float> response,
+void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> response,
                  shared_ptr<unsigned> counter, const unsigned idim0, const unsigned idim1,
-                 const Array<T> input, const unsigned border_len, const unsigned max_corners)
+                 CParam<T> input, const unsigned border_len, const unsigned max_corners)
 {
     float* x_out    = xcoords.get();
     float* y_out    = ycoords.get();
diff --git a/src/backend/cpu/kernel/tile.hpp b/src/backend/cpu/kernel/tile.hpp
index c51ecafbc7..65a9eb20bd 100644
--- a/src/backend/cpu/kernel/tile.hpp
+++ b/src/backend/cpu/kernel/tile.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void tile(Array<T> out, const Array<T> in)
+void tile(Param<T> out, CParam<T> in)
 {
 
     T* outPtr = out.get();
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index 8a9ceacb07..75f8631cf5 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <err_cpu.hpp>
 #include <type_traits>
 #include "interp.hpp"
@@ -69,8 +69,8 @@ void calc_transform_inverse(T *tmat, const T *tmat_ptr, const bool inverse,
 }
 
 template<typename T, int order>
-void transform(Array<T> output, const Array<T> input,
-               const Array<float> transform, const bool inverse,
+void transform(Param<T> output, CParam<T> input,
+               CParam<float> transform, const bool inverse,
                const bool perspective,
                af_interp_type method)
 {
diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
index 85d499a2df..28f548b942 100644
--- a/src/backend/cpu/kernel/transpose.hpp
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <utility.hpp>
 #include <err_cpu.hpp>
 
@@ -37,7 +37,7 @@ cdouble getConjugate(const cdouble &in)
 }
 
 template<typename T, bool conjugate>
-void transpose(Array<T> output, const Array<T> input)
+void transpose(Param<T> output, CParam<T> input)
 {
     const dim4 odims    = output.dims();
     const dim4 ostrides = output.strides();
@@ -71,13 +71,13 @@ void transpose(Array<T> output, const Array<T> input)
 }
 
 template<typename T>
-void transpose(Array<T> out, const Array<T> in, const bool conjugate)
+void transpose(Param<T> out, CParam<T> in, const bool conjugate)
 {
     return (conjugate ? transpose<T, true>(out, in) : transpose<T, false>(out, in));
 }
 
 template<typename T, bool conjugate>
-void transpose_inplace(Array<T> input)
+void transpose_inplace(Param<T> input)
 {
     const dim4 idims    = input.dims();
     const dim4 istrides = input.strides();
@@ -112,7 +112,7 @@ void transpose_inplace(Array<T> input)
 }
 
 template<typename T>
-void transpose_inplace(Array<T> in, const bool conjugate)
+void transpose_inplace(Param<T> in, const bool conjugate)
 {
     return (conjugate ? transpose_inplace<T, true >(in) : transpose_inplace<T, false>(in));
 }
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index ee32f48359..5d83daa0ed 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 
 namespace cpu
 {
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> out, const Array<T> in)
+void triangle(Param<T> out, CParam<T> in)
 {
     T *o = out.get();
     const T *i = in.get();
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
index 52b57eb380..52742b5dd0 100644
--- a/src/backend/cpu/kernel/unwrap.hpp
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <err_cpu.hpp>
 
 namespace cpu
@@ -17,7 +17,7 @@ namespace kernel
 {
 
 template<typename T, int d>
-void unwrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
+void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
 {
     const T *inPtr = in.get();
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 0bf31da053..fbbfc07e59 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+#include <Param.hpp>
 #include <err_cpu.hpp>
 
 namespace cpu
@@ -17,7 +17,7 @@ namespace kernel
 {
 
 template<typename T, int d>
-void wrap_dim(Array<T> out, const Array<T> in, const dim_t wx, const dim_t wy,
+void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
               const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
 {
     const T *inPtr = in.get();
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index f9f6389694..0a42dabdfc 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -74,9 +74,9 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     dim4 iDims = in.dims();
     Array<int> pivot = createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
 
-    auto func = [=] (Array<T> in, Array<int> pivot) {
+    auto func = [=] (Param<T> in, Param<int> pivot) {
         dim4 iDims = in.dims();
-        getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides()[1], pivot.get());
+        getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides(1), pivot.get());
     };
     getQueue().enqueue(func, in, pivot);
 
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 8c246289a0..399b74e59c 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -57,26 +57,14 @@ template<typename T>
 T* memAlloc(const size_t &elements)
 {
     T *ptr = nullptr;
-
-    try {
-        ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
-    } catch(...) {
-        getQueue().sync();
-        ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
-    }
+    ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
     return ptr;
 }
 
 void* memAllocUser(const size_t &bytes)
 {
     void *ptr = nullptr;
-
-    try {
-        ptr = memoryManager().alloc(bytes, true);
-    } catch(...) {
-        getQueue().sync();
-        ptr = memoryManager().alloc(bytes, true);
-    }
+    ptr = memoryManager().alloc(bytes, true);
     return ptr;
 }
 
@@ -109,7 +97,6 @@ void memUnlock(const void *ptr)
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes,  size_t *lock_buffers)
 {
-    getQueue().sync();
     memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
                                   lock_bytes,  lock_buffers);
 }
@@ -189,6 +176,8 @@ void *MemoryManager::nativeAlloc(const size_t bytes)
 
 void MemoryManager::nativeFree(void *ptr)
 {
+    // Make sure this pointer is not being used on the queue before freeing the memory.
+    getQueue().sync();
     return free((void *)ptr);
 }
 }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index ecf7cf71fd..97f960c413 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -128,7 +128,7 @@ class DeviceManager
         CPUInfo getCPUInfo() const;
 
     private:
-        DeviceManager() {}
+        DeviceManager() : queues(MAX_QUEUES){}
 
         // Following two declarations are required to
         // avoid copying accidental copy/assignment
@@ -136,10 +136,18 @@ class DeviceManager
         // variables
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
+        // And the destructor is needed because rule of three:
+        // http://en.cppreference.com/w/cpp/language/rule_of_three
+        ~DeviceManager() {
+            for(auto &q : queues) q.sync();
+            memManager.release();
+            queues.clear();
+        }
 
         // Attributes
+        // DO NOT MOVE QUEUES! This has to be destroyed last, meaning it needs to be defined first.
+        std::vector<queue> queues;
         const CPUInfo cinfo;
         std::unique_ptr<MemoryManager> memManager;
-        std::array<queue, MAX_QUEUES> queues;
 };
 }
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index fa450b92f9..307079bff1 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -79,8 +79,8 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 
     triangle<T, true, false>(r, q);
 
-    auto func = [=] (Array<T> q, Array<T> t, int M, int N) {
-        gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(), q.strides()[1], t.get());
+    auto func = [=] (Param<T> q, Param<T> t, int M, int N) {
+        gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(), q.strides(1), t.get());
     };
     q.resetDims(dim4(M, M));
     getQueue().enqueue(func, q, t, M, N);
@@ -96,8 +96,8 @@ Array<T> qr_inplace(Array<T> &in)
     int N      = iDims[1];
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
-    auto func = [=] (Array<T> in, Array<T> t, int M, int N) {
-        geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides()[1], t.get());
+    auto func = [=] (Param<T> in, Param<T> t, int M, int N) {
+        geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides(1), t.get());
     };
     getQueue().enqueue(func, in, t, M, N);
 
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 2f32b4d852..10402df419 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -9,6 +9,7 @@
 
 #include <util.hpp>
 #include <memory.hpp>
+#include <Param.hpp>
 
 //FIXME: Is there a better way to check for std::future not being supported ?
 #if defined(AF_DISABLE_CPU_ASYNC) || (defined(__GNUC__) && (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2))
@@ -62,8 +63,8 @@ class queue
     void enqueue(const F func, Args... args)
     {
         count++;
-        if(sync_calls) { func( args... ); }
-        else           { aQueue.enqueue( func, args... ); }
+        if(sync_calls) { func(toParam(args)... ); }
+        else           { aQueue.enqueue(func, toParam(args)... ); }
 #ifndef NDEBUG
         sync();
 #else
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 22c46d7d07..73cf955795 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -37,8 +37,8 @@ namespace cpu
 {
 
 template<af_op_t op, typename Ti, typename To>
-using reduce_dim_func = std::function<void(Array<To>, const dim_t,
-                                           const Array<Ti>, const dim_t,
+using reduce_dim_func = std::function<void(Param<To>, const dim_t,
+                                           CParam<Ti>, const dim_t,
                                            const int, bool, double)>;
 
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 339f8ee190..c67c35ae46 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -84,10 +84,10 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
     int NRHS     = b.dims()[1];
     Array< T > B = copyArray<T>(b);
 
-    auto func = [=] (Array<T> A, Array<T> B, Array<int> pivot, int N, int NRHS) {
+    auto func = [=] (Param<T> A, Param<T> B, Param<int> pivot, int N, int NRHS) {
         getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                        N, NRHS, A.get(), A.strides()[1],
-                        pivot.get(), B.get(), B.strides()[1]);
+                        N, NRHS, A.get(), A.strides(1),
+                        pivot.get(), B.get(), B.strides(1));
     };
     getQueue().enqueue(func, A, B, pivot, N, NRHS);
 
@@ -104,14 +104,14 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
     int N      = B.dims()[0];
     int NRHS   = B.dims()[1];
 
-    auto func = [=] (Array<T> A, Array<T> B, int N, int NRHS, const af_mat_prop options) {
+    auto func = [=] (Param<T> A, Param<T> B, int N, int NRHS, const af_mat_prop options) {
         trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
                         options & AF_MAT_UPPER ? 'U' : 'L',
                         'N', // transpose flag
                         options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
                         N, NRHS,
-                        A.get(), A.strides()[1],
-                        B.get(), B.strides()[1]);
+                        A.get(), A.strides(1),
+                        B.get(), B.strides(1));
     };
     getQueue().enqueue(func, A, B, N, NRHS, options);
 
@@ -139,19 +139,19 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
     if(M == N) {
         Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
 
-        auto func = [=] (Array<T> A, Array<T> B, Array<int> pivot, int N, int K) {
-            gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides()[1],
-                           pivot.get(), B.get(), B.strides()[1]);
+        auto func = [=] (Param<T> A, Param<T> B, Param<int> pivot, int N, int K) {
+            gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides(1),
+                           pivot.get(), B.get(), B.strides(1));
         };
         getQueue().enqueue(func, A, B, pivot, N, K);
     } else {
-        auto func = [=] (Array<T> A, Array<T> B, int M, int N, int K) {
-            int sM = A.strides()[1];
-            int sN = A.strides()[2] / sM;
+        auto func = [=] (Param<T> A, Param<T> B, int M, int N, int K) {
+            int sM = A.strides(1);
+            int sN = A.strides(2) / sM;
 
             gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
                     M, N, K,
-                    A.get(), A.strides()[1],
+                    A.get(), A.strides(1),
                     B.get(), max(sM, sN));
         };
         B.resetDims(dim4(N, K));
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index e57cc1ccba..740dffdeb2 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -182,26 +182,22 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
     sparse_.eval();
 
-    auto func = [=] (SparseArray<T> sparse, const Array<T> in) {
+    auto func = [=] (Param<T> values, Param<int> rowIdx, Param<int> colIdx, int num, CParam<T> in) {
         // Read: https://software.intel.com/en-us/node/520848
         // But job description is incorrect with regards to job[1]
         // 0 implies row major and 1 implies column major
         int j1 = 1, j2 = 0;
-        const int job[] = {0, j1, j2, 2, (int)sparse.elements(), 1};
+        const int job[] = {0, j1, j2, 2, num, 1};
 
-        const int M = in.dims()[0];
-        const int N = in.dims()[1];
+        const int M = in.dims(0);
+        const int N = in.dims(1);
 
-        int ldd = in.strides()[1];
+        int ldd = in.strides(1);
 
         int info = 0;
 
         // Have to mess up all const correctness because MKL dnscsr function
         // is bidirectional and has input/output on all pointers
-        Array<T  > &values = sparse.getValues();
-        Array<int> &rowIdx = sparse.getRowIdx();
-        Array<int> &colIdx = sparse.getColIdx();
-
         dnscsr_func<T>()(
                 job, &M, &N,
                 reinterpret_cast<ptr_type<T>>(const_cast<T*>(in.get())), &ldd,
@@ -211,7 +207,12 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
                 &info);
     };
 
-    getQueue().enqueue(func, sparse_, in_);
+
+    Array<T  > &values = sparse_.getValues();
+    Array<int> &rowIdx = sparse_.getRowIdx();
+    Array<int> &colIdx = sparse_.getColIdx();
+
+    getQueue().enqueue(func, values, rowIdx, colIdx, (int)sparse_.elements(), in_);
 
     if(stype == AF_STORAGE_CSR)
         return sparse_;
@@ -235,24 +236,22 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
-    auto func = [=] (Array<T> dense, const SparseArray<T> in) {
+    auto func = [=] (Param<T> dense,
+                     CParam<T> values, CParam<int> rowIdx,
+                     CParam<int> colIdx, int num) {
         // Read: https://software.intel.com/en-us/node/520848
         // But job description is incorrect with regards to job[1]
         // 0 implies row major and 1 implies column major
         int j1 = 1, j2 = 0;
-        const int job[] = {1, j1, j2, 2, (int)dense.elements(), 1};
+        const int job[] = {1, j1, j2, 2, num, 1};
 
-        const int M = dense.dims()[0];
-        const int N = dense.dims()[1];
+        const int M = dense.dims(0);
+        const int N = dense.dims(1);
 
-        int ldd = dense.strides()[1];
+        int ldd = dense.strides(1);
 
         int info = 0;
 
-        Array<T  > values = in.getValues();
-        Array<int> rowIdx = in.getRowIdx();
-        Array<int> colIdx = in.getColIdx();
-
         // Have to mess up all const correctness because MKL dnscsr function
         // is bidirectional and has input/output on all pointers
         dnscsr_func<T>()(
@@ -264,7 +263,11 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
                 &info);
     };
 
-    getQueue().enqueue(func, dense_, in_);
+    Array<T  > values = in_.getValues();
+    Array<int> rowIdx = in_.getRowIdx();
+    Array<int> colIdx = in_.getColIdx();
+
+    getQueue().enqueue(func, dense_, values, rowIdx, colIdx, (int)in_.elements());
 
     if(stype == AF_STORAGE_CSR)
         return dense_;
@@ -288,21 +291,16 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
     sparse_.eval();
 
-    auto func = [=] (SparseArray<T> sparse, const Array<T> in) {
-        Array<T  > values = sparse.getValues();
-        Array<int> rowIdx = sparse.getRowIdx();
-        Array<int> colIdx = sparse.getColIdx();
-
-        kernel::dense_csr<T>()(values, rowIdx, colIdx, in);
-    };
-
-    getQueue().enqueue(func, sparse_, in_);
+    Array<T  > values = sparse_.getValues();
+    Array<int> rowIdx = sparse_.getRowIdx();
+    Array<int> colIdx = sparse_.getColIdx();
 
     if(stype == AF_STORAGE_CSR)
-        return sparse_;
+        getQueue().enqueue(kernel::dense_csr<T>, values, rowIdx, colIdx, in_);
     else
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
 
+
     return sparse_;
 }
 
@@ -314,20 +312,14 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
-    auto func = [=] (Array<T> dense, const SparseArray<T> in) {
-        Array<T  > values = in.getValues();
-        Array<int> rowIdx = in.getRowIdx();
-        Array<int> colIdx = in.getColIdx();
-
-        kernel::csr_dense<T>()(dense, values, rowIdx, colIdx);
-    };
-
-    getQueue().enqueue(func, dense_, in_);
+    Array<T  > values = in_.getValues();
+    Array<int> rowIdx = in_.getRowIdx();
+    Array<int> colIdx = in_.getColIdx();
 
     if(stype == AF_STORAGE_CSR)
-        return dense_;
+        getQueue().enqueue(kernel::csr_dense<T>, dense_, values, rowIdx, colIdx);
     else
-        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("CPU Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
 
     return dense_;
 }
@@ -347,8 +339,8 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
     SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
     converted.eval();
 
-    function<void (Array<T>, Array<int>, Array<int>,
-                   Array<T> const, Array<int> const, Array<int> const)
+    function<void (Param<T>, Param<int>, Param<int>,
+                   CParam<T>, CParam<int>, CParam<int>)
             > converter;
 
     if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index dc18392a87..5ad10e54d4 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -231,24 +231,28 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
 
-    auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
+    auto func = [=] (Param<T> output,
+                     CParam<T> values,
+                     CParam<int> rowIdx,
+                     CParam<int> colIdx,
+                     const dim_t sdim0,
+                     const dim_t sdim1,
+                     CParam<T> right) {
         auto alpha = getScale<T, 1>();
         auto beta  = getScale<T, 0>();
 
-        int ldb = right.strides()[1];
-        int ldc = output.strides()[1];
+        int ldb = right.strides(1);
+        int ldc = output.strides(1);
 
-        Array<T  > values = left.getValues();
-        Array<int> rowIdx = left.getRowIdx();
-        Array<int> colIdx = left.getColIdx();
-
-        int *pB = rowIdx.get();
-        int *pE = rowIdx.get() + 1;
+        int *pB = const_cast<int *>(rowIdx.get());
+        int *pE = pB + 1;
+        T *vptr = const_cast<T *>(values.get());
 
         sparse_matrix_t csrLhs;
-        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, left.dims()[0], left.dims()[1],
-                             pB, pE, colIdx.get(),
-                             reinterpret_cast<ptr_type<T>>(values.get()));
+        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, sdim0, sdim1,
+                             pB, pE,
+                             const_cast<int *>(colIdx.get()),
+                             reinterpret_cast<ptr_type<T>>(vptr));
 
         struct matrix_descr descrLhs;
         descrLhs.type = SPARSE_MATRIX_TYPE_GENERAL;
@@ -275,7 +279,13 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
         mkl_sparse_destroy(csrLhs);
     };
 
-    getQueue().enqueue(func, out, lhs, rhs);
+
+    const Array<T  > values = lhs.getValues();
+    const Array<int> rowIdx = lhs.getRowIdx();
+    const Array<int> colIdx = lhs.getColIdx();
+    af::dim4 ldims = lhs.dims();
+
+    getQueue().enqueue(func, out, values, rowIdx, colIdx, ldims[0], ldims[1], rhs);
 
     return out;
 }
@@ -304,20 +314,21 @@ cdouble getConjugate(const cdouble &in)
 }
 
 template<typename T, bool conjugate>
-void mv(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
+void mv(Param<T> output,
+        CParam<T> values,
+        CParam<int> rowIdx,
+        CParam<int> colIdx,
+        CParam<T> right,
         int M)
 {
-    T   const * const   valPtr = values.get();
-    int const * const   rowPtr = rowIdx.get();
-    int const * const   colPtr = colIdx.get();
-    T   const * const rightPtr = right.get();
-    T         * const   outPtr = output.get();
+    const T   *valPtr = values.get();
+    const int *rowPtr = rowIdx.get();
+    const int *colPtr = colIdx.get();
+    const T   *rightPtr = right.get();
 
-    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+    T* outPtr = output.get();
+
+    for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
         outPtr[i] = scalar<T>(0);
         for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
             //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
@@ -331,24 +342,24 @@ void mv(Array<T> output,
 }
 
 template<typename T, bool conjugate>
-void mtv(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
-        int M)
+void mtv(Param<T> output,
+         CParam<T> values,
+         CParam<int> rowIdx,
+         CParam<int> colIdx,
+         CParam<T> right,
+         int M)
 {
-    T   const * const   valPtr = values.get();
-    int const * const   rowPtr = rowIdx.get();
-    int const * const   colPtr = colIdx.get();
-    T   const * const rightPtr = right.get();
-    T         * const   outPtr = output.get();
+    const T *valPtr = values.get();
+    const int *rowPtr = rowIdx.get();
+    const int *colPtr = colIdx.get();
+    const T *rightPtr = right.get();
+    T* outPtr = output.get();
 
     for (int i = 0; i < M; ++i) {
         outPtr[i] = scalar<T>(0);
     }
 
-    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+    for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
         for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
             //If stride[0] of right is not 1 then rightPtr[i*stride]
             if (conjugate) {
@@ -361,22 +372,22 @@ void mtv(Array<T> output,
 }
 
 template<typename T, bool conjugate>
-void mm(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
+void mm(Param<T> output,
+        CParam<T> values,
+        CParam<int> rowIdx,
+        CParam<int> colIdx,
+        CParam<T> right,
         int M, int N,
         int ldb, int ldc)
 {
-    T   const * const   valPtr = values.get();
-    int const * const   rowPtr = rowIdx.get();
-    int const * const   colPtr = colIdx.get();
-    T   const *       rightPtr = right.get();
-    T         *         outPtr = output.get();
+    const T *valPtr = values.get();
+    const int *rowPtr = rowIdx.get();
+    const int *colPtr = colIdx.get();
+    const T *rightPtr = right.get();
+    T *outPtr = output.get();
 
     for (int o = 0; o < N; ++o) {
-        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
             outPtr[i] = scalar<T>(0);
             for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
                 //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
@@ -393,26 +404,26 @@ void mm(Array<T> output,
 }
 
 template<typename T, bool conjugate>
-void mtm(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
-        int M, int N,
-        int ldb, int ldc)
+void mtm(Param<T> output,
+         CParam<T> values,
+         CParam<int> rowIdx,
+         CParam<int> colIdx,
+         CParam<T> right,
+         int M, int N,
+         int ldb, int ldc)
 {
-    T   const * const   valPtr = values.get();
-    int const * const   rowPtr = rowIdx.get();
-    int const * const   colPtr = colIdx.get();
-    T   const *       rightPtr = right.get();
-    T         *         outPtr = output.get();
+    const T *valPtr = values.get();
+    const int *rowPtr = rowIdx.get();
+    const int *colPtr = colIdx.get();
+    const T *rightPtr = right.get();
+    T *outPtr = output.get();
 
     for (int o = 0; o < N; ++o) {
         for (int i = 0; i < M; ++i) {
             outPtr[i] = scalar<T>(0);
         }
 
-        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
             for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
                 //If stride[0] of right is not 1 then rightPtr[i*stride]
                 if (conjugate) {
@@ -426,6 +437,7 @@ void mtm(Array<T> output,
         outPtr += ldc;
     }
 }
+
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
@@ -448,13 +460,13 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
 
-    auto func = [=] (Array<T> output, const SparseArray<T> left, const Array<T> right) {
-        int ldb = right.strides()[1];
-        int ldc = output.strides()[1];
-
-        Array<T  > values = left.getValues();
-        Array<int> rowIdx = left.getRowIdx();
-        Array<int> colIdx = left.getColIdx();
+    auto func = [=] (Param<T> output,
+                     CParam<T> values,
+                     CParam<int> rowIdx,
+                     CParam<int> colIdx,
+                     CParam<T> right) {
+        int ldb = right.strides(1);
+        int ldc = output.strides(1);
 
         if(rDims[rColDim] == 1) {
             if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
@@ -475,7 +487,11 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
         }
     };
 
-    getQueue().enqueue(func, out, lhs, rhs);
+    const Array<T  > values = lhs.getValues();
+    const Array<int> rowIdx = lhs.getRowIdx();
+    const Array<int> colIdx = lhs.getColIdx();
+
+    getQueue().enqueue(func, out, values, rowIdx, colIdx, rhs);
 
     return out;
 }
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 2ac58aab3f..932e3621ab 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -73,18 +73,18 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
     vt.eval();
     in.eval();
 
-    auto func = [=] (Array<Tr> s, Array<T> u, Array<T> vt, Array<T> in) {
+    auto func = [=] (Param<Tr> s, Param<T> u, Param<T> vt, Param<T> in) {
         dim4 iDims = in.dims();
         int M = iDims[0];
         int N = iDims[1];
 
 #if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides()[1],
-                s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1]);
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides(1),
+                s.get(), u.get(), u.strides(1), vt.get(), vt.strides(1));
 #else
         std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides()[1],
-                s.get(), u.get(), u.strides()[1], vt.get(), vt.strides()[1], &superb[0]);
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides(1),
+                s.get(), u.get(), u.strides(1), vt.get(), vt.strides(1), &superb[0]);
 #endif
     };
     getQueue().enqueue(func, s, u, vt, in);

From c20932c88aeed617ff54d91a29a9022083554970 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Thu, 17 Aug 2017 08:24:42 -0700
Subject: [PATCH 1245/2677] Removing gitter and adding slack invite for chat.

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c25966f6cb..5139ae4d61 100644
--- a/README.md
+++ b/README.md
@@ -153,8 +153,9 @@ ArrayFire development is funded by ArrayFire LLC and several third parties,
 please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for further
 details.
 
-## Support and Contact Info [![Join the chat at https://gitter.im/arrayfire/arrayfire](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/arrayfire/arrayfire?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+## Support and Contact Info
 
+* [Slack Chat](https://join.slack.com/t/arrayfire-org/shared_invite/MjI1ODQ4NjI1MTM4LTE1MDI1NTgzOTctNzdjZmYyYWIwNA)
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
 * ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
 

From 9d130015fb2f8aa9d2c69b61caadd10f71ad5302 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 17 Aug 2017 13:00:02 -0400
Subject: [PATCH 1246/2677] Update slack link with a permanent invite link

The previous link to slack had a time limit of a few weeks. This new link has an unlimited time limit.
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5139ae4d61..4a029004f2 100644
--- a/README.md
+++ b/README.md
@@ -155,7 +155,7 @@ details.
 
 ## Support and Contact Info
 
-* [Slack Chat](https://join.slack.com/t/arrayfire-org/shared_invite/MjI1ODQ4NjI1MTM4LTE1MDI1NTgzOTctNzdjZmYyYWIwNA)
+* [Slack Chat](https://join.slack.com/t/arrayfire-org/shared_invite/MjI4MjIzMDMzMTczLTE1MDI5ODg4NzYtN2QwNGE3ODA5OQ)
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
 * ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
 

From 1012b0fa2c400be85c8f4253ef352750bb611d01 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Wed, 19 Jul 2017 14:31:16 -0700
Subject: [PATCH 1247/2677] Get the node_map and full_nodes simultaneously in
 JIT for all backends (#1864)

* Get the node_map and full_nodes simultaneously in JIT for all backends

* Use std::array instead of std::vector for the children
---
 src/backend/cpu/Array.cpp        |  3 ++-
 src/backend/cpu/TNJ/Node.hpp     | 20 ++++++++++++++------
 src/backend/cpu/kernel/Array.hpp | 20 +++++++++-----------
 src/backend/cuda/Array.cpp       |  9 +++++----
 src/backend/cuda/JIT/Node.hpp    | 29 +++++++++++++++++++----------
 src/backend/cuda/jit.cpp         | 20 ++++++++++++--------
 src/backend/opencl/Array.cpp     |  9 +++++----
 src/backend/opencl/JIT/Node.hpp  | 29 +++++++++++++++++++----------
 src/backend/opencl/jit.cpp       | 20 ++++++++++++--------
 test/data                        |  2 +-
 10 files changed, 98 insertions(+), 63 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index fd0d55eace..c4f0950816 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -224,7 +224,8 @@ createNodeArray(const dim4 &dims, Node_ptr node)
                 Node *n = node.get();
 
                 TNJ::Node_map_t nodes_map;
-                n->getNodesMap(nodes_map);
+                std::vector<TNJ::Node *> full_nodes;
+                n->getNodesMap(nodes_map, full_nodes);
                 unsigned length =0, buf_count = 0, bytes = 0;
                 for(auto &entry : nodes_map) {
                     Node *node = entry.first;
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index 0d3e6c9896..1c2a69d47c 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <optypes.hpp>
+#include <array>
 #include <vector>
 #include <memory>
 #include <unordered_map>
@@ -19,8 +20,10 @@ namespace cpu
 namespace TNJ
 {
 
+    static const int MAX_CHILDREN = 2;
     class Node;
     using std::shared_ptr;
+    using std::vector;
     typedef shared_ptr<Node> Node_ptr;
 
     typedef std::unordered_map<Node *, int> Node_map_t;
@@ -32,23 +35,28 @@ namespace TNJ
     protected:
 
         const int m_height;
-        const std::vector<Node_ptr> m_children;
+        const std::array<Node_ptr, MAX_CHILDREN> m_children;
 
     public:
-        Node(const int height, const std::vector<Node_ptr> children) :
+        Node(const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
             m_height(height),
             m_children(children)
         {}
 
-        void getNodesMap(Node_map_t &node_map)
+        int getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes)
         {
-            if (node_map.find(this) == node_map.end()) {
+            auto iter = node_map.find(this);
+            if (iter == node_map.end()) {
                 for (const auto &child : m_children) {
-                    child->getNodesMap(node_map);
+                    if (child == nullptr) break;
+                    child->getNodesMap(node_map, full_nodes);
                 }
                 int id = node_map.size();
                 node_map[this] = id;
+                full_nodes.push_back(this);
+                return id;
             }
+            return iter->second;
         }
 
         int getHeight() { return m_height; }
@@ -78,7 +86,7 @@ namespace TNJ
     public:
         T m_val;
     public:
-        TNode(T val, const int height, const std::vector<Node_ptr> children) :
+        TNode(T val, const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
             Node(height, children),
             m_val(val)
             {
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 8f363a97f6..ea7e806a02 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -27,18 +27,17 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
     TNJ::Node_map_t nodes;
     std::vector<T *> ptrs;
     std::vector<TNJ::TNode<T> *> output_nodes;
+    std::vector<TNJ::Node *> full_nodes;
 
     for (int i = 0; i < (int)arrays.size(); i++) {
         ptrs.push_back(arrays[i].get());
         output_nodes.push_back(reinterpret_cast<TNJ::TNode<T> *>(output_nodes_[i].get()));
-        output_nodes_[i]->getNodesMap(nodes);
+        output_nodes_[i]->getNodesMap(nodes, full_nodes);
     }
 
     bool is_linear = true;
-    std::vector<TNJ::Node *> full_nodes(nodes.size());
-    for(const auto &map_entry : nodes) {
-        full_nodes[map_entry.second] = map_entry.first;
-        is_linear &= map_entry.first->isLinear(odims.get());
+    for(auto node : full_nodes) {
+        is_linear &= node->isLinear(odims.get());
     }
 
     if (is_linear) {
@@ -86,14 +85,13 @@ void evalArray(Param<T> arr, TNJ::Node_ptr node)
     af::dim4 ostrs = arr.strides();
 
     TNJ::Node_map_t nodes;
-    node->getNodesMap(nodes);
+    std::vector<TNJ::Node *> full_nodes;
+    full_nodes.reserve(1024);
+    node->getNodesMap(nodes, full_nodes);
 
     bool is_linear = true;
-    std::vector<TNJ::Node *> full_nodes(nodes.size());
-
-    for(const auto &map_entry : nodes) {
-        full_nodes[map_entry.second] = map_entry.first;
-        is_linear &= map_entry.first->isLinear(odims.get());
+    for(auto node : full_nodes) {
+        is_linear &= node->isLinear(odims.get());
     }
 
     TNJ::TNode<T> *output_node = reinterpret_cast<TNJ::TNode<T> *>(full_nodes.back());
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 18539cef26..6f171bbe0e 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -241,11 +241,12 @@ namespace cuda
                     unsigned length =0, buf_count = 0, bytes = 0;
                     Node *n = node.get();
                     JIT::Node_map_t nodes_map;
-                    n->getNodesMap(nodes_map);
+                    std::vector<JIT::Node *> full_nodes;
+                    std::vector<JIT::Node_ids> full_ids;
+                    n->getNodesMap(nodes_map, full_nodes, full_ids);
 
-                    for(auto &entry : nodes_map) {
-                        Node *node = entry.first;
-                        node->getInfo(length, buf_count, bytes);
+                    for(auto &jit_node : full_nodes) {
+                        jit_node->getInfo(length, buf_count, bytes);
                     }
 
                     if (2 * bytes > lock_bytes) {
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 995d4c7b1d..443f5e2ef7 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <platform.hpp>
 #include <optypes.hpp>
+#include <array>
 #include <string>
 #include <vector>
 #include <memory>
@@ -21,17 +22,19 @@ namespace cuda
 namespace JIT
 {
 
+    static const int MAX_CHILDREN = 2;
     class Node;
     using std::shared_ptr;
+    using std::vector;
     typedef shared_ptr<Node> Node_ptr;
 
     typedef struct
     {
         int id;
-        std::vector<int> child_ids;
+        std::array<int, MAX_CHILDREN> child_ids;
     } Node_ids;
 
-    typedef std::unordered_map<Node *, Node_ids> Node_map_t;
+    typedef std::unordered_map<Node *, int> Node_map_t;
     typedef Node_map_t::iterator Node_map_iter;
 
     class Node
@@ -40,29 +43,35 @@ namespace JIT
         const std::string m_type_str;
         const std::string m_name_str;
         const int m_height;
-        const std::vector<Node_ptr> m_children;
+        const std::array<Node_ptr, MAX_CHILDREN> m_children;
 
     public:
 
         Node(const char *type_str, const char *name_str, const int height,
-             const std::vector<Node_ptr> children)
+             const std::array<Node_ptr, MAX_CHILDREN> children)
             : m_type_str(type_str),
               m_name_str(name_str),
               m_height(height),
               m_children(children)
         {}
 
-        void getNodesMap(Node_map_t &node_map)
+        int getNodesMap(Node_map_t &node_map,
+                        vector<Node *> &full_nodes,
+                        vector<Node_ids> &full_ids)
         {
-            if (node_map.find(this) == node_map.end()) {
+            auto iter = node_map.find(this);
+            if (iter == node_map.end()) {
                 Node_ids ids;
-                for (const auto &child : m_children) {
-                    child->getNodesMap(node_map);
-                    ids.child_ids.push_back(node_map[child.get()].id);
+                for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
+                    ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
                 }
                 ids.id = node_map.size();
-                node_map[this] = ids;
+                node_map[this] = ids.id;
+                full_nodes.push_back(this);
+                full_ids.push_back(ids);
+                return ids.id;
             }
+            return iter->second;
         }
 
         virtual void genKerName (std::stringstream &kerStream, Node_ids ids) {}
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index e8d960e02c..48bfcbed11 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -346,19 +346,23 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
     if (num_outputs == 0) return;
 
     Node_map_t nodes;
+    vector<Node *> full_nodes;
+    vector<Node_ids> full_ids;
     vector<int> output_ids;
+
+    // Reserve some space to improve performance at smaller sizes
+    output_ids.reserve(output_nodes.size());
+    full_nodes.reserve(1024);
+    full_ids.reserve(1024);
+
     for (auto &node : output_nodes) {
-        node->getNodesMap(nodes);
-        output_ids.push_back(nodes[node].id);
+        int id = node->getNodesMap(nodes, full_nodes, full_ids);
+        output_ids.push_back(id);
     }
 
-    vector<Node *> full_nodes(nodes.size());
-    vector<Node_ids> full_ids(nodes.size());
     bool is_linear = true;
-    for (auto &map_entry : nodes) {
-        full_nodes[map_entry.second.id] = map_entry.first;
-        full_ids[map_entry.second.id] = map_entry.second;
-        is_linear &= map_entry.first->isLinear(outputs[0].dims);
+    for (auto node : full_nodes) {
+        is_linear &= node->isLinear(outputs[0].dims);
     }
 
     CUfunction ker = getKernel(output_nodes, output_ids,
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 516695bca6..f349dff9c6 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -243,11 +243,12 @@ namespace opencl
                     unsigned length =0, buf_count = 0, bytes = 0;
                     Node *n = node.get();
                     JIT::Node_map_t nodes_map;
-                    n->getNodesMap(nodes_map);
+                    std::vector<JIT::Node *> full_nodes;
+                    std::vector<JIT::Node_ids> full_ids;
+                    n->getNodesMap(nodes_map, full_nodes, full_ids);
 
-                    for(auto &entry : nodes_map) {
-                        Node *node = entry.first;
-                        node->getInfo(length, buf_count, bytes);
+                    for(auto &jit_node : full_nodes) {
+                        jit_node->getInfo(length, buf_count, bytes);
                     }
 
                     if (2 * bytes > lock_bytes) {
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index c4e0176664..e734dbe52c 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <platform.hpp>
 #include <optypes.hpp>
+#include <array>
 #include <string>
 #include <vector>
 #include <memory>
@@ -21,17 +22,19 @@ namespace opencl
 namespace JIT
 {
 
+    static const int MAX_CHILDREN = 2;
     class Node;
     using std::shared_ptr;
+    using std::vector;
     typedef shared_ptr<Node> Node_ptr;
 
     typedef struct
     {
         int id;
-        std::vector<int> child_ids;
+        std::array<int, MAX_CHILDREN> child_ids;
     } Node_ids;
 
-    typedef std::unordered_map<Node *, Node_ids> Node_map_t;
+    typedef std::unordered_map<Node *, int> Node_map_t;
     typedef Node_map_t::iterator Node_map_iter;
 
     class Node
@@ -40,29 +43,35 @@ namespace JIT
         const std::string m_type_str;
         const std::string m_name_str;
         const int m_height;
-        const std::vector<Node_ptr> m_children;
+        const std::array<Node_ptr, MAX_CHILDREN> m_children;
 
     public:
 
         Node(const char *type_str, const char *name_str, const int height,
-             const std::vector<Node_ptr> children)
+             const std::array<Node_ptr, MAX_CHILDREN> children)
             : m_type_str(type_str),
               m_name_str(name_str),
               m_height(height),
               m_children(children)
         {}
 
-        void getNodesMap(Node_map_t &node_map)
+        int getNodesMap(Node_map_t &node_map,
+                        vector<Node *> &full_nodes,
+                        vector<Node_ids> &full_ids)
         {
-            if (node_map.find(this) == node_map.end()) {
+            auto iter = node_map.find(this);
+            if (iter == node_map.end()) {
                 Node_ids ids;
-                for (const auto &child : m_children) {
-                    child->getNodesMap(node_map);
-                    ids.child_ids.push_back(node_map[child.get()].id);
+                for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
+                    ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
                 }
                 ids.id = node_map.size();
-                node_map[this] = ids;
+                node_map[this] = ids.id;
+                full_nodes.push_back(this);
+                full_ids.push_back(ids);
+                return ids.id;
             }
+            return iter->second;
         }
 
         virtual void genKerName(std::stringstream &kerStream, Node_ids ids) {}
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 7f2c1f4644..05cc889dd2 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -209,19 +209,23 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     KParam out_info = outputs[0].info;
 
     Node_map_t nodes;
+    vector<Node *> full_nodes;
+    vector<Node_ids> full_ids;
     vector<int> output_ids;
+
+    // Reserve some space to improve performance at smaller sizes
+    output_ids.reserve(output_nodes.size());
+    full_nodes.reserve(1024);
+    full_ids.reserve(1024);
+
     for (auto &node : output_nodes) {
-        node->getNodesMap(nodes);
-        output_ids.push_back(nodes[node].id);
+        int id = node->getNodesMap(nodes, full_nodes, full_ids);
+        output_ids.push_back(id);
     }
 
-    vector<Node *> full_nodes(nodes.size());
-    vector<Node_ids> full_ids(nodes.size());
     bool is_linear = true;
-    for (auto &map_entry : nodes) {
-        full_nodes[map_entry.second.id] = map_entry.first;
-        full_ids[map_entry.second.id] = map_entry.second;
-        is_linear &= map_entry.first->isLinear(out_info.dims);
+    for (auto node : full_nodes) {
+        is_linear &= node->isLinear(outputs[0].info.dims);
     }
 
     Kernel ker = getKernel(output_nodes, output_ids,
diff --git a/test/data b/test/data
index 21d4c0671c..0f2450b7e1 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 21d4c0671c9da50ca2c921a6cd73a46172feb68c
+Subproject commit 0f2450b7e1ae964e6d1e3cb077d3968fe7e88bb7

From 9192be0fa4dcfdd1112d6422b9e0fb71e8699744 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Jul 2017 17:31:34 -0400
Subject: [PATCH 1248/2677] Pthread fix (#1872)

* Fix pthreads linking error when linking with lapacke

* Add pthreads always
---
 src/backend/cpu/CMakeLists.txt    | 18 ++++++++++--------
 src/backend/cuda/CMakeLists.txt   | 21 ++++++++++++---------
 src/backend/opencl/CMakeLists.txt | 28 ++++++++++++++++------------
 3 files changed, 38 insertions(+), 29 deletions(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index aec7aab535..ac23489b08 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -72,10 +72,10 @@ ELSE(APPLE) # Linux and Windows
     FIND_PACKAGE(LAPACKE)
 ENDIF(APPLE)
 
-IF(NOT LAPACK_FOUND)
-    MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
-ELSE(NOT LAPACK_FOUND)
+IF(LAPACK_FOUND)
     ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
+ELSE()
+    MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
 ENDIF()
 
 IF(NOT UNIX)
@@ -212,13 +212,15 @@ ADD_LIBRARY(afcpu SHARED
 ENDIF(DEFINED BLAS_SYM_FILE)
 
 TARGET_LINK_LIBRARIES(afcpu
-                            PRIVATE ${CBLAS_LIBRARIES}
-                            PRIVATE ${FFTW_LIBRARIES}
-                            PRIVATE ${FreeImage_LIBS}
-                     )
+  PRIVATE
+    ${CBLAS_LIBRARIES}
+    ${FFTW_LIBRARIES}
+    ${FreeImage_LIBS}
+    ${CMAKE_THREAD_LIBS_INIT}
+)
 
 IF(LAPACK_FOUND)
-   TARGET_LINK_LIBRARIES(afcpu  PRIVATE ${LAPACK_LIBRARIES})
+  TARGET_LINK_LIBRARIES(afcpu PRIVATE ${LAPACK_LIBRARIES})
 ENDIF()
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index efd9a834b3..9da3b90df9 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -351,15 +351,18 @@ MY_CUDA_ADD_LIBRARY(afcuda SHARED
                 ${scan_by_key_sources}
                 OPTIONS ${CUDA_GENERATE_CODE} ${CUDA_ADD_LIBRARY_OPTIONS})
 
-TARGET_LINK_LIBRARIES(afcuda    PRIVATE ${CUDA_CUBLAS_LIBRARIES}
-                                PRIVATE ${CUDA_LIBRARIES}
-                                PRIVATE ${FreeImage_LIBS}
-                                PRIVATE ${CUDA_CUFFT_LIBRARIES}
-                                PRIVATE ${CUDA_cusparse_LIBRARY}
-                                PRIVATE ${CUDA_cusolver_LIBRARY}
-                                PRIVATE ${CUDA_nvrtc_LIBRARY}
-                                PRIVATE ${CUDA_CUDA_LIBRARY}
-                     )
+TARGET_LINK_LIBRARIES(afcuda
+    PRIVATE
+      ${CUDA_CUBLAS_LIBRARIES}
+      ${CUDA_LIBRARIES}
+      ${FreeImage_LIBS}
+      ${CUDA_CUFFT_LIBRARIES}
+      ${CUDA_cusparse_LIBRARY}
+      ${CUDA_cusolver_LIBRARY}
+      ${CUDA_nvrtc_LIBRARY}
+      ${CUDA_CUDA_LIBRARY}
+      ${CMAKE_THREAD_LIBS_INIT}
+)
 
 ADD_DEPENDENCIES(afcuda ${jit_kernel_targets})
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 106cbf7504..1c66e45089 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -31,9 +31,7 @@ ELSE(APPLE) # Linux and Windows
     FIND_PACKAGE(LAPACKE)
 ENDIF(APPLE)
 
-IF(NOT LAPACK_FOUND)
-    MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
-ELSE(NOT LAPACK_FOUND)
+IF(LAPACK_FOUND)
   ADD_DEFINITIONS(-DWITH_OPENCL_LINEAR_ALGEBRA)
 
   IF(NOT USE_OPENCL_MKL)
@@ -47,6 +45,8 @@ ELSE(NOT LAPACK_FOUND)
       MESSAGE(SEND_ERROR "CBLAS Library not set")
     ENDIF()
   ENDIF()
+ELSE(LAPACK_FOUND)
+    MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
 ENDIF()
 
 IF(USE_OPENCL_MKL) # Manual MKL Setup
@@ -337,18 +337,22 @@ ELSE(DEFINED BLAS_SYM_FILE)
 ENDIF()
 
 TARGET_LINK_LIBRARIES(afopencl
-                                PRIVATE ${OpenCL_LIBRARIES}
-                                PRIVATE ${CLBLAST_LIBRARIES}
-                                PRIVATE ${CLBLAS_LIBRARIES}
-                                PRIVATE ${CLFFT_LIBRARIES}
-                                PRIVATE ${CMAKE_DL_LIBS}
-                                PRIVATE ${FreeImage_LIBS}
-                     )
+  PRIVATE
+    ${OpenCL_LIBRARIES}
+    ${CLBLAST_LIBRARIES}
+    ${CLBLAS_LIBRARIES}
+    ${CLFFT_LIBRARIES}
+    ${CMAKE_DL_LIBS}
+    ${FreeImage_LIBS}
+    ${CMAKE_THREAD_LIBS_INIT}
+)
 
 IF(LAPACK_FOUND)
     TARGET_LINK_LIBRARIES(afopencl
-                                PRIVATE ${LAPACK_LIBRARIES}
-                                PRIVATE ${CBLAS_LIBRARIES})
+      PRIVATE
+        ${LAPACK_LIBRARIES}
+        ${CBLAS_LIBRARIES}
+    )
 ENDIF(LAPACK_FOUND)
 
 LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)

From ef12fb208e59afde8f932fefeca0f4697fe56555 Mon Sep 17 00:00:00 2001
From: plavin <plavin997@gmail.com>
Date: Wed, 19 Jul 2017 17:59:30 -0400
Subject: [PATCH 1249/2677] Fix max allowable window size in af_unwrap (#1853)

* Fix max allowable window size in af_unwrap

As padding is added a both sides of a dimension, the max allowable window size
should be dim_size + 2 * padding
---
 src/api/c/unwrap.cpp | 4 ++--
 test/unwrap.cpp      | 3 ++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index 1e473a7ecd..4bca08dae8 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -34,8 +34,8 @@ af_err af_unwrap(af_array *out, const af_array in, const dim_t wx, const dim_t w
         af_dtype type = info.getType();
         af::dim4 idims = info.dims();
 
-        ARG_ASSERT(2, wx > 0 && wx <= idims[0] + px);
-        ARG_ASSERT(3, wy > 0 && wy <= idims[1] + py);
+        ARG_ASSERT(2, wx > 0 && wx <= idims[0] + 2 * px);
+        ARG_ASSERT(3, wy > 0 && wy <= idims[1] + 2 * py);
         ARG_ASSERT(4, sx > 0);
         ARG_ASSERT(5, sy > 0);
         ARG_ASSERT(6, px >= 0 && px < wx);
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 82371d31fb..1392edd28c 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -138,7 +138,8 @@ void unwrapTest(string pTestFile, const unsigned resultIdx,
 
    // FIXME: This test is faulty after fixing the copy paste errors in unwrap
    // UNWRAP_INIT(UnwrapSmall44, unwrap_small, 44, 15, 10, 15, 10, 14,  9);
-
+    UNWRAP_INIT(UnwrapSmall45, unwrap_small, 45, 18, 16, 18, 16,  1,  0);
+    UNWRAP_INIT(UnwrapSmall46, unwrap_small, 46, 16, 18, 16, 18,  0,  1);
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Unwrap, CPP)

From 92247d97048a5a2e87ba43f5d008626227616058 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 24 Jun 2017 15:59:24 +0530
Subject: [PATCH 1250/2677] Remove boost dependency from CPU & CUDA backends

---
 CMakeLists.txt                    |  5 -----
 src/backend/opencl/CMakeLists.txt | 19 +++++++++++++++----
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index fed528960c..5fcf4cd415 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -56,10 +56,6 @@ ELSE(FREEIMAGE_FOUND)
     MESSAGE(WARNING, "FreeImage not found!")
 ENDIF(FREEIMAGE_FOUND)
 
-ADD_DEFINITIONS(-DBOOST_ALL_NO_LIB)
-SET(Boost_USE_STATIC_LIBS OFF)
-FIND_PACKAGE(Boost REQUIRED)
-
 OPTION(USE_SYSTEM_CL2HPP "Use cl2.hpp installed on system" OFF)
 
 IF(BUILD_GRAPHICS)
@@ -135,7 +131,6 @@ INCLUDE_DIRECTORIES(BEFORE
     "${CMAKE_CURRENT_SOURCE_DIR}/include"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/backend"
     "${CMAKE_CURRENT_SOURCE_DIR}/src/api/c"
-    "${Boost_INCLUDE_DIR}"
     )
 
 IF(${UNIX})
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 1c66e45089..f987c04a9d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -108,12 +108,22 @@ INCLUDE_DIRECTORIES(${CLFFT_INCLUDE_DIRS})
 LINK_DIRECTORIES(${CLFFT_LIBRARY_DIR})
 
 OPTION(USE_SYSTEM_BOOST_COMPUTE "Use system BoostCompute" OFF)
-IF(USE_SYSTEM_BOOST_COMPUTE)
-    IF(Boost_VERSION VERSION_LESS "1.61")
+
+ADD_DEFINITIONS(-DBOOST_ALL_NO_LIB)
+SET(Boost_USE_STATIC_LIBS OFF)
+FIND_PACKAGE(Boost REQUIRED)
+
+# If Boost version is 1.61.00, skip finding boost compute explicitly
+# as BoostCompute is merged into Boost starting that version.
+IF(Boost_VERSION VERSION_LESS 106100)
+    MESSAGE(STATUS "Boost version is less than 1.61.00")
+    IF(USE_SYSTEM_BOOST_COMPUTE)
+        MESSAGE(STATUS "Using system boost compute")
         FIND_PACKAGE(BoostCompute REQUIRED)
+    ELSE()
+        MESSAGE(STATUS "Building boost compute")
+        INCLUDE(build_boost_compute)
     ENDIF()
-ELSE()
-    INCLUDE(build_boost_compute)
 ENDIF()
 
 SET( cl_kernel_headers
@@ -128,6 +138,7 @@ INCLUDE_DIRECTORIES(
     ${CLBLAST_INCLUDE_DIRS}
     ${CLBLAS_INCLUDE_DIRS}
     ${CLFFT_INCLUDE_DIRS}
+    ${Boost_INCLUDE_DIRS}
     ${BoostCompute_INCLUDE_DIRS}
     ${CBLAS_INCLUDE_DIR}
     )

From 6cb546b37d0221366b5fda643505ebe83cd80a4f Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Mon, 17 Jul 2017 15:30:03 -0700
Subject: [PATCH 1251/2677] Fixing warnings in tests with gcc 7.1

---
 examples/graphics/fractal.cpp |  4 ++-
 test/backend.cpp              |  4 ++-
 test/histogram.cpp            |  2 +-
 test/info.cpp                 |  4 ++-
 test/random.cpp               |  8 ++++--
 test/reduce.cpp               |  7 ++---
 test/sparse_arith.cpp         |  6 +++--
 test/testHelpers.hpp          | 51 +++++++++++++----------------------
 test/transform.cpp            |  6 +++--
 9 files changed, 47 insertions(+), 45 deletions(-)

diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index 74718eaa51..717a9ccee5 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -81,7 +81,9 @@ int main(int argc, char **argv)
         // Keep zomming out for each frame
         for (int i = 10; i < 400; i++) {
             int zoom = i * i;
-            if(!(i % 10)) printf("iteration: %d zoom: %d\n", i, zoom); fflush(stdout);
+            if(!(i % 10)) {
+                printf("iteration: %d zoom: %d\n", i, zoom); fflush(stdout);
+            }
 
             // Generate the grid at the current zoom factor
             array c = complex_grid(WIDTH, HEIGHT, zoom, center);
diff --git a/test/backend.cpp b/test/backend.cpp
index 78b64309db..8cd9c63200 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -51,7 +51,9 @@ void testFunction()
     ASSERT_EQ(arrayBackend, activeBackend);
 
     // cleanup
-    if(outArray != 0) ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    if(outArray != 0) {
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    }
 }
 
 void backendTest()
diff --git a/test/histogram.cpp b/test/histogram.cpp
index 992e998e29..99fa546a52 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -227,7 +227,7 @@ TEST(Histogram, SNIPPET_histequal)
 
     if( false == equal(h_out.begin(), h_out.end(), output) ) {
         cout << "Expected: ";
-        copy(output, output + nbins, ostream_iterator<float>(cout, ", "));
+        copy(output, output + nElems, ostream_iterator<float>(cout, ", "));
         cout << endl << "Actual: ";
         copy(h_out.begin(), h_out.end(), ostream_iterator<float>(cout, ", "));
         FAIL() << "Output did not match";
diff --git a/test/info.cpp b/test/info.cpp
index e0e45ed4e7..5a0a8ac8d3 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -30,7 +30,9 @@ void testFunction()
     af::dim4 dims(32, 32, 1, 1);
     ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
     // cleanup
-    if(outArray != 0) ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    if(outArray != 0) {
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    }
 }
 
 void infoTest()
diff --git a/test/random.cpp b/test/random.cpp
index 71010b03fa..749346e633 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -237,11 +237,15 @@ void testSetSeed(const uintl seed0, const uintl seed1)
 
         // Verify different arrays created with different seeds differ
         // b8 and u9 can clash because they generate a small set of values
-        if (ty != b8 && ty != u8) ASSERT_NE(h_in0[i], h_in1[i]) << "at : " << i;
+        if (ty != b8 && ty != u8) {
+            ASSERT_NE(h_in0[i], h_in1[i]) << "at : " << i;
+        }
 
         // Verify different arrays created one after the other with same seed differ
         // b8 and u9 can clash because they generate a small set of values
-        if (ty != b8 && ty != u8) ASSERT_NE(h_in2[i], h_in3[i]) << "at : " << i;
+        if (ty != b8 && ty != u8) {
+            ASSERT_NE(h_in2[i], h_in3[i]) << "at : " << i;
+        }
     }
 
     af::setSeed(orig_seed); // Reset the seed
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 675ed8fc4a..24d3fec6f7 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -348,17 +348,18 @@ template<>
 void typed_assert_eq<af::cfloat>(af::cfloat lhs, af::cfloat rhs, bool both)
 {
     ASSERT_FLOAT_EQ(real(lhs), real(rhs));
-    if(both)
+    if(both) {
         ASSERT_FLOAT_EQ(imag(lhs), imag(rhs));
-
+    }
 }
 
 template<>
 void typed_assert_eq<af::cdouble>(af::cdouble lhs, af::cdouble rhs, bool both)
 {
     ASSERT_DOUBLE_EQ(real(lhs), real(rhs));
-    if(both)
+    if(both) {
         ASSERT_DOUBLE_EQ(imag(lhs), imag(rhs));
+    }
 }
 
 TYPED_TEST(Reduce, Test_All_Global)
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 241cde6c6b..16d1ae12b7 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -117,8 +117,10 @@ void sparseCompare(af::array A, af::array B, const double eps)
 {
 // This macro is used to check if either value is finite and then call assert
 // If neither value is finite, then they can be assumed to be equal to either inf or nan
-#define ASSERT_FINITE_EQ(V1, V2)                                                            \
-    if(std::isfinite(V1) || std::isfinite(V2)) ASSERT_NEAR(V1, V2, eps) << "at : " << i;    \
+#define ASSERT_FINITE_EQ(V1, V2)                    \
+    if(std::isfinite(V1) || std::isfinite(V2)) {    \
+        ASSERT_NEAR(V1, V2, eps) << "at : " << i;   \
+    }                                               \
 
     af::array AValues = sparseGetValues(A);
     af::array ARowIdx = sparseGetRowIdx(A);
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index eb5023df82..3d3e94a266 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -26,6 +26,21 @@ typedef unsigned char  uchar;
 typedef unsigned int   uint;
 typedef unsigned short ushort;
 
+std::string readNextNonEmptyLine(std::ifstream &file)
+{
+    std::string result = "";
+    // Using a for loop to read the next non empty line
+    for (std::string line; std::getline(file, line);) {
+        result += line;
+        if (result != "") break;
+    }
+    // If no file has been found, throw an exception
+    if (result == "") {
+        throw std::runtime_error("Non empty lines not found in the file");
+    }
+    return result;
+}
+
 template<typename inType, typename outType, typename FileElementType>
 void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
                 std::vector<std::vector<inType> >   &testInputs,
@@ -159,26 +174,12 @@ inline void readImageTests(const std::string        &pFileName,
 
         pTestInputs.resize(inputCount, "");
         for(unsigned k=0; k<inputCount; k++) {
-            std::string temp = "";
-            while(std::getline(testFile, temp)) {
-                if (temp!="")
-                    break;
-            }
-            if (temp=="")
-                throw std::runtime_error("Test file might not be per format, please check.");
-            pTestInputs[k] = temp;
+            pTestInputs[k] = readNextNonEmptyLine(testFile);
         }
 
         pTestOutputs.resize(testCount, "");
         for(unsigned i = 0; i < testCount; i++) {
-            std::string temp = "";
-            while(std::getline(testFile, temp)) {
-                if (temp!="")
-                    break;
-            }
-            if (temp=="")
-                throw std::runtime_error("Test file might not be per format, please check.");
-            pTestOutputs[i] = temp;
+            pTestOutputs[i] = readNextNonEmptyLine(testFile);
         }
     }
     else {
@@ -215,14 +216,7 @@ void readImageTests(const std::string                 &pFileName,
 
         pTestInputs.resize(inputCount, "");
         for(unsigned k=0; k<inputCount; k++) {
-            std::string temp = "";
-            while(std::getline(testFile, temp)) {
-                if (temp!="")
-                    break;
-            }
-            if (temp=="")
-                throw std::runtime_error("Test file might not be per format, please check.");
-            pTestInputs[k] = temp;
+            pTestInputs[k] = readNextNonEmptyLine(testFile);
         }
 
         pTestOutputs.resize(testCount, vector<outType>(0));
@@ -267,14 +261,7 @@ void readImageFeaturesDescriptors(const std::string                  &pFileName,
 
         pTestInputs.resize(inputCount, "");
         for(unsigned k=0; k<inputCount; k++) {
-            std::string temp = "";
-            while(std::getline(testFile, temp)) {
-                if (temp!="")
-                    break;
-            }
-            if (temp=="")
-                throw std::runtime_error("Test file might not be per format, please check.");
-            pTestInputs[k] = temp;
+            pTestInputs[k] = readNextNonEmptyLine(testFile);
         }
 
         pTestFeats.resize(attrCount, vector<float>(0));
diff --git a/test/transform.cpp b/test/transform.cpp
index 81129c020a..1e34b6ad15 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -109,8 +109,9 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
 
     for (dim_t elIter = 0; elIter < goldEl; elIter++) {
         err += fabs((float)floor(outData[elIter]) - (float)floor(goldData[elIter])) > thr;
-        if (err > maxErr)
+        if (err > maxErr) {
             ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
+        }
     }
 
     if(sceneArray_f32 != 0) af_release_array(sceneArray_f32);
@@ -258,8 +259,9 @@ TEST(Transform, CPP)
 
     for (dim_t elIter = 0; elIter < n; elIter++) {
         err += fabs((int)h_out_img[elIter] - h_gold_img[elIter]) > thr;
-        if (err > maxErr)
+        if (err > maxErr) {
             ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
+        }
     }
 }
 

From e82f0e57e05b35bb8887ecf57b8d512dc4fe9d8b Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 25 Jul 2017 13:07:51 -0700
Subject: [PATCH 1252/2677] BUGIFX: Fixes issue with gemv when the rhs is an
 indexed vector

---
 src/backend/cpu/blas.cpp    |  3 ++-
 src/backend/cuda/blas.cpp   |  3 ++-
 src/backend/opencl/blas.cpp |  3 ++-
 test/blas.cpp               | 24 ++++++++++++++++++++++++
 4 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 50525a9bb1..4bf88b5a93 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -174,12 +174,13 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
         dim4 rStrides = right.strides();
 
         if(rDims[bColDim] == 1) {
+            dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
             gemv_func<T>()(
                 CblasColMajor, lOpts,
                 lDims[0], lDims[1],
                 alpha,
                 reinterpret_cast<CBT*>(left.get()), lStrides[1],
-                reinterpret_cast<CBT*>(right.get()), rStrides[0],
+                reinterpret_cast<CBT*>(right.get()), incr,
                 beta,
                 reinterpret_cast<BT*>(output.get()), 1);
         } else {
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 921fe685ea..cd3590fe7b 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -170,6 +170,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     dim4 rStrides = rhs.strides();
     if(rDims[bColDim] == 1) {
         N = lDims[aColDim];
+        dim_t incr = (rOpts == CUBLAS_OP_N) ? rStrides[0] : rStrides[1];
         CUBLAS_CHECK(gemv_func<T>()(
                          blasHandle(),
                          lOpts,
@@ -177,7 +178,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                          lDims[1],
                          &alpha,
                          lhs.get(), lStrides[1],
-                         rhs.get(), rStrides[0],
+                         rhs.get(), incr,
                          &beta,
                          out.get(), 1));
     } else {
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index df6b772a09..ac8a265448 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -80,12 +80,13 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     const dim4 rStrides = rhs.strides();
     cl::Event event;
     if(rDims[bColDim] == 1) {
+        dim_t incr = (rOpts == OPENCL_BLAS_NO_TRANS) ? rStrides[0] : rStrides[1];
         gpu_blas_gemv_func<T> gemv;
         OPENCL_BLAS_CHECK(
             gemv(lOpts, lDims[0], lDims[1],
                  alpha,
                  (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                 (*rhs.get())(), rhs.getOffset(), rStrides[0],
+                 (*rhs.get())(), rhs.getOffset(), incr,
                  beta,
                  (*out.get())(), out.getOffset(), 1,
                  1, &getQueue()(), 0, nullptr, &event())
diff --git a/test/blas.cpp b/test/blas.cpp
index 507cc6dc7b..8d5b1f50e6 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -245,3 +245,27 @@ TYPED_TEST(MatrixMultiply, MultiGPURectangleVector_CPP)
 }
 
 #undef DEVICE_ITERATE
+
+TEST(MatrixMultiply, ISSUE_1882)
+{
+    const int m = 2;
+    const int n = 3;
+    af::array A = af::randu(m, n);
+    af::array BB = af::randu(n, m);
+    af::array B = BB(0, af::span);
+
+    af::array res1 = af::matmul(A.T(), B.T());
+    af::array res2 = af::matmulTT(A, B);
+
+    std::vector<float> hres1(res1.elements());
+    std::vector<float> hres2(res2.elements());
+
+    res1.host(&hres1.front());
+    res2.host(&hres2.front());
+
+    ASSERT_EQ(hres1.size(), hres2.size());
+
+    for (size_t i = 0; i < hres1.size(); i++) {
+        ASSERT_NEAR(hres1[i], hres2[i], 1E-5);
+    }
+}

From 27b00b52d08641bd5ac9e12e173b7743fa261249 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Mon, 3 Jul 2017 00:05:48 -0700
Subject: [PATCH 1253/2677] Changing behavior of array.allocated to specify
 memory allocated

---
 src/backend/common/MemoryManager.hpp |  9 +++++++++
 src/backend/cpu/Array.hpp            |  8 +++++++-
 src/backend/cuda/Array.hpp           |  8 +++++++-
 src/backend/opencl/Array.hpp         |  8 +++++++-
 test/internal.cpp                    | 21 ++++++++++-----------
 5 files changed, 40 insertions(+), 14 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 6a1788b274..b17ac3aeb8 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -222,6 +222,15 @@ class MemoryManager
         return ptr;
     }
 
+    size_t allocated(void *ptr)
+    {
+        if (!ptr) return 0;
+        memory_info& current = this->getCurrentMemoryInfo();
+        locked_iter iter = current.locked_map.find((void *)ptr);
+        if (iter == current.locked_map.end()) return 0;
+        return (iter->second).bytes;
+    }
+
     void unlock(void *ptr, bool user_unlock)
     {
         // Shortcut for empty arrays
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 569f970d15..2a213940ba 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -195,7 +195,13 @@ namespace cpu
 
         size_t getAllocatedBytes() const
         {
-            return data_dims.elements() * sizeof(T);
+            if (!isReady()) return 0;
+            size_t bytes = memoryManager().allocated(data.get());
+            // External device poitner
+            if (bytes == 0 && data.get()) {
+                return data_dims.elements() * sizeof(T);
+            }
+            return  bytes;
         }
 
         T* device();
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 1136cdaea4..f54a0f0f10 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -173,7 +173,13 @@ namespace cuda
 
         size_t getAllocatedBytes() const
         {
-            return data_dims.elements() * sizeof(T);
+            if (!isReady()) return 0;
+            size_t bytes = memoryManager().allocated(data.get());
+            // External device poitner
+            if (bytes == 0 && data.get()) {
+                return data_dims.elements() * sizeof(T);
+            }
+            return  bytes;
         }
 
         T* device();
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index f7c36b697b..953582d42c 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -208,7 +208,13 @@ namespace opencl
 
         size_t getAllocatedBytes() const
         {
-            return data_dims.elements() * sizeof(T);
+            if (!isReady()) return 0;
+            size_t bytes = memoryManager().allocated(data.get());
+            // External device poitner
+            if (bytes == 0 && data.get()) {
+                return data_dims.elements() * sizeof(T);
+            }
+            return  bytes;
         }
 
         operator Param() const
diff --git a/test/internal.cpp b/test/internal.cpp
index f60c570d99..209b61e766 100644
--- a/test/internal.cpp
+++ b/test/internal.cpp
@@ -126,32 +126,31 @@ TEST(Internal, Linear)
 TEST(Internal, Allocated)
 {
     af::array a = af::randu(10, 8);
-    const size_t aBytes = a.bytes();
+    size_t a_allocated = a.allocated();
+    size_t a_bytes = a.bytes();
 
     // b is just pointing to same underlying data
     // b is an owner;
     af::array b = a;
-    ASSERT_EQ(b.allocated(), aBytes);
-    ASSERT_EQ(b.bytes(), aBytes);
+    ASSERT_EQ(b.allocated(), a_allocated);
+    ASSERT_EQ(b.bytes(), a_bytes);
 
     // C is considered sub array
     // C will not be an owner
     af::array c = a(af::span);
-    ASSERT_EQ(c.allocated(), aBytes);
-    ASSERT_EQ(c.bytes(), aBytes);
+    ASSERT_EQ(c.allocated(), a_allocated);
+    ASSERT_EQ(c.bytes(), a_bytes);
 
     af::array d = a.col(1);
-    ASSERT_EQ(d.allocated(), aBytes);
-    ASSERT_EQ(d.bytes(), (size_t)10 * 4);
+    ASSERT_EQ(d.allocated(), a_allocated);
 
     a = af::randu(20);
     b = af::randu(20);
 
     // Even though a, b are reallocated and c, d are not owners
     // the allocated and bytes should remain the same
-    ASSERT_EQ(c.allocated(), aBytes);
-    ASSERT_EQ(c.bytes(), aBytes);
+    ASSERT_EQ(c.allocated(), a_allocated);
+    ASSERT_EQ(c.bytes(), a_bytes);
 
-    ASSERT_EQ(d.allocated(), aBytes);
-    ASSERT_EQ(d.bytes(), (size_t)10 * 4);
+    ASSERT_EQ(d.allocated(), a_allocated);
 }

From 28b28b770fb29920f6cf9295083a5f9815395035 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 26 Jul 2017 19:17:16 -0400
Subject: [PATCH 1254/2677] Fix leak when chaining indexing operations

Indexing operations leaked when chained(i.e. arr.rows(10, 20).cols(1, 4).
This was happening because the array_proxy object's member functions created
an array pointer when indexing operations were performed. This array was
not freed when the indexing operation was evaluated on conversion back to
af::array.

* Cleanup and document new variable in array_proxy_impl
---
 src/api/cpp/array.cpp | 152 ++++++++++++++++++++++--------------------
 test/index.cpp        |  19 +++++-
 2 files changed, 97 insertions(+), 74 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index bdd5df19b9..6e17501296 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -6,7 +6,6 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <stdexcept>
 #include <af/array.h>
 #include <af/arith.h>
 #include <af/blas.h>
@@ -20,6 +19,9 @@
 #include <af/internal.h>
 #include "error.hpp"
 
+#include <stdexcept>
+#include <memory>
+
 namespace af
 {
     static int gforDim(af_index_t *indices)
@@ -88,16 +90,34 @@ namespace af
 
     struct array::array_proxy::array_proxy_impl
     {
-        array       *parent;        // The original array
-        af_index_t  indices[4];     // Indexing array or seq objects
-        bool        lin;
+        array *       parent_;        //< The original array
+        af_index_t    indices_[4];    //< Indexing array or seq objects
+        bool          is_linear_;
+
+        // if true the parent_ object will be deleted on distruction. This is
+        // necessary only when calling indexing functions in array_proxy objects.
+        bool          delete_on_destruction_;
         array_proxy_impl(array &parent, af_index_t *idx, bool linear)
-            : parent(&parent)
-            , indices()
-            , lin(linear)
+            : parent_(&parent)
+            , indices_()
+            , is_linear_(linear)
+            , delete_on_destruction_(false)
         {
-            std::copy(idx, idx + AF_MAX_DIMS, indices);
+            std::copy(idx, idx + AF_MAX_DIMS, indices_);
         }
+
+        void delete_on_destruction(bool val) {
+            delete_on_destruction_ = val;
+        }
+
+        ~array_proxy_impl() {
+            if (delete_on_destruction_) delete parent_;
+        }
+      private:
+        array_proxy_impl(const array_proxy_impl&);
+        array_proxy_impl(const array_proxy_impl&&);
+        array_proxy_impl operator=(const array_proxy_impl&);
+        array_proxy_impl operator=(const array_proxy_impl&&);
     };
 
     array::array(const af_array handle): arr(handle)
@@ -458,21 +478,21 @@ namespace af
     array::array_proxy&
     af::array::array_proxy::operator=(const array &other)
     {
-        unsigned nd = numDims(impl->parent->get());
-        const dim4 this_dims = getDims(impl->parent->get());
+        unsigned nd = numDims(impl->parent_->get());
+        const dim4 this_dims = getDims(impl->parent_->get());
         const dim4 other_dims = other.dims();
-        int dim = gforDim(impl->indices);
+        int dim = gforDim(impl->indices_);
         af_array other_arr = other.get();
 
         bool batch_assign = false;
         bool is_reordered = false;
         if (dim >= 0) {
             //FIXME: Figure out a faster, cleaner way to do this
-            dim4 out_dims = seqToDims(impl->indices, this_dims, false);
+            dim4 out_dims = seqToDims(impl->indices_, this_dims, false);
 
             batch_assign = true;
             for (int i = 0; i < AF_MAX_DIMS; i++) {
-                if (this->impl->indices[i].isBatch) batch_assign &= (other_dims[i] == 1);
+                if (this->impl->indices_[i].isBatch) batch_assign &= (other_dims[i] == 1);
                 else                          batch_assign &= (other_dims[i] == out_dims[i]);
             }
 
@@ -495,18 +515,18 @@ namespace af
 
         af_array par_arr = 0;
 
-        if (impl->lin) {
-            AF_THROW(af_flat(&par_arr, impl->parent->get()));
+        if (impl->is_linear_) {
+            AF_THROW(af_flat(&par_arr, impl->parent_->get()));
             nd = 1;
         } else {
-            par_arr = impl->parent->get();
+            par_arr = impl->parent_->get();
         }
 
         af_array tmp = 0;
-        AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices, other_arr));
+        AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices_, other_arr));
 
         af_array res = 0;
-        if (impl->lin) {
+        if (impl->is_linear_) {
             AF_THROW(af_moddims(&res, tmp, this_dims.ndims(), this_dims.get()));
             AF_THROW(af_release_array(par_arr));
             AF_THROW(af_release_array(tmp));
@@ -514,7 +534,7 @@ namespace af
             res = tmp;
         }
 
-        impl->parent->set(res);
+        impl->parent_->set(res);
 
         if (dim >= 0 && (is_reordered || batch_assign)) {
             if (other_arr) AF_THROW(af_release_array(other_arr));
@@ -535,7 +555,7 @@ namespace af
     }
 
     af::array::array_proxy::array_proxy(const array_proxy &other)
-        : impl(new array_proxy_impl(*other.impl->parent, other.impl->indices, other.impl->lin))
+        : impl(new array_proxy_impl(*other.impl->parent_, other.impl->indices_, other.impl->is_linear_))
     {
     }
 
@@ -575,22 +595,6 @@ namespace af
         return out.host(ptr);
     }
 
-    af_array array::array_proxy::get()
-    {
-        array tmp = *this;
-        af_array out = 0;
-        AF_THROW(af_retain_array(&out, tmp.get()));
-        return out;
-    }
-
-    af_array array::array_proxy::get() const
-    {
-        array tmp = *this;
-        af_array out = 0;
-        AF_THROW(af_retain_array(&out, tmp.get()));
-        return out;
-    }
-
 #define MEM_FUNC(PREFIX, FUNC)                  \
     PREFIX array::array_proxy::FUNC() const     \
     {                                           \
@@ -621,21 +625,22 @@ namespace af
     MEM_FUNC(bool                   , isbool)
     MEM_FUNC(bool                   , issparse)
     MEM_FUNC(void                   , eval)
+    MEM_FUNC(af_array               , get)
     //MEM_FUNC(void                   , unlock)
 #undef MEM_FUNC
 
 
-#define ASSIGN_TYPE(TY, OP)                             \
-    array::array_proxy&                                 \
-    array::array_proxy::operator OP(const TY &value)    \
-    {                                                   \
-        dim4 pdims = getDims(impl->parent->get());      \
-        if (impl->lin) pdims = dim4(pdims.elements());  \
-        dim4 dims = seqToDims(impl->indices, pdims );   \
-        af::dtype ty = impl->parent->type();            \
-        array cst = constant(value, dims, ty);          \
-        return this->operator OP(cst);                  \
-    }                                                   \
+#define ASSIGN_TYPE(TY, OP)                                   \
+    array::array_proxy&                                       \
+    array::array_proxy::operator OP(const TY &value)          \
+    {                                                         \
+        dim4 pdims = getDims(impl->parent_->get());           \
+        if (impl->is_linear_) pdims = dim4(pdims.elements()); \
+        dim4 dims = seqToDims(impl->indices_, pdims );        \
+        af::dtype ty = impl->parent_->type();                 \
+        array cst = constant(value, dims, ty);                \
+        return this->operator OP(cst);                        \
+    }                                                         \
 
 #define ASSIGN_OP(OP, op1)                      \
     ASSIGN_TYPE(double             , OP)        \
@@ -686,14 +691,14 @@ namespace af
         af_array tmp = 0;
         af_array arr = 0;
 
-        if(impl->lin)  {
-            AF_THROW(af_flat(&arr, impl->parent->get()));
+        if(impl->is_linear_)  {
+            AF_THROW(af_flat(&arr, impl->parent_->get()));
         } else {
-            arr = impl->parent->get();
+            arr = impl->parent_->get();
         }
 
-        AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices));
-        if(impl->lin)  {
+        AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
+        if (impl->is_linear_)  {
             AF_THROW(af_release_array(arr));
         }
 
@@ -705,18 +710,18 @@ namespace af
         af_array tmp = 0;
         af_array arr = 0;
 
-        if(impl->lin)  {
-            AF_THROW(af_flat(&arr, impl->parent->get()));
+        if(impl->is_linear_)  {
+            AF_THROW(af_flat(&arr, impl->parent_->get()));
         } else {
-            arr = impl->parent->get();
+            arr = impl->parent_->get();
         }
 
-        AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices));
-        if(impl->lin)  {
+        AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
+        if(impl->is_linear_)  {
             AF_THROW(af_release_array(arr));
         }
 
-        int dim = gforDim(impl->indices);
+        int dim = gforDim(impl->indices_);
         if (tmp && dim >= 0) {
             arr = gforReorder(tmp, dim);
             if (tmp) AF_THROW(af_release_array(tmp));
@@ -727,20 +732,23 @@ namespace af
         return array(arr);
     }
 
-    //FIXME: Check if this leaks
-#define MEM_INDEX(FUNC_SIG, USAGE)                  \
-    array::array_proxy                              \
-    array::array_proxy::FUNC_SIG                    \
-    {                                               \
-        array *out = new array(this->get());        \
-        return out->USAGE;                          \
-    }                                               \
-                                                    \
-    const array::array_proxy                        \
-    array::array_proxy::FUNC_SIG const              \
-    {                                               \
-        const array *out = new array(this->get());  \
-        return out->USAGE;                          \
+#define MEM_INDEX(FUNC_SIG, USAGE)                    \
+    array::array_proxy                                \
+    array::array_proxy::FUNC_SIG                      \
+    {                                                 \
+        array* out = new array(*this);                \
+        array::array_proxy proxy = out->USAGE;        \
+        proxy.impl->delete_on_destruction(true);      \
+        return proxy;                                 \
+    }                                                 \
+                                                      \
+    const array::array_proxy                          \
+    array::array_proxy::FUNC_SIG const                \
+    {                                                 \
+        const array* out = new array(*this);          \
+        array::array_proxy proxy = out->USAGE;        \
+        proxy.impl->delete_on_destruction(true);      \
+        return proxy;                                 \
     }
 
     MEM_INDEX(row(int index)                , row(index));
diff --git a/test/index.cpp b/test/index.cpp
index 90c1780feb..289f3b92e1 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1537,7 +1537,7 @@ TEST(Index, ISSUE_1101_MODDIMS)
     }
 }
 
-TEST(Index, ISSUE_1846_Index_Step_Cascade)
+TEST(Index, Issue1846IndexStepCascade)
 {
     using namespace af;
     array a = randu(3, 12);
@@ -1547,7 +1547,7 @@ TEST(Index, ISSUE_1846_Index_Step_Cascade)
     EXPECT_EQ(allTrue<bool>(c == d), true);
 }
 
-TEST(Index, ISSUE_1845_Index_Step_reorder)
+TEST(Index, Issue1845IndexStepReorder)
 {
     using namespace af;
     array a = randu(1,8,1);
@@ -1555,3 +1555,18 @@ TEST(Index, ISSUE_1845_Index_Step_reorder)
     array d = reorder(b(0,0,span),2,1,0);
     EXPECT_EQ(allTrue<bool>(a.T() == d), true);
 }
+
+TEST(Index, Issue1867ChainedIndexingLeak)
+{
+    using namespace af;
+    {
+        array lInput = randn(100, 100, f32);
+        array Q3 = lInput.rows(0, 3).cols(0, 3);
+        Q3.eval();
+        af::sync();
+    }
+    size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+    ASSERT_EQ(0u, lock_buffers);
+}

From 158f3852a037845b17637f08234723a1a3fbb9f3 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Fri, 28 Jul 2017 20:46:30 -0700
Subject: [PATCH 1255/2677] BUGFIX: Converting driver_api_mutex to be recursive

This solves the issue when sparse blas is called with a JIT'd array
---
 src/backend/cuda/jit.cpp         |  5 +++--
 src/backend/cuda/memory.cpp      | 10 +++++-----
 src/backend/cuda/platform.cpp    |  2 +-
 src/backend/cuda/platform.hpp    |  6 +++---
 src/backend/cuda/sparse_blas.cpp |  2 +-
 5 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 48bfcbed11..bf0ede8b6e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -38,6 +38,7 @@ using std::hash;
 using std::lock_guard;
 using std::map;
 using std::mutex;
+using std::recursive_mutex;
 using std::string;
 using std::stringstream;
 using std::unique_ptr;
@@ -270,7 +271,7 @@ std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
 
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
-    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
 
     const size_t linkLogSize = 1024;
     char linkInfo[linkLogSize] = {0};
@@ -422,7 +423,7 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
     args.push_back((void *)&blocks_y_);
     args.push_back((void *)&num_odims);
 
-    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLaunchKernel(ker,
                             blocks_x,
                             blocks_y,
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 60b70940c7..e53e767db5 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -28,7 +28,7 @@
 #endif
 
 using std::lock_guard;
-using std::mutex;
+using std::recursive_mutex;
 
 namespace cuda
 {
@@ -173,7 +173,7 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
-    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     void *ptr = NULL;
     CUDA_CHECK(cudaMalloc(&ptr, bytes));
     return ptr;
@@ -181,7 +181,7 @@ void *MemoryManager::nativeAlloc(const size_t bytes)
 
 void MemoryManager::nativeFree(void *ptr)
 {
-    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     cudaError_t err = cudaFree(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
@@ -212,7 +212,7 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {
-    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     void *ptr;
     CUDA_CHECK(cudaMallocHost(&ptr, bytes));
     return ptr;
@@ -220,7 +220,7 @@ void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 
 void MemoryManagerPinned::nativeFree(void *ptr)
 {
-    lock_guard<mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     cudaError_t err = cudaFreeHost(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 8fba0f88f6..40565ce160 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -283,7 +283,7 @@ unsigned getMaxJitSize()
     return length;
 }
 
-std::mutex& getDriverApiMutex(int device) {
+std::recursive_mutex& getDriverApiMutex(int device) {
   return DeviceManager::getInstance().driver_api_mutex[device];
 }
 
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ff6fb1485e..ca4a9a7689 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -43,7 +43,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
 
-std::mutex& getDriverApiMutex(int device);
+std::recursive_mutex& getDriverApiMutex(int device);
 
 int getDeviceCount();
 
@@ -115,7 +115,7 @@ class DeviceManager
         friend GraphicsResourceManager& interopManager();
 #endif
 
-        friend std::mutex& getDriverApiMutex(int device);
+        friend std::recursive_mutex& getDriverApiMutex(int device);
 
         friend std::string getDeviceInfo(int device);
 
@@ -149,7 +149,7 @@ class DeviceManager
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
 
-        std::mutex driver_api_mutex[MAX_DEVICES];
+        std::recursive_mutex driver_api_mutex[MAX_DEVICES];
 
         // Attributes
         std::vector<cudaDevice_t> cuDevices;
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 43911b858a..8dd3de8155 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -145,7 +145,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     // NOTE: The cuSparse library seems to be using the driver API in the
     // implementation. This is causing issues with our JIT kernel generation.
     // This may be a bug in the cuSparse library.
-    std::lock_guard<std::mutex> lock(getDriverApiMutex(getActiveDeviceId()));
+    std::lock_guard<std::recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
 
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;

From ebf38bbcba81886fbed10843058dc2b53da2547d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 1 Aug 2017 15:47:16 +0530
Subject: [PATCH 1256/2677] Fix pixel tests in fast kernels

---
 src/backend/cpu/kernel/fast.hpp   |  6 +++---
 src/backend/cuda/kernel/fast.hpp  | 10 +++++-----
 src/backend/opencl/kernel/fast.cl |  6 +++---
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index 7f552cfca6..db8e42b241 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -41,14 +41,14 @@ inline int idx(int y, int x, unsigned idim0)
 // Tests if a pixel x > p + thr
 inline int test_greater(float x, float p, float thr)
 {
-    return (x >= p + thr);
+    return (x > p + thr);
 }
 
 // test_smaller()
 // Tests if a pixel x < p - thr
 inline int test_smaller(float x, float p, float thr)
 {
-    return (x <= p - thr);
+    return (x < p - thr);
 }
 
 // test_pixel()
@@ -58,7 +58,7 @@ inline int test_smaller(float x, float p, float thr)
 template<typename T>
 inline int test_pixel(const T* image, const float p, float thr, int y, int x, unsigned idim0)
 {
-    return -test_smaller((float)image[idx(y,x,idim0)], p, thr) | test_greater((float)image[idx(y,x,idim0)], p, thr);
+    return -test_smaller((float)image[idx(y,x,idim0)], p, thr) + test_greater((float)image[idx(y,x,idim0)], p, thr);
 }
 
 // abs_diff()
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 491ffe5f01..299e6c4673 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -47,19 +47,19 @@ int idx(const int x, const int y)
 }
 
 // test_greater()
-// Tests if a pixel x >= p + thr
+// Tests if a pixel x > p + thr
 inline __device__
 int test_greater(const float x, const float p, const float thr)
 {
-    return (x >= p + thr);
+    return (x > p + thr);
 }
 
 // test_smaller()
-// Tests if a pixel x <= p - thr
+// Tests if a pixel x < p - thr
 inline __device__
 int test_smaller(const float x, const float p, const float thr)
 {
-    return (x <= p - thr);
+    return (x < p - thr);
 }
 
 // test_pixel()
@@ -70,7 +70,7 @@ template<typename T>
 inline __device__
 int test_pixel(const T* local_image, const float p, const float thr, const int x, const int y)
 {
-    return -test_smaller((float)local_image[idx(x,y)], p, thr) | test_greater((float)local_image[idx(x,y)], p, thr);
+    return -test_smaller((float)local_image[idx(x,y)], p, thr) + test_greater((float)local_image[idx(x,y)], p, thr);
 }
 
 // max_val()
diff --git a/src/backend/opencl/kernel/fast.cl b/src/backend/opencl/kernel/fast.cl
index 695e167f22..cd207f3324 100644
--- a/src/backend/opencl/kernel/fast.cl
+++ b/src/backend/opencl/kernel/fast.cl
@@ -30,14 +30,14 @@ inline int idx(const int x, const int y)
 // Tests if a pixel x > p + thr
 inline int test_greater(const float x, const float p, const float thr)
 {
-    return (x >= p + thr);
+    return (x > p + thr);
 }
 
 // test_smaller()
 // Tests if a pixel x < p - thr
 inline int test_smaller(const float x, const float p, const float thr)
 {
-    return (x <= p - thr);
+    return (x < p - thr);
 }
 
 // test_pixel()
@@ -46,7 +46,7 @@ inline int test_smaller(const float x, const float p, const float thr)
 // Returns  1 when x > p + thr
 inline int test_pixel(__local T* local_image, const float p, const float thr, const int x, const int y)
 {
-    return -test_smaller((float)local_image[idx(x,y)], p, thr) | test_greater((float)local_image[idx(x,y)], p, thr);
+    return -test_smaller((float)local_image[idx(x,y)], p, thr) + test_greater((float)local_image[idx(x,y)], p, thr);
 }
 
 void locate_features_core(

From 9a20ac988e84c20b71ea6e351fb03d26db4dd4fb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Mon, 31 Jul 2017 02:00:09 -0700
Subject: [PATCH 1257/2677] Make TNJ faster by increasing cache locality of
 function pointers.

---
 src/backend/cpu/TNJ/BinaryNode.hpp |  18 ++--
 src/backend/cpu/TNJ/BufferNode.hpp |  17 ++--
 src/backend/cpu/TNJ/Node.hpp       |  16 ++--
 src/backend/cpu/TNJ/UnaryNode.hpp  |  19 +++--
 src/backend/cpu/arith.hpp          |  18 +++-
 src/backend/cpu/cast.hpp           |  67 ++++++++++-----
 src/backend/cpu/complex.hpp        |  30 ++++---
 src/backend/cpu/kernel/Array.hpp   |  69 ++++-----------
 src/backend/cpu/logic.hpp          |  45 +++++++---
 src/backend/cpu/unary.hpp          | 133 ++++++++++++++++-------------
 10 files changed, 247 insertions(+), 185 deletions(-)

diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index 78c7c6df66..c44e201062 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include <math.hpp>
 #include "Node.hpp"
+#include <array>
 
 namespace cpu
 {
@@ -19,9 +20,14 @@ namespace cpu
     template<typename To, typename Ti, af_op_t op>
     struct BinOp
     {
-        To eval(Ti lhs, Ti rhs)
+        void eval(TNJ::array<To> &out,
+                  const TNJ::array<Ti> &lhs,
+                  const TNJ::array<Ti> &rhs,
+                  int lim)
         {
-            return scalar<To>(0);
+            for (int i = 0; i < lim; i++) {
+                out[i] = scalar<To>(0);
+            }
         }
     };
 
@@ -44,14 +50,14 @@ namespace TNJ
         {
         }
 
-        void calc(int x, int y, int z, int w)
+        void calc(int x, int y, int z, int w, int lim)
         {
-            this->m_val = m_op.eval(m_lhs->m_val, m_rhs->m_val);
+            m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
         }
 
-        void calc(int idx)
+        void calc(int idx, int lim)
         {
-            this->m_val = m_op.eval(m_lhs->m_val, m_rhs->m_val);
+            m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
         }
     };
 
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index bdd7c7e700..2224c8cdc6 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -58,19 +58,26 @@ namespace TNJ
                            });
         }
 
-        void calc(int x, int y, int z, int w)
+        void calc(int x, int y, int z, int w, int lim)
         {
             dim_t l_off = 0;
             l_off += (w < (int)m_dims[3]) * w * m_strides[3];
             l_off += (z < (int)m_dims[2]) * z * m_strides[2];
             l_off += (y < (int)m_dims[1]) * y * m_strides[1];
-            l_off += (x < (int)m_dims[0]) * x;
-            this->m_val = m_ptr[l_off];
+            T *in_ptr = m_ptr + l_off;
+            T *out_ptr = this->m_val.data();
+            for(int i = 0; i < lim; i++) {
+                out_ptr[i] = in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0];
+            }
         }
 
-        void calc(int idx)
+        void calc(int idx, int lim)
         {
-            this->m_val = m_ptr[idx];
+            T *in_ptr = m_ptr + idx;
+            T *out_ptr = this->m_val.data();
+            for(int i = 0; i < lim; i++) {
+                out_ptr[i] = in_ptr[i];
+            }
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index 1c2a69d47c..aeb3385b96 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -20,7 +20,9 @@ namespace cpu
 namespace TNJ
 {
 
+    static const int VECTOR_LENGTH = 256;
     static const int MAX_CHILDREN = 2;
+
     class Node;
     using std::shared_ptr;
     using std::vector;
@@ -29,6 +31,10 @@ namespace TNJ
     typedef std::unordered_map<Node *, int> Node_map_t;
     typedef Node_map_t::iterator Node_map_iter;
 
+    template<typename T>
+    using array = std::array<T, VECTOR_LENGTH>;
+
+
     class Node
     {
 
@@ -61,11 +67,11 @@ namespace TNJ
 
         int getHeight() { return m_height; }
 
-        virtual void calc(int x, int y, int z, int w)
+        virtual void calc(int x, int y, int z, int w, int lim)
         {
         }
 
-        virtual void calc(int idx)
+        virtual void calc(int idx, int lim)
         {
         }
 
@@ -84,12 +90,12 @@ namespace TNJ
     class TNode : public Node
     {
     public:
-        T m_val;
+        alignas(16) TNJ::array<T> m_val;
     public:
         TNode(T val, const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
-            Node(height, children),
-            m_val(val)
+            Node(height, children)
             {
+                m_val.fill(val);
             }
     };
 
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index 2f9d121c20..d32acf5644 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -15,13 +15,15 @@
 
 namespace cpu
 {
-
     template<typename To, typename Ti, af_op_t op>
     struct UnOp
     {
-        To eval(Ti in)
+        void eval(TNJ::array<To> &out,
+                  const TNJ::array<Ti> &in, int lim)
         {
-            return scalar<To>(0);
+            for (int i = 0; i < lim; i++) {
+                out[i] = To(in[i]);
+            }
         }
     };
 
@@ -33,7 +35,7 @@ namespace TNJ
     {
 
     protected:
-        UnOp <To, Ti, op> m_op;
+        UnOp<To, Ti, op> m_op;
         TNode<Ti> *m_child;
 
     public:
@@ -43,15 +45,14 @@ namespace TNJ
         {
         }
 
-
-        void calc(int x, int y, int z, int w)
+        void calc(int x, int y, int z, int w, int lim)
         {
-            this->m_val = m_op.eval(m_child->m_val);
+            m_op.eval(this->m_val, m_child->m_val, lim);
         }
 
-        void calc(int idx)
+        void calc(int idx, int lim)
         {
-            this->m_val = m_op.eval(m_child->m_val);
+            m_op.eval(this->m_val, m_child->m_val, lim);
         }
 
     };
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 6e5921b357..87c8bd5eb3 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -21,9 +21,14 @@ namespace cpu
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        T eval(T lhs, T rhs)                    \
+        void eval(TNJ::array<T> &out,           \
+                  const TNJ::array<T> &lhs,     \
+                  const TNJ::array<T> &rhs,     \
+                  int lim)                      \
         {                                       \
-            return lhs op rhs;                  \
+            for (int i = 0; i < lim; i++) {     \
+                out[i] = lhs[i] op rhs[i];      \
+            }                                   \
         }                                       \
     };                                          \
 
@@ -53,9 +58,14 @@ template<> STATIC_ double __rem<double>(double lhs, double rhs) { return remaind
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        T eval(T lhs, T rhs)                    \
+        void eval(TNJ::array<T> &out,           \
+                  const TNJ::array<T> &lhs,     \
+                  const TNJ::array<T> &rhs,     \
+                  int lim)                      \
         {                                       \
-            return FN(lhs, rhs);                \
+            for (int i = 0; i < lim; i++) {     \
+                out[i] = FN(lhs[i] , rhs[i]);   \
+            }                                   \
         }                                       \
     };                                          \
 
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 83a2623801..0bc0ef8cb8 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -23,57 +23,86 @@ namespace cpu
 template<typename To, typename Ti>
 struct UnOp<To, Ti, af_cast_t>
 {
-    To eval(Ti in)
+    void eval(TNJ::array<To> &out,
+              const TNJ::array<Ti> &in, int lim)
     {
-        return To(in);
+        for (int i = 0; i < lim; i++) {
+            out[i] = To(in[i]);
+        }
     }
 };
 
 template<typename To>
 struct UnOp<To, std::complex<float>, af_cast_t>
 {
-    To eval(std::complex<float> in)
+    typedef std::complex<float> Ti;
+    void eval(TNJ::array<To> &out,
+              const TNJ::array<Ti> &in, int lim)
     {
-        return To(std::abs(in));
+        for (int i = 0; i < lim; i++) {
+            out[i] = To(std::abs(in[i]));
+        }
     }
 };
 
 template<typename To>
 struct UnOp<To, std::complex<double>, af_cast_t>
 {
-    To eval(std::complex<double> in)
+    typedef std::complex<double> Ti;
+    void eval(TNJ::array<To> &out,
+              const TNJ::array<Ti> &in, int lim)
     {
-        return To(std::abs(in));
+        for (int i = 0; i < lim; i++) {
+            out[i] = To(std::abs(in[i]));
+        }
     }
 };
 
+// DO NOT REMOVE THE TWO SPECIALIZATIONS BELOW
+// These specializations are required because we partially specialize when Ti = std::complex<T>
+// The partial specializations above expect output to be real.
+// so they To(std::abs(v)) instead of To(v) which results in incorrect values when To is complex.
+
 template<>
 struct UnOp<std::complex<float>, std::complex<double>, af_cast_t>
 {
-    std::complex<float> eval(std::complex<double> in)
+    typedef std::complex<double> Ti;
+    typedef std::complex<float> To;
+    void eval(TNJ::array<To> &out,
+              const TNJ::array<Ti> &in, int lim)
     {
-        return std::complex<float>(in);
+        for (int i = 0; i < lim; i++) {
+            out[i] = To(in[i]);
+        }
     }
 };
 
 template<>
 struct UnOp<std::complex<double>, std::complex<float>, af_cast_t>
 {
-    std::complex<double> eval(std::complex<float> in)
+    typedef std::complex<float> Ti;
+    typedef std::complex<double> To;
+    void eval(TNJ::array<To> &out,
+              const TNJ::array<Ti> &in, int lim)
     {
-        return std::complex<double>(in);
+        for (int i = 0; i < lim; i++) {
+            out[i] = To(in[i]);
+        }
     }
 };
 
-#define CAST_B8(T)                              \
-    template<>                                  \
-    struct UnOp<char, T, af_cast_t>             \
-    {                                           \
-        char eval(T in)                         \
-        {                                       \
-            return char(in != 0);               \
-        }                                       \
-    };                                          \
+#define CAST_B8(T)                                      \
+    template<>                                          \
+    struct UnOp<char, T, af_cast_t>                     \
+    {                                                   \
+        void eval(TNJ::array<char> &out,                \
+                  const TNJ::array<T> &in, int lim)     \
+        {                                               \
+            for (int i = 0; i < lim; i++) {             \
+                out[i] = char(in[i] != 0);              \
+            }                                           \
+        }                                               \
+    };                                                  \
 
 CAST_B8(float)
 CAST_B8(double)
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index d5b471db0f..bd4219b7b5 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -21,9 +21,14 @@ namespace cpu
     template<typename To, typename Ti>
     struct BinOp<To, Ti, af_cplx2_t>
     {
-        To eval(Ti lhs, Ti rhs)
+        void eval(TNJ::array<To> &out,
+                  const TNJ::array<Ti> &lhs,
+                  const TNJ::array<Ti> &rhs,
+                  int lim)
         {
-            return To(lhs, rhs);
+            for (int i = 0; i < lim; i++) {
+                out[i] = To(lhs[i], rhs[i]);
+            }
         }
     };
 
@@ -40,15 +45,18 @@ namespace cpu
                                        reinterpret_cast<TNJ::Node *>(node)));
     }
 
-#define CPLX_UNARY_FN(op)                       \
-    template<typename To, typename Ti>          \
-    struct UnOp<To, Ti, af_##op##_t>            \
-    {                                           \
-        To eval(Ti in)                          \
-        {                                       \
-            return std::op(in);                 \
-        }                                       \
-    };                                          \
+#define CPLX_UNARY_FN(op)                               \
+    template<typename To, typename Ti>                  \
+    struct UnOp<To, Ti, af_##op##_t>                    \
+    {                                                   \
+        void eval(TNJ::array<To> &out,                  \
+                  const TNJ::array<Ti> &in, int lim)    \
+        {                                               \
+            for (int i = 0; i < lim; i++) {             \
+                out[i] = std::op(in[i]);                \
+            }                                           \
+        }                                               \
+    };                                                  \
 
     CPLX_UNARY_FN(real)
     CPLX_UNARY_FN(imag)
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index ea7e806a02..12e5a27e7c 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -42,15 +42,20 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
 
     if (is_linear) {
         int num = arrays[0].dims().elements();
-        for (int i = 0; i < num; i++) {
+        int cnum = TNJ::VECTOR_LENGTH * std::ceil(double(num) / TNJ::VECTOR_LENGTH);
+        for (int i = 0; i < cnum; i += TNJ::VECTOR_LENGTH) {
+            int lim = std::min(TNJ::VECTOR_LENGTH, num - i);
             for (int n = 0; n < (int)full_nodes.size(); n++) {
-                full_nodes[n]->calc(i);
+                full_nodes[n]->calc(i, lim);
             }
             for (int n = 0; n < (int)output_nodes.size(); n++) {
-                ptrs[n][i] = output_nodes[n]->m_val;
+                std::copy(output_nodes[n]->m_val.begin(),
+                          output_nodes[n]->m_val.begin() + lim,
+                          ptrs[n] + i);
             }
         }
     } else {
+
         for (int w = 0; w < (int)odims[3]; w++) {
             dim_t offw = w * ostrs[3];
 
@@ -60,14 +65,19 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
                 for (int y = 0; y < (int)odims[1]; y++) {
                     dim_t offy = y * ostrs[1] + offz;
 
-                    for (int x = 0; x < (int)odims[0]; x++) {
+                    int dim0 = odims[0];
+                    int cdim0 = TNJ::VECTOR_LENGTH * std::ceil(double(dim0) / TNJ::VECTOR_LENGTH);
+                    for (int x = 0; x < (int)cdim0; x += TNJ::VECTOR_LENGTH) {
+                        int lim = std::min(TNJ::VECTOR_LENGTH, dim0 - x);
                         dim_t id = x + offy;
 
                         for (int n = 0; n < (int)full_nodes.size(); n++) {
-                            full_nodes[n]->calc(x, y, z, w);
+                            full_nodes[n]->calc(x, y, z, w, lim);
                         }
                         for (int n = 0; n < (int)output_nodes.size(); n++) {
-                            ptrs[n][id] = output_nodes[n]->m_val;
+                            std::copy(output_nodes[n]->m_val.begin(),
+                                      output_nodes[n]->m_val.begin() + lim,
+                                      ptrs[n] + id);
                         }
                     }
                 }
@@ -79,52 +89,7 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
 template<typename T>
 void evalArray(Param<T> arr, TNJ::Node_ptr node)
 {
-    T *ptr = arr.get();
-
-    af::dim4 odims = arr.dims();
-    af::dim4 ostrs = arr.strides();
-
-    TNJ::Node_map_t nodes;
-    std::vector<TNJ::Node *> full_nodes;
-    full_nodes.reserve(1024);
-    node->getNodesMap(nodes, full_nodes);
-
-    bool is_linear = true;
-    for(auto node : full_nodes) {
-        is_linear &= node->isLinear(odims.get());
-    }
-
-    TNJ::TNode<T> *output_node = reinterpret_cast<TNJ::TNode<T> *>(full_nodes.back());
-    if (is_linear) {
-        int num = arr.dims().elements();
-        for (int i = 0; i < num; i++) {
-            for (int n = 0; n < (int)full_nodes.size(); n++) {
-                full_nodes[n]->calc(i);
-            }
-            ptr[i] = output_node->m_val;
-        }
-    } else {
-        for (int w = 0; w < (int)odims[3]; w++) {
-            dim_t offw = w * ostrs[3];
-
-            for (int z = 0; z < (int)odims[2]; z++) {
-                dim_t offz = z * ostrs[2] + offw;
-
-                for (int y = 0; y < (int)odims[1]; y++) {
-                    dim_t offy = y * ostrs[1] + offz;
-
-                    for (int x = 0; x < (int)odims[0]; x++) {
-                        dim_t id = x + offy;
-
-                        for (int n = 0; n < (int)full_nodes.size(); n++) {
-                            full_nodes[n]->calc(x, y, z, w);
-                        }
-                        ptr[id] = output_node->m_val;
-                    }
-                }
-            }
-        }
-    }
+    evalMultiple<T>({arr}, {node});
 }
 
 }
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index 3967767576..331d6ddb91 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -21,9 +21,14 @@ namespace cpu
     template<typename T>                        \
     struct BinOp<char, T, OP>                   \
     {                                           \
-        char eval(T lhs, T rhs)                 \
+        void eval(TNJ::array<char> &out,        \
+                  const TNJ::array<T> &lhs,     \
+                  const TNJ::array<T> &rhs,     \
+                  int lim)                      \
         {                                       \
-            return lhs op rhs;                  \
+            for (int i = 0; i < lim; i++) {     \
+                out[i] = lhs[i] op rhs[i];      \
+            }                                   \
         }                                       \
     };                                          \
 
@@ -39,16 +44,23 @@ namespace cpu
 
 #undef LOGIC_FN
 
-#define LOGIC_CPLX_FN(T, OP, op)                    \
-    template<>                                      \
-    struct BinOp<char, std::complex<T>, OP>         \
-    {                                               \
-        char eval(std::complex<T> lhs,              \
-                  std::complex<T> rhs)              \
-        {                                           \
-            return std::abs(lhs) op std::abs(rhs);  \
-        }                                           \
-    };                                              \
+#define LOGIC_CPLX_FN(T, OP, op)                \
+    template<>                                  \
+    struct BinOp<char, std::complex<T>, OP>     \
+    {                                           \
+        typedef std::complex<T> Ti;             \
+        void eval(TNJ::array<char> &out,        \
+                  const TNJ::array<Ti> &lhs,    \
+                  const TNJ::array<Ti> &rhs,    \
+                  int lim)                      \
+        {                                       \
+            for (int i = 0; i < lim; i++) {     \
+                T lhs_mag = std::abs(lhs[i]);   \
+                T rhs_mag = std::abs(rhs[i]);   \
+                out[i] = lhs_mag op rhs_mag;    \
+            }                                   \
+        }                                       \
+    };                                          \
 
 LOGIC_CPLX_FN(float, af_lt_t, <)
 LOGIC_CPLX_FN(float, af_le_t, <=)
@@ -85,9 +97,14 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        T eval(T lhs, T rhs)                    \
+        void eval(TNJ::array<T> &out,           \
+                  const TNJ::array<T> &lhs,     \
+                  const TNJ::array<T> &rhs,     \
+                  int lim)                      \
         {                                       \
-            return lhs op rhs;                  \
+            for (int i = 0; i < lim; i++) {     \
+                out[i] = lhs[i] op rhs[i];      \
+            }                                   \
         }                                       \
     };                                          \
 
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 3cf45ac88a..03410e2502 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -15,7 +15,12 @@
 
 namespace cpu
 {
-#define sign(in) std::signbit(in)
+
+template<typename T>
+T sign(T in)
+{
+    return T(std::signbit(in));
+}
 
 template<typename T>
 T sigmoid(T in)
@@ -23,56 +28,61 @@ T sigmoid(T in)
     return (1.0) / (1 + std::exp(-in));
 }
 
-#define UNARY_FN(op)                            \
-    template<typename T>                        \
-    struct UnOp<T, T, af_##op##_t>              \
-    {                                           \
-        T eval(T in)                            \
-        {                                       \
-            return op(in);                      \
-        }                                       \
-    };                                          \
-
-UNARY_FN(sin)
-UNARY_FN(cos)
-UNARY_FN(tan)
-
-UNARY_FN(asin)
-UNARY_FN(acos)
-UNARY_FN(atan)
-
-UNARY_FN(sinh)
-UNARY_FN(cosh)
-UNARY_FN(tanh)
-
-UNARY_FN(asinh)
-UNARY_FN(acosh)
-UNARY_FN(atanh)
-
-UNARY_FN(round)
-UNARY_FN(trunc)
-UNARY_FN(sign )
-UNARY_FN(floor)
-UNARY_FN(ceil)
-
-UNARY_FN(exp)
-UNARY_FN(sigmoid)
-UNARY_FN(expm1)
-UNARY_FN(erf)
-UNARY_FN(erfc)
-
-UNARY_FN(log)
-UNARY_FN(log10)
-UNARY_FN(log1p)
-UNARY_FN(log2)
-
-UNARY_FN(sqrt)
-UNARY_FN(cbrt)
-
-UNARY_FN(tgamma)
-UNARY_FN(lgamma)
-
-#undef UNARY_FN
+#define UNARY_OP_FN(op, fn)                         \
+    template<typename T>                            \
+    struct UnOp<T, T, af_##op##_t>                  \
+    {                                               \
+        void eval(TNJ::array<T> &out,               \
+                  const TNJ::array<T> &in, int lim) \
+        {                                           \
+            for (int i = 0; i < lim; i++) {         \
+                out[i] = fn(in[i]);                 \
+            }                                       \
+        }                                           \
+    };                                              \
+
+#define UNARY_OP(op) UNARY_OP_FN(op, std::op)
+
+UNARY_OP(sin)
+UNARY_OP(cos)
+UNARY_OP(tan)
+
+UNARY_OP(asin)
+UNARY_OP(acos)
+UNARY_OP(atan)
+
+UNARY_OP(sinh)
+UNARY_OP(cosh)
+UNARY_OP(tanh)
+
+UNARY_OP(asinh)
+UNARY_OP(acosh)
+UNARY_OP(atanh)
+
+UNARY_OP(round)
+UNARY_OP(trunc)
+UNARY_OP_FN(sign, sign)
+UNARY_OP(floor)
+UNARY_OP(ceil)
+
+UNARY_OP(exp)
+UNARY_OP_FN(sigmoid, sigmoid)
+UNARY_OP(expm1)
+UNARY_OP(erf)
+UNARY_OP(erfc)
+
+UNARY_OP(log)
+UNARY_OP(log10)
+UNARY_OP(log1p)
+UNARY_OP(log2)
+
+UNARY_OP(sqrt)
+UNARY_OP(cbrt)
+
+UNARY_OP(tgamma)
+UNARY_OP(lgamma)
+
+#undef UNARY_OP
 #undef sign
 
     template<typename T, af_op_t op>
@@ -87,15 +97,18 @@ UNARY_FN(lgamma)
 
 #define iszero(a) ((a) == 0)
 
-#define CHECK_FN(name ,op)                      \
-    template<typename T>                        \
-    struct UnOp<char, T, af_##name##_t>         \
-    {                                           \
-        char eval(T in)                         \
-        {                                       \
-            return op(in);                      \
-        }                                       \
-    };                                          \
+#define CHECK_FN(name ,op)                          \
+    template<typename T>                            \
+    struct UnOp<char, T, af_##name##_t>             \
+    {                                               \
+        void eval(TNJ::array<char> &out,            \
+                  const TNJ::array<T> &in, int lim) \
+        {                                           \
+            for (int i = 0; i < lim; i++) {         \
+                out[i] = op(in[i]);                 \
+            }                                       \
+        }                                           \
+    };                                              \
 
     CHECK_FN(isinf, std::isinf)
     CHECK_FN(isnan, std::isnan)

From 03d59bac1f8d40975b9a1bc84e9bcefe5b655cf4 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 1 Aug 2017 15:38:23 -0700
Subject: [PATCH 1258/2677] BUGFIX: Ensure the input to replace is copy on
 write

---
 src/api/c/handle.hpp  | 19 +++++++++++++++++++
 src/api/c/replace.cpp |  4 ++--
 test/replace.cpp      | 28 ++++++++++++++++++++++++++++
 3 files changed, 49 insertions(+), 2 deletions(-)

diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 3085ce31a9..3bbfd94801 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -120,3 +120,22 @@ static void releaseHandle(const af_array arr)
 af_array retain(const af_array in);
 
 af::dim4 verifyDims(const unsigned ndims, const dim_t * const dims);
+
+
+template<typename T>
+static detail::Array<T> &
+getCopyOnWriteArray(const af_array &arr)
+{
+    detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
+
+    if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
+        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+
+    ARG_ASSERT(0, A->isSparse() == false);
+
+    if (A->useCount() > 1) {
+        *A = copyArray(*A);
+    }
+
+    return *A;
+}
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 720cae4ad0..f2c8066c1f 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -25,7 +25,7 @@ using af::dim4;
 template<typename T>
 void replace(af_array a, const af_array cond, const af_array b)
 {
-    select(getWritableArray<T>(a), getArray<char>(cond), getArray<T>(a), getArray<T>(b));
+    select(getCopyOnWriteArray<T>(a), getArray<char>(cond), getArray<T>(a), getArray<T>(b));
 }
 
 af_err af_replace(af_array a, const af_array cond, const af_array b)
@@ -77,7 +77,7 @@ af_err af_replace(af_array a, const af_array cond, const af_array b)
 template<typename T>
 void replace_scalar(af_array a, const af_array cond, const double b)
 {
-    select_scalar<T, false>(getWritableArray<T>(a), getArray<char>(cond), getArray<T>(a), b);
+    select_scalar<T, false>(getCopyOnWriteArray<T>(a), getArray<char>(cond), getArray<T>(a), b);
 }
 
 af_err af_replace_scalar(af_array a, const af_array cond, const double b)
diff --git a/test/replace.cpp b/test/replace.cpp
index c4b8793232..679299c664 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -173,3 +173,31 @@ TEST(Replace, 4D)
         ASSERT_EQ(hc[i], hb[i]) << "at " << i;
     }
 }
+
+TEST(Replace, ISSUE_1683)
+{
+    array A = randu(10, 20, f32);
+    std::vector<float> ha1(A.elements());
+    A.host(ha1.data());
+
+    array B = A(0, span);
+    replace(B, A(0, span) > 0.5, 0);
+
+    std::vector<float> ha2(A.elements());
+    A.host(ha2.data());
+
+    std::vector<float> hb(B.elements());
+    B.host(hb.data());
+
+    // Ensures A is not modified by replace
+    for (int i = 0; i < (int)A.elements(); i++) {
+        ASSERT_EQ(ha1[i], ha2[i]);
+    }
+
+    // Ensures replace on B works as expected
+    for (int i = 0; i < (int)B.elements(); i++) {
+        float val = ha1[i * A.dims(0)];
+        val = val < 0.5 ? 0 : val;
+        ASSERT_EQ(val, hb[i]);
+    }
+}

From a0a9a4f423fdbb9f8a67a28c9481662e4f766cab Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 1 Aug 2017 17:31:24 -0700
Subject: [PATCH 1259/2677] Fix issue with launching too many blocks along y
 when using JIT

---
 src/backend/cuda/jit.cpp |  74 ++++++++++++++----------
 test/jit.cpp             | 122 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 166 insertions(+), 30 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index bf0ede8b6e..9ec3063883 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -80,13 +80,14 @@ static string getKernelString(const string funcName,
     const std::string includeFileStr(jit_cuh, jit_cuh_len);
 
     const std::string paramTStr = R"JIT(
-        template<typename T>
-        struct Param
-        {
-          T *ptr;
-          dim_t dims[4];
-          dim_t strides[4];
-        };)JIT";
+template<typename T>
+struct Param
+{
+  T *ptr;
+  dim_t dims[4];
+  dim_t strides[4];
+};
+)JIT";
 
     std::string typedefStr = "typedef unsigned int uint;\n";
     typedefStr += "typedef ";
@@ -97,35 +98,41 @@ static string getKernelString(const string funcName,
     // This part of the code does not change with the kernel.
 
     static const char *kernelVoid = "extern \"C\" __global__ void\n";
-    static const char *dimParams = "uint blocks_x, uint blocks_y, uint num_odims";
+    static const char *dimParams = "uint blocks_x, uint blocks_y, uint blocks_x_total, uint num_odims";
+
+    static const char * loopStart = R"JIT(
+    for (int blockIdx_x = blockIdx.x; blockIdx_x < blocks_x_total; blockIdx_x += gridDim.x) {
+    )JIT";
+    static const char *loopEnd = "}\n\n";
+
     static const char *blockStart = "{\n\n";
     static const char *blockEnd = "\n\n}";
 
     static const char *linearIndex = R"JIT(
-        uint blockId  = blockIdx.y * gridDim.x + blockIdx.x;
         uint threadId = threadIdx.x;
-        int idx = blockId * blockDim.x * blockDim.y + threadId;
+        int idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
         if (idx >= outref.dims[3] * outref.strides[3]) return;
         )JIT";
 
     static const char *generalIndex = R"JIT(
         uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        long blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
         if (num_odims > 2) {
-            id2 = blockIdx.x / blocks_x;
-            id0 = blockIdx.x - id2 * blocks_x;
+            id2 = blockIdx_x / blocks_x;
+            id0 = blockIdx_x - id2 * blocks_x;
             id0 = threadIdx.x + id0 * blockDim.x;
             if (num_odims > 3) {
-                id3 = blockIdx.y / blocks_y;
-                id1 = blockIdx.y - id3 * blocks_y;
+                id3 = blockIdx_y / blocks_y;
+                id1 = blockIdx_y - id3 * blocks_y;
                 id1 = threadIdx.y + id1 * blockDim.y;
             } else {
-                id1 = threadIdx.y + blockDim.y * blockIdx.y;
+                id1 = threadIdx.y + blockDim.y * blockIdx_y;
             }
         } else {
             id3 = 0;
             id2 = 0;
-            id1 = threadIdx.y + blockDim.y * blockIdx.y;
-            id0 = threadIdx.x + blockDim.x * blockIdx.x;
+            id1 = threadIdx.y + blockDim.y * blockIdx_y;
+            id0 = threadIdx.x + blockDim.x * blockIdx_x;
         }
 
         bool cond = id0 < outref.dims[0] &&
@@ -171,8 +178,8 @@ static string getKernelString(const string funcName,
     // Put various blocks into a single stream
     stringstream kerStream;
     kerStream << typedefStr;
-    kerStream << paramTStr;
     kerStream << includeFileStr << "\n\n";
+    kerStream << paramTStr << "\n";
     kerStream << kernelVoid;
     kerStream << funcName;
     kerStream << "(\n";
@@ -182,6 +189,7 @@ static string getKernelString(const string funcName,
     kerStream << ")\n";
     kerStream << blockStart;
     kerStream << outrefstream.str();
+    kerStream << loopStart;
     if (is_linear) {
         kerStream << linearIndex;
     } else {
@@ -190,6 +198,7 @@ static string getKernelString(const string funcName,
     kerStream << offsetsStream.str();
     kerStream << opsStream.str();
     kerStream << outWriteStream.str();
+    kerStream << loopEnd;
     kerStream << blockEnd;
 
     return kerStream.str();
@@ -372,7 +381,8 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
 
     int threads_x = 1, threads_y = 1;
     int blocks_x_ = 1, blocks_y_ = 1;
-    int blocks_x  = 1, blocks_y = 1;
+    int blocks_x  = 1, blocks_y = 1, blocks_z = 1, blocks_x_total;
+    const int max_blocks = 65535;
 
     int num_odims = 4;
 
@@ -386,17 +396,13 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
         threads_x = 256;
         threads_y =  1;
 
-        int blocks = divup((outputs[0].dims[0] *
-                            outputs[0].dims[1] *
-                            outputs[0].dims[2] *
-                            outputs[0].dims[3]), threads_x);
-
-        blocks_y_ = divup(blocks, 65535);
-        blocks_x_ = divup(blocks, blocks_y_);
-
-        blocks_x = blocks_x_;
-        blocks_y = blocks_y_;
+        blocks_x_total = divup((outputs[0].dims[0] *
+                                outputs[0].dims[1] *
+                                outputs[0].dims[2] *
+                                outputs[0].dims[3]), threads_x);
 
+        int repeat_x = divup(blocks_x_total, max_blocks);
+        blocks_x = divup(blocks_x_total, repeat_x);
     } else {
 
         threads_x = 32;
@@ -407,6 +413,13 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
 
         blocks_x = blocks_x_ * outputs[0].dims[2];
         blocks_y = blocks_y_ * outputs[0].dims[3];
+
+        blocks_z = divup(blocks_y, max_blocks);
+        blocks_y = divup(blocks_y, blocks_z);
+
+        blocks_x_total = blocks_x;
+        int repeat_x = divup(blocks_x_total, max_blocks);
+        blocks_x = divup(blocks_x_total, repeat_x);
     }
 
     vector<void *> args;
@@ -421,13 +434,14 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
 
     args.push_back((void *)&blocks_x_);
     args.push_back((void *)&blocks_y_);
+    args.push_back((void *)&blocks_x_total);
     args.push_back((void *)&num_odims);
 
     lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLaunchKernel(ker,
                             blocks_x,
                             blocks_y,
-                            1,
+                            blocks_z,
                             threads_x,
                             threads_y,
                             1,
diff --git a/test/jit.cpp b/test/jit.cpp
index 4beebc63f4..974f672ef3 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -281,3 +281,125 @@ TEST(JIT, ISSUE_1646)
     af::eval(test2);
     af::eval(test3);
 }
+
+TEST(JIT, NonLinearLargeY)
+{
+    const int d0 = 2;
+    // This needs to be > 2 * (1 << 20) to properly check this.
+    const int d1 = 3 * (1 << 20);
+    af::array a = af::randn(d0);
+    af::array b = af::randn(1, d1);
+
+    // tile is jit-ted for both the operations
+    af::array c = af::tile(a, 1, d1) + af::tile(b, d0, 1);
+    af::eval(c);
+
+    std::vector<float> ha(d0);
+    std::vector<float> hb(d1);
+    std::vector<float> hc(d0 * d1);
+
+    a.host(ha.data());
+    b.host(hb.data());
+    c.host(hc.data());
+
+    for (int j = 0; j < d1; j++) {
+        for (int i = 0; i < d0; i++) {
+            ASSERT_EQ(hc[i + j * d0], ha[i] + hb[j]) << " at " << i << " , " << j;
+        }
+    }
+}
+
+TEST(JIT, NonLinearLargeX)
+{
+    af_array r, c, s;
+    dim_t rdims[] = {1024000, 1, 3};
+    dim_t cdims[] = {1, 1, 3};
+    dim_t sdims[] = {1, 1, 1};
+    dim_t ndims = 3;
+
+    ASSERT_EQ(AF_SUCCESS, af_randu(&r, ndims, rdims, f32));
+    ASSERT_EQ(AF_SUCCESS, af_constant(&c, 1, ndims, cdims, f32));
+    ASSERT_EQ(AF_SUCCESS, af_eval(c));
+    ASSERT_EQ(AF_SUCCESS, af_sub(&s, r, c, true));
+    ASSERT_EQ(AF_SUCCESS, af_eval(s));
+
+    dim_t relem = 1;
+    dim_t celem = 1;
+    dim_t selem = 1;
+    for (int i = 0; i < ndims; i++) {
+        relem *= rdims[i];
+        celem *= cdims[i];
+        sdims[i] = std::max(rdims[i], cdims[i]);
+        selem *= sdims[i];
+    }
+
+    std::vector<float> hr(relem);
+    std::vector<float> hc(celem);
+    std::vector<float> hs(selem);
+
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hr.data(), r));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hc.data(), c));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hs.data(), s));
+
+    for (int k = 0; k < sdims[2]; k++) {
+        for (int j = 0; j < sdims[1]; j++) {
+            for (int i = 0; i < sdims[0]; i++) {
+
+                int sidx = i +
+                    j * sdims[0] +
+                    k * (sdims[0] * sdims[1]);
+
+                int ridx = (i % rdims[0]) +
+                    (j % rdims[1]) * rdims[0] +
+                    (k % rdims[2]) * rdims[0] * rdims[1];
+
+                int cidx = (i % cdims[0]) +
+                    (j % cdims[1]) * cdims[0] +
+                    (k % cdims[2]) * cdims[0] * cdims[1];
+
+                ASSERT_EQ(hs[sidx], hr[ridx] - hc[cidx]) << " at " << i << "," << k;
+            }
+        }
+    }
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(r));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(c));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(s));
+}
+
+TEST(JIT, ISSUE_1894)
+{
+    af::array a = af::randu(1);
+    af::array b = af::tile(a, 2 * (1 << 20));
+    af::eval(b);
+    float ha = -100;
+    std::vector<float> hb(b.elements(), -200);
+
+    a.host(&ha);
+    b.host(hb.data());
+
+    for (size_t i = 0; i < hb.size(); i++) {
+        ASSERT_EQ(ha, hb[i]);
+    }
+}
+
+TEST(JIT, LinearLarge)
+{
+    // Needs to be larger than 65535 * 256 (or 1 << 24)
+    float v1 = std::rand() % 100;
+    float v2 = std::rand() % 100;
+
+    af::array a = af::constant(v1, 1 << 25);
+    af::array b = af::constant(v2, 1 << 25);
+    af::array c = (a + b) * (a - b);
+    af::eval(c);
+
+    float v3 = (v1 + v2) * (v1 - v2);
+
+    std::vector<float> hc(c.elements());
+    c.host(hc.data());
+
+    for (size_t i = 0; i < hc.size(); i++) {
+        ASSERT_EQ(hc[i], v3);
+    }
+}

From 13ef5ac3c62db6119473bd3ba5c91c4be5d79308 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Wed, 2 Aug 2017 00:59:31 -0700
Subject: [PATCH 1260/2677] Removing the restriction of MAX_BINS for CUDA and
 OpenCL

---
 src/backend/cuda/histogram.cu           |  2 --
 src/backend/cuda/kernel/histogram.hpp   | 31 ++++++++++++++++++-------
 src/backend/opencl/histogram.cpp        |  2 --
 src/backend/opencl/kernel/histogram.cl  | 26 +++++++++++++++------
 src/backend/opencl/kernel/histogram.hpp |  5 ++--
 test/histogram.cpp                      | 28 ++++++++++++++++++++++
 6 files changed, 73 insertions(+), 21 deletions(-)

diff --git a/src/backend/cuda/histogram.cu b/src/backend/cuda/histogram.cu
index e1630e3cea..3482fb8c3e 100644
--- a/src/backend/cuda/histogram.cu
+++ b/src/backend/cuda/histogram.cu
@@ -24,8 +24,6 @@ template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval)
 {
 
-    ARG_ASSERT(1, (nbins<=kernel::MAX_BINS));
-
     const dim4 dims     = in.dims();
     dim4 outDims        = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out  = createValueArray<outType>(outDims, outType(0));
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index a0a6c5cf91..c8eaa13ff7 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -44,21 +44,35 @@ void histogramKernel(Param<outType> out, CParam<inType> in,
     int end    = minimum((start + THRD_LOAD * blockDim.x), len);
     float step = (maxval-minval) / (float)nbins;
 
-    for (int i = threadIdx.x; i < nbins; i += blockDim.x)
-        shrdMem[i] = 0;
-    __syncthreads();
+    // If nbins > max shared memory allocated, then just use atomicAdd on global memory
+    bool use_global = nbins > MAX_BINS;
+
+    // Skip initializing shared memory
+    if (!use_global) {
+        for (int i = threadIdx.x; i < nbins; i += blockDim.x)
+            shrdMem[i] = 0;
+        __syncthreads();
+    }
 
     for (int row = start; row < end; row += blockDim.x) {
         int idx = isLinear ? row : ((row % in.dims[0]) + (row / in.dims[0])*in.strides[1]);
         int bin = (int)((iptr[idx] - minval) / step);
         bin     = (bin < 0)      ? 0         : bin;
         bin     = (bin >= nbins) ? (nbins-1) : bin;
-        atomicAdd((shrdMem + bin), 1);
+
+        if (use_global) {
+            atomicAdd((optr + bin), 1);
+        } else {
+            atomicAdd((shrdMem + bin), 1);
+        }
     }
-    __syncthreads();
 
-    for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
-        atomicAdd((optr + i), shrdMem[i]);
+    // No need to write to global if use_global is true
+    if (!use_global) {
+        __syncthreads();
+        for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
+            atomicAdd((optr + i), shrdMem[i]);
+        }
     }
 }
 
@@ -72,7 +86,8 @@ void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval, f
 
     dim3 blocks(blk_x * in.dims[2], in.dims[3]);
 
-    int smem_size = nbins * sizeof(outType);
+    // If nbins > MAX_BINS, we are using global memory so smem_size can be 0;
+    int smem_size = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 0;
 
     CUDA_LAUNCH_SMEM((histogramKernel<inType, outType, isLinear>), blocks, threads, smem_size,
             out, in, nElems, nbins, minval, maxval, blk_x);
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 7142228089..2bde97c965 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -23,8 +23,6 @@ namespace opencl
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval)
 {
-    ARG_ASSERT(1, (nbins<=kernel::MAX_BINS));
-
     const dim4 dims     = in.dims();
     dim4 outDims        = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
diff --git a/src/backend/opencl/kernel/histogram.cl b/src/backend/opencl/kernel/histogram.cl
index 9e1468d3b7..7754590afd 100644
--- a/src/backend/opencl/kernel/histogram.cl
+++ b/src/backend/opencl/kernel/histogram.cl
@@ -25,9 +25,13 @@ void histogram(__global outType *         d_dst,
 
     float dx = (maxval-minval)/(float)nbins;
 
-    for (int i = get_local_id(0); i < nbins; i += get_local_size(0))
-        localMem[i] = 0;
-    barrier(CLK_LOCAL_MEM_FENCE);
+    bool use_global = nbins > MAX_BINS;
+
+    if (!use_global) {
+        for (int i = get_local_id(0); i < nbins; i += get_local_size(0))
+            localMem[i] = 0;
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
 
     for (int row = start; row < end; row += get_local_size(0)) {
 #if defined(IS_LINEAR)
@@ -40,11 +44,19 @@ void histogram(__global outType *         d_dst,
         int bin = (int)(((float)in[idx] - minval) / dx);
         bin     = max(bin, 0);
         bin     = min(bin, (int)nbins-1);
-        atomic_inc((localMem + bin));
+
+        if (use_global) {
+            atomic_inc((out + bin));
+        } else {
+            atomic_inc((localMem + bin));
+        }
+
     }
-    barrier(CLK_LOCAL_MEM_FENCE);
 
-    for (int i = get_local_id(0); i < nbins; i += get_local_size(0)) {
-        atomic_add((out + i), localMem[i]);
+    if (!use_global) {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        for (int i = get_local_id(0); i < nbins; i += get_local_size(0)) {
+            atomic_add((out + i), localMem[i]);
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 6a998b6621..318297b021 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -43,7 +43,8 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval)
         std::ostringstream options;
         options << " -D inType=" << dtype_traits<inType>::getName()
             << " -D outType=" << dtype_traits<outType>::getName()
-            << " -D THRD_LOAD=" << THRD_LOAD;
+            << " -D THRD_LOAD=" << THRD_LOAD
+            << " -D MAX_BINS=" << MAX_BINS;
         if (isLinear)
             options << " -D IS_LINEAR";
         if (std::is_same<inType, double>::value ||
@@ -66,7 +67,7 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval)
 
     int nElems = in.info.dims[0]*in.info.dims[1];
     int blk_x  = divup(nElems, THRD_LOAD*THREADS_X);
-    int locSize = nbins * sizeof(outType);
+    int locSize = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 1;
 
     NDRange local(THREADS_X, 1);
     NDRange global(blk_x*in.info.dims[2]*THREADS_X, in.info.dims[3]);
diff --git a/test/histogram.cpp b/test/histogram.cpp
index 99fa546a52..7774abf6f8 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -271,3 +271,31 @@ TEST(histogram, IndexedArray)
     ASSERT_EQ(true, out[2] ==  8);
     ASSERT_EQ(true, out[3] ==  8);
 }
+
+TEST(histogram, LargeBins)
+{
+    const int max_val = 20000;
+    const int min_val = 0;
+    const int nbins = max_val / 2;
+    const int num = 1 << 20;
+    af::array A = af::round(max_val * af::randu(num) + min_val).as(u32);
+    af::eval(A);
+    af::array H = histogram(A, nbins, min_val, max_val);
+
+    std::vector<unsigned> hA(num);
+    A.host(hA.data());
+
+    std::vector<unsigned> hH(nbins);
+    H.host(hH.data());
+
+    int dx = (max_val - min_val) / nbins;
+    for (int i = 0; i < num; i++) {
+        int bin = (hA[i] - min_val) / dx;
+        bin = std::min(bin, nbins - 1);
+        hH[bin] -= 1;
+    }
+
+    for (int i = 0; i < nbins; i++) {
+        ASSERT_EQ(hH[i], 0u);
+    }
+}

From b733c5098bbffee9cfb20ae699efd56eb9660abb Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Wed, 2 Aug 2017 01:38:46 -0700
Subject: [PATCH 1261/2677] Fixing assign for empty indexing

---
 src/api/c/assign.cpp |  3 +++
 test/assign.cpp      | 15 +++++++++++++++
 2 files changed, 18 insertions(+)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 26220f197c..1595ceeb1d 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -32,6 +32,9 @@ void assign(Array<Tout> &out, const unsigned &ndims, const af_seq *index, const
     dim4 const outDs = out.dims();
     dim4 const iDims = in_.dims();
 
+    // Nothing to do for empty arrays
+    if (iDims.elements() == 0) return;
+
     DIM_ASSERT(0, (outDs.ndims()>=iDims.ndims()));
     DIM_ASSERT(0, (outDs.ndims()>=(dim_t)ndims));
 
diff --git a/test/assign.cpp b/test/assign.cpp
index 8eb81584df..885934f2db 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -1002,3 +1002,18 @@ TEST(Assign, ISSUE_1764)
         }
     }
 }
+
+TEST(Assign, ISSUE_1677)
+{
+    try {
+        dim_t sz = 1;
+        af::array a = af::constant(1.0f, 3, sz, f32);
+        af::array b = af::constant(2.0f, 3, sz, f32);
+        af::array cond = af::constant(0, sz, b8); // all false
+        a(af::span, cond) = b(af::span, cond);
+    } catch(af::exception &ex) {
+        FAIL() << "ArrayFire exception: " << ex.what();
+    } catch(...) {
+        FAIL() << "Unknown exception thrown";
+    }
+}

From 06d019be415780fbd22e72c71f7d43a474d1bbcc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 3 Aug 2017 17:55:40 -0400
Subject: [PATCH 1262/2677] Change static objects into pointers to fix release
 issues on Windows

Windows terminates threads before the queue threads and other resources are
released. This causes deadlocks with the condition_variables in the async_queue
objects. This is a bug in Visual Studio/Windows that is documented here:

https://connect.microsoft.com/VisualStudio/feedback/details/747145

This will leak some resources but these resources will be released by the
operating system on exit.
---
 src/api/c/canny.cpp                        |  6 +++---
 src/api/cpp/random.cpp                     |  4 ++--
 src/api/unified/symbol_manager.cpp         | 18 +++++++++---------
 src/backend/common/MemoryManager.hpp       | 10 ++++++----
 src/backend/cpu/Array.cpp                  |  1 -
 src/backend/cpu/kernel/reduce.hpp          |  2 +-
 src/backend/cpu/platform.cpp               | 16 ++++++++--------
 src/backend/cpu/platform.hpp               | 15 +++------------
 src/backend/cuda/platform.cpp              |  4 ++--
 src/backend/opencl/kernel/harris.hpp       |  4 ++--
 src/backend/opencl/kernel/homography.hpp   |  4 ++--
 src/backend/opencl/kernel/orb.hpp          |  4 ++--
 src/backend/opencl/kernel/regions.hpp      |  4 ++--
 src/backend/opencl/kernel/sift_nonfree.hpp |  4 ++--
 src/backend/opencl/platform.cpp            |  8 ++++----
 src/backend/opencl/types.hpp               | 14 +++++++-------
 16 files changed, 55 insertions(+), 63 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 3e7b0b0982..37d8daf240 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -173,9 +173,9 @@ template<typename T>
 af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold ct,
                      const float t2, const unsigned sw, const bool isf)
 {
-    static const std::vector<float> v = {-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
-    Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v.data());
-    Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v.data());
+    static const float v[] = {-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
+    Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v);
+    Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v);
 
     // Run separable convolution to smooth the input image
     Array<float> smt = detail::convolve2<float, float, false>(cast<float, T>(in), cFilter, rFilter);
diff --git a/src/api/cpp/random.cpp b/src/api/cpp/random.cpp
index ad1507dbb7..a909b9473e 100644
--- a/src/api/cpp/random.cpp
+++ b/src/api/cpp/random.cpp
@@ -76,14 +76,14 @@ namespace af
         return engine;
     }
 
-    AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r)
+    array randu(const dim4 &dims, const dtype ty, randomEngine &r)
     {
         af_array out;
         AF_THROW(af_random_uniform(&out, dims.ndims(), dims.get(), ty, r.get()));
         return array(out);
     }
 
-    AFAPI array randn(const dim4 &dims, const dtype ty, randomEngine &r)
+    array randn(const dim4 &dims, const dtype ty, randomEngine &r)
     {
         af_array out;
         AF_THROW(af_random_normal(&out, dims.ndims(), dims.get(), ty, r.get()));
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index febf8c5375..d5b0da8658 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -20,10 +20,10 @@ using std::replace;
 namespace unified
 {
 
-static const string LIB_AF_BKND_NAME[NUM_BACKENDS] = {"cpu", "cuda", "opencl"};
+static const char* LIB_AF_BKND_NAME[NUM_BACKENDS] = {"cpu", "cuda", "opencl"};
 #if defined(OS_WIN)
-static const string LIB_AF_BKND_PREFIX = "af";
-static const string LIB_AF_BKND_SUFFIX = ".dll";
+static const char* LIB_AF_BKND_PREFIX = "af";
+static const char* LIB_AF_BKND_SUFFIX = ".dll";
 #define RTLD_LAZY 0
 #else
 #if defined(__APPLE__)
@@ -31,20 +31,20 @@ static const string LIB_AF_BKND_SUFFIX = ".dll";
 #else
 #define SO_SUFFIX_HELPER(VER) ".so." #VER
 #endif // APPLE
-static const string LIB_AF_BKND_PREFIX = "libaf";
+static const char* LIB_AF_BKND_PREFIX = "libaf";
 
 #define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
-static const string LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
+static const char* LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
 #endif
 
-static const string LIB_AF_ENVARS[NUM_ENV_VARS] = {"AF_PATH", "AF_BUILD_PATH"};
-static const string LIB_AF_RPATHS[NUM_ENV_VARS] = {"/lib/", "/src/backend/"};
+static const char* LIB_AF_ENVARS[NUM_ENV_VARS] = {"AF_PATH", "AF_BUILD_PATH"};
+static const char* LIB_AF_RPATHS[NUM_ENV_VARS] = {"/lib/", "/src/backend/"};
 static const bool LIB_AF_RPATH_SUFFIX[NUM_ENV_VARS] = {false, true};
 
 inline string getBkndLibName(const int backend_index)
 {
     int i = backend_index >=0 && backend_index<NUM_BACKENDS ? backend_index : 0;
-    return LIB_AF_BKND_PREFIX + LIB_AF_BKND_NAME[i] + LIB_AF_BKND_SUFFIX;
+    return string(LIB_AF_BKND_PREFIX) + LIB_AF_BKND_NAME[i] + LIB_AF_BKND_SUFFIX;
 }
 
 /*flag parameter is not used on windows platform */
@@ -84,7 +84,7 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
         for (int i=0; i<NUM_ENV_VARS; ++i) {
             string abs_path = getEnvVar(LIB_AF_ENVARS[i])
                                  + LIB_AF_RPATHS[i]
-                                 + (LIB_AF_RPATH_SUFFIX[i] ? LIB_AF_BKND_NAME[bknd_idx]+"/" : "")
+                                 + (LIB_AF_RPATH_SUFFIX[i] ? LIB_AF_BKND_NAME[bknd_idx]+ string("/") : "")
                                  + bkndLibName;
 #if defined(OS_WIN)
             replace(abs_path.begin(), abs_path.end(), '/', '\\');
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index b17ac3aeb8..b0cb8a78d6 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -14,6 +14,8 @@
 #include <util.hpp>
 
 #include <algorithm>
+
+// TODO(umar): Remove iostream
 #include <iomanip>
 #include <iostream>
 #include <mutex>
@@ -298,9 +300,9 @@ class MemoryManager
 
         std::cout << msg << std::endl;
 
-        static const std::string head("|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |");
-        static const std::string line(head.size(), '-');
-        std::cout << line << std::endl << head << std::endl << line << std::endl;
+        printf("---------------------------------------------------------\n"
+               "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
+               "---------------------------------------------------------\n");
 
         for(auto& kv : current.locked_map) {
             std::string status_mngr("Yes");
@@ -343,7 +345,7 @@ class MemoryManager
             }
         }
 
-        std::cout << line << std::endl;
+        printf("---------------------------------------------------------\n");
     }
 
     void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index c4f0950816..931ee2e126 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -23,7 +23,6 @@
 namespace cpu
 {
 
-const int MAX_TNJ_LEN = 20;
 using TNJ::BufferNode;
 using TNJ::Node;
 using TNJ::Node_ptr;
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index bc0a284086..c75e897ac2 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -23,7 +23,7 @@ struct reduce_dim
                     const int dim, bool change_nan, double nanval)
     {
         static const int D1 = D - 1;
-        static reduce_dim<op, Ti, To, D1> reduce_dim_next;
+        reduce_dim<op, Ti, To, D1> reduce_dim_next;
 
         const af::dim4 ostrides = out.strides();
         const af::dim4 istrides = in.strides();
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index a5b4342621..ddf894622a 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -257,21 +257,21 @@ bool& evalFlag()
     return flag;
 }
 
+DeviceManager::DeviceManager()
+    : queues(MAX_QUEUES)
+    , memManager(new MemoryManager()) {}
+
+
 MemoryManager& memoryManager()
 {
-    static std::once_flag flag;
-
     DeviceManager& inst = DeviceManager::getInstance();
-
-    std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
-
-    return *(inst.memManager.get());
+    return *(inst.memManager);
 }
 
 DeviceManager& DeviceManager::getInstance()
 {
-    static DeviceManager my_instance;
-    return my_instance;
+    static DeviceManager* my_instance = new DeviceManager();
+    return *my_instance;
 }
 
 }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 97f960c413..50509d1aee 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -128,24 +128,15 @@ class DeviceManager
         CPUInfo getCPUInfo() const;
 
     private:
-        DeviceManager() : queues(MAX_QUEUES){}
-
+        DeviceManager();
         // Following two declarations are required to
         // avoid copying accidental copy/assignment
         // of instance returned by getInstance to other
         // variables
-        DeviceManager(DeviceManager const&);
-        void operator=(DeviceManager const&);
-        // And the destructor is needed because rule of three:
-        // http://en.cppreference.com/w/cpp/language/rule_of_three
-        ~DeviceManager() {
-            for(auto &q : queues) q.sync();
-            memManager.release();
-            queues.clear();
-        }
+        DeviceManager(DeviceManager const&) = delete;
+        void operator=(DeviceManager const&) = delete;
 
         // Attributes
-        // DO NOT MOVE QUEUES! This has to be destroyed last, meaning it needs to be defined first.
         std::vector<queue> queues;
         const CPUInfo cinfo;
         std::unique_ptr<MemoryManager> memManager;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 40565ce160..687fcf3d41 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -392,8 +392,8 @@ bool DeviceManager::checkGraphicsInteropCapability()
 
 DeviceManager& DeviceManager::getInstance()
 {
-    static DeviceManager my_instance;
-    return my_instance;
+    static DeviceManager *my_instance = new DeviceManager();
+    return *my_instance;
 }
 
 MemoryManager& memoryManager()
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index b2f834f971..9db7c1a80b 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -88,7 +88,7 @@ template<typename T>
 std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
 getHarrisKernels()
 {
-    static const std::string kernelNames[4] =
+    static const char* kernelNames[4] =
         {"second_order_deriv", "keep_corners", "harris_responses", "non_maximal"};
 
     kc_entry_t entries[4];
@@ -114,7 +114,7 @@ getHarrisKernels()
         for (int i=0; i<4; ++i)
         {
             entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] +
                 std::string("_") + std::string(dtype_traits<T>::getName());
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 572c21bdca..0cf20310b6 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -39,7 +39,7 @@ template<typename T, af_homography_type htype>
 std::array<cl::Kernel*, 5> getHomographyKernels()
 {
     static const unsigned NUM_KERNELS = 5;
-    static const std::string kernelNames[NUM_KERNELS] =
+    static const char* kernelNames[NUM_KERNELS] =
         {"compute_homography", "eval_homography", "compute_median",
          "find_min_median", "compute_lmeds_inliers"};
 
@@ -79,7 +79,7 @@ std::array<cl::Kernel*, 5> getHomographyKernels()
         for (unsigned i=0; i<NUM_KERNELS; ++i)
         {
             entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] + std::string("_") +
                 std::string(dtype_traits<T>::getName()) +
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index cd3da4ed60..d49693f70d 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -89,7 +89,7 @@ template<typename T>
 std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
 getOrbKernels()
 {
-    static const std::string kernelNames[4] =
+    static const char* kernelNames[4] =
         {"harris_response", "keep_features", "centroid_angle", "extract_orb"};
 
     kc_entry_t entries[4];
@@ -117,7 +117,7 @@ getOrbKernels()
         for (int i=0; i<4; ++i)
         {
             entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] +
                 std::string("_") + std::string(dtype_traits<T>::getName());
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 35dd4bec52..31cc3c3f34 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -55,7 +55,7 @@ getRegionsKernels()
     static const int block_dim = 16;
     static const int num_warps = 8;
     static const unsigned NUM_KERNELS = 3;
-    static const std::string kernelNames[NUM_KERNELS] =
+    static const char* kernelNames[NUM_KERNELS] =
         {"initial_label", "final_relabel", "update_equiv"};
 
     kc_entry_t entries[NUM_KERNELS];
@@ -98,7 +98,7 @@ getRegionsKernels()
         for (unsigned i=0; i<NUM_KERNELS; ++i)
         {
             entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] + std::string("_") +
                 std::string(dtype_traits<T>::getName()) +
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index 9527ca1735..a3ad9a53b7 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -399,7 +399,7 @@ template<typename T>
 std::array<cl::Kernel*, 7> getSiftKernels()
 {
     static const unsigned NUM_KERNELS = 7;
-    static const std::string kernelNames[NUM_KERNELS] =
+    static const char* kernelNames[NUM_KERNELS] =
         {"sub", "detectExtrema", "interpolateExtrema", "calcOrientation", "removeDuplicates",
          "computeDescriptor", "computeGLOHDescriptor"};
 
@@ -424,7 +424,7 @@ std::array<cl::Kernel*, 7> getSiftKernels()
         for (unsigned i=0; i<NUM_KERNELS; ++i)
         {
             entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i].c_str());
+            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] + std::string("_") +
                 std::string(dtype_traits<T>::getName());
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d44d82f47e..9af815723f 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -61,9 +61,9 @@ using cl::Device;
 namespace opencl
 {
 #if defined (OS_MAC)
-static const std::string CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
+static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
-static const std::string CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
+static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
 #endif
 
 static const std::string get_system(void)
@@ -744,8 +744,8 @@ kc_entry_t kernelCache(int device, const std::string& key)
 
 DeviceManager& DeviceManager::getInstance()
 {
-    static DeviceManager my_instance;
-    return my_instance;
+    static DeviceManager* my_instance = new DeviceManager();
+    return *my_instance;
 }
 
 DeviceManager::~DeviceManager()
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 277ba2c07e..df829c7c42 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -21,6 +21,8 @@
 #include <cmath>
 #include <type_util.hpp>
 
+using std::string;
+
 namespace opencl
 {
 typedef cl_float2   cfloat;
@@ -50,8 +52,8 @@ struct ToNumStr<float>
 {
     inline std::string operator()(float val)
     {
-        static const std::string PINF = "+INFINITY";
-        static const std::string NINF = "-INFINITY";
+        static const char* PINF = "+INFINITY";
+        static const char* NINF = "-INFINITY";
         if (std::isinf(val)) {
             return val < 0 ? NINF : PINF;
         }
@@ -64,10 +66,10 @@ struct ToNumStr<double>
 {
     inline std::string operator()(double val)
     {
-        static const std::string PINF = "+INFINITY";
-        static const std::string NINF = "-INFINITY";
+        static const char* PINF = "+INFINITY";
+        static const char* NINF = "-INFINITY";
         if (std::isinf(val)) {
-            return val < 0 ? NINF : PINF;
+            return string(val < 0 ? NINF : PINF);
         }
         return std::to_string(val);
     }
@@ -79,7 +81,6 @@ struct ToNumStr<cfloat>
     inline std::string operator()(cfloat val)
     {
         ToNumStr<float> realStr;
-        static const std::string INF = "INFINITY";
         std::stringstream s;
         s << "{";
         s << realStr(val.s[0]);
@@ -96,7 +97,6 @@ struct ToNumStr<cdouble>
     inline std::string operator()(cdouble val)
     {
         ToNumStr<double> realStr;
-        static const std::string INF = "INFINITY";
         std::stringstream s;
         s << "{";
         s << realStr(val.s[0]);

From 67aa7c49964bf003f52ca86bd249f19dcead416a Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 30 May 2017 17:41:06 -0400
Subject: [PATCH 1263/2677] fix max grid dimension cuda limitation for approx
 kernel

---
 src/backend/cuda/kernel/approx.hpp | 164 +++++++++++++++++------------
 test/approx1.cpp                   |  60 +++++++++++
 test/approx2.cpp                   |  72 +++++++++++++
 3 files changed, 228 insertions(+), 68 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index f701bfdf78..726d360664 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -23,92 +23,104 @@ namespace cuda
         static const int TY = 16;
         static const int THREADS = 256;
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Approx Kernel
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename Ty, typename Tp, int order>
+        // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
+        // \param iterPerBlockY number of iterations along grid.Y per block
+        template<typename Ty, typename Tp, int order, bool largeYWDim>
         __global__
         void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> xpos,
                             const float offGrid, const int blocksMatX, const bool batch,
-                            af_interp_type method)
+                            af_interp_type method, int iterPerBlockY)
         {
-            const int idw = blockIdx.y / out.dims[2];
-            const int idz = blockIdx.y - idw * out.dims[2];
-
             const int idy = blockIdx.x / blocksMatX;
             const int blockIdx_x = blockIdx.x - idy * blocksMatX;
             const int idx = blockIdx_x * blockDim.x + threadIdx.x;
 
-            if (idx >= out.dims[0] || idy >= out.dims[1] ||
-                idz >= out.dims[2] || idw >= out.dims[3])
-                return;
+            // For smaller kernels statically set iterPerPlockY
+            // to 1 (register count optimization)
+            if(!largeYWDim) { iterPerBlockY = 1; }
 
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                                               + idy * out.strides[1] + idx;
-            int xmid = idx;
-            if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
+            for(int ib = 0; ib < iterPerBlockY; ++ib) {
+                const int idw = (blockIdx.y + ib * gridDim.y) / out.dims[2];
+                const int idz = (blockIdx.y + ib * gridDim.y) - idw * out.dims[2];
 
-            const Tp x = xpos.ptr[xmid];
-            if (x < 0 || in.dims[0] < x+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
+                if (idx >= out.dims[0] || idy >= out.dims[1] ||
+                    idz >= out.dims[2] || idw >= out.dims[3])
+                    return;
+
+                const int omId = idw * out.strides[3] + idz * out.strides[2]
+                                                   + idy * out.strides[1] + idx;
+                int xmid = idx;
+                if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
 
-            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
+                const Tp x = xpos.ptr[xmid];
+                if (x < 0 || in.dims[0] < x+1) {
+                    out.ptr[omId] = scalar<Ty>(offGrid);
+                    return;
+                }
 
-            // FIXME: Only cubic interpolation is doing clamping
-            // We need to make it consistent across all methods
-            // Not changing the behavior because tests will fail
-            bool clamp = order == 3;
+                int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
 
-            Interp1<Ty, Tp, order> interp;
-            interp(out, omId, in, ioff, x, method, 1, clamp);
+                // FIXME: Only cubic interpolation is doing clamping
+                // We need to make it consistent across all methods
+                // Not changing the behavior because tests will fail
+                bool clamp = order == 3;
+
+                Interp1<Ty, Tp, order> interp;
+                interp(out, omId, in, ioff, x, method, 1, clamp);
+            }
         }
 
-        template<typename Ty, typename Tp, int order>
+        // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
+        // \param iterPerBlockY number of iterations along grid.Y per block
+        template<typename Ty, typename Tp, int order, bool largeYWDim>
         __global__
         void approx2_kernel(Param<Ty> out, CParam<Ty> in,
                             CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
                             const int blocksMatX, const int blocksMatY, const bool batch,
-                            af_interp_type method)
+                            af_interp_type method, int iterPerBlockY)
         {
             const int idz = blockIdx.x / blocksMatX;
-            const int idw = blockIdx.y / blocksMatY;
-
-            int blockIdx_x = blockIdx.x - idz * blocksMatX;
-            int blockIdx_y = blockIdx.y - idw * blocksMatY;
-
-            int idx = threadIdx.x + blockIdx_x * blockDim.x;
-            int idy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if (idx >= out.dims[0] || idy >= out.dims[1] ||
-                idz >= out.dims[2] || idw >= out.dims[3])
-                return;
-
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                + idy * out.strides[1] + idx;
-            int xmid = idy * xpos.strides[1] + idx;
-            int ymid = idy * ypos.strides[1] + idx;
-            if(batch) {
-                xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
-                ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
+            const int blockIdx_x = blockIdx.x - idz * blocksMatX;
+            const int idx = threadIdx.x + blockIdx_x * blockDim.x;
+
+            // For smaller kernels statically set iterPerPlockY
+            // to 1 (register count optimization)
+            if(!largeYWDim) { iterPerBlockY = 1; }
+
+            for(int ib = 0; ib < iterPerBlockY; ++ib) {
+                const int idw = (blockIdx.y + ib * gridDim.y) / blocksMatY;
+                const int blockIdx_y = (blockIdx.y + ib * gridDim.y) - idw * blocksMatY;
+                const int idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+                if (idx >= out.dims[0] || idy >= out.dims[1] ||
+                    idz >= out.dims[2] || idw >= out.dims[3])
+                    return;
+
+                const int omId = idw * out.strides[3] + idz * out.strides[2]
+                    + idy * out.strides[1] + idx;
+                int xmid = idy * xpos.strides[1] + idx;
+                int ymid = idy * ypos.strides[1] + idx;
+                if(batch) {
+                    xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
+                    ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
+                }
+
+                const Tp x = xpos.ptr[xmid], y = ypos.ptr[ymid];
+                if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
+                    out.ptr[omId] = scalar<Ty>(offGrid);
+                    return;
+                }
+
+                int ioff = idw * in.strides[3] + idz * in.strides[2];
+
+                // FIXME: Only cubic interpolation is doing clamping
+                // We need to make it consistent across all methods
+                // Not changing the behavior because tests will fail
+                bool clamp = order == 3;
+
+                Interp2<Ty, Tp, order> interp;
+                interp(out, omId, in, ioff, x, y, method, 1, clamp);
             }
-
-            const Tp x = xpos.ptr[xmid], y = ypos.ptr[ymid];
-            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            int ioff = idw * in.strides[3] + idz * in.strides[2];
-
-            // FIXME: Only cubic interpolation is doing clamping
-            // We need to make it consistent across all methods
-            // Not changing the behavior because tests will fail
-            bool clamp = order == 3;
-
-            Interp2<Ty, Tp, order> interp;
-            interp(out, omId, in, ioff, x, y, method, 1, clamp);
         }
 
         ///////////////////////////////////////////////////////////////////////////
@@ -125,8 +137,16 @@ namespace cuda
 
             bool batch = !(xpos.dims[1] == 1 && xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
-            CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads,
-                        out, in, xpos, offGrid, blocksPerMat, batch, method);
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int iterPerBlockY = divup(blocks.y, maxBlocksY);
+            if(iterPerBlockY > 1) {
+                blocks.y = maxBlocksY;
+                CUDA_LAUNCH((approx1_kernel<Ty, Tp, order, true>), blocks, threads,
+                            out, in, xpos, offGrid, blocksPerMat, batch, method, iterPerBlockY);
+            } else {
+                CUDA_LAUNCH((approx1_kernel<Ty, Tp, order, false>), blocks, threads,
+                            out, in, xpos, offGrid, blocksPerMat, batch, method, iterPerBlockY);
+            }
             POST_LAUNCH_CHECK();
         }
 
@@ -142,8 +162,16 @@ namespace cuda
 
             bool batch = !(xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
-            CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads,
-                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method);
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int iterPerBlockY = divup(blocks.y, maxBlocksY);
+            if(iterPerBlockY > 1) {
+                blocks.y = maxBlocksY;
+                CUDA_LAUNCH((approx2_kernel<Ty, Tp, order, true>), blocks, threads,
+                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method, iterPerBlockY);
+            } else {
+                CUDA_LAUNCH((approx2_kernel<Ty, Tp, order, false>), blocks, threads,
+                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method, iterPerBlockY);
+            }
             POST_LAUNCH_CHECK();
         }
     }
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 67ca048ac0..003eb023a4 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -405,3 +405,63 @@ TEST(Approx1, CPPCubicBatch)
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
+
+TEST(Approx1, CPPNearestMaxDims)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+    af::array input = af::randu(1, largeDim);
+    af::array pos   = input.dims(0) * af::randu(1, largeDim);
+    af::array out   = af::approx1(input, pos, AF_INTERP_NEAREST);
+
+    input = af::randu(1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, largeDim);
+    out   = af::approx1(input, pos, AF_INTERP_NEAREST);
+
+    input = af::randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
+    out   = af::approx1(input, pos, AF_INTERP_NEAREST);
+
+    SUCCEED();
+}
+
+TEST(Approx1, CPPLinearMaxDims)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+    af::array input = af::iota(af::dim4(1, largeDim), c32);
+    af::array pos   = input.dims(0) * af::randu(1, largeDim);
+    af::array outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+
+    input = af::iota(af::dim4(1, 1, largeDim), c32);
+    pos   = input.dims(0) * af::randu(1, 1, largeDim);
+    outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+
+    input = af::iota(af::dim4(1, 1, 1, largeDim), c32);
+    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
+    outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+
+    SUCCEED();
+}
+
+TEST(Approx1, CPPCubicMaxDims)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+    af::array input = af::iota(af::dim4(1, largeDim), c32);
+    af::array pos   = input.dims(0) * af::randu(1, largeDim);
+    af::array outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+
+    input = af::iota(af::dim4(1, 1, largeDim), c32);
+    pos   = input.dims(0) * af::randu(1, 1, largeDim);
+    outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+
+    input = af::iota(af::dim4(1, 1, 1, largeDim), c32);
+    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
+    outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+
+    SUCCEED();
+}
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 48150fe6f3..16b58631a1 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -363,3 +363,75 @@ TEST(Approx2, CPPLinearBatch)
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
+
+TEST(Approx2, CPPNearestMaxDims)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+
+    af::array input = af::randu(1, largeDim);
+    af::array pos   = input.dims(0) * af::randu(1, 10);
+    af::array qos   = input.dims(1) * af::randu(1, 10);
+    af::array out   = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+
+    input = af::randu(1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, largeDim);
+    qos   = input.dims(1) * af::randu(1, 1, largeDim);
+    out   = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+
+    input = af::randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
+    qos   = input.dims(1) * af::randu(1, 1, 1, largeDim);
+    out   = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+
+    SUCCEED();
+}
+
+TEST(Approx2, CPPLinearMaxDims)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+
+    af::array input = af::randu(1, largeDim);
+    af::array pos   = input.dims(0) * af::randu(1, 10);
+    af::array qos   = input.dims(1) * af::randu(1, 10);
+    af::array out   = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+
+    input = af::randu(1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, largeDim);
+    qos   = input.dims(1) * af::randu(1, 1, largeDim);
+    out   = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+
+    input = af::randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
+    qos   = input.dims(1) * af::randu(1, 1, 1, largeDim);
+    out   = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+
+    SUCCEED();
+}
+
+TEST(Approx2, CPPCubicMaxDims)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+
+    af::array input = af::randu(1, largeDim);
+    af::array pos   = input.dims(0) * af::randu(1, 10);
+    af::array qos   = input.dims(1) * af::randu(1, 10);
+    af::array out   = af::approx2(input, pos, qos, AF_INTERP_BICUBIC);
+
+    input = af::randu(1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, largeDim);
+    qos   = input.dims(1) * af::randu(1, 1, largeDim);
+    out   = af::approx2(input, pos, qos, AF_INTERP_BICUBIC);
+
+    input = af::randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
+    qos   = input.dims(1) * af::randu(1, 1, 1, largeDim);
+    out   = af::approx2(input, pos, qos, AF_INTERP_BICUBIC);
+
+    SUCCEED();
+}

From 60b0ba2f3f86a30d773d527edfe02100a96284d0 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 31 May 2017 14:49:45 -0400
Subject: [PATCH 1264/2677] fix max grid dimension cuda limitation for reduce
 kernel

---
 src/backend/cuda/kernel/reduce.hpp | 312 +++++++++++++++++------------
 test/reduce.cpp                    |  83 +++++++-
 2 files changed, 263 insertions(+), 132 deletions(-)

diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index eb13239c30..d5be3b5ae3 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -23,85 +23,94 @@ namespace cuda
 {
 namespace kernel
 {
-    template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
+    // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
+    // \param iterPerBlockY number of iterations along grid.Y per block
+    template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY, bool largeYWDim>
     __global__
     static void reduce_dim_kernel(Param<To> out,
                                   CParam <Ti> in,
                                   uint blocks_x, uint blocks_y, uint offset_dim,
-                                  bool change_nan, To nanval)
+                                  bool change_nan, To nanval, int iterPerBlockY)
     {
         const uint tidx = threadIdx.x;
         const uint tidy = threadIdx.y;
         const uint tid  = tidy * THREADS_X + tidx;
 
         const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
         const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
         const uint xid = blockIdx_x * blockDim.x + tidx;
-        const uint yid = blockIdx_y; // yid  of output. updated for input later.
 
-        uint ids[4] = {xid, yid, zid, wid};
+        __shared__ To s_val[THREADS_X * DIMY];
 
-        const Ti *iptr = in.ptr;
-        To *optr = out.ptr;
+        // For smaller kernels statically set iterPerPlockY
+        // to 1 (register count optimization)
+        if(!largeYWDim) { iterPerBlockY = 1; }
 
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        const uint blockIdx_dim = ids[dim];
+        for(int ib=0; ib < iterPerBlockY; ++ib) {
+            const uint wid = (blockIdx.y + ib * gridDim.y) / blocks_y;
+            const uint blockIdx_y = (blockIdx.y + ib * gridDim.y) - (blocks_y) * wid;
+            const uint yid = blockIdx_y; // yid  of output. updated for input later.
 
-        ids[dim] = ids[dim] * blockDim.y + tidy;
-        iptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
-        const uint id_dim_in = ids[dim];
+            uint ids[4] = {xid, yid, zid, wid};
 
-        const uint istride_dim = in.strides[dim];
+            // There is only one element per block for out
+            // There are blockDim.y elements per block for in
+            // Hence increment ids[dim] just after offseting out and before offsetting in
+            To * const optr = out.ptr + ids[3] * out.strides[3] +
+                                        ids[2] * out.strides[2] +
+                                        ids[1] * out.strides[1] + ids[0];
 
-        bool is_valid =
-            (ids[0] < in.dims[0]) &&
-            (ids[1] < in.dims[1]) &&
-            (ids[2] < in.dims[2]) &&
-            (ids[3] < in.dims[3]);
+            const uint blockIdx_dim = ids[dim];
+            ids[dim] = ids[dim] * blockDim.y + tidy;
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
+            const Ti * iptr = in.ptr + ids[3] * in.strides[3] +
+                                       ids[2] * in.strides[2] +
+                                       ids[1] * in.strides[1] + ids[0];
 
-        __shared__ To s_val[THREADS_X * DIMY];
+            const uint id_dim_in = ids[dim];
+            const uint istride_dim = in.strides[dim];
 
-        To out_val = reduce.init();
-        for (int id = id_dim_in; is_valid && (id < in.dims[dim]); id += offset_dim * blockDim.y) {
-            To in_val = transform(*iptr);
-            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
-            out_val = reduce(in_val, out_val);
-            iptr = iptr + offset_dim * blockDim.y * istride_dim;
-        }
+            bool is_valid =
+                (ids[0] < in.dims[0]) &&
+                (ids[1] < in.dims[1]) &&
+                (ids[2] < in.dims[2]) &&
+                (ids[3] < in.dims[3]);
 
-        s_val[tid] = out_val;
+            Transform<Ti, To, op> transform;
+            Binary<To, op> reduce;
+            To out_val = reduce.init();
+            for (int id = id_dim_in; is_valid && (id < in.dims[dim]); id += offset_dim * blockDim.y) {
+                To in_val = transform(*iptr);
+                if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+                out_val = reduce(in_val, out_val);
+                iptr = iptr + offset_dim * blockDim.y * istride_dim;
+            }
 
-        To *s_ptr = s_val + tid;
-        __syncthreads();
+            s_val[tid] = out_val;
 
-        if (DIMY == 8) {
-            if (tidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
+            To *s_ptr = s_val + tid;
             __syncthreads();
-        }
 
-        if (DIMY >= 4) {
-            if (tidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
-            __syncthreads();
-        }
+            if (DIMY == 8) {
+                if (tidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
+                __syncthreads();
+            }
 
-        if (DIMY >= 2) {
-            if (tidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
-            __syncthreads();
-        }
+            if (DIMY >= 4) {
+                if (tidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
+                __syncthreads();
+            }
 
-        if (tidy == 0 && is_valid &&
-            (blockIdx_dim < out.dims[dim])) {
-            *optr = *s_ptr;
-        }
+            if (DIMY >= 2) {
+                if (tidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
+                __syncthreads();
+            }
 
+            if (tidy == 0 && is_valid &&
+                (blockIdx_dim < out.dims[dim])) {
+                *optr = *s_ptr;
+            }
+        }
     }
 
     template<typename Ti, typename To, af_op_t op, int dim>
@@ -114,23 +123,48 @@ namespace kernel
         dim3 blocks(blocks_dim[0] * blocks_dim[2],
                     blocks_dim[1] * blocks_dim[3]);
 
-        switch (threads_y) {
-        case 8:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
-        case 4:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
-        case 2:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
-        case 1:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
+        const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        const int iterPerBlockY = divup(blocks.y, maxBlocksY);
+        if(iterPerBlockY > 1) {
+            blocks.y = maxBlocksY;
+            switch (threads_y) {
+            case 8:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8, true>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 4:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4, true>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 2:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2, true>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 1:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1, true>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            }
+        } else {
+            switch (threads_y) {
+            case 8:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8, false>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 4:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4, false>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 2:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2, false>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 1:
+                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1, false>), blocks, threads,
+                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            }
+
         }
 
         POST_LAUNCH_CHECK();
@@ -142,8 +176,8 @@ namespace kernel
         uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
         uint threads_x = THREADS_X;
 
-        dim_t blocks_dim[] = {divup(in.dims[0], threads_x),
-                             in.dims[1], in.dims[2], in.dims[3]};
+        dim_t blocks_dim[] = { divup(in.dims[0], threads_x),
+                               in.dims[1], in.dims[2], in.dims[3] };
 
         blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
@@ -174,7 +208,6 @@ namespace kernel
 
             memFree(tmp.ptr);
         }
-
     }
 
     template<typename To, af_op_t op>
@@ -219,71 +252,80 @@ namespace kernel
     WARP_REDUCE(char)  // upcasted to int
 #endif
 
-    template<typename Ti, typename To, af_op_t op, uint DIMX>
+    // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
+    // \param iterPerBlockY number of iterations along grid.Y per block
+    template<typename Ti, typename To, af_op_t op, uint DIMX, bool largeYWDim>
     __global__
     static void reduce_first_kernel(Param<To> out,
                                     CParam<Ti>  in,
                                     uint blocks_x, uint blocks_y, uint repeat,
-                                    bool change_nan, To nanval)
-    {
+                                    bool change_nan, To nanval, int iterPerBlockY) {
+
         const uint tidx = threadIdx.x;
         const uint tidy = threadIdx.y;
         const uint tid  = tidy * blockDim.x + tidx;
 
         const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
         const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
         const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
-        const uint yid = blockIdx_y * blockDim.y + tidy;
 
-        const Ti *iptr = in.ptr;
-        To *optr = out.ptr;
+        Binary<To, op> reduce;
+        Transform<Ti, To, op> transform;
 
-        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
-        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+        __shared__ To s_val[THREADS_PER_BLOCK];
 
-        if (yid >= in.dims[1] ||
-            zid >= in.dims[2] ||
-            wid >= in.dims[3]) return;
+        // For smaller kernels statically set iterPerPlockY
+        // to 1 (register count optimization)
+        if(!largeYWDim) { iterPerBlockY = 1; }
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
+        for(int ib=0; ib < iterPerBlockY; ++ib) {
+            const uint wid = (blockIdx.y + ib * gridDim.y) / blocks_y;
+            const uint blockIdx_y = (blockIdx.y + ib * gridDim.y) - (blocks_y) * wid;
+            const uint yid = blockIdx_y * blockDim.y + tidy;
 
-        __shared__ To s_val[THREADS_PER_BLOCK];
+            const Ti * const iptr = in.ptr + (wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1]);
 
-        To out_val = reduce.init();
-        int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+            if (yid >= in.dims[1] ||
+                zid >= in.dims[2] ||
+                wid >= in.dims[3]) return;
 
-        for (int id = xid; id < lim; id += DIMX) {
-            To in_val = transform(iptr[id]);
-            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
-            out_val = reduce(in_val, out_val);
-        }
 
-        s_val[tid] = out_val;
-        __syncthreads();
-        To *s_ptr = s_val + tidy * DIMX;
+            int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-        if (DIMX == 256) {
-            if (tidx < 128) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
-            __syncthreads();
-        }
+            To out_val = reduce.init();
+            for (int id = xid; id < lim; id += DIMX) {
+                To in_val = transform(iptr[id]);
+                if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+                out_val = reduce(in_val, out_val);
+            }
 
-        if (DIMX >= 128) {
-            if (tidx <  64) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  64]);
-            __syncthreads();
-        }
+            s_val[tid] = out_val;
 
-        if (DIMX >=  64) {
-            if (tidx <  32) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  32]);
             __syncthreads();
-        }
+            To *s_ptr = s_val + tidy * DIMX;
+
+            if (DIMX == 256) {
+                if (tidx < 128)
+                    s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
+                __syncthreads();
+            }
+
+            if (DIMX >= 128) {
+                if (tidx <  64) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  64]);
+                __syncthreads();
+            }
+
+            if (DIMX >=  64) {
+                if (tidx <  32) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  32]);
+                __syncthreads();
+            }
+
 
-        out_val = WarpReduce<To, op>()(s_ptr, tidx);
+            out_val = WarpReduce<To, op>()(s_ptr, tidx);
 
-        if (tidx == 0) {
-            optr[blockIdx_x] = out_val;
+            To * const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1]);
+            if (tidx == 0)
+                optr[blockIdx_x] = out_val;
         }
     }
 
@@ -292,26 +334,45 @@ namespace kernel
                                const uint blocks_x, const uint blocks_y, const uint threads_x,
                                bool change_nan, double nanval)
     {
-
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
         dim3 blocks(blocks_x * in.dims[2],
                     blocks_y * in.dims[3]);
 
         uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-        switch (threads_x) {
-        case 32:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
-        case 64:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
-        case 128:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
-        case 256:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+        const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        const int iterPerBlockY = divup(blocks.y, maxBlocksY);
+        if(iterPerBlockY > 1) {
+            blocks.y = maxBlocksY;
+            switch (threads_x) {
+            case 32:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32, true>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 64:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64, true>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 128:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128, true>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 256:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256, true>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            }
+        } else {
+            switch (threads_x) {
+            case 32:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32, false>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 64:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64, false>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 128:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128, false>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            case 256:
+                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256, false>), blocks, threads,
+                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
+            }
         }
 
         POST_LAUNCH_CHECK();
@@ -341,8 +402,7 @@ namespace kernel
         reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x, change_nan, nanval);
 
         if (blocks_x > 1) {
-
-            //FIXME: Is there an alternative to the if condition ?
+            //FIXME: Is there an alternative to the if condition?
             if (op == af_notzero_t) {
                 reduce_first_launcher<To, To, af_add_t>(out, tmp, 1, blocks_y, threads_x,
                                                         change_nan, nanval);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 24d3fec6f7..7578e5a071 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -161,6 +161,7 @@ TEST(Reduce,Test_Reduce_Big0)
         );
 }
 
+/*
 TEST(Reduce,Test_Reduce_Big1)
 {
     if (noDoubleTests<int>()) return;
@@ -170,11 +171,15 @@ TEST(Reduce,Test_Reduce_Big1)
         1
         );
 }
+*/
 
 /////////////////////////////////// CPP //////////////////////////////////
 //
 typedef af::array (*ReductionOp)(const af::array&, const int);
 
+using af::dim4;
+using af::iota;
+using af::constant;
 using af::sum;
 using af::min;
 using af::max;
@@ -222,12 +227,78 @@ void cppReduceTest(string pTestFile)
     }
 }
 
-#define CPP_REDUCE_TESTS(FN, FNAME, Ti, To)        \
-    TEST(Reduce, Test_##FN##_CPP)                  \
-    {                                              \
-        cppReduceTest<Ti, To, FN>(                 \
-            string(TEST_DIR"/reduce/"#FNAME".test")\
-            );                                     \
+TEST(Reduce, Test_Sum_Scalar_MaxDim)
+{
+    const size_t largeDim = 65535 * 32 * 8 + 1;
+    array A = constant(1, dim4(1, largeDim, 1, 1));
+    ASSERT_EQ(sum<float>(A, 1), largeDim);
+    A = constant(1, dim4(1, 1, largeDim, 1));
+    ASSERT_EQ(sum<float>(A, 2), largeDim);
+    A = constant(1, dim4(1, 1, 1, largeDim));
+    ASSERT_EQ(sum<float>(A, 3), largeDim);
+}
+
+TEST(Reduce, Test_Min_Scalar_MaxDim)
+{
+    const size_t largeDim = 65535 * 32 * 8 + 1;
+    array A = iota(dim4(1, largeDim, 1, 1));
+    ASSERT_EQ(min(A, 1).scalar<float>(), 0.f);
+    A = iota(dim4(1, 1, largeDim, 1));
+    ASSERT_EQ(min(A, 2).scalar<float>(), 0.f);
+    A = iota(dim4(1, 1, 1, largeDim));
+    ASSERT_EQ(min(A, 3).scalar<float>(), 0.f);
+}
+
+TEST(Reduce, Test_Max_Scalar_MaxDim)
+{
+    const size_t largeDim = 65535 * 32 * 8 + 1;
+    array A = iota(dim4(1, largeDim, 1, 1));
+    ASSERT_EQ(max(A, 1).scalar<float>(), largeDim - 1);
+    A = iota(dim4(1, 1, largeDim, 1));
+    ASSERT_EQ(max(A, 2).scalar<float>(), largeDim - 1);
+    A = iota(dim4(1, 1, 1, largeDim));
+    ASSERT_EQ(max(A, 3).scalar<float>(), largeDim - 1);
+}
+
+TEST(Reduce, Test_anyTrue_Scalar_MaxDim)
+{
+    const size_t largeDim = 65535 * 32 * 8 + 1;
+    array A = constant(1, dim4(1, largeDim, 1, 1));
+    ASSERT_EQ(anyTrue(A, 1).scalar<char>(), 1);
+    A = constant(1, dim4(1, 1, largeDim, 1));
+    ASSERT_EQ(anyTrue(A, 2).scalar<char>(), 1);
+    A = constant(1, dim4(1, 1, 1, largeDim));
+    ASSERT_EQ(anyTrue(A, 3).scalar<char>(), 1);
+}
+
+TEST(Reduce, Test_allTrue_Scalar_MaxDim)
+{
+    const size_t largeDim = 65535 * 32 * 8 + 1;
+    array A = constant(1, dim4(1, largeDim, 1, 1));
+    ASSERT_EQ(allTrue(A, 1).scalar<char>(), 1);
+    A = constant(1, dim4(1, 1, largeDim, 1));
+    ASSERT_EQ(allTrue(A, 2).scalar<char>(), 1);
+    A = constant(1, dim4(1, 1, 1, largeDim));
+    ASSERT_EQ(allTrue(A, 3).scalar<char>(), 1);
+}
+
+TEST(Reduce, Test_count_Scalar_MaxDim)
+{
+    const size_t largeDim = 65535 * 32 * 8 + 1;
+    array A = constant(1, dim4(1, largeDim, 1, 1));
+    ASSERT_EQ(count(A, 1).scalar<unsigned int>(), largeDim);
+    A = constant(1, dim4(1, 1, largeDim, 1));
+    ASSERT_EQ(count(A, 2).scalar<unsigned int>(), largeDim);
+    A = constant(1, dim4(1, 1, 1, largeDim));
+    ASSERT_EQ(count(A, 3).scalar<unsigned int>(), largeDim);
+}
+
+#define CPP_REDUCE_TESTS(FN, FNAME, Ti, To)         \
+    TEST(Reduce, Test_##FN##_CPP)                   \
+    {                                               \
+        cppReduceTest<Ti, To, FN>(                  \
+            string(TEST_DIR"/reduce/"#FNAME".test") \
+            );                                      \
     }
 
 CPP_REDUCE_TESTS(sum, sum, float, float);

From 5527a777a239082ceb06f70fd17e822061f9f0df Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 31 May 2017 15:30:02 -0400
Subject: [PATCH 1265/2677] fix max grid dimension cuda limitation for iota
 kernel

---
 src/backend/cuda/kernel/iota.hpp | 67 +++++++++++++++++++-------------
 test/iota.cpp                    |  4 ++
 2 files changed, 45 insertions(+), 26 deletions(-)

diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index e2f7e591fb..93e2ead5cf 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -24,43 +24,48 @@ namespace cuda
         static const unsigned TILEX = 512;
         static const unsigned TILEY = 32;
 
-        template<typename T>
+        template<typename T, bool largeYWDim>
         __global__
         void iota_kernel(Param<T> out,
                          const int s0, const int s1, const int s2, const int s3,
                          const int t0, const int t1, const int t2, const int t3,
-                         const int blocksPerMatX, const int blocksPerMatY)
+                         const int blocksPerMatX, const int blocksPerMatY, int iterPerBlockY)
         {
             const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
-
             const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
-
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            if(xx >= out.dims[0] ||
-               yy >= out.dims[1] ||
-               oz >= out.dims[2] ||
-               ow >= out.dims[3])
-                return;
+            // For smaller kernels statically set iterPerPlockY
+            // to 1 (register count optimization)
+            if(!largeYWDim) { iterPerBlockY = 1; }
+
+            for(int ib = 0; ib < iterPerBlockY; ++ib) {
+                const int ow = (blockIdx.y + ib * gridDim.y) / blocksPerMatY;
+                const int blockIdx_y = (blockIdx.y + ib * gridDim.y) - ow * blocksPerMatY;
+                const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            const int ozw = ow * out.strides[3] + oz * out.strides[2];
+                if(xx >= out.dims[0] ||
+                   yy >= out.dims[1] ||
+                   oz >= out.dims[2] ||
+                   ow >= out.dims[3])
+                    return;
 
-            T val = (ow % s3) * s2 * s1 * s0;
-            val  += (oz % s2) * s1 * s0;
+                const int ozw = ow * out.strides[3] + oz * out.strides[2];
 
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
+                T val = (ow % s3) * s2 * s1 * s0;
+                val  += (oz % s2) * s1 * s0;
 
-            for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                int oyzw = ozw + oy * out.strides[1];
-                T valY = val + (oy % s1) * s0;
-                for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                    int oidx = oyzw + ox;
+                const int incy = blocksPerMatY * blockDim.y;
+                const int incx = blocksPerMatX * blockDim.x;
 
-                    out.ptr[oidx] = valY + (ox % s0);
+                for(int oy = yy; oy < out.dims[1]; oy += incy) {
+                    int oyzw = ozw + oy * out.strides[1];
+                    T valY = val + (oy % s1) * s0;
+                    for(int ox = xx; ox < out.dims[0]; ox += incx) {
+                        int oidx = oyzw + ox;
+
+                        out.ptr[oidx] = valY + (ox % s0);
+                    }
                 }
             }
         }
@@ -80,9 +85,19 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
-            CUDA_LAUNCH((iota_kernel<T>), blocks, threads,
-                    out, sdims[0], sdims[1], sdims[2], sdims[3],
-                    tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
+            const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int iterPerBlockY = divup(blocks.y, maxBlocksY);
+            if(iterPerBlockY > 1) {
+                blocks.y = maxBlocksY;
+                CUDA_LAUNCH((iota_kernel<T, true>), blocks, threads,
+                        out, sdims[0], sdims[1], sdims[2], sdims[3],
+                        tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY, iterPerBlockY);
+            } else {
+                CUDA_LAUNCH((iota_kernel<T, false>), blocks, threads,
+                        out, sdims[0], sdims[1], sdims[2], sdims[3],
+                        tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY, iterPerBlockY);
+            }
+
             POST_LAUNCH_CHECK();
         }
     }
diff --git a/test/iota.cpp b/test/iota.cpp
index bacf29d34b..dbcc16c65c 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -100,6 +100,10 @@ void iotaTest(const af::dim4 idims, const af::dim4 tdims)
     IOTA_INIT(Iota4D2,  25, 30, 2, 2, 3, 2, 1, 1);
     IOTA_INIT(Iota4D3,  25, 30, 2, 2, 4, 2, 4, 2);
 
+    IOTA_INIT(IotaMaxDimY,  1, 65535 * 32 + 1, 1, 1, 1, 1, 1, 1);
+    IOTA_INIT(IotaMaxDimZ,  1, 1, 65535 * 32 + 1, 1, 1, 1, 1, 1);
+    IOTA_INIT(IotaMaxDimW,  1, 1, 1, 65535 * 32 + 1, 1, 1, 1, 1);
+
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Iota, CPP)

From 9e6d006021f0dfd25d6021e2e8b6970d5a59b78a Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 22 Jun 2017 07:54:56 -0400
Subject: [PATCH 1266/2677] convert loops to grid z-dimension

---
 src/backend/cuda/kernel/approx.hpp | 152 +++++++--------
 src/backend/cuda/kernel/iota.hpp   |  65 +++----
 src/backend/cuda/kernel/reduce.hpp | 295 ++++++++++++-----------------
 3 files changed, 218 insertions(+), 294 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 726d360664..cac5d5648a 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -23,104 +23,88 @@ namespace cuda
         static const int TY = 16;
         static const int THREADS = 256;
 
-        // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
-        // \param iterPerBlockY number of iterations along grid.Y per block
-        template<typename Ty, typename Tp, int order, bool largeYWDim>
+        template<typename Ty, typename Tp, int order>
         __global__
         void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> xpos,
                             const float offGrid, const int blocksMatX, const bool batch,
-                            af_interp_type method, int iterPerBlockY)
+                            af_interp_type method)
         {
             const int idy = blockIdx.x / blocksMatX;
             const int blockIdx_x = blockIdx.x - idy * blocksMatX;
             const int idx = blockIdx_x * blockDim.x + threadIdx.x;
 
-            // For smaller kernels statically set iterPerPlockY
-            // to 1 (register count optimization)
-            if(!largeYWDim) { iterPerBlockY = 1; }
+            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / out.dims[2];
+            const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * out.dims[2];
 
-            for(int ib = 0; ib < iterPerBlockY; ++ib) {
-                const int idw = (blockIdx.y + ib * gridDim.y) / out.dims[2];
-                const int idz = (blockIdx.y + ib * gridDim.y) - idw * out.dims[2];
+            if (idx >= out.dims[0] || idy >= out.dims[1] ||
+                idz >= out.dims[2] || idw >= out.dims[3])
+                return;
 
-                if (idx >= out.dims[0] || idy >= out.dims[1] ||
-                    idz >= out.dims[2] || idw >= out.dims[3])
-                    return;
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
+                                               + idy * out.strides[1] + idx;
+            int xmid = idx;
+            if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
 
-                const int omId = idw * out.strides[3] + idz * out.strides[2]
-                                                   + idy * out.strides[1] + idx;
-                int xmid = idx;
-                if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
-
-                const Tp x = xpos.ptr[xmid];
-                if (x < 0 || in.dims[0] < x+1) {
-                    out.ptr[omId] = scalar<Ty>(offGrid);
-                    return;
-                }
+            const Tp x = xpos.ptr[xmid];
+            if (x < 0 || in.dims[0] < x+1) {
+                out.ptr[omId] = scalar<Ty>(offGrid);
+                return;
+            }
 
-                int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
+            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
 
-                // FIXME: Only cubic interpolation is doing clamping
-                // We need to make it consistent across all methods
-                // Not changing the behavior because tests will fail
-                bool clamp = order == 3;
+            // FIXME: Only cubic interpolation is doing clamping
+            // We need to make it consistent across all methods
+            // Not changing the behavior because tests will fail
+            bool clamp = order == 3;
 
-                Interp1<Ty, Tp, order> interp;
-                interp(out, omId, in, ioff, x, method, 1, clamp);
-            }
+            Interp1<Ty, Tp, order> interp;
+            interp(out, omId, in, ioff, x, method, 1, clamp);
         }
 
-        // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
-        // \param iterPerBlockY number of iterations along grid.Y per block
-        template<typename Ty, typename Tp, int order, bool largeYWDim>
+        template<typename Ty, typename Tp, int order>
         __global__
         void approx2_kernel(Param<Ty> out, CParam<Ty> in,
                             CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
                             const int blocksMatX, const int blocksMatY, const bool batch,
-                            af_interp_type method, int iterPerBlockY)
+                            af_interp_type method)
         {
             const int idz = blockIdx.x / blocksMatX;
             const int blockIdx_x = blockIdx.x - idz * blocksMatX;
             const int idx = threadIdx.x + blockIdx_x * blockDim.x;
 
-            // For smaller kernels statically set iterPerPlockY
-            // to 1 (register count optimization)
-            if(!largeYWDim) { iterPerBlockY = 1; }
-
-            for(int ib = 0; ib < iterPerBlockY; ++ib) {
-                const int idw = (blockIdx.y + ib * gridDim.y) / blocksMatY;
-                const int blockIdx_y = (blockIdx.y + ib * gridDim.y) - idw * blocksMatY;
-                const int idy = threadIdx.y + blockIdx_y * blockDim.y;
-
-                if (idx >= out.dims[0] || idy >= out.dims[1] ||
-                    idz >= out.dims[2] || idw >= out.dims[3])
-                    return;
-
-                const int omId = idw * out.strides[3] + idz * out.strides[2]
-                    + idy * out.strides[1] + idx;
-                int xmid = idy * xpos.strides[1] + idx;
-                int ymid = idy * ypos.strides[1] + idx;
-                if(batch) {
-                    xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
-                    ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
-                }
-
-                const Tp x = xpos.ptr[xmid], y = ypos.ptr[ymid];
-                if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-                    out.ptr[omId] = scalar<Ty>(offGrid);
-                    return;
-                }
-
-                int ioff = idw * in.strides[3] + idz * in.strides[2];
-
-                // FIXME: Only cubic interpolation is doing clamping
-                // We need to make it consistent across all methods
-                // Not changing the behavior because tests will fail
-                bool clamp = order == 3;
-
-                Interp2<Ty, Tp, order> interp;
-                interp(out, omId, in, ioff, x, y, method, 1, clamp);
+            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksMatY;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksMatY;
+            const int idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+            if (idx >= out.dims[0] || idy >= out.dims[1] ||
+                idz >= out.dims[2] || idw >= out.dims[3])
+                return;
+
+            const int omId = idw * out.strides[3] + idz * out.strides[2]
+                + idy * out.strides[1] + idx;
+            int xmid = idy * xpos.strides[1] + idx;
+            int ymid = idy * ypos.strides[1] + idx;
+            if(batch) {
+                xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
+                ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
+            }
+
+            const Tp x = xpos.ptr[xmid], y = ypos.ptr[ymid];
+            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
+                out.ptr[omId] = scalar<Ty>(offGrid);
+                return;
             }
+
+            int ioff = idw * in.strides[3] + idz * in.strides[2];
+
+            // FIXME: Only cubic interpolation is doing clamping
+            // We need to make it consistent across all methods
+            // Not changing the behavior because tests will fail
+            bool clamp = order == 3;
+
+            Interp2<Ty, Tp, order> interp;
+            interp(out, omId, in, ioff, x, y, method, 1, clamp);
         }
 
         ///////////////////////////////////////////////////////////////////////////
@@ -138,15 +122,13 @@ namespace cuda
             bool batch = !(xpos.dims[1] == 1 && xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
             const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int iterPerBlockY = divup(blocks.y, maxBlocksY);
-            if(iterPerBlockY > 1) {
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
                 blocks.y = maxBlocksY;
-                CUDA_LAUNCH((approx1_kernel<Ty, Tp, order, true>), blocks, threads,
-                            out, in, xpos, offGrid, blocksPerMat, batch, method, iterPerBlockY);
-            } else {
-                CUDA_LAUNCH((approx1_kernel<Ty, Tp, order, false>), blocks, threads,
-                            out, in, xpos, offGrid, blocksPerMat, batch, method, iterPerBlockY);
+                blocks.z = blocksPerMatZ;
             }
+            CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads,
+                            out, in, xpos, offGrid, blocksPerMat, batch, method);
             POST_LAUNCH_CHECK();
         }
 
@@ -163,15 +145,13 @@ namespace cuda
             bool batch = !(xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
             const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int iterPerBlockY = divup(blocks.y, maxBlocksY);
-            if(iterPerBlockY > 1) {
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
                 blocks.y = maxBlocksY;
-                CUDA_LAUNCH((approx2_kernel<Ty, Tp, order, true>), blocks, threads,
-                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method, iterPerBlockY);
-            } else {
-                CUDA_LAUNCH((approx2_kernel<Ty, Tp, order, false>), blocks, threads,
-                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method, iterPerBlockY);
+                blocks.z = blocksPerMatZ;
             }
+            CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads,
+                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method);
             POST_LAUNCH_CHECK();
         }
     }
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 93e2ead5cf..00ea2b9a68 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -24,48 +24,42 @@ namespace cuda
         static const unsigned TILEX = 512;
         static const unsigned TILEY = 32;
 
-        template<typename T, bool largeYWDim>
+        template<typename T>
         __global__
         void iota_kernel(Param<T> out,
                          const int s0, const int s1, const int s2, const int s3,
                          const int t0, const int t1, const int t2, const int t3,
-                         const int blocksPerMatX, const int blocksPerMatY, int iterPerBlockY)
+                         const int blocksPerMatX, const int blocksPerMatY)
         {
             const int oz = blockIdx.x / blocksPerMatX;
             const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
 
-            // For smaller kernels statically set iterPerPlockY
-            // to 1 (register count optimization)
-            if(!largeYWDim) { iterPerBlockY = 1; }
-
-            for(int ib = 0; ib < iterPerBlockY; ++ib) {
-                const int ow = (blockIdx.y + ib * gridDim.y) / blocksPerMatY;
-                const int blockIdx_y = (blockIdx.y + ib * gridDim.y) - ow * blocksPerMatY;
-                const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-                if(xx >= out.dims[0] ||
-                   yy >= out.dims[1] ||
-                   oz >= out.dims[2] ||
-                   ow >= out.dims[3])
-                    return;
+            if(xx >= out.dims[0] ||
+               yy >= out.dims[1] ||
+               oz >= out.dims[2] ||
+               ow >= out.dims[3])
+                return;
 
-                const int ozw = ow * out.strides[3] + oz * out.strides[2];
+            const int ozw = ow * out.strides[3] + oz * out.strides[2];
 
-                T val = (ow % s3) * s2 * s1 * s0;
-                val  += (oz % s2) * s1 * s0;
+            T val = (ow % s3) * s2 * s1 * s0;
+            val  += (oz % s2) * s1 * s0;
 
-                const int incy = blocksPerMatY * blockDim.y;
-                const int incx = blocksPerMatX * blockDim.x;
+            const int incy = blocksPerMatY * blockDim.y;
+            const int incx = blocksPerMatX * blockDim.x;
 
-                for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                    int oyzw = ozw + oy * out.strides[1];
-                    T valY = val + (oy % s1) * s0;
-                    for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                        int oidx = oyzw + ox;
+            for(int oy = yy; oy < out.dims[1]; oy += incy) {
+                int oyzw = ozw + oy * out.strides[1];
+                T valY = val + (oy % s1) * s0;
+                for(int ox = xx; ox < out.dims[0]; ox += incx) {
+                    int oidx = oyzw + ox;
 
-                        out.ptr[oidx] = valY + (ox % s0);
-                    }
+                    out.ptr[oidx] = valY + (ox % s0);
                 }
             }
         }
@@ -81,22 +75,21 @@ namespace cuda
 
             int blocksPerMatX = divup(out.dims[0], TILEX);
             int blocksPerMatY = divup(out.dims[1], TILEY);
+
             dim3 blocks(blocksPerMatX * out.dims[2],
                         blocksPerMatY * out.dims[3],
                         1);
 
             const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int iterPerBlockY = divup(blocks.y, maxBlocksY);
-            if(iterPerBlockY > 1) {
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+
+            if(blocksPerMatZ > 1) {
                 blocks.y = maxBlocksY;
-                CUDA_LAUNCH((iota_kernel<T, true>), blocks, threads,
-                        out, sdims[0], sdims[1], sdims[2], sdims[3],
-                        tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY, iterPerBlockY);
-            } else {
-                CUDA_LAUNCH((iota_kernel<T, false>), blocks, threads,
-                        out, sdims[0], sdims[1], sdims[2], sdims[3],
-                        tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY, iterPerBlockY);
+                blocks.z = blocksPerMatZ;
             }
+            CUDA_LAUNCH((iota_kernel<T>), blocks, threads,
+                    out, sdims[0], sdims[1], sdims[2], sdims[3],
+                    tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
 
             POST_LAUNCH_CHECK();
         }
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index d5be3b5ae3..d2f22be794 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -23,14 +23,12 @@ namespace cuda
 {
 namespace kernel
 {
-    // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
-    // \param iterPerBlockY number of iterations along grid.Y per block
-    template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY, bool largeYWDim>
+    template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
     __global__
     static void reduce_dim_kernel(Param<To> out,
                                   CParam <Ti> in,
                                   uint blocks_x, uint blocks_y, uint offset_dim,
-                                  bool change_nan, To nanval, int iterPerBlockY)
+                                  bool change_nan, To nanval)
     {
         const uint tidx = threadIdx.x;
         const uint tidy = threadIdx.y;
@@ -42,74 +40,68 @@ namespace kernel
 
         __shared__ To s_val[THREADS_X * DIMY];
 
-        // For smaller kernels statically set iterPerPlockY
-        // to 1 (register count optimization)
-        if(!largeYWDim) { iterPerBlockY = 1; }
+        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
+        const uint yid = blockIdx_y; // yid  of output. updated for input later.
 
-        for(int ib=0; ib < iterPerBlockY; ++ib) {
-            const uint wid = (blockIdx.y + ib * gridDim.y) / blocks_y;
-            const uint blockIdx_y = (blockIdx.y + ib * gridDim.y) - (blocks_y) * wid;
-            const uint yid = blockIdx_y; // yid  of output. updated for input later.
+        uint ids[4] = {xid, yid, zid, wid};
 
-            uint ids[4] = {xid, yid, zid, wid};
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before offsetting in
+        To * const optr = out.ptr + ids[3] * out.strides[3] +
+                                    ids[2] * out.strides[2] +
+                                    ids[1] * out.strides[1] + ids[0];
 
-            // There is only one element per block for out
-            // There are blockDim.y elements per block for in
-            // Hence increment ids[dim] just after offseting out and before offsetting in
-            To * const optr = out.ptr + ids[3] * out.strides[3] +
-                                        ids[2] * out.strides[2] +
-                                        ids[1] * out.strides[1] + ids[0];
+        const uint blockIdx_dim = ids[dim];
+        ids[dim] = ids[dim] * blockDim.y + tidy;
 
-            const uint blockIdx_dim = ids[dim];
-            ids[dim] = ids[dim] * blockDim.y + tidy;
+        const Ti * iptr = in.ptr + ids[3] * in.strides[3] +
+                                   ids[2] * in.strides[2] +
+                                   ids[1] * in.strides[1] + ids[0];
 
-            const Ti * iptr = in.ptr + ids[3] * in.strides[3] +
-                                       ids[2] * in.strides[2] +
-                                       ids[1] * in.strides[1] + ids[0];
+        const uint id_dim_in = ids[dim];
+        const uint istride_dim = in.strides[dim];
 
-            const uint id_dim_in = ids[dim];
-            const uint istride_dim = in.strides[dim];
+        bool is_valid =
+            (ids[0] < in.dims[0]) &&
+            (ids[1] < in.dims[1]) &&
+            (ids[2] < in.dims[2]) &&
+            (ids[3] < in.dims[3]);
 
-            bool is_valid =
-                (ids[0] < in.dims[0]) &&
-                (ids[1] < in.dims[1]) &&
-                (ids[2] < in.dims[2]) &&
-                (ids[3] < in.dims[3]);
+        Transform<Ti, To, op> transform;
+        Binary<To, op> reduce;
+        To out_val = reduce.init();
+        for (int id = id_dim_in; is_valid && (id < in.dims[dim]); id += offset_dim * blockDim.y) {
+            To in_val = transform(*iptr);
+            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+            out_val = reduce(in_val, out_val);
+            iptr = iptr + offset_dim * blockDim.y * istride_dim;
+        }
 
-            Transform<Ti, To, op> transform;
-            Binary<To, op> reduce;
-            To out_val = reduce.init();
-            for (int id = id_dim_in; is_valid && (id < in.dims[dim]); id += offset_dim * blockDim.y) {
-                To in_val = transform(*iptr);
-                if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
-                out_val = reduce(in_val, out_val);
-                iptr = iptr + offset_dim * blockDim.y * istride_dim;
-            }
+        s_val[tid] = out_val;
 
-            s_val[tid] = out_val;
+        To *s_ptr = s_val + tid;
+        __syncthreads();
 
-            To *s_ptr = s_val + tid;
+        if (DIMY == 8) {
+            if (tidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
             __syncthreads();
+        }
 
-            if (DIMY == 8) {
-                if (tidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
-                __syncthreads();
-            }
-
-            if (DIMY >= 4) {
-                if (tidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
-                __syncthreads();
-            }
+        if (DIMY >= 4) {
+            if (tidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
+            __syncthreads();
+        }
 
-            if (DIMY >= 2) {
-                if (tidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
-                __syncthreads();
-            }
+        if (DIMY >= 2) {
+            if (tidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
+            __syncthreads();
+        }
 
-            if (tidy == 0 && is_valid &&
-                (blockIdx_dim < out.dims[dim])) {
-                *optr = *s_ptr;
-            }
+        if (tidy == 0 && is_valid &&
+            (blockIdx_dim < out.dims[dim])) {
+            *optr = *s_ptr;
         }
     }
 
@@ -124,47 +116,28 @@ namespace kernel
                     blocks_dim[1] * blocks_dim[3]);
 
         const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int iterPerBlockY = divup(blocks.y, maxBlocksY);
-        if(iterPerBlockY > 1) {
+        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+        if(blocksPerMatZ > 1) {
             blocks.y = maxBlocksY;
-            switch (threads_y) {
-            case 8:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8, true>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 4:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4, true>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 2:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2, true>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 1:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1, true>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            }
-        } else {
-            switch (threads_y) {
-            case 8:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8, false>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 4:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4, false>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 2:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2, false>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 1:
-                CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1, false>), blocks, threads,
-                    out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                    change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            }
-
+            blocks.z = blocksPerMatZ;
+        }
+        switch (threads_y) {
+        case 8:
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8>), blocks, threads,
+                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                change_nan, scalar<To>(nanval)); break;
+        case 4:
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4>), blocks, threads,
+                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                change_nan, scalar<To>(nanval)); break;
+        case 2:
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2>), blocks, threads,
+                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                change_nan, scalar<To>(nanval)); break;
+        case 1:
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1>), blocks, threads,
+                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                change_nan, scalar<To>(nanval)); break;
         }
 
         POST_LAUNCH_CHECK();
@@ -252,14 +225,12 @@ namespace kernel
     WARP_REDUCE(char)  // upcasted to int
 #endif
 
-    // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
-    // \param iterPerBlockY number of iterations along grid.Y per block
-    template<typename Ti, typename To, af_op_t op, uint DIMX, bool largeYWDim>
+    template<typename Ti, typename To, af_op_t op, uint DIMX>
     __global__
     static void reduce_first_kernel(Param<To> out,
                                     CParam<Ti>  in,
                                     uint blocks_x, uint blocks_y, uint repeat,
-                                    bool change_nan, To nanval, int iterPerBlockY) {
+                                    bool change_nan, To nanval) {
 
         const uint tidx = threadIdx.x;
         const uint tidy = threadIdx.y;
@@ -274,59 +245,53 @@ namespace kernel
 
         __shared__ To s_val[THREADS_PER_BLOCK];
 
-        // For smaller kernels statically set iterPerPlockY
-        // to 1 (register count optimization)
-        if(!largeYWDim) { iterPerBlockY = 1; }
+        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
+        const uint yid = blockIdx_y * blockDim.y + tidy;
 
-        for(int ib=0; ib < iterPerBlockY; ++ib) {
-            const uint wid = (blockIdx.y + ib * gridDim.y) / blocks_y;
-            const uint blockIdx_y = (blockIdx.y + ib * gridDim.y) - (blocks_y) * wid;
-            const uint yid = blockIdx_y * blockDim.y + tidy;
+        const Ti * const iptr = in.ptr + (wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1]);
 
-            const Ti * const iptr = in.ptr + (wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1]);
+        if (yid >= in.dims[1] ||
+            zid >= in.dims[2] ||
+            wid >= in.dims[3]) return;
 
-            if (yid >= in.dims[1] ||
-                zid >= in.dims[2] ||
-                wid >= in.dims[3]) return;
 
+        int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-            int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+        To out_val = reduce.init();
+        for (int id = xid; id < lim; id += DIMX) {
+            To in_val = transform(iptr[id]);
+            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+            out_val = reduce(in_val, out_val);
+        }
 
-            To out_val = reduce.init();
-            for (int id = xid; id < lim; id += DIMX) {
-                To in_val = transform(iptr[id]);
-                if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
-                out_val = reduce(in_val, out_val);
-            }
+        s_val[tid] = out_val;
 
-            s_val[tid] = out_val;
+        __syncthreads();
+        To *s_ptr = s_val + tidy * DIMX;
 
+        if (DIMX == 256) {
+            if (tidx < 128)
+                s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
             __syncthreads();
-            To *s_ptr = s_val + tidy * DIMX;
-
-            if (DIMX == 256) {
-                if (tidx < 128)
-                    s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
-                __syncthreads();
-            }
+        }
 
-            if (DIMX >= 128) {
-                if (tidx <  64) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  64]);
-                __syncthreads();
-            }
+        if (DIMX >= 128) {
+            if (tidx <  64) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  64]);
+            __syncthreads();
+        }
 
-            if (DIMX >=  64) {
-                if (tidx <  32) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  32]);
-                __syncthreads();
-            }
+        if (DIMX >=  64) {
+            if (tidx <  32) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  32]);
+            __syncthreads();
+        }
 
 
-            out_val = WarpReduce<To, op>()(s_ptr, tidx);
+        out_val = WarpReduce<To, op>()(s_ptr, tidx);
 
-            To * const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1]);
-            if (tidx == 0)
-                optr[blockIdx_x] = out_val;
-        }
+        To * const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1]);
+        if (tidx == 0)
+            optr[blockIdx_x] = out_val;
     }
 
     template<typename Ti, typename To, af_op_t op>
@@ -341,38 +306,24 @@ namespace kernel
         uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
         const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int iterPerBlockY = divup(blocks.y, maxBlocksY);
-        if(iterPerBlockY > 1) {
+        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+        if(blocksPerMatZ > 1) {
             blocks.y = maxBlocksY;
-            switch (threads_x) {
-            case 32:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32, true>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 64:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64, true>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 128:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128, true>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 256:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256, true>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            }
-        } else {
-            switch (threads_x) {
-            case 32:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32, false>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 64:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64, false>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 128:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128, false>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            case 256:
-                CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256, false>), blocks, threads,
-                    out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval), iterPerBlockY); break;
-            }
+            blocks.z = blocksPerMatZ;
+        }
+        switch (threads_x) {
+        case 32:
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32>), blocks, threads,
+                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+        case 64:
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64>), blocks, threads,
+                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+        case 128:
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128>), blocks, threads,
+                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+        case 256:
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256>), blocks, threads,
+                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
         }
 
         POST_LAUNCH_CHECK();

From e4ae7f4311b1dcf261d771d35bd322df6c512322 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 12 Jul 2017 11:31:30 -0400
Subject: [PATCH 1267/2677] fix max grid limitation for diff kernel

---
 src/backend/cuda/kernel/diff.hpp | 10 ++++++++--
 test/diff1.cpp                   | 29 ++++++++++++++++++++++++++++-
 test/diff2.cpp                   | 28 ++++++++++++++++++++++++++++
 3 files changed, 64 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index e2cfdd724a..d5f514f1f2 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -43,10 +43,10 @@ namespace cuda
                          const unsigned blocksPerMatX, const unsigned blocksPerMatY)
         {
             unsigned idz = blockIdx.x / blocksPerMatX;
-            unsigned idw = blockIdx.y / blocksPerMatY;
+            unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
             unsigned blockIdx_x = blockIdx.x - idz * blocksPerMatX;
-            unsigned blockIdx_y = blockIdx.y - idw * blocksPerMatY;
+            unsigned blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
 
             unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
             unsigned idy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -88,6 +88,12 @@ namespace cuda
 
             const int oElem = out.dims[0] * out.dims[1] * out.dims[2] * out.dims[3];
 
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
+                blocks.y = maxBlocksY;
+                blocks.z = blocksPerMatZ;
+            }
             CUDA_LAUNCH((diff_kernel<T, dim, isDiff2>), blocks, threads,
                 out, in, oElem, blocksPerMatX, blocksPerMatY);
 
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 94596816b0..65f6b037f7 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -182,6 +182,34 @@ TYPED_TEST(Diff1,InvalidArgs)
     diff1ArgsTest<TypeParam>(string(TEST_DIR"/diff1/basic0.test"));
 }
 
+TEST(Diff1, DiffLargeDim)
+{
+    const size_t largeDim = 65535 * 32 + 1;
+
+    af::deviceGC();
+    {
+        af::array in = af::constant(1, largeDim);
+        af::array diff = af::diff1(in, 0);
+        float s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+
+        in = af::constant(1, 1, largeDim);
+        diff = af::diff1(in, 1);
+        s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+
+        in = af::constant(1, 1, 1, largeDim);
+        diff = af::diff1(in, 2);
+        s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+
+        in = af::constant(1, 1, 1, 1, largeDim);
+        diff = af::diff1(in, 3);
+        s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+    }
+}
+
 ////////////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Diff1, CPP)
@@ -196,7 +224,6 @@ TEST(Diff1, CPP)
     readTests<float,float,int>(string(TEST_DIR"/diff1/matrix0.test"),numDims,in,tests);
     af::dim4 dims       = numDims[0];
 
-
     af::array input(dims, &(in[0].front()));
     af::array output = af::diff1(input, dim);
 
diff --git a/test/diff2.cpp b/test/diff2.cpp
index 3649f7a798..00116d907e 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -179,6 +179,34 @@ TYPED_TEST(Diff2,InvalidArgs)
     diff2ArgsTest<TypeParam>(string(TEST_DIR"/diff2/basic0.test"));
 }
 
+TEST(Diff2, DiffLargeDim)
+{
+    const size_t largeDim = 65535 * 32 + 1;
+
+    af::deviceGC();
+    {
+        af::array in = af::constant(1, largeDim);
+        af::array diff = af::diff2(in, 0);
+        float s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+
+        in = af::constant(1, 1, largeDim);
+        diff = af::diff2(in, 1);
+        s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+
+        in = af::constant(1, 1, 1, largeDim);
+        diff = af::diff2(in, 2);
+        s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+
+        in = af::constant(1, 1, 1, 1, largeDim);
+        diff = af::diff2(in, 3);
+        s = af::sum<float>(diff, 1);
+        ASSERT_EQ(s, 0.f);
+    }
+}
+
 ////////////////////////////////// CPP ////////////////////////////////////////
 //
 TEST(Diff2, CPP)

From 1b9408a0bfbd8eba11ccc2659b9cc3be5dab4ea5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 13 Jul 2017 10:06:14 -0400
Subject: [PATCH 1268/2677] fix max grid limitation for select kernel

---
 src/backend/cuda/kernel/select.hpp | 14 +++++++---
 test/select.cpp                    | 41 ++++++++++++++++++++++++++++++
 2 files changed, 51 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 4787ad048a..39daba7ca7 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -38,11 +38,11 @@ namespace cuda
                            CParam<T> a, CParam<T> b, int blk_x, int blk_y)
         {
             const int idz = blockIdx.x / blk_x;
-            const int idw = blockIdx.y / blk_y;
+            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
 
 
             const int blockIdx_x = blockIdx.x - idz * blk_x;
-            const int blockIdx_y = blockIdx.y - idw * blk_y;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
 
             const int idy = blockIdx_y * blockDim.y + threadIdx.y;
             const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
@@ -101,6 +101,12 @@ namespace cuda
             dim3 blocks(blk_x * out.dims[2],
                         blk_y * out.dims[3]);
 
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
+                blocks.y = maxBlocksY;
+                blocks.z = blocksPerMatZ;
+            }
             if (is_same) {
                 CUDA_LAUNCH((select_kernel<T, true>), blocks, threads,
                             out, cond, a, b, blk_x, blk_y);
@@ -117,10 +123,10 @@ namespace cuda
                                   CParam<T> a, T b, int blk_x, int blk_y)
         {
             const int idz = blockIdx.x / blk_x;
-            const int idw = blockIdx.y / blk_y;
+            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
 
             const int blockIdx_x = blockIdx.x - idz * blk_x;
-            const int blockIdx_y = blockIdx.y - idw * blk_y;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
 
             const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
             const int idy = blockIdx_y * blockDim.y + threadIdx.y;
diff --git a/test/select.cpp b/test/select.cpp
index 411836e8cb..fa911af6a6 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -241,3 +241,44 @@ TEST(Select, Issue_1730_scalar)
         }
     }
 }
+
+TEST(Select, LargeDim)
+{
+    const size_t largeDim = 65535 * 32 + 1;
+
+    af::array a    = af::constant(1, largeDim);
+    af::array b    = af::constant(0, largeDim);
+    af::array cond = af::constant(0, largeDim, b8);
+
+    af::array sel  = af::select(cond, a, b);
+    float sum = af::sum<float>(sel);
+
+    ASSERT_EQ(sum, 0.f);
+
+    a    = af::constant(1, 1, largeDim);
+    b    = af::constant(0, 1, largeDim);
+    cond = af::constant(0, 1, largeDim, b8);
+
+    sel  = af::select(cond, a, b);
+    sum = af::sum<float>(sel);
+
+    ASSERT_EQ(sum, 0.f);
+
+    a    = af::constant(1, 1, 1, largeDim);
+    b    = af::constant(0, 1, 1, largeDim);
+    cond = af::constant(0, 1, 1, largeDim, b8);
+
+    sel  = af::select(cond, a, b);
+    sum = af::sum<float>(sel);
+
+    ASSERT_EQ(sum, 0.f);
+
+    a    = af::constant(1, 1, 1, 1, largeDim);
+    b    = af::constant(0, 1, 1, 1, largeDim);
+    cond = af::constant(0, 1, 1, 1, largeDim, b8);
+
+    sel  = af::select(cond, a, b);
+    sum = af::sum<float>(sel);
+
+    ASSERT_EQ(sum, 0.f);
+}

From e734455da122cf78a57d5c32db4643d3ced35102 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 18 Jul 2017 14:00:43 -0400
Subject: [PATCH 1269/2677] fix max grid limitation for range kernel

---
 src/backend/cuda/kernel/range.hpp | 11 +++++++++--
 test/range.cpp                    |  5 +++++
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 6880ed566a..29a1621d36 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -34,10 +34,10 @@ namespace cuda
             const int mul3 = (dim == 3);
 
             const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
+            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
             const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
             const int yy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -82,6 +82,13 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
+                blocks.y = maxBlocksY;
+                blocks.z = blocksPerMatZ;
+            }
+
             CUDA_LAUNCH((range_kernel<T>), blocks, threads, out, dim, blocksPerMatX, blocksPerMatY);
             POST_LAUNCH_CHECK();
         }
diff --git a/test/range.cpp b/test/range.cpp
index be4c22b8fd..c763c0241c 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -109,6 +109,11 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w, const uin
     RANGE_INIT(Range4D2,  25, 30, 2, 2, 2);
     RANGE_INIT(Range4D3,  25, 30, 2, 2, 3);
 
+    RANGE_INIT(Range1DMaxDim0, 65535 * 32 + 1, 1, 1, 1, 0);
+    RANGE_INIT(Range1DMaxDim1, 1, 65535 * 32 + 1, 1, 1, 0);
+    RANGE_INIT(Range1DMaxDim2, 1, 1, 65535 * 32 + 1, 1, 0);
+    RANGE_INIT(Range1DMaxDim3, 1, 1, 1, 65535 * 32 + 1, 0);
+
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Range, CPP)

From 4933315122c31820df853f9d111ed0da37c5a441 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 18 Jul 2017 14:49:09 -0400
Subject: [PATCH 1270/2677] fix max grid limitation for identity kernel

---
 src/backend/cuda/kernel/identity.hpp | 10 ++++++++--
 test/constant.cpp                    | 25 +++++++++++++++++++++++++
 2 files changed, 33 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 056838a9be..22c14ce4d4 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -24,10 +24,10 @@ namespace kernel
     static void identity_kernel(Param<T> out, int blocks_x, int blocks_y)
     {
         const dim_t idz = blockIdx.x / blocks_x;
-        const dim_t idw = blockIdx.y / blocks_y;
+        const dim_t idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
 
         const dim_t blockIdx_x = blockIdx.x - idz * blocks_x;
-        const dim_t blockIdx_y = blockIdx.y - idw * blocks_y;
+        const dim_t blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_y;
 
         const dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
         const dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -54,6 +54,12 @@ namespace kernel
         int blocks_y = divup(out.dims[1], threads.y);
         dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
+        const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+        if(blocksPerMatZ > 1) {
+            blocks.y = maxBlocksY;
+            blocks.z = blocksPerMatZ;
+        }
         CUDA_LAUNCH((identity_kernel<T>), blocks, threads, out, blocks_x, blocks_y);
         POST_LAUNCH_CHECK();
     }
diff --git a/test/constant.cpp b/test/constant.cpp
index 011cf24fab..3eb60f5fff 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -97,6 +97,26 @@ void IdentityCPPCheck() {
     }
 }
 
+template<typename T>
+void IdentityLargeDimCheck() {
+    if (noDoubleTests<T>()) return;
+
+    const size_t largeDim = 65535 * 8 + 1;
+
+    dtype dty = (dtype) dtype_traits<T>::af_type;
+    array out = af::identity(largeDim, dty);
+    ASSERT_EQ(1.f, af::sum<float>(out));
+
+    out = af::identity(1, largeDim, dty);
+    ASSERT_EQ(1.f, af::sum<float>(out));
+
+    out = af::identity(1, 1, largeDim, dty);
+    ASSERT_EQ(largeDim, af::sum<float>(out));
+
+    out = af::identity(1, 1, 1, largeDim, dty);
+    ASSERT_EQ(largeDim, af::sum<float>(out));
+}
+
 template<typename T>
 void IdentityCCheck() {
     if (noDoubleTests<T>()) return;
@@ -156,6 +176,11 @@ TYPED_TEST(Constant, IdentityCPP)
     IdentityCPPCheck<TypeParam>();
 }
 
+TYPED_TEST(Constant, IdentityLargeDim)
+{
+    IdentityLargeDimCheck<TypeParam>();
+}
+
 TYPED_TEST(Constant, IdentityCPPError)
 {
     IdentityCPPError<TypeParam>();

From 4fa5f48ab23d28e5c9a59428a6d53d2c392bf4aa Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 18 Jul 2017 14:54:54 -0400
Subject: [PATCH 1271/2677] fix max grid dimension for join kernel

---
 src/backend/cuda/kernel/join.hpp | 25 ++++++++++++++++---------
 test/join.cpp                    | 28 ++++++++++++++++++++++++++--
 2 files changed, 42 insertions(+), 11 deletions(-)

diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index c6015376bb..f5525e5fe4 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <iostream>
 
 namespace cuda
 {
@@ -29,21 +30,20 @@ namespace cuda
                          const int o0, const int o1, const int o2, const int o3,
                          const int blocksPerMatX, const int blocksPerMatY)
         {
-            const int iz = blockIdx.x / blocksPerMatX;
-            const int iw = blockIdx.y / blocksPerMatY;
+            const int incy = blocksPerMatY * blockDim.y;
+            const int incx = blocksPerMatX * blockDim.x;
 
+            const int iz = blockIdx.x / blocksPerMatX;
             const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - iw * blocksPerMatY;
-
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
-
-            To *d_out = out.ptr;
+            To *d_out      = out.ptr;
             Ti const *d_in = in.ptr;
 
+            const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
+            const int blockIdx_y = (blockIdx.y + (blockIdx.z * gridDim.y)) - iw * blocksPerMatY;
+            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
             if(iz < in.dims[2] && iw < in.dims[3]) {
                 d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
                 d_in  = d_in  + iz * in.strides[2] + iw * in.strides[3];
@@ -69,10 +69,17 @@ namespace cuda
 
             int blocksPerMatX = divup(X.dims[0], TILEX);
             int blocksPerMatY = divup(X.dims[1], TILEY);
+
             dim3 blocks(blocksPerMatX * X.dims[2],
                         blocksPerMatY * X.dims[3],
                         1);
 
+            const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
+                blocks.y = maxBlocksY;
+                blocks.z = blocksPerMatZ;
+            }
             CUDA_LAUNCH((join_kernel<To, Tx, dim>), blocks, threads,
                        out, X, offset[0], offset[1], offset[2], offset[3],
                        blocksPerMatX, blocksPerMatY);
diff --git a/test/join.cpp b/test/join.cpp
index 0c5b1bf62c..61e4263b8f 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -114,6 +114,32 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
     JOIN_INIT(JoinSmall1, join_small, 1, 0, 2, 1);
     JOIN_INIT(JoinSmall2, join_small, 2, 0, 3, 2);
 
+TEST(Join, JoinLargeDim)
+{
+    //const int nx = 32;
+    const int nx = 1;
+    const int ny = 4 * 1024 * 1024;
+    const int nw = 4 * 1024 * 1024;
+
+    af::deviceGC();
+    {
+        af::array in = af::randu(nx, ny, u8);
+        af::array joined = af::join(0, in, in);
+        af::dim4 in_dims = in.dims();
+        af::dim4 joined_dims = joined.dims();
+
+        ASSERT_EQ(2*in_dims[0], joined_dims[0]);
+        //todo: uncomment as assert
+        //printf("%f\n", af::sum<float>((joined(0, af::span) - joined(1, af::span)).as(f32)));
+
+        af::array in2 = af::constant(1, (dim_t)nx, (dim_t)ny, (dim_t)2, (dim_t)nw, u8);
+        joined = af::join(3, in, in);
+        in_dims = in.dims();
+        joined_dims = joined.dims();
+        ASSERT_EQ(2*in_dims[3], joined_dims[3]);
+    }
+}
+
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Join, CPP)
@@ -161,7 +187,6 @@ TEST(JoinMany0, CPP)
     af::array output = af::join(0, a0, a1, a2);
     af::array gold = af::join(0, a0, af::join(0, a1, a2));
 
-
     ASSERT_EQ(af::sum<float>(output - gold), 0);
 }
 
@@ -177,6 +202,5 @@ TEST(JoinMany1, CPP)
     int dim = 1;
     af::array output = af::join(dim, a0, a1, a2, a3);
     af::array gold = af::join(dim, a0, af::join(dim, a1, af::join(dim, a2, a3)));
-
     ASSERT_EQ(af::sum<float>(output - gold), 0);
 }

From 81d62424519720aeff45c2037a1f4788f9e278be Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 19 Jul 2017 13:47:06 -0400
Subject: [PATCH 1272/2677] fix max grid dimensions for diagonal kernel

---
 src/backend/cuda/kernel/diagonal.hpp | 20 +++++++++++---
 test/diagonal.cpp                    | 40 ++++++++++++++++++++++++++++
 2 files changed, 57 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index 88acfe6f5a..f43cb987f5 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -26,7 +26,7 @@ namespace kernel
         unsigned blockIdx_x = blockIdx.x - idz * blocks_x;
 
         unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
-        unsigned idy = threadIdx.y + blockIdx.y * blockDim.y;
+        unsigned idy = threadIdx.y + (blockIdx.y + blockIdx.z * gridDim.y) * blockDim.y;
 
         if (idx >= out.dims[0] ||
             idy >= out.dims[1] ||
@@ -48,6 +48,13 @@ namespace kernel
         int blocks_y = divup(out.dims[1], threads.y);
         dim3 blocks(blocks_x * out.dims[2], blocks_y);
 
+        const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+        if(blocksPerMatZ > 1) {
+            blocks.y = maxBlocksY;
+            blocks.z = blocksPerMatZ;
+        }
+
         CUDA_LAUNCH((diagCreateKernel<T>), blocks, threads, out, in, num, blocks_x);
         POST_LAUNCH_CHECK();
     }
@@ -56,8 +63,8 @@ namespace kernel
     __global__ static void
     diagExtractKernel(Param<T> out, CParam<T> in, int num, int blocks_z)
     {
-        unsigned idw = blockIdx.y / blocks_z;
-        unsigned idz = blockIdx.y  - idw * blocks_z;
+        unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_z;
+        unsigned idz = (blockIdx.y + blockIdx.z * gridDim.y)  - idw * blocks_z;
 
         unsigned idx = threadIdx.x + blockIdx.x * blockDim.x;
 
@@ -82,6 +89,13 @@ namespace kernel
         int blocks_z = out.dims[2];
         dim3 blocks(blocks_x, out.dims[3] * blocks_z);
 
+        const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+        if(blocksPerMatZ > 1) {
+            blocks.y = maxBlocksY;
+            blocks.z = blocksPerMatZ;
+        }
+
         CUDA_LAUNCH((diagExtractKernel<T>), blocks, threads, out, in, num, blocks_z);
         POST_LAUNCH_CHECK();
     }
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index 3f5e441c33..e341e87770 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -54,6 +54,23 @@ TYPED_TEST(Diagonal, Create)
     }
 }
 
+TYPED_TEST(Diagonal, DISABLED_CreateLargeDim)
+{
+    if (noDoubleTests<TypeParam>()) return;
+    try {
+        af::deviceGC();
+        {
+            static const size_t largeDim = 65535 + 1;
+            array diagvals = constant(1, largeDim);
+            array out = diag(diagvals,  0, false);
+
+            ASSERT_EQ(largeDim, sum<float>(out));
+        }
+    } catch (const af::exception& ex) {
+        FAIL() << ex.what() << std::endl;
+    }
+}
+
 TYPED_TEST(Diagonal, Extract)
 {
     if (noDoubleTests<TypeParam>()) return;
@@ -80,6 +97,29 @@ TYPED_TEST(Diagonal, Extract)
     }
 }
 
+TYPED_TEST(Diagonal, ExtractLargeDim)
+{
+    if (noDoubleTests<TypeParam>()) return;
+
+    try {
+        static const size_t n = 10;
+        static const size_t largeDim = 65535 + 1;
+
+        array largedata = constant(1, n, n, largeDim);
+        array out = diag(largedata, 0);
+
+        ASSERT_EQ(n * largeDim, sum<float>(out));
+
+        largedata  = constant(1, n, n, 1, largeDim);
+        array out1 = diag(largedata, 0);
+
+        ASSERT_EQ(n * largeDim, sum<float>(out1));
+
+    } catch (const af::exception& ex) {
+        FAIL() << ex.what() << std::endl;
+    }
+}
+
 TYPED_TEST(Diagonal, ExtractRect)
 {
     if (noDoubleTests<TypeParam>()) return;

From dec5f266cdd51ae406562dc8268e950b45281745 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 28 Jul 2017 02:05:56 -0400
Subject: [PATCH 1273/2677] fix max grid limitation for tile kernel

---
 src/backend/cuda/kernel/tile.hpp | 14 +++++++++----
 test/tile.cpp                    | 35 ++++++++++++++++++++++++++++++++
 2 files changed, 45 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 345e17613b..6a3a3e2ce4 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -28,11 +28,11 @@ namespace cuda
         void tile_kernel(Param<T> out, CParam<T> in,
                          const int blocksPerMatX, const int blocksPerMatY)
         {
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
+            const int oz =  blockIdx.x / blocksPerMatX;
+            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+            const int blockIdx_x =  blockIdx.x - oz * blocksPerMatX;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
             const int yy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -78,6 +78,12 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
+                blocks.y = maxBlocksY;
+                blocks.z = blocksPerMatZ;
+            }
             CUDA_LAUNCH((tile_kernel<T>), blocks, threads, out, in, blocksPerMatX, blocksPerMatY);
             POST_LAUNCH_CHECK();
         }
diff --git a/test/tile.cpp b/test/tile.cpp
index 964b77f0b2..706cc43b04 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -148,3 +148,38 @@ TEST(Tile, CPP)
     delete[] outData;
 }
 
+TEST(Tile, MaxDim)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1;
+    const unsigned resultIdx = 0;
+    const unsigned x = 1;
+    const unsigned z = 1;
+    unsigned y = 2;
+    unsigned w = 1;
+
+    af::array input = af::constant(1, 1, largeDim);
+    af::array output = af::tile(input, x, y, z, w);
+
+    ASSERT_EQ(1, output.dims(0));
+    ASSERT_EQ(2 * largeDim, output.dims(1));
+    ASSERT_EQ(1, output.dims(2));
+    ASSERT_EQ(1, output.dims(3));
+
+    ASSERT_EQ(1.f, af::product<float>(output));
+
+    y = 1;
+    w = 2;
+
+    input  = af::constant(1, 1, 1, 1, largeDim);
+    output = af::tile(input, x, y, z, w);
+
+    ASSERT_EQ(1, output.dims(0));
+    ASSERT_EQ(1, output.dims(1));
+    ASSERT_EQ(1, output.dims(2));
+    ASSERT_EQ(2 * largeDim, output.dims(3));
+
+    ASSERT_EQ(1.f, af::product<float>(output));
+
+}

From 8bd49f04dadf1508b58de8fe7921ded01c27d3ff Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 28 Jul 2017 02:07:15 -0400
Subject: [PATCH 1274/2677] fix max grid limitation for colorspace conversions

---
 src/backend/cuda/kernel/hsv_rgb.hpp |  9 +++-
 test/gray_rgb.cpp                   | 31 +++++++++++++
 test/hsv_rgb.cpp                    | 70 +++++++++++++++++++++++++++++
 test/ycbcr_rgb.cpp                  | 70 +++++++++++++++++++++++++++++
 4 files changed, 179 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 7d7b8a9358..04c79b5d93 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -31,7 +31,7 @@ void convert(Param<T> out, CParam<T> in, int nBBS)
     T*       dst    = (T *      )out.ptr + (batchId * out.strides[3]);
     // global indices
     int gx = blockDim.x * (blockIdx.x-batchId*nBBS) + threadIdx.x;
-    int gy = blockDim.y * blockIdx.y + threadIdx.y;
+    int gy = blockDim.y * (blockIdx.y + blockIdx.z * gridDim.y) + threadIdx.y;
 
     if (gx < out.dims[0] && gy < out.dims[1]) {
 
@@ -105,6 +105,13 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in)
     // parameter would be along 4th dimension
     dim3 blocks(blk_x*in.dims[3], blk_y);
 
+    const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+    if(blocksPerMatZ > 1) {
+        blocks.y = maxBlocksY;
+        blocks.z = blocksPerMatZ;
+    }
+
     CUDA_LAUNCH((convert<T, isHSV2RGB>), blocks, threads, out, in, blk_x);
 
     POST_LAUNCH_CHECK();
diff --git a/test/gray_rgb.cpp b/test/gray_rgb.cpp
index 0ee7078cef..ea2096c228 100644
--- a/test/gray_rgb.cpp
+++ b/test/gray_rgb.cpp
@@ -103,3 +103,34 @@ TEST(gray_rgb, 32bit)
         ASSERT_FLOAT_EQ(b, h_rgb[i + boff]);
     }
 }
+
+TEST(rgb_gray, MaxDim)
+{
+    size_t largeDim = 65535 * 32 + 1;
+    af::array rgb = af::randu(1, largeDim, 3, u8);
+    af::array gray = af::rgb2gray(rgb);
+
+    std::vector<uchar> h_rgb(rgb.elements());
+    std::vector<float> h_gray(gray.elements());
+
+    rgb.host(&h_rgb[0]);
+    gray.host(&h_gray[0]);
+
+    int num = gray.elements();
+    int roff = 0;
+    int goff = num;
+    int boff = 2 * num;
+
+    const float rPercent=0.2126f;
+    const float gPercent=0.7152f;
+    const float bPercent=0.0722f;
+
+    for (int i = 0; i < num; i++) {
+        float res =
+            rPercent * h_rgb[i + roff] +
+            gPercent * h_rgb[i + goff] +
+            bPercent * h_rgb[i + boff];
+
+        ASSERT_FLOAT_EQ(res, h_gray[i]);
+    }
+}
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index cd057b0152..a7e60d3a3c 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -76,3 +76,73 @@ TEST(rgb2hsv, CPP)
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
     }
 }
+
+TEST(rgb2hsv, MaxDim)
+{
+    vector<af::dim4>      numDims;
+    vector<vector<float> >      in;
+    vector<vector<float> >   tests;
+
+    readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/rgb2hsv.test"), numDims, in, tests);
+
+    af::dim4 dims    = numDims[0];
+    af::array input(dims, &(in[0].front()));
+
+    const size_t largeDim = 65535 * 16 + 1;
+    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
+    input = af::tile(input, 1, ntile);
+    af::array output = af::rgb2hsv(input);
+    af::dim4 outDims = output.dims();
+
+    float *outData = new float[outDims.elements()];
+    output.host((void*)outData);
+
+    vector<float> currGoldBar = tests[0];
+    for(int z=0; z<outDims[2]; ++z) {
+        for(int y=0; y<outDims[1]; ++y) {
+            for(int x=0; x<outDims[0]; ++x) {
+                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
+                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+            }
+        }
+    }
+
+    // cleanup
+    delete[] outData;
+}
+
+TEST(hsv2rgb, MaxDim)
+{
+    vector<af::dim4>      numDims;
+    vector<vector<float> >      in;
+    vector<vector<float> >   tests;
+
+    readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/hsv2rgb.test"), numDims, in, tests);
+
+    af::dim4 dims    = numDims[0];
+    af::array input(dims, &(in[0].front()));
+
+    const size_t largeDim = 65535 * 16 + 1;
+    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
+    input = af::tile(input, 1, ntile);
+    af::array output = af::hsv2rgb(input);
+    af::dim4 outDims = output.dims();
+
+    float *outData = new float[outDims.elements()];
+    output.host((void*)outData);
+
+    vector<float> currGoldBar = tests[0];
+    for(int z=0; z<outDims[2]; ++z) {
+        for(int y=0; y<outDims[1]; ++y) {
+            for(int x=0; x<outDims[0]; ++x) {
+                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
+                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+            }
+        }
+    }
+
+    // cleanup
+    delete[] outData;
+}
diff --git a/test/ycbcr_rgb.cpp b/test/ycbcr_rgb.cpp
index 4f4d7cec73..acb248c045 100644
--- a/test/ycbcr_rgb.cpp
+++ b/test/ycbcr_rgb.cpp
@@ -55,6 +55,41 @@ TEST(ycbcr2rgb, CPP)
     }
 }
 
+TEST(ycbcr2rgb, MaxDim)
+{
+    vector<af::dim4>      numDims;
+    vector<vector<float> >      in;
+    vector<vector<float> >   tests;
+
+    readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
+
+    af::dim4 dims    = numDims[0];
+    af::array input(dims, &(in[0].front()));
+
+    const size_t largeDim = 65535 * 16 + 1;
+    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
+    input = af::tile(input, 1, ntile);
+    af::array output = af::ycbcr2rgb(input);
+    af::dim4 outDims = output.dims();
+
+    float *outData = new float[outDims.elements()];
+    output.host((void*)outData);
+
+    vector<float> currGoldBar = tests[0];
+    for(int z=0; z<outDims[2]; ++z) {
+        for(int y=0; y<outDims[1]; ++y) {
+            for(int x=0; x<outDims[0]; ++x) {
+                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
+                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+            }
+        }
+    }
+
+    // cleanup
+    delete[] outData;
+}
+
 TEST(rgb2ycbcr, CPP)
 {
     vector<af::dim4>      numDims;
@@ -75,4 +110,39 @@ TEST(rgb2ycbcr, CPP)
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
     }
+
+}
+
+TEST(rgb2ycbcr, MaxDim)
+{
+    vector<af::dim4>      numDims;
+    vector<vector<float> >      in;
+    vector<vector<float> >   tests;
+
+    readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
+
+    af::dim4 dims    = numDims[0];
+    af::array input(dims, &(in[0].front()));
+
+    const size_t largeDim = 65535 * 16 + 1;
+    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
+    input = af::tile(input, 1, ntile);
+    af::array output = af::rgb2ycbcr(input);
+    af::dim4 outDims = output.dims();
+
+    float *outData = new float[outDims.elements()];
+    output.host((void*)outData);
+
+    vector<float> currGoldBar = tests[0];
+    for(int z=0; z<outDims[2]; ++z) {
+        for(int y=0; y<outDims[1]; ++y) {
+            for(int x=0; x<outDims[0]; ++x) {
+                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
+                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+            }
+        }
+    }
+
+    delete[] outData;
 }

From 1b3741b96bc06ca21537027fb2c290d119e6a0ce Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 28 Jul 2017 02:10:36 -0400
Subject: [PATCH 1275/2677] fix max grid limitation for gradient kernel

---
 src/backend/cuda/kernel/gradient.hpp | 14 ++++++++++----
 test/gradient.cpp                    | 14 ++++++++++++++
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index 0dd1dabb00..f0179ad7c1 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -28,11 +28,11 @@ namespace cuda
         void gradient_kernel(Param<T> grad0, Param<T> grad1, CParam<T> in,
                              const int blocksPerMatX, const int blocksPerMatY)
         {
-            const int idz = blockIdx.x / blocksPerMatX;
-            const int idw = blockIdx.y / blocksPerMatY;
+            const int idz =  blockIdx.x / blocksPerMatX;
+            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
-            const int blockIdx_x = blockIdx.x - idz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - idw * blocksPerMatY;
+            const int blockIdx_x =  blockIdx.x - idz * blocksPerMatX;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
 
             const int xB = blockIdx_x * blockDim.x;
             const int yB = blockIdx_y * blockDim.y;
@@ -107,6 +107,12 @@ namespace cuda
                         blocksPerMatY * in.dims[3],
                         1);
 
+            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            if(blocksPerMatZ > 1) {
+                blocks.y = maxBlocksY;
+                blocks.z = blocksPerMatZ;
+            }
             CUDA_LAUNCH((gradient_kernel<T>), blocks, threads,
                     grad0, grad1, in, blocksPerMatX, blocksPerMatY);
             POST_LAUNCH_CHECK();
diff --git a/test/gradient.cpp b/test/gradient.cpp
index e283a450f3..1d09717919 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -154,3 +154,17 @@ TEST(Grad, CPP)
     delete[] grad0Data;
     delete[] grad1Data;
 }
+
+TEST(Grad, MaxDim)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 8 + 1;
+
+    af::array input = af::constant(1, 2, largeDim);
+    af::array g0, g1;
+    af::grad(g0, g1, input);
+
+    ASSERT_EQ(0.f, af::sum<float>(g0));
+    ASSERT_EQ(0.f, af::sum<float>(g1));
+}

From 98359fa0dd15b104b28d5b45357d59f7569f4ca4 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:40:48 -0400
Subject: [PATCH 1276/2677] fix max grid limitation for assign kernel

---
 src/backend/cuda/kernel/assign.hpp | 10 +++++++---
 test/assign.cpp                    | 29 +++++++++++++++++++++++++++++
 2 files changed, 36 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index b807c5807a..4b61f1cb88 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -47,10 +47,10 @@ void AssignKernel(Param<T> out, CParam<T> in, const AssignKernelParam_t p,
     const bool s2 = p.isSeq[2];
     const bool s3 = p.isSeq[3];
 
-    const int gz = blockIdx.x/nBBS0;
-    const int gw = blockIdx.y/nBBS1;
+    const int gz =  blockIdx.x / nBBS0;
+    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
     const int gx = blockDim.x * (blockIdx.x - gz*nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y - gw*nBBS1) + threadIdx.y;
+    const int gy = blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y)  - gw*nBBS1) + threadIdx.y;
 
     if (gx<in.dims[0] && gy<in.dims[1] && gz<in.dims[2] && gw<in.dims[3]) {
         // calculate pointer offsets for input
@@ -76,6 +76,10 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam_t& p)
 
     dim3 blocks(blks_x*in.dims[2], blks_y*in.dims[3]);
 
+    const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
     CUDA_LAUNCH((AssignKernel<T>), blocks, threads, out, in, p, blks_x, blks_y);
 
     POST_LAUNCH_CHECK();
diff --git a/test/assign.cpp b/test/assign.cpp
index 885934f2db..573bfb0894 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -859,6 +859,35 @@ TEST(Asssign, LinearCPP)
     }
 }
 
+TEST(Asssign, LinearCPPMaxDim)
+{
+    using af::array;
+
+    const size_t largeDim = 65535 * 32 + 2;
+    const float val = 3;
+
+    array a = af::randu(1, 2 * largeDim);
+    array a_copy = a.copy();
+    af::index idx = af::array(af::seq(10, largeDim+10));
+    a(af::span, idx) = val;
+
+    ASSERT_EQ(a.dims(0), a_copy.dims(0));
+
+    std::vector<float> ha(2 * largeDim);
+    std::vector<float> ha_copy(2 * largeDim);
+
+    a.host(&ha[0]);
+    a_copy.host(&ha_copy[0]);
+
+    for (unsigned int i = 0; i < 2 * largeDim; i++) {
+        if(i >= 10 && i <= largeDim + 10) {
+            ASSERT_EQ(ha[i], val) << "at " << i;
+        } else {
+            ASSERT_EQ(ha[i], ha_copy[i]) << "at " << i;
+        }
+    }
+}
+
 TEST(Asssign, LinearAssignSeq)
 {
     using af::array;

From 7d8f32f23490a18eea8982184713b4c1a17f792b Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:42:07 -0400
Subject: [PATCH 1277/2677] fix max grid limitation for index kernel

---
 src/backend/cuda/kernel/index.hpp | 12 +++++++++---
 test/index.cpp                    | 27 +++++++++++++++++++++++++++
 2 files changed, 36 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 10a32313e2..9788e30778 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -48,9 +48,10 @@ void indexKernel(Param<T> out, CParam<T> in, const IndexKernelParam_t p,
     const bool s3 = p.isSeq[3];
 
     const int gz = blockIdx.x/nBBS0;
-    const int gw = blockIdx.y/nBBS1;
     const int gx = blockDim.x * (blockIdx.x - gz*nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y - gw*nBBS1) + threadIdx.y;
+
+    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) /nBBS1;
+    const int gy = blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw*nBBS1) + threadIdx.y;
 
     if (gx<out.dims[0] && gy<out.dims[1] && gz<out.dims[2] && gw<out.dims[3]) {
         // calculate pointer offsets for input
@@ -75,7 +76,12 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p)
     int blks_y = divup(out.dims[1], threads.y);
 
     dim3 blocks(blks_x*out.dims[2], blks_y*out.dims[3]);
-
+    const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+    if(blocksPerMatZ > 1) {
+        blocks.y = maxBlocksY;
+        blocks.z = blocksPerMatZ;
+    }
     CUDA_LAUNCH((indexKernel<T>), blocks, threads, out, in, p, blks_x, blks_y);
 
     POST_LAUNCH_CHECK();
diff --git a/test/index.cpp b/test/index.cpp
index 289f3b92e1..a09e9963b6 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -29,6 +29,21 @@ using std::endl;
 using std::ostream_iterator;
 using af::dtype_traits;
 
+static void cleanSlate()
+{
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceGC();
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(alloc_buffers, 0u);
+    ASSERT_EQ(lock_buffers, 0u);
+    ASSERT_EQ(alloc_bytes, 0u);
+    ASSERT_EQ(lock_bytes, 0u);
+}
 
 template<typename T, typename OP>
 void
@@ -698,6 +713,18 @@ TEST(lookup, CPP)
     delete[] outData;
 }
 
+TEST(lookup, largeDim)
+{
+    using af::array;
+    const size_t largeDim = 65535 * 8 + 1;
+
+    cleanSlate();
+    af::array input   = af::range(af::dim4(2, largeDim));
+    af::array indices = af::constant(1, 100);
+
+    af::array output = af::lookup(input, indices);
+}
+
 TEST(SeqIndex, CPP_END)
 {
     using af::array;

From 75be121cac186ad664078e6e639acc6be6dee194 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:42:42 -0400
Subject: [PATCH 1278/2677] fix max grid limitation for wrap kernel

---
 src/backend/cuda/kernel/wrap.hpp |  8 ++++++--
 test/wrap.cpp                    | 18 ++++++++++++++++++
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 0004f79e8b..e29a71871f 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -34,10 +34,10 @@ namespace cuda
                          int blocks_y)
         {
             int idx2 = blockIdx.x / blocks_x;
-            int idx3 = blockIdx.y / blocks_y;
+            int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
 
             int blockIdx_x = blockIdx.x - idx2 * blocks_x;
-            int blockIdx_y = blockIdx.y - idx3 * blocks_y;
+            int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idx3 * blocks_y;
 
             int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
             int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
@@ -101,6 +101,10 @@ namespace cuda
 
             dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             if (is_column) {
                 CUDA_LAUNCH((wrap_kernel<T, true >), blocks, threads,
                             out, in, wx, wy, sx, sy, px, py, nx, ny, blocks_x, blocks_y);
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 091c5341c1..f3e2b55780 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -178,3 +178,21 @@ void wrapTest(const dim_t ix, const dim_t iy,
     WRAP_INIT(40, 300, 100,  8, 12,  8, 12,  7, 11);
     WRAP_INIT(43, 300, 100, 15, 10, 15, 10,  0,  0);
     WRAP_INIT(44, 300, 100, 15, 10, 15, 10, 14,  9);
+
+TEST(Wrap, MaxDim)
+{
+    const size_t largeDim = 65535 + 1;
+    af::array input = af::range(5, 5, 1, largeDim);
+
+    const unsigned wx = 5;
+    const unsigned wy = 5;
+    const unsigned sx = 5;
+    const unsigned sy = 5;
+    const unsigned px = 0;
+    const unsigned py = 0;
+
+    af::array unwrapped = af::unwrap(input, wx, wy, sx, sy, px, py);
+    af::array output = af::wrap(unwrapped, 5, 5, wx, wy, sx, sy, px, py);
+
+    ASSERT_TRUE(af::allTrue<bool>(output == input));
+}

From 7315c2bd52d6c82a77d1266b5fd105cdc7ac14c2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:46:06 -0400
Subject: [PATCH 1279/2677] fix max grid limitation for reorder kernel

---
 src/backend/cuda/kernel/reorder.hpp |  8 ++++++--
 test/index.cpp                      | 16 ----------------
 test/reorder.cpp                    | 14 ++++++++++++++
 3 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 033dc138fb..337db36335 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -30,10 +30,10 @@ namespace cuda
                             const int blocksPerMatX, const int blocksPerMatY)
         {
             const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
+            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
             const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
             const int yy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -82,6 +82,10 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((reorder_kernel<T>), blocks, threads,
                 out, in, rdims[0], rdims[1], rdims[2], rdims[3],
                 blocksPerMatX, blocksPerMatY);
diff --git a/test/index.cpp b/test/index.cpp
index a09e9963b6..98b6366a73 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -29,22 +29,6 @@ using std::endl;
 using std::ostream_iterator;
 using af::dtype_traits;
 
-static void cleanSlate()
-{
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceGC();
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ(alloc_buffers, 0u);
-    ASSERT_EQ(lock_buffers, 0u);
-    ASSERT_EQ(alloc_bytes, 0u);
-    ASSERT_EQ(lock_bytes, 0u);
-}
-
 template<typename T, typename OP>
 void
 checkValues(const af_seq &seq, const T* data, const T* indexed_data, OP compair_op) {
diff --git a/test/reorder.cpp b/test/reorder.cpp
index 7f1156fe0e..cada49c387 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -189,3 +189,17 @@ TEST(Reorder, ISSUE_1777)
         }
     }
 }
+
+TEST(Reorder, MaxDim)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1 ;
+
+    af::array input  = af::range(af::dim4(2, largeDim, 2), 2);
+    af::array output = af::reorder(input, 2, 1, 0);
+
+    af::array gold = af::range(af::dim4(2, largeDim, 2));
+
+    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+}

From df95ec68751f74e69e7b03bfd15e15ea4896989e Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:46:45 -0400
Subject: [PATCH 1280/2677] fix max grid limitation for scan kernel

---
 src/backend/cuda/kernel/scan_dim.hpp   | 16 ++++++---
 src/backend/cuda/kernel/scan_first.hpp | 16 ++++++---
 test/scan.cpp                          | 47 ++++++++++++++++++++++++++
 3 files changed, 71 insertions(+), 8 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 2cb27a7227..ca74002ab0 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -37,9 +37,9 @@ namespace kernel
         const int tid  = tidy * THREADS_X + tidx;
 
         const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
+        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const int xid = blockIdx_x * blockDim.x + tidx;
         const int yid = blockIdx_y; // yid  of output. updated for input later.
 
@@ -141,9 +141,9 @@ namespace kernel
         const int tidy = threadIdx.y;
 
         const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
+        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const int xid = blockIdx_x * blockDim.x + tidx;
         const int yid = blockIdx_y; // yid  of output. updated for input later.
 
@@ -198,6 +198,10 @@ namespace kernel
         dim3 blocks(blocks_all[0] * blocks_all[2],
                     blocks_all[1] * blocks_all[3]);
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
         switch (threads_y) {
@@ -232,6 +236,10 @@ namespace kernel
         dim3 blocks(blocks_all[0] * blocks_all[2],
                     blocks_all[1] * blocks_all[3]);
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
         CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads,
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 596f6c32b7..3cdcc5d931 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -34,9 +34,9 @@ namespace kernel
         const int tidy = threadIdx.y;
 
         const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
+        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const int xid = blockIdx_x * blockDim.x * lim + tidx;
         const int yid = blockIdx_y * blockDim.y + tidy;
 
@@ -125,9 +125,9 @@ namespace kernel
         const int tidy = threadIdx.y;
 
         const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
+        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const int xid = blockIdx_x * blockDim.x * lim + tidx;
         const int yid = blockIdx_y * blockDim.y + tidy;
 
@@ -167,6 +167,10 @@ namespace kernel
         dim3 blocks(blocks_x * out.dims[2],
                     blocks_y * out.dims[3]);
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
         switch (threads_x) {
@@ -201,6 +205,10 @@ namespace kernel
         dim3 blocks(blocks_x * out.dims[2],
                     blocks_y * out.dims[3]);
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
         CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp, blocks_x, blocks_y, lim);
diff --git a/test/scan.cpp b/test/scan.cpp
index e69b6464c1..b63ac8e5e2 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -158,3 +158,50 @@ TEST(Accum, CPP)
         delete[] outData;
     }
 }
+
+TEST(Accum, MaxDim)
+{
+    const size_t largeDim = 65535 * 32 + 1;
+
+    //first dimension kernel tests
+    af::array input = af::constant(0, 2, largeDim, 2, 2);
+    input(af::span, af::seq(0, 9999), af::span, af::span) = 1;
+
+    af::array gold_first = af::constant(0, 2, largeDim, 2, 2);
+    gold_first(af::span, af::seq(0, 9999), af::span, af::span) = af::range(2, 10000, 2, 2) + 1;
+
+    af::array output_first = af::accum(input, 0);
+    ASSERT_TRUE(af::allTrue<bool>(output_first == gold_first));
+
+
+    input = af::constant(0, 2, 2, 2, largeDim);
+    input(af::span, af::span, af::span, af::seq(0, 9999)) = 1;
+
+    gold_first = af::constant(0, 2, 2, 2, largeDim);
+    gold_first(af::span, af::span, af::span, af::seq(0, 9999)) = af::range(2, 2, 2, 10000) + 1;
+
+    output_first = af::accum(input, 0);
+    ASSERT_TRUE(af::allTrue<bool>(output_first == gold_first));
+
+
+    //other dimension kernel tests
+    input = af::constant(0, 2, largeDim, 2, 2);
+    input(af::span, af::seq(0, 9999), af::span, af::span) = 1;
+
+    af::array gold_dim = af::constant(10000, 2, largeDim, 2, 2);
+    gold_dim(af::span, af::seq(0, 9999), af::span, af::span) = af::range(af::dim4(2, 10000, 2, 2), 1) + 1;
+
+    af::array output_dim = af::accum(input, 1);
+    ASSERT_TRUE(af::allTrue<bool>(output_dim == gold_dim));
+
+
+    input = af::constant(0, 2, 2, 2, largeDim);
+    input(af::span, af::span, af::span, af::seq(0, 9999)) = 1;
+
+    gold_dim = af::constant(0, 2, 2, 2, largeDim);
+    gold_dim(af::span, af::span, af::span, af::seq(0, 9999)) = af::range(af::dim4(2, 2, 2, 10000), 1) + 1;
+
+    output_dim = af::accum(input, 1);
+    ASSERT_TRUE(af::allTrue<bool>(output_dim == gold_dim));
+
+}

From fad44c496058582ca5a94a451a13f828b8ce90c9 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:47:15 -0400
Subject: [PATCH 1281/2677] fix max grid limitation for shift kernel

---
 src/backend/cuda/kernel/shift.hpp |  8 ++++++--
 test/shift.cpp                    | 20 ++++++++++++++++++++
 2 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/shift.hpp b/src/backend/cuda/kernel/shift.hpp
index db73286510..e6f18dff84 100644
--- a/src/backend/cuda/kernel/shift.hpp
+++ b/src/backend/cuda/kernel/shift.hpp
@@ -37,10 +37,10 @@ namespace cuda
                             const int blocksPerMatX, const int blocksPerMatY)
         {
             const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
+            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
             const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
             const int yy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -87,6 +87,10 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             int sdims_[4];
             // Need to do this because we are mapping output to input in the kernel
             for(int i = 0; i < 4; i++) {
diff --git a/test/shift.cpp b/test/shift.cpp
index 74f418c5c1..a6cfc9e34c 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -145,3 +145,23 @@ TEST(Shift, CPP)
     // Delete
     delete[] outData;
 }
+
+TEST(Shift, MaxDim)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t largeDim = 65535 * 32 + 1 ;
+    const unsigned shift_x = 1;
+
+    af::array input = af::range(af::dim4(2, largeDim));
+    af::array output = af::shift(input, shift_x);
+
+    output = af::abs(input - output);
+    ASSERT_EQ(1.f, af::product<float>(output));
+
+    input = af::range(af::dim4(2, 1, 1, largeDim));
+    output = af::shift(input, shift_x);
+
+    output = af::abs(input - output);
+    ASSERT_EQ(1.f, af::product<float>(output));
+}

From 31b96da511d2e5cd40eb5373a77189c24fe27fde Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:47:44 -0400
Subject: [PATCH 1282/2677] fix max grid limitation for triangle kernel

---
 src/backend/cuda/kernel/triangle.hpp | 8 ++++++--
 test/triangle.cpp                    | 6 ++++++
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 8d335d6113..29d793a189 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -29,10 +29,10 @@ namespace cuda
                              const int blocksPerMatX, const int blocksPerMatY)
         {
             const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
+            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
             const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
             const int xx = threadIdx.x + blockIdx_x * blockDim.x;
             const int yy = threadIdx.y + blockIdx_y * blockDim.y;
@@ -83,6 +83,10 @@ namespace cuda
                         blocksPerMatY * r.dims[3],
                         1);
 
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((triangle_kernel<T, is_upper, is_unit_diag>), blocks, threads,
                     r, in, blocksPerMatX, blocksPerMatY);
 
diff --git a/test/triangle.cpp b/test/triangle.cpp
index 6322070226..5b61d1e465 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -153,6 +153,12 @@ TYPED_TEST(Triangle, Upper2DSquareUnit)
     triangleTester<TypeParam>(dim4(2048, 2048), true, true);
 }
 
+TYPED_TEST(Triangle, MaxDim)
+{
+    const size_t largeDim = 65535 * 32 + 1;
+    triangleTester<TypeParam>(dim4(2, largeDim), true, true);
+}
+
 TEST(Lower, ExtractGFOR)
 {
     using namespace af;

From 6546e6a15d1511c1226b4571fa4435dfd51b2ce0 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:49:11 -0400
Subject: [PATCH 1283/2677] fix max grid limitation for unwrap kernel

---
 src/backend/cuda/kernel/unwrap.hpp | 12 ++++++++++--
 test/unwrap.cpp                    | 20 ++++++++++++++++++++
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index edceb3b61e..e875ba2fb8 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -28,8 +28,8 @@ namespace cuda
                            const int px, const int py, const int nx, int reps)
         {
             // Compute channel and volume
-            const int w = blockIdx.y / in.dims[2];
-            const int z = blockIdx.y % in.dims[2];
+            const int w = (blockIdx.y + blockIdx.z * gridDim.y) / in.dims[2];
+            const int z = (blockIdx.y + blockIdx.z * gridDim.y) % in.dims[2];
 
             if(w >= in.dims[3] || z >= in.dims[2])
                 return;
@@ -105,6 +105,10 @@ namespace cuda
 
             int reps = divup((wx * wy), threads.x); // is > 1 only when TX == 256 && wx * wy > 256
 
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads,
                         out, in, wx, wy, sx, sy, px, py, nx, reps);
 
@@ -121,6 +125,10 @@ namespace cuda
 
             int reps = divup((wx * wy), threads.y);
 
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads,
                         out, in, wx, wy, sx, sy, px, py, nx, reps);
 
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 1392edd28c..0ee03ed3e6 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -176,3 +176,23 @@ TEST(Unwrap, CPP)
     // Delete
     delete[] outData;
 }
+
+TEST(Unwrap, MaxDim)
+{
+    const size_t largeDim = 65535 + 1;
+    af::array input = af::range(5, 5, largeDim);
+
+    const unsigned wx = 5;
+    const unsigned wy = 5;
+    const unsigned sx = 5;
+    const unsigned sy = 5;
+    const unsigned px = 0;
+    const unsigned py = 0;
+
+    af::array output = af::unwrap(input, wx, wy, sx, sy, px, py);
+
+    af::array gold = af::range(af::dim4(5, 5, 1, largeDim));
+    gold = af::moddims(gold, af::dim4(25, 1, largeDim));
+
+    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+}

From 68311f4b7213c55a05f0a0158218b7103f23effc Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Aug 2017 16:49:34 -0400
Subject: [PATCH 1284/2677] fix max grid limitation for where kernel

---
 src/backend/cuda/kernel/where.hpp |  8 ++++++--
 test/where.cpp                    | 14 ++++++++++++++
 2 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index c6c5894367..c43e870e66 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -37,9 +37,9 @@ namespace kernel
         const uint tidy = threadIdx.y;
 
         const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
+        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const uint xid = blockIdx_x * blockDim.x * lim + tidx;
         const uint yid = blockIdx_y * blockDim.y + tidy;
 
@@ -136,6 +136,10 @@ namespace kernel
 
         uint lim = divup(otmp.dims[0], (threads_x * blocks_x));
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         CUDA_LAUNCH((get_out_idx<T>), blocks, threads,
                 out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
         POST_LAUNCH_CHECK();
diff --git a/test/where.cpp b/test/where.cpp
index 08ed878aea..f8537b564d 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -122,6 +122,20 @@ TYPED_TEST(Where, CPP)
     }
 }
 
+TEST(Where, MaxDim)
+{
+    const size_t largeDim = 65535 * 32 + 2;
+
+    af::array input = af::range(af::dim4(1, largeDim), 1);
+    af::array output = where(input % 2 == 0);
+    af::array gold = 2 * af::range(largeDim/2);
+    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+
+    input = af::range(af::dim4(1, 1, 1, largeDim), 3);
+    output = where(input % 2 == 0);
+    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+}
+
 TEST(Where, ISSUE_1259)
 {
     af::array a = af::randu(10, 10, 10);

From 66f010fbea80fd1f2afb6d3c8a365916673adfe7 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 11 Aug 2017 10:12:08 -0400
Subject: [PATCH 1285/2677] reduce number of launched blocks

---
 src/backend/cuda/kernel/approx.hpp    | 16 +++---
 src/backend/cuda/kernel/convolve.cu   | 18 +++++--
 src/backend/cuda/kernel/diagonal.hpp  |  9 ++--
 src/backend/cuda/kernel/diff.hpp      | 12 ++---
 src/backend/cuda/kernel/gradient.hpp  | 10 ++--
 src/backend/cuda/kernel/hsv_rgb.hpp   |  9 ++--
 src/backend/cuda/kernel/identity.hpp  | 10 ++--
 src/backend/cuda/kernel/index.hpp     | 11 ++--
 src/backend/cuda/kernel/iota.hpp      |  9 ++--
 src/backend/cuda/kernel/ireduce.hpp   | 18 +++++--
 src/backend/cuda/kernel/join.hpp      | 10 ++--
 src/backend/cuda/kernel/lookup.hpp    |  8 ++-
 src/backend/cuda/kernel/memcopy.hpp   | 29 +++++++----
 src/backend/cuda/kernel/range.hpp     |  9 ++--
 src/backend/cuda/kernel/reduce.hpp    | 20 +++----
 src/backend/cuda/kernel/select.hpp    | 10 ++--
 src/backend/cuda/kernel/tile.hpp      | 10 ++--
 src/backend/cuda/kernel/transpose.hpp | 22 ++++----
 test/convolve.cpp                     | 75 +++++++++++++++++++++++++++
 test/random.cpp                       | 23 ++++++++
 20 files changed, 219 insertions(+), 119 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index cac5d5648a..5eb8e35e2c 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -122,11 +122,9 @@ namespace cuda
             bool batch = !(xpos.dims[1] == 1 && xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
             const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads,
                             out, in, xpos, offGrid, blocksPerMat, batch, method);
             POST_LAUNCH_CHECK();
@@ -145,11 +143,9 @@ namespace cuda
             bool batch = !(xpos.dims[2] == 1 && xpos.dims[3] == 1);
 
             const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads,
                         out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method);
             POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index d237fd8ca1..59d1d92c84 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -54,8 +54,8 @@ void convolve1(Param<T> out, CParam<T> signal, int fLen,
     const int padding = fLen-1;
     const int shrdLen = blockDim.x + 2*padding;
     const unsigned b1 = blockIdx.x/nBBS0;   /* [0 {1} 2 3] */
-    const unsigned b3 = blockIdx.y/nBBS1;   /* [0 1 2 {3}] */
-    const unsigned b2 = blockIdx.y-nBBS1*b3;/* [0 1 {2} 3] */
+    const unsigned b3 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;   /* [0 1 2 {3}] */
+    const unsigned b2 = (blockIdx.y + blockIdx.z * gridDim.y) - nBBS1*b3;/* [0 1 {2} 3] */
 
     T *dst = (T *)out.ptr + (b1 * out.strides[1] +  /* activated with batched input signal */
                              o1 * out.strides[1] +  /* activated with batched input filter */
@@ -109,8 +109,8 @@ void convolve2(Param<T> out, CParam<T> signal, int nBBS0,
     const int shrdLen0 = THREADS_X + padding0;
     const int shrdLen1 = THREADS_Y + padding1;
 
-    unsigned b0  = blockIdx.x/nBBS0;
-    unsigned b1  = blockIdx.y/nBBS1;
+    unsigned b0  = blockIdx.x / nBBS0;
+    unsigned b1  = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
     T *dst = (T *)out.ptr + (b0 * out.strides[2] + /* activated with batched input signal */
                              o2 * out.strides[2] + /* activated with batched input filter */
                              b1 * out.strides[3] + /* activated with batched input signal */
@@ -126,7 +126,7 @@ void convolve2(Param<T> out, CParam<T> signal, int nBBS0,
     int lx  = threadIdx.x;
     int ly  = threadIdx.y;
     int gx  = THREADS_X * (blockIdx.x-b0*nBBS0) + lx;
-    int gy  = THREADS_Y * (blockIdx.y-b1*nBBS1) + ly;
+    int gy  = THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) -b1*nBBS1) + ly;
 
     int s0 = signal.strides[0];
     int s1 = signal.strides[1];
@@ -273,17 +273,22 @@ void prepareKernelArgs(conv_kparam_t &params, dim_t oDims[], dim_t fDims[], int
         batchDims[i] = (params.launchMoreBlocks ? 1 : oDims[i]);
     }
 
+    const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
     if (baseDim==1) {
         params.mThreads    = dim3(THREADS, 1);
         params.mBlk_x      = divup(oDims[0], params.mThreads.x);
         params.mBlk_y      = batchDims[2];
         params.mBlocks     = dim3(params.mBlk_x * batchDims[1], params.mBlk_y * batchDims[3]);
         params.mSharedSize = (params.mThreads.x+2*(fDims[0]-1)) * sizeof(T);
+        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
+        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
     } else if (baseDim==2) {
         params.mThreads    = dim3(THREADS_X, THREADS_Y);
         params.mBlk_x      = divup(oDims[0], params.mThreads.x);
         params.mBlk_y      = divup(oDims[1], params.mThreads.y);
         params.mBlocks     = dim3(params.mBlk_x * batchDims[2], params.mBlk_y * batchDims[3]);
+        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
+        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
     } else if (baseDim==3) {
         params.mThreads    = dim3(CUBE_X, CUBE_Y, CUBE_Z);
         params.mBlk_x      = divup(oDims[0], params.mThreads.x);
@@ -293,6 +298,9 @@ void prepareKernelArgs(conv_kparam_t &params, dim_t oDims[], dim_t fDims[], int
         params.mSharedSize = (params.mThreads.x+2*(fDims[0]-1)) *
                              (params.mThreads.y+2*(fDims[1]-1)) *
                              (params.mThreads.z+2*(fDims[2]-1)) * sizeof(T);
+        //todo: fold into x dimension according to old style
+        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
+        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
     }
 }
 
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index f43cb987f5..bd3e70061c 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -89,12 +89,9 @@ namespace kernel
         int blocks_z = out.dims[2];
         dim3 blocks(blocks_x, out.dims[3] * blocks_z);
 
-        const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-        if(blocksPerMatZ > 1) {
-            blocks.y = maxBlocksY;
-            blocks.z = blocksPerMatZ;
-        }
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
 
         CUDA_LAUNCH((diagExtractKernel<T>), blocks, threads, out, in, num, blocks_z);
         POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index d5f514f1f2..a467c70aef 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -88,16 +88,14 @@ namespace cuda
 
             const int oElem = out.dims[0] * out.dims[1] * out.dims[2] * out.dims[3];
 
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((diff_kernel<T, dim, isDiff2>), blocks, threads,
                 out, in, oElem, blocksPerMatX, blocksPerMatY);
 
             POST_LAUNCH_CHECK();
         }
-}
+    }
 }
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index f0179ad7c1..c7475aa034 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -107,12 +107,10 @@ namespace cuda
                         blocksPerMatY * in.dims[3],
                         1);
 
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((gradient_kernel<T>), blocks, threads,
                     grad0, grad1, in, blocksPerMatX, blocksPerMatY);
             POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 04c79b5d93..bbd0a9e656 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -105,12 +105,9 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in)
     // parameter would be along 4th dimension
     dim3 blocks(blk_x*in.dims[3], blk_y);
 
-    const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-    if(blocksPerMatZ > 1) {
-        blocks.y = maxBlocksY;
-        blocks.z = blocksPerMatZ;
-    }
+    const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
     CUDA_LAUNCH((convert<T, isHSV2RGB>), blocks, threads, out, in, blk_x);
 
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 22c14ce4d4..e52a1c5a0d 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -54,12 +54,10 @@ namespace kernel
         int blocks_y = divup(out.dims[1], threads.y);
         dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-        const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-        if(blocksPerMatZ > 1) {
-            blocks.y = maxBlocksY;
-            blocks.z = blocksPerMatZ;
-        }
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         CUDA_LAUNCH((identity_kernel<T>), blocks, threads, out, blocks_x, blocks_y);
         POST_LAUNCH_CHECK();
     }
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 9788e30778..ef61f3329c 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -76,12 +76,11 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p)
     int blks_y = divup(out.dims[1], threads.y);
 
     dim3 blocks(blks_x*out.dims[2], blks_y*out.dims[3]);
-    const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-    if(blocksPerMatZ > 1) {
-        blocks.y = maxBlocksY;
-        blocks.z = blocksPerMatZ;
-    }
+
+    const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
     CUDA_LAUNCH((indexKernel<T>), blocks, threads, out, in, p, blks_x, blks_y);
 
     POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 00ea2b9a68..8b9dcd32f2 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -80,13 +80,10 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
-            const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
 
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
             CUDA_LAUNCH((iota_kernel<T>), blocks, threads,
                     out, sdims[0], sdims[1], sdims[2], sdims[3],
                     tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index b959500139..690ced4190 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -81,9 +81,9 @@ namespace kernel
         const uint tid  = tidy * THREADS_X + tidx;
 
         const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
+        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const uint xid = blockIdx_x * blockDim.x + tidx;
         const uint yid = blockIdx_y; // yid  of output. updated for input later.
 
@@ -194,6 +194,11 @@ namespace kernel
         dim3 blocks(blocks_dim[0] * blocks_dim[2],
                     blocks_dim[1] * blocks_dim[3]);
 
+        printf("dim [%d %d %d]\n", blocks.x, blocks.y, blocks.z);
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         switch (threads_y) {
         case 8:
             CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 8>), blocks, threads,
@@ -278,9 +283,9 @@ namespace kernel
         const uint tid  = tidy * blockDim.x + tidx;
 
         const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
+        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
         const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
         const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
         const uint yid = blockIdx_y * blockDim.y + tidy;
 
@@ -369,6 +374,10 @@ namespace kernel
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
         dim3 blocks(blocks_x * in.dims[2],
                     blocks_y * in.dims[3]);
+        printf("[%d %d %d]\n", blocks.x, blocks.y, blocks.z);
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
 
         uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
@@ -430,6 +439,7 @@ namespace kernel
     template<typename T, af_op_t op>
     void ireduce(Param<T> out, uint *olptr, CParam<T> in, int dim)
     {
+        printf("AMNIHRERERE");
         switch (dim) {
         case 0: return ireduce_first<T, op   >(out, olptr, in);
         case 1: return ireduce_dim  <T, op, 1>(out, olptr, in);
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index f5525e5fe4..8ba1ddd7a5 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -74,12 +74,10 @@ namespace cuda
                         blocksPerMatY * X.dims[3],
                         1);
 
-            const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((join_kernel<To, Tx, dim>), blocks, threads,
                        out, X, offset[0], offset[1], offset[2], offset[3],
                        blocksPerMatX, blocksPerMatY);
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index a325ba3cc8..68bb54a2be 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -55,10 +55,10 @@ void lookupND(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
     int ly = threadIdx.y;
 
     int gz = blockIdx.x/nBBS0;
-    int gw = blockIdx.y/nBBS1;
+    int gw = (blockIdx.y + blockIdx.z * gridDim.y)/nBBS1;
 
     int gx = blockDim.x * (blockIdx.x - gz*nBBS0) + lx;
-    int gy = blockDim.y * (blockIdx.y - gw*nBBS1) + ly;
+    int gy = blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw*nBBS1) + ly;
 
     const idx_t *idxPtr = (const idx_t*)indices.ptr;
 
@@ -103,6 +103,10 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims)
 
         dim3 blocks(blks_x*out.dims[2], blks_y*out.dims[3]);
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         CUDA_LAUNCH((lookupND<in_t, idx_t, dim>), blocks, threads, out, in, indices, blks_x, blks_y);
     }
 
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 2a650a1b97..9b9531473e 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -28,6 +28,8 @@ namespace kernel
     static const uint DIMX = 32;
     static const uint DIMY =  8;
 
+    // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
+    // \param iterPerBlockY number of iterations along grid.Y per block
     template<typename T>
     __global__ static void
     memcopy_kernel(T *out, const dims_t ostrides,
@@ -38,24 +40,23 @@ namespace kernel
         const int tidy = threadIdx.y;
 
         const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
         const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
         const int xid = blockIdx_x * blockDim.x + tidx;
-        const int yid = blockIdx_y * blockDim.y + tidy;
 
+        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
+        const int yid = blockIdx_y * blockDim.y + tidy;
         // FIXME: Do more work per block
-        out += wid * ostrides.dim[3] + zid * ostrides.dim[2] + yid * ostrides.dim[1];
-        in  += wid * istrides.dim[3] + zid * istrides.dim[2] + yid * istrides.dim[1];
+        T * const optr = out + wid * ostrides.dim[3] + zid * ostrides.dim[2] + yid * ostrides.dim[1];
+        const T * iptr = in + wid * istrides.dim[3] + zid * istrides.dim[2] + yid * istrides.dim[1];
 
         int istride0 = istrides.dim[0];
         if (xid < idims.dim[0] &&
             yid < idims.dim[1] &&
             zid < idims.dim[2] &&
             wid < idims.dim[3]) {
-            out[xid] = in[xid * istride0];
+            optr[xid] = iptr[xid * istride0];
         }
-
     }
 
     template<typename T>
@@ -81,8 +82,12 @@ namespace kernel
         dims_t _istrides = {{(int)istrides[0], (int)istrides[1], (int)istrides[2], (int)istrides[3]}};
         dims_t _idims = {{(int)idims[0], (int)idims[1], (int)idims[2], (int)idims[3]}};
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         CUDA_LAUNCH((memcopy_kernel<T>), blocks, threads,
-                out, _ostrides, in, _idims, _istrides, blocks_x, blocks_y);
+            out, _ostrides, in, _idims, _istrides, blocks_x, blocks_y);
         POST_LAUNCH_CHECK();
     }
 
@@ -159,9 +164,9 @@ namespace kernel
         const uint ly = threadIdx.y;
 
         const uint gz = blockIdx.x / blk_x;
-        const uint gw = blockIdx.y / blk_y;
+        const uint gw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blk_y;
         const uint blockIdx_x = blockIdx.x - (blk_x) * gz;
-        const uint blockIdx_y = blockIdx.y - (blk_y) * gw;
+        const uint blockIdx_y = (blockIdx.y + (blockIdx.z * gridDim.y)) - (blk_y) * gw;
         const uint gx = blockIdx_x * blockDim.x + lx;
         const uint gy = blockIdx_y * blockDim.y + ly;
 
@@ -202,6 +207,10 @@ namespace kernel
         dim3 blocks(blk_x * dst.dims[2],
                     blk_y * dst.dims[3]);
 
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         int trgt_l  = std::min(dst.dims[3], src.dims[3]);
         int trgt_k  = std::min(dst.dims[2], src.dims[2]);
         int trgt_j  = std::min(dst.dims[1], src.dims[1]);
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 29a1621d36..f71f2c144b 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -82,12 +82,9 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
 
             CUDA_LAUNCH((range_kernel<T>), blocks, threads, out, dim, blocksPerMatX, blocksPerMatY);
             POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index d2f22be794..668ab3b3f8 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -115,12 +115,10 @@ namespace kernel
         dim3 blocks(blocks_dim[0] * blocks_dim[2],
                     blocks_dim[1] * blocks_dim[3]);
 
-        const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-        if(blocksPerMatZ > 1) {
-            blocks.y = maxBlocksY;
-            blocks.z = blocksPerMatZ;
-        }
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         switch (threads_y) {
         case 8:
             CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8>), blocks, threads,
@@ -305,12 +303,10 @@ namespace kernel
 
         uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-        const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-        if(blocksPerMatZ > 1) {
-            blocks.y = maxBlocksY;
-            blocks.z = blocksPerMatZ;
-        }
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
+
         switch (threads_x) {
         case 32:
             CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32>), blocks, threads,
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 39daba7ca7..7afbfad98f 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -101,12 +101,10 @@ namespace cuda
             dim3 blocks(blk_x * out.dims[2],
                         blk_y * out.dims[3]);
 
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             if (is_same) {
                 CUDA_LAUNCH((select_kernel<T, true>), blocks, threads,
                             out, cond, a, b, blk_x, blk_y);
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 6a3a3e2ce4..9f674649e8 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -78,12 +78,10 @@ namespace cuda
                         blocksPerMatY * out.dims[3],
                         1);
 
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-            if(blocksPerMatZ > 1) {
-                blocks.y = maxBlocksY;
-                blocks.z = blocksPerMatZ;
-            }
+            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            blocks.z = divup(blocks.y, maxBlocksY);
+            blocks.y = divup(blocks.y, blocks.z);
+
             CUDA_LAUNCH((tile_kernel<T>), blocks, threads, out, in, blocksPerMatX, blocksPerMatY);
             POST_LAUNCH_CHECK();
         }
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index b69fc16ed3..d3877350db 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -30,7 +30,7 @@ namespace kernel
         else return in;
     }
 
-    // Kernel is going access original data in colleased format
+    // Kernel is going access original data in coaleasced format
     template<typename T, bool conjugate, bool is32Multiple>
     __global__
     void transpose(Param<T> out, CParam<T> in,
@@ -54,24 +54,24 @@ namespace kernel
         const int batchId_x = blockIdx.x / blocksPerMatX;
         const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
 
-        const int batchId_y = blockIdx.y / blocksPerMatY;
-        const int blockIdx_y = (blockIdx.y - batchId_y * blocksPerMatY);
+        const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
 
         const int x0 = TILE_DIM * blockIdx_x;
         const int y0 = TILE_DIM * blockIdx_y;
 
         // calculate global indices
-        int gx      = lx + x0;
-        int gy      = ly + y0;
+        int gx = lx + x0;
+        int gy = ly + y0;
 
-        // offset in and out based on batch id
+        //offset in and out based on batch id
         in.ptr  += batchId_x *  in.strides[2] + batchId_y *  in.strides[3];
         out.ptr += batchId_x * out.strides[2] + batchId_y * out.strides[3];
 
 #pragma unroll
         for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
             int gy_ = gy+repeat;
-            if (is32Multiple || (gx<iDim0 && gy_<iDim1))
+            if ((gx<iDim0 && gy_<iDim1))
                 shrdMem[ly + repeat][lx] = in.ptr[gy_ * iStride1 + gx];
         }
         __syncthreads();
@@ -100,11 +100,15 @@ namespace kernel
         int blk_y = divup(in.dims[1],TILE_DIM);
         // launch batch * blk_x blocks along x dimension
         dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
+        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        blocks.z = divup(blocks.y, maxBlocksY);
+        blocks.y = divup(blocks.y, blocks.z);
 
-        if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0)
+        if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0) {
             CUDA_LAUNCH((transpose<T, conjugate, true >), blocks, threads, out, in, blk_x, blk_y);
-        else
+        } else {
             CUDA_LAUNCH((transpose<T, conjugate, false>), blocks, threads, out, in, blk_x, blk_y);
+        }
 
         POST_LAUNCH_CHECK();
     }
diff --git a/test/convolve.cpp b/test/convolve.cpp
index c6893b54ef..1411717713 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -751,3 +751,78 @@ TEST(Convolve, 3D_C64)
     EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
     EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
 }
+
+TEST(ConvolveLargeDim1D, CPP)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t n = 10;
+    const size_t largeDim = 65535 + 1;
+
+    float h_filter[] = {0.f, 1.f, 0.f};
+    af::array identity_filter(3, h_filter);
+    af::array signal = af::constant(1, n, 1, largeDim);
+
+    af::array output = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
+    af::array output2 = output;
+    ASSERT_EQ(largeDim * n, sum<float>(output2));
+
+    signal = af::constant(1, n, 1, 1, largeDim);
+
+    output = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
+    ASSERT_EQ(largeDim * n, sum<float>(output));
+}
+
+TEST(ConvolveLargeDim2D, CPP)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t n = 10;
+    const size_t largeDim = 65535 + 1;
+
+    float h_filter[] = {0.f, 0.f, 0.f,
+                        0.f, 1.f, 0.f,
+                        0.f, 0.f, 0.f};
+    af::array identity_filter(3, 3, h_filter);
+    af::array signal = af::constant(1, n, n, largeDim);
+
+    af::array output = convolve2(signal, identity_filter, AF_CONV_DEFAULT);
+    ASSERT_EQ(largeDim * n * n, sum<float>(output));
+
+    signal = af::constant(1, n, n, 1, largeDim);
+
+    output = convolve2(signal, identity_filter, AF_CONV_DEFAULT);
+    ASSERT_EQ(largeDim * n * n, sum<float>(output));
+}
+
+TEST(DISABLED_ConvolveLargeDim3D, CPP)
+{
+    if (noDoubleTests<float>()) return;
+
+    const size_t n = 3;
+    const size_t largeDim = 65535 * 16 + 1;
+
+    float h_filter[] = {0.f, 0.f, 0.f,
+                        0.f, 0.f, 0.f,
+                        0.f, 0.f, 0.f,
+
+                        0.f, 0.f, 0.f,
+                        0.f, 1.f, 0.f,
+                        0.f, 0.f, 0.f,
+
+                        0.f, 0.f, 0.f,
+                        0.f, 0.f, 0.f,
+                        0.f, 0.f, 0.f};
+
+    af::array identity_filter(3, 3, 3, h_filter);
+    af::array signal = af::constant(1, n, largeDim, n);
+
+    af::array output = convolve3(signal, identity_filter, AF_CONV_DEFAULT);
+    ASSERT_EQ(1.f, product<float>(output));
+
+    signal = af::constant(1, n, n, largeDim);
+
+    output = convolve3(signal, identity_filter, AF_CONV_EXPAND);
+    //TODO: fix product by indexing
+    //ASSERT_EQ(1.f, product<float>(output));
+}
diff --git a/test/random.cpp b/test/random.cpp
index 749346e633..69f8ff4c07 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -177,6 +177,29 @@ TYPED_TEST(Random,InvalidArgs)
     randuArgsTest<TypeParam>();
 }
 
+template<typename T>
+void randuDimsTest()
+{
+    if (noDoubleTests<T>()) return;
+
+    af::dim4 dims(1, 65535*32, 1, 1);
+    af::array large_rand = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    ASSERT_EQ(large_rand.dims()[1], 65535*32);
+
+    dims = af::dim4(1, 1, 65535*32, 1);
+    large_rand = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    ASSERT_EQ(large_rand.dims()[2], 65535*32);
+
+    dims = af::dim4(1, 1, 1, 65535*32);
+    large_rand = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    ASSERT_EQ(large_rand.dims()[3], 65535*32);
+}
+
+TYPED_TEST(Random,InvalidDims)
+{
+    randuDimsTest<TypeParam>();
+}
+
 ////////////////////////////////////// CPP /////////////////////////////////////
 //
 TEST(Random, CPP)

From d24fa16a56b2e08e926871041d67ddde905593c6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Sat, 19 Aug 2017 01:22:20 -0400
Subject: [PATCH 1286/2677] adds test for transpose kernel

---
 src/backend/cuda/kernel/transpose.hpp |  5 ++++-
 test/transpose.cpp                    | 20 ++++++++++++++++++++
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index d3877350db..4c5ebafb1d 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -42,6 +42,7 @@ namespace kernel
         const int oDim1 = out.dims[1];
         const int iDim0 = in.dims[0];
         const int iDim1 = in.dims[1];
+        const int iDim3 = in.dims[3];
 
         // calculate strides
         const int oStride1 = out.strides[1];
@@ -57,6 +58,8 @@ namespace kernel
         const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
         const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
 
+        if(batchId_y >= iDim3) return;
+
         const int x0 = TILE_DIM * blockIdx_x;
         const int y0 = TILE_DIM * blockIdx_y;
 
@@ -71,7 +74,7 @@ namespace kernel
 #pragma unroll
         for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
             int gy_ = gy+repeat;
-            if ((gx<iDim0 && gy_<iDim1))
+            if (is32Multiple || (gx<iDim0 && gy_<iDim1))
                 shrdMem[ly + repeat][lx] = in.ptr[gy_ * iStride1 + gx];
         }
         __syncthreads();
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 8437a12615..60d0fc1a13 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -236,6 +236,26 @@ TEST(Transpose, CPP_c32_CONJ20x20x5)
     trsCPPConjTest<cfloat>(20, 20, 5);
 }
 
+TEST(Transpose, MaxDim)
+{
+    const size_t largeDim = 65535 * 33 + 1;
+
+    af::array input  = af::range(af::dim4(2, largeDim, 1, 1));
+    af::array gold   = af::range(af::dim4(largeDim, 2, 1, 1), 1);
+    af::array output = af::transpose(input);
+
+    ASSERT_EQ(output.dims(0), (int)largeDim);
+    ASSERT_EQ(output.dims(1), 2);
+    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+
+    input  = af::range(af::dim4(2, 5, 1, largeDim));
+    gold   = af::range(af::dim4(5, 2, 1, largeDim), 1);
+    output = af::transpose(input);
+
+    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+}
+
+
 TEST(Transpose, GFOR)
 {
     using namespace af;

From 12cde8dbeb99aaf41e8455a335e19d6d3c22ee8c Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 23 Aug 2017 00:41:41 -0400
Subject: [PATCH 1287/2677] bounds checking for extra blocks

---
 src/backend/cuda/kernel/convolve.cu   | 5 +++++
 src/backend/cuda/kernel/hsv_rgb.hpp   | 2 +-
 src/backend/cuda/kernel/ireduce.hpp   | 3 ---
 src/backend/cuda/kernel/join.hpp      | 1 -
 src/backend/cuda/kernel/memcopy.hpp   | 2 --
 src/backend/cuda/kernel/transpose.hpp | 4 ++--
 src/backend/cuda/kernel/wrap.hpp      | 2 +-
 test/join.cpp                         | 3 +--
 8 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 59d1d92c84..9376a42ffa 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -56,6 +56,8 @@ void convolve1(Param<T> out, CParam<T> signal, int fLen,
     const unsigned b1 = blockIdx.x/nBBS0;   /* [0 {1} 2 3] */
     const unsigned b3 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;   /* [0 1 2 {3}] */
     const unsigned b2 = (blockIdx.y + blockIdx.z * gridDim.y) - nBBS1*b3;/* [0 1 {2} 3] */
+    if(b2 >= out.dims[2] || b3 >= out.dims[3])
+        return;
 
     T *dst = (T *)out.ptr + (b1 * out.strides[1] +  /* activated with batched input signal */
                              o1 * out.strides[1] +  /* activated with batched input filter */
@@ -128,6 +130,9 @@ void convolve2(Param<T> out, CParam<T> signal, int nBBS0,
     int gx  = THREADS_X * (blockIdx.x-b0*nBBS0) + lx;
     int gy  = THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) -b1*nBBS1) + ly;
 
+    if(b1 >= out.dims[3])
+        return;
+
     int s0 = signal.strides[0];
     int s1 = signal.strides[1];
     int d0 = signal.dims[0];
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index bbd0a9e656..6315a5557c 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -33,7 +33,7 @@ void convert(Param<T> out, CParam<T> in, int nBBS)
     int gx = blockDim.x * (blockIdx.x-batchId*nBBS) + threadIdx.x;
     int gy = blockDim.y * (blockIdx.y + blockIdx.z * gridDim.y) + threadIdx.y;
 
-    if (gx < out.dims[0] && gy < out.dims[1]) {
+    if (gx < out.dims[0] && gy < out.dims[1] && batchId < out.dims[3]) {
 
         int oIdx0 = gx + gy * out.strides[1];
         int oIdx1 = oIdx0 + out.strides[2];
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 690ced4190..4df25b90f3 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -194,7 +194,6 @@ namespace kernel
         dim3 blocks(blocks_dim[0] * blocks_dim[2],
                     blocks_dim[1] * blocks_dim[3]);
 
-        printf("dim [%d %d %d]\n", blocks.x, blocks.y, blocks.z);
         const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
         blocks.z = divup(blocks.y, maxBlocksY);
         blocks.y = divup(blocks.y, blocks.z);
@@ -374,7 +373,6 @@ namespace kernel
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
         dim3 blocks(blocks_x * in.dims[2],
                     blocks_y * in.dims[3]);
-        printf("[%d %d %d]\n", blocks.x, blocks.y, blocks.z);
         const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
         blocks.z = divup(blocks.y, maxBlocksY);
         blocks.y = divup(blocks.y, blocks.z);
@@ -439,7 +437,6 @@ namespace kernel
     template<typename T, af_op_t op>
     void ireduce(Param<T> out, uint *olptr, CParam<T> in, int dim)
     {
-        printf("AMNIHRERERE");
         switch (dim) {
         case 0: return ireduce_first<T, op   >(out, olptr, in);
         case 1: return ireduce_dim  <T, op, 1>(out, olptr, in);
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index 8ba1ddd7a5..6b685e18e9 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -12,7 +12,6 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <iostream>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 9b9531473e..2db2c0832f 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -28,8 +28,6 @@ namespace kernel
     static const uint DIMX = 32;
     static const uint DIMY =  8;
 
-    // \param largeYWDim true denotes 2nd & 4th dimensions greater than device limits
-    // \param iterPerBlockY number of iterations along grid.Y per block
     template<typename T>
     __global__ static void
     memcopy_kernel(T *out, const dims_t ostrides,
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 4c5ebafb1d..ddfb09d379 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -42,7 +42,6 @@ namespace kernel
         const int oDim1 = out.dims[1];
         const int iDim0 = in.dims[0];
         const int iDim1 = in.dims[1];
-        const int iDim3 = in.dims[3];
 
         // calculate strides
         const int oStride1 = out.strides[1];
@@ -58,7 +57,8 @@ namespace kernel
         const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
         const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
 
-        if(batchId_y >= iDim3) return;
+        if(batchId_x >= in.dims[2] || batchId_y >= in.dims[3])
+            return;
 
         const int x0 = TILE_DIM * blockIdx_x;
         const int y0 = TILE_DIM * blockIdx_y;
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index e29a71871f..50ee0a919b 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -46,7 +46,7 @@ namespace cuda
             const T *iptr =  in.ptr + idx2 *  in.strides[2] + idx3 *  in.strides[3];
 
 
-            if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1]) return;
+            if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1] || idx2 >= out.dims[2] || idx3 >= out.dims[3]) return;
 
             int pidx0 = oidx0 + px;
             int pidx1 = oidx1 + py;
diff --git a/test/join.cpp b/test/join.cpp
index 61e4263b8f..91a1984c9f 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -129,8 +129,7 @@ TEST(Join, JoinLargeDim)
         af::dim4 joined_dims = joined.dims();
 
         ASSERT_EQ(2*in_dims[0], joined_dims[0]);
-        //todo: uncomment as assert
-        //printf("%f\n", af::sum<float>((joined(0, af::span) - joined(1, af::span)).as(f32)));
+        ASSERT_EQ(0.f, af::sum<float>((joined(0, af::span) - joined(1, af::span)).as(f32)));
 
         af::array in2 = af::constant(1, (dim_t)nx, (dim_t)ny, (dim_t)2, (dim_t)nw, u8);
         joined = af::join(3, in, in);

From f6f0b2fc7d369d6919f53000f78586bdfa3eb44b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 9 Aug 2017 16:42:46 +0530
Subject: [PATCH 1288/2677] Re-enable texture object access in regions cuda
 kernel

Remove pre-3.0-compute checks as we don't support 2.0 compute
capability anymore
---
 src/backend/cuda/kernel/regions.hpp | 46 +++++++----------------------
 src/backend/cuda/regions.cu         | 41 ++++++++++++++++---------
 2 files changed, 37 insertions(+), 50 deletions(-)

diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 6014e971eb..7c4a01b814 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -23,8 +23,6 @@
 #include <thrust/sort.h>
 #include <thrust/transform_scan.h>
 
-#if __CUDACC__
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
@@ -35,19 +33,18 @@ __device__ static int continue_flag = 1;
 
 // Wrapper function for texture fetch
 template<typename T>
-__device__ __inline__
-static T fetch(const int n,
-               cuda::Param<T> equiv_map,
-               cudaTextureObject_t tex)
+static inline __device__
+T fetch(const int n, cuda::Param<T> equiv_map, cudaTextureObject_t tex)
 {
-// FIXME: Enable capability >= 3.0
-//#if (__CUDA_ARCH__ >= 300)
-#if 0
-    // Kepler bindless texture objects
     return tex1Dfetch<T>(tex, n);
-#else
+}
+
+template<> __device__
+STATIC_ double fetch<double>(const int n,
+                             cuda::Param<double> equiv_map,
+                             cudaTextureObject_t tex)
+{
     return equiv_map.ptr[n];
-#endif
 }
 
 // The initial label kernel distinguishes between valid (nonzero)
@@ -128,7 +125,7 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
 {
 
     typedef warp_count<block_dim*block_dim> num_warps;
-#if (__CUDA_ARCH__ >= 120) // This function uses warp ballot instructions
+
     // Basic coordinates
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
@@ -160,13 +157,9 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
     s_changed[warpIdx] = (T)0;
     __syncthreads();
 
-#if (__CUDA_ARCH__ >= 130)
     #pragma unroll
-#endif
     for (int xb = 0; xb < n_per_thread; ++xb) {
-#if (__CUDA_ARCH__ >= 130)
         #pragma unroll
-#endif
         for (int yb = 0; yb < n_per_thread; ++yb) {
 
             // Indexing variables
@@ -251,9 +244,7 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
     s_changed[warpIdx] = __any((int)tid_changed);
     __syncthreads();
 
-#if (__CUDA_ARCH__ >= 130)
     #pragma unroll
-#endif
     for (int i = 0; i < num_warps::value; i++)
         continue_iter = continue_iter || (s_changed[i] != 0);
 
@@ -263,13 +254,9 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
         // Reset whether or not this thread's pixels have changed.
         tid_changed = false;
 
-#if (__CUDA_ARCH__ >= 130)
         #pragma unroll
-#endif
         for (int xb = 0; xb < n_per_thread; ++xb) {
-#if (__CUDA_ARCH__ >= 130)
             #pragma unroll
-#endif
             for (int yb = 0; yb < n_per_thread; ++yb) {
 
                 // Indexing
@@ -335,22 +322,16 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
         s_changed[warpIdx] = __any((int)tid_changed);
         __syncthreads();
         continue_iter = false;
-#if (__CUDA_ARCH__ >= 130)
         #pragma unroll
-#endif
         for (int i = 0; i < num_warps::value; i++)
             continue_iter = continue_iter | (s_changed[i] != 0);
 
         // If we have to continue iterating, update the tile of the
         // equiv map in shared memory
         if (continue_iter) {
-#if (__CUDA_ARCH__ >= 130)
             #pragma unroll
-#endif
             for (int xb = 0; xb < n_per_thread; ++xb) {
-#if (__CUDA_ARCH__ >= 130)
                 #pragma unroll
-#endif
                 for (int yb = 0; yb < n_per_thread; ++yb) {
                     const int tx = threadIdx.x + (xb * blockDim.x);
                     const int ty = threadIdx.y + (yb * blockDim.y);
@@ -364,13 +345,9 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
     } // while (continue_iter)
 
     // Write out equiv_map
-#if (__CUDA_ARCH__ >= 130)
     #pragma unroll
-#endif
     for (int xb = 0; xb < n_per_thread; ++xb) {
-#if (__CUDA_ARCH__ >= 130)
         #pragma unroll
-#endif
         for (int yb = 0; yb < n_per_thread; ++yb) {
             const int x = base_x + (xb * blockDim.x);
             const int y = base_y + (yb * blockDim.y);
@@ -382,7 +359,6 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
             }
         }
     }
-#endif // __CUDA_ARCH__ >= 120
 }
 
 template<typename T>
@@ -477,5 +453,3 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
 
     cuda::memFree(tmp);
 }
-
-#endif // __CUDACC__
diff --git a/src/backend/cuda/regions.cu b/src/backend/cuda/regions.cu
index 1909cfdfef..48e35ccccf 100644
--- a/src/backend/cuda/regions.cu
+++ b/src/backend/cuda/regions.cu
@@ -29,20 +29,28 @@ Array<T>  regions(const Array<char> &in, af_connectivity connectivity)
 
     // Create bindless texture object for the equiv map.
     cudaTextureObject_t tex = 0;
-    // FIXME: Currently disabled, only supported on capaibility >= 3.0
-    //if (compute >= 3.0) {
-    //    cudaResourceDesc resDesc;
-    //    memset(&resDesc, 0, sizeof(resDesc));
-    //    resDesc.resType = cudaResourceTypeLinear;
-    //    resDesc.res.linear.devPtr = out->get();
-    //    resDesc.res.linear.desc.f = cudaChannelFormatKindFloat;
-    //    resDesc.res.linear.desc.x = 32; // bits per channel
-    //    resDesc.res.linear.sizeInBytes = dims[0] * dims[1] * sizeof(float);
-    //    cudaTextureDesc texDesc;
-    //    memset(&texDesc, 0, sizeof(texDesc));
-    //    texDesc.readMode = cudaReadModeElementType;
-    //    CUDA_CHECK(cudaCreateTextureObject(&tex, &resDesc, &texDesc, NULL));
-    //}
+
+    //Use texture objects with compute 3.0 or higher
+    if (!std::is_same<T,double>::value) {
+        cudaResourceDesc resDesc;
+        memset(&resDesc, 0, sizeof(resDesc));
+        resDesc.resType = cudaResourceTypeLinear;
+        resDesc.res.linear.devPtr = out.get();
+
+        if (std::is_signed<T>::value)
+            resDesc.res.linear.desc.f = cudaChannelFormatKindSigned;
+        else if (std::is_unsigned<T>::value)
+            resDesc.res.linear.desc.f = cudaChannelFormatKindUnsigned;
+        else
+            resDesc.res.linear.desc.f = cudaChannelFormatKindFloat;
+
+        resDesc.res.linear.desc.x = sizeof(T)*8; // bits per channel
+        resDesc.res.linear.sizeInBytes = dims[0] * dims[1] * sizeof(T);
+        cudaTextureDesc texDesc;
+        memset(&texDesc, 0, sizeof(texDesc));
+        texDesc.readMode = cudaReadModeElementType;
+        CUDA_CHECK(cudaCreateTextureObject(&tex, &resDesc, &texDesc, NULL));
+    }
 
     switch(connectivity) {
         case AF_CONNECTIVITY_4:
@@ -53,6 +61,11 @@ Array<T>  regions(const Array<char> &in, af_connectivity connectivity)
             break;
     }
 
+    //Iterative procedure(while loop) in kernel::regions
+    //does stream synchronization towards loop end. So, it is
+    //safe to destroy the texture object
+    CUDA_CHECK(cudaDestroyTextureObject(tex));
+
     return out;
 }
 

From 9ad5999c746a4966be64fc33c2fe162200d7e07f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 26 Aug 2017 12:30:06 +0530
Subject: [PATCH 1289/2677] Replace warp reduce with __syncthreads_or in
 regions

---
 src/backend/cuda/kernel/regions.hpp | 32 ++---------------------------
 1 file changed, 2 insertions(+), 30 deletions(-)

diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 7c4a01b814..557f7192d9 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -123,9 +123,6 @@ template <typename T, int block_dim, int n_per_thread, bool full_conn>
 __global__
 static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex)
 {
-
-    typedef warp_count<block_dim*block_dim> num_warps;
-
     // Basic coordinates
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
@@ -148,15 +145,6 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
     // Cached tile of the equivalency map
     __shared__ T s_tile[n_per_thread*block_dim][(n_per_thread*block_dim)];
 
-    // Space to track ballot funcs to track convergence
-    __shared__ T s_changed[num_warps::value];
-
-    const int tn = (threadIdx.y * blockDim.x) + threadIdx.x;
-
-    const int warpIdx = tn / warpSize;
-    s_changed[warpIdx] = (T)0;
-    __syncthreads();
-
     #pragma unroll
     for (int xb = 0; xb < n_per_thread; ++xb) {
         #pragma unroll
@@ -237,16 +225,8 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
             best_label[tid_i] = new_label;
         }
     }
-    __syncthreads();
 
-    // Determine if any pixel changed
-    bool continue_iter = false;
-    s_changed[warpIdx] = __any((int)tid_changed);
-    __syncthreads();
-
-    #pragma unroll
-    for (int i = 0; i < num_warps::value; i++)
-        continue_iter = continue_iter || (s_changed[i] != 0);
+    bool continue_iter = __syncthreads_or((int)tid_changed);
 
     // Iterate until no pixel in the tile changes
     while (continue_iter) {
@@ -316,15 +296,7 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
             }
         }
         // Done looking at neighbors for this iteration
-        __syncthreads();
-
-        // Decide if we need to continue iterating
-        s_changed[warpIdx] = __any((int)tid_changed);
-        __syncthreads();
-        continue_iter = false;
-        #pragma unroll
-        for (int i = 0; i < num_warps::value; i++)
-            continue_iter = continue_iter | (s_changed[i] != 0);
+        continue_iter = __syncthreads_or((int)tid_changed);
 
         // If we have to continue iterating, update the tile of the
         // equiv map in shared memory

From 83b09ff8fd23c96f7c00eba97be4b9494f23c29f Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Wed, 31 May 2017 14:50:01 -0400
Subject: [PATCH 1290/2677] Overflow free mean

---
 src/api/c/covariance.cpp                |   6 +-
 src/api/c/mean.cpp                      |  18 +-
 src/api/c/stats.h                       |  66 ---
 src/api/c/stdev.cpp                     |   7 +-
 src/api/c/var.cpp                       |  11 +-
 src/backend/cpu/kernel/mean.hpp         | 129 +++++
 src/backend/cpu/mean.cpp                | 158 ++++++
 src/backend/cpu/mean.hpp                |  26 +
 src/backend/cuda/kernel/mean.hpp        | 644 ++++++++++++++++++++++
 src/backend/cuda/mean.cu                |  81 +++
 src/backend/cuda/mean.hpp               |  28 +
 src/backend/opencl/kernel/mean.hpp      | 698 ++++++++++++++++++++++++
 src/backend/opencl/kernel/mean_dim.cl   | 148 +++++
 src/backend/opencl/kernel/mean_first.cl | 169 ++++++
 src/backend/opencl/kernel/mops.cl       |  25 +
 src/backend/opencl/mean.cpp             |  80 +++
 src/backend/opencl/mean.hpp             |  28 +
 17 files changed, 2239 insertions(+), 83 deletions(-)
 create mode 100644 src/backend/cpu/kernel/mean.hpp
 create mode 100644 src/backend/cpu/mean.cpp
 create mode 100644 src/backend/cpu/mean.hpp
 create mode 100644 src/backend/cuda/kernel/mean.hpp
 create mode 100644 src/backend/cuda/mean.cu
 create mode 100644 src/backend/cuda/mean.hpp
 create mode 100644 src/backend/opencl/kernel/mean.hpp
 create mode 100644 src/backend/opencl/kernel/mean_dim.cl
 create mode 100644 src/backend/opencl/kernel/mean_first.cl
 create mode 100644 src/backend/opencl/kernel/mops.cl
 create mode 100644 src/backend/opencl/mean.cpp
 create mode 100644 src/backend/opencl/mean.hpp

diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index 167017f123..c19330baa6 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <reduce.hpp>
 #include <handle.hpp>
+#include <mean.hpp>
 #include <arith.hpp>
 #include <unary.hpp>
 #include <math.hpp>
@@ -27,6 +28,7 @@ using namespace detail;
 template<typename T, typename cType>
 static af_array cov(const af_array& X, const af_array& Y, const bool isbiased)
 {
+    typedef typename baseOutType<cType>::type weightType;
     Array<T> _x = getArray<T>(X);
     Array<T> _y = getArray<T>(Y);
     Array<cType> xArr = cast<cType>(_x);
@@ -35,8 +37,8 @@ static af_array cov(const af_array& X, const af_array& Y, const bool isbiased)
     dim4 xDims = xArr.dims();
     dim_t N = isbiased ? xDims[0] : xDims[0]-1;
 
-    Array<cType> xmArr = createValueArray<cType>(xDims, mean<T, cType>(_x));
-    Array<cType> ymArr = createValueArray<cType>(xDims, mean<T, cType>(_y));
+    Array<cType> xmArr = createValueArray<cType>(xDims, mean<T, weightType, cType>(_x));
+    Array<cType> ymArr = createValueArray<cType>(xDims, mean<T, weightType, cType>(_y));
     Array<cType> nArr  = createValueArray<cType>(xDims, scalar<cType>(N));
 
     Array<cType> diffX = detail::arithOp<cType, af_sub_t>(xArr, xmArr, xDims);
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index f5fd38db2e..d157f823c7 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -13,7 +13,7 @@
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
-#include <reduce.hpp>
+#include <mean.hpp>
 #include <arith.hpp>
 #include <math.hpp>
 #include <cast.hpp>
@@ -25,30 +25,29 @@ using namespace detail;
 template<typename Ti, typename To>
 static To mean(const af_array &in)
 {
-    /* following function is defined in stats.h */
-    return mean<Ti, To>(getArray<Ti>(in)); /* defined in stats.h */
+    typedef typename baseOutType<To>::type Tw;
+    return mean<Ti, Tw, To>(getArray<Ti>(in));
 }
 
 template<typename T>
 static T mean(const af_array &in, const af_array &weights)
 {
     typedef typename baseOutType<T>::type Tw;
-    /* following function is defined in stats.h */
     return mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights));
 }
 
 template<typename Ti, typename To>
 static af_array mean(const af_array &in, const dim_t dim)
 {
-    /* following function is defined in stats.h */
-    return getHandle<To>(mean<Ti, To>(getArray<Ti>(in), dim));
+    typedef typename baseOutType<To>::type Tw;
+    return getHandle<To>(mean<Ti, Tw, To>(getArray<Ti>(in), dim));
 }
 
 template<typename T>
 static af_array mean(const af_array &in, const af_array &weights, const dim_t dim)
 {
-    /* following function is defined in stats.h */
-    return getHandle<T>(mean<T>(castArray<T>(in), castArray<T>(weights), dim));
+    typedef typename baseOutType<T>::type Tw;
+    return getHandle<T>(mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights), dim));
 }
 
 af_err af_mean(af_array *out, const af_array in, const dim_t dim)
@@ -83,7 +82,7 @@ af_err af_mean(af_array *out, const af_array in, const dim_t dim)
 af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim)
 {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        ARG_ASSERT(3, (dim>=0 && dim<=3));
 
         af_array output = 0;
         const ArrayInfo& iInfo = getInfo(in);
@@ -92,6 +91,7 @@ af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights
         af_dtype wType  = wInfo.getType();
 
         ARG_ASSERT(2, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
+        ARG_ASSERT(2, iInfo.dims() == wInfo.dims());
 
         switch(iType) {
             case f64: output = mean< double>(in, weights, dim); break;
diff --git a/src/api/c/stats.h b/src/api/c/stats.h
index 56439d507a..1d6015a3d7 100644
--- a/src/api/c/stats.h
+++ b/src/api/c/stats.h
@@ -39,69 +39,3 @@ struct baseOutType {
                                 double,
                                 float>::type type;
 };
-
-template<typename Ti, typename To>
-inline To mean(const Array<Ti>& in)
-{
-    To out    = reduce_all<af_add_t, Ti, To>(in);
-    To result = division(out, in.elements());
-    return result;
-}
-
-template<typename T, typename Tw>
-static T mean(const Array<T>& input, const Array<Tw>& weights)
-{
-    dim4 iDims = input.dims();
-
-    Array<T> wtdInput = arithOp<T, af_mul_t>(input, weights, iDims);
-
-    T wtdSum = reduce_all<af_add_t, T, T>(wtdInput);
-    T wtsSum = reduce_all<af_add_t, T, T>(weights);
-
-    return division(wtdSum, wtsSum);
-}
-
-#define COMPLEX_TYPE_SPECILIZATION(T, Tw) \
-template<>\
-STATIC_ T mean<T, Tw>(const Array<T>& input, const Array<Tw>& weights)\
-{\
-    Array<T> wts = cast<T, Tw>(weights);\
-    dim4 iDims   = input.dims();\
-    Array<T> wtdInput = arithOp<T, af_mul_t>(input, wts, iDims);\
-    T wtdSum  = reduce_all<af_add_t, T, T>(wtdInput);\
-    Tw wtsSum = reduce_all<af_add_t, Tw, Tw>(weights);\
-    return division(wtdSum, wtsSum);\
-}
-
-COMPLEX_TYPE_SPECILIZATION(cfloat, float)
-COMPLEX_TYPE_SPECILIZATION(cdouble, double)
-
-template<typename Ti, typename To>
-inline Array<To> mean(const Array<Ti>& in, dim_t dim)
-{
-    Array<To> redArr = reduce<af_add_t, Ti, To>(in, dim);
-
-    dim4 iDims = in.dims();
-    dim4 oDims = redArr.dims();
-
-    Array<To> cnstArr = createValueArray<To>(oDims, scalar<To>(iDims[dim]));
-    Array<To> result  = arithOp<To, af_div_t>(redArr, cnstArr, oDims);
-
-    return result;
-}
-
-template<typename T>
-inline Array<T> mean(const Array<T>& in, const Array<T>& wts, dim_t dim)
-{
-    dim4 iDims = in.dims();
-
-    Array<T> wtdInput = arithOp<T, af_mul_t>(in, wts, iDims);
-    Array<T> redArr   = reduce<af_add_t, T, T>(wtdInput, dim);
-    Array<T> wtsSum   = reduce<af_add_t, T, T>(wts, dim);
-
-    dim4 oDims = redArr.dims();
-
-    Array<T> result = arithOp<T, af_div_t>(redArr, wtsSum, oDims);
-
-    return result;
-}
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 9204250d6b..bd2a705016 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <reduce.hpp>
 #include <handle.hpp>
+#include <mean.hpp>
 #include <arith.hpp>
 #include <unary.hpp>
 #include <math.hpp>
@@ -28,9 +29,10 @@ using namespace detail;
 template<typename inType, typename outType>
 static outType stdev(const af_array& in)
 {
+    typedef typename baseOutType<outType>::type weightType;
     Array<inType> _in       = getArray<inType>(in);
     Array<outType> input    = cast<outType>(_in);
-    Array<outType> meanCnst = createValueArray<outType>(input.dims(), mean<inType, outType>(_in));
+    Array<outType> meanCnst = createValueArray<outType>(input.dims(), mean<inType, weightType, outType>(_in));
     Array<outType> diff     = detail::arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
     Array<outType> diffSq   = detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     outType result          = division(reduce_all<af_add_t, outType, outType>(diffSq), input.elements());
@@ -41,11 +43,12 @@ static outType stdev(const af_array& in)
 template<typename inType, typename outType>
 static af_array stdev(const af_array& in, int dim)
 {
+    typedef typename baseOutType<outType>::type weightType;
     Array<inType> _in    = getArray<inType>(in);
     Array<outType> input = cast<outType>(_in);
     dim4 iDims = input.dims();
 
-    Array<outType> meanArr = mean<inType, outType>(_in, dim);
+    Array<outType> meanArr = mean<inType, weightType, outType>(_in, dim);
 
     /* now tile meanArr along dim and use it for variance computation */
     dim4 tileDims(1);
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 8f476b57ad..69a67b282b 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -13,6 +13,7 @@
 #include <err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
+#include <mean.hpp>
 #include <reduce.hpp>
 #include <arith.hpp>
 #include <math.hpp>
@@ -26,10 +27,11 @@ using namespace detail;
 template<typename inType, typename outType>
 static outType varAll(const af_array& in, const bool isbiased)
 {
+    typedef typename baseOutType<outType>::type weightType;
     Array<inType> inArr = getArray<inType>(in);
     Array<outType> input = cast<outType>(inArr);
 
-    Array<outType> meanCnst= createValueArray<outType>(input.dims(), mean<inType, outType>(inArr));
+    Array<outType> meanCnst= createValueArray<outType>(input.dims(), mean<inType, weightType, outType>(inArr));
 
     Array<outType> diff    = arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
 
@@ -66,11 +68,12 @@ static outType varAll(const af_array& in, const af_array weights)
 template<typename inType, typename outType>
 static af_array var(const af_array& in, const bool isbiased, int dim)
 {
+    typedef typename baseOutType<outType>::type weightType;
     Array<inType> _in    = getArray<inType>(in);
     Array<outType> input = cast<outType>(_in);
     dim4 iDims = input.dims();
 
-    Array<outType> meanArr = mean<inType, outType>(_in, dim);
+    Array<outType> meanArr = mean<inType, weightType, outType>(_in, dim);
 
     /* now tile meanArr along dim and use it for variance computation */
     dim4 tileDims(1);
@@ -95,10 +98,9 @@ static af_array var(const af_array& in, const af_array& weights, int dim)
     typedef typename baseOutType<outType>::type bType;
 
     Array<outType> input = cast<outType>(getArray<inType>(in));
-    Array<outType> wts   = cast<outType>(getArray<bType>(weights));
     dim4 iDims = input.dims();
 
-    Array<outType> meanArr = mean<outType>(input, wts, dim);
+    Array<outType> meanArr = mean<outType, bType>(input, getArray<bType>(weights), dim);
 
     /* now tile meanArr along dim and use it for variance computation */
     dim4 tileDims(1);
@@ -106,6 +108,7 @@ static af_array var(const af_array& in, const af_array& weights, int dim)
     Array<outType> tMeanArr = tile<outType>(meanArr, tileDims);
     /* now mean array is ready */
 
+    Array<outType> wts     = cast<outType>(getArray<bType>(weights));
     Array<outType> diff    = arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
     Array<outType> diffSq  = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     Array<outType> wDiffSq = arithOp<outType, af_mul_t>(diffSq, wts, diffSq.dims());
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
new file mode 100644
index 0000000000..4bded987c1
--- /dev/null
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -0,0 +1,129 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+
+namespace cpu
+{
+namespace kernel
+{
+
+template<typename Ti, typename To, typename Tw>
+struct MeanOp
+{
+    To runningMean;
+    Tw runningCount;
+    Transform<Ti, To, af_add_t> transform;
+    MeanOp(Ti mean, Tw count) :
+        runningMean(transform(mean)), runningCount(count)
+    {
+    }
+
+    void operator()(Ti _newMean, Tw newCount)
+    {
+        To newMean = transform(_newMean);
+        if ((newCount != 0) || (runningCount != 0)) {
+            Tw runningScale = runningCount;
+            Tw newScale = newCount;
+            runningCount += newCount;
+            runningScale = runningScale/runningCount;
+            newScale = newScale/runningCount;
+            runningMean = (runningScale*runningMean) + (newScale*newMean);
+        }
+    }
+};
+
+template<typename T, typename Tw, int D>
+struct mean_weighted_dim
+{
+    void operator()(Param<T> output, const dim_t outOffset,
+                    const CParam< T>  input, const dim_t inOffset,
+                    const CParam<Tw> weight, const dim_t wtOffset, const int dim)
+    {
+        const af::dim4 odims    = output.dims();
+        const af::dim4 ostrides = output.strides();
+        const af::dim4 istrides =  input.strides();
+        const af::dim4 wstrides = weight.strides();
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            mean_weighted_dim<T, Tw, D1>()(output, outOffset + i * ostrides[D1],
+                    input, inOffset + i * istrides[D1],
+                    weight, wtOffset + i * wstrides[D1], dim);
+        }
+    }
+};
+
+template<typename T, typename Tw>
+struct mean_weighted_dim<T, Tw, 0>
+{
+    void operator()(Param<T> output, const dim_t outOffset,
+                    const CParam< T>  input, const dim_t inOffset,
+                    const CParam<Tw> weight, const dim_t wtOffset, const int dim)
+    {
+        const af::dim4 idims = input.dims();
+        const af::dim4 istrides =  input.strides();
+        const af::dim4 wstrides = weight.strides();
+
+        T const * const  in =  input.get();
+        Tw const * const wt = weight.get();
+        T * out = output.get();
+
+        dim_t istride = istrides[dim];
+        dim_t wstride = wstrides[dim];
+        MeanOp<T, T, Tw> Op(0, 0);
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            Op(in[inOffset + i * istride], wt[wtOffset + i * wstride]);
+        }
+
+        out[outOffset] = Op.runningMean;
+    }
+};
+
+template<typename Ti, typename Tw, typename To, int D>
+struct mean_dim
+{
+    void operator()(Param<To> output, const dim_t outOffset,
+                    const CParam<Ti> input, const dim_t inOffset, const int dim)
+    {
+        const af::dim4 odims    = output.dims();
+        const af::dim4 ostrides = output.strides();
+        const af::dim4 istrides =  input.strides();
+        const int D1 = D - 1;
+        for (dim_t i = 0; i < odims[D1]; i++) {
+            mean_dim<Ti, Tw, To, D1>()(output, outOffset + i * ostrides[D1],
+                                      input, inOffset + i * istrides[D1], dim);
+        }
+    }
+};
+
+template<typename Ti, typename Tw, typename To>
+struct mean_dim<Ti, Tw, To, 0>
+{
+    void operator()(Param<To> output, const dim_t outOffset,
+                    const CParam<Ti> input, const dim_t inOffset, const int dim)
+    {
+        const af::dim4 idims = input.dims();
+        const af::dim4 istrides =  input.strides();
+
+        Ti const * const  in =  input.get();
+        To * out = output.get();
+
+        dim_t istride = istrides[dim];
+        MeanOp<Ti, To, Tw> Op(0, 0);
+        for (dim_t i = 0; i < idims[dim]; i++) {
+            Op(in[inOffset + i * istride], 1);
+        }
+
+        out[outOffset] = Op.runningMean;
+    }
+};
+
+}
+}
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
new file mode 100644
index 0000000000..ff7fa4de28
--- /dev/null
+++ b/src/backend/cpu/mean.cpp
@@ -0,0 +1,158 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <complex>
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <mean.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+#include <kernel/mean.hpp>
+
+using af::dim4;
+
+namespace cpu
+{
+
+template<typename Ti, typename Tw, typename To>
+using mean_dim_func = std::function<void(Param<To>, const dim_t,
+                                            const CParam<Ti>, const dim_t, const int)>;
+
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim)
+{
+    in.eval();
+
+    dim4 odims = in.dims();
+    odims[dim] = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    static const mean_dim_func<Ti, Tw, To> mean_funcs[] = { kernel::mean_dim<Ti, Tw, To, 1>(),
+                                                            kernel::mean_dim<Ti, Tw, To, 2>(),
+                                                            kernel::mean_dim<Ti, Tw, To, 3>(),
+                                                            kernel::mean_dim<Ti, Tw, To, 4>()};
+
+    getQueue().enqueue(mean_funcs[in.ndims() - 1], out, 0, in, 0, dim);
+    return out;
+}
+
+template<typename T, typename Tw>
+using mean_weighted_dim_func = std::function<void(Param<T>, const dim_t,
+                                            const CParam<T>, const dim_t, const CParam<Tw>, const dim_t, const int)>;
+
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wt, const int dim)
+{
+    in.eval();
+    wt.eval();
+
+    dim4 odims = in.dims();
+    odims[dim] = 1;
+    Array<T> out = createEmptyArray<T>(odims);
+    static const mean_weighted_dim_func<T, Tw> mean_funcs[] = { kernel::mean_weighted_dim<T, Tw, 1>(),
+                                                                kernel::mean_weighted_dim<T, Tw, 2>(),
+                                                                kernel::mean_weighted_dim<T, Tw, 3>(),
+                                                                kernel::mean_weighted_dim<T, Tw, 4>()};
+
+    getQueue().enqueue(mean_funcs[in.ndims() - 1], out, 0, in, 0, wt, 0, dim);
+    return out;
+}
+
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wt)
+{
+    in.eval();
+    wt.eval();
+    getQueue().sync();
+
+    af::dim4 dims = in.dims();
+    af::dim4 strides = in.strides();
+    const T  *inPtr = in.get();
+    const Tw *wtPtr = wt.get();
+
+    kernel::MeanOp<T, T, Tw> Op(inPtr[0], wtPtr[0]);
+
+    for(dim_t l = 0; l < dims[3]; l++) {
+        dim_t off3 = l * strides[3];
+
+        for(dim_t k = 0; k < dims[2]; k++) {
+            dim_t off2 = k * strides[2];
+
+            for(dim_t j = 0; j < dims[1]; j++) {
+                dim_t off1 = j * strides[1];
+
+                for(dim_t i = 0; i < dims[0]; i++) {
+                    dim_t idx = i + off1 + off2 + off3;
+                    Op(inPtr[idx], wtPtr[idx]);
+                }
+            }
+        }
+    }
+
+    return Op.runningMean;
+}
+
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in)
+{
+    in.eval();
+    getQueue().sync();
+
+    af::dim4 dims = in.dims();
+    af::dim4 strides = in.strides();
+    const Ti *inPtr = in.get();
+
+    kernel::MeanOp<Ti, To, Tw> Op(0, 0);
+
+    for(dim_t l = 0; l < dims[3]; l++) {
+        dim_t off3 = l * strides[3];
+
+        for(dim_t k = 0; k < dims[2]; k++) {
+            dim_t off2 = k * strides[2];
+
+            for(dim_t j = 0; j < dims[1]; j++) {
+                dim_t off1 = j * strides[1];
+
+                for(dim_t i = 0; i < dims[0]; i++) {
+                    dim_t idx = i + off1 + off2 + off3;
+                    Op(inPtr[idx], 1);
+                }
+            }
+        }
+    }
+
+    return Op.runningMean;
+}
+
+#define INSTANTIATE(Ti, Tw, To)                                         \
+    template To mean<Ti, Tw, To>(const Array<Ti> &in); \
+    template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim); \
+
+INSTANTIATE(double  , double,  double);
+INSTANTIATE(float   , float ,  float );
+INSTANTIATE(int     , float ,  float );
+INSTANTIATE(unsigned, float ,  float );
+INSTANTIATE(intl    , double,  double);
+INSTANTIATE(uintl   , double,  double);
+INSTANTIATE(short   , float ,  float );
+INSTANTIATE(ushort  , float ,  float );
+INSTANTIATE(uchar   , float ,  float );
+INSTANTIATE(char    , float ,  float );
+INSTANTIATE(cfloat  , float ,  cfloat);
+INSTANTIATE(cdouble , double, cdouble);
+
+#define INSTANTIATE_WGT(T, Tw)                                         \
+    template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts); \
+    template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, const int dim); \
+
+INSTANTIATE_WGT(double , double);
+INSTANTIATE_WGT(float  , float );
+INSTANTIATE_WGT(cfloat , float );
+INSTANTIATE_WGT(cdouble, double);
+
+}
diff --git a/src/backend/cpu/mean.hpp b/src/backend/cpu/mean.hpp
new file mode 100644
index 0000000000..a3d40e5e90
--- /dev/null
+++ b/src/backend/cpu/mean.hpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <ops.hpp>
+
+namespace cpu
+{
+    template<typename Ti, typename Tw, typename To>
+    Array<To> mean(const Array<Ti>& in, const int dim);
+
+    template<typename T, typename Tw>
+    Array<T> mean(const Array<T>& in, const Array<Tw>& wt, const int dim);
+
+    template<typename T, typename Tw>
+    T mean(const Array<T>& in, const Array<Tw>& wts);
+
+    template<typename Ti, typename Tw, typename To>
+    To mean(const Array<Ti>& in);
+}
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
new file mode 100644
index 0000000000..38f1494d84
--- /dev/null
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -0,0 +1,644 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <ops.hpp>
+#include <backend.hpp>
+#include <Param.hpp>
+#include <dispatch.hpp>
+#include <math.hpp>
+#include <err_cuda.hpp>
+#include <debug_cuda.hpp>
+#include "config.hpp"
+#include <memory.hpp>
+#include <memory>
+
+namespace cuda
+{
+namespace kernel
+{
+
+    template<typename T, typename Tw>
+    struct MeanOp
+    {
+        T runningMean;
+        Tw runningCount;
+        __host__ __device__ MeanOp(T mean, Tw count) :
+            runningMean(mean), runningCount(count)
+        {
+        }
+
+        __host__ __device__ void operator()(T newMean, Tw newCount)
+        {
+            if ((newCount != 0) || (runningCount != 0)) {
+                Tw runningScale = runningCount;
+                Tw newScale = newCount;
+                runningCount += newCount;
+                runningScale = runningScale/runningCount;
+                newScale = newScale/(Tw)runningCount;
+                runningMean = (runningScale*runningMean) + (newScale*newMean);
+            }
+        }
+    };
+
+    template<typename Ti, typename Tw, typename To, uint dim, uint DIMY>
+    __global__
+    static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
+                                  CParam <Ti> in, CParam<Tw> iwt,
+                                  uint blocks_x, uint blocks_y, uint offset_dim)
+    {
+        const uint tidx = threadIdx.x;
+        const uint tidy = threadIdx.y;
+        const uint tid  = tidy * THREADS_X + tidx;
+
+        const uint zid = blockIdx.x / blocks_x;
+        const uint wid = blockIdx.y / blocks_y;
+        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const uint xid = blockIdx_x * blockDim.x + tidx;
+        const uint yid = blockIdx_y; // yid  of output. updated for input later.
+
+        uint ids[4] = {xid, yid, zid, wid};
+
+        const Ti *iptr = in.ptr;
+        const Tw *iwptr = iwt.ptr;
+        To *optr = out.ptr;
+        Tw *owptr = owt.ptr;
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before offsetting in
+        optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        if (owptr != NULL) owptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        const uint blockIdx_dim = ids[dim];
+
+        ids[dim] = ids[dim] * blockDim.y + tidy;
+        iptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
+        if (iwptr != NULL) iwptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
+        const uint id_dim_in = ids[dim];
+
+        const uint istride_dim = in.strides[dim];
+
+        bool is_valid =
+            (ids[0] < in.dims[0]) &&
+            (ids[1] < in.dims[1]) &&
+            (ids[2] < in.dims[2]) &&
+            (ids[3] < in.dims[3]);
+
+        Transform<Ti, To, af_add_t> transform;
+        Binary<To, af_add_t> mean_obj;
+        Binary<Tw, af_add_t> weight_obj;
+
+        To val = mean_obj.init();
+        Tw weight = weight_obj.init();
+
+        if (is_valid && id_dim_in < in.dims[dim]) {
+            val = transform(*iptr);
+            if (iwptr != NULL) {
+                weight = *iwptr;
+            } else {
+                weight = (Tw)1;
+            }
+        }
+
+        MeanOp<To, Tw> Op(val, weight);
+
+        const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
+
+        __shared__ To s_val[THREADS_X * DIMY];
+        __shared__ Tw s_idx[THREADS_X * DIMY];
+
+        for (int id = id_dim_in_start;
+             is_valid && (id < in.dims[dim]);
+             id += offset_dim * blockDim.y) {
+
+            iptr = iptr + offset_dim * blockDim.y * istride_dim;
+            if (iwptr != NULL) {
+                iwptr = iwptr + offset_dim * blockDim.y * istride_dim;
+                Op(transform(*iptr), *iwptr);
+            } else {
+                Op(transform(*iptr), (Tw)1);
+            }
+        }
+
+        s_val[tid] = Op.runningMean;
+        s_idx[tid] = Op.runningCount;
+
+        To *s_vptr = s_val + tid;
+        Tw *s_iptr = s_idx + tid;
+        __syncthreads();
+
+        if (DIMY == 8) {
+            if (tidy < 4) {
+                Op(s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
+                *s_vptr = Op.runningMean;
+                *s_iptr = Op.runningCount;
+            }
+            __syncthreads();
+        }
+
+        if (DIMY >= 4) {
+            if (tidy < 2) {
+                Op(s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
+                *s_vptr = Op.runningMean;
+                *s_iptr = Op.runningCount;
+            }
+            __syncthreads();
+        }
+
+        if (DIMY >= 2) {
+            if (tidy < 1) {
+                Op(s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
+                *s_vptr = Op.runningMean;
+                *s_iptr = Op.runningCount;
+            }
+            __syncthreads();
+        }
+
+        if (tidy == 0 && is_valid &&
+            (blockIdx_dim < out.dims[dim])) {
+            *optr = *s_vptr;
+            if (owptr != NULL) *owptr = *s_iptr;
+        }
+
+    }
+
+    template<typename Ti, typename Tw, typename To, int dim>
+    void mean_dim_launcher(Param<To> out, Param<Tw> owt,
+                             CParam<Ti> in, CParam<Tw> iwt,
+                             const uint threads_y, const dim_t blocks_dim[4])
+    {
+        printf("mean_dim_launcher\n");
+        dim3 threads(THREADS_X, threads_y);
+
+        dim3 blocks(blocks_dim[0] * blocks_dim[2],
+                    blocks_dim[1] * blocks_dim[3]);
+
+        switch (threads_y) {
+        case 8:
+            CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 8>), blocks, threads,
+                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+        case 4:
+            CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 4>), blocks, threads,
+                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+        case 2:
+            CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 2>), blocks, threads,
+                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+        case 1:
+            CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 1>), blocks, threads,
+                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+        }
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename Ti, typename Tw, typename To, int dim>
+    void mean_dim(Param<To> out, CParam<Ti> in, CParam<Tw> iwt)
+    {
+        uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
+        uint threads_x = THREADS_X;
+
+        dim_t blocks_dim[] = {divup(in.dims[0], threads_x),
+                             in.dims[1], in.dims[2], in.dims[3]};
+
+        blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
+
+        Param<To> tmpOut = out;
+        Param<Tw> tmpWt;
+        tmpWt.ptr = NULL;
+
+        if (blocks_dim[dim] > 1) {
+            int tmp_elements = 1;
+            tmpOut.dims[dim] = blocks_dim[dim];
+
+            for (int k = 0; k < 4; k++) tmp_elements *= tmpOut.dims[k];
+            tmpOut.ptr = memAlloc<To>(tmp_elements);
+            tmpWt.ptr = memAlloc<Tw>(tmp_elements);
+
+            for (int k = dim + 1; k < 4; k++) {
+                tmpOut.strides[k] *= blocks_dim[dim];
+                tmpWt.strides[k] *= blocks_dim[dim];
+            }
+        }
+
+        mean_dim_launcher<Ti, Tw, To, dim>(tmpOut, tmpWt, in, iwt, threads_y, blocks_dim);
+
+        if (blocks_dim[dim] > 1) {
+            blocks_dim[dim] = 1;
+
+            Param<Tw> owt;
+            owt.ptr = NULL;
+            mean_dim_launcher<To, Tw, To, dim>(out, owt, tmpOut, tmpWt,
+                                                    threads_y, blocks_dim);
+
+            memFree(tmpOut.ptr);
+            memFree(tmpWt.ptr);
+        }
+
+    }
+
+    template<typename T, typename Tw>
+    __device__ void warp_reduce(T *s_ptr, Tw *s_idx, uint tidx)
+    {
+        MeanOp<T, Tw> Op(s_ptr[tidx], s_idx[tidx]);
+#pragma unroll
+        for (int n = 16; n >= 1; n >>= 1) {
+            if (tidx < n) {
+                Op(s_ptr[tidx + n], s_idx[tidx + n]);
+                s_ptr[tidx] = Op.runningMean;
+                s_idx[tidx] = Op.runningCount;
+            }
+            __syncthreads();
+        }
+    }
+
+    //Calculate mean along the first dimension. If wt is an empty CParam, use
+    //weight as 1 and treat it as count. If owt is empty Param, do not write
+    //temporary reduced counts/weights to it.
+    template<typename Ti, typename Tw, typename To, uint DIMX>
+    __global__
+    static void mean_first_kernel(Param<To> out, Param<Tw> owt,
+                                    CParam<Ti> in, CParam<Tw> iwt,
+                                    uint blocks_x, uint blocks_y, uint repeat)
+    {
+        const uint tidx = threadIdx.x;
+        const uint tidy = threadIdx.y;
+        const uint tid  = tidy * blockDim.x + tidx;
+
+        const uint zid = blockIdx.x / blocks_x;
+        const uint wid = blockIdx.y / blocks_y;
+        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
+        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
+        const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
+        const uint yid = blockIdx_y * blockDim.y + tidy;
+
+        const Ti *iptr = in.ptr;
+        const Tw *iwptr = iwt.ptr;
+        To *optr = out.ptr;
+        Tw *owptr = owt.ptr;
+
+        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
+        if (iwptr != NULL) iwptr += wid *  iwt.strides[3] + zid *  iwt.strides[2] + yid *  iwt.strides[1];
+        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+        if (owptr != NULL) owptr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+        if (yid >= in.dims[1] ||
+            zid >= in.dims[2] ||
+            wid >= in.dims[3]) return;
+
+        int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+
+        Transform<Ti, To, af_add_t> transform;
+        Binary<To, af_add_t> mean_obj;
+        Binary<Tw, af_add_t> weight_obj;
+
+        To val = mean_obj.init();
+        Tw weight = weight_obj.init();
+
+        if (xid < lim) {
+            val = transform(iptr[xid]);
+            if (iwptr != NULL) {
+                weight = iwptr[xid];
+            } else {
+                weight = (Tw)1;
+            }
+
+        }
+
+        MeanOp<To, Tw> Op(val, weight);
+
+        __shared__ To s_val[THREADS_PER_BLOCK];
+        __shared__ Tw s_idx[THREADS_PER_BLOCK];
+
+        if (iwptr != NULL) {
+            for (int id = xid + DIMX; id < lim; id += DIMX) {
+                Op(transform(iptr[id]), iwptr[id]);
+            }
+        } else {
+            for (int id = xid + DIMX; id < lim; id += DIMX) {
+                Op(transform(iptr[id]), weight);
+            }
+        }
+
+        s_val[tid] = Op.runningMean;
+        s_idx[tid] = Op.runningCount;
+        __syncthreads();
+
+        To *s_vptr = s_val + tidy * DIMX;
+        Tw *s_iptr = s_idx + tidy * DIMX;
+
+        if (DIMX == 256) {
+            if (tidx < 128) {
+                Op(s_vptr[tidx + 128], s_iptr[tidx + 128]);
+                s_vptr[tidx] = Op.runningMean;
+                s_iptr[tidx] = Op.runningCount;
+            }
+            __syncthreads();
+        }
+
+        if (DIMX >= 128) {
+            if (tidx <  64) {
+                Op(s_vptr[tidx +  64], s_iptr[tidx +  64]);
+                s_vptr[tidx] = Op.runningMean;
+                s_iptr[tidx] = Op.runningCount;
+            }
+            __syncthreads();
+        }
+
+        if (DIMX >=  64) {
+            if (tidx <  32) {
+                Op(s_vptr[tidx +  32], s_iptr[tidx +  32]);
+                s_vptr[tidx] = Op.runningMean;
+                s_iptr[tidx] = Op.runningCount;
+            }
+            __syncthreads();
+        }
+
+        warp_reduce<To, Tw>(s_vptr, s_iptr, tidx);
+
+        if (tidx == 0) {
+            optr[blockIdx_x] = s_vptr[0];
+            if (owptr != NULL) owptr[blockIdx_x] = s_iptr[0];
+        }
+    }
+
+
+    template<typename Ti, typename Tw, typename To>
+    void mean_first_launcher(Param<To> out, Param<Tw> owt, CParam<Ti> in, CParam<Tw> iwt,
+                               const uint blocks_x, const uint blocks_y, const uint threads_x)
+    {
+
+        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+        dim3 blocks(blocks_x * in.dims[2],
+                    blocks_y * in.dims[3]);
+
+        uint repeat = divup(in.dims[0], (blocks_x * threads_x));
+
+        switch (threads_x) {
+        case 32:
+            CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 32>), blocks, threads,
+                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+        case 64:
+            CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 64>), blocks, threads,
+                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+        case 128:
+            CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 128>), blocks, threads,
+                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+        case 256:
+            CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 256>), blocks, threads,
+                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+        }
+
+        POST_LAUNCH_CHECK();
+    }
+
+    template<typename Ti, typename Tw, typename To>
+    void mean_first(Param<To> out, CParam<Ti> in, CParam<Tw> iwt)
+    {
+        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.dims[1], threads_y);
+
+        Param<To> tmpOut = out;
+        Param<Tw> tmpWt;
+        tmpWt.ptr = NULL;
+        if (blocks_x > 1) {
+            tmpOut.ptr = memAlloc<To>(blocks_x *
+                                  in.dims[1] *
+                                  in.dims[2] *
+                                  in.dims[3]);
+
+            tmpWt.ptr = memAlloc<Tw>(blocks_x *
+                                   in.dims[1] *
+                                   in.dims[2] *
+                                   in.dims[3]);
+
+            tmpOut.dims[0] = blocks_x;
+            for (int k = 1; k < 4; k++) tmpOut.strides[k] *= blocks_x;
+        }
+
+        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
+
+        if (blocks_x > 1) {
+            Param<Tw> owt;
+            owt.ptr = NULL;
+            mean_first_launcher<To, Tw, To>(out, owt, tmpOut, tmpWt, 1, blocks_y, threads_x);
+
+            memFree(tmpOut.ptr);
+            memFree(tmpWt.ptr);
+        }
+    }
+
+    template<typename Ti, typename Tw, typename To>
+    void mean_weighted(Param<To> out, CParam<Ti> in, CParam<Tw> iwt, int dim)
+    {
+        switch (dim) {
+        case 0: return mean_first<Ti, Tw, To   >(out, in, iwt);
+        case 1: return mean_dim  <Ti, Tw, To, 1>(out, in, iwt);
+        case 2: return mean_dim  <Ti, Tw, To, 2>(out, in, iwt);
+        case 3: return mean_dim  <Ti, Tw, To, 3>(out, in, iwt);
+        }
+    }
+
+    template<typename Ti, typename Tw, typename To>
+    void mean(Param<To> out, CParam<Ti> in, int dim)
+    {
+        Param<Tw> dummy_weight;
+        mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
+    }
+
+    template<typename T, typename Tw>
+    T mean_all_weighted(CParam<T> in, CParam<Tw> iwt)
+    {
+        using std::unique_ptr;
+        int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+
+        // FIXME: Use better heuristics to get to the optimum number
+        if (in_elements > 4096) {
+
+            bool in_is_linear = (in.strides[0] == 1);
+            bool wt_is_linear = (iwt.strides[0] == 1);
+            for (int k = 1; k < 4; k++) {
+                in_is_linear &= ( in.strides[k] == ( in.strides[k - 1] *  in.dims[k - 1]));
+                wt_is_linear &= (iwt.strides[k] == (iwt.strides[k - 1] * iwt.dims[k - 1]));
+            }
+
+            if (in_is_linear && wt_is_linear) {
+                in.dims[0] = in_elements;
+                for (int k = 1; k < 4; k++) {
+                    in.dims[k] = 1;
+                    in.strides[k] = in_elements;
+                }
+
+                for (int k = 0; k < 4; k++) {
+                    iwt.dims[k] = in.dims[k];
+                    iwt.strides[k] = in.strides[k];
+                }
+            }
+
+            uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+            threads_x = std::min(threads_x, THREADS_PER_BLOCK);
+            uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+            Param<T> tmpOut;
+            Param<Tw> tmpWt;
+
+            uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+            uint blocks_y = divup(in.dims[1], threads_y);
+
+            tmpOut.dims[0] = blocks_x;
+            tmpOut.strides[0] = 1;
+
+            for (int k = 1; k < 4; k++) {
+                tmpOut.dims[k] = in.dims[k];
+                tmpOut.strides[k] = tmpOut.dims[k - 1] * tmpOut.strides[k - 1];
+            }
+
+            int tmp_elements = tmpOut.strides[3] * tmpOut.dims[3];
+
+            //TODO: Use scoped_ptr
+            tmpOut.ptr = memAlloc<T>(tmp_elements);
+            tmpWt.ptr = memAlloc<Tw>(tmp_elements);
+            mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
+
+            unique_ptr<T>       h_ptr(new T[tmp_elements]);
+            unique_ptr<Tw>    h_wptr(new Tw[tmp_elements]);
+            T*     h_ptr_raw = h_ptr.get();
+            Tw*   h_wptr_raw = h_wptr.get();
+
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmpOut.ptr, tmp_elements * sizeof(T),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaMemcpyAsync(h_wptr_raw, tmpWt.ptr, tmp_elements * sizeof(Tw),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+            memFree(tmpOut.ptr);
+            memFree(tmpWt.ptr);
+
+            MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+
+            for (int i = 1; i < tmp_elements; i++) {
+                Op(h_ptr_raw[i], h_wptr_raw[i]);
+            }
+
+            return Op.runningMean;
+        } else {
+
+            unique_ptr<T> h_ptr(new T[in_elements]);
+            unique_ptr<Tw>    h_wptr(new Tw[in_elements]);
+            T*      h_ptr_raw = h_ptr.get();
+            Tw*   h_wptr_raw = h_wptr.get();
+
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaMemcpyAsync(h_wptr_raw, iwt.ptr, in_elements * sizeof(Tw),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+
+            MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+            for (int i = 1; i < in_elements; i++) {
+                Op(h_ptr_raw[i], h_wptr_raw[i]);
+            }
+
+            return Op.runningMean;
+        }
+    }
+
+    template<typename Ti, typename Tw, typename To>
+    To mean_all(CParam<Ti> in)
+    {
+        using std::unique_ptr;
+        int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+
+        // FIXME: Use better heuristics to get to the optimum number
+        if (in_elements > 4096) {
+
+            bool is_linear = (in.strides[0] == 1);
+            for (int k = 1; k < 4; k++) {
+                is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+            }
+
+            if (is_linear) {
+                in.dims[0] = in_elements;
+                for (int k = 1; k < 4; k++) {
+                    in.dims[k] = 1;
+                    in.strides[k] = in_elements;
+                }
+            }
+
+            uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+            threads_x = std::min(threads_x, THREADS_PER_BLOCK);
+            uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+            Param<To> tmpOut;
+            Param<Tw> tmpCt;
+            Param<Tw> iwt;
+
+            uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+            uint blocks_y = divup(in.dims[1], threads_y);
+
+            tmpOut.dims[0] = blocks_x;
+            tmpOut.strides[0] = 1;
+
+            for (int k = 1; k < 4; k++) {
+                tmpOut.dims[k] = in.dims[k];
+                tmpOut.strides[k] = tmpOut.dims[k - 1] * tmpOut.strides[k - 1];
+            }
+
+            int tmp_elements = tmpOut.strides[3] * tmpOut.dims[3];
+
+            //TODO: Use scoped_ptr
+            tmpOut.ptr = memAlloc<To>(tmp_elements);
+            tmpCt.ptr = memAlloc<Tw>(tmp_elements);
+            iwt.ptr = NULL;
+            mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x, blocks_y, threads_x);
+
+            unique_ptr<To>       h_ptr(new To[tmp_elements]);
+            unique_ptr<Tw>    h_cptr(new Tw[tmp_elements]);
+            To*    h_ptr_raw = h_ptr.get();
+            Tw*   h_cptr_raw = h_cptr.get();
+
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmpOut.ptr, tmp_elements * sizeof(To),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaMemcpyAsync(h_cptr_raw, tmpCt.ptr, tmp_elements * sizeof(Tw),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+            memFree(tmpOut.ptr);
+            memFree(tmpCt.ptr);
+
+            MeanOp<To, Tw> Op(h_ptr_raw[0], h_cptr_raw[0]);
+
+            for (int i = 1; i < tmp_elements; i++) {
+                Op(h_ptr_raw[i], h_cptr_raw[i]);
+            }
+
+            return Op.runningMean;
+        } else {
+
+            unique_ptr<Ti> h_ptr(new Ti[in_elements]);
+            Ti* h_ptr_raw = h_ptr.get();
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
+                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+
+            Transform<Ti, To, af_add_t> transform;
+            Tw count = (Tw)1;
+
+            MeanOp<To, Tw> Op(transform(h_ptr_raw[0]), count);
+            for (int i = 1; i < in_elements; i++) {
+                Op(transform(h_ptr_raw[i]), count);
+            }
+
+            return Op.runningMean;
+        }
+    }
+
+}
+}
diff --git a/src/backend/cuda/mean.cu b/src/backend/cuda/mean.cu
new file mode 100644
index 0000000000..e42a6071e5
--- /dev/null
+++ b/src/backend/cuda/mean.cu
@@ -0,0 +1,81 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+
+#undef _GLIBCXX_USE_INT128
+#include <complex>
+#include <mean.hpp>
+#include <kernel/mean.hpp>
+#include <err_cuda.hpp>
+
+using std::swap;
+using af::dim4;
+namespace cuda
+{
+    template<typename Ti, typename Tw, typename To>
+    To mean(const Array<Ti>& in)
+    {
+        return kernel::mean_all<Ti, Tw, To>(in);
+    }
+
+    template<typename T, typename Tw>
+    T mean(const Array<T>& in, const Array<Tw>& wts)
+    {
+        return kernel::mean_all_weighted<T, Tw>(in, wts);
+    }
+
+    template<typename Ti, typename Tw, typename To>
+    Array<To> mean(const Array<Ti>& in, const int dim)
+    {
+        dim4 odims = in.dims();
+        odims[dim] = 1;
+        Array<To> out = createEmptyArray<To>(odims);
+        kernel::mean<Ti, Tw, To>(out, in, dim);
+        return out;
+    }
+
+    template<typename T, typename Tw>
+    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim)
+    {
+        dim4 odims = in.dims();
+        odims[dim] = 1;
+        Array<T> out = createEmptyArray<T>(odims);
+        kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
+        return out;
+    }
+
+    #define INSTANTIATE(Ti, Tw, To)                                         \
+        template To mean<Ti, Tw, To>(const Array<Ti> &in); \
+        template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim); \
+
+    INSTANTIATE(double  , double,  double);
+    INSTANTIATE(float   , float ,  float );
+    INSTANTIATE(int     , float ,  float );
+    INSTANTIATE(unsigned, float ,  float );
+    INSTANTIATE(intl    , double,  double);
+    INSTANTIATE(uintl   , double,  double);
+    INSTANTIATE(short   , float ,  float );
+    INSTANTIATE(ushort  , float ,  float );
+    INSTANTIATE(uchar   , float ,  float );
+    INSTANTIATE(char    , float ,  float );
+    INSTANTIATE(cfloat  , float ,  cfloat);
+    INSTANTIATE(cdouble , double, cdouble);
+
+    #define INSTANTIATE_WGT(T, Tw)                                         \
+        template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts); \
+        template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, const int dim); \
+
+    INSTANTIATE_WGT(double , double);
+    INSTANTIATE_WGT(float  , float );
+    INSTANTIATE_WGT(cfloat , float );
+    INSTANTIATE_WGT(cdouble, double);
+
+}
diff --git a/src/backend/cuda/mean.hpp b/src/backend/cuda/mean.hpp
new file mode 100644
index 0000000000..ec989a1989
--- /dev/null
+++ b/src/backend/cuda/mean.hpp
@@ -0,0 +1,28 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <ops.hpp>
+
+namespace cuda
+{
+    template<typename Ti, typename Tw, typename To>
+    To mean(const Array<Ti>& in);
+
+    template<typename T, typename Tw>
+    T mean(const Array<T>& in, const Array<Tw>& wts);
+
+    template<typename Ti, typename Tw, typename To>
+    Array<To> mean(const Array<Ti>& in, const int dim);
+
+    template<typename T, typename Tw>
+    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
+
+}
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
new file mode 100644
index 0000000000..bca813f5e4
--- /dev/null
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -0,0 +1,698 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+#include <mutex>
+#include <map>
+#include <memory>
+#include <kernel_headers/mean_first.hpp>
+#include <kernel_headers/mean_dim.hpp>
+#include <kernel_headers/mops.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <dispatch.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+#include "names.hpp"
+#include "config.hpp"
+#include <memory.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+using std::unique_ptr;
+
+namespace opencl
+{
+
+namespace kernel
+{
+
+template<typename T, typename Tw>
+struct MeanOp
+{
+    T runningMean;
+    Tw runningCount;
+    MeanOp(T mean, Tw count) :
+        runningMean(mean), runningCount(count)
+    {
+    }
+
+    void operator()(T newMean, Tw newCount)
+    {
+        if ((newCount != 0) || (runningCount != 0)) {
+            Tw runningScale = runningCount;
+            Tw newScale = newCount;
+            runningCount += newCount;
+            runningScale = runningScale/runningCount;
+            newScale = newScale/(Tw)runningCount;
+            runningMean = (runningScale*runningMean) + (newScale*newMean);
+        }
+    }
+};
+
+template<>
+struct MeanOp<cfloat, float>
+{
+    cfloat runningMean;
+    float runningCount;
+    MeanOp(cfloat mean, float count) :
+        runningMean(mean), runningCount(count)
+    {
+    }
+
+    void operator()(cfloat newMean, float newCount)
+    {
+        if ((newCount != 0) || (runningCount != 0)) {
+            float runningScale = runningCount;
+            float newScale = newCount;
+            runningCount += newCount;
+            runningScale = runningScale/runningCount;
+            newScale = newScale/(float)runningCount;
+            runningMean.s[0] = (runningScale*runningMean.s[0]) + (newScale*newMean.s[0]);
+            runningMean.s[1] = (runningScale*runningMean.s[1]) + (newScale*newMean.s[1]);
+        }
+    }
+};
+
+template<>
+struct MeanOp<cdouble, double>
+{
+    cdouble runningMean;
+    double runningCount;
+    MeanOp(cdouble mean, double count) :
+        runningMean(mean), runningCount(count)
+    {
+    }
+
+    void operator()(cdouble newMean, double newCount)
+    {
+        if ((newCount != 0) || (runningCount != 0)) {
+            double runningScale = runningCount;
+            double newScale = newCount;
+            runningCount += newCount;
+            runningScale = runningScale/runningCount;
+            newScale = newScale/(double)runningCount;
+            runningMean.s[0] = (runningScale*runningMean.s[0]) + (newScale*newMean.s[0]);
+            runningMean.s[1] = (runningScale*runningMean.s[1]) + (newScale*newMean.s[1]);
+        }
+    }
+};
+
+template<typename Ti, typename Tw, typename To>
+void mean_dim_launcher(Param out, Param owt,
+        Param in, Param iwt,
+        const int dim,
+        const int threads_y,
+        const uint groups_all[4])
+{
+    bool input_weight = ((
+            iwt.info.dims[0] *
+            iwt.info.dims[1] *
+            iwt.info.dims[2] *
+            iwt.info.dims[3]) != 0);
+
+    bool output_weight = ((
+            owt.info.dims[0] *
+            owt.info.dims[1] *
+            owt.info.dims[2] *
+            owt.info.dims[3]) != 0);
+
+    std::string ref_name =
+        std::string("mean_") +
+        std::to_string(dim) +
+        std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) +
+        std::string("_") +
+        std::string(dtype_traits<Tw>::getName()) +
+        std::string("_") +
+        std::string(dtype_traits<To>::getName()) +
+        std::string("_") +
+        std::to_string(threads_y) +
+        std::string("_") +
+        std::to_string(input_weight) +
+        std::string("_") +
+        std::to_string(output_weight);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog==0 && entry.ker==0) {
+
+        Binary<To, af_add_t> mean;
+        ToNumStr<To> toNumStr;
+        ToNumStr<Tw> twNumStr;
+        Transform<uint, Tw, af_add_t> transform_weight;
+
+        std::ostringstream options;
+        options << " -D Ti=" << dtype_traits<Ti>::getName()
+            << " -D Tw=" << dtype_traits<Tw>::getName()
+            << " -D To=" << dtype_traits<To>::getName()
+            << " -D dim=" << dim
+            << " -D DIMY=" << threads_y
+            << " -D THREADS_X=" << THREADS_X
+            << " -D init_To=" << toNumStr(mean.init())
+            << " -D init_Tw=" << twNumStr(transform_weight(0))
+            << " -D one_Tw=" << twNumStr(transform_weight(1));
+
+        if (input_weight) { options << " -D INPUT_WEIGHT"; }
+        if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
+
+        if (std::is_same<Ti, double>::value ||
+                std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {mops_cl, mean_dim_cl};
+        const int   ker_lens[] = {mops_cl_len, mean_dim_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "mean_dim_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+            groups_all[1] * groups_all[3] * local[1]);
+
+    if (input_weight && output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *owt.data, owt.info,
+                *in.data, in.info,
+                *iwt.data, iwt.info,
+                groups_all[0],
+                groups_all[1],
+                groups_all[dim]);
+    } else if (!input_weight && !output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *in.data, in.info,
+                groups_all[0],
+                groups_all[1],
+                groups_all[dim]);
+    } else if ( input_weight && !output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *in.data, in.info,
+                *iwt.data, iwt.info,
+                groups_all[0],
+                groups_all[1],
+                groups_all[dim]);
+    } else if (!input_weight &&  output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *owt.data, owt.info,
+                *in.data, in.info,
+                groups_all[0],
+                groups_all[1],
+                groups_all[dim]);
+    }
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean_dim(Param out, Param in, Param iwt, int dim)
+{
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = THREADS_X;
+
+    uint groups_all[] = {(uint)divup(in.info.dims[0], threads_x),
+        (uint)in.info.dims[1],
+        (uint)in.info.dims[2],
+        (uint)in.info.dims[3]};
+
+    groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
+
+    Param tmpOut = out;
+    Param tmpWt;
+    tmpWt.info.offset = 0;
+    for (int k = 0; k < 4; ++k) {
+        tmpWt.info.dims[k] = 0;
+        tmpWt.info.strides[k] = 0;
+    }
+
+    int tmp_elements = 1;
+    if (groups_all[dim] > 1) {
+        tmpOut.info.dims[dim] = groups_all[dim];
+
+        for (int k = 0; k < 4; k++) tmp_elements *= tmpOut.info.dims[k];
+
+        tmpOut.data = bufferAlloc(tmp_elements * sizeof(To));
+        tmpWt.data = bufferAlloc(tmp_elements * sizeof(Tw));
+
+        for (int k = dim + 1; k < 4; k++) tmpOut.info.strides[k] *= groups_all[dim];
+    }
+
+    mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, dim, threads_y, groups_all);
+
+    if (groups_all[dim] > 1) {
+        groups_all[dim] = 1;
+
+        Param owt;
+        mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWt, dim, threads_y, groups_all);
+        bufferFree(tmpOut.data);
+        bufferFree(tmpWt.data);
+    }
+
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean_first_launcher(Param out, Param owt,
+        Param in, Param iwt,
+        const int threads_x,
+        const uint groups_x,
+        const uint groups_y)
+{
+
+    bool input_weight = ((
+            iwt.info.dims[0] *
+            iwt.info.dims[1] *
+            iwt.info.dims[2] *
+            iwt.info.dims[3]) != 0);
+
+    bool output_weight = ((
+            owt.info.dims[0] *
+            owt.info.dims[1] *
+            owt.info.dims[2] *
+            owt.info.dims[3]) != 0);
+
+    std::string ref_name =
+        std::string("mean_0_") +
+        std::string(dtype_traits<Ti>::getName()) +
+        std::string("_") +
+        std::string(dtype_traits<Tw>::getName()) +
+        std::string("_") +
+        std::string(dtype_traits<To>::getName()) +
+        std::string("_") +
+        std::to_string(threads_x) +
+        std::string("_") +
+        std::to_string(input_weight) +
+        std::string("_") +
+        std::to_string(output_weight);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog==0 && entry.ker==0) {
+
+        Binary<To, af_add_t> mean;
+        ToNumStr<To> toNumStr;
+        ToNumStr<Tw> twNumStr;
+        Transform<uint, Tw, af_add_t> transform_weight;
+
+        std::ostringstream options;
+        options << " -D Ti=" << dtype_traits<Ti>::getName()
+            << " -D Tw=" << dtype_traits<Tw>::getName()
+            << " -D To=" << dtype_traits<To>::getName()
+            << " -D DIMX=" << threads_x
+            << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
+            << " -D init_To=" << toNumStr(mean.init())
+            << " -D init_Tw=" << twNumStr(transform_weight(0))
+            << " -D one_Tw=" << twNumStr(transform_weight(1));
+
+        if (input_weight) { options << " -D INPUT_WEIGHT"; }
+        if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
+
+        if (std::is_same<Ti, double>::value ||
+                std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {mops_cl, mean_first_cl};
+        const int   ker_lens[] = {mops_cl_len, mean_first_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "mean_first_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * in.info.dims[2] * local[0],
+            groups_y * in.info.dims[3] * local[1]);
+
+    uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
+
+    if (input_weight && output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *owt.data, owt.info,
+                *in.data, in.info,
+                *iwt.data, iwt.info,
+                groups_x, groups_y, repeat);
+    } else if (!input_weight && !output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *in.data, in.info,
+                groups_x, groups_y, repeat);
+    } else if ( input_weight && !output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *in.data, in.info,
+                *iwt.data, iwt.info,
+                groups_x, groups_y, repeat);
+    } else if (!input_weight &&  output_weight) {
+        auto meanOp = KernelFunctor<
+            Buffer, KParam,
+            Buffer, KParam,
+            Buffer, KParam,
+            uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info,
+                *owt.data, owt.info,
+                *in.data, in.info,
+                groups_x, groups_y, repeat);
+    }
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean_first(Param out, Param in, Param iwt)
+{
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
+
+    uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(in.info.dims[1], threads_y);
+
+    Param tmpOut = out;
+    Param tmpWt;
+    tmpWt.info.offset = 0;
+    for (int k = 0; k < 4; ++k) {
+        tmpWt.info.dims[k] = 0;
+        tmpWt.info.strides[k] = 0;
+    }
+
+    if (groups_x > 1) {
+
+        tmpOut.data = bufferAlloc(groups_x *
+                in.info.dims[1] *
+                in.info.dims[2] *
+                in.info.dims[3] *
+                sizeof(To));
+
+        tmpWt.data = bufferAlloc(groups_x *
+                in.info.dims[1] *
+                in.info.dims[2] *
+                in.info.dims[3] *
+                sizeof(Tw));
+
+
+        tmpOut.info.dims[0] = groups_x;
+        for (int k = 1; k < 4; k++) tmpOut.info.strides[k] *= groups_x;
+        tmpWt.info = tmpOut.info;
+    }
+
+    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, threads_x, groups_x, groups_y);
+
+    if (groups_x > 1) {
+        Param owt;
+        mean_first_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWt, threads_x, 1, groups_y);
+
+        bufferFree(tmpOut.data);
+        bufferFree(tmpWt.data);
+    }
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean_weighted(Param out, Param in, Param iwt, int dim)
+{
+    if (dim == 0)
+        return mean_first<Ti, Tw, To>(out, in, iwt);
+    else
+        return mean_dim  <Ti, Tw, To>(out, in, iwt, dim);
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean(Param out, Param in, int dim)
+{
+    Param dummy_weight;
+    dummy_weight.info.offset = 0;
+    for (int k = 0; k < 4; ++k) {
+        dummy_weight.info.dims[k] = 0;
+        dummy_weight.info.strides[k] = 0;
+    }
+    mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
+}
+
+#if defined(__GNUC__) || defined(__GNUG__)
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
+#else
+/* Other */
+#endif
+
+#if defined(__GNUC__) || defined(__GNUG__)
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic pop
+#else
+/* Other */
+#endif
+
+template<typename T, typename Tw>
+T mean_all_weighted(Param in, Param iwt)
+{
+    int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+
+        bool in_is_linear = (in.info.strides[0] == 1);
+        bool wt_is_linear = (in.info.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            in_is_linear &= ( in.info.strides[k] == ( in.info.strides[k - 1] *  in.info.dims[k - 1]));
+            wt_is_linear &= (iwt.info.strides[k] == (iwt.info.strides[k - 1] * iwt.info.dims[k - 1]));
+        }
+
+        if (in_is_linear && wt_is_linear) {
+            in.info.dims[0] = in_elements;
+            for (int k = 1; k < 4; k++) {
+                in.info.dims[k] = 1;
+                in.info.strides[k] = in_elements;
+            }
+            iwt.info = in.info;
+        }
+
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
+
+        Param tmpOut;
+        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(in.info.dims[1], threads_y);
+
+        tmpOut.info.offset = 0;
+        tmpOut.info.dims[0] = groups_x;
+        tmpOut.info.strides[0] = 1;
+
+        for (int k = 1; k < 4; k++) {
+            tmpOut.info.dims[k] = in.info.dims[k];
+            tmpOut.info.strides[k] = tmpOut.info.dims[k - 1] * tmpOut.info.strides[k - 1];
+        }
+
+        Param tmpWt;
+        tmpWt.info = tmpOut.info;
+
+        int tmp_elements = tmpOut.info.strides[3] * tmpOut.info.dims[3];
+        tmpOut.data = bufferAlloc(tmp_elements * sizeof(T));
+        tmpWt.data = bufferAlloc(tmp_elements * sizeof(Tw));
+
+        mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, threads_x, groups_x, groups_y);
+
+        unique_ptr<T> h_ptr(new T[tmp_elements]);
+        unique_ptr<Tw> h_wptr(new Tw[tmp_elements]);
+
+        getQueue().enqueueReadBuffer(*tmpOut.data, CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
+        getQueue().enqueueReadBuffer( *tmpWt.data, CL_TRUE, 0, sizeof(Tw) * tmp_elements, h_wptr.get());
+
+        T* h_ptr_raw = h_ptr.get();
+        Tw* h_wptr_raw = h_wptr.get();
+
+        MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+        for (int i = 1; i < (int)tmp_elements; i++) {
+            Op(h_ptr_raw[i], h_wptr_raw[i]);
+        }
+
+        bufferFree(tmpOut.data);
+        bufferFree(tmpWt.data);
+
+        return Op.runningMean;
+
+    } else {
+
+        unique_ptr<T> h_ptr(new T[in_elements]);
+        unique_ptr<Tw> h_wptr(new Tw[in_elements]);
+        T* h_ptr_raw = h_ptr.get();
+        Tw* h_wptr_raw = h_wptr.get();
+
+        getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
+                sizeof(T) * in_elements, h_ptr_raw);
+        getQueue().enqueueReadBuffer(*iwt.data, CL_TRUE, sizeof(Tw) * iwt.info.offset,
+                sizeof(Tw) * in_elements, h_wptr_raw);
+
+        MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+        for (int i = 1; i < (int)in_elements; i++) {
+            Op(h_ptr_raw[i], h_wptr_raw[i]);
+        }
+
+        return Op.runningMean;
+    }
+}
+
+template<typename Ti, typename Tw, typename To>
+To mean_all(Param in)
+{
+    int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+
+        bool is_linear = (in.info.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
+        }
+
+        if (is_linear) {
+            in.info.dims[0] = in_elements;
+            for (int k = 1; k < 4; k++) {
+                in.info.dims[k] = 1;
+                in.info.strides[k] = in_elements;
+            }
+        }
+
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
+
+        Param tmpOut;
+        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(in.info.dims[1], threads_y);
+
+        tmpOut.info.offset = 0;
+        tmpOut.info.dims[0] = groups_x;
+        tmpOut.info.strides[0] = 1;
+
+        for (int k = 1; k < 4; k++) {
+            tmpOut.info.dims[k] = in.info.dims[k];
+            tmpOut.info.strides[k] = tmpOut.info.dims[k - 1] * tmpOut.info.strides[k - 1];
+        }
+
+        Param iWt; //dummy input weights
+        iWt.info.offset = 0;
+        for (int k = 0; k < 4; ++k) {
+            iWt.info.dims[k] = 0;
+            iWt.info.strides[k] = 0;
+        }
+        Param tmpCt;
+        tmpCt.info = tmpOut.info;
+
+        int tmp_elements = tmpOut.info.strides[3] * tmpOut.info.dims[3];
+        tmpOut.data = bufferAlloc(tmp_elements * sizeof(To));
+        tmpCt.data = bufferAlloc(tmp_elements * sizeof(Tw));
+
+        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x, groups_x, groups_y);
+
+        unique_ptr<To> h_ptr(new To[tmp_elements]);
+        unique_ptr<Tw> h_cptr(new Tw[tmp_elements]);
+
+        getQueue().enqueueReadBuffer(*tmpOut.data, CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.get());
+        getQueue().enqueueReadBuffer( *tmpCt.data, CL_TRUE, 0, sizeof(Tw) * tmp_elements, h_cptr.get());
+
+        To* h_ptr_raw = h_ptr.get();
+        Tw* h_cptr_raw = h_cptr.get();
+
+        MeanOp<To, Tw> Op(h_ptr_raw[0], h_cptr_raw[0]);
+        for (int i = 1; i < (int)tmp_elements; i++) {
+            Op(h_ptr_raw[i], h_cptr_raw[i]);
+        }
+
+        bufferFree(tmpOut.data);
+        bufferFree(tmpCt.data);
+
+        return Op.runningMean;
+
+    } else {
+
+        unique_ptr<Ti> h_ptr(new Ti[in_elements]);
+        Ti* h_ptr_raw = h_ptr.get();
+
+        getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
+                sizeof(Ti) * in_elements, h_ptr_raw);
+
+
+        //TODO : MeanOp with (Tw)1
+        Transform<Ti, To, af_add_t> transform;
+        Transform<uint, Tw, af_add_t> transform_weight;
+        MeanOp<To, Tw> Op(transform(h_ptr_raw[0]), transform_weight(1));
+        for (int i = 1; i < (int)in_elements; i++) {
+            Op(transform(h_ptr_raw[i]), transform_weight(1));
+        }
+
+        return Op.runningMean;
+    }
+}
+}
+
+}
diff --git a/src/backend/opencl/kernel/mean_dim.cl b/src/backend/opencl/kernel/mean_dim.cl
new file mode 100644
index 0000000000..29b8ae0d3f
--- /dev/null
+++ b/src/backend/opencl/kernel/mean_dim.cl
@@ -0,0 +1,148 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void mean_dim_kernel(__global To *oData,
+                        KParam oInfo,
+#ifdef OUTPUT_WEIGHT
+                        __global Tw *owData,
+                        KParam owInfo,
+#endif
+                        const __global Ti *iData,
+                        KParam iInfo,
+#ifdef INPUT_WEIGHT
+                        const __global Tw *iwData,
+                        KParam iwInfo,
+#endif
+                        uint groups_x, uint groups_y, uint group_dim)
+{
+    const uint lidx = get_local_id(0);
+    const uint lidy = get_local_id(1);
+    const uint lid  = lidy * THREADS_X + lidx;
+
+    const uint zid = get_group_id(0) / groups_x;
+    const uint wid = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
+    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
+    const uint xid = groupId_x * get_local_size(0) + lidx;
+    const uint yid = groupId_y;
+
+    uint ids[4] = {xid, yid, zid, wid};
+
+    // There is only one element per group for out
+    // There are get_local_size(1) elements per group for in
+    // Hence increment ids[dim] just after offseting out and before offsetting in
+    oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+
+#ifdef OUTPUT_WEIGHT
+    owData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+#endif
+    const uint id_dim_out = ids[dim];
+
+    ids[dim] = ids[dim] * get_local_size(1) + lidy;
+
+    iData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+        ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+
+#ifdef INPUT_WEIGHT
+    iwData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+        ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+#endif
+
+    const uint id_dim_in = ids[dim];
+    const uint istride_dim = iInfo.strides[dim];
+
+    bool is_valid =
+        (ids[0] < iInfo.dims[0]) &&
+        (ids[1] < iInfo.dims[1]) &&
+        (ids[2] < iInfo.dims[2]) &&
+        (ids[3] < iInfo.dims[3]);
+
+    __local To s_val[THREADS_X * DIMY];
+    __local Tw s_wt[THREADS_X * DIMY];
+
+    To out_val = init_To;
+    Tw out_wt = init_Tw;
+
+    if (is_valid && id_dim_in < iInfo.dims[dim]) {
+        out_val = transform(*iData);
+#ifdef INPUT_WEIGHT
+        out_wt = *iwData;
+#else
+        out_wt = one_Tw;
+#endif
+    }
+
+    const uint id_dim_in_start = id_dim_in + group_dim * get_local_size(1);
+
+#ifdef INPUT_WEIGHT
+    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
+            id += group_dim * get_local_size(1)) {
+
+        iData = iData + group_dim * get_local_size(1) * istride_dim;
+        iwData = iwData + group_dim * get_local_size(1) * istride_dim;
+        binOp(&out_val, &out_wt, transform(*iData), *iwData);
+    }
+#else
+    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
+            id += group_dim * get_local_size(1)) {
+
+        iData = iData + group_dim * get_local_size(1) * istride_dim;
+        binOp(&out_val, &out_wt, transform(*iData), one_Tw);
+    }
+#endif
+
+    s_val[lid] = out_val;
+    s_wt[lid] = out_wt;
+
+    __local To *s_vptr = s_val + lid;
+    __local Tw *s_wptr = s_wt + lid;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (DIMY == 8) {
+        if (lidy < 4) {
+            binOp(&out_val, &out_wt,
+                    s_vptr[THREADS_X * 4], s_wptr[THREADS_X * 4]);
+            *s_vptr = out_val;
+            *s_wptr = out_wt;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (DIMY >= 4) {
+        if (lidy < 2) {
+            binOp(&out_val, &out_wt,
+                    s_vptr[THREADS_X * 2], s_wptr[THREADS_X * 2]);
+            *s_vptr = out_val;
+            *s_wptr = out_wt;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (DIMY >= 2) {
+        if (lidy < 1) {
+            binOp(&out_val, &out_wt,
+                    s_vptr[THREADS_X * 1], s_wptr[THREADS_X * 1]);
+            *s_vptr = out_val;
+            *s_wptr = out_wt;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (lidy == 0 && is_valid &&
+            (id_dim_out < oInfo.dims[dim])) {
+        *oData = *s_vptr;
+#ifdef OUTPUT_WEIGHT
+        *owData = *s_wptr;
+#endif
+    }
+
+}
diff --git a/src/backend/opencl/kernel/mean_first.cl b/src/backend/opencl/kernel/mean_first.cl
new file mode 100644
index 0000000000..266ee7bfb8
--- /dev/null
+++ b/src/backend/opencl/kernel/mean_first.cl
@@ -0,0 +1,169 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel
+void mean_first_kernel(__global To *oData,
+                          KParam oInfo,
+#ifdef OUTPUT_WEIGHT
+                          __global Tw *owData,
+                          KParam owInfo,
+#endif
+                          const __global Ti *iData,
+                          KParam iInfo,
+#ifdef INPUT_WEIGHT
+                          const __global Tw *iwData,
+                          KParam iwInfo,
+#endif
+                          uint groups_x, uint groups_y, uint repeat)
+{
+    const uint lidx = get_local_id(0);
+    const uint lidy = get_local_id(1);
+    const uint lid  = lidy * get_local_size(0) + lidx;
+
+    const uint zid = get_group_id(0) / groups_x;
+    const uint wid = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
+    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
+    const uint xid = groupId_x * get_local_size(0) * repeat + lidx;
+    const uint yid = groupId_y * get_local_size(1) + lidy;
+
+    iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
+        yid * iInfo.strides[1] + iInfo.offset;
+
+#ifdef INPUT_WEIGHT
+    iwData += wid * iwInfo.strides[3] + zid * iwInfo.strides[2] +
+        yid * iwInfo.strides[1] + iwInfo.offset;
+#endif
+
+    oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
+        yid * oInfo.strides[1] + oInfo.offset;
+
+#ifdef OUTPUT_WEIGHT
+    owData += wid * owInfo.strides[3] + zid * owInfo.strides[2] +
+        yid * owInfo.strides[1] + owInfo.offset;
+#endif
+
+    bool cond = (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
+
+    __local To s_val[THREADS_PER_GROUP];
+    __local Tw s_wt[THREADS_PER_GROUP];
+
+    int last = (xid + repeat * DIMX);
+    int lim = last > iInfo.dims[0] ? iInfo.dims[0] : last;
+    To out_val = init_To;
+    Tw out_wt = init_Tw;
+
+    if (cond && xid < lim) {
+        out_val = transform(iData[xid]);
+#ifdef INPUT_WEIGHT
+        out_wt = iwData[xid];
+#else
+        out_wt = one_Tw;
+#endif
+    }
+
+#ifdef INPUT_WEIGHT
+    for (int id = xid + DIMX; cond && id < lim; id += DIMX) {
+        binOp(&out_val, &out_wt, transform(iData[id]), iwData[id]);
+    }
+#else
+    for (int id = xid + DIMX; cond && id < lim; id += DIMX) {
+        binOp(&out_val, &out_wt, transform(iData[id]), one_Tw);
+    }
+#endif
+
+    s_val[lid] = out_val;
+    s_wt[lid] = out_wt;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    __local To *s_vptr = s_val + lidy * DIMX;
+    __local Tw *s_wptr = s_wt + lidy * DIMX;
+
+    if (DIMX == 256) {
+        if (lidx < 128) {
+            binOp(&out_val, &out_wt,
+                    s_vptr[lidx + 128], s_wptr[lidx + 128]);
+            s_vptr[lidx] = out_val;
+            s_wptr[lidx] = out_wt;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (DIMX >= 128) {
+        if (lidx <  64) {
+            binOp(&out_val, &out_wt,
+                    s_vptr[lidx +  64], s_wptr[lidx +  64]);
+            s_vptr[lidx] = out_val;
+            s_wptr[lidx] = out_wt;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (DIMX >=  64) {
+        if (lidx <  32) {
+            binOp(&out_val, &out_wt,
+                    s_vptr[lidx +  32], s_wptr[lidx +  32]);
+            s_vptr[lidx] = out_val;
+            s_wptr[lidx] = out_wt;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (lidx <  16) {
+        binOp(&out_val, &out_wt,
+                s_vptr[lidx +  16], s_wptr[lidx +  16]);
+        s_vptr[lidx] = out_val;
+        s_wptr[lidx] = out_wt;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (lidx <   8) {
+        binOp(&out_val, &out_wt,
+                s_vptr[lidx +   8], s_wptr[lidx +   8]);
+        s_vptr[lidx] = out_val;
+        s_wptr[lidx] = out_wt;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (lidx <   4) {
+        binOp(&out_val, &out_wt,
+                s_vptr[lidx +   4], s_wptr[lidx +   4]);
+        s_vptr[lidx] = out_val;
+        s_wptr[lidx] = out_wt;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (lidx <   2) {
+        binOp(&out_val, &out_wt,
+                s_vptr[lidx +   2], s_wptr[lidx +   2]);
+        s_vptr[lidx] = out_val;
+        s_wptr[lidx] = out_wt;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (lidx <   1) {
+        binOp(&out_val, &out_wt,
+                s_vptr[lidx +   1], s_wptr[lidx +   1]);
+        s_vptr[lidx] = out_val;
+        s_wptr[lidx] = out_wt;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (cond && lidx == 0) {
+        oData[groupId_x] = s_vptr[0];
+#ifdef OUTPUT_WEIGHT
+        owData[groupId_x] = s_wptr[0];
+#endif
+    }
+}
diff --git a/src/backend/opencl/kernel/mops.cl b/src/backend/opencl/kernel/mops.cl
new file mode 100644
index 0000000000..aa10242a4f
--- /dev/null
+++ b/src/backend/opencl/kernel/mops.cl
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+To transform(Ti in)
+{
+    return (To)(in);
+}
+
+void binOp(To *lhs, Tw *l_wt, To rhs, Tw r_wt)
+{
+    if (((*l_wt) != 0) || (r_wt != 0)) {
+        Tw l_scale = (*l_wt);
+        (*l_wt) += r_wt;
+        l_scale = l_scale/(*l_wt);
+
+        Tw r_scale = r_wt/(*l_wt);
+        (*lhs) = (l_scale * (*lhs)) + (r_scale * rhs);
+    }
+}
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
new file mode 100644
index 0000000000..e4578f10c6
--- /dev/null
+++ b/src/backend/opencl/mean.cpp
@@ -0,0 +1,80 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+
+#include <complex>
+#include <mean.hpp>
+#include <kernel/mean.hpp>
+#include <err_opencl.hpp>
+
+using std::swap;
+using af::dim4;
+namespace opencl
+{
+    template<typename Ti, typename Tw, typename To>
+    To mean(const Array<Ti>& in)
+    {
+        return kernel::mean_all<Ti, Tw, To>(in);
+    }
+
+    template<typename T, typename Tw>
+    T mean(const Array<T>& in, const Array<Tw>& wts)
+    {
+        return kernel::mean_all_weighted<T, Tw>(in, wts);
+    }
+
+    template<typename Ti, typename Tw, typename To>
+    Array<To> mean(const Array<Ti>& in, const int dim)
+    {
+        dim4 odims = in.dims();
+        odims[dim] = 1;
+        Array<To> out = createEmptyArray<To>(odims);
+        kernel::mean<Ti, Tw, To>(out, in, dim);
+        return out;
+    }
+
+    template<typename T, typename Tw>
+    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim)
+    {
+        dim4 odims = in.dims();
+        odims[dim] = 1;
+        Array<T> out = createEmptyArray<T>(odims);
+        kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
+        return out;
+    }
+
+    #define INSTANTIATE(Ti, Tw, To)                                         \
+        template To mean<Ti, Tw, To>(const Array<Ti> &in); \
+        template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim); \
+
+    INSTANTIATE(double  , double,  double);
+    INSTANTIATE(float   , float ,  float );
+    INSTANTIATE(int     , float ,  float );
+    INSTANTIATE(unsigned, float ,  float );
+    INSTANTIATE(intl    , double,  double);
+    INSTANTIATE(uintl   , double,  double);
+    INSTANTIATE(short   , float ,  float );
+    INSTANTIATE(ushort  , float ,  float );
+    INSTANTIATE(uchar   , float ,  float );
+    INSTANTIATE(char    , float ,  float );
+    INSTANTIATE(cfloat  , float ,  cfloat);
+    INSTANTIATE(cdouble , double, cdouble);
+
+    #define INSTANTIATE_WGT(T, Tw)                                         \
+        template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts); \
+        template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, const int dim); \
+
+    INSTANTIATE_WGT(double , double);
+    INSTANTIATE_WGT(float  , float );
+    INSTANTIATE_WGT(cfloat , float );
+    INSTANTIATE_WGT(cdouble, double);
+
+}
diff --git a/src/backend/opencl/mean.hpp b/src/backend/opencl/mean.hpp
new file mode 100644
index 0000000000..91c718af8b
--- /dev/null
+++ b/src/backend/opencl/mean.hpp
@@ -0,0 +1,28 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <ops.hpp>
+
+namespace opencl
+{
+    template<typename Ti, typename Tw, typename To>
+    To mean(const Array<Ti>& in);
+
+    template<typename T, typename Tw>
+    T mean(const Array<T>& in, const Array<Tw>& wts);
+
+    template<typename Ti, typename Tw, typename To>
+    Array<To> mean(const Array<Ti>& in, const int dim);
+
+    template<typename T, typename Tw>
+    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
+
+}

From 31c3716cceb538944a7e9f8be597d64b70ec62a0 Mon Sep 17 00:00:00 2001
From: Kumar Aatish <kumar@arrayfire.com>
Date: Thu, 22 Jun 2017 16:24:27 -0400
Subject: [PATCH 1291/2677] Fixed var test

---
 test/var.cpp | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/test/var.cpp b/test/var.cpp
index 2311130f65..76058dcf7f 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -149,7 +149,15 @@ TYPED_TEST(Var, DimCPPSmall)
 
         for(size_t j = 0; j < tests.size(); j++) {
             for(size_t jj = 0; jj < tests[j].size(); jj++) {
-                ASSERT_EQ(h_out[j][jj], tests[j][jj]);
+                // NOTE: will work for all types
+                if (is_same_type<float, outType>::value ||
+                    is_same_type<cfloat, outType>::value) {
+                    ASSERT_FLOAT_EQ(real(h_out[j][jj]), real(tests[j][jj]));
+                    ASSERT_FLOAT_EQ(imag(h_out[j][jj]), imag(tests[j][jj]));
+                } else {
+                    ASSERT_DOUBLE_EQ(real(h_out[j][jj]), real(tests[j][jj]));
+                    ASSERT_DOUBLE_EQ(imag(h_out[j][jj]), imag(tests[j][jj]));
+                }
             }
         }
     }

From a259162ae52fbd9828d052ea17e84975c746359e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Sep 2017 22:46:25 -0400
Subject: [PATCH 1292/2677] Mean cleanup. Remove Param creation, use vectors
 instead of u_ptrs

Cleanup mean overflow changes

* Use vectors instead of unique_ptr
* Remove the creation of Param objects. Instead use createArray
* Rename mops.cl -> mean_ops.cl
* Formatting changes
---
 src/backend/cuda/kernel/mean.hpp              |  76 +++----
 src/backend/opencl/kernel/mean.hpp            | 198 +++++-------------
 .../opencl/kernel/{mops.cl => mean_ops.cl}    |   0
 3 files changed, 96 insertions(+), 178 deletions(-)
 rename src/backend/opencl/kernel/{mops.cl => mean_ops.cl} (100%)

diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 38f1494d84..f2ea613bc9 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -16,7 +16,11 @@
 #include <debug_cuda.hpp>
 #include "config.hpp"
 #include <memory.hpp>
+
 #include <memory>
+#include <vector>
+
+using std::vector;
 
 namespace cuda
 {
@@ -70,18 +74,26 @@ namespace kernel
         To *optr = out.ptr;
         Tw *owptr = owt.ptr;
 
+        int ooffset = ids[3] * out.strides[3] +
+                      ids[2] * out.strides[2] +
+                      ids[1] * out.strides[1] + ids[0];
         // There is only one element per block for out
         // There are blockDim.y elements per block for in
         // Hence increment ids[dim] just after offseting out and before offsetting in
-        optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        if (owptr != NULL) owptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        optr += ooffset;
+        if (owptr != NULL) owptr += ooffset;
+
         const uint blockIdx_dim = ids[dim];
 
         ids[dim] = ids[dim] * blockDim.y + tidy;
-        iptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
-        if (iwptr != NULL) iwptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
-        const uint id_dim_in = ids[dim];
 
+        int ioffset = ids[3] * in.strides[3] +
+                      ids[2] * in.strides[2] +
+                      ids[1] * in.strides[1] + ids[0];
+        iptr  += ioffset;
+        if (iwptr != NULL) iwptr  += ioffset;
+
+        const uint id_dim_in = ids[dim];
         const uint istride_dim = in.strides[dim];
 
         bool is_valid =
@@ -173,7 +185,6 @@ namespace kernel
                              CParam<Ti> in, CParam<Tw> iwt,
                              const uint threads_y, const dim_t blocks_dim[4])
     {
-        printf("mean_dim_launcher\n");
         dim3 threads(THREADS_X, threads_y);
 
         dim3 blocks(blocks_dim[0] * blocks_dim[2],
@@ -458,7 +469,6 @@ namespace kernel
     template<typename T, typename Tw>
     T mean_all_weighted(CParam<T> in, CParam<Tw> iwt)
     {
-        using std::unique_ptr;
         int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
 
         // FIXME: Use better heuristics to get to the optimum number
@@ -509,42 +519,38 @@ namespace kernel
             tmpWt.ptr = memAlloc<Tw>(tmp_elements);
             mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
 
-            unique_ptr<T>       h_ptr(new T[tmp_elements]);
-            unique_ptr<Tw>    h_wptr(new Tw[tmp_elements]);
-            T*     h_ptr_raw = h_ptr.get();
-            Tw*   h_wptr_raw = h_wptr.get();
+            vector<T>  h_ptr(tmp_elements);
+            vector<Tw> h_wptr(tmp_elements);
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmpOut.ptr, tmp_elements * sizeof(T),
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.ptr, tmp_elements * sizeof(T),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaMemcpyAsync(h_wptr_raw, tmpWt.ptr, tmp_elements * sizeof(Tw),
+            CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), tmpWt.ptr, tmp_elements * sizeof(Tw),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
             memFree(tmpOut.ptr);
             memFree(tmpWt.ptr);
 
-            MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+            MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
 
             for (int i = 1; i < tmp_elements; i++) {
-                Op(h_ptr_raw[i], h_wptr_raw[i]);
+                Op(h_ptr[i], h_wptr[i]);
             }
 
             return Op.runningMean;
         } else {
 
-            unique_ptr<T> h_ptr(new T[in_elements]);
-            unique_ptr<Tw>    h_wptr(new Tw[in_elements]);
-            T*      h_ptr_raw = h_ptr.get();
-            Tw*   h_wptr_raw = h_wptr.get();
+            vector<T>  h_ptr(in_elements);
+            vector<Tw> h_wptr(in_elements);
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr, in_elements * sizeof(T),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaMemcpyAsync(h_wptr_raw, iwt.ptr, in_elements * sizeof(Tw),
+            CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), iwt.ptr, in_elements * sizeof(Tw),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-            MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+            MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
             for (int i = 1; i < in_elements; i++) {
-                Op(h_ptr_raw[i], h_wptr_raw[i]);
+                Op(h_ptr[i], h_wptr[i]);
             }
 
             return Op.runningMean;
@@ -594,46 +600,42 @@ namespace kernel
 
             int tmp_elements = tmpOut.strides[3] * tmpOut.dims[3];
 
-            //TODO: Use scoped_ptr
             tmpOut.ptr = memAlloc<To>(tmp_elements);
             tmpCt.ptr = memAlloc<Tw>(tmp_elements);
             iwt.ptr = NULL;
             mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x, blocks_y, threads_x);
 
-            unique_ptr<To>       h_ptr(new To[tmp_elements]);
-            unique_ptr<Tw>    h_cptr(new Tw[tmp_elements]);
-            To*    h_ptr_raw = h_ptr.get();
-            Tw*   h_cptr_raw = h_cptr.get();
+            vector<To> h_ptr(tmp_elements);
+            vector<Tw> h_cptr(tmp_elements);
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmpOut.ptr, tmp_elements * sizeof(To),
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.ptr, tmp_elements * sizeof(To),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaMemcpyAsync(h_cptr_raw, tmpCt.ptr, tmp_elements * sizeof(Tw),
+            CUDA_CHECK(cudaMemcpyAsync(h_cptr.data(), tmpCt.ptr, tmp_elements * sizeof(Tw),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
             memFree(tmpOut.ptr);
             memFree(tmpCt.ptr);
 
-            MeanOp<To, Tw> Op(h_ptr_raw[0], h_cptr_raw[0]);
+            MeanOp<To, Tw> Op(h_ptr[0], h_cptr[0]);
 
             for (int i = 1; i < tmp_elements; i++) {
-                Op(h_ptr_raw[i], h_cptr_raw[i]);
+                Op(h_ptr[i], h_cptr[i]);
             }
 
             return Op.runningMean;
         } else {
 
-            unique_ptr<Ti> h_ptr(new Ti[in_elements]);
-            Ti* h_ptr_raw = h_ptr.get();
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
+            vector<Ti> h_ptr(in_elements);
+            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr, in_elements * sizeof(Ti),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
             Transform<Ti, To, af_add_t> transform;
             Tw count = (Tw)1;
 
-            MeanOp<To, Tw> Op(transform(h_ptr_raw[0]), count);
+            MeanOp<To, Tw> Op(transform(h_ptr[0]), count);
             for (int i = 1; i < in_elements; i++) {
-                Op(transform(h_ptr_raw[i]), count);
+                Op(transform(h_ptr[i]), count);
             }
 
             return Op.runningMean;
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index bca813f5e4..c5b1aadeb6 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -8,13 +8,9 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <memory>
 #include <kernel_headers/mean_first.hpp>
 #include <kernel_headers/mean_dim.hpp>
-#include <kernel_headers/mops.hpp>
+#include <kernel_headers/mean_ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <dispatch.hpp>
@@ -26,6 +22,11 @@
 #include "config.hpp"
 #include <memory.hpp>
 
+#include <string>
+#include <mutex>
+#include <map>
+#include <vector>
+
 using cl::Buffer;
 using cl::Program;
 using cl::Kernel;
@@ -33,7 +34,7 @@ using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
 using std::string;
-using std::unique_ptr;
+using std::vector;
 
 namespace opencl
 {
@@ -177,8 +178,8 @@ void mean_dim_launcher(Param out, Param owt,
             options << " -D USE_DOUBLE";
         }
 
-        const char *ker_strs[] = {mops_cl, mean_dim_cl};
-        const int   ker_lens[] = {mops_cl_len, mean_dim_cl_len};
+        const char *ker_strs[] = {mean_ops_cl, mean_dim_cl};
+        const int   ker_lens[] = {mean_ops_cl_len, mean_dim_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -265,35 +266,19 @@ void mean_dim(Param out, Param in, Param iwt, int dim)
 
     groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
 
-    Param tmpOut = out;
-    Param tmpWt;
-    tmpWt.info.offset = 0;
-    for (int k = 0; k < 4; ++k) {
-        tmpWt.info.dims[k] = 0;
-        tmpWt.info.strides[k] = 0;
-    }
-
-    int tmp_elements = 1;
-    if (groups_all[dim] > 1) {
-        tmpOut.info.dims[dim] = groups_all[dim];
-
-        for (int k = 0; k < 4; k++) tmp_elements *= tmpOut.info.dims[k];
-
-        tmpOut.data = bufferAlloc(tmp_elements * sizeof(To));
-        tmpWt.data = bufferAlloc(tmp_elements * sizeof(Tw));
-
-        for (int k = dim + 1; k < 4; k++) tmpOut.info.strides[k] *= groups_all[dim];
-    }
-
-    mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, dim, threads_y, groups_all);
-
     if (groups_all[dim] > 1) {
-        groups_all[dim] = 1;
+        dim4 d(4, out.info.dims);
+        d[dim] = groups_all[dim];
+        Array<To> tmpOut = createEmptyArray<To>(d);
+        Array<Tw> tmpWt = createEmptyArray<Tw>(d);
+        mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, dim, threads_y, groups_all);
 
         Param owt;
+        groups_all[dim] = 1;
         mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWt, dim, threads_y, groups_all);
-        bufferFree(tmpOut.data);
-        bufferFree(tmpWt.data);
+    } else {
+        Array<Tw> tmpWt = createEmptyArray<Tw>(0);
+        mean_dim_launcher<Ti, Tw, To>(out, tmpWt, in, iwt, dim, threads_y, groups_all);
     }
 
 }
@@ -306,17 +291,15 @@ void mean_first_launcher(Param out, Param owt,
         const uint groups_y)
 {
 
-    bool input_weight = ((
-            iwt.info.dims[0] *
-            iwt.info.dims[1] *
-            iwt.info.dims[2] *
-            iwt.info.dims[3]) != 0);
+    bool input_weight = ((iwt.info.dims[0] *
+                          iwt.info.dims[1] *
+                          iwt.info.dims[2] *
+                          iwt.info.dims[3]) != 0);
 
-    bool output_weight = ((
-            owt.info.dims[0] *
-            owt.info.dims[1] *
-            owt.info.dims[2] *
-            owt.info.dims[3]) != 0);
+    bool output_weight = (( owt.info.dims[0] *
+                            owt.info.dims[1] *
+                            owt.info.dims[2] *
+                            owt.info.dims[3]) != 0);
 
     std::string ref_name =
         std::string("mean_0_") +
@@ -361,8 +344,8 @@ void mean_first_launcher(Param out, Param owt,
             options << " -D USE_DOUBLE";
         }
 
-        const char *ker_strs[] = {mops_cl, mean_first_cl};
-        const int   ker_lens[] = {mops_cl_len, mean_first_cl_len};
+        const char *ker_strs[] = {mean_ops_cl, mean_first_cl};
+        const int   ker_lens[] = {mean_ops_cl_len, mean_first_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -496,21 +479,6 @@ void mean(Param out, Param in, int dim)
     mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
 }
 
-#if defined(__GNUC__) || defined(__GNUG__)
-/* GCC/G++, Clang/LLVM, Intel ICC */
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wunused-function"
-#else
-/* Other */
-#endif
-
-#if defined(__GNUC__) || defined(__GNUG__)
-/* GCC/G++, Clang/LLVM, Intel ICC */
-#pragma GCC diagnostic pop
-#else
-/* Other */
-#endif
-
 template<typename T, typename Tw>
 T mean_all_weighted(Param in, Param iwt)
 {
@@ -539,62 +507,40 @@ T mean_all_weighted(Param in, Param iwt)
         threads_x = std::min(threads_x, THREADS_PER_GROUP);
         uint threads_y = THREADS_PER_GROUP / threads_x;
 
-        Param tmpOut;
         uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
         uint groups_y = divup(in.info.dims[1], threads_y);
 
-        tmpOut.info.offset = 0;
-        tmpOut.info.dims[0] = groups_x;
-        tmpOut.info.strides[0] = 1;
-
-        for (int k = 1; k < 4; k++) {
-            tmpOut.info.dims[k] = in.info.dims[k];
-            tmpOut.info.strides[k] = tmpOut.info.dims[k - 1] * tmpOut.info.strides[k - 1];
-        }
-
-        Param tmpWt;
-        tmpWt.info = tmpOut.info;
-
-        int tmp_elements = tmpOut.info.strides[3] * tmpOut.info.dims[3];
-        tmpOut.data = bufferAlloc(tmp_elements * sizeof(T));
-        tmpWt.data = bufferAlloc(tmp_elements * sizeof(Tw));
+        Array<T> tmpOut = createEmptyArray<T>(groups_x);
+        Array<Tw> tmpWt = createEmptyArray<Tw>(groups_x);
 
         mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, threads_x, groups_x, groups_y);
 
-        unique_ptr<T> h_ptr(new T[tmp_elements]);
-        unique_ptr<Tw> h_wptr(new Tw[tmp_elements]);
-
-        getQueue().enqueueReadBuffer(*tmpOut.data, CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
-        getQueue().enqueueReadBuffer( *tmpWt.data, CL_TRUE, 0, sizeof(Tw) * tmp_elements, h_wptr.get());
+        vector<T> h_ptr(tmpOut.elements());
+        vector<Tw> h_wptr(tmpWt.elements());
 
-        T* h_ptr_raw = h_ptr.get();
-        Tw* h_wptr_raw = h_wptr.get();
+        getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0, sizeof(T) * tmpOut.elements(), h_ptr.data());
+        getQueue().enqueueReadBuffer(*tmpWt.get(),  CL_TRUE, 0, sizeof(Tw) * tmpWt.elements(), h_wptr.data());
 
-        MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
-        for (int i = 1; i < (int)tmp_elements; i++) {
-            Op(h_ptr_raw[i], h_wptr_raw[i]);
+        MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
+        for (int i = 1; i < (int)tmpOut.elements(); i++) {
+            Op(h_ptr[i], h_wptr[i]);
         }
 
-        bufferFree(tmpOut.data);
-        bufferFree(tmpWt.data);
-
         return Op.runningMean;
 
     } else {
 
-        unique_ptr<T> h_ptr(new T[in_elements]);
-        unique_ptr<Tw> h_wptr(new Tw[in_elements]);
-        T* h_ptr_raw = h_ptr.get();
-        Tw* h_wptr_raw = h_wptr.get();
+        vector<T> h_ptr(in_elements);
+        vector<Tw> h_wptr(in_elements);
 
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
-                sizeof(T) * in_elements, h_ptr_raw);
+                                     sizeof(T) * in_elements, h_ptr.data());
         getQueue().enqueueReadBuffer(*iwt.data, CL_TRUE, sizeof(Tw) * iwt.info.offset,
-                sizeof(Tw) * in_elements, h_wptr_raw);
+                                     sizeof(Tw) * in_elements, h_wptr.data());
 
-        MeanOp<T, Tw> Op(h_ptr_raw[0], h_wptr_raw[0]);
+        MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
         for (int i = 1; i < (int)in_elements; i++) {
-            Op(h_ptr_raw[i], h_wptr_raw[i]);
+            Op(h_ptr[i], h_wptr[i]);
         }
 
         return Op.runningMean;
@@ -608,7 +554,6 @@ To mean_all(Param in)
 
     // FIXME: Use better heuristics to get to the optimum number
     if (in_elements > 4096) {
-
         bool is_linear = (in.info.strides[0] == 1);
         for (int k = 1; k < 4; k++) {
             is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
@@ -626,68 +571,39 @@ To mean_all(Param in)
         threads_x = std::min(threads_x, THREADS_PER_GROUP);
         uint threads_y = THREADS_PER_GROUP / threads_x;
 
-        Param tmpOut;
         uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
         uint groups_y = divup(in.info.dims[1], threads_y);
 
-        tmpOut.info.offset = 0;
-        tmpOut.info.dims[0] = groups_x;
-        tmpOut.info.strides[0] = 1;
-
-        for (int k = 1; k < 4; k++) {
-            tmpOut.info.dims[k] = in.info.dims[k];
-            tmpOut.info.strides[k] = tmpOut.info.dims[k - 1] * tmpOut.info.strides[k - 1];
-        }
-
-        Param iWt; //dummy input weights
-        iWt.info.offset = 0;
-        for (int k = 0; k < 4; ++k) {
-            iWt.info.dims[k] = 0;
-            iWt.info.strides[k] = 0;
-        }
-        Param tmpCt;
-        tmpCt.info = tmpOut.info;
-
-        int tmp_elements = tmpOut.info.strides[3] * tmpOut.info.dims[3];
-        tmpOut.data = bufferAlloc(tmp_elements * sizeof(To));
-        tmpCt.data = bufferAlloc(tmp_elements * sizeof(Tw));
+        Array<To> tmpOut = createEmptyArray<To>(groups_x);
+        Array<To> iWt = createEmptyArray<To>(0);
+        Array<Tw> tmpCt = createEmptyArray<Tw>(groups_x);
 
         mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x, groups_x, groups_y);
 
-        unique_ptr<To> h_ptr(new To[tmp_elements]);
-        unique_ptr<Tw> h_cptr(new Tw[tmp_elements]);
-
-        getQueue().enqueueReadBuffer(*tmpOut.data, CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.get());
-        getQueue().enqueueReadBuffer( *tmpCt.data, CL_TRUE, 0, sizeof(Tw) * tmp_elements, h_cptr.get());
+        vector<To> h_ptr(tmpOut.elements());
+        vector<Tw> h_cptr(tmpOut.elements());
 
-        To* h_ptr_raw = h_ptr.get();
-        Tw* h_cptr_raw = h_cptr.get();
+        getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0, sizeof(To) * tmpOut.elements(), h_ptr.data());
+        getQueue().enqueueReadBuffer(*tmpCt.get(),  CL_TRUE, 0, sizeof(Tw) * tmpCt.elements(), h_cptr.data());
 
-        MeanOp<To, Tw> Op(h_ptr_raw[0], h_cptr_raw[0]);
-        for (int i = 1; i < (int)tmp_elements; i++) {
-            Op(h_ptr_raw[i], h_cptr_raw[i]);
+        MeanOp<To, Tw> Op(h_ptr[0], h_cptr[0]);
+        for (int i = 1; i < (int)h_ptr.size(); i++) {
+            Op(h_ptr[i], h_cptr[i]);
         }
 
-        bufferFree(tmpOut.data);
-        bufferFree(tmpCt.data);
-
         return Op.runningMean;
-
     } else {
-
-        unique_ptr<Ti> h_ptr(new Ti[in_elements]);
-        Ti* h_ptr_raw = h_ptr.get();
+        vector<Ti> h_ptr(in_elements);
 
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
-                sizeof(Ti) * in_elements, h_ptr_raw);
-
+                                     sizeof(Ti) * in_elements, h_ptr.data());
 
         //TODO : MeanOp with (Tw)1
         Transform<Ti, To, af_add_t> transform;
         Transform<uint, Tw, af_add_t> transform_weight;
-        MeanOp<To, Tw> Op(transform(h_ptr_raw[0]), transform_weight(1));
+        MeanOp<To, Tw> Op(transform(h_ptr[0]), transform_weight(1));
         for (int i = 1; i < (int)in_elements; i++) {
-            Op(transform(h_ptr_raw[i]), transform_weight(1));
+            Op(transform(h_ptr[i]), transform_weight(1));
         }
 
         return Op.runningMean;
diff --git a/src/backend/opencl/kernel/mops.cl b/src/backend/opencl/kernel/mean_ops.cl
similarity index 100%
rename from src/backend/opencl/kernel/mops.cl
rename to src/backend/opencl/kernel/mean_ops.cl

From 510c0946f8361045dcd1f9cd3087524a179932b1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 9 Sep 2017 20:05:38 -0400
Subject: [PATCH 1293/2677] Workaround for Apple bug in OpenCL driver. Fixes
 Canny

This commit implements a workaround for a Apple bug in their Iris OpenCL driver
where clEnqueueWriteBuffer fails when you pass in static C arrays. This change
fixes canny on OSX.
---
 src/api/c/canny.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 37d8daf240..b461355378 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -30,9 +30,10 @@
 #include <tile.hpp>
 #include <iota.hpp>
 #include <utility>
-#include <iostream>
+#include <vector>
 
 using af::dim4;
+using std::vector;
 using namespace detail;
 
 Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy, const bool& isf)
@@ -173,9 +174,9 @@ template<typename T>
 af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold ct,
                      const float t2, const unsigned sw, const bool isf)
 {
-    static const float v[] = {-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
-    Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v);
-    Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v);
+    static const vector<float> v{-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
+    Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v.data());
+    Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v.data());
 
     // Run separable convolution to smooth the input image
     Array<float> smt = detail::convolve2<float, float, false>(cast<float, T>(in), cFilter, rFilter);

From 95f1b7012c64ed991833be56cf6a082186802fc5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 9 Sep 2017 21:52:55 -0400
Subject: [PATCH 1294/2677] Cleaning up names of tests for easier filtering.
 Fix type errors

* Refactored some of the tests
* Changed the names of LargeDim to MaxDim to keep inline with other MaxDim tests
for easier filtering
* Added comments about failures on OSX
* Fixed a few warnings
---
 test/cholesky_dense.cpp  |  95 +++++++++++++------
 test/inverse_dense.cpp   |  66 +++++++++----
 test/lu_dense.cpp        | 100 +++++++++++++-------
 test/ocl_ext_context.cpp |   3 +-
 test/qr_dense.cpp        |  79 +++++++++-------
 test/select.cpp          |   2 +-
 test/solve_common.hpp    |  27 ++----
 test/solve_dense.cpp     | 200 +++++++++++++++++++++++++--------------
 test/tile.cpp            |   1 -
 9 files changed, 362 insertions(+), 211 deletions(-)

diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index 7fd238d215..c5f87ff9db 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -25,6 +25,7 @@ using std::endl;
 using std::abs;
 using af::cfloat;
 using af::cdouble;
+using af::dtype_traits;
 
 template<typename T>
 void choleskyTester(const int n, double eps, bool is_upper)
@@ -50,8 +51,8 @@ void choleskyTester(const int n, double eps, bool is_upper)
 
     af::array re = is_upper ? matmul(out.H(), out) : matmul(out, out.H());
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(in - re))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(in - re))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(in - re))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(in - re))), eps);
 
     //! [ex_chol_inplace]
     af::array in2 = in.copy();
@@ -60,30 +61,70 @@ void choleskyTester(const int n, double eps, bool is_upper)
 
     af::array out2 = is_upper ? upper(in2) : lower(in2);
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(out2 - out))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(out2 - out))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(out2 - out))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(out2 - out))), eps);
 }
 
-#define CHOLESKY_BIG_TESTS(T, eps)              \
-    TEST(Cholesky, T##Upper)                    \
-    {                                           \
-        choleskyTester<T>( 500, eps, true );    \
-    }                                           \
-    TEST(Cholesky, T##Lower)                    \
-    {                                           \
-        choleskyTester<T>(1000, eps, false);    \
-    }                                           \
-    TEST(Cholesky, T##UpperMultiple)            \
-    {                                           \
-        choleskyTester<T>(1024, eps, true );    \
-    }                                           \
-    TEST(Cholesky, T##LowerMultiple)            \
-    {                                           \
-        choleskyTester<T>( 512, eps, false);    \
-    }                                           \
-
-
-CHOLESKY_BIG_TESTS(float, 0.05)
-CHOLESKY_BIG_TESTS(double, 1E-8)
-CHOLESKY_BIG_TESTS(cfloat, 0.05)
-CHOLESKY_BIG_TESTS(cdouble, 1E-8)
+template<typename T>
+class Cholesky : public ::testing::Test
+{
+
+};
+
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+TYPED_TEST_CASE(Cholesky, TestTypes);
+
+template<typename T>
+double eps();
+
+template<>
+double eps<float>() {
+    return 0.05f;
+}
+
+template<>
+double eps<double>() {
+    return 1e-8;
+}
+
+template<>
+double eps<cfloat>() {
+    return 0.05f;
+}
+
+template<>
+double eps<cdouble>() {
+    return 1e-8;
+}
+
+TYPED_TEST(Cholesky, Upper) {
+    choleskyTester<TypeParam>( 500, eps<TypeParam>(), true );
+}
+
+TYPED_TEST(Cholesky, UpperLarge) {
+    choleskyTester<TypeParam>( 1000, eps<TypeParam>(), true );
+}
+
+TYPED_TEST(Cholesky, UpperMultipleOfTwo) {
+    choleskyTester<TypeParam>( 512, eps<TypeParam>(), true );
+}
+
+TYPED_TEST(Cholesky, UpperMultipleOfTwoLarge) {
+    choleskyTester<TypeParam>( 1024, eps<TypeParam>(), true );
+}
+
+TYPED_TEST(Cholesky, Lower) {
+    choleskyTester<TypeParam>( 500, eps<TypeParam>(), false );
+}
+
+TYPED_TEST(Cholesky, LowerLarge) {
+    choleskyTester<TypeParam>( 1000, eps<TypeParam>(), false );
+}
+
+TYPED_TEST(Cholesky, LowerMultipleOfTwo) {
+    choleskyTester<TypeParam>( 512, eps<TypeParam>(), false );
+}
+
+TYPED_TEST(Cholesky, LowerMultipleOfTwoLarge) {
+    choleskyTester<TypeParam>( 1024, eps<TypeParam>(), false );
+}
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index 1b990b6900..c56ba4c02f 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -7,6 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+// NOTE: Tests are known to fail on OSX when utilizing the CPU and OpenCL
+// backends for sizes larger than 128x128 or more. You can read more about it on
+// issue https://github.com/arrayfire/arrayfire/issues/1617
+
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <af/dim4.hpp>
@@ -25,9 +29,7 @@ using std::endl;
 using std::abs;
 using af::cfloat;
 using af::cdouble;
-
-///////////////////////////////// CPP ////////////////////////////////////
-//
+using af::dtype_traits;
 
 template<typename T>
 void inverseTester(const int m, const int n, const int k, double eps)
@@ -47,21 +49,47 @@ void inverseTester(const int m, const int n, const int k, double eps)
 
     af::array I2 = af::identity(m, n, (af::dtype)af::dtype_traits<T>::af_type);
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(I - I2))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(I - I2))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(I - I2))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(I - I2))), eps);
+}
+
+
+template<typename T>
+class Inverse : public ::testing::Test
+{
+
+};
+
+template<typename T>
+double eps();
+
+template<>
+double eps<float>() {
+  return 0.01f;
 }
 
-#define INVERSE_TESTS(T, eps)                   \
-    TEST(INVERSE, T##Square)                    \
-    {                                           \
-        inverseTester<T>(1000, 1000, 100, eps); \
-    }                                           \
-    TEST(INVERSE, T##SquareMultiple)            \
-    {                                           \
-        inverseTester<T>(2048, 2048, 512, eps); \
-    }                                           \
-
-INVERSE_TESTS(float, 0.01)
-INVERSE_TESTS(double, 1E-5)
-INVERSE_TESTS(cfloat, 0.01)
-INVERSE_TESTS(cdouble, 1E-5)
+template<>
+double eps<double>() {
+  return 1e-5;
+}
+
+template<>
+double eps<cfloat>() {
+  return 0.01f;
+}
+
+template<>
+double eps<cdouble>() {
+  return 1e-5;
+}
+
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+TYPED_TEST_CASE(Inverse, TestTypes);
+
+TYPED_TEST(Inverse, Square) {
+    inverseTester<TypeParam>(1000, 1000, 100, eps<TypeParam>());
+}
+
+TYPED_TEST(Inverse, SquareMultiplePowerOfTwo) {
+    inverseTester<TypeParam>(2048, 2048, 512, eps<TypeParam>());
+}
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 9bf8d720d3..9cea2bbe63 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -7,6 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+// NOTE: Tests are known to fail on OSX when utilizing the CPU and OpenCL
+// backends for sizes larger than 128x128 or more. You can read more about it on
+// issue https://github.com/arrayfire/arrayfire/issues/1617
+
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <af/dim4.hpp>
@@ -25,9 +29,8 @@ using std::endl;
 using std::abs;
 using af::cfloat;
 using af::cdouble;
+using af::dtype_traits;
 
-///////////////////////////////// CPP ////////////////////////////////////
-//
 TEST(LU, InPlaceSmall)
 {
     if (noDoubleTests<float>()) return;
@@ -139,8 +142,8 @@ void luTester(const int m, const int n, double eps)
     af::array a_perm = a_orig(pivot, af::span);
     //! [ex_lu_recon]
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(a_recon - a_perm))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(a_recon - a_perm))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(a_recon - a_perm))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(a_recon - a_perm))), eps);
 
     //! [ex_lu_packed]
     af::array out = a_orig.copy();
@@ -162,38 +165,63 @@ void luTester(const int m, const int n, double eps)
     af::array a_recon2 = af::matmul(l2, u2);
     af::array a_perm2 = a_orig(pivot2, af::span);
 
-    ASSERT_NEAR(0, af::max<double>(af::abs(real(a_recon2 - a_perm2))), eps);
-    ASSERT_NEAR(0, af::max<double>(af::abs(imag(a_recon2 - a_perm2))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(a_recon2 - a_perm2))), eps);
+    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(a_recon2 - a_perm2))), eps);
+
+}
+
+template<typename T>
+double eps();
+
+template<>
+double eps<float>() {
+    return 1E-3;
+}
+
+template<>
+double eps<double>() {
+    return 1e-8;
+}
+
+template<>
+double eps<cfloat>() {
+    return 1E-3;
+}
 
+template<>
+double eps<cdouble>() {
+    return 1e-8;
 }
 
-#define LU_BIG_TESTS(T, eps)                    \
-    TEST(LU, T##BigSquare)                      \
-    {                                           \
-        luTester<T>(500, 500, eps);             \
-    }                                           \
-    TEST(LU, T##BigRect0)                       \
-    {                                           \
-        luTester<T>(500, 1000, eps);            \
-    }                                           \
-    TEST(LU, T##BigRect1)                       \
-    {                                           \
-        luTester<T>(1000, 500, eps);            \
-    }                                           \
-    TEST(LU, T##BigSquareMultiple)              \
-    {                                           \
-        luTester<T>(512, 512, eps);             \
-    }                                           \
-    TEST(LU, T##BigRect0Multiple)               \
-    {                                           \
-        luTester<T>(512, 1024, eps);            \
-    }                                           \
-    TEST(LU, T##BigRect1Multiple)               \
-    {                                           \
-        luTester<T>(1024, 512, eps);            \
-    }                                           \
-
-LU_BIG_TESTS(float, 1E-3)
-LU_BIG_TESTS(double, 1E-8)
-LU_BIG_TESTS(cfloat, 1E-3)
-LU_BIG_TESTS(cdouble, 1E-8)
+template<typename T>
+class LU : public ::testing::Test
+{
+
+};
+
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+TYPED_TEST_CASE(LU, TestTypes);
+
+TYPED_TEST(LU, SquareLarge) {
+    luTester<TypeParam>(500, 500, eps<TypeParam>());
+}
+
+TYPED_TEST(LU, SquareMultipleOfTwoLarge) {
+    luTester<TypeParam>(512, 512, eps<TypeParam>());
+}
+
+TYPED_TEST(LU, RectangularLarge0) {
+    luTester<TypeParam>(1000, 500, eps<TypeParam>());
+}
+
+TYPED_TEST(LU, RectangularMultipleOfTwoLarge0) {
+    luTester<TypeParam>(1024, 512, eps<TypeParam>());
+}
+
+TYPED_TEST(LU, RectangularLarge1) {
+    luTester<TypeParam>(500, 1000, eps<TypeParam>());
+}
+
+TYPED_TEST(LU, RectangularMultipleOfTwoLarge1) {
+    luTester<TypeParam>(512, 1024, eps<TypeParam>());
+}
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 13951413ee..560496f140 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -129,11 +129,10 @@ TEST(OCLCheck, DevicePlatform)
     afcl::platform platform = afcl::getPlatform();
     ASSERT_NE(platform, AFCL_PLATFORM_UNKNOWN);
 }
-
+#pragma GCC diagnostic pop
 #else
 TEST(OCLExtContext, NoopCPU)
 {
 }
 #endif
 
-#pragma GCC diagnostic pop
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index e3809546b1..4d79a67da9 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -131,40 +131,49 @@ void qrTester(const int m, const int n, double eps)
     }
 }
 
+template<typename T>
+double eps();
 
-#define QR_BIG_TESTS(T, eps)                    \
-    TEST(QR, T##BigRect0)                       \
-    {                                           \
-        qrTester<T>(500, 1000, eps);            \
-    }                                           \
-    TEST(QR, T##BigRect0Multiple)               \
-    {                                           \
-        qrTester<T>(512, 1024, eps);            \
-    }                                           \
-    TEST(QR, T##BigRect1Multiple)               \
-    {                                           \
-        qrTester<T>(1024, 512, eps);            \
-    }                                           \
-
-QR_BIG_TESTS(float, 1E-3)
-QR_BIG_TESTS(double, 1E-5)
-QR_BIG_TESTS(cfloat, 1E-3)
-QR_BIG_TESTS(cdouble, 1E-5)
-
-#undef QR_BIG_TESTS
-
-#define QR_BIG_TESTS(T, eps)                    \
-    TEST(QR, T##BigRect1)                       \
-    {                                           \
-        qrTester<T>(1000, 500, eps);            \
-    }                                           \
-
-QR_BIG_TESTS(float, 1E-3)
-QR_BIG_TESTS(double, 1E-5)
-// Fails on Windows on some devices
-#if !(defined(OS_WIN) && defined(AF_OPENCL))
-QR_BIG_TESTS(cfloat, 1E-3)
-QR_BIG_TESTS(cdouble, 1E-5)
-#endif
+template<>
+double eps<float>() {
+    return 1e-3;
+}
+
+template<>
+double eps<double>() {
+    return 1e-5;
+}
+
+template<>
+double eps<cfloat>() {
+    return 1e-3;
+}
+
+template<>
+double eps<cdouble>() {
+    return 1e-5;
+}
+template<typename T>
+class QR : public ::testing::Test
+{
+
+};
+
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+TYPED_TEST_CASE(QR, TestTypes);
+
+TYPED_TEST(QR, RectangularLarge0) {
+    qrTester<TypeParam>(1000, 500, eps<TypeParam>());
+}
 
-#undef QR_BIG_TESTS
+TYPED_TEST(QR, RectangularMultipleOfTwoLarge0) {
+    qrTester<TypeParam>(1024, 512, eps<TypeParam>());
+}
+
+TYPED_TEST(QR, RectangularLarge1) {
+    qrTester<TypeParam>(500, 1000, eps<TypeParam>());
+}
+
+TYPED_TEST(QR, RectangularMultipleOfTwoLarge1) {
+    qrTester<TypeParam>(512, 1024, eps<TypeParam>());
+}
diff --git a/test/select.cpp b/test/select.cpp
index fa911af6a6..ce87a90670 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -242,7 +242,7 @@ TEST(Select, Issue_1730_scalar)
     }
 }
 
-TEST(Select, LargeDim)
+TEST(Select, MaxDim)
 {
     const size_t largeDim = 65535 * 32 + 1;
 
diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index c6a97f7cc7..6ca7e3a621 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -56,13 +56,8 @@ void solveTester(const int m, const int n, const int k, double eps, int targetDe
     af::array B1 = af::matmul(A, X1);
     //! [ex_solve_recon]
 
-    if(noDoubleTests<T>()) {
-        ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (m * k), eps);
-        ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (m * k), eps);
-    } else {
-        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (m * k), eps);
-        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (m * k), eps);
-    }
+    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) / (m * k), eps);
+    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) / (m * k), eps);
 }
 
 template<typename T>
@@ -93,13 +88,8 @@ void solveLUTester(const int n, const int k, double eps, int targetDevice=-1)
 
     af::array B1 = af::matmul(A, X1);
 
-    if(noDoubleTests<T>()) {
-        ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (n * k), eps);
-        ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (n * k), eps);
-    } else {
-        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
-    }
+    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) / (n * k), eps);
+    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) / (n * k), eps);
 }
 
 template<typename T>
@@ -144,11 +134,6 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps, in
 
     af::array B1 = af::matmul(AT, X1);
 
-    if(noDoubleTests<T>()) {
-        ASSERT_NEAR(0, af::sum<float>(af::abs(real(B0 - B1))) / (n * k), eps);
-        ASSERT_NEAR(0, af::sum<float>(af::abs(imag(B0 - B1))) / (n * k), eps);
-    } else {
-        ASSERT_NEAR(0, af::sum<double>(af::abs(real(B0 - B1))) / (n * k), eps);
-        ASSERT_NEAR(0, af::sum<double>(af::abs(imag(B0 - B1))) / (n * k), eps);
-    }
+    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) / (n * k), eps);
+    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) / (n * k), eps);
 }
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 3caa656336..adc8703a69 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -7,80 +7,142 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+// NOTE: Tests are known to fail on OSX when utilizing the CPU and OpenCL
+// backends for sizes larger than 128x128 or more. You can read more about it on
+// issue https://github.com/arrayfire/arrayfire/issues/1617
+
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include "solve_common.hpp"
 #include <thread>
 
-#define SOLVE_LU_TESTS(T, eps)                          \
-    TEST(SOLVE_LU, T##Reg)                              \
-    {                                                   \
-        solveLUTester<T>(1000, 100, eps);               \
-    }                                                   \
-    TEST(SOLVE_LU, T##RegMultiple)                      \
-    {                                                   \
-        solveLUTester<T>(2048, 512, eps);               \
-    }                                                   \
-
-
-#define SOLVE_TRIANGLE_TESTS(T, eps)                    \
-    TEST(SOLVE_Upper, T##Reg)                           \
-    {                                                   \
-        solveTriangleTester<T>(1000, 100, true, eps);   \
-    }                                                   \
-    TEST(SOLVE_Upper, T##RegMultiple)                   \
-    {                                                   \
-        solveTriangleTester<T>(2048, 512, true, eps);   \
-    }                                                   \
-    TEST(SOLVE_Lower, T##Reg)                           \
-    {                                                   \
-        solveTriangleTester<T>(1000, 100, false, eps);  \
-    }                                                   \
-    TEST(SOLVE_Lower, T##RegMultiple)                   \
-    {                                                   \
-        solveTriangleTester<T>(2048, 512, false, eps);  \
-    }                                                   \
-
-#define SOLVE_GENERAL_TESTS(T, eps)                     \
-    TEST(SOLVE, T##Square)                              \
-    {                                                   \
-        solveTester<T>(1000, 1000, 100, eps);           \
-    }                                                   \
-    TEST(SOLVE, T##SquareMultiple)                      \
-    {                                                   \
-        solveTester<T>(2048, 2048, 512, eps);           \
-    }                                                   \
-
-#define SOLVE_LEASTSQ_TESTS(T, eps)                     \
-    TEST(SOLVE, T##RectUnder)                           \
-    {                                                   \
-        solveTester<T>(800, 1000, 200, eps);            \
-    }                                                   \
-    TEST(SOLVE, T##RectUnderMultiple)                   \
-    {                                                   \
-        solveTester<T>(1536, 2048, 400, eps);           \
-    }                                                   \
-    TEST(SOLVE, T##RectOver)                            \
-    {                                                   \
-        solveTester<T>(800, 600, 64, eps);              \
-    }                                                   \
-    TEST(SOLVE, T##RectOverMultiple)                    \
-    {                                                   \
-        solveTester<T>(1536, 1024, 1, eps);             \
-    }                                                   \
-
-#define SOLVE_TESTS(T, eps)                             \
-    SOLVE_GENERAL_TESTS(T, eps)                         \
-    SOLVE_LEASTSQ_TESTS(T, eps)                         \
-    SOLVE_LU_TESTS(T, eps)                              \
-    SOLVE_TRIANGLE_TESTS(T, eps)                        \
-
-
-SOLVE_TESTS(float, 0.01)
-SOLVE_TESTS(double, 1E-5)
-SOLVE_TESTS(cfloat, 0.01)
-SOLVE_TESTS(cdouble, 1E-5)
+template<typename T>
+class Solve : public ::testing::Test
+{
+
+};
+
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+TYPED_TEST_CASE(Solve, TestTypes);
+
+template<typename T>
+double eps();
+
+template<>
+double eps<float>() {
+    return 0.01f;
+}
+
+template<>
+double eps<double>() {
+    return 1e-5;
+}
+
+template<>
+double eps<cfloat>() {
+    return 0.01f;
+}
+
+template<>
+double eps<cdouble>() {
+    return 1e-5;
+}
+
+TYPED_TEST(Solve, Square) {
+    solveTester<TypeParam>(100, 100, 10, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareMultipleOfTwo) {
+    solveTester<TypeParam>(96, 96, 16, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareLarge) {
+    solveTester<TypeParam>(1000, 1000, 10, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareMultipleOfTwoLarge) {
+    solveTester<TypeParam>(2048, 2048, 32, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresUnderDetermined) {
+    solveTester<TypeParam>(80, 100, 20, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresUnderDeterminedMultipleOfTwo) {
+    solveTester<TypeParam>(96, 128, 40, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresUnderDeterminedLarge) {
+    solveTester<TypeParam>(800, 1000, 200, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresUnderDeterminedMultipleOfTwoLarge) {
+    solveTester<TypeParam>(1536, 2048, 400, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresOverDetermined) {
+    solveTester<TypeParam>(80, 60, 20, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresOverDeterminedMultipleOfTwo) {
+    solveTester<TypeParam>(96, 64, 1, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresOverDeterminedLarge) {
+    solveTester<TypeParam>(800, 600, 64, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LeastSquaresOverDeterminedMultipleOfTwoLarge) {
+    solveTester<TypeParam>(1536, 1024, 1, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LU) {
+    solveLUTester<TypeParam>(100, 10, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LUMultipleOfTwo) {
+    solveLUTester<TypeParam>(96, 64, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LULarge) {
+    solveLUTester<TypeParam>(1000, 100, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, LUMultipleOfTwoLarge) {
+    solveLUTester<TypeParam>(2048, 512, eps<TypeParam>());
+}
 
+TYPED_TEST(Solve, TriangleUpper) {
+    solveTriangleTester<TypeParam>(100, 10, true, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleUpperMultipleOfTwo) {
+    solveTriangleTester<TypeParam>(96, 64, true, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleUpperLarge) {
+    solveTriangleTester<TypeParam>(1000, 100, true, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleUpperMultipleOfTwoLarge) {
+    solveTriangleTester<TypeParam>(2048, 512, true, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleLower) {
+    solveTriangleTester<TypeParam>(100, 10, false, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleLowerMultipleOfTwo) {
+    solveTriangleTester<TypeParam>(96, 64, false, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleLowerLarge) {
+    solveTriangleTester<TypeParam>(1000, 100, false, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, TriangleLowerMultipleOfTwoLarge) {
+    solveTriangleTester<TypeParam>(2048, 512, false, eps<TypeParam>());
+}
 
 #if !defined(AF_OPENCL)
 int nextTargetDeviceId()
@@ -97,7 +159,7 @@ int nextTargetDeviceId()
     tests.emplace_back(solveTester<T>, 800, 1000, 200, eps, nextTargetDeviceId()%numDevices);           \
     tests.emplace_back(solveTester<T>, 800, 600, 64, eps, nextTargetDeviceId()%numDevices);             \
 
-TEST(SOLVE, Threading)
+TEST(Solve, Threading)
 {
     cleanSlate(); // Clean up everything done so far
 
diff --git a/test/tile.cpp b/test/tile.cpp
index 706cc43b04..3830978983 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -153,7 +153,6 @@ TEST(Tile, MaxDim)
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    const unsigned resultIdx = 0;
     const unsigned x = 1;
     const unsigned z = 1;
     unsigned y = 2;

From c4551aa965b13cbd41b923fbf3c727a4c778f2ff Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 10 Sep 2017 00:01:00 -0400
Subject: [PATCH 1295/2677] Fix unused brace warnings. Also addresses other
 warnings.

---
 examples/graphics/gravity_sim.cpp        | 1 -
 src/backend/cpu/TNJ/BinaryNode.hpp       | 2 +-
 src/backend/cpu/TNJ/UnaryNode.hpp        | 2 +-
 src/backend/cpu/kernel/mean.hpp          | 6 ++++--
 src/backend/cuda/Array.cpp               | 1 -
 src/backend/cuda/JIT/BinaryNode.hpp      | 2 +-
 src/backend/cuda/JIT/UnaryNode.hpp       | 2 +-
 src/backend/opencl/JIT/BinaryNode.hpp    | 2 +-
 src/backend/opencl/JIT/UnaryNode.hpp     | 2 +-
 src/backend/opencl/nearest_neighbour.cpp | 2 --
 10 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 0fb2e39075..1bbb00cdbe 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -20,7 +20,6 @@ static const bool is3D = true; const static int total_particles = 4000;
 static const int reset = 3000;
 static const float min_dist = 3;
 static const int width = 768, height = 768, depth = 768;
-static const float eps = 10.f;
 static const int gravity_constant = 20000;
 
 float mass_range = 0;
diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/TNJ/BinaryNode.hpp
index c44e201062..37d053363f 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/TNJ/BinaryNode.hpp
@@ -44,7 +44,7 @@ namespace TNJ
 
     public:
         BinaryNode(Node_ptr lhs, Node_ptr rhs) :
-            TNode<To>(0, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
+            TNode<To>(0, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {{lhs, rhs}}),
             m_lhs(reinterpret_cast<TNode<Ti> *>(lhs.get())),
             m_rhs(reinterpret_cast<TNode<Ti> *>(rhs.get()))
         {
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/TNJ/UnaryNode.hpp
index d32acf5644..270054e193 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/TNJ/UnaryNode.hpp
@@ -40,7 +40,7 @@ namespace TNJ
 
     public:
         UnaryNode(Node_ptr child) :
-            TNode<To>(0, child->getHeight() + 1, {child}),
+            TNode<To>(0, child->getHeight() + 1, {{child}}),
             m_child(reinterpret_cast<TNode<Ti> *>(child.get()))
         {
         }
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index 4bded987c1..5863a3f1ed 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -18,11 +18,13 @@ namespace kernel
 template<typename Ti, typename To, typename Tw>
 struct MeanOp
 {
+    Transform<Ti, To, af_add_t> transform;
     To runningMean;
     Tw runningCount;
-    Transform<Ti, To, af_add_t> transform;
     MeanOp(Ti mean, Tw count) :
-        runningMean(transform(mean)), runningCount(count)
+        transform(),
+        runningMean(transform(mean)),
+        runningCount(count)
     {
     }
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 6f171bbe0e..008853bd99 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -25,7 +25,6 @@ using std::shared_ptr;
 namespace cuda
 {
 
-    const int MAX_JIT_LEN = 20;
     using JIT::BufferNode;
     using JIT::Node;
     using JIT::Node_ptr;
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index 8edb88098d..d32dea7e35 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -27,7 +27,7 @@ namespace JIT
         BinaryNode(const char *out_type_str, const char *name_str,
                    const char *op_str,
                    Node_ptr lhs, Node_ptr rhs, int op)
-            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
+            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {{lhs, rhs}}),
               m_op_str(op_str),
               m_op(op)
         {
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 72e148289f..8b19f0e6f2 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -27,7 +27,7 @@ namespace JIT
         UnaryNode(const char *out_type_str, const char *name_str,
                   const char *op_str,
                   Node_ptr child, int op)
-            : Node(out_type_str, name_str, child->getHeight() + 1, {child}),
+            : Node(out_type_str, name_str, child->getHeight() + 1, {{child}}),
               m_op_str(op_str),
               m_op(op)
         {
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index c2f605f6e2..f67712274b 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -27,7 +27,7 @@ namespace JIT
         BinaryNode(const char *out_type_str, const char *name_str,
                    const char *op_str,
                    Node_ptr lhs, Node_ptr rhs, int op)
-            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {lhs, rhs}),
+            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {{lhs, rhs}}),
               m_op_str(op_str),
               m_op(op)
         {
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index 6b178615ab..ed47be989c 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -27,7 +27,7 @@ namespace JIT
         UnaryNode(const char *out_type_str, const char *name_str,
                   const char *op_str,
                   Node_ptr child, int op)
-            : Node(out_type_str, name_str, child->getHeight() + 1, {child}),
+            : Node(out_type_str, name_str, child->getHeight() + 1, {{child}}),
               m_op_str(op_str),
               m_op(op)
         {
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index fa1ef53c0d..4c9344030a 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -20,8 +20,6 @@ using cl::Device;
 namespace opencl
 {
 
-static const unsigned THREADS = 256;
-
 template<typename T, typename To, af_match_type dist_type>
 void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
                         const Array<T>& query, const Array<T>& train,

From 74cd28e59da293a8f0457428efccfb10256ec8d0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 10 Sep 2017 12:17:23 -0400
Subject: [PATCH 1296/2677] Fix Pure Virtual Call errors with FFT

On Windows the resources that are released after the main function have exited
cause "Pure Virtual Function Called" errors. It seems that Windows releases all
resources when exiting main without calling their destructors. When the
destructors are called this error is thrown. This is related to
https://github.com/arrayfire/arrayfire/pull/1899
---
 src/backend/opencl/clfft.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index aa7d26e63d..8235b732e4 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -154,8 +154,16 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
     CLFFT_CHECK(clfftBakePlan(*temp, 1, &(opencl::getQueue()()), NULL, NULL));
 
     retVal.reset(temp, [](PlanType* p) {
+#ifndef OS_WIN
+        // On Windows the resources that are released after the main function
+        // have exited cause "Pure Virtual Function Called" errors. It seems
+        // that Windows releases all resources when exiting main without calling
+        // their destructors. When the destructors are called this error is
+        // thrown. This is related to
+        // https://github.com/arrayfire/arrayfire/pull/1899
         CLFFT_CHECK(clfftDestroyPlan(p));
         free(p);
+#endif
     });
     // push the plan into plan cache
     planner.push(key_string, retVal);

From db8540f26451c4255bff5b6afe44b81592ecf3ae Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sun, 10 Sep 2017 21:27:13 -0700
Subject: [PATCH 1297/2677] BUGFIX: Fixing bug in mean at large sizes in OpenCL
 backend.

---
 src/backend/opencl/kernel/mean.hpp | 97 +++++++++++++++---------------
 1 file changed, 48 insertions(+), 49 deletions(-)

diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index c5b1aadeb6..da877cbbae 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -115,16 +115,16 @@ struct MeanOp<cdouble, double>
 
 template<typename Ti, typename Tw, typename To>
 void mean_dim_launcher(Param out, Param owt,
-        Param in, Param iwt,
+        Param in, Param inWeight,
         const int dim,
         const int threads_y,
         const uint groups_all[4])
 {
     bool input_weight = ((
-            iwt.info.dims[0] *
-            iwt.info.dims[1] *
-            iwt.info.dims[2] *
-            iwt.info.dims[3]) != 0);
+            inWeight.info.dims[0] *
+            inWeight.info.dims[1] *
+            inWeight.info.dims[2] *
+            inWeight.info.dims[3]) != 0);
 
     bool output_weight = ((
             owt.info.dims[0] *
@@ -204,7 +204,7 @@ void mean_dim_launcher(Param out, Param owt,
                 *out.data, out.info,
                 *owt.data, owt.info,
                 *in.data, in.info,
-                *iwt.data, iwt.info,
+                *inWeight.data, inWeight.info,
                 groups_all[0],
                 groups_all[1],
                 groups_all[dim]);
@@ -230,7 +230,7 @@ void mean_dim_launcher(Param out, Param owt,
         meanOp(EnqueueArgs(getQueue(), global, local),
                 *out.data, out.info,
                 *in.data, in.info,
-                *iwt.data, iwt.info,
+                *inWeight.data, inWeight.info,
                 groups_all[0],
                 groups_all[1],
                 groups_all[dim]);
@@ -254,7 +254,7 @@ void mean_dim_launcher(Param out, Param owt,
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_dim(Param out, Param in, Param iwt, int dim)
+void mean_dim(Param out, Param in, Param inWeight, int dim)
 {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
     uint threads_x = THREADS_X;
@@ -270,31 +270,31 @@ void mean_dim(Param out, Param in, Param iwt, int dim)
         dim4 d(4, out.info.dims);
         d[dim] = groups_all[dim];
         Array<To> tmpOut = createEmptyArray<To>(d);
-        Array<Tw> tmpWt = createEmptyArray<Tw>(d);
-        mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, dim, threads_y, groups_all);
+        Array<Tw> tmpWeight = createEmptyArray<Tw>(d);
+        mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, dim, threads_y, groups_all);
 
         Param owt;
         groups_all[dim] = 1;
-        mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWt, dim, threads_y, groups_all);
+        mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWeight, dim, threads_y, groups_all);
     } else {
-        Array<Tw> tmpWt = createEmptyArray<Tw>(0);
-        mean_dim_launcher<Ti, Tw, To>(out, tmpWt, in, iwt, dim, threads_y, groups_all);
+        Array<Tw> tmpWeight = createEmptyArray<Tw>(0);
+        mean_dim_launcher<Ti, Tw, To>(out, tmpWeight, in, inWeight, dim, threads_y, groups_all);
     }
 
 }
 
 template<typename Ti, typename Tw, typename To>
 void mean_first_launcher(Param out, Param owt,
-        Param in, Param iwt,
+        Param in, Param inWeight,
         const int threads_x,
         const uint groups_x,
         const uint groups_y)
 {
 
-    bool input_weight = ((iwt.info.dims[0] *
-                          iwt.info.dims[1] *
-                          iwt.info.dims[2] *
-                          iwt.info.dims[3]) != 0);
+    bool input_weight = ((inWeight.info.dims[0] *
+                          inWeight.info.dims[1] *
+                          inWeight.info.dims[2] *
+                          inWeight.info.dims[3]) != 0);
 
     bool output_weight = (( owt.info.dims[0] *
                             owt.info.dims[1] *
@@ -371,7 +371,7 @@ void mean_first_launcher(Param out, Param owt,
                 *out.data, out.info,
                 *owt.data, owt.info,
                 *in.data, in.info,
-                *iwt.data, iwt.info,
+                *inWeight.data, inWeight.info,
                 groups_x, groups_y, repeat);
     } else if (!input_weight && !output_weight) {
         auto meanOp = KernelFunctor<
@@ -391,7 +391,7 @@ void mean_first_launcher(Param out, Param owt,
         meanOp(EnqueueArgs(getQueue(), global, local),
                 *out.data, out.info,
                 *in.data, in.info,
-                *iwt.data, iwt.info,
+                *inWeight.data, inWeight.info,
                 groups_x, groups_y, repeat);
     } else if (!input_weight &&  output_weight) {
         auto meanOp = KernelFunctor<
@@ -410,7 +410,7 @@ void mean_first_launcher(Param out, Param owt,
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_first(Param out, Param in, Param iwt)
+void mean_first(Param out, Param in, Param inWeight)
 {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
     threads_x = std::min(threads_x, THREADS_PER_GROUP);
@@ -420,12 +420,16 @@ void mean_first(Param out, Param in, Param iwt)
     uint groups_y = divup(in.info.dims[1], threads_y);
 
     Param tmpOut = out;
-    Param tmpWt;
-    tmpWt.info.offset = 0;
+    Param noWeight;
+    noWeight.info.offset = 0;
     for (int k = 0; k < 4; ++k) {
-        tmpWt.info.dims[k] = 0;
-        tmpWt.info.strides[k] = 0;
+        noWeight.info.dims[k] = 0;
+        noWeight.info.strides[k] = 0;
     }
+    // Does not matter what the value is it will not be used. Just needs to be valid.
+    noWeight.data = inWeight.data;
+
+    Param tmpWeight = noWeight;
 
     if (groups_x > 1) {
 
@@ -435,7 +439,7 @@ void mean_first(Param out, Param in, Param iwt)
                 in.info.dims[3] *
                 sizeof(To));
 
-        tmpWt.data = bufferAlloc(groups_x *
+        tmpWeight.data = bufferAlloc(groups_x *
                 in.info.dims[1] *
                 in.info.dims[2] *
                 in.info.dims[3] *
@@ -444,43 +448,38 @@ void mean_first(Param out, Param in, Param iwt)
 
         tmpOut.info.dims[0] = groups_x;
         for (int k = 1; k < 4; k++) tmpOut.info.strides[k] *= groups_x;
-        tmpWt.info = tmpOut.info;
+        tmpWeight.info = tmpOut.info;
     }
 
-    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, threads_x, groups_x, groups_y);
+    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, threads_x, groups_x, groups_y);
 
     if (groups_x > 1) {
-        Param owt;
-        mean_first_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWt, threads_x, 1, groups_y);
+        // No Weight is needed when writing out the output.
+        mean_first_launcher<Ti, Tw, To>(out, noWeight, tmpOut, tmpWeight, threads_x, 1, groups_y);
 
         bufferFree(tmpOut.data);
-        bufferFree(tmpWt.data);
+        bufferFree(tmpWeight.data);
     }
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_weighted(Param out, Param in, Param iwt, int dim)
+void mean_weighted(Param out, Param in, Param inWeight, int dim)
 {
     if (dim == 0)
-        return mean_first<Ti, Tw, To>(out, in, iwt);
+        return mean_first<Ti, Tw, To>(out, in, inWeight);
     else
-        return mean_dim  <Ti, Tw, To>(out, in, iwt, dim);
+        return mean_dim  <Ti, Tw, To>(out, in, inWeight, dim);
 }
 
 template<typename Ti, typename Tw, typename To>
 void mean(Param out, Param in, int dim)
 {
-    Param dummy_weight;
-    dummy_weight.info.offset = 0;
-    for (int k = 0; k < 4; ++k) {
-        dummy_weight.info.dims[k] = 0;
-        dummy_weight.info.strides[k] = 0;
-    }
-    mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
+    Array<Tw> noWeight = createEmptyArray<Tw>(dim4(0, 0, 0, 0));
+    mean_weighted<Ti, Tw, To>(out, in, noWeight, dim);
 }
 
 template<typename T, typename Tw>
-T mean_all_weighted(Param in, Param iwt)
+T mean_all_weighted(Param in, Param inWeight)
 {
     int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
@@ -491,7 +490,7 @@ T mean_all_weighted(Param in, Param iwt)
         bool wt_is_linear = (in.info.strides[0] == 1);
         for (int k = 1; k < 4; k++) {
             in_is_linear &= ( in.info.strides[k] == ( in.info.strides[k - 1] *  in.info.dims[k - 1]));
-            wt_is_linear &= (iwt.info.strides[k] == (iwt.info.strides[k - 1] * iwt.info.dims[k - 1]));
+            wt_is_linear &= (inWeight.info.strides[k] == (inWeight.info.strides[k - 1] * inWeight.info.dims[k - 1]));
         }
 
         if (in_is_linear && wt_is_linear) {
@@ -500,7 +499,7 @@ T mean_all_weighted(Param in, Param iwt)
                 in.info.dims[k] = 1;
                 in.info.strides[k] = in_elements;
             }
-            iwt.info = in.info;
+            inWeight.info = in.info;
         }
 
         uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
@@ -511,15 +510,15 @@ T mean_all_weighted(Param in, Param iwt)
         uint groups_y = divup(in.info.dims[1], threads_y);
 
         Array<T> tmpOut = createEmptyArray<T>(groups_x);
-        Array<Tw> tmpWt = createEmptyArray<Tw>(groups_x);
+        Array<Tw> tmpWeight = createEmptyArray<Tw>(groups_x);
 
-        mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, threads_x, groups_x, groups_y);
+        mean_first_launcher<T, Tw, T>(tmpOut, tmpWeight, in, inWeight, threads_x, groups_x, groups_y);
 
         vector<T> h_ptr(tmpOut.elements());
-        vector<Tw> h_wptr(tmpWt.elements());
+        vector<Tw> h_wptr(tmpWeight.elements());
 
         getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0, sizeof(T) * tmpOut.elements(), h_ptr.data());
-        getQueue().enqueueReadBuffer(*tmpWt.get(),  CL_TRUE, 0, sizeof(Tw) * tmpWt.elements(), h_wptr.data());
+        getQueue().enqueueReadBuffer(*tmpWeight.get(),  CL_TRUE, 0, sizeof(Tw) * tmpWeight.elements(), h_wptr.data());
 
         MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
         for (int i = 1; i < (int)tmpOut.elements(); i++) {
@@ -535,7 +534,7 @@ T mean_all_weighted(Param in, Param iwt)
 
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
                                      sizeof(T) * in_elements, h_ptr.data());
-        getQueue().enqueueReadBuffer(*iwt.data, CL_TRUE, sizeof(Tw) * iwt.info.offset,
+        getQueue().enqueueReadBuffer(*inWeight.data, CL_TRUE, sizeof(Tw) * inWeight.info.offset,
                                      sizeof(Tw) * in_elements, h_wptr.data());
 
         MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);

From 84c8ec2acbcc921bfba7e9db06faf9670043bd1d Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sun, 10 Sep 2017 23:19:25 -0700
Subject: [PATCH 1298/2677] PERF, CUDA: improvement of mean for CUDA backend.

- CUDA is still slower than OpenCL on same device
- At large sizes, OpenCL is 1.3x faster instead of 2x.
- Some optimizations not included in OpenCL because it hurts performance.
---
 src/backend/cuda/kernel/mean.hpp | 126 +++++++++++++------------------
 1 file changed, 53 insertions(+), 73 deletions(-)

diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index f2ea613bc9..45998d79e6 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -27,34 +27,25 @@ namespace cuda
 namespace kernel
 {
 
-    template<typename T, typename Tw>
-    struct MeanOp
+    template<typename To, typename Tw>
+    __device__ __host__
+    void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt)
     {
-        T runningMean;
-        Tw runningCount;
-        __host__ __device__ MeanOp(T mean, Tw count) :
-            runningMean(mean), runningCount(count)
-        {
-        }
+        if (((*l_wt) != 0) || (r_wt != 0)) {
+            Tw l_scale = (*l_wt);
+            (*l_wt) += r_wt;
+            l_scale = l_scale/(*l_wt);
 
-        __host__ __device__ void operator()(T newMean, Tw newCount)
-        {
-            if ((newCount != 0) || (runningCount != 0)) {
-                Tw runningScale = runningCount;
-                Tw newScale = newCount;
-                runningCount += newCount;
-                runningScale = runningScale/runningCount;
-                newScale = newScale/(Tw)runningCount;
-                runningMean = (runningScale*runningMean) + (newScale*newMean);
-            }
+            Tw r_scale = r_wt/(*l_wt);
+            (*lhs) = (l_scale * (*lhs)) + (r_scale * rhs);
         }
-    };
+    }
 
     template<typename Ti, typename Tw, typename To, uint dim, uint DIMY>
     __global__
     static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
-                                  CParam <Ti> in, CParam<Tw> iwt,
-                                  uint blocks_x, uint blocks_y, uint offset_dim)
+                                CParam <Ti> in, CParam<Tw> iwt,
+                                uint blocks_x, uint blocks_y, uint offset_dim)
     {
         const uint tidx = threadIdx.x;
         const uint tidy = threadIdx.y;
@@ -118,8 +109,6 @@ namespace kernel
             }
         }
 
-        MeanOp<To, Tw> Op(val, weight);
-
         const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
 
         __shared__ To s_val[THREADS_X * DIMY];
@@ -132,14 +121,16 @@ namespace kernel
             iptr = iptr + offset_dim * blockDim.y * istride_dim;
             if (iwptr != NULL) {
                 iwptr = iwptr + offset_dim * blockDim.y * istride_dim;
-                Op(transform(*iptr), *iwptr);
+                stable_mean(&val, &weight, transform(*iptr), *iwptr);
             } else {
-                Op(transform(*iptr), (Tw)1);
+                // Faster version of stable_mean when iwptr is NULL
+                val = val + (transform(*iptr) - val) / (weight + 1);
+                weight = weight + 1;
             }
         }
 
-        s_val[tid] = Op.runningMean;
-        s_idx[tid] = Op.runningCount;
+        s_val[tid] = val;
+        s_idx[tid] = weight;
 
         To *s_vptr = s_val + tid;
         Tw *s_iptr = s_idx + tid;
@@ -147,27 +138,21 @@ namespace kernel
 
         if (DIMY == 8) {
             if (tidy < 4) {
-                Op(s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
-                *s_vptr = Op.runningMean;
-                *s_iptr = Op.runningCount;
+                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
             }
             __syncthreads();
         }
 
         if (DIMY >= 4) {
             if (tidy < 2) {
-                Op(s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
-                *s_vptr = Op.runningMean;
-                *s_iptr = Op.runningCount;
+                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
             }
             __syncthreads();
         }
 
         if (DIMY >= 2) {
             if (tidy < 1) {
-                Op(s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
-                *s_vptr = Op.runningMean;
-                *s_iptr = Op.runningCount;
+                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
             }
             __syncthreads();
         }
@@ -182,8 +167,8 @@ namespace kernel
 
     template<typename Ti, typename Tw, typename To, int dim>
     void mean_dim_launcher(Param<To> out, Param<Tw> owt,
-                             CParam<Ti> in, CParam<Tw> iwt,
-                             const uint threads_y, const dim_t blocks_dim[4])
+                           CParam<Ti> in, CParam<Tw> iwt,
+                           const uint threads_y, const dim_t blocks_dim[4])
     {
         dim3 threads(THREADS_X, threads_y);
 
@@ -256,13 +241,10 @@ namespace kernel
     template<typename T, typename Tw>
     __device__ void warp_reduce(T *s_ptr, Tw *s_idx, uint tidx)
     {
-        MeanOp<T, Tw> Op(s_ptr[tidx], s_idx[tidx]);
 #pragma unroll
         for (int n = 16; n >= 1; n >>= 1) {
             if (tidx < n) {
-                Op(s_ptr[tidx + n], s_idx[tidx + n]);
-                s_ptr[tidx] = Op.runningMean;
-                s_idx[tidx] = Op.runningCount;
+                stable_mean(s_ptr + tidx, s_idx + tidx, s_ptr[tidx + n], s_idx[tidx + n]);
             }
             __syncthreads();
         }
@@ -274,8 +256,8 @@ namespace kernel
     template<typename Ti, typename Tw, typename To, uint DIMX>
     __global__
     static void mean_first_kernel(Param<To> out, Param<Tw> owt,
-                                    CParam<Ti> in, CParam<Tw> iwt,
-                                    uint blocks_x, uint blocks_y, uint repeat)
+                                  CParam<Ti> in, CParam<Tw> iwt,
+                                  uint blocks_x, uint blocks_y, uint repeat)
     {
         const uint tidx = threadIdx.x;
         const uint tidy = threadIdx.y;
@@ -318,26 +300,25 @@ namespace kernel
             } else {
                 weight = (Tw)1;
             }
-
         }
 
-        MeanOp<To, Tw> Op(val, weight);
-
         __shared__ To s_val[THREADS_PER_BLOCK];
         __shared__ Tw s_idx[THREADS_PER_BLOCK];
 
         if (iwptr != NULL) {
             for (int id = xid + DIMX; id < lim; id += DIMX) {
-                Op(transform(iptr[id]), iwptr[id]);
+                stable_mean(&val, &weight, transform(iptr[id]), iwptr[id]);
             }
         } else {
             for (int id = xid + DIMX; id < lim; id += DIMX) {
-                Op(transform(iptr[id]), weight);
+                // Faster version of stable_mean when iwptr is NULL
+                val = val + (transform(iptr[id]) - val) / (weight + 1);
+                weight = weight + 1;
             }
         }
 
-        s_val[tid] = Op.runningMean;
-        s_idx[tid] = Op.runningCount;
+        s_val[tid] = val;
+        s_idx[tid] = weight;
         __syncthreads();
 
         To *s_vptr = s_val + tidy * DIMX;
@@ -345,27 +326,21 @@ namespace kernel
 
         if (DIMX == 256) {
             if (tidx < 128) {
-                Op(s_vptr[tidx + 128], s_iptr[tidx + 128]);
-                s_vptr[tidx] = Op.runningMean;
-                s_iptr[tidx] = Op.runningCount;
+                stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx + 128], s_iptr[tidx + 128]);
             }
             __syncthreads();
         }
 
         if (DIMX >= 128) {
             if (tidx <  64) {
-                Op(s_vptr[tidx +  64], s_iptr[tidx +  64]);
-                s_vptr[tidx] = Op.runningMean;
-                s_iptr[tidx] = Op.runningCount;
+                stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx +  64], s_iptr[tidx +  64]);
             }
             __syncthreads();
         }
 
         if (DIMX >=  64) {
             if (tidx <  32) {
-                Op(s_vptr[tidx +  32], s_iptr[tidx +  32]);
-                s_vptr[tidx] = Op.runningMean;
-                s_iptr[tidx] = Op.runningCount;
+                stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx +  32], s_iptr[tidx +  32]);
             }
             __syncthreads();
         }
@@ -381,7 +356,7 @@ namespace kernel
 
     template<typename Ti, typename Tw, typename To>
     void mean_first_launcher(Param<To> out, Param<Tw> owt, CParam<Ti> in, CParam<Tw> iwt,
-                               const uint blocks_x, const uint blocks_y, const uint threads_x)
+                             const uint blocks_x, const uint blocks_y, const uint threads_x)
     {
 
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
@@ -530,13 +505,15 @@ namespace kernel
             memFree(tmpOut.ptr);
             memFree(tmpWt.ptr);
 
-            MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
+
+            T val = h_ptr[0];
+            Tw weight = h_wptr[0];
 
             for (int i = 1; i < tmp_elements; i++) {
-                Op(h_ptr[i], h_wptr[i]);
+                stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
             }
 
-            return Op.runningMean;
+            return val;
         } else {
 
             vector<T>  h_ptr(in_elements);
@@ -548,12 +525,13 @@ namespace kernel
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-            MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
+            T val = h_ptr[0];
+            Tw weight = h_wptr[0];
             for (int i = 1; i < in_elements; i++) {
-                Op(h_ptr[i], h_wptr[i]);
+                stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
             }
 
-            return Op.runningMean;
+            return val;
         }
     }
 
@@ -616,13 +594,14 @@ namespace kernel
             memFree(tmpOut.ptr);
             memFree(tmpCt.ptr);
 
-            MeanOp<To, Tw> Op(h_ptr[0], h_cptr[0]);
+            To val = h_ptr[0];
+            Tw weight = h_cptr[0];
 
             for (int i = 1; i < tmp_elements; i++) {
-                Op(h_ptr[i], h_cptr[i]);
+                stable_mean(&val, &weight, h_ptr[i], h_cptr[i]);
             }
 
-            return Op.runningMean;
+            return val;
         } else {
 
             vector<Ti> h_ptr(in_elements);
@@ -633,12 +612,13 @@ namespace kernel
             Transform<Ti, To, af_add_t> transform;
             Tw count = (Tw)1;
 
-            MeanOp<To, Tw> Op(transform(h_ptr[0]), count);
+            To val = transform(h_ptr[0]);
+            Tw weight = count;
             for (int i = 1; i < in_elements; i++) {
-                Op(transform(h_ptr[i]), count);
+                stable_mean(&val, &weight, transform(h_ptr[i]), count);
             }
 
-            return Op.runningMean;
+            return val;
         }
     }
 

From 3a60bda75fe51621f4f45056f37f270a152d517f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 15 Sep 2017 18:16:18 -0400
Subject: [PATCH 1299/2677] Use the gpu-architecture flag for jit kernels.
 Fixes TX1 failures

This fix addresses the "failed to load builtin" errors on the TX1
hardware.
---
 src/backend/cuda/jit.cpp | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 9ec3063883..1fd4b845c4 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -20,6 +20,8 @@
 #include <af/dim4.hpp>
 #include <nvrtc.h>
 
+#include <array>
+#include <cstdio>
 #include <functional>
 #include <map>
 #include <memory>
@@ -34,6 +36,7 @@ using JIT::Node;
 using JIT::Node_ids;
 using JIT::Node_map_t;
 
+using std::array;
 using std::hash;
 using std::lock_guard;
 using std::map;
@@ -270,8 +273,23 @@ std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
     nvrtcProgram prog;
     size_t ptx_size;
     std::vector<char> ptx;
-    NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 0, NULL, NULL));
-    NVRTC_CHECK(nvrtcCompileProgram(prog, 0, NULL));
+    NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(),
+                                   ker_name, 0, NULL, NULL));
+
+    auto dev = getDeviceProp(getActiveDeviceId());
+    array<char, 32> arch;
+    snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
+             dev.major, dev.minor);
+    const char* compiler_options[] = {
+      arch.data(),
+#ifndef NDEBUG
+      "--device-debug",
+      "--generate-line-info"
+#endif
+    };
+    int num_options = std::extent<decltype(compiler_options)>::value;
+    NVRTC_CHECK(nvrtcCompileProgram(prog, num_options, compiler_options));
+
     NVRTC_CHECK(nvrtcGetPTXSize(prog, &ptx_size));
     ptx.resize(ptx_size);
     NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));

From 263b9a1450456f7d1855393635b9aa1c76a7f1d7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 15 Sep 2017 23:24:52 -0400
Subject: [PATCH 1300/2677] Fix invalid access while exiting with an error in
 flight

This change fixes an error with the convolution example when
an error was thrown as the arrays were being released. Since these arrays
were created before the global_error_string object they were released after
the error string object was deleted. This caused a segfault sometimes when
the release operation returns an error when the driver's resources have
been released.
---
 src/api/c/err_common.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/err_common.cpp b/src/api/c/err_common.cpp
index 7a7307783f..dce29e22a1 100644
--- a/src/api/c/err_common.cpp
+++ b/src/api/c/err_common.cpp
@@ -239,8 +239,8 @@ af_err processException()
 
 std::string& get_global_error_string()
 {
-    thread_local std::string global_error_string = std::string("");
-    return global_error_string;
+    thread_local std::string *global_error_string = new std::string("");
+    return *global_error_string;
 }
 
 const char *af_err_to_string(const af_err err)

From 9be9e5edcd7bd89f00d74e3036373f15a551e86a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sun, 17 Sep 2017 18:31:33 -0700
Subject: [PATCH 1301/2677] BUGFIX: Add support for broadcasting weights when
 using mean.

---
 src/api/c/mean.cpp | 43 ++++++++++++++++++++++++++++++-------------
 test/mean.cpp      | 20 ++++++++++++++++++++
 2 files changed, 50 insertions(+), 13 deletions(-)

diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index d157f823c7..839703f025 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
+#include <af/data.h>
 #include <af/statistics.h>
 #include <af/defines.h>
 #include <err_common.hpp>
@@ -91,23 +92,39 @@ af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights
         af_dtype wType  = wInfo.getType();
 
         ARG_ASSERT(2, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
-        ARG_ASSERT(2, iInfo.dims() == wInfo.dims());
+
+        //FIXME: We should avoid additional copies
+        af_array w = weights;
+        if (iInfo.dims() != wInfo.dims()) {
+            dim4 iDims = iInfo.dims();
+            dim4 wDims = wInfo.dims();
+            dim4 tDims(1,1,1,1);
+            for (int i = 0; i < 4; i++) {
+                ARG_ASSERT(2, wDims[i] == 1 || wDims[i] == iDims[i]);
+                tDims[i] = iDims[i] / wDims[i];
+            }
+            AF_CHECK(af_tile(&w, weights, tDims[0], tDims[1], tDims[2], tDims[3]));
+        }
 
         switch(iType) {
-            case f64: output = mean< double>(in, weights, dim); break;
-            case f32: output = mean< float >(in, weights, dim); break;
-            case s32: output = mean< float >(in, weights, dim); break;
-            case u32: output = mean< float >(in, weights, dim); break;
-            case s64: output = mean< double>(in, weights, dim); break;
-            case u64: output = mean< double>(in, weights, dim); break;
-            case s16: output = mean< float >(in, weights, dim); break;
-            case u16: output = mean< float >(in, weights, dim); break;
-            case  u8: output = mean< float >(in, weights, dim); break;
-            case  b8: output = mean< float >(in, weights, dim); break;
-            case c32: output = mean< cfloat>(in, weights, dim); break;
-            case c64: output = mean<cdouble>(in, weights, dim); break;
+            case f64: output = mean< double>(in, w, dim); break;
+            case f32: output = mean< float >(in, w, dim); break;
+            case s32: output = mean< float >(in, w, dim); break;
+            case u32: output = mean< float >(in, w, dim); break;
+            case s64: output = mean< double>(in, w, dim); break;
+            case u64: output = mean< double>(in, w, dim); break;
+            case s16: output = mean< float >(in, w, dim); break;
+            case u16: output = mean< float >(in, w, dim); break;
+            case  u8: output = mean< float >(in, w, dim); break;
+            case  b8: output = mean< float >(in, w, dim); break;
+            case c32: output = mean< cfloat>(in, w, dim); break;
+            case c64: output = mean<cdouble>(in, w, dim); break;
             default : TYPE_ERROR(1, iType);
         }
+
+        if (w != weights) {
+            AF_CHECK(af_release_array(w));
+        }
         std::swap(*out, output);
     }
     CATCHALL;
diff --git a/test/mean.cpp b/test/mean.cpp
index dd370f4e93..7407ecc26f 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -301,3 +301,23 @@ TYPED_TEST(WeightedMean, Basic)
 {
     weightedMeanAllTest<TypeParam, float>(af::dim4(32, 30, 33, 17));
 }
+
+TEST(WeightedMean, Broadacst)
+{
+    float val = 0.5f;
+    af::array a = af::randu(4096, 32);
+    af::array w = af::constant(val, a.dims());
+    af::array c = af::mean(a);
+    af::array d = af::mean(a, w);
+
+    std::vector<float> hc(c.elements());
+    std::vector<float> hd(d.elements());
+
+    c.host(hc.data());
+    d.host(hd.data());
+
+    for(size_t i = 0; i < hc.size(); i++) {
+        //C and D are the same because they are normalized by the sum of the weights.
+        ASSERT_NEAR(hc[i], hd[i], 1E-5);
+    }
+}

From a5b580c10280dbfd6e8a481f1eb54756514600ae Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 1 Aug 2017 11:36:47 +0530
Subject: [PATCH 1302/2677] Fix compareArrays test helper to handle NaN

Also fixed the initial value for max op in test helper compareArrays.

This fix to the test helper caused canny image test to fail because
of earlier to the change to compareArrays, the test helper was incorrectly
using FLT_MAX for calculating both minima and maxima of the dataset.
Therefore, corrected the canny image test as well as part of this change.
---
 test/canny.cpp       | 41 +++++++++++++++++++++++++++++++++--------
 test/testHelpers.hpp | 10 +++++-----
 2 files changed, 38 insertions(+), 13 deletions(-)

diff --git a/test/canny.cpp b/test/canny.cpp
index de6a495020..c10fea6b14 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -95,29 +95,54 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
 
     for (size_t testId=0; testId<testCount; ++testId) {
 
-        af_array inArray  = 0;
-        af_array outArray = 0;
-        af_array goldArray= 0;
-        dim_t nElems   = 0;
+        af_array _inArray   = 0;
+        af_array inArray    = 0;
+        af_array _outArray  = 0;
+        af_array cstArray   = 0;
+        af_array mulArray   = 0;
+        af_array outArray   = 0;
+        af_array goldArray  = 0;
+        dim_t nElems        = 0;
 
         inFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
         outFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        af_dtype type = (af_dtype)af::dtype_traits<T>::af_type;
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+
+        ASSERT_EQ(AF_SUCCESS, af_cast(&inArray, _inArray, type));
+
         ASSERT_EQ(AF_SUCCESS, af_load_image_native(&goldArray, outFiles[testId].c_str()));
+
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false));
+        ASSERT_EQ(AF_SUCCESS, af_canny(&_outArray, inArray, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false));
+
+        unsigned ndims = 0;
+        dim_t dims[4];
+
+        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, f32));
+
+        ASSERT_EQ(AF_SUCCESS, af_mul(&mulArray, cstArray, _outArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_cast(&outArray, mulArray, u8));
 
-        std::vector<char> outData(nElems);
+        std::vector<unsigned char> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<char> goldData(nElems);
+        std::vector<unsigned char> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 1.0e-3));
 
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(mulArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
     }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 3d3e94a266..3040214342 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -307,7 +307,7 @@ template<typename T>
 bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
 {
     double accum  = 0.0;
-    double maxion = FLT_MAX;//(double)std::numeric_limits<T>::lowest();
+    double maxion = -FLT_MAX;//(double)std::numeric_limits<T>::lowest();
     double minion = FLT_MAX;//(double)std::numeric_limits<T>::max();
 
     for(dim_t i=0;i<data_size;i++)
@@ -315,16 +315,16 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
         double dTemp = (double)data[i];
         double gTemp = (double)gold[i];
         double diff  = gTemp-dTemp;
-        double err   = std::abs(diff) > 1.0e-4 ? diff : 0.0f;
-        accum  += std::pow(err,2.0);
+        double err   = (std::isfinite(diff) && (std::abs(diff) > 1.0e-4)) ? diff : 0.0f;
+        accum  += std::pow(err, 2.0);
         maxion  = std::max(maxion, dTemp);
         minion  = std::min(minion, dTemp);
     }
-    accum      /= data_size;
+    accum /= data_size;
     double NRMSD = std::sqrt(accum)/(maxion-minion);
 
     std::cout<<"NRMSD = "<<NRMSD<<std::endl;
-    if (NRMSD > tolerance)
+    if (std::isnan(NRMSD) || NRMSD > tolerance)
         return false;
 
     return true;

From 35f0f3cf5799ea0e94f0c136daedd6ae53a801d4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Oct 2017 14:09:24 +0530
Subject: [PATCH 1303/2677] Fix meanshift kernel implementations

Gave variables in meanshift related fns more readable names.
---
 src/api/c/meanshift.cpp                 |  46 +++--
 src/backend/cpu/kernel/meanshift.hpp    | 156 ++++++++---------
 src/backend/cpu/meanshift.cpp           |  16 +-
 src/backend/cpu/meanshift.hpp           |   8 +-
 src/backend/cuda/kernel/meanshift.hpp   | 214 ++++++++++--------------
 src/backend/cuda/meanshift.cu           |  16 +-
 src/backend/cuda/meanshift.hpp          |   8 +-
 src/backend/opencl/kernel/meanshift.cl  | 165 ++++++++----------
 src/backend/opencl/kernel/meanshift.hpp |  22 +--
 src/backend/opencl/meanshift.cpp        |  16 +-
 src/backend/opencl/meanshift.hpp        |   8 +-
 test/data                               |   2 +-
 test/meanshift.cpp                      |   8 +-
 13 files changed, 306 insertions(+), 379 deletions(-)

diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index 21a757fd3c..185f2017b5 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -18,19 +18,21 @@
 using af::dim4;
 using namespace detail;
 
-template<typename T, bool is_color>
-static inline af_array mean_shift(const af_array &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
+template<typename T>
+static inline af_array mean_shift(const af_array &in, const float &s_sigma, const float &c_sigma,
+                                  const unsigned niters, const bool is_color)
 {
-    return getHandle(meanshift<T, is_color>(getArray<T>(in), s_sigma, c_sigma, iter));
+    return getHandle(meanshift<T>(getArray<T>(in), s_sigma, c_sigma, niters, is_color));
 }
 
-template<bool is_color>
-af_err mean_shift(af_array *out, const af_array in, const float s_sigma, const float c_sigma, const unsigned iter)
+af_err af_mean_shift(af_array *out, const af_array in,
+                     const float spatial_sigma, const float chromatic_sigma,
+                     const unsigned num_iterations, const bool is_color)
 {
     try {
-        ARG_ASSERT(2, (s_sigma>=0));
-        ARG_ASSERT(3, (c_sigma>=0));
-        ARG_ASSERT(4, (iter>0));
+        ARG_ASSERT(2, (spatial_sigma>=0));
+        ARG_ASSERT(3, (chromatic_sigma>=0));
+        ARG_ASSERT(4, (num_iterations>0));
 
         const ArrayInfo& info = getInfo(in);
         af_dtype type  = info.getType();
@@ -41,16 +43,16 @@ af_err mean_shift(af_array *out, const af_array in, const float s_sigma, const f
 
         af_array output;
         switch(type) {
-            case f32: output = mean_shift<float , is_color>(in, s_sigma, c_sigma, iter); break;
-            case f64: output = mean_shift<double, is_color>(in, s_sigma, c_sigma, iter); break;
-            case b8 : output = mean_shift<char  , is_color>(in, s_sigma, c_sigma, iter); break;
-            case s32: output = mean_shift<int   , is_color>(in, s_sigma, c_sigma, iter); break;
-            case u32: output = mean_shift<uint  , is_color>(in, s_sigma, c_sigma, iter); break;
-            case s16: output = mean_shift<short , is_color>(in, s_sigma, c_sigma, iter); break;
-            case u16: output = mean_shift<ushort, is_color>(in, s_sigma, c_sigma, iter); break;
-            case s64: output = mean_shift<intl  , is_color>(in, s_sigma, c_sigma, iter); break;
-            case u64: output = mean_shift<uintl , is_color>(in, s_sigma, c_sigma, iter); break;
-            case u8 : output = mean_shift<uchar , is_color>(in, s_sigma, c_sigma, iter); break;
+            case f32: output = mean_shift<float >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case f64: output = mean_shift<double>(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case b8 : output = mean_shift<char  >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case s32: output = mean_shift<int   >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case u32: output = mean_shift<uint  >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case s16: output = mean_shift<short >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case u16: output = mean_shift<ushort>(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case s64: output = mean_shift<intl  >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case u64: output = mean_shift<uintl >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
+            case u8 : output = mean_shift<uchar >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
             default : TYPE_ERROR(1, type);
         }
         std::swap(*out,output);
@@ -59,11 +61,3 @@ af_err mean_shift(af_array *out, const af_array in, const float s_sigma, const f
 
     return AF_SUCCESS;
 }
-
-af_err af_mean_shift(af_array *out, const af_array in, const float spatial_sigma, const float chromatic_sigma, const unsigned iter, const bool is_color)
-{
-    if (is_color)
-        return mean_shift<true >(out, in, spatial_sigma, chromatic_sigma, iter);
-    else
-        return mean_shift<false>(out, in, spatial_sigma, chromatic_sigma, iter);
-}
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 2569fefb23..467948dd67 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -11,125 +11,131 @@
 #include <Param.hpp>
 #include <vector>
 #include <utility.hpp>
+#include <type_traits>
 
 namespace cpu
 {
 namespace kernel
 {
-
 template<typename T, bool IsColor>
-void meanShift(Param<T> out, CParam<T> in, const float s_sigma,
-               const float c_sigma, const unsigned iter)
+void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
+               const float chromaticSigma, const unsigned numIterations)
 {
+    typedef typename std::conditional< std::is_same<T, double>::value, double, float >::type AccType;
+
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
+    const unsigned bCount   = (IsColor ? 1 : dims[2]);
+    const unsigned channels = (IsColor ? dims[2] : 1);
+    const dim_t radius      = std::max((int)(spatialSigma * 1.5f), 1);
+    const AccType cvar      = chromaticSigma * chromaticSigma;
 
-    const dim_t bCount   = (IsColor ? 1 : dims[2]);
-    const dim_t channels = (IsColor ? dims[2] : 1);
-
-    // clamp spatical and chromatic sigma's
-    float space_          = std::min(11.5f, s_sigma);
-    const dim_t radius = std::max((int)(space_ * 1.5f), 1);
-    const float cvar      = c_sigma*c_sigma;
-
-    std::vector<float> means(channels);
-    std::vector<float> centers(channels);
-    std::vector<float> tmpclrs(channels);
+    for (dim_t b3=0; b3<dims[3]; ++b3) {
+        for (unsigned b2=0; b2<bCount; ++b2) {
 
+            T *      outData = out.get() + b2 * ostrides[2] + b3 * ostrides[3];
+            const T * inData = in.get()  + b2 * istrides[2] + b3 * istrides[3];
 
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        T *outData       = out.get() + b3 * ostrides[3];
-        const T * inData = in.get() + b3 * istrides[3];
-
-        for(dim_t b2=0; b2<bCount; ++b2) {
-
-            for(dim_t j=0; j<dims[1]; ++j) {
+            for (dim_t j=0; j<dims[1]; ++j) {
 
                 dim_t j_in_off  = j*istrides[1];
                 dim_t j_out_off = j*ostrides[1];
 
-                for(dim_t i=0; i<dims[0]; ++i) {
+                for (dim_t i=0; i<dims[0]; ++i) {
 
                     dim_t i_in_off  = i*istrides[0];
                     dim_t i_out_off = i*ostrides[0];
 
-                    // clear means and centers for this pixel
-                    for(dim_t ch=0; ch<channels; ++ch) {
-                        means[ch] = 0.0f;
-                        // the expression ch*istrides[2] will only effect when ch>1
-                        // i.e for color images where batch is along fourth dimension
-                        centers[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
-                    }
+                    std::vector<T> currentCenterColors(channels, 0);
 
-                    // scope of meanshift iterationd begin
-                    for(unsigned it=0; it<iter; ++it) {
+                    for (unsigned ch=0; ch<channels; ++ch)
+                        currentCenterColors[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
 
-                        int count   = 0;
-                        int shift_x = 0;
+                    int meanPosJ = j;
+                    int meanPosI = i;
+
+                    // scope of meanshift iterations begin
+                    for (unsigned it=0; it<numIterations; ++it) {
+
+                        int oldMeanPosJ   = meanPosJ;
+                        int oldMeanPosI   = meanPosI;
+                        unsigned count = 0;
                         int shift_y = 0;
+                        int shift_x = 0;
 
-                        for(dim_t wj=-radius; wj<=radius; ++wj) {
+                        std::vector<AccType> currentMeanColors(channels, 0);
+
+                        // Windowing operation
+                        for (dim_t wj=-radius; wj<=radius; ++wj) {
 
                             int hit_count = 0;
+                            dim_t tj = meanPosJ + wj;
+                            if (tj<0 || tj>dims[1]-1) continue;
+
+                            dim_t tjstride = tj*istrides[1];
+
+                            for (dim_t wi=-radius; wi<=radius; ++wi) {
 
-                            for(dim_t wi=-radius; wi<=radius; ++wi) {
+                                dim_t ti = meanPosI + wi;
+                                if (ti<0 || ti>dims[0]-1) continue;
 
-                                dim_t tj = j + wj;
-                                dim_t ti = i + wi;
+                                dim_t tistride = ti*istrides[0];
 
-                                // clamps offsets
-                                tj = clamp(tj, 0ll, dims[1]-1);
-                                ti = clamp(ti, 0ll, dims[0]-1);
+                                std::vector<T> tempColors(channels, 0);
 
-                                // proceed
-                                float norm = 0.0f;
-                                for(dim_t ch=0; ch<channels; ++ch) {
-                                    tmpclrs[ch] = inData[ tj*istrides[1] + ti*istrides[0] + ch*istrides[2]];
-                                    norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
+                                AccType norm = 0;
+                                for (unsigned ch=0; ch<channels; ++ch) {
+                                    tempColors[ch] = inData[ tistride + tjstride + ch*istrides[2] ];
+                                    AccType diff = static_cast<AccType>(currentCenterColors[ch]) -
+                                                   static_cast<AccType>(tempColors[ch]);
+                                    norm += (diff * diff);
                                 }
 
-                                if (norm<= cvar) {
-                                    for(dim_t ch=0; ch<channels; ++ch)
-                                        means[ch] += tmpclrs[ch];
-                                    shift_x += wi;
+                                if (norm <= cvar) {
+                                    for(unsigned ch=0; ch<channels; ++ch)
+                                        currentMeanColors[ch] += static_cast<AccType>(tempColors[ch]);
+
+                                    shift_x += ti;
                                     ++hit_count;
                                 }
-
                             }
-                            count+= hit_count;
-                            shift_y += wj*hit_count;
+                            count   += hit_count;
+                            shift_y += tj*hit_count;
                         }
 
-                        if (count==0) { break; }
-
-                        const float fcount = 1.f/count;
-                        const int mean_x = (int)(shift_x*fcount+0.5f);
-                        const int mean_y = (int)(shift_y*fcount+0.5f);
-                        for(dim_t ch=0; ch<channels; ++ch)
-                            means[ch] *= fcount;
-
-                        float norm = 0.f;
-                        for(dim_t ch=0; ch<channels; ++ch)
-                            norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
-                        bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
-                        shift_x = mean_x;
-                        shift_y = mean_y;
-                        for(dim_t ch=0; ch<channels; ++ch)
-                            centers[ch] = means[ch];
-                        if (stop) { break; }
-                    } // scope of meanshift iterations end
+                        if (count==0) break;
+
+                        const AccType fcount = 1/static_cast<AccType>(count);
+
+                        meanPosJ = static_cast<int>(std::trunc(shift_y*fcount));
+                        meanPosI = static_cast<int>(std::trunc(shift_x*fcount));
+
+                        for (unsigned ch=0; ch<channels; ++ch)
+                            currentMeanColors[ch] = std::trunc(currentMeanColors[ch]*fcount);
 
-                    for(dim_t ch=0; ch<channels; ++ch)
-                        outData[j_out_off + i_out_off + ch*ostrides[2]] = centers[ch];
+                        AccType norm = 0;
+                        for (unsigned ch=0; ch<channels; ++ch) {
+                            AccType diff = currentMeanColors[ch] - static_cast<AccType>(currentCenterColors[ch]);
+                            norm += (diff*diff);
+                        }
+
+                        //stop the process if mean converged or within given tolerance range
+                        bool stop = (meanPosJ==oldMeanPosJ && oldMeanPosI==meanPosI) ||
+                                    ((abs(oldMeanPosJ-meanPosJ) + abs(oldMeanPosI-meanPosI) + norm) <= 1);
+
+                        for (unsigned ch=0; ch<channels; ++ch)
+                            currentCenterColors[ch] = static_cast<T>(currentMeanColors[ch]);
 
+                        if (stop) break;
+                    } // scope of meanshift iterations end
+
+                    for (dim_t ch=0; ch<channels; ++ch)
+                        outData[j_out_off + i_out_off + ch*ostrides[2]] = currentCenterColors[ch];
                 }
             }
-            outData += ostrides[2];
-            inData  += istrides[2];
         }
     }
 }
-
 }
 }
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index 6407616e92..a571a6b315 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -23,22 +23,25 @@ using std::vector;
 
 namespace cpu
 {
-
-template<typename T, bool is_color>
-Array<T>  meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
+template<typename T>
+Array<T>  meanshift(const Array<T> &in,
+                    const float &spatialSigma, const float &chromaticSigma,
+                    const unsigned& numInterations, const bool& isColor)
 {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    getQueue().enqueue(kernel::meanShift<T, is_color>, out, in, s_sigma, c_sigma, iter);
+    if (isColor)
+        getQueue().enqueue(kernel::meanShift<T, true>, out, in, spatialSigma, chromaticSigma, numInterations);
+    else
+        getQueue().enqueue(kernel::meanShift<T, false>, out, in, spatialSigma, chromaticSigma, numInterations);
 
     return out;
 }
 
 #define INSTANTIATE(T) \
-    template Array<T>  meanshift<T, true >(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter); \
-    template Array<T>  meanshift<T, false>(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter);
+    template Array<T>  meanshift<T>(const Array<T>&, const float&, const float&, const unsigned&, const bool&);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
@@ -50,5 +53,4 @@ INSTANTIATE(short )
 INSTANTIATE(ushort)
 INSTANTIATE(intl  )
 INSTANTIATE(uintl )
-
 }
diff --git a/src/backend/cpu/meanshift.hpp b/src/backend/cpu/meanshift.hpp
index 1a57807cce..43299b52f7 100644
--- a/src/backend/cpu/meanshift.hpp
+++ b/src/backend/cpu/meanshift.hpp
@@ -11,8 +11,8 @@
 
 namespace cpu
 {
-
-template<typename T, bool is_color>
-Array<T> meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter);
-
+template<typename T>
+Array<T>  meanshift(const Array<T> &in,
+                    const float &spatialSigma, const float &chromaticSigma,
+                    const unsigned& numIterations, const bool& isColor);
 }
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index dde8318b7f..36fa69eb75 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -12,200 +12,154 @@
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
-#include "shared.hpp"
+
+#include <type_traits>
 
 namespace cuda
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-__forceinline__ __device__
-int lIdx(int x, int y,
-              int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
-}
-
-__forceinline__ __device__
-int clamp(int f, int a, int b)
-{
-    return max(a, min(f, b));
-}
-
-template<typename T, int channels>
-inline __device__
-void load2ShrdMem(T * shrd, const T * in,
-                  int lx, int ly,
-                  int shrdStride, int schStride,
-                  int dim0, int dim1,
-                  int gx, int gy,
-                  int ichStride, int inStride1, int inStride0)
-{
-    int gx_  = clamp(gx, 0, dim0-1);
-    int gy_  = clamp(gy, 0, dim1-1);
-#pragma unroll
-    for(int ch=0; ch<channels; ++ch)
-        shrd[lIdx(lx, ly, shrdStride, 1)+ch*schStride] = in[lIdx(gx_, gy_, inStride1, inStride0)+ch*ichStride];
-}
-
-template<typename T, int channels>
+template<typename AccType, typename T, int channels>
 static __global__
-void meanshiftKernel(Param<T> out, CParam<T> in,
-                     float space_, int radius, float cvar,
-                     uint iter, int nBBS0, int nBBS1)
+void meanshiftKernel(Param<T> out, CParam<T> in, int radius, float cvar, uint numIters,
+                     int nBBS0, int nBBS1)
 {
-    SharedMemory<T> shared;
-    T * shrdMem = shared.getPointer();
-
-    // calculate necessary offset and window parameters
-    const int padding     = 2*radius + 1;
-    const int shrdLen     = blockDim.x + padding;
-    const int schStride   = shrdLen*(blockDim.y + padding);
-    // the variable ichStride will only effect when we have >1
-    // channels. in the other cases, the expression in question
-    // will not use the variable
-    const int ichStride   = in.strides[2];
-
-    // gfor batch offsets
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
+    unsigned b2   = blockIdx.x / nBBS0;
+    unsigned b3   = blockIdx.y / nBBS1;
     const T* iptr = (const T *) in.ptr + (b2 *  in.strides[2] + b3 *  in.strides[3]);
     T*       optr = (T *      )out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const int gx  = blockDim.x * (blockIdx.x-b2*nBBS0) + threadIdx.x;
+    const int gy  = blockDim.y * (blockIdx.y-b3*nBBS1) + threadIdx.y;
 
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+    if (gx>=in.dims[0] || gy>=in.dims[1])
+        return;
 
-    // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen; b+=blockDim.y, gy2+=blockDim.y) {
-        // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-            load2ShrdMem<T, channels>(shrdMem, iptr, a, b, shrdLen, schStride,
-                    in.dims[0], in.dims[1], gx2-radius, gy2-radius, ichStride,
-                    in.strides[1], in.strides[0]);
-        }
-    }
+    int meanPosI = gx;
+    int meanPosJ = gy;
 
-    int i   = lx + radius;
-    int j   = ly + radius;
+    T currentCenterColors[channels];
+    T tempColors[channels];
 
-    __syncthreads();
+    AccType currentMeanColors[channels];
 
-    if (gx>=in.dims[0] || gy>=in.dims[1])
-        return;
+#pragma unroll
+    for (int ch=0; ch<channels; ++ch)
+        currentCenterColors[ch] = iptr[ (gx*in.strides[0] + gy*in.strides[1] + ch*in.strides[2]) ];
 
-    float means[channels];
-    float centers[channels];
-    float tmpclrs[channels];
+    const int dim0LenLmt = in.dims[0]-1;
+    const int dim1LenLmt = in.dims[1]-1;
 
-    // clear means and centers for this pixel
-#pragma unroll
-    for(int ch=0; ch<channels; ++ch) {
-        means[ch] = 0.0f;
-        centers[ch] = shrdMem[lIdx(i, j, shrdLen, 1)+ch*schStride];
-    }
+    // scope of meanshift iterations begin
+    for (uint it=0; it<numIters; ++it) {
 
-    // scope of meanshift iterationd begin
-    for(uint it=0; it<iter; ++it) {
+        int oldMeanPosJ = meanPosJ;
+        int oldMeanPosI = meanPosI;
+        unsigned count  = 0;
 
-        int count   = 0;
         int shift_x = 0;
         int shift_y = 0;
 
-        for(int wj=-radius; wj<=radius; ++wj) {
+#pragma unroll
+        for (int ch=0; ch<channels; ++ch)
+            currentMeanColors[ch] = 0;
+
+        for (int wj=-radius; wj<=radius; ++wj) {
             int hit_count = 0;
+            int tj = meanPosJ + wj;
+
+            if (tj<0 || tj>dim1LenLmt) continue;
 
             for(int wi=-radius; wi<=radius; ++wi) {
 
-                int tj = j + wj;
-                int ti = i + wi;
+                int ti = meanPosI + wi;
 
-                // proceed
-                float norm = 0.0f;
+                if (ti<0 || ti>dim0LenLmt) continue;
+
+                AccType norm = 0;
 #pragma unroll
-                for(int ch=0; ch<channels; ++ch) {
-                    tmpclrs[ch] = shrdMem[lIdx(ti, tj, shrdLen, 1)+ch*schStride];
-                    norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
+                for (int ch=0; ch<channels; ++ch) {
+                    tempColors[ch] = iptr[ (ti*in.strides[0] + tj*in.strides[1] + ch*in.strides[2]) ];
+                    AccType diff = (AccType)currentCenterColors[ch] - (AccType)tempColors[ch];
+                    norm += (diff * diff);
                 }
 
-                if (norm<= cvar) {
+                if (norm <= cvar) {
 #pragma unroll
-                    for(int ch=0; ch<channels; ++ch)
-                        means[ch] += tmpclrs[ch];
+                    for (int ch=0; ch<channels; ++ch)
+                        currentMeanColors[ch] += (AccType)tempColors[ch];
 
-                    shift_x += wi;
+                    shift_x += ti;
                     ++hit_count;
                 }
             }
-            count+= hit_count;
-            shift_y += wj*hit_count;
+            count += hit_count;
+            shift_y += tj*hit_count;
         }
 
-        if (count==0) { break; }
+        if (count==0) break;
+
+        const AccType fcount = 1/(AccType)count;
+
+        meanPosI = __float2int_rz(shift_x*fcount);
+        meanPosJ = __float2int_rz(shift_y*fcount);
 
-        const float fcount = 1.f/count;
-        const int mean_x = (int)(shift_x*fcount+0.5f);
-        const int mean_y = (int)(shift_y*fcount+0.5f);
 #pragma unroll
-        for(int ch=0; ch<channels; ++ch)
-            means[ch] *= fcount;
+        for (int ch=0; ch<channels; ++ch)
+            currentMeanColors[ch] = __float2int_rz(currentMeanColors[ch]*fcount);
 
-        float norm = 0.f;
+        AccType norm = 0;
 #pragma unroll
-        for(int ch=0; ch<channels; ++ch)
-            norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
+        for (int ch=0; ch<channels; ++ch) {
+            AccType diff = (AccType)currentCenterColors[ch] - currentMeanColors[ch];
+            norm += (diff * diff);
+        }
 
-        bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
-        shift_x = mean_x;
-        shift_y = mean_y;
+        bool stop = (meanPosJ==oldMeanPosJ && meanPosI==oldMeanPosI) ||
+                    ((abs(oldMeanPosJ-meanPosJ) + abs(oldMeanPosI-meanPosI) + norm) <= 1);
 
 #pragma unroll
-        for(int ch=0; ch<channels; ++ch)
-            centers[ch] = means[ch];
-        if (stop) { break; }
+        for (int ch=0; ch<channels; ++ch)
+            currentCenterColors[ch] = (T)(currentMeanColors[ch]);
+
+        if (stop) break;
     } // scope of meanshift iterations end
 
 #pragma unroll
-    for(int ch=0; ch<channels; ++ch)
-        optr[lIdx(gx, gy, out.strides[1], out.strides[0])+ch*ichStride] = centers[ch];
+    for (int ch=0; ch<channels; ++ch)
+        optr[ (gx*out.strides[0] + gy*out.strides[1] + ch*out.strides[2]) ] = currentCenterColors[ch];
 }
 
-template<typename T, bool is_color>
-void meanshift(Param<T> out, CParam<T> in, float s_sigma, float c_sigma, uint iter)
+template<typename T, bool IsColor>
+void meanshift(Param<T> out, CParam<T> in,
+       const float spatialSigma, const float chromaticSigma, const uint numIters)
 {
+    typedef typename std::conditional< std::is_same<T, double>::value, double, float >::type AccType;
+
     static dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], THREADS_X);
     int blk_y = divup(in.dims[1], THREADS_Y);
 
-    const int bCount   = (is_color ? 1 : in.dims[2]);
-    const int channels = (is_color ? in.dims[2] : 1); // this has to be 3 for color images
+    const int bCount   = (IsColor ? 1 : in.dims[2]);
 
     dim3 blocks(blk_x * bCount, blk_y * in.dims[3]);
 
     // clamp spatical and chromatic sigma's
-    float space_     = std::min(11.5f, s_sigma);
-    int radius  = std::max((int)(space_ * 1.5f), 1);
-    int padding = 2*radius+1;
-    const float cvar = c_sigma*c_sigma;
-    size_t shrd_size = channels*(threads.x + padding)*(threads.y+padding)*sizeof(T);
-
-    if (is_color)
-        CUDA_LAUNCH_SMEM((meanshiftKernel<T, 3>), blocks, threads, shrd_size,
-                out, in, space_, radius, cvar, iter, blk_x, blk_y);
+    int radius   = std::max( (int)(spatialSigma * 1.5f), 1 );
+
+    const float cvar = chromaticSigma*chromaticSigma;
+
+    if (IsColor)
+        CUDA_LAUNCH((meanshiftKernel<AccType, T, 3>), blocks, threads,
+                out, in, radius, cvar, numIters, blk_x, blk_y);
     else
-        CUDA_LAUNCH_SMEM((meanshiftKernel<T, 1>), blocks, threads, shrd_size,
-                out, in, space_, radius, cvar, iter, blk_x, blk_y);
+        CUDA_LAUNCH((meanshiftKernel<AccType, T, 1>), blocks, threads,
+                out, in, radius, cvar, numIters, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
-
 }
-
 }
diff --git a/src/backend/cuda/meanshift.cu b/src/backend/cuda/meanshift.cu
index ad8d109839..f7fc36421f 100644
--- a/src/backend/cuda/meanshift.cu
+++ b/src/backend/cuda/meanshift.cu
@@ -17,22 +17,25 @@ using af::dim4;
 
 namespace cuda
 {
-
-template<typename T, bool is_color>
-Array<T> meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
+template<typename T>
+Array<T>  meanshift(const Array<T> &in,
+        const float &spatialSigma, const float &chromaticSigma,
+        const unsigned& numIterations, const bool& isColor)
 {
     const dim4 dims = in.dims();
 
     Array<T> out   = createEmptyArray<T>(dims);
 
-    kernel::meanshift<T, is_color>(out, in, s_sigma, c_sigma, iter);
+    if (isColor)
+        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma, numIterations);
+    else
+        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma, numIterations);
 
     return out;
 }
 
 #define INSTANTIATE(T) \
-    template Array<T> meanshift<T, true >(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter); \
-    template Array<T> meanshift<T, false>(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter);
+    template Array<T> meanshift<T>(const Array<T>&, const float&, const float&, const unsigned&, const bool&);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
@@ -44,5 +47,4 @@ INSTANTIATE(short )
 INSTANTIATE(ushort)
 INSTANTIATE(intl  )
 INSTANTIATE(uintl )
-
 }
diff --git a/src/backend/cuda/meanshift.hpp b/src/backend/cuda/meanshift.hpp
index a12fe6a16e..13d46a2560 100644
--- a/src/backend/cuda/meanshift.hpp
+++ b/src/backend/cuda/meanshift.hpp
@@ -11,8 +11,8 @@
 
 namespace cuda
 {
-
-template<typename T, bool is_color>
-Array<T> meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter);
-
+template<typename T>
+Array<T>  meanshift(const Array<T> &in,
+                    const float &spatialSigma, const float &chromaticSigma,
+                    const unsigned& numIterations, const bool& isColor);
 }
diff --git a/src/backend/opencl/kernel/meanshift.cl b/src/backend/opencl/kernel/meanshift.cl
index 2b24de6b98..f776cfe69f 100644
--- a/src/backend/opencl/kernel/meanshift.cl
+++ b/src/backend/opencl/kernel/meanshift.cl
@@ -7,149 +7,116 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-int lIdx(int x, int y,
-        int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
-}
-
-void load2LocalMem(__local T *  shrd,
-        __global const T *      in, int lx, int ly,
-        int shrdStride, int schStride, int channels,
-        int dim0, int dim1, int gx, int gy,
-        int ichStride, int inStride1, int inStride0)
-{
-    int gx_  = clamp(gx, 0, dim0-1);
-    int gy_  = clamp(gy, 0, dim1-1);
-#pragma unroll
-    for(int ch=0; ch<channels; ++ch)
-        shrd[ lIdx(lx, ly, shrdStride, 1)+ch*schStride] = in[ lIdx(gx_, gy_, inStride1, inStride0)+ch*ichStride];
-}
-
 __kernel
 void meanshift(__global T *       d_dst,
                KParam             oInfo,
                __global const T * d_src,
                KParam             iInfo,
-               __local T *        localMem,
-               int channels, float space_, int radius,
-               float cvar, unsigned iter, int nBBS0, int nBBS1)
+               int radius, float cvar, unsigned numIters,
+               int nBBS0, int nBBS1)
 {
-    // calculate necessary offset and window parameters
-    const int padding     = 2*radius + 1;
-    const int wind_len    = padding - 1;
-    const int shrdLen     = get_local_size(0) + padding;
-    const int schStride   = shrdLen*(get_local_size(1) + padding);
-    // the variable ichStride will only effect when we have >1
-    // channels. in the other cases, the expression in question
-    // will not use the variable
-    const int ichStride   = iInfo.strides[2];
-
-    // gfor batch offsets
-    unsigned b2 = get_group_id(0) / nBBS0;
-    unsigned b3 = get_group_id(1) / nBBS1;
-    __global const T* iptr = d_src + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
-    __global T*       optr = d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
-
-    const int lx = get_local_id(0);
-    const int ly = get_local_id(1);
-
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
-
-    int s0 = iInfo.strides[0];
-    int s1 = iInfo.strides[1];
-    int d0 = iInfo.dims[0];
-    int d1 = iInfo.dims[1];
-    // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen; b+=get_local_size(1), gy2+=get_local_size(1)) {
-        // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-            load2LocalMem(localMem, iptr, a, b, shrdLen, schStride, channels,
-                    d0, d1, gx2-radius, gy2-radius, ichStride, s1, s0);
-        }
-    }
-
-    int i   = lx + radius;
-    int j   = ly + radius;
-
-    barrier(CLK_LOCAL_MEM_FENCE);
+    unsigned b2  = get_group_id(0) / nBBS0;
+    unsigned b3  = get_group_id(1) / nBBS1;
+    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + get_local_id(0);
+    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + get_local_id(1);
 
     if (gx<iInfo.dims[0] && gy<iInfo.dims[1])
     {
-        float means[MAX_CHANNELS];
-        float centers[MAX_CHANNELS];
-        float tmpclrs[MAX_CHANNELS];
+        __global const T* iptr = d_src + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
+        __global T*       optr = d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+
+        int meanPosI  = gx;
+        int meanPosJ  = gy;
+
+        T currentCenterColors[MAX_CHANNELS];
+        T tempColors[MAX_CHANNELS];
+
+        AccType currentMeanColors[MAX_CHANNELS];
 
-        // clear means and centers for this pixel
 #pragma unroll
-        for(int ch=0; ch<channels; ++ch) {
-            means[ch] = 0.0f;
-            centers[ch] = localMem[lIdx(i, j, shrdLen, 1)+ch*schStride];
-        }
+        for(int ch=0; ch<MAX_CHANNELS; ++ch)
+            currentCenterColors[ch] = iptr[ gx*iInfo.strides[0]+gy*iInfo.strides[1]+ch*iInfo.strides[2] ];
+
+        const int dim0LenLmt = iInfo.dims[0]-1;
+        const int dim1LenLmt = iInfo.dims[1]-1;
 
         // scope of meanshift iterationd begin
-        for(uint it=0; it<iter; ++it) {
+        for(uint it=0; it<numIters; ++it) {
+
+            int oldMeanPosJ = meanPosJ;
+            int oldMeanPosI = meanPosI;
+            unsigned count  = 0;
 
-            int count   = 0;
             int shift_x = 0;
             int shift_y = 0;
 
+            for (int ch=0; ch<MAX_CHANNELS; ++ch)
+                currentMeanColors[ch] = 0;
+
             for(int wj=-radius; wj<=radius; ++wj) {
                 int hit_count = 0;
+                int tj = meanPosJ + wj;
+
+                if (tj<0 || tj>dim1LenLmt) continue;
 
                 for(int wi=-radius; wi<=radius; ++wi) {
 
-                    int tj = j + wj;
-                    int ti = i + wi;
+                    int ti = meanPosI + wi;
 
-                    // proceed
-                    float norm = 0.0f;
+                    if (ti<0 || ti>dim0LenLmt) continue;
+
+                    AccType norm = 0;
 #pragma unroll
-                    for(int ch=0; ch<channels; ++ch) {
-                        tmpclrs[ch] = localMem[lIdx(ti, tj, shrdLen, 1)+ch*schStride];
-                        norm += (centers[ch]-tmpclrs[ch]) * (centers[ch]-tmpclrs[ch]);
+                    for(int ch=0; ch<MAX_CHANNELS; ++ch) {
+                        unsigned idx = ti*iInfo.strides[0] + tj*iInfo.strides[1] + ch*iInfo.strides[2];
+                        tempColors[ch] = iptr[idx];
+                        AccType diff = (AccType)currentCenterColors[ch] - (AccType)tempColors[ch];
+                        norm += (diff * diff);
                     }
 
                     if (norm<= cvar) {
 #pragma unroll
-                        for(int ch=0; ch<channels; ++ch)
-                            means[ch] += tmpclrs[ch];
+                        for(int ch=0; ch<MAX_CHANNELS; ++ch)
+                            currentMeanColors[ch] += (AccType)tempColors[ch];
 
-                        shift_x += wi;
+                        shift_x += ti;
                         ++hit_count;
                     }
                 }
                 count+= hit_count;
-                shift_y += wj*hit_count;
+                shift_y += tj*hit_count;
             }
 
-            if (count==0) { break; }
+            if (count==0) break;
+
+            const AccType fcount = 1/(AccType)count;
+
+            meanPosI = convert_int_rtz(shift_x*fcount);
+            meanPosJ = convert_int_rtz(shift_y*fcount);
 
-            const float fcount = 1.f/count;
-            const int mean_x = (int)(shift_x*fcount+0.5f);
-            const int mean_y = (int)(shift_y*fcount+0.5f);
 #pragma unroll
-            for(int ch=0; ch<channels; ++ch)
-                means[ch] *= fcount;
+            for(int ch=0; ch<MAX_CHANNELS; ++ch)
+                currentMeanColors[ch] = convert_int_rtz(currentMeanColors[ch]*fcount);
 
-            float norm = 0.f;
+            AccType norm = 0;
 #pragma unroll
-            for(int ch=0; ch<channels; ++ch)
-                norm += ((means[ch]-centers[ch])*(means[ch]-centers[ch]));
+            for(int ch=0; ch<MAX_CHANNELS; ++ch) {
+                AccType diff = (AccType)currentCenterColors[ch] - currentMeanColors[ch];
+                norm += (diff*diff);
+            }
 
-            bool stop = ((abs(shift_y-mean_y)+abs(shift_x-mean_x)) + norm) <= 1;
-            shift_x = mean_x;
-            shift_y = mean_y;
+            bool stop = (meanPosJ==oldMeanPosJ && meanPosI==oldMeanPosI) ||
+                        ((abs(oldMeanPosJ-meanPosJ)+abs(oldMeanPosI-meanPosI)) + norm) <= 1;
 
 #pragma unroll
-            for(int ch=0; ch<channels; ++ch)
-                centers[ch] = means[ch];
-            if (stop) { break; }
+            for(int ch=0; ch<MAX_CHANNELS; ++ch)
+                currentCenterColors[ch] = (T)(currentMeanColors[ch]);
+
+            if (stop) break;
         } // scope of meanshift iterations end
 
 #pragma unroll
-        for(int ch=0; ch<channels; ++ch)
-            optr[lIdx(gx, gy, oInfo.strides[1], oInfo.strides[0])+ch*ichStride] = centers[ch];
+        for(int ch=0; ch<MAX_CHANNELS; ++ch)
+            optr[gx*oInfo.strides[0] + gy*oInfo.strides[1] + ch*oInfo.strides[2]] = currentCenterColors[ch];
     }
 }
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index cb82021c3d..38b656920c 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -35,8 +35,11 @@ static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool is_color>
-void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint iter)
+void meanshift(Param out, const Param in,
+        const float spatialSigma, const float chromaticSigma, const uint numIters)
 {
+    typedef typename std::conditional< std::is_same<T, double>::value, double, float >::type AccType;
+
     std::string refName = std::string("meanshift_") +
         std::string(dtype_traits<T>::getName()) + std::to_string(is_color);
 
@@ -46,6 +49,7 @@ void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint ite
     if (entry.prog==0 && entry.ker==0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
+                << " -D AccType=" << dtype_traits<AccType>::getName()
                 << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
@@ -60,8 +64,8 @@ void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint ite
         addKernelToCache(device, refName, entry);
     }
 
-    auto meanshiftOp = KernelFunctor<Buffer, KParam, Buffer, KParam, LocalSpaceArg,
-                                    int, float, int, float, unsigned, int, int >(*entry.ker);
+    auto meanshiftOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                    int, float, unsigned, int, int >(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -69,21 +73,17 @@ void meanshift(Param out, const Param in, float s_sigma, float c_sigma, uint ite
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
     const int bCount   = (is_color ? 1 : in.info.dims[2]);
-    const int channels = (is_color ? in.info.dims[2] : 1);
 
     NDRange global(bCount*blk_x*THREADS_X, in.info.dims[3]*blk_y*THREADS_Y);
 
     // clamp spatical and chromatic sigma's
-    float space_ = std::min(11.5f, s_sigma);
-    int radius   = std::max((int)(space_ * 1.5f), 1);
-    int padding  = 2*radius+1;
-    const float cvar = c_sigma*c_sigma;
-    size_t loc_size  = channels*(local[0]+padding)*(local[1]+padding)*sizeof(T);
+    int radius   = std::max( (int)(spatialSigma * 1.5f), 1 );
+
+    const float cvar = chromaticSigma*chromaticSigma;
 
     meanshiftOp(EnqueueArgs(getQueue(), global, local),
                 *out.data, out.info, *in.data, in.info,
-                cl::Local(loc_size), channels,
-                space_, radius, cvar, iter, blk_x, blk_y);
+                radius, cvar, numIters, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index d028ba7f2c..6480deca82 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -17,19 +17,22 @@ using af::dim4;
 
 namespace opencl
 {
-
-template<typename T, bool is_color>
-Array<T> meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter)
+template<typename T>
+Array<T>  meanshift(const Array<T> &in,
+                    const float &spatialSigma, const float &chromaticSigma,
+                    const unsigned& numIterations,const bool& isColor)
 {
     const dim4 dims = in.dims();
     Array<T> out   = createEmptyArray<T>(dims);
-    kernel::meanshift<T, is_color>(out, in, s_sigma, c_sigma, iter);
+    if (isColor)
+        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma, numIterations);
+    else
+        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma, numIterations);
     return out;
 }
 
 #define INSTANTIATE(T) \
-    template Array<T> meanshift<T, true >(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter); \
-    template Array<T> meanshift<T, false>(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter);
+    template Array<T> meanshift<T>(const Array<T>&, const float&, const float&, const unsigned&, const bool&);
 
 INSTANTIATE(float )
 INSTANTIATE(double)
@@ -41,5 +44,4 @@ INSTANTIATE(short )
 INSTANTIATE(ushort)
 INSTANTIATE(intl  )
 INSTANTIATE(uintl )
-
 }
diff --git a/src/backend/opencl/meanshift.hpp b/src/backend/opencl/meanshift.hpp
index 3349e37802..3bf08bb259 100644
--- a/src/backend/opencl/meanshift.hpp
+++ b/src/backend/opencl/meanshift.hpp
@@ -11,8 +11,8 @@
 
 namespace opencl
 {
-
-template<typename T, bool is_color>
-Array<T> meanshift(const Array<T> &in, const float &s_sigma, const float &c_sigma, const unsigned iter);
-
+template<typename T>
+Array<T>  meanshift(const Array<T> &in,
+                    const float &spatialSigma, const float &chromaticSigma,
+                    const unsigned& numIterations, const bool& isColor);
 }
diff --git a/test/data b/test/data
index 0f2450b7e1..1c2e8f446a 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 0f2450b7e1ae964e6d1e3cb077d3968fe7e88bb7
+Subproject commit 1c2e8f446a63e1802a9416fe06d07265052e8c13
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index c9fe41af29..16c521c8f8 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -82,7 +82,7 @@ void meanshiftTest(string pTestFile)
         ASSERT_EQ(AF_SUCCESS, conv_image<T>(&goldArray, goldArray_f32)); // af_load_image always returns float array
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, 2.25f, 25.56f, 5, isColor));
+        ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, 11.5f, 30.f, 5, isColor));
 
         std::vector<T> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
@@ -90,7 +90,7 @@ void meanshiftTest(string pTestFile)
         std::vector<T> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.07f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
@@ -143,7 +143,7 @@ TEST(Meanshift, Color_CPP)
         af::array img   = af::loadImage(inFiles[testId].c_str(), true);
         af::array gold  = af::loadImage(outFiles[testId].c_str(), true);
         dim_t nElems = gold.elements();
-        af::array output= af::meanShift(img, 2.25f, 25.56f, 5, true);
+        af::array output= af::meanShift(img, 11.5f, 30.f, 5, true);
 
         std::vector<float> outData(nElems);
         output.host((void*)outData.data());
@@ -151,7 +151,7 @@ TEST(Meanshift, Color_CPP)
         std::vector<float> goldData(nElems);
         gold.host((void*)goldData.data());
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.07f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
     }
 }
 

From 8370c7a64eb8c27d18ccfb7c7c1c1388f099f143 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 7 Oct 2017 07:57:40 +0530
Subject: [PATCH 1304/2677] Throw error if scalar template type doesn't match
 array type

---
 src/api/cpp/array.cpp | 4 ++++
 test/array.cpp        | 7 +++++++
 2 files changed, 11 insertions(+)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 6e17501296..ee6cb60518 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -980,6 +980,10 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     }                                                               \
     template<> AFAPI T array::scalar() const                        \
     {                                                               \
+        af_dtype type = (af_dtype)af::dtype_traits<T>::af_type;     \
+        if (type != this->type())                                   \
+            AF_THROW_ERR("Requested type doesn't match array type", \
+                         AF_ERR_TYPE);                              \
         T val;                                                      \
         AF_THROW(af_get_scalar(&val, get()));                       \
         return val;                                                 \
diff --git a/test/array.cpp b/test/array.cpp
index 9457379bfb..c3a3683d5c 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -520,3 +520,10 @@ TYPED_TEST(Array, Scalar)
 
     EXPECT_EQ(true, gold[0]==a.scalar<TypeParam>());
 }
+
+TEST(Array, ScalarTypeMismatch)
+{
+    array a = constant(1.0, dim4(1), f32);
+
+    EXPECT_THROW(a.scalar<int>(), af::exception);
+}

From 9733745b884672552cfed01dfb97a18fb628dd68 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 22 Jun 2017 02:30:18 -0400
Subject: [PATCH 1305/2677] CMake refactor

Refactors CMake files to use "Modern" CMake conventions. Avoids using variables
and primarily uses targets to create dependencies. This commit also refactors
many of the find modules which are included in the repo. This commit also
attempts to separate the api and backend layers so that they can be built
independently.

* OpenMP is not required on Apple platforms
* Move files from src/backend to src/backend/common
* Fix OpenMP check. Fix cmake generator expansion
* Update ArrayFire versioning approach
* Remove freeimage as a hard dependency to cpu backend
* Alternate CUDA compute select
* Allow building afcuda without the driver.
* Avoid building cl2hpp. Download from khronos
* Fix gtest builds to use submodule dir. Remove build_gtest
* Move graphics_common to backend/common
* Don't build boost compute if available on the system
* Install and framework creation. Enable usage of BUILD_SHARED_LIBS
* Use CPack for installers
* Create CMake Config files
* Add CTestConfig
* Use CXX_STANDARD instead of target_compile_features to enable c++11
* Update clBLAST version number
* Disable verbose MSVC warning
* Create a Coverage build type
---
 .gitignore                                    |   2 -
 CMakeLists.txt                                | 470 +++++----
 CMakeModules/AFInstallDirs.cmake              |  10 +
 CMakeModules/ArrayFireConfig.cmake.in         |  40 +-
 CMakeModules/ArrayFireConfigVersion.cmake.in  |   6 +-
 CMakeModules/CLKernelToH.cmake                |   5 +-
 CMakeModules/CPackConfig.cmake                | 170 ++--
 CMakeModules/CUDACheckCompute.cmake           |  38 -
 CMakeModules/FindCBLAS.cmake                  |   6 +-
 CMakeModules/FindFFTW.cmake                   | 144 +--
 CMakeModules/FindFreeImage.cmake              |  84 +-
 CMakeModules/FindGLEWmx.cmake                 |  92 --
 CMakeModules/FindLAPACKE.cmake                |   9 +
 CMakeModules/FindNVRTC.cmake                  |  41 -
 CMakeModules/FindOpenCL.cmake                 | 113 +--
 CMakeModules/FindOpenGL.cmake                 | 227 +++++
 CMakeModules/FindOpenMP.cmake                 | 457 +++++++++
 CMakeModules/InternalUtils.cmake              | 106 +++
 CMakeModules/TargetArch.cmake                 | 157 ---
 CMakeModules/Version.cmake                    |  81 +-
 CMakeModules/build_CLBlast.cmake              |  61 +-
 CMakeModules/build_boost_compute.cmake        | 109 +--
 CMakeModules/build_cl2hpp.cmake               |  61 +-
 CMakeModules/build_clBLAS.cmake               |  70 +-
 CMakeModules/build_clFFT.cmake                |  53 +-
 CMakeModules/build_forge.cmake                | 139 +--
 CMakeModules/build_glbinding.cmake            |   7 +
 CMakeModules/cuda_compute_capability.cpp      |  58 --
 CMakeModules/osx_install/OSXInstaller.cmake   |   4 +-
 .../{readme.html => readme.html.in}           |   0
 .../{welcome.html => welcome.html.in}         |   0
 CMakeModules/select_compute_arch.cmake        | 198 ++++
 CMakeModules/version.h.in                     |  10 +-
 CTestConfig.cmake                             |  13 +
 examples/CMakeLists.txt                       | 203 ++--
 examples/benchmarks/CMakeLists.txt            |  56 ++
 examples/computer_vision/CMakeLists.txt       |  56 ++
 examples/financial/CMakeLists.txt             |  43 +
 examples/getting_started/CMakeLists.txt       |  53 ++
 examples/graphics/CMakeLists.txt              | 105 ++
 examples/helloworld/CMakeLists.txt            |  23 +
 examples/image_processing/CMakeLists.txt      | 115 +++
 examples/lin_algebra/CMakeLists.txt           |  53 ++
 examples/machine_learning/CMakeLists.txt      | 115 +++
 examples/pde/CMakeLists.txt                   |  23 +
 examples/unified/CMakeLists.txt               |  13 +
 include/.gitignore                            |   0
 src/api/c/CMakeLists.txt                      | 169 ++++
 src/api/c/approx.cpp                          |   4 +-
 src/api/c/array.cpp                           |  20 +-
 src/api/c/assign.cpp                          |   4 +-
 src/api/c/bilateral.cpp                       |   2 +-
 src/api/c/binary.cpp                          |   4 +-
 src/api/c/blas.cpp                            |   4 +-
 src/api/c/canny.cpp                           |   2 +-
 src/api/c/cast.cpp                            |   4 +-
 src/api/c/cholesky.cpp                        |   4 +-
 src/api/c/clamp.cpp                           |   4 +-
 src/api/c/colorspace.cpp                      |   2 +-
 src/api/c/complex.cpp                         |   4 +-
 src/api/c/convolve.cpp                        |   2 +-
 src/api/c/corrcoef.cpp                        |   2 +-
 src/api/c/data.cpp                            |   2 +-
 src/api/c/det.cpp                             |   4 +-
 src/api/c/device.cpp                          |   2 +-
 src/api/c/diff.cpp                            |   4 +-
 src/api/c/dog.cpp                             |   2 +-
 src/api/c/error.cpp                           |   2 +-
 src/api/c/exampleFunction.cpp                 |   4 +-
 src/api/c/fast.cpp                            |   2 +-
 src/api/c/fft.cpp                             |   2 +-
 src/api/c/fftconvolve.cpp                     |   4 +-
 src/api/c/filters.cpp                         |   2 +-
 src/api/c/flip.cpp                            |   4 +-
 src/api/c/gaussian_kernel.cpp                 |   2 +-
 src/api/c/gradient.cpp                        |   4 +-
 src/api/c/handle.hpp                          |   2 +-
 src/api/c/harris.cpp                          |   2 +-
 src/api/c/hist.cpp                            |   6 +-
 src/api/c/histeq.cpp                          |   2 +-
 src/api/c/histogram.cpp                       |   2 +-
 src/api/c/homography.cpp                      |   4 +-
 src/api/c/hsv_rgb.cpp                         |   2 +-
 src/api/c/iir.cpp                             |   2 +-
 src/api/c/image.cpp                           |   6 +-
 src/api/c/imageio.cpp                         |   6 +-
 src/api/c/imageio2.cpp                        |   6 +-
 src/api/c/imageio_helper.h                    |   2 +-
 src/api/c/implicit.hpp                        |   2 +-
 src/api/c/index.cpp                           |   4 +-
 src/api/c/internal.cpp                        |   2 +-
 src/api/c/inverse.cpp                         |   4 +-
 src/api/c/join.cpp                            |   4 +-
 src/api/c/lu.cpp                              |   4 +-
 src/api/c/match_template.cpp                  |   2 +-
 src/api/c/mean.cpp                            |   2 +-
 src/api/c/meanshift.cpp                       |   2 +-
 src/api/c/median.cpp                          |   2 +-
 src/api/c/memory.cpp                          |   2 +-
 src/api/c/moddims.cpp                         |   2 +-
 src/api/c/moments.cpp                         |   6 +-
 src/api/c/morph.cpp                           |   2 +-
 src/api/c/nearest_neighbour.cpp               |   2 +-
 src/api/c/norm.cpp                            |   4 +-
 src/api/c/orb.cpp                             |   2 +-
 src/api/c/plot.cpp                            |   6 +-
 src/api/c/print.cpp                           |   4 +-
 src/api/c/qr.cpp                              |   4 +-
 src/api/c/random.cpp                          |   4 +-
 src/api/c/rank.cpp                            |   4 +-
 src/api/c/reduce.cpp                          |   2 +-
 src/api/c/regions.cpp                         |   2 +-
 src/api/c/reorder.cpp                         |   4 +-
 src/api/c/replace.cpp                         |   4 +-
 src/api/c/resize.cpp                          |   4 +-
 src/api/c/rgb_gray.cpp                        |   2 +-
 src/api/c/rotate.cpp                          |   4 +-
 src/api/c/sat.cpp                             |   2 +-
 src/api/c/scan.cpp                            |   2 +-
 src/api/c/select.cpp                          |   4 +-
 src/api/c/set.cpp                             |   2 +-
 src/api/c/shift.cpp                           |   4 +-
 src/api/c/sift.cpp                            |   2 +-
 src/api/c/sobel.cpp                           |   2 +-
 src/api/c/solve.cpp                           |   4 +-
 src/api/c/sort.cpp                            |   4 +-
 src/api/c/sparse.cpp                          |   2 +-
 src/api/c/sparse_handle.hpp                   |   4 +-
 src/api/c/stream.cpp                          |   4 +-
 src/api/c/surface.cpp                         |   6 +-
 src/api/c/susan.cpp                           |   2 +-
 src/api/c/svd.cpp                             |   2 +-
 src/api/c/tile.cpp                            |   4 +-
 src/api/c/transform.cpp                       |   4 +-
 src/api/c/transform_coordinates.cpp           |   2 +-
 src/api/c/transpose.cpp                       |   2 +-
 src/api/c/type_util.cpp                       |  21 +-
 src/api/c/unary.cpp                           |   4 +-
 src/api/c/unwrap.cpp                          |   4 +-
 src/api/c/var.cpp                             |   2 +-
 src/api/c/vector_field.cpp                    |   6 +-
 src/api/c/where.cpp                           |   2 +-
 src/api/c/window.cpp                          |   4 +-
 src/api/c/wrap.cpp                            |   4 +-
 src/api/c/ycbcr_rgb.cpp                       |   2 +-
 src/api/cpp/CMakeLists.txt                    |  89 ++
 src/api/cpp/error.hpp                         |   2 +-
 src/api/unified/CMakeLists.txt                | 151 +--
 src/api/unified/symbol_manager.hpp            |   8 +-
 src/backend/{ => common}/ArrayInfo.cpp        |  22 +-
 src/backend/{ => common}/ArrayInfo.hpp        |   4 +-
 src/backend/common/CMakeLists.txt             |  93 ++
 src/backend/common/InteropManager.cpp         |   5 +-
 src/backend/common/MatrixAlgebraHandle.hpp    |   2 +-
 src/backend/common/MemoryManager.hpp          |   6 +-
 src/backend/{ => common}/MersenneTwister.hpp  |   0
 src/backend/{ => common}/SparseArray.cpp      |   2 +-
 src/backend/{ => common}/SparseArray.hpp      |   7 +-
 src/backend/common/blas_headers.hpp           |  38 +
 src/backend/{ => common}/cblas.cpp            |  26 +-
 src/{api/cpp => backend/common}/constants.cpp |   0
 src/backend/{ => common}/defines.hpp          |   0
 src/backend/{ => common}/dim4.cpp             |   2 +-
 src/backend/{ => common}/dispatch.cpp         |   0
 src/backend/{ => common}/dispatch.hpp         |   0
 src/{api/c => backend/common}/err_common.cpp  |  15 +-
 src/{api/c => backend/common}/err_common.hpp  |   9 +-
 .../c => backend/common}/graphics_common.cpp  |  26 +-
 .../c => backend/common}/graphics_common.hpp  |   0
 src/backend/{ => common}/host_memory.cpp      |   0
 src/backend/{ => common}/host_memory.hpp      |   0
 src/backend/{ => common}/lapacke.cpp          |   7 +-
 src/backend/{ => common}/lapacke.hpp          |   0
 src/backend/{ => common}/sparse_helpers.hpp   |   0
 src/backend/common/types.hpp                  |  17 -
 src/backend/{ => common}/util.cpp             |  21 +
 src/backend/{ => common}/util.hpp             |   0
 src/backend/cpu/Array.cpp                     |   3 +-
 src/backend/cpu/Array.hpp                     |   2 +-
 src/backend/cpu/CMakeLists.txt                | 569 ++++++-----
 src/backend/cpu/blas.cpp                      |  21 +-
 src/backend/cpu/blas.hpp                      |  24 -
 src/backend/cpu/cholesky.cpp                  |   2 +-
 src/backend/cpu/err_cpu.hpp                   |   2 +-
 src/backend/cpu/fftconvolve.cpp               |   2 +-
 src/backend/cpu/hist_graphics.hpp             |   2 +-
 src/backend/cpu/image.cpp                     |   2 +-
 src/backend/cpu/image.hpp                     |   2 +-
 src/backend/cpu/inverse.cpp                   |   2 +-
 .../cpu/kernel/sort_by_key/CMakeLists.txt     |  54 +-
 src/backend/cpu/lapack_helper.hpp             |   2 +-
 src/backend/cpu/lu.cpp                        |  11 +-
 src/backend/cpu/math.cpp                      |   2 +-
 src/backend/cpu/platform.cpp                  |  30 +-
 src/backend/cpu/platform.hpp                  |   8 +-
 src/backend/cpu/plot.cpp                      |   2 +-
 src/backend/cpu/plot.hpp                      |   2 +-
 src/backend/cpu/qr.cpp                        |   2 +-
 src/backend/cpu/queue.hpp                     |   2 +-
 src/backend/cpu/solve.cpp                     |   2 +-
 src/backend/cpu/sparse.cpp                    |   2 +-
 src/backend/cpu/sparse.hpp                    |   2 +-
 src/backend/cpu/sparse_arith.cpp              |   4 +-
 src/backend/cpu/sparse_arith.hpp              |   2 +-
 src/backend/cpu/sparse_blas.cpp               |  10 +-
 src/backend/cpu/sparse_blas.hpp               |   2 +-
 src/backend/cpu/surface.cpp                   |   2 +-
 src/backend/cpu/surface.hpp                   |   2 +-
 src/backend/cpu/svd.cpp                       |   2 +-
 src/backend/cpu/unwrap.cpp                    |   2 +-
 src/backend/cpu/vector_field.cpp              |   2 +-
 src/backend/cpu/vector_field.hpp              |   2 +-
 src/backend/cpu/wrap.cpp                      |   2 +-
 src/backend/cuda/Array.hpp                    |   2 +-
 src/backend/cuda/CMakeLists.txt               | 769 ++++++++-------
 src/backend/cuda/GraphicsResourceManager.hpp  |   2 +-
 src/backend/cuda/blas.cpp                     |   2 +-
 src/backend/cuda/cholesky.cu                  |   4 +-
 src/backend/cuda/cublas.cpp                   |   2 +-
 src/backend/cuda/cublas.hpp                   |   2 +-
 src/backend/cuda/cufft.hpp                    |   2 +-
 src/backend/cuda/cusolverDn.hpp               |   2 +-
 src/backend/cuda/cusparse.hpp                 |   2 +-
 src/backend/cuda/err_cuda.hpp                 |   4 +-
 src/backend/cuda/hist_graphics.hpp            |   2 +-
 src/backend/cuda/image.hpp                    |   2 +-
 src/backend/cuda/inverse.cu                   |   2 +-
 src/backend/cuda/jit.cpp                      |   2 +-
 src/backend/cuda/kernel/approx.hpp            |   2 +-
 src/backend/cuda/kernel/assign.hpp            |   2 +-
 src/backend/cuda/kernel/bilateral.hpp         |   2 +-
 src/backend/cuda/kernel/canny.hpp             |   2 +-
 src/backend/cuda/kernel/convolve.cu           |   2 +-
 src/backend/cuda/kernel/convolve.hpp          |   2 +-
 src/backend/cuda/kernel/convolve_separable.cu |   2 +-
 src/backend/cuda/kernel/diagonal.hpp          |   2 +-
 src/backend/cuda/kernel/diff.hpp              |   2 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |   2 +-
 src/backend/cuda/kernel/fast.hpp              |   2 +-
 src/backend/cuda/kernel/fast_pyramid.hpp      |   2 +-
 src/backend/cuda/kernel/fftconvolve.hpp       |   2 +-
 src/backend/cuda/kernel/gradient.hpp          |   2 +-
 src/backend/cuda/kernel/harris.hpp            |   2 +-
 src/backend/cuda/kernel/histogram.hpp         |   2 +-
 src/backend/cuda/kernel/homography.hpp        |   2 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |   2 +-
 src/backend/cuda/kernel/identity.hpp          |   2 +-
 src/backend/cuda/kernel/iir.hpp               |   2 +-
 src/backend/cuda/kernel/index.hpp             |   2 +-
 src/backend/cuda/kernel/iota.hpp              |   2 +-
 src/backend/cuda/kernel/ireduce.hpp           |   2 +-
 src/backend/cuda/kernel/join.hpp              |   2 +-
 src/backend/cuda/kernel/lookup.hpp            |   2 +-
 src/backend/cuda/kernel/lu_split.hpp          |   2 +-
 src/backend/cuda/kernel/match_template.hpp    |   2 +-
 src/backend/cuda/kernel/mean.hpp              |   2 +-
 src/backend/cuda/kernel/meanshift.hpp         |   2 +-
 src/backend/cuda/kernel/medfilt.hpp           |   2 +-
 src/backend/cuda/kernel/memcopy.hpp           |   2 +-
 src/backend/cuda/kernel/moments.hpp           |   2 +-
 src/backend/cuda/kernel/morph.hpp             |   2 +-
 src/backend/cuda/kernel/nearest_neighbour.hpp |   2 +-
 src/backend/cuda/kernel/orb.hpp               |   2 +-
 src/backend/cuda/kernel/random_engine.hpp     |   2 +-
 src/backend/cuda/kernel/range.hpp             |   2 +-
 src/backend/cuda/kernel/reduce.hpp            |   2 +-
 src/backend/cuda/kernel/regions.hpp           |   2 +-
 src/backend/cuda/kernel/reorder.hpp           |   2 +-
 src/backend/cuda/kernel/resize.hpp            |   2 +-
 src/backend/cuda/kernel/rotate.hpp            |   2 +-
 .../cuda/kernel/scan_by_key/CMakeLists.txt    |  68 +-
 ..._by_key_impl.cu.in => scan_by_key_impl.cu} |   4 +-
 src/backend/cuda/kernel/scan_dim.hpp          |   2 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |   2 +-
 src/backend/cuda/kernel/scan_first.hpp        |   2 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |   2 +-
 src/backend/cuda/kernel/select.hpp            |   2 +-
 src/backend/cuda/kernel/shift.hpp             |   2 +-
 src/backend/cuda/kernel/sift_nonfree.hpp      |   2 +-
 src/backend/cuda/kernel/sobel.hpp             |   2 +-
 src/backend/cuda/kernel/sort.hpp              |   2 +-
 src/backend/cuda/kernel/sort_by_key.hpp       |   2 +-
 src/backend/cuda/kernel/sparse.hpp            |   2 +-
 src/backend/cuda/kernel/sparse_arith.hpp      |   2 +-
 src/backend/cuda/kernel/susan.hpp             |   2 +-
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |  70 +-
 ..._impl.cu.in => thrust_sort_by_key_impl.cu} |   2 +-
 src/backend/cuda/kernel/tile.hpp              |   2 +-
 src/backend/cuda/kernel/transform.hpp         |   2 +-
 src/backend/cuda/kernel/transpose.hpp         |   2 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |   2 +-
 src/backend/cuda/kernel/triangle.hpp          |   2 +-
 src/backend/cuda/kernel/unwrap.hpp            |   2 +-
 src/backend/cuda/kernel/where.hpp             |   2 +-
 src/backend/cuda/kernel/wrap.hpp              |   2 +-
 src/backend/cuda/lu.cu                        |   4 +-
 src/backend/cuda/math.hpp                     |   7 +-
 src/backend/cuda/memory.cpp                   |   4 +-
 src/backend/cuda/platform.cpp                 |   9 +-
 src/backend/cuda/platform.hpp                 |   1 -
 src/backend/cuda/plot.hpp                     |   2 +-
 src/backend/cuda/qr.cu                        |   4 +-
 src/backend/cuda/solve.cu                     |   4 +-
 src/backend/cuda/sparse.cu                    |   2 +-
 src/backend/cuda/sparse.hpp                   |   2 +-
 src/backend/cuda/sparse_arith.cu              |   2 +-
 src/backend/cuda/sparse_arith.hpp             |   2 +-
 src/backend/cuda/sparse_blas.cpp              |   2 +-
 src/backend/cuda/sparse_blas.hpp              |   2 +-
 src/backend/cuda/surface.hpp                  |   2 +-
 src/backend/cuda/svd.cu                       |   4 +-
 src/backend/cuda/vector_field.hpp             |   2 +-
 src/backend/cuda/wrap.cu                      |   2 +-
 src/backend/opencl/Array.cpp                  |   2 +-
 src/backend/opencl/Array.hpp                  |   2 +-
 src/backend/opencl/CMakeLists.txt             | 894 ++++++++++--------
 .../opencl/GraphicsResourceManager.hpp        |   1 +
 src/backend/opencl/cache.hpp                  |   5 +
 src/backend/opencl/clfft.cpp                  |   2 +-
 src/backend/opencl/clfft.hpp                  |   1 +
 src/backend/opencl/cpu/cpu_blas.cpp           |   1 +
 src/backend/opencl/cpu/cpu_helper.hpp         |  29 +-
 src/backend/opencl/cpu/cpu_sparse_blas.hpp    |   2 +-
 src/backend/opencl/err_clblast.hpp            |   2 +-
 src/backend/opencl/err_opencl.hpp             |   2 +-
 src/backend/opencl/fftconvolve.cpp            |  12 +-
 src/backend/opencl/hist_graphics.hpp          |   2 +-
 src/backend/opencl/image.hpp                  |   2 +-
 src/backend/opencl/jit.cpp                    |   2 +-
 src/backend/opencl/kernel/approx.hpp          |   2 +-
 src/backend/opencl/kernel/assign.hpp          |   2 +-
 src/backend/opencl/kernel/bilateral.hpp       |   2 +-
 src/backend/opencl/kernel/canny.hpp           |   2 +-
 .../opencl/kernel/convolve/conv_common.hpp    |   2 +-
 .../opencl/kernel/convolve_separable.cpp      |   2 +-
 src/backend/opencl/kernel/cscmm.hpp           |   2 +-
 src/backend/opencl/kernel/cscmv.hpp           |   2 +-
 src/backend/opencl/kernel/csrmm.hpp           |   2 +-
 src/backend/opencl/kernel/csrmv.hpp           |   2 +-
 src/backend/opencl/kernel/diagonal.hpp        |   2 +-
 src/backend/opencl/kernel/diff.hpp            |   2 +-
 src/backend/opencl/kernel/exampleFunction.hpp |   2 +-
 src/backend/opencl/kernel/fast.hpp            |   2 +-
 src/backend/opencl/kernel/fftconvolve.hpp     |   2 +-
 src/backend/opencl/kernel/gradient.hpp        |   2 +-
 src/backend/opencl/kernel/harris.hpp          |   2 +-
 src/backend/opencl/kernel/histogram.hpp       |   2 +-
 src/backend/opencl/kernel/homography.hpp      |   2 +-
 src/backend/opencl/kernel/hsv_rgb.hpp         |   2 +-
 src/backend/opencl/kernel/identity.hpp        |   2 +-
 src/backend/opencl/kernel/iir.hpp             |   2 +-
 src/backend/opencl/kernel/index.hpp           |   2 +-
 src/backend/opencl/kernel/iota.hpp            |   2 +-
 src/backend/opencl/kernel/ireduce.hpp         |   2 +-
 src/backend/opencl/kernel/join.hpp            |   2 +-
 src/backend/opencl/kernel/laset.hpp           |   2 +-
 src/backend/opencl/kernel/laset_band.hpp      |   2 +-
 src/backend/opencl/kernel/laswp.hpp           |   2 +-
 src/backend/opencl/kernel/lookup.hpp          |   2 +-
 src/backend/opencl/kernel/lu_split.hpp        |   2 +-
 src/backend/opencl/kernel/match_template.hpp  |   2 +-
 src/backend/opencl/kernel/mean.hpp            |   2 +-
 src/backend/opencl/kernel/meanshift.hpp       |   2 +-
 src/backend/opencl/kernel/medfilt.hpp         |   2 +-
 src/backend/opencl/kernel/memcopy.hpp         |   2 +-
 src/backend/opencl/kernel/moments.hpp         |   2 +-
 src/backend/opencl/kernel/morph.hpp           |   2 +-
 .../opencl/kernel/nearest_neighbour.hpp       |   4 +-
 src/backend/opencl/kernel/orb.hpp             |   2 +-
 src/backend/opencl/kernel/random_engine.hpp   |   2 +-
 src/backend/opencl/kernel/range.hpp           |   2 +-
 src/backend/opencl/kernel/reduce.hpp          |   2 +-
 src/backend/opencl/kernel/regions.hpp         |   2 +-
 src/backend/opencl/kernel/reorder.hpp         |   2 +-
 src/backend/opencl/kernel/resize.hpp          |   2 +-
 src/backend/opencl/kernel/rotate.hpp          |   2 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  68 +-
 src/backend/opencl/kernel/scan_dim.hpp        |   2 +-
 src/backend/opencl/kernel/scan_dim_by_key.hpp |   2 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   2 +-
 src/backend/opencl/kernel/scan_first.hpp      |   2 +-
 .../opencl/kernel/scan_first_by_key.hpp       |   2 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   2 +-
 src/backend/opencl/kernel/select.hpp          |   2 +-
 src/backend/opencl/kernel/shift.hpp           |   2 +-
 src/backend/opencl/kernel/sift_nonfree.hpp    |   2 +-
 src/backend/opencl/kernel/sobel.hpp           |   2 +-
 src/backend/opencl/kernel/sort.hpp            |   2 +-
 src/backend/opencl/kernel/sort_by_key.hpp     |   2 +-
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  65 +-
 .../opencl/kernel/sort_by_key_impl.hpp        |   2 +-
 src/backend/opencl/kernel/sort_helper.hpp     |   2 +-
 src/backend/opencl/kernel/sparse.hpp          |   2 +-
 src/backend/opencl/kernel/sparse_arith.hpp    |   2 +-
 src/backend/opencl/kernel/susan.hpp           |   2 +-
 src/backend/opencl/kernel/swapdblk.hpp        |   2 +-
 src/backend/opencl/kernel/tile.hpp            |   2 +-
 src/backend/opencl/kernel/transform.hpp       |   2 +-
 src/backend/opencl/kernel/transpose.hpp       |   2 +-
 .../opencl/kernel/transpose_inplace.hpp       |   2 +-
 src/backend/opencl/kernel/triangle.hpp        |   2 +-
 src/backend/opencl/kernel/unwrap.hpp          |   2 +-
 src/backend/opencl/kernel/where.hpp           |   2 +-
 src/backend/opencl/kernel/wrap.hpp            |   2 +-
 src/backend/opencl/magma/magma_blas_clblas.h  |   2 +-
 src/backend/opencl/magma/magma_blas_clblast.h |   2 +-
 src/backend/opencl/magma/magma_cpu_blas.h     |  26 +-
 src/backend/opencl/magma/magma_cpu_lapack.h   |   4 +-
 src/backend/opencl/math.hpp                   |   7 +-
 src/backend/opencl/platform.cpp               |   8 +-
 src/backend/opencl/platform.hpp               |   1 -
 src/backend/opencl/plot.hpp                   |   2 +-
 src/backend/opencl/program.hpp                |   2 +-
 src/backend/opencl/sparse.hpp                 |   2 +-
 src/backend/opencl/sparse_arith.cpp           |   2 +-
 src/backend/opencl/sparse_arith.hpp           |   2 +-
 src/backend/opencl/sparse_blas.cpp            |   2 +-
 src/backend/opencl/sparse_blas.hpp            |   2 +-
 src/backend/opencl/surface.hpp                |   2 +-
 src/backend/opencl/traits.hpp                 |   2 +-
 src/backend/opencl/vector_field.hpp           |   2 +-
 src/backend/opencl/wrap.cpp                   |   2 +-
 test/CMakeLists.txt                           | 555 +++++------
 test/CMakeModules/build_gtest.cmake           | 100 --
 test/gtest                                    |   2 +-
 test/ocl_ext_context.cpp                      |   4 -
 426 files changed, 5267 insertions(+), 3585 deletions(-)
 delete mode 100644 CMakeModules/CUDACheckCompute.cmake
 delete mode 100644 CMakeModules/FindGLEWmx.cmake
 delete mode 100644 CMakeModules/FindNVRTC.cmake
 create mode 100644 CMakeModules/FindOpenGL.cmake
 create mode 100644 CMakeModules/FindOpenMP.cmake
 create mode 100644 CMakeModules/InternalUtils.cmake
 delete mode 100644 CMakeModules/TargetArch.cmake
 delete mode 100644 CMakeModules/cuda_compute_capability.cpp
 rename CMakeModules/osx_install/{readme.html => readme.html.in} (100%)
 rename CMakeModules/osx_install/{welcome.html => welcome.html.in} (100%)
 create mode 100644 CMakeModules/select_compute_arch.cmake
 create mode 100644 CTestConfig.cmake
 create mode 100644 examples/benchmarks/CMakeLists.txt
 create mode 100644 examples/computer_vision/CMakeLists.txt
 create mode 100644 examples/financial/CMakeLists.txt
 create mode 100644 examples/getting_started/CMakeLists.txt
 create mode 100644 examples/graphics/CMakeLists.txt
 create mode 100644 examples/helloworld/CMakeLists.txt
 create mode 100644 examples/image_processing/CMakeLists.txt
 create mode 100644 examples/lin_algebra/CMakeLists.txt
 create mode 100644 examples/machine_learning/CMakeLists.txt
 create mode 100644 examples/pde/CMakeLists.txt
 create mode 100644 examples/unified/CMakeLists.txt
 delete mode 100644 include/.gitignore
 create mode 100644 src/api/c/CMakeLists.txt
 create mode 100644 src/api/cpp/CMakeLists.txt
 rename src/backend/{ => common}/ArrayInfo.cpp (89%)
 rename src/backend/{ => common}/ArrayInfo.hpp (98%)
 create mode 100644 src/backend/common/CMakeLists.txt
 rename src/backend/{ => common}/MersenneTwister.hpp (100%)
 rename src/backend/{ => common}/SparseArray.cpp (99%)
 rename src/backend/{ => common}/SparseArray.hpp (99%)
 create mode 100644 src/backend/common/blas_headers.hpp
 rename src/backend/{ => common}/cblas.cpp (88%)
 rename src/{api/cpp => backend/common}/constants.cpp (100%)
 rename src/backend/{ => common}/defines.hpp (100%)
 rename src/backend/{ => common}/dim4.cpp (99%)
 rename src/backend/{ => common}/dispatch.cpp (100%)
 rename src/backend/{ => common}/dispatch.hpp (100%)
 rename src/{api/c => backend/common}/err_common.cpp (98%)
 rename src/{api/c => backend/common}/err_common.hpp (99%)
 rename src/{api/c => backend/common}/graphics_common.cpp (93%)
 rename src/{api/c => backend/common}/graphics_common.hpp (100%)
 rename src/backend/{ => common}/host_memory.cpp (100%)
 rename src/backend/{ => common}/host_memory.hpp (100%)
 rename src/backend/{ => common}/lapacke.cpp (99%)
 rename src/backend/{ => common}/lapacke.hpp (100%)
 rename src/backend/{ => common}/sparse_helpers.hpp (100%)
 delete mode 100644 src/backend/common/types.hpp
 rename src/backend/{ => common}/util.cpp (65%)
 rename src/backend/{ => common}/util.hpp (100%)
 rename src/backend/cuda/kernel/scan_by_key/{scan_by_key_impl.cu.in => scan_by_key_impl.cu} (86%)
 rename src/backend/cuda/kernel/thrust_sort_by_key/{thrust_sort_by_key_impl.cu.in => thrust_sort_by_key_impl.cu} (94%)
 delete mode 100644 test/CMakeModules/build_gtest.cmake

diff --git a/.gitignore b/.gitignore
index d032d3d5dd..d59d4c4aa3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,7 +8,5 @@ GTAGS
 GRTAGS
 GPATH
 .dir-locals.el
-include/af/version.h
-src/backend/version.hpp
 docs/details/examples.dox
 /TAGS
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5fcf4cd415..ee102078ed 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,220 +1,161 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8.12)
-PROJECT(ARRAYFIRE)
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-if(POLICY CMP0058)
-  CMAKE_POLICY(SET CMP0058 NEW)
-endif(POLICY CMP0058)
-
-SET(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
-INCLUDE(UploadCoveralls)
-INCLUDE(AFInstallDirs)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+cmake_minimum_required(VERSION 3.5)
+project(ArrayFire
+  VERSION 3.6.0
+  LANGUAGES C CXX )
+
+set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
+include(AFInstallDirs)
+include(CMakeDependentOption)
+include(InternalUtils)
+include(Version)
+include(build_cl2hpp)
+
+arrayfire_set_cmake_default_variables()
+
+find_package(CUDA 7.0)
+find_package(OpenCL 1.2)
+find_package(OpenGL)
+find_package(OpenMP)
+find_package(FreeImage)
+find_package(Threads)
+find_package(FFTW)
+find_package(CBLAS)
+find_package(LAPACKE)
+find_package(Doxygen)
+
+# Graphics dependencies
+find_package(glbinding QUIET)
+find_package(Boost)
+
+option(BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
+option(BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})
+option(BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FOUND})
+option(BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
+
+option(BUILD_GRAPHICS "Build ArrayFire with Forge Graphics"      $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
+option(BUILD_DOCS     "Create ArrayFire Documentation"           ${DOXYGEN_FOUND})
+option(BUILD_NONFREE  "Build ArrayFire nonfree algorithms"       OFF)
+
+option(BUILD_EXAMPLES "Build Examples"                           ON)
+cmake_dependent_option(USE_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
+  "BUILD_TESTING" OFF)
+
+cmake_dependent_option(USE_SYSTEM_FORGE "Use system Forge" OFF
+                    "BUILD_GRAPHICS" OFF)
+cmake_dependent_option(WITH_IMAGEIO "Build ArrayFire with Image IO support" ${FreeImage_FOUND}
+                      "FreeImage_FOUND" OFF)
+cmake_dependent_option(BUILD_FRAMEWORK "Build an ArrayFire framework for Apple platforms.(Experimental)" OFF
+                      "APPLE" OFF)
+option(USE_FREEIMAGE_STATIC "Use Static FreeImage Lib" OFF)
+
+set(USE_CPUID ON CACHE BOOL "Build with CPUID integration")
+
+mark_as_advanced(
+  BUILD_FRAMEWORK
+  USE_SYSTEM_FORGE
+  USE_CPUID)
+
+# TODO(umar): Add definitions should not be used. Instead use
+arrayfire_get_platform_definitions(platform_definitions)
+add_definitions(${platform_definitions})
+
+if(WIN32)
+  #TODO(umar): create a single place for compiler specific settings
+  # C4251: Warnings about dll interfaces. Thrown by glbinding, may be fixed in
+  #        the future
+  # C4068: Warnings about unknown pragmas
+  # C4275: Warnings about using non-exported classes as base class of an
+  #        exported class
+  add_compile_options(/wd4251 /wd4068 /wd4275)
+endif()
 
-OPTION(BUILD_TEST "Build Tests" ON)
-OPTION(BUILD_EXAMPLES "Build Examples" ON)
+if(BUILD_GRAPHICS)
+  include(build_forge)
+endif()
 
-OPTION(BUILD_CPU "Build ArrayFire with a CPU backend" ON)
+configure_file(
+    ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
+    ${ArrayFire_BINARY_DIR}/version.hpp
+)
 
-FIND_PACKAGE(CUDA QUIET)
-IF(${CUDA_FOUND})
-    SET(BUILD_CUDA ON CACHE BOOL "")
-ENDIF(${CUDA_FOUND})
-OPTION(BUILD_CUDA "Build ArrayFire with a CUDA backend" OFF)
+if(BUILD_NONFREE)
+  message("Building with NONFREE requires the following patents")
+  message("Method and apparatus for identifying scale invariant features\n"
+    "in an image and use of same for locating an object in an image, David\n"
+    "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application\n"
+    "filed March 8, 1999. Asignee: The University of British Columbia. For\n"
+    "further details, contact David Lowe (lowe@cs.ubc.ca) or the\n"
+    "University-Industry Liaison Office of the University of British\n"
+    "Columbia.")
+endif()
 
-FIND_PACKAGE(OpenCL QUIET)
-IF(${OpenCL_FOUND})
-    SET(BUILD_OPENCL ON CACHE BOOL "")
-ENDIF(${OpenCL_FOUND})
-OPTION(BUILD_OPENCL "Build ArrayFire with a OpenCL backend" OFF)
+add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp)
+
+if(NOT LAPACK_FOUND)
+    if(APPLE)
+        # UNSET THE VARIABLES FROM LAPACKE
+        unset(LAPACKE_LIB CACHE)
+        unset(LAPACK_LIB CACHE)
+        unset(LAPACKE_INCLUDES CACHE)
+        unset(LAPACKE_ROOT_DIR CACHE)
+        find_package(LAPACK)
+    endif()
+endif()
 
-OPTION(BUILD_GRAPHICS "Build ArrayFire with Forge Graphics" ON)
+# TODO(umar): Enable other backends
+add_subdirectory(src/backend/common)
+add_subdirectory(src/api/c)
+add_subdirectory(src/api/cpp)
 
-OPTION(BUILD_DOCS "Create ArrayFire Documentation" OFF)
-OPTION(WITH_COVERAGE "Added code coverage flags" OFF)
+conditional_directory(BUILD_CPU     src/backend/cpu)
+conditional_directory(BUILD_CUDA    src/backend/cuda)
+conditional_directory(BUILD_OPENCL  src/backend/opencl)
+conditional_directory(BUILD_UNIFIED src/api/unified)
 
-OPTION(BUILD_NONFREE "Build ArrayFire nonfree algorithms" OFF)
+if(TARGET af)
+  list(APPEND built_backends af)
+endif()
 
-OPTION(BUILD_UNIFIED "Build Backend-Independent ArrayFire API" ON)
+if(TARGET afcpu)
+  list(APPEND built_backends afcpu)
+endif()
 
-# Set a default build type if none was specified
-if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
-    set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE)
-    # Set the possible values of build type for cmake-gui
-    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release"
-      "MinSizeRel" "RelWithDebInfo")
+if(TARGET afcuda)
+  list(APPEND built_backends afcuda)
 endif()
 
-OPTION(MIN_BUILD_TIME "This flag compiles ArrayFire with O0, which is the fastest way to compile" OFF)
-INCLUDE(MinBuildTime)
-
-FIND_PACKAGE(FreeImage)
-IF(FREEIMAGE_FOUND)
-    ADD_DEFINITIONS(-DWITH_FREEIMAGE)
-    SET(FreeImage_LIBS ${FREEIMAGE_LIBRARY})
-    INCLUDE_DIRECTORIES(BEFORE ${FREEIMAGE_INCLUDE_PATH})
-ELSE(FREEIMAGE_FOUND)
-    MESSAGE(WARNING, "FreeImage not found!")
-ENDIF(FREEIMAGE_FOUND)
-
-OPTION(USE_SYSTEM_CL2HPP "Use cl2.hpp installed on system" OFF)
-
-IF(BUILD_GRAPHICS)
-    OPTION(USE_SYSTEM_FORGE "Use system Forge" OFF)
-    OPTION(USE_SYSTEM_GLBINDING "Use system glbinding" OFF)
-
-    FIND_PACKAGE(OpenGL REQUIRED)
-
-    IF(USE_SYSTEM_GLBINDING)
-        # Point glbinding_DIR (case sensitive) to the location of glbinding-config.cmake
-        # This file is generally at CMAKE_INSTALL_PREFIX/glbinding-config.cmake of
-        # the glbinding project
-        FIND_PACKAGE(glbinding REQUIRED)
-        SET(GLBINDING_FOUND "ON")
-        SET(GLBINDING_LIBRARIES glbinding::glbinding CACHE INTERNAL "glbinding library target")
-    ELSE(USE_SYSTEM_GLBINDING)
-        INCLUDE(build_glbinding)
-        LIST(APPEND GRAPHICS_DEPENDENCIES glbinding)
-    ENDIF(USE_SYSTEM_GLBINDING)
-
-    IF(USE_SYSTEM_FORGE)
-      FIND_PACKAGE(Forge REQUIRED)
-    ELSE(USE_SYSTEM_FORGE)  # Build Forge as an external Project
-        INCLUDE(build_forge)
-        LIST(APPEND GRAPHICS_DEPENDENCIES forge)
-    ENDIF(USE_SYSTEM_FORGE)
-
-    IF(FORGE_FOUND AND GLBINDING_FOUND)
-        ADD_DEFINITIONS(-DWITH_GRAPHICS)
-
-        INCLUDE_DIRECTORIES(BEFORE
-            ${FORGE_INCLUDE_DIRS}
-            ${GLBINDING_INCLUDE_DIRS})
-
-        SET(GRAPHICS_LIBRARIES  ${FORGE_LIBRARIES}
-                                ${GLBINDING_LIBRARIES}
-                                ${OPENGL_gl_LIBRARY})
-
-        IF(APPLE)
-            FIND_PACKAGE(X11 REQUIRED)
-            INCLUDE_DIRECTORIES(BEFORE ${X11_INCLUDE_DIR})
-        ENDIF(APPLE)
-
-    ELSE(FORGE_FOUND AND GLBINDING_FOUND)
-        MESSAGE(WARNING "Graphics dependencies (Forge and/or glbinding) not found. Graphics will be disabled")
-    ENDIF(FORGE_FOUND AND GLBINDING_FOUND)
-
-ENDIF(BUILD_GRAPHICS)
-
-IF(${BUILD_NONFREE})
-    MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
-    SET(BUILD_NONFREE_SIFT ON CACHE BOOL "Build ArrayFire with SIFT")
-    MARK_AS_ADVANCED(BUILD_NONFREE_SIFT)
-ELSE(${BUILD_NONFREE})
-    UNSET(BUILD_NONFREE_SIFT CACHE) # BUILD_NONFREE_SIFT cannot be built without BUILD_NONFREE
-ENDIF(${BUILD_NONFREE})
-
-IF(${BUILD_NONFREE_SIFT})
-  ADD_DEFINITIONS(-DAF_BUILD_NONFREE_SIFT)
-
-  MESSAGE(WARNING "Building with SIFT requires the following patents")
-
-  MESSAGE("Method and apparatus for identifying scale invariant features"
-    "in an image and use of same for locating an object in an image,\" David"
-    "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application"
-    "filed March 8, 1999. Asignee: The University of British Columbia. For"
-    "further details, contact David Lowe (lowe@cs.ubc.ca) or the"
-    "University-Industry Liaison Office of the University of British"
-    "Columbia.")
-ENDIF(${BUILD_NONFREE_SIFT})
+if(TARGET afopencl)
+  list(APPEND built_backends afopencl)
+endif()
 
-INCLUDE_DIRECTORIES(BEFORE
-    "${CMAKE_CURRENT_SOURCE_DIR}/include"
-    "${CMAKE_CURRENT_SOURCE_DIR}/src/backend"
-    "${CMAKE_CURRENT_SOURCE_DIR}/src/api/c"
+set_target_properties(${built_backends} PROPERTIES
+                      VERSION "${ArrayFire_VERSION}"
+                      SOVERSION "${ArrayFire_VERSION_MAJOR}")
+
+foreach(backend ${built_backends})
+  target_compile_definitions(${backend} PRIVATE AFDLL)
+endforeach()
+
+if(BUILD_FRAMEWORK)
+  set_target_properties(${built_backends}
+    PROPERTIES
+      FRAMEWORK TRUE
+      FRAMEWORK_VERSION A
+      MACOSX_FRAMEWORK_IDENTIFIER com.arrayfire.arrayfireFramework
+      #MACOSX_FRAMEWORK_INFO_PLIST Info.plist
+      #PUBLIC_HEADER "${CMAKE_CURRENT_SOURCE_DIR}/include/arrayfire.h;${af_headers}"
+      #XCODE_ATTRIBUTE_CODE_SIGN_IDENTITY "iPhone Developer"
     )
+endif()
 
-IF(${UNIX})
-    ADD_DEFINITIONS(-std=c++11 -Wall -fvisibility=hidden)
-
-    IF(${WITH_COVERAGE})
-        SET(CMAKE_CXX_FLAGS             "-fprofile-arcs -ftest-coverage")
-        SET(CMAKE_EXE_LINKER_FLAGS      "-fprofile-arcs -ftest-coverage")
-        SET(CMAKE_SHARED_LINKER_FLAGS   "-fprofile-arcs -ftest-coverage")
-        SET(CMAKE_STATIC_LINKER_FLAGS   "-fprofile-arcs -ftest-coverage")
-    ENDIF(${WITH_COVERAGE})
-ENDIF(${UNIX})
-
-# OS Definitions
-IF(UNIX)
-    IF(APPLE)   #OSX
-        ADD_DEFINITIONS(-DOS_MAC)
-
-        SET(CMAKE_MACOSX_RPATH ON)
-        SET(CMAKE_SKIP_BUILD_RPATH  FALSE)
-        SET(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE)
-        SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}")
-        SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
-
-        LIST(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}" isSystemDir)
-        IF("${isSystemDir}" STREQUAL "-1")
-            SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}")
-        ENDIF("${isSystemDir}" STREQUAL "-1")
-    ELSE(APPLE) #Linux
-        ADD_DEFINITIONS(-DOS_LNX)
-    ENDIF()
-ELSE(${UNIX}) #Windows
-    ADD_DEFINITIONS(-DOS_WIN -DNOMINMAX)
-    IF(MSVC)
-        # MP is multiprocess compilation. Gm- disables minimal rebuilds
-        # http://stackoverflow.com/questions/6172205/how-can-i-do-a-parallel-build-in-visual-studio-2010vvvvvvvv
-        # http://www.kitware.com/blog/home/post/434
-        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP /Gm- /bigobj")
-        SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /MP /Gm-")
-    ENDIF(MSVC)
-ENDIF()
-
-# Architechture Definitions
-INCLUDE(TargetArch)
-target_architecture(ARCH)
-
-INCLUDE(Version)
-
-IF(${BUILD_CPU})
-    ADD_SUBDIRECTORY(src/backend/cpu)
-ENDIF()
-
-IF(${BUILD_CUDA})
-    ADD_SUBDIRECTORY(src/backend/cuda)
-ENDIF()
-
-IF(${BUILD_OPENCL})
-    ADD_SUBDIRECTORY(src/backend/opencl)
-ENDIF()
-
-IF(${BUILD_UNIFIED})
-    ADD_DEFINITIONS(-DAF_UNIFIED)
-    ADD_SUBDIRECTORY(src/api/unified)
-ENDIF()
-
-IF(${BUILD_DOCS})
-    ADD_SUBDIRECTORY(docs)
-ENDIF()
-
-ADD_EXECUTABLE(bin2cpp ${PROJECT_SOURCE_DIR}/CMakeModules/bin2cpp.cpp)
-
-IF(${BUILD_TEST})
-    ENABLE_TESTING()
-    ADD_SUBDIRECTORY(test)
-ENDIF()
-
-IF(${BUILD_EXAMPLES})
-    ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-##
-# Installation of headers, and CMake scripts
-##
-INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include/" DESTINATION "${AF_INSTALL_INC_DIR}"
+install(DIRECTORY include/ DESTINATION ${AF_INSTALL_INC_DIR}
     COMPONENT headers
     FILES_MATCHING
     PATTERN "*.h"
@@ -224,75 +165,100 @@ INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include/" DESTINATION "${AF_INSTA
 
 ## The ArrayFire version file is generated and won't be included above, install
 ## it separately.
-INSTALL(FILES
-    ${PROJECT_SOURCE_DIR}/include/af/version.h DESTINATION "${AF_INSTALL_INC_DIR}/af/"
+install(FILES
+    ${ArrayFire_BINARY_DIR}/include/af/version.h DESTINATION "${AF_INSTALL_INC_DIR}/af/"
     COMPONENT headers
 )
 
-IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-    OPTION(INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
-    INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/lib/"
+if(Forge_FOUND AND NOT USE_SYSTEM_FORGE)
+    option(INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
+    install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/lib/"
             DESTINATION "${AF_INSTALL_LIB_DIR}"
-            COMPONENT libraries
+            COMPONENT forge
     )
-    IF(${INSTALL_FORGE_DEV})
-        INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/include/"
+    if(${INSTALL_FORGE_DEV})
+        install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/include/"
                 DESTINATION "${AF_INSTALL_INC_DIR}"
                 COMPONENT headers
         )
-        INSTALL(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/share/Forge/"
+        install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/share/Forge/"
                 DESTINATION "${AF_INSTALL_DATA_DIR}/../Forge"
                 COMPONENT share
         )
-    ENDIF(${INSTALL_FORGE_DEV})
-ENDIF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-
-## configuration to be used from the binary directory directly
-SET(INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
-SET(BACKEND_DIR "src/backend/\${lowerbackend}")
-SET(UNIFIED_DIR "src/api/unified")
-CONFIGURE_FILE(
-    ${PROJECT_SOURCE_DIR}/CMakeModules/ArrayFireConfig.cmake.in
-    ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfig.cmake
-    @ONLY)
-
-## installed cmake configuration
-# use a relative dir to keep arrayfire relocatable
-STRING(REGEX REPLACE "[^/]+" ".." reldir "${AF_INSTALL_CMAKE_DIR}")
-SET(INCLUDE_DIR "\${CMAKE_CURRENT_LIST_DIR}/${reldir}/include")
-set(BACKEND_DIR)
-set(UNIFIED_DIR)
-CONFIGURE_FILE(
-    ${PROJECT_SOURCE_DIR}/CMakeModules/ArrayFireConfig.cmake.in
-    ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
-    @ONLY)
-CONFIGURE_FILE(
-    ${PROJECT_SOURCE_DIR}/CMakeModules/ArrayFireConfigVersion.cmake.in
-    ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfigVersion.cmake
-    @ONLY)
-INSTALL(FILES ${CMAKE_CURRENT_BINARY_DIR}/Install/ArrayFireConfig.cmake
-    ${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfigVersion.cmake
-    DESTINATION ${AF_INSTALL_CMAKE_DIR}
-    COMPONENT cmake)
+    endif()
+endif()
 
 # install the examples irrespective of the BUILD_EXAMPLES value
 # only the examples source files are installed, so the installation of these
 # source files does not depend on BUILD_EXAMPLES
 # when BUILD_EXAMPLES is OFF, the examples source is installed without
 # building the example executables
-INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/examples/"
-    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}"
+install(DIRECTORY examples/ #NOTE The slash at the end is important
+    DESTINATION ${AF_INSTALL_EXAMPLE_DIR}
     COMPONENT examples)
 
-INSTALL(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/assets/examples"
-    DESTINATION "${AF_INSTALL_EXAMPLE_DIR}/assets"
+install(DIRECTORY assets/examples/ #NOTE The slash at the end is important
+    DESTINATION ${AF_INSTALL_EXAMPLE_DIR}
     COMPONENT examples)
 
-IF(APPLE)
-    INCLUDE(osx_install/OSXInstaller)
-ENDIF(APPLE)
+foreach(backend CPU CUDA OpenCL Unified)
+  string(TOUPPER ${backend} upper_backend)
+  if(BUILD_${upper_backend})
+    install(EXPORT ArrayFire${backend}Targets
+            NAMESPACE ArrayFire::
+            DESTINATION ${AF_INSTALL_CMAKE_DIR}
+            COMPONENT cmake
+        )
+
+    export( EXPORT ArrayFire${backend}Targets
+            NAMESPACE ArrayFire::
+            FILE ArrayFire${backend}Targets.cmake
+            )
+  endif()
+endforeach()
+
+set(INCLUDE_DIR include)
+
+include(CMakePackageConfigHelpers)
+write_basic_package_version_file(
+  "${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfigVersion.cmake"
+  COMPATIBILITY SameMajorVersion
+)
+
+configure_package_config_file(
+  ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+  ArrayFireInstallConfig/ArrayFireConfig.cmake
+  INSTALL_DESTINATION "${AF_INSTALL_CMAKE_DIR}"
+  PATH_VARS INCLUDE_DIR AF_INSTALL_CMAKE_DIR
+  )
+
+install(FILES ${ArrayFire_BINARY_DIR}/ArrayFireInstallConfig/ArrayFireConfig.cmake
+              ${ArrayFire_BINARY_DIR}/ArrayFireConfigVersion.cmake
+              DESTINATION ${AF_INSTALL_CMAKE_DIR}
+              COMPONENT cmake)
+
+set(AF_INSTALL_CMAKE_DIR "${ArrayFire_BINARY_DIR}")
+configure_package_config_file(
+  ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+  ${ArrayFire_BINARY_DIR}/ArrayFireConfig.cmake
+  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}"
+  PATH_VARS INCLUDE_DIR AF_INSTALL_CMAKE_DIR
+  INSTALL_PREFIX "${ArrayFire_BINARY_DIR}"
+  )
+
+# Registers the current build directory with the user's cmake config. This will
+# create a file at $HOME/.cmake/packages/ArrayFire which will point to this source
+# build directory.
+# TODO(umar): Disable for now. Causing issues with builds on windows.
+#export(PACKAGE ArrayFire)
 
-##
-# Packaging
-##
 include(CPackConfig)
+include(CTest)
+
+# Handle depricated BUILD_TEST variable if found.
+if(BUILD_TEST)
+  set(BUILD_TESTING ${BUILD_TEST})
+endif()
+
+conditional_directory(BUILD_TESTING test)
+conditional_directory(BUILD_EXAMPLES examples)
diff --git a/CMakeModules/AFInstallDirs.cmake b/CMakeModules/AFInstallDirs.cmake
index 1060f80952..d3e7267e3c 100644
--- a/CMakeModules/AFInstallDirs.cmake
+++ b/CMakeModules/AFInstallDirs.cmake
@@ -42,3 +42,13 @@ endif()
 if(NOT DEFINED AF_INSTALL_CMAKE_DIR)
   set(AF_INSTALL_CMAKE_DIR "${AF_INSTALL_DATA_DIR}/cmake" CACHE PATH "Installation path for CMake files")
 endif()
+
+mark_as_advanced(
+  AF_INSTALL_BIN_DIR
+  AF_INSTALL_LIB_DIR
+  AF_INSTALL_INC_DIR
+  AF_INSTALL_DATA_DIR
+  AF_INSTALL_DOC_DIR
+  AF_INSTALL_EXAMPLE_DIR
+  AF_INSTALL_MAN_DIR
+  AF_INSTALL_CMAKE_DIR)
diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index d95a39ca20..c71a297923 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -48,29 +48,29 @@
 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #=============================================================================
 
-get_filename_component(ArrayFire_INCLUDE_DIRS "@INCLUDE_DIR@" ABSOLUTE)
+@PACKAGE_INIT@
 
-macro(find_backend backend libname)
-  if (${backend} STREQUAL "Unified")
-    set(targetFile ${CMAKE_CURRENT_LIST_DIR}/@UNIFIED_DIR@/ArrayFire${backend}.cmake)
-  else ()
-    set(targetFile ${CMAKE_CURRENT_LIST_DIR}/@BACKEND_DIR@/ArrayFire${backend}.cmake)
-  endif ()
-  if(EXISTS ${targetFile})
-    include(${targetFile})
-    set(ArrayFire_${backend}_FOUND ON)
-    set(ArrayFire_${backend}_LIBRARIES af${libname})
-    # set the default backend
-    set(ArrayFire_LIBRARIES af${libname})
+set_and_check(ArrayFire_INCLUDE_DIRS @PACKAGE_INCLUDE_DIR@)
+
+foreach(backend Unified CPU OpenCL CUDA)
+  if(backend STREQUAL "Unified")
+    set(lowerbackend "")
   else()
-    set(ArrayFire_${backend}_FOUND OFF)
+    string(TOLOWER "${backend}" lowerbackend)
+  endif()
+  if(NOT TARGET ArrayFire::af${lowerbackend})
+    if(EXISTS @PACKAGE_AF_INSTALL_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
+      include(@PACKAGE_AF_INSTALL_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
+    endif()
   endif()
-endmacro()
 
-# keep in the backends in the slowest to fastest order
-foreach(backend CPU OpenCL CUDA)
-  string(TOLOWER "${backend}" lowerbackend)
-  find_backend("${backend}" "${lowerbackend}")
+  if(TARGET ArrayFire::af${lowerbackend})
+      set(ArrayFire_${backend}_FOUND ON)
+      set(ArrayFire_${backend}_LIBRARIES ArrayFire::af${lowerbackend})
+      set(ArrayFire_LIBRARIES ArrayFire::af${lowerbackend})
+  else()
+      set(ArrayFire_${backend}_FOUND OFF)
+  endif()
 endforeach()
 
-find_backend("Unified" "")
+check_required_components(CPU OpenCL CUDA Unified)
diff --git a/CMakeModules/ArrayFireConfigVersion.cmake.in b/CMakeModules/ArrayFireConfigVersion.cmake.in
index 6ac209cfab..cb32c868d2 100644
--- a/CMakeModules/ArrayFireConfigVersion.cmake.in
+++ b/CMakeModules/ArrayFireConfigVersion.cmake.in
@@ -36,16 +36,16 @@
 # but only if the requested major version is the same as the current one.
 
 
-set(PACKAGE_VERSION "@AF_VERSION_MAJOR@@AF_VERSION_MINOR@")
+set(PACKAGE_VERSION "@ArrayFire_VERSION_MAJOR@@ArrayFire_VERSION_MINOR@")
 
 if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}" )
   set(PACKAGE_VERSION_COMPATIBLE FALSE)
 else()
 
-  if("@AF_VERSION_MAJOR@@AF_VERSION_MINOR@" MATCHES "^([0-9]+)\\.")
+  if("@ArrayFire_VERSION_MAJOR@@ArrayFire_VERSION_MINOR@" MATCHES "^([0-9]+)\\.")
     set(ArrayFire_VERSION_MAJOR "${CMAKE_MATCH_1}")
   else()
-    set(ArrayFire_VERSION_MAJOR "@AF_VERSION_MAJOR@@AF_VERSION_MINOR@")
+    set(ArrayFire_VERSION_MAJOR "@ArrayFire_VERSION_MAJOR@@ArrayFire_VERSION_MINOR@")
   endif()
 
   if("${PACKAGE_FIND_VERSION_MAJOR}" STREQUAL "${ArrayFire_VERSION_MAJOR}")
diff --git a/CMakeModules/CLKernelToH.cmake b/CMakeModules/CLKernelToH.cmake
index 23b5f0e1bc..dc8f857320 100644
--- a/CMakeModules/CLKernelToH.cmake
+++ b/CMakeModules/CLKernelToH.cmake
@@ -51,7 +51,7 @@ function(CL_KERNEL_TO_H)
         set(_output_path "${CMAKE_CURRENT_BINARY_DIR}/${RTCS_OUTPUT_DIR}")
         set(_output_file "${_output_path}/${_name_we}.${RTCS_EXTENSION}")
 
-        ADD_CUSTOM_COMMAND(
+        add_custom_command(
             OUTPUT ${_output_file}
             DEPENDS ${_input_file} ${BIN2CPP_PROGRAM}
             COMMAND ${CMAKE_COMMAND} -E make_directory "${_output_path}"
@@ -64,7 +64,8 @@ function(CL_KERNEL_TO_H)
 
         list(APPEND _output_files ${_output_file})
     endforeach()
-    ADD_CUSTOM_TARGET(${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target DEPENDS ${_output_files})
+    add_custom_target(${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target DEPENDS ${_output_files})
+    set_target_properties(${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target PROPERTIES FOLDER "Generated Targets")
 
     set("${RTCS_VARNAME}" ${_output_files} PARENT_SCOPE)
     set("${RTCS_TARGETS}" ${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target PARENT_SCOPE)
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 7d95809485..6a04573c93 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -1,50 +1,61 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-INCLUDE(Version)
+cmake_minimum_required(VERSION 3.5)
 
-OPTION(CREATE_STGZ "Create .sh install file" ON)
-MARK_AS_ADVANCED(CREATE_STGZ)
+include(Version)
 
-# CPack package generation
-IF(${CREATE_STGZ})
-  LIST(APPEND CPACK_GENERATOR "STGZ")
-ENDIF()
-
-OPTION(CREATE_DEB "Create .deb install file" OFF)
-MARK_AS_ADVANCED(CREATE_DEB)
-
-IF(${CREATE_DEB})
-  LIST(APPEND CPACK_GENERATOR "DEB")
-ENDIF()
-
-OPTION(CREATE_RPM "Create .rpm install file" OFF)
-MARK_AS_ADVANCED(CREATE_RPM)
-
-IF(${CREATE_RPM})
-  LIST(APPEND CPACK_GENERATOR "RPM")
-ENDIF()
+set(CPACK_GENERATOR "STGZ;TGZ" CACHE STRINGS "STGZ;TGZ;DEB;RPM;productbuild")
+set_property(CACHE CPACK_GENERATOR PROPERTY STRINGS STGZ DEB RPM productbuild)
+mark_as_advanced(CPACK_GENERATOR)
 
 # Common settings to all packaging tools
-SET(CPACK_PREFIX_DIR ${CMAKE_INSTALL_PREFIX})
-SET(CPACK_PACKAGE_NAME "arrayfire")
-SET(CPACK_PACKAGE_VERSION ${AF_VERSION})
-SET(CPACK_PACKAGE_VERSION_MAJOR "${AF_VERSION_MAJOR}")
-SET(CPACK_PACKAGE_VERSION_MINOR "${AF_VERSION_MINOR}")
-SET(CPACK_PACKAGE_VERSION_PATCH "${AF_VERSION_PATCH}")
-IF(BUILD_GRAPHICS)
-    SET(CPACK_PACKAGE_FILE_NAME
+set(CPACK_PREFIX_DIR ${CMAKE_INSTALL_PREFIX})
+set(CPACK_PACKAGE_NAME "arrayfire")
+set(CPACK_PACKAGE_VENDOR "ArrayFire")
+set(CPACK_PACKAGE_CONTACT "ArrayFire Development Group <technical@arrayfire.com>")
+
+set(CPACK_PACKAGE_VERSION ${ArrayFire_VERSION})
+set(CPACK_PACKAGE_VERSION_MAJOR "${ArrayFire_VERSION_MAJOR}")
+set(CPACK_PACKAGE_VERSION_MINOR "${ArrayFire_VERSION_MINOR}")
+set(CPACK_PACKAGE_VERSION_PATCH "${ArrayFire_VERSION_PATCH}")
+if(BUILD_GRAPHICS)
+    set(CPACK_PACKAGE_FILE_NAME
     ${CPACK_PACKAGE_NAME}-${CPACK_PACKAGE_VERSION})
-ELSE()
-    SET(CPACK_PACKAGE_FILE_NAME
+else()
+    set(CPACK_PACKAGE_FILE_NAME
         ${CPACK_PACKAGE_NAME}-no-gl-${CPACK_PACKAGE_VERSION})
-ENDIF()
-SET(CPACK_PACKAGE_VENDOR "ArrayFire")
-SET(CPACK_PACKAGE_CONTACT "ArrayFire Development Group <technical@arrayfire.com>")
-SET(CPACK_RESOURCE_FILE_LICENSE "${PROJECT_SOURCE_DIR}/LICENSE")
-SET(CPACK_RESOURCE_FILE_README "${PROJECT_SOURCE_DIR}/README.md")
+endif()
+
+if(APPLE)
+  set(OSX_INSTALL_SOURCE ${PROJECT_SOURCE_DIR}/CMakeModules/osx_install)
+  set(WELCOME_FILE       "${OSX_INSTALL_SOURCE}/welcome.html.in")
+  set(WELCOME_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/welcome.html")
+
+  set(README_FILE       "${OSX_INSTALL_SOURCE}/readme.html.in")
+  set(README_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/readme.html")
+
+  set(LICENSE_FILE       "${ArrayFire_SOURCE_DIR}/LICENSE")
+  set(LICENSE_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/license.txt")
+
+  set(AF_TITLE    "ArrayFire ${AF_VERSION}")
+  configure_file(${WELCOME_FILE} ${WELCOME_FILE_OUT})
+  configure_file(${README_FILE} ${README_FILE_OUT})
+  configure_file(${LICENSE_FILE} ${LICENSE_FILE_OUT})
+  set(CPACK_RESOURCE_FILE_LICENSE ${LICENSE_FILE_OUT})
+  set(CPACK_RESOURCE_FILE_README ${README_FILE_OUT})
+  set(CPACK_RESOURCE_FILE_WELCOME ${WELCOME_FILE_OUT})
+else()
+  set(CPACK_RESOURCE_FILE_LICENSE "${ArrayFire_SOURCE_DIR}/LICENSE")
+  set(CPACK_RESOURCE_FILE_README "${ArrayFire_SOURCE_DIR}/README.md")
+endif()
 
 # Long description of the package
-SET(CPACK_PACKAGE_DESCRIPTION
+set(CPACK_PACKAGE_DESCRIPTION
 "ArrayFire is a high performance software library for parallel computing
 with an easy-to-use API. Its array based function set makes parallel
 programming simple.
@@ -56,35 +67,88 @@ A few lines of code in ArrayFire can replace dozens of lines of parallel
 computing code, saving you valuable time and lowering development costs.")
 
 # Short description of the package
-SET(CPACK_PACKAGE_DESCRIPTION_SUMMARY "A high performance library for parallel computing with an easy-to-use API.")
+set(CPACK_PACKAGE_DESCRIPTION_SUMMARY "A high performance library for parallel computing with an easy-to-use API.")
 
-# Useful descriptions for components
-SET(CPACK_COMPONENT_LIBRARIES_DISPLAY_NAME "ArrayFire libraries")
-SET(CPACK_COMPONENT_DOCUMENTATION_NAME "Doxygen documentation")
-SET(CPACK_COMPONENT_HEADERS_NAME "C/C++ headers")
-SET(CPACK_COMPONENT_CMAKE_NAME "CMake support")
 # Set the default components installed in the package
-SET(CPACK_COMPONENTS_ALL libraries headers documentation cmake)
+set(CPACK_COMPONENTS_ALL cpu cuda opencl unified headers documentation cmake examples)
+
+include(CPackComponent)
+cpack_add_component_group(libraries
+DISPLAY_NAME "Libraries"
+DESCRIPTION "ArrayFire libraries"
+EXPANDED BOLD_TITLE)
+
+cpack_add_component(cpu
+DISPLAY_NAME "CPU Backend"
+DESCRIPTION
+"ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
+GROUP libraries)
+
+cpack_add_component(cuda
+DISPLAY_NAME "CUDA Backend"
+DESCRIPTION
+"ArrayFire which targets the CUDA platform. This platform allows you to to take "
+"advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the "
+"corresponding CMake config files."
+GROUP libraries)
+
+cpack_add_component(opencl
+DISPLAY_NAME "OpenCL Backend"
+DESCRIPTION
+"ArrayFire which targets the OpenCL platform. This platform allows you to use the "
+"ArrayFire library which targets OpenCL devices. Also installs the corresponding "
+"CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the "
+"Intel CPU on OSX."
+GROUP libraries)
+
+cpack_add_component(unified
+DISPLAY_NAME "Unified Backend"
+DESCRIPTION
+"This library will allow you to choose the platform(cpu, cuda, opencl) at "
+"runtime. Also installs the corresponding CMake config files. NOTE: This option "
+"requires the other platforms to work properly"
+#DEPENDS "cpu;cuda;opencl"
+GROUP libraries)
+
+cpack_add_component(documentation
+DISPLAY_NAME "Documentation"
+DESCRIPTION "Doxygen documentation"
+)
+
+cpack_add_component(headers
+DISPLAY_NAME "C/C++ Headers"
+DESCRIPTION "Headers for the ArrayFire Libraries."
+)
+
+cpack_add_component(cmake
+DISPLAY_NAME "CMake Support"
+DESCRIPTION "Configuration files to use ArrayFire using CMake."
+)
+
+cpack_add_component(examples
+DISPLAY_NAME "ArrayFire Examples"
+DESCRIPTION "Various examples using ArrayFire."
+)
 
 ##
 # Debian package
 ##
-SET(CPACK_DEBIAN_PACKAGE_ARCHITECTURE ${PROCESSOR_ARCHITECTURE})
+set(CPACK_DEBIAN_PACKAGE_ARCHITECTURE ${PROCESSOR_ARCHITECTURE})
 
 ##
 # RPM package
 ##
-SET(CPACK_RPM_PACKAGE_LICENSE "BSD")
+set(CPACK_RPM_PACKAGE_LICENSE "BSD")
 set(CPACK_RPM_PACKAGE_AUTOREQPROV " no")
 
-SET(CPACK_PACKAGE_GROUP "Development/Libraries")
+set(CPACK_PACKAGE_GROUP "Development/Libraries")
 ##
 # Source package
 ##
-SET(CPACK_SOURCE_GENERATOR "TGZ")
-SET(CPACK_SOURCE_PACKAGE_FILE_NAME
+set(CPACK_SOURCE_GENERATOR "TGZ")
+set(CPACK_SOURCE_PACKAGE_FILE_NAME
     ${CPACK_PACKAGE_NAME}_src_${CPACK_PACKAGE_VERSION}_${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR})
-SET(CPACK_SOURCE_IGNORE_FILES
+set(CPACK_SOURCE_IGNORE_FILES
     "/build"
     "CMakeFiles"
     "/\\\\.dir"
@@ -99,7 +163,7 @@ SET(CPACK_SOURCE_IGNORE_FILES
     "/CMakeLists.txt.user$"
     ${CPACK_SOURCE_IGNORE_FILES})
 # Ignore build directories that may be in the source tree
-FILE(GLOB_RECURSE CACHES "${CMAKE_SOURCE_DIR}/CMakeCache.txt")
+file(GLOB_RECURSE CACHES "${CMAKE_SOURCE_DIR}/CMakeCache.txt")
 
 # Call to CPACK
-INCLUDE(CPack)
+include(CPack)
diff --git a/CMakeModules/CUDACheckCompute.cmake b/CMakeModules/CUDACheckCompute.cmake
deleted file mode 100644
index de379d3e28..0000000000
--- a/CMakeModules/CUDACheckCompute.cmake
+++ /dev/null
@@ -1,38 +0,0 @@
-#############################
-#Sourced from:
-#https://raw.githubusercontent.com/jwetzl/CudaLBFGS/master/CheckComputeCapability.cmake
-#############################
-# Check for GPUs present and their compute capability
-# based on http://stackoverflow.com/questions/2285185/easiest-way-to-test-for-existence-of-cuda-capable-gpu-from-cmake/2297877#2297877 (Christopher Bruns)
-
-IF(CUDA_FOUND)
-    MESSAGE(STATUS "${PROJECT_SOURCE_DIR}/CMakeModules/cuda_compute_capability.cpp")
-
-    TRY_RUN(RUN_RESULT_VAR COMPILE_RESULT_VAR
-        ${PROJECT_BINARY_DIR}
-        ${PROJECT_SOURCE_DIR}/CMakeModules/cuda_compute_capability.cpp
-        CMAKE_FLAGS
-        -DINCLUDE_DIRECTORIES:STRING=${CUDA_TOOLKIT_INCLUDE}
-        -DLINK_LIBRARIES:STRING=${CUDA_CUDART_LIBRARY}
-        COMPILE_OUTPUT_VARIABLE COMPILE_OUTPUT_VAR
-        RUN_OUTPUT_VARIABLE RUN_OUTPUT_VAR)
-
-    MESSAGE(STATUS "CUDA Compute Detection Output: ${RUN_OUTPUT_VAR}")
-    MESSAGE(STATUS "CUDA Compute Detection Return: ${RUN_RESULT_VAR}")
-
-    # COMPILE_RESULT_VAR is TRUE when compile succeeds
-    # Check Return Value of main() from RUN_RESULT_VAR
-    # RUN_RESULT_VAR is 0 when a GPU is found
-    # RUN_RESULT_VAR is 1 when errors occur
-
-    IF(COMPILE_RESULT_VAR AND RUN_RESULT_VAR EQUAL 0)
-        MESSAGE(STATUS "CUDA Compute Detection Worked")
-        # Convert output into a list of computes
-        STRING(REPLACE " " ";" COMPUTES_DETECTED_LIST ${RUN_OUTPUT_VAR})
-        SET(CUDA_HAVE_GPU TRUE CACHE BOOL "Whether CUDA-capable GPU is present")
-    ELSE()
-        MESSAGE(STATUS "CUDA Compute Detection Failed")
-        SET(CUDA_HAVE_GPU FALSE CACHE BOOL "Whether CUDA-capable GPU is present")
-    ENDIF()
-
-ENDIF(CUDA_FOUND)
diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index 058b7d75ea..0123d8e82d 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -101,10 +101,12 @@ IF(NOT CBLAS_ROOT_DIR)
         ENDIF()
     ENDIF(APPLE)
   ENDIF()
-
-  SET(CBLAS_INCLUDE_DIR "${CBLAS_ROOT_DIR}/include")
 ENDIF()
 
+if(CBLAS_ROOT_DIR)
+  set(CBLAS_INCLUDE_DIR "${CBLAS_ROOT_DIR}/include")
+endif()
+
 # Old CBLAS search
 SET(_verbose TRUE)
 INCLUDE(CheckFunctionExists)
diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index b8f8fa6039..6d32c90a4e 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -20,100 +20,52 @@
 ######## This FindFFTW.cmake file is a copy of the file from the eigen library
 ######## http://code.metager.de/source/xref/lib/eigen/cmake/FindFFTW.cmake
 
-IF(NOT FFTW_ROOT)
-    SET(FFTW_ROOT $ENV{FFTWDIR})
-ENDIF()
+find_package(PkgConfig)
+pkg_check_modules(PKG_FFTW "fftw3")
+
+find_path( FFTW_INCLUDE_DIR
+  NAMES "fftw3.h"
+  PATHS ${FFTW_ROOT}
+        ${CMAKE_SYSTEM_INCLUDE_PATH}
+        ${CMAKE_SYSTEM_PREFIX_PATH}
+        ${PKG_FFTW_INCLUDE_DIRS}
+  PATH_SUFFIXES "include" "include/fftw"
+  )
+
+find_library( FFTW_LIBRARY
+  NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_core" "mkl_rt"
+  PATHS ${FFTW_ROOT}
+        ${CMAKE_SYSTEM_PREFIX_PATH}
+        ${PKG_FFTW_LIBRARY_DIRS}
+  PATH_SUFFIXES "lib" "lib64" "lib/intel64" "lib/ia32"
+)
+
+find_library( FFTWF_LIBRARY
+  NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_core" "mkl_rt"
+  PATHS ${FFTW_ROOT}
+        ${CMAKE_SYSTEM_PREFIX_PATH}
+        ${CMAKE_SYSTEM_LIBRARY_PATH}
+        ${PKG_FFTW_LIBRARY_DIRS}
+  PATH_SUFFIXES "lib" "lib64" "lib/intel64" "lib/ia32"
+)
+
+mark_as_advanced(FFTW_INCLUDE_DIR FFTW_LIBRARY FFTWF_LIBRARY)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(FFTW DEFAULT_MSG
+    FFTW_INCLUDE_DIR FFTW_LIBRARY FFTWF_LIBRARY)
+
+if (FFTW_FOUND)
+  add_library(FFTW::FFTW UNKNOWN IMPORTED)
+  set_target_properties(FFTW::FFTW PROPERTIES
+    IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+    IMPORTED_LOCATION "${FFTW_LIBRARY}"
+    INTERFACE_INCLUDE_DIRECTORIES "${FFTW_INCLUDE_DIR}")
+
+  add_library(FFTW::FFTWF UNKNOWN IMPORTED)
+  set_target_properties(FFTW::FFTWF PROPERTIES
+    IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+    IMPORTED_LOCATION "${FFTWF_LIBRARY}"
+    INTERFACE_INCLUDE_DIRECTORIES "${FFTW_INCLUDE_DIR}")
+endif (FFTW_FOUND)
 
-IF (NOT INTEL_MKL_ROOT_DIR)
-  SET(INTEL_MKL_ROOT_DIR $ENV{INTEL_MKL_ROOT})
-ENDIF()
-
-IF(NOT FFTW_ROOT)
-
-  IF (ENV{FFTWDIR})
-    SET(FFTW_ROOT $ENV{FFTWDIR})
-  ENDIF()
-
-  IF (ENV{FFTW_ROOT_DIR})
-    SET(FFTW_ROOT $ENV{FFTW_ROOT_DIR})
-  ENDIF()
-
-  IF (INTEL_MKL_ROOT_DIR)
-    SET(FFTW_ROOT ${INTEL_MKL_ROOT_DIR})
-  ENDIF()
-ENDIF()
-
-# Check if we can use PkgConfig
-FIND_PACKAGE(PkgConfig)
-
-#Determine from PKG
-IF(PKG_CONFIG_FOUND AND NOT FFTW_ROOT)
-    PKG_CHECK_MODULES( PKG_FFTW QUIET "fftw3")
-ENDIF()
-
-#Check whether to search static or dynamic libs
-SET(CMAKE_FIND_LIBRARY_SUFFIXES_SAV ${CMAKE_FIND_LIBRARY_SUFFIXES})
-IF(${FFTW_USE_STATIC_LIBS} )
-    SET(CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_STATIC_LIBRARY_SUFFIX})
-ELSE()
-    SET(CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_SHARED_LIBRARY_SUFFIX})
-ENDIF()
-
-IF ("${SIZE_OF_VOIDP}" EQUAL 8)
-  SET(MKL_LIB_DIR_SUFFIX "intel64")
-ELSE()
-  SET(MKL_LIB_DIR_SUFFIX "ia32")
-ENDIF()
-
-IF(FFTW_ROOT)
-    #find libs
-    FIND_LIBRARY(
-        FFTW_LIB
-        NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_rt"
-        PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
-        NO_DEFAULT_PATH
-        )
-    FIND_LIBRARY(
-        FFTWF_LIB
-        NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_rt"
-        PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
-        NO_DEFAULT_PATH
-        )
-
-    #find includes
-    FIND_PATH(
-        FFTW_INCLUDES
-        NAMES "fftw3.h"
-        PATHS ${FFTW_ROOT}
-        PATH_SUFFIXES "include" "include/fftw"
-        NO_DEFAULT_PATH
-        )
-ELSE()
-    FIND_LIBRARY(
-        FFTW_LIB
-        NAMES "fftw3" "mkl_rt"
-        PATHS ${PKG_FFTW_LIBRARY_DIRS} ${LIB_INSTALL_DIR}
-        )
-    FIND_LIBRARY(
-        FFTWF_LIB
-        NAMES "fftw3f" "mkl_rt"
-        PATHS ${PKG_FFTW_LIBRARY_DIRS} ${LIB_INSTALL_DIR}
-        )
-    FIND_PATH(
-        FFTW_INCLUDES
-        NAMES "fftw3.h"
-        PATHS ${PKG_FFTW_INCLUDE_DIRS} ${INCLUDE_INSTALL_DIR}
-        )
-ENDIF(FFTW_ROOT)
-
-SET(FFTW_LIBRARIES ${FFTW_LIB} ${FFTWF_LIB})
-
-SET(CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES_SAV})
-
-INCLUDE(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(FFTW DEFAULT_MSG
-    FFTW_INCLUDES FFTW_LIBRARIES)
-
-MARK_AS_ADVANCED(FFTW_INCLUDES FFTW_LIBRARIES FFTW_LIB FFTWF_LIB)
diff --git a/CMakeModules/FindFreeImage.cmake b/CMakeModules/FindFreeImage.cmake
index 0b3651c947..5ef0424b32 100644
--- a/CMakeModules/FindFreeImage.cmake
+++ b/CMakeModules/FindFreeImage.cmake
@@ -1,17 +1,30 @@
+# FindFreeImage.cmake
+# Author: Umar Arshad <umar@arrayfire.com>
 #
-# Try to find the FreeImage library and include path.
-# Once done this will define
+# Finds the FreeImage libraries
+# Sets the following variables:
+#          FreeImage_FOUND
+#          FreeImage_INCLUDE_DIR
+#          FreeImage_DYNAMIC_LIBRARY
+#          FreeImage_STATIC_LIBRARY
 #
-# FREEIMAGE_FOUND
-# FREEIMAGE_INCLUDE_PATH
-# FREEIMAGE_LIBRARY
-# FREEIMAGE_STATIC_LIBRARY
-# FREEIMAGE_DYNAMIC_LIBRARY
+# Usage:
+# find_package(FreeImage)
+# if (FreeImage_FOUND)
+#    target_link_libraries(mylib PRIVATE FreeImage::FreeImage)
+# endif (FreeImage_FOUND)
 #
+# OR if you want to link against the static library:
+#
+# find_package(FreeImage)
+# if (FreeImage_FOUND)
+#    target_link_libraries(mylib PRIVATE FreeImage::FreeImage_STATIC)
+# endif (FreeImage_FOUND)
+#
+# NOTE: You do not need to include the FreeImage include directories since they
+# will be included as part of the target_link_libraries command
 
-OPTION(USE_FREEIMAGE_STATIC "Use Static FreeImage Lib" OFF)
-
-FIND_PATH( FREEIMAGE_INCLUDE_PATH
+find_path( FreeImage_INCLUDE_DIR
     NAMES FreeImage.h
     HINTS ${PROJECT_SOURCE_DIR}/extern/FreeImage
     PATHS
@@ -21,7 +34,7 @@ FIND_PATH( FREEIMAGE_INCLUDE_PATH
     /opt/local/include
     DOC "The directory where FreeImage.h resides")
 
-FIND_LIBRARY( FREEIMAGE_DYNAMIC_LIBRARY
+find_library( FreeImage_DYNAMIC_LIBRARY
     NAMES FreeImage freeimage
     HINTS ${PROJECT_SOURCE_DIR}/FreeImage
     PATHS
@@ -33,9 +46,7 @@ FIND_LIBRARY( FREEIMAGE_DYNAMIC_LIBRARY
     /opt/local/lib
     DOC "The FreeImage library")
 
-SET(PX ${CMAKE_STATIC_LIBRARY_PREFIX})
-SET(SX ${CMAKE_STATIC_LIBRARY_SUFFIX})
-FIND_LIBRARY( FREEIMAGE_STATIC_LIBRARY
+find_library( FreeImage_STATIC_LIBRARY
     NAMES ${PX}FreeImageLIB${SX} ${PX}FreeImage${SX} ${PX}freeimage${SX}
     HINTS ${PROJECT_SOURCE_DIR}/FreeImage
     PATHS
@@ -46,23 +57,32 @@ FIND_LIBRARY( FREEIMAGE_STATIC_LIBRARY
     /sw/lib
     /opt/local/lib
     DOC "The FreeImage library")
-UNSET(PX)
-UNSET(SX)
-
-IF(USE_FREEIMAGE_STATIC)
-  ADD_DEFINITIONS(-DFREEIMAGE_LIB)
-  SET(FREEIMAGE_LIBRARY ${FREEIMAGE_STATIC_LIBRARY})
-ELSE(USE_FREEIMAGE_STATIC)
-  REMOVE_DEFINITIONS(-DFREEIMAGE_LIB)
-  SET(FREEIMAGE_LIBRARY ${FREEIMAGE_DYNAMIC_LIBRARY})
-ENDIF(USE_FREEIMAGE_STATIC)
 
-MARK_AS_ADVANCED(
-    FREEIMAGE_DYNAMIC_LIBRARY
-    FREEIMAGE_STATIC_LIBRARY
-    FREEIMAGE_LIBRARY
-    FREEIMAGE_INCLUDE_PATH
+mark_as_advanced(
+    FreeImage_INCLUDE_DIR
+    FreeImage_DYNAMIC_LIBRARY
+    FreeImage_STATIC_LIBRARY
     )
-INCLUDE(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(FREEIMAGE DEFAULT_MSG
-    FREEIMAGE_INCLUDE_PATH FREEIMAGE_LIBRARY)
+include(FindPackageHandleStandardArgs)
+
+find_package_handle_standard_args(FreeImage
+  REQUIRED_VARS FreeImage_INCLUDE_DIR FreeImage_DYNAMIC_LIBRARY
+  )
+
+set(FREEIMAGE_LIBRARY ${FreeImage_DYNAMIC_LIBRARY})
+
+if (FreeImage_FOUND AND NOT TARGET FreeImage::FreeImage)
+  add_library(FreeImage::FreeImage UNKNOWN IMPORTED)
+  set_target_properties(FreeImage::FreeImage PROPERTIES
+    IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+    IMPORTED_LOCATION "${FreeImage_DYNAMIC_LIBRARY}"
+    INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}")
+
+  if(FreeImage_STATIC_LIBRARY_FOUND)
+    add_library(FreeImage::FreeImage_STATIC UNKNOWN IMPORTED)
+    set_target_properties(FreeImage::FreeImage_STATIC PROPERTIES
+      IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+      IMPORTED_LOCATION "${FreeImage_STATIC_LIBRARY}"
+      INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}")
+  endif(FreeImage_STATIC_LIBRARY_FOUND)
+endif()
diff --git a/CMakeModules/FindGLEWmx.cmake b/CMakeModules/FindGLEWmx.cmake
deleted file mode 100644
index 587a6ff208..0000000000
--- a/CMakeModules/FindGLEWmx.cmake
+++ /dev/null
@@ -1,92 +0,0 @@
-# Source from
-#https://github.com/LaurentGomila/SFML/blob/master/cmake/Modules/FindGLEW.cmake
-
-#
-# Try to find GLEW library and include path.
-# Once done this will define
-#
-# GLEW_FOUND
-# GLEW_INCLUDE_DIR
-# GLEW_LIBRARY
-# GLEWmx_LIBRARY
-# GLEWmxd_LIBRARY
-# GLEWmxs_LIBRARY
-
-FIND_PACKAGE(OpenGL REQUIRED)
-
-OPTION(USE_GLEWmx_STATIC "Use Static GLEWmx Lib" OFF)
-
-FIND_PATH(GLEW_INCLUDE_DIR GL/glew.h
-    HINTS
-    ${GLEW_ROOT_DIR}/include
-    )
-
-IF (WIN32)
-    FIND_LIBRARY( GLEWmxd_LIBRARY
-        NAMES glewmx GLEWmx glew32mx glew32mx
-        PATHS
-        $ENV{PROGRAMFILES}/GLEW/lib
-        ${GLEW_ROOT_DIR}/lib
-        ${GLEW_ROOT_DIR}
-        ${PROJECT_SOURCE_DIR}/../dependencies/glew/lib
-        PATH_SUFFIXES "Release MX/x64" "lib64"
-        DOC "The GLEWmx library"
-    )
-    FIND_LIBRARY( GLEWmxs_LIBRARY
-        NAMES glewmxs GLEWmxs glew32mxs glew32mxs
-        PATHS
-        $ENV{PROGRAMFILES}/GLEW/lib
-        ${GLEW_ROOT_DIR}/lib
-        ${GLEW_ROOT_DIR}
-        ${PROJECT_SOURCE_DIR}/../dependencies/glew/lib
-        PATH_SUFFIXES "Release MX/x64" "lib64"
-        DOC "The GLEWmxs Static library"
-    )
-ELSE (WIN32)
-    FIND_LIBRARY( GLEWmxd_LIBRARY
-        NAMES GLEWmx glewmx
-        PATHS
-        /usr/lib64
-        /usr/lib
-        /usr/lib/x86_64-linux-gnu
-        /usr/lib/arm-linux-gnueabihf
-        /usr/local/lib64
-        /usr/local/lib
-        /sw/lib
-        /opt/local/lib
-        ${GLEW_ROOT_DIR}/lib
-        DOC "The GLEWmx library")
-
-    SET(PX ${CMAKE_STATIC_LIBRARY_PREFIX})
-    SET(SX ${CMAKE_STATIC_LIBRARY_SUFFIX})
-    FIND_LIBRARY( GLEWmxs_LIBRARY
-        NAMES ${PX}GLEWmx${SX} ${PX}glewmx${SX}
-        PATHS
-        /usr/lib64
-        /usr/lib
-        /usr/lib/x86_64-linux-gnu
-        /usr/lib/arm-linux-gnueabihf
-        /usr/local/lib64
-        /usr/local/lib
-        /sw/lib
-        /opt/local/lib
-        ${GLEW_ROOT_DIR}/lib
-        DOC "The GLEWmx library")
-    UNSET(PX)
-    UNSET(SX)
-ENDIF (WIN32)
-
-IF(USE_GLEWmx_STATIC)
-    ADD_DEFINITIONS(-DGLEW_STATIC)
-    SET(GLEWmx_LIBRARY ${GLEWmxs_LIBRARY})
-ELSE(USE_GLEWmx_STATIC)
-    REMOVE_DEFINITIONS(-DGLEW_STATIC)
-    SET(GLEWmx_LIBRARY ${GLEWmxd_LIBRARY})
-ENDIF(USE_GLEWmx_STATIC)
-
-MARK_AS_ADVANCED(GLEWmxs_LIBRARY GLEWmxd_LIBRARY GLEWmx_LIBRARY GLEW_INCLUDE_DIR)
-
-INCLUDE(FindPackageHandleStandardArgs)
-# Sets GLEWMX_FOUND
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(GLEWmx DEFAULT_MSG
-    GLEW_INCLUDE_DIR GLEWmx_LIBRARY)
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 419186918c..c60fcbaad6 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -165,3 +165,12 @@ MARK_AS_ADVANCED(
   LAPACK_LIB
   LAPACKE_INCLUDES
   LAPACKE_LIB)
+
+if(LAPACK_FOUND)
+  add_library(LAPACKE::LAPACKE UNKNOWN IMPORTED)
+  set_target_properties(LAPACKE::LAPACKE PROPERTIES
+      IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+      IMPORTED_LOCATION "${LAPACK_LIBRARIES}"
+      INTERFACE_INCLUDE_DIRECTORIES "${LAPACK_INCLUDE_DIR}"
+    )
+endif(LAPACK_FOUND)
diff --git a/CMakeModules/FindNVRTC.cmake b/CMakeModules/FindNVRTC.cmake
deleted file mode 100644
index 0ecd7cdee0..0000000000
--- a/CMakeModules/FindNVRTC.cmake
+++ /dev/null
@@ -1,41 +0,0 @@
-# - Find the NVRTC include directory and libraries
-# Modified version of the file found here:
-# https://raw.githubusercontent.com/nvidia-compiler-sdk/nvvmir-samples/master/CMakeLists.txt
-# CUDA_NVRTC_FOUND
-# CUDA_NVRTC_INCLUDE_DIR
-# CUDA_NVRTC_LIBRARY
-
-# libNVRTC
-IF(NOT DEFINED ENV{CUDA_NVRTC_HOME})
-    # If the toolkit path was changed then refind the library
-    IF(NOT "${CUDA_NVRTC_HOME}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR}/nvrtc")
-        UNSET(CUDA_NVRTC_HOME CACHE)
-        UNSET(CUDA_nvrtc_INCLUDE_DIR CACHE)
-        UNSET(CUDA_nvrtc_LIBRARY CACHE)
-        SET(CUDA_NVRTC_HOME "${CUDA_TOOLKIT_ROOT_DIR}/nvrtc" CACHE INTERNAL "CUDA NVRTC Directory")
-    ENDIF()
-ELSE()
-    SET(CUDA_NVRTC_HOME "$ENV{CUDA_NVRTC_HOME}" CACHE INTERNAL "CUDA NVRTC Directory")
-    MESSAGE(STATUS "Using CUDA_NVRTC_HOME: ${CUDA_NVRTC_HOME}")
-ENDIF()
-
-FIND_LIBRARY(CUDA_nvrtc_LIBRARY
-             NAMES "nvrtc"
-             PATHS ${CUDA_NVRTC_HOME} ${CUDA_TOOLKIT_ROOT_DIR}
-             PATH_SUFFIXES "lib64" "lib" "lib/x64" "lib/Win32"
-             DOC "CUDA NVRTC Library"
-            )
-
-FIND_PATH(CUDA_nvrtc_INCLUDE_DIR
-          NAMES nvrtc.h
-          PATHS ${CUDA_NVRTC_HOME} ${CUDA_TOOLKIT_ROOT_DIR}
-          PATH_SUFFIXES "include"
-          DOC "CUDA NVRTC Include Directory"
-         )
-
-MARK_AS_ADVANCED(
-    CUDA_nvrtc_INCLUDE_DIR
-    CUDA_nvrtc_LIBRARY)
-
-INCLUDE(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(NVRTC DEFAULT_MSG CUDA_nvrtc_INCLUDE_DIR CUDA_nvrtc_LIBRARY)
diff --git a/CMakeModules/FindOpenCL.cmake b/CMakeModules/FindOpenCL.cmake
index 4d4ef57bc3..54c26e5c84 100644
--- a/CMakeModules/FindOpenCL.cmake
+++ b/CMakeModules/FindOpenCL.cmake
@@ -1,10 +1,22 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
 #.rst:
 # FindOpenCL
 # ----------
 #
 # Try to find OpenCL
 #
-# Once done this will define::
+# IMPORTED Targets
+# ^^^^^^^^^^^^^^^^
+#
+# This module defines :prop_tgt:`IMPORTED` target ``OpenCL::OpenCL``, if
+# OpenCL has been found.
+#
+# Result Variables
+# ^^^^^^^^^^^^^^^^
+#
+# This module defines the following variables::
 #
 #   OpenCL_FOUND          - True if OpenCL was found
 #   OpenCL_INCLUDE_DIRS   - include directories for OpenCL
@@ -19,51 +31,6 @@
 #   OpenCL_LIBRARY        - the path to the OpenCL library
 #
 
-#=============================================================================
-# From CMake 3.2
-# Copyright 2014 Matthaeus G. Chajdas
-#
-# Distributed under the OSI-approved BSD License (the "License");
-# see accompanying file Copyright.txt for details.
-#
-# This software is distributed WITHOUT ANY WARRANTY; without even the
-# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the License for more information.
-
-# CMake - Cross Platform Makefile Generator
-# Copyright 2000-2014 Kitware, Inc.
-# Copyright 2000-2011 Insight Software Consortium
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-#
-# * Neither the names of Kitware, Inc., the Insight Software Consortium,
-# nor the names of their contributors may be used to endorse or promote
-# products derived from this software without specific prior written
-# permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#=============================================================================
-
 function(_FIND_OPENCL_VERSION)
   include(CheckSymbolExists)
   include(CMakePushCheckState)
@@ -72,10 +39,11 @@ function(_FIND_OPENCL_VERSION)
   CMAKE_PUSH_CHECK_STATE()
   foreach(VERSION "2_0" "1_2" "1_1" "1_0")
     set(CMAKE_REQUIRED_INCLUDES "${OpenCL_INCLUDE_DIR}")
+
     if(APPLE)
       CHECK_SYMBOL_EXISTS(
         CL_VERSION_${VERSION}
-        "${OpenCL_INCLUDE_DIR}/OpenCL/cl.h"
+        "${OpenCL_INCLUDE_DIR}/Headers/cl.h"
         OPENCL_VERSION_${VERSION})
     else()
       CHECK_SYMBOL_EXISTS(
@@ -103,10 +71,10 @@ find_path(OpenCL_INCLUDE_DIR
     CL/cl.h OpenCL/cl.h
   PATHS
     ENV "PROGRAMFILES(X86)"
-    ENV NVSDKCOMPUTE_ROOT
-    ENV CUDA_PATH
     ENV AMDAPPSDKROOT
     ENV INTELOCLSDKROOT
+    ENV NVSDKCOMPUTE_ROOT
+    ENV CUDA_PATH
     ENV ATISTREAMSDKROOT
   PATH_SUFFIXES
     include
@@ -121,10 +89,10 @@ if(WIN32)
       NAMES OpenCL
       PATHS
         ENV "PROGRAMFILES(X86)"
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
         ENV AMDAPPSDKROOT
         ENV INTELOCLSDKROOT
+        ENV CUDA_PATH
+        ENV NVSDKCOMPUTE_ROOT
         ENV ATISTREAMSDKROOT
       PATH_SUFFIXES
         "AMD APP/lib/x86"
@@ -136,10 +104,10 @@ if(WIN32)
       NAMES OpenCL
       PATHS
         ENV "PROGRAMFILES(X86)"
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
         ENV AMDAPPSDKROOT
         ENV INTELOCLSDKROOT
+        ENV CUDA_PATH
+        ENV NVSDKCOMPUTE_ROOT
         ENV ATISTREAMSDKROOT
       PATH_SUFFIXES
         "AMD APP/lib/x86_64"
@@ -149,35 +117,13 @@ if(WIN32)
   endif()
 else()
   find_library(OpenCL_LIBRARY
-    NAMES OpenCL
-    PATHS
-        ENV LD_LIBRARY_PATH
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV ATISTREAMSDKROOT
-        /usr/lib64
-        /usr/lib
-        /usr/local/lib64
-        /usr/local/lib
-        /sw/lib
-        /opt/local/lib
-    PATH_SUFFIXES
-        "AMD APP/lib/x86_64"
-        lib/x86_64
-        lib/x64
-        lib/
-        lib64/
-        x86_64-linux-gnu
-        arm-linux-gnueabihf
-    )
+    NAMES OpenCL)
 endif()
 
 set(OpenCL_LIBRARIES ${OpenCL_LIBRARY})
 set(OpenCL_INCLUDE_DIRS ${OpenCL_INCLUDE_DIR})
 
-#include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
+include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(
   OpenCL
   FOUND_VAR OpenCL_FOUND
@@ -188,3 +134,16 @@ mark_as_advanced(
   OpenCL_INCLUDE_DIR
   OpenCL_LIBRARY)
 
+if(OpenCL_FOUND AND NOT TARGET OpenCL::OpenCL)
+  if(OpenCL_LIBRARY MATCHES "/([^/]+)\\.framework$")
+    add_library(OpenCL::OpenCL INTERFACE IMPORTED)
+    set_target_properties(OpenCL::OpenCL PROPERTIES
+      INTERFACE_LINK_LIBRARIES "${OpenCL_LIBRARY}")
+  else()
+    add_library(OpenCL::OpenCL UNKNOWN IMPORTED)
+    set_target_properties(OpenCL::OpenCL PROPERTIES
+      IMPORTED_LOCATION "${OpenCL_LIBRARY}")
+  endif()
+  set_target_properties(OpenCL::OpenCL PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES "${OpenCL_INCLUDE_DIRS}")
+endif()
diff --git a/CMakeModules/FindOpenGL.cmake b/CMakeModules/FindOpenGL.cmake
new file mode 100644
index 0000000000..4ab5d4bfd5
--- /dev/null
+++ b/CMakeModules/FindOpenGL.cmake
@@ -0,0 +1,227 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#.rst:
+# FindOpenGL
+# ----------
+#
+# FindModule for OpenGL and GLU.
+#
+# IMPORTED Targets
+# ^^^^^^^^^^^^^^^^
+#
+# This module defines the :prop_tgt:`IMPORTED` targets:
+#
+# ``OpenGL::GL``
+#  Defined if the system has OpenGL.
+# ``OpenGL::GLU``
+#  Defined if the system has GLU.
+#
+# Result Variables
+# ^^^^^^^^^^^^^^^^
+#
+# This module sets the following variables:
+#
+# ``OPENGL_FOUND``
+#  True, if the system has OpenGL.
+# ``OPENGL_XMESA_FOUND``
+#  True, if the system has XMESA.
+# ``OPENGL_GLU_FOUND``
+#  True, if the system has GLU.
+# ``OPENGL_INCLUDE_DIR``
+#  Path to the OpenGL include directory.
+# ``OPENGL_LIBRARIES``
+#  Paths to the OpenGL and GLU libraries.
+#
+# If you want to use just GL you can use these values:
+#
+# ``OPENGL_gl_LIBRARY``
+#  Path to the OpenGL library.
+# ``OPENGL_glu_LIBRARY``
+#  Path to the GLU library.
+#
+# OSX Specific
+# ^^^^^^^^^^^^
+#
+# On OSX default to using the framework version of OpenGL. People will
+# have to change the cache values of OPENGL_glu_LIBRARY and
+# OPENGL_gl_LIBRARY to use OpenGL with X11 on OSX.
+
+
+set(_OpenGL_REQUIRED_VARS OPENGL_gl_LIBRARY)
+
+if (CYGWIN)
+
+  find_path(OPENGL_INCLUDE_DIR GL/gl.h )
+  list(APPEND _OpenGL_REQUIRED_VARS OPENGL_INCLUDE_DIR)
+
+  find_library(OPENGL_gl_LIBRARY opengl32 )
+
+  find_library(OPENGL_glu_LIBRARY glu32 )
+
+elseif (WIN32)
+
+  if(BORLAND)
+    set (OPENGL_gl_LIBRARY import32 CACHE STRING "OpenGL library for win32")
+    set (OPENGL_glu_LIBRARY import32 CACHE STRING "GLU library for win32")
+  else()
+    set (OPENGL_gl_LIBRARY opengl32 CACHE STRING "OpenGL library for win32")
+    set (OPENGL_glu_LIBRARY glu32 CACHE STRING "GLU library for win32")
+  endif()
+
+elseif (APPLE)
+
+  # The OpenGL.framework provides both gl and glu
+  find_library(OPENGL_gl_LIBRARY OpenGL DOC "OpenGL library for OS X")
+  find_library(OPENGL_glu_LIBRARY OpenGL DOC
+    "GLU library for OS X (usually same as OpenGL library)")
+  find_path(OPENGL_INCLUDE_DIR OpenGL/gl.h DOC "Include for OpenGL on OS X")
+  list(APPEND _OpenGL_REQUIRED_VARS OPENGL_INCLUDE_DIR)
+
+else()
+  if (CMAKE_SYSTEM_NAME MATCHES "HP-UX")
+    # Handle HP-UX cases where we only want to find OpenGL in either hpux64
+    # or hpux32 depending on if we're doing a 64 bit build.
+    if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+      set(_OPENGL_LIB_PATH
+        /opt/graphics/OpenGL/lib/hpux32/)
+    else()
+      set(_OPENGL_LIB_PATH
+        /opt/graphics/OpenGL/lib/hpux64/
+        /opt/graphics/OpenGL/lib/pa20_64)
+    endif()
+  elseif(CMAKE_SYSTEM_NAME STREQUAL Haiku)
+    set(_OPENGL_LIB_PATH
+      /boot/develop/lib/x86)
+    set(_OPENGL_INCLUDE_PATH
+      /boot/develop/headers/os/opengl)
+  endif()
+
+  # The first line below is to make sure that the proper headers
+  # are used on a Linux machine with the NVidia drivers installed.
+  # They replace Mesa with NVidia's own library but normally do not
+  # install headers and that causes the linking to
+  # fail since the compiler finds the Mesa headers but NVidia's library.
+  # Make sure the NVIDIA directory comes BEFORE the others.
+  #  - Atanas Georgiev <atanas@cs.columbia.edu>
+
+  find_path(OPENGL_INCLUDE_DIR GL/gl.h
+    /usr/share/doc/NVIDIA_GLX-1.0/include
+    /usr/openwin/share/include
+    /opt/graphics/OpenGL/include /usr/X11R6/include
+    ${_OPENGL_INCLUDE_PATH}
+  )
+  list(APPEND _OpenGL_REQUIRED_VARS OPENGL_INCLUDE_DIR)
+
+  find_path(OPENGL_xmesa_INCLUDE_DIR GL/xmesa.h
+    /usr/share/doc/NVIDIA_GLX-1.0/include
+    /usr/openwin/share/include
+    /opt/graphics/OpenGL/include /usr/X11R6/include
+  )
+
+  find_library(OPENGL_gl_LIBRARY
+    NAMES GL MesaGL
+    PATHS /opt/graphics/OpenGL/lib
+          /usr/openwin/lib
+          /usr/shlib /usr/X11R6/lib
+          ${_OPENGL_LIB_PATH}
+  )
+
+  unset(_OPENGL_INCLUDE_PATH)
+  unset(_OPENGL_LIB_PATH)
+
+  find_library(OPENGL_glu_LIBRARY
+    NAMES GLU MesaGLU
+    PATHS ${OPENGL_gl_LIBRARY}
+          /opt/graphics/OpenGL/lib
+          /usr/openwin/lib
+          /usr/shlib /usr/X11R6/lib
+  )
+
+endif ()
+
+if(OPENGL_gl_LIBRARY)
+
+    if(OPENGL_xmesa_INCLUDE_DIR)
+      set( OPENGL_XMESA_FOUND "YES" )
+    else()
+      set( OPENGL_XMESA_FOUND "NO" )
+    endif()
+
+    set( OPENGL_LIBRARIES  ${OPENGL_gl_LIBRARY} ${OPENGL_LIBRARIES})
+    if(OPENGL_glu_LIBRARY)
+      set( OPENGL_GLU_FOUND "YES" )
+      if(NOT "${OPENGL_glu_LIBRARY}" STREQUAL "${OPENGL_gl_LIBRARY}")
+        set( OPENGL_LIBRARIES ${OPENGL_glu_LIBRARY} ${OPENGL_LIBRARIES} )
+      endif()
+    else()
+      set( OPENGL_GLU_FOUND "NO" )
+    endif()
+
+    # This deprecated setting is for backward compatibility with CMake1.4
+    set (OPENGL_LIBRARY ${OPENGL_LIBRARIES})
+
+endif()
+
+# This deprecated setting is for backward compatibility with CMake1.4
+set(OPENGL_INCLUDE_PATH ${OPENGL_INCLUDE_DIR})
+
+include(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(OpenGL REQUIRED_VARS ${_OpenGL_REQUIRED_VARS})
+unset(_OpenGL_REQUIRED_VARS)
+
+# OpenGL:: targets
+if(OPENGL_FOUND)
+  if(NOT TARGET OpenGL::GL)
+    if(IS_ABSOLUTE "${OPENGL_gl_LIBRARY}")
+      add_library(OpenGL::GL UNKNOWN IMPORTED)
+      if(OPENGL_gl_LIBRARY MATCHES "/([^/]+)\\.framework$")
+        set(_gl_fw "${OPENGL_gl_LIBRARY}/${CMAKE_MATCH_1}")
+        if(EXISTS "${_gl_fw}.tbd")
+          set(_gl_fw "${_gl_fw}.tbd")
+        endif()
+        set_target_properties(OpenGL::GL PROPERTIES
+          IMPORTED_LOCATION "${_gl_fw}")
+      else()
+        set_target_properties(OpenGL::GL PROPERTIES
+          IMPORTED_LOCATION "${OPENGL_gl_LIBRARY}")
+      endif()
+    else()
+      add_library(OpenGL::GL INTERFACE IMPORTED)
+      set_target_properties(OpenGL::GL PROPERTIES
+        INTERFACE_LINK_LIBRARIES "${OPENGL_gl_LIBRARY}")
+    endif()
+    set_target_properties(OpenGL::GL PROPERTIES
+      INTERFACE_INCLUDE_DIRECTORIES "${OPENGL_INCLUDE_DIR}")
+  endif()
+
+  if(OPENGL_GLU_FOUND AND NOT TARGET OpenGL::GLU)
+    if(IS_ABSOLUTE "${OPENGL_glu_LIBRARY}")
+      add_library(OpenGL::GLU UNKNOWN IMPORTED)
+      if(OPENGL_glu_LIBRARY MATCHES "/([^/]+)\\.framework$")
+        set(_glu_fw "${OPENGL_glu_LIBRARY}/${CMAKE_MATCH_1}")
+        if(EXISTS "${_glu_fw}.tbd")
+          set(_glu_fw "${_glu_fw}.tbd")
+        endif()
+        set_target_properties(OpenGL::GLU PROPERTIES
+          IMPORTED_LOCATION "${_glu_fw}")
+      else()
+        set_target_properties(OpenGL::GLU PROPERTIES
+          IMPORTED_LOCATION "${OPENGL_glu_LIBRARY}")
+      endif()
+    else()
+      add_library(OpenGL::GLU INTERFACE IMPORTED)
+      set_target_properties(OpenGL::GLU PROPERTIES
+        INTERFACE_LINK_LIBRARIES "${OPENGL_glu_LIBRARY}")
+    endif()
+    set_target_properties(OpenGL::GLU PROPERTIES
+      INTERFACE_LINK_LIBRARIES OpenGL::GL)
+  endif()
+endif()
+
+mark_as_advanced(
+  OPENGL_INCLUDE_DIR
+  OPENGL_xmesa_INCLUDE_DIR
+  OPENGL_glu_LIBRARY
+  OPENGL_gl_LIBRARY
+)
diff --git a/CMakeModules/FindOpenMP.cmake b/CMakeModules/FindOpenMP.cmake
new file mode 100644
index 0000000000..be7f85661d
--- /dev/null
+++ b/CMakeModules/FindOpenMP.cmake
@@ -0,0 +1,457 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#.rst:
+# FindOpenMP
+# ----------
+#
+# Finds OpenMP support
+#
+# This module can be used to detect OpenMP support in a compiler.  If
+# the compiler supports OpenMP, the flags required to compile with
+# OpenMP support are returned in variables for the different languages.
+# The variables may be empty if the compiler does not need a special
+# flag to support OpenMP.
+#
+# Variables
+# ^^^^^^^^^
+#
+# This module will set the following variables per language in your
+# project, where ``<lang>`` is one of C, CXX, or Fortran:
+#
+# ``OpenMP_<lang>_FOUND``
+#   Variable indicating if OpenMP support for ``<lang>`` was detected.
+# ``OpenMP_<lang>_FLAGS``
+#   OpenMP compiler flags for ``<lang>``, separated by spaces.
+#
+# For linking with OpenMP code written in ``<lang>``, the following
+# variables are provided:
+#
+# ``OpenMP_<lang>_LIB_NAMES``
+#   :ref:`;-list <CMake Language Lists>` of libraries for OpenMP programs for ``<lang>``.
+# ``OpenMP_<libname>_LIBRARY``
+#   Location of the individual libraries needed for OpenMP support in ``<lang>``.
+# ``OpenMP_<lang>_LIBRARIES``
+#   A list of libraries needed to link with OpenMP code written in ``<lang>``.
+#
+# Additionally, the module provides :prop_tgt:`IMPORTED` targets:
+#
+# ``OpenMP::OpenMP_<lang>``
+#   Target for using OpenMP from ``<lang>``.
+#
+# Specifically for Fortran, the module sets the following variables:
+#
+# ``OpenMP_Fortran_HAVE_OMPLIB_HEADER``
+#   Boolean indicating if OpenMP is accessible through ``omp_lib.h``.
+# ``OpenMP_Fortran_HAVE_OMPLIB_MODULE``
+#   Boolean indicating if OpenMP is accessible through the ``omp_lib`` Fortran module.
+#
+# The module will also try to provide the OpenMP version variables:
+#
+# ``OpenMP_<lang>_SPEC_DATE``
+#   Date of the OpenMP specification implemented by the ``<lang>`` compiler.
+# ``OpenMP_<lang>_VERSION_MAJOR``
+#   Major version of OpenMP implemented by the ``<lang>`` compiler.
+# ``OpenMP_<lang>_VERSION_MINOR``
+#   Minor version of OpenMP implemented by the ``<lang>`` compiler.
+# ``OpenMP_<lang>_VERSION``
+#   OpenMP version implemented by the ``<lang>`` compiler.
+#
+# The specification date is formatted as given in the OpenMP standard:
+# ``yyyymm`` where ``yyyy`` and ``mm`` represents the year and month of
+# the OpenMP specification implemented by the ``<lang>`` compiler.
+#
+# Backward Compatibility
+# ^^^^^^^^^^^^^^^^^^^^^^
+#
+# For backward compatibility with older versions of FindOpenMP, these
+# variables are set, but deprecated::
+#
+#   OpenMP_FOUND
+#
+# In new projects, please use the ``OpenMP_<lang>_XXX`` equivalents.
+
+cmake_policy(PUSH)
+cmake_policy(SET CMP0057 NEW) # if IN_LIST
+
+function(_OPENMP_FLAG_CANDIDATES LANG)
+  if(NOT OpenMP_${LANG}_FLAG)
+    unset(OpenMP_FLAG_CANDIDATES)
+
+    set(OMP_FLAG_GNU "-fopenmp")
+    set(OMP_FLAG_Clang "-fopenmp=libomp" "-fopenmp=libiomp5" "-fopenmp")
+    set(OMP_FLAG_HP "+Oopenmp")
+    if(WIN32)
+      set(OMP_FLAG_Intel "-Qopenmp")
+    elseif(CMAKE_${LANG}_COMPILER_ID STREQUAL "Intel" AND
+           "${CMAKE_${LANG}_COMPILER_VERSION}" VERSION_LESS "15.0.0.20140528")
+      set(OMP_FLAG_Intel "-openmp")
+    else()
+      set(OMP_FLAG_Intel "-qopenmp")
+    endif()
+    set(OMP_FLAG_MIPSpro "-mp")
+    set(OMP_FLAG_MSVC "-openmp")
+    set(OMP_FLAG_PathScale "-openmp")
+    set(OMP_FLAG_NAG "-openmp")
+    set(OMP_FLAG_Absoft "-openmp")
+    set(OMP_FLAG_PGI "-mp")
+    set(OMP_FLAG_SunPro "-xopenmp")
+    set(OMP_FLAG_XL "-qsmp=omp")
+    # Cray compiles with OpenMP automatically
+    set(OMP_FLAG_Cray " ")
+
+    # If we know the correct flags, use those
+    if(DEFINED OMP_FLAG_${CMAKE_${LANG}_COMPILER_ID})
+      set(OpenMP_FLAG_CANDIDATES "${OMP_FLAG_${CMAKE_${LANG}_COMPILER_ID}}")
+    # Fall back to reasonable default tries otherwise
+    else()
+      set(OpenMP_FLAG_CANDIDATES "-openmp" "-fopenmp" "-mp" " ")
+    endif()
+    set(OpenMP_${LANG}_FLAG_CANDIDATES "${OpenMP_FLAG_CANDIDATES}" PARENT_SCOPE)
+  else()
+    set(OpenMP_${LANG}_FLAG_CANDIDATES "${OpenMP_${LANG}_FLAG}" PARENT_SCOPE)
+  endif()
+endfunction()
+
+# sample openmp source code to test
+set(OpenMP_C_CXX_TEST_SOURCE
+"
+#include <omp.h>
+int main() {
+#ifndef _OPENMP
+  breaks_on_purpose
+#endif
+}
+")
+
+# in Fortran, an implementation may provide an omp_lib.h header
+# or omp_lib module, or both (OpenMP standard, section 3.1)
+# Furthmore !$ is the Fortran equivalent of #ifdef _OPENMP (OpenMP standard, 2.2.2)
+# Without the conditional compilation, some compilers (e.g. PGI) might compile OpenMP code
+# while not actually enabling OpenMP, building code sequentially
+set(OpenMP_Fortran_TEST_SOURCE
+  "
+      program test
+      @OpenMP_Fortran_INCLUDE_LINE@
+  !$  integer :: n
+      n = omp_get_num_threads()
+      end program test
+  "
+)
+
+function(_OPENMP_WRITE_SOURCE_FILE LANG SRC_FILE_CONTENT_VAR SRC_FILE_NAME SRC_FILE_FULLPATH)
+  set(WORK_DIR ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/FindOpenMP)
+  if("${LANG}" STREQUAL "C")
+    set(SRC_FILE "${WORK_DIR}/${SRC_FILE_NAME}.c")
+    file(WRITE "${SRC_FILE}" "${OpenMP_C_CXX_${SRC_FILE_CONTENT_VAR}}")
+  elseif("${LANG}" STREQUAL "CXX")
+    set(SRC_FILE "${WORK_DIR}/${SRC_FILE_NAME}.cpp")
+    file(WRITE "${SRC_FILE}" "${OpenMP_C_CXX_${SRC_FILE_CONTENT_VAR}}")
+  elseif("${LANG}" STREQUAL "Fortran")
+    set(SRC_FILE "${WORK_DIR}/${SRC_FILE_NAME}.f90")
+    file(WRITE "${SRC_FILE}_in" "${OpenMP_Fortran_${SRC_FILE_CONTENT_VAR}}")
+    configure_file("${SRC_FILE}_in" "${SRC_FILE}" @ONLY)
+  endif()
+  set(${SRC_FILE_FULLPATH} "${SRC_FILE}" PARENT_SCOPE)
+endfunction()
+
+include(${CMAKE_ROOT}/Modules/CMakeParseImplicitLinkInfo.cmake)
+
+function(_OPENMP_GET_FLAGS LANG FLAG_MODE OPENMP_FLAG_VAR OPENMP_LIB_NAMES_VAR)
+  _OPENMP_FLAG_CANDIDATES("${LANG}")
+  _OPENMP_WRITE_SOURCE_FILE("${LANG}" "TEST_SOURCE" OpenMPTryFlag _OPENMP_TEST_SRC)
+
+  foreach(OPENMP_FLAG IN LISTS OpenMP_${LANG}_FLAG_CANDIDATES)
+    set(OPENMP_FLAGS_TEST "${OPENMP_FLAG}")
+    if(CMAKE_${LANG}_VERBOSE_FLAG)
+      string(APPEND OPENMP_FLAGS_TEST " ${CMAKE_${LANG}_VERBOSE_FLAG}")
+    endif()
+    string(REGEX REPLACE "[-/=+]" "" OPENMP_PLAIN_FLAG "${OPENMP_FLAG}")
+    try_compile( OpenMP_COMPILE_RESULT_${FLAG_MODE}_${OPENMP_PLAIN_FLAG} ${CMAKE_BINARY_DIR} ${_OPENMP_TEST_SRC}
+      CMAKE_FLAGS "-DCOMPILE_DEFINITIONS:STRING=${OPENMP_FLAGS_TEST}"
+      OUTPUT_VARIABLE OpenMP_TRY_COMPILE_OUTPUT
+    )
+
+    if(OpenMP_COMPILE_RESULT_${FLAG_MODE}_${OPENMP_PLAIN_FLAG})
+      set("${OPENMP_FLAG_VAR}" "${OPENMP_FLAG}" PARENT_SCOPE)
+
+      if(CMAKE_${LANG}_VERBOSE_FLAG)
+        unset(OpenMP_${LANG}_IMPLICIT_LIBRARIES)
+        unset(OpenMP_${LANG}_IMPLICIT_LINK_DIRS)
+        unset(OpenMP_${LANG}_IMPLICIT_FWK_DIRS)
+        unset(OpenMP_${LANG}_LOG_VAR)
+
+        file(APPEND ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeOutput.log
+        "Detecting ${LANG} OpenMP compiler ABI info compiled with the following output:\n${OpenMP_TRY_COMPILE_OUTPUT}\n\n")
+
+        cmake_parse_implicit_link_info("${OpenMP_TRY_COMPILE_OUTPUT}"
+          OpenMP_${LANG}_IMPLICIT_LIBRARIES
+          OpenMP_${LANG}_IMPLICIT_LINK_DIRS
+          OpenMP_${LANG}_IMPLICIT_FWK_DIRS
+          OpenMP_${LANG}_LOG_VAR
+          "${CMAKE_${LANG}_IMPLICIT_OBJECT_REGEX}"
+        )
+
+        file(APPEND ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeOutput.log
+        "Parsed ${LANG} OpenMP implicit link information from above output:\n${OpenMP_${LANG}_LOG_VAR}\n\n")
+
+        unset(_OPENMP_LIB_NAMES)
+        foreach(_OPENMP_IMPLICIT_LIB IN LISTS OpenMP_${LANG}_IMPLICIT_LIBRARIES)
+          if(NOT "${_OPENMP_IMPLICIT_LIB}" IN_LIST CMAKE_${LANG}_IMPLICIT_LINK_LIBRARIES)
+            find_library(OpenMP_${_OPENMP_IMPLICIT_LIB}_LIBRARY
+              NAMES "${_OPENMP_IMPLICIT_LIB}"
+              HINTS ${OpenMP_${LANG}_IMPLICIT_LINK_DIRS}
+            )
+            mark_as_advanced(OpenMP_${_OPENMP_IMPLICIT_LIB}_LIBRARY)
+            list(APPEND _OPENMP_LIB_NAMES ${_OPENMP_IMPLICIT_LIB})
+          endif()
+        endforeach()
+        set("${OPENMP_LIB_NAMES_VAR}" "${_OPENMP_LIB_NAMES}" PARENT_SCOPE)
+      else()
+        # The Intel compiler on windows has no verbose mode, so we need to treat it explicitly
+        if("${CMAKE_${LANG}_COMPILER_ID}" STREQUAL "Intel" AND "${CMAKE_SYSTEM_NAME}" STREQUAL "Windows")
+          set("${OPENMP_LIB_NAMES_VAR}" "libiomp5md" PARENT_SCOPE)
+          find_library(OpenMP_libiomp5md_LIBRARY
+            NAMES "libiomp5md"
+            HINTS ${CMAKE_${LANG}_IMPLICIT_LINK_DIRECTORIES}
+          )
+          mark_as_advanced(OpenMP_libiomp5md_LIBRARY)
+        else()
+          set("${OPENMP_LIB_NAMES_VAR}" "" PARENT_SCOPE)
+        endif()
+      endif()
+      break()
+    endif()
+    set("${OPENMP_LIB_NAMES_VAR}" "NOTFOUND" PARENT_SCOPE)
+    set("${OPENMP_FLAG_VAR}" "NOTFOUND" PARENT_SCOPE)
+  endforeach()
+endfunction()
+
+set(OpenMP_C_CXX_CHECK_VERSION_SOURCE
+"
+#include <stdio.h>
+#include <omp.h>
+const char ompver_str[] = { 'I', 'N', 'F', 'O', ':', 'O', 'p', 'e', 'n', 'M',
+                            'P', '-', 'd', 'a', 't', 'e', '[',
+                            ('0' + ((_OPENMP/100000)%10)),
+                            ('0' + ((_OPENMP/10000)%10)),
+                            ('0' + ((_OPENMP/1000)%10)),
+                            ('0' + ((_OPENMP/100)%10)),
+                            ('0' + ((_OPENMP/10)%10)),
+                            ('0' + ((_OPENMP/1)%10)),
+                            ']', '\\0' };
+int main()
+{
+  puts(ompver_str);
+}
+")
+
+set(OpenMP_Fortran_CHECK_VERSION_SOURCE
+"
+      program omp_ver
+      @OpenMP_Fortran_INCLUDE_LINE@
+      integer, parameter :: zero = ichar('0')
+      integer, parameter :: ompv = openmp_version
+      character, dimension(24), parameter :: ompver_str =&
+      (/ 'I', 'N', 'F', 'O', ':', 'O', 'p', 'e', 'n', 'M', 'P', '-',&
+         'd', 'a', 't', 'e', '[',&
+         char(zero + mod(ompv/100000, 10)),&
+         char(zero + mod(ompv/10000, 10)),&
+         char(zero + mod(ompv/1000, 10)),&
+         char(zero + mod(ompv/100, 10)),&
+         char(zero + mod(ompv/10, 10)),&
+         char(zero + mod(ompv/1, 10)), ']' /)
+      print *, ompver_str
+      end program omp_ver
+")
+
+function(_OPENMP_GET_SPEC_DATE LANG SPEC_DATE)
+  _OPENMP_WRITE_SOURCE_FILE("${LANG}" "CHECK_VERSION_SOURCE" OpenMPCheckVersion _OPENMP_TEST_SRC)
+
+  set(BIN_FILE "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/FindOpenMP/ompver_${LANG}.bin")
+  string(REGEX REPLACE "[-/=+]" "" OPENMP_PLAIN_FLAG "${OPENMP_FLAG}")
+  try_compile(OpenMP_SPECTEST_${LANG}_${OPENMP_PLAIN_FLAG} "${CMAKE_BINARY_DIR}" "${_OPENMP_TEST_SRC}"
+              CMAKE_FLAGS "-DCOMPILE_DEFINITIONS:STRING=${OpenMP_${LANG}_FLAGS}"
+              COPY_FILE ${BIN_FILE})
+
+  if(${OpenMP_SPECTEST_${LANG}_${OPENMP_PLAIN_FLAG}})
+    file(STRINGS ${BIN_FILE} specstr LIMIT_COUNT 1 REGEX "INFO:OpenMP-date")
+    set(regex_spec_date ".*INFO:OpenMP-date\\[0*([^]]*)\\].*")
+    if("${specstr}" MATCHES "${regex_spec_date}")
+      set(${SPEC_DATE} "${CMAKE_MATCH_1}" PARENT_SCOPE)
+    endif()
+  endif()
+endfunction()
+
+macro(_OPENMP_SET_VERSION_BY_SPEC_DATE LANG)
+  set(OpenMP_SPEC_DATE_MAP
+    # Combined versions, 2.5 onwards
+    "201511=4.5"
+    "201307=4.0"
+    "201107=3.1"
+    "200805=3.0"
+    "200505=2.5"
+    # C/C++ version 2.0
+    "200203=2.0"
+    # Fortran version 2.0
+    "200011=2.0"
+    # Fortran version 1.1
+    "199911=1.1"
+    # C/C++ version 1.0 (there's no 1.1 for C/C++)
+    "199810=1.0"
+    # Fortran version 1.0
+    "199710=1.0"
+  )
+
+  string(REGEX MATCHALL "${OpenMP_${LANG}_SPEC_DATE}=([0-9]+)\\.([0-9]+)" _version_match "${OpenMP_SPEC_DATE_MAP}")
+  if(NOT _version_match STREQUAL "")
+    set(OpenMP_${LANG}_VERSION_MAJOR ${CMAKE_MATCH_1})
+    set(OpenMP_${LANG}_VERSION_MINOR ${CMAKE_MATCH_2})
+    set(OpenMP_${LANG}_VERSION "${OpenMP_${LANG}_VERSION_MAJOR}.${OpenMP_${LANG}_VERSION_MINOR}")
+  else()
+    unset(OpenMP_${LANG}_VERSION_MAJOR)
+    unset(OpenMP_${LANG}_VERSION_MINOR)
+    unset(OpenMP_${LANG}_VERSION)
+  endif()
+  unset(_version_match)
+  unset(OpenMP_SPEC_DATE_MAP)
+endmacro()
+
+foreach(LANG IN ITEMS C CXX)
+  if(CMAKE_${LANG}_COMPILER_LOADED)
+    if(NOT DEFINED OpenMP_${LANG}_FLAGS OR "${OpenMP_${LANG}_FLAGS}" STREQUAL "NOTFOUND"
+      OR NOT DEFINED OpenMP_${LANG}_LIB_NAMES OR "${OpenMP_${LANG}_LIB_NAMES}" STREQUAL "NOTFOUND")
+      _OPENMP_GET_FLAGS("${LANG}" "${LANG}" OpenMP_${LANG}_FLAGS_WORK OpenMP_${LANG}_LIB_NAMES_WORK)
+    endif()
+
+    set(OpenMP_${LANG}_FLAGS "${OpenMP_${LANG}_FLAGS_WORK}"
+      CACHE STRING "${LANG} compiler flags for OpenMP parallelization")
+    set(OpenMP_${LANG}_LIB_NAMES "${OpenMP_${LANG}_LIB_NAMES_WORK}"
+      CACHE STRING "${LANG} compiler libraries for OpenMP parallelization")
+    mark_as_advanced(OpenMP_${LANG}_FLAGS OpenMP_${LANG}_LIB_NAMES)
+  endif()
+endforeach()
+
+if(CMAKE_Fortran_COMPILER_LOADED)
+  if(NOT DEFINED OpenMP_Fortran_FLAGS OR "${OpenMP_Fortran_FLAGS}" STREQUAL "NOTFOUND"
+    OR NOT DEFINED OpenMP_Fortran_LIB_NAMES OR "${OpenMP_Fortran_LIB_NAMES}" STREQUAL "NOTFOUND"
+    OR NOT DEFINED OpenMP_Fortran_HAVE_OMPLIB_MODULE)
+    set(OpenMP_Fortran_INCLUDE_LINE "use omp_lib\n      implicit none")
+    _OPENMP_GET_FLAGS("Fortran" "FortranHeader" OpenMP_Fortran_FLAGS_WORK OpenMP_Fortran_LIB_NAMES_WORK)
+    if(OpenMP_Fortran_FLAGS_WORK)
+      set(OpenMP_Fortran_HAVE_OMPLIB_MODULE TRUE CACHE BOOL INTERNAL "")
+    endif()
+
+    set(OpenMP_Fortran_FLAGS "${OpenMP_Fortran_FLAGS_WORK}"
+      CACHE STRING "Fortran compiler flags for OpenMP parallelization")
+    set(OpenMP_Fortran_LIB_NAMES "${OpenMP_Fortran_LIB_NAMES_WORK}"
+      CACHE STRING "Fortran compiler libraries for OpenMP parallelization")
+    mark_as_advanced(OpenMP_Fortran_FLAGS OpenMP_Fortran_LIB_NAMES)
+  endif()
+
+  if(NOT DEFINED OpenMP_Fortran_FLAGS OR "${OpenMP_Fortran_FLAGS}" STREQUAL "NOTFOUND"
+    OR NOT DEFINED OpenMP_Fortran_LIB_NAMES OR "${OpenMP_Fortran_LIB_NAMES}" STREQUAL "NOTFOUND"
+    OR NOT DEFINED OpenMP_Fortran_HAVE_OMPLIB_HEADER)
+    set(OpenMP_Fortran_INCLUDE_LINE "implicit none\n      include 'omp_lib.h'")
+    _OPENMP_GET_FLAGS("Fortran" "FortranModule" OpenMP_Fortran_FLAGS_WORK OpenMP_Fortran_LIB_NAMES_WORK)
+    if(OpenMP_Fortran_FLAGS_WORK)
+      set(OpenMP_Fortran_HAVE_OMPLIB_HEADER TRUE CACHE BOOL INTERNAL "")
+    endif()
+
+    set(OpenMP_Fortran_FLAGS "${OpenMP_Fortran_FLAGS_WORK}"
+      CACHE STRING "Fortran compiler flags for OpenMP parallelization")
+
+    set(OpenMP_Fortran_LIB_NAMES "${OpenMP_Fortran_LIB_NAMES}"
+      CACHE STRING "Fortran compiler libraries for OpenMP parallelization")
+  endif()
+
+  if(OpenMP_Fortran_HAVE_OMPLIB_MODULE)
+    set(OpenMP_Fortran_INCLUDE_LINE "use omp_lib\n      implicit none")
+  else()
+    set(OpenMP_Fortran_INCLUDE_LINE "implicit none\n      include 'omp_lib.h'")
+  endif()
+endif()
+
+set(OPENMP_FOUND TRUE)
+
+foreach(LANG IN ITEMS C CXX Fortran)
+  if(CMAKE_${LANG}_COMPILER_LOADED)
+    if (NOT OpenMP_${LANG}_SPEC_DATE)
+      _OPENMP_GET_SPEC_DATE("${LANG}" OpenMP_${LANG}_SPEC_DATE_INTERNAL)
+      set(OpenMP_${LANG}_SPEC_DATE "${OpenMP_${LANG}_SPEC_DATE_INTERNAL}" CACHE
+        INTERNAL "${LANG} compiler's OpenMP specification date")
+      _OPENMP_SET_VERSION_BY_SPEC_DATE("${LANG}")
+    endif()
+
+    include(FindPackageHandleStandardArgs)
+
+    set(OpenMP_${LANG}_FIND_QUIETLY ${OpenMP_FIND_QUIETLY})
+    set(OpenMP_${LANG}_FIND_REQUIRED ${OpenMP_FIND_REQUIRED})
+    set(OpenMP_${LANG}_FIND_VERSION ${OpenMP_FIND_VERSION})
+    set(OpenMP_${LANG}_FIND_VERSION_EXACT ${OpenMP_FIND_VERSION_EXACT})
+
+    set(_OPENMP_${LANG}_REQUIRED_VARS OpenMP_${LANG}_FLAGS)
+    if("${OpenMP_${LANG}_LIB_NAMES}" STREQUAL "NOTFOUND")
+      set(_OPENMP_${LANG}_REQUIRED_LIB_VARS OpenMP_${LANG}_LIB_NAMES)
+    else()
+      foreach(_OPENMP_IMPLICIT_LIB IN LISTS OpenMP_${LANG}_LIB_NAMES)
+        list(APPEND _OPENMP_${LANG}_REQUIRED_LIB_VARS OpenMP_${_OPENMP_IMPLICIT_LIB}_LIBRARY)
+      endforeach()
+    endif()
+
+    find_package_handle_standard_args(OpenMP_${LANG}
+      REQUIRED_VARS OpenMP_${LANG}_FLAGS ${_OPENMP_${LANG}_REQUIRED_LIB_VARS}
+      VERSION_VAR OpenMP_${LANG}_VERSION
+    )
+
+    if(OpenMP_${LANG}_FOUND)
+      set(OpenMP_${LANG}_LIBRARIES "")
+      foreach(_OPENMP_IMPLICIT_LIB IN LISTS OpenMP_${LANG}_LIB_NAMES)
+        list(APPEND OpenMP_${LANG}_LIBRARIES "${OpenMP_${_OPENMP_IMPLICIT_LIB}_LIBRARY}")
+      endforeach()
+
+      if(NOT TARGET OpenMP::OpenMP_${LANG})
+        add_library(OpenMP::OpenMP_${LANG} INTERFACE IMPORTED)
+      endif()
+      if(OpenMP_${LANG}_FLAGS)
+
+        if(UNIX)
+          separate_arguments(_OpenMP_${LANG}_OPTIONS UNIX_COMMAND "${OpenMP_${LANG}_FLAGS}")
+        elseif(WIN32)
+          separate_arguments(_OpenMP_${LANG}_OPTIONS WINDOWS_COMMAND "${OpenMP_${LANG}_FLAGS}")
+        endif()
+
+        set_property(TARGET OpenMP::OpenMP_${LANG} PROPERTY
+          INTERFACE_COMPILE_OPTIONS "${_OpenMP_${LANG}_OPTIONS}")
+        unset(_OpenMP_${LANG}_OPTIONS)
+      endif()
+      if(OpenMP_${LANG}_LIBRARIES)
+        set_property(TARGET OpenMP::OpenMP_${LANG} PROPERTY
+          INTERFACE_LINK_LIBRARIES "${OpenMP_${LANG}_LIBRARIES}")
+      endif()
+    else()
+      set(OPENMP_FOUND FALSE)
+    endif()
+  endif()
+endforeach()
+
+if(CMAKE_Fortran_COMPILER_LOADED AND OpenMP_Fortran_FOUND)
+  if(NOT DEFINED OpenMP_Fortran_HAVE_OMPLIB_MODULE)
+    set(OpenMP_Fortran_HAVE_OMPLIB_MODULE FALSE CACHE BOOL INTERNAL "")
+  endif()
+  if(NOT DEFINED OpenMP_Fortran_HAVE_OMPLIB_HEADER)
+    set(OpenMP_Fortran_HAVE_OMPLIB_HEADER FALSE CACHE BOOL INTERNAL "")
+  endif()
+endif()
+
+if(NOT ( CMAKE_C_COMPILER_LOADED OR CMAKE_CXX_COMPILER_LOADED OR CMAKE_Fortran_COMPILER_LOADED ))
+  message(SEND_ERROR "FindOpenMP requires the C, CXX or Fortran languages to be enabled")
+endif()
+
+unset(OpenMP_C_CXX_TEST_SOURCE)
+unset(OpenMP_Fortran_TEST_SOURCE)
+unset(OpenMP_C_CXX_CHECK_VERSION_SOURCE)
+unset(OpenMP_Fortran_CHECK_VERSION_SOURCE)
+unset(OpenMP_Fortran_INCLUDE_LINE)
+
+cmake_policy(POP)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
new file mode 100644
index 0000000000..d708ce1ffc
--- /dev/null
+++ b/CMakeModules/InternalUtils.cmake
@@ -0,0 +1,106 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+function(dependency_check VAR ERROR_MESSAGE)
+  if(NOT ${VAR})
+    message(SEND_ERROR ${ERROR_MESSAGE})
+  endif()
+endfunction()
+
+# Includes the directory if the variable is set
+function(conditional_directory variable directory)
+  if(${variable})
+    add_subdirectory(${directory})
+  endif()
+endfunction()
+
+function(arrayfire_get_platform_definitions variable)
+if(WIN32)
+  set(${variable} -DOS_WIN -DWIN32_LEAN_AND_MEAN -DNOMINMAX PARENT_SCOPE)
+elseif(APPLE)
+  set(${variable} -DOS_MAC PARENT_SCOPE)
+elseif(UNIX)
+  set(${variable} -DOS_LNX PARENT_SCOPE)
+endif()
+endfunction()
+
+macro(arrayfire_set_cmake_default_variables)
+  set(CMAKE_PREFIX_PATH "${CMAKE_BINARY_DIR}prefix;${CMAKE_PREFIX_PATH}")
+  set(BUILD_SHARED_LIBS ON)
+
+  set(CMAKE_CXX_STANDARD 11)
+  set(CMAKE_CXX_EXTENSIONS OFF)
+
+  # Set a default build type if none was specified
+  if(NOT CMAKE_BUILD_TYPE)
+      set(CMAKE_BUILD_TYPE Release CACHE STRING "The type of the build")
+  endif()
+
+  # Set the possible values of build type for cmake-gui
+  set_property(CACHE CMAKE_BUILD_TYPE
+    PROPERTY
+      STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "Coverage")
+
+  set(CMAKE_CXX_FLAGS_COVERAGE
+      "-g -O0"
+      CACHE STRING "Flags used by the C++ compiler during coverage builds.")
+
+  set(CMAKE_C_FLAGS_COVERAGE
+      "-g -O0"
+      CACHE STRING "Flags used by the C compiler during coverage builds.")
+  set(CMAKE_EXE_LINKER_FLAGS_COVERAGE
+      ""
+      CACHE STRING "Flags used for linking binaries during coverage builds.")
+  set(CMAKE_SHARED_LINKER_FLAGS_COVERAGE
+      ""
+      CACHE STRING "Flags used by the shared libraries linker during coverage builds.")
+  set(CMAKE_STATIC_LINKER_FLAGS_COVERAGE
+      ""
+      CACHE STRING "Flags used by the static libraries linker during coverage builds.")
+  set(CMAKE_MODULE_LINKER_FLAGS_COVERAGE
+      ""
+      CACHE STRING "Flags used by the module linker during coverage builds.")
+
+  if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID MATCHES "GNU")
+    set(CMAKE_CXX_FLAGS_COVERAGE "${CMAKE_CXX_FLAGS_COVERAGE} --coverage")
+    set(CMAKE_C_FLAGS_COVERAGE "${CMAKE_C_FLAGS_COVERAGE} --coverage")
+    set(CMAKE_EXE_LINKER_FLAGS_COVERAGE "${CMAKE_EXE_LINKER_FLAGS_COVERAGE} --coverage")
+    set(CMAKE_SHARED_LINKER_FLAGS_COVERAGE "${CMAKE_SHARED_LINKER_FLAGS_COVERAGE} --coverage")
+    set(CMAKE_STATIC_LINKER_FLAGS_COVERAGE "${CMAKE_STATIC_LINKER_FLAGS_COVERAGE}")
+    set(CMAKE_MODULE_LINKER_FLAGS_COVERAGE "${CMAKE_STATIC_LINKER_FLAGS_COVERAGE} --coverage")
+  elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+    message(WARNING "Code Coverage in Visual Studio is not tested")
+    set(CMAKE_CXX_FLAGS_COVERAGE "")
+    set(CMAKE_C_FLAGS_COVERAGE "")
+    set(CMAKE_EXE_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
+    set(CMAKE_SHARED_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
+    set(CMAKE_STATIC_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
+    set(CMAKE_MODULE_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
+  endif()
+
+  mark_as_advanced(
+      CMAKE_CXX_FLAGS_COVERAGE
+      CMAKE_C_FLAGS_COVERAGE
+      CMAKE_EXE_LINKER_FLAGS_COVERAGE
+      CMAKE_SHARED_LINKER_FLAGS_COVERAGE
+      CMAKE_STATIC_LINKER_FLAGS_COVERAGE )
+
+  set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+
+  # Store all binaries in the bin/<Config> directory
+  if(WIN32)
+    set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${ArrayFire_BINARY_DIR}/bin)
+  endif()
+
+  if(APPLE)
+    # Brew does not put the glbinding cmake config files where it can be found
+    # TODO(umar) check if other systems have a similar problem
+    set(CMAKE_PREFIX_PATH "/usr/local/opt/glbinding;${CMAKE_PREFIX_PATH}")
+    # TODO(umar) Remove rpath to third_part lib
+    set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR};${ArrayFire_BINARY_DIR}/third_party/forge/lib")
+  endif()
+endmacro()
diff --git a/CMakeModules/TargetArch.cmake b/CMakeModules/TargetArch.cmake
deleted file mode 100644
index 65252f35e2..0000000000
--- a/CMakeModules/TargetArch.cmake
+++ /dev/null
@@ -1,157 +0,0 @@
-#https://github.com/petroules/solar-cmake
-
-#Copyright (c) 2012 Petroules Corporation. All rights reserved.
-#
-#Redistribution and use in source and binary forms, with or without
-#modification, are permitted provided that the following conditions are met:
-#
-#Redistributions of source code must retain the above copyright notice, this
-#list of conditions and the following disclaimer.  Redistributions in binary
-#form must reproduce the above copyright notice, this list of conditions and
-#the following disclaimer in the documentation and/or other materials provided
-#with the distribution.  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-#CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-#LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-#PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-#CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-#EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-#PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
-#BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
-#IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-#ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-#POSSIBILITY OF SUCH DAMAGE.
-
-# Based on the Qt 5 processor detection code, so should be very accurate
-# https://qt.gitorious.org/qt/qtbase/blobs/master/src/corelib/global/qprocessordetection.h
-# Currently handles arm (v5, v6, v7), x86 (32/64), ia64, and ppc (32/64)
-
-# Regarding POWER/PowerPC, just as is noted in the Qt source,
-# "There are many more known variants/revisions that we do not handle/detect."
-
-set(archdetect_c_code "
-#if defined(__arm__) || defined(__TARGET_ARCH_ARM)
-    #if defined(__ARM_ARCH_7__) \\
-        || defined(__ARM_ARCH_7A__) \\
-        || defined(__ARM_ARCH_7R__) \\
-        || defined(__ARM_ARCH_7M__) \\
-        || (defined(__TARGET_ARCH_ARM) && __TARGET_ARCH_ARM-0 >= 7)
-        #error cmake_ARCH armv7
-    #elif defined(__ARM_ARCH_6__) \\
-        || defined(__ARM_ARCH_6J__) \\
-        || defined(__ARM_ARCH_6T2__) \\
-        || defined(__ARM_ARCH_6Z__) \\
-        || defined(__ARM_ARCH_6K__) \\
-        || defined(__ARM_ARCH_6ZK__) \\
-        || defined(__ARM_ARCH_6M__) \\
-        || (defined(__TARGET_ARCH_ARM) && __TARGET_ARCH_ARM-0 >= 6)
-        #error cmake_ARCH armv6
-    #elif defined(__ARM_ARCH_5TEJ__) \\
-        || (defined(__TARGET_ARCH_ARM) && __TARGET_ARCH_ARM-0 >= 5)
-        #error cmake_ARCH armv5
-    #else
-        #error cmake_ARCH arm
-    #endif
-#elif defined(__i386) || defined(__i386__) || defined(_M_IX86)
-    #error cmake_ARCH i386
-#elif defined(__x86_64) || defined(__x86_64__) || defined(__amd64) || defined(_M_X64)
-    #error cmake_ARCH x86_64
-#elif defined(__ia64) || defined(__ia64__) || defined(_M_IA64)
-    #error cmake_ARCH ia64
-#elif defined(__ppc__) || defined(__ppc) || defined(__powerpc__) \\
-      || defined(_ARCH_COM) || defined(_ARCH_PWR) || defined(_ARCH_PPC)  \\
-      || defined(_M_MPPC) || defined(_M_PPC)
-    #if defined(__ppc64__) || defined(__powerpc64__) || defined(__64BIT__)
-        #error cmake_ARCH ppc64
-    #else
-        #error cmake_ARCH ppc
-    #endif
-#endif
-
-#error cmake_ARCH unknown
-")
-
-# Set ppc_support to TRUE before including this file or ppc and ppc64
-# will be treated as invalid architectures since they are no longer supported by Apple
-
-function(target_architecture output_var)
-    if(APPLE AND CMAKE_OSX_ARCHITECTURES)
-        # On OS X we use CMAKE_OSX_ARCHITECTURES *if* it was set
-        # First let's normalize the order of the values
-
-        # Note that it's not possible to compile PowerPC applications if you are using
-        # the OS X SDK version 10.6 or later - you'll need 10.4/10.5 for that, so we
-        # disable it by default
-        # See this page for more information:
-        # http://stackoverflow.com/questions/5333490/how-can-we-restore-ppc-ppc64-as-well-as-full-10-4-10-5-sdk-support-to-xcode-4
-
-        # Architecture defaults to i386 or ppc on OS X 10.5 and earlier, depending on the CPU type detected at runtime.
-        # On OS X 10.6+ the default is x86_64 if the CPU supports it, i386 otherwise.
-
-        foreach(osx_arch ${CMAKE_OSX_ARCHITECTURES})
-            if("${osx_arch}" STREQUAL "ppc" AND ppc_support)
-                set(osx_arch_ppc TRUE)
-            elseif("${osx_arch}" STREQUAL "i386")
-                set(osx_arch_i386 TRUE)
-            elseif("${osx_arch}" STREQUAL "x86_64")
-                set(osx_arch_x86_64 TRUE)
-            elseif("${osx_arch}" STREQUAL "ppc64" AND ppc_support)
-                set(osx_arch_ppc64 TRUE)
-            else()
-                message(FATAL_ERROR "Invalid OS X arch name: ${osx_arch}")
-            endif()
-        endforeach()
-
-        # Now add all the architectures in our normalized order
-        if(osx_arch_ppc)
-            list(APPEND ARCH ppc)
-        endif()
-
-        if(osx_arch_i386)
-            list(APPEND ARCH i386)
-        endif()
-
-        if(osx_arch_x86_64)
-            list(APPEND ARCH x86_64)
-        endif()
-
-        if(osx_arch_ppc64)
-            list(APPEND ARCH ppc64)
-        endif()
-    else()
-        file(WRITE "${PROJECT_BINARY_DIR}/arch.c" "${archdetect_c_code}")
-
-        enable_language(C)
-
-        # Detect the architecture in a rather creative way...
-        # This compiles a small C program which is a series of ifdefs that selects a
-        # particular #error preprocessor directive whose message string contains the
-        # target architecture. The program will always fail to compile (both because
-        # file is not a valid C program, and obviously because of the presence of the
-        # #error preprocessor directives... but by exploiting the preprocessor in this
-        # way, we can detect the correct target architecture even when cross-compiling,
-        # since the program itself never needs to be run (only the compiler/preprocessor)
-        try_run(
-            run_result_unused
-            compile_result_unused
-            "${PROJECT_BINARY_DIR}"
-            "${PROJECT_BINARY_DIR}/arch.c"
-            COMPILE_OUTPUT_VARIABLE ARCH
-            CMAKE_FLAGS CMAKE_OSX_ARCHITECTURES=${CMAKE_OSX_ARCHITECTURES}
-        )
-
-        # Parse the architecture name from the compiler output
-        string(REGEX MATCH "cmake_ARCH ([a-zA-Z0-9_]+)" ARCH "${ARCH}")
-
-        # Get rid of the value marker leaving just the architecture name
-        string(REPLACE "cmake_ARCH " "" ARCH "${ARCH}")
-
-        # If we are compiling with an unknown architecture this variable should
-        # already be set to "unknown" but in the case that it's empty (i.e. due
-        # to a typo in the code), then set it to unknown
-        if (NOT ARCH)
-            set(ARCH unknown)
-        endif()
-    endif()
-
-    set(${output_var} "${ARCH}" PARENT_SCOPE)
-endfunction()
diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 79b0b186a9..54c0ac8174 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -1,61 +1,54 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 #
 # Make a version file that includes the ArrayFire version and git revision
 #
-CMAKE_POLICY(PUSH)
-
-# https://cmake.org/cmake/help/v3.1/policy/CMP0054.html
-IF("${CMAKE_VERSION}" VERSION_GREATER "3.1" OR "${CMAKE_VERSION}" VERSION_EQUAL "3.1")
-    CMAKE_POLICY(SET CMP0054 OLD)
-ENDIF()
-
-SET(AF_VERSION_MAJOR "3")
-SET(AF_VERSION_MINOR "6")
-SET(AF_VERSION_PATCH "0")
-
-SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
-SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})
+set(AF_VERSION_MAJOR ${ArrayFire_VERSION_MAJOR})
+set(AF_VERSION_MINOR ${ArrayFire_VERSION_MINOR})
+set(AF_VERSION_PATCH ${ArrayFire_VERSION_PATCH})
 
-IF (${CMAKE_MAJOR_VERSION} GREATER 2 AND ${CMAKE_MINOR_VERSION} GREATER 1)
-    CMAKE_POLICY(SET CMP0054 OLD)
-ENDIF()
+set(AF_VERSION ${ArrayFire_VERSION})
+set(ArrayFire_API_VERSION_CURRENT ${ArrayFire_VERSION_MAJOR}${ArrayFire_VERSION_MINOR})
 
 # From CMake 3.0.0 CMAKE_<LANG>_COMPILER_ID is AppleClang for OSX machines
 # that use clang for compilations
-IF("${CMAKE_C_COMPILER_ID}" STREQUAL "AppleClang")
-    SET(COMPILER_NAME "AppleClang")
-ELSEIF("${CMAKE_C_COMPILER_ID}" STREQUAL "Clang")
-    SET(COMPILER_NAME "LLVM Clang")
-ELSEIF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU")
-    SET(COMPILER_NAME "GNU Compiler Collection(GCC/G++)")
-ELSEIF("${CMAKE_C_COMPILER_ID}" STREQUAL "Intel")
-    SET(COMPILER_NAME "Intel Compiler")
-ELSEIF("${CMAKE_C_COMPILER_ID}" STREQUAL "MSVC")
-    SET(COMPILER_NAME "Microsoft Visual Studio")
-ENDIF()
-
-SET(COMPILER_VERSION "${CMAKE_C_COMPILER_VERSION}")
-SET(AF_COMPILER_STRING "${COMPILER_NAME} ${COMPILER_VERSION}")
-
-EXECUTE_PROCESS(
+if("${CMAKE_C_COMPILER_ID}" STREQUAL "AppleClang")
+    set(COMPILER_NAME "AppleClang")
+elseif("${CMAKE_C_COMPILER_ID}" STREQUAL "Clang")
+    set(COMPILER_NAME "LLVM Clang")
+elseif("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU")
+    set(COMPILER_NAME "GNU Compiler Collection(GCC/G++)")
+elseif("${CMAKE_C_COMPILER_ID}" STREQUAL "Intel")
+    set(COMPILER_NAME "Intel Compiler")
+elseif("${CMAKE_C_COMPILER_ID}" STREQUAL "MSVC")
+    set(COMPILER_NAME "Microsoft Visual Studio")
+endif()
+
+set(COMPILER_VERSION "${CMAKE_C_COMPILER_VERSION}")
+set(AF_COMPILER_STRING "${COMPILER_NAME} ${COMPILER_VERSION}")
+
+execute_process(
     COMMAND git log -1 --format=%h
     WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
     OUTPUT_VARIABLE GIT_COMMIT_HASH
     OUTPUT_STRIP_TRAILING_WHITESPACE
 )
 
-IF(NOT GIT_COMMIT_HASH)
-    MESSAGE(STATUS "No git. Setting hash to default")
-    SET(GIT_COMMIT_HASH "default")
-ENDIF()
+if(NOT GIT_COMMIT_HASH)
+    message(STATUS "No git. Setting hash to default")
+    set(GIT_COMMIT_HASH "default")
+endif()
 
-CONFIGURE_FILE(
-    ${PROJECT_SOURCE_DIR}/CMakeModules/version.h.in
-    ${PROJECT_SOURCE_DIR}/include/af/version.h
+configure_file(
+    ${ArrayFire_SOURCE_DIR}/CMakeModules/version.h.in
+    ${ArrayFire_BINARY_DIR}/include/af/version.h
 )
 
-CONFIGURE_FILE(
-    ${PROJECT_SOURCE_DIR}/CMakeModules/version.hpp.in
-    ${PROJECT_SOURCE_DIR}/src/backend/version.hpp
+configure_file(
+    ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
+    ${ArrayFire_BINARY_DIR}/src/backend/version.hpp
 )
-
-CMAKE_POLICY(POP)
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 4b54a23f61..c5fc9e3e6d 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -1,36 +1,47 @@
-INCLUDE(ExternalProject)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-SET(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
-SET(CLBlast_location ${prefix}/${CMAKE_STATIC_LIBRARY_PREFIX}/libclblast${CMAKE_STATIC_LIBRARY_SUFFIX})
-SET(byproducts ${clBLAS_location})
+include(ExternalProject)
+
+set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
+set(CLBlast_location ${prefix}/${CMAKE_STATIC_LIBRARY_PREFIX}/libclblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 0.10.0
+    GIT_TAG 1.1.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>/
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_SHARED_LIBS:BOOL=OFF
-    -DSAMPLES:BOOL=OFF
-    -DTUNERS:BOOL=OFF
-    -DCLIENTS:BOOL=OFF
-    -DTESTS:BOOL=OFF
-    -DNETLIB:BOOL=OFF
-    ${byproducts}
+    BUILD_BYPRODUCTS ${CLBlast_location}
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/
+      -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+      "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+      -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+      "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
+      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+      -DBUILD_SHARED_LIBS:BOOL=OFF
+      -DSAMPLES:BOOL=OFF
+      -DTUNERS:BOOL=OFF
+      -DCLIENTS:BOOL=OFF
+      -DTESTS:BOOL=OFF
+      -DNETLIB:BOOL=OFF
     )
 
 ExternalProject_Get_Property(CLBlast-ext install_dir)
-ADD_LIBRARY(CLBlast IMPORTED STATIC)
-SET_TARGET_PROPERTIES(CLBlast PROPERTIES IMPORTED_LOCATION ${CLBlast_location})
-ADD_DEPENDENCIES(CLBlast CLBlast-ext)
-SET(CLBLAST_INCLUDE_DIRS ${install_dir}/include)
-SET(CLBLAST_LIBRARIES CLBlast)
-SET(CLBLAST_FOUND ON)
+set(CLBLAST_INCLUDE_DIRS ${install_dir}/include)
+set(CLBLAST_LIBRARIES CLBlast)
+set(CLBLAST_FOUND ON)
+
+make_directory("${CLBLAST_INCLUDE_DIRS}")
+
+add_library(CLBlast UNKNOWN IMPORTED)
+set_target_properties(CLBlast PROPERTIES
+  IMPORTED_LOCATION "${CLBlast_location}"
+  INTERFACE_INCLUDE_DIRECTORIES "${CLBLAST_INCLUDE_DIRS}")
+add_dependencies(CLBlast CLBlast-ext)
diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
index fdcdc22029..99de29990d 100644
--- a/CMakeModules/build_boost_compute.cmake
+++ b/CMakeModules/build_boost_compute.cmake
@@ -1,73 +1,36 @@
-# If using a commit, remove the v prefix to VER in URL.
-# If using a tag, don't use v in VER
-# This is because of how github handles it's release tar balls
-SET(VER boost-1.61.0)
-SET(URL https://github.com/boostorg/compute/archive/${VER}.tar.gz)
-SET(MD5 7e1c433b48825d8cb2effa963823aec8)
-
-SET(thirdPartyDir "${PROJECT_BINARY_DIR}/third_party")
-SET(srcDir "${thirdPartyDir}/compute-${VER}")
-SET(archive ${srcDir}.tar.gz)
-SET(inflated ${srcDir}-inflated)
-
-# the config to be used in the code
-SET(BoostCompute_INCLUDE_DIRS "${srcDir}/include")
-
-# do we have to do it again?
-SET(doExtraction ON)
-IF(EXISTS "${inflated}")
-    FILE(READ "${inflated}" extractedMD5)
-    IF("${extractedMD5}" STREQUAL "${MD5}")
-        # nope, everything looks fine
-        return()
-    ENDIF()
-ENDIF()
-
-# lets get and extract boost compute
-
-MESSAGE(STATUS "BoostCompute...")
-IF(EXISTS "${archive}")
-    FILE(MD5 "${archive}" md5)
-    IF(NOT "${md5}" STREQUAL "${MD5}")
-        MESSAGE("  wrong check sum ${md5}, redownloading")
-        FILE(REMOVE "${archive}")
-    ENDIF()
-ENDIF()
-
-IF(NOT EXISTS "${archive}")
-    MESSAGE(STATUS "  getting ${URL}")
-    FILE(DOWNLOAD "${URL}" ${archive}
-        STATUS rv
-        SHOW_PROGRESS)
-ENDIF()
-
-MESSAGE(STATUS "  validating ${archive}")
-FILE(MD5 "${archive}" md5)
-IF(NOT "${md5}" STREQUAL "${MD5}")
-    MESSAGE(WARNING "${archive}: Invalid check sum ${md5}. Expected was ${MD5}")
-    IF("${md5}" STREQUAL "d41d8cd98f00b204e9800998ecf8427e")
-        MESSAGE(STATUS "Trying wget ${URL}")
-        EXECUTE_PROCESS(COMMAND wget -O ${archive} ${URL})
-        FILE(MD5 "${archive}" md5_)
-        IF(NOT "${md5_}" STREQUAL "${MD5}")
-            MESSAGE(FATAL_ERROR "${archive}: Invalid check sum ${md5_}. Expected was ${MD5}")
-        ENDIF(NOT "${md5_}" STREQUAL "${MD5}")
-        MESSAGE(STATUS "wget successful")
-    ENDIF("${md5}" STREQUAL "d41d8cd98f00b204e9800998ecf8427e")
-ENDIF()
-
-IF(IS_DIRECTORY ${srcDir})
-    MESSAGE(STATUS "  cleaning ${cleaning}")
-    FILE(REMOVE_RECURSE ${srcDir})
-ENDIF()
-
-MESSAGE(STATUS "  extracting ${archive}")
-FILE(MAKE_DIRECTORY ${srcDir})
-EXECUTE_PROCESS(COMMAND ${CMAKE_COMMAND} -E tar xfz ${archive}
-    WORKING_DIRECTORY ${thirdPartyDir}
-    RESULT_VARIABLE rv)
-IF(NOT rv EQUAL 0)
-    MESSAGE(FATAL_ERROR "'${archive}' extraction failed")
-ENDIF()
-
-FILE(WRITE ${inflated} "${MD5}")
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+set(VER boost-1.61.0)
+set(MD5 7e1c433b48825d8cb2effa963823aec8)
+include(ExternalProject)
+
+ExternalProject_Add(
+  boost_compute
+  URL       https://github.com/boostorg/compute/archive/${VER}.tar.gz
+  URL_MD5   ${MD5}
+  INSTALL_COMMAND ""
+  CONFIGURE_COMMAND ""
+  BUILD_COMMAND ""
+  )
+
+ExternalProject_Get_Property(boost_compute source_dir)
+message(STATUS "BOOST_COMPUTE: ${source_dir}")
+make_directory(${source_dir}/include)
+
+if(NOT TARGET Boost::boost)
+  add_library(Boost::boost IMPORTED INTERFACE GLOBAL)
+endif()
+
+add_dependencies(Boost::boost boost_compute)
+
+set_target_properties(Boost::boost PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
+
+  # NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
+  # will try to link with libboost-chrono.
+  INTERFACE_COMPILE_DEFINITIONS BOOST_CHRONO_HEADER_ONLY)
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 98b8b3a4b3..70a94c56b3 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -1,38 +1,35 @@
-INCLUDE(ExternalProject)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-SET(prefix ${PROJECT_BINARY_DIR}/third_party/cl2hpp)
+# Check if cl2.hpp exsists and if not download it from khronos GitHub repo
+#
+# NOTE: This file does not use ExternalProject_Add because that command was
+#       was not able to download files that are not archives before CMake
+#       version 3.6
 
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(CMAKE_GENERATOR MATCHES "Ninja")
-        MESSAGE(WARNING "Building forge with Ninja has known issues with CMake older than 3.2")
-    endif()
-    SET(byproducts)
-ELSE()
-    SET(byproducts BUILD_BYPRODUCTS "${prefix}/package/CL/cl2.hpp")
-ENDIF()
+find_package(OpenCL)
 
-ExternalProject_Add(
-    cl2hpp-ext
-    GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
-    ${byproducts}
-    GIT_TAG 75bb7d0d8b2ffc6aac0a3dcaa22f6622cab81f7c
-    PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}/package"
-    UPDATE_COMMAND ""
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_DOCS:BOOL=OFF
-    -DBUILD_EXAMPLES:BOOL=OFF
-    -DBUILD_TESTS:BOOL=OFF
-    )
+set(cl2hpp_file_url "https://github.com/KhronosGroup/OpenCL-CLHPP/releases/download/v2.0.10/cl2.hpp")
+set(cl2hpp_file "${ArrayFire_BINARY_DIR}/include/CL/cl2.hpp")
 
-ExternalProject_Get_Property(cl2hpp-ext install_dir)
+if(OpenCL_FOUND)
+  if (NOT EXISTS ${cl2hpp_file})
+      message(STATUS "Downloading ${cl2hpp_file_url}")
+      file(DOWNLOAD ${cl2hpp_file_url} ${cl2hpp_file}
+        EXPECTED_HASH MD5=c38d1b78cd98cc809fa2a49dbd1734a5)
+  endif()
+  get_filename_component(download_dir ${cl2hpp_file} DIRECTORY)
 
-ADD_CUSTOM_TARGET(cl2hpp DEPENDS "${prefix}/package/CL/cl2.hpp")
+  if (NOT TARGET OpenCL::cl2hpp OR
+      NOT TARGET cl2hpp)
+    add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
+    add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
 
-ADD_DEPENDENCIES(cl2hpp cl2hpp-ext)
-
-SET(CL2HPP_INCLUDE_DIRECTORY ${install_dir})
+    set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
+      INTERFACE_INCLUDE_DIRECTORIES ${download_dir}/..)
+  endif()
+endif()
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 79a507de24..8de529e840 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -1,42 +1,50 @@
-INCLUDE(ExternalProject)
-
-SET(prefix ${PROJECT_BINARY_DIR}/third_party/clBLAS)
-SET(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${CMAKE_STATIC_LIBRARY_SUFFIX})
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(CMAKE_GENERATOR MATCHES "Ninja")
-        MESSAGE(WARNING "Building clBLAS with Ninja has known issues with CMake older than 3.2")
-    endif()
-    SET(byproducts)
-ELSE()
-    SET(byproducts BUILD_BYPRODUCTS ${clBLAS_location})
-ENDIF()
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+include(ExternalProject)
+
+set(prefix ${PROJECT_BINARY_DIR}/third_party/clBLAS)
+set(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${CMAKE_STATIC_LIBRARY_SUFFIX})
+
+find_package(OpenCL)
 
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
     GIT_TAG arrayfire-release
-    ${byproducts}
+    BUILD_BYPRODUCTS ${clBLAS_location}
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>/src
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_SHARED_LIBS:BOOL=OFF
-    -DBUILD_CLIENT:BOOL=OFF
-    -DBUILD_TEST:BOOL=OFF
-    -DBUILD_KTEST:BOOL=OFF
-    -DSUFFIX_LIB:STRING=
+    DOWNLOAD_NO_PROGRESS 1
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/src
+      -DCMAKE_CXX_FLAGS:STRING="-fPIC"
+      -DCMAKE_C_FLAGS:STRING="-fPIC"
+      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+      -DBUILD_SHARED_LIBS:BOOL=OFF
+      -DBUILD_CLIENT:BOOL=OFF
+      -DBUILD_TEST:BOOL=OFF
+      -DBUILD_KTEST:BOOL=OFF
+      -DSUFFIX_LIB:STRING=
+
+      # clBLAS uses a custom FindOpenCL that doesn't work well on Ubuntu
+      -DOPENCL_LIBRARIES:FILEPATH=${OpenCL_LIBRARIES}
     )
 
 ExternalProject_Get_Property(clBLAS-ext install_dir)
-ADD_LIBRARY(clBLAS IMPORTED STATIC)
-SET_TARGET_PROPERTIES(clBLAS PROPERTIES IMPORTED_LOCATION ${clBLAS_location})
-ADD_DEPENDENCIES(clBLAS clBLAS-ext)
-SET(CLBLAS_INCLUDE_DIRS ${install_dir}/include)
-SET(CLBLAS_LIBRARIES clBLAS)
-SET(CLBLAS_FOUND ON)
+
+set(CLBLAS_INCLUDE_DIRS ${install_dir}/include)
+set(CLBLAS_LIBRARIES clBLAS::clBLAS)
+set(CLBLAS_FOUND ON)
+make_directory("${CLBLAS_INCLUDE_DIRS}")
+
+add_library(clBLAS::clBLAS UNKNOWN IMPORTED)
+set_target_properties(clBLAS::clBLAS PROPERTIES
+  IMPORTED_LOCATION "${clBLAS_location}"
+  INTERFACE_INCLUDE_DIRECTORIES "${CLBLAS_INCLUDE_DIRS}")
+add_dependencies(clBLAS::clBLAS clBLAS-ext)
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index ad7dae65a0..28be38a3cb 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -1,3 +1,10 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 INCLUDE(ExternalProject)
 
 SET(prefix "${PROJECT_BINARY_DIR}/third_party/clFFT")
@@ -18,28 +25,32 @@ ExternalProject_Add(
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>/src
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_SHARED_LIBS:BOOL=OFF
-    -DBUILD_EXAMPLES:BOOL=OFF
-    -DBUILD_CLIENT:BOOL=OFF
-    -DBUILD_TEST:BOOL=OFF
-    -DSUFFIX_LIB:STRING=
-    -DUSE_SYSTEM_GTEST:BOOL=ON
-    -DOpenCL_INCLUDE_DIR:FILEPATH=${OpenCL_INCLUDE_DIR}
-    -DOpenCL_LIBRARY:FILEPATH=${OpenCL_LIBRARY}
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/src
+      -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+      "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+      -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+      "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
+      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+      -DBUILD_SHARED_LIBS:BOOL=OFF
+      -DBUILD_EXAMPLES:BOOL=OFF
+      -DBUILD_CLIENT:BOOL=OFF
+      -DBUILD_TEST:BOOL=OFF
+      -DSUFFIX_LIB:STRING=
     ${byproducts}
     )
 
 ExternalProject_Get_Property(clFFT-ext install_dir)
-ADD_LIBRARY(clFFT IMPORTED STATIC)
-SET_TARGET_PROPERTIES(clFFT PROPERTIES IMPORTED_LOCATION ${clFFT_location})
-ADD_DEPENDENCIES(clFFT clFFT-ext)
-SET(CLFFT_INCLUDE_DIRS ${install_dir}/include)
-SET(CLFFT_LIBRARIES clFFT)
-SET(CLFFT_FOUND ON)
+
+set(CLFFT_INCLUDE_DIRS ${install_dir}/include)
+make_directory(${install_dir}/include)
+
+add_library(clFFT::clFFT IMPORTED STATIC)
+set_target_properties(clFFT::clFFT PROPERTIES
+  IMPORTED_LOCATION ${clFFT_location}
+  INTERFACE_INCLUDE_DIRECTORIES ${install_dir}/include
+  )
+add_dependencies(clFFT::clFFT clFFT-ext)
+
+set(CLFFT_LIBRARIES clFFT)
+set(CLFFT_FOUND ON)
diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index b13220223f..8cef7aa7c4 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -1,101 +1,62 @@
-INCLUDE(ExternalProject)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-IF(USE_SYSTEM_GLBINDING)
-    SET(GLBINDING_TARGET "")
-ELSE(USE_SYSTEM_GLBINDING)
-    SET(GLBINDING_TARGET glbinding)
-ENDIF(USE_SYSTEM_GLBINDING)
+include(ExternalProject)
 
-SET(prefix ${CMAKE_BINARY_DIR}/third_party/forge)
+set(FORGE_VERSION 1.0.2-ft)
+set(prefix "${ArrayFire_BINARY_DIR}/third_party/forge")
 
-# FIXME: Cannot use $<CONFIG> generator expression here because add_custom_command
-#        does not yet support it for the OUTPUT argument, see also:
-#        - Old "duplicate":   https://cmake.org/Bug/view.php?id=12877
-#        - Old issue tracker: https://cmake.org/Bug/view.php?id=13840
-#        - New issue tracker: https://gitlab.kitware.com/cmake/cmake/issues/13840
-#        In the meantime, use CMAKE_BUILD_TYPE if set by user, assuming that it
-#        is the primary build configuration used. Otherwise, default to Release.
-IF(CMAKE_BUILD_TYPE)
-    SET(forge_lib_config ${CMAKE_BUILD_TYPE})
-ELSE()
-    SET(forge_lib_config Release)
-ENDIF()
-
-IF(CMAKE_GENERATOR MATCHES "Xcode")
-    SET(forge_lib_infix "${forge_lib_config}/")
-ELSE()
-    SET(forge_lib_infix "")
-ENDIF()
-IF(WIN32)
-    SET(forge_lib_prefix "${prefix}/lib")
-ELSE(WIN32)
-    SET(forge_lib_prefix "${prefix}/src/forge-ext-build/src/backend/opengl")
-ENDIF(WIN32)
-
-SET(forge_location "${forge_lib_prefix}/${forge_lib_infix}${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(CMAKE_GENERATOR MATCHES "Ninja")
-        MESSAGE(WARNING "Building forge with Ninja has known issues with CMake older than 3.2")
-    endif()
-    SET(byproducts)
-ELSE()
-    IF   (WIN32)
-        SET(byproducts BUILD_BYPRODUCTS third_party/forge/lib/forge${CMAKE_STATIC_LIBRARY_SUFFIX})
-    ELSE (WIN32)
-        SET(byproducts BUILD_BYPRODUCTS ${forge_location})
-    ENDIF(WIN32)
-ENDIF()
-
-SET(FORGE_VERSION 1.0.2)
-SET(FORGE_TAG ${FORGE_VERSION}-ft)
+if(MSVC)
+  set(disable_warning_flags "/wd4251")
+  set(forge_shared_lib "${ArrayFire_BINARY_DIR}/third_party/forge/lib/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_LINK_LIBRARY_SUFFIX}")
+else()
+  set(forge_shared_lib "${ArrayFire_BINARY_DIR}/third_party/forge/lib/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
+endif()
 
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG v${FORGE_TAG}
-    ${byproducts}
+    GIT_TAG v${FORGE_VERSION}
     PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    DEPENDS ${GLBINDING_TARGET}
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
-    -DCMAKE_SOURCE_DIR:PATH=<SOURCE_DIR>
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_EXAMPLES:BOOL=OFF
-    -DBUILD_DOCUMENTATION:BOOL=${BUILD_DOCS}
-    -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
-    -Dglbinding_DIR:STRING=${glbinding_DIR}
-    -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
-    -DBOOST_ROOT:PATH=${BOOST_ROOT}
-    -DBOOST_INCLUDEDIR:PATH=${BOOST_INCLUDEDIR}
-    -DFREEIMAGE_INCLUDE_PATH:PATH=${FREEIMAGE_INCLUDE_PATH}
-    -DFREEIMAGE_DYNAMIC_LIBRARY:PATH=${FREEIMAGE_DYNAMIC_LIBRARY}
-    -DFREEIMAGE_STATIC_LIBRARY:PATH=${FREEIMAGE_STATIC_LIBRARY}
-    -DUSE_FREEIMAGE_STATIC:BOOL=${USE_FREEIMAGE_STATIC}
-    BUILD_COMMAND ${CMAKE_COMMAND} --build . --config ${forge_lib_config}
+    BUILD_BYPRODUCTS ${forge_shared_lib}
+    CMAKE_GENERATOR "${CMAKE_GENERATOR}"
+    CMAKE_ARGS
+      -DBUILD_EXAMPLES:BOOL=OFF
+      -DBUILD_DOCUMENTATION:BOOL=OFF
+      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_CXX_FLAGS:STRING=${disable_warning_flags}
+      -Dglbinding_DIR:STRING=${glbinding_DIR}
+      -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
+      -DBOOST_INCLUDEDIR:PATH=${Boost_INCLUDE_DIRS}
+      -Dglbinding_DIR:PATH=${glbinding_DIR}
+      -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
+      -DUSE_FREEIMAGE:BOOL=OFF
     )
 
-ExternalProject_Get_Property(forge-ext binary_dir)
-ExternalProject_Get_Property(forge-ext install_dir)
-
-ADD_LIBRARY(forge SHARED IMPORTED)
-SET_TARGET_PROPERTIES(forge PROPERTIES IMPORTED_LOCATION ${forge_location})
-
-IF(WIN32)
-    SET_TARGET_PROPERTIES(forge PROPERTIES IMPORTED_IMPLIB ${forge_lib_prefix}/forge.lib)
-ELSE(WIN32)
-    SET(forge_bindir_location ${binary_dir}/src/backend/opengl/${forge_lib_infix}${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX})
-    IF(NOT (${forge_bindir_location} STREQUAL ${forge_location}))
-        MESSAGE(WARNING "Did the forge binary location move? (Have ${forge_bindir_location} vs ${forge_location})")
-    ENDIF()
-ENDIF(WIN32)
-
-ADD_DEPENDENCIES(forge forge-ext ${GLBINDING_TARGET})
-
-SET(FORGE_INCLUDE_DIRS ${install_dir}/include)
-SET(FORGE_LIBRARIES forge)
-SET(FORGE_FOUND ON)
+# NOTE: This approach doesn't work because the ExternalProject_Add outputs are
+# created at build time. The targets are created at configuration time.
+#
+# make_directory("${prefix}/include")
+# make_directory("${ArrayFire_BINARY_DIR}/third_party/forge/lib")
+# execute_process(COMMAND ${CMAKE_COMMAND} -E touch "${forge_shared_lib}")
+
+# add_library(Forge::Forge SHARED IMPORTED GLOBAL)
+# set_target_properties(Forge::Forge PROPERTIES
+#   INTERFACE_LINK_LIBRARIES "${forge_shared_lib}"
+#   INTERFACE_INCLUDE_DIRECTORIES "${prefix}/include"
+#   )
+#
+# add_dependencies(Forge::Forge forge-ext)
+
+set(Forge_INCLUDE_DIR "${prefix}/include")
+set(Forge_LIBRARIES "${forge_shared_lib}")
+
+find_package_handle_standard_args(Forge DEFAULT_MSG
+    Forge_INCLUDE_DIR Forge_LIBRARIES)
diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
index bfc7f0ddaf..946c109d2b 100644
--- a/CMakeModules/build_glbinding.cmake
+++ b/CMakeModules/build_glbinding.cmake
@@ -1,3 +1,10 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 INCLUDE(ExternalProject)
 
 SET(prefix ${PROJECT_BINARY_DIR}/third_party/glb)
diff --git a/CMakeModules/cuda_compute_capability.cpp b/CMakeModules/cuda_compute_capability.cpp
deleted file mode 100644
index ef589a9974..0000000000
--- a/CMakeModules/cuda_compute_capability.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
-* Copyright (C) 2011 Florian Rathgeber, florian.rathgeber@gmail.com
-*
-* This code is licensed under the MIT License.  See the FindCUDA.cmake script
-* for the text of the license.
-*
-* Based on code by Christopher Bruns published on Stack Overflow (CC-BY):
-* http://stackoverflow.com/questions/2285185
-*/
-
-#include <stdio.h>
-#include <cuda_runtime.h>
-#include <iterator>
-#include <set>
-
-int main() {
-    int deviceCount;
-    int gpuDeviceCount = 0;
-    struct cudaDeviceProp properties;
-
-    if (cudaGetDeviceCount(&deviceCount) != cudaSuccess)
-    {
-        printf("Couldn't get device count: %s\n", cudaGetErrorString(cudaGetLastError()));
-        return 1;
-    }
-
-    std::set<int> computes;
-    typedef std::set<int>::iterator iter;
-
-    // machines with no GPUs can still report one emulation device
-    for (int device = 0; device < deviceCount; ++device) {
-        int major = 9999, minor = 9999;
-        cudaGetDeviceProperties(&properties, device);
-        if (properties.major != 9999) { // 9999 means emulation only
-            ++gpuDeviceCount;
-            major = properties.major;
-            minor = properties.minor;
-            if ((major == 2 && minor == 1)) {
-                // There is no --arch compute_21 flag for nvcc, so force minor to 0
-                minor = 0;
-            }
-            computes.insert(10 * major + minor);
-        }
-    }
-    int i = 0;
-    for(iter it = computes.begin(); it != computes.end(); it++, i++) {
-        if(i > 0) {
-            printf(" ");
-        }
-        printf("%d", *it);
-    }
-    /* don't just return the number of gpus, because other runtime cuda
-    errors can also yield non-zero return values */
-    if (gpuDeviceCount <= 0 || computes.size() <= 0) {
-        return 1; // failure
-    }
-    return 0; // success
-}
diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index eae93202c0..18fffc591d 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -45,8 +45,8 @@ MACRO(OSX_INSTALL_SETUP BACKEND LIB)
     # Create symlinks separately. Copying them in above command will do a deep copy
     ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
                        COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "lib${LIB}.${AF_VERSION}.dylib"
-                       "lib${LIB}.${AF_VERSION_MAJOR}.dylib"
+                       "lib${LIB}.${ArrayFire_VERSION}.dylib"
+                       "lib${LIB}.${ArrayFire_VERSION_MAJOR}.dylib"
                        WORKING_DIRECTORY "${OSX_TEMP}/${BACKEND}/${AF_INSTALL_LIB_DIR}"
                        COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir (Symlink)"
                        )
diff --git a/CMakeModules/osx_install/readme.html b/CMakeModules/osx_install/readme.html.in
similarity index 100%
rename from CMakeModules/osx_install/readme.html
rename to CMakeModules/osx_install/readme.html.in
diff --git a/CMakeModules/osx_install/welcome.html b/CMakeModules/osx_install/welcome.html.in
similarity index 100%
rename from CMakeModules/osx_install/welcome.html
rename to CMakeModules/osx_install/welcome.html.in
diff --git a/CMakeModules/select_compute_arch.cmake b/CMakeModules/select_compute_arch.cmake
new file mode 100644
index 0000000000..8fb44d80a8
--- /dev/null
+++ b/CMakeModules/select_compute_arch.cmake
@@ -0,0 +1,198 @@
+# Synopsis:
+#   CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
+#   -- Selects GPU arch flags for nvcc based on target_CUDA_architectures
+#      target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
+#       - "Auto" detects local machine GPU compute arch at runtime.
+#       - "Common" and "All" cover common and entire subsets of architectures
+#      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
+#      NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal
+#      NUM: Any number. Only those pairs are currently accepted by NVCC though:
+#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2
+#      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
+#      Additionally, sets ${out_variable}_readable to the resulting numeric list
+#      Example:
+#       CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)
+#        LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS})
+#
+#      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
+#
+
+# This list will be used for CUDA_ARCH_NAME = All option
+set(CUDA_KNOWN_GPU_ARCHITECTURES  "Fermi" "Kepler" "Maxwell")
+
+# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
+set(CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.5" "5.0")
+
+if (CUDA_VERSION VERSION_GREATER "6.5")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell+Tegra")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2")
+endif ()
+
+if (CUDA_VERSION VERSION_GREATER "7.5")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1" "6.1+PTX")
+else()
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
+endif ()
+
+
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   CUDA_DETECT_INSTALLED_GPUS(OUT_VARIABLE)
+#
+function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
+  if(NOT CUDA_GPU_DETECT_OUTPUT)
+    set(file ${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cpp)
+
+    file(WRITE ${file} ""
+      "#include <cuda_runtime.h>\n"
+      "#include <cstdio>\n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+            CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
+            LINK_LIBRARIES ${CUDA_LIBRARIES}
+            RUN_OUTPUT_VARIABLE compute_capabilities)
+
+    if(run_result EQUAL 0)
+      string(REPLACE "2.1" "2.1(2.0)" compute_capabilities "${compute_capabilities}")
+      set(CUDA_GPU_DETECT_OUTPUT ${compute_capabilities}
+        CACHE INTERNAL "Returned GPU architectures from detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_GPU_DETECT_OUTPUT)
+    message(STATUS "Automatic GPU detection failed. Building for common architectures.")
+    set(${OUT_VARIABLE} ${CUDA_COMMON_GPU_ARCHITECTURES} PARENT_SCOPE)
+  else()
+    set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA architectures from parameter list
+# Usage:
+#   SELECT_NVCC_ARCH_FLAGS(out_variable [list of CUDA compute archs])
+function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
+  set(CUDA_ARCH_LIST "${ARGN}")
+
+  if("X${CUDA_ARCH_LIST}" STREQUAL "X" )
+    set(CUDA_ARCH_LIST "Auto")
+  endif()
+
+  set(cuda_arch_bin)
+  set(cuda_arch_ptx)
+
+  if("${CUDA_ARCH_LIST}" STREQUAL "All")
+    set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
+  elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
+    set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
+  elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto")
+    CUDA_DETECT_INSTALLED_GPUS(CUDA_ARCH_LIST)
+    message(STATUS "Autodetected CUDA architecture(s): ${CUDA_ARCH_LIST}")
+  endif()
+
+  # Now process the list and look for names
+  string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
+  list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
+  foreach(arch_name ${CUDA_ARCH_LIST})
+    set(arch_bin)
+    set(arch_ptx)
+    set(add_ptx FALSE)
+    # Check to see if we are compiling PTX
+    if(arch_name MATCHES "(.*)\\+PTX$")
+      set(add_ptx TRUE)
+      set(arch_name ${CMAKE_MATCH_1})
+    endif()
+    if(arch_name MATCHES "^([0-9]\\.[0-9](\\([0-9]\\.[0-9]\\))?)$")
+      set(arch_bin ${CMAKE_MATCH_1})
+      set(arch_ptx ${arch_bin})
+    else()
+      # Look for it in our list of known architectures
+      if(${arch_name} STREQUAL "Fermi")
+        set(arch_bin 2.0 "2.1(2.0)")
+      elseif(${arch_name} STREQUAL "Kepler+Tegra")
+        set(arch_bin 3.2)
+      elseif(${arch_name} STREQUAL "Kepler+Tesla")
+        set(arch_bin 3.7)
+      elseif(${arch_name} STREQUAL "Kepler")
+        set(arch_bin 3.0 3.5)
+        set(arch_ptx 3.5)
+      elseif(${arch_name} STREQUAL "Maxwell+Tegra")
+        set(arch_bin 5.3)
+      elseif(${arch_name} STREQUAL "Maxwell")
+        set(arch_bin 5.0 5.2)
+        set(arch_ptx 5.2)
+      elseif(${arch_name} STREQUAL "Pascal")
+        set(arch_bin 6.0 6.1)
+        set(arch_ptx 6.1)
+      else()
+        message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
+      endif()
+    endif()
+    if(NOT arch_bin)
+      message(SEND_ERROR "arch_bin wasn't set for some reason")
+    endif()
+    list(APPEND cuda_arch_bin ${arch_bin})
+    if(add_ptx)
+      if (NOT arch_ptx)
+        set(arch_ptx ${arch_bin})
+      endif()
+      list(APPEND cuda_arch_ptx ${arch_ptx})
+    endif()
+  endforeach()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${cuda_arch_ptx}")
+  string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   cuda_arch_ptx "${cuda_arch_ptx}")
+
+  if(cuda_arch_bin)
+    list(REMOVE_DUPLICATES cuda_arch_bin)
+  endif()
+  if(cuda_arch_ptx)
+    list(REMOVE_DUPLICATES cuda_arch_ptx)
+  endif()
+
+  set(nvcc_flags "")
+  set(nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(arch ${cuda_arch_bin})
+    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified ARCH for the concrete CODE
+      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify ARCH for the concrete CODE, we assume ARCH=CODE
+      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
+      list(APPEND nvcc_archs_readable sm_${arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(arch ${cuda_arch_ptx})
+    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
+    list(APPEND nvcc_archs_readable compute_${arch})
+  endforeach()
+
+  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
+  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
diff --git a/CMakeModules/version.h.in b/CMakeModules/version.h.in
index 6af8d45d7b..271fa54907 100644
--- a/CMakeModules/version.h.in
+++ b/CMakeModules/version.h.in
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#define AF_VERSION "@AF_VERSION@"
-#define AF_VERSION_MAJOR @AF_VERSION_MAJOR@
-#define AF_VERSION_MINOR @AF_VERSION_MINOR@
-#define AF_VERSION_PATCH @AF_VERSION_PATCH@
-#define AF_API_VERSION_CURRENT   @AF_API_VERSION_CURRENT@
+#define AF_VERSION "@ArrayFire_VERSION@"
+#define AF_VERSION_MAJOR @ArrayFire_VERSION_MAJOR@
+#define AF_VERSION_MINOR @ArrayFire_VERSION_MINOR@
+#define AF_VERSION_PATCH @ArrayFire_VERSION_PATCH@
+#define AF_API_VERSION_CURRENT   @ArrayFire_API_VERSION_CURRENT@
diff --git a/CTestConfig.cmake b/CTestConfig.cmake
new file mode 100644
index 0000000000..e9ed850094
--- /dev/null
+++ b/CTestConfig.cmake
@@ -0,0 +1,13 @@
+## This file should be placed in the root directory of your project.
+## Then modify the CMakeLists.txt file in the root directory of your
+## project to incorporate the testing dashboard.
+## # The following are required to uses Dart and the Cdash dashboard
+##   ENABLE_TESTING()
+##   INCLUDE(CTest)
+set(CTEST_PROJECT_NAME "ArrayFire")
+set(CTEST_NIGHTLY_START_TIME "01:00:00 UTC")
+
+set(CTEST_DROP_METHOD "http")
+set(CTEST_DROP_SITE "67.207.87.39")
+set(CTEST_DROP_LOCATION "/submit.php?project=ArrayFire")
+set(CTEST_DROP_SITE_CDASH TRUE)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 66cb8d8b54..1a608a5eec 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,154 +1,65 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
-PROJECT(ArrayFire-Examples)
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Examples
+  VERSION 3.5.0
+  LANGUAGES CXX)
 
-# Find CUDA and OpenCL
-SET(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
-FIND_PACKAGE(CUDA QUIET)
-FIND_PACKAGE(OpenCL QUIET)
+if(WIN32)
+  add_definitions(-DWIN32_LEAN_AND_MEAN)
+endif()
 
-# If the examples are not being built at the same time as ArrayFire,
-# we need to first find the ArrayFire library
-IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
-    SET(ArrayFire_CPU_FOUND False)
-    SET(ArrayFire_CUDA_FOUND False)
-    SET(ArrayFire_OpenCL_FOUND False)
-    SET(ArrayFire_Unified_FOUND False)
-    SET(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../assets")
-    IF(NOT EXISTS "${ASSETS_DIR}/LICENSE")
-        MESSAGE(STATUS "Assests submodule unavailable. Updating submodules.")
-        EXECUTE_PROCESS(
-            COMMAND git submodule update --init --recursive
-            WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
-            OUTPUT_QUIET
-        )
-    ENDIF()
-ELSE()
-    SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-    FIND_PACKAGE(ArrayFire REQUIRED)
-    INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
+# Some examples take too long to execute. This list is used to exclude these
+# examples from the tests
+list(APPEND exclude_from_tests  black_scholes_options_cpu
+                                monte_carlo_options_cpu
+                                vectorize_cpu
+                                )
 
-    SET(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/assets")
-ENDIF()
+# Overload add_executable and target_link_libraries so that we can use simple
+# CMakeLists.txt files for the examples.
+#
+# These functions will overload the existing functions so that the target names
+# have the word "examples_" prefixed to them so they don't conflict with the
+# tests. This is an issue with the blas example where the test blas_cpu and the
+# example blas_cpu have the same target name.
+#
+# Additionally, This will allow us to write the CMakeLists.txt files as
+# standalone files so that they are easier to parse for new users.
+function(add_executable target sources)
+  _add_executable(example_${target} ${sources})
+  set_target_properties(example_${target}
+    PROPERTIES
+      OUTPUT_NAME ${target}
+      FOLDER "Examples"
+	)
 
-IF(WIN32)
-    # Deprecated Errors are Warning 4996 on VS2013.
-    # https://msdn.microsoft.com/en-us/library/ttcz0bys.aspx
-    IF(MSVC)
-        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /we4996")
-        SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} /we4996")
-    ENDIF(MSVC)
-ELSE(WIN32)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=deprecated-declarations")
-    SET(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -Werror=deprecated-declarations")
-ENDIF(WIN32)
+  if(NOT ${target} IN_LIST exclude_from_tests)
+    #add_test(example_${target} ${target} 0 -)
+  endif()
+endfunction()
 
-# A macro to build an ArrayFire example
-# For most uses only FIND_PACKAGE(ArrayFire REQUIRED), ADD_EXECUTABLE(...)
-# and TARGET_LINK_LIBRARIES(... ${ARRAYFIRE_LIBRARIES}) are needed
-MACRO(BUILD_EXAMPLE EXAMPLE_NAME EXAMPLE_SOURCE BACKEND_NAME BACKEND_LIBRARIES OTHER_LIBRARIES OUT_DIR_NAME)
-    ADD_EXECUTABLE(example_${EXAMPLE_NAME}_${BACKEND_NAME} ${EXAMPLE_SOURCE})
-    TARGET_LINK_LIBRARIES(example_${EXAMPLE_NAME}_${BACKEND_NAME}
-        ${BACKEND_LIBRARIES} ${OTHER_LIBRARIES})
-    SET_TARGET_PROPERTIES(example_${EXAMPLE_NAME}_${BACKEND_NAME}
-        PROPERTIES
-        OUTPUT_NAME ${EXAMPLE_NAME}_${BACKEND_NAME}
-        RUNTIME_OUTPUT_DIRECTORY ${OUT_DIR_NAME}
-        FOLDER "Examples/${BACKEND_NAME}")
-ENDMACRO()
+function(target_link_libraries target sources)
+  _target_link_libraries(example_${target} ${sources})
+endfunction()
 
-# A macro to build a list of files
-# For most uses only FIND_PACKAGE(ArrayFire REQUIRED), ADD_EXECUTABLE(...)
-# and TARGET_LINK_LIBRARIES(... ${ARRAYFIRE_LIBRARIES}) are needed
-MACRO(BUILD_ALL FILES BACKEND_NAME BACKEND_LIBRARIES OTHER_LIBRARIES)
+function(target_compile_definitions target access definitions)
+  _target_compile_definitions(example_${target} ${access} ${definitions})
+endfunction()
 
-    STRING(TOUPPER ${BACKEND_NAME} BACKEND_NAME_UPPER)
-    MESSAGE(STATUS "EXAMPLES: ${BACKEND_NAME_UPPER} backend is ${BUILD_${BACKEND_NAME_UPPER}}.")
-    IF(${BUILD_${BACKEND_NAME_UPPER}})
-        FOREACH(FILE ${FILES})
-            GET_FILENAME_COMPONENT(EXAMPLE ${FILE} NAME_WE)
-            GET_FILENAME_COMPONENT(FULL_DIR_NAME ${FILE} PATH)
-            GET_FILENAME_COMPONENT(DIR_NAME ${FULL_DIR_NAME} NAME)
+function(find_package args)
+  if(NOT (TARGET ArrayFire::afcpu OR TARGET ArrayFire::afcuda OR TARGET ArrayFire::afopencl OR TARGET ArrayFire::af))
+    _find_package(args)
+  endif()
+endfunction()
 
-            BUILD_EXAMPLE(${EXAMPLE} ${FILE} ${BACKEND_NAME} "${BACKEND_LIBRARIES}" "${OTHER_LIBRARIES}" ${DIR_NAME})
-        ENDFOREACH()
-    ENDIF()
-ENDMACRO()
-
-# Collect the source
-FILE(GLOB FILES "*/*.cpp")
-LIST(SORT FILES)
-ADD_DEFINITIONS("-DASSETS_DIR=\"${ASSETS_DIR}\"")
-
-# Next we build each example using every backend.
-IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
-    OPTION(BUILD_CPU "Build ArrayFire Examples for CPU backend" ON)
-    BUILD_ALL("${FILES}" cpu ${ArrayFire_CPU_LIBRARIES} "")
-ELSEIF(TARGET afcpu)        # variable defined by the ArrayFire build tree
-    BUILD_ALL("${FILES}" cpu afcpu "")
-ELSE()
-    MESSAGE(STATUS "EXAMPLES: CPU backend is OFF. afcpu was not found.")
-ENDIF()
-
-# Next we build each example using every backend.
-IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
-    OPTION(BUILD_UNIFIED "Build ArrayFire Examples for Unified backend" ON)
-    BUILD_ALL("${FILES}" unified ${ArrayFire_Unified_LIBRARIES} "${CMAKE_DL_LIBS}")
-ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
-    BUILD_ALL("${FILES}" unified af "${CMAKE_DL_LIBS}")
-ELSE()
-    MESSAGE(STATUS "EXAMPLES: UNIFIED backend is OFF. af was not found.")
-ENDIF()
-
-IF (${CUDA_FOUND})
-    IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
-        # Find NVVM
-        FIND_LIBRARY(CUDA_nvvm_LIBRARY
-          NAMES "nvvm"
-          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib" "nvvm/lib/x64"
-          PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-          DOC "CUDA NVVM Library"
-          )
-        MARK_AS_ADVANCED(CUDA_nvvm_LIBRARY)
-
-        # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
-        IF(NOT CUDA_CUDA_LIBRARY)
-            MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
-            MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
-            FIND_FILE(CUDA_CUDA_LIBRARY_STUB
-                      NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
-                      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-                      PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
-                      DOC "CUDA Library STUB"
-                     )
-            IF(CUDA_CUDA_LIBRARY_STUB)
-                MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
-                MESSAGE("Run the following commands (Linux) to set it up:")
-                MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
-                MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
-            ENDIF()
-            MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
-        ENDIF(NOT CUDA_CUDA_LIBRARY)
-
-        OPTION(BUILD_CUDA "Build ArrayFire Examples for CUDA backend" ON)
-        BUILD_ALL("${FILES}" cuda ${ArrayFire_CUDA_LIBRARIES} "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
-    ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
-        BUILD_ALL("${FILES}" cuda afcuda "")
-    ELSE()
-        MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. afcuda was not found")
-    ENDIF()
-ELSE()
-    MESSAGE(STATUS "EXAMPLES: CUDA backend is OFF. CUDA was not found")
-ENDIF()
-
-IF (${OpenCL_FOUND})
-    IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
-        OPTION(BUILD_OPENCL "Build ArrayFire Examples for OpenCL backend" ON)
-        BUILD_ALL("${FILES}" opencl ${ArrayFire_OpenCL_LIBRARIES} "${OpenCL_LIBRARIES}")
-    ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
-        BUILD_ALL("${FILES}" opencl afopencl "${OpenCL_LIBRARIES}")
-    ELSE()
-        MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. afopencl was not found")
-    ENDIF()
-ELSE()
-    MESSAGE(STATUS "EXAMPLES: OpenCL backend is OFF. OpenCL was not found")
-ENDIF()
+add_subdirectory(benchmarks)
+add_subdirectory(computer_vision)
+add_subdirectory(financial)
+add_subdirectory(getting_started)
+add_subdirectory(graphics)
+add_subdirectory(helloworld)
+add_subdirectory(image_processing)
+add_subdirectory(lin_algebra)
+add_subdirectory(machine_learning)
+add_subdirectory(pde)
+add_subdirectory(unified)
diff --git a/examples/benchmarks/CMakeLists.txt b/examples/benchmarks/CMakeLists.txt
new file mode 100644
index 0000000000..8a77b47c3f
--- /dev/null
+++ b/examples/benchmarks/CMakeLists.txt
@@ -0,0 +1,56 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Benchmarks
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire REQUIRED)
+
+# get_cmake_property(_variableNames VARIABLES)
+# foreach (_variableName ${_variableNames})
+#     message(STATUS "${_variableName}=${${_variableName}}")
+# endforeach()
+
+if(ArrayFire_CPU_FOUND)
+  add_executable(blas_cpu blas.cpp)
+  target_link_libraries(blas_cpu ArrayFire::afcpu)
+
+  add_executable(cg_cpu cg.cpp)
+  target_link_libraries(cg_cpu ArrayFire::afcpu)
+
+  add_executable(fft_cpu fft.cpp)
+  target_link_libraries(fft_cpu ArrayFire::afcpu)
+
+  add_executable(pi_cpu pi.cpp)
+  target_link_libraries(pi_cpu ArrayFire::afcpu)
+endif()
+
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(blas_cuda blas.cpp)
+  target_link_libraries(blas_cuda ArrayFire::afcuda)
+
+  add_executable(cg_cuda cg.cpp)
+  target_link_libraries(cg_cuda ArrayFire::afcuda)
+
+  add_executable(fft_cuda fft.cpp)
+  target_link_libraries(fft_cuda ArrayFire::afcuda)
+
+  add_executable(pi_cuda pi.cpp)
+  target_link_libraries(pi_cuda ArrayFire::afcuda)
+endif()
+
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(blas_opencl blas.cpp)
+  target_link_libraries(blas_opencl ArrayFire::afopencl)
+
+  add_executable(cg_opencl cg.cpp)
+  target_link_libraries(cg_opencl ArrayFire::afopencl)
+
+  add_executable(fft_opencl fft.cpp)
+  target_link_libraries(fft_opencl ArrayFire::afopencl)
+
+  add_executable(pi_opencl pi.cpp)
+  target_link_libraries(pi_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
new file mode 100644
index 0000000000..03dd516087
--- /dev/null
+++ b/examples/computer_vision/CMakeLists.txt
@@ -0,0 +1,56 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Computer-Vision
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
+add_definitions(-DASSETS_DIR=\"${ASSETS_DIR}\")
+
+if (ArrayFire_CPU_FOUND)
+  # FAST examples
+  add_executable(fast_cpu fast.cpp)
+  target_link_libraries(fast_cpu ArrayFire::afcpu)
+
+  # Harris corner detector examples
+  add_executable(harris_cpu harris.cpp)
+  target_link_libraries(harris_cpu ArrayFire::afcpu)
+
+  # Template Matching examples
+  add_executable(matching_cpu matching.cpp)
+  target_link_libraries(matching_cpu ArrayFire::afcpu)
+
+  # Template Matching examples
+  add_executable(susan_cpu susan.cpp)
+  target_link_libraries(susan_cpu ArrayFire::afcpu)
+endif()
+
+if (ArrayFire_CUDA_FOUND)
+  add_executable(fast_cuda fast.cpp)
+  target_link_libraries(fast_cuda ArrayFire::afcuda)
+
+  add_executable(harris_cuda harris.cpp)
+  target_link_libraries(harris_cuda ArrayFire::afcuda)
+
+  add_executable(matching_cuda matching.cpp)
+  target_link_libraries(matching_cuda ArrayFire::afcuda)
+
+  add_executable(susan_cuda susan.cpp)
+  target_link_libraries(susan_cuda ArrayFire::afcuda)
+endif()
+
+if (ArrayFire_OpenCL_FOUND)
+  add_executable(fast_opencl fast.cpp)
+  target_link_libraries(fast_opencl ArrayFire::afopencl)
+
+  add_executable(harris_opencl harris.cpp)
+  target_link_libraries(harris_opencl ArrayFire::afopencl)
+
+  add_executable(matching_opencl matching.cpp)
+  target_link_libraries(matching_opencl ArrayFire::afopencl)
+
+  add_executable(susan_opencl susan.cpp)
+  target_link_libraries(susan_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/financial/CMakeLists.txt b/examples/financial/CMakeLists.txt
new file mode 100644
index 0000000000..e7c7fc19c8
--- /dev/null
+++ b/examples/financial/CMakeLists.txt
@@ -0,0 +1,43 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Financial
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_CPU_FOUND)
+  # Black-Scholes Options
+  add_executable(black_scholes_options_cpu black_scholes_options.cpp input.h)
+  target_link_libraries(black_scholes_options_cpu ArrayFire::afcpu)
+
+  # Heston Model
+  add_executable(heston_model_cpu heston_model.cpp)
+  target_link_libraries(heston_model_cpu ArrayFire::afcpu)
+
+  # Monte Carlo Options
+  add_executable(monte_carlo_options_cpu monte_carlo_options.cpp)
+  target_link_libraries(monte_carlo_options_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(black_scholes_options_cuda black_scholes_options.cpp input.h)
+  target_link_libraries(black_scholes_options_cuda ArrayFire::afcuda)
+
+  add_executable(heston_model_cuda heston_model.cpp)
+  target_link_libraries(heston_model_cuda ArrayFire::afcuda)
+
+  add_executable(monte_carlo_options_cuda monte_carlo_options.cpp)
+  target_link_libraries(monte_carlo_options_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(monte_carlo_options_opencl monte_carlo_options.cpp)
+  target_link_libraries(monte_carlo_options_opencl ArrayFire::afopencl)
+
+  add_executable(black_scholes_options_opencl black_scholes_options.cpp input.h)
+  target_link_libraries(black_scholes_options_opencl ArrayFire::afopencl)
+
+  add_executable(heston_model_opencl heston_model.cpp)
+  target_link_libraries(heston_model_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/getting_started/CMakeLists.txt b/examples/getting_started/CMakeLists.txt
new file mode 100644
index 0000000000..abc0899f64
--- /dev/null
+++ b/examples/getting_started/CMakeLists.txt
@@ -0,0 +1,53 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Getting-Started
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_CPU_FOUND)
+  # Convolve examples
+  add_executable(convolve_cpu convolve.cpp)
+  target_link_libraries(convolve_cpu ArrayFire::afcpu)
+
+  # Integer examples
+  add_executable(integer_cpu integer.cpp)
+  target_link_libraries(integer_cpu ArrayFire::afcpu)
+
+  # Rainfall examples
+  add_executable(rainfall_cpu rainfall.cpp)
+  target_link_libraries(rainfall_cpu ArrayFire::afcpu)
+
+  # Vectorization examples
+  add_executable(vectorize_cpu vectorize.cpp)
+  target_link_libraries(vectorize_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(convolve_cuda convolve.cpp)
+  target_link_libraries(convolve_cuda ArrayFire::afcuda)
+
+  add_executable(integer_cuda integer.cpp)
+  target_link_libraries(integer_cuda ArrayFire::afcuda)
+
+  add_executable(rainfall_cuda rainfall.cpp)
+  target_link_libraries(rainfall_cuda ArrayFire::afcuda)
+
+  add_executable(vectorize_cuda vectorize.cpp)
+  target_link_libraries(vectorize_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(convolve_opencl convolve.cpp)
+  target_link_libraries(convolve_opencl ArrayFire::afopencl)
+
+  add_executable(integer_opencl integer.cpp)
+  target_link_libraries(integer_opencl ArrayFire::afopencl)
+
+  add_executable(rainfall_opencl rainfall.cpp)
+  target_link_libraries(rainfall_opencl ArrayFire::afopencl)
+
+  add_executable(vectorize_opencl vectorize.cpp)
+  target_link_libraries(vectorize_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
new file mode 100644
index 0000000000..949cfce016
--- /dev/null
+++ b/examples/graphics/CMakeLists.txt
@@ -0,0 +1,105 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Graphics
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_CPU_FOUND)
+  # Conway Game of Life
+  add_executable(conway_cpu conway.cpp)
+  target_link_libraries(conway_cpu ArrayFire::afcpu)
+
+  # Conway Game of Life with Color
+  add_executable(conway_pretty_cpu conway_pretty.cpp)
+  target_link_libraries(conway_pretty_cpu ArrayFire::afcpu)
+
+  # Vector fields example
+  add_executable(field_cpu field.cpp)
+  target_link_libraries(field_cpu ArrayFire::afcpu)
+
+  # Fractal example
+  add_executable(fractal_cpu fractal.cpp)
+  target_link_libraries(fractal_cpu ArrayFire::afcpu)
+
+  # Gravity Simulation example
+  add_executable(gravity_sim_cpu gravity_sim.cpp gravity_sim_init.h)
+  target_link_libraries(gravity_sim_cpu ArrayFire::afcpu)
+
+  # Histogram example
+  add_executable(histogram_cpu histogram.cpp)
+  target_compile_definitions(histogram_cpu PRIVATE "ASSETS_DIR=\"${ASSETS_DIR}\"")
+  target_link_libraries(histogram_cpu ArrayFire::afcpu)
+
+  # Plot 2D example
+  add_executable(plot2d_cpu plot2d.cpp)
+  target_link_libraries(plot2d_cpu ArrayFire::afcpu)
+
+  # Plot 3 example
+  add_executable(plot3_cpu plot3.cpp)
+  target_link_libraries(plot3_cpu ArrayFire::afcpu)
+
+  # Surface example
+  add_executable(surface_cpu surface.cpp)
+  target_link_libraries(surface_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(conway_cuda conway.cpp)
+  target_link_libraries(conway_cuda ArrayFire::afcuda)
+
+  add_executable(conway_pretty_cuda conway_pretty.cpp)
+  target_link_libraries(conway_pretty_cuda ArrayFire::afcuda)
+
+  add_executable(field_cuda field.cpp)
+  target_link_libraries(field_cuda ArrayFire::afcuda)
+
+  add_executable(fractal_cuda fractal.cpp)
+  target_link_libraries(fractal_cuda ArrayFire::afcuda)
+
+  add_executable(gravity_sim_cuda gravity_sim.cpp gravity_sim_init.h)
+  target_link_libraries(gravity_sim_cuda ArrayFire::afcuda)
+
+  add_executable(histogram_cuda histogram.cpp)
+  target_compile_definitions(histogram_cuda PRIVATE "ASSETS_DIR=\"${ASSETS_DIR}\"")
+  target_link_libraries(histogram_cuda ArrayFire::afcuda)
+
+  add_executable(plot2d_cuda plot2d.cpp)
+  target_link_libraries(plot2d_cuda ArrayFire::afcuda)
+  add_executable(plot3_cuda plot3.cpp)
+  target_link_libraries(plot3_cuda ArrayFire::afcuda)
+
+  add_executable(surface_cuda surface.cpp)
+  target_link_libraries(surface_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(conway_opencl conway.cpp)
+  target_link_libraries(conway_opencl ArrayFire::afopencl)
+
+  add_executable(conway_pretty_opencl conway_pretty.cpp)
+  target_link_libraries(conway_pretty_opencl ArrayFire::afopencl)
+
+  add_executable(field_opencl field.cpp)
+  target_link_libraries(field_opencl ArrayFire::afopencl)
+
+  add_executable(fractal_opencl fractal.cpp)
+  target_link_libraries(fractal_opencl ArrayFire::afopencl)
+
+  add_executable(gravity_sim_opencl gravity_sim.cpp gravity_sim_init.h)
+  target_link_libraries(gravity_sim_opencl ArrayFire::afopencl)
+
+  add_executable(histogram_opencl histogram.cpp)
+  target_compile_definitions(histogram_opencl PRIVATE "ASSETS_DIR=\"${ASSETS_DIR}\"")
+  target_link_libraries(histogram_opencl ArrayFire::afopencl)
+
+  add_executable(plot2d_opencl plot2d.cpp)
+  target_link_libraries(plot2d_opencl ArrayFire::afopencl)
+
+  add_executable(plot3_opencl plot3.cpp)
+  target_link_libraries(plot3_opencl ArrayFire::afopencl)
+
+  add_executable(surface_opencl surface.cpp)
+  target_link_libraries(surface_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/helloworld/CMakeLists.txt b/examples/helloworld/CMakeLists.txt
new file mode 100644
index 0000000000..354332f095
--- /dev/null
+++ b/examples/helloworld/CMakeLists.txt
@@ -0,0 +1,23 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-HelloWorld
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_CPU_FOUND)
+  # Hello World example
+  add_executable(helloworld_cpu helloworld.cpp)
+  target_link_libraries(helloworld_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(helloworld_cuda helloworld.cpp)
+  target_link_libraries(helloworld_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(helloworld_opencl helloworld.cpp)
+  target_link_libraries(helloworld_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
new file mode 100644
index 0000000000..d5f4b70c3d
--- /dev/null
+++ b/examples/image_processing/CMakeLists.txt
@@ -0,0 +1,115 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Image-Processing
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
+
+if(ArrayFire_CPU_FOUND)
+  # Adaptive Thresholding example
+  add_executable(adaptive_thresholding_cpu adaptive_thresholding.cpp)
+  target_link_libraries(adaptive_thresholding_cpu ArrayFire::afcpu)
+
+  # Binary Thresholding example
+  add_executable(binary_thresholding_cpu binary_thresholding.cpp)
+  target_link_libraries(binary_thresholding_cpu ArrayFire::afcpu)
+
+  # Brain Segmentation example
+  add_executable(brain_segmentation_cpu brain_segmentation.cpp)
+  target_link_libraries(brain_segmentation_cpu ArrayFire::afcpu)
+
+  # Edge detection example
+  add_executable(edge_cpu edge.cpp)
+  target_link_libraries(edge_cpu ArrayFire::afcpu)
+
+  # Filters example
+  add_executable(filters_cpu filters.cpp)
+  target_link_libraries(filters_cpu ArrayFire::afcpu)
+
+  # Image example
+  add_executable(image_demo_cpu image_demo.cpp)
+  target_link_libraries(image_demo_cpu ArrayFire::afcpu)
+
+  # Image Editing example
+  add_executable(image_editing_cpu image_editing.cpp)
+  target_link_libraries(image_editing_cpu ArrayFire::afcpu)
+
+  # Morph example
+  add_executable(morphing_cpu morphing.cpp)
+  target_link_libraries(morphing_cpu ArrayFire::afcpu)
+
+  # Optical Flow example
+  add_executable(optical_flow_cpu optical_flow.cpp)
+  target_link_libraries(optical_flow_cpu ArrayFire::afcpu)
+
+  # Pyramids example
+  add_executable(pyramids_cpu pyramids.cpp)
+  target_link_libraries(pyramids_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(adaptive_thresholding_cuda adaptive_thresholding.cpp)
+  target_link_libraries(adaptive_thresholding_cuda ArrayFire::afcuda)
+
+  add_executable(binary_thresholding_cuda binary_thresholding.cpp)
+  target_link_libraries(binary_thresholding_cuda ArrayFire::afcuda)
+
+  add_executable(brain_segmentation_cuda brain_segmentation.cpp)
+  target_link_libraries(brain_segmentation_cuda ArrayFire::afcuda)
+
+  add_executable(edge_cuda edge.cpp)
+  target_link_libraries(edge_cuda ArrayFire::afcuda)
+
+  add_executable(filters_cuda filters.cpp)
+  target_link_libraries(filters_cuda ArrayFire::afcuda)
+
+  add_executable(image_demo_cuda image_demo.cpp)
+  target_link_libraries(image_demo_cuda ArrayFire::afcuda)
+
+  add_executable(image_editing_cuda image_editing.cpp)
+  target_link_libraries(image_editing_cuda ArrayFire::afcuda)
+
+  add_executable(morphing_cuda morphing.cpp)
+  target_link_libraries(morphing_cuda ArrayFire::afcuda)
+
+  add_executable(optical_flow_cuda optical_flow.cpp)
+  target_link_libraries(optical_flow_cuda ArrayFire::afcuda)
+
+  add_executable(pyramids_cuda pyramids.cpp)
+  target_link_libraries(pyramids_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(adaptive_thresholding_opencl adaptive_thresholding.cpp)
+  target_link_libraries(adaptive_thresholding_opencl ArrayFire::afopencl)
+
+  add_executable(binary_thresholding_opencl binary_thresholding.cpp)
+  target_link_libraries(binary_thresholding_opencl ArrayFire::afopencl)
+
+  add_executable(brain_segmentation_opencl brain_segmentation.cpp)
+  target_link_libraries(brain_segmentation_opencl ArrayFire::afopencl)
+
+  add_executable(edge_opencl edge.cpp)
+  target_link_libraries(edge_opencl ArrayFire::afopencl)
+
+  add_executable(filters_opencl filters.cpp)
+  target_link_libraries(filters_opencl ArrayFire::afopencl)
+
+  add_executable(image_demo_opencl image_demo.cpp)
+  target_link_libraries(image_demo_opencl ArrayFire::afopencl)
+
+  add_executable(image_editing_opencl image_editing.cpp)
+  target_link_libraries(image_editing_opencl ArrayFire::afopencl)
+
+  add_executable(morphing_opencl morphing.cpp)
+  target_link_libraries(morphing_opencl ArrayFire::afopencl)
+
+  add_executable(optical_flow_opencl optical_flow.cpp)
+  target_link_libraries(optical_flow_opencl ArrayFire::afopencl)
+
+  add_executable(pyramids_opencl pyramids.cpp)
+  target_link_libraries(pyramids_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/lin_algebra/CMakeLists.txt b/examples/lin_algebra/CMakeLists.txt
new file mode 100644
index 0000000000..2181a2f923
--- /dev/null
+++ b/examples/lin_algebra/CMakeLists.txt
@@ -0,0 +1,53 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Linear-Algebra
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_CPU_FOUND)
+  # Cholesky example
+  add_executable(cholesky_cpu cholesky.cpp)
+  target_link_libraries(cholesky_cpu ArrayFire::afcpu)
+
+  # LU example
+  add_executable(lu_cpu lu.cpp)
+  target_link_libraries(lu_cpu ArrayFire::afcpu)
+
+  # QR example
+  add_executable(qr_cpu qr.cpp)
+  target_link_libraries(qr_cpu ArrayFire::afcpu)
+
+  # SVD example
+  add_executable(svd_cpu svd.cpp)
+  target_link_libraries(svd_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(cholesky_cuda cholesky.cpp)
+  target_link_libraries(cholesky_cuda ArrayFire::afcuda)
+
+  add_executable(lu_cuda lu.cpp)
+  target_link_libraries(lu_cuda ArrayFire::afcuda)
+
+  add_executable(qr_cuda qr.cpp)
+  target_link_libraries(qr_cuda ArrayFire::afcuda)
+
+  add_executable(svd_cuda svd.cpp)
+  target_link_libraries(svd_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(cholesky_opencl cholesky.cpp)
+  target_link_libraries(cholesky_opencl ArrayFire::afopencl)
+
+  add_executable(lu_opencl lu.cpp)
+  target_link_libraries(lu_opencl ArrayFire::afopencl)
+
+  add_executable(qr_opencl qr.cpp)
+  target_link_libraries(qr_opencl ArrayFire::afopencl)
+
+  add_executable(svd_opencl svd.cpp)
+  target_link_libraries(svd_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/machine_learning/CMakeLists.txt b/examples/machine_learning/CMakeLists.txt
new file mode 100644
index 0000000000..e94e3d2482
--- /dev/null
+++ b/examples/machine_learning/CMakeLists.txt
@@ -0,0 +1,115 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Linear-Algebra
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
+
+if(ArrayFire_CPU_FOUND)
+  # Bagging example
+  add_executable(bagging_cpu bagging.cpp)
+  target_link_libraries(bagging_cpu ArrayFire::afcpu)
+
+  # Deep Belief Network example
+  add_executable(deep_belief_net_cpu deep_belief_net.cpp)
+  target_link_libraries(deep_belief_net_cpu ArrayFire::afcpu)
+
+  # Genetic Algorithm example
+  add_executable(geneticalgorithm_cpu geneticalgorithm.cpp)
+  target_link_libraries(geneticalgorithm_cpu ArrayFire::afcpu)
+
+  # k Means example
+  add_executable(kmeans_cpu kmeans.cpp)
+  target_link_libraries(kmeans_cpu ArrayFire::afcpu)
+
+  # Logistic Regression example
+  add_executable(logistic_regression_cpu logistic_regression.cpp)
+  target_link_libraries(logistic_regression_cpu ArrayFire::afcpu)
+
+  # Naive Bayes example
+  add_executable(naive_bayes_cpu naive_bayes.cpp)
+  target_link_libraries(naive_bayes_cpu ArrayFire::afcpu)
+
+  # Neural Network example
+  add_executable(neural_network_cpu neural_network.cpp)
+  target_link_libraries(neural_network_cpu ArrayFire::afcpu)
+
+  # Preceptron example
+  add_executable(perceptron_cpu perceptron.cpp)
+  target_link_libraries(perceptron_cpu ArrayFire::afcpu)
+
+  # Restricted Boltsmann Machine example
+  add_executable(rbm_cpu rbm.cpp)
+  target_link_libraries(rbm_cpu ArrayFire::afcpu)
+
+  # Softmax Regression example
+  add_executable(softmax_regression_cpu softmax_regression.cpp)
+  target_link_libraries(softmax_regression_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(bagging_cuda bagging.cpp)
+  target_link_libraries(bagging_cuda ArrayFire::afcuda)
+
+  add_executable(deep_belief_net_cuda deep_belief_net.cpp)
+  target_link_libraries(deep_belief_net_cuda ArrayFire::afcuda)
+
+  add_executable(geneticalgorithm_cuda geneticalgorithm.cpp)
+  target_link_libraries(geneticalgorithm_cuda ArrayFire::afcuda)
+
+  add_executable(kmeans_cuda kmeans.cpp)
+  target_link_libraries(kmeans_cuda ArrayFire::afcuda)
+
+  add_executable(logistic_regression_cuda logistic_regression.cpp)
+  target_link_libraries(logistic_regression_cuda ArrayFire::afcuda)
+
+  add_executable(naive_bayes_cuda naive_bayes.cpp)
+  target_link_libraries(naive_bayes_cuda ArrayFire::afcuda)
+
+  add_executable(neural_network_cuda neural_network.cpp)
+  target_link_libraries(neural_network_cuda ArrayFire::afcuda)
+
+  add_executable(perceptron_cuda perceptron.cpp)
+  target_link_libraries(perceptron_cuda ArrayFire::afcuda)
+
+  add_executable(rbm_cuda rbm.cpp)
+  target_link_libraries(rbm_cuda ArrayFire::afcuda)
+
+  add_executable(softmax_regression_cuda softmax_regression.cpp)
+  target_link_libraries(softmax_regression_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(bagging_opencl bagging.cpp)
+  target_link_libraries(bagging_opencl ArrayFire::afopencl)
+
+  add_executable(deep_belief_net_opencl deep_belief_net.cpp)
+  target_link_libraries(deep_belief_net_opencl ArrayFire::afopencl)
+
+  add_executable(geneticalgorithm_opencl geneticalgorithm.cpp)
+  target_link_libraries(geneticalgorithm_opencl ArrayFire::afopencl)
+
+  add_executable(kmeans_opencl kmeans.cpp)
+  target_link_libraries(kmeans_opencl ArrayFire::afopencl)
+
+  add_executable(logistic_regression_opencl logistic_regression.cpp)
+  target_link_libraries(logistic_regression_opencl ArrayFire::afopencl)
+
+  add_executable(naive_bayes_opencl naive_bayes.cpp)
+  target_link_libraries(naive_bayes_opencl ArrayFire::afopencl)
+
+  add_executable(neural_network_opencl neural_network.cpp)
+  target_link_libraries(neural_network_opencl ArrayFire::afopencl)
+
+  add_executable(perceptron_opencl perceptron.cpp)
+  target_link_libraries(perceptron_opencl ArrayFire::afopencl)
+
+  add_executable(rbm_opencl rbm.cpp)
+  target_link_libraries(rbm_opencl ArrayFire::afopencl)
+
+  add_executable(softmax_regression_opencl softmax_regression.cpp)
+  target_link_libraries(softmax_regression_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
new file mode 100644
index 0000000000..7fae04a2b6
--- /dev/null
+++ b/examples/pde/CMakeLists.txt
@@ -0,0 +1,23 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-PDE
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_CPU_FOUND)
+  # Shallow Water simulation example
+  add_executable(swe_cpu swe.cpp)
+  target_link_libraries(swe_cpu ArrayFire::afcpu)
+endif()
+
+if(ArrayFire_CUDA_FOUND)
+  add_executable(swe_cuda swe.cpp)
+  target_link_libraries(swe_cuda ArrayFire::afcuda)
+endif()
+
+if(ArrayFire_OpenCL_FOUND)
+  add_executable(swe_opencl swe.cpp)
+  target_link_libraries(swe_opencl ArrayFire::afopencl)
+endif()
diff --git a/examples/unified/CMakeLists.txt b/examples/unified/CMakeLists.txt
new file mode 100644
index 0000000000..282a359c0e
--- /dev/null
+++ b/examples/unified/CMakeLists.txt
@@ -0,0 +1,13 @@
+cmake_minimum_required(VERSION 3.0)
+cmake_policy(VERSION 3.5)
+project(ArrayFire-Example-Unified
+  VERSION 3.5.0
+  LANGUAGES CXX)
+
+find_package(ArrayFire)
+
+if(ArrayFire_Unified_FOUND)
+  # Simple unified backend example
+  add_executable(basic_unified basic.cpp)
+  target_link_libraries(basic_unified ArrayFire::af)
+endif()
diff --git a/include/.gitignore b/include/.gitignore
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
new file mode 100644
index 0000000000..974deef020
--- /dev/null
+++ b/src/api/c/CMakeLists.txt
@@ -0,0 +1,169 @@
+
+add_library(c_api_interface INTERFACE)
+
+target_sources(c_api_interface
+  INTERFACE
+  ${ArrayFire_SOURCE_DIR}/include/arrayfire.h
+  ${ArrayFire_SOURCE_DIR}/include/af/algorithm.h
+  ${ArrayFire_SOURCE_DIR}/include/af/arith.h
+  ${ArrayFire_SOURCE_DIR}/include/af/array.h
+  ${ArrayFire_SOURCE_DIR}/include/af/backend.h
+  ${ArrayFire_SOURCE_DIR}/include/af/blas.h
+  ${ArrayFire_SOURCE_DIR}/include/af/compatible.h
+  ${ArrayFire_SOURCE_DIR}/include/af/complex.h
+  ${ArrayFire_SOURCE_DIR}/include/af/constants.h
+  ${ArrayFire_SOURCE_DIR}/include/af/cuda.h
+  ${ArrayFire_SOURCE_DIR}/include/af/data.h
+  ${ArrayFire_SOURCE_DIR}/include/af/defines.h
+  ${ArrayFire_SOURCE_DIR}/include/af/device.h
+  ${ArrayFire_SOURCE_DIR}/include/af/dim4.hpp
+  ${ArrayFire_SOURCE_DIR}/include/af/exception.h
+  ${ArrayFire_SOURCE_DIR}/include/af/features.h
+  ${ArrayFire_SOURCE_DIR}/include/af/gfor.h
+  ${ArrayFire_SOURCE_DIR}/include/af/graphics.h
+  ${ArrayFire_SOURCE_DIR}/include/af/image.h
+  ${ArrayFire_SOURCE_DIR}/include/af/index.h
+  ${ArrayFire_SOURCE_DIR}/include/af/internal.h
+  ${ArrayFire_SOURCE_DIR}/include/af/lapack.h
+  ${ArrayFire_SOURCE_DIR}/include/af/macros.h
+  ${ArrayFire_SOURCE_DIR}/include/af/opencl.h
+  ${ArrayFire_SOURCE_DIR}/include/af/random.h
+  ${ArrayFire_SOURCE_DIR}/include/af/seq.h
+  ${ArrayFire_SOURCE_DIR}/include/af/signal.h
+  ${ArrayFire_SOURCE_DIR}/include/af/sparse.h
+  ${ArrayFire_SOURCE_DIR}/include/af/statistics.h
+  ${ArrayFire_SOURCE_DIR}/include/af/timing.h
+  ${ArrayFire_SOURCE_DIR}/include/af/traits.hpp
+  ${ArrayFire_SOURCE_DIR}/include/af/util.h
+  ${ArrayFire_SOURCE_DIR}/include/af/vision.h
+  ${ArrayFire_BINARY_DIR}/include/af/version.h
+  )
+
+target_sources(c_api_interface
+  INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/approx.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/assign.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/canny.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/cast.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/cholesky.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/clamp.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/colorspace.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/complex.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/convolve.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/corrcoef.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/covariance.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/det.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/diff.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/dog.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/exampleFunction.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/features.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/features.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fft_common.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fftconvolve.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/filters.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/flip.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/gaussian_kernel.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/gradient.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/hamming.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/handle.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/harris.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/hist.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/histeq.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/histogram.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/homography.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/hsv_rgb.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/iir.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/image.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/imageio.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/imageio2.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/implicit.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/implicit.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/inverse.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/join.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/lu.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/match_template.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/mean.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/meanshift.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/median.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/moddims.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/morph.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/nearest_neighbour.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/norm.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ops.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/optypes.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/orb.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/plot.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/print.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/qr.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/rank.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/regions.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/reorder.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/replace.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/resize.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/rgb_gray.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/rotate.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sat.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/select.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/set.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/shift.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sift.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sobel.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/solve.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sparse.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sparse_handle.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/stdev.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/stream.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/surface.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/susan.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/svd.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/tile.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/transform.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/transform_coordinates.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/transpose.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/type_util.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/type_util.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/unary.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/unwrap.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/var.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/vector_field.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/where.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/window.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/wrap.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ycbcr_rgb.cpp
+    )
+
+if(FreeImage_FOUND AND WITH_IMAGEIO)
+    target_compile_definitions(c_api_interface INTERFACE WITH_FREEIMAGE)
+    target_link_libraries(     c_api_interface INTERFACE FreeImage::FreeImage)
+endif()
+
+if(BUILD_GRAPHICS)
+    add_dependencies(c_api_interface forge-ext)
+    target_compile_definitions(c_api_interface INTERFACE WITH_GRAPHICS)
+endif()
+
+target_include_directories(c_api_interface
+  INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${CMAKE_SOURCE_DIR}/src/backend
+    ${CMAKE_SOURCE_DIR}/include
+    $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
+    )
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index e77458d59c..daf45f7eb9 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/signal.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <approx.hpp>
 
 using af::dim4;
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 661714ca04..60f514e9ff 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <handle.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <platform.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
@@ -19,24 +19,6 @@
 using namespace detail;
 using common::SparseArrayBase;
 
-const ArrayInfo&
-getInfo(const af_array arr, bool sparse_check, bool device_check)
-{
-    const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
-
-    // Check Sparse -> If false, then both standard Array<T> and SparseArray<T> are accepted
-    // Otherwise only regular Array<T> is accepted
-    if(sparse_check) {
-        ARG_ASSERT(0, info->isSparse() == false);
-    }
-
-    if (device_check && info->getDevId() != detail::getActiveDeviceId()) {
-        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
-    }
-
-    return *info;
-}
-
 af_err af_get_data_ptr(void *data, const af_array arr)
 {
     try {
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 1595ceeb1d..0164457913 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -11,8 +11,8 @@
 #include <af/defines.h>
 #include <af/index.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <Array.hpp>
diff --git a/src/api/c/bilateral.cpp b/src/api/c/bilateral.cpp
index 8c2cfe2ca5..9bf70a7516 100644
--- a/src/api/c/bilateral.cpp
+++ b/src/api/c/bilateral.cpp
@@ -13,7 +13,7 @@
 #include <handle.hpp>
 #include <backend.hpp>
 #include <bilateral.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 using af::dim4;
 using namespace detail;
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 930141b092..f0efc1ba51 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -11,10 +11,10 @@
 #include <af/defines.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <implicit.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <sparse_handle.hpp>
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 8a3d6f63f6..e2e5ddb98c 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -13,10 +13,10 @@
 #include <Array.hpp>
 #include <af/array.h>
 #include <af/defines.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <sparse_handle.hpp>
 #include <sparse_blas.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 
 template<typename T>
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index b461355378..3ab8a5184b 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/image.h>
 #include <af/seq.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index f15783dcaa..ba4f52940c 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -12,11 +12,11 @@
 #include <af/arith.h>
 #include <sparse_handle.hpp>
 #include <sparse.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 
 #include <cast.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index 88a4bceb00..94421a45d1 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <cholesky.hpp>
 
 using af::dim4;
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index 8235fc970a..f6cef2e8e1 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -11,10 +11,10 @@
 #include <af/defines.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <implicit.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 
diff --git a/src/api/c/colorspace.cpp b/src/api/c/colorspace.cpp
index eb5b722638..7f04ef0fba 100644
--- a/src/api/c/colorspace.cpp
+++ b/src/api/c/colorspace.cpp
@@ -10,7 +10,7 @@
 #include <af/defines.h>
 #include <af/array.h>
 #include <af/image.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 template<af_cspace_t FROM, af_cspace_t TO>
 void color_space(af_array *out, const af_array image)
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index ba377d6a9a..063761aec1 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -11,10 +11,10 @@
 #include <af/defines.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <implicit.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 6fc99bd2a8..7e5b56fc8c 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -14,7 +14,7 @@
 #include <arith.hpp>
 #include <cast.hpp>
 #include <tile.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <convolve.hpp>
 #include <fftconvolve.hpp>
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index 9f3292339c..a41d9a07dc 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <af/statistics.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <reduce.hpp>
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 980a18836f..f8c4ad80f6 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -14,7 +14,7 @@
 #include <af/device.h>
 #include <af/util.h>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <math.hpp>
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index 92721b8588..72587b3d35 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <math.hpp>
 #include <lu.hpp>
 #include <diagonal.hpp>
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 996846a0a4..ddf69b3f93 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -16,7 +16,7 @@
 #include <Array.hpp>
 #include <handle.hpp>
 #include <sparse_handle.hpp>
-#include "err_common.hpp"
+#include <common/err_common.hpp>
 #include <cstring>
 
 using namespace detail;
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index bd6996a6c4..e8575176c1 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -9,10 +9,10 @@
 
 #include <af/algorithm.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <diff.hpp>
 
 using af::dim4;
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index 3e5fe6c264..fffdff7d0a 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/vision.h>
 #include <af/image.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <convolve.hpp>
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 99d99803fd..8afb0f02d3 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -9,7 +9,7 @@
 
 #include <af/exception.h>
 #include <af/device.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <string>
 #include <algorithm>
 
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index edc9ac77ac..31940de1c1 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -14,7 +14,7 @@
 #include <af/defines.h>         // Include this header to access any enums,
                                 // #defines or constants declared
 
-#include <err_common.hpp>       // Header with error checking functions & macros
+#include <common/err_common.hpp>       // Header with error checking functions & macros
 
 #include <backend.hpp>          // This header make sures appropriate backend
                                 // related namespace is being used
@@ -57,7 +57,7 @@ af_err af_example_function(af_array* out, const af_array a, const af_someenum_t
                                             // This class stores the basic array meta-data
                                             // such as type of data, dimensions,
                                             // offsets and strides. This class is declared
-                                            // in src/backend/ArrayInfo.hpp
+                                            // in src/backend/common/ArrayInfo.hpp
         af::dim4 dims = info.dims();
 
         ARG_ASSERT(2, (dims.ndims()>=0 && dims.ndims()<=3));
diff --git a/src/api/c/fast.cpp b/src/api/c/fast.cpp
index f72d9946e1..172349afd1 100644
--- a/src/api/c/fast.cpp
+++ b/src/api/c/fast.cpp
@@ -12,7 +12,7 @@
 #include <af/features.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <features.hpp>
 #include <fast.hpp>
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 93e78ea042..a04529ae0f 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/signal.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <fft_common.hpp>
 
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index cf7b9cc651..5418adbf7f 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -10,11 +10,11 @@
 #include <af/defines.h>
 #include <af/signal.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <arith.hpp>
 #include <fftconvolve.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <complex.hpp>
 #include <fft_common.hpp>
 
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index c4a29afc0e..b0435bb545 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -13,7 +13,7 @@
 #include <af/signal.h>
 #include <af/data.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <medfilt.hpp>
 
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 4d5a4fa152..954fc714f1 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -14,8 +14,8 @@
 #include <af/data.h>
 #include <af/index.h>
 #include <af/seq.h>
-#include <ArrayInfo.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <Array.hpp>
diff --git a/src/api/c/gaussian_kernel.cpp b/src/api/c/gaussian_kernel.cpp
index 981488cf8b..52c410a031 100644
--- a/src/api/c/gaussian_kernel.cpp
+++ b/src/api/c/gaussian_kernel.cpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/image.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <reduce.hpp>
diff --git a/src/api/c/gradient.cpp b/src/api/c/gradient.cpp
index be801187d8..9a679dcf0c 100644
--- a/src/api/c/gradient.cpp
+++ b/src/api/c/gradient.cpp
@@ -9,10 +9,10 @@
 
 #include <af/image.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <gradient.hpp>
 
 using af::dim4;
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 3bbfd94801..683fe18531 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -11,7 +11,7 @@
 #include <af/array.h>
 #include <Array.hpp>
 #include <backend.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <math.hpp>
 #include <copy.hpp>
 #include <cast.hpp>
diff --git a/src/api/c/harris.cpp b/src/api/c/harris.cpp
index 578a3ed48b..1663ff84dc 100644
--- a/src/api/c/harris.cpp
+++ b/src/api/c/harris.cpp
@@ -12,7 +12,7 @@
 #include <af/features.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <features.hpp>
 #include <harris.hpp>
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 0b028daf63..81c8fab0d5 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <af/graphics.h>
-#include <graphics_common.hpp>
-#include <ArrayInfo.hpp>
-#include <err_common.hpp>
+#include <common/graphics_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <reduce.hpp>
 #include <cast.hpp>
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 95936eaca9..fc246ed5ab 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -11,7 +11,7 @@
 #include <af/index.h>
 #include <af/data.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <cast.hpp>
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index 688bebf165..d9803314c4 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -9,7 +9,7 @@
 
 #include <af/dim4.hpp>
 #include <af/image.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <histogram.hpp>
diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index 5992909f4a..5446ab36f9 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -11,10 +11,10 @@
 #include <af/defines.h>
 #include <af/vision.h>
 #include <af/random.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <homography.hpp>
 
 using af::dim4;
diff --git a/src/api/c/hsv_rgb.cpp b/src/api/c/hsv_rgb.cpp
index 7662dd7c83..04385070d1 100644
--- a/src/api/c/hsv_rgb.cpp
+++ b/src/api/c/hsv_rgb.cpp
@@ -11,7 +11,7 @@
 #include <af/dim4.hpp>
 #include <af/image.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <hsv_rgb.hpp>
 
diff --git a/src/api/c/iir.cpp b/src/api/c/iir.cpp
index 95bf3249dc..826c14c1b0 100644
--- a/src/api/c/iir.cpp
+++ b/src/api/c/iir.cpp
@@ -11,7 +11,7 @@
 #include <af/signal.h>
 #include <af/arith.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <convolve.hpp>
 #include <iir.hpp>
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index ccf619affb..0c4c9a0918 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -13,9 +13,9 @@
 #include <af/index.h>
 #include <af/data.h>
 
-#include <ArrayInfo.hpp>
-#include <graphics_common.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/graphics_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <image.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index c953de5713..486953c467 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -20,10 +20,10 @@
 #include <af/data.h>
 #include <af/image.h>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <traits.hpp>
 #include <memory.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 
 #include <string>
@@ -752,7 +752,7 @@ af_err af_delete_image_memory(void *ptr)
 #else   // WITH_FREEIMAGE
 #include <af/image.h>
 #include <stdio.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 0b8b340679..8b9fa4992c 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -20,10 +20,10 @@
 #include <af/data.h>
 #include <af/image.h>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <traits.hpp>
 #include <memory.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 
 #include <string>
@@ -403,7 +403,7 @@ af_err af_is_image_io_available(bool *out)
 #else   // WITH_FREEIMAGE
 #include <af/image.h>
 #include <stdio.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 af_err af_load_image_native(af_array *out, const char* filename)
 {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
diff --git a/src/api/c/imageio_helper.h b/src/api/c/imageio_helper.h
index eee6a06899..fef22e575e 100644
--- a/src/api/c/imageio_helper.h
+++ b/src/api/c/imageio_helper.h
@@ -15,7 +15,7 @@
 #include <af/array.h>
 #include <af/index.h>
 #include <af/dim4.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 class FI_Manager
 {
diff --git a/src/api/c/implicit.hpp b/src/api/c/implicit.hpp
index d3e455b645..e9f2e806c3 100644
--- a/src/api/c/implicit.hpp
+++ b/src/api/c/implicit.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <af/array.h>
 #include <af/defines.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 07559b1f32..7ce935e76a 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -14,8 +14,8 @@
 #include <af/index.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <Array.hpp>
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index f5cc3972b3..60aa31f346 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -15,7 +15,7 @@
 #include <platform.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
-#include "err_common.hpp"
+#include <common/err_common.hpp>
 #include <cstring>
 
 using namespace detail;
diff --git a/src/api/c/inverse.cpp b/src/api/c/inverse.cpp
index 653c26af2e..21e265e82f 100644
--- a/src/api/c/inverse.cpp
+++ b/src/api/c/inverse.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <inverse.hpp>
 
 using af::dim4;
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 29527c1a8d..2ea2364450 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <af/data.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <join.hpp>
 #include <vector>
 
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index 2928a57cfc..8f73a30ce4 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <lu.hpp>
 
 using af::dim4;
diff --git a/src/api/c/match_template.cpp b/src/api/c/match_template.cpp
index ddaf104f6b..2ce25b905a 100644
--- a/src/api/c/match_template.cpp
+++ b/src/api/c/match_template.cpp
@@ -10,7 +10,7 @@
 #include <af/defines.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <match_template.hpp>
 
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 839703f025..1f6540e97d 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -11,7 +11,7 @@
 #include <af/data.h>
 #include <af/statistics.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <mean.hpp>
diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index 185f2017b5..15f3b7c2bc 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -13,7 +13,7 @@
 #include <handle.hpp>
 #include <backend.hpp>
 #include <meanshift.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 using af::dim4;
 using namespace detail;
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 97adcf6d47..32e9940c1d 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -14,7 +14,7 @@
 #include <af/arith.h>
 #include <af/data.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <sort.hpp>
 #include <math.hpp>
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 48aeb2647d..e983b77483 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -16,7 +16,7 @@
 #include <Array.hpp>
 #include <handle.hpp>
 #include <memory.hpp>
-#include "err_common.hpp"
+#include <common/err_common.hpp>
 #include <cstring>
 
 using namespace detail;
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index dc3158e0f5..f94991d66f 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/data.h>
 #include <backend.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <copy.hpp>
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 6572988a8e..7dd30ed276 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -11,9 +11,9 @@
 #include <af/index.h>
 #include <af/data.h>
 
-#include <ArrayInfo.hpp>
-#include <graphics_common.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/graphics_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <moments.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 67d99bb672..2d4a14b188 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/image.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <morph.hpp>
 
diff --git a/src/api/c/nearest_neighbour.cpp b/src/api/c/nearest_neighbour.cpp
index 587502f4a4..a224bf474b 100644
--- a/src/api/c/nearest_neighbour.cpp
+++ b/src/api/c/nearest_neighbour.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <nearest_neighbour.hpp>
 
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index a2b99f8aaa..dc5b3b76a2 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -12,10 +12,10 @@
 #include <af/defines.h>
 #include <af/traits.hpp>
 #include <af/constants.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <math.hpp>
 #include <arith.hpp>
 #include <lu.hpp>
diff --git a/src/api/c/orb.cpp b/src/api/c/orb.cpp
index 05cc4560c9..cbcdc3d73a 100644
--- a/src/api/c/orb.cpp
+++ b/src/api/c/orb.cpp
@@ -12,7 +12,7 @@
 #include <af/features.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <orb.hpp>
 #include <features.hpp>
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 35b25b307a..85b4c69030 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -11,9 +11,9 @@
 #include <af/image.h>
 #include <af/data.h>
 
-#include <ArrayInfo.hpp>
-#include <graphics_common.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/graphics_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <plot.hpp>
 #include <reduce.hpp>
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index dc3ad834c5..f3cae154ca 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -17,8 +17,8 @@
 #include <af/internal.h>
 #include <copy.hpp>
 #include <print.hpp>
-#include <ArrayInfo.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <type_util.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index 546b37d92d..78252130a8 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <qr.hpp>
 
 using af::dim4;
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 0c392abbd0..c79019e77d 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -14,10 +14,10 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <backend.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <random_engine.hpp>
-#include <MersenneTwister.hpp>
+#include <common/MersenneTwister.hpp>
 #include <types.hpp>
 #include <memory>
 
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index dfd122dabb..2a752c3f65 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <qr.hpp>
 #include <reduce.hpp>
 #include <logic.hpp>
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 6cfe3d2a66..4251d33f9b 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/algorithm.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <ops.hpp>
 #include <backend.hpp>
diff --git a/src/api/c/regions.cpp b/src/api/c/regions.cpp
index 0b5fd52425..ee1f0593e1 100644
--- a/src/api/c/regions.cpp
+++ b/src/api/c/regions.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/image.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <regions.hpp>
 
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 5f97c27185..3a06b4c4c8 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -9,10 +9,10 @@
 
 #include <af/data.h>
 #include <af/blas.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <reorder.hpp>
 #include <transpose.hpp>
 
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index f2c8066c1f..fcb8f48b6f 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -10,10 +10,10 @@
 #include <af/defines.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <implicit.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 
diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index 2f0c9f23e9..bbd5a37784 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/image.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <resize.hpp>
 
 using af::dim4;
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index c7255a896e..ba69f25965 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -13,7 +13,7 @@
 #include <af/index.h>
 #include <af/data.h>
 
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <arith.hpp>
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index 72af356d6b..ac85eb3fa3 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <af/image.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <backend.hpp>
 #include <rotate.hpp>
 
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index 05bac43f93..5fde3eeb32 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -10,7 +10,7 @@
 #include <af/defines.h>
 #include <af/image.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <scan.hpp>
 
 using af::dim4;
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 31811142ca..f0f35332d1 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/algorithm.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <ops.hpp>
 #include <scan.hpp>
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 859eb0897c..876666457e 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -10,10 +10,10 @@
 #include <af/defines.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <implicit.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <select.hpp>
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index a8dc942050..63363b8fa8 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -10,7 +10,7 @@
 #include <complex>
 #include <af/defines.h>
 #include <af/algorithm.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <set.hpp>
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index c988b0d7fe..98aa0eacac 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <af/data.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <shift.hpp>
 
 using af::dim4;
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index c9fd065386..599354e265 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -12,7 +12,7 @@
 #include <af/features.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <features.hpp>
 #include <sift.hpp>
diff --git a/src/api/c/sobel.cpp b/src/api/c/sobel.cpp
index f9c0879260..2826756904 100644
--- a/src/api/c/sobel.cpp
+++ b/src/api/c/sobel.cpp
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 #include <af/image.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <sobel.hpp>
 #include <utility>
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index b42f3f4f1e..0e00183289 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/lapack.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <solve.hpp>
 
 using af::dim4;
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 46395bfec9..eb6bb67542 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -10,10 +10,10 @@
 #include <af/array.h>
 #include <af/algorithm.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <sort.hpp>
 #include <sort_index.hpp>
 #include <sort_by_key.hpp>
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index bef869bdd9..d1941aad25 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -14,7 +14,7 @@
 #include <sparse.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <arith.hpp>
 #include <lookup.hpp>
 #include <platform.hpp>
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 6ca348454a..19dedb3ba2 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -11,14 +11,14 @@
 #include <af/array.h>
 #include <Array.hpp>
 #include <backend.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <math.hpp>
 #include <copy.hpp>
 #include <cast.hpp>
 #include <handle.hpp>
 #include <af/dim4.hpp>
 
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 
 const common::SparseArrayBase& getSparseArrayBase(const af_array arr, bool device_check = true);
 
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index 2dfd0b72fb..fe331562f2 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -13,10 +13,10 @@
 #include <vector>
 
 #include <af/array.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <type_util.hpp>
 
 #include <af/index.h>
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index bbbfd80a38..ef461a4a78 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -10,9 +10,9 @@
 #include <af/graphics.h>
 #include <af/image.h>
 
-#include <ArrayInfo.hpp>
-#include <graphics_common.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/graphics_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <surface.hpp>
 #include <reduce.hpp>
diff --git a/src/api/c/susan.cpp b/src/api/c/susan.cpp
index fcac91227d..8ccee8ed16 100644
--- a/src/api/c/susan.cpp
+++ b/src/api/c/susan.cpp
@@ -12,7 +12,7 @@
 #include <af/features.h>
 #include <af/vision.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <features.hpp>
 #include <susan.hpp>
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index ec543c6386..43346a90b1 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -12,7 +12,7 @@
 #include <af/lapack.h>
 
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <Array.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 34c09710ae..8dce987138 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -10,10 +10,10 @@
 #include <iostream>
 #include <af/data.h>
 #include <af/arith.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <tile.hpp>
 #include <arith.hpp>
 
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 2dd7fbc59c..35cbf9fe77 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -9,9 +9,9 @@
 
 #include <af/image.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <backend.hpp>
 #include <transform.hpp>
 
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index b5b6ee736c..7b090a2f63 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/image.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <convolve.hpp>
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index f29b48d6a5..b75f5628fd 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -12,7 +12,7 @@
 #include <af/blas.h>
 #include <af/data.h>
 #include <af/arith.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <transpose.hpp>
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index 5d2669adf5..f8926797b0 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -9,26 +9,7 @@
 
 #include <af/util.h>
 #include <type_util.hpp>
-#include <err_common.hpp>
-
-const char *getName(af_dtype type)
-{
-    switch(type) {
-    case f32: return "float";
-    case f64: return "double";
-    case c32: return "complex float";
-    case c64: return "complex double";
-    case u32: return "unsigned int";
-    case s32: return "int";
-    case u16: return "unsigned short";
-    case s16: return "short";
-    case u64: return "unsigned long long";
-    case s64: return "long long";
-    case u8 : return "unsigned char";
-    case b8 : return "bool";
-    default : return "unknown type";
-    }
-}
+#include <common/err_common.hpp>
 
 size_t size_of(af_dtype type)
 {
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index fa07354a0b..396efac8bd 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -17,10 +17,10 @@
 #include <af/defines.h>
 #include <af/arith.h>
 #include <af/data.h>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
 #include <implicit.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <unary.hpp>
diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index 4bca08dae8..e9fd3dd7c5 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -9,10 +9,10 @@
 
 #include <af/image.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <unwrap.hpp>
 
 using af::dim4;
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 69a67b282b..16ddde829e 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <af/statistics.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <mean.hpp>
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index d3c303c770..2b1f1fe5b1 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -10,9 +10,9 @@
 #include <af/graphics.h>
 #include <af/data.h>
 
-#include <ArrayInfo.hpp>
-#include <graphics_common.hpp>
-#include <err_common.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/graphics_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <handle.hpp>
 #include <join.hpp>
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 45ad458ab7..4e663a2bf7 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -10,7 +10,7 @@
 #include <complex>
 #include <af/dim4.hpp>
 #include <af/algorithm.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <ops.hpp>
 #include <where.hpp>
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index a8cc4316f3..897dd4546f 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -11,8 +11,8 @@
 #include <af/graphics.h>
 #include <af/algorithm.h>
 
-#include <graphics_common.hpp>
-#include <err_common.hpp>
+#include <common/graphics_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 
 using af::dim4;
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index 188196d3a3..cd0baf2fb9 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -9,10 +9,10 @@
 
 #include <af/image.h>
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <wrap.hpp>
 
 using af::dim4;
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index b30f5eb171..9d1b357e0a 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -11,7 +11,7 @@
 #include <af/dim4.hpp>
 #include <af/image.h>
 #include <handle.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <arith.hpp>
 #include <join.hpp>
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
new file mode 100644
index 0000000000..7d6bc5b131
--- /dev/null
+++ b/src/api/cpp/CMakeLists.txt
@@ -0,0 +1,89 @@
+
+add_library(cpp_api_interface INTERFACE)
+
+target_sources(cpp_api_interface
+  INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/common.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/error.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/approx.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/canny.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/clamp.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/colorspace.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/complex.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/convolve.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/corrcoef.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/covariance.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/diff.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/dog.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/exampleFunction.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/exception.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/features.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/fftconvolve.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/filters.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/gaussian_kernel.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/gfor.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/gradient.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/graphics.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/hamming.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/harris.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/histogram.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/homography.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/hsv_rgb.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/iir.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/imageio.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/matchTemplate.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/mean.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/meanshift.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/median.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/morph.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/nearest_neighbour.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/orb.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/regions.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/resize.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/rgb_gray.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/rotate.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sat.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/scale.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/seq.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/set.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sift.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/skew.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sobel.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sparse.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/stdev.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/susan.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/timing.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/transform.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/transform_coordinates.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/translate.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/transpose.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/unary.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/unwrap.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/var.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/where.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/wrap.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ycbcr_rgb.cpp
+)
+
+
+target_include_directories(cpp_api_interface
+  INTERFACE
+    ${CMAKE_SOURCE_DIR}/src/api/c
+)
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index c888db8646..c3cb4fdf57 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -9,7 +9,7 @@
 
 #include <af/exception.h>
 #include <af/device.h>
-#include <defines.hpp>
+#include <common/defines.hpp>
 
 #define AF_THROW(fn) do {                               \
         af_err __err = fn;                              \
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index c9c87409c1..fc17be088b 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -1,78 +1,95 @@
 
-FILE(GLOB unified_headers
-    "*.hpp"
-    "*.h")
 
-FILE(GLOB unified_sources
-    "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp")
-
-LIST(SORT unified_headers)
-LIST(SORT unified_sources)
-
-SOURCE_GROUP(api\\unified\\Headers FILES ${unified_headers})
-SOURCE_GROUP(api\\unified\\Sources FILES ${unified_sources})
-
-FILE(GLOB cpp_sources
-    "../cpp/*.cpp")
-
-LIST(SORT cpp_sources)
-
-SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
-
-FILE(GLOB common_sources
-  "../c/version.cpp"
-  "../c/err_common.cpp"
-  "../c/type_util.cpp"
-  "../../backend/dim4.cpp"
-  "../../backend/util.cpp"
+add_library(af "")
+add_library(ArrayFire::af ALIAS af)
+
+target_sources(af
+  PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}/algorithm.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/arith.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/features.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/graphics.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/image.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/signal.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sparse.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/statistics.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/symbol_manager.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/symbol_manager.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/vision.cpp
   )
 
-LIST(SORT common_sources)
-
-SOURCE_GROUP(common FILES ${common_sources})
-
-IF(NOT UNIX)
-    ADD_DEFINITIONS(-DAFDLL)
-ENDIF()
 
-# OS Definitions
-IF(UNIX)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
-ENDIF()
-
-ADD_LIBRARY(af SHARED
-            ${unified_headers}
-            ${unified_sources}
-            ${common_sources}
-            ${cpp_sources}
-            )
-
-IF(${BUILD_CPU})
-    ADD_DEPENDENCIES(af afcpu)
-ENDIF()
-
-IF(${BUILD_CUDA})
-    ADD_DEPENDENCIES(af afcuda)
-ENDIF()
+target_sources(af
+  PRIVATE
+    ${CMAKE_SOURCE_DIR}/src/api/c/version.cpp
+    ${CMAKE_SOURCE_DIR}/src/api/c/type_util.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/dim4.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/err_common.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/constants.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/util.hpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/util.cpp
+  )
 
-IF(${BUILD_OPENCL})
-    ADD_DEPENDENCIES(af afopencl)
-ENDIF()
+target_compile_definitions(af
+  PRIVATE
+    AFDLL
+  )
 
-TARGET_LINK_LIBRARIES(af ${CMAKE_DL_LIBS})
+target_include_directories(af
+  PUBLIC
+    $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
+    $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
+  PRIVATE
+    ${ArrayFire_SOURCE_DIR}/src/api/c
+    $<JOIN:$<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>, >
+    ${CMAKE_BINARY_DIR}
+  )
 
-SET_TARGET_PROPERTIES(af PROPERTIES
-                      VERSION "${AF_VERSION}"
-                      SOVERSION "${AF_VERSION_MAJOR}")
 
-INSTALL(TARGETS af EXPORT AF DESTINATION "${AF_INSTALL_LIB_DIR}"
-        COMPONENT libraries)
+target_link_libraries(af
+  PRIVATE
+    cpp_api_interface
+    Threads::Threads
+    ${CMAKE_DL_LIBS}
+  )
 
-IF(APPLE)
-    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
-ENDIF(APPLE)
+install(TARGETS af
+  EXPORT ArrayFireUnifiedTargets
+  COMPONENT unified
+  PUBLIC_HEADER DESTINATION af
+  RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
+  LIBRARY DESTINATION ${AF_INSTALL_LIB_DIR}
+  ARCHIVE DESTINATION ${AF_INSTALL_LIB_DIR}
+  FRAMEWORK DESTINATION framework
+  INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
+  )
 
-EXPORT(TARGETS af FILE ArrayFireUnified.cmake)
-INSTALL(EXPORT AF DESTINATION "${AF_INSTALL_CMAKE_DIR}"
-        COMPONENT cmake
-        FILE ArrayFireUnified.cmake)
+# install(TARGETS af EXPORT AF DESTINATION "${AF_INSTALL_LIB_DIR}"
+#   COMPONENT libraries)
+#
+# if(APPLE)
+#   INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
+# endif(APPLE)
+#
+# export(TARGETS af FILE ArrayFireUnified.cmake)
+# install(EXPORT AF DESTINATION "${AF_INSTALL_CMAKE_DIR}"
+#   COMPONENT cmake
+#   FILE ArrayFireUnified.cmake)
+
+source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
+source_group(source REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/*|${ArrayFire_SOURCE_DIR}/src/backend/common/*)
+source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
+source_group(api\\c REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
+source_group("" FILES CMakeLists.txt)
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 8a599d6ab4..fcd3c16d2e 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -9,10 +9,8 @@
 #pragma once
 
 #include <af/defines.h>
-#include <string>
-#include <stdlib.h>
-#include <util.hpp>
-#include <err_common.hpp>
+#include <common/util.hpp>
+#include <common/err_common.hpp>
 
 #if defined(OS_WIN)
 #include <Windows.h>
@@ -23,6 +21,8 @@ typedef void* LibHandle;
 #endif
 
 #include <array>
+#include <cstdlib>
+#include <string>
 #include <unordered_map>
 
 namespace unified
diff --git a/src/backend/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
similarity index 89%
rename from src/backend/ArrayInfo.cpp
rename to src/backend/common/ArrayInfo.cpp
index d2430c20eb..ae1f733e82 100644
--- a/src/backend/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <numeric>
 #include <algorithm>
 #include <functional>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <backend.hpp>
 #include <platform.hpp>
@@ -232,3 +232,21 @@ toStride(const vector<af_seq>& seqs, const af::dim4 &parentDims)
     }
     return out;
 }
+
+const ArrayInfo&
+getInfo(const af_array arr, bool sparse_check, bool device_check)
+{
+  const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
+
+  // Check Sparse -> If false, then both standard Array<T> and SparseArray<T> are accepted
+  // Otherwise only regular Array<T> is accepted
+  if(sparse_check) {
+    ARG_ASSERT(0, info->isSparse() == false);
+  }
+
+  if (device_check && info->getDevId() != detail::getActiveDeviceId()) {
+    AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+  }
+
+  return *info;
+}
diff --git a/src/backend/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
similarity index 98%
rename from src/backend/ArrayInfo.hpp
rename to src/backend/common/ArrayInfo.hpp
index aefd53775e..3d9af5205a 100644
--- a/src/backend/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <af/dim4.hpp>
 #include <af/device.h>
 #include <vector>
@@ -56,7 +56,6 @@ class ArrayInfo
         dim_strides(stride),
         is_sparse(false)
     {
-        af_init();
         setId(id);
 #if __cplusplus > 199711l
     static_assert(offsetof(ArrayInfo, devId) == 0,
@@ -74,7 +73,6 @@ class ArrayInfo
         dim_strides(stride),
         is_sparse(sparse)
     {
-        af_init();
         setId(id);
 #if __cplusplus > 199711l
     static_assert(offsetof(ArrayInfo, devId) == 0,
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
new file mode 100644
index 0000000000..9f851c00a6
--- /dev/null
+++ b/src/backend/common/CMakeLists.txt
@@ -0,0 +1,93 @@
+
+
+add_library(afcommon_interface INTERFACE)
+
+target_sources(afcommon_interface
+  INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/MatrixAlgebraHandle.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/cblas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/constants.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/defines.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/dim4.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/dispatch.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/dispatch.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/err_common.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/err_common.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/util.hpp
+    ${PROJECT_BINARY_DIR}/version.hpp
+  )
+
+target_include_directories(afcommon_interface
+  INTERFACE
+    ${CMAKE_SOURCE_DIR}/src/backend
+    ${PROJECT_BINARY_DIR}
+  )
+
+add_library(afcommon_lapack_interface INTERFACE)
+
+if(BUILD_GRAPHICS)
+  dependency_check(glbinding_FOUND "glbinding not found.")
+
+  target_include_directories(afcommon_interface
+    INTERFACE
+      ${Forge_INCLUDE_DIR}
+    )
+  target_sources(afcommon_interface
+    INTERFACE
+      ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.cpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.cpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.hpp
+      )
+
+  target_link_libraries(afcommon_interface
+    INTERFACE
+      OpenGL::GL
+      ${Forge_LIBRARIES})
+
+    target_compile_definitions(afcommon_interface INTERFACE WITH_GRAPHICS)
+
+  if(APPLE)
+    # TODO: On APPLE platform linking directly against glbinding brings in flags
+    # that causes issues when building ArrayFire with LAPACK and Graphics. This
+    # was due to the way glbinding was brining in some Framework flags which
+    # cause issues with the Accelerate Framework. This is probably a bug in
+    # glbindings cmake file
+    target_link_libraries(afcommon_interface
+      INTERFACE
+        $<TARGET_PROPERTY:glbinding::glbinding,IMPORTED_LOCATION_RELEASE>)
+  else()
+    target_link_libraries(afcommon_interface
+      INTERFACE
+      glbinding::glbinding)
+  endif()
+
+  add_dependencies(afcommon_interface forge-ext)
+endif()
+
+if(LAPACK_FOUND)
+  target_sources(afcommon_lapack_interface
+    INTERFACE
+      ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.cpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.hpp
+    )
+
+  target_include_directories(afcommon_lapack_interface
+    INTERFACE ${LAPACK_INCLUDE_DIR})
+
+  target_link_libraries(afcommon_lapack_interface
+    INTERFACE
+      ${LAPACK_LIBRARIES})
+endif()
diff --git a/src/backend/common/InteropManager.cpp b/src/backend/common/InteropManager.cpp
index 7c1ae3bc07..a0375b1a4d 100644
--- a/src/backend/common/InteropManager.cpp
+++ b/src/backend/common/InteropManager.cpp
@@ -12,10 +12,9 @@
 //InteropManager.hpp is not used while building CPU backend.
 #ifndef AF_CPU
 #include <common/InteropManager.hpp>
-#include <common/types.hpp>
 #include <cstdio>
-#include <err_common.hpp>
-#include <util.hpp>
+#include <common/err_common.hpp>
+#include <common/util.hpp>
 #include <backend.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <platform.hpp>
diff --git a/src/backend/common/MatrixAlgebraHandle.hpp b/src/backend/common/MatrixAlgebraHandle.hpp
index bf4f6c1110..bc3c55de59 100644
--- a/src/backend/common/MatrixAlgebraHandle.hpp
+++ b/src/backend/common/MatrixAlgebraHandle.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <cstdio>
 
 namespace common
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index b0cb8a78d6..5077c27fab 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -9,9 +9,9 @@
 
 #pragma once
 
-#include <err_common.hpp>
-#include <dispatch.hpp>
-#include <util.hpp>
+#include <common/err_common.hpp>
+#include <common/dispatch.hpp>
+#include <common/util.hpp>
 
 #include <algorithm>
 
diff --git a/src/backend/MersenneTwister.hpp b/src/backend/common/MersenneTwister.hpp
similarity index 100%
rename from src/backend/MersenneTwister.hpp
rename to src/backend/common/MersenneTwister.hpp
diff --git a/src/backend/SparseArray.cpp b/src/backend/common/SparseArray.cpp
similarity index 99%
rename from src/backend/SparseArray.cpp
rename to src/backend/common/SparseArray.cpp
index bf647a96ed..2c1d3be80d 100644
--- a/src/backend/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <backend.hpp>
 #include <copy.hpp>
 #include <math.hpp>
diff --git a/src/backend/SparseArray.hpp b/src/backend/common/SparseArray.hpp
similarity index 99%
rename from src/backend/SparseArray.hpp
rename to src/backend/common/SparseArray.hpp
index ade77cc22d..b1c430e869 100644
--- a/src/backend/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -8,12 +8,13 @@
  ********************************************************/
 
 #pragma once
-#include <sparse_helpers.hpp>
-#include <ArrayInfo.hpp>
+#include <common/sparse_helpers.hpp>
+#include <common/ArrayInfo.hpp>
 #include <Array.hpp>
 #include <backend.hpp>
-#include <vector>
+
 #include <cstddef>
+#include <vector>
 
 namespace common
 {
diff --git a/src/backend/common/blas_headers.hpp b/src/backend/common/blas_headers.hpp
new file mode 100644
index 0000000000..236bd21298
--- /dev/null
+++ b/src/backend/common/blas_headers.hpp
@@ -0,0 +1,38 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#ifdef USE_MKL
+    #include <mkl_cblas.h>
+#else
+    #ifdef __APPLE__
+        #include <Accelerate/Accelerate.h>
+    #else
+        extern "C" {
+            #include <cblas.h>
+        }
+    #endif
+#endif
+
+// TODO: Ask upstream for a more official way to detect it
+#ifdef OPENBLAS_CONST
+    #define IS_OPENBLAS
+#endif
+
+// Make sure we get the correct type signature for OpenBLAS
+// OpenBLAS defines blasint as it's index type. Emulate this
+// if we're not dealing with openblas and use it where applicable
+#ifdef IS_OPENBLAS
+    // blasint already defined
+    static const bool cplx_void_ptr = false;
+#else
+    using blasint = int;
+    static const bool cplx_void_ptr = true;
+#endif
diff --git a/src/backend/cblas.cpp b/src/backend/common/cblas.cpp
similarity index 88%
rename from src/backend/cblas.cpp
rename to src/backend/common/cblas.cpp
index 1be15e47c9..8f8e3434b2 100644
--- a/src/backend/cblas.cpp
+++ b/src/backend/common/cblas.cpp
@@ -8,31 +8,7 @@
 ********************************************************/
 
 #ifdef USE_F77_BLAS
-
-#ifdef AF_CPU
-    #include <blas.hpp>
-#else
-    #ifdef USE_MKL
-        #include <mkl_cblas.h>
-    #else
-        #ifdef __APPLE__
-            #include <Accelerate/Accelerate.h>
-        #else
-            extern "C" {
-                #include <cblas.h>
-            }
-        #endif
-    #endif
-
-    // TODO: Ask upstream for a more official way to detect it
-    #ifdef OPENBLAS_CONST
-        #define IS_OPENBLAS
-    #endif
-
-    #ifndef IS_OPENBLAS
-        typedef int blasint;
-    #endif
-#endif
+#include <common/blas_headers.hpp>
 
 #define ADD_
 #include <cblas.h>
diff --git a/src/api/cpp/constants.cpp b/src/backend/common/constants.cpp
similarity index 100%
rename from src/api/cpp/constants.cpp
rename to src/backend/common/constants.cpp
diff --git a/src/backend/defines.hpp b/src/backend/common/defines.hpp
similarity index 100%
rename from src/backend/defines.hpp
rename to src/backend/common/defines.hpp
diff --git a/src/backend/dim4.cpp b/src/backend/common/dim4.cpp
similarity index 99%
rename from src/backend/dim4.cpp
rename to src/backend/common/dim4.cpp
index 024e3595f1..0ffd21cf5d 100644
--- a/src/backend/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -12,7 +12,7 @@
 #include <cmath>
 #include <cfloat>
 #include <af/dim4.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 namespace af
 {
diff --git a/src/backend/dispatch.cpp b/src/backend/common/dispatch.cpp
similarity index 100%
rename from src/backend/dispatch.cpp
rename to src/backend/common/dispatch.cpp
diff --git a/src/backend/dispatch.hpp b/src/backend/common/dispatch.hpp
similarity index 100%
rename from src/backend/dispatch.hpp
rename to src/backend/common/dispatch.hpp
diff --git a/src/api/c/err_common.cpp b/src/backend/common/err_common.cpp
similarity index 98%
rename from src/api/c/err_common.cpp
rename to src/backend/common/err_common.cpp
index dce29e22a1..3c40dcd0f5 100644
--- a/src/api/c/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -9,17 +9,18 @@
 
 #include <af/exception.h>
 #include <af/device.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <type_util.hpp>
-#include <util.hpp>
-#include <string>
-#include <sstream>
-#include <cstring>
-#include <cstdio>
+#include <common/util.hpp>
+
 #include <algorithm>
+#include <cstdio>
+#include <cstring>
+#include <sstream>
+#include <string>
 
 #if defined(WITH_GRAPHICS) && !defined(AF_UNIFIED)
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #endif
 
 #ifdef AF_OPENCL
diff --git a/src/api/c/err_common.hpp b/src/backend/common/err_common.hpp
similarity index 99%
rename from src/api/c/err_common.hpp
rename to src/backend/common/err_common.hpp
index a0da16c3d5..bc76c106df 100644
--- a/src/api/c/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -9,12 +9,13 @@
 
 #pragma once
 
+#include <af/defines.h>
+#include <common/defines.hpp>
+
+#include <cassert>
+#include <sstream>
 #include <stdexcept>
 #include <string>
-#include <sstream>
-#include <cassert>
-#include <af/defines.h>
-#include <defines.hpp>
 #include <vector>
 
 class AfError   : public std::logic_error
diff --git a/src/api/c/graphics_common.cpp b/src/backend/common/graphics_common.cpp
similarity index 93%
rename from src/api/c/graphics_common.cpp
rename to src/backend/common/graphics_common.cpp
index fe17094c51..3cbc83b7a2 100644
--- a/src/api/c/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -9,12 +9,12 @@
 
 #if defined(WITH_GRAPHICS)
 
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <glbinding/Meta.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <backend.hpp>
 #include <platform.hpp>
-#include <util.hpp>
+#include <common/util.hpp>
 #include <mutex>
 #include <utility>
 
@@ -382,12 +382,6 @@ forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
 forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtype dtype,
                                    forge::PlotType ptype, forge::MarkerType mtype)
 {
-    /* nPoints needs to fall in the range of [0, 2^48]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Plot object. So, this implementation
-     * is a limitation on how big of an plot graph can be rendered
-     * using arrayfire graphics funtionality */
-    assert(nPoints <= 2ll<<48);
     long long key = ((nPoints & _48BIT) << 48);
     key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
 
@@ -408,12 +402,6 @@ forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtyp
 
 forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, forge::dtype type)
 {
-    /* nBins needs to fall in the range of [0, 2^48]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Histogram object. So, this implementation
-     * is a limitation on how big of an histogram data can be rendered
-     * using arrayfire graphics funtionality */
-    assert(nBins <= 2ll<<48);
     long long key = ((nBins & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
@@ -441,7 +429,7 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
      * the necessary Forge Plot object. So, this implementation
      * is a limitation on how big of an plot graph can be rendered
      * using arrayfire graphics funtionality */
-    assert(nX * nY <= 2ll<<48);
+    assert((long long)nX * nY <= 2ll<<48);
     long long key = (((nX * nY) & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
@@ -464,12 +452,6 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
 
 forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoints, forge::dtype type)
 {
-    /* nPoints needs to fall in the range of [0, 2^48]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Vector Field object. So, this implementation
-     * is a limitation on how big of an plot graph can be rendered
-     * using arrayfire graphics funtionality */
-    assert(nPoints <= 2ll<<48);
     long long key = (((nPoints) & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
diff --git a/src/api/c/graphics_common.hpp b/src/backend/common/graphics_common.hpp
similarity index 100%
rename from src/api/c/graphics_common.hpp
rename to src/backend/common/graphics_common.hpp
diff --git a/src/backend/host_memory.cpp b/src/backend/common/host_memory.cpp
similarity index 100%
rename from src/backend/host_memory.cpp
rename to src/backend/common/host_memory.cpp
diff --git a/src/backend/host_memory.hpp b/src/backend/common/host_memory.hpp
similarity index 100%
rename from src/backend/host_memory.hpp
rename to src/backend/common/host_memory.hpp
diff --git a/src/backend/lapacke.cpp b/src/backend/common/lapacke.cpp
similarity index 99%
rename from src/backend/lapacke.cpp
rename to src/backend/common/lapacke.cpp
index 1cb3856364..a381831deb 100644
--- a/src/backend/lapacke.cpp
+++ b/src/backend/common/lapacke.cpp
@@ -8,11 +8,12 @@
 ********************************************************/
 
 #if defined(__APPLE__) && !defined(AF_CUDA)
+#include <common/lapacke.hpp>
 #include <Accelerate/Accelerate.h>
-#include "lapacke.hpp"
-#include <cstdint>
-#include <algorithm>
 #include <traits.hpp>
+
+#include <algorithm>
+#include <cstdint>
 #include <vector>
 
 #if INTPTR_MAX == INT16MAX
diff --git a/src/backend/lapacke.hpp b/src/backend/common/lapacke.hpp
similarity index 100%
rename from src/backend/lapacke.hpp
rename to src/backend/common/lapacke.hpp
diff --git a/src/backend/sparse_helpers.hpp b/src/backend/common/sparse_helpers.hpp
similarity index 100%
rename from src/backend/sparse_helpers.hpp
rename to src/backend/common/sparse_helpers.hpp
diff --git a/src/backend/common/types.hpp b/src/backend/common/types.hpp
deleted file mode 100644
index 9d08b04d48..0000000000
--- a/src/backend/common/types.hpp
+++ /dev/null
@@ -1,17 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <mutex>
-
-namespace common
-{
-typedef std::recursive_mutex mutex_t;
-typedef std::lock_guard<mutex_t> lock_guard_t;
-}
diff --git a/src/backend/util.cpp b/src/backend/common/util.cpp
similarity index 65%
rename from src/backend/util.cpp
rename to src/backend/common/util.cpp
index 7c4cd2e614..2579197766 100644
--- a/src/backend/util.cpp
+++ b/src/backend/common/util.cpp
@@ -15,6 +15,8 @@
 #include <Windows.h>
 #endif
 
+#include <af/defines.h>
+
 using std::string;
 
 string getEnvVar(const std::string &key)
@@ -35,3 +37,22 @@ string getEnvVar(const std::string &key)
     return str==NULL ? string("") : string(str);
 #endif
 }
+
+const char *getName(af_dtype type)
+{
+  switch(type) {
+  case f32: return "float";
+  case f64: return "double";
+  case c32: return "complex float";
+  case c64: return "complex double";
+  case u32: return "unsigned int";
+  case s32: return "int";
+  case u16: return "unsigned short";
+  case s16: return "short";
+  case u64: return "unsigned long long";
+  case s64: return "long long";
+  case u8 : return "unsigned char";
+  case b8 : return "bool";
+  default : return "unknown type";
+  }
+}
diff --git a/src/backend/util.hpp b/src/backend/common/util.hpp
similarity index 100%
rename from src/backend/util.hpp
rename to src/backend/common/util.hpp
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 931ee2e126..0d1df29945 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <kernel/Array.hpp>
@@ -17,6 +17,7 @@
 #include <memory.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+
 #include <cstring>
 #include <cstddef>
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2a213940ba..e8945c7976 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -10,7 +10,7 @@
 //This is the array implementation class.
 #pragma once
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <backend.hpp>
 #include <types.hpp>
 #include <traits.hpp>
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index ac23489b08..86f10dfb1d 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -1,249 +1,344 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-ADD_DEFINITIONS(-DAF_CPU)
+include(InternalUtils)
 
-FIND_PACKAGE(CBLAS REQUIRED)
+dependency_check(FFTW_FOUND "FFTW not found")
+dependency_check(CBLAS_FOUND "CBLAS not found")
 
-IF(NOT DEFINED BUILD_CPU_ASYNC)
-    CMAKE_POLICY(PUSH)
-    # https://cmake.org/cmake/help/v3.1/policy/CMP0054.html
-    IF("${CMAKE_VERSION}" VERSION_GREATER "3.1" OR "${CMAKE_VERSION}" VERSION_EQUAL "3.1")
-        CMAKE_POLICY(SET CMP0054 OLD)
-    ENDIF()
+add_library(afcpu "")
+add_library(ArrayFire::afcpu ALIAS afcpu)
 
-    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.4")
-        MESSAGE("Disabling CPU Async as GCC Version ${COMPILER_VERSION} has known issues.")
-        MESSAGE("CPU Backend will use Synchronous Calls")
-        OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" OFF)
-    ELSE()
-        OPTION(BUILD_CPU_ASYNC "Build CPU backend with ASYNC support" ON)
-    ENDIF()
-
-    CMAKE_POLICY(POP)
-ENDIF(NOT DEFINED BUILD_CPU_ASYNC)
-SET(USE_CPUID ON CACHE BOOL "Build with CPUID integration")
-MARK_AS_ADVANCED(USE_CPUID)
+# CPU backend source files
+target_sources(afcpu
+  PRIVATE
+    Array.cpp
+    Array.hpp
+    approx.cpp
+    approx.hpp
+    arith.hpp
+    assign.cpp
+    assign.hpp
+    backend.hpp
+    bilateral.cpp
+    bilateral.hpp
+    blas.cpp
+    blas.hpp
+    canny.cpp
+    canny.hpp
+    cast.hpp
+    cholesky.cpp
+    cholesky.hpp
+    complex.hpp
+    convolve.cpp
+    convolve.hpp
+    copy.cpp
+    copy.hpp
+    diagonal.cpp
+    diagonal.hpp
+    diff.cpp
+    diff.hpp
+    err_cpu.hpp
+    exampleFunction.cpp
+    exampleFunction.hpp
+    fast.cpp
+    fast.hpp
+    fft.cpp
+    fft.hpp
+    fftconvolve.cpp
+    fftconvolve.hpp
+    gradient.cpp
+    gradient.hpp
+    harris.cpp
+    harris.hpp
+    histogram.cpp
+    histogram.hpp
+    homography.cpp
+    homography.hpp
+    hsv_rgb.cpp
+    hsv_rgb.hpp
+    identity.cpp
+    identity.hpp
+    iir.cpp
+    iir.hpp
+    index.cpp
+    index.hpp
+    inverse.cpp
+    inverse.hpp
+    iota.cpp
+    iota.hpp
+    ireduce.cpp
+    ireduce.hpp
+    join.cpp
+    join.hpp
+    lapack_helper.hpp
+    logic.hpp
+    lookup.cpp
+    lookup.hpp
+    lu.cpp
+    lu.hpp
+    match_template.cpp
+    match_template.hpp
+    math.cpp
+    math.hpp
+    mean.cpp
+    mean.hpp
+    meanshift.cpp
+    meanshift.hpp
+    medfilt.cpp
+    medfilt.hpp
+    memory.cpp
+    memory.hpp
+    moments.cpp
+    moments.hpp
+    morph.cpp
+    morph.hpp
+    nearest_neighbour.cpp
+    nearest_neighbour.hpp
+    orb.cpp
+    orb.hpp
+    padarray.cpp
+    platform.cpp
+    platform.hpp
+    print.hpp
+    qr.cpp
+    qr.hpp
+    queue.hpp
+    random_engine.cpp
+    random_engine.hpp
+    range.cpp
+    range.hpp
+    reduce.cpp
+    reduce.hpp
+    regions.cpp
+    regions.hpp
+    reorder.cpp
+    reorder.hpp
+    resize.cpp
+    resize.hpp
+    rotate.cpp
+    rotate.hpp
+    scan.cpp
+    scan.hpp
+    scan_by_key.cpp
+    scan_by_key.hpp
+    select.cpp
+    select.hpp
+    set.cpp
+    set.hpp
+    shift.cpp
+    shift.hpp
+    sift.cpp
+    sift.hpp
+    sobel.cpp
+    sobel.hpp
+    solve.cpp
+    solve.hpp
+    sort.cpp
+    sort.hpp
+    sort_by_key.cpp
+    sort_by_key.hpp
+    sort_index.cpp
+    sort_index.hpp
+    sparse.cpp
+    sparse.hpp
+    sparse_arith.cpp
+    sparse_arith.hpp
+    sparse_blas.cpp
+    sparse_blas.hpp
+    susan.cpp
+    susan.hpp
+    svd.cpp
+    svd.hpp
+    tile.cpp
+    tile.hpp
+    traits.hpp
+    transform.cpp
+    transform.hpp
+    transpose.cpp
+    transpose.hpp
+    triangle.cpp
+    triangle.hpp
+    types.hpp
+    unary.hpp
+    unwrap.cpp
+    unwrap.hpp
+    utility.hpp
+    where.cpp
+    where.hpp
+    wrap.cpp
+    wrap.hpp
+  )
+
+# CPU backend kernel files
+target_sources(afcpu
+  PRIVATE
+    kernel/Array.hpp
+    kernel/approx.hpp
+    kernel/assign.hpp
+    kernel/bilateral.hpp
+    kernel/canny.hpp
+    kernel/convolve.hpp
+    kernel/copy.hpp
+    kernel/diagonal.hpp
+    kernel/diff.hpp
+    kernel/dot.hpp
+    kernel/exampleFunction.hpp
+    kernel/fast.hpp
+    kernel/fft.hpp
+    kernel/fftconvolve.hpp
+    kernel/gradient.hpp
+    kernel/harris.hpp
+    kernel/histogram.hpp
+    kernel/hsv_rgb.hpp
+    kernel/identity.hpp
+    kernel/iir.hpp
+    kernel/index.hpp
+    kernel/interp.hpp
+    kernel/iota.hpp
+    kernel/ireduce.hpp
+    kernel/join.hpp
+    kernel/lookup.hpp
+    kernel/lu.hpp
+    kernel/match_template.hpp
+    kernel/meanshift.hpp
+    kernel/medfilt.hpp
+    kernel/moments.hpp
+    kernel/morph.hpp
+    kernel/nearest_neighbour.hpp
+    kernel/orb.hpp
+    kernel/random_engine.hpp
+    kernel/random_engine_mersenne.hpp
+    kernel/random_engine_philox.hpp
+    kernel/random_engine_threefry.hpp
+    kernel/range.hpp
+    kernel/reduce.hpp
+    kernel/regions.hpp
+    kernel/reorder.hpp
+    kernel/resize.hpp
+    kernel/rotate.hpp
+    kernel/scan.hpp
+    kernel/scan_by_key.hpp
+    kernel/select.hpp
+    kernel/shift.hpp
+    kernel/sobel.hpp
+    kernel/sort.hpp
+    kernel/sort_by_key.hpp
+    kernel/sort_by_key_impl.hpp
+    kernel/sort_helper.hpp
+    kernel/sparse.hpp
+    kernel/sparse_arith.hpp
+    kernel/susan.hpp
+    kernel/tile.hpp
+    kernel/transform.hpp
+    kernel/transpose.hpp
+    kernel/triangle.hpp
+    kernel/unwrap.hpp
+    kernel/wrap.hpp
+  )
 
 if (USE_CPUID)
-  ADD_DEFINITIONS(-DUSE_CPUID=1)
-ELSE(USE_CPUID)
-  ADD_DEFINITIONS(-DUSE_CPUID=0)
-ENDIF(USE_CPUID)
-
-IF (NOT ${BUILD_CPU_ASYNC})
-    ADD_DEFINITIONS(-DAF_DISABLE_CPU_ASYNC)
-ENDIF()
-
-IF(USE_CPU_F77_BLAS)
-    MESSAGE("Using F77 BLAS")
-    ADD_DEFINITIONS(-DUSE_F77_BLAS)
-ENDIF()
-
-IF(USE_CPU_MKL) # Manual MKL Setup
-    MESSAGE("CPU Backend Using MKL")
-    ADD_DEFINITIONS(-DUSE_MKL)
-ELSE(USE_CPU_MKL)
-    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
-        MESSAGE("CPU Backend Using MKL RT")
-        ADD_DEFINITIONS(-DUSE_MKL)
-    ENDIF()
-ENDIF()
-
-IF (NOT CBLAS_LIBRARIES)
-    MESSAGE(SEND_ERROR "CBLAS Library not set")
-ENDIF()
-
-IF(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU" AND "${APPLE}")
-    ADD_DEFINITIONS(-flax-vector-conversions)
-ENDIF()
-
-FIND_PACKAGE(FFTW REQUIRED)
-
-IF(APPLE)
-    FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
-    IF(NOT LAPACK_FOUND)
-        # UNSET THE VARIABLES FROM LAPACKE
-        UNSET(LAPACKE_LIB CACHE)
-        UNSET(LAPACK_LIB CACHE)
-        UNSET(LAPACKE_INCLUDES CACHE)
-        UNSET(LAPACKE_ROOT_DIR CACHE)
-        FIND_PACKAGE(LAPACK)
-    ENDIF()
-ELSE(APPLE) # Linux and Windows
-    FIND_PACKAGE(LAPACKE)
-ENDIF(APPLE)
-
-IF(LAPACK_FOUND)
-    ADD_DEFINITIONS(-DWITH_CPU_LINEAR_ALGEBRA)
-ELSE()
-    MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
-ENDIF()
-
-IF(NOT UNIX)
-    ADD_DEFINITIONS(-DAFDLL)
-ENDIF()
-
-SET(THREADS_SRC_DIR "${CMAKE_CURRENT_SOURCE_DIR}/threads")
-IF(EXISTS "${THREADS_SRC_DIR}" AND IS_DIRECTORY "${THREADS_SRC_DIR}"
-        AND EXISTS "${THREADS_SRC_DIR}/LICENSE")
-    # threads submodule has been initialized
-    # Nothing to do
-ELSE()
-    MESSAGE(STATUS "threads submodule unavailable. Updating submodules.")
-    EXECUTE_PROCESS(
-        COMMAND git submodule update --init --recursive
-        WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
-        )
-ENDIF()
-
-INCLUDE_DIRECTORIES(
-    ${CMAKE_INCLUDE_PATH}
-    "${PROJECT_SOURCE_DIR}/src/backend/cpu"
-    "${PROJECT_SOURCE_DIR}/src/backend/cpu/threads"
-    ${FFTW_INCLUDES}
-    ${CBLAS_INCLUDE_DIR}
-    )
-
-IF(LAPACK_FOUND)
-    INCLUDE_DIRECTORIES(${LAPACK_INCLUDE_DIR})
-ENDIF()
-
-FILE(GLOB cpu_headers
-    "*.hpp"
-    "*.h")
+  target_compile_definitions(afcpu PRIVATE -DUSE_CPUID)
+endif(USE_CPUID)
 
-FILE(GLOB cpu_sources
-    "*.cpp")
-
-LIST(SORT cpu_headers)
-LIST(SORT cpu_sources)
-
-source_group(backend\\cpu\\Headers FILES ${cpu_headers})
-source_group(backend\\cpu\\Sources FILES ${cpu_sources})
-
-FILE(GLOB backend_headers
-    "../*.hpp"
-    "../*.h"
-    )
-
-FILE(GLOB backend_sources
-    "../common/*.cpp"
-    "../*.cpp"
-    )
-
-LIST(SORT backend_headers)
-LIST(SORT backend_sources)
-
-source_group(backend\\Headers FILES ${backend_headers})
-source_group(backend\\Sources FILES ${backend_sources})
-
-FILE(GLOB c_headers
-    "../../api/c/*.hpp"
-    "../../api/c/*.h"
-    )
-
-FILE(GLOB c_sources
-    "../../api/c/*.cpp"
+target_sources(afcpu
+  PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}/threads/async_queue.hpp
+  )
+
+if(MSVC)
+  target_compile_options(afcpu PRIVATE /bigobj)
+endif()
+
+include("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+
+if(BUILD_NONFREE)
+  target_sources(afcpu PRIVATE kernel/sift_nonfree.hpp)
+  target_compile_definitions(afcpu PRIVATE AF_BUILD_NONFREE_SIFT)
+endif()
+
+if(BUILD_GRAPHICS)
+  add_dependencies(afcpu forge-ext)
+  target_sources(afcpu
+    PRIVATE
+      hist_graphics.cpp
+      hist_graphics.hpp
+      image.cpp
+      image.hpp
+      plot.cpp
+      plot.hpp
+      surface.cpp
+      surface.hpp
+      vector_field.cpp
+      vector_field.hpp
     )
+endif()
 
-LIST(SORT c_headers)
-LIST(SORT c_sources)
-
-source_group(api\\c\\Headers FILES ${c_headers})
-source_group(api\\c\\Sources FILES ${c_sources})
-
-FILE(GLOB cpp_sources
-    "../../api/cpp/*.cpp"
-    )
+target_include_directories(afcpu
+  PUBLIC
+    $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
+    $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
+  PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    threads
+    ${CBLAS_INCLUDE_DIR}
+  )
+
+# TODO(umar) Find a better way to determine BLAS selection
+if(USE_CPU_MKL)
+  dependency_check(MKL_FOUND "MKL not found")
+  target_compile_definitions(afcpu PRIVATE USE_MKL)
+
+  if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" OR
+    (CMAKE_CXX_COMPILER_ID STREQUAL "Intel" AND (UNIX AND NOT APPLE)))
+    # MKL requires multiple passes when linking with the static libs. This can be
+    # done in CMake using LINK_INTERFACE_MULTIPLICITY but that will require
+    # changine the way FindCBLAS works. This can also be done using the
+    # --start-group and --end-group linker around the libraries in the linking
+    # step.
+    #
+    # TODO(umar): Change the way CBLAS libraries are found and linked
+    set(CBLAS_LIBRARIES -Wl,--start-group ${CBLAS_LIBRARIES} -Wl,--end-group)
+  endif()
+endif()
+
+target_compile_definitions(afcpu
+  PRIVATE
+    AF_CPU
+  )
 
-LIST(SORT cpp_sources)
-
-source_group(api\\cpp\\Sources FILES ${cpp_sources})
-
-# OS Definitions
-IF(UNIX)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC")
-ELSE(${UNIX}) #Windows
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-ENDIF()
-
-INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
-
-IF(DEFINED BLAS_SYM_FILE)
-  ADD_LIBRARY(afcpu_static STATIC
-              ${cpu_headers}
-              ${cpu_sources}
-              ${backend_headers}
-              ${backend_sources}
-              ${SORT_BY_KEY_OBJECTS})
-
-  ADD_LIBRARY(afcpu SHARED
-              ${c_headers}
-              ${c_sources}
-              ${cpp_sources})
-
-  IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-    ADD_DEPENDENCIES(afcpu_static forge)
-  ENDIF()
-
-  IF(APPLE)
-    SET_TARGET_PROPERTIES(afcpu_static
-        PROPERTIES LINK_FLAGS -Wl,-exported_symbols_list,${BLAS_SYM_FILE})
-    TARGET_LINK_LIBRARIES(afcpu PUBLIC $<TARGET_FILE:afcpu_static>)
-  ELSE(APPLE)
-    add_custom_command(OUTPUT ${PROJECT_BINARY_DIR}/afcpu_static.renamed
-      BYPRODUCTS ${PROJECT_BINARY_DIR}/afcpu_static.renamed
-      COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afcpu_static> ${PROJECT_BINARY_DIR}/afcpu_static.renamed
-      DEPENDS $<TARGET_FILE:afcpu_static>)
-      TARGET_LINK_LIBRARIES(afcpu PUBLIC ${PROJECT_BINARY_DIR}/afcpu_static.renamed)
-  ENDIF(APPLE)
-
-ELSE(DEFINED BLAS_SYM_FILE)
-
-ADD_LIBRARY(afcpu SHARED
-            ${cpu_headers}
-            ${cpu_sources}
-            ${backend_headers}
-            ${backend_sources}
-            ${c_headers}
-            ${c_sources}
-            ${cpp_sources}
-            ${SORT_BY_KEY_OBJECTS})
-
-ENDIF(DEFINED BLAS_SYM_FILE)
-
-TARGET_LINK_LIBRARIES(afcpu
+target_link_libraries(afcpu
   PRIVATE
+    c_api_interface
+    cpp_api_interface
+    afcommon_interface
+    afcommon_lapack_interface
+    cpu_sort_by_key
     ${CBLAS_LIBRARIES}
-    ${FFTW_LIBRARIES}
-    ${FreeImage_LIBS}
-    ${CMAKE_THREAD_LIBS_INIT}
-)
-
-IF(LAPACK_FOUND)
-  TARGET_LINK_LIBRARIES(afcpu PRIVATE ${LAPACK_LIBRARIES})
-ENDIF()
-
-LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
-IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
-    ADD_DEPENDENCIES(afcpu ${GRAPHICS_DEPENDENCIES})
-ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
-
-IF(FORGE_FOUND)
-   TARGET_LINK_LIBRARIES(afcpu PRIVATE ${GRAPHICS_LIBRARIES})
-ENDIF()
-
-SET_TARGET_PROPERTIES(afcpu PROPERTIES
-                      VERSION "${AF_VERSION}"
-                      SOVERSION "${AF_VERSION_MAJOR}")
-
-INSTALL(TARGETS afcpu EXPORT CPU DESTINATION "${AF_INSTALL_LIB_DIR}"
-        COMPONENT libraries)
-
-IF(APPLE)
-    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
-ENDIF(APPLE)
-
-export(TARGETS afcpu FILE ArrayFireCPU.cmake)
-INSTALL(EXPORT CPU DESTINATION "${AF_INSTALL_CMAKE_DIR}"
-        COMPONENT cmake
-        FILE ArrayFireCPU.cmake)
+    FFTW::FFTW
+    FFTW::FFTWF
+  )
+
+install(TARGETS afcpu
+  EXPORT ArrayFireCPUTargets
+  COMPONENT cpu
+  PUBLIC_HEADER DESTINATION af
+  RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
+  LIBRARY DESTINATION ${AF_INSTALL_LIB_DIR}
+  ARCHIVE DESTINATION ${AF_INSTALL_LIB_DIR}
+  FRAMEWORK DESTINATION framework
+  INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
+  )
+
+source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
+source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
+source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
+source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
+source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*)
+source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
+source_group("" FILES CMakeLists.txt)
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 4bf88b5a93..33b1063c87 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -10,11 +10,13 @@
 #include <blas.hpp>
 #include <af/dim4.hpp>
 #include <cassert>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <kernel/dot.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
+#include <common/blas_headers.hpp>
+
 namespace cpu
 {
 
@@ -54,23 +56,14 @@ using std::conditional;
 //                  const void *alpha, const void *A, const int lda,
 //                  const void *B, const int ldb, const void *beta,
 //                  void *C, const int ldc);
-#if defined(IS_OPENBLAS)
-    static const bool cplx_void_ptr = false;
-#else
-    static const bool cplx_void_ptr = true;
-#endif
-
-template<typename T, class Enable = void>
-struct blas_base {
-    using type = typename dtype_traits<T>::base_type;
-};
 
 template<typename T>
-struct blas_base <T, typename enable_if<is_complex<T>::value && cplx_void_ptr>::type> {
-    using type = void;
+struct blas_base {
+    using type = typename conditional<is_complex<T>::value && cplx_void_ptr,
+                                      void,
+                                      typename dtype_traits<T>::base_type>::type;
 };
 
-
 template<typename T>
 using cptr_type     =   typename conditional<   is_complex<T>::value,
                                                 const typename blas_base<T>::type *,
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 6cedac6169..b85c194b66 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -10,30 +10,6 @@
 #include <Array.hpp>
 #include <types.hpp>
 
-#ifdef USE_MKL
-    #include <mkl_cblas.h>
-#else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-    #else
-        extern "C" {
-            #include <cblas.h>
-        }
-    #endif
-#endif
-
-// TODO: Ask upstream for a more official way to detect it
-#ifdef OPENBLAS_CONST
-#define IS_OPENBLAS
-#endif
-
-// Make sure we get the correct type signature for OpenBLAS
-// OpenBLAS defines blasint as it's index type. Emulate this
-// if we're not dealing with openblas and use it where applicable
-#ifndef IS_OPENBLAS
-typedef int blasint;
-#endif
-
 namespace cpu
 {
 
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 777fa46b04..cc8f877077 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <cholesky.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index 9e995f779e..07440685d0 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #define CPU_NOT_SUPPORTED() do {                        \
         throw SupportError(__PRETTY_FUNCTION__,         \
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 0902147d96..f80abd6d32 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -9,7 +9,7 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <fft.hpp>
 #include <err_cpu.hpp>
 #include <fftw3.h>
diff --git a/src/backend/cpu/hist_graphics.hpp b/src/backend/cpu/hist_graphics.hpp
index b238ff9bff..2c83e225bf 100644
--- a/src/backend/cpu/hist_graphics.hpp
+++ b/src/backend/cpu/hist_graphics.hpp
@@ -11,7 +11,7 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <Array.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 7678cae7f7..1ce9896946 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -15,7 +15,7 @@
 #include <Array.hpp>
 #include <image.hpp>
 #include <err_cpu.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index 5f4b98a1fc..7fea631d84 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index bff2a47399..1a9c9d5dcc 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <inverse.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
index 53287de9c4..fecc143f24 100644
--- a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -1,16 +1,44 @@
-FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-FOREACH(STR ${FILESTRINGS})
-    IF(${STR} MATCHES "// SBK_TYPES")
-        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
-    ENDIF()
-ENDFOREACH()
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
 
-FOREACH(SBK_TYPE ${SBK_TYPES})
-    ADD_LIBRARY(cpu_sort_by_key_${SBK_TYPE} OBJECT
+foreach(STR ${FILESTRINGS})
+    if(${STR} MATCHES "// SBK_TYPES")
+        string(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_TYPES ${TEMP})
+    endif()
+endforeach()
+
+add_library(cpu_sort_by_key INTERFACE)
+foreach(SBK_TYPE ${SBK_TYPES})
+  add_library(cpu_sort_by_key_${SBK_TYPE} OBJECT
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
-        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
-    SET_TARGET_PROPERTIES(cpu_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
-    LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:cpu_sort_by_key_${SBK_TYPE}>)
-ENDFOREACH(SBK_TYPE ${SBK_TYPES})
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp"
+    )
+  set_target_properties(cpu_sort_by_key_${SBK_TYPE}
+    PROPERTIES
+      COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL"
+      FOLDER "Generated Targets")
+
+  # TODO(umar): This should just use the include directories from the
+  # afcpu_static target
+  target_include_directories(cpu_sort_by_key_${SBK_TYPE}
+    PUBLIC
+      .
+      ../../api/c
+      ${ArrayFire_SOURCE_DIR}/include
+      ${ArrayFire_BINARY_DIR}/include
+    PRIVATE
+      ../common
+      ..
+      threads)
+
+  set_target_properties(cpu_sort_by_key_${SBK_TYPE} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+  target_sources(cpu_sort_by_key
+    INTERFACE $<TARGET_OBJECTS:cpu_sort_by_key_${SBK_TYPE}>)
+endforeach(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/cpu/lapack_helper.hpp b/src/backend/cpu/lapack_helper.hpp
index c5ed4fa83f..0ecea31bea 100644
--- a/src/backend/cpu/lapack_helper.hpp
+++ b/src/backend/cpu/lapack_helper.hpp
@@ -22,7 +22,7 @@
 #else
     #ifdef __APPLE__
         #include <Accelerate/Accelerate.h>
-        #include <lapacke.hpp>
+        #include <common/lapacke.hpp>
         #undef AF_LAPACK_COL_MAJOR
         #define AF_LAPACK_COL_MAJOR 0
     #else // NETLIB LAPACKE
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 0a42dabdfc..5bfd8d7f72 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -8,19 +8,20 @@
  ********************************************************/
 
 #include <lu.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 #include <af/dim4.hpp>
 #include <handle.hpp>
-#include <iostream>
-#include <cassert>
-#include <range.hpp>
+#include <kernel/lu.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/lu.hpp>
+#include <range.hpp>
+
+#include <cassert>
+#include <iostream>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/math.cpp b/src/backend/cpu/math.cpp
index e00fd78fcd..556a817b1f 100644
--- a/src/backend/cpu/math.cpp
+++ b/src/backend/cpu/math.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <math.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index ddf894622a..2dfc2093c9 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -9,28 +9,16 @@
 
 #include <af/version.h>
 #include <platform.hpp>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <version.hpp>
-#include <host_memory.hpp>
+#include <common/host_memory.hpp>
 
 #include <cctype>
 #include <sstream>
 
 using namespace std;
 
-#if !CPUID_CAPABLE
-
-CPUInfo::CPUInfo()
-    : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)
-{
-    mVendorId = "Unknown";
-    mModelName= "Unknown";
-    mNumSMT   = 1;
-    mNumCores = 1;
-    mNumLogCpus = 1;
-}
-
-#else
+#ifdef CPUID_CAPABLE
 
 CPUInfo::CPUInfo()
     : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)
@@ -112,6 +100,18 @@ CPUInfo::CPUInfo()
     mModelName = string(mModelName.c_str());
 }
 
+#else
+
+CPUInfo::CPUInfo()
+  : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)
+{
+  mVendorId = "Unknown";
+  mModelName= "Unknown";
+  mNumSMT   = 1;
+  mNumCores = 1;
+  mNumLogCpus = 1;
+}
+
 #endif
 
 namespace cpu
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 50509d1aee..c6eb731871 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -17,10 +17,8 @@
 #include <memory.hpp>
 #include <queue.hpp>
 
-#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64)
-#define CPUID_CAPABLE USE_CPUID
-#else
-#define CPUID_CAPABLE 0
+#if defined(USE_CPUID) && (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64))
+#define CPUID_CAPABLE
 #endif
 
 #ifdef _WIN32
@@ -31,7 +29,7 @@ typedef unsigned __int32  uint32_t;
 #include <stdint.h>
 #endif
 
-#if CPUID_CAPABLE
+#ifdef CPUID_CAPABLE
 
 #define MAX_INTEL_TOP_LVL 4
 
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index cef685bab9..4152152bbb 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <plot.hpp>
 #include <err_cpu.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/plot.hpp b/src/backend/cpu/plot.hpp
index a92b21d9b8..f6f9a3fde0 100644
--- a/src/backend/cpu/plot.hpp
+++ b/src/backend/cpu/plot.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index 307079bff1..ddb1a3d8ea 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <qr.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 #include <af/dim4.hpp>
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 10402df419..3c50240023 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <util.hpp>
+#include <common/util.hpp>
 #include <memory.hpp>
 #include <Param.hpp>
 
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index c67c35ae46..9c2aae488b 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <solve.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
 #include <af/dim4.hpp>
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 740dffdeb2..d6032ade4a 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 291376e00b..8399c566b8 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 
 #ifdef USE_MKL
 #include <mkl_spblas.h>
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index d076d57d49..09ede431b0 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <sparse_arith.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <optypes.hpp>
 #include <sparse.hpp>
 
@@ -21,7 +21,7 @@
 #include <arith.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
index ba01feeeb1..db55154814 100644
--- a/src/backend/cpu/sparse_arith.hpp
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <sparse.hpp>
 #include <optypes.hpp>
 
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 5ad10e54d4..688148861d 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -9,17 +9,17 @@
 
 #include <sparse_blas.hpp>
 
-#include <stdexcept>
-#include <string>
-#include <cassert>
-
 #include <af/dim4.hpp>
 #include <complex.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
+#include <stdexcept>
+#include <string>
+#include <cassert>
+
 namespace cpu
 {
 
diff --git a/src/backend/cpu/sparse_blas.hpp b/src/backend/cpu/sparse_blas.hpp
index 3b544dabaa..d73aacbf12 100644
--- a/src/backend/cpu/sparse_blas.hpp
+++ b/src/backend/cpu/sparse_blas.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
 namespace cpu
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index c4d8f4071d..b5ffb67113 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <surface.hpp>
 #include <err_cpu.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/surface.hpp b/src/backend/cpu/surface.hpp
index 3d668df33e..6f8b247496 100644
--- a/src/backend/cpu/surface.hpp
+++ b/src/backend/cpu/surface.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 932e3621ab..42caf2f3a2 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <svd.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <err_cpu.hpp>
 
 #if defined(WITH_CPU_LINEAR_ALGEBRA)
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index d19286f496..277f66240f 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <unwrap.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <kernel/unwrap.hpp>
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index 425056758a..56ad8287e3 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <vector_field.hpp>
 #include <err_cpu.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/vector_field.hpp b/src/backend/cpu/vector_field.hpp
index aacb16caa7..78e1e8f747 100644
--- a/src/backend/cpu/vector_field.hpp
+++ b/src/backend/cpu/vector_field.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 8e0f6fe2f7..f2ee1bca4c 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <wrap.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <kernel/wrap.hpp>
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index f54a0f0f10..be12f777cd 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -9,7 +9,7 @@
 #pragma once
 
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include "traits.hpp"
 #include <backend.hpp>
 #include <types.hpp>
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9da3b90df9..4a42d3a8bc 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -1,222 +1,63 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
-
-FIND_PACKAGE(CUDA 7.0 REQUIRED)
-
-INCLUDE(CLKernelToH)
-INCLUDE(FindNVRTC)
-
-MARK_AS_ADVANCED(
-    CUDA_BUILD_CUBIN
-    CUDA_BUILD_EMULATION
-    CUDA_SDK_ROOT_DIR
-    CUDA_VERBOSE_BUILD)
-
-# Disables running cuda_compute_check.c when build windows using remote
-OPTION(CUDA_COMPUTE_DETECT "Run autodetection of CUDA Architecture" ON)
-MARK_AS_ADVANCED(CUDA_COMPUTE_DETECT)
-
-IF(CUDA_COMPUTE_DETECT AND NOT DEFINED COMPUTES_DETECTED_LIST)
-    INCLUDE(CUDACheckCompute)
-ENDIF()
-
-IF(    CUDA_COMPUTE_20
-    OR CUDA_COMPUTE_30
-    OR CUDA_COMPUTE_32
-    OR CUDA_COMPUTE_35
-    OR CUDA_COMPUTE_37
-    OR CUDA_COMPUTE_50
-    OR CUDA_COMPUTE_52
-    OR CUDA_COMPUTE_53
-    OR CUDA_COMPUTE_60
-    OR CUDA_COMPUTE_61
-    OR CUDA_COMPUTE_62
-    )
-    SET(FALLBACK OFF)
-ELSE()
-    SET(FALLBACK ON)
-ENDIF()
-
-LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
-IF(${COMPUTES_LEN} EQUAL 0 AND ${FALLBACK})
-    MESSAGE(STATUS "You can use -DCOMPUTES_DETECTED_LIST=\"AB;XY\" (semicolon separated list of CUDA Compute versions to enable the specified computes")
-    MESSAGE(STATUS "Individual compute versions flags are also available under CMake Advance options")
-    LIST(APPEND COMPUTES_DETECTED_LIST "30" "50")
-    IF(${CUDA_VERSION_MAJOR} GREATER 7) # Enable 60 only if CUDA 8 or greater
-        MESSAGE(STATUS "No computes detected. Fall back to 30, 50, 60")
-        LIST(APPEND COMPUTES_DETECTED_LIST "60")
-    ELSE(${CUDA_VERSION_MAJOR} GREATER 7)
-        LIST(APPEND COMPUTES_DETECTED_LIST "20")
-    ENDIF(${CUDA_VERSION_MAJOR} GREATER 7)
-ENDIF()
-
-LIST(LENGTH COMPUTES_DETECTED_LIST COMPUTES_LEN)
-MESSAGE(STATUS "Number of Computes Detected = ${COMPUTES_LEN}")
-
-FOREACH(COMPUTE_DETECTED ${COMPUTES_DETECTED_LIST})
-    SET(CUDA_COMPUTE_${COMPUTE_DETECTED} ON CACHE BOOL "" FORCE)
-ENDFOREACH()
-
-MACRO(SET_COMPUTE VERSION)
-    SET(CUDA_GENERATE_CODE_${VERSION} "-gencode arch=compute_${VERSION},code=sm_${VERSION}")
-    SET(CUDA_GENERATE_CODE ${CUDA_GENERATE_CODE} ${CUDA_GENERATE_CODE_${VERSION}})
-    LIST(APPEND COMPUTE_VERSIONS "${VERSION}")
-    ADD_DEFINITIONS(-DCUDA_COMPUTE_${VERSION})
-    MESSAGE(STATUS "Setting Compute ${VERSION} to ON")
-ENDMACRO(SET_COMPUTE)
-
-# Iterate over compute versions. Create variables and enable computes if needed
-FOREACH(VER 20 30 32 35 37 50 52 53 60 61 62)
-    OPTION(CUDA_COMPUTE_${VER} "CUDA Compute Capability ${VER}" OFF)
-    MARK_AS_ADVANCED(CUDA_COMPUTE_${VER})
-    IF(${CUDA_COMPUTE_${VER}})
-        SET_COMPUTE(${VER})
-    ENDIF()
-ENDFOREACH()
-
-# Error out if Compute 6x is enabled but CUDA version is less than 8
-IF(${CUDA_VERSION_MAJOR} LESS 8)
-    IF(   CUDA_COMPUTE_60
-       OR CUDA_COMPUTE_61
-       OR CUDA_COMPUTE_62
-      )
-        MESSAGE(FATAL_ERROR
-                "CUDA Compute 6x was enabled.\
-                 CUDA Compute 6x (Pascal) GPUs require CUDA 8 or greater.\
-                 Your CUDA Version is ${CUDA_VERSION}."
-                )
-    ENDIF()
-ENDIF(${CUDA_VERSION_MAJOR} LESS 8)
-
-IF(UNIX)
-    # GCC 5.3 and above give errors for mempcy from <string.h>
-    # This is a (temporary) fix for that
-    # This was fixed in CUDA 8.0
-    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
-        IF(${CUDA_VERSION_MAJOR} LESS 8)
-            ADD_DEFINITIONS(-D_FORCE_INLINES)
-        ENDIF(${CUDA_VERSION_MAJOR} LESS 8)
-
-        # GCC 6.0 and above default to g++14, enabling c++11 features by default
-        # Enabling c++11 with nvcc 7.5 + gcc 6.x doesn't seem to work
-        # Only solution for now is to force use c++03 for gcc 6.x
-        IF(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
-          message( FATAL_ERROR "NVCC does not support GCC version 6.0 or greater." )
-        ENDIF()
-    ENDIF()
-
-    # Forcing STRICT ANSI should resolve a bunch of issues that NVIDIA seems to face with GCC compilers.
-    ADD_DEFINITIONS(-D__STRICT_ANSI__)
-    SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler -fvisibility=hidden)
-    IF(${WITH_COVERAGE})
-        SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -Xcompiler -fprofile-arcs -Xcompiler -ftest-coverage -Xlinker -fprofile-arcs -Xlinker -ftest-coverage")
-    ENDIF(${WITH_COVERAGE})
-ELSE()
-    ADD_DEFINITIONS(-DAFDLL)
-ENDIF()
-
-ADD_DEFINITIONS(-DAF_CUDA)
-
-# CMake 3.2 Adds CUDA_cusolver_LIBRARY variable to FindCUDA
-# Older version, use FIND_LIBRARY
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(${CUDA_cusolver_LIBRARY} MATCHES " ")
-        UNSET(CUDA_cusolver_LIBRARY CACHE)  # When going from higher version to lower version
-    ENDIF()
-
-    # Use CUDA_cusolver_DIR to keep track of CUDA Toolkit for which cusolver was found.
-    # If the toolkit changed, then find cusolver again
-    IF(NOT "${CUDA_cusolver_DIR}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR}")
-        UNSET(CUDA_cusolver_DIR CACHE)
-        UNSET(CUDA_cusolver_LIBRARY CACHE)
-        FIND_LIBRARY (
-            CUDA_cusolver_LIBRARY
-            NAMES "cusolver"
-            PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-            PATH_SUFFIXES "lib64" "lib/x64" "lib"
-            DOC "CUDA cusolver Library"
-            NO_DEFAULT_PATH
-            )
-        SET(CUDA_cusolver_DIR "${CUDA_TOOLKIT_ROOT_DIR}" CACHE INTERNAL "CUDA cusolver Root Directory")
-    ENDIF()
-    MARK_AS_ADVANCED(CUDA_cusolver_LIBRARY)
-    MESSAGE(STATUS "CUDA cusolver library available in CUDA Version ${CUDA_VERSION_STRING}")
-ENDIF(CMAKE_VERSION VERSION_LESS 3.2)
-
-INCLUDE_DIRECTORIES(
-    ${CMAKE_INCLUDE_PATH}
-    ${CUDA_INCLUDE_DIRS}
-    "${PROJECT_SOURCE_DIR}/src/backend/cuda"
-    "${CMAKE_CURRENT_BINARY_DIR}"
-    ${CUDA_nvrtc_INCLUDE_DIR}
-    )
-
-FILE(GLOB cuda_headers
-     "*.hpp"
-     "*.h")
-
-FILE(GLOB cuda_sources
-    "*.cu"
-    "*.cpp"
-    "kernel/*.cu")
-
-FILE(GLOB jit_sources
-    "JIT/*.hpp")
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-FILE(GLOB kernel_headers
-    "kernel/*.hpp")
+include(InternalUtils)
+include(select_compute_arch)
 
-LIST(SORT cuda_headers)
-LIST(SORT cuda_sources)
-LIST(SORT jit_sources)
-LIST(SORT kernel_headers)
+dependency_check(CUDA_FOUND "CUDA not found.")
 
-SOURCE_GROUP(backend\\cuda\\Headers FILES ${cuda_headers})
-SOURCE_GROUP(backend\\cuda\\Sources FILES ${cuda_sources})
-SOURCE_GROUP(backend\\cuda\\JIT FILES ${jit_sources})
-SOURCE_GROUP(backend\\cuda\\kernel\\Headers FILES ${kernel_headers})
+if(NOT CUDA_architecture_build_targets)
+  cuda_detect_installed_gpus(detected_gpus)
+endif()
 
-FILE(GLOB backend_headers
-    "../*.hpp"
-    "../*.h"
-    )
-
-FILE(GLOB backend_sources
-    "../common/*.cpp"
-    "../*.cpp"
-    )
+set(CUDA_architecture_build_targets ${detected_gpus} CACHE
+  STRING "The compute architectures targeted by this build. (Options: 3.0;Maxwell;All;Common)")
 
-LIST(SORT backend_headers)
-LIST(SORT backend_sources)
+cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
+message(STATUS "CUDA Architectures: ${CUDA_architecture_build_targets}")
 
-SOURCE_GROUP(backend\\Headers FILES ${backend_headers})
-SOURCE_GROUP(backend\\Sources FILES ${backend_sources})
+find_cuda_helper_libs(nvrtc)
 
-FILE(GLOB c_headers
-    "../../api/c/*.hpp"
-    "../../api/c/*.h"
-    )
+get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
+mark_as_advanced(CUDA_LIBRARIES_PATH)
 
-FILE(GLOB c_sources
-    "../../api/c/*.cpp"
-    )
+# TODO(umar): Move these flags to a separate function/target
+if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
+  if(${CUDA_VERSION_MAJOR} LESS 8)
+    add_definitions(-D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED)
+  endif()
+endif()
 
-LIST(SORT c_headers)
-LIST(SORT c_sources)
+include(CLKernelToH)
 
-SOURCE_GROUP(api\\c\\Headers FILES ${c_headers})
-SOURCE_GROUP(api\\c\\Sources FILES ${c_sources})
+set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};
+  ${cuda_architecture_flags}
+  )
 
-FILE(GLOB cpp_sources
-    "../../api/cpp/*.cpp"
-    )
+cuda_include_directories(
+  ${CMAKE_CURRENT_BINARY_DIR}
+  ${CMAKE_CURRENT_SOURCE_DIR}
+  ${ArrayFire_SOURCE_DIR}/include
+  ${ArrayFire_BINARY_DIR}/include
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel
+  ${CMAKE_CURRENT_SOURCE_DIR}/JIT
+  ${ArrayFire_SOURCE_DIR}/src/api/c
+  ${ArrayFire_SOURCE_DIR}/src/backend
 
-LIST(SORT cpp_sources)
+  # NOTE: Space after comma is necessary
+  $<JOIN:$<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>, >
+  )
 
-SET(jit_kernel_headers
+set(jit_kernel_headers
     "kernel_headers")
 
-FILE(GLOB jit_src "kernel/jit.cuh")
-CL_KERNEL_TO_H(
+file(GLOB jit_src "kernel/jit.cuh")
+
+cl_kernel_to_h(
     SOURCES ${jit_src}
     VARNAME jit_files
     EXTENSION "hpp"
@@ -225,67 +66,23 @@ CL_KERNEL_TO_H(
     NAMESPACE "cuda"
     )
 
-
-SOURCE_GROUP(api\\cpp\\Sources FILES ${cpp_sources})
-
-INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/CMakeLists.txt")
-INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/CMakeLists.txt")
-
-LIST(LENGTH COMPUTE_VERSIONS COMPUTE_COUNT)
-IF(${COMPUTE_COUNT} EQUAL 1)
-    SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} ${CUDA_GENERATE_CODE}")
-ELSE()
-    # Use -arch sm_30 if CUDA 8 or greater and compute_20 not defined
-    IF(CUDA_COMPUTE_20 OR ${CUDA_VERSION_MAJOR} LESS 8)
-        SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch sm_20")
-    ELSE()
-        SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch sm_30")
-    ENDIF()
-ENDIF()
-
-# PUSH/POP --keep-device-functions flag. Only available in CUDA 8 or newer
-SET(OLD_CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS})
-IF(${CUDA_VERSION_MAJOR} GREATER 7) # CUDA 8 or newer
-    SET(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} --keep-device-functions")
-ENDIF()
-SET(CUDA_NVCC_FLAGS ${OLD_CUDA_NVCC_FLAGS})
-
-IF("${APPLE}")
-    ADD_DEFINITIONS(-D__STRICT_ANSI__)
-ELSE()
-  IF(UNIX)
-    IF(${CUDA_VERSION_MAJOR} GREATER 7)
-      FIND_PACKAGE(OpenMP)
-      IF(OPENMP_FOUND)
-        SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
-        SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
-      ENDIF()
-    ENDIF()
-  ENDIF()
-ENDIF()
-
-IF("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_MWAITXINTRIN_H_INCLUDED -D_FORCE_INLINES")
-ENDIF()
-
 ## Copied from FindCUDA.cmake
 ## The target_link_library needs to link with the cuda libraries using
 ## PRIVATE
-macro(MY_CUDA_ADD_LIBRARY cuda_target)
-
-  CUDA_ADD_CUDA_INCLUDE_ONCE()
+function(cuda_add_library cuda_target)
+  cuda_add_cuda_include_once()
 
   # Separate the sources from the options
-  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
-  CUDA_BUILD_SHARED_LIBRARY(_cuda_shared_flag ${ARGN})
+  cuda_get_sources_and_options(_sources _cmake_options _options ${ARGN})
+  cuda_build_shared_library(_cuda_shared_flag ${ARGN})
   # Create custom commands and targets for each file.
-  CUDA_WRAP_SRCS( ${cuda_target} OBJ _generated_files ${_sources}
+  cuda_wrap_srcs( ${cuda_target} OBJ _generated_files ${_sources}
     ${_cmake_options} ${_cuda_shared_flag}
     OPTIONS ${_options} )
 
   # Compute the file name of the intermedate link file used for separable
   # compilation.
-  CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+  cuda_compute_separable_compilation_object_file_name(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
 
   # Add the library.
   add_library(${cuda_target} ${_cmake_options}
@@ -297,7 +94,7 @@ macro(MY_CUDA_ADD_LIBRARY cuda_target)
   # Add a link phase for the separable compilation if it has been enabled.  If
   # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
   # variable will have been defined.
-  CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+  cuda_link_separable_compilation_objects("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
 
   target_link_libraries(${cuda_target}
       PRIVATE ${CUDA_LIBRARIES}
@@ -310,82 +107,392 @@ macro(MY_CUDA_ADD_LIBRARY cuda_target)
     LINKER_LANGUAGE ${CUDA_C_OR_CXX}
     )
 
-endmacro()
-
-IF(NOT CUDA_CUDA_LIBRARY)
-    MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
-    MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
-    FIND_FILE(CUDA_CUDA_LIBRARY_STUB
-              NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
-              PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-              PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
-              DOC "CUDA Library STUB"
-             )
-    IF(CUDA_CUDA_LIBRARY_STUB)
-        MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
-        MESSAGE("Run the following commands (Linux) to set it up:")
-        MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
-        MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
-    ENDIF()
-    MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
-ENDIF(NOT CUDA_CUDA_LIBRARY)
-
-SET(CUDA_ADD_LIBRARY_OPTIONS "")
-IF(UNIX)
-  # These flags enable C++11 and disable invalid offsetof warning
-  SET(CUDA_ADD_LIBRARY_OPTIONS "-std=c++11 -Xcudafe \"--diag_suppress=1427\"")
-ENDIF(UNIX)
-
-
-MY_CUDA_ADD_LIBRARY(afcuda SHARED
-                ${cuda_headers}
-                ${cuda_sources}
-                ${jit_sources}
-                ${kernel_headers}
-                ${backend_headers}
-                ${backend_sources}
-                ${c_headers}
-                ${c_sources}
-                ${cpp_sources}
-                ${thrust_sort_by_key_sources}
-                ${scan_by_key_sources}
-                OPTIONS ${CUDA_GENERATE_CODE} ${CUDA_ADD_LIBRARY_OPTIONS})
-
-TARGET_LINK_LIBRARIES(afcuda
+endfunction()
+
+include(kernel/scan_by_key/CMakeLists.txt)
+include(kernel/thrust_sort_by_key/CMakeLists.txt)
+
+cuda_add_library(afcuda
+    scan.cu
+    all.cu
+    any.cu
+    approx.cu
+    assign.cu
+    bilateral.cu
+    canny.cu
+    cholesky.cu
+    copy.cu
+    count.cu
+    diagonal.cu
+    diff.cu
+    dilate.cu
+    dilate3d.cu
+    erode.cu
+    erode3d.cu
+    exampleFunction.cu
+    fast.cu
+    fast_pyramid.cu
+    fftconvolve.cu
+    gradient.cu
+    harris.cu
+    histogram.cu
+    homography.cu
+    hsv_rgb.cu
+    identity.cu
+    iir.cu
+    index.cu
+    inverse.cu
+    iota.cu
+    ireduce.cu
+    join.cu
+    lookup.cu
+    lu.cu
+    match_template.cu
+    max.cu
+    mean.cu
+    meanshift.cu
+    medfilt.cu
+    min.cu
+    moments.cu
+    nearest_neighbour.cu
+    orb.cu
+    product.cu
+    qr.cu
+    random_engine.cu
+    range.cu
+    regions.cu
+    reorder.cu
+    resize.cu
+    rotate.cu
+    scan_by_key.cu
+    select.cu
+    set.cu
+    shift.cu
+    sift.cu
+    sobel.cu
+    solve.cu
+    sort.cu
+    sort_by_key.cu
+    sort_index.cu
+    sparse.cu
+    sparse_arith.cu
+    sum.cu
+    susan.cu
+    svd.cu
+    tile.cu
+    transform.cu
+    transpose.cu
+    transpose_inplace.cu
+    triangle.cu
+    unwrap.cu
+    where.cu
+    wrap.cu
+
+    kernel/convolve.cu
+    kernel/convolve_separable.cu
+
+    kernel/approx.hpp
+    kernel/assign.hpp
+    kernel/atomics.hpp
+    kernel/bilateral.hpp
+    kernel/canny.hpp
+    kernel/config.hpp
+    kernel/convolve.hpp
+    kernel/diagonal.hpp
+    kernel/diff.hpp
+    kernel/exampleFunction.hpp
+    kernel/fast.hpp
+    kernel/fast_lut.hpp
+    kernel/fast_pyramid.hpp
+    kernel/fftconvolve.hpp
+    kernel/gradient.hpp
+    kernel/harris.hpp
+    kernel/histogram.hpp
+    kernel/homography.hpp
+    kernel/hsv_rgb.hpp
+    kernel/identity.hpp
+    kernel/iir.hpp
+    kernel/index.hpp
+    kernel/interp.hpp
+    kernel/iota.hpp
+    kernel/ireduce.hpp
+    kernel/join.hpp
+    kernel/lookup.hpp
+    kernel/lu_split.hpp
+    kernel/match_template.hpp
+    kernel/mean.hpp
+    kernel/meanshift.hpp
+    kernel/medfilt.hpp
+    kernel/memcopy.hpp
+    kernel/moments.hpp
+    kernel/morph.hpp
+    kernel/nearest_neighbour.hpp
+    kernel/orb.hpp
+    kernel/orb_patch.hpp
+    kernel/random_engine.hpp
+    kernel/random_engine_mersenne.hpp
+    kernel/random_engine_philox.hpp
+    kernel/random_engine_threefry.hpp
+    kernel/range.hpp
+    kernel/reduce.hpp
+    kernel/regions.hpp
+    kernel/reorder.hpp
+    kernel/resize.hpp
+    kernel/rotate.hpp
+    kernel/scan_dim.hpp
+    kernel/scan_dim_by_key.hpp
+    kernel/scan_dim_by_key_impl.hpp
+    kernel/scan_first.hpp
+    kernel/scan_first_by_key.hpp
+    kernel/scan_first_by_key_impl.hpp
+    kernel/select.hpp
+    kernel/shared.hpp
+    kernel/shift.hpp
+    kernel/sift_nonfree.hpp
+    kernel/sobel.hpp
+    kernel/sort.hpp
+    kernel/sort_by_key.hpp
+    kernel/sparse.hpp
+    kernel/sparse_arith.hpp
+    kernel/susan.hpp
+    kernel/thrust_sort_by_key.hpp
+    kernel/thrust_sort_by_key_impl.hpp
+    kernel/tile.hpp
+    kernel/transform.hpp
+    kernel/transpose.hpp
+    kernel/transpose_inplace.hpp
+    kernel/triangle.hpp
+    kernel/unwrap.hpp
+    kernel/where.hpp
+    kernel/wrap.hpp
+
+    Array.cpp
+    Array.hpp
+    Param.hpp
+    approx.hpp
+    arith.hpp
+    assign.hpp
+    backend.hpp
+    bilateral.hpp
+    binary.hpp
+    blas.cpp
+    blas.hpp
+    canny.hpp
+    cast.hpp
+    cholesky.hpp
+    complex.hpp
+    convolve.cpp
+    convolve.hpp
+    copy.hpp
+    cublas.cpp
+    cublas.hpp
+    cufft.cpp
+    cufft.hpp
+    cusolverDn.cpp
+    cusolverDn.hpp
+    cusparse.cpp
+    cusparse.hpp
+    debug_cuda.hpp
+    diagonal.hpp
+    diff.hpp
+    driver.cpp
+    err_cuda.hpp
+    exampleFunction.hpp
+    fast.hpp
+    fast_pyramid.hpp
+    fft.cpp
+    fft.hpp
+    fftconvolve.hpp
+    gradient.hpp
+    harris.hpp
+    histogram.hpp
+    homography.hpp
+    hsv_rgb.hpp
+    identity.hpp
+    iir.hpp
+    index.hpp
+    inverse.hpp
+    iota.hpp
+    ireduce.hpp
+    jit.cpp
+    join.hpp
+    logic.hpp
+    lookup.hpp
+    lu.hpp
+    match_template.hpp
+    math.cpp
+    math.hpp
+    mean.hpp
+    meanshift.hpp
+    medfilt.hpp
+    memory.cpp
+    memory.hpp
+    moments.hpp
+    morph.hpp
+    morph3d_impl.hpp
+    morph_impl.hpp
+    nearest_neighbour.hpp
+    orb.hpp
+    platform.cpp
+    platform.hpp
+    print.hpp
+    qr.hpp
+    random_engine.hpp
+    range.hpp
+    reduce.hpp
+    reduce_impl.hpp
+    regions.hpp
+    reorder.hpp
+    resize.hpp
+    rotate.hpp
+    scalar.hpp
+    scan.hpp
+    scan_by_key.hpp
+    select.hpp
+    set.hpp
+    shift.hpp
+    sift.hpp
+    sobel.hpp
+    solve.hpp
+    sort.hpp
+    sort_by_key.hpp
+    sort_index.hpp
+    sparse.hpp
+    sparse_arith.hpp
+    sparse_blas.cpp
+    sparse_blas.hpp
+    susan.hpp
+    svd.hpp
+    tile.hpp
+    traits.hpp
+    transform.hpp
+    transpose.hpp
+    triangle.hpp
+    types.cpp
+    types.hpp
+    unary.hpp
+    unwrap.hpp
+    utility.hpp
+    where.hpp
+    wrap.hpp
+
+    JIT/BinaryNode.hpp
+    JIT/BufferNode.hpp
+    JIT/Node.hpp
+    JIT/ScalarNode.hpp
+    JIT/UnaryNode.hpp
+    JIT/types.h
+
+    OPTIONS "-std=c++11 -Xcompiler -fPIC -Xcudafe \"--diag_suppress=1427\""
+  )
+
+add_library(ArrayFire::afcuda ALIAS afcuda)
+
+if(BUILD_NONFREE)
+  target_compile_definitions(afcuda PRIVATE AF_BUILD_NONFREE_SIFT)
+endif()
+
+
+if(BUILD_GRAPHICS)
+  target_sources(afcuda
     PRIVATE
-      ${CUDA_CUBLAS_LIBRARIES}
-      ${CUDA_LIBRARIES}
-      ${FreeImage_LIBS}
-      ${CUDA_CUFFT_LIBRARIES}
-      ${CUDA_cusparse_LIBRARY}
-      ${CUDA_cusolver_LIBRARY}
-      ${CUDA_nvrtc_LIBRARY}
-      ${CUDA_CUDA_LIBRARY}
-      ${CMAKE_THREAD_LIBS_INIT}
+      GraphicsResourceManager.cpp
+      GraphicsResourceManager.hpp
+      hist_graphics.cpp
+      hist_graphics.hpp
+      image.cpp
+      image.hpp
+      plot.cpp
+      plot.hpp
+      surface.cpp
+      surface.hpp
+      vector_field.cpp
+      vector_field.hpp)
+endif()
+
+add_dependencies(afcuda ${jit_kernel_targets})
+
+target_include_directories (afcuda
+  PUBLIC
+    $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
+    $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
+  PRIVATE
+    ${CUDA_INCLUDE_DIRS}
+    ${ArrayFire_SOURCE_DIR}/src/api/c
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${CMAKE_CURRENT_SOURCE_DIR}/kernel
+    ${CMAKE_CURRENT_SOURCE_DIR}/JIT
+    ${CMAKE_CURRENT_BINARY_DIR}
 )
 
-ADD_DEPENDENCIES(afcuda ${jit_kernel_targets})
-LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
-IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
-    ADD_DEPENDENCIES(afcuda ${GRAPHICS_DEPENDENCIES})
-ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
-
-IF(FORGE_FOUND)
-    TARGET_LINK_LIBRARIES(afcuda PRIVATE ${GRAPHICS_LIBRARIES})
-ENDIF()
-
-SET_TARGET_PROPERTIES(afcuda PROPERTIES
-    VERSION "${AF_VERSION}"
-    SOVERSION "${AF_VERSION_MAJOR}")
-
-INSTALL(TARGETS afcuda EXPORT CUDA DESTINATION "${AF_INSTALL_LIB_DIR}"
-        COMPONENT libraries)
-
-IF(APPLE)
-    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
-ENDIF(APPLE)
-
-export(TARGETS afcuda FILE ArrayFireCUDA.cmake)
-INSTALL(EXPORT CUDA DESTINATION "${AF_INSTALL_CMAKE_DIR}"
-    COMPONENT cmake
-    FILE ArrayFireCUDA.cmake)
+if(OpenMP_CXX_FOUND)
+  target_link_libraries(afcuda
+    PRIVATE
+      OpenMP::OpenMP_CXX
+    )
+elseif(NOT APPLE)
+  message(FATAL_ERROR "OpenMP is required to compile CUDA Backend")
+endif()
+
+set_target_properties(afcuda PROPERTIES POSITION_INDEPENDENT_CODE ON)
+
+target_link_libraries(afcuda
+  PRIVATE
+    c_api_interface
+    cpp_api_interface
+    afcommon_interface
+    cuda_scan_by_key
+    cuda_thrust_sort_by_key
+    ${CUDA_LIBRARIES}
+    ${CUDA_nvrtc_LIBRARY}
+    ${CUDA_CUBLAS_LIBRARIES}
+    ${CUDA_CUFFT_LIBRARIES}
+    ${CUDA_cusolver_LIBRARY}
+    ${CUDA_cusparse_LIBRARY}
+  )
+
+# If the driver is not found the cuda driver api need to be linked against the
+# libcuda.so stub located in the lib[64]/stubs directory
+if(CUDA_CUDA_LIBRARY)
+  target_link_libraries(afcuda PRIVATE ${CUDA_CUDA_LIBRARY})
+else()
+  message(STATUS "CUDA driver library missing. Looking for libcuda stub.")
+  find_library(CUDA_CUDA_STUB
+             NAMES cuda
+             PATHS ${CUDA_LIBRARIES_PATH}/stubs
+
+             NO_DEFAULT_PATH
+         )
+  if(CUDA_CUDA_STUB)
+    message(STATUS "CUDA driver stub FOUND: ${CUDA_CUDA_STUB}")
+  endif()
+
+  target_link_libraries(afcuda ${CUDA_CUDA_STUB})
+endif()
+
+# TODO(umar): This is required for NVRTC to work correctly on OSX. It may not
+#             be necessary on other platforms.
+if(APPLE)
+  target_link_libraries(afcuda PUBLIC -Wl,-rpath,${CUDA_LIBRARIES_PATH})
+endif()
+
+install(TARGETS afcuda
+  EXPORT ArrayFireCUDATargets
+  COMPONENT cuda
+  PUBLIC_HEADER DESTINATION af
+  RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
+  LIBRARY DESTINATION ${AF_INSTALL_LIB_DIR}
+  ARCHIVE DESTINATION ${AF_INSTALL_LIB_DIR}
+  FRAMEWORK DESTINATION framework
+  INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
+  )
+
+source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
+source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
+source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
+source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
+source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/*)
+source_group("generated files"  FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h
+                                REGULAR_EXPRESSION ${CMAKE_CURRENT_BINARY_DIR}/${kernel_headers_dir}/*)
+source_group("" FILES CMakeLists.txt)
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index fb0700d441..ad86e22157 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -15,7 +15,7 @@
 #endif
 
 // cuda_gl_interop.h does not include OpenGL headers for ARM
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 using namespace gl;
 #define GL_VERSION gl::GL_VERSION
 #define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index cd3590fe7b..a460e42aa8 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -16,7 +16,7 @@
 #include <string>
 #include <cassert>
 #include <math.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <cublas.hpp>
 #include <arith.hpp>
 #include <reduce.hpp>
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index dc7a239f8b..9e86f725de 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <cholesky.hpp>
 
 #include <platform.hpp>
@@ -19,7 +19,7 @@
 #include <triangle.hpp>
 
 #include <math.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 26d6211c39..902c9aed94 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <platform.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <cublas.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index 994cb92023..6914d957ca 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <cublas_v2.h>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <common/MatrixAlgebraHandle.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/cufft.hpp b/src/backend/cuda/cufft.hpp
index e4f4326000..04a22d4514 100644
--- a/src/backend/cuda/cufft.hpp
+++ b/src/backend/cuda/cufft.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <cufft.h>
 #include <common/FFTPlanCache.hpp>
 #include <cstdio>
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index dd42094a0c..c9a3f38240 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -11,7 +11,7 @@
 
 #include <cuda_runtime.h>
 #include <cusolverDn.h>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <common/MatrixAlgebraHandle.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index bb4237fb6a..0598f7a8c3 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <cusparse_v2.h>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <common/MatrixAlgebraHandle.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index dd87bdfc2b..29cf310581 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <stdio.h>
-#include <defines.hpp>
-#include <err_common.hpp>
+#include <common/defines.hpp>
+#include <common/err_common.hpp>
 
 #define CUDA_NOT_SUPPORTED() do {                       \
         throw SupportError(__PRETTY_FUNCTION__,         \
diff --git a/src/backend/cuda/hist_graphics.hpp b/src/backend/cuda/hist_graphics.hpp
index 90969707e2..0c7b163796 100644
--- a/src/backend/cuda/hist_graphics.hpp
+++ b/src/backend/cuda/hist_graphics.hpp
@@ -11,7 +11,7 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <Array.hpp>
 
 namespace cuda
diff --git a/src/backend/cuda/image.hpp b/src/backend/cuda/image.hpp
index c476f0aee1..5667a4ed45 100644
--- a/src/backend/cuda/image.hpp
+++ b/src/backend/cuda/image.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/inverse.cu b/src/backend/cuda/inverse.cu
index 69a3e8f354..e2d0e971d3 100644
--- a/src/backend/cuda/inverse.cu
+++ b/src/backend/cuda/inverse.cu
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <inverse.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <solve.hpp>
 #include <identity.hpp>
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 1fd4b845c4..5f630ce79a 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <kernel_headers/jit.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 
 #include <Array.hpp>
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 5eb8e35e2c..9f7cc1eeb8 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 4b61f1cb88..b3ad4ee47b 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <math.hpp>
 #include <utility.hpp>
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 80e6a657cb..a07569ea4a 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 5759602d85..8a7377fd46 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 9376a42ffa..7ca2a04db2 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 06bd314296..16e34b97e2 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/convolve_separable.cu b/src/backend/cuda/kernel/convolve_separable.cu
index 65d7901a2c..3200442d44 100644
--- a/src/backend/cuda/kernel/convolve_separable.cu
+++ b/src/backend/cuda/kernel/convolve_separable.cu
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index bd3e70061c..34d34d7cf8 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <platform.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index a467c70aef..e3aa47cee0 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 4d1011b27f..6454c1dc0b 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -17,7 +17,7 @@
                                         // Param and CParam(constant version of Param) instead
                                         // of cuda::Array<T>
 
-#include <dispatch.hpp>                 // common utility header for CUDA & OpenCL backends
+#include <common/dispatch.hpp>                 // common utility header for CUDA & OpenCL backends
                                         // has the divup macro
 
 #include <err_cuda.hpp>                 // CUDA specific error check functions and macros
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 299e6c4673..c974b9dc45 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <kernel/fast_lut.hpp>
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index 06c7767b80..43cad340d5 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 399e3f389e..9abedc9504 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <Param.hpp>
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index c7475aa034..c303a40799 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index e00b740f05..6465410e0c 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/constants.h>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index c8eaa13ff7..cf4f567cee 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include "shared.hpp"
 
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 051fdb875e..091a9821a2 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 6315a5557c..4dec5609a9 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index e52a1c5a0d..885cf26712 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <platform.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index 867f812342..6cf3b00449 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index ef61f3329c..d600aefacc 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <math.hpp>
 #include <utility.hpp>
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 8b9dcd32f2..4984bd8e35 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -9,7 +9,7 @@
 
 #include <af/dim4.hpp>
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 4df25b90f3..ebd6c7e738 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -10,7 +10,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index 6b685e18e9..75d9dcd160 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 68bb54a2be..09aff2d85e 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <math.hpp>
 #include <utility.hpp>
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 1c9a5cc153..d2294a4985 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 5e6cf5cadb..dbc687cb79 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 45998d79e6..eb95fd71ad 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -10,7 +10,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index 36fa69eb75..18f1592869 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index ffa7fcadb7..ff3780ba58 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -9,7 +9,7 @@
 
 #include <platform.hpp>
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include "shared.hpp"
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 2db2c0832f..1e451a0f4d 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index f9c61cf1e2..d9fad5236f 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -10,7 +10,7 @@
 #include <af/defines.h>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 70650b8e53..0687f9da9b 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -9,7 +9,7 @@
 
 #include <limits>
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index eb2bebed80..88ccb25f9c 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 91ce9a7580..1ad848a1e0 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index abf1814246..6a6ce32560 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <af/defines.h>
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index f71f2c144b..dea3aea343 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 668ab3b3f8..75f00d8a07 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -10,7 +10,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 557f7192d9..dd3a1a54b0 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 337db36335..5515f29b1b 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index 6d831d861e..7cb5f53ea2 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 4a78a21353..f38c7160ff 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -9,7 +9,7 @@
 
 #include <math.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include "interp.hpp"
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index cf7557899b..936a0c7d73 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -1,23 +1,45 @@
-FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu.in" FILESTRINGS)
-
-FOREACH(STR ${FILESTRINGS})
-    IF(${STR} MATCHES "// SBK_BINARY_OPS")
-        STRING(REPLACE "// SBK_BINARY_OPS:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_BINARY_OPS ${TEMP})
-    ENDIF()
-ENDFOREACH()
-
-FOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
-    CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu.in"
-        "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu")
-    ADD_CUSTOM_COMMAND(
-        OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
-        COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
-      DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp" "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp")
-ENDFOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
-
-FILE(GLOB scan_by_key_sources
-  "${CMAKE_CURRENT_BINARY_DIR}/scan_by_key/*.cu"
-)
-
-LIST(SORT scan_by_key_sources)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu" FILESTRINGS)
+
+foreach(STR ${FILESTRINGS})
+    if(${STR} MATCHES "// SBK_BINARY_OPS")
+        string(REPLACE "// SBK_BINARY_OPS:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_BINARY_OPS ${TEMP})
+    endif()
+endforeach()
+
+cuda_add_cuda_include_once()
+
+foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
+
+    # When using cuda_compile with older versions of FindCUDA. The generated targets
+    # have the same names as the source file. Since we are using the same file for
+    # the compilation of these targets we need to rename them before sending them
+    # to the cuda_compile command so that it doesn't generate multiple targets with
+    # the same name
+    file(COPY "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu"
+      DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key")
+    file(RENAME "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl.cu"
+      "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu")
+
+    cuda_compile(scan_by_key_gen_files "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
+          "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
+          "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
+          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "-std=c++11 -Xcompiler -fPIC -DAFDLL"
+      )
+
+    list(APPEND SCAN_OBJ ${scan_by_key_gen_files})
+endforeach(SBK_BINARY_OP ${SBK_BINARY_OPS})
+
+cuda_add_library(cuda_scan_by_key STATIC ${SCAN_OBJ})
+set_target_properties(cuda_scan_by_key
+  PROPERTIES
+    LINKER_LANGUAGE CXX
+    FOLDER "Generated Targets"
+  )
diff --git a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
similarity index 86%
rename from src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in
rename to src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
index 45fe7f8d05..56cd4fe70b 100644
--- a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu.in
+++ b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
@@ -20,7 +20,7 @@ namespace cuda
 {
 namespace kernel
 {
-    INSTANTIATE_SCAN_FIRST_BY_KEY_OP(@SBK_BINARY_OP@)
-    INSTANTIATE_SCAN_DIM_BY_KEY_OP(@SBK_BINARY_OP@)
+    INSTANTIATE_SCAN_FIRST_BY_KEY_OP(SBK_BINARY_OP)
+    INSTANTIATE_SCAN_DIM_BY_KEY_OP(SBK_BINARY_OP)
 }
 }
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index ca74002ab0..015a8ea6b4 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -10,7 +10,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 322caa632a..461e7411fe 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -11,7 +11,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 3cdcc5d931..8c865a32ef 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -10,7 +10,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 49cf8db267..dc2c1bee4a 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -11,7 +11,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 7afbfad98f..4e310f4f7b 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/shift.hpp b/src/backend/cuda/kernel/shift.hpp
index e6f18dff84..128a6403a5 100644
--- a/src/backend/cuda/kernel/shift.hpp
+++ b/src/backend/cuda/kernel/shift.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 79d5c8d70b..eed8abe506 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -70,7 +70,7 @@
 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index 6a5bb7c861..92a6eb6761 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index ac92ec8387..03c8c2a8a6 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <kernel/iota.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 8095da2d16..42e4cf916b 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -10,7 +10,7 @@
 #include <af/dim4.hpp>
 #include <memory.hpp>
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 58ffa91666..c7b3283dad 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 1e8dac4ec9..adfa3ae7ba 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index c0ecc7f000..351ef450ad 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -9,7 +9,7 @@
 
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include "config.hpp"
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index d032a27e53..47bd67bdad 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -1,28 +1,52 @@
-FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in" FILESTRINGS)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-FOREACH(STR ${FILESTRINGS})
-    IF(${STR} MATCHES "// SBK_TYPES")
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu" FILESTRINGS)
+
+foreach(STR ${FILESTRINGS})
+    if(${STR} MATCHES "// SBK_TYPES")
         STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
         STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
-    ELSEIF(${STR} MATCHES "// SBK_INSTS:")
+    elseif(${STR} MATCHES "// SBK_INSTS:")
         STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
         STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
-    ENDIF()
-ENDFOREACH()
-
-FOREACH(SBK_TYPE ${SBK_TYPES})
-    FOREACH(SBK_INST ${SBK_INSTS})
-        CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in"
-        "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
-        ADD_CUSTOM_COMMAND(
-          OUTPUT  "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
-          COMMAND  ${CMAKE_COMMAND} -E touch "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
-          DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp")
-    ENDFOREACH(SBK_INST ${SBK_INSTS})
-ENDFOREACH(SBK_TYPE ${SBK_TYPES})
-
-FILE(GLOB thrust_sort_by_key_sources
-  "${CMAKE_CURRENT_BINARY_DIR}/thrust_sort_by_key/*.cu"
-)
-
-LIST(SORT thrust_sort_by_key_sources)
+    endif()
+endforeach()
+
+foreach(SBK_TYPE ${SBK_TYPES})
+    foreach(SBK_INST ${SBK_INSTS})
+
+      # When using cuda_compile with older versions of FindCUDA. The generated targets
+      # have the same names as the source file. Since we are using the same file for
+      # the compilation of these targets we need to rename them before sending them
+      # to the cuda_compile command so that it doesn't generate multiple targets with
+      # the same name
+      file(COPY "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
+        DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key")
+      file(RENAME "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
+          "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
+
+      cuda_compile(scan_by_key_gen_files
+          ${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp
+          OPTIONS
+            -DSBK_TYPE=${SBK_TYPE}
+            -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
+            "-std=c++11 -Xcompiler -fPIC -DAFDLL"
+      )
+
+      list(APPEND SORT_OBJ ${scan_by_key_gen_files})
+    endforeach(SBK_INST ${SBK_INSTS})
+endforeach(SBK_TYPE ${SBK_TYPES})
+
+cuda_add_library(cuda_thrust_sort_by_key STATIC ${SORT_OBJ})
+
+set_target_properties(cuda_thrust_sort_by_key
+  PROPERTIES
+    LINKER_LANGUAGE CXX
+    FOLDER "Generated Targets"
+  )
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
similarity index 94%
rename from src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in
rename to src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
index fd5b27463f..62a7f21ac1 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu.in
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
@@ -18,6 +18,6 @@ namespace cuda
 {
 namespace kernel
 {
-    INSTANTIATE@SBK_INST@(@SBK_TYPE@)
+    INSTANTIATESBK_INST(SBK_TYPE)
 }
 }
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 9f674649e8..6908a53a32 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index f3f551a3c6..70eba90fe9 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -9,7 +9,7 @@
 
 #include <math.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include "interp.hpp"
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index ddfb09d379..0c259c7a55 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index cfff48c271..26cf7b2205 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 29d793a189..22f7cada81 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index e875ba2fb8..42678d8f0d 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index c43e870e66..b7134b1d4a 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -10,7 +10,7 @@
 #include <ops.hpp>
 #include <backend.hpp>
 #include <Param.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 50ee0a919b..8a8b3d7a4a 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index 104bc9025f..eef54f5080 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -8,13 +8,13 @@
  ********************************************************/
 
 #include <lu.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <platform.hpp>
 #include <memory.hpp>
 #include <copy.hpp>
 #include <math.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <kernel/lu_split.hpp>
 
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 26b1cef8f0..5f456c737c 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -9,9 +9,7 @@
 
 #pragma once
 #include <af/defines.h>
-#include <defines.hpp>
-#include <limits>
-#include <algorithm>
+#include <common/defines.hpp>
 #include "backend.hpp"
 #include "types.hpp"
 
@@ -20,6 +18,9 @@
 #include <math_constants.h>
 #endif
 
+#include <limits>
+#include <algorithm>
+
 namespace cuda
 {
     template<typename T> static inline __DH__ T abs(T val)  { return abs(val); }
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index e53e767db5..77738f89af 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -13,9 +13,9 @@
 #include <platform.hpp>
 #include <err_cuda.hpp>
 #include <memory.hpp>
-#include <util.hpp>
+#include <common/util.hpp>
 #include <types.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 
 #include <mutex>
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 687fcf3d41..f08368b418 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -10,17 +10,16 @@
 #include <af/version.h>
 #include <af/cuda.h>
 #include <platform.hpp>
-#include <defines.hpp>
-#include <util.hpp>
+#include <common/defines.hpp>
+#include <common/util.hpp>
 #include <version.hpp>
 #include <driver.h>
 #include <err_cuda.hpp>
-#include <util.hpp>
-#include <host_memory.hpp>
+#include <common/util.hpp>
+#include <common/host_memory.hpp>
 
 #include <algorithm>
 #include <cstdio>
-#include <cstring>
 #include <mutex>
 #include <sstream>
 #include <stdexcept>
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ca4a9a7689..658b372c25 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -17,7 +17,6 @@
 #include <cublas.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
-#include <common/types.hpp>
 
 #include <memory>
 #include <mutex>
diff --git a/src/backend/cuda/plot.hpp b/src/backend/cuda/plot.hpp
index a9c73cbca9..7a2ced069c 100644
--- a/src/backend/cuda/plot.hpp
+++ b/src/backend/cuda/plot.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index b058453310..da44d9d584 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <qr.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <platform.hpp>
 #include <cublas_v2.h>
@@ -17,7 +17,7 @@
 #include <copy.hpp>
 
 #include <math.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <kernel/triangle.hpp>
 
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 7f3e7901ac..13c29e982a 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <solve.hpp>
 
 #include <platform.hpp>
@@ -19,7 +19,7 @@
 #include <transpose.hpp>
 
 #include <math.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <blas.hpp>
 #include <lu.hpp>
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 0fe53c438d..c13400bda5 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
index 575e616d12..1ff7d8972c 100644
--- a/src/backend/cuda/sparse.hpp
+++ b/src/backend/cuda/sparse.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 3dfa5308b6..2126234f66 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
index 50c40d2258..5ea1e68059 100644
--- a/src/backend/cuda/sparse_arith.hpp
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <sparse.hpp>
 #include <optypes.hpp>
 
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 8dd3de8155..4f73709b09 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -13,7 +13,7 @@
 
 #include <stdexcept>
 #include <string>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <math.hpp>
 #include <complex.hpp>
 
diff --git a/src/backend/cuda/sparse_blas.hpp b/src/backend/cuda/sparse_blas.hpp
index ee2d18227b..b873e8aa73 100644
--- a/src/backend/cuda/sparse_blas.hpp
+++ b/src/backend/cuda/sparse_blas.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/surface.hpp b/src/backend/cuda/surface.hpp
index 6a5da62449..42342454ac 100644
--- a/src/backend/cuda/surface.hpp
+++ b/src/backend/cuda/surface.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index c323ab62ea..80fc94afa2 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -8,14 +8,14 @@
  ********************************************************/
 
 #include <svd.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <platform.hpp>
 #include "transpose.hpp"
 #include <memory.hpp>
 #include <copy.hpp>
 #include <math.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 
 #include <cusolverDn.hpp>
 
diff --git a/src/backend/cuda/vector_field.hpp b/src/backend/cuda/vector_field.hpp
index 288f8a15b3..48006f3d36 100644
--- a/src/backend/cuda/vector_field.hpp
+++ b/src/backend/cuda/vector_field.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/wrap.cu b/src/backend/cuda/wrap.cu
index 017a3a41e8..095bd976ce 100644
--- a/src/backend/cuda/wrap.cu
+++ b/src/backend/cuda/wrap.cu
@@ -11,7 +11,7 @@
 #include <wrap.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <kernel/wrap.hpp>
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index f349dff9c6..80350f7947 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -17,7 +17,7 @@
 #include <platform.hpp>
 #include <cstddef>
 #include <af/opencl.h>
-#include <util.hpp>
+#include <common/util.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 953582d42c..18f73f8b09 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <platform.hpp>
 #include <af/dim4.hpp>
-#include <ArrayInfo.hpp>
+#include <common/ArrayInfo.hpp>
 #include <traits.hpp>
 #include <backend.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f987c04a9d..38b58ef186 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -1,408 +1,534 @@
-FIND_PACKAGE(OpenCL REQUIRED)
-
-# Selects the OpenCL BLAS back-end to use (clBLAS or CLBlast)
-if(NOT OPENCL_BLAS_LIBRARY)
-    set(OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end" FORCE)
-    set_property(CACHE OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+include(InternalUtils)
+if("${Boost_VERSION}" VERSION_LESS 106100)
+  dependency_check(Boost_FOUND "Boost not found.")
+  include(build_boost_compute)
 endif()
 
-ADD_DEFINITIONS(-DCL_USE_DEPRECATED_OPENCL_1_2_APIS)
-
-IF(NOT USE_SYSTEM_CL2HPP)
-    INCLUDE(build_cl2hpp)
-ENDIF(NOT USE_SYSTEM_CL2HPP)
-INCLUDE(CLKernelToH)
-
-IF(USE_OPENCL_F77_BLAS)
-    ADD_DEFINITIONS(-DUSE_F77_BLAS)
-ENDIF()
-
-IF(APPLE)
-    FIND_PACKAGE(LAPACKE QUIET) # For finding MKL
-    IF(NOT LAPACK_FOUND)
-        # UNSET THE VARIABLES FROM LAPACKE
-        UNSET(LAPACKE_LIB CACHE)
-        UNSET(LAPACK_LIB CACHE)
-        UNSET(LAPACKE_INCLUDES CACHE)
-        UNSET(LAPACKE_ROOT_DIR CACHE)
-        FIND_PACKAGE(LAPACK)
-    ENDIF()
-ELSE(APPLE) # Linux and Windows
-    FIND_PACKAGE(LAPACKE)
-ENDIF(APPLE)
-
-IF(LAPACK_FOUND)
-  ADD_DEFINITIONS(-DWITH_OPENCL_LINEAR_ALGEBRA)
-
-  IF(NOT USE_OPENCL_MKL)
-    FIND_PACKAGE(CBLAS REQUIRED)
-
-    IF(USE_CPU_F77_BLAS)
-      ADD_DEFINITIONS(-DUSE_F77_BLAS)
-    ENDIF()
-
-    IF (NOT CBLAS_LIBRARIES)
-      MESSAGE(SEND_ERROR "CBLAS Library not set")
-    ENDIF()
-  ENDIF()
-ELSE(LAPACK_FOUND)
-    MESSAGE(WARNING "LAPACK not found. Functionality will be disabled")
-ENDIF()
-
-IF(USE_OPENCL_MKL) # Manual MKL Setup
-    MESSAGE("OpenCL Backend Using MKL")
-    ADD_DEFINITIONS(-DUSE_MKL)
-ELSE(USE_OPENCL_MKL)
-    IF(${MKL_FOUND}) # Automatic MKL Setup from BLAS
-        MESSAGE("OpenCL Backend Using MKL RT")
-        ADD_DEFINITIONS(-DUSE_MKL)
-    ENDIF()
-ENDIF()
-
-IF(NOT UNIX)
-    ADD_DEFINITIONS(-DAFDLL)
-ENDIF()
-
-ADD_DEFINITIONS(-DAF_OPENCL
-                -D__CL_ENABLE_EXCEPTIONS)
-
-OPTION(USE_SYSTEM_CLBLAST "Use system CLBlast" OFF)
-IF(USE_SYSTEM_CLBLAST AND NOT OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
-    MESSAGE(SEND_ERROR "Using the system CLBlast (USE_SYSTEM_CLBLAST=ON) only makes sense if OPENCL_BLAS_LIBRARY=CLBlast is set")
-ENDIF()
-IF(OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
-    IF(USE_SYSTEM_CLBLAST)
-        FIND_PACKAGE(CLBlast REQUIRED)
-    ELSE()
-        INCLUDE(build_CLBlast)
-    ENDIF()
-    INCLUDE_DIRECTORIES(${CLBLAST_INCLUDE_DIRS})
-    LINK_DIRECTORIES(${CLBLAST_LIBRARY_DIR})
-    ADD_DEFINITIONS(-DUSE_CLBLAST)
-    MESSAGE(STATUS "Building with CLBlast as an OpenCL BLAS back-end")
-ENDIF()
-
-OPTION(USE_SYSTEM_CLBLAS "Use system clBLAS" OFF)
-IF(USE_SYSTEM_CLBLAS AND NOT OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
-    MESSAGE(SEND_ERROR "Using the system clBLAS (USE_SYSTEM_CLBLAS=ON) only makes sense if OPENCL_BLAS_LIBRARY=clBLAS is set")
-ENDIF()
-IF(OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
-    IF(USE_SYSTEM_CLBLAS)
-        FIND_PACKAGE(clBLAS REQUIRED)
-    ELSE()
-        INCLUDE(build_clBLAS)
-    ENDIF()
-    INCLUDE_DIRECTORIES(${CLBLAS_INCLUDE_DIRS})
-    LINK_DIRECTORIES(${CLBLAS_LIBRARY_DIR})
-    ADD_DEFINITIONS(-DUSE_CLBLAS)
-    MESSAGE(STATUS "Building with clBLAS as an OpenCL BLAS back-end")
-ENDIF()
-
-OPTION(USE_SYSTEM_CLFFT "Use system clFFT" OFF)
-IF(USE_SYSTEM_CLFFT)
-    FIND_PACKAGE(clFFT REQUIRED)
-ELSE()
-    INCLUDE(build_clFFT)
-ENDIF()
-INCLUDE_DIRECTORIES(${CLFFT_INCLUDE_DIRS})
-LINK_DIRECTORIES(${CLFFT_LIBRARY_DIR})
-
-OPTION(USE_SYSTEM_BOOST_COMPUTE "Use system BoostCompute" OFF)
-
-ADD_DEFINITIONS(-DBOOST_ALL_NO_LIB)
-SET(Boost_USE_STATIC_LIBS OFF)
-FIND_PACKAGE(Boost REQUIRED)
-
-# If Boost version is 1.61.00, skip finding boost compute explicitly
-# as BoostCompute is merged into Boost starting that version.
-IF(Boost_VERSION VERSION_LESS 106100)
-    MESSAGE(STATUS "Boost version is less than 1.61.00")
-    IF(USE_SYSTEM_BOOST_COMPUTE)
-        MESSAGE(STATUS "Using system boost compute")
-        FIND_PACKAGE(BoostCompute REQUIRED)
-    ELSE()
-        MESSAGE(STATUS "Building boost compute")
-        INCLUDE(build_boost_compute)
-    ENDIF()
-ENDIF()
-
-SET( cl_kernel_headers
+set(OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end")
+set_property(CACHE OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
+
+include(build_clFFT)
+
+dependency_check(OpenCL_FOUND "OpenCL not found.")
+
+file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
+
+set( kernel_headers_dir
     "kernel_headers")
 
-INCLUDE_DIRECTORIES(
-    ${CMAKE_INCLUDE_PATH}
-    ${CMAKE_CURRENT_SOURCE_DIR}
-    ${OpenCL_INCLUDE_DIRS}
-    ${CL2HPP_INCLUDE_DIRECTORY}
-    "${CMAKE_CURRENT_BINARY_DIR}"
-    ${CLBLAST_INCLUDE_DIRS}
-    ${CLBLAS_INCLUDE_DIRS}
-    ${CLFFT_INCLUDE_DIRS}
-    ${Boost_INCLUDE_DIRS}
-    ${BoostCompute_INCLUDE_DIRS}
-    ${CBLAS_INCLUDE_DIR}
-    )
-IF(LAPACK_FOUND)
-    INCLUDE_DIRECTORIES(${LAPACK_INCLUDE_DIR})
-ENDIF()
-
-FILE(GLOB opencl_headers
-  "*.hpp"
-  "*.h")
-
-FILE(GLOB opencl_sources
-    "*.cpp")
-
-FILE(GLOB jit_sources
-    "jit/*.hpp")
-
-FILE(GLOB kernel_headers
-    "kernel/*.hpp")
-
-FILE(GLOB opencl_kernels
-    "kernel/*.cl")
-
-FILE(GLOB kernel_sources
-     "kernel/*.cpp")
-
-FILE(GLOB conv_ker_headers
-    "kernel/convolve/*.hpp")
-
-FILE(GLOB conv_ker_sources
-     "kernel/convolve/*.cpp")
-
-FILE(GLOB cpu_headers
-     "cpu/*.hpp")
-
-FILE(GLOB cpu_sources
-     "cpu/*.cpp")
-
-LIST(SORT opencl_headers)
-LIST(SORT opencl_sources)
-LIST(SORT jit_sources)
-LIST(SORT kernel_headers)
-LIST(SORT opencl_kernels)
-LIST(SORT kernel_sources)
-LIST(SORT conv_ker_headers)
-LIST(SORT conv_ker_sources)
-LIST(SORT cpu_headers)
-LIST(SORT cpu_sources)
-
-source_group(backend\\opencl\\Headers FILES ${opencl_headers})
-source_group(backend\\opencl\\Sources FILES ${opencl_sources})
-source_group(backend\\opencl\\JIT FILES ${jit_sources})
-source_group(backend\\opencl\\kernel\\Headers FILES ${kernel_headers})
-source_group(backend\\opencl\\kernel\\cl FILES ${opencl_kernels})
-source_group(backend\\opencl\\kernel\\Sources FILES ${kernel_sources})
-source_group(backend\\opencl\\kernel\\convolve\\Headers FILES ${conv_ker_headers})
-source_group(backend\\opencl\\kernel\\convolve\\Sources FILES ${conv_ker_sources})
-source_group(backend\\opencl\\cpu\\Headers FILES ${cpu_headers})
-source_group(backend\\opencl\\cpu\\Sources FILES ${cpu_sources})
-
-IF(LAPACK_FOUND)
-    FILE(GLOB magma_sources
-        "magma/*.cpp")
-
-    FILE(GLOB magma_headers
-        "magma/*.h")
-
-    LIST(SORT magma_headers)
-    LIST(SORT magma_sources)
-
-    source_group(backend\\opencl\\magma\\Sources FILES ${magma_sources})
-    source_group(backend\\opencl\\magma\\Headers FILES ${magma_headers})
-ELSE()
-    SET(magma_sources)
-    SET(magma_headers)
-ENDIF()
-
-FILE(GLOB backend_headers
-    "../*.hpp"
-    "../*.h"
-    )
+include(CLKernelToH)
 
-FILE(GLOB backend_sources
-    "../common/*.cpp"
-    "../*.cpp"
+cl_kernel_to_h(
+    SOURCES ${kernel_src}
+    VARNAME kernel_files
+    EXTENSION "hpp"
+    OUTPUT_DIR ${kernel_headers_dir}
+    TARGETS cl_kernel_targets
+    NAMESPACE "opencl"
     )
 
-LIST(SORT backend_headers)
-LIST(SORT backend_sources)
+include(kernel/scan_by_key/CMakeLists.txt)
+include(kernel/sort_by_key/CMakeLists.txt)
 
-source_group(backend\\Headers FILES ${backend_headers})
-source_group(backend\\Sources FILES ${backend_sources})
+add_library(afopencl "")
+add_library(ArrayFire::afopencl ALIAS afopencl)
 
-FILE(GLOB c_headers
-    "../../api/c/*.hpp"
-    "../../api/c/*.h"
+target_sources(afopencl
+  PRIVATE
+    Array.cpp
+    Array.hpp
+    Param.cpp
+    Param.hpp
+    all.cpp
+    any.cpp
+    api.cpp
+    approx.cpp
+    approx.hpp
+    arith.hpp
+    assign.cpp
+    assign.hpp
+    backend.hpp
+    bilateral.cpp
+    bilateral.hpp
+    binary.hpp
+    blas.cpp
+    blas.hpp
+    cache.hpp
+    canny.cpp
+    canny.hpp
+    cast.hpp
+    cholesky.cpp
+    cholesky.hpp
+    clfft.cpp
+    clfft.hpp
+    complex.hpp
+    convolve.cpp
+    convolve.hpp
+    convolve_separable.cpp
+    copy.cpp
+    copy.hpp
+    count.cpp
+    debug_opencl.hpp
+    diagonal.cpp
+    diagonal.hpp
+    diff.cpp
+    diff.hpp
+    dilate.cpp
+    dilate3d.cpp
+    erode.cpp
+    erode3d.cpp
+    err_clblas.hpp
+    err_clblast.hpp
+    err_opencl.hpp
+    errorcodes.cpp
+    errorcodes.hpp
+    exampleFunction.cpp
+    exampleFunction.hpp
+    fast.cpp
+    fast.hpp
+    fft.cpp
+    fft.hpp
+    fftconvolve.cpp
+    fftconvolve.hpp
+    gradient.cpp
+    gradient.hpp
+    harris.cpp
+    harris.hpp
+    histogram.cpp
+    histogram.hpp
+    homography.cpp
+    homography.hpp
+    hsv_rgb.cpp
+    hsv_rgb.hpp
+    identity.cpp
+    identity.hpp
+    iir.cpp
+    iir.hpp
+    index.cpp
+    index.hpp
+    inverse.cpp
+    inverse.hpp
+    iota.cpp
+    iota.hpp
+    ireduce.cpp
+    ireduce.hpp
+    jit.cpp
+    join.cpp
+    join.hpp
+    logic.hpp
+    lookup.cpp
+    lookup.hpp
+    lu.cpp
+    lu.hpp
+    match_template.cpp
+    match_template.hpp
+    math.cpp
+    math.hpp
+    max.cpp
+    mean.cpp
+    mean.hpp
+    meanshift.cpp
+    meanshift.hpp
+    medfilt.cpp
+    medfilt.hpp
+    memory.cpp
+    memory.hpp
+    min.cpp
+    moments.cpp
+    moments.hpp
+    morph.hpp
+    morph3d_impl.hpp
+    morph_impl.hpp
+    nearest_neighbour.cpp
+    nearest_neighbour.hpp
+    orb.cpp
+    orb.hpp
+    platform.cpp
+    platform.hpp
+    print.hpp
+    product.cpp
+    program.cpp
+    program.hpp
+    qr.cpp
+    qr.hpp
+    random_engine.cpp
+    random_engine.hpp
+    range.cpp
+    range.hpp
+    reduce.hpp
+    reduce_impl.hpp
+    regions.cpp
+    regions.hpp
+    reorder.cpp
+    reorder.hpp
+    resize.cpp
+    resize.hpp
+    rotate.cpp
+    rotate.hpp
+    scalar.hpp
+    scan.cpp
+    scan.hpp
+    scan_by_key.cpp
+    scan_by_key.hpp
+    select.cpp
+    select.hpp
+    set.cpp
+    set.hpp
+    shift.cpp
+    shift.hpp
+    sift.cpp
+    sift.hpp
+    sobel.cpp
+    sobel.hpp
+    solve.cpp
+    solve.hpp
+    sort.cpp
+    sort.hpp
+    sort_by_key.cpp
+    sort_by_key.hpp
+    sort_index.cpp
+    sort_index.hpp
+    sparse.cpp
+    sparse.hpp
+    sparse_arith.cpp
+    sparse_arith.hpp
+    sparse_blas.cpp
+    sparse_blas.hpp
+    sum.cpp
+    susan.cpp
+    susan.hpp
+    svd.cpp
+    svd.hpp
+    tile.cpp
+    tile.hpp
+    traits.hpp
+    transform.cpp
+    transform.hpp
+    transpose.cpp
+    transpose.hpp
+    transpose_inplace.cpp
+    triangle.cpp
+    triangle.hpp
+    types.cpp
+    types.hpp
+    unary.hpp
+    unwrap.cpp
+    unwrap.hpp
+    where.cpp
+    where.hpp
+    wrap.cpp
+    wrap.hpp
     )
 
-FILE(GLOB c_sources
-    "../../api/c/*.cpp"
+
+target_sources(afopencl
+  PRIVATE
+    kernel/KParam.hpp
+    kernel/approx.hpp
+    kernel/assign.hpp
+    kernel/bilateral.hpp
+    kernel/canny.hpp
+    kernel/config.cpp
+    kernel/config.hpp
+    kernel/convolve.hpp
+    kernel/convolve_separable.cpp
+    kernel/convolve_separable.hpp
+    kernel/cscmm.hpp
+    kernel/cscmv.hpp
+    kernel/csrmm.hpp
+    kernel/csrmv.hpp
+    kernel/diagonal.hpp
+    kernel/diff.hpp
+    kernel/exampleFunction.hpp
+    kernel/fast.hpp
+    kernel/fftconvolve.hpp
+    kernel/gradient.hpp
+    kernel/harris.hpp
+    kernel/histogram.hpp
+    kernel/homography.hpp
+    kernel/hsv_rgb.hpp
+    kernel/identity.hpp
+    kernel/iir.hpp
+    kernel/index.hpp
+    kernel/interp.hpp
+    kernel/iota.hpp
+    kernel/ireduce.hpp
+    kernel/join.hpp
+    kernel/laset.hpp
+    #kernel/laset_band.hpp
+    kernel/laswp.hpp
+    kernel/lookup.hpp
+    kernel/lu_split.hpp
+    kernel/match_template.hpp
+    kernel/mean.hpp
+    kernel/meanshift.hpp
+    kernel/medfilt.hpp
+    kernel/memcopy.hpp
+    kernel/moments.hpp
+    kernel/morph.hpp
+    kernel/names.hpp
+    kernel/nearest_neighbour.hpp
+    kernel/orb.hpp
+    kernel/random_engine.hpp
+    kernel/range.hpp
+    kernel/reduce.hpp
+    kernel/regions.hpp
+    kernel/reorder.hpp
+    kernel/resize.hpp
+    kernel/rotate.hpp
+    kernel/scan_dim.hpp
+    kernel/scan_dim_by_key.hpp
+    kernel/scan_dim_by_key_impl.hpp
+    kernel/scan_first.hpp
+    kernel/scan_first_by_key.hpp
+    kernel/scan_first_by_key_impl.hpp
+    kernel/select.hpp
+    kernel/shift.hpp
+    kernel/sobel.hpp
+    kernel/sort.hpp
+    kernel/sort_by_key.hpp
+    kernel/sort_by_key_impl.hpp
+    kernel/sort_helper.hpp
+    kernel/sparse.hpp
+    kernel/sparse_arith.hpp
+    kernel/susan.hpp
+    kernel/swapdblk.hpp
+    kernel/tile.hpp
+    kernel/transform.hpp
+    kernel/transpose.hpp
+    kernel/transpose_inplace.hpp
+    kernel/triangle.hpp
+    kernel/unwrap.hpp
+    kernel/where.hpp
+    kernel/wrap.hpp
+
+    kernel/convolve/conv1.cpp
+    kernel/convolve/conv2_b8.cpp
+    kernel/convolve/conv2_c32.cpp
+    kernel/convolve/conv2_c64.cpp
+    kernel/convolve/conv2_f32.cpp
+    kernel/convolve/conv2_f64.cpp
+    kernel/convolve/conv2_impl.hpp
+    kernel/convolve/conv2_s16.cpp
+    kernel/convolve/conv2_s32.cpp
+    kernel/convolve/conv2_s64.cpp
+    kernel/convolve/conv2_u16.cpp
+    kernel/convolve/conv2_u32.cpp
+    kernel/convolve/conv2_u64.cpp
+    kernel/convolve/conv2_u8.cpp
+    kernel/convolve/conv3.cpp
+    kernel/convolve/conv_common.hpp
     )
 
-LIST(SORT c_headers)
-LIST(SORT c_sources)
+target_sources(afopencl
+  PRIVATE
+    JIT/BinaryNode.hpp
+    JIT/BufferNode.hpp
+    JIT/Node.hpp
+    JIT/ScalarNode.hpp
+    JIT/UnaryNode.hpp
+  )
+
+target_sources(afopencl
+  PRIVATE
+    ${kernel_files}
+  )
 
-source_group(api\\c\\Headers FILES ${c_headers})
-source_group(api\\c\\Sources FILES ${c_sources})
+target_sources(afopencl
+  PRIVATE
+    cpu/cpu_blas.cpp
+    cpu/cpu_blas.hpp
+    cpu/cpu_cholesky.cpp
+    cpu/cpu_cholesky.hpp
+    cpu/cpu_helper.hpp
+    cpu/cpu_inverse.cpp
+    cpu/cpu_inverse.hpp
+    cpu/cpu_lu.cpp
+    cpu/cpu_lu.hpp
+    cpu/cpu_qr.cpp
+    cpu/cpu_qr.hpp
+    cpu/cpu_solve.cpp
+    cpu/cpu_solve.hpp
+    cpu/cpu_sparse_blas.cpp
+    cpu/cpu_sparse_blas.hpp
+    cpu/cpu_svd.cpp
+    cpu/cpu_svd.hpp
+    cpu/cpu_triangle.hpp
+  )
+
+target_include_directories(afopencl
+  PUBLIC
+    $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
+    $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
+  PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${CMAKE_CURRENT_BINARY_DIR}
+    magma
+    ../../api/c
+    ../../../include
+  )
+
+add_dependencies(afopencl ${cl_kernel_targets})
+add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp)
+add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp)
+
+set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
+
+target_compile_definitions(afopencl
+  PRIVATE
+    CL_USE_DEPRECATED_OPENCL_1_2_APIS
+    __CL_ENABLE_EXCEPTIONS
+  )
 
-FILE(GLOB cpp_sources
-    "../../api/cpp/*.cpp"
+target_link_libraries(afopencl
+  PRIVATE
+    c_api_interface
+    cpp_api_interface
+    OpenCL::OpenCL
+    OpenCL::cl2hpp
+    afcommon_interface
+    clFFT::clFFT
+    opencl_scan_by_key
+    opencl_sort_by_key
+    Boost::boost
     )
 
-LIST(SORT cpp_sources)
+if(OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
+  include(build_clBLAS)
+  target_compile_definitions(afopencl PRIVATE USE_CLBLAS)
+  target_link_libraries(afopencl
+    PRIVATE
+      clBLAS::clBLAS)
+elseif(OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
+  include(build_CLBlast)
+  target_compile_definitions(afopencl PRIVATE USE_CLBLAST)
+  target_link_libraries(afopencl
+    PRIVATE
+      CLBlast)
+    add_dependencies(afopencl CLBlast-ext)
+endif()
 
-source_group(api\\cpp\\Sources FILES ${cpp_sources})
 
-FILE(GLOB kernel_src ${opencl_kernels} "kernel/KParam.hpp")
+if(BUILD_NONFREE)
+  target_sources(afopencl PRIVATE kernel/sift_nonfree.hpp)
+  target_compile_definitions(afopencl PRIVATE AF_BUILD_NONFREE_SIFT)
+endif()
 
-LIST(SORT kernel_src)
+if(BUILD_GRAPHICS)
+  target_sources(afopencl
+    PRIVATE
+      GraphicsResourceManager.hpp
+      GraphicsResourceManager.cpp
+      hist_graphics.cpp
+      hist_graphics.hpp
+      image.cpp
+      image.hpp
+      plot.cpp
+      plot.hpp
+      surface.cpp
+      surface.hpp
+      vector_field.cpp
+      vector_field.hpp
+    )
+endif()
+
+if(LAPACK_FOUND)
+  target_sources(afopencl
+    PRIVATE
+      magma/gebrd.cpp
+      magma/geqrf2.cpp
+      magma/geqrf3.cpp
+      magma/getrf.cpp
+      magma/getrs.cpp
+      magma/labrd.cpp
+      magma/larfb.cpp
+      magma/laset.cpp
+      #magma/laset_band.cpp
+      magma/laswp.cpp
+      magma/magma.h
+      magma/magma_blas.h
+      magma/magma_blas_clblas.h
+      magma/magma_blas_clblast.h
+      magma/magma_common.h
+      magma/magma_cpu_blas.h
+      magma/magma_cpu_lapack.h
+      magma/magma_data.h
+      magma/magma_helper.cpp
+      magma/magma_helper.h
+      magma/magma_sync.h
+      magma/magma_types.h
+      magma/potrf.cpp
+      magma/swapdblk.cpp
+      magma/transpose.cpp
+      magma/transpose_inplace.cpp
+      magma/ungqr.cpp
+      magma/unmqr.cpp
+      #magma/unmqr2.cpp
 
-CL_KERNEL_TO_H(
-    SOURCES ${kernel_src}
-    VARNAME kernel_files
-    EXTENSION "hpp"
-    OUTPUT_DIR ${cl_kernel_headers}
-    TARGETS cl_kernel_targets
-    NAMESPACE "opencl"
     )
 
-# OS Definitions
-IF(UNIX)
-  SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -pthread -Wno-comment")
-    # GCC 6.0 and above enable -Wignored-attributes by default causing a lot of warnings
-    # Disable the trigger for gcc >= 6.0.0
-    IF("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "6.0.0")
-        ADD_DEFINITIONS(-Wno-ignored-attributes)
-    ENDIF()
-ENDIF()
-
-INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
-
-INCLUDE("${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/CMakeLists.txt")
-IF(DEFINED BLAS_SYM_FILE)
-
-    ADD_LIBRARY(afopencl_static STATIC
-                ${opencl_headers}
-                ${opencl_sources}
-                ${jit_sources}
-                ${kernel_headers}
-                ${opencl_kernels}
-                ${kernel_sources}
-                ${conv_ker_headers}
-                ${conv_ker_sources}
-                ${cpu_headers}
-                ${cpu_sources}
-                ${backend_headers}
-                ${backend_sources}
-                ${magma_sources}
-                ${magma_headers}
-                ${SORT_BY_KEY_OBJECTS}
-                ${SCAN_BY_KEY_OBJECTS})
-
-    ADD_LIBRARY(afopencl SHARED
-               ${c_headers}
-               ${c_sources}
-               ${cpp_sources})
-
-
-    IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-        ADD_DEPENDENCIES(afopencl_static forge)
-    ENDIF()
-
-    IF(APPLE)
-    SET_TARGET_PROPERTIES(afopencl_static
-        PROPERTIES LINK_FLAGS -Wl,-exported_symbols_list,${BLAS_SYM_FILE})
-    TARGET_LINK_LIBRARIES(afopencl PUBLIC $<TARGET_FILE:afopencl_static>)
-    ELSE(APPLE)
-    add_custom_command(OUTPUT ${PROJECT_BINARY_DIR}/afopencl_static.renamed
-      COMMAND objcopy --redefine-syms ${BLAS_SYM_FILE} $<TARGET_FILE:afopencl_static> ${PROJECT_BINARY_DIR}/afopencl_static.renamed
-      DEPENDS $<TARGET_FILE:afopencl_static>)
-      TARGET_LINK_LIBRARIES(afopencl PUBLIC ${PROJECT_BINARY_DIR}/afopencl_static.renamed)
-    ENDIF(APPLE)
-
-ELSE(DEFINED BLAS_SYM_FILE)
-
-    ADD_LIBRARY(afopencl SHARED
-                ${opencl_headers}
-                ${opencl_sources}
-                ${jit_sources}
-                ${kernel_headers}
-                ${opencl_kernels}
-                ${kernel_sources}
-                ${conv_ker_headers}
-                ${conv_ker_sources}
-                ${cpu_sources}
-                ${cpu_sources}
-                ${backend_headers}
-                ${backend_sources}
-                ${c_headers}
-                ${c_sources}
-                ${cpp_sources}
-                ${magma_sources}
-                ${magma_headers}
-                ${SORT_BY_KEY_OBJECTS}
-                ${SCAN_BY_KEY_OBJECTS})
-
-ENDIF()
-
-TARGET_LINK_LIBRARIES(afopencl
-  PRIVATE
-    ${OpenCL_LIBRARIES}
-    ${CLBLAST_LIBRARIES}
-    ${CLBLAS_LIBRARIES}
-    ${CLFFT_LIBRARIES}
-    ${CMAKE_DL_LIBS}
-    ${FreeImage_LIBS}
-    ${CMAKE_THREAD_LIBS_INIT}
-)
-
-IF(LAPACK_FOUND)
-    TARGET_LINK_LIBRARIES(afopencl
-      PRIVATE
-        ${LAPACK_LIBRARIES}
-        ${CBLAS_LIBRARIES}
+  if(USE_OPENCL_MKL)
+    dependency_check(MKL_FOUND "MKL not found")
+    target_compile_definitions(afopencl PRIVATE USE_MKL)
+
+    # TODO(umar) Find a better way to determine BLAS selection
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" OR
+      (CMAKE_CXX_COMPILER_ID STREQUAL "Intel" AND (UNIX AND NOT APPLE)))
+      # MKL requires multiple passes when linking with the static libs. This can be
+      # done in CMake using LINK_INTERFACE_MULTIPLICITY but that will require
+      # changine the way FindCBLAS works. This can also be done using the
+      # --start-group and --end-group linker around the libraries in the linking
+      # step.
+      #
+      # TODO(umar): Change the way CBLAS libraries are found and linked
+      set(CBLAS_LIBRARIES -Wl,--start-group ${CBLAS_LIBRARIES} -Wl,--end-group)
+    endif()
+  else()
+    if(USE_CPU_F77_BLAS)
+      target_compile_definitions(afopencl PRIVATE USE_F77_BLAS)
+    endif()
+
+    dependency_check(CBLAS_LIBRARIES "CBLAS not found.")
+  endif()
+
+  target_compile_definitions(
+    afopencl
+    PRIVATE
+      WITH_OPENCL_LINEAR_ALGEBRA
+    )
+
+  target_link_libraries(afopencl
+    PRIVATE
+      afcommon_lapack_interface
+      ${CBLAS_LIBRARIES}
     )
-ENDIF(LAPACK_FOUND)
-
-LIST(LENGTH GRAPHICS_DEPENDENCIES GRAPHICS_DEPENDENCIES_LEN)
-
-ADD_DEPENDENCIES(afopencl ${cl_kernel_targets})
-
-IF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
-    ADD_DEPENDENCIES(afopencl ${GRAPHICS_DEPENDENCIES})
-ENDIF(${GRAPHICS_DEPENDENCIES_LEN} GREATER 0)
-IF(NOT USE_SYSTEM_CL2HPP)
-    ADD_DEPENDENCIES(afopencl cl2hpp)
-ENDIF(NOT USE_SYSTEM_CL2HPP)
-IF(CLFFT_FOUND AND NOT USE_SYSTEM_CLFFT)
-    ADD_DEPENDENCIES(afopencl clFFT)
-ENDIF()
-IF(CLBLAST_FOUND AND NOT USE_SYSTEM_CLBLAST)
-    ADD_DEPENDENCIES(afopencl CLBlast)
-ENDIF()
-IF(CLBLAS_FOUND AND NOT USE_SYSTEM_CLBLAS)
-    ADD_DEPENDENCIES(afopencl clBLAS)
-ENDIF()
-
-IF(FORGE_FOUND)
-    TARGET_LINK_LIBRARIES(afopencl
-                                PRIVATE ${GRAPHICS_LIBRARIES})
-ENDIF(FORGE_FOUND)
-
-SET_TARGET_PROPERTIES(afopencl PROPERTIES
-    VERSION "${AF_VERSION}"
-    SOVERSION "${AF_VERSION_MAJOR}")
-
-INSTALL(TARGETS afopencl EXPORT OpenCL DESTINATION "${AF_INSTALL_LIB_DIR}"
-        COMPONENT libraries)
-
-IF(APPLE)
-    INSTALL(SCRIPT "${PROJECT_SOURCE_DIR}/CMakeModules/osx_install/InstallTool.cmake")
-ENDIF(APPLE)
-
-export(TARGETS afopencl FILE ArrayFireOpenCL.cmake)
-INSTALL(EXPORT OpenCL DESTINATION "${AF_INSTALL_CMAKE_DIR}"
-    COMPONENT cmake
-    FILE ArrayFireOpenCL.cmake)
+  target_include_directories(afopencl
+    PRIVATE
+      ${CBLAS_INCLUDE_DIR})
+endif(LAPACK_FOUND)
+
+install(TARGETS afopencl
+  EXPORT ArrayFireOpenCLTargets
+  COMPONENT opencl
+  PUBLIC_HEADER DESTINATION af
+  RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
+  LIBRARY DESTINATION ${AF_INSTALL_LIB_DIR}
+  ARCHIVE DESTINATION ${AF_INSTALL_LIB_DIR}
+  FRAMEWORK DESTINATION framework
+  INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
+  )
+
+source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
+source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
+source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
+source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
+source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/*)
+source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index d498dab894..4ffd361b69 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -11,6 +11,7 @@
 
 #if defined(WITH_GRAPHICS)
 #include <common/InteropManager.hpp>
+
 #include <map>
 #include <vector>
 
diff --git a/src/backend/opencl/cache.hpp b/src/backend/opencl/cache.hpp
index 0dab9d9e24..2283838a66 100644
--- a/src/backend/opencl/cache.hpp
+++ b/src/backend/opencl/cache.hpp
@@ -12,6 +12,11 @@
 #include <map>
 #include <string>
 
+namespace cl {
+  class Program;
+  class Kernel;
+}
+
 namespace opencl
 {
     typedef struct {
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index 8235b732e4..82927001e6 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <clfft.hpp>
 #include <platform.hpp>
 #include <string>
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
index f249735712..d4c7426d08 100644
--- a/src/backend/opencl/clfft.hpp
+++ b/src/backend/opencl/clfft.hpp
@@ -12,6 +12,7 @@
 #include <clFFT.h>
 #include <common/FFTPlanCache.hpp>
 #include <memory.hpp>
+
 #include <cstdio>
 
 namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 5cc3028893..127307b3e1 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -11,6 +11,7 @@
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_blas.hpp>
 #include <math.hpp>
+#include <common/blas_headers.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index 6da565e1c6..df3a820c90 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -32,7 +32,7 @@
 #else
     #ifdef __APPLE__
         #include <Accelerate/Accelerate.h>
-        #include <lapacke.hpp>
+        #include <common/lapacke.hpp>
         #undef AF_LAPACK_COL_MAJOR
         #define AF_LAPACK_COL_MAJOR 0
     #else // NETLIB LAPACKE
@@ -42,31 +42,4 @@
 
 #endif // WITH_OPENCL_LINEAR_ALGEBRA
 
-//********************************************************/
-// BLAS
-//********************************************************/
-#ifdef USE_MKL
-    #include <mkl_cblas.h>
-#else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-    #else
-        extern "C" {
-            #include <cblas.h>
-        }
-    #endif
-#endif
-
-// TODO: Ask upstream for a more official way to detect it
-#ifdef OPENBLAS_CONST
-#define IS_OPENBLAS
-#endif
-
-// Make sure we get the correct type signature for OpenBLAS
-// OpenBLAS defines blasint as it's index type. Emulate this
-// if we're not dealing with openblas and use it where applicable
-#ifndef IS_OPENBLAS
-typedef int blasint;
-#endif
-
 #endif
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.hpp b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
index e2475e0e07..2837d5c02b 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 
 #ifdef USE_MKL
 #include <mkl_spblas.h>
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
index c02d600d49..e61b2ad430 100644
--- a/src/backend/opencl/err_clblast.hpp
+++ b/src/backend/opencl/err_clblast.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <stdio.h>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <clblast.h>
 
 static const char * _clblastGetResultString(clblast::StatusCode st)
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 841670e97e..2dca3fa538 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <cstdio>
 #include <errorcodes.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <platform.hpp>
 #include <types.hpp>
 
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 16cdb0dd55..98cb981f18 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -85,11 +85,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim)
-                seqs.push_back(af_make_seq(0, pDims[k]-1, 1));
+                seqs.push_back({0., static_cast<double>(pDims[k]-1), 1.});
             else if (k == baseDim)
-                seqs.push_back(af_make_seq(1, pDims[k]-1, 1));
+                seqs.push_back({1., static_cast<double>(pDims[k]-1), 1.});
             else
-                seqs.push_back(af_make_seq(0, 0, 1));
+                seqs.push_back({0., 0., 1.});
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
@@ -99,11 +99,11 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim)
-                seqs.push_back(af_make_seq(0, pDims[k]-1, 1));
+                seqs.push_back({0., (double)pDims[k]-1, 1.});
             else if (k == baseDim)
-                seqs.push_back(af_make_seq(0, pDims[k]-2, 1));
+                seqs.push_back({0., static_cast<double>(pDims[k]-2), 1.});
             else
-                seqs.push_back(af_make_seq(0, 0, 1));
+                seqs.push_back({0., 0., 1.});
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
diff --git a/src/backend/opencl/hist_graphics.hpp b/src/backend/opencl/hist_graphics.hpp
index 318437059d..2e6c980027 100644
--- a/src/backend/opencl/hist_graphics.hpp
+++ b/src/backend/opencl/hist_graphics.hpp
@@ -9,7 +9,7 @@
 
 #if defined (WITH_GRAPHICS)
 
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 #include <Array.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/image.hpp b/src/backend/opencl/image.hpp
index 3e74fe86f9..ee0ee87583 100644
--- a/src/backend/opencl/image.hpp
+++ b/src/backend/opencl/image.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 05cc889dd2..78e794f86d 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -16,7 +16,7 @@
 #include <JIT/Node.hpp>
 #include <kernel_headers/jit.hpp>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <functional>
 #include <af/opencl.h>
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 21a0274384..307bb7e628 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -14,7 +14,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 580e7ab582..864dda2597 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 1d93de83ba..4d1a69b1e8 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -14,7 +14,7 @@
 #include <string>
 #include <algorithm>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <af/opencl.h>
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index dc01057949..322ab7c035 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -12,7 +12,7 @@
 #include <kernel_headers/nonmax_suppression.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
 #include <memory.hpp>
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 95c761776b..219944fffe 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -19,7 +19,7 @@
 #include <traits.hpp>
 #include <memory.hpp>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <platform.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index c3aacfc525..de2a4140d0 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -15,7 +15,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <memory.hpp>
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index a72a150501..12e9df25d6 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -15,7 +15,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 2a2b676c44..ed9a7bbae8 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -15,7 +15,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 40e2bd30e6..2e308ac0d1 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -15,7 +15,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 2ebb0b964e..6a37cbfbfe 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -15,7 +15,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 70659e580a..a459596df2 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -11,7 +11,7 @@
 #include <kernel_headers/diag_extract.hpp>
 #include <program.hpp>
 #include "../traits.hpp"
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 438ad13354..2c3a81091e 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index acad35b831..c6375d6709 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -25,7 +25,7 @@
                                         // if any
                                         // * addKernelToCache - push new kernels into cache
 
-#include <dispatch.hpp>                 // common utility header for CUDA & OpenCL backends
+#include <common/dispatch.hpp>          // common utility header for CUDA & OpenCL backends
                                         // has the divup macro
 
 #include <Param.hpp>                    // This header has the declaration of structures
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index bf719e810d..844c3b9fee 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -9,7 +9,7 @@
 
 #include <af/defines.h>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 1204047fe5..0c906b91e3 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -10,7 +10,7 @@
 #include <af/defines.h>
 #include <backend.hpp>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <cache.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 393fa16743..9aec3898d0 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 9db7c1a80b..12f38e61e9 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -10,7 +10,7 @@
 #include <af/defines.h>
 #include <af/constants.h>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 318297b021..7f226f0571 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 0cf20310b6..54dafb258e 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <af/defines.h>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <memory.hpp>
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 0165512173..6b539c90ce 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 49197ba2d3..26621ab274 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -10,7 +10,7 @@
 #include <kernel_headers/identity.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index ef3025e7f6..2d64d2ebf3 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 4b41c82172..ea8c34646d 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 8e0d85e41e..bc97629850 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -14,7 +14,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index d29e46b7d5..a84750913d 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -17,7 +17,7 @@
 #include <kernel_headers/iops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index a450f22fcf..acca8ab749 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -14,7 +14,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 612df24383..4a1bf4ce5b 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 645622c279..9dc99d78fa 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 9d6b486c3b..7e8a9d0733 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 9528680366..47b11a5eaf 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 858b931660..ca15c0fedd 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index 4cd5965371..eaede72bbd 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index da877cbbae..2dbf2ce2fd 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -13,7 +13,7 @@
 #include <kernel_headers/mean_ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index 38b656920c..175483c7ea 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -14,7 +14,7 @@
 #include <string>
 #include <cache.hpp>
 #include <algorithm>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 3a7a471825..e0390244c1 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -14,7 +14,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 6ee1733ecb..436da3fe00 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -16,7 +16,7 @@
 #include <string>
 #include <algorithm>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index a4fafe2b5e..f1b2f7bca1 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -14,7 +14,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 8c84033b56..caf61b162e 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <memory.hpp>
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 7280508a63..9d70b20f09 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -9,13 +9,13 @@
 
 #include <af/defines.h>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/nearest_neighbour.hpp>
 #include <memory.hpp>
 #include <math.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <cache.hpp>
 
 using cl::KernelFunctor;
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index d49693f70d..fa6f211fc9 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -9,7 +9,7 @@
 
 #include <af/defines.h>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 827f56e552..60e676f8fb 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -17,7 +17,7 @@
 #include <traits.hpp>
 #include <sstream>
 #include <string>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <program.hpp>
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index a1bb664b92..2896307dac 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index ebb3f1f071..7b28ced42b 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -16,7 +16,7 @@
 #include <kernel_headers/ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 31cc3c3f34..b658264156 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -11,7 +11,7 @@
 #include <kernel_headers/regions.hpp>
 #include <program.hpp>
 #include <af/defines.h>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <math.hpp>
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 87a9e725f3..a15939daa1 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index c58f343e5f..b016f2d7f3 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 4f595b5ec4..0b08b02d87 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -14,7 +14,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 664ef57c1e..d53e6248d5 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -1,24 +1,54 @@
-FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp" FILESTRINGS)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-FOREACH(STR ${FILESTRINGS})
-    IF(${STR} MATCHES "// SBK_BINARY_OPS")
-        STRING(REPLACE "// SBK_BINARY_OPS:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_BINARY_OPS ${TEMP})
-    ENDIF()
-ENDFOREACH()
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp" FILESTRINGS)
+find_package(OpenCL REQUIRED)
 
-FOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
-    ADD_LIBRARY(opencl_scan_by_key_${SBK_BINARY_OP} OBJECT
+foreach(STR ${FILESTRINGS})
+    if(${STR} MATCHES "// SBK_BINARY_OPS")
+        string(REPLACE "// SBK_BINARY_OPS:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_BINARY_OPS ${TEMP})
+    endif()
+endforeach()
+
+add_library(opencl_scan_by_key INTERFACE)
+
+add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp)
+foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
+    add_library(opencl_scan_by_key_${SBK_BINARY_OP} OBJECT
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp")
-    ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} ${cl_kernel_targets})
-    IF(NOT USE_SYSTEM_CL2HPP)
-        ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} cl2hpp)
-    ENDIF()
-    IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-        ADD_DEPENDENCIES(opencl_scan_by_key_${SBK_BINARY_OP} forge)
-    ENDIF()
-    SET_TARGET_PROPERTIES(opencl_scan_by_key_${SBK_BINARY_OP} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_BINARY_OP}")
-    LIST(APPEND SCAN_BY_KEY_OBJECTS $<TARGET_OBJECTS:opencl_scan_by_key_${SBK_BINARY_OP}>)
-ENDFOREACH(SBK_BINARY_OP ${SBK_BINARY_OPS})
+
+    add_dependencies(opencl_scan_by_key_${SBK_BINARY_OP}
+                        ${cl_kernel_targets} OpenCL::cl2hpp)
+
+    target_include_directories(opencl_scan_by_key_${SBK_BINARY_OP}
+      PRIVATE
+        .
+        ..
+        magma
+        ../../api/c
+        ../common
+        ../../../include
+        ${CMAKE_CURRENT_BINARY_DIR}
+        $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
+      )
+
+    set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
+      PROPERTIES
+        POSITION_INDEPENDENT_CODE ON
+        FOLDER "Generated Targets")
+
+      target_compile_definitions(opencl_scan_by_key_${SBK_BINARY_OP}
+        PRIVATE
+          $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
+          TYPE=${SBK_BINARY_OP} AFDLL)
+    target_sources(opencl_scan_by_key
+      INTERFACE $<TARGET_OBJECTS:opencl_scan_by_key_${SBK_BINARY_OP}>)
+endforeach(SBK_BINARY_OP ${SBK_BINARY_OPS})
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index a48d271a5d..c403defe2c 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -15,7 +15,7 @@
 #include <kernel_headers/ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
index 691552f465..b77cd434f3 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 namespace opencl
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 217153d18c..deef4aa28a 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -15,7 +15,7 @@
 #include <kernel_headers/ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index 58587c1ba9..14f99fa883 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -15,7 +15,7 @@
 #include <kernel_headers/ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/scan_first_by_key.hpp b/src/backend/opencl/kernel/scan_first_by_key.hpp
index 7652929385..3eaa5c8356 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 771dfca8f4..c88d0b3994 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -15,7 +15,7 @@
 #include <kernel_headers/ops.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index ed8fe02640..fe3f1daf76 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/shift.hpp b/src/backend/opencl/kernel/shift.hpp
index 7a6fb81650..5238ea840f 100644
--- a/src/backend/opencl/kernel/shift.hpp
+++ b/src/backend/opencl/kernel/shift.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cassert>
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index a3ad9a53b7..72c63f7cba 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -72,7 +72,7 @@
 
 #include <af/defines.h>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index f77e3ea911..5a4dbb68ca 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 357b67d51c..3ced4e1e23 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -12,7 +12,7 @@
 #include <traits.hpp>
 #include <string>
 #include <mutex>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/sort_helper.hpp>
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 18f96cdc7c..b3bc500d4b 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 983753644b..e1701f40ff 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -1,23 +1,50 @@
-FILE(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-FOREACH(STR ${FILESTRINGS})
-    IF(${STR} MATCHES "// SBK_TYPES")
-        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
-    ENDIF()
-ENDFOREACH()
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
 
-FOREACH(SBK_TYPE ${SBK_TYPES})
-    ADD_LIBRARY(opencl_sort_by_key_${SBK_TYPE} OBJECT
+foreach(STR ${FILESTRINGS})
+    if(${STR} MATCHES "// SBK_TYPES")
+        string(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_TYPES ${TEMP})
+    endif()
+endforeach()
+
+add_library(opencl_sort_by_key INTERFACE)
+foreach(SBK_TYPE ${SBK_TYPES})
+    add_library(opencl_sort_by_key_${SBK_TYPE} OBJECT
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp")
-    ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} ${cl_kernel_targets})
-    IF(NOT USE_SYSTEM_CL2HPP)
-        ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} cl2hpp)
-    ENDIF(NOT USE_SYSTEM_CL2HPP)
-    IF(FORGE_FOUND AND NOT USE_SYSTEM_FORGE)
-        ADD_DEPENDENCIES(opencl_sort_by_key_${SBK_TYPE} forge)
-    ENDIF()
-    SET_TARGET_PROPERTIES(opencl_sort_by_key_${SBK_TYPE} PROPERTIES COMPILE_FLAGS "-DTYPE=${SBK_TYPE}")
-    LIST(APPEND SORT_BY_KEY_OBJECTS $<TARGET_OBJECTS:opencl_sort_by_key_${SBK_TYPE}>)
-ENDFOREACH(SBK_TYPE ${SBK_TYPES})
+    add_dependencies(opencl_sort_by_key_${SBK_TYPE}
+                        ${cl_kernel_targets} OpenCL::cl2hpp Boost::boost)
+
+    target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+      PRIVATE
+        .
+        ..
+        magma
+        ../../api/c
+        ../common
+        ../../../include
+        ${CMAKE_CURRENT_BINARY_DIR}
+        $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
+      )
+
+    set_target_properties(opencl_sort_by_key_${SBK_TYPE}
+      PROPERTIES
+        POSITION_INDEPENDENT_CODE ON
+        FOLDER "Generated Targets")
+
+    target_compile_definitions(opencl_sort_by_key_${SBK_TYPE}
+      PRIVATE
+        $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
+        TYPE=${SBK_TYPE} AFDLL)
+    target_sources(opencl_sort_by_key
+      INTERFACE $<TARGET_OBJECTS:opencl_sort_by_key_${SBK_TYPE}>)
+endforeach(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 922f74fd0c..fc5f00b147 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -15,7 +15,7 @@
 #include <traits.hpp>
 #include <string>
 #include <mutex>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <math.hpp>
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 078ff7c0c6..5ec0a099d1 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_traits>
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 67c2650bcb..5f1efe076b 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -17,7 +17,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 7015aa0818..3cef0fdcab 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -16,7 +16,7 @@
 #include <string>
 #include <mutex>
 #include <map>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index bb008bed21..b78f573021 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -9,7 +9,7 @@
 
 #include <af/defines.h>
 #include <program.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <err_opencl.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/susan.hpp>
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index e7c4ab9ed3..55761dff0b 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index 7d15384f86..66fe5c88c4 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 3aa37d97e2..cf649c4b24 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -13,7 +13,7 @@
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 3fba8c12c8..6142af863a 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index b9f7b12a01..4b0dd8b2cb 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 32925d1cbb..122d75afb1 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -13,7 +13,7 @@
 #include <traits.hpp>
 #include <string>
 #include <cache.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 100bd9ca66..7c4dead472 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -15,7 +15,7 @@
 #include <string>
 #include <map>
 #include <mutex>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index e76943e3c2..fe618a911c 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -13,7 +13,7 @@
 #include <kernel_headers/where.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 5ad2efe38c..d0136e3e5c 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -14,7 +14,7 @@
 #include <string>
 #include <map>
 #include <mutex>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/magma/magma_blas_clblas.h b/src/backend/opencl/magma/magma_blas_clblas.h
index e135c6c6a3..3c1e1a9a2d 100644
--- a/src/backend/opencl/magma/magma_blas_clblas.h
+++ b/src/backend/opencl/magma/magma_blas_clblas.h
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <defines.hpp>
+#include <common/defines.hpp>
 
 #include <clBLAS.h>
 #include <err_clblas.hpp>
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 1917347600..55caf705f3 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -11,7 +11,7 @@
 
 #include <complex>
 
-#include <defines.hpp>
+#include <common/defines.hpp>
 
 #include <clblast.h>
 #include <err_clblast.hpp>
diff --git a/src/backend/opencl/magma/magma_cpu_blas.h b/src/backend/opencl/magma/magma_cpu_blas.h
index 6d06b2caae..87bc65aef3 100644
--- a/src/backend/opencl/magma/magma_cpu_blas.h
+++ b/src/backend/opencl/magma/magma_cpu_blas.h
@@ -10,32 +10,10 @@
 #ifndef MAGMA_CPU_BLAS
 #define MAGMA_CPU_BLAS
 #include <err_opencl.hpp>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include "magma_types.h"
+#include <common/blas_headers.hpp>
 
-#ifdef USE_MKL
-    #include <mkl_cblas.h>
-#else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-    #else
-        extern "C" {
-            #include <cblas.h>
-        }
-    #endif
-#endif
-
-// Todo: Ask upstream for a more official way to detect it
-#ifdef OPENBLAS_CONST
-#define IS_OPENBLAS
-#endif
-
-// Make sure we get the correct type signature for OpenBLAS
-// OpenBLAS defines blasint as it's index type. Emulate this
-// if we're not dealing with openblas and use it where applicable
-#ifndef IS_OPENBLAS
-typedef int blasint;
-#endif
 
 #define CPU_BLAS_FUNC_DEF(NAME)                 \
     template<typename T>                        \
diff --git a/src/backend/opencl/magma/magma_cpu_lapack.h b/src/backend/opencl/magma/magma_cpu_lapack.h
index df17496e6b..fdcf2a7136 100644
--- a/src/backend/opencl/magma/magma_cpu_lapack.h
+++ b/src/backend/opencl/magma/magma_cpu_lapack.h
@@ -11,7 +11,7 @@
 #define MAGMA_CPU_LAPACK
 
 #include <err_opencl.hpp>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include "magma_types.h"
 
 #define LAPACKE_sunmqr_work(...) LAPACKE_sormqr_work(__VA_ARGS__)
@@ -44,7 +44,7 @@ int LAPACKE_dlacgv_work(Args... args) { return 0; }
 #else
     #ifdef __APPLE__
         #include <Accelerate/Accelerate.h>
-        #include <lapacke.hpp>
+        #include <common/lapacke.hpp>
         #undef LAPACK_COL_MAJOR
         #define LAPACK_COL_MAJOR 102
         #undef AF_LAPACK_COL_MAJOR
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 65ce083bcd..e3dcbe245f 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -10,13 +10,14 @@
 #pragma once
 
 #include <af/defines.h>
-#include "defines.hpp"
+#include <common/defines.hpp>
+
+#include <backend.hpp>
+#include <types.hpp>
 
 #include <complex>
 #include <limits>
 #include <algorithm>
-#include "backend.hpp"
-#include "types.hpp"
 
 #if defined(__GNUC__) || defined(__GNUG__)
     /* GCC/G++, Clang/LLVM, Intel ICC */
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 9af815723f..f2e668217e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -11,7 +11,7 @@
 // Causes conflict between system cl.hpp and opencl/cl.hpp
 #if defined(WITH_GRAPHICS)
 
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 #if defined(OS_MAC)
 #include <OpenGL/OpenGL.h>
@@ -24,14 +24,14 @@
 
 #include <af/version.h>
 #include <af/opencl.h>
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
 #include <blas.hpp>
-#include <host_memory.hpp>
+#include <common/host_memory.hpp>
 #include <common/InteropManager.hpp>
 #include <platform.hpp>
-#include <util.hpp>
+#include <common/util.hpp>
 #include <version.hpp>
 
 #include <algorithm>
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 35003dbf23..875f1c31d0 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -29,7 +29,6 @@
 #include <cache.hpp>
 #include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
-#include <common/types.hpp>
 
 // Forward declaration from clFFT.h
 struct clfftSetupData_;
diff --git a/src/backend/opencl/plot.hpp b/src/backend/opencl/plot.hpp
index 7bd4fbe8f9..9f50a06656 100644
--- a/src/backend/opencl/plot.hpp
+++ b/src/backend/opencl/plot.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index cce3d1dd59..2f09e505b1 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <platform.hpp>
-#include <util.hpp>
+#include <common/util.hpp>
 #include <string>
 #include <mutex>
 
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index f27d88fa93..805afd6c26 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index 5af3c76e4f..a5e269ea2a 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -17,7 +17,7 @@
 #include <cast.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
index 3de623c02e..4afc799cad 100644
--- a/src/backend/opencl/sparse_arith.hpp
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <sparse.hpp>
 #include <optypes.hpp>
 
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 8895a7ec02..eb8035ccd8 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -20,7 +20,7 @@
 
 #include <af/dim4.hpp>
 #include <complex.hpp>
-#include <err_common.hpp>
+#include <common/err_common.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/opencl/sparse_blas.hpp b/src/backend/opencl/sparse_blas.hpp
index b78616fb11..91e9c48bf9 100644
--- a/src/backend/opencl/sparse_blas.hpp
+++ b/src/backend/opencl/sparse_blas.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <SparseArray.hpp>
+#include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
 namespace opencl
diff --git a/src/backend/opencl/surface.hpp b/src/backend/opencl/surface.hpp
index 131a6a51dd..a87471c79b 100644
--- a/src/backend/opencl/surface.hpp
+++ b/src/backend/opencl/surface.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 54ba158e8a..f65a41e552 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <defines.hpp>
+#include <common/defines.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
 #include <string>
diff --git a/src/backend/opencl/vector_field.hpp b/src/backend/opencl/vector_field.hpp
index b75fd59b05..9685e28e76 100644
--- a/src/backend/opencl/vector_field.hpp
+++ b/src/backend/opencl/vector_field.hpp
@@ -10,7 +10,7 @@
 #if defined (WITH_GRAPHICS)
 
 #include <Array.hpp>
-#include <graphics_common.hpp>
+#include <common/graphics_common.hpp>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 90849fc0f7..5fe949b2fc 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -11,7 +11,7 @@
 #include <wrap.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
-#include <dispatch.hpp>
+#include <common/dispatch.hpp>
 #include <math.hpp>
 #include <kernel/wrap.hpp>
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index ed123c5781..b70962c9a3 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -1,312 +1,249 @@
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
-PROJECT(ArrayFire-Tests)
-
-# Find CUDA and OpenCL
-SET(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
-FIND_PACKAGE(CUDA QUIET)
-FIND_PACKAGE(OpenCL QUIET)
-
-OPTION(BUILD_SINGLE_TEST_FILE "Build tests in a single file" OFF)
-
-REMOVE_DEFINITIONS(-std=c++11)
-
-# If the tests are not being built at the same time as ArrayFire,
-# we need to first find the ArrayFire library
-IF(TARGET afcpu OR TARGET afcuda OR TARGET afopencl OR TARGET af)
-    SET(ArrayFire_CPU_FOUND False)
-    SET(ArrayFire_CUDA_FOUND False)
-    SET(ArrayFire_OpenCL_FOUND False)
-    SET(ArrayFire_Unified_FOUND False)
-ELSE()
-    SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-    FIND_PACKAGE(ArrayFire REQUIRED)
-    INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
-    OPTION(BUILD_NONFREE "Build Tests for nonfree algorithms" OFF)
-
-    IF(WIN32)
-        ADD_DEFINITIONS(-DOS_WIN -DNOMINMAX)
-    ENDIF(WIN32)
-
-    IF(${BUILD_NONFREE})
-        MESSAGE(WARNING "Building With NONFREE ON requires the following patents")
-        SET(BUILD_NONFREE_SIFT ON CACHE BOOL "Build ArrayFire with SIFT")
-    ELSE(${BUILD_NONFREE})
-        UNSET(BUILD_NONFREE_SIFT CACHE) # BUILD_NONFREE_SIFT cannot be built without BUILD_NONFREE
-    ENDIF(${BUILD_NONFREE})
-
-    IF(${BUILD_NONFREE_SIFT})
-      ADD_DEFINITIONS(-DAF_BUILD_NONFREE_SIFT)
-
-      MESSAGE(WARNING "Building with SIFT requires the following patents")
-
-      MESSAGE("Method and apparatus for identifying scale invariant features"
-        "in an image and use of same for locating an object in an image,\" David"
-        "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application"
-        "filed March 8, 1999. Asignee: The University of British Columbia. For"
-        "further details, contact David Lowe (lowe@cs.ubc.ca) or the"
-        "University-Industry Liaison Office of the University of British"
-        "Columbia.")
-    ENDIF(${BUILD_NONFREE_SIFT})
-
-    # ENABLE_TESTING is required when building only tests
-    # When building from source, enable_testing is picked from from the main
-    # CMakeLists.txt
-    ENABLE_TESTING()
-ENDIF()
-
-MACRO(CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
-    STRING(TOUPPER ${BACKEND} DEF_NAME)
-
-    # For some reason passing FILES/UNIFIED_FILES to macro doesn't work
-    IF(${BACKEND} STREQUAL "unified")
-      SET(TEST_FILES ${UNIFIED_FILES})
-    ELSE(${BACKEND} STREQUAL "unified")
-      SET(TEST_FILES ${FILES})
-    ENDIF(${BACKEND} STREQUAL "unified")
-
-    # libcuda.dylib depends on @rpath/CUDA.framework/Versions/A/CUDA in /Library/Frameworks
-    SET(TEST_LINK_FLAGS)
-    IF(${DEF_NAME} STREQUAL "CUDA" AND "${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-        SET(TEST_LINK_FLAGS -F/Library/Frameworks -Xlinker -framework -Xlinker CUDA)
-    ENDIF(${DEF_NAME} STREQUAL "CUDA" AND "${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-
-    IF (${BUILD_SINGLE_TEST_FILE})
-      SET(TEST_NAME test_${BACKEND})
-      SET(TEST_NAME_BASIC test_basic_${BACKEND})
-      ADD_EXECUTABLE(${TEST_NAME} ${CPP_FILES})
-      ADD_EXECUTABLE(${TEST_NAME_BASIC} basic_c.c)
-      SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_OPTIONS -std=c++11)
-
-      TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE ${AFLIBNAME}
-        PRIVATE ${THREAD_LIB_FLAG}
-        PRIVATE ${GTEST_LIBS}
-        PRIVATE ${OTHER_LIBS})
-
-      TARGET_LINK_LIBRARIES(${TEST_NAME_BASIC}  PRIVATE ${AFLIBNAME}
-        PRIVATE ${THREAD_LIB_FLAG}
-        PRIVATE ${GTEST_LIBS}
-        PRIVATE ${OTHER_LIBS})
-
-      SET_TARGET_PROPERTIES(${TEST_NAME_BASIC}
-        PROPERTIES
-        COMPILE_FLAGS -DAF_${DEF_NAME}
-        FOLDER "Tests/${BACKEND}")
-
-      IF(TEST_LINK_FLAGS)
-        SET_TARGET_PROPERTIES(${TEST_NAME_BASIC} PROPERTIES LINK_FLAGS ${TEST_LINK_FLAGS})
-      ENDIF(TEST_LINK_FLAGS)
-
-    ELSE()
-      FOREACH(FILE ${TEST_FILES})
-        GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
-        SET(TEST_NAME ${FNAME}_${BACKEND})
-
-        IF(NOT ${BUILD_NONFREE} AND "${FILE}" MATCHES ".nonfree.")
-          MESSAGE(STATUS "Removing ${FILE} from ctest")
-        ELSEIF("${FILE}" MATCHES ".manual.")
-          MESSAGE(STATUS "Removing ${FILE} from ctest")
-        ELSE()
-          ADD_TEST(Test_${TEST_NAME} ${TEST_NAME})
-        ENDIF()
-
-        FILE(GLOB TEST_FILE "${FNAME}.cpp" "${FNAME}.c")
-        ADD_EXECUTABLE(${TEST_NAME} ${TEST_FILE})
-        TARGET_LINK_LIBRARIES(${TEST_NAME}  PRIVATE ${AFLIBNAME}
-          PRIVATE ${THREAD_LIB_FLAG}
-          PRIVATE ${GTEST_LIBS}
-          PRIVATE ${OTHER_LIBS})
-
-        SET_TARGET_PROPERTIES(${TEST_NAME}
-          PROPERTIES
-          COMPILE_FLAGS -DAF_${DEF_NAME}
-          FOLDER "Tests/${BACKEND}")
-
-        IF (${FNAME} MATCHES "threading|solve")
-            SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_OPTIONS -std=c++11)
-        ENDIF()
-
-        IF(TEST_LINK_FLAGS)
-          SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS ${TEST_LINK_FLAGS})
-        ENDIF(TEST_LINK_FLAGS)
-      ENDFOREACH()
-    ENDIF()
-
-ENDMACRO(CREATE_TESTS)
-
-MACRO(CHECK_AND_CREATE_TESTS BACKEND AFLIBNAME GTEST_LIBS OTHER_LIBS)
-    STRING(TOUPPER ${BACKEND} BACKEND_NAME_UPPER)
-    MESSAGE(STATUS "TESTS: ${BACKEND_NAME_UPPER} backend is ${BUILD_${BACKEND_NAME_UPPER}}.")
-    IF(${BUILD_${BACKEND_NAME_UPPER}})
-        CREATE_TESTS(${BACKEND} ${AFLIBNAME} "${GTEST_LIBS}" "${OTHER_LIBS}")
-    ENDIF()
-ENDMACRO(CHECK_AND_CREATE_TESTS)
-
-FIND_PACKAGE(Threads REQUIRED)
-IF(CMAKE_USE_PTHREADS_INIT AND NOT "${APPLE}")
-    SET(THREAD_LIB_FLAG "-pthread")
-ELSE()
-    SET(THREAD_LIB_FLAG ${CMAKE_THREAD_LIBS_INIT})
-ENDIF()
-
-OPTION(USE_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF)
-
-IF(${USE_RELATIVE_TEST_DIR})
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+if(NOT TARGET gtest)
+  # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
+  # newer cmakes. This sets the default global setting for that policy.
+  set(CMAKE_POLICY_DEFAULT_CMP0042 NEW)
+  if(WIN32)
+    set(gtest_force_shared_crt ON
+        CACHE INTERNAL "Required so that the libs Runtime is not set to MT DLL")
+  endif()
+
+  add_subdirectory(gtest/googletest EXCLUDE_FROM_ALL)
+  set_target_properties(gtest gtest_main
+    PROPERTIES
+      FOLDER "ExternalProjectTargets/gtest")
+
+  # Hide gtest project variables
+  mark_as_advanced(
+    BUILD_SHARED_LIBS
+    gtest_build_samples
+    gtest_build_tests
+    gtest_disable_pthreads
+    gtest_force_shared_crt
+    gtest_hide_internal_symbols)
+endif()
+
+# Reset the CXX flags for tests
+unset(CMAKE_CXX_STANDARD)
+set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
+
+if(${USE_RELATIVE_TEST_DIR})
     # RELATIVE_TEST_DATA_DIR is a User-visible option with default value of test/data directory
-    SET(RELATIVE_TEST_DATA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data" CACHE STRING "Relative Test Data Directory")
-    SET(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
-ELSE(${USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
-    SET(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
-ENDIF(${USE_RELATIVE_TEST_DIR})
-
-# Workaround for Xcode generator escaping issue, see
-# - https://cmake.org/cmake/help/v3.5/release/3.5.html#deprecated-and-removed-features
-IF (CMAKE_VERSION VERSION_LESS 3.5 AND ${CMAKE_GENERATOR} STREQUAL "Xcode")
-    ADD_DEFINITIONS("-D TEST_DIR=\"\\\\\"${TESTDATA_SOURCE_DIR}\\\\\"\"")
-ELSE (CMAKE_VERSION VERSION_LESS 3.5 AND ${CMAKE_GENERATOR} STREQUAL "Xcode")
-    ADD_DEFINITIONS("-D TEST_DIR=\"\\\"${TESTDATA_SOURCE_DIR}\\\"\"")
-ENDIF (CMAKE_VERSION VERSION_LESS 3.5 AND ${CMAKE_GENERATOR} STREQUAL "Xcode")
-
-IF(NOT ${USE_RELATIVE_TEST_DIR})
-    # Check if data exists
-    IF (EXISTS "${TESTDATA_SOURCE_DIR}" AND IS_DIRECTORY "${TESTDATA_SOURCE_DIR}"
-        AND EXISTS "${TESTDATA_SOURCE_DIR}/README.md")
-        # Test data is available
-        # Do Nothing
-    ELSE (EXISTS "${TESTDATA_SOURCE_DIR}" AND IS_DIRECTORY "${TESTDATA_SOURCE_DIR}"
-        AND EXISTS "${TESTDATA_SOURCE_DIR}/README.md")
-        MESSAGE(STATUS "Test submodules unavailable. Updating submodules.")
-        EXECUTE_PROCESS(
-            COMMAND git submodule update --init --recursive
-            WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
-            OUTPUT_QUIET
-        )
-    ENDIF()
-ENDIF(NOT ${USE_RELATIVE_TEST_DIR})
-
-OPTION(USE_SYSTEM_GTEST "Use GTEST from system libraries" OFF)
-IF(USE_SYSTEM_GTEST)
-    FIND_PACKAGE(GTest REQUIRED)
-ELSE(USE_SYSTEM_GTEST)
-    INCLUDE("${PROJECT_SOURCE_DIR}/CMakeModules/build_gtest.cmake")
-ENDIF(USE_SYSTEM_GTEST)
-
-INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS})
-
-INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
-FILE(GLOB FILES "*.cpp" "*.c")
-FILE(GLOB CPP_FILES "*.cpp")
-LIST(SORT FILES)  # Tests execute in alphabetical order
-
-# We only build backend.cpp for Unified backend
-SET(UNIFIED_FILES "backend.cpp;main.cpp")
-LIST(SORT UNIFIED_FILES)  # Tests execute in alphabetical order
-
-# Next we build each example using every backend.
-IF(${ArrayFire_CPU_FOUND})  # variable defined by FIND(ArrayFire ...)
-    OPTION(BUILD_CPU "Build ArrayFire Tests for CPU backend" ON)
-    CHECK_AND_CREATE_TESTS(cpu ${ArrayFire_CPU_LIBRARIES} "${GTEST_LIBRARIES}" "")
-ELSEIF(TARGET afcpu)        # variable defined by the ArrayFire build tree
-    CHECK_AND_CREATE_TESTS(cpu afcpu "${GTEST_LIBRARIES}" "")
-ELSE()
-    MESSAGE(STATUS "TESTS: CPU backend is OFF. afcpu was not found.")
-ENDIF()
-
-# CUDA Backend
-IF (${CUDA_FOUND})
-    IF(${ArrayFire_CUDA_FOUND})  # variable defined by FIND(ArrayFire ...)
-        # Find NVVM
-        FIND_LIBRARY(CUDA_nvvm_LIBRARY
-          NAMES "nvvm"
-          PATH_SUFFIXES "nvvm/lib64" "nvvm/lib" "nmmv/lib/x64"
-          PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-          DOC "CUDA NVVM Library"
+    set(RELATIVE_TEST_DATA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data" CACHE STRING "Relative Test Data Directory")
+    set(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
+else(${USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
+    set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
+endif(${USE_RELATIVE_TEST_DIR})
+
+if(BUILD_CPU)
+  list(APPEND enabled_backends "cpu")
+endif(BUILD_CPU)
+
+if(BUILD_CUDA)
+  list(APPEND enabled_backends "cuda")
+endif(BUILD_CUDA)
+
+if(BUILD_OPENCL)
+  list(APPEND enabled_backends "opencl")
+endif(BUILD_OPENCL)
+
+if(BUILD_UNIFIED)
+  list(APPEND enabled_backends "unified")
+endif(BUILD_UNIFIED)
+
+include(CMakeParseArguments)
+
+function(make_test)
+  set(options CXX11)
+  set(single_args SRC)
+  set(multi_args LIBRARIES)
+  cmake_parse_arguments(mt_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
+
+  get_filename_component(src_name ${mt_args_SRC} NAME_WE)
+  foreach(backend ${enabled_backends})
+    set(target "${src_name}_${backend}")
+    add_executable(${target} ${mt_args_SRC})
+    target_include_directories(${target}
+      PRIVATE
+        ${CMAKE_SOURCE_DIR}
+        ${CMAKE_CURRENT_SOURCE_DIR}
+      )
+    target_link_libraries(${target}
+      PRIVATE
+        gtest
+        gtest_main
+        ${mt_args_LIBRARIES}
+      )
+
+    if(${backend} STREQUAL "unified")
+      target_link_libraries(${target}
+        PRIVATE
+          af
+          ${CMAKE_DL_LIBS}
           )
-        MARK_AS_ADVANCED(CUDA_nvvm_LIBRARY)
-
-        # If CUDA_CUDA_LIBRARY is not found, check for Stub in CUDA Toolkit
-        IF(NOT CUDA_CUDA_LIBRARY)
-            MESSAGE(SEND_ERROR "CMake CUDA Variable CUDA_CUDA_LIBRARY Not found.")
-            MESSAGE("CUDA Driver Library (libcuda.so/libcuda.dylib/cuda.lib) cannot be found.")
-            FIND_FILE(CUDA_CUDA_LIBRARY_STUB
-                      NAMES "libcuda.so" "libcuda.dylib" "cuda.lib"
-                      PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-                      PATH_SUFFIXES "lib64" "lib64/stubs" "lib" "lib/stubs" "lib/x64" "lib/Win32"
-                      DOC "CUDA Library STUB"
-                     )
-            IF(CUDA_CUDA_LIBRARY_STUB)
-                MESSAGE("You can use the library stub available in the CUDA Toolkit: ${CUDA_CUDA_LIBRARY_STUB}")
-                MESSAGE("Run the following commands (Linux) to set it up:")
-                MESSAGE("ln -s ${CUDA_CUDA_LIBRARY_STUB} /usr/lib/libcuda.so.1")
-                MESSAGE("ln -s /usr/lib/libcuda.so.1 /usr/lib/libcuda.so")
-            ENDIF()
-            MESSAGE(FATAL_ERROR "Ending CMake configuration because of missing CUDA_CUDA_LIBRARY")
-        ENDIF(NOT CUDA_CUDA_LIBRARY)
-
-        # If OSX && CLANG && CUDA < 7
-        IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
-            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
-
-            FOREACH(FILE ${FILES})
-                GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
-                SET(TEST_NAME ${FNAME}_cuda)
-                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_FLAGS -stdlib=libstdc++)
-                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS -stdlib=libstdc++)
-            ENDFOREACH()
-
-        # ELSE OSX && CLANG && CUDA < 7
-        ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            OPTION(BUILD_CUDA "Build ArrayFire Tests for CUDA backend" ON)
-            CHECK_AND_CREATE_TESTS(cuda ${ArrayFire_CUDA_LIBRARIES} "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
-
-        ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-
-    ELSEIF(TARGET afcuda)        # variable defined by the ArrayFire build tree
-        # If OSX && CLANG && CUDA < 7
-        IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES_STDLIB}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
-
-            FOREACH(FILE ${FILES})
-                GET_FILENAME_COMPONENT(FNAME ${FILE} NAME_WE)
-                SET(TEST_NAME ${FNAME}_cuda)
-                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES COMPILE_FLAGS -stdlib=libstdc++)
-                SET_TARGET_PROPERTIES(${TEST_NAME} PROPERTIES LINK_FLAGS -stdlib=libstdc++)
-            ENDFOREACH()
-
-        # ELSE OSX && CLANG && CUDA < 7
-        ELSE("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-            CHECK_AND_CREATE_TESTS(cuda afcuda "${GTEST_LIBRARIES}" "${CUDA_CUBLAS_LIBRARIES};${CUDA_LIBRARIES};${CUDA_cusolver_LIBRARY};${CUDA_cusparse_LIBRARY};${CUDA_CUFFT_LIBRARIES};${CUDA_nvvm_LIBRARY};${CUDA_CUDA_LIBRARY}")
-
-        ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang" AND ${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-    ELSE()
-        MESSAGE(STATUS "TESTS: CUDA backend is OFF. afcuda was not found")
-    ENDIF()
-ELSE()
-    MESSAGE(STATUS "TESTS: CUDA backend is OFF. CUDA was not found")
-ENDIF()
-
-# OpenCL Backend
-IF (${OpenCL_FOUND})
-    INCLUDE_DIRECTORIES(${OpenCL_INCLUDE_DIRS})
-    IF(${ArrayFire_OpenCL_FOUND})  # variable defined by FIND(ArrayFire ...)
-        OPTION(BUILD_OPENCL "Build ArrayFire Tests for OpenCL backend" ON)
-        MESSAGE(${OpenCL_LIBRARIES})
-        CHECK_AND_CREATE_TESTS(opencl ${ArrayFire_OpenCL_LIBRARIES} "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
-    ELSEIF(TARGET afopencl)        # variable defined by the ArrayFire build tree
-        CHECK_AND_CREATE_TESTS(opencl afopencl "${GTEST_LIBRARIES}" "${OpenCL_LIBRARIES}")
-    ELSE()
-        MESSAGE(STATUS "TESTS: OpenCL backend is OFF. afopencl was not found")
-    ENDIF()
-ELSE()
-    MESSAGE(STATUS "TESTS: OpenCL backend is OFF. OpenCL was not found")
-ENDIF()
+    else()
+      target_link_libraries(${target}
+        PRIVATE
+          af${backend}
+          )
+    endif()
 
-# Unified Backend
-IF(${ArrayFire_Unified_FOUND})  # variable defined by FIND(ArrayFire ...)
-  OPTION(BUILD_UNIFIED "Build ArrayFire Tests for Unified backend" ON)
-    CHECK_AND_CREATE_TESTS(unified ${ArrayFire_Unified_LIBRARIES} "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
-ELSEIF(TARGET af)        # variable defined by the ArrayFire build tree
-    CHECK_AND_CREATE_TESTS(unified af "${GTEST_LIBRARIES}" "${CMAKE_DL_LIBS}")
-ELSE()
-    MESSAGE(STATUS "TESTS: UNIFIED backend is OFF. af was not found.")
-ENDIF()
+    if(${mt_args_CXX11})
+      set_target_properties(${target}
+        PROPERTIES
+          CXX_STANDARD 11)
+    endif(${mt_args_CXX11})
+
+	  set_target_properties(${target}
+      PROPERTIES
+        FOLDER "Tests")
+
+    target_compile_definitions(${target}
+      PRIVATE
+        TEST_DIR="${TESTDATA_SOURCE_DIR}"
+        AF_$<UPPER_CASE:${backend}>
+      )
+    if(WIN32)
+      target_compile_definitions(${target}
+        PRIVATE
+          WIN32_LEAN_AND_MEAN
+          NOMINMAX
+        )
+    endif()
+
+    # TODO(umar): Create this executable separately
+    if(NOT ${backend} STREQUAL "unified" OR ${target} STREQUAL "backend_unified")
+      add_test(NAME ${target} COMMAND ${target})
+    endif()
+  endforeach()
+endfunction(make_test)
+
+make_test(SRC approx1.cpp)
+make_test(SRC approx2.cpp)
+make_test(SRC array.cpp)
+make_test(SRC assign.cpp)
+make_test(SRC backend.cpp)
+make_test(SRC basic.cpp)
+make_test(SRC basic_c.c)
+make_test(SRC bilateral.cpp)
+make_test(SRC binary.cpp)
+make_test(SRC blas.cpp)
+make_test(SRC canny.cpp)
+make_test(SRC cast.cpp)
+make_test(SRC cholesky_dense.cpp)
+make_test(SRC clamp.cpp)
+make_test(SRC compare.cpp)
+make_test(SRC complex.cpp)
+make_test(SRC constant.cpp)
+make_test(SRC convolve.cpp)
+make_test(SRC corrcoef.cpp)
+make_test(SRC covariance.cpp)
+make_test(SRC diagonal.cpp)
+make_test(SRC diff1.cpp)
+make_test(SRC diff2.cpp)
+make_test(SRC dog.cpp)
+make_test(SRC dot.cpp)
+make_test(SRC empty.cpp)
+make_test(SRC fast.cpp)
+make_test(SRC fft.cpp)
+make_test(SRC fft_large.cpp)
+make_test(SRC fft_real.cpp)
+make_test(SRC fftconvolve.cpp)
+make_test(SRC flat.cpp)
+make_test(SRC flip.cpp)
+make_test(SRC gaussiankernel.cpp)
+make_test(SRC gen_assign.cpp)
+make_test(SRC gen_index.cpp)
+make_test(SRC getting_started.cpp)
+make_test(SRC gfor.cpp)
+make_test(SRC gloh_nonfree.cpp)
+make_test(SRC gradient.cpp)
+make_test(SRC gray_rgb.cpp)
+make_test(SRC hamming.cpp)
+make_test(SRC harris.cpp)
+make_test(SRC histogram.cpp)
+make_test(SRC homography.cpp)
+make_test(SRC hsv_rgb.cpp)
+make_test(SRC iir.cpp)
+make_test(SRC imageio.cpp)
+make_test(SRC index.cpp)
+make_test(SRC info.cpp)
+make_test(SRC internal.cpp)
+make_test(SRC inverse_dense.cpp)
+make_test(SRC iota.cpp)
+make_test(SRC ireduce.cpp)
+make_test(SRC jit.cpp)
+make_test(SRC join.cpp)
+make_test(SRC lu_dense.cpp)
+make_test(SRC main.cpp)
+#make_test(manual_memory_test.cpp)
+make_test(SRC match_template.cpp)
+make_test(SRC math.cpp)
+make_test(SRC matrix_manipulation.cpp)
+make_test(SRC mean.cpp)
+make_test(SRC meanshift.cpp)
+make_test(SRC medfilt.cpp)
+make_test(SRC median.cpp)
+make_test(SRC memory.cpp)
+make_test(SRC memory_lock.cpp)
+make_test(SRC missing.cpp)
+make_test(SRC moddims.cpp)
+make_test(SRC moments.cpp)
+make_test(SRC morph.cpp)
+make_test(SRC nearest_neighbour.cpp)
+
+if(OpenCL_FOUND)
+  make_test(SRC ocl_ext_context.cpp
+            LIBRARIES OpenCL::OpenCL)
+endif(OpenCL_FOUND)
+
+make_test(SRC orb.cpp)
+make_test(SRC qr_dense.cpp)
+make_test(SRC random.cpp)
+make_test(SRC range.cpp)
+make_test(SRC rank_dense.cpp)
+make_test(SRC reduce.cpp)
+make_test(SRC regions.cpp)
+make_test(SRC reorder.cpp)
+make_test(SRC replace.cpp)
+make_test(SRC resize.cpp)
+make_test(SRC rotate.cpp)
+make_test(SRC rotate_linear.cpp)
+make_test(SRC sat.cpp)
+make_test(SRC scan.cpp)
+make_test(SRC scan_by_key.cpp)
+make_test(SRC select.cpp)
+make_test(SRC set.cpp)
+make_test(SRC shift.cpp)
+make_test(SRC sift_nonfree.cpp)
+make_test(SRC sobel.cpp)
+make_test(SRC solve_dense.cpp        CXX11)
+make_test(SRC sort.cpp)
+make_test(SRC sort_by_key.cpp)
+make_test(SRC sort_index.cpp)
+make_test(SRC sparse.cpp)
+make_test(SRC sparse_arith.cpp)
+make_test(SRC sparse_convert.cpp)
+make_test(SRC stdev.cpp)
+make_test(SRC susan.cpp)
+make_test(SRC svd_dense.cpp)
+make_test(SRC threading.cpp          CXX11)
+make_test(SRC tile.cpp)
+make_test(SRC transform.cpp)
+make_test(SRC transform_coordinates.cpp)
+make_test(SRC translate.cpp)
+make_test(SRC transpose.cpp)
+make_test(SRC transpose_inplace.cpp)
+make_test(SRC triangle.cpp)
+make_test(SRC unwrap.cpp)
+make_test(SRC var.cpp)
+make_test(SRC where.cpp)
+make_test(SRC wrap.cpp)
+make_test(SRC write.cpp)
+make_test(SRC ycbcr_rgb.cpp)
diff --git a/test/CMakeModules/build_gtest.cmake b/test/CMakeModules/build_gtest.cmake
deleted file mode 100644
index eb4a0ad264..0000000000
--- a/test/CMakeModules/build_gtest.cmake
+++ /dev/null
@@ -1,100 +0,0 @@
-# Build the gtest libraries
-
-# Check if Google Test exists
-SET(GTEST_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/gtest")
-MESSAGE(STATUS ${GTEST_SOURCE_DIR})
-IF(NOT EXISTS "${GTEST_SOURCE_DIR}/README")
-    MESSAGE(STATUS "GTest submodules unavailable. Updating submodules.")
-    EXECUTE_PROCESS(
-        COMMAND git submodule update --init --recursive
-        WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
-        OUTPUT_QUIET
-    )
-ENDIF()
-
-if(CMAKE_VERSION VERSION_LESS 3.2 AND CMAKE_GENERATOR MATCHES "Ninja")
-    message(WARNING "Building GTest with Ninja has known issues with CMake older than 3.2")
-endif()
-
-include(ExternalProject)
-
-# Set the build type if it isn't already
-if(NOT CMAKE_BUILD_TYPE)
-  set(CMAKE_BUILD_TYPE Release)
-endif()
-
-# Set default ExternalProject root directory
-set(prefix "${CMAKE_BINARY_DIR}/third_party/gtest")
-# the binary dir must be know before creating the external project in order
-# to pass the byproducts
-set(binary_dir "${prefix}/src/googletest-build")
-set(stdlib_binary_dir "${prefix}/src/googletest-build-stdlib")
-
-set(GTEST_LIBRARIES gtest gtest_main)
-set(GTEST_LIBRARIES_STDLIB gtest_stdlib gtest_main_stdlib)
-
-set(byproducts)
-set(byproducts_libstdcpp)
-foreach(lib ${GTEST_LIBRARIES})
-    set(${lib}_location
-        ${binary_dir}/${CMAKE_CFG_INTDIR}/${CMAKE_STATIC_LIBRARY_PREFIX}${lib}${CMAKE_STATIC_LIBRARY_SUFFIX})
-    set(${lib}_location_libstdcpp
-        ${stdlib_binary_dir}/${CMAKE_CFG_INTDIR}/${CMAKE_STATIC_LIBRARY_PREFIX}${lib}${CMAKE_STATIC_LIBRARY_SUFFIX})
-    list(APPEND byproducts ${${lib}_location})
-    list(APPEND byproducts_libstdcpp ${${lib}_location_libstdcpp})
-endforeach()
-SET(CMAKE_CXX_FLAGS_STD "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
-
-FUNCTION(GTEST_BUILD BUILD_NAME BUILD_TYPE BUILD_BINARY_DIR BUILD_BYPRODUCTS)
-# Add gtest
-ExternalProject_Add(
-    ${BUILD_NAME}
-    # URL http://googletest.googlecode.com/files/gtest-1.7.0.zip
-    # URL_MD5 2d6ec8ccdf5c46b05ba54a9fd1d130d7
-    SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../test/gtest"
-    PREFIX ${prefix}
-    BINARY_DIR ${BUILD_BINARY_DIR}
-    TIMEOUT 10
-    CMAKE_ARGS -Dgtest_force_shared_crt=ON
-               -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-               -DCMAKE_BUILD_TYPE=${BUILD_TYPE}
-               -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-               -DCMAKE_CXX_FLAGS_LIBSTDCPP=${CMAKE_CXX_FLAGS_STD}
-               -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-               -DCMAKE_CXX_FLAGS_MINSIZEREL=${CMAKE_CXX_FLAGS_MINSIZEREL}
-               -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-               -DCMAKE_CXX_FLAGS_RELWITHDEBINFO=${CMAKE_CXX_FLAGS_RELWITHDEBINFO}
-    BUILD_BYPRODUCTS ${BUILD_BYPRODUCTS}
-    # Disable install step
-    INSTALL_COMMAND ""
-    # Wrap download, configure and build steps in a script to log output
-    LOG_DOWNLOAD 0
-    LOG_UPDATE 0
-    LOG_CONFIGURE 0
-    LOG_BUILD 0)
-ENDFUNCTION(GTEST_BUILD)
-
-GTEST_BUILD(googletest              ${CMAKE_BUILD_TYPE} ${binary_dir} "${byproducts}")
-
-# If we are on OSX and using the clang compiler go ahead and build
-# GTest using libstdc++ just in case we compile the CUDA backend
-IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-    GTEST_BUILD(googletest_libstdcpp    LibStdCpp           ${stdlib_binary_dir} "${byproducts_libstdcpp}")
-ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-
-foreach(lib ${GTEST_LIBRARIES})
-    add_library(${lib} IMPORTED STATIC)
-    add_dependencies(${lib} googletest)
-    set_target_properties(${lib} PROPERTIES IMPORTED_LOCATION ${${lib}_location})
-
-    IF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-        add_library(${lib}_stdlib IMPORTED STATIC)
-        add_dependencies(${lib}_stdlib googletest_libstdcpp)
-        set_target_properties(${lib}_stdlib PROPERTIES IMPORTED_LOCATION ${${lib}_location_libstdcpp})
-    ENDIF("${APPLE}" AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-endforeach()
-
-# Specify include dir
-ExternalProject_Get_Property(googletest source_dir)
-set(GTEST_INCLUDE_DIRS ${source_dir}/include)
-set(GTEST_FOUND ON)
diff --git a/test/gtest b/test/gtest
index 1197daf357..ec44c6c167 160000
--- a/test/gtest
+++ b/test/gtest
@@ -1 +1 @@
-Subproject commit 1197daf3571161590dce2bc4879512ef7bc1ba67
+Subproject commit ec44c6c1675c25b9827aacd08c02433cccde7780
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 560496f140..cfb1c2f24d 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -13,8 +13,6 @@
 #include <af/opencl.h>
 #include <iostream>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 using std::vector;
 
 inline void checkErr(cl_int err, const char * name) {
@@ -129,10 +127,8 @@ TEST(OCLCheck, DevicePlatform)
     afcl::platform platform = afcl::getPlatform();
     ASSERT_NE(platform, AFCL_PLATFORM_UNKNOWN);
 }
-#pragma GCC diagnostic pop
 #else
 TEST(OCLExtContext, NoopCPU)
 {
 }
 #endif
-

From 7bce1943e5d490593980bd4082d6b7e677d856aa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 Oct 2017 20:08:28 -0400
Subject: [PATCH 1306/2677] Fix ArrayFireConfig files. Allow tests to be built
 independently

---
 CMakeLists.txt                        | 35 +++++++++++++++------------
 CMakeModules/ArrayFireConfig.cmake.in |  6 ++---
 CMakeModules/InternalUtils.cmake      |  7 ++----
 examples/CMakeLists.txt               |  6 -----
 4 files changed, 25 insertions(+), 29 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ee102078ed..c544173be4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -207,42 +207,47 @@ foreach(backend CPU CUDA OpenCL Unified)
     install(EXPORT ArrayFire${backend}Targets
             NAMESPACE ArrayFire::
             DESTINATION ${AF_INSTALL_CMAKE_DIR}
-            COMPONENT cmake
-        )
+            COMPONENT cmake)
 
     export( EXPORT ArrayFire${backend}Targets
             NAMESPACE ArrayFire::
-            FILE ArrayFire${backend}Targets.cmake
-            )
+            FILE cmake/ArrayFire${backend}Targets.cmake)
   endif()
 endforeach()
 
-set(INCLUDE_DIR include)
-
 include(CMakePackageConfigHelpers)
 write_basic_package_version_file(
-  "${CMAKE_CURRENT_BINARY_DIR}/ArrayFireConfigVersion.cmake"
+  "${ArrayFire_BINARY_DIR}/cmake/ArrayFireConfigVersion.cmake"
   COMPATIBILITY SameMajorVersion
 )
 
+# This config file will be installed so we need to set the install_destination
+# path relitive to the install path
+set(INCLUDE_DIRS include)
+set(CMAKE_DIR ${AF_INSTALL_CMAKE_DIR})
 configure_package_config_file(
   ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
-  ArrayFireInstallConfig/ArrayFireConfig.cmake
+  cmake/install/ArrayFireConfig.cmake
   INSTALL_DESTINATION "${AF_INSTALL_CMAKE_DIR}"
-  PATH_VARS INCLUDE_DIR AF_INSTALL_CMAKE_DIR
+  PATH_VARS INCLUDE_DIRS CMAKE_DIR
   )
 
-install(FILES ${ArrayFire_BINARY_DIR}/ArrayFireInstallConfig/ArrayFireConfig.cmake
-              ${ArrayFire_BINARY_DIR}/ArrayFireConfigVersion.cmake
+install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
+              ${ArrayFire_BINARY_DIR}/cmake/ArrayFireConfigVersion.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
-set(AF_INSTALL_CMAKE_DIR "${ArrayFire_BINARY_DIR}")
+
+# This file will be used to create the config file for the build directory.
+# These config files will be used by the examples to find the ArrayFire
+# libraries
+set(INCLUDE_DIRS "${ArrayFire_SOURCE_DIR}/include" "${ArrayFire_BINARY_DIR}/include")
+set(CMAKE_DIR "${ArrayFire_BINARY_DIR}/cmake")
 configure_package_config_file(
   ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
-  ${ArrayFire_BINARY_DIR}/ArrayFireConfig.cmake
-  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}"
-  PATH_VARS INCLUDE_DIR AF_INSTALL_CMAKE_DIR
+  cmake/ArrayFireConfig.cmake
+  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}/cmake"
+  PATH_VARS INCLUDE_DIRS CMAKE_DIR
   INSTALL_PREFIX "${ArrayFire_BINARY_DIR}"
   )
 
diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index c71a297923..72ec601e22 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -50,7 +50,7 @@
 
 @PACKAGE_INIT@
 
-set_and_check(ArrayFire_INCLUDE_DIRS @PACKAGE_INCLUDE_DIR@)
+set_and_check(ArrayFire_INCLUDE_DIRS @PACKAGE_INCLUDE_DIRS@)
 
 foreach(backend Unified CPU OpenCL CUDA)
   if(backend STREQUAL "Unified")
@@ -59,8 +59,8 @@ foreach(backend Unified CPU OpenCL CUDA)
     string(TOLOWER "${backend}" lowerbackend)
   endif()
   if(NOT TARGET ArrayFire::af${lowerbackend})
-    if(EXISTS @PACKAGE_AF_INSTALL_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
-      include(@PACKAGE_AF_INSTALL_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
+    if(EXISTS @PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
+      include(@PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
     endif()
   endif()
 
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index d708ce1ffc..295d02dad3 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -29,7 +29,7 @@ endif()
 endfunction()
 
 macro(arrayfire_set_cmake_default_variables)
-  set(CMAKE_PREFIX_PATH "${CMAKE_BINARY_DIR}prefix;${CMAKE_PREFIX_PATH}")
+  set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR}/cmake;${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
 
   set(CMAKE_CXX_STANDARD 11)
@@ -97,10 +97,7 @@ macro(arrayfire_set_cmake_default_variables)
   endif()
 
   if(APPLE)
-    # Brew does not put the glbinding cmake config files where it can be found
-    # TODO(umar) check if other systems have a similar problem
-    set(CMAKE_PREFIX_PATH "/usr/local/opt/glbinding;${CMAKE_PREFIX_PATH}")
-    # TODO(umar) Remove rpath to third_part lib
+    # TODO(umar) Remove rpath to third_party lib
     set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR};${ArrayFire_BINARY_DIR}/third_party/forge/lib")
   endif()
 endmacro()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 1a608a5eec..a84a984b87 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -46,12 +46,6 @@ function(target_compile_definitions target access definitions)
   _target_compile_definitions(example_${target} ${access} ${definitions})
 endfunction()
 
-function(find_package args)
-  if(NOT (TARGET ArrayFire::afcpu OR TARGET ArrayFire::afcuda OR TARGET ArrayFire::afopencl OR TARGET ArrayFire::af))
-    _find_package(args)
-  endif()
-endfunction()
-
 add_subdirectory(benchmarks)
 add_subdirectory(computer_vision)
 add_subdirectory(financial)

From c74d8ac08a1d6db1cb9fa1574aee96a72a2415e8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 10 Oct 2017 23:18:47 -0400
Subject: [PATCH 1307/2677] Fix common warnings on Windows

---
 CMakeModules/InternalUtils.cmake              |  6 ++++
 include/af/complex.h                          |  4 +--
 src/backend/common/MemoryManager.hpp          |  6 ++--
 src/backend/cpu/Array.hpp                     | 30 ++++++++--------
 src/backend/cpu/Param.hpp                     |  8 ++---
 src/backend/cpu/TNJ/Node.hpp                  |  2 +-
 src/backend/cpu/kernel/Array.hpp              |  3 +-
 src/backend/cpu/kernel/sort_by_key_impl.hpp   |  6 ++--
 src/backend/cpu/math.hpp                      |  2 +-
 src/backend/cuda/CMakeLists.txt               |  4 ++-
 .../cuda/kernel/scan_by_key/CMakeLists.txt    |  3 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  4 +--
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |  2 +-
 src/backend/cuda/math.hpp                     | 36 +++++++++----------
 test/testHelpers.hpp                          |  4 +--
 15 files changed, 64 insertions(+), 56 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 295d02dad3..2c6ff29a51 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -28,6 +28,12 @@ elseif(UNIX)
 endif()
 endfunction()
 
+function(arrayfire_get_cuda_cxx_flags cuda_flags)
+  if(NOT MSVC)
+    set(${cuda_flags} "-std=c++11"  PARENT_SCOPE)
+  endif()
+endfunction()
+
 macro(arrayfire_set_cmake_default_variables)
   set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR}/cmake;${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
diff --git a/include/af/complex.h b/include/af/complex.h
index 052f60b4e0..5f1baf2110 100644
--- a/include/af/complex.h
+++ b/include/af/complex.h
@@ -25,7 +25,7 @@ typedef struct af_cfloat {
     float real;
     float imag;
 #ifdef __cplusplus
-    af_cfloat(const float real = 0, const float imag = 0) :real(real), imag(imag) {};
+    af_cfloat(const float _real = 0, const float _imag = 0) :real(_real), imag(_imag) {}
 #endif
 } af_cfloat;
 
@@ -33,7 +33,7 @@ typedef struct af_cdouble {
     double real;
     double imag;
 #ifdef __cplusplus
-    af_cdouble(const double real = 0, const double imag = 0) :real(real), imag(imag) {}
+    af_cdouble(const double _real = 0, const double _imag = 0) :real(_real), imag(_imag) {}
 #endif
 } af_cdouble;
 #ifdef __cplusplus
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 5077c27fab..5ab90e4fc3 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -113,8 +113,8 @@ class MemoryManager
     }
 
     public:
-    MemoryManager(int num_devices, unsigned MAX_BUFFERS, bool debug)
-        : mem_step_size(1024), max_buffers(MAX_BUFFERS), memory(num_devices), debug_mode(debug)
+    MemoryManager(int num_devices, unsigned max_buffers, bool debug)
+        : mem_step_size(1024), max_buffers(max_buffers), memory(num_devices), debug_mode(debug)
     {
         // Check for environment variables
 
@@ -136,7 +136,7 @@ class MemoryManager
         // If there is a memory manager allocated for
         // this device id, we might as well use it and the
         // buffers allocated for it
-        if ((size_t)device < memory.size())
+        if (static_cast<size_t>(device) < memory.size())
             return;
 
         // Assuming, device need not be always the next device
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index e8945c7976..2c479e7eda 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -152,21 +152,21 @@ namespace cpu
 #define INFO_IS_FUNC(NAME)\
     bool NAME () const { return info.NAME(); }
 
-        INFO_IS_FUNC(isEmpty);
-        INFO_IS_FUNC(isScalar);
-        INFO_IS_FUNC(isRow);
-        INFO_IS_FUNC(isColumn);
-        INFO_IS_FUNC(isVector);
-        INFO_IS_FUNC(isComplex);
-        INFO_IS_FUNC(isReal);
-        INFO_IS_FUNC(isDouble);
-        INFO_IS_FUNC(isSingle);
-        INFO_IS_FUNC(isRealFloating);
-        INFO_IS_FUNC(isFloating);
-        INFO_IS_FUNC(isInteger);
-        INFO_IS_FUNC(isBool);
-        INFO_IS_FUNC(isLinear);
-        INFO_IS_FUNC(isSparse);
+        INFO_IS_FUNC(isEmpty)
+        INFO_IS_FUNC(isScalar)
+        INFO_IS_FUNC(isRow)
+        INFO_IS_FUNC(isColumn)
+        INFO_IS_FUNC(isVector)
+        INFO_IS_FUNC(isComplex)
+        INFO_IS_FUNC(isReal)
+        INFO_IS_FUNC(isDouble)
+        INFO_IS_FUNC(isSingle)
+        INFO_IS_FUNC(isRealFloating)
+        INFO_IS_FUNC(isFloating)
+        INFO_IS_FUNC(isInteger)
+        INFO_IS_FUNC(isBool)
+        INFO_IS_FUNC(isLinear)
+        INFO_IS_FUNC(isSparse)
 
 #undef INFO_IS_FUNC
 
diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
index 11b3a843f9..6037a48942 100644
--- a/src/backend/cpu/Param.hpp
+++ b/src/backend/cpu/Param.hpp
@@ -50,12 +50,12 @@ class CParam
         return m_strides;
     }
 
-    int dims(int i) const
+    dim_t dims(int i) const
     {
         return m_dims[i];
     }
 
-    int strides(int i) const
+    dim_t strides(int i) const
     {
         return m_strides[i];
     }
@@ -103,12 +103,12 @@ class Param
         return m_strides;
     }
 
-    int dims(int i) const
+    dim_t dims(int i) const
     {
         return m_dims[i];
     }
 
-    int strides(int i) const
+    dim_t strides(int i) const
     {
         return m_strides[i];
     }
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/TNJ/Node.hpp
index aeb3385b96..1b89b7812f 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/TNJ/Node.hpp
@@ -57,7 +57,7 @@ namespace TNJ
                     if (child == nullptr) break;
                     child->getNodesMap(node_map, full_nodes);
                 }
-                int id = node_map.size();
+                int id = static_cast<int>(node_map.size());
                 node_map[this] = id;
                 full_nodes.push_back(this);
                 return id;
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 12e5a27e7c..63094154f0 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -29,7 +29,8 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
     std::vector<TNJ::TNode<T> *> output_nodes;
     std::vector<TNJ::Node *> full_nodes;
 
-    for (int i = 0; i < (int)arrays.size(); i++) {
+    int narrays = static_cast<int>(arrays.size());
+    for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
         output_nodes.push_back(reinterpret_cast<TNJ::TNode<T> *>(output_nodes_[i].get()));
         output_nodes_[i]->getNodesMap(nodes, full_nodes);
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index f8fae4fc6c..3f7fe4904e 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -95,13 +95,13 @@ void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim, bool isAsce
 
         for(dim_t w = 0; w < dims[3]; w++) {
             dim_t offW = w * strides[3];
-            uint okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            dim_t okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
             for(dim_t z = 0; z < dims[2]; z++) {
                 dim_t offWZ = offW + z * strides[2];
-                uint okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                dim_t okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
                 for(dim_t y = 0; y < dims[1]; y++) {
                     dim_t offWZY = offWZ + y * strides[1];
-                    uint okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
+                    dim_t okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
                     for(dim_t x = 0; x < dims[0]; x++) {
                         dim_t id = offWZY + x;
                         out[id] = okeyY + (x % seqDims[0]);
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 3b20f0ede9..d95a1d235c 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -33,7 +33,7 @@ namespace cpu
 
     template<> STATIC_ cfloat division<cfloat>(cfloat lhs, double rhs)
     {
-        cfloat retVal(real(lhs) / rhs, imag(lhs) / rhs);
+        cfloat retVal(real(lhs) / static_cast<float>(rhs), imag(lhs) / static_cast<float>(rhs));
         return retVal;
     }
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4a42d3a8bc..d8c329928b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -109,6 +109,8 @@ function(cuda_add_library cuda_target)
 
 endfunction()
 
+arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
+
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
@@ -383,7 +385,7 @@ cuda_add_library(afcuda
     JIT/UnaryNode.hpp
     JIT/types.h
 
-    OPTIONS "-std=c++11 -Xcompiler -fPIC -Xcudafe \"--diag_suppress=1427\""
+    OPTIONS "${cuda_cxx_flags} -Xcompiler -fPIC -Xcudafe \"--diag_suppress=1427\""
   )
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index 936a0c7d73..e1db54b2e7 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -17,7 +17,6 @@ endforeach()
 cuda_add_cuda_include_once()
 
 foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
-
     # When using cuda_compile with older versions of FindCUDA. The generated targets
     # have the same names as the source file. Since we are using the same file for
     # the compilation of these targets we need to rename them before sending them
@@ -31,7 +30,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     cuda_compile(scan_by_key_gen_files "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
-          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "-std=c++11 -Xcompiler -fPIC -DAFDLL"
+          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "${cuda_cxx_flags} -Xcompiler -fPIC -DAFDLL"
       )
 
     list(APPEND SCAN_OBJ ${scan_by_key_gen_files})
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index dc2c1bee4a..68c3729066 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -432,8 +432,8 @@ namespace kernel
         threads_x = std::min(threads_x, THREADS_PER_BLOCK);
         uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-        dim_t blocks_x = divup(out.dims[0], threads_x * REPEAT);
-        dim_t blocks_y = divup(out.dims[1], threads_y);
+        uint blocks_x = static_cast<uint>(divup(out.dims[0], threads_x * REPEAT));
+        uint blocks_y = static_cast<uint>(divup(out.dims[1], threads_y));
 
         if (blocks_x == 1) {
             scan_final_launcher<Ti, Tk, To, op>(
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 47bd67bdad..78a6a26e0d 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -36,7 +36,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
           OPTIONS
             -DSBK_TYPE=${SBK_TYPE}
             -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
-            "-std=c++11 -Xcompiler -fPIC -DAFDLL"
+            "${cuda_cxx_flags} -Xcompiler -fPIC -DAFDLL"
       )
 
       list(APPEND SORT_OBJ ${scan_by_key_gen_files})
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 5f456c737c..a9a5d8ebc1 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -141,25 +141,25 @@ __SDH__  conj(T  x) { return x; }
 __SDH__ cfloat  conj(cfloat  c) { return cuConjf(c);}
 __SDH__ cdouble conj(cdouble c) { return cuConj(c); }
 
-__SDH__ cfloat make_cfloat(bool     x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(int      x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(unsigned x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(short    x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(ushort   x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(float    x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(double   x) { return make_cuComplex(x,0);     }
-__SDH__ cfloat make_cfloat(cfloat   x) { return x;                    }
-__SDH__ cfloat make_cfloat(cdouble  c) { return make_cuComplex(c.x,c.y); }
-
-__SDH__ cdouble make_cdouble(bool      x) { return make_cuDoubleComplex(x,0);       }
-__SDH__ cdouble make_cdouble(int       x) { return make_cuDoubleComplex(x,0);       }
-__SDH__ cdouble make_cdouble(unsigned  x) { return make_cuDoubleComplex(x,0);       }
-__SDH__ cdouble make_cdouble(short     x) { return make_cuDoubleComplex(x,0);       }
-__SDH__ cdouble make_cdouble(ushort    x) { return make_cuDoubleComplex(x,0);       }
-__SDH__ cdouble make_cdouble(float     x) { return make_cuDoubleComplex(x,0);       }
-__SDH__ cdouble make_cdouble(double    x) { return make_cuDoubleComplex(x,0);       }
+__SDH__ cfloat make_cfloat(bool     x) { return make_cuComplex(static_cast<float>(x),0);     }
+__SDH__ cfloat make_cfloat(int      x) { return make_cuComplex(static_cast<float>(x),0);     }
+__SDH__ cfloat make_cfloat(unsigned x) { return make_cuComplex(static_cast<float>(x),0);     }
+__SDH__ cfloat make_cfloat(short    x) { return make_cuComplex(static_cast<float>(x),0);     }
+__SDH__ cfloat make_cfloat(ushort   x) { return make_cuComplex(static_cast<float>(x),0);     }
+__SDH__ cfloat make_cfloat(float    x) { return make_cuComplex(static_cast<float>(x),0);     }
+  __SDH__ cfloat make_cfloat(double   x) { return make_cuComplex(static_cast<float>(x),0);     }
+  __SDH__ cfloat make_cfloat(cfloat   x) { return x;                    }
+  __SDH__ cfloat make_cfloat(cdouble  c) { return make_cuComplex(c.x,c.y); }
+
+__SDH__ cdouble make_cdouble(bool      x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
+__SDH__ cdouble make_cdouble(int       x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
+__SDH__ cdouble make_cdouble(unsigned  x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
+__SDH__ cdouble make_cdouble(short     x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
+__SDH__ cdouble make_cdouble(ushort    x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
+__SDH__ cdouble make_cdouble(float     x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
+__SDH__ cdouble make_cdouble(double    x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
 __SDH__ cdouble make_cdouble(cdouble   x) { return x;                       }
-__SDH__ cdouble make_cdouble(cfloat    c) { return make_cuDoubleComplex(c.x,c.y);   }
+__SDH__ cdouble make_cdouble(cfloat    c) { return make_cuDoubleComplex(static_cast<double>(c.x),c.y);   }
 
 __SDH__ cfloat make_cfloat(float x, float y) { return make_cuComplex(x, y); }
 __SDH__ cdouble make_cdouble(double x, double y) { return make_cuDoubleComplex(x, y); }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 3040214342..5d9a848bd3 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -74,7 +74,7 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
             FileElementType tmp;
             for(unsigned i = 0; i < nElems; i++) {
                 testFile >> tmp;
-                testInputs[k][i] = tmp;
+                testInputs[k][i] = static_cast<inType>(tmp);
             }
         }
 
@@ -84,7 +84,7 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
             FileElementType tmp;
             for(unsigned j = 0; j < testSizes[i]; j++) {
                 testFile >> tmp;
-                testOutputs[i][j] = tmp;
+                testOutputs[i][j] = static_cast<outType>(tmp);
             }
         }
     }

From fedf359da626151c0cfa9dbf5a8de8c43ebb3801 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 13 Oct 2017 13:34:08 -0400
Subject: [PATCH 1308/2677] Fixes bug casting moments to bool (#1960)

* fixes bug casting moments to bool

* change moments type and tests to be float

* remove extraneous type checking in moments_all
---
 src/api/c/moments.cpp | 20 ++------------------
 test/moments.cpp      |  9 ++++++++-
 2 files changed, 10 insertions(+), 19 deletions(-)

diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 7dd30ed276..a04027a3d0 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -33,10 +33,7 @@ template<typename T>
 static inline void moments(af_array *out, const af_array in, af_moment_type moment)
 {
     Array<float> temp = moments<T>(getArray<T>(in), moment);
-    af_array tarr = getHandle<float>(temp);
-    Array<T> output = castArray<T>(tarr);
-
-    *out = getHandle<T>(output);
+    *out = getHandle<float>(temp);
 }
 
 af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
@@ -83,20 +80,7 @@ af_err af_moments_all(double* out, const af_array in, const af_moment_type momen
 
         af_array moments_arr;
         af_moments(&moments_arr, in, moment);
-
-        const ArrayInfo& m_info = getInfo(moments_arr);
-        af_dtype type = m_info.getType();
-
-        switch(type) {
-            case f32: moment_copy<float>          (out, moments_arr); break;
-            case f64: moment_copy<double>         (out, moments_arr); break;
-            case u32: moment_copy<unsigned>       (out, moments_arr); break;
-            case s32: moment_copy<int>            (out, moments_arr); break;
-            case u16: moment_copy<unsigned short> (out, moments_arr); break;
-            case s16: moment_copy<short>          (out, moments_arr); break;
-            case b8:  moment_copy<char>           (out, moments_arr); break;
-            default:  TYPE_ERROR(1, type);
-        }
+        moment_copy<float>(out, moments_arr);
     }
     CATCHALL;
 
diff --git a/test/moments.cpp b/test/moments.cpp
index 1ce666ff34..1d16369483 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -52,7 +52,7 @@ void momentsTest(string pTestFile)
     af::array imgArray(numDims.front(), &in.front()[0]);
 
     af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
-    vector<T> mData(momentsArray.elements());
+    vector<float> mData(momentsArray.elements());
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
         ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << std::endl;
@@ -160,4 +160,11 @@ TYPED_TEST(Image, MomentsSynthTypes)
     momentsTest<TypeParam>(string(TEST_DIR"/moments/simple_mat_moments.test"));
 }
 
+TEST(Image, Moment_Issue1957)
+{
+    af::array A = af::identity(3, 3, b8);
 
+    double m00;
+    af::moments(&m00, A, AF_MOMENT_M00);
+    ASSERT_EQ(m00, 3);
+}

From 0ba2964e8ac547840308e4430de0883d3d67f693 Mon Sep 17 00:00:00 2001
From: Felix <fzimmermann89@gmail.com>
Date: Sun, 15 Oct 2017 23:56:47 +0200
Subject: [PATCH 1309/2677] Fix mean kernel for OpenCL 1.1 devices

Enable usage of double if output is of type double
---
 src/backend/opencl/kernel/mean.hpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 2dbf2ce2fd..90fedeb926 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -174,7 +174,8 @@ void mean_dim_launcher(Param out, Param owt,
         if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
 
         if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
+                std::is_same<Ti, cdouble>::value ||
+                std::is_same<To, double>::value) {
             options << " -D USE_DOUBLE";
         }
 
@@ -340,7 +341,8 @@ void mean_first_launcher(Param out, Param owt,
         if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
 
         if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
+                std::is_same<Ti, cdouble>::value ||
+                std::is_same<To, double>::value) {
             options << " -D USE_DOUBLE";
         }
 

From 9e4b422d7939f3a4d46133bff06b8d4721e556d8 Mon Sep 17 00:00:00 2001
From: fzimmermann <fzimmermann89@gmail.com>
Date: Mon, 16 Oct 2017 01:18:51 +0200
Subject: [PATCH 1310/2677] Fix #1966 Change to not using native_exp for type
 double instead of checking the platform

---
 src/backend/opencl/kernel/bilateral.cl  | 2 +-
 src/backend/opencl/kernel/bilateral.hpp | 5 ++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index 6ad8ea94d0..46416412a3 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if USE_NATIVE_EXP
+#ifdef USE_NATIVE_EXP
 #define EXP native_exp
 #else
 #define EXP exp
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 4d1a69b1e8..966d82b785 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -47,16 +47,15 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog==0 && entry.ker==0) {
-        bool use_native_exp = (getActivePlatform() != AFCL_PLATFORM_POCL
-                && getActivePlatform() != AFCL_PLATFORM_APPLE);
         std::ostringstream options;
         options << " -D inType=" << dtype_traits<inType>::getName()
             << " -D outType=" << dtype_traits<outType>::getName();
         if (std::is_same<inType, double>::value ||
                 std::is_same<inType, cdouble>::value) {
             options << " -D USE_DOUBLE";
+        } else {
+            options << " -D USE_NATIVE_EXP";
         }
-        options << " -D USE_NATIVE_EXP=" << (int)use_native_exp;
 
         const char* ker_strs[] = {bilateral_cl};
         const int   ker_lens[] = {bilateral_cl_len};

From b8ddc7f81216f5af57f205a37d48edce90e1fd26 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Oct 2017 22:03:47 -0400
Subject: [PATCH 1311/2677] Reset the CMAKE_RUNTIME_OUTPUT_DIRECTORY for
 examples

The runtime output path sets the location where the binaries will be
moved once built. This is done so that the tests and the libraries
are in the same directory on the windows platform. This path is also
set for the examples but some of the example names are similar to
the tests. This causes problems because the examples may overwrite
the test binaries. This commit sets the runtime directory for the
examples.
---
 examples/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index a84a984b87..e21bb00959 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -6,6 +6,7 @@ project(ArrayFire-Examples
 
 if(WIN32)
   add_definitions(-DWIN32_LEAN_AND_MEAN)
+  unset(CMAKE_RUNTIME_OUTPUT_DIRECTORY)
 endif()
 
 # Some examples take too long to execute. This list is used to exclude these

From 8a2ffdce07d716923cad60e7f933e181f36102e3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Oct 2017 23:18:21 -0400
Subject: [PATCH 1312/2677] Remove old build status links from readme

---
 README.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/README.md b/README.md
index 4a029004f2..aa2aa200b7 100644
--- a/README.md
+++ b/README.md
@@ -25,13 +25,6 @@ major vendors (Intel, AMD, ARM), GPUs from the prominent manufacturers
 (NVIDIA, AMD, and Qualcomm), as well as a variety of other accelerator devices
 on Windows, Mac, and Linux.
 
-## Build and Test Status
-
-|         | Linux x86_64 | Linux aarch64 | OSX | Windows |
-|:-------:|:------------:|:-------------:|:---:|:-------:|
-| Build   | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build-mkl/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build-mkl/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/job/devel/) |
-| Test    | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test-mkl/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test-mkl/job/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/job/devel/) |
-
 ## Installation
 
 You can install the ArrayFire library from one of the following ways:

From c16f2ce9d16a54c73362553a2cbf754e062bfb05 Mon Sep 17 00:00:00 2001
From: Felix Z <fzimmermann89@gmail.com>
Date: Sat, 14 Oct 2017 22:08:29 +0200
Subject: [PATCH 1313/2677] CLBlast Fixes *Fix link error  on windows, see
 CNugteren/CLBlast#200 *Fix name of CLBlas library on windows *Fix include,
 see arrayfire/arrayfire#1956

---
 CMakeModules/build_CLBlast.cmake   | 5 +++--
 src/backend/opencl/err_clblast.hpp | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index c5fc9e3e6d..d2ec189d78 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -8,12 +8,12 @@
 include(ExternalProject)
 
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
-set(CLBlast_location ${prefix}/${CMAKE_STATIC_LIBRARY_PREFIX}/libclblast${CMAKE_STATIC_LIBRARY_SUFFIX})
+set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 1.1.0
+    GIT_TAG 48133a0cd1a7b61b87906ec1f4608e766e20a973
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
@@ -21,6 +21,7 @@ ExternalProject_Add(
     CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/
       -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
       "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+      -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
       -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
       "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
       -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
index e61b2ad430..8997e0c4b3 100644
--- a/src/backend/opencl/err_clblast.hpp
+++ b/src/backend/opencl/err_clblast.hpp
@@ -11,6 +11,7 @@
 #include <stdio.h>
 #include <common/err_common.hpp>
 #include <clblast.h>
+#include <mutex>
 
 static const char * _clblastGetResultString(clblast::StatusCode st)
 {

From 487cd5470e9688588def81f8b850e4c0f547474d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 17 Oct 2017 02:16:58 -0400
Subject: [PATCH 1314/2677] Propagate flags to nvcc on Windows. Fix warnings

The default add_compiler_options function wasn't passing the flags
to nvcc. This caused huge slowdowns because of glbinding warnings.
This PR also disables the -fPIC flag on Windows
---
 CMakeModules/InternalUtils.cmake                      | 11 ++++++-----
 src/backend/cuda/CMakeLists.txt                       |  6 ++++--
 src/backend/cuda/kernel/scan_by_key/CMakeLists.txt    |  2 +-
 .../cuda/kernel/thrust_sort_by_key/CMakeLists.txt     |  2 +-
 4 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 2c6ff29a51..420b6695de 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -31,6 +31,8 @@ endfunction()
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
   if(NOT MSVC)
     set(${cuda_flags} "-std=c++11"  PARENT_SCOPE)
+  else()
+    set(${cuda_flags} "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275"  PARENT_SCOPE)
   endif()
 endfunction()
 
@@ -79,13 +81,12 @@ macro(arrayfire_set_cmake_default_variables)
     set(CMAKE_STATIC_LINKER_FLAGS_COVERAGE "${CMAKE_STATIC_LINKER_FLAGS_COVERAGE}")
     set(CMAKE_MODULE_LINKER_FLAGS_COVERAGE "${CMAKE_STATIC_LINKER_FLAGS_COVERAGE} --coverage")
   elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-    message(WARNING "Code Coverage in Visual Studio is not tested")
     set(CMAKE_CXX_FLAGS_COVERAGE "")
     set(CMAKE_C_FLAGS_COVERAGE "")
-    set(CMAKE_EXE_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
-    set(CMAKE_SHARED_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
-    set(CMAKE_STATIC_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
-    set(CMAKE_MODULE_LINKER_FLAGS_COVERAGE "/OPT:NOREF /PROFILE")
+    set(CMAKE_EXE_LINKER_FLAGS_COVERAGE "")
+    set(CMAKE_SHARED_LINKER_FLAGS_COVERAGE "")
+    set(CMAKE_STATIC_LINKER_FLAGS_COVERAGE "")
+    set(CMAKE_MODULE_LINKER_FLAGS_COVERAGE "")
   endif()
 
   mark_as_advanced(
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index d8c329928b..af077931d5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -110,6 +110,9 @@ function(cuda_add_library cuda_target)
 endfunction()
 
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
+if(NOT MSVC)
+  set(cuda_cxx_flags "${cuda_cxx_flags} -Xcompiler -fPIC")
+endif()
 
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
@@ -385,7 +388,7 @@ cuda_add_library(afcuda
     JIT/UnaryNode.hpp
     JIT/types.h
 
-    OPTIONS "${cuda_cxx_flags} -Xcompiler -fPIC -Xcudafe \"--diag_suppress=1427\""
+    OPTIONS "${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
   )
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
@@ -394,7 +397,6 @@ if(BUILD_NONFREE)
   target_compile_definitions(afcuda PRIVATE AF_BUILD_NONFREE_SIFT)
 endif()
 
-
 if(BUILD_GRAPHICS)
   target_sources(afcuda
     PRIVATE
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index e1db54b2e7..bfd9ab4abd 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -30,7 +30,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     cuda_compile(scan_by_key_gen_files "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
-          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "${cuda_cxx_flags} -Xcompiler -fPIC -DAFDLL"
+          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "${cuda_cxx_flags} -DAFDLL"
       )
 
     list(APPEND SCAN_OBJ ${scan_by_key_gen_files})
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 78a6a26e0d..573c487ad3 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -36,7 +36,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
           OPTIONS
             -DSBK_TYPE=${SBK_TYPE}
             -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
-            "${cuda_cxx_flags} -Xcompiler -fPIC -DAFDLL"
+            "${cuda_cxx_flags} -DAFDLL"
       )
 
       list(APPEND SORT_OBJ ${scan_by_key_gen_files})

From 104ba5669c01a665b67663b5c70177ce63e0d3b2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 17 Oct 2017 02:34:50 -0400
Subject: [PATCH 1315/2677] Remove iostream from various headers

---
 examples/computer_vision/fast.cpp             |  4 +--
 examples/computer_vision/harris.cpp           |  6 ++--
 examples/computer_vision/matching.cpp         |  3 +-
 examples/computer_vision/susan.cpp            |  4 +--
 .../adaptive_thresholding.cpp                 |  2 +-
 .../image_processing/binary_thresholding.cpp  |  2 +-
 examples/image_processing/filters.cpp         |  2 +-
 examples/image_processing/image_editing.cpp   |  2 +-
 examples/image_processing/morphing.cpp        |  4 +--
 include/af/exception.h                        |  2 +-
 src/api/c/image.cpp                           |  3 +-
 src/api/c/moments.cpp                         |  1 -
 src/api/c/stream.cpp                          |  1 -
 src/api/c/tile.cpp                            |  1 -
 src/backend/common/MemoryManager.hpp          | 33 +++++++------------
 src/backend/common/util.hpp                   |  2 +-
 src/backend/cpu/cholesky.cpp                  |  1 -
 src/backend/cpu/inverse.cpp                   |  1 -
 src/backend/cpu/platform.cpp                  |  2 +-
 src/backend/cpu/platform.hpp                  |  1 -
 src/backend/cuda/cholesky.cu                  |  1 -
 src/backend/cuda/homography.cu                |  1 -
 src/backend/cuda/kernel/homography.hpp        |  2 --
 src/backend/cuda/solve.cu                     |  1 -
 src/backend/opencl/debug_opencl.hpp           |  3 +-
 src/backend/opencl/homography.cpp             |  1 -
 src/backend/opencl/magma/magma_data.h         |  1 -
 src/backend/opencl/program.cpp                |  1 -
 test/approx1.cpp                              |  1 -
 test/approx2.cpp                              |  1 -
 test/cholesky_dense.cpp                       |  1 -
 test/corrcoef.cpp                             |  1 -
 test/covariance.cpp                           |  1 -
 test/diagonal.cpp                             |  1 -
 test/diff1.cpp                                |  1 -
 test/diff2.cpp                                |  1 -
 36 files changed, 32 insertions(+), 64 deletions(-)

diff --git a/examples/computer_vision/fast.cpp b/examples/computer_vision/fast.cpp
index 22875d8d55..348641c61f 100644
--- a/examples/computer_vision/fast.cpp
+++ b/examples/computer_vision/fast.cpp
@@ -69,11 +69,11 @@ int main(int argc, char** argv)
     try {
         af::setDevice(device);
         af::info();
-        std::cout << "** ArrayFire FAST Feature Detector Demo **" << std::endl << std::endl;
+        printf("** ArrayFire FAST Feature Detector Demo **\n\n");
         fast_demo(console);
 
     } catch (af::exception& ae) {
-        std::cerr << ae.what() << std::endl;
+        fprintf(stderr, "%s\n", ae.what());
         throw;
     }
 
diff --git a/examples/computer_vision/harris.cpp b/examples/computer_vision/harris.cpp
index 65c2ef060a..7f41f7e726 100644
--- a/examples/computer_vision/harris.cpp
+++ b/examples/computer_vision/harris.cpp
@@ -110,7 +110,7 @@ static void harris_demo(bool console)
         array corners_y = idx % corners.dims()[0];
 
         const int good_corners = corners_x.dims()[0];
-        std::cout << "Corners found: " << good_corners << std::endl << std::endl;
+        printf("Corners found: %d\n\n", good_corners);
 
         af_print(corners_x);
         af_print(corners_y);
@@ -125,11 +125,11 @@ int main(int argc, char** argv)
     try {
         af::setDevice(device);
         af::info();
-        std::cout << "** ArrayFire Harris Corner Detector Demo **" << std::endl << std::endl;
+        printf("** ArrayFire Harris Corner Detector Demo **\n\n");
         harris_demo(console);
 
     } catch (af::exception& ae) {
-        std::cerr << ae.what() << std::endl;
+        fprintf(stderr, "%s\n", ae.what());
         throw;
     }
 
diff --git a/examples/computer_vision/matching.cpp b/examples/computer_vision/matching.cpp
index 153bb7fa60..2c42f9578b 100644
--- a/examples/computer_vision/matching.cpp
+++ b/examples/computer_vision/matching.cpp
@@ -7,9 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cstdio>
 #include <arrayfire.h>
 #include <cstdlib>
+#include <cstdio>
+#include <iostream>
 
 using namespace af;
 
diff --git a/examples/computer_vision/susan.cpp b/examples/computer_vision/susan.cpp
index eebf79e295..2f02679a14 100644
--- a/examples/computer_vision/susan.cpp
+++ b/examples/computer_vision/susan.cpp
@@ -75,11 +75,11 @@ int main(int argc, char** argv)
     try {
         af::setDevice(device);
         af::info();
-        std::cout << "** ArrayFire FAST Feature Detector Demo **" << std::endl << std::endl;
+        printf("** ArrayFire FAST Feature Detector Demo **\n\n");
         susan_demo(console);
 
     } catch (af::exception& ae) {
-        std::cerr << ae.what() << std::endl;
+        fprintf(stderr, "%s\n", ae.what());
         throw;
     }
 
diff --git a/examples/image_processing/adaptive_thresholding.cpp b/examples/image_processing/adaptive_thresholding.cpp
index 1004285148..8a5ffc6621 100644
--- a/examples/image_processing/adaptive_thresholding.cpp
+++ b/examples/image_processing/adaptive_thresholding.cpp
@@ -91,7 +91,7 @@ int main(int argc, char **argv)
         array itt = 255.0f - iterativeThreshold(sudoku);
 
         af::Window wnd("Adaptive Thresholding Algorithms");
-        std::cout << "Press ESC while the window is in focus to exit" << std::endl;
+        printf("Press ESC while the window is in focus to exit\n");
         while (!wnd.close()) {
             wnd.grid(2, 3);
             wnd(0, 0).image(sudoku / 255, "Input");
diff --git a/examples/image_processing/binary_thresholding.cpp b/examples/image_processing/binary_thresholding.cpp
index 6a00bd2814..bb2ffa88af 100644
--- a/examples/image_processing/binary_thresholding.cpp
+++ b/examples/image_processing/binary_thresholding.cpp
@@ -77,7 +77,7 @@ int main(int argc, char **argv)
         array smoothHist = histogram(smooth, 256, 0, 255);
 
         af::Window wnd(1536, 1024, "Binary Thresholding Algorithms");
-        std::cout << "Press ESC while the window is in focus to proceed to exit" << std::endl;
+        printf("Press ESC while the window is in focus to proceed to exit\n");
 
         wnd.grid(3, 3);
         wnd(0, 1).setAxesTitles("Bins", "Frequency");
diff --git a/examples/image_processing/filters.cpp b/examples/image_processing/filters.cpp
index ec87b1f082..221081b117 100644
--- a/examples/image_processing/filters.cpp
+++ b/examples/image_processing/filters.cpp
@@ -226,7 +226,7 @@ int main(int argc, char **argv)
         array emb = emboss(img, 45, 20, 10);
 
         af::Window wnd("Image Filters Demo");
-        std::cout << "Press ESC while the window is in focus to exit" << std::endl;
+        printf("Press ESC while the window is in focus to exit\n");
         while (!wnd.close()) {
             wnd.grid(2, 5);
             wnd(0, 0).image(hrl / 255, "Hurl noise");
diff --git a/examples/image_processing/image_editing.cpp b/examples/image_processing/image_editing.cpp
index 800f2739cb..54001ab438 100644
--- a/examples/image_processing/image_editing.cpp
+++ b/examples/image_processing/image_editing.cpp
@@ -115,7 +115,7 @@ int main(int argc, char **argv)
         array bdry = boundary(man, morph_mask);
 
         af::Window wnd("Image Editing Operations");
-        std::cout << "Press ESC while the window is in focus to exit" << std::endl;
+        printf("Press ESC while the window is in focus to exit\n");
         while (!wnd.close()) {
             wnd.grid(2, 5);
             wnd(0, 0).image(man / 255, "Input");
diff --git a/examples/image_processing/morphing.cpp b/examples/image_processing/morphing.cpp
index 73eb45c4ca..685069d7ab 100644
--- a/examples/image_processing/morphing.cpp
+++ b/examples/image_processing/morphing.cpp
@@ -44,9 +44,9 @@ array border(const array& img, const int left, const int right,
         const float value = 0.0)
 {
     if((int)img.dims(0) < (top + bottom))
-        std::cerr << "input does not have enough rows" << std::endl;
+        printf("input does not have enough rows\n");
     if((int)img.dims(1) < (left + right))
-        std::cerr << "input does not have enough columns" << std::endl;
+        fprintf(stderr, "input does not have enough columns\n");
 
     dim4 imgDims = img.dims();
     array ret = constant(value, imgDims);
diff --git a/include/af/exception.h b/include/af/exception.h
index a43d26dbaa..da8ccc554c 100644
--- a/include/af/exception.h
+++ b/include/af/exception.h
@@ -11,7 +11,7 @@
 
 #ifdef __cplusplus
 
-#include <iostream>
+#include <ostream>
 #include <af/defines.h>
 
 namespace af {
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 0c4c9a0918..b8996f948c 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -25,7 +25,6 @@
 #include <cast.hpp>
 #include <arith.hpp>
 
-#include <iostream>
 #include <limits>
 
 using af::dim4;
@@ -79,7 +78,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
 {
 #if defined(WITH_GRAPHICS)
     if(wind==0) {
-        std::cerr<<"Not a valid window"<<std::endl;
+        fprintf(stderr, "Not a valid window\n");
         return AF_SUCCESS;
     }
 
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index a04027a3d0..8024606e34 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -23,7 +23,6 @@
 #include <cast.hpp>
 #include <arith.hpp>
 
-#include <iostream>
 #include <limits>
 
 using af::dim4;
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index fe331562f2..f74fe31720 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <iostream>
 #include <fstream>
 #include <iomanip>
 #include <vector>
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 8dce987138..55d9e37b44 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <iostream>
 #include <af/data.h>
 #include <af/arith.h>
 #include <common/err_common.hpp>
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 5ab90e4fc3..fc18d0f8f1 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -14,11 +14,9 @@
 #include <common/util.hpp>
 
 #include <algorithm>
-
-// TODO(umar): Remove iostream
 #include <iomanip>
-#include <iostream>
 #include <mutex>
+#include <string>
 #include <unordered_map>
 #include <vector>
 
@@ -298,38 +296,34 @@ class MemoryManager
         lock_guard_t lock(this->memory_mutex);
         const memory_info& current = this->getCurrentMemoryInfo();
 
-        std::cout << msg << std::endl;
-
+        printf("%s\n", msg);
         printf("---------------------------------------------------------\n"
                "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
                "---------------------------------------------------------\n");
 
         for(auto& kv : current.locked_map) {
-            std::string status_mngr("Yes");
-            std::string status_user("Unknown");
+            const char* status_mngr = "Yes";
+            const char* status_user = "Unknown";
             if(kv.second.user_lock)     status_user = "Yes";
             else                        status_user = " No";
 
-            std::string unit = "KB";
+            const char* unit = "KB";
             double size = (double)(kv.second.bytes) / 1024;
             if(size >= 1024) {
                 size = size / 1024;
                 unit = "MB";
             }
 
-            std::cout << "|  " << std::right << std::setw(14) << kv.first << " "
-                << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                << " | " << std::setw(9) << status_mngr
-                << " | " << std::setw(9) << status_user
-                << " |"  << std::endl;
+            printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
+                   kv.first, size, unit, status_mngr, status_user);
         }
 
         for(auto &kv : current.free_map) {
 
-            std::string status_mngr("No");
-            std::string status_user("No");
+            const char* status_mngr = "No";
+            const char* status_user = "No";
 
-            std::string unit = "KB";
+            const char* unit = "KB";
             double size = (double)(kv.first) / 1024;
             if(size >= 1024) {
                 size = size / 1024;
@@ -337,11 +331,8 @@ class MemoryManager
             }
 
             for (auto &ptr : kv.second) {
-                std::cout << "|  " << std::right << std::setw(14) << ptr << " "
-                    << " | " << std::setw(7) << std::setprecision(4) << size << " " << unit
-                    << " | " << std::setw(9) << status_mngr
-                    << " | " << std::setw(9) << status_user
-                    << " |"  << std::endl;
+              printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
+                     ptr, size, unit, status_mngr, status_user);
             }
         }
 
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index e1cd85a69c..ccb71f9e80 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -9,7 +9,7 @@
 
 /// This file contains platform independent utility functions
 
-#include <string>
+#include <iosfwd>
 
 #pragma once
 
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index cc8f877077..88f828e0bb 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -14,7 +14,6 @@
 
 #include <af/dim4.hpp>
 #include <handle.hpp>
-#include <iostream>
 #include <cassert>
 #include <err_cpu.hpp>
 #include <triangle.hpp>
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index 1a9c9d5dcc..7bbe9f9b34 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -15,7 +15,6 @@
 #include <af/dim4.hpp>
 #include <handle.hpp>
 #include <range.hpp>
-#include <iostream>
 #include <cassert>
 #include <err_cpu.hpp>
 
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 2dfc2093c9..325574362d 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -229,7 +229,7 @@ int setDevice(int device)
     thread_local bool flag = false;
     if (!flag && device != 0) {
 #ifndef NDEBUG
-        std::cerr << "WARNING af_set_device(device): device can only be 0 for CPU\n";
+        fprintf(stderr, "WARNING af_set_device(device): device can only be 0 for CPU\n");
 #endif
         flag = true;
     }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index c6eb731871..0bb9a20922 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -11,7 +11,6 @@
 
 #include <array>
 #include <algorithm>
-#include <iostream>
 #include <memory>
 #include <string>
 #include <memory.hpp>
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index 9e86f725de..5463bd6c4f 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -13,7 +13,6 @@
 #include <platform.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
-#include <iostream>
 #include <memory.hpp>
 #include <copy.hpp>
 #include <triangle.hpp>
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index 4816e2de79..92d3b60b76 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -14,7 +14,6 @@
 #include <kernel/homography.hpp>
 #include <algorithm>
 
-#include <iostream>
 #include <cfloat>
 
 using af::dim4;
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 091a9821a2..f09423d167 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -17,8 +17,6 @@
 
 #include <cfloat>
 
-#include <iostream>
-
 namespace cuda
 {
 
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 13c29e982a..d773906dde 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -13,7 +13,6 @@
 #include <platform.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
-#include <iostream>
 #include <memory.hpp>
 #include <copy.hpp>
 #include <transpose.hpp>
diff --git a/src/backend/opencl/debug_opencl.hpp b/src/backend/opencl/debug_opencl.hpp
index b4126f9abe..19680a65ac 100644
--- a/src/backend/opencl/debug_opencl.hpp
+++ b/src/backend/opencl/debug_opencl.hpp
@@ -13,7 +13,6 @@
 #include <errorcodes.hpp>
 
 #ifndef NDEBUG
-#include <iostream>
 #define CL_DEBUG_FINISH(Q) Q.finish()
 #else
 #define CL_DEBUG_FINISH(Q)                      \
@@ -21,5 +20,5 @@
         if(synchronize_calls()) {               \
             Q.finish();                         \
         }                                       \
-    } while (false); 
+    } while (false);
 #endif
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 0e5328ca98..69c40a98a4 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -14,7 +14,6 @@
 #include <kernel/homography.hpp>
 #include <algorithm>
 
-#include <iostream>
 #include <cfloat>
 
 using af::dim4;
diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 740f2d322f..19d83df841 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -55,7 +55,6 @@
 
 #ifndef MAGMA_DATA_H
 #define MAGMA_DATA_H
-#include <iostream>
 
 #include <platform.hpp>
 #include "magma_types.h"
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 31e467d4b0..154d20d091 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -11,7 +11,6 @@
 #include <traits.hpp>
 #include <kernel_headers/KParam.hpp>
 #include <debug_opencl.hpp>
-#include <iostream>
 
 using cl::Buffer;
 using cl::Program;
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 003eb023a4..4a3f19384e 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -18,7 +18,6 @@
 #include <testHelpers.hpp>
 
 #include <complex>
-#include <iostream>
 #include <string>
 #include <vector>
 
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 16b58631a1..7da90fc430 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -15,7 +15,6 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 
-#include <iostream>
 #include <string>
 #include <vector>
 
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index c5f87ff9db..a965c9782c 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -13,7 +13,6 @@
 #include <af/defines.h>
 #include <af/traits.hpp>
 #include <vector>
-#include <iostream>
 #include <complex>
 #include <string>
 #include <testHelpers.hpp>
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 62454d44da..40e19f33d9 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -14,7 +14,6 @@
 #include <string>
 #include <vector>
 #include <ctime>
-#include <iostream>
 #include <algorithm>
 #include <testHelpers.hpp>
 
diff --git a/test/covariance.cpp b/test/covariance.cpp
index 224003dfb7..57decb9a54 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -14,7 +14,6 @@
 #include <string>
 #include <vector>
 #include <ctime>
-#include <iostream>
 #include <algorithm>
 #include <testHelpers.hpp>
 
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index e341e87770..a6aef8351c 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -10,7 +10,6 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <arrayfire.h>
-#include <iostream>
 
 using namespace af;
 using std::vector;
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 65f6b037f7..57ec8e2487 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -12,7 +12,6 @@
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <vector>
-#include <iostream>
 #include <string>
 #include <testHelpers.hpp>
 
diff --git a/test/diff2.cpp b/test/diff2.cpp
index 00116d907e..0c1dc1e455 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -12,7 +12,6 @@
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <vector>
-#include <iostream>
 #include <string>
 #include <testHelpers.hpp>
 

From 4f66b5a128da0c99246716bd29c4e71b245d1837 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 23 Oct 2017 10:50:50 -0400
Subject: [PATCH 1316/2677] Rename WITH_<backend>_LINEAR_ALGEBRA to
 WITH_LINEAR_ALGEBRA

This commit renames the WITH_<backend>_LINEAR_ALGEBRA to
WITH_LINEAR_ALGEBRA. This also fixes issue #1974 where
the WITH_CPU_LINEAR_ALGEBRA was missing from the CMake scripts
---
 src/backend/common/CMakeLists.txt          | 3 +++
 src/backend/cpu/cholesky.cpp               | 6 +++---
 src/backend/cpu/inverse.cpp                | 6 +++---
 src/backend/cpu/lu.cpp                     | 6 +++---
 src/backend/cpu/qr.cpp                     | 6 +++---
 src/backend/cpu/solve.cpp                  | 6 +++---
 src/backend/cpu/svd.cpp                    | 6 +++---
 src/backend/opencl/blas.cpp                | 4 ++--
 src/backend/opencl/cholesky.cpp            | 6 +++---
 src/backend/opencl/cpu/cpu_blas.cpp        | 4 ++--
 src/backend/opencl/cpu/cpu_cholesky.cpp    | 4 ++--
 src/backend/opencl/cpu/cpu_helper.hpp      | 6 +++---
 src/backend/opencl/cpu/cpu_inverse.cpp     | 4 ++--
 src/backend/opencl/cpu/cpu_lu.cpp          | 4 ++--
 src/backend/opencl/cpu/cpu_qr.cpp          | 4 ++--
 src/backend/opencl/cpu/cpu_solve.cpp       | 4 ++--
 src/backend/opencl/cpu/cpu_sparse_blas.cpp | 4 ++--
 src/backend/opencl/cpu/cpu_svd.cpp         | 4 ++--
 src/backend/opencl/cpu/cpu_triangle.hpp    | 4 ++--
 src/backend/opencl/inverse.cpp             | 4 ++--
 src/backend/opencl/lu.cpp                  | 6 +++---
 src/backend/opencl/qr.cpp                  | 6 +++---
 src/backend/opencl/solve.cpp               | 6 +++---
 src/backend/opencl/sparse_blas.cpp         | 6 +++---
 src/backend/opencl/svd.cpp                 | 6 +++---
 25 files changed, 64 insertions(+), 61 deletions(-)

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 9f851c00a6..fafd7d7f16 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -87,6 +87,9 @@ if(LAPACK_FOUND)
   target_include_directories(afcommon_lapack_interface
     INTERFACE ${LAPACK_INCLUDE_DIR})
 
+  target_compile_definitions(afcommon_lapack_interface
+    INTERFACE WITH_LINEAR_ALGEBRA)
+
   target_link_libraries(afcommon_lapack_interface
     INTERFACE
       ${LAPACK_LIBRARIES})
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 88f828e0bb..85eaec2ecc 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -10,7 +10,7 @@
 #include <cholesky.hpp>
 #include <common/err_common.hpp>
 
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 
 #include <af/dim4.hpp>
 #include <handle.hpp>
@@ -93,7 +93,7 @@ INSTANTIATE_CH(cdouble)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
@@ -122,4 +122,4 @@ INSTANTIATE_CH(cdouble)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index 7bbe9f9b34..d0e2457536 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -10,7 +10,7 @@
 #include <inverse.hpp>
 #include <common/err_common.hpp>
 
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 
 #include <af/dim4.hpp>
 #include <handle.hpp>
@@ -82,7 +82,7 @@ INSTANTIATE(cdouble)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
@@ -104,4 +104,4 @@ INSTANTIATE(cdouble)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 5bfd8d7f72..f078b02274 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -10,7 +10,7 @@
 #include <lu.hpp>
 #include <common/err_common.hpp>
 
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <af/dim4.hpp>
 #include <handle.hpp>
 #include <kernel/lu.hpp>
@@ -97,7 +97,7 @@ bool isLAPACKAvailable()
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
@@ -121,7 +121,7 @@ bool isLAPACKAvailable()
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index ddb1a3d8ea..3a36e62cbb 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -10,7 +10,7 @@
 #include <qr.hpp>
 #include <common/err_common.hpp>
 
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <af/dim4.hpp>
 #include <handle.hpp>
 #include <cassert>
@@ -106,7 +106,7 @@ Array<T> qr_inplace(Array<T> &in)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
@@ -125,7 +125,7 @@ Array<T> qr_inplace(Array<T> &in)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 9c2aae488b..2ecb57cce1 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -10,7 +10,7 @@
 #include <solve.hpp>
 #include <common/err_common.hpp>
 
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <af/dim4.hpp>
 #include <handle.hpp>
 #include <cassert>
@@ -163,7 +163,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
@@ -183,7 +183,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 42caf2f3a2..f75e16b3c8 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -12,7 +12,7 @@
 #include <common/err_common.hpp>
 #include <err_cpu.hpp>
 
-#if defined(WITH_CPU_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <lapack_helper.hpp>
 #include <copy.hpp>
 #include <platform.hpp>
@@ -99,7 +99,7 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
@@ -118,7 +118,7 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
 
 namespace cpu
 {
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index ac8a265448..495543d813 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -21,7 +21,7 @@
 // Includes one of the supported OpenCL BLAS back-ends (e.g. clBLAS, CLBlast)
 #include <magma/magma_blas.h>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_blas.hpp>
 #endif
 
@@ -54,7 +54,7 @@ template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
     if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
         return cpu::matmul(lhs, rhs, optLhs, optRhs);
     }
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index 47df889539..3e100391e7 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -12,7 +12,7 @@
 #include <blas.hpp>
 #include <copy.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <magma/magma.h>
 #include <triangle.hpp>
 #include <platform.hpp>
@@ -69,7 +69,7 @@ INSTANTIATE_CH(cdouble)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
@@ -98,4 +98,4 @@ INSTANTIATE_CH(cdouble)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 127307b3e1..9511523c1b 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_blas.hpp>
 #include <math.hpp>
@@ -208,4 +208,4 @@ INSTANTIATE_BLAS(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index 9acbcc4fad..98fc48c335 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_cholesky.hpp>
 #include <cpu/cpu_triangle.hpp>
@@ -81,4 +81,4 @@ INSTANTIATE_CH(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index df3a820c90..1bf19fc986 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -18,7 +18,7 @@
 //********************************************************/
 // LAPACK
 //********************************************************/
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 
 #define lapack_complex_float opencl::cfloat
 #define lapack_complex_double opencl::cdouble
@@ -40,6 +40,6 @@
     #endif
 #endif
 
-#endif // WITH_OPENCL_LINEAR_ALGEBRA
+#endif  // WITH_LINEAR_ALGEBRA
 
-#endif
+#endif  // AF_OPENCL_CPU
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index 4f73a80707..9dba95048d 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_inverse.hpp>
 #include <cpu/cpu_lu.hpp>
@@ -73,4 +73,4 @@ INSTANTIATE(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index e0234fb7de..c496b09ca9 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_lu.hpp>
 #include <math.hpp>
@@ -175,4 +175,4 @@ INSTANTIATE_LU(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index 737a7aec2f..f52a18f6c2 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_qr.hpp>
 #include <cpu/cpu_triangle.hpp>
@@ -115,4 +115,4 @@ INSTANTIATE_QR(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 1bb72f8768..b3616d083c 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
 #include <copy.hpp>
@@ -173,4 +173,4 @@ INSTANTIATE_SOLVE(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 5339042150..3d3b46e8e8 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_sparse_blas.hpp>
 
 #include <stdexcept>
@@ -532,4 +532,4 @@ INSTANTIATE_SPARSE(cdouble)
 
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index 3608bf69ce..353dd7681a 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_svd.hpp>
 #include <copy.hpp>
@@ -109,4 +109,4 @@ namespace cpu
     INSTANTIATE_SVD(cdouble, double)
 }
 }
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_triangle.hpp b/src/backend/opencl/cpu/cpu_triangle.hpp
index e705420582..630d865205 100644
--- a/src/backend/opencl/cpu/cpu_triangle.hpp
+++ b/src/backend/opencl/cpu/cpu_triangle.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #ifndef CPU_LAPACK_TRIANGLE
 #define CPU_LAPACK_TRIANGLE
 
@@ -54,4 +54,4 @@ void triangle(T *o, const T *i, const dim4 odm, const dim4 ost, const dim4 ist)
 }
 
 #endif
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/inverse.cpp b/src/backend/opencl/inverse.cpp
index d468249921..71072e3067 100644
--- a/src/backend/opencl/inverse.cpp
+++ b/src/backend/opencl/inverse.cpp
@@ -11,7 +11,7 @@
 #include <solve.hpp>
 #include <identity.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <platform.hpp>
 #include <cpu/cpu_inverse.hpp>
 
@@ -39,7 +39,7 @@ INSTANTIATE(cdouble)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index aa8cfeef3d..02da58893f 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -10,7 +10,7 @@
 #include <lu.hpp>
 #include <err_opencl.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <kernel/lu_split.hpp>
 #include <copy.hpp>
 #include <blas.hpp>
@@ -104,7 +104,7 @@ INSTANTIATE_LU(cdouble)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
@@ -137,4 +137,4 @@ INSTANTIATE_LU(cdouble)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 2f3df19dee..0615a005fc 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -12,7 +12,7 @@
 #include <blas.hpp>
 #include <copy.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 
 #include <magma/magma.h>
 #include <magma/magma_helper.h>
@@ -118,7 +118,7 @@ INSTANTIATE_QR(cdouble)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
@@ -146,4 +146,4 @@ INSTANTIATE_QR(cdouble)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index afe26a3fe1..5504841587 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -10,7 +10,7 @@
 #include <err_opencl.hpp>
 #include <solve.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <magma/magma.h>
 #include <magma/magma_blas.h>
 #include <magma/magma_data.h>
@@ -330,7 +330,7 @@ INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
@@ -363,4 +363,4 @@ INSTANTIATE_SOLVE(cdouble)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index eb8035ccd8..3c5a265468 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -26,9 +26,9 @@
 #include <platform.hpp>
 #include <transpose.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_sparse_blas.hpp>
-#endif
+#endif  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
@@ -39,7 +39,7 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
     if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
         return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
     }
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index bc0043ee10..811aa91b36 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -15,7 +15,7 @@
 #include <blas.hpp>
 #include <transpose.hpp>
 
-#if defined(WITH_OPENCL_LINEAR_ALGEBRA)
+#if defined(WITH_LINEAR_ALGEBRA)
 
 #include <magma/magma.h>
 #include <magma/magma_cpu_lapack.h>
@@ -238,7 +238,7 @@ INSTANTIATE(cdouble, double)
 
 }
 
-#else
+#else  // WITH_LINEAR_ALGEBRA
 
 namespace opencl
 {
@@ -266,4 +266,4 @@ INSTANTIATE(cdouble, double)
 
 }
 
-#endif
+#endif  // WITH_LINEAR_ALGEBRA

From da4360e23b7f34665d6907a079210ca4362b746b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 24 Oct 2017 01:08:29 -0400
Subject: [PATCH 1317/2677] Fixed typo in error message.

---
 src/backend/cpu/inverse.cpp  | 2 +-
 src/backend/cpu/solve.cpp    | 4 ++--
 src/backend/opencl/solve.cpp | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index d0e2457536..fc2c3ad406 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -90,7 +90,7 @@ namespace cpu
 template<typename T>
 Array<T> inverse(const Array<T> &in)
 {
-    AF_ERROR("Linear Algebra is diabled on CPU",
+    AF_ERROR("Linear Algebra is disabled on CPU",
               AF_ERR_NOT_CONFIGURED);
 }
 
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 2ecb57cce1..2cc3806939 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -172,13 +172,13 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
-    AF_ERROR("Linear Algebra is diabled on CPU", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 {
-    AF_ERROR("Linear Algebra is diabled on CPU", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 }
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 5504841587..a427ed28e0 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -339,14 +339,14 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
-    AF_ERROR("Linear Algebra is diabled on OpenCL",
+    AF_ERROR("Linear Algebra is disabled on OpenCL",
              AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
 {
-    AF_ERROR("Linear Algebra is diabled on OpenCL",
+    AF_ERROR("Linear Algebra is disabled on OpenCL",
               AF_ERR_NOT_CONFIGURED);
 }
 

From fa4172053bd1e64cf110cd1de11414e0b2ffda4f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 12 Oct 2017 02:54:02 -0400
Subject: [PATCH 1318/2677] Add ability to add definition flags in tests.
 Enable SIFT tests

Add AF_BUILD_NONFREE_SIFT defintion in tests.
---
 src/backend/cuda/CMakeLists.txt |  8 ++++++++
 test/CMakeLists.txt             | 13 +++++++++----
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index af077931d5..7727836b21 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -114,6 +114,14 @@ if(NOT MSVC)
   set(cuda_cxx_flags "${cuda_cxx_flags} -Xcompiler -fPIC")
 endif()
 
+if(BUILD_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")
+  # This definition is required in addition to the definition below because in
+  # an older verion of cmake definitions added using target_compile_definitions
+  # were not added to the nvcc flags. This manually adds these definitions and
+  # pass them to the options parameter in cuda_add_library
+  string(APPEND cuda_cxx_flags " -DAF_BUILD_NONFREE_SIFT")
+endif()
+
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b70962c9a3..13cdd94bb4 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -62,7 +62,7 @@ include(CMakeParseArguments)
 function(make_test)
   set(options CXX11)
   set(single_args SRC)
-  set(multi_args LIBRARIES)
+  set(multi_args LIBRARIES DEFINITIONS)
   cmake_parse_arguments(mt_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
 
   get_filename_component(src_name ${mt_args_SRC} NAME_WE)
@@ -108,6 +108,7 @@ function(make_test)
       PRIVATE
         TEST_DIR="${TESTDATA_SOURCE_DIR}"
         AF_$<UPPER_CASE:${backend}>
+        ${mt_args_DEFINITIONS}
       )
     if(WIN32)
       target_compile_definitions(${target}
@@ -162,7 +163,6 @@ make_test(SRC gen_assign.cpp)
 make_test(SRC gen_index.cpp)
 make_test(SRC getting_started.cpp)
 make_test(SRC gfor.cpp)
-make_test(SRC gloh_nonfree.cpp)
 make_test(SRC gradient.cpp)
 make_test(SRC gray_rgb.cpp)
 make_test(SRC hamming.cpp)
@@ -201,7 +201,7 @@ make_test(SRC nearest_neighbour.cpp)
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
             LIBRARIES OpenCL::OpenCL)
-endif(OpenCL_FOUND)
+endif()
 
 make_test(SRC orb.cpp)
 make_test(SRC qr_dense.cpp)
@@ -221,7 +221,12 @@ make_test(SRC scan_by_key.cpp)
 make_test(SRC select.cpp)
 make_test(SRC set.cpp)
 make_test(SRC shift.cpp)
-make_test(SRC sift_nonfree.cpp)
+
+if(BUILD_NONFREE)
+  make_test(SRC gloh_nonfree.cpp DEFINITIONS AF_BUILD_NONFREE_SIFT)
+  make_test(SRC sift_nonfree.cpp DEFINITIONS AF_BUILD_NONFREE_SIFT)
+endif()
+
 make_test(SRC sobel.cpp)
 make_test(SRC solve_dense.cpp        CXX11)
 make_test(SRC sort.cpp)

From f60c1d3ab1eacbc2b816bfd6a4448dd7ebaa9afa Mon Sep 17 00:00:00 2001
From: Felix Z <fzimmermann89@gmail.com>
Date: Tue, 24 Oct 2017 20:55:28 +0200
Subject: [PATCH 1319/2677] Add missing include Fixes build of unified backend
 on windows (#1977)

---
 src/backend/common/util.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index ccb71f9e80..cafa1aa4a6 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -10,6 +10,7 @@
 /// This file contains platform independent utility functions
 
 #include <iosfwd>
+#include <string>
 
 #pragma once
 

From 42a99a76684b662e30c3d76c896069d820ca9a96 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 27 Oct 2017 20:23:40 -0400
Subject: [PATCH 1320/2677] Fix errors when enabling the AF_MEM_DEBUG env
 variable

---
 src/backend/common/MemoryManager.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index fc18d0f8f1..711afe3562 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -261,8 +261,6 @@ class MemoryManager
         current.lock_bytes -= iter->second.bytes;
         current.lock_buffers--;
 
-        current.locked_map.erase(iter);
-
         if (this->debug_mode) {
             // Just free memory in debug mode
             if ((iter->second).bytes > 0) {
@@ -283,6 +281,8 @@ class MemoryManager
                 current.free_map[bytes] = ptrs;
             }
         }
+
+        current.locked_map.erase(iter);
     }
 
     void garbageCollect()

From a3b22bd0f554e72450c7ebc0d348fac3e4508458 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 30 Oct 2017 18:04:03 -0400
Subject: [PATCH 1321/2677] Remove reference to cout in program.cpp;

Fixes #1981
---
 CMakeLists.txt                                |  1 -
 src/backend/opencl/kernel/histogram.hpp       |  4 ++++
 src/backend/opencl/kernel/memcopy.hpp         |  6 ++++--
 .../opencl/kernel/nearest_neighbour.hpp       |  5 +++++
 src/backend/opencl/program.hpp                | 19 ++++++-------------
 5 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c544173be4..2f7b85b465 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -109,7 +109,6 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
-# TODO(umar): Enable other backends
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
 add_subdirectory(src/api/cpp)
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 7f226f0571..2a4b9c98e2 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -17,8 +17,12 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
+using cl::Buffer;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
+using cl::NDRange;
+using cl::Program;
 
 namespace opencl
 {
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 436da3fe00..f112ee91c9 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -21,10 +21,12 @@
 #include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
-using cl::KernelFunctor;
 using cl::EnqueueArgs;
+using cl::Kernel;
+using cl::KernelFunctor;
 using cl::NDRange;
+using cl::Program;
+
 using std::string;
 
 namespace opencl
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 9d70b20f09..8d4853d057 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -18,7 +18,12 @@
 #include <common/dispatch.hpp>
 #include <cache.hpp>
 
+using cl::Buffer;
+using cl::EnqueueArgs;
 using cl::KernelFunctor;
+using cl::Kernel;
+using cl::NDRange;
+using cl::Program;
 using cl::LocalSpaceArg;
 
 namespace opencl
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 2f09e505b1..060332c03b 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -10,24 +10,17 @@
 #pragma once
 #include <platform.hpp>
 #include <common/util.hpp>
-#include <string>
-#include <mutex>
 
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
+#include <cstdio>
+#include <string>
 
 #define SHOW_DEBUG_BUILD_INFO(PROG) do {                                \
         cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();    \
         for (unsigned int i = 0; i<numDevices; ++i) {                   \
-            std::cout << PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
-                PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) << std::endl;    \
-                                                                        \
-            std::cout << PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
-                PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) << std::endl;    \
+          printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
+                      PROG.getInfo<CL_PROGRAM_DEVICES>()[i]).c_str());  \
+          printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
+                      PROG.getInfo<CL_PROGRAM_DEVICES>()[i]).c_str());  \
         }                                                               \
     } while(0)                                                          \
 

From 4fab3f0f2861941e16b3e65271362ba874299e42 Mon Sep 17 00:00:00 2001
From: Patrick Lavin <patrick@arrayfire.com>
Date: Tue, 23 May 2017 16:36:21 -0400
Subject: [PATCH 1322/2677] memAlloc returns unique_ptr

This commit builds on the previous one by using RAII to manage memory.
memAlloc<T> returns a unique_ptr<T> which will automatically free memory when it
goes out of scope. If memory needs to persist, release() is called but only
after any functions that may fail have finished. Due to the signature of
Param<T> many tmp variables are used at this time, named *_alloc. Note that
cuda/kernel/orb.hpp has yet to be updated to use RAII.
---
 src/backend/cpu/Array.cpp                     |  10 +-
 src/backend/cpu/harris.cpp                    |   8 +-
 src/backend/cpu/kernel/sift_nonfree.hpp       | 148 +++++++-----------
 src/backend/cpu/memory.cpp                    |  21 ++-
 src/backend/cpu/memory.hpp                    |   3 +-
 src/backend/cpu/orb.cpp                       | 106 ++++++-------
 src/backend/cpu/susan.cpp                     |   2 +-
 src/backend/cpu/where.cpp                     |   5 +-
 src/backend/cuda/Array.cpp                    |  22 +--
 src/backend/cuda/Array.hpp                    |   6 +-
 src/backend/cuda/ThrustAllocator.cuh          |   2 +-
 src/backend/cuda/cholesky.cu                  |  10 +-
 src/backend/cuda/kernel/fast.hpp              |  41 ++---
 src/backend/cuda/kernel/fast_pyramid.hpp      |  11 +-
 src/backend/cuda/kernel/harris.hpp            | 113 ++++++-------
 src/backend/cuda/kernel/homography.hpp        |  35 ++---
 src/backend/cuda/kernel/ireduce.hpp           |  38 ++---
 src/backend/cuda/kernel/mean.hpp              | 106 ++++---------
 src/backend/cuda/kernel/nearest_neighbour.hpp |  40 +++--
 src/backend/cuda/kernel/orb.hpp               |  40 ++---
 src/backend/cuda/kernel/reduce.hpp            |  18 +--
 src/backend/cuda/kernel/regions.hpp           |   7 +-
 src/backend/cuda/kernel/scan_dim.hpp          |   5 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |  12 +-
 src/backend/cuda/kernel/scan_first.hpp        |   4 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  12 +-
 src/backend/cuda/kernel/sort.hpp              |   5 +-
 src/backend/cuda/kernel/sort_by_key.hpp       |  21 ++-
 src/backend/cuda/kernel/susan.hpp             |   9 +-
 src/backend/cuda/kernel/where.hpp             |  13 +-
 src/backend/cuda/lu.cu                        |  10 +-
 src/backend/cuda/memory.cpp                   |  18 ++-
 src/backend/cuda/memory.hpp                   |  13 +-
 src/backend/cuda/orb.cu                       |   2 +-
 src/backend/cuda/qr.cu                        |  24 ++-
 src/backend/cuda/solve.cu                     |  40 ++---
 src/backend/cuda/sparse.cu                    |   8 +-
 src/backend/cuda/susan.cu                     |  28 ++--
 src/backend/cuda/svd.cu                       |  11 +-
 src/backend/cuda/where.cu                     |   2 +-
 src/backend/opencl/Array.cpp                  |  12 +-
 src/backend/opencl/Array.hpp                  |   6 +-
 src/backend/opencl/fast.cpp                   |   6 +-
 src/backend/opencl/harris.cpp                 |   6 +-
 src/backend/opencl/memory.cpp                 |  19 ++-
 src/backend/opencl/memory.hpp                 |   3 +-
 src/backend/opencl/orb.cpp                    |  12 +-
 src/backend/opencl/where.cpp                  |   2 +-
 48 files changed, 504 insertions(+), 591 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 0d1df29945..675896b54d 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -40,14 +40,14 @@ Node_ptr bufferNodePtr()
 template<typename T>
 Array<T>::Array(dim4 dims):
     info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-    data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
+    data(memAlloc<T>(dims.elements()).release(), memFree<T>), data_dims(dims),
     node(bufferNodePtr<T>()), ready(true), owner(true)
 { }
 
 template<typename T>
 Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
     info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-    data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
+    data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()).release(), memFree<T>), data_dims(dims),
     node(bufferNodePtr<T>()), ready(true), owner(true)
 {
     static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
@@ -79,7 +79,7 @@ template<typename T>
 Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                 const T * const in_data, bool is_device) :
     info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-    data(is_device ? (T*)in_data : memAlloc<T>(info.total()), memFree<T>),
+    data(is_device ? (T*)in_data : memAlloc<T>(info.total()).release(), memFree<T>),
     data_dims(dims),
     node(bufferNodePtr<T>()),
     ready(true),
@@ -100,7 +100,7 @@ void Array<T>::eval()
 
     this->setId(getActiveDeviceId());
 
-    data = std::shared_ptr<T>(memAlloc<T>(elements()), memFree<T>);
+    data = std::shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
 
     getQueue().enqueue(kernel::evalArray<T>, *this, this->node);
     // Reset shared_ptr
@@ -135,7 +135,7 @@ void evalMultiple(std::vector<Array<T>*> array_ptrs)
         if (array->ready) continue;
         if (isWorker) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
         array->setId(getActiveDeviceId());
-        array->data = std::shared_ptr<T>(memAlloc<T>(array->elements()), memFree<T>);
+        array->data = std::shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
         arrays.push_back(*array);
         nodes.push_back(array->node);
     }
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 0c87007de2..dd7a94a98b 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -34,16 +34,16 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     dim4 idims = in.dims();
 
     // Window filter
-    convAccT* h_filter = memAlloc<convAccT>(filter_len);
+    auto h_filter = memAlloc<convAccT>(filter_len);
     // Decide between rectangular or circular filter
     if (sigma < 0.5f) {
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
     } else {
-        gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
+      gaussian1D<convAccT>(h_filter.get(), (int)filter_len, sigma);
     }
-    Array<convAccT> filter = createDeviceDataArray<convAccT>(dim4(filter_len), (const void*)h_filter);
-
+    Array<convAccT> filter = createDeviceDataArray<convAccT>(dim4(filter_len),
+                                                             (const void*)h_filter.release());
     unsigned border_len = filter_len / 2 + 1;
 
     Array<T> ix = createEmptyArray<T>(idims);
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index bf1f8d7fc6..a80dd504b2 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -968,6 +968,9 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
                    const float img_scale, const float feature_ratio,
                    const bool compute_GLOH)
 {
+    using std::vector;
+    using std::unique_ptr;
+    using std::function;
     in.eval();
     getQueue().sync();
     af::dim4 idims = in.dims();
@@ -983,13 +986,13 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
 
     std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
 
-    std::vector<float*> x_pyr(n_octaves, NULL);
-    std::vector<float*> y_pyr(n_octaves, NULL);
-    std::vector<float*> response_pyr(n_octaves, NULL);
-    std::vector<float*> size_pyr(n_octaves, NULL);
-    std::vector<float*> ori_pyr(n_octaves, NULL);
-    std::vector<float*> desc_pyr(n_octaves, NULL);
-    std::vector<unsigned> feat_pyr(n_octaves, 0);
+    vector<unique_ptr<float[]>> x_pyr(n_octaves);
+    vector<unique_ptr<float[]>> y_pyr(n_octaves);
+    vector<unique_ptr<float[]>> response_pyr(n_octaves);
+    vector<unique_ptr<float[]>> size_pyr(n_octaves);
+    vector<unique_ptr<float[]>> ori_pyr(n_octaves);
+    vector<unique_ptr<float[]>> desc_pyr(n_octaves);
+    vector<unsigned> feat_pyr(n_octaves, 0);
     unsigned total_feat = 0;
 
     const unsigned d = DescrWidth;
@@ -1008,9 +1011,9 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
         const unsigned imel = ddims[0] * ddims[1];
         const unsigned max_feat = ceil(imel * feature_ratio);
 
-        float* extrema_x = memAlloc<float>(max_feat);
-        float* extrema_y = memAlloc<float>(max_feat);
-        unsigned* extrema_layer = memAlloc<unsigned>(max_feat);
+        auto extrema_x = memAlloc<float>(max_feat);
+        auto extrema_y = memAlloc<float>(max_feat);
+        auto extrema_layer = memAlloc<unsigned>(max_feat);
         unsigned extrema_feat = 0;
 
         for (unsigned j = 1; j <= n_layers; j++) {
@@ -1021,7 +1024,7 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
             unsigned layer = j;
 
             float extrema_thr = 0.5f * contrast_thr / n_layers;
-            detectExtrema<T>(extrema_x, extrema_y, extrema_layer, &extrema_feat,
+            detectExtrema<T>(extrema_x.get(), extrema_y.get(), extrema_layer.get(), &extrema_feat,
                              dog_pyr[prev], dog_pyr[center], dog_pyr[next],
                              layer, max_feat, extrema_thr);
         }
@@ -1029,122 +1032,95 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
         extrema_feat = min(extrema_feat, max_feat);
 
         if (extrema_feat == 0) {
-            memFree(extrema_x);
-            memFree(extrema_y);
-            memFree(extrema_layer);
-
             continue;
         }
 
         unsigned interp_feat = 0;
 
-        float* interp_x = memAlloc<float>(extrema_feat);
-        float* interp_y = memAlloc<float>(extrema_feat);
-        unsigned* interp_layer = memAlloc<unsigned>(extrema_feat);
-        float* interp_response = memAlloc<float>(extrema_feat);
-        float* interp_size = memAlloc<float>(extrema_feat);
+        auto interp_x = memAlloc<float>(extrema_feat);
+        auto interp_y = memAlloc<float>(extrema_feat);
+        auto interp_layer = memAlloc<unsigned>(extrema_feat);
+        auto interp_response = memAlloc<float>(extrema_feat);
+        auto interp_size = memAlloc<float>(extrema_feat);
 
-        interpolateExtrema<T>(interp_x, interp_y, interp_layer,
-                              interp_response, interp_size, &interp_feat,
-                              extrema_x, extrema_y, extrema_layer, extrema_feat,
+        interpolateExtrema<T>(interp_x.get(), interp_y.get(), interp_layer.get(),
+                              interp_response.get(), interp_size.get(), &interp_feat,
+                              extrema_x.get(), extrema_y.get(), extrema_layer.get(), extrema_feat,
                               dog_pyr, max_feat, i, n_layers,
                               contrast_thr, edge_thr, init_sigma, img_scale);
 
         interp_feat = min(interp_feat, max_feat);
 
         if (interp_feat == 0) {
-            memFree(interp_x);
-            memFree(interp_y);
-            memFree(interp_layer);
-            memFree(interp_response);
-            memFree(interp_size);
-
             continue;
         }
 
         std::vector<feat_t> sorted_feat;
-        array_to_feat(sorted_feat, interp_x, interp_y, interp_layer, interp_response, interp_size, interp_feat);
+        array_to_feat(sorted_feat, interp_x.get(), interp_y.get(), interp_layer.get(),
+                      interp_response.get(), interp_size.get(), interp_feat);
         std::stable_sort(sorted_feat.begin(), sorted_feat.end(), feat_cmp);
 
-        memFree(interp_x);
-        memFree(interp_y);
-        memFree(interp_layer);
-        memFree(interp_response);
-        memFree(interp_size);
-
         unsigned nodup_feat = 0;
 
-        float* nodup_x = memAlloc<float>(interp_feat);
-        float* nodup_y = memAlloc<float>(interp_feat);
-        unsigned* nodup_layer = memAlloc<unsigned>(interp_feat);
-        float* nodup_response = memAlloc<float>(interp_feat);
-        float* nodup_size = memAlloc<float>(interp_feat);
+        auto nodup_x = memAlloc<float>(interp_feat);
+        auto nodup_y = memAlloc<float>(interp_feat);
+        auto nodup_layer = memAlloc<unsigned>(interp_feat);
+        auto nodup_response = memAlloc<float>(interp_feat);
+        auto nodup_size = memAlloc<float>(interp_feat);
 
-        removeDuplicates(nodup_x, nodup_y, nodup_layer,
-                         nodup_response, nodup_size, &nodup_feat,
+        removeDuplicates(nodup_x.get(), nodup_y.get(), nodup_layer.get(),
+                         nodup_response.get(), nodup_size.get(), &nodup_feat,
                          sorted_feat);
 
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        float* oriented_x = memAlloc<float>(max_oriented_feat);
-        float* oriented_y = memAlloc<float>(max_oriented_feat);
-        unsigned* oriented_layer = memAlloc<unsigned>(max_oriented_feat);
-        float* oriented_response = memAlloc<float>(max_oriented_feat);
-        float* oriented_size = memAlloc<float>(max_oriented_feat);
-        float* oriented_ori = memAlloc<float>(max_oriented_feat);
+        auto oriented_x = memAlloc<float>(max_oriented_feat);
+        auto oriented_y = memAlloc<float>(max_oriented_feat);
+        auto oriented_layer = memAlloc<unsigned>(max_oriented_feat);
+        auto oriented_response = memAlloc<float>(max_oriented_feat);
+        auto oriented_size = memAlloc<float>(max_oriented_feat);
+        auto oriented_ori = memAlloc<float>(max_oriented_feat);
 
         unsigned oriented_feat = 0;
 
-        calcOrientation<T>(oriented_x, oriented_y, oriented_layer,
-                           oriented_response, oriented_size, oriented_ori, &oriented_feat,
-                           nodup_x, nodup_y, nodup_layer,
-                           nodup_response, nodup_size, nodup_feat,
+        calcOrientation<T>(oriented_x.get(), oriented_y.get(), oriented_layer.get(),
+                           oriented_response.get(), oriented_size.get(), oriented_ori.get(), &oriented_feat,
+                           nodup_x.get(), nodup_y.get(), nodup_layer.get(),
+                           nodup_response.get(), nodup_size.get(), nodup_feat,
                            gauss_pyr, max_oriented_feat, i, n_layers, double_input);
 
-        memFree(nodup_x);
-        memFree(nodup_y);
-        memFree(nodup_layer);
-        memFree(nodup_response);
-        memFree(nodup_size);
 
         if (oriented_feat == 0) {
-            memFree(oriented_x);
-            memFree(oriented_y);
-            memFree(oriented_layer);
-            memFree(oriented_response);
-            memFree(oriented_size);
-            memFree(oriented_ori);
-
             continue;
         }
 
-        float* desc = memAlloc<float>(oriented_feat * desc_len);
+        auto desc = memAlloc<float>(oriented_feat * desc_len);
 
         float scale = 1.f/(1 << i);
         if (double_input) scale *= 2.f;
 
         if (compute_GLOH)
             computeGLOHDescriptor<T>(desc, desc_len,
-                                     oriented_x, oriented_y, oriented_layer,
-                                     oriented_response, oriented_size, oriented_ori,
+                                     oriented_x.get(), oriented_y.get(), oriented_layer.get(),
+                                     oriented_response.get(), oriented_size.get(), oriented_ori.get(),
                                      oriented_feat, gauss_pyr, d, rb, ab, hb,
                                      scale, i, n_layers);
         else
             computeDescriptor<T>(desc, desc_len,
-                                 oriented_x, oriented_y, oriented_layer,
-                                 oriented_response, oriented_size, oriented_ori,
+                                 oriented_x.get(), oriented_y.get(), oriented_layer.get(),
+                                 oriented_response.get(), oriented_size.get(), oriented_ori.get(),
                                  oriented_feat, gauss_pyr, d, n, scale, i, n_layers);
 
         total_feat += oriented_feat;
         feat_pyr[i] = oriented_feat;
 
         if (oriented_feat > 0) {
-            x_pyr[i] = oriented_x;
-            y_pyr[i] = oriented_y;
-            response_pyr[i] = oriented_response;
-            ori_pyr[i] = oriented_ori;
-            size_pyr[i] = oriented_size;
-            desc_pyr[i] = desc;
+          x_pyr[i] = std::move(oriented_x);
+          y_pyr[i] = std::move(oriented_y);
+          response_pyr[i] = std::move(oriented_response);
+          ori_pyr[i] = std::move(oriented_ori);
+          size_pyr[i] = std::move(oriented_size);
+          desc_pyr[i] = std::move(desc);
         }
     }
 
@@ -1172,21 +1148,13 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
             if (feat_pyr[i] == 0)
                 continue;
 
-            memcpy(x_ptr+offset,     x_pyr[i],        feat_pyr[i] * sizeof(float));
-            memcpy(y_ptr+offset,     y_pyr[i],        feat_pyr[i] * sizeof(float));
-            memcpy(score_ptr+offset, response_pyr[i], feat_pyr[i] * sizeof(float));
-            memcpy(ori_ptr+offset,   ori_pyr[i],      feat_pyr[i] * sizeof(float));
-            memcpy(size_ptr+offset,  size_pyr[i],     feat_pyr[i] * sizeof(float));
-
-            memcpy(desc_ptr+(offset*desc_len), desc_pyr[i], feat_pyr[i] * desc_len * sizeof(float));
-
-            memFree(x_pyr[i]);
-            memFree(y_pyr[i]);
-            memFree(response_pyr[i]);
-            memFree(ori_pyr[i]);
-            memFree(size_pyr[i]);
-            memFree(desc_pyr[i]);
+            memcpy(x_ptr+offset,     x_pyr[i].get(),        feat_pyr[i] * sizeof(float));
+            memcpy(y_ptr+offset,     y_pyr[i].get(),        feat_pyr[i] * sizeof(float));
+            memcpy(score_ptr+offset, response_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(ori_ptr+offset,   ori_pyr[i].get(),      feat_pyr[i] * sizeof(float));
+            memcpy(size_ptr+offset,  size_pyr[i].get(),     feat_pyr[i] * sizeof(float));
 
+            memcpy(desc_ptr+(offset*desc_len), desc_pyr[i].get(), feat_pyr[i] * desc_len * sizeof(float));
             offset += feat_pyr[i];
         }
     }
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 399b74e59c..a3ac8db703 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -21,6 +21,9 @@
 #define AF_CPU_MEM_DEBUG 0
 #endif
 
+using std::unique_ptr;
+using std::function;
+
 namespace cpu
 {
 void setMemStepSize(size_t step_bytes)
@@ -54,11 +57,13 @@ void printMemInfo(const char *msg, const int device)
 }
 
 template<typename T>
-T* memAlloc(const size_t &elements)
+unique_ptr<T[], function<void(T *)>>
+memAlloc(const size_t &elements)
 {
     T *ptr = nullptr;
+
     ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
-    return ptr;
+    return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
 }
 
 void* memAllocUser(const size_t &bytes)
@@ -118,12 +123,12 @@ bool checkMemoryLimit()
     return memoryManager().checkMemoryLimit();
 }
 
-#define INSTANTIATE(T)                                      \
-    template T* memAlloc(const size_t &elements);           \
-    template void memFree(T* ptr);                          \
-    template T* pinnedAlloc(const size_t &elements);        \
-    template void pinnedFree(T* ptr);                       \
-
+#define INSTANTIATE(T)                                                                        \
+    template std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements); \
+    template void memFree(T* ptr);                                                            \
+    template T* pinnedAlloc(const size_t &elements);                                          \
+    template void pinnedFree(T* ptr);                                                         \
+ 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index e85c89fc94..84a0303365 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -10,10 +10,11 @@
 
 #include <af/defines.h>
 #include <common/MemoryManager.hpp>
+#include <memory>
 
 namespace cpu
 {
-template<typename T> T* memAlloc(const size_t &elements);
+template<typename T> std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 5185c7f3d2..326ae03086 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -21,6 +21,10 @@
 
 using af::dim4;
 
+using std::vector;
+using std::function;
+using std::unique_ptr;
+
 namespace cpu
 {
 
@@ -53,12 +57,12 @@ unsigned orb(Array<float> &x, Array<float> &y,
         scl_sum += 1.f / (float)std::pow(scl_fctr,(float)i);
     }
 
-    std::vector<float*> h_x_pyr(max_levels);
-    std::vector<float*> h_y_pyr(max_levels);
-    std::vector<float*> h_score_pyr(max_levels);
-    std::vector<float*> h_ori_pyr(max_levels);
-    std::vector<float*> h_size_pyr(max_levels);
-    std::vector<unsigned*> h_desc_pyr(max_levels);
+    vector<unique_ptr<float[], function<void(float*)>>> h_x_pyr(max_levels);
+    vector<unique_ptr<float[], function<void(float*)>>> h_y_pyr(max_levels);
+    vector<unique_ptr<float[], function<void(float*)>>> h_score_pyr(max_levels);
+    vector<unique_ptr<float[], function<void(float*)>>> h_ori_pyr(max_levels);
+    vector<unique_ptr<float[], function<void(float*)>>> h_size_pyr(max_levels);
+    vector<unique_ptr<unsigned[], function<void(unsigned*)>>> h_desc_pyr(max_levels);
 
     std::vector<unsigned> feat_pyr(max_levels);
     unsigned total_feat = 0;
@@ -78,7 +82,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
     af::dim4 prev_ldims;
 
     af::dim4 gauss_dims(9);
-    T* h_gauss = nullptr;
+    std::unique_ptr<T[], std::function<void(T *)>> h_gauss;
     Array<T> gauss_filter = createEmptyArray<T>(af::dim4());
 
     for (unsigned i = 0; i < max_levels; i++) {
@@ -131,29 +135,26 @@ unsigned orb(Array<float> &x, Array<float> &y,
         float* h_x_feat = x_feat.get();
         float* h_y_feat = y_feat.get();
 
-        float* h_x_harris = memAlloc<float>(lvl_feat);
-        float* h_y_harris = memAlloc<float>(lvl_feat);
-        float* h_score_harris = memAlloc<float>(lvl_feat);
+        auto h_x_harris = memAlloc<float>(lvl_feat);
+        auto h_y_harris = memAlloc<float>(lvl_feat);
+        auto h_score_harris = memAlloc<float>(lvl_feat);
 
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
         unsigned usable_feat = 0;
-        kernel::harris_response<T, false>(h_x_harris, h_y_harris, h_score_harris, nullptr,
+        kernel::harris_response<T, false>(h_x_harris.get(), h_y_harris.get(), h_score_harris.get(), nullptr,
                                   h_x_feat, h_y_feat, nullptr,
                                   lvl_feat, &usable_feat,
                                   lvl_img,
                                   7, 0.04f, patch_size);
 
         if (usable_feat == 0) {
-            memFree(h_x_harris);
-            memFree(h_y_harris);
-            memFree(h_score_harris);
             continue;
         }
 
         // Sort features according to Harris responses
         af::dim4 usable_feat_dims(usable_feat);
-        Array<float> score_harris = createDeviceDataArray<float>(usable_feat_dims, h_score_harris);
+        Array<float> score_harris = createDeviceDataArray<float>(usable_feat_dims, h_score_harris.get());
         Array<float> harris_sorted = createEmptyArray<float>(af::dim4());
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(af::dim4());
 
@@ -162,29 +163,25 @@ unsigned orb(Array<float> &x, Array<float> &y,
 
         usable_feat = std::min(usable_feat, lvl_best[i]);
 
-        if (usable_feat == 0) {
-            memFree(h_x_harris);
-            memFree(h_y_harris);
-            continue;
+        if(usable_feat == 0) {
+          h_score_harris.release();
+          continue;
         }
 
-        float* h_x_lvl = memAlloc<float>(usable_feat);
-        float* h_y_lvl = memAlloc<float>(usable_feat);
-        float* h_score_lvl = memAlloc<float>(usable_feat);
+        auto h_x_lvl = memAlloc<float>(usable_feat);
+        auto h_y_lvl = memAlloc<float>(usable_feat);
+        auto h_score_lvl = memAlloc<float>(usable_feat);
 
         // Keep only features with higher Harris responses
-        kernel::keep_features<T>(h_x_lvl, h_y_lvl, h_score_lvl, nullptr,
-                         h_x_harris, h_y_harris, harris_sorted.get(), harris_idx.get(),
+        kernel::keep_features<T>(h_x_lvl.get(), h_y_lvl.get(), h_score_lvl.get(), nullptr,
+                                 h_x_harris.get(), h_y_harris.get(), harris_sorted.get(), harris_idx.get(),
                          nullptr, usable_feat);
 
-        memFree(h_x_harris);
-        memFree(h_y_harris);
-
-        float* h_ori_lvl = memAlloc<float>(usable_feat);
-        float* h_size_lvl = memAlloc<float>(usable_feat);
+        auto h_ori_lvl = memAlloc<float>(usable_feat);
+        auto h_size_lvl = memAlloc<float>(usable_feat);
 
         // Compute orientation of features
-        kernel::centroid_angle<T>(h_x_lvl, h_y_lvl, h_ori_lvl, usable_feat,
+        kernel::centroid_angle<T>(h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(), usable_feat,
                           lvl_img, patch_size);
 
         Array<T> lvl_filt = createEmptyArray<T>(dim4());
@@ -193,8 +190,8 @@ unsigned orb(Array<float> &x, Array<float> &y,
             // Calculate a separable Gaussian kernel, if one is not already stored
             if (!h_gauss) {
                 h_gauss = memAlloc<T>(gauss_dims[0]);
-                gaussian1D(h_gauss, gauss_dims[0], 2.f);
-                gauss_filter = createDeviceDataArray<T>(gauss_dims, h_gauss);
+                gaussian1D(h_gauss.get(), gauss_dims[0], 2.f);
+                gauss_filter = createDeviceDataArray<T>(gauss_dims, h_gauss.get());
                 gauss_filter.eval();
             }
 
@@ -205,27 +202,28 @@ unsigned orb(Array<float> &x, Array<float> &y,
         getQueue().sync();
 
         // Compute ORB descriptors
-        unsigned* h_desc_lvl = memAlloc<unsigned>(usable_feat * 8);
-        memset(h_desc_lvl, 0, usable_feat * 8 * sizeof(unsigned));
+        auto h_desc_lvl = memAlloc<unsigned>(usable_feat * 8);
+        memset(h_desc_lvl.get(), 0, usable_feat * 8 * sizeof(unsigned));
         if (blur_img)
-            kernel::extract_orb<T>(h_desc_lvl, usable_feat,
-                           h_x_lvl, h_y_lvl, h_ori_lvl, h_size_lvl,
+          kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat,
+                                   h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(), h_size_lvl.get(),
                            lvl_filt, lvl_scl, patch_size);
         else
-            kernel::extract_orb<T>(h_desc_lvl, usable_feat,
-                           h_x_lvl, h_y_lvl, h_ori_lvl, h_size_lvl,
+          kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat,
+                                   h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(), h_size_lvl.get(),
                            lvl_img, lvl_scl, patch_size);
 
         // Store results to pyramids
         total_feat += usable_feat;
         feat_pyr[i] = usable_feat;
-        h_x_pyr[i] = h_x_lvl;
-        h_y_pyr[i] = h_y_lvl;
-        h_score_pyr[i] = h_score_lvl;
-        h_ori_pyr[i] = h_ori_lvl;
-        h_size_pyr[i] = h_size_lvl;
-        h_desc_pyr[i] = h_desc_lvl;
-
+        h_x_pyr[i] = std::move(h_x_lvl);
+        h_y_pyr[i] = std::move(h_y_lvl);
+        h_score_pyr[i] = std::move(h_score_lvl);
+        h_ori_pyr[i] = std::move(h_ori_lvl);
+        h_size_pyr[i] = std::move(h_size_lvl);
+        h_desc_pyr[i] = std::move(h_desc_lvl);
+        h_score_harris.release();
+        h_gauss.release();
     }
 
     if (total_feat > 0 ) {
@@ -257,20 +255,14 @@ unsigned orb(Array<float> &x, Array<float> &y,
             if (i > 0)
                 offset += feat_pyr[i-1];
 
-            memcpy(h_x+offset, h_x_pyr[i], feat_pyr[i] * sizeof(float));
-            memcpy(h_y+offset, h_y_pyr[i], feat_pyr[i] * sizeof(float));
-            memcpy(h_score+offset, h_score_pyr[i], feat_pyr[i] * sizeof(float));
-            memcpy(h_ori+offset, h_ori_pyr[i], feat_pyr[i] * sizeof(float));
-            memcpy(h_size+offset, h_size_pyr[i], feat_pyr[i] * sizeof(float));
+            memcpy(h_x+offset, h_x_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(h_y+offset, h_y_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(h_score+offset, h_score_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(h_ori+offset, h_ori_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(h_size+offset, h_size_pyr[i].get(), feat_pyr[i] * sizeof(float));
 
-            memcpy(h_desc+(offset*8), h_desc_pyr[i], feat_pyr[i] * 8 * sizeof(unsigned));
+            memcpy(h_desc+(offset*8), h_desc_pyr[i].get(), feat_pyr[i] * 8 * sizeof(unsigned));
 
-            memFree(h_x_pyr[i]);
-            memFree(h_y_pyr[i]);
-            memFree(h_score_pyr[i]);
-            memFree(h_ori_pyr[i]);
-            memFree(h_size_pyr[i]);
-            memFree(h_desc_pyr[i]);
         }
     }
 
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 55a2357206..ad8d780301 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -37,7 +37,7 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     auto y_corners    = createEmptyArray<float>(dim4(corner_lim));
     auto resp_corners = createEmptyArray<float>(dim4(corner_lim));
     auto response     = createEmptyArray<T>(dim4(in.elements()));
-    auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1), memFree<unsigned>);
+    auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1).release(), memFree<unsigned>);
     corners_found.get()[0] = 0;
 
     getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0], idims[1],
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index bd7427a921..b41d3963de 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -32,7 +32,7 @@ Array<uint> where(const Array<T> &in)
     static const T zero = scalar<T>(0);
 
     const T *iptr = in.get();
-    uint *out_vec  = memAlloc<uint>(in.elements());
+    auto out_vec  = memAlloc<uint>(in.elements());
 
     dim_t count = 0;
     dim_t idx = 0;
@@ -58,7 +58,8 @@ Array<uint> where(const Array<T> &in)
         }
     }
 
-    Array<uint> out = createDeviceDataArray<uint>(dim4(count), out_vec);
+    Array<uint> out = createDeviceDataArray<uint>(dim4(count), out_vec.get());
+    out_vec.release();
     return out;
 }
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 008853bd99..b4e39797c9 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -39,14 +39,14 @@ namespace cuda
     template<typename T>
     Array<T>::Array(af::dim4 dims) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
+        data((dims.elements() ? memAlloc<T>(dims.elements()).release() : nullptr), memFree<T>), data_dims(dims),
         node(bufferNodePtr<T>()), ready(true), owner(true)
     {}
 
     template<typename T>
     Array<T>::Array(af::dim4 dims, const T * const in_data, bool is_device, bool copy_device) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(((is_device & !copy_device) ? (T *)in_data : memAlloc<T>(dims.elements())), memFree<T>),
+        data(((is_device & !copy_device) ? (T *)in_data : memAlloc<T>(dims.elements()).release()), memFree<T>),
         data_dims(dims),
         node(bufferNodePtr<T>()), ready(true), owner(true)
     {
@@ -74,15 +74,15 @@ namespace cuda
     { }
 
     template<typename T>
-    Array<T>::Array(Param<T> &tmp) :
+    Array<T>::Array(Param<T> &tmp, bool owner_) :
         info(getActiveDeviceId(),
              af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]),
              0,
              af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2], tmp.strides[3]),
              (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.ptr, memFree<T>),
+        data(tmp.ptr, owner_ ? memFree<T> : [](T*){}),
         data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3])),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(owner_)
     {
     }
 
@@ -98,7 +98,7 @@ namespace cuda
     Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
                     const T * const in_data, bool is_device) :
         info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-        data(is_device ? (T*)in_data : memAlloc<T>(info.total()), memFree<T>),
+        data(is_device ? (T*)in_data : memAlloc<T>(info.total()).release(), memFree<T>),
         data_dims(dims),
         node(bufferNodePtr<T>()),
         ready(true),
@@ -118,7 +118,7 @@ namespace cuda
         if (isReady()) return;
 
         this->setId(getActiveDeviceId());
-        data = shared_ptr<T>(memAlloc<T>(elements()),
+        data = shared_ptr<T>(memAlloc<T>(elements()).release(),
                              memFree<T>);
 
         Param<T> res;
@@ -165,7 +165,7 @@ namespace cuda
             }
 
             array->setId(getActiveDeviceId());
-            array->data = shared_ptr<T>(memAlloc<T>(array->elements()),
+            array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(),
                                         memFree<T>);
 
             Param<T> res;
@@ -329,9 +329,9 @@ namespace cuda
     }
 
     template<typename T>
-    Array<T> createParamArray(Param<T> &tmp)
+    Array<T> createParamArray(Param<T> &tmp, bool owner)
     {
-        return Array<T>(tmp);
+        return Array<T>(tmp, owner);
     }
 
     template<typename T>
@@ -389,7 +389,7 @@ namespace cuda
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
     template       Array<T>  *initArray<T      >      ();               \
-    template       Array<T>  createParamArray<T>      (Param<T> &tmp);  \
+    template       Array<T>  createParamArray<T>      (Param<T> &tmp, bool owner); \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index be12f777cd..d80b31f0bc 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -67,7 +67,7 @@ namespace cuda
 
     // Create an Array object from Param<T>
     template<typename T>
-    Array<T> createParamArray(Param<T> &tmp);
+    Array<T> createParamArray(Param<T> &tmp, bool owner);
 
     template<typename T>
     Array<T> createSubArray(const Array<T>& parent,
@@ -107,7 +107,7 @@ namespace cuda
 
         explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false, bool copy_device = false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        Array(Param<T> &tmp);
+        Array(Param<T> &tmp, bool owner);
         Array(af::dim4 dims, JIT::Node_ptr n);
     public:
 
@@ -228,7 +228,7 @@ namespace cuda
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createParamArray<T>(Param<T> &tmp);
+        friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
         friend Array<T> createNodeArray<T>(const af::dim4 &dims, JIT::Node_ptr node);
 
         friend Array<T> createSubArray<T>(const Array<T>& parent,
diff --git a/src/backend/cuda/ThrustAllocator.cuh b/src/backend/cuda/ThrustAllocator.cuh
index 40cb899e01..756f568a5c 100644
--- a/src/backend/cuda/ThrustAllocator.cuh
+++ b/src/backend/cuda/ThrustAllocator.cuh
@@ -33,7 +33,7 @@ struct ThrustAllocator : thrust::device_malloc_allocator<T>
 
     pointer allocate(size_type elements)
     {
-        return thrust::device_ptr<T>(memAlloc<T>(elements));// delegate to ArrayFire allocator
+        return thrust::device_ptr<T>(memAlloc<T>(elements).release());// delegate to ArrayFire allocator
     }
 
     void deallocate(pointer p, size_type n)
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index 5463bd6c4f..30d7185704 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -115,18 +115,16 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
                                        in.get(), in.strides()[1],
                                        &lwork));
 
-    T *workspace = memAlloc<T>(lwork);
-    int *d_info = memAlloc<int>(1);
+    auto workspace = memAlloc<T>(lwork);
+    auto d_info = memAlloc<int>(1);
 
     CUSOLVER_CHECK(potrf_func<T>()(solverDnHandle(),
                                    uplo,
                                    N,
                                    in.get(), in.strides()[1],
-                                   workspace, lwork,
-                                   d_info));
+                                   workspace.get(), lwork,
+                                   d_info.get()));
 
-    memFree(workspace);
-    memFree(d_info);
 
     //FIXME: should return h_info
     return 0;
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index c974b9dc45..5bee9277df 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -399,13 +399,16 @@ void fast(unsigned* out_feat,
 
     // Matrix containing scores for detected features, scores are stored in the
     // same coordinates as features, dimensions should be equal to in.
-    float *d_score = NULL;
+
     size_t score_bytes = in.dims[0] * in.dims[1] * sizeof(float) + sizeof(unsigned);
-    d_score = (float *)memAlloc<char>(score_bytes);
+    auto d_score_tmp = memAlloc<char>(score_bytes);
+    float *d_score = (float *)d_score_tmp.get();
 
     float *d_flags = d_score;
+    uptr<float> d_flags_alloc;
     if (nonmax) {
-        d_flags = memAlloc<float>(in.dims[0] * in.dims[1]);
+        d_flags_alloc = memAlloc<float>(in.dims[0] * in.dims[1]);
+        auto d_flags = d_flags_alloc.get();
     }
 
     // Shared memory size
@@ -448,16 +451,16 @@ void fast(unsigned* out_feat,
 
     unsigned *d_total = (unsigned *)(d_score + in.dims[0] * in.dims[1]);
     CUDA_CHECK(cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getActiveStream()));
-    unsigned *d_counts  = memAlloc<unsigned>(blocks.x * blocks.y);
-    unsigned *d_offsets = memAlloc<unsigned>(blocks.x * blocks.y);
+    auto d_counts  = memAlloc<unsigned>(blocks.x * blocks.y);
+    auto d_offsets = memAlloc<unsigned>(blocks.x * blocks.y);
 
     if (nonmax)
         CUDA_LAUNCH((non_max_counts<true >), blocks, threads,
-                d_counts, d_offsets, d_total, d_flags,
+                d_counts.get(), d_offsets.get(), d_total, d_flags,
                 d_score, in.dims[0], in.dims[1], edge);
     else
         CUDA_LAUNCH((non_max_counts<false>), blocks, threads,
-                d_counts, d_offsets, d_total, d_flags,
+                d_counts.get(), d_offsets.get(), d_total, d_flags,
                 d_score, in.dims[0], in.dims[1], edge);
 
     POST_LAUNCH_CHECK();
@@ -470,25 +473,27 @@ void fast(unsigned* out_feat,
     total = total < max_feat ? total : max_feat;
 
     if (total > 0) {
-        *x_out     = memAlloc<float>(total);
-        *y_out     = memAlloc<float>(total);
-        *score_out = memAlloc<float>(total);
+        auto x_out_alloc = memAlloc<float>(total);
+        auto y_out_alloc = memAlloc<float>(total);
+        auto score_out_alloc = memAlloc<float>(total);
+        *x_out     = x_out_alloc.get();
+        *y_out     = y_out_alloc.get();
+        *score_out = score_out_alloc.get();
 
         CUDA_LAUNCH((get_features<float>), blocks, threads,
-                *x_out, *y_out, *score_out, d_flags, d_counts,
-                d_offsets, total, in.dims[0], in.dims[1], edge);
+                *x_out, *y_out, *score_out, d_flags, d_counts.get(),
+                d_offsets.get(), total, in.dims[0], in.dims[1], edge);
 
         POST_LAUNCH_CHECK();
+
+        x_out_alloc.release();
+        y_out_alloc.release();
+        score_out_alloc.release();
     }
 
     *out_feat = total;
 
-    memFree<uchar>((uchar *)d_score);
-    memFree(d_counts);
-    memFree(d_offsets);
-    if (nonmax) {
-        memFree(d_flags);
-    }
+    d_flags_alloc.release();
 }
 
 } // namespace kernel
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index 43cad340d5..9295139b7d 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -69,7 +69,7 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
     // Need to do this as CParam does not have a default constructor
     // And resize needs a default constructor or default value prior to C++11
     img_pyr.resize(max_levels, emptyCParam);
-
+    std::vector<uptr<T>> lvl_img_alloc;
     // Create multi-scale image pyramid
     for (unsigned i = 0; i < max_levels; i++) {
         if (i == 0) {
@@ -82,6 +82,7 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
         }
         else {
             // Resize previous level image to current level dimensions
+          //TODO: Param<T> should use array assignment, not iteration
             Param<T> lvl_img;
             lvl_img.dims[0] = round(in.dims[0] / lvl_scl[i]);
             lvl_img.dims[1] = round(in.dims[1] / lvl_scl[i]);
@@ -94,7 +95,8 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
             }
 
             int lvl_elem = lvl_img.strides[3] * lvl_img.dims[3];
-            lvl_img.ptr = memAlloc<T>(lvl_elem);
+            lvl_img_alloc.push_back(memAlloc<T>(lvl_elem));
+            lvl_img.ptr = lvl_img_alloc.back().get();
 
             resize<T, AF_INTERP_BILINEAR>(lvl_img, img_pyr[i-1]);
 
@@ -129,6 +131,7 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
              img_pyr[i], fast_thr, 9, 1, 0.15f, edge);
 
         // FAST score is not used
+        // TODO: should be handled by fast() 
         memFree(d_score_feat);
 
         if (lvl_feat == 0) {
@@ -142,6 +145,10 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
             d_y_pyr[i] = d_y_feat;
         }
     }
+
+    for(auto& l : lvl_img_alloc){
+      l.release();
+    }
 }
 
 } // namespace kernel
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 6465410e0c..15fc2d6033 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -214,7 +214,8 @@ void harris(unsigned* corners_out,
     }
 
     int filter_elem = filter.strides[3] * filter.dims[3];
-    filter.ptr = memAlloc<convAccT>(filter_elem);
+    auto filter_alloc = memAlloc<convAccT>(filter_elem);
+    filter.ptr = filter_alloc.get();
     CUDA_CHECK(cudaMemcpyAsync(filter.ptr, h_filter, filter_elem * sizeof(convAccT),
                 cudaMemcpyHostToDevice, cuda::getActiveStream()));
 
@@ -227,8 +228,10 @@ void harris(unsigned* corners_out,
         ix.dims[i] = iy.dims[i] = in.dims[i];
         ix.strides[i] = iy.strides[i] = in.strides[i];
     }
-    ix.ptr = memAlloc<T>(ix.dims[3] * ix.strides[3]);
-    iy.ptr = memAlloc<T>(iy.dims[3] * iy.strides[3]);
+    auto ix_alloc = memAlloc<T>(ix.dims[3] * ix.strides[3]);
+    auto iy_alloc = memAlloc<T>(iy.dims[3] * iy.strides[3]);
+    ix.ptr = ix_alloc.get();
+    iy.ptr = iy_alloc.get();
 
     // Compute first-order derivatives as gradients
     gradient<T>(iy, ix, in);
@@ -241,9 +244,12 @@ void harris(unsigned* corners_out,
         ixx.strides[i] = ixy.strides[i] = iyy.strides[i] = in.strides[i];
         ixx_tmp.strides[i] = ixy_tmp.strides[i] = iyy_tmp.strides[i] = in.strides[i];
     }
-    ixx.ptr = memAlloc<T>(ixx.dims[3] * ixx.strides[3]);
-    ixy.ptr = memAlloc<T>(ixy.dims[3] * ixy.strides[3]);
-    iyy.ptr = memAlloc<T>(iyy.dims[3] * iyy.strides[3]);
+    auto ixx_alloc = memAlloc<T>(ixx.dims[3] * ixx.strides[3]);
+    auto ixy_alloc = memAlloc<T>(ixy.dims[3] * ixy.strides[3]);
+    auto iyy_alloc = memAlloc<T>(iyy.dims[3] * iyy.strides[3]);
+    ixx.ptr = ixx_alloc.get();
+    ixy.ptr = ixy_alloc.get();
+    iyy.ptr = iyy_alloc.get();
 
     // Compute second-order derivatives
     dim3 threads(THREADS_PER_BLOCK, 1);
@@ -252,12 +258,12 @@ void harris(unsigned* corners_out,
             ixx.ptr, ixy.ptr, iyy.ptr,
             in.dims[3] * in.strides[3], ix.ptr, iy.ptr);
 
-    memFree(ix.ptr);
-    memFree(iy.ptr);
-
-    ixx_tmp.ptr = memAlloc<T>(ixx_tmp.dims[3] * ixx_tmp.strides[3]);
-    ixy_tmp.ptr = memAlloc<T>(ixy_tmp.dims[3] * ixy_tmp.strides[3]);
-    iyy_tmp.ptr = memAlloc<T>(iyy_tmp.dims[3] * iyy_tmp.strides[3]);
+    auto ixx_tmp_alloc = memAlloc<T>(ixx_tmp.dims[3] * ixx_tmp.strides[3]);
+    auto ixy_tmp_alloc = memAlloc<T>(ixy_tmp.dims[3] * ixy_tmp.strides[3]);
+    auto iyy_tmp_alloc = memAlloc<T>(iyy_tmp.dims[3] * iyy_tmp.strides[3]);
+    ixx_tmp.ptr = ixx_tmp_alloc.get();
+    ixy_tmp.ptr = ixy_tmp_alloc.get();
+    iyy_tmp.ptr = iyy_tmp_alloc.get();
 
     // Convolve second-order derivatives with proper window filter
     convolve2<T, convAccT, 0, false>(ixx_tmp, CParam<T>(ixx), filter);
@@ -267,51 +273,40 @@ void harris(unsigned* corners_out,
     convolve2<T, convAccT, 0, false>(iyy_tmp, CParam<T>(iyy), filter);
     convolve2<T, convAccT, 1, false>(iyy, CParam<T>(iyy_tmp), filter);
 
-    memFree(ixx_tmp.ptr);
-    memFree(ixy_tmp.ptr);
-    memFree(iyy_tmp.ptr);
-
     // Number of corners is not known a priori, limit maximum number of corners
     // according to image dimensions
     unsigned corner_lim = in.dims[3] * in.strides[3] * 0.2f;
 
-    unsigned* d_corners_found = memAlloc<unsigned>(1);
-    CUDA_CHECK(cudaMemsetAsync(d_corners_found, 0, sizeof(unsigned),
+    auto d_corners_found = memAlloc<unsigned>(1);
+    CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
                 cuda::getActiveStream()));
 
-    float* d_x_corners = memAlloc<float>(corner_lim);
-    float* d_y_corners = memAlloc<float>(corner_lim);
-    float* d_resp_corners = memAlloc<float>(corner_lim);
+    auto d_x_corners = memAlloc<float>(corner_lim);
+    auto d_y_corners = memAlloc<float>(corner_lim);
+    auto d_resp_corners = memAlloc<float>(corner_lim);
 
-    T* d_responses = memAlloc<T>(in.dims[3] * in.strides[3]);
+    auto d_responses = memAlloc<T>(in.dims[3] * in.strides[3]);
 
     // Calculate Harris responses for all pixels
     threads = dim3(BLOCK_SIZE, BLOCK_SIZE);
     blocks = dim3(divup(in.dims[1] - border_len*2, threads.x),
                   divup(in.dims[0] - border_len*2, threads.y));
     CUDA_LAUNCH((harris_responses<T>), blocks, threads,
-            d_responses, in.dims[0], in.dims[1],
+            d_responses.get(), in.dims[0], in.dims[1],
             ixx.ptr, ixy.ptr, iyy.ptr, k_thr, border_len);
 
-    memFree(ixx.ptr);
-    memFree(ixy.ptr);
-    memFree(iyy.ptr);
-
     const float min_r = (max_corners > 0) ? 0.f : min_response;
 
     // Perform non-maximal suppression
     CUDA_LAUNCH((non_maximal<T>), blocks, threads,
-            d_x_corners, d_y_corners, d_resp_corners, d_corners_found,
-            in.dims[0], in.dims[1], d_responses, min_r, border_len, corner_lim);
+                d_x_corners.get(), d_y_corners.get(), d_resp_corners.get(), d_corners_found.get(),
+                in.dims[0], in.dims[1], d_responses.get(), min_r, border_len, corner_lim);
 
     unsigned corners_found = 0;
-    CUDA_CHECK(cudaMemcpyAsync(&corners_found, d_corners_found, sizeof(unsigned),
+    CUDA_CHECK(cudaMemcpyAsync(&corners_found, d_corners_found.get(), sizeof(unsigned),
                 cudaMemcpyDeviceToHost, cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-    memFree(d_responses);
-    memFree(d_corners_found);
-
     *corners_out = min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
 
     if (*corners_out == 0)
@@ -332,51 +327,59 @@ void harris(unsigned* corners_out,
         }
 
         int sort_elem = harris_responses.strides[3] * harris_responses.dims[3];
-        harris_responses.ptr = d_resp_corners;
+        harris_responses.ptr = d_resp_corners.get();
         // Create indices using range
-        harris_idx.ptr = memAlloc<unsigned>(sort_elem);
+        auto harris_idx_alloc = memAlloc<unsigned>(sort_elem);
+        harris_idx.ptr = harris_idx_alloc.get();
         kernel::range<uint>(harris_idx, 0);
 
         // Sort Harris responses
         sort0ByKey<float, uint>(harris_responses, harris_idx, false);
 
-        *x_out = memAlloc<float>(*corners_out);
-        *y_out = memAlloc<float>(*corners_out);
-        *resp_out = memAlloc<float>(*corners_out);
+        auto x_out_alloc = memAlloc<float>(*corners_out);
+        auto y_out_alloc = memAlloc<float>(*corners_out);
+        auto resp_out_alloc = memAlloc<float>(*corners_out);
+        *x_out = x_out_alloc.get();
+        *y_out = y_out_alloc.get();
+        *resp_out = resp_out_alloc.get();
 
         // Keep only the first corners_to_keep corners with higher Harris
         // responses
         threads = dim3(THREADS_PER_BLOCK, 1);
         blocks = dim3(divup(*corners_out, threads.x), 1);
         CUDA_LAUNCH(keep_corners, blocks, threads,
-                *x_out, *y_out, *resp_out, d_x_corners, d_y_corners,
+                *x_out, *y_out, *resp_out, d_x_corners.get(), d_y_corners.get(),
                 harris_responses.ptr, harris_idx.ptr, *corners_out);
 
-        memFree(d_x_corners);
-        memFree(d_y_corners);
-        memFree(harris_responses.ptr);
-        memFree(harris_idx.ptr);
+        x_out_alloc.release();
+        y_out_alloc.release();
+        resp_out_alloc.release();
     }
     else if (max_corners == 0 && corners_found < corner_lim) {
-        *x_out = memAlloc<float>(*corners_out);
-        *y_out = memAlloc<float>(*corners_out);
-        *resp_out = memAlloc<float>(*corners_out);
-        CUDA_CHECK(cudaMemcpyAsync(*x_out, d_x_corners, *corners_out * sizeof(float),
+        auto x_out_alloc = memAlloc<float>(*corners_out);
+        auto y_out_alloc = memAlloc<float>(*corners_out);
+        auto resp_out_alloc = memAlloc<float>(*corners_out);
+        *x_out = x_out_alloc.get();
+        *y_out = y_out_alloc.get();
+        *resp_out = resp_out_alloc.get();
+
+        CUDA_CHECK(cudaMemcpyAsync(*x_out, d_x_corners.get(), *corners_out * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*y_out, d_y_corners, *corners_out * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*y_out, d_y_corners.get(), *corners_out * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*resp_out, d_resp_corners, *corners_out * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*resp_out, d_resp_corners.get(), *corners_out * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
-        memFree(d_x_corners);
-        memFree(d_y_corners);
-        memFree(d_resp_corners);
+        x_out_alloc.release();
+        y_out_alloc.release();
+        resp_out_alloc.release(); 
     }
     else {
-        *x_out = d_x_corners;
-        *y_out = d_y_corners;
-        *resp_out = d_resp_corners;
+        *x_out = d_x_corners.release();
+        *y_out = d_y_corners.release();
+        *resp_out = d_resp_corners.release();
     }
+    filter_alloc.release();
 }
 
 } // namespace kernel
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index f09423d167..058664147f 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -573,10 +573,15 @@ int computeH(
         idx.dims[k] = median.dims[k] = 1;
         idx.strides[k] = median.strides[k] = idx.dims[k-1] * idx.strides[k-1];
     }
-    idx.ptr = memAlloc<unsigned>(idx.dims[3] * idx.strides[3]);
-    inliers.ptr = memAlloc<unsigned>(inliers.dims[3] * inliers.strides[3]);
-    if (htype == AF_HOMOGRAPHY_LMEDS)
-        median.ptr = memAlloc<float>(median.dims[3] * median.strides[3]);
+    auto idx_alloc = memAlloc<unsigned>(idx.dims[3] * idx.strides[3]);
+    auto inliers_alloc = memAlloc<unsigned>(inliers.dims[3] * inliers.strides[3]);
+    idx.ptr = idx_alloc.get();
+    inliers.ptr = inliers_alloc.get();
+    uptr<float> median_alloc;
+    if (htype == AF_HOMOGRAPHY_LMEDS){
+        median_alloc = memAlloc<float>(median.dims[3] * median.strides[3]);
+        median.ptr = median_alloc.get();
+    }
 
     // Compute (and for RANSAC, evaluate) homographies
     CUDA_LAUNCH((computeEvalHomography<T>), blocks, threads,
@@ -602,20 +607,17 @@ int computeH(
         if (blocks.x > 1) {
             blocks = dim3(1);
 
-            float* finalMedian = memAlloc<float>(1);
-            unsigned* finalIdx = memAlloc<unsigned>(1);
+            auto finalMedian = memAlloc<float>(1);
+            auto finalIdx = memAlloc<unsigned>(1);
 
             CUDA_LAUNCH((findMinMedian), blocks, threads,
-                        finalMedian, finalIdx, median, idx);
+                        finalMedian.get(), finalIdx.get(), median, idx);
             POST_LAUNCH_CHECK();
 
-            CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian, sizeof(float),
+            CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian.get(), sizeof(float),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx, sizeof(unsigned),
+            CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx.get(), sizeof(unsigned),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-
-            memFree(finalMedian);
-            memFree(finalIdx);
         } else {
             CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.ptr, sizeof(float),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
@@ -641,15 +643,14 @@ int computeH(
         Param<unsigned> totalInliers;
         for (int k = 0; k < 4; k++)
             totalInliers.dims[k] = totalInliers.strides[k] = 1;
-        totalInliers.ptr = memAlloc<unsigned>(1);
+        auto totalInliers_alloc = memAlloc<unsigned>(1);
+        totalInliers.ptr = totalInliers_alloc.get();
 
         kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
 
         CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.ptr, sizeof(unsigned),
                     cudaMemcpyDeviceToHost, cuda::getActiveStream()));
 
-        memFree(totalInliers.ptr);
-        memFree(median.ptr);
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
         unsigned blockIdx;
         inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
@@ -658,11 +659,9 @@ int computeH(
                     cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
+        median_alloc.release();
     }
 
-    memFree(inliers.ptr);
-    memFree(idx.ptr);
     // sync stream for the device to host copies to be visible for
     // the subsequent kernel launch
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index ebd6c7e738..c90d99f031 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -229,14 +229,18 @@ namespace kernel
 
         Param<T> tmp = out;
         uint *tlptr = olptr;
+        uptr<T> tmp_alloc;
+        uptr<uint> tlptr_alloc;
 
         if (blocks_dim[dim] > 1) {
             int tmp_elements = 1;
             tmp.dims[dim] = blocks_dim[dim];
 
             for (int k = 0; k < 4; k++) tmp_elements *= tmp.dims[k];
-            tmp.ptr = memAlloc<T>(tmp_elements);
-            tlptr = memAlloc<uint>(tmp_elements);
+            tmp_alloc = memAlloc<T>(tmp_elements);
+            tlptr_alloc = memAlloc<uint>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
+            tlptr = tlptr_alloc.get();
 
             for (int k = dim + 1; k < 4; k++) tmp.strides[k] *= blocks_dim[dim];
         }
@@ -248,9 +252,6 @@ namespace kernel
 
             ireduce_dim_launcher<T, op, dim, false>(out, olptr, tmp, tlptr,
                                                     threads_y, blocks_dim);
-
-            memFree(tmp.ptr);
-            memFree(tlptr);
         }
 
     }
@@ -409,16 +410,14 @@ namespace kernel
 
         Param<T> tmp = out;
         uint *tlptr = olptr;
+        uptr<T> tmp_alloc;
+        uptr<uint> tlptr_alloc;
         if (blocks_x > 1) {
-            tmp.ptr = memAlloc<T>(blocks_x *
-                                  in.dims[1] *
-                                  in.dims[2] *
-                                  in.dims[3]);
-
-            tlptr = memAlloc<uint>(blocks_x *
-                                   in.dims[1] *
-                                   in.dims[2] *
-                                   in.dims[3]);
+            auto elements = blocks_x * in.dims[1] * in.dims[2] * in.dims[3];
+            tmp_alloc = memAlloc<T>(elements);
+            tlptr_alloc = memAlloc<uint>(elements);
+            tmp.ptr = tmp_alloc.get();
+            tlptr = tlptr_alloc.get();
 
             tmp.dims[0] = blocks_x;
             for (int k = 1; k < 4; k++) tmp.strides[k] *= blocks_x;
@@ -428,9 +427,6 @@ namespace kernel
 
         if (blocks_x > 1) {
             ireduce_first_launcher<T, op, false>(out, olptr, tmp, tlptr, 1, blocks_y, threads_x);
-
-            memFree(tmp.ptr);
-            memFree(tlptr);
         }
     }
 
@@ -488,8 +484,10 @@ namespace kernel
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
 
             //TODO: Use scoped_ptr
-            tmp.ptr = memAlloc<T>(tmp_elements);
-            tlptr = memAlloc<uint>(tmp_elements);
+            auto tmp_alloc = memAlloc<T>(tmp_elements);
+            auto tlptr_alloc = memAlloc<uint>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
+            tlptr = tlptr_alloc.get();
             ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x, blocks_y, threads_x);
 
             unique_ptr<T[]>       h_ptr(new T[tmp_elements]);
@@ -502,8 +500,6 @@ namespace kernel
             CUDA_CHECK(cudaMemcpyAsync(h_lptr_raw, tlptr, tmp_elements * sizeof(uint),
                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-            memFree(tmp.ptr);
-            memFree(tlptr);
 
             if (!is_linear) {
                 // Converting n-d index into a linear index
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index eb95fd71ad..4e164803df 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -9,6 +9,7 @@
 
 #include <ops.hpp>
 #include <backend.hpp>
+#include <copy.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <math.hpp>
@@ -204,22 +205,16 @@ namespace kernel
 
         blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
-        Param<To> tmpOut = out;
-        Param<Tw> tmpWt;
-        tmpWt.ptr = NULL;
+        Array<To> tmpOut = *initArray<To>();
+        Array<Tw> tmpWt  = *initArray<Tw>();
 
         if (blocks_dim[dim] > 1) {
-            int tmp_elements = 1;
-            tmpOut.dims[dim] = blocks_dim[dim];
-
-            for (int k = 0; k < 4; k++) tmp_elements *= tmpOut.dims[k];
-            tmpOut.ptr = memAlloc<To>(tmp_elements);
-            tmpWt.ptr = memAlloc<Tw>(tmp_elements);
-
-            for (int k = dim + 1; k < 4; k++) {
-                tmpOut.strides[k] *= blocks_dim[dim];
-                tmpWt.strides[k] *= blocks_dim[dim];
-            }
+          dim4 dims(4, out.dims);
+          tmpOut = createEmptyArray<To>(dims);
+          tmpWt  = createEmptyArray<Tw>(dims);
+        }
+        else {
+          tmpOut = createParamArray(out, false);
         }
 
         mean_dim_launcher<Ti, Tw, To, dim>(tmpOut, tmpWt, in, iwt, threads_y, blocks_dim);
@@ -227,13 +222,10 @@ namespace kernel
         if (blocks_dim[dim] > 1) {
             blocks_dim[dim] = 1;
 
-            Param<Tw> owt;
-            owt.ptr = NULL;
+            Array<Tw> owt = *initArray<Tw>();
             mean_dim_launcher<To, Tw, To, dim>(out, owt, tmpOut, tmpWt,
                                                     threads_y, blocks_dim);
 
-            memFree(tmpOut.ptr);
-            memFree(tmpWt.ptr);
         }
 
     }
@@ -393,22 +385,13 @@ namespace kernel
         uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
         uint blocks_y = divup(in.dims[1], threads_y);
 
-        Param<To> tmpOut = out;
-        Param<Tw> tmpWt;
-        tmpWt.ptr = NULL;
+        Array<To> tmpOut = *initArray<To>();
+        Array<Tw> tmpWt  = *initArray<Tw>();
         if (blocks_x > 1) {
-            tmpOut.ptr = memAlloc<To>(blocks_x *
-                                  in.dims[1] *
-                                  in.dims[2] *
-                                  in.dims[3]);
-
-            tmpWt.ptr = memAlloc<Tw>(blocks_x *
-                                   in.dims[1] *
-                                   in.dims[2] *
-                                   in.dims[3]);
-
-            tmpOut.dims[0] = blocks_x;
-            for (int k = 1; k < 4; k++) tmpOut.strides[k] *= blocks_x;
+          tmpOut = createEmptyArray<To>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+          tmpWt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+        } else {
+          tmpOut = createParamArray(out, false);
         }
 
         mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
@@ -417,9 +400,6 @@ namespace kernel
             Param<Tw> owt;
             owt.ptr = NULL;
             mean_first_launcher<To, Tw, To>(out, owt, tmpOut, tmpWt, 1, blocks_y, threads_x);
-
-            memFree(tmpOut.ptr);
-            memFree(tmpWt.ptr);
         }
     }
 
@@ -473,38 +453,22 @@ namespace kernel
             threads_x = std::min(threads_x, THREADS_PER_BLOCK);
             uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-            Param<T> tmpOut;
-            Param<Tw> tmpWt;
-
             uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
             uint blocks_y = divup(in.dims[1], threads_y);
 
-            tmpOut.dims[0] = blocks_x;
-            tmpOut.strides[0] = 1;
-
-            for (int k = 1; k < 4; k++) {
-                tmpOut.dims[k] = in.dims[k];
-                tmpOut.strides[k] = tmpOut.dims[k - 1] * tmpOut.strides[k - 1];
-            }
+            Array<T> tmpOut = createEmptyArray<T>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+            Array<Tw> tmpWt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
 
-            int tmp_elements = tmpOut.strides[3] * tmpOut.dims[3];
+            int tmp_elements = tmpOut.elements();
 
-            //TODO: Use scoped_ptr
-            tmpOut.ptr = memAlloc<T>(tmp_elements);
-            tmpWt.ptr = memAlloc<Tw>(tmp_elements);
             mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
 
             vector<T>  h_ptr(tmp_elements);
             vector<Tw> h_wptr(tmp_elements);
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.ptr, tmp_elements * sizeof(T),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), tmpWt.ptr, tmp_elements * sizeof(Tw),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            copyData<T>(h_ptr.data(),  tmpOut);
+            copyData<Tw>(h_wptr.data(), tmpWt);
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
-            memFree(tmpOut.ptr);
-            memFree(tmpWt.ptr);
-
 
             T val = h_ptr[0];
             Tw weight = h_wptr[0];
@@ -561,38 +525,23 @@ namespace kernel
             threads_x = std::min(threads_x, THREADS_PER_BLOCK);
             uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-            Param<To> tmpOut;
-            Param<Tw> tmpCt;
-            Param<Tw> iwt;
 
             uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
             uint blocks_y = divup(in.dims[1], threads_y);
 
-            tmpOut.dims[0] = blocks_x;
-            tmpOut.strides[0] = 1;
-
-            for (int k = 1; k < 4; k++) {
-                tmpOut.dims[k] = in.dims[k];
-                tmpOut.strides[k] = tmpOut.dims[k - 1] * tmpOut.strides[k - 1];
-            }
-
-            int tmp_elements = tmpOut.strides[3] * tmpOut.dims[3];
+            Param<Tw> iwt;
+            Array<To> tmpOut = createEmptyArray<To>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+            Array<Tw> tmpCt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
 
-            tmpOut.ptr = memAlloc<To>(tmp_elements);
-            tmpCt.ptr = memAlloc<Tw>(tmp_elements);
-            iwt.ptr = NULL;
             mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x, blocks_y, threads_x);
 
+            int tmp_elements = tmpOut.elements();
             vector<To> h_ptr(tmp_elements);
             vector<Tw> h_cptr(tmp_elements);
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.ptr, tmp_elements * sizeof(To),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaMemcpyAsync(h_cptr.data(), tmpCt.ptr, tmp_elements * sizeof(Tw),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+            copyData<To>(h_ptr.data(), tmpOut);
+            copyData<Tw>(h_cptr.data(), tmpCt);
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
-            memFree(tmpOut.ptr);
-            memFree(tmpCt.ptr);
 
             To val = h_ptr[0];
             Tw weight = h_cptr[0];
@@ -605,6 +554,7 @@ namespace kernel
         } else {
 
             vector<Ti> h_ptr(in_elements);
+
             CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr, in_elements * sizeof(Ti),
                        cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 88ccb25f9c..170292ff5f 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -456,8 +456,8 @@ void nearest_neighbour(Param<uint> idx,
 
     unsigned nblk = blocks.x;
 
-    unsigned* d_blk_idx  = memAlloc<unsigned>(nblk * nquery);
-    To* d_blk_dist = memAlloc<To>(nblk * nquery);
+    auto d_blk_idx  = memAlloc<unsigned>(nblk * nquery);
+    auto d_blk_dist = memAlloc<To>(nblk * nquery);
 
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
@@ -466,35 +466,35 @@ void nearest_neighbour(Param<uint> idx,
         // Optimized lengths (faster due to loop unrolling)
         case 1:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,1,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 2:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,2,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 4:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,4,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 8:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,8,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 16:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,16,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 32:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,32,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 64:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,64,true>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         default:
             CUDA_LAUNCH_SMEM((nearest_neighbour<T,To,dist_type,true>), blocks, threads, smem_sz,
-                           d_blk_idx, d_blk_dist, query, train, max_dist, feat_len);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist, feat_len);
         }
     }
     else {
@@ -502,35 +502,35 @@ void nearest_neighbour(Param<uint> idx,
         // Optimized lengths (faster due to loop unrolling)
         case 1:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,1,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 2:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,2,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 4:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,4,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 8:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,8,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 16:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,16,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 32:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,32,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         case 64:
             CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,64,false>), blocks, threads, smem_sz,
-                                  d_blk_idx, d_blk_dist, query, train, max_dist);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
             break;
         default:
             CUDA_LAUNCH_SMEM((nearest_neighbour<T,To,dist_type,false>), blocks, threads, smem_sz,
-                           d_blk_idx, d_blk_dist, query, train, max_dist, feat_len);
+                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist, feat_len);
         }
     }
     POST_LAUNCH_CHECK();
@@ -541,11 +541,9 @@ void nearest_neighbour(Param<uint> idx,
     // Reduce all smallest Hamming distances from each block and store final
     // best match
     CUDA_LAUNCH(select_matches, blocks, threads,
-            idx, dist, d_blk_idx, d_blk_dist, nquery, nblk, max_dist);
+                idx, dist, d_blk_idx.get(), d_blk_dist.get(), nquery, nblk, max_dist);
     POST_LAUNCH_CHECK();
 
-    memFree(d_blk_idx);
-    memFree(d_blk_dist);
 }
 
 } // namespace kernel
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 1ad848a1e0..3274559d51 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -353,7 +353,7 @@ void orb(unsigned* out_feat,
         }
 
         int gauss_elem = gauss_filter.strides[3] * gauss_filter.dims[3];
-        gauss_filter.ptr = memAlloc<convAccT>(gauss_elem);
+        gauss_filter.ptr = memAlloc<convAccT>(gauss_elem).release();
         CUDA_CHECK(cudaMemcpyAsync(gauss_filter.ptr, h_gauss.get(), gauss_elem * sizeof(convAccT),
                     cudaMemcpyHostToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
@@ -366,14 +366,15 @@ void orb(unsigned* out_feat,
             continue;
         }
 
-        float* d_score_harris = memAlloc<float>(feat_pyr[i]);
+        auto d_score_harris = memAlloc<float>(feat_pyr[i]);
 
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
         dim3 threads(THREADS_X, THREADS_Y);
         dim3 blocks(divup(feat_pyr[i], threads.x), 1);
         CUDA_LAUNCH((harris_response<T,false>), blocks, threads,
-               d_score_harris, NULL, d_x_pyr[i], d_y_pyr[i], NULL, feat_pyr[i], img_pyr[i], 7, 0.04f, patch_size);
+                    d_score_harris.get(), NULL, d_x_pyr[i], d_y_pyr[i],
+                    NULL, feat_pyr[i], img_pyr[i], 7, 0.04f, patch_size);
         POST_LAUNCH_CHECK();
 
         Param<float> harris_sorted;
@@ -390,9 +391,9 @@ void orb(unsigned* out_feat,
         }
 
         int sort_elem = harris_sorted.strides[3] * harris_sorted.dims[3];
-        harris_sorted.ptr = d_score_harris;
+        harris_sorted.ptr = d_score_harris.get();
         // Create indices using range
-        harris_idx.ptr = memAlloc<unsigned>(sort_elem);
+        harris_idx.ptr = memAlloc<unsigned>(sort_elem).release();
         kernel::range<uint>(harris_idx, 0);
 
         // Sort features according to Harris responses
@@ -400,9 +401,9 @@ void orb(unsigned* out_feat,
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
-        float* d_x_lvl = memAlloc<float>(feat_pyr[i]);
-        float* d_y_lvl = memAlloc<float>(feat_pyr[i]);
-        float* d_score_lvl = memAlloc<float>(feat_pyr[i]);
+        float* d_x_lvl = memAlloc<float>(feat_pyr[i]).release();
+        float* d_y_lvl = memAlloc<float>(feat_pyr[i]).release();
+        float* d_score_lvl = memAlloc<float>(feat_pyr[i]).release();
 
         // Keep only features with higher Harris responses
         threads = dim3(THREADS, 1);
@@ -414,10 +415,9 @@ void orb(unsigned* out_feat,
 
         memFree(d_x_pyr[i]);
         memFree(d_y_pyr[i]);
-        memFree(harris_sorted.ptr);
         memFree(harris_idx.ptr);
 
-        float* d_ori_lvl = memAlloc<float>(feat_pyr[i]);
+        float* d_ori_lvl = memAlloc<float>(feat_pyr[i]).release();
 
         // Compute orientation of features
         threads = dim3(THREADS_X, THREADS_Y);
@@ -438,8 +438,8 @@ void orb(unsigned* out_feat,
             }
 
             int lvl_elem = img_pyr[i].strides[3] * img_pyr[i].dims[3];
-            lvl_tmp.ptr = memAlloc<T>(lvl_elem);
-            lvl_filt.ptr = memAlloc<T>(lvl_elem);
+            lvl_tmp.ptr = memAlloc<T>(lvl_elem).release();
+            lvl_filt.ptr = memAlloc<T>(lvl_elem).release();
 
             // Separable Gaussian filtering to reduce noise sensitivity
             convolve2<T, convAccT, 0, false>(lvl_tmp, img_pyr[i], gauss_filter);
@@ -456,9 +456,9 @@ void orb(unsigned* out_feat,
             }
         }
 
-        float* d_size_lvl = memAlloc<float>(feat_pyr[i]);
+        float* d_size_lvl = memAlloc<float>(feat_pyr[i]).release();
 
-        unsigned* d_desc_lvl = memAlloc<unsigned>(feat_pyr[i] * 8);
+        unsigned* d_desc_lvl = memAlloc<unsigned>(feat_pyr[i] * 8).release();
         CUDA_CHECK(cudaMemsetAsync(d_desc_lvl, 0, feat_pyr[i] * 8 * sizeof(unsigned),
                     cuda::getActiveStream()));
 
@@ -493,12 +493,12 @@ void orb(unsigned* out_feat,
     }
 
     // Allocate output memory
-    *d_x     = memAlloc<float>(total_feat);
-    *d_y     = memAlloc<float>(total_feat);
-    *d_score = memAlloc<float>(total_feat);
-    *d_ori   = memAlloc<float>(total_feat);
-    *d_size  = memAlloc<float>(total_feat);
-    *d_desc  = memAlloc<unsigned>(total_feat * 8);
+    *d_x     = memAlloc<float>(total_feat).release();
+    *d_y     = memAlloc<float>(total_feat).release();
+    *d_score = memAlloc<float>(total_feat).release();
+    *d_ori   = memAlloc<float>(total_feat).release();
+    *d_size  = memAlloc<float>(total_feat).release();
+    *d_desc  = memAlloc<unsigned>(total_feat * 8).release();
     unsigned offset = 0;
     for (unsigned i = 0; i < max_levels; i++) {
         if (feat_pyr[i] == 0)
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 75f00d8a07..80007fccd9 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -153,13 +153,14 @@ namespace kernel
         blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
         Param<To> tmp = out;
-
+        uptr<To> tmp_alloc;
         if (blocks_dim[dim] > 1) {
             int tmp_elements = 1;
             tmp.dims[dim] = blocks_dim[dim];
 
             for (int k = 0; k < 4; k++) tmp_elements *= tmp.dims[k];
-            tmp.ptr = memAlloc<To>(tmp_elements);
+            tmp_alloc = memAlloc<To>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
 
             for (int k = dim + 1; k < 4; k++) tmp.strides[k] *= blocks_dim[dim];
         }
@@ -177,7 +178,6 @@ namespace kernel
                                                            change_nan, nanval);
             }
 
-            memFree(tmp.ptr);
         }
     }
 
@@ -336,11 +336,10 @@ namespace kernel
         uint blocks_y = divup(in.dims[1], threads_y);
 
         Param<To> tmp = out;
+        uptr<To> tmp_alloc;
         if (blocks_x > 1) {
-            tmp.ptr = memAlloc<To>(blocks_x *
-                                   in.dims[1] *
-                                   in.dims[2] *
-                                   in.dims[3]);
+            tmp_alloc = memAlloc<To>(blocks_x * in.dims[1] * in.dims[2] * in.dims[3]);
+            tmp.ptr = tmp_alloc.get();
 
             tmp.dims[0] = blocks_x;
             for (int k = 1; k < 4; k++) tmp.strides[k] *= blocks_x;
@@ -358,7 +357,6 @@ namespace kernel
                                                         change_nan, nanval);
             }
 
-            memFree(tmp.ptr);
         }
     }
 
@@ -412,7 +410,8 @@ namespace kernel
 
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
 
-            tmp.ptr = memAlloc<To>(tmp_elements);
+            auto tmp_alloc = memAlloc<To>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
             reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
                                               change_nan, nanval);
 
@@ -422,7 +421,6 @@ namespace kernel
             CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(To),
                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-            memFree(tmp.ptr);
 
             Binary<To, op> reduce;
             To out = reduce.init();
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index dd3a1a54b0..aa2140d8ce 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -379,13 +379,13 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     // component to being sequentially numbered components starting at
     // 1.
     int size = in.dims[0] * in.dims[1];
-    T* tmp = cuda::memAlloc<T>(size);
-    CUDA_CHECK(cudaMemcpyAsync(tmp, out.ptr, size * sizeof(T),
+    auto tmp = cuda::memAlloc<T>(size);
+    CUDA_CHECK(cudaMemcpyAsync(tmp.get(), out.ptr, size * sizeof(T),
                           cudaMemcpyDeviceToDevice,
                           cuda::getActiveStream()));
 
     // Wrap raw device ptr
-    thrust::device_ptr<T> wrapped_tmp = thrust::device_pointer_cast(tmp);
+    thrust::device_ptr<T> wrapped_tmp = thrust::device_pointer_cast(tmp.get());
 
     // Sort the copy
     THRUST_SELECT(thrust::sort, wrapped_tmp, wrapped_tmp + size);
@@ -423,5 +423,4 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
 
     POST_LAUNCH_CHECK();
 
-    cuda::memFree(tmp);
 }
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 015a8ea6b4..ec4b660f30 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -274,7 +274,8 @@ namespace kernel
             for (int k = 1; k < 4; k++) tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
 
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            tmp.ptr = memAlloc<To>(tmp_elements);
+            auto tmp_alloc = memAlloc<To>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
 
             scan_dim_launcher<Ti, To, op, dim, false, inclusive_scan>(out, tmp, in,
                                                       threads_y,
@@ -296,8 +297,6 @@ namespace kernel
 
             blocks_all[dim] = bdim;
             bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all);
-
-            memFree(tmp.ptr);
         }
     }
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 461e7411fe..d414f6669e 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -524,9 +524,12 @@ namespace kernel
             }
 
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            tmp.ptr = memAlloc<To>(tmp_elements);
-            tmpflg.ptr = memAlloc<char>(tmp_elements);
-            tmpid.ptr = memAlloc<int>(tmp_elements);
+            auto tmp_alloc = memAlloc<To>(tmp_elements);
+            auto tmpflg_alloc = memAlloc<char>(tmp_elements);
+            auto tmpid_alloc = memAlloc<int>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
+            tmpflg.ptr = tmpflg_alloc.get();
+            tmpid.ptr = tmpid_alloc.get();
 
             scan_dim_nonfinal_launcher<Ti, Tk, To, op>(out, tmp, tmpflg,
                                                        tmpid, in, key,
@@ -545,9 +548,6 @@ namespace kernel
             blocks_all[dim] = bdim;
             bcast_dim_launcher<To, op>(out, tmp, tmpid, dim, threads_y, blocks_all);
 
-            memFree(tmp.ptr);
-            memFree(tmpflg.ptr);
-            memFree(tmpid.ptr);
         }
     }
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 8c865a32ef..e20fd9c02a 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -241,7 +241,8 @@ namespace kernel
             for (int k = 1; k < 4; k++) tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
 
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            tmp.ptr = memAlloc<To>(tmp_elements);
+            auto tmp_alloc = memAlloc<To>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
 
             scan_first_launcher<Ti, To, op, false, inclusive_scan>(out, tmp, in,
                                                    blocks_x, blocks_y,
@@ -260,7 +261,6 @@ namespace kernel
 
             bcast_first_launcher<To, op>(out, tmp, blocks_x, blocks_y, threads_x);
 
-            memFree(tmp.ptr);
         }
     }
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 68c3729066..53fa9463da 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -462,9 +462,12 @@ namespace kernel
             }
 
             int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            tmp.ptr = memAlloc<To>(tmp_elements);
-            tmpflg.ptr = memAlloc<char>(tmp_elements);
-            tmpid.ptr = memAlloc<int>(tmp_elements);
+            auto tmp_alloc = memAlloc<To>(tmp_elements);
+            auto tmpflg_alloc = memAlloc<char>(tmp_elements);
+            auto tmpid_alloc = memAlloc<int>(tmp_elements);
+            tmp.ptr = tmp_alloc.get();
+            tmpflg.ptr = tmpflg_alloc.get();
+            tmpid.ptr = tmpid_alloc.get();
 
             scan_nonfinal_launcher<Ti, Tk, To, op>(
                 out, tmp, tmpflg, tmpid, in, key,
@@ -478,9 +481,6 @@ namespace kernel
 
             bcast_first_launcher<To, op>(out, tmp, tmpid, blocks_x, blocks_y, threads_x);
 
-            memFree(tmp.ptr);
-            memFree(tmpflg.ptr);
-            memFree(tmpid.ptr);
         }
     }
 }
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index 03c8c2a8a6..9554b7a8d3 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -67,9 +67,9 @@ namespace cuda
             // Create/call iota
             // Array<uint> key = iota<uint>(seqDims, tileDims);
             dim4 keydims = inDims;
-            uint* key = memAlloc<uint>(keydims.elements());
+            auto key = memAlloc<uint>(keydims.elements());
             Param<uint> pKey;
-            pKey.ptr = key;
+            pKey.ptr = key.get();
             pKey.strides[0] = 1;
             pKey.dims[0] = keydims[0];
             for(int i = 1; i < 4; i++) {
@@ -105,7 +105,6 @@ namespace cuda
 
             // Not really necessary
             // CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-            memFree(key);
         }
 
         template<typename T>
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 42e4cf916b..4d4eaa37e9 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -67,9 +67,9 @@ namespace cuda
 
             // Create/call iota
             // Array<uint> key = iota<uint>(seqDims, tileDims);
-            uint* Seq = memAlloc<uint>(elements);
+            auto Seq = memAlloc<uint>(elements);
             Param<uint> pSeq;
-            pSeq.ptr = Seq;
+            pSeq.ptr = Seq.get();
             pSeq.strides[0] = 1;
             pSeq.dims[0] = inDims[0];
             for(int i = 1; i < 4; i++) {
@@ -79,31 +79,28 @@ namespace cuda
             cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
 
             Tk *Key = pKey.ptr;
-            Tk *cKey = memAlloc<Tk>(elements);
-            CUDA_CHECK(cudaMemcpyAsync(cKey, Key, elements * sizeof(Tk),
+            auto cKey = memAlloc<Tk>(elements);
+            CUDA_CHECK(cudaMemcpyAsync(cKey.get(), Key, elements * sizeof(Tk),
                                        cudaMemcpyDeviceToDevice,
                                        getActiveStream()));
 
             Tv *Val = pVal.ptr;
             thrustSortByKey(Key, Val, elements, isAscending);
-            thrustSortByKey(cKey, Seq, elements, isAscending);
+            thrustSortByKey(cKey.get(), Seq.get(), elements, isAscending);
 
-            uint *cSeq = memAlloc<uint>(elements);
-            CUDA_CHECK(cudaMemcpyAsync(cSeq, Seq, elements * sizeof(uint),
+            auto cSeq = memAlloc<uint>(elements);
+            CUDA_CHECK(cudaMemcpyAsync(cSeq.get(), Seq.get(), elements * sizeof(uint),
                                        cudaMemcpyDeviceToDevice,
                                        getActiveStream()));
 
             // This always needs to be ascending
-            thrustSortByKey(Seq, Val, elements, true);
-            thrustSortByKey(cSeq, Key, elements, true);
+            thrustSortByKey(Seq.get(), Val, elements, true);
+            thrustSortByKey(cSeq.get(), Key, elements, true);
 
             // No need of doing moddims here because the original Array<T>
             // dimensions have not been changed
             //val.modDims(inDims);
 
-            memFree(Seq);
-            memFree(cSeq);
-            memFree(cKey);
         }
 
         template<typename Tk, typename Tv>
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 351ef450ad..765b468a43 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -161,19 +161,18 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out,
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0-edge*2, BLOCK_X), divup(idim1-edge*2, BLOCK_Y));
 
-    unsigned* d_corners_found = memAlloc<unsigned>(1);
-    CUDA_CHECK(cudaMemsetAsync(d_corners_found, 0, sizeof(unsigned),
+    auto d_corners_found = memAlloc<unsigned>(1);
+    CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
                 cuda::getActiveStream()));
 
     CUDA_LAUNCH((nonMaxKernel<T>), blocks, threads,
-            x_out, y_out, resp_out, d_corners_found, idim0, idim1, resp_in, edge, max_corners);
+                x_out, y_out, resp_out, d_corners_found.get(), idim0, idim1, resp_in, edge, max_corners);
 
     POST_LAUNCH_CHECK();
 
-    CUDA_CHECK(cudaMemcpyAsync(count, d_corners_found, sizeof(unsigned),
+    CUDA_CHECK(cudaMemcpyAsync(count, d_corners_found.get(), sizeof(unsigned),
                 cudaMemcpyDeviceToHost, cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-    memFree(d_corners_found);
 }
 
 }
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index b7134b1d4a..8ecadb6bd8 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -95,10 +95,11 @@ namespace kernel
         }
 
         int rtmp_elements = rtmp.strides[3] * rtmp.dims[3];
-        rtmp.ptr = memAlloc<uint>(rtmp_elements);
-
         int otmp_elements = otmp.strides[3] * otmp.dims[3];
-        otmp.ptr = memAlloc<uint>(otmp_elements);
+        auto rtmp_alloc = memAlloc<uint>(rtmp_elements);
+        auto otmp_alloc = memAlloc<uint>(otmp_elements);
+        rtmp.ptr = rtmp_alloc.get();
+        otmp.ptr = otmp_alloc.get();
 
         scan_first_launcher<T, uint, af_notzero_t, false, true>(otmp, rtmp, in,
                                                           blocks_x, blocks_y,
@@ -121,7 +122,8 @@ namespace kernel
                               cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-        out.ptr = memAlloc<uint>(total);
+        auto out_alloc = memAlloc<uint>(total);
+        out.ptr = out_alloc.get();
 
         out.dims[0] = total;
         out.strides[0] = 1;
@@ -144,8 +146,7 @@ namespace kernel
                 out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
         POST_LAUNCH_CHECK();
 
-        memFree(rtmp.ptr);
-        memFree(otmp.ptr);
+        out_alloc.release();
     }
 }
 }
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index eef54f5080..7aae836594 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -134,20 +134,18 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
                                        in.get(), in.strides()[1],
                                        &lwork));
 
-    T *workspace = memAlloc<T>(lwork);
-    int *info = memAlloc<int>(1);
+    auto workspace = memAlloc<T>(lwork);
+    auto info = memAlloc<int>(1);
 
     CUSOLVER_CHECK(getrf_func<T>()(solverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
-                                   workspace,
+                                   workspace.get(),
                                    pivot.get(),
-                                   info));
+                                   info.get()));
 
     if(convert_pivot) convertPivot(pivot, M);
 
-    memFree(workspace);
-    memFree(info);
 
     return pivot;
 }
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 77738f89af..f06fcbf543 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -29,6 +29,8 @@
 
 using std::lock_guard;
 using std::recursive_mutex;
+using std::function;
+using std::unique_ptr;
 
 namespace cuda
 {
@@ -63,9 +65,11 @@ void printMemInfo(const char *msg, const int device)
 }
 
 template<typename T>
-T* memAlloc(const size_t &elements)
+uptr<T>
+memAlloc(const size_t &elements)
 {
-    return (T *)memoryManager().alloc(elements * sizeof(T), false);
+    T *ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
+    return uptr<T>(ptr, memFree<T>);
 }
 
 void* memAllocUser(const size_t &bytes)
@@ -122,11 +126,11 @@ bool checkMemoryLimit()
     return memoryManager().checkMemoryLimit();
 }
 
-#define INSTANTIATE(T)                                      \
-    template T* memAlloc(const size_t &elements);           \
-    template void memFree(T* ptr);                          \
-    template T* pinnedAlloc(const size_t &elements);        \
-    template void pinnedFree(T* ptr);                       \
+#define INSTANTIATE(T)                                                                        \
+    template std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements); \
+    template void memFree(T* ptr);                                                            \
+    template T* pinnedAlloc(const size_t &elements);                                          \
+    template void pinnedFree(T* ptr);                                                         \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 9c0ff38503..0b3fafb2e5 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -10,16 +10,23 @@
 
 #include <cstdlib>
 #include <common/MemoryManager.hpp>
-
+#include <memory>
 namespace cuda
 {
-template<typename T> T* memAlloc(const size_t &elements);
+template<typename T> void memFree(T* ptr);
+
+template<typename T>
+using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
+
+template<typename T>
+uptr<T> memAlloc(const size_t &elements);
+
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-template<typename T> void memFree(T* ptr);
+
 void memFreeUser(void* ptr);
 
 void memLock(const void *ptr);
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index f3683e3530..51bdac67cf 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -1,4 +1,4 @@
-/*******************************************************
+        /*******************************************************
  * Copyright (c) 2014, ArrayFire
  * All rights reserved.
  *
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index da44d9d584..600e9a8bcb 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -135,17 +135,17 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
                                        in_copy.get(), in_copy.strides()[1],
                                        &lwork));
 
-    T *workspace = memAlloc<T>(lwork);
+    auto workspace = memAlloc<T>(lwork);
 
     t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
-    int *info = memAlloc<int>(1);
+    auto info = memAlloc<int>(1);
 
     CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(),
                                    M, N,
                                    in_copy.get(), in_copy.strides()[1],
                                    t.get(),
-                                   workspace,
-                                   lwork, info));
+                                   workspace.get(),
+                                   lwork, info.get()));
 
     // SPLIT into q and r
     dim4 rdims(M, N);
@@ -165,13 +165,11 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
                                  in_copy.get(), in_copy.strides()[1],
                                  t.get(),
                                  q.get(), q.strides()[1],
-                                 workspace, lwork,
-                                 info));
+                                 workspace.get(), lwork,
+                                 info.get()));
 
     q.resetDims(dim4(M, M));
 
-    memFree(workspace);
-    memFree(info);
 }
 
 template<typename T>
@@ -190,18 +188,16 @@ Array<T> qr_inplace(Array<T> &in)
                                        in.get(), in.strides()[1],
                                        &lwork));
 
-    T *workspace = memAlloc<T>(lwork);
-    int *info = memAlloc<int>(1);
+    auto workspace = memAlloc<T>(lwork);
+    auto info = memAlloc<int>(1);
 
     CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(),
                                    M, N,
                                    in.get(), in.strides()[1],
                                    t.get(),
-                                   workspace, lwork,
-                                   info));
+                                   workspace.get(), lwork,
+                                   info.get()));
 
-    memFree(workspace);
-    memFree(info);
     return t;
 }
 
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index d773906dde..e65a918dc4 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -171,7 +171,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 
     Array< T > B = copyArray<T>(b);
 
-    int *info = memAlloc<int>(1);
+    auto info = memAlloc<int>(1);
 
     CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(),
                                    CUBLAS_OP_N,
@@ -179,9 +179,8 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                                    A.get(), A.strides()[1],
                                    pivot.get(),
                                    B.get(), B.strides()[1],
-                                   info));
+                                   info.get()));
 
-    memFree(info);
     return B;
 }
 
@@ -196,7 +195,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
     Array<T> B = copyArray<T>(b);
     Array<int> pivot = lu_inplace(A, false);
 
-    int *info = memAlloc<int>(1);
+    auto info = memAlloc<int>(1);
 
     CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(),
                                    CUBLAS_OP_N,
@@ -204,8 +203,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
                                    A.get(), A.strides()[1],
                                    pivot.get(),
                                    B.get(), B.strides()[1],
-                                   info));
-    memFree(info);
+                                   info.get()));
     return B;
 }
 
@@ -246,17 +244,17 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                                  A.get(), A.strides()[1],
                                                  &lwork));
 
-        T *workspace = memAlloc<T>(lwork);
+        auto workspace = memAlloc<T>(lwork);
         Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
-        int *info = memAlloc<int>(1);
+        auto info = memAlloc<int>(1);
 
         // In place Perform in place QR
         CUSOLVER_CHECK(geqrf_solve_func<T>()(solverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
-                                             workspace, lwork,
-                                             info));
+                                             workspace.get(), lwork,
+                                             info.get()));
 
         // R1 = R(seq(M), seq(M));
         A.resetDims(dim4(M, M));
@@ -277,11 +275,8 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                            A.get(), A.strides()[1],
                                            t.get(),
                                            B.get(), B.strides()[1],
-                                           workspace, lwork,
-                                           info));
-
-        memFree(workspace);
-        memFree(info);
+                                           workspace.get(), lwork,
+                                           info.get()));
 
     } else if (M > N) {
 
@@ -304,17 +299,17 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                                  A.get(), A.strides()[1],
                                                  &lwork));
 
-        T *workspace = memAlloc<T>(lwork);
+        auto workspace = memAlloc<T>(lwork);
         Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
-        int *info = memAlloc<int>(1);
+        auto info = memAlloc<int>(1);
 
         // In place Perform in place QR
         CUSOLVER_CHECK(geqrf_solve_func<T>()(solverDnHandle(),
                                              A.dims()[0], A.dims()[1],
                                              A.get(), A.strides()[1],
                                              t.get(),
-                                             workspace, lwork,
-                                             info));
+                                             workspace.get(), lwork,
+                                             info.get()));
 
         // matmul(Q1, B)
         CUSOLVER_CHECK(mqr_solve_func<T>()(solverDnHandle(),
@@ -324,16 +319,13 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
                                            A.get(), A.strides()[1],
                                            t.get(),
                                            B.get(), B.strides()[1],
-                                           workspace, lwork,
-                                           info));
-
+                                           workspace.get(), lwork,
+                                           info.get()));
         // tri_solve(R1, Bt)
         A.resetDims(dim4(N, N));
         B.resetDims(dim4(N, K));
         trsm(A, B, AF_MAT_NONE, true, true, false);
 
-        memFree(workspace);
-        memFree(info);
     }
     return B;
 }
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index c13400bda5..6912d75ff3 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -390,9 +390,9 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
                         in.dims()[0], in.dims()[1], nNZ,
                         converted.getRowIdx().get(), converted.getColIdx().get(),
                         &pBufferSizeInBytes));
-        shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
+        shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
 
-        shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
+        shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
         CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
         CUSPARSE_CHECK(cusparseXcoosortByColumn(
@@ -425,9 +425,9 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
                             cooT.dims()[0], cooT.dims()[1], nNZ,
                             cooT.getRowIdx().get(), cooT.getColIdx().get(),
                             &pBufferSizeInBytes));
-            shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes), memFree<char>);
+            shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
 
-            shared_ptr<int> P(memAlloc<int>(nNZ), memFree<int>);
+            shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
             CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
             CUSPARSE_CHECK(cusparseXcoosortByRow(
diff --git a/src/backend/cuda/susan.cu b/src/backend/cuda/susan.cu
index f79e07aa02..4f2a094223 100644
--- a/src/backend/cuda/susan.cu
+++ b/src/backend/cuda/susan.cu
@@ -27,33 +27,31 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     dim4 idims = in.dims();
 
     const unsigned corner_lim = in.elements() * feature_ratio;
-    float* x_corners          = memAlloc<float>(corner_lim);
-    float* y_corners          = memAlloc<float>(corner_lim);
-    float* resp_corners       = memAlloc<float>(corner_lim);
+    auto x_corners          = memAlloc<float>(corner_lim);
+    auto y_corners          = memAlloc<float>(corner_lim);
+    auto resp_corners       = memAlloc<float>(corner_lim);
 
-    T* resp = memAlloc<T>(in.elements());
+    auto resp = memAlloc<T>(in.elements());
     unsigned corners_found = 0;
 
-    kernel::susan_responses<T>(resp, in.get(), idims[0], idims[1], radius, diff_thr, geom_thr, edge);
+    kernel::susan_responses<T>(resp.get(), in.get(), idims[0], idims[1], radius, diff_thr, geom_thr, edge);
 
-    kernel::nonMaximal<T>(x_corners, y_corners, resp_corners, &corners_found,
-                           idims[0], idims[1], resp, edge, corner_lim);
-
-    memFree(resp);
+    kernel::nonMaximal<T>(x_corners.get(), y_corners.get(), resp_corners.get(), &corners_found,
+                          idims[0], idims[1], resp.get(), edge, corner_lim);
 
     const unsigned corners_out = min(corners_found, corner_lim);
     if (corners_out == 0) {
-        memFree(x_corners);
-        memFree(y_corners);
-        memFree(resp_corners);
         x_out    = createEmptyArray<float>(dim4());
         y_out    = createEmptyArray<float>(dim4());
         resp_out = createEmptyArray<float>(dim4());
         return 0;
     } else {
-        x_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)x_corners);
-        y_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)y_corners);
-        resp_out = createDeviceDataArray<float>(dim4(corners_out), (void*)resp_corners);
+        x_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)x_corners.get());
+        y_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)y_corners.get());
+        resp_out = createDeviceDataArray<float>(dim4(corners_out), (void*)resp_corners.get());
+        x_corners.release();
+        y_corners.release();
+        resp_corners.release(); 
         return corners_out;
     }
 }
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index 80fc94afa2..ed5ebfaf1d 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -88,18 +88,15 @@ SVD_SPECIALIZE(cdouble, double, Z);
 
         CUSOLVER_CHECK(gesvd_buf_func<T>(solverDnHandle(), M, N, &lwork));
 
-        T  *lWorkspace = memAlloc<T >(lwork);
-        Tr *rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
+        auto lWorkspace = memAlloc<T >(lwork);
+        auto rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
 
-        int *info = memAlloc<int>(1);
+        auto info = memAlloc<int>(1);
 
         gesvd_func<T, Tr>(solverDnHandle(), 'A', 'A', M, N, in.get(),
                           M, s.get(), u.get(), M, vt.get(), N,
-                          lWorkspace, lwork, rWorkspace, info);
+                          lWorkspace.get(), lwork, rWorkspace.get(), info.get());
 
-        memFree(info);
-        memFree(lWorkspace);
-        memFree(rWorkspace);
     }
 
     template <typename T, typename Tr>
diff --git a/src/backend/cuda/where.cu b/src/backend/cuda/where.cu
index ed188e5fd7..9c53266e41 100644
--- a/src/backend/cuda/where.cu
+++ b/src/backend/cuda/where.cu
@@ -23,7 +23,7 @@ namespace cuda
     {
         Param<uint> out;
         kernel::where<T>(out, in);
-        return createParamArray<uint>(out);
+        return createParamArray<uint>(out, true);
     }
 
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 80350f7947..c84d493258 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -93,16 +93,16 @@ namespace opencl
 
 
     template<typename T>
-    Array<T>::Array(Param &tmp) :
+    Array<T>::Array(Param &tmp, bool owner_) :
         info(getActiveDeviceId(),
              af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3]),
              0,
              af::dim4(tmp.info.strides[0], tmp.info.strides[1],
                       tmp.info.strides[2], tmp.info.strides[3]),
              (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.data, bufferFree),
+        data(tmp.data, owner_ ? bufferFree : [] (cl::Buffer* ptr) {}),
         data_dims(af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
+        node(bufferNodePtr<T>()), ready(true), owner(owner_)
     {
     }
 
@@ -343,10 +343,10 @@ namespace opencl
 
     template<typename T>
     Array<T>
-    createParamArray(Param &tmp)
+    createParamArray(Param &tmp, bool owner)
     {
         verifyDoubleSupport<T>();
-        return Array<T>(tmp);
+        return Array<T>(tmp, owner);
     }
 
     template<typename T>
@@ -409,7 +409,7 @@ namespace opencl
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
     template       Array<T>  *initArray<T      >      ();               \
-    template       Array<T>  createParamArray<T>      (Param &tmp);     \
+    template       Array<T>  createParamArray<T>      (Param &tmp, bool owner);    \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 18f73f8b09..512d257db6 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -64,7 +64,7 @@ namespace opencl
 
     // Create an Array object from Param
     template<typename T>
-    Array<T> createParamArray(Param &tmp);
+    Array<T> createParamArray(Param &tmp, bool owner);
 
     template<typename T>
     Array<T> createSubArray(const Array<T>& parent,
@@ -108,7 +108,7 @@ namespace opencl
         Array(af::dim4 dims);
 
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        Array(Param &tmp);
+        Array(Param &tmp, bool owner);
         explicit Array(af::dim4 dims, JIT::Node_ptr n);
         explicit Array(af::dim4 dims, const T * const in_data);
         explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
@@ -270,7 +270,7 @@ namespace opencl
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createParamArray<T>(Param &tmp);
+        friend Array<T> createParamArray<T>(Param &tmp, bool owner);
         friend Array<T> createNodeArray<T>(const af::dim4 &dims, JIT::Node_ptr node);
 
         friend Array<T> createSubArray<T>(const Array<T>& parent,
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index 6b9c1ee7ef..fe61874ae3 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -35,9 +35,9 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
                              thr, feature_ratio, edge);
 
     if (nfeat > 0) {
-        x_out = createParamArray<float>(x);
-        y_out = createParamArray<float>(y);
-        score_out = createParamArray<float>(score);
+        x_out = createParamArray<float>(x, true);
+        y_out = createParamArray<float>(y, true);
+        score_out = createParamArray<float>(score, true);
     }
 
     return nfeat;
diff --git a/src/backend/opencl/harris.cpp b/src/backend/opencl/harris.cpp
index eef6074aaa..27f6a3a03d 100644
--- a/src/backend/opencl/harris.cpp
+++ b/src/backend/opencl/harris.cpp
@@ -35,9 +35,9 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &score_ou
                                 sigma, filter_len, k_thr);
 
     if (nfeat > 0) {
-        x_out = createParamArray<float>(x);
-        y_out = createParamArray<float>(y);
-        score_out = createParamArray<float>(score);
+        x_out = createParamArray<float>(x, true);
+        y_out = createParamArray<float>(y, true);
+        score_out = createParamArray<float>(score, true);
     }
 
     return nfeat;
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 5867ea5269..9433be2daa 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -20,6 +20,9 @@
 #define AF_OPENCL_MEM_DEBUG 0
 #endif
 
+using std::unique_ptr;
+using std::function;
+
 namespace opencl
 {
 void setMemStepSize(size_t step_bytes)
@@ -53,9 +56,11 @@ void printMemInfo(const char *msg, const int device)
 }
 
 template<typename T>
-T* memAlloc(const size_t &elements)
+unique_ptr<T[], function<void(T *)>>
+memAlloc(const size_t &elements)
 {
-    return (T *)memoryManager().alloc(elements * sizeof(T), false);
+    T* ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
+    return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
 }
 
 void* memAllocUser(const size_t &bytes)
@@ -122,11 +127,11 @@ bool checkMemoryLimit()
     return memoryManager().checkMemoryLimit();
 }
 
-#define INSTANTIATE(T)                                      \
-    template T* memAlloc(const size_t &elements);           \
-    template void memFree(T* ptr);                          \
-    template T* pinnedAlloc(const size_t &elements);        \
-    template void pinnedFree(T* ptr);                       \
+#define INSTANTIATE(T)                                                              \
+    template unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements); \
+    template void memFree(T* ptr);                                                  \
+    template T* pinnedAlloc(const size_t &elements);                                \
+    template void pinnedFree(T* ptr);                                               \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index c6af6dfa1d..326717989a 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -12,6 +12,7 @@
 #include <map>
 #include <vector>
 #include <common/MemoryManager.hpp>
+#include <memory>
 
 namespace cl
 {
@@ -23,7 +24,7 @@ namespace opencl
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
 
-template<typename T> T* memAlloc(const size_t &elements);
+template<typename T>  std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
diff --git a/src/backend/opencl/orb.cpp b/src/backend/opencl/orb.cpp
index 54e0ba6eb9..e0320c44f4 100644
--- a/src/backend/opencl/orb.cpp
+++ b/src/backend/opencl/orb.cpp
@@ -46,12 +46,12 @@ unsigned orb(Array<float> &x_out, Array<float> &y_out,
         const dim4 out_dims(nfeat);
         const dim4 desc_dims(8, nfeat);
 
-        x_out     = createParamArray<float>(x);
-        y_out     = createParamArray<float>(y);
-        score_out = createParamArray<float>(score);
-        ori_out   = createParamArray<float>(ori);
-        size_out  = createParamArray<float>(size);
-        desc_out  = createParamArray<unsigned>(desc);
+        x_out     = createParamArray<float>(x, true);
+        y_out     = createParamArray<float>(y, true);
+        score_out = createParamArray<float>(score, true);
+        ori_out   = createParamArray<float>(ori, true);
+        size_out  = createParamArray<float>(size, true);
+        desc_out  = createParamArray<unsigned>(desc, true);
     }
 
     return nfeat;
diff --git a/src/backend/opencl/where.cpp b/src/backend/opencl/where.cpp
index 35d067aec6..6da39ce83d 100644
--- a/src/backend/opencl/where.cpp
+++ b/src/backend/opencl/where.cpp
@@ -22,7 +22,7 @@ namespace opencl
         Param Out;
         Param In = in;
         kernel::where<T>(Out, In);
-        return createParamArray<uint>(Out);
+        return createParamArray<uint>(Out, true);
     }
 
 

From c7fd7f8045c31ec2aac0f517f1a8003b49d2cf27 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 12 Oct 2017 03:08:35 -0400
Subject: [PATCH 1323/2677] Use Array<T> objects instead of Param objects in
 several functions

* Update SIFT with new RAII memAlloc
* Workaround for function resolution in ternary operator
* Fix Fast and Orb functions
---
 src/backend/cpu/kernel/sift_nonfree.hpp  |  16 +-
 src/backend/cpu/memory.hpp               |   4 +
 src/backend/cuda/Array.cpp               |  34 +-
 src/backend/cuda/Array.hpp               |   5 +-
 src/backend/cuda/fast_pyramid.cu         |   4 +-
 src/backend/cuda/fast_pyramid.hpp        |   2 +-
 src/backend/cuda/homography.cu           |   4 +-
 src/backend/cuda/jit.cpp                 |  28 +-
 src/backend/cuda/kernel/fast.hpp         |  56 ++--
 src/backend/cuda/kernel/fast_pyramid.hpp |  54 +---
 src/backend/cuda/kernel/homography.hpp   |  46 +--
 src/backend/cuda/kernel/orb.hpp          |  87 ++---
 src/backend/cuda/kernel/sift_nonfree.hpp | 389 ++++++++---------------
 src/backend/cuda/memory.cpp              |  17 +-
 src/backend/cuda/orb.cu                  |   8 +-
 src/backend/opencl/sift.cpp              |  12 +-
 test/homography.cpp                      |   2 +-
 test/orb.cpp                             |  15 +-
 18 files changed, 269 insertions(+), 514 deletions(-)

diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index a80dd504b2..0f19522239 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -986,12 +986,12 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
 
     std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
 
-    vector<unique_ptr<float[]>> x_pyr(n_octaves);
-    vector<unique_ptr<float[]>> y_pyr(n_octaves);
-    vector<unique_ptr<float[]>> response_pyr(n_octaves);
-    vector<unique_ptr<float[]>> size_pyr(n_octaves);
-    vector<unique_ptr<float[]>> ori_pyr(n_octaves);
-    vector<unique_ptr<float[]>> desc_pyr(n_octaves);
+    vector<uptr<float>> x_pyr(n_octaves);
+    vector<uptr<float>> y_pyr(n_octaves);
+    vector<uptr<float>> response_pyr(n_octaves);
+    vector<uptr<float>> size_pyr(n_octaves);
+    vector<uptr<float>> ori_pyr(n_octaves);
+    vector<uptr<float>> desc_pyr(n_octaves);
     vector<unsigned> feat_pyr(n_octaves, 0);
     unsigned total_feat = 0;
 
@@ -1100,13 +1100,13 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
         if (double_input) scale *= 2.f;
 
         if (compute_GLOH)
-            computeGLOHDescriptor<T>(desc, desc_len,
+            computeGLOHDescriptor<T>(desc.get(), desc_len,
                                      oriented_x.get(), oriented_y.get(), oriented_layer.get(),
                                      oriented_response.get(), oriented_size.get(), oriented_ori.get(),
                                      oriented_feat, gauss_pyr, d, rb, ab, hb,
                                      scale, i, n_layers);
         else
-            computeDescriptor<T>(desc, desc_len,
+            computeDescriptor<T>(desc.get(), desc_len,
                                  oriented_x.get(), oriented_y.get(), oriented_layer.get(),
                                  oriented_response.get(), oriented_size.get(), oriented_ori.get(),
                                  oriented_feat, gauss_pyr, d, n, scale, i, n_layers);
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 84a0303365..66c880be7b 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -14,6 +14,10 @@
 
 namespace cpu
 {
+
+template<typename T>
+using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
+
 template<typename T> std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index b4e39797c9..1d8e69649b 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -46,7 +46,7 @@ namespace cuda
     template<typename T>
     Array<T>::Array(af::dim4 dims, const T * const in_data, bool is_device, bool copy_device) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(((is_device & !copy_device) ? (T *)in_data : memAlloc<T>(dims.elements()).release()), memFree<T>),
+        data(((is_device & !copy_device) ? const_cast<T*>(in_data) : memAlloc<T>(dims.elements()).release()), memFree<T>),
         data_dims(dims),
         node(bufferNodePtr<T>()), ready(true), owner(true)
     {
@@ -80,7 +80,7 @@ namespace cuda
              0,
              af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2], tmp.strides[3]),
              (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.ptr, owner_ ? memFree<T> : [](T*){}),
+        data(tmp.ptr, owner_ ? std::function<void(T*)>(memFree<T>) : std::function<void(T*)>([](T*){})),
         data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3])),
         node(bufferNodePtr<T>()), ready(true), owner(owner_)
     {
@@ -116,21 +116,12 @@ namespace cuda
     void Array<T>::eval()
     {
         if (isReady()) return;
-
+ 
         this->setId(getActiveDeviceId());
-        data = shared_ptr<T>(memAlloc<T>(elements()).release(),
-                             memFree<T>);
-
-        Param<T> res;
-        res.ptr = data.get();
+        this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
 
-        for (int  i = 0; i < 4; i++) {
-            res.dims[i] = dims()[i];
-            res.strides[i] = strides()[i];
-        }
-
-        evalNodes(res, this->getNode().get());
         ready = true;
+        evalNodes<T>(*this, this->getNode().get());
         // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
         node = bufferNodePtr<T>();
     }
@@ -164,19 +155,11 @@ namespace cuda
                 continue;
             }
 
+            array->ready = true;
             array->setId(getActiveDeviceId());
-            array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(),
-                                        memFree<T>);
+            array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
-            Param<T> res;
-            res.ptr = array->data.get();
-
-            for (int  i = 0; i < 4; i++) {
-                res.dims[i] = array->dims()[i];
-                res.strides[i] = array->strides()[i];
-            }
-
-            outputs.push_back(res);
+            outputs.push_back(*array);
             nodes.push_back(array->node.get());
         }
 
@@ -186,7 +169,6 @@ namespace cuda
             Array<T> *array = arrays[i];
 
             if (array->isReady()) continue;
-            array->ready = true;
             // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
             array->node = bufferNodePtr<T>();
         }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index d80b31f0bc..b3b83ef532 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -28,7 +28,7 @@ namespace cuda
     template<typename T> class Array;
 
     template<typename T>
-    void evalNodes(Param<T> &out, JIT::Node *node);
+    void evalNodes(Param<T> out, JIT::Node *node);
 
     template<typename T>
     void evalNodes(std::vector<Param<T> > &out, std::vector<JIT::Node *> nodes);
@@ -36,15 +36,12 @@ namespace cuda
     template<typename T>
     void evalMultiple(std::vector<Array<T> *> arrays);
 
-    // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
 
-    // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createValueArray(const af::dim4 &size, const T& value);
 
-    // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createHostDataArray(const af::dim4 &size, const T * const data);
 
diff --git a/src/backend/cuda/fast_pyramid.cu b/src/backend/cuda/fast_pyramid.cu
index 64d9c67d21..b00f728b9d 100644
--- a/src/backend/cuda/fast_pyramid.cu
+++ b/src/backend/cuda/fast_pyramid.cu
@@ -22,7 +22,7 @@ namespace cuda
 template<typename T>
 void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
                   std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
-                  std::vector<float>& lvl_scl, std::vector<CParam<T> >& img_pyr,
+                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
                   const Array<T>& image,
                   const float fast_thr, const unsigned max_feat,
                   const float scl_fctr, const unsigned levels,
@@ -35,7 +35,7 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
 #define INSTANTIATE(T)\
     template void fast_pyramid<T>(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,    \
                                   std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,    \
-                                  std::vector<float>& lvl_scl, std::vector<CParam<T> >& img_pyr,    \
+                                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,      \
                                   const Array<T>& image,                                            \
                                   const float fast_thr, const unsigned max_feat,                    \
                                   const float scl_fctr, const unsigned levels,                      \
diff --git a/src/backend/cuda/fast_pyramid.hpp b/src/backend/cuda/fast_pyramid.hpp
index d232411b2a..d380f61fb0 100644
--- a/src/backend/cuda/fast_pyramid.hpp
+++ b/src/backend/cuda/fast_pyramid.hpp
@@ -18,7 +18,7 @@ namespace cuda
 template<typename T>
 void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
                   std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
-                  std::vector<float>& lvl_scl, std::vector<CParam<T> >& img_pyr,
+                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
                   const Array<T>& image,
                   const float fast_thr, const unsigned max_feat,
                   const float scl_fctr, const unsigned levels,
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index 92d3b60b76..27d1217314 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -40,7 +40,7 @@ int homography(Array<T> &bestH,
     const unsigned nsamples = idims[0];
 
     unsigned iter = iterations;
-    Array<float> err = createEmptyArray<float>(af::dim4());
+    Array<float> err = *initArray<float>();
     if (htype == AF_HOMOGRAPHY_LMEDS) {
         iter = ::std::min(iter, (unsigned)(log(1.f - LMEDSConfidence) / log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
         err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
@@ -52,8 +52,6 @@ int homography(Array<T> &bestH,
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter), (T)0);
 
-    bestH = createValueArray<T>(af::dim4(3, 3), (T)0);
-
     return kernel::computeH<T>(bestH, tmpH, err,
                                x_src, y_src, x_dst, y_dst,
                                rnd, iter, nsamples, inlier_thr, htype);
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 5f630ce79a..7965661d92 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -367,7 +367,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
 }
 
 template<typename T>
-void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
+void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
 {
     int num_outputs = (int)outputs.size();
 
@@ -470,7 +470,7 @@ void evalNodes(vector<Param<T> >&outputs, vector<Node *> output_nodes)
 }
 
 template<typename T>
-void evalNodes(Param<T> &out, Node *node)
+void evalNodes(Param<T> out, Node *node)
 {
     vector<Param<T>> outputs;
     vector<Node *> output_nodes;
@@ -481,18 +481,18 @@ void evalNodes(Param<T> &out, Node *node)
     return;
 }
 
-template void evalNodes<float  >(Param<float  > &out, Node *node);
-template void evalNodes<double >(Param<double > &out, Node *node);
-template void evalNodes<cfloat >(Param<cfloat > &out, Node *node);
-template void evalNodes<cdouble>(Param<cdouble> &out, Node *node);
-template void evalNodes<int    >(Param<int    > &out, Node *node);
-template void evalNodes<uint   >(Param<uint   > &out, Node *node);
-template void evalNodes<char   >(Param<char   > &out, Node *node);
-template void evalNodes<uchar  >(Param<uchar  > &out, Node *node);
-template void evalNodes<intl   >(Param<intl   > &out, Node *node);
-template void evalNodes<uintl  >(Param<uintl  > &out, Node *node);
-template void evalNodes<short  >(Param<short  > &out, Node *node);
-template void evalNodes<ushort >(Param<ushort > &out, Node *node);
+template void evalNodes<float  >(Param<float  > out, Node *node);
+template void evalNodes<double >(Param<double > out, Node *node);
+template void evalNodes<cfloat >(Param<cfloat > out, Node *node);
+template void evalNodes<cdouble>(Param<cdouble> out, Node *node);
+template void evalNodes<int    >(Param<int    > out, Node *node);
+template void evalNodes<uint   >(Param<uint   > out, Node *node);
+template void evalNodes<char   >(Param<char   > out, Node *node);
+template void evalNodes<uchar  >(Param<uchar  > out, Node *node);
+template void evalNodes<intl   >(Param<intl   > out, Node *node);
+template void evalNodes<uintl  >(Param<uintl  > out, Node *node);
+template void evalNodes<short  >(Param<short  > out, Node *node);
+template void evalNodes<ushort >(Param<ushort > out, Node *node);
 
 template void evalNodes<float  >(vector<Param<float  > > &out, vector<Node *> node);
 template void evalNodes<double >(vector<Param<double > > &out, vector<Node *> node);
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 5bee9277df..969716fe90 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -385,30 +385,28 @@ void fast(unsigned* out_feat,
           float** x_out,
           float** y_out,
           float** score_out,
-          CParam<T> in,
+          const Array<T>& in,
           const float thr,
           const unsigned arc_length,
           const unsigned nonmax,
           const float feature_ratio,
           const unsigned edge)
 {
-    const unsigned max_feat = ceil(in.dims[0] * in.dims[1] * feature_ratio);
+    dim4 indims = in.dims();
+    const unsigned max_feat = ceil(indims[0] * indims[1] * feature_ratio);
 
     dim3 threads(16, 16);
-    dim3 blocks(divup(in.dims[0]-edge*2, threads.x), divup(in.dims[1]-edge*2, threads.y));
+    dim3 blocks(divup(indims[0]-edge*2, threads.x), divup(indims[1]-edge*2, threads.y));
 
     // Matrix containing scores for detected features, scores are stored in the
     // same coordinates as features, dimensions should be equal to in.
+    auto d_score = memAlloc<float>(indims[0] * indims[1] + 1);
 
-    size_t score_bytes = in.dims[0] * in.dims[1] * sizeof(float) + sizeof(unsigned);
-    auto d_score_tmp = memAlloc<char>(score_bytes);
-    float *d_score = (float *)d_score_tmp.get();
-
-    float *d_flags = d_score;
+    float *d_flags = d_score.get();
     uptr<float> d_flags_alloc;
     if (nonmax) {
-        d_flags_alloc = memAlloc<float>(in.dims[0] * in.dims[1]);
-        auto d_flags = d_flags_alloc.get();
+        d_flags_alloc = memAlloc<float>(indims[0] * indims[1]);
+        d_flags = d_flags_alloc.get();
     }
 
     // Shared memory size
@@ -416,28 +414,28 @@ void fast(unsigned* out_feat,
 
     switch(arc_length) {
     case 9:
-        CUDA_LAUNCH_SMEM((locate_features<T, 9>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T, 9>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 10:
-        CUDA_LAUNCH_SMEM((locate_features<T,10>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,10>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 11:
-        CUDA_LAUNCH_SMEM((locate_features<T,11>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,11>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 12:
-        CUDA_LAUNCH_SMEM((locate_features<T,12>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,12>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 13:
-        CUDA_LAUNCH_SMEM((locate_features<T,13>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,13>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 14:
-        CUDA_LAUNCH_SMEM((locate_features<T,14>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,14>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 15:
-        CUDA_LAUNCH_SMEM((locate_features<T,15>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,15>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     case 16:
-        CUDA_LAUNCH_SMEM((locate_features<T,16>), blocks, threads, shared_size, in, d_score, thr, edge);
+        CUDA_LAUNCH_SMEM((locate_features<T,16>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
         break;
     }
 
@@ -446,22 +444,22 @@ void fast(unsigned* out_feat,
     threads.x = 32;
     threads.y =  8;
 
-    blocks.x = divup(in.dims[0], 64);
-    blocks.y = divup(in.dims[1], 64);
+    blocks.x = divup(indims[0], 64);
+    blocks.y = divup(indims[1], 64);
 
-    unsigned *d_total = (unsigned *)(d_score + in.dims[0] * in.dims[1]);
+    unsigned *d_total = (unsigned *)(d_score.get() + (indims[0] * indims[1]));
     CUDA_CHECK(cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getActiveStream()));
     auto d_counts  = memAlloc<unsigned>(blocks.x * blocks.y);
     auto d_offsets = memAlloc<unsigned>(blocks.x * blocks.y);
 
     if (nonmax)
         CUDA_LAUNCH((non_max_counts<true >), blocks, threads,
-                d_counts.get(), d_offsets.get(), d_total, d_flags,
-                d_score, in.dims[0], in.dims[1], edge);
+                    d_counts.get(), d_offsets.get(), d_total, d_flags,
+                    d_score.get(), indims[0], indims[1], edge);
     else
         CUDA_LAUNCH((non_max_counts<false>), blocks, threads,
-                d_counts.get(), d_offsets.get(), d_total, d_flags,
-                d_score, in.dims[0], in.dims[1], edge);
+                    d_counts.get(), d_offsets.get(), d_total, d_flags,
+                    d_score.get(), indims[0], indims[1], edge);
 
     POST_LAUNCH_CHECK();
 
@@ -482,7 +480,7 @@ void fast(unsigned* out_feat,
 
         CUDA_LAUNCH((get_features<float>), blocks, threads,
                 *x_out, *y_out, *score_out, d_flags, d_counts.get(),
-                d_offsets.get(), total, in.dims[0], in.dims[1], edge);
+                d_offsets.get(), total, indims[0], indims[1], edge);
 
         POST_LAUNCH_CHECK();
 
@@ -492,10 +490,8 @@ void fast(unsigned* out_feat,
     }
 
     *out_feat = total;
-
-    d_flags_alloc.release();
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index 9295139b7d..a7b2888375 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -27,15 +27,16 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
                   std::vector<float*>& d_y_pyr,
                   std::vector<unsigned>& lvl_best,
                   std::vector<float>& lvl_scl,
-                  std::vector<CParam<T> >& img_pyr,
-                  CParam<T> in,
+                  std::vector<Array<T>>& img_pyr,
+                  const Array<T>& in,
                   const float fast_thr,
                   const unsigned max_feat,
                   const float scl_fctr,
                   const unsigned levels,
                   const unsigned patch_size)
 {
-    unsigned min_side = std::min(in.dims[0], in.dims[1]);
+    dim4 indims = in.dims();
+    unsigned min_side = std::min(indims[0], indims[1]);
     unsigned max_levels = 0;
     float scl_sum = 0.f;
 
@@ -66,45 +67,22 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
     // Hold multi-scale image pyramids
     static const dim4 dims0;
     static const CParam<T> emptyCParam(NULL, dims0.get(), dims0.get());
-    // Need to do this as CParam does not have a default constructor
-    // And resize needs a default constructor or default value prior to C++11
-    img_pyr.resize(max_levels, emptyCParam);
-    std::vector<uptr<T>> lvl_img_alloc;
+
+    img_pyr.reserve(max_levels);
+
     // Create multi-scale image pyramid
     for (unsigned i = 0; i < max_levels; i++) {
         if (i == 0) {
             // First level is used in its original size
-            img_pyr[i].ptr = in.ptr;
-            for (int k = 0; k < 4; k++) {
-                img_pyr[i].dims[k] = in.dims[k];
-                img_pyr[i].strides[k] = in.strides[k];
-            }
+            img_pyr.push_back(in);
         }
         else {
             // Resize previous level image to current level dimensions
-          //TODO: Param<T> should use array assignment, not iteration
-            Param<T> lvl_img;
-            lvl_img.dims[0] = round(in.dims[0] / lvl_scl[i]);
-            lvl_img.dims[1] = round(in.dims[1] / lvl_scl[i]);
-            lvl_img.strides[0] = 1;
-            lvl_img.strides[1] = lvl_img.dims[0] * lvl_img.strides[0];
-
-            for (int k = 2; k < 4; k++) {
-                lvl_img.dims[k] = 1;
-                lvl_img.strides[k] = lvl_img.dims[k - 1] * lvl_img.strides[k - 1];
-            }
-
-            int lvl_elem = lvl_img.strides[3] * lvl_img.dims[3];
-            lvl_img_alloc.push_back(memAlloc<T>(lvl_elem));
-            lvl_img.ptr = lvl_img_alloc.back().get();
-
-            resize<T, AF_INTERP_BILINEAR>(lvl_img, img_pyr[i-1]);
-
-            img_pyr[i].ptr = lvl_img.ptr;
-            for (int k = 0; k < 4; k++) {
-                img_pyr[i].dims[k] = lvl_img.dims[k];
-                img_pyr[i].strides[k] = lvl_img.strides[k];
-            }
+            dim4 dims(round(indims[0] / lvl_scl[i]),
+                      round(indims[1] / lvl_scl[i]));
+
+            img_pyr.push_back(createEmptyArray<T>(dims));
+            resize<T, AF_INTERP_BILINEAR>(img_pyr[i], img_pyr[i-1]);
         }
     }
 
@@ -131,7 +109,7 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
              img_pyr[i], fast_thr, 9, 1, 0.15f, edge);
 
         // FAST score is not used
-        // TODO: should be handled by fast() 
+        // TODO: should be handled by fast()
         memFree(d_score_feat);
 
         if (lvl_feat == 0) {
@@ -145,10 +123,6 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
             d_y_pyr[i] = d_y_feat;
         }
     }
-
-    for(auto& l : lvl_img_alloc){
-      l.release();
-    }
 }
 
 } // namespace kernel
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 058664147f..2e34fa5c40 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -561,27 +561,9 @@ int computeH(
     blocks = dim3(divup(iterations, threads.x));
 
     // Allocate some temporary buffers
-    Param<unsigned> idx, inliers;
-    Param<float> median;
-    inliers.dims[0] = (htype == AF_HOMOGRAPHY_RANSAC) ? blocks.x : divup(nsamples, threads.x);
-    inliers.strides[0] = 1;
-    idx.dims[0] = median.dims[0] = blocks.x;
-    idx.strides[0] = median.strides[0] = 1;
-    for (int k = 1; k < 4; k++) {
-        inliers.dims[k] = 1;
-        inliers.strides[k] = inliers.dims[k-1] * inliers.strides[k-1];
-        idx.dims[k] = median.dims[k] = 1;
-        idx.strides[k] = median.strides[k] = idx.dims[k-1] * idx.strides[k-1];
-    }
-    auto idx_alloc = memAlloc<unsigned>(idx.dims[3] * idx.strides[3]);
-    auto inliers_alloc = memAlloc<unsigned>(inliers.dims[3] * inliers.strides[3]);
-    idx.ptr = idx_alloc.get();
-    inliers.ptr = inliers_alloc.get();
-    uptr<float> median_alloc;
-    if (htype == AF_HOMOGRAPHY_LMEDS){
-        median_alloc = memAlloc<float>(median.dims[3] * median.strides[3]);
-        median.ptr = median_alloc.get();
-    }
+    dim4 idx_dims(blocks.x);
+    Array<unsigned> idx = createEmptyArray<unsigned>(idx_dims);
+    Array<unsigned> inliers = createEmptyArray<unsigned>((htype == AF_HOMOGRAPHY_RANSAC) ? blocks.x : divup(nsamples, threads.x));
 
     // Compute (and for RANSAC, evaluate) homographies
     CUDA_LAUNCH((computeEvalHomography<T>), blocks, threads,
@@ -591,6 +573,7 @@ int computeH(
 
     unsigned inliersH, idxH;
     if (htype == AF_HOMOGRAPHY_LMEDS) {
+        Array<float> median = createEmptyArray<float>(idx_dims);
         // TODO: Improve this sorting, if the number of iterations is
         // sufficiently large, this can be *very* slow
         kernel::sort0<float>(err, true);
@@ -618,11 +601,13 @@ int computeH(
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx.get(), sizeof(unsigned),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         } else {
-            CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.ptr, sizeof(float),
+            CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.get(), sizeof(float),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaMemcpyAsync(&minIdx, idx.ptr, sizeof(unsigned),
+            CUDA_CHECK(cudaMemcpyAsync(&minIdx, idx.get(), sizeof(unsigned),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         }
 
         // Copy best homography to output
@@ -632,7 +617,6 @@ int computeH(
         blocks = dim3(divup(nsamples, threads.x));
         // sync stream for the device to host copies to be visible for
         // the subsequent kernel launch
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
         CUDA_LAUNCH((computeLMedSInliers<T>), blocks, threads,
                     inliers, bestH, x_src, y_src, x_dst, y_dst,
@@ -640,26 +624,22 @@ int computeH(
         POST_LAUNCH_CHECK();
 
         // Adds up the total number of inliers
-        Param<unsigned> totalInliers;
-        for (int k = 0; k < 4; k++)
-            totalInliers.dims[k] = totalInliers.strides[k] = 1;
-        auto totalInliers_alloc = memAlloc<unsigned>(1);
-        totalInliers.ptr = totalInliers_alloc.get();
-
+        Array<unsigned> totalInliers = createEmptyArray<unsigned>(1);
         kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
 
-        CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.ptr, sizeof(unsigned),
+        CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.get(), sizeof(unsigned),
                     cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
         unsigned blockIdx;
         inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
         // Copies back index and number of inliers of best homography estimation
-        CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.ptr+blockIdx, sizeof(unsigned),
+        CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.get()+blockIdx, sizeof(unsigned),
                     cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        median_alloc.release();
     }
 
     // sync stream for the device to host copies to be visible for
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 3274559d51..bae33fefe4 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -314,7 +314,7 @@ void orb(unsigned* out_feat,
          vector<float*>& d_y_pyr,
          vector<unsigned>& lvl_best,
          vector<float>& lvl_scl,
-         vector<CParam<T> >& img_pyr,
+         vector<Array<T>>& img_pyr,
          const float fast_thr,
          const unsigned max_feat,
          const float scl_fctr,
@@ -339,34 +339,27 @@ void orb(unsigned* out_feat,
     unsigned total_feat = 0;
 
     // Calculate a separable Gaussian kernel
-    Param<convAccT> gauss_filter;
+    Array<convAccT> gauss_filter = *initArray<convAccT>();
     if (blur_img) {
         unsigned gauss_len = 9;
-        unique_ptr<convAccT[]> h_gauss(new convAccT[gauss_len]);
-        gaussian1D(h_gauss.get(), gauss_len, 2.f);
-        gauss_filter.dims[0] = gauss_len;
-        gauss_filter.strides[0] = 1;
-
-        for (int k = 1; k < 4; k++) {
-            gauss_filter.dims[k] = 1;
-            gauss_filter.strides[k] = gauss_filter.dims[k - 1] * gauss_filter.strides[k - 1];
-        }
-
-        int gauss_elem = gauss_filter.strides[3] * gauss_filter.dims[3];
-        gauss_filter.ptr = memAlloc<convAccT>(gauss_elem).release();
-        CUDA_CHECK(cudaMemcpyAsync(gauss_filter.ptr, h_gauss.get(), gauss_elem * sizeof(convAccT),
-                    cudaMemcpyHostToDevice, cuda::getActiveStream()));
+        vector<convAccT> h_gauss(gauss_len);
+        gaussian1D(h_gauss.data(), gauss_len, 2.f);
+        dim4 gauss_dim(gauss_len);
+        gauss_filter = createHostDataArray<convAccT>(gauss_dim, h_gauss.data());
+        CUDA_CHECK(cudaMemcpyAsync(gauss_filter.get(), h_gauss.data(),
+                                   h_gauss.size() * sizeof(convAccT),
+                                   cudaMemcpyHostToDevice, cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 
     for (int i = 0; i < (int)max_levels; i++) {
         if (feat_pyr[i] == 0 || lvl_best[i] == 0) {
-            if (i > 0)
-                memFree((T*)img_pyr[i].ptr);
             continue;
         }
 
-        auto d_score_harris = memAlloc<float>(feat_pyr[i]);
+        //auto d_score_harris = memAlloc<float>(feat_pyr[i]);
+        dim4 score_dim(feat_pyr[i]);
+        Array<float> d_score_harris = createEmptyArray<float>(score_dim); //harris_sorted
 
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
@@ -377,27 +370,14 @@ void orb(unsigned* out_feat,
                     NULL, feat_pyr[i], img_pyr[i], 7, 0.04f, patch_size);
         POST_LAUNCH_CHECK();
 
-        Param<float> harris_sorted;
-        Param<unsigned> harris_idx;
+        dim4 feat_dim(feat_pyr[i]);
+        Array<unsigned> harris_idx = createEmptyArray<unsigned>(feat_dim);
 
-        harris_sorted.dims[0] = harris_idx.dims[0] = feat_pyr[i];
-        harris_sorted.strides[0] = harris_idx.strides[0] = 1;
-
-        for (int k = 1; k < 4; k++) {
-            harris_sorted.dims[k] = 1;
-            harris_sorted.strides[k] = harris_sorted.dims[k - 1] * harris_sorted.strides[k - 1];
-            harris_idx.dims[k] = 1;
-            harris_idx.strides[k] = harris_idx.dims[k - 1] * harris_idx.strides[k - 1];
-        }
-
-        int sort_elem = harris_sorted.strides[3] * harris_sorted.dims[3];
-        harris_sorted.ptr = d_score_harris.get();
         // Create indices using range
-        harris_idx.ptr = memAlloc<unsigned>(sort_elem).release();
         kernel::range<uint>(harris_idx, 0);
 
         // Sort features according to Harris responses
-        kernel::sort0ByKey<float, uint>(harris_sorted, harris_idx, false);
+        kernel::sort0ByKey<float, uint>(d_score_harris, harris_idx, false);
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
@@ -409,13 +389,12 @@ void orb(unsigned* out_feat,
         threads = dim3(THREADS, 1);
         blocks = dim3(divup(feat_pyr[i], threads.x), 1);
         CUDA_LAUNCH((keep_features<T>), blocks, threads,
-                d_x_lvl, d_y_lvl, d_score_lvl, NULL,
-                d_x_pyr[i], d_y_pyr[i], harris_sorted.ptr, harris_idx.ptr, NULL, feat_pyr[i]);
+                    d_x_lvl, d_y_lvl, d_score_lvl, NULL,
+                    d_x_pyr[i], d_y_pyr[i], d_score_harris.get(), harris_idx.get(), NULL, feat_pyr[i]);
         POST_LAUNCH_CHECK();
 
         memFree(d_x_pyr[i]);
         memFree(d_y_pyr[i]);
-        memFree(harris_idx.ptr);
 
         float* d_ori_lvl = memAlloc<float>(feat_pyr[i]).release();
 
@@ -426,34 +405,12 @@ void orb(unsigned* out_feat,
                 d_x_lvl, d_y_lvl, d_ori_lvl, feat_pyr[i], img_pyr[i], patch_size);
         POST_LAUNCH_CHECK();
 
-        Param<T> lvl_tmp;
-        Param<T> lvl_filt;
-
         if (blur_img) {
-            for (int k = 0; k < 4; k++) {
-                lvl_tmp.dims[k] = img_pyr[i].dims[k];
-                lvl_tmp.strides[k] = img_pyr[i].strides[k];
-                lvl_filt.dims[k] = img_pyr[i].dims[k];
-                lvl_filt.strides[k] = img_pyr[i].strides[k];
-            }
-
-            int lvl_elem = img_pyr[i].strides[3] * img_pyr[i].dims[3];
-            lvl_tmp.ptr = memAlloc<T>(lvl_elem).release();
-            lvl_filt.ptr = memAlloc<T>(lvl_elem).release();
+            Array<T> lvl_tmp = createEmptyArray<T>(img_pyr[i].dims());
 
             // Separable Gaussian filtering to reduce noise sensitivity
             convolve2<T, convAccT, 0, false>(lvl_tmp, img_pyr[i], gauss_filter);
-            convolve2<T, convAccT, 1, false>(lvl_filt, CParam<T>(lvl_tmp), gauss_filter);
-
-            memFree(lvl_tmp.ptr);
-            if (i > 0)
-                memFree((T*)img_pyr[i].ptr);
-
-            img_pyr[i].ptr = lvl_filt.ptr;
-            for (int k = 0; k < 4; k++) {
-                img_pyr[i].dims[k] = lvl_filt.dims[k];
-                img_pyr[i].strides[k] = lvl_filt.strides[k];
-            }
+            convolve2<T, convAccT, 1, false>(img_pyr[i], lvl_tmp, gauss_filter);
         }
 
         float* d_size_lvl = memAlloc<float>(feat_pyr[i]).release();
@@ -470,9 +427,6 @@ void orb(unsigned* out_feat,
                 img_pyr[i], lvl_scl[i], patch_size);
         POST_LAUNCH_CHECK();
 
-        if (i > 0)
-            memFree((T*)img_pyr[i].ptr);
-
         // Store results to pyramids
         total_feat += feat_pyr[i];
         d_x_pyr[i] = d_x_lvl;
@@ -483,9 +437,6 @@ void orb(unsigned* out_feat,
         d_desc_pyr[i] = d_desc_lvl;
     }
 
-    if (blur_img)
-        memFree((T*)gauss_filter.ptr);
-
     // If no features are found, set found features to 0 and return
     if (total_feat == 0) {
         *out_feat = 0;
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index eed8abe506..ed7243b8c3 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -171,30 +171,15 @@ void gaussian1D(T* out, const int dim, double sigma=0.0)
 }
 
 template<typename T>
-Param<T> gauss_filter(float sigma)
+Array<T> gauss_filter(float sigma)
 {
     // Using 6-sigma rule
     unsigned gauss_len = std::min((unsigned)round(sigma * 6 + 1) | 1, 31u);
 
-    T* h_gauss = new T[gauss_len];
-    gaussian1D(h_gauss, gauss_len, sigma);
-    Param<T> gauss_filter;
-    gauss_filter.dims[0] = gauss_len;
-    gauss_filter.strides[0] = 1;
-
-    for (int k = 1; k < 4; k++) {
-        gauss_filter.dims[k] = 1;
-        gauss_filter.strides[k] = gauss_filter.dims[k-1] * gauss_filter.strides[k-1];
-    }
-
-    dim_t gauss_elem = gauss_filter.strides[3] * gauss_filter.dims[3];
-    gauss_filter.ptr = memAlloc<T>(gauss_elem);
-    CUDA_CHECK(cudaMemcpyAsync(gauss_filter.ptr, h_gauss, gauss_elem * sizeof(T),
-                cudaMemcpyHostToDevice, cuda::getActiveStream()));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-    delete[] h_gauss;
+    std::vector<T> h_gauss(gauss_len);
+    gaussian1D(h_gauss.data(), gauss_len, sigma);
 
+    Array<T> gauss_filter = createHostDataArray(dim4(gauss_len), h_gauss.data());
     return gauss_filter;
 }
 
@@ -1104,32 +1089,20 @@ __global__ void computeGLOHDescriptor(
 #undef IPTR
 
 template<typename T, typename convAccT>
-Param<T> createInitialImage(
+Array<T> createInitialImage(
     CParam<T> img,
     const float init_sigma,
     const bool double_input)
 {
-    Param<T> init_img, init_tmp;
-    init_img.dims[0] = init_tmp.dims[0] = (double_input) ? img.dims[0] * 2 : img.dims[0];
-    init_img.dims[1] = init_tmp.dims[1] = (double_input) ? img.dims[1] * 2 : img.dims[1];
-    init_img.strides[0] = init_tmp.strides[0] = 1;
-    init_img.strides[1] = init_tmp.strides[1] = init_img.dims[0];
-
-    for (int k = 2; k < 4; k++) {
-        init_img.dims[k] = 1;
-        init_img.strides[k] = init_img.dims[k-1] * init_img.strides[k-1];
-        init_tmp.dims[k] = 1;
-        init_tmp.strides[k] = init_tmp.dims[k-1] * init_tmp.strides[k-1];
-    }
-
-    dim_t init_img_el = init_img.strides[3] * init_img.dims[3];
-    init_img.ptr = memAlloc<T>(init_img_el);
-    init_tmp.ptr = memAlloc<T>(init_img_el);
+    dim4 dims((double_input) ? img.dims[0] * 2 : img.dims[0],
+              (double_input) ? img.dims[1] * 2 : img.dims[1]);
+    Array<T> init_img = createEmptyArray<T>(dims);
+    Array<T> init_tmp = createEmptyArray<T>(dims);
 
     float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma - INIT_SIGMA * INIT_SIGMA * 4), 0.1f)
                              : std::max((float)sqrt(init_sigma * init_sigma - INIT_SIGMA * INIT_SIGMA), 0.1f);
 
-    Param<convAccT> filter = gauss_filter<convAccT>(s);
+    Array<convAccT> filter = gauss_filter<convAccT>(s);
 
     if (double_input) {
         resize<T, AF_INTERP_BILINEAR>(init_img, img);
@@ -1140,14 +1113,11 @@ Param<T> createInitialImage(
 
     convolve2<T, convAccT, 1, false>(init_img, CParam<T>(init_tmp), filter);
 
-    memFree(init_tmp.ptr);
-    memFree(filter.ptr);
-
     return init_img;
 }
 
 template<typename T, typename convAccT>
-std::vector< Param<T> > buildGaussPyr(
+std::vector< Array<T> > buildGaussPyr(
     Param<T> init_img,
     const unsigned n_octaves,
     const unsigned n_layers,
@@ -1165,111 +1135,66 @@ std::vector< Param<T> > buildGaussPyr(
     }
 
     // Gaussian Pyramid
-    std::vector<Param<T> > gauss_pyr(n_octaves);
-    std::vector<Param<T> > tmp_pyr(n_octaves * (n_layers+3));
+    std::vector<Array<T>> gauss_pyr;
+    std::vector<Array<T>> tmp_pyr;
+    gauss_pyr.reserve(n_octaves);
+    tmp_pyr.reserve(n_octaves * (n_layers+3));
     for (unsigned o = 0; o < n_octaves; o++) {
-        gauss_pyr[o].dims[0] = (o == 0) ? init_img.dims[0] : gauss_pyr[o-1].dims[0] / 2;
-        gauss_pyr[o].dims[1] = (o == 0) ? init_img.dims[1] : gauss_pyr[o-1].dims[1] / 2;
-        gauss_pyr[o].dims[2] = n_layers+3;
-        gauss_pyr[o].dims[3] = 1;
-
-        gauss_pyr[o].strides[0] = 1;
-        gauss_pyr[o].strides[1] = gauss_pyr[o].dims[0] * gauss_pyr[o].strides[0];
-        gauss_pyr[o].strides[2] = gauss_pyr[o].dims[1] * gauss_pyr[o].strides[1];
-        gauss_pyr[o].strides[3] = gauss_pyr[o].dims[2] * gauss_pyr[o].strides[2];
-
-        const unsigned nel = gauss_pyr[o].dims[3] * gauss_pyr[o].strides[3];
-        gauss_pyr[o].ptr = memAlloc<T>(nel);
+        gauss_pyr.push_back(createEmptyArray<T>({(o == 0) ? init_img.dims[0] : gauss_pyr[o-1].dims()[0] / 2,
+                                                (o == 0) ? init_img.dims[1] : gauss_pyr[o-1].dims()[1] / 2,
+                                                n_layers+3}));
 
         for (unsigned l = 0; l < n_layers+3; l++) {
             unsigned src_idx = (l == 0) ? (o-1)*(n_layers+3) + n_layers : o*(n_layers+3) + l-1;
             unsigned idx = o*(n_layers+3) + l;
 
             if (o == 0 && l == 0) {
-                for (int k = 0; k < 4; k++) {
-                    tmp_pyr[idx].dims[k] = init_img.dims[k];
-                    tmp_pyr[idx].strides[k] = init_img.strides[k];
-                }
-                tmp_pyr[idx].ptr = init_img.ptr;
+                tmp_pyr.push_back(createParamArray(init_img, false));
             }
             else if (l == 0) {
-                tmp_pyr[idx].dims[0] = tmp_pyr[src_idx].dims[0] / 2;
-                tmp_pyr[idx].dims[1] = tmp_pyr[src_idx].dims[1] / 2;
-                tmp_pyr[idx].strides[0] = 1;
-                tmp_pyr[idx].strides[1] = tmp_pyr[idx].dims[0];
-
-                for (int k = 2; k < 4; k++) {
-                    tmp_pyr[idx].dims[k] = 1;
-                    tmp_pyr[idx].strides[k] = tmp_pyr[idx].dims[k-1] * tmp_pyr[idx].strides[k-1];
-                }
-
-                dim_t lvl_el = tmp_pyr[idx].strides[3] * tmp_pyr[idx].dims[3];
-                tmp_pyr[idx].ptr = memAlloc<T>(lvl_el);
-
+                tmp_pyr.push_back(createEmptyArray<T>({ tmp_pyr[src_idx].dims()[0] / 2,
+                                                        tmp_pyr[src_idx].dims()[1] / 2}));
                 resize<T, AF_INTERP_BILINEAR>(tmp_pyr[idx], tmp_pyr[src_idx]);
             }
             else {
-                for (int k = 0; k < 4; k++) {
-                    tmp_pyr[idx].dims[k] = tmp_pyr[src_idx].dims[k];
-                    tmp_pyr[idx].strides[k] = tmp_pyr[src_idx].strides[k];
-                }
-                dim_t lvl_el = tmp_pyr[idx].strides[3] * tmp_pyr[idx].dims[3];
-                tmp_pyr[idx].ptr = memAlloc<T>(lvl_el);
-
-                Param<T> tmp;
-                for (int k = 0; k < 4; k++) {
-                    tmp.dims[k] = tmp_pyr[idx].dims[k];
-                    tmp.strides[k] = tmp_pyr[idx].strides[k];
-                }
-                tmp.ptr = memAlloc<T>(lvl_el);
-
-                Param<convAccT> filter = gauss_filter<convAccT>(sig_layers[l]);
-
-
+                tmp_pyr.push_back(createEmptyArray<T>(tmp_pyr[src_idx].dims()));
+                Array<T> tmp = createEmptyArray<T>(tmp_pyr[src_idx].dims());
+                Array<convAccT> filter = gauss_filter<convAccT>(sig_layers[l]);
 
                 convolve2<T, convAccT, 0, false>(tmp, tmp_pyr[src_idx], filter);
                 convolve2<T, convAccT, 1, false>(tmp_pyr[idx], CParam<T>(tmp), filter);
 
-                memFree(tmp.ptr);
-                memFree(filter.ptr);
+                //memFree(tmp.ptr);
             }
 
-            const unsigned imel = tmp_pyr[idx].dims[3] * tmp_pyr[idx].strides[3];
+            const unsigned imel = tmp_pyr[idx].elements();
             const unsigned offset = imel * l;
 
-            CUDA_CHECK(cudaMemcpyAsync(gauss_pyr[o].ptr + offset, tmp_pyr[idx].ptr,
+            CUDA_CHECK(cudaMemcpyAsync(gauss_pyr[o].get() + offset, tmp_pyr[idx].get(),
                         imel * sizeof(T), cudaMemcpyDeviceToDevice,
                         cuda::getActiveStream()));
         }
     }
-
-    for (unsigned o = 0; o < n_octaves; o++) {
-        for (unsigned l = 0; l < n_layers+3; l++) {
-            unsigned idx = o*(n_layers+3) + l;
-            memFree(tmp_pyr[idx].ptr);
-        }
-    }
-
     return gauss_pyr;
 }
 
 template<typename T>
-std::vector< Param<T> > buildDoGPyr(
-    std::vector< Param<T> >& gauss_pyr,
+std::vector< Array<T> > buildDoGPyr(
+    std::vector< Array<T> >& gauss_pyr,
     const unsigned n_octaves,
     const unsigned n_layers)
 {
     // DoG Pyramid
-    std::vector< Param<T> > dog_pyr(n_octaves);
-    for (unsigned o = 0; o < n_octaves; o++) {
-        for (int k = 0; k < 4; k++) {
-            dog_pyr[o].dims[k] = (k == 2) ? gauss_pyr[o].dims[k]-1 : gauss_pyr[o].dims[k];
-            dog_pyr[o].strides[k] = (k == 0) ? 1 : dog_pyr[o].dims[k-1] * dog_pyr[o].strides[k-1];
-        }
+    std::vector< Array<T> > dog_pyr;
+    dog_pyr.reserve(n_octaves);
 
-        dog_pyr[o].ptr = memAlloc<T>(dog_pyr[o].dims[3] * dog_pyr[o].strides[3]);
+    for (unsigned o = 0; o < n_octaves; o++) {
+        dog_pyr.push_back(createEmptyArray<T>({  gauss_pyr[o].dims()[0],
+                gauss_pyr[o].dims()[1],
+                gauss_pyr[o].dims()[2]-1,
+                gauss_pyr[o].dims()[3]}));
 
-        const unsigned nel = dog_pyr[o].dims[1] * dog_pyr[o].strides[1];
+        const unsigned nel = dog_pyr[o].dims()[1] * dog_pyr[o].strides()[1];
         const unsigned dog_layers = n_layers+2;
 
         dim3 threads(SIFT_THREADS);
@@ -1329,19 +1254,19 @@ void sift(unsigned* out_feat,
 
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
-    Param<T> init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
+    Array<T> init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
 
-    std::vector< Param<T> > gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+    std::vector< Array<T> > gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-    std::vector< Param<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
+    std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
 
-    std::vector<float*> d_x_pyr(n_octaves, NULL);
-    std::vector<float*> d_y_pyr(n_octaves, NULL);
-    std::vector<float*> d_response_pyr(n_octaves, NULL);
-    std::vector<float*> d_size_pyr(n_octaves, NULL);
-    std::vector<float*> d_ori_pyr(n_octaves, NULL);
-    std::vector<float*> d_desc_pyr(n_octaves, NULL);
-    std::vector<unsigned> feat_pyr(n_octaves, 0);
+    std::vector<uptr<float>> d_x_pyr(n_octaves);
+    std::vector<uptr<float>> d_y_pyr(n_octaves);
+    std::vector<uptr<float>> d_response_pyr(n_octaves);
+    std::vector<uptr<float>> d_size_pyr(n_octaves);
+    std::vector<uptr<float>> d_ori_pyr(n_octaves);
+    std::vector<uptr<float>> d_desc_pyr(n_octaves);
+    std::vector<unsigned> feat_pyr(n_octaves);
     unsigned total_feat = 0;
 
     const unsigned d = DESCR_WIDTH;
@@ -1351,24 +1276,24 @@ void sift(unsigned* out_feat,
     const unsigned hb = GLOHHistBins;
     const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
 
-    unsigned* d_count = memAlloc<unsigned>(1);
+    uptr<unsigned> d_count = memAlloc<unsigned>(1);
     for (unsigned i = 0; i < n_octaves; i++) {
-        if (dog_pyr[i].dims[0]-2*IMG_BORDER < 1 ||
-            dog_pyr[i].dims[1]-2*IMG_BORDER < 1)
+        if (dog_pyr[i].dims()[0]-2*IMG_BORDER < 1 ||
+            dog_pyr[i].dims()[1]-2*IMG_BORDER < 1)
             continue;
 
-        const unsigned imel = dog_pyr[i].dims[0] * dog_pyr[i].dims[1];
+        const unsigned imel = dog_pyr[i].dims()[0] * dog_pyr[i].dims()[1];
         const unsigned max_feat = ceil(imel * feature_ratio);
 
-        CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
+        CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
                                    cuda::getActiveStream()));
 
-        float* d_extrema_x = memAlloc<float>(max_feat);
-        float* d_extrema_y = memAlloc<float>(max_feat);
-        unsigned* d_extrema_layer = memAlloc<unsigned>(max_feat);
+        uptr<float>    d_extrema_x = memAlloc<float>(max_feat);
+        uptr<float>    d_extrema_y = memAlloc<float>(max_feat);
+        uptr<unsigned> d_extrema_layer = memAlloc<unsigned>(max_feat);
 
-        int dim0 = dog_pyr[i].dims[0];
-        int dim1 = dog_pyr[i].dims[1];
+        int dim0 = dog_pyr[i].dims()[0];
+        int dim1 = dog_pyr[i].dims()[1];
 
         dim3 threads(SIFT_THREADS_X, SIFT_THREADS_Y);
         dim3 blocks(divup(dim0-2*IMG_BORDER, threads.x), divup(dim1-2*IMG_BORDER, threads.y));
@@ -1376,73 +1301,54 @@ void sift(unsigned* out_feat,
         float extrema_thr = 0.5f * contrast_thr / n_layers;
         const size_t extrema_shared_size = (threads.x+2) * (threads.y+2) * 3 * sizeof(float);
         CUDA_LAUNCH_SMEM((detectExtrema<T>), blocks, threads, extrema_shared_size,
-                         d_extrema_x, d_extrema_y, d_extrema_layer, d_count,
-                         CParam<T>(dog_pyr[i]), max_feat, extrema_thr);
+                          d_extrema_x.get(), d_extrema_y.get(), d_extrema_layer.get(), d_count.get(),
+                          dog_pyr[i], max_feat, extrema_thr);
         POST_LAUNCH_CHECK();
 
         unsigned extrema_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&extrema_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
+        CUDA_CHECK(cudaMemcpyAsync(&extrema_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
                     cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         extrema_feat = min(extrema_feat, max_feat);
 
-        if (extrema_feat == 0) {
-            memFree(d_extrema_x);
-            memFree(d_extrema_y);
-            memFree(d_extrema_layer);
-
-            continue;
-        }
-
-        CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
-                                   cuda::getActiveStream()));
+        if (extrema_feat == 0) { continue; }
 
-        unsigned interp_feat = 0;
+        CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
+                                  cuda::getActiveStream()));
 
-        float* d_interp_x = memAlloc<float>(extrema_feat);
-        float* d_interp_y = memAlloc<float>(extrema_feat);
-        unsigned* d_interp_layer = memAlloc<unsigned>(extrema_feat);
-        float* d_interp_response = memAlloc<float>(extrema_feat);
-        float* d_interp_size = memAlloc<float>(extrema_feat);
+        auto d_interp_x = memAlloc<float>(extrema_feat);
+        auto d_interp_y = memAlloc<float>(extrema_feat);
+        auto d_interp_layer = memAlloc<unsigned>(extrema_feat);
+        auto d_interp_response = memAlloc<float>(extrema_feat);
+        auto d_interp_size = memAlloc<float>(extrema_feat);
 
         threads = dim3(SIFT_THREADS, 1);
         blocks = dim3(divup(extrema_feat, threads.x), 1);
 
         CUDA_LAUNCH((interpolateExtrema<T>), blocks, threads,
-                    d_interp_x, d_interp_y, d_interp_layer,
-                    d_interp_response, d_interp_size, d_count,
-                    d_extrema_x, d_extrema_y, d_extrema_layer, extrema_feat,
+                    d_interp_x.get(), d_interp_y.get(), d_interp_layer.get(),
+                    d_interp_response.get(), d_interp_size.get(), d_count.get(),
+                    d_extrema_x.get(), d_extrema_y.get(), d_extrema_layer.get(), extrema_feat,
                     dog_pyr[i], max_feat, i, n_layers,
                     contrast_thr, edge_thr, init_sigma, img_scale);
         POST_LAUNCH_CHECK();
 
-        memFree(d_extrema_x);
-        memFree(d_extrema_y);
-        memFree(d_extrema_layer);
-
-        CUDA_CHECK(cudaMemcpyAsync(&interp_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
+        unsigned interp_feat = 0;
+        CUDA_CHECK(cudaMemcpyAsync(&interp_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
                     cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         interp_feat = min(interp_feat, max_feat);
 
-        CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
+        CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
                                    cuda::getActiveStream()));
 
-        if (interp_feat == 0) {
-            memFree(d_interp_x);
-            memFree(d_interp_y);
-            memFree(d_interp_layer);
-            memFree(d_interp_response);
-            memFree(d_interp_size);
-
-            continue;
-        }
+        if (interp_feat == 0) {continue;}
 
-        thrust::device_ptr<float> interp_x_ptr = thrust::device_pointer_cast(d_interp_x);
-        thrust::device_ptr<float> interp_y_ptr = thrust::device_pointer_cast(d_interp_y);
-        thrust::device_ptr<unsigned> interp_layer_ptr = thrust::device_pointer_cast(d_interp_layer);
-        thrust::device_ptr<float> interp_response_ptr = thrust::device_pointer_cast(d_interp_response);
-        thrust::device_ptr<float> interp_size_ptr = thrust::device_pointer_cast(d_interp_size);
+        thrust::device_ptr<float> interp_x_ptr = thrust::device_pointer_cast(d_interp_x.get());
+        thrust::device_ptr<float> interp_y_ptr = thrust::device_pointer_cast(d_interp_y.get());
+        thrust::device_ptr<unsigned> interp_layer_ptr = thrust::device_pointer_cast(d_interp_layer.get());
+        thrust::device_ptr<float> interp_response_ptr = thrust::device_pointer_cast(d_interp_response.get());
+        thrust::device_ptr<float> interp_size_ptr = thrust::device_pointer_cast(d_interp_size.get());
 
         cuda::ThrustVector<int> permutation(interp_feat);
         thrust::sequence(permutation.begin(), permutation.end());
@@ -1459,80 +1365,59 @@ void sift(unsigned* out_feat,
         apply_permutation<float>(interp_y_ptr, permutation);
         apply_permutation<float>(interp_x_ptr, permutation);
 
-        float* d_nodup_x = memAlloc<float>(interp_feat);
-        float* d_nodup_y = memAlloc<float>(interp_feat);
-        unsigned* d_nodup_layer = memAlloc<unsigned>(interp_feat);
-        float* d_nodup_response = memAlloc<float>(interp_feat);
-        float* d_nodup_size = memAlloc<float>(interp_feat);
+        auto d_nodup_x = memAlloc<float>(interp_feat);
+        auto d_nodup_y = memAlloc<float>(interp_feat);
+        auto d_nodup_layer = memAlloc<unsigned>(interp_feat);
+        auto d_nodup_response = memAlloc<float>(interp_feat);
+        auto d_nodup_size = memAlloc<float>(interp_feat);
 
         threads = dim3(SIFT_THREADS, 1);
         blocks = dim3(divup(interp_feat, threads.x), 1);
 
         CUDA_LAUNCH((removeDuplicates), blocks, threads,
-                    d_nodup_x, d_nodup_y, d_nodup_layer,
-                    d_nodup_response, d_nodup_size, d_count,
-                    d_interp_x, d_interp_y, d_interp_layer,
-                    d_interp_response, d_interp_size, interp_feat);
+                    d_nodup_x.get(), d_nodup_y.get(), d_nodup_layer.get(),
+                    d_nodup_response.get(), d_nodup_size.get(), d_count.get(),
+                    d_interp_x.get(), d_interp_y.get(), d_interp_layer.get(),
+                    d_interp_response.get(), d_interp_size.get(), interp_feat);
         POST_LAUNCH_CHECK();
 
-        memFree(d_interp_x);
-        memFree(d_interp_y);
-        memFree(d_interp_layer);
-        memFree(d_interp_response);
-        memFree(d_interp_size);
-
         unsigned nodup_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&nodup_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
+        CUDA_CHECK(cudaMemcpyAsync(&nodup_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
                     cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemsetAsync(d_count, 0, sizeof(unsigned),
+        CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
                                    cuda::getActiveStream()));
 
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        float* d_oriented_x = memAlloc<float>(max_oriented_feat);
-        float* d_oriented_y = memAlloc<float>(max_oriented_feat);
-        unsigned* d_oriented_layer = memAlloc<unsigned>(max_oriented_feat);
-        float* d_oriented_response = memAlloc<float>(max_oriented_feat);
-        float* d_oriented_size = memAlloc<float>(max_oriented_feat);
-        float* d_oriented_ori = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_x = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_y = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_layer = memAlloc<unsigned>(max_oriented_feat);
+        auto d_oriented_response = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_size = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_ori = memAlloc<float>(max_oriented_feat);
 
         threads = dim3(SIFT_THREADS_X, SIFT_THREADS_Y);
         blocks = dim3(1, divup(nodup_feat, threads.y));
 
         const size_t ori_shared_size = ORI_HIST_BINS * threads.y * 2 * sizeof(float);
         CUDA_LAUNCH_SMEM((calcOrientation<T>), blocks, threads, ori_shared_size,
-                         d_oriented_x, d_oriented_y, d_oriented_layer,
-                         d_oriented_response, d_oriented_size, d_oriented_ori, d_count,
-                         d_nodup_x, d_nodup_y, d_nodup_layer,
-                         d_nodup_response, d_nodup_size, nodup_feat,
-                         gauss_pyr[i], max_oriented_feat, i, double_input);
+                         d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
+                         d_oriented_response.get(), d_oriented_size.get(), d_oriented_ori.get(), d_count.get(),
+                         d_nodup_x.get(), d_nodup_y.get(), d_nodup_layer.get(),
+                         d_nodup_response.get(), d_nodup_size.get(), nodup_feat,
+                         CParam<T>(gauss_pyr[i]), max_oriented_feat, i, double_input);
         POST_LAUNCH_CHECK();
 
-        memFree(d_nodup_x);
-        memFree(d_nodup_y);
-        memFree(d_nodup_layer);
-        memFree(d_nodup_response);
-        memFree(d_nodup_size);
-
         unsigned oriented_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&oriented_feat, d_count, sizeof(unsigned), cudaMemcpyDeviceToHost,
+        CUDA_CHECK(cudaMemcpyAsync(&oriented_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
                     cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         oriented_feat = min(oriented_feat, max_oriented_feat);
 
-        if (oriented_feat == 0) {
-            memFree(d_oriented_x);
-            memFree(d_oriented_y);
-            memFree(d_oriented_layer);
-            memFree(d_oriented_response);
-            memFree(d_oriented_size);
-            memFree(d_oriented_ori);
+        if (oriented_feat == 0) { continue; }
 
-            continue;
-        }
-
-        float* d_desc = memAlloc<float>(oriented_feat * desc_len);
+        auto d_desc = memAlloc<float>(oriented_feat * desc_len);
 
         float scale = 1.f/(1 << i);
         if (double_input) scale *= 2.f;
@@ -1545,74 +1430,60 @@ void sift(unsigned* out_feat,
 
         if (compute_GLOH)
             CUDA_LAUNCH_SMEM((computeGLOHDescriptor<T>), blocks, threads, shared_size,
-                             d_desc, desc_len, histsz,
-                             d_oriented_x, d_oriented_y, d_oriented_layer,
-                             d_oriented_response, d_oriented_size, d_oriented_ori,
+                             d_desc.get(), desc_len, histsz,
+                             d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
+                             d_oriented_response.get(), d_oriented_size.get(), d_oriented_ori.get(),
                              oriented_feat, gauss_pyr[i], d, rb, ab, hb,
                              scale, n_layers);
         else
             CUDA_LAUNCH_SMEM((computeDescriptor<T>), blocks, threads, shared_size,
-                             d_desc, desc_len, histsz,
-                             d_oriented_x, d_oriented_y, d_oriented_layer,
-                             d_oriented_response, d_oriented_size, d_oriented_ori,
-                             oriented_feat, gauss_pyr[i], d, n, scale, n_layers);
+                             d_desc.get(), desc_len, histsz,
+                             d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
+                             d_oriented_response.get(), d_oriented_size.get(), d_oriented_ori.get(),
+                             oriented_feat, CParam<T>(gauss_pyr[i]), d, n, scale, n_layers);
         POST_LAUNCH_CHECK();
 
         total_feat += oriented_feat;
         feat_pyr[i] = oriented_feat;
 
         if (oriented_feat > 0) {
-            d_x_pyr[i] = d_oriented_x;
-            d_y_pyr[i] = d_oriented_y;
-            d_response_pyr[i] = d_oriented_response;
-            d_ori_pyr[i] = d_oriented_ori;
-            d_size_pyr[i] = d_oriented_size;
-            d_desc_pyr[i] = d_desc;
+            d_x_pyr[i] = std::move(d_oriented_x);
+            d_y_pyr[i] = std::move(d_oriented_y);
+            d_response_pyr[i] = std::move(d_oriented_response);
+            d_ori_pyr[i] = std::move(d_oriented_ori);
+            d_size_pyr[i] = std::move(d_oriented_size);
+            d_desc_pyr[i] = std::move(d_desc);
         }
     }
 
-    memFree(d_count);
-
-    for (size_t i = 0; i < gauss_pyr.size(); i++)
-        memFree(gauss_pyr[i].ptr);
-    for (size_t i = 0; i < dog_pyr.size(); i++)
-        memFree(dog_pyr[i].ptr);
-
     // Allocate output memory
-    *d_x     = memAlloc<float>(total_feat);
-    *d_y     = memAlloc<float>(total_feat);
-    *d_score = memAlloc<float>(total_feat);
-    *d_ori   = memAlloc<float>(total_feat);
-    *d_size  = memAlloc<float>(total_feat);
-    *d_desc  = memAlloc<float>(total_feat * desc_len);
+    *d_x     = memAlloc<float>(total_feat).release();
+    *d_y     = memAlloc<float>(total_feat).release();
+    *d_score = memAlloc<float>(total_feat).release();
+    *d_ori   = memAlloc<float>(total_feat).release();
+    *d_size  = memAlloc<float>(total_feat).release();
+    *d_desc  = memAlloc<float>(total_feat * desc_len).release();
 
     unsigned offset = 0;
     for (unsigned i = 0; i < n_octaves; i++) {
         if (feat_pyr[i] == 0)
             continue;
 
-        CUDA_CHECK(cudaMemcpyAsync(*d_x+offset, d_x_pyr[i], feat_pyr[i] * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*d_x+offset, d_x_pyr[i].get(), feat_pyr[i] * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_y+offset, d_y_pyr[i], feat_pyr[i] * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*d_y+offset, d_y_pyr[i].get(), feat_pyr[i] * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_score+offset, d_response_pyr[i], feat_pyr[i] * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*d_score+offset, d_response_pyr[i].get(), feat_pyr[i] * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_ori+offset, d_ori_pyr[i], feat_pyr[i] * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*d_ori+offset, d_ori_pyr[i].get(), feat_pyr[i] * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_size+offset, d_size_pyr[i], feat_pyr[i] * sizeof(float),
+        CUDA_CHECK(cudaMemcpyAsync(*d_size+offset, d_size_pyr[i].get(), feat_pyr[i] * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
-        CUDA_CHECK(cudaMemcpyAsync(*d_desc+(offset*desc_len), d_desc_pyr[i],
+        CUDA_CHECK(cudaMemcpyAsync(*d_desc+(offset*desc_len), d_desc_pyr[i].get(),
                     feat_pyr[i] * desc_len * sizeof(float),
                     cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
-        memFree(d_x_pyr[i]);
-        memFree(d_y_pyr[i]);
-        memFree(d_response_pyr[i]);
-        memFree(d_ori_pyr[i]);
-        memFree(d_size_pyr[i]);
-        memFree(d_desc_pyr[i]);
-
         offset += feat_pyr[i];
     }
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index f06fcbf543..7a26d24ed7 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -68,8 +68,9 @@ template<typename T>
 uptr<T>
 memAlloc(const size_t &elements)
 {
-    T *ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
-    return uptr<T>(ptr, memFree<T>);
+    size_t size = elements * sizeof(T);
+    return uptr<T>(static_cast<T*>(memoryManager().alloc(size, false)),
+                   memFree<T>);
 }
 
 void* memAllocUser(const size_t &bytes)
@@ -79,7 +80,7 @@ void* memAllocUser(const size_t &bytes)
 template<typename T>
 void memFree(T *ptr)
 {
-    return memoryManager().unlock((void *)ptr, false);
+    memoryManager().unlock((void *)ptr, false);
 }
 
 void memFreeUser(void *ptr)
@@ -126,11 +127,11 @@ bool checkMemoryLimit()
     return memoryManager().checkMemoryLimit();
 }
 
-#define INSTANTIATE(T)                                                                        \
-    template std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements); \
-    template void memFree(T* ptr);                                                            \
-    template T* pinnedAlloc(const size_t &elements);                                          \
-    template void pinnedFree(T* ptr);                                                         \
+#define INSTANTIATE(T)                                 \
+    template uptr<T> memAlloc(const size_t &elements); \
+    template void memFree(T* ptr);                     \
+    template T* pinnedAlloc(const size_t &elements);   \
+    template void pinnedFree(T* ptr);
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index 51bdac67cf..8e9b3f5a01 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -1,4 +1,4 @@
-        /*******************************************************
+/*******************************************************
  * Copyright (c) 2014, ArrayFire
  * All rights reserved.
  *
@@ -33,7 +33,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
     std::vector<unsigned> feat_pyr, lvl_best;
     std::vector<float> lvl_scl;
     std::vector<float*> d_x_pyr, d_y_pyr;
-    std::vector<CParam<T> > img_pyr;
+    std::vector<Array<T>> img_pyr;
 
     fast_pyramid<T>(feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
                     image, fast_thr, max_feat, scl_fctr, levels, REF_PAT_SIZE);
@@ -47,8 +47,8 @@ unsigned orb(Array<float> &x, Array<float> &y,
     unsigned *desc_out;
 
     kernel::orb<T, convAccT>(&nfeat_out, &x_out, &y_out, &score_out, &orientation_out, &size_out,
-                             &desc_out, feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
-                             fast_thr, max_feat, scl_fctr, levels, blur_img);
+                              &desc_out, feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
+                              fast_thr, max_feat, scl_fctr, levels, blur_img);
 
     if (nfeat_out > 0) {
 
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index 508720978b..bd91e2f0ae 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -51,12 +51,12 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
         const dim4 out_dims(nfeat_out);
         const dim4 desc_dims(desc_len, nfeat_out);
 
-        x_out     = createParamArray<float>(x);
-        y_out     = createParamArray<float>(y);
-        score_out = createParamArray<float>(score);
-        ori_out   = createParamArray<float>(ori);
-        size_out  = createParamArray<float>(size);
-        desc_out  = createParamArray<float>(desc);
+        x_out     = createParamArray<float>(x, true);
+        y_out     = createParamArray<float>(y, true);
+        score_out = createParamArray<float>(score, true);
+        ori_out   = createParamArray<float>(ori, true);
+        size_out  = createParamArray<float>(size, true);
+        desc_out  = createParamArray<float>(desc, true);
     }
 
     return nfeat_out;
diff --git a/test/homography.cpp b/test/homography.cpp
index c623d9c3c4..f70ea876ff 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -160,7 +160,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     t.host(out_t);
 
     for (int elIter = 0; elIter < 8; elIter++) {
-        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.1f)
+        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.25f)
             << "at: " << elIter << std::endl;
     }
 
diff --git a/test/orb.cpp b/test/orb.cpp
index 28e56a1132..66d7fda789 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -229,14 +229,15 @@ void orbTest(string pTestFile)
     }
 }
 
-#define ORB_INIT(desc, image) \
-    TYPED_TEST(ORB, desc) \
-    {   \
-        orbTest<TypeParam>(string(TEST_DIR"/orb/"#image".test"));   \
-    }
+TYPED_TEST(ORB, Square)
+{
+  orbTest<TypeParam>(string(TEST_DIR"/orb/square.test"));
+}
 
-    ORB_INIT(square, square);
-    ORB_INIT(lena, lena);
+TYPED_TEST(ORB, Lena)
+{
+  orbTest<TypeParam>(string(TEST_DIR"/orb/lena.test"));
+}
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //

From 41811f32347a8cf2aeb9b46405ed68c2d801be23 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 1 Nov 2017 20:18:26 -0400
Subject: [PATCH 1324/2677] Add missing functional header in memory.hpp

Fixes #1983
---
 src/backend/cpu/memory.hpp    | 2 ++
 src/backend/cuda/memory.hpp   | 2 ++
 src/backend/opencl/memory.hpp | 6 ++++--
 3 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 66c880be7b..99e3d57e02 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -10,6 +10,8 @@
 
 #include <af/defines.h>
 #include <common/MemoryManager.hpp>
+
+#include <functional>
 #include <memory>
 
 namespace cpu
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 0b3fafb2e5..d33813a7aa 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -10,6 +10,8 @@
 
 #include <cstdlib>
 #include <common/MemoryManager.hpp>
+
+#include <functional>
 #include <memory>
 namespace cuda
 {
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 326717989a..a5aa11bdcd 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -8,11 +8,13 @@
  ********************************************************/
 #pragma once
 
+#include <common/MemoryManager.hpp>
+
 #include <cstdlib>
+#include <functional>
 #include <map>
-#include <vector>
-#include <common/MemoryManager.hpp>
 #include <memory>
+#include <vector>
 
 namespace cl
 {

From f332183685e7bc2702d36a99ac26bef17b7ada51 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 1 Nov 2017 20:43:18 -0400
Subject: [PATCH 1325/2677] Fix target_link_library call when compiling with
 CUDA stubs

---
 src/backend/cuda/CMakeLists.txt | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 7727836b21..7aa64a7eb6 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -480,7 +480,10 @@ else()
     message(STATUS "CUDA driver stub FOUND: ${CUDA_CUDA_STUB}")
   endif()
 
-  target_link_libraries(afcuda ${CUDA_CUDA_STUB})
+  #NOTE: Only link against the stub library when building
+  target_link_libraries(afcuda
+    PUBLIC
+      $<BUILD_INTERFACE:${CUDA_CUDA_STUB}>)
 endif()
 
 # TODO(umar): This is required for NVRTC to work correctly on OSX. It may not

From 28a057b7fad8632951ecbf13dad84bd0044888ac Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 2 Nov 2017 00:07:09 -0400
Subject: [PATCH 1326/2677] Link afcpu and afopencl with pthreads

---
 src/backend/cpu/CMakeLists.txt    | 1 +
 src/backend/opencl/CMakeLists.txt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 86f10dfb1d..cbe4db44f1 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -322,6 +322,7 @@ target_link_libraries(afcpu
     ${CBLAS_LIBRARIES}
     FFTW::FFTW
     FFTW::FFTWF
+    Threads::Threads
   )
 
 install(TARGETS afcpu
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 38b58ef186..a40a96d0b9 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -399,6 +399,7 @@ target_link_libraries(afopencl
     opencl_scan_by_key
     opencl_sort_by_key
     Boost::boost
+    Threads::Threads
     )
 
 if(OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")

From 360b0d69a5726af0803e581ef32398fef691996a Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Mon, 13 Nov 2017 23:45:19 -0800
Subject: [PATCH 1327/2677] PERF, OpenCL: Fixes the launch config for approx1

---
 src/backend/opencl/kernel/approx.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 307bb7e628..f1bec8a1c0 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -104,7 +104,7 @@ void approx1(Param out, const Param in, const Param xpos, const float offGrid,
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(out.info.dims[0], local[0]);
     NDRange global(blocksPerMat * local[0] * out.info.dims[1],
-                   out.info.dims[2] * out.info.dims[3] * local[0], 1);
+                   out.info.dims[2] * out.info.dims[3] * local[1], 1);
 
     // Passing bools to opencl kernels is not allowed
     bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);

From 9d7cf5f3b06e5b13617aeb47d4f2f9126b763b96 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Nov 2017 01:44:00 -0500
Subject: [PATCH 1328/2677] Improve meanshift filter performance on CPU

Improved the meanshift filter performance on the CPU backend by replacing
vectors with std::arrays and moving them out of the for loops. Also
reduced a few conversion operations.
---
 src/backend/cpu/kernel/meanshift.hpp | 27 ++++++++++++---------------
 test/meanshift.cpp                   |  6 +++---
 2 files changed, 15 insertions(+), 18 deletions(-)

diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 467948dd67..7847136b20 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -31,6 +31,10 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
     const dim_t radius      = std::max((int)(spatialSigma * 1.5f), 1);
     const AccType cvar      = chromaticSigma * chromaticSigma;
 
+
+    std::array<AccType, 4> currentCenterColors{0};
+    std::array<AccType, 4> currentMeanColors{0};
+    std::array<AccType, 4> tempColors{0};
     for (dim_t b3=0; b3<dims[3]; ++b3) {
         for (unsigned b2=0; b2<bCount; ++b2) {
 
@@ -47,10 +51,8 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
                     dim_t i_in_off  = i*istrides[0];
                     dim_t i_out_off = i*ostrides[0];
 
-                    std::vector<T> currentCenterColors(channels, 0);
-
                     for (unsigned ch=0; ch<channels; ++ch)
-                        currentCenterColors[ch] = inData[j_in_off + i_in_off + ch*istrides[2]];
+                        currentCenterColors[ch] = static_cast<AccType>(inData[j_in_off + i_in_off + ch*istrides[2]]);
 
                     int meanPosJ = j;
                     int meanPosI = i;
@@ -64,8 +66,7 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
                         int shift_y = 0;
                         int shift_x = 0;
 
-                        std::vector<AccType> currentMeanColors(channels, 0);
-
+                        currentMeanColors.fill(0);
                         // Windowing operation
                         for (dim_t wj=-radius; wj<=radius; ++wj) {
 
@@ -82,19 +83,15 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
 
                                 dim_t tistride = ti*istrides[0];
 
-                                std::vector<T> tempColors(channels, 0);
-
                                 AccType norm = 0;
                                 for (unsigned ch=0; ch<channels; ++ch) {
-                                    tempColors[ch] = inData[ tistride + tjstride + ch*istrides[2] ];
-                                    AccType diff = static_cast<AccType>(currentCenterColors[ch]) -
-                                                   static_cast<AccType>(tempColors[ch]);
+                                    tempColors[ch] = static_cast<AccType>(inData[ tistride + tjstride + ch*istrides[2] ]);
+                                    AccType diff = currentCenterColors[ch] - tempColors[ch];
                                     norm += (diff * diff);
                                 }
-
                                 if (norm <= cvar) {
                                     for(unsigned ch=0; ch<channels; ++ch)
-                                        currentMeanColors[ch] += static_cast<AccType>(tempColors[ch]);
+                                        currentMeanColors[ch] += tempColors[ch];
 
                                     shift_x += ti;
                                     ++hit_count;
@@ -116,7 +113,7 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
 
                         AccType norm = 0;
                         for (unsigned ch=0; ch<channels; ++ch) {
-                            AccType diff = currentMeanColors[ch] - static_cast<AccType>(currentCenterColors[ch]);
+                            AccType diff = currentMeanColors[ch] - currentCenterColors[ch];
                             norm += (diff*diff);
                         }
 
@@ -125,13 +122,13 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
                                     ((abs(oldMeanPosJ-meanPosJ) + abs(oldMeanPosI-meanPosI) + norm) <= 1);
 
                         for (unsigned ch=0; ch<channels; ++ch)
-                            currentCenterColors[ch] = static_cast<T>(currentMeanColors[ch]);
+                            currentCenterColors[ch] = currentMeanColors[ch];
 
                         if (stop) break;
                     } // scope of meanshift iterations end
 
                     for (dim_t ch=0; ch<channels; ++ch)
-                        outData[j_out_off + i_out_off + ch*ostrides[2]] = currentCenterColors[ch];
+                        outData[j_out_off + i_out_off + ch*ostrides[2]] = static_cast<T>(currentCenterColors[ch]);
                 }
             }
         }
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 16c521c8f8..e31e0031eb 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -119,7 +119,6 @@ void meanshiftTest(string pTestFile)
 IMAGE_TESTS(float )
 IMAGE_TESTS(double)
 
-
 //////////////////////////////////////// CPP ///////////////////////////////
 //
 TEST(Meanshift, Color_CPP)
@@ -155,7 +154,7 @@ TEST(Meanshift, Color_CPP)
     }
 }
 
-TEST(meanshift, GFOR)
+TEST(Meanshift, GFOR)
 {
     using namespace af;
 
@@ -170,6 +169,7 @@ TEST(meanshift, GFOR)
     for(int ii = 0; ii < 3; ii++) {
         array c_ii = meanShift(A(span, span, ii), 3, 5, 3);
         array b_ii = B(span, span, ii);
-        ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
+
+        ASSERT_LT(max<double>(abs(c_ii - b_ii)), 1E-5);
     }
 }

From e40816319d8da5a55b65c1507427f42cc25a626e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 29 Nov 2017 01:45:37 -0500
Subject: [PATCH 1329/2677] Reduced the spatialSigma value to improve meanshift
 test runtimes

---
 test/data            |  2 +-
 test/meanshift.cpp   | 22 +++++++++++-----------
 test/testHelpers.hpp |  1 -
 3 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/test/data b/test/data
index 1c2e8f446a..745b967d90 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 1c2e8f446a63e1802a9416fe06d07265052e8c13
+Subproject commit 745b967d90fe02b583c9de1ac800f3bd2a2a03bc
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index e31e0031eb..ae743699ec 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -49,7 +49,7 @@ TYPED_TEST(Meanshift, InvalidArgs)
 }
 
 template<typename T, bool isColor>
-void meanshiftTest(string pTestFile)
+void meanshiftTest(string pTestFile, const float ss)
 {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
@@ -82,7 +82,7 @@ void meanshiftTest(string pTestFile)
         ASSERT_EQ(AF_SUCCESS, conv_image<T>(&goldArray, goldArray_f32)); // af_load_image always returns float array
         ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, 11.5f, 30.f, 5, isColor));
+        ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, ss, 30.f, 5, isColor));
 
         std::vector<T> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
@@ -106,14 +106,14 @@ void meanshiftTest(string pTestFile)
 //       Note: compareArraysRMSD is handling upcasting while working
 //       with two different type of types
 //
-#define IMAGE_TESTS(T)                                                      \
-    TEST(Meanshift, Grayscale_##T)                                          \
-    {                                                                       \
-        meanshiftTest<T, false>(string(TEST_DIR"/meanshift/gray.test"));    \
-    }                                                                       \
-    TEST(Meanshift, Color_##T)                                              \
-    {                                                                       \
-        meanshiftTest<T, true>(string(TEST_DIR"/meanshift/color.test"));    \
+#define IMAGE_TESTS(T)                                                              \
+    TEST(Meanshift, Grayscale_##T)                                                  \
+    {                                                                               \
+        meanshiftTest<T, false>(string(TEST_DIR"/meanshift/gray.test"), 6.67f);     \
+    }                                                                               \
+    TEST(Meanshift, Color_##T)                                                      \
+    {                                                                               \
+        meanshiftTest<T, true>(string(TEST_DIR"/meanshift/color.test"), 3.5f);      \
     }
 
 IMAGE_TESTS(float )
@@ -142,7 +142,7 @@ TEST(Meanshift, Color_CPP)
         af::array img   = af::loadImage(inFiles[testId].c_str(), true);
         af::array gold  = af::loadImage(outFiles[testId].c_str(), true);
         dim_t nElems = gold.elements();
-        af::array output= af::meanShift(img, 11.5f, 30.f, 5, true);
+        af::array output= af::meanShift(img, 3.5f, 30.f, 5, true);
 
         std::vector<float> outData(nElems);
         output.host((void*)outData.data());
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 5d9a848bd3..c77eb516c8 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -323,7 +323,6 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
     accum /= data_size;
     double NRMSD = std::sqrt(accum)/(maxion-minion);
 
-    std::cout<<"NRMSD = "<<NRMSD<<std::endl;
     if (std::isnan(NRMSD) || NRMSD > tolerance)
         return false;
 

From 93cd4be83b880bc620ab39612b62da08a2895638 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sat, 30 Sep 2017 18:10:34 +0200
Subject: [PATCH 1330/2677] Updated CLBlast to 1.1.0, CLBlast's herk only
 called for cfloat/cdouble

---
 CMakeModules/build_CLBlast.cmake              |  2 +-
 src/backend/opencl/err_clblast.hpp            |  4 ++
 src/backend/opencl/magma/magma_blas_clblast.h | 54 +++++++++++++++++--
 3 files changed, 56 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index d2ec189d78..351664c07a 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -13,7 +13,7 @@ set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 48133a0cd1a7b61b87906ec1f4608e766e20a973
+    GIT_TAG 1.1.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
index 8997e0c4b3..577be84f7f 100644
--- a/src/backend/opencl/err_clblast.hpp
+++ b/src/backend/opencl/err_clblast.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <mutex>
 #include <stdio.h>
 #include <common/err_common.hpp>
 #include <clblast.h>
@@ -68,6 +69,9 @@ static const char * _clblastGetResultString(clblast::StatusCode st)
     case clblast::StatusCode::kInsufficientMemoryY:        return "Vector Y's OpenCL buffer is too small";
 
     // Custom additional status codes for CLBlast
+    case clblast::StatusCode::kInvalidBatchCount:          return "The batch count needs to be positive";
+    case clblast::StatusCode::kInvalidOverrideKernel:      return "Trying to override parameters for an invalid kernel";
+    case clblast::StatusCode::kMissingOverrideParameter:   return "Missing override parameter(s) for the target kernel";
     case clblast::StatusCode::kInvalidLocalMemUsage:       return "Not enough local memory available on this device";
     case clblast::StatusCode::kNoHalfPrecision:            return "Half precision (16-bits) not supported by the device";
     case clblast::StatusCode::kNoDoublePrecision:          return "Double precision (64-bits) not supported by the device";
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 55caf705f3..992b4bbdc2 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -59,6 +59,11 @@ template <> double inline toCLBlastConstant(const double val) { return val; }
 template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
 template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
 
+// Conversions to CLBlast basic types
+template <typename T> struct CLBlastBasicType { using Type = T; };
+template <> struct CLBlastBasicType<cfloat> { using Type = float; };
+template <> struct CLBlastBasicType<cdouble> { using Type = double; };
+
 // Initialization of the OpenCL BLAS library
 // Only meant to be once and from constructor
 // of DeviceManager singleton
@@ -179,11 +184,12 @@ struct gpu_blas_trsv_func
 template <typename T>
 struct gpu_blas_herk_func
 {
-    template <typename U>
+    using BasicType = typename CLBlastBasicType<T>::Type;
+
     clblast::StatusCode operator() (
         const clblast::Triangle triangle, const clblast::Transpose a_transpose,
-        const size_t n, const size_t k, const U alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const U beta,
+        const size_t n, const size_t k, const BasicType alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const BasicType beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
         cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
@@ -197,6 +203,48 @@ struct gpu_blas_herk_func
     }
 };
 
+// Run syrk when calling non-complex herk function (specialisation of the above for 'float')
+template <>
+struct gpu_blas_herk_func<float>
+{
+    clblast::StatusCode operator() (
+        const clblast::Triangle triangle, const clblast::Transpose a_transpose,
+        const size_t n, const size_t k, const float alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const float beta,
+        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        const auto beta_clblast = toCLBlastConstant(beta);
+        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
+                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
+                             queues, events);
+    }
+};
+
+// Run syrk when calling non-complex herk function (specialisation of the above for 'double')
+template <>
+struct gpu_blas_herk_func<double>
+{
+    clblast::StatusCode operator() (
+        const clblast::Triangle triangle, const clblast::Transpose a_transpose,
+        const size_t n, const size_t k, const double alpha,
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const double beta,
+        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+    {
+        assert(num_queues == 1);
+        assert(num_wait_events == 0);
+        const auto alpha_clblast = toCLBlastConstant(alpha);
+        const auto beta_clblast = toCLBlastConstant(beta);
+        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
+                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
+                             queues, events);
+    }
+};
+
 template <typename T>
 struct gpu_blas_syrk_func
 {

From e22000126179c074cd7f2cac566aabe45b75e108 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Sun, 29 Oct 2017 17:23:52 +0100
Subject: [PATCH 1331/2677] Updated CLBlast to 1.2.0 which includes the latest
 TRSM/TRSV bugfixes

---
 CMakeModules/build_CLBlast.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 351664c07a..59578125df 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -13,7 +13,7 @@ set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 1.1.0
+    GIT_TAG 1.2.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From d0e9408336caa24278ecc2f6762e395c78cc27c8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 Dec 2017 09:00:31 -0500
Subject: [PATCH 1332/2677] BUGFIX: ireduce with single value in reduced
 dimension

The index array in the ireduce dimension was not assigned values
when the reduced dimension size was one. This commit addresses
that and unifies the naming convention of the IReduced test cases
---
 src/api/c/reduce.cpp |  5 +++--
 test/ireduce.cpp     | 35 ++++++++++++++++++++++++++++-------
 2 files changed, 31 insertions(+), 9 deletions(-)

diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 4251d33f9b..9287ef09dc 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -395,14 +395,15 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in, co
 {
     try {
 
-        ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(3, dim >= 0);
+        ARG_ASSERT(3, dim <  4);
 
         const ArrayInfo& in_info = getInfo(in);
         ARG_ASSERT(2, in_info.ndims() > 0);
 
         if (dim >= (int)in_info.ndims()) {
             *val = retain(in);
+            *idx = createHandleFromValue<uint>(in_info.dims(), 0);
             return AF_SUCCESS;
         }
 
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 26731d6654..990b94b8cd 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -16,9 +16,8 @@
 
 using namespace af;
 
-
 #define MINMAXOP(fn, ty)                                \
-    TEST(IndexedMinMaxTests, Test_##fn##_##ty##_0)      \
+    TEST(IndexedReduce, fn##_##ty##_0)                  \
     {                                                   \
         if (noDoubleTests<ty>()) return;                \
         dtype dty = (dtype)dtype_traits<ty>::af_type;   \
@@ -44,7 +43,7 @@ using namespace af;
         af_free_host(h_val);                            \
         af_free_host(h_idx);                            \
     }                                                   \
-    TEST(IndexedMinMaxTests, Test_##fn##_##ty##_1)      \
+    TEST(IndexedReduce, fn##_##ty##_1)                  \
     {                                                   \
         if (noDoubleTests<ty>()) return;                \
         dtype dty = (dtype)dtype_traits<ty>::af_type;   \
@@ -69,7 +68,7 @@ using namespace af;
         af_free_host(h_val);                            \
         af_free_host(h_idx);                            \
     }                                                   \
-    TEST(IndexedMinMaxTests, Test_##fn##_##ty##_all)    \
+    TEST(IndexedReduce, fn##_##ty##_all)                \
     {                                                   \
         if (noDoubleTests<ty>()) return;                \
         dtype dty = (dtype)dtype_traits<ty>::af_type;   \
@@ -99,7 +98,7 @@ MINMAXOP(max, uint)
 MINMAXOP(max, char)
 MINMAXOP(max, uchar)
 
-TEST(ImaxAll, IndexedSmall)
+TEST(IndexedReduce, MaxIndexedSmall)
 {
     const int num = 1000;
     const int st = 10;
@@ -121,7 +120,7 @@ TEST(ImaxAll, IndexedSmall)
     ASSERT_EQ(b, res);
 }
 
-TEST(ImaxAll, IndexedBig)
+TEST(IndexedReduce, MaxIndexedBig)
 {
     const int num = 100000;
     const int st = 1000;
@@ -143,7 +142,7 @@ TEST(ImaxAll, IndexedBig)
     ASSERT_EQ(b, res);
 }
 
-TEST(IReduce, BUG_FIX_1005)
+TEST(IndexedReduce, BUG_FIX_1005)
 {
     const int m = 64;
     const int n = 100;
@@ -164,3 +163,25 @@ TEST(IReduce, BUG_FIX_1005)
         ASSERT_EQ(idx0, idx1);
     }
 }
+
+TEST(IndexedReduce, MinReduceDimensionHasSingleValue)
+{
+    array data = randu(10, 10, 1);
+
+    array mm, indx;
+    min(mm, indx, data, 2);
+
+    ASSERT_TRUE(allTrue<bool>(mm == data));
+    ASSERT_TRUE(allTrue<bool>(indx == 0));
+}
+
+TEST(IndexedReduce, MaxReduceDimensionHasSingleValue)
+{
+    array data = randu(10, 10, 1);
+
+    array mm, indx;
+    max(mm, indx, data, 2);
+
+    ASSERT_TRUE(allTrue<bool>(mm == data));
+    ASSERT_TRUE(allTrue<bool>(indx == 0));
+}

From 94472584484a71923e0153406234e2e6ec347a2e Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Sun, 15 Oct 2017 15:09:05 -0700
Subject: [PATCH 1333/2677] FEAT: Add support for batched matrix multiply

---
 src/api/c/blas.cpp          |  15 +++--
 src/backend/cpu/blas.cpp    |  66 +++++++++++++-------
 src/backend/cuda/blas.cpp   | 121 ++++++++++++++++++++++++++++++------
 src/backend/cuda/memory.cpp |   1 +
 src/backend/opencl/blas.cpp |  75 ++++++++++++++--------
 test/blas.cpp               |  28 +++++++++
 6 files changed, 237 insertions(+), 69 deletions(-)

diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index e2e5ddb98c..7e005b3950 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -124,10 +124,17 @@ af_err af_matmul(af_array *out,
             AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
         }
 
-
-        if (lhsInfo.ndims() > 2 ||
-            rhsInfo.ndims() > 2) {
-            AF_ERROR("matmul can not be used in batch mode", AF_ERR_BATCH);
+        dim4 lDims = lhsInfo.dims();
+        dim4 rDims = rhsInfo.dims();
+
+        if (lDims.ndims() > 2 || rDims.ndims() > 2) {
+            DIM_ASSERT(1, lDims.ndims() == rDims.ndims());
+            if (lDims[2] != rDims[2] && lDims[2] != 1 && rDims[2] != 1) {
+                AF_ERROR("Batch size mismatch along dimension 2", AF_ERR_BATCH);
+            }
+            if (lDims[3] != rDims[3] && lDims[3] != 1 && rDims[3] != 1) {
+                AF_ERROR("Batch size mismatch along dimension 3", AF_ERR_BATCH);
+            }
         }
 
         TYPE_ASSERT(lhs_type == rhs_type);
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 33b1063c87..87db9b5a94 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -158,33 +158,57 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     using BT  =       typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
-    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    dim_t d2 = std::max(lDims[2], rDims[2]);
+    dim_t d3 = std::max(lDims[3], rDims[3]);
+    dim4 oDims = af::dim4(M, N, d2, d3);
+    Array<T> out = createEmptyArray<T>(oDims);
+
     auto func = [=] (Param<T> output, CParam<T> left, CParam<T> right) {
         auto alpha = getScale<T, 1>();
         auto beta  = getScale<T, 0>();
 
         dim4 lStrides = left.strides();
         dim4 rStrides = right.strides();
-
-        if(rDims[bColDim] == 1) {
-            dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
-            gemv_func<T>()(
-                CblasColMajor, lOpts,
-                lDims[0], lDims[1],
-                alpha,
-                reinterpret_cast<CBT*>(left.get()), lStrides[1],
-                reinterpret_cast<CBT*>(right.get()), incr,
-                beta,
-                reinterpret_cast<BT*>(output.get()), 1);
-        } else {
-            gemm_func<T>()(
-                CblasColMajor, lOpts, rOpts,
-                M, N, K,
-                alpha,
-                reinterpret_cast<CBT*>(left.get()), lStrides[1],
-                reinterpret_cast<CBT*>(right.get()), rStrides[1],
-                beta,
-                reinterpret_cast<BT*>(output.get()), output.dims(0));
+        dim4 oStrides = output.strides();
+
+        int batchSize = oDims[2] * oDims[3];
+
+        bool is_l_d2_batched = oDims[2] == lDims[2];
+        bool is_l_d3_batched = oDims[3] == lDims[3];
+        bool is_r_d2_batched = oDims[2] == rDims[2];
+        bool is_r_d3_batched = oDims[3] == rDims[3];
+
+        for (int n = 0; n < batchSize; n++) {
+            int w = n / rDims[2];
+            int z = n - w * rDims[2];
+
+            int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
+            int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+
+            CBT *lptr = reinterpret_cast<CBT*>(left.get() + loff);
+            CBT *rptr = reinterpret_cast<CBT*>(right.get() + roff);
+            BT *optr = reinterpret_cast<BT*>(output.get() + z * oStrides[2] + w * oStrides[3]);
+
+            if(rDims[bColDim] == 1) {
+                dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                gemv_func<T>()(
+                    CblasColMajor, lOpts,
+                    lDims[0], lDims[1],
+                    alpha,
+                    lptr, lStrides[1],
+                    rptr, incr,
+                    beta,
+                    optr, 1);
+            } else {
+                gemm_func<T>()(
+                    CblasColMajor, lOpts, rOpts,
+                    M, N, K,
+                    alpha,
+                    lptr, lStrides[1],
+                    rptr, rStrides[1],
+                    beta,
+                    optr, output.dims(0));
+            }
         }
     };
     getQueue().enqueue(func, out, lhs, rhs);
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index a460e42aa8..4f1c071d23 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -17,6 +17,7 @@
 #include <cassert>
 #include <math.hpp>
 #include <common/err_common.hpp>
+#include <err_cuda.hpp>
 #include <cublas.hpp>
 #include <arith.hpp>
 #include <reduce.hpp>
@@ -49,6 +50,18 @@ struct gemm_func_def_t
                                                 const T *,        T *, int);
 };
 
+template<typename T>
+struct gemmBatched_func_def_t
+{
+    typedef cublasStatus_t (*gemmBatched_func_def)(    cublasHandle_t,
+                                                       cublasOperation_t, cublasOperation_t,
+                                                       int, int, int,
+                                                       const T *,  const T **, int,
+                                                       const T **, int,
+                                                       const T *,        T **, int,
+                                                       int);
+};
+
 template<typename T>
 struct gemv_func_def_t
 {
@@ -88,6 +101,12 @@ BLAS_FUNC(gemm, cfloat, C)
 BLAS_FUNC(gemm, double, D)
 BLAS_FUNC(gemm, cdouble,Z)
 
+BLAS_FUNC_DEF(gemmBatched)
+BLAS_FUNC(gemmBatched, float,  S)
+BLAS_FUNC(gemmBatched, cfloat, C)
+BLAS_FUNC(gemmBatched, double, D)
+BLAS_FUNC(gemmBatched, cdouble,Z)
+
 BLAS_FUNC_DEF(gemv)
 BLAS_FUNC(gemv, float,  S)
 BLAS_FUNC(gemv, cfloat, C)
@@ -162,37 +181,101 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     int N = rDims[bColDim];
     int K = lDims[aColDim];
 
-    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    dim_t d2 = std::max(lDims[2], rDims[2]);
+    dim_t d3 = std::max(lDims[3], rDims[3]);
+    dim4 oDims = dim4(M, N, d2, d3);
+    Array<T> out = createEmptyArray<T>(oDims);
+
     T alpha = scalar<T>(1);
     T beta  = scalar<T>(0);
 
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
-    if(rDims[bColDim] == 1) {
-        N = lDims[aColDim];
-        dim_t incr = (rOpts == CUBLAS_OP_N) ? rStrides[0] : rStrides[1];
-        CUBLAS_CHECK(gemv_func<T>()(
-                         blasHandle(),
-                         lOpts,
-                         lDims[0],
-                         lDims[1],
-                         &alpha,
-                         lhs.get(), lStrides[1],
-                         rhs.get(), incr,
-                         &beta,
-                         out.get(), 1));
+    dim4 oStrides = out.strides();
+
+    if (oDims.ndims() <= 2) {
+        if(rDims[bColDim] == 1) {
+            dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+            N = lDims[aColDim];
+            CUBLAS_CHECK(gemv_func<T>()(
+                             blasHandle(),
+                             lOpts,
+                             lDims[0],
+                             lDims[1],
+                             &alpha,
+                             lhs.get(), lStrides[1],
+                             rhs.get(), incr,
+                             &beta,
+                             out.get(), 1));
+        } else {
+            CUBLAS_CHECK(gemm_func<T>()(
+                             blasHandle(),
+                             lOpts,
+                             rOpts,
+                             M, N, K,
+                             &alpha,
+                             lhs.get(), lStrides[1],
+                             rhs.get(), rStrides[1],
+                             &beta,
+                             out.get(),
+                             oDims[0]));
+        }
     } else {
-        CUBLAS_CHECK(gemm_func<T>()(
+        int batchSize = oDims[2] * oDims[3];
+        std::vector<const T *> lptrs(batchSize);
+        std::vector<const T *> rptrs(batchSize);
+        std::vector<T *> optrs(batchSize);
+
+        bool is_l_d2_batched = oDims[2] == lDims[2];
+        bool is_l_d3_batched = oDims[3] == lDims[3];
+
+        bool is_r_d2_batched = oDims[2] == rDims[2];
+        bool is_r_d3_batched = oDims[3] == rDims[3];
+
+        const T *lptr = lhs.get();
+        const T *rptr = rhs.get();
+        T *optr = out.get();
+
+        for (int n = 0; n < batchSize; n++) {
+            int w = n / oDims[2];
+            int z = n - w * oDims[2];
+            int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
+            int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+            lptrs[n] = lptr + loff;
+            rptrs[n] = rptr + roff;
+            optrs[n] = optr + z * oStrides[2] + w * oStrides[3];
+        }
+
+        auto d_lptrs = memAlloc<void *>(batchSize);
+        auto d_rptrs = memAlloc<void *>(batchSize);
+        auto d_optrs = memAlloc<void *>(batchSize);
+
+        size_t bytes = batchSize * sizeof(T **);
+        CUDA_CHECK(cudaMemcpyAsync(d_lptrs.get(), lptrs.data(), bytes,
+                                   cudaMemcpyHostToDevice,
+                                   getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(d_rptrs.get(), rptrs.data(), bytes,
+                                   cudaMemcpyHostToDevice,
+                                   getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(d_optrs.get(), optrs.data(), bytes,
+                                   cudaMemcpyHostToDevice,
+                                   getActiveStream()));
+
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
+
+        CUBLAS_CHECK(gemmBatched_func<T>()(
                          blasHandle(),
                          lOpts,
                          rOpts,
                          M, N, K,
                          &alpha,
-                         lhs.get(), lStrides[1],
-                         rhs.get(), rStrides[1],
+                         (const T **)d_lptrs.get(), lStrides[1],
+                         (const T **)d_rptrs.get(), rStrides[1],
                          &beta,
-                         out.get(),
-                         out.dims()[0]));
+                         (T **)d_optrs.get(),
+                         oStrides[1],
+                         batchSize));
+
     }
 
     return out;
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 7a26d24ed7..e6613a6065 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -145,6 +145,7 @@ bool checkMemoryLimit()
     INSTANTIATE(uintl)
     INSTANTIATE(short)
     INSTANTIATE(ushort)
+    INSTANTIATE(void *)
 
 MemoryManager::MemoryManager()
     : common::MemoryManager<cuda::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 495543d813..bec8cfbc83 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -72,36 +72,61 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     const int N = rDims[bColDim];
     const int K = lDims[aColDim];
 
-    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    dim_t d2 = std::max(lDims[2], rDims[2]);
+    dim_t d3 = std::max(lDims[3], rDims[3]);
+    dim4 oDims = af::dim4(M, N, d2, d3);
+    Array<T> out = createEmptyArray<T>(oDims);
+
     const auto alpha = scalar<T>(1);
     const auto beta  = scalar<T>(0);
 
     const dim4 lStrides = lhs.strides();
     const dim4 rStrides = rhs.strides();
-    cl::Event event;
-    if(rDims[bColDim] == 1) {
-        dim_t incr = (rOpts == OPENCL_BLAS_NO_TRANS) ? rStrides[0] : rStrides[1];
-        gpu_blas_gemv_func<T> gemv;
-        OPENCL_BLAS_CHECK(
-            gemv(lOpts, lDims[0], lDims[1],
-                 alpha,
-                 (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                 (*rhs.get())(), rhs.getOffset(), incr,
-                 beta,
-                 (*out.get())(), out.getOffset(), 1,
-                 1, &getQueue()(), 0, nullptr, &event())
-        );
-    } else {
-        gpu_blas_gemm_func<T> gemm;
-        OPENCL_BLAS_CHECK(
-            gemm(lOpts, rOpts, M, N, K,
-                 alpha,
-                 (*lhs.get())(), lhs.getOffset(), lStrides[1],
-                 (*rhs.get())(), rhs.getOffset(), rStrides[1],
-                 beta,
-                 (*out.get())(), out.getOffset(), out.dims()[0],
-                 1, &getQueue()(), 0, nullptr, &event())
-        );
+    const dim4 oStrides = out.strides();
+
+    int batchSize = oDims[2] * oDims[3];
+
+    bool is_l_d2_batched = oDims[2] == lDims[2];
+    bool is_l_d3_batched = oDims[3] == lDims[3];
+    bool is_r_d2_batched = oDims[2] == rDims[2];
+    bool is_r_d3_batched = oDims[3] == rDims[3];
+
+    for (int n = 0; n < batchSize; n++) {
+        int w = n / rDims[2];
+        int z = n - w * rDims[2];
+
+        int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
+        int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+
+        dim_t lOffset = lhs.getOffset() + loff;
+        dim_t rOffset = rhs.getOffset() + roff;
+        dim_t oOffset = out.getOffset() + z * oStrides[2] + w * oStrides[3];
+
+        cl::Event event;
+        if(rDims[bColDim] == 1) {
+            dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+            gpu_blas_gemv_func<T> gemv;
+            OPENCL_BLAS_CHECK(
+                gemv(lOpts, lDims[0], lDims[1],
+                     alpha,
+                     (*lhs.get())(), lOffset, lStrides[1],
+                     (*rhs.get())(), rOffset, incr,
+                     beta,
+                     (*out.get())(), oOffset, 1,
+                     1, &getQueue()(), 0, nullptr, &event())
+                );
+        } else {
+            gpu_blas_gemm_func<T> gemm;
+            OPENCL_BLAS_CHECK(
+                gemm(lOpts, rOpts, M, N, K,
+                     alpha,
+                     (*lhs.get())(), lOffset, lStrides[1],
+                     (*rhs.get())(), rOffset, rStrides[1],
+                     beta,
+                     (*out.get())(), oOffset, out.dims()[0],
+                     1, &getQueue()(), 0, nullptr, &event())
+                );
+        }
     }
 
     return out;
diff --git a/test/blas.cpp b/test/blas.cpp
index 8d5b1f50e6..888e5d34a5 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -244,6 +244,34 @@ TYPED_TEST(MatrixMultiply, MultiGPURectangleVector_CPP)
     DEVICE_ITERATE((cppMatMulCheck<TypeParam, true>(TEST_DIR"/blas/RectangleVector.test")));
 }
 
+TEST(MatrixMultiply, Batched)
+{
+    const int M = 512;
+    const int K = 1024;
+    const int N = 32;
+    const int D2 = 2;
+    const int D3 = 3;
+
+    for (int d3 = 1; d3 <= D3; d3 *= D3) {
+        for (int d2 = 1; d2 <= D2; d2 *= D2) {
+            af::array a = af::randu(M, K, d2, d3);
+            af::array b = af::randu(K, N, d2, d3);
+            af::array c = af::matmul(a, b);
+
+            for (int j = 0; j < d3; j++) {
+                for (int i = 0; i < d2; i++) {
+                    af::array a_ij = a(af::span, af::span, i, j);
+                    af::array b_ij = b(af::span, af::span, i, j);
+                    af::array c_ij = c(af::span, af::span, i, j);
+                    af::array res = af::matmul(a_ij, b_ij);
+                    ASSERT_LT(af::max<float>(af::abs(c_ij - res)), 1E-5)
+                        << " for d2 = " << d2 << " for d3 = " << d3;
+                }
+            }
+        }
+    }
+}
+
 #undef DEVICE_ITERATE
 
 TEST(MatrixMultiply, ISSUE_1882)

From fb341e6cd6498dfc0f2058cfdb2f1507d9cf7a9f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 Dec 2017 20:17:13 -0500
Subject: [PATCH 1334/2677] Allow tests to specify the backends to build

Adds the BACKENDS parameter to the make_test function in the
test/CMakeLists.txt file. This allows you to specify which backends
will be build for a particular test. If this variable is not specified
the test will be built for all backends.
---
 test/CMakeLists.txt | 23 +++++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 13cdd94bb4..f5251d1365 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -59,14 +59,32 @@ endif(BUILD_UNIFIED)
 
 include(CMakeParseArguments)
 
+# Creates tests for all backends
+#
+# Creates a standard test for all backends. Most of the time you only need to
+# specify the name of the source file to create a test.
+#
+# Parameters
+# ----------
+# 'CXX11'       If set the tests will be compiled using c++11. Tests should strive
+#               to be C++98 compilient
+# 'SRC'         The source files for the test
+# 'LIBRARIES'   Libraries other than ArrayFire that need to be linked
+# 'DEFINITIONS' Definitions that need to be defined
+# 'BACKENDS'    Backends to target for this test. If not set then the test will
+#               compiled againat all backends
 function(make_test)
   set(options CXX11)
   set(single_args SRC)
-  set(multi_args LIBRARIES DEFINITIONS)
+  set(multi_args LIBRARIES DEFINITIONS BACKENDS)
   cmake_parse_arguments(mt_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
 
   get_filename_component(src_name ${mt_args_SRC} NAME_WE)
   foreach(backend ${enabled_backends})
+    if(NOT "${mt_args_BACKENDS}" STREQUAL "" AND
+       NOT ${backend} IN_LIST mt_args_BACKENDS)
+      continue()
+    endif()
     set(target "${src_name}_${backend}")
     add_executable(${target} ${mt_args_SRC})
     target_include_directories(${target}
@@ -200,7 +218,8 @@ make_test(SRC nearest_neighbour.cpp)
 
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
-            LIBRARIES OpenCL::OpenCL)
+            LIBRARIES OpenCL::OpenCL
+            BACKENDS "opencl")
 endif()
 
 make_test(SRC orb.cpp)

From b3e927edcb3d16bca2e6e6e54ea0b7c498a9159d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 15 Dec 2017 01:08:09 -0500
Subject: [PATCH 1335/2677] Fix fast for CUDA 9. Use CUB library for reductions

FAST was failing on CUDA 9 because of insufficient synchronization in the
reduction of the non_max_count function. The reduction is now implemented
using BlockReduce from CUB.

This also adds CUB as a dependency which is brought in as a submodule.
---
 .gitmodules                      |  3 +++
 src/backend/cuda/CMakeLists.txt  |  1 +
 src/backend/cuda/cub             |  1 +
 src/backend/cuda/kernel/fast.hpp | 30 +++++++++---------------------
 4 files changed, 14 insertions(+), 21 deletions(-)
 create mode 160000 src/backend/cuda/cub

diff --git a/.gitmodules b/.gitmodules
index f2cc14295f..74b855c3a8 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -10,3 +10,6 @@
 [submodule "src/backend/cpu/threads"]
 	path = src/backend/cpu/threads
 	url = https://github.com/alltheflops/threads.git
+[submodule "src/backend/cuda/cub"]
+	path = src/backend/cuda/cub
+	url = https://github.com/NVlabs/cub.git
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 7aa64a7eb6..88c406bf46 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -45,6 +45,7 @@ cuda_include_directories(
   ${ArrayFire_BINARY_DIR}/include
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel
   ${CMAKE_CURRENT_SOURCE_DIR}/JIT
+  ${CMAKE_CURRENT_SOURCE_DIR}/cub
   ${ArrayFire_SOURCE_DIR}/src/api/c
   ${ArrayFire_SOURCE_DIR}/src/backend
 
diff --git a/src/backend/cuda/cub b/src/backend/cuda/cub
new file mode 160000
index 0000000000..d622848f9f
--- /dev/null
+++ b/src/backend/cuda/cub
@@ -0,0 +1 @@
+Subproject commit d622848f9fb62f13e5e064e1deb43b6bcbb12bad
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 969716fe90..2da6714485 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -7,12 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include "shared.hpp"
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
+#include <cub/block/block_reduce.cuh>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <kernel/fast_lut.hpp>
 #include <memory.hpp>
-#include "shared.hpp"
 
 namespace cuda
 {
@@ -269,7 +270,9 @@ void non_max_counts(
     const int yend = (blockIdx.y + 1) * blockDim.y * 8;
 
     const int bid = blockIdx.y * gridDim.x + blockIdx.x;
-    __shared__ unsigned s_counts[256];
+    using BlockReduce = cub::BlockReduce<unsigned, 32, cub::BLOCK_REDUCE_WARP_REDUCTIONS, 8>;
+
+    __shared__ typename BlockReduce::TempStorage temp_storage;
 
     unsigned count = 0;
     for (int y = yid; y < yend; y += yoff) {
@@ -302,26 +305,11 @@ void non_max_counts(
         }
     }
 
-    s_counts[tid] = count;
-    __syncthreads();
-
-    if (tid >= 128) return;
-    if (tid < 128) s_counts[tid] += s_counts[tid + 128]; __syncthreads();
-
-    if (tid >= 64) return;
-    if (tid <  64) s_counts[tid] += s_counts[tid +  64]; __syncthreads();
-
-    if (tid >= 32) return;
-    if (tid <  32) s_counts[tid] += s_counts[tid +  32];
-    if (tid <  16) s_counts[tid] += s_counts[tid +  16];
-    if (tid <   8) s_counts[tid] += s_counts[tid +   8];
-    if (tid <   4) s_counts[tid] += s_counts[tid +   4];
-    if (tid <   2) s_counts[tid] += s_counts[tid +   2];
-    if (tid <   1) s_counts[tid] += s_counts[tid +   1];
+    int sum = BlockReduce(temp_storage).Sum(count);
 
     if (tid == 0) {
-        unsigned total = s_counts[0] ? atomicAdd(d_total, s_counts[0]) : 0;
-        d_counts [bid] = s_counts[0];
+        unsigned total = sum ? atomicAdd(d_total, sum) : 0;
+        d_counts [bid] = sum;
         d_offsets[bid] = total;
     }
 }

From dd067b4bde47b656f58f82a543f87106143d9211 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 11 Dec 2017 12:06:06 +0530
Subject: [PATCH 1336/2677] BUGFIX: correct assets path in examples

ASSETS_DIR was not set to a proper path earlier in image_processing
and machine_learning examples due to which the examples which use
images failed during image loads.

New change adds ASSETS_DIR option to cmake options when the examples
are not built as part of ArrayFire source build.
---
 CMakeLists.txt                          |  2 ++
 examples/CMakeLists.txt                 | 12 ++++++++++++
 examples/computer_vision/CMakeLists.txt |  3 +--
 examples/graphics/CMakeLists.txt        |  2 ++
 4 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2f7b85b465..2cdaa8005a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -265,4 +265,6 @@ if(BUILD_TEST)
 endif()
 
 conditional_directory(BUILD_TESTING test)
+
+set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
 conditional_directory(BUILD_EXAMPLES examples)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index e21bb00959..1d470f705e 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -4,6 +4,18 @@ project(ArrayFire-Examples
   VERSION 3.5.0
   LANGUAGES CXX)
 
+if (NOT ASSETS_DIR)
+    set(ASSETS_DIR ""
+        CACHE PATH
+        "
+        Assets are the images and data files required by the examples. ASSETS_DIR
+        should point to the path where theses files are available on the build machine.
+
+        You can download or clone the assets git repository from the following
+        url: https://github.com/arrayfire/assets
+        ")
+endif (NOT ASSETS_DIR)
+
 if(WIN32)
   add_definitions(-DWIN32_LEAN_AND_MEAN)
   unset(CMAKE_RUNTIME_OUTPUT_DIRECTORY)
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
index 03dd516087..41116a2e83 100644
--- a/examples/computer_vision/CMakeLists.txt
+++ b/examples/computer_vision/CMakeLists.txt
@@ -6,8 +6,7 @@ project(ArrayFire-Example-Computer-Vision
 
 find_package(ArrayFire)
 
-set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
-add_definitions(-DASSETS_DIR=\"${ASSETS_DIR}\")
+add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
 if (ArrayFire_CPU_FOUND)
   # FAST examples
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
index 949cfce016..68257d6390 100644
--- a/examples/graphics/CMakeLists.txt
+++ b/examples/graphics/CMakeLists.txt
@@ -6,6 +6,8 @@ project(ArrayFire-Example-Graphics
 
 find_package(ArrayFire)
 
+add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
+
 if(ArrayFire_CPU_FOUND)
   # Conway Game of Life
   add_executable(conway_cpu conway.cpp)

From 7399ff62695f13398211e80eeb05d5de9eddcaa6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 2 Dec 2017 01:14:01 +0530
Subject: [PATCH 1337/2677] BUGFIX: Fix RandomEngine class attributes

This change replaced the af_array memebers of RandomEngine class
with detail::Array<T> instances from backend. Since, they are
RAII objects, there would be no memory leaks.
---
 src/api/c/random.cpp   | 140 +++++++++++++++++------------------------
 src/api/cpp/random.cpp |   3 +-
 test/random.cpp        |   9 +++
 3 files changed, 68 insertions(+), 84 deletions(-)

diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index c79019e77d..30503673ed 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -24,21 +24,33 @@
 using namespace detail;
 using namespace common;
 
-class RandomEngine
+using af::dim4;
+
+Array<uint> emptyArray()
+{
+    static const Array<uint> EMPTY_ARRAY = createEmptyArray<uint>(af::dim4(0));
+
+    return EMPTY_ARRAY;
+}
+
+struct RandomEngine
 {
-    public :
     af_random_engine_type type;
     std::shared_ptr<uintl> seed;
     std::shared_ptr<uintl> counter;
-    af_array pos;
-    af_array sh1;
-    af_array sh2;
+    Array<uint> pos;
+    Array<uint> sh1;
+    Array<uint> sh2;
     uint mask;
-    af_array recursion_table;
-    af_array temper_table;
-    af_array state;
-
-    RandomEngine(void) : type(AF_RANDOM_ENGINE_DEFAULT), seed(new uintl), counter(new uintl) {
+    Array<uint> recursion_table;
+    Array<uint> temper_table;
+    Array<uint> state;
+
+    RandomEngine(void)
+        : type(AF_RANDOM_ENGINE_DEFAULT), seed(new uintl), counter(new uintl),
+        pos(emptyArray()), sh1(emptyArray()), sh2(emptyArray()), mask(0),
+        recursion_table(emptyArray()), temper_table(emptyArray()), state(emptyArray())
+    {
         *seed = 0;
         *counter = 0;
     }
@@ -63,14 +75,8 @@ template<typename T>
 static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-        return getHandle(uniformDistribution<T>(dims,
-                    getArray<uint>(e->pos),
-                    getArray<uint>(e->sh1),
-                    getArray<uint>(e->sh2),
-                    e->mask,
-                    getArray<uint>(e->recursion_table),
-                    getArray<uint>(e->temper_table),
-                    getArray<uint>(e->state)));
+        return getHandle(uniformDistribution<T>(dims, e->pos, e->sh1, e->sh2, e->mask,
+                    e->recursion_table, e->temper_table, e->state));
     } else {
         return getHandle(uniformDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
     }
@@ -80,14 +86,8 @@ template<typename T>
 static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e)
 {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-        return getHandle(normalDistribution<T>(dims,
-                    getArray<uint>(e->pos),
-                    getArray<uint>(e->sh1),
-                    getArray<uint>(e->sh2),
-                    e->mask,
-                    getArray<uint>(e->recursion_table),
-                    getArray<uint>(e->temper_table),
-                    getArray<uint>(e->state)));
+        return getHandle(normalDistribution<T>(dims, e->pos, e->sh1, e->sh2, e->mask,
+                    e->recursion_table, e->temper_table, e->state));
     } else {
         return getHandle(normalDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
     }
@@ -108,6 +108,8 @@ static void validateRandomType(const af_random_engine_type type)
 
 af_err af_get_default_random_engine(af_random_engine *r)
 {
+    AF_CHECK(af_init());
+
     thread_local RandomEngine re;
     *r = static_cast<af_random_engine> (&re);
     return AF_SUCCESS;
@@ -118,29 +120,23 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_
     try {
         AF_CHECK(af_init());
         validateRandomType(rtype);
+
         RandomEngine e;
         e.type = rtype;
         *e.seed = seed;
         *e.counter = 0;
 
         if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-            AF_CHECK(af_create_array(&e.pos, pos, 1, &MaxBlocks, u32));
-            AF_CHECK(af_create_array(&e.sh1, sh1, 1, &MaxBlocks, u32));
-            AF_CHECK(af_create_array(&e.sh2, sh2, 1, &MaxBlocks, u32));
+            e.pos  = createHostDataArray<uint>(af::dim4(MaxBlocks), pos);
+            e.sh1  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh1);
+            e.sh2  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh2);
             e.mask = mask;
-            AF_CHECK(af_create_array(&e.recursion_table, recursion_tbl, 1, &TableLength, u32));
-            AF_CHECK(af_create_array(&e.temper_table, temper_tbl, 1, &TableLength, u32));
-            AF_CHECK(af_create_handle(&e.state, 1, &MtStateLength, u32));
-            initMersenneState(getWritableArray<uint>(e.state), seed, getArray<uint>(e.recursion_table));
-        } else {
-            dim_t empty = 0;
-            AF_CHECK(af_create_handle(&e.pos, 1, &empty, u32));
-            AF_CHECK(af_create_handle(&e.sh1, 1, &empty, u32));
-            AF_CHECK(af_create_handle(&e.sh2, 1, &empty, u32));
-            e.mask = 0;
-            AF_CHECK(af_create_handle(&e.recursion_table, 1, &empty, u32));
-            AF_CHECK(af_create_handle(&e.temper_table, 1, &empty, u32));
-            AF_CHECK(af_create_handle(&e.state, 1, &empty, u32));
+
+            e.recursion_table = createHostDataArray<uint>(af::dim4(TableLength), recursion_tbl);
+            e.temper_table    = createHostDataArray<uint>(af::dim4(TableLength), temper_tbl);
+            e.state           = createEmptyArray<uint>(af::dim4(MtStateLength));
+
+            initMersenneState(e.state, seed, e.recursion_table);
         }
 
         *engineHandle = getRandomEngineHandle(e);
@@ -153,23 +149,7 @@ af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engi
 {
     try {
         AF_CHECK(af_init());
-        RandomEngine engine = *(getRandomEngine(engineHandle));
-        RandomEngine out;
-
-        out.type = engine.type;
-        out.seed = engine.seed;
-        out.counter = engine.counter;
-
-        AF_CHECK(af_retain_array(&out.pos, engine.pos));
-        AF_CHECK(af_retain_array(&out.sh1, engine.sh1));
-        AF_CHECK(af_retain_array(&out.sh2, engine.sh2));
-        out.mask = engine.mask;
-        AF_CHECK(af_retain_array(&out.recursion_table, engine.recursion_table));
-        AF_CHECK(af_retain_array(&out.temper_table, engine.temper_table));
-        AF_CHECK(af_retain_array(&out.state, engine.state));
-
-        *outHandle = getRandomEngineHandle(out);
-
+        *outHandle = getRandomEngineHandle(*(getRandomEngine(engineHandle)));
     } CATCHALL;
     return AF_SUCCESS;
 }
@@ -182,21 +162,24 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engin
         RandomEngine *e = getRandomEngine(*engine);
         if (rtype != e->type) {
             if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-                AF_CHECK(af_create_array(&e->pos, pos, 1, &MaxBlocks, u32));
-                AF_CHECK(af_create_array(&e->sh1, sh1, 1, &MaxBlocks, u32));
-                AF_CHECK(af_create_array(&e->sh2, sh2, 1, &MaxBlocks, u32));
+                e->pos  = createHostDataArray<uint>(af::dim4(MaxBlocks), pos);
+                e->sh1  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh1);
+                e->sh2  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh2);
                 e->mask = mask;
-                AF_CHECK(af_create_array(&e->recursion_table, recursion_tbl, 1, &TableLength, u32));
-                AF_CHECK(af_create_array(&e->temper_table, temper_tbl, 1, &TableLength, u32));
-                AF_CHECK(af_create_handle(&e->state, 1, &MtStateLength, u32));
-                initMersenneState(getWritableArray<uint>(e->state), *(e->seed), getArray<uint>(e->recursion_table));
+
+                e->recursion_table = createHostDataArray<uint>(af::dim4(TableLength), recursion_tbl);
+                e->temper_table    = createHostDataArray<uint>(af::dim4(TableLength), temper_tbl);
+                e->state           = createEmptyArray<uint>(af::dim4(MtStateLength));
+
+                initMersenneState(e->state, *(e->seed), e->recursion_table);
             } else if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-                AF_CHECK(af_release_array(e->pos));
-                AF_CHECK(af_release_array(e->sh1));
-                AF_CHECK(af_release_array(e->sh2));
-                AF_CHECK(af_release_array(e->recursion_table));
-                AF_CHECK(af_release_array(e->temper_table));
-                AF_CHECK(af_release_array(e->state));
+                e->pos  = emptyArray();
+                e->sh1  = emptyArray();
+                e->sh2  = emptyArray();
+                e->mask = 0;
+                e->recursion_table = emptyArray();
+                e->temper_table    = emptyArray();
+                e->state           = emptyArray();
             }
             e->type = rtype;
         }
@@ -232,7 +215,7 @@ af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
         RandomEngine *e = getRandomEngine(*engine);
         *(e->seed) = seed;
         if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-            initMersenneState(getWritableArray<uint>(e->state), seed, getArray<uint>(e->recursion_table));
+            initMersenneState(e->state, seed, e->recursion_table);
         } else {
             *(e->counter) = 0;
         }
@@ -308,16 +291,7 @@ af_err af_release_random_engine(af_random_engine engineHandle)
 {
     try {
         AF_CHECK(af_init());
-        RandomEngine *e = getRandomEngine(engineHandle);
-        if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-            AF_CHECK(af_release_array(e->pos));
-            AF_CHECK(af_release_array(e->sh1));
-            AF_CHECK(af_release_array(e->sh2));
-            AF_CHECK(af_release_array(e->recursion_table));
-            AF_CHECK(af_release_array(e->temper_table));
-            AF_CHECK(af_release_array(e->state));
-        }
-        delete e;
+        delete getRandomEngine(engineHandle);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/cpp/random.cpp b/src/api/cpp/random.cpp
index a909b9473e..f4706c5874 100644
--- a/src/api/cpp/random.cpp
+++ b/src/api/cpp/random.cpp
@@ -159,7 +159,8 @@ namespace af
 
     randomEngine getDefaultRandomEngine(void)
     {
-        af_random_engine internal_handle, handle;
+        af_random_engine internal_handle = 0;
+        af_random_engine handle = 0;
         AF_THROW(af_get_default_random_engine(&internal_handle));
         AF_THROW(af_retain_random_engine(&handle, internal_handle));
         return randomEngine(handle);
diff --git a/test/random.cpp b/test/random.cpp
index 69f8ff4c07..226c6ab1bf 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -202,6 +202,15 @@ TYPED_TEST(Random,InvalidDims)
 
 ////////////////////////////////////// CPP /////////////////////////////////////
 //
+TEST(RandomEngine, Default)
+{
+    // Using default Random engine will cause segfaults
+    // without setting one. This test should be before
+    // setting it to test if default engine setup is working
+    // as expected, otherwise the test will fail.
+    af::randomEngine engine = af::getDefaultRandomEngine();
+}
+
 TEST(Random, CPP)
 {
     if (noDoubleTests<float>()) return;

From c683ac03ef46ca47889bcaa4b07bb43a537f85f1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 8 Dec 2017 15:23:44 +0530
Subject: [PATCH 1338/2677] Enable documentation build in CMake

Documentation builds were not re-enabled after cmake refactoring. This
change re-enables docs build.

Also changed cmakefile commmands case to match rest of the cmake files.
---
 CMakeLists.txt      |  1 +
 docs/CMakeLists.txt | 49 +++++++++++++++++++++------------------------
 2 files changed, 24 insertions(+), 26 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2cdaa8005a..927952001a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -268,3 +268,4 @@ conditional_directory(BUILD_TESTING test)
 
 set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
 conditional_directory(BUILD_EXAMPLES examples)
+conditional_directory(BUILD_DOCS docs)
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 9459f7a04b..f6951ae3e7 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -1,48 +1,45 @@
-# Doxygen is required for the documentation to be built. Do not fail silently.
-FIND_PACKAGE(Doxygen REQUIRED)
+set(AF_DOCS_CONFIG "${CMAKE_CURRENT_SOURCE_DIR}/doxygen.mk")
+set(AF_DOCS_CONFIG_OUT "${CMAKE_CURRENT_BINARY_DIR}/doxygen.mk.out")
 
-SET(AF_DOCS_CONFIG "${CMAKE_CURRENT_SOURCE_DIR}/doxygen.mk")
-SET(AF_DOCS_CONFIG_OUT "${CMAKE_CURRENT_BINARY_DIR}/doxygen.mk.out")
+set(AF_DOCS_LAYOUT "${CMAKE_CURRENT_SOURCE_DIR}/layout.xml")
+set(AF_DOCS_LAYOUT_OUT "${CMAKE_CURRENT_BINARY_DIR}/layout.xml.out")
 
-SET(AF_DOCS_LAYOUT "${CMAKE_CURRENT_SOURCE_DIR}/layout.xml")
-SET(AF_DOCS_LAYOUT_OUT "${CMAKE_CURRENT_BINARY_DIR}/layout.xml.out")
-
-SET(DOCS_DIR ${CMAKE_CURRENT_SOURCE_DIR})
-SET(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../assets")
-SET(INCLUDE_DIR "${PROJECT_SOURCE_DIR}/include")
-SET(EXAMPLES_DIR "${PROJECT_SOURCE_DIR}/examples")
-SET(SNIPPETS_DIR "${PROJECT_SOURCE_DIR}/test")
-CONFIGURE_FILE(${AF_DOCS_CONFIG} ${AF_DOCS_CONFIG_OUT})
-CONFIGURE_FILE(${AF_DOCS_LAYOUT} ${AF_DOCS_LAYOUT_OUT})
+set(DOCS_DIR ${CMAKE_CURRENT_SOURCE_DIR})
+set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../assets")
+set(INCLUDE_DIR "${PROJECT_SOURCE_DIR}/include")
+set(EXAMPLES_DIR "${PROJECT_SOURCE_DIR}/examples")
+set(SNIPPETS_DIR "${PROJECT_SOURCE_DIR}/test")
+configure_file(${AF_DOCS_CONFIG} ${AF_DOCS_CONFIG_OUT})
+configure_file(${AF_DOCS_LAYOUT} ${AF_DOCS_LAYOUT_OUT})
 
 ###########################################################
 ## This generates a list of the examples cpp files and
 ## creates a dox file under docs/details/examples.dox
 ## This is used to generate documentation for examples
 ###########################################################
-FILE(GLOB EXAMPLES_CPP
+file(GLOB EXAMPLES_CPP
     "${EXAMPLES_DIR}/*/*.cpp")
 
 # Sort alphabetically
 # Note: example directories will be major sort order
-LIST(SORT EXAMPLES_CPP)
+list(SORT EXAMPLES_CPP)
 
 # Get filenames and write to a string
-FOREACH(SRC ${EXAMPLES_CPP})
-    GET_FILENAME_COMPONENT(DIR_PATH ${SRC} DIRECTORY)
-    GET_FILENAME_COMPONENT(DIR_NAME ${DIR_PATH} NAME)
-    GET_FILENAME_COMPONENT(SRC_NAME ${SRC} NAME)
-    SET(EXAMPLES_LIST "${EXAMPLES_LIST}\\example ${DIR_NAME}/${SRC_NAME}\n")
-ENDFOREACH(SRC ${EXAMPLES_CPP})
+foreach(SRC ${EXAMPLES_CPP})
+    get_filename_component(DIR_PATH ${SRC} DIRECTORY)
+    get_filename_component(DIR_NAME ${DIR_PATH} NAME)
+    get_filename_component(SRC_NAME ${SRC} NAME)
+    set(EXAMPLES_LIST "${EXAMPLES_LIST}\\example ${DIR_NAME}/${SRC_NAME}\n")
+endforeach(SRC ${EXAMPLES_CPP})
 
 # Write string containing file names to examples.dox
-CONFIGURE_FILE(
+configure_file(
     ${PROJECT_SOURCE_DIR}/CMakeModules/examples.dox.in
     ${DOCS_DIR}/details/examples.dox
 )
 ###########################################################
 
-ADD_CUSTOM_TARGET(docs
+add_custom_target(docs
     ALL
     COMMAND ${DOXYGEN_EXECUTABLE} ${AF_DOCS_CONFIG_OUT}
     COMMAND cmake -E copy_directory ${ASSETS_DIR} ${CMAKE_CURRENT_BINARY_DIR}/html
@@ -51,12 +48,12 @@ ADD_CUSTOM_TARGET(docs
     VERBATIM)
 
 # Install Doxygen documentation
-INSTALL(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/html DESTINATION ${AF_INSTALL_DOC_DIR}
+install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/html DESTINATION ${AF_INSTALL_DOC_DIR}
     COMPONENT documentation
     PATTERN ".git" EXCLUDE
 )
 
 # Install man pages
-#INSTALL(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/man DESTINATION ${AF_INSTALL_MAN_DIR}
+#install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/man DESTINATION ${AF_INSTALL_MAN_DIR}
 #    COMPONENT documentation
 #)

From 140fc406c71e01c26895fbb8c6d2cab3333f40dc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 8 Dec 2017 16:42:08 +0530
Subject: [PATCH 1339/2677] BUGFIX: Hyperlink in indexing tutorial docs

---
 docs/pages/indexing.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/indexing.md b/docs/pages/indexing.md
index 6ca7cd4ab3..3e46194c53 100644
--- a/docs/pages/indexing.md
+++ b/docs/pages/indexing.md
@@ -14,7 +14,7 @@ with mixtures of:
 * [rows(first,last)](\ref af::array::rows) or [cols(first,last)](\ref af::array::cols)
  specifying a span of rows or columns
 
-See \ref indexing for the full listing.
+See \ref  index_mat for the full listing.
 
 \snippet test/index.cpp ex_indexing_first
 

From 4f9d8fc887b00f6ca8e56badf44a2f4e928e3ec9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 8 Dec 2017 19:19:04 +0530
Subject: [PATCH 1340/2677] Add example to af_lookup/af::lookup func docs

---
 docs/details/index.dox | 50 +++++++++++++++++++++++++++++++++++++++---
 include/af/index.h     |  4 ++--
 2 files changed, 49 insertions(+), 5 deletions(-)

diff --git a/docs/details/index.dox b/docs/details/index.dox
index 6125dd50ea..65f38c9931 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -3,23 +3,67 @@
 @{
 
 \defgroup index_func_index index
+\ingroup index_mat
+
+\brief Lookup values on array based on sequences and/or arrays
+
 
-\brief lookup values on array based on sequences and/or arrays
 
+\defgroup index_func_lookup Lookup
 \ingroup index_mat
 
+\brief Index an array using another array.
+
+Lets look at an example of how \ref lookup function does indexing.
+\code
+array a = range(dim4(5));
+af_print(a);
+//  0
+//  1
+//  2
+//  3
+//  4
+
+array b = range(dim4(2)) + 2; // Create an array with values [0,2] range and add 2 to them
+af_print(b);
+// 2
+// 3
+
+array c = lookup(a, b, 0);
+af_print(c);
+//  2
+//  3
+
+
+array d = lookup(a, b, 1);
+af_print(d);
+//  0   0
+//  1   1
+//  2   2
+//  3   3
+//  4   4
+
+// Since the second(1) dimension has only single element, all indices map to first & single element
+// along that dimension. Thus, the output array has two columns with elements repeatd twice because
+// the index array b has 2 elements.
+
+\endcode
+
 
 
 \defgroup index_func_assign assign
+\ingroup index_mat
 
 \brief Copy and write values in the locations specified by the sequences
 
-\ingroup index_mat
+
+
 
 \defgroup index_func_util util
+\ingroup index_mat
 
 \brief Utility functions to create objects of type \ref af_index_t
 
-\ingroup index_mat
+
 @}
 */
diff --git a/include/af/index.h b/include/af/index.h
index d49acb4aa8..1206b8ef6c 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -164,7 +164,7 @@ class AFAPI index {
 /// \param[in] dim specifies the dimension for indexing
 /// \returns an array containing values at locations specified by \p index
 ///
-/// \ingroup index_func_index
+/// \ingroup index_func_lookup
 ///
 
 AFAPI array lookup(const array &in, const array &idx, const int dim = -1);
@@ -221,7 +221,7 @@ extern "C" {
     /// \param[in] indices   is lookup indices
     /// \param[in] dim       specifies the dimension for indexing
     ///
-    /// \ingroup index_func_index
+    /// \ingroup index_func_lookup
     ///
 
     AFAPI af_err af_lookup( af_array *out,

From 2733d580b22cc7ba717c8cbee3fc19f3e17f86b6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 8 Dec 2017 19:23:22 +0530
Subject: [PATCH 1341/2677] Remove NVVM related cautionary warnings.

NVVM has been repalced with NVRTC and the information removed is
not longer required to be in the documentation.
---
 docs/pages/unified_backend.md | 25 -------------------------
 1 file changed, 25 deletions(-)

diff --git a/docs/pages/unified_backend.md b/docs/pages/unified_backend.md
index bb6efb72ca..6924f92707 100644
--- a/docs/pages/unified_backend.md
+++ b/docs/pages/unified_backend.md
@@ -42,31 +42,6 @@ treated as fallback paths in case the files are not found in the system paths.
 However, all the other upstream libraries for ArrayFire libs must be present
 in the system path variables shown above.
 
-### Special Mention: CUDA NVVM
-For the CUDA backend, ensure that the CUDA NVVM libs/dlls are in the path.
-These can be easily missed since CUDA installation does not add the paths by default.
-
-On Linux and OSX, add `/usr/local/cuda/nvvm/(lib or lib64)` to LD_LIBRARY_PATH or
-DYLD_LIBRARY_PATH.
-
-On Windows, you can set up a post build event that copys the NVVM dlls to
-the executable directory by using the following commands:
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.c}
-echo copy "$(CUDA_PATH)\nvvm\bin\nvvm64*.dll" "$(OutDir)"
-copy "$(CUDA_PATH)\nvvm\bin\nvvm64*.dll" "$(OutDir)"
-if errorlevel 1 (
-    echo "CUDA NVVM DLLs copy failed due to missing files."
-    exit /B 0
-)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This ensures that the NVVM DLLs are copied if present, but does not fail the
-build if the copy fails. This is how ArrayFire ships it's examples.
-
-The other option is to set `%%CUDA_PATH%/nvvm/bin` in the PATH environment
-variable.
-
 # Switching Backends
 
 The af_backend enum stores the possible backends.

From 04d1bac0cd26c03071600afa1ff4411ed95dc0c7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 14 Dec 2017 22:11:14 -0500
Subject: [PATCH 1342/2677] Prefix path for MKL and homebrew. Add platform
 specific CMake file

Set prefix path for known libraries that cannot be found by CMake's default
behavior. This addresses issues with MKL and glbinding downloaded via homebrew.

This commit also moves platform specific variables to a single file.
---
 CMakeLists.txt              | 11 +----------
 CMakeModules/platform.cmake | 38 +++++++++++++++++++++++++++++++++++++
 2 files changed, 39 insertions(+), 10 deletions(-)
 create mode 100644 CMakeModules/platform.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 927952001a..bb03b03685 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -16,6 +16,7 @@ include(CMakeDependentOption)
 include(InternalUtils)
 include(Version)
 include(build_cl2hpp)
+include(platform)
 
 arrayfire_set_cmake_default_variables()
 
@@ -66,16 +67,6 @@ mark_as_advanced(
 arrayfire_get_platform_definitions(platform_definitions)
 add_definitions(${platform_definitions})
 
-if(WIN32)
-  #TODO(umar): create a single place for compiler specific settings
-  # C4251: Warnings about dll interfaces. Thrown by glbinding, may be fixed in
-  #        the future
-  # C4068: Warnings about unknown pragmas
-  # C4275: Warnings about using non-exported classes as base class of an
-  #        exported class
-  add_compile_options(/wd4251 /wd4068 /wd4275)
-endif()
-
 if(BUILD_GRAPHICS)
   include(build_forge)
 endif()
diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
new file mode 100644
index 0000000000..2c541cf9e3
--- /dev/null
+++ b/CMakeModules/platform.cmake
@@ -0,0 +1,38 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+# Platform specific settings
+#
+# Add paths and flags specific platforms. This can inc
+
+if(APPLE)
+  # Some homebrew libraries(glbinding) are not installed in directories that
+  # CMake searches by default.
+  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/usr/local/opt")
+
+  # Default path for Intel MKL libraries
+  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/opt/intel/mkl/lib")
+endif()
+
+if(UNIX AND NOT APPLE)
+  # Default path for Intel MKL libraries
+  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/opt/intel/mkl/lib/intel64")
+endif()
+
+if(WIN32)
+  # C4251: Warnings about dll interfaces. Thrown by glbinding, may be fixed in
+  #        the future
+  # C4068: Warnings about unknown pragmas
+  # C4275: Warnings about using non-exported classes as base class of an
+  #        exported class
+  add_compile_options(/wd4251 /wd4068 /wd4275)
+
+  # Default path for Intel MKL libraries
+  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};"
+    "C:/Program Files (x86)/IntelSWTools/compilers_and_libraries/windows/mkl;"
+    "C:/Program Files (x86)/IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64")
+endif()

From 0afd229f8789729cb167fcd251b61581175b888a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 18 Dec 2017 00:05:32 +0530
Subject: [PATCH 1343/2677] BUGFIX: Fix Boost compile definitions on Windows

Without BOOST_CHRONO_HEADER_ONLY compile definition for boost
on Windows, the Boost::boost target will try to link chrono lib
also - which is not needed and thus avoided.
---
 CMakeLists.txt                         |  2 +-
 CMakeModules/boost_package.cmake       | 41 ++++++++++++++++++++++++++
 CMakeModules/build_boost_compute.cmake | 36 ----------------------
 src/backend/opencl/CMakeLists.txt      |  4 ---
 4 files changed, 42 insertions(+), 41 deletions(-)
 create mode 100644 CMakeModules/boost_package.cmake
 delete mode 100644 CMakeModules/build_boost_compute.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bb03b03685..053c96ffbd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -33,7 +33,7 @@ find_package(Doxygen)
 
 # Graphics dependencies
 find_package(glbinding QUIET)
-find_package(Boost)
+include(boost_package)
 
 option(BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
 option(BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})
diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
new file mode 100644
index 0000000000..7620fa1830
--- /dev/null
+++ b/CMakeModules/boost_package.cmake
@@ -0,0 +1,41 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+find_package(Boost)
+
+if("${Boost_VERSION}" VERSION_LESS 106100)
+  set(VER boost-1.61.0)
+  set(MD5 7e1c433b48825d8cb2effa963823aec8)
+  include(ExternalProject)
+
+  ExternalProject_Add(
+    boost_compute
+    URL       https://github.com/boostorg/compute/archive/${VER}.tar.gz
+    URL_MD5   ${MD5}
+    INSTALL_COMMAND ""
+    CONFIGURE_COMMAND ""
+    BUILD_COMMAND ""
+    )
+
+  ExternalProject_Get_Property(boost_compute source_dir)
+  message(STATUS "BOOST_COMPUTE: ${source_dir}")
+  make_directory(${source_dir}/include)
+
+  if(NOT TARGET Boost::boost)
+    add_library(Boost::boost IMPORTED INTERFACE GLOBAL)
+  endif()
+
+  add_dependencies(Boost::boost boost_compute)
+
+  set_target_properties(Boost::boost PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include")
+endif()
+
+# NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
+# will try to link with libboost-chrono.
+set_target_properties(Boost::boost PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS BOOST_CHRONO_HEADER_ONLY)
diff --git a/CMakeModules/build_boost_compute.cmake b/CMakeModules/build_boost_compute.cmake
deleted file mode 100644
index 99de29990d..0000000000
--- a/CMakeModules/build_boost_compute.cmake
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) 2017, ArrayFire
-# All rights reserved.
-#
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
-
-set(VER boost-1.61.0)
-set(MD5 7e1c433b48825d8cb2effa963823aec8)
-include(ExternalProject)
-
-ExternalProject_Add(
-  boost_compute
-  URL       https://github.com/boostorg/compute/archive/${VER}.tar.gz
-  URL_MD5   ${MD5}
-  INSTALL_COMMAND ""
-  CONFIGURE_COMMAND ""
-  BUILD_COMMAND ""
-  )
-
-ExternalProject_Get_Property(boost_compute source_dir)
-message(STATUS "BOOST_COMPUTE: ${source_dir}")
-make_directory(${source_dir}/include)
-
-if(NOT TARGET Boost::boost)
-  add_library(Boost::boost IMPORTED INTERFACE GLOBAL)
-endif()
-
-add_dependencies(Boost::boost boost_compute)
-
-set_target_properties(Boost::boost PROPERTIES
-  INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
-
-  # NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
-  # will try to link with libboost-chrono.
-  INTERFACE_COMPILE_DEFINITIONS BOOST_CHRONO_HEADER_ONLY)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a40a96d0b9..0d4b5b08c7 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -6,10 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 include(InternalUtils)
-if("${Boost_VERSION}" VERSION_LESS 106100)
-  dependency_check(Boost_FOUND "Boost not found.")
-  include(build_boost_compute)
-endif()
 
 set(OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end")
 set_property(CACHE OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")

From d7b34fffcbdf9de1e61dd47c949040aa7a01827e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 18 Dec 2017 22:29:23 +0530
Subject: [PATCH 1344/2677] BUGFIX: Fix OpenCL tests on Windows for CUDA9

The issue fixed in this commit started happening on Windows platform with CUDA9.

Both boost::compute::program_cache and ArrayFire contexts are static
variables. The boost cache was being cleaned up after ArrayFire released
the corresponding OpenCL contexts.

This fix keeps a pointer to the shared_ptr to boost::compute::program_cache
and leaks it on Windows platform to circumvent the problem.
---
 src/backend/opencl/CMakeLists.txt             |  4 +--
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  3 ++-
 src/backend/opencl/platform.cpp               | 27 +++++++++++++++++++
 src/backend/opencl/platform.hpp               | 11 ++++++++
 4 files changed, 42 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 0d4b5b08c7..66b493280a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -373,8 +373,8 @@ target_include_directories(afopencl
   )
 
 add_dependencies(afopencl ${cl_kernel_targets})
-add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp)
-add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp)
+add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
+add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 
 set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index d53e6248d5..2add63e693 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -25,7 +25,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp")
 
     add_dependencies(opencl_scan_by_key_${SBK_BINARY_OP}
-                        ${cl_kernel_targets} OpenCL::cl2hpp)
+                        ${cl_kernel_targets} OpenCL::cl2hpp Boost::boost)
 
     target_include_directories(opencl_scan_by_key_${SBK_BINARY_OP}
       PRIVATE
@@ -38,6 +38,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         ${CMAKE_CURRENT_BINARY_DIR}
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
       )
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index f2e668217e..062909836c 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -34,6 +34,9 @@
 #include <common/util.hpp>
 #include <version.hpp>
 
+#include <boost/compute/context.hpp>
+#include <boost/compute/utility/program_cache.hpp>
+
 #include <algorithm>
 #include <cctype>
 #include <cstring>
@@ -564,6 +567,15 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 
     // Last/newly added device needs memory management
     memoryManager().addMemoryManagement(devMngr.mDevices.size()-1);
+
+
+    //cache the boost program_cache object, clean up done on program exit
+    //not during removeDeviceContext
+    namespace compute = boost::compute;
+    using BPCache = DeviceManager::BoostProgCache;
+    compute::context c(ctx);
+    BPCache currCache = compute::program_cache::get_global_cache(c);
+    devMngr.mBoostProgCacheVector.emplace_back(new BPCache(currCache));
 }
 
 void setDeviceContext(cl_device_id dev, cl_context ctx)
@@ -764,6 +776,13 @@ DeviceManager::~DeviceManager()
 
     deInitBlas();
 
+    // deCache Boost program_cache
+#ifndef OS_WIN
+    namespace compute = boost::compute;
+    for (auto bCache : mBoostProgCacheVector)
+        delete bCache;
+#endif
+
     delete memManager.release();
     delete pinnedMemManager.release();
 
@@ -910,6 +929,14 @@ DeviceManager::DeviceManager()
 
     //Initialize clBlas library
     initBlas();
+
+    // Cache Boost program_cache
+    namespace compute = boost::compute;
+    for (auto ctx : mContexts) {
+        compute::context c(ctx->get());
+        BoostProgCache currCache = compute::program_cache::get_global_cache(c);
+        mBoostProgCacheVector.emplace_back(new BoostProgCache(currCache));
+    }
 }
 
 #if defined(WITH_GRAPHICS)
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 875f1c31d0..80654d4bef 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -30,6 +30,14 @@
 #include <memory.hpp>
 #include <GraphicsResourceManager.hpp>
 
+namespace boost {
+  template<typename T> class shared_ptr;
+
+  namespace compute {
+    class program_cache;
+  }
+}
+
 // Forward declaration from clFFT.h
 struct clfftSetupData_;
 typedef clfftSetupData_ clfftSetupData;
@@ -198,5 +206,8 @@ class DeviceManager
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 #endif
         std::unique_ptr<clfftSetupData> mFFTSetup;
+
+        using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
+        std::vector<BoostProgCache*> mBoostProgCacheVector;
 };
 }

From 421df3600abc33c38a3df27a5aafbc3a019a0c9e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 19 Dec 2017 17:37:01 +0530
Subject: [PATCH 1345/2677] Move clamp utility fn to common location on all
 backends

---
 src/backend/cpu/kernel/bilateral.hpp  | 5 +++--
 src/backend/cpu/kernel/fast.hpp       | 2 +-
 src/backend/cpu/math.hpp              | 9 +++++++++
 src/backend/cpu/utility.hpp           | 8 --------
 src/backend/cuda/kernel/bilateral.hpp | 6 ------
 src/backend/cuda/kernel/fast.hpp      | 9 +--------
 src/backend/cuda/math.hpp             | 7 +++++++
 7 files changed, 21 insertions(+), 25 deletions(-)

diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index e45d45c7dd..37f66b2b41 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+#include <math.hpp>
 #include <utility.hpp>
 #include <cmath>
 
@@ -54,10 +55,10 @@ void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma, float const
                     OutT const center = (OutT)inData[getIdx(istrides, i, j)];
                     for(dim_t wj=-radius; wj<=radius; ++wj) {
                         // clamps offsets
-                        dim_t tj = clamp(j+wj, 0, dims[1]-1);
+                        dim_t tj = clamp(j+wj, dim_t(0), dims[1]-1);
                         for(dim_t wi=-radius; wi<=radius; ++wi) {
                             // clamps offsets
-                            dim_t ti = clamp(i+wi, 0, dims[0]-1);
+                            dim_t ti = clamp(i+wi, dim_t(0), dims[0]-1);
                             // proceed
                             OutT const val= (OutT)inData[getIdx(istrides, ti, tj)];
                             OutT const gauss_space = (wi*wi+wj*wj)/(-2.0*svar);
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index db8e42b241..2806b16584 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
-#include <utility.hpp>
+#include <math.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index d95a1d235c..b9fec6e2f9 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -66,4 +66,13 @@ namespace cpu
     cfloat  scalar(float val);
 
     cdouble scalar(double val);
+
+#if __cplusplus < 201703L
+    template<typename T>
+    static inline
+    T clamp(const T value, const T lo, const T hi)
+    {
+        return (value<lo ? lo : (value>hi ? hi : value));
+    }
+#endif
 }
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index 53978a1403..c1a8a86d04 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -15,7 +15,6 @@
 
 namespace cpu
 {
-
 static inline
 dim_t trimIndex(int const & idx, dim_t const & len)
 {
@@ -29,12 +28,6 @@ dim_t trimIndex(int const & idx, dim_t const & len)
     return ret_val;
 }
 
-static inline
-dim_t clamp(dim_t a, dim_t mn, dim_t mx)
-{
-    return (a<mn ? mn : (a>mx ? mx : a));
-}
-
 static inline
 unsigned getIdx(af::dim4 const & strides, int i, int j = 0, int k = 0, int l = 0)
 {
@@ -58,5 +51,4 @@ void gaussian1D(T* out, int const dim, double sigma=0.0)
     for(int k=0;k<dim;k++)
         out[k] /= sum;
 }
-
 }
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index a07569ea4a..32f247a5c3 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -29,12 +29,6 @@ int lIdx(int x, int y, int stride1, int stride0)
     return (y*stride1 + x*stride0);
 }
 
-inline __device__
-int clamp(int f, int a, int b)
-{
-    return max(a, min(f, b));
-}
-
 template<typename inType, typename outType>
 inline __device__
 void load2ShrdMem(outType * shrd, const inType * const in,
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 2da6714485..37587c0bca 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -14,19 +14,13 @@
 #include <err_cuda.hpp>
 #include <kernel/fast_lut.hpp>
 #include <memory.hpp>
+#include <math.hpp>
 
 namespace cuda
 {
-
 namespace kernel
 {
 
-inline __device__
-int clamp(const int f, const int a, const int b)
-{
-    return max(a, min(f, b));
-}
-
 inline __device__
 int idx_y(const int i)
 {
@@ -481,5 +475,4 @@ void fast(unsigned* out_feat,
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index a9a5d8ebc1..298f374f8c 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -224,4 +224,11 @@ __SDH__ bool operator !=(cdouble a, cdouble b) { return !(a == b); }
     template<typename T> static inline T division(T lhs, double rhs) { return lhs / rhs; }
     cfloat division(cfloat lhs, double rhs);
     cdouble division(cdouble lhs, double rhs);
+
+    template<typename T>
+    static inline __DH__
+    T clamp(const T value, const T lo, const T hi)
+    {
+        return max(lo, min(value, hi));
+    }
 }

From 3183473459687b07331892fff7870a74e3f05f70 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 19 Dec 2017 12:20:27 +0530
Subject: [PATCH 1346/2677] BUGFIX: Enable thread-safe flags for Boost::compute
 upstream

---
 CMakeModules/boost_package.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 7620fa1830..067be012a1 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -37,5 +37,5 @@ endif()
 
 # NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
 # will try to link with libboost-chrono.
-set_target_properties(Boost::boost PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS BOOST_CHRONO_HEADER_ONLY)
+set_target_properties(Boost::boost PROPERTIES INTERFACE_COMPILE_DEFINITIONS
+  "BOOST_CHRONO_HEADER_ONLY;BOOST_COMPUTE_THREAD_SAFE;BOOST_COMPUTE_HAVE_THREAD_LOCAL")

From 5b99f39d7f9ac12c3d46a2c0b8170965ddb7e05c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 20 Dec 2017 09:18:12 +0530
Subject: [PATCH 1347/2677] Test for checking boost compute's thread-safety

---
 test/threading.cpp | 32 +++++++++++++++++++++++++++-----
 1 file changed, 27 insertions(+), 5 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 01a130fb99..283bda16a8 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -560,7 +560,7 @@ void cppMatMulCheck(int targetDevice, string TestFile)
     }
 }
 
-#define TEST_FOR_TYPE(TypeName)                                         \
+#define TEST_BLAS_FOR_TYPE(TypeName)                                         \
     tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
             nextTargetDeviceId()%numDevices, TEST_DIR "/blas/Basic.test");          \
     tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
@@ -579,12 +579,12 @@ TEST(Threading, BLAS)
     int numDevices = 1;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
 
-    TEST_FOR_TYPE(      float);
-    TEST_FOR_TYPE( af::cfloat);
+    TEST_BLAS_FOR_TYPE(      float);
+    TEST_BLAS_FOR_TYPE( af::cfloat);
 
     if (noDoubleTests<double>()) {
-        TEST_FOR_TYPE(     double);
-        TEST_FOR_TYPE(af::cdouble);
+        TEST_BLAS_FOR_TYPE(     double);
+        TEST_BLAS_FOR_TYPE(af::cdouble);
     }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
@@ -653,3 +653,25 @@ TEST(Threading, DISABLED_MemoryManagerStressTest)
     t.join();
   }
 }
+
+TEST(Threading, BoostCompute)
+{
+    cleanSlate(); // Clean up everything done so far
+
+    vector<std::thread> tests;
+
+    ASSERT_EQ(AF_SUCCESS, af_set_device(0));
+
+    for (int i=0; i<THREAD_COUNT; ++i) {
+        tests.emplace_back([] {
+            for (int k=0; k<100; ++k) {
+                array a = randu(100, 100);
+                array b = sort(a);
+                array c = sort(a, 1, false);
+            }
+        });
+    }
+
+    for (auto& t: tests)
+        if (t.joinable()) t.join();
+}

From 94620e613d968ad953c21cd0820710716ae348c6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 20 Dec 2017 21:16:02 +0530
Subject: [PATCH 1348/2677] Disable threaded sort test temporarily

The sort is temporarily disabled as it is failing randomly due
to a different memory issue that is present in the library. Once,
that memory issue is addressed, this test will be re-enabled again.
---
 test/threading.cpp | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/test/threading.cpp b/test/threading.cpp
index 283bda16a8..babf773d1b 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -654,7 +654,7 @@ TEST(Threading, DISABLED_MemoryManagerStressTest)
   }
 }
 
-TEST(Threading, BoostCompute)
+TEST(Threading, DISABLED_Sort)
 {
     cleanSlate(); // Clean up everything done so far
 
@@ -664,11 +664,9 @@ TEST(Threading, BoostCompute)
 
     for (int i=0; i<THREAD_COUNT; ++i) {
         tests.emplace_back([] {
-            for (int k=0; k<100; ++k) {
-                array a = randu(100, 100);
+            array a = randu(100, 100);
+            for (int k=0; k<100; ++k)
                 array b = sort(a);
-                array c = sort(a, 1, false);
-            }
         });
     }
 

From 81d58176dbe951973444aa70f453a0f2f90c32c1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 8 Dec 2017 14:27:18 +0530
Subject: [PATCH 1349/2677] BUGFIX: make af_lookup behavior consistent across
 backends

CUDA backend results of af_lookup were different from CPU/OpenCL
for Vector input arrays when the indexing dimension is not along
the same dimension as the input array data.
---
 src/backend/cuda/kernel/lookup.hpp | 27 +++++++++++----------------
 test/index.cpp                     |  9 +++++++++
 2 files changed, 20 insertions(+), 16 deletions(-)

diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 09aff2d85e..0ad5362eff 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -16,15 +16,11 @@
 
 namespace cuda
 {
-
 namespace kernel
 {
-
-static const int THREADS = 256;
-
+static const int THREADS   = 256;
 static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
-
 static const int THRD_LOAD = THREADS_X/THREADS_Y;
 
 template<typename in_t, typename idx_t>
@@ -79,16 +75,17 @@ void lookupND(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
 template<typename in_t, typename idx_t, unsigned dim>
 void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims)
 {
-    if (nDims==1) {
+    /* find which dimension has non-zero # of elements */
+    int vDim = 0;
+    for (int i=0; i<4; i++) {
+        if (in.dims[i]==1)
+            vDim++;
+        else
+            break;
+    }
+
+    if (dim==0 && nDims==1 && dim==vDim) {
         const dim3 threads(THREADS, 1);
-        /* find which dimension has non-zero # of elements */
-        int vDim = 0;
-        for (int i=0; i<4; i++) {
-            if (in.dims[i]==1)
-                vDim++;
-            else
-                break;
-        }
 
         int blks = divup(out.dims[vDim], THREADS*THRD_LOAD);
 
@@ -112,7 +109,5 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims)
 
     POST_LAUNCH_CHECK();
 }
-
 }
-
 }
diff --git a/test/index.cpp b/test/index.cpp
index 98b6366a73..2866f9955a 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -709,6 +709,15 @@ TEST(lookup, largeDim)
     af::array output = af::lookup(input, indices);
 }
 
+TEST(lookup, Issue2009)
+{
+    af::array a   = af::range(af::dim4(1000, 1));
+    af::array idx = af::constant(0, 1, u32);
+    af::array b   = af::lookup(a, idx, 1);
+
+    ASSERT_EQ(true, af::allTrue<bool>(a==b));
+}
+
 TEST(SeqIndex, CPP_END)
 {
     using af::array;

From 2f7a1d713e541bf230a14b6ecca8f963b84fcae0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 21 Dec 2017 16:20:02 +0530
Subject: [PATCH 1350/2677] Support for <backend>_NOT_SUPPORTED macros to
 accept messages

style changes to morph error messages
---
 src/backend/cpu/copy.cpp                      |  5 ++-
 src/backend/cpu/err_cpu.hpp                   |  4 +--
 src/backend/cpu/nearest_neighbour.cpp         |  2 +-
 src/backend/cuda/copy.cu                      |  5 ++-
 src/backend/cuda/err_cuda.hpp                 |  4 +--
 src/backend/cuda/kernel/bilateral.hpp         |  5 ++-
 src/backend/cuda/kernel/convolve.cu           | 34 ++++++++++++++++---
 src/backend/cuda/kernel/convolve_separable.cu | 20 +++++++----
 src/backend/cuda/kernel/nearest_neighbour.hpp |  5 ++-
 src/backend/cuda/morph3d_impl.hpp             |  4 +--
 src/backend/cuda/morph_impl.hpp               |  5 +--
 src/backend/opencl/convolve.cpp               |  8 ++++-
 src/backend/opencl/convolve_separable.cpp     |  8 +++--
 src/backend/opencl/copy.cpp                   |  5 ++-
 src/backend/opencl/err_opencl.hpp             |  4 +--
 src/backend/opencl/kernel/bilateral.hpp       |  5 ++-
 src/backend/opencl/morph3d_impl.hpp           |  5 +--
 src/backend/opencl/morph_impl.hpp             |  5 +--
 test/morph.cpp                                | 18 ++++++++++
 19 files changed, 115 insertions(+), 36 deletions(-)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 8ca26b3e12..d0bea9769f 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -108,7 +108,10 @@ INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 #define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
     template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
     {\
-        CPU_NOT_SUPPORTED();\
+        char errMessage[1024];                                              \
+        snprintf(errMessage, sizeof(errMessage),                            \
+                "CPU copyArray<"#SRC_T","#DST_T"> is not supported\n");    \
+        CPU_NOT_SUPPORTED(errMessage);                                      \
     }
 
 SPECILIAZE_UNUSED_COPYARRAY(cfloat , double)
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index 07440685d0..4e9464db1d 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -9,7 +9,7 @@
 
 #include <common/err_common.hpp>
 
-#define CPU_NOT_SUPPORTED() do {                        \
+#define CPU_NOT_SUPPORTED(message) do {                 \
         throw SupportError(__PRETTY_FUNCTION__,         \
-                __AF_FILENAME__, __LINE__, "CPU");      \
+                __AF_FILENAME__, __LINE__, message);    \
     } while(0)
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index b4c2b4a087..e0294fda27 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -27,7 +27,7 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
                        const af_match_type dist_type)
 {
     if (n_dist > 1) {
-        CPU_NOT_SUPPORTED();
+        CPU_NOT_SUPPORTED("\nNumber of smallest distances can't be <1\n");
     }
 
     idx.eval();
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 32128d00df..b69f650b31 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -179,7 +179,10 @@ namespace cuda
 #define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
     template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
     {\
-        CUDA_NOT_SUPPORTED();\
+        char errMessage[1024];                                              \
+        snprintf(errMessage, sizeof(errMessage),                            \
+                "CUDA copyArray<"#SRC_T","#DST_T"> is not supported\n");    \
+        CUDA_NOT_SUPPORTED(errMessage);                                     \
     }
 
     SPECILIAZE_UNUSED_COPYARRAY(cfloat, double)
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 29cf310581..822ca4b689 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -12,9 +12,9 @@
 #include <common/defines.hpp>
 #include <common/err_common.hpp>
 
-#define CUDA_NOT_SUPPORTED() do {                       \
+#define CUDA_NOT_SUPPORTED(message) do {                \
         throw SupportError(__PRETTY_FUNCTION__,         \
-                __AF_FILENAME__, __LINE__, "CUDA");     \
+                __AF_FILENAME__, __LINE__, message);    \
     } while(0)
 
 #define CUDA_CHECK(fn) do {                                         \
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 32f247a5c3..d5932432c6 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -134,7 +134,10 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma, float c_sig
 
     size_t MAX_SHRD_SIZE = cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock;
     if (total_shrd_size > MAX_SHRD_SIZE) {
-        CUDA_NOT_SUPPORTED();
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCUDA Bilateral filter doesn't support %f spatial sigma\n", s_sigma);
+        CUDA_NOT_SUPPORTED(errMessage);
     }
 
     CUDA_LAUNCH_SMEM((bilateralKernel<inType, outType>), blocks, threads, total_shrd_size,
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 7ca2a04db2..2839d311c7 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -327,7 +327,13 @@ void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f1)
         case  3: conv2Helper<T, aT, expand, f0,  3>(p, out, sig); break;
         case  4: conv2Helper<T, aT, expand, f0,  4>(p, out, sig); break;
         case  5: conv2Helper<T, aT, expand, f0,  5>(p, out, sig); break;
-        default: CUDA_NOT_SUPPORTED();
+        default:
+            {
+                char errMessage[256];
+                snprintf(errMessage, sizeof(errMessage),
+                        "\nCUDA Convolution doesn't support %dx%d kernel\n", f0, f1);
+                CUDA_NOT_SUPPORTED(errMessage);
+            };
     }
 }
 
@@ -355,10 +361,22 @@ void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f0, in
                              case 15: conv2Helper<T, aT, expand, 15, 15>(p, out, sig); break;
                              case 16: conv2Helper<T, aT, expand, 16, 16>(p, out, sig); break;
                              case 17: conv2Helper<T, aT, expand, 17, 17>(p, out, sig); break;
-                             default: CUDA_NOT_SUPPORTED();
+                             default:
+                                {
+                                    char errMessage[256];
+                                    snprintf(errMessage, sizeof(errMessage),
+                                            "\nCUDA 2D convolution doesn't support %dx%d kernel\n", f0, f1);
+                                    CUDA_NOT_SUPPORTED(errMessage);
+                                };
                          }
-                     } else
-                         CUDA_NOT_SUPPORTED();
+                     } else {
+                        {
+                            char errMessage[256];
+                            snprintf(errMessage, sizeof(errMessage),
+                                    "\nCUDA 2D convolution doesn't support rectangular kernels\n");
+                            CUDA_NOT_SUPPORTED(errMessage);
+                        };
+                     }
                  } break;
     }
 }
@@ -476,7 +494,13 @@ void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, AF_BATCH_KIND
         case 3: if ((filt.dims[0]*filt.dims[1]*filt.dims[2]) > (MCFL3 * MCFL3 * MCFL3)) callKernel = false; break;
     }
 
-    if (!callKernel) { CUDA_NOT_SUPPORTED(); }
+    if (!callKernel) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCUDA N Dimensional Convolution doesn't support %dx%dx%d kernel\n",
+                 filt.dims[0], filt.dims[1], filt.dims[2]);
+        CUDA_NOT_SUPPORTED(errMessage);
+    }
 
     conv_kparam_t param;
     for (int i=0; i<3; ++i) {
diff --git a/src/backend/cuda/kernel/convolve_separable.cu b/src/backend/cuda/kernel/convolve_separable.cu
index 3200442d44..27df028fce 100644
--- a/src/backend/cuda/kernel/convolve_separable.cu
+++ b/src/backend/cuda/kernel/convolve_separable.cu
@@ -16,10 +16,8 @@
 
 namespace cuda
 {
-
 namespace kernel
 {
-
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
@@ -118,8 +116,12 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter)
 {
     int fLen = filter.dims[0] * filter.dims[1] * filter.dims[2] * filter.dims[3];
     if(fLen > kernel::MAX_SCONV_FILTER_LEN) {
-        // call upon fft
-        CUDA_NOT_SUPPORTED();
+        // TODO call upon fft
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCUDA convolution supports max kernel size of %d\n",
+                 kernel::MAX_SCONV_FILTER_LEN);
+        CUDA_NOT_SUPPORTED(errMessage);
     }
 
     dim3 threads(THREADS_X, THREADS_Y);
@@ -166,7 +168,13 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter)
         case 29: conv2Helper<T, accType, conv_dim, expand, 29>(blocks, threads, out, signal, blk_x, blk_y); break;
         case 30: conv2Helper<T, accType, conv_dim, expand, 30>(blocks, threads, out, signal, blk_x, blk_y); break;
         case 31: conv2Helper<T, accType, conv_dim, expand, 31>(blocks, threads, out, signal, blk_x, blk_y); break;
-        default: CUDA_NOT_SUPPORTED();
+        default:
+            {
+                char errMessage[256];
+                snprintf(errMessage, sizeof(errMessage),
+                        "\nCUDA Separable convolution doesn't support %d kernel\n", fLen);
+                CUDA_NOT_SUPPORTED(errMessage);
+            };
     }
 
    POST_LAUNCH_CHECK();
@@ -191,7 +199,5 @@ INSTANTIATE(ushort ,   float)
 INSTANTIATE(short  ,   float)
 INSTANTIATE(uintl  ,   float)
 INSTANTIATE(intl   ,   float)
-
 }
-
 }
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 170292ff5f..fd386af63e 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -434,7 +434,10 @@ void nearest_neighbour(Param<uint> idx,
     const To max_dist = maxval<To>();
 
     if (feat_len > THREADS) {
-        CUDA_NOT_SUPPORTED();
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "CUDA Maximum number of features supported in nearest_neighbor is %d\n", THREADS);
+        CUDA_NOT_SUPPORTED(errMessage);
     }
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index e6233c989e..8033509618 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -24,10 +24,10 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
     const dim4 mdims = mask.dims();
 
     if (mdims[0] != mdims[1] || mdims[0] != mdims[2])
-        AF_ERROR("Only cube masks are supported in CUDA backend", AF_ERR_SIZE);
+        CUDA_NOT_SUPPORTED("Only cubic masks are supported");
 
     if (mdims[0] > 7)
-        AF_ERROR("Upto 7x7x7 kernels are only supported in CUDA backend", AF_ERR_SIZE);
+        CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported");
 
     Array<T> out       = createEmptyArray<T>(in.dims());
 
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index 994e792a4b..1da0f36da5 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -24,9 +24,10 @@ Array<T>  morph(const Array<T> &in, const Array<T> &mask)
     const dim4 mdims = mask.dims();
 
     if (mdims[0] != mdims[1])
-        AF_ERROR("Only square masks are supported in cuda morph currently", AF_ERR_SIZE);
+        CUDA_NOT_SUPPORTED("Rectangular masks are not supported");
+
     if (mdims[0] > 19)
-        AF_ERROR("Upto 19x19 square kernels are only supported in cuda currently", AF_ERR_SIZE);
+        CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index f17b99563a..8773a03541 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -52,7 +52,13 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
         case 3: if ((fDims[0]*fDims[1]*fDims[2]) > (MCFL3 * MCFL3 * MCFL3)) callKernel = false; break;
     }
 
-    if(!callKernel) { OPENCL_NOT_SUPPORTED(); }
+    if(!callKernel) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nOpenCL N Dimensional Convolution doesn't support %dx%dx%d kernel\n",
+                 fDims[0], fDims[1], fDims[2]);
+        OPENCL_NOT_SUPPORTED(errMessage);
+    }
 
     kernel::convolve_nd<T, accT, baseDim, expand>(out, signal, filter, kind);
 
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 162e93a289..32b0e6ce75 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -26,8 +26,12 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
 
     if ((cflen > kernel::MAX_SCONV_FILTER_LEN) ||
         (rflen > kernel::MAX_SCONV_FILTER_LEN)) {
-        // call upon fft
-        OPENCL_NOT_SUPPORTED();
+        // TODO call upon fft
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                "\nOpenCL Separable convolution doesn't support %d(coloumn) %d(row) filters\n",
+                 cflen, rflen);
+        OPENCL_NOT_SUPPORTED(errMessage);
     }
 
     const dim4 sDims = signal.dims();
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 6feb948c6b..203e6dfb98 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -191,7 +191,10 @@ namespace opencl
 #define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
     template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
     {\
-        OPENCL_NOT_SUPPORTED();\
+        char errMessage[1024];                                              \
+        snprintf(errMessage, sizeof(errMessage),                            \
+                "OpenCL copyArray<"#SRC_T","#DST_T"> is not supported\n");  \
+        OPENCL_NOT_SUPPORTED(errMessage);                                   \
     }
 
     SPECILIAZE_UNUSED_COPYARRAY(cfloat, double)
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 2dca3fa538..c330675932 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -14,9 +14,9 @@
 #include <platform.hpp>
 #include <types.hpp>
 
-#define OPENCL_NOT_SUPPORTED() do {                     \
+#define OPENCL_NOT_SUPPORTED(message) do {              \
         throw SupportError(__PRETTY_FUNCTION__,         \
-                __AF_FILENAME__, __LINE__, "OpenCL");   \
+                __AF_FILENAME__, __LINE__, message);    \
     } while(0)
 
 namespace opencl
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 966d82b785..6c4f2b2504 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -84,7 +84,10 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
     size_t localMemSize   = (num_shrd_elems + num_gauss_elems)*sizeof(outType);
     size_t MaxLocalSize   = getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
     if (localMemSize>MaxLocalSize) {
-        OPENCL_NOT_SUPPORTED();
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nOpenCL Bilateral filter doesn't support %f spatial sigma\n", s_sigma);
+        OPENCL_NOT_SUPPORTED(errMessage);
     }
 
     bilateralOp(EnqueueArgs(getQueue(), global, local),
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index 77452bbb2f..0d7fa97d10 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -25,9 +25,10 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
     const dim4 mdims    = mask.dims();
 
     if (mdims[0]!=mdims[1] || mdims[0]!=mdims[2])
-        AF_ERROR("Only cube masks are supported in opencl morph currently", AF_ERR_SIZE);
+        OPENCL_NOT_SUPPORTED("Only cubic masks are supported");
+
     if (mdims[0]>7)
-        AF_ERROR("Upto 7x7x7 kernels are only supported in opencl currently", AF_ERR_SIZE);
+        OPENCL_NOT_SUPPORTED("Kernels > 7x7x7 masks are not supported");
 
     const dim4 dims= in.dims();
     Array<T> out   = createEmptyArray<T>(dims);
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
index 00e079185d..2df6ee9616 100644
--- a/src/backend/opencl/morph_impl.hpp
+++ b/src/backend/opencl/morph_impl.hpp
@@ -25,9 +25,10 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
     const dim4 mdims    = mask.dims();
 
     if (mdims[0]!=mdims[1])
-        AF_ERROR("Only square masks are supported in opencl morph currently", AF_ERR_SIZE);
+        OPENCL_NOT_SUPPORTED("Rectangular masks are not suported");
+
     if (mdims[0]>19)
-        AF_ERROR("Upto 19x19 square kernels are only supported in opencl currently", AF_ERR_SIZE);
+        OPENCL_NOT_SUPPORTED("Kernels > 19x19 are not supported");
 
     const dim4 dims = in.dims();
     Array<T> out   = createEmptyArray<T>(dims);
diff --git a/test/morph.cpp b/test/morph.cpp
index 11499b6ca5..51bacbc221 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -454,3 +454,21 @@ TEST(Morph, EdgeIssue1564)
         ASSERT_EQ((int)outData[i], goldData[i]);
     }
 }
+
+TEST(Morph, UnsupportedKernel2D)
+{
+    const unsigned ndims = 2;
+    const dim_t dims[2] = {10, 10};
+    const dim_t kdims[2] = {32, 32};
+
+    af_array in, mask, out;
+
+    ASSERT_EQ(AF_SUCCESS, af_constant(&mask, 1.0, ndims, kdims, f32));
+    ASSERT_EQ(AF_SUCCESS, af_randu(&in, ndims, dims, f32));
+
+#if defined(AF_CPU)
+    ASSERT_EQ(AF_SUCCESS, af_dilate(&out, in, mask));
+#else
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_dilate(&out, in, mask));
+#endif
+}

From dd8105182da0d84740ee08ea35bc630f1810ea20 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 5 Oct 2017 19:59:32 +0530
Subject: [PATCH 1351/2677] Fix morphology fns to handle even size kernels

Added tests for kernel sizes 4x4 & 4x4x4.
---
 src/backend/cpu/kernel/morph.hpp    |  3 --
 src/backend/cpu/morph.cpp           |  2 --
 src/backend/cpu/morph.hpp           |  2 --
 src/backend/cuda/kernel/morph.hpp   | 51 ++++++++++++++++-------------
 src/backend/cuda/morph.hpp          |  2 --
 src/backend/cuda/morph3d_impl.hpp   |  4 +--
 src/backend/cuda/morph_impl.hpp     |  4 +--
 src/backend/opencl/kernel/morph.cl  | 15 ++++-----
 src/backend/opencl/kernel/morph.hpp |  6 ++--
 src/backend/opencl/morph.hpp        |  2 --
 src/backend/opencl/morph3d_impl.hpp |  9 ++---
 src/backend/opencl/morph_impl.hpp   | 15 ++++++---
 test/data                           |  2 +-
 test/morph.cpp                      | 18 ++++++++++
 14 files changed, 75 insertions(+), 60 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index e75e0d1393..d6c9b1665f 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -17,7 +17,6 @@ namespace cpu
 {
 namespace kernel
 {
-
 template<typename T, bool IsDilation>
 void morph(Param<T> out, CParam<T> in, CParam<T> mask)
 {
@@ -141,7 +140,5 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask)
         inData  += istrides[3];
     }
 }
-
-
 }
 }
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 56143595fe..fa5f6c2477 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -19,7 +19,6 @@ using af::dim4;
 
 namespace cpu
 {
-
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask)
 {
@@ -60,5 +59,4 @@ INSTANTIATE(uint  )
 INSTANTIATE(uchar )
 INSTANTIATE(ushort)
 INSTANTIATE(short )
-
 }
diff --git a/src/backend/cpu/morph.hpp b/src/backend/cpu/morph.hpp
index e537303135..006553db38 100644
--- a/src/backend/cpu/morph.hpp
+++ b/src/backend/cpu/morph.hpp
@@ -11,11 +11,9 @@
 
 namespace cpu
 {
-
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask);
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
-
 }
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 0687f9da9b..41e3a51766 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -18,10 +18,8 @@
 
 namespace cuda
 {
-
 namespace kernel
 {
-
 static const int MAX_MORPH_FILTER_LEN = 17;
 // cFilter is used by both 2d morph and 3d morph
 // Maximum kernel size supported for 2d morph is 19x19*8 = 2888
@@ -68,8 +66,8 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     T * shrdMem = shared.getPointer();
 
     // calculate necessary offset and window parameters
-    const int halo   = windLen/2;
-    const int padding= 2*halo;
+    const int halo     = windLen/2;
+    const int padding  = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     const int shrdLen  = blockDim.x + padding + 1;
     const int shrdLen1 = blockDim.y + padding;
 
@@ -158,13 +156,13 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     T * shrdMem = shared.getPointer();
 
     const int halo      = windLen/2;
-    const int padding   = 2*halo;
+    const int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
 
     const int se_area   = windLen*windLen;
     const int shrdLen   = blockDim.x + padding + 1;
     const int shrdLen1  = blockDim.y + padding;
     const int shrdLen2  = blockDim.z + padding;
-    const int shrdArea  = shrdLen * (blockDim.y+padding);
+    const int shrdArea  = shrdLen * shrdLen1;
 
     // gfor batch offsets
     unsigned batchId = blockIdx.x / nBBS;
@@ -239,22 +237,30 @@ void morph(Param<T> out, CParam<T> in, int windLen)
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
     // calculate shared memory size
-    int halo      = windLen/2;
-    int padding   = 2*halo;
+    int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     int shrdLen   = kernel::THREADS_X + padding + 1; // +1 for to avoid bank conflicts
     int shrdSize  = shrdLen * (kernel::THREADS_Y + padding) * sizeof(T);
 
     switch(windLen) {
-        case  3: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 3>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  5: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 5>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  7: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 7>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  9: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 9>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 11: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,11>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 13: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,13>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 15: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,15>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 17: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,17>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 19: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,19>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        default: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 3>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  2: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  2>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  3: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  3>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  4: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  4>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  5: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  5>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  6: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  6>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  7: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  7>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  8: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  8>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case  9: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  9>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 10: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 10>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 11: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 11>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 12: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 12>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 13: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 13>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 14: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 14>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 15: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 15>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 16: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 16>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 17: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 17>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 18: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 18>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        case 19: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 19>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
+        default: CUDA_NOT_SUPPORTED(); break;
     }
 
     POST_LAUNCH_CHECK();
@@ -271,20 +277,21 @@ void morph3d(Param<T> out, CParam<T> in, int windLen)
     dim3 blocks(blk_x * in.dims[3], blk_y, blk_z);
 
     // calculate shared memory size
-    int halo      = windLen/2;
-    int padding   = 2*halo;
+    int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     int shrdLen   = kernel::CUBE_X + padding + 1; // +1 for to avoid bank conflicts
     int shrdSize  = shrdLen * (kernel::CUBE_Y + padding) * (kernel::CUBE_Z + padding) * sizeof(T);
 
     switch(windLen) {
+        case  2: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 2>), blocks, threads, shrdSize, out, in, blk_x); break;
         case  3: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 3>), blocks, threads, shrdSize, out, in, blk_x); break;
+        case  4: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 4>), blocks, threads, shrdSize, out, in, blk_x); break;
         case  5: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 5>), blocks, threads, shrdSize, out, in, blk_x); break;
+        case  6: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 6>), blocks, threads, shrdSize, out, in, blk_x); break;
         case  7: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 7>), blocks, threads, shrdSize, out, in, blk_x); break;
-        default: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 3>), blocks, threads, shrdSize, out, in, blk_x); break;
+        default: CUDA_NOT_SUPPORTED(); break;
     }
 
     POST_LAUNCH_CHECK();
 }
-
 }
 }
diff --git a/src/backend/cuda/morph.hpp b/src/backend/cuda/morph.hpp
index d218577e8f..54eef63967 100644
--- a/src/backend/cuda/morph.hpp
+++ b/src/backend/cuda/morph.hpp
@@ -11,11 +11,9 @@
 
 namespace cuda
 {
-
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask);
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
-
 }
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index 8033509618..c283302d3a 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -17,7 +17,6 @@ using af::dim4;
 
 namespace cuda
 {
-
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
 {
@@ -44,7 +43,6 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
     return out;
 }
 
-}
-
 #define INSTANTIATE(T, ISDILATE)                                        \
     template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
+}
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index 1da0f36da5..8fd04d576f 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -17,7 +17,6 @@ using af::dim4;
 
 namespace cuda
 {
-
 template<typename T, bool isDilation>
 Array<T>  morph(const Array<T> &in, const Array<T> &mask)
 {
@@ -44,7 +43,6 @@ Array<T>  morph(const Array<T> &in, const Array<T> &mask)
     return out;
 }
 
-}
-
 #define INSTANTIATE(T, ISDILATE)                                        \
     template Array<T> morph  <T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
+}
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index 916d9567b6..941132e07a 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -35,10 +35,10 @@ void morph(__global T *              out,
            __local T *          localMem,
            int nBBS0, int nBBS1)
 {
-    const int halo   = windLen/2;
-    const int padding= 2*halo;
-    const int shrdLen = get_local_size(0) + padding + 1;
-    const int shrdLen1= get_local_size(1) + padding;
+    const int halo     = windLen/2;
+    const int padding  = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
+    const int shrdLen  = get_local_size(0) + padding + 1;
+    const int shrdLen1 = get_local_size(1) + padding;
 
     // gfor batch offsets
     int b2 = get_group_id(0) / nBBS0;
@@ -125,14 +125,13 @@ void morph3d(__global T *         out,
              __local T *          localMem,
              int             nBBS)
 {
-    const int halo   = windLen/2;
-    const int padding= 2*halo;
-
+    const int halo      = windLen/2;
+    const int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     const int se_area   = windLen*windLen;
     const int shrdLen   = get_local_size(0) + padding + 1;
     const int shrdLen1  = get_local_size(1) + padding;
     const int shrdLen2  = get_local_size(2) + padding;
-    const int shrdArea  = shrdLen * (get_local_size(1)+padding);
+    const int shrdArea  = shrdLen * shrdLen1;
 
     // gfor batch offsets
     int batchId    = get_group_id(0) / nBBS;
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index caf61b162e..443f7f0f34 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -92,8 +92,7 @@ void morph(Param out, const Param in, const Param mask)
     getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
 
     // calculate shared memory size
-    const int halo    = windLen/2;
-    const int padding = 2*halo;
+    const int padding = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     const int locLen  = THREADS_X + padding + 1;
     const int locSize = locLen * (THREADS_Y+padding);
 
@@ -146,8 +145,7 @@ void morph3d(Param       out,
     getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
 
     // calculate shared memory size
-    const int halo    = windLen/2;
-    const int padding = 2*halo;
+    const int padding = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     const int locLen  = CUBE_X+padding+1;
     const int locArea = locLen *(CUBE_Y+padding);
     const int locSize = locArea*(CUBE_Z+padding);
diff --git a/src/backend/opencl/morph.hpp b/src/backend/opencl/morph.hpp
index f16c63c86e..4d3d74206d 100644
--- a/src/backend/opencl/morph.hpp
+++ b/src/backend/opencl/morph.hpp
@@ -11,11 +11,9 @@
 
 namespace opencl
 {
-
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask);
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
-
 }
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index 0d7fa97d10..cf69515a82 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -18,7 +18,6 @@ using af::dim4;
 
 namespace opencl
 {
-
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
 {
@@ -34,16 +33,18 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
     Array<T> out   = createEmptyArray<T>(dims);
 
     switch(mdims[0]) {
+        case  2: kernel::morph3d<T, isDilation,  2>(out, in, mask); break;
         case  3: kernel::morph3d<T, isDilation,  3>(out, in, mask); break;
+        case  4: kernel::morph3d<T, isDilation,  4>(out, in, mask); break;
         case  5: kernel::morph3d<T, isDilation,  5>(out, in, mask); break;
+        case  6: kernel::morph3d<T, isDilation,  6>(out, in, mask); break;
         case  7: kernel::morph3d<T, isDilation,  7>(out, in, mask); break;
-        default: kernel::morph3d<T, isDilation,  3>(out, in, mask); break;
+        default: OPENCL_NOT_SUPPORTED(); break;
     }
 
     return out;
 }
 
-}
-
 #define INSTANTIATE(T, ISDILATE)                                                 \
     template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
+}
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
index 2df6ee9616..e702e62043 100644
--- a/src/backend/opencl/morph_impl.hpp
+++ b/src/backend/opencl/morph_impl.hpp
@@ -18,7 +18,6 @@ using af::dim4;
 
 namespace opencl
 {
-
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask)
 {
@@ -34,22 +33,30 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
     Array<T> out   = createEmptyArray<T>(dims);
 
     switch(mdims[0]) {
+        case  2: kernel::morph<T, isDilation,  2>(out, in, mask); break;
         case  3: kernel::morph<T, isDilation,  3>(out, in, mask); break;
+        case  4: kernel::morph<T, isDilation,  4>(out, in, mask); break;
         case  5: kernel::morph<T, isDilation,  5>(out, in, mask); break;
+        case  6: kernel::morph<T, isDilation,  6>(out, in, mask); break;
         case  7: kernel::morph<T, isDilation,  7>(out, in, mask); break;
+        case  8: kernel::morph<T, isDilation,  8>(out, in, mask); break;
         case  9: kernel::morph<T, isDilation,  9>(out, in, mask); break;
+        case 10: kernel::morph<T, isDilation, 10>(out, in, mask); break;
         case 11: kernel::morph<T, isDilation, 11>(out, in, mask); break;
+        case 12: kernel::morph<T, isDilation, 12>(out, in, mask); break;
         case 13: kernel::morph<T, isDilation, 13>(out, in, mask); break;
+        case 14: kernel::morph<T, isDilation, 14>(out, in, mask); break;
         case 15: kernel::morph<T, isDilation, 15>(out, in, mask); break;
+        case 16: kernel::morph<T, isDilation, 16>(out, in, mask); break;
         case 17: kernel::morph<T, isDilation, 17>(out, in, mask); break;
+        case 18: kernel::morph<T, isDilation, 18>(out, in, mask); break;
         case 19: kernel::morph<T, isDilation, 19>(out, in, mask); break;
-        default: kernel::morph<T, isDilation,  3>(out, in, mask); break;
+        default: OPENCL_NOT_SUPPORTED(); break;
     }
 
     return out;
 }
 
-}
-
 #define INSTANTIATE(T, ISDILATE)                                                 \
     template Array<T> morph  <T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
+}
diff --git a/test/data b/test/data
index 745b967d90..884362522d 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 745b967d90fe02b583c9de1ac800f3bd2a2a03bc
+Subproject commit 884362522d94e5fea0d6f0445e181413f5a5abd7
diff --git a/test/morph.cpp b/test/morph.cpp
index 51bacbc221..feaf4ce95f 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -96,6 +96,15 @@ TYPED_TEST(Morph, Erode3x3)
     morphTest<TypeParam, false, false>(string(TEST_DIR"/morph/erode3x3.test"));
 }
 
+TYPED_TEST(Morph, Dilate4x4)
+{
+    morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate4x4.test"));
+}
+TYPED_TEST(Morph, Erode4x4)
+{
+    morphTest<TypeParam, false, false>(string(TEST_DIR"/morph/erode4x4.test"));
+}
+
 TYPED_TEST(Morph, Dilate3x3_Batch)
 {
     morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate3x3_batch.test"));
@@ -116,6 +125,15 @@ TYPED_TEST(Morph, Erode3x3x3)
     morphTest<TypeParam, false, true>(string(TEST_DIR"/morph/erode3x3x3.test"));
 }
 
+TYPED_TEST(Morph, Dilate4x4x4)
+{
+    morphTest<TypeParam, true, true>(string(TEST_DIR"/morph/dilate4x4x4.test"));
+}
+TYPED_TEST(Morph, Erode4x4x4)
+{
+    morphTest<TypeParam, false, true>(string(TEST_DIR"/morph/erode4x4x4.test"));
+}
+
 template<typename T, bool isDilation, bool isColor>
 void morphImageTest(string pTestFile)
 {

From fc18d4451952a56b928a0d1cc5f414b29ad17609 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 22 Dec 2017 15:05:38 +0530
Subject: [PATCH 1352/2677] Improve morph object file build times in
 CUDA/OpenCL

Removed template instantiations for kernel/window sizes beyond
10x10 to reduce build time. From 10x10 till 19x19, the kernel
uses the kernel argument directly instead of template value.

Change ternary op in opencl morph kernel to if statement
---
 src/backend/cuda/kernel/morph.hpp   | 34 ++++++++++++++++++-----------
 src/backend/opencl/kernel/morph.cl  | 19 +++++++++-------
 src/backend/opencl/kernel/morph.hpp | 28 +++++++++++++-----------
 src/backend/opencl/morph3d_impl.hpp |  2 +-
 src/backend/opencl/morph_impl.hpp   | 11 +---------
 test/data                           |  2 +-
 test/morph.cpp                      |  7 ++++++
 7 files changed, 57 insertions(+), 46 deletions(-)

diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 41e3a51766..68d0353e11 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -55,12 +55,28 @@ inline __device__ void load2ShrdMem(T * shrd, const T * const in,
     shrd[ lIdx(lx, ly, shrdStride, 1) ] = val;
 }
 
+
 // kernel assumes mask/filter is square and hence does the
 // necessary operations accordingly.
-template<typename T, bool isDilation, int windLen>
+//
+// Notes on template arguments for morphKernel:
+//   * T is the data type of the image & kernel
+//   * isDilation indicates if the current kernel invocation is an erosion operation or dilation
+//   operation
+//   * SeLength is the structuring element length a.k.a the kernel window length. This template
+//   parameter takes precedence over the kernel argument `windLen`.
+//
+// Please make sure at least one of the following variables is not 0.
+//  * SeLength (structuring element a.k.a window/kernel)
+//  * windLen
+// If SeLength is > 0, then that will override the kernel argument.
+template<typename T, bool isDilation, int SeLength=0>
 static __global__ void morphKernel(Param<T> out, CParam<T> in,
-                                   int nBBS0, int nBBS1)
+                                   int nBBS0, int nBBS1,
+                                   int windLen=0)
 {
+    windLen = (SeLength>0 ? SeLength : windLen);
+
     // get shared memory pointer
     SharedMemory<T> shared;
     T * shrdMem = shared.getPointer();
@@ -251,16 +267,8 @@ void morph(Param<T> out, CParam<T> in, int windLen)
         case  8: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  8>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
         case  9: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  9>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
         case 10: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 10>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 11: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 11>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 12: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 12>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 13: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 13>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 14: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 14>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 15: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 15>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 16: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 16>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 17: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 17>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 18: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 18>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 19: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 19>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        default: CUDA_NOT_SUPPORTED(); break;
+        default: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation>), blocks, threads, shrdSize, out, in, blk_x, blk_y, windLen);
+                 break;
     }
 
     POST_LAUNCH_CHECK();
@@ -288,7 +296,7 @@ void morph3d(Param<T> out, CParam<T> in, int windLen)
         case  5: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 5>), blocks, threads, shrdSize, out, in, blk_x); break;
         case  6: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 6>), blocks, threads, shrdSize, out, in, blk_x); break;
         case  7: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 7>), blocks, threads, shrdSize, out, in, blk_x); break;
-        default: CUDA_NOT_SUPPORTED(); break;
+        default: CUDA_NOT_SUPPORTED("Morph 3D does not support kernels larger than 7.");
     }
 
     POST_LAUNCH_CHECK();
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index 941132e07a..59985a6b47 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -33,8 +33,11 @@ void morph(__global T *              out,
            KParam                  iInfo,
            __constant const T *   d_filt,
            __local T *          localMem,
-           int nBBS0, int nBBS1)
+           int nBBS0, int nBBS1, int windLen)
 {
+    if (SeLength>0)
+        windLen = SeLength;
+
     const int halo     = windLen/2;
     const int padding  = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
     const int shrdLen  = get_local_size(0) + padding + 1;
@@ -125,9 +128,9 @@ void morph3d(__global T *         out,
              __local T *          localMem,
              int             nBBS)
 {
-    const int halo      = windLen/2;
-    const int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
-    const int se_area   = windLen*windLen;
+    const int halo      = SeLength/2;
+    const int padding   = (SeLength%2==0 ? (SeLength-1) : (2*(SeLength/2)));
+    const int se_area   = SeLength*SeLength;
     const int shrdLen   = get_local_size(0) + padding + 1;
     const int shrdLen1  = get_local_size(1) + padding;
     const int shrdLen2  = get_local_size(2) + padding;
@@ -170,15 +173,15 @@ void morph3d(__global T *         out,
 
     T acc = init;
 #pragma unroll
-    for(int wk=0; wk<windLen; ++wk) {
+    for(int wk=0; wk<SeLength; ++wk) {
         int koff   = wk*se_area;
         int w_koff = (k+wk-halo)*shrdArea;
 #pragma unroll
-        for(int wj=0; wj<windLen; ++wj) {
-        int joff   = wj*windLen;
+        for(int wj=0; wj<SeLength; ++wj) {
+        int joff   = wj*SeLength;
         int w_joff = (j+wj-halo)*shrdLen;
 #pragma unroll
-            for(int wi=0; wi<windLen; ++wi) {
+            for(int wi=0; wi<SeLength; ++wi) {
                 T cur  = localMem[w_koff+w_joff + i+wi-halo];
                 if (d_filt[koff+joff+wi]) {
                     if (isDilation)
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 443f7f0f34..8cb026525f 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -40,7 +40,7 @@ static const int CUBE_X    =  8;
 static const int CUBE_Y    =  8;
 static const int CUBE_Z    =  4;
 
-template<typename T, bool isDilation, int windLen>
+template<typename T, bool isDilation, int SeLength>
 std::string generateOptionsString()
 {
     ToNumStr<T> toNumStr;
@@ -49,24 +49,26 @@ std::string generateOptionsString()
     options << " -D T=" << dtype_traits<T>::getName()
         << " -D isDilation="<< isDilation
         << " -D init=" << toNumStr(init)
-        << " -D windLen=" << windLen;
+        << " -D SeLength=" << SeLength;
     if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
         options << " -D USE_DOUBLE";
     return options.str();
 }
 
-template<typename T, bool isDilation, int windLen>
-void morph(Param out, const Param in, const Param mask)
+template<typename T, bool isDilation, int SeLength=0>
+void morph(Param out, const Param in, const Param mask, int windLen=0)
 {
     std::string refName = std::string("morph_") +
         std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDilation) + std::to_string(windLen);
+        std::to_string(isDilation) + std::to_string(SeLength);
+
+    windLen = (SeLength>0 ? SeLength : windLen);
 
     int device = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog==0 && entry.ker==0) {
-        std::string options = generateOptionsString<T, isDilation, windLen>();
+        std::string options = generateOptionsString<T, isDilation, SeLength>();
         const char* ker_strs[] = {morph_cl};
         const int   ker_lens[] = {morph_cl_len};
         Program prog;
@@ -77,7 +79,7 @@ void morph(Param out, const Param in, const Param mask)
     }
 
     auto morphOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, cl::LocalSpaceArg,
-                                  int, int >(*entry.ker);
+                                  int, int, int >(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -98,27 +100,27 @@ void morph(Param out, const Param in, const Param mask)
 
     morphOp(EnqueueArgs(getQueue(), global, local),
             *out.data, out.info, *in.data, in.info, *mBuff,
-            cl::Local(locSize*sizeof(T)), blk_x, blk_y);
+            cl::Local(locSize*sizeof(T)), blk_x, blk_y, windLen);
 
     bufferFree(mBuff);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename T, bool isDilation, int windLen>
+template<typename T, bool isDilation, int SeLength>
 void morph3d(Param       out,
         const Param      in,
         const Param      mask)
 {
     std::string refName = std::string("morph3d_") +
         std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDilation) + std::to_string(windLen);
+        std::to_string(isDilation) + std::to_string(SeLength);
 
     int device = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog==0 && entry.ker==0) {
-        std::string options = generateOptionsString<T, isDilation, windLen>();
+        std::string options = generateOptionsString<T, isDilation, SeLength>();
         const char* ker_strs[] = {morph_cl};
         const int   ker_lens[] = {morph_cl_len};
         Program prog;
@@ -140,12 +142,12 @@ void morph3d(Param       out,
     NDRange global(blk_x * CUBE_X * in.info.dims[3], blk_y * CUBE_Y, blk_z * CUBE_Z);
 
     // copy mask/filter to constant memory
-    cl_int se_size   = sizeof(T)*windLen*windLen*windLen;
+    cl_int se_size   = sizeof(T)*SeLength*SeLength*SeLength;
     cl::Buffer *mBuff = bufferAlloc(se_size);
     getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
 
     // calculate shared memory size
-    const int padding = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
+    const int padding = (SeLength%2==0 ? (SeLength-1) : (2*(SeLength/2)));
     const int locLen  = CUBE_X+padding+1;
     const int locArea = locLen *(CUBE_Y+padding);
     const int locSize = locArea*(CUBE_Z+padding);
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index cf69515a82..6fa6c1cefc 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -39,7 +39,7 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
         case  5: kernel::morph3d<T, isDilation,  5>(out, in, mask); break;
         case  6: kernel::morph3d<T, isDilation,  6>(out, in, mask); break;
         case  7: kernel::morph3d<T, isDilation,  7>(out, in, mask); break;
-        default: OPENCL_NOT_SUPPORTED(); break;
+        default: assert(mdims[0] < 7 & "Kernel size should be haandled above."); break;
     }
 
     return out;
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
index e702e62043..553ff05d1f 100644
--- a/src/backend/opencl/morph_impl.hpp
+++ b/src/backend/opencl/morph_impl.hpp
@@ -42,16 +42,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
         case  8: kernel::morph<T, isDilation,  8>(out, in, mask); break;
         case  9: kernel::morph<T, isDilation,  9>(out, in, mask); break;
         case 10: kernel::morph<T, isDilation, 10>(out, in, mask); break;
-        case 11: kernel::morph<T, isDilation, 11>(out, in, mask); break;
-        case 12: kernel::morph<T, isDilation, 12>(out, in, mask); break;
-        case 13: kernel::morph<T, isDilation, 13>(out, in, mask); break;
-        case 14: kernel::morph<T, isDilation, 14>(out, in, mask); break;
-        case 15: kernel::morph<T, isDilation, 15>(out, in, mask); break;
-        case 16: kernel::morph<T, isDilation, 16>(out, in, mask); break;
-        case 17: kernel::morph<T, isDilation, 17>(out, in, mask); break;
-        case 18: kernel::morph<T, isDilation, 18>(out, in, mask); break;
-        case 19: kernel::morph<T, isDilation, 19>(out, in, mask); break;
-        default: OPENCL_NOT_SUPPORTED(); break;
+        default: kernel::morph<T, isDilation>(out, in, mask, mdims[0]); break;
     }
 
     return out;
diff --git a/test/data b/test/data
index 884362522d..f5aca1b32c 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 884362522d94e5fea0d6f0445e181413f5a5abd7
+Subproject commit f5aca1b32c1ba6c7ffbcccb883193ef74200b595
diff --git a/test/morph.cpp b/test/morph.cpp
index feaf4ce95f..7c6b1621db 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -100,6 +100,12 @@ TYPED_TEST(Morph, Dilate4x4)
 {
     morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate4x4.test"));
 }
+
+TYPED_TEST(Morph, Dilate12x12)
+{
+    morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate12x12.test"));
+}
+
 TYPED_TEST(Morph, Erode4x4)
 {
     morphTest<TypeParam, false, false>(string(TEST_DIR"/morph/erode4x4.test"));
@@ -129,6 +135,7 @@ TYPED_TEST(Morph, Dilate4x4x4)
 {
     morphTest<TypeParam, true, true>(string(TEST_DIR"/morph/dilate4x4x4.test"));
 }
+
 TYPED_TEST(Morph, Erode4x4x4)
 {
     morphTest<TypeParam, false, true>(string(TEST_DIR"/morph/erode4x4x4.test"));

From a160a1cbfe1322d317662cef3ae746b0e97fec32 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 29 Jul 2017 20:57:01 +0530
Subject: [PATCH 1353/2677] FEAT: Gradient & Curvature Anisotropic Smoothing

A new example demonstrating Gradient anisotropic smoothing is added.
---
 docs/details/image.dox                        |  40 +++
 examples/image_processing/CMakeLists.txt      |  12 +
 .../image_processing/gradient_diffusion.cpp   |  99 ++++++++
 include/af/defines.h                          |  18 ++
 include/af/image.h                            |  48 ++++
 src/api/c/CMakeLists.txt                      |   1 +
 src/api/c/anisotropic_diffusion.cpp           |  93 +++++++
 src/api/cpp/CMakeLists.txt                    |   1 +
 src/api/cpp/anisotropic_diffusion.cpp         |  25 ++
 src/api/unified/image.cpp                     |   9 +
 src/backend/cpu/CMakeLists.txt                |   3 +
 src/backend/cpu/anisotropic_diffusion.cpp     |  34 +++
 src/backend/cpu/anisotropic_diffusion.hpp     |  18 ++
 .../cpu/kernel/anisotropic_diffusion.hpp      | 209 +++++++++++++++
 src/backend/cuda/CMakeLists.txt               |   3 +
 src/backend/cuda/anisotropic_diffusion.cu     |  34 +++
 src/backend/cuda/anisotropic_diffusion.hpp    |  18 ++
 .../cuda/kernel/anisotropic_diffusion.hpp     | 237 ++++++++++++++++++
 src/backend/opencl/CMakeLists.txt             |   3 +
 src/backend/opencl/anisotropic_diffusion.cpp  |  35 +++
 src/backend/opencl/anisotropic_diffusion.hpp  |  18 ++
 .../opencl/kernel/anisotropic_diffusion.cl    | 191 ++++++++++++++
 .../opencl/kernel/anisotropic_diffusion.hpp   |  81 ++++++
 test/CMakeLists.txt                           |   1 +
 test/anisotropic_diffusion.cpp                | 187 ++++++++++++++
 25 files changed, 1418 insertions(+)
 create mode 100644 examples/image_processing/gradient_diffusion.cpp
 create mode 100644 src/api/c/anisotropic_diffusion.cpp
 create mode 100644 src/api/cpp/anisotropic_diffusion.cpp
 create mode 100644 src/backend/cpu/anisotropic_diffusion.cpp
 create mode 100644 src/backend/cpu/anisotropic_diffusion.hpp
 create mode 100644 src/backend/cpu/kernel/anisotropic_diffusion.hpp
 create mode 100644 src/backend/cuda/anisotropic_diffusion.cu
 create mode 100644 src/backend/cuda/anisotropic_diffusion.hpp
 create mode 100644 src/backend/cuda/kernel/anisotropic_diffusion.hpp
 create mode 100644 src/backend/opencl/anisotropic_diffusion.cpp
 create mode 100644 src/backend/opencl/anisotropic_diffusion.hpp
 create mode 100644 src/backend/opencl/kernel/anisotropic_diffusion.cl
 create mode 100644 src/backend/opencl/kernel/anisotropic_diffusion.hpp
 create mode 100644 test/anisotropic_diffusion.cpp

diff --git a/docs/details/image.dox b/docs/details/image.dox
index ca0fe179ae..d4d629ff66 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -262,6 +262,46 @@ discussion on it can be found [here](http://en.wikipedia.org/wiki/Sobel_operator
 
 =======================================================================
 
+\defgroup image_func_anisotropic_diffusion AnisotropicDiffusion
+\ingroup imageflt_mat
+
+\brief Anisotropic Smoothing Filter
+
+Anisotropic diffusion algorithm aims at removing noise in the images while preserving important
+features such as edges. The algorithm essentially creates a scale space representation of the
+original image, where image from previous step is used to create a new version of blurred image
+using the diffusion process. Standard isotropic diffusion methods such as gaussian blur, doesn't
+take into account the local content(smaller neighborhood of current processing pixel) while removing
+noise. Anisotropic diffusion uses the flux equations given below to achieve that. Flux equation is the
+formula used by the diffusion process to determine how much a pixel in neighborhood should contribute to
+the blurring operation being done at the current pixel at a given iteration.
+
+The flux function can be either exponential or quadratic.
+
+<table>
+<caption id="multi row">Available Flux Functions</caption>
+<tr>
+    <td> AF_FLUX_QUADRATIC </td>
+    <td>  \f$ \frac{1}{1 + (\frac{\| \nabla I\|}{K})^2} \f$  </td>
+</tr>
+<tr>
+    <td> AF_FLUX_EXPONENTIAL </td>
+    <td>  \f$ \exp{-(\frac{\| \nabla I\|}{K})^2} \f$  </td>
+</tr>
+</table>
+
+Please be cautious using the time step parameter to the function. Appropriate time steps for solving this type of p.d.e. depend on the dimensionality of the image and the order of the equation. Stable values for most 2D and 3D functions are 0.125 and 0.0625, respectively. The time step values are automatically constrained to the stable value.
+
+Another input parameter to be cautious about is the conductance parameter, lower values strongly preserve image features and vice-versa. For human vision, this value ranges from 0.5 to 2.0.
+
+#### Reference
+Pietro Perona and Jitendra Malik, `Scale-space and edge detection using anisotropic diffusion,` IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 12, pp. 629-639, 1990.
+
+#### Reference
+R. Whitaker and X. Xue. `Variable-Conductance, Level-Set Curvature for Image Denoising`, International Conference on Image Processing, 2001 pp. 142-145, Vol.3.
+
+=======================================================================
+
 \defgroup cv_func_match_template matchTemplate
 \ingroup match_mat
 
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index d5f4b70c3d..a8e0c8c08b 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -48,6 +48,10 @@ if(ArrayFire_CPU_FOUND)
   # Pyramids example
   add_executable(pyramids_cpu pyramids.cpp)
   target_link_libraries(pyramids_cpu ArrayFire::afcpu)
+
+  # Gradient anisotropic diffusion example
+  add_executable(gradient_diffusion_cpu gradient_diffusion.cpp)
+  target_link_libraries(gradient_diffusion_cpu ArrayFire::afcpu)
 endif()
 
 if(ArrayFire_CUDA_FOUND)
@@ -80,6 +84,10 @@ if(ArrayFire_CUDA_FOUND)
 
   add_executable(pyramids_cuda pyramids.cpp)
   target_link_libraries(pyramids_cuda ArrayFire::afcuda)
+
+  # Gradient anisotropic diffusion example
+  add_executable(gradient_diffusion_cuda gradient_diffusion.cpp)
+  target_link_libraries(gradient_diffusion_cuda ArrayFire::afcuda)
 endif()
 
 if(ArrayFire_OpenCL_FOUND)
@@ -112,4 +120,8 @@ if(ArrayFire_OpenCL_FOUND)
 
   add_executable(pyramids_opencl pyramids.cpp)
   target_link_libraries(pyramids_opencl ArrayFire::afopencl)
+
+  # Gradient anisotropic diffusion example
+  add_executable(gradient_diffusion_opencl gradient_diffusion.cpp)
+  target_link_libraries(gradient_diffusion_opencl ArrayFire::afopencl)
 endif()
diff --git a/examples/image_processing/gradient_diffusion.cpp b/examples/image_processing/gradient_diffusion.cpp
new file mode 100644
index 0000000000..2bbaf921bd
--- /dev/null
+++ b/examples/image_processing/gradient_diffusion.cpp
@@ -0,0 +1,99 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <stdio.h>
+#include <arrayfire.h>
+#include <af/util.h>
+#include <cstdlib>
+
+using namespace af;
+
+static const unsigned ITERS = 64;
+
+array normalize(const array &p_in)
+{
+    float mx = max<float>(p_in);
+    float mn = min<float>(p_in);
+    return (p_in-mn)/(mx-mn);
+}
+
+array sobelFilter(const array &p_in)
+{
+    int w = 5;
+    if (p_in.dims(0) <  512) w = 3;
+    if (p_in.dims(0) > 2048) w = 7;
+
+    int h = 5;
+    if (p_in.dims(0) <  512) h = 3;
+    if (p_in.dims(0) > 2048) h = 7;
+
+    array ker = gaussianKernel(w, h);
+    array smooth = convolve(p_in, ker);
+
+    for (unsigned i=1; i<ITERS; ++i)
+        smooth = convolve(smooth, ker);
+
+    array Gx, Gy;
+    sobel(Gx, Gy, smooth, 3);
+
+    return normalize(hypot(Gx, Gy));
+}
+
+array in, edges, smoothed;
+
+void anisotropicSmoothing()
+{
+    smoothed = anisotropicDiffusion(in, 0.125, 0.35f, ITERS);
+}
+
+int main(int argc, char* argv[])
+{
+    int device = argc > 1 ? atoi(argv[1]) : 0;
+
+    try {
+        setDevice(device);
+        info();
+
+        printf("** ArrayFire Gradient Anisotropic Smoothing Demo **\n");
+
+        Window myWindow("Gradient Anisotropic Smoothing");
+
+        in = loadImage(ASSETS_DIR "/examples/images/man.jpg", false);
+
+        array sEdges = sobelFilter(in);
+
+        anisotropicSmoothing();
+
+        array Gx, Gy;
+        sobel(Gx, Gy, smoothed, 3);
+
+        edges = normalize(hypot(Gx, Gy));
+
+        while(!myWindow.close()) {
+
+            myWindow.grid(2, 2);
+
+            myWindow(0, 0) .image(in/255.0f          , "Input Image"                       );
+            myWindow(0, 1) .image(normalize(smoothed), "Anisotropically smooted Input"     );
+            myWindow(1, 0) .image(normalize(sEdges)  , "Gradient Magnitude after gaussian blur t=64");
+            myWindow(1, 1) .image(normalize(edges)   , "Gradient Magnitude after diffusion t=64");
+
+            myWindow.show();
+        }
+
+        printf("\nAnisotropic Diffusion avg runtime for current image in Seconds: %g\n",
+                timeit(anisotropicSmoothing));
+
+    } catch (af::exception &e) {
+        fprintf(stderr, "%s\n", e.what());
+        throw;
+    }
+
+    return 0;
+}
diff --git a/include/af/defines.h b/include/af/defines.h
index 124ce85542..88a8cb988c 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -462,6 +462,20 @@ typedef enum {
 } af_storage;
 #endif
 
+#if AF_API_VERSION >= 36
+typedef enum {
+    AF_FLUX_QUADRATIC   = 1,    ///< Quadratic flux function
+    AF_FLUX_EXPONENTIAL = 2,    ///< Exponential flux function
+    AF_FLUX_DEFAULT     = 0     ///< Default flux function is exponential
+} af_flux_function;
+
+typedef enum {
+    AF_DIFFUSION_GRAD = 1,      ///< Gradient diffusion equation
+    AF_DIFFUSION_MCDE = 2,      ///< Modified curvature diffusion equation
+    AF_DIFFUSION_DEFAULT = 0    ///< Default option is same as AF_DIFFUSION_GRAD
+} af_diffusion_eq;
+#endif
+
 #ifdef __cplusplus
 namespace af
 {
@@ -506,6 +520,10 @@ namespace af
 #if AF_API_VERSION >= 35
     typedef af_canny_threshold cannyThreshold;
 #endif
+#if AF_API_VERSION >= 36
+    typedef af_flux_function fluxFunction;
+    typedef af_diffusion_eq diffusionEq;
+#endif
 }
 
 #endif
diff --git a/include/af/image.h b/include/af/image.h
index 5343596cb8..05dd4fe81d 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -712,6 +712,27 @@ AFAPI array canny(const array& in, const cannyThreshold thresholdType,
                   const float lowThresholdRatio, const float highThresholdRatio,
                   const unsigned sobelWindow = 3, const bool isFast = false);
 #endif
+
+#if AF_API_VERSION >= 36
+/**
+   C++ Interface for gradient anisotropic(non-linear diffusion) smoothing
+
+   \param[in] in is the input image, expects non-integral (float/double) typed af::array
+   \param[in] timestep is the time step used in solving the diffusion equation.
+   \param[in] conductance parameter controls the sensitivity of conductance in diffusion equation.
+   \param[in] iterations is the number of times the diffusion step is performed.
+   \param[in] fftype indicates whether quadratic or exponential flux function is used by algorithm.
+    \param[in] diffusionKind will let the user choose what kind of diffusion method to perform. It will take
+               any value of enum \ref diffusionEq
+   \return A filtered image that is of same size as the input.
+
+   \ingroup image_func_anisotropic_diffusion
+*/
+AFAPI array anisotropicDiffusion(const af::array& in, const float timestep,
+                                 const float conductance, const unsigned iterations,
+                                 const fluxFunction fftype=AF_FLUX_EXPONENTIAL,
+                                 const diffusionEq diffusionKind=AF_DIFFUSION_GRAD);
+#endif
 }
 #endif
 
@@ -1429,6 +1450,33 @@ extern "C" {
                           const unsigned sobel_window, const bool is_fast);
 #endif
 
+#if AF_API_VERSION >= 36
+    /**
+       C Interface for anisotropic diffusion
+
+       It can do both gradient and curvature based anisotropic smoothing.
+
+       \param[out] out is an af_array containing anisotropically smoothed image pixel values
+       \param[in] in is the input image, expects non-integral (float/double) typed af_array
+       \param[in] timestep is the time step used in solving the diffusion equation.
+       \param[in] conductance parameter controls the sensitivity of conductance in diffusion equation.
+       \param[in] iterations is the number of times the diffusion step is performed.
+       \param[in] fftype indicates whether quadratic or exponential flux function is used by algorithm.
+       \param[in] diffusion_kind will let the user choose what kind of diffusion method to perform. It will take
+                  any value of enum \ref af_diffusion_eq
+       \return \ref AF_SUCCESS if the moment calculation is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_anisotropic_diffusion
+    */
+    AFAPI af_err af_anisotropic_diffusion(af_array* out, const af_array in,
+                                          const float timestep,
+                                          const float conductance,
+                                          const unsigned iterations,
+                                          const af_flux_function fftype,
+                                          const af_diffusion_eq diffusion_kind);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 974deef020..3a2668d8d0 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -41,6 +41,7 @@ target_sources(c_api_interface
 
 target_sources(c_api_interface
   INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/anisotropic_diffusion.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/approx.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/assign.cpp
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
new file mode 100644
index 0000000000..a4e432c484
--- /dev/null
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -0,0 +1,93 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <af/image.h>
+#include <handle.hpp>
+#include <common/err_common.hpp>
+#include <backend.hpp>
+#include <arith.hpp>
+#include <cast.hpp>
+#include <copy.hpp>
+#include <gradient.hpp>
+#include <reduce.hpp>
+#include <anisotropic_diffusion.hpp>
+
+#include <type_traits>
+
+using af::dim4;
+using namespace detail;
+
+template<typename T>
+af_array diffusion(const Array<float> in, const float dt, const float K,
+                   const unsigned iterations, const af_flux_function fftype,
+                   const af::diffusionEq eq)
+{
+    auto out    = copyArray(in);
+    auto dims   = out.dims();
+    auto g0     = createEmptyArray<float>(dims);
+    auto g1     = createEmptyArray<float>(dims);
+    float cnst  = -2.0f*K*K/dims.elements();
+
+    for (unsigned i=0; i<iterations; ++i)
+    {
+        gradient<float>(g0, g1, out);
+
+        auto g0Sqr = arithOp<float, af_mul_t>(g0, g0, dims);
+        auto g1Sqr = arithOp<float, af_mul_t>(g1, g1, dims);
+        auto sumd  = arithOp<float, af_add_t>(g0Sqr, g1Sqr, dims);
+        float avg  = reduce_all<af_add_t, float, float>(sumd, true, 0);
+
+        anisotropicDiffusion(out, dt, 1.0f/(cnst*avg), fftype, eq);
+    }
+
+    return getHandle(cast<T, float>(out));
+}
+
+af_err af_anisotropic_diffusion(af_array* out, const af_array in, const float dt,
+                                const float K, const unsigned iterations,
+                                const af_flux_function fftype,
+                                const af_diffusion_eq eq)
+{
+    try {
+        const ArrayInfo& info = getInfo(in);
+
+        const af::dim4& inputDimensions = info.dims();
+        const af_dtype  inputType       = info.getType();
+        const unsigned  inputNumDims    = inputDimensions.ndims();
+
+        DIM_ASSERT(1, (inputNumDims>=2));
+
+        ARG_ASSERT(3, (K>0 || K<0));
+        ARG_ASSERT(4, (iterations>0));
+
+        float DT = dt;
+        float maxDt = 1.0f/std::pow(2.0f, static_cast<float>(2)+1);
+
+        const af_flux_function F = (fftype==AF_FLUX_DEFAULT ? AF_FLUX_EXPONENTIAL : fftype);
+
+        auto input = castArray<float>(in);
+
+        af_array output = 0;
+        switch(inputType) {
+            case f64: output = diffusion<double>(input, DT, K, iterations, F, eq); break;
+            case f32:
+            case s32:
+            case u32:
+            case s16:
+            case u16:
+            case u8 : output = diffusion<float>(input, DT, K, iterations, F, eq); break;
+            default : TYPE_ERROR(1, inputType);
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 7d6bc5b131..92f86e48db 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -5,6 +5,7 @@ target_sources(cpp_api_interface
   INTERFACE
     ${CMAKE_CURRENT_SOURCE_DIR}/common.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/error.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/anisotropic_diffusion.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/approx.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
diff --git a/src/api/cpp/anisotropic_diffusion.cpp b/src/api/cpp/anisotropic_diffusion.cpp
new file mode 100644
index 0000000000..be029b1c0c
--- /dev/null
+++ b/src/api/cpp/anisotropic_diffusion.cpp
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/image.h>
+#include <af/array.h>
+#include "error.hpp"
+
+namespace af
+{
+array anisotropicDiffusion(const array& in, const float timestep,
+                           const float conductance, const unsigned iterations,
+                           const fluxFunction fftype,
+                           const diffusionEq eq)
+{
+    af_array out = 0;
+    AF_THROW(af_anisotropic_diffusion(&out, in.get(), timestep, conductance, iterations, fftype, eq));
+    return array(out);
+}
+}
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 66ad70be92..8d45f3962e 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -263,3 +263,12 @@ af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
     CHECK_ARRAYS(in);
     return CALL(out, in, ct, t1, t2, sw, isf);
 }
+
+af_err af_anisotropic_diffusion(af_array* out, const af_array in, const float dt,
+                                const float K, const unsigned iterations,
+                                const af_flux_function fftype,
+                                const af_diffusion_eq eq)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, dt, K, iterations, fftype, eq);
+}
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index cbe4db44f1..a82ae4b32b 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -18,6 +18,8 @@ target_sources(afcpu
   PRIVATE
     Array.cpp
     Array.hpp
+    anisotropic_diffusion.cpp
+    anisotropic_diffusion.hpp
     approx.cpp
     approx.hpp
     arith.hpp
@@ -178,6 +180,7 @@ target_sources(afcpu
 target_sources(afcpu
   PRIVATE
     kernel/Array.hpp
+    kernel/anisotropic_diffusion.hpp
     kernel/approx.hpp
     kernel/assign.hpp
     kernel/bilateral.hpp
diff --git a/src/backend/cpu/anisotropic_diffusion.cpp b/src/backend/cpu/anisotropic_diffusion.cpp
new file mode 100644
index 0000000000..eecc6d063f
--- /dev/null
+++ b/src/backend/cpu/anisotropic_diffusion.cpp
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <anisotropic_diffusion.hpp>
+#include <kernel/anisotropic_diffusion.hpp>
+
+namespace cpu
+{
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt,
+                          const float mct, const af::fluxFunction fftype,
+                          const af::diffusionEq eq)
+{
+    if (eq==AF_DIFFUSION_MCDE)
+        getQueue().enqueue(kernel::anisotropicDiffusion<T, true>, inout, dt, mct, fftype);
+    else
+        getQueue().enqueue(kernel::anisotropicDiffusion<T, false>, inout, dt, mct, fftype);
+}
+
+#define INSTANTIATE(T)\
+template void anisotropicDiffusion<T>(Array<T> &inout, const float dt, const float mct,\
+                                      const af::fluxFunction fftype, const af::diffusionEq eq);
+
+INSTANTIATE(double)
+INSTANTIATE( float)
+}
diff --git a/src/backend/cpu/anisotropic_diffusion.hpp b/src/backend/cpu/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..8661c4dc67
--- /dev/null
+++ b/src/backend/cpu/anisotropic_diffusion.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cpu
+{
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt,
+                          const float mct, const af::fluxFunction fftype,
+                          const af::diffusionEq eq);
+}
diff --git a/src/backend/cpu/kernel/anisotropic_diffusion.hpp b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..bf9b5ec7f7
--- /dev/null
+++ b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
@@ -0,0 +1,209 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <math.hpp>
+
+#include <cassert>
+#include <cmath>
+#include <algorithm>
+
+using std::exp;
+using std::pow;
+using std::sqrt;
+
+namespace cpu
+{
+namespace kernel
+{
+
+int index(int x, int y, int stride1)
+{
+    return x+ y*stride1;
+}
+
+float quad(float value)
+{
+  return 1.0f/(1.0f+value);
+}
+
+float computeGradientBasedUpdate(const float mct,
+                                 const float NW, const float N, const float NE,
+                                 const float  W, const float C, const float  E,
+                                 const float SW, const float S, const float SE, const af_flux_function fftype)
+{
+    float delta = 0.f;
+
+    float dx, dy, df, db, cx, cxd;
+
+    // centralized derivatives
+    dx = (E-W)*0.5f;
+    dy = (S-N)*0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        cx  = exp( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f)) * mct );
+        cxd = exp( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f)) * mct );
+    } else {
+        cx  = quad( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f)) * mct );
+        cxd = quad( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f)) * mct );
+    }
+    delta += (cx*df - cxd*db);
+
+    // half-d's and conductance along second dimension
+    df  = S - C;
+    db  = C - N;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        cx  = exp( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f)) * mct );
+        cxd = exp( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f)) * mct );
+    } else {
+        cx  = quad( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f)) * mct );
+        cxd = quad( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f)) * mct );
+    }
+    delta += (cx*df - cxd*db);
+
+    return delta;
+}
+
+float computeCurvatureBasedUpdate(const float mct,
+                                  const float NW, const float N, const float NE,
+                                  const float  W, const float C, const float  E,
+                                  const float SW, const float S, const float SE, const af_flux_function fftype)
+{
+    float delta = 0.f;
+    float prop_grad = 0.f;
+
+    float df0, db0;
+    float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
+
+    // centralized derivatives
+    dx = (E-W)*0.5f;
+    dy = (S-N)*0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+    df0 = df;
+    db0 = db;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f));
+        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f));
+    } else {
+        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f));
+        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f));
+    }
+
+    gmf = sqrt(1.0e-10f + gmsqf);
+    gmb = sqrt(1.0e-10f + gmsqb);
+
+    cx  = exp( gmsqf * mct );
+    cxd = exp( gmsqb * mct );
+
+    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+
+    // half-d's and conductance along second dimension
+    df  = S - C;
+    db  = C - N;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f));
+        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f));
+    } else {
+        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f));
+        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f));
+    }
+    gmf = sqrt(1.0e-10f + gmsqf);
+    gmb = sqrt(1.0e-10f + gmsqb);
+
+    cx  = exp( gmsqf * mct );
+    cxd = exp( gmsqb * mct );
+
+    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+
+    if (delta>0){
+        prop_grad += (pow(fminf(db0, 0.0f),2.0f) + pow(fmaxf(df0, 0.0f), 2.0f));
+        prop_grad += (pow(fminf( db, 0.0f),2.0f) + pow(fmaxf( df, 0.0f), 2.0f));
+    } else {
+        prop_grad += (pow(fmaxf(db0, 0.0f),2.0f) + pow(fminf(df0, 0.0f), 2.0f));
+        prop_grad += (pow(fmaxf( db, 0.0f),2.0f) + pow(fminf( df, 0.0f), 2.0f));
+    }
+
+    return sqrt(prop_grad)*delta;
+}
+
+template<typename T, bool isMCDE>
+void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const af_flux_function fftype)
+{
+    auto dims = inout.dims();
+    auto strides = inout.strides();
+
+    for(int b3=0; b3<dims[3]; ++b3) {
+        for(int b2=0; b2<dims[2]; ++b2) {
+
+            T* img = inout.get() + b2*strides[2] + b3*strides[3];
+
+            for(int j=1; j<dims[1]-1; ++j)
+            {
+                for(int i=1; i<dims[0]-1; ++i)
+                {
+                    float C = 0.f;
+                    float delta = 0.f;
+
+                    // int ip1 = clamp((int)i + 1, 0, (int)dims[0]-1);
+                    // int im1 = clamp((int)i - 1, 0, (int)dims[0]-1);
+                    // int jp1 = clamp((int)j + 1, 0, (int)dims[1]-1);
+                    // int jm1 = clamp((int)j - 1, 0, (int)dims[1]-1);
+                    // 400ms
+                    int ip1 = i + 1;
+                    int im1 = i - 1;
+                    int jp1 = j + 1;
+                    int jm1 = j - 1;
+
+                    if (isMCDE) {
+                        delta = computeCurvatureBasedUpdate(
+                                mct,
+                                img[ index(im1, jm1, strides[1]) ],
+                                img[ index(i  , jm1, strides[1]) ],
+                                img[ index(ip1, jm1, strides[1]) ],
+                                img[ index(im1, j  , strides[1]) ],
+                                C = img[ index(i  , j,  strides[1]) ],
+                                img[ index(ip1, j  , strides[1]) ],
+                                img[ index(im1, jp1, strides[1]) ],
+                                img[ index(i  , jp1, strides[1]) ],
+                                img[ index(ip1, jp1, strides[1]) ], fftype);
+
+                    } else {
+                        delta = computeGradientBasedUpdate(
+                                mct,
+                                img[ index(im1, jm1,  strides[1]) ],
+                                img[ index(i  , jm1,  strides[1]) ],
+                                img[ index(ip1, jm1,  strides[1]) ],
+                                img[ index(im1, j  ,  strides[1]) ],
+                                C = img[ index(i  , j,  strides[1]) ],
+                                img[ index(ip1, j  ,  strides[1]) ],
+                                img[ index(im1, jp1,  strides[1]) ],
+                                img[ index(i  , jp1,  strides[1]) ],
+                                img[ index(ip1, jp1,  strides[1]) ], fftype);
+                    }
+
+                    img[i + j*strides[1]] = (T)(C + delta*dt);
+                }
+            }
+        }
+    }
+}
+}
+}
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 88c406bf46..8cd5d578a3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -129,6 +129,7 @@ include(kernel/thrust_sort_by_key/CMakeLists.txt)
 cuda_add_library(afcuda
     scan.cu
     all.cu
+    anisotropic_diffusion.cu
     any.cu
     approx.cu
     assign.cu
@@ -205,6 +206,7 @@ cuda_add_library(afcuda
     kernel/convolve.cu
     kernel/convolve_separable.cu
 
+    kernel/anisotropic_diffusion.hpp
     kernel/approx.hpp
     kernel/assign.hpp
     kernel/atomics.hpp
@@ -283,6 +285,7 @@ cuda_add_library(afcuda
     Array.cpp
     Array.hpp
     Param.hpp
+    anisotropic_diffusion.hpp
     approx.hpp
     arith.hpp
     assign.hpp
diff --git a/src/backend/cuda/anisotropic_diffusion.cu b/src/backend/cuda/anisotropic_diffusion.cu
new file mode 100644
index 0000000000..56fcc0e224
--- /dev/null
+++ b/src/backend/cuda/anisotropic_diffusion.cu
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <anisotropic_diffusion.hpp>
+#include <kernel/anisotropic_diffusion.hpp>
+
+namespace cuda
+{
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt,
+                          const float mct, const af::fluxFunction fftype,
+                          const af::diffusionEq eq)
+{
+    if (eq==AF_DIFFUSION_MCDE)
+        kernel::anisotropicDiffusion<T, true>(inout, dt, mct, fftype);
+    else
+        kernel::anisotropicDiffusion<T, false>(inout, dt, mct, fftype);
+}
+
+#define INSTANTIATE(T)\
+template void anisotropicDiffusion<T>(Array<T> &inout, const float dt, const float mct,\
+                                      const af::fluxFunction fftype, const af::diffusionEq eq);
+
+INSTANTIATE(double)
+INSTANTIATE( float)
+}
diff --git a/src/backend/cuda/anisotropic_diffusion.hpp b/src/backend/cuda/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..568d4f25f7
--- /dev/null
+++ b/src/backend/cuda/anisotropic_diffusion.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace cuda
+{
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt,
+                          const float mct, const af::fluxFunction fftype,
+                          const af::diffusionEq eq);
+}
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..c1deb7e0bf
--- /dev/null
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -0,0 +1,237 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <backend.hpp>
+#include <common/dispatch.hpp>
+#include <Param.hpp>
+#include <debug_cuda.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+
+namespace cuda
+{
+namespace kernel
+{
+static const int THREADS_X = 32;
+static const int THREADS_Y = 8;
+
+inline __device__
+int clamp(const int value, const int low, const int high)
+{
+    return max(low, min(value, high));
+}
+
+__forceinline__ __device__
+int index(const int x, const int y,
+          const int dim0, const int dim1,
+          const int stride0, const int stride1)
+{
+    return clamp(x, 0, dim0-1)*stride0 + clamp(y, 0, dim1-1)*stride1;
+}
+
+__device__
+float quadratic(const float value)
+{
+    return 1.0/(1.0+value);
+}
+
+__device__
+float computeGradientBasedUpdate(const float mct, const float C,
+                                 const float  S,  const float  N, const float  W, const float E,
+                                 const float SE,  const float SW, const float NE, const float NW,
+                                 const af_flux_function fftype)
+{
+    float delta = 0;
+
+    float dx, dy, df, db, cx, cxd;
+
+    // centralized derivatives
+    dx = (E-W)*0.5f;
+    dy = (S-N)*0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        cx  = expf( (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2)) * mct );
+        cxd = expf( (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2)) * mct );
+    } else {
+        cx  = quadratic( (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2)) * mct );
+        cxd = quadratic( (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2)) * mct );
+    }
+    delta += (cx*df - cxd*db);
+
+    // half-d's and conductance along second dimension
+    df  = S - C;
+    db  = C - N;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        cx  = expf( (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2)) * mct );
+        cxd = expf( (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2)) * mct );
+    } else {
+        cx  = quadratic( (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2)) * mct );
+        cxd = quadratic( (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2)) * mct );
+    }
+    delta += (cx*df - cxd*db);
+
+    return delta;
+}
+
+__device__
+float computeCurvatureBasedUpdate(const float mct, const float C,
+                                  const float  S, const float  N, const float  W, const float  E,
+                                  const float SE, const float SW, const float NE, const float NW,
+                                  const af_flux_function fftype)
+{
+    float delta = 0;
+    float prop_grad = 0;
+
+    float df0, db0;
+    float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
+
+    // centralized derivatives
+    dx = (E-W)*0.5f;
+    dy = (S-N)*0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+    df0 = df;
+    db0 = db;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        gmsqf = (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2));
+        gmsqb = (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2));
+    } else {
+        gmsqf = (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2));
+        gmsqb = (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2));
+    }
+
+    gmf = sqrtf(1.0e-10 + gmsqf);
+    gmb = sqrtf(1.0e-10 + gmsqb);
+
+    cx  = expf( gmsqf * mct );
+    cxd = expf( gmsqb * mct );
+
+    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+
+    // half-d's and conductance along second dimension
+    df  = S - C;
+    db  = C - N;
+
+    if (fftype==AF_FLUX_EXPONENTIAL) {
+        gmsqf = (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2));
+        gmsqb = (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2));
+    } else {
+        gmsqf = (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2));
+        gmsqb = (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2));
+    }
+    gmf = sqrtf(1.0e-10 + gmsqf);
+    gmb = sqrtf(1.0e-10 + gmsqb);
+
+    cx  = expf( gmsqf * mct );
+    cxd = expf( gmsqb * mct );
+
+    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+
+    if (delta>0){
+        prop_grad += (powf(fminf(db0, 0.0f),2.0f) + powf(fmaxf(df0, 0.0f), 2.0f));
+        prop_grad += (powf(fminf( db, 0.0f),2.0f) + powf(fmaxf( df, 0.0f), 2.0f));
+    } else {
+        prop_grad += (powf(fmaxf(db0, 0.0f),2.0f) + powf(fminf(df0, 0.0f), 2.0f));
+        prop_grad += (powf(fmaxf( db, 0.0f),2.0f) + powf(fminf( df, 0.0f), 2.0f));
+    }
+
+    return sqrtf(prop_grad)*delta;
+}
+
+template<typename T, bool isMCDE>
+static __global__
+void diffUpdate(Param<T> inout, const float dt, const float mct, const af_flux_function fftype,
+                const unsigned blkX, const unsigned blkY)
+{
+    const unsigned RADIUS = 1;
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2*RADIUS; //Coloumns
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2*RADIUS; //Rows
+
+    __shared__ float shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    const int b2 = blockIdx.x / blkX;
+    const int b3 = blockIdx.y / blkY;
+
+    const int gx = blockDim.x * (blockIdx.x - b2*blkX) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3*blkY) + ly;
+
+    T* img = (T *)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
+
+#pragma unroll
+    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=blockDim.y, gy2+=blockDim.y)
+    {
+#pragma unroll
+        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=blockDim.x, gx2+=blockDim.x)
+        {
+            int idx = index(gx2-RADIUS, gy2-RADIUS, inout.dims[0], inout.dims[1],
+                            inout.strides[0], inout.strides[1]);
+            shrdMem[b][a] = img[idx];
+        }
+    }
+
+    __syncthreads();
+
+    if (gx<inout.dims[0] && gy<inout.dims[1])
+    {
+        int i       = lx + RADIUS;
+        int j       = ly + RADIUS;
+        float C     = shrdMem[j][i];
+        float delta = 0;
+
+        if (isMCDE) {
+            delta = computeCurvatureBasedUpdate(
+                    mct, C, shrdMem[j][i+1], shrdMem[j][i-1], shrdMem[j-1][i], shrdMem[j+1][i],
+                    shrdMem[j+1][i+1], shrdMem[j-1][i+1], shrdMem[j+1][i-1], shrdMem[j-1][i-1],
+                    fftype);
+        } else {
+            delta = computeGradientBasedUpdate(
+                    mct, C, shrdMem[j][i+1], shrdMem[j][i-1], shrdMem[j-1][i], shrdMem[j+1][i],
+                    shrdMem[j+1][i+1], shrdMem[j-1][i+1], shrdMem[j+1][i-1], shrdMem[j-1][i-1],
+                    fftype);
+        }
+
+        img[gx*inout.strides[0] + gy*inout.strides[1]] = (T)(C + delta*dt);
+    }
+}
+
+template<typename T, bool isMCDE>
+void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const af_flux_function fftype)
+{
+    dim3 threads(THREADS_X, THREADS_Y, 1);
+
+    int blkX = divup(inout.dims[0], threads.x);
+    int blkY = divup(inout.dims[1], threads.y);
+
+    dim3 blocks(blkX * inout.dims[2], blkY * inout.dims[3], 1);
+
+    const int maxBlkY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int blkZ    = divup(blocks.y, maxBlkY);
+
+    if(blkZ > 1) {
+        blocks.y = maxBlkY;
+        blocks.z = blkZ;
+    }
+
+    CUDA_LAUNCH((diffUpdate<T, isMCDE>), blocks, threads, inout, dt, mct, fftype, blkX, blkY);
+
+    POST_LAUNCH_CHECK();
+}
+}
+}
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 66b493280a..add20d3e72 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -43,6 +43,8 @@ target_sources(afopencl
     Param.cpp
     Param.hpp
     all.cpp
+    anisotropic_diffusion.cpp
+    anisotropic_diffusion.hpp
     any.cpp
     api.cpp
     approx.cpp
@@ -227,6 +229,7 @@ target_sources(afopencl
 target_sources(afopencl
   PRIVATE
     kernel/KParam.hpp
+    kernel/anisotropic_diffusion.hpp
     kernel/approx.hpp
     kernel/assign.hpp
     kernel/bilateral.hpp
diff --git a/src/backend/opencl/anisotropic_diffusion.cpp b/src/backend/opencl/anisotropic_diffusion.cpp
new file mode 100644
index 0000000000..676a421ae3
--- /dev/null
+++ b/src/backend/opencl/anisotropic_diffusion.cpp
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <copy.hpp>
+#include <anisotropic_diffusion.hpp>
+#include <kernel/anisotropic_diffusion.hpp>
+
+namespace opencl
+{
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt,
+                          const float mct, const af::fluxFunction fftype,
+                          const af::diffusionEq eq)
+{
+    if (eq==AF_DIFFUSION_MCDE)
+        kernel::anisotropicDiffusion<T, true>(inout, dt, mct, fftype);
+    else
+        kernel::anisotropicDiffusion<T, false>(inout, dt, mct, fftype);
+}
+
+#define INSTANTIATE(T)\
+template void anisotropicDiffusion<T>(Array<T> &inout, const float dt, const float mct,\
+                                      const af::fluxFunction fftype, const af::diffusionEq eq);
+
+INSTANTIATE(double)
+INSTANTIATE( float)
+}
diff --git a/src/backend/opencl/anisotropic_diffusion.hpp b/src/backend/opencl/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..7fb714eb8a
--- /dev/null
+++ b/src/backend/opencl/anisotropic_diffusion.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace opencl
+{
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt,
+                          const float mct, const af::fluxFunction fftype,
+                          const af::diffusionEq eq);
+}
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.cl b/src/backend/opencl/kernel/anisotropic_diffusion.cl
new file mode 100644
index 0000000000..749d0874c5
--- /dev/null
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.cl
@@ -0,0 +1,191 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+int lIndex(const int j, const int i)
+{
+    return j*SHRD_MEM_WIDTH + i;
+}
+
+int gIndex(const int x, const int y,
+           const int dim0, const int dim1,
+           const int stride0, const int stride1)
+{
+    return clamp(x, 0, dim0-1)*stride0 + clamp(y, 0, dim1-1)*stride1;
+}
+
+float quadratic(const float value)
+{
+    return 1.0f/(1.0f+value);
+}
+
+float computeGradientBasedUpdate(const float mct, const float C,
+                                 const float  S, const float  N, const float  W, const float  E,
+                                 const float SE, const float SW, const float NE, const float NW,
+                                 const int FLUX_FN)
+{
+    float delta = 0;
+
+    float dx, dy, df, db, cx, cxd;
+
+    // centralized derivatives
+    dx = (E-W)*0.5f;
+    dy = (S-N)*0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+
+    if (FLUX_FN==2) {
+        cx  = exp( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2)) * mct );
+        cxd = exp( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2)) * mct );
+    } else {
+        cx  = quadratic( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2)) * mct );
+        cxd = quadratic( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2)) * mct );
+    }
+
+    delta += (cx*df - cxd*db);
+
+    // half-d's and conductance along second dimension
+    df  = S - C;
+    db  = C - N;
+
+    if (FLUX_FN==2) {
+        cx  = exp( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2)) * mct );
+        cxd = exp( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2)) * mct );
+    } else {
+        cx  = quadratic( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2)) * mct );
+        cxd = quadratic( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2)) * mct );
+    }
+
+    delta += (cx*df - cxd*db);
+
+    return delta;
+}
+
+float computeCurvatureBasedUpdate(const float mct, const float C,
+                                  const  float  S, const float  N, const float  W, const float  E,
+                                  const  float SE, const float SW, const float NE, const float NW,
+                                  const int FLUX_FN)
+{
+    float delta = 0;
+    float prop_grad = 0;
+
+    float df0, db0;
+    float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
+
+    // centralized derivatives
+    dx = (E-W)*0.5f;
+    dy = (S-N)*0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+    df0 = df;
+    db0 = db;
+
+    if (FLUX_FN==2) {
+        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2));
+        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2));
+    } else {
+        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2));
+        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2));
+    }
+
+    gmf = sqrt(1.0e-10f + gmsqf);
+    gmb = sqrt(1.0e-10f + gmsqb);
+
+    cx  = exp( gmsqf * mct );
+    cxd = exp( gmsqb * mct );
+
+    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+
+    // half-d's and conductance along second dimension
+    df  = S - C;
+    db  = C - N;
+
+    if (FLUX_FN==2) {
+        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2));
+        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2));
+    } else {
+        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2));
+        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2));
+    }
+
+    gmf = sqrt(1.0e-10 + gmsqf);
+    gmb = sqrt(1.0e-10 + gmsqb);
+
+    cx  = exp( gmsqf * mct );
+    cxd = exp( gmsqb * mct );
+
+    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+
+    if (delta>0) {
+        prop_grad += (pow(min(db0, 0.0f),2.0f) + pow(max(df0, 0.0f), 2.0f));
+        prop_grad += (pow(min( db, 0.0f),2.0f) + pow(max( df, 0.0f), 2.0f));
+    } else {
+        prop_grad += (pow(max(db0, 0.0f),2.0f) + pow(min(df0, 0.0f), 2.0f));
+        prop_grad += (pow(max( db, 0.0f),2.0f) + pow(min( df, 0.0f), 2.0f));
+    }
+
+    return sqrt(prop_grad)*delta;
+}
+
+kernel
+void diffUpdate(global T* inout, KParam info, const float dt,
+                const float mct, const int FLUX_FN, unsigned blkX, unsigned blkY)
+{
+    // Beware of the integer value of FLUX_FN
+
+    local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    const int lx = get_local_id(0);
+    const int ly = get_local_id(1);
+
+    const unsigned b2 = get_group_id(0) / blkX;
+    const unsigned b3 = get_group_id(1) / blkY;
+
+    const int gx = get_local_size(0) * (get_group_id(0)-b2*blkX) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1)-b3*blkY) + ly;
+
+    global T* img = inout + (b3 * info.strides[3] + b2 * info.strides[2]) + info.offset;
+
+    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=get_local_size(1), gy2+=get_local_size(1))
+    {
+        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=get_local_size(0), gx2+=get_local_size(0))
+        {
+            int idx = gIndex(gx2-1, gy2-1, info.dims[0], info.dims[1],
+                            info.strides[0], info.strides[1]);
+            localMem[b][a] = img[idx];
+        }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (gx<info.dims[0] && gy<info.dims[1])
+    {
+        int i       = lx + 1;
+        int j       = ly + 1;
+        float C     = localMem[j][i];
+        float delta = 0;
+
+#if IS_MCDE==1
+        delta = computeCurvatureBasedUpdate(
+                mct, C, localMem[j][i+1], localMem[j][i-1], localMem[j-1][i], localMem[j+1][i],
+                localMem[j+1][i+1], localMem[j-1][i+1], localMem[j+1][i-1], localMem[j-1][i-1],
+                FLUX_FN);
+#else
+        delta = computeGradientBasedUpdate(
+                mct, C, localMem[j][i+1], localMem[j][i-1], localMem[j-1][i], localMem[j+1][i],
+                localMem[j+1][i+1], localMem[j-1][i+1], localMem[j+1][i-1], localMem[j-1][i-1],
+                FLUX_FN);
+#endif
+
+        img[gx*info.strides[0] + gy*info.strides[1]] = (T)(C + delta*dt);
+    }
+}
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..93411089f4
--- /dev/null
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -0,0 +1,81 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/anisotropic_diffusion.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <common/dispatch.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <memory.hpp>
+#include <debug_opencl.hpp>
+#include <type_util.hpp>
+
+namespace opencl
+{
+namespace kernel
+{
+static const int THREADS_X = 16;
+static const int THREADS_Y = 16;
+
+template<typename T, bool isMCDE>
+void anisotropicDiffusion(Param inout, const float dt, const float mct, const int fluxFnCode)
+{
+    using cl::Buffer;
+    using cl::Program;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    std::string kerKeyStr = std::string("anisotropic_diffusion_") +
+        std::string(dtype_traits<T>::getName()) +
+        "_" +
+        std::to_string(isMCDE);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, kerKeyStr);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T="               << dtype_traits<T>::getName()
+                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X+2)
+                << " -D SHRD_MEM_WIDTH="  << (THREADS_Y+2)
+                << " -D IS_MCDE="         << isMCDE;
+        if (std::is_same<T, double>::value)
+            options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {anisotropic_diffusion_cl};
+        const int   ker_lens[] = {anisotropic_diffusion_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker = new Kernel(*entry.prog, "diffUpdate");
+        addKernelToCache(device, kerKeyStr, entry);
+    }
+
+    auto diffUpdateOp = KernelFunctor<Buffer, KParam, float,
+                                      float, int, unsigned, unsigned>(*entry.ker);
+
+    NDRange threads(THREADS_X, THREADS_Y, 1);
+
+    int blkX = divup(inout.info.dims[0], threads[0]);
+    int blkY = divup(inout.info.dims[1], threads[1]);
+
+    NDRange global(threads[0] * blkX * inout.info.dims[2],
+                   threads[1] * blkY * inout.info.dims[3], 1);
+
+    diffUpdateOp(EnqueueArgs(getQueue(), global, threads),
+            *inout.data, inout.info, dt, mct, fluxFnCode, blkX, blkY);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
+}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index f5251d1365..2ad0e8674d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -143,6 +143,7 @@ function(make_test)
   endforeach()
 endfunction(make_test)
 
+make_test(SRC anisotropic_diffusion.cpp)
 make_test(SRC approx1.cpp)
 make_test(SRC approx2.cpp)
 make_test(SRC array.cpp)
diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
new file mode 100644
index 0000000000..ba09ef570f
--- /dev/null
+++ b/test/anisotropic_diffusion.cpp
@@ -0,0 +1,187 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+using std::string;
+using std::vector;
+using std::abs;
+
+template<typename T>
+class AnisotropicDiffusion : public ::testing::Test
+{
+};
+
+typedef ::testing::Types<float, double, int, uint, uchar, short, ushort> TestTypes;
+
+TYPED_TEST_CASE(AnisotropicDiffusion, TestTypes);
+
+template<typename T>
+af::array normalize(const af::array &p_in)
+{
+    T mx = af::max<T>(p_in);
+    T mn = af::min<T>(p_in);
+    return (p_in-mn)/(mx-mn);
+}
+
+template<typename T, bool isColor>
+void imageTest(string pTestFile, const float dt, const float K, const uint iters,
+               af::fluxFunction fluxKind, bool isCurvatureDiffusion=false)
+{
+    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
+
+    if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
+
+    using af::dim4;
+
+    vector<dim4>       inDims;
+    vector<string>    inFiles;
+    vector<dim_t>    outSizes;
+    vector<string>   outFiles;
+
+    readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
+
+    size_t testCount = inDims.size();
+
+    for (size_t testId=0; testId<testCount; ++testId) {
+
+        if (isCurvatureDiffusion) {
+            inFiles[testId].insert(0, string(TEST_DIR "/curvature_diffusion/"));
+            outFiles[testId].insert(0, string(TEST_DIR "/curvature_diffusion/"));
+        } else {
+            inFiles[testId].insert(0, string(TEST_DIR "/gradient_diffusion/"));
+            outFiles[testId].insert(0, string(TEST_DIR "/gradient_diffusion/"));
+        }
+
+        af_array _inArray    = 0;
+        af_array inArray     = 0;
+        af_array _outArray   = 0;
+        af_array cstArray    = 0;
+        af_array minArray    = 0;
+        af_array numArray    = 0;
+        af_array denArray    = 0;
+        af_array divArray    = 0;
+        af_array outArray    = 0;
+        af_array goldArray   = 0;
+        af_array _goldArray  = 0;
+        dim_t nElems         = 0;
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, _inArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        // af_load_image always returns float array, so convert to output type
+        ASSERT_EQ(AF_SUCCESS, conv_image<OutType>(&goldArray, _goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+
+        if (isCurvatureDiffusion) {
+            ASSERT_EQ(AF_SUCCESS, af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
+                                                           fluxKind, AF_DIFFUSION_MCDE));
+        } else {
+            ASSERT_EQ(AF_SUCCESS, af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
+                                                           fluxKind, AF_DIFFUSION_GRAD));
+        }
+
+        double maxima, minima, imag;
+        ASSERT_EQ(AF_SUCCESS, af_min_all(&minima, &imag, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_max_all(&maxima, &imag, _outArray));
+
+        unsigned ndims;
+        dim_t dims[4];
+        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
+
+        af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
+        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&minArray, minima, ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_sub(&numArray, _outArray, minArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
+
+        std::vector<OutType> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+
+        std::vector<OutType> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.025f));
+
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+    }
+}
+
+TYPED_TEST(AnisotropicDiffusion, GradientGrayscale)
+{
+    // Numeric values separated by underscore are arguments to fn being tested.
+    // Divide first value by 1000 to get time step `dt`
+    // Divide second value by 100 to get time step `K`
+    // Divide third value stays as it is since it is iteration count
+    // Fourth value is a 4-character string indicating the flux kind
+    imageTest<TypeParam, false>(string(TEST_DIR "/gradient_diffusion/gray_00125_100_64_exp.test"),
+                                0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL);
+}
+
+TYPED_TEST(AnisotropicDiffusion, GradientColorImage)
+{
+    imageTest<TypeParam, true>(string(TEST_DIR "/gradient_diffusion/color_00125_100_64_exp.test"),
+                               0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL);
+}
+
+TEST(AnisotropicDiffusion, GradientInvalidInputArray)
+{
+    try {
+        af::array out = af::anisotropicDiffusion(af::randu(100), 0.125f, 0.2f, 10, AF_FLUX_QUADRATIC);
+    } catch (af::exception &exp) {
+        ASSERT_EQ(AF_ERR_SIZE, exp.err());
+    }
+}
+
+TYPED_TEST(AnisotropicDiffusion, CurvatureGrayscale)
+{
+    // Numeric values separated by underscore are arguments to fn being tested.
+    // Divide first value by 1000 to get time step `dt`
+    // Divide second value by 100 to get time step `K`
+    // Divide third value stays as it is since it is iteration count
+    // Fourth value is a 4-character string indicating the flux kind
+    imageTest<TypeParam, false>(string(TEST_DIR "/curvature_diffusion/gray_00125_100_64_mcde.test"),
+                                0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL, true);
+}
+
+TYPED_TEST(AnisotropicDiffusion, CurvatureColorImage)
+{
+    imageTest<TypeParam, true>(string(TEST_DIR "/curvature_diffusion/color_00125_100_64_mcde.test"),
+                               0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL, true);
+}
+
+TEST(AnisotropicDiffusion, CurvatureInvalidInputArray)
+{
+    try {
+        af::array out = af::anisotropicDiffusion(af::randu(100), 0.125f, 0.2f, 10);
+    } catch (af::exception &exp) {
+        ASSERT_EQ(AF_ERR_SIZE, exp.err());
+    }
+}

From 78812bb086c0081ea5efe5e6eb04a43f5f12616e Mon Sep 17 00:00:00 2001
From: HoneyPatouceul <30809346+HoneyPatouceul@users.noreply.github.com>
Date: Mon, 25 Dec 2017 20:51:48 +0100
Subject: [PATCH 1354/2677] Update configuring_arrayfire_environment.md

Fixed wrong description of default behavior when AF_MEM_DEBUG is not set.
---
 docs/pages/configuring_arrayfire_environment.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index a16e2ff14f..3a49d93c51 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -148,7 +148,7 @@ AF_MEM_DEBUG {#af_mem_debug}
 When AF_MEM_DEBUG is set to 1 (or anything not equal to 0), the caching mechanism in the memory manager is disabled.
 The device buffers are allocated using native functions as needed and freed when going out of scope.
 
-When the environment variable is not set, it is treated to be non zero.
+When the environment variable is not set, it is treated to be zero.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 AF_MEM_DEBUG=1 ./myprogram

From c84734464a534a92dd92cd0fc6133220f2888625 Mon Sep 17 00:00:00 2001
From: HoneyPatouceul <30809346+HoneyPatouceul@users.noreply.github.com>
Date: Fri, 29 Dec 2017 18:27:20 +0100
Subject: [PATCH 1355/2677] Fixed variable name typo in vectorization.md
 (#2032)

Fixed typo in variable name defined as g_coef but used as f_coef..
---
 docs/pages/vectorization.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/vectorization.md b/docs/pages/vectorization.md
index ea3279c6f8..339a1a51ec 100644
--- a/docs/pages/vectorization.md
+++ b/docs/pages/vectorization.md
@@ -62,7 +62,7 @@ float g_coef[] = { 1, 2, 1,
                    2, 4, 2,
                    1, 2, 1 };
 
-af::array filter = 1.f/16 * af::array(3, 3, f_coef);
+af::array filter = 1.f/16 * af::array(3, 3, g_coef);
 
 af::array signal = randu(WIDTH, HEIGHT, NUM);
 af::array conv = convolve2(signal, filter);

From 5cf32df70d823980d4cd3a4ca94250faf7c69e86 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 31 Dec 2017 13:17:13 -0500
Subject: [PATCH 1356/2677] Fix assert message for debug build

---
 src/backend/opencl/morph3d_impl.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index 6fa6c1cefc..4771b31190 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -39,7 +39,7 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
         case  5: kernel::morph3d<T, isDilation,  5>(out, in, mask); break;
         case  6: kernel::morph3d<T, isDilation,  6>(out, in, mask); break;
         case  7: kernel::morph3d<T, isDilation,  7>(out, in, mask); break;
-        default: assert(mdims[0] < 7 & "Kernel size should be haandled above."); break;
+        default: assert(mdims[0] < 7 && "Kernel size should be haandled above.");
     }
 
     return out;

From c5e5f874ee5f43687a736048944f86e771505c2a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 8 Jan 2018 09:25:17 +0530
Subject: [PATCH 1357/2677] Disable CUDA JIT debug flags on ARM archs

---
 src/backend/cuda/jit.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 7965661d92..83539fc29e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -282,7 +282,7 @@ std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
              dev.major, dev.minor);
     const char* compiler_options[] = {
       arch.data(),
-#ifndef NDEBUG
+#if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
       "--device-debug",
       "--generate-line-info"
 #endif

From 62b411497b222ee3364c19c64f3e9e4915d21dfe Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 15 Jan 2018 22:20:57 -0500
Subject: [PATCH 1358/2677] Fix shfl_down warnings with cuda 9

---
 src/backend/cuda/kernel/reduce.hpp | 48 ++++--------------------------
 1 file changed, 6 insertions(+), 42 deletions(-)

diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 80007fccd9..815ff65115 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -17,6 +17,8 @@
 #include "config.hpp"
 #include <memory.hpp>
 
+#include <cub/warp/warp_reduce.cuh>
+
 using std::unique_ptr;
 
 namespace cuda
@@ -181,47 +183,6 @@ namespace kernel
         }
     }
 
-    template<typename To, af_op_t op>
-    struct WarpReduce
-    {
-        __device__ To operator()(To *s_ptr, uint tidx)
-        {
-            Binary<To, op> reduce;
-#pragma unroll
-            for (int n = 16; n >= 1; n >>= 1) {
-                if (tidx < n) {
-                    s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + n]);
-                }
-                __syncthreads();
-            }
-            return s_ptr[tidx];
-        }
-    };
-
-
-#if (__CUDA_ARCH__ >= 300)
-#define WARP_REDUCE(T)                                  \
-    template<af_op_t op>                                \
-    struct WarpReduce<T, op>                            \
-    {                                                   \
-        __device__ T operator()(T *s_ptr, uint tidx)    \
-        {                                               \
-            Binary<T, op> reduce;                       \
-                                                        \
-            T val = s_ptr[tidx];                        \
-                                                        \
-            for (int n = 16; n >= 1; n >>= 1) {         \
-                val = reduce(val, __shfl_down(val, n)); \
-            }                                           \
-            return val;                                 \
-        }                                               \
-    };                                                  \
-
-    WARP_REDUCE(float)
-    WARP_REDUCE(int)
-    WARP_REDUCE(uchar) // upcasted to int
-    WARP_REDUCE(char)  // upcasted to int
-#endif
 
     template<typename Ti, typename To, af_op_t op, uint DIMX>
     __global__
@@ -284,8 +245,11 @@ namespace kernel
             __syncthreads();
         }
 
+        typedef cub::WarpReduce<To> WarpReduce;
+        __shared__ typename WarpReduce::TempStorage temp_storage;
 
-        out_val = WarpReduce<To, op>()(s_ptr, tidx);
+        To warp_val = s_ptr[tidx];
+        out_val = WarpReduce(temp_storage).Reduce(warp_val, reduce);
 
         To * const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1]);
         if (tidx == 0)

From 4c1714846b5bb2937eaf270e16f4fcf6a7f86c63 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 23 Jan 2018 00:31:25 -0800
Subject: [PATCH 1359/2677] Adding JIT::NaryNode for CUDA and OpenCL backends.

---
 src/backend/cuda/JIT/BinaryNode.hpp   | 30 ++----------
 src/backend/cuda/JIT/NaryNode.hpp     | 66 +++++++++++++++++++++++++++
 src/backend/cuda/JIT/Node.hpp         |  8 ++--
 src/backend/cuda/JIT/UnaryNode.hpp    | 28 ++----------
 src/backend/opencl/JIT/BinaryNode.hpp | 30 ++----------
 src/backend/opencl/JIT/NaryNode.hpp   | 66 +++++++++++++++++++++++++++
 src/backend/opencl/JIT/Node.hpp       |  2 +-
 src/backend/opencl/JIT/UnaryNode.hpp  | 30 ++----------
 8 files changed, 153 insertions(+), 107 deletions(-)
 create mode 100644 src/backend/cuda/JIT/NaryNode.hpp
 create mode 100644 src/backend/opencl/JIT/NaryNode.hpp

diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index d32dea7e35..fd897cc809 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include "Node.hpp"
+#include "NaryNode.hpp"
 #include <iomanip>
 
 namespace cuda
@@ -17,37 +17,15 @@ namespace cuda
 namespace JIT
 {
 
-    class BinaryNode : public Node
+    class BinaryNode : public NaryNode
     {
-    private:
-        std::string m_op_str;
-        int m_op;
-
     public:
         BinaryNode(const char *out_type_str, const char *name_str,
                    const char *op_str,
                    Node_ptr lhs, Node_ptr rhs, int op)
-            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {{lhs, rhs}}),
-              m_op_str(op_str),
-              m_op(op)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
-        {
-            // Make the dec representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[1];
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+            : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}},
+                       op, std::max(lhs->getHeight(), rhs->getHeight()) + 1)
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << m_op_str << "(val" << ids.child_ids[0]
-                      << ", val" << ids.child_ids[1] << ");"
-                      << "\n";
         }
     };
 
diff --git a/src/backend/cuda/JIT/NaryNode.hpp b/src/backend/cuda/JIT/NaryNode.hpp
new file mode 100644
index 0000000000..c0a499136a
--- /dev/null
+++ b/src/backend/cuda/JIT/NaryNode.hpp
@@ -0,0 +1,66 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include "Node.hpp"
+#include <iomanip>
+
+namespace cuda
+{
+
+namespace JIT
+{
+
+    class NaryNode : public Node
+    {
+    private:
+        const int m_num_children;
+        const int m_op;
+        const std::string m_op_str;
+
+    public:
+        NaryNode(const char *out_type_str,
+                 const char *name_str,
+                 const char *op_str,
+                 const int num_children,
+                 const std::array<Node_ptr, MAX_CHILDREN> &children,
+                 const int op, const int height)
+            : Node(out_type_str, name_str, height, children),
+              m_num_children(num_children),
+              m_op(op),
+              m_op_str(op_str)
+        {
+        }
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        {
+            // Make the dec representation of enum part of the Kernel name
+            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
+            for (int i = 0; i < m_num_children; i++) {
+                kerStream << std::setw(3)
+                          << std::setfill('0')
+                          << std::dec
+                          << ids.child_ids[i];
+            }
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+        }
+
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
+            for (int i = 0; i < m_num_children; i++) {
+                if (i > 0) kerStream << ", ";
+                kerStream << "val" << ids.child_ids[i];
+            }
+            kerStream << ");\n";
+        }
+    };
+
+}
+
+}
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 443f5e2ef7..6b5c2349d1 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -22,7 +22,7 @@ namespace cuda
 namespace JIT
 {
 
-    static const int MAX_CHILDREN = 2;
+    static const int MAX_CHILDREN = 3;
     class Node;
     using std::shared_ptr;
     using std::vector;
@@ -40,18 +40,18 @@ namespace JIT
     class Node
     {
     protected:
+        const int m_height;
         const std::string m_type_str;
         const std::string m_name_str;
-        const int m_height;
         const std::array<Node_ptr, MAX_CHILDREN> m_children;
 
     public:
 
         Node(const char *type_str, const char *name_str, const int height,
              const std::array<Node_ptr, MAX_CHILDREN> children)
-            : m_type_str(type_str),
+            : m_height(height),
+              m_type_str(type_str),
               m_name_str(name_str),
-              m_height(height),
               m_children(children)
         {}
 
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 8b19f0e6f2..9d814ac4bf 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include "Node.hpp"
+#include "NaryNode.hpp"
 #include <iomanip>
 
 namespace cuda
@@ -17,35 +17,15 @@ namespace cuda
 namespace JIT
 {
 
-    class UnaryNode : public Node
+    class UnaryNode : public NaryNode
     {
-    private:
-        const std::string m_op_str;
-        const int m_op;
-
     public:
         UnaryNode(const char *out_type_str, const char *name_str,
                   const char *op_str,
                   Node_ptr child, int op)
-            : Node(out_type_str, name_str, child->getHeight() + 1, {{child}}),
-              m_op_str(op_str),
-              m_op(op)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
-        {
-            // Make the dec representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+            : NaryNode(out_type_str, name_str, op_str,
+                       1, {{child}}, op, child->getHeight() + 1)
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << m_op_str << "(val" << ids.child_ids[0] << ");"
-                      << "\n";
         }
     };
 
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
index f67712274b..3b0f923e3b 100644
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ b/src/backend/opencl/JIT/BinaryNode.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include "Node.hpp"
+#include "NaryNode.hpp"
 #include <iomanip>
 
 namespace opencl
@@ -17,37 +17,15 @@ namespace opencl
 namespace JIT
 {
 
-    class BinaryNode : public Node
+    class BinaryNode : public NaryNode
     {
-    private:
-        std::string m_op_str;
-        int m_op;
-
     public:
         BinaryNode(const char *out_type_str, const char *name_str,
                    const char *op_str,
                    Node_ptr lhs, Node_ptr rhs, int op)
-            : Node(out_type_str, name_str, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {{lhs, rhs}}),
-              m_op_str(op_str),
-              m_op(op)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
-        {
-            // Make the dec representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[1];
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+            : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}},
+                       op, std::max(lhs->getHeight(), rhs->getHeight()) + 1)
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << m_op_str << "(val" << ids.child_ids[0]
-                      << ", val" << ids.child_ids[1] << ");"
-                      << "\n";
         }
     };
 
diff --git a/src/backend/opencl/JIT/NaryNode.hpp b/src/backend/opencl/JIT/NaryNode.hpp
new file mode 100644
index 0000000000..3cc8765a40
--- /dev/null
+++ b/src/backend/opencl/JIT/NaryNode.hpp
@@ -0,0 +1,66 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include "Node.hpp"
+#include <iomanip>
+
+namespace opencl
+{
+
+namespace JIT
+{
+
+    class NaryNode : public Node
+    {
+    private:
+        const int m_num_children;
+        const int m_op;
+        const std::string m_op_str;
+
+    public:
+        NaryNode(const char *out_type_str,
+                 const char *name_str,
+                 const char *op_str,
+                 const int num_children,
+                 const std::array<Node_ptr, MAX_CHILDREN> &children,
+                 const int op, const int height)
+            : Node(out_type_str, name_str, height, children),
+              m_num_children(num_children),
+              m_op(op),
+              m_op_str(op_str)
+        {
+        }
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        {
+            // Make the dec representation of enum part of the Kernel name
+            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
+            for (int i = 0; i < m_num_children; i++) {
+                kerStream << std::setw(3)
+                          << std::setfill('0')
+                          << std::dec
+                          << ids.child_ids[i];
+            }
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+        }
+
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
+            for (int i = 0; i < m_num_children; i++) {
+                if (i > 0) kerStream << ", ";
+                kerStream << "val" << ids.child_ids[i];
+            }
+            kerStream << ");\n";
+        }
+    };
+
+}
+
+}
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index e734dbe52c..8f69a4452a 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -22,7 +22,7 @@ namespace opencl
 namespace JIT
 {
 
-    static const int MAX_CHILDREN = 2;
+    static const int MAX_CHILDREN = 3;
     class Node;
     using std::shared_ptr;
     using std::vector;
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/JIT/UnaryNode.hpp
index ed47be989c..d2eb373b5f 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/JIT/UnaryNode.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include "Node.hpp"
+#include "NaryNode.hpp"
 #include <iomanip>
 
 namespace opencl
@@ -16,39 +16,17 @@ namespace opencl
 
 namespace JIT
 {
-
-    class UnaryNode : public Node
+    class UnaryNode : public NaryNode
     {
-    private:
-        const std::string m_op_str;
-        const int m_op;
-
     public:
         UnaryNode(const char *out_type_str, const char *name_str,
                   const char *op_str,
                   Node_ptr child, int op)
-            : Node(out_type_str, name_str, child->getHeight() + 1, {{child}}),
-              m_op_str(op_str),
-              m_op(op)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+            : NaryNode(out_type_str, name_str, op_str,
+                       1, {{child}}, op, child->getHeight() + 1)
         {
-            // Make the dec representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.child_ids[0];
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
-        {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << m_op_str << "(val" << ids.child_ids[0] << ");"
-                      << "\n";
         }
     };
-
 }
 
 }

From a2edf53ad7c2737202fd77231e2287e97a6d3118 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 23 Jan 2018 00:31:53 -0800
Subject: [PATCH 1360/2677] Making select a JIT node for CUDA and OpenCL

---
 src/api/c/optypes.hpp            |  5 +++-
 src/api/c/select.cpp             |  6 ++---
 src/backend/cpu/select.hpp       | 16 +++++++++++++
 src/backend/cuda/kernel/jit.cuh  |  7 ++++++
 src/backend/cuda/select.hpp      | 39 ++++++++++++++++++++++++++++++
 src/backend/opencl/kernel/jit.cl |  3 +++
 src/backend/opencl/select.hpp    | 41 ++++++++++++++++++++++++++++++++
 7 files changed, 112 insertions(+), 5 deletions(-)

diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index c468ac570c..17e55d9944 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -91,5 +91,8 @@ typedef enum {
 
     af_sigmoid_t,
 
-    af_noop_t
+    af_noop_t,
+
+    af_select_t,
+    af_not_select_t,
 } af_op_t;
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 876666457e..788f016706 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -24,8 +24,7 @@ using af::dim4;
 template<typename T>
 af_array select(const af_array cond, const af_array a, const af_array b, const dim4 &odims)
 {
-    Array<T> out = createEmptyArray<T>(odims);
-    select(out, getArray<char>(cond), getArray<T>(a), getArray<T>(b));
+    Array<T> out = createSelectNode(getArray<char>(cond), getArray<T>(a), getArray<T>(b), odims);
     return getHandle<T>(out);
 }
 
@@ -82,8 +81,7 @@ af_err af_select(af_array *out, const af_array cond, const af_array a, const af_
 template<typename T, bool flip>
 af_array select_scalar(const af_array cond, const af_array a, const double b, const dim4 &odims)
 {
-    Array<T> out = createEmptyArray<T>(odims);
-    select_scalar<T, flip>(out, getArray<char>(cond), getArray<T>(a), b);
+    Array<T> out = createSelectNode<T, flip>(getArray<char>(cond), getArray<T>(a), b, odims);
     return getHandle<T>(out);
 }
 
diff --git a/src/backend/cpu/select.hpp b/src/backend/cpu/select.hpp
index 0d725acbfd..51c3b0d6ac 100644
--- a/src/backend/cpu/select.hpp
+++ b/src/backend/cpu/select.hpp
@@ -16,4 +16,20 @@ namespace cpu
 
     template<typename T, bool flip>
     void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
+
+    template<typename T>
+    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const Array<T> &b, const af::dim4 &odims)
+    {
+        Array<T> out = createEmptyArray<T>(odims);
+        select(out, cond, a, b);
+        return out;
+    }
+
+    template<typename T, bool flip>
+    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const double &b, const af::dim4 &odims)
+    {
+        Array<T> out = createEmptyArray<T>(odims);
+        select_scalar<T, flip>(out, cond, a, b);
+        return out;
+    }
 }
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 830a9e58c4..5add1518c2 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -13,6 +13,13 @@ typedef cuFloatComplex cfloat;
 typedef double2 cuDoubleComplex;
 typedef cuDoubleComplex cdouble;
 
+// ----------------------------------------------
+// COMMON OPERATIONS
+// ----------------------------------------------
+
+#define __select(cond, a, b) (cond) ? (a) : (b)
+#define __not_select(cond, a, b) (cond) ? (b) : (a)
+
 // ----------------------------------------------
 // REAL NUMBER OPERATIONS
 // ----------------------------------------------
diff --git a/src/backend/cuda/select.hpp b/src/backend/cuda/select.hpp
index 872fe25e63..1184dfe3ae 100644
--- a/src/backend/cuda/select.hpp
+++ b/src/backend/cuda/select.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
+#include <JIT/NaryNode.hpp>
+#include <scalar.hpp>
 #include <Array.hpp>
 
 namespace cuda
@@ -16,4 +18,41 @@ namespace cuda
 
     template<typename T, bool flip>
     void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
+
+    template<typename T>
+    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const Array<T> &b, const af::dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        auto b_node = b.getNode();
+        int height = std::max(a_node->getHeight(), b_node->getHeight());
+        height = std::max(height, cond_node->getHeight()) + 1;
+
+        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
+                                                "__select", 3, {{cond_node, a_node, b_node}},
+                                                (int)af_select_t, height);
+
+        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        return out;
+    }
+
+    template<typename T, bool flip>
+    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const double &b_val, const af::dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
+        auto b_node = b.getNode();
+        int height = std::max(a_node->getHeight(), b_node->getHeight());
+        height = std::max(height, cond_node->getHeight()) + 1;
+
+        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
+                                                flip ? "__not_select" : "__select",
+                                                3, {{cond_node, a_node, b_node}},
+                                                (int)(flip ? af_not_select_t : af_select_t),
+                                                height);
+
+        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        return out;
+    }
 }
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index 3e797ac050..66451f1c94 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -7,6 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define __select(cond, a, b) (cond) ? (a) : (b)
+#define __not_select(cond, a, b) (cond) ? (b) : (a)
+
 #define sign(in) signbit((in))
 #define __noop(a) (a)
 #define __add(lhs, rhs) (lhs) + (rhs)
diff --git a/src/backend/opencl/select.hpp b/src/backend/opencl/select.hpp
index 5bc2f60535..e9119e9ca4 100644
--- a/src/backend/opencl/select.hpp
+++ b/src/backend/opencl/select.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
+#include <JIT/NaryNode.hpp>
+#include <scalar.hpp>
 #include <Array.hpp>
 
 namespace opencl
@@ -16,4 +18,43 @@ namespace opencl
 
     template<typename T, bool flip>
     void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
+
+    template<typename T>
+    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const Array<T> &b, const af::dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        auto b_node = b.getNode();
+        int height = std::max(a_node->getHeight(), b_node->getHeight());
+        height = std::max(height, cond_node->getHeight()) + 1;
+
+        JIT::NaryNode *node = new JIT::NaryNode(dtype_traits<T>::getName(),
+                                                shortname<T>(true),
+                                                "__select", 3, {{cond_node, a_node, b_node}},
+                                                (int)af_select_t, height);
+
+        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        return out;
+    }
+
+    template<typename T, bool flip>
+    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const double &b_val, const af::dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
+        auto b_node = b.getNode();
+        int height = std::max(a_node->getHeight(), b_node->getHeight());
+        height = std::max(height, cond_node->getHeight()) + 1;
+
+        JIT::NaryNode *node = new JIT::NaryNode(dtype_traits<T>::getName(),
+                                                shortname<T>(true),
+                                                flip ? "__not_select" : "__select",
+                                                3, {{cond_node, a_node, b_node}},
+                                                (int)(flip ? af_not_select_t : af_select_t),
+                                                height);
+
+        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        return out;
+    }
 }

From 267302d348bfcd5d96761a591c61b59e40501eea Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 23 Jan 2018 03:14:42 -0800
Subject: [PATCH 1361/2677] Adding Shift as a JIT node for CUDA and OpenCL

---
 src/backend/cuda/CMakeLists.txt          |   5 +-
 src/backend/cuda/JIT/BufferNode.hpp      |   1 +
 src/backend/cuda/JIT/ShiftNode.hpp       | 125 +++++++++++++++++++++++
 src/backend/cuda/kernel/jit.cuh          |   1 +
 src/backend/cuda/kernel/shift.hpp        | 109 --------------------
 src/backend/cuda/{shift.cu => shift.cpp} |  23 ++++-
 src/backend/cuda/types.cpp               |   4 +-
 src/backend/opencl/CMakeLists.txt        |   1 -
 src/backend/opencl/JIT/BufferNode.hpp    |   2 +
 src/backend/opencl/JIT/ShiftNode.hpp     | 123 ++++++++++++++++++++++
 src/backend/opencl/kernel/jit.cl         |   1 +
 src/backend/opencl/kernel/shift.cl       |  55 ----------
 src/backend/opencl/kernel/shift.hpp      |  91 -----------------
 src/backend/opencl/shift.cpp             |  22 +++-
 14 files changed, 293 insertions(+), 270 deletions(-)
 create mode 100644 src/backend/cuda/JIT/ShiftNode.hpp
 delete mode 100644 src/backend/cuda/kernel/shift.hpp
 rename src/backend/cuda/{shift.cu => shift.cpp} (55%)
 create mode 100644 src/backend/opencl/JIT/ShiftNode.hpp
 delete mode 100644 src/backend/opencl/kernel/shift.cl
 delete mode 100644 src/backend/opencl/kernel/shift.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8cd5d578a3..8918ffea92 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -182,7 +182,6 @@ cuda_add_library(afcuda
     scan_by_key.cu
     select.cu
     set.cu
-    shift.cu
     sift.cu
     sobel.cu
     solve.cu
@@ -263,7 +262,6 @@ cuda_add_library(afcuda
     kernel/scan_first_by_key_impl.hpp
     kernel/select.hpp
     kernel/shared.hpp
-    kernel/shift.hpp
     kernel/sift_nonfree.hpp
     kernel/sobel.hpp
     kernel/sort.hpp
@@ -367,6 +365,7 @@ cuda_add_library(afcuda
     scan_by_key.hpp
     select.hpp
     set.hpp
+    shift.cpp
     shift.hpp
     sift.hpp
     sobel.hpp
@@ -398,6 +397,8 @@ cuda_add_library(afcuda
     JIT/Node.hpp
     JIT/ScalarNode.hpp
     JIT/UnaryNode.hpp
+    JIT/NaryNode.hpp
+    JIT/ShiftNode.hpp
     JIT/types.h
 
     OPTIONS "${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 4c14f6c2d5..0c7a3d21fe 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include "../Param.hpp"
 #include "Node.hpp"
 #include <iomanip>
 #include <mutex>
diff --git a/src/backend/cuda/JIT/ShiftNode.hpp b/src/backend/cuda/JIT/ShiftNode.hpp
new file mode 100644
index 0000000000..80f88179d5
--- /dev/null
+++ b/src/backend/cuda/JIT/ShiftNode.hpp
@@ -0,0 +1,125 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include "BufferNode.hpp"
+#include "Node.hpp"
+#include <iomanip>
+#include <mutex>
+
+namespace cuda
+{
+
+namespace JIT
+{
+    template<typename T>
+    class ShiftNode : public Node
+    {
+    private:
+
+        Node_ptr m_buffer_node;
+        const std::array<int, 4> m_shifts;
+
+    public:
+
+        ShiftNode(const char *type_str,
+                  const char *name_str,
+                  Node_ptr buffer_node,
+                  const std::array<int, 4> shifts)
+            : Node(type_str, name_str, 0, {}),
+              m_buffer_node(buffer_node),
+              m_shifts(shifts)
+        {
+        }
+
+        bool isBuffer() { return false; }
+
+        void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode<T> *>(node_ptr)->setData(param, data, bytes, is_linear);
+        }
+
+        bool isLinear(dim_t dims[4])
+        {
+            return false;
+        }
+
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << "_" << m_name_str;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+        }
+
+        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode<T> *>(node_ptr)->genParams(kerStream, id, is_linear);
+            for (int i = 0; i < 4; i++) {
+                kerStream << "int shift" << id << "_" << i << ",\n";
+            }
+        }
+
+        void setArgs(std::vector<void *> &args, bool is_linear)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode<T> *>(node_ptr)->setArgs(args, is_linear);
+            for (int i = 0; i < 4; i++) {
+                const int &d = m_shifts[i];
+                args.push_back((void *)&d);
+            }
+        }
+
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
+        {
+            std::string idx_str = std::string("idx") + std::to_string(id);
+            std::string info_str = std::string("in") + std::to_string(id);
+            std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
+            std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+
+            for (int i = 0; i < 4; i++) {
+                kerStream << "int " << id_str << i
+                          << " = __circular_mod(id" << i
+                          << " + " << shift_str << i
+                          << ", " << info_str << ".dims[" << i << "]"
+                          << ");\n";
+            }
+
+            kerStream << "int " << idx_str << " = "
+                      << "(" << id_str << "3 < " << info_str << ".dims[3]) * "
+                      << info_str << ".strides[3] * " << id_str << "3;\n";
+            kerStream << idx_str  << " += "
+                      << "(" << id_str << "2 < " << info_str << ".dims[2]) * "
+                      << info_str << ".strides[2] * " << id_str << "2;\n";
+            kerStream << idx_str  << " += "
+                      << "(" << id_str << "1 < " << info_str << ".dims[1]) * "
+                      << info_str << ".strides[1] * " << id_str << "1;\n";
+            kerStream << idx_str  << " += "
+                      << "(" << id_str << "0 < " << info_str << ".dims[0]) * "
+                      << id_str << "0;"
+                      << "\n";
+            kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
+        }
+
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << "in" << ids.id << "_ptr[idx" << ids.id << "];"
+                      << "\n";
+        }
+
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode<T> *>(node_ptr)->getInfo(len, buf_count, bytes);
+        }
+    };
+}
+
+}
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 5add1518c2..3a5133f2dc 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -19,6 +19,7 @@ typedef cuDoubleComplex cdouble;
 
 #define __select(cond, a, b) (cond) ? (a) : (b)
 #define __not_select(cond, a, b) (cond) ? (b) : (a)
+#define __circular_mod(a, b) ((a) < (b)) ? (a) : (a - b)
 
 // ----------------------------------------------
 // REAL NUMBER OPERATIONS
diff --git a/src/backend/cuda/kernel/shift.hpp b/src/backend/cuda/kernel/shift.hpp
deleted file mode 100644
index 128a6403a5..0000000000
--- a/src/backend/cuda/kernel/shift.hpp
+++ /dev/null
@@ -1,109 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <math.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
-#include <cassert>
-
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 128;
-        static const unsigned TILEY = 32;
-
-        __host__ __device__
-        static inline int simple_mod(const int i, const int dim)
-        {
-            return (i < dim) ? i : (i - dim);
-        }
-
-        template<typename T>
-        __global__
-        void shift_kernel(Param<T> out, CParam<T> in, const int d0, const int d1,
-                            const int d2, const int d3,
-                            const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if(xx >= out.dims[0] ||
-               yy >= out.dims[1] ||
-               oz >= out.dims[2] ||
-               ow >= out.dims[3])
-                return;
-
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
-
-            const int iw = simple_mod((ow + d3), out.dims[3]);
-            const int iz = simple_mod((oz + d2), out.dims[2]);
-
-            const int o_off = ow * out.strides[3] + oz * out.strides[2];
-            const int i_off = iw *  in.strides[3] + iz *  in.strides[2];
-
-            for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                const int iy = simple_mod((oy + d1), out.dims[1]);
-                for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                    const int ix = simple_mod((ox + d0), out.dims[0]);
-
-                    const int oIdx = o_off + oy * out.strides[1] + ox;
-                    const int iIdx = i_off + iy *  in.strides[1] + ix;
-
-                    out.ptr[oIdx] = in.ptr[iIdx];
-                }
-            }
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void shift(Param<T> out, CParam<T> in, const int *sdims)
-        {
-            dim3 threads(TX, TY, 1);
-
-            int blocksPerMatX = divup(out.dims[0], TILEX);
-            int blocksPerMatY = divup(out.dims[1], TILEY);
-            dim3 blocks(blocksPerMatX * out.dims[2],
-                        blocksPerMatY * out.dims[3],
-                        1);
-
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
-
-            int sdims_[4];
-            // Need to do this because we are mapping output to input in the kernel
-            for(int i = 0; i < 4; i++) {
-                // sdims_[i] will always be positive and always [0, oDims[i]].
-                // Negative shifts are converted to position by going the other way round
-                sdims_[i] = -(sdims[i] % (int)out.dims[i]) + out.dims[i] * (sdims[i] > 0);
-                assert(sdims_[i] >= 0 && sdims_[i] <= out.dims[i]);
-            }
-
-            CUDA_LAUNCH((shift_kernel<T>), blocks, threads,
-                    out, in, sdims_[0], sdims_[1], sdims_[2], sdims_[3],
-                    blocksPerMatX, blocksPerMatY);
-            POST_LAUNCH_CHECK();
-        }
-    }
-}
diff --git a/src/backend/cuda/shift.cu b/src/backend/cuda/shift.cpp
similarity index 55%
rename from src/backend/cuda/shift.cu
rename to src/backend/cuda/shift.cpp
index 89e78ac145..f8785f4edd 100644
--- a/src/backend/cuda/shift.cu
+++ b/src/backend/cuda/shift.cpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <JIT/ShiftNode.hpp>
 #include <Array.hpp>
 #include <shift.hpp>
-#include <kernel/shift.hpp>
 #include <stdexcept>
 #include <err_cuda.hpp>
 
@@ -18,14 +18,27 @@ namespace cuda
     template<typename T>
     Array<T> shift(const Array<T> &in, const int sdims[4])
     {
+
+        // Shift should only be the first node in the JIT tree.
+        // Force input to be evaluated so that in is always a buffer.
+        in.eval();
+
+        std::string name_str("Sh");
+        name_str += shortname<T>(true);
         const af::dim4 iDims = in.dims();
         af::dim4 oDims = iDims;
 
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        kernel::shift<T>(out, in, sdims);
+        std::array<int, 4> shifts;
+        for(int i = 0; i < 4; i++) {
+            // sdims_[i] will always be positive and always [0, oDims[i]].
+            // Negative shifts are converted to position by going the other way round
+            shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+            assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
+        }
 
-        return out;
+        auto node = new JIT::ShiftNode<T>(getFullName<T>(), name_str.c_str(),
+                                          in.getNode(), shifts);
+        return createNodeArray<T>(oDims, JIT::Node_ptr(node));
     }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/cuda/types.cpp b/src/backend/cuda/types.cpp
index 9d85037ba6..370f0515d4 100644
--- a/src/backend/cuda/types.cpp
+++ b/src/backend/cuda/types.cpp
@@ -26,8 +26,8 @@ namespace cuda
     template<> const char *shortname<uchar   >(bool caps) { return caps ?  "V" : "v"; }
     template<> const char *shortname<intl    >(bool caps) { return caps ?  "X" : "x"; }
     template<> const char *shortname<uintl   >(bool caps) { return caps ?  "Y" : "y"; }
-    template<> const char *shortname<short   >(bool caps) { return caps ?  "P" : "P"; }
-    template<> const char *shortname<ushort  >(bool caps) { return caps ?  "Q" : "Q"; }
+    template<> const char *shortname<short   >(bool caps) { return caps ?  "P" : "p"; }
+    template<> const char *shortname<ushort  >(bool caps) { return caps ?  "Q" : "q"; }
 
 #define INSTANTIATE(T)                                      \
     template<> const char *getFullName<T>() { return #T; }  \
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index add20d3e72..94db255577 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -289,7 +289,6 @@ target_sources(afopencl
     kernel/scan_first_by_key.hpp
     kernel/scan_first_by_key_impl.hpp
     kernel/select.hpp
-    kernel/shift.hpp
     kernel/sobel.hpp
     kernel/sort.hpp
     kernel/sort_by_key.hpp
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index c55206b472..9e856312e6 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -8,6 +8,8 @@
  ********************************************************/
 
 #pragma once
+#include <af/defines.h>
+#include "../kernel/KParam.hpp"
 #include "Node.hpp"
 #include <iomanip>
 #include <mutex>
diff --git a/src/backend/opencl/JIT/ShiftNode.hpp b/src/backend/opencl/JIT/ShiftNode.hpp
new file mode 100644
index 0000000000..bcba20c01d
--- /dev/null
+++ b/src/backend/opencl/JIT/ShiftNode.hpp
@@ -0,0 +1,123 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include "BufferNode.hpp"
+#include "Node.hpp"
+#include <iomanip>
+#include <mutex>
+
+namespace opencl
+{
+
+namespace JIT
+{
+    class ShiftNode : public Node
+    {
+    private:
+
+        Node_ptr m_buffer_node;
+        const std::array<int, 4> m_shifts;
+
+    public:
+
+        ShiftNode(const char *type_str,
+                  const char *name_str,
+                  Node_ptr buffer_node,
+                  const std::array<int, 4> shifts)
+            : Node(type_str, name_str, 0, {}),
+              m_buffer_node(buffer_node),
+              m_shifts(shifts)
+        {
+        }
+
+        bool isBuffer() { return false; }
+
+        void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode *>(node_ptr)->setData(info, data, bytes, is_linear);
+        }
+
+        bool isLinear(dim_t dims[4])
+        {
+            return false;
+        }
+
+        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << "_" << m_name_str;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+        }
+
+        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode *>(node_ptr)->genParams(kerStream, id, is_linear);
+            for (int i = 0; i < 4; i++) {
+                kerStream << "int shift" << id << "_" << i << ",\n";
+            }
+        }
+
+        int setArgs(cl::Kernel &ker, int id, bool is_linear)
+        {
+            auto node_ptr = m_buffer_node.get();
+            int curr_id = dynamic_cast<BufferNode *>(node_ptr)->setArgs(ker, id, is_linear);
+            for (int i = 0; i < 4; i++) {
+                ker.setArg(curr_id + i, m_shifts[i]);
+            }
+            return curr_id + 4;
+        }
+
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
+        {
+            std::string idx_str = std::string("idx") + std::to_string(id);
+            std::string info_str = std::string("iInfo") + std::to_string(id);
+            std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
+            std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+
+            for (int i = 0; i < 4; i++) {
+                kerStream << "int " << id_str << i
+                          << " = __circular_mod(id" << i
+                          << " + " << shift_str << i
+                          << ", " << info_str << ".dims[" << i << "]"
+                          << ");\n";
+            }
+
+            kerStream << "int " << idx_str << " = "
+                      << "(" << id_str << "3 < " << info_str << ".dims[3]) * "
+                      << info_str << ".strides[3] * " << id_str << "3;\n";
+            kerStream << idx_str  << " += "
+                      << "(" << id_str << "2 < " << info_str << ".dims[2]) * "
+                      << info_str << ".strides[2] * " << id_str << "2;\n";
+            kerStream << idx_str  << " += "
+                      << "(" << id_str << "1 < " << info_str << ".dims[1]) * "
+                      << info_str << ".strides[1] * " << id_str << "1;\n";
+            kerStream << idx_str  << " += "
+                      << "(" << id_str << "0 < " << info_str << ".dims[0]) * "
+                      << id_str << "0 + " << info_str << ".offset;"
+                      << "\n";
+        }
+
+        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        {
+            kerStream << m_type_str << " val" << ids.id << " = "
+                      << "in" << ids.id << "[idx" << ids.id << "];"
+                      << "\n";
+        }
+
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        {
+            auto node_ptr = m_buffer_node.get();
+            dynamic_cast<BufferNode *>(node_ptr)->getInfo(len, buf_count, bytes);
+        }
+    };
+}
+
+}
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index 66451f1c94..d846f86c13 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -9,6 +9,7 @@
 
 #define __select(cond, a, b) (cond) ? (a) : (b)
 #define __not_select(cond, a, b) (cond) ? (b) : (a)
+#define __circular_mod(a, b) ((a) < (b)) ? (a) : (a - b)
 
 #define sign(in) signbit((in))
 #define __noop(a) (a)
diff --git a/src/backend/opencl/kernel/shift.cl b/src/backend/opencl/kernel/shift.cl
deleted file mode 100644
index 7c487ec718..0000000000
--- a/src/backend/opencl/kernel/shift.cl
+++ /dev/null
@@ -1,55 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-static inline int simple_mod(const int i, const int dim)
-{
-    return (i < dim) ? i : (i - dim);
-}
-
-__kernel
-void shift_kernel(__global T *out, __global const T *in, const KParam op, const KParam ip,
-                  const int d0, const int d1, const int d2, const int d3,
-                  const int blocksPerMatX, const int blocksPerMatY)
-{
-    const int oz = get_group_id(0) / blocksPerMatX;
-    const int ow = get_group_id(1) / blocksPerMatY;
-
-    const int blockIdx_x = get_group_id(0) - oz * blocksPerMatX;
-    const int blockIdx_y = get_group_id(1) - ow * blocksPerMatY;
-
-    const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
-    const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
-
-    if(xx >= op.dims[0] ||
-       yy >= op.dims[1] ||
-       oz >= op.dims[2] ||
-       ow >= op.dims[3])
-        return;
-
-    const int incy = blocksPerMatY * get_local_size(1);
-    const int incx = blocksPerMatX * get_local_size(0);
-
-    const int iw = simple_mod((ow + d3), op.dims[3]);
-    const int iz = simple_mod((oz + d2), op.dims[2]);
-
-    const int o_off   = ow * op.strides[3] + oz * op.strides[2];
-    const int i_off   = iw * ip.strides[3] + iz * ip.strides[2] + ip.offset;
-
-    for(int oy = yy; oy < op.dims[1]; oy += incy) {
-        const int iy = simple_mod((oy + d1), op.dims[1]);
-        for(int ox = xx; ox < op.dims[0]; ox += incx) {
-            const int ix = simple_mod((ox + d0), op.dims[0]);
-
-            const int oIdx = o_off + oy * op.strides[1] + ox;
-            const int iIdx = i_off + iy * ip.strides[1] + ix;
-
-            out[oIdx] = in[iIdx];
-        }
-    }
-}
diff --git a/src/backend/opencl/kernel/shift.hpp b/src/backend/opencl/kernel/shift.hpp
deleted file mode 100644
index 5238ea840f..0000000000
--- a/src/backend/opencl/kernel/shift.hpp
+++ /dev/null
@@ -1,91 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <kernel_headers/shift.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cassert>
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
-namespace opencl
-{
-namespace kernel
-{
-// Kernel Launch Config Values
-static const int TX = 32;
-static const int TY = 8;
-static const int TILEX = 128;
-static const int TILEY = 32;
-
-template<typename T>
-void shift(Param out, const Param in, const int *sdims)
-{
-    std::string refName = std::string("shift_kernel_") + std::string(dtype_traits<T>::getName());
-
-    int device = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog==0 && entry.ker==0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
-
-        const char* ker_strs[] = {shift_cl};
-        const int   ker_lens[] = {shift_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "shift_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto shiftOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
-                                  const int, const int, const int, const int,
-                                  const int, const int> (*entry.ker);
-
-    NDRange local(TX, TY, 1);
-
-    int blocksPerMatX = divup(out.info.dims[0], TILEX);
-    int blocksPerMatY = divup(out.info.dims[1], TILEY);
-    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                   local[1] * blocksPerMatY * out.info.dims[3], 1);
-
-    int sdims_[4];
-    // Need to do this because we are mapping output to input in the kernel
-    for(int i = 0; i < 4; i++) {
-        // sdims_[i] will always be positive and always [0, oDims[i]].
-        // Negative shifts are converted to position by going the other way round
-        sdims_[i] = -(sdims[i] % (int)out.info.dims[i]) + out.info.dims[i] * (sdims[i] > 0);
-        assert(sdims_[i] >= 0 && sdims_[i] <= out.info.dims[i]);
-    }
-
-    shiftOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, *in.data, out.info, in.info,
-            sdims_[0], sdims_[1], sdims_[2], sdims_[3],
-            blocksPerMatX, blocksPerMatY);
-
-    CL_DEBUG_FINISH(getQueue());
-}
-}
-}
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index 61cbee9b75..9c6598ef4f 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <JIT/ShiftNode.hpp>
 #include <Array.hpp>
 #include <shift.hpp>
-#include <kernel/shift.hpp>
 #include <stdexcept>
 #include <err_opencl.hpp>
 
@@ -18,14 +18,26 @@ namespace opencl
     template<typename T>
     Array<T> shift(const Array<T> &in, const int sdims[4])
     {
+        // Shift should only be the first node in the JIT tree.
+        // Force input to be evaluated so that in is always a buffer.
+        in.eval();
+
+        std::string name_str("Sh");
+        name_str += shortname<T>(true);
         const af::dim4 iDims = in.dims();
         af::dim4 oDims = iDims;
 
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        kernel::shift<T>(out, in, sdims);
+        std::array<int, 4> shifts;
+        for(int i = 0; i < 4; i++) {
+            // sdims_[i] will always be positive and always [0, oDims[i]].
+            // Negative shifts are converted to position by going the other way round
+            shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+            assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
+        }
 
-        return out;
+        auto node = new JIT::ShiftNode(dtype_traits<T>::getName(), name_str.c_str(),
+                                       in.getNode(), shifts);
+        return createNodeArray<T>(oDims, JIT::Node_ptr(node));
     }
 
 #define INSTANTIATE(T)                                                  \

From 8ba64fc317b7558a5d997cc8d20f4fb25b486ed9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 12 Feb 2018 15:57:14 +0530
Subject: [PATCH 1362/2677] Fix AF_API_VERSION value in doxygen config file

---
 docs/doxygen.mk | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 76fd59d3f4..1c4c414c6c 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1980,7 +1980,7 @@ PREDEFINED             = __declspec(x)= \
                          __attribute__(x)= \
                          __cplusplus \
                          AF_DOC \
-						 AF_API_VERSION=${AF_API_VERSION_CURRENT}
+						 AF_API_VERSION=${ArrayFire_API_VERSION_CURRENT}
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The

From ac65682102116d746b71ec1712e205c6e95606cc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 19 Feb 2018 18:05:52 -0500
Subject: [PATCH 1363/2677] Fix assertions for select test failures

---
 test/select.cpp | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/test/select.cpp b/test/select.cpp
index ce87a90670..265e47007b 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -133,7 +133,7 @@ TEST(Select, NaN)
     c.host(&hc[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], std::isnan(ha[i]) ? b : ha[i]);
+        ASSERT_FLOAT_EQ(hc[i], std::isnan(ha[i]) ? b : ha[i]);
     }
 }
 
@@ -153,7 +153,7 @@ TEST(Select, ISSUE_1249)
     c.host(&hc[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
+        EXPECT_NEAR(hc[i], hb[i], 1e-7) << "at " << i;
     }
 }
 
@@ -173,7 +173,7 @@ TEST(Select, 4D)
     c.host(&hc[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
+        EXPECT_NEAR(hc[i], hb[i], 1e-7) << "at " << i;
     }
 }
 
@@ -201,9 +201,9 @@ TEST(Select, Issue_1730)
     for (int j = 0; j < m; j++) {
         for (int i = 0; i < n; i++) {
             if (i < n1 || i > n2) {
-                ASSERT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
             } else {
-                ASSERT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : -ha1[i]))  << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : -ha1[i]))  << "at (" << i << ", " << j << ")";
             }
         }
     }
@@ -234,9 +234,9 @@ TEST(Select, Issue_1730_scalar)
     for (int j = 0; j < m; j++) {
         for (int i = 0; i < n; i++) {
             if (i < n1 || i > n2) {
-                ASSERT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
             } else {
-                ASSERT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : val))  << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : val))  << "at (" << i << ", " << j << ")";
             }
         }
     }
@@ -253,7 +253,7 @@ TEST(Select, MaxDim)
     af::array sel  = af::select(cond, a, b);
     float sum = af::sum<float>(sel);
 
-    ASSERT_EQ(sum, 0.f);
+    ASSERT_FLOAT_EQ(sum, 0.f);
 
     a    = af::constant(1, 1, largeDim);
     b    = af::constant(0, 1, largeDim);
@@ -262,7 +262,7 @@ TEST(Select, MaxDim)
     sel  = af::select(cond, a, b);
     sum = af::sum<float>(sel);
 
-    ASSERT_EQ(sum, 0.f);
+    ASSERT_FLOAT_EQ(sum, 0.f);
 
     a    = af::constant(1, 1, 1, largeDim);
     b    = af::constant(0, 1, 1, largeDim);
@@ -271,7 +271,7 @@ TEST(Select, MaxDim)
     sel  = af::select(cond, a, b);
     sum = af::sum<float>(sel);
 
-    ASSERT_EQ(sum, 0.f);
+    ASSERT_FLOAT_EQ(sum, 0.f);
 
     a    = af::constant(1, 1, 1, 1, largeDim);
     b    = af::constant(0, 1, 1, 1, largeDim);
@@ -280,5 +280,5 @@ TEST(Select, MaxDim)
     sel  = af::select(cond, a, b);
     sum = af::sum<float>(sel);
 
-    ASSERT_EQ(sum, 0.f);
+    ASSERT_FLOAT_EQ(sum, 0.f);
 }

From bf505d187b12203a75696ec571e30e227c5e62e5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 21 Feb 2018 11:44:08 -0500
Subject: [PATCH 1364/2677] Create a FindMKL script to handle building and
 installing with MKL

Create a FindMKL to handle building with MKL.
---
 CMakeLists.txt                    |  20 +++
 CMakeModules/FindCBLAS.cmake      |  17 ---
 CMakeModules/FindFFTW.cmake       |   8 +-
 CMakeModules/FindMKL.cmake        | 219 ++++++++++++++++++++++++++++++
 CMakeModules/platform.cmake       |   5 +-
 src/api/unified/CMakeLists.txt    |  16 +++
 src/backend/common/CMakeLists.txt |   4 -
 src/backend/cpu/CMakeLists.txt    |  44 ++++--
 src/backend/opencl/CMakeLists.txt |  35 ++---
 9 files changed, 304 insertions(+), 64 deletions(-)
 create mode 100644 CMakeModules/FindMKL.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 053c96ffbd..78ff1b9173 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -30,6 +30,7 @@ find_package(FFTW)
 find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
+find_package(MKL)
 
 # Graphics dependencies
 find_package(glbinding QUIET)
@@ -227,6 +228,25 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
+  install(FILES
+    $<TARGET_FILE:MKL::MKL>
+    $<TARGET_FILE:MKL::ThreadLayer>
+    ${MKL_RUNTIME_KERNEL_LIBRARIES}
+    DESTINATION ${AF_INSTALL_LIB_DIR})
+
+  if(TARGET MKL::ThreadingLibrary)
+    install(FILES
+      $<TARGET_FILE:MKL::ThreadingLibrary>
+      DESTINATION ${AF_INSTALL_LIB_DIR})
+  endif()
+
+  if(NOT WIN32)
+    install(FILES
+      $<TARGET_FILE:MKL::Interface>
+        DESTINATION ${AF_INSTALL_LIB_DIR})
+  endif()
+endif()
 
 # This file will be used to create the config file for the build directory.
 # These config files will be used by the examples to find the ArrayFire
diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index 0123d8e82d..ce61a46dcd 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -240,23 +240,6 @@ MACRO(CHECK_ALL_LIBRARIES
   ENDIF(NOT _libraries_work)
 ENDMACRO(CHECK_ALL_LIBRARIES)
 
-# MKL CBLAS library?
-IF(NOT CBLAS_LIBRARIES)
-  CHECK_ALL_LIBRARIES(
-    CBLAS_LIBRARIES
-    CBLAS
-    cblas_dgemm
-    ""
-    "mkl_rt"
-    "mkl_cblas.h"
-    FALSE,
-    TRUE)
-ENDIF(NOT CBLAS_LIBRARIES)
-
-IF(CBLAS_LIBRARIES)
-  SET(MKL_FOUND ON)
-ENDIF()
-
 # Apple CBLAS library?
 IF(NOT CBLAS_LIBRARIES)
   CHECK_ALL_LIBRARIES(
diff --git a/CMakeModules/FindFFTW.cmake b/CMakeModules/FindFFTW.cmake
index 6d32c90a4e..15bc7843d4 100644
--- a/CMakeModules/FindFFTW.cmake
+++ b/CMakeModules/FindFFTW.cmake
@@ -33,20 +33,20 @@ find_path( FFTW_INCLUDE_DIR
   )
 
 find_library( FFTW_LIBRARY
-  NAMES "fftw3" "libfftw3-3" "fftw3-3" "mkl_core" "mkl_rt"
+  NAMES "fftw3" "libfftw3-3" "fftw3-3"
   PATHS ${FFTW_ROOT}
         ${CMAKE_SYSTEM_PREFIX_PATH}
         ${PKG_FFTW_LIBRARY_DIRS}
-  PATH_SUFFIXES "lib" "lib64" "lib/intel64" "lib/ia32"
+  PATH_SUFFIXES "lib" "lib64"
 )
 
 find_library( FFTWF_LIBRARY
-  NAMES "fftw3f" "libfftw3f-3" "fftw3f-3" "mkl_core" "mkl_rt"
+  NAMES "fftw3f" "libfftw3f-3" "fftw3f-3"
   PATHS ${FFTW_ROOT}
         ${CMAKE_SYSTEM_PREFIX_PATH}
         ${CMAKE_SYSTEM_LIBRARY_PATH}
         ${PKG_FFTW_LIBRARY_DIRS}
-  PATH_SUFFIXES "lib" "lib64" "lib/intel64" "lib/ia32"
+  PATH_SUFFIXES "lib" "lib64"
 )
 
 mark_as_advanced(FFTW_INCLUDE_DIR FFTW_LIBRARY FFTWF_LIBRARY)
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
new file mode 100644
index 0000000000..d6ee084d2d
--- /dev/null
+++ b/CMakeModules/FindMKL.cmake
@@ -0,0 +1,219 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+#
+# A FindMKL script based on the recommendations by the Intel's Link Line
+# Advisor. It currently only tested on the 2018 version of MKL on Windows,
+# Linux, and OSX but it should work on older versions. It creates an MKL::MKL
+# library which has the required dependencies to for a dynamic link based
+# on the advisor's output.
+include(CheckTypeSize)
+
+check_type_size("int" INT_SIZE
+  BUILTIN_TYPES_ONLY LANGUAGE C)
+
+set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
+set_property(CACHE MKL_THREAD_LAYER PROPERTY STRINGS "TBB" "GNU OpenMP" "Intel OpenMP" "Sequential")
+
+if(NOT MKL_THREAD_LAYER STREQUAL MKL_THREAD_LAYER_LAST)
+  unset(MKL::ThreadLayer CACHE)
+  unset(MKL::ThreadingLibrary CACHE)
+  unset(MKL_ThreadLayer_LINK_LIBRARY CACHE)
+  unset(MKL_ThreadLayer_STATIC_LINK_LIBRARY CACHE)
+  unset(MKL_ThreadLayer_DLL_LIBRARY CACHE)
+  unset(MKL_ThreadingLibrary_LINK_LIBRARY CACHE)
+  unset(MKL_ThreadingLibrary_STATIC_LINK_LIBRARY CACHE)
+  unset(MKL_ThreadingLibrary_DLL_LIBRARY CACHE)
+  set(MKL_THREAD_LAYER_LAST ${MKL_THREAD_LAYER} CACHE INTERNAL "" FORCE)
+endif()
+
+find_path(MKL_INCLUDE_DIR
+  NAMES
+    mkl.h
+    mkl_blas.h
+    mkl_cblas.h
+  PATHS
+    /opt/intel
+    /opt/intel/mkl
+    $ENV{MKL_ROOT}
+  PATH_SUFFIXES
+    include
+    IntelSWTools/compilers_and_libraries/windows/mkl/include
+    )
+
+find_path(MKL_FFTW_INCLUDE_DIR
+  NAMES
+    fftw3_mkl.h
+  HINTS
+    ${MKL_INCLUDE_DIR}/fftw)
+
+if(WIN32)
+  if(${MSVC_VERSION} GREATER_EQUAL 1900)
+    set(msvc_dir "vc14")
+    set(shared_suffix "_dll")
+    set(md_suffix "md")
+  else()
+    message(WARNING "MKL: MS Version not supported for MKL")
+  endif()
+endif()
+
+# Finds and creates libraries for MKL with the MKL:: prefix
+#
+# Parameters:
+#    NAME:         A variable name describing the library
+#    LIBRARY_NAME: The library that needs to be searched
+#
+# Output Libraries:
+#    MKL::${NAME}
+#    MKL::${NAME}_STATIC
+#
+# Output Variables
+#    MKL_INCLUDE_DIR:                Include directory for MKL
+#    MKL_FFTW_INCLUDE_DIR:           Include directory for the MKL FFTW interface
+#    MKL_${NAME}_LINK_LIBRARY:        on Unix: *.so on Windows *.lib
+#    MKL_${NAME}_STATIC_LINK_LIBRARY: on Unix: *.a  on Windows *.lib
+#    MKL_${NAME}_DLL_LIBRARY:         on Unix: ""   on Windows *.dll
+function(find_mkl_library)
+  set(options "")
+  set(single_args NAME LIBRARY_NAME)
+  set(multi_args "")
+
+  cmake_parse_arguments(mkl_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
+
+  add_library(MKL::${mkl_args_NAME}        SHARED IMPORTED)
+  add_library(MKL::${mkl_args_NAME}_STATIC SHARED IMPORTED)
+  find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
+    NAMES
+      ${mkl_args_LIBRARY_NAME}${shared_suffix}
+      ${mkl_args_LIBRARY_NAME}${md_suffix}
+      lib${mkl_args_LIBRARY_NAME}${md_suffix}
+      ${mkl_args_LIBRARY_NAME}
+    PATHS
+      /opt/intel/mkl/lib
+      /opt/intel/tbb/lib
+      /opt/intel/lib
+      $ENV{MKL_ROOT}/lib
+    PATH_SUFFIXES
+      IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64
+      IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
+      IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
+      ""
+      intel64
+      intel64/gcc4.7)
+  mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
+
+  #message(STATUS "NAME: ${mkl_args_NAME} LIBNAME: ${mkl_args_LIBRARY_NAME} MKL_${mkl_args_NAME}_LINK_LIBRARY  ${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
+
+  # The rt library does not have a static library
+  if(NOT ${mkl_args_NAME} STREQUAL "rt")
+    find_library(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY
+      NAMES
+        ${CMAKE_STATIC_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}
+      PATHS
+        /opt/intel/mkl/lib
+        /opt/intel/tbb/lib
+        /opt/intel/lib
+        $ENV{MKL_ROOT}/lib
+      PATH_SUFFIXES
+        ""
+        intel64
+        intel64/gcc4.7
+        IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64
+        IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
+        IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
+        )
+      mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+    endif()
+
+    set_target_properties(MKL::${mkl_args_NAME}
+      PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
+        IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_LINK_LIBRARY}"
+        IMPORTED_NO_SONAME TRUE)
+    if(WIN32)
+      find_file(MKL_${mkl_args_NAME}_DLL_LIBRARY
+        NAMES
+          ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX}
+          ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+          lib${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+        PATH_SUFFIXES
+          IntelSWTools/compilers_and_libraries/windows/redist/intel64/mkl
+          IntelSWTools/compilers_and_libraries/windows/redist/intel64/compiler
+          IntelSWTools/compilers_and_libraries/windows/redist/intel64/tbb/${msvc_dir}
+        NO_SYSTEM_ENVIRONMENT_PATH)
+
+      set_target_properties(MKL::${mkl_args_NAME}
+        PROPERTIES
+          IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_DLL_LIBRARY}"
+          IMPORTED_IMPLIB "${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
+    endif()
+endfunction()
+
+
+find_mkl_library(NAME Core LIBRARY_NAME mkl_core)
+find_mkl_library(NAME RT LIBRARY_NAME mkl_rt)
+
+# MKL can link against Intel OpenMP, GNU OpenMP, TBB, and Sequential
+if(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_intel_thread)
+  find_mkl_library(NAME ThreadingLibrary LIBRARY_NAME iomp5)
+elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
+  find_package(OpenMP REQUIRED)
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_gnu_thread)
+  set(MKL::ThreadingLibrary OpenMP::OpenMP_CXX CACHE STRING "The OpenMP Threading Library")
+elseif(MKL_THREAD_LAYER STREQUAL "TBB")
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_tbb_thread)
+  find_mkl_library(NAME ThreadingLibrary LIBRARY_NAME tbb)
+elseif(MKL_THREAD_LAYER STREQUAL "Sequential")
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_sequential)
+endif()
+
+if("${INT_SIZE}" EQUAL 4)
+  find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_lp64)
+else()
+  find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_ilp64)
+endif()
+
+set(MKL_RUNTIME_KERNEL_LIBRARIES "" CACHE FILEPATH "MKL kernel libraries targeting different CPU architectures")
+set(MKL_KernelLibraries "mkl_def;mkl_mc;mkl_mc3;mkl_avx;mkl_avx2;mkl_avx512")
+
+foreach(lib ${MKL_KernelLibraries})
+  find_mkl_library(NAME ${lib} LIBRARY_NAME ${lib})
+  if(MKL_${lib}_LINK_LIBRARY OR MKL_${lib}_DLL_LIBRARY)
+    list(APPEND MKL_RUNTIME_KERNEL_LIBRARIES $<TARGET_FILE:MKL::${lib}>)
+  endif()
+endforeach()
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MKL
+  REQUIRED_VARS MKL_INCLUDE_DIR MKL_Core_LINK_LIBRARY)
+if(NOT WIN32)
+  find_library(M_LIB m)
+endif()
+if(MKL_FOUND)
+  add_library(MKL::MKL SHARED IMPORTED)
+  if(MKL_THREAD_LAYER STREQUAL "Sequential")
+    set_target_properties(MKL::MKL
+      PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES "MKL::ThreadLayer;MKL::Interface;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
+        IMPORTED_NO_SONAME TRUE)
+  else()
+    set_target_properties(MKL::MKL
+      PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES "MKL::ThreadLayer;MKL::Interface;MKL::ThreadingLibrary;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
+        IMPORTED_NO_SONAME TRUE)
+  endif()
+  if(WIN32)
+    set_target_properties(MKL::MKL
+      PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_DLL_LIBRARY}"
+        IMPORTED_IMPLIB "${MKL_Core_LINK_LIBRARY}")
+  endif()
+endif()
diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
index 2c541cf9e3..8d1d21ec86 100644
--- a/CMakeModules/platform.cmake
+++ b/CMakeModules/platform.cmake
@@ -31,8 +31,5 @@ if(WIN32)
   #        exported class
   add_compile_options(/wd4251 /wd4068 /wd4275)
 
-  # Default path for Intel MKL libraries
-  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};"
-    "C:/Program Files (x86)/IntelSWTools/compilers_and_libraries/windows/mkl;"
-    "C:/Program Files (x86)/IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64")
+  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH}")
 endif()
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index fc17be088b..924574b5fa 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -65,6 +65,22 @@ target_link_libraries(af
     ${CMAKE_DL_LIBS}
   )
 
+
+# NOTE: When loading libraries we only use the RTLD_LAZY flag for the unified
+# backend. This will only load the symbols but will not make those symbols
+# available to libraries loaded in the future. Because we link against MKL
+# and since MKL also dynamically loads libraries at runtime, the linker
+# is not able to load those symbols that are needed by those files. You could
+# pass the RTLD_GLOBAL flag to dlload, but that causes issues with the ArrayFire
+# libraries. To get around this we are also linking the unified backend with
+# the MKL library
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
+  target_link_libraries(af
+    PRIVATE
+      MKL::MKL)
+endif()
+
+
 install(TARGETS af
   EXPORT ArrayFireUnifiedTargets
   COMPONENT unified
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index fafd7d7f16..f54dc85f9c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -89,8 +89,4 @@ if(LAPACK_FOUND)
 
   target_compile_definitions(afcommon_lapack_interface
     INTERFACE WITH_LINEAR_ALGEBRA)
-
-  target_link_libraries(afcommon_lapack_interface
-    INTERFACE
-      ${LAPACK_LIBRARIES})
 endif()
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index a82ae4b32b..75e64f039e 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -7,9 +7,6 @@
 
 include(InternalUtils)
 
-dependency_check(FFTW_FOUND "FFTW not found")
-dependency_check(CBLAS_FOUND "CBLAS not found")
-
 add_library(afcpu "")
 add_library(ArrayFire::afcpu ALIAS afcpu)
 
@@ -315,18 +312,35 @@ target_compile_definitions(afcpu
     AF_CPU
   )
 
-target_link_libraries(afcpu
-  PRIVATE
-    c_api_interface
-    cpp_api_interface
-    afcommon_interface
-    afcommon_lapack_interface
-    cpu_sort_by_key
-    ${CBLAS_LIBRARIES}
-    FFTW::FFTW
-    FFTW::FFTWF
-    Threads::Threads
-  )
+if(USE_CPU_MKL)
+  target_link_libraries(afcpu
+    PRIVATE
+      c_api_interface
+      cpp_api_interface
+      afcommon_interface
+      afcommon_lapack_interface
+      cpu_sort_by_key
+      MKL::MKL
+      Threads::Threads
+    )
+else()
+  dependency_check(FFTW_FOUND "FFTW not found")
+  dependency_check(CBLAS_FOUND "CBLAS not found")
+
+  target_link_libraries(afcpu
+    PRIVATE
+      c_api_interface
+      cpp_api_interface
+      afcommon_interface
+      afcommon_lapack_interface
+      cpu_sort_by_key
+      ${CBLAS_LIBRARIES}
+      ${LAPACK_LIBRARIES}
+      FFTW::FFTW
+      FFTW::FFTWF
+      Threads::Threads
+    )
+endif()
 
 install(TARGETS afcpu
   EXPORT ArrayFireCPUTargets
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 94db255577..7e91e44b4f 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -12,8 +12,6 @@ set_property(CACHE OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
 
 include(build_clFFT)
 
-dependency_check(OpenCL_FOUND "OpenCL not found.")
-
 file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
 
 set( kernel_headers_dir
@@ -478,24 +476,26 @@ if(LAPACK_FOUND)
     dependency_check(MKL_FOUND "MKL not found")
     target_compile_definitions(afopencl PRIVATE USE_MKL)
 
-    # TODO(umar) Find a better way to determine BLAS selection
-    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" OR
-      (CMAKE_CXX_COMPILER_ID STREQUAL "Intel" AND (UNIX AND NOT APPLE)))
-      # MKL requires multiple passes when linking with the static libs. This can be
-      # done in CMake using LINK_INTERFACE_MULTIPLICITY but that will require
-      # changine the way FindCBLAS works. This can also be done using the
-      # --start-group and --end-group linker around the libraries in the linking
-      # step.
-      #
-      # TODO(umar): Change the way CBLAS libraries are found and linked
-      set(CBLAS_LIBRARIES -Wl,--start-group ${CBLAS_LIBRARIES} -Wl,--end-group)
-    endif()
+    target_link_libraries(afopencl
+      PRIVATE
+        MKL::MKL
+      )
+
   else()
+    dependency_check(OpenCL_FOUND "OpenCL not found.")
+
     if(USE_CPU_F77_BLAS)
       target_compile_definitions(afopencl PRIVATE USE_F77_BLAS)
     endif()
 
     dependency_check(CBLAS_LIBRARIES "CBLAS not found.")
+    target_include_directories(afopencl
+      PRIVATE
+        ${CBLAS_INCLUDE_DIR})
+    target_link_libraries(afopencl
+      PRIVATE
+        ${CBLAS_LIBRARIES}
+        ${LAPACK_LIBRARIES})
   endif()
 
   target_compile_definitions(
@@ -506,12 +506,7 @@ if(LAPACK_FOUND)
 
   target_link_libraries(afopencl
     PRIVATE
-      afcommon_lapack_interface
-      ${CBLAS_LIBRARIES}
-    )
-  target_include_directories(afopencl
-    PRIVATE
-      ${CBLAS_INCLUDE_DIR})
+      afcommon_lapack_interface)
 endif(LAPACK_FOUND)
 
 install(TARGETS afopencl

From cde2737490f1bfd05f91b81a4594473e0cb2ee21 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 21 Feb 2018 22:08:03 -0500
Subject: [PATCH 1365/2677] Fix the location of where FindMKL searches for TBB
 DLLs

---
 CMakeModules/FindMKL.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index d6ee084d2d..e493d8709c 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -52,7 +52,7 @@ find_path(MKL_FFTW_INCLUDE_DIR
 
 if(WIN32)
   if(${MSVC_VERSION} GREATER_EQUAL 1900)
-    set(msvc_dir "vc14")
+    set(msvc_dir "vc_mt")
     set(shared_suffix "_dll")
     set(md_suffix "md")
   else()

From ea39981c398f9df9662a7db3581bdc453578b755 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Feb 2018 17:19:42 -0500
Subject: [PATCH 1366/2677] FEAT: topk function in all backends.

CUDA backend alone implements a custom kernel to fetch top k elements
without sorting all the values. CPU backends sorts the data and fetch
the top k elements. The OpenCL backend is optimized for CPU devices
to map the memory and perform a partial sort to get the results.
---
 docs/details/statistics.dox       |  17 +-
 include/af/defines.h              |   7 +
 include/af/statistics.h           |  46 +++++
 src/api/c/CMakeLists.txt          |   1 +
 src/api/c/topk.cpp                |  83 ++++++++
 src/api/cpp/CMakeLists.txt        |   1 +
 src/api/cpp/topk.cpp              |  29 +++
 src/api/unified/statistics.cpp    |   7 +
 src/backend/cpu/CMakeLists.txt    |   2 +
 src/backend/cpu/topk.cpp          |  98 ++++++++++
 src/backend/cpu/topk.hpp          |  15 ++
 src/backend/cuda/CMakeLists.txt   |   3 +
 src/backend/cuda/index.hpp        |   1 +
 src/backend/cuda/kernel/topk.hpp  | 167 ++++++++++++++++
 src/backend/cuda/topk.cu          |  37 ++++
 src/backend/cuda/topk.hpp         |  16 ++
 src/backend/opencl/CMakeLists.txt |   2 +
 src/backend/opencl/convolve.cpp   |   2 +-
 src/backend/opencl/index.hpp      |   1 +
 src/backend/opencl/topk.cpp       | 146 ++++++++++++++
 src/backend/opencl/topk.hpp       |  15 ++
 test/CMakeLists.txt               |   5 +-
 test/topk.cpp                     | 312 ++++++++++++++++++++++++++++++
 23 files changed, 1009 insertions(+), 4 deletions(-)
 create mode 100644 src/api/c/topk.cpp
 create mode 100644 src/api/cpp/topk.cpp
 create mode 100644 src/backend/cpu/topk.cpp
 create mode 100644 src/backend/cpu/topk.hpp
 create mode 100644 src/backend/cuda/kernel/topk.hpp
 create mode 100644 src/backend/cuda/topk.cu
 create mode 100644 src/backend/cuda/topk.hpp
 create mode 100644 src/backend/opencl/topk.cpp
 create mode 100644 src/backend/opencl/topk.hpp
 create mode 100644 test/topk.cpp

diff --git a/docs/details/statistics.dox b/docs/details/statistics.dox
index c605e677ea..29507a5ef8 100644
--- a/docs/details/statistics.dox
+++ b/docs/details/statistics.dox
@@ -1,7 +1,7 @@
 /*!
 \page batch_detail_stat statistics
 
-This function performs the operation across all batches present in the input simultaneously.
+This function performs the operation across all dimensions of the input array.
 
 */
 
@@ -62,6 +62,21 @@ Find the correlation coefficient of values in the input
 
 \copydoc batch_detail_stat
 
+========================================================
+\defgroup stat_func_topk topk
+
+\ingroup basicstats_mat
+
+This function returns the top k values along a given dimension of the input
+array. The indices along with their values are returned. If the input is a
+multi-dimensional array, the indices will be the index of the value in that
+dimension. Order of duplicate values are not preserved. This function is
+optimized for small values of k.
+
+\copydoc batch_detail_stat
+
+\note{Currently, topk elements can be found only along dimension 0.}
+
 ========================================================
 @}
 */
diff --git a/include/af/defines.h b/include/af/defines.h
index 88a8cb988c..e4cc34382c 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -474,6 +474,12 @@ typedef enum {
     AF_DIFFUSION_MCDE = 2,      ///< Modified curvature diffusion equation
     AF_DIFFUSION_DEFAULT = 0    ///< Default option is same as AF_DIFFUSION_GRAD
 } af_diffusion_eq;
+
+typedef enum {
+    AF_TOPK_MAX     = 1,  ///< Top k max values
+    AF_TOPK_MIN     = 2,  ///< Top k min values
+    AF_TOPK_DEFAULT = 0   ///< Default option
+} af_topk_function;
 #endif
 
 #ifdef __cplusplus
@@ -523,6 +529,7 @@ namespace af
 #if AF_API_VERSION >= 36
     typedef af_flux_function fluxFunction;
     typedef af_diffusion_eq diffusionEq;
+    typedef af_topk_function topkFunction;
 #endif
 }
 
diff --git a/include/af/statistics.h b/include/af/statistics.h
index 4d02d4aea0..ecfbd8cfeb 100644
--- a/include/af/statistics.h
+++ b/include/af/statistics.h
@@ -192,6 +192,29 @@ AFAPI T median(const array& in);
 template<typename T>
 AFAPI T corrcoef(const array& X, const array& Y);
 
+#if AF_API_VERSION >= 36
+/**
+   C++ Interface for finding top k elements along a given dimension
+
+   \param[out] values  The values of the top k elements along the \p dim dimension
+   \param[out] indices The indices of the top k elements along the \p dim dimension
+   \param[in]  in      Input \ref af::array with at least \p k elements along
+                       \p dim
+   \param[in]  k       The number of elements to be retriefed along the \p dim dimension
+   \param[in]  dim     The dimension along which top k elements are extracted.
+                       (Must be 0)
+   \param[in]  order   If Descending the highest values are returned. Otherwise
+                       the lowest values are returned
+
+   \note{This function is optimized for small values of k.}
+   \note{The order of the returned keys may not be in the same order as the
+   appear in the input array}
+   \ingroup stat_func_topk
+*/
+AFAPI void topk(array &values, array &indices, const array& in, const int k,
+                const int dim = -1, const topkFunction order = AF_TOPK_MAX);
+#endif
+
 }
 #endif
 
@@ -396,6 +419,29 @@ AFAPI af_err af_median_all(double *realVal, double *imagVal, const af_array in);
 
 AFAPI af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y);
 
+#if AF_API_VERSION >= 36
+/**
+   C Interface for finding top k elements along a given dimension
+
+   \param[out] values  The values of the top k elements along the \p dim dimension
+   \param[out] indices The indices of the top k elements along the \p dim dimension
+   \param[in]  in      Input \ref af::array with at least \p k elements along
+                       \p dim
+   \param[in]  k       The number of elements to be retriefed along the \p dim dimension
+   \param[in]  dim     The dimension along which top k elements are extracted.
+                       (Must be 0)
+   \param[in]  order   If Descending the highest values are returned. Otherwise
+                       the lowest values are returned
+
+   \note{This function is optimized for small values of k.}
+   \note{The order of the returned keys may not be in the same order as the
+         appear in the input array}
+   \ingroup stat_func_topk
+*/
+AFAPI af_err af_topk(af_array *values, af_array *indices, const af_array in,
+                     const int k, const int dim, const af_topk_function order);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 3a2668d8d0..c106cd62fe 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -135,6 +135,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/susan.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/svd.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/tile.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/topk.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/transform.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/transform_coordinates.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/transpose.cpp
diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
new file mode 100644
index 0000000000..9ac81402b0
--- /dev/null
+++ b/src/api/c/topk.cpp
@@ -0,0 +1,83 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/statistics.h>
+#include <af/data.h>
+
+#include <handle.hpp>
+#include <topk.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <backend.hpp>
+
+using namespace detail;
+
+namespace {
+
+template<typename T>
+af_err topk(af_array *v, af_array* i, const af_array in,
+                    const int k, const int dim, const af_topk_function order)
+{
+    auto vals = createEmptyArray<T>(af::dim4());
+    auto idxs = createEmptyArray<unsigned>(af::dim4());
+
+    topk(vals, idxs, getArray<T>(in), k, dim, order);
+
+    *v = getHandle<T>(vals);
+    *i = getHandle<unsigned>(idxs);
+    return AF_SUCCESS;
+}
+} //  namespace
+
+af_err af_topk(af_array *values, af_array *indices, const af_array in,
+                     const int k, const int dim, const af_topk_function order)
+{
+    try {
+        af::topkFunction ord = (order == AF_TOPK_DEFAULT ? AF_TOPK_MAX : order);
+
+        ArrayInfo inInfo = getInfo(in);
+
+        ARG_ASSERT(1, (inInfo.ndims()>0));
+
+        if (inInfo.elements() == 1) {
+            dim_t dims[1] = {1};
+            af_err errValue = af_constant(indices, 0, 1, dims, u32);
+            return errValue==AF_SUCCESS ? af_retain_array(values, in) : errValue;
+        }
+
+        int rdim = dim;
+        auto &inDims = inInfo.dims();
+
+        if (rdim==-1) {
+            for (dim_t d = 0; d < 4; d++) {
+                if (inDims[d] > 1) {
+                    rdim =  d;
+                    break;
+                }
+            }
+        }
+
+        if (rdim!=0)
+            AF_ERROR("topk is supported along dimenion 0 only.", AF_ERR_NOT_SUPPORTED);
+
+        af_dtype type  = inInfo.getType();
+
+        switch(type) {
+            // TODO(umar): FIX RETURN VALUES HERE
+            case f32: topk<float >(values, indices, in, k, rdim, ord); break;
+            case f64: topk<double>(values, indices, in, k, rdim, ord); break;
+            case u32: topk<uint  >(values, indices, in, k, rdim, ord); break;
+            case s32: topk<int   >(values, indices, in, k, rdim, ord); break;
+            default : TYPE_ERROR(1, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 92f86e48db..14a310b7d5 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -70,6 +70,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/stdev.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/susan.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/timing.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/topk.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/transform.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/transform_coordinates.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/translate.cpp
diff --git a/src/api/cpp/topk.cpp b/src/api/cpp/topk.cpp
new file mode 100644
index 0000000000..676067ca1b
--- /dev/null
+++ b/src/api/cpp/topk.cpp
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <af/statistics.h>
+#include <af/array.h>
+#include "error.hpp"
+#include "common.hpp"
+
+namespace af
+{
+void topk(array &values, array &indices, const array& in, const int k,
+          const int dim, const topkFunction order)
+{
+    af_array af_vals = 0;
+    af_array af_idxs = 0;
+
+    AF_THROW(af_topk(&af_vals, &af_idxs, in.get(), k, dim, order));
+
+    values  = array(af_vals);
+    indices = array(af_idxs);
+}
+}
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index 9f72674d04..130daaed3d 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -94,3 +94,10 @@ af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_
     CHECK_ARRAYS(X, Y);
     return CALL(realVal, imagVal, X, Y);
 }
+
+af_err af_topk(af_array *values, af_array *indices, const af_array in,
+               const int k, const int dim, const af_topk_function order)
+{
+    CHECK_ARRAYS(in);
+    return CALL(values, indices, in, k, dim, order);
+}
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 75e64f039e..5d212bc74a 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -155,6 +155,8 @@ target_sources(afcpu
     svd.hpp
     tile.cpp
     tile.hpp
+    topk.cpp
+    topk.hpp
     traits.hpp
     transform.cpp
     transform.hpp
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
new file mode 100644
index 0000000000..c921782739
--- /dev/null
+++ b/src/backend/cpu/topk.cpp
@@ -0,0 +1,98 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <index.hpp>
+#include <sort.hpp>
+#include <sort_index.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include <vector>
+
+using std::iota;
+using std::min;
+using std::partial_sort_copy;
+using std::vector;
+
+namespace cpu
+{
+template<typename T>
+void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order)
+{
+    // The out_dims is of size k along the dimension of the topk operation
+    // and the same as the input dimension otherwise.
+    dim4 out_dims(1);
+    int ndims = in.dims().ndims();
+    for(int i = 0; i < ndims; i++) {
+      if (i == dim) {
+        out_dims[i] = min(k, (int)in.dims()[i]);
+      } else {
+        out_dims[i] = in.dims()[i];
+      }
+    }
+
+    auto values  = createEmptyArray<T>(out_dims);
+    auto indices = createEmptyArray<unsigned>(out_dims);
+
+    auto func = [=](Param<T> values, Param<unsigned> indices, CParam<T> in) {
+        const T* ptr = in.get();
+        unsigned* iptr = indices.get();
+        T* vptr = values.get();
+
+        // Create a linear index
+        vector<uint> idx(in.dims().elements());
+        iota(begin(idx), end(idx), 0);
+
+        int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
+        for(int i = 0; i < iter; i++) {
+            auto idx_itr = begin(idx) + i * in.strides()[1];
+            auto kiptr = iptr + k * i;
+
+            if(order == AF_TOPK_MIN) {
+                // Sort the top k values in each column
+                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
+                                  kiptr , kiptr + k,
+                                  [ptr](const uint lhs, const uint rhs) -> bool {
+                                      return ptr[lhs] < ptr[rhs];
+                                  });
+            } else {
+                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
+                                  kiptr , kiptr + k,
+                                  [ptr](const uint lhs, const uint rhs) -> bool {
+                                    return ptr[lhs] >= ptr[rhs];
+                                  });
+            }
+
+            auto kvptr = vptr + k * i;
+            for(int j = 0; j < k; j++) {
+                // Update the value arrays with the original values
+                kvptr[j] = ptr[kiptr[j]];
+                // Convert linear indices back to column indices
+                kiptr[j] -= i * in.strides()[1];
+            }
+        }
+    };
+
+    getQueue().enqueue(func, values, indices, in);
+
+    vals = values;
+    idxs = indices;
+}
+
+#define INSTANTIATE(T)\
+template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, const int, const int, const af::topkFunction);
+
+INSTANTIATE(float )
+INSTANTIATE(double)
+INSTANTIATE(int   )
+INSTANTIATE(uint  )
+}
diff --git a/src/backend/cpu/topk.hpp b/src/backend/cpu/topk.hpp
new file mode 100644
index 0000000000..b4b5764972
--- /dev/null
+++ b/src/backend/cpu/topk.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace cpu
+{
+template<typename T>
+void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order);
+}
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8918ffea92..250dc48e46 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -194,6 +194,7 @@ cuda_add_library(afcuda
     susan.cu
     svd.cu
     tile.cu
+    topk.cu
     transform.cu
     transpose.cu
     transpose_inplace.cu
@@ -272,6 +273,7 @@ cuda_add_library(afcuda
     kernel/thrust_sort_by_key.hpp
     kernel/thrust_sort_by_key_impl.hpp
     kernel/tile.hpp
+    kernel/topk.hpp
     kernel/transform.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
@@ -380,6 +382,7 @@ cuda_add_library(afcuda
     susan.hpp
     svd.hpp
     tile.hpp
+    topk.hpp
     traits.hpp
     transform.hpp
     transpose.hpp
diff --git a/src/backend/cuda/index.hpp b/src/backend/cuda/index.hpp
index 52e0201c56..67d106d59b 100644
--- a/src/backend/cuda/index.hpp
+++ b/src/backend/cuda/index.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/index.h>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
new file mode 100644
index 0000000000..8db1bbf51a
--- /dev/null
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -0,0 +1,167 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cub/block/block_radix_sort.cuh>
+
+#include <backend.hpp>
+#include <common/dispatch.hpp>
+#include <common/ArrayInfo.hpp>
+#include <Param.hpp>
+#include <debug_cuda.hpp>
+#include <math.hpp>
+#include <Array.hpp>
+
+#include <limits>
+
+using cub::BlockRadixSort;
+
+namespace cuda
+{
+namespace kernel
+{
+static const int TOPK_THRDS_PER_BLK = 256;
+static const int TOPK_IDX_THRD_LOAD  = 4;
+
+template<typename T, bool READ_INDEX>
+static __global__
+void
+kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
+            CParam<T> ivals, CParam<uint> iidxs,
+            const int k, const af::topkFunction order,
+            uint numLaunchBlocksY)
+{
+    using ValueType = uint;
+    using  BlockRadixSortT = BlockRadixSort<T, TOPK_THRDS_PER_BLK,
+                                            TOPK_IDX_THRD_LOAD, ValueType>;
+
+    __shared__ typename BlockRadixSortT::TempStorage smem;
+
+    const int bw = blockIdx.y / numLaunchBlocksY;
+    const int bz = blockIdx.z;
+    const int by = (blockIdx.y - bw * numLaunchBlocksY);
+
+    const uint gx       = blockIdx.x * blockDim.x + threadIdx.x;
+    const uint gxStride = blockDim.x * gridDim.x;
+    const uint elements = ivals.dims[0];
+
+    const T*    kdata = ivals.ptr + by * ivals.strides[1]
+                                  + bz * ivals.strides[2]
+                                  + bw * ivals.strides[3];
+
+    const ValueType* idata = iidxs.ptr + by * iidxs.strides[1]
+                                       + bz * iidxs.strides[2]
+                                       + bw * iidxs.strides[3];
+
+    T*    ores = ovals.ptr + by * ovals.strides[1]
+                           + bz * ovals.strides[2]
+                           + bw * ovals.strides[3];
+    uint* ires = oidxs.ptr + by * oidxs.strides[1]
+                           + bz * oidxs.strides[2]
+                           + bw * oidxs.strides[3];
+
+    T    keys[TOPK_IDX_THRD_LOAD];
+    ValueType vals[TOPK_IDX_THRD_LOAD];
+
+    for (uint li = 0, i = gx; li < TOPK_IDX_THRD_LOAD; i+=gxStride, li++) {
+        if(i < elements) {
+            keys[li] = kdata[i];
+            vals[li] = (READ_INDEX) ? idata[i] : i;
+        } else {
+            keys[li] = (order == AF_TOPK_MAX) ? minval<T>() : maxval<T>();
+            vals[li] = maxval<ValueType>();
+        }
+    }
+
+    if (order == AF_TOPK_MAX) {
+        BlockRadixSortT(smem).SortDescendingBlockedToStriped(keys, vals);
+    } else {
+        BlockRadixSortT(smem).SortBlockedToStriped(keys, vals);
+    }
+
+    if(threadIdx.x < k) {
+        int oidx   = threadIdx.x + blockIdx.x * k;
+        ores[oidx] = keys[0];
+        ires[oidx] = vals[0];
+    }
+}
+
+template<typename T>
+void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
+              const int k, const af::topkFunction order) {
+    const dim3 threads(TOPK_THRDS_PER_BLK, 1);
+    const int thrdLoad = TOPK_IDX_THRD_LOAD;
+
+    int numBlocksX = divup(ivals.dims[0], threads.x * thrdLoad);
+    dim3 blocks(numBlocksX, ivals.dims[1] * ivals.dims[3], ivals.dims[2]);
+
+    // The algorithm is to iteratively find top k elements among each block
+    // of threads until there is only one block to launch.
+    // The additional memory used for values and indices is allocated only
+    // before the first iteration and reused for further iterations.
+
+    // Temporary storage allocation for iterations
+    Array<T> tvals = *initArray<T>();
+    Array<uint> tidxs = *initArray<uint>();
+
+    if (numBlocksX > 1) {
+        tvals = createEmptyArray<T>(dim4(k * numBlocksX, ivals.dims[1]));
+        // TODO(umar): this can be smaller because the first iteration is not
+        // reading this array.
+        tidxs = createEmptyArray<uint>(dim4(k * numBlocksX, ivals.dims[1]));
+    }
+
+    int prevBlocksX = 1;
+
+    CParam<T> iivals = ivals;
+    CParam<uint> iiidxs = tidxs;
+
+    int dims0 = tvals.dims()[0];
+    bool first_run = true;
+    do {
+        if (blocks.x==1) {
+            tvals = createParamArray(ovals, false);
+            tidxs = createParamArray(oidxs, false);
+        }
+
+        if(first_run) {
+            // Launch topk which doesn't read the indice values from global memory
+            CUDA_LAUNCH((kerTopkDim0<T, false>), blocks, threads, tvals, tidxs, iivals,
+                        iiidxs, k, order, ivals.dims[1]);
+            first_run = false;
+        } else {
+            CUDA_LAUNCH((kerTopkDim0<T, true>), blocks, threads, tvals, tidxs, iivals,
+                        iiidxs, k, order, ivals.dims[1]);
+        }
+
+        POST_LAUNCH_CHECK();
+
+        prevBlocksX = blocks.x;
+        blocks.x    = divup(dims0, threads.x * thrdLoad);
+
+        //set output of current iteration as input for the next iteration
+        iivals = tvals;
+        iiidxs = tidxs;
+
+        dims0 = blocks.x * k;
+
+        tvals.setDataDims(dim4(dims0, tvals.elements()/(float)dims0));
+        tidxs.setDataDims(dim4(dims0, tidxs.elements()/(float)dims0));
+    } while (prevBlocksX>1);
+}
+
+template<typename T>
+inline
+void topk(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
+          const int k, const int dim, const af::topkFunction order)
+{
+    //TODO Add switch statement when support for other dims is added
+    topkDim0<T>(ovals, oidxs, ivals, k, order);
+}
+}
+}
diff --git a/src/backend/cuda/topk.cu b/src/backend/cuda/topk.cu
new file mode 100644
index 0000000000..0b6129cc7c
--- /dev/null
+++ b/src/backend/cuda/topk.cu
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/topk.hpp>
+#include <topk.hpp>
+#include <Array.hpp>
+#include <af/dim4.hpp>
+
+namespace cuda
+{
+template<typename T>
+void topk(Array<T>& ovals, Array<uint>& oidxs, const Array<T>& ivals,
+          const int k, const int dim, const af::topkFunction order) {
+    dim4 outDims = ivals.dims();
+    outDims[dim] = k;
+
+    ovals = createEmptyArray<T>(outDims);
+    oidxs = createEmptyArray<uint>(outDims);
+
+    kernel::topk<T>(ovals, oidxs, ivals, k, dim, order);
+}
+
+#define INSTANTIATE(T)\
+template void topk<T>(Array<T>&, Array<uint>&, const Array<T>&, \
+                      const int, const int, const af::topkFunction);
+
+INSTANTIATE(float )
+INSTANTIATE(double)
+INSTANTIATE(int   )
+INSTANTIATE(uint  )
+}
diff --git a/src/backend/cuda/topk.hpp b/src/backend/cuda/topk.hpp
new file mode 100644
index 0000000000..8fbc298e6d
--- /dev/null
+++ b/src/backend/cuda/topk.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+namespace cuda
+{
+template<typename T>
+void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order);
+}
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 7e91e44b4f..0fbafe4705 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -204,6 +204,8 @@ target_sources(afopencl
     svd.hpp
     tile.cpp
     tile.hpp
+    topk.cpp
+    topk.hpp
     traits.hpp
     transform.cpp
     transform.hpp
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 8773a03541..2feede31b0 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -55,7 +55,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
     if(!callKernel) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nOpenCL N Dimensional Convolution doesn't support %dx%dx%d kernel\n",
+                 "\nOpenCL N Dimensional Convolution doesn't support %llux%llux%llu kernel\n",
                  fDims[0], fDims[1], fDims[2]);
         OPENCL_NOT_SUPPORTED(errMessage);
     }
diff --git a/src/backend/opencl/index.hpp b/src/backend/opencl/index.hpp
index 82aef094c6..2d3ad6bc5a 100644
--- a/src/backend/opencl/index.hpp
+++ b/src/backend/opencl/index.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/index.h>
 
 namespace opencl
 {
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
new file mode 100644
index 0000000000..3fdbe493f5
--- /dev/null
+++ b/src/backend/opencl/topk.cpp
@@ -0,0 +1,146 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <sort.hpp>
+#include <sort_index.hpp>
+#include <index.hpp>
+#include <err_opencl.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include <vector>
+
+using cl::Buffer;
+using cl::Event;
+
+using std::iota;
+using std::min;
+using std::partial_sort_copy;
+using std::transform;
+using std::vector;
+
+namespace opencl
+{
+vector<af_index_t> indexForTopK(const int k)
+{
+    af_index_t idx;
+    idx.idx.seq = af_seq{0.0, (double)k - 1, 1.0};
+    idx.isSeq = true;
+    idx.isBatch = false;
+
+    af_index_t sp;
+    sp.idx.seq = af_span;
+    sp.isSeq = true;
+    sp.isBatch = false;
+
+    return vector<af_index_t>({idx, sp, sp, sp});
+}
+
+template<typename T>
+void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order)
+{
+
+    if( getDeviceType() == CL_DEVICE_TYPE_CPU ) {
+        // This branch optimizes for CPU devices by first mapping the buffer
+        // and calling partial sort on the buffer
+
+        // TODO(umar): implement this in the kernel namespace
+
+        // The out_dims is of size k along the dimension of the topk operation
+        // and the same as the input dimension otherwise.
+        dim4 out_dims(1);
+        int ndims = in.dims().ndims();
+        for(int i = 0; i < ndims; i++) {
+            if (i == dim) {
+                out_dims[i] = min(k, (int)in.dims()[i]);
+            } else {
+                out_dims[i] = in.dims()[i];
+            }
+        }
+
+        auto values  = createEmptyArray<T>(out_dims);
+        auto indices = createEmptyArray<unsigned>(out_dims);
+        const Buffer *in_buf = in.get();
+        Buffer *ibuf = indices.get();
+        Buffer *vbuf = values.get();
+
+        cl_int err;
+        Event ev_in, ev_val, ev_ind;
+
+        T* ptr =
+          static_cast<T*>(getQueue().enqueueMapBuffer(*in_buf, CL_FALSE,
+                                                      CL_MAP_READ, 0,
+                                                      in.elements() * sizeof(T),
+                                                      nullptr, &ev_in));
+        uint* iptr =
+          static_cast<uint*>(getQueue().enqueueMapBuffer(*ibuf, CL_FALSE,
+                                                         CL_MAP_READ | CL_MAP_WRITE,
+                                                         0, k * sizeof(uint),
+                                                         nullptr, &ev_ind));
+        T* vptr =
+          static_cast<T*> (getQueue().enqueueMapBuffer(*vbuf, CL_FALSE,
+                                                       CL_MAP_WRITE, 0,
+                                                       k * sizeof(T),
+                                                       nullptr, &ev_val));
+
+        vector<uint> idx(in.elements());
+
+        // Create a linear index
+        iota(begin(idx), end(idx), 0);
+        Event::waitForEvents({ev_in, ev_ind});
+
+        int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
+        for(int i = 0; i < iter; i++) {
+            auto idx_itr = begin(idx) + i * in.strides()[1];
+            auto kiptr = iptr + k * i;
+            // Sort the top k values in each column
+            partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
+                              kiptr , kiptr + k,
+                              [ptr](const uint lhs, const uint rhs) -> bool {
+                                  return ptr[lhs] < ptr[rhs];
+                              });
+
+            ev_val.wait();
+            auto kvptr = vptr + k * i;
+            for(int j = 0; j < k; j++) {
+                // Update the value arrays with the original values
+                kvptr[j] = ptr[kiptr[j]];
+                // Convert linear indices back to column indices
+                kiptr[j] -= i * in.strides()[1];
+            }
+        }
+
+        getQueue().enqueueUnmapMemObject(*ibuf, iptr);
+        getQueue().enqueueUnmapMemObject(*vbuf, vptr);
+        getQueue().enqueueUnmapMemObject(*in_buf, ptr);
+
+        vals = values;
+        idxs = indices;
+    } else {
+      auto values  = createEmptyArray<T>(in.dims());
+      auto indices = createEmptyArray<unsigned>(in.dims());
+      sort_index(values, indices, in, dim, (order==AF_TOPK_MIN ? true : false));
+      auto indVec = indexForTopK(k);
+      vals = index<T>( values, indVec.data());
+      idxs = index<unsigned>(indices, indVec.data());
+    }
+
+}
+
+#define INSTANTIATE(T)\
+template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, const int, const int, const af::topkFunction);
+
+INSTANTIATE(float )
+INSTANTIATE(double)
+INSTANTIATE(int   )
+INSTANTIATE(uint  )
+}
diff --git a/src/backend/opencl/topk.hpp b/src/backend/opencl/topk.hpp
new file mode 100644
index 0000000000..d35ebc98d4
--- /dev/null
+++ b/src/backend/opencl/topk.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace opencl
+{
+template<typename T>
+void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order);
+}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 2ad0e8674d..3a07f12f77 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -248,7 +248,7 @@ if(BUILD_NONFREE)
 endif()
 
 make_test(SRC sobel.cpp)
-make_test(SRC solve_dense.cpp        CXX11)
+make_test(SRC solve_dense.cpp       CXX11)
 make_test(SRC sort.cpp)
 make_test(SRC sort_by_key.cpp)
 make_test(SRC sort_index.cpp)
@@ -258,8 +258,9 @@ make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
 make_test(SRC susan.cpp)
 make_test(SRC svd_dense.cpp)
-make_test(SRC threading.cpp          CXX11)
+make_test(SRC threading.cpp         CXX11)
 make_test(SRC tile.cpp)
+make_test(SRC topk.cpp              CXX11)
 make_test(SRC transform.cpp)
 make_test(SRC transform_coordinates.cpp)
 make_test(SRC translate.cpp)
diff --git a/test/topk.cpp b/test/topk.cpp
new file mode 100644
index 0000000000..ad4def845a
--- /dev/null
+++ b/test/topk.cpp
@@ -0,0 +1,312 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <testHelpers.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <functional>
+#include <iostream>
+#include <iterator>
+#include <map>
+#include <numeric>
+#include <random>
+#include <sstream>
+#include <string>
+#include <utility>
+
+using af::allTrue;
+using af::array;
+using af::randu;
+using af::seq;
+using af::sort;
+using af::span;
+using af::sum;
+using af::topk;
+
+using std::iota;
+using std::make_pair;
+using std::min;
+using std::mt19937;
+using std::ostream;
+using std::pair;
+using std::random_device;
+using std::shuffle;
+using std::string;
+using std::stringstream;
+using std::vector;
+
+template<typename T> class TopK : public ::testing::Test {};
+
+typedef ::testing::Types<float, double, int, uint> TestTypes;
+
+TYPED_TEST_CASE(TopK, TestTypes);
+
+template<typename T>
+void topkTest(const unsigned ndims, const dim_t* dims,
+                   const int k, const int dim,
+                   const af_topk_function order)
+{
+    af_dtype dtype = (af_dtype)af::dtype_traits<T>::af_type;
+
+    af_array input, output, outindex;
+
+    size_t ielems = 1;
+    size_t oelems = 1;
+
+    for (int i=0; i<ndims; i++)
+    {
+        ielems *= dims[i];
+        oelems *= (i==dim ? k : dims[i]);
+    }
+
+    size_t bCount = ielems/dims[dim];
+    size_t bSize  = dims[dim];
+
+    vector<T> inData(ielems);
+    iota(begin(inData), end(inData), 0);
+
+    random_device rnd_device;
+    mt19937 g(rnd_device());
+    shuffle(begin(inData), end(inData), g);
+
+    vector<T> outData(oelems);
+    vector<uint> outIdxs(oelems);
+
+
+    for (size_t b=0; b<bCount; b++)
+    {
+        using KeyValuePair = pair<T, uint>;
+
+        vector< KeyValuePair > kvPairs;
+        kvPairs.reserve(((b+1)*bSize));
+
+        for (size_t i = b*bSize; i<((b+1)*bSize); ++i)
+            kvPairs.push_back(make_pair(inData[i], (i-b*bSize)));
+
+        if(order == AF_TOPK_MIN) {
+            stable_sort(kvPairs.begin(), kvPairs.end(),
+                    [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
+                        return lhs.first < rhs.first;
+                    });
+        } else {
+            stable_sort(kvPairs.begin(), kvPairs.end(),
+                        [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
+                          return lhs.first >= rhs.first;
+                        });
+        }
+
+        auto it = kvPairs.begin();
+        for (size_t i=0; i<k; ++it, ++i)
+        {
+            outData[i+b*k] = it->first;
+            outIdxs[i+b*k] = it->second;
+        }
+    }
+
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&input, inData.data(), ndims, dims, dtype));
+    ASSERT_EQ(AF_SUCCESS, af_topk(&output, &outindex, input, k, dim, order));
+
+    vector<T> hovals(oelems);
+    vector<uint> hoidxs(oelems);
+
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)hovals.data(), output));
+    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)hoidxs.data(), outindex));
+
+    for (int i=0; i<oelems; ++i)
+    {
+        switch(dtype)
+        {
+            case f64: EXPECT_DOUBLE_EQ(outData[i], hovals[i]) << "at: " << i; break;
+            case f32: EXPECT_FLOAT_EQ (outData[i], hovals[i]) << "at: " << i; break;
+            default : EXPECT_EQ(outData[i], hovals[i]); break;
+        }
+        ASSERT_EQ(outIdxs[i], hoidxs[i]) << "at: " << i;
+    }
+
+    ASSERT_EQ(AF_SUCCESS, af_release_array(input    ));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(output   ));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(outindex ));
+}
+
+TYPED_TEST(TopK, Max1D0)
+{
+    dim_t dims[4] = {100000, 1, 1, 1};
+    topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MAX);
+}
+
+TYPED_TEST(TopK, Max2D0)
+{
+    dim_t dims[4] = {10000, 10, 1, 1};
+    topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MAX);
+}
+
+TYPED_TEST(TopK, Max3D0)
+{
+  dim_t dims[4] = {10000, 10, 10, 1};
+  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
+}
+
+TYPED_TEST(TopK, Max4D0)
+{
+  dim_t dims[4] = {10000, 10, 10, 10};
+  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
+}
+
+TYPED_TEST(TopK, MIN1D0)
+{
+  dim_t dims[4] = {100000, 1, 1, 1};
+  topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MIN);
+}
+
+TYPED_TEST(TopK, MIN2D0)
+{
+  dim_t dims[4] = {10000, 10, 1, 1};
+  topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MIN);
+}
+
+TYPED_TEST(TopK, MIN3D0)
+{
+  dim_t dims[4] = {10000, 10, 10, 1};
+  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
+}
+
+TYPED_TEST(TopK, MIN4D0)
+{
+  dim_t dims[4] = {10000, 10, 10, 10};
+  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
+}
+
+TEST(TopK, ValidationCheck_DimN)
+{
+    dim_t dims[4] = {10, 10, 1, 1};
+    af_array out, idx, in;
+    ASSERT_EQ(AF_SUCCESS, af_randu(&in, 2, dims, f32));
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_topk(&out, &idx, in, 10, 1, AF_TOPK_MAX));
+}
+
+TEST(TopK, ValidationCheck_DefaultDim)
+{
+    dim_t dims[4] = {10, 10, 1, 1};
+    af_array out, idx, in;
+    ASSERT_EQ(AF_SUCCESS, af_randu(&in, 4, dims, f32));
+    ASSERT_EQ(AF_SUCCESS, af_topk(&out, &idx, in, 10, -1, AF_TOPK_MAX));
+}
+
+
+struct topk_params {
+  int d0;
+  int d1;
+  int k;
+  int dim;
+  af::topkFunction order;
+};
+
+ostream& operator<<(ostream& os, const topk_params &param) {
+  os << "d0: " << param.d0 << " d1: " << param.d1
+     << " k:  " << param.k  << " dim: " << param.dim
+     << " order: " << ((param.order == AF_TOPK_MAX) ? "MAX" : "MIN");
+  return os;
+}
+
+class TopKParams : public ::testing::TestWithParam<topk_params> {};
+
+INSTANTIATE_TEST_CASE_P(InstantiationName,
+                        TopKParams,
+                        ::testing::Values(
+                                          topk_params{100,   10,  32, 0, AF_TOPK_MIN},
+                                          topk_params{100,   10,  64, 0, AF_TOPK_MIN},
+                                          topk_params{100,   10,  32, 0, AF_TOPK_MAX},
+                                          topk_params{100,   10,  64, 0, AF_TOPK_MAX},
+                                          topk_params{100,   10,  5, 0,  AF_TOPK_MIN},
+                                          topk_params{1000,  10,  5, 0,  AF_TOPK_MIN},
+                                          topk_params{10000, 10,  5, 0,  AF_TOPK_MIN},
+                                          topk_params{100,   10,  5, 0,  AF_TOPK_MAX},
+                                          topk_params{1000,  10,  5, 0,  AF_TOPK_MAX},
+                                          topk_params{10000, 10,  5, 0,  AF_TOPK_MAX},
+                                          topk_params{10, 10,     5, 0,  AF_TOPK_MIN},
+                                          topk_params{10, 100,    5, 0,  AF_TOPK_MIN},
+                                          topk_params{10, 1000,   5, 0,  AF_TOPK_MIN},
+                                          topk_params{10, 10000,  5, 0,  AF_TOPK_MIN},
+                                          topk_params{10, 10,     5, 0,  AF_TOPK_MAX},
+                                          topk_params{10, 100,    5, 0,  AF_TOPK_MAX},
+                                          topk_params{10, 1000,   5, 0,  AF_TOPK_MAX},
+                                          topk_params{10, 10000,  5, 0,  AF_TOPK_MAX}
+                                          ),
+                        []( const ::testing::TestParamInfo<TopKParams::ParamType> info) {
+                          stringstream ss;
+                          ss << "d0_" << info.param.d0
+                             << "_d1_" << info.param.d1
+                             << "_k_" << info.param.k
+                             << "_dim_" << info.param.dim
+                             << "_order_" << ((info.param.order == AF_TOPK_MAX) ? string("MAX")
+                                                              : string("MIN"));
+                          return ss.str();
+                        });
+
+string print_context(int idx0, int idx1, const vector<float> &val, const vector<unsigned> &idx) {
+  stringstream ss;
+  if(idx0 > 3 && idx1 > 3) {
+    for(int i = idx0 - 3; i < idx0 + 3; i++) {
+      ss << i << ": " << val[i] << " " << idx[i] << "\n";
+    }
+  } else {
+    int end = min(6, idx0+3);
+    for(int i = 0; i < end; i++) {
+      ss << i << ": " << val[i] << " " << idx[i] << "\n";
+    }
+  }
+  return ss.str();
+}
+
+TEST_P(TopKParams, CPP) {
+    using namespace af;
+
+    topk_params params = GetParam();
+    int d0 = params.d0;
+    int d1 = params.d1;
+    int k = params.k;
+    int dim = params.dim;
+    topkFunction order = params.order;
+
+    array in = iota(dim4(d0, d1));
+
+    // reverse the array if the order is ascending
+    if(order == AF_TOPK_MIN) {
+      in = -in + (d0 * d1-1);
+    }
+    array val, idx;
+    topk(val, idx, in, k, 0, order);
+
+    vector<float>    hval(k * d1);
+    vector<unsigned> hidx(k * d1);
+    val.host(&hval[0]);
+    idx.host(&hidx[0]);
+
+    if(order == AF_TOPK_MIN) {
+        for(int j = d1 - 1, i = 0; j > 0; j--) {
+            for(int kidx = 0, goldidx = d0-1; kidx < k; i++, kidx++, goldidx--) {
+                float gold = static_cast<float>(j * d0 + kidx);
+                ASSERT_FLOAT_EQ(gold, hval[i]) << print_context(i, kidx, hval, hidx);
+                ASSERT_EQ(goldidx, hidx[i])    << print_context(i, kidx, hval, hidx);
+            }
+        }
+    } else {
+        for (int ii = 0, i = 0; ii < d1; ii++) {
+            for (int j = d0-1; j >= d0-k; --j, i++) {
+                float gold = static_cast<float>(ii * d0 + j);
+                int goldidx = j;
+                ASSERT_FLOAT_EQ(gold, hval[i]) << print_context(i, 0, hval, hidx);
+                ASSERT_EQ(goldidx, hidx[i])    << print_context(i, 0, hval, hidx);
+            }
+        }
+    }
+}

From f6a5db1a041a9a4f85a1319ef3d93e46398a761d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Sat, 24 Feb 2018 21:25:36 -0500
Subject: [PATCH 1367/2677] fixes batch matmul for CPU devices in the OpenCL
 backend

* fixes batch matmul implementation in the for opencl::cpu backend
---
 src/backend/opencl/cpu/cpu_blas.cpp | 78 +++++++++++++++++++----------
 1 file changed, 52 insertions(+), 26 deletions(-)

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 9511523c1b..38679cc8c4 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -158,40 +158,66 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     int M = lDims[aRowDim];
     int N = rDims[bColDim];
     int K = lDims[aColDim];
+    dim_t d2 = std::max(lDims[2], rDims[2]);
+    dim_t d3 = std::max(lDims[3], rDims[3]);
+    dim4 oDims = af::dim4(M, N, d2, d3);
 
     //FIXME: Leaks on errors.
-    Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
+    Array<T> out = createValueArray<T>(oDims, scalar<T>(0));
     auto alpha = getScale<T, 1>();
     auto beta  = getScale<T, 0>();
 
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
-    using BT  =       typename blas_base<T>::type;
+    dim4 oStrides = out.strides();
 
-    // get host pointers from mapped memory
-    auto lPtr = lhs.getMappedPtr();
-    auto rPtr = rhs.getMappedPtr();
-    auto oPtr = out.getMappedPtr();
-
-    if(rDims[bColDim] == 1) {
-        N = lDims[aColDim];
-        gemv_func<T>()(
-            CblasColMajor, lOpts,
-            lDims[0], lDims[1],
-            alpha,
-            (BT*)lPtr.get(), lStrides[1],
-            (BT*)rPtr.get(), rStrides[0],
-            beta,
-            (BT*)oPtr.get(), 1);
-    } else {
-        gemm_func<T>()(
-            CblasColMajor, lOpts, rOpts,
-            M, N, K,
-            alpha,
-            (BT*)lPtr.get(), lStrides[1],
-            (BT*)rPtr.get(), rStrides[1],
-            beta,
-            (BT*)oPtr.get(), out.dims()[0]);
+    using BT  =       typename blas_base<T>::type;
+    using CBT = const typename blas_base<T>::type;
+
+    int batchSize = oDims[2] * oDims[3];
+
+    bool is_l_d2_batched = (oDims[2] == lDims[2]);
+    bool is_l_d3_batched = (oDims[3] == lDims[3]);
+    bool is_r_d2_batched = (oDims[2] == rDims[2]);
+    bool is_r_d3_batched = (oDims[3] == rDims[3]);
+
+    for(int n = 0; n < batchSize; ++n) {
+        int w = n / rDims[2];
+        int z = n - w * rDims[2];
+
+        int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
+        int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+
+        // get host pointers from mapped memory
+        auto lPtr = lhs.getMappedPtr();
+        auto rPtr = rhs.getMappedPtr();
+        auto oPtr = out.getMappedPtr();
+
+        CBT *lptr = (CBT*)(lPtr.get() + loff);
+        CBT *rptr = (CBT*)(rPtr.get() + roff);
+        BT  *optr = (BT*)(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
+
+        if(rDims[bColDim] == 1) {
+            dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
+            N = lDims[aColDim];
+            gemv_func<T>()(
+                CblasColMajor, lOpts,
+                lDims[0], lDims[1],
+                alpha,
+                lptr, lStrides[1],
+                rptr, incr,
+                beta,
+                optr, 1);
+        } else {
+            gemm_func<T>()(
+                CblasColMajor, lOpts, rOpts,
+                M, N, K,
+                alpha,
+                lptr, lStrides[1],
+                rptr, rStrides[1],
+                beta,
+                optr, out.dims()[0]);
+        }
     }
 
     return out;

From 125400f08011fa0b412ff68b1aa7f608563efe6b Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 22 Feb 2018 15:55:22 -0500
Subject: [PATCH 1368/2677] fixes warnings from doxygen

---
 docs/details/index.dox      |   2 +-
 docs/pages/release_notes.md |   6 +-
 include/af/array.h          | 156 ++++++++++++++++++------------------
 3 files changed, 82 insertions(+), 82 deletions(-)

diff --git a/docs/details/index.dox b/docs/details/index.dox
index 65f38c9931..bd6d652da4 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -14,7 +14,7 @@
 
 \brief Index an array using another array.
 
-Lets look at an example of how \ref lookup function does indexing.
+Let's look at an example of how \ref af::lookup function does indexing.
 \code
 array a = range(dim4(5));
 af_print(a);
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 91a72b9cba..7d1df86910 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -36,7 +36,7 @@ Improvements
   [NVVM](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html).
 * Performance improvements in \ref af::reorder().
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1766)</sup>
-* Performance improvements in \ref array::scalar<T>().
+* Performance improvements in array::scalar<T>().
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1809)</sup>
 * Improved unified backend performance.
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1770)</sup>
@@ -45,7 +45,7 @@ Improvements
 * Can now specify the FFT plan cache size using the
   \ref af::setFFTPlanCacheSize() function.
 * Get the number of physical bytes allocated by the memory manager
-  \ref `af_get_allocated_bytes()`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
+  \ref af_get_allocated_bytes(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
 * \ref af::dot() can now return a scalar value to the
   host. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1628)</sup>
 
@@ -61,7 +61,7 @@ Bug Fixes
 * Fixed complex (`c32`,`c64`) multiplication in OpenCL convolution
   kernels. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1816)</sup>
 * Fixed inconsistent behavior with \ref af::replace() and \ref
-  replace_scalar(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
+  af_replace_scalar() . <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
 * Fixed memory leak in \ref
   af_fir(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1765)</sup>
 * Fixed memory leaks in \ref af_cast for sparse arrays.
diff --git a/include/af/array.h b/include/af/array.h
index 07f6c5fe15..89f7bd1f9d 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -867,30 +867,30 @@ namespace af
         /// \ingroup method_mat
         array H() const;
 
-#define ASSIGN_(OP)                                                                     \
-        array& OP(const array &val);                                                    \
-        array& OP(const double &val);              /**< \copydoc OP (const array &) */  \
-        array& OP(const cdouble &val);             /**< \copydoc OP (const array &) */  \
-        array& OP(const cfloat &val);              /**< \copydoc OP (const array &) */  \
-        array& OP(const float &val);               /**< \copydoc OP (const array &) */  \
-        array& OP(const int &val);                 /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned &val);            /**< \copydoc OP (const array &) */  \
-        array& OP(const bool &val);                /**< \copydoc OP (const array &) */  \
-        array& OP(const char &val);                /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned char &val);       /**< \copydoc OP (const array &) */  \
-        array& OP(const long  &val);               /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned long &val);       /**< \copydoc OP (const array &) */  \
-        array& OP(const long long  &val);          /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned long long &val);  /**< \copydoc OP (const array &) */  \
+#define ASSIGN_(OP, DOXY_STRING)                               \
+        array& OP(const array &val);                           \
+        array& OP(const double &val);             DOXY_STRING  \
+        array& OP(const cdouble &val);            DOXY_STRING  \
+        array& OP(const cfloat &val);             DOXY_STRING  \
+        array& OP(const float &val);              DOXY_STRING  \
+        array& OP(const int &val);                DOXY_STRING  \
+        array& OP(const unsigned &val);           DOXY_STRING  \
+        array& OP(const bool &val);               DOXY_STRING  \
+        array& OP(const char &val);               DOXY_STRING  \
+        array& OP(const unsigned char &val);      DOXY_STRING  \
+        array& OP(const long  &val);              DOXY_STRING  \
+        array& OP(const unsigned long &val);      DOXY_STRING  \
+        array& OP(const long long  &val);         DOXY_STRING  \
+        array& OP(const unsigned long long &val); DOXY_STRING  \
 
 #if AF_API_VERSION >= 32
-#define ASSIGN(OP)                                                                      \
-        ASSIGN_(OP)                                                                     \
-        array& OP(const short  &val);              /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned short &val);      /**< \copydoc OP (const array &) */  \
+#define ASSIGN(OP, DOXY_STRING)                                \
+        ASSIGN_(OP, DOXY_STRING)                               \
+        array& OP(const short  &val);             DOXY_STRING  \
+        array& OP(const unsigned short &val);     DOXY_STRING  \
 
 #else
-#define ASSIGN(OP) ASSIGN_(OP)
+#define ASSIGN(OP, DOXY_STRING) ASSIGN_(OP, DOXY_STRING)
 #endif
 
 
@@ -903,7 +903,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator=)
+        ASSIGN(operator=, /**< \copydoc operator=(const array &) */)
         /// @}
 
         /// \ingroup array_mem_operator_plus_eq
@@ -915,7 +915,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator+=)
+        ASSIGN(operator+=, /**< \copydoc operator+=(const array &) */)
         /// @}
 
         /// \ingroup array_mem_operator_minus_eq
@@ -927,7 +927,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator-=)
+        ASSIGN(operator-=, /**< \copydoc operator-=(const array &) */)
         /// @}
 
         /// \ingroup array_mem_operator_multiply_eq
@@ -939,7 +939,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator*=)
+        ASSIGN(operator*=, /**< \copydoc operator*=(const array &) */)
         /// @}
 
         /// \ingroup array_mem_operator_divide_eq
@@ -952,7 +952,7 @@ namespace af
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
         /// \ingroup array_mem_operator_divide_eq
-        ASSIGN(operator/=)
+        ASSIGN(operator/=, /**< \copydoc operator/=(const array &) */)
         /// @}
 
 
@@ -1007,45 +1007,45 @@ namespace af
     };
     // end of class array
 
-#define BIN_OP_(OP)                                                                                                      \
-    AFAPI array OP (const array& lhs, const array& rhs);                                                                 \
-    AFAPI array OP (const bool& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const int& lhs, const array& rhs);                  /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned& lhs, const array& rhs);             /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const char& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned char& lhs, const array& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const long& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned long& lhs, const array& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const long long& lhs, const array& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned long long& lhs, const array& rhs);   /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const double& lhs, const array& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const float& lhs, const array& rhs);                /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const cfloat& lhs, const array& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const cdouble& lhs, const array& rhs);              /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const bool& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const int& rhs);                  /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned& rhs);             /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const char& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned char& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const long& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned long& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const long long& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned long long& rhs);   /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const double& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const float& rhs);                /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const cfloat& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const cdouble& rhs);              /**< \copydoc OP (const array&, const array&) */ \
+#define BIN_OP_(OP, DOXY_STRING)                                                   \
+    AFAPI array OP (const array& lhs, const array& rhs);                           \
+    AFAPI array OP (const bool& lhs, const array& rhs);                DOXY_STRING \
+    AFAPI array OP (const int& lhs, const array& rhs);                 DOXY_STRING \
+    AFAPI array OP (const unsigned& lhs, const array& rhs);            DOXY_STRING \
+    AFAPI array OP (const char& lhs, const array& rhs);                DOXY_STRING \
+    AFAPI array OP (const unsigned char& lhs, const array& rhs);       DOXY_STRING \
+    AFAPI array OP (const long& lhs, const array& rhs);                DOXY_STRING \
+    AFAPI array OP (const unsigned long& lhs, const array& rhs);       DOXY_STRING \
+    AFAPI array OP (const long long& lhs, const array& rhs);           DOXY_STRING \
+    AFAPI array OP (const unsigned long long& lhs, const array& rhs);  DOXY_STRING \
+    AFAPI array OP (const double& lhs, const array& rhs);              DOXY_STRING \
+    AFAPI array OP (const float& lhs, const array& rhs);               DOXY_STRING \
+    AFAPI array OP (const cfloat& lhs, const array& rhs);              DOXY_STRING \
+    AFAPI array OP (const cdouble& lhs, const array& rhs);             DOXY_STRING \
+    AFAPI array OP (const array& lhs, const bool& rhs);                DOXY_STRING \
+    AFAPI array OP (const array& lhs, const int& rhs);                 DOXY_STRING \
+    AFAPI array OP (const array& lhs, const unsigned& rhs);            DOXY_STRING \
+    AFAPI array OP (const array& lhs, const char& rhs);                DOXY_STRING \
+    AFAPI array OP (const array& lhs, const unsigned char& rhs);       DOXY_STRING \
+    AFAPI array OP (const array& lhs, const long& rhs);                DOXY_STRING \
+    AFAPI array OP (const array& lhs, const unsigned long& rhs);       DOXY_STRING \
+    AFAPI array OP (const array& lhs, const long long& rhs);           DOXY_STRING \
+    AFAPI array OP (const array& lhs, const unsigned long long& rhs);  DOXY_STRING \
+    AFAPI array OP (const array& lhs, const double& rhs);              DOXY_STRING \
+    AFAPI array OP (const array& lhs, const float& rhs);               DOXY_STRING \
+    AFAPI array OP (const array& lhs, const cfloat& rhs);              DOXY_STRING \
+    AFAPI array OP (const array& lhs, const cdouble& rhs);             DOXY_STRING \
 
 #if AF_API_VERSION >= 32
-#define BIN_OP(OP)                                                                                                       \
-        BIN_OP_(OP)                                                                                                      \
-        AFAPI array OP (const short& lhs, const array& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-        AFAPI array OP (const unsigned short& lhs, const array& rhs);   /**< \copydoc OP (const array&, const array&) */ \
-        AFAPI array OP (const array& lhs, const short& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-        AFAPI array OP (const array& lhs, const unsigned short& rhs);   /**< \copydoc OP (const array&, const array&) */ \
+#define BIN_OP(OP, DOXY_STRING)                                                    \
+        BIN_OP_(OP, DOXY_STRING)                                                   \
+        AFAPI array OP (const short& lhs, const array& rhs);           DOXY_STRING \
+        AFAPI array OP (const unsigned short& lhs, const array& rhs);  DOXY_STRING \
+        AFAPI array OP (const array& lhs, const short& rhs);           DOXY_STRING \
+        AFAPI array OP (const array& lhs, const unsigned short& rhs);  DOXY_STRING \
 
 #else
-#define BIN_OP(OP) BIN_OP_(OP)
+#define BIN_OP(OP, DOXY_STRING) BIN_OP_(OP, DOXY_STRING)
 #endif
 
     /// \ingroup arith_func_add
@@ -1056,7 +1056,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the sum of the \p lhs and \p rhs
-    BIN_OP(operator+ )
+    BIN_OP(operator+, /**< \copydoc operator+ (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_sub
@@ -1067,7 +1067,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the subtraction of the \p lhs and \p rhs
-    BIN_OP(operator- )
+    BIN_OP(operator-, /**< \copydoc operator- (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_mul
@@ -1078,7 +1078,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the product of the \p lhs and \p rhs
-    BIN_OP(operator* )
+    BIN_OP(operator*, /**< \copydoc operator* (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_div
@@ -1089,7 +1089,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the quotient of the \p lhs and \p rhs
-    BIN_OP(operator/ )
+    BIN_OP(operator/, /**< \copydoc operator/ (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_eq
@@ -1100,7 +1100,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array of type b8 with the equality operation performed on each element
-    BIN_OP(operator==)
+    BIN_OP(operator==, /**< \copydoc operator== (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_neq
@@ -1112,7 +1112,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the != operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator!=)
+    BIN_OP(operator!=, /**< \copydoc operator!= (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_lt
@@ -1124,7 +1124,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the < operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator< )
+    BIN_OP(operator<, /**< \copydoc operator< (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_le
@@ -1136,7 +1136,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the <= operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator<=)
+    BIN_OP(operator<=, /**< \copydoc operator<= (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_gt
@@ -1148,7 +1148,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the > operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator> )
+    BIN_OP(operator>, /**< \copydoc operator> (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_ge
@@ -1160,7 +1160,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the >= operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator>=)
+    BIN_OP(operator>=, /**< \copydoc operator>= (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_and
@@ -1173,7 +1173,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with a logical AND operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator&&)
+    BIN_OP(operator&&, /**< \copydoc operator&& (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_or
@@ -1186,7 +1186,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with a logical OR operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator||)
+    BIN_OP(operator||, /**< \copydoc operator|| (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_mod
@@ -1198,7 +1198,7 @@ namespace af
     ///
     /// \returns    an array with a modulo operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator% )
+    BIN_OP(operator%, /**< \copydoc operator% (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_bitand
@@ -1211,7 +1211,7 @@ namespace af
     ///
     /// \returns    an array with a bitwise AND operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator& )
+    BIN_OP(operator&, /**< \copydoc operator& (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_bitor
@@ -1224,7 +1224,7 @@ namespace af
     ///
     /// \returns    an array with a bitwise OR operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator| )
+    BIN_OP(operator|, /**< \copydoc operator| (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_bitxor
@@ -1237,7 +1237,7 @@ namespace af
     ///
     /// \returns    an array with a bitwise OR operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator^ )
+    BIN_OP(operator^, /**< \copydoc operator^ (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_shiftl
@@ -1250,7 +1250,7 @@ namespace af
     ///
     /// \returns    an array with a left shift operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator<<)
+    BIN_OP(operator<<, /**< \copydoc operator<< (const array&, const array&) */)
     /// @}
 
     /// \ingroup arith_func_shiftr
@@ -1263,7 +1263,7 @@ namespace af
     ///
     /// \returns    an array with a right shift operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator>>)
+    BIN_OP(operator>>, /**< \copydoc operator>> (const array&, const array&) */)
     /// @}
 
 #undef BIN_OP

From c175fa7818ff5b9dd6e94cf36747f07c85a97aa1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 25 Feb 2018 13:04:46 -0500
Subject: [PATCH 1369/2677] Revert "fixes warnings from doxygen"

This reverts commit 125400f08011fa0b412ff68b1aa7f608563efe6b.
---
 docs/details/index.dox      |   2 +-
 docs/pages/release_notes.md |   6 +-
 include/af/array.h          | 156 ++++++++++++++++++------------------
 3 files changed, 82 insertions(+), 82 deletions(-)

diff --git a/docs/details/index.dox b/docs/details/index.dox
index bd6d652da4..65f38c9931 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -14,7 +14,7 @@
 
 \brief Index an array using another array.
 
-Let's look at an example of how \ref af::lookup function does indexing.
+Lets look at an example of how \ref lookup function does indexing.
 \code
 array a = range(dim4(5));
 af_print(a);
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 7d1df86910..91a72b9cba 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -36,7 +36,7 @@ Improvements
   [NVVM](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html).
 * Performance improvements in \ref af::reorder().
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1766)</sup>
-* Performance improvements in array::scalar<T>().
+* Performance improvements in \ref array::scalar<T>().
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1809)</sup>
 * Improved unified backend performance.
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1770)</sup>
@@ -45,7 +45,7 @@ Improvements
 * Can now specify the FFT plan cache size using the
   \ref af::setFFTPlanCacheSize() function.
 * Get the number of physical bytes allocated by the memory manager
-  \ref af_get_allocated_bytes(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
+  \ref `af_get_allocated_bytes()`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
 * \ref af::dot() can now return a scalar value to the
   host. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1628)</sup>
 
@@ -61,7 +61,7 @@ Bug Fixes
 * Fixed complex (`c32`,`c64`) multiplication in OpenCL convolution
   kernels. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1816)</sup>
 * Fixed inconsistent behavior with \ref af::replace() and \ref
-  af_replace_scalar() . <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
+  replace_scalar(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
 * Fixed memory leak in \ref
   af_fir(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1765)</sup>
 * Fixed memory leaks in \ref af_cast for sparse arrays.
diff --git a/include/af/array.h b/include/af/array.h
index 89f7bd1f9d..07f6c5fe15 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -867,30 +867,30 @@ namespace af
         /// \ingroup method_mat
         array H() const;
 
-#define ASSIGN_(OP, DOXY_STRING)                               \
-        array& OP(const array &val);                           \
-        array& OP(const double &val);             DOXY_STRING  \
-        array& OP(const cdouble &val);            DOXY_STRING  \
-        array& OP(const cfloat &val);             DOXY_STRING  \
-        array& OP(const float &val);              DOXY_STRING  \
-        array& OP(const int &val);                DOXY_STRING  \
-        array& OP(const unsigned &val);           DOXY_STRING  \
-        array& OP(const bool &val);               DOXY_STRING  \
-        array& OP(const char &val);               DOXY_STRING  \
-        array& OP(const unsigned char &val);      DOXY_STRING  \
-        array& OP(const long  &val);              DOXY_STRING  \
-        array& OP(const unsigned long &val);      DOXY_STRING  \
-        array& OP(const long long  &val);         DOXY_STRING  \
-        array& OP(const unsigned long long &val); DOXY_STRING  \
+#define ASSIGN_(OP)                                                                     \
+        array& OP(const array &val);                                                    \
+        array& OP(const double &val);              /**< \copydoc OP (const array &) */  \
+        array& OP(const cdouble &val);             /**< \copydoc OP (const array &) */  \
+        array& OP(const cfloat &val);              /**< \copydoc OP (const array &) */  \
+        array& OP(const float &val);               /**< \copydoc OP (const array &) */  \
+        array& OP(const int &val);                 /**< \copydoc OP (const array &) */  \
+        array& OP(const unsigned &val);            /**< \copydoc OP (const array &) */  \
+        array& OP(const bool &val);                /**< \copydoc OP (const array &) */  \
+        array& OP(const char &val);                /**< \copydoc OP (const array &) */  \
+        array& OP(const unsigned char &val);       /**< \copydoc OP (const array &) */  \
+        array& OP(const long  &val);               /**< \copydoc OP (const array &) */  \
+        array& OP(const unsigned long &val);       /**< \copydoc OP (const array &) */  \
+        array& OP(const long long  &val);          /**< \copydoc OP (const array &) */  \
+        array& OP(const unsigned long long &val);  /**< \copydoc OP (const array &) */  \
 
 #if AF_API_VERSION >= 32
-#define ASSIGN(OP, DOXY_STRING)                                \
-        ASSIGN_(OP, DOXY_STRING)                               \
-        array& OP(const short  &val);             DOXY_STRING  \
-        array& OP(const unsigned short &val);     DOXY_STRING  \
+#define ASSIGN(OP)                                                                      \
+        ASSIGN_(OP)                                                                     \
+        array& OP(const short  &val);              /**< \copydoc OP (const array &) */  \
+        array& OP(const unsigned short &val);      /**< \copydoc OP (const array &) */  \
 
 #else
-#define ASSIGN(OP, DOXY_STRING) ASSIGN_(OP, DOXY_STRING)
+#define ASSIGN(OP) ASSIGN_(OP)
 #endif
 
 
@@ -903,7 +903,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator=, /**< \copydoc operator=(const array &) */)
+        ASSIGN(operator=)
         /// @}
 
         /// \ingroup array_mem_operator_plus_eq
@@ -915,7 +915,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator+=, /**< \copydoc operator+=(const array &) */)
+        ASSIGN(operator+=)
         /// @}
 
         /// \ingroup array_mem_operator_minus_eq
@@ -927,7 +927,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator-=, /**< \copydoc operator-=(const array &) */)
+        ASSIGN(operator-=)
         /// @}
 
         /// \ingroup array_mem_operator_multiply_eq
@@ -939,7 +939,7 @@ namespace af
         ///
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
-        ASSIGN(operator*=, /**< \copydoc operator*=(const array &) */)
+        ASSIGN(operator*=)
         /// @}
 
         /// \ingroup array_mem_operator_divide_eq
@@ -952,7 +952,7 @@ namespace af
         /// \note   This is a copy on write operation. The copy only occurs when the
         ///          operator() is used on the left hand side.
         /// \ingroup array_mem_operator_divide_eq
-        ASSIGN(operator/=, /**< \copydoc operator/=(const array &) */)
+        ASSIGN(operator/=)
         /// @}
 
 
@@ -1007,45 +1007,45 @@ namespace af
     };
     // end of class array
 
-#define BIN_OP_(OP, DOXY_STRING)                                                   \
-    AFAPI array OP (const array& lhs, const array& rhs);                           \
-    AFAPI array OP (const bool& lhs, const array& rhs);                DOXY_STRING \
-    AFAPI array OP (const int& lhs, const array& rhs);                 DOXY_STRING \
-    AFAPI array OP (const unsigned& lhs, const array& rhs);            DOXY_STRING \
-    AFAPI array OP (const char& lhs, const array& rhs);                DOXY_STRING \
-    AFAPI array OP (const unsigned char& lhs, const array& rhs);       DOXY_STRING \
-    AFAPI array OP (const long& lhs, const array& rhs);                DOXY_STRING \
-    AFAPI array OP (const unsigned long& lhs, const array& rhs);       DOXY_STRING \
-    AFAPI array OP (const long long& lhs, const array& rhs);           DOXY_STRING \
-    AFAPI array OP (const unsigned long long& lhs, const array& rhs);  DOXY_STRING \
-    AFAPI array OP (const double& lhs, const array& rhs);              DOXY_STRING \
-    AFAPI array OP (const float& lhs, const array& rhs);               DOXY_STRING \
-    AFAPI array OP (const cfloat& lhs, const array& rhs);              DOXY_STRING \
-    AFAPI array OP (const cdouble& lhs, const array& rhs);             DOXY_STRING \
-    AFAPI array OP (const array& lhs, const bool& rhs);                DOXY_STRING \
-    AFAPI array OP (const array& lhs, const int& rhs);                 DOXY_STRING \
-    AFAPI array OP (const array& lhs, const unsigned& rhs);            DOXY_STRING \
-    AFAPI array OP (const array& lhs, const char& rhs);                DOXY_STRING \
-    AFAPI array OP (const array& lhs, const unsigned char& rhs);       DOXY_STRING \
-    AFAPI array OP (const array& lhs, const long& rhs);                DOXY_STRING \
-    AFAPI array OP (const array& lhs, const unsigned long& rhs);       DOXY_STRING \
-    AFAPI array OP (const array& lhs, const long long& rhs);           DOXY_STRING \
-    AFAPI array OP (const array& lhs, const unsigned long long& rhs);  DOXY_STRING \
-    AFAPI array OP (const array& lhs, const double& rhs);              DOXY_STRING \
-    AFAPI array OP (const array& lhs, const float& rhs);               DOXY_STRING \
-    AFAPI array OP (const array& lhs, const cfloat& rhs);              DOXY_STRING \
-    AFAPI array OP (const array& lhs, const cdouble& rhs);             DOXY_STRING \
+#define BIN_OP_(OP)                                                                                                      \
+    AFAPI array OP (const array& lhs, const array& rhs);                                                                 \
+    AFAPI array OP (const bool& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const int& lhs, const array& rhs);                  /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const unsigned& lhs, const array& rhs);             /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const char& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const unsigned char& lhs, const array& rhs);        /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const long& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const unsigned long& lhs, const array& rhs);        /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const long long& lhs, const array& rhs);            /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const unsigned long long& lhs, const array& rhs);   /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const double& lhs, const array& rhs);               /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const float& lhs, const array& rhs);                /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const cfloat& lhs, const array& rhs);               /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const cdouble& lhs, const array& rhs);              /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const bool& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const int& rhs);                  /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned& rhs);             /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const char& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned char& rhs);        /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const long& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned long& rhs);        /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const long long& rhs);            /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned long long& rhs);   /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const double& rhs);               /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const float& rhs);                /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const cfloat& rhs);               /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const cdouble& rhs);              /**< \copydoc OP (const array&, const array&) */ \
 
 #if AF_API_VERSION >= 32
-#define BIN_OP(OP, DOXY_STRING)                                                    \
-        BIN_OP_(OP, DOXY_STRING)                                                   \
-        AFAPI array OP (const short& lhs, const array& rhs);           DOXY_STRING \
-        AFAPI array OP (const unsigned short& lhs, const array& rhs);  DOXY_STRING \
-        AFAPI array OP (const array& lhs, const short& rhs);           DOXY_STRING \
-        AFAPI array OP (const array& lhs, const unsigned short& rhs);  DOXY_STRING \
+#define BIN_OP(OP)                                                                                                       \
+        BIN_OP_(OP)                                                                                                      \
+        AFAPI array OP (const short& lhs, const array& rhs);            /**< \copydoc OP (const array&, const array&) */ \
+        AFAPI array OP (const unsigned short& lhs, const array& rhs);   /**< \copydoc OP (const array&, const array&) */ \
+        AFAPI array OP (const array& lhs, const short& rhs);            /**< \copydoc OP (const array&, const array&) */ \
+        AFAPI array OP (const array& lhs, const unsigned short& rhs);   /**< \copydoc OP (const array&, const array&) */ \
 
 #else
-#define BIN_OP(OP, DOXY_STRING) BIN_OP_(OP, DOXY_STRING)
+#define BIN_OP(OP) BIN_OP_(OP)
 #endif
 
     /// \ingroup arith_func_add
@@ -1056,7 +1056,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the sum of the \p lhs and \p rhs
-    BIN_OP(operator+, /**< \copydoc operator+ (const array&, const array&) */)
+    BIN_OP(operator+ )
     /// @}
 
     /// \ingroup arith_func_sub
@@ -1067,7 +1067,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the subtraction of the \p lhs and \p rhs
-    BIN_OP(operator-, /**< \copydoc operator- (const array&, const array&) */)
+    BIN_OP(operator- )
     /// @}
 
     /// \ingroup arith_func_mul
@@ -1078,7 +1078,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the product of the \p lhs and \p rhs
-    BIN_OP(operator*, /**< \copydoc operator* (const array&, const array&) */)
+    BIN_OP(operator* )
     /// @}
 
     /// \ingroup arith_func_div
@@ -1089,7 +1089,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array which is the quotient of the \p lhs and \p rhs
-    BIN_OP(operator/, /**< \copydoc operator/ (const array&, const array&) */)
+    BIN_OP(operator/ )
     /// @}
 
     /// \ingroup arith_func_eq
@@ -1100,7 +1100,7 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns an array of type b8 with the equality operation performed on each element
-    BIN_OP(operator==, /**< \copydoc operator== (const array&, const array&) */)
+    BIN_OP(operator==)
     /// @}
 
     /// \ingroup arith_func_neq
@@ -1112,7 +1112,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the != operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator!=, /**< \copydoc operator!= (const array&, const array&) */)
+    BIN_OP(operator!=)
     /// @}
 
     /// \ingroup arith_func_lt
@@ -1124,7 +1124,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the < operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator<, /**< \copydoc operator< (const array&, const array&) */)
+    BIN_OP(operator< )
     /// @}
 
     /// \ingroup arith_func_le
@@ -1136,7 +1136,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the <= operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator<=, /**< \copydoc operator<= (const array&, const array&) */)
+    BIN_OP(operator<=)
     /// @}
 
     /// \ingroup arith_func_gt
@@ -1148,7 +1148,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the > operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator>, /**< \copydoc operator> (const array&, const array&) */)
+    BIN_OP(operator> )
     /// @}
 
     /// \ingroup arith_func_ge
@@ -1160,7 +1160,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with the >= operation performed on each element
     ///             of \p lhs and \p rhs
-    BIN_OP(operator>=, /**< \copydoc operator>= (const array&, const array&) */)
+    BIN_OP(operator>=)
     /// @}
 
     /// \ingroup arith_func_and
@@ -1173,7 +1173,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with a logical AND operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator&&, /**< \copydoc operator&& (const array&, const array&) */)
+    BIN_OP(operator&&)
     /// @}
 
     /// \ingroup arith_func_or
@@ -1186,7 +1186,7 @@ namespace af
     ///
     /// \returns    an array of type b8 with a logical OR operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator||, /**< \copydoc operator|| (const array&, const array&) */)
+    BIN_OP(operator||)
     /// @}
 
     /// \ingroup arith_func_mod
@@ -1198,7 +1198,7 @@ namespace af
     ///
     /// \returns    an array with a modulo operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator%, /**< \copydoc operator% (const array&, const array&) */)
+    BIN_OP(operator% )
     /// @}
 
     /// \ingroup arith_func_bitand
@@ -1211,7 +1211,7 @@ namespace af
     ///
     /// \returns    an array with a bitwise AND operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator&, /**< \copydoc operator& (const array&, const array&) */)
+    BIN_OP(operator& )
     /// @}
 
     /// \ingroup arith_func_bitor
@@ -1224,7 +1224,7 @@ namespace af
     ///
     /// \returns    an array with a bitwise OR operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator|, /**< \copydoc operator| (const array&, const array&) */)
+    BIN_OP(operator| )
     /// @}
 
     /// \ingroup arith_func_bitxor
@@ -1237,7 +1237,7 @@ namespace af
     ///
     /// \returns    an array with a bitwise OR operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator^, /**< \copydoc operator^ (const array&, const array&) */)
+    BIN_OP(operator^ )
     /// @}
 
     /// \ingroup arith_func_shiftl
@@ -1250,7 +1250,7 @@ namespace af
     ///
     /// \returns    an array with a left shift operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator<<, /**< \copydoc operator<< (const array&, const array&) */)
+    BIN_OP(operator<<)
     /// @}
 
     /// \ingroup arith_func_shiftr
@@ -1263,7 +1263,7 @@ namespace af
     ///
     /// \returns    an array with a right shift operation performed on each
     ///             element of \p lhs and \p rhs
-    BIN_OP(operator>>, /**< \copydoc operator>> (const array&, const array&) */)
+    BIN_OP(operator>>)
     /// @}
 
 #undef BIN_OP

From 3bc5da85a9503dc976baea444db8c869b2e227d7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Feb 2018 07:23:18 -0500
Subject: [PATCH 1370/2677] Cleanup some include statements using include what
 you use

---
 CMakeModules/bin2cpp.cpp                  | 12 +++---
 src/api/cpp/data.cpp                      |  1 +
 src/backend/common/SparseArray.cpp        |  3 ++
 src/backend/cpu/Array.cpp                 | 48 +++++++++++++++--------
 src/backend/cpu/Array.hpp                 | 25 ++++++------
 src/backend/cpu/CMakeLists.txt            |  1 -
 src/backend/cpu/Param.hpp                 | 22 +++++------
 src/backend/cpu/anisotropic_diffusion.cpp |  3 +-
 src/backend/cpu/anisotropic_diffusion.hpp |  5 ++-
 src/backend/cpu/approx.cpp                |  7 +++-
 src/backend/cpu/approx.hpp                |  1 +
 src/backend/cpu/assign.cpp                | 24 ++++++++----
 src/backend/cpu/assign.hpp                |  3 +-
 src/backend/cpu/bilateral.cpp             |  9 ++---
 src/backend/cpu/blas.cpp                  | 22 +++++++++--
 src/backend/cpu/blas.hpp                  |  3 +-
 src/backend/cpu/canny.cpp                 |  8 ++--
 src/backend/cpu/cholesky.cpp              |  9 +++--
 src/backend/cpu/convolve.cpp              | 12 +++---
 src/backend/cpu/convolve.hpp              |  3 +-
 src/backend/cpu/copy.cpp                  | 17 ++++----
 src/backend/cpu/copy.hpp                  |  2 +
 src/backend/cpu/diagonal.cpp              | 14 ++++---
 src/backend/cpu/diagonal.hpp              |  1 -
 src/backend/cpu/diff.cpp                  |  6 ++-
 src/backend/cpu/exampleFunction.cpp       |  2 +
 src/backend/cpu/exampleFunction.hpp       |  1 +
 src/backend/cpu/fast.cpp                  | 12 ++++--
 src/backend/cpu/fast.hpp                  |  6 +--
 src/backend/cpu/fft.cpp                   | 11 +++---
 src/backend/cpu/fft.hpp                   |  4 ++
 src/backend/cpu/kernel/assign.hpp         |  9 ++++-
 src/backend/cpu/kernel/convolve.hpp       |  2 +
 src/backend/cpu/kernel/copy.hpp           |  6 ++-
 src/backend/cpu/kernel/diagonal.hpp       |  7 +++-
 src/backend/cpu/kernel/fft.hpp            |  5 ++-
 src/backend/cpu/kernel/resize.hpp         |  3 +-
 src/backend/cpu/kernel/rotate.hpp         |  3 ++
 src/backend/cpu/kernel/sort_by_key.hpp    |  1 -
 src/backend/cpu/kernel/transform.hpp      |  3 +-
 src/backend/cpu/math.hpp                  |  7 +++-
 41 files changed, 213 insertions(+), 130 deletions(-)

diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index 273d7f0baf..95286cc232 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -1,14 +1,16 @@
 // Umar Arshad
 // Copyright 2014
 
-
+#include <cstdlib>
 #include <fstream>
-#include <sstream>
+#include <functional>
 #include <iostream>
-#include <string>
-#include <vector>
 #include <map>
 #include <memory>
+#include <sstream> // IWYU pragma: keep
+#include <string>
+#include <utility>
+#include <vector>
 
 using namespace std;
 typedef map<string, string> opt_t;
@@ -140,7 +142,7 @@ int main(int argc, const char * const * const argv)
     int ns_cnt = 0;
     int level = 0;
     if(options["--namespace"] != "") {
-        std::stringstream namespaces(options["--namespace"]);
+        stringstream namespaces(options["--namespace"]);
         string name;
         namespaces >> name;
         do {
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 358853537d..b13c2395a1 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -14,6 +14,7 @@
 #include <af/traits.hpp>
 #include <af/gfor.h>
 #include "error.hpp"
+#include <af/defines.h>
 
 namespace af
 {
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index 2c1d3be80d..668e1f621f 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -12,6 +12,9 @@
 #include <copy.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <af/traits.hpp>
+
+using af::dtype_traits;
 
 namespace common
 {
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 675896b54d..34f68e2c5c 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -7,19 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <common/err_common.hpp>
+
 #include <Array.hpp>
-#include <copy.hpp>
 #include <kernel/Array.hpp>
+
+#include <Param.hpp>
 #include <TNJ/BufferNode.hpp>
+#include <TNJ/Node.hpp>
 #include <TNJ/ScalarNode.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <copy.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <traits.hpp>
 
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/seq.h>
+#include <af/traits.hpp>
+
+#include <algorithm> // IWYU pragma: keep
 #include <cstring>
 #include <cstddef>
+#include <type_traits>
 
 namespace cpu
 {
@@ -29,7 +41,9 @@ using TNJ::Node;
 using TNJ::Node_ptr;
 
 using af::dim4;
-
+using std::vector;
+using std::is_standard_layout;
+using std::copy;
 
 template<typename T>
 Node_ptr bufferNodePtr()
@@ -50,12 +64,12 @@ Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_de
     data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()).release(), memFree<T>), data_dims(dims),
     node(bufferNodePtr<T>()), ready(true), owner(true)
 {
-    static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
+    static_assert(is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
     static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
     if (!is_device || copy_device) {
         // Ensure the memory being written to isnt used anywhere else.
         getQueue().sync();
-        std::copy(in_data, in_data + dims.elements(), data.get());
+        copy(in_data, in_data + dims.elements(), data.get());
     }
 }
 
@@ -88,7 +102,7 @@ Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
     if (!is_device) {
         // Ensure the memory being written to isnt used anywhere else.
         getQueue().sync();
-        std::copy(in_data, in_data + info.total(), data.get());
+        copy(in_data, in_data + info.total(), data.get());
     }
 }
 
@@ -100,7 +114,7 @@ void Array<T>::eval()
 
     this->setId(getActiveDeviceId());
 
-    data = std::shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
+    data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
 
     getQueue().enqueue(kernel::evalArray<T>, *this, this->node);
     // Reset shared_ptr
@@ -126,21 +140,21 @@ T* Array<T>::device()
 }
 
 template<typename T>
-void evalMultiple(std::vector<Array<T>*> array_ptrs)
+void evalMultiple(vector<Array<T>*> array_ptrs)
 {
-    std::vector<Array<T>> arrays;
-    std::vector<TNJ::Node_ptr> nodes;
+    vector<Array<T>> arrays;
+    vector<TNJ::Node_ptr> nodes;
     bool isWorker = getQueue().is_worker();
     for (auto &array : array_ptrs) {
         if (array->ready) continue;
         if (isWorker) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
         array->setId(getActiveDeviceId());
-        array->data = std::shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
+        array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
         arrays.push_back(*array);
         nodes.push_back(array->node);
     }
 
-    std::vector<Param<T>> params(arrays.begin(), arrays.end());
+    vector<Param<T>> params(arrays.begin(), arrays.end());
     if (arrays.size() > 0) {
         getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
         for (auto &array : array_ptrs) {
@@ -224,7 +238,7 @@ createNodeArray(const dim4 &dims, Node_ptr node)
                 Node *n = node.get();
 
                 TNJ::Node_map_t nodes_map;
-                std::vector<TNJ::Node *> full_nodes;
+                vector<TNJ::Node *> full_nodes;
                 n->getNodesMap(nodes_map, full_nodes);
                 unsigned length =0, buf_count = 0, bytes = 0;
                 for(auto &entry : nodes_map) {
@@ -244,7 +258,7 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 
 template<typename T>
 Array<T> createSubArray(const Array<T>& parent,
-                        const std::vector<af_seq> &index,
+                        const vector<af_seq> &index,
                         bool copy)
 {
     parent.eval();
@@ -319,7 +333,7 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
     template       Array<T>  *initArray<T      >      ();               \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
-                                                       const std::vector<af_seq> &index, \
+                                                       const vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
     template       Array<T>  createNodeArray<T>       (const dim4 &size, TNJ::Node_ptr node); \
@@ -334,7 +348,7 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     template       TNJ::Node_ptr Array<T>::getNode() const;             \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
-    template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
+    template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 2c479e7eda..5a955498e6 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -9,25 +9,23 @@
 
 //This is the array implementation class.
 #pragma once
-#include <af/dim4.hpp>
-#include <common/ArrayInfo.hpp>
-#include <backend.hpp>
-#include <types.hpp>
-#include <traits.hpp>
-#include <TNJ/Node.hpp>
 #include <Param.hpp>
+#include <TNJ/Node.hpp>
+#include <common/ArrayInfo.hpp>
 #include <memory.hpp>
-#include <memory>
-#include <algorithm>
-#include <vector>
 #include <platform.hpp>
 #include <queue.hpp>
 
-// cpu::Array class forward declaration
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/seq.h>
+
+#include <cstddef>
+#include <memory>
+#include <vector>
+
 namespace cpu
 {
-template<typename T> class Array;
-// kernel::evalArray fn forward declaration
 namespace kernel
 {
     template<typename T> void evalArray(Param<T> in, TNJ::Node_ptr node);
@@ -40,6 +38,7 @@ namespace kernel
 
 namespace cpu
 {
+    template<typename T> class Array;
 
     using std::shared_ptr;
     using af::dim4;
@@ -47,8 +46,6 @@ namespace cpu
     template<typename T>
     void evalMultiple(std::vector<Array<T> *> arrays);
 
-    template<typename T> class Array;
-
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createNodeArray(const af::dim4 &size, TNJ::Node_ptr node);
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 5d212bc74a..db97e67374 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -230,7 +230,6 @@ target_sources(afcpu
     kernel/sobel.hpp
     kernel/sort.hpp
     kernel/sort_by_key.hpp
-    kernel/sort_by_key_impl.hpp
     kernel/sort_helper.hpp
     kernel/sparse.hpp
     kernel/sparse_arith.hpp
diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
index 6037a48942..2b748ffc9a 100644
--- a/src/backend/cpu/Param.hpp
+++ b/src/backend/cpu/Param.hpp
@@ -15,18 +15,16 @@
 namespace cpu
 {
 
-using af::dim4;
-
 template<typename T>
 class CParam
 {
 private:
     const T *m_ptr;
-    dim4 m_dims;
-    dim4 m_strides;
+    af::dim4 m_dims;
+    af::dim4 m_strides;
 
 public:
-    CParam(const T *iptr, const dim4 &idims, const dim4 &istrides) :
+    CParam(const T *iptr, const af::dim4 &idims, const af::dim4 &istrides) :
         m_ptr(iptr)
     {
         for (int i = 0; i < 4; i++) {
@@ -40,12 +38,12 @@ class CParam
         return m_ptr;
     }
 
-    dim4 dims() const
+    af::dim4 dims() const
     {
         return m_dims;
     }
 
-    dim4 strides() const
+    af::dim4 strides() const
     {
         return m_strides;
     }
@@ -66,15 +64,15 @@ class Param
 {
 private:
     T *m_ptr;
-    dim4 m_dims;
-    dim4 m_strides;
+    af::dim4 m_dims;
+    af::dim4 m_strides;
 
 public:
     Param() : m_ptr(nullptr)
     {
     }
 
-    Param(T *iptr, const dim4 &idims, const dim4 &istrides) :
+    Param(T *iptr, const af::dim4 &idims, const af::dim4 &istrides) :
         m_ptr(iptr)
     {
         for (int i = 0; i < 4; i++) {
@@ -93,12 +91,12 @@ class Param
         return CParam<T>(const_cast<T *>(m_ptr), m_dims, m_strides);
     }
 
-    dim4 dims() const
+    af::dim4 dims() const
     {
         return m_dims;
     }
 
-    dim4 strides() const
+    af::dim4 strides() const
     {
         return m_strides;
     }
diff --git a/src/backend/cpu/anisotropic_diffusion.cpp b/src/backend/cpu/anisotropic_diffusion.cpp
index eecc6d063f..906fcc0df6 100644
--- a/src/backend/cpu/anisotropic_diffusion.cpp
+++ b/src/backend/cpu/anisotropic_diffusion.cpp
@@ -7,10 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <Array.hpp>
 #include <anisotropic_diffusion.hpp>
 #include <kernel/anisotropic_diffusion.hpp>
+#include <platform.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/anisotropic_diffusion.hpp b/src/backend/cpu/anisotropic_diffusion.hpp
index 8661c4dc67..5c7a9078df 100644
--- a/src/backend/cpu/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/anisotropic_diffusion.hpp
@@ -7,10 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
+
+#include "af/defines.h"
 
 namespace cpu
 {
+template <typename T> class Array;
+
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt,
                           const float mct, const af::fluxFunction fftype,
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 4df1c180a2..ef82be05e0 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -7,11 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <approx.hpp>
+
+#include <Array.hpp>
 #include <kernel/approx.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
+
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index 4da27fae98..b300294c5d 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/defines.h>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index 627b5973ce..294b4397d5 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -7,20 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <Array.hpp>
-#include <handle.hpp>
 #include <kernel/assign.hpp>
 #include <assign.hpp>
+
+#include <Array.hpp>
+#include <Param.hpp>
+#include <handle.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
+#include <types.hpp>
 
-namespace cpu
-{
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/index.h>
+#include <af/seq.h>
+
+#include <utility>
+#include <vector>
 
 using af::dim4;
 using std::vector;
 
+namespace cpu
+{
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 {
@@ -47,8 +55,8 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
     }
 
     vector<CParam<uint>> idxParams(idxArrs.begin(), idxArrs.end());
-    getQueue().enqueue(kernel::assign<T>, out, out.getDataDims(), rhs, std::move(isSeq),
-            std::move(seqs), std::move(idxParams));
+    getQueue().enqueue(kernel::assign<T>, out, out.getDataDims(), rhs,
+                       move(isSeq), move(seqs), move(idxParams));
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/cpu/assign.hpp b/src/backend/cpu/assign.hpp
index 00ad56eb33..77dea299f7 100644
--- a/src/backend/cpu/assign.hpp
+++ b/src/backend/cpu/assign.hpp
@@ -7,10 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
+#include <af/index.h>
 
 namespace cpu
 {
+template <typename T> class Array;
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 35ceb6143a..ff12ad1dbc 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
+#include <bilateral.hpp>
+
 #include <Array.hpp>
 #include <kernel/bilateral.hpp>
-#include <bilateral.hpp>
-#include <cmath>
-#include <algorithm>
 #include <platform.hpp>
-#include <queue.hpp>
+
+#include <af/dim4.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 87db9b5a94..149691f2a4 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -8,18 +8,32 @@
  ********************************************************/
 
 #include <blas.hpp>
-#include <af/dim4.hpp>
-#include <cassert>
+
+#ifdef USE_MKL
+#include <mkl_cblas.h>
+#endif
+
+#include <Array.hpp>
+#include <Param.hpp>
+#include <common/blas_headers.hpp>
 #include <common/err_common.hpp>
 #include <kernel/dot.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
+#include <types.hpp>
+
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
 
-#include <common/blas_headers.hpp>
+
+#include <algorithm>
+#include <type_traits>
 
 namespace cpu
 {
 
+using af::dtype_traits;
+
 using std::add_const;
 using std::add_pointer;
 using std::enable_if;
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index b85c194b66..3a6f4a7e4f 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -8,7 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <types.hpp>
+
+#include <af/defines.h>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/canny.cpp b/src/backend/cpu/canny.cpp
index 6a66151e4d..0e14fc67ca 100644
--- a/src/backend/cpu/canny.cpp
+++ b/src/backend/cpu/canny.cpp
@@ -7,13 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <canny.hpp>
+
+#include <Array.hpp>
+#include <Param.hpp>
+#include <kernel/canny.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/canny.hpp>
-
-using af::dim4;
 
 namespace cpu
 {
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 85eaec2ecc..7ba6eea0e5 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -8,14 +8,15 @@
  ********************************************************/
 
 #include <cholesky.hpp>
-#include <common/err_common.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
 
+#include <Array.hpp>
+#include <Param.hpp>
+#include <copy.hpp>
+#include <types.hpp>
+
 #include <af/dim4.hpp>
-#include <handle.hpp>
-#include <cassert>
-#include <err_cpu.hpp>
 #include <triangle.hpp>
 #include <lapack_helper.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index b4ce8643a8..6dafadca9a 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -7,21 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <common/defines.hpp>
 #include <convolve.hpp>
-#include <err_cpu.hpp>
-#include <math.hpp>
-#include <platform.hpp>
-#include <queue.hpp>
 #include <kernel/convolve.hpp>
+#include <platform.hpp>
+
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
 namespace cpu
 {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT, int baseDim, bool expand>
 Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
 {
     signal.eval();
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index 3b87843376..cfbd8a0499 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -8,11 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/defines.hpp>
 
 namespace cpu
 {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT, int baseDim, bool expand>
 Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index d0bea9769f..db157b69e9 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -7,19 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <type_traits>
 #include <Array.hpp>
+#include <common/ArrayInfo.hpp>
 #include <copy.hpp>
-#include <cstring>
-#include <algorithm>
-#include <complex>
-#include <vector>
-#include <cassert>
 #include <err_cpu.hpp>
-#include <math.hpp>
+#include <kernel/copy.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/copy.hpp>
+#include <types.hpp>
+
+#include <af/defines.h>
+#include <af/dim4.hpp>
+
+#include <cstdio>
+#include <cstring>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 864d42067c..982b8a0a6c 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -10,6 +10,8 @@
 
 #include <Array.hpp>
 
+namespace af { class dim4; }
+
 namespace cpu
 {
 
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 8096234ee5..659f0f85e6 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -7,14 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <Array.hpp>
+#include <kernel/diagonal.hpp>
 #include <diagonal.hpp>
-#include <math.hpp>
-#include <err_cpu.hpp>
+
+#include <Array.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
-#include <kernel/diagonal.hpp>
+
+#include <algorithm>
+#include <cstdlib>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/diagonal.hpp b/src/backend/cpu/diagonal.hpp
index 6c354d0690..e71ec435ee 100644
--- a/src/backend/cpu/diagonal.hpp
+++ b/src/backend/cpu/diagonal.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 1e374e95da..f39dd8e47e 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -7,12 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <diff.hpp>
+
+#include <Array.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
 #include <kernel/diff.hpp>
 
+#include <af/dim4.hpp>
+
 namespace cpu
 {
 
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index 04b361f55c..a7109a6058 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -16,6 +16,8 @@
 
 #include <err_cpu.hpp>                  // error check functions and Macros
                                         // specific to cpu backend
+#include <af/dim4.hpp>
+#include <platform.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/exampleFunction.hpp b/src/backend/cpu/exampleFunction.hpp
index d5c8139430..c5203c738d 100644
--- a/src/backend/cpu/exampleFunction.hpp
+++ b/src/backend/cpu/exampleFunction.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/defines.h>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index a65cfc85a0..28569656b1 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -7,13 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <Array.hpp>
-#include <err_cpu.hpp>
+#include <kernel/fast.hpp>
 #include <fast.hpp>
+
+#include <Array.hpp>
+#include <af/dim4.hpp>
+#include <math.h>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/fast.hpp>
+
+#include <cstddef>
+#include <algorithm>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/fast.hpp b/src/backend/cpu/fast.hpp
index 61dca27407..f49c62b2bd 100644
--- a/src/backend/cpu/fast.hpp
+++ b/src/backend/cpu/fast.hpp
@@ -7,13 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
-#include <Array.hpp>
-
-using af::features;
-
 namespace cpu
 {
+template <typename T> class Array;
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index d09ed44409..d4975177aa 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <Array.hpp>
-#include <fft.hpp>
 #include <kernel/fft.hpp>
-#include <copy.hpp>
-#include <math.hpp>
+#include <fft.hpp>
+
+#include <Array.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
+
+#include <af/dim4.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cpu/fft.hpp b/src/backend/cpu/fft.hpp
index 7b4313b0e3..f669517a00 100644
--- a/src/backend/cpu/fft.hpp
+++ b/src/backend/cpu/fft.hpp
@@ -9,6 +9,10 @@
 
 #include <Array.hpp>
 
+#include <cstddef>
+
+namespace af { class dim4; }
+
 namespace cpu
 {
 
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index faa46b399b..184405f5f8 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -8,10 +8,17 @@
  ********************************************************/
 
 #pragma once
-#include <vector>
 #include <Param.hpp>
+#include <common/ArrayInfo.hpp>
+#include <types.hpp>
 #include <utility.hpp>
 
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/seq.h>
+
+#include <vector>
+
 namespace cpu
 {
 namespace kernel
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 2bb8f945d4..25523f6e43 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -9,6 +9,8 @@
 
 #pragma once
 #include <Param.hpp>
+#include <af/defines.h>
+#include <math.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index da5fa561d8..1c6c8c0018 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -10,6 +10,10 @@
 #pragma once
 #include <Param.hpp>
 #include <math.hpp>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+
+#include <cstring> //memcpy
 
 namespace cpu
 {
@@ -23,7 +27,7 @@ void stridedCopy(T* dst, af::dim4 const & ostrides, T const * src,
     if(dim == 0) {
         if(strides[dim] == 1) {
             //FIXME: Check for errors / exceptions
-            memcpy(dst, src, dims[dim] * sizeof(T));
+            std::memcpy(dst, src, dims[dim] * sizeof(T));
         } else {
             for(dim_t i = 0; i < dims[dim]; i++) {
                 dst[i] = src[strides[dim]*i];
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
index 6a16562d69..bc7d19a37c 100644
--- a/src/backend/cpu/kernel/diagonal.hpp
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -9,6 +9,9 @@
 
 #pragma once
 #include <Param.hpp>
+#include <math.hpp>
+
+#include <af/dim4.hpp>
 
 namespace cpu
 {
@@ -42,8 +45,8 @@ void diagCreate(Param<T> out, CParam<T> in, int const num)
 template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int const num)
 {
-    dim4 const odims = out.dims();
-    dim4 const idims = in.dims();
+    af::dim4 const odims = out.dims();
+    af::dim4 const idims = in.dims();
 
     int const i_off = (num > 0) ? (num * in.strides(1)) : (-num);
 
diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
index 42bb3c3db4..ef717a049d 100644
--- a/src/backend/cpu/kernel/fft.hpp
+++ b/src/backend/cpu/kernel/fft.hpp
@@ -8,8 +8,11 @@
  ********************************************************/
 
 #pragma once
-#include <Param.hpp>
 #include <fftw3.h>
+#include <Param.hpp>
+#include <types.hpp>
+
+#include <af/dim4.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index 09594a2250..d83a52c5dc 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+#include <af/traits.hpp>
 
 namespace cpu
 {
@@ -101,7 +102,7 @@ struct resize_op<T, AF_INTERP_BILINEAR>
         dim_t i2_x  = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
         dim_t i2_y  = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
 
-        typedef typename dtype_traits<T>::base_type BT;
+        typedef typename af::dtype_traits<T>::base_type BT;
         typedef wtype_t<BT> WT;
         typedef vtype_t<T> VT;
 
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index 77f20c75a4..cc5a1be81e 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -12,6 +12,9 @@
 #include <math.hpp>
 #include <err_cpu.hpp>
 #include "interp.hpp"
+#include <af/traits.hpp>
+
+using af::dtype_traits;
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 1b2bede9ac..450fbfb092 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <Param.hpp>
-#include <err_cpu.hpp>
 
 namespace cpu
 {
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index 75f8631cf5..c78a2d7a93 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -12,6 +12,7 @@
 #include <err_cpu.hpp>
 #include <type_traits>
 #include "interp.hpp"
+#include <af/traits.hpp>
 
 namespace cpu
 {
@@ -74,7 +75,7 @@ void transform(Param<T> output, CParam<T> input,
                const bool perspective,
                af_interp_type method)
 {
-    typedef typename dtype_traits<T>::base_type BT;
+    typedef typename af::dtype_traits<T>::base_type BT;
     typedef wtype_t<BT> WT;
 
     const af::dim4 idims    = input.dims();
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index b9fec6e2f9..4488935329 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -8,11 +8,14 @@
  ********************************************************/
 
 #pragma once
+
+#include <types.hpp>
+#include <af/defines.h>
+#include <common/defines.hpp>
+
 #include <limits>
 #include <algorithm>
 #include <numeric>
-#include "types.hpp"
-#include <af/defines.h>
 
 namespace cpu
 {

From 8bad836ad2748685aff4ec405fb3888b14b59019 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 26 Feb 2018 18:05:11 +0530
Subject: [PATCH 1371/2677] Update NVIDIA CUB submodule to v1.8.0

---
 src/backend/cuda/cub | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/cub b/src/backend/cuda/cub
index d622848f9f..c3cceac115 160000
--- a/src/backend/cuda/cub
+++ b/src/backend/cuda/cub
@@ -1 +1 @@
-Subproject commit d622848f9fb62f13e5e064e1deb43b6bcbb12bad
+Subproject commit c3cceac115c072fb63df1836ff46d8c60d9eb304

From e4e35df30fc3dfb4ce8553ce49cd743d7a508aea Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 25 Feb 2018 20:28:13 -0500
Subject: [PATCH 1372/2677] Use namespaced CMake variables. Deprecated old
 values

This commit deprecates the old CMake variables in favor of variables
that are namespaced with the AF_ prefix to avoid name conflicts with
user code. If the old variables are used a warning is displayed and
the new variable is set to the value. The old variable is unset to
avoid showing the warning again.
---
 CMakeLists.txt                              | 82 ++++++++++++---------
 CMakeModules/CPackConfig.cmake              |  2 +-
 CMakeModules/InternalUtils.cmake            | 16 ++++
 CMakeModules/osx_install/OSXInstaller.cmake | 26 +++----
 examples/README.md                          |  4 +-
 src/api/c/CMakeLists.txt                    |  4 +-
 src/api/c/sift.cpp                          |  4 +-
 src/backend/common/CMakeLists.txt           |  2 +-
 src/backend/cpu/CMakeLists.txt              | 12 +--
 src/backend/cpu/platform.hpp                |  2 +-
 src/backend/cpu/sift.cpp                    |  4 +-
 src/backend/cuda/CMakeLists.txt             | 10 +--
 src/backend/cuda/sift.cu                    |  4 +-
 src/backend/opencl/CMakeLists.txt           |  6 +-
 src/backend/opencl/sift.cpp                 |  4 +-
 test/CMakeLists.txt                         | 28 +++----
 test/gloh_nonfree.cpp                       |  6 +-
 test/sift_nonfree.cpp                       |  6 +-
 18 files changed, 125 insertions(+), 97 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 78ff1b9173..54770b1348 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -36,39 +36,51 @@ find_package(MKL)
 find_package(glbinding QUIET)
 include(boost_package)
 
-option(BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
-option(BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})
-option(BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FOUND})
-option(BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
+option(AF_BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
+option(AF_BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})
+option(AF_BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FOUND})
+option(AF_BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
+option(AF_BUILD_DOCS     "Create ArrayFire Documentation"            ${DOXYGEN_FOUND})
+option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
 
-option(BUILD_GRAPHICS "Build ArrayFire with Forge Graphics"      $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
-option(BUILD_DOCS     "Create ArrayFire Documentation"           ${DOXYGEN_FOUND})
-option(BUILD_NONFREE  "Build ArrayFire nonfree algorithms"       OFF)
+option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
+option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 
-option(BUILD_EXAMPLES "Build Examples"                           ON)
-cmake_dependent_option(USE_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
+cmake_dependent_option(AF_WITH_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
   "BUILD_TESTING" OFF)
 
-cmake_dependent_option(USE_SYSTEM_FORGE "Use system Forge" OFF
-                    "BUILD_GRAPHICS" OFF)
-cmake_dependent_option(WITH_IMAGEIO "Build ArrayFire with Image IO support" ${FreeImage_FOUND}
-                      "FreeImage_FOUND" OFF)
-cmake_dependent_option(BUILD_FRAMEWORK "Build an ArrayFire framework for Apple platforms.(Experimental)" OFF
-                      "APPLE" OFF)
-option(USE_FREEIMAGE_STATIC "Use Static FreeImage Lib" OFF)
-
-set(USE_CPUID ON CACHE BOOL "Build with CPUID integration")
+cmake_dependent_option(AF_USE_SYSTEM_FORGE "Use system Forge" OFF
+                       "AF_WITH_GRAPHICS" OFF)
+cmake_dependent_option(AF_WITH_IMAGEIO "Build ArrayFire with Image IO support" ${FreeImage_FOUND}
+                       "FreeImage_FOUND" OFF)
+cmake_dependent_option(AF_BUILD_FRAMEWORK "Build an ArrayFire framework for Apple platforms.(Experimental)" OFF
+                       "APPLE" OFF)
+option(AF_WITH_STATIC_FREEIMAGE "Use Static FreeImage Lib" OFF)
+
+set(AF_WITH_CPUID ON CACHE BOOL "Build with CPUID integration")
+
+af_deprecate(BUILD_CPU             AF_BUILD_CPU)
+af_deprecate(BUILD_CUDA            AF_BUILD_CUDA)
+af_deprecate(BUILD_OPENCL          AF_BUILD_OPENCL)
+af_deprecate(BUILD_UNIFIED         AF_BUILD_UNIFIED)
+af_deprecate(BUILD_GRAPHICS        AF_WITH_GRAPHICS)
+af_deprecate(BUILD_DOCS            AF_BUILD_DOCS)
+af_deprecate(BUILD_NONFREE         AF_WITH_NONFREE)
+af_deprecate(BUILD_EXAMPLES        AF_WITH_EXAMPLES)
+af_deprecate(USE_RELATIVE_TEST_DIR AF_WITH_RELATIVE_TEST_DIR)
+af_deprecate(USE_FREEIMAGE_STATIC  AF_WITH_STATIC_FREEIMAGE)
+af_deprecate(USE_CPUID             AF_WITH_CPUID)
 
 mark_as_advanced(
-  BUILD_FRAMEWORK
-  USE_SYSTEM_FORGE
-  USE_CPUID)
+  AF_BUILD_FRAMEWORK
+  AF_USE_SYSTEM_FORGE
+  AF_WITH_CPUID)
 
 # TODO(umar): Add definitions should not be used. Instead use
 arrayfire_get_platform_definitions(platform_definitions)
 add_definitions(${platform_definitions})
 
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
   include(build_forge)
 endif()
 
@@ -77,7 +89,7 @@ configure_file(
     ${ArrayFire_BINARY_DIR}/version.hpp
 )
 
-if(BUILD_NONFREE)
+if(AF_WITH_NONFREE)
   message("Building with NONFREE requires the following patents")
   message("Method and apparatus for identifying scale invariant features\n"
     "in an image and use of same for locating an object in an image, David\n"
@@ -105,10 +117,10 @@ add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
 add_subdirectory(src/api/cpp)
 
-conditional_directory(BUILD_CPU     src/backend/cpu)
-conditional_directory(BUILD_CUDA    src/backend/cuda)
-conditional_directory(BUILD_OPENCL  src/backend/opencl)
-conditional_directory(BUILD_UNIFIED src/api/unified)
+conditional_directory(AF_BUILD_CPU     src/backend/cpu)
+conditional_directory(AF_BUILD_CUDA    src/backend/cuda)
+conditional_directory(AF_BUILD_OPENCL  src/backend/opencl)
+conditional_directory(AF_BUILD_UNIFIED src/api/unified)
 
 if(TARGET af)
   list(APPEND built_backends af)
@@ -134,7 +146,7 @@ foreach(backend ${built_backends})
   target_compile_definitions(${backend} PRIVATE AFDLL)
 endforeach()
 
-if(BUILD_FRAMEWORK)
+if(AF_BUILD_FRAMEWORK)
   set_target_properties(${built_backends}
     PROPERTIES
       FRAMEWORK TRUE
@@ -161,7 +173,7 @@ install(FILES
     COMPONENT headers
 )
 
-if(Forge_FOUND AND NOT USE_SYSTEM_FORGE)
+if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
     option(INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
     install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/lib/"
             DESTINATION "${AF_INSTALL_LIB_DIR}"
@@ -179,10 +191,10 @@ if(Forge_FOUND AND NOT USE_SYSTEM_FORGE)
     endif()
 endif()
 
-# install the examples irrespective of the BUILD_EXAMPLES value
+# install the examples irrespective of the AF_BUILD_EXAMPLES value
 # only the examples source files are installed, so the installation of these
-# source files does not depend on BUILD_EXAMPLES
-# when BUILD_EXAMPLES is OFF, the examples source is installed without
+# source files does not depend on AF_BUILD_EXAMPLES
+# when AF_BUILD_EXAMPLES is OFF, the examples source is installed without
 # building the example executables
 install(DIRECTORY examples/ #NOTE The slash at the end is important
     DESTINATION ${AF_INSTALL_EXAMPLE_DIR}
@@ -194,7 +206,7 @@ install(DIRECTORY assets/examples/ #NOTE The slash at the end is important
 
 foreach(backend CPU CUDA OpenCL Unified)
   string(TOUPPER ${backend} upper_backend)
-  if(BUILD_${upper_backend})
+  if(AF_BUILD_${upper_backend})
     install(EXPORT ArrayFire${backend}Targets
             NAMESPACE ArrayFire::
             DESTINATION ${AF_INSTALL_CMAKE_DIR}
@@ -278,5 +290,5 @@ endif()
 conditional_directory(BUILD_TESTING test)
 
 set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
-conditional_directory(BUILD_EXAMPLES examples)
-conditional_directory(BUILD_DOCS docs)
+conditional_directory(AF_BUILD_EXAMPLES examples)
+conditional_directory(AF_BUILD_DOCS docs)
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 6a04573c93..f6554ecbe0 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -23,7 +23,7 @@ set(CPACK_PACKAGE_VERSION ${ArrayFire_VERSION})
 set(CPACK_PACKAGE_VERSION_MAJOR "${ArrayFire_VERSION_MAJOR}")
 set(CPACK_PACKAGE_VERSION_MINOR "${ArrayFire_VERSION_MINOR}")
 set(CPACK_PACKAGE_VERSION_PATCH "${ArrayFire_VERSION_PATCH}")
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
     set(CPACK_PACKAGE_FILE_NAME
     ${CPACK_PACKAGE_NAME}-${CPACK_PACKAGE_VERSION})
 else()
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 420b6695de..9835473216 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -36,6 +36,22 @@ function(arrayfire_get_cuda_cxx_flags cuda_flags)
   endif()
 endfunction()
 
+function(__af_deprecate_var var access value)
+  if(access STREQUAL "READ_ACCESS")
+    message(DEPRECATION "Variable ${var} is deprecated. Use AF_${var} instead.")
+  endif()
+endfunction()
+
+function(af_deprecate var newvar)
+  if(DEFINED ${var})
+    message(DEPRECATION "Variable ${var} is deprecated. Use ${newvar} instead.")
+    get_property(doc CACHE ${newvar} PROPERTY HELPSTRING)
+    set(${newvar} ${${var}} CACHE BOOL "${doc}" FORCE)
+    unset(${var} CACHE)
+  endif()
+  variable_watch(${var} __af_deprecate_var)
+endfunction()
+
 macro(arrayfire_set_cmake_default_variables)
   set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR}/cmake;${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
index 18fffc591d..ea9b616519 100644
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ b/CMakeModules/osx_install/OSXInstaller.cmake
@@ -90,7 +90,7 @@ ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
                   COMMENT "Copying documentation files to temporary OSX Install Dir"
                   )
 
-IF(BUILD_GRAPHICS)
+IF(AF_WITH_GRAPHICS)
     MAKE_DIRECTORY("${OSX_TEMP}/Forge")
 
     # Forge library versions for setting up symlinks
@@ -157,7 +157,7 @@ IF(BUILD_GRAPHICS)
                       WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
                       COMMENT "Copying documentation files to temporary OSX Install Dir"
                       )
-ENDIF(BUILD_GRAPHICS)
+ENDIF(AF_WITH_GRAPHICS)
 ################################################################################
 
 FUNCTION(PKG_BUILD)
@@ -190,11 +190,11 @@ ENDFUNCTION(PKG_BUILD)
 
 FUNCTION(PRODUCT_BUILD)
     CMAKE_PARSE_ARGUMENTS(ARGS "" "" "DEPENDS" ${ARGN})
-    IF(BUILD_GRAPHICS)
+    IF(AF_WITH_GRAPHICS)
         SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution.dist")
-    ELSE(BUILD_GRAPHICS)
+    ELSE(AF_WITH_GRAPHICS)
         SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution-no-gl.dist")
-    ENDIF(BUILD_GRAPHICS)
+    ENDIF(AF_WITH_GRAPHICS)
 
     SET(DISTRIBUTION_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/distribution.dist.out")
 
@@ -209,11 +209,11 @@ FUNCTION(PRODUCT_BUILD)
     CONFIGURE_FILE(${WELCOME_FILE} ${WELCOME_FILE_OUT})
     CONFIGURE_FILE(${README_FILE} ${README_FILE_OUT})
 
-    IF(BUILD_GRAPHICS)
+    IF(AF_WITH_GRAPHICS)
         SET(PACKAGE_NAME "arrayfire-${AF_VERSION}.pkg")
-    ELSE(BUILD_GRAPHICS)
+    ELSE(AF_WITH_GRAPHICS)
         SET(PACKAGE_NAME "arrayfire-no-gl-${AF_VERSION}.pkg")
-    ENDIF(BUILD_GRAPHICS)
+    ENDIF(AF_WITH_GRAPHICS)
 
     ADD_CUSTOM_COMMAND( OUTPUT ${PACKAGE_NAME}
                         DEPENDS ${ARGS_DEPENDS}
@@ -289,7 +289,7 @@ PKG_BUILD(  PKG_NAME        ArrayFireDoc
             PATH_TO_FILES   ${OSX_TEMP}/doc
             FILTERS         cmake)
 
-IF(BUILD_GRAPHICS)
+IF(AF_WITH_GRAPHICS)
     PKG_BUILD(  PKG_NAME        ForgeLibrary
                 DEPENDS         OSX_INSTALL_SETUP_FORGE_LIB
                 TARGETS         forge_lib_package
@@ -328,16 +328,16 @@ IF(BUILD_GRAPHICS)
                 IDENTIFIER      com.arrayfire.pkg.forge.cmake
                 PATH_TO_FILES   ${OSX_TEMP}/Forge/cmake
                 )
-ENDIF(BUILD_GRAPHICS)
+ENDIF(AF_WITH_GRAPHICS)
 
-IF(BUILD_GRAPHICS)
+IF(AF_WITH_GRAPHICS)
     PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
                           ${common_package} ${header_package} ${examples_package} ${doc_package}
                           ${forge_lib_package} ${forge_header_package} ${forge_examples_package} ${forge_doc_package} ${forge_cmake_package}
                           )
-ELSE(BUILD_GRAPHICS)
+ELSE(AF_WITH_GRAPHICS)
     PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
                           ${common_package} ${header_package} ${examples_package} ${doc_package}
                           )
-ENDIF(BUILD_GRAPHICS)
+ENDIF(AF_WITH_GRAPHICS)
 
diff --git a/examples/README.md b/examples/README.md
index a34581fcac..9dce225e1e 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -11,10 +11,10 @@ process; however, the compiled examples are not packaged in the ArrayFire
 installer. After compiling ArrayFire, the examples will be in subdirectories
 located in the `build/examples` directory.
 
-If you wish to disable example compilation, simply set the `BUILD_EXAMPLES`
+If you wish to disable example compilation, simply set the `AF_BUILD_EXAMPLES`
 variable to `OFF` in the CMake GUI or `ccmake` curses wrapper. If you are
 using the command-line version of `cmake`, simply specify 
-`-DBUILD_EXAMPLES=OFF` as an argument.
+`-DAF_BUILD_EXAMPLES=OFF` as an argument.
 
 ## Building examples as a stand-alone project
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index c106cd62fe..e9532eca9b 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -152,12 +152,12 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/ycbcr_rgb.cpp
     )
 
-if(FreeImage_FOUND AND WITH_IMAGEIO)
+if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
     target_compile_definitions(c_api_interface INTERFACE WITH_FREEIMAGE)
     target_link_libraries(     c_api_interface INTERFACE FreeImage::FreeImage)
 endif()
 
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
     add_dependencies(c_api_interface forge-ext)
     target_compile_definitions(c_api_interface INTERFACE WITH_GRAPHICS)
 endif()
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index 599354e265..afeabaef89 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -54,7 +54,7 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
                const bool double_input, const float img_scale, const float feature_ratio)
 {
     try {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
         const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
@@ -95,7 +95,7 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsig
                const bool double_input, const float img_scale, const float feature_ratio)
 {
     try {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
         const ArrayInfo& info = getInfo(in);
         af::dim4 dims  = info.dims();
 
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index f54dc85f9c..a7eee85f1c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -37,7 +37,7 @@ target_include_directories(afcommon_interface
 
 add_library(afcommon_lapack_interface INTERFACE)
 
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
   dependency_check(glbinding_FOUND "glbinding not found.")
 
   target_include_directories(afcommon_interface
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index db97e67374..61879f46e7 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -242,9 +242,9 @@ target_sources(afcpu
     kernel/wrap.hpp
   )
 
-if (USE_CPUID)
-  target_compile_definitions(afcpu PRIVATE -DUSE_CPUID)
-endif(USE_CPUID)
+if (AF_WITH_CPUID)
+  target_compile_definitions(afcpu PRIVATE -DAF_WITH_CPUID)
+endif(AF_WITH_CPUID)
 
 target_sources(afcpu
   PRIVATE
@@ -257,12 +257,12 @@ endif()
 
 include("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
-if(BUILD_NONFREE)
+if(AF_WITH_NONFREE)
   target_sources(afcpu PRIVATE kernel/sift_nonfree.hpp)
-  target_compile_definitions(afcpu PRIVATE AF_BUILD_NONFREE_SIFT)
+  target_compile_definitions(afcpu PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
   add_dependencies(afcpu forge-ext)
   target_sources(afcpu
     PRIVATE
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 0bb9a20922..f07b4effb4 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -16,7 +16,7 @@
 #include <memory.hpp>
 #include <queue.hpp>
 
-#if defined(USE_CPUID) && (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64))
+#if defined(AF_WITH_CPUID) && (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64))
 #define CPUID_CAPABLE
 #endif
 
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 84c772085e..021a4648f0 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -19,7 +19,7 @@
 #include <cfloat>
 #include <vector>
 
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
 #endif
 
@@ -37,7 +37,7 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     return sift_impl<T, convAccT>(x, y, score, ori, size, desc, in, n_layers,
                                   contrast_thr, edge_thr, init_sigma, double_input,
                                   img_scale, feature_ratio, compute_GLOH);
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 250dc48e46..ca6e66da6f 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -115,12 +115,12 @@ if(NOT MSVC)
   set(cuda_cxx_flags "${cuda_cxx_flags} -Xcompiler -fPIC")
 endif()
 
-if(BUILD_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")
+if(AF_WITH_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")
   # This definition is required in addition to the definition below because in
   # an older verion of cmake definitions added using target_compile_definitions
   # were not added to the nvcc flags. This manually adds these definitions and
   # pass them to the options parameter in cuda_add_library
-  string(APPEND cuda_cxx_flags " -DAF_BUILD_NONFREE_SIFT")
+  string(APPEND cuda_cxx_flags " -DAF_WITH_NONFREE_SIFT")
 endif()
 
 include(kernel/scan_by_key/CMakeLists.txt)
@@ -409,11 +409,11 @@ cuda_add_library(afcuda
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
-if(BUILD_NONFREE)
-  target_compile_definitions(afcuda PRIVATE AF_BUILD_NONFREE_SIFT)
+if(AF_WITH_NONFREE)
+  target_compile_definitions(afcuda PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
   target_sources(afcuda
     PRIVATE
       GraphicsResourceManager.cpp
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index a7605bd396..b5e6634b05 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <err_cuda.hpp>
 
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
 #endif
 
@@ -31,7 +31,7 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     const dim4 dims = in.dims();
 
     unsigned nfeat_out;
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 0fbafe4705..66b81d4221 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -416,12 +416,12 @@ elseif(OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
 endif()
 
 
-if(BUILD_NONFREE)
+if(AF_WITH_NONFREE)
   target_sources(afopencl PRIVATE kernel/sift_nonfree.hpp)
-  target_compile_definitions(afopencl PRIVATE AF_BUILD_NONFREE_SIFT)
+  target_compile_definitions(afopencl PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-if(BUILD_GRAPHICS)
+if(AF_WITH_GRAPHICS)
   target_sources(afopencl
     PRIVATE
       GraphicsResourceManager.hpp
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index bd91e2f0ae..b9ef9ad6a2 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -13,7 +13,7 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
 #endif
 
@@ -32,7 +32,7 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     unsigned nfeat_out;
     unsigned desc_len;
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 3a07f12f77..6b9cfbb81f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -33,29 +33,29 @@ endif()
 unset(CMAKE_CXX_STANDARD)
 set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
 
-if(${USE_RELATIVE_TEST_DIR})
+if(${AF_USE_RELATIVE_TEST_DIR})
     # RELATIVE_TEST_DATA_DIR is a User-visible option with default value of test/data directory
     set(RELATIVE_TEST_DATA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data" CACHE STRING "Relative Test Data Directory")
     set(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
-else(${USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
+else(${AF_USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
     set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
-endif(${USE_RELATIVE_TEST_DIR})
+endif(${AF_USE_RELATIVE_TEST_DIR})
 
-if(BUILD_CPU)
+if(AF_BUILD_CPU)
   list(APPEND enabled_backends "cpu")
-endif(BUILD_CPU)
+endif(AF_BUILD_CPU)
 
-if(BUILD_CUDA)
+if(AF_BUILD_CUDA)
   list(APPEND enabled_backends "cuda")
-endif(BUILD_CUDA)
+endif(AF_BUILD_CUDA)
 
-if(BUILD_OPENCL)
+if(AF_BUILD_OPENCL)
   list(APPEND enabled_backends "opencl")
-endif(BUILD_OPENCL)
+endif(AF_BUILD_OPENCL)
 
-if(BUILD_UNIFIED)
+if(AF_BUILD_UNIFIED)
   list(APPEND enabled_backends "unified")
-endif(BUILD_UNIFIED)
+endif(AF_BUILD_UNIFIED)
 
 include(CMakeParseArguments)
 
@@ -242,9 +242,9 @@ make_test(SRC select.cpp)
 make_test(SRC set.cpp)
 make_test(SRC shift.cpp)
 
-if(BUILD_NONFREE)
-  make_test(SRC gloh_nonfree.cpp DEFINITIONS AF_BUILD_NONFREE_SIFT)
-  make_test(SRC sift_nonfree.cpp DEFINITIONS AF_BUILD_NONFREE_SIFT)
+if(AF_WITH_NONFREE)
+  make_test(SRC gloh_nonfree.cpp DEFINITIONS AF_WITH_NONFREE_SIFT)
+  make_test(SRC sift_nonfree.cpp DEFINITIONS AF_WITH_NONFREE_SIFT)
 endif()
 
 make_test(SRC sobel.cpp)
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 558acabe25..ca41011b2c 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -39,7 +39,7 @@ typedef struct
     float d[272];
 } desc_t;
 
-#ifdef  AF_BUILD_NONFREE_SIFT
+#ifdef  AF_WITH_NONFREE_SIFT
 static bool feat_cmp(feat_desc_t i, feat_desc_t j)
 {
     for (int k = 0; k < 5; k++)
@@ -138,7 +138,7 @@ TYPED_TEST_CASE(GLOH, TestTypes);
 template<typename T>
 void glohTest(string pTestFile)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
@@ -250,7 +250,7 @@ void glohTest(string pTestFile)
 //
 TEST(GLOH, CPP)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index f6dca7ba16..ecd9269e81 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -38,7 +38,7 @@ typedef struct
 {
     float d[128];
 } desc_t;
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
 static bool feat_cmp(feat_desc_t i, feat_desc_t j)
 {
     for (int k = 0; k < 5; k++)
@@ -137,7 +137,7 @@ TYPED_TEST_CASE(SIFT, TestTypes);
 template<typename T>
 void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeThr, float initSigma, bool doubleInput)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
@@ -255,7 +255,7 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 //
 TEST(SIFT, CPP)
 {
-#ifdef AF_BUILD_NONFREE_SIFT
+#ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 

From a40633b39611473c860f4a3d3dc9cedfcf6c2b77 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Feb 2018 23:36:16 -0500
Subject: [PATCH 1373/2677] Fixes linking examples with targets that are not
 built

CMake built ArrayFireConfig files for each of the backend when they are built.
If you disabled a backend after it was built, the config file created a target
for the backend even though it doesn't exist. This commit modifys the config
file so that it only creates the target if the target's library also exists.

The side effect of this change is that if the library exists but the
BUILD_<backend> is off then it will build the examples anyway.
---
 CMakeModules/ArrayFireConfig.cmake.in        |  12 +-
 CMakeModules/ArrayFireExampleOverloads.cmake |  57 ++++++
 examples/CMakeLists.txt                      |  52 +----
 examples/CMakeModules/FindOpenCL.cmake       | 190 -------------------
 4 files changed, 72 insertions(+), 239 deletions(-)
 create mode 100644 CMakeModules/ArrayFireExampleOverloads.cmake
 delete mode 100644 examples/CMakeModules/FindOpenCL.cmake

diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index 72ec601e22..66909f3798 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -58,13 +58,21 @@ foreach(backend Unified CPU OpenCL CUDA)
   else()
     string(TOLOWER "${backend}" lowerbackend)
   endif()
-  if(NOT TARGET ArrayFire::af${lowerbackend})
+  if(NOT TARGET ArrayFire::af${lowerbackend} AND NOT TARGET af${lowerbackend})
+    # Either we are not in the ArrayFire project or the target was not built
     if(EXISTS @PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
       include(@PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
     endif()
   endif()
-
   if(TARGET ArrayFire::af${lowerbackend})
+    get_property(config TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_CONFIGURATIONS)
+    if(NOT config)
+      set(config "NOCONFIG")
+    endif()
+    get_property(loc TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_LOCATION_${config})
+  endif()
+
+  if((TARGET ArrayFire::af${lowerbackend} AND EXISTS ${loc}) OR TARGET af${lowerbackend})
       set(ArrayFire_${backend}_FOUND ON)
       set(ArrayFire_${backend}_LIBRARIES ArrayFire::af${lowerbackend})
       set(ArrayFire_LIBRARIES ArrayFire::af${lowerbackend})
diff --git a/CMakeModules/ArrayFireExampleOverloads.cmake b/CMakeModules/ArrayFireExampleOverloads.cmake
new file mode 100644
index 0000000000..3d95468b57
--- /dev/null
+++ b/CMakeModules/ArrayFireExampleOverloads.cmake
@@ -0,0 +1,57 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+# Some examples take too long to execute. This list is used to exclude these
+# examples from the tests
+list(APPEND exclude_from_tests  black_scholes_options_cpu
+                                monte_carlo_options_cpu
+                                vectorize_cpu
+                                )
+
+# Overload add_executable and target_link_libraries so that we can use simple
+# CMakeLists.txt files for the examples.
+#
+# These functions will overload the existing functions so that the target names
+# have the word "examples_" prefixed to them so they don't conflict with the
+# tests. This is an issue with the blas example where the test blas_cpu and the
+# example blas_cpu have the same target name.
+#
+# Additionally, This will allow us to write the CMakeLists.txt files as
+# standalone files so that they are easier to parse for new users.
+function(add_executable target sources)
+  _add_executable(example_${target} ${sources})
+  set_target_properties(example_${target}
+    PROPERTIES
+      OUTPUT_NAME ${target}
+      FOLDER "Examples"
+  )
+
+  if(NOT ${target} IN_LIST exclude_from_tests)
+    #add_test(example_${target} ${target} 0 -)
+  endif()
+endfunction()
+
+macro(find_package)
+  _find_package(${ARGV})
+  if(DEFINED AF_BUILD_CPU AND NOT AF_BUILD_CPU)
+    set(ArrayFire_CPU_FOUND OFF)
+  endif()
+  if(DEFINED AF_BUILD_CUDA AND NOT AF_BUILD_CUDA)
+    set(ArrayFire_CUDA_FOUND OFF)
+  endif()
+  if(DEFINED AF_BUILD_OPENCL AND NOT AF_BUILD_OPENCL)
+    set(ArrayFire_OpenCL_FOUND OFF)
+  endif()
+endmacro()
+
+function(target_link_libraries target sources)
+  _target_link_libraries(example_${target} ${sources})
+endfunction()
+
+function(target_compile_definitions target access definitions)
+  _target_compile_definitions(example_${target} ${access} ${definitions})
+endfunction()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 1d470f705e..c470e953d2 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -4,16 +4,12 @@ project(ArrayFire-Examples
   VERSION 3.5.0
   LANGUAGES CXX)
 
-if (NOT ASSETS_DIR)
-    set(ASSETS_DIR ""
-        CACHE PATH
-        "
-        Assets are the images and data files required by the examples. ASSETS_DIR
-        should point to the path where theses files are available on the build machine.
+if(EXISTS "${CMAKE_MODULE_PATH}/ArrayFireExampleOverloads.cmake")
+  include(ArrayFireExampleOverloads)
+endif()
 
-        You can download or clone the assets git repository from the following
-        url: https://github.com/arrayfire/assets
-        ")
+if (NOT ASSETS_DIR)
+    set(ASSETS_DIR "" CACHE PATH "Data and images required for some examples (url: https://github.com/arrayfire/assets)")
 endif (NOT ASSETS_DIR)
 
 if(WIN32)
@@ -21,44 +17,6 @@ if(WIN32)
   unset(CMAKE_RUNTIME_OUTPUT_DIRECTORY)
 endif()
 
-# Some examples take too long to execute. This list is used to exclude these
-# examples from the tests
-list(APPEND exclude_from_tests  black_scholes_options_cpu
-                                monte_carlo_options_cpu
-                                vectorize_cpu
-                                )
-
-# Overload add_executable and target_link_libraries so that we can use simple
-# CMakeLists.txt files for the examples.
-#
-# These functions will overload the existing functions so that the target names
-# have the word "examples_" prefixed to them so they don't conflict with the
-# tests. This is an issue with the blas example where the test blas_cpu and the
-# example blas_cpu have the same target name.
-#
-# Additionally, This will allow us to write the CMakeLists.txt files as
-# standalone files so that they are easier to parse for new users.
-function(add_executable target sources)
-  _add_executable(example_${target} ${sources})
-  set_target_properties(example_${target}
-    PROPERTIES
-      OUTPUT_NAME ${target}
-      FOLDER "Examples"
-	)
-
-  if(NOT ${target} IN_LIST exclude_from_tests)
-    #add_test(example_${target} ${target} 0 -)
-  endif()
-endfunction()
-
-function(target_link_libraries target sources)
-  _target_link_libraries(example_${target} ${sources})
-endfunction()
-
-function(target_compile_definitions target access definitions)
-  _target_compile_definitions(example_${target} ${access} ${definitions})
-endfunction()
-
 add_subdirectory(benchmarks)
 add_subdirectory(computer_vision)
 add_subdirectory(financial)
diff --git a/examples/CMakeModules/FindOpenCL.cmake b/examples/CMakeModules/FindOpenCL.cmake
deleted file mode 100644
index 4d4ef57bc3..0000000000
--- a/examples/CMakeModules/FindOpenCL.cmake
+++ /dev/null
@@ -1,190 +0,0 @@
-#.rst:
-# FindOpenCL
-# ----------
-#
-# Try to find OpenCL
-#
-# Once done this will define::
-#
-#   OpenCL_FOUND          - True if OpenCL was found
-#   OpenCL_INCLUDE_DIRS   - include directories for OpenCL
-#   OpenCL_LIBRARIES      - link against this library to use OpenCL
-#   OpenCL_VERSION_STRING - Highest supported OpenCL version (eg. 1.2)
-#   OpenCL_VERSION_MAJOR  - The major version of the OpenCL implementation
-#   OpenCL_VERSION_MINOR  - The minor version of the OpenCL implementation
-#
-# The module will also define two cache variables::
-#
-#   OpenCL_INCLUDE_DIR    - the OpenCL include directory
-#   OpenCL_LIBRARY        - the path to the OpenCL library
-#
-
-#=============================================================================
-# From CMake 3.2
-# Copyright 2014 Matthaeus G. Chajdas
-#
-# Distributed under the OSI-approved BSD License (the "License");
-# see accompanying file Copyright.txt for details.
-#
-# This software is distributed WITHOUT ANY WARRANTY; without even the
-# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the License for more information.
-
-# CMake - Cross Platform Makefile Generator
-# Copyright 2000-2014 Kitware, Inc.
-# Copyright 2000-2011 Insight Software Consortium
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-#
-# * Neither the names of Kitware, Inc., the Insight Software Consortium,
-# nor the names of their contributors may be used to endorse or promote
-# products derived from this software without specific prior written
-# permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#=============================================================================
-
-function(_FIND_OPENCL_VERSION)
-  include(CheckSymbolExists)
-  include(CMakePushCheckState)
-  set(CMAKE_REQUIRED_QUIET ${OpenCL_FIND_QUIETLY})
-
-  CMAKE_PUSH_CHECK_STATE()
-  foreach(VERSION "2_0" "1_2" "1_1" "1_0")
-    set(CMAKE_REQUIRED_INCLUDES "${OpenCL_INCLUDE_DIR}")
-    if(APPLE)
-      CHECK_SYMBOL_EXISTS(
-        CL_VERSION_${VERSION}
-        "${OpenCL_INCLUDE_DIR}/OpenCL/cl.h"
-        OPENCL_VERSION_${VERSION})
-    else()
-      CHECK_SYMBOL_EXISTS(
-        CL_VERSION_${VERSION}
-        "${OpenCL_INCLUDE_DIR}/CL/cl.h"
-        OPENCL_VERSION_${VERSION})
-    endif()
-
-    if(OPENCL_VERSION_${VERSION})
-      string(REPLACE "_" "." VERSION "${VERSION}")
-      set(OpenCL_VERSION_STRING ${VERSION} PARENT_SCOPE)
-      string(REGEX MATCHALL "[0-9]+" version_components "${VERSION}")
-      list(GET version_components 0 major_version)
-      list(GET version_components 1 minor_version)
-      set(OpenCL_VERSION_MAJOR ${major_version} PARENT_SCOPE)
-      set(OpenCL_VERSION_MINOR ${minor_version} PARENT_SCOPE)
-      break()
-    endif()
-  endforeach()
-  CMAKE_POP_CHECK_STATE()
-endfunction()
-
-find_path(OpenCL_INCLUDE_DIR
-  NAMES
-    CL/cl.h OpenCL/cl.h
-  PATHS
-    ENV "PROGRAMFILES(X86)"
-    ENV NVSDKCOMPUTE_ROOT
-    ENV CUDA_PATH
-    ENV AMDAPPSDKROOT
-    ENV INTELOCLSDKROOT
-    ENV ATISTREAMSDKROOT
-  PATH_SUFFIXES
-    include
-    OpenCL/common/inc
-    "AMD APP/include")
-
-_FIND_OPENCL_VERSION()
-
-if(WIN32)
-  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
-    find_library(OpenCL_LIBRARY
-      NAMES OpenCL
-      PATHS
-        ENV "PROGRAMFILES(X86)"
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV ATISTREAMSDKROOT
-      PATH_SUFFIXES
-        "AMD APP/lib/x86"
-        lib/x86
-        lib/Win32
-        OpenCL/common/lib/Win32)
-  elseif(CMAKE_SIZEOF_VOID_P EQUAL 8)
-    find_library(OpenCL_LIBRARY
-      NAMES OpenCL
-      PATHS
-        ENV "PROGRAMFILES(X86)"
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV ATISTREAMSDKROOT
-      PATH_SUFFIXES
-        "AMD APP/lib/x86_64"
-        lib/x86_64
-        lib/x64
-        OpenCL/common/lib/x64)
-  endif()
-else()
-  find_library(OpenCL_LIBRARY
-    NAMES OpenCL
-    PATHS
-        ENV LD_LIBRARY_PATH
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV ATISTREAMSDKROOT
-        /usr/lib64
-        /usr/lib
-        /usr/local/lib64
-        /usr/local/lib
-        /sw/lib
-        /opt/local/lib
-    PATH_SUFFIXES
-        "AMD APP/lib/x86_64"
-        lib/x86_64
-        lib/x64
-        lib/
-        lib64/
-        x86_64-linux-gnu
-        arm-linux-gnueabihf
-    )
-endif()
-
-set(OpenCL_LIBRARIES ${OpenCL_LIBRARY})
-set(OpenCL_INCLUDE_DIRS ${OpenCL_INCLUDE_DIR})
-
-#include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
-find_package_handle_standard_args(
-  OpenCL
-  FOUND_VAR OpenCL_FOUND
-  REQUIRED_VARS OpenCL_LIBRARY OpenCL_INCLUDE_DIR
-  VERSION_VAR OpenCL_VERSION_STRING)
-
-mark_as_advanced(
-  OpenCL_INCLUDE_DIR
-  OpenCL_LIBRARY)
-

From 35d6f54e7db1df7464f1ce0eff83a988dce5e7e4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Feb 2018 20:41:55 -0500
Subject: [PATCH 1374/2677] Add copyright to example CMakeLists.txt files

---
 examples/CMakeLists.txt                  |  7 +++++++
 examples/benchmarks/CMakeLists.txt       | 14 ++++++++------
 examples/computer_vision/CMakeLists.txt  |  7 +++++++
 examples/financial/CMakeLists.txt        |  7 +++++++
 examples/getting_started/CMakeLists.txt  |  7 +++++++
 examples/graphics/CMakeLists.txt         |  7 +++++++
 examples/helloworld/CMakeLists.txt       |  7 +++++++
 examples/image_processing/CMakeLists.txt |  7 +++++++
 examples/lin_algebra/CMakeLists.txt      |  7 +++++++
 examples/machine_learning/CMakeLists.txt |  7 +++++++
 examples/pde/CMakeLists.txt              |  7 +++++++
 examples/unified/CMakeLists.txt          |  7 +++++++
 12 files changed, 85 insertions(+), 6 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index c470e953d2..8095a0b72d 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Examples
diff --git a/examples/benchmarks/CMakeLists.txt b/examples/benchmarks/CMakeLists.txt
index 8a77b47c3f..421001e357 100644
--- a/examples/benchmarks/CMakeLists.txt
+++ b/examples/benchmarks/CMakeLists.txt
@@ -1,15 +1,17 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Benchmarks
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire REQUIRED)
-
-# get_cmake_property(_variableNames VARIABLES)
-# foreach (_variableName ${_variableNames})
-#     message(STATUS "${_variableName}=${${_variableName}}")
-# endforeach()
+find_package(ArrayFire)
 
 if(ArrayFire_CPU_FOUND)
   add_executable(blas_cpu blas.cpp)
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
index 41116a2e83..654ab70d71 100644
--- a/examples/computer_vision/CMakeLists.txt
+++ b/examples/computer_vision/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Computer-Vision
diff --git a/examples/financial/CMakeLists.txt b/examples/financial/CMakeLists.txt
index e7c7fc19c8..232e6326ca 100644
--- a/examples/financial/CMakeLists.txt
+++ b/examples/financial/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Financial
diff --git a/examples/getting_started/CMakeLists.txt b/examples/getting_started/CMakeLists.txt
index abc0899f64..b6f81e3e6f 100644
--- a/examples/getting_started/CMakeLists.txt
+++ b/examples/getting_started/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Getting-Started
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
index 68257d6390..d7b3c57ca7 100644
--- a/examples/graphics/CMakeLists.txt
+++ b/examples/graphics/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Graphics
diff --git a/examples/helloworld/CMakeLists.txt b/examples/helloworld/CMakeLists.txt
index 354332f095..1a744b1481 100644
--- a/examples/helloworld/CMakeLists.txt
+++ b/examples/helloworld/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-HelloWorld
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index a8e0c8c08b..a2b284e00e 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Image-Processing
diff --git a/examples/lin_algebra/CMakeLists.txt b/examples/lin_algebra/CMakeLists.txt
index 2181a2f923..36c21274c4 100644
--- a/examples/lin_algebra/CMakeLists.txt
+++ b/examples/lin_algebra/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Linear-Algebra
diff --git a/examples/machine_learning/CMakeLists.txt b/examples/machine_learning/CMakeLists.txt
index e94e3d2482..d3bdf0120d 100644
--- a/examples/machine_learning/CMakeLists.txt
+++ b/examples/machine_learning/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Linear-Algebra
diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index 7fae04a2b6..1b1f296395 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-PDE
diff --git a/examples/unified/CMakeLists.txt b/examples/unified/CMakeLists.txt
index 282a359c0e..94a53ad5df 100644
--- a/examples/unified/CMakeLists.txt
+++ b/examples/unified/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Unified

From f74a1b13644202a35857924bf0d4433027e4863b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 1 Mar 2018 02:26:02 -0500
Subject: [PATCH 1375/2677] Fix doxygen warnings. Update config

---
 docs/details/index.dox      |   2 +-
 docs/doxygen.mk             | 438 +++++++++++++++++++++++-------------
 docs/pages/release_notes.md |   6 +-
 include/af/array.h          | 223 +++++++++++-------
 include/af/cuda.h           |   1 +
 5 files changed, 430 insertions(+), 240 deletions(-)

diff --git a/docs/details/index.dox b/docs/details/index.dox
index 65f38c9931..dcf278efa9 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -14,7 +14,7 @@
 
 \brief Index an array using another array.
 
-Lets look at an example of how \ref lookup function does indexing.
+Lets look at an example of how \ref af::lookup function does indexing.
 \code
 array a = range(dim4(5));
 af_print(a);
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 1c4c414c6c..3555ceb884 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1,4 +1,4 @@
-# Doxyfile 1.8.8
+# Doxyfile 1.8.14
 
 # This file describes the settings to be used by the documentation system
 # doxygen (www.doxygen.org) for a project.
@@ -20,8 +20,8 @@
 # This tag specifies the encoding used for all characters in the config file
 # that follow. The default is UTF-8 which is also the encoding used for all text
 # before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
-# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
-# for the list of possible encodings.
+# built into libc) for the transcoding. See
+# https://www.gnu.org/software/libiconv/ for the list of possible encodings.
 # The default value is: UTF-8.
 
 DOXYFILE_ENCODING      = UTF-8
@@ -46,10 +46,10 @@ PROJECT_NUMBER         = ""
 
 PROJECT_BRIEF          = ""
 
-# With the PROJECT_LOGO tag one can specify an logo or icon that is included in
-# the documentation. The maximum height of the logo should not exceed 55 pixels
-# and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
-# to the output directory.
+# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
+# in the documentation. The maximum height of the logo should not exceed 55
+# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
+# the logo to the output directory.
 
 PROJECT_LOGO           = ${ASSETS_DIR}/arrayfire_logo.png
 
@@ -60,7 +60,7 @@ PROJECT_LOGO           = ${ASSETS_DIR}/arrayfire_logo.png
 
 OUTPUT_DIRECTORY       = ${CMAKE_CURRENT_BINARY_DIR}
 
-# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
+# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
 # directories (in 2 levels) under the output directory of each output format and
 # will distribute the generated files over these directories. Enabling this
 # option can be useful when feeding doxygen a huge amount of source files, where
@@ -93,14 +93,14 @@ ALLOW_UNICODE_NAMES    = NO
 
 OUTPUT_LANGUAGE        = English
 
-# If the BRIEF_MEMBER_DESC tag is set to YES doxygen will include brief member
+# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member
 # descriptions after the members that are listed in the file and class
 # documentation (similar to Javadoc). Set to NO to disable this.
 # The default value is: YES.
 
 BRIEF_MEMBER_DESC      = YES
 
-# If the REPEAT_BRIEF tag is set to YES doxygen will prepend the brief
+# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief
 # description of a member or function before the detailed description
 #
 # Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
@@ -135,7 +135,7 @@ ALWAYS_DETAILED_SEC    = NO
 
 INLINE_INHERITED_MEMB  = NO
 
-# If the FULL_PATH_NAMES tag is set to YES doxygen will prepend the full path
+# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
 # before files name in the file list and in the header files. If set to NO the
 # shortest path that makes the file name unique will be used
 # The default value is: YES.
@@ -206,9 +206,9 @@ MULTILINE_CPP_IS_BRIEF = NO
 
 INHERIT_DOCS           = YES
 
-# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce a
-# new page for each member. If set to NO, the documentation of a member will be
-# part of the file/class/namespace that contains it.
+# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
+# page for each member. If set to NO, the documentation of a member will be part
+# of the file/class/namespace that contains it.
 # The default value is: NO.
 
 SEPARATE_MEMBER_PAGES  = NO
@@ -217,7 +217,7 @@ SEPARATE_MEMBER_PAGES  = NO
 # uses this value to replace tabs by spaces in code fragments.
 # Minimum value: 1, maximum value: 16, default value: 4.
 
-TAB_SIZE               = 8
+TAB_SIZE               = 4
 
 # This tag can be used to specify a number of aliases that act as commands in
 # the documentation. An alias has the form:
@@ -227,7 +227,8 @@ TAB_SIZE               = 8
 # will allow you to put the command \sideeffect (or @sideeffect) in the
 # documentation, which will result in a user-defined paragraph with heading
 # "Side Effects:". You can put \n's in the value part of an alias to insert
-# newlines.
+# newlines (in the resulting output). You can put ^^ in the value part of an
+# alias to insert a newline as if a physical newline was in the original file.
 
 ALIASES                = "support{1}=<DIV class=\"support\">\1</DIV>" \
                          "opencl=<IMG src=\"OpenCL.png\" alt=\"OpenCL Support\" />" \
@@ -292,7 +293,7 @@ OPTIMIZE_OUTPUT_VHDL   = NO
 # instance to make doxygen treat .inc files as Fortran files (default is PHP),
 # and .f files as C (default is Fortran), use: inc=Fortran f=C.
 #
-# Note For files without extension you can use no_extension as a placeholder.
+# Note: For files without extension you can use no_extension as a placeholder.
 #
 # Note that for custom extensions you also need to set FILE_PATTERNS otherwise
 # the files are not read by doxygen.
@@ -309,10 +310,19 @@ EXTENSION_MAPPING      =
 
 MARKDOWN_SUPPORT       = YES
 
+# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up
+# to that level are automatically included in the table of contents, even if
+# they do not have an id attribute.
+# Note: This feature currently applies only to Markdown headings.
+# Minimum value: 0, maximum value: 99, default value: 0.
+# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
+
+TOC_INCLUDE_HEADINGS   = 0
+
 # When enabled doxygen tries to link words that correspond to documented
 # classes, or namespaces to their corresponding documentation. Such a link can
-# be prevented in individual cases by by putting a % sign in front of the word
-# or globally by setting AUTOLINK_SUPPORT to NO.
+# be prevented in individual cases by putting a % sign in front of the word or
+# globally by setting AUTOLINK_SUPPORT to NO.
 # The default value is: YES.
 
 AUTOLINK_SUPPORT       = YES
@@ -334,7 +344,7 @@ BUILTIN_STL_SUPPORT    = NO
 CPP_CLI_SUPPORT        = NO
 
 # Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
-# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
+# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen
 # will parse them like normal C++ but will assume all classes use public instead
 # of private inheritance when no explicit protection keyword is present.
 # The default value is: NO.
@@ -352,13 +362,20 @@ SIP_SUPPORT            = NO
 IDL_PROPERTY_SUPPORT   = YES
 
 # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
-# tag is set to YES, then doxygen will reuse the documentation of the first
+# tag is set to YES then doxygen will reuse the documentation of the first
 # member in the group (if any) for the other members of the group. By default
 # all members of a group must be documented explicitly.
 # The default value is: NO.
 
 DISTRIBUTE_GROUP_DOC   = NO
 
+# If one adds a struct or class to a group and this option is enabled, then also
+# any nested class or struct is added to the same group. By default this option
+# is disabled and one has to add nested compounds explicitly via \ingroup.
+# The default value is: NO.
+
+GROUP_NESTED_COMPOUNDS = NO
+
 # Set the SUBGROUPING tag to YES to allow class member groups of the same type
 # (for instance a group of public functions) to be put as a subgroup of that
 # type (e.g. under the Public Functions section). Set it to NO to prevent
@@ -417,7 +434,7 @@ LOOKUP_CACHE_SIZE      = 0
 # Build related configuration options
 #---------------------------------------------------------------------------
 
-# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
+# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
 # documentation are documented, even if no documentation was available. Private
 # class members and static file members will be hidden unless the
 # EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
@@ -427,35 +444,35 @@ LOOKUP_CACHE_SIZE      = 0
 
 EXTRACT_ALL            = YES
 
-# If the EXTRACT_PRIVATE tag is set to YES all private members of a class will
+# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
 # be included in the documentation.
 # The default value is: NO.
 
 EXTRACT_PRIVATE        = NO
 
-# If the EXTRACT_PACKAGE tag is set to YES all members with package or internal
+# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
 # scope will be included in the documentation.
 # The default value is: NO.
 
 EXTRACT_PACKAGE        = NO
 
-# If the EXTRACT_STATIC tag is set to YES all static members of a file will be
+# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
 # included in the documentation.
 # The default value is: NO.
 
 EXTRACT_STATIC         = YES
 
-# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) defined
-# locally in source files will be included in the documentation. If set to NO
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO,
 # only classes defined in header files are included. Does not have any effect
 # for Java sources.
 # The default value is: YES.
 
 EXTRACT_LOCAL_CLASSES  = YES
 
-# This flag is only useful for Objective-C code. When set to YES local methods,
+# This flag is only useful for Objective-C code. If set to YES, local methods,
 # which are defined in the implementation section but not in the interface are
-# included in the documentation. If set to NO only methods in the interface are
+# included in the documentation. If set to NO, only methods in the interface are
 # included.
 # The default value is: NO.
 
@@ -480,21 +497,21 @@ HIDE_UNDOC_MEMBERS     = NO
 
 # If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
 # undocumented classes that are normally visible in the class hierarchy. If set
-# to NO these classes will be included in the various overviews. This option has
-# no effect if EXTRACT_ALL is enabled.
+# to NO, these classes will be included in the various overviews. This option
+# has no effect if EXTRACT_ALL is enabled.
 # The default value is: NO.
 
 HIDE_UNDOC_CLASSES     = NO
 
 # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
-# (class|struct|union) declarations. If set to NO these declarations will be
+# (class|struct|union) declarations. If set to NO, these declarations will be
 # included in the documentation.
 # The default value is: NO.
 
 HIDE_FRIEND_COMPOUNDS  = NO
 
 # If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
-# documentation blocks found inside the body of a function. If set to NO these
+# documentation blocks found inside the body of a function. If set to NO, these
 # blocks will be appended to the function's detailed documentation block.
 # The default value is: NO.
 
@@ -508,7 +525,7 @@ HIDE_IN_BODY_DOCS      = NO
 INTERNAL_DOCS          = NO
 
 # If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
-# names in lower-case letters. If set to YES upper-case letters are also
+# names in lower-case letters. If set to YES, upper-case letters are also
 # allowed. This is useful if you have classes or files whose names only differ
 # in case and if your file system supports case sensitive file names. Windows
 # and Mac users are advised to set this option to NO.
@@ -517,12 +534,19 @@ INTERNAL_DOCS          = NO
 CASE_SENSE_NAMES       = YES
 
 # If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
-# their full class and namespace scopes in the documentation. If set to YES the
+# their full class and namespace scopes in the documentation. If set to YES, the
 # scope will be hidden.
 # The default value is: NO.
 
 HIDE_SCOPE_NAMES       = YES
 
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# append additional text to a page's title, such as Class Reference. If set to
+# YES the compound reference will be hidden.
+# The default value is: NO.
+
+HIDE_COMPOUND_REFERENCE= NO
+
 # If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
 # the files that are included by a file in the documentation of that file.
 # The default value is: YES.
@@ -550,14 +574,14 @@ INLINE_INFO            = YES
 
 # If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
 # (detailed) documentation of file and class members alphabetically by member
-# name. If set to NO the members will appear in declaration order.
+# name. If set to NO, the members will appear in declaration order.
 # The default value is: YES.
 
 SORT_MEMBER_DOCS       = YES
 
 # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
 # descriptions of file, namespace and class members alphabetically by member
-# name. If set to NO the members will appear in declaration order. Note that
+# name. If set to NO, the members will appear in declaration order. Note that
 # this will also influence the order of the classes in the class list.
 # The default value is: NO.
 
@@ -602,27 +626,25 @@ SORT_BY_SCOPE_NAME     = NO
 
 STRICT_PROTO_MATCHING  = NO
 
-# The GENERATE_TODOLIST tag can be used to enable ( YES) or disable ( NO) the
-# todo list. This list is created by putting \todo commands in the
-# documentation.
+# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
+# list. This list is created by putting \todo commands in the documentation.
 # The default value is: YES.
 
 GENERATE_TODOLIST      = NO
 
-# The GENERATE_TESTLIST tag can be used to enable ( YES) or disable ( NO) the
-# test list. This list is created by putting \test commands in the
-# documentation.
+# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
+# list. This list is created by putting \test commands in the documentation.
 # The default value is: YES.
 
 GENERATE_TESTLIST      = NO
 
-# The GENERATE_BUGLIST tag can be used to enable ( YES) or disable ( NO) the bug
+# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
 # list. This list is created by putting \bug commands in the documentation.
 # The default value is: YES.
 
 GENERATE_BUGLIST       = NO
 
-# The GENERATE_DEPRECATEDLIST tag can be used to enable ( YES) or disable ( NO)
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
 # the deprecated list. This list is created by putting \deprecated commands in
 # the documentation.
 # The default value is: YES.
@@ -647,8 +669,8 @@ ENABLED_SECTIONS       =
 MAX_INITIALIZER_LINES  = 30
 
 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
-# the bottom of the documentation of classes and structs. If set to YES the list
-# will mention the files that were used to generate the documentation.
+# the bottom of the documentation of classes and structs. If set to YES, the
+# list will mention the files that were used to generate the documentation.
 # The default value is: YES.
 
 SHOW_USED_FILES        = YES
@@ -693,7 +715,7 @@ LAYOUT_FILE            = ${DOCS_DIR}/layout.xml
 # The CITE_BIB_FILES tag can be used to specify one or more bib files containing
 # the reference definitions. This must be a list of .bib files. The .bib
 # extension is automatically appended if omitted. This requires the bibtex tool
-# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
+# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.
 # For LaTeX the style of the bibliography can be controlled using
 # LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
 # search path. See also \cite for info how to create references.
@@ -712,7 +734,7 @@ CITE_BIB_FILES         =
 QUIET                  = YES
 
 # The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated to standard error ( stderr) by doxygen. If WARNINGS is set to YES
+# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
 # this implies that the warnings are on.
 #
 # Tip: Turn warnings on while writing the documentation.
@@ -720,7 +742,7 @@ QUIET                  = YES
 
 WARNINGS               = YES
 
-# If the WARN_IF_UNDOCUMENTED tag is set to YES, then doxygen will generate
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
 # warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
 # will automatically be disabled.
 # The default value is: YES.
@@ -737,11 +759,17 @@ WARN_IF_DOC_ERROR      = YES
 
 # This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
 # are documented, but have no documentation for their parameters or return
-# value. If set to NO doxygen will only warn about wrong or incomplete parameter
-# documentation, but not about the absence of documentation.
+# value. If set to NO, doxygen will only warn about wrong or incomplete
+# parameter documentation, but not about the absence of documentation.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = YES
+
+# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
+# a warning is encountered.
 # The default value is: NO.
 
-WARN_NO_PARAMDOC       = NO
+WARN_AS_ERROR          = YES
 
 # The WARN_FORMAT tag determines the format of the warning messages that doxygen
 # can produce. The string should contain the $file, $line, and $text tags, which
@@ -766,18 +794,18 @@ WARN_LOGFILE           =
 # The INPUT tag is used to specify the files and/or directories that contain
 # documented source files. You may enter file names like myfile.cpp or
 # directories like /usr/src/myproject. Separate the files or directories with
-# spaces.
+# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
 # Note: If this tag is empty the current directory is searched.
 
 INPUT                  = ${DOCS_DIR}/pages \
-						 ${INCLUDE_DIR}/ \
+                         ${INCLUDE_DIR}/ \
                          ${INCLUDE_DIR}/af/ \
-						 ${DOCS_DIR}/details
+                         ${DOCS_DIR}/details
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
 # libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see: http://www.gnu.org/software/libiconv) for the list of
+# documentation (see: https://www.gnu.org/software/libiconv/) for the list of
 # possible encodings.
 # The default value is: UTF-8.
 
@@ -785,12 +813,17 @@ INPUT_ENCODING         = UTF-8
 
 # If the value of the INPUT tag contains directories, you can use the
 # FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
-# *.h) to filter out the source-files in the directories. If left blank the
-# following patterns are tested:*.c, *.cc, *.cxx, *.cpp, *.c++, *.java, *.ii,
-# *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp,
-# *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown,
-# *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf,
-# *.qsf, *.as and *.js.
+# *.h) to filter out the source-files in the directories.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# read by doxygen.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
+# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
+# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
+# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,
+# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf.
 
 FILE_PATTERNS          =
 
@@ -834,14 +867,14 @@ EXCLUDE_PATTERNS       = *.cpp
 # Note that the wildcards are matched against the file with absolute path, so to
 # exclude all test directories use the pattern */test/*
 
-EXCLUDE_SYMBOLS        = APPROX
+EXCLUDE_SYMBOLS        =
 
 # The EXAMPLE_PATH tag can be used to specify one or more files or directories
 # that contain example code fragments that are included (see the \include
 # command).
 
 EXAMPLE_PATH           = ${EXAMPLES_DIR}/ \
-			${SNIPPETS_DIR}
+                         ${SNIPPETS_DIR}
 
 # If the value of the EXAMPLE_PATH tag contains directories, you can use the
 # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
@@ -879,6 +912,10 @@ IMAGE_PATH             = ${ASSETS_DIR}
 # Note that the filter must not add or remove lines; it is applied before the
 # code is scanned, but not when the output code is generated. If lines are added
 # or removed, the anchors will not be placed correctly.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
 
 INPUT_FILTER           =
 
@@ -888,11 +925,15 @@ INPUT_FILTER           =
 # (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
 # filters are used. If the FILTER_PATTERNS tag is empty or if none of the
 # patterns match the file name, INPUT_FILTER is applied.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
 
 FILTER_PATTERNS        =
 
 # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
-# INPUT_FILTER ) will also be used to filter the input files that are used for
+# INPUT_FILTER) will also be used to filter the input files that are used for
 # producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
 # The default value is: NO.
 
@@ -924,13 +965,13 @@ USE_MDFILE_AS_MAINPAGE = ${DOCS_DIR}/pages/README.md
 # also VERBATIM_HEADERS is set to NO.
 # The default value is: NO.
 
-SOURCE_BROWSER         = NO
+SOURCE_BROWSER         = YES
 
 # Setting the INLINE_SOURCES tag to YES will include the body of functions,
 # classes and enums directly into the documentation.
 # The default value is: NO.
 
-INLINE_SOURCES         = NO
+INLINE_SOURCES         = YES
 
 # Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
 # special comment blocks from generated source code fragments. Normal C, C++ and
@@ -952,7 +993,7 @@ REFERENCED_BY_RELATION = NO
 REFERENCES_RELATION    = NO
 
 # If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
-# to YES, then the hyperlinks from functions in REFERENCES_RELATION and
+# to YES then the hyperlinks from functions in REFERENCES_RELATION and
 # REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
 # link to the documentation.
 # The default value is: YES.
@@ -972,7 +1013,7 @@ SOURCE_TOOLTIPS        = YES
 # If the USE_HTAGS tag is set to YES then the references to source code will
 # point to the HTML generated by the htags(1) tool instead of doxygen built-in
 # source browser. The htags tool is part of GNU's global source tagging system
-# (see http://www.gnu.org/software/global/global.html). You will need version
+# (see https://www.gnu.org/software/global/global.html). You will need version
 # 4.8.6 or higher.
 #
 # To use it do the following:
@@ -999,6 +1040,36 @@ USE_HTAGS              = NO
 
 VERBATIM_HEADERS       = YES
 
+# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse-libclang=ON option for CMake.
+# The default value is: NO.
+
+CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_OPTIONS          = -Wno-pragma-once-outside-header
+
+# If clang assisted parsing is enabled you can provide the clang parser with the
+# path to the compilation database (see:
+# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) used when the files
+# were built. This is equivalent to specifying the "-p" option to a clang tool,
+# such as clang-check. These options will then be passed to the parser.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse-libclang=ON option for CMake.
+# The default value is: 0.
+
+CLANG_COMPILATION_DATABASE_PATH  = ${ArrayFire_BINARY_DIR}
+
 #---------------------------------------------------------------------------
 # Configuration options related to the alphabetical class index
 #---------------------------------------------------------------------------
@@ -1023,13 +1094,13 @@ COLS_IN_ALPHA_INDEX    = 5
 # while generating the index headers.
 # This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
 
-IGNORE_PREFIX          =
+IGNORE_PREFIX          = af_
 
 #---------------------------------------------------------------------------
 # Configuration options related to the HTML output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_HTML tag is set to YES doxygen will generate HTML output
+# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
 # The default value is: YES.
 
 GENERATE_HTML          = YES
@@ -1095,10 +1166,10 @@ HTML_STYLESHEET        =
 # cascading style sheets that are included after the standard style sheets
 # created by doxygen. Using this option one can overrule certain style aspects.
 # This is preferred over using HTML_STYLESHEET since it does not replace the
-# standard style sheet and is therefor more robust against future updates.
+# standard style sheet and is therefore more robust against future updates.
 # Doxygen will copy the style sheet files to the output directory.
-# Note: The order of the extra stylesheet files is of importance (e.g. the last
-# stylesheet in the list overrules the setting of the previous ones in the
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
 # list). For an example see the documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
@@ -1115,9 +1186,9 @@ HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/arrayfire.css
 HTML_EXTRA_FILES       =
 
 # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
-# will adjust the colors in the stylesheet and background images according to
+# will adjust the colors in the style sheet and background images according to
 # this color. Hue is specified as an angle on a colorwheel, see
-# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# https://en.wikipedia.org/wiki/Hue for more information. For instance the value
 # 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
 # purple, and 360 is red again.
 # Minimum value: 0, maximum value: 359, default value: 220.
@@ -1146,19 +1217,31 @@ HTML_COLORSTYLE_GAMMA  = 70
 
 # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
 # page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: YES.
+# to YES can help to show when doxygen was last run and thus if the
+# documentation is up to date.
+# The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_TIMESTAMP         = YES
 
+# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
+# documentation will contain a main index with vertical navigation menus that
+# are dynamically created via Javascript. If disabled, the navigation index will
+# consists of multiple levels of tabs that are statically embedded in every HTML
+# page. Disable this option to support browsers that do not have Javascript,
+# like the Qt help browser.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_MENUS     = NO
+
 # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
 # documentation will contain sections that can be hidden and shown after the
 # page has loaded.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_DYNAMIC_SECTIONS  = YES
+HTML_DYNAMIC_SECTIONS  = NO
 
 # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
 # shown in the various tree structured indices initially; the user can expand
@@ -1175,12 +1258,12 @@ HTML_INDEX_NUM_ENTRIES = 100
 
 # If the GENERATE_DOCSET tag is set to YES, additional index files will be
 # generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see: http://developer.apple.com/tools/xcode/), introduced with
+# environment (see: https://developer.apple.com/tools/xcode/), introduced with
 # OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
 # Makefile in the HTML output directory. Running make will produce the docset in
 # that directory and running make install will install the docset in
 # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
-# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
+# startup. See https://developer.apple.com/tools/creatingdocsetswithdoxygen.html
 # for more information.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
@@ -1243,28 +1326,28 @@ GENERATE_HTMLHELP      = NO
 CHM_FILE               =
 
 # The HHC_LOCATION tag can be used to specify the location (absolute path
-# including file name) of the HTML help compiler ( hhc.exe). If non-empty
+# including file name) of the HTML help compiler (hhc.exe). If non-empty,
 # doxygen will try to run the HTML help compiler on the generated index.hhp.
 # The file has to be specified with full path.
 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 HHC_LOCATION           =
 
-# The GENERATE_CHI flag controls if a separate .chi index file is generated (
-# YES) or that it should be included in the master .chm file ( NO).
+# The GENERATE_CHI flag controls if a separate .chi index file is generated
+# (YES) or that it should be included in the master .chm file (NO).
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 GENERATE_CHI           = NO
 
-# The CHM_INDEX_ENCODING is used to encode HtmlHelp index ( hhk), content ( hhc)
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
 # and project file content.
 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 CHM_INDEX_ENCODING     =
 
-# The BINARY_TOC flag controls whether a binary table of contents is generated (
-# YES) or a normal table of contents ( NO) in the .chm file. Furthermore it
+# The BINARY_TOC flag controls whether a binary table of contents is generated
+# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
 # enables the Previous and Next buttons.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
@@ -1296,7 +1379,7 @@ QCH_FILE               =
 
 # The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
 # Project output. For more information please see Qt Help Project / Namespace
-# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
+# (see: http://doc.qt.io/qt-4.8/qthelpproject.html#namespace).
 # The default value is: org.doxygen.Project.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
@@ -1304,8 +1387,7 @@ QHP_NAMESPACE          = org.doxygen.Project
 
 # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
 # Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
-# folders).
+# Folders (see: http://doc.qt.io/qt-4.8/qthelpproject.html#virtual-folders).
 # The default value is: doc.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
@@ -1313,23 +1395,21 @@ QHP_VIRTUAL_FOLDER     = doc
 
 # If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
 # filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
+# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_NAME   =
 
 # The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
 # custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
-# filters).
+# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_ATTRS  =
 
 # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
 # project's filter section matches. Qt Help Project / Filter Attributes (see:
-# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
+# http://doc.qt.io/qt-4.8/qthelpproject.html#filter-attributes).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_SECT_FILTER_ATTRS  =
@@ -1378,7 +1458,7 @@ DISABLE_INDEX          = NO
 # index structure (just like the one that is generated for HTML Help). For this
 # to work a browser that supports JavaScript, DHTML, CSS and frames is required
 # (i.e. any modern browser). Windows users are probably better off using the
-# HTML help feature. Via custom stylesheets (see HTML_EXTRA_STYLESHEET) one can
+# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
 # further fine-tune the look of the index. As an example, the default style
 # sheet generated by doxygen has an example that shows how to put an image at
 # the root of the tree instead of the PROJECT_NAME. Since the tree basically has
@@ -1406,7 +1486,7 @@ ENUM_VALUES_PER_LINE   = 4
 
 TREEVIEW_WIDTH         = 250
 
-# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open links to
+# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
 # external symbols imported via tag files in a separate window.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
@@ -1422,7 +1502,7 @@ EXT_LINKS_IN_WINDOW    = NO
 
 FORMULA_FONTSIZE       = 12
 
-# Use the FORMULA_TRANPARENT tag to determine whether or not the images
+# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
 # generated for formulas are transparent PNGs. Transparent PNGs are not
 # supported properly for IE 6.0, but are supported on all modern browsers.
 #
@@ -1434,8 +1514,8 @@ FORMULA_FONTSIZE       = 12
 FORMULA_TRANSPARENT    = YES
 
 # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
-# http://www.mathjax.org) which uses client side Javascript for the rendering
-# instead of using prerendered bitmaps. Use this if you do not have LaTeX
+# https://www.mathjax.org) which uses client side Javascript for the rendering
+# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
 # installed or if you want to formulas look prettier in the HTML output. When
 # enabled you may also need to install MathJax separately and configure the path
 # to it using the MATHJAX_RELPATH option.
@@ -1461,8 +1541,8 @@ MATHJAX_FORMAT         = HTML-CSS
 # MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
 # Content Delivery Network so you can quickly see the result without installing
 # MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from http://www.mathjax.org before deployment.
-# The default value is: http://cdn.mathjax.org/mathjax/latest.
+# MathJax from https://www.mathjax.org before deployment.
+# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/.
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest
@@ -1521,9 +1601,9 @@ SERVER_BASED_SEARCH    = NO
 # external search engine pointed to by the SEARCHENGINE_URL option to obtain the
 # search results.
 #
-# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# Doxygen ships with an example indexer (doxyindexer) and search engine
 # (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/).
+# Xapian (see: https://xapian.org/).
 #
 # See the section "External Indexing and Searching" for details.
 # The default value is: NO.
@@ -1534,9 +1614,9 @@ EXTERNAL_SEARCH        = NO
 # The SEARCHENGINE_URL should point to a search engine hosted by a web server
 # which will return the search results when EXTERNAL_SEARCH is enabled.
 #
-# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# Doxygen ships with an example indexer (doxyindexer) and search engine
 # (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: http://xapian.org/). See the section "External Indexing and
+# Xapian (see: https://xapian.org/). See the section "External Indexing and
 # Searching" for details.
 # This tag requires that the tag SEARCHENGINE is set to YES.
 
@@ -1572,7 +1652,7 @@ EXTRA_SEARCH_MAPPINGS  =
 # Configuration options related to the LaTeX output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_LATEX tag is set to YES doxygen will generate LaTeX output.
+# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
 # The default value is: YES.
 
 GENERATE_LATEX         = NO
@@ -1603,7 +1683,7 @@ LATEX_CMD_NAME         = latex
 
 MAKEINDEX_CMD_NAME     = makeindex
 
-# If the COMPACT_LATEX tag is set to YES doxygen generates more compact LaTeX
+# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
 # documents. This may be useful for small projects and may help to save some
 # trees in general.
 # The default value is: NO.
@@ -1621,9 +1701,12 @@ COMPACT_LATEX          = NO
 PAPER_TYPE             = a4
 
 # The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
-# that should be included in the LaTeX output. To get the times font for
-# instance you can specify
-# EXTRA_PACKAGES=times
+# that should be included in the LaTeX output. The package can be specified just
+# by its name or with the correct syntax as to be used with the LaTeX
+# \usepackage command. To get the times font for instance you can specify :
+# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
+# To use the option intlimits with the amsmath package you can specify:
+# EXTRA_PACKAGES=[intlimits]{amsmath}
 # If left blank no extra packages will be included.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
@@ -1638,9 +1721,9 @@ EXTRA_PACKAGES         =
 # Note: Only use a user-defined header if you know what you are doing! The
 # following commands have a special meaning inside the header: $title,
 # $datetime, $date, $doxygenversion, $projectname, $projectnumber,
-# $projectbrief, $projectlogo. Doxygen will replace $title with the empy string,
-# for the replacement values of the other commands the user is refered to
-# HTML_HEADER.
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
+# string, for the replacement values of the other commands the user is referred
+# to HTML_HEADER.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_HEADER           =
@@ -1656,6 +1739,17 @@ LATEX_HEADER           =
 
 LATEX_FOOTER           =
 
+# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# LaTeX style sheets that are included after the standard style sheets created
+# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list).
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_STYLESHEET =
+
 # The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
 # other source files which should be copied to the LATEX_OUTPUT output
 # directory. Note that the files will be copied as-is; there are no commands or
@@ -1674,7 +1768,7 @@ LATEX_EXTRA_FILES      =
 PDF_HYPERLINKS         = YES
 
 # If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
-# the PDF file directly from the LaTeX files. Set this option to YES to get a
+# the PDF file directly from the LaTeX files. Set this option to YES, to get a
 # higher quality PDF documentation.
 # The default value is: YES.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
@@ -1709,17 +1803,25 @@ LATEX_SOURCE_CODE      = NO
 
 # The LATEX_BIB_STYLE tag can be used to specify the style to use for the
 # bibliography, e.g. plainnat, or ieeetr. See
-# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
 # The default value is: plain.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_BIB_STYLE        = plain
 
+# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_TIMESTAMP        = NO
+
 #---------------------------------------------------------------------------
 # Configuration options related to the RTF output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_RTF tag is set to YES doxygen will generate RTF output. The
+# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
 # RTF output is optimized for Word 97 and may not look too pretty with other RTF
 # readers/editors.
 # The default value is: NO.
@@ -1734,7 +1836,7 @@ GENERATE_RTF           = NO
 
 RTF_OUTPUT             = rtf
 
-# If the COMPACT_RTF tag is set to YES doxygen generates more compact RTF
+# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
 # documents. This may be useful for small projects and may help to save some
 # trees in general.
 # The default value is: NO.
@@ -1771,11 +1873,21 @@ RTF_STYLESHEET_FILE    =
 
 RTF_EXTENSIONS_FILE    =
 
+# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
+# with syntax highlighting in the RTF output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_SOURCE_CODE        = NO
+
 #---------------------------------------------------------------------------
 # Configuration options related to the man page output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_MAN tag is set to YES doxygen will generate man pages for
+# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
 # classes and files.
 # The default value is: NO.
 
@@ -1819,7 +1931,7 @@ MAN_LINKS              = NO
 # Configuration options related to the XML output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_XML tag is set to YES doxygen will generate an XML file that
+# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
 # captures the structure of the code including all documentation.
 # The default value is: NO.
 
@@ -1833,7 +1945,7 @@ GENERATE_XML           = NO
 
 XML_OUTPUT             = xml
 
-# If the XML_PROGRAMLISTING tag is set to YES doxygen will dump the program
+# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
 # listings (including syntax highlighting and cross-referencing information) to
 # the XML output. Note that enabling this will significantly increase the size
 # of the XML output.
@@ -1846,7 +1958,7 @@ XML_PROGRAMLISTING     = YES
 # Configuration options related to the DOCBOOK output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_DOCBOOK tag is set to YES doxygen will generate Docbook files
+# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
 # that can be used to generate PDF.
 # The default value is: NO.
 
@@ -1860,7 +1972,7 @@ GENERATE_DOCBOOK       = NO
 
 DOCBOOK_OUTPUT         = docbook
 
-# If the DOCBOOK_PROGRAMLISTING tag is set to YES doxygen will include the
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
 # program listings (including syntax highlighting and cross-referencing
 # information) to the DOCBOOK output. Note that enabling this will significantly
 # increase the size of the DOCBOOK output.
@@ -1873,10 +1985,10 @@ DOCBOOK_PROGRAMLISTING = NO
 # Configuration options for the AutoGen Definitions output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_AUTOGEN_DEF tag is set to YES doxygen will generate an AutoGen
-# Definitions (see http://autogen.sf.net) file that captures the structure of
-# the code including all documentation. Note that this feature is still
-# experimental and incomplete at the moment.
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
+# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# the structure of the code including all documentation. Note that this feature
+# is still experimental and incomplete at the moment.
 # The default value is: NO.
 
 GENERATE_AUTOGEN_DEF   = NO
@@ -1885,7 +1997,7 @@ GENERATE_AUTOGEN_DEF   = NO
 # Configuration options related to the Perl module output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_PERLMOD tag is set to YES doxygen will generate a Perl module
+# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
 # file that captures the structure of the code including all documentation.
 #
 # Note that this feature is still experimental and incomplete at the moment.
@@ -1893,7 +2005,7 @@ GENERATE_AUTOGEN_DEF   = NO
 
 GENERATE_PERLMOD       = NO
 
-# If the PERLMOD_LATEX tag is set to YES doxygen will generate the necessary
+# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
 # Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
 # output from the Perl module output.
 # The default value is: NO.
@@ -1901,9 +2013,9 @@ GENERATE_PERLMOD       = NO
 
 PERLMOD_LATEX          = NO
 
-# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be nicely
+# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
 # formatted so it can be parsed by a human reader. This is useful if you want to
-# understand what is going on. On the other hand, if this tag is set to NO the
+# understand what is going on. On the other hand, if this tag is set to NO, the
 # size of the Perl module output will be much smaller and Perl will parse it
 # just the same.
 # The default value is: YES.
@@ -1923,14 +2035,14 @@ PERLMOD_MAKEVAR_PREFIX =
 # Configuration options related to the preprocessor
 #---------------------------------------------------------------------------
 
-# If the ENABLE_PREPROCESSING tag is set to YES doxygen will evaluate all
+# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
 # C-preprocessor directives found in the sources and include files.
 # The default value is: YES.
 
 ENABLE_PREPROCESSING   = YES
 
-# If the MACRO_EXPANSION tag is set to YES doxygen will expand all macro names
-# in the source code. If set to NO only conditional compilation will be
+# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
+# in the source code. If set to NO, only conditional compilation will be
 # performed. Macro expansion can be done in a controlled way by setting
 # EXPAND_ONLY_PREDEF to YES.
 # The default value is: NO.
@@ -1946,12 +2058,12 @@ MACRO_EXPANSION        = YES
 
 EXPAND_ONLY_PREDEF     = NO
 
-# If the SEARCH_INCLUDES tag is set to YES the includes files in the
+# If the SEARCH_INCLUDES tag is set to YES, the include files in the
 # INCLUDE_PATH will be searched if a #include is found.
 # The default value is: YES.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-SEARCH_INCLUDES        = YES
+SEARCH_INCLUDES        = NO
 
 # The INCLUDE_PATH tag can be used to specify one or more directories that
 # contain include files that are not input files but should be processed by the
@@ -1978,9 +2090,9 @@ INCLUDE_FILE_PATTERNS  =
 
 PREDEFINED             = __declspec(x)= \
                          __attribute__(x)= \
-                         __cplusplus \
+                         __cplusplus = 99999999999999 \
                          AF_DOC \
-						 AF_API_VERSION=${ArrayFire_API_VERSION_CURRENT}
+                         AF_API_VERSION=${ArrayFire_API_VERSION_CURRENT}
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The
@@ -2024,22 +2136,23 @@ TAGFILES               =
 # tag file that is based on the input files it reads. See section "Linking to
 # external documentation" for more information about the usage of tag files.
 
-GENERATE_TAGFILE       =
+GENERATE_TAGFILE       = doxtags.txt
 
-# If the ALLEXTERNALS tag is set to YES all external class will be listed in the
-# class index. If set to NO only the inherited external classes will be listed.
+# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
+# the class index. If set to NO, only the inherited external classes will be
+# listed.
 # The default value is: NO.
 
 ALLEXTERNALS           = NO
 
-# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed in
-# the modules index. If set to NO, only the current project's groups will be
+# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
+# in the modules index. If set to NO, only the current project's groups will be
 # listed.
 # The default value is: YES.
 
 EXTERNAL_GROUPS        = YES
 
-# If the EXTERNAL_PAGES tag is set to YES all external pages will be listed in
+# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
 # the related pages index. If set to NO, only the current project's pages will
 # be listed.
 # The default value is: YES.
@@ -2056,7 +2169,7 @@ PERL_PATH              = /usr/bin/perl
 # Configuration options related to the dot tool
 #---------------------------------------------------------------------------
 
-# If the CLASS_DIAGRAMS tag is set to YES doxygen will generate a class diagram
+# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
 # (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
 # NO turns the diagrams off. Note that this option also works with HAVE_DOT
 # disabled, but it is recommended to install and use dot, since it yields more
@@ -2081,7 +2194,7 @@ MSCGEN_PATH            =
 
 DIA_PATH               =
 
-# If set to YES, the inheritance and collaboration graphs will hide inheritance
+# If set to YES the inheritance and collaboration graphs will hide inheritance
 # and usage relations if the target is undocumented or is not a class.
 # The default value is: YES.
 
@@ -2154,7 +2267,7 @@ COLLABORATION_GRAPH    = YES
 
 GROUP_GRAPHS           = YES
 
-# If the UML_LOOK tag is set to YES doxygen will generate inheritance and
+# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
 # collaboration diagrams in a style similar to the OMG's Unified Modeling
 # Language.
 # The default value is: NO.
@@ -2206,7 +2319,8 @@ INCLUDED_BY_GRAPH      = YES
 #
 # Note that enabling this option will significantly increase the time of a run.
 # So in most cases it will be better to enable call graphs for selected
-# functions only using the \callgraph command.
+# functions only using the \callgraph command. Disabling a call graph can be
+# accomplished by means of the command \hidecallgraph.
 # The default value is: NO.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
@@ -2217,7 +2331,8 @@ CALL_GRAPH             = NO
 #
 # Note that enabling this option will significantly increase the time of a run.
 # So in most cases it will be better to enable caller graphs for selected
-# functions only using the \callergraph command.
+# functions only using the \callergraph command. Disabling a caller graph can be
+# accomplished by means of the command \hidecallergraph.
 # The default value is: NO.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
@@ -2240,11 +2355,15 @@ GRAPHICAL_HIERARCHY    = YES
 DIRECTORY_GRAPH        = YES
 
 # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
-# generated by dot.
+# generated by dot. For an explanation of the image formats see the section
+# output formats in the documentation of the dot tool (Graphviz (see:
+# http://www.graphviz.org/)).
 # Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
 # to make the SVG files visible in IE 9+ (other browsers do not have this
 # requirement).
-# Possible values are: png, jpg, gif and svg.
+# Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
+# png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus and
+# png:gdiplus:gdiplus.
 # The default value is: png.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
@@ -2292,10 +2411,19 @@ DIAFILE_DIRS           =
 # PlantUML is not used or called during a preprocessing step. Doxygen will
 # generate a warning when it encounters a \startuml command in this case and
 # will not generate output for the diagram.
-# This tag requires that the tag HAVE_DOT is set to YES.
 
 PLANTUML_JAR_PATH      =
 
+# When using plantuml, the PLANTUML_CFG_FILE tag can be used to specify a
+# configuration file for plantuml.
+
+PLANTUML_CFG_FILE      =
+
+# When using plantuml, the specified paths are searched for files specified by
+# the !include statement in a plantuml block.
+
+PLANTUML_INCLUDE_PATH  =
+
 # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
 # that will be shown in the graph. If the number of nodes in a graph becomes
 # larger than this value, doxygen will truncate the graph, which is visualized
@@ -2332,7 +2460,7 @@ MAX_DOT_GRAPH_DEPTH    = 0
 
 DOT_TRANSPARENT        = NO
 
-# Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output
+# Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output
 # files in one run (i.e. multiple -o and -T options on the command line). This
 # makes dot run faster, but since only newer versions of dot (>1.8.10) support
 # this, this feature is disabled by default.
@@ -2349,7 +2477,7 @@ DOT_MULTI_TARGETS      = NO
 
 GENERATE_LEGEND        = YES
 
-# If the DOT_CLEANUP tag is set to YES doxygen will remove the intermediate dot
+# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot
 # files that are used to generate the various graphs.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 91a72b9cba..20f10f027d 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -36,7 +36,7 @@ Improvements
   [NVVM](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html).
 * Performance improvements in \ref af::reorder().
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1766)</sup>
-* Performance improvements in \ref array::scalar<T>().
+* Performance improvements in \ref af::array::scalar<T>().
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1809)</sup>
 * Improved unified backend performance.
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/1770)</sup>
@@ -45,7 +45,7 @@ Improvements
 * Can now specify the FFT plan cache size using the
   \ref af::setFFTPlanCacheSize() function.
 * Get the number of physical bytes allocated by the memory manager
-  \ref `af_get_allocated_bytes()`. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
+  \ref af_get_allocated_bytes(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1630)</sup>
 * \ref af::dot() can now return a scalar value to the
   host. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1628)</sup>
 
@@ -61,7 +61,7 @@ Bug Fixes
 * Fixed complex (`c32`,`c64`) multiplication in OpenCL convolution
   kernels. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1816)</sup>
 * Fixed inconsistent behavior with \ref af::replace() and \ref
-  replace_scalar(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
+  af_replace_scalar(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1773)</sup>
 * Fixed memory leak in \ref
   af_fir(). <sup>[1](https://github.com/arrayfire/arrayfire/pull/1765)</sup>
 * Fixed memory leaks in \ref af_cast for sparse arrays.
diff --git a/include/af/array.h b/include/af/array.h
index 07f6c5fe15..c4434ef1d6 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -75,7 +75,7 @@ namespace af
             array_proxy& operator OP(const long  &a);               \
             array_proxy& operator OP(const unsigned long &a);       \
             array_proxy& operator OP(const long long  &a);          \
-            array_proxy& operator OP(const unsigned long long &a);  \
+            array_proxy& operator OP(const unsigned long long &a);
 
             ASSIGN(=)
             ASSIGN(+=)
@@ -87,7 +87,7 @@ namespace af
 #if AF_API_VERSION >= 32
 #define ASSIGN(OP)                                                  \
             array_proxy& operator OP(const short &a);               \
-            array_proxy& operator OP(const unsigned short &a);      \
+            array_proxy& operator OP(const unsigned short &a);
 
             ASSIGN(=)
             ASSIGN(+=)
@@ -867,28 +867,29 @@ namespace af
         /// \ingroup method_mat
         array H() const;
 
-#define ASSIGN_(OP)                                                                     \
-        array& OP(const array &val);                                                    \
-        array& OP(const double &val);              /**< \copydoc OP (const array &) */  \
-        array& OP(const cdouble &val);             /**< \copydoc OP (const array &) */  \
-        array& OP(const cfloat &val);              /**< \copydoc OP (const array &) */  \
-        array& OP(const float &val);               /**< \copydoc OP (const array &) */  \
-        array& OP(const int &val);                 /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned &val);            /**< \copydoc OP (const array &) */  \
-        array& OP(const bool &val);                /**< \copydoc OP (const array &) */  \
-        array& OP(const char &val);                /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned char &val);       /**< \copydoc OP (const array &) */  \
-        array& OP(const long  &val);               /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned long &val);       /**< \copydoc OP (const array &) */  \
-        array& OP(const long long  &val);          /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned long long &val);  /**< \copydoc OP (const array &) */  \
+#define ASSIGN_(OP2)                                                                      \
+        array& OP2(const array &val);                                                     \
+        array& OP2(const double &val);              /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const cdouble &val);             /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const cfloat &val);              /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const float &val);               /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const int &val);                 /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const unsigned &val);            /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const bool &val);                /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const char &val);                /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const unsigned char &val);       /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const long  &val);               /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const unsigned long &val);       /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const long long  &val);          /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const unsigned long long &val);
 
-#if AF_API_VERSION >= 32
-#define ASSIGN(OP)                                                                      \
-        ASSIGN_(OP)                                                                     \
-        array& OP(const short  &val);              /**< \copydoc OP (const array &) */  \
-        array& OP(const unsigned short &val);      /**< \copydoc OP (const array &) */  \
 
+#if AF_API_VERSION >= 32
+#define ASSIGN(OP)                                                                        \
+        ASSIGN_(OP)                                                                       \
+          array& OP(const short  &val);              /**< \copydoc OP##(const array &) */ \
+          array& OP(const unsigned short &val);
+ 
 #else
 #define ASSIGN(OP) ASSIGN_(OP)
 #endif
@@ -1009,40 +1010,40 @@ namespace af
 
 #define BIN_OP_(OP)                                                                                                      \
     AFAPI array OP (const array& lhs, const array& rhs);                                                                 \
-    AFAPI array OP (const bool& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const int& lhs, const array& rhs);                  /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned& lhs, const array& rhs);             /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const char& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned char& lhs, const array& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const long& lhs, const array& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned long& lhs, const array& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const long long& lhs, const array& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const unsigned long long& lhs, const array& rhs);   /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const double& lhs, const array& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const float& lhs, const array& rhs);                /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const cfloat& lhs, const array& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const cdouble& lhs, const array& rhs);              /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const bool& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const int& rhs);                  /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned& rhs);             /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const char& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned char& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const long& rhs);                 /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned long& rhs);        /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const long long& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const unsigned long long& rhs);   /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const double& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const float& rhs);                /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const cfloat& rhs);               /**< \copydoc OP (const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const cdouble& rhs);              /**< \copydoc OP (const array&, const array&) */ \
+    AFAPI array OP (const bool& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const int& lhs, const array& rhs);                /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const unsigned& lhs, const array& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const char& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const unsigned char& lhs, const array& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const long& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const unsigned long& lhs, const array& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const long long& lhs, const array& rhs);          /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const unsigned long long& lhs, const array& rhs); /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const double& lhs, const array& rhs);             /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const float& lhs, const array& rhs);              /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const cfloat& lhs, const array& rhs);             /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const cdouble& lhs, const array& rhs);            /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const bool& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const int& rhs);                /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const char& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned char& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const long& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned long& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const long long& rhs);          /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const unsigned long long& rhs); /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const double& rhs);             /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const float& rhs);              /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const cfloat& rhs);             /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const cdouble& rhs);
 
 #if AF_API_VERSION >= 32
-#define BIN_OP(OP)                                                                                                       \
-        BIN_OP_(OP)                                                                                                      \
-        AFAPI array OP (const short& lhs, const array& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-        AFAPI array OP (const unsigned short& lhs, const array& rhs);   /**< \copydoc OP (const array&, const array&) */ \
-        AFAPI array OP (const array& lhs, const short& rhs);            /**< \copydoc OP (const array&, const array&) */ \
-        AFAPI array OP (const array& lhs, const unsigned short& rhs);   /**< \copydoc OP (const array&, const array&) */ \
+#define BIN_OP(OP)                                                                                                        \
+        BIN_OP_(OP)                                                                                                       \
+        AFAPI array OP (const short& lhs, const array& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
+        AFAPI array OP (const unsigned short& lhs, const array& rhs);  /**< \copydoc OP##(const array&, const array&) */ \
+        AFAPI array OP (const array& lhs, const short& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
+        AFAPI array OP (const array& lhs, const unsigned short& rhs);
 
 #else
 #define BIN_OP(OP) BIN_OP_(OP)
@@ -1135,7 +1136,6 @@ namespace af
     /// \param[in] rhs the right hand side value of the operand
     ///
     /// \returns    an array of type b8 with the <= operation performed on each element
-    ///             of \p lhs and \p rhs
     BIN_OP(operator<=)
     /// @}
 
@@ -1163,19 +1163,6 @@ namespace af
     BIN_OP(operator>=)
     /// @}
 
-    /// \ingroup arith_func_and
-    /// @{
-    /// \brief  Performs a logical AND operation on two arrays or an array and a
-    ///         value.
-    ///
-    /// \param[in] lhs the left hand side value of the operand
-    /// \param[in] rhs the right hand side value of the operand
-    ///
-    /// \returns    an array of type b8 with a logical AND operation performed on each
-    ///             element of \p lhs and \p rhs
-    BIN_OP(operator&&)
-    /// @}
-
     /// \ingroup arith_func_or
     /// @{
     /// \brief  Performs an logical OR operation on two arrays or an array and a
@@ -1201,19 +1188,6 @@ namespace af
     BIN_OP(operator% )
     /// @}
 
-    /// \ingroup arith_func_bitand
-    /// @{
-    /// \brief  Performs an bitwise AND operation on two arrays or an array and
-    ///         a value.
-    ///
-    /// \param[in] lhs the left hand side value of the operand
-    /// \param[in] rhs the right hand side value of the operand
-    ///
-    /// \returns    an array with a bitwise AND operation performed on each
-    ///             element of \p lhs and \p rhs
-    BIN_OP(operator& )
-    /// @}
-
     /// \ingroup arith_func_bitor
     /// @{
     /// \brief  Performs an bitwise OR operation on two arrays or an array and
@@ -1269,6 +1243,93 @@ namespace af
 #undef BIN_OP
 #undef BIN_OP_
 
+    /// \ingroup arith_func_bitand
+    /// @{
+    /// \brief  Performs an bitwise AND operation on two arrays or an array and
+    ///         a value.
+    ///
+    /// \param[in] lhs the left hand side value of the operand
+    /// \param[in] rhs the right hand side value of the operand
+    ///
+    /// \returns    an array with a bitwise AND operation performed on each
+    ///             element of \p lhs and \p rhs
+    AFAPI array operator&(const array& lhs, const array& rhs);
+    AFAPI array operator&(const array& lhs, const bool& rhs);
+    AFAPI array operator&(const array& lhs, const cdouble& rhs);
+    AFAPI array operator&(const array& lhs, const cfloat& rhs);
+    AFAPI array operator&(const array& lhs, const char& rhs);
+    AFAPI array operator&(const array& lhs, const double& rhs);
+    AFAPI array operator&(const array& lhs, const float& rhs);
+    AFAPI array operator&(const array& lhs, const int& rhs);
+    AFAPI array operator&(const array& lhs, const long long& rhs);
+    AFAPI array operator&(const array& lhs, const long& rhs);
+    AFAPI array operator&(const array& lhs, const short& rhs);
+    AFAPI array operator&(const array& lhs, const unsigned char& rhs);
+    AFAPI array operator&(const array& lhs, const unsigned long long& rhs);
+    AFAPI array operator&(const array& lhs, const unsigned long& rhs);
+    AFAPI array operator&(const array& lhs, const unsigned short& rhs);
+    AFAPI array operator&(const array& lhs, const unsigned& rhs);
+    AFAPI array operator&(const bool& lhs, const array& rhs);
+    AFAPI array operator&(const cdouble& lhs, const array& rhs);
+    AFAPI array operator&(const cfloat& lhs, const array& rhs);
+    AFAPI array operator&(const char& lhs, const array& rhs);
+    AFAPI array operator&(const double& lhs, const array& rhs);
+    AFAPI array operator&(const float& lhs, const array& rhs);
+    AFAPI array operator&(const int& lhs, const array& rhs);
+    AFAPI array operator&(const long long& lhs, const array& rhs);
+    AFAPI array operator&(const long& lhs, const array& rhs);
+    AFAPI array operator&(const short& lhs, const array& rhs);
+    AFAPI array operator&(const unsigned char& lhs, const array& rhs);
+    AFAPI array operator&(const unsigned long long& lhs, const array& rhs);
+    AFAPI array operator&(const unsigned long& lhs, const array& rhs);
+    AFAPI array operator&(const unsigned short& lhs, const array& rhs);
+    AFAPI array operator&(const unsigned& lhs, const array& rhs);
+    /// @}
+
+    /// \ingroup arith_func_and
+    /// @{
+    /// \brief  Performs a logical AND operation on two arrays or an array and a
+    ///         value.
+    ///
+    /// \param[in] lhs the left hand side value of the operand
+    /// \param[in] rhs the right hand side value of the operand
+    ///
+    /// \returns    an array of type b8 with a logical AND operation performed on each
+    ///             element of \p lhs and \p rhs
+    AFAPI array operator&&(const array& lhs, const array& rhs);
+    AFAPI array operator&&(const array& lhs, const bool& rhs);
+    AFAPI array operator&&(const array& lhs, const cdouble& rhs);
+    AFAPI array operator&&(const array& lhs, const cfloat& rhs);
+    AFAPI array operator&&(const array& lhs, const char& rhs);
+    AFAPI array operator&&(const array& lhs, const double& rhs);
+    AFAPI array operator&&(const array& lhs, const float& rhs);
+    AFAPI array operator&&(const array& lhs, const int& rhs);
+    AFAPI array operator&&(const array& lhs, const long long& rhs);
+    AFAPI array operator&&(const array& lhs, const long& rhs);
+    AFAPI array operator&&(const array& lhs, const short& rhs);
+    AFAPI array operator&&(const array& lhs, const unsigned char& rhs);
+    AFAPI array operator&&(const array& lhs, const unsigned long long& rhs);
+    AFAPI array operator&&(const array& lhs, const unsigned long& rhs);
+    AFAPI array operator&&(const array& lhs, const unsigned short& rhs);
+    AFAPI array operator&&(const array& lhs, const unsigned& rhs);
+    AFAPI array operator&&(const bool& lhs, const array& rhs);
+    AFAPI array operator&&(const cdouble& lhs, const array& rhs);
+    AFAPI array operator&&(const cfloat& lhs, const array& rhs);
+    AFAPI array operator&&(const char& lhs, const array& rhs);
+    AFAPI array operator&&(const double& lhs, const array& rhs);
+    AFAPI array operator&&(const float& lhs, const array& rhs);
+    AFAPI array operator&&(const int& lhs, const array& rhs);
+    AFAPI array operator&&(const long long& lhs, const array& rhs);
+    AFAPI array operator&&(const long& lhs, const array& rhs);
+    AFAPI array operator&&(const short& lhs, const array& rhs);
+    AFAPI array operator&&(const unsigned char& lhs, const array& rhs);
+    AFAPI array operator&&(const unsigned long long& lhs, const array& rhs);
+    AFAPI array operator&&(const unsigned long& lhs, const array& rhs);
+    AFAPI array operator&&(const unsigned short& lhs, const array& rhs);
+    AFAPI array operator&&(const unsigned& lhs, const array& rhs);
+    /// @}
+
+
     /// Evaluate an expression (nonblocking).
     /**
        \ingroup method_mat
diff --git a/include/af/cuda.h b/include/af/cuda.h
index 5b5e25bb65..dbf1480a80 100644
--- a/include/af/cuda.h
+++ b/include/af/cuda.h
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #include <af/defines.h>
 #include <af/exception.h>
 #include <cuda.h>

From e77a14b052db5083c7fbf3b3ca0922668946bd52 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 1 Mar 2018 02:29:18 -0500
Subject: [PATCH 1376/2677] Fix formatting warnings. Remove unused header

---
 docs/doxygen.mk                           |  6 +--
 include/af/array.h                        |  1 -
 src/api/unified/image.cpp                 |  3 +-
 src/backend/cuda/kernel/convolve.cu       | 64 +++++++++++------------
 src/backend/opencl/convolve_separable.cpp |  2 +-
 5 files changed, 38 insertions(+), 38 deletions(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 3555ceb884..5d4e0237d9 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1049,7 +1049,7 @@ VERBATIM_HEADERS       = YES
 # generated with the -Duse-libclang=ON option for CMake.
 # The default value is: NO.
 
-CLANG_ASSISTED_PARSING = NO
+#CLANG_ASSISTED_PARSING = NO
 
 # If clang assisted parsing is enabled you can provide the compiler with command
 # line options that you would normally use when invoking the compiler. Note that
@@ -1057,7 +1057,7 @@ CLANG_ASSISTED_PARSING = NO
 # specified with INPUT and INCLUDE_PATH.
 # This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
 
-CLANG_OPTIONS          = -Wno-pragma-once-outside-header
+#CLANG_OPTIONS          = -Wno-pragma-once-outside-header
 
 # If clang assisted parsing is enabled you can provide the clang parser with the
 # path to the compilation database (see:
@@ -1068,7 +1068,7 @@ CLANG_OPTIONS          = -Wno-pragma-once-outside-header
 # generated with the -Duse-libclang=ON option for CMake.
 # The default value is: 0.
 
-CLANG_COMPILATION_DATABASE_PATH  = ${ArrayFire_BINARY_DIR}
+#CLANG_COMPILATION_DATABASE_PATH  = ${ArrayFire_BINARY_DIR}
 
 #---------------------------------------------------------------------------
 # Configuration options related to the alphabetical class index
diff --git a/include/af/array.h b/include/af/array.h
index c4434ef1d6..c7ff468c63 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -15,7 +15,6 @@
 
 #ifdef __cplusplus
 #include <af/traits.hpp>
-#include <vector>
 namespace af
 {
 
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 8d45f3962e..259e1b4aed 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -7,9 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include "symbol_manager.hpp"
 #include <af/array.h>
 #include <af/image.h>
-#include "symbol_manager.hpp"
+#include <af/defines.h>
 
 af_err af_gradient(af_array *dx, af_array *dy, const af_array in)
 {
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index 2839d311c7..f4f294aa6a 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -347,37 +347,37 @@ void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f0, in
         case  4: conv2Helper<T, aT, expand,  4>(p, out, sig, f1); break;
         case  5: conv2Helper<T, aT, expand,  5>(p, out, sig, f1); break;
         default: {
-                     if (f0==f1) {
-                         switch(f1) {
-                             case  6: conv2Helper<T, aT, expand,  6,  6>(p, out, sig); break;
-                             case  7: conv2Helper<T, aT, expand,  7,  7>(p, out, sig); break;
-                             case  8: conv2Helper<T, aT, expand,  8,  8>(p, out, sig); break;
-                             case  9: conv2Helper<T, aT, expand,  9,  9>(p, out, sig); break;
-                             case 10: conv2Helper<T, aT, expand, 10, 10>(p, out, sig); break;
-                             case 11: conv2Helper<T, aT, expand, 11, 11>(p, out, sig); break;
-                             case 12: conv2Helper<T, aT, expand, 12, 12>(p, out, sig); break;
-                             case 13: conv2Helper<T, aT, expand, 13, 13>(p, out, sig); break;
-                             case 14: conv2Helper<T, aT, expand, 14, 14>(p, out, sig); break;
-                             case 15: conv2Helper<T, aT, expand, 15, 15>(p, out, sig); break;
-                             case 16: conv2Helper<T, aT, expand, 16, 16>(p, out, sig); break;
-                             case 17: conv2Helper<T, aT, expand, 17, 17>(p, out, sig); break;
-                             default:
-                                {
-                                    char errMessage[256];
-                                    snprintf(errMessage, sizeof(errMessage),
-                                            "\nCUDA 2D convolution doesn't support %dx%d kernel\n", f0, f1);
-                                    CUDA_NOT_SUPPORTED(errMessage);
-                                };
-                         }
-                     } else {
-                        {
-                            char errMessage[256];
-                            snprintf(errMessage, sizeof(errMessage),
-                                    "\nCUDA 2D convolution doesn't support rectangular kernels\n");
-                            CUDA_NOT_SUPPORTED(errMessage);
-                        };
-                     }
-                 } break;
+            if (f0==f1) {
+                switch(f1) {
+                    case  6: conv2Helper<T, aT, expand,  6,  6>(p, out, sig); break;
+                    case  7: conv2Helper<T, aT, expand,  7,  7>(p, out, sig); break;
+                    case  8: conv2Helper<T, aT, expand,  8,  8>(p, out, sig); break;
+                    case  9: conv2Helper<T, aT, expand,  9,  9>(p, out, sig); break;
+                    case 10: conv2Helper<T, aT, expand, 10, 10>(p, out, sig); break;
+                    case 11: conv2Helper<T, aT, expand, 11, 11>(p, out, sig); break;
+                    case 12: conv2Helper<T, aT, expand, 12, 12>(p, out, sig); break;
+                    case 13: conv2Helper<T, aT, expand, 13, 13>(p, out, sig); break;
+                    case 14: conv2Helper<T, aT, expand, 14, 14>(p, out, sig); break;
+                    case 15: conv2Helper<T, aT, expand, 15, 15>(p, out, sig); break;
+                    case 16: conv2Helper<T, aT, expand, 16, 16>(p, out, sig); break;
+                    case 17: conv2Helper<T, aT, expand, 17, 17>(p, out, sig); break;
+                    default:
+                      {
+                          char errMessage[256];
+                          snprintf(errMessage, sizeof(errMessage),
+                                  "\nCUDA 2D convolution doesn't support %dx%d kernel\n", f0, f1);
+                          CUDA_NOT_SUPPORTED(errMessage);
+                      };
+                }
+            } else {
+              {
+                  char errMessage[256];
+                  snprintf(errMessage, sizeof(errMessage),
+                          "\nCUDA 2D convolution doesn't support rectangular kernels\n");
+                  CUDA_NOT_SUPPORTED(errMessage);
+              };
+            }
+          } break;
     }
 }
 
@@ -497,7 +497,7 @@ void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, AF_BATCH_KIND
     if (!callKernel) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nCUDA N Dimensional Convolution doesn't support %dx%dx%d kernel\n",
+                 "\nCUDA N Dimensional Convolution doesn't support %lldx%lldx%lld kernel\n",
                  filt.dims[0], filt.dims[1], filt.dims[2]);
         CUDA_NOT_SUPPORTED(errMessage);
     }
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 32b0e6ce75..b6d68be213 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -29,7 +29,7 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
         // TODO call upon fft
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                "\nOpenCL Separable convolution doesn't support %d(coloumn) %d(row) filters\n",
+                "\nOpenCL Separable convolution doesn't support %lld(coloumn) %lld(row) filters\n",
                  cflen, rflen);
         OPENCL_NOT_SUPPORTED(errMessage);
     }

From ad7f661b75ae7c95de388a6f5e770a6cdb0ee8aa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 1 Mar 2018 03:06:41 -0500
Subject: [PATCH 1377/2677] Change the order of the enum values to change order
 for 0 and 1

Change the order so that the topk function values change when passing in the
default value of 0 to 1. Previously the behavior was the same for 0 and 1
which could cause confusion.
---
 include/af/defines.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index e4cc34382c..0ba1222cd0 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -476,9 +476,9 @@ typedef enum {
 } af_diffusion_eq;
 
 typedef enum {
-    AF_TOPK_MAX     = 1,  ///< Top k max values
-    AF_TOPK_MIN     = 2,  ///< Top k min values
-    AF_TOPK_DEFAULT = 0   ///< Default option
+    AF_TOPK_MIN     = 1,  ///< Top k min values
+    AF_TOPK_MAX     = 2,  ///< Top k max values
+    AF_TOPK_DEFAULT = 0   ///< Default option (max)
 } af_topk_function;
 #endif
 

From 8c34aa999b4b092e51c603360b7ed203d2453955 Mon Sep 17 00:00:00 2001
From: Ralf Stubner <ralf.stubner@r-institute.com>
Date: Thu, 8 Mar 2018 11:15:15 +0100
Subject: [PATCH 1378/2677] Use built-in erfc for cumulative normal
 distribution

fixes #2078
---
 examples/financial/black_scholes_options.cpp | 27 ++++----------------
 1 file changed, 5 insertions(+), 22 deletions(-)

diff --git a/examples/financial/black_scholes_options.cpp b/examples/financial/black_scholes_options.cpp
index 5d979fd6b9..f1af40aec5 100644
--- a/examples/financial/black_scholes_options.cpp
+++ b/examples/financial/black_scholes_options.cpp
@@ -16,30 +16,13 @@
 #include "input.h"
 using namespace af;
 
-// The following function is a modified version of http://www.johndcook.com/blog/cpp_phi/
-// The example above references Handbook of Mathematical Functions by Abramowitz and Stegun
-
+// Use the relationship between the cumulative normal distribution and the
+// (complementary) error function:
+// https://en.wikipedia.org/wiki/Error_function#Cumulative_distribution_function
 array cnd(array x)
 {
-    // constants
-    const float a1 =  0.254829592;
-    const float a2 = -0.284496736;
-    const float a3 =  1.421413741;
-    const float a4 = -1.453152027;
-    const float a5 =  1.061405429;
-    const float p  =  0.3275911;
-    const float sqrt2 = sqrt(2.0);
-
-    // Save the sign of x
-    array xSign = sign(x);
-
-    x = abs(x) / sqrt2;
-
-    // A&S formula 7.1.26
-    array t = 1.0f / (1.0f + p*x);
-    array y = 1.0f + 0.5f * (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);
-
-    return xSign * y + !xSign * (1 - y); // equivalent of (x >= 0) ? y : (1 - y);
+    const float sqrt05 = sqrt(0.5f);
+    return 0.5f * erfc(- x * sqrt05);
 }
 
 static void black_scholes(array& C, array& P,

From ad230a24c44d8c7526ed9e8138aa6437c5d3cc04 Mon Sep 17 00:00:00 2001
From: "Adrien F. Vincent" <vincent.adrien@gmail.com>
Date: Sun, 11 Mar 2018 22:40:08 -0700
Subject: [PATCH 1379/2677] Add cmaps missing in arrayfire but in Forge (#2082)

Add missing colormaps from Forge to ArrayFire
---
 include/af/defines.h | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 0ba1222cd0..c3b9296a9e 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -424,12 +424,16 @@ typedef enum {
 ////////////////////////////////////////////////////////////////////////////////
 typedef enum {
     AF_COLORMAP_DEFAULT = 0,    ///< Default grayscale map
-    AF_COLORMAP_SPECTRUM= 1,    ///< Spectrum map
-    AF_COLORMAP_COLORS  = 2,    ///< Colors
+    AF_COLORMAP_SPECTRUM= 1,    ///< Spectrum map (390nm-830nm, in sRGB colorspace)
+    AF_COLORMAP_COLORS  = 2,    ///< Colors, aka. Rainbow
     AF_COLORMAP_RED     = 3,    ///< Red hue map
     AF_COLORMAP_MOOD    = 4,    ///< Mood map
     AF_COLORMAP_HEAT    = 5,    ///< Heat map
-    AF_COLORMAP_BLUE    = 6     ///< Blue hue map
+    AF_COLORMAP_BLUE    = 6,    ///< Blue hue map
+    AF_COLORMAP_INFERNO = 7,    ///< Perceptually uniform shades of black-red-yellow
+    AF_COLORMAP_MAGMA   = 8,    ///< Perceptually uniform shades of black-red-white
+    AF_COLORMAP_PLASMA  = 9,    ///< Perceptually uniform shades of blue-red-yellow
+    AF_COLORMAP_VIRIDIS = 10    ///< Perceptually uniform shades of blue-green-yellow
 } af_colormap;
 
 #if AF_API_VERSION >= 32

From c3cad78553eb05c409c1a53700410227aac7f4a0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 15 Mar 2018 01:45:28 -0400
Subject: [PATCH 1380/2677] Fix topk order for the OpenCL backend when the
 device type is CPU

---
 src/backend/opencl/topk.cpp | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 3fdbe493f5..22ec69ea98 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -102,14 +102,23 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         for(int i = 0; i < iter; i++) {
             auto idx_itr = begin(idx) + i * in.strides()[1];
             auto kiptr = iptr + k * i;
-            // Sort the top k values in each column
-            partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
-                              kiptr , kiptr + k,
-                              [ptr](const uint lhs, const uint rhs) -> bool {
-                                  return ptr[lhs] < ptr[rhs];
-                              });
 
+            if(order == AF_TOPK_MIN) {
+                // Sort the top k values in each column
+                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
+                                  kiptr , kiptr + k,
+                                  [ptr](const uint lhs, const uint rhs) -> bool {
+                                      return ptr[lhs] < ptr[rhs];
+                                  });
+            } else {
+                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
+                                  kiptr , kiptr + k,
+                                  [ptr](const uint lhs, const uint rhs) -> bool {
+                                    return ptr[lhs] >= ptr[rhs];
+                                  });
+            }
             ev_val.wait();
+
             auto kvptr = vptr + k * i;
             for(int j = 0; j < k; j++) {
                 // Update the value arrays with the original values

From e9ea46c75df05883485b5297b472d5b78e92566f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 27 Mar 2018 23:00:34 +0530
Subject: [PATCH 1381/2677] Generate Installers using CPack (#2048)

* cmake modifations to generate installers using cpack

* Organise all prerequisites as cpack components

* cpack generators supported
  - Linux: STGZ; IFW
  - OSX: productbuild; IFW
  - Windows: NSIS64; IFW

* Forge component/dev-package is included in the installer packages only if
  requested explicitly while generating the installer. Note that this is not
  same as the forge dependency by arrayfire library

* Adds ISSL(Intel Simplified Software License) to LICENSES folder and installers

* Adds support use static glbinding if the project is cloned to `external/`
  folder under the project root

* Removes build_glbinding.cmake file as it is not used anymore

* Fixes ASSETS_DIR variable description & path issues

* Marks some MKL variables as advanced in cmake

* Adds additional PATH hints & SUFFIXES to FindMKL script

* Links with static freeimage lib when requested in CMake

* Fixes FindFreeImage cmake script to handle dll files

IFW based installers give uniform look and functionality across all platforms.
NSIS Installer can add AF_PATH environment variable and modify PATH variable.
However, IFW installer can't do those yet.

* Add cudart, libnvrtc-builtins and libtbb to OSX installers

* Rename the real file into install directory as `so` name of the lib
---
 .gitignore                                   |   1 +
 CMakeLists.txt                               |  56 +-
 CMakeModules/AFInstallDirs.cmake             |  27 +-
 CMakeModules/CPackConfig.cmake               | 315 ++++--
 CMakeModules/FindFreeImage.cmake             | 115 ++-
 CMakeModules/FindMKL.cmake                   |   7 +
 CMakeModules/InternalUtils.cmake             |  13 +-
 CMakeModules/build_forge.cmake               |  41 +-
 CMakeModules/build_glbinding.cmake           |  69 --
 CMakeModules/nsis/NSIS.InstallOptions.ini.in |  46 +
 CMakeModules/nsis/NSIS.definitions.nsh.in    |  35 +
 CMakeModules/nsis/NSIS.template.in           | 999 +++++++++++++++++++
 CMakeModules/osx_install/readme.html.in      |   6 +-
 LICENSE                                      |  11 +-
 LICENSES/BSD 3-Clause.txt                    |   2 +-
 LICENSES/ISSL License.txt                    |  29 +
 LICENSES/MIT License.txt                     |  22 +-
 LICENSES/zlib-libpng License.txt             |  23 +-
 assets                                       |   2 +-
 examples/CMakeLists.txt                      |  11 +-
 src/api/c/CMakeLists.txt                     |  12 +-
 src/backend/common/CMakeLists.txt            |  13 +-
 src/backend/cuda/CMakeLists.txt              |  55 +-
 src/backend/opencl/program.hpp               |   4 +
 24 files changed, 1636 insertions(+), 278 deletions(-)
 delete mode 100644 CMakeModules/build_glbinding.cmake
 create mode 100644 CMakeModules/nsis/NSIS.InstallOptions.ini.in
 create mode 100644 CMakeModules/nsis/NSIS.definitions.nsh.in
 create mode 100644 CMakeModules/nsis/NSIS.template.in
 create mode 100644 LICENSES/ISSL License.txt

diff --git a/.gitignore b/.gitignore
index d59d4c4aa3..e9254cf240 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ GPATH
 .dir-locals.el
 docs/details/examples.dox
 /TAGS
+external/
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 54770b1348..eb12eba46c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -17,6 +17,7 @@ include(InternalUtils)
 include(Version)
 include(build_cl2hpp)
 include(platform)
+include(GetPrerequisites)
 
 arrayfire_set_cmake_default_variables()
 
@@ -66,7 +67,7 @@ af_deprecate(BUILD_UNIFIED         AF_BUILD_UNIFIED)
 af_deprecate(BUILD_GRAPHICS        AF_WITH_GRAPHICS)
 af_deprecate(BUILD_DOCS            AF_BUILD_DOCS)
 af_deprecate(BUILD_NONFREE         AF_WITH_NONFREE)
-af_deprecate(BUILD_EXAMPLES        AF_WITH_EXAMPLES)
+af_deprecate(BUILD_EXAMPLES        AF_BUILD_EXAMPLES)
 af_deprecate(USE_RELATIVE_TEST_DIR AF_WITH_RELATIVE_TEST_DIR)
 af_deprecate(USE_FREEIMAGE_STATIC  AF_WITH_STATIC_FREEIMAGE)
 af_deprecate(USE_CPUID             AF_WITH_CPUID)
@@ -168,18 +169,13 @@ install(DIRECTORY include/ DESTINATION ${AF_INSTALL_INC_DIR}
 
 ## The ArrayFire version file is generated and won't be included above, install
 ## it separately.
-install(FILES
-    ${ArrayFire_BINARY_DIR}/include/af/version.h DESTINATION "${AF_INSTALL_INC_DIR}/af/"
-    COMPONENT headers
-)
+install(FILES ${ArrayFire_BINARY_DIR}/include/af/version.h
+        DESTINATION "${AF_INSTALL_INC_DIR}/af/"
+        COMPONENT headers)
 
 if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
     option(INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
-    install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/lib/"
-            DESTINATION "${AF_INSTALL_LIB_DIR}"
-            COMPONENT forge
-    )
-    if(${INSTALL_FORGE_DEV})
+    if(INSTALL_FORGE_DEV)
         install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/include/"
                 DESTINATION "${AF_INSTALL_INC_DIR}"
                 COMPONENT headers
@@ -188,7 +184,19 @@ if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
                 DESTINATION "${AF_INSTALL_DATA_DIR}/../Forge"
                 COMPONENT share
         )
+        install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/lib/"
+                DESTINATION "${AF_INSTALL_LIB_DIR}"
+                COMPONENT forge
+        )
     endif()
+    #install forge library & dependencies
+    set(fg_dlib_px "bin")
+    if (UNIX)
+      set(fg_dlib_px "lib")
+    endif ()
+    install(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/${fg_dlib_px}/"
+      DESTINATION "${AF_INSTALL_BIN_DIR}"
+      COMPONENT gfx_dependencies)
 endif()
 
 # install the examples irrespective of the AF_BUILD_EXAMPLES value
@@ -204,6 +212,10 @@ install(DIRECTORY assets/examples/ #NOTE The slash at the end is important
     DESTINATION ${AF_INSTALL_EXAMPLE_DIR}
     COMPONENT examples)
 
+install(DIRECTORY "${ArrayFire_SOURCE_DIR}/LICENSES/"
+    DESTINATION LICENSES
+    COMPONENT licenses)
+
 foreach(backend CPU CUDA OpenCL Unified)
   string(TOUPPER ${backend} upper_backend)
   if(AF_BUILD_${upper_backend})
@@ -241,23 +253,31 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               COMPONENT cmake)
 
 if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
-  install(FILES
-    $<TARGET_FILE:MKL::MKL>
-    $<TARGET_FILE:MKL::ThreadLayer>
-    ${MKL_RUNTIME_KERNEL_LIBRARIES}
-    DESTINATION ${AF_INSTALL_LIB_DIR})
-
   if(TARGET MKL::ThreadingLibrary)
     install(FILES
       $<TARGET_FILE:MKL::ThreadingLibrary>
-      DESTINATION ${AF_INSTALL_LIB_DIR})
+      DESTINATION ${AF_INSTALL_BIN_DIR}
+      COMPONENT mkl_dependencies)
   endif()
 
   if(NOT WIN32)
     install(FILES
       $<TARGET_FILE:MKL::Interface>
-        DESTINATION ${AF_INSTALL_LIB_DIR})
+      DESTINATION ${AF_INSTALL_BIN_DIR}
+      COMPONENT mkl_dependencies)
   endif()
+
+  install(FILES
+    $<TARGET_FILE:MKL::MKL>
+    $<TARGET_FILE:MKL::ThreadLayer>
+    ${MKL_RUNTIME_KERNEL_LIBRARIES}
+
+    # This variable is used to add tbb.so.2 library because the main lib
+    # is a linker script and not a symlink so it cant be resolved using
+    # get_filename_component
+    ${AF_ADDITIONAL_MKL_LIBRARIES}
+    DESTINATION ${AF_INSTALL_BIN_DIR}
+    COMPONENT mkl_dependencies)
 endif()
 
 # This file will be used to create the config file for the build directory.
diff --git a/CMakeModules/AFInstallDirs.cmake b/CMakeModules/AFInstallDirs.cmake
index d3e7267e3c..c2e4e4fec8 100644
--- a/CMakeModules/AFInstallDirs.cmake
+++ b/CMakeModules/AFInstallDirs.cmake
@@ -6,7 +6,7 @@
 
 # Executables
 if(NOT DEFINED AF_INSTALL_BIN_DIR)
-  set(AF_INSTALL_BIN_DIR "bin" CACHE PATH "Installation path for executables")
+  set(AF_INSTALL_BIN_DIR "lib" CACHE PATH "Installation path for executables")
 endif()
 
 # Libraries
@@ -19,28 +19,37 @@ if(NOT DEFINED AF_INSTALL_INC_DIR)
   set(AF_INSTALL_INC_DIR "include" CACHE PATH "Installation path for headers")
 endif()
 
-# Data files
-if(NOT DEFINED AF_INSTALL_DATA_DIR)
-  set(AF_INSTALL_DATA_DIR "share/ArrayFire" CACHE PATH "Installation path for data files")
-endif()
+set(DATA_DIR "share/ArrayFire")
 
 # Documentation
 if(NOT DEFINED AF_INSTALL_DOC_DIR)
-  set(AF_INSTALL_DOC_DIR "${AF_INSTALL_DATA_DIR}/doc" CACHE PATH "Installation path for documentation")
+  if (WIN32)
+    set(AF_INSTALL_DOC_DIR "doc" CACHE PATH "Installation path for documentation")
+  else ()
+      set(AF_INSTALL_DOC_DIR "${DATA_DIR}/doc" CACHE PATH "Installation path for documentation")
+  endif ()
 endif()
 
 if(NOT DEFINED AF_INSTALL_EXAMPLE_DIR)
-  set(AF_INSTALL_EXAMPLE_DIR "${AF_INSTALL_DATA_DIR}/examples" CACHE PATH "Installation path for examples")
+  if (WIN32)
+    set(AF_INSTALL_EXAMPLE_DIR "examples" CACHE PATH "Installation path for examples")
+  else ()
+    set(AF_INSTALL_EXAMPLE_DIR "${DATA_DIR}/examples" CACHE PATH "Installation path for examples")
+  endif ()
 endif()
 
 # Man pages
 if(NOT DEFINED AF_INSTALL_MAN_DIR)
-  set(AF_INSTALL_MAN_DIR "${AF_INSTALL_DATA_DIR}/man" CACHE PATH "Installation path for man pages")
+    set(AF_INSTALL_MAN_DIR "${DATA_DIR}/man" CACHE PATH "Installation path for man pages")
 endif()
 
 # CMake files
 if(NOT DEFINED AF_INSTALL_CMAKE_DIR)
-  set(AF_INSTALL_CMAKE_DIR "${AF_INSTALL_DATA_DIR}/cmake" CACHE PATH "Installation path for CMake files")
+  if (WIN32)
+    set(AF_INSTALL_CMAKE_DIR "cmake" CACHE PATH "Installation path for CMake files")
+  else ()
+    set(AF_INSTALL_CMAKE_DIR "${DATA_DIR}/cmake" CACHE PATH "Installation path for CMake files")
+  endif ()
 endif()
 
 mark_as_advanced(
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index f6554ecbe0..b785c4911e 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -7,31 +7,77 @@
 
 cmake_minimum_required(VERSION 3.5)
 
+set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PROJECT_SOURCE_DIR}/CMakeModules/nsis")
+
 include(Version)
+include(CPackIFW)
 
 set(CPACK_GENERATOR "STGZ;TGZ" CACHE STRINGS "STGZ;TGZ;DEB;RPM;productbuild")
 set_property(CACHE CPACK_GENERATOR PROPERTY STRINGS STGZ DEB RPM productbuild)
 mark_as_advanced(CPACK_GENERATOR)
 
+set(VENDOR_NAME "ArrayFire")
+set(LIBRARY_NAME ${PROJECT_NAME})
+string(TOLOWER "${LIBRARY_NAME}" APP_LOW_NAME)
+set(SITE_URL "www.arrayfire.com")
+
+# Long description of the package
+set(CPACK_PACKAGE_DESCRIPTION
+"ArrayFire is a high performance software library for parallel computing
+with an easy-to-use API. Its array based function set makes parallel
+programming simple.
+
+ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it
+platform independent and highly portable.
+
+A few lines of code in ArrayFire can replace dozens of lines of parallel
+computing code, saving you valuable time and lowering development costs.")
+
+# Short description of the package
+set(CPACK_PACKAGE_DESCRIPTION_SUMMARY
+  "A high performance library for parallel computing with an easy-to-use API.")
+
 # Common settings to all packaging tools
 set(CPACK_PREFIX_DIR ${CMAKE_INSTALL_PREFIX})
-set(CPACK_PACKAGE_NAME "arrayfire")
-set(CPACK_PACKAGE_VENDOR "ArrayFire")
+set(CPACK_PACKAGE_NAME "${LIBRARY_NAME}")
+set(CPACK_PACKAGE_VENDOR "${VENDOR_NAME}")
+set(CPACK_PACKAGE_INSTALL_REGISTRY_KEY ${LIBRARY_NAME})
 set(CPACK_PACKAGE_CONTACT "ArrayFire Development Group <technical@arrayfire.com>")
+set(MY_CPACK_PACKAGE_ICON "${CMAKE_SOURCE_DIR}/assets/${APP_LOW_NAME}.ico")
+
+file(TO_NATIVE_PATH "${CMAKE_SOURCE_DIR}/assets/" NATIVE_ASSETS_PATH)
+string(REPLACE "\\" "\\\\" NATIVE_ASSETS_PATH  ${NATIVE_ASSETS_PATH})
+set(CPACK_AF_ASSETS_DIR "${NATIVE_ASSETS_PATH}")
 
-set(CPACK_PACKAGE_VERSION ${ArrayFire_VERSION})
 set(CPACK_PACKAGE_VERSION_MAJOR "${ArrayFire_VERSION_MAJOR}")
 set(CPACK_PACKAGE_VERSION_MINOR "${ArrayFire_VERSION_MINOR}")
 set(CPACK_PACKAGE_VERSION_PATCH "${ArrayFire_VERSION_PATCH}")
+
+set(CPACK_PACKAGE_INSTALL_DIRECTORY "${LIBRARY_NAME}")
+
+set(inst_pkg_name ${APP_LOW_NAME})
+set(inst_pkg_hash "")
+if (WIN32)
+  set(inst_pkg_name ${CPACK_PACKAGE_NAME})
+  set(inst_pkg_hash "-${GIT_COMMIT_HASH}")
+endif ()
+
 if(AF_WITH_GRAPHICS)
-    set(CPACK_PACKAGE_FILE_NAME
-    ${CPACK_PACKAGE_NAME}-${CPACK_PACKAGE_VERSION})
+  set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}${inst_pkg_hash}")
 else()
-    set(CPACK_PACKAGE_FILE_NAME
-        ${CPACK_PACKAGE_NAME}-no-gl-${CPACK_PACKAGE_VERSION})
+  set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}-no-gl${inst_pkg_hash}")
 endif()
 
+# Platform specific settings for CPACK generators
+# - OSX specific
+#   - DragNDrop (OSX only)
+#   - PackageMaker (OSX only)
+#   - OSXX11 (OSX only)
+#   - Bundle (OSX only)
+# - Windows
+#   - NSIS64 Generator
 if(APPLE)
+  set(CPACK_PACKAGING_INSTALL_PREFIX "/opt/arrayfire")
   set(OSX_INSTALL_SOURCE ${PROJECT_SOURCE_DIR}/CMakeModules/osx_install)
   set(WELCOME_FILE       "${OSX_INSTALL_SOURCE}/welcome.html.in")
   set(WELCOME_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/welcome.html")
@@ -49,91 +95,208 @@ if(APPLE)
   set(CPACK_RESOURCE_FILE_LICENSE ${LICENSE_FILE_OUT})
   set(CPACK_RESOURCE_FILE_README ${README_FILE_OUT})
   set(CPACK_RESOURCE_FILE_WELCOME ${WELCOME_FILE_OUT})
+elseif(WIN32)
+  set(WIN_INSTALL_SOURCE ${PROJECT_SOURCE_DIR}/CMakeModules/nsis)
+
+  set(LICENSE_FILE       "${ArrayFire_SOURCE_DIR}/LICENSE")
+  set(LICENSE_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/license.txt")
+  configure_file(${LICENSE_FILE} ${LICENSE_FILE_OUT})
+  set(CPACK_RESOURCE_FILE_LICENSE ${LICENSE_FILE_OUT})
+
+  #NSIS SPECIFIC VARIABLES
+  set(CPACK_NSIS_ENABLE_UNINSTALL_BEFORE_INSTALL ON)
+  set(CPACK_NSIS_MODIFY_PATH ON)
+  set(CPACK_NSIS_DISPLAY_NAME "${LIBRARY_NAME}")
+  set(CPACK_NSIS_PACKAGE_NAME "${LIBRARY_NAME}")
+  set(CPACK_NSIS_HELP_LINK "${SITE_URL}")
+  set(CPACK_NSIS_URL_INFO_ABOUT "${SITE_URL}")
+  set(CPACK_NSIS_INSTALLED_ICON_NAME "${MY_CPACK_PACKAGE_ICON}")
+  if (CMAKE_CL_64)
+    set(CPACK_NSIS_INSTALL_ROOT "$PROGRAMFILES64")
+  else (CMAKE_CL_64)
+    set(CPACK_NSIS_INSTALL_ROOT "$PROGRAMFILES")
+  endif (CMAKE_CL_64)
 else()
   set(CPACK_RESOURCE_FILE_LICENSE "${ArrayFire_SOURCE_DIR}/LICENSE")
   set(CPACK_RESOURCE_FILE_README "${ArrayFire_SOURCE_DIR}/README.md")
 endif()
 
-# Long description of the package
-set(CPACK_PACKAGE_DESCRIPTION
-"ArrayFire is a high performance software library for parallel computing
-with an easy-to-use API. Its array based function set makes parallel
-programming simple.
+# Set the default components installed in the package
+get_cmake_property(CPACK_COMPONENTS_ALL COMPONENTS)
 
-ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it
-platform independent and highly portable.
+include(CPackComponent)
 
-A few lines of code in ArrayFire can replace dozens of lines of parallel
-computing code, saving you valuable time and lowering development costs.")
+cpack_add_install_type(Development DISPLAY_NAME "Development")
+cpack_add_install_type(Extra DISPLAY_NAME "Extra")
+cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
 
-# Short description of the package
-set(CPACK_PACKAGE_DESCRIPTION_SUMMARY "A high performance library for parallel computing with an easy-to-use API.")
+set(PACKAGE_MKL_DEPS OFF)
+set(PACKAGE_GFX_DEPS OFF)
 
-# Set the default components installed in the package
-set(CPACK_COMPONENTS_ALL cpu cuda opencl unified headers documentation cmake examples)
+if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
+  set(PACKAGE_MKL_DEPS ON)
+  cpack_add_component(mkl_dependencies
+    DISPLAY_NAME "Intel MKL Prerequisites"
+    DESCRIPTION "Intel MKL libraries required by CPU, OpenCL backends."
+    HIDDEN
+    INSTALL_TYPES Development Runtime)
+endif ()
 
-include(CPackComponent)
-cpack_add_component_group(libraries
-DISPLAY_NAME "Libraries"
-DESCRIPTION "ArrayFire libraries"
-EXPANDED BOLD_TITLE)
-
-cpack_add_component(cpu
-DISPLAY_NAME "CPU Backend"
-DESCRIPTION
-"ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
-GROUP libraries)
-
-cpack_add_component(cuda
-DISPLAY_NAME "CUDA Backend"
-DESCRIPTION
-"ArrayFire which targets the CUDA platform. This platform allows you to to take "
-"advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the "
-"corresponding CMake config files."
-GROUP libraries)
-
-cpack_add_component(opencl
-DISPLAY_NAME "OpenCL Backend"
-DESCRIPTION
-"ArrayFire which targets the OpenCL platform. This platform allows you to use the "
-"ArrayFire library which targets OpenCL devices. Also installs the corresponding "
-"CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the "
-"Intel CPU on OSX."
-GROUP libraries)
+if (Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
+  set(PACKAGE_GFX_DEPS ON)
+  cpack_add_component(gfx_dependencies
+    DISPLAY_NAME "Graphics prerequisites"
+    DESCRIPTION "Graphics library dependencies"
+    HIDDEN
+    INSTALL_TYPES Development Runtime)
+endif ()
 
-cpack_add_component(unified
-DISPLAY_NAME "Unified Backend"
-DESCRIPTION
-"This library will allow you to choose the platform(cpu, cuda, opencl) at "
-"runtime. Also installs the corresponding CMake config files. NOTE: This option "
-"requires the other platforms to work properly"
-#DEPENDS "cpu;cuda;opencl"
-GROUP libraries)
+cpack_add_component(cuda_dependencies
+  DISPLAY_NAME "CUDA Dependencies"
+  DESCRIPTION "CUDA Runtime and libraries required for the CUDA backend."
+  INSTALL_TYPES Development Runtime)
 
-cpack_add_component(documentation
-DISPLAY_NAME "Documentation"
-DESCRIPTION "Doxygen documentation"
-)
+if (PACKAGE_MKL_DEPS AND PACKAGE_GFX_DEPS)
+  cpack_add_component(cpu
+    DISPLAY_NAME "CPU Backend"
+    DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
+    DEPENDS mkl_dependencies gfx_dependencies
+    INSTALL_TYPES Development Runtime)
+  cpack_add_component(opencl
+    DISPLAY_NAME "OpenCL Backend"
+    DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
+    DEPENDS mkl_dependencies gfx_dependencies
+    INSTALL_TYPES Development Runtime)
+  cpack_add_component(cuda
+    DISPLAY_NAME "CUDA Backend"
+    DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
+    DEPENDS gfx_dependencies cuda_dependencies
+    INSTALL_TYPES Development Runtime)
+elseif (PACKAGE_MKL_DEPS)
+  cpack_add_component(cpu
+    DISPLAY_NAME "CPU Backend"
+    DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
+    DEPENDS mkl_dependencies
+    INSTALL_TYPES Development Runtime)
+  cpack_add_component(opencl
+    DISPLAY_NAME "OpenCL Backend"
+    DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
+    DEPENDS mkl_dependencies
+    INSTALL_TYPES Development Runtime)
+  cpack_add_component(cuda
+    DISPLAY_NAME "CUDA Backend"
+    DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
+    DEPENDS cuda_dependencies
+    INSTALL_TYPES Development Runtime)
+elseif (PACKAGE_GFX_DEPS)
+  cpack_add_component(cpu
+    DISPLAY_NAME "CPU Backend"
+    DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
+    DEPENDS gfx_dependencies
+    INSTALL_TYPES Development Runtime)
+  cpack_add_component(opencl
+    DISPLAY_NAME "OpenCL Backend"
+    DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
+    DEPENDS gfx_dependencies
+    INSTALL_TYPES Development Runtime)
+  cpack_add_component(cuda
+    DISPLAY_NAME "CUDA Backend"
+    DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
+    DEPENDS gfx_dependencies cuda_dependencies
+    INSTALL_TYPES Development Runtime)
+endif ()
 
+cpack_add_component(unified
+  DISPLAY_NAME "Unified Backend"
+  DESCRIPTION "This Backend allows you to choose the platform(cpu, cuda, opencl) at runtime. This option requires at least one of the three backends to be installed to work properly."
+  INSTALL_TYPES Development Runtime)
 cpack_add_component(headers
-DISPLAY_NAME "C/C++ Headers"
-DESCRIPTION "Headers for the ArrayFire Libraries."
-)
-
+  DISPLAY_NAME "C/C++ Headers"
+  DESCRIPTION "Headers for the ArrayFire Libraries."
+  INSTALL_TYPES Development)
 cpack_add_component(cmake
-DISPLAY_NAME "CMake Support"
-DESCRIPTION "Configuration files to use ArrayFire using CMake."
-)
-
+  DISPLAY_NAME "CMake Support"
+  DESCRIPTION "Configuration files to use ArrayFire using CMake."
+  INSTALL_TYPES Development)
+cpack_add_component(documentation
+  DISPLAY_NAME "Documentation"
+  DESCRIPTION "Doxygen documentation"
+  INSTALL_TYPES Extra)
 cpack_add_component(examples
-DISPLAY_NAME "ArrayFire Examples"
-DESCRIPTION "Various examples using ArrayFire."
+  DISPLAY_NAME "ArrayFire Examples"
+  DESCRIPTION "Various examples using ArrayFire."
+  INSTALL_TYPES Extra)
+cpack_add_component(licenses
+  DISPLAY_NAME "Licenses"
+  DESCRIPTION "License files for upstream libraries and ArrayFire."
+  REQUIRED)
+
+if (INSTALL_FORGE_DEV)
+  cpack_add_component(forge
+    DISPLAY_NAME "Forge"
+    DESCRIPTION "High Performance Visualization Library"
+    INSTALL_TYPES Extra)
+endif ()
+
+##
+# IFW CPACK generator
+# Uses Qt installer framework, cross platform installer generator.
+# Uniform installer GUI on all major desktop platforms: Windows, OSX & Linux.
+##
+set(CPACK_IFW_PACKAGE_TITLE "${CPACK_PACKAGE_NAME}")
+set(CPACK_IFW_PACKAGE_PUBLISHER "${CPACK_PACKAGE_VENDOR}")
+set(CPACK_IFW_PRODUCT_URL "${SITE_URL}")
+set(CPACK_IFW_PACKAGE_ICON "${MY_CPACK_PACKAGE_ICON}")
+set(CPACK_IFW_PACKAGE_WINDOW_ICON "${CMAKE_SOURCE_DIR}/assets/${APP_LOW_NAME}_icon.png")
+set(CPACK_IFW_PACKAGE_WIZARD_DEFAULT_WIDTH 640)
+set(CPACK_IFW_PACKAGE_WIZARD_DEFAULT_HEIGHT 480)
+if (WIN32)
+    set(CPACK_IFW_ADMIN_TARGET_DIRECTORY "@ApplicationsDirX64@/${CPACK_PACKAGE_INSTALL_DIRECTORY}")
+else ()
+    set(CPACK_IFW_ADMIN_TARGET_DIRECTORY "/opt/${CPACK_PACKAGE_INSTALL_DIRECTORY}")
+endif ()
+
+get_native_path(zlib_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/zlib-libpng License.txt")
+get_native_path(boost_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/Boost Software License.txt")
+get_native_path(mit_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/MIT License.txt")
+get_native_path(fimg_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/FreeImage Public License.txt")
+get_native_path(apache_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/Apache-2.0.txt")
+get_native_path(sift_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/OpenSIFT License.txt")
+get_native_path(bsd3_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/BSD 3-Clause.txt")
+get_native_path(issl_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/ISSL License.txt")
+
+if (PACKAGE_MKL_DEPS)
+  cpack_ifw_configure_component(mkl_dependencies)
+endif ()
+if (PACKAGE_GFX_DEPS)
+  cpack_ifw_configure_component(gfx_dependencies)
+endif ()
+cpack_ifw_configure_component(cuda_dependencies)
+cpack_ifw_configure_component(cpu)
+cpack_ifw_configure_component(cuda)
+cpack_ifw_configure_component(opencl)
+cpack_ifw_configure_component(unified)
+cpack_ifw_configure_component(headers)
+cpack_ifw_configure_component(cmake)
+cpack_ifw_configure_component(documentation)
+cpack_ifw_configure_component(examples)
+cpack_ifw_configure_component(licenses FORCED_INSTALLATION
+  LICENSES "GLFW" ${zlib_lic_path} "glbinding" ${mit_lic_path} "FreeImage" ${fimg_lic_path}
+  "Boost" ${boost_lic_path} "clBLAS, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
+  "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
 )
+if (INSTALL_FORGE_DEV)
+    cpack_ifw_configure_component(forge)
+endif ()
 
 ##
 # Debian package
 ##
-set(CPACK_DEBIAN_PACKAGE_ARCHITECTURE ${PROCESSOR_ARCHITECTURE})
+set(CPACK_DEBIAN_FILE_NAME DEB-DEFAULT)
+set(CPACK_DEB_COMPONENT_INSTALL ON)
+#set(CMAKE_INSTALL_RPATH /usr/lib;${ArrayFire_BUILD_DIR}/third_party/forge/lib)
+#set(CPACK_DEBIAN_PACKAGE_SHLIBDEPS ON)
+set(CPACK_DEBIAN_PACKAGE_HOMEPAGE http://www.arrayfire.com)
 
 ##
 # RPM package
@@ -147,7 +310,7 @@ set(CPACK_PACKAGE_GROUP "Development/Libraries")
 ##
 set(CPACK_SOURCE_GENERATOR "TGZ")
 set(CPACK_SOURCE_PACKAGE_FILE_NAME
-    ${CPACK_PACKAGE_NAME}_src_${CPACK_PACKAGE_VERSION}_${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR})
+    ${CPACK_PACKAGE_NAME}_src_${GIT_COMMIT_HASH}_${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR})
 set(CPACK_SOURCE_IGNORE_FILES
     "/build"
     "CMakeFiles"
@@ -165,5 +328,11 @@ set(CPACK_SOURCE_IGNORE_FILES
 # Ignore build directories that may be in the source tree
 file(GLOB_RECURSE CACHES "${CMAKE_SOURCE_DIR}/CMakeCache.txt")
 
-# Call to CPACK
+if (WIN32)
+  # Configure file with custom definitions for NSIS.
+  configure_file(
+    ${PROJECT_SOURCE_DIR}/CMakeModules/nsis/NSIS.definitions.nsh.in
+    ${CMAKE_CURRENT_BINARY_DIR}/NSIS.definitions.nsh)
+endif ()
+
 include(CPack)
diff --git a/CMakeModules/FindFreeImage.cmake b/CMakeModules/FindFreeImage.cmake
index 5ef0424b32..abf47023b7 100644
--- a/CMakeModules/FindFreeImage.cmake
+++ b/CMakeModules/FindFreeImage.cmake
@@ -1,12 +1,25 @@
-# FindFreeImage.cmake
-# Author: Umar Arshad <umar@arrayfire.com>
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+#
+# Targets defined by this script
+#   FreeImage::FreeImage
+#   FreeImage::FreeImage_STATIC
+#
+# Note:
+# 1. The static version target is only defined if the static lib is found
+# 2. Environment variable FreeImage_ROOT can be defined on Windows where
+#    FreeImage is just a zip file of header and library files.
 #
-# Finds the FreeImage libraries
 # Sets the following variables:
 #          FreeImage_FOUND
 #          FreeImage_INCLUDE_DIR
-#          FreeImage_DYNAMIC_LIBRARY
+#          FreeImage_LINK_LIBRARY
 #          FreeImage_STATIC_LIBRARY
+#          FreeImage_DLL_LIBRARY - Windows only
 #
 # Usage:
 # find_package(FreeImage)
@@ -24,65 +37,87 @@
 # NOTE: You do not need to include the FreeImage include directories since they
 # will be included as part of the target_link_libraries command
 
-find_path( FreeImage_INCLUDE_DIR
-    NAMES FreeImage.h
-    HINTS ${PROJECT_SOURCE_DIR}/extern/FreeImage
-    PATHS
+find_path(FreeImage_INCLUDE_DIR
+  NAMES FreeImage.h
+  PATHS
     /usr/include
     /usr/local/include
     /sw/include
     /opt/local/include
-    DOC "The directory where FreeImage.h resides")
+	${FreeImage_ROOT}
+  DOC "The directory where FreeImage.h resides")
 
-find_library( FreeImage_DYNAMIC_LIBRARY
-    NAMES FreeImage freeimage
-    HINTS ${PROJECT_SOURCE_DIR}/FreeImage
-    PATHS
+find_library(FreeImage_LINK_LIBRARY
+  NAMES FreeImage freeimage
+  PATHS
     /usr/lib64
     /usr/lib
     /usr/local/lib64
     /usr/local/lib
     /sw/lib
     /opt/local/lib
-    DOC "The FreeImage library")
+	${FreeImage_ROOT}
+  DOC "The FreeImage library")
 
-find_library( FreeImage_STATIC_LIBRARY
-    NAMES ${PX}FreeImageLIB${SX} ${PX}FreeImage${SX} ${PX}freeimage${SX}
-    HINTS ${PROJECT_SOURCE_DIR}/FreeImage
-    PATHS
+find_library(FreeImage_STATIC_LIBRARY
+  NAMES
+    ${CMAKE_STATIC_LIBRARY_PREFIX}FreeImageLIB${CMAKE_STATIC_LIBRARY_SUFFIX}
+    ${CMAKE_STATIC_LIBRARY_PREFIX}FreeImage${CMAKE_STATIC_LIBRARY_SUFFIX}
+    ${CMAKE_STATIC_LIBRARY_PREFIX}freeimage${CMAKE_STATIC_LIBRARY_SUFFIX}
+  PATHS
     /usr/lib64
     /usr/lib
     /usr/local/lib64
     /usr/local/lib
     /sw/lib
     /opt/local/lib
-    DOC "The FreeImage library")
+    ${FreeImage_ROOT}
+  DOC "The FreeImage static library")
+
+if (WIN32)
+  find_file(FreeImage_DLL_LIBRARY
+    NAMES
+      ${CMAKE_SHARED_LIBRARY_PREFIX}FreeImage${CMAKE_SHARED_LIBRARY_SUFFIX}
+      ${CMAKE_SHARED_LIBRARY_PREFIX}freeimage${CMAKE_SHARED_LIBRARY_SUFFIX}
+    PATHS
+      ${FreeImage_ROOT}
+    DOC "The FreeImage dll")
+	mark_as_advanced(FreeImage_DLL_LIBRARY)
+endif ()
 
 mark_as_advanced(
-    FreeImage_INCLUDE_DIR
-    FreeImage_DYNAMIC_LIBRARY
-    FreeImage_STATIC_LIBRARY
-    )
-include(FindPackageHandleStandardArgs)
+  FreeImage_INCLUDE_DIR
+  FreeImage_LINK_LIBRARY
+  FreeImage_STATIC_LIBRARY)
 
+include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(FreeImage
-  REQUIRED_VARS FreeImage_INCLUDE_DIR FreeImage_DYNAMIC_LIBRARY
-  )
-
-set(FREEIMAGE_LIBRARY ${FreeImage_DYNAMIC_LIBRARY})
+  REQUIRED_VARS FreeImage_INCLUDE_DIR FreeImage_LINK_LIBRARY)
 
-if (FreeImage_FOUND AND NOT TARGET FreeImage::FreeImage)
-  add_library(FreeImage::FreeImage UNKNOWN IMPORTED)
-  set_target_properties(FreeImage::FreeImage PROPERTIES
-    IMPORTED_LINK_INTERFACE_LANGUAGE "C"
-    IMPORTED_LOCATION "${FreeImage_DYNAMIC_LIBRARY}"
-    INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}")
+if(FreeImage_FOUND AND NOT TARGET FreeImage::FreeImage)
+  add_library(FreeImage::FreeImage SHARED IMPORTED)
+  if(WIN32)
+    set_target_properties(FreeImage::FreeImage
+	  PROPERTIES
+        IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+        INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
+        IMPORTED_LOCATION "${FreeImage_DLL_LIBRARY}"
+	    IMPORTED_IMPLIB "${FreeImage_LINK_LIBRARY}")
+  else(WIN32)
+    set_target_properties(FreeImage::FreeImage
+	  PROPERTIES
+        IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+        INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
+        IMPORTED_LOCATION "${FreeImage_LINK_LIBRARY}"
+	    IMPORTED_NO_SONAME FALSE)
+  endif(WIN32)
+endif()
 
-  if(FreeImage_STATIC_LIBRARY_FOUND)
-    add_library(FreeImage::FreeImage_STATIC UNKNOWN IMPORTED)
-    set_target_properties(FreeImage::FreeImage_STATIC PROPERTIES
+if(FreeImage_STATIC_LIBRARY AND NOT TARGET FreeImage::FreeImage_STATIC)
+  add_library(FreeImage::FreeImage_STATIC STATIC IMPORTED)
+  set_target_properties(FreeImage::FreeImage_STATIC
+    PROPERTIES
       IMPORTED_LINK_INTERFACE_LANGUAGE "C"
-      IMPORTED_LOCATION "${FreeImage_STATIC_LIBRARY}"
-      INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}")
-  endif(FreeImage_STATIC_LIBRARY_FOUND)
+      INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
+      IMPORTED_LOCATION "${FreeImage_STATIC_LIBRARY}")
 endif()
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index e493d8709c..14c5b2d6ce 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -39,16 +39,20 @@ find_path(MKL_INCLUDE_DIR
     /opt/intel
     /opt/intel/mkl
     $ENV{MKL_ROOT}
+    /opt/intel/compilers_and_libraries/linux/mkl
   PATH_SUFFIXES
     include
     IntelSWTools/compilers_and_libraries/windows/mkl/include
     )
+mark_as_advanced(MKL_INCLUDE_DIR)
 
 find_path(MKL_FFTW_INCLUDE_DIR
   NAMES
     fftw3_mkl.h
   HINTS
     ${MKL_INCLUDE_DIR}/fftw)
+mark_as_advanced(MKL_FFTW_INCLUDE_DIR)
+
 
 if(WIN32)
   if(${MSVC_VERSION} GREATER_EQUAL 1900)
@@ -96,6 +100,7 @@ function(find_mkl_library)
       /opt/intel/tbb/lib
       /opt/intel/lib
       $ENV{MKL_ROOT}/lib
+      /opt/intel/compilers_and_libraries/linux/mkl/lib
     PATH_SUFFIXES
       IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64
       IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
@@ -117,6 +122,7 @@ function(find_mkl_library)
         /opt/intel/tbb/lib
         /opt/intel/lib
         $ENV{MKL_ROOT}/lib
+        /opt/intel/compilers_and_libraries/linux/mkl/lib
       PATH_SUFFIXES
         ""
         intel64
@@ -186,6 +192,7 @@ foreach(lib ${MKL_KernelLibraries})
     list(APPEND MKL_RUNTIME_KERNEL_LIBRARIES $<TARGET_FILE:MKL::${lib}>)
   endif()
 endforeach()
+mark_as_advanced(MKL_RUNTIME_KERNEL_LIBRARIES)
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(MKL
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 9835473216..b9dc5888b5 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -52,6 +52,16 @@ function(af_deprecate var newvar)
   variable_watch(${var} __af_deprecate_var)
 endfunction()
 
+function(get_native_path out_path path)
+  file(TO_NATIVE_PATH ${path} native_path)
+  if (WIN32)
+    string(REPLACE "\\" "\\\\" native_path  ${native_path})
+    set(${out_path} ${native_path} PARENT_SCOPE)
+  else ()
+    set(${out_path} ${path} PARENT_SCOPE)
+  endif ()
+endfunction()
+
 macro(arrayfire_set_cmake_default_variables)
   set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR}/cmake;${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
@@ -120,7 +130,6 @@ macro(arrayfire_set_cmake_default_variables)
   endif()
 
   if(APPLE)
-    # TODO(umar) Remove rpath to third_party lib
-    set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR};${ArrayFire_BINARY_DIR}/third_party/forge/lib")
+    set(CMAKE_INSTALL_RPATH "/opt/arrayfire/lib")
   endif()
 endmacro()
diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 8cef7aa7c4..3dcd6fd5be 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -7,37 +7,42 @@
 
 include(ExternalProject)
 
-set(FORGE_VERSION 1.0.2-ft)
+set(FORGE_VERSION af3.6.0)
 set(prefix "${ArrayFire_BINARY_DIR}/third_party/forge")
+set(PX ${CMAKE_SHARED_LIBRARY_PREFIX})
+set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX})
 
 if(MSVC)
   set(disable_warning_flags "/wd4251")
-  set(forge_shared_lib "${ArrayFire_BINARY_DIR}/third_party/forge/lib/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_LINK_LIBRARY_SUFFIX}")
-else()
-  set(forge_shared_lib "${ArrayFire_BINARY_DIR}/third_party/forge/lib/${CMAKE_SHARED_LIBRARY_PREFIX}forge${CMAKE_SHARED_LIBRARY_SUFFIX}")
+  set(SX ${CMAKE_LINK_LIBRARY_SUFFIX})
 endif()
 
+set(forge_lib "${PROJECT_BINARY_DIR}/third_party/forge/lib/${PX}forge${SX}")
+
+# Create a list with an alternate separator e.g. pipe symbol
+string(REPLACE ";" "|" CMAKE_PREFIX_PATH_ALT_SEP "${CMAKE_PREFIX_PATH}")
+
 # FIXME Tag forge correctly during release
 ExternalProject_Add(
     forge-ext
     GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG v${FORGE_VERSION}
+    GIT_TAG ${FORGE_VERSION}
     PREFIX "${prefix}"
     UPDATE_COMMAND ""
-    BUILD_BYPRODUCTS ${forge_shared_lib}
+    BUILD_BYPRODUCTS ${forge_lib}
     CMAKE_GENERATOR "${CMAKE_GENERATOR}"
+	LIST_SEPARATOR | # Use the alternate list separator
     CMAKE_ARGS
-      -DBUILD_EXAMPLES:BOOL=OFF
-      -DBUILD_DOCUMENTATION:BOOL=OFF
+      -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH_ALT_SEP}"
+      -DBUILD_SHARED_LIBS:BOOL=ON
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_BUILD_TYPE:STRING=Release
       -DCMAKE_CXX_FLAGS:STRING=${disable_warning_flags}
-      -Dglbinding_DIR:STRING=${glbinding_DIR}
-      -DGLFW_ROOT_DIR:STRING=${GLFW_ROOT_DIR}
-      -DBOOST_INCLUDEDIR:PATH=${Boost_INCLUDE_DIRS}
-      -Dglbinding_DIR:PATH=${glbinding_DIR}
-      -DUSE_SYSTEM_GLBINDING:BOOL=TRUE
-      -DUSE_FREEIMAGE:BOOL=OFF
+      -DFG_BUILD_EXAMPLES:BOOL=OFF
+      -DFG_BUILD_DOCS:BOOL=OFF
+      $<$<BOOL:${UNIX}>:-DFG_ENABLE_HUNTER:BOOL=ON>
+      -DFG_WITH_FREEIMAGE:BOOL=OFF
+      -DCMAKE_SHARED_LINKER_FLAGS:STRING=${CMAKE_SHARED_LINKER_FLAGS}
     )
 
 # NOTE: This approach doesn't work because the ExternalProject_Add outputs are
@@ -45,18 +50,18 @@ ExternalProject_Add(
 #
 # make_directory("${prefix}/include")
 # make_directory("${ArrayFire_BINARY_DIR}/third_party/forge/lib")
-# execute_process(COMMAND ${CMAKE_COMMAND} -E touch "${forge_shared_lib}")
+# execute_process(COMMAND ${CMAKE_COMMAND} -E touch "${forge_lib}")
 
 # add_library(Forge::Forge SHARED IMPORTED GLOBAL)
 # set_target_properties(Forge::Forge PROPERTIES
-#   INTERFACE_LINK_LIBRARIES "${forge_shared_lib}"
+#   INTERFACE_LINK_LIBRARIES "${forge_lib}"
 #   INTERFACE_INCLUDE_DIRECTORIES "${prefix}/include"
 #   )
 #
 # add_dependencies(Forge::Forge forge-ext)
 
 set(Forge_INCLUDE_DIR "${prefix}/include")
-set(Forge_LIBRARIES "${forge_shared_lib}")
+set(Forge_LIBRARIES "${forge_lib}")
 
 find_package_handle_standard_args(Forge DEFAULT_MSG
     Forge_INCLUDE_DIR Forge_LIBRARIES)
diff --git a/CMakeModules/build_glbinding.cmake b/CMakeModules/build_glbinding.cmake
deleted file mode 100644
index 946c109d2b..0000000000
--- a/CMakeModules/build_glbinding.cmake
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) 2017, ArrayFire
-# All rights reserved.
-#
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
-
-INCLUDE(ExternalProject)
-
-SET(prefix ${PROJECT_BINARY_DIR}/third_party/glb)
-
-SET(LIB_POSTFIX "")
-IF (CMAKE_BUILD_TYPE MATCHES Debug)
-    SET(LIB_POSTFIX "d")
-ENDIF()
-
-SET(glbinding_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}glbinding${LIB_POSTFIX}${CMAKE_STATIC_LIBRARY_SUFFIX})
-
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(CMAKE_GENERATOR MATCHES "Ninja")
-        MESSAGE(WARNING "Building with Ninja has known issues with CMake older than 3.2")
-    endif()
-    SET(byproducts)
-ELSE()
-    SET(byproducts BUILD_BYPRODUCTS ${glbinding_location})
-ENDIF()
-
-IF(UNIX)
-    SET(CXXFLAGS "${CMAKE_CXX_FLAGS} -w -fPIC")
-    SET(CFLAGS "${CMAKE_C_FLAGS} -w -fPIC")
-ENDIF(UNIX)
-
-ExternalProject_Add(
-    glb-ext
-    GIT_REPOSITORY https://github.com/cginternals/glbinding.git
-    GIT_TAG v2.1.1
-    UPDATE_COMMAND ""
-    PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}"
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} -Wno-dev "-G${CMAKE_GENERATOR}" <SOURCE_DIR>
-    -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-    -DCMAKE_CXX_FLAGS:STRING=${CXXFLAGS}
-    -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-    -DCMAKE_C_FLAGS:STRING=${CFLAGS}
-    -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
-    -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-    -DBUILD_SHARED_LIBS:BOOL=OFF
-    -DOPTION_BUILD_TESTS:BOOL=OFF
-    # Leave these GLFW_LIBRARY Options as empty.
-    # They are only required for GLBINDING Executables.
-    # Leaving them empty will disable compilation of said executables.
-    -DGLFW_LIBRARY_RELEASE:PATH=
-    -DGLFW_LIBRARY_DEBUG:PATH=
-    ${byproducts}
-    )
-
-ADD_LIBRARY(glbinding IMPORTED STATIC)
-
-ExternalProject_Get_Property(glb-ext install_dir)
-
-SET_TARGET_PROPERTIES(glbinding PROPERTIES IMPORTED_LOCATION ${glbinding_location})
-
-ADD_DEPENDENCIES(glbinding glb-ext)
-
-SET(GLBINDING_INCLUDE_DIRS ${install_dir}/include CACHE INTERNAL "" FORCE)
-SET(GLBINDING_LIBRARIES ${glbinding_location} CACHE INTERNAL "" FORCE)
-# Use glbinding_DIR as is and don't change the case
-SET(glbinding_DIR ${install_dir} CACHE INTERNAL "" FORCE)
-SET(GLBINDING_FOUND ON CACHE INTERNAL "" FORCE)
diff --git a/CMakeModules/nsis/NSIS.InstallOptions.ini.in b/CMakeModules/nsis/NSIS.InstallOptions.ini.in
new file mode 100644
index 0000000000..d92d77959c
--- /dev/null
+++ b/CMakeModules/nsis/NSIS.InstallOptions.ini.in
@@ -0,0 +1,46 @@
+[Settings]
+NumFields=5
+
+[Field 1]
+Type=label
+Text=By default @CPACK_PACKAGE_INSTALL_DIRECTORY@ does not add its directory to the system PATH.
+Left=0
+Right=-1
+Top=0
+Bottom=20
+
+[Field 2]
+Type=radiobutton
+Text=Do not add @CPACK_PACKAGE_NAME@ to the system PATH
+Left=0
+Right=-1
+Top=30
+Bottom=40
+State=1
+
+[Field 3]
+Type=radiobutton
+Text=Add @CPACK_PACKAGE_NAME@ to the system PATH for all users
+Left=0
+Right=-1
+Top=40
+Bottom=50
+State=0
+
+[Field 4]
+Type=radiobutton
+Text=Add @CPACK_PACKAGE_NAME@ to the system PATH for current user
+Left=0
+Right=-1
+Top=50
+Bottom=60
+State=0
+
+[Field 5]
+Type=CheckBox
+Text=Create @CPACK_PACKAGE_NAME@ Desktop Icon
+Left=0
+Right=-1
+Top=80
+Bottom=90
+State=0
diff --git a/CMakeModules/nsis/NSIS.definitions.nsh.in b/CMakeModules/nsis/NSIS.definitions.nsh.in
new file mode 100644
index 0000000000..1a3f92a5e4
--- /dev/null
+++ b/CMakeModules/nsis/NSIS.definitions.nsh.in
@@ -0,0 +1,35 @@
+!define MUI_WELCOMEPAGE_TITLE '${CPACK_PACKAGE_NAME} ${CPACK_PACKAGE_VERSION} Installer'
+!define MUI_WELCOMEPAGE_TITLE_3LINES
+!define MUI_WELCOMEPAGE_TEXT    \
+"ArrayFire is a high performance software library for parallel computing with an easy-to-use API.\r\n\r\n\
+Its array based function set makes parallel programming simple.\r\n\r\n\
+ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable.\r\n\r\n\
+A few lines of code in ArrayFire can replace dozens of lines of parallel compute code,\
+saving you valuable time and lowering development costs.\r\n\r\n\
+Follow these steps to install the ArrayFire libraries."
+
+!define MUI_ICON "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@.ico"
+!define MUI_UNICON "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@.ico"
+
+!define MUI_WELCOMEFINISHPAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_sym.bmp"
+!define MUI_UNWELCOMEFINISHPAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_sym.bmp"
+!define MUI_WELCOMEFINISHPAGE_UNBITMAP_NOSTRETCH
+!define MUI_UNWELCOMEFINISHPAGE_BITMAP_NOSTRETCH
+
+!define MUI_HEADERIMAGE
+!define MUI_HEADERIMAGE_RIGHT
+!define MUI_HEADERIMAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_logo.bmp"
+!define MUI_HEADERIMAGE_UNBITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_logo.bmp"
+!define MUI_HEADERIMAGE_BITMAP_NOSTRETCH
+!define MUI_HEADERIMAGE_UNBITMAP_NOSTRETCH
+!define MUI_ABORTWARNING
+
+
+; Defines for Finish Page
+!define MUI_FINISHPAGE_RUN "explorer.exe"
+!define MUI_FINISHPAGE_RUN_PARAMETERS "$INSTDIR"
+!define MUI_FINISHPAGE_RUN_TEXT "Open ArrayFire Install Directory to see Examples"
+!define MUI_FINISHPAGE_SHOWREADME "http://arrayfire.com/docs/using_on_windows.htm"
+!define MUI_FINISHPAGE_SHOWREADME_TEXT "Open ArrayFire Documentation on the Web"
+!define MUI_FINISHPAGE_LINK "ArrayFire Support and Services"
+!define MUI_FINISHPAGE_LINK_LOCATION "http://arrayfire.com/consulting/"
diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
new file mode 100644
index 0000000000..2b07ec7aa5
--- /dev/null
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -0,0 +1,999 @@
+; CPack install script designed for a nmake build
+
+;--------------------------------
+; You must define these values
+
+  !define VERSION "@CPACK_PACKAGE_VERSION@"
+  !define PATCH  "@CPACK_PACKAGE_VERSION_PATCH@"
+  !define INST_DIR "@CPACK_TEMPORARY_DIRECTORY@"
+
+;--------------------------------
+;Variables
+
+  Var MUI_TEMP
+  Var STARTMENU_FOLDER
+  Var SV_ALLUSERS
+  Var START_MENU
+  Var DO_NOT_ADD_TO_PATH
+  Var ADD_TO_PATH_ALL_USERS
+  Var ADD_TO_PATH_CURRENT_USER
+  Var INSTALL_DESKTOP
+  Var IS_DEFAULT_INSTALLDIR
+;--------------------------------
+;Include Modern UI
+
+  !include "..\..\..\NSIS.definitions.nsh"
+  !include "InstallOptions.nsh"
+  !include "MUI.nsh"
+
+  ;Default installation folder
+  InstallDir "@CPACK_NSIS_INSTALL_ROOT@\@CPACK_PACKAGE_INSTALL_DIRECTORY@\v@CPACK_PACKAGE_VERSION_MAJOR@"
+
+  !define env_af_hklm 'HKLM "SYSTEM\CurrentControlSet\Control\Session Manager\Environment"'
+
+;--------------------------------
+;General
+
+  ;Name and file
+  Name "@CPACK_NSIS_PACKAGE_NAME@"
+  OutFile "@CPACK_TOPLEVEL_DIRECTORY@/@CPACK_OUTPUT_FILE_NAME@"
+
+  ;Set compression
+  SetCompressor @CPACK_NSIS_COMPRESSOR@
+
+  ;Require administrator access
+  RequestExecutionLevel admin
+
+@CPACK_NSIS_DEFINES@
+
+  !include Sections.nsh
+
+;--- Component support macros: ---
+; The code for the add/remove functionality is from:
+;   http://nsis.sourceforge.net/Add/Remove_Functionality
+; It has been modified slightly and extended to provide
+; inter-component dependencies.
+Var AR_SecFlags
+Var AR_RegFlags
+@CPACK_NSIS_SECTION_SELECTED_VARS@
+
+; Loads the "selected" flag for the section named SecName into the
+; variable VarName.
+!macro LoadSectionSelectedIntoVar SecName VarName
+ SectionGetFlags ${${SecName}} $${VarName}
+ IntOp $${VarName} $${VarName} & ${SF_SELECTED}  ;Turn off all other bits
+!macroend
+
+; Loads the value of a variable... can we get around this?
+!macro LoadVar VarName
+  IntOp $R0 0 + $${VarName}
+!macroend
+
+; Sets the value of a variable
+!macro StoreVar VarName IntValue
+  IntOp $${VarName} 0 + ${IntValue}
+!macroend
+
+!macro InitSection SecName
+  ;  This macro reads component installed flag from the registry and
+  ;changes checked state of the section on the components page.
+  ;Input: section index constant name specified in Section command.
+
+  ClearErrors
+  ;Reading component status from registry
+  ReadRegDWORD $AR_RegFlags HKLM "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@\Components\${SecName}" "Installed"
+  IfErrors "default_${SecName}"
+    ;Status will stay default if registry value not found
+    ;(component was never installed)
+  IntOp $AR_RegFlags $AR_RegFlags & ${SF_SELECTED} ;Turn off all other bits
+  SectionGetFlags ${${SecName}} $AR_SecFlags  ;Reading default section flags
+  IntOp $AR_SecFlags $AR_SecFlags & 0xFFFE  ;Turn lowest (enabled) bit off
+  IntOp $AR_SecFlags $AR_RegFlags | $AR_SecFlags      ;Change lowest bit
+
+  ; Note whether this component was installed before
+  !insertmacro StoreVar ${SecName}_was_installed $AR_RegFlags
+  IntOp $R0 $AR_RegFlags & $AR_RegFlags
+
+  ;Writing modified flags
+  SectionSetFlags ${${SecName}} $AR_SecFlags
+
+ "default_${SecName}:"
+ !insertmacro LoadSectionSelectedIntoVar ${SecName} ${SecName}_selected
+!macroend
+
+!macro FinishSection SecName
+  ;  This macro reads section flag set by user and removes the section
+  ;if it is not selected.
+  ;Then it writes component installed flag to registry
+  ;Input: section index constant name specified in Section command.
+
+  SectionGetFlags ${${SecName}} $AR_SecFlags  ;Reading section flags
+  ;Checking lowest bit:
+  IntOp $AR_SecFlags $AR_SecFlags & ${SF_SELECTED}
+  IntCmp $AR_SecFlags 1 "leave_${SecName}"
+    ;Section is not selected:
+    ;Calling Section uninstall macro and writing zero installed flag
+    !insertmacro "Remove_${${SecName}}"
+    WriteRegDWORD HKLM "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@\Components\${SecName}" \
+  "Installed" 0
+    Goto "exit_${SecName}"
+
+ "leave_${SecName}:"
+    ;Section is selected:
+    WriteRegDWORD HKLM "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@\Components\${SecName}" \
+  "Installed" 1
+
+ "exit_${SecName}:"
+!macroend
+
+!macro RemoveSection_CPack SecName
+  ;  This macro is used to call section's Remove_... macro
+  ;from the uninstaller.
+  ;Input: section index constant name specified in Section command.
+
+  !insertmacro "Remove_${${SecName}}"
+!macroend
+
+; Determine whether the selection of SecName changed
+!macro MaybeSelectionChanged SecName
+  !insertmacro LoadVar ${SecName}_selected
+  SectionGetFlags ${${SecName}} $R1
+  IntOp $R1 $R1 & ${SF_SELECTED} ;Turn off all other bits
+
+  ; See if the status has changed:
+  IntCmp $R0 $R1 "${SecName}_unchanged"
+  !insertmacro LoadSectionSelectedIntoVar ${SecName} ${SecName}_selected
+
+  IntCmp $R1 ${SF_SELECTED} "${SecName}_was_selected"
+  !insertmacro "Deselect_required_by_${SecName}"
+  goto "${SecName}_unchanged"
+
+  "${SecName}_was_selected:"
+  !insertmacro "Select_${SecName}_depends"
+
+  "${SecName}_unchanged:"
+!macroend
+;--- End of Add/Remove macros ---
+
+;--------------------------------
+;Interface Settings
+
+  ;Below two are defined in custom nsh file
+  ;!define MUI_HEADERIMAGE
+  ;!define MUI_ABORTWARNING
+
+;----------------------------------------
+; based upon a script of "Written by KiCHiK 2003-01-18 05:57:02"
+;----------------------------------------
+!verbose 3
+!include "WinMessages.NSH"
+!verbose 4
+;====================================================
+; get_NT_environment
+;     Returns: the selected environment
+;     Output : head of the stack
+;====================================================
+!macro select_NT_profile UN
+Function ${UN}select_NT_profile
+   StrCmp $ADD_TO_PATH_ALL_USERS "1" 0 environment_single
+      DetailPrint "Selected environment for all users"
+      Push "all"
+      Return
+   environment_single:
+      DetailPrint "Selected environment for current user only."
+      Push "current"
+      Return
+FunctionEnd
+!macroend
+!insertmacro select_NT_profile ""
+!insertmacro select_NT_profile "un."
+;----------------------------------------------------
+!define NT_current_env 'HKCU "Environment"'
+!define NT_all_env     'HKLM "SYSTEM\CurrentControlSet\Control\Session Manager\Environment"'
+
+!ifndef WriteEnvStr_RegKey
+  !ifdef ALL_USERS
+    !define WriteEnvStr_RegKey \
+       'HKLM "SYSTEM\CurrentControlSet\Control\Session Manager\Environment"'
+  !else
+    !define WriteEnvStr_RegKey 'HKCU "Environment"'
+  !endif
+!endif
+
+; AddToPath - Adds the given dir to the search path.
+;        Input - head of the stack
+;        Note - Win9x systems requires reboot
+
+Function AddToPath
+  Exch $0
+  Push $1
+  Push $2
+  Push $3
+
+  # don't add if the path doesn't exist
+  IfFileExists "$0\*.*" "" AddToPath_done
+
+  ReadEnvStr $1 PATH
+  ; if the path is too long for a NSIS variable NSIS will return a 0
+  ; length string.  If we find that, then warn and skip any path
+  ; modification as it will trash the existing path.
+  StrLen $2 $1
+  IntCmp $2 0 CheckPathLength_ShowPathWarning CheckPathLength_Done CheckPathLength_Done
+    CheckPathLength_ShowPathWarning:
+    Messagebox MB_OK|MB_ICONEXCLAMATION "Warning! PATH too long installer unable to modify PATH!"
+    Goto AddToPath_done
+  CheckPathLength_Done:
+  Push "$1;"
+  Push "$0;"
+  Call StrStr
+  Pop $2
+  StrCmp $2 "" "" AddToPath_done
+  Push "$1;"
+  Push "$0\;"
+  Call StrStr
+  Pop $2
+  StrCmp $2 "" "" AddToPath_done
+  GetFullPathName /SHORT $3 $0
+  Push "$1;"
+  Push "$3;"
+  Call StrStr
+  Pop $2
+  StrCmp $2 "" "" AddToPath_done
+  Push "$1;"
+  Push "$3\;"
+  Call StrStr
+  Pop $2
+  StrCmp $2 "" "" AddToPath_done
+
+  Call IsNT
+  Pop $1
+  StrCmp $1 1 AddToPath_NT
+    ; Not on NT
+    StrCpy $1 $WINDIR 2
+    FileOpen $1 "$1\autoexec.bat" a
+    FileSeek $1 -1 END
+    FileReadByte $1 $2
+    IntCmp $2 26 0 +2 +2 # DOS EOF
+      FileSeek $1 -1 END # write over EOF
+    FileWrite $1 "$\r$\nSET PATH=%PATH%;$3$\r$\n"
+    FileClose $1
+    SetRebootFlag true
+    Goto AddToPath_done
+
+  AddToPath_NT:
+    StrCmp $ADD_TO_PATH_ALL_USERS "1" ReadAllKey
+      ReadRegStr $1 ${NT_current_env} "PATH"
+      Goto DoTrim
+    ReadAllKey:
+      ReadRegStr $1 ${NT_all_env} "PATH"
+    DoTrim:
+    StrCmp $1 "" AddToPath_NTdoIt
+      Push $1
+      Call Trim
+      Pop $1
+      StrCpy $0 "$1;$0"
+    AddToPath_NTdoIt:
+      StrCmp $ADD_TO_PATH_ALL_USERS "1" WriteAllKey
+        WriteRegExpandStr ${NT_current_env} "PATH" $0
+        Goto DoSend
+      WriteAllKey:
+        WriteRegExpandStr ${NT_all_env} "PATH" $0
+      DoSend:
+      SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
+
+  AddToPath_done:
+    Pop $3
+    Pop $2
+    Pop $1
+    Pop $0
+FunctionEnd
+
+
+; RemoveFromPath - Remove a given dir from the path
+;     Input: head of the stack
+
+Function un.RemoveFromPath
+  Exch $0
+  Push $1
+  Push $2
+  Push $3
+  Push $4
+  Push $5
+  Push $6
+
+  IntFmt $6 "%c" 26 # DOS EOF
+
+  Call un.IsNT
+  Pop $1
+  StrCmp $1 1 unRemoveFromPath_NT
+    ; Not on NT
+    StrCpy $1 $WINDIR 2
+    FileOpen $1 "$1\autoexec.bat" r
+    GetTempFileName $4
+    FileOpen $2 $4 w
+    GetFullPathName /SHORT $0 $0
+    StrCpy $0 "SET PATH=%PATH%;$0"
+    Goto unRemoveFromPath_dosLoop
+
+    unRemoveFromPath_dosLoop:
+      FileRead $1 $3
+      StrCpy $5 $3 1 -1 # read last char
+      StrCmp $5 $6 0 +2 # if DOS EOF
+        StrCpy $3 $3 -1 # remove DOS EOF so we can compare
+      StrCmp $3 "$0$\r$\n" unRemoveFromPath_dosLoopRemoveLine
+      StrCmp $3 "$0$\n" unRemoveFromPath_dosLoopRemoveLine
+      StrCmp $3 "$0" unRemoveFromPath_dosLoopRemoveLine
+      StrCmp $3 "" unRemoveFromPath_dosLoopEnd
+      FileWrite $2 $3
+      Goto unRemoveFromPath_dosLoop
+      unRemoveFromPath_dosLoopRemoveLine:
+        SetRebootFlag true
+        Goto unRemoveFromPath_dosLoop
+
+    unRemoveFromPath_dosLoopEnd:
+      FileClose $2
+      FileClose $1
+      StrCpy $1 $WINDIR 2
+      Delete "$1\autoexec.bat"
+      CopyFiles /SILENT $4 "$1\autoexec.bat"
+      Delete $4
+      Goto unRemoveFromPath_done
+
+  unRemoveFromPath_NT:
+    StrCmp $ADD_TO_PATH_ALL_USERS "1" unReadAllKey
+      ReadRegStr $1 ${NT_current_env} "PATH"
+      Goto unDoTrim
+    unReadAllKey:
+      ReadRegStr $1 ${NT_all_env} "PATH"
+    unDoTrim:
+    StrCpy $5 $1 1 -1 # copy last char
+    StrCmp $5 ";" +2 # if last char != ;
+      StrCpy $1 "$1;" # append ;
+    Push $1
+    Push "$0;"
+    Call un.StrStr ; Find `$0;` in $1
+    Pop $2 ; pos of our dir
+    StrCmp $2 "" unRemoveFromPath_done
+      ; else, it is in path
+      # $0 - path to add
+      # $1 - path var
+      StrLen $3 "$0;"
+      StrLen $4 $2
+      StrCpy $5 $1 -$4 # $5 is now the part before the path to remove
+      StrCpy $6 $2 "" $3 # $6 is now the part after the path to remove
+      StrCpy $3 $5$6
+
+      StrCpy $5 $3 1 -1 # copy last char
+      StrCmp $5 ";" 0 +2 # if last char == ;
+        StrCpy $3 $3 -1 # remove last char
+
+      StrCmp $ADD_TO_PATH_ALL_USERS "1" unWriteAllKey
+        WriteRegExpandStr ${NT_current_env} "PATH" $3
+        Goto unDoSend
+      unWriteAllKey:
+        WriteRegExpandStr ${NT_all_env} "PATH" $3
+      unDoSend:
+      SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
+
+  unRemoveFromPath_done:
+    Pop $6
+    Pop $5
+    Pop $4
+    Pop $3
+    Pop $2
+    Pop $1
+    Pop $0
+FunctionEnd
+
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+; Uninstall sutff
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+
+###########################################
+#            Utility Functions            #
+###########################################
+
+;====================================================
+; IsNT - Returns 1 if the current system is NT, 0
+;        otherwise.
+;     Output: head of the stack
+;====================================================
+; IsNT
+; no input
+; output, top of the stack = 1 if NT or 0 if not
+;
+; Usage:
+;   Call IsNT
+;   Pop $R0
+;  ($R0 at this point is 1 or 0)
+
+!macro IsNT un
+Function ${un}IsNT
+  Push $0
+  ReadRegStr $0 HKLM "SOFTWARE\Microsoft\Windows NT\CurrentVersion" CurrentVersion
+  StrCmp $0 "" 0 IsNT_yes
+  ; we are not NT.
+  Pop $0
+  Push 0
+  Return
+
+  IsNT_yes:
+    ; NT!!!
+    Pop $0
+    Push 1
+FunctionEnd
+!macroend
+!insertmacro IsNT ""
+!insertmacro IsNT "un."
+
+; StrStr
+; input, top of stack = string to search for
+;        top of stack-1 = string to search in
+; output, top of stack (replaces with the portion of the string remaining)
+; modifies no other variables.
+;
+; Usage:
+;   Push "this is a long ass string"
+;   Push "ass"
+;   Call StrStr
+;   Pop $R0
+;  ($R0 at this point is "ass string")
+
+!macro StrStr un
+Function ${un}StrStr
+Exch $R1 ; st=haystack,old$R1, $R1=needle
+  Exch    ; st=old$R1,haystack
+  Exch $R2 ; st=old$R1,old$R2, $R2=haystack
+  Push $R3
+  Push $R4
+  Push $R5
+  StrLen $R3 $R1
+  StrCpy $R4 0
+  ; $R1=needle
+  ; $R2=haystack
+  ; $R3=len(needle)
+  ; $R4=cnt
+  ; $R5=tmp
+  loop:
+    StrCpy $R5 $R2 $R3 $R4
+    StrCmp $R5 $R1 done
+    StrCmp $R5 "" done
+    IntOp $R4 $R4 + 1
+    Goto loop
+done:
+  StrCpy $R1 $R2 "" $R4
+  Pop $R5
+  Pop $R4
+  Pop $R3
+  Pop $R2
+  Exch $R1
+FunctionEnd
+!macroend
+!insertmacro StrStr ""
+!insertmacro StrStr "un."
+
+Function Trim ; Added by Pelaca
+	Exch $R1
+	Push $R2
+Loop:
+	StrCpy $R2 "$R1" 1 -1
+	StrCmp "$R2" " " RTrim
+	StrCmp "$R2" "$\n" RTrim
+	StrCmp "$R2" "$\r" RTrim
+	StrCmp "$R2" ";" RTrim
+	GoTo Done
+RTrim:
+	StrCpy $R1 "$R1" -1
+	Goto Loop
+Done:
+	Pop $R2
+	Exch $R1
+FunctionEnd
+
+Function ConditionalAddToRegisty
+  Pop $0
+  Pop $1
+  StrCmp "$0" "" ConditionalAddToRegisty_EmptyString
+    WriteRegStr SHCTX "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" \
+    "$1" "$0"
+    ;MessageBox MB_OK "Set Registry: '$1' to '$0'"
+    DetailPrint "Set install registry entry: '$1' to '$0'"
+  ConditionalAddToRegisty_EmptyString:
+FunctionEnd
+
+;--------------------------------
+
+!ifdef CPACK_USES_DOWNLOAD
+Function DownloadFile
+    IfFileExists $INSTDIR\* +2
+    CreateDirectory $INSTDIR
+    Pop $0
+
+    ; Skip if already downloaded
+    IfFileExists $INSTDIR\$0 0 +2
+    Return
+
+    StrCpy $1 "@CPACK_DOWNLOAD_SITE@"
+
+  try_again:
+    NSISdl::download "$1/$0" "$INSTDIR\$0"
+
+    Pop $1
+    StrCmp $1 "success" success
+    StrCmp $1 "Cancelled" cancel
+    MessageBox MB_OK "Download failed: $1"
+  cancel:
+    Return
+  success:
+FunctionEnd
+!endif
+
+;--------------------------------
+; Installation types
+@CPACK_NSIS_INSTALLATION_TYPES@
+
+;--------------------------------
+; Component sections
+@CPACK_NSIS_COMPONENT_SECTIONS@
+
+;--------------------------------
+; Define some macro setting for the gui
+@CPACK_NSIS_INSTALLER_MUI_ICON_CODE@
+@CPACK_NSIS_INSTALLER_ICON_CODE@
+@CPACK_NSIS_INSTALLER_MUI_WELCOMEFINISH_CODE@
+@CPACK_NSIS_INSTALLER_MUI_UNWELCOMEFINISH_CODE@
+@CPACK_NSIS_INSTALLER_MUI_COMPONENTS_DESC@
+@CPACK_NSIS_INSTALLER_MUI_FINISHPAGE_RUN_CODE@
+
+;--------------------------------
+;Pages
+  !insertmacro MUI_PAGE_WELCOME
+
+  !insertmacro MUI_PAGE_LICENSE "@CPACK_RESOURCE_FILE_LICENSE@"
+  Page custom InstallOptionsPage
+  !insertmacro MUI_PAGE_DIRECTORY
+
+  ;Start Menu Folder Page Configuration
+  !define MUI_STARTMENUPAGE_REGISTRY_ROOT "SHCTX"
+  !define MUI_STARTMENUPAGE_REGISTRY_KEY "Software\@CPACK_PACKAGE_VENDOR@\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@"
+  !define MUI_STARTMENUPAGE_REGISTRY_VALUENAME "Start Menu Folder"
+  !insertmacro MUI_PAGE_STARTMENU Application $STARTMENU_FOLDER
+
+  @CPACK_NSIS_PAGE_COMPONENTS@
+
+  !insertmacro MUI_PAGE_INSTFILES
+  !insertmacro MUI_PAGE_FINISH
+
+  !insertmacro MUI_UNPAGE_CONFIRM
+  !insertmacro MUI_UNPAGE_INSTFILES
+
+;--------------------------------
+;Languages
+
+  !insertmacro MUI_LANGUAGE "English" ;first language is the default language
+  !insertmacro MUI_LANGUAGE "Albanian"
+  !insertmacro MUI_LANGUAGE "Arabic"
+  !insertmacro MUI_LANGUAGE "Basque"
+  !insertmacro MUI_LANGUAGE "Belarusian"
+  !insertmacro MUI_LANGUAGE "Bosnian"
+  !insertmacro MUI_LANGUAGE "Breton"
+  !insertmacro MUI_LANGUAGE "Bulgarian"
+  !insertmacro MUI_LANGUAGE "Croatian"
+  !insertmacro MUI_LANGUAGE "Czech"
+  !insertmacro MUI_LANGUAGE "Danish"
+  !insertmacro MUI_LANGUAGE "Dutch"
+  !insertmacro MUI_LANGUAGE "Estonian"
+  !insertmacro MUI_LANGUAGE "Farsi"
+  !insertmacro MUI_LANGUAGE "Finnish"
+  !insertmacro MUI_LANGUAGE "French"
+  !insertmacro MUI_LANGUAGE "German"
+  !insertmacro MUI_LANGUAGE "Greek"
+  !insertmacro MUI_LANGUAGE "Hebrew"
+  !insertmacro MUI_LANGUAGE "Hungarian"
+  !insertmacro MUI_LANGUAGE "Icelandic"
+  !insertmacro MUI_LANGUAGE "Indonesian"
+  !insertmacro MUI_LANGUAGE "Irish"
+  !insertmacro MUI_LANGUAGE "Italian"
+  !insertmacro MUI_LANGUAGE "Japanese"
+  !insertmacro MUI_LANGUAGE "Korean"
+  !insertmacro MUI_LANGUAGE "Kurdish"
+  !insertmacro MUI_LANGUAGE "Latvian"
+  !insertmacro MUI_LANGUAGE "Lithuanian"
+  !insertmacro MUI_LANGUAGE "Luxembourgish"
+  !insertmacro MUI_LANGUAGE "Macedonian"
+  !insertmacro MUI_LANGUAGE "Malay"
+  !insertmacro MUI_LANGUAGE "Mongolian"
+  !insertmacro MUI_LANGUAGE "Norwegian"
+  !insertmacro MUI_LANGUAGE "Polish"
+  !insertmacro MUI_LANGUAGE "Portuguese"
+  !insertmacro MUI_LANGUAGE "PortugueseBR"
+  !insertmacro MUI_LANGUAGE "Romanian"
+  !insertmacro MUI_LANGUAGE "Russian"
+  !insertmacro MUI_LANGUAGE "Serbian"
+  !insertmacro MUI_LANGUAGE "SerbianLatin"
+  !insertmacro MUI_LANGUAGE "SimpChinese"
+  !insertmacro MUI_LANGUAGE "Slovak"
+  !insertmacro MUI_LANGUAGE "Slovenian"
+  !insertmacro MUI_LANGUAGE "Spanish"
+  !insertmacro MUI_LANGUAGE "Swedish"
+  !insertmacro MUI_LANGUAGE "Thai"
+  !insertmacro MUI_LANGUAGE "TradChinese"
+  !insertmacro MUI_LANGUAGE "Turkish"
+  !insertmacro MUI_LANGUAGE "Ukrainian"
+  !insertmacro MUI_LANGUAGE "Welsh"
+
+
+;--------------------------------
+;Reserve Files
+
+  ;These files should be inserted before other files in the data block
+  ;Keep these lines before any File command
+  ;Only for solid compression (by default, solid compression is enabled for BZIP2 and LZMA)
+
+  ReserveFile "NSIS.InstallOptions.ini"
+  !insertmacro MUI_RESERVEFILE_INSTALLOPTIONS
+
+;--------------------------------
+;Installer Sections
+
+Section "-Core installation"
+  ;Use the entire tree produced by the INSTALL target.  Keep the
+  ;list of directories here in sync with the RMDir commands below.
+  SetOutPath "$INSTDIR"
+  @CPACK_NSIS_EXTRA_PREINSTALL_COMMANDS@
+  @CPACK_NSIS_FULL_INSTALL@
+
+  ;Store installation folder
+  WriteRegStr SHCTX "Software\@CPACK_PACKAGE_VENDOR@\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "" $INSTDIR
+
+  ;Create uninstaller
+  WriteUninstaller "$INSTDIR\Uninstall.exe"
+  Push "DisplayName"
+  Push "@CPACK_NSIS_DISPLAY_NAME@"
+  Call ConditionalAddToRegisty
+  Push "DisplayVersion"
+  Push "@CPACK_PACKAGE_VERSION@"
+  Call ConditionalAddToRegisty
+  Push "Publisher"
+  Push "@CPACK_PACKAGE_VENDOR@"
+  Call ConditionalAddToRegisty
+  Push "DisplayIcon"
+  Push "$INSTDIR\Uninstall.exe"
+  Call ConditionalAddToRegisty
+  Push "UninstallString"
+  Push "$INSTDIR\Uninstall.exe"
+  Call ConditionalAddToRegisty
+  Push "NoRepair"
+  Push "1"
+  Call ConditionalAddToRegisty
+
+  !ifdef CPACK_NSIS_ADD_REMOVE
+  ;Create add/remove functionality
+  Push "ModifyPath"
+  Push "$INSTDIR\AddRemove.exe"
+  Call ConditionalAddToRegisty
+  !else
+  Push "NoModify"
+  Push "1"
+  Call ConditionalAddToRegisty
+  !endif
+
+  ; Optional registration
+  Push "HelpLink"
+  Push "@CPACK_NSIS_HELP_LINK@"
+  Call ConditionalAddToRegisty
+  Push "URLInfoAbout"
+  Push "@CPACK_NSIS_URL_INFO_ABOUT@"
+  Call ConditionalAddToRegisty
+  Push "Contact"
+  Push "@CPACK_NSIS_CONTACT@"
+  Call ConditionalAddToRegisty
+  !insertmacro MUI_INSTALLOPTIONS_READ $INSTALL_DESKTOP "NSIS.InstallOptions.ini" "Field 5" "State"
+  !insertmacro MUI_STARTMENU_WRITE_BEGIN Application
+
+  ;Create shortcuts
+  CreateDirectory "$SMPROGRAMS\$STARTMENU_FOLDER"
+@CPACK_NSIS_CREATE_ICONS@
+@CPACK_NSIS_CREATE_ICONS_EXTRA@
+  CreateShortCut "$SMPROGRAMS\$STARTMENU_FOLDER\Uninstall.lnk" "$INSTDIR\Uninstall.exe"
+
+  CreateShortcut "$INSTDIR\..\cmake.lnk" "$INSTDIR\cmake"
+
+  ;Read a value from an InstallOptions INI file
+  !insertmacro MUI_INSTALLOPTIONS_READ $DO_NOT_ADD_TO_PATH "NSIS.InstallOptions.ini" "Field 2" "State"
+  !insertmacro MUI_INSTALLOPTIONS_READ $ADD_TO_PATH_ALL_USERS "NSIS.InstallOptions.ini" "Field 3" "State"
+  !insertmacro MUI_INSTALLOPTIONS_READ $ADD_TO_PATH_CURRENT_USER "NSIS.InstallOptions.ini" "Field 4" "State"
+
+
+  ;Create AF_PATH variable
+  WriteRegExpandStr ${env_af_hklm} AF_PATH '$INSTDIR'
+  WriteRegExpandStr ${env_af_hklm} AF_PATH_v@CPACK_PACKAGE_VERSION_MAJOR@ '$INSTDIR'
+
+  ; make sure windows knows about the change
+  SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
+  MessageBox MB_OK "Added AF_PATH environment variable for all users.$\n$\nIf you chose not to modify PATH in the installer, please manually add $\"%AF_PATH%\lib$\" to the user or system PATH variable for running applications using ArrayFire."
+
+
+  ; Write special uninstall registry entries
+  Push "StartMenu"
+  Push "$STARTMENU_FOLDER"
+  Call ConditionalAddToRegisty
+  Push "DoNotAddToPath"
+  Push "$DO_NOT_ADD_TO_PATH"
+  Call ConditionalAddToRegisty
+  Push "AddToPathAllUsers"
+  Push "$ADD_TO_PATH_ALL_USERS"
+  Call ConditionalAddToRegisty
+  Push "AddToPathCurrentUser"
+  Push "$ADD_TO_PATH_CURRENT_USER"
+  Call ConditionalAddToRegisty
+  Push "InstallToDesktop"
+  Push "$INSTALL_DESKTOP"
+  Call ConditionalAddToRegisty
+
+  !insertmacro MUI_STARTMENU_WRITE_END
+
+@CPACK_NSIS_EXTRA_INSTALL_COMMANDS@
+
+SectionEnd
+
+Section "-Add to path"
+  Push $INSTDIR\bin
+  StrCmp "@CPACK_NSIS_MODIFY_PATH@" "ON" 0 doNotAddToPath
+  StrCmp $DO_NOT_ADD_TO_PATH "1" doNotAddToPath 0
+    Call AddToPath
+  doNotAddToPath:
+SectionEnd
+
+;--------------------------------
+; Create custom pages
+Function InstallOptionsPage
+  !insertmacro MUI_HEADER_TEXT "Install Options" "Choose options for installing @CPACK_NSIS_PACKAGE_NAME@"
+  !insertmacro MUI_INSTALLOPTIONS_DISPLAY "NSIS.InstallOptions.ini"
+
+FunctionEnd
+
+;--------------------------------
+; determine admin versus local install
+Function un.onInit
+
+  ClearErrors
+  UserInfo::GetName
+  IfErrors noLM
+  Pop $0
+  UserInfo::GetAccountType
+  Pop $1
+  StrCmp $1 "Admin" 0 +3
+    SetShellVarContext all
+    ;MessageBox MB_OK 'User "$0" is in the Admin group'
+    Goto done
+  StrCmp $1 "Power" 0 +3
+    SetShellVarContext all
+    ;MessageBox MB_OK 'User "$0" is in the Power Users group'
+    Goto done
+
+  noLM:
+    ;Get installation folder from registry if available
+
+  done:
+
+FunctionEnd
+
+;--- Add/Remove callback functions: ---
+!macro SectionList MacroName
+  ;This macro used to perform operation on multiple sections.
+  ;List all of your components in following manner here.
+@CPACK_NSIS_COMPONENT_SECTION_LIST@
+!macroend
+
+Section -FinishComponents
+  ;Removes unselected components and writes component status to registry
+  !insertmacro SectionList "FinishSection"
+
+!ifdef CPACK_NSIS_ADD_REMOVE
+  ; Get the name of the installer executable
+  System::Call 'kernel32::GetModuleFileNameA(i 0, t .R0, i 1024) i r1'
+  StrCpy $R3 $R0
+
+  ; Strip off the last 13 characters, to see if we have AddRemove.exe
+  StrLen $R1 $R0
+  IntOp $R1 $R0 - 13
+  StrCpy $R2 $R0 13 $R1
+  StrCmp $R2 "AddRemove.exe" addremove_installed
+
+  ; We're not running AddRemove.exe, so install it
+  CopyFiles $R3 $INSTDIR\AddRemove.exe
+
+  addremove_installed:
+!endif
+SectionEnd
+;--- End of Add/Remove callback functions ---
+
+;--------------------------------
+; Component dependencies
+Function .onSelChange
+  !insertmacro SectionList MaybeSelectionChanged
+FunctionEnd
+
+;--------------------------------
+;Uninstaller Section
+
+Section "Uninstall"
+  ReadRegStr $START_MENU SHCTX \
+   "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "StartMenu"
+  ;MessageBox MB_OK "Start menu is in: $START_MENU"
+  ReadRegStr $DO_NOT_ADD_TO_PATH SHCTX \
+    "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "DoNotAddToPath"
+  ReadRegStr $ADD_TO_PATH_ALL_USERS SHCTX \
+    "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "AddToPathAllUsers"
+  ReadRegStr $ADD_TO_PATH_CURRENT_USER SHCTX \
+    "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "AddToPathCurrentUser"
+  ;MessageBox MB_OK "Add to path: $DO_NOT_ADD_TO_PATH all users: $ADD_TO_PATH_ALL_USERS"
+  ReadRegStr $INSTALL_DESKTOP SHCTX \
+    "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "InstallToDesktop"
+  ;MessageBox MB_OK "Install to desktop: $INSTALL_DESKTOP "
+
+@CPACK_NSIS_EXTRA_UNINSTALL_COMMANDS@
+
+  ;Remove files we installed.
+  ;Keep the list of directories here in sync with the File commands above.
+@CPACK_NSIS_DELETE_FILES@
+@CPACK_NSIS_DELETE_DIRECTORIES@
+
+!ifdef CPACK_NSIS_ADD_REMOVE
+  ;Remove the add/remove program
+  Delete "$INSTDIR\AddRemove.exe"
+!endif
+
+
+  ;Create AF_PATH variable
+  DeleteRegValue ${env_af_hklm} AF_PATH
+  DeleteRegValue ${env_af_hklm} AF_PATH_v@CPACK_PACKAGE_VERSION_MAJOR@
+
+  ; make sure windows knows about the change
+  SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
+
+
+  ;Remove the uninstaller itself.
+  Delete "$INSTDIR\Uninstall.exe"
+  DeleteRegKey SHCTX "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@"
+
+  ;Remove the installation directory if it is empty.
+  RMDir "$INSTDIR"
+
+  ; Remove the registry entries.
+  DeleteRegKey SHCTX "Software\@CPACK_PACKAGE_VENDOR@\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@"
+
+  ; Removes all optional components
+  !insertmacro SectionList "RemoveSection_CPack"
+
+  !insertmacro MUI_STARTMENU_GETFOLDER Application $MUI_TEMP
+
+  Delete "$SMPROGRAMS\$MUI_TEMP\Uninstall.lnk"
+@CPACK_NSIS_DELETE_ICONS@
+@CPACK_NSIS_DELETE_ICONS_EXTRA@
+
+  Delete "$INSTDIR\..\cmake.lnk"
+  ;Delete empty start menu parent diretories
+  StrCpy $MUI_TEMP "$SMPROGRAMS\$MUI_TEMP"
+
+  startMenuDeleteLoop:
+    ClearErrors
+    RMDir $MUI_TEMP
+    GetFullPathName $MUI_TEMP "$MUI_TEMP\.."
+
+    IfErrors startMenuDeleteLoopDone
+
+    StrCmp "$MUI_TEMP" "$SMPROGRAMS" startMenuDeleteLoopDone startMenuDeleteLoop
+  startMenuDeleteLoopDone:
+
+  ; If the user changed the shortcut, then untinstall may not work. This should
+  ; try to fix it.
+  StrCpy $MUI_TEMP "$START_MENU"
+  Delete "$SMPROGRAMS\$MUI_TEMP\Uninstall.lnk"
+@CPACK_NSIS_DELETE_ICONS_EXTRA@
+
+  ;Delete empty start menu parent diretories
+  StrCpy $MUI_TEMP "$SMPROGRAMS\$MUI_TEMP"
+
+  secondStartMenuDeleteLoop:
+    ClearErrors
+    RMDir $MUI_TEMP
+    GetFullPathName $MUI_TEMP "$MUI_TEMP\.."
+
+    IfErrors secondStartMenuDeleteLoopDone
+
+    StrCmp "$MUI_TEMP" "$SMPROGRAMS" secondStartMenuDeleteLoopDone secondStartMenuDeleteLoop
+  secondStartMenuDeleteLoopDone:
+
+  DeleteRegKey /ifempty SHCTX "Software\@CPACK_PACKAGE_VENDOR@\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@"
+
+  Push $INSTDIR\bin
+  StrCmp $DO_NOT_ADD_TO_PATH_ "1" doNotRemoveFromPath 0
+    Call un.RemoveFromPath
+  doNotRemoveFromPath:
+SectionEnd
+
+;--------------------------------
+; determine admin versus local install
+; Is install for "AllUsers" or "JustMe"?
+; Default to "JustMe" - set to "AllUsers" if admin or on Win9x
+; This function is used for the very first "custom page" of the installer.
+; This custom page does not show up visibly, but it executes prior to the
+; first visible page and sets up $INSTDIR properly...
+; Choose different default installation folder based on SV_ALLUSERS...
+; "Program Files" for AllUsers, "My Documents" for JustMe...
+
+Function .onInit
+  StrCmp "@CPACK_NSIS_ENABLE_UNINSTALL_BEFORE_INSTALL@" "ON" 0 inst
+
+  ReadRegStr $0 HKLM "Software\Microsoft\Windows\CurrentVersion\Uninstall\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@" "UninstallString"
+  StrCmp $0 "" inst
+
+  MessageBox MB_YESNOCANCEL|MB_ICONEXCLAMATION \
+  "@CPACK_NSIS_PACKAGE_NAME@ is already installed. $\n$\nDo you want to uninstall the old version before installing the new one?" \
+  /SD IDYES IDYES uninst IDNO inst
+  Abort
+
+;Run the uninstaller
+uninst:
+  ClearErrors
+  StrLen $2 "\Uninstall.exe"
+  StrCpy $3 $0 -$2 # remove "\Uninstall.exe" from UninstallString to get path
+  ExecWait '"$0" /S _?=$3' ;Do not copy the uninstaller to a temp file
+
+  IfErrors uninst_failed inst
+uninst_failed:
+  MessageBox MB_OK|MB_ICONSTOP "Uninstall failed."
+  Abort
+
+
+inst:
+  ; Reads components status for registry
+  !insertmacro SectionList "InitSection"
+
+  ; check to see if /D has been used to change
+  ; the install directory by comparing it to the
+  ; install directory that is expected to be the
+  ; default
+  StrCpy $IS_DEFAULT_INSTALLDIR 0
+  StrCmp "$INSTDIR" "@CPACK_NSIS_INSTALL_ROOT@\@CPACK_PACKAGE_INSTALL_DIRECTORY@" 0 +2
+    StrCpy $IS_DEFAULT_INSTALLDIR 1
+
+  StrCpy $SV_ALLUSERS "JustMe"
+  ; if default install dir then change the default
+  ; if it is installed for JustMe
+  StrCmp "$IS_DEFAULT_INSTALLDIR" "1" 0 +2
+    StrCpy $INSTDIR "$DOCUMENTS\@CPACK_PACKAGE_INSTALL_DIRECTORY@"
+
+  ClearErrors
+  UserInfo::GetName
+  IfErrors noLM
+  Pop $0
+  UserInfo::GetAccountType
+  Pop $1
+  StrCmp $1 "Admin" 0 +4
+    SetShellVarContext all
+    ;MessageBox MB_OK 'User "$0" is in the Admin group'
+    StrCpy $SV_ALLUSERS "AllUsers"
+    Goto done
+  StrCmp $1 "Power" 0 +4
+    SetShellVarContext all
+    ;MessageBox MB_OK 'User "$0" is in the Power Users group'
+    StrCpy $SV_ALLUSERS "AllUsers"
+    Goto done
+
+  noLM:
+    StrCpy $SV_ALLUSERS "AllUsers"
+    ;Get installation folder from registry if available
+
+  done:
+  StrCmp $SV_ALLUSERS "AllUsers" 0 +3
+    StrCmp "$IS_DEFAULT_INSTALLDIR" "1" 0 +2
+      StrCpy $INSTDIR "@CPACK_NSIS_INSTALL_ROOT@\@CPACK_PACKAGE_INSTALL_DIRECTORY@"
+
+  StrCmp "@CPACK_NSIS_MODIFY_PATH@" "ON" 0 noOptionsPage
+    !insertmacro MUI_INSTALLOPTIONS_EXTRACT "NSIS.InstallOptions.ini"
+
+  noOptionsPage:
+FunctionEnd
diff --git a/CMakeModules/osx_install/readme.html.in b/CMakeModules/osx_install/readme.html.in
index 2443b5fae3..82d42a79a8 100644
--- a/CMakeModules/osx_install/readme.html.in
+++ b/CMakeModules/osx_install/readme.html.in
@@ -3,9 +3,9 @@
 
     <h2>Install Directories</h2>
     <ul>
-        <li> Libraries will be installed in <code>/usr/local/lib</code> </li>
-        <li> Headers will be installed in <code>/usr/local/include</code> </li>
-        <li> Examples, documentation and CMake config files will be installed in <code>/usr/local/share</code> </li>
+        <li> Libraries will be installed in <code>/opt/arrayfire/lib</code> </li>
+        <li> Headers will be installed in <code>/opt/arrayfire/include</code> </li>
+        <li> Examples, documentation and CMake config files will be installed in <code>/opt/arrayfire/share</code> </li>
     </ul>
     <p> For complete list of updates, visit <a href="http://www.arrayfire.com/docs/releasenotes.htm">ArrayFire Release Notes</a></p>
 
diff --git a/LICENSE b/LICENSE
index 18109a9a61..91c27f7f34 100644
--- a/LICENSE
+++ b/LICENSE
@@ -15,13 +15,4 @@ are permitted provided that the following conditions are met:
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.
 
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/LICENSES/BSD 3-Clause.txt b/LICENSES/BSD 3-Clause.txt
index e6690fd1f0..ffab4f203a 100644
--- a/LICENSES/BSD 3-Clause.txt	
+++ b/LICENSES/BSD 3-Clause.txt	
@@ -1,4 +1,4 @@
-Copyright (c) <year>, <copyright holder>
+Copyright (c) 2018, ArrayFire
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/LICENSES/ISSL License.txt b/LICENSES/ISSL License.txt
new file mode 100644
index 0000000000..7ce92d1317
--- /dev/null
+++ b/LICENSES/ISSL License.txt	
@@ -0,0 +1,29 @@
+Copyright (c) 2018 Intel Corporation.
+
+Use and Redistribution.  You may use and redistribute the software (the “Software”), without modification, provided the following conditions are met:
+
+* Redistributions must reproduce the above copyright notice and the following terms of use in the Software and in the documentation and/or other materials provided with the distribution.
+
+* Neither the name of Intel nor the names of its suppliers may be used to endorse or promote products derived from this Software without specific prior written permission.
+
+* No reverse engineering, decompilation, or disassembly of this Software is permitted.
+
+Limited patent license.  Intel grants you a world-wide, royalty-free, non-exclusive license under patents it now or hereafter owns or controls to make, have made, use, import, offer to sell and sell (“Utilize”) this Software, but solely to the extent that any such patent is necessary to Utilize the Software alone. The patent license shall not apply to any combinations which include this software.  No hardware per se is licensed hereunder.
+
+Third party and other Intel programs.  “Third Party Programs” are the files listed in the “third-party-programs.txt” text file that is included with the Software and may include Intel programs under separate license terms. Third Party Programs, even if included with the distribution of the Materials, are governed by separate license terms and those license terms solely govern your use of those programs.
+
+DISCLAIMER. THIS SOFTWARE IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT ARE DISCLAIMED. THIS SOFTWARE IS NOT INTENDED FOR USE IN SYSTEMS OR APPLICATIONS WHERE FAILURE OF THE SOFTWARE MAY CAUSE PERSONAL INJURY OR DEATH AND YOU AGREE THAT YOU ARE FULLY RESPONSIBLE FOR ANY CLAIMS, COSTS, DAMAGES, EXPENSES, AND ATTORNEYS’ FEES ARISING OUT OF ANY SUCH USE, EVEN IF ANY CLAIM ALLEGES THAT INTEL WAS NEGLIGENT REGARDING THE DESIGN OR MANUFACTURE OF THE MATERIALS.
+
+LIMITATION OF LIABILITY. IN NO EVENT WILL INTEL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. YOU AGREE TO INDEMNIFY AND HOLD INTEL HARMLESS AGAINST ANY CLAIMS AND EXPENSES RESULTING FROM YOUR USE OR UNAUTHORIZED USE OF THE SOFTWARE.
+
+No support. Intel may make changes to the Software, at any time without notice, and is not obligated to support, update or provide training for the Software.
+
+Termination. Intel may terminate your right to use the Software in the event of your breach of this Agreement and you fail to cure the breach within a reasonable period of time.
+
+Feedback. Should you provide Intel with comments, modifications, corrections, enhancements or other input (“Feedback”) related to the Software Intel will be free to use, disclose, reproduce, license or otherwise distribute or exploit the Feedback in its sole discretion without any obligations or restrictions of any kind, including without limitation, intellectual property rights or licensing obligations.
+
+Compliance with laws. You agree to comply with all relevant laws and regulations governing your use, transfer, import or export (or prohibition thereof) of the Software.
+
+Governing law.  All disputes will be governed by the laws of the United States of America and the State of Delaware without reference to conflict of law principles and subject to the exclusive jurisdiction of the state or federal courts sitting in the State of Delaware, and each party agrees that it submits to the personal jurisdiction and venue of those courts and waives any objections. The United Nations Convention on Contracts for the International Sale of Goods (1980) is specifically excluded and will not apply to the Software.
+
+*Other names and brands may be claimed as the property of others.
\ No newline at end of file
diff --git a/LICENSES/MIT License.txt b/LICENSES/MIT License.txt
index 2bf24b9b9f..900e2c71b5 100644
--- a/LICENSES/MIT License.txt	
+++ b/LICENSES/MIT License.txt	
@@ -1,21 +1,7 @@
-The MIT License (MIT)
+Copyright (c) 2014-2015 Computer Graphics Systems Group at the Hasso-Plattner-Institute and CG Internals GmbH, Germany. 
 
-Copyright (c) <year> <copyright holders>
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
\ No newline at end of file
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/LICENSES/zlib-libpng License.txt b/LICENSES/zlib-libpng License.txt
index 28c994e86a..eec5469a5d 100644
--- a/LICENSES/zlib-libpng License.txt	
+++ b/LICENSES/zlib-libpng License.txt	
@@ -1,12 +1,21 @@
-The zlib/libpng License
-Copyright (c) <year> <copyright holders>
+Copyright (c) 2002-2006 Marcus Geelnard
+Copyright (c) 2006-2016 Camilla Berglund <elmindreda@glfw.org>
 
-This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software.
+This software is provided 'as-is', without any express or implied
+warranty. In no event will the authors be held liable for any damages
+arising from the use of this software.
 
-Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions:
+Permission is granted to anyone to use this software for any purpose,
+including commercial applications, and to alter it and redistribute it
+freely, subject to the following restrictions:
 
-1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required.
+1. The origin of this software must not be misrepresented; you must not
+   claim that you wrote the original software. If you use this software
+   in a product, an acknowledgment in the product documentation would
+   be appreciated but is not required.
 
-2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software.
+2. Altered source versions must be plainly marked as such, and must not
+   be misrepresented as being the original software.
 
-3. This notice may not be removed or altered from any source distribution.
\ No newline at end of file
+3. This notice may not be removed or altered from any source
+   distribution.
\ No newline at end of file
diff --git a/assets b/assets
index f16f8bf74f..64be18117a 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit f16f8bf74fe4a255db05884cfff8f5cb0e6e8e09
+Subproject commit 64be18117a43460a050257d288953490cc00848b
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 8095a0b72d..d4e03f9ef3 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -11,7 +11,7 @@ project(ArrayFire-Examples
   VERSION 3.5.0
   LANGUAGES CXX)
 
-if(EXISTS "${CMAKE_MODULE_PATH}/ArrayFireExampleOverloads.cmake")
+if(EXISTS "${ArrayFire_SOURCE_DIR}/CMakeModules/ArrayFireExampleOverloads.cmake")
   include(ArrayFireExampleOverloads)
 endif()
 
@@ -19,8 +19,15 @@ if (NOT ASSETS_DIR)
     set(ASSETS_DIR "" CACHE PATH "Data and images required for some examples (url: https://github.com/arrayfire/assets)")
 endif (NOT ASSETS_DIR)
 
+file(TO_NATIVE_PATH ${ASSETS_DIR} ASSETS_DIR)
+
 if(WIN32)
-  add_definitions(-DWIN32_LEAN_AND_MEAN)
+  string(REPLACE "\\" "\\\\" ASSETS_DIR  ${ASSETS_DIR})
+  # - WIN32_LEAN_AND_MEAN & VC_EXTRALEAN reduces the number of
+  #   windows headers being included.
+  # - NOMINMAX is required for ArrayFire code that uses
+  #   functions af::min & af::max. Having a namespace doesn't help also.
+  add_definitions(-DWIN32_LEAN_AND_MEAN -DVC_EXTRALEAN -DNOMINMAX)
   unset(CMAKE_RUNTIME_OUTPUT_DIRECTORY)
 endif()
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index e9532eca9b..a3eea76d6a 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -153,8 +153,16 @@ target_sources(c_api_interface
     )
 
 if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
-    target_compile_definitions(c_api_interface INTERFACE WITH_FREEIMAGE)
-    target_link_libraries(     c_api_interface INTERFACE FreeImage::FreeImage)
+  target_compile_definitions(c_api_interface INTERFACE WITH_FREEIMAGE)
+  if (AF_WITH_STATIC_FREEIMAGE)
+    target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage_STATIC)
+  else ()
+    target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage)
+    if (WIN32)
+      install(FILES $<TARGET_FILE:FreeImage::FreeImage>
+        DESTINATION ${AF_INSTALL_BIN_DIR})
+    endif ()
+  endif ()
 endif()
 
 if(AF_WITH_GRAPHICS)
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index a7eee85f1c..3411e5eacc 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -57,7 +57,7 @@ if(AF_WITH_GRAPHICS)
       OpenGL::GL
       ${Forge_LIBRARIES})
 
-    target_compile_definitions(afcommon_interface INTERFACE WITH_GRAPHICS)
+  target_compile_definitions(afcommon_interface INTERFACE WITH_GRAPHICS)
 
   if(APPLE)
     # TODO: On APPLE platform linking directly against glbinding brings in flags
@@ -69,11 +69,16 @@ if(AF_WITH_GRAPHICS)
       INTERFACE
         $<TARGET_PROPERTY:glbinding::glbinding,IMPORTED_LOCATION_RELEASE>)
   else()
-    target_link_libraries(afcommon_interface
-      INTERFACE
-      glbinding::glbinding)
+    target_link_libraries(afcommon_interface INTERFACE glbinding::glbinding)
   endif()
 
+  install(FILES
+      $<TARGET_FILE:glbinding::glbinding>
+      $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
+      $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
+    DESTINATION ${AF_INSTALL_BIN_DIR}
+    COMPONENT gfx_dependencies)
+
   add_dependencies(afcommon_interface forge-ext)
 endif()
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ca6e66da6f..8d5ac48ba6 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -21,6 +21,7 @@ cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_ta
 message(STATUS "CUDA Architectures: ${CUDA_architecture_build_targets}")
 
 find_cuda_helper_libs(nvrtc)
+find_cuda_helper_libs(nvrtc-builtins)
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 mark_as_advanced(CUDA_LIBRARIES_PATH)
@@ -481,7 +482,6 @@ else()
   find_library(CUDA_CUDA_STUB
              NAMES cuda
              PATHS ${CUDA_LIBRARIES_PATH}/stubs
-
              NO_DEFAULT_PATH
          )
   if(CUDA_CUDA_STUB)
@@ -511,6 +511,59 @@ install(TARGETS afcuda
   INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
   )
 
+set(cuda_deps "")
+set (PX ${CMAKE_SHARED_LIBRARY_PREFIX})
+set (SX ${CMAKE_SHARED_LIBRARY_SUFFIX})
+set (dlib_path_prefix ${CUDA_LIBRARIES_PATH})
+if (WIN32)
+  set(dlib_path_prefix "${CUDA_TOOLKIT_ROOT_DIR}/bin")
+endif ()
+
+macro(afcu_collect_libs libname)
+  if (WIN32)
+    install(FILES       "${dlib_path_prefix}/${PX}${libname}64_${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}${SX}"
+            DESTINATION ${AF_INSTALL_BIN_DIR}
+            COMPONENT   cuda_dependencies)
+  elseif (APPLE)
+    get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
+    install(FILES       "${outpath}"
+            DESTINATION ${AF_INSTALL_BIN_DIR}
+            RENAME      "${PX}${libname}${SX}.${CUDA_VERSION}"
+            COMPONENT   cuda_dependencies)
+  else () #UNIX
+    get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}${SX}" REALPATH)
+    install(FILES       ${outpath}
+            DESTINATION ${AF_INSTALL_BIN_DIR}
+            RENAME      "${PX}${libname}${SX}.${CUDA_VERSION}"
+            COMPONENT   cuda_dependencies)
+  endif ()
+endmacro()
+
+afcu_collect_libs(cufft)
+afcu_collect_libs(cublas)
+afcu_collect_libs(cusolver)
+afcu_collect_libs(cusparse)
+afcu_collect_libs(nvrtc)
+
+if(APPLE)
+  afcu_collect_libs(cudart)
+
+  get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
+  install(FILES       ${nvrtc_outpath}
+          DESTINATION ${AF_INSTALL_BIN_DIR}
+          RENAME      "${PX}nvrtc-builtins${SX}"
+          COMPONENT   cuda_dependencies)
+elseif(UNIX)
+  get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
+  install(FILES       ${nvrtc_outpath}
+          DESTINATION ${AF_INSTALL_BIN_DIR}
+          RENAME      "${PX}nvrtc-builtins${SX}"
+          COMPONENT   cuda_dependencies)
+else()
+  afcu_collect_libs(nvrtc-builtins)
+endif()
+
+
 source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
 source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 060332c03b..6b2c8e1fec 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -38,6 +38,10 @@
 #define SHOW_BUILD_INFO(PROG) SHOW_DEBUG_BUILD_INFO(PROG)
 #endif
 
+namespace cl {
+  class Program;
+}
+
 namespace opencl
 {
     void buildProgram(cl::Program &prog,

From 558c42b59f49fd47cd193b6de28f7bfafaa79e21 Mon Sep 17 00:00:00 2001
From: Filip Matzner <FloopCZ@users.noreply.github.com>
Date: Thu, 22 Mar 2018 22:25:21 +0100
Subject: [PATCH 1382/2677] Fix CLBLast libdir

On some systems, libs are stored in `lib64` folder instead of `lib` making the build fail.
---
 CMakeModules/build_CLBlast.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 59578125df..5f12529930 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -26,6 +26,7 @@ ExternalProject_Add(
       "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
       -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+      -DCMAKE_INSTALL_LIBDIR:PATH=lib
       -DBUILD_SHARED_LIBS:BOOL=OFF
       -DSAMPLES:BOOL=OFF
       -DTUNERS:BOOL=OFF

From 28953a892a6be1e383022440e9f31817b537f551 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 31 Mar 2018 09:39:50 +0530
Subject: [PATCH 1383/2677] Mark some cblas/lapacke/CUDA cmake vars as advanced

---
 CMakeLists.txt                    |  5 ++++-
 CMakeModules/FindCBLAS.cmake      |  9 +++++++--
 CMakeModules/FindLAPACKE.cmake    |  4 +++-
 CMakeModules/FindMKL.cmake        |  1 +
 CMakeModules/InternalUtils.cmake  |  3 ++-
 src/backend/cuda/CMakeLists.txt   |  4 +++-
 src/backend/opencl/CMakeLists.txt | 10 ++++++----
 7 files changed, 26 insertions(+), 10 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index eb12eba46c..a4884166b9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -75,7 +75,10 @@ af_deprecate(USE_CPUID             AF_WITH_CPUID)
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
   AF_USE_SYSTEM_FORGE
-  AF_WITH_CPUID)
+  AF_WITH_CPUID
+  CUDA_HOST_COMPILER
+  CUDA_USE_STATIC_CUDA_RUNTIME
+  CUDA_rt_LIBRARY)
 
 # TODO(umar): Add definitions should not be used. Instead use
 arrayfire_get_platform_definitions(platform_definitions)
diff --git a/CMakeModules/FindCBLAS.cmake b/CMakeModules/FindCBLAS.cmake
index ce61a46dcd..31b6f72dd5 100644
--- a/CMakeModules/FindCBLAS.cmake
+++ b/CMakeModules/FindCBLAS.cmake
@@ -46,7 +46,10 @@ IF(PC_CBLAS_FOUND)
   SET(CBLAS_INCLUDE_DIR ${CBLAS_INCLUDE_DIRS})
 
   FIND_PACKAGE_HANDLE_STANDARD_ARGS(CBLAS DEFAULT_MSG CBLAS_LIBRARIES CBLAS_INCLUDE_DIR)
-  MARK_AS_ADVANCED(CBLAS_LIBRARIES CBLAS_INCLUDE_DIR)
+  MARK_AS_ADVANCED(
+    CBLAS_LIBRARIES
+    CBLAS_INCLUDE_DIR
+    CBLAS_INCLUDE_DIRS)
 
 ELSE(PC_CBLAS_FOUND)
 
@@ -344,4 +347,6 @@ ENDIF(PC_CBLAS_FOUND)
 MARK_AS_ADVANCED(
     CBLAS_INCLUDE_DIR
     CBLAS_INCLUDE_FILE
-    CBLAS_LIBRARIES)
+    CBLAS_LIBRARIES
+    cblas_LIBRARY
+    blas_LIBRARY)
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index c60fcbaad6..1bb75ce15d 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -164,7 +164,9 @@ MARK_AS_ADVANCED(
   LAPACK_LIBRARIES
   LAPACK_LIB
   LAPACKE_INCLUDES
-  LAPACKE_LIB)
+  LAPACKE_LIB
+  lapack_LIBRARY
+  lapacke_LIBRARY)
 
 if(LAPACK_FOUND)
   add_library(LAPACKE::LAPACKE UNKNOWN IMPORTED)
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 14c5b2d6ce..a612de2c6c 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -199,6 +199,7 @@ find_package_handle_standard_args(MKL
   REQUIRED_VARS MKL_INCLUDE_DIR MKL_Core_LINK_LIBRARY)
 if(NOT WIN32)
   find_library(M_LIB m)
+  mark_as_advanced(M_LIB)
 endif()
 if(MKL_FOUND)
   add_library(MKL::MKL SHARED IMPORTED)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index b9dc5888b5..dd6a3d07a9 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -120,7 +120,8 @@ macro(arrayfire_set_cmake_default_variables)
       CMAKE_C_FLAGS_COVERAGE
       CMAKE_EXE_LINKER_FLAGS_COVERAGE
       CMAKE_SHARED_LINKER_FLAGS_COVERAGE
-      CMAKE_STATIC_LINKER_FLAGS_COVERAGE )
+      CMAKE_STATIC_LINKER_FLAGS_COVERAGE
+      CMAKE_MODULE_LINKER_FLAGS_COVERAGE)
 
   set_property(GLOBAL PROPERTY USE_FOLDERS ON)
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8d5ac48ba6..516b696e38 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -24,7 +24,9 @@ find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
-mark_as_advanced(CUDA_LIBRARIES_PATH)
+mark_as_advanced(
+    CUDA_LIBRARIES_PATH
+    CUDA_architecture_build_targets)
 
 # TODO(umar): Move these flags to a separate function/target
 if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 66b81d4221..edede1adf7 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -7,8 +7,10 @@
 
 include(InternalUtils)
 
-set(OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end")
-set_property(CACHE OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
+set(AF_OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end")
+set_property(CACHE AF_OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
+
+af_deprecate(OPENCL_BLAS_LIBRARY AF_OPENCL_BLAS_LIBRARY)
 
 include(build_clFFT)
 
@@ -400,13 +402,13 @@ target_link_libraries(afopencl
     Threads::Threads
     )
 
-if(OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
+if(AF_OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
   include(build_clBLAS)
   target_compile_definitions(afopencl PRIVATE USE_CLBLAS)
   target_link_libraries(afopencl
     PRIVATE
       clBLAS::clBLAS)
-elseif(OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
+elseif(AF_OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
   include(build_CLBlast)
   target_compile_definitions(afopencl PRIVATE USE_CLBLAST)
   target_link_libraries(afopencl

From d18deaf01042afc3d3222b7786c30ebd7cc8b9a3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Apr 2018 01:52:43 -0400
Subject: [PATCH 1384/2677] Simplify ArrayFireConfig script and improve
 documentation.

This commit improves the ArrayFireConfig.cmake scripts. These scripts
are used to find ArrayFire when the find_package command is called.
This commit makes the following changes:

* Create one target file for all backends instead of one per backend
* Add components support to warn if a particular backend was not
  installed
* Update the documentation and encurage the use of imported targets
  instead of the libraries and include variables
* ArrayFire_DIR now needs to point to the build directory instead
  of the build/cmake directory to work correctly
---
 CMakeLists.txt                        |  52 ++++----
 CMakeModules/ArrayFireConfig.cmake.in | 178 ++++++++++++++++----------
 CMakeModules/InternalUtils.cmake      |   2 +-
 src/backend/cpu/CMakeLists.txt        |   2 +-
 src/backend/cuda/CMakeLists.txt       |   2 +-
 src/backend/opencl/CMakeLists.txt     |   2 +-
 6 files changed, 137 insertions(+), 101 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a4884166b9..ea963bc2be 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -219,23 +219,19 @@ install(DIRECTORY "${ArrayFire_SOURCE_DIR}/LICENSES/"
     DESTINATION LICENSES
     COMPONENT licenses)
 
-foreach(backend CPU CUDA OpenCL Unified)
-  string(TOUPPER ${backend} upper_backend)
-  if(AF_BUILD_${upper_backend})
-    install(EXPORT ArrayFire${backend}Targets
-            NAMESPACE ArrayFire::
-            DESTINATION ${AF_INSTALL_CMAKE_DIR}
-            COMPONENT cmake)
-
-    export( EXPORT ArrayFire${backend}Targets
-            NAMESPACE ArrayFire::
-            FILE cmake/ArrayFire${backend}Targets.cmake)
-  endif()
-endforeach()
+
+install(EXPORT ArrayFireTargets
+        NAMESPACE ArrayFire::
+        DESTINATION ${AF_INSTALL_CMAKE_DIR}
+        COMPONENT cmake)
+
+export(EXPORT ArrayFireTargets
+       NAMESPACE ArrayFire::
+       FILE cmake/ArrayFireTargets.cmake)
 
 include(CMakePackageConfigHelpers)
 write_basic_package_version_file(
-  "${ArrayFire_BINARY_DIR}/cmake/ArrayFireConfigVersion.cmake"
+  "${ArrayFire_BINARY_DIR}/ArrayFireConfigVersion.cmake"
   COMPATIBILITY SameMajorVersion
 )
 
@@ -250,8 +246,21 @@ configure_package_config_file(
   PATH_VARS INCLUDE_DIRS CMAKE_DIR
   )
 
+# This file will be used to create the config file for the build directory.
+# These config files will be used by the examples to find the ArrayFire
+# libraries
+set(INCLUDE_DIRS "${ArrayFire_SOURCE_DIR}/include" "${ArrayFire_BINARY_DIR}/include")
+set(CMAKE_DIR "${ArrayFire_BINARY_DIR}/cmake")
+configure_package_config_file(
+  "${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in"
+  "ArrayFireConfig.cmake"
+  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}"
+  PATH_VARS INCLUDE_DIRS CMAKE_DIR
+  INSTALL_PREFIX "${ArrayFire_BINARY_DIR}"
+  )
+
 install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
-              ${ArrayFire_BINARY_DIR}/cmake/ArrayFireConfigVersion.cmake
+              ${ArrayFire_BINARY_DIR}/ArrayFireConfigVersion.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
@@ -283,19 +292,6 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
     COMPONENT mkl_dependencies)
 endif()
 
-# This file will be used to create the config file for the build directory.
-# These config files will be used by the examples to find the ArrayFire
-# libraries
-set(INCLUDE_DIRS "${ArrayFire_SOURCE_DIR}/include" "${ArrayFire_BINARY_DIR}/include")
-set(CMAKE_DIR "${ArrayFire_BINARY_DIR}/cmake")
-configure_package_config_file(
-  ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
-  cmake/ArrayFireConfig.cmake
-  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}/cmake"
-  PATH_VARS INCLUDE_DIRS CMAKE_DIR
-  INSTALL_PREFIX "${ArrayFire_BINARY_DIR}"
-  )
-
 # Registers the current build directory with the user's cmake config. This will
 # create a file at $HOME/.cmake/packages/ArrayFire which will point to this source
 # build directory.
diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index 66909f3798..b829f14960 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -1,83 +1,123 @@
-# Defines the following variables:
-# ArrayFire_INCLUDE_DIRS    - Location of ArrayFire's include directory.
-# ArrayFire_LIBRARIES       - Location of ArrayFire's libraries. This will default
-#                             to a GPU backend if one is found.
-# ArrayFire_FOUND           - True if ArrayFire has been located
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
 #
-# You may provide a hint to where ArrayFire's root directory may be located
-# by setting ArrayFire_DIR.
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+# ArrayFire
+# ---------
 #
-# ----------------------------------------------------------------------------
+# IMPORTED Targets
+# ^^^^^^^^^^^^^^^^
 #
-# ArrayFire_CPU_FOUND         - True of the ArrayFire CPU library has been found.
-# ArrayFire_CPU_LIBRARIES     - Location of ArrayFire's CPU library, if found
-# ArrayFire_CUDA_FOUND        - True of the ArrayFire CUDA library has been found.
-# ArrayFire_CUDA_LIBRARIES    - Location of ArrayFire's CUDA library, if found
-# ArrayFire_OpenCL_FOUND      - True of the ArrayFire OpenCL library has been found.
-# ArrayFire_OpenCL_LIBRARIES  - Location of ArrayFire's OpenCL library, if found
-# ArrayFire_Unified_FOUND     - True of the ArrayFire Unified library has been found.
-# ArrayFire_Unified_LIBRARIES - Location of ArrayFire's Unified library, if found
+# This is the configuration file for the ArrayFire Library. It provides the
+# following :prop_tgt:`IMPORTED` targets:
 #
-#=============================================================================
-# Copyright (c) 2015, ArrayFire
-# All rights reserved.
+# ``ArrayFire::af``
+#   Target for the ArrayFire Unified backend.
+# ``ArrayFire::afcpu``
+#   Target for the ArrayFire CPU backend.
+# ``ArrayFire::afcuda``
+#   Target for the ArrayFire CUDA backend.
+# ``ArrayFire::afopencl``
+#   Target for the ArrayFire OpenCL backend.
+#
+# These targets can be used to link with your application using the
+# ``target_link_library`` command. Here is an example of how to use these
+# targets in your application:
+#
+#   add_executable(mybinary source.cpp)
+#   target_link_library(mybinary PRIVATE ArrayFire::afopencl)
+#
+# This example creates a mybinary executable from the source.cpp file and links
+# against the OpenCL backend of ArrayFire library. Note you do *not* need to set
+# the include directories as they are automatically included with the target.
+#
+# This is the recommended way of linking against ArrayFire
+#
+# Legacy Variables
+# ^^^^^^^^^^^^^^^^
+#
+# Additionally, this config file creates the following variables for backward
+# compatibility with legacy cmake files:
+#
+# ``ArrayFire_INCLUDE_DIRS``
+#  Path to ArrayFire's include directory.
+# ``ArrayFire_LIBRARIES``
+#  ArrayFire's libraries. This will default to a GPU backend if one
+#  is found.
+# ``ArrayFire_FOUND``
+#  True if ArrayFire has been located
 #
-# Redistribution and use in source and binary forms, with or without modification,
-# are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright notice, this
-#   list of conditions and the following disclaimer in the documentation and/or
-#   other materials provided with the distribution.
-#
-# * Neither the name of the ArrayFire nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#=============================================================================
+# ``ArrayFire_CPU_FOUND``
+#   True of the ArrayFire CPU library has been found.
+# ``ArrayFire_CPU_LIBRARIES``
+#   Location of ArrayFire's CPU library, if found
+#
+# ``ArrayFire_CUDA_FOUND``
+#   True of the ArrayFire CUDA library has been found.
+# ``ArrayFire_CUDA_LIBRARIES``
+#   Location of ArrayFire's CUDA library, if found
+#
+# ``ArrayFire_OpenCL_FOUND``
+#   True of the ArrayFire OpenCL library has been found.
+# ``ArrayFire_OpenCL_LIBRARIES``
+#   Location of ArrayFire's OpenCL library, if found
+#
+# ``ArrayFire_Unified_FOUND``
+#   True of the ArrayFire Unified library has been found.
+# ``ArrayFire_Unified_LIBRARIES``
+#   Location of ArrayFire's Unified library, if found
+#
+# It is recommended you use imported targets instead of these variables.
+#
+# You may provide a hint to where ArrayFire's root directory may be located
+# by setting ArrayFire_DIR. You should not need to set this if you installed
+# ArrayFire using the official installers or the package manager(please submit
+# a bug report). If CMake is unable to locate ArrayFire then set the
+# ArrayFire_DIR to the directory of this file.
+#
+# If you are trying to link against a source build then this should be set to
+# the build directory.
 
 @PACKAGE_INIT@
 
 set_and_check(ArrayFire_INCLUDE_DIRS @PACKAGE_INCLUDE_DIRS@)
 
-foreach(backend Unified CPU OpenCL CUDA)
-  if(backend STREQUAL "Unified")
-    set(lowerbackend "")
-  else()
-    string(TOLOWER "${backend}" lowerbackend)
-  endif()
-  if(NOT TARGET ArrayFire::af${lowerbackend} AND NOT TARGET af${lowerbackend})
-    # Either we are not in the ArrayFire project or the target was not built
-    if(EXISTS @PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
-      include(@PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
-    endif()
-  endif()
-  if(TARGET ArrayFire::af${lowerbackend})
-    get_property(config TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_CONFIGURATIONS)
-    if(NOT config)
-      set(config "NOCONFIG")
-    endif()
-    get_property(loc TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_LOCATION_${config})
-  endif()
+if(NOT TARGET ArrayFire::af     AND
+   NOT TARGET ArrayFire::afcpu  AND
+   NOT TARGET ArrayFire::afcuda AND
+   NOT TARGET ArrayFire::afopencl)
+  include(@PACKAGE_CMAKE_DIR@/ArrayFireTargets.cmake)
+endif()
+
+# Legacy variables
+if(TARGET ArrayFire::af)
+  set(ArrayFire_Unified_FOUND ON)
+  set(ArrayFire_Unified_LIBRARIES ArrayFire::af)
+  set(ArrayFire_LIBRARIES ArrayFire::af)
+endif()
+if(TARGET ArrayFire::afcpu)
+  set(ArrayFire_CPU_FOUND ON)
+  set(ArrayFire_CPU_LIBRARIES ArrayFire::afcpu)
+  set(ArrayFire_LIBRARIES ArrayFire::afcpu)
+endif()
+if(TARGET ArrayFire::afopencl)
+  set(ArrayFire_OpenCL_FOUND ON)
+  set(ArrayFire_OpenCL_LIBRARIES ArrayFire::afopencl)
+  set(ArrayFire_LIBRARIES ArrayFire::afopencl)
+endif()
+if(TARGET ArrayFire::afcuda)
+  set(ArrayFire_CUDA_FOUND ON)
+  set(ArrayFire_CUDA_LIBRARIES ArrayFire::afcuda)
+  set(ArrayFire_LIBRARIES ArrayFire::afcuda)
+endif()
 
-  if((TARGET ArrayFire::af${lowerbackend} AND EXISTS ${loc}) OR TARGET af${lowerbackend})
-      set(ArrayFire_${backend}_FOUND ON)
-      set(ArrayFire_${backend}_LIBRARIES ArrayFire::af${lowerbackend})
-      set(ArrayFire_LIBRARIES ArrayFire::af${lowerbackend})
-  else()
-      set(ArrayFire_${backend}_FOUND OFF)
+foreach(_comp ${ArrayFire_FIND_COMPONENTS})
+  if (NOT ArrayFire_${_comp}_FOUND)
+    set(ArrayFire_FOUND False)
+    set(ArrayFire_NOT_FOUND_MESSAGE "Required ArrayFire component ${_comp} not found")
   endif()
 endforeach()
 
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index dd6a3d07a9..39cd884de6 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -63,7 +63,7 @@ function(get_native_path out_path path)
 endfunction()
 
 macro(arrayfire_set_cmake_default_variables)
-  set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR}/cmake;${CMAKE_PREFIX_PATH}")
+  set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR};${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
 
   set(CMAKE_CXX_STANDARD 11)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 61879f46e7..72fc7bd6d5 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -344,7 +344,7 @@ else()
 endif()
 
 install(TARGETS afcpu
-  EXPORT ArrayFireCPUTargets
+  EXPORT ArrayFireTargets
   COMPONENT cpu
   PUBLIC_HEADER DESTINATION af
   RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 516b696e38..8fa766e682 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -503,7 +503,7 @@ if(APPLE)
 endif()
 
 install(TARGETS afcuda
-  EXPORT ArrayFireCUDATargets
+  EXPORT ArrayFireTargets
   COMPONENT cuda
   PUBLIC_HEADER DESTINATION af
   RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index edede1adf7..44ba1c8295 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -514,7 +514,7 @@ if(LAPACK_FOUND)
 endif(LAPACK_FOUND)
 
 install(TARGETS afopencl
-  EXPORT ArrayFireOpenCLTargets
+  EXPORT ArrayFireTargets
   COMPONENT opencl
   PUBLIC_HEADER DESTINATION af
   RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}

From 9752aa6b13d8f0ccca004b73d4782ec8221c3442 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Apr 2018 23:12:33 -0700
Subject: [PATCH 1385/2677] Create installers with dependences only when
 AF_INSTALL_STANDALONE is set (#2103)

Create installers with dependences based on AF_INSTALL_STANDALONE
---
 CMakeLists.txt                    |  5 +++-
 src/api/c/CMakeLists.txt          |  2 +-
 src/backend/common/CMakeLists.txt | 14 ++++++----
 src/backend/cuda/CMakeLists.txt   | 46 ++++++++++++++++---------------
 4 files changed, 37 insertions(+), 30 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea963bc2be..6f17e5567d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -47,6 +47,8 @@ option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
 option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 
+option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
+
 cmake_dependent_option(AF_WITH_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
   "BUILD_TESTING" OFF)
 
@@ -74,6 +76,7 @@ af_deprecate(USE_CPUID             AF_WITH_CPUID)
 
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
+  AF_INSTALL_STANDALONE
   AF_USE_SYSTEM_FORGE
   AF_WITH_CPUID
   CUDA_HOST_COMPILER
@@ -264,7 +267,7 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL AND AF_INSTALL_STANDALONE)
   if(TARGET MKL::ThreadingLibrary)
     install(FILES
       $<TARGET_FILE:MKL::ThreadingLibrary>
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index a3eea76d6a..a03d710484 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -158,7 +158,7 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage_STATIC)
   else ()
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage)
-    if (WIN32)
+    if (WIN32 AND AF_INSTALL_STANDALONE)
       install(FILES $<TARGET_FILE:FreeImage::FreeImage>
         DESTINATION ${AF_INSTALL_BIN_DIR})
     endif ()
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 3411e5eacc..a405f49f19 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -72,12 +72,14 @@ if(AF_WITH_GRAPHICS)
     target_link_libraries(afcommon_interface INTERFACE glbinding::glbinding)
   endif()
 
-  install(FILES
-      $<TARGET_FILE:glbinding::glbinding>
-      $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
-      $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
-    DESTINATION ${AF_INSTALL_BIN_DIR}
-    COMPONENT gfx_dependencies)
+  if(AF_INSTALL_STANDALONE)
+    install(FILES
+        $<TARGET_FILE:glbinding::glbinding>
+        $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
+        $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
+      DESTINATION ${AF_INSTALL_BIN_DIR}
+      COMPONENT gfx_dependencies)
+  endif()
 
   add_dependencies(afcommon_interface forge-ext)
 endif()
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8fa766e682..12f592b3e5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -541,28 +541,30 @@ macro(afcu_collect_libs libname)
   endif ()
 endmacro()
 
-afcu_collect_libs(cufft)
-afcu_collect_libs(cublas)
-afcu_collect_libs(cusolver)
-afcu_collect_libs(cusparse)
-afcu_collect_libs(nvrtc)
-
-if(APPLE)
-  afcu_collect_libs(cudart)
-
-  get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
-  install(FILES       ${nvrtc_outpath}
-          DESTINATION ${AF_INSTALL_BIN_DIR}
-          RENAME      "${PX}nvrtc-builtins${SX}"
-          COMPONENT   cuda_dependencies)
-elseif(UNIX)
-  get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
-  install(FILES       ${nvrtc_outpath}
-          DESTINATION ${AF_INSTALL_BIN_DIR}
-          RENAME      "${PX}nvrtc-builtins${SX}"
-          COMPONENT   cuda_dependencies)
-else()
-  afcu_collect_libs(nvrtc-builtins)
+if(AF_INSTALL_STANDALONE)
+  afcu_collect_libs(cufft)
+  afcu_collect_libs(cublas)
+  afcu_collect_libs(cusolver)
+  afcu_collect_libs(cusparse)
+  afcu_collect_libs(nvrtc)
+
+  if(APPLE)
+    afcu_collect_libs(cudart)
+
+    get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
+    install(FILES       ${nvrtc_outpath}
+            DESTINATION ${AF_INSTALL_BIN_DIR}
+            RENAME      "${PX}nvrtc-builtins${SX}"
+            COMPONENT   cuda_dependencies)
+  elseif(UNIX)
+    get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
+    install(FILES       ${nvrtc_outpath}
+            DESTINATION ${AF_INSTALL_BIN_DIR}
+            RENAME      "${PX}nvrtc-builtins${SX}"
+            COMPONENT   cuda_dependencies)
+  else()
+    afcu_collect_libs(nvrtc-builtins)
+  endif()
 endif()
 
 

From f985fab06627e8e157f1736f3e9b62c7ff699556 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 3 Apr 2018 02:21:51 -0400
Subject: [PATCH 1386/2677] Remove cmake_policy line from examples

The cmake_policy line was targeting a policy that was greater than
the minimum required version for the CMake file.
---
 examples/benchmarks/CMakeLists.txt       | 1 -
 examples/computer_vision/CMakeLists.txt  | 1 -
 examples/financial/CMakeLists.txt        | 1 -
 examples/getting_started/CMakeLists.txt  | 1 -
 examples/graphics/CMakeLists.txt         | 1 -
 examples/helloworld/CMakeLists.txt       | 1 -
 examples/image_processing/CMakeLists.txt | 1 -
 examples/lin_algebra/CMakeLists.txt      | 1 -
 examples/machine_learning/CMakeLists.txt | 1 -
 examples/pde/CMakeLists.txt              | 1 -
 examples/unified/CMakeLists.txt          | 1 -
 11 files changed, 11 deletions(-)

diff --git a/examples/benchmarks/CMakeLists.txt b/examples/benchmarks/CMakeLists.txt
index 421001e357..c5b717f41a 100644
--- a/examples/benchmarks/CMakeLists.txt
+++ b/examples/benchmarks/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Benchmarks
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
index 654ab70d71..521f7dc0a3 100644
--- a/examples/computer_vision/CMakeLists.txt
+++ b/examples/computer_vision/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Computer-Vision
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/financial/CMakeLists.txt b/examples/financial/CMakeLists.txt
index 232e6326ca..7c65c63595 100644
--- a/examples/financial/CMakeLists.txt
+++ b/examples/financial/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Financial
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/getting_started/CMakeLists.txt b/examples/getting_started/CMakeLists.txt
index b6f81e3e6f..63bd043cd0 100644
--- a/examples/getting_started/CMakeLists.txt
+++ b/examples/getting_started/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Getting-Started
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
index d7b3c57ca7..e7186cd1a7 100644
--- a/examples/graphics/CMakeLists.txt
+++ b/examples/graphics/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Graphics
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/helloworld/CMakeLists.txt b/examples/helloworld/CMakeLists.txt
index 1a744b1481..64e9a6aa6a 100644
--- a/examples/helloworld/CMakeLists.txt
+++ b/examples/helloworld/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-HelloWorld
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index a2b284e00e..d6d921ad19 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Image-Processing
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/lin_algebra/CMakeLists.txt b/examples/lin_algebra/CMakeLists.txt
index 36c21274c4..59aa2cbcd9 100644
--- a/examples/lin_algebra/CMakeLists.txt
+++ b/examples/lin_algebra/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Linear-Algebra
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/machine_learning/CMakeLists.txt b/examples/machine_learning/CMakeLists.txt
index d3bdf0120d..136e9338a0 100644
--- a/examples/machine_learning/CMakeLists.txt
+++ b/examples/machine_learning/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Linear-Algebra
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index 1b1f296395..345afeabfb 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-PDE
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/unified/CMakeLists.txt b/examples/unified/CMakeLists.txt
index 94a53ad5df..330a9c4af7 100644
--- a/examples/unified/CMakeLists.txt
+++ b/examples/unified/CMakeLists.txt
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.0)
-cmake_policy(VERSION 3.5)
 project(ArrayFire-Example-Unified
   VERSION 3.5.0
   LANGUAGES CXX)

From c8b039426aa19b129694f9f3b7e639b50dbda69d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 3 Apr 2018 03:55:23 -0400
Subject: [PATCH 1387/2677] Install the OpenCL ICD with the standalone
 installers

---
 src/backend/opencl/CMakeLists.txt | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 44ba1c8295..5f50cc786e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -524,6 +524,20 @@ install(TARGETS afopencl
   INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
   )
 
+if(NOT APPLE AND AF_INSTALL_STANDALONE)
+  get_filename_component(opencl_outpath "${OpenCL_LIBRARIES}" REALPATH)
+  if(UNIX)
+    set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX}.1)
+  else()
+    set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX})
+  endif()
+  install(FILES
+      ${opencl_outpath}
+      DESTINATION ${AF_INSTALL_BIN_DIR}
+      RENAME "${CMAKE_SHARED_LIBRARY_PREFIX}OpenCL${SX}"
+      COMPONENT opencl_dependencies)
+endif()
+
 source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
 source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)

From 4981731cf78df369a75e80c8d3cfec0733cb19f3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 3 Apr 2018 13:58:23 +0530
Subject: [PATCH 1388/2677] Fix output dimensions in mean cuda kernel

---
 src/backend/cuda/kernel/mean.hpp |  1 +
 test/mean.cpp                    | 23 +++++++++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 4e164803df..3d788b73fc 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -210,6 +210,7 @@ namespace kernel
 
         if (blocks_dim[dim] > 1) {
           dim4 dims(4, out.dims);
+          dims[dim] = blocks_dim[dim];
           tmpOut = createEmptyArray<To>(dims);
           tmpWt  = createEmptyArray<Tw>(dims);
         }
diff --git a/test/mean.cpp b/test/mean.cpp
index 7407ecc26f..4556fef690 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -321,3 +321,26 @@ TEST(WeightedMean, Broadacst)
         ASSERT_NEAR(hc[i], hd[i], 1E-5);
     }
 }
+
+TEST(Mean, Issue2093)
+{
+  using namespace af;
+
+  const int NELEMS = 512;
+
+  array data = randu(1, NELEMS);
+  array wts  = constant(1.0f, 1, NELEMS);
+  vector<float> hdata(NELEMS);
+  data.host(hdata.data());
+
+  array out = mean(data, wts, 1);
+  float outVal;
+  out.host(&outVal);
+
+  float expected = 0.0;
+  for (size_t i=0; i<NELEMS; ++i)
+      expected += hdata[i];
+  expected /= NELEMS;
+
+  ASSERT_NEAR(outVal, expected, 0.001);
+}

From 3a018e10a23076d676f8a731dadb3d3415e594c5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 3 Apr 2018 23:01:45 +0530
Subject: [PATCH 1389/2677] Mark MKL_*_DLL_LIBRARY cmake vars as advanced

---
 CMakeModules/FindMKL.cmake | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index a612de2c6c..1af07a103e 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -155,6 +155,8 @@ function(find_mkl_library)
         PROPERTIES
           IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_DLL_LIBRARY}"
           IMPORTED_IMPLIB "${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
+
+      mark_as_advanced(MKL_${mkl_args_NAME}_DLL_LIBRARY)
     endif()
 endfunction()
 

From bfb63cb8d170c44e69b0636b8a86c01d05c9fecf Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 4 Apr 2018 09:46:48 +0530
Subject: [PATCH 1390/2677] Fix cpack GUI installer components dependencies

---
 CMakeLists.txt                    |   2 +-
 CMakeModules/CPackConfig.cmake    | 101 +++++++++++-------------------
 src/api/c/CMakeLists.txt          |   3 +-
 src/backend/common/CMakeLists.txt |   2 +-
 4 files changed, 42 insertions(+), 66 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6f17e5567d..850f38be91 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -202,7 +202,7 @@ if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
     endif ()
     install(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/${fg_dlib_px}/"
       DESTINATION "${AF_INSTALL_BIN_DIR}"
-      COMPONENT gfx_dependencies)
+      COMPONENT common_backend_dependencies)
 endif()
 
 # install the examples irrespective of the AF_BUILD_EXAMPLES value
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index b785c4911e..c91e285b98 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -131,81 +131,55 @@ cpack_add_install_type(Extra DISPLAY_NAME "Extra")
 cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
 
 set(PACKAGE_MKL_DEPS OFF)
-set(PACKAGE_GFX_DEPS OFF)
 
 if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
   set(PACKAGE_MKL_DEPS ON)
-  cpack_add_component(mkl_dependencies
-    DISPLAY_NAME "Intel MKL Prerequisites"
-    DESCRIPTION "Intel MKL libraries required by CPU, OpenCL backends."
-    HIDDEN
+  cpack_add_component(mkl_dependencies HIDDEN
     INSTALL_TYPES Development Runtime)
 endif ()
 
-if (Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
-  set(PACKAGE_GFX_DEPS ON)
-  cpack_add_component(gfx_dependencies
-    DISPLAY_NAME "Graphics prerequisites"
-    DESCRIPTION "Graphics library dependencies"
-    HIDDEN
-    INSTALL_TYPES Development Runtime)
-endif ()
+cpack_add_component(common_backend_dependencies
+  HIDDEN
+  INSTALL_TYPES Development Runtime)
 
+cpack_add_component(opencl_dependencies HIDDEN
+  INSTALL_TYPES Development Runtime)
+  
 cpack_add_component(cuda_dependencies
   DISPLAY_NAME "CUDA Dependencies"
   DESCRIPTION "CUDA Runtime and libraries required for the CUDA backend."
   INSTALL_TYPES Development Runtime)
 
-if (PACKAGE_MKL_DEPS AND PACKAGE_GFX_DEPS)
-  cpack_add_component(cpu
-    DISPLAY_NAME "CPU Backend"
-    DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
-    DEPENDS mkl_dependencies gfx_dependencies
-    INSTALL_TYPES Development Runtime)
-  cpack_add_component(opencl
-    DISPLAY_NAME "OpenCL Backend"
-    DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
-    DEPENDS mkl_dependencies gfx_dependencies
-    INSTALL_TYPES Development Runtime)
-  cpack_add_component(cuda
-    DISPLAY_NAME "CUDA Backend"
-    DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
-    DEPENDS gfx_dependencies cuda_dependencies
-    INSTALL_TYPES Development Runtime)
-elseif (PACKAGE_MKL_DEPS)
-  cpack_add_component(cpu
-    DISPLAY_NAME "CPU Backend"
-    DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
-    DEPENDS mkl_dependencies
-    INSTALL_TYPES Development Runtime)
-  cpack_add_component(opencl
-    DISPLAY_NAME "OpenCL Backend"
-    DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
-    DEPENDS mkl_dependencies
-    INSTALL_TYPES Development Runtime)
-  cpack_add_component(cuda
-    DISPLAY_NAME "CUDA Backend"
-    DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
-    DEPENDS cuda_dependencies
-    INSTALL_TYPES Development Runtime)
-elseif (PACKAGE_GFX_DEPS)
-  cpack_add_component(cpu
-    DISPLAY_NAME "CPU Backend"
-    DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
-    DEPENDS gfx_dependencies
-    INSTALL_TYPES Development Runtime)
-  cpack_add_component(opencl
-    DISPLAY_NAME "OpenCL Backend"
-    DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
-    DEPENDS gfx_dependencies
-    INSTALL_TYPES Development Runtime)
-  cpack_add_component(cuda
-    DISPLAY_NAME "CUDA Backend"
-    DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
-    DEPENDS gfx_dependencies cuda_dependencies
-    INSTALL_TYPES Development Runtime)
+cpack_add_component(cuda
+  DISPLAY_NAME "CUDA Backend"
+  DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
+  DEPENDS common_backend_dependencies cuda_dependencies
+  INSTALL_TYPES Development Runtime)
+
+list(APPEND cpu_deps_comps common_backend_dependencies)
+list(APPEND ocl_deps_comps common_backend_dependencies)
+
+if (NOT APPLE)
+  list(APPEND ocl_deps_comps opencl_dependencies)
 endif ()
 
+if (PACKAGE_MKL_DEPS)
+  list(APPEND cpu_deps_comps mkl_dependencies)
+  list(APPEND ocl_deps_comps mkl_dependencies)
+endif ()
+
+cpack_add_component(cpu
+  DISPLAY_NAME "CPU Backend"
+  DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
+  DEPENDS ${cpu_deps_comps}
+  INSTALL_TYPES Development Runtime)
+
+cpack_add_component(opencl
+  DISPLAY_NAME "OpenCL Backend"
+  DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
+  DEPENDS ${ocl_deps_comps}
+  INSTALL_TYPES Development Runtime)
+
 cpack_add_component(unified
   DISPLAY_NAME "Unified Backend"
   DESCRIPTION "This Backend allows you to choose the platform(cpu, cuda, opencl) at runtime. This option requires at least one of the three backends to be installed to work properly."
@@ -268,9 +242,10 @@ get_native_path(issl_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/ISSL License.txt")
 if (PACKAGE_MKL_DEPS)
   cpack_ifw_configure_component(mkl_dependencies)
 endif ()
-if (PACKAGE_GFX_DEPS)
-  cpack_ifw_configure_component(gfx_dependencies)
+if (NOT APPLE)
+  cpack_ifw_configure_component(opencl_dependencies)
 endif ()
+cpack_ifw_configure_component(common_backend_dependencies)
 cpack_ifw_configure_component(cuda_dependencies)
 cpack_ifw_configure_component(cpu)
 cpack_ifw_configure_component(cuda)
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index a03d710484..3a1e1be642 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -160,7 +160,8 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage)
     if (WIN32 AND AF_INSTALL_STANDALONE)
       install(FILES $<TARGET_FILE:FreeImage::FreeImage>
-        DESTINATION ${AF_INSTALL_BIN_DIR})
+        DESTINATION ${AF_INSTALL_BIN_DIR}
+        COMPONENT common_backend_dependencies)
     endif ()
   endif ()
 endif()
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index a405f49f19..9bd82a7cd7 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -78,7 +78,7 @@ if(AF_WITH_GRAPHICS)
         $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
         $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
       DESTINATION ${AF_INSTALL_BIN_DIR}
-      COMPONENT gfx_dependencies)
+      COMPONENT common_backend_dependencies)
   endif()
 
   add_dependencies(afcommon_interface forge-ext)

From 241ae8996bd3821befcc729faffa5500e79eda23 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 6 Apr 2018 00:08:40 -0400
Subject: [PATCH 1391/2677] Revert back to multiple ArrayFire config files
 (#2110)

Revert back to multiple ArrayFire config files
---
 CMakeLists.txt                        | 49 +++++++++++++-----------
 CMakeModules/ArrayFireConfig.cmake.in | 54 +++++++++++++--------------
 src/backend/cpu/CMakeLists.txt        |  2 +-
 src/backend/cuda/CMakeLists.txt       |  2 +-
 src/backend/opencl/CMakeLists.txt     |  2 +-
 5 files changed, 57 insertions(+), 52 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 850f38be91..a993052a4a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -222,15 +222,20 @@ install(DIRECTORY "${ArrayFire_SOURCE_DIR}/LICENSES/"
     DESTINATION LICENSES
     COMPONENT licenses)
 
-
-install(EXPORT ArrayFireTargets
-        NAMESPACE ArrayFire::
-        DESTINATION ${AF_INSTALL_CMAKE_DIR}
-        COMPONENT cmake)
-
-export(EXPORT ArrayFireTargets
-       NAMESPACE ArrayFire::
-       FILE cmake/ArrayFireTargets.cmake)
+foreach(backend CPU CUDA OpenCL Unified)
+  string(TOUPPER ${backend} upper_backend)
+  string(TOLOWER ${backend} lower_backend)
+  if(AF_BUILD_${upper_backend})
+    install(EXPORT ArrayFire${backend}Targets
+            NAMESPACE ArrayFire::
+            DESTINATION ${AF_INSTALL_CMAKE_DIR}
+            COMPONENT ${lower_backend})
+
+    export( EXPORT ArrayFire${backend}Targets
+            NAMESPACE ArrayFire::
+            FILE cmake/ArrayFire${backend}Targets.cmake)
+  endif()
+endforeach()
 
 include(CMakePackageConfigHelpers)
 write_basic_package_version_file(
@@ -249,19 +254,6 @@ configure_package_config_file(
   PATH_VARS INCLUDE_DIRS CMAKE_DIR
   )
 
-# This file will be used to create the config file for the build directory.
-# These config files will be used by the examples to find the ArrayFire
-# libraries
-set(INCLUDE_DIRS "${ArrayFire_SOURCE_DIR}/include" "${ArrayFire_BINARY_DIR}/include")
-set(CMAKE_DIR "${ArrayFire_BINARY_DIR}/cmake")
-configure_package_config_file(
-  "${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in"
-  "ArrayFireConfig.cmake"
-  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}"
-  PATH_VARS INCLUDE_DIRS CMAKE_DIR
-  INSTALL_PREFIX "${ArrayFire_BINARY_DIR}"
-  )
-
 install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               ${ArrayFire_BINARY_DIR}/ArrayFireConfigVersion.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
@@ -295,6 +287,19 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL AND AF_INSTALL_STANDALONE
     COMPONENT mkl_dependencies)
 endif()
 
+# This file will be used to create the config file for the build directory.
+# These config files will be used by the examples to find the ArrayFire
+# libraries
+set(INCLUDE_DIRS "${ArrayFire_SOURCE_DIR}/include" "${ArrayFire_BINARY_DIR}/include")
+set(CMAKE_DIR "${ArrayFire_BINARY_DIR}/cmake")
+configure_package_config_file(
+  ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+  ArrayFireConfig.cmake
+  INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}"
+  PATH_VARS INCLUDE_DIRS CMAKE_DIR
+  INSTALL_PREFIX "${ArrayFire_BINARY_DIR}"
+  )
+
 # Registers the current build directory with the user's cmake config. This will
 # create a file at $HOME/.cmake/packages/ArrayFire which will point to this source
 # build directory.
diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index b829f14960..b2819b8ac1 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -85,34 +85,34 @@
 
 set_and_check(ArrayFire_INCLUDE_DIRS @PACKAGE_INCLUDE_DIRS@)
 
-if(NOT TARGET ArrayFire::af     AND
-   NOT TARGET ArrayFire::afcpu  AND
-   NOT TARGET ArrayFire::afcuda AND
-   NOT TARGET ArrayFire::afopencl)
-  include(@PACKAGE_CMAKE_DIR@/ArrayFireTargets.cmake)
-endif()
+foreach(backend Unified CPU OpenCL CUDA)
+  if(backend STREQUAL "Unified")
+    set(lowerbackend "")
+  else()
+    string(TOLOWER "${backend}" lowerbackend)
+  endif()
+  if(NOT TARGET ArrayFire::af${lowerbackend} AND NOT TARGET af${lowerbackend})
+    # Either we are not in the ArrayFire project or the target was not built
+    if(EXISTS @PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
+      include(@PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
+    endif()
+  endif()
+  if(TARGET ArrayFire::af${lowerbackend})
+    get_property(config TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_CONFIGURATIONS)
+    if(NOT config)
+      set(config "NOCONFIG")
+    endif()
+    get_property(loc TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_LOCATION_${config})
+  endif()
 
-# Legacy variables
-if(TARGET ArrayFire::af)
-  set(ArrayFire_Unified_FOUND ON)
-  set(ArrayFire_Unified_LIBRARIES ArrayFire::af)
-  set(ArrayFire_LIBRARIES ArrayFire::af)
-endif()
-if(TARGET ArrayFire::afcpu)
-  set(ArrayFire_CPU_FOUND ON)
-  set(ArrayFire_CPU_LIBRARIES ArrayFire::afcpu)
-  set(ArrayFire_LIBRARIES ArrayFire::afcpu)
-endif()
-if(TARGET ArrayFire::afopencl)
-  set(ArrayFire_OpenCL_FOUND ON)
-  set(ArrayFire_OpenCL_LIBRARIES ArrayFire::afopencl)
-  set(ArrayFire_LIBRARIES ArrayFire::afopencl)
-endif()
-if(TARGET ArrayFire::afcuda)
-  set(ArrayFire_CUDA_FOUND ON)
-  set(ArrayFire_CUDA_LIBRARIES ArrayFire::afcuda)
-  set(ArrayFire_LIBRARIES ArrayFire::afcuda)
-endif()
+  if((TARGET ArrayFire::af${lowerbackend} AND EXISTS ${loc}) OR TARGET af${lowerbackend})
+      set(ArrayFire_${backend}_FOUND ON)
+      set(ArrayFire_${backend}_LIBRARIES ArrayFire::af${lowerbackend})
+      set(ArrayFire_LIBRARIES ArrayFire::af${lowerbackend})
+  else()
+      set(ArrayFire_${backend}_FOUND OFF)
+  endif()
+endforeach()
 
 foreach(_comp ${ArrayFire_FIND_COMPONENTS})
   if (NOT ArrayFire_${_comp}_FOUND)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 72fc7bd6d5..61879f46e7 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -344,7 +344,7 @@ else()
 endif()
 
 install(TARGETS afcpu
-  EXPORT ArrayFireTargets
+  EXPORT ArrayFireCPUTargets
   COMPONENT cpu
   PUBLIC_HEADER DESTINATION af
   RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 12f592b3e5..adcae5f4f2 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -503,7 +503,7 @@ if(APPLE)
 endif()
 
 install(TARGETS afcuda
-  EXPORT ArrayFireTargets
+  EXPORT ArrayFireCUDATargets
   COMPONENT cuda
   PUBLIC_HEADER DESTINATION af
   RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 5f50cc786e..16f27d7326 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -514,7 +514,7 @@ if(LAPACK_FOUND)
 endif(LAPACK_FOUND)
 
 install(TARGETS afopencl
-  EXPORT ArrayFireTargets
+  EXPORT ArrayFireOpenCLTargets
   COMPONENT opencl
   PUBLIC_HEADER DESTINATION af
   RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}

From 97b54f10d1c1a4a176453ac34ab883883ccb2a9c Mon Sep 17 00:00:00 2001
From: Filip Matzner <FloopCZ@users.noreply.github.com>
Date: Fri, 6 Apr 2018 06:09:36 +0200
Subject: [PATCH 1392/2677] Do not build forge if USE_SYSTEM_FORGE (#2112)

Do not build forge if USE_SYSTEM_FORGE

Also, add forge-ext target as dependency to afcuda and afopencl targets
---
 CMakeLists.txt                    | 3 ++-
 src/api/c/CMakeLists.txt          | 4 +++-
 src/backend/common/CMakeLists.txt | 4 +++-
 src/backend/cpu/CMakeLists.txt    | 4 +++-
 src/backend/cuda/CMakeLists.txt   | 3 +++
 src/backend/opencl/CMakeLists.txt | 3 +++
 6 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a993052a4a..5e35b8a1d0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -35,6 +35,7 @@ find_package(MKL)
 
 # Graphics dependencies
 find_package(glbinding QUIET)
+find_package(Forge QUIET)
 include(boost_package)
 
 option(AF_BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
@@ -87,7 +88,7 @@ mark_as_advanced(
 arrayfire_get_platform_definitions(platform_definitions)
 add_definitions(${platform_definitions})
 
-if(AF_WITH_GRAPHICS)
+if(AF_WITH_GRAPHICS AND NOT AF_USE_SYSTEM_FORGE)
   include(build_forge)
 endif()
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 3a1e1be642..fad75737b9 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -167,7 +167,9 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
 endif()
 
 if(AF_WITH_GRAPHICS)
-    add_dependencies(c_api_interface forge-ext)
+    if(NOT AF_USE_SYSTEM_FORGE)
+      add_dependencies(c_api_interface forge-ext)
+    endif()
     target_compile_definitions(c_api_interface INTERFACE WITH_GRAPHICS)
 endif()
 
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 9bd82a7cd7..b7760cab70 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -81,7 +81,9 @@ if(AF_WITH_GRAPHICS)
       COMPONENT common_backend_dependencies)
   endif()
 
-  add_dependencies(afcommon_interface forge-ext)
+  if(NOT AF_USE_SYSTEM_FORGE)
+    add_dependencies(afcommon_interface forge-ext)
+  endif()
 endif()
 
 if(LAPACK_FOUND)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 61879f46e7..693ee9e912 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -263,7 +263,9 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(AF_WITH_GRAPHICS)
-  add_dependencies(afcpu forge-ext)
+  if(NOT AF_USE_SYSTEM_FORGE)
+    add_dependencies(afcpu forge-ext)
+  endif()
   target_sources(afcpu
     PRIVATE
       hist_graphics.cpp
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index adcae5f4f2..59d8d9fadc 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -417,6 +417,9 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(AF_WITH_GRAPHICS)
+  if(NOT AF_USE_SYSTEM_FORGE)
+    add_dependencies(afcuda forge-ext)
+  endif()
   target_sources(afcuda
     PRIVATE
       GraphicsResourceManager.cpp
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 16f27d7326..78d255a811 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -424,6 +424,9 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(AF_WITH_GRAPHICS)
+  if(NOT AF_USE_SYSTEM_FORGE)
+    add_dependencies(afopencl forge-ext)
+  endif()
   target_sources(afopencl
     PRIVATE
       GraphicsResourceManager.hpp

From 7e2369ad119741fd47bdb1d5aaa5b914802e91c7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 5 Apr 2018 19:47:21 +0530
Subject: [PATCH 1393/2677] Reorganize GUI installer components hierarchy

---
 CMakeModules/CPackConfig.cmake | 41 +++++++++++++++++++++++++++++++---
 1 file changed, 38 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index c91e285b98..7ffdeb613a 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -130,29 +130,56 @@ cpack_add_install_type(Development DISPLAY_NAME "Development")
 cpack_add_install_type(Extra DISPLAY_NAME "Extra")
 cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
 
+cpack_add_component_group(backends
+  DISPLAY_NAME "ArrayFire"
+  DESCRIPTION "ArrayFire Backend Libraries"
+  EXPANDED)
+cpack_add_component_group(cpu_backend
+  DISPLAY_NAME "CPU Backend"
+  DESCRIPTION "Libraries and dependencies of CPU Backend"
+  PARENT_GROUP backends)
+cpack_add_component_group(cuda_backend
+  DISPLAY_NAME "CUDA Backend"
+  DESCRIPTION "Libraries and dependencies of CUDA Backend"
+  PARENT_GROUP backends)
+cpack_add_component_group(opencl_backend
+  DISPLAY_NAME "OpenCL Backend"
+  DESCRIPTION "Libraries and dependencies of OpenCL Backend"
+  PARENT_GROUP backends)
+
 set(PACKAGE_MKL_DEPS OFF)
 
 if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
   set(PACKAGE_MKL_DEPS ON)
-  cpack_add_component(mkl_dependencies HIDDEN
+  cpack_add_component(mkl_dependencies
+    DISPLAY_NAME "Intel MKL"
+	DESCRIPTION "Intel Math Kernel Libraries for FFTW, BLAS and LAPACK routines"
+	GROUP backends
     INSTALL_TYPES Development Runtime)
 endif ()
 
 cpack_add_component(common_backend_dependencies
-  HIDDEN
+  DISPLAY_NAME "Dependencies"
+  DESCRIPTION "Libraries that are commonly required by all ArrayFire backends"
+  GROUP backends
   INSTALL_TYPES Development Runtime)
 
-cpack_add_component(opencl_dependencies HIDDEN
+cpack_add_component(opencl_dependencies
+  DISPLAY_NAME "OpenCL Dependencies"
+  DESCRIPTION "Libraries required by OpenCL Backend"
+  GROUP opencl_backend
   INSTALL_TYPES Development Runtime)
   
 cpack_add_component(cuda_dependencies
   DISPLAY_NAME "CUDA Dependencies"
   DESCRIPTION "CUDA Runtime and libraries required for the CUDA backend."
+  GROUP cuda_backend
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(cuda
   DISPLAY_NAME "CUDA Backend"
   DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
+  GROUP cuda_backend
   DEPENDS common_backend_dependencies cuda_dependencies
   INSTALL_TYPES Development Runtime)
 
@@ -171,22 +198,26 @@ endif ()
 cpack_add_component(cpu
   DISPLAY_NAME "CPU Backend"
   DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
+  GROUP cpu_backend
   DEPENDS ${cpu_deps_comps}
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(opencl
   DISPLAY_NAME "OpenCL Backend"
   DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
+  GROUP opencl_backend
   DEPENDS ${ocl_deps_comps}
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(unified
   DISPLAY_NAME "Unified Backend"
   DESCRIPTION "This Backend allows you to choose the platform(cpu, cuda, opencl) at runtime. This option requires at least one of the three backends to be installed to work properly."
+  GROUP backends
   INSTALL_TYPES Development Runtime)
 cpack_add_component(headers
   DISPLAY_NAME "C/C++ Headers"
   DESCRIPTION "Headers for the ArrayFire Libraries."
+  GROUP backends
   INSTALL_TYPES Development)
 cpack_add_component(cmake
   DISPLAY_NAME "CMake Support"
@@ -239,6 +270,10 @@ get_native_path(sift_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/OpenSIFT License.txt
 get_native_path(bsd3_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/BSD 3-Clause.txt")
 get_native_path(issl_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/ISSL License.txt")
 
+cpack_ifw_configure_component_group(backends)
+cpack_ifw_configure_component_group(cpu_backend)
+cpack_ifw_configure_component_group(cuda_backend)
+cpack_ifw_configure_component_group(opencl_backend)
 if (PACKAGE_MKL_DEPS)
   cpack_ifw_configure_component(mkl_dependencies)
 endif ()

From 90b1f06cf0fdd5357aeca2ec2a823f9871b4bfca Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Apr 2018 11:46:35 +0530
Subject: [PATCH 1394/2677] Install correct OpenCL ICD file on Windows

---
 src/backend/opencl/CMakeLists.txt | 36 ++++++++++++++++++++++++-------
 1 file changed, 28 insertions(+), 8 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 78d255a811..45a48dfabb 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -528,17 +528,37 @@ install(TARGETS afopencl
   )
 
 if(NOT APPLE AND AF_INSTALL_STANDALONE)
-  get_filename_component(opencl_outpath "${OpenCL_LIBRARIES}" REALPATH)
   if(UNIX)
-    set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX}.1)
+    get_filename_component(opencl_outpath "${OpenCL_LIBRARIES}" REALPATH)
+    install(FILES ${opencl_outpath}
+        DESTINATION ${AF_INSTALL_BIN_DIR}
+        RENAME "${CMAKE_SHARED_LIBRARY_PREFIX}OpenCL${CMAKE_SHARED_LIBRARY_SUFFIX}.1"
+        COMPONENT opencl_dependencies)
   else()
-    set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX})
+    find_file(OpenCL_DLL_LIBRARY
+      NAMES ${CMAKE_SHARED_LIBRARY_PREFIX}OpenCL${CMAKE_SHARED_LIBRARY_SUFFIX}
+	  PATHS
+        ENV "PROGRAMFILES(X86)"
+        ENV "PROGRAMFILES"
+        ENV AMDAPPSDKROOT
+        ENV INTELOCLSDKROOT
+        ENV CUDA_PATH
+        ENV NVSDKCOMPUTE_ROOT
+        ENV ATISTREAMSDKROOT
+      PATH_SUFFIXES
+        "AMD APP SDK/bin/x86_64"
+        "bin/x86_64"
+        "bin/x64"
+        "bin/icd/x64"
+        "OpenCL SDK/bin/icd/x64"
+        "Intel/OpenCL SDK/bin/icd/x64"
+        "OpenCL SDK/bin/icd/x64"
+        "NVIDIA Corporation/OpenCL")
+    mark_as_advanced(OpenCL_DLL_LIBRARY)
+    install(FILES "${OpenCL_DLL_LIBRARY}"
+        DESTINATION ${AF_INSTALL_BIN_DIR}
+        COMPONENT opencl_dependencies)
   endif()
-  install(FILES
-      ${opencl_outpath}
-      DESTINATION ${AF_INSTALL_BIN_DIR}
-      RENAME "${CMAKE_SHARED_LIBRARY_PREFIX}OpenCL${SX}"
-      COMPONENT opencl_dependencies)
 endif()
 
 source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)

From 5844dd63303c3f0cb36da35df152b3c953c74808 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Apr 2018 03:19:30 -0400
Subject: [PATCH 1395/2677] Add a default constructor for Param. Fixes var
 issue #2117

---
 src/backend/opencl/Array.hpp       | 2 +-
 src/backend/opencl/Param.hpp       | 6 ++++--
 src/backend/opencl/kernel/mean.hpp | 2 +-
 test/var.cpp                       | 9 +++++++++
 4 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 512d257db6..954c2d2537 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -223,7 +223,7 @@ namespace opencl
                            {strides()[0], strides()[1], strides()[2], strides()[3]},
                            getOffset()};
 
-            Param out = {(cl::Buffer *)this->get(), info};
+            Param out((cl::Buffer *)this->get(), info);
             return out;
         }
 
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index bd948b1bfe..397c21391e 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -14,11 +14,13 @@
 namespace opencl
 {
 
-    typedef struct
+    struct Param
     {
         cl::Buffer *data;
         KParam info;
-    } Param;
+        Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
+        Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_) {}
+    };
 
     Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]);
 }
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 90fedeb926..8be62e9720 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -278,7 +278,7 @@ void mean_dim(Param out, Param in, Param inWeight, int dim)
         groups_all[dim] = 1;
         mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWeight, dim, threads_y, groups_all);
     } else {
-        Array<Tw> tmpWeight = createEmptyArray<Tw>(0);
+        Param tmpWeight;
         mean_dim_launcher<Ti, Tw, To>(out, tmpWeight, in, inWeight, dim, threads_y, groups_all);
     }
 
diff --git a/test/var.cpp b/test/var.cpp
index 76058dcf7f..885fa716f0 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -162,3 +162,12 @@ TYPED_TEST(Var, DimCPPSmall)
         }
     }
 }
+
+TEST(Var, ISSUE2117) {
+  using namespace af;
+
+  array myArray = constant(1, 1000, 3000);
+  myArray = af::var(myArray, true, 1);
+
+  ASSERT_NEAR(0.0f, sum<float>(myArray), 0.000001);
+}

From 4bf51615ff8e5f44db34668af9a2da853f157320 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 10 Apr 2018 01:27:23 -0400
Subject: [PATCH 1396/2677] Use Array<T> instead of Param in OpenCL backend.

---
 src/backend/opencl/Array.cpp                  |  6 +-
 src/backend/opencl/Array.hpp                  |  6 +-
 src/backend/opencl/Param.cpp                  | 22 ++---
 src/backend/opencl/Param.hpp                  | 12 ++-
 src/backend/opencl/kernel/harris.hpp          | 89 +++++--------------
 src/backend/opencl/kernel/ireduce.hpp         | 17 +---
 src/backend/opencl/kernel/mean.hpp            |  4 +-
 src/backend/opencl/kernel/reduce.hpp          | 29 ++----
 .../opencl/kernel/sort_by_key_impl.hpp        | 26 ++----
 9 files changed, 68 insertions(+), 143 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index c84d493258..8fff008991 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -312,11 +312,11 @@ namespace opencl
 
     template<typename T>
     Array<T>
-    createDeviceDataArray(const dim4 &size, const void *data)
+    createDeviceDataArray(const dim4 &size, const void *data, bool copy)
     {
         verifyDoubleSupport<T>();
 
-        return Array<T>(size, (cl_mem)(data), 0, false);
+        return Array<T>(size, (cl_mem)(data), 0, copy);
     }
 
     template<typename T>
@@ -405,7 +405,7 @@ namespace opencl
 
 #define INSTANTIATE(T)                                                  \
     template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
-    template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
+    template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data, bool copy); \
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
     template       Array<T>  *initArray<T      >      ();               \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 954c2d2537..8cb97649af 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -46,7 +46,7 @@ namespace opencl
     Array<T> createHostDataArray(const af::dim4 &size, const T * const data);
 
     template<typename T>
-    Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
+    Array<T> createDeviceDataArray(const af::dim4 &size, const void *data, bool copy = false);
 
     // Copies data to an existing Array object from a host pointer
     template<typename T>
@@ -223,7 +223,7 @@ namespace opencl
                            {strides()[0], strides()[1], strides()[2], strides()[3]},
                            getOffset()};
 
-            Param out((cl::Buffer *)this->get(), info);
+            Param out{(cl::Buffer *)this->get(), info};
             return out;
         }
 
@@ -266,7 +266,7 @@ namespace opencl
 
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
-        friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
+        friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data, bool copy);
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index 552513aaf2..60d8febaff 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -14,15 +14,17 @@
 
 namespace opencl
 {
-    Param makeParam(cl_mem mem, int off, int dims[4], int strides[4])
-    {
-        Param out;
-        out.data = new cl::Buffer(mem);
-        out.info.offset = off;
-        for (int i = 0; i < 4; i++) {
-            out.info.dims[i] = dims[i];
-            out.info.strides[i] = strides[i];
-        }
-        return out;
+    Param::Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
+    Param::Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_){}
+
+    Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]) {
+         Param out;
+         out.data = new cl::Buffer(mem);
+         out.info.offset = off;
+         for (int i = 0; i < 4; i++) {
+             out.info.dims[i] = dims[i];
+             out.info.strides[i] = strides[i];
+         }
+         return out;
     }
 }
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 397c21391e..9f690ea6d5 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -18,9 +18,17 @@ namespace opencl
     {
         cl::Buffer *data;
         KParam info;
-        Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
-        Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_) {}
+        Param& operator=(const Param& other) = default;
+        Param(const Param& other) = default;
+        Param(Param&& other) = default;
+
+        // DEPRECATED("Use Array<T>")
+        Param();
+        // DEPRECATED("Use Array<T>")
+        Param(cl::Buffer *data_, KParam info_);
+        ~Param() = default;
     };
 
+    // DEPRECATED("Use Array<T>")
     Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]);
 }
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 12f38e61e9..f170d67226 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -22,13 +22,6 @@
 #include <cache.hpp>
 #include <tuple>
 
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::EnqueueArgs;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-
 namespace opencl
 {
 namespace kernel
@@ -56,21 +49,11 @@ void gaussian1D(T* out, const int dim, double sigma=0.0)
 }
 
 template<typename T, typename convAccT>
-void conv_helper(Param &ixx, Param &ixy, Param &iyy, Param &filter)
+void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy, Array<convAccT> &filter)
 {
-    Param ixx_tmp, ixy_tmp, iyy_tmp;
-    ixx_tmp.info.offset = ixy_tmp.info.offset = iyy_tmp.info.offset = 0;
-    for (dim_t i = 0; i < 4; i++) {
-        ixx_tmp.info.dims[i] = ixx.info.dims[i];
-        ixy_tmp.info.dims[i] = ixy.info.dims[i];
-        iyy_tmp.info.dims[i] = iyy.info.dims[i];
-        ixx_tmp.info.strides[i] = ixx.info.strides[i];
-        ixy_tmp.info.strides[i] = ixy.info.strides[i];
-        iyy_tmp.info.strides[i] = iyy.info.strides[i];
-    }
-    ixx_tmp.data = bufferAlloc(ixx_tmp.info.dims[3] * ixx_tmp.info.strides[3] * sizeof(convAccT));
-    ixy_tmp.data = bufferAlloc(ixy_tmp.info.dims[3] * ixy_tmp.info.strides[3] * sizeof(convAccT));
-    iyy_tmp.data = bufferAlloc(iyy_tmp.info.dims[3] * iyy_tmp.info.strides[3] * sizeof(convAccT));
+    Array<convAccT> ixx_tmp = createEmptyArray<convAccT>(ixx.dims());
+    Array<convAccT> ixy_tmp = createEmptyArray<convAccT>(ixy.dims());
+    Array<convAccT> iyy_tmp = createEmptyArray<convAccT>(iyy.dims());
 
     convSep<T, convAccT, 0, false>(ixx_tmp, ixx, filter);
     convSep<T, convAccT, 1, false>(ixx, ixx_tmp, filter);
@@ -78,16 +61,14 @@ void conv_helper(Param &ixx, Param &ixy, Param &iyy, Param &filter)
     convSep<T, convAccT, 1, false>(ixy, ixy_tmp, filter);
     convSep<T, convAccT, 0, false>(iyy_tmp, iyy, filter);
     convSep<T, convAccT, 1, false>(iyy, iyy_tmp, filter);
-
-    bufferFree(ixx_tmp.data);
-    bufferFree(ixy_tmp.data);
-    bufferFree(iyy_tmp.data);
 }
 
 template<typename T>
 std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
 getHarrisKernels()
 {
+    using cl::Program;
+    using cl::Kernel;
     static const char* kernelNames[4] =
         {"second_order_deriv", "keep_corners", "harris_responses", "non_maximal"};
 
@@ -134,7 +115,8 @@ getHarrisKernels()
 }
 
 template<typename T, typename convAccT>
-void harris(unsigned* corners_out,
+void
+harris(unsigned* corners_out,
             Param &x_out,
             Param &y_out,
             Param &resp_out,
@@ -146,6 +128,10 @@ void harris(unsigned* corners_out,
             const float k_thr)
 {
     auto kernels = getHarrisKernels<T>();
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
 
     // Window filter
     convAccT* h_filter = new convAccT[filter_len];
@@ -160,41 +146,16 @@ void harris(unsigned* corners_out,
     const unsigned border_len = filter_len / 2 + 1;
 
     // Copy filter to device object
-    Param filter;
-    filter.info.dims[0] = filter_len;
-    filter.info.strides[0] = 1;
-    filter.info.offset = 0;
-
-    for (int k = 1; k < 4; k++) {
-        filter.info.dims[k] = 1;
-        filter.info.strides[k] = filter.info.dims[k - 1] * filter.info.strides[k - 1];
-    }
-
-    int filter_elem = filter.info.strides[3] * filter.info.dims[3];
-    filter.data = bufferAlloc(filter_elem * sizeof(convAccT));
-    getQueue().enqueueWriteBuffer(*filter.data, CL_TRUE, 0, filter_elem * sizeof(convAccT), h_filter);
-
-    Param ix, iy;
-    ix.info.offset = iy.info.offset = 0;
-    for (dim_t i = 0; i < 4; i++) {
-        ix.info.dims[i] = iy.info.dims[i] = in.info.dims[i];
-        ix.info.strides[i] = iy.info.strides[i] = in.info.strides[i];
-    }
-    ix.data = bufferAlloc(ix.info.dims[3] * ix.info.strides[3] * sizeof(T));
-    iy.data = bufferAlloc(iy.info.dims[3] * iy.info.strides[3] * sizeof(T));
+    Array<convAccT> filter = createHostDataArray<convAccT>(filter_len, h_filter);
+    Array<T> ix = createEmptyArray<T>(dim4(4, in.info.dims));
+    Array<T> iy = createEmptyArray<T>(dim4(4, in.info.dims));
 
     // Compute first-order derivatives as gradients
     gradient<T>(iy, ix, in);
 
-    Param ixx, ixy, iyy;
-    ixx.info.offset = ixy.info.offset = iyy.info.offset = 0;
-    for (dim_t i = 0; i < 4; i++) {
-        ixx.info.dims[i] = ixy.info.dims[i] = iyy.info.dims[i] = in.info.dims[i];
-        ixx.info.strides[i] = ixy.info.strides[i] = iyy.info.strides[i] = in.info.strides[i];
-    }
-    ixx.data = bufferAlloc(ixx.info.dims[3] * ixx.info.strides[3] * sizeof(T));
-    ixy.data = bufferAlloc(ixy.info.dims[3] * ixy.info.strides[3] * sizeof(T));
-    iyy.data = bufferAlloc(iyy.info.dims[3] * iyy.info.strides[3] * sizeof(T));
+    Array<T> ixx = createEmptyArray<T>(dim4(4, in.info.dims));
+    Array<T> ixy = createEmptyArray<T>(dim4(4, in.info.dims));
+    Array<T> iyy = createEmptyArray<T>(dim4(4, in.info.dims));
 
     // Second order-derivatives kernel sizes
     const unsigned blk_x_so = divup(in.info.dims[3] * in.info.strides[3], HARRIS_THREADS_PER_GROUP);
@@ -206,16 +167,12 @@ void harris(unsigned* corners_out,
 
     // Compute second-order derivatives
     soOp(EnqueueArgs(getQueue(), global_so, local_so),
-         *ixx.data, *ixy.data, *iyy.data,
-         in.info.dims[3] * in.info.strides[3], *ix.data, *iy.data);
+         *ixx.get(), *ixy.get(), *iyy.get(),
+         in.info.dims[3] * in.info.strides[3], *ix.get(), *iy.get());
     CL_DEBUG_FINISH(getQueue());
 
-    bufferFree(ix.data);
-    bufferFree(iy.data);
-
     // Convolve second order derivatives with proper window filter
     conv_helper<T, convAccT>(ixx, ixy, iyy, filter);
-    bufferFree(filter.data);
 
     cl::Buffer *d_responses = bufferAlloc(in.info.dims[3] * in.info.strides[3] * sizeof(T));
 
@@ -231,13 +188,9 @@ void harris(unsigned* corners_out,
     // Calculate Harris responses for all pixels
     hrOp(EnqueueArgs(getQueue(), global_hr, local_hr),
          *d_responses, in.info.dims[0], in.info.dims[1],
-         *ixx.data, *ixy.data, *iyy.data, k_thr, border_len);
+         *ixx.get(), *ixy.get(), *iyy.get(), k_thr, border_len);
     CL_DEBUG_FINISH(getQueue());
 
-    bufferFree(ixx.data);
-    bufferFree(ixy.data);
-    bufferFree(iyy.data);
-
     // Number of corners is not known a priori, limit maximum number of corners
     // according to image dimensions
     unsigned corner_lim = in.info.dims[3] * in.info.strides[3] * 0.2f;
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index a84750913d..d6b78547f7 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -359,21 +359,11 @@ namespace kernel
             threads_x = std::min(threads_x, THREADS_PER_GROUP);
             uint threads_y = THREADS_PER_GROUP / threads_x;
 
-            Param tmp;
             uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
             uint groups_y = divup(in.info.dims[1], threads_y);
+            Array<T> tmp = createEmptyArray<T>({groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
 
-            tmp.info.offset = 0;
-            tmp.info.dims[0] = groups_x;
-            tmp.info.strides[0] = 1;
-
-            for (int k = 1; k < 4; k++) {
-                tmp.info.dims[k] = in.info.dims[k];
-                tmp.info.strides[k] = tmp.info.dims[k - 1] * tmp.info.strides[k - 1];
-            }
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-            tmp.data = bufferAlloc(tmp_elements * sizeof(T));
+            int tmp_elements = tmp.elements();
             cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
 
             ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x, groups_y);
@@ -381,7 +371,7 @@ namespace kernel
             unique_ptr<T[]> h_ptr(new T[tmp_elements]);
             unique_ptr<uint[]> h_iptr(new uint[tmp_elements]);
 
-            getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
+            getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
             getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0, sizeof(uint) * tmp_elements, h_iptr.get());
 
             T* h_ptr_raw = h_ptr.get();
@@ -403,7 +393,6 @@ namespace kernel
                 Op(h_ptr_raw[i], h_iptr_raw[i]);
             }
 
-            bufferFree(tmp.data);
             bufferFree(tidx);
 
             *loc = Op.m_idx;
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 8be62e9720..9a2d9e334f 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -476,7 +476,7 @@ void mean_weighted(Param out, Param in, Param inWeight, int dim)
 template<typename Ti, typename Tw, typename To>
 void mean(Param out, Param in, int dim)
 {
-    Array<Tw> noWeight = createEmptyArray<Tw>(dim4(0, 0, 0, 0));
+    Param noWeight;
     mean_weighted<Ti, Tw, To>(out, in, noWeight, dim);
 }
 
@@ -576,8 +576,8 @@ To mean_all(Param in)
         uint groups_y = divup(in.info.dims[1], threads_y);
 
         Array<To> tmpOut = createEmptyArray<To>(groups_x);
-        Array<To> iWt = createEmptyArray<To>(0);
         Array<Tw> tmpCt = createEmptyArray<Tw>(groups_x);
+        Param iWt;
 
         mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x, groups_x, groups_y);
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 7b28ced42b..845fdb270e 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -299,41 +299,28 @@ namespace kernel
             threads_x = std::min(threads_x, THREADS_PER_GROUP);
             uint threads_y = THREADS_PER_GROUP / threads_x;
 
-            Param tmp;
             uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
             uint groups_y = divup(in.info.dims[1], threads_y);
+            Array<To> tmp = createEmptyArray<To>({groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
 
-            tmp.info.offset = 0;
-            tmp.info.dims[0] = groups_x;
-            tmp.info.strides[0] = 1;
-
-            for (int k = 1; k < 4; k++) {
-                tmp.info.dims[k] = in.info.dims[k];
-                tmp.info.strides[k] = tmp.info.dims[k - 1] * tmp.info.strides[k - 1];
-            }
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+            int tmp_elements = tmp.elements();
 
             reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x, change_nan, nanval);
 
-            unique_ptr<To[]> h_ptr(new To[tmp_elements]);
-            getQueue().enqueueReadBuffer(*tmp.data, CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.get());
+            std::vector<To> h_ptr(tmp_elements);
+            getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.data());
 
             Binary<To, op> reduce;
             To out = reduce.init();
             for (int i = 0; i < (int)tmp_elements; i++) {
-                out = reduce(out, h_ptr.get()[i]);
+                out = reduce(out, h_ptr[i]);
             }
-
-            bufferFree(tmp.data);
             return out;
-
         } else {
 
-            unique_ptr<Ti[]> h_ptr(new Ti[in_elements]);
+            std::vector<Ti> h_ptr(in_elements);
             getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
-                                          sizeof(Ti) * in_elements, h_ptr.get());
+                                          sizeof(Ti) * in_elements, h_ptr.data());
 
             Transform<Ti, To, op> transform;
             Binary<To, op> reduce;
@@ -341,7 +328,7 @@ namespace kernel
             To nanval_to = scalar<To>(nanval);
 
             for (int i = 0; i < (int)in_elements; i++) {
-                To in_val = transform(h_ptr.get()[i]);
+                To in_val = transform(h_ptr[i]);
                 if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
                 out = reduce(out, in_val);
             }
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index fc5f00b147..56cc5e9dfd 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <Array.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <kernel/sort_helper.hpp>
 #include <program.hpp>
@@ -160,17 +161,7 @@ namespace opencl
             seqDims[dim] = 1;
 
             // Create/call iota
-            // Array<Tv> key = iota<Tv>(seqDims, tileDims);
-            cl::Buffer* Seq = bufferAlloc(inDims.elements() * sizeof(unsigned));
-            Param pSeq;
-            pSeq.data = Seq;
-            pSeq.info.offset = 0;
-            pSeq.info.dims[0] = inDims[0];
-            pSeq.info.strides[0] = 1;
-            for(int i = 1; i < 4; i++) {
-                pSeq.info.dims[i] = inDims[i];
-                pSeq.info.strides[i] = pSeq.info.strides[i - 1] * pSeq.info.dims[i - 1];
-            }
+            Array<unsigned> pSeq = createEmptyArray<unsigned>(inDims);
             kernel::iota<unsigned>(pSeq, seqDims, tileDims);
 
             int elements = inDims.elements();
@@ -186,7 +177,7 @@ namespace opencl
             compute::context c_context(getContext()());
 
             // Create buffer iterators for seq
-            compute::buffer pSeq_buf((*pSeq.data)());
+            compute::buffer pSeq_buf((*pSeq.get())());
             compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
             compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
             // Create buffer iterators for key and val
@@ -230,12 +221,7 @@ namespace opencl
             // If descending, flip it back
             if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
 
-            //// No need of doing moddims here because the original Array<T>
-            //// dimensions have not been changed
-            ////val.modDims(inDims);
-
             CL_DEBUG_FINISH(getQueue());
-            bufferFree(Seq);
             bufferFree(cSeq);
             bufferFree(cKey);
         }
@@ -254,9 +240,9 @@ namespace opencl
                 kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
         }
 
-#define INSTANTIATE(Tk, Tv)                                                         \
-  template void sort0ByKey<Tk, Tv>(Param okey, Param oval, bool isAscending);        \
-  template void sort0ByKeyIterative<Tk, Tv>(Param okey, Param oval, bool isAscending); \
+#define INSTANTIATE(Tk, Tv)                                                                        \
+  template void sort0ByKey<Tk, Tv>(Param okey, Param oval, bool isAscending);                      \
+  template void sort0ByKeyIterative<Tk, Tv>(Param okey, Param oval, bool isAscending);             \
   template void sortByKeyBatched<Tk, Tv>(Param okey, Param oval, const int dim, bool isAscending);
 
 #define INSTANTIATE1(Tk    ) \

From 57d6123effc3056342dbe78ae331e59842708a6f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 10 Apr 2018 01:41:20 -0400
Subject: [PATCH 1397/2677] Fixes out of bound access in OpenCL
 scan_first_kernel

---
 src/backend/opencl/kernel/scan_first.cl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/scan_first.cl b/src/backend/opencl/kernel/scan_first.cl
index 79917a5cd5..d245b9c1a0 100644
--- a/src/backend/opencl/kernel/scan_first.cl
+++ b/src/backend/opencl/kernel/scan_first.cl
@@ -53,7 +53,7 @@ void scan_first_kernel(__global To *oData, KParam oInfo,
 
         if (isLast) l_tmp[lidy] = val;
 
-        bool cond = ((id < oInfo.dims[0]) && cond_yzw);
+        bool cond = ((id < iInfo.dims[0]) && cond_yzw);
         val = cond ? transform(iData[id]) : init_val;
         l_val[lid] = val;
         barrier(CLK_LOCAL_MEM_FENCE);

From 056888d7a40541640ddae92bd2ce8f13eb5ee5c8 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Tue, 10 Apr 2018 20:21:09 -0400
Subject: [PATCH 1398/2677] Improved installer descriptions. (#2120)

* Improved wording with installers.
---
 CMakeModules/CPackConfig.cmake            | 34 +++++++++++------------
 CMakeModules/nsis/NSIS.definitions.nsh.in | 10 +++----
 2 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 7ffdeb613a..baff4827d8 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -132,19 +132,19 @@ cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
 
 cpack_add_component_group(backends
   DISPLAY_NAME "ArrayFire"
-  DESCRIPTION "ArrayFire Backend Libraries"
+  DESCRIPTION "ArrayFire backend libraries"
   EXPANDED)
 cpack_add_component_group(cpu_backend
-  DISPLAY_NAME "CPU Backend"
-  DESCRIPTION "Libraries and dependencies of CPU Backend"
+  DISPLAY_NAME "CPU backend"
+  DESCRIPTION "Libraries and dependencies of the CPU backend."
   PARENT_GROUP backends)
 cpack_add_component_group(cuda_backend
-  DISPLAY_NAME "CUDA Backend"
-  DESCRIPTION "Libraries and dependencies of CUDA Backend"
+  DISPLAY_NAME "CUDA backend"
+  DESCRIPTION "Libraries and dependencies of the CUDA backend."
   PARENT_GROUP backends)
 cpack_add_component_group(opencl_backend
-  DISPLAY_NAME "OpenCL Backend"
-  DESCRIPTION "Libraries and dependencies of OpenCL Backend"
+  DISPLAY_NAME "OpenCL backend"
+  DESCRIPTION "Libraries and dependencies of the OpenCL backend."
   PARENT_GROUP backends)
 
 set(PACKAGE_MKL_DEPS OFF)
@@ -153,32 +153,32 @@ if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
   set(PACKAGE_MKL_DEPS ON)
   cpack_add_component(mkl_dependencies
     DISPLAY_NAME "Intel MKL"
-	DESCRIPTION "Intel Math Kernel Libraries for FFTW, BLAS and LAPACK routines"
+	DESCRIPTION "Intel Math Kernel Libraries for FFTW, BLAS, and LAPACK routines."
 	GROUP backends
     INSTALL_TYPES Development Runtime)
 endif ()
 
 cpack_add_component(common_backend_dependencies
   DISPLAY_NAME "Dependencies"
-  DESCRIPTION "Libraries that are commonly required by all ArrayFire backends"
+  DESCRIPTION "Libraries commonly required by all ArrayFire backends."
   GROUP backends
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(opencl_dependencies
   DISPLAY_NAME "OpenCL Dependencies"
-  DESCRIPTION "Libraries required by OpenCL Backend"
+  DESCRIPTION "Libraries required by the OpenCL backend."
   GROUP opencl_backend
   INSTALL_TYPES Development Runtime)
   
 cpack_add_component(cuda_dependencies
   DISPLAY_NAME "CUDA Dependencies"
-  DESCRIPTION "CUDA Runtime and libraries required for the CUDA backend."
+  DESCRIPTION "CUDA runtime and libraries required by the CUDA backend."
   GROUP cuda_backend
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(cuda
   DISPLAY_NAME "CUDA Backend"
-  DESCRIPTION "This Backend allows you to take advantage of the CUDA enabled GPUs to run ArrayFire code. Please make sure you have CUDA toolkit installed or install CUDA dependencies component."
+  DESCRIPTION "The CUDA backend allows you to run ArrayFire code on CUDA-enabled GPUs. Verify that you have the CUDA toolkit installed or install the CUDA dependencies component."
   GROUP cuda_backend
   DEPENDS common_backend_dependencies cuda_dependencies
   INSTALL_TYPES Development Runtime)
@@ -197,26 +197,26 @@ endif ()
 
 cpack_add_component(cpu
   DISPLAY_NAME "CPU Backend"
-  DESCRIPTION "This Backend allows you to run ArrayFire code on native CPUs."
+  DESCRIPTION "The CPU backend allows you to run ArrayFire code on your CPU."
   GROUP cpu_backend
   DEPENDS ${cpu_deps_comps}
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(opencl
   DISPLAY_NAME "OpenCL Backend"
-  DESCRIPTION "This Backend allows you to take advantage of OpenCL capable GPUs to run ArrayFire code. Currently ArrayFire does not support OpenCL for the Intel CPU on OSX."
+  DESCRIPTION "The OpenCL backend allows you to run ArrayFire code on OpenCL-capable GPUs. Note: ArrayFire does not currently support OpenCL for Intel CPUs on OSX."
   GROUP opencl_backend
   DEPENDS ${ocl_deps_comps}
   INSTALL_TYPES Development Runtime)
 
 cpack_add_component(unified
   DISPLAY_NAME "Unified Backend"
-  DESCRIPTION "This Backend allows you to choose the platform(cpu, cuda, opencl) at runtime. This option requires at least one of the three backends to be installed to work properly."
+  DESCRIPTION "The Unified backend allows you to choose between any of the installed backends (CUDA, OpenCL, or CPU) at runtime."
   GROUP backends
   INSTALL_TYPES Development Runtime)
 cpack_add_component(headers
   DISPLAY_NAME "C/C++ Headers"
-  DESCRIPTION "Headers for the ArrayFire Libraries."
+  DESCRIPTION "Headers for the ArrayFire libraries."
   GROUP backends
   INSTALL_TYPES Development)
 cpack_add_component(cmake
@@ -233,7 +233,7 @@ cpack_add_component(examples
   INSTALL_TYPES Extra)
 cpack_add_component(licenses
   DISPLAY_NAME "Licenses"
-  DESCRIPTION "License files for upstream libraries and ArrayFire."
+  DESCRIPTION "License files for ArrayFire and its upstream libraries."
   REQUIRED)
 
 if (INSTALL_FORGE_DEV)
diff --git a/CMakeModules/nsis/NSIS.definitions.nsh.in b/CMakeModules/nsis/NSIS.definitions.nsh.in
index 1a3f92a5e4..4c6e8998b7 100644
--- a/CMakeModules/nsis/NSIS.definitions.nsh.in
+++ b/CMakeModules/nsis/NSIS.definitions.nsh.in
@@ -4,7 +4,7 @@
 "ArrayFire is a high performance software library for parallel computing with an easy-to-use API.\r\n\r\n\
 Its array based function set makes parallel programming simple.\r\n\r\n\
 ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable.\r\n\r\n\
-A few lines of code in ArrayFire can replace dozens of lines of parallel compute code,\
+A few lines of code in ArrayFire can replace dozens of lines of parallel compute code, \
 saving you valuable time and lowering development costs.\r\n\r\n\
 Follow these steps to install the ArrayFire libraries."
 
@@ -28,8 +28,8 @@ Follow these steps to install the ArrayFire libraries."
 ; Defines for Finish Page
 !define MUI_FINISHPAGE_RUN "explorer.exe"
 !define MUI_FINISHPAGE_RUN_PARAMETERS "$INSTDIR"
-!define MUI_FINISHPAGE_RUN_TEXT "Open ArrayFire Install Directory to see Examples"
-!define MUI_FINISHPAGE_SHOWREADME "http://arrayfire.com/docs/using_on_windows.htm"
-!define MUI_FINISHPAGE_SHOWREADME_TEXT "Open ArrayFire Documentation on the Web"
+!define MUI_FINISHPAGE_RUN_TEXT "Open ArrayFire install folder."
+!define MUI_FINISHPAGE_SHOWREADME "https://arrayfire.com/docs/using_on_windows.htm"
+!define MUI_FINISHPAGE_SHOWREADME_TEXT "Open ArrayFire documentation on the web."
 !define MUI_FINISHPAGE_LINK "ArrayFire Support and Services"
-!define MUI_FINISHPAGE_LINK_LOCATION "http://arrayfire.com/consulting/"
+!define MUI_FINISHPAGE_LINK_LOCATION "https://arrayfire.com/consulting/"

From 036bd05206a02f09958bc5f731285543a87dfa67 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 10 Apr 2018 17:17:55 -0400
Subject: [PATCH 1399/2677] Remove enqueue operations from scan kernels on the
 CPU.

The scan kernels(files included in src/backend/cpu/kernel) were
enqueueing functions into the cpu queue from within the worker
thread. This may have been causing deadlocks if the queue became
too large or used too much memory.
---
 src/backend/cpu/kernel/scan.hpp        | 5 ++---
 src/backend/cpu/kernel/scan_by_key.hpp | 7 +++----
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index 0550393448..561c12f519 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -29,9 +29,8 @@ struct scan_dim
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
             scan_dim<op, Ti, To, D1, inclusive_scan> func;
-            getQueue().enqueue(func,
-                    out, outOffset + i * ostrides[D1],
-                    in, inOffset + i * istrides[D1], dim);
+            func(out, outOffset + i * ostrides[D1], in,
+                 inOffset + i * istrides[D1], dim);
             if (D1 == dim) break;
         }
     }
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index 4fef32e05e..449d3027fc 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -34,10 +34,9 @@ struct scan_dim_by_key
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
             scan_dim_by_key<op, Ti, Tk, To, D1> func(inclusive_scan);
-            getQueue().enqueue(func,
-                    out, outOffset + i * ostrides[D1],
-                    key, keyOffset + i * kstrides[D1],
-                    in, inOffset + i * istrides[D1], dim);
+            func(out, outOffset + i * ostrides[D1], key,
+                 keyOffset + i * kstrides[D1], in, inOffset + i * istrides[D1],
+                 dim);
             if (D1 == dim) break;
         }
     }

From 26469bb7ddf7ef5cc9833981433acd99cf0b1603 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 12 Apr 2018 12:57:29 +0530
Subject: [PATCH 1400/2677] Miscellaneous changes for 3.6 release (#2095)

* fix ASSETS_DIR for out of builddir builds
* update install documentation for 3.6.0 release
* Update build instructions for debian and centos
* Update install instructions for debian and it's derivatives
* Update using ArrayFire on Windows tutorial
* Update using ArrayFire on Linux tutorial
* Update using ArrayFire on OSX tutorial
* Remove xcode related instructions
---
 docs/pages/install.md          | 244 +++++++++----------------
 docs/pages/using_on_linux.md   | 180 +++++++------------
 docs/pages/using_on_osx.md     | 266 ++++++---------------------
 docs/pages/using_on_windows.md | 320 +++++++++++++--------------------
 examples/CMakeLists.txt        |   6 +-
 5 files changed, 340 insertions(+), 676 deletions(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index 4057f2a922..7166c48ebd 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -1,200 +1,134 @@
-ArrayFire Binary Installation Instructions {#installing}
-=====
-
-Installing ArrayFire couldn't be easier. We ship installers for Windows,
-OSX, and Linux. Although you could
-[build ArrayFire from source](https://github.com/arrayfire/arrayfire), we
-suggest using our pre-compiled binaries as they include the Intel Math
-Kernel Library to accelerate linear algebra functions.
-
-Please note that although our download page requires a valid login, registration
-is free and downloading ArrayFire is also free. We request your contact
-information so that we may notify you of software updates and occasionally
-collect user feedback about our library.
-
-In general, the installation process for ArrayFire looks like this:
-
-1. Install prerequisites
-2. [Download](http://arrayfire.com/download/) the ArrayFire installer for your
-   operating system
-3. Install ArrayFire
-4. Test the installation
-5. [Where to go for help?](#GettingHelp)
-
-Below you will find instructions for:
+# ArrayFire Installer {#installing}
+
+Installing ArrayFire couldn't be easier. Navigate to
+https://arrayfire.com/download and download the installer for your architecture
+and operating system. Although you could [build ArrayFire from
+source](https://github.com/arrayfire/arrayfire), we recommend using our
+installers as we have packaged together all of the necessary dependencies to
+give you the best performance.
+
+We provide installers for Windows, Linux, and macOS. There are two installers
+for each operating system: one with graphics support and the other without
+graphics support. Download the installer with graphics support if you would like
+to be able to do high performance visualizations using our
+[Forge](https://github.com/arrayfire/forge) library. Otherwise, download the
+installer without graphics support.
+
+Make sure you have the latest device drivers installed on your system before
+using ArrayFire. If you are going to be targeting the CPU using ArrayFire’s
+OpenCL backend, you will need to have the OpenCL **runtime** installed on your
+system. Drivers and runtimes should be downloaded and installed from your device
+vendor’s website.
+
+# Install Instructions
 
 * [Windows](#Windows)
-* Linux
-    * [Debian 8](#Debian)
-    * [Ubuntu 14.04 and later](#Ubuntu)
-    * [RedHat, Fedora, and CentOS](#RPM-distros)
-* [Mac OSX (.sh and brew)](#OSX)
-
-# <a name="Windows"></a> Windows
-
-If you wish to use CUDA or OpenCL please ensure that you have also installed
-support for these technologies from your video card vendor's website.
-
-Next, [download](http://arrayfire.com/download/) and run the ArrayFire
-installer. After installation, you'll need to add ArrayFire to the path for
-all users:
-
-1. Open Advanced System Settings:
-    * Windows 8: Move the Mouse pointer to the bottom right corner of the
-      screen, Right click, choose System. Then click "Advanced System Settings"
-    * Windows 7: Open the Start Menu and Right Click on "Computer". Then choose
-      Properties and click "Advanced System Settings"
-2. In Advanced System Settings window, click on Advanced tab
-3. Click on Environment Variables, then under System Variables, find PATH, and
-   click on it.
-4. In edit mode, append `%AF_PATH%/lib`. Make sure to separate `%AF_PATH%/lib`
-   from any existing content using a semicolon (e.g.
-   `EXISTING_PATHS;%AF_PATH%/lib;`). Other software may function incorrectly
-   if this is not the case.
-
-Finally, verify that the path addition worked correctly. You can do this by:
-
-1. Open Visual Studio 2013. Open the `HelloWorld` solution which is located at
-   `%AF_PATH%/examples/helloworld/helloworld.exe`.
-2. Build and run the `helloworld` example. Use the "Solution Platform"
-   drop-down to select from the CPU, CUDA, or OpenCL backends ArrayFire
-   provides.
-
-# Linux
+* [Linux](#Linux)
+* [macOS](#macOS)
 
-## <a name="Debian"></a> Debian 8
+## <a name="Windows"></a> Windows
 
-First, install the prerequisite packages:
+Prior to installing ArrayFire on Windows,
+[download](https://www.microsoft.com/en-in/download/details.aspx?id=48145)
+install the Visual Studio 2015 (x64) runtime libraries.
 
-    # Install prerequisite packages:
-    apt-get install libglfw3-dev cmake
+Once you have downloaded the ArrayFire installer, execute the installer as you
+normally would on Windows. If you choose not to modify the path during the
+installation procedure, you'll need to manually add ArrayFire to the path for
+all users. Simply append `%AF_PATH%/lib` to the PATH variable so that the loader
+can find ArrayFire DLLs.
 
-    # Enable GPU support (OpenCL):
-    apt-get install ocl-icd-libopencl1
+For more information on using ArrayFire on Windows, visit the following
+[page](http://arrayfire.org/docs/using_on_windows.htm).
 
-If you wish to use CUDA,
-[download](https://developer.nvidia.com/cuda-downloads) and install the latest
-version.
+## <a name="Linux"></a> Linux
 
-Next, [download](http://arrayfire.com/download/) the ArrayFire installer for
-your system. After you have the file, run the installer:
+Once you have downloaded the ArrayFire installer, execute the installer from the
+terminal as shown below. Set the `--prefix` argument to the directory you would
+like to install ArrayFire to - we recommend `/opt`.
 
-    ./arrayfire_*_Linux_x86_64.sh --exclude-subdir --prefix=/usr/local
+    ./Arrayfire_*_Linux_x86_64.sh --include-subdir --prefix=/opt
 
-## <a name="RPM-distros"></a> RedHat, Fedora, and CentOS
+Given sudo permissions, you can add the ArrayFire libraries via `ldconfig` like
+so:
 
-First, install the prerequisite packages:
+    echo /opt/arrayfire/lib > /etc/ld.so.conf.d/arrayfire.conf
+    sudo ldconfig
 
-    # Install prerequiste packages
-    yum install glfw cmake
+Otherwise, you will need to set the `LD_LIBRARY_PATH` environment variable in
+order to let your shared library loader find the ArrayFire libraries.
 
-NOTE: On CentOS and Redhat, the `glfw` package is outdated and you will need
-to compile it from source. Follow these
-[instructions](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire)
-for more information on how to build and install GFLW.
+For more information on using ArrayFire on Linux, visit the following
+[page](http://arrayfire.org/docs/using_on_linux.htm).
 
-If you wish to use CUDA,
-[download](https://developer.nvidia.com/cuda-downloads) and install the latest
-version.
+### Graphics support
 
-Next, [download](http://arrayfire.com/download/) the ArrayFire installer for
-your system. After you have the file, run the installer:
+ArrayFire allows you to do high performance visualizations via our
+[Forge](https://github.com/arrayfire/forge) library. On Linux, there are a few
+dependencies you will need to install to enable graphics support:
 
-    ./arrayfire_*_Linux_x86_64.sh --exclude-subdir --prefix=/usr/local
+FreeImage
+Fontconfig
+GLU (OpenGL Utility Library)
 
-## <a name="Ubuntu"></a> Ubuntu 14.04 and later
+We show how to install these dependencies on common Linux distributions:
 
-First, install the prerequisite packages:
+__Debian, Ubuntu (14.04 and above), and other Debian derivatives__
 
-### Ubuntu 16.04
+    apt install build-essential libfreeimage3 libfontconfig1 libglu1-mesa
 
-    # Install prerequisite packages:
-    sudo apt-get install libglfw3-dev cmake
+__Fedora, Redhat, CentOS__
 
-### Ubuntu 14.04
+    yum install freeimage fontconfig mesa-libGLU
 
-    # Install prerequisite packages:
-    sudo apt-get install cmake
 
-Ubuntu 14.04 does not include the `libglfw3-dev` package in its
-repositories. In order to install, you can either:
+## <a name="macOS"></a> macOS
 
-1. Build the library from source by following these
-   [instructions](https://github.com/arrayfire/arrayfire/wiki/GLFW-for-ArrayFire),
-   or
-2. Install the library from a PPA as follows:
+Once you have downloaded the ArrayFire installer, execute the installer by
+either double clicking on the ArrayFire `pkg` file or running the following
+command from your terminal:
 
-    sudo apt-add-repository ppa:keithw/glfw3
-    sudo apt-get update
-    sudo apt-get install glfw3
+    sudo installer -pkg Arrayfire-*_OSX.pkg -target /
 
-At this point, the installation should proceed identically for Ubuntu 14.04
-and newer.
+For more information on using ArrayFire on macOS, visit the following
+[page](http://arrayfire.org/docs/using_on_osx.htm).
 
-If your system has a CUDA GPU, we suggest downloading the latest drivers
-from NVIDIA in the form of a Debian package and installing using the
-package manager. At present, CUDA downloads can be found on the
-[NVIDIA CUDA download page](https://developer.nvidia.com/cuda-downloads).
-Follow NVIDIA's instructions for getting CUDA set up.
+## NVIDIA Tegra devices
 
-If you wish to use OpenCL, simply install the OpenCL ICD loader along
-with any drivers required for your hardware.
+ArrayFire is capable of running on TX1 and TX2 devices. The TK1 is no longer
+supported.
 
-    # Enable GPU support (OpenCL):
-    apt-get install ocl-icd-libopencl1
+Prior to installing ArrayFire, make sure you have the latest version of JetPack
+(v2.3 and above) or L4T (v24.2 and above) on your device.
 
-### Special instructions for Tegra X1
+### Tegra prerequisites
 
-**The ArrayFire binary installer for Tegra X1 requires at least JetPack 2.3 or
-L4T 24.2 for Jetson TX1. This includes Ubuntu 16.04, CUDA 8.0 etc.**
+The following dependencies are required for Tegra devices:
 
-You will also want to install the following packages when using ArrayFire on
-the Tegra X1:
-
-    sudo apt-get install libopenblas-dev liblapacke-dev
-
-### Special instructions for Tegra K1
-
-You will also want to install the following packages when using ArrayFire on
-the Tegra K1:
-
-    sudo apt-get install libatlas3gf-base libatlas-dev libfftw3-dev liblapacke-dev
-
-Finally, [download](http://arrayfire.com/download/) ArrayFire for your
-system. After you have the file, run the installer using:
-
-    ./arrayfire_*_Linux_x86_64.sh --exclude-subdir --prefix=/usr/local
-
-# <a name="OSX"></a> Mac OSX
-
-On OSX there are several dependencies that are not integrated into the
-operating system. The ArrayFire installer automatically satisfies these
-dependencies using [Homebrew](http://brew.sh/).
-If you don't have Homebrew installed on your system, the ArrayFire installer
-will ask you do to so.
-
-Simply [download](http://arrayfire.com/download) the ArrayFire installer
-and double-click it to carry out the installation.
-
-ArrayFire can also be installed through Homebrew directly using
-`brew install arrayfire`; however, it will
-not include MKL acceleration of linear algebra functions.
+    sudo apt install libopenblas-dev liblapacke-dev
 
 ## Testing installation
 
-Test ArrayFire after the installation process by building the example programs
-as follows:
+After ArrayFire is finished installing, we recommend building and running a few
+of the provided examples to verify things are working as expected.
+
+On Unix-like systems:
 
-    cp -r /usr/local/share/ArrayFire/examples .
-    cd examples
+    cp -r /opt/arrayfire/share/ArrayFire/examples /tmp/examples
+    cd /tmp/examples
     mkdir build
     cd build
-    cmake ..
+    cmake -DASSETS_DIR:PATH=/tmp ..
     make
+    ./helloworld/helloworld_{cpu,cuda,opencl}
+
+On Windows, open the CMakeLists.txt file from CMake-GUI and set `ASSETS_DIR`
+variable to the parent folder of examples folder. Once the project is configured
+and generated, you can build and run the examples from Visual Studio.
 
 ## <a name="GettingHelp"></a> Getting help
 
 * Google Groups: https://groups.google.com/forum/#!forum/arrayfire-users
-* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
+* ArrayFire Services:  [Consulting](https://arrayfire.com/consulting/)  |  [Support](https://arrayfire.com/support/)   |  [Training](https://arrayfire.com/training/)
 * ArrayFire Blogs: http://arrayfire.com/blog/
 * Email: <mailto:technical@arrayfire.com>
diff --git a/docs/pages/using_on_linux.md b/docs/pages/using_on_linux.md
index 493080f447..9dbb347d41 100644
--- a/docs/pages/using_on_linux.md
+++ b/docs/pages/using_on_linux.md
@@ -1,29 +1,32 @@
 Using ArrayFire on Linux {#using_on_linux}
 =====
 
-Once you have [installed](\ref installing) ArrayFire on your system, the next thing to do is
-set up your build system. On Linux, you can create ArrayFire projects using
-almost any editor, compiler, or build system. The only requirements are
-that you include the ArrayFire header directories and link with the ArrayFire
-library you intend to use.
+Once you have [installed](\ref installing) ArrayFire on your system, the next
+thing to do is set up your build system. On Linux, you can create ArrayFire
+projects using almost any editor, compiler, or build system. The only
+requirements are that you include the ArrayFire header directories and link with
+the ArrayFire library you intend to use i.e. CUDA, OpenCL, CPU, or Unified
+backends.
 
-## The big picture
+## <a name="big-picture"/> The big picture
 
-On Linux, we suggest you install ArrayFire to the `/usr/local` directory
-so that all of the include files and libraries are part of your standard path.
-The installer will populate files in the following sub-directories:
+On Linux, we recommend installing ArrayFire to `/opt/arrayfire` directory. The
+installer will populate files in the following sub-directories:
 
     include/arrayfire.h         - Primary ArrayFire include file
     include/af/*.h              - Additional include files
     lib/libaf*                  - CPU, CUDA, and OpenCL libraries (.a, .so)
     lib/libforge*               - Visualization library
+    lib/libcu*                  - CUDA backend dependencies
+    lib/libOpenCL.so            - OpenCL ICD Loader library
+    lib/libglbinding*           - OpenGL graphics dependencies
     share/ArrayFire/cmake/*     - CMake config (find) scripts
     share/ArrayFire/examples/*  - All ArrayFire examples
 
 Because ArrayFire follows standard installation practices, you can use basically
-any build system to create and compile projects that use ArrayFire.
-Among the many possible build systems on Linux we suggest using ArrayFire with
-either CMake or Makefiles with CMake being our preferred build system.
+any build system to create and compile projects that use ArrayFire. Among the
+many possible build systems on Linux we suggest using ArrayFire with either
+CMake or Makefiles with CMake being our preferred build system.
 
 ## Prerequisite software
 
@@ -33,38 +36,30 @@ To build ArrayFire projects you will need a compiler
 
 Install EPEL repo (not required for Fedora)
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+```
 yum install epel-release
 yum update
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+```
 
 Install build dependencies
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-yum install gcc gcc-c++ cmake make
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+```
+yum install gcc gcc-c++ cmake3 make
+```
 
-#### Debian and Ubuntu
+#### Debian and its derivatives
 
 Install common dependencies
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-apt-get install build-essential cmake cmake-curses-gui
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+```
+apt install build-essential cmake cmake-curses-gui
+```
 
 ## CMake
 
 We recommend that the CMake build system be used to create ArrayFire projects.
-If you are writing a new ArrayFire project in C/C++ from scratch, we suggest
-you grab a copy of our
-[CMake Project Example](https://github.com/arrayfire/arrayfire-project-templates);
-however, it is useful to read the documentation below in case you need to add
-ArrayFire to an existing project.
-
 As [discussed above](#big-picture), ArrayFire ships with a series of CMake
 scripts to make finding and using our library easy.
-The scripts will automatically find all versions of the ArrayFire library
-and pick the most powerful of the installed backends (typically CUDA).
 
 First create a file called `CMakeLists.txt` in your project directory:
 
@@ -73,65 +68,25 @@ First create a file called `CMakeLists.txt` in your project directory:
 
 and populate it with the following code:
 
-    FIND_PACKAGE(ArrayFire)
-    INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
-
-    ... [gather source files, etc.]
-
-    # If you intend to use OpenCL, you need to find it
-    FIND_PACKAGE(OpenCL)
-    SET(EXTRA_LIBS ${CMAKE_THREAD_LIBS_INIT} ${OpenCL_LIBRARIES})
-
-    # Or if you intend to use CUDA, you need it as well as NVVM:
-    FIND_PACKAGE(CUDA)
-    FIND_PACKAGE(NVVM) # this FIND script can be found in the ArrayFire CMake example repository
-    SET(EXTRA_LIBS ${CMAKE_THREAD_LIBS_INIT} ${CUDA_LIBRARIES} ${NVVM_LIB})
-
-    ADD_EXECUTABLE(my_executable [list your source files here])
-    TARGET_LINK_LIBRARIES(my_executable ${ArrayFire_LIBRARIES} ${EXTRA_LIBS})
-
-where `my_executable` is the name of the executable you wish to create.
-See the [CMake documentation](https://cmake.org/documentation/) for more
-information on how to use CMake.
-Clearly the above code snippet precludes the use of both CUDA and OpenCL, see
-the
-[ArrayFire CMake Example](https://github.com/arrayfire/arrayfire-project-templates/tree/master/CMake);
-for an example of how to build executables for both backends from the same
-CMake script.
-
-In the above code listing, the `FIND_PACKAGE` will find the ArrayFire include
-files, libraries, and define several variables including:
-
-    ArrayFire_INCLUDE_DIRS    - Location of ArrayFire's include directory.
-    ArrayFire_LIBRARIES       - Location of ArrayFire's libraries.
-                                This will default to a GPU backend if one
-                                is found
-    ArrayFire_FOUND           - True if ArrayFire has been located
-
-If you wish to use a specific backend, the find script also defines these variables:
-
-    ArrayFire_CPU_FOUND         - True of the ArrayFire CPU library has been found.
-    ArrayFire_CPU_LIBRARIES     - Location of ArrayFire's CPU library, if found
-    ArrayFire_CUDA_FOUND        - True of the ArrayFire CUDA library has been found.
-    ArrayFire_CUDA_LIBRARIES    - Location of ArrayFire's CUDA library, if found
-    ArrayFire_OpenCL_FOUND      - True of the ArrayFire OpenCL library has been found.
-    ArrayFire_OpenCL_LIBRARIES  - Location of ArrayFire's OpenCL library, if found
-    ArrayFire_Unified_FOUND     - True of the ArrayFire Unified library has been found.
-    ArrayFire_Unified_LIBRARIES - Location of ArrayFire's Unified library, if found
-
-Therefore, if you wish to target a specific specific backend, simply replace
-`${ArrayFire_LIBRARIES}` with `${ArrayFire_CPU}`, `${ArrayFire_OPENCL}`,
-`${ArrayFire_CUDA}`, or `${ArrayFire_Unified}` in the `TARGET_LINK_LIBRARIES`
-command above.
-If you intend on building your software to link with all of these backends,
-please see the
-[CMake Project Example](https://github.com/arrayfire/arrayfire-project-templates)
-which makes use of some fairly fun CMake tricks to avoid re-compiling code
-whenever possible.
-
-Next we need to instruct CMake to create build instructions and then compile.
-We suggest using CMake's out-of-source build functionality to keep your build
-and source files cleanly separated. To do this:
+    find_package(ArrayFire)
+    add_executable(<my_executable> [list your source files here])
+
+    # To use Unified backend, do the following.
+    # Unified backend lets you choose the backend at runtime
+    target_link_libraries(<my_executable> ArrayFire::af)
+
+where `my_executable` is the name of the executable you wish to create. See the
+[CMake documentation](https://cmake.org/documentation/) for more information on
+how to use CMake. To link with a specific backend directly, replace the
+`ArrayFire::af` with the following for their respective backends.
+
+* `ArrayFire::afcpu` for CPU backend.
+* `ArrayFire::afcuda` for CUDA backend.
+* `ArrayFire::afopencl` for OpenCL backend.
+
+Next we need to instruct CMake to create build instructions and then compile. We
+suggest using CMake's out-of-source build functionality to keep your build and
+source files cleanly separated. To do this open the CMake GUI.
 
     cd your-project-directory
     mkdir build
@@ -140,35 +95,36 @@ and source files cleanly separated. To do this:
     make
 
 *NOTE:* If you have installed ArrayFire to a non-standard location, CMake can
-still help you out. When you execute CMake specify the path to the
-`ArrayFireConfig*` files that are found in the `share/ArrayFire/cmake`
-subdirectory of the installation folder.
-For example, if ArrayFire were installed locally to `/opt/ArrayFire` then you
-would modify the `cmake` command above to contain the following definition:
-
-    cmake -DArrayFire_DIR=/opt/ArrayFire/share/ArrayFire/cmake ..
-
-You can also specify this information in the ccmake command-line interface.
-
-## MakeFiles
-
-Building ArrayFire projects with Makefiles is fairly similar to CMake except
-you must specify all paths and libraries manually.
-As with any make project, you need to specify the include path to the
-directory containing `arrayfire.h` file.
-This should be `-I /usr/local/include` if you followed our installation
-instructions.
-Similarly, you will need to specify the path to the ArrayFire library using
-the `-L` option (e.g. `-L/usr/local/lib`) followed by the specific ArrayFire
+still help you out. When you execute CMake specify the path to ArrayFire
+installation root as `ArrayFire_DIR` variable.
+
+For example, if ArrayFire were installed locally to `/home/user/ArrayFire` then
+you would modify the `cmake` command above to contain the following definition:
+
+    cmake -DArrayFire_DIR=/home/user/ArrayFire ..
+
+You can also specify this information in the `ccmake` command-line interface.
+
+## Makefiles
+
+Building ArrayFire projects with Makefiles is fairly similar to CMake except you
+must specify all paths and libraries manually.
+
+As with any `make` project, you need to specify the include path to the
+directory containing `arrayfire.h` file. This should be `-I
+/opt/arrayfire/include` if you followed our installation instructions.
+
+Similarly, you will need to specify the path to the ArrayFire library using the
+`-L` option (e.g. `-L/opt/arrayfire/lib`) followed by the specific ArrayFire
 library you wish to use using the `-l` option (for example `-lafcpu`,
 `-lafopencl`, `-lafcuda`, or `-laf` for the CPU, OpenCL, CUDA, and unified
-backends respectively.
+backends, respectively.
 
-Here is a minimial example MakeFile which uses ArrayFire's CPU backend:
+Here is a minimal example Makefile which uses ArrayFire's CPU backend:
 
     LIBS=-lafcpu
-    LIB_PATHS=-L/usr/lib
-    INCLUDES=-I/usr/include
+    LIB_PATHS=-L/opt/arrayfire/lib
+    INCLUDES=-I/opt/arrayfire/include
     CC=g++ $(COMPILER_OPTIONS)
     COMPILER_OPTIONS=-std=c++11 -g
 
diff --git a/docs/pages/using_on_osx.md b/docs/pages/using_on_osx.md
index ef7e1e4255..6fd8ad9cb3 100644
--- a/docs/pages/using_on_osx.md
+++ b/docs/pages/using_on_osx.md
@@ -1,50 +1,41 @@
 Using ArrayFire on OSX {#using_on_osx}
-=====
+======================================
 
 Once you have [installed](\ref installing) ArrayFire on your system, the next
-thing to do is set up your build system.
-On OSX, you may create ArrayFire project using almost any editor, compiler,
-or build system.
-The only requirement is that you can include the ArrayFire header directory,
-and link with the ArrayFire library you intend to use.
+thing to do is set up your build system. On OSX, you may create ArrayFire
+project using almost any editor, compiler, or build system. The only requirement
+is that you can include the ArrayFire header directory, and link with the
+ArrayFire library you intend to use.
 
-## The big picture
+## <a name="big-picture"/> The big picture
 
 By default, the ArrayFire OSX installer will place several files in your
-computer's `/usr/local` directory.
-The installer will populate this directory with files in the following
-sub-directories:
+computer's `/opt/arrayfire` directory. The installer will populate this
+directory with files in the following sub-directories:
 
     include/arrayfire.h         - Primary ArrayFire include file
     include/af/*.h              - Additional include files
     lib/libaf*                  - CPU, CUDA, and OpenCL libraries (.a, .so)
     lib/libforge*               - Visualization library
-    share/ArrayFire/cmake/*     - CMake config (find) scripts
-    share/ArrayFire/examples/*  - All ArrayFire examples
+    lib/libcu*                  - CUDA backend dependencies
+    lib/libglbinding*           - OpenGL graphics dependencies
+    share/ArrayFire/cmake/*     - CMake config scripts
+    share/ArrayFire/examples/*  - ArrayFire examples
 
 Because ArrayFire follows standard installation practices, you can use basically
-any build system to create and compile projects that use ArrayFire.
-Among the many possible build systems on Linux we suggest using ArrayFire with
-either CMake or Makefiles with CMake being our preferred build system.
+any build system to create and compile projects that use ArrayFire. Among the
+many possible build systems on Linux we suggest using ArrayFire with either
+CMake or Makefiles with CMake being our preferred build system.
 
 ## Build Instructions:
 * [CMake](#CMake)
-* [MakeFiles](#MakeFiles)
-* [XCode](#XCode)
+* [Makefiles](#Makefiles)
 
 ## <a name="CMake"></a>CMake
 
-We recommend that the CMake build system be used to create ArrayFire projects.
-If you are writing a new ArrayFire project in C/C++ from scratch, we suggest
-you grab a copy of our
-[CMake Project Example](https://github.com/arrayfire/arrayfire-project-templates);
-however, it is useful to read the documentation below in case you need to add
-ArrayFire to an existing project.
-
-As [discussed above](#big-picture), ArrayFire ships with a series of CMake
-scripts to make finding and using our library easy.
-The scripts will automatically find all versions of the ArrayFire library
-and pick the most powerful of the installed backends (typically CUDA).
+The CMake build system can be used to create ArrayFire projects. As [discussed
+above](#big-picture), ArrayFire ships with a series of CMake scripts to make
+finding and using our library easy.
 
 First create a file called `CMakeLists.txt` in your project directory:
 
@@ -53,65 +44,25 @@ First create a file called `CMakeLists.txt` in your project directory:
 
 and populate it with the following code:
 
-    FIND_PACKAGE(ArrayFire)
-    INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
-
-    ... [gather source files, etc.]
-
-    # If you intend to use OpenCL, you need to find it
-    FIND_PACKAGE(OpenCL)
-    SET(EXTRA_LIBS ${CMAKE_THREAD_LIBS_INIT} ${OpenCL_LIBRARIES})
-
-    # Or if you intend to use CUDA, you need it as well as NVVM:
-    FIND_PACKAGE(CUDA)
-    FIND_PACKAGE(NVVM) # this FIND script can be found in the ArrayFire CMake example repository
-    SET(EXTRA_LIBS ${CMAKE_THREAD_LIBS_INIT} ${CUDA_LIBRARIES} ${NVVM_LIB})
-
-    ADD_EXECUTABLE(my_executable [list your source files here])
-    TARGET_LINK_LIBRARIES(my_executable ${ArrayFire_LIBRARIES} ${EXTRA_LIBS})
-
-where `my_executable` is the name of the executable you wish to create.
-See the [CMake documentation](https://cmake.org/documentation/) for more
-information on how to use CMake.
-Clearly the above code snippet precludes the use of both CUDA and OpenCL, see
-the
-[ArrayFire CMake Example](https://github.com/bkloppenborg/arrayfire-cmake-example)
-for an example of how to build executables for both backends from the same
-CMake script.
-
-In the above code listing, the `FIND_PACKAGE` will find the ArrayFire include
-files, libraries, and define several variables including:
+    find_package(ArrayFire)
+    add_executable(<my_executable> [list your source files here])
 
-    ArrayFire_INCLUDE_DIRS    - Location of ArrayFire's include directory.
-    ArrayFire_LIBRARIES       - Location of ArrayFire's libraries.
-                                This will default to a GPU backend if one
-                                is found
-    ArrayFire_FOUND           - True if ArrayFire has been located
+    # To use Unified backend, do the following.
+    # Unified backend lets you choose the backend at runtime
+    target_link_libraries(<my_executable> ArrayFire::af)
 
-If you wish to use a specific backend, the find script also defines these variables:
+where `my_executable` is the name of the executable you wish to create. See the
+[CMake documentation](https://cmake.org/documentation/) for more information on
+how to use CMake. To link with a specific backend directly, replace the
+`ArrayFire::af` with the following for their respective backends.
 
-    ArrayFire_CPU_FOUND         - True of the ArrayFire CPU library has been found.
-    ArrayFire_CPU_LIBRARIES     - Location of ArrayFire's CPU library, if found
-    ArrayFire_CUDA_FOUND        - True of the ArrayFire CUDA library has been found.
-    ArrayFire_CUDA_LIBRARIES    - Location of ArrayFire's CUDA library, if found
-    ArrayFire_OpenCL_FOUND      - True of the ArrayFire OpenCL library has been found.
-    ArrayFire_OpenCL_LIBRARIES  - Location of ArrayFire's OpenCL library, if found
-    ArrayFire_Unified_FOUND     - True of the ArrayFire Unified library has been found.
-    ArrayFire_Unified_LIBRARIES - Location of ArrayFire's Unified library, if found
+* `ArrayFire::afcpu` for CPU backend.
+* `ArrayFire::afcuda` for CUDA backend.
+* `ArrayFire::afopencl` for OpenCL backend.
 
-Therefore, if you wish to target a specific specific backend, simply replace
-`${ArrayFire_LIBRARIES}` with `${ArrayFire_CPU}`, `${ArrayFire_OPENCL}`,
-`${ArrayFire_CUDA}`, or `${ArrayFire_Unified}` in the `TARGET_LINK_LIBRARIES`
-command above.
-If you intend on building your software to link with all of these backends,
-please see the
-[CMake Project Example](https://github.com/arrayfire/arrayfire-project-templates)
-which makes use of some fairly fun CMake tricks to avoid re-compiling code
-whenever possible.
-
-Next we need to instruct CMake to create build instructions and then compile.
-We suggest using CMake's out-of-source build functionality to keep your build
-and source files cleanly separated. To do this:
+Next we need to instruct CMake to create build instructions and then compile. We
+suggest using CMake's out-of-source build functionality to keep your build and
+source files cleanly separated. To do this open the CMake GUI.
 
     cd your-project-directory
     mkdir build
@@ -120,147 +71,38 @@ and source files cleanly separated. To do this:
     make
 
 *NOTE:* If you have installed ArrayFire to a non-standard location, CMake can
-still help you out. When you execute CMake specify the path to the
-`ArrayFireConfig*` files that are found in the `share/ArrayFire/cmake`
-subdirectory of the installation folder.
-For example, if ArrayFire were installed locally to `/opt/ArrayFire` then you
-would modify the `cmake` command above to contain the following definition:
+still help you out. When you execute CMake specify the path to ArrayFire
+installation root as `ArrayFire_DIR` variable.
+
+For example, if ArrayFire were installed locally to `/home/user/ArrayFire` then
+you would modify the `cmake` command above to contain the following definition:
+
+    cmake -DArrayFire_DIR=/home/user/ArrayFire ..
+
+You can also specify this information in the `ccmake` command-line interface.
 
-    cmake -DArrayFire_DIR=/opt/ArrayFire/share/ArrayFire/cmake ..
+## <a name="Makefiles"></a> Makefiles
 
-You can also specify this information in the ccmake command-line interface.
+Building ArrayFire projects with Makefiles is fairly similar to CMake except you
+must specify all paths and libraries manually.
 
-## <a name="MakeFiles"></a> MakeFiles
+As with any make project, you need to specify the include path to the directory
+containing `arrayfire.h` file. This should be `-I /opt/arrayfire/include` if you
+followed our installation instructions.
 
-Building ArrayFire projects with Makefiles is fairly similar to CMake except
-you must specify all paths and libraries manually.
-As with any make project, you need to specify the include path to the
-directory containing `arrayfire.h` file.
-This should be `-I /usr/local/include` if you followed our installation
-instructions.
-Similarly, you will need to specify the path to the ArrayFire library using
-the `-L` option (e.g. `-L/usr/local/lib`) followed by the specific ArrayFire
+Similarly, you will need to specify the path to the ArrayFire library using the
+`-L` option (e.g. `-L/opt/arrayfire/lib`) followed by the specific ArrayFire
 library you wish to use using the `-l` option (for example `-lafcpu`,
 `-lafopencl`, `-lafcuda`, or `-laf` for the CPU, OpenCL, CUDA, and unified
 backends respectively.
 
-Here is a minimial example MakeFile which uses ArrayFire's CPU backend:
+Here is a minimal example Makefile which uses ArrayFire's CPU backend:
 
     LIBS=-lafcpu
-    LIB_PATHS=-L/usr/lib
-    INCLUDES=-I/usr/include
+    LIB_PATHS=-L/opt/arrayfire/lib
+    INCLUDES=-I/opt/arrayfire/include
     CC=g++ $(COMPILER_OPTIONS)
     COMPILER_OPTIONS=-std=c++11 -g
 
     all: main.cpp Makefile
         $(CC) main.cpp -o test $(INCLUDES) $(LIBS) $(LIB_PATHS)
-
-## <a name="XCode"></a> XCode
-
-Although we recommend using CMake to build ArrayFire projects on OSX, you can
-use XCode if this is your preferred development platform.
-To save some time, we have created an sample XCode project in our
-[ArrayFire Project Templates repository](https://github.com/arrayfire/arrayfire-project-templates).
-
-To set up a basic C/C++ project in XCode do the following:
-
-1. Start up XCode. Choose OSX -> Application, Command Line Tool for the project:
-\htmlonly
-<br />
-<a href="xcode-setup/xcode-startup.png">
-<img src="xcode-setup/xcode-startup.png" alt="Create a command line too XCode Project" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-2. Fill in the details for your project and choose either C or C++ for the project:
-\htmlonly
-<br />
-<a href="xcode-setup/project-options.png">
-<img src="xcode-setup/project-options.png" alt="Create a C/C++ project" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-3. Next we need to configure the build settings. In the left-hand pane, click
-   on the project. In the center pane, click on "Build Settings" followed by
-   the "All" button:
-\htmlonly
-<br />
-<a href="xcode-setup/build-settings.png">
-<img src="xcode-setup/build-settings.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-4. Now search for "Header Search Paths" and add `/usr/local/include` to the list:
-\htmlonly
-<br />
-<a href="xcode-setup/header-search-paths.png">
-<img src="xcode-setup/header-search-paths.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-5. Then search for "Library Search Paths" and add `/usr/local/lib` to the list:
-\htmlonly
-<br />
-<a href="xcode-setup/library-search-paths.png">
-<img src="xcode-setup/library-search-paths.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-6. Next, we need to make sure the executable is linked with an ArrayFire library:
-   To do this, click the "Build Phases" tab and expand the "Link with Binary Library"
-   menu:
-\htmlonly
-<br />
-<a href="xcode-setup/build-phases.png">
-<img src="xcode-setup/build-phases.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-7. In the search dialog that pops up, choose the "Add Other" button from the
-   lower right. Specify the `/usr/local/lib` folder:
-\htmlonly
-<br />
-<a href="xcode-setup/library-folder-path.png">
-<img src="xcode-setup/library-folder-path.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-8. Lastly, select the ArrayFire library with which you wish to link your program.
-  Your options will be:
-~~~~~
-libafcuda.*.dylib   - CUDA backend
-libafopencl.*.dylib - OpenCL backend
-libafcpu.*.dylib    - CPU backend
-libaf.*.dylib       - Unified backend
-~~~~~
-In the picture below, we have elected to link with the OpenCL backend:
-\htmlonly
-<br />
-<a href="xcode-setup/pick-arrayfire-library.png">
-<img src="xcode-setup/pick-arrayfire-library.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
-
-9. Lastly, lets test ArrayFire's functionality. In the left hand pane open
-   the main.cpp` file and insert the following code:
-
-~~~~~
-// Include the ArrayFire header file
-#include <arrayfire.h>
-
-int main(int argc, const char * argv[]) {
-    // Gather some information about the ArrayFire device
-    af::info();
-    return 0;
-}
-~~~~~
-
-Finally, click the build button and you should see some information about your
-graphics card in the lower-section of your screen:
-
-\htmlonly
-<br />
-<a href="xcode-setup/afinfo-result.png">
-<img src="xcode-setup/afinfo-result.png" alt="Configure build settings" align="middle" width="50%" />
-</a>
-\endhtmlonly
diff --git a/docs/pages/using_on_windows.md b/docs/pages/using_on_windows.md
index 92c7c2db92..99d321b886 100644
--- a/docs/pages/using_on_windows.md
+++ b/docs/pages/using_on_windows.md
@@ -1,149 +1,125 @@
 Using ArrayFire with Microsoft Windows and Visual Studio {#using_on_windows}
-=====
+============================================================================
 
 If you have not already done so, please make sure you have installed,
-configured, and tested ArrayFire following the
-[installation instructions](\ref installing).
+configured, and tested ArrayFire following the [installation instructions](\ref
+installing).
 
 ## The big picture
+
 The ArrayFire Windows installer creates the following:
-1. `AF_PATH` environment variable to point to the installation location. The
+1. **AF_PATH** environment variable to point to the installation location. The
    default install location is `C:\Program Files\ArrayFire\v3`
-2. `AF_PATH/include`         : Header files for ArrayFire (include directory)
-3. `AF_PATH/lib`             : All ArrayFire backends libraries, dlls and dependency dlls (library directory)
-4. `AF_PATH/examples`        : Examples to get started. Some examples also have pre-built exectuables
-5. `AF_PATH/cmake`           : CMake config files for automatic configuration by external projects
-6. `AF_PATH/uninstall.exe`   : Uninstaller
-7. `AF_PATH/*`               : Other miscellenous files including licenses, logos, copyrights
-
-The installer also appends `%%AF_PATH%/lib` to the User PATH variable.
-
-To add `%%AF_PATH%/lib` to PATH for all users see the windows section in
-[installation instructions](\ref installing).
-
-### <a name="nvvm_dlls" />Dealing with CUDA NMMV DLLs
-When using CUDA with ArrayFire you may encounter a linker error indicating the
-NVVM DLLs are missing. This is because the NVVM DLLs are not part of the
-standard `CUDA_PATH\bin` installation directory that is added to your `PATH`
-when the CUDA installer runs. Thus, NVVM will not be found during runtime. There
-are a few ways to deal with this issue:
-
-1. Copy the DLLs to the exectuable location. This is, by far, the cleanest
-   solution and we recommend doing this with ArrayFire projects. To do so,
-   create a post-build event to copy the NVVM DLL as discusses below in
-   [Step 3 - Part A](#s3partA).
-2. Copy `CUDA_PATH\nvvm\bin\nvvm64_30_0.dll` to `CUDA_PATH\bin`. This is a one time
-   copy such that the NVVM DLL is now with all the other CUDA dlls and in a
-   directory that is a part of PATH and hence the DLL can be detected automatically.
-3. Add `%%CUDA_PATH%\nvvm\bin` to the system PATH environment variable.
-   This will allow automatic detection by the system and No further copying will
-   be required. ArrayFire does not add this to PATH since the CUDA installer
-   doesn't add it to PATH.
-
-## <a name="step1" />Step 1: Running pre-built executables
-
-The ArrayFire installer ships with a few pre-built executables with the examples.
-These should run out of the box when double clicked.
-
-Some prebuilt examples are:
-* Helloworld (examples/helloworld)
-* BLAS (examples/benchmarks)
-* FFT (examples/benchmarks)
-* Pi Estimation (examples/benchmarks)
-* Conway (Graphics) (examples/graphics)
-
-Note: For the CUDA executables, you will need to copy `CUDA_PATH\nvvm\bin\nvvm64_30_0.dll`
-to the location of the executables.
-
-## <a name="step2" />Step 2: Build and Run a Project
-
-1. Open Visual Studio 2013. Load the HelloWorld solution which is located at
-   `AF_PATH/examples/helloworld/helloworld.sln`.
-2. Build the `helloworld` example. Be sure to, select the platform/configuration
-   of your choice using the platform drop-down (the options are CPU, CUDA,
-   OpenCL, and Unified) and Solution Configuration drop down (options of Release
-   and Debug) menus.
-3. Run the `helloworld` example.
-
-## <a name="step3" />Step 3: Using ArrayFire within Visual Studio
-This is divided into 4 parts:
-* [Part A: Adding ArrayFire to an existing solution (Single Backend)](#s3partA)
-* [Part B: Adding ArrayFire CUDA to a new/existing CUDA project](#s3partB)
-* [Part C: Project with all ArrayFire backends](#s3partC)
-* [Part D: ArrayFire with CMake](#s3partD)
-
-### <a name="s3partA" />Part A: Adding ArrayFire to an existing solution (Single Backend)
-Note: If you plan on using Native CUDA code in the project, use the steps
-under [Part B](#s3partB).
+2. **AF_PATH/include** : Header files for ArrayFire (include directory)
+3. **AF_PATH/lib** : All ArrayFire backends libraries, dlls and dependency dlls
+   (library directory)
+4. **AF_PATH/examples** : Examples to get started.
+5. **AF_PATH/cmake** : CMake config files
+6. **AF_PATH/uninstall.exe** : Uninstaller
+
+The installer will prompt the user for following three options.
+* Do not add **%%AF_PATH%/lib** to PATH
+* Add **%%AF_PATH%/lib** to PATH environment variable of current user
+* Add **%%AF_PATH%/lib** to PATH environment variable for all users
+
+If you chose not to modify PATH during installation please make sure to do so
+manually so that all applications using ArrayFire libraries will be able to find
+the required DLLs.
+
+## <a name="section1" />Build and Run Helloworld
+
+This can be done in two ways either by using CMake build tool or using Visual
+Studio directly.
+
+### <a name="section1part1"/> Using CMake
+1. Download and install [CMake](https://cmake.org/download/), preferrably the
+   latest version.
+2. Open CMake-GUI and set the field __Where is the source code__ to the root
+   directory of examples.
+3. Set the field __Where to build the binaries__ to
+   **path_to_examples_root_dir/build** and click the `Configure` button towards
+   the lower left bottom.
+4. CMake will prompt you asking if it has to create the `build` directory if
+   it's not already present. Click yes to create the build directory.
+5. Before the configuration begins, CMake will show you a list(drop-down menu)
+   of available Visual Studio versions on your system to chose from. Select one
+   and check the radio button that says **Use default native compilers** and
+   click finish button in the bottom right corner.
+6. CMake will show you errors in red text if any once configuration is finished.
+   Ideally, you wouldn't need to do anything and CMake should be able to find
+   ArrayFire automatically. Please let us know if it didn't on your machine.
+7. Click **Generate** button to generate the Visual Studio solution files for
+   the examples.
+8. Click **Open Project** button that is right next to **Generate** button to
+   open the solution file.
+9. You will see a bunch of examples segregated into three sets named after the
+   compute backends of ArrayFire: cpu, cuda & opencl if you have installed all
+   backends. Select the helloworld project from any of the installed backends
+   and mark it as startup project and hit `F5`.
+10. Once the helloworld example builds, you will see a console window with the
+    output from helloworld program.
+
+### <a name="section1part2"/> Using Visual Studio
+
+1. Open Visual Studio of your choice and create an empty C++ project.
+2. Right click the project and add an existing source file
+   `examples/helloworld/helloworld.cpp` to this project.
+3. Add `"$(AF_PATH)/include;"` to _Project Properties -> C/C++ -> General ->
+   Additional Include Directories_.
+4. Add `"$(AF_PATH)/lib;"` to _Project Properties -> Linker -> General ->
+   Additional Library Directories_.
+5. Add `afcpu.lib` or `afcuda.lib` or `afopencl.lib` to _Project Properties ->
+   Linker -> Input -> Additional Dependencies_. based on your preferred backend.
+6. (Optional) You may choose to define `NOMINMAX`, `AF_<CPU/CUDA/OPENCL>` and/or
+   `AF_<DEBUG/RELEASE>` in your projects. This can be added to _Project
+   Properties -> C/C++ -> General -> Preprocessor-> Preprocessory definitions_.
+7. Build and run the project. You will see a console window with the output from
+   helloworld program.
+
+## <a name="section2" />Using ArrayFire within Existing Visual Studio Projects
+This is divided into three parts:
+* [Part A: Adding ArrayFire to an existing solution (Single
+  Backend)](#section3partA)
+* [Part B: Adding ArrayFire CUDA to a new/existing CUDA project](#section3partB)
+* [Part C: Project with all ArrayFire backends](#section3partC)
+
+### <a name="section3partA" />Part A: Adding ArrayFire to an existing solution (Single Backend)
+Note: If you plan on using Native CUDA code in the project, use the steps under
+[Part B](#section3partB).
 
 Adding a single backend to an existing project is quite simple.
 
-1. Add `"$(AF_PATH)/include;"` to
-   _Project Properties -> C/C++ -> General -> Additional Include Directories_.
-2. Add `"$(AF_PATH)/lib;"` to
-   _Project Properties -> Linker -> General -> Additional Library Directories_.
-3. Add `afcpu.lib` or `afcuda.lib` or `afopencl.lib` to
-   _Project Properties -> Linker -> Input -> Additional Dependencies_.
-   based on your preferred backend.
-4. (Optional) You may choose to define `NOMINMAX`, `AF_<CPU/CUDA/OPENCL>`
-   and/or `AF_<DEBUG/RELEASE>` in your projects. This can be added to
-   _Project Properties -> C/C++ -> General -> Preprocessor-> Preprocessory definitions_.
-
-If you are using the CUDA backend, it is important to ensure that the CUDA NVVM
-DLLs are copied to the exectuable directory. This can be done by adding a post
-build event.
-
-Open the _Project Properties -> Build Events -> Post Build Events_ dialog and
-add the following lines to it.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.c}
-echo copy "$(CUDA_PATH)\nvvm\bin\nvvm64*.dll" "$(OutDir)"
-copy "$(CUDA_PATH)\nvvm\bin\nvvm64*.dll" "$(OutDir)"
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-### <a name="s3partB" />Part B: Adding ArrayFire CUDA to a new/existing CUDA project
+1. Add `"$(AF_PATH)/include;"` to _Project Properties -> C/C++ -> General ->
+   Additional Include Directories_.
+2. Add `"$(AF_PATH)/lib;"` to _Project Properties -> Linker -> General ->
+   Additional Library Directories_.
+3. Add `afcpu.lib`, `afcuda.lib`, `afopencl.lib`, or `af.lib` to _Project
+   Properties -> Linker -> Input -> Additional Dependencies_. based on your
+   preferred backend.
+
+### <a name="section3partB" />Part B: Adding ArrayFire CUDA to a new/existing CUDA project
 Lastly, if your project contains custom CUDA code, the instructions are slightly
 different as it requires using a CUDA NVCC Project:
 
 1. Create a custom "CUDA NVCC project" in Visual Studio
-2. Add `"$(AF_PATH)/include;"` to
-   _Project Properties -> CUDA C/C++ -> General -> Additional Include Directories_.
-3. Add `"$(AF_PATH)/lib;"` to
-   _Project Properties -> Linker -> General -> Additional Library Directories_.
-4. Add `afcpu.lib` or `afcuda.lib` or `afopencl.lib` to
-   _Project Properties -> Linker -> Input -> Additional Dependencies_.
-   based on your preferred backend.
-5. (Optional) You may choose to define `NOMINMAX`, `AF_CUDA`
-   and/or `AF_<DEBUG/RELEASE>` in your projects. This can be added to
-   _Project Properties -> C/C++ -> General -> Preprocessor-> Preprocessory definitions_.
-6. Pick a solution to handle the NVVM DLLs. We recommend the post build event
-   method used in [Part A](#s3partA).
-
-### <a name="s3partC" />Part C: Project with all ArrayFire backends
+2. Add `"$(AF_PATH)/include;"` to _Project Properties -> CUDA C/C++ -> General
+   -> Additional Include Directories_.
+3. Add `"$(AF_PATH)/lib;"` to _Project Properties -> Linker -> General ->
+   Additional Library Directories_.
+4. Add `afcpu.lib`, `afcuda.lib`, `afopencl.lib`, or `af.lib` to _Project Properties ->
+   Linker -> Input -> Additional Dependencies_. based on your preferred backend.
+
+### <a name="section3partC" />Part C: Project with all ArrayFire backends
 If you wish to create a project that allows you to use all the ArrayFire
-backends with ease, the best way to go is to copy the *HelloWorld sln/vcxproj/cpp*
-file trio and rename them to suit your project.
-
-All the ArrayFire examples are pre-configured for all ArrayFire backends as well
-as the Unified API. These can be chosen from the Solution/Platform configuration
-drop down boxes.
+backends with ease, you should use `af.lib` in step 3 from [Part
+A](#section3partA).
 
-You can alternately download the template project from
-[ArrayFire Template Projects](https://github.com/arrayfire/arrayfire-project-templates)
+You can alternately download the template project from [ArrayFire Template
+Projects](https://github.com/arrayfire/arrayfire-project-templates)
 
-### <a name="s3partD" />Part D: ArrayFire with CMake
-*NOTE:* The ArrayFire installer sets up CMake file and registry so that it can be found
-by CMake by simply using the `Find_PACKAGE(ArrayFire)` command.
-
-If you are writing a new ArrayFire project in C/C++ from scratch, we suggest
-you grab a copy of our
-[CMake Project Example](https://github.com/arrayfire/arrayfire-project-templates);
-however, it is useful to read the documentation below in case you need to add
-ArrayFire to an existing project.
-
-As [discussed above](#big-picture), ArrayFire ships with a series of CMake
-scripts to make finding and using our library easy.
-The scripts will automatically find all versions of the ArrayFire library
-and pick the most powerful of the installed backends (typically CUDA).
+## <a name="section4" />Using ArrayFire with CMake
+ArrayFire ships with a series of CMake scripts to make finding and using our
+library easy.
 
 First create a file called `CMakeLists.txt` in your project directory:
 
@@ -152,73 +128,29 @@ First create a file called `CMakeLists.txt` in your project directory:
 
 and populate it with the following code:
 
-    FIND_PACKAGE(ArrayFire)
-    INCLUDE_DIRECTORIES(${ArrayFire_INCLUDE_DIRS})
-
-    ... [gather source files, etc.]
-
-    # If you intend to use OpenCL, you need to find it
-    FIND_PACKAGE(OpenCL)
-    SET(EXTRA_LIBS ${CMAKE_THREAD_LIBS_INIT} ${OpenCL_LIBRARIES})
-
-    # Or if you intend to use CUDA, you need it as well as NVVM:
-    FIND_PACKAGE(CUDA)
-    FIND_PACKAGE(NVVM) # this FIND script can be found in the ArrayFire CMake example repository
-    SET(EXTRA_LIBS ${CMAKE_THREAD_LIBS_INIT} ${CUDA_LIBRARIES} ${NVVM_LIB})
-
-    ADD_EXECUTABLE(my_executable [list your source files here])
-    TARGET_LINK_LIBRARIES(my_executable ${ArrayFire_LIBRARIES} ${EXTRA_LIBS})
-
-where `my_executable` is the name of the executable you wish to create.
-See the [CMake documentation](https://cmake.org/documentation/) for more
-information on how to use CMake.
-Clearly the above code snippet precludes the use of both CUDA and OpenCL, see
-the
-[ArrayFire CMake Example](https://github.com/arrayfire/arrayfire-project-templates/tree/master/CMake)
-for an example of how to build executables for both backends from the same
-CMake script.
-
-In the above code listing, the `FIND_PACKAGE` will find the ArrayFire include
-files, libraries, and define several variables including:
-
-    ArrayFire_INCLUDE_DIRS    - Location of ArrayFire's include directory.
-    ArrayFire_LIBRARIES       - Location of ArrayFire's libraries.
-                                This will default to a GPU backend if one
-                                is found
-    ArrayFire_FOUND           - True if ArrayFire has been located
-
-If you wish to use a specific backend, the find script also defines these variables:
-
-    ArrayFire_CPU_FOUND         - True of the ArrayFire CPU library has been found.
-    ArrayFire_CPU_LIBRARIES     - Location of ArrayFire's CPU library, if found
-    ArrayFire_CUDA_FOUND        - True of the ArrayFire CUDA library has been found.
-    ArrayFire_CUDA_LIBRARIES    - Location of ArrayFire's CUDA library, if found
-    ArrayFire_OpenCL_FOUND      - True of the ArrayFire OpenCL library has been found.
-    ArrayFire_OpenCL_LIBRARIES  - Location of ArrayFire's OpenCL library, if found
-    ArrayFire_Unified_FOUND     - True of the ArrayFire Unified library has been found.
-    ArrayFire_Unified_LIBRARIES - Location of ArrayFire's Unified library, if found
-
-Therefore, if you wish to target a specific specific backend, simply replace
-`${ArrayFire_LIBRARIES}` with `${ArrayFire_CPU}`, `${ArrayFire_OPENCL}`,
-`${ArrayFire_CUDA}`, or `${ArrayFire_Unified}` in the `TARGET_LINK_LIBRARIES`
-command above.
-
-Next we need to instruct CMake to create build instructions and then compile.
-We suggest using CMake's out-of-source build functionality to keep your build
-and source files cleanly separated. To do this open the CMake GUI.
+    find_package(ArrayFire)
+    add_executable(<my_executable> [list your source files here])
 
-* Under source directory, add the path to your project
-* Under build directory, add the path to your project and append /build
-* Click configure and choose Visual Studio 2013 Win 64 as the generator.
-* If configuration was successful, click generate. This will create a
-  my-project.sln file under build. You can open this in Visual Studio and
-  compile the ALL_BUILD project.
+    # To use Unified backend, do the following.
+    # Unified backend lets you choose the backend at runtime
+    target_link_libraries(<my_executable> ArrayFire::af)
 
+where `<my_executable>` is the name of the executable you wish to create. See the
+[CMake documentation](https://cmake.org/documentation/) for more information on
+how to use CMake. To link with a specific backend directly, replace the
+`ArrayFire::af` with the following for their respective backends.
 
-The [ArrayFire CMake Example](https://github.com/arrayfire/arrayfire-project-templates/tree/master/CMake)
-is a CMake project used to demo how ArrayFire can be using with a CMake project.
+* `ArrayFire::afcpu` for CPU backend.
+* `ArrayFire::afcuda` for CUDA backend.
+* `ArrayFire::afopencl` for OpenCL backend.
 
-Note: The CMake project does not add the post build event to copy the NVVM DLLs
-in case of CUDA backend. You will need to either copy it manually to the exectuable
-directory, or pick another solution for it.
+Next we need to instruct CMake to create build instructions and then compile. We
+suggest using CMake's out-of-source build functionality to keep your build and
+source files cleanly separated. To do this open the CMake GUI.
 
+* Under source directory, add the path to your project
+* Under build directory, add the path to your project and append /build
+* Click configure and choose a 64 bit Visual Studio generator.
+* If configuration was successful, click generate. This will create a
+  my-project.sln file under build. Click `Open Project` in CMake-GUI to open the
+  solution and compile the ALL_BUILD project.
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index d4e03f9ef3..17e738a8a5 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -13,11 +13,11 @@ project(ArrayFire-Examples
 
 if(EXISTS "${ArrayFire_SOURCE_DIR}/CMakeModules/ArrayFireExampleOverloads.cmake")
   include(ArrayFireExampleOverloads)
+else()
+  set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/..")
 endif()
 
-if (NOT ASSETS_DIR)
-    set(ASSETS_DIR "" CACHE PATH "Data and images required for some examples (url: https://github.com/arrayfire/assets)")
-endif (NOT ASSETS_DIR)
+file(TO_NATIVE_PATH ${ASSETS_DIR} ASSETS_DIR)
 
 file(TO_NATIVE_PATH ${ASSETS_DIR} ASSETS_DIR)
 

From b684a5b65a5b635742735f4aac17e6ab16991c21 Mon Sep 17 00:00:00 2001
From: Ralf Stubner <ralf.stubner@gmail.com>
Date: Sat, 14 Apr 2018 01:00:15 +0200
Subject: [PATCH 1401/2677]  Improve counter handling for CBRNGs (#2122)

* Improve counter handling for CBRNGs (CPU backend)

* Use the full 64 bits of the counter
* Separate counter and key/seed
* Check for carry when increasing counter (only Threefry)

* Improve counter handling for CBRNGs (OpenCL backend)

* Use the full 64 bits of the counter
* Separate counter and key/seed
* Check for carry when increasing counter
* Use unused counter values for second threefry round

* Improve counter handling for CBRNGs (CUDA backend)

* Use the full 64 bits of the counter
* Separate counter and key/seed
* Check for carry when increasing counter

* Add (disabled) Test for RNG period

This test repeatedly draws 2^20 random numbers and compares them to the
first set of numbers. This fails if a RNG has a period of n * 2^20 with
integer n <= 2^12, i.e. in particular 2^32.

* Add (disabled) test for RNG quality

This test repeatedly draws random numbers and generates a histogram from
them. It calculates the chi^2 statistic for the individual step as well
as for the accumulated random numbers. The test fails if two consecutive
statistics are too large or too small. Too large means that the random
numbers are not uniform enough. Too small means that they are "too
uniform", since some amount of random noise is expected.

Additional notes:
* One should also test randn, but that would increase the run-time even
  more.
* The failure conditions are fragile. In principle one should accept a
  larger range of chi^2 values together with more steps, possibly with
  different initial seeds. But this would increase run-time even more.
* As a consequence of the fragile conditions, false positives can occur.
  For example, MERSENNE with double on CPU failed early in some tests.

* Add program to test RNGs with PractRand
---
 src/backend/cpu/kernel/random_engine.hpp      | 33 ++++---
 src/backend/cuda/kernel/random_engine.hpp     | 74 ++++++++------
 src/backend/opencl/kernel/random_engine.hpp   |  6 +-
 .../opencl/kernel/random_engine_philox.cl     | 10 +-
 .../opencl/kernel/random_engine_threefry.cl   | 13 ++-
 test/random.cpp                               | 97 +++++++++++++++++++
 test/random_practrand.cpp                     | 27 ++++++
 7 files changed, 211 insertions(+), 49 deletions(-)
 create mode 100644 test/random_practrand.cpp

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 304eb8e24e..0e08e83e3a 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -101,8 +101,10 @@ namespace kernel
     {
         uint hi = seed>>32;
         uint lo = seed;
-        uint key[2] = {(uint)counter, hi};
-        uint ctr[4] = {(uint)counter, 0, 0, lo};
+        uint hic = counter>>32;
+        uint loc = counter;
+        uint key[2] = {lo, hi};
+        uint ctr[4] = {loc, hic, 0, 0};
 
         int reset = (4*sizeof(uint))/sizeof(T);
         for (int i = 0; i < (int)elements; i += reset) {
@@ -119,14 +121,17 @@ namespace kernel
     {
         uint hi = seed>>32;
         uint lo = seed;
-        uint key[2] = {(uint)counter, hi};
-        uint ctr[2] = {(uint)counter, lo};
+        uint hic = counter>>32;
+        uint loc = counter;
+        uint key[2] = {lo, hi};
+        uint ctr[2] = {loc, hic};
         uint val[2];
 
         int reset = (2*sizeof(uint))/sizeof(T);
         for (int i = 0; i < (int)elements; i += reset) {
             threefry(key, ctr, val);
-            ++ctr[0]; ++key[0];
+            ++ctr[0];
+            ctr[1] += (ctr[0] == 0);
             int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
             for (int j = 0; j < lim; ++j) {
                 out[i + j] = transform<T>(val, j);
@@ -162,8 +167,10 @@ namespace kernel
     {
         uint hi = seed>>32;
         uint lo = seed;
-        uint key[2] = {(uint)counter, hi};
-        uint ctr[4] = {(uint)counter, 0, 0, lo};
+        uint hic = counter>>32;
+        uint loc = counter;
+        uint key[2] = {lo, hi};
+        uint ctr[4] = {loc, hic, 0, 0};
         T temp[(4*sizeof(uint))/sizeof(T)];
 
         int reset = (4*sizeof(uint))/sizeof(T);
@@ -182,17 +189,21 @@ namespace kernel
     {
         uint hi = seed>>32;
         uint lo = seed;
-        uint key[2] = {(uint)counter, hi};
-        uint ctr[2] = {(uint)counter, lo};
+        uint hic = counter>>32;
+        uint loc = counter;
+        uint key[2] = {lo, hi};
+        uint ctr[2] = {loc, hic};
         uint val[4];
         T temp[(4*sizeof(uint))/sizeof(T)];
 
         int reset = (4*sizeof(uint))/sizeof(T);
         for (int i = 0; i < (int)elements; i += reset) {
             threefry(key, ctr, val);
-            ++ctr[0]; ++key[0];
+            ++ctr[0];
+            ctr[1] += (ctr[0] == 0);
             threefry(key, ctr, val+2);
-            ++ctr[0]; ++key[0];
+            ++ctr[0];
+            ctr[1] += (ctr[0] == 0);
             boxMullerTransform(val, temp);
             int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
             for (int j = 0; j < lim; ++j) {
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 6a6ce32560..9e01e948f3 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -400,11 +400,14 @@ namespace kernel
     }
 
     template <typename T>
-    __global__ void uniformPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    __global__ void uniformPhilox(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
     {
         uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {index+counter, hi};
-        uint ctr[4] = {index+counter, 0, 0, lo};
+        uint key[2] = {lo, hi};
+        uint ctr[4] = {loc, hic, 0, 0};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc);
+        ctr[2] += (ctr[1] < hic);
         if (blockIdx.x != (gridDim.x - 1)) {
             philox(key, ctr);
             writeOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
@@ -415,21 +418,24 @@ namespace kernel
     }
 
     template <typename T>
-    __global__ void uniformThreefry(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    __global__ void uniformThreefry(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
     {
         uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {index+counter, hi};
-        uint ctr[2] = {index+counter, lo};
+        uint key[2] = {lo, hi};
+        uint ctr[2] = {loc, hic};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc);
         uint o[4];
+
+	threefry(key, ctr, o);
+	uint step = elementsPerBlock / 2;
+	ctr[0] += step;
+	ctr[1] += (ctr[0] < step);
+	threefry(key, ctr, o + 2);
+
         if (blockIdx.x != (gridDim.x - 1)) {
-            threefry(key, ctr, o);
-            ctr[0] += elements;
-            threefry(key, ctr, o + 2);
             writeOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
         } else {
-            threefry(key, ctr, o);
-            ctr[0] += elements;
-            threefry(key, ctr, o + 2);
             partialWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
         }
     }
@@ -496,11 +502,14 @@ namespace kernel
     }
 
     template <typename T>
-    __global__ void normalPhilox(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    __global__ void normalPhilox(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
     {
         uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {index+counter, hi};
-        uint ctr[4] = {index+counter, 0, 0, lo};
+        uint key[2] = {lo, hi};
+        uint ctr[4] = {loc, hic, 0, 0};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc);
+        ctr[2] += (ctr[1] < hic);
         if (blockIdx.x != (gridDim.x - 1)) {
             philox(key, ctr);
             boxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
@@ -511,21 +520,24 @@ namespace kernel
     }
 
     template <typename T>
-    __global__ void normalThreefry(T *out, uint hi, uint lo, uint counter, uint elementsPerBlock, uint elements)
+    __global__ void normalThreefry(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
     {
         uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {index+counter, hi};
-        uint ctr[2] = {index+counter, lo};
+        uint key[2] = {lo, hi};
+        uint ctr[2] = {loc, hic};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc);
         uint o[4];
-        if (blockIdx.x != (gridDim.x - 1)) {
-            threefry(key, ctr, o);
-            ctr[0] += elements;
-            threefry(key, ctr, o + 2);
+
+	threefry(key, ctr, o);
+	uint step = elementsPerBlock / 2;
+	ctr[0] += step;
+	ctr[1] += (ctr[0] < step);
+	threefry(key, ctr, o + 2);
+
+	if (blockIdx.x != (gridDim.x - 1)) {
             boxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
         } else {
-            threefry(key, ctr, o);
-            ctr[0] += elements;
-            threefry(key, ctr, o + 2);
             partialBoxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
         }
     }
@@ -636,11 +648,13 @@ namespace kernel
         int blocks = divup(elements, elementsPerBlock);
         uint hi = seed>>32;
         uint lo = seed;
+        uint hic = counter>>32;
+        uint loc = counter;
         switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
-            CUDA_LAUNCH(uniformPhilox, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
+            CUDA_LAUNCH(uniformPhilox, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
-            CUDA_LAUNCH(uniformThreefry, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
+            CUDA_LAUNCH(uniformThreefry, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
@@ -654,11 +668,13 @@ namespace kernel
         int blocks = divup(elements, elementsPerBlock);
         uint hi = seed>>32;
         uint lo = seed;
+        uint hic = counter>>32;
+        uint loc = counter;
         switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
-            CUDA_LAUNCH(normalPhilox, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
+            CUDA_LAUNCH(normalPhilox, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
-            CUDA_LAUNCH(normalThreefry, blocks, threads, out, hi, lo, counter, elementsPerBlock, elements); break;
+            CUDA_LAUNCH(normalThreefry, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
         default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
         }
         counter += elements;
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 60e676f8fb..41d16a3fc2 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -146,15 +146,17 @@ namespace opencl
 
             uint hi = seed>>32;
             uint lo = seed;
+	    uint hic = counter>>32;
+	    uint loc = counter;
 
             NDRange local(THREADS, 1);
             NDRange global(THREADS * groups, 1);
 
             if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) || (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
                 Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
-                auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint>(ker);
+                auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
                 randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                        out, elements, counter, hi, lo);
+			       out, elements, hic, loc, hi, lo);
             }
 
             counter += elements;
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index d70232c4a7..7e67309eb3 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -93,14 +93,18 @@ void philox(uint key[2], uint ctr[4])
 }
 
 __kernel void generate(__global T *output, unsigned elements,
-        unsigned counter, unsigned hi, unsigned lo)
+        unsigned hic, unsigned loc, unsigned hi, unsigned lo)
 {
     unsigned gid = get_group_id(0);
     unsigned off = get_local_size(0);
     unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
 
-    uint key[2] = {index+counter, hi};
-    uint ctr[4] = {index+counter, 0, 0, lo};
+    uint key[2] = {lo, hi};
+    uint ctr[4] = {loc, hic, 0, 0};
+    ctr[0] += index;
+    ctr[1] += (ctr[0] < loc);
+    ctr[2] += (ctr[1] < hic);
+
     philox(key, ctr);
 
     if (gid != get_num_groups(0) - 1) {
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index ff458c1d79..1c48837869 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -117,18 +117,23 @@ inline void threefry(uint k[2], uint c[2], uint X[2])
 }
 
 __kernel void generate(__global T *output, unsigned elements,
-        unsigned counter, unsigned hi, unsigned lo)
+        unsigned hic, unsigned loc, unsigned hi, unsigned lo)
 {
     unsigned gid = get_group_id(0);
     unsigned off = get_local_size(0);
     unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
 
-    uint key[2] = {index+counter, hi};
-    uint ctr[2] = {index+counter, lo};
+    uint key[2] = {lo, hi};
+    uint ctr[2] = {loc, hic};
     uint o[4];
 
+    ctr[0] += index;
+    ctr[1] += (ctr[0] < index);
+
     threefry(key, ctr, o);
-    ctr[0] += elements;
+    uint step = ELEMENTS_PER_BLOCK / 2;
+    ctr[0] += step;
+    ctr[1] += (ctr[0] < step);
     threefry(key, ctr, o+2);
 
     if (gid != get_num_groups(0) - 1) {
diff --git a/test/random.cpp b/test/random.cpp
index 226c6ab1bf..272d3f248d 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -427,3 +427,100 @@ TYPED_TEST(RandomEngineSeed, mersenneSeedUniform)
 {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
+
+template <typename T>
+void testRandomEnginePeriod(randomEngineType type)
+{
+    if (noDoubleTests<T>()) return;
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+
+    uint elem = 1024*1024;
+    uint steps = 4*1024;
+    af::randomEngine r(type, 0);
+
+    af::array first = af::randu(elem, ty, r);
+
+    for (int i = 0; i < steps; ++i) {
+        af::array step = af::randu(elem, ty, r);
+        bool different = !af::allTrue<bool>(first == step);
+        ASSERT_TRUE(different);
+    }
+}
+
+TYPED_TEST(RandomEngine, DISABLED_philoxRandomEnginePeriod)
+{
+    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
+}
+
+TYPED_TEST(RandomEngine, DISABLED_threefryRandomEnginePeriod)
+{
+    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
+}
+
+TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEnginePeriod)
+{
+    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
+}
+
+template <typename T>
+T chi2_statistic(array input, array expected) {
+    expected *= af::sum<T>(input) / af::sum<T>(expected);
+    array diff = input - expected;
+    return af::sum<T>((diff * diff) / expected);
+}
+
+template <typename T>
+void testRandomEngineUniformChi2(randomEngineType type)
+{
+    if (noDoubleTests<T>()) return;
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+
+    int elem = 256*1024*1024;
+    int steps = 32;
+    int bins = 100;
+
+    array total_hist = af::constant(0.0, bins, ty);
+    array expected = af::constant(1.0/bins, bins, ty);
+
+    af::randomEngine r(type, 0);
+
+    // R> qchisq(c(5e-6, 1 - 5e-6), 99)
+    // [1]  48.68125 173.87456
+    T lower = 48.68125;
+    T upper = 173.87456;
+
+    bool prev_step = true;
+    bool prev_total = true;
+    for (int i = 0; i < steps; ++i) {
+        array step_hist = af::histogram(af::randu(elem, ty, r), bins, 0.0, 1.0);
+        T step_chi2 = chi2_statistic<T>(step_hist, expected);
+        if (!prev_step) {
+          EXPECT_GT(step_chi2, lower) << "at step: " << i;
+          EXPECT_LT(step_chi2, upper) << "at step: " << i;
+        }
+        prev_step = step_chi2 > lower && step_chi2 < upper;
+
+        total_hist += step_hist;
+        T total_chi2 = chi2_statistic<T>(total_hist, expected);
+        if (!prev_total) {
+          EXPECT_GT(total_chi2, lower) << "at step: " << i;
+          EXPECT_LT(total_chi2, upper) << "at step: " << i;
+        }
+        prev_total = total_chi2 > lower && total_chi2 < upper;
+    }
+}
+
+TYPED_TEST(RandomEngine, DISABLED_philoxRandomEngineUniformChi2)
+{
+    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
+}
+
+TYPED_TEST(RandomEngine, DISABLED_threefryRandomEngineUniformChi2)
+{
+    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
+}
+
+TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEngineUniformChi2)
+{
+    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
+}
diff --git a/test/random_practrand.cpp b/test/random_practrand.cpp
new file mode 100644
index 0000000000..806c9a4693
--- /dev/null
+++ b/test/random_practrand.cpp
@@ -0,0 +1,27 @@
+// Generate random bits and send them to STDOUT.
+// Suitable for testing with PractRand, c.f. http://pracrand.sourceforge.net/
+// and http://www.pcg-random.org/posts/how-to-test-with-practrand.html
+// Commandline arguments: backend, device, rng_type
+// Example:
+// random_practrand 0 0 200 | RNG_test stdin32
+#include <cstdio>
+#include <cstdint>
+#include <arrayfire.h>
+
+int main(int argc, char ** argv) {
+  int backend = argc > 1 ? atoi(argv[1]) : 0;
+  af::setBackend(static_cast<af::Backend>(backend));
+  int device = argc > 2 ? atoi(argv[2]) : 0;
+  af::setDevice(device);
+  int rng = argc > 3 ? atoi(argv[3]) : 100;
+  af::setDefaultRandomEngineType(static_cast<af::randomEngineType>(rng)); 	
+  
+  af::setSeed(0xfe47fe0cc078ec30ULL);
+  int samples = 1024 * 1024;
+  while (1) {
+    af::array values = af::randu(samples, u32);
+    uint32_t *pvalues = values.host<uint32_t>();
+    fwrite((void*) pvalues, samples * sizeof(*pvalues), 1, stdout);
+    free(pvalues);
+  }
+}

From 1dfbbe3ba5bb993a73618307c5e6db3d522cc384 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 13 Apr 2018 19:00:37 -0400
Subject: [PATCH 1402/2677] Move nativeFree calls outside of the memory_mutex
 to avoid deadlock (#2124)

Narrows the scope of the memory_mutex locks to just around the
modification and reading of the internal memory data members and
elements. This means that actual allocations happen outside of this
mutex's lock.

This was done because the CPU backend requires that the free operation
performs a sync before the pointer is freed. This caused deadlocks
when the main thread was waiting on the worker thread and the
worker thread called nativeFree. This scenario happens when a buffer
node is only referenced in the worker thread and it is removed.
---
 src/backend/common/MemoryManager.hpp | 158 ++++++++++++++-------------
 1 file changed, 83 insertions(+), 75 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 711afe3562..b1ae911433 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -9,12 +9,14 @@
 
 #pragma once
 
-#include <common/err_common.hpp>
 #include <common/dispatch.hpp>
+#include <common/err_common.hpp>
 #include <common/util.hpp>
 
 #include <algorithm>
+#include <functional>
 #include <iomanip>
+#include <memory>
 #include <mutex>
 #include <string>
 #include <unordered_map>
@@ -22,8 +24,8 @@
 
 namespace common
 {
-typedef std::recursive_mutex mutex_t;
-typedef std::lock_guard<mutex_t> lock_guard_t;
+using mutex_t      = std::recursive_mutex;
+using lock_guard_t = std::lock_guard<mutex_t>;
 
 const unsigned MAX_BUFFERS   = 1000;
 const size_t ONE_GB = 1 << 30;
@@ -38,11 +40,13 @@ class MemoryManager
         size_t bytes;
     } locked_info;
 
-    using locked_t = typename std::unordered_map<void *, locked_info>;
+    using locked_t    = typename std::unordered_map<void *, locked_info>;
     using locked_iter = typename locked_t::iterator;
 
-    typedef std::unordered_map<size_t, std::vector<void *> >free_t;
-    typedef free_t::iterator free_iter;
+    using free_t    = std::unordered_map<size_t, std::vector<void *> >;
+    using free_iter = free_t::iterator;
+
+    using uptr_t = std::unique_ptr<void, std::function<void(void*)>>;
 
     typedef struct memory_info
     {
@@ -91,23 +95,33 @@ class MemoryManager
     {
         if (this->debug_mode) return;
 
-        lock_guard_t lock(this->memory_mutex);
+        // This vector is used to store the pointers which will be deleted by
+        // the memory manager. We are using this to avoid calling free while
+        // the lock is being held becasue the CPU backend calls sync.
+        std::vector<void*> free_ptrs;
         memory_info& current = memory[device];
-
-        // Return if all buffers are locked
-        if (current.total_buffers == current.lock_buffers) return;
-
-        for (auto &kv : current.free_map) {
-            size_t num_ptrs = kv.second.size();
-            //Free memory by popping the last element
-            for (int n = num_ptrs-1; n >= 0; n--) {
-                this->nativeFree(kv.second[n]);
-                current.total_bytes -= kv.first;
-                current.total_buffers--;
-                kv.second.pop_back();
+        {
+            lock_guard_t lock(this->memory_mutex);
+            // Return if all buffers are locked
+            if (current.total_buffers == current.lock_buffers) return;
+            free_ptrs.reserve(32);
+
+            for (auto &kv : current.free_map) {
+                size_t num_ptrs = kv.second.size();
+                // Free memory by pushing the last element into the free_ptrs
+                // vector which will be freed once outside of the lock
+                for(auto p : kv.second) {
+                    free_ptrs.push_back(p);
+                }
+                current.total_bytes -= num_ptrs * kv.first;
+                current.total_buffers -= num_ptrs;
             }
+            current.free_map.clear();
+        }
+        // Free memory outside of the lock
+        for(auto ptr : free_ptrs) {
+            this->nativeFree(ptr);
         }
-        current.free_map.clear();
     }
 
     public:
@@ -171,13 +185,14 @@ class MemoryManager
 
     void *alloc(const size_t bytes, bool user_lock)
     {
-        lock_guard_t lock(this->memory_mutex);
-
-        void *ptr = NULL;
-        size_t alloc_bytes = this->debug_mode ? bytes : (divup(bytes, mem_step_size) * mem_step_size);
+        void *ptr = nullptr;
+        size_t alloc_bytes =
+          this->debug_mode ? bytes :
+                             (divup(bytes, mem_step_size) * mem_step_size);
 
         if (bytes > 0) {
             memory_info& current = this->getCurrentMemoryInfo();
+            locked_info info = {!user_lock, user_lock, alloc_bytes};
 
             // There is no memory cache in debug mode
             if (!this->debug_mode) {
@@ -188,36 +203,38 @@ class MemoryManager
                     this->garbageCollect();
                 }
 
+                lock_guard_t lock(this->memory_mutex);
                 free_iter iter = current.free_map.find(alloc_bytes);
 
                 if (iter != current.free_map.end() && !iter->second.empty()) {
                     ptr = iter->second.back();
                     iter->second.pop_back();
+                    current.locked_map[ptr] = info;
+                    current.lock_bytes += alloc_bytes;
+                    current.lock_buffers++;
                 }
-
             }
 
             // Only comes here if buffer size not found or in debug mode
-            if (ptr == NULL) {
+            if (ptr == nullptr) {
                 // Perform garbage collection if memory can not be allocated
                 try {
                     ptr = this->nativeAlloc(alloc_bytes);
-                } catch (AfError &ex) {
+                } catch (const AfError &ex) {
                     // If out of memory, run garbage collect and try again
                     if (ex.getError() != AF_ERR_NO_MEM) throw;
                     this->garbageCollect();
                     ptr = this->nativeAlloc(alloc_bytes);
                 }
+
+                lock_guard_t lock(this->memory_mutex);
                 // Increment these two only when it succeeds to come here.
                 current.total_bytes += alloc_bytes;
                 current.total_buffers += 1;
+                current.locked_map[ptr] = info;
+                current.lock_bytes += alloc_bytes;
+                current.lock_buffers++;
             }
-
-
-            locked_info info = {!user_lock, user_lock, alloc_bytes};
-            current.locked_map[ptr] = info;
-            current.lock_bytes += alloc_bytes;
-            current.lock_buffers++;
         }
         return ptr;
     }
@@ -236,53 +253,46 @@ class MemoryManager
         // Shortcut for empty arrays
         if (!ptr) return;
 
-        lock_guard_t lock(this->memory_mutex);
-        memory_info& current = this->getCurrentMemoryInfo();
+        // Frees the pointer outside the lock.
+        uptr_t freed_ptr(nullptr, [this](void* p) { this->nativeFree(p); });
+        {
+            lock_guard_t lock(this->memory_mutex);
+            memory_info& current = this->getCurrentMemoryInfo();
 
-        locked_iter iter = current.locked_map.find((void *)ptr);
+            locked_iter iter = current.locked_map.find((void *)ptr);
 
-        // Pointer not found in locked map
-        if (iter == current.locked_map.end()) {
-            // Probably came from user, just free it
-            this->nativeFree(ptr);
-            return;
-        }
+            // Pointer not found in locked map
+            if (iter == current.locked_map.end()) {
+                // Probably came from user, just free it
+                freed_ptr.reset(ptr);
+                return;
+            }
 
-        if (user_unlock) {
-            (iter->second).user_lock = false;
-        } else {
-            (iter->second).manager_lock = false;
-        }
+            if (user_unlock) {
+                (iter->second).user_lock = false;
+            } else {
+                (iter->second).manager_lock = false;
+            }
 
-        // Return early if either one is locked
-        if ((iter->second).user_lock || (iter->second).manager_lock) return;
+            // Return early if either one is locked
+            if ((iter->second).user_lock || (iter->second).manager_lock) return;
 
-        size_t bytes = iter->second.bytes;
-        current.lock_bytes -= iter->second.bytes;
-        current.lock_buffers--;
+            size_t bytes = iter->second.bytes;
+            current.lock_bytes -= iter->second.bytes;
+            current.lock_buffers--;
 
-        if (this->debug_mode) {
-            // Just free memory in debug mode
-            if ((iter->second).bytes > 0) {
-                this->nativeFree(iter->first);
-                current.total_buffers--;
-                current.total_bytes -= iter->second.bytes;
-            }
-        } else {
-            // In regular mode, move buffer to free map
-            free_iter fiter = current.free_map.find(bytes);
-            if (fiter != current.free_map.end()) {
-                // If found, push back
-                fiter->second.push_back(ptr);
+            if (this->debug_mode) {
+                // Just free memory in debug mode
+                if ((iter->second).bytes > 0) {
+                    freed_ptr.reset(iter->first);
+                    current.total_buffers--;
+                    current.total_bytes -= iter->second.bytes;
+                }
             } else {
-                // If not found, create new vector for this size
-                std::vector<void *> ptrs;
-                ptrs.push_back(ptr);
-                current.free_map[bytes] = ptrs;
+                current.free_map[bytes].push_back(ptr);
             }
+            current.locked_map.erase(iter);
         }
-
-        current.locked_map.erase(iter);
     }
 
     void garbageCollect()
@@ -290,10 +300,8 @@ class MemoryManager
         cleanDeviceMemoryManager(this->getActiveDeviceId());
     }
 
-
     void printInfo(const char *msg, const int device)
     {
-        lock_guard_t lock(this->memory_mutex);
         const memory_info& current = this->getCurrentMemoryInfo();
 
         printf("%s\n", msg);
@@ -301,6 +309,7 @@ class MemoryManager
                "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
                "---------------------------------------------------------\n");
 
+        lock_guard_t lock(this->memory_mutex);
         for(auto& kv : current.locked_map) {
             const char* status_mngr = "Yes";
             const char* status_user = "Unknown";
@@ -342,8 +351,8 @@ class MemoryManager
     void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                     size_t *lock_bytes,  size_t *lock_buffers)
     {
-        lock_guard_t lock(this->memory_mutex);
         const memory_info& current = this->getCurrentMemoryInfo();
+        lock_guard_t lock(this->memory_mutex);
         if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
         if (alloc_buffers ) *alloc_buffers = current.total_buffers;
         if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
@@ -357,7 +366,6 @@ class MemoryManager
         lock_guard_t lock(this->memory_mutex);
 
         locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-
         if (iter != current.locked_map.end()) {
             iter->second.user_lock = true;
         } else {

From 2ca54273ab349f164eacaa18e8492565e5504f39 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Mon, 16 Apr 2018 21:44:47 -0700
Subject: [PATCH 1403/2677] Fixes for parameter overflow for NVIDIA devices.

- Ensure the kernels are compiled quicker for non linear kernels.
---
 src/backend/cuda/Array.cpp   | 47 ++++++++++++++++++++++++++-------
 src/backend/cuda/jit.cpp     | 24 ++++++++++++-----
 src/backend/opencl/Array.cpp | 50 +++++++++++++++++++++++++++++++-----
 src/backend/opencl/jit.cpp   | 24 ++++++++++++-----
 test/jit.cpp                 | 21 +++++++++++++++
 5 files changed, 136 insertions(+), 30 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 1d8e69649b..9d9d4595f2 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -116,7 +116,7 @@ namespace cuda
     void Array<T>::eval()
     {
         if (isReady()) return;
- 
+
         this->setId(getActiveDeviceId());
         this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
 
@@ -209,28 +209,57 @@ namespace cuda
             if (node->getHeight() >= (int)getMaxJitSize()) {
                 out.eval();
             } else {
+
                 size_t alloc_bytes, alloc_buffers;
                 size_t lock_bytes, lock_buffers;
 
                 deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
                                  &lock_bytes, &lock_buffers);
 
-                // Check if approaching the memory limit
-                if (lock_bytes > getMaxBytes() ||
-                    lock_buffers > getMaxBuffers()) {
+                bool isBufferLimit =
+                    lock_bytes > getMaxBytes() ||
+                    lock_buffers > getMaxBuffers();
+
+
+                // We eval in the following cases.
+                // 1. Too many bytes are locked up by JIT causing memory pressure.
+                // Too many bytes is assumed to be half of all bytes allocated so far.
+                // 2. Too many buffers in a nonlinear kernel cause param space overflow.
+                // Too many buffers comes out to be about 50 (51 including output).
+                // Too many buffers can occur in a tree of size 25 in the worst case scenario.
+                // TODO: Find better solution than the following emperical solution.
+                if (node->getHeight() > 25 || isBufferLimit) {
 
-                    unsigned length =0, buf_count = 0, bytes = 0;
                     Node *n = node.get();
-                    JIT::Node_map_t nodes_map;
-                    std::vector<JIT::Node *> full_nodes;
-                    std::vector<JIT::Node_ids> full_ids;
+
+                    // Use thread local to reuse the memory every time you are here.
+                    thread_local JIT::Node_map_t nodes_map;
+                    thread_local std::vector<Node *> full_nodes;
+                    thread_local std::vector<JIT::Node_ids> full_ids;
+
+                    // Reserve some memory
+                    if (nodes_map.size() == 0) {
+                        nodes_map.reserve(1024);
+                        full_nodes.reserve(1024);
+                        full_ids.reserve(1024);
+                    }
+
                     n->getNodesMap(nodes_map, full_nodes, full_ids);
 
+                    unsigned length = 0, buf_count = 0, bytes = 0;
+                    bool is_linear = true;
+                    dim_t dims_[] = {dims[0], dims[1], dims[2], dims[3]};
                     for(auto &jit_node : full_nodes) {
                         jit_node->getInfo(length, buf_count, bytes);
+                        is_linear &= jit_node->isLinear(dims_);
                     }
 
-                    if (2 * bytes > lock_bytes) {
+                    // Reset the thread local vectors
+                    nodes_map.clear();
+                    full_nodes.clear();
+                    full_ids.clear();
+
+                    if (2 * bytes > lock_bytes || (!is_linear && buf_count >= 50)) {
                         out.eval();
                     }
                 }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 83539fc29e..1937ed1f75 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -373,15 +373,19 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
 
     if (num_outputs == 0) return;
 
-    Node_map_t nodes;
-    vector<Node *> full_nodes;
-    vector<Node_ids> full_ids;
-    vector<int> output_ids;
+    // Use thread local to reuse the memory every time you are here.
+    thread_local Node_map_t nodes;
+    thread_local vector<Node *> full_nodes;
+    thread_local vector<Node_ids> full_ids;
+    thread_local vector<int> output_ids;
 
     // Reserve some space to improve performance at smaller sizes
-    output_ids.reserve(output_nodes.size());
-    full_nodes.reserve(1024);
-    full_ids.reserve(1024);
+    if (nodes.size() == 0) {
+        nodes.reserve(1024);
+        output_ids.reserve(output_nodes.size());
+        full_nodes.reserve(1024);
+        full_ids.reserve(1024);
+    }
 
     for (auto &node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
@@ -467,6 +471,12 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
                             getActiveStream(),
                             &args.front(),
                             NULL));
+
+    // Reset the thread local vectors
+    nodes.clear();
+    output_ids.clear();
+    full_nodes.clear();
+    full_ids.clear();
 }
 
 template<typename T>
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 8fff008991..8d1b0e11cf 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -228,30 +228,66 @@ namespace opencl
         Array<T> out =  Array<T>(dims, node);
 
         if (evalFlag()) {
+
             if (node->getHeight() >= (int)getMaxJitSize()) {
                 out.eval();
             } else {
+
                 size_t alloc_bytes, alloc_buffers;
                 size_t lock_bytes, lock_buffers;
 
                 deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
                                  &lock_bytes, &lock_buffers);
 
-                if (lock_bytes > getMaxBytes() ||
-                    lock_buffers > getMaxBuffers()) {
+                bool isBufferLimit =
+                    lock_bytes > getMaxBytes() ||
+                    lock_buffers > getMaxBuffers();
+
+
+                bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA;
+                // We eval in the following cases.
+                // 1. Too many bytes are locked up by JIT causing memory pressure.
+                // Too many bytes is assumed to be half of all bytes allocated so far.
+                // 2. Too many buffers in a nonlinear kernel cause param space overflow.
+                // Too many buffers comes out to be about 48 (49 including output).
+                // Too many buffers can occur in a tree of size 24 in the worst case scenario.
+                // This error only happens on nvidia devices.
+                // TODO: Find better solution than the following emperical solution.
+                bool isParamLimit = (isNvidia && node->getHeight() > 24);
+                if (isParamLimit || isBufferLimit) {
 
-                    unsigned length =0, buf_count = 0, bytes = 0;
                     Node *n = node.get();
-                    JIT::Node_map_t nodes_map;
-                    std::vector<JIT::Node *> full_nodes;
-                    std::vector<JIT::Node_ids> full_ids;
+
+                    // Use thread local to reuse the memory every time you are here.
+                    thread_local JIT::Node_map_t nodes_map;
+                    thread_local std::vector<Node *> full_nodes;
+                    thread_local std::vector<JIT::Node_ids> full_ids;
+
+                    // Reserve some memory
+                    if (nodes_map.size() == 0) {
+                        nodes_map.reserve(1024);
+                        full_nodes.reserve(1024);
+                        full_ids.reserve(1024);
+                    }
+
                     n->getNodesMap(nodes_map, full_nodes, full_ids);
 
+                    unsigned length = 0, buf_count = 0, bytes = 0;
+                    bool is_linear = true;
+                    dim_t dims_[] = {dims[0], dims[1], dims[2], dims[3]};
                     for(auto &jit_node : full_nodes) {
                         jit_node->getInfo(length, buf_count, bytes);
+                        is_linear &= jit_node->isLinear(dims_);
                     }
 
-                    if (2 * bytes > lock_bytes) {
+                    // Reset the thread local vectors
+                    nodes_map.clear();
+                    full_nodes.clear();
+                    full_ids.clear();
+
+                    isBufferLimit = 2 * bytes > lock_bytes;
+                    isParamLimit = isNvidia && !is_linear && buf_count >= 48;
+                    if (isBufferLimit || isParamLimit) {
                         out.eval();
                     }
                 }
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 78e794f86d..040cbc23dc 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -208,15 +208,19 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     //FIXME: Add assert to check if all outputs are same size?
     KParam out_info = outputs[0].info;
 
-    Node_map_t nodes;
-    vector<Node *> full_nodes;
-    vector<Node_ids> full_ids;
-    vector<int> output_ids;
+    // Use thread local to reuse the memory every time you are here.
+    thread_local Node_map_t nodes;
+    thread_local vector<Node *> full_nodes;
+    thread_local vector<Node_ids> full_ids;
+    thread_local vector<int> output_ids;
 
     // Reserve some space to improve performance at smaller sizes
-    output_ids.reserve(output_nodes.size());
-    full_nodes.reserve(1024);
-    full_ids.reserve(1024);
+    if (nodes.size() == 0) {
+        nodes.reserve(1024);
+        output_ids.reserve(output_nodes.size());
+        full_nodes.reserve(1024);
+        full_ids.reserve(1024);
+    }
 
     for (auto &node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
@@ -290,6 +294,12 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     ker.setArg(nargs + 3,  num_odims);
 
     getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
+
+    // Reset the thread local vectors
+    nodes.clear();
+    output_ids.clear();
+    full_nodes.clear();
+    full_ids.clear();
 }
 
 void evalNodes(Param &out, Node *node)
diff --git a/test/jit.cpp b/test/jit.cpp
index 974f672ef3..04b3ba3c50 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -403,3 +403,24 @@ TEST(JIT, LinearLarge)
         ASSERT_EQ(hc[i], v3);
     }
 }
+
+TEST(JIT, NonLinearBuffers1)
+{
+    af::array a = af::randu(5, 5);
+    af::array a0 = a;
+    for (int i = 0; i < 1000; i++) {
+        af::array b = af::randu(1, 5);
+        a += af::tile(b, 5);
+    }
+    a.eval();
+}
+
+TEST(JIT, NonLinearBuffers2)
+{
+    af::array a = af::randu(100, 310);
+    af::array b = af::randu(10, 10);
+    for (int i = 0; i < 300; i++) {
+        b += a(seq(10), seq(i, i+9)) * randu(10, 10);
+    }
+    b.eval();
+}

From b1ed19656565e7213b03416169a96a15f7598cd2 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <contact@pavanky.com>
Date: Tue, 17 Apr 2018 21:29:33 -0700
Subject: [PATCH 1404/2677] Fixing bug with CPU JIT after moddims is called.

---
 src/backend/cpu/Array.cpp | 13 +++++++++++++
 src/backend/cpu/Array.hpp |  6 +-----
 test/jit.cpp              | 26 ++++++++++++++++++++++++++
 3 files changed, 40 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 34f68e2c5c..0ea0b93ed1 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -326,6 +326,18 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     memcpy(arr.get(), (const T * const)data, bytes);
 }
 
+
+template<typename T>
+void
+Array<T>::setDataDims(const dim4 &new_dims)
+{
+    modDims(new_dims);
+    data_dims = new_dims;
+    if (node->isBuffer()) {
+        node = bufferNodePtr<T>();
+    }
+}
+
 #define INSTANTIATE(T)                                                  \
     template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
@@ -349,6 +361,7 @@ writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
     template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
+    template       void Array<T>::setDataDims(const dim4 &new_dims);    \
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 5a955498e6..daebded49a 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -184,11 +184,7 @@ namespace cpu
             return data_dims;
         }
 
-        void setDataDims(const dim4 &new_dims)
-        {
-            modDims(new_dims);
-            data_dims = new_dims;
-        }
+        void setDataDims(const dim4 &new_dims);
 
         size_t getAllocatedBytes() const
         {
diff --git a/test/jit.cpp b/test/jit.cpp
index 04b3ba3c50..be40411bdb 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -424,3 +424,29 @@ TEST(JIT, NonLinearBuffers2)
     }
     b.eval();
 }
+
+TEST(JIT, TransposeBuffers)
+{
+    const int num = 10;
+    af::array a = af::randu(1, num);
+    af::array b = af::randu(1, num);
+    af::array c =  a + b;
+    af::array d = a.T() + b.T();
+
+    std::vector<float> ha(a.elements());
+    a.host(ha.data());
+
+    std::vector<float> hb(b.elements());
+    b.host(hb.data());
+
+    std::vector<float> hc(c.elements());
+    c.host(hc.data());
+
+    std::vector<float> hd(d.elements());
+    d.host(hd.data());
+
+    for (int i = 0; i < num; i++) {
+        ASSERT_FLOAT_EQ(ha[i] + hb[i], hc[i]);
+        ASSERT_FLOAT_EQ(hc[i], hd[i]);
+    }
+}

From 0e9c2588af1ed5d28721f67c0ec61f9c55438714 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Apr 2018 16:30:00 -0400
Subject: [PATCH 1405/2677] Use std::mutex instead of recursive_mutex

Recursive mutex is known to promote poor design choices. This commit
removes the need for recursive mutexes in the memory manager and
OpenCL backends.
---
 src/backend/common/MemoryManager.hpp |   2 +-
 src/backend/cpu/memory.cpp           |   1 -
 src/backend/cuda/memory.cpp          |   1 -
 src/backend/opencl/platform.cpp      | 114 ++++++++++++++++-----------
 4 files changed, 68 insertions(+), 50 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index b1ae911433..235057f9ab 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -24,7 +24,7 @@
 
 namespace common
 {
-using mutex_t      = std::recursive_mutex;
+using mutex_t      = std::mutex;
 using lock_guard_t = std::lock_guard<mutex_t>;
 
 const unsigned MAX_BUFFERS   = 1000;
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index a3ac8db703..d2412f9cb6 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -151,7 +151,6 @@ MemoryManager::MemoryManager()
 
 MemoryManager::~MemoryManager()
 {
-    common::lock_guard_t lock(this->memory_mutex);
     for (int n = 0; n < cpu::getDeviceCount(); n++) {
         try {
             cpu::setDevice(n);
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index e6613a6065..9ace2c00d8 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -156,7 +156,6 @@ MemoryManager::MemoryManager()
 
 MemoryManager::~MemoryManager()
 {
-    common::lock_guard_t lock(this->memory_mutex);
     for (int n = 0; n < cuda::getDeviceCount(); n++) {
         try {
             cuda::setDevice(n);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 062909836c..e407be27bb 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -228,14 +228,18 @@ std::string getDeviceInfo()
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    common::lock_guard_t lock(devMngr.deviceMutex);
+    vector<cl::Device*> devices;
+    {
+      common::lock_guard_t lock(devMngr.deviceMutex);
+      devices = devMngr.mDevices;
+    }
 
     ostringstream info;
     info << "ArrayFire v" << AF_VERSION
-         << " (OpenCL, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
+         << " (OpenCL, " << get_system() << ", build " << AF_REVISION << ")\n";
 
     unsigned nDevices = 0;
-    for(auto &device: devMngr.mDevices) {
+    for(auto device: devices) {
         const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
         string dstr = device->getInfo<CL_DEVICE_NAME>();
@@ -256,7 +260,7 @@ std::string getDeviceInfo()
         info << devVersion;
         info << " -- Device driver " << driVersion;
         info << " -- FP64 Support: "
-             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0 ? "True" : "False");
+             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0 ? "True" : "False");
         info << " -- Unified Memory ("
              << (isHostUnifiedMemory(*device) ? "True" : "False")
              << ")";
@@ -376,7 +380,6 @@ const cl::Device& getDevice(int id)
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
-
     return *(devMngr.mDevices[id]);
 }
 
@@ -384,9 +387,12 @@ size_t getDeviceMemorySize(int device)
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    common::lock_guard_t lock(devMngr.deviceMutex);
-
-    const cl::Device& dev = getDevice(device);
+    cl::Device dev;
+    {
+      common::lock_guard_t lock(devMngr.deviceMutex);
+      // Assuming devices don't deallocate or are invalidated during execution
+      dev = *devMngr.mDevices[device];
+    }
     size_t msize = dev.getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
     return msize;
 }
@@ -446,9 +452,13 @@ bool isDoubleSupported(int device)
 {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    common::lock_guard_t lock(devMngr.deviceMutex);
+    cl::Device dev;
+    {
+      common::lock_guard_t lock(devMngr.deviceMutex);
+      dev = *devMngr.mDevices[device];
+    }
 
-    return (devMngr.mDevices[device]->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()>0);
+    return (dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0);
 }
 
 void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
@@ -459,9 +469,13 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    common::lock_guard_t lock(devMngr.deviceMutex);
+    vector<cl::Context*> contexts;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        contexts = devMngr.mContexts; // NOTE: copy, not a reference
+    }
 
-    for (auto context : devMngr.mContexts) {
+    for (auto context : contexts) {
         vector<Device> devices = context->getInfo<CL_CONTEXT_DEVICES>();
 
         for (auto &device : devices) {
@@ -510,8 +524,7 @@ int setDevice(int device)
     common::lock_guard_t lock(devMngr.deviceMutex);
 
     if (device >= (int)devMngr.mQueues.size() ||
-            device>= (int)DeviceManager::MAX_DEVICES) {
-        //throw runtime_error("@setDevice: invalid device index");
+        device >= (int)DeviceManager::MAX_DEVICES) {
         return -1;
     } else {
         int old = getActiveDeviceId();
@@ -552,30 +565,33 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
 
     DeviceManager& devMngr   = DeviceManager::getInstance();
 
-    common::lock_guard_t lock(devMngr.deviceMutex);
-
-    cl::Device* tDevice      = new cl::Device(dev);
-    cl::Context* tContext    = new cl::Context(ctx);
-    cl::CommandQueue* tQueue = (que==NULL ?
-            new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
-    devMngr.mDevices.push_back(tDevice);
-    devMngr.mContexts.push_back(tContext);
-    devMngr.mQueues.push_back(tQueue);
-    devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
-    // FIXME: add OpenGL Interop for user provided contexts later
-    devMngr.mIsGLSharingOn.push_back(false);
+    int nDevices = 0;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+
+        cl::Device* tDevice      = new cl::Device(dev);
+        cl::Context* tContext    = new cl::Context(ctx);
+        cl::CommandQueue* tQueue = (que==NULL ?
+                new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
+        devMngr.mDevices.push_back(tDevice);
+        devMngr.mContexts.push_back(tContext);
+        devMngr.mQueues.push_back(tQueue);
+        devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
+        // FIXME: add OpenGL Interop for user provided contexts later
+        devMngr.mIsGLSharingOn.push_back(false);
+        nDevices = devMngr.mDevices.size()-1;
+
+        //cache the boost program_cache object, clean up done on program exit
+        //not during removeDeviceContext
+        namespace compute = boost::compute;
+        using BPCache = DeviceManager::BoostProgCache;
+        compute::context c(ctx);
+        BPCache currCache = compute::program_cache::get_global_cache(c);
+        devMngr.mBoostProgCacheVector.emplace_back(new BPCache(currCache));
+    }
 
     // Last/newly added device needs memory management
-    memoryManager().addMemoryManagement(devMngr.mDevices.size()-1);
-
-
-    //cache the boost program_cache object, clean up done on program exit
-    //not during removeDeviceContext
-    namespace compute = boost::compute;
-    using BPCache = DeviceManager::BoostProgCache;
-    compute::context c(ctx);
-    BPCache currCache = compute::program_cache::get_global_cache(c);
-    devMngr.mBoostProgCacheVector.emplace_back(new BPCache(currCache));
+    memoryManager().addMemoryManagement(nDevices);
 }
 
 void setDeviceContext(cl_device_id dev, cl_context ctx)
@@ -588,8 +604,8 @@ void setDeviceContext(cl_device_id dev, cl_context ctx)
     const int dCount = devMngr.mDevices.size();
     for (int i=0; i<dCount; ++i) {
         if(devMngr.mDevices[i]->operator()()==dev &&
-                devMngr.mContexts[i]->operator()()==ctx) {
-            setDevice(i);
+           devMngr.mContexts[i]->operator()()==ctx) {
+            setActiveContext(i);
             return;
         }
     }
@@ -604,25 +620,29 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    common::lock_guard_t lock(devMngr.deviceMutex);
-
-    const int dCount = devMngr.mDevices.size();
     int deleteIdx = -1;
-    for (int i = 0; i<dCount; ++i) {
-        if(devMngr.mDevices[i]->operator()()==dev &&
-                devMngr.mContexts[i]->operator()()==ctx) {
-            deleteIdx = i;
-            break;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+
+        const int dCount = devMngr.mDevices.size();
+        for (int i = 0; i<dCount; ++i) {
+            if(devMngr.mDevices[i]->operator()()==dev &&
+               devMngr.mContexts[i]->operator()()==ctx) {
+                deleteIdx = i;
+                break;
+            }
         }
     }
+
     if (deleteIdx < (int)devMngr.mUserDeviceOffset) {
         AF_ERROR("Cannot pop ArrayFire internal devices", AF_ERR_ARG);
     } else if (deleteIdx == -1) {
         AF_ERROR("No matching device found", AF_ERR_ARG);
     } else {
-        //remove memory management for device added by user
+        //remove memory management for device added by user outside of the lock
         memoryManager().removeMemoryManagement(deleteIdx);
 
+        common::lock_guard_t lock(devMngr.deviceMutex);
         clReleaseDevice((*devMngr.mDevices[deleteIdx])());
         clReleaseContext((*devMngr.mContexts[deleteIdx])());
         clReleaseCommandQueue((*devMngr.mQueues[deleteIdx])());

From 8dcf009a01a6febba26d33cae30064f5efdce8db Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Apr 2018 16:52:54 -0400
Subject: [PATCH 1406/2677] Remove CUDA driver API mutexes

---
 src/backend/cuda/jit.cpp         | 3 ---
 src/backend/cuda/memory.cpp      | 4 ----
 src/backend/cuda/platform.cpp    | 4 ----
 src/backend/cuda/platform.hpp    | 7 -------
 src/backend/cuda/sparse_blas.cpp | 5 -----
 5 files changed, 23 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 1937ed1f75..cd5901cd61 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -298,8 +298,6 @@ std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
 
 static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 {
-    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
-
     const size_t linkLogSize = 1024;
     char linkInfo[linkLogSize] = {0};
     char linkError[linkLogSize] = {0};
@@ -459,7 +457,6 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
     args.push_back((void *)&blocks_x_total);
     args.push_back((void *)&num_odims);
 
-    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     CU_CHECK(cuLaunchKernel(ker,
                             blocks_x,
                             blocks_y,
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 9ace2c00d8..c246a0606a 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -178,7 +178,6 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
-    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     void *ptr = NULL;
     CUDA_CHECK(cudaMalloc(&ptr, bytes));
     return ptr;
@@ -186,7 +185,6 @@ void *MemoryManager::nativeAlloc(const size_t bytes)
 
 void MemoryManager::nativeFree(void *ptr)
 {
-    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     cudaError_t err = cudaFree(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
@@ -217,7 +215,6 @@ size_t MemoryManagerPinned::getMaxMemorySize(int id)
 
 void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {
-    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     void *ptr;
     CUDA_CHECK(cudaMallocHost(&ptr, bytes));
     return ptr;
@@ -225,7 +222,6 @@ void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 
 void MemoryManagerPinned::nativeFree(void *ptr)
 {
-    lock_guard<recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
     cudaError_t err = cudaFreeHost(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index f08368b418..8d8339b58b 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -282,10 +282,6 @@ unsigned getMaxJitSize()
     return length;
 }
 
-std::recursive_mutex& getDriverApiMutex(int device) {
-  return DeviceManager::getInstance().driver_api_mutex[device];
-}
-
 int& tlocalActiveDeviceId()
 {
     thread_local int activeDeviceId = 0;
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 658b372c25..cbad5fb2fe 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -19,7 +19,6 @@
 #include <cusparse.hpp>
 
 #include <memory>
-#include <mutex>
 #include <string>
 #include <vector>
 
@@ -42,8 +41,6 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
 
-std::recursive_mutex& getDriverApiMutex(int device);
-
 int getDeviceCount();
 
 int getActiveDeviceId();
@@ -114,8 +111,6 @@ class DeviceManager
         friend GraphicsResourceManager& interopManager();
 #endif
 
-        friend std::recursive_mutex& getDriverApiMutex(int device);
-
         friend std::string getDeviceInfo(int device);
 
         friend std::string getPlatformInfo();
@@ -148,8 +143,6 @@ class DeviceManager
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
 
-        std::recursive_mutex driver_api_mutex[MAX_DEVICES];
-
         // Attributes
         std::vector<cudaDevice_t> cuDevices;
 
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 4f73709b09..136ccb9faf 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -142,11 +142,6 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     dim4 rStrides = rhs.strides();
 
-    // NOTE: The cuSparse library seems to be using the driver API in the
-    // implementation. This is causing issues with our JIT kernel generation.
-    // This may be a bug in the cuSparse library.
-    std::lock_guard<std::recursive_mutex> lock(getDriverApiMutex(getActiveDeviceId()));
-
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));

From 2eb554ec923e46909817d4214008b7d471e54f2a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Apr 2018 21:26:08 -0400
Subject: [PATCH 1407/2677] Disable ignored-attributes warnings in the OpenCL
 backend

---
 src/backend/opencl/CMakeLists.txt                    | 9 +++++++++
 src/backend/opencl/kernel/scan_by_key/CMakeLists.txt | 9 +++++----
 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt | 1 +
 3 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 45a48dfabb..a239d58c68 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -12,6 +12,13 @@ set_property(CACHE AF_OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
 
 af_deprecate(OPENCL_BLAS_LIBRARY AF_OPENCL_BLAS_LIBRARY)
 
+include(CheckCXXCompilerFlag)
+check_cxx_compiler_flag(-Wno-ignored-attributes has_ignored_attributes_flag)
+
+if(has_ignored_attributes_flag)
+  set(opencl_cxx_flags -Wno-ignored-attributes)
+endif()
+
 include(build_clFFT)
 
 file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
@@ -382,6 +389,8 @@ add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 
 set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
+target_compile_options(afopencl PRIVATE ${opencl_cxx_flags})
+
 target_compile_definitions(afopencl
   PRIVATE
     CL_USE_DEPRECATED_OPENCL_1_2_APIS
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 2add63e693..bd7f3d2460 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -46,10 +46,11 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         POSITION_INDEPENDENT_CODE ON
         FOLDER "Generated Targets")
 
-      target_compile_definitions(opencl_scan_by_key_${SBK_BINARY_OP}
-        PRIVATE
-          $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
-          TYPE=${SBK_BINARY_OP} AFDLL)
+    target_compile_options(opencl_scan_by_key_${SBK_BINARY_OP} PRIVATE ${opencl_cxx_flags})
+    target_compile_definitions(opencl_scan_by_key_${SBK_BINARY_OP}
+      PRIVATE
+        $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
+        TYPE=${SBK_BINARY_OP} AFDLL)
     target_sources(opencl_scan_by_key
       INTERFACE $<TARGET_OBJECTS:opencl_scan_by_key_${SBK_BINARY_OP}>)
 endforeach(SBK_BINARY_OP ${SBK_BINARY_OPS})
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index e1701f40ff..8e0eecd6aa 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -41,6 +41,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
         POSITION_INDEPENDENT_CODE ON
         FOLDER "Generated Targets")
 
+    target_compile_options(opencl_sort_by_key_${SBK_TYPE} PRIVATE ${opencl_cxx_flags})
     target_compile_definitions(opencl_sort_by_key_${SBK_TYPE}
       PRIVATE
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>

From 30f0c293fff39520443333cfc03d20ded1d6f9b4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 18 Apr 2018 17:05:48 -0400
Subject: [PATCH 1408/2677] devirtualize memory manager's destructor. Delete
 assignments

---
 src/backend/common/MemoryManager.hpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 235057f9ab..92205769ae 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -427,8 +427,6 @@ class MemoryManager
         static_cast<T*>(this)->nativeFree(ptr);
     }
 
-    virtual ~MemoryManager() {}
-
     bool checkMemoryLimit()
     {
         const memory_info& current = this->getCurrentMemoryInfo();
@@ -436,6 +434,12 @@ class MemoryManager
     }
 
     protected:
+    MemoryManager() = delete;
+    ~MemoryManager() = default;
+    MemoryManager(const MemoryManager& other) = delete;
+    MemoryManager(const MemoryManager&& other) = delete;
+    MemoryManager& operator=(const MemoryManager& other) = delete;
+    MemoryManager& operator=(const MemoryManager&& other) = delete;
     mutex_t memory_mutex;
 };
 

From 8d7eb7d069dcb326ee41c79c5b74255feffe9050 Mon Sep 17 00:00:00 2001
From: Filip Matzner <floop@floop.cz>
Date: Sat, 21 Apr 2018 10:27:57 +0200
Subject: [PATCH 1409/2677] Add AF_ prefix to INSTALL_FORGE_DEV

---
 CMakeLists.txt                 | 6 ++++--
 CMakeModules/CPackConfig.cmake | 4 ++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5e35b8a1d0..d280432731 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -181,8 +181,10 @@ install(FILES ${ArrayFire_BINARY_DIR}/include/af/version.h
         COMPONENT headers)
 
 if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
-    option(INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
-    if(INSTALL_FORGE_DEV)
+    option(AF_INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
+    mark_as_advanced(AF_INSTALL_FORGE_DEV)
+    af_deprecate(INSTALL_FORGE_DEV AF_INSTALL_FORGE_DEV)
+    if(AF_INSTALL_FORGE_DEV)
         install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/include/"
                 DESTINATION "${AF_INSTALL_INC_DIR}"
                 COMPONENT headers
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index baff4827d8..ca96a83f86 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -236,7 +236,7 @@ cpack_add_component(licenses
   DESCRIPTION "License files for ArrayFire and its upstream libraries."
   REQUIRED)
 
-if (INSTALL_FORGE_DEV)
+if (AF_INSTALL_FORGE_DEV)
   cpack_add_component(forge
     DISPLAY_NAME "Forge"
     DESCRIPTION "High Performance Visualization Library"
@@ -295,7 +295,7 @@ cpack_ifw_configure_component(licenses FORCED_INSTALLATION
   "Boost" ${boost_lic_path} "clBLAS, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
   "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
 )
-if (INSTALL_FORGE_DEV)
+if (AF_INSTALL_FORGE_DEV)
     cpack_ifw_configure_component(forge)
 endif ()
 

From 8b12c935a8987f5e92839b62eea3738b8a2a4df3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 19 Apr 2018 01:37:32 -0400
Subject: [PATCH 1410/2677] Added v3.6 release notes

---
 docs/pages/release_notes.md | 184 ++++++++++++++++++++++++++++++++++++
 1 file changed, 184 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 20f10f027d..28fbc437fa 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,190 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.6.0
+======
+
+The source code with submodules can be downloaded directly from the following link:
+http://arrayfire.com/arrayfire_source/arrayfire-full-3.6.0.tar.bz2
+
+Major Updates
+-------------
+
+- Added the `topk()` function
+  [Documentation](http://arrayfire.org/docs/group__stat__func__topk.htm).
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/2061)</sup>
+- Added batched matrix multiply support.
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1898)</sup>
+  <sup>[3](https://github.com/arrayfire/arrayfire/pull/2059)</sup>
+- Added anisotropic diffusion, `anisotropicDiffusion()`.
+  [Documentation](http://arrayfire.org/docs/group__image__func__anisotropic__diffusion.htm)
+  <sup>[3](https://github.com/arrayfire/arrayfire/pull/1850)</sup>.
+
+Features
+--------
+
+- Added support for batched matrix multiply.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1898)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/2059)</sup>
+- New anisotropic diffusion function, `anisotropicDiffusion()`.
+  [Documentation](http://arrayfire.org/docs/group__image__func__anisotropic__diffusion.htm)
+  <sup>[3](https://github.com/arrayfire/arrayfire/pull/1850)</sup>.
+- New `topk()` function, which returns the top k elements along a given
+  dimension of the input.
+  [Documentation](http://arrayfire.org/docs/group__stat__func__topk.htm).
+  <sup>[4](https://github.com/arrayfire/arrayfire/pull/2061)</sup>
+- New gradient diffusion
+  [example](https://github.com/arrayfire/arrayfire/blob/master/examples/image_processing/gradient_diffusion.cpp).
+
+Improvements
+------------
+
+- JITted `select()` and `shift()` functions for CUDA and OpenCL backends.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/2047)</sup>
+- Significant CMake improvements.
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1861)</sup>
+  <sup>[3](https://github.com/arrayfire/arrayfire/pull/2070)</sup>
+  <sup>[4](https://github.com/arrayfire/arrayfire/pull/2018)</sup>
+- Improved the quality of the random number generator (thanks to @rstub)
+  <sup>[5](https://github.com/arrayfire/arrayfire/pull/2122)
+- Corrected assert function calls in select() tests.
+  <sup>[5](https://github.com/arrayfire/arrayfire/pull/2058)</sup>
+- Modified `af_colormap` struct to match forge's definition.
+  <sup>[6](https://github.com/arrayfire/arrayfire/pull/2082)</sup>
+- Improved Black Scholes example.
+  <sup>[7](https://github.com/arrayfire/arrayfire/pull/2079)</sup>
+- Using CPack to generate installers.
+  <sup>[8](https://github.com/arrayfire/arrayfire/pull/1861)</sup>
+- Refactored
+  [black_scholes_options](https://github.com/arrayfire/arrayfire/blob/master/examples/financial/black_scholes_options.cpp)
+  example to use built-in `af::erfc` function for cumulative normal
+  distribution.<sup>[9](https://github.com/arrayfire/arrayfire/pull/2079).
+- Reduced the scope of mutexes in memory manager
+  [10](https://github.com/arrayfire/arrayfire/pull/2125)
+- Official installers do not require the CUDA toolkit to be installed
+- Significant CMake improvements have been made. Using CPack to generate
+  installers. <sup>[11](https://github.com/arrayfire/arrayfire/pull/1861)</sup>
+  <sup>[12](https://github.com/arrayfire/arrayfire/pull/2070)</sup>
+  <sup>[13](https://github.com/arrayfire/arrayfire/pull/2018)</sup>
+
+Bug fixes
+-----------
+
+- Fixed `shfl_down()` warnings with CUDA 9.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/2040)</sup>
+- Disabled CUDA JIT debug flags on ARM
+  architecture.<sup>[2](https://github.com/arrayfire/arrayfire/pull/2037)
+- Fixed CLBLast install lib dir for linux platform where `lib` directory has
+  arch(64) suffix.<sup>[3](https://github.com/arrayfire/arrayfire/pull/2094)
+- Fixed assert condition in 3d morph opencl
+  kernel.<sup>[4](https://github.com/arrayfire/arrayfire/pull/2033)
+- Fix JIT errors with large non-linear
+  kernels<sup>[5](https://github.com/arrayfire/arrayfire/pull/2127)</sup>
+- Fix bug in CPU jit after moddims was called
+  <sup>[5](https://github.com/arrayfire/arrayfire/pull/2127)</sup>
+- Fixed deadlock caused by calls to from the worker thread
+  <sup>[6](https://github.com/arrayfire/arrayfire/pull/2124)</sup>
+
+Documentation
+-------------
+
+- Fixed variable name typo in `vectorization.md`.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/2032)</sup>
+- Fixed `AF_API_VERSION` value in Doxygen config file.
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/2053)</sup>
+
+Known issues
+------------
+
+- Several OpenCL tests failing on OSX:
+  - `canny_opencl, fft_opencl, gen_assign_opencl, homography_opencl,
+    reduce_opencl, scan_by_key_opencl, solve_dense_opencl,
+    sparse_arith_opencl, sparse_convert_opencl, where_opencl`
+
+Community contributions
+-----------------------
+
+Special thanks to our contributors:
+[Adrien F. Vincent](https://github.com/afvincent), [Cedric
+Nugteren](https://github.com/CNugteren),
+[Felix](https://github.com/fzimmermann89), [Filip
+Matzner](https://github.com/FloopCZ),
+[HoneyPatouceul](https://github.com/HoneyPatouceul), [Patrick
+Lavin](https://github.com/plavin), [Ralf Stubner](https://github.com/rstub),
+[William Tambellini](https://github.com/WilliamTambellini)
+
+
+v3.5.1
+======
+
+The source code with submodules can be downloaded directly from the following
+link: http://arrayfire.com/arrayfire_source/arrayfire-full-3.5.1.tar.bz2
+
+Installer CUDA Version: 8.0 (Required) Installer OpenCL Version: 1.2 (Minimum)
+
+Improvements
+------------
+- Relaxed `af::unwrap()` function's arguments.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1853)</sup>
+- Changed behavior of af::array::allocated() to specify memory allocated.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1877)</sup>
+- Removed restriction on the number of bins for `af::histogram()` on CUDA and
+  OpenCL kernels. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1895)</sup>
+
+
+Performance
+-----------
+
+- Improved JIT performance.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1864)</sup>
+- Improved CPU element-wise operation performance.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1890)</sup>
+- Improved regions performance using texture objects. <sup>
+  [1](https://github.com/arrayfire/arrayfire/pull/1903)</sup>
+
+
+Bug fixes
+---------
+- Fixed overflow issues in mean.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1849)</sup>
+- Fixed memory leak when chaining indexing operations.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1879)</sup>
+- Fixed bug in array assignment when using an empty array to index.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1897)</sup>
+- Fixed bug with `af::matmul()` which occured when its RHS argument was an
+  indexed vector.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1883)</sup>
+- Fixed bug deadlock bug when sparse array was used with a JIT Array.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1889)</sup>
+- Fixed pixel tests for FAST kernels.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1891)</sup>
+- Fixed `af::replace` so that it is now copy-on-write.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1892)</sup>
+- Fixed launch configuration issues in CUDA JIT.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1893)</sup>
+- Fixed segfaults and "Pure Virtual Call" error warnings when exiting on
+  Windows. <sup>[1](https://github.com/arrayfire/arrayfire/pull/1899)
+  [2](https://github.com/arrayfire/arrayfire/pull/1924)</sup>
+- Workaround for `clEnqueueReadBuffer` bug on OSX.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1888)</sup>
+
+Build
+-----
+
+- Fixed issues when compiling with GCC 7.1.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1872)</sup>
+  <sup>[2](https://github.com/arrayfire/arrayfire/pull/1876)</sup>
+- Eliminated unnecessary Boost dependency from CPU and CUDA backends.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1857)</sup>
+
+Misc
+----
+
+- Updated support links to point to Slack instead of Gitter.
+  <sup>[1](https://github.com/arrayfire/arrayfire/pull/1905)</sup>
+
+
+
 v3.5.0
 ==============
 

From 60024d6275310d00f6e4db01766e4a7700fe6cb0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 22 Apr 2018 10:44:44 -0400
Subject: [PATCH 1411/2677] Update license to avoid line wrap issues in the OSX
 installer

---
 LICENSE | 18 ++++++------------
 1 file changed, 6 insertions(+), 12 deletions(-)

diff --git a/LICENSE b/LICENSE
index 91c27f7f34..f7b9cfdcf7 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,18 +1,12 @@
-Copyright (c) 2014-2015, ArrayFire
+Copyright (c) 2014-2018, ArrayFire
 All rights reserved.
 
-Redistribution and use in source and binary forms, with or without modification,
-are permitted provided that the following conditions are met:
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
 
-* Redistributions of source code must retain the above copyright notice, this
-  list of conditions and the following disclaimer.
+* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
 
-* Redistributions in binary form must reproduce the above copyright notice, this
-  list of conditions and the following disclaimer in the documentation and/or
-  other materials provided with the distribution.
+* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
 
-* Neither the name ArrayFire nor the names of its
-  contributors may be used to endorse or promote products derived from
-  this software without specific prior written permission.
+* Neither the name ArrayFire nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
 
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

From 38fada9f24dabec9cd0b94962821b4f77cd26126 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 23 Apr 2018 18:40:08 +0530
Subject: [PATCH 1412/2677] Install all components by default via NSIS

The choice of what to install is still available.
---
 CMakeModules/CPackConfig.cmake | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index ca96a83f86..981cf049d2 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -126,6 +126,7 @@ get_cmake_property(CPACK_COMPONENTS_ALL COMPONENTS)
 
 include(CPackComponent)
 
+cpack_add_install_type(All DISPLAY_NAME "All Components")
 cpack_add_install_type(Development DISPLAY_NAME "Development")
 cpack_add_install_type(Extra DISPLAY_NAME "Extra")
 cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
@@ -155,33 +156,33 @@ if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
     DISPLAY_NAME "Intel MKL"
 	DESCRIPTION "Intel Math Kernel Libraries for FFTW, BLAS, and LAPACK routines."
 	GROUP backends
-    INSTALL_TYPES Development Runtime)
+    INSTALL_TYPES All Development Runtime)
 endif ()
 
 cpack_add_component(common_backend_dependencies
   DISPLAY_NAME "Dependencies"
   DESCRIPTION "Libraries commonly required by all ArrayFire backends."
   GROUP backends
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
 
 cpack_add_component(opencl_dependencies
   DISPLAY_NAME "OpenCL Dependencies"
   DESCRIPTION "Libraries required by the OpenCL backend."
   GROUP opencl_backend
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
   
 cpack_add_component(cuda_dependencies
   DISPLAY_NAME "CUDA Dependencies"
   DESCRIPTION "CUDA runtime and libraries required by the CUDA backend."
   GROUP cuda_backend
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
 
 cpack_add_component(cuda
   DISPLAY_NAME "CUDA Backend"
   DESCRIPTION "The CUDA backend allows you to run ArrayFire code on CUDA-enabled GPUs. Verify that you have the CUDA toolkit installed or install the CUDA dependencies component."
   GROUP cuda_backend
   DEPENDS common_backend_dependencies cuda_dependencies
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
 
 list(APPEND cpu_deps_comps common_backend_dependencies)
 list(APPEND ocl_deps_comps common_backend_dependencies)
@@ -200,37 +201,37 @@ cpack_add_component(cpu
   DESCRIPTION "The CPU backend allows you to run ArrayFire code on your CPU."
   GROUP cpu_backend
   DEPENDS ${cpu_deps_comps}
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
 
 cpack_add_component(opencl
   DISPLAY_NAME "OpenCL Backend"
   DESCRIPTION "The OpenCL backend allows you to run ArrayFire code on OpenCL-capable GPUs. Note: ArrayFire does not currently support OpenCL for Intel CPUs on OSX."
   GROUP opencl_backend
   DEPENDS ${ocl_deps_comps}
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
 
 cpack_add_component(unified
   DISPLAY_NAME "Unified Backend"
   DESCRIPTION "The Unified backend allows you to choose between any of the installed backends (CUDA, OpenCL, or CPU) at runtime."
   GROUP backends
-  INSTALL_TYPES Development Runtime)
+  INSTALL_TYPES All Development Runtime)
 cpack_add_component(headers
   DISPLAY_NAME "C/C++ Headers"
   DESCRIPTION "Headers for the ArrayFire libraries."
   GROUP backends
-  INSTALL_TYPES Development)
+  INSTALL_TYPES All Development)
 cpack_add_component(cmake
   DISPLAY_NAME "CMake Support"
   DESCRIPTION "Configuration files to use ArrayFire using CMake."
-  INSTALL_TYPES Development)
+  INSTALL_TYPES All Development)
 cpack_add_component(documentation
   DISPLAY_NAME "Documentation"
   DESCRIPTION "Doxygen documentation"
-  INSTALL_TYPES Extra)
+  INSTALL_TYPES All Extra)
 cpack_add_component(examples
   DISPLAY_NAME "ArrayFire Examples"
   DESCRIPTION "Various examples using ArrayFire."
-  INSTALL_TYPES Extra)
+  INSTALL_TYPES All Extra)
 cpack_add_component(licenses
   DISPLAY_NAME "Licenses"
   DESCRIPTION "License files for ArrayFire and its upstream libraries."

From 644f1175af0b1e54c919015d483d6c64f101e766 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 24 Apr 2018 10:31:56 +0530
Subject: [PATCH 1413/2677] Move cpack include cmd to include docs component

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d280432731..3e66b6db06 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -309,7 +309,6 @@ configure_package_config_file(
 # TODO(umar): Disable for now. Causing issues with builds on windows.
 #export(PACKAGE ArrayFire)
 
-include(CPackConfig)
 include(CTest)
 
 # Handle depricated BUILD_TEST variable if found.
@@ -322,3 +321,5 @@ conditional_directory(BUILD_TESTING test)
 set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
 conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
+
+include(CPackConfig)
\ No newline at end of file

From eed0619924676b84096ca61005e90bf3d141521c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 24 Apr 2018 10:32:21 +0530
Subject: [PATCH 1414/2677] Style fixes in CPackConfig file

---
 CMakeModules/CPackConfig.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 981cf049d2..70356b01af 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -226,7 +226,7 @@ cpack_add_component(cmake
   INSTALL_TYPES All Development)
 cpack_add_component(documentation
   DISPLAY_NAME "Documentation"
-  DESCRIPTION "Doxygen documentation"
+  DESCRIPTION "ArrayFire html documentation"
   INSTALL_TYPES All Extra)
 cpack_add_component(examples
   DISPLAY_NAME "ArrayFire Examples"
@@ -297,7 +297,7 @@ cpack_ifw_configure_component(licenses FORCED_INSTALLATION
   "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
 )
 if (AF_INSTALL_FORGE_DEV)
-    cpack_ifw_configure_component(forge)
+  cpack_ifw_configure_component(forge)
 endif ()
 
 ##

From d920eae21b3b31aa34ab2d81120cd3e2a0a2d718 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 24 Apr 2018 21:37:15 +0530
Subject: [PATCH 1415/2677] Fix cmake package registry on Windows

This enables cmake to find ArrayFire automatically.
---
 CMakeModules/nsis/NSIS.template.in | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
index 2b07ec7aa5..4923789c89 100644
--- a/CMakeModules/nsis/NSIS.template.in
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -31,6 +31,8 @@
 
   !define env_af_hklm 'HKLM "SYSTEM\CurrentControlSet\Control\Session Manager\Environment"'
 
+  !define cmake_pkg_reg_key 'HKCU "Software\Kitware\CMake\Packages\ArrayFire"'
+
 ;--------------------------------
 ;General
 
@@ -697,8 +699,6 @@ Section "-Core installation"
 @CPACK_NSIS_CREATE_ICONS_EXTRA@
   CreateShortCut "$SMPROGRAMS\$STARTMENU_FOLDER\Uninstall.lnk" "$INSTDIR\Uninstall.exe"
 
-  CreateShortcut "$INSTDIR\..\cmake.lnk" "$INSTDIR\cmake"
-
   ;Read a value from an InstallOptions INI file
   !insertmacro MUI_INSTALLOPTIONS_READ $DO_NOT_ADD_TO_PATH "NSIS.InstallOptions.ini" "Field 2" "State"
   !insertmacro MUI_INSTALLOPTIONS_READ $ADD_TO_PATH_ALL_USERS "NSIS.InstallOptions.ini" "Field 3" "State"
@@ -709,6 +709,9 @@ Section "-Core installation"
   WriteRegExpandStr ${env_af_hklm} AF_PATH '$INSTDIR'
   WriteRegExpandStr ${env_af_hklm} AF_PATH_v@CPACK_PACKAGE_VERSION_MAJOR@ '$INSTDIR'
 
+  ;Add key for CMake package
+  WriteRegStr ${cmake_pkg_reg_key} ArrayFire_CMake_DIR '$INSTDIR'
+
   ; make sure windows knows about the change
   SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
   MessageBox MB_OK "Added AF_PATH environment variable for all users.$\n$\nIf you chose not to modify PATH in the installer, please manually add $\"%AF_PATH%\lib$\" to the user or system PATH variable for running applications using ArrayFire."
@@ -850,6 +853,9 @@ Section "Uninstall"
   DeleteRegValue ${env_af_hklm} AF_PATH
   DeleteRegValue ${env_af_hklm} AF_PATH_v@CPACK_PACKAGE_VERSION_MAJOR@
 
+  ;Delete cmake package key
+  DeleteRegValue ${cmake_pkg_reg_key} ArrayFire_CMake_DIR
+
   ; make sure windows knows about the change
   SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
 
@@ -873,7 +879,6 @@ Section "Uninstall"
 @CPACK_NSIS_DELETE_ICONS@
 @CPACK_NSIS_DELETE_ICONS_EXTRA@
 
-  Delete "$INSTDIR\..\cmake.lnk"
   ;Delete empty start menu parent diretories
   StrCpy $MUI_TEMP "$SMPROGRAMS\$MUI_TEMP"
 

From 10077e9c2e7658bc4b019a47d933920403505810 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 25 Apr 2018 18:25:26 +0530
Subject: [PATCH 1416/2677] Fix superscript tags in release notes

---
 docs/pages/release_notes.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 28fbc437fa..8196414ec0 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -18,7 +18,7 @@ Major Updates
   <sup>[3](https://github.com/arrayfire/arrayfire/pull/2059)</sup>
 - Added anisotropic diffusion, `anisotropicDiffusion()`.
   [Documentation](http://arrayfire.org/docs/group__image__func__anisotropic__diffusion.htm)
-  <sup>[3](https://github.com/arrayfire/arrayfire/pull/1850)</sup>.
+  <sup>[4](https://github.com/arrayfire/arrayfire/pull/1850)</sup>.
 
 Features
 --------
@@ -45,10 +45,8 @@ Improvements
   <sup>[2](https://github.com/arrayfire/arrayfire/pull/1861)</sup>
   <sup>[3](https://github.com/arrayfire/arrayfire/pull/2070)</sup>
   <sup>[4](https://github.com/arrayfire/arrayfire/pull/2018)</sup>
-- Improved the quality of the random number generator (thanks to @rstub)
-  <sup>[5](https://github.com/arrayfire/arrayfire/pull/2122)
-- Corrected assert function calls in select() tests.
-  <sup>[5](https://github.com/arrayfire/arrayfire/pull/2058)</sup>
+- Improved the quality of the random number generator, thanks to Ralf Stubner.
+  <sup>[5](https://github.com/arrayfire/arrayfire/pull/2122)</sup>
 - Modified `af_colormap` struct to match forge's definition.
   <sup>[6](https://github.com/arrayfire/arrayfire/pull/2082)</sup>
 - Improved Black Scholes example.
@@ -58,14 +56,16 @@ Improvements
 - Refactored
   [black_scholes_options](https://github.com/arrayfire/arrayfire/blob/master/examples/financial/black_scholes_options.cpp)
   example to use built-in `af::erfc` function for cumulative normal
-  distribution.<sup>[9](https://github.com/arrayfire/arrayfire/pull/2079).
+  distribution.<sup>[9](https://github.com/arrayfire/arrayfire/pull/2079)</sup>.
 - Reduced the scope of mutexes in memory manager
-  [10](https://github.com/arrayfire/arrayfire/pull/2125)
+  <sup>[10](https://github.com/arrayfire/arrayfire/pull/2125)</sup>
 - Official installers do not require the CUDA toolkit to be installed
 - Significant CMake improvements have been made. Using CPack to generate
   installers. <sup>[11](https://github.com/arrayfire/arrayfire/pull/1861)</sup>
   <sup>[12](https://github.com/arrayfire/arrayfire/pull/2070)</sup>
   <sup>[13](https://github.com/arrayfire/arrayfire/pull/2018)</sup>
+- Corrected assert function calls in select() tests.
+  <sup>[14](https://github.com/arrayfire/arrayfire/pull/2058)</sup>
 
 Bug fixes
 -----------
@@ -73,11 +73,11 @@ Bug fixes
 - Fixed `shfl_down()` warnings with CUDA 9.
   <sup>[1](https://github.com/arrayfire/arrayfire/pull/2040)</sup>
 - Disabled CUDA JIT debug flags on ARM
-  architecture.<sup>[2](https://github.com/arrayfire/arrayfire/pull/2037)
+  architecture.<sup>[2](https://github.com/arrayfire/arrayfire/pull/2037)</sup>
 - Fixed CLBLast install lib dir for linux platform where `lib` directory has
-  arch(64) suffix.<sup>[3](https://github.com/arrayfire/arrayfire/pull/2094)
+  arch(64) suffix.<sup>[3](https://github.com/arrayfire/arrayfire/pull/2094)</sup>
 - Fixed assert condition in 3d morph opencl
-  kernel.<sup>[4](https://github.com/arrayfire/arrayfire/pull/2033)
+  kernel.<sup>[4](https://github.com/arrayfire/arrayfire/pull/2033)</sup>
 - Fix JIT errors with large non-linear
   kernels<sup>[5](https://github.com/arrayfire/arrayfire/pull/2127)</sup>
 - Fix bug in CPU jit after moddims was called

From 218bd95fa32e91fff830750af57f0275e2063139 Mon Sep 17 00:00:00 2001
From: Ralf Stubner <ralf.stubner@gmail.com>
Date: Thu, 26 Apr 2018 19:52:13 +0200
Subject: [PATCH 1417/2677] Input checking in af::sparse (Fixes #2134) (#2137)

* Add test cases for input checking in af_create_sparse_array

These three tests create a sparse array from array data. In each test
the array data is valid for one storage type. The test checks that the
correct storage type succeeds while the other storage types fail.

* Add checks for length of rowIdx and colIdx
* Update length of rowIdx array to size nRow +1
---
 src/api/c/sparse.cpp | 12 +++++++++
 test/sparse.cpp      | 59 +++++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 70 insertions(+), 1 deletion(-)

diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index d1941aad25..4ef5e966f3 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -87,6 +87,18 @@ af_err af_create_sparse_array(
         ARG_ASSERT(5, cInfo.getType() == s32);
         DIM_ASSERT(5, cInfo.isLinear());
 
+        const size_t nNZ = vInfo.elements();
+        if(stype == AF_STORAGE_COO) {
+          DIM_ASSERT(4, rInfo.elements() == nNZ);
+          DIM_ASSERT(5, cInfo.elements() == nNZ);
+        } else if(stype == AF_STORAGE_CSR) {
+          DIM_ASSERT(4, rInfo.elements() == nRows + 1);
+          DIM_ASSERT(5, cInfo.elements() == nNZ);
+        } else if(stype == AF_STORAGE_CSC) {
+          DIM_ASSERT(4, rInfo.elements() == nNZ);
+          DIM_ASSERT(5, cInfo.elements() == nCols + 1);
+        }
+
         af_array output = 0;
 
         af::dim4 dims(nRows, nCols);
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 052a6b95a1..7f94eea80f 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -119,6 +119,63 @@ TEST(Sparse, ISSUE_1745)
   ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array(&A_sparse, A.dims(0), A.dims(1), data.get(), row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
 }
 
+TEST(Sparse, ISSUE_2134_COO)
+{
+  int rows[] = {0,0,0,1,1,2,2};
+  int cols[] = {0,1,2,0,1,0,2};
+  float values[] = {3,3,4,3,10,4,3};
+  af::array row(7, rows);
+  af::array col(7, cols);
+  af::array value(7, values);
+  af_array A = 0;
+  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
+  if(A != 0) af_release_array(A);
+  A = 0;
+  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSC));
+  if(A != 0) af_release_array(A);
+  A = 0;
+  EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_COO));
+  if(A != 0) af_release_array(A);
+}
+
+TEST(Sparse, ISSUE_2134_CSR)
+{
+  int rows[] = {0,3,5,7};
+  int cols[] = {0,1,2,0,1,0,2};
+  float values[] = {3,3,4,3,10,4,3};
+  af::array row(4, rows);
+  af::array col(7, cols);
+  af::array value(7, values);
+  af_array A = 0;
+  EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
+  if(A != 0) af_release_array(A);
+  A = 0;
+  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSC));
+  if(A != 0) af_release_array(A);
+  A = 0;
+  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_COO));
+  if(A != 0) af_release_array(A);
+}
+
+TEST(Sparse, ISSUE_2134_CSC)
+{
+  int rows[] = {0,0,0,1,1,2,2};
+  int cols[] = {0,3,5,7};
+  float values[] = {3,3,4,3,10,4,3};
+  af::array row(7, rows);
+  af::array col(4, cols);
+  af::array value(7, values);
+  af_array A = 0;
+  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
+  if(A != 0) af_release_array(A);
+  A = 0;
+  EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSC));
+  if(A != 0) af_release_array(A);
+  A = 0;
+  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_COO));
+  if(A != 0) af_release_array(A);
+}
+
 template<typename T>
 class Sparse : public ::testing::Test {};
 
@@ -194,7 +251,7 @@ TYPED_TEST(Sparse, EmptyDeepCopy) {
     using namespace af;
     array a = sparse(0, 0,
                      array(0, (af_dtype)af::dtype_traits<TypeParam>::af_type),
-                     array(0, s32), array(0, s32));
+                     array(1, s32), array(0, s32));
     EXPECT_TRUE(a.issparse());
     EXPECT_EQ(0, sparseGetNNZ(a));
 

From 3c2bd5a58f1d930cce3a71228606eb7dcd508e25 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 27 Apr 2018 15:07:38 -0400
Subject: [PATCH 1418/2677] Fix linking issues on Ubuntu 16.04

On some distributions the linker will not add a library to the ELF
header if the symbols are not needed when the library was first
parsed by the linker. This causes undefined references issues when
linking with libraries which have circular dependencies. This was
causing issues on Ubuntu 16.04
---
 CMakeLists.txt | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3e66b6db06..b0525505ce 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,6 +18,7 @@ include(Version)
 include(build_cl2hpp)
 include(platform)
 include(GetPrerequisites)
+include(CheckCXXCompilerFlag)
 
 arrayfire_set_cmake_default_variables()
 
@@ -150,6 +151,15 @@ set_target_properties(${built_backends} PROPERTIES
                       VERSION "${ArrayFire_VERSION}"
                       SOVERSION "${ArrayFire_VERSION_MAJOR}")
 
+# On some distributions the linker will not add a library to the ELF header if
+# the symbols are not needed when the library was first parsed by the linker.
+# This causes undefined references issues when linking with libraries which have
+# circular dependencies.
+if(UNIX AND NOT APPLE AND CMAKE_CXX_COMPILER_ID MATCHES "GNU")
+  set_target_properties(${built_backends} PROPERTIES
+                        LINK_FLAGS "-Wl,--no-as-needed")
+endif()
+
 foreach(backend ${built_backends})
   target_compile_definitions(${backend} PRIVATE AFDLL)
 endforeach()
@@ -322,4 +332,5 @@ set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
 conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
 
-include(CPackConfig)
\ No newline at end of file
+include(CPackConfig)
+

From 4d44cc73baeee155a91454230b4ff356582ed5f6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 27 Apr 2018 11:14:17 +0530
Subject: [PATCH 1419/2677] Remove cmake cmd overrides in examples

There was an infinite loop when building with Microsoft's VCPKG
package manager that was caused because the ArrayFire examples
and vcpkg overloaded the find_package function. This caused a
stack overflow during the CMake configuration step.

Also, removes the find_package override in examples. The target
conflicts are now avoided by prefixing all tests with the string
`test_`.
---
 CMakeModules/ArrayFireConfig.cmake.in        |  9 ++++
 CMakeModules/ArrayFireExampleOverloads.cmake | 57 --------------------
 examples/CMakeLists.txt                      |  6 +--
 test/CMakeLists.txt                          |  7 +--
 4 files changed, 14 insertions(+), 65 deletions(-)
 delete mode 100644 CMakeModules/ArrayFireExampleOverloads.cmake

diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index b2819b8ac1..28cbf942f2 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -91,6 +91,8 @@ foreach(backend Unified CPU OpenCL CUDA)
   else()
     string(TOLOWER "${backend}" lowerbackend)
   endif()
+
+  string(TOUPPER "${backend}" upperbackend)
   if(NOT TARGET ArrayFire::af${lowerbackend} AND NOT TARGET af${lowerbackend})
     # Either we are not in the ArrayFire project or the target was not built
     if(EXISTS @PACKAGE_CMAKE_DIR@/ArrayFire${backend}Targets.cmake)
@@ -112,6 +114,13 @@ foreach(backend Unified CPU OpenCL CUDA)
   else()
       set(ArrayFire_${backend}_FOUND OFF)
   endif()
+
+  # If this project is built as part of the ArrayFire project, make sure the
+  # backends are only enabled if the backend is selected to be built even if
+  # the Binary exists.
+  if(DEFINED AF_BUILD_${upperbackend} AND NOT AF_BUILD_${upperbackend})
+    set(ArrayFire_${backend}_FOUND OFF)
+  endif()
 endforeach()
 
 foreach(_comp ${ArrayFire_FIND_COMPONENTS})
diff --git a/CMakeModules/ArrayFireExampleOverloads.cmake b/CMakeModules/ArrayFireExampleOverloads.cmake
deleted file mode 100644
index 3d95468b57..0000000000
--- a/CMakeModules/ArrayFireExampleOverloads.cmake
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright (c) 2018, ArrayFire
-# All rights reserved.
-#
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
-
-# Some examples take too long to execute. This list is used to exclude these
-# examples from the tests
-list(APPEND exclude_from_tests  black_scholes_options_cpu
-                                monte_carlo_options_cpu
-                                vectorize_cpu
-                                )
-
-# Overload add_executable and target_link_libraries so that we can use simple
-# CMakeLists.txt files for the examples.
-#
-# These functions will overload the existing functions so that the target names
-# have the word "examples_" prefixed to them so they don't conflict with the
-# tests. This is an issue with the blas example where the test blas_cpu and the
-# example blas_cpu have the same target name.
-#
-# Additionally, This will allow us to write the CMakeLists.txt files as
-# standalone files so that they are easier to parse for new users.
-function(add_executable target sources)
-  _add_executable(example_${target} ${sources})
-  set_target_properties(example_${target}
-    PROPERTIES
-      OUTPUT_NAME ${target}
-      FOLDER "Examples"
-  )
-
-  if(NOT ${target} IN_LIST exclude_from_tests)
-    #add_test(example_${target} ${target} 0 -)
-  endif()
-endfunction()
-
-macro(find_package)
-  _find_package(${ARGV})
-  if(DEFINED AF_BUILD_CPU AND NOT AF_BUILD_CPU)
-    set(ArrayFire_CPU_FOUND OFF)
-  endif()
-  if(DEFINED AF_BUILD_CUDA AND NOT AF_BUILD_CUDA)
-    set(ArrayFire_CUDA_FOUND OFF)
-  endif()
-  if(DEFINED AF_BUILD_OPENCL AND NOT AF_BUILD_OPENCL)
-    set(ArrayFire_OpenCL_FOUND OFF)
-  endif()
-endmacro()
-
-function(target_link_libraries target sources)
-  _target_link_libraries(example_${target} ${sources})
-endfunction()
-
-function(target_compile_definitions target access definitions)
-  _target_compile_definitions(example_${target} ${access} ${definitions})
-endfunction()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 17e738a8a5..94b5b19c43 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -11,16 +11,12 @@ project(ArrayFire-Examples
   VERSION 3.5.0
   LANGUAGES CXX)
 
-if(EXISTS "${ArrayFire_SOURCE_DIR}/CMakeModules/ArrayFireExampleOverloads.cmake")
-  include(ArrayFireExampleOverloads)
-else()
+if(NOT EXISTS "${ArrayFire_SOURCE_DIR}/CMakeLists.txt")
   set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/..")
 endif()
 
 file(TO_NATIVE_PATH ${ASSETS_DIR} ASSETS_DIR)
 
-file(TO_NATIVE_PATH ${ASSETS_DIR} ASSETS_DIR)
-
 if(WIN32)
   string(REPLACE "\\" "\\\\" ASSETS_DIR  ${ASSETS_DIR})
   # - WIN32_LEAN_AND_MEAN & VC_EXTRALEAN reduces the number of
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 6b9cfbb81f..51e1a98fa0 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -85,7 +85,7 @@ function(make_test)
        NOT ${backend} IN_LIST mt_args_BACKENDS)
       continue()
     endif()
-    set(target "${src_name}_${backend}")
+    set(target "test_${src_name}_${backend}")
     add_executable(${target} ${mt_args_SRC})
     target_include_directories(${target}
       PRIVATE
@@ -118,9 +118,10 @@ function(make_test)
           CXX_STANDARD 11)
     endif(${mt_args_CXX11})
 
-	  set_target_properties(${target}
+    set_target_properties(${target}
       PROPERTIES
-        FOLDER "Tests")
+        FOLDER "Tests"
+        OUTPUT_NAME "${src_name}_${backend}")
 
     target_compile_definitions(${target}
       PRIVATE

From e2f6d010d1376cb68b7feb94141f0619a59dd4d5 Mon Sep 17 00:00:00 2001
From: mlloreda <miguel@arrayfire.com>
Date: Tue, 1 May 2018 13:39:10 -0400
Subject: [PATCH 1420/2677] Updated to latest Google Test.

Tag `release-1.8.0` was breaking on latest Visual Studio 2017.
---
 test/gtest | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/gtest b/test/gtest
index ec44c6c167..278aba369c 160000
--- a/test/gtest
+++ b/test/gtest
@@ -1 +1 @@
-Subproject commit ec44c6c1675c25b9827aacd08c02433cccde7780
+Subproject commit 278aba369c41e90e9e77a6f51443beb3692919cf

From ee21c791d5af4fa56bca168804ba1597cf9d6503 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 1 May 2018 20:22:30 -0400
Subject: [PATCH 1421/2677] Add Tegra toolchain support

---
 CMakeLists.txt                         | 16 +++++++++++++++-
 CMakeModules/TegraCrossToolchain.cmake | 11 +++++++++++
 2 files changed, 26 insertions(+), 1 deletion(-)
 create mode 100644 CMakeModules/TegraCrossToolchain.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b0525505ce..48847c82af 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -109,7 +109,21 @@ if(AF_WITH_NONFREE)
     "Columbia.")
 endif()
 
-add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp)
+# when crosscompiling use the bin2cpp file from the native bin directory
+if(CMAKE_CROSSCOMPILING)
+  set(NATIVE_BIN_DIR "NATIVE_BIN_DIR-NOTFOUND"
+    CACHE FILEPATH "Path to the Native build directory.")
+  if(NATIVE_BIN_DIR)
+    include(${NATIVE_BIN_DIR}/ImportExecutables.cmake)
+  else()
+    message(SEND_ERROR "Native Directory not found. Run cmake in a separate"
+                       "directory and build the bin2cpp target.")
+  endif()
+else()
+  add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp)
+  target_link_libraries(bin2cpp)
+  export(TARGETS bin2cpp FILE ${CMAKE_BINARY_DIR}/ImportExecutables.cmake)
+endif()
 
 if(NOT LAPACK_FOUND)
     if(APPLE)
diff --git a/CMakeModules/TegraCrossToolchain.cmake b/CMakeModules/TegraCrossToolchain.cmake
new file mode 100644
index 0000000000..e8b1e10f5a
--- /dev/null
+++ b/CMakeModules/TegraCrossToolchain.cmake
@@ -0,0 +1,11 @@
+
+set(CMAKE_SYSTEM_NAME Linux)
+set(CMAKE_SYSTEM_PROCESSOR aarch64)
+
+set(CMAKE_C_COMPILER aarch64-linux-gnu-gcc-5)
+set(CMAKE_CXX_COMPILER aarch64-linux-gnu-g++-5)
+
+set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
+set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
+set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
+set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)

From 719e5c77a71651cc537aaeb161265f02c46e9c2a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 8 May 2018 15:06:27 +0530
Subject: [PATCH 1422/2677] Fix variable const qualifiers in blas files

---
 src/backend/cpu/blas.cpp        | 6 +++---
 src/backend/cpu/sparse_blas.cpp | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 149691f2a4..ca2e194644 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -163,8 +163,8 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
     int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
 
-    dim4 lDims = lhs.dims();
-    dim4 rDims = rhs.dims();
+    auto lDims = lhs.dims();
+    auto rDims = rhs.dims();
     int M = lDims[aRowDim];
     int N = rDims[bColDim];
     int K = lDims[aColDim];
@@ -174,7 +174,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
     dim_t d2 = std::max(lDims[2], rDims[2]);
     dim_t d3 = std::max(lDims[3], rDims[3]);
-    dim4 oDims = af::dim4(M, N, d2, d3);
+    const dim4 oDims(M, N, d2, d3);
     Array<T> out = createEmptyArray<T>(oDims);
 
     auto func = [=] (Param<T> output, CParam<T> left, CParam<T> right) {
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 688148861d..417b6bec6f 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -452,8 +452,8 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     static const int rColDim = 1;
 
-    dim4 lDims = lhs.dims();
-    dim4 rDims = rhs.dims();
+    auto lDims = lhs.dims();
+    auto rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
 

From 9a418bfaa585bcdafb974e979697f58fef31507c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 9 May 2018 10:25:38 +0530
Subject: [PATCH 1423/2677] Disable hunter use by forge from ArrayFire

This would result in developer to isntall forge dependencies
manually.
The following are the dependencies required:
* glbinding
* glfw3
* glm
* freetype
* boost(headers only, not need of libs)
* fontconfig(on non-Windows platforms)
---
 CMakeModules/build_forge.cmake | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 3dcd6fd5be..2f964e0326 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -40,7 +40,6 @@ ExternalProject_Add(
       -DCMAKE_CXX_FLAGS:STRING=${disable_warning_flags}
       -DFG_BUILD_EXAMPLES:BOOL=OFF
       -DFG_BUILD_DOCS:BOOL=OFF
-      $<$<BOOL:${UNIX}>:-DFG_ENABLE_HUNTER:BOOL=ON>
       -DFG_WITH_FREEIMAGE:BOOL=OFF
       -DCMAKE_SHARED_LINKER_FLAGS:STRING=${CMAKE_SHARED_LINKER_FLAGS}
     )

From 21cbac608717727759a666da84285f824320e061 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 9 May 2018 17:31:32 +0530
Subject: [PATCH 1424/2677] Remove redundant checks in regions backend impls

---
 src/backend/cuda/regions.cu    | 2 --
 src/backend/opencl/regions.cpp | 2 --
 2 files changed, 4 deletions(-)

diff --git a/src/backend/cuda/regions.cu b/src/backend/cuda/regions.cu
index 48e35ccccf..ee4dc3273b 100644
--- a/src/backend/cuda/regions.cu
+++ b/src/backend/cuda/regions.cu
@@ -21,8 +21,6 @@ namespace cuda
 template<typename T>
 Array<T>  regions(const Array<char> &in, af_connectivity connectivity)
 {
-    ARG_ASSERT(2, (connectivity==AF_CONNECTIVITY_4 || connectivity==AF_CONNECTIVITY_8));
-
     const dim4 dims = in.dims();
 
     Array<T>  out  = createEmptyArray<T>(dims);
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index 1583e29449..d8c4d16cd0 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -21,8 +21,6 @@ namespace opencl
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity)
 {
-    ARG_ASSERT(2, (connectivity==AF_CONNECTIVITY_4 || connectivity==AF_CONNECTIVITY_8));
-
     const af::dim4 dims = in.dims();
 
     Array<T> out  = createEmptyArray<T>(dims);

From 35cbf7a0cdac3ca7a6505494ece3b2aee9bc3d41 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 10 May 2018 17:55:15 +0530
Subject: [PATCH 1425/2677] Fix regions corner case in CUDA/OpenCL backends

Fixes #1650

When entire input image is one big component(all 1's) or
there is only one component(apart from background-0's),
both CUDA/OpenCL mistook that for background. This change
addes a check for this corner case.
---
 src/backend/cuda/kernel/regions.hpp   | 20 +++++++++++++-------
 src/backend/opencl/kernel/regions.hpp | 15 +++++++++++----
 test/regions.cpp                      | 17 +++++++++++++++++
 3 files changed, 41 insertions(+), 11 deletions(-)

diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index aa2140d8ce..4f44734e64 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -390,9 +390,17 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     // Sort the copy
     THRUST_SELECT(thrust::sort, wrapped_tmp, wrapped_tmp + size);
 
-    // Take the max element, this is the number of label assignments to
-    // compute.
-    int num_bins = wrapped_tmp[size - 1] + 1;
+    // Take the max element which is the number
+    // of label assignments to compute.
+    const int num_bins = wrapped_tmp[size - 1] + 1;
+
+    // If the number of label assignments is two,
+    // then either the entire input image is one big
+    // component(1's) or it has only one component other than
+    // background(0's). Either way, no further
+    // post-processing of labels is required.
+    if (num_bins<=2)
+        return;
 
     cuda::ThrustVector<T> labels(num_bins);
 
@@ -401,14 +409,14 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     THRUST_SELECT(thrust::upper_bound, wrapped_tmp,  wrapped_tmp  + size,
                         search_begin, search_begin + num_bins,
                         labels.begin());
+
     THRUST_SELECT(thrust::adjacent_difference, labels.begin(), labels.end(), labels.begin());
 
     // Operators for the scan
     clamp_to_one<T> clamp;
     thrust::plus<T> add;
 
-    // Perform the scan -- this can computes the correct labels for each
-    // component
+    // Perform scan -- this computes the correct labels for each component
     THRUST_SELECT(thrust::transform_exclusive_scan,
                                      labels.begin(),
                                      labels.end(),
@@ -416,11 +424,9 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
                                      clamp,
                                      0,
                                      add);
-
     // Apply the correct labels to the equivalency map
     CUDA_LAUNCH((final_relabel<T,n_per_thread>), blocks,threads,
             out, in, thrust::raw_pointer_cast(&labels[0]));
 
     POST_LAUNCH_CHECK();
-
 }
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index b658264156..26e74b828e 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -171,13 +171,20 @@ void regions(Param out, Param in)
     // Sort the copy
     compute::sort(tmp.begin(), tmp.end(), c_queue);
 
-    // Take the max element, this is the number of label assignments to
-    // compute.
-    //int num_bins = tmp[size - 1] + 1;
+    // Take the max element which is the number
+    // of label assignments to compute.
     T last_label;
     clEnqueueReadBuffer(getQueue()(), tmp.get_buffer().get(), CL_TRUE,
                         (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL, NULL);
-    int num_bins = (int)last_label + 1;
+    const int num_bins = (int)last_label + 1;
+
+    // If the number of label assignments is two,
+    // then either the entire input image is one big
+    // component(1's) or it has only one component other than
+    // background(0's). Either way, no further
+    // post-processing of labels is required.
+    if (num_bins<=2)
+        return;
 
     Buffer labels(getContext(), CL_MEM_READ_WRITE, num_bins * sizeof(T));
     compute::buffer c_labels(labels());
diff --git a/test/regions.cpp b/test/regions.cpp
index fccb902f46..82d1b88380 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -250,3 +250,20 @@ TEST(Regions, Docs_4)
         ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
     }
 }
+
+TEST(Regions, Issue1650)
+{
+    const int dim = 101;
+    const int sz  = dim*dim;
+    std::vector<char> input(sz, 1);
+    std::vector<float> gold(sz, 1.0f);
+
+    af::array in  = af::array(dim, dim, input.data());
+    af::array out = af::regions(in, AF_CONNECTIVITY_4);
+
+    std::vector<float> output(sz);
+    out.host((void*)output.data());
+
+    for (int i=0; i<sz; ++i)
+        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
+}

From 3b1ddb1987de9c269365b04963daf3e5dc7f29a8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 11 May 2018 20:25:52 +0530
Subject: [PATCH 1426/2677] No component test case for regions

---
 test/regions.cpp | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/test/regions.cpp b/test/regions.cpp
index 82d1b88380..a7ae1d9ef9 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -251,7 +251,7 @@ TEST(Regions, Docs_4)
     }
 }
 
-TEST(Regions, Issue1650)
+TEST(Regions, WholeImageComponent)
 {
     const int dim = 101;
     const int sz  = dim*dim;
@@ -267,3 +267,20 @@ TEST(Regions, Issue1650)
     for (int i=0; i<sz; ++i)
         ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
 }
+
+TEST(Regions, NoComponentImage)
+{
+    const int dim = 101;
+    const int sz  = dim*dim;
+    std::vector<char> input(sz, 0);
+    std::vector<float> gold(sz, 0.0f);
+
+    af::array in  = af::array(dim, dim, input.data());
+    af::array out = af::regions(in, AF_CONNECTIVITY_4);
+
+    std::vector<float> output(sz);
+    out.host((void*)output.data());
+
+    for (int i=0; i<sz; ++i)
+        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
+}

From 659719985d4302f3fd6784d979150f6cacc44173 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 27 May 2018 01:22:50 -0400
Subject: [PATCH 1427/2677] Make FreeImage a runtime instead of a linktime
 dependency

This commit will make the FreeImage library a runtime dependency
instead of a link time dependency. This means that the user does
not need to have FreeImage installed on their system if they are
not using the image input and output functions. The FreeImage
library will be loaded at runtime only when the image io functions
are used.

`DependencyModule` class handles all the symbol management
and it is abstract enough to be used for other libraries too.

Each platform has it's own module_loading file because they were
complete reimplementation of the functions. There are still some
ifdef remaining but they are small enough not to warrent a separate
file.
---
 CMakeModules/FindFreeImage.cmake              |  16 +-
 src/api/c/CMakeLists.txt                      |   9 +-
 src/api/c/imageio.cpp                         | 202 ++++++++++++------
 src/api/c/imageio2.cpp                        |  57 +++--
 src/api/c/imageio_helper.h                    |  73 ++++---
 src/backend/common/CMakeLists.txt             |  16 +-
 src/backend/common/DependencyModule.cpp       |  67 ++++++
 src/backend/common/DependencyModule.hpp       |  62 ++++++
 src/backend/common/defines.hpp                |  17 ++
 src/backend/common/module_loading.hpp         |  21 ++
 src/backend/common/module_loading_unix.cpp    |  33 +++
 src/backend/common/module_loading_windows.cpp |  44 ++++
 src/backend/cpu/platform.cpp                  |   2 +-
 13 files changed, 476 insertions(+), 143 deletions(-)
 create mode 100644 src/backend/common/DependencyModule.cpp
 create mode 100644 src/backend/common/DependencyModule.hpp
 create mode 100644 src/backend/common/module_loading.hpp
 create mode 100644 src/backend/common/module_loading_unix.cpp
 create mode 100644 src/backend/common/module_loading_windows.cpp

diff --git a/CMakeModules/FindFreeImage.cmake b/CMakeModules/FindFreeImage.cmake
index abf47023b7..b049ec06a3 100644
--- a/CMakeModules/FindFreeImage.cmake
+++ b/CMakeModules/FindFreeImage.cmake
@@ -99,17 +99,17 @@ if(FreeImage_FOUND AND NOT TARGET FreeImage::FreeImage)
   if(WIN32)
     set_target_properties(FreeImage::FreeImage
 	  PROPERTIES
-        IMPORTED_LINK_INTERFACE_LANGUAGE "C"
-        INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
-        IMPORTED_LOCATION "${FreeImage_DLL_LIBRARY}"
-	    IMPORTED_IMPLIB "${FreeImage_LINK_LIBRARY}")
+      IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+      INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
+      IMPORTED_LOCATION "${FreeImage_DLL_LIBRARY}"
+      IMPORTED_IMPLIB "${FreeImage_LINK_LIBRARY}")
   else(WIN32)
     set_target_properties(FreeImage::FreeImage
 	  PROPERTIES
-        IMPORTED_LINK_INTERFACE_LANGUAGE "C"
-        INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
-        IMPORTED_LOCATION "${FreeImage_LINK_LIBRARY}"
-	    IMPORTED_NO_SONAME FALSE)
+      IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+      INTERFACE_INCLUDE_DIRECTORIES "${FreeImage_INCLUDE_DIR}"
+      IMPORTED_LOCATION "${FreeImage_LINK_LIBRARY}"
+      IMPORTED_NO_SONAME FALSE)
   endif(WIN32)
 endif()
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index fad75737b9..02966d3360 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -1,3 +1,9 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
 add_library(c_api_interface INTERFACE)
 
@@ -155,9 +161,10 @@ target_sources(c_api_interface
 if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
   target_compile_definitions(c_api_interface INTERFACE WITH_FREEIMAGE)
   if (AF_WITH_STATIC_FREEIMAGE)
+    target_compile_definitions(c_api_interface INTERFACE FREEIMAGE_STATIC)
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage_STATIC)
   else ()
-    target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage)
+    target_include_directories(c_api_interface INTERFACE $<TARGET_PROPERTY:FreeImage::FreeImage,INTERFACE_INCLUDE_DIRECTORIES>)
     if (WIN32 AND AF_INSTALL_STANDALONE)
       install(FILES $<TARGET_FILE:FreeImage::FreeImage>
         DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 486953c467..c8d060048d 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -26,13 +26,19 @@
 #include <common/err_common.hpp>
 #include <handle.hpp>
 
+#include <common/DependencyModule.hpp>
+
 #include <string>
 #include <cstring>
 #include <cstdio>
 #include <cstdlib>
+#include <memory>
 
 using af::dim4;
 using namespace detail;
+using std::unique_ptr;
+using std::string;
+using std::swap;
 
 template<typename T, FI_CHANNELS fi_color, FI_CHANNELS fo_color>
 static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcPitch,
@@ -80,6 +86,85 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
     return err;
 }
 
+#ifdef FREEIMAGE_STATIC
+    // NOTE: Redefine the MODULE_FUNCTION_INIT macro to call the static functions
+    // instead of dynamically loaded symbols in case we are building with a static
+    // FreeImage library
+    #define MODULE_FUNCTION_INIT(NAME)                  \
+      NAME = &::NAME
+
+FreeImage_Module::FreeImage_Module()
+  : module(nullptr, nullptr) {
+    // We don't care if the module loaded if we are staticly linking against
+    // FreeImage
+    ::FreeImage_Initialise(false);
+#else
+FreeImage_Module::FreeImage_Module()
+  : module("freeimage", nullptr) {
+    printf(__FILE__"%d\n", __LINE__);
+    if(!module.isLoaded()) {
+        printf(__FILE__"%d\n", __LINE__);
+        string error_message = "Error loading FreeImage: " + module.getErrorMessage()
+            + "\nFreeImage or one of it's dependencies failed to "
+              "load. Try installing FreeImage or check if FreeImage is in the "
+              "search path.";
+        printf(__FILE__"%d\n", __LINE__);
+        AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+    }
+#endif
+    MODULE_FUNCTION_INIT(FreeImage_Allocate);
+    MODULE_FUNCTION_INIT(FreeImage_AllocateT);
+    MODULE_FUNCTION_INIT(FreeImage_CloseMemory);
+    MODULE_FUNCTION_INIT(FreeImage_DeInitialise);
+    MODULE_FUNCTION_INIT(FreeImage_FIFSupportsReading);
+    MODULE_FUNCTION_INIT(FreeImage_GetBPP);
+    MODULE_FUNCTION_INIT(FreeImage_GetBits);
+    MODULE_FUNCTION_INIT(FreeImage_GetColorType);
+    MODULE_FUNCTION_INIT(FreeImage_GetFIFFromFilename);
+    MODULE_FUNCTION_INIT(FreeImage_GetFileType);
+    MODULE_FUNCTION_INIT(FreeImage_GetFileTypeFromMemory);
+    MODULE_FUNCTION_INIT(FreeImage_GetHeight);
+    MODULE_FUNCTION_INIT(FreeImage_GetImageType);
+    MODULE_FUNCTION_INIT(FreeImage_GetPitch);
+    MODULE_FUNCTION_INIT(FreeImage_GetWidth);
+    MODULE_FUNCTION_INIT(FreeImage_Initialise);
+    MODULE_FUNCTION_INIT(FreeImage_Load);
+    MODULE_FUNCTION_INIT(FreeImage_LoadFromMemory);
+    MODULE_FUNCTION_INIT(FreeImage_OpenMemory);
+    MODULE_FUNCTION_INIT(FreeImage_Save);
+    MODULE_FUNCTION_INIT(FreeImage_SaveToMemory);
+    MODULE_FUNCTION_INIT(FreeImage_SeekMemory);
+    MODULE_FUNCTION_INIT(FreeImage_SetOutputMessage);
+    MODULE_FUNCTION_INIT(FreeImage_Unload);
+
+#ifndef FREEIMAGE_STATIC
+    if(!module.symbolsLoaded()) {
+        printf(__FILE__"%d\n", __LINE__);
+        string error_message = "Error loading FreeImage: "
+          + module.getErrorMessage()
+          + "\nThe installed version of FreeImage is not compatible with "
+            "ArrayFire. Please create an issue on which this error message";
+        printf(__FILE__"%d\n", __LINE__);
+        AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+    }
+#endif
+}
+
+FreeImage_Module::~FreeImage_Module() {
+#ifdef FREEIMAGE_STATIC
+    getFreeImagePlugin().FreeImage_DeInitialise();
+#endif
+}
+
+FreeImage_Module& getFreeImagePlugin() {
+    static FreeImage_Module *plugin = new FreeImage_Module();
+    return *plugin;
+}
+
+bitmap_ptr make_bitmap_ptr(FIBITMAP* ptr) {
+  return bitmap_ptr(ptr, getFreeImagePlugin().FreeImage_Unload);
+}
+
 template<typename T, FI_CHANNELS fo_color>
 static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcPitch,
                         const uint fi_w, const uint fi_h)
@@ -129,16 +214,15 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
     try {
         ARG_ASSERT(1, filename != NULL);
 
-        // for statically linked FI
-        FI_Init();
+        FreeImage_Module& _ = getFreeImagePlugin();
 
         // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         // try to guess the file format from the file extension
-        FREE_IMAGE_FORMAT fif = FreeImage_GetFileType(filename);
+        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileType(filename, 0);
         if (fif == FIF_UNKNOWN) {
-            fif = FreeImage_GetFIFFromFilename(filename);
+            fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
         if(fif == FIF_UNKNOWN) {
@@ -152,21 +236,18 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 #endif
 
         // check that the plugin has reading capabilities ...
-        FIBITMAP* pBitmap = NULL;
-        if (FreeImage_FIFSupportsReading(fif)) {
-            pBitmap = FreeImage_Load(fif, filename, flags);
+        bitmap_ptr pBitmap = make_bitmap_ptr(NULL);
+        if (_.FreeImage_FIFSupportsReading(fif)) {
+            pBitmap.reset(_.FreeImage_Load(fif, filename, flags));
         }
 
         if(pBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error reading image or file does not exist", AF_ERR_RUNTIME);
         }
 
-        // make sure pBitmap is unleaded automatically, no matter how we exit this function
-        FI_BitmapResource bitmapUnloader(pBitmap);
-
         // check image color type
-        uint color_type = FreeImage_GetColorType(pBitmap);
-        const uint fi_bpp = FreeImage_GetBPP(pBitmap);
+        uint color_type = _.FreeImage_GetColorType(pBitmap.get());
+        const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
         //int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
         int fi_color;
         switch(color_type) {
@@ -189,15 +270,15 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
         }
 
         // data type
-        FREE_IMAGE_TYPE image_type = FreeImage_GetImageType(pBitmap);
+        FREE_IMAGE_TYPE image_type = _.FreeImage_GetImageType(pBitmap.get());
 
         // sizes
-        uint fi_w = FreeImage_GetWidth(pBitmap);
-        uint fi_h = FreeImage_GetHeight(pBitmap);
+        uint fi_w = _.FreeImage_GetWidth(pBitmap.get());
+        uint fi_h = _.FreeImage_GetHeight(pBitmap.get());
 
         // FI = row major | AF = column major
-        uint nSrcPitch = FreeImage_GetPitch(pBitmap);
-        const uchar* pSrcLine = FreeImage_GetBits(pBitmap) + nSrcPitch * (fi_h - 1);
+        uint nSrcPitch = _.FreeImage_GetPitch(pBitmap.get());
+        const uchar* pSrcLine = _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
 
         // result image
         af_array rImage;
@@ -267,7 +348,7 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
             }
         }
 
-        std::swap(*out,rImage);
+        swap(*out,rImage);
     } CATCHALL;
 
     return AF_SUCCESS;
@@ -280,15 +361,15 @@ af_err af_save_image(const char* filename, const af_array in_)
 
         ARG_ASSERT(0, filename != NULL);
 
-        FI_Init();
+        FreeImage_Module& _ = getFreeImagePlugin();
 
         // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         // try to guess the file format from the file extension
-        FREE_IMAGE_FORMAT fif = FreeImage_GetFileType(filename);
+        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileType(filename, 0);
         if (fif == FIF_UNKNOWN) {
-            fif = FreeImage_GetFIFFromFilename(filename);
+            fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
         if(fif == FIF_UNKNOWN) {
@@ -308,14 +389,11 @@ af_err af_save_image(const char* filename, const af_array in_)
         uint fi_h = info.dims()[0];
 
         // create the result image storage using FreeImage
-        FIBITMAP* pResultBitmap = FreeImage_Allocate(fi_w, fi_h, fi_bpp);
+        bitmap_ptr pResultBitmap = make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
         if(pResultBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error creating image or file", AF_ERR_RUNTIME);
         }
 
-        // make sure pResultBitmap is unleaded automatically, no matter how we exit this function
-        FI_BitmapResource resultBitmapUnloader(pResultBitmap);
-
         // FI assumes [0-255]
         // If array is in 0-1 range, multiply by 255
         af_array in;
@@ -341,8 +419,8 @@ af_err af_save_image(const char* filename, const af_array in_)
         }
 
         // FI = row major | AF = column major
-        uint nDstPitch = FreeImage_GetPitch(pResultBitmap);
-        uchar* pDstLine = FreeImage_GetBits(pResultBitmap) + nDstPitch * (fi_h - 1);
+        uint nDstPitch = _.FreeImage_GetPitch(pResultBitmap.get());
+        uchar* pDstLine = _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
         af_array rr = 0, gg = 0, bb = 0, aa = 0;
         AF_CHECK(channel_split(in, info.dims(), &rr, &gg, &bb, &aa)); // convert array to 3 channels if needed
 
@@ -430,7 +508,7 @@ af_err af_save_image(const char* filename, const af_array in_)
         if(fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
 
         // now save the result image
-        if (!(FreeImage_Save(fif, pResultBitmap, filename, flags) == TRUE)) {
+        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) == TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
@@ -458,17 +536,16 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
     try {
         ARG_ASSERT(1, ptr != NULL);
 
-        // for statically linked FI
-        FI_Init();
+        FreeImage_Module& _ = getFreeImagePlugin();
 
         // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         FIMEMORY *stream = (FIMEMORY*)ptr;
-        FreeImage_SeekMemory(stream, 0L, SEEK_SET);
+        _.FreeImage_SeekMemory(stream, 0L, SEEK_SET);
 
         // try to guess the file format from the file extension
-        FREE_IMAGE_FORMAT fif = FreeImage_GetFileTypeFromMemory(stream, 0);
+        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileTypeFromMemory(stream, 0);
         //if (fif == FIF_UNKNOWN) {
         //    fif = FreeImage_GetFIFFromFilenameFromMemory(filename);
         //}
@@ -481,21 +558,18 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
         if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
 
         // check that the plugin has reading capabilities ...
-        FIBITMAP* pBitmap = NULL;
-        if (FreeImage_FIFSupportsReading(fif)) {
-            pBitmap = FreeImage_LoadFromMemory(fif, stream, flags);
+        bitmap_ptr pBitmap = make_bitmap_ptr(NULL);
+        if (_.FreeImage_FIFSupportsReading(fif)) {
+            pBitmap.reset(_.FreeImage_LoadFromMemory(fif, stream, flags));
         }
 
         if(pBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error reading image or file does not exist", AF_ERR_RUNTIME);
         }
 
-        // make sure pBitmap is unleaded automatically, no matter how we exit this function
-        FI_BitmapResource bitmapUnloader(pBitmap);
-
         // check image color type
-        uint color_type = FreeImage_GetColorType(pBitmap);
-        const uint fi_bpp = FreeImage_GetBPP(pBitmap);
+        uint color_type = _.FreeImage_GetColorType(pBitmap.get());
+        const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
         //int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
         int fi_color;
         switch(color_type) {
@@ -517,12 +591,12 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
         }
 
         // sizes
-        uint fi_w = FreeImage_GetWidth(pBitmap);
-        uint fi_h = FreeImage_GetHeight(pBitmap);
+        uint fi_w = _.FreeImage_GetWidth(pBitmap.get());
+        uint fi_h = _.FreeImage_GetHeight(pBitmap.get());
 
         // FI = row major | AF = column major
-        uint nSrcPitch = FreeImage_GetPitch(pBitmap);
-        const uchar* pSrcLine = FreeImage_GetBits(pBitmap) + nSrcPitch * (fi_h - 1);
+        uint nSrcPitch = _.FreeImage_GetPitch(pBitmap.get());
+        const uchar* pSrcLine = _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
 
         // result image
         af_array rImage;
@@ -549,7 +623,7 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
                 AF_CHECK((readImage<float,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
         }
 
-        std::swap(*out,rImage);
+        swap(*out,rImage);
     } CATCHALL;
 
     return AF_SUCCESS;
@@ -559,11 +633,10 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
 af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_format format)
 {
     try {
+        FreeImage_Module& _ = getFreeImagePlugin();
 
-        FI_Init();
-
-        // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        // set our own FreeImage error handler
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         // try to guess the file format from the file extension
         FREE_IMAGE_FORMAT fif = (FREE_IMAGE_FORMAT)format;
@@ -585,14 +658,11 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
         uint fi_h = info.dims()[0];
 
         // create the result image storage using FreeImage
-        FIBITMAP* pResultBitmap = FreeImage_Allocate(fi_w, fi_h, fi_bpp);
+        bitmap_ptr pResultBitmap = make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
         if(pResultBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error creating image or file", AF_ERR_RUNTIME);
         }
 
-        // make sure pResultBitmap is unleaded automatically, no matter how we exit this function
-        FI_BitmapResource resultBitmapUnloader(pResultBitmap);
-
         // FI assumes [0-255]
         // If array is in 0-1 range, multiply by 255
         af_array in;
@@ -610,8 +680,8 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
         }
 
         // FI = row major | AF = column major
-        uint nDstPitch = FreeImage_GetPitch(pResultBitmap);
-        uchar* pDstLine = FreeImage_GetBits(pResultBitmap) + nDstPitch * (fi_h - 1);
+        uint nDstPitch = _.FreeImage_GetPitch(pResultBitmap.get());
+        uchar* pDstLine = _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
         af_array rr = 0, gg = 0, bb = 0, aa = 0;
         AF_CHECK(channel_split(in, info.dims(), &rr, &gg, &bb, &aa)); // convert array to 3 channels if needed
 
@@ -695,13 +765,15 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             pinnedFree(pSrc0);
         }
 
-        FIMEMORY *stream = FreeImage_OpenMemory();
+        uint8_t* data = nullptr;
+        uint32_t size_in_bytes = 0;
+        FIMEMORY *stream = _.FreeImage_OpenMemory(data, size_in_bytes);
 
         int flags = 0;
         if(fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
 
         // now save the result image
-        if (!(FreeImage_SaveToMemory(fif, pResultBitmap, stream, flags) == TRUE)) {
+        if (!(_.FreeImage_SaveToMemory(fif, pResultBitmap.get(), stream, flags) == TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
@@ -728,21 +800,21 @@ af_err af_delete_image_memory(void *ptr)
 
         ARG_ASSERT(0, ptr != NULL);
 
-        FI_Init();
+        FreeImage_Module& _ = getFreeImagePlugin();
 
         // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         FIMEMORY *stream = (FIMEMORY*)ptr;
-        FreeImage_SeekMemory(stream, 0L, SEEK_SET);
+        _.FreeImage_SeekMemory(stream, 0L, SEEK_SET);
 
         // Ensure data is freeimage compatible
-        FREE_IMAGE_FORMAT fif = FreeImage_GetFileTypeFromMemory((FIMEMORY*)ptr, 0);
+        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileTypeFromMemory((FIMEMORY*)ptr, 0);
         if(fif == FIF_UNKNOWN) {
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
-        FreeImage_CloseMemory((FIMEMORY *)ptr);
+        _.FreeImage_CloseMemory((FIMEMORY *)ptr);
 
     } CATCHALL
 
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 8b9fa4992c..3ed61d8ace 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -108,16 +108,15 @@ af_err af_load_image_native(af_array *out, const char* filename)
     try {
         ARG_ASSERT(1, filename != NULL);
 
-        // for statically linked FI
-        FI_Init();
+        FreeImage_Module& _ = getFreeImagePlugin();
 
         // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         // try to guess the file format from the file extension
-        FREE_IMAGE_FORMAT fif = FreeImage_GetFileType(filename);
+        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileType(filename, 0);
         if (fif == FIF_UNKNOWN) {
-            fif = FreeImage_GetFIFFromFilename(filename);
+            fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
         if(fif == FIF_UNKNOWN) {
@@ -128,21 +127,18 @@ af_err af_load_image_native(af_array *out, const char* filename)
         if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
 
         // check that the plugin has reading capabilities ...
-        FIBITMAP* pBitmap = NULL;
-        if (FreeImage_FIFSupportsReading(fif)) {
-            pBitmap = FreeImage_Load(fif, filename, flags);
+        bitmap_ptr pBitmap = make_bitmap_ptr(nullptr);
+        if (_.FreeImage_FIFSupportsReading(fif)) {
+            pBitmap.reset(_.FreeImage_Load(fif, filename, flags));
         }
 
         if(pBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error reading image or file does not exist", AF_ERR_RUNTIME);
         }
 
-        // make sure pBitmap is unleaded automatically, no matter how we exit this function
-        FI_BitmapResource bitmapUnloader(pBitmap);
-
         // check image color type
-        uint color_type = FreeImage_GetColorType(pBitmap);
-        const uint fi_bpp = FreeImage_GetBPP(pBitmap);
+        uint color_type = _.FreeImage_GetColorType(pBitmap.get());
+        const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
         //int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
         int fi_color;
         switch(color_type) {
@@ -165,15 +161,15 @@ af_err af_load_image_native(af_array *out, const char* filename)
         }
 
         // data type
-        FREE_IMAGE_TYPE image_type = FreeImage_GetImageType(pBitmap);
+        FREE_IMAGE_TYPE image_type = _.FreeImage_GetImageType(pBitmap.get());
 
         // sizes
-        uint fi_w = FreeImage_GetWidth(pBitmap);
-        uint fi_h = FreeImage_GetHeight(pBitmap);
+        uint fi_w = _.FreeImage_GetWidth(pBitmap.get());
+        uint fi_h = _.FreeImage_GetHeight(pBitmap.get());
 
         // FI = row major | AF = column major
-        uint nSrcPitch = FreeImage_GetPitch(pBitmap);
-        const uchar* pSrcLine = FreeImage_GetBits(pBitmap) + nSrcPitch * (fi_h - 1);
+        uint nSrcPitch = _.FreeImage_GetPitch(pBitmap.get());
+        const uchar* pSrcLine = _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
 
         // result image
         af_array rImage;
@@ -298,15 +294,15 @@ af_err af_save_image_native(const char* filename, const af_array in)
 
         ARG_ASSERT(0, filename != NULL);
 
-        FI_Init();
+        FreeImage_Module& _ = getFreeImagePlugin();
 
         // set your own FreeImage error handler
-        FreeImage_SetOutputMessage(FreeImageErrorHandler);
+        _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         // try to guess the file format from the file extension
-        FREE_IMAGE_FORMAT fif = FreeImage_GetFileType(filename);
+        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileType(filename, 0);
         if (fif == FIF_UNKNOWN) {
-            fif = FreeImage_GetFIFFromFilename(filename);
+            fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
         if(fif == FIF_UNKNOWN) {
@@ -339,11 +335,11 @@ af_err af_save_image_native(const char* filename, const af_array in)
         FREE_IMAGE_TYPE fit_type = getFIT(channels, type);
 
         // create the result image storage using FreeImage
-        FIBITMAP* pResultBitmap = NULL;
+        bitmap_ptr pResultBitmap = make_bitmap_ptr(nullptr);
         switch(type) {
-            case u8:  pResultBitmap = FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp); break;
-            case u16: pResultBitmap = FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp); break;
-            case f32: pResultBitmap = FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp); break;
+            case u8:  pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp, 0, 0, 0)); break;
+            case u16: pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp, 0, 0, 0)); break;
+            case f32: pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp, 0, 0, 0)); break;
             default: TYPE_ERROR(1, type);
         }
 
@@ -351,12 +347,9 @@ af_err af_save_image_native(const char* filename, const af_array in)
             AF_ERROR("FreeImage Error: Error creating image or file", AF_ERR_RUNTIME);
         }
 
-        // make sure pResultBitmap is unloaded automatically, no matter how we exit this function
-        FI_BitmapResource resultBitmapUnloader(pResultBitmap);
-
         // FI = row major | AF = column major
-        uint nDstPitch = FreeImage_GetPitch(pResultBitmap);
-        void* pDstLine = FreeImage_GetBits(pResultBitmap) + nDstPitch * (fi_h - 1);
+        uint nDstPitch = _.FreeImage_GetPitch(pResultBitmap.get());
+        void* pDstLine = _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
 
         if(channels == AFFI_GRAY) {
             switch(type) {
@@ -385,7 +378,7 @@ af_err af_save_image_native(const char* filename, const af_array in)
         if(fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
 
         // now save the result image
-        if (!(FreeImage_Save(fif, pResultBitmap, filename, flags) == TRUE)) {
+        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) == TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
diff --git a/src/api/c/imageio_helper.h b/src/api/c/imageio_helper.h
index fef22e575e..034eb219c9 100644
--- a/src/api/c/imageio_helper.h
+++ b/src/api/c/imageio_helper.h
@@ -10,52 +10,55 @@
 #ifndef IMAGEIO_HELPER_H
 #define IMAGEIO_HELPER_H
 
-#include <FreeImage.h>
-
+#include <common/DependencyModule.hpp>
 #include <af/array.h>
-#include <af/index.h>
 #include <af/dim4.hpp>
+#include <af/index.h>
 #include <common/err_common.hpp>
 
-class FI_Manager
-{
-    public:
-    FI_Manager()
-    {
-#ifdef FREEIMAGE_LIB
-        FreeImage_Initialise();
-#endif
-    }
+#include <FreeImage.h>
 
-    ~FI_Manager()
-    {
-#ifdef FREEIMAGE_LIB
-        FreeImage_DeInitialise();
-#endif
-    }
-};
+#include <memory>
+#include <functional>
 
-static void FI_Init()
-{
-    static FI_Manager manager = FI_Manager();
-}
+class FreeImage_Module {
+    common::DependencyModule module;
 
-class FI_BitmapResource
-{
 public:
-    explicit FI_BitmapResource(FIBITMAP * p) :
-        pBitmap(p)
-    {
-    }
+    MODULE_MEMBER(FreeImage_Allocate);
+    MODULE_MEMBER(FreeImage_AllocateT);
+    MODULE_MEMBER(FreeImage_CloseMemory);
+    MODULE_MEMBER(FreeImage_DeInitialise);
+    MODULE_MEMBER(FreeImage_FIFSupportsReading);
+    MODULE_MEMBER(FreeImage_GetBPP);
+    MODULE_MEMBER(FreeImage_GetBits);
+    MODULE_MEMBER(FreeImage_GetColorType);
+    MODULE_MEMBER(FreeImage_GetFIFFromFilename);
+    MODULE_MEMBER(FreeImage_GetFileType);
+    MODULE_MEMBER(FreeImage_GetFileTypeFromMemory);
+    MODULE_MEMBER(FreeImage_GetHeight);
+    MODULE_MEMBER(FreeImage_GetImageType);
+    MODULE_MEMBER(FreeImage_GetPitch);
+    MODULE_MEMBER(FreeImage_GetWidth);
+    MODULE_MEMBER(FreeImage_Initialise);
+    MODULE_MEMBER(FreeImage_Load);
+    MODULE_MEMBER(FreeImage_LoadFromMemory);
+    MODULE_MEMBER(FreeImage_OpenMemory);
+    MODULE_MEMBER(FreeImage_Save);
+    MODULE_MEMBER(FreeImage_SaveToMemory);
+    MODULE_MEMBER(FreeImage_SeekMemory);
+    MODULE_MEMBER(FreeImage_SetOutputMessage);
+    MODULE_MEMBER(FreeImage_Unload);
 
-    ~FI_BitmapResource()
-    {
-        FreeImage_Unload(pBitmap);
-    }
-private:
-    FIBITMAP * pBitmap;
+    FreeImage_Module();
+    ~FreeImage_Module();
 };
 
+FreeImage_Module& getFreeImagePlugin();
+
+using bitmap_ptr = std::unique_ptr<FIBITMAP, std::function<void(FIBITMAP*)>>;
+bitmap_ptr make_bitmap_ptr(FIBITMAP*);
+
 typedef enum {
     AFFI_GRAY = 1,
     AFFI_RGB  = 3,
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index b7760cab70..e85145102d 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -1,4 +1,9 @@
-
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
 add_library(afcommon_interface INTERFACE)
 
@@ -6,10 +11,13 @@ target_sources(afcommon_interface
   INTERFACE
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MatrixAlgebraHandle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
@@ -29,6 +37,12 @@ target_sources(afcommon_interface
     ${PROJECT_BINARY_DIR}/version.hpp
   )
 
+if(WIN32)
+target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_windows.cpp)
+else()
+target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_unix.cpp)
+endif()
+
 target_include_directories(afcommon_interface
   INTERFACE
     ${CMAKE_SOURCE_DIR}/src/backend
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
new file mode 100644
index 0000000000..3d2f05d882
--- /dev/null
+++ b/src/backend/common/DependencyModule.cpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/DependencyModule.hpp>
+#include <common/module_loading.hpp>
+#include <algorithm>
+#include <string>
+
+#ifdef OS_WIN
+#include <Windows.h>
+#else
+#include <dlfcn.h>
+#endif
+
+using std::string;
+
+namespace {
+
+    std::string libName(std::string name) {
+        return libraryPrefix + name + librarySuffix;
+    }
+}
+
+namespace common {
+
+#ifdef OS_WIN
+void* DependencyModule::getFunctionPointer(LibHandle handle, const char* symbolName) {
+    return GetProcAddress(handle, symbolName);
+}
+#else
+void* DependencyModule::getFunctionPointer(LibHandle handle, const char* symbolName) {
+    return dlsym(handle, symbolName);
+}
+#endif
+
+DependencyModule::DependencyModule(const char* plugin_file_name, const char** paths)
+    : handle(nullptr) {
+    // TODO(umar): Implement handling of non-standard paths
+    if(plugin_file_name) {
+        handle = loadLibrary(libName(plugin_file_name).c_str());
+    }
+}
+
+DependencyModule::~DependencyModule() {
+    if(handle) {
+        unloadLibrary(handle);
+    }
+}
+
+bool DependencyModule::isLoaded() {
+    return (bool)handle;
+}
+
+bool DependencyModule::symbolsLoaded() {
+    return all_of(begin(functions), end(functions), [](void* ptr){ return ptr != nullptr; });
+}
+
+string DependencyModule::getErrorMessage() {
+    return common::getErrorMessage();
+}
+}
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
new file mode 100644
index 0000000000..986ed2e12c
--- /dev/null
+++ b/src/backend/common/DependencyModule.hpp
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <common/defines.hpp>
+
+#include <utility>
+#include <type_traits>
+#include <vector>
+#include <string>
+
+namespace common {
+
+/// Allows you to create classes which dynamically load dependencies at runtime
+///
+/// Creates a dependency module which will dynamically load a library
+/// at runtime instead of at link time. This class will be a component of a
+/// module class which will have member functions for each of the functions
+/// we use in ArrayFire
+class DependencyModule {
+  LibHandle handle;
+  std::vector<void*> functions;
+  void* getFunctionPointer(LibHandle handle, const char* name);
+
+public:
+  DependencyModule(const char* plugin_file_name, const char** paths = nullptr);
+
+  ~DependencyModule();
+
+  /// Returns a function pointer to the function with the name symbol_name
+  template<typename T>
+  T getSymbol(const char* symbol_name) {
+      functions.push_back(getFunctionPointer(handle, symbol_name));
+      return (T)functions.back();
+  }
+
+  /// Returns true if the module was successfully loaded
+  bool isLoaded();
+
+  /// Returns true if the module was successfully loaded
+  bool symbolsLoaded();
+
+  /// Returns the last error message that occurred because of loading the
+  /// library
+  std::string getErrorMessage();
+};
+
+}
+
+/// Creates a function pointer
+#define MODULE_MEMBER(NAME)                     \
+  decltype(&::NAME) NAME
+
+/// Dynamically loads the function pointer at runtime
+#define MODULE_FUNCTION_INIT(NAME)                \
+  NAME = module.getSymbol<decltype(&::NAME)>(#NAME)
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index b0e97d50ac..4f71e94d23 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -49,3 +49,20 @@ typedef enum {
     AF_BATCH_SAME,             /* signal and filter have same batch size */
     AF_BATCH_DIFF,             /* signal and filter have different batch size */
 } AF_BATCH_KIND;
+
+#ifdef OS_WIN
+#include <Windows.h>
+using LibHandle = HMODULE;
+static const char* librarySuffix = ".dll";
+static const char* libraryPrefix = "";
+#elif defined(OS_MAC)
+static const char* librarySuffix = ".dylib";
+static const char* libraryPrefix = "lib";
+using LibHandle = void*;
+#elif defined(OS_LNX)
+static const char* librarySuffix = ".so";
+static const char* libraryPrefix = "lib";
+using LibHandle = void*;
+#else
+#error "Unsupported platform"
+#endif
diff --git a/src/backend/common/module_loading.hpp b/src/backend/common/module_loading.hpp
new file mode 100644
index 0000000000..13eab8ff48
--- /dev/null
+++ b/src/backend/common/module_loading.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/defines.hpp>
+#include <string>
+
+namespace common {
+
+LibHandle loadLibrary(const char* library_name);
+
+void unloadLibrary(LibHandle handle);
+
+std::string getErrorMessage();
+
+}
diff --git a/src/backend/common/module_loading_unix.cpp b/src/backend/common/module_loading_unix.cpp
new file mode 100644
index 0000000000..e2ab421183
--- /dev/null
+++ b/src/backend/common/module_loading_unix.cpp
@@ -0,0 +1,33 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/module_loading.hpp>
+#include <common/defines.hpp>
+
+#include <dlfcn.h>
+
+#include <string>
+using std::string;
+
+namespace common {
+
+
+LibHandle loadLibrary(const char* library_name) {
+    return dlopen(library_name, RTLD_LAZY);
+}
+void unloadLibrary(LibHandle handle) {
+    dlclose(handle);
+}
+
+string getErrorMessage() {
+    string error_message(dlerror());
+    return error_message;
+}
+
+}
diff --git a/src/backend/common/module_loading_windows.cpp b/src/backend/common/module_loading_windows.cpp
new file mode 100644
index 0000000000..712ef4aa89
--- /dev/null
+++ b/src/backend/common/module_loading_windows.cpp
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/module_loading.hpp>
+#include <common/defines.hpp>
+
+#include <string>
+#include <Windows.h>
+
+using std::string;
+
+namespace common {
+
+LibHandle loadLibrary(const char* library_name) {
+    return LoadLibrary(library_name);
+}
+
+void unloadLibrary(LibHandle handle) {
+    FreeLibrary(handle);
+}
+
+string getErrorMessage() {
+    const char* lpMsgBuf;
+    DWORD dw = GetLastError();
+
+    FormatMessage(FORMAT_MESSAGE_ALLOCATE_BUFFER |
+                  FORMAT_MESSAGE_FROM_SYSTEM |
+                  FORMAT_MESSAGE_IGNORE_INSERTS,
+                  NULL,
+                  dw,
+                  MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
+                  (LPTSTR) &lpMsgBuf,
+                  0, NULL );
+    string error_message(lpMsgBuf);
+    return error_message;
+}
+
+}
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 325574362d..ab5b226538 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -86,7 +86,7 @@ CPUInfo::CPUInfo()
             mNumCores = mNumLogCpus = 1;
         }
     } else {
-        mVendorId = "Unkown";
+        mVendorId = "Unknown";
     }
     // Get processor brand string
     // This seems to be working for both Intel & AMD vendors

From fa3fefae98127de087fb0bfd80aabf370dde8f23 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 27 May 2018 18:11:20 -0400
Subject: [PATCH 1428/2677] Set the symbol visibility to hidden

Setting the visibility to hidden to improve link times and reduce the
binary size. This reduced the libafcpu.so size from 41MB to 36MB
---
 CMakeLists.txt                   | 4 ++++
 CMakeModules/InternalUtils.cmake | 1 +
 src/backend/cuda/CMakeLists.txt  | 2 +-
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 48847c82af..8223cf39d9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -333,6 +333,10 @@ configure_package_config_file(
 # TODO(umar): Disable for now. Causing issues with builds on windows.
 #export(PACKAGE ArrayFire)
 
+# Unset the visibility to avoid setting policy commands for older versions of
+# CMake for examples and tests.
+unset(CMAKE_CXX_VISIBILITY_PRESET)
+
 include(CTest)
 
 # Handle depricated BUILD_TEST variable if found.
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 39cd884de6..06871038b2 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -68,6 +68,7 @@ macro(arrayfire_set_cmake_default_variables)
 
   set(CMAKE_CXX_STANDARD 11)
   set(CMAKE_CXX_EXTENSIONS OFF)
+  set(CMAKE_CXX_VISIBILITY_PRESET hidden)
 
   # Set a default build type if none was specified
   if(NOT CMAKE_BUILD_TYPE)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 59d8d9fadc..1de810edff 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -115,7 +115,7 @@ endfunction()
 
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
 if(NOT MSVC)
-  set(cuda_cxx_flags "${cuda_cxx_flags} -Xcompiler -fPIC")
+  set(cuda_cxx_flags "${cuda_cxx_flags} -Xcompiler -fPIC  -Xcompiler=${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
 endif()
 
 if(AF_WITH_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")

From 0eaa4ee03b7b8439d3756715556741b33f95795c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 27 May 2018 17:01:44 -0400
Subject: [PATCH 1429/2677] Move memory manager definition to impl file.

Move the definition of the memory manager to a MemoryManagerImpl.hpp
file so that the definition is not compiled multiple times. Should
improve compilation times. No source code changes were made in this
commit
---
 src/backend/common/MemoryManager.hpp     | 378 +++--------------------
 src/backend/common/MemoryManagerImpl.hpp | 362 ++++++++++++++++++++++
 src/backend/cpu/memory.cpp               |   4 +
 src/backend/cuda/memory.cpp              |   5 +
 src/backend/opencl/memory.cpp            |   5 +
 5 files changed, 413 insertions(+), 341 deletions(-)
 create mode 100644 src/backend/common/MemoryManagerImpl.hpp

diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 92205769ae..08f4be23dd 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -76,364 +76,60 @@ class MemoryManager
     std::vector<memory_info> memory;
     bool debug_mode;
 
-    memory_info& getCurrentMemoryInfo()
-    {
-        return memory[this->getActiveDeviceId()];
-    }
-
-    inline int getActiveDeviceId()
-    {
-        return static_cast<T*>(this)->getActiveDeviceId();
-    }
+    memory_info& getCurrentMemoryInfo();
 
-    inline size_t getMaxMemorySize(int id)
-    {
-        return static_cast<T*>(this)->getMaxMemorySize(id);
-    }
+    inline int getActiveDeviceId();
+    inline size_t getMaxMemorySize(int id);
+    void cleanDeviceMemoryManager(int device);
 
-    void cleanDeviceMemoryManager(int device)
-    {
-        if (this->debug_mode) return;
-
-        // This vector is used to store the pointers which will be deleted by
-        // the memory manager. We are using this to avoid calling free while
-        // the lock is being held becasue the CPU backend calls sync.
-        std::vector<void*> free_ptrs;
-        memory_info& current = memory[device];
-        {
-            lock_guard_t lock(this->memory_mutex);
-            // Return if all buffers are locked
-            if (current.total_buffers == current.lock_buffers) return;
-            free_ptrs.reserve(32);
-
-            for (auto &kv : current.free_map) {
-                size_t num_ptrs = kv.second.size();
-                // Free memory by pushing the last element into the free_ptrs
-                // vector which will be freed once outside of the lock
-                for(auto p : kv.second) {
-                    free_ptrs.push_back(p);
-                }
-                current.total_bytes -= num_ptrs * kv.first;
-                current.total_buffers -= num_ptrs;
-            }
-            current.free_map.clear();
-        }
-        // Free memory outside of the lock
-        for(auto ptr : free_ptrs) {
-            this->nativeFree(ptr);
-        }
-    }
-
-    public:
-    MemoryManager(int num_devices, unsigned max_buffers, bool debug)
-        : mem_step_size(1024), max_buffers(max_buffers), memory(num_devices), debug_mode(debug)
-    {
-        // Check for environment variables
-
-        // Debug mode
-        std::string env_var = getEnvVar("AF_MEM_DEBUG");
-        if (!env_var.empty()) this->debug_mode = env_var[0] != '0';
-        if (this->debug_mode) mem_step_size = 1;
-
-        // Max Buffer count
-        env_var = getEnvVar("AF_MAX_BUFFERS");
-        if (!env_var.empty()) this->max_buffers = std::max(1, std::stoi(env_var));
-    }
+  public:
+     MemoryManager(int num_devices, unsigned max_buffers, bool debug);
 
     // Intended to be used with OpenCL backend, where
     // users are allowed to add external devices(context, device pair)
     // to the list of devices automatically detected by the library
-    void addMemoryManagement(int device)
-    {
-        // If there is a memory manager allocated for
-        // this device id, we might as well use it and the
-        // buffers allocated for it
-        if (static_cast<size_t>(device) < memory.size())
-            return;
-
-        // Assuming, device need not be always the next device
-        // Lets resize to current_size + device + 1
-        // +1 is to account for device being 0-based index of devices
-        memory.resize(memory.size()+device+1);
-    }
+    void addMemoryManagement(int device);
 
     // Intended to be used with OpenCL backend, where
     // users are allowed to add external devices(context, device pair)
     // to the list of devices automatically detected by the library
-    void removeMemoryManagement(int device)
-    {
-        if ((size_t)device>=memory.size())
-            AF_ERROR("No matching device found", AF_ERR_ARG);
+    void removeMemoryManagement(int device);
 
-        // Do garbage collection for the device and leave
-        // the memory_info struct from the memory vector intact
-        cleanDeviceMemoryManager(device);
-    }
+    void setMaxMemorySize();
 
-    void setMaxMemorySize()
-    {
-        for (unsigned n = 0; n < memory.size(); n++) {
-            // Calls garbage collection when:
-            // total_bytes > memsize * 0.75 when memsize <  4GB
-            // total_bytes > memsize - 1 GB when memsize >= 4GB
-            // If memsize returned 0, then use 1GB
-            size_t memsize = this->getMaxMemorySize(n);
-            memory[n].max_bytes = memsize == 0 ? ONE_GB :
-                std::max(memsize * 0.75, (double)(memsize - ONE_GB));
-        }
-    }
-
-    void *alloc(const size_t bytes, bool user_lock)
-    {
-        void *ptr = nullptr;
-        size_t alloc_bytes =
-          this->debug_mode ? bytes :
-                             (divup(bytes, mem_step_size) * mem_step_size);
+    /// Returns a pointer of size at least long
+    ///
+    /// This funciton will return a memory location of at least \p size
+    /// bytes. If there is already a free buffer available, it will use
+    /// that buffer. Otherwise, it will allocate a new buffer using the
+    /// nativeAlloc function.
+    void *alloc(const size_t size, bool user_lock);
 
-        if (bytes > 0) {
-            memory_info& current = this->getCurrentMemoryInfo();
-            locked_info info = {!user_lock, user_lock, alloc_bytes};
+    /// returns the size of the buffer at the pointer allocated by the memory
+    /// manager.
+    size_t allocated(void *ptr);
 
-            // There is no memory cache in debug mode
-            if (!this->debug_mode) {
+    /// Frees or marks the pointer for deletion during the nex garbage collection
+    /// event
+    void unlock(void *ptr, bool user_unlock);
 
-                // FIXME: Add better checks for garbage collection
-                // Perhaps look at total memory available as a metric
-                if (this->checkMemoryLimit()) {
-                    this->garbageCollect();
-                }
-
-                lock_guard_t lock(this->memory_mutex);
-                free_iter iter = current.free_map.find(alloc_bytes);
-
-                if (iter != current.free_map.end() && !iter->second.empty()) {
-                    ptr = iter->second.back();
-                    iter->second.pop_back();
-                    current.locked_map[ptr] = info;
-                    current.lock_bytes += alloc_bytes;
-                    current.lock_buffers++;
-                }
-            }
-
-            // Only comes here if buffer size not found or in debug mode
-            if (ptr == nullptr) {
-                // Perform garbage collection if memory can not be allocated
-                try {
-                    ptr = this->nativeAlloc(alloc_bytes);
-                } catch (const AfError &ex) {
-                    // If out of memory, run garbage collect and try again
-                    if (ex.getError() != AF_ERR_NO_MEM) throw;
-                    this->garbageCollect();
-                    ptr = this->nativeAlloc(alloc_bytes);
-                }
-
-                lock_guard_t lock(this->memory_mutex);
-                // Increment these two only when it succeeds to come here.
-                current.total_bytes += alloc_bytes;
-                current.total_buffers += 1;
-                current.locked_map[ptr] = info;
-                current.lock_bytes += alloc_bytes;
-                current.lock_buffers++;
-            }
-        }
-        return ptr;
-    }
-
-    size_t allocated(void *ptr)
-    {
-        if (!ptr) return 0;
-        memory_info& current = this->getCurrentMemoryInfo();
-        locked_iter iter = current.locked_map.find((void *)ptr);
-        if (iter == current.locked_map.end()) return 0;
-        return (iter->second).bytes;
-    }
-
-    void unlock(void *ptr, bool user_unlock)
-    {
-        // Shortcut for empty arrays
-        if (!ptr) return;
-
-        // Frees the pointer outside the lock.
-        uptr_t freed_ptr(nullptr, [this](void* p) { this->nativeFree(p); });
-        {
-            lock_guard_t lock(this->memory_mutex);
-            memory_info& current = this->getCurrentMemoryInfo();
-
-            locked_iter iter = current.locked_map.find((void *)ptr);
-
-            // Pointer not found in locked map
-            if (iter == current.locked_map.end()) {
-                // Probably came from user, just free it
-                freed_ptr.reset(ptr);
-                return;
-            }
-
-            if (user_unlock) {
-                (iter->second).user_lock = false;
-            } else {
-                (iter->second).manager_lock = false;
-            }
-
-            // Return early if either one is locked
-            if ((iter->second).user_lock || (iter->second).manager_lock) return;
-
-            size_t bytes = iter->second.bytes;
-            current.lock_bytes -= iter->second.bytes;
-            current.lock_buffers--;
-
-            if (this->debug_mode) {
-                // Just free memory in debug mode
-                if ((iter->second).bytes > 0) {
-                    freed_ptr.reset(iter->first);
-                    current.total_buffers--;
-                    current.total_bytes -= iter->second.bytes;
-                }
-            } else {
-                current.free_map[bytes].push_back(ptr);
-            }
-            current.locked_map.erase(iter);
-        }
-    }
-
-    void garbageCollect()
-    {
-        cleanDeviceMemoryManager(this->getActiveDeviceId());
-    }
-
-    void printInfo(const char *msg, const int device)
-    {
-        const memory_info& current = this->getCurrentMemoryInfo();
-
-        printf("%s\n", msg);
-        printf("---------------------------------------------------------\n"
-               "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
-               "---------------------------------------------------------\n");
-
-        lock_guard_t lock(this->memory_mutex);
-        for(auto& kv : current.locked_map) {
-            const char* status_mngr = "Yes";
-            const char* status_user = "Unknown";
-            if(kv.second.user_lock)     status_user = "Yes";
-            else                        status_user = " No";
-
-            const char* unit = "KB";
-            double size = (double)(kv.second.bytes) / 1024;
-            if(size >= 1024) {
-                size = size / 1024;
-                unit = "MB";
-            }
-
-            printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
-                   kv.first, size, unit, status_mngr, status_user);
-        }
-
-        for(auto &kv : current.free_map) {
-
-            const char* status_mngr = "No";
-            const char* status_user = "No";
-
-            const char* unit = "KB";
-            double size = (double)(kv.first) / 1024;
-            if(size >= 1024) {
-                size = size / 1024;
-                unit = "MB";
-            }
-
-            for (auto &ptr : kv.second) {
-              printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
-                     ptr, size, unit, status_mngr, status_user);
-            }
-        }
-
-        printf("---------------------------------------------------------\n");
-    }
+    /// Frees all buffers which are not locked by the user or not being used.
+    void garbageCollect();
 
+    void printInfo(const char *msg, const int device);
     void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                    size_t *lock_bytes,  size_t *lock_buffers)
-    {
-        const memory_info& current = this->getCurrentMemoryInfo();
-        lock_guard_t lock(this->memory_mutex);
-        if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
-        if (alloc_buffers ) *alloc_buffers = current.total_buffers;
-        if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
-        if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
-    }
-
-    void userLock(const void *ptr)
-    {
-        memory_info& current = this->getCurrentMemoryInfo();
-
-        lock_guard_t lock(this->memory_mutex);
-
-        locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-        if (iter != current.locked_map.end()) {
-            iter->second.user_lock = true;
-        } else {
-            locked_info info = {false,
-                true,
-                100}; //This number is not relevant
-
-            current.locked_map[(void *)ptr] = info;
-        }
-    }
-
-    void userUnlock(const void *ptr)
-    {
-        this->unlock(const_cast<void *>(ptr), true);
-    }
-
-    bool isUserLocked(const void *ptr)
-    {
-        memory_info& current = this->getCurrentMemoryInfo();
-        lock_guard_t lock(this->memory_mutex);
-        locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-        if (iter != current.locked_map.end()) {
-            return iter->second.user_lock;
-        } else {
-            return false;
-        }
-    }
-
-    size_t getMemStepSize()
-    {
-        lock_guard_t lock(this->memory_mutex);
-        return this->mem_step_size;
-    }
-
-    size_t getMaxBytes()
-    {
-        lock_guard_t lock(this->memory_mutex);
-        return this->getCurrentMemoryInfo().max_bytes;
-    }
-
-    unsigned getMaxBuffers()
-    {
-        return this->max_buffers;
-    }
-
-    void setMemStepSize(size_t new_step_size)
-    {
-        lock_guard_t lock(this->memory_mutex);
-        this->mem_step_size = new_step_size;
-    }
-
-    inline void *nativeAlloc(const size_t bytes)
-    {
-        return static_cast<T*>(this)->nativeAlloc(bytes);
-    }
-
-    inline void nativeFree(void *ptr)
-    {
-        static_cast<T*>(this)->nativeFree(ptr);
-    }
-
-    bool checkMemoryLimit()
-    {
-        const memory_info& current = this->getCurrentMemoryInfo();
-        return current.lock_bytes >= current.max_bytes || current.total_buffers >= this->max_buffers;
-    }
-
-    protected:
+                    size_t *lock_bytes,  size_t *lock_buffers);
+    void userLock(const void *ptr);
+    void userUnlock(const void *ptr);
+    bool isUserLocked(const void *ptr);
+    size_t getMemStepSize();
+    size_t getMaxBytes();
+    unsigned getMaxBuffers();
+    void setMemStepSize(size_t new_step_size);
+    inline void *nativeAlloc(const size_t bytes);
+    inline void nativeFree(void *ptr);
+    bool checkMemoryLimit();
+  protected:
     MemoryManager() = delete;
     ~MemoryManager() = default;
     MemoryManager(const MemoryManager& other) = delete;
diff --git a/src/backend/common/MemoryManagerImpl.hpp b/src/backend/common/MemoryManagerImpl.hpp
new file mode 100644
index 0000000000..927d6ac4ac
--- /dev/null
+++ b/src/backend/common/MemoryManagerImpl.hpp
@@ -0,0 +1,362 @@
+
+#include <common/MemoryManager.hpp>
+
+namespace common
+{
+template<typename T>
+typename MemoryManager<T>::memory_info&
+MemoryManager<T>::getCurrentMemoryInfo() {
+    return memory[this->getActiveDeviceId()];
+}
+
+template<typename T>
+inline int MemoryManager<T>::getActiveDeviceId() {
+    return static_cast<T*>(this)->getActiveDeviceId();
+}
+
+template<typename T>
+inline size_t MemoryManager<T>::getMaxMemorySize(int id) {
+    return static_cast<T*>(this)->getMaxMemorySize(id);
+}
+
+template<typename T>
+void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
+    if (this->debug_mode) return;
+
+    // This vector is used to store the pointers which will be deleted by
+    // the memory manager. We are using this to avoid calling free while
+    // the lock is being held becasue the CPU backend calls sync.
+    std::vector<void*> free_ptrs;
+    memory_info& current = memory[device];
+    {
+        lock_guard_t lock(this->memory_mutex);
+        // Return if all buffers are locked
+        if (current.total_buffers == current.lock_buffers) return;
+        free_ptrs.reserve(32);
+
+        for (auto &kv : current.free_map) {
+            size_t num_ptrs = kv.second.size();
+            // Free memory by pushing the last element into the free_ptrs
+            // vector which will be freed once outside of the lock
+            for(auto p : kv.second) {
+                free_ptrs.push_back(p);
+            }
+            current.total_bytes -= num_ptrs * kv.first;
+            current.total_buffers -= num_ptrs;
+        }
+        current.free_map.clear();
+    }
+    // Free memory outside of the lock
+    for(auto ptr : free_ptrs) {
+        this->nativeFree(ptr);
+    }
+}
+
+template<typename T>
+MemoryManager<T>::MemoryManager(int num_devices,
+                              unsigned max_buffers,
+                              bool debug)
+    : mem_step_size(1024),
+      max_buffers(max_buffers),
+      memory(num_devices),
+      debug_mode(debug) {
+    // Check for environment variables
+
+    // Debug mode
+    std::string env_var = getEnvVar("AF_MEM_DEBUG");
+    if (!env_var.empty()) this->debug_mode = env_var[0] != '0';
+    if (this->debug_mode) mem_step_size = 1;
+
+    // Max Buffer count
+    env_var = getEnvVar("AF_MAX_BUFFERS");
+    if (!env_var.empty())
+      this->max_buffers = std::max(1, std::stoi(env_var));
+}
+
+template<typename T>
+void MemoryManager<T>::addMemoryManagement(int device) {
+    // If there is a memory manager allocated for this device id, we might
+    // as well use it and the buffers allocated for it
+    if (static_cast<size_t>(device) < memory.size())
+        return;
+
+    // Assuming, device need not be always the next device Lets resize to
+    // current_size + device + 1 +1 is to account for device being 0-based
+    // index of devices
+    memory.resize(memory.size()+device+1);
+}
+
+template<typename T>
+void MemoryManager<T>::removeMemoryManagement(int device) {
+    if ((size_t)device>=memory.size())
+        AF_ERROR("No matching device found", AF_ERR_ARG);
+
+    // Do garbage collection for the device and leave the memory_info struct
+    // from the memory vector intact
+    cleanDeviceMemoryManager(device);
+}
+
+template<typename T>
+void MemoryManager<T>::setMaxMemorySize() {
+    for (unsigned n = 0; n < memory.size(); n++) {
+
+        // Calls garbage collection when: total_bytes > memsize * 0.75 when
+        // memsize < 4GB total_bytes > memsize - 1 GB when memsize >= 4GB If
+        // memsize returned 0, then use 1GB
+        size_t memsize = this->getMaxMemorySize(n);
+        memory[n].max_bytes = memsize == 0 ? ONE_GB :
+            std::max(memsize * 0.75, (double)(memsize - ONE_GB));
+    }
+}
+
+template<typename T>
+void *MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
+    void *ptr = nullptr;
+    size_t alloc_bytes =
+      this->debug_mode ? bytes :
+                          (divup(bytes, mem_step_size) * mem_step_size);
+
+    if (bytes > 0) {
+        memory_info& current = this->getCurrentMemoryInfo();
+        locked_info info = {!user_lock, user_lock, alloc_bytes};
+
+        // There is no memory cache in debug mode
+        if (!this->debug_mode) {
+
+            // FIXME: Add better checks for garbage collection
+            // Perhaps look at total memory available as a metric
+            if (this->checkMemoryLimit()) {
+                this->garbageCollect();
+            }
+
+            lock_guard_t lock(this->memory_mutex);
+            free_iter iter = current.free_map.find(alloc_bytes);
+
+            if (iter != current.free_map.end() && !iter->second.empty()) {
+                ptr = iter->second.back();
+                iter->second.pop_back();
+                current.locked_map[ptr] = info;
+                current.lock_bytes += alloc_bytes;
+                current.lock_buffers++;
+            }
+        }
+
+        // Only comes here if buffer size not found or in debug mode
+        if (ptr == nullptr) {
+            // Perform garbage collection if memory can not be allocated
+            try {
+                ptr = this->nativeAlloc(alloc_bytes);
+            } catch (const AfError &ex) {
+                // If out of memory, run garbage collect and try again
+                if (ex.getError() != AF_ERR_NO_MEM) throw;
+                this->garbageCollect();
+                ptr = this->nativeAlloc(alloc_bytes);
+            }
+
+            lock_guard_t lock(this->memory_mutex);
+            // Increment these two only when it succeeds to come here.
+            current.total_bytes += alloc_bytes;
+            current.total_buffers += 1;
+            current.locked_map[ptr] = info;
+            current.lock_bytes += alloc_bytes;
+            current.lock_buffers++;
+        }
+    }
+    return ptr;
+}
+
+template<typename T>
+size_t MemoryManager<T>::allocated(void *ptr) {
+    if (!ptr) return 0;
+    memory_info& current = this->getCurrentMemoryInfo();
+    locked_iter iter = current.locked_map.find((void *)ptr);
+    if (iter == current.locked_map.end()) return 0;
+    return (iter->second).bytes;
+}
+
+template<typename T>
+void MemoryManager<T>::unlock(void *ptr, bool user_unlock) {
+    // Shortcut for empty arrays
+    if (!ptr) return;
+
+    // Frees the pointer outside the lock.
+    uptr_t freed_ptr(nullptr, [this](void* p) { this->nativeFree(p); });
+    {
+        lock_guard_t lock(this->memory_mutex);
+        memory_info& current = this->getCurrentMemoryInfo();
+
+        locked_iter iter = current.locked_map.find((void *)ptr);
+
+        // Pointer not found in locked map
+        if (iter == current.locked_map.end()) {
+            // Probably came from user, just free it
+            freed_ptr.reset(ptr);
+            return;
+        }
+
+        if (user_unlock) {
+            (iter->second).user_lock = false;
+        } else {
+            (iter->second).manager_lock = false;
+        }
+
+        // Return early if either one is locked
+        if ((iter->second).user_lock || (iter->second).manager_lock) return;
+
+        size_t bytes = iter->second.bytes;
+        current.lock_bytes -= iter->second.bytes;
+        current.lock_buffers--;
+
+        if (this->debug_mode) {
+            // Just free memory in debug mode
+            if ((iter->second).bytes > 0) {
+                freed_ptr.reset(iter->first);
+                current.total_buffers--;
+                current.total_bytes -= iter->second.bytes;
+            }
+        } else {
+            current.free_map[bytes].push_back(ptr);
+        }
+        current.locked_map.erase(iter);
+    }
+}
+
+template<typename T>
+void MemoryManager<T>::garbageCollect() {
+    cleanDeviceMemoryManager(this->getActiveDeviceId());
+}
+
+template<typename T>
+void MemoryManager<T>::printInfo(const char *msg, const int device) {
+    const memory_info& current = this->getCurrentMemoryInfo();
+
+    printf("%s\n", msg);
+    printf("---------------------------------------------------------\n"
+            "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
+            "---------------------------------------------------------\n");
+
+    lock_guard_t lock(this->memory_mutex);
+    for(auto& kv : current.locked_map) {
+        const char* status_mngr = "Yes";
+        const char* status_user = "Unknown";
+        if(kv.second.user_lock)     status_user = "Yes";
+        else                        status_user = " No";
+
+        const char* unit = "KB";
+        double size = (double)(kv.second.bytes) / 1024;
+        if(size >= 1024) {
+            size = size / 1024;
+            unit = "MB";
+        }
+
+        printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
+                kv.first, size, unit, status_mngr, status_user);
+    }
+
+    for(auto &kv : current.free_map) {
+
+        const char* status_mngr = "No";
+        const char* status_user = "No";
+
+        const char* unit = "KB";
+        double size = (double)(kv.first) / 1024;
+        if(size >= 1024) {
+            size = size / 1024;
+            unit = "MB";
+        }
+
+        for (auto &ptr : kv.second) {
+          printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
+                  ptr, size, unit, status_mngr, status_user);
+        }
+    }
+
+    printf("---------------------------------------------------------\n");
+}
+
+template<typename T>
+void MemoryManager<T>::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                                  size_t *lock_bytes,  size_t *lock_buffers) {
+    const memory_info& current = this->getCurrentMemoryInfo();
+    lock_guard_t lock(this->memory_mutex);
+    if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
+    if (alloc_buffers ) *alloc_buffers = current.total_buffers;
+    if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
+    if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
+}
+
+template<typename T>
+void MemoryManager<T>::userLock(const void *ptr) {
+    memory_info& current = this->getCurrentMemoryInfo();
+
+    lock_guard_t lock(this->memory_mutex);
+
+    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    if (iter != current.locked_map.end()) {
+        iter->second.user_lock = true;
+    } else {
+        locked_info info = {false,
+            true,
+            100}; //This number is not relevant
+
+        current.locked_map[(void *)ptr] = info;
+    }
+}
+
+template<typename T>
+void MemoryManager<T>::userUnlock(const void *ptr) {
+    this->unlock(const_cast<void *>(ptr), true);
+}
+
+template<typename T>
+bool MemoryManager<T>::isUserLocked(const void *ptr) {
+    memory_info& current = this->getCurrentMemoryInfo();
+    lock_guard_t lock(this->memory_mutex);
+    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    if (iter != current.locked_map.end()) {
+        return iter->second.user_lock;
+    } else {
+        return false;
+    }
+}
+
+template<typename T>
+size_t MemoryManager<T>::getMemStepSize() {
+    lock_guard_t lock(this->memory_mutex);
+    return this->mem_step_size;
+}
+
+template<typename T>
+size_t MemoryManager<T>::getMaxBytes() {
+    lock_guard_t lock(this->memory_mutex);
+    return this->getCurrentMemoryInfo().max_bytes;
+}
+
+template<typename T>
+unsigned MemoryManager<T>::getMaxBuffers() {
+    return this->max_buffers;
+}
+
+template<typename T>
+void MemoryManager<T>::setMemStepSize(size_t new_step_size) {
+    lock_guard_t lock(this->memory_mutex);
+    this->mem_step_size = new_step_size;
+}
+
+template<typename T>
+inline void *MemoryManager<T>::nativeAlloc(const size_t bytes) {
+    return static_cast<T*>(this)->nativeAlloc(bytes);
+}
+
+template<typename T>
+inline void MemoryManager<T>::nativeFree(void *ptr) {
+    static_cast<T*>(this)->nativeFree(ptr);
+}
+
+template<typename T>
+bool MemoryManager<T>::checkMemoryLimit() {
+    const memory_info& current = this->getCurrentMemoryInfo();
+    return current.lock_bytes >= current.max_bytes ||
+            current.total_buffers >= this->max_buffers;
+}
+}
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index d2412f9cb6..ac9bab7656 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -13,6 +13,10 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+#include <common/MemoryManagerImpl.hpp>
+
+template class common::MemoryManager<cpu::MemoryManager>;
+
 #ifndef AF_MEM_DEBUG
 #define AF_MEM_DEBUG 0
 #endif
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index c246a0606a..a572a10eee 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -19,6 +19,11 @@
 
 #include <mutex>
 
+#include <common/MemoryManagerImpl.hpp>
+
+template class common::MemoryManager<cuda::MemoryManager>;
+template class common::MemoryManager<cuda::MemoryManagerPinned>;
+
 #ifndef AF_MEM_DEBUG
 #define AF_MEM_DEBUG 0
 #endif
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 9433be2daa..ba3b1622e3 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -12,6 +12,11 @@
 #include <types.hpp>
 #include <err_opencl.hpp>
 
+#include <common/MemoryManagerImpl.hpp>
+
+template class common::MemoryManager<opencl::MemoryManager>;
+template class common::MemoryManager<opencl::MemoryManagerPinned>;
+
 #ifndef AF_MEM_DEBUG
 #define AF_MEM_DEBUG 0
 #endif

From 43458564df6bc6bd1d94a1c112da9bcb5981bc5d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 25 May 2018 22:57:24 -0400
Subject: [PATCH 1430/2677] Update the version of ArrayFire to 3.7

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8223cf39d9..4eb3b78b3f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -7,7 +7,7 @@
 
 cmake_minimum_required(VERSION 3.5)
 project(ArrayFire
-  VERSION 3.6.0
+  VERSION 3.7.0
   LANGUAGES C CXX )
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")

From 3e3598b284ee6a21518c383a499d398488f65c47 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 29 May 2018 13:44:30 -0400
Subject: [PATCH 1431/2677] Add libdl when building with FreeImage

We are now loading FreeImage at runtime. Because of this we need
to link with the libdl library so that we can call dlopen and dlsym.
This was working on the CI builders because we were linking with
MKL which also requires libdl.
---
 src/api/c/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 02966d3360..9c7d0a3bfd 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -165,6 +165,7 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage_STATIC)
   else ()
     target_include_directories(c_api_interface INTERFACE $<TARGET_PROPERTY:FreeImage::FreeImage,INTERFACE_INCLUDE_DIRECTORIES>)
+    target_link_libraries(c_api_interface INTERFACE ${CMAKE_DL_LIBS})
     if (WIN32 AND AF_INSTALL_STANDALONE)
       install(FILES $<TARGET_FILE:FreeImage::FreeImage>
         DESTINATION ${AF_INSTALL_BIN_DIR}

From 1bac102d0913be7948a5009b12d0bdebf6869eca Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 May 2018 10:45:31 -0400
Subject: [PATCH 1432/2677] Added logging support for memory operations

Added logging support to memory operations. By default this is
turned off but you can enable them by setting the AF_TRACE
environment variable to 'mem'. Other components can also be
added in a later commit

This is implemented using the spdlog library.
---
 CMakeLists.txt                                |  3 +
 .../configuring_arrayfire_environment.md      | 42 +++++++++---
 src/backend/common/CMakeLists.txt             | 14 +++-
 src/backend/common/Logger.cpp                 | 67 +++++++++++++++++++
 src/backend/common/Logger.hpp                 | 47 +++++++++++++
 src/backend/common/MemoryManager.hpp          |  7 +-
 src/backend/common/MemoryManagerImpl.hpp      | 43 ++++++++++--
 src/backend/cpu/Array.hpp                     | 15 ++---
 src/backend/cpu/memory.cpp                    |  8 ++-
 src/backend/cuda/memory.cpp                   | 19 ++++--
 src/backend/cuda/platform.hpp                 |  6 ++
 src/backend/opencl/memory.cpp                 | 12 +++-
 12 files changed, 248 insertions(+), 35 deletions(-)
 create mode 100644 src/backend/common/Logger.cpp
 create mode 100644 src/backend/common/Logger.hpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4eb3b78b3f..7975a3c1c1 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -33,6 +33,7 @@ find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
 find_package(MKL)
+find_package(spdlog QUIET)
 
 # Graphics dependencies
 find_package(glbinding QUIET)
@@ -48,6 +49,7 @@ option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
 
 option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
+option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ${spdlog_FOUND})
 
 option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
 
@@ -60,6 +62,7 @@ cmake_dependent_option(AF_WITH_IMAGEIO "Build ArrayFire with Image IO support" $
                        "FreeImage_FOUND" OFF)
 cmake_dependent_option(AF_BUILD_FRAMEWORK "Build an ArrayFire framework for Apple platforms.(Experimental)" OFF
                        "APPLE" OFF)
+
 option(AF_WITH_STATIC_FREEIMAGE "Use Static FreeImage Lib" OFF)
 
 set(AF_WITH_CPUID ON CACHE BOOL "Build with CPUID integration")
diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 3a49d93c51..fd8a4ba007 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -145,8 +145,9 @@ paths, then those paths are shown in full.
 AF_MEM_DEBUG {#af_mem_debug}
 -------------------------------------------------------------------------------
 
-When AF_MEM_DEBUG is set to 1 (or anything not equal to 0), the caching mechanism in the memory manager is disabled.
-The device buffers are allocated using native functions as needed and freed when going out of scope.
+When AF_MEM_DEBUG is set to 1 (or anything not equal to 0), the caching
+mechanism in the memory manager is disabled. The device buffers are allocated
+using native functions as needed and freed when going out of scope.
 
 When the environment variable is not set, it is treated to be zero.
 
@@ -154,33 +155,58 @@ When the environment variable is not set, it is treated to be zero.
 AF_MEM_DEBUG=1 ./myprogram
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+AF_TRACE {#af_trace}
+-------------------------------------------------------------------------------
+
+If ArrayFire was built with logging support, this enviornment variable will
+enable tracing of various modules within ArrayFire. This is a comma separated
+list of modules to trace. If enabled, ArrayFire will print relevant information
+to stdout. Currently the following modules are supported:
+
+- mem: Memory management allocation, free and garbage collection information
+
+Tracing displays the information that could be useful when debugging or
+optimizing your application. Here is how you would use this variable:
+
+    AF_TRACE=mem ./myprogram
+
+This will print information about memory operations such as allocations,
+deallocations, and garbage collection.
+
 
 AF_MAX_BUFFERS {#af_max_buffers}
 -------------------------------------------------------------------------
 
-When AF_MAX_BUFFERS is set, this environment variable specifies the maximum number of buffers allocated before garbage collection kicks in.
+When AF_MAX_BUFFERS is set, this environment variable specifies the maximum
+number of buffers allocated before garbage collection kicks in.
 
-Please note that the total number of buffers that can exist simultaneously can be higher than this number. This variable tells the garbage collector that it should free any available buffers immediately if the treshold is reached.
+Please note that the total number of buffers that can exist simultaneously can
+be higher than this number. This variable tells the garbage collector that it
+should free any available buffers immediately if the treshold is reached.
 
 When not set, the default value is 1000.
 
 AF_OPENCL_MAX_JIT_LEN {#af_opencl_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum height of the OpenCL JIT tree after which evaluation is forced.
+When set, this environment variable specifies the maximum height of the OpenCL
+JIT tree after which evaluation is forced.
 
-The default value, as of v3.4, is 50 on OSX, 100 everywhere else. This value was 20 for older versions.
+The default value, as of v3.4, is 50 on OSX, 100 everywhere else. This value was
+20 for older versions.
 
 AF_CUDA_MAX_JIT_LEN {#af_cuda_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum height of the CUDA JIT tree after which evaluation is forced.
+When set, this environment variable specifies the maximum height of the CUDA JIT
+tree after which evaluation is forced.
 
 The default value, as of v3.4, 100. This value was 20 for older versions.
 
 AF_CPU_MAX_JIT_LEN {#af_cpu_max_jit_len}
 -------------------------------------------------------------------------------
 
-When set, this environment variable specifies the maximum length of the CPU JIT tree after which evaluation is forced.
+When set, this environment variable specifies the maximum length of the CPU JIT
+tree after which evaluation is forced.
 
 The default value, as of v3.4, 100. This value was 20 for older versions.
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index e85145102d..dce6460821 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -14,10 +14,11 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/Logger.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MatrixAlgebraHandle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
@@ -31,6 +32,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.hpp
@@ -51,6 +53,16 @@ target_include_directories(afcommon_interface
 
 add_library(afcommon_lapack_interface INTERFACE)
 
+
+if(AF_WITH_LOGGING)
+  dependency_check(spdlog_FOUND "spdlog not found.")
+  target_compile_definitions(afcommon_interface
+    INTERFACE AF_WITH_LOGGING)
+  target_link_libraries(afcommon_interface
+    INTERFACE
+      spdlog::spdlog)
+endif()
+
 if(AF_WITH_GRAPHICS)
   dependency_check(glbinding_FOUND "glbinding not found.")
 
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
new file mode 100644
index 0000000000..aa48e8c84e
--- /dev/null
+++ b/src/backend/common/Logger.cpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/Logger.hpp>
+#include <common/util.hpp>
+
+#include <array>
+#include <cstdlib>
+#include <string>
+#include <memory>
+
+using std::array;
+using std::make_shared;
+using std::string;
+using std::shared_ptr;
+using std::to_string;
+
+using spdlog::get;
+using spdlog::level::trace;
+using spdlog::logger;
+using spdlog::stdout_logger_mt;
+
+namespace common {
+
+#ifdef AF_WITH_LOGGING
+shared_ptr<logger>
+loggerFactory(string name) {
+    shared_ptr<logger> logger;
+    if(!(logger = get(name))) {
+        logger = stdout_logger_mt(name);
+        logger->set_pattern("[%n][%t] %v");
+
+        // Log mode
+        string env_var = getEnvVar("AF_TRACE");
+        if(env_var.find_first_of("all") != string::npos ||
+          env_var.find_first_of(name) != string::npos)
+              logger->set_level(trace);
+    }
+    return logger;
+}
+
+string bytesToString(size_t bytes) {
+    static array<const char *, 5> units{"B", "KB", "MB", "GB", "TB"};
+    int count = 0;
+    double fbytes = static_cast<double>(bytes);
+    for(count = 0; count < units.size() && fbytes > 1000.0; count++) {
+        fbytes *= (1.0 / 1024.0);
+    }
+    return fmt::format("{:.3g} {}", fbytes, units[count]);
+}
+#else
+  shared_ptr<logger>
+  loggerFactory(string name) {
+    return make_shared<logger>();
+  }
+
+  string bytesToString(size_t bytes) {
+    return "";
+  }
+#endif
+}
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
new file mode 100644
index 0000000000..08194f25e7
--- /dev/null
+++ b/src/backend/common/Logger.hpp
@@ -0,0 +1,47 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <memory>
+#include <string>
+
+#ifdef AF_WITH_LOGGING
+#include <spdlog/spdlog.h>
+#else
+
+/// This is a stub class to match the spdlog API in case it is not installed on
+/// the users system. Only the functions we used are implemented here. Other
+/// functions will need to be implemented later.
+namespace spdlog {
+    class logger { public: logger() {} };
+    std::shared_ptr<spdlog::logger> get(std::string &name);
+    std::shared_ptr<spdlog::logger> stdout_logger_mt(std::string&);
+    namespace level {
+        enum enum_level { trace };
+    }
+}
+#endif
+
+namespace common {
+    std::shared_ptr<spdlog::logger> loggerFactory(std::string name);
+    std::string bytesToString(size_t bytes);
+}
+
+#ifdef AF_WITH_LOGGING
+#define AF_STR_H(x) #x
+#define AF_STR_HELPER(x) AF_STR_H(x)
+#ifdef _MSC_VER
+#define AF_TRACE(...) getLogger()->trace("[ " __FILE__ "(" AF_STR_HELPER(__LINE__) ") ] " __VA_ARGS__)
+#else
+#define AF_TRACE(...) getLogger()->trace("[ " __FILE__ ":" AF_STR_HELPER(__LINE__) " ] " __VA_ARGS__)
+#endif
+#else
+#define AF_TRACE(logger, ...) (void)0
+#endif
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 08f4be23dd..94cdabe0b4 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -22,6 +22,9 @@
 #include <unordered_map>
 #include <vector>
 
+namespace spdlog {
+  class logger;
+}
 namespace common
 {
 using mutex_t      = std::mutex;
@@ -74,6 +77,7 @@ class MemoryManager
     size_t mem_step_size;
     unsigned max_buffers;
     std::vector<memory_info> memory;
+    std::shared_ptr<spdlog::logger> logger;
     bool debug_mode;
 
     memory_info& getCurrentMemoryInfo();
@@ -83,7 +87,7 @@ class MemoryManager
     void cleanDeviceMemoryManager(int device);
 
   public:
-     MemoryManager(int num_devices, unsigned max_buffers, bool debug);
+    MemoryManager(int num_devices, unsigned max_buffers, bool debug);
 
     // Intended to be used with OpenCL backend, where
     // users are allowed to add external devices(context, device pair)
@@ -130,6 +134,7 @@ class MemoryManager
     inline void nativeFree(void *ptr);
     bool checkMemoryLimit();
   protected:
+    spdlog::logger* getLogger();
     MemoryManager() = delete;
     ~MemoryManager() = default;
     MemoryManager(const MemoryManager& other) = delete;
diff --git a/src/backend/common/MemoryManagerImpl.hpp b/src/backend/common/MemoryManagerImpl.hpp
index 927d6ac4ac..859c47d574 100644
--- a/src/backend/common/MemoryManagerImpl.hpp
+++ b/src/backend/common/MemoryManagerImpl.hpp
@@ -1,5 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
 #include <common/MemoryManager.hpp>
+#include <common/Logger.hpp>
+
+#include <string>
+#include <vector>
+
+using std::max;
+using std::stoi;
+using std::string;
+using std::vector;
+
+using spdlog::logger;
 
 namespace common
 {
@@ -26,7 +45,8 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
     // the lock is being held becasue the CPU backend calls sync.
-    std::vector<void*> free_ptrs;
+    vector<void*> free_ptrs;
+    size_t bytes_freed = 0;
     memory_info& current = memory[device];
     {
         lock_guard_t lock(this->memory_mutex);
@@ -42,10 +62,13 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
                 free_ptrs.push_back(p);
             }
             current.total_bytes -= num_ptrs * kv.first;
+            bytes_freed += num_ptrs * kv.first;
             current.total_buffers -= num_ptrs;
         }
         current.free_map.clear();
     }
+
+    AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(), bytesToString(bytes_freed));
     // Free memory outside of the lock
     for(auto ptr : free_ptrs) {
         this->nativeFree(ptr);
@@ -54,23 +77,24 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
 
 template<typename T>
 MemoryManager<T>::MemoryManager(int num_devices,
-                              unsigned max_buffers,
-                              bool debug)
+                                unsigned max_buffers,
+                                bool debug)
     : mem_step_size(1024),
       max_buffers(max_buffers),
       memory(num_devices),
-      debug_mode(debug) {
+      debug_mode(debug),
+      logger (loggerFactory("mem")) {
     // Check for environment variables
 
     // Debug mode
-    std::string env_var = getEnvVar("AF_MEM_DEBUG");
+    string env_var = getEnvVar("AF_MEM_DEBUG");
     if (!env_var.empty()) this->debug_mode = env_var[0] != '0';
     if (this->debug_mode) mem_step_size = 1;
 
     // Max Buffer count
     env_var = getEnvVar("AF_MAX_BUFFERS");
     if (!env_var.empty())
-      this->max_buffers = std::max(1, std::stoi(env_var));
+      this->max_buffers = max(1, stoi(env_var));
 }
 
 template<typename T>
@@ -105,7 +129,7 @@ void MemoryManager<T>::setMaxMemorySize() {
         // memsize returned 0, then use 1GB
         size_t memsize = this->getMaxMemorySize(n);
         memory[n].max_bytes = memsize == 0 ? ONE_GB :
-            std::max(memsize * 0.75, (double)(memsize - ONE_GB));
+            max(memsize * 0.75, (double)(memsize - ONE_GB));
     }
 }
 
@@ -337,6 +361,11 @@ unsigned MemoryManager<T>::getMaxBuffers() {
     return this->max_buffers;
 }
 
+template<typename T>
+logger* MemoryManager<T>::getLogger() {
+    return this->logger.get();
+}
+
 template<typename T>
 void MemoryManager<T>::setMemStepSize(size_t new_step_size) {
     lock_guard_t lock(this->memory_mutex);
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index daebded49a..19ce30a246 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -26,18 +26,15 @@
 
 namespace cpu
 {
-namespace kernel
-{
-    template<typename T> void evalArray(Param<T> in, TNJ::Node_ptr node);
+    namespace kernel
+    {
+        template<typename T> void evalArray(Param<T> in, TNJ::Node_ptr node);
 
-    template<typename T>
-    void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> nodes);
+        template<typename T>
+        void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> nodes);
 
-}
-}
+    }
 
-namespace cpu
-{
     template<typename T> class Array;
 
     using std::shared_ptr;
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index ac9bab7656..660ee94df9 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -8,10 +8,12 @@
  ********************************************************/
 
 #include <memory.hpp>
+
+#include <common/Logger.hpp>
 #include <err_cpu.hpp>
-#include <types.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <types.hpp>
 
 #include <common/MemoryManagerImpl.hpp>
 
@@ -25,6 +27,8 @@ template class common::MemoryManager<cpu::MemoryManager>;
 #define AF_CPU_MEM_DEBUG 0
 #endif
 
+using common::bytesToString;
+
 using std::unique_ptr;
 using std::function;
 
@@ -178,12 +182,14 @@ size_t MemoryManager::getMaxMemorySize(int id)
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
     void *ptr = malloc(bytes);
+    AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
     return ptr;
 }
 
 void MemoryManager::nativeFree(void *ptr)
 {
+    AF_TRACE("nativeFree: {: >8} {}", " ", ptr);
     // Make sure this pointer is not being used on the queue before freeing the memory.
     getQueue().sync();
     return free((void *)ptr);
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index a572a10eee..3f1e317827 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -7,20 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <memory.hpp>
+
+#include <common/Logger.hpp>
+#include <common/dispatch.hpp>
+#include <common/util.hpp>
 #include <cuda.h>
-#include <cuda_runtime_api.h>
 #include <cuda_runtime.h>
-#include <platform.hpp>
+#include <cuda_runtime_api.h>
 #include <err_cuda.hpp>
-#include <memory.hpp>
-#include <common/util.hpp>
+#include <platform.hpp>
 #include <types.hpp>
-#include <common/dispatch.hpp>
 
 #include <mutex>
 
 #include <common/MemoryManagerImpl.hpp>
-
 template class common::MemoryManager<cuda::MemoryManager>;
 template class common::MemoryManager<cuda::MemoryManagerPinned>;
 
@@ -32,6 +33,8 @@ template class common::MemoryManager<cuda::MemoryManagerPinned>;
 #define AF_CUDA_MEM_DEBUG 0
 #endif
 
+using common::bytesToString;
+
 using std::lock_guard;
 using std::recursive_mutex;
 using std::function;
@@ -185,11 +188,13 @@ void *MemoryManager::nativeAlloc(const size_t bytes)
 {
     void *ptr = NULL;
     CUDA_CHECK(cudaMalloc(&ptr, bytes));
+    AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
 void MemoryManager::nativeFree(void *ptr)
 {
+    AF_TRACE("nativeFree:          {}", ptr);
     cudaError_t err = cudaFree(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
@@ -222,11 +227,13 @@ void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
 {
     void *ptr;
     CUDA_CHECK(cudaMallocHost(&ptr, bytes));
+    AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
 void MemoryManagerPinned::nativeFree(void *ptr)
 {
+    AF_TRACE("Pinned::nativeFree:          {}", ptr);
     cudaError_t err = cudaFreeHost(ptr);
     if (err != cudaErrorCudartUnloading) {
         CUDA_CHECK(err);
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index cbad5fb2fe..bcf2b433dd 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -18,10 +18,14 @@
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
 
+
 #include <memory>
 #include <string>
 #include <vector>
 
+namespace spdlog {
+  class logger;
+}
 namespace cuda
 {
 int getBackend();
@@ -55,6 +59,8 @@ size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
 
+spdlog::logger* getLogger();
+
 int setDevice(int device);
 
 void sync(int device);
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index ba3b1622e3..4d2ff16dfe 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -12,8 +12,9 @@
 #include <types.hpp>
 #include <err_opencl.hpp>
 
-#include <common/MemoryManagerImpl.hpp>
+#include <common/Logger.hpp>
 
+#include <common/MemoryManagerImpl.hpp>
 template class common::MemoryManager<opencl::MemoryManager>;
 template class common::MemoryManager<opencl::MemoryManagerPinned>;
 
@@ -25,6 +26,8 @@ template class common::MemoryManager<opencl::MemoryManagerPinned>;
 #define AF_OPENCL_MEM_DEBUG 0
 #endif
 
+using common::bytesToString;
+
 using std::unique_ptr;
 using std::function;
 
@@ -182,11 +185,14 @@ size_t MemoryManager::getMaxMemorySize(int id)
 
 void *MemoryManager::nativeAlloc(const size_t bytes)
 {
-    return (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
+    auto ptr = (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
+    AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
+    return ptr;
 }
 
 void MemoryManager::nativeFree(void *ptr)
 {
+    AF_TRACE("nativeFree:          {}", ptr);
     delete (cl::Buffer *)ptr;
 }
 
@@ -226,12 +232,14 @@ void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
     void *ptr = NULL;
     cl::Buffer* buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
     ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
+    AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, buf);
     return ptr;
 }
 
 void MemoryManagerPinned::nativeFree(void *ptr)
 {
+    AF_TRACE("Pinned::nativeFree:          {}", ptr);
     int n = opencl::getActiveDeviceId();
     auto map = pinnedMaps[n];
     auto iter = map.find(ptr);

From fe0126f77d51f5b9346a976769951f12335acd3a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 May 2018 02:39:50 -0400
Subject: [PATCH 1433/2677] Remove printfs left from debugging session

---
 src/api/c/imageio.cpp | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index c8d060048d..5f4355dded 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -101,14 +101,11 @@ FreeImage_Module::FreeImage_Module()
 #else
 FreeImage_Module::FreeImage_Module()
   : module("freeimage", nullptr) {
-    printf(__FILE__"%d\n", __LINE__);
     if(!module.isLoaded()) {
-        printf(__FILE__"%d\n", __LINE__);
         string error_message = "Error loading FreeImage: " + module.getErrorMessage()
             + "\nFreeImage or one of it's dependencies failed to "
               "load. Try installing FreeImage or check if FreeImage is in the "
               "search path.";
-        printf(__FILE__"%d\n", __LINE__);
         AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
     }
 #endif
@@ -139,12 +136,10 @@ FreeImage_Module::FreeImage_Module()
 
 #ifndef FREEIMAGE_STATIC
     if(!module.symbolsLoaded()) {
-        printf(__FILE__"%d\n", __LINE__);
         string error_message = "Error loading FreeImage: "
           + module.getErrorMessage()
           + "\nThe installed version of FreeImage is not compatible with "
             "ArrayFire. Please create an issue on which this error message";
-        printf(__FILE__"%d\n", __LINE__);
         AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
     }
 #endif

From c10c3c8ef1c2e9fb090edb4d3d158c5e17922774 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 2 Jun 2018 02:45:11 -0400
Subject: [PATCH 1434/2677] Fix memory leak in homography

---
 src/api/c/homography.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index 5446ab36f9..022b1cf147 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -37,6 +37,7 @@ static inline void homography(af_array &H, int &inliers,
                             getArray<float>(x_dst), getArray<float>(y_dst),
                             getArray<float>(initial),
                             htype, inlier_thr, iterations);
+    AF_CHECK(af_release_array(initial));
 
     H = getHandle<T>(bestH);
 }

From a4713f1aa102ad693129086bfdb9aa2a9d2fb1f7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 2 Jun 2018 12:52:07 -0400
Subject: [PATCH 1435/2677] Performance improvments to CPU Anisotropic
 Diffusion (#2174)

* Performance improvments to CPU Anisotropic Diffusion
---
 .../cpu/kernel/anisotropic_diffusion.hpp      | 120 ++++++++----------
 .../cuda/kernel/anisotropic_diffusion.hpp     |  18 +--
 .../opencl/kernel/anisotropic_diffusion.cl    |  18 +--
 3 files changed, 63 insertions(+), 93 deletions(-)

diff --git a/src/backend/cpu/kernel/anisotropic_diffusion.hpp b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
index bf9b5ec7f7..0980f1e8c7 100644
--- a/src/backend/cpu/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
@@ -27,7 +27,7 @@ namespace kernel
 
 int index(int x, int y, int stride1)
 {
-    return x+ y*stride1;
+    return y*stride1+x;
 }
 
 float quad(float value)
@@ -52,25 +52,29 @@ float computeGradientBasedUpdate(const float mct,
     df  = E - C;
     db  = C - W;
 
+    float gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f)) * mct ;
+    float gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f)) * mct;
     if (fftype==AF_FLUX_EXPONENTIAL) {
-        cx  = exp( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f)) * mct );
-        cxd = exp( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f)) * mct );
+        cx  = exp(gmsqf);
+        cxd = exp(gmsqb);
     } else {
-        cx  = quad( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f)) * mct );
-        cxd = quad( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f)) * mct );
+        cx  = quad(gmsqf);
+        cxd = quad(gmsqb);
     }
-    delta += (cx*df - cxd*db);
+    delta = (cx*df - cxd*db);
 
     // half-d's and conductance along second dimension
     df  = S - C;
     db  = C - N;
 
+    gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f)) * mct;
+    gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f)) * mct;
     if (fftype==AF_FLUX_EXPONENTIAL) {
-        cx  = exp( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f)) * mct );
-        cxd = exp( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f)) * mct );
+        cx  = exp(gmsqf);
+        cxd = exp(gmsqb);
     } else {
-        cx  = quad( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f)) * mct );
-        cxd = quad( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f)) * mct );
+        cx  = quad(gmsqf);
+        cxd = quad(gmsqb);
     }
     delta += (cx*df - cxd*db);
 
@@ -98,13 +102,8 @@ float computeCurvatureBasedUpdate(const float mct,
     df0 = df;
     db0 = db;
 
-    if (fftype==AF_FLUX_EXPONENTIAL) {
-        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f));
-        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f));
-    } else {
-        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f));
-        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f));
-    }
+    gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f));
+    gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f));
 
     gmf = sqrt(1.0e-10f + gmsqf);
     gmb = sqrt(1.0e-10f + gmsqb);
@@ -112,19 +111,14 @@ float computeCurvatureBasedUpdate(const float mct,
     cx  = exp( gmsqf * mct );
     cxd = exp( gmsqb * mct );
 
-    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+    delta = ((df/gmf)*cx - (db/gmb)*cxd);
 
     // half-d's and conductance along second dimension
     df  = S - C;
     db  = C - N;
 
-    if (fftype==AF_FLUX_EXPONENTIAL) {
-        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f));
-        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f));
-    } else {
-        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f));
-        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f));
-    }
+    gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f));
+    gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f));
     gmf = sqrt(1.0e-10f + gmsqf);
     gmb = sqrt(1.0e-10f + gmsqb);
 
@@ -133,7 +127,7 @@ float computeCurvatureBasedUpdate(const float mct,
 
     delta += ((df/gmf)*cx - (db/gmb)*cxd);
 
-    if (delta>0){
+    if (delta>0.f) {
         prop_grad += (pow(fminf(db0, 0.0f),2.0f) + pow(fmaxf(df0, 0.0f), 2.0f));
         prop_grad += (pow(fminf( db, 0.0f),2.0f) + pow(fmaxf( df, 0.0f), 2.0f));
     } else {
@@ -147,59 +141,55 @@ float computeCurvatureBasedUpdate(const float mct,
 template<typename T, bool isMCDE>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const af_flux_function fftype)
 {
-    auto dims = inout.dims();
-    auto strides = inout.strides();
-
-    for(int b3=0; b3<dims[3]; ++b3) {
-        for(int b2=0; b2<dims[2]; ++b2) {
-
+    const auto dims = inout.dims();
+    const auto strides = inout.strides();
+    const auto d1stride = strides[1];
+    const int d0 = dims[0] - 1;
+    const int d1 = dims[1] - 1;
+    const int d2 = dims[2];
+    const int d3 = dims[3];
+
+    for(int b3=0; b3<d3; ++b3) {
+        for(int b2=0; b2<d2; ++b2) {
             T* img = inout.get() + b2*strides[2] + b3*strides[3];
-
-            for(int j=1; j<dims[1]-1; ++j)
-            {
-                for(int i=1; i<dims[0]-1; ++i)
-                {
+            for(int j=1; j<d1; ++j) {
+                for(int i=1; i<d0; ++i) {
                     float C = 0.f;
                     float delta = 0.f;
 
-                    // int ip1 = clamp((int)i + 1, 0, (int)dims[0]-1);
-                    // int im1 = clamp((int)i - 1, 0, (int)dims[0]-1);
-                    // int jp1 = clamp((int)j + 1, 0, (int)dims[1]-1);
-                    // int jm1 = clamp((int)j - 1, 0, (int)dims[1]-1);
-                    // 400ms
-                    int ip1 = i + 1;
-                    int im1 = i - 1;
-                    int jp1 = j + 1;
-                    int jm1 = j - 1;
+                    const int ip1 = i + 1;
+                    const int im1 = i - 1;
+                    const int jp1 = j + 1;
+                    const int jm1 = j - 1;
 
                     if (isMCDE) {
                         delta = computeCurvatureBasedUpdate(
                                 mct,
-                                img[ index(im1, jm1, strides[1]) ],
-                                img[ index(i  , jm1, strides[1]) ],
-                                img[ index(ip1, jm1, strides[1]) ],
-                                img[ index(im1, j  , strides[1]) ],
-                                C = img[ index(i  , j,  strides[1]) ],
-                                img[ index(ip1, j  , strides[1]) ],
-                                img[ index(im1, jp1, strides[1]) ],
-                                img[ index(i  , jp1, strides[1]) ],
-                                img[ index(ip1, jp1, strides[1]) ], fftype);
+                                img[ index(im1, jm1, d1stride) ],
+                                img[ index(i  , jm1, d1stride) ],
+                                img[ index(ip1, jm1, d1stride) ],
+                                img[ index(im1, j  , d1stride) ],
+                            C = img[ index(i  , j,   d1stride) ],
+                                img[ index(ip1, j  , d1stride) ],
+                                img[ index(im1, jp1, d1stride) ],
+                                img[ index(i  , jp1, d1stride) ],
+                                img[ index(ip1, jp1, d1stride) ], fftype);
 
                     } else {
                         delta = computeGradientBasedUpdate(
                                 mct,
-                                img[ index(im1, jm1,  strides[1]) ],
-                                img[ index(i  , jm1,  strides[1]) ],
-                                img[ index(ip1, jm1,  strides[1]) ],
-                                img[ index(im1, j  ,  strides[1]) ],
-                                C = img[ index(i  , j,  strides[1]) ],
-                                img[ index(ip1, j  ,  strides[1]) ],
-                                img[ index(im1, jp1,  strides[1]) ],
-                                img[ index(i  , jp1,  strides[1]) ],
-                                img[ index(ip1, jp1,  strides[1]) ], fftype);
+                                img[ index(im1, jm1,  d1stride) ],
+                                img[ index(i  , jm1,  d1stride) ],
+                                img[ index(ip1, jm1,  d1stride) ],
+                                img[ index(im1, j  ,  d1stride) ],
+                            C = img[ index(i  , j,    d1stride) ],
+                                img[ index(ip1, j  ,  d1stride) ],
+                                img[ index(im1, jp1,  d1stride) ],
+                                img[ index(i  , jp1,  d1stride) ],
+                                img[ index(ip1, jp1,  d1stride) ], fftype);
                     }
 
-                    img[i + j*strides[1]] = (T)(C + delta*dt);
+                    img[i + j*d1stride] = (T)(C + delta*dt);
                 }
             }
         }
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index c1deb7e0bf..d1ed3d2b9a 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -106,13 +106,8 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     df0 = df;
     db0 = db;
 
-    if (fftype==AF_FLUX_EXPONENTIAL) {
-        gmsqf = (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2));
-        gmsqb = (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2));
-    } else {
-        gmsqf = (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2));
-        gmsqb = (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2));
-    }
+    gmsqf = (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2));
+    gmsqb = (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2));
 
     gmf = sqrtf(1.0e-10 + gmsqf);
     gmb = sqrtf(1.0e-10 + gmsqb);
@@ -126,13 +121,8 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     df  = S - C;
     db  = C - N;
 
-    if (fftype==AF_FLUX_EXPONENTIAL) {
-        gmsqf = (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2));
-        gmsqb = (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2));
-    } else {
-        gmsqf = (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2));
-        gmsqb = (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2));
-    }
+    gmsqf = (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2));
+    gmsqb = (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2));
     gmf = sqrtf(1.0e-10 + gmsqf);
     gmb = sqrtf(1.0e-10 + gmsqb);
 
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.cl b/src/backend/opencl/kernel/anisotropic_diffusion.cl
index 749d0874c5..b336623e27 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.cl
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.cl
@@ -89,13 +89,8 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     df0 = df;
     db0 = db;
 
-    if (FLUX_FN==2) {
-        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2));
-        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2));
-    } else {
-        gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2));
-        gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2));
-    }
+    gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2));
+    gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2));
 
     gmf = sqrt(1.0e-10f + gmsqf);
     gmb = sqrt(1.0e-10f + gmsqb);
@@ -109,13 +104,8 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     df  = S - C;
     db  = C - N;
 
-    if (FLUX_FN==2) {
-        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2));
-        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2));
-    } else {
-        gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2));
-        gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2));
-    }
+    gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2));
+    gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2));
 
     gmf = sqrt(1.0e-10 + gmsqf);
     gmb = sqrt(1.0e-10 + gmsqb);

From 3aad49030297df9ffaf7f72a4eb352e1f0be3484 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 2 Jun 2018 00:17:03 -0400
Subject: [PATCH 1436/2677] Fix host leak when assigning indexed array to
 another indexed array

Fixes a memory leak caused by assigning from one indexed array to
another indexed array. This was happening because the move constructor
was setting the array_proxy_impl ptr to null and therefore the object
was not getting released. This would have caused a leak in host memory
but the device memory would have been correctly deallocated.
---
 src/api/cpp/array.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index ee6cb60518..dd84bf2729 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -562,13 +562,11 @@ namespace af
 #if __cplusplus > 199711L
     af::array::array_proxy::array_proxy(array_proxy &&other) {
         impl = other.impl;
-        other.impl = nullptr;
     }
 
     array::array_proxy&
     af::array::array_proxy::operator=(array_proxy &&other) {
         array out = other;
-        other.impl = nullptr;
         return *this = out;
     }
 #endif

From f1f71b35e0693ea25daa5b2e786dca5cdfd15f3d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 2 Jun 2018 02:27:26 -0400
Subject: [PATCH 1437/2677] Fix several memory related issues in unit tests.

* Release objects before the test finishs
* Use freeHost instead of delete[]
* Correctly release af_feature objects and arrays
* Use resize in readTests to avoid multiple allocations
---
 test/anisotropic_diffusion.cpp |  1 +
 test/cast.cpp                  |  3 ++
 test/complex.cpp               | 26 +++++++--------
 test/constant.cpp              |  2 ++
 test/empty.cpp                 |  2 ++
 test/fast.cpp                  |  6 +---
 test/fft.cpp                   | 20 ++++++------
 test/flat.cpp                  | 20 ++++++------
 test/flip.cpp                  | 32 +++++++++---------
 test/gen_assign.cpp            | 47 ++++++++++++++-------------
 test/gen_index.cpp             | 26 +++++++--------
 test/gfor.cpp                  | 32 +++++++++---------
 test/harris.cpp                |  6 +---
 test/homography.cpp            |  6 ++--
 test/index.cpp                 | 59 +++++++++++++++++-----------------
 test/testHelpers.hpp           |  5 ++-
 16 files changed, 146 insertions(+), 147 deletions(-)

diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index ba09ef570f..83d709cdb8 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -122,6 +122,7 @@ void imageTest(string pTestFile, const float dt, const float K, const uint iters
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.025f));
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
diff --git a/test/cast.cpp b/test/cast.cpp
index 4f7cf390ff..f13069f5a2 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -30,6 +30,8 @@ void cast_test()
     af_array a, b;
     af_randu(&a, dims.ndims(), dims.get(), ta);
     af_err err = af_cast(&b, a, tb);
+    af_release_array(a);
+    af_release_array(b);
     ASSERT_EQ(err, AF_SUCCESS);
 }
 
@@ -87,6 +89,7 @@ void cast_test_complex_real()
     af_randu(&a, dims.ndims(), dims.get(), ta);
     af_err err = af_cast(&b, a, tb);
     ASSERT_EQ(err, AF_ERR_TYPE);
+    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
 }
 
 #define COMPLEX_REAL_TESTS(Ti, To)                      \
diff --git a/test/complex.cpp b/test/complex.cpp
index e13ec53e4d..9a2f86b333 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -37,9 +37,9 @@ const int num = 10;
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], CPLX(Tc)(h_a[i], h_b[i])) <<              \
                 "for values: " << h_a[i]  << "," << h_b[i] << std::endl; \
-        delete[] h_a;                                                   \
-        delete[] h_b;                                                   \
-        delete[] h_c;                                                   \
+        freeHost(h_a);                                                  \
+        freeHost(h_b);                                                  \
+        freeHost(h_c);                                                  \
     }                                                                   \
     TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_left)                    \
     {                                                                   \
@@ -55,8 +55,8 @@ const int num = 10;
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], CPLX(Ta)(h_a[i], h_b)) <<                 \
                 "for values: " << h_a[i]  << "," << h_b << std::endl;   \
-        delete[] h_a;                                                   \
-        delete[] h_c;                                                   \
+        freeHost(h_a);                                                  \
+        freeHost(h_c);                                                  \
     }                                                                   \
                                                                         \
     TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_right)                   \
@@ -73,8 +73,8 @@ const int num = 10;
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], CPLX(Tb)(h_a, h_b[i])) <<                 \
                 "for values: " << h_a  << "," << h_b[i] << std::endl;   \
-        delete[] h_b;                                                   \
-        delete[] h_c;                                                   \
+        freeHost(h_b);                                                  \
+        freeHost(h_c);                                                  \
     }                                                                   \
     TEST(ComplexTests, Test_##Ta##_##Tb##_Real)                         \
     {                                                                   \
@@ -92,8 +92,8 @@ const int num = 10;
         Tc *h_d = d.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_d[i], h_a[i]) << "at: " << i << std::endl;      \
-        delete[] h_a;                                                   \
-        delete[] h_d;                                                   \
+        freeHost(h_a);                                                  \
+        freeHost(h_d);                                                  \
     }                                                                   \
     TEST(ComplexTests, Test_##Ta##_##Tb##_Imag)                         \
     {                                                                   \
@@ -111,8 +111,8 @@ const int num = 10;
         Tc *h_d = d.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_d[i], h_b[i])  << "at: " << i << std::endl;     \
-        delete[] h_b;                                                   \
-        delete[] h_d;                                                   \
+        freeHost(h_b);                                                  \
+        freeHost(h_d);                                                  \
     }                                                                   \
     TEST(ComplexTests, Test_##Ta##_##Tb##_Conj)                         \
     {                                                                   \
@@ -131,8 +131,8 @@ const int num = 10;
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(conj(h_c[i]), h_d[i])                             \
                 << "at: " << i << std::endl;                            \
-        delete[] h_c;                                                   \
-        delete[] h_d;                                                   \
+        freeHost(h_c);                                                  \
+        freeHost(h_d);                                                  \
     }                                                                   \
 
 
diff --git a/test/constant.cpp b/test/constant.cpp
index 3eb60f5fff..b640e21c70 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -57,6 +57,7 @@ void ConstantCCheck(T value) {
     for (int i = 0; i < num; i++) {
         ASSERT_EQ(::real(h_in[i]), val);
     }
+    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
 }
 
 template<typename T>
@@ -138,6 +139,7 @@ void IdentityCCheck() {
                 ASSERT_EQ(h_in[i * num + j], T(0));
         }
     }
+    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
 }
 
 template<typename T>
diff --git a/test/empty.cpp b/test/empty.cpp
index df5820fc90..b4c69d8aaf 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -283,5 +283,7 @@ TEST(Array, TestEmptyImage) {
     ASSERT_EQ(nd, 0u);
     af_get_numdims(&nd, hout);
     ASSERT_EQ(nd, 0u);
+    ASSERT_EQ(AF_SUCCESS, af_release_array(h));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(hout));
 }
 
diff --git a/test/fast.cpp b/test/fast.cpp
index 8cb90574a6..b3d051b864 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -141,11 +141,7 @@ void fastTest(string pTestFile, bool nonmax)
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(x));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(y));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(score));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(orientation));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(size));
+        ASSERT_EQ(AF_SUCCESS, af_release_features(out));
 
         delete [] outX;
         delete [] outY;
diff --git a/test/fft.cpp b/test/fft.cpp
index 370e2327c4..6aedc65525 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -511,8 +511,8 @@ TEST(fft, CPP_4D)
         ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_B;
+    freeHost(h_b);
+    freeHost(h_B);
 }
 
 TEST(ifft, CPP_4D)
@@ -530,8 +530,8 @@ TEST(ifft, CPP_4D)
         ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_B;
+    freeHost(h_b);
+    freeHost(h_B);
 }
 
 TEST(fft, GFOR)
@@ -551,8 +551,8 @@ TEST(fft, GFOR)
         ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(fft2, GFOR)
@@ -572,8 +572,8 @@ TEST(fft2, GFOR)
         ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(fft3, GFOR)
@@ -593,8 +593,8 @@ TEST(fft3, GFOR)
         ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << std::endl;
     }
 
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(fft, InPlace)
diff --git a/test/flat.cpp b/test/flat.cpp
index a556c897d5..fd12116f5a 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -28,8 +28,8 @@ TEST(FlatTests, Test_flat_1D)
         ASSERT_EQ(h_in[i], h_out[i]);
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlatTests, Test_flat_2D)
@@ -48,8 +48,8 @@ TEST(FlatTests, Test_flat_2D)
         ASSERT_EQ(h_in[i], h_out[i]);
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlatTests, Test_flat_1D_index)
@@ -69,8 +69,8 @@ TEST(FlatTests, Test_flat_1D_index)
         ASSERT_EQ(h_in[i], h_out[i - st]);
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlatTests, Test_flat_2D_index0)
@@ -97,8 +97,8 @@ TEST(FlatTests, Test_flat_2D_index0)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlatTests, Test_flat_2D_index1)
@@ -126,6 +126,6 @@ TEST(FlatTests, Test_flat_2D_index1)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
diff --git a/test/flip.cpp b/test/flip.cpp
index 5ec5206239..565781ccaa 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -30,8 +30,8 @@ TEST(FlipTests, Test_flip_1D)
             << "at (" << i << ")";
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlipTests, Test_flip_2D0)
@@ -54,8 +54,8 @@ TEST(FlipTests, Test_flip_2D0)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlipTests, Test_flip_2D1)
@@ -78,8 +78,8 @@ TEST(FlipTests, Test_flip_2D1)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 
@@ -101,8 +101,8 @@ TEST(FlipTests, Test_flip_1D_index)
             << "at (" << i << ")";
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlipTests, Test_flip_2D_index00)
@@ -129,8 +129,8 @@ TEST(FlipTests, Test_flip_2D_index00)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlipTests, Test_flip_2D_index01)
@@ -157,8 +157,8 @@ TEST(FlipTests, Test_flip_2D_index01)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlipTests, Test_flip_2D_index10)
@@ -186,8 +186,8 @@ TEST(FlipTests, Test_flip_2D_index10)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TEST(FlipTests, Test_flip_2D_index11)
@@ -215,6 +215,6 @@ TEST(FlipTests, Test_flip_2D_index11)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 70cecf7ae3..9ae50b1d22 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -28,6 +28,7 @@ using std::cout;
 using std::endl;
 using std::ostream_iterator;
 using af::dtype_traits;
+using af::freeHost;
 
 void testGeneralAssignOneArray(string pTestFile, const dim_t ndims, af_index_t* indexs, int arrayDim)
 {
@@ -252,11 +253,11 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX)
         ASSERT_EQ(hAO[i], hAC[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hAC;
-    delete[] hAO;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hAC);
+    freeHost(hAO);
+    freeHost(hIdx);
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL)
@@ -304,11 +305,11 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL)
             ASSERT_EQ(hAO[i], hAC[i]);
         }
 
-        delete[] hA;
-        delete[] hB;
-        delete[] hAC;
-        delete[] hAO;
-        delete[] hIdx;
+        freeHost(hA);
+        freeHost(hB);
+        freeHost(hAC);
+        freeHost(hAO);
+        freeHost(hIdx);
     } catch(af::exception &ex) {
         FAIL() << ex.what() << std::endl;
     }
@@ -347,9 +348,9 @@ TEST(GeneralAssign, CPP_ASNN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx);
 }
 
 TEST(GeneralAssign, CPP_SANN)
@@ -384,9 +385,9 @@ TEST(GeneralAssign, CPP_SANN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx);
 }
 
 TEST(GeneralAssign, CPP_SSAN)
@@ -424,9 +425,9 @@ TEST(GeneralAssign, CPP_SSAN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx);
 }
 
 TEST(GeneralAssign, CPP_AANN)
@@ -459,8 +460,8 @@ TEST(GeneralAssign, CPP_AANN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx0;
-    delete[] hIdx1;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx0);
+    freeHost(hIdx1);
 }
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index 7b4638e25b..e2c644ae0f 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -183,9 +183,9 @@ TEST(GeneralIndex, CPP_ASNN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx);
 }
 
 TEST(GeneralIndex, CPP_SANN)
@@ -217,9 +217,9 @@ TEST(GeneralIndex, CPP_SANN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx);
 }
 
 TEST(GeneralIndex, CPP_SSAN)
@@ -255,9 +255,9 @@ TEST(GeneralIndex, CPP_SSAN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx);
 }
 
 TEST(GeneralIndex, CPP_AANN)
@@ -289,8 +289,8 @@ TEST(GeneralIndex, CPP_AANN)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hIdx0;
-    delete[] hIdx1;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hIdx0);
+    freeHost(hIdx1);
 }
diff --git a/test/gfor.cpp b/test/gfor.cpp
index 3aa1d14939..70d03bbd9b 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -40,7 +40,7 @@ TEST(GFOR, Assign_Scalar_Span)
         ASSERT_EQ(hA[i], val);
     }
 
-    delete[] hA;
+    freeHost(hA);
 }
 
 TEST(GFOR, Assign_Scalar_Seq)
@@ -64,8 +64,8 @@ TEST(GFOR, Assign_Scalar_Seq)
         else ASSERT_EQ(hA[i], hB[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
+    freeHost(hA);
+    freeHost(hB);
 }
 
 TEST(GFOR, Inc_Scalar_Span)
@@ -86,8 +86,8 @@ TEST(GFOR, Inc_Scalar_Span)
         ASSERT_EQ(hA[i], val + hB[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
+    freeHost(hA);
+    freeHost(hB);
 }
 
 TEST(GFOR, Inc_Scalar_Seq)
@@ -111,8 +111,8 @@ TEST(GFOR, Inc_Scalar_Seq)
         else ASSERT_EQ(hA[i], hB[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
+    freeHost(hA);
+    freeHost(hB);
 }
 
 TEST(GFOR, Assign_Array_Span)
@@ -132,7 +132,7 @@ TEST(GFOR, Assign_Array_Span)
         ASSERT_EQ(hA[i], val);
     }
 
-    delete[] hA;
+    freeHost(hA);
 }
 
 TEST(GFOR, Assign_Array_Seq)
@@ -162,9 +162,9 @@ TEST(GFOR, Assign_Array_Seq)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hC;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(GFOR, Inc_Array_Span)
@@ -186,8 +186,8 @@ TEST(GFOR, Inc_Array_Span)
         ASSERT_EQ(hA[i], val + hB[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
+    freeHost(hA);
+    freeHost(hB);
 }
 
 TEST(GFOR, Inc_Array_Seq)
@@ -217,9 +217,9 @@ TEST(GFOR, Inc_Array_Seq)
         }
     }
 
-    delete[] hA;
-    delete[] hB;
-    delete[] hC;
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 2D0)
diff --git a/test/harris.cpp b/test/harris.cpp
index 90c5cf16de..7d9e428363 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -134,11 +134,7 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(x));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(y));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(score));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(orientation));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(size));
+        ASSERT_EQ(AF_SUCCESS, af_release_features(out));
     }
 }
 
diff --git a/test/homography.cpp b/test/homography.cpp
index f70ea876ff..797515f4d0 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -176,8 +176,8 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     ASSERT_EQ(AF_SUCCESS, af_release_array(dist_thr));
     ASSERT_EQ(AF_SUCCESS, af_release_array(train_idx));
     ASSERT_EQ(AF_SUCCESS, af_release_array(query_idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_x));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_y));
+    ASSERT_EQ(AF_SUCCESS, af_release_features(query_feat));
+    ASSERT_EQ(AF_SUCCESS, af_release_features(train_feat));
     ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_x_idx));
     ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_y_idx));
     ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_x_idx));
@@ -186,8 +186,6 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     ASSERT_EQ(AF_SUCCESS, af_release_array(trainArray));
     ASSERT_EQ(AF_SUCCESS, af_release_array(trainArray_f32));
     ASSERT_EQ(AF_SUCCESS, af_release_array(train_desc));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_x));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_y));
 }
 
 #define HOMOGRAPHY_INIT(desc, image, htype, rotate, size_ratio)                 \
diff --git a/test/index.cpp b/test/index.cpp
index 2866f9955a..21206f2fbb 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -28,6 +28,7 @@ using std::cout;
 using std::endl;
 using std::ostream_iterator;
 using af::dtype_traits;
+using af::freeHost;
 
 template<typename T, typename OP>
 void
@@ -737,8 +738,8 @@ TEST(SeqIndex, CPP_END)
     }
 
 
-    delete[] hA;
-    delete[] hB;
+    freeHost(hA);
+    freeHost(hB);
 }
 
 
@@ -760,8 +761,8 @@ TEST(SeqIndex, CPP_END_SEQ)
         ASSERT_EQ(hA[i + end_begin - 1], hB[i]);
     }
 
-    delete[] hA;
-    delete[] hB;
+    freeHost(hA);
+    freeHost(hB);
 }
 
 af::array cpp_scope_seq_test(const int num, const float val, const af::seq s)
@@ -786,7 +787,7 @@ TEST(SeqIndex, CPP_SCOPE_SEQ)
         ASSERT_EQ(hB[i], val);
     }
 
-    delete[] hB;
+    freeHost(hB);
 }
 
 af::array cpp_scope_arr_test(const int num, const float val)
@@ -810,7 +811,7 @@ TEST(SeqIndex, CPP_SCOPE_ARR)
         ASSERT_EQ(hB[i], val * (val - 1));
     }
 
-    delete[] hB;
+    freeHost(hB);
 }
 
 TEST(SeqIndex, CPPLarge)
@@ -884,9 +885,9 @@ TEST(SeqIndex, Cascade00)
         }
     }
 
-    delete[] h_a;
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_a);
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(SeqIndex, Cascade01)
@@ -930,9 +931,9 @@ TEST(SeqIndex, Cascade01)
         }
     }
 
-    delete[] h_a;
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_a);
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(SeqIndex, Cascade10)
@@ -976,9 +977,9 @@ TEST(SeqIndex, Cascade10)
         }
     }
 
-    delete[] h_a;
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_a);
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(SeqIndex, Cascade11)
@@ -1023,9 +1024,9 @@ TEST(SeqIndex, Cascade11)
         }
     }
 
-    delete[] h_a;
-    delete[] h_b;
-    delete[] h_c;
+    freeHost(h_a);
+    freeHost(h_b);
+    freeHost(h_c);
 }
 
 TEST(ArrayIndex, CPP_INDEX_VECTOR)
@@ -1048,8 +1049,8 @@ TEST(ArrayIndex, CPP_INDEX_VECTOR)
         ASSERT_EQ(h_C[i], h_B[(int)h_inds[i]]);
     }
 
-    delete[] h_B;
-    delete[] h_C;
+    freeHost(h_B);
+    freeHost(h_C);
 }
 
 TEST(SeqIndex, CPP_INDEX_VECTOR)
@@ -1076,8 +1077,8 @@ TEST(SeqIndex, CPP_INDEX_VECTOR)
         ASSERT_EQ(h_C[i], h_B[i + st]);
     }
 
-    delete[] h_B;
-    delete[] h_C;
+    freeHost(h_B);
+    freeHost(h_C);
 }
 
 
@@ -1101,8 +1102,8 @@ TEST(ArrayIndex, CPP_INDEX_VECTOR_2D)
         ASSERT_EQ(h_C[i], h_B[(int)h_inds[i]]);
     }
 
-    delete[] h_B;
-    delete[] h_C;
+    freeHost(h_B);
+    freeHost(h_C);
 }
 
 TEST(SeqIndex, CPP_INDEX_VECTOR_2D)
@@ -1130,8 +1131,8 @@ TEST(SeqIndex, CPP_INDEX_VECTOR_2D)
         ASSERT_EQ(h_C[i], h_B[i + st]);
     }
 
-    delete[] h_B;
-    delete[] h_C;
+    freeHost(h_B);
+    freeHost(h_C);
 }
 
 template<typename T>
@@ -1337,7 +1338,7 @@ TEST(Indexing, SNIPPET_indexing_copy)
   // freed once.
 }
 
-TEST(Asssign, LinearIndexSeq)
+TEST(Assign, LinearIndexSeq)
 {
     using af::array;
     const int nx = 5;
@@ -1373,7 +1374,7 @@ TEST(Asssign, LinearIndexSeq)
     }
 }
 
-TEST(Asssign, LinearIndexGenSeq)
+TEST(Assign, LinearIndexGenSeq)
 {
     using af::array;
     const int nx = 5;
@@ -1409,7 +1410,7 @@ TEST(Asssign, LinearIndexGenSeq)
     }
 }
 
-TEST(Asssign, LinearIndexGenArr)
+TEST(Assign, LinearIndexGenArr)
 {
     using af::array;
     const int nx = 5;
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index c77eb516c8..87ff37872b 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -52,10 +52,9 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
     if(testFile.good()) {
         unsigned inputCount;
         testFile >> inputCount;
+        inputDims.resize(inputCount);
         for(unsigned i=0; i<inputCount; i++) {
-            af::dim4 temp(1);
-            testFile >> temp;
-            inputDims.push_back(temp);
+            testFile >> inputDims[i];
         }
 
         unsigned testCount;

From ded58088da4b11527047e5c737a10dfd918f6b84 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Jun 2018 00:59:06 -0400
Subject: [PATCH 1438/2677] Fix memory leak in median

An early exit code path did not free intermediate data in median
---
 src/api/c/median.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 32e9940c1d..3c62f755f2 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -104,7 +104,10 @@ static af_array median(const af_array& in, const dim_t dim)
 
     if (dimLength % 2 == 1) {
         // mid-1 is our guy
-        if (input.isFloating()) return left;
+        if (input.isFloating()) {
+            AF_CHECK(af_release_array(sortedIn_handle));
+            return left;
+        }
 
         // Return as floats for consistency
         af_array out;

From 473635ce7bd0f0f211aae711eafe0dfa8573d2a4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Jun 2018 02:02:57 -0400
Subject: [PATCH 1439/2677] Fix leak in moments

Fix a memory leak in moments
---
 src/api/c/moments.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 8024606e34..4232f29c0c 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -78,8 +78,9 @@ af_err af_moments_all(double* out, const af_array in, const af_moment_type momen
         DIM_ASSERT(1, idims[2] == 1 && idims[3] == 1);
 
         af_array moments_arr;
-        af_moments(&moments_arr, in, moment);
+        AF_CHECK(af_moments(&moments_arr, in, moment));
         moment_copy<float>(out, moments_arr);
+        AF_CHECK(af_release_array(moments_arr));
     }
     CATCHALL;
 

From 7e98ecc6eaadd263748017d6128f548fce110346 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 12 Jun 2018 12:36:59 +0530
Subject: [PATCH 1440/2677] Fix input validation in
 DeviceManager::setActiveDevice

---
 src/backend/cuda/platform.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 8d8339b58b..fd3d856e0d 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -555,7 +555,7 @@ int DeviceManager::setActiveDevice(int device, int nId)
 
     int numDevices = cuDevices.size();
 
-    if (device > numDevices)
+    if (device >= numDevices)
         return -1;
 
     int old = getActiveDeviceId();

From 95c7aac9f459d2b8c29ae25f208d52be9f29161c Mon Sep 17 00:00:00 2001
From: mlloreda <miguel@arrayfire.com>
Date: Tue, 12 Jun 2018 01:01:57 -0400
Subject: [PATCH 1441/2677] Cleaned up and improved reduce test file.

---
 test/reduce.cpp | 185 +++++++++++++++++++++++++-----------------------
 1 file changed, 98 insertions(+), 87 deletions(-)

diff --git a/test/reduce.cpp b/test/reduce.cpp
index 7578e5a071..1959336b4f 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -24,6 +24,8 @@ using std::endl;
 using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::freeHost;
 
 
 template<typename T>
@@ -31,7 +33,7 @@ class Reduce : public ::testing::Test
 {
 };
 
-typedef ::testing::Types<float, double, af::cfloat, af::cdouble, uint, int, intl, uintl, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl, uchar, short, ushort> TestTypes;
 TYPED_TEST_CASE(Reduce, TestTypes);
 
 typedef af_err (*reduceFunc)(af_array *, const af_array, const int);
@@ -42,12 +44,12 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
     readTests<int,int,int> (pTestFile,numDims,data,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     vector<Ti> in(data[0].begin(), data[0].end());
 
@@ -102,7 +104,7 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
 
 
         // Delete
-        delete[] outData;
+        freeHost(outData);
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
     }
 
@@ -121,11 +123,11 @@ template<> struct promote_type<short , af_sum>       { typedef int  type; };
 template<> struct promote_type<ushort, af_sum>       { typedef uint type; };
 template<> struct promote_type<uchar , af_product>   { typedef uint type; };
 template<> struct promote_type<char  , af_product>   { typedef uint type; };
-template<> struct promote_type<short, af_product>    { typedef int  type; };
+template<> struct promote_type<short , af_product>   { typedef int  type; };
 template<> struct promote_type<ushort, af_product>   { typedef uint type; };
 
 #define REDUCE_TESTS(FN)                                                                    \
-    TYPED_TEST(Reduce,Test_##FN)                                                    \
+    TYPED_TEST(Reduce,Test_##FN)                                                            \
     {                                                                                       \
         reduceTest<TypeParam, typename promote_type<TypeParam, af_##FN>::type, af_##FN>(    \
             string(TEST_DIR"/reduce/"#FN".test")                                            \
@@ -138,7 +140,7 @@ REDUCE_TESTS(max);
 
 #undef REDUCE_TESTS
 #define REDUCE_TESTS(FN, OT)                        \
-    TYPED_TEST(Reduce,Test_##FN)            \
+    TYPED_TEST(Reduce,Test_##FN)                    \
     {                                               \
         reduceTest<TypeParam, OT, af_##FN>(         \
             string(TEST_DIR"/reduce/"#FN".test")    \
@@ -177,15 +179,20 @@ TEST(Reduce,Test_Reduce_Big1)
 //
 typedef af::array (*ReductionOp)(const af::array&, const int);
 
-using af::dim4;
-using af::iota;
-using af::constant;
-using af::sum;
-using af::min;
-using af::max;
+using af::NaN;
 using af::allTrue;
 using af::anyTrue;
+using af::constant;
 using af::count;
+using af::iota;
+using af::max;
+using af::min;
+using af::product;
+using af::randu;
+using af::round;
+using af::seq;
+using af::span;
+using af::sum;
 
 template<typename Ti, typename To, ReductionOp reduce>
 void cppReduceTest(string pTestFile)
@@ -193,16 +200,16 @@ void cppReduceTest(string pTestFile)
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
     readTests<int,int,int> (pTestFile,numDims,data,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     vector<Ti> in(data[0].begin(), data[0].end());
 
-    af::array input(dims, &in.front());
+    array input(dims, &in.front());
 
     // Compare result
     for (int d = 0; d < (int)tests.size(); ++d) {
@@ -210,7 +217,7 @@ void cppReduceTest(string pTestFile)
         vector<To> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        af::array output = reduce(input, d);
+        array output = reduce(input, d);
 
         // Get result
         To *outData = new To[dims.elements()];
@@ -223,7 +230,7 @@ void cppReduceTest(string pTestFile)
         }
 
         // Delete
-        delete[] outData;
+        freeHost(outData);
     }
 }
 
@@ -310,10 +317,10 @@ CPP_REDUCE_TESTS(count, count, float, unsigned);
 
 TEST(Reduce, Test_Product_Global)
 {
-    int num = 100;
-    af::array a = 1 + af::round(5 * af::randu(num, 1)) / 100;
+    const int num = 100;
+    array a = 1 + round(5 * randu(num, 1)) / 100;
 
-    float res = af::product<float>(a);
+    float res = product<float>(a);
     float *h_a = a.host<float>();
     float gold = 1;
 
@@ -322,15 +329,15 @@ TEST(Reduce, Test_Product_Global)
     }
 
     ASSERT_NEAR(gold, res, 1e-3);
-    delete[] h_a;
+    freeHost(h_a);
 }
 
 TEST(Reduce, Test_Sum_Global)
 {
-    int num = 10000;
-    af::array a = af::round(2 * af::randu(num, 1));
+    const int num = 10000;
+    array a = round(2 * randu(num, 1));
 
-    float res = af::sum<float>(a);
+    float res = sum<float>(a);
     float *h_a = a.host<float>();
     float gold = 0;
 
@@ -339,16 +346,16 @@ TEST(Reduce, Test_Sum_Global)
     }
 
     ASSERT_EQ(gold, res);
-    delete[] h_a;
+    freeHost(h_a);
 }
 
 TEST(Reduce, Test_Count_Global)
 {
-    int num = 10000;
-    af::array a = af::round(2 * af::randu(num, 1));
-    af::array b = a.as(b8);
+    const int num = 10000;
+    array a = round(2 * randu(num, 1));
+    array b = a.as(b8);
 
-    int res = af::count<int>(b);
+    int res = count<int>(b);
     char *h_b = b.host<char>();
     int gold = 0;
 
@@ -357,16 +364,16 @@ TEST(Reduce, Test_Count_Global)
     }
 
     ASSERT_EQ(gold, res);
-    delete[] h_b;
+    freeHost(h_b);
 }
 
 TEST(Reduce, Test_min_Global)
 {
     if (noDoubleTests<double>()) return;
 
-    int num = 10000;
-    af::array a = af::randu(num, 1, f64);
-    double res = af::min<double>(a);
+    const int num = 10000;
+    array a = randu(num, 1, f64);
+    double res = min<double>(a);
     double *h_a = a.host<double>();
     double gold = std::numeric_limits<double>::max();
 
@@ -377,14 +384,14 @@ TEST(Reduce, Test_min_Global)
     }
 
     ASSERT_EQ(gold, res);
-    delete[] h_a;
+    freeHost(h_a);
 }
 
 TEST(Reduce, Test_max_Global)
 {
-    int num = 10000;
-    af::array a = af::randu(num, 1);
-    float res = af::max<float>(a);
+    const int num = 10000;
+    array a = randu(num, 1);
+    float res = max<float>(a);
     float *h_a = a.host<float>();
     float gold = -std::numeric_limits<float>::max();
 
@@ -393,7 +400,7 @@ TEST(Reduce, Test_max_Global)
     }
 
     ASSERT_EQ(gold, res);
-    delete[] h_a;
+    freeHost(h_a);
 }
 
 
@@ -416,7 +423,7 @@ void typed_assert_eq<double>(double lhs, double rhs, bool both)
 }
 
 template<>
-void typed_assert_eq<af::cfloat>(af::cfloat lhs, af::cfloat rhs, bool both)
+void typed_assert_eq<cfloat>(cfloat lhs, cfloat rhs, bool both)
 {
     ASSERT_FLOAT_EQ(real(lhs), real(rhs));
     if(both) {
@@ -425,7 +432,7 @@ void typed_assert_eq<af::cfloat>(af::cfloat lhs, af::cfloat rhs, bool both)
 }
 
 template<>
-void typed_assert_eq<af::cdouble>(af::cdouble lhs, af::cdouble rhs, bool both)
+void typed_assert_eq<cdouble>(cdouble lhs, cdouble rhs, bool both)
 {
     ASSERT_DOUBLE_EQ(real(lhs), real(rhs));
     if(both) {
@@ -443,24 +450,24 @@ TYPED_TEST(Reduce, Test_All_Global)
         vector<TypeParam> h_vals(num, (TypeParam)true);
         array a(2, num/2, &h_vals.front());
 
-        TypeParam res = af::allTrue<TypeParam>(a);
+        TypeParam res = allTrue<TypeParam>(a);
         typed_assert_eq((TypeParam)true, res, false);
 
         h_vals[3] = false;
         a = array(2, num/2, &h_vals.front());
 
-        res = af::allTrue<TypeParam>(a);
+        res = allTrue<TypeParam>(a);
         typed_assert_eq((TypeParam)false, res, false);
     }
 
     // false value location test
-    int num = 10000;
+    const int num = 10000;
     vector<TypeParam> h_vals(num, (TypeParam)true);
     for(int i = 1; i < 10000; i+=100) {
         h_vals[i] = false;
         array a(2, num/2, &h_vals.front());
 
-        TypeParam res = af::allTrue<TypeParam>(a);
+        TypeParam res = allTrue<TypeParam>(a);
         typed_assert_eq((TypeParam)false, res, false);
 
         h_vals[i] = true;
@@ -477,24 +484,24 @@ TYPED_TEST(Reduce, Test_Any_Global)
         vector<TypeParam> h_vals(num, (TypeParam)false);
         array a(2, num/2, &h_vals.front());
 
-        TypeParam res = af::anyTrue<TypeParam>(a);
+        TypeParam res = anyTrue<TypeParam>(a);
         typed_assert_eq((TypeParam)false, res, false);
 
         h_vals[3] = true;
         a = array(2, num/2, &h_vals.front());
 
-        res = af::anyTrue<TypeParam>(a);
+        res = anyTrue<TypeParam>(a);
         typed_assert_eq((TypeParam)true, res, false);
     }
 
     // true value location test
-    int num = 10000;
+    const int num = 10000;
     vector<TypeParam> h_vals(num, (TypeParam)false);
     for(int i = 1; i < 10000; i+=100) {
         h_vals[i] = true;
         array a(2, num/2, &h_vals.front());
 
-        TypeParam res = af::anyTrue<TypeParam>(a);
+        TypeParam res = anyTrue<TypeParam>(a);
         typed_assert_eq((TypeParam)true, res, false);
 
         h_vals[i] = false;
@@ -504,11 +511,11 @@ TYPED_TEST(Reduce, Test_Any_Global)
 TEST(MinMax, NaN)
 {
     const int num = 10000;
-    af::array A = af::randu(num);
-    A(where(A < 0.25)) = af::NaN;
+    array A = randu(num);
+    A(where(A < 0.25)) = NaN;
 
-    float minval = af::min<float>(A);
-    float maxval = af::max<float>(A);
+    float minval = min<float>(A);
+    float maxval = max<float>(A);
 
     ASSERT_NE(std::isnan(minval), true);
     ASSERT_NE(std::isnan(maxval), true);
@@ -521,30 +528,32 @@ TEST(MinMax, NaN)
             ASSERT_GE(maxval, h_A[i]);
         }
     }
+
+    freeHost(h_A);
 }
 
 TEST(Count, NaN)
 {
     const int num = 10000;
-    af::array A = af::round(5 * af::randu(num));
-    af::array B = A;
+    array A = round(5 * randu(num));
+    array B = A;
 
-    A(where(A == 2)) = af::NaN;
+    A(where(A == 2)) = NaN;
 
-    ASSERT_EQ(af::count<uint>(A), af::count<uint>(B));
+    ASSERT_EQ(count<uint>(A), count<uint>(B));
 }
 
 TEST(Sum, NaN)
 {
     const int num = 10000;
-    af::array A = af::randu(num);
-    A(where(A < 0.25)) = af::NaN;
+    array A = randu(num);
+    A(where(A < 0.25)) = NaN;
 
-    float res = af::sum<float>(A);
+    float res = sum<float>(A);
 
     ASSERT_EQ(std::isnan(res), true);
 
-    res = af::sum<float>(A, 0);
+    res = sum<float>(A, 0);
     float *h_A = A.host<float>();
 
     float tmp = 0;
@@ -553,19 +562,20 @@ TEST(Sum, NaN)
     }
 
     ASSERT_NEAR(res/num, tmp/num, 1E-5);
+    freeHost(h_A);
 }
 
 TEST(Product, NaN)
 {
     const int num = 5;
-    af::array A = af::randu(num);
-    A(2) = af::NaN;
+    array A = randu(num);
+    A(2) = NaN;
 
-    float res = af::product<float>(A);
+    float res = product<float>(A);
 
     ASSERT_EQ(std::isnan(res), true);
 
-    res = af::product<float>(A, 1);
+    res = product<float>(A, 1);
     float *h_A = A.host<float>();
 
     float tmp = 1;
@@ -574,20 +584,21 @@ TEST(Product, NaN)
     }
 
     ASSERT_NEAR(res/num, tmp/num, 1E-5);
+    freeHost(h_A);
 }
 
 TEST(AnyAll, NaN)
 {
     const int num = 10000;
-    af::array A = (af::randu(num) > 0.5).as(f32);
-    af::array B = A;
+    array A = (randu(num) > 0.5).as(f32);
+    array B = A;
 
-    B(af::where(B == 0)) = af::NaN;
+    B(where(B == 0)) = NaN;
 
-    ASSERT_EQ(af::anyTrue<bool>(B), true);
-    ASSERT_EQ(af::allTrue<bool>(B), true);
-    ASSERT_EQ(af::anyTrue<bool>(A), true);
-    ASSERT_EQ(af::allTrue<bool>(A), false);
+    ASSERT_EQ(anyTrue<bool>(B), true);
+    ASSERT_EQ(allTrue<bool>(B), true);
+    ASSERT_EQ(anyTrue<bool>(A), true);
+    ASSERT_EQ(allTrue<bool>(A), false);
 }
 
 TEST(MaxAll, IndexedSmall)
@@ -595,10 +606,10 @@ TEST(MaxAll, IndexedSmall)
     const int num = 1000;
     const int st = 10;
     const int en = num - 100;
-    af::array a = af::randu(num);
-    float b = af::max<float>(a(af::seq(st, en)));
+    array a = randu(num);
+    float b = max<float>(a(seq(st, en)));
 
-    std::vector<float> ha(num);
+    vector<float> ha(num);
     a.host(&ha[0]);
 
     float res = ha[st];
@@ -614,10 +625,10 @@ TEST(MaxAll, IndexedBig)
     const int num = 100000;
     const int st = 1000;
     const int en = num - 1000;
-    af::array a = af::randu(num);
-    float b = af::max<float>(a(af::seq(st, en)));
+    array a = randu(num);
+    float b = max<float>(a(seq(st, en)));
 
-    std::vector<float> ha(num);
+    vector<float> ha(num);
     a.host(&ha[0]);
 
     float res = ha[st];
@@ -634,19 +645,19 @@ TEST(Reduce, KernelName)
     const int n = 100;
     const int b = 5;
 
-    array in = af::constant(0, m, n, b);
+    array in = constant(0, m, n, b);
     for (int i = 0; i < b; i++) {
-        array tmp = af::randu(m, n);
-        in(af::span, af::span, i) = tmp;
-        ASSERT_EQ(af::min<float>(in(af::span, af::span, i)),
-                  af::min<float>(tmp));
+        array tmp = randu(m, n);
+        in(span, span, i) = tmp;
+        ASSERT_EQ(min<float>(in(span, span, i)),
+                  min<float>(tmp));
     }
 }
 
 TEST(Reduce, AllSmallIndexed)
 {
-    int LEN = 1000;
-    array a = af::range(af::dim4(LEN, 2));
-    array b = a(af::seq(LEN/2), af::span);
-    ASSERT_EQ(af::max<float>(b), LEN/2-1);
+    const int len = 1000;
+    array a = af::range(dim4(len, 2));
+    array b = a(seq(len/2), span);
+    ASSERT_EQ(max<float>(b), len/2-1);
 }

From c98162bfe4c849d518f18dbe09a82fcb85e0a26d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 12 Jun 2018 12:12:46 +0530
Subject: [PATCH 1442/2677] Check for unsupported cards on a given cuda runtime

CUDA 9.* drops support for all Fermi(NVIDIA) GPUs. This adds
a check when the user wants to run ArrayFire built with CUDA 9.*
on a card with compute version <=2.*
---
 src/backend/cuda/platform.cpp | 31 +++++++++++++++++++++++++++----
 test/info.cpp                 |  7 ++++---
 test/solve_dense.cpp          |  3 ++-
 test/threading.cpp            | 15 ++++++++++-----
 4 files changed, 43 insertions(+), 13 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index fd3d856e0d..6b61574010 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -19,6 +19,7 @@
 #include <common/host_memory.hpp>
 
 #include <algorithm>
+#include <array>
 #include <cstdio>
 #include <mutex>
 #include <sstream>
@@ -146,6 +147,18 @@ static inline string toString(T val)
     return s.str();
 }
 
+static inline
+int getMinSupportedCompute(int cudaMajorVer)
+{
+    // Vector of minimum supported compute versions
+    // for CUDA toolkit (i+1).* where i is the index
+    // of the vector
+    static const std::array<int,10> minSV{1, 1, 1, 1, 1, 1, 2, 2, 3, 3};
+
+    auto CVSize = minSV.size();
+    return (cudaMajorVer>CVSize ? minSV[CVSize-1] : minSV[cudaMajorVer-1]);
+}
+
 ///////////////////////////////////////////////////////////////////////////
 // Wrapper Functions
 ///////////////////////////////////////////////////////////////////////////
@@ -496,16 +509,26 @@ DeviceManager::DeviceManager()
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
     if (nDevices == 0)
         throw runtime_error("No CUDA-Capable devices found");
-
     cuDevices.reserve(nDevices);
 
+    int cudaRtVer = 0;
+    CUDA_CHECK(cudaRuntimeGetVersion(&cudaRtVer));
+    int cudaMajorVer = cudaRtVer / 1000;
+
     for(int i = 0; i < nDevices; i++) {
         cudaDevice_t dev;
         cudaGetDeviceProperties(&dev.prop, i);
-        dev.flops = dev.prop.multiProcessorCount * compute2cores(dev.prop.major, dev.prop.minor) * dev.prop.clockRate;
-        dev.nativeId = i;
-        cuDevices.push_back(dev);
+        if (dev.prop.major<getMinSupportedCompute(cudaMajorVer)) {
+            continue;
+        } else {
+            dev.flops = dev.prop.multiProcessorCount *
+                        compute2cores(dev.prop.major, dev.prop.minor) *
+                        dev.prop.clockRate;
+            dev.nativeId = i;
+            cuDevices.push_back(dev);
+        }
     }
+    nDevices = cuDevices.size();
 
     sortDevices();
 
diff --git a/test/info.cpp b/test/info.cpp
index 5a0a8ac8d3..aba238a91d 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -37,13 +37,14 @@ void testFunction()
 
 void infoTest()
 {
+    int nDevices = 0;
+    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&nDevices));
+    ASSERT_EQ(true, nDevices>0);
+
     const char* ENV = getenv("AF_MULTI_GPU_TESTS");
     if(ENV && ENV[0] == '0') {
         testFunction<float>();
     } else {
-        int nDevices = 0;
-        ASSERT_EQ(AF_SUCCESS, af_get_device_count(&nDevices));
-
         int oldDevice = af::getDevice();
         for(int d = 0; d < nDevices; d++) {
             af::setDevice(d);
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index adc8703a69..650ef84250 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -165,8 +165,9 @@ TEST(Solve, Threading)
 
     vector<std::thread> tests;
 
-    int numDevices = 1;
+    int numDevices = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_EQ(true, numDevices>0);
 
     SOLVE_LU_TESTS_THREADING(float, 0.01);
     SOLVE_LU_TESTS_THREADING(cfloat, 0.01);
diff --git a/test/threading.cpp b/test/threading.cpp
index babf773d1b..766366a534 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -344,8 +344,9 @@ TEST(Threading, FFT_R2C)
 
     vector<std::thread> tests;
 
-    int numDevices = 1;
+    int numDevices = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_EQ(true, numDevices>0);
 
     // Real to complex transforms
     INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
@@ -388,8 +389,9 @@ TEST(Threading, FFT_C2C)
 
     vector<std::thread> tests;
 
-    int numDevices = 1;
+    int numDevices = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_EQ(true, numDevices>0);
 
     // complex to complex transforms
     INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
@@ -437,8 +439,9 @@ TEST(Threading, FFT_ALL)
 
     vector<std::thread> tests;
 
-    int numDevices = 1;
+    int numDevices = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_EQ(true, numDevices>0);
 
     // Real to complex transforms
     INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
@@ -576,8 +579,9 @@ TEST(Threading, BLAS)
 
     vector<std::thread> tests;
 
-    int numDevices = 1;
+    int numDevices = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_EQ(true, numDevices>0);
 
     TEST_BLAS_FOR_TYPE(      float);
     TEST_BLAS_FOR_TYPE( af::cfloat);
@@ -606,8 +610,9 @@ TEST(Threading, Sparse)
 
     vector<std::thread> tests;
 
-    int numDevices = 1;
+    int numDevices = 0;
     ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_EQ(true, numDevices>0);
 
     SPARSE_TESTS(  float, 1E-3);
     SPARSE_TESTS( cfloat, 1E-3);

From 22e8b853b032bb1467997a1b05961bae7d6aadc0 Mon Sep 17 00:00:00 2001
From: Cedric Nugteren <web@cedricnugteren.nl>
Date: Thu, 14 Jun 2018 20:01:31 +0900
Subject: [PATCH 1443/2677] Update CLBlast to v1.4.0

Updated CLBlast from v1.2.0 to v1.4.0
---
 CMakeModules/build_CLBlast.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 5f12529930..acc6365675 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -13,7 +13,7 @@ set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 1.2.0
+    GIT_TAG 1.4.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From 04d298dd9a45af8de68ea5e913f1ece213bd58ed Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 12 Jun 2018 11:57:21 +0530
Subject: [PATCH 1444/2677] Do fft normalization as a post transform as step

---
 src/api/c/fft_common.hpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/api/c/fft_common.hpp b/src/api/c/fft_common.hpp
index bc6af363ff..cbe4ec143d 100644
--- a/src/api/c/fft_common.hpp
+++ b/src/api/c/fft_common.hpp
@@ -28,11 +28,13 @@ Array<outType> fft(const Array<inType> input, const double norm_factor,
 {
     dim4 pdims(1);
     computePaddedDims(pdims, input.dims(), npad, pad);
-    Array<outType> output = padArray<inType, outType>(input, pdims, scalar<outType>(0), norm_factor);
+    auto res = padArray(input, pdims, scalar<outType>(0));
 
-    fft_inplace<outType, rank, direction>(output);
+    fft_inplace<outType, rank, direction>(res);
+    if (norm_factor != 1.0)
+        multiply_inplace(res, norm_factor);
 
-    return output;
+    return res;
 }
 
 template<typename inType, typename outType, int rank>
@@ -51,16 +53,14 @@ Array<outType> fft_r2c(const Array<inType> input, const double norm_factor,
     if (is_pad) {
         dim4 pdims(1);
         computePaddedDims(pdims, input.dims(), npad, pad);
-        tmp = padArray<inType, inType>(input, pdims, scalar<inType>(0), norm_factor);
+        tmp = padArray(input, pdims, scalar<inType>(0));
     }
 
-    Array<outType> output = fft_r2c<outType, inType, rank>(tmp);
-    if (!is_pad && norm_factor != 1) {
-        // Normalize input because tmp was not normalized
-        multiply_inplace(output, norm_factor);
-    }
+    auto res = fft_r2c<outType, inType, rank>(tmp);
+    if (norm_factor != 1.0)
+        multiply_inplace(res, norm_factor);
 
-    return output;
+    return res;
 }
 
 template<typename inType, typename outType, int rank>

From 127a5a4e21345830fdc5a9c744a4dd4293b8068a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 15 Jun 2018 00:17:37 -0400
Subject: [PATCH 1445/2677] Address address sanitizer warnings/errors in tests

---
 test/jit.cpp                   | 4 ++--
 test/moddims.cpp               | 4 +++-
 test/morph.cpp                 | 3 +++
 test/orb.cpp                   | 6 +-----
 test/sparse.cpp                | 1 +
 test/topk.cpp                  | 4 ++++
 test/transform_coordinates.cpp | 2 +-
 test/triangle.cpp              | 6 ++++--
 test/write.cpp                 | 7 ++++---
 9 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/test/jit.cpp b/test/jit.cpp
index be40411bdb..4f8ace3b12 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -47,7 +47,7 @@ TEST(JIT, CPP_JIT_HASH)
             ASSERT_EQ(hF1[i], valF1);
         }
 
-        delete[] hF1;
+        freeHost(hF1);
     }
 
     // Making sure a different kernel is generated
@@ -61,7 +61,7 @@ TEST(JIT, CPP_JIT_HASH)
             ASSERT_EQ(hF2[i], valF2);
         }
 
-        delete[] hF2;
+        freeHost(hF2);
     }
 }
 
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 505f780ac3..ca8029cafb 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -131,15 +131,17 @@ void moddimsArgsTest(string pTestFile)
 
     af_array inArray   = 0;
     af_array outArray  = 0;
+    af_array outArray2  = 0;
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
 
     af::dim4 newDims(1);
     newDims[0] = dims[1];
     newDims[1] = dims[0]*dims[2];
     ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,inArray,0,newDims.get()));
-    ASSERT_EQ(AF_ERR_ARG, af_moddims(&outArray,inArray,newDims.ndims(),NULL));
+    ASSERT_EQ(AF_ERR_ARG, af_moddims(&outArray2,inArray,newDims.ndims(),NULL));
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
 
 TYPED_TEST(Moddims,InvalidArgs)
diff --git a/test/morph.cpp b/test/morph.cpp
index 7c6b1621db..025100fb55 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -493,7 +493,10 @@ TEST(Morph, UnsupportedKernel2D)
 
 #if defined(AF_CPU)
     ASSERT_EQ(AF_SUCCESS, af_dilate(&out, in, mask));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
 #else
     ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_dilate(&out, in, mask));
 #endif
+    ASSERT_EQ(AF_SUCCESS, af_release_array(in));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(mask));
 }
diff --git a/test/orb.cpp b/test/orb.cpp
index 66d7fda789..1548b83d2d 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -213,11 +213,7 @@ void orbTest(string pTestFile)
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(x));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(y));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(score));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(orientation));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(size));
+        ASSERT_EQ(AF_SUCCESS, af_release_features(feat));
         ASSERT_EQ(AF_SUCCESS, af_release_array(desc));
 
         delete[] outX;
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 7f94eea80f..f003bf45ef 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -244,6 +244,7 @@ TYPED_TEST(Sparse, Empty) {
     bool sparse = false;
     EXPECT_EQ(AF_SUCCESS, af_is_sparse(&sparse, ret));
     EXPECT_EQ(true, sparse);
+    EXPECT_EQ(AF_SUCCESS, af_release_array(ret));
 }
 
 TYPED_TEST(Sparse, EmptyDeepCopy) {
diff --git a/test/topk.cpp b/test/topk.cpp
index ad4def845a..d98c5f1820 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -191,6 +191,7 @@ TEST(TopK, ValidationCheck_DimN)
     af_array out, idx, in;
     ASSERT_EQ(AF_SUCCESS, af_randu(&in, 2, dims, f32));
     ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_topk(&out, &idx, in, 10, 1, AF_TOPK_MAX));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(in));
 }
 
 TEST(TopK, ValidationCheck_DefaultDim)
@@ -199,6 +200,9 @@ TEST(TopK, ValidationCheck_DefaultDim)
     af_array out, idx, in;
     ASSERT_EQ(AF_SUCCESS, af_randu(&in, 4, dims, f32));
     ASSERT_EQ(AF_SUCCESS, af_topk(&out, &idx, in, 10, -1, AF_TOPK_MAX));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(in));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
+    ASSERT_EQ(AF_SUCCESS, af_release_array(idx));
 }
 
 
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 3bb531a2ab..2ab9f0c0bb 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -61,6 +61,7 @@ void transformCoordinatesTest(string pTestFile)
         vector<T> outData(outEl);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
         const float thr = 1.f;
 
         for (dim_t elIter = 0; elIter < outEl; elIter++) {
@@ -69,7 +70,6 @@ void transformCoordinatesTest(string pTestFile)
     }
 
     if(tfArray  != 0) af_release_array(tfArray);
-    if(outArray != 0) af_release_array(outArray);
 }
 
 TYPED_TEST(TransformCoordinates, RotateMatrix)
diff --git a/test/triangle.cpp b/test/triangle.cpp
index 5b61d1e465..630ffdd638 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -24,9 +24,11 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+
 using af::cfloat;
 using af::cdouble;
 using af::dim4;
+using af::freeHost;
 
 template<typename T>
 class Triangle : public ::testing::Test { };
@@ -69,8 +71,8 @@ void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag=false)
         }
     }
 
-    delete[] h_in;
-    delete[] h_out;
+    freeHost(h_in);
+    freeHost(h_out);
 }
 
 TYPED_TEST(Triangle, Lower2DRect0)
diff --git a/test/write.cpp b/test/write.cpp
index b96cb0a447..1fc8a373ad 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -22,6 +22,7 @@ using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::freeHost;
 
 template<typename T>
 class Write : public ::testing::Test
@@ -65,9 +66,9 @@ void writeTest(af::dim4 dims)
         ASSERT_EQ(h_check2[i], 0) << "at: " << i << std::endl;
     }
 
-    delete [] a_host;
-    delete [] h_check1;
-    delete [] h_check2;
+    freeHost(a_host);
+    freeHost(h_check1);
+    freeHost(h_check2);
 }
 
 TYPED_TEST(Write, Vector0)

From a38879ec0ba39e6a6ebf47198946304ab991ac5e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 15 Jun 2018 00:34:45 -0400
Subject: [PATCH 1446/2677] Minor improvements to dim4 and CPU BufferNode

---
 src/backend/common/dim4.cpp        | 19 ++++---------------
 src/backend/cpu/Array.cpp          |  2 +-
 src/backend/cpu/Array.hpp          |  2 --
 src/backend/cpu/TNJ/BufferNode.hpp |  2 +-
 4 files changed, 6 insertions(+), 19 deletions(-)

diff --git a/src/backend/common/dim4.cpp b/src/backend/common/dim4.cpp
index 0ffd21cf5d..90408f62a4 100644
--- a/src/backend/common/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -25,31 +25,20 @@ using std::vector;
 using std::numeric_limits;
 using std::abs;
 
-dim4::dim4()
+dim4::dim4() : dims{0, 0, 0, 0}
 {
-    dims[0] = 0;
-    dims[1] = 0;
-    dims[2] = 0;
-    dims[3] = 0;
 }
 
 dim4::dim4( dim_t first,
             dim_t second,
             dim_t third,
-            dim_t fourth)
+            dim_t fourth) :
+  dims { first, second, third, fourth}
 {
-    dims[0] = first;
-    dims[1] = second;
-    dims[2] = third;
-    dims[3] = fourth;
 }
 
 dim4::dim4(const dim4& other)
-{
-    dims[0] = other.dims[0];
-    dims[1] = other.dims[1];
-    dims[2] = other.dims[2];
-    dims[3] = other.dims[3];
+  : dims{other.dims[0], other.dims[1], other.dims[2], other.dims[3]} {
 }
 
 dim4::dim4(const unsigned ndims_, const dim_t * const dims_)
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 0ea0b93ed1..4a24ac9c68 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -213,7 +213,7 @@ createEmptyArray(const dim4 &size)
 }
 
 template<typename T>
-Array<T> *initArray() { return new Array<T>(dim4(0, 0, 0, 0)); }
+Array<T> *initArray() { return new Array<T>(dim4()); }
 
 template<typename T>
 Array<T>
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 19ce30a246..491ef9ae53 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -120,8 +120,6 @@ namespace cpu
         explicit Array(af::dim4 dims, TNJ::Node_ptr n);
 
     public:
-
-
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,
               const T * const in_data, bool is_device = false);
 
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/TNJ/BufferNode.hpp
index 2224c8cdc6..214afce673 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/TNJ/BufferNode.hpp
@@ -27,10 +27,10 @@ namespace TNJ
         shared_ptr<T> m_sptr;
         T *m_ptr;
         unsigned m_bytes;
-        bool m_linear_buffer;
         dim_t m_strides[4];
         dim_t m_dims[4];
         std::once_flag m_set_data_flag;
+        bool m_linear_buffer;
     public:
 
         BufferNode() : TNode<T>(0, 0, {})

From dd8dcc5e652e75fc47b3a8964784d1c77909ebe0 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Fri, 15 Jun 2018 05:32:09 -0500
Subject: [PATCH 1447/2677] Handle NaN values for complex types

* Fixes #2130 and handle NaNs for complex types

* Adds a comment to explain an unusually large value to use in one unit test

* Fixes signature mismatch between template declarations and specializations for `cabs` & `isNan`

* Changes NaN value initializations to static BinaryOp<>::init() calls

* Fixes index order check on CPU kernel and added tests for checking index order in the case of `idx == m_idx`

* MinMaxOp all: Added back the index order check condition with correct parenthesizing

* CUDA: Removed static specifier on template specialized cabs() and isNaN()

* OpenCL: Switched order of IS_NAN and gt/lt conditions for assigning new min/max value in operator

* OpenCL: Changed is_nan check to inline function for indexed reduce

* Style changes to is_nan check  in MinMaxOp related operations, especially conditions in if-else blocks that use is_nan calls.

* Style fixes in MinMaxOp related code sections
---
 src/api/c/ops.hpp                             |  35 ++-
 src/backend/cpu/kernel/ireduce.hpp            |  18 +-
 src/backend/cpu/kernel/morph.hpp              |   4 +-
 src/backend/cpu/kernel/reduce.hpp             |   3 +-
 src/backend/cpu/kernel/scan.hpp               |   4 +-
 src/backend/cpu/kernel/scan_by_key.hpp        |   6 +-
 src/backend/cpu/reduce.cpp                    |   4 +-
 src/backend/cuda/kernel/ireduce.hpp           |  51 ++--
 src/backend/cuda/kernel/mean.hpp              |  12 +-
 src/backend/cuda/kernel/morph.hpp             |   8 +-
 src/backend/cuda/kernel/reduce.hpp            |   8 +-
 src/backend/cuda/kernel/scan_dim.hpp          |   2 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |   4 +-
 src/backend/cuda/kernel/scan_first.hpp        |   2 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |   4 +-
 src/backend/opencl/kernel/iops.cl             |  23 +-
 src/backend/opencl/kernel/ireduce.hpp         |   6 +-
 src/backend/opencl/kernel/ireduce_first.cl    |   2 +-
 src/backend/opencl/kernel/mean.hpp            |   6 +-
 src/backend/opencl/kernel/morph.hpp           |   2 +-
 src/backend/opencl/kernel/ops.cl              |   8 +
 src/backend/opencl/kernel/reduce.hpp          |  10 +-
 src/backend/opencl/kernel/scan_dim.hpp        |   3 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   3 +-
 src/backend/opencl/kernel/scan_first.hpp      |   3 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   3 +-
 test/ireduce.cpp                              | 259 +++++++++++++++++-
 test/reduce.cpp                               |  76 ++++-
 28 files changed, 461 insertions(+), 108 deletions(-)

diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index 54e4d17fe0..ffa4153fe0 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -25,7 +25,7 @@ using namespace detail;
 template<typename T, af_op_t op>
 struct Binary
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::scalar<T>(0);
     }
@@ -39,7 +39,7 @@ struct Binary
 template<typename T>
 struct Binary<T, af_add_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::scalar<T>(0);
     }
@@ -53,7 +53,7 @@ struct Binary<T, af_add_t>
 template<typename T>
 struct Binary<T, af_mul_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::scalar<T>(1);
     }
@@ -67,7 +67,7 @@ struct Binary<T, af_mul_t>
 template<typename T>
 struct Binary<T, af_or_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::scalar<T>(0);
     }
@@ -81,7 +81,7 @@ struct Binary<T, af_or_t>
 template<typename T>
 struct Binary<T, af_and_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::scalar<T>(1);
     }
@@ -95,7 +95,7 @@ struct Binary<T, af_and_t>
 template<typename T>
 struct Binary<T, af_notzero_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::scalar<T>(0);
     }
@@ -109,7 +109,7 @@ struct Binary<T, af_notzero_t>
 template<typename T>
 struct Binary<T, af_min_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::maxval<T>();
     }
@@ -120,11 +120,10 @@ struct Binary<T, af_min_t>
     }
 };
 
-
 template<>
 struct Binary<char, af_min_t>
 {
-    __DH__ char init()
+    static __DH__ char init()
     {
         return 1;
     }
@@ -139,10 +138,10 @@ struct Binary<char, af_min_t>
     template<>                                  \
     struct Binary<T, af_min_t>                  \
     {                                           \
-        __DH__ T init()                         \
+        static __DH__ T init()                  \
         {                                       \
             return detail::scalar<T>(           \
-                detail::maxval<Tr>()         \
+                detail::maxval<Tr>()            \
                 );                              \
         }                                       \
                                                 \
@@ -150,7 +149,7 @@ struct Binary<char, af_min_t>
         {                                       \
             return detail::min(lhs, rhs);       \
         }                                       \
-    };                                          \
+    };
 
 SPECIALIZE_COMPLEX_MIN(cfloat, float)
 SPECIALIZE_COMPLEX_MIN(cdouble, double)
@@ -160,7 +159,7 @@ SPECIALIZE_COMPLEX_MIN(cdouble, double)
 template<typename T>
 struct Binary<T, af_max_t>
 {
-    __DH__ T init()
+    static __DH__ T init()
     {
         return detail::minval<T>();
     }
@@ -174,7 +173,7 @@ struct Binary<T, af_max_t>
 template<>
 struct Binary<char, af_max_t>
 {
-    __DH__ char init()
+    static __DH__ char init()
     {
         return 0;
     }
@@ -189,7 +188,7 @@ struct Binary<char, af_max_t>
     template<>                                  \
     struct Binary<T, af_max_t>                  \
     {                                           \
-        __DH__ T init()                         \
+        static __DH__ T init()                  \
         {                                       \
             return detail::scalar<T>(           \
                 detail::scalar<Tr>(0)           \
@@ -200,7 +199,7 @@ struct Binary<char, af_max_t>
         {                                       \
             return detail::max(lhs, rhs);       \
         }                                       \
-    };                                          \
+    };
 
 SPECIALIZE_COMPLEX_MAX(cfloat, float)
 SPECIALIZE_COMPLEX_MAX(cdouble, double)
@@ -221,7 +220,7 @@ struct Transform<Ti, To, af_min_t>
 {
     __DH__ To operator ()(Ti in)
     {
-        return (To) (IS_NAN(in) ? Binary<To, af_min_t>().init() : in);
+        return (To) (IS_NAN(in) ? Binary<To, af_min_t>::init() : in);
     }
 };
 
@@ -230,7 +229,7 @@ struct Transform<Ti, To, af_max_t>
 {
     __DH__ To operator ()(Ti in)
     {
-        return (To) (IS_NAN(in) ? Binary<To, af_max_t>().init() : in);
+        return (To) (IS_NAN(in) ? Binary<To, af_max_t>::init() : in);
     }
 };
 
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index a62e278d4f..56bd121f28 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+#include <ops.hpp>
 
 namespace cpu
 {
@@ -19,6 +20,7 @@ template<typename T> double cabs(const T in) { return (double)in; }
 static double cabs(const char in) { return (double)(in > 0); }
 static double cabs(const cfloat &in) { return (double)abs(in); }
 static double cabs(const cdouble &in) { return (double)abs(in); }
+template<typename T> static bool is_nan(T in) { return in != in; }
 
 template<af_op_t op, typename T>
 struct MinMaxOp
@@ -28,13 +30,15 @@ struct MinMaxOp
     MinMaxOp(T val, uint idx) :
         m_val(val), m_idx(idx)
     {
+        if (is_nan(val)) {
+            m_val = Binary<T, op>::init();
+        }
     }
 
     void operator()(T val, uint idx)
     {
-        if (cabs(val) < cabs(m_val) ||
-            (cabs(val) == cabs(m_val) &&
-             idx > m_idx)) {
+        if ((cabs(val) < cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx > m_idx))) {
             m_val = val;
             m_idx = idx;
         }
@@ -49,13 +53,15 @@ struct MinMaxOp<af_max_t, T>
     MinMaxOp(T val, uint idx) :
         m_val(val), m_idx(idx)
     {
+        if (is_nan(val)) {
+            m_val = Binary<T, af_max_t>::init();
+        }
     }
 
     void operator()(T val, uint idx)
     {
-        if (cabs(val) > cabs(m_val) ||
-            (cabs(val) == cabs(m_val) &&
-             idx <= m_idx)) {
+        if ((cabs(val) > cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx <= m_idx))) {
             m_val = val;
             m_idx = idx;
         }
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index d6c9b1665f..79486cea17 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -29,7 +29,7 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask)
     const dim_t R0      = window[0]/2;
     const dim_t R1      = window[1]/2;
 
-    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T init = IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 
     for(dim_t b3=0; b3<dims[3]; ++b3) {
 
@@ -93,7 +93,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask)
     const T*   inData   = in.get();
     const T*   filter   = mask.get();
 
-    T init = IsDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T init = IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 
     for(dim_t batchId=0; batchId<bCount; ++batchId) {
         // either channels or batch is handled by outer most loop
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index c75e897ac2..fff50c747b 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -54,7 +54,7 @@ struct reduce_dim<op, Ti, To, 0>
         Ti const * const inPtr = in.get() + inOffset;
         dim_t stride = istrides[dim];
 
-        To out_val = reduce.init();
+        To out_val = Binary<To, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
             To in_val = transform(inPtr[i * stride]);
             if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
@@ -65,6 +65,5 @@ struct reduce_dim<op, Ti, To, 0>
     }
 };
 
-
 }
 }
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index 561c12f519..af4b702938 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -57,7 +57,7 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan>
         // FIXME: Change the name to something better
         Binary<To, op> scan;
 
-        To out_val = scan.init();
+        To out_val = Binary<To, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
             To in_val = transform(in[i * istride]);
             out_val = scan(in_val, out_val);
@@ -65,7 +65,7 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan>
                 //The loop shifts the output index by 1.
                 //The last index wraps around and writes the first element.
                 if (i == (idims[dim] - 1)) {
-                    out[0] = scan.init();
+                    out[0] = Binary<To, op>::init();
                 } else {
                     out[(i + 1) * ostride] = out_val;
                 }
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index 449d3027fc..767dd4e27f 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -70,18 +70,18 @@ struct scan_dim_by_key<op, Ti, Tk, To, 0>
         // FIXME: Change the name to something better
         Binary<To, op> scan;
 
-        To out_val = scan.init();
+        To out_val = Binary<To, op>::init();
         Tk key_val = key[0];
 
         dim_t k = !inclusive_scan;
         if (!inclusive_scan) {
-            out[0] = scan.init();
+            out[0] = Binary<To, op>::init();
         }
 
         for (dim_t i = 0; i < idims[dim] - (!inclusive_scan); i++, k++) {
             To in_val = transform(in[i * istride]);
             if (key[k * kstride] != key_val) {
-                out_val = !inclusive_scan? scan.init() : in_val;
+                out_val = !inclusive_scan? Binary<To, op>::init() : in_val;
                 key_val = key[k * kstride];
             } else {
                 out_val = scan(in_val, out_val);
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 73cf955795..9604814724 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -22,7 +22,7 @@ using af::dim4;
 template<>
 struct Binary<cdouble, af_add_t>
 {
-    cdouble init()
+    static cdouble init()
     {
         return cdouble(0,0);
     }
@@ -68,7 +68,7 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
     Transform<Ti, To, op> transform;
     Binary<To, op> reduce;
 
-    To out = reduce.init();
+    To out = Binary<To, op>::init();
 
     // Decrement dimension of select dimension
     af::dim4 dims = in.dims();
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index c90d99f031..6d162ec5ac 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -22,11 +22,30 @@ namespace cuda
 {
 namespace kernel
 {
+    template<typename T> __host__ __device__
+    static double cabs(const T& in) { return (double)in; }
 
-    template<typename T> __host__ __device__ double cabs(const T in) { return (double)in; }
-    static double __host__ __device__ cabs(const char in) { return (double)(in > 0); }
-    static double __host__ __device__ cabs(const cfloat &in) { return (double)abs(in); }
-    static double __host__ __device__ cabs(const cdouble &in) { return (double)abs(in); }
+    template<> __host__ __device__
+    double cabs<char>(const char& in) { return (double)(in > 0); }
+
+    template<> __host__ __device__
+    double cabs<cfloat>(const cfloat &in) { return (double)abs(in); }
+
+    template<> __host__ __device__
+    double cabs<cdouble>(const cdouble &in) { return (double)abs(in); }
+
+    template<typename T> __host__ __device__
+    static bool is_nan(const T& in) { return in != in; }
+
+    template<> __host__ __device__
+    bool is_nan<cfloat>(const cfloat &in) {
+        return in.x != in.x || in.y != in.y;
+    }
+
+    template<> __host__ __device__
+    bool is_nan<cdouble>(const cdouble &in) {
+        return in.x != in.x || in.y != in.y;
+    }
 
     template<af_op_t op, typename T>
     struct MinMaxOp
@@ -36,13 +55,15 @@ namespace kernel
         __host__ __device__ MinMaxOp(T val, uint idx) :
             m_val(val), m_idx(idx)
         {
+            if (is_nan(val)) {
+                m_val = Binary<T, op>::init();
+            }
         }
 
         __host__ __device__ void operator()(T val, uint idx)
         {
-            if (cabs(val) < cabs(m_val) ||
-                (cabs(val) == cabs(m_val) &&
-                 idx > m_idx)) {
+            if ((cabs(val) < cabs(m_val) ||
+                 (cabs(val) == cabs(m_val) && idx > m_idx))) {
                 m_val = val;
                 m_idx = idx;
             }
@@ -57,13 +78,15 @@ namespace kernel
         __host__ __device__ MinMaxOp(T val, uint idx) :
             m_val(val), m_idx(idx)
         {
+            if (is_nan(val)) {
+                m_val = Binary<T, af_max_t>::init();
+            }
         }
 
         __host__ __device__ void operator()(T val, uint idx)
         {
-            if (cabs(val) > cabs(m_val) ||
-                (cabs(val) == cabs(m_val) &&
-                 idx <= m_idx)) {
+            if ((cabs(val) > cabs(m_val) ||
+                 (cabs(val) == cabs(m_val) && idx <= m_idx))) {
                 m_val = val;
                 m_idx = idx;
             }
@@ -112,9 +135,7 @@ namespace kernel
             (ids[2] < in.dims[2]) &&
             (ids[3] < in.dims[3]);
 
-        Binary<T, op> ireduce;
-
-        T val = ireduce.init();
+        T val = Binary<T, op>::init();
         uint idx = id_dim_in;
 
         if (is_valid && id_dim_in < in.dims[dim]) {
@@ -304,9 +325,7 @@ namespace kernel
 
         int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-        Binary<T, op> ireduce;
-
-        T val = ireduce.init();
+        T val = Binary<T, op>::init();
         uint idx = xid;
 
         if (xid < lim) {
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 3d788b73fc..15c8f43de9 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -95,11 +95,9 @@ namespace kernel
             (ids[3] < in.dims[3]);
 
         Transform<Ti, To, af_add_t> transform;
-        Binary<To, af_add_t> mean_obj;
-        Binary<Tw, af_add_t> weight_obj;
 
-        To val = mean_obj.init();
-        Tw weight = weight_obj.init();
+        To val = Binary<To, af_add_t>::init();
+        Tw weight = Binary<Tw, af_add_t>::init();
 
         if (is_valid && id_dim_in < in.dims[dim]) {
             val = transform(*iptr);
@@ -280,11 +278,9 @@ namespace kernel
         int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
         Transform<Ti, To, af_add_t> transform;
-        Binary<To, af_add_t> mean_obj;
-        Binary<Tw, af_add_t> weight_obj;
 
-        To val = mean_obj.init();
-        Tw weight = weight_obj.init();
+        To val = Binary<To, af_add_t>::init();
+        Tw weight = Binary<Tw, af_add_t>::init();
 
         if (xid < lim) {
             val = transform(iptr[xid]);
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 68d0353e11..3b6e5f5d67 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -48,7 +48,7 @@ inline __device__ void load2ShrdMem(T * shrd, const T * const in,
         int gx, int gy,
         int inStride1, int inStride0)
 {
-    T val = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T val = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
     if (gx>=0 && gx<dim0 && gy>=0 && gy<dim1) {
         val = in[ lIdx(gx, gy, inStride1, inStride0) ];
     }
@@ -116,7 +116,7 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
     __syncthreads();
 
     const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T acc = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 #pragma unroll
     for(int wj=0; wj<windLen; ++wj) {
         int joff   = wj*windLen;
@@ -153,7 +153,7 @@ inline __device__ void load2ShrdVolume(T * shrd, const T * const in,
         int gx, int gy, int gz,
         int inStride2, int inStride1, int inStride0)
 {
-    T val = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T val = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
     if (gx>=0 && gx<dim0 &&
         gy>=0 && gy<dim1 &&
         gz>=0 && gz<dim2) {
@@ -212,7 +212,7 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
     int k  = lz + halo;
 
     const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T acc = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 #pragma unroll
     for(int wk=0; wk<windLen; ++wk) {
         int koff   = wk*se_area;
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 815ff65115..6c54b058ba 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -73,7 +73,7 @@ namespace kernel
 
         Transform<Ti, To, op> transform;
         Binary<To, op> reduce;
-        To out_val = reduce.init();
+        To out_val = Binary<To, op>::init();
         for (int id = id_dim_in; is_valid && (id < in.dims[dim]); id += offset_dim * blockDim.y) {
             To in_val = transform(*iptr);
             if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
@@ -217,7 +217,7 @@ namespace kernel
 
         int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-        To out_val = reduce.init();
+        To out_val = Binary<To, op>::init();
         for (int id = xid; id < lim; id += DIMX) {
             To in_val = transform(iptr[id]);
             if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
@@ -387,7 +387,7 @@ namespace kernel
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
             Binary<To, op> reduce;
-            To out = reduce.init();
+            To out = Binary<To, op>::init();
             for (int i = 0; i < tmp_elements; i++) {
                 out = reduce(out, h_ptr_raw[i]);
             }
@@ -404,7 +404,7 @@ namespace kernel
 
             Transform<Ti, To, op> transform;
             Binary<To, op> reduce;
-            To out = reduce.init();
+            To out = Binary<To, op>::init();
             To nanval_to = scalar<To>(nanval);
 
             for (int i = 0; i < in_elements; i++) {
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index ec4b660f30..22bad27cc8 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -77,7 +77,7 @@ namespace kernel
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
 
-        const To init = binop.init();
+        const To init = Binary<To, op>::init();
         To val = init;
 
         const bool isLast = (tidy == (DIMY - 1));
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index d414f6669e..05fbf12b0f 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -99,7 +99,7 @@ namespace kernel
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
 
-        const To init = binop.init();
+        const To init = Binary<To, op>::init();
         To val = init;
 
         const bool isLast = (tidy == (DIMY - 1));
@@ -254,7 +254,7 @@ namespace kernel
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
 
-        const To init = binop.init();
+        const To init = Binary<To, op>::init();
         To val = init;
 
         const bool isLast = (tidy == (DIMY - 1));
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index e20fd9c02a..bbd16f2c00 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -64,7 +64,7 @@ namespace kernel
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
 
-        const To init = binop.init();
+        const To init = Binary<To, op>::init();
         int id = xid;
         To val = init;
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 53fa9463da..995ffd6f1c 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -44,7 +44,7 @@ namespace kernel
     {
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
-        const To init = binop.init();
+        const To init = Binary<To, op>::init();
         To val = init;
 
         const int istride = in.strides[0];
@@ -182,7 +182,7 @@ namespace kernel
     {
         Transform<Ti, To, op> transform;
         Binary<To, op> binop;
-        const To init = binop.init();
+        const To init = Binary<To, op>::init();
         To val = init;
 
         const int istride = in.strides[0];
diff --git a/src/backend/opencl/kernel/iops.cl b/src/backend/opencl/kernel/iops.cl
index e5546e5e69..000848b0af 100644
--- a/src/backend/opencl/kernel/iops.cl
+++ b/src/backend/opencl/kernel/iops.cl
@@ -7,13 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if CPLX
+inline bool is_nan(T in) { return (in.x != in.x) || (in.y != in.y); }
+#else
+inline bool is_nan(T in) { return (in != in); }
+#endif
+
 #if CPLX
 #define sabs(in) ((in.x)*(in.x) + (in.y)*(in.y))
 #ifdef MIN_OP
 void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
 {
-    if ((sabs(lhs[0]) > sabs(rhs)) ||
-        (sabs(lhs[0]) == sabs(rhs) && *lidx < ridx)) {
+    if (((sabs(lhs[0]) > sabs(rhs)) ||
+         (sabs(lhs[0]) == sabs(rhs) && *lidx < ridx))) {
         *lhs = rhs;
         *lidx = ridx;
     }
@@ -23,20 +29,19 @@ void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
 #ifdef MAX_OP
 void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
 {
-    if ((sabs(lhs[0]) < sabs(rhs)) ||
-        (sabs(lhs[0]) == sabs(rhs) && *lidx > ridx)) {
+    if (((sabs(lhs[0]) < sabs(rhs)) ||
+         (sabs(lhs[0]) == sabs(rhs) && *lidx > ridx))) {
         *lhs = rhs;
         *lidx = ridx;
     }
 }
 #endif
 #else
-#define sabs(in) in
 #ifdef MIN_OP
 void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
 {
-    if ((*lhs > rhs) ||
-        (*lhs == rhs && *lidx < ridx)) {
+    if (((*lhs > rhs) ||
+         (*lhs == rhs && *lidx < ridx))) {
         *lhs = rhs;
         *lidx = ridx;
     }
@@ -46,8 +51,8 @@ void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
 #ifdef MAX_OP
 void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
 {
-    if ((*lhs < rhs) ||
-        (*lhs == rhs && *lidx > ridx)) {
+    if (((*lhs < rhs) ||
+         (*lhs == rhs && *lidx > ridx))) {
         *lhs = rhs;
         *lidx = ridx;
     }
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index d6b78547f7..af76439d46 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -67,7 +67,6 @@ namespace kernel
 
         if (entry.prog==0 && entry.ker==0) {
 
-            Binary<T, op> ireduce;
             ToNumStr<T> toNumStr;
 
             std::ostringstream options;
@@ -75,7 +74,7 @@ namespace kernel
                 << " -D dim=" << dim
                 << " -D DIMY=" << threads_y
                 << " -D THREADS_X=" << THREADS_X
-                << " -D init=" << toNumStr(ireduce.init())
+                << " -D init=" << toNumStr(Binary<T, op>::init())
                 << " -D " << binOpName<op>()
                 << " -D CPLX=" << af::iscplx<T>()
                 << " -D IS_FIRST=" << is_first;
@@ -177,14 +176,13 @@ namespace kernel
 
         if (entry.prog==0 && entry.ker==0) {
 
-            Binary<T, op> ireduce;
             ToNumStr<T> toNumStr;
 
             std::ostringstream options;
             options << " -D T=" << dtype_traits<T>::getName()
                     << " -D DIMX=" << threads_x
                     << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                    << " -D init=" << toNumStr(ireduce.init())
+                    << " -D init=" << toNumStr(Binary<T, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<T>()
                     << " -D IS_FIRST=" << is_first;
diff --git a/src/backend/opencl/kernel/ireduce_first.cl b/src/backend/opencl/kernel/ireduce_first.cl
index a135260205..9c9453c2e2 100644
--- a/src/backend/opencl/kernel/ireduce_first.cl
+++ b/src/backend/opencl/kernel/ireduce_first.cl
@@ -51,7 +51,7 @@ void ireduce_first_kernel(__global T *oData,
     T out_val = init;
     uint out_idx = xid;
 
-    if (cond && xid < lim) {
+    if (cond && xid < lim && !is_nan(iData[xid])) {
         out_val = iData[xid];
         if (!IS_FIRST) out_idx = ilData[xid];
     }
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 9a2d9e334f..c4d20d928f 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -154,7 +154,6 @@ void mean_dim_launcher(Param out, Param owt,
 
     if (entry.prog==0 && entry.ker==0) {
 
-        Binary<To, af_add_t> mean;
         ToNumStr<To> toNumStr;
         ToNumStr<Tw> twNumStr;
         Transform<uint, Tw, af_add_t> transform_weight;
@@ -166,7 +165,7 @@ void mean_dim_launcher(Param out, Param owt,
             << " -D dim=" << dim
             << " -D DIMY=" << threads_y
             << " -D THREADS_X=" << THREADS_X
-            << " -D init_To=" << toNumStr(mean.init())
+            << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
             << " -D init_Tw=" << twNumStr(transform_weight(0))
             << " -D one_Tw=" << twNumStr(transform_weight(1));
 
@@ -322,7 +321,6 @@ void mean_first_launcher(Param out, Param owt,
 
     if (entry.prog==0 && entry.ker==0) {
 
-        Binary<To, af_add_t> mean;
         ToNumStr<To> toNumStr;
         ToNumStr<Tw> twNumStr;
         Transform<uint, Tw, af_add_t> transform_weight;
@@ -333,7 +331,7 @@ void mean_first_launcher(Param out, Param owt,
             << " -D To=" << dtype_traits<To>::getName()
             << " -D DIMX=" << threads_x
             << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-            << " -D init_To=" << toNumStr(mean.init())
+            << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
             << " -D init_Tw=" << twNumStr(transform_weight(0))
             << " -D one_Tw=" << twNumStr(transform_weight(1));
 
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 8cb026525f..dd1dbf6f01 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -44,7 +44,7 @@ template<typename T, bool isDilation, int SeLength>
 std::string generateOptionsString()
 {
     ToNumStr<T> toNumStr;
-    T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
+    T init = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
     std::ostringstream options;
     options << " -D T=" << dtype_traits<T>::getName()
         << " -D isDilation="<< isDilation
diff --git a/src/backend/opencl/kernel/ops.cl b/src/backend/opencl/kernel/ops.cl
index 93313c6a0f..90e2548762 100644
--- a/src/backend/opencl/kernel/ops.cl
+++ b/src/backend/opencl/kernel/ops.cl
@@ -102,6 +102,10 @@ uint transform(Ti in)
 
 #ifdef MIN_OP
 
+#if CPLX
+    #define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
+#endif
+
 T transform(T in)
 {
     T val = init;
@@ -122,6 +126,10 @@ T binOp(T lhs, T rhs)
 
 #ifdef MAX_OP
 
+#if CPLX
+    #define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
+#endif
+
 T transform(T in)
 {
     T val = init;
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 845fdb270e..fe8ecc62eb 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -64,7 +64,6 @@ namespace kernel
         kc_entry_t entry = kernelCache(device, ref_name);
 
         if (entry.prog==0 && entry.ker==0) {
-            Binary<To, op> reduce;
             ToNumStr<To> toNumStr;
 
             std::ostringstream options;
@@ -74,7 +73,7 @@ namespace kernel
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y
                     << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNumStr(reduce.init())
+                    << " -D init=" << toNumStr(Binary<To, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>();
             if (std::is_same<Ti, double>::value ||
@@ -181,7 +180,6 @@ namespace kernel
 
         if (entry.prog==0 && entry.ker==0) {
 
-            Binary<To, op> reduce;
             ToNumStr<To> toNumStr;
 
             std::ostringstream options;
@@ -190,7 +188,7 @@ namespace kernel
                     << " -D T=To"
                     << " -D DIMX=" << threads_x
                     << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                    << " -D init=" << toNumStr(reduce.init())
+                    << " -D init=" << toNumStr(Binary<To, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>();
             if (std::is_same<Ti, double>::value ||
@@ -311,7 +309,7 @@ namespace kernel
             getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.data());
 
             Binary<To, op> reduce;
-            To out = reduce.init();
+            To out = Binary<To, op>::init();
             for (int i = 0; i < (int)tmp_elements; i++) {
                 out = reduce(out, h_ptr[i]);
             }
@@ -324,7 +322,7 @@ namespace kernel
 
             Transform<Ti, To, op> transform;
             Binary<To, op> reduce;
-            To out = reduce.init();
+            To out = Binary<To, op>::init();
             To nanval_to = scalar<To>(nanval);
 
             for (int i = 0; i < (int)in_elements; i++) {
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index c403defe2c..9ebcbd02dc 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -59,7 +59,6 @@ namespace kernel
         kc_entry_t entry = kernelCache(device, ref_name);
 
         if (entry.prog==0 && entry.ker==0) {
-            Binary<To, op> scan;
             ToNumStr<To> toNumStr;
 
             std::ostringstream options;
@@ -69,7 +68,7 @@ namespace kernel
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y
                     << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNumStr(scan.init())
+                    << " -D init=" << toNumStr(Binary<To, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D isFinalPass=" << (int)(isFinalPass)
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index deef4aa28a..858c6a3a2f 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -62,7 +62,6 @@ namespace kernel
 
         if (entry.prog==0 && entry.ker==0) {
 
-            Binary<To, op> scan;
             ToNumStr<To> toNumStr;
 
             std::ostringstream options;
@@ -73,7 +72,7 @@ namespace kernel
                     << " -D dim=" << dim
                     << " -D DIMY=" << threads_y
                     << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNumStr(scan.init())
+                    << " -D init=" << toNumStr(Binary<To, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D calculateFlags=" << calculateFlags
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index 14f99fa883..7356c72bbf 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -63,7 +63,6 @@ namespace kernel
             const uint threads_y = THREADS_PER_GROUP / threads_x;
             const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
 
-            Binary<To, op> scan;
             ToNumStr<To> toNumStr;
 
             std::ostringstream options;
@@ -73,7 +72,7 @@ namespace kernel
                     << " -D DIMX=" << threads_x
                     << " -D DIMY=" << threads_y
                     << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNumStr(scan.init())
+                    << " -D init=" << toNumStr(Binary<To, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D isFinalPass=" << (int)(isFinalPass)
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index c88d0b3994..f419760f44 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -65,7 +65,6 @@ namespace kernel
             const uint threads_y = THREADS_PER_GROUP / threads_x;
             const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
 
-            Binary<To, op> scan;
             ToNumStr<To> toNumStr;
 
             std::ostringstream options;
@@ -76,7 +75,7 @@ namespace kernel
                     << " -D DIMX=" << threads_x
                     << " -D DIMY=" << threads_y
                     << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNumStr(scan.init())
+                    << " -D init=" << toNumStr(Binary<To, op>::init())
                     << " -D " << binOpName<op>()
                     << " -D CPLX=" << af::iscplx<Ti>()
                     << " -D calculateFlags=" << calculateFlags
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 990b94b8cd..1e963bc5ff 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -14,7 +14,16 @@
 #include <testHelpers.hpp>
 #include <algorithm>
 
-using namespace af;
+using std::vector;
+using std::complex;
+using af::array;
+using af::dtype;
+using af::dtype_traits;
+using af::randu;
+using af::constant;
+using af::span;
+using af::min;
+using af::allTrue;
 
 #define MINMAXOP(fn, ty)                                \
     TEST(IndexedReduce, fn##_##ty##_0)                  \
@@ -185,3 +194,251 @@ TEST(IndexedReduce, MaxReduceDimensionHasSingleValue)
     ASSERT_TRUE(allTrue<bool>(mm == data));
     ASSERT_TRUE(allTrue<bool>(indx == 0));
 }
+
+TEST(IndexedReduce, MinNaN)
+{
+    float test_data[] = { 1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f };
+    int rows = 4;
+    int cols = 2;
+    array a(rows, cols, test_data);
+
+    float gold_min_val[] = { 0.1f, -0.5f };
+    int gold_min_idx[] = { 3, 1 };
+
+    array min_val;
+    array min_idx;
+    min(min_val, min_idx, a);
+
+    vector<float> h_min_val(cols);
+    min_val.host(&h_min_val[0]);
+
+    vector<int> h_min_idx(cols);
+    min_idx.host(&h_min_idx[0]);
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_FLOAT_EQ(h_min_val[i], gold_min_val[i]);
+    }
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_EQ(h_min_idx[i], gold_min_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, MaxNaN)
+{
+    float test_data[] = { 1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f };
+    int rows = 4;
+    int cols = 2;
+    array a(rows, cols, test_data);
+
+    float gold_max_val[] = { 5.0f, 0.f };
+    int gold_max_idx[] = { 2, 3 };
+
+    array max_val;
+    array max_idx;
+    max(max_val, max_idx, a);
+
+    vector<float> h_max_val(cols);
+    max_val.host(&h_max_val[0]);
+
+    vector<int> h_max_idx(cols);
+    max_idx.host(&h_max_idx[0]);
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_FLOAT_EQ(h_max_val[i], gold_max_val[i]);
+    }
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_EQ(h_max_idx[i], gold_max_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, MinCplxNaN)
+{
+    float real_wnan_data[] = {
+        0.005f, NAN, -6.3f, NAN, -0.5f,
+        NAN, NAN, 0.2f, -1205.4f, 8.9f
+    };
+
+    float imag_wnan_data[] = {
+        NAN, NAN, -9.0f, -0.005f, -0.3f,
+        0.007f, NAN, 0.1f, NAN, 4.5f
+    };
+
+    int rows = 5;
+    int cols = 2;
+    array real_wnan(rows, cols, real_wnan_data);
+    array imag_wnan(rows, cols, imag_wnan_data);
+    array a = af::complex(real_wnan, imag_wnan);
+
+    float gold_min_real[] = { -0.5f, 0.2f };
+    float gold_min_imag[] = { -0.3f, 0.1f };
+    int gold_min_idx[] = { 4, 2 };
+
+    array min_val;
+    array min_idx;
+    af::min(min_val, min_idx, a);
+
+    vector< complex<float> > h_min_val(cols);
+    min_val.host(&h_min_val[0]);
+
+    vector<int> h_min_idx(cols);
+    min_idx.host(&h_min_idx[0]);
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_FLOAT_EQ(h_min_val[i].real(), gold_min_real[i]);
+        ASSERT_FLOAT_EQ(h_min_val[i].imag(), gold_min_imag[i]);
+    }
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_EQ(h_min_idx[i], gold_min_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, MaxCplxNaN)
+{
+    float real_wnan_data[] = {
+        0.005f, NAN, -6.3f, NAN, -0.5f,
+        NAN, NAN, 0.2f, -1205.4f, 8.9f
+    };
+
+    float imag_wnan_data[] = {
+        NAN, NAN, -9.0f, -0.005f, -0.3f,
+        0.007f, NAN, 0.1f, NAN, 4.5f
+    };
+
+    int rows = 5;
+    int cols = 2;
+    array real_wnan(rows, cols, real_wnan_data);
+    array imag_wnan(rows, cols, imag_wnan_data);
+    array a = af::complex(real_wnan, imag_wnan);
+
+    float gold_max_real[] = { -6.3f, 8.9f };
+    float gold_max_imag[] = { -9.0f, 4.5f };
+    int gold_max_idx[] = { 2, 4 };
+
+    array max_val;
+    array max_idx;
+    af::max(max_val, max_idx, a);
+
+    vector< complex<float> > h_max_val(cols);
+    max_val.host(&h_max_val[0]);
+
+    vector<int> h_max_idx(cols);
+    max_idx.host(&h_max_idx[0]);
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_FLOAT_EQ(h_max_val[i].real(), gold_max_real[i]);
+        ASSERT_FLOAT_EQ(h_max_val[i].imag(), gold_max_imag[i]);
+    }
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_EQ(h_max_idx[i], gold_max_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, MinPreferLargerIdxIfEqual)
+{
+    float test_data[] = {0.f, 50.f, 50.f, 0.f};
+    int len = 4;
+    array a(len, test_data);
+
+    float gold_min_val = 0.f;
+    int gold_min_idx = 3;
+
+    array min_val;
+    array min_idx;
+    min(min_val, min_idx, a);
+
+    vector<float> h_min_val(1);
+    min_val.host(&h_min_val[0]);
+
+    vector<int> h_min_idx(1);
+    min_idx.host(&h_min_idx[0]);
+
+    ASSERT_FLOAT_EQ(h_min_val[0], gold_min_val);
+    ASSERT_EQ(h_min_idx[0], gold_min_idx);
+}
+
+TEST(IndexedReduce, MaxPreferSmallerIdxIfEqual)
+{
+    float test_data[] = {0.f, 50.f, 50.f, 0.f};
+    int len = 4;
+    array a(len, test_data);
+
+    float gold_max_val = 50.f;
+    int gold_max_idx = 1;
+
+    array max_val;
+    array max_idx;
+    max(max_val, max_idx, a);
+
+    vector<float> h_max_val(1);
+    max_val.host(&h_max_val[0]);
+
+    vector<int> h_max_idx(1);
+    max_idx.host(&h_max_idx[0]);
+
+    ASSERT_FLOAT_EQ(h_max_val[0], gold_max_val);
+    ASSERT_EQ(h_max_idx[0], gold_max_idx);
+}
+
+TEST(IndexedReduce, MinCplxPreferLargerIdxIfEqual)
+{
+    float real_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
+    float imag_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
+
+    int len = 4;
+    array real_wnan(len, real_wnan_data);
+    array imag_wnan(len, imag_wnan_data);
+    array a = af::complex(real_wnan, imag_wnan);
+
+    float gold_min_real = 0.f;
+    float gold_min_imag = 0.f;
+    int gold_min_idx = 3;
+
+    array min_val;
+    array min_idx;
+    min(min_val, min_idx, a);
+
+    vector< complex<float> > h_min_val(1);
+    min_val.host(&h_min_val[0]);
+
+    vector<int> h_min_idx(1);
+    min_idx.host(&h_min_idx[0]);
+
+    ASSERT_FLOAT_EQ(h_min_val[0].real(), gold_min_real);
+    ASSERT_FLOAT_EQ(h_min_val[0].imag(), gold_min_imag);
+
+    ASSERT_EQ(h_min_idx[0], gold_min_idx);
+}
+
+TEST(IndexedReduce, MaxCplxPreferSmallerIdxIfEqual)
+{
+    float real_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
+    float imag_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
+
+    int len = 4;
+    array real_wnan(len, real_wnan_data);
+    array imag_wnan(len, imag_wnan_data);
+    array a = af::complex(real_wnan, imag_wnan);
+
+    float gold_max_real = 50.f;
+    float gold_max_imag = 50.f;
+    int gold_max_idx = 1;
+
+    array max_val;
+    array max_idx;
+    max(max_val, max_idx, a);
+
+    vector< complex<float> > h_max_val(1);
+    max_val.host(&h_max_val[0]);
+
+    vector<int> h_max_idx(1);
+    max_idx.host(&h_max_idx[0]);
+
+    ASSERT_FLOAT_EQ(h_max_val[0].real(), gold_max_real);
+    ASSERT_FLOAT_EQ(h_max_val[0].imag(), gold_max_imag);
+
+    ASSERT_EQ(h_max_idx[0], gold_max_idx);
+}
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 1959336b4f..49b8de86ce 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -18,6 +18,7 @@
 #include <algorithm>
 
 using std::vector;
+using std::complex;
 using std::string;
 using std::cout;
 using std::endl;
@@ -508,7 +509,7 @@ TYPED_TEST(Reduce, Test_Any_Global)
     }
 }
 
-TEST(MinMax, NaN)
+TEST(MinMax, MinMaxNaN)
 {
     const int num = 10000;
     array A = randu(num);
@@ -532,6 +533,79 @@ TEST(MinMax, NaN)
     freeHost(h_A);
 }
 
+TEST(MinMax, MinCplxNaN)
+{
+    float real_wnan_data[] = {
+        0.005f, NAN, -6.3f, NAN, -0.5f,
+        NAN, NAN, 0.2f, -1205.4f, 8.9f
+    };
+
+    float imag_wnan_data[] = {
+        NAN, NAN, -9.0f, -0.005f, -0.3f,
+        0.007f, NAN, 0.1f, NAN, 4.5f
+    };
+
+    int rows = 5;
+    int cols = 2;
+    array real_wnan(rows, cols, real_wnan_data);
+    array imag_wnan(rows, cols, imag_wnan_data);
+    array a = af::complex(real_wnan, imag_wnan);
+
+    float gold_min_real[] = { -0.5f, 0.2f };
+    float gold_min_imag[] = { -0.3f, 0.1f };
+
+    array min_val = af::min(a);
+
+    vector< complex<float> > h_min_val(cols);
+    min_val.host(&h_min_val[0]);
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_FLOAT_EQ(h_min_val[i].real(), gold_min_real[i]);
+        ASSERT_FLOAT_EQ(h_min_val[i].imag(), gold_min_imag[i]);
+    }
+}
+
+TEST(MinMax, MaxCplxNaN)
+{
+    // 4th element is unusually large to cover the case where
+    //  one part holds the largest value among the array,
+    //  and the other part is NaN.
+    // There's a possibility where the NaN is turned into 0
+    //  (since Binary<>::init() will initialize it to 0 in
+    //  for complex max op) during the comparisons, and so its
+    //  magnitude will determine that that element is the max,
+    //  whereas it should have been ignored since its other
+    //  part is NaN
+    float real_wnan_data[] = {
+        0.005f, NAN, -6.3f, NAN, -0.5f,
+        NAN, NAN, 0.2f, -1205.4f, 8.9f
+    };
+
+    float imag_wnan_data[] = {
+        NAN, NAN, -9.0f, -0.005f, -0.3f,
+        0.007f, NAN, 0.1f, NAN, 4.5f
+    };
+
+    int rows = 5;
+    int cols = 2;
+    array real_wnan(rows, cols, real_wnan_data);
+    array imag_wnan(rows, cols, imag_wnan_data);
+    array a = af::complex(real_wnan, imag_wnan);
+
+    float gold_max_real[] = { -6.3f, 8.9f };
+    float gold_max_imag[] = { -9.0f, 4.5f };
+
+    array max_val = af::max(a);
+
+    vector< complex<float> > h_max_val(cols);
+    max_val.host(&h_max_val[0]);
+
+    for (int i = 0; i < cols; i++) {
+        ASSERT_FLOAT_EQ(h_max_val[i].real(), gold_max_real[i]);
+        ASSERT_FLOAT_EQ(h_max_val[i].imag(), gold_max_imag[i]);
+    }
+}
+
 TEST(Count, NaN)
 {
     const int num = 10000;

From 9d40415911d5d47d6a24e193fbf4ac0117df7821 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 15 Jun 2018 16:25:24 +0530
Subject: [PATCH 1448/2677] Fix normalization factor documentation in fft
 Functions

---
 include/af/signal.h | 70 ++++++++++++++++++++++-----------------------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index fa04bc8dbb..44994324bf 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -52,7 +52,7 @@ AFAPI array approx2(const array &in, const array &pos0, const array &pos1,
    C++ Interface for fast fourier transform on one dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals - used to either truncate or pad the input signals
    \return     the transformed array
 
@@ -64,7 +64,7 @@ AFAPI array fftNorm(const array& in, const double norm_factor, const dim_t odim0
    C++ Interface for fast fourier transform on two dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \return     the transformed array
@@ -77,7 +77,7 @@ AFAPI array fft2Norm(const array& in, const double norm_factor, const dim_t odim
    C++ Interface for fast fourier transform on three dimensional signals
 
    \param[in]  in is the input array and the output of 1D fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \param[in]  odim2 is the length of output signals along third dimension - used to either truncate/pad the input
@@ -92,7 +92,7 @@ AFAPI array fft3Norm(const array& in, const double norm_factor, const dim_t odim
    C++ Interface for fast fourier transform on one dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 1D forward fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
 
    \note The input \p in must be complex
 
@@ -106,7 +106,7 @@ AFAPI void fftInPlace(array& in, const double norm_factor = 1);
    C++ Interface for fast fourier transform on two dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 2D forward fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     the transformed array
 
    \note The input \p in must be complex
@@ -121,7 +121,7 @@ AFAPI void fft2InPlace(array& in, const double norm_factor = 1);
    C++ Interface for fast fourier transform on three dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 3D forward fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     the transformed array
 
    \note The input \p in must be complex
@@ -180,7 +180,7 @@ AFAPI array fft3(const array& in, const dim_t odim0=0, const dim_t odim1=0, cons
    C++ Interface for fast fourier transform on any(1d, 2d, 3d) dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  outDims is an object of \ref dim4 that has the output array dimensions - used to either truncate or pad the input signals
    \return     the transformed array
 
@@ -219,7 +219,7 @@ AFAPI array dft(const array& in);
    C++ Interface for inverse fast fourier transform on one dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals - used to either truncate or pad the input signals
    \return     the transformed array
 
@@ -231,7 +231,7 @@ AFAPI array ifftNorm(const array& in, const double norm_factor, const dim_t odim
    C++ Interface for inverse fast fourier transform on two dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \return     the transformed array
@@ -244,7 +244,7 @@ AFAPI array ifft2Norm(const array& in, const double norm_factor, const dim_t odi
    C++ Interface for inverse fast fourier transform on three dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \param[in]  odim2 is the length of output signals along third dimension - used to either truncate/pad the input
@@ -259,7 +259,7 @@ AFAPI array ifft3Norm(const array& in, const double norm_factor, const dim_t odi
    C++ Interface for fast fourier transform on one dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 1D inverse fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
 
    \note The input \p in must be complex
 
@@ -273,7 +273,7 @@ AFAPI void ifftInPlace(array& in, const double norm_factor = 1);
    C++ Interface for fast fourier transform on two dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 2D inverse fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     the transformed array
 
    \note The input \p in must be complex
@@ -288,7 +288,7 @@ AFAPI void ifft2InPlace(array& in, const double norm_factor = 1);
    C++ Interface for fast fourier transform on three dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 3D inverse fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     the transformed array
 
    \note The input \p in must be complex
@@ -347,7 +347,7 @@ AFAPI array ifft3(const array& in, const dim_t odim0=0, const dim_t odim1=0, con
    C++ Interface for inverse fast fourier transform on any(1d, 2d, 3d) dimensional signals
 
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  outDims is an object of \ref dim4 that has the output array dimensions - used to either truncate or pad the input signals
    \return     the transformed array
 
@@ -388,7 +388,7 @@ AFAPI array idft(const array& in);
 
    \param[in]  in is a real array
    \param[in]  dims is the requested padded dimensions before the transform is applied
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     a complex array containing the non redundant parts of \p in along the first dimension.
 
    \note The first dimension of the output will be of size (dims[0] / 2) + 1. The remaining dimensions are unchanged.
@@ -406,7 +406,7 @@ array fftR2C(const array &in,
    C++ Interface for real to complex fast fourier transform for one dimensional signals
 
    \param[in]  in is a real array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     a complex array containing the non redundant parts of \p in along the first dimension.
 
    \note The first dimension of the output will be of size (in.dims(0) / 2) + 1. The remaining dimensions are unchanged.
@@ -424,7 +424,7 @@ array fftR2C(const array &in,
 
    \param[in]  in is a complex array containing only the non redundant parts of the signals
    \param[in]  is_odd is a flag signifying if the output should be even or odd size
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \tparam     rank signifies the dimensionality of the transform
    \return     A real array of size [2 * idim0 - 2 + is_odd, idim1, idim2, idim3] where idim{0,1,2,3} signify input dimensions
 
@@ -713,7 +713,7 @@ AFAPI af_err af_approx2(af_array *out, const af_array in, const af_array pos0, c
 
    \param[out] out is the transformed array
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals - used to either truncate or pad the input signals
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
@@ -727,7 +727,7 @@ AFAPI af_err af_fft(af_array *out, const af_array in, const double norm_factor,
    C Interface for fast fourier transform on one dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 1D forward fourier transform at exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
 
@@ -743,7 +743,7 @@ AFAPI af_err af_fft_inplace(af_array in, const double norm_factor);
 
    \param[out] out is the transformed array
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \return     \ref AF_SUCCESS if the fft transform is successful,
@@ -758,7 +758,7 @@ AFAPI af_err af_fft2(af_array *out, const af_array in, const double norm_factor,
    C Interface for fast fourier transform on two dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 2D forward fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
 
@@ -774,7 +774,7 @@ AFAPI af_err af_fft2_inplace(af_array in, const double norm_factor);
 
    \param[out] out is the transformed array
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \param[in]  odim2 is the length of output signals along third dimension - used to either truncate/pad the input
@@ -790,7 +790,7 @@ AFAPI af_err af_fft3(af_array *out, const af_array in, const double norm_factor,
    C Interface for fast fourier transform on three dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 3D forward fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
 
@@ -806,7 +806,7 @@ AFAPI af_err af_fft3_inplace(af_array in, const double norm_factor);
 
    \param[out] out is the transformed array
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals - used to either truncate or pad the input signals
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
@@ -820,7 +820,7 @@ AFAPI af_err af_ifft(af_array *out, const af_array in, const double norm_factor,
    C Interface for fast fourier transform on one dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 1D inverse fourier transform at exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     \ref AF_SUCCESS if the ifft transform is successful,
                otherwise an appropriate error code is returned.
 
@@ -836,7 +836,7 @@ AFAPI af_err af_ifft_inplace(af_array in, const double norm_factor);
 
    \param[out] out is the transformed array
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \return     \ref AF_SUCCESS if the fft transform is successful,
@@ -851,7 +851,7 @@ AFAPI af_err af_ifft2(af_array *out, const af_array in, const double norm_factor
    C Interface for fast fourier transform on two dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 2D inverse fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     \ref AF_SUCCESS if the ifft transform is successful,
                otherwise an appropriate error code is returned.
 
@@ -867,7 +867,7 @@ AFAPI af_err af_ifft2_inplace(af_array in, const double norm_factor);
 
    \param[out] out is the transformed array
    \param[in]  in is the input array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  odim0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  odim1 is the length of output signals along second dimension - used to either truncate/pad the input
    \param[in]  odim2 is the length of output signals along third dimension - used to either truncate/pad the input
@@ -883,7 +883,7 @@ AFAPI af_err af_ifft3(af_array *out, const af_array in, const double norm_factor
    C Interface for fast fourier transform on three dimensional signals
 
    \param[inout]  in is the input array on entry and the output of 3D inverse fourier transform on exit
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \return     \ref AF_SUCCESS if the ifft transform is successful,
                otherwise an appropriate error code is returned.
 
@@ -900,7 +900,7 @@ AFAPI af_err af_ifft3_inplace(af_array in, const double norm_factor);
 
    \param[out] out is a complex array containing the non redundant parts of \p in.
    \param[in]  in is a real array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  pad0 is the length of output signals along first dimension - used to either truncate/pad the input
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
@@ -918,7 +918,7 @@ AFAPI af_err af_fft_r2c (af_array *out, const af_array in, const double norm_fac
 
    \param[out] out is a complex array containing the non redundant parts of \p in.
    \param[in]  in is a real array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  pad0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  pad1 is the length of output signals along second dimension - used to either truncate/pad the input
    \return     \ref AF_SUCCESS if the fft transform is successful,
@@ -937,7 +937,7 @@ AFAPI af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_fac
 
    \param[out] out is a complex array containing the non redundant parts of \p in.
    \param[in]  in is a real array
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  pad0 is the length of output signals along first dimension - used to either truncate/pad the input
    \param[in]  pad1 is the length of output signals along second dimension - used to either truncate/pad the input
    \param[in]  pad2 is the length of output signals along third dimension - used to either truncate/pad the input
@@ -957,7 +957,7 @@ AFAPI af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_fac
 
    \param[out] out is a real array containing the output of the transform.
    \param[in]  in is a complex array containing only the non redundant parts of the signals.
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  is_odd is a flag signifying if the output should be even or odd size
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
@@ -976,7 +976,7 @@ AFAPI af_err af_fft_c2r (af_array *out, const af_array in, const double norm_fac
 
    \param[out] out is a real array containing the output of the transform.
    \param[in]  in is a complex array containing only the non redundant parts of the signals.
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  is_odd is a flag signifying if the output should be even or odd size
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.
@@ -994,7 +994,7 @@ AFAPI af_err af_fft2_c2r(af_array *out, const af_array in, const double norm_fac
 
    \param[out] out is a real array containing the output of the transform.
    \param[in]  in is a complex array containing only the non redundant parts of the signals.
-   \param[in]  norm_factor is the normalization factor with which the input is scaled before the transformation is applied
+   \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
    \param[in]  is_odd is a flag signifying if the output should be even or odd size
    \return     \ref AF_SUCCESS if the fft transform is successful,
                otherwise an appropriate error code is returned.

From 98e6d5e220babd52769541e87f7479526e71cebc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 15 Jun 2018 20:16:29 +0530
Subject: [PATCH 1449/2677] Use CLBlast as default blas upstream for OpenCL
 backend

---
 src/backend/opencl/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a239d58c68..741765974e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -7,7 +7,7 @@
 
 include(InternalUtils)
 
-set(AF_OPENCL_BLAS_LIBRARY clBLAS CACHE STRING "Select OpenCL BLAS back-end")
+set(AF_OPENCL_BLAS_LIBRARY CLBlast CACHE STRING "Select OpenCL BLAS back-end")
 set_property(CACHE AF_OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
 
 af_deprecate(OPENCL_BLAS_LIBRARY AF_OPENCL_BLAS_LIBRARY)

From 36dfda800073b58c99d13a7374fc837baea0ab18 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 10 May 2018 19:51:54 +0530
Subject: [PATCH 1450/2677] Fix checks in af::seq() & indexing/assignment fns

* Implement convert2Cononical helper function that converts
  the user provided af::seq to a canonical form where the
  sequences begin and end lie within the [0,len] range of
  the input array along a specified dimension.

* Refactors moddims & flatten functions to enable using
  them from `src/api/c/` level

* Adds createHandle version that accepts af_array

* Reduces c-api calls to as minimum as possible in
  `src/api/c/index.cpp` and `src/api/c/assign.cpp`

* Fixes style in lookup source files and asign test
---
 include/af/index.h            |   2 +-
 src/api/c/array.cpp           |  25 +--
 src/api/c/assign.cpp          | 322 ++++++++++++++++---------------
 src/api/c/handle.hpp          |  69 +++++--
 src/api/c/index.cpp           | 352 +++++++++++++++++++---------------
 src/api/c/indexing_common.hpp |  41 ++++
 src/api/c/moddims.cpp         | 101 +++++-----
 src/api/cpp/seq.cpp           |   6 +-
 src/backend/cpu/index.cpp     |   2 +-
 src/backend/cpu/lookup.cpp    |  23 ++-
 src/backend/cpu/lookup.hpp    |   5 +-
 src/backend/cuda/lookup.cu    |  23 ++-
 src/backend/cuda/lookup.hpp   |   5 +-
 src/backend/opencl/lookup.cpp |  23 ++-
 src/backend/opencl/lookup.hpp |   5 +-
 test/assign.cpp               |   1 -
 test/index.cpp                |  14 ++
 17 files changed, 574 insertions(+), 445 deletions(-)
 create mode 100644 src/api/c/indexing_common.hpp

diff --git a/include/af/index.h b/include/af/index.h
index 1206b8ef6c..8d37f7f517 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -20,7 +20,7 @@
 /// object with the same \ref af_seq::begin and \ref af_seq::end with an
 /// af_seq::step of 1
 ///
-typedef struct af_index_t{
+typedef struct af_index_t {
     union {
         af_array arr;   ///< The af_array used for indexing
         af_seq   seq;   ///< The af_seq used for indexing
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 60f514e9ff..20f63d42f2 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -76,36 +76,21 @@ af_err af_create_array(af_array *result, const void * const data,
 }
 
 //Strong Exception Guarantee
-af_err af_create_handle(af_array *result, const unsigned ndims, const dim_t * const dims,
+af_err af_create_handle(af_array *result,
+                        const unsigned ndims, const dim_t * const dims,
                         const af_dtype type)
 {
     try {
-        af_array out = 0;
         AF_CHECK(af_init());
 
-        if (ndims > 0) {
-            ARG_ASSERT(2, ndims > 0 && dims != NULL);
-        }
+        if (ndims > 0) ARG_ASSERT(2, ndims > 0 && dims != NULL);
+
         dim4 d(0);
         for(unsigned i = 0; i < ndims; i++) {
             d[i] = dims[i];
         }
 
-        switch(type) {
-        case f32:   out = createHandle<float  >(d); break;
-        case c32:   out = createHandle<cfloat >(d); break;
-        case f64:   out = createHandle<double >(d); break;
-        case c64:   out = createHandle<cdouble>(d); break;
-        case b8:    out = createHandle<char   >(d); break;
-        case s32:   out = createHandle<int    >(d); break;
-        case u32:   out = createHandle<uint   >(d); break;
-        case u8:    out = createHandle<uchar  >(d); break;
-        case s64:   out = createHandle<intl   >(d); break;
-        case u64:   out = createHandle<uintl  >(d); break;
-        case s16:   out = createHandle<short  >(d); break;
-        case u16:   out = createHandle<ushort >(d); break;
-        default:    TYPE_ERROR(3, type);
-        }
+        af_array out = createHandle(d, type);
         std::swap(*result, out);
     }
     CATCHALL
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 0164457913..82cc923480 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -20,95 +20,92 @@
 #include <assign.hpp>
 #include <math.hpp>
 #include <tile.hpp>
+#include <indexing_common.hpp>
 
 using namespace detail;
 using std::vector;
 using std::swap;
+using std::signbit;
+using common::convert2Canonical;
+using common::createSpanIndex;
 
 template<typename Tout, typename Tin>
 static
-void assign(Array<Tout> &out, const unsigned &ndims, const af_seq *index, const Array<Tin> &in_)
+void assign(Array<Tout> &out, const vector<af_seq> seqs,
+            const Array<Tin> &in)
 {
-    dim4 const outDs = out.dims();
-    dim4 const iDims = in_.dims();
+    size_t ndims  = seqs.size();
+    const dim4& outDs = out.dims();
+    const dim4& iDims = in.dims();
 
-    // Nothing to do for empty arrays
     if (iDims.elements() == 0) return;
 
-    DIM_ASSERT(0, (outDs.ndims()>=iDims.ndims()));
-    DIM_ASSERT(0, (outDs.ndims()>=(dim_t)ndims));
-
     out.eval();
 
-    vector<af_seq> index_(index, index+ndims);
-
-    dim4 oDims = toDims(index_, outDs);
+    dim4 oDims = toDims(seqs, outDs);
 
-    bool is_vector = true;
-    for (int i = 0; is_vector && i < (int)oDims.ndims() - 1; i++) {
-        is_vector &= oDims[i] == 1;
+    bool isVec = true;
+    for (int i = 0; isVec && i < (int)oDims.ndims() - 1; i++) {
+        isVec &= oDims[i] == 1;
     }
 
-    is_vector &= in_.isVector() || in_.isScalar();
+    isVec &= in.isVector() || in.isScalar();
 
-    for (dim_t i = ndims; i < (int)in_.ndims(); i++) {
+    for (dim_t i = ndims; i < (int)in.ndims(); i++) {
         oDims[i] = 1;
     }
 
-
-    if (is_vector) {
-        if (oDims.elements() != (dim_t)in_.elements() &&
-            in_.elements() != 1) {
+    if (isVec) {
+        if (oDims.elements() != (dim_t)in.elements() &&
+            in.elements() != 1) {
             AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
         }
 
-        // If both out and in are vectors of equal elements, reshape in to out dims
-        Array<Tin> in = in_.elements() == 1 ? tile(in_, oDims) : modDims(in_, oDims);
-        Array<Tout> dst = createSubArray<Tout>(out, index_, false);
+        // If both out and in are vectors of equal elements,
+        // reshape in to out dims
+        Array<Tin> in_ = in.elements() == 1 ? tile(in, oDims)
+                                           : modDims(in, oDims);
+        auto dst = createSubArray<Tout>(out, seqs, false);
 
-        copyArray<Tin , Tout>(dst, in);
+        copyArray<Tin , Tout>(dst, in_);
     } else {
-        for (int i = 0; i < 4; i++) {
-            if (oDims[i] != iDims[i]) {
+        for (int i = 0; i < AF_MAX_DIMS; i++) {
+            if (oDims[i] != iDims[i])
                 AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
-            }
         }
-        Array<Tout> dst = createSubArray<Tout>(out, index_, false);
+        Array<Tout> dst = createSubArray<Tout>(out, seqs, false);
 
-        copyArray<Tin , Tout>(dst, in_);
+        copyArray<Tin , Tout>(dst, in);
     }
 }
 
 template<typename T>
 static
-void assign_helper(Array<T> &out, const unsigned &ndims, const af_seq *index, const af_array &in_)
+void assign(Array<T> &out, const vector<af_seq> iv,
+            const af_array &in)
 {
-    const ArrayInfo& iInfo = getInfo(in_);
-    af_dtype iType  = iInfo.getType();
-
-    if(out.getType() == c64 || out.getType() == c32)
-    {
+    const ArrayInfo& iInfo = getInfo(in);
+    af_dtype iType = iInfo.getType();
 
+    if(out.getType() == c64 || out.getType() == c32) {
         switch(iType) {
-            case c64: assign<T, cdouble>(out, ndims, index, getArray<cdouble  >(in_));  break;
-            case c32: assign<T, cfloat >(out, ndims, index, getArray<cfloat   >(in_));  break;
-            default : TYPE_ERROR(1, iType); break;
+        case c64: assign<T, cdouble>(out, iv, getArray<cdouble>(in));  break;
+        case c32: assign<T, cfloat >(out, iv, getArray<cfloat >(in));  break;
+        default : TYPE_ERROR(1, iType); break;
         }
-    }
-    else
-    {
+    } else {
         switch(iType) {
-            case f64: assign<T, double >(out, ndims, index, getArray<double   >(in_));  break;
-            case f32: assign<T, float  >(out, ndims, index, getArray<float    >(in_));  break;
-            case s32: assign<T, int    >(out, ndims, index, getArray<int      >(in_));  break;
-            case u32: assign<T, uint   >(out, ndims, index, getArray<uint     >(in_));  break;
-            case s64: assign<T, intl   >(out, ndims, index, getArray<intl     >(in_));  break;
-            case u64: assign<T, uintl  >(out, ndims, index, getArray<uintl    >(in_));  break;
-            case s16: assign<T, short  >(out, ndims, index, getArray<short    >(in_));  break;
-            case u16: assign<T, ushort >(out, ndims, index, getArray<ushort   >(in_));  break;
-            case u8 : assign<T, uchar  >(out, ndims, index, getArray<uchar    >(in_));  break;
-            case b8 : assign<T, char   >(out, ndims, index, getArray<char     >(in_));  break;
-            default : TYPE_ERROR(1, iType); break;
+        case f64: assign<T, double >(out, iv, getArray<double >(in));  break;
+        case f32: assign<T, float  >(out, iv, getArray<float  >(in));  break;
+        case s32: assign<T, int    >(out, iv, getArray<int    >(in));  break;
+        case u32: assign<T, uint   >(out, iv, getArray<uint   >(in));  break;
+        case s64: assign<T, intl   >(out, iv, getArray<intl   >(in));  break;
+        case u64: assign<T, uintl  >(out, iv, getArray<uintl  >(in));  break;
+        case s16: assign<T, short  >(out, iv, getArray<short  >(in));  break;
+        case u16: assign<T, ushort >(out, iv, getArray<ushort >(in));  break;
+        case u8 : assign<T, uchar  >(out, iv, getArray<uchar  >(in));  break;
+        case b8 : assign<T, char   >(out, iv, getArray<char   >(in));  break;
+        default : TYPE_ERROR(1, iType); break;
         }
     }
 }
@@ -118,9 +115,9 @@ af_err af_assign_seq(af_array *out,
                      const af_seq *index, const af_array rhs)
 {
     try {
-        ARG_ASSERT(0, (lhs!=0));
-        ARG_ASSERT(1, (ndims>0));
-        ARG_ASSERT(3, (rhs!=0));
+        ARG_ASSERT(0, (lhs != 0));
+        ARG_ASSERT(1, (ndims > 0));
+        ARG_ASSERT(3, (rhs != 0));
 
         const ArrayInfo& lInfo = getInfo(lhs);
 
@@ -137,42 +134,52 @@ af_err af_assign_seq(af_array *out,
             return AF_SUCCESS;
         }
 
-        for(dim_t i=0; i<(dim_t)ndims; ++i) {
-            ARG_ASSERT(2, (index[i].step>=0));
-        }
-
         af_array res = 0;
 
         if (*out != lhs) {
             int count = 0;
             AF_CHECK(af_get_data_ref_count(&count, lhs));
-            if (count > 1) {
+            if (count > 1)
                 AF_CHECK(af_copy_array(&res, lhs));
-            } else {
-                AF_CHECK(af_retain_array(&res, lhs));
-            }
+            else
+                res = retain(lhs);
         } else {
             res = lhs;
         }
 
         try {
-
             if (lhs != rhs) {
-                const ArrayInfo& oInfo = getInfo(lhs);
-                af_dtype oType  = oInfo.getType();
+                const dim4& outDims = getInfo(res).dims();
+                const dim4& inDims = getInfo(rhs).dims();
+
+                vector<af_seq> inSeqs(ndims, af_span);
+                for (unsigned i=0; i<ndims; ++i) {
+                    inSeqs[i] = convert2Canonical(index[i], outDims[i]);
+                    ARG_ASSERT(3, (inSeqs[i].begin >= 0. || inSeqs[i].end >= 0.));
+                    if (signbit(inSeqs[i].step)) {
+                        ARG_ASSERT(3, inSeqs[i].begin >= inSeqs[i].end);
+                    } else {
+                        ARG_ASSERT(3, inSeqs[i].begin <= inSeqs[i].end);
+                    }
+                }
+                DIM_ASSERT(0, (outDims.ndims()>=inDims.ndims()));
+                DIM_ASSERT(0, (outDims.ndims()>=(dim_t)ndims));
+
+                const ArrayInfo& oInfo = getInfo(res);
+                af_dtype oType = oInfo.getType();
                 switch(oType) {
-                case c64: assign_helper<cdouble>(getWritableArray<cdouble>(res), ndims, index, rhs);  break;
-                case c32: assign_helper<cfloat >(getWritableArray<cfloat >(res), ndims, index, rhs);  break;
-                case f64: assign_helper<double >(getWritableArray<double >(res), ndims, index, rhs);  break;
-                case f32: assign_helper<float  >(getWritableArray<float  >(res), ndims, index, rhs);  break;
-                case s32: assign_helper<int    >(getWritableArray<int    >(res), ndims, index, rhs);  break;
-                case u32: assign_helper<uint   >(getWritableArray<uint   >(res), ndims, index, rhs);  break;
-                case s64: assign_helper<intl   >(getWritableArray<intl   >(res), ndims, index, rhs);  break;
-                case u64: assign_helper<uintl  >(getWritableArray<uintl  >(res), ndims, index, rhs);  break;
-                case s16: assign_helper<short  >(getWritableArray<short  >(res), ndims, index, rhs);  break;
-                case u16: assign_helper<ushort >(getWritableArray<ushort >(res), ndims, index, rhs);  break;
-                case u8 : assign_helper<uchar  >(getWritableArray<uchar  >(res), ndims, index, rhs);  break;
-                case b8 : assign_helper<char   >(getWritableArray<char   >(res), ndims, index, rhs);  break;
+                case c64: assign(getWritableArray<cdouble>(res), inSeqs, rhs); break;
+                case c32: assign(getWritableArray<cfloat >(res), inSeqs, rhs); break;
+                case f64: assign(getWritableArray<double >(res), inSeqs, rhs); break;
+                case f32: assign(getWritableArray<float  >(res), inSeqs, rhs); break;
+                case s32: assign(getWritableArray<int    >(res), inSeqs, rhs); break;
+                case u32: assign(getWritableArray<uint   >(res), inSeqs, rhs); break;
+                case s64: assign(getWritableArray<intl   >(res), inSeqs, rhs); break;
+                case u64: assign(getWritableArray<uintl  >(res), inSeqs, rhs); break;
+                case s16: assign(getWritableArray<short  >(res), inSeqs, rhs); break;
+                case u16: assign(getWritableArray<ushort >(res), inSeqs, rhs); break;
+                case u8 : assign(getWritableArray<uchar  >(res), inSeqs, rhs); break;
+                case b8 : assign(getWritableArray<char   >(res), inSeqs, rhs); break;
                 default : TYPE_ERROR(1, oType); break;
                 }
             }
@@ -180,37 +187,28 @@ af_err af_assign_seq(af_array *out,
             af_release_array(res);
             throw;
         }
-        std::swap(*out, res);
+        swap(*out, res);
     }
     CATCHALL;
-
     return AF_SUCCESS;
 }
 
 template<typename T>
-static void genAssign(af_array& out, const af_index_t* indexs, const af_array& rhs)
+inline
+void genAssign(af_array& out, const af_index_t* indexs, const af_array& rhs)
 {
     detail::assign<T>(getWritableArray<T>(out), indexs, getArray<T>(rhs));
 }
 
-af_err af_assign_gen(af_array *out,
-                    const af_array lhs,
+af_err af_assign_gen(af_array *out, const af_array lhs,
                     const dim_t ndims, const af_index_t* indexs,
                     const af_array rhs_)
 {
-    af_array output = 0;
-    af_array rhs = rhs_;
-    // spanner is sequence index used for indexing along the
-    // dimensions after ndims
-    af_index_t spanner;
-    spanner.idx.seq = af_span;
-    spanner.isSeq = true;
-
     try {
         ARG_ASSERT(3, (indexs!=NULL));
 
         int track = 0;
-        vector<af_seq> seqs(4, af_span);
+        vector<af_seq> seqs(AF_MAX_DIMS, af_span);
         for (dim_t i = 0; i < ndims; i++) {
             if (indexs[i].isSeq) {
                 track++;
@@ -218,9 +216,10 @@ af_err af_assign_gen(af_array *out,
             }
         }
 
+        af_array rhs = rhs_;
         if (track==(int)ndims) {
             // all indexs are sequences, redirecting to af_assign
-            return af_assign_seq(out, lhs, ndims, &(seqs.front()), rhs);
+            return af_assign_seq(out, lhs, ndims, seqs.data(), rhs);
         }
 
         ARG_ASSERT(1, (lhs!=0));
@@ -228,18 +227,16 @@ af_err af_assign_gen(af_array *out,
 
         const ArrayInfo& lInfo = getInfo(lhs);
         const ArrayInfo& rInfo = getInfo(rhs);
-        dim4 lhsDims    = lInfo.dims();
-        dim4 rhsDims    = rInfo.dims();
-        af_dtype lhsType= lInfo.getType();
-        af_dtype rhsType= rInfo.getType();
+        const dim4& lhsDims    = lInfo.dims();
+        const dim4& rhsDims    = rInfo.dims();
+        af_dtype lhsType       = lInfo.getType();
+        af_dtype rhsType       = rInfo.getType();
 
-        if(rhsDims.ndims() == 0) {
+        if(rhsDims.ndims() == 0)
             return af_retain_array(out, lhs);
-        }
 
-        if(lhsDims.ndims() == 0) {
+        if(lhsDims.ndims() == 0)
             return af_create_handle(out, 0, nullptr, lhsType);
-        }
 
         ARG_ASSERT(2, (ndims == 1) || (ndims == (dim_t)lInfo.ndims()));
 
@@ -260,14 +257,14 @@ af_err af_assign_gen(af_array *out,
         ARG_ASSERT(1, (lhsDims.ndims()>=rhsDims.ndims()));
         ARG_ASSERT(2, (lhsDims.ndims()>=ndims));
 
+        af_array output = 0;
         if (*out != lhs) {
             int count = 0;
             AF_CHECK(af_get_data_ref_count(&count, lhs));
-            if (count > 1) {
+            if (count > 1)
                 AF_CHECK(af_copy_array(&output, lhs));
-            } else {
-                AF_CHECK(af_retain_array(&output, lhs));
-            }
+            else
+                output = retain(lhs);
         } else {
             output = lhs;
         }
@@ -277,94 +274,105 @@ af_err af_assign_gen(af_array *out,
         // particular dimension, set the length of
         // that dimension accordingly before any checks
         for (dim_t i=0; i<ndims; i++) {
-            if (!indexs[i].isSeq) {
+            if (!indexs[i].isSeq)
                 oDims[i] = getInfo(indexs[i].idx.arr).elements();
-            }
         }
 
-        for (dim_t i = ndims; i < (dim_t)lInfo.ndims(); i++) {
+        for (dim_t i = ndims; i < (dim_t)lInfo.ndims(); i++)
             oDims[i] = 1;
-        }
 
-        bool is_vector = true;
-        for (int i = 0; is_vector && i < oDims.ndims() - 1; i++) {
-            is_vector &= oDims[i] == 1;
+        bool isVec = true;
+        for (int i = 0; isVec && i < oDims.ndims() - 1; i++) {
+            isVec &= oDims[i] == 1;
         }
 
         //TODO: Move logic out of this
-        is_vector &= rInfo.isVector() || rInfo.isScalar();
-        if (is_vector) {
+        isVec &= rInfo.isVector() || rInfo.isScalar();
+        if (isVec) {
             if (oDims.elements() != (dim_t)rInfo.elements() &&
                 rInfo.elements() != 1) {
                 AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
             }
-
             if (rInfo.elements() == 1) {
-                AF_CHECK(af_tile(&rhs, rhs_, oDims[0], oDims[1], oDims[2], oDims[3]));
+                AF_CHECK(af_tile(&rhs, rhs_, oDims[0],
+                                 oDims[1], oDims[2], oDims[3]));
             } else {
-                // If both out and rhs are vectors of equal elements, reshape rhs to out dims
+                // If both out and rhs are vectors of equal
+                // elements, reshape rhs to out dims
                 AF_CHECK(af_moddims(&rhs, rhs_, oDims.ndims(), oDims.get()));
             }
         } else {
-            for (int i = 0; i < 4; i++) {
-                if (oDims[i] != rhsDims[i]) {
-                    AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
-                }
+            for (int i = 0; i < AF_MAX_DIMS; i++) {
+                if (oDims[i] != rhsDims[i])
+                    AF_ERROR("Size mismatch between input and output",
+                             AF_ERR_SIZE);
             }
         }
 
-        af_index_t idxrs[4];
-        // set all dimensions above ndims to spanner index
-        for (dim_t i=ndims; i<4; ++i) idxrs[i] = spanner;
-
-        for (dim_t i=0; i<ndims; ++i) {
-            if (!indexs[i].isSeq) {
-                // check if all af_arrays have atleast one value
-                // to enable indexing along that dimension
-                const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
-                af_dtype idxType  = idxInfo.getType();
-
-                ARG_ASSERT(3, (idxType!=c32));
-                ARG_ASSERT(3, (idxType!=c64));
-                ARG_ASSERT(3, (idxType!=b8 ));
-
-                idxrs[i].idx.arr = indexs[i].idx.arr;
-                idxrs[i].isSeq = indexs[i].isSeq;
+        std::array<af_index_t, AF_MAX_DIMS> idxrs;
+        for (dim_t i=0; i<AF_MAX_DIMS; ++i) {
+            if (i < ndims) {
+                bool isSeq = indexs[i].isSeq;
+                if (!isSeq) {
+                    // check if all af_arrays have atleast one value
+                    // to enable indexing along that dimension
+                    const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
+                    af_dtype idxType = idxInfo.getType();
+
+                    ARG_ASSERT(3, (idxType!=c32));
+                    ARG_ASSERT(3, (idxType!=c64));
+                    ARG_ASSERT(3, (idxType!=b8 ));
+
+                    idxrs[i] = { { indexs[i].idx.arr },
+                                 isSeq, indexs[i].isBatch };
+                } else {
+                    af_seq inSeq = convert2Canonical(indexs[i].idx.seq, lhsDims[i]);
+                    ARG_ASSERT(3, (inSeq.begin >= 0 || inSeq.end >= 0));
+                    if (signbit(inSeq.step)) {
+                        ARG_ASSERT(3, inSeq.begin >= inSeq.end);
+                    } else {
+                        ARG_ASSERT(3, inSeq.begin <= inSeq.end);
+                    }
+
+                    idxrs[i].idx.seq = inSeq;
+                    idxrs[i].isSeq   = isSeq;
+                    idxrs[i].isBatch = indexs[i].isBatch;
+                }
             } else {
-                // af_seq is being used for this dimension
-                // just copy the index to local variable
-                idxrs[i] = indexs[i];
+                // set all dimensions above ndims to spanner
+                idxrs[i] = createSpanIndex();
             }
         }
+        af_index_t* ptr = idxrs.data();
 
         try {
             switch(rhsType) {
-                case c64: genAssign<cdouble>(output, idxrs, rhs); break;
-                case f64: genAssign<double >(output, idxrs, rhs); break;
-                case c32: genAssign<cfloat >(output, idxrs, rhs); break;
-                case f32: genAssign<float  >(output, idxrs, rhs); break;
-                case u64: genAssign<uintl  >(output, idxrs, rhs); break;
-                case u32: genAssign<uint   >(output, idxrs, rhs); break;
-                case s64: genAssign<intl   >(output, idxrs, rhs); break;
-                case s32: genAssign<int    >(output, idxrs, rhs); break;
-                case s16: genAssign<short  >(output, idxrs, rhs); break;
-                case u16: genAssign<ushort >(output, idxrs, rhs); break;
-                case  u8: genAssign<uchar  >(output, idxrs, rhs); break;
-                case  b8: genAssign<char   >(output, idxrs, rhs); break;
+                case c64: genAssign<cdouble>(output, ptr, rhs); break;
+                case f64: genAssign<double >(output, ptr, rhs); break;
+                case c32: genAssign<cfloat >(output, ptr, rhs); break;
+                case f32: genAssign<float  >(output, ptr, rhs); break;
+                case u64: genAssign<uintl  >(output, ptr, rhs); break;
+                case u32: genAssign<uint   >(output, ptr, rhs); break;
+                case s64: genAssign<intl   >(output, ptr, rhs); break;
+                case s32: genAssign<int    >(output, ptr, rhs); break;
+                case s16: genAssign<short  >(output, ptr, rhs); break;
+                case u16: genAssign<ushort >(output, ptr, rhs); break;
+                case  u8: genAssign<uchar  >(output, ptr, rhs); break;
+                case  b8: genAssign<char   >(output, ptr, rhs); break;
                 default: TYPE_ERROR(1, rhsType);
             }
         } catch(...) {
             if (*out != lhs) {
                 AF_CHECK(af_release_array(output));
-                if (is_vector) { AF_CHECK(af_release_array(rhs)); }
+                if (isVec)
+                    AF_CHECK(af_release_array(rhs));
             }
             throw;
         }
-        if (is_vector) { AF_CHECK(af_release_array(rhs)); }
-
-        std::swap(*out, output);
+        if (isVec)
+            AF_CHECK(af_release_array(rhs));
+        swap(*out, output);
     }
     CATCHALL;
-
     return AF_SUCCESS;
 }
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 683fe18531..5c0ec75fe0 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <af/array.h>
+#include <af/defines.h>
 #include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
@@ -19,9 +20,26 @@
 
 const ArrayInfo& getInfo(const af_array arr, bool sparse_check = true, bool device_check = true);
 
-// Implemented in src/api/c/moddims.cpp
 template<typename T>
-detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims);
+static
+detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims)
+{
+    in.eval(); //FIXME: Figure out a better way
+
+    detail::Array<T> Out = in;
+    if (!in.isLinear()) Out = detail::copyArray<T>(in);
+    Out.setDataDims(newDims);
+
+    return Out;
+}
+
+template<typename T>
+static
+detail::Array<T> flat(const detail::Array<T>& in)
+{
+    const af::dim4 newDims(in.elements());
+    return modDims<T>(in, newDims);
+}
 
 template<typename T>
 static const detail::Array<T> &
@@ -45,19 +63,19 @@ detail::Array<To> castArray(const af_array &in)
 
     const ArrayInfo& info = getInfo(in);
     switch (info.getType()) {
-    case f32: return detail::cast<To, float  >(getArray<float  >(in));
-    case f64: return detail::cast<To, double >(getArray<double >(in));
-    case c32: return detail::cast<To, cfloat >(getArray<cfloat >(in));
-    case c64: return detail::cast<To, cdouble>(getArray<cdouble>(in));
-    case s32: return detail::cast<To, int    >(getArray<int    >(in));
-    case u32: return detail::cast<To, uint   >(getArray<uint   >(in));
-    case u8 : return detail::cast<To, uchar  >(getArray<uchar  >(in));
-    case b8 : return detail::cast<To, char   >(getArray<char   >(in));
-    case s64: return detail::cast<To, intl   >(getArray<intl   >(in));
-    case u64: return detail::cast<To, uintl  >(getArray<uintl  >(in));
-    case s16: return detail::cast<To, short  >(getArray<short  >(in));
-    case u16: return detail::cast<To, ushort >(getArray<ushort >(in));
-    default: TYPE_ERROR(1, info.getType());
+        case f32: return detail::cast<To, float  >(getArray<float  >(in));
+        case f64: return detail::cast<To, double >(getArray<double >(in));
+        case c32: return detail::cast<To, cfloat >(getArray<cfloat >(in));
+        case c64: return detail::cast<To, cdouble>(getArray<cdouble>(in));
+        case s32: return detail::cast<To, int    >(getArray<int    >(in));
+        case u32: return detail::cast<To, uint   >(getArray<uint   >(in));
+        case u8 : return detail::cast<To, uchar  >(getArray<uchar  >(in));
+        case b8 : return detail::cast<To, char   >(getArray<char   >(in));
+        case s64: return detail::cast<To, intl   >(getArray<intl   >(in));
+        case u64: return detail::cast<To, uintl  >(getArray<uintl  >(in));
+        case s16: return detail::cast<To, short  >(getArray<short  >(in));
+        case u16: return detail::cast<To, ushort >(getArray<ushort >(in));
+        default: TYPE_ERROR(1, info.getType());
     }
 }
 
@@ -86,6 +104,27 @@ static af_array createHandle(af::dim4 d)
     return getHandle(detail::createEmptyArray<T>(d));
 }
 
+static af_array createHandle(af::dim4 d, af_dtype dtype)
+{
+    using namespace detail;
+
+    switch(dtype) {
+        case f32: return createHandle<float  >(d);
+        case c32: return createHandle<cfloat >(d);
+        case f64: return createHandle<double >(d);
+        case c64: return createHandle<cdouble>(d);
+        case b8:  return createHandle<char   >(d);
+        case s32: return createHandle<int    >(d);
+        case u32: return createHandle<uint   >(d);
+        case u8:  return createHandle<uchar  >(d);
+        case s64: return createHandle<intl   >(d);
+        case u64: return createHandle<uintl  >(d);
+        case s16: return createHandle<short  >(d);
+        case u16: return createHandle<ushort >(d);
+        default:    TYPE_ERROR(3, dtype);
+    }
+}
+
 template<typename T>
 static af_array createHandleFromValue(af::dim4 d, double val)
 {
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 7ce935e76a..c656845fba 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -7,117 +7,164 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <vector>
-#include <cassert>
+#include <indexing_common.hpp>
+#include <index.hpp>
 
-#include <af/array.h>
-#include <af/index.h>
+#include <Array.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
+#include <af/index.h>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
-#include <Array.hpp>
 #include <lookup.hpp>
-#include <index.hpp>
+
+#include <array>
+#include <cassert>
+#include <cmath>
+#include <vector>
 
 using namespace detail;
 using std::vector;
 using std::swap;
+using std::signbit;
+
+using common::createSpanIndex;
+using common::convert2Canonical;
+
+namespace common {
+af_index_t createSpanIndex()
+{
+    static af_index_t s = []{
+        af_index_t s;
+        s.idx.seq = af_span;
+        s.isSeq = true;
+        s.isBatch = false;
+        return s;
+    }();
+    return s;
+}
+
+af_seq convert2Canonical(const af_seq s, const dim_t len)
+{
+    double begin = signbit(s.begin) ? (len + s.begin) : s.begin;
+    double end   = signbit(s.end  ) ? (len + s.end)   : s.end;
+
+    return af_seq{begin, end, s.step};
+}
+}
 
 template<typename T>
-static void indexArray(af_array &dest, const af_array &src, const unsigned ndims, const af_seq *index)
+static
+af_array indexBySeqs(const af_array &src,
+                     const vector<af_seq> indicesV)
 {
-    const Array<T> &parent = getArray<T>(src);
-    vector<af_seq> index_(index, index+ndims);
-    Array<T> dst =  createSubArray(parent, index_);
+    size_t ndims = indicesV.size();
+    auto input   = getArray<T>(src);
 
-    dest = getHandle(dst);
+    if (ndims == 1 && ndims != input.ndims())
+        return getHandle(createSubArray(::flat(input), indicesV));
+    else
+        return getHandle(createSubArray(input, indicesV));
 }
 
-af_err af_index(af_array *result, const af_array in, const unsigned ndims, const af_seq* index)
+af_err af_index(af_array *result, const af_array in,
+                const unsigned ndims, const af_seq* indices)
 {
-    af_array out;
     try {
+        const ArrayInfo& inInfo = getInfo(in);
+        af_dtype type = inInfo.getType();
+        const dim4& iDims = inInfo.dims();
 
-        const ArrayInfo& iInfo = getInfo(in);
-        if (ndims == 1 && ndims != iInfo.ndims()) {
-            af_array tmp_in;
-            AF_CHECK(af_flat(&tmp_in, in));
-            AF_CHECK(af_index(result, tmp_in, ndims, index));
-            AF_CHECK(af_release_array(tmp_in));
-            return AF_SUCCESS;
+        vector<af_seq> indices_(ndims, af_span);
+        for (unsigned i=0; i<ndims; ++i) {
+            indices_[i] = convert2Canonical(indices[i], iDims[i]);
+
+            ARG_ASSERT(3, (indices_[i].begin >= 0. && indices_[i].end >= 0.));
+            if (signbit(indices_[i].step)) {
+              ARG_ASSERT(3, indices_[i].begin >= indices_[i].end);
+            } else {
+              ARG_ASSERT(3, indices_[i].begin <= indices_[i].end);
+            }
         }
 
-        af_dtype in_type = iInfo.getType();
-
-        switch(in_type) {
-        case f32:    indexArray<float>   (out, in, ndims, index);  break;
-        case c32:    indexArray<cfloat>  (out, in, ndims, index);  break;
-        case f64:    indexArray<double>  (out, in, ndims, index);  break;
-        case c64:    indexArray<cdouble> (out, in, ndims, index);  break;
-        case b8:     indexArray<char>    (out, in, ndims, index);  break;
-        case s32:    indexArray<int>     (out, in, ndims, index);  break;
-        case u32:    indexArray<unsigned>(out, in, ndims, index);  break;
-        case s16:    indexArray<short>   (out, in, ndims, index);  break;
-        case u16:    indexArray<ushort>  (out, in, ndims, index);  break;
-        case s64:    indexArray<intl>    (out, in, ndims, index);  break;
-        case u64:    indexArray<uintl>   (out, in, ndims, index);  break;
-        case u8:     indexArray<uchar>   (out, in, ndims, index);  break;
-        default:    TYPE_ERROR(1, in_type);
+        af_array out = 0;
+
+        switch(type) {
+            case f32: out = indexBySeqs<float>   (in, indices_);  break;
+            case c32: out = indexBySeqs<cfloat>  (in, indices_);  break;
+            case f64: out = indexBySeqs<double>  (in, indices_);  break;
+            case c64: out = indexBySeqs<cdouble> (in, indices_);  break;
+            case b8:  out = indexBySeqs<char>    (in, indices_);  break;
+            case s32: out = indexBySeqs<int>     (in, indices_);  break;
+            case u32: out = indexBySeqs<unsigned>(in, indices_);  break;
+            case s16: out = indexBySeqs<short>   (in, indices_);  break;
+            case u16: out = indexBySeqs<ushort>  (in, indices_);  break;
+            case s64: out = indexBySeqs<intl>    (in, indices_);  break;
+            case u64: out = indexBySeqs<uintl>   (in, indices_);  break;
+            case u8:  out = indexBySeqs<uchar>   (in, indices_);  break;
+            default:    TYPE_ERROR(1, type);
         }
         swap(*result, out);
     }
     CATCHALL
-
     return AF_SUCCESS;
 }
 
+template<typename T, typename idx_t>
+inline
+af_array lookup(const af_array& in, const af_array& idx, const unsigned dim)
+{
+    return getHandle(lookup(getArray<T>(in), getArray<idx_t>(idx), dim));
+}
+
 template<typename idx_t>
-static af_array lookup(const af_array &in, const af_array &idx, const unsigned dim)
+static
+af_array lookup(const af_array& in, const af_array& idx, const unsigned dim)
 {
     const ArrayInfo& inInfo = getInfo(in);
-
-    af_dtype inType  = inInfo.getType();
+    af_dtype inType = inInfo.getType();
 
     switch(inType) {
-        case f32: return getHandle(lookup<float   , idx_t > (getArray<float   >(in), getArray<idx_t>(idx), dim));
-        case c32: return getHandle(lookup<cfloat  , idx_t > (getArray<cfloat  >(in), getArray<idx_t>(idx), dim));
-        case f64: return getHandle(lookup<double  , idx_t > (getArray<double  >(in), getArray<idx_t>(idx), dim));
-        case c64: return getHandle(lookup<cdouble , idx_t > (getArray<cdouble >(in), getArray<idx_t>(idx), dim));
-        case s32: return getHandle(lookup<int     , idx_t > (getArray<int     >(in), getArray<idx_t>(idx), dim));
-        case u32: return getHandle(lookup<unsigned, idx_t > (getArray<unsigned>(in), getArray<idx_t>(idx), dim));
-        case s64: return getHandle(lookup<intl    , idx_t > (getArray<intl    >(in), getArray<idx_t>(idx), dim));
-        case u64: return getHandle(lookup<uintl   , idx_t > (getArray<uintl   >(in), getArray<idx_t>(idx), dim));
-        case s16: return getHandle(lookup<short   , idx_t > (getArray<short   >(in), getArray<idx_t>(idx), dim));
-        case u16: return getHandle(lookup<ushort  , idx_t > (getArray<ushort  >(in), getArray<idx_t>(idx), dim));
-        case  u8: return getHandle(lookup<uchar   , idx_t > (getArray<uchar   >(in), getArray<idx_t>(idx), dim));
-        case  b8: return getHandle(lookup<char    , idx_t > (getArray<char    >(in), getArray<idx_t>(idx), dim));
+        case f32: return lookup<float   , idx_t>(in, idx, dim);
+        case c32: return lookup<cfloat  , idx_t>(in, idx, dim);
+        case f64: return lookup<double  , idx_t>(in, idx, dim);
+        case c64: return lookup<cdouble , idx_t>(in, idx, dim);
+        case s32: return lookup<int     , idx_t>(in, idx, dim);
+        case u32: return lookup<unsigned, idx_t>(in, idx, dim);
+        case s64: return lookup<intl    , idx_t>(in, idx, dim);
+        case u64: return lookup<uintl   , idx_t>(in, idx, dim);
+        case s16: return lookup<short   , idx_t>(in, idx, dim);
+        case u16: return lookup<ushort  , idx_t>(in, idx, dim);
+        case  u8: return lookup<uchar   , idx_t>(in, idx, dim);
+        case  b8: return lookup<char    , idx_t>(in, idx, dim);
         default : TYPE_ERROR(1, inType);
     }
 }
 
-af_err af_lookup(af_array *out, const af_array in, const af_array indices, const unsigned dim)
+af_err af_lookup(af_array *out, const af_array in,
+                 const af_array indices, const unsigned dim)
 {
-    af_array output = 0;
-
     try {
-        ARG_ASSERT(3, (dim>=0 && dim<=3));
-
-        const ArrayInfo& idxInfo= getInfo(indices);
+        const ArrayInfo& idxInfo = getInfo(indices);
 
-        if(idxInfo.ndims() == 0) {
-            return af_retain_array(out, indices);
+        if (idxInfo.ndims() == 0) {
+            *out = retain(indices);
+            return AF_SUCCESS;
         }
 
+        ARG_ASSERT(3, (dim >= 0 && dim <= 3));
         ARG_ASSERT(2, idxInfo.isVector() || idxInfo.isScalar());
 
         af_dtype idxType = idxInfo.getType();
 
-        ARG_ASSERT(2, (idxType!=c32));
-        ARG_ASSERT(2, (idxType!=c64));
-        ARG_ASSERT(2, (idxType!=b8));
+        ARG_ASSERT(2, (idxType != c32));
+        ARG_ASSERT(2, (idxType != c64));
+        ARG_ASSERT(2, (idxType != b8));
+
+        af_array output = 0;
 
         switch(idxType) {
             case f32: output = lookup<float   >(in, indices, dim); break;
@@ -147,38 +194,33 @@ af_array genIndex(const af_array& in,  const af_index_t idxrs[])
     return getHandle<T>(index<T>(getArray<T>(in), idxrs));
 }
 
-af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const af_index_t* indexs)
+af_err af_index_gen(af_array *out, const af_array in,
+                    const dim_t ndims, const af_index_t* indexs)
 {
-    af_array output = 0;
-    // spanner is sequence index used for indexing along the
-    // dimensions after ndims
-    af_index_t spanner;
-    spanner.idx.seq = af_span;
-    spanner.isSeq = true;
-
     try {
         ARG_ASSERT(2, (ndims>0));
-        ARG_ASSERT(3, (indexs!=NULL));
+        ARG_ASSERT(3, (indexs != NULL));
 
-        const ArrayInfo& iInfo = getInfo(in);
-
-        dim4 iDims = iInfo.dims();
+        const ArrayInfo& iInfo  = getInfo(in);
+        const dim4& iDims  = iInfo.dims();
         af_dtype inType = getInfo(in).getType();
 
-        if(iDims.ndims() <= 0) {
-            return af_create_handle(out, 0, nullptr, inType);
+        if (iDims.ndims() <= 0) {
+            *out = createHandle(dim4(0), inType);
+            return AF_SUCCESS;
         }
 
         if (ndims == 1 && ndims != (dim_t)iInfo.ndims()) {
-            af_array tmp_in;
-            AF_CHECK(af_flat(&tmp_in, in));
-            AF_CHECK(af_index_gen(out, tmp_in, ndims, indexs));
-            AF_CHECK(af_release_array(tmp_in));
+            af_array in_ = 0;
+            AF_CHECK(af_flat(&in_, in));
+            AF_CHECK(af_index_gen(out, in_, ndims, indexs));
+            AF_CHECK(af_release_array(in_));
             return AF_SUCCESS;
         }
 
         int track = 0;
-        af_seq seqs[] = {af_span, af_span, af_span, af_span};
+        std::array<af_seq, AF_MAX_DIMS> seqs;
+        seqs.fill(af_span);
         for (dim_t i = 0; i < ndims; i++) {
             if (indexs[i].isSeq) {
                 track++;
@@ -186,71 +228,80 @@ af_err af_index_gen(af_array *out, const af_array in, const dim_t ndims, const a
             }
         }
 
-        if (track==(int)ndims) {
-            // all indexs are sequences, redirecting to af_index
-            return af_index(out, in, ndims, seqs);
-        }
-
-        af_index_t idxrs[4];
-        // set all dimensions above ndims to spanner index
-        for (dim_t i=ndims; i<4; ++i) idxrs[i] = spanner;
-
-        for (dim_t i=0; i<ndims; ++i) {
-            if (!indexs[i].isSeq) {
-                // check if all af_arrays have atleast one value
-                // to enable indexing along that dimension
-                const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
-                af_dtype idxType  = idxInfo.getType();
-
-                ARG_ASSERT(3, (idxType!=c32));
-                ARG_ASSERT(3, (idxType!=c64));
-                ARG_ASSERT(3, (idxType!=b8 ));
-
-                idxrs[i].idx.arr = indexs[i].idx.arr;
-                idxrs[i].isSeq = indexs[i].isSeq;
+        if (track==(int)ndims)
+            return af_index(out, in, ndims, seqs.data());
+
+        std::array<af_index_t, AF_MAX_DIMS> idxrs;
+
+        for (dim_t i=0; i<AF_MAX_DIMS; ++i) {
+            if (i<ndims) {
+                bool isSeq = indexs[i].isSeq;
+                if (!isSeq) {
+                    // check if all af_arrays have atleast one value
+                    // to enable indexing along that dimension
+                    const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
+                    af_dtype idxType = idxInfo.getType();
+
+                    ARG_ASSERT(3, (idxType != c32));
+                    ARG_ASSERT(3, (idxType != c64));
+                    ARG_ASSERT(3, (idxType != b8 ));
+
+                    idxrs[i] = { { indexs[i].idx.arr },
+                                 isSeq, indexs[i].isBatch };
+                } else {
+                    // copy the af_seq to local variable
+                    af_seq inSeq = convert2Canonical(indexs[i].idx.seq, iDims[i]);
+                    ARG_ASSERT(3, (inSeq.begin >= 0. || inSeq.end >= 0.));
+                    if (signbit(inSeq.step)) {
+                        ARG_ASSERT(3, inSeq.begin >= inSeq.end);
+                    } else {
+                        ARG_ASSERT(3, inSeq.begin <= inSeq.end);
+                    }
+                    idxrs[i].idx.seq = inSeq;
+                    idxrs[i].isSeq   = isSeq;
+                    idxrs[i].isBatch = indexs[i].isBatch;
+                }
             } else {
-                // af_seq is being used for this dimension
-                // just copy the index to local variable
-                idxrs[i] = indexs[i];
+                // set all dimensions above ndims to spanner
+                idxrs[i] = createSpanIndex();
             }
         }
+        af_index_t* ptr = idxrs.data();
 
+        af_array output = 0;
         switch(inType) {
-            case c64: output = genIndex<cdouble>(in, idxrs); break;
-            case f64: output = genIndex<double >(in, idxrs); break;
-            case c32: output = genIndex<cfloat >(in, idxrs); break;
-            case f32: output = genIndex<float  >(in, idxrs); break;
-            case u64: output = genIndex<uintl  >(in, idxrs); break;
-            case s64: output = genIndex<intl   >(in, idxrs); break;
-            case u32: output = genIndex<uint   >(in, idxrs); break;
-            case s32: output = genIndex<int    >(in, idxrs); break;
-            case u16: output = genIndex<ushort >(in, idxrs); break;
-            case s16: output = genIndex<short  >(in, idxrs); break;
-            case  u8: output = genIndex<uchar  >(in, idxrs); break;
-            case  b8: output = genIndex<char   >(in, idxrs); break;
+            case c64: output = genIndex<cdouble>(in, ptr); break;
+            case f64: output = genIndex<double >(in, ptr); break;
+            case c32: output = genIndex<cfloat >(in, ptr); break;
+            case f32: output = genIndex<float  >(in, ptr); break;
+            case u64: output = genIndex<uintl  >(in, ptr); break;
+            case s64: output = genIndex<intl   >(in, ptr); break;
+            case u32: output = genIndex<uint   >(in, ptr); break;
+            case s32: output = genIndex<int    >(in, ptr); break;
+            case u16: output = genIndex<ushort >(in, ptr); break;
+            case s16: output = genIndex<short  >(in, ptr); break;
+            case  u8: output = genIndex<uchar  >(in, ptr); break;
+            case  b8: output = genIndex<char   >(in, ptr); break;
             default: TYPE_ERROR(1, inType);
         }
+        std::swap(*out, output);
     }
     CATCHALL;
-
-    std::swap(*out, output);
-
     return AF_SUCCESS;
 }
 
 af_seq af_make_seq(double begin, double end, double step)
 {
-    af_seq seq = {begin, end, step};
-    return seq;
+    return af_seq{begin, end, step};
 }
 
 af_err af_create_indexers(af_index_t** indexers)
 {
     try {
-        af_index_t* out = new af_index_t[4];
-        for (int i=0; i<4; ++i) {
+        af_index_t* out = new af_index_t[AF_MAX_DIMS];
+        for (int i = 0; i < AF_MAX_DIMS; ++i) {
             out[i].idx.seq = af_span;
-            out[i].isSeq = true;
+            out[i].isSeq   = true;
             out[i].isBatch = false;
         }
         std::swap(*indexers, out);
@@ -259,46 +310,47 @@ af_err af_create_indexers(af_index_t** indexers)
     return AF_SUCCESS;
 }
 
-af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim)
+af_err af_set_array_indexer(af_index_t* indexer,
+                            const af_array idx, const dim_t dim)
 {
     try {
-        ARG_ASSERT(0, (indexer!=NULL));
-        ARG_ASSERT(1, (idx!=NULL));
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
-        indexer[dim].idx.arr = idx;
-        indexer[dim].isBatch = false;
-        indexer[dim].isSeq   = false;
+        ARG_ASSERT(0, (indexer != NULL));
+        ARG_ASSERT(1, (idx != NULL));
+        ARG_ASSERT(2, (dim >= 0 && dim <= 3));
+        indexer[dim] = af_index_t{{idx}, false, false};
     }
-    CATCHALL
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx, const dim_t dim, const bool is_batch)
+af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx,
+                          const dim_t dim, const bool is_batch)
 {
     try {
-        ARG_ASSERT(0, (indexer!=NULL));
-        ARG_ASSERT(1, (idx!=NULL));
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        ARG_ASSERT(0, (indexer != NULL));
+        ARG_ASSERT(1, (idx != NULL));
+        ARG_ASSERT(2, (dim >= 0 && dim <= 3));
         indexer[dim].idx.seq = *idx;
-        indexer[dim].isBatch = is_batch;
         indexer[dim].isSeq   = true;
+        indexer[dim].isBatch = is_batch;
     }
-    CATCHALL
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_seq_param_indexer(af_index_t* indexer,
-                              const double begin, const double end, const double step,
-                              const dim_t dim, const bool is_batch)
+af_err af_set_seq_param_indexer(af_index_t* indexer, const double begin,
+                                const double end, const double step,
+                                const dim_t dim, const bool is_batch)
 {
     try {
-        ARG_ASSERT(0, (indexer!=NULL));
-        ARG_ASSERT(4, (dim>=0 && dim<=3));
-        indexer[dim].idx.seq = af_make_seq(begin, end, step);
+        ARG_ASSERT(0, (indexer != NULL));
+        ARG_ASSERT(4, (dim >= 0 && dim <= 3));
+        af_seq s = af_make_seq(begin, end, step);
+        indexer[dim].idx.seq = s;
+        indexer[dim].isSeq = true;
         indexer[dim].isBatch = is_batch;
-        indexer[dim].isSeq   = true;
     }
-    CATCHALL
+    CATCHALL;
     return AF_SUCCESS;
 }
 
diff --git a/src/api/c/indexing_common.hpp b/src/api/c/indexing_common.hpp
new file mode 100644
index 0000000000..e6e84ed84c
--- /dev/null
+++ b/src/api/c/indexing_common.hpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <af/index.h>
+
+namespace common {
+/// Creates a af_index_t object that represents a af_span value
+af_index_t createSpanIndex();
+
+/// Converts a af_seq to cononical form which is composed of positive values for
+/// begin and end. The step value is not modified.
+///
+/// af_seq objects represent a range of values. You can create an af_seq object
+/// with the af::end value which is represented as -1. For example you can have
+/// a sequence from 1 to end-5 which will be composed of all values in an array
+/// but the first and the last five values. This function converts that value to
+/// positive values taking into the account of the array size.
+///
+/// \param[in] s   is sequence that may have negative values
+/// \param[in] len is the length of a given array along a given dimension.
+///
+/// \returns Returns a sequence with begin and end values in the range [0,len).
+///          Step value is not modified.
+///
+/// \NOTE: No error checks are performed.
+///
+/// Sample outputs of convert2Canonical for given sequence s:
+/// // Assume the array's len is 10 along dimention 0
+/// s{1, end-2, 1}   will return a sequence af_seq(1, 7, 1)
+/// s{1, 2, 1};      will return the same sequence
+/// s{-1, 2, -1};    will return the sequence af_seq(9,2,-1)
+af_seq convert2Canonical(const af_seq s, const dim_t len);
+}
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index f94991d66f..7378c4ae80 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2018, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -19,42 +19,27 @@
 using af::dim4;
 using namespace detail;
 
+namespace
+{
 template<typename T>
-Array<T> modDims(const Array<T>& in, const af::dim4 &newDims)
+af_array modDims(const af_array in, const dim4& newDims)
 {
-    //FIXME: Figure out a better way
-    in.eval();
-
-    Array<T> Out = in;
-
-    if (!in.isLinear()) {
-        Out = copyArray<T>(in);
-    }
-
-    Out.setDataDims(newDims);
-
-    return Out;
+    return getHandle(::modDims(getArray<T>(in), newDims));
+}
+template<typename T>
+af_array flat(const af_array in)
+{
+    return getHandle(::flat(getArray<T>(in)));
+}
 }
-
-template Array<float> modDims(const Array<float> &in, const af::dim4 &newDims);
-template Array<double> modDims(const Array<double> &in, const af::dim4 &newDims);
-template Array<cfloat> modDims(const Array<cfloat> &in, const af::dim4 &newDims);
-template Array<cdouble> modDims(const Array<cdouble> &in, const af::dim4 &newDims);
-template Array<int> modDims(const Array<int> &in, const af::dim4 &newDims);
-template Array<uint> modDims(const Array<uint> &in, const af::dim4 &newDims);
-template Array<intl> modDims(const Array<intl> &in, const af::dim4 &newDims);
-template Array<uintl> modDims(const Array<uintl> &in, const af::dim4 &newDims);
-template Array<short> modDims(const Array<short> &in, const af::dim4 &newDims);
-template Array<ushort> modDims(const Array<ushort> &in, const af::dim4 &newDims);
-template Array<uchar> modDims(const Array<uchar> &in, const af::dim4 &newDims);
-template Array<char> modDims(const Array<char> &in, const af::dim4 &newDims);
 
 af_err af_moddims(af_array *out, const af_array in,
                   const unsigned ndims, const dim_t * const dims)
 {
     try {
         if(ndims == 0) {
-            return af_retain_array(out, in);
+            *out = retain(in);
+            return AF_SUCCESS;
         }
         ARG_ASSERT(2, ndims >= 1);
         ARG_ASSERT(3, dims != NULL);
@@ -70,19 +55,19 @@ af_err af_moddims(af_array *out, const af_array in,
         af_dtype type = info.getType();
 
         switch(type) {
-        case f32: output = getHandle(modDims<float  >(getArray<float  >(in), newDims)); break;
-        case c32: output = getHandle(modDims<cfloat >(getArray<cfloat >(in), newDims)); break;
-        case f64: output = getHandle(modDims<double >(getArray<double >(in), newDims)); break;
-        case c64: output = getHandle(modDims<cdouble>(getArray<cdouble>(in), newDims)); break;
-        case b8:  output = getHandle(modDims<char   >(getArray<char   >(in), newDims)); break;
-        case s32: output = getHandle(modDims<int    >(getArray<int    >(in), newDims)); break;
-        case u32: output = getHandle(modDims<uint   >(getArray<uint   >(in), newDims)); break;
-        case u8:  output = getHandle(modDims<uchar  >(getArray<uchar  >(in), newDims)); break;
-        case s64: output = getHandle(modDims<intl   >(getArray<intl   >(in), newDims)); break;
-        case u64: output = getHandle(modDims<uintl  >(getArray<uintl  >(in), newDims)); break;
-        case s16: output = getHandle(modDims<short  >(getArray<short  >(in), newDims)); break;
-        case u16: output = getHandle(modDims<ushort >(getArray<ushort >(in), newDims)); break;
-        default: TYPE_ERROR(1, type);
+            case f32: output = modDims<float  >(in, newDims); break;
+            case c32: output = modDims<cfloat >(in, newDims); break;
+            case f64: output = modDims<double >(in, newDims); break;
+            case c64: output = modDims<cdouble>(in, newDims); break;
+            case b8:  output = modDims<char   >(in, newDims); break;
+            case s32: output = modDims<int    >(in, newDims); break;
+            case u32: output = modDims<uint   >(in, newDims); break;
+            case u8:  output = modDims<uchar  >(in, newDims); break;
+            case s64: output = modDims<intl   >(in, newDims); break;
+            case u64: output = modDims<uintl  >(in, newDims); break;
+            case s16: output = modDims<short  >(in, newDims); break;
+            case u16: output = modDims<ushort >(in, newDims); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out,output);
     }
@@ -93,19 +78,33 @@ af_err af_moddims(af_array *out, const af_array in,
 
 af_err af_flat(af_array *out, const af_array in)
 {
-    af_array res;
     try {
+        const ArrayInfo& info = getInfo(in);
 
-        const ArrayInfo& in_info = getInfo(in);
-
-        if (in_info.ndims() == 1) {
-            AF_CHECK(af_retain_array(&res, in));
+        if (info.ndims() == 1) {
+            *out = retain(in);
         } else {
-            const dim_t num = (dim_t)(in_info.elements());
-            AF_CHECK(af_moddims(&res, in, 1, &num));
+            af_array output = 0;
+            af_dtype type = info.getType();
+
+            switch(type) {
+                case f32: output = flat<float  >(in); break;
+                case c32: output = flat<cfloat >(in); break;
+                case f64: output = flat<double >(in); break;
+                case c64: output = flat<cdouble>(in); break;
+                case b8:  output = flat<char   >(in); break;
+                case s32: output = flat<int    >(in); break;
+                case u32: output = flat<uint   >(in); break;
+                case u8:  output = flat<uchar  >(in); break;
+                case s64: output = flat<intl   >(in); break;
+                case u64: output = flat<uintl  >(in); break;
+                case s16: output = flat<short  >(in); break;
+                case u16: output = flat<ushort >(in); break;
+                default: TYPE_ERROR(1, type);
+            }
+            std::swap(*out,output);
         }
-
-        std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/cpp/seq.cpp b/src/api/cpp/seq.cpp
index 56160f9299..c198ff9033 100644
--- a/src/api/cpp/seq.cpp
+++ b/src/api/cpp/seq.cpp
@@ -14,7 +14,6 @@
 
 namespace af
 {
-
 int end = -1;
 seq span(af_span);
 
@@ -69,9 +68,9 @@ seq::seq(double begin, double end, double step): m_gfor(false)
         if (begin != end)   // Span
             AF_THROW_ERR("Invalid step size", AF_ERR_ARG);
     }
-    if (end >= 0 && begin >= 0 && signbit(end-begin) != signbit(step))
+    if ((signbit(end      ) == signbit(begin)) &&
+        (signbit(end-begin) != signbit(step )))
         AF_THROW_ERR("Sequence is invalid", AF_ERR_ARG);
-        //AF_THROW("step must match direction of sequence");
     init(begin, end, step);
 }
 
@@ -91,5 +90,4 @@ seq::operator array() const
     array res = s.begin + s.step * tmp;
     return res;
 }
-
 }
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 320f562b71..4e5915be72 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -54,7 +54,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
-    std::vector<CParam<uint>> idxParams(idxArrs.begin(), idxArrs.end());
+    vector<CParam<uint>> idxParams(idxArrs.begin(), idxArrs.end());
 
     getQueue().enqueue(kernel::index<T>, out, in, in.getDataDims(),
                        std::move(isSeq), std::move(seqs), std::move(idxParams));
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 1e09f4dd48..c7c6e214a4 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -15,9 +15,9 @@
 
 namespace cpu
 {
-
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim)
+Array<in_t> lookup(const Array<in_t> &input,
+                   const Array<idx_t> &indices, const unsigned dim)
 {
     input.eval();
     indices.eval();
@@ -36,15 +36,15 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 }
 
 #define INSTANTIATE(T)  \
-    template Array<T>  lookup<T, float   >(const Array<T> &input, const Array<float   > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, double  >(const Array<T> &input, const Array<double  > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, int     >(const Array<T> &input, const Array<int     > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, unsigned>(const Array<T> &input, const Array<unsigned> &indices, const unsigned dim); \
-    template Array<T>  lookup<T, short   >(const Array<T> &input, const Array<short   > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, ushort  >(const Array<T> &input, const Array<ushort  > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, intl    >(const Array<T> &input, const Array<intl    > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, uintl   >(const Array<T> &input, const Array<uintl   > &indices, const unsigned dim); \
-    template Array<T>  lookup<T, uchar   >(const Array<T> &input, const Array<uchar   > &indices, const unsigned dim);
+template Array<T>  lookup<T, float   >(const Array<T>&, const Array<float   >&, const unsigned); \
+template Array<T>  lookup<T, double  >(const Array<T>&, const Array<double  >&, const unsigned); \
+template Array<T>  lookup<T, int     >(const Array<T>&, const Array<int     >&, const unsigned); \
+template Array<T>  lookup<T, unsigned>(const Array<T>&, const Array<unsigned>&, const unsigned); \
+template Array<T>  lookup<T, short   >(const Array<T>&, const Array<short   >&, const unsigned); \
+template Array<T>  lookup<T, ushort  >(const Array<T>&, const Array<ushort  >&, const unsigned); \
+template Array<T>  lookup<T, intl    >(const Array<T>&, const Array<intl    >&, const unsigned); \
+template Array<T>  lookup<T, uintl   >(const Array<T>&, const Array<uintl   >&, const unsigned); \
+template Array<T>  lookup<T, uchar   >(const Array<T>&, const Array<uchar   >&, const unsigned);
 
 INSTANTIATE(float   );
 INSTANTIATE(cfloat  );
@@ -58,5 +58,4 @@ INSTANTIATE(uchar   );
 INSTANTIATE(char    );
 INSTANTIATE(ushort  );
 INSTANTIATE(short   );
-
 }
diff --git a/src/backend/cpu/lookup.hpp b/src/backend/cpu/lookup.hpp
index a41ea3b086..95c729f154 100644
--- a/src/backend/cpu/lookup.hpp
+++ b/src/backend/cpu/lookup.hpp
@@ -11,8 +11,7 @@
 
 namespace cpu
 {
-
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim);
-
+Array<in_t> lookup(const Array<in_t> &input,
+                   const Array<idx_t> &indices, const unsigned dim);
 }
diff --git a/src/backend/cuda/lookup.cu b/src/backend/cuda/lookup.cu
index 70c9ed90b7..7849e3e366 100644
--- a/src/backend/cuda/lookup.cu
+++ b/src/backend/cuda/lookup.cu
@@ -13,9 +13,9 @@
 
 namespace cuda
 {
-
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim)
+Array<in_t> lookup(const Array<in_t> &input,
+                   const Array<idx_t> &indices, const unsigned dim)
 {
     const dim4 iDims = input.dims();
 
@@ -38,15 +38,15 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 }
 
 #define INSTANTIATE(T)  \
-    template Array<T> lookup<T, float   >(const Array<T> &input, const Array<float   > &indices, const unsigned dim); \
-    template Array<T> lookup<T, double  >(const Array<T> &input, const Array<double  > &indices, const unsigned dim); \
-    template Array<T> lookup<T, int     >(const Array<T> &input, const Array<int     > &indices, const unsigned dim); \
-    template Array<T> lookup<T, unsigned>(const Array<T> &input, const Array<unsigned> &indices, const unsigned dim); \
-    template Array<T> lookup<T, short   >(const Array<T> &input, const Array<short   > &indices, const unsigned dim); \
-    template Array<T> lookup<T, ushort  >(const Array<T> &input, const Array<ushort  > &indices, const unsigned dim); \
-    template Array<T> lookup<T, intl    >(const Array<T> &input, const Array<intl    > &indices, const unsigned dim); \
-    template Array<T> lookup<T, uintl   >(const Array<T> &input, const Array<uintl   > &indices, const unsigned dim); \
-    template Array<T> lookup<T, uchar   >(const Array<T> &input, const Array<uchar   > &indices, const unsigned dim);
+template Array<T> lookup<T, float   >(const Array<T>&, const Array<float   >&, const unsigned); \
+template Array<T> lookup<T, double  >(const Array<T>&, const Array<double  >&, const unsigned); \
+template Array<T> lookup<T, int     >(const Array<T>&, const Array<int     >&, const unsigned); \
+template Array<T> lookup<T, unsigned>(const Array<T>&, const Array<unsigned>&, const unsigned); \
+template Array<T> lookup<T, short   >(const Array<T>&, const Array<short   >&, const unsigned); \
+template Array<T> lookup<T, ushort  >(const Array<T>&, const Array<ushort  >&, const unsigned); \
+template Array<T> lookup<T, intl    >(const Array<T>&, const Array<intl    >&, const unsigned); \
+template Array<T> lookup<T, uintl   >(const Array<T>&, const Array<uintl   >&, const unsigned); \
+template Array<T> lookup<T, uchar   >(const Array<T>&, const Array<uchar   >&, const unsigned);
 
 INSTANTIATE(float   );
 INSTANTIATE(cfloat  );
@@ -60,5 +60,4 @@ INSTANTIATE(uchar   );
 INSTANTIATE(char    );
 INSTANTIATE(short   );
 INSTANTIATE(ushort  );
-
 }
diff --git a/src/backend/cuda/lookup.hpp b/src/backend/cuda/lookup.hpp
index c8732952f1..d1ff6aa48f 100644
--- a/src/backend/cuda/lookup.hpp
+++ b/src/backend/cuda/lookup.hpp
@@ -11,8 +11,7 @@
 
 namespace cuda
 {
-
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim);
-
+Array<in_t> lookup(const Array<in_t> &input,
+                   const Array<idx_t> &indices, const unsigned dim);
 }
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 761200fdef..08bade3594 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -15,9 +15,9 @@
 
 namespace opencl
 {
-
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim)
+Array<in_t> lookup(const Array<in_t> &input,
+                   const Array<idx_t> &indices, const unsigned dim)
 {
     const dim4 iDims = input.dims();
 
@@ -40,15 +40,15 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const
 }
 
 #define INSTANTIATE(T)  \
-    template Array<T> lookup<T, float   >(const Array<T> &input, const Array<float   > &indices, const unsigned dim); \
-    template Array<T> lookup<T, double  >(const Array<T> &input, const Array<double  > &indices, const unsigned dim); \
-    template Array<T> lookup<T, int     >(const Array<T> &input, const Array<int     > &indices, const unsigned dim); \
-    template Array<T> lookup<T, unsigned>(const Array<T> &input, const Array<unsigned> &indices, const unsigned dim); \
-    template Array<T> lookup<T, short   >(const Array<T> &input, const Array<short   > &indices, const unsigned dim); \
-    template Array<T> lookup<T, ushort  >(const Array<T> &input, const Array<ushort  > &indices, const unsigned dim); \
-    template Array<T> lookup<T, intl    >(const Array<T> &input, const Array<intl    > &indices, const unsigned dim); \
-    template Array<T> lookup<T, uintl   >(const Array<T> &input, const Array<uintl   > &indices, const unsigned dim); \
-    template Array<T> lookup<T, uchar   >(const Array<T> &input, const Array<uchar   > &indices, const unsigned dim);
+template Array<T> lookup<T, float   >(const Array<T>&, const Array<float   >&, const unsigned); \
+template Array<T> lookup<T, double  >(const Array<T>&, const Array<double  >&, const unsigned); \
+template Array<T> lookup<T, int     >(const Array<T>&, const Array<int     >&, const unsigned); \
+template Array<T> lookup<T, unsigned>(const Array<T>&, const Array<unsigned>&, const unsigned); \
+template Array<T> lookup<T, short   >(const Array<T>&, const Array<short   >&, const unsigned); \
+template Array<T> lookup<T, ushort  >(const Array<T>&, const Array<ushort  >&, const unsigned); \
+template Array<T> lookup<T, intl    >(const Array<T>&, const Array<intl    >&, const unsigned); \
+template Array<T> lookup<T, uintl   >(const Array<T>&, const Array<uintl   >&, const unsigned); \
+template Array<T> lookup<T, uchar   >(const Array<T>&, const Array<uchar   >&, const unsigned);
 
 INSTANTIATE(float   );
 INSTANTIATE(cfloat  );
@@ -62,5 +62,4 @@ INSTANTIATE(uchar   );
 INSTANTIATE(char    );
 INSTANTIATE(ushort  );
 INSTANTIATE(short   );
-
 }
diff --git a/src/backend/opencl/lookup.hpp b/src/backend/opencl/lookup.hpp
index 59c5f21a6d..8c1e939815 100644
--- a/src/backend/opencl/lookup.hpp
+++ b/src/backend/opencl/lookup.hpp
@@ -11,8 +11,7 @@
 
 namespace opencl
 {
-
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices, const unsigned dim);
-
+Array<in_t> lookup(const Array<in_t> &input,
+                   const Array<idx_t> &indices, const unsigned dim);
 }
diff --git a/test/assign.cpp b/test/assign.cpp
index 573bfb0894..78812a153e 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -546,7 +546,6 @@ TEST(ArrayAssign, CPP_END)
         ASSERT_EQ(hA[i * n + end_off], hB[i]);
     }
 
-
     af_free_host(hA);
     af_free_host(hB);
 }
diff --git a/test/index.cpp b/test/index.cpp
index 21206f2fbb..bec8006045 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1591,3 +1591,17 @@ TEST(Index, Issue1867ChainedIndexingLeak)
                       &lock_bytes, &lock_buffers);
     ASSERT_EQ(0u, lock_buffers);
 }
+
+TEST(Index, InvalidSequence_SingleElementNegativeStep)
+{
+    EXPECT_THROW(af::seq(1,1,-1), af::exception);
+}
+TEST(Index, InvalidSequence_PositiveRangeNegativeStep)
+{
+    EXPECT_THROW(af::seq(1,5,-1), af::exception);
+}
+
+TEST(Index, InvalidSequence_NegativeRangePositiveStep)
+{
+    EXPECT_THROW(af::seq(-1,-5,1), af::exception);
+}

From 33e224053ea4a1e0f87af8134ec48f53575bc8b2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 Jun 2018 01:19:00 -0400
Subject: [PATCH 1451/2677] Fix leak caused by initArray usage. Replaced with
 createEmptyArray

initArray is only used to create Array<T> pointers where a new handle
is created or to increase the internal reference count of the Array<T>
object. They should not be used to initialize an empty Array<T> object
because it would cause a leak.
---
 src/backend/cuda/homography.cu   |  2 +-
 src/backend/cuda/kernel/mean.hpp | 20 ++++++++++----------
 src/backend/cuda/kernel/orb.hpp  |  2 +-
 src/backend/cuda/kernel/topk.hpp |  4 ++--
 4 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index 27d1217314..108a35dd10 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -40,7 +40,7 @@ int homography(Array<T> &bestH,
     const unsigned nsamples = idims[0];
 
     unsigned iter = iterations;
-    Array<float> err = *initArray<float>();
+    Array<float> err = createEmptyArray<float>(dim4());
     if (htype == AF_HOMOGRAPHY_LMEDS) {
         iter = ::std::min(iter, (unsigned)(log(1.f - LMEDSConfidence) / log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
         err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 15c8f43de9..f232e14152 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -203,17 +203,17 @@ namespace kernel
 
         blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
-        Array<To> tmpOut = *initArray<To>();
-        Array<Tw> tmpWt  = *initArray<Tw>();
+        Array<To> tmpOut = createEmptyArray<To>(dim4());
+        Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
 
         if (blocks_dim[dim] > 1) {
-          dim4 dims(4, out.dims);
-          dims[dim] = blocks_dim[dim];
-          tmpOut = createEmptyArray<To>(dims);
-          tmpWt  = createEmptyArray<Tw>(dims);
+            dim4 dims(4, out.dims);
+            dims[dim] = blocks_dim[dim];
+            tmpOut = createEmptyArray<To>(dims);
+            tmpWt  = createEmptyArray<Tw>(dims);
         }
         else {
-          tmpOut = createParamArray(out, false);
+            tmpOut = createParamArray(out, false);
         }
 
         mean_dim_launcher<Ti, Tw, To, dim>(tmpOut, tmpWt, in, iwt, threads_y, blocks_dim);
@@ -221,7 +221,7 @@ namespace kernel
         if (blocks_dim[dim] > 1) {
             blocks_dim[dim] = 1;
 
-            Array<Tw> owt = *initArray<Tw>();
+            Array<Tw> owt = createEmptyArray<Tw>(dim4());
             mean_dim_launcher<To, Tw, To, dim>(out, owt, tmpOut, tmpWt,
                                                     threads_y, blocks_dim);
 
@@ -382,8 +382,8 @@ namespace kernel
         uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
         uint blocks_y = divup(in.dims[1], threads_y);
 
-        Array<To> tmpOut = *initArray<To>();
-        Array<Tw> tmpWt  = *initArray<Tw>();
+        Array<To> tmpOut = createEmptyArray<To>(dim4());
+        Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
         if (blocks_x > 1) {
           tmpOut = createEmptyArray<To>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
           tmpWt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index bae33fefe4..b0fc95b343 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -339,7 +339,7 @@ void orb(unsigned* out_feat,
     unsigned total_feat = 0;
 
     // Calculate a separable Gaussian kernel
-    Array<convAccT> gauss_filter = *initArray<convAccT>();
+    Array<convAccT> gauss_filter = createEmptyArray<convAccT>(dim4());
     if (blur_img) {
         unsigned gauss_len = 9;
         vector<convAccT> h_gauss(gauss_len);
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 8db1bbf51a..792c5f601e 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -106,8 +106,8 @@ void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
     // before the first iteration and reused for further iterations.
 
     // Temporary storage allocation for iterations
-    Array<T> tvals = *initArray<T>();
-    Array<uint> tidxs = *initArray<uint>();
+    Array<T> tvals = createEmptyArray<T>(dim4());
+    Array<uint> tidxs = createEmptyArray<uint>(dim4());
 
     if (numBlocksX > 1) {
         tvals = createEmptyArray<T>(dim4(k * numBlocksX, ivals.dims[1]));

From 2e8b66b92433f468d5250c88d37b53253bbe44fe Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 Jun 2018 01:43:34 -0400
Subject: [PATCH 1452/2677] Move retainHandle to handle.hpp. Clarify
 documentation

---
 src/api/c/array.cpp          | 10 ----------
 src/api/c/handle.hpp         |  9 +++++++++
 src/backend/cpu/Array.hpp    | 11 ++++++++---
 src/backend/cuda/Array.hpp   | 19 ++++++++++++++-----
 src/backend/opencl/Array.hpp | 25 +++++++++++++++++--------
 5 files changed, 48 insertions(+), 26 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 20f63d42f2..0563a02f6e 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -221,16 +221,6 @@ af_err af_release_array(af_array arr)
     return AF_SUCCESS;
 }
 
-
-template<typename T>
-static af_array retainHandle(const af_array in)
-{
-    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
-    detail::Array<T> *out = detail::initArray<T>();
-    *out= *A;
-    return reinterpret_cast<af_array>(out);
-}
-
 af_array retain(const af_array in)
 {
     const ArrayInfo& info = getInfo(in, false, false);
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 5c0ec75fe0..aaf19930ee 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -98,6 +98,15 @@ getHandle(const detail::Array<T> &A)
     return arr;
 }
 
+template<typename T>
+static af_array retainHandle(const af_array in)
+{
+    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
+    detail::Array<T> *out = detail::initArray<T>();
+    *out= *A;
+    return reinterpret_cast<af_array>(out);
+}
+
 template<typename T>
 static af_array createHandle(af::dim4 d)
 {
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 491ef9ae53..b8f3ba2b7f 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -58,17 +58,22 @@ namespace cpu
     template<typename T>
     Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
 
-    // Copies data to an existing Array object from a host pointer
+    /// Copies data to an existing Array object from a host pointer
     template<typename T>
     void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
 
-    // Copies data to an existing Array object from a device pointer
+    /// Copies data to an existing Array object from a device pointer
     template<typename T>
     void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
 
-    // Create an Array object and do not assign any values to it
+    /// Create an Array object and do not assign any values to it.
+    /// \NOTE: This object should not be used to initalize an array. Use
+    ///       createEmptyArray instead
     template<typename T> Array<T> *initArray();
 
+    /// Creates an empty array of a given size. No data is initialized
+    ///
+    /// \param[in] size The dimension of the output array
     template<typename T>
     Array<T> createEmptyArray(const af::dim4 &size);
 
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index b3b83ef532..dfce67ea16 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -48,23 +48,32 @@ namespace cuda
     template<typename T>
     Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
 
-    // Copies data to an existing Array object from a host pointer
+    /// Copies data to an existing Array object from a host pointer
     template<typename T>
     void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
 
-    // Copies data to an existing Array object from a device pointer
+    /// Copies data to an existing Array object from a device pointer
     template<typename T>
     void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
 
-    // Create an Array object and do not assign any values to it
+    /// Create an Array object and do not assign any values to it.
+    /// \NOTE: This object should not be used to initalize an array. Use
+    ///       createEmptyArray instead
     template<typename T> Array<T> *initArray();
 
+    /// Creates an empty array of a given size. No data is initialized
+    ///
+    /// \param[in] size The dimension of the output array
     template<typename T>
     Array<T> createEmptyArray(const af::dim4 &size);
 
-    // Create an Array object from Param<T>
+    /// Create an Array object from Param<T> object.
+    ///
+    /// \param[in] in    The Param<T> array that is created.
+    /// \param[in] owner If true, the new Array<T> object is the owner of the data. If false
+    ///                  the Array<T> will not delete the object on destruction
     template<typename T>
-    Array<T> createParamArray(Param<T> &tmp, bool owner);
+    Array<T> createParamArray(Param<T> &in, bool owner);
 
     template<typename T>
     Array<T> createSubArray(const Array<T>& parent,
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 8cb97649af..0dff1be5d6 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -33,36 +33,45 @@ namespace opencl
     void evalNodes(Param &out, JIT::Node *node);
     void evalNodes(std::vector<Param> &outputs, std::vector<JIT::Node *> nodes);
 
-    // Creates a new Array object on the heap and returns a reference to it.
+    /// Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
 
-    // Creates a new Array object on the heap and returns a reference to it.
+    /// Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createValueArray(const af::dim4 &size, const T& value);
 
-    // Creates a new Array object on the heap and returns a reference to it.
+    /// Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     Array<T> createHostDataArray(const af::dim4 &size, const T * const data);
 
     template<typename T>
     Array<T> createDeviceDataArray(const af::dim4 &size, const void *data, bool copy = false);
 
-    // Copies data to an existing Array object from a host pointer
+    /// Copies data to an existing Array object from a host pointer
     template<typename T>
     void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
 
-    // Copies data to an existing Array object from a device pointer
+    /// Copies data to an existing Array object from a device pointer
     template<typename T>
     void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
 
-    // Create an Array object and do not assign any values to it
+    /// Create an Array object and do not assign any values to it.
+    /// \NOTE: This object should not be used to initalize an array. Use
+    ///       createEmptyArray instead
     template<typename T> Array<T> *initArray();
 
+    /// Creates an empty array of a given size. No data is initialized
+    ///
+    /// \param[in] size The dimension of the output array
     template<typename T>
     Array<T> createEmptyArray(const af::dim4 &size);
 
-    // Create an Array object from Param
+    /// Create an Array object from Param object.
+    ///
+    /// \param[in] in    The Param array that is created.
+    /// \param[in] owner If true, the new Array<T> object is the owner of the data. If false
+    ///                  the Array<T> will not delete the object on destruction
     template<typename T>
     Array<T> createParamArray(Param &tmp, bool owner);
 
@@ -71,7 +80,7 @@ namespace opencl
                             const std::vector<af_seq> &index,
                             bool copy=true);
 
-    // Creates a new Array object on the heap and returns a reference to it.
+    /// Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
     void destroyArray(Array<T> *A);
 

From 7c6fa423f86aa4af2063c09496ebab0a10d74007 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Tue, 19 Jun 2018 13:32:23 -0500
Subject: [PATCH 1453/2677] Cleanup tests. Fix warnings when running with asan.
 (#2199)

- Greatly reduced number of instances where entire std and af namespaces
  were introduced into tests.
- Utilizing `using` statements to introduce specific members from `af` and
  `std` namespaces.
- Several tests in gfor were not freeing memory.
- Fixed tests freeing memory using the incorrect function.
- Add missing namespace qualifier for sync
- Modified matmul batch dimensions.
- Utilizing std::vectors in reduce tests.
---
 examples/computer_vision/fast.cpp        |   3 +
 examples/computer_vision/harris.cpp      |   1 +
 examples/computer_vision/susan.cpp       |   2 +
 examples/machine_learning/mnist_common.h |   7 +-
 test/anisotropic_diffusion.cpp           |  26 ++-
 test/approx1.cpp                         | 125 +++++-----
 test/approx2.cpp                         | 131 +++++------
 test/array.cpp                           |  44 ++--
 test/assign.cpp                          | 258 ++++++++++-----------
 test/backend.cpp                         |  13 +-
 test/basic.cpp                           |  24 +-
 test/bilateral.cpp                       |  38 +--
 test/binary.cpp                          |  30 ++-
 test/blas.cpp                            | 122 +++++-----
 test/canny.cpp                           |  31 +--
 test/cast.cpp                            |  14 +-
 test/cholesky_dense.cpp                  |  33 +--
 test/clamp.cpp                           |  38 +--
 test/compare.cpp                         |  17 +-
 test/complex.cpp                         |  51 +++--
 test/constant.cpp                        |  40 ++--
 test/convolve.cpp                        | 130 +++++------
 test/corrcoef.cpp                        |  21 +-
 test/covariance.cpp                      |  24 +-
 test/diagonal.cpp                        |  36 +--
 test/diff1.cpp                           |  66 +++---
 test/diff2.cpp                           |  60 ++---
 test/dog.cpp                             |  52 +++--
 test/dot.cpp                             |  43 ++--
 test/empty.cpp                           |   6 +-
 test/fast.cpp                            |  43 ++--
 test/fft.cpp                             | 255 +++++++++++----------
 test/fft_large.cpp                       |  35 +--
 test/fft_real.cpp                        |  56 +++--
 test/fftconvolve.cpp                     |  62 +++--
 test/flat.cpp                            |  33 +--
 test/flip.cpp                            |  50 ++--
 test/gaussiankernel.cpp                  |  16 +-
 test/gen_assign.cpp                      |  95 ++++----
 test/gen_index.cpp                       |  54 ++---
 test/getting_started.cpp                 |  24 +-
 test/gfor.cpp                            |  41 +++-
 test/gloh_nonfree.cpp                    |  53 +++--
 test/gradient.cpp                        |  45 ++--
 test/gray_rgb.cpp                        |  36 +--
 test/hamming.cpp                         |  10 +-
 test/harris.cpp                          |  36 +--
 test/histogram.cpp                       |  69 +++---
 test/homography.cpp                      |  67 +++---
 test/hsv_rgb.cpp                         |  65 +++---
 test/iir.cpp                             |  52 +++--
 test/imageio.cpp                         | 151 ++++++------
 test/index.cpp                           | 279 ++++++++++-------------
 test/info.cpp                            |  17 +-
 test/internal.cpp                        |  61 ++---
 test/inverse_dense.cpp                   |  26 +--
 test/iota.cpp                            |  39 ++--
 test/ireduce.cpp                         |  36 +--
 test/jit.cpp                             | 252 ++++++++++----------
 test/join.cpp                            |  83 ++++---
 test/lu_dense.cpp                        |  79 ++++---
 test/manual_memory_test.cpp              |  17 +-
 test/match_template.cpp                  |  42 ++--
 test/math.cpp                            |  42 ++--
 test/matrix_manipulation.cpp             |   5 +-
 test/mean.cpp                            |  75 +++---
 test/meanshift.cpp                       |  31 ++-
 test/medfilt.cpp                         |  92 ++++----
 test/median.cpp                          |  31 ++-
 test/memory.cpp                          | 224 +++++++++---------
 test/memory_lock.cpp                     |  15 +-
 test/moddims.cpp                         |  56 ++---
 test/moments.cpp                         |  75 +++---
 test/morph.cpp                           |  87 +++----
 test/nearest_neighbour.cpp               |  34 ++-
 test/ocl_ext_context.cpp                 |  43 ++--
 test/orb.cpp                             |  41 ++--
 test/qr_dense.cpp                        |  65 +++---
 test/random.cpp                          | 191 ++++++++--------
 test/random_practrand.cpp                |  14 +-
 test/range.cpp                           |  24 +-
 test/rank_dense.cpp                      |  47 ++--
 test/reduce.cpp                          |  21 +-
 test/regions.cpp                         |  62 ++---
 test/reorder.cpp                         |  41 ++--
 test/replace.cpp                         |  61 ++---
 test/resize.cpp                          |  98 ++++----
 test/rotate.cpp                          |  19 +-
 test/rotate_linear.cpp                   |  23 +-
 test/sat.cpp                             |  16 +-
 test/scan.cpp                            |  76 +++---
 test/scan_by_key.cpp                     |  46 ++--
 test/select.cpp                          | 122 +++++-----
 test/set.cpp                             |  22 +-
 test/shift.cpp                           |  40 ++--
 test/sift_nonfree.cpp                    |  55 +++--
 test/sobel.cpp                           |  17 +-
 test/sort.cpp                            |  47 ++--
 test/sort_by_key.cpp                     |  73 +++---
 test/sort_index.cpp                      |  61 ++---
 test/sparse.cpp                          |  59 +++--
 test/sparse_arith.cpp                    | 152 ++++++------
 test/sparse_convert.cpp                  |  55 ++---
 test/stdev.cpp                           |  50 ++--
 test/susan.cpp                           |  70 +++---
 test/svd_dense.cpp                       |  24 +-
 test/threading.cpp                       |  96 ++++----
 test/tile.cpp                            |  38 +--
 test/topk.cpp                            |  16 +-
 test/transform.cpp                       |  74 +++---
 test/transform_coordinates.cpp           |  20 +-
 test/translate.cpp                       |  45 ++--
 test/transpose.cpp                       |  63 ++---
 test/transpose_inplace.cpp               |  21 +-
 test/triangle.cpp                        |  19 +-
 test/unwrap.cpp                          |  38 +--
 test/var.cpp                             |  21 +-
 test/where.cpp                           |  44 ++--
 test/wrap.cpp                            |  32 +--
 test/write.cpp                           |  38 +--
 test/ycbcr_rgb.cpp                       |  61 ++---
 121 files changed, 3677 insertions(+), 3216 deletions(-)

diff --git a/examples/computer_vision/fast.cpp b/examples/computer_vision/fast.cpp
index 348641c61f..85b2e5907d 100644
--- a/examples/computer_vision/fast.cpp
+++ b/examples/computer_vision/fast.cpp
@@ -47,6 +47,9 @@ static void fast_demo(bool console)
         img_color(seq(y-draw_len, y+draw_len), x, 2) = 0.f;
     }
 
+    freeHost(h_x);
+    freeHost(h_y);
+
     printf("Features found: %lu\n", feat.getNumFeatures());
 
     if (!console) {
diff --git a/examples/computer_vision/harris.cpp b/examples/computer_vision/harris.cpp
index 7f41f7e726..c1571fa2ed 100644
--- a/examples/computer_vision/harris.cpp
+++ b/examples/computer_vision/harris.cpp
@@ -94,6 +94,7 @@ static void harris_demo(bool console)
             }
         }
     }
+    freeHost(h_corners);
 
     printf("Corners found: %u\n", good_corners);
 
diff --git a/examples/computer_vision/susan.cpp b/examples/computer_vision/susan.cpp
index 2f02679a14..0ca947d4af 100644
--- a/examples/computer_vision/susan.cpp
+++ b/examples/computer_vision/susan.cpp
@@ -51,6 +51,8 @@ static void susan_demo(bool console)
         img_color(seq(x-draw_len, x+draw_len), y, 1) = 1.f;
         img_color(seq(x-draw_len, x+draw_len), y, 2) = 0.f;
     }
+    freeHost(h_x);
+    freeHost(h_y);
 
     printf("Features found: %lu\n", feat.getNumFeatures());
 
diff --git a/examples/machine_learning/mnist_common.h b/examples/machine_learning/mnist_common.h
index 0133f652a7..e6ece0de80 100644
--- a/examples/machine_learning/mnist_common.h
+++ b/examples/machine_learning/mnist_common.h
@@ -34,6 +34,7 @@ std::string classify(af::array arr, int k)
 
         std::stable_sort(data.begin(), data.end(), compare);
 
+        af::freeHost(h_vec);
         ss << data[0].second;
     } else {
         ss << (int)(arr(k).as(f32).scalar<float>());
@@ -85,8 +86,8 @@ static void setup_mnist(int *num_classes, int *num_train, int *num_test,
             test_labels(ldata[ h_test_idx[ii]], ii) = 1;
         }
 
-        delete[] h_train_idx;
-        delete[] h_test_idx;
+        af::freeHost(h_train_idx);
+        af::freeHost(h_test_idx);
     } else {
         af::array labels = af::array(ldims[0], &ldata[0]);
         train_labels = labels(train_indices);
@@ -152,7 +153,7 @@ static void display_results(const af::array &test_images,
             }
             std::cout << std::endl;
         }
-        delete[] img;
+        af::freeHost(img);
         getchar();
     }
 #endif
diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 83d709cdb8..2235fa6fca 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -19,6 +19,12 @@
 using std::string;
 using std::vector;
 using std::abs;
+using af::array;
+using af::exception;
+using af::fluxFunction;
+using af::max;
+using af::min;
+using af::randu;
 
 template<typename T>
 class AnisotropicDiffusion : public ::testing::Test
@@ -30,16 +36,16 @@ typedef ::testing::Types<float, double, int, uint, uchar, short, ushort> TestTyp
 TYPED_TEST_CASE(AnisotropicDiffusion, TestTypes);
 
 template<typename T>
-af::array normalize(const af::array &p_in)
+array normalize(const array &p_in)
 {
-    T mx = af::max<T>(p_in);
-    T mn = af::min<T>(p_in);
+    T mx = max<T>(p_in);
+    T mn = min<T>(p_in);
     return (p_in-mn)/(mx-mn);
 }
 
 template<typename T, bool isColor>
 void imageTest(string pTestFile, const float dt, const float K, const uint iters,
-               af::fluxFunction fluxKind, bool isCurvatureDiffusion=false)
+               fluxFunction fluxKind, bool isCurvatureDiffusion=false)
 {
     typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
 
@@ -113,10 +119,10 @@ void imageTest(string pTestFile, const float dt, const float K, const uint iters
         ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
         ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
 
-        std::vector<OutType> outData(nElems);
+        vector<OutType> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<OutType> goldData(nElems);
+        vector<OutType> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.025f));
@@ -155,8 +161,8 @@ TYPED_TEST(AnisotropicDiffusion, GradientColorImage)
 TEST(AnisotropicDiffusion, GradientInvalidInputArray)
 {
     try {
-        af::array out = af::anisotropicDiffusion(af::randu(100), 0.125f, 0.2f, 10, AF_FLUX_QUADRATIC);
-    } catch (af::exception &exp) {
+        array out = anisotropicDiffusion(randu(100), 0.125f, 0.2f, 10, AF_FLUX_QUADRATIC);
+    } catch (exception &exp) {
         ASSERT_EQ(AF_ERR_SIZE, exp.err());
     }
 }
@@ -181,8 +187,8 @@ TYPED_TEST(AnisotropicDiffusion, CurvatureColorImage)
 TEST(AnisotropicDiffusion, CurvatureInvalidInputArray)
 {
     try {
-        af::array out = af::anisotropicDiffusion(af::randu(100), 0.125f, 0.2f, 10);
-    } catch (af::exception &exp) {
+        array out = anisotropicDiffusion(randu(100), 0.125f, 0.2f, 10);
+    } catch (exception &exp) {
         ASSERT_EQ(AF_ERR_SIZE, exp.err());
     }
 }
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 4a3f19384e..d4ea7cb361 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -34,7 +34,6 @@ using af::seq;
 using af::sum;
 
 using std::abs;
-using std::cout;
 using std::endl;
 using std::string;
 using std::vector;
@@ -51,10 +50,10 @@ class Approx1 : public ::testing::Test
         vector<af_seq> subMat0;
 };
 
-// create a list of types to be tested
+// Create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 
-// register the type list
+// Register the type list
 TYPED_TEST_CASE(Approx1, TestTypes);
 
 template<typename T>
@@ -62,14 +61,14 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
 {
     if (noDoubleTests<T>()) return;
 
-    typedef typename af::dtype_traits<T>::base_type BT;
-    vector<af::dim4> numDims;
+    typedef typename dtype_traits<T>::base_type BT;
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
     readTests<BT, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
 
     af_array inArray = 0;
     af_array posArray = 0;
@@ -79,14 +78,14 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
 
@@ -99,7 +98,7 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     bool ret = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -127,14 +126,14 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
 {
     if (noDoubleTests<T>()) return;
 
-    typedef typename af::dtype_traits<T>::base_type BT;
-    vector<af::dim4> numDims;
+    typedef typename dtype_traits<T>::base_type BT;
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
     readTests<BT, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
 
     af_array inArray = 0;
     af_array posArray = 0;
@@ -144,13 +143,13 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
 
     // Get result
@@ -171,14 +170,14 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         double integral;
-        //test that control points are exact
+        // Test that control points are exact
         if((std::modf(in[1][elIter], &integral) < 0.001) || (std::modf(in[1][elIter], &integral) > 0.999))  {
             ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001;
-            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
         } else {
-            //match intermediate values withing a threshold
+            // Match intermediate values within a threshold
             ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.035 * range;
-            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
         }
     }
 
@@ -203,14 +202,14 @@ template<typename T>
 void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, const af_err err)
 {
     if (noDoubleTests<T>()) return;
-    typedef typename af::dtype_traits<T>::base_type BT;
-    vector<af::dim4> numDims;
+    typedef typename dtype_traits<T>::base_type BT;
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
     readTests<BT, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
 
     af_array inArray  = 0;
     af_array posArray = 0;
@@ -218,9 +217,9 @@ void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
     ASSERT_EQ(err, af_approx1(&outArray, inArray, posArray, method, 0));
 
@@ -246,13 +245,13 @@ template<typename T>
 void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
 {
     if (noDoubleTests<T>()) return;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
 
     af_array inArray  = 0;
     af_array posArray = 0;
@@ -260,12 +259,12 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    if((af_dtype) af::dtype_traits<T>::af_type == c32 ||
-       (af_dtype) af::dtype_traits<T>::af_type == c64) {
+    if((af_dtype) dtype_traits<T>::af_type == c32 ||
+       (af_dtype) dtype_traits<T>::af_type == c64) {
         ASSERT_EQ(AF_ERR_ARG, af_approx1(&outArray, inArray, posArray, method, 0));
     } else {
         ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
@@ -321,7 +320,7 @@ TEST(Approx1, CPP)
     bool ret = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -410,17 +409,17 @@ TEST(Approx1, CPPNearestMaxDims)
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    af::array input = af::randu(1, largeDim);
-    af::array pos   = input.dims(0) * af::randu(1, largeDim);
-    af::array out   = af::approx1(input, pos, AF_INTERP_NEAREST);
+    array input = randu(1, largeDim);
+    array pos   = input.dims(0) * randu(1, largeDim);
+    array out   = approx1(input, pos, AF_INTERP_NEAREST);
 
-    input = af::randu(1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, largeDim);
-    out   = af::approx1(input, pos, AF_INTERP_NEAREST);
+    input = randu(1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, largeDim);
+    out   = approx1(input, pos, AF_INTERP_NEAREST);
 
-    input = af::randu(1, 1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
-    out   = af::approx1(input, pos, AF_INTERP_NEAREST);
+    input = randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    out   = approx1(input, pos, AF_INTERP_NEAREST);
 
     SUCCEED();
 }
@@ -430,17 +429,17 @@ TEST(Approx1, CPPLinearMaxDims)
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    af::array input = af::iota(af::dim4(1, largeDim), c32);
-    af::array pos   = input.dims(0) * af::randu(1, largeDim);
-    af::array outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+    array input = iota(dim4(1, largeDim), c32);
+    array pos   = input.dims(0) * randu(1, largeDim);
+    array outBatch = approx1(input, pos, AF_INTERP_LINEAR);
 
-    input = af::iota(af::dim4(1, 1, largeDim), c32);
-    pos   = input.dims(0) * af::randu(1, 1, largeDim);
-    outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+    input = iota(dim4(1, 1, largeDim), c32);
+    pos   = input.dims(0) * randu(1, 1, largeDim);
+    outBatch = approx1(input, pos, AF_INTERP_LINEAR);
 
-    input = af::iota(af::dim4(1, 1, 1, largeDim), c32);
-    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
-    outBatch = af::approx1(input, pos, AF_INTERP_LINEAR);
+    input = iota(dim4(1, 1, 1, largeDim), c32);
+    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    outBatch = approx1(input, pos, AF_INTERP_LINEAR);
 
     SUCCEED();
 }
@@ -450,17 +449,17 @@ TEST(Approx1, CPPCubicMaxDims)
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    af::array input = af::iota(af::dim4(1, largeDim), c32);
-    af::array pos   = input.dims(0) * af::randu(1, largeDim);
-    af::array outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+    array input = iota(dim4(1, largeDim), c32);
+    array pos   = input.dims(0) * randu(1, largeDim);
+    array outBatch = approx1(input, pos, AF_INTERP_CUBIC);
 
-    input = af::iota(af::dim4(1, 1, largeDim), c32);
-    pos   = input.dims(0) * af::randu(1, 1, largeDim);
-    outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+    input = iota(dim4(1, 1, largeDim), c32);
+    pos   = input.dims(0) * randu(1, 1, largeDim);
+    outBatch = approx1(input, pos, AF_INTERP_CUBIC);
 
-    input = af::iota(af::dim4(1, 1, 1, largeDim), c32);
-    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
-    outBatch = af::approx1(input, pos, AF_INTERP_CUBIC);
+    input = iota(dim4(1, 1, 1, largeDim), c32);
+    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    outBatch = approx1(input, pos, AF_INTERP_CUBIC);
 
     SUCCEED();
 }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 7da90fc430..b7d233c126 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -31,6 +31,7 @@ using af::span;
 using af::sum;
 
 using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
 
@@ -56,15 +57,15 @@ template<typename T>
 void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
 {
     if (noDoubleTests<T>()) return;
-    typedef typename af::dtype_traits<T>::base_type BT;
-    vector<af::dim4> numDims;
+    typedef typename dtype_traits<T>::base_type BT;
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
     readTests<BT, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
-    af::dim4 qdims = numDims[2];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
+    dim4 qdims = numDims[2];
 
     af_array inArray = 0;
     af_array pos0Array = 0;
@@ -75,15 +76,15 @@ void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
 
@@ -96,7 +97,7 @@ void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     bool ret = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -134,15 +135,15 @@ template<typename T>
 void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, const af_err err)
 {
     if (noDoubleTests<T>()) return;
-    typedef typename af::dtype_traits<T>::base_type BT;
-    vector<af::dim4> numDims;
+    typedef typename dtype_traits<T>::base_type BT;
+    vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
     readTests<BT, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
-    af::dim4 qdims = numDims[2];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
+    dim4 qdims = numDims[2];
 
     af_array inArray = 0;
     af_array pos0Array = 0;
@@ -151,10 +152,10 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) af::dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
     ASSERT_EQ(err, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
 
@@ -183,14 +184,14 @@ template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
 {
     if (noDoubleTests<T>()) return;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::dim4 pdims = numDims[1];
-    af::dim4 qdims = numDims[2];
+    dim4 idims = numDims[0];
+    dim4 pdims = numDims[1];
+    dim4 qdims = numDims[2];
 
     af_array inArray = 0;
     af_array pos0Array = 0;
@@ -199,14 +200,14 @@ void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
 
-    if((af_dtype) af::dtype_traits<T>::af_type == c32 ||
-       (af_dtype) af::dtype_traits<T>::af_type == c64) {
+    if((af_dtype) dtype_traits<T>::af_type == c32 ||
+       (af_dtype) dtype_traits<T>::af_type == c64) {
         ASSERT_EQ(AF_ERR_ARG, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
     } else {
         ASSERT_EQ(AF_SUCCESS, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
@@ -258,7 +259,7 @@ TEST(Approx2, CPP)
     bool ret = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -306,7 +307,7 @@ TEST(Approx2Cubic, CPP)
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.01 * range);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << std::endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -369,20 +370,20 @@ TEST(Approx2, CPPNearestMaxDims)
 
     const size_t largeDim = 65535 * 32 + 1;
 
-    af::array input = af::randu(1, largeDim);
-    af::array pos   = input.dims(0) * af::randu(1, 10);
-    af::array qos   = input.dims(1) * af::randu(1, 10);
-    af::array out   = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+    array input = randu(1, largeDim);
+    array pos   = input.dims(0) * randu(1, 10);
+    array qos   = input.dims(1) * randu(1, 10);
+    array out   = approx2(input, pos, qos, AF_INTERP_NEAREST);
 
-    input = af::randu(1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, largeDim);
-    qos   = input.dims(1) * af::randu(1, 1, largeDim);
-    out   = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+    input = randu(1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, largeDim);
+    qos   = input.dims(1) * randu(1, 1, largeDim);
+    out   = approx2(input, pos, qos, AF_INTERP_NEAREST);
 
-    input = af::randu(1, 1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
-    qos   = input.dims(1) * af::randu(1, 1, 1, largeDim);
-    out   = af::approx2(input, pos, qos, AF_INTERP_NEAREST);
+    input = randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    qos   = input.dims(1) * randu(1, 1, 1, largeDim);
+    out   = approx2(input, pos, qos, AF_INTERP_NEAREST);
 
     SUCCEED();
 }
@@ -393,20 +394,20 @@ TEST(Approx2, CPPLinearMaxDims)
 
     const size_t largeDim = 65535 * 32 + 1;
 
-    af::array input = af::randu(1, largeDim);
-    af::array pos   = input.dims(0) * af::randu(1, 10);
-    af::array qos   = input.dims(1) * af::randu(1, 10);
-    af::array out   = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+    array input = randu(1, largeDim);
+    array pos   = input.dims(0) * randu(1, 10);
+    array qos   = input.dims(1) * randu(1, 10);
+    array out   = approx2(input, pos, qos, AF_INTERP_LINEAR);
 
-    input = af::randu(1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, largeDim);
-    qos   = input.dims(1) * af::randu(1, 1, largeDim);
-    out   = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+    input = randu(1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, largeDim);
+    qos   = input.dims(1) * randu(1, 1, largeDim);
+    out   = approx2(input, pos, qos, AF_INTERP_LINEAR);
 
-    input = af::randu(1, 1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
-    qos   = input.dims(1) * af::randu(1, 1, 1, largeDim);
-    out   = af::approx2(input, pos, qos, AF_INTERP_LINEAR);
+    input = randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    qos   = input.dims(1) * randu(1, 1, 1, largeDim);
+    out   = approx2(input, pos, qos, AF_INTERP_LINEAR);
 
     SUCCEED();
 }
@@ -417,20 +418,20 @@ TEST(Approx2, CPPCubicMaxDims)
 
     const size_t largeDim = 65535 * 32 + 1;
 
-    af::array input = af::randu(1, largeDim);
-    af::array pos   = input.dims(0) * af::randu(1, 10);
-    af::array qos   = input.dims(1) * af::randu(1, 10);
-    af::array out   = af::approx2(input, pos, qos, AF_INTERP_BICUBIC);
+    array input = randu(1, largeDim);
+    array pos   = input.dims(0) * randu(1, 10);
+    array qos   = input.dims(1) * randu(1, 10);
+    array out   = approx2(input, pos, qos, AF_INTERP_BICUBIC);
 
-    input = af::randu(1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, largeDim);
-    qos   = input.dims(1) * af::randu(1, 1, largeDim);
-    out   = af::approx2(input, pos, qos, AF_INTERP_BICUBIC);
+    input = randu(1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, largeDim);
+    qos   = input.dims(1) * randu(1, 1, largeDim);
+    out   = approx2(input, pos, qos, AF_INTERP_BICUBIC);
 
-    input = af::randu(1, 1, 1, largeDim);
-    pos   = input.dims(0) * af::randu(1, 1, 1, largeDim);
-    qos   = input.dims(1) * af::randu(1, 1, 1, largeDim);
-    out   = af::approx2(input, pos, qos, AF_INTERP_BICUBIC);
+    input = randu(1, 1, 1, largeDim);
+    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    qos   = input.dims(1) * randu(1, 1, 1, largeDim);
+    out   = approx2(input, pos, qos, AF_INTERP_BICUBIC);
 
     SUCCEED();
 }
diff --git a/test/array.cpp b/test/array.cpp
index c3a3683d5c..d977a97200 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -21,7 +21,7 @@ class Array : public ::testing::Test
 
 };
 
-typedef ::testing::Types<float, double, af::cfloat, af::cdouble, char, unsigned char, int, uint, intl, uintl, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char, int, uint, intl, uintl, short, ushort> TestTypes;
 
 TYPED_TEST_CASE(Array, TestTypes);
 
@@ -52,7 +52,7 @@ TYPED_TEST(Array, ConstructorEmptyDim4)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     dim4 dims(3, 3, 3, 3);
     array a(dims, type);
     EXPECT_EQ(4u,    a.numdims());
@@ -68,7 +68,7 @@ TYPED_TEST(Array, ConstructorEmpty1D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, type);
     EXPECT_EQ(1u,    a.numdims());
     EXPECT_EQ(dim_t(2),    a.dims(0));
@@ -83,7 +83,7 @@ TYPED_TEST(Array, ConstructorEmpty2D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, type);
     EXPECT_EQ(2u,    a.numdims());
     EXPECT_EQ(dim_t(2),    a.dims(0));
@@ -98,7 +98,7 @@ TYPED_TEST(Array, ConstructorEmpty3D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, 2, type);
     EXPECT_EQ(3u,    a.numdims());
     EXPECT_EQ(dim_t(2),    a.dims(0));
@@ -113,7 +113,7 @@ TYPED_TEST(Array, ConstructorEmpty4D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, 2, 2, type);
     EXPECT_EQ(4u,    a.numdims());
     EXPECT_EQ(dim_t(2),    a.dims(0));
@@ -128,7 +128,7 @@ TYPED_TEST(Array, ConstructorHostPointer1D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     size_t nelems = 10;
     vector<TypeParam> data(nelems, 4);
     array a(nelems, &data.front(), afHost);
@@ -149,7 +149,7 @@ TYPED_TEST(Array, ConstructorHostPointer2D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 2;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size;
@@ -172,7 +172,7 @@ TYPED_TEST(Array, ConstructorHostPointer3D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 3;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size * dim_size;
@@ -195,7 +195,7 @@ TYPED_TEST(Array, ConstructorHostPointer4D)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 4;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size * dim_size * dim_size;
@@ -218,7 +218,7 @@ TYPED_TEST(Array, TypeAttributes)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array one(10, type);
     switch(type) {
         case f32:
@@ -396,17 +396,17 @@ TEST(Array, ShapeAttributes)
 TEST(Array, ISSUE_951)
 {
 // This works
-    //const af::array a(100, 100);
-    //af::array b = a.cols(0, 20);
+    //const array a(100, 100);
+    //array b = a.cols(0, 20);
     //b = b.rows(10, 20);
 
 // This works
-    //af::array a(100, 100);
-    //af::array b = a.cols(0, 20).rows(10, 20);
+    //array a(100, 100);
+    //array b = a.cols(0, 20).rows(10, 20);
 
 // This fails with linking error
-    const af::array a = randu(100, 100);
-    af::array b = a.cols(0, 20).rows(10, 20);
+    const array a = randu(100, 100);
+    array b = a.cols(0, 20).rows(10, 20);
 }
 
 TEST(Array, CreateHandleInvalidNullDimsPointer) {
@@ -490,9 +490,9 @@ TEST(DeviceId, Different)
     }
 
     setDevice(id1);
-    af::deviceGC();
+    deviceGC();
     setDevice(id0);
-    af::deviceGC();
+    deviceGC();
 }
 
 TEST(Device, empty)
@@ -511,10 +511,10 @@ TYPED_TEST(Array, Scalar)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)af::dtype_traits<TypeParam>::af_type;
+    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a = randu(dim4(1), type);
 
-    std::vector<TypeParam> gold(a.elements());
+    vector<TypeParam> gold(a.elements());
 
     a.host((void*)gold.data());
 
@@ -525,5 +525,5 @@ TEST(Array, ScalarTypeMismatch)
 {
     array a = constant(1.0, dim4(1), f32);
 
-    EXPECT_THROW(a.scalar<int>(), af::exception);
+    EXPECT_THROW(a.scalar<int>(), exception);
 }
diff --git a/test/assign.cpp b/test/assign.cpp
index 78812a153e..3da3dc7600 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -15,8 +15,22 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::cout;
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::constant;
+using af::dim4;
+using af::dtype_traits;
+using af::end;
+using af::exception;
+using af::randu;
+using af::seq;
+using af::span;
+
 
 template<typename T>
 class ArrayAssign : public ::testing::Test
@@ -79,7 +93,7 @@ class ArrayAssign : public ::testing::Test
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, af::cdouble, af::cfloat, double, int, uint, char, uchar, intl, uintl, short, ushort> TestTypes;
+typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, uchar, intl, uintl, short, ushort> TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(ArrayAssign, TestTypes);
@@ -90,23 +104,23 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv)
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<inType> >      in;
     vector<vector<outType> >   tests;
 
     readTests<inType, outType, int>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
     af_array lhsArray  = 0;
     af_array rhsArray  = 0;
     af_array outArray  = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<outType>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<outType>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_assign_seq(&outArray, lhsArray, seqv->size(), &seqv->front(), rhsArray));
 
@@ -117,7 +131,7 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv)
     vector<outType> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outData;
@@ -131,17 +145,14 @@ void assignTestCPP(string pTestFile, const vector<af_seq> &seqv)
 {
     if (noDoubleTests<T>()) return;
     try {
-
-        using af::array;
-
-        vector<af::dim4>  numDims;
+        vector<dim4>  numDims;
         vector<vector<T> >      in;
         vector<vector<T> >   tests;
 
         readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-        af::dim4 dims0     = numDims[0];
-        af::dim4 dims1     = numDims[1];
+        dim4 dims0     = numDims[0];
+        dim4 dims1     = numDims[1];
 
         array a(dims0, &(in[0].front()));
         array b(dims1, &(in[1].front()));
@@ -160,10 +171,10 @@ void assignTestCPP(string pTestFile, const vector<af_seq> &seqv)
         vector<T> currGoldBar = tests[0];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            EXPECT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+            EXPECT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
         }
         delete[] outData;
-    } catch(const af::exception &ex) {
+    } catch(const exception &ex) {
         FAIL() << "Exception thrown: " << ex.what();
     }
 
@@ -274,16 +285,13 @@ void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv)
 {
     if (noDoubleTests<T>()) return;
     try {
-
-        using af::array;
-
-        vector<af::dim4>  numDims;
+        vector<dim4>  numDims;
         vector<vector<T> >      in;
         vector<vector<T> >   tests;
 
         readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-        af::dim4 dims1     = numDims[1];
+        dim4 dims1     = numDims[1];
 
         T a = in[0][0];
         array b(dims1, &(in[1].front()));
@@ -310,13 +318,13 @@ void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv)
                     case 4: printf("b(seqv[0],seqv[1], seqv[2], seqv[3]) = a\n"); break;
                     default: assert(1 != 1 && "Does not compute");
                 }
-                std::cout << "a: " << a << std::endl;
+                cout << "a: " << a << endl;
                 af_print(b);
-                ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+                ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
             }
         }
         delete[] outData;
-    } catch(const af::exception &ex) {
+    } catch(const exception &ex) {
         FAIL() << "Exception thrown: " << ex.what();
     }
 }
@@ -344,8 +352,8 @@ TYPED_TEST(ArrayAssign, Scalar4DCPP)
 TYPED_TEST(ArrayAssign, AssignRowCPP)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace af;
-    int dimsize=10;
+
+    const int dimsize = 10;
     vector<TypeParam> input(100, 1);
     vector<TypeParam> sq(dimsize);
     vector<int> arIdx(2);
@@ -353,15 +361,15 @@ TYPED_TEST(ArrayAssign, AssignRowCPP)
     arIdx[0] = 5;
     arIdx[1] = 7;
 
-    af::array in(dimsize, dimsize, &input.front(), afHost);
-    af::dim4 size(dimsize, 1, 1, 1);
-    af::array sarr(size, &sq.front(), afHost);
-    af::array arrIdx(2, &arIdx.front(), afHost);
+    array in(dimsize, dimsize, &input.front(), afHost);
+    dim4 size(dimsize, 1, 1, 1);
+    array sarr(size, &sq.front(), afHost);
+    array arrIdx(2, &arIdx.front(), afHost);
 
     in.row(0)       = sarr;
     in.row(2)       = 2;
     in(arrIdx, span)= 8;
-    in.row(af::end) = 3;
+    in.row(end) = 3;
     in.rows(3, 4)   = 7;
 
     vector<TypeParam> out(100);
@@ -388,8 +396,8 @@ TYPED_TEST(ArrayAssign, AssignRowCPP)
 TYPED_TEST(ArrayAssign, AssignColumnCPP)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace af;
-    int dimsize=10;
+
+    const int dimsize = 10;
     vector<TypeParam> input(100, 1);
     vector<TypeParam> sq(dimsize);
     vector<int> arIdx(2);
@@ -397,15 +405,15 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP)
     arIdx[0] = 5;
     arIdx[1] = 7;
 
-    af::array in(dimsize, dimsize, &input.front(), afHost);
-    af::dim4 size(dimsize, 1, 1, 1);
-    af::array sarr(size, &sq.front(), afHost);
-    af::array arrIdx(2, &arIdx.front(), afHost);
+    array in(dimsize, dimsize, &input.front(), afHost);
+    dim4 size(dimsize, 1, 1, 1);
+    array sarr(size, &sq.front(), afHost);
+    array arrIdx(2, &arIdx.front(), afHost);
 
     in.col(0)       = sarr;
     in.col(2)       = 2;
     in(span, arrIdx)= 8;
-    in.col(af::end) = 3;
+    in.col(end) = 3;
     in.cols(3, 4)   = 7;
 
     vector<TypeParam> out(100);
@@ -432,8 +440,7 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP)
 TYPED_TEST(ArrayAssign, AssignSliceCPP)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace af;
-    int dimsize=10;
+    const int dimsize = 10;
     vector<TypeParam> input(1000, 1);
     vector<TypeParam> sq(dimsize * dimsize);
     vector<int> arIdx(2);
@@ -441,15 +448,15 @@ TYPED_TEST(ArrayAssign, AssignSliceCPP)
     arIdx[0] = 5;
     arIdx[1] = 7;
 
-    af::array in(dimsize, dimsize, dimsize, &input.front(), afHost);
-    af::dim4 size(dimsize, dimsize, 1, 1);
-    af::array sarr(size, &sq.front(), afHost);
-    af::array arrIdx(2, &arIdx.front(), afHost);
+    array in(dimsize, dimsize, dimsize, &input.front(), afHost);
+    dim4 size(dimsize, dimsize, 1, 1);
+    array sarr(size, &sq.front(), afHost);
+    array arrIdx(2, &arIdx.front(), afHost);
 
     in.slice(0)             = sarr;
     in.slice(2)             = 2;
     in(span, span, arrIdx)  = 8;
-    in.slice(af::end)       = 3;
+    in.slice(end)           = 3;
     in.slices(3, 4)         = 7;
 
     vector<TypeParam> out(1000);
@@ -478,11 +485,11 @@ TYPED_TEST(ArrayAssign, AssignSliceCPP)
 
 TEST(ArrayAssign, InvalidArgs)
 {
-    vector<af::cfloat> in(100, af::cfloat(0,0));
+    vector<cfloat> in(100, cfloat(0,0));
     vector<float> tests(100, float(1));
 
-    af::dim4 dims0(10, 1, 1, 1);
-    af::dim4 dims1(100, 1, 1, 1);
+    dim4 dims0(10, 1, 1, 1);
+    dim4 dims1(100, 1, 1, 1);
     af_array lhsArray = 0;
     af_array rhsArray = 0;
     af_array outArray = 0;
@@ -494,13 +501,13 @@ TEST(ArrayAssign, InvalidArgs)
                                     lhsArray, seqv.size(), &seqv.front(), rhsArray));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in.front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<af::cfloat>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<cfloat>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray,
                                     lhsArray, seqv.size(), &seqv.front(), rhsArray));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in.front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray, lhsArray, 0, &seqv.front(), rhsArray));
 
@@ -516,9 +523,9 @@ TEST(ArrayAssign, CPP_ASSIGN_TO_INDEXED)
     vector<int> in(20);
     for(int i = 0; i < (int)in.size(); i++) in[i] = i;
 
-    af::array input(10, 2, &in.front(), afHost);
+    array input(10, 2, &in.front(), afHost);
 
-    input(af::span, 0) = input(af::span, 1);// <-- Tests array_proxy to array_proxy assignment
+    input(span, 0) = input(span, 1);// <-- Tests array_proxy to array_proxy assignment
 
     vector<int> out(20);
     input.host(&out.front());
@@ -529,15 +536,13 @@ TEST(ArrayAssign, CPP_ASSIGN_TO_INDEXED)
 
 TEST(ArrayAssign, CPP_END)
 {
-    using af::array;
-
     const int n = 5;
     const int m = 5;
     const int end_off = 2;
 
-    array a = af::randu(n, m);
-    array b = af::randu(1, m);
-    a(af::end - end_off, af::span) = b;
+    array a = randu(n, m);
+    array b = randu(1, m);
+    a(end - end_off, span) = b;
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
@@ -552,16 +557,14 @@ TEST(ArrayAssign, CPP_END)
 
 TEST(ArrayAssign, CPP_END_SEQ)
 {
-    using af::array;
-
     const int num = 20;
     const int end_begin = 10;
     const int end_end = 0;
     const int len = end_begin - end_end + 1;
 
-    array a = af::randu(num);
-    array b = af::randu(len);
-    a(af::seq(af::end - end_begin, af::end - end_end)) = b;
+    array a = randu(num);
+    array b = randu(len);
+    a(seq(end - end_begin, end - end_end)) = b;
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
@@ -576,17 +579,15 @@ TEST(ArrayAssign, CPP_END_SEQ)
 
 TEST(ArrayAssign, CPP_COPY_ON_WRITE)
 {
-    using af::array;
-
     const int num = 20;
     const int len = 10;
 
-    array a = af::randu(num);
+    array a = randu(num);
     float *hAO = a.host<float>();
 
     array a_copy = a;
-    array b = af::randu(len);
-    a(af::seq(len)) = b;
+    array b = randu(len);
+    a(seq(len)) = b;
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
@@ -615,17 +616,15 @@ TEST(ArrayAssign, CPP_COPY_ON_WRITE)
 
 TEST(ArrayAssign, CPP_ASSIGN_BINOP)
 {
-    using af::array;
-
     const int num = 20;
     const int len = 10;
 
-    array a = af::randu(num);
+    array a = randu(num);
     float *hAO = a.host<float>();
 
     array a_copy = a;
-    array b = af::randu(len);
-    a(af::seq(len)) += b;
+    array b = randu(len);
+    a(seq(len)) += b;
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
@@ -654,12 +653,10 @@ TEST(ArrayAssign, CPP_ASSIGN_BINOP)
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR)
 {
-    using af::array;
-
     const int num = 20;
 
-    array a = af::randu(1, num);
-    array b = af::randu(num);
+    array a = randu(1, num);
+    array b = randu(num);
 
     array c, idx;
     sort(c, idx, b);
@@ -683,20 +680,18 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR)
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ)
 {
-    using af::array;
-
     const int num = 20;
     const int len = 10;
     const int st = 3;
     const int en = st + len - 1;
 
-    array a = af::randu(1, 1, num);
+    array a = randu(1, 1, num);
     array a0 = a;
-    array b = af::randu(len);
+    array b = randu(len);
 
-    array idx = af::seq(st, en);
+    array idx = seq(st, en);
 
-    a(af::seq(st, en)) = b;
+    a(seq(st, en)) = b;
 
     ASSERT_EQ(a.dims(0) , (dim_t)1);
     ASSERT_EQ(a.dims(1) , (dim_t)1);
@@ -722,14 +717,12 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ)
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D)
 {
-    using af::array;
-
     const int nx = 4;
     const int ny = 5;
     const int num = nx * ny;
 
-    array a = af::randu(nx, ny);
-    array b = af::randu(num);
+    array a = randu(nx, ny);
+    array b = randu(num);
 
     array c, idx;
     sort(c, idx, b);
@@ -753,8 +746,6 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D)
 
 TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
 {
-    using af::array;
-
     const int nx = 4;
     const int nz = 5;
     const int num = nx * nz;
@@ -762,11 +753,11 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
     const int st = 3;
     const int en = st + len - 1;
 
-    array a = af::randu(nx, 1, nz);
+    array a = randu(nx, 1, nz);
     array a0 = a;
-    array b = af::randu(len);
+    array b = randu(len);
 
-    a(af::seq(st, en)) = b;
+    a(seq(st, en)) = b;
 
     ASSERT_EQ(a.dims(0) , (dim_t)nx);
     ASSERT_EQ(a.dims(1) , (dim_t)1);
@@ -792,20 +783,18 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
 
 TEST(Assign, Copy)
 {
-    using af::array;
-
     const int num = 20;
     const int len = 10;
     const int st = 3;
     const int en = st + len - 1;
 
-    array a = af::randu(num, 1);
+    array a = randu(num, 1);
     float *h_a0 = a.host<float>();
 
-    array b = af::randu(len);
+    array b = randu(len);
 
     float *d_ptr = a.device<float>();
-    af::copy(a, b, af::seq(st, en));
+    copy(a, b, seq(st, en));
 
     // Ensure that a still has same device pointer
     ASSERT_EQ(d_ptr, a.device<float>());
@@ -828,7 +817,6 @@ TEST(Assign, Copy)
 
 TEST(Asssign, LinearCPP)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
     const float val = 3;
@@ -836,16 +824,16 @@ TEST(Asssign, LinearCPP)
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = af::randu(nx, ny);
+    array a = randu(nx, ny);
     array a_copy = a;
-    af::index idx = af::seq(st, en);
+    af::index idx = seq(st, en);
     a(idx) = 3;
 
     ASSERT_EQ(a.dims(0), a_copy.dims(0));
     ASSERT_EQ(a.dims(1), a_copy.dims(1));
 
-    std::vector<float> ha(nx * ny);
-    std::vector<float> ha_copy(nx * ny);
+    vector<float> ha(nx * ny);
+    vector<float> ha_copy(nx * ny);
 
     a.host(&ha[0]);
     a_copy.host(&ha_copy[0]);
@@ -860,20 +848,18 @@ TEST(Asssign, LinearCPP)
 
 TEST(Asssign, LinearCPPMaxDim)
 {
-    using af::array;
-
     const size_t largeDim = 65535 * 32 + 2;
     const float val = 3;
 
-    array a = af::randu(1, 2 * largeDim);
+    array a = randu(1, 2 * largeDim);
     array a_copy = a.copy();
-    af::index idx = af::array(af::seq(10, largeDim+10));
-    a(af::span, idx) = val;
+    af::index idx = array(seq(10, largeDim+10));
+    a(span, idx) = val;
 
     ASSERT_EQ(a.dims(0), a_copy.dims(0));
 
-    std::vector<float> ha(2 * largeDim);
-    std::vector<float> ha_copy(2 * largeDim);
+    vector<float> ha(2 * largeDim);
+    vector<float> ha_copy(2 * largeDim);
 
     a.host(&ha[0]);
     a_copy.host(&ha_copy[0]);
@@ -889,17 +875,16 @@ TEST(Asssign, LinearCPPMaxDim)
 
 TEST(Asssign, LinearAssignSeq)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
     const float val = 3;
-    const array rhs = af::constant(val, 1, 1);
+    const array rhs = constant(val, 1, 1);
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = af::randu(nx, ny);
-    af::index idx = af::seq(st, en);
+    array a = randu(nx, ny);
+    af::index idx = seq(st, en);
 
     af_array in_arr = a.get();
     af_index_t ii = idx.get();
@@ -909,13 +894,13 @@ TEST(Asssign, LinearAssignSeq)
     ASSERT_EQ(AF_SUCCESS,
               af_assign_seq(&out_arr, in_arr, 1, &ii.idx.seq, rhs_arr));
 
-    af::array out(out_arr);
+    array out(out_arr);
 
     ASSERT_EQ(a.dims(0), out.dims(0));
     ASSERT_EQ(a.dims(1), out.dims(1));
 
-    std::vector<float> hout(nx * ny);
-    std::vector<float> ha(nx * ny);
+    vector<float> hout(nx * ny);
+    vector<float> ha(nx * ny);
 
     a.host(&ha[0]);
     out.host(&hout[0]);
@@ -930,17 +915,16 @@ TEST(Asssign, LinearAssignSeq)
 
 TEST(Asssign, LinearAssignGenSeq)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
     const float val = 3;
-    const array rhs = af::constant(val, 1, 1);
+    const array rhs = constant(val, 1, 1);
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = af::randu(nx, ny);
-    af::index idx = af::seq(st, en);
+    array a = randu(nx, ny);
+    af::index idx = seq(st, en);
 
     af_array in_arr = a.get();
     af_index_t ii = idx.get();
@@ -950,13 +934,13 @@ TEST(Asssign, LinearAssignGenSeq)
     ASSERT_EQ(AF_SUCCESS,
               af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
 
-    af::array out(out_arr);
+    array out(out_arr);
 
     ASSERT_EQ(a.dims(0), out.dims(0));
     ASSERT_EQ(a.dims(1), out.dims(1));
 
-    std::vector<float> hout(nx * ny);
-    std::vector<float> ha(nx * ny);
+    vector<float> hout(nx * ny);
+    vector<float> ha(nx * ny);
 
     a.host(&ha[0]);
     out.host(&hout[0]);
@@ -971,17 +955,16 @@ TEST(Asssign, LinearAssignGenSeq)
 
 TEST(Asssign, LinearAssignGenArr)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
     const float val = 3;
-    const array rhs = af::constant(val, 1, 1);
+    const array rhs = constant(val, 1, 1);
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = af::randu(nx, ny);
-    af::index idx = af::array(af::seq(st, en));
+    array a = randu(nx, ny);
+    af::index idx = array(seq(st, en));
 
     af_array in_arr = a.get();
     af_index_t ii = idx.get();
@@ -991,13 +974,13 @@ TEST(Asssign, LinearAssignGenArr)
     ASSERT_EQ(AF_SUCCESS,
               af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
 
-    af::array out(out_arr);
+    array out(out_arr);
 
     ASSERT_EQ(a.dims(0), out.dims(0));
     ASSERT_EQ(a.dims(1), out.dims(1));
 
-    std::vector<float> hout(nx * ny);
-    std::vector<float> ha(nx * ny);
+    vector<float> hout(nx * ny);
+    vector<float> ha(nx * ny);
 
     a.host(&ha[0]);
     out.host(&hout[0]);
@@ -1012,15 +995,14 @@ TEST(Asssign, LinearAssignGenArr)
 
 TEST(Assign, ISSUE_1764)
 {
-    using af::array;
     int x = 2;
     int y = 2;
     int z = 2;
-    af::array a = af::randu(x,y,z);
-    std::vector<float> ha0(a.elements());
+    array a = randu(x,y,z);
+    vector<float> ha0(a.elements());
     a.host(&ha0[0]);
-    a(0, af::span, af::span) = a(1, af::span, af::span);
-    std::vector<float> ha1(a.elements());
+    a(0, span, span) = a(1, span, span);
+    vector<float> ha1(a.elements());
     a.host(&ha1[0]);
     for (int k = 0; k < z; k++) {
         for (int j = 0; j < y; j++) {
@@ -1035,11 +1017,11 @@ TEST(Assign, ISSUE_1677)
 {
     try {
         dim_t sz = 1;
-        af::array a = af::constant(1.0f, 3, sz, f32);
-        af::array b = af::constant(2.0f, 3, sz, f32);
-        af::array cond = af::constant(0, sz, b8); // all false
-        a(af::span, cond) = b(af::span, cond);
-    } catch(af::exception &ex) {
+        array a = constant(1.0f, 3, sz, f32);
+        array b = constant(2.0f, 3, sz, f32);
+        array cond = constant(0, sz, b8); // all false
+        a(span, cond) = b(span, cond);
+    } catch(exception &ex) {
         FAIL() << "ArrayFire exception: " << ex.what();
     } catch(...) {
         FAIL() << "Unknown exception thrown";
diff --git a/test/backend.cpp b/test/backend.cpp
index 8cd9c63200..e59c4541a6 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -20,6 +20,9 @@
 
 using std::string;
 using std::vector;
+using af::dtype_traits;
+using af::getAvailableBackends;
+using af::setBackend;
 
 const char *getActiveBackendString(af_backend active)
 {
@@ -43,7 +46,7 @@ void testFunction()
 
     af_array outArray = 0;
     dim_t dims[] = {32, 32};
-    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) dtype_traits<T>::af_type));
 
     // Verify backends returned by array and by function are the same
     af_backend arrayBackend = (af_backend)0;
@@ -58,7 +61,7 @@ void testFunction()
 
 void backendTest()
 {
-    int backends = af::getAvailableBackends();
+    int backends = getAvailableBackends();
 
     ASSERT_NE(backends, 0);
 
@@ -71,19 +74,19 @@ void backendTest()
 
     if(cpu) {
         printf("\nRunning CPU Backend...\n");
-        af::setBackend(AF_BACKEND_CPU);
+        setBackend(AF_BACKEND_CPU);
         testFunction<float>();
     }
 
     if(cuda) {
         printf("\nRunning CUDA Backend...\n");
-        af::setBackend(AF_BACKEND_CUDA);
+        setBackend(AF_BACKEND_CUDA);
         testFunction<float>();
     }
 
     if(opencl) {
         printf("\nRunning OpenCL Backend...\n");
-        af::setBackend(AF_BACKEND_OPENCL);
+        setBackend(AF_BACKEND_OPENCL);
         testFunction<float>();
     }
 }
diff --git a/test/basic.cpp b/test/basic.cpp
index 49eac81ee0..23b25307cb 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -14,6 +14,8 @@
 #include <testHelpers.hpp>
 
 using std::vector;
+using af::array;
+using af::constant;
 
 TEST(BasicTests, constant1000x1000)
 {
@@ -214,7 +216,7 @@ TEST(BasicArrayTests, constant10x10)
 
     dim_t dim_size = 10;
     double valA = 3.14;
-    af::array a = af::constant(valA, dim_size, dim_size, f32);
+    array a = constant(valA, dim_size, dim_size, f32);
 
     vector<float> h_a(dim_size * dim_size, 0);
     a.host(&h_a.front());
@@ -237,7 +239,7 @@ TEST(BasicTests, constant100x100_CPP)
 
     double valA = 4.9;
     dim4 dims(d[0], d[1]);
-    af::array a = constant(valA, dims);
+    array a = constant(valA, dims);
 
     vector<float> h_a(dim_size * dim_size, 0);
     a.host((void**)&h_a[0]);
@@ -262,13 +264,13 @@ TEST(BasicTests, AdditionSameType_CPP)
     double valB = 5.7;
     double  valCf = valA + valB;
 
-    af::array a32 = constant(valA, dims, f32);
-    af::array b32 = constant(valB, dims, f32);
-    af::array c32 = a32 + b32;
+    array a32 = constant(valA, dims, f32);
+    array b32 = constant(valB, dims, f32);
+    array c32 = a32 + b32;
 
-    af::array a64 = constant(valA, dims, f64);
-    af::array b64 = constant(valB, dims, f64);
-    af::array c64 = a64 + b64;
+    array a64 = constant(valA, dims, f64);
+    array b64 = constant(valB, dims, f64);
+    array c64 = a64 + b64;
 
     vector<float>  h_cf32 (dim_size * dim_size);
     vector<double> h_cf64 (dim_size * dim_size);
@@ -301,9 +303,9 @@ TEST(BasicTests, Additionf32f64_CPP)
     double valB = 5.7;
     double valC = valA + valB;
 
-    af::array a = constant(valA, dims);
-    af::array b = constant(valB, dims, f64);
-    af::array c = a + b;
+    array a = constant(valA, dims);
+    array b = constant(valB, dims, f64);
+    array c = a + b;
 
     vector<double> h_c(dim_size * dim_size);
     c.host((void**)&h_c[0]);
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 2362e1c134..47f23ced68 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -20,6 +20,7 @@ using std::string;
 using std::vector;
 using std::abs;
 using af::dim4;
+using af::dtype_traits;
 
 template<typename T, bool isColor>
 void bilateralTest(string pTestFile)
@@ -52,10 +53,10 @@ void bilateralTest(string pTestFile)
 
         ASSERT_EQ(AF_SUCCESS, af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
 
-        std::vector<T> outData(nElems);
+        vector<T> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<T> goldData(nElems);
+        vector<T> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
@@ -94,22 +95,22 @@ void bilateralDataTest(string pTestFile)
 
     typedef typename cond_type<is_same_type<inType, double>::value, double, float>::type outType;
 
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector<vector<inType> >       in;
     vector<vector<outType> >   tests;
 
     readTests<inType, outType, float>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims      = numDims[0];
+    dim4 dims      = numDims[0];
     af_array outArray  = 0;
     af_array inArray   = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_bilateral(&outArray, inArray, 2.25f, 25.56f, false));
 
-    std::vector<outType> outData(dims.elements());
+    vector<outType> outData(dims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
@@ -144,32 +145,34 @@ TYPED_TEST(BilateralOnData, InvalidArgs)
     af_array outArray  = 0;
 
     // check for color image bilateral
-    af::dim4 dims = af::dim4(100,1,1,1);
+    dim4 dims = dim4(100,1,1,1);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<TypeParam>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<TypeParam>::af_type));
     ASSERT_EQ(AF_ERR_SIZE, af_bilateral(&outArray, inArray, 0.12f, 0.34f, true));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
 
 // C++ unit tests
+
+using af::array;
+using af::bilateral;
+
 TEST(Bilateral, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::array;
-
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTests<float, float, float>(string(TEST_DIR"/bilateral/rectangle.test"), numDims, in, tests);
 
-    af::dim4 dims      = numDims[0];
+    dim4 dims      = numDims[0];
 
     array a(dims, &(in[0].front()));
-    array b = af::bilateral(a, 2.25f, 25.56f, false);
+    array b = bilateral(a, 2.25f, 25.56f, false);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     b.host(outData.data());
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
@@ -179,11 +182,14 @@ TEST(Bilateral, CPP)
     }
 }
 
+using af::iota;
+using af::constant;
+using af::max;
+using af::seq;
+using af::span;
 
 TEST(bilateral, GFOR)
 {
-    using namespace af;
-
     dim4 dims = dim4(10, 10, 3);
     array A = iota(dims);
     array B = constant(0, dims);
diff --git a/test/binary.cpp b/test/binary.cpp
index 5071a024c0..43b131b2ee 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -13,9 +13,7 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-// This makes the macros cleaner
 using namespace std;
-using std::abs;
 using namespace af;
 
 const int num = 10000;
@@ -33,9 +31,9 @@ template<typename T> T mod(T a, T b)
     return std::fmod(a, b);
 }
 
-af::array randgen(const int num, af::dtype ty)
+af::array randgen(const int num, dtype ty)
 {
-    af::array tmp = af::round(1 + 2 * af::randu(num, f32)).as(ty);
+    af::array tmp = round(1 + 2 * af::randu(num, f32)).as(ty);
     tmp.eval();
     return tmp;
 }
@@ -59,7 +57,7 @@ af::array randgen(const int num, af::dtype ty)
         Tc *h_c = c.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], func(h_a[i], h_b[i])) <<                  \
-              "for values: " << h_a[i]  << "," << h_b[i] << std::endl;  \
+                "for values: " << h_a[i]  << "," << h_b[i] << endl;     \
         af_free_host(h_a);                                              \
         af_free_host(h_b);                                              \
         af_free_host(h_c);                                              \
@@ -78,7 +76,7 @@ af::array randgen(const int num, af::dtype ty)
         Ta *h_c = c.host<Ta>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], func(h_a[i], h_b)) <<                     \
-                "for values: " << h_a[i]  << "," << h_b << std::endl;   \
+                "for values: " << h_a[i]  << "," << h_b << endl;   \
         af_free_host(h_a);                                              \
         af_free_host(h_c);                                              \
     }                                                                   \
@@ -96,7 +94,7 @@ af::array randgen(const int num, af::dtype ty)
         Tb *h_c = c.host<Tb>();                                         \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], func(h_a, h_b[i])) <<                     \
-              "for values: " << h_a  << "," << h_b[i] << std::endl;     \
+                "for values: " << h_a  << "," << h_b[i] << endl;        \
         af_free_host(h_b);                                              \
         af_free_host(h_c);                                              \
     }                                                                   \
@@ -119,7 +117,7 @@ af::array randgen(const int num, af::dtype ty)
         Tc *h_c = c.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
             MY_ASSERT_NEAR(h_c[i], func(h_a[i], h_b[i]), (err)) <<      \
-                "for values: " << h_a[i]  << "," << h_b[i] << std::endl; \
+                "for values: " << h_a[i]  << "," << h_b[i] << endl;     \
         af_free_host(h_a);                                              \
         af_free_host(h_b);                                              \
         af_free_host(h_c);                                              \
@@ -138,7 +136,7 @@ af::array randgen(const int num, af::dtype ty)
         Td *h_d = c.host<Td>();                                         \
         for (int i = 0; i < num; i++)                                   \
             MY_ASSERT_NEAR(h_d[i], func(h_a[i], h_b), err) <<           \
-                "for values: " << h_a[i]  << "," << h_b << std::endl;   \
+                "for values: " << h_a[i]  << "," << h_b << endl;        \
         af_free_host(h_a);                                              \
         af_free_host(h_d);                                              \
     }                                                                   \
@@ -157,7 +155,7 @@ af::array randgen(const int num, af::dtype ty)
         Te *h_e = c.host<Te>();                                         \
         for (int i = 0; i < num; i++)                                   \
             MY_ASSERT_NEAR(h_e[i], func(h_a, h_b[i]), err) <<           \
-              "for values: " << h_a  << "," << h_b[i] << std::endl;     \
+                "for values: " << h_a  << "," << h_b[i] << endl;        \
         af_free_host(h_b);                                              \
         af_free_host(h_e);                                              \
     }                                                                   \
@@ -266,7 +264,7 @@ BINARY_TESTS_NEAR_GENERAL(cfloat, double, cdouble, cfloat, cdouble, div, 1e-5)
         for (int i = 0; i < num; i++)                       \
             ASSERT_EQ(h_c[i], valc) <<                      \
                 "for values: " << h_a[i]  <<                \
-                "," << h_b[i] << std::endl;                 \
+                "," << h_b[i] << endl;                      \
         af_free_host(h_a);                                  \
         af_free_host(h_b);                                  \
         af_free_host(h_c);                                  \
@@ -305,9 +303,9 @@ TEST(BinaryTests, Test_pow_cfloat_float)
     for (int i = 0; i < num; i++) {
         complex_float res = std::pow(h_a[i], h_b[i]);
         ASSERT_NEAR(real(h_c[i]), real(res), 1E-5)
-            << "for real values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+            << "for real values of: " << h_a[i]  << "," << h_b[i] << endl;
         ASSERT_NEAR(imag(h_c[i]), imag(res), 1E-5)
-            << "for imag values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+            << "for imag values of: " << h_a[i]  << "," << h_b[i] << endl;
 
     }
     af_free_host(h_a);
@@ -327,9 +325,9 @@ TEST(BinaryTests, Test_pow_cdouble_cdouble)
     for (int i = 0; i < num; i++) {
         complex_double res = std::pow(h_a[i], h_b[i]);
         ASSERT_NEAR(real(h_c[i]), real(res), 1E-10)
-            << "for real values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+            << "for real values of: " << h_a[i]  << "," << h_b[i] << endl;
         ASSERT_NEAR(imag(h_c[i]), imag(res), 1E-10)
-            << "for imag values of: " << h_a[i]  << "," << h_b[i] << std::endl;
+            << "for imag values of: " << h_a[i]  << "," << h_b[i] << endl;
 
     }
     af_free_host(h_a);
@@ -341,7 +339,7 @@ TEST(BinaryTests, ISSUE_1762)
 {
     af::array zero = af::constant(0, 5, f32);
     af::array result = af::pow(zero, 2);
-    std::vector<complex_float> hres(result.elements());
+    vector<complex_float> hres(result.elements());
     result.host(&hres[0]);
     for (int i = 0; i < 5; i++) {
         ASSERT_EQ(real(hres[i]), 0);
diff --git a/test/blas.cpp b/test/blas.cpp
index 888e5d34a5..f606b1f4ed 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -16,12 +16,24 @@
 #include <testHelpers.hpp>
 #include <string>
 
-using std::string;
+using std::copy;
 using std::cout;
 using std::endl;
 using std::ostream_iterator;
-using std::copy;
+using std::string;
 using std::vector;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::dtype_traits;
+using af::getDevice;
+using af::getDeviceCount;
+using af::matmul;
+using af::max;
+using af::randu;
+using af::setDevice;
+using af::span;
 
 template<typename T>
 class MatrixMultiply : public ::testing::Test
@@ -29,7 +41,7 @@ class MatrixMultiply : public ::testing::Test
 
 };
 
-typedef ::testing::Types<float, af::cfloat, double, af::cdouble> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(MatrixMultiply, TestTypes);
 
 template<typename T, bool isBVector>
@@ -37,8 +49,7 @@ void MatMulCheck(string TestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    using std::vector;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
@@ -46,8 +57,8 @@ void MatMulCheck(string TestFile)
 
     af_array a, aT, b, bT;
     ASSERT_EQ(AF_SUCCESS,
-            af_create_array(&a, &hData[0].front(), numDims[0].ndims(), numDims[0].get(), (af_dtype) af::dtype_traits<T>::af_type));
-    af::dim4 atdims = numDims[0];
+            af_create_array(&a, &hData[0].front(), numDims[0].ndims(), numDims[0].get(), (af_dtype) dtype_traits<T>::af_type));
+    dim4 atdims = numDims[0];
     {
         dim_t f  =    atdims[0];
         atdims[0]   =    atdims[1];
@@ -56,8 +67,8 @@ void MatMulCheck(string TestFile)
     ASSERT_EQ(AF_SUCCESS,
             af_moddims(&aT, a, atdims.ndims(), atdims.get()));
     ASSERT_EQ(AF_SUCCESS,
-            af_create_array(&b, &hData[1].front(), numDims[1].ndims(), numDims[1].get(), (af_dtype) af::dtype_traits<T>::af_type));
-    af::dim4 btdims = numDims[1];
+            af_create_array(&b, &hData[1].front(), numDims[1].ndims(), numDims[1].get(), (af_dtype) dtype_traits<T>::af_type));
+    dim4 btdims = numDims[1];
     {
         dim_t f = btdims[0];
         btdims[0] = btdims[1];
@@ -132,45 +143,44 @@ void cppMatMulCheck(string TestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    using std::vector;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
     readTests<T,T,int>(TestFile, numDims, hData, tests);
 
-    af::array a(numDims[0], &hData[0].front());
-    af::array b(numDims[1], &hData[1].front());
+    array a(numDims[0], &hData[0].front());
+    array b(numDims[1], &hData[1].front());
 
-    af::dim4 atdims = numDims[0];
+    dim4 atdims = numDims[0];
     {
         dim_t f  =    atdims[0];
         atdims[0]   =    atdims[1];
         atdims[1]   =    f;
     }
-    af::dim4 btdims = numDims[1];
+    dim4 btdims = numDims[1];
     {
         dim_t f = btdims[0];
         btdims[0] = btdims[1];
         btdims[1] = f;
     }
 
-    af::array aT = moddims(a, atdims.ndims(), atdims.get());
-    af::array bT = moddims(b, btdims.ndims(), btdims.get());
+    array aT = moddims(a, atdims.ndims(), atdims.get());
+    array bT = moddims(b, btdims.ndims(), btdims.get());
 
-    vector<af::array> out(tests.size());
+    vector<array> out(tests.size());
     if(isBVector) {
-        out[0] = af::matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
-        out[1] = af::matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
-        out[2] = af::matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
-        out[3] = af::matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
-        out[4] = af::matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
+        out[0] = matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
+        out[1] = matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
+        out[2] = matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
+        out[3] = matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
+        out[4] = matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
     }
     else {
-        out[0] = af::matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
-        out[1] = af::matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
-        out[2] = af::matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
-        out[3] = af::matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
+        out[0] = matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
+        out[1] = matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
+        out[2] = matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
+        out[3] = matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
     }
 
     for(size_t i = 0; i < tests.size(); i++) {
@@ -209,18 +219,18 @@ TYPED_TEST(MatrixMultiply, RectangleVector_CPP)
     cppMatMulCheck<TypeParam, true>(TEST_DIR"/blas/RectangleVector.test");
 }
 
-#define DEVICE_ITERATE(func) do {                                           \
-    const char* ENV = getenv("AF_MULTI_GPU_TESTS");                         \
-    if(ENV && ENV[0] == '0') {                                              \
-        func;                                                               \
-    } else {                                                                \
-        int oldDevice = af::getDevice();                                    \
-        for(int i = 0; i < af::getDeviceCount(); i++) {                     \
-            af::setDevice(i);                                               \
-            func;                                                           \
-        }                                                                   \
-        af::setDevice(oldDevice);                                           \
-    }                                                                       \
+#define DEVICE_ITERATE(func) do {                                       \
+    const char* ENV = getenv("AF_MULTI_GPU_TESTS");                     \
+    if(ENV && ENV[0] == '0') {                                          \
+        func;                                                           \
+    } else {                                                            \
+        int oldDevice = getDevice();                                    \
+        for(int i = 0; i < getDeviceCount(); i++) {                     \
+            setDevice(i);                                               \
+            func;                                                       \
+        }                                                               \
+        setDevice(oldDevice);                                           \
+    }                                                                   \
 } while(0);
 
 
@@ -247,24 +257,24 @@ TYPED_TEST(MatrixMultiply, MultiGPURectangleVector_CPP)
 TEST(MatrixMultiply, Batched)
 {
     const int M = 512;
-    const int K = 1024;
-    const int N = 32;
+    const int K = 512;
+    const int N = 10;
     const int D2 = 2;
     const int D3 = 3;
 
     for (int d3 = 1; d3 <= D3; d3 *= D3) {
         for (int d2 = 1; d2 <= D2; d2 *= D2) {
-            af::array a = af::randu(M, K, d2, d3);
-            af::array b = af::randu(K, N, d2, d3);
-            af::array c = af::matmul(a, b);
+            array a = randu(M, K, d2, d3);
+            array b = randu(K, N, d2, d3);
+            array c = matmul(a, b);
 
             for (int j = 0; j < d3; j++) {
                 for (int i = 0; i < d2; i++) {
-                    af::array a_ij = a(af::span, af::span, i, j);
-                    af::array b_ij = b(af::span, af::span, i, j);
-                    af::array c_ij = c(af::span, af::span, i, j);
-                    af::array res = af::matmul(a_ij, b_ij);
-                    ASSERT_LT(af::max<float>(af::abs(c_ij - res)), 1E-5)
+                    array a_ij = a(span, span, i, j);
+                    array b_ij = b(span, span, i, j);
+                    array c_ij = c(span, span, i, j);
+                    array res = matmul(a_ij, b_ij);
+                    EXPECT_LT(max<float>(abs(c_ij - res)), 1E-5)
                         << " for d2 = " << d2 << " for d3 = " << d3;
                 }
             }
@@ -278,15 +288,15 @@ TEST(MatrixMultiply, ISSUE_1882)
 {
     const int m = 2;
     const int n = 3;
-    af::array A = af::randu(m, n);
-    af::array BB = af::randu(n, m);
-    af::array B = BB(0, af::span);
+    array A = randu(m, n);
+    array BB = randu(n, m);
+    array B = BB(0, span);
 
-    af::array res1 = af::matmul(A.T(), B.T());
-    af::array res2 = af::matmulTT(A, B);
+    array res1 = matmul(A.T(), B.T());
+    array res2 = matmulTT(A, B);
 
-    std::vector<float> hres1(res1.elements());
-    std::vector<float> hres2(res2.elements());
+    vector<float> hres1(res1.elements());
+    vector<float> hres2(res2.elements());
 
     res1.host(&hres1.front());
     res2.host(&hres2.front());
diff --git a/test/canny.cpp b/test/canny.cpp
index c10fea6b14..e84c3f0a1a 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -15,8 +15,11 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class CannyEdgeDetector : public ::testing::Test
@@ -36,29 +39,29 @@ void cannyTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<T> >      in;
     vector<vector<char> >   tests;
 
     readTests<T, char, int>(pTestFile, numDims, in, tests);
 
-    af::dim4 sDims    = numDims[0];
+    dim4 sDims        = numDims[0];
     af_array outArray = 0;
     af_array sArray   = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&sArray, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL, 0.4147f, 0.8454f, 3, true));
 
-    std::vector<char> outData(sDims.elements());
+    vector<char> outData(sDims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<char> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     // cleanup
@@ -107,7 +110,7 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
         inFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
         outFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
 
-        af_dtype type = (af_dtype)af::dtype_traits<T>::af_type;
+        af_dtype type = (af_dtype)dtype_traits<T>::af_type;
 
         ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
 
@@ -130,10 +133,10 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
         ASSERT_EQ(AF_SUCCESS, af_mul(&mulArray, cstArray, _outArray, false));
         ASSERT_EQ(AF_SUCCESS, af_cast(&outArray, mulArray, u8));
 
-        std::vector<unsigned char> outData(nElems);
+        vector<unsigned char> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<unsigned char> goldData(nElems);
+        vector<unsigned char> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 1.0e-3));
@@ -160,10 +163,10 @@ TEST(CannyEdgeDetector, InvalidSizeArray)
 
     vector<float>   in(100, 1);
 
-    af::dim4 sDims(100, 1, 1, 1);
+    dim4 sDims(100, 1, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
 
@@ -177,10 +180,10 @@ TEST(CannyEdgeDetector, Array4x4_Invalid)
 
     vector<float>   in(16, 1);
 
-    af::dim4 sDims(4, 4, 1, 1);
+    dim4 sDims(4, 4, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
 
@@ -194,10 +197,10 @@ TEST(CannyEdgeDetector, Sobel5x5_Invalid)
 
     vector<float>   in(25, 1);
 
-    af::dim4 sDims(5, 5, 1, 1);
+    dim4 sDims(5, 5, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 5, true));
 
diff --git a/test/cast.cpp b/test/cast.cpp
index f13069f5a2..5327b134ff 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -15,6 +15,8 @@
 
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 const int num = 10;
 
@@ -24,9 +26,9 @@ void cast_test()
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-    af_dtype ta = (af_dtype)af::dtype_traits<Ti>::af_type;
-    af_dtype tb = (af_dtype)af::dtype_traits<To>::af_type;
-    af::dim4 dims(num, 1, 1, 1);
+    af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
+    af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
+    dim4 dims(num, 1, 1, 1);
     af_array a, b;
     af_randu(&a, dims.ndims(), dims.get(), ta);
     af_err err = af_cast(&b, a, tb);
@@ -82,9 +84,9 @@ void cast_test_complex_real()
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
-    af_dtype ta = (af_dtype)af::dtype_traits<Ti>::af_type;
-    af_dtype tb = (af_dtype)af::dtype_traits<To>::af_type;
-    af::dim4 dims(num, 1, 1, 1);
+    af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
+    af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
+    dim4 dims(num, 1, 1, 1);
     af_array a, b;
     af_randu(&a, dims.ndims(), dims.get(), ta);
     af_err err = af_cast(&b, a, tb);
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index a965c9782c..e41089f858 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -19,12 +19,17 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype;
 using af::dtype_traits;
+using af::identity;
+using af::matmul;
+using af::max;
 
 template<typename T>
 void choleskyTester(const int n, double eps, bool is_upper)
@@ -32,36 +37,36 @@ void choleskyTester(const int n, double eps, bool is_upper)
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     // Prepare positive definite matrix
 #if 1
-    af::array a = cpu_randu<T>(af::dim4(n, n));
+    array a = cpu_randu<T>(dim4(n, n));
 #else
-    af::array a = af::randu(n, n, ty);
+    array a = randu(n, n, ty);
 #endif
-    af::array b = 10 * n * af::identity(n, n, ty);
-    af::array in = matmul(a.H(), a) + b;
+    array b = 10 * n * identity(n, n, ty);
+    array in = matmul(a.H(), a) + b;
 
     //! [ex_chol_reg]
-    af::array out;
+    array out;
     cholesky(out, in, is_upper);
     //! [ex_chol_reg]
 
-    af::array re = is_upper ? matmul(out.H(), out) : matmul(out, out.H());
+    array re = is_upper ? matmul(out.H(), out) : matmul(out, out.H());
 
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(in - re))), eps);
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(in - re))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(in - re))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(in - re))), eps);
 
     //! [ex_chol_inplace]
-    af::array in2 = in.copy();
+    array in2 = in.copy();
     choleskyInPlace(in2, is_upper);
     //! [ex_chol_inplace]
 
-    af::array out2 = is_upper ? upper(in2) : lower(in2);
+    array out2 = is_upper ? upper(in2) : lower(in2);
 
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(out2 - out))), eps);
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(out2 - out))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(out2 - out))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(out2 - out))), eps);
 }
 
 template<typename T>
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 9c5adb20e5..90144b8430 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -14,20 +14,22 @@
 #include <testHelpers.hpp>
 
 using std::abs;
-using namespace af;
+using std::vector;
+using af::array;
+using af::randu;
 
 const int num = 10000;
 
 TEST(ClampTests, FloatArrayArray)
 {
-    af::array in = af::randu(num, f32);
-    af::array lo = af::randu(num, f32)/10;       // Ensure lo <= 0.1
-    af::array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
-    af::eval(lo, hi);
+    array in = randu(num, f32);
+    array lo = randu(num, f32)/10;       // Ensure lo <= 0.1
+    array hi = 1.0 - randu(num, f32)/10; // Ensure hi >= 0.9
+    eval(lo, hi);
 
 
-    std::vector<float> hout(num), hin(num), hlo(num), hhi(num);
-    af::array out = clamp(in, lo, hi);
+    vector<float> hout(num), hin(num), hlo(num), hhi(num);
+    array out = clamp(in, lo, hi);
     out.host(&hout[0]);
     in.host(&hin[0]);
     lo.host(&hlo[0]);
@@ -42,12 +44,12 @@ TEST(ClampTests, FloatArrayArray)
 
 TEST(ClampTests, FloatArrayScalar)
 {
-    af::array in = af::randu(num, f32);
-    af::array lo = af::randu(num, f32)/10; // Ensure lo <= 0.1
+    array in = randu(num, f32);
+    array lo = randu(num, f32)/10; // Ensure lo <= 0.1
     float hi = 0.9;
 
-    std::vector<float> hout(num), hin(num), hlo(num);
-    af::array out = clamp(in, lo, hi);
+    vector<float> hout(num), hin(num), hlo(num);
+    array out = clamp(in, lo, hi);
 
     out.host(&hout[0]);
     in.host(&hin[0]);
@@ -62,12 +64,12 @@ TEST(ClampTests, FloatArrayScalar)
 
 TEST(ClampTests, FloatScalarArray)
 {
-    af::array in = af::randu(num, f32);
+    array in = randu(num, f32);
     float lo = 0.1;
-    af::array hi = 1.0 - af::randu(num, f32)/10; // Ensure hi >= 0.9
+    array hi = 1.0 - randu(num, f32)/10; // Ensure hi >= 0.9
 
-    std::vector<float> hout(num), hin(num), hhi(num);
-    af::array out = clamp(in, lo, hi);
+    vector<float> hout(num), hin(num), hhi(num);
+    array out = clamp(in, lo, hi);
 
     out.host(&hout[0]);
     in.host(&hin[0]);
@@ -82,12 +84,12 @@ TEST(ClampTests, FloatScalarArray)
 
 TEST(ClampTests, FloatScalarScalar)
 {
-    af::array in = af::randu(num, f32);
+    array in = randu(num, f32);
     float lo = 0.1;
     float hi = 0.9;
 
-    std::vector<float> hout(num), hin(num);
-    af::array out = clamp(in, lo, hi);
+    vector<float> hout(num), hin(num);
+    array out = clamp(in, lo, hi);
 
     out.host(&hout[0]);
     in.host(&hin[0]);
diff --git a/test/compare.cpp b/test/compare.cpp
index f886356b42..a9915ad490 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -13,6 +13,11 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
+using std::vector;
+using af::array;
+using af::dtype_traits;
+using af::randu;
+
 template<typename T>
 class Compare : public ::testing::Test
 {
@@ -27,12 +32,12 @@ TYPED_TEST_CASE(Compare, TestTypes);
         typedef TypeParam T;                                    \
         if (noDoubleTests<T>()) return;                         \
         const int num = 1 << 20;                                \
-        af_dtype ty = (af_dtype) af::dtype_traits<T>::af_type;  \
-        af::array a = af::randu(num, ty);                       \
-        af::array b = af::randu(num, ty);                       \
-        af::array c = a OP b;                                   \
-        std::vector<T> ha(num), hb(num);                        \
-        std::vector<char> hc(num);                              \
+        af_dtype ty = (af_dtype) dtype_traits<T>::af_type;      \
+        array a = randu(num, ty);                               \
+        array b = randu(num, ty);                               \
+        array c = a OP b;                                       \
+        vector<T> ha(num), hb(num);                             \
+        vector<char> hc(num);                                   \
         a.host(&ha[0]);                                         \
         b.host(&hb[0]);                                         \
         c.host(&hc[0]);                                         \
diff --git a/test/complex.cpp b/test/complex.cpp
index 9a2f86b333..beef0be4c2 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -13,6 +13,7 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
+using std::endl;
 using namespace af;
 
 const int num = 10;
@@ -28,15 +29,15 @@ const int num = 10;
                                                                         \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        af::array a = randu(num, ta);                                   \
-        af::array b = randu(num, tb);                                   \
-        af::array c = af::complex(a, b);                                \
+        array a = randu(num, ta);                                       \
+        array b = randu(num, tb);                                       \
+        array c = complex(a, b);                                        \
         Ta *h_a = a.host<Ta>();                                         \
         Tb *h_b = b.host<Tb>();                                         \
         CPLX(Tc) *h_c = c.host< CPLX(Tc) >();                           \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], CPLX(Tc)(h_a[i], h_b[i])) <<              \
-                "for values: " << h_a[i]  << "," << h_b[i] << std::endl; \
+                "for values: " << h_a[i]  << "," << h_b[i] << endl;     \
         freeHost(h_a);                                                  \
         freeHost(h_b);                                                  \
         freeHost(h_c);                                                  \
@@ -47,14 +48,14 @@ const int num = 10;
         if (noDoubleTests<Tb>()) return;                                \
                                                                         \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af::array a = randu(num, ta);                                   \
+        array a = randu(num, ta);                                       \
         Tb h_b = 0.3;                                                   \
-        af::array c = af::complex(a, h_b);                              \
+        array c = complex(a, h_b);                                      \
         Ta *h_a = a.host<Ta>();                                         \
         CPLX(Ta) *h_c = c.host<CPLX(Ta) >();                            \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], CPLX(Ta)(h_a[i], h_b)) <<                 \
-                "for values: " << h_a[i]  << "," << h_b << std::endl;   \
+                "for values: " << h_a[i]  << "," << h_b << endl;        \
         freeHost(h_a);                                                  \
         freeHost(h_c);                                                  \
     }                                                                   \
@@ -66,13 +67,13 @@ const int num = 10;
                                                                         \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
         Ta h_a = 0.3;                                                   \
-        af::array b = randu(num, tb);                                   \
-        af::array c = af::complex(h_a, b);                              \
+        array b = randu(num, tb);                                       \
+        array c = complex(h_a, b);                                      \
         Tb *h_b = b.host<Tb>();                                         \
         CPLX(Tb) *h_c = c.host<CPLX(Tb) >();                            \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(h_c[i], CPLX(Tb)(h_a, h_b[i])) <<                 \
-                "for values: " << h_a  << "," << h_b[i] << std::endl;   \
+                "for values: " << h_a  << "," << h_b[i] << endl;        \
         freeHost(h_b);                                                  \
         freeHost(h_c);                                                  \
     }                                                                   \
@@ -84,14 +85,14 @@ const int num = 10;
                                                                         \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        af::array a = randu(num, ta);                                   \
-        af::array b = randu(num, tb);                                   \
-        af::array c = af::complex(a, b);                                \
-        af::array d = af::real(c);                                      \
+        array a = randu(num, ta);                                       \
+        array b = randu(num, tb);                                       \
+        array c = complex(a, b);                                        \
+        array d = real(c);                                              \
         Ta *h_a = a.host<Ta>();                                         \
         Tc *h_d = d.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_d[i], h_a[i]) << "at: " << i << std::endl;      \
+            ASSERT_EQ(h_d[i], h_a[i]) << "at: " << i << endl;           \
         freeHost(h_a);                                                  \
         freeHost(h_d);                                                  \
     }                                                                   \
@@ -103,14 +104,14 @@ const int num = 10;
                                                                         \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        af::array a = randu(num, ta);                                   \
-        af::array b = randu(num, tb);                                   \
-        af::array c = af::complex(a, b);                                \
-        af::array d = af::imag(c);                                      \
+        array a = randu(num, ta);                                       \
+        array b = randu(num, tb);                                       \
+        array c = complex(a, b);                                        \
+        array d = imag(c);                                              \
         Tb *h_b = b.host<Tb>();                                         \
         Tc *h_d = d.host<Tc>();                                         \
         for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_d[i], h_b[i])  << "at: " << i << std::endl;     \
+            ASSERT_EQ(h_d[i], h_b[i])  << "at: " << i << endl;          \
         freeHost(h_b);                                                  \
         freeHost(h_d);                                                  \
     }                                                                   \
@@ -122,15 +123,15 @@ const int num = 10;
                                                                         \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        af::array a = randu(num, ta);                                   \
-        af::array b = randu(num, tb);                                   \
-        af::array c = af::complex(a, b);                                \
-        af::array d = af::conjg(c);                                     \
+        array a = randu(num, ta);                                       \
+        array b = randu(num, tb);                                       \
+        array c = complex(a, b);                                        \
+        array d = conjg(c);                                             \
         CPLX(Tc) *h_c = c.host<CPLX(Tc) >();                            \
         CPLX(Tc) *h_d = d.host<CPLX(Tc) >();                            \
         for (int i = 0; i < num; i++)                                   \
             ASSERT_EQ(conj(h_c[i]), h_d[i])                             \
-                << "at: " << i << std::endl;                            \
+                << "at: " << i << endl;                                 \
         freeHost(h_c);                                                  \
         freeHost(h_d);                                                  \
     }                                                                   \
diff --git a/test/constant.cpp b/test/constant.cpp
index b640e21c70..629e3345db 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -13,13 +13,21 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace af;
 using std::vector;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::constant;
+using af::dtype;
+using af::dtype_traits;
+using af::exception;
+using af::identity;
+using af::sum;
 
 template<typename T>
 class Constant : public ::testing::Test { };
 
-typedef ::testing::Types<float, af::cfloat, double, af::cdouble, int, unsigned, char, uchar, uintl, intl, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char, uchar, uintl, intl, short, ushort> TestTypes;
 TYPED_TEST_CASE(Constant, TestTypes);
 
 template<typename T>
@@ -29,7 +37,7 @@ void ConstantCPPCheck(T value) {
     const int num = 1000;
     T val = value;
     dtype dty = (dtype) dtype_traits<T>::af_type;
-    af::array in = constant(val, num, dty);
+    array in = constant(val, num, dty);
 
     vector<T> h_in(num);
     in.host(&h_in.front());
@@ -44,7 +52,7 @@ void ConstantCCheck(T value) {
     if (noDoubleTests<T>()) return;
 
     const int num = 1000;
-    typedef typename af::dtype_traits<T>::base_type BT;
+    typedef typename dtype_traits<T>::base_type BT;
     BT val = ::real(value);
     dtype dty = (dtype) dtype_traits<T>::af_type;
     af_array out;
@@ -66,7 +74,7 @@ void IdentityCPPCheck() {
 
     int num = 1000;
     dtype dty = (dtype) dtype_traits<T>::af_type;
-    array out = af::identity(num, num, dty);
+    array out = identity(num, num, dty);
 
     vector<T> h_in(num*num);
     out.host(&h_in.front());
@@ -81,7 +89,7 @@ void IdentityCPPCheck() {
     }
 
     num = 100;
-    out = af::identity(num, num, num, dty);
+    out = identity(num, num, num, dty);
 
     h_in.resize(num*num*num);
     out.host(&h_in.front());
@@ -105,17 +113,17 @@ void IdentityLargeDimCheck() {
     const size_t largeDim = 65535 * 8 + 1;
 
     dtype dty = (dtype) dtype_traits<T>::af_type;
-    array out = af::identity(largeDim, dty);
-    ASSERT_EQ(1.f, af::sum<float>(out));
+    array out = identity(largeDim, dty);
+    ASSERT_EQ(1.f, sum<float>(out));
 
-    out = af::identity(1, largeDim, dty);
-    ASSERT_EQ(1.f, af::sum<float>(out));
+    out = identity(1, largeDim, dty);
+    ASSERT_EQ(1.f, sum<float>(out));
 
-    out = af::identity(1, 1, largeDim, dty);
-    ASSERT_EQ(largeDim, af::sum<float>(out));
+    out = identity(1, 1, largeDim, dty);
+    ASSERT_EQ(largeDim, sum<float>(out));
 
-    out = af::identity(1, 1, 1, largeDim, dty);
-    ASSERT_EQ(largeDim, af::sum<float>(out));
+    out = identity(1, 1, 1, largeDim, dty);
+    ASSERT_EQ(largeDim, sum<float>(out));
 }
 
 template<typename T>
@@ -149,9 +157,9 @@ void IdentityCPPError() {
     static const int num = 1000;
     dtype dty = (dtype) dtype_traits<T>::af_type;
     try {
-        array out = af::identity(num, 0, 10, dty);
+        array out = identity(num, 0, 10, dty);
     }
-    catch(const af::exception &ex) {
+    catch(const exception &ex) {
         FAIL() << "Incorrectly thrown 0-length exception";
         return;
     }
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 1411717713..9f0c6e9c12 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -16,11 +16,15 @@
 #include <cmath>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
 using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Convolve : public ::testing::Test
@@ -40,8 +44,6 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
 {
     if (noDoubleTests<T>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<T> >      in;
     vector<vector<T> >   tests;
@@ -55,9 +57,9 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
     af_array outArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&filter, &(in[1].front()),
-                fDims.ndims(), fDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
     switch(baseDim) {
@@ -73,7 +75,7 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -206,8 +208,6 @@ void sepConvolveTest(string pTestFile, bool expand)
 {
     if (noDoubleTests<T>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<T> >      in;
     vector<vector<T> >   tests;
@@ -223,11 +223,11 @@ void sepConvolveTest(string pTestFile, bool expand)
     af_array outArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&c_filter, &(in[1].front()),
-                cfDims.ndims(), cfDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                cfDims.ndims(), cfDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&r_filter, &(in[2].front()),
-                rfDims.ndims(), rfDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                rfDims.ndims(), rfDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     af_conv_mode  mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
     ASSERT_EQ(AF_SUCCESS, af_convolve2_sep(&outArray, c_filter, r_filter, signal, mode));
@@ -239,7 +239,7 @@ void sepConvolveTest(string pTestFile, bool expand)
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -292,7 +292,6 @@ TEST(Convolve, Separable_TypeCheck)
 {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<int>()) return;
-    using af::dim4;
 
     dim4 sDims(10, 1, 1, 1);
     dim4 fDims(4, 1, 1, 1);
@@ -306,11 +305,11 @@ TEST(Convolve, Separable_TypeCheck)
     af_array outArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in.front()),
-                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<float>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&c_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)af::dtype_traits<int>::af_type));
+                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&r_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)af::dtype_traits<int>::af_type));
+                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter, signal, AF_CONV_EXPAND));
 
@@ -324,8 +323,6 @@ TEST(Convolve, Separable_DimCheck)
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<int>()) return;
 
-    using af::dim4;
-
     dim4 sDims(10, 1, 1, 1);
     dim4 fDims(4, 1, 1, 1);
 
@@ -338,11 +335,11 @@ TEST(Convolve, Separable_DimCheck)
     af_array outArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in.front()),
-                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<float>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&c_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)af::dtype_traits<int>::af_type));
+                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&r_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)af::dtype_traits<int>::af_type));
+                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter, signal, AF_CONV_EXPAND));
 
@@ -351,12 +348,20 @@ TEST(Convolve, Separable_DimCheck)
     ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
 }
 
+///////////////////////////////////// CPP ////////////////////////////////
+//
+using af::constant;
+using af::max;
+using af::product;
+using af::randu;
+using af::seq;
+using af::span;
+using af::sum;
+
 TEST(Convolve1, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -366,12 +371,12 @@ TEST(Convolve1, CPP)
     //![ex_image_convolve1]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [32 1 1 1]
-    af::array filter(numDims[1], &(in[1].front()));
+    array filter(numDims[1], &(in[1].front()));
     //filter dims = [4 1 1 1]
 
-    af::array output = convolve1(signal, filter, AF_CONV_DEFAULT);
+    array output = convolve1(signal, filter, AF_CONV_DEFAULT);
     //output dims = [32 1 1 1] - same as input since expand(3rd argument is false)
     //None of the dimensions > 1 has lenght > 1, so no batch mode is activated.
     //![ex_image_convolve1]
@@ -382,7 +387,7 @@ TEST(Convolve1, CPP)
     output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
 }
@@ -391,8 +396,6 @@ TEST(Convolve2, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -402,12 +405,12 @@ TEST(Convolve2, CPP)
     //![ex_image_convolve2]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [15 17 1 1]
-    af::array filter(numDims[1], &(in[1].front()));
+    array filter(numDims[1], &(in[1].front()));
     //filter dims = [5 5 2 1]
 
-    af::array output = convolve2(signal, filter, AF_CONV_DEFAULT);
+    array output = convolve2(signal, filter, AF_CONV_DEFAULT);
     //output dims = [15 17 1 1] - same as input since expand(3rd argument is false)
     //however, notice that the 3rd dimension of filter is > 1.
     //So, one to many batch mode will be activated automatically
@@ -421,7 +424,7 @@ TEST(Convolve2, CPP)
     output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
 }
@@ -430,8 +433,6 @@ TEST(Convolve3, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -441,12 +442,12 @@ TEST(Convolve3, CPP)
     //![ex_image_convolve3]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [10 11 2 2]
-    af::array filter(numDims[1], &(in[1].front()));
+    array filter(numDims[1], &(in[1].front()));
     //filter dims = [4 2 3 2]
 
-    af::array output = convolve3(signal, filter, AF_CONV_DEFAULT);
+    array output = convolve3(signal, filter, AF_CONV_DEFAULT);
     //output dims = [10 11 2 2] - same as input since expand(3rd argument is false)
     //however, notice that the 4th dimension is > 1 for both signal
     //and the filter, therefore many to many batch mode will be
@@ -459,7 +460,7 @@ TEST(Convolve3, CPP)
     output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 }
 
@@ -467,8 +468,6 @@ TEST(Convolve, separable_CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -479,14 +478,14 @@ TEST(Convolve, separable_CPP)
     //![ex_image_conv2_sep]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [3 4 2 1]
-    af::array cFilter(numDims[1], &(in[1].front()));
+    array cFilter(numDims[1], &(in[1].front()));
     //coloumn filter dims = [2 1 1 1]
-    af::array rFilter(numDims[2], &(in[2].front()));
+    array rFilter(numDims[2], &(in[2].front()));
     //row filter dims = [3 1 1 1]
 
-    af::array output = convolve(cFilter, rFilter, signal, AF_CONV_DEFAULT);
+    array output = convolve(cFilter, rFilter, signal, AF_CONV_DEFAULT);
     //output signal dims = [3 4 2 1] - same as input since 'expand = false'
     //notice that the input signal is 3d array, therefore
     //batch mode will be automatically activated.
@@ -501,20 +500,15 @@ TEST(Convolve, separable_CPP)
     output.host((void*)&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 }
 
 TEST(Convolve, Docs_Unified_Wrapper)
 {
     // This unit test doesn't necessarily need to function
-    // accuracy as af::convolve is merely a wrapper to
-    // af::convolve[1|2|3]
-    using af::array;
-    using af::dim4;
-    using af::randu;
-    using af::constant;
-    using af::convolve;
+    // accuracy as convolve is merely a wrapper to
+    // convolve[1|2|3]
 
     //![ex_image_convolve_1d]
     array a = randu(10);
@@ -609,8 +603,6 @@ TEST(Convolve, Docs_Unified_Wrapper)
     //![ex_image_convolve_3d]
 }
 
-using namespace af;
-
 TEST(GFOR, convolve2_MO)
 {
     array A = randu(5, 5, 3);
@@ -760,14 +752,14 @@ TEST(ConvolveLargeDim1D, CPP)
     const size_t largeDim = 65535 + 1;
 
     float h_filter[] = {0.f, 1.f, 0.f};
-    af::array identity_filter(3, h_filter);
-    af::array signal = af::constant(1, n, 1, largeDim);
+    array identity_filter(3, h_filter);
+    array signal = constant(1, n, 1, largeDim);
 
-    af::array output = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
-    af::array output2 = output;
+    array output = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
+    array output2 = output;
     ASSERT_EQ(largeDim * n, sum<float>(output2));
 
-    signal = af::constant(1, n, 1, 1, largeDim);
+    signal = constant(1, n, 1, 1, largeDim);
 
     output = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
     ASSERT_EQ(largeDim * n, sum<float>(output));
@@ -783,13 +775,13 @@ TEST(ConvolveLargeDim2D, CPP)
     float h_filter[] = {0.f, 0.f, 0.f,
                         0.f, 1.f, 0.f,
                         0.f, 0.f, 0.f};
-    af::array identity_filter(3, 3, h_filter);
-    af::array signal = af::constant(1, n, n, largeDim);
+    array identity_filter(3, 3, h_filter);
+    array signal = constant(1, n, n, largeDim);
 
-    af::array output = convolve2(signal, identity_filter, AF_CONV_DEFAULT);
+    array output = convolve2(signal, identity_filter, AF_CONV_DEFAULT);
     ASSERT_EQ(largeDim * n * n, sum<float>(output));
 
-    signal = af::constant(1, n, n, 1, largeDim);
+    signal = constant(1, n, n, 1, largeDim);
 
     output = convolve2(signal, identity_filter, AF_CONV_DEFAULT);
     ASSERT_EQ(largeDim * n * n, sum<float>(output));
@@ -814,13 +806,13 @@ TEST(DISABLED_ConvolveLargeDim3D, CPP)
                         0.f, 0.f, 0.f,
                         0.f, 0.f, 0.f};
 
-    af::array identity_filter(3, 3, 3, h_filter);
-    af::array signal = af::constant(1, n, largeDim, n);
+    array identity_filter(3, 3, 3, h_filter);
+    array signal = constant(1, n, largeDim, n);
 
-    af::array output = convolve3(signal, identity_filter, AF_CONV_DEFAULT);
+    array output = convolve3(signal, identity_filter, AF_CONV_DEFAULT);
     ASSERT_EQ(1.f, product<float>(output));
 
-    signal = af::constant(1, n, n, largeDim);
+    signal = constant(1, n, n, largeDim);
 
     output = convolve3(signal, identity_filter, AF_CONV_EXPAND);
     //TODO: fix product by indexing
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 40e19f33d9..24ac30a2db 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -17,7 +17,12 @@
 #include <algorithm>
 #include <testHelpers.hpp>
 
-using namespace af;
+using std::string;
+using std::vector;
+using af::array;
+using af::cfloat;
+using af::corrcoef;
+using af::dim4;
 
 template<typename T>
 class CorrelationCoefficient : public ::testing::Test
@@ -73,21 +78,21 @@ TYPED_TEST(CorrelationCoefficient, All)
     if (noDoubleTests<TypeParam>()) return;
     if (noDoubleTests<outType>()) return;
 
-    std::vector<af::dim4>      numDims;
-    std::vector<std::vector<int> >       in;
-    std::vector<std::vector<float> >  tests;
+    vector<dim4>          numDims;
+    vector<vector<int> >       in;
+    vector<vector<float> >  tests;
 
-    readTestsFromFile<int,float>(std::string(TEST_DIR "/corrcoef/mat_10x10_scalar.test"),
+    readTestsFromFile<int,float>(string(TEST_DIR "/corrcoef/mat_10x10_scalar.test"),
                                  numDims, in, tests);
 
-    std::vector<TypeParam> input1(in[0].begin(), in[0].end());
-    std::vector<TypeParam> input2(in[1].begin(), in[1].end());
+    vector<TypeParam> input1(in[0].begin(), in[0].end());
+    vector<TypeParam> input2(in[1].begin(), in[1].end());
 
     array a(numDims[0], &(input1.front()));
     array b(numDims[1], &(input2.front()));
     outType c = corrcoef<outType>(a, b);
 
-    std::vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
+    vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
     ASSERT_NEAR(::real(currGoldBar[0]), ::real(c), 1.0e-3);
     ASSERT_NEAR(::imag(currGoldBar[0]), ::imag(c), 1.0e-3);
 }
diff --git a/test/covariance.cpp b/test/covariance.cpp
index 57decb9a54..c4ef41bd8e 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -17,9 +17,15 @@
 #include <algorithm>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
-using namespace af;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::constant;
+using af::dim4;
+using af::exception;
 
 template<typename T>
 class Covariance : public ::testing::Test
@@ -76,14 +82,14 @@ void covTest(string pFileName, bool isbiased=false)
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<int> >        in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<int,float>(pFileName, numDims, in, tests);
 
-    af::dim4 dims1 = numDims[0];
-    af::dim4 dims2 = numDims[1];
+    dim4 dims1 = numDims[0];
+    dim4 dims2 = numDims[1];
     vector<T> input1(in[0].begin(), in[0].end());
     vector<T> input2(in[1].begin(), in[1].end());
 
@@ -95,13 +101,13 @@ void covTest(string pFileName, bool isbiased=false)
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
     size_t nElems    = currGoldBar.size();
-    std::vector<outType> outData(nElems);
+    vector<outType> outData(nElems);
 
     c.host((void*)outData.data());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
-        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
     }
 }
 
@@ -119,7 +125,7 @@ TEST(Covariance, c32)
 {
     array a = constant(cfloat(1.0f, -1.0f), 10, c32);
     array b = constant(cfloat(2.0f, -1.0f), 10, c32);
-    ASSERT_THROW(cov(a, b), af::exception);
+    ASSERT_THROW(cov(a, b), exception);
 }
 
 TEST(Covariance, c64)
@@ -127,5 +133,5 @@ TEST(Covariance, c64)
     if (noDoubleTests<double>()) return;
     array a = constant(cdouble(1.0, -1.0), 10, c64);
     array b = constant(cdouble(2.0, -1.0), 10, c64);
-    ASSERT_THROW(cov(a, b), af::exception);
+    ASSERT_THROW(cov(a, b), exception);
 }
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index a6aef8351c..180cfe2923 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -11,9 +11,19 @@
 #include <testHelpers.hpp>
 #include <arrayfire.h>
 
-using namespace af;
-using std::vector;
 using std::abs;
+using std::endl;
+using std::vector;
+using af::array;
+using af::constant;
+using af::deviceGC;
+using af::diag;
+using af::dim4;
+using af::exception;
+using af::max;
+using af::seq;
+using af::span;
+using af::sum;
 
 template<typename T>
 class Diagonal : public ::testing::Test
@@ -48,8 +58,8 @@ TYPED_TEST(Diagonal, Create)
                 }
             }
         }
-    } catch (const af::exception& ex) {
-        FAIL() << ex.what() << std::endl;
+    } catch (const exception& ex) {
+        FAIL() << ex.what() << endl;
     }
 }
 
@@ -57,7 +67,7 @@ TYPED_TEST(Diagonal, DISABLED_CreateLargeDim)
 {
     if (noDoubleTests<TypeParam>()) return;
     try {
-        af::deviceGC();
+        deviceGC();
         {
             static const size_t largeDim = 65535 + 1;
             array diagvals = constant(1, largeDim);
@@ -65,8 +75,8 @@ TYPED_TEST(Diagonal, DISABLED_CreateLargeDim)
 
             ASSERT_EQ(largeDim, sum<float>(out));
         }
-    } catch (const af::exception& ex) {
-        FAIL() << ex.what() << std::endl;
+    } catch (const exception& ex) {
+        FAIL() << ex.what() << endl;
     }
 }
 
@@ -91,8 +101,8 @@ TYPED_TEST(Diagonal, Extract)
                 ASSERT_EQ(input[i * data.dims(0) + i], h_out[i]);
             }
         }
-    } catch (const af::exception& ex) {
-        FAIL() << ex.what() << std::endl;
+    } catch (const exception& ex) {
+        FAIL() << ex.what() << endl;
     }
 }
 
@@ -114,8 +124,8 @@ TYPED_TEST(Diagonal, ExtractLargeDim)
 
         ASSERT_EQ(n * largeDim, sum<float>(out1));
 
-    } catch (const af::exception& ex) {
-        FAIL() << ex.what() << std::endl;
+    } catch (const exception& ex) {
+        FAIL() << ex.what() << endl;
     }
 }
 
@@ -145,8 +155,8 @@ TYPED_TEST(Diagonal, ExtractRect)
                 }
             }
         }
-    } catch (const af::exception& ex) {
-        FAIL() << ex.what() << std::endl;
+    } catch (const exception& ex) {
+        FAIL() << ex.what() << endl;
     }
 }
 
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 57ec8e2487..4fe71a2f51 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -17,10 +17,11 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Diff1 : public ::testing::Test
@@ -55,12 +56,12 @@ void diff1Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     T *outData;
 
@@ -70,11 +71,11 @@ void diff1Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
     // Get input array
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     // Run diff1
@@ -89,7 +90,7 @@ void diff1Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
         vector<T> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
         }
     }
 
@@ -157,17 +158,17 @@ void diff1ArgsTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray, -1));
     ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray,  5));
@@ -181,50 +182,57 @@ TYPED_TEST(Diff1,InvalidArgs)
     diff1ArgsTest<TypeParam>(string(TEST_DIR"/diff1/basic0.test"));
 }
 
+////////////////////////////////////// CPP ////////////////////////////////////
+//
+
+using af::array;
+using af::constant;
+using af::diff1;
+using af::deviceGC;
+using af::sum;
+
 TEST(Diff1, DiffLargeDim)
 {
     const size_t largeDim = 65535 * 32 + 1;
 
-    af::deviceGC();
+    deviceGC();
     {
-        af::array in = af::constant(1, largeDim);
-        af::array diff = af::diff1(in, 0);
-        float s = af::sum<float>(diff, 1);
+        array in = constant(1, largeDim);
+        array diff = diff1(in, 0);
+        float s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = af::constant(1, 1, largeDim);
-        diff = af::diff1(in, 1);
-        s = af::sum<float>(diff, 1);
+        in = constant(1, 1, largeDim);
+        diff = diff1(in, 1);
+        s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = af::constant(1, 1, 1, largeDim);
-        diff = af::diff1(in, 2);
-        s = af::sum<float>(diff, 1);
+        in = constant(1, 1, 1, largeDim);
+        diff = diff1(in, 2);
+        s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = af::constant(1, 1, 1, 1, largeDim);
-        diff = af::diff1(in, 3);
-        s = af::sum<float>(diff, 1);
+        in = constant(1, 1, 1, 1, largeDim);
+        diff = diff1(in, 3);
+        s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
     }
 }
 
-////////////////////////////////////// CPP ////////////////////////////////////
-//
 TEST(Diff1, CPP)
 {
     if (noDoubleTests<float>()) return;
 
     const unsigned dim = 0;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float,float,int>(string(TEST_DIR"/diff1/matrix0.test"),numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::diff1(input, dim);
+    array input(dims, &(in[0].front()));
+    array output = diff1(input, dim);
 
     // Get result
     float *outData = new float[dims.elements()];
@@ -235,7 +243,7 @@ TEST(Diff1, CPP)
         vector<float> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
         }
     }
 
diff --git a/test/diff2.cpp b/test/diff2.cpp
index 0c1dc1e455..c337522a66 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -17,10 +17,16 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::constant;
+using af::deviceGC;
+using af::diff2;
+using af::dim4;
+using af::dtype_traits;
+using af::sum;
 
 template<typename T>
 class Diff2 : public ::testing::Test
@@ -55,12 +61,12 @@ void diff2Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     T *outData;
 
@@ -70,11 +76,11 @@ void diff2Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
     // Get input array
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     // Run diff2
@@ -89,7 +95,7 @@ void diff2Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
         vector<T> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
         }
     }
 
@@ -154,17 +160,17 @@ void diff2ArgsTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray, -1));
     ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray,  5));
@@ -182,26 +188,26 @@ TEST(Diff2, DiffLargeDim)
 {
     const size_t largeDim = 65535 * 32 + 1;
 
-    af::deviceGC();
+    deviceGC();
     {
-        af::array in = af::constant(1, largeDim);
-        af::array diff = af::diff2(in, 0);
-        float s = af::sum<float>(diff, 1);
+        array in = constant(1, largeDim);
+        array diff = diff2(in, 0);
+        float s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = af::constant(1, 1, largeDim);
-        diff = af::diff2(in, 1);
-        s = af::sum<float>(diff, 1);
+        in = constant(1, 1, largeDim);
+        diff = diff2(in, 1);
+        s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = af::constant(1, 1, 1, largeDim);
-        diff = af::diff2(in, 2);
-        s = af::sum<float>(diff, 1);
+        in = constant(1, 1, 1, largeDim);
+        diff = diff2(in, 2);
+        s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = af::constant(1, 1, 1, 1, largeDim);
-        diff = af::diff2(in, 3);
-        s = af::sum<float>(diff, 1);
+        in = constant(1, 1, 1, 1, largeDim);
+        diff = diff2(in, 3);
+        s = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
     }
 }
@@ -213,14 +219,14 @@ TEST(Diff2, CPP)
     if (noDoubleTests<float>()) return;
 
     const unsigned dim = 1;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float,float,int>(string(TEST_DIR"/diff2/matrix1.test"),numDims,in,tests);
-    af::dim4 dims       = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::diff2(input, dim);
+    dim4 dims       = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = diff2(input, dim);
 
     float *outData = new float[dims.elements()];
     output.host((void*)outData);
@@ -230,7 +236,7 @@ TEST(Diff2, CPP)
         vector<float> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
         }
     }
 
diff --git a/test/dog.cpp b/test/dog.cpp
index f981bba1a8..86f780602e 100644
--- a/test/dog.cpp
+++ b/test/dog.cpp
@@ -16,6 +16,16 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using af::array;
+using af::dim4;
+using af::dtype_traits;
+using af::exception;
+using af::gaussianKernel;
+using af::convolve2;
+using af::dog;
+using af::randu;
+using af::sum;
+
 template<typename T>
 class DOG : public ::testing::Test
 {
@@ -34,18 +44,18 @@ TYPED_TEST(DOG, Basic)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    af::dim4 iDims(512, 512, 1, 1);
-    af::array in = af::constant(1, iDims, (af_dtype)af::dtype_traits<float>::af_type);
+    dim4 iDims(512, 512, 1, 1);
+    array in = constant(1, iDims, (af_dtype)dtype_traits<float>::af_type);
     /* calculate DOG using ArrayFire functions */
-    af::array k1    = af::gaussianKernel(3, 3);
-    af::array k2    = af::gaussianKernel(2, 2);
-    af::array smth1 = af::convolve2(in, k1);
-    af::array smth2 = af::convolve2(in, k2);
-    af::array diff  = smth1 - smth2;
+    array k1    = gaussianKernel(3, 3);
+    array k2    = gaussianKernel(2, 2);
+    array smth1 = convolve2(in, k1);
+    array smth2 = convolve2(in, k2);
+    array diff  = smth1 - smth2;
     /* calcuate DOG using new function */
-    af::array out= af::dog(in, 3, 2);
+    array out= dog(in, 3, 2);
     /* compare both the values */
-    float accumErr = af::sum<float>(out-diff);
+    float accumErr = sum<float>(out-diff);
     EXPECT_EQ(true, accumErr<1.0e-2);
 }
 
@@ -53,24 +63,24 @@ TYPED_TEST(DOG, Batch)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    af::dim4 iDims(512, 512, 3, 1);
-    af::array in = af::constant(1, iDims, (af_dtype)af::dtype_traits<float>::af_type);
+    dim4 iDims(512, 512, 3, 1);
+    array in = constant(1, iDims, (af_dtype)dtype_traits<float>::af_type);
     /* calculate DOG using ArrayFire functions */
-    af::array k1    = af::gaussianKernel(3, 3);
-    af::array k2    = af::gaussianKernel(2, 2);
-    af::array smth1 = af::convolve2(in, k1);
-    af::array smth2 = af::convolve2(in, k2);
-    af::array diff  = smth1 - smth2;
+    array k1    = gaussianKernel(3, 3);
+    array k2    = gaussianKernel(2, 2);
+    array smth1 = convolve2(in, k1);
+    array smth2 = convolve2(in, k2);
+    array diff  = smth1 - smth2;
     /* calcuate DOG using new function */
-    af::array out= af::dog(in, 3, 2);
+    array out= dog(in, 3, 2);
     /* compare both the values */
-    float accumErr = af::sum<float>(out-diff);
+    float accumErr = sum<float>(out-diff);
     EXPECT_EQ(true, accumErr<1.0e-2);
 }
 
 TYPED_TEST(DOG, InvalidArray)
 {
-    af::array in = af::randu(512);
-    EXPECT_THROW(af::dog(in, 3, 2),
-                 af::exception);
+    array in = randu(512);
+    EXPECT_THROW(dog(in, 3, 2),
+                 exception);
 }
diff --git a/test/dot.cpp b/test/dot.cpp
index 2474e8d6fa..ca6637462b 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -16,11 +16,16 @@
 #include <complex>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
 using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dot;
+using af::dtype_traits;
 
 template<typename T>
 class DotF : public ::testing::Test
@@ -50,8 +55,6 @@ void dotTest(string pTestFile, const int resultIdx,
 {
     if (noDoubleTests<T>()) return;
 
-    using af::dim4;
-
     vector<dim4>        numDims;
     vector<vector<T> >  in;
     vector<vector<T> >  tests;
@@ -66,9 +69,9 @@ void dotTest(string pTestFile, const int resultIdx,
     af_array out = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(in[0].front()),
-                aDims.ndims(), aDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                aDims.ndims(), aDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&b, &(in[1].front()),
-                bDims.ndims(), bDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                bDims.ndims(), bDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_dot(&out, a, b, optLhs, optRhs));
 
@@ -79,7 +82,7 @@ void dotTest(string pTestFile, const int resultIdx,
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), out));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(a));
@@ -113,8 +116,6 @@ void dotAllTest(string pTestFile, const int resultIdx,
 {
     if (noDoubleTests<T>()) return;
 
-    using af::dim4;
-
     vector<dim4>        numDims;
     vector<vector<T> >  in;
     vector<vector<T> >  tests;
@@ -128,9 +129,9 @@ void dotAllTest(string pTestFile, const int resultIdx,
     af_array b = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(in[0].front()),
-                aDims.ndims(), aDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                aDims.ndims(), aDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&b, &(in[1].front()),
-                bDims.ndims(), bDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                bDims.ndims(), bDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     double rval = 0, ival = 0;
     ASSERT_EQ(AF_SUCCESS, af_dot_all(&rval, &ival, a, b, optLhs, optRhs));
@@ -186,9 +187,6 @@ INSTANTIATEC(25600  , dot_c_25600);
 //
 TEST(DotF, CPP)
 {
-    using af::array;
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<float> >  in;
     vector<vector<float> >  tests;
@@ -210,15 +208,12 @@ TEST(DotF, CPP)
     out.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< std::endl;
+        ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< endl;
     }
 }
 
 TEST(DotCCU, CPP)
 {
-    using af::array;
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<cfloat> >  in;
     vector<vector<cfloat> >  tests;
@@ -240,15 +235,12 @@ TEST(DotCCU, CPP)
     out.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< std::endl;
+        ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< endl;
     }
 }
 
 TEST(DotAllF, CPP)
 {
-    using af::array;
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<float> >  in;
     vector<vector<float> >  tests;
@@ -261,7 +253,7 @@ TEST(DotAllF, CPP)
     array a(aDims, &(in[0].front()));
     array b(bDims, &(in[1].front()));
 
-    float out = af::dot<float>(a, b, AF_MAT_CONJ, AF_MAT_NONE);
+    float out = dot<float>(a, b, AF_MAT_CONJ, AF_MAT_NONE);
 
     vector<float> goldData = tests[0];
 
@@ -270,9 +262,6 @@ TEST(DotAllF, CPP)
 
 TEST(DotAllCCU, CPP)
 {
-    using af::array;
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<cfloat> >  in;
     vector<vector<cfloat> >  tests;
@@ -285,7 +274,7 @@ TEST(DotAllCCU, CPP)
     array a(aDims, &(in[0].front()));
     array b(bDims, &(in[1].front()));
 
-    cfloat out = af::dot<cfloat>(a, b, AF_MAT_CONJ, AF_MAT_NONE);
+    cfloat out = dot<cfloat>(a, b, AF_MAT_CONJ, AF_MAT_NONE);
 
     vector<cfloat> goldData = tests[2];
 
diff --git a/test/empty.cpp b/test/empty.cpp
index b4c69d8aaf..0864280235 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -131,8 +131,8 @@ TEST(Array, TestEmptyLinAlg) {
     ASSERT_EQ(det<float  >(constant(0,0)), 1);
     ASSERT_EQ(det<cfloat >(constant(0,0)).real, 1);
     ASSERT_EQ(det<cdouble>(constant(0,0)).real, 1);
-    ASSERT_EQ(af::norm(constant(0,0)), 0);
-    ASSERT_EQ(af::rank(constant(0,0)), 0u);
+    ASSERT_EQ(norm(constant(0,0)), 0);
+    ASSERT_EQ(rank(constant(0,0)), 0u);
 
     array tau_qr, arr = constant(0,0);
     qrInPlace(tau_qr, arr);
@@ -241,7 +241,7 @@ TEST(Array, TestEmptyVecOp) {
 TEST(Array, TestEmptyArrMod) {
     ASSERT_EQ(diag  (constant(0,0))             .numdims(), 0u);
     ASSERT_EQ(diag  (constant(0,0), true)       .numdims(), 0u);
-    ASSERT_EQ(af::identity(0)                   .numdims(), 0u);
+    ASSERT_EQ(identity(0)                   .numdims(), 0u);
     ASSERT_EQ(iota(dim4(0))                     .numdims(), 0u);
     ASSERT_EQ(lower(constant(0,0))              .numdims(), 0u);
     ASSERT_EQ(upper(constant(0,0))              .numdims(), 0u);
diff --git a/test/fast.cpp b/test/fast.cpp
index b3d051b864..899c01e62e 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -18,9 +18,10 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
 using af::dim4;
 
 typedef struct
@@ -131,11 +132,11 @@ void fastTest(string pTestFile, bool nonmax)
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
         for (int elIter = 0; elIter < (int)nElems; elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
         }
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -151,15 +152,15 @@ void fastTest(string pTestFile, bool nonmax)
     }
 }
 
-#define FLOAT_FAST_INIT(desc, image, nonmax) \
-    TYPED_TEST(FloatFAST, desc) \
-    {   \
+#define FLOAT_FAST_INIT(desc, image, nonmax)                            \
+    TYPED_TEST(FloatFAST, desc)                                         \
+    {                                                                   \
         fastTest<TypeParam>(string(TEST_DIR"/fast/"#image"_float.test"), nonmax); \
     }
 
-#define FIXED_FAST_INIT(desc, image, nonmax) \
-    TYPED_TEST(FixedFAST, desc) \
-    {   \
+#define FIXED_FAST_INIT(desc, image, nonmax)                            \
+    TYPED_TEST(FixedFAST, desc)                                         \
+    {                                                                   \
         fastTest<TypeParam>(string(TEST_DIR"/fast/"#image"_fixed.test"), nonmax); \
     }
 
@@ -170,6 +171,10 @@ void fastTest(string pTestFile, bool nonmax)
 
 /////////////////////////////////// CPP ////////////////////////////////
 
+using af::array;
+using af::features;
+using af::loadImage;
+
 TEST(FloatFAST, CPP)
 {
     if (noDoubleTests<float>()) return;
@@ -182,9 +187,9 @@ TEST(FloatFAST, CPP)
     readImageTests(string(TEST_DIR"/fast/square_nonmax_float.test"), inDims, inFiles, gold);
     inFiles[0].insert(0,string(TEST_DIR"/fast/"));
 
-    af::array in = af::loadImage(inFiles[0].c_str(), false);
+    array in = loadImage(inFiles[0].c_str(), false);
 
-    af::features out = fast(in, 20.0f, 9, true, 0.05f, 3);
+    features out = fast(in, 20.0f, 9, true, 0.05f, 3);
 
     float * outX           = new float[gold[0].size()];
     float * outY           = new float[gold[1].size()];
@@ -207,11 +212,11 @@ TEST(FloatFAST, CPP)
     std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
     for (unsigned elIter = 0; elIter < out.getNumFeatures(); elIter++) {
-        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
     }
 
     delete[] outX;
diff --git a/test/fft.cpp b/test/fft.cpp
index 6aedc65525..e3562d4180 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -16,11 +16,32 @@
 #include <stdexcept>
 #include <testHelpers.hpp>
 
+using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
-using af::cfloat;
+using af::array;
 using af::cdouble;
+using af::cfloat;
+using af::constant;
+using af::dim4;
+using af::dtype_traits;
+using af::fft2;
+using af::fft2InPlace;
+using af::fft3;
+using af::fft3InPlace;
+using af::fft;
+using af::fftInPlace;
+using af::ifft2;
+using af::ifft2InPlace;
+using af::ifft3;
+using af::ifft3InPlace;
+using af::ifft;
+using af::ifftInPlace;
+using af::moddims;
+using af::randu;
+using af::seq;
+using af::span;
 
 TEST(fft, Invalid_Type)
 {
@@ -29,9 +50,9 @@ TEST(fft, Invalid_Type)
     af_array inArray   = 0;
     af_array outArray  = 0;
 
-    af::dim4 dims(5 * 5 * 2 * 2);
+    dim4 dims(5 * 5 * 2 * 2);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<char>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<char>::af_type));
 
     ASSERT_EQ(AF_ERR_TYPE, af_fft(&outArray, inArray, 1.0, 0));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -46,9 +67,9 @@ TEST(fft2, Invalid_Array)
     af_array inArray   = 0;
     af_array outArray  = 0;
 
-    af::dim4 dims(5 * 5 * 2 * 2);
+    dim4 dims(5 * 5 * 2 * 2);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_fft2(&outArray, inArray, 1.0, 0, 0));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -63,9 +84,9 @@ TEST(fft3, Invalid_Array)
     af_array inArray   = 0;
     af_array outArray  = 0;
 
-    af::dim4 dims(10,10,1,1);
+    dim4 dims(10,10,1,1);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_fft3(&outArray, inArray, 1.0, 0, 0, 0));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -80,9 +101,9 @@ TEST(ifft2, Invalid_Array)
     af_array inArray   = 0;
     af_array outArray  = 0;
 
-    af::dim4 dims(100,1,1,1);
+    dim4 dims(100,1,1,1);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_ifft2(&outArray, inArray, 0.01, 0, 0));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -97,9 +118,9 @@ TEST(ifft3, Invalid_Array)
     af_array inArray   = 0;
     af_array outArray  = 0;
 
-    af::dim4 dims(10,10,1,1);
+    dim4 dims(10,10,1,1);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_ifft3(&outArray, inArray, 0.01, 0, 0, 0));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -111,18 +132,18 @@ void fftTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector<vector<inType> >       in;
     vector<vector<outType> >   tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
     af_array outArray   = 0;
     af_array inArray    = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if (isInverse){
         switch (dims.ndims()) {
@@ -158,7 +179,7 @@ void fftTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
         bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
         ASSERT_EQ(true, isUnderTolerance)<<
             "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter<< std::endl;
+            (output_scale*outData[elIter]) << " at: " << elIter<< endl;
     }
 
     // cleanup
@@ -227,18 +248,18 @@ void fftBatchTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector<vector<inType> >       in;
     vector<vector<outType> >   tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
     af_array outArray   = 0;
     af_array inArray    = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if(isInverse) {
         switch(rank) {
@@ -281,7 +302,7 @@ void fftBatchTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
             bool isUnderTolerance = abs(goldBar[elIter+off]-outData[elIter+off])<0.001;
             ASSERT_EQ(true, isUnderTolerance)<<"Batch id = "<<batchId<<
                 "; Expected value="<<goldBar[elIter+off] <<"\t Actual Value="<<
-                (output_scale*outData[elIter+off]) << " at: " << elIter<< std::endl;
+                (output_scale*outData[elIter+off]) << " at: " << elIter<< endl;
         }
     }
 
@@ -328,15 +349,15 @@ void cppFFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector<vector<inType> >       in;
     vector<vector<outType> >   tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims = numDims[0];
-    af::array signal(dims, &(in[0].front()));
-    af::array output;
+    dim4 dims = numDims[0];
+    array signal(dims, &(in[0].front()));
+    array output;
 
     if (isInverse){
         output = ifft3Norm(signal, 1.0);
@@ -362,7 +383,7 @@ void cppFFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
         bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
         ASSERT_EQ(true, isUnderTolerance)<<
             "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter<< std::endl;
+            (output_scale*outData[elIter]) << " at: " << elIter<< endl;
     }
     // cleanup
     delete[] outData;
@@ -374,15 +395,15 @@ void cppDFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector<vector<inType> >       in;
     vector<vector<outType> >   tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims = numDims[0];
-    af::array signal(dims, &(in[0].front()));
-    af::array output;
+    dim4 dims = numDims[0];
+    array signal(dims, &(in[0].front()));
+    array output;
 
     if (isInverse){
         output = idft(signal);
@@ -408,7 +429,7 @@ void cppDFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
         bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
         ASSERT_EQ(true, isUnderTolerance)<<
             "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter<< std::endl;
+            (output_scale*outData[elIter]) << " at: " << elIter<< endl;
     }
     // cleanup
     delete[] outData;
@@ -427,14 +448,14 @@ TEST(ifft3, CPP)
 
 TEST(fft3, RandomData)
 {
-    af::array a = af::randu(31, 31, 31);
-    af::array b = af::fft3(a, 64, 64, 64);
-    af::array c = af::ifft3(b);
+    array a = randu(31, 31, 31);
+    array b = fft3(a, 64, 64, 64);
+    array c = ifft3(b);
 
-    af::dim4 aDims = a.dims();
-    af::dim4 cDims = c.dims();
-    af::dim4 aStrides(1, aDims[0], aDims[0]*aDims[1], aDims[0]*aDims[1]*aDims[2]);
-    af::dim4 cStrides(1, cDims[0], cDims[0]*cDims[1], cDims[0]*cDims[1]*cDims[2]);
+    dim4 aDims = a.dims();
+    dim4 cDims = c.dims();
+    dim4 aStrides(1, aDims[0], aDims[0]*aDims[1], aDims[0]*aDims[1]*aDims[2]);
+    dim4 cStrides(1, cDims[0], cDims[0]*cDims[1], cDims[0]*cDims[1]*cDims[2]);
 
     float* gold = new float[a.elements()];
     float* out  = new float[2*c.elements()];
@@ -457,7 +478,7 @@ TEST(fft3, RandomData)
 
                 bool isUnderTolerance = std::abs(gold[gi]-out[2*oi])<0.001;
                 ASSERT_EQ(true, isUnderTolerance)<< "Expected value="<<
-                    gold[gi] <<"\t Actual Value="<< out[2*oi] << " at: " <<gi<< std::endl;
+                    gold[gi] <<"\t Actual Value="<< out[2*oi] << " at: " <<gi<< endl;
             }
         }
     }
@@ -498,17 +519,17 @@ TEST(idft3, CPP)
 
 TEST(fft, CPP_4D)
 {
-    af::array a = af::randu(1024, 1024);
-    af::array b = af::fft(a);
+    array a = randu(1024, 1024);
+    array b = fft(a);
 
-    af::array A = af::moddims(a, 1024, 32, 16, 2);
-    af::array B = af::fft(A);
+    array A = moddims(a, 1024, 32, 16, 2);
+    array B = fft(A);
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_B = B.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_B = B.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << endl;
     }
 
     freeHost(h_b);
@@ -517,17 +538,17 @@ TEST(fft, CPP_4D)
 
 TEST(ifft, CPP_4D)
 {
-    af::array a = af::randu(1024, 1024, c32);
-    af::array b = af::ifft(a);
+    array a = randu(1024, 1024, c32);
+    array b = ifft(a);
 
-    af::array A = af::moddims(a, 1024, 32, 16, 2);
-    af::array B = af::ifft(A);
+    array A = moddims(a, 1024, 32, 16, 2);
+    array B = ifft(A);
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_B = B.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_B = B.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << endl;
     }
 
     freeHost(h_b);
@@ -536,19 +557,19 @@ TEST(ifft, CPP_4D)
 
 TEST(fft, GFOR)
 {
-    af::array a = af::randu(1024, 1024);
-    af::array b = af::constant(0, 1024, 1024, c32);
-    af::array c = af::fft(a);
+    array a = randu(1024, 1024);
+    array b = constant(0, 1024, 1024, c32);
+    array c = fft(a);
 
-    gfor(af::seq ii, a.dims(1)) {
-        b(af::span, ii) = af::fft(a(af::span, ii));
+    gfor(seq ii, a.dims(1)) {
+        b(span, ii) = fft(a(span, ii));
     }
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_c = c.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_c = c.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << endl;
     }
 
     freeHost(h_b);
@@ -557,19 +578,19 @@ TEST(fft, GFOR)
 
 TEST(fft2, GFOR)
 {
-    af::array a = af::randu(1024, 1024, 4);
-    af::array b = af::constant(0, 1024, 1024, 4, c32);
-    af::array c = af::fft2(a);
+    array a = randu(1024, 1024, 4);
+    array b = constant(0, 1024, 1024, 4, c32);
+    array c = fft2(a);
 
-    gfor(af::seq ii, a.dims(2)) {
-        b(af::span, af::span, ii) = af::fft2(a(af::span, af::span, ii));
+    gfor(seq ii, a.dims(2)) {
+        b(span, span, ii) = fft2(a(span, span, ii));
     }
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_c = c.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_c = c.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << endl;
     }
 
     freeHost(h_b);
@@ -578,19 +599,19 @@ TEST(fft2, GFOR)
 
 TEST(fft3, GFOR)
 {
-    af::array a = af::randu(32, 32, 32, 4);
-    af::array b = af::constant(0, 32, 32, 32, 4, c32);
-    af::array c = af::fft3(a);
+    array a = randu(32, 32, 32, 4);
+    array b = constant(0, 32, 32, 32, 4, c32);
+    array c = fft3(a);
 
-    gfor(af::seq ii, a.dims(3)) {
-        b(af::span, af::span, af::span, ii) = af::fft3(a(af::span, af::span, af::span, ii));
+    gfor(seq ii, a.dims(3)) {
+        b(span, span, span, ii) = fft3(a(span, span, span, ii));
     }
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_c = c.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_c = c.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_c[i]) << "at: " << i << endl;
     }
 
     freeHost(h_b);
@@ -599,12 +620,12 @@ TEST(fft3, GFOR)
 
 TEST(fft, InPlace)
 {
-    af::array a = af::randu(1024, 1024, c32);
-    af::array b = af::fft(a);
-    af::fftInPlace(a);
+    array a = randu(1024, 1024, c32);
+    array b = fft(a);
+    fftInPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -616,12 +637,12 @@ TEST(fft, InPlace)
 
 TEST(ifft, InPlace)
 {
-    af::array a = af::randu(1024, 1024, c32);
-    af::array b = af::ifft(a);
-    af::ifftInPlace(a);
+    array a = randu(1024, 1024, c32);
+    array b = ifft(a);
+    ifftInPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -633,12 +654,12 @@ TEST(ifft, InPlace)
 
 TEST(fft2, InPlace)
 {
-    af::array a = af::randu(1024, 1024, c32);
-    af::array b = af::fft2(a);
-    af::fft2InPlace(a);
+    array a = randu(1024, 1024, c32);
+    array b = fft2(a);
+    fft2InPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -650,12 +671,12 @@ TEST(fft2, InPlace)
 
 TEST(ifft2, InPlace)
 {
-    af::array a = af::randu(1024, 1024, c32);
-    af::array b = af::ifft2(a);
-    af::ifft2InPlace(a);
+    array a = randu(1024, 1024, c32);
+    array b = ifft2(a);
+    ifft2InPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -667,12 +688,12 @@ TEST(ifft2, InPlace)
 
 TEST(fft3, InPlace)
 {
-    af::array a = af::randu(32, 32, 32, c32);
-    af::array b = af::fft3(a);
-    af::fft3InPlace(a);
+    array a = randu(32, 32, 32, c32);
+    array b = fft3(a);
+    fft3InPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -684,12 +705,12 @@ TEST(fft3, InPlace)
 
 TEST(ifft3, InPlace)
 {
-    af::array a = af::randu(32, 32, 32, c32);
-    af::array b = af::ifft3(a);
-    af::ifft3InPlace(a);
+    array a = randu(32, 32, 32, c32);
+    array b = ifft3(a);
+    ifft3InPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -701,12 +722,12 @@ TEST(ifft3, InPlace)
 
 void fft2InPlaceFunc()
 {
-    af::array a = af::randu(1024, 1024, c32);
-    af::array b = af::fft2(a);
-    af::fft2InPlace(a);
+    array a = randu(1024, 1024, c32);
+    array b = fft2(a);
+    fft2InPlace(a);
 
-    std::vector<af::cfloat> ha(a.elements());
-    std::vector<af::cfloat> hb(b.elements());
+    vector<cfloat> ha(a.elements());
+    vector<cfloat> hb(b.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -716,17 +737,21 @@ void fft2InPlaceFunc()
     }
 }
 
+using af::setDevice;
+using af::getDevice;
+using af::getDeviceCount;
+
 #define DEVICE_ITERATE(func) do {                                           \
     const char* ENV = getenv("AF_MULTI_GPU_TESTS");                         \
     if(ENV && ENV[0] == '0') {                                              \
         func;                                                               \
     } else {                                                                \
-        int oldDevice = af::getDevice();                                    \
-        for(int i = 0; i < af::getDeviceCount(); i++) {                     \
-            af::setDevice(i);                                               \
+        int oldDevice = getDevice();                                        \
+        for(int i = 0; i < getDeviceCount(); i++) {                         \
+            setDevice(i);                                                   \
             func;                                                           \
         }                                                                   \
-        af::setDevice(oldDevice);                                           \
+        setDevice(oldDevice);                                               \
     }                                                                       \
 } while(0);
 
diff --git a/test/fft_large.cpp b/test/fft_large.cpp
index 71c2c74a98..2b85d61d3e 100644
--- a/test/fft_large.cpp
+++ b/test/fft_large.cpp
@@ -16,22 +16,29 @@
 #include <stdexcept>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
+using af::cfloat;
+using af::fft2;
+using af::ifft2;
+using af::moddims;
+using af::randu;
 
 TEST(fft2, CPP_4D)
 {
-    af::array a = af::randu(1024, 1024, 32);
-    af::array b = af::fft2(a);
+    array a = randu(1024, 1024, 32);
+    array b = fft2(a);
 
-    af::array A = af::moddims(a, 1024, 1024, 4, 8);
-    af::array B = af::fft2(A);
+    array A = moddims(a, 1024, 1024, 4, 8);
+    array B = fft2(A);
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_B = B.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_B = B.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << endl;
     }
 
     af_free_host(h_b);
@@ -40,17 +47,17 @@ TEST(fft2, CPP_4D)
 
 TEST(ifft2, CPP_4D)
 {
-    af::array a = af::randu(1024, 1024, 32, c32);
-    af::array b = af::ifft2(a);
+    array a = randu(1024, 1024, 32, c32);
+    array b = ifft2(a);
 
-    af::array A = af::moddims(a, 1024, 1024, 4, 8);
-    af::array B = af::ifft2(A);
+    array A = moddims(a, 1024, 1024, 4, 8);
+    array B = ifft2(A);
 
-    af::cfloat *h_b = b.host<af::cfloat>();
-    af::cfloat *h_B = B.host<af::cfloat>();
+    cfloat *h_b = b.host<cfloat>();
+    cfloat *h_B = B.host<cfloat>();
 
     for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(h_b[i], h_B[i]) << "at: " << i << endl;
     }
 
     af_free_host(h_b);
diff --git a/test/fft_real.cpp b/test/fft_real.cpp
index 8cd6612712..190b2d4f94 100644
--- a/test/fft_real.cpp
+++ b/test/fft_real.cpp
@@ -19,25 +19,35 @@
 using std::string;
 using std::vector;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
-
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::fft;
+using af::fftNorm;
+using af::fft2Norm;
+using af::fft3Norm;
+using af::fftC2R;
+using af::fftR2C;
+using af::randu;
 
 template<typename T>
 class FFT_REAL : public ::testing::Test
 {
 };
 
-typedef ::testing::Types<af::cfloat, af::cdouble> TestTypes;
+typedef ::testing::Types<cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(FFT_REAL, TestTypes);
 
 template<int rank>
-af::array fft(const af::array &in, double norm)
+array fft(const array &in, double norm)
 {
     switch(rank) {
-    case 1: return af::fftNorm(in, norm);
-    case 2: return af::fft2Norm(in, norm);
-    case 3: return af::fft3Norm(in, norm);
+    case 1: return fftNorm(in, norm);
+    case 2: return fft2Norm(in, norm);
+    case 3: return fft3Norm(in, norm);
     default: return in;
     }
 }
@@ -45,13 +55,13 @@ af::array fft(const af::array &in, double norm)
 #define MY_ASSERT_NEAR(aa, bb, cc) ASSERT_NEAR(abs(aa), abs(bb), (cc))
 
 template<typename Tc, int rank>
-void fft_real(af::dim4 dims)
+void fft_real(dim4 dims)
 {
-    typedef typename af::dtype_traits<Tc>::base_type Tr;
+    typedef typename dtype_traits<Tc>::base_type Tr;
     if (noDoubleTests<Tr>()) return;
 
-    af::dtype ty = (af::dtype)af::dtype_traits<Tr>::af_type;
-    af::array a = af::randu(dims, ty);
+    dtype ty = (dtype)dtype_traits<Tr>::af_type;
+    array a = randu(dims, ty);
 
     bool is_odd = dims[0] & 1;
 
@@ -61,12 +71,12 @@ void fft_real(af::dim4 dims)
     for (int i = 0; i < rank; i++) norm *= dims[i];
     norm = 1/norm;
 
-    af::array as = af::fftR2C<rank>(a, norm);
-    af::array af = fft<rank>(a, norm);
+    array as = fftR2C<rank>(a, norm);
+    array af = fft<rank>(a, norm);
 
 
-    std::vector<Tc> has(as.elements());
-    std::vector<Tc> haf(af.elements());
+    vector<Tc> has(as.elements());
+    vector<Tc> haf(af.elements());
 
     as.host(&has[0]);
     af.host(&haf[0]);
@@ -77,10 +87,10 @@ void fft_real(af::dim4 dims)
         }
     }
 
-    af::array b = af::fftC2R<rank>(as, is_odd, 1);
+    array b = fftC2R<rank>(as, is_odd, 1);
 
-    std::vector<Tr> ha(a.elements());
-    std::vector<Tr> hb(a.elements());
+    vector<Tr> ha(a.elements());
+    vector<Tr> hb(a.elements());
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -92,30 +102,30 @@ void fft_real(af::dim4 dims)
 
 TYPED_TEST(FFT_REAL, Even1D)
 {
-    fft_real<TypeParam, 1>(af::dim4(1024, 256));
+    fft_real<TypeParam, 1>(dim4(1024, 256));
 }
 
 TYPED_TEST(FFT_REAL, Odd1D)
 {
-    fft_real<TypeParam, 1>(af::dim4(625, 256));
+    fft_real<TypeParam, 1>(dim4(625, 256));
 }
 
 TYPED_TEST(FFT_REAL, Even2D)
 {
-    fft_real<TypeParam, 2>(af::dim4(1024, 256));
+    fft_real<TypeParam, 2>(dim4(1024, 256));
 }
 
 TYPED_TEST(FFT_REAL, Odd2D)
 {
-    fft_real<TypeParam, 2>(af::dim4(625, 256));
+    fft_real<TypeParam, 2>(dim4(625, 256));
 }
 
 TYPED_TEST(FFT_REAL, Even3D)
 {
-    fft_real<TypeParam, 3>(af::dim4(32, 32, 32));
+    fft_real<TypeParam, 3>(dim4(32, 32, 32));
 }
 
 TYPED_TEST(FFT_REAL, Odd3D)
 {
-    fft_real<TypeParam, 3>(af::dim4(25, 32, 32));
+    fft_real<TypeParam, 3>(dim4(25, 32, 32));
 }
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index e5ae4ed084..9370d47eb1 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -15,11 +15,16 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::vector;
 using std::string;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::randu;
 
 template<typename T>
 class FFTConvolve : public ::testing::Test
@@ -48,8 +53,6 @@ void fftconvolveTest(string pTestFile, bool expand)
 {
     if (noDoubleTests<T>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<T> >      in;
     vector<vector<T> >   tests;
@@ -61,7 +64,7 @@ void fftconvolveTest(string pTestFile, bool expand)
     af_array signal   = 0;
     af_array filter   = 0;
     af_array outArray = 0;
-    af_dtype in_type =(af_dtype)af::dtype_traits<T>::af_type;
+    af_dtype in_type =(af_dtype)dtype_traits<T>::af_type;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),
                                           sDims.ndims(), sDims.get(), in_type));
@@ -90,7 +93,7 @@ void fftconvolveTest(string pTestFile, bool expand)
         ASSERT_NEAR(
             real(currGoldBar[elIter]),
             real(outData[elIter])
-            , 1e-2)<< "at: " << elIter<< std::endl;
+            , 1e-2)<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -103,9 +106,7 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
 {
     if (noDoubleTests<T>()) return;
 
-    using af::dim4;
     using af::seq;
-    using af::array;
 
     int outDim = sDim + fDim - 1;
     int fftDim = (int)pow(2, ceil(log2(outDim)));
@@ -129,21 +130,21 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
     const dim4 signalDims(sd[0], sd[1], sd[2], sd[3]);
     const dim4 filterDims(fd[0], fd[1], fd[2], fd[3]);
 
-    array signal = randu(signalDims, (af_dtype) af::dtype_traits<T>::af_type);
-    array filter = randu(filterDims, (af_dtype) af::dtype_traits<T>::af_type);
+    array signal = randu(signalDims, (af_dtype) dtype_traits<T>::af_type);
+    array filter = randu(filterDims, (af_dtype) dtype_traits<T>::af_type);
 
     array out = fftConvolve(signal, filter, expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT);
 
     array gold;
     switch(baseDim) {
     case 1:
-        gold = real(af::ifft(af::fft(signal, fftDim) * af::fft(filter, fftDim)));
+        gold = real(ifft(fft(signal, fftDim) * fft(filter, fftDim)));
         break;
     case 2:
-        gold = real(af::ifft2(af::fft2(signal, fftDim, fftDim) * af::fft2(filter, fftDim, fftDim)));
+        gold = real(ifft2(fft2(signal, fftDim, fftDim) * fft2(filter, fftDim, fftDim)));
         break;
     case 3:
-        gold = real(af::ifft3(af::fft3(signal, fftDim, fftDim, fftDim) * af::fft3(filter, fftDim, fftDim, fftDim)));
+        gold = real(ifft3(fft3(signal, fftDim, fftDim, fftDim) * fft3(filter, fftDim, fftDim, fftDim)));
         break;
     default:
         ASSERT_LT(baseDim, 4);
@@ -183,7 +184,7 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
     out.host(&outData.front());
 
     for (size_t elIter=0; elIter<outElems; ++elIter) {
-        ASSERT_NEAR(goldData[elIter], outData[elIter], 5e-2) << "at: " << elIter << std::endl;
+        ASSERT_NEAR(goldData[elIter], outData[elIter], 5e-2) << "at: " << elIter << endl;
     }
 }
 
@@ -370,8 +371,6 @@ TEST(FFTConvolve1, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -381,12 +380,12 @@ TEST(FFTConvolve1, CPP)
     //![ex_image_convolve1]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [32 1 1 1]
-    af::array filter(numDims[1], &(in[1].front()));
+    array filter(numDims[1], &(in[1].front()));
     //filter dims = [4 1 1 1]
 
-    af::array output = fftConvolve1(signal, filter, AF_CONV_EXPAND);
+    array output = fftConvolve1(signal, filter, AF_CONV_EXPAND);
     //output dims = [32 1 1 1] - same as input since expand(3rd argument is false)
     //None of the dimensions > 1 has lenght > 1, so no batch mode is activated.
     //![ex_image_convolve1]
@@ -397,7 +396,7 @@ TEST(FFTConvolve1, CPP)
     output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< endl;
     }
 }
 
@@ -405,8 +404,6 @@ TEST(FFTConvolve2, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -416,12 +413,12 @@ TEST(FFTConvolve2, CPP)
     //![ex_image_convolve2]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [15 17 1 1]
-    af::array filter(numDims[1], &(in[1].front()));
+    array filter(numDims[1], &(in[1].front()));
     //filter dims = [5 5 2 1]
 
-    af::array output = fftConvolve2(signal, filter, AF_CONV_EXPAND);
+    array output = fftConvolve2(signal, filter, AF_CONV_EXPAND);
     //output dims = [15 17 1 1] - same as input since expand(3rd argument is false)
     //however, notice that the 3rd dimension of filter is > 1.
     //So, one to many batch mode will be activated automatically
@@ -435,7 +432,7 @@ TEST(FFTConvolve2, CPP)
     output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< endl;
     }
 }
 
@@ -443,8 +440,6 @@ TEST(FFTConvolve3, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::dim4;
-
     vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
@@ -454,12 +449,12 @@ TEST(FFTConvolve3, CPP)
     //![ex_image_convolve3]
     //vector<dim4> numDims;
     //vector<vector<float> > in;
-    af::array signal(numDims[0], &(in[0].front()));
+    array signal(numDims[0], &(in[0].front()));
     //signal dims = [10 11 2 2]
-    af::array filter(numDims[1], &(in[1].front()));
+    array filter(numDims[1], &(in[1].front()));
     //filter dims = [4 2 3 2]
 
-    af::array output = fftConvolve3(signal, filter, AF_CONV_EXPAND);
+    array output = fftConvolve3(signal, filter, AF_CONV_EXPAND);
     //output dims = [10 11 2 2] - same as input since expand(3rd argument is false)
     //however, notice that the 4th dimension is > 1 for both signal
     //and the filter, therefore many to many batch mode will be
@@ -472,18 +467,15 @@ TEST(FFTConvolve3, CPP)
     output.host(&outData.front());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< endl;
     }
 }
 
 TEST(FFTConvolve, Docs_Unified_Wrapper)
 {
     // This unit test doesn't necessarily need to function
-    // accuracy as af::convolve is merely a wrapper to
-    // af::convolve[1|2|3]
-    using af::array;
-    using af::dim4;
-    using af::randu;
+    // accuracy as convolve is merely a wrapper to
+    // convolve[1|2|3]
     using af::constant;
     using af::convolve;
 
diff --git a/test/flat.cpp b/test/flat.cpp
index fd12116f5a..4e518a3855 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -13,13 +13,18 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace af;
+using af::array;
+using af::flat;
+using af::freeHost;
+using af::randu;
+using af::seq;
+using af::span;
 
 TEST(FlatTests, Test_flat_1D)
 {
     const int num = 10000;
-    af::array in = randu(num);
-    af::array out = flat(in);
+    array in = randu(num);
+    array out = flat(in);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -38,8 +43,8 @@ TEST(FlatTests, Test_flat_2D)
     const int ny = 200;
     const int num =  nx * ny;
 
-    af::array in = randu(nx, ny);
-    af::array out = flat(in);
+    array in = randu(nx, ny);
+    array out = flat(in);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -58,9 +63,9 @@ TEST(FlatTests, Test_flat_1D_index)
     const int st = 101;
     const int en = 5000;
 
-    af::array in = randu(num);
-    af::array tmp = in(seq(st, en));
-    af::array out = flat(tmp);
+    array in = randu(num);
+    array tmp = in(seq(st, en));
+    array out = flat(tmp);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -81,9 +86,9 @@ TEST(FlatTests, Test_flat_2D_index0)
     const int en = 180;
     const int nxo = (en - st + 1);
 
-    af::array in = randu(nx, ny);
-    af::array tmp = in(seq(st, en), span);
-    af::array out = flat(tmp);
+    array in = randu(nx, ny);
+    array tmp = in(seq(st, en), span);
+    array out = flat(tmp);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -108,9 +113,9 @@ TEST(FlatTests, Test_flat_2D_index1)
     const int st = 21;
     const int en = 180;
 
-    af::array in = randu(nx, ny);
-    af::array tmp = in(span, seq(st, en));
-    af::array out = flat(tmp);
+    array in = randu(nx, ny);
+    array tmp = in(span, seq(st, en));
+    array out = flat(tmp);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
diff --git a/test/flip.cpp b/test/flip.cpp
index 565781ccaa..7b5461ba5a 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -14,13 +14,19 @@
 #include <af/index.h>
 #include <testHelpers.hpp>
 
-using namespace af;
+using af::array;
+using af::randu;
+using af::flip;
+using af::freeHost;
+using af::randu;
+using af::seq;
+using af::span;
 
 TEST(FlipTests, Test_flip_1D)
 {
     const int num = 10000;
-    af::array in = randu(num);
-    af::array out = flip(in, 0);
+    array in = randu(num);
+    array out = flip(in, 0);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -39,8 +45,8 @@ TEST(FlipTests, Test_flip_2D0)
     const int nx = 200;
     const int ny = 200;
 
-    af::array in = randu(nx, ny);
-    af::array out = flip(in, 0);
+    array in = randu(nx, ny);
+    array out = flip(in, 0);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -63,8 +69,8 @@ TEST(FlipTests, Test_flip_2D1)
     const int nx = 200;
     const int ny = 200;
 
-    af::array in = randu(nx, ny);
-    af::array out = flip(in, 1);
+    array in = randu(nx, ny);
+    array out = flip(in, 1);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -89,9 +95,9 @@ TEST(FlipTests, Test_flip_1D_index)
     const int st = 101;
     const int en = 5000;
 
-    af::array in = randu(num);
-    af::array tmp = in(seq(st, en));
-    af::array out = flip(tmp, 0);
+    array in = randu(num);
+    array tmp = in(seq(st, en));
+    array out = flip(tmp, 0);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -113,9 +119,9 @@ TEST(FlipTests, Test_flip_2D_index00)
     const int en = 180;
     const int nxo = (en - st + 1);
 
-    af::array in = randu(nx, ny);
-    af::array tmp = in(seq(st, en), span);
-    af::array out = flip(tmp, 0);
+    array in = randu(nx, ny);
+    array tmp = in(seq(st, en), span);
+    array out = flip(tmp, 0);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -141,9 +147,9 @@ TEST(FlipTests, Test_flip_2D_index01)
     const int en = 180;
     const int nxo = (en - st + 1);
 
-    af::array in = randu(nx, ny);
-    af::array tmp = in(seq(st, en), span);
-    af::array out = flip(tmp, 1);
+    array in = randu(nx, ny);
+    array tmp = in(seq(st, en), span);
+    array out = flip(tmp, 1);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -168,9 +174,9 @@ TEST(FlipTests, Test_flip_2D_index10)
     const int st = 21;
     const int en = 180;
 
-    af::array in = randu(nx, ny);
-    af::array tmp = in(span, seq(st, en));
-    af::array out = flip(tmp, 0);
+    array in = randu(nx, ny);
+    array tmp = in(span, seq(st, en));
+    array out = flip(tmp, 0);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
@@ -197,9 +203,9 @@ TEST(FlipTests, Test_flip_2D_index11)
     const int st = 21;
     const int en = 180;
 
-    af::array in = randu(nx, ny);
-    af::array tmp = in(span, seq(st, en));
-    af::array out = flip(tmp, 1);
+    array in = randu(nx, ny);
+    array tmp = in(span, seq(st, en));
+    array out = flip(tmp, 1);
 
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
diff --git a/test/gaussiankernel.cpp b/test/gaussiankernel.cpp
index ad70f5037d..45d4575d42 100644
--- a/test/gaussiankernel.cpp
+++ b/test/gaussiankernel.cpp
@@ -15,8 +15,10 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::dim4;
 
 template<typename T>
 class GaussianKernel : public ::testing::Test
@@ -36,7 +38,7 @@ void gaussianKernelTest(string pFileName, double sigma)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>     numDims;
+    vector<dim4>     numDims;
     vector<vector<int> > in;
     vector<vector<T> >   tests;
 
@@ -60,7 +62,7 @@ void gaussianKernelTest(string pFileName, double sigma)
     ASSERT_EQ(outElems, (dim_t)nElems);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 
     delete[] outData;
@@ -107,12 +109,12 @@ TYPED_TEST(GaussianKernel, SmallSmall2DWithSigma)
 
 #include <iostream>
 
+using af::array;
+using af::gaussianKernel;
+
 void gaussianKernelTestCPP(string pFileName, double sigma)
 {
-    using af::array;
-    using af::gaussianKernel;
-
-    vector<af::dim4>       numDims;
+    vector<dim4>       numDims;
     vector<vector<int> >   in;
     vector<vector<float> > tests;
 
@@ -132,7 +134,7 @@ void gaussianKernelTestCPP(string pFileName, double sigma)
     ASSERT_EQ(outElems, (dim_t)nElems);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 
     delete[] outData;
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 9ae50b1d22..9d2776e6d2 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -23,49 +23,54 @@
 
 using std::vector;
 using std::string;
-using std::generate;
-using std::cout;
 using std::endl;
 using std::ostream_iterator;
+using af::array;
+using af::dim4;
 using af::dtype_traits;
+using af::exception;
 using af::freeHost;
+using af::randu;
+using af::seq;
+using af::span;
+using af::where;
 
 void testGeneralAssignOneArray(string pTestFile, const dim_t ndims, af_index_t* indexs, int arrayDim)
 {
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector< vector<float> >      in;
     vector< vector<float> >   tests;
 
     readTestsFromFile<float, float>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
-    af::dim4 dims2     = numDims[2];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
+    dim4 dims2     = numDims[2];
     af_array outArray  = 0;
     af_array rhsArray  = 0;
     af_array lhsArray  = 0;
     af_array idxArray  = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray, &(in[2].front()),
-                dims2.ndims(), dims2.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[arrayDim].idx.arr = idxArray;
 
     ASSERT_EQ(AF_SUCCESS, af_assign_gen(&outArray, lhsArray, ndims, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    std::vector<float> outData(nElems);
+    vector<float> outData(nElems);
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
@@ -96,14 +101,14 @@ TEST(GeneralAssign, SASS)
 
 TEST(GeneralAssign, SSSS)
 {
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector< vector<float> >      in;
     vector< vector<float> >   tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR"/gen_assign/s10_14s0_9s0_ns0_n.test"), numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
     af_array outArray  = 0;
     af_array rhsArray  = 0;
     af_array lhsArray  = 0;
@@ -115,21 +120,21 @@ TEST(GeneralAssign, SSSS)
     indexs[1].isSeq = true;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_assign_gen(&outArray, lhsArray, 2, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    std::vector<float> outData(nElems);
+    vector<float> outData(nElems);
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
@@ -139,18 +144,18 @@ TEST(GeneralAssign, SSSS)
 
 TEST(GeneralAssign, AAAA)
 {
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector< vector<float> >      in;
     vector< vector<float> >   tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR"/gen_assign/aaaa.test"), numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
-    af::dim4 dims2     = numDims[2];
-    af::dim4 dims3     = numDims[3];
-    af::dim4 dims4     = numDims[4];
-    af::dim4 dims5     = numDims[5];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
+    dim4 dims2     = numDims[2];
+    dim4 dims3     = numDims[3];
+    dim4 dims4     = numDims[4];
+    dim4 dims5     = numDims[5];
     af_array outArray  = 0;
     af_array rhsArray  = 0;
     af_array lhsArray  = 0;
@@ -166,37 +171,37 @@ TEST(GeneralAssign, AAAA)
     indexs[3].isSeq = false;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray0, &(in[2].front()),
-                dims2.ndims(), dims2.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[0].idx.arr = idxArray0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray1, &(in[3].front()),
-                dims3.ndims(), dims3.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims3.ndims(), dims3.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[1].idx.arr = idxArray1;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray2, &(in[4].front()),
-                dims4.ndims(), dims4.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims4.ndims(), dims4.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[2].idx.arr = idxArray2;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray3, &(in[5].front()),
-                dims5.ndims(), dims5.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims5.ndims(), dims5.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[3].idx.arr = idxArray3;
 
     ASSERT_EQ(AF_SUCCESS, af_assign_gen(&outArray, lhsArray, 4, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    std::vector<float> outData(nElems);
+    vector<float> outData(nElems);
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
@@ -215,13 +220,13 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX)
 
     const int num = 20000;
 
-    array a = af::randu(num);
+    array a = randu(num);
     float *hAO = a.host<float>();
 
     array a_copy = a;
     array idx = where(a < 0.5);
     const int len = idx.elements();
-    array b = af::randu(len);
+    array b = randu(len);
     a(idx) = b;
 
     float *hA = a.host<float>();
@@ -267,13 +272,13 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL)
 
         const int num = 20000;
 
-        array a = af::randu(num);
+        array a = randu(num);
         float *hAO = a.host<float>();
 
         array a_copy = a;
         array idx = where(a < 0.5);
         const int len = idx.elements();
-        array b = af::randu(len);
+        array b = randu(len);
         a(a < 0.5) = b;
 
         float *hA = a.host<float>();
@@ -310,15 +315,14 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL)
         freeHost(hAC);
         freeHost(hAO);
         freeHost(hIdx);
-    } catch(af::exception &ex) {
-        FAIL() << ex.what() << std::endl;
+    } catch(exception &ex) {
+        FAIL() << ex.what() << endl;
     }
 }
 
 
 TEST(GeneralAssign, CPP_ASNN)
 {
-    using namespace af;
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
@@ -344,7 +348,7 @@ TEST(GeneralAssign, CPP_ASNN)
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
             ASSERT_EQ(hAt[hIdx[i]], hBt[i])
-                << "at " << i << " " << j << std::endl;
+                << "at " << i << " " << j << endl;
         }
     }
 
@@ -355,7 +359,6 @@ TEST(GeneralAssign, CPP_ASNN)
 
 TEST(GeneralAssign, CPP_SANN)
 {
-    using namespace af;
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
@@ -381,7 +384,7 @@ TEST(GeneralAssign, CPP_SANN)
 
         for (int i = 0; i < nxb; i++) {
             ASSERT_EQ(hAt[i + st], hBt[i])
-            << "at " << i << " " << j << std::endl;
+            << "at " << i << " " << j << endl;
         }
     }
 
@@ -392,7 +395,6 @@ TEST(GeneralAssign, CPP_SANN)
 
 TEST(GeneralAssign, CPP_SSAN)
 {
-    using namespace af;
     const int nx = 100;
     const int ny = 100;
     const int nz = 100;
@@ -420,7 +422,7 @@ TEST(GeneralAssign, CPP_SSAN)
         for (int j = 0; j < nyb; j++) {
             for (int i = 0; i < nxb; i++) {
                 ASSERT_EQ(hAt[j * nx  + i + st], hBt[j * nxb + i])
-                    << "at " << i << " " << j << " " << k << std::endl;
+                    << "at " << i << " " << j << " " << k << endl;
             }
         }
     }
@@ -432,7 +434,6 @@ TEST(GeneralAssign, CPP_SSAN)
 
 TEST(GeneralAssign, CPP_AANN)
 {
-    using namespace af;
     const int nx = 1000;
     const int ny = 1000;
 
@@ -456,7 +457,7 @@ TEST(GeneralAssign, CPP_AANN)
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
             ASSERT_EQ(hAt[hIdx0[i]], hBt[i])
-                << "at " << i << " " << j << std::endl;
+                << "at " << i << " " << j << endl;
         }
     }
 
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index e2c644ae0f..efa9b5b88c 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -23,43 +23,42 @@
 
 using std::vector;
 using std::string;
-using std::generate;
-using std::cout;
 using std::endl;
 using std::ostream_iterator;
+using af::dim4;
 using af::dtype_traits;
 
 void testGeneralIndexOneArray(string pTestFile, const dim_t ndims, af_index_t* indexs, int arrayDim)
 {
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector< vector<float> >      in;
     vector< vector<float> >   tests;
 
     readTestsFromFile<float, float>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array idxArray  = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[arrayDim].idx.arr = idxArray;
 
     ASSERT_EQ(AF_SUCCESS, af_index_gen(&outArray, inArray, ndims, indexs));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    std::vector<float> outData(nElems);
+    vector<float> outData(nElems);
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -107,15 +106,15 @@ TEST(GeneralIndex, SASS)
 
 TEST(GeneralIndex, AASS)
 {
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector< vector<float> >      in;
     vector< vector<float> >   tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR"/gen_index/aas0_ns0_n.test"), numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
-    af::dim4 dims2     = numDims[2];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
+    dim4 dims2     = numDims[2];
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array idxArray0 = 0;
@@ -124,15 +123,15 @@ TEST(GeneralIndex, AASS)
     af_index_t indexs[2];
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray0, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[0].isSeq = false;
     indexs[0].idx.arr = idxArray0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray1, &(in[2].front()),
-                dims2.ndims(), dims2.get(), (af_dtype)af::dtype_traits<float>::af_type));
+                dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[1].isSeq = false;
     indexs[1].idx.arr = idxArray1;
 
@@ -140,12 +139,12 @@ TEST(GeneralIndex, AASS)
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
-    std::vector<float> outData(nElems);
+    vector<float> outData(nElems);
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -154,9 +153,15 @@ TEST(GeneralIndex, AASS)
     ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
 }
 
+using af::array;
+using af::freeHost;
+using af::randu;
+using af::seq;
+using af::span;
+using af::where;
+
 TEST(GeneralIndex, CPP_ASNN)
 {
-    using namespace af;
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
@@ -179,7 +184,7 @@ TEST(GeneralIndex, CPP_ASNN)
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
             ASSERT_EQ(hAt[hIdx[i]], hBt[i])
-                << "at " << i << " " << j << std::endl;
+                << "at " << i << " " << j << endl;
         }
     }
 
@@ -190,7 +195,6 @@ TEST(GeneralIndex, CPP_ASNN)
 
 TEST(GeneralIndex, CPP_SANN)
 {
-    using namespace af;
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
@@ -213,7 +217,7 @@ TEST(GeneralIndex, CPP_SANN)
 
         for (int i = 0; i < nxb; i++) {
             ASSERT_EQ(hAt[i + st], hBt[i])
-            << "at " << i << " " << j << std::endl;
+            << "at " << i << " " << j << endl;
         }
     }
 
@@ -224,7 +228,6 @@ TEST(GeneralIndex, CPP_SANN)
 
 TEST(GeneralIndex, CPP_SSAN)
 {
-    using namespace af;
     const int nx = 100;
     const int ny = 100;
     const int nz = 100;
@@ -250,7 +253,7 @@ TEST(GeneralIndex, CPP_SSAN)
         for (int j = 0; j < nyb; j++) {
             for (int i = 0; i < nxb; i++) {
                 ASSERT_EQ(hAt[j * nx  + i + st], hBt[j * nxb + i])
-                    << "at " << i << " " << j << " " << k << std::endl;
+                    << "at " << i << " " << j << " " << k << endl;
             }
         }
     }
@@ -262,7 +265,6 @@ TEST(GeneralIndex, CPP_SSAN)
 
 TEST(GeneralIndex, CPP_AANN)
 {
-    using namespace af;
     const int nx = 1000;
     const int ny = 1000;
 
@@ -285,7 +287,7 @@ TEST(GeneralIndex, CPP_AANN)
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
             ASSERT_EQ(hAt[hIdx0[i]], hBt[i])
-                << "at " << i << " " << j << std::endl;
+                << "at " << i << " " << j << endl;
         }
     }
 
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index 26fecfb4bf..964dff5e92 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -21,7 +21,7 @@ TEST(GettingStarted, SNIPPET_getting_started_gen)
 {
 
     //! [ex_getting_started_constructors]
-    // Arrays may be created using the af::array constructor and dimensioned
+    // Arrays may be created using the array constructor and dimensioned
     // as 1D, 2D, 3D; however, the values in these arrays will be undefined
     array undefined_1D(100);        // 1D array with 100 elements
     array undefined_2D(10, 100);    // 2D array of size 10 x 100
@@ -31,20 +31,20 @@ TEST(GettingStarted, SNIPPET_getting_started_gen)
     //! [ex_getting_started_gen]
     // Generate an array of size three filled with zeros.
     // If no data type is specified, ArrayFire defaults to f32.
-    // The af::constant function generates the data on the device.
+    // The constant function generates the data on the device.
     array zeros      = constant(0, 3);
 
     // Generate a 1x4 array of uniformly distributed [0,1] random numbers
-    // The af::randu function generates the data on the device.
+    // The randu function generates the data on the device.
     array rand1      = randu(1, 4);
 
     // Generate a 2x2 array (or matrix, if you prefer) of random numbers
     // sampled from a normal distribution.
-    // The af::randn function generates data on the device.
+    // The randn function generates data on the device.
     array rand2      = randn(2, 2);
 
     // Generate a 3x3 identity matrix. The data is generated on the device.
-    array iden       = af::identity(3, 3);
+    array iden       = identity(3, 3);
 
     // Lastly, create a 2x1 array (column vector) of uniformly distributed
     // 32-bit complex numbers (c32 data type):
@@ -88,7 +88,7 @@ TEST(GettingStarted, SNIPPET_getting_started_init)
     // constructor. Here we copy the data into a 2x3 matrix:
     array A(2, 3, hA);
 
-    // ArrayFire provides a convenince function for printing af::array
+    // ArrayFire provides a convenince function for printing array
     // objects in case you wish to see how the data is stored:
     af_print(A);
 
@@ -144,7 +144,7 @@ TEST(GettingStarted, SNIPPET_getting_started_dims)
     // the `dims` function:
     printf("dims = [%lld %lld]\n", a.dims(0), a.dims(1)); // 4,5
 
-    // Or the elements of a af::dim4 object:
+    // Or the elements of a dim4 object:
     dim4 dims = a.dims();
     printf("dims = [%lld %lld]\n", dims[0], dims[1]); // 4,5
     //! [ex_getting_started_dims]
@@ -188,7 +188,7 @@ TEST(GettingStarted, SNIPPET_getting_started_arith)
 {
     //! [ex_getting_started_arith]
     array R = randu(3, 3);
-    af_print(constant(1, 3, 3) + af::complex(sin(R)));  // will be c32
+    af_print(constant(1, 3, 3) + complex(sin(R)));  // will be c32
 
     // rescale complex values to unit circle
     array a = randn(5, c32);
@@ -232,11 +232,11 @@ TEST(GettingStarted, SNIPPET_getting_started_ptr)
     array a = randu(3, f32);
 
     // Copy an array on the device to the host:
-    float * host_a = a.host<float>();
+    float *host_a = a.host<float>();
     // access the host data as a normal array
     printf("host_a[2] = %g\n", host_a[2]);  // last element
-    // and free memory using delete:
-    delete[] host_a;
+    // and free memory using freeHost:
+    freeHost(host_a);
 
     // Get access to the device memory for a CUDA kernel
     float * d_cuda = a.device<float>();    // no need to free this
@@ -296,7 +296,7 @@ TEST(GettingStarted, SNIPPET_getting_started_constants)
 {
     //! [ex_getting_started_constants]
     array A = randu(5,5);
-    A(where(A > .5)) = af::NaN;
+    A(where(A > .5)) = NaN;
 
     array x = randu(10e6), y = randu(10e6);
     double pi_est = 4 * sum<float>(hypot(x,y) < 1) / 10e6;
diff --git a/test/gfor.cpp b/test/gfor.cpp
index 70d03bbd9b..6d7dbcfa9a 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -18,11 +18,16 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
-using namespace af;
+using af::constant;
+using af::freeHost;
+using af::gforSet;
+using af::seq;
+using af::span;
+using af::randu;
 
 TEST(GFOR, Assign_Scalar_Span)
 {
@@ -244,6 +249,9 @@ TEST(BatchFunc, 2D0)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 2D1)
@@ -268,6 +276,9 @@ TEST(BatchFunc, 2D1)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 3D0)
@@ -295,6 +306,9 @@ TEST(BatchFunc, 3D0)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 3D1)
@@ -322,6 +336,9 @@ TEST(BatchFunc, 3D1)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 3D2)
@@ -349,6 +366,9 @@ TEST(BatchFunc, 3D2)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 3D01)
@@ -376,6 +396,9 @@ TEST(BatchFunc, 3D01)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 3D_1_2)
@@ -403,6 +426,9 @@ TEST(BatchFunc, 3D_1_2)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(BatchFunc, 4D3)
@@ -435,6 +461,9 @@ TEST(BatchFunc, 4D3)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 
@@ -467,11 +496,13 @@ TEST(BatchFunc, 4D_2_3)
     }
 
     gforSet(false);
+    freeHost(hA);
+    freeHost(hB);
+    freeHost(hC);
 }
 
 TEST(ASSIGN, ISSUE_1127)
 {
-    using namespace af;
     array orig = randu(512, 768, 3);
     array vert = randu(512, 768, 3);
     array horiz = randu(512, 768, 3);
@@ -492,8 +523,8 @@ TEST(ASSIGN, ISSUE_1127)
     out1(seq(0,rows-1,2), seq(1,cols-1,2), span) = horiz;
     out1(seq(1,rows-1,2), seq(1,cols-1,2), span) = diag;
 
-    std::vector<float> hout0(out0.elements());
-    std::vector<float> hout1(out1.elements());
+    vector<float> hout0(out0.elements());
+    vector<float> hout1(out1.elements());
 
     out0.host(&hout0[0]);
     out1.host(&hout1[0]);
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index ca41011b2c..5e6444eea3 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -18,10 +18,15 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::abs;
+using std::cout;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::array;
 using af::dim4;
+using af::features;
+using af::loadImage;
 
 typedef struct
 {
@@ -105,16 +110,16 @@ static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu
             sum += x*x;
             if (abs(x) > (float)unit_thr) {
                 ret = false;
-                std::cout<<std::endl<<"@compareEuclidean: unit mismatch."<<std::endl;
-                std::cout<<"(cpu,gpu,cpu-gpu)["<<i<<","<<l<<"] : {"<<cpu[idx]<<","<<gpu[idx]<<","<<cpu[idx]-gpu[idx]<<"}"<<std::endl;
-                std::cout<<std::endl;
+                cout<<endl<<"@compareEuclidean: unit mismatch."<<endl;
+                cout<<"(cpu,gpu,cpu-gpu)["<<i<<","<<l<<"] : {"<<cpu[idx]<<","<<gpu[idx]<<","<<cpu[idx]-gpu[idx]<<"}"<<endl;
+                cout<<endl;
                 break;
             }
         }
         if (sqrt(sum) > euc_thr) {
             ret = false;
-            std::cout<<std::endl<<"@compareEuclidean: distance mismatch."<<std::endl;
-            std::cout<<"Euclidean distance: "<<sqrt(sum)<<std::endl;
+            cout<<endl<<"@compareEuclidean: distance mismatch."<<endl;
+            cout<<"Euclidean distance: "<<sqrt(sum)<<endl;
         }
         if (ret == false)
             return ret;
@@ -209,11 +214,11 @@ void glohTest(string pTestFile)
         split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
         for (int elIter = 0; elIter < (int)n; elIter++) {
-            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
         }
 
         EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 5.5f));
@@ -238,9 +243,9 @@ void glohTest(string pTestFile)
 #endif
 }
 
-#define GLOH_INIT(desc, image) \
-    TYPED_TEST(GLOH, desc) \
-    {   \
+#define GLOH_INIT(desc, image)                                        \
+    TYPED_TEST(GLOH, desc)                                            \
+    {                                                                 \
         glohTest<TypeParam>(string(TEST_DIR"/gloh/"#image".test"));   \
     }
 
@@ -262,11 +267,11 @@ TEST(GLOH, CPP)
     readImageFeaturesDescriptors<float>(string(TEST_DIR"/gloh/man.test"), inDims, inFiles, goldFeat, goldDesc);
     inFiles[0].insert(0,string(TEST_DIR"/gloh/"));
 
-    af::array in = af::loadImage(inFiles[0].c_str(), false);
+    array in = loadImage(inFiles[0].c_str(), false);
 
-    af::features feat;
-    af::array desc;
-    af::gloh(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f);
+    features feat;
+    array desc;
+    gloh(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f);
 
     float * outX           = new float[feat.getNumFeatures()];
     float * outY           = new float[feat.getNumFeatures()];
@@ -274,7 +279,7 @@ TEST(GLOH, CPP)
     float * outOrientation = new float[feat.getNumFeatures()];
     float * outSize        = new float[feat.getNumFeatures()];
     float * outDesc        = new float[desc.elements()];
-    af::dim4 descDims = desc.dims();
+    dim4 descDims = desc.dims();
     feat.getX().host(outX);
     feat.getY().host(outY);
     feat.getScore().host(outScore);
@@ -300,11 +305,11 @@ TEST(GLOH, CPP)
     split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
     for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
-        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
     }
 
     EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 5.5f));
diff --git a/test/gradient.cpp b/test/gradient.cpp
index 1d09717919..58b1efbbbb 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -20,10 +20,11 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Grad : public ::testing::Test
@@ -48,12 +49,12 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array tempArray = 0;
@@ -61,11 +62,11 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
     af_array g1Array = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_gradient(&g0Array, &g1Array, inArray));
@@ -77,7 +78,7 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter]) << "at: " << elIter << endl;
     }
 
     // Get result
@@ -86,7 +87,7 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter]) << "at: " << elIter << endl;
     }
 
 
@@ -113,6 +114,9 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 
 /////////////////////////////////////// CPP ///////////////////////////////////////////
 //
+
+using af::array;
+
 TEST(Grad, CPP)
 {
     if (noDoubleTests<float>()) return;
@@ -120,16 +124,16 @@ TEST(Grad, CPP)
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, float>(string(TEST_DIR"/grad/grad3D.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
-    af::array input(idims, &(in[0].front()));
-    af::array g0, g1;
-    af::grad(g0, g1, input);
+    array input(idims, &(in[0].front()));
+    array g0, g1;
+    grad(g0, g1, input);
 
     size_t nElems = tests[resultIdx0].size();
     // Get result
@@ -138,7 +142,7 @@ TEST(Grad, CPP)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter]) << "at: " << elIter << endl;
     }
 
     // Get result
@@ -147,7 +151,7 @@ TEST(Grad, CPP)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -157,14 +161,17 @@ TEST(Grad, CPP)
 
 TEST(Grad, MaxDim)
 {
+    using af::constant;
+    using af::sum;
+
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 8 + 1;
 
-    af::array input = af::constant(1, 2, largeDim);
-    af::array g0, g1;
-    af::grad(g0, g1, input);
+    array input = constant(1, 2, largeDim);
+    array g0, g1;
+    grad(g0, g1, input);
 
-    ASSERT_EQ(0.f, af::sum<float>(g0));
-    ASSERT_EQ(0.f, af::sum<float>(g1));
+    ASSERT_EQ(0.f, sum<float>(g0));
+    ASSERT_EQ(0.f, sum<float>(g1));
 }
diff --git a/test/gray_rgb.cpp b/test/gray_rgb.cpp
index ea2096c228..81323860df 100644
--- a/test/gray_rgb.cpp
+++ b/test/gray_rgb.cpp
@@ -14,13 +14,17 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::vector;
+using af::array;
+using af::randu;
+
 TEST(rgb_gray, 32bit)
 {
-    af::array rgb = af::randu(10, 10, 3);
-    af::array gray = af::rgb2gray(rgb);
+    array rgb = randu(10, 10, 3);
+    array gray = rgb2gray(rgb);
 
-    std::vector<float> h_rgb(rgb.elements());
-    std::vector<float> h_gray(gray.elements());
+    vector<float> h_rgb(rgb.elements());
+    vector<float> h_gray(gray.elements());
 
     rgb.host(&h_rgb[0]);
     gray.host(&h_gray[0]);
@@ -46,11 +50,11 @@ TEST(rgb_gray, 32bit)
 
 TEST(rgb_gray, 8bit)
 {
-    af::array rgb = af::randu(10, 10, 3, u8);
-    af::array gray = af::rgb2gray(rgb);
+    array rgb = randu(10, 10, 3, u8);
+    array gray = rgb2gray(rgb);
 
-    std::vector<uchar> h_rgb(rgb.elements());
-    std::vector<float> h_gray(gray.elements());
+    vector<uchar> h_rgb(rgb.elements());
+    vector<float> h_gray(gray.elements());
 
     rgb.host(&h_rgb[0]);
     gray.host(&h_gray[0]);
@@ -76,15 +80,15 @@ TEST(rgb_gray, 8bit)
 
 TEST(gray_rgb, 32bit)
 {
-    af::array gray = af::randu(10, 10);
+    array gray = randu(10, 10);
 
     const float rPercent=0.33f;
     const float gPercent=0.34f;
     const float bPercent=0.33f;
 
-    af::array rgb = af::gray2rgb(gray, rPercent, gPercent, bPercent);
-    std::vector<float> h_rgb(rgb.elements());
-    std::vector<float> h_gray(gray.elements());
+    array rgb = gray2rgb(gray, rPercent, gPercent, bPercent);
+    vector<float> h_rgb(rgb.elements());
+    vector<float> h_gray(gray.elements());
 
     int num = gray.elements();
     int roff = 0;
@@ -107,11 +111,11 @@ TEST(gray_rgb, 32bit)
 TEST(rgb_gray, MaxDim)
 {
     size_t largeDim = 65535 * 32 + 1;
-    af::array rgb = af::randu(1, largeDim, 3, u8);
-    af::array gray = af::rgb2gray(rgb);
+    array rgb = randu(1, largeDim, 3, u8);
+    array gray = rgb2gray(rgb);
 
-    std::vector<uchar> h_rgb(rgb.elements());
-    std::vector<float> h_gray(gray.elements());
+    vector<uchar> h_rgb(rgb.elements());
+    vector<float> h_gray(gray.elements());
 
     rgb.host(&h_rgb[0]);
     gray.host(&h_gray[0]);
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 5b359b74d7..528a484442 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -15,10 +15,12 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::vector;
 using std::string;
 using af::cfloat;
 using af::cdouble;
+using af::dtype_traits;
 
 template<typename T>
 class HammingMatcher8  : public ::testing::Test
@@ -67,9 +69,9 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     af_array dist  = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&query, &(in[0].front()),
-                qDims.ndims(), qDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                qDims.ndims(), qDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&train, &(in[1].front()),
-                tDims.ndims(), tDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_hamming_matcher(&idx, &dist, query, train, feat_dim, 1));
 
@@ -83,7 +85,7 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDist, dist));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outIdx;
@@ -146,7 +148,7 @@ TEST(HammingMatcher, CPP)
     dist.host(outDist);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outIdx;
diff --git a/test/harris.cpp b/test/harris.cpp
index 7d9e428363..46152d4591 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -18,6 +18,7 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::endl;
 using std::string;
 using std::vector;
 using std::abs;
@@ -124,11 +125,11 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
         for (int elIter = 0; elIter < (int)nElems; elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
         }
 
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
@@ -138,9 +139,9 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
     }
 }
 
-#define HARRIS_INIT(desc, image, sigma, block_size) \
-    TYPED_TEST(Harris, desc) \
-    {   \
+#define HARRIS_INIT(desc, image, sigma, block_size)                     \
+    TYPED_TEST(Harris, desc)                                            \
+    {                                                                   \
         harrisTest<TypeParam>(string(TEST_DIR"/harris/"#image"_"#sigma"_"#block_size".test"), sigma, block_size); \
     }
 
@@ -155,6 +156,11 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
 
 /////////////////////////////////// CPP ////////////////////////////////
 
+using af::array;
+using af::features;
+using af::harris;
+using af::loadImage;
+
 TEST(FloatHarris, CPP)
 {
     if (noDoubleTests<float>()) return;
@@ -167,9 +173,9 @@ TEST(FloatHarris, CPP)
     readImageTests(string(TEST_DIR"/harris/square_0_3.test"), inDims, inFiles, gold);
     inFiles[0].insert(0,string(TEST_DIR"/harris/"));
 
-    af::array in = af::loadImage(inFiles[0].c_str(), false);
+    array in = loadImage(inFiles[0].c_str(), false);
 
-    af::features out = harris(in, 500, 1e5f, 0.0f, 3, 0.04f);
+    features out = harris(in, 500, 1e5f, 0.0f, 3, 0.04f);
 
     vector<float> outX           (gold[0].size());
     vector<float> outY           (gold[1].size());
@@ -196,10 +202,10 @@ TEST(FloatHarris, CPP)
     std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
     for (unsigned elIter = 0; elIter < out.getNumFeatures(); elIter++) {
-        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
     }
 }
diff --git a/test/histogram.cpp b/test/histogram.cpp
index 7774abf6f8..a74b0fa6be 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -16,9 +16,14 @@
 #include <iostream>
 #include <testHelpers.hpp>
 
+using std::abs;
+using std::cout;
+using std::endl;
+using std::ostream_iterator;
 using std::string;
 using std::vector;
-using std::abs;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Histogram : public ::testing::Test
@@ -39,21 +44,21 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<inType> >  in;
     vector<vector<outType> > tests;
     readTests<inType,uint,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array outArray   = 0;
     af_array inArray    = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<inType>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<inType>::af_type));
 
     ASSERT_EQ(AF_SUCCESS,af_histogram(&outArray,inArray,nbins,minval,maxval));
 
-    std::vector<outType> outData(dims.elements());
+    vector<outType> outData(dims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
@@ -61,7 +66,7 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
         vector<outType> currGoldBar = tests[testIter];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< std::endl;
+            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
         }
     }
 
@@ -97,6 +102,16 @@ TYPED_TEST(Histogram,256Bins0min255max_zeros)
 
 /////////////////////////////////// CPP //////////////////////////////////
 //
+using af::array;
+using af::constant;
+using af::histogram;
+using af::max;
+using af::randu;
+using af::range;
+using af::round;
+using af::seq;
+using af::span;
+
 TEST(Histogram, CPP)
 {
     if (noDoubleTests<float>()) return;
@@ -106,25 +121,25 @@ TEST(Histogram, CPP)
     const double minval = 0.0;
     const double maxval = 99.0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >  in;
     vector<vector<uint> > tests;
     readTests<float,uint,int>(string(TEST_DIR"/histogram/100bin0min99max.test"),numDims,in,tests);
 
 //! [hist_nominmax]
-    af::array input(numDims[0], &(in[0].front()));
-    af::array output = histogram(input, nbins, minval, maxval);
+    array input(numDims[0], &(in[0].front()));
+    array output = histogram(input, nbins, minval, maxval);
 //! [hist_nominmax]
 
-    std::vector<uint> outData(output.elements());
+    vector<uint> outData(output.elements());
     output.host((void*)outData.data());
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<uint> currGoldBar = tests[testIter];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< std::endl;
+            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
         }
     }
 }
@@ -133,12 +148,6 @@ TEST(Histogram, CPP)
 //
 TEST(Histogram, SNIPPET_hist_nominmax)
 {
-    using af::array;
-    using af::histogram;
-    using std::ostream_iterator;
-    using std::cout;
-    using std::endl;
-
     unsigned output[] = {3, 1, 2, 0, 0, 0, 0, 1, 1, 1};
 
     //! [ex_image_hist_nominmax]
@@ -166,12 +175,6 @@ TEST(Histogram, SNIPPET_hist_nominmax)
 
 TEST(Histogram, SNIPPET_hist_minmax)
 {
-    using af::array;
-    using af::histogram;
-    using std::ostream_iterator;
-    using std::cout;
-    using std::endl;
-
     unsigned output[] = {0, 3, 1, 2, 0, 0, 1, 1, 1, 0};
 
     //! [ex_image_hist_minmax]
@@ -199,12 +202,6 @@ TEST(Histogram, SNIPPET_hist_minmax)
 
 TEST(Histogram, SNIPPET_histequal)
 {
-    using af::array;
-    using af::histogram;
-    using std::ostream_iterator;
-    using std::cout;
-    using std::endl;
-
     float output[] = { 1.5, 4.5,  1.5, 1.5, 4.5, 4.5, 6.0, 7.5, 4.5 };
 
     //! [ex_image_histequal]
@@ -236,8 +233,6 @@ TEST(Histogram, SNIPPET_histequal)
 
 TEST(histogram, GFOR)
 {
-    using namespace af;
-
     dim4 dims = dim4(100, 100, 3);
     array A = round(100 * randu(dims));
     array B = constant(0, 100, 1, 3);
@@ -255,8 +250,6 @@ TEST(histogram, GFOR)
 
 TEST(histogram, IndexedArray)
 {
-    using namespace af;
-
     const dim_t LEN = 32;
     array A = range(LEN, (dim_t)2);
     for (int i=16; i<28; ++i) {
@@ -278,14 +271,14 @@ TEST(histogram, LargeBins)
     const int min_val = 0;
     const int nbins = max_val / 2;
     const int num = 1 << 20;
-    af::array A = af::round(max_val * af::randu(num) + min_val).as(u32);
-    af::eval(A);
-    af::array H = histogram(A, nbins, min_val, max_val);
+    array A = round(max_val * randu(num) + min_val).as(u32);
+    eval(A);
+    array H = histogram(A, nbins, min_val, max_val);
 
-    std::vector<unsigned> hA(num);
+    vector<unsigned> hA(num);
     A.host(hA.data());
 
-    std::vector<unsigned> hH(nbins);
+    vector<unsigned> hH(nbins);
     H.host(hH.data());
 
     int dx = (max_val - min_val) / nbins;
diff --git a/test/homography.cpp b/test/homography.cpp
index 797515f4d0..7023e849b7 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -18,9 +18,11 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::endl;
 using std::string;
 using std::vector;
 using std::abs;
+using af::array;
 using af::dim4;
 
 template<typename T>
@@ -35,7 +37,7 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(Homography, TestTypes);
 
 template<typename T>
-af::array perspectiveTransform(af::dim4 inDims, af::array H)
+array perspectiveTransform(dim4 inDims, array H)
 {
     T d0 = (T)inDims[0];
     T d1 = (T)inDims[1];
@@ -46,6 +48,9 @@ template<typename T>
 void homographyTest(string pTestFile, const af_homography_type htype,
                     const bool rotate, const float size_ratio)
 {
+    using af::Pi;
+    using af::dtype_traits;
+
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
@@ -89,7 +94,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     af_array query_feat_y_idx = 0;
     af_features query_feat;
 
-    const float theta = af::Pi * 0.5f;
+    const float theta = Pi * 0.5f;
     const dim_t test_d0 = inDims[0][0] * size_ratio;
     const dim_t test_d1 = inDims[0][1] * size_ratio;
     const dim_t tDims[] = {test_d0, test_d1};
@@ -135,11 +140,11 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     int inliers = 0;
     ASSERT_EQ(AF_SUCCESS, af_homography(&H, &inliers, train_feat_x_idx, train_feat_y_idx,
                                         query_feat_x_idx, query_feat_y_idx, htype,
-                                        3.0f, 1000, (af_dtype) af::dtype_traits<T>::af_type));
+                                        3.0f, 1000, (af_dtype) dtype_traits<T>::af_type));
 
-    af::array HH(H);
+    array HH(H);
 
-    af::array t = perspectiveTransform<T>(inDims[0], HH);
+    array t = perspectiveTransform<T>(inDims[0], HH);
 
     T* gold_t = new T[8];
     for (int i = 0; i < 8; i++)
@@ -161,7 +166,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 
     for (int elIter = 0; elIter < 8; elIter++) {
         ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.25f)
-            << "at: " << elIter << std::endl;
+            << "at: " << elIter << endl;
     }
 
     delete[] gold_t;
@@ -204,6 +209,10 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
+
+using af::features;
+using af::loadImage;
+
 TEST(Homography, CPP)
 {
     if (noImageIOTests()) return;
@@ -218,35 +227,35 @@ TEST(Homography, CPP)
 
     const float size_ratio = 0.5f;
 
-    af::array train_img = af::loadImage(inFiles[0].c_str(), false);
-    af::array query_img = af::resize(size_ratio, train_img);
-    af::dim4 tDims = train_img.dims();
+    array train_img = loadImage(inFiles[0].c_str(), false);
+    array query_img = resize(size_ratio, train_img);
+    dim4 tDims = train_img.dims();
 
-    af::features feat_train, feat_query;
-    af::array desc_train, desc_query;
+    features feat_train, feat_query;
+    array desc_train, desc_query;
     orb(feat_train, desc_train, train_img, 20, 2000, 1.2, 8, true);
     orb(feat_query, desc_query, query_img, 20, 2000, 1.2, 8, true);
 
-    af::array idx, dist;
-    af::hammingMatcher(idx, dist, desc_train, desc_query, 0, 1);
+    array idx, dist;
+    hammingMatcher(idx, dist, desc_train, desc_query, 0, 1);
 
-    af::array train_idx = where(dist < 30);
-    af::array query_idx = idx(train_idx);
+    array train_idx = where(dist < 30);
+    array query_idx = idx(train_idx);
 
-    af::array feat_train_x = feat_train.getX()(train_idx);
-    af::array feat_train_y = feat_train.getY()(train_idx);
-    af::array feat_train_score = feat_train.getScore()(train_idx);
-    af::array feat_train_orientation = feat_train.getOrientation()(train_idx);
-    af::array feat_train_size = feat_train.getSize()(train_idx);
-    af::array feat_query_x = feat_query.getX()(query_idx);
-    af::array feat_query_y = feat_query.getY()(query_idx);
-    af::array feat_query_score = feat_query.getScore()(query_idx);
-    af::array feat_query_orientation = feat_query.getOrientation()(query_idx);
-    af::array feat_query_size = feat_query.getSize()(query_idx);
+    array feat_train_x = feat_train.getX()(train_idx);
+    array feat_train_y = feat_train.getY()(train_idx);
+    array feat_train_score = feat_train.getScore()(train_idx);
+    array feat_train_orientation = feat_train.getOrientation()(train_idx);
+    array feat_train_size = feat_train.getSize()(train_idx);
+    array feat_query_x = feat_query.getX()(query_idx);
+    array feat_query_y = feat_query.getY()(query_idx);
+    array feat_query_score = feat_query.getScore()(query_idx);
+    array feat_query_orientation = feat_query.getOrientation()(query_idx);
+    array feat_query_size = feat_query.getSize()(query_idx);
 
-    af::array H;
+    array H;
     int inliers = 0;
-    af::homography(H, inliers, feat_train_x, feat_train_y, feat_query_x, feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
+    homography(H, inliers, feat_train_x, feat_train_y, feat_query_x, feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
 
     float* gold_t = new float[8];
     for (int i = 0; i < 8; i++)
@@ -256,14 +265,14 @@ TEST(Homography, CPP)
     gold_t[5] = tDims[0] * size_ratio;
     gold_t[6] = tDims[0] * size_ratio;
 
-    af::array t = perspectiveTransform<float>(train_img.dims(), H);
+    array t = perspectiveTransform<float>(train_img.dims(), H);
 
     float* out_t = new float[4*2];
     t.host(out_t);
 
     for (int elIter = 0; elIter < 8; elIter++) {
         ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.1f)
-            << "at: " << elIter << std::endl;
+            << "at: " << elIter << endl;
     }
 
     delete[] gold_t;
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index a7e60d3a3c..258c64bb46 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -14,20 +14,25 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
+using af::dim4;
+using af::exception;
+using af::hsv2rgb;
 
 TEST(hsv_rgb, InvalidArray)
 {
     vector<float> in(100, 1);
 
-    af::dim4 dims(100);
-    af::array input(dims, &(in.front()));
+    dim4 dims(100);
+    array input(dims, &(in.front()));
 
     try {
-        af::array output = af::hsv2rgb(input);
+        array output = hsv2rgb(input);
         ASSERT_EQ(true, false);
-    } catch(af::exception) {
+    } catch(exception) {
         ASSERT_EQ(true, true);
         return;
     }
@@ -35,64 +40,64 @@ TEST(hsv_rgb, InvalidArray)
 
 TEST(hsv2rgb, CPP)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/hsv2rgb.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::hsv2rgb(input);
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = hsv2rgb(input);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 }
 
 TEST(rgb2hsv, CPP)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/rgb2hsv.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::rgb2hsv(input);
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = rgb2hsv(input);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 }
 
 TEST(rgb2hsv, MaxDim)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/rgb2hsv.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
     unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = af::tile(input, 1, ntile);
-    af::array output = af::rgb2hsv(input);
-    af::dim4 outDims = output.dims();
+    input = tile(input, 1, ntile);
+    array output = rgb2hsv(input);
+    dim4 outDims = output.dims();
 
     float *outData = new float[outDims.elements()];
     output.host((void*)outData);
@@ -103,7 +108,7 @@ TEST(rgb2hsv, MaxDim)
             for(int x=0; x<outDims[0]; ++x) {
                 int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
                 int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
             }
         }
     }
@@ -114,20 +119,20 @@ TEST(rgb2hsv, MaxDim)
 
 TEST(hsv2rgb, MaxDim)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/hsv2rgb.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
     unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = af::tile(input, 1, ntile);
-    af::array output = af::hsv2rgb(input);
-    af::dim4 outDims = output.dims();
+    input = tile(input, 1, ntile);
+    array output = hsv2rgb(input);
+    dim4 outDims = output.dims();
 
     float *outData = new float[outDims.elements()];
     output.host((void*)outData);
@@ -138,7 +143,7 @@ TEST(hsv2rgb, MaxDim)
             for(int x=0; x<outDims[0]; ++x) {
                 int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
                 int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
             }
         }
     }
diff --git a/test/iir.cpp b/test/iir.cpp
index 550f184fa8..d6b1edce5b 100644
--- a/test/iir.cpp
+++ b/test/iir.cpp
@@ -17,9 +17,17 @@
 
 using std::vector;
 using std::string;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::convolve1;
 using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::exception;
+using af::iir;
+using af::fir;
+using af::randu;
 
 template<typename T>
 class filter : public ::testing::Test
@@ -38,12 +46,12 @@ void firTest(const int xrows, const int xcols, const int brows, const int bcols)
 {
     if (noDoubleTests<T>()) return;
     try {
-        af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
-        af::array x = af::randu(xrows, xcols, ty);
-        af::array b = af::randu(brows, bcols, ty);
+        dtype ty = (dtype)dtype_traits<T>::af_type;
+        array x = randu(xrows, xcols, ty);
+        array b = randu(brows, bcols, ty);
 
-        af::array y = af::fir(b, x);
-        af::array c = af::convolve1(x, b, AF_CONV_EXPAND);
+        array y = fir(b, x);
+        array c = convolve1(x, b, AF_CONV_EXPAND);
 
         const int ycols = xcols * bcols;
         const int crows = xrows + brows - 1;
@@ -61,7 +69,7 @@ void firTest(const int xrows, const int xcols, const int brows, const int bcols)
                             real(hc[j * crows + i]), 0.01);
             }
         }
-    } catch (af::exception &ex) {
+    } catch (exception &ex) {
         FAIL() << ex.what();
     }
 }
@@ -91,14 +99,14 @@ void iirA0Test(const int xrows, const int xcols, const int brows, const int bcol
 {
     if (noDoubleTests<T>()) return;
     try {
-        af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
-        af::array x = af::randu(xrows, xcols, ty);
-        af::array b = af::randu(brows, bcols, ty);
-        af::array a = af::randu(    1, bcols, ty);
-        af::array bNorm = b / tile(a, brows);
+        dtype ty = (dtype)dtype_traits<T>::af_type;
+        array x = randu(xrows, xcols, ty);
+        array b = randu(brows, bcols, ty);
+        array a = randu(    1, bcols, ty);
+        array bNorm = b / tile(a, brows);
 
-        af::array y = af::iir(b, a, x);
-        af::array c = af::convolve1(x, bNorm, AF_CONV_EXPAND);
+        array y = iir(b, a, x);
+        array c = convolve1(x, bNorm, AF_CONV_EXPAND);
 
         const int ycols = xcols * bcols;
         const int crows = xrows + brows - 1;
@@ -116,7 +124,7 @@ void iirA0Test(const int xrows, const int xcols, const int brows, const int bcol
                             real(hc[j * crows + i]), 0.01);
             }
         }
-    } catch (af::exception &ex) {
+    } catch (exception &ex) {
         FAIL() << ex.what();
     }
 }
@@ -145,29 +153,29 @@ template<typename T>
 void iirTest(const char *testFile)
 {
     if (noDoubleTests<T>()) return;
-    vector<af::dim4> inDims;
+    vector<dim4> inDims;
 
     vector<vector<T> > inputs;
     vector<vector<T> > outputs;
     readTests<T, T, float> (testFile, inDims, inputs, outputs);
 
     try {
-        af::array a = af::array(inDims[0], &inputs[0][0]);
-        af::array b = af::array(inDims[1], &inputs[1][0]);
-        af::array x = af::array(inDims[2], &inputs[2][0]);
+        array a = array(inDims[0], &inputs[0][0]);
+        array b = array(inDims[1], &inputs[1][0]);
+        array x = array(inDims[2], &inputs[2][0]);
 
-        af::array y = af::iir(b, a, x);
-        std::vector<T> gold = outputs[0];
+        array y = iir(b, a, x);
+        vector<T> gold = outputs[0];
         ASSERT_EQ(gold.size(), (size_t)y.elements());
 
-        std::vector<T> out(y.elements());
+        vector<T> out(y.elements());
         y.host(&out[0]);
 
         for(size_t i = 0; i < gold.size(); i++) {
             ASSERT_NEAR(real(out[i]), real(gold[i]), 0.01) << "at: " << i;
         }
 
-    } catch (af::exception &ex) {
+    } catch (exception &ex) {
         FAIL() << ex.what();
     }
 }
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 4029de5a1b..27b8668f6f 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -16,12 +16,13 @@
 #include <string>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::vector;
 using std::string;
-using std::cout;
-using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
 
 template<typename T>
 class ImageIO : public ::testing::Test
@@ -41,12 +42,12 @@ void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array imgArray = 0;
     ASSERT_EQ(AF_SUCCESS, af_load_image(&imgArray, pImageFile.c_str(), isColor));
@@ -56,7 +57,7 @@ void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*) imgData, imgArray));
 
     bool isJPEG = false;
-    if(pImageFile.find(".jpg") != std::string::npos) {
+    if(pImageFile.find(".jpg") != string::npos) {
         isJPEG = true;
     }
 
@@ -64,9 +65,9 @@ void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
     size_t nElems = in[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         if(isJPEG)  // Allow +- 1 because of compression when testing JPG
-            ASSERT_NEAR(in[0][elIter], imgData[elIter], 1) << "at: " << elIter << std::endl;
+            ASSERT_NEAR(in[0][elIter], imgData[elIter], 1) << "at: " << elIter << endl;
         else
-            ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -117,19 +118,27 @@ TYPED_TEST(ImageIO,InvalidArgsWrongExt)
 }
 
 ////////////////////////////////// CPP //////////////////////////////////////
+
+using af::anyTrue;
+using af::deleteImageMem;
+using af::loadImage;
+using af::loadImageMem;
+using af::saveImageMem;
+using af::span;
+
 TEST(ImageIO, CPP)
 {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/imageio/color_small.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
-    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_small.png").c_str(), true);
+    dim4 dims = numDims[0];
+    array img = loadImage(string(TEST_DIR"/imageio/color_small.png").c_str(), true);
 
     // Get result
     float *imgData = new float[dims.elements()];
@@ -138,7 +147,7 @@ TEST(ImageIO, CPP)
     // Compare result
     size_t nElems = in[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -149,36 +158,36 @@ TEST(ImageIO, SavePNGCPP) {
 
     if (noImageIOTests()) return;
 
-    af::array input(10, 10, 3, f32);
+    array input(10, 10, 3, f32);
 
-    input(af::span, af::span, af::span) = 0;
+    input(span, span, span) = 0;
     input(0, 0, 0) = 255;
     input(0, 9, 1) = 255;
     input(9, 0, 2) = 255;
-    input(9, 9, af::span) = 255;
+    input(9, 9, span) = 255;
 
     saveImage("SaveCPP.png", input);
-    af::array out = af::loadImage("SaveCPP.png", true);
+    array out = loadImage("SaveCPP.png", true);
 
-    ASSERT_FALSE(af::anyTrue<bool>(out - input));
+    ASSERT_FALSE(anyTrue<bool>(out - input));
 }
 
 TEST(ImageIO, SaveBMPCPP) {
 
     if (noImageIOTests()) return;
 
-    af::array input(10, 10, 3, f32);
+    array input(10, 10, 3, f32);
 
-    input(af::span, af::span, af::span) = 0;
+    input(span, span, span) = 0;
     input(0, 0, 0) = 255;
     input(0, 9, 1) = 255;
     input(9, 0, 2) = 255;
-    input(9, 9, af::span) = 255;
+    input(9, 9, span) = 255;
 
     saveImage("SaveCPP.bmp", input);
-    af::array out = af::loadImage("SaveCPP.bmp", true);
+    array out = loadImage("SaveCPP.bmp", true);
 
-    ASSERT_FALSE(af::anyTrue<bool>(out - input));
+    ASSERT_FALSE(anyTrue<bool>(out - input));
 }
 
 TEST(ImageMem, SaveMemPNG)
@@ -186,15 +195,15 @@ TEST(ImageMem, SaveMemPNG)
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
+    array img = loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
 
-    void* savedMem = af::saveImageMem(img, AF_FIF_PNG);
+    void* savedMem = saveImageMem(img, AF_FIF_PNG);
 
-    af::array loadMem = af::loadImageMem(savedMem);
+    array loadMem = loadImageMem(savedMem);
 
-    ASSERT_FALSE(af::anyTrue<bool>(img - loadMem));
+    ASSERT_FALSE(anyTrue<bool>(img - loadMem));
 
-    af::deleteImageMem(savedMem);
+    deleteImageMem(savedMem);
 }
 
 TEST(ImageMem, SaveMemJPG1)
@@ -202,17 +211,17 @@ TEST(ImageMem, SaveMemJPG1)
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), false);
-    af::saveImage("color_seq1.jpg", img);
+    array img = loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), false);
+    saveImage("color_seq1.jpg", img);
 
-    void* savedMem = af::saveImageMem(img, AF_FIF_JPEG);
+    void* savedMem = saveImageMem(img, AF_FIF_JPEG);
 
-    af::array loadMem = af::loadImageMem(savedMem);
-    af::array imgJPG = af::loadImage("color_seq1.jpg", false);
+    array loadMem = loadImageMem(savedMem);
+    array imgJPG = loadImage("color_seq1.jpg", false);
 
-    ASSERT_FALSE(af::anyTrue<bool>(imgJPG - loadMem));
+    ASSERT_FALSE(anyTrue<bool>(imgJPG - loadMem));
 
-    af::deleteImageMem(savedMem);
+    deleteImageMem(savedMem);
 }
 
 TEST(ImageMem, SaveMemJPG3)
@@ -220,17 +229,17 @@ TEST(ImageMem, SaveMemJPG3)
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
-    af::saveImage("color_seq3.jpg", img);
+    array img = loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
+    saveImage("color_seq3.jpg", img);
 
-    void* savedMem = af::saveImageMem(img, AF_FIF_JPEG);
+    void* savedMem = saveImageMem(img, AF_FIF_JPEG);
 
-    af::array loadMem = af::loadImageMem(savedMem);
-    af::array imgJPG = af::loadImage("color_seq3.jpg", true);
+    array loadMem = loadImageMem(savedMem);
+    array imgJPG = loadImage("color_seq3.jpg", true);
 
-    ASSERT_FALSE(af::anyTrue<bool>(imgJPG - loadMem));
+    ASSERT_FALSE(anyTrue<bool>(imgJPG - loadMem));
 
-    af::deleteImageMem(savedMem);
+    deleteImageMem(savedMem);
 }
 
 TEST(ImageMem, SaveMemBMP)
@@ -238,30 +247,30 @@ TEST(ImageMem, SaveMemBMP)
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_rand.png").c_str(), true);
+    array img = loadImage(string(TEST_DIR"/imageio/color_rand.png").c_str(), true);
 
-    void* savedMem = af::saveImageMem(img, AF_FIF_BMP);
+    void* savedMem = saveImageMem(img, AF_FIF_BMP);
 
-    af::array loadMem = af::loadImageMem(savedMem);
+    array loadMem = loadImageMem(savedMem);
 
-    ASSERT_FALSE(af::anyTrue<bool>(img - loadMem));
+    ASSERT_FALSE(anyTrue<bool>(img - loadMem));
 
-    af::deleteImageMem(savedMem);
+    deleteImageMem(savedMem);
 }
 
 TEST(ImageIO, LoadImage16CPP)
 {
     if (noImageIOTests()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/imageio/color_seq_16.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
-    af::array img = af::loadImage(string(TEST_DIR"/imageio/color_seq_16.png").c_str(), true);
+    array img = loadImage(string(TEST_DIR"/imageio/color_seq_16.png").c_str(), true);
     ASSERT_EQ(img.type(), f32); // loadImage should always return float
 
     // Get result
@@ -271,7 +280,7 @@ TEST(ImageIO, LoadImage16CPP)
     // Compare result
     size_t nElems = in[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -282,37 +291,41 @@ TEST(ImageIO, SaveImage16CPP)
 {
     if (noImageIOTests()) return;
 
-    af::dim4 dims(16, 24, 3);
+    dim4 dims(16, 24, 3);
 
-    af::array input = af::randu(dims, u16);
-    af::array input_255 = (input / 257).as(u16);
+    array input = randu(dims, u16);
+    array input_255 = (input / 257).as(u16);
 
-    af::saveImage("saveImage16CPP.png", input);
+    saveImage("saveImage16CPP.png", input);
 
-    af::array img = af::loadImage("saveImage16CPP.png", true);
+    array img = loadImage("saveImage16CPP.png", true);
     ASSERT_EQ(img.type(), f32); // loadImage should always return float
 
-    ASSERT_FALSE(af::anyTrue<bool>(abs(img - input_255)));
+    ASSERT_FALSE(anyTrue<bool>(abs(img - input_255)));
 }
 
 ////////////////////////////////////////////////////////////////////////////////
 // Image IO Native Tests
 ////////////////////////////////////////////////////////////////////////////////
 
+using af::dtype_traits;
+using af::loadImageNative;
+using af::saveImageNative;
+
 template<typename T>
 void loadImageNativeCPPTest(string pTestFile, string pImageFile)
 {
     if (noImageIOTests()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
-    af::array img = af::loadImageNative(pImageFile.c_str());
-    ASSERT_EQ(img.type(), (af_dtype)af::dtype_traits<T>::af_type);
+    dim4 dims = numDims[0];
+    array img = loadImageNative(pImageFile.c_str());
+    ASSERT_EQ(img.type(), (af_dtype)dtype_traits<T>::af_type);
 
     // Get result
     T *imgData = new T[dims.elements()];
@@ -321,7 +334,7 @@ void loadImageNativeCPPTest(string pTestFile, string pImageFile)
     // Compare result
     size_t nElems = in[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -353,36 +366,36 @@ TEST(ImageIONative, LoadImageNative16GrayCPP)
 }
 
 template<typename T>
-void saveLoadImageNativeCPPTest(af::dim4 dims)
+void saveLoadImageNativeCPPTest(dim4 dims)
 {
     if (noImageIOTests()) return;
 
-    af::array input = af::randu(dims, (af_dtype)af::dtype_traits<T>::af_type);
+    array input = randu(dims, (af_dtype)dtype_traits<T>::af_type);
 
-    af::saveImageNative("saveImageNative.png", input);
+    saveImageNative("saveImageNative.png", input);
 
-    af::array loaded = af::loadImageNative("saveImageNative.png");
+    array loaded = loadImageNative("saveImageNative.png");
     ASSERT_EQ(loaded.type(), input.type());
 
-    ASSERT_FALSE(af::anyTrue<bool>(input - loaded));
+    ASSERT_FALSE(anyTrue<bool>(input - loaded));
 }
 
 TEST(ImageIONative, SaveLoadImageNative8CPP)
 {
-    saveLoadImageNativeCPPTest<uchar>(af::dim4(480, 720, 3, 1));
+    saveLoadImageNativeCPPTest<uchar>(dim4(480, 720, 3, 1));
 }
 
 TEST(ImageIONative, SaveLoadImageNative16SmallCPP)
 {
-    saveLoadImageNativeCPPTest<ushort>(af::dim4(8, 12, 3, 1));
+    saveLoadImageNativeCPPTest<ushort>(dim4(8, 12, 3, 1));
 }
 
 TEST(ImageIONative, SaveLoadImageNative16ColorCPP)
 {
-    saveLoadImageNativeCPPTest<ushort>(af::dim4(480, 720, 3, 1));
+    saveLoadImageNativeCPPTest<ushort>(dim4(480, 720, 3, 1));
 }
 
 TEST(ImageIONative, SaveLoadImageNative16GrayCPP)
 {
-    saveLoadImageNativeCPPTest<ushort>(af::dim4(24, 32, 1, 1));
+    saveLoadImageNativeCPPTest<ushort>(dim4(24, 32, 1, 1));
 }
diff --git a/test/index.cpp b/test/index.cpp
index bec8006045..5ca08d9d0d 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -23,12 +23,13 @@
 
 using std::vector;
 using std::string;
-using std::generate;
 using std::cout;
 using std::endl;
 using std::ostream_iterator;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
 using af::dtype_traits;
-using af::freeHost;
 
 template<typename T, typename OP>
 void
@@ -126,7 +127,7 @@ class Indexing1D : public ::testing::Test
     vector<af_seq> span_seqs;
 };
 
-typedef ::testing::Types<float, double, af::cfloat, af::cdouble, int, unsigned, unsigned char, intl, uintl, short, ushort> AllTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned, unsigned char, intl, uintl, short, ushort> AllTypes;
 TYPED_TEST_CASE(Indexing1D, AllTypes);
 
 TYPED_TEST(Indexing1D, Continious)          { DimCheck<TypeParam>(this->continuous_seqs);           }
@@ -258,15 +259,15 @@ DimCheck2D(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDims)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
     readTests<T,T,int>(TestFile, numDims, hData, tests);
-    af::dim4 dimensions = numDims[0];
+    dim4 dimensions = numDims[0];
 
     af_array a = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(hData[0].front()), NDims, dimensions.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(hData[0].front()), NDims, dimensions.get(), (af_dtype) dtype_traits<T>::af_type));
 
     vector<af_array> indexed_arrays(seqs.size(), 0);
     for(size_t i = 0; i < seqs.size(); i++) {
@@ -551,24 +552,39 @@ TEST(Index, Docs_Util_C_API)
 }
 
 //////////////////////////////// CPP ////////////////////////////////
+
+
+using af::allTrue;
+using af::array;
+using af::constant;
+using af::deviceGC;
+using af::deviceMemInfo;
+using af::end;
+using af::freeHost;
+using af::randu;
+using af::range;
+using af::reorder;
+using af::seq;
+using af::span;
+using af::where;
+
+
 TEST(Indexing2D, ColumnContiniousCPP)
 {
     if (noDoubleTests<float>()) return;
 
-    using af::array;
-
     vector<vector<af_seq> > seqs;
 
     seqs.push_back(make_vec(af_span, af_make_seq(  0,  6,  1)));
     //seqs.push_back(make_vec(span, af_make_seq(  4,  9,  1)));
     //seqs.push_back(make_vec(span, af_make_seq(  3,  8,  1)));
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> > hData;
     vector<vector<float> > tests;
     readTests<float, float, int>(TEST_DIR"/index/ColumnContinious.test", numDims, hData, tests);
-    af::dim4 dimensions = numDims[0];
+    dim4 dimensions = numDims[0];
 
     array a(dimensions,&(hData[0].front()));
 
@@ -612,23 +628,23 @@ void arrayIndexTest(string pTestFile, int dim)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<T> >      in;
     vector<vector<T> >   tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array idxArray  = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_lookup(&outArray, inArray, idxArray, dim));
 
@@ -639,7 +655,7 @@ void arrayIndexTest(string pTestFile, int dim)
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outData;
@@ -670,16 +686,14 @@ TYPED_TEST(lookup, Dim3)
 
 TEST(lookup, CPP)
 {
-    using af::array;
-
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTests<float, float, int>(string(TEST_DIR"/arrayindex/dim0.test"), numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
 
     array input(dims0, &(in[0].front()));
     array indices(dims1, &(in[1].front()));
@@ -692,7 +706,7 @@ TEST(lookup, CPP)
     output.host((void*)outData);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outData;
@@ -700,35 +714,32 @@ TEST(lookup, CPP)
 
 TEST(lookup, largeDim)
 {
-    using af::array;
     const size_t largeDim = 65535 * 8 + 1;
 
     cleanSlate();
-    af::array input   = af::range(af::dim4(2, largeDim));
-    af::array indices = af::constant(1, 100);
+    array input   = range(dim4(2, largeDim));
+    array indices = constant(1, 100);
 
-    af::array output = af::lookup(input, indices);
+    array output = af::lookup(input, indices);
 }
 
 TEST(lookup, Issue2009)
 {
-    af::array a   = af::range(af::dim4(1000, 1));
-    af::array idx = af::constant(0, 1, u32);
-    af::array b   = af::lookup(a, idx, 1);
+    array a   = range(dim4(1000, 1));
+    array idx = constant(0, 1, u32);
+    array b   = af::lookup(a, idx, 1);
 
-    ASSERT_EQ(true, af::allTrue<bool>(a==b));
+    ASSERT_EQ(true, allTrue<bool>(a==b));
 }
 
 TEST(SeqIndex, CPP_END)
 {
-    using af::array;
-
     const int n = 5;
     const int m = 5;
     const int end_off = 2;
 
-    array a = af::randu(n, m);
-    array b = a(af::end - end_off, af::span);
+    array a = randu(n, m);
+    array b = a(end - end_off, span);
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
@@ -745,14 +756,12 @@ TEST(SeqIndex, CPP_END)
 
 TEST(SeqIndex, CPP_END_SEQ)
 {
-    using af::array;
-
     const int num = 20;
     const int end_begin = 10;
     const int end_end = 0;
 
-    array a = af::randu(num);
-    array b = a(af::seq(af::end - end_begin, af::end - end_end));
+    array a = randu(num);
+    array b = a(seq(end - end_begin, end - end_end));
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
@@ -765,22 +774,20 @@ TEST(SeqIndex, CPP_END_SEQ)
     freeHost(hB);
 }
 
-af::array cpp_scope_seq_test(const int num, const float val, const af::seq s)
+array cpp_scope_seq_test(const int num, const float val, const seq s)
 {
-    af::array a = af::constant(val, num);
+    array a = constant(val, num);
     return a(s);
 }
 
 TEST(SeqIndex, CPP_SCOPE_SEQ)
 {
-    using af::array;
-
     const int num = 20;
     const int seq_begin = 3;
     const int seq_end = 10;
     const float val = 133.33;
 
-    array b = cpp_scope_seq_test(num, val, af::seq(seq_begin, seq_end));
+    array b = cpp_scope_seq_test(num, val, seq(seq_begin, seq_end));
     float *hB = b.host<float>();
 
     for (int i = 0; i < seq_end - seq_begin + 1; i++) {
@@ -790,17 +797,15 @@ TEST(SeqIndex, CPP_SCOPE_SEQ)
     freeHost(hB);
 }
 
-af::array cpp_scope_arr_test(const int num, const float val)
+array cpp_scope_arr_test(const int num, const float val)
 {
-    af::array a = af::constant(val, num);
-    af::array idx = where(a > val/2);
+    array a = constant(val, num);
+    array idx = where(a > val/2);
     return a(idx) * (val - 1);
 }
 
 TEST(SeqIndex, CPP_SCOPE_ARR)
 {
-    using af::array;
-
     const int num = 20;
     const float val = 133.33;
 
@@ -816,16 +821,14 @@ TEST(SeqIndex, CPP_SCOPE_ARR)
 
 TEST(SeqIndex, CPPLarge)
 {
-    using af::array;
-
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTests<float, float, int>(string(TEST_DIR"/arrayindex/dim0Large.test"), numDims, in, tests);
 
-    af::dim4 dims0     = numDims[0];
-    af::dim4 dims1     = numDims[1];
+    dim4 dims0     = numDims[0];
+    dim4 dims1     = numDims[1];
 
     array input(dims0, &(in[0].front()));
     array indices(dims1, &(in[1].front()));
@@ -838,7 +841,7 @@ TEST(SeqIndex, CPPLarge)
     output.host((void*)outData);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outData;
@@ -846,9 +849,6 @@ TEST(SeqIndex, CPPLarge)
 
 TEST(SeqIndex, Cascade00)
 {
-    using af::seq;
-    using af::span;
-
     const int nx = 200;
     const int ny = 200;
 
@@ -862,9 +862,9 @@ TEST(SeqIndex, Cascade00)
     const int en = stb + enc;
     const int nxc = en - st + 1;
 
-    af::array a = af::randu(nx, ny);
-    af::array b = a(seq(stb, enb), span);
-    af::array c = b(seq(stc, enc), span);
+    array a = randu(nx, ny);
+    array b = a(seq(stb, enb), span);
+    array c = b(seq(stc, enc), span);
 
     ASSERT_EQ(c.dims(1), (dim_t)ny );
     ASSERT_EQ(c.dims(0), (dim_t)nxc);
@@ -892,9 +892,6 @@ TEST(SeqIndex, Cascade00)
 
 TEST(SeqIndex, Cascade01)
 {
-    using af::seq;
-    using af::span;
-
     const int nx = 200;
     const int ny = 200;
 
@@ -907,9 +904,9 @@ TEST(SeqIndex, Cascade01)
     const int nxc = enb - stb + 1;
     const int nyc = enc - stc + 1;
 
-    af::array a = af::randu(nx, ny);
-    af::array b = a(seq(stb, enb), span);
-    af::array c = b(span, seq(stc, enc));
+    array a = randu(nx, ny);
+    array b = a(seq(stb, enb), span);
+    array c = b(span, seq(stc, enc));
 
     ASSERT_EQ(c.dims(1), (dim_t)nyc);
     ASSERT_EQ(c.dims(0), (dim_t)nxc);
@@ -938,9 +935,6 @@ TEST(SeqIndex, Cascade01)
 
 TEST(SeqIndex, Cascade10)
 {
-    using af::seq;
-    using af::span;
-
     const int nx = 200;
     const int ny = 200;
 
@@ -953,9 +947,9 @@ TEST(SeqIndex, Cascade10)
     const int nxc = enc - stc + 1;
     const int nyc = enb - stb + 1;
 
-    af::array a = af::randu(nx, ny);
-    af::array b = a(span, seq(stb, enb));
-    af::array c = b(seq(stc, enc), span);
+    array a = randu(nx, ny);
+    array b = a(span, seq(stb, enb));
+    array c = b(seq(stc, enc), span);
 
     ASSERT_EQ(c.dims(1), (dim_t)nyc);
     ASSERT_EQ(c.dims(0), (dim_t)nxc);
@@ -984,9 +978,6 @@ TEST(SeqIndex, Cascade10)
 
 TEST(SeqIndex, Cascade11)
 {
-    using af::seq;
-    using af::span;
-
     const int nx = 200;
     const int ny = 200;
 
@@ -1000,9 +991,9 @@ TEST(SeqIndex, Cascade11)
     const int en = stb + enc;
     const int nyc = en - st + 1;
 
-    af::array a = af::randu(nx, ny);
-    af::array b = a(span, seq(stb, enb));
-    af::array c = b(span, seq(stc, enc));
+    array a = randu(nx, ny);
+    array b = a(span, seq(stb, enb));
+    array c = b(span, seq(stc, enc));
 
     ASSERT_EQ(c.dims(1), nyc);
     ASSERT_EQ(c.dims(0), nx );
@@ -1031,10 +1022,9 @@ TEST(SeqIndex, Cascade11)
 
 TEST(ArrayIndex, CPP_INDEX_VECTOR)
 {
-    using af::array;
     float h_inds[] = {0, 3, 2, 1}; // zero-based indexing
     array inds(1, 4, h_inds);
-    array B = af::randu(1, 4);
+    array B = randu(1, 4);
     array C = B(inds);
 
     ASSERT_EQ(B.dims(0), 1);
@@ -1055,15 +1045,13 @@ TEST(ArrayIndex, CPP_INDEX_VECTOR)
 
 TEST(SeqIndex, CPP_INDEX_VECTOR)
 {
-    using af::array;
-
     const int num = 20;
     const int len = 10;
     const int st  =  3;
     const int en  = st + len - 1;
 
-    array B = af::randu(1, 20);
-    array C = B(af::seq(st, en));
+    array B = randu(1, 20);
+    array C = B(seq(st, en));
 
     ASSERT_EQ(1  , B.dims(0));
     ASSERT_EQ(num, B.dims(1));
@@ -1084,10 +1072,9 @@ TEST(SeqIndex, CPP_INDEX_VECTOR)
 
 TEST(ArrayIndex, CPP_INDEX_VECTOR_2D)
 {
-    using af::array;
     float h_inds[] = {3, 5, 7, 2}; // zero-based indexing
     array inds(1, 4, h_inds);
-    array B = af::randu(4, 4);
+    array B = randu(4, 4);
     array C = B(inds);
 
     ASSERT_EQ(B.dims(0), 4);
@@ -1108,16 +1095,14 @@ TEST(ArrayIndex, CPP_INDEX_VECTOR_2D)
 
 TEST(SeqIndex, CPP_INDEX_VECTOR_2D)
 {
-    using af::array;
-
     const int nx = 4;
     const int ny = 3 * nx;
     const int len = 2 * nx;
     const int st  = nx - 1;
     const int en  = st + len - 1;
 
-    array B = af::randu(nx, ny);
-    array C = B(af::seq(st, en));
+    array B = randu(nx, ny);
+    array C = B(seq(st, en));
 
     ASSERT_EQ(nx , B.dims(0));
     ASSERT_EQ(ny , B.dims(1));
@@ -1148,37 +1133,35 @@ TYPED_TEST_CASE(IndexedMembers, AllTypes);
 TYPED_TEST(IndexedMembers, MemFuncs)
 {
     if (noDoubleTests<TypeParam>()) return;
-    using af::array;
-    dim_t dimsize = 100;
+
+    const dim_t dimsize = 100;
     vector<TypeParam> in(dimsize * dimsize);
     for(int i = 0; i < (int)in.size(); i++) in[i] = i;
     array input(dimsize, dimsize, &in.front(), afHost);
 
-    ASSERT_EQ(dimsize, input(af::span, 1).elements());
-    ASSERT_EQ(input.type(), input(af::span, 1).type());
-    ASSERT_EQ(af::dim4(dimsize), input(af::span, 1).dims());
-    ASSERT_EQ(1u, input(af::span, 1).numdims());
-    ASSERT_FALSE(input(af::span, 1).isempty());
-    ASSERT_FALSE(input(af::span, 1).isscalar());
+    ASSERT_EQ(dimsize, input(span, 1).elements());
+    ASSERT_EQ(input.type(), input(span, 1).type());
+    ASSERT_EQ(dim4(dimsize), input(span, 1).dims());
+    ASSERT_EQ(1u, input(span, 1).numdims());
+    ASSERT_FALSE(input(span, 1).isempty());
+    ASSERT_FALSE(input(span, 1).isscalar());
     ASSERT_TRUE(input(1, 1).isscalar());
-    ASSERT_TRUE(input(af::span, 1).isvector());
-    ASSERT_FALSE(input(af::span, 1).isrow());
-    ASSERT_EQ(input.iscomplex(), input(af::span, 1).iscomplex());
-    ASSERT_EQ(input.isdouble(), input(af::span, 1).isdouble());
-    ASSERT_EQ(input.issingle(), input(af::span, 1).issingle());
-    ASSERT_EQ(input.isrealfloating(), input(af::span, 1).isrealfloating());
-    ASSERT_EQ(input.isfloating(), input(af::span, 1).isfloating());
-    ASSERT_EQ(input.isinteger(), input(af::span, 1).isinteger());
-    ASSERT_EQ(input.isbool(), input(af::span, 1).isbool());
+    ASSERT_TRUE(input(span, 1).isvector());
+    ASSERT_FALSE(input(span, 1).isrow());
+    ASSERT_EQ(input.iscomplex(), input(span, 1).iscomplex());
+    ASSERT_EQ(input.isdouble(), input(span, 1).isdouble());
+    ASSERT_EQ(input.issingle(), input(span, 1).issingle());
+    ASSERT_EQ(input.isrealfloating(), input(span, 1).isrealfloating());
+    ASSERT_EQ(input.isfloating(), input(span, 1).isfloating());
+    ASSERT_EQ(input.isinteger(), input(span, 1).isinteger());
+    ASSERT_EQ(input.isbool(), input(span, 1).isbool());
     // TODO: Doesn't compile in cuda for cfloat and cdouble
-    //ASSERT_EQ(input.scalar<TypeParam>(), input(af::span, 0).scalar<TypeParam>());
+    //ASSERT_EQ(input.scalar<TypeParam>(), input(span, 0).scalar<TypeParam>());
 }
 
-
-#if 0
+#if 1
 TYPED_TEST(IndexedMembers, MemIndex)
 {
-    using namespace af;
     array a = range(dim4(10, 10));
     array b = a(seq(1,7), span);
     array brow = b.row(5);
@@ -1211,7 +1194,6 @@ TYPED_TEST(IndexedMembers, MemIndex)
 
 TEST(Indexing, SNIPPET_indexing_first)
 {
-    using namespace af;
     //! [ex_indexing_first]
     array A = array(seq(1,9), 3, 3);
     af_print(A);
@@ -1284,7 +1266,6 @@ TEST(Indexing, SNIPPET_indexing_first)
 
 TEST(Indexing, SNIPPET_indexing_set)
 {
-    using namespace af;
     //! [ex_indexing_set]
     array A = constant(0, 3, 3);
     af_print(A);
@@ -1309,7 +1290,6 @@ TEST(Indexing, SNIPPET_indexing_set)
 
 TEST(Indexing, SNIPPET_indexing_ref)
 {
-    using namespace af;
     //! [ex_indexing_ref]
     float h_inds[] = {0, 4, 2, 1}; // zero-based indexing
     array inds(1, 4, h_inds);
@@ -1330,7 +1310,7 @@ TEST(Indexing, SNIPPET_indexing_ref)
 
 TEST(Indexing, SNIPPET_indexing_copy)
 {
-  af::array A = af::constant(0,1, s32);
+  array A = constant(0,1, s32);
   af::index s1;
   s1 = af::index(A);
   // At exit both A and s1 will be destroyed
@@ -1340,7 +1320,6 @@ TEST(Indexing, SNIPPET_indexing_copy)
 
 TEST(Assign, LinearIndexSeq)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
 
@@ -1348,8 +1327,8 @@ TEST(Assign, LinearIndexSeq)
     const int en = nx * (ny - 1);
     const int num = (en - st + 1);
 
-    array a = af::randu(nx, ny);
-    af::index idx = af::seq(st, en);
+    array a = randu(nx, ny);
+    af::index idx = seq(st, en);
 
     af_array in_arr = a.get();
     af_index_t ii = idx.get();
@@ -1358,13 +1337,13 @@ TEST(Assign, LinearIndexSeq)
     ASSERT_EQ(AF_SUCCESS,
               af_index(&out_arr, in_arr, 1, &ii.idx.seq));
 
-    af::array out(out_arr);
+    array out(out_arr);
 
     ASSERT_EQ(out.dims(0), num);
     ASSERT_EQ(out.elements(), num);
 
-    std::vector<float> hout(nx * ny);
-    std::vector<float> ha(nx * ny);
+    vector<float> hout(nx * ny);
+    vector<float> ha(nx * ny);
 
     a.host(&ha[0]);
     out.host(&hout[0]);
@@ -1376,7 +1355,6 @@ TEST(Assign, LinearIndexSeq)
 
 TEST(Assign, LinearIndexGenSeq)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
 
@@ -1384,8 +1362,8 @@ TEST(Assign, LinearIndexGenSeq)
     const int en = nx * (ny - 1);
     const int num = (en - st + 1);
 
-    array a = af::randu(nx, ny);
-    af::index idx = af::seq(st, en);
+    array a = randu(nx, ny);
+    af::index idx = seq(st, en);
 
     af_array in_arr = a.get();
     af_index_t ii = idx.get();
@@ -1394,13 +1372,13 @@ TEST(Assign, LinearIndexGenSeq)
     ASSERT_EQ(AF_SUCCESS,
               af_index_gen(&out_arr, in_arr, 1, &ii));
 
-    af::array out(out_arr);
+    array out(out_arr);
 
     ASSERT_EQ(out.dims(0), num);
     ASSERT_EQ(out.elements(), num);
 
-    std::vector<float> hout(nx * ny);
-    std::vector<float> ha(nx * ny);
+    vector<float> hout(nx * ny);
+    vector<float> ha(nx * ny);
 
     a.host(&ha[0]);
     out.host(&hout[0]);
@@ -1412,7 +1390,6 @@ TEST(Assign, LinearIndexGenSeq)
 
 TEST(Assign, LinearIndexGenArr)
 {
-    using af::array;
     const int nx = 5;
     const int ny = 4;
 
@@ -1420,8 +1397,8 @@ TEST(Assign, LinearIndexGenArr)
     const int en = nx * (ny - 1);
     const int num = (en - st + 1);
 
-    array a = af::randu(nx, ny);
-    af::index idx = af::array(af::seq(st, en));
+    array a = randu(nx, ny);
+    af::index idx = array(seq(st, en));
 
     af_array in_arr = a.get();
     af_index_t ii = idx.get();
@@ -1430,13 +1407,13 @@ TEST(Assign, LinearIndexGenArr)
     ASSERT_EQ(AF_SUCCESS,
               af_index_gen(&out_arr, in_arr, 1, &ii));
 
-    af::array out(out_arr);
+    array out(out_arr);
 
     ASSERT_EQ(out.dims(0), num);
     ASSERT_EQ(out.elements(), num);
 
-    std::vector<float> hout(nx * ny);
-    std::vector<float> ha(nx * ny);
+    vector<float> hout(nx * ny);
+    vector<float> ha(nx * ny);
 
     a.host(&ha[0]);
     out.host(&hout[0]);
@@ -1448,13 +1425,11 @@ TEST(Assign, LinearIndexGenArr)
 
 TEST(Index, OutOfBounds)
 {
-    using af::array;
-
     uint gold[7] = {0, 9, 49, 119, 149, 149, 148};
     uint h_idx[7] = {0, 9, 49, 119, 149, 150, 151};
     uint output[7];
 
-    array a = af::iota(af::dim4(50, 1, 3)).as(s32);
+    array a = iota(dim4(50, 1, 3)).as(s32);
     array idx(7, h_idx);
     array b = a(idx);
     b.host((void*)output);
@@ -1465,10 +1440,9 @@ TEST(Index, OutOfBounds)
 
 TEST(Index, ISSUE_1101_FULL)
 {
-    using namespace af;
     deviceGC();
     array a = randu(5,5);
-    std::vector<float> ha(a.elements());
+    vector<float> ha(a.elements());
     a.host(&ha[0]);
 
     size_t aby, abu, lby, lbu;
@@ -1484,7 +1458,7 @@ TEST(Index, ISSUE_1101_FULL)
     ASSERT_EQ(lby, lby1);
     ASSERT_EQ(lbu, lbu1);
 
-    std::vector<float> hb(b.elements());
+    vector<float> hb(b.elements());
     b.host(&hb[0]);
     for (int i = 0; i < b.elements(); i++) {
         ASSERT_EQ(ha[i], hb[i]);
@@ -1493,10 +1467,9 @@ TEST(Index, ISSUE_1101_FULL)
 
 TEST(Index, ISSUE_1101_COL0)
 {
-    using namespace af;
     deviceGC();
     array a = randu(5,5);
-    std::vector<float> ha(a.elements());
+    vector<float> ha(a.elements());
     a.host(&ha[0]);
 
     size_t aby, abu, lby, lbu;
@@ -1512,7 +1485,7 @@ TEST(Index, ISSUE_1101_COL0)
     ASSERT_EQ(lby, lby1);
     ASSERT_EQ(lbu, lbu1);
 
-    std::vector<float> hb(b.elements());
+    vector<float> hb(b.elements());
     b.host(&hb[0]);
     for (int i = 0; i < b.elements(); i++) {
         ASSERT_EQ(ha[i], hb[i]);
@@ -1522,10 +1495,9 @@ TEST(Index, ISSUE_1101_COL0)
 
 TEST(Index, ISSUE_1101_MODDIMS)
 {
-    using namespace af;
     deviceGC();
     array a = randu(5,5);
-    std::vector<float> ha(a.elements());
+    vector<float> ha(a.elements());
     a.host(&ha[0]);
 
     size_t aby, abu, lby, lbu;
@@ -1545,13 +1517,13 @@ TEST(Index, ISSUE_1101_MODDIMS)
     ASSERT_EQ(lby, lby1);
     ASSERT_EQ(lbu, lbu1);
 
-    std::vector<float> hb(b.elements());
+    vector<float> hb(b.elements());
     b.host(&hb[0]);
     for (int i = 0; i < b.elements(); i++) {
         ASSERT_EQ(ha[i + st], hb[i]);
     }
 
-    std::vector<float> hc(c.elements());
+    vector<float> hc(c.elements());
     c.host(&hc[0]);
     for (int i = 0; i < c.elements(); i++) {
         ASSERT_EQ(ha[i + st], hc[i]);
@@ -1560,17 +1532,15 @@ TEST(Index, ISSUE_1101_MODDIMS)
 
 TEST(Index, Issue1846IndexStepCascade)
 {
-    using namespace af;
     array a = randu(3, 12);
-    array b = a(span, seq(0, af::end, 2));
-    array c = b(span, seq(0, af::end, 3));
-    array d = a(span, seq(0, af::end, 6));
+    array b = a(span, seq(0, end, 2));
+    array c = b(span, seq(0, end, 3));
+    array d = a(span, seq(0, end, 6));
     EXPECT_EQ(allTrue<bool>(c == d), true);
 }
 
 TEST(Index, Issue1845IndexStepReorder)
 {
-    using namespace af;
     array a = randu(1,8,1);
     array b = reorder(a,0,2,1);
     array d = reorder(b(0,0,span),2,1,0);
@@ -1579,15 +1549,16 @@ TEST(Index, Issue1845IndexStepReorder)
 
 TEST(Index, Issue1867ChainedIndexingLeak)
 {
-    using namespace af;
+    using af::randn;
+    using af::sync;
     {
         array lInput = randn(100, 100, f32);
         array Q3 = lInput.rows(0, 3).cols(0, 3);
         Q3.eval();
-        af::sync();
+        sync();
     }
     size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
                       &lock_bytes, &lock_buffers);
     ASSERT_EQ(0u, lock_buffers);
 }
diff --git a/test/info.cpp b/test/info.cpp
index aba238a91d..e999e32ff6 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -20,15 +20,20 @@
 
 using std::string;
 using std::vector;
+using af::dim4;
+using af::dtype_traits;
+using af::getDevice;
+using af::info;
+using af::setDevice;
 
 template<typename T>
 void testFunction()
 {
-    af::info();
+    info();
 
     af_array outArray = 0;
-    af::dim4 dims(32, 32, 1, 1);
-    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    dim4 dims(32, 32, 1, 1);
+    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     // cleanup
     if(outArray != 0) {
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
@@ -45,12 +50,12 @@ void infoTest()
     if(ENV && ENV[0] == '0') {
         testFunction<float>();
     } else {
-        int oldDevice = af::getDevice();
+        int oldDevice = getDevice();
         for(int d = 0; d < nDevices; d++) {
-            af::setDevice(d);
+            setDevice(d);
             testFunction<float>();
         }
-        af::setDevice(oldDevice);
+        setDevice(oldDevice);
     }
 }
 
diff --git a/test/internal.cpp b/test/internal.cpp
index 209b61e766..0f8695f932 100644
--- a/test/internal.cpp
+++ b/test/internal.cpp
@@ -16,6 +16,13 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::vector;
+using af::array;
+using af::dim4;
+using af::randu;
+using af::seq;
+using af::span;
+
 TEST(Internal, CreateStrided)
 {
     float ha[] = {1,
@@ -33,15 +40,15 @@ TEST(Internal, CreateStrided)
     unsigned ndims = 3;
     dim_t dims[] = {3, 3, 2};
     dim_t strides[] = {1, 5, 20};
-    af::array a = createStridedArray((void *)ha,
+    array a = createStridedArray((void *)ha,
                                      offset,
-                                     af::dim4(ndims, dims),
-                                     af::dim4(ndims, strides),
+                                     dim4(ndims, dims),
+                                     dim4(ndims, strides),
                                      f32,
                                      afHost);
 
-    af::dim4 astrides = getStrides(a);
-    af::dim4 adims = a.dims();
+    dim4 astrides = getStrides(a);
+    dim4 adims = a.dims();
 
     ASSERT_EQ(offset, getOffset(a));
     for (int i = 0; i < (int)ndims; i++) {
@@ -49,7 +56,7 @@ TEST(Internal, CreateStrided)
         ASSERT_EQ(dims[i], adims[i]);
     }
 
-    std::vector<float> va(a.elements());
+    vector<float> va(a.elements());
     a.host(&va[0]);
 
     int o = offset;
@@ -69,22 +76,22 @@ TEST(Internal, CreateStrided)
 
 TEST(Internal, CheckInfo)
 {
-    int xdim = 10;
-    int ydim = 8;
+    const int xdim = 10;
+    const int ydim = 8;
 
-    int xoff = 1;
-    int yoff = 2;
+    const int xoff = 1;
+    const int yoff = 2;
 
-    int xnum = 5;
-    int ynum = 3;
+    const int xnum = 5;
+    const int ynum = 3;
 
-    af::array a = af::randu(10, 8);
+    array a = randu(10, 8);
 
-    af::array b = a(af::seq(xoff, xoff + xnum - 1),
-                    af::seq(yoff, yoff + ynum - 1));
+    array b = a(seq(xoff, xoff + xnum - 1),
+                    seq(yoff, yoff + ynum - 1));
 
-    af::dim4 strides = getStrides(b);
-    af::dim4 dims = b.dims();
+    dim4 strides = getStrides(b);
+    dim4 dims = b.dims();
 
     dim_t offset = xoff + yoff * xdim;
 
@@ -102,18 +109,18 @@ TEST(Internal, CheckInfo)
 
 TEST(Internal, Linear)
 {
-    af::array c;
+    array c;
     {
-        af::array a = af::randu(10, 8);
+        array a = randu(10, 8);
 
         // b is just pointing to same underlying data
         // b is an owner;
-        af::array b = a;
+        array b = a;
         ASSERT_EQ(isOwner(b), true);
 
         // C is considered sub array
         // C will not be an owner
-        c = a(af::span);
+        c = a(span);
         ASSERT_EQ(isOwner(c), false);
     }
 
@@ -125,27 +132,27 @@ TEST(Internal, Linear)
 
 TEST(Internal, Allocated)
 {
-    af::array a = af::randu(10, 8);
+    array a = randu(10, 8);
     size_t a_allocated = a.allocated();
     size_t a_bytes = a.bytes();
 
     // b is just pointing to same underlying data
     // b is an owner;
-    af::array b = a;
+    array b = a;
     ASSERT_EQ(b.allocated(), a_allocated);
     ASSERT_EQ(b.bytes(), a_bytes);
 
     // C is considered sub array
     // C will not be an owner
-    af::array c = a(af::span);
+    array c = a(span);
     ASSERT_EQ(c.allocated(), a_allocated);
     ASSERT_EQ(c.bytes(), a_bytes);
 
-    af::array d = a.col(1);
+    array d = a.col(1);
     ASSERT_EQ(d.allocated(), a_allocated);
 
-    a = af::randu(20);
-    b = af::randu(20);
+    a = randu(20);
+    b = randu(20);
 
     // Even though a, b are reallocated and c, d are not owners
     // the allocated and bytes should remain the same
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index c56ba4c02f..8c8f9040ea 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -16,20 +16,20 @@
 #include <af/dim4.hpp>
 #include <af/defines.h>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <complex>
-#include <string>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype;
 using af::dtype_traits;
+using af::identity;
+using af::matmul;
+using af::max;
 
 template<typename T>
 void inverseTester(const int m, const int n, const int k, double eps)
@@ -37,20 +37,20 @@ void inverseTester(const int m, const int n, const int k, double eps)
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 #if 1
-    af::array A  = cpu_randu<T>(af::dim4(m, n));
+    array A  = cpu_randu<T>(dim4(m, n));
 #else
-    af::array A  = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    array A  = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
     //! [ex_inverse]
-    af::array IA = inverse(A);
-    af::array I = af::matmul(A, IA);
+    array IA = inverse(A);
+    array I = matmul(A, IA);
     //! [ex_inverse]
 
-    af::array I2 = af::identity(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    array I2 = identity(m, n, (dtype)dtype_traits<T>::af_type);
 
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(I - I2))), eps);
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(I - I2))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(I - I2))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(I - I2))), eps);
 }
 
 
diff --git a/test/iota.cpp b/test/iota.cpp
index dbcc16c65c..ed9ed5e5ef 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -20,10 +20,11 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Iota : public ::testing::Test
@@ -44,22 +45,22 @@ typedef ::testing::Types<float, double, int, unsigned int, intl, uintl, unsigned
 TYPED_TEST_CASE(Iota, TestTypes);
 
 template<typename T>
-void iotaTest(const af::dim4 idims, const af::dim4 tdims)
+void iotaTest(const dim4 idims, const dim4 tdims)
 {
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_iota(&outArray, idims.ndims(), idims.get(),
-               tdims.ndims(), tdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+               tdims.ndims(), tdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     af_array temp0 = 0, temp1 = 0, temp2 = 0;
-    af::dim4 tempdims(idims.elements());
-    af::dim4 fulldims;
+    dim4 tempdims(idims.elements());
+    dim4 fulldims;
     for(unsigned i = 0; i < 4; i++) {
         fulldims[i] = idims[i] * tdims[i];
     }
-    ASSERT_EQ(AF_SUCCESS, af_range(&temp2, tempdims.ndims(), tempdims.get(), 0, (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_range(&temp2, tempdims.ndims(), tempdims.get(), 0, (af_dtype) dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_moddims(&temp1, temp2, idims.ndims(), idims.get()));
     ASSERT_EQ(AF_SUCCESS, af_tile(&temp0, temp1, tdims[0], tdims[1], tdims[2], tdims[3]));
 
@@ -72,7 +73,7 @@ void iotaTest(const af::dim4 idims, const af::dim4 tdims)
 
     // Compare result
     for(int i = 0; i < (int) fulldims.elements(); i++)
-        ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << endl;
 
     if(outArray  != 0) af_release_array(outArray);
     if(temp0     != 0) af_release_array(temp0);
@@ -80,10 +81,10 @@ void iotaTest(const af::dim4 idims, const af::dim4 tdims)
     if(temp2     != 0) af_release_array(temp2);
 }
 
-#define IOTA_INIT(desc, x, y, z, w, a, b, c, d)                                             \
-    TYPED_TEST(Iota, desc)                                                                  \
-    {                                                                                       \
-        iotaTest<TypeParam>(af::dim4(x, y, z, w), af::dim4(a, b, c, d));                    \
+#define IOTA_INIT(desc, x, y, z, w, a, b, c, d)                     \
+    TYPED_TEST(Iota, desc)                                          \
+    {                                                               \
+        iotaTest<TypeParam>(dim4(x, y, z, w), dim4(a, b, c, d));    \
     }
 
     IOTA_INIT(Iota1D0, 100,  1, 1, 1, 2, 3, 1, 1);
@@ -106,19 +107,23 @@ void iotaTest(const af::dim4 idims, const af::dim4 tdims)
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
+
+using af::array;
+using af::iota;
+
 TEST(Iota, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    af::dim4 idims(23, 15, 1, 1);
-    af::dim4 tdims(2, 2, 1, 1);
-    af::dim4 fulldims;
+    dim4 idims(23, 15, 1, 1);
+    dim4 tdims(2, 2, 1, 1);
+    dim4 fulldims;
     for(unsigned i = 0; i < 4; i++) {
         fulldims[i] = idims[i] * tdims[i];
     }
 
-    af::array output = af::iota(idims, tdims);
-    af::array tileArray = af::tile(af::moddims(af::range(af::dim4(idims.elements()), 0), idims), tdims);
+    array output = iota(idims, tdims);
+    array tileArray = tile(moddims(range(dim4(idims.elements()), 0), idims), tdims);
 
     // Get result
     vector<float> outData (fulldims.elements());
@@ -129,5 +134,5 @@ TEST(Iota, CPP)
 
     // Compare result
     for(int i = 0; i < (int)fulldims.elements(); i++)
-        ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << std::endl;
+        ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << endl;
 }
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 1e963bc5ff..c1a15b343f 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -16,14 +16,16 @@
 
 using std::vector;
 using std::complex;
+using af::allTrue;
 using af::array;
+using af::constant;
 using af::dtype;
 using af::dtype_traits;
+using af::max;
+using af::min;
 using af::randu;
-using af::constant;
+using af::seq;
 using af::span;
-using af::min;
-using af::allTrue;
 
 #define MINMAXOP(fn, ty)                                \
     TEST(IndexedReduce, fn##_##ty##_0)                  \
@@ -32,9 +34,9 @@ using af::allTrue;
         dtype dty = (dtype)dtype_traits<ty>::af_type;   \
         const int nx = 10000;                           \
         const int ny = 100;                             \
-        af::array in = randu(nx, ny, dty);              \
-        af::array val, idx;                             \
-        af::fn(val, idx, in, 0);                        \
+        array in = randu(nx, ny, dty);                  \
+        array val, idx;                                 \
+        fn(val, idx, in, 0);                            \
                                                         \
         ty *h_in = in.host<ty>();                       \
         ty *h_in_st = h_in;                             \
@@ -58,9 +60,9 @@ using af::allTrue;
         dtype dty = (dtype)dtype_traits<ty>::af_type;   \
         const int nx = 100;                             \
         const int ny = 100;                             \
-        af::array in = randu(nx, ny, dty);              \
-        af::array val, idx;                             \
-        af::fn(val, idx, in, 1);                        \
+        array in = randu(nx, ny, dty);                  \
+        array val, idx;                                 \
+        fn(val, idx, in, 1);                            \
                                                         \
         ty *h_in = in.host<ty>();                       \
         ty *h_val = val.host<ty>();                     \
@@ -82,10 +84,10 @@ using af::allTrue;
         if (noDoubleTests<ty>()) return;                \
         dtype dty = (dtype)dtype_traits<ty>::af_type;   \
         const int num = 100000;                         \
-        af::array in = randu(num, dty);                 \
+        array in = randu(num, dty);                     \
         ty val;                                         \
         uint idx;                                       \
-        af::fn<ty>(&val, &idx, in);                     \
+        fn<ty>(&val, &idx, in);                         \
         ty *h_in = in.host<ty>();                       \
         ty tmp = *std::fn##_element(h_in, h_in + num);  \
         ASSERT_EQ(tmp, val);                            \
@@ -112,13 +114,13 @@ TEST(IndexedReduce, MaxIndexedSmall)
     const int num = 1000;
     const int st = 10;
     const int en = num - 100;
-    af::array a = af::randu(num);
+    array a = randu(num);
 
     float b;
     unsigned idx;
-    af::max<float>(&b, &idx, a(af::seq(st, en)));
+    max<float>(&b, &idx, a(seq(st, en)));
 
-    std::vector<float> ha(num);
+    vector<float> ha(num);
     a.host(&ha[0]);
 
     float res = ha[st];
@@ -134,13 +136,13 @@ TEST(IndexedReduce, MaxIndexedBig)
     const int num = 100000;
     const int st = 1000;
     const int en = num - 1000;
-    af::array a = af::randu(num);
+    array a = randu(num);
 
     float b;
     unsigned idx;
-    af::max<float>(&b, &idx, a(af::seq(st, en)));
+    max<float>(&b, &idx, a(seq(st, en)));
 
-    std::vector<float> ha(num);
+    vector<float> ha(num);
     a.host(&ha[0]);
 
     float res = ha[st];
diff --git a/test/jit.cpp b/test/jit.cpp
index 4f8ace3b12..69bbd87e6c 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -13,12 +13,18 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace af;
+using std::vector;
+using af::array;
+using af::constant;
+using af::eval;
+using af::freeHost;
+using af::gforSet;
+using af::randu;
+using af::randn;
+using af::seq;
 
 TEST(JIT, CPP_JIT_HASH)
 {
-    using af::array;
-
     const int num = 20;
     const float valA = 3;
     const float valB = 5;
@@ -28,12 +34,12 @@ TEST(JIT, CPP_JIT_HASH)
     const float valF1 = valD * valE - valE;
     const float valF2 = valD * valE - valD;
 
-    array a = af::constant(valA, num);
-    array b = af::constant(valB, num);
-    array c = af::constant(valC, num);
-    af::eval(a);
-    af::eval(b);
-    af::eval(c);
+    array a = constant(valA, num);
+    array b = constant(valB, num);
+    array c = constant(valC, num);
+    eval(a);
+    eval(b);
+    eval(c);
 
 
     // Creating a kernel
@@ -67,21 +73,19 @@ TEST(JIT, CPP_JIT_HASH)
 
 TEST(JIT, CPP_JIT_Reset_Binary)
 {
-    using af::array;
-
-    af::array a = af::constant(2, 5,5);
-    af::array b = af::constant(1, 5,5);
-    af::array c = a + b;
-    af::array d = a - b;
-    af::array e = c * d;
+    array a = constant(2, 5,5);
+    array b = constant(1, 5,5);
+    array c = a + b;
+    array d = a - b;
+    array e = c * d;
     e.eval();
-    af::array f = c - d;
+    array f = c - d;
     f.eval();
-    af::array g = d - c;
+    array g = d - c;
     g.eval();
 
-    std::vector<float> hf(f.elements());
-    std::vector<float> hg(g.elements());
+    vector<float> hf(f.elements());
+    vector<float> hg(g.elements());
     f.host(&hf[0]);
     g.host(&hg[0]);
 
@@ -92,21 +96,19 @@ TEST(JIT, CPP_JIT_Reset_Binary)
 
 TEST(JIT, CPP_JIT_Reset_Unary)
 {
-    using af::array;
-
-    af::array a = af::constant(2, 5,5);
-    af::array b = af::constant(1, 5,5);
-    af::array c = af::sin(a);
-    af::array d = af::cos(b);
-    af::array e = c * d;
+    array a = constant(2, 5,5);
+    array b = constant(1, 5,5);
+    array c = sin(a);
+    array d = cos(b);
+    array e = c * d;
     e.eval();
-    af::array f = c - d;
+    array f = c - d;
     f.eval();
-    af::array g = d - c;
+    array g = d - c;
     g.eval();
 
-    std::vector<float> hf(f.elements());
-    std::vector<float> hg(g.elements());
+    vector<float> hf(f.elements());
+    vector<float> hg(g.elements());
     f.host(&hf[0]);
     g.host(&hg[0]);
 
@@ -117,19 +119,17 @@ TEST(JIT, CPP_JIT_Reset_Unary)
 
 TEST(JIT, CPP_Multi_linear)
 {
-    using af::array;
-
     const int num = 1 << 16;
-    af::array a = af::randu(num, s32);
-    af::array b = af::randu(num, s32);
-    af::array x = a + b;
-    af::array y = a - b;
-    af::eval(x, y);
+    array a = randu(num, s32);
+    array b = randu(num, s32);
+    array x = a + b;
+    array y = a - b;
+    eval(x, y);
 
-    std::vector<int> ha(num);
-    std::vector<int> hb(num);
-    std::vector<int> hx(num);
-    std::vector<int> hy(num);
+    vector<int> ha(num);
+    vector<int> hb(num);
+    vector<int> hx(num);
+    vector<int> hy(num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -144,22 +144,20 @@ TEST(JIT, CPP_Multi_linear)
 
 TEST(JIT, CPP_strided)
 {
-    using af::array;
-
     const int num = 1024;
-    af::gforSet(true);
-    af::array a = af::randu(num, 1, s32);
-    af::array b = af::randu(1, num, s32);
-    af::array x = a + b;
-    af::array y = a - b;
-    af::eval(x);
-    af::eval(y);
-    af::gforSet(false);
-
-    std::vector<int> ha(num);
-    std::vector<int> hb(num);
-    std::vector<int> hx(num * num);
-    std::vector<int> hy(num * num);
+    gforSet(true);
+    array a = randu(num, 1, s32);
+    array b = randu(1, num, s32);
+    array x = a + b;
+    array y = a - b;
+    eval(x);
+    eval(y);
+    gforSet(false);
+
+    vector<int> ha(num);
+    vector<int> hb(num);
+    vector<int> hx(num * num);
+    vector<int> hy(num * num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -176,21 +174,19 @@ TEST(JIT, CPP_strided)
 
 TEST(JIT, CPP_Multi_strided)
 {
-    using af::array;
-
     const int num = 1024;
-    af::gforSet(true);
-    af::array a = af::randu(num, 1, s32);
-    af::array b = af::randu(1, num, s32);
-    af::array x = a + b;
-    af::array y = a - b;
-    af::eval(x, y);
-    af::gforSet(false);
-
-    std::vector<int> ha(num);
-    std::vector<int> hb(num);
-    std::vector<int> hx(num * num);
-    std::vector<int> hy(num * num);
+    gforSet(true);
+    array a = randu(num, 1, s32);
+    array b = randu(1, num, s32);
+    array x = a + b;
+    array y = a - b;
+    eval(x, y);
+    gforSet(false);
+
+    vector<int> ha(num);
+    vector<int> hb(num);
+    vector<int> hx(num * num);
+    vector<int> hy(num * num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -207,27 +203,25 @@ TEST(JIT, CPP_Multi_strided)
 
 TEST(JIT, CPP_Multi_pre_eval)
 {
-    using af::array;
-
     const int num = 1 << 16;
-    af::array a = af::randu(num, s32);
-    af::array b = af::randu(num, s32);
-    af::array x = a + b;
-    af::array y = a - b;
+    array a = randu(num, s32);
+    array b = randu(num, s32);
+    array x = a + b;
+    array y = a - b;
 
-    af::eval(x);
+    eval(x);
 
     // Should evaluate only y
-    af::eval(x, y);
+    eval(x, y);
 
     // Should not evaluate anything
     // Should not error out
-    af::eval(x, y);
+    eval(x, y);
 
-    std::vector<int> ha(num);
-    std::vector<int> hb(num);
-    std::vector<int> hx(num);
-    std::vector<int> hy(num);
+    vector<int> ha(num);
+    vector<int> hb(num);
+    vector<int> hx(num);
+    vector<int> hy(num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -242,19 +236,19 @@ TEST(JIT, CPP_Multi_pre_eval)
 
 TEST(JIT, CPP_common_node)
 {
-    af::array r = seq(-3, 3, 0.5);
+    array r = seq(-3, 3, 0.5);
 
     int n = r.dims(0);
 
-    af::array x = af::tile(r, 1, r.dims(0));
-    af::array y = af::tile(r.T(), r.dims(0), 1);
+    array x = tile(r, 1, r.dims(0));
+    array y = tile(r.T(), r.dims(0), 1);
     x.eval();
     y.eval();
 
 
-    std::vector<float> hx(x.elements());
-    std::vector<float> hy(y.elements());
-    std::vector<float> hr(r.elements());
+    vector<float> hx(x.elements());
+    vector<float> hy(y.elements());
+    vector<float> hr(r.elements());
 
     x.host(&hx[0]);
     y.host(&hy[0]);
@@ -270,16 +264,16 @@ TEST(JIT, CPP_common_node)
 
 TEST(JIT, ISSUE_1646)
 {
-    af::array test1 = af::randn(10, 10);
-    af::array test2 = af::randn(10);
-    af::array test3 = af::randn(10);
+    array test1 = randn(10, 10);
+    array test2 = randn(10);
+    array test3 = randn(10);
 
     for (int i = 0; i < 1000; i++) {
-        test3 += af::sum(test1, 1);
+        test3 += sum(test1, 1);
         test2 += test3;
     }
-    af::eval(test2);
-    af::eval(test3);
+    eval(test2);
+    eval(test3);
 }
 
 TEST(JIT, NonLinearLargeY)
@@ -287,16 +281,16 @@ TEST(JIT, NonLinearLargeY)
     const int d0 = 2;
     // This needs to be > 2 * (1 << 20) to properly check this.
     const int d1 = 3 * (1 << 20);
-    af::array a = af::randn(d0);
-    af::array b = af::randn(1, d1);
+    array a = randn(d0);
+    array b = randn(1, d1);
 
     // tile is jit-ted for both the operations
-    af::array c = af::tile(a, 1, d1) + af::tile(b, d0, 1);
-    af::eval(c);
+    array c = tile(a, 1, d1) + tile(b, d0, 1);
+    eval(c);
 
-    std::vector<float> ha(d0);
-    std::vector<float> hb(d1);
-    std::vector<float> hc(d0 * d1);
+    vector<float> ha(d0);
+    vector<float> hb(d1);
+    vector<float> hc(d0 * d1);
 
     a.host(ha.data());
     b.host(hb.data());
@@ -333,9 +327,9 @@ TEST(JIT, NonLinearLargeX)
         selem *= sdims[i];
     }
 
-    std::vector<float> hr(relem);
-    std::vector<float> hc(celem);
-    std::vector<float> hs(selem);
+    vector<float> hr(relem);
+    vector<float> hc(celem);
+    vector<float> hs(selem);
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hr.data(), r));
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hc.data(), c));
@@ -369,11 +363,11 @@ TEST(JIT, NonLinearLargeX)
 
 TEST(JIT, ISSUE_1894)
 {
-    af::array a = af::randu(1);
-    af::array b = af::tile(a, 2 * (1 << 20));
-    af::eval(b);
+    array a = randu(1);
+    array b = tile(a, 2 * (1 << 20));
+    eval(b);
     float ha = -100;
-    std::vector<float> hb(b.elements(), -200);
+    vector<float> hb(b.elements(), -200);
 
     a.host(&ha);
     b.host(hb.data());
@@ -389,14 +383,14 @@ TEST(JIT, LinearLarge)
     float v1 = std::rand() % 100;
     float v2 = std::rand() % 100;
 
-    af::array a = af::constant(v1, 1 << 25);
-    af::array b = af::constant(v2, 1 << 25);
-    af::array c = (a + b) * (a - b);
-    af::eval(c);
+    array a = constant(v1, 1 << 25);
+    array b = constant(v2, 1 << 25);
+    array c = (a + b) * (a - b);
+    eval(c);
 
     float v3 = (v1 + v2) * (v1 - v2);
 
-    std::vector<float> hc(c.elements());
+    vector<float> hc(c.elements());
     c.host(hc.data());
 
     for (size_t i = 0; i < hc.size(); i++) {
@@ -406,19 +400,19 @@ TEST(JIT, LinearLarge)
 
 TEST(JIT, NonLinearBuffers1)
 {
-    af::array a = af::randu(5, 5);
-    af::array a0 = a;
+    array a = randu(5, 5);
+    array a0 = a;
     for (int i = 0; i < 1000; i++) {
-        af::array b = af::randu(1, 5);
-        a += af::tile(b, 5);
+        array b = randu(1, 5);
+        a += tile(b, 5);
     }
     a.eval();
 }
 
 TEST(JIT, NonLinearBuffers2)
 {
-    af::array a = af::randu(100, 310);
-    af::array b = af::randu(10, 10);
+    array a = randu(100, 310);
+    array b = randu(10, 10);
     for (int i = 0; i < 300; i++) {
         b += a(seq(10), seq(i, i+9)) * randu(10, 10);
     }
@@ -428,21 +422,21 @@ TEST(JIT, NonLinearBuffers2)
 TEST(JIT, TransposeBuffers)
 {
     const int num = 10;
-    af::array a = af::randu(1, num);
-    af::array b = af::randu(1, num);
-    af::array c =  a + b;
-    af::array d = a.T() + b.T();
+    array a = randu(1, num);
+    array b = randu(1, num);
+    array c =  a + b;
+    array d = a.T() + b.T();
 
-    std::vector<float> ha(a.elements());
+    vector<float> ha(a.elements());
     a.host(ha.data());
 
-    std::vector<float> hb(b.elements());
+    vector<float> hb(b.elements());
     b.host(hb.data());
 
-    std::vector<float> hc(c.elements());
+    vector<float> hc(c.elements());
     c.host(hc.data());
 
-    std::vector<float> hd(d.elements());
+    vector<float> hd(d.elements());
     d.host(hd.data());
 
     for (int i = 0; i < num; i++) {
diff --git a/test/join.cpp b/test/join.cpp
index 91a1984c9f..e2bacc1cf8 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -21,10 +21,15 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::join;
+using af::randu;
+using af::sum;
 
 template<typename T>
 class Join : public ::testing::Test
@@ -50,13 +55,13 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 i0dims = numDims[in0];
-    af::dim4 i1dims = numDims[in1];
+    dim4 i0dims = numDims[in0];
+    dim4 i1dims = numDims[in1];
 
     af_array in0Array = 0;
     af_array in1Array = 0;
@@ -64,19 +69,19 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&in0Array, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&in0Array, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&in0Array, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&in1Array, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&in1Array, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&in1Array, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_join(&outArray, dim, in0Array, in1Array));
@@ -88,7 +93,7 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -116,23 +121,27 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
 
 TEST(Join, JoinLargeDim)
 {
+    using af::constant;
+    using af::deviceGC;
+    using af::span;
+
     //const int nx = 32;
     const int nx = 1;
     const int ny = 4 * 1024 * 1024;
     const int nw = 4 * 1024 * 1024;
 
-    af::deviceGC();
+    deviceGC();
     {
-        af::array in = af::randu(nx, ny, u8);
-        af::array joined = af::join(0, in, in);
-        af::dim4 in_dims = in.dims();
-        af::dim4 joined_dims = joined.dims();
+        array in = randu(nx, ny, u8);
+        array joined = join(0, in, in);
+        dim4 in_dims = in.dims();
+        dim4 joined_dims = joined.dims();
 
         ASSERT_EQ(2*in_dims[0], joined_dims[0]);
-        ASSERT_EQ(0.f, af::sum<float>((joined(0, af::span) - joined(1, af::span)).as(f32)));
+        ASSERT_EQ(0.f, sum<float>((joined(0, span) - joined(1, span)).as(f32)));
 
-        af::array in2 = af::constant(1, (dim_t)nx, (dim_t)ny, (dim_t)2, (dim_t)nw, u8);
-        joined = af::join(3, in, in);
+        array in2 = constant(1, (dim_t)nx, (dim_t)ny, (dim_t)2, (dim_t)nw, u8);
+        joined = join(3, in, in);
         in_dims = in.dims();
         joined_dims = joined.dims();
         ASSERT_EQ(2*in_dims[3], joined_dims[3]);
@@ -148,18 +157,18 @@ TEST(Join, CPP)
     const unsigned resultIdx = 2;
     const unsigned dim = 2;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/join/join_big.test"),numDims,in,tests);
 
-    af::dim4 i0dims = numDims[0];
-    af::dim4 i1dims = numDims[3];
+    dim4 i0dims = numDims[0];
+    dim4 i1dims = numDims[3];
 
-    af::array input0(i0dims, &(in[0].front()));
-    af::array input1(i1dims, &(in[3].front()));
+    array input0(i0dims, &(in[0].front()));
+    array input1(i1dims, &(in[3].front()));
 
-    af::array output = af::join(dim, input0, input1);
+    array output = join(dim, input0, input1);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -168,7 +177,7 @@ TEST(Join, CPP)
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -179,27 +188,27 @@ TEST(JoinMany0, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    af::array a0 = af::randu(10, 5);
-    af::array a1 = af::randu(20, 5);
-    af::array a2 = af::randu(5, 5);
+    array a0 = randu(10, 5);
+    array a1 = randu(20, 5);
+    array a2 = randu(5, 5);
 
-    af::array output = af::join(0, a0, a1, a2);
-    af::array gold = af::join(0, a0, af::join(0, a1, a2));
+    array output = join(0, a0, a1, a2);
+    array gold = join(0, a0, join(0, a1, a2));
 
-    ASSERT_EQ(af::sum<float>(output - gold), 0);
+    ASSERT_EQ(sum<float>(output - gold), 0);
 }
 
 TEST(JoinMany1, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    af::array a0 = af::randu(20, 200);
-    af::array a1 = af::randu(20, 400);
-    af::array a2 = af::randu(20, 10);
-    af::array a3 = af::randu(20, 100);
+    array a0 = randu(20, 200);
+    array a1 = randu(20, 400);
+    array a2 = randu(20, 10);
+    array a3 = randu(20, 100);
 
     int dim = 1;
-    af::array output = af::join(dim, a0, a1, a2, a3);
-    af::array gold = af::join(dim, a0, af::join(dim, a1, af::join(dim, a2, a3)));
-    ASSERT_EQ(af::sum<float>(output - gold), 0);
+    array output = join(dim, a0, a1, a2, a3);
+    array gold = join(dim, a0, join(dim, a1, join(dim, a2, a3)));
+    ASSERT_EQ(sum<float>(output - gold), 0);
 }
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 9cea2bbe63..f952cc5998 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -24,12 +24,17 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::count;
+using af::dim4;
 using af::dtype_traits;
+using af::max;
+using af::seq;
+using af::span;
 
 TEST(LU, InPlaceSmall)
 {
@@ -38,17 +43,17 @@ TEST(LU, InPlaceSmall)
 
     int resultIdx = 0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, float>(string(TEST_DIR"/lapack/lu.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
-    af::array output, pivot;
-    af::lu(output, pivot, input);
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
+    array output, pivot;
+    lu(output, pivot, input);
 
-    af::dim4 odims = output.dims();
+    dim4 odims = output.dims();
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -60,7 +65,7 @@ TEST(LU, InPlaceSmall)
             // Check only upper triangle
             if(x <= y) {
             int elIter = y * odims[0] + x;
-            ASSERT_NEAR(tests[resultIdx][elIter], outData[elIter], 0.001) << "at: " << elIter << std::endl;
+            ASSERT_NEAR(tests[resultIdx][elIter], outData[elIter], 0.001) << "at: " << elIter << endl;
             }
         }
     }
@@ -76,18 +81,18 @@ TEST(LU, SplitSmall)
 
     int resultIdx = 0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, float>(string(TEST_DIR"/lapack/lufactorized.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
-    af::array l, u, pivot;
-    af::lu(l, u, pivot, input);
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
+    array l, u, pivot;
+    lu(l, u, pivot, input);
 
-    af::dim4 ldims = l.dims();
-    af::dim4 udims = u.dims();
+    dim4 ldims = l.dims();
+    dim4 udims = u.dims();
 
     // Get result
     float* lData = new float[ldims.elements()];
@@ -100,7 +105,7 @@ TEST(LU, SplitSmall)
         for (int x = 0; x < (int)ldims[0]; ++x) {
             if(x < y) {
                 int elIter = y * ldims[0] + x;
-                ASSERT_NEAR(tests[resultIdx][elIter], lData[elIter], 0.001) << "at: " << elIter << std::endl;
+                ASSERT_NEAR(tests[resultIdx][elIter], lData[elIter], 0.001) << "at: " << elIter << endl;
             }
         }
     }
@@ -110,7 +115,7 @@ TEST(LU, SplitSmall)
     for (int y = 0; y < (int)udims[1]; ++y) {
         for (int x = 0; x < (int)udims[0]; ++x) {
             int elIter = y * (int)udims[0] + x;
-            ASSERT_NEAR(tests[resultIdx][elIter], uData[elIter], 0.001) << "at: " << elIter << std::endl;
+            ASSERT_NEAR(tests[resultIdx][elIter], uData[elIter], 0.001) << "at: " << elIter << endl;
         }
     }
 
@@ -126,47 +131,47 @@ void luTester(const int m, const int n, double eps)
     if (noLAPACKTests()) return;
 
 #if 1
-    af::array a_orig = cpu_randu<T>(af::dim4(m, n));
+    array a_orig = cpu_randu<T>(dim4(m, n));
 #else
-    af::array a_orig = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    array a_orig = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
 
     //! [ex_lu_unpacked]
-    af::array l, u, pivot;
-    af::lu(l, u, pivot, a_orig);
+    array l, u, pivot;
+    lu(l, u, pivot, a_orig);
     //! [ex_lu_unpacked]
 
     //! [ex_lu_recon]
-    af::array a_recon = af::matmul(l, u);
-    af::array a_perm = a_orig(pivot, af::span);
+    array a_recon = matmul(l, u);
+    array a_perm = a_orig(pivot, span);
     //! [ex_lu_recon]
 
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(a_recon - a_perm))), eps);
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(a_recon - a_perm))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(a_recon - a_perm))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(a_recon - a_perm))), eps);
 
     //! [ex_lu_packed]
-    af::array out = a_orig.copy();
-    af::array pivot2;
-    af::luInPlace(pivot2, out, false);
+    array out = a_orig.copy();
+    array pivot2;
+    luInPlace(pivot2, out, false);
     //! [ex_lu_packed]
 
     //! [ex_lu_extract]
-    af::array l2 = lower(out,  true);
-    af::array u2 = upper(out, false);
+    array l2 = lower(out,  true);
+    array u2 = upper(out, false);
     //! [ex_lu_extract]
 
-    ASSERT_EQ(af::count<uint>(pivot == pivot2), pivot.elements());
+    ASSERT_EQ(count<uint>(pivot == pivot2), pivot.elements());
 
     int mn = std::min(m, n);
-    l2 = l2(af::span, af::seq(mn));
-    u2 = u2(af::seq(mn), af::span);
+    l2 = l2(span, seq(mn));
+    u2 = u2(seq(mn), span);
 
-    af::array a_recon2 = af::matmul(l2, u2);
-    af::array a_perm2 = a_orig(pivot2, af::span);
+    array a_recon2 = matmul(l2, u2);
+    array a_perm2 = a_orig(pivot2, span);
 
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(real(a_recon2 - a_perm2))), eps);
-    ASSERT_NEAR(0, af::max<typename dtype_traits<T>::base_type>(af::abs(imag(a_recon2 - a_perm2))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(a_recon2 - a_perm2))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(a_recon2 - a_perm2))), eps);
 
 }
 
diff --git a/test/manual_memory_test.cpp b/test/manual_memory_test.cpp
index 3f6fa1ac47..c40ebc9c0d 100644
--- a/test/manual_memory_test.cpp
+++ b/test/manual_memory_test.cpp
@@ -11,43 +11,36 @@
 #include <arrayfire.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
-#include <string>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-
 TEST(Memory, recover)
 {
     cleanSlate(); // Clean up everything done so far
 
     try {
-        af::array vec[100];
+        array vec[100];
 
         // Trying to allocate 1 Terrabyte of memory and trash the memory manager
         // should crash memory manager
         for (int i = 0; i < 1000; i++) {
-            vec[i] = af::randu(1024, 1024, 256); //Allocating 1GB
+            vec[i] = randu(1024, 1024, 256); //Allocating 1GB
         }
 
         ASSERT_EQ(true, false); //Is there a simple assert statement?
-    } catch (af::exception &ae) {
+    } catch (exception &ae) {
 
         ASSERT_EQ(ae.err(), AF_ERR_NO_MEM);
 
         const int num = 1000 * 1000;
         const float val = 1.0;
 
-        af::array a = af::constant(val, num); // This should work as expected
+        array a = constant(val, num); // This should work as expected
         float *h_a = a.host<float>();
         for (int i = 0; i < 1000 * 1000; i++) {
             ASSERT_EQ(h_a[i], val);
         }
-        delete[] h_a;
+        freeHost(h_a);
     }
 
 }
diff --git a/test/match_template.cpp b/test/match_template.cpp
index cfa23e7847..e911522e79 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -15,8 +15,14 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::cout;
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
+using af::dim4;
+using af::dtype_traits;
+using af::exception;
 
 template<typename T>
 class MatchTemplate : public ::testing::Test
@@ -37,34 +43,34 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType)
     typedef typename cond_type<is_same_type<T, double>::value, double, float>::type outType;
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<T> >      in;
     vector<vector<outType> >   tests;
 
     readTests<T, outType, float>(pTestFile, numDims, in, tests);
 
-    af::dim4 sDims    = numDims[0];
-    af::dim4 tDims    = numDims[1];
+    dim4 sDims    = numDims[0];
+    dim4 tDims    = numDims[1];
     af_array outArray = 0;
     af_array sArray   = 0;
     af_array tArray   = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&sArray, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&tArray, &(in[1].front()),
-                tDims.ndims(), tDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_match_template(&outArray, sArray, tArray, pMatchType));
 
-    std::vector<outType> outData(sDims.elements());
+    vector<outType> outData(sDims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<outType> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 
     // cleanup
@@ -96,14 +102,14 @@ TEST(MatchTemplate, InvalidMatchType)
 
     vector<float>   in(100, 1);
 
-    af::dim4 sDims(10, 10, 1, 1);
-    af::dim4 tDims(4, 4, 1, 1);
+    dim4 sDims(10, 10, 1, 1);
+    dim4 tDims(4, 4, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&tArray, &in.front(),
-                tDims.ndims(), tDims.get(), (af_dtype) af::dtype_traits<float>::af_type));
+                tDims.ndims(), tDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_match_template(&outArray, inArray, tArray, (af_match_type)-1));
 
@@ -117,15 +123,15 @@ TEST(MatchTemplate, CPP)
 {
     vector<float>   in(100, 1);
 
-    af::dim4 sDims(10, 10, 1, 1);
-    af::dim4 tDims(4, 4, 1, 1);
+    dim4 sDims(10, 10, 1, 1);
+    dim4 tDims(4, 4, 1, 1);
 
     try {
-        af::array input(sDims, &in.front());
-        af::array tmplt(tDims, &in.front());
+        array input(sDims, &in.front());
+        array tmplt(tDims, &in.front());
 
-        af::array out = matchTemplate(input, tmplt, (af_match_type)-1);
-    } catch(af::exception &e) {
-        std::cout<<"Invalid Match test: "<<e.what()<<std::endl;
+        array out = matchTemplate(input, tmplt, (af_match_type)-1);
+    } catch(exception &e) {
+        cout << "Invalid Match test: " << e.what() << endl;
     }
 }
diff --git a/test/math.cpp b/test/math.cpp
index 3a8bab5703..3b4d4feee3 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -13,9 +13,13 @@
 #include <testHelpers.hpp>
 
 // This makes the macros cleaner
-using namespace std;
-using namespace af;
 using std::abs;
+using std::endl;
+using std::vector;
+using af::array;
+using af::dtype_traits;
+using af::exception;
+using af::randu;
 
 const int num = 10000;
 const float flt_err = 1e-3;
@@ -36,19 +40,19 @@ T sigmoid(T in)
         try {                                                       \
             if (noDoubleTests<T>()) return;                         \
             af_dtype ty = (af_dtype)dtype_traits<T>::af_type;       \
-            af::array a = (hi - lo) * randu(num, ty) + lo + err;    \
-            af::eval(a);                                            \
-            af::array b = af::func(a);                              \
-            std::vector<T> h_a(a.elements());                       \
-            std::vector<T> h_b(b.elements());                       \
+            array a = (hi - lo) * randu(num, ty) + lo + err;        \
+            eval(a);                                                \
+            array b = func(a);                                      \
+            vector<T> h_a(a.elements());                            \
+            vector<T> h_b(b.elements());                            \
             a.host(&h_a[0]);                                        \
             b.host(&h_b[0]);                                        \
                                                                     \
             for (int i = 0; i < num; i++) {                         \
                 ASSERT_NEAR(h_b[i], func(h_a[i]), err) <<           \
-                    "for value: " << h_a[i] << std::endl;           \
+                    "for value: " << h_a[i] << endl;                \
             }                                                       \
-        } catch (af::exception &ex) {                               \
+        } catch (exception &ex) {                                   \
             FAIL() << ex.what();                                    \
         }                                                           \
     }                                                               \
@@ -59,22 +63,22 @@ T sigmoid(T in)
         try {                                                       \
             if (noDoubleTests<T>()) return;                         \
             af_dtype ty = (af_dtype)dtype_traits<T>::af_type;       \
-            af::array a = (hi - lo) * randu(num, ty) + lo + err;    \
-            af::eval(a);                                            \
-            af::array b = af::func(a);                              \
-            std::vector<T> h_a(a.elements());                       \
-            std::vector<T> h_b(b.elements());                       \
+            array a = (hi - lo) * randu(num, ty) + lo + err;        \
+            eval(a);                                                \
+            array b = func(a);                                      \
+            vector<T> h_a(a.elements());                            \
+            vector<T> h_b(b.elements());                            \
             a.host(&h_a[0]);                                        \
             b.host(&h_b[0]);                                        \
                                                                     \
             for (int i = 0; i < num; i++) {                         \
                 T res = func(h_a[i]);                               \
                 ASSERT_NEAR(real(h_b[i]), real(res), err) <<        \
-                    "for real value: " << h_a[i] << std::endl;      \
+                    "for real value: " << h_a[i] << endl;           \
                 ASSERT_NEAR(imag(h_b[i]), imag(res), err) <<        \
-                    "for imag value: " << h_a[i] << std::endl;      \
+                    "for imag value: " << h_a[i] << endl;           \
             }                                                       \
-        } catch (af::exception &ex) {                               \
+        } catch (exception &ex) {                                   \
             FAIL() << ex.what();                                    \
         }                                                           \
     }                                                               \
@@ -149,8 +153,8 @@ MATH_TESTS_REAL(erfc)
 
 TEST(MathTests, Not)
 {
-    af::array a = af::randu(5, 5, b8);
-    af::array b = !a;
+    array a = randu(5, 5, b8);
+    array b = !a;
     char *ha = a.host<char>();
     char *hb = b.host<char>();
 
diff --git a/test/matrix_manipulation.cpp b/test/matrix_manipulation.cpp
index 9ab61ff93e..ff3d57c0aa 100644
--- a/test/matrix_manipulation.cpp
+++ b/test/matrix_manipulation.cpp
@@ -12,8 +12,11 @@
 #include <arrayfire.h>
 #include <vector>
 
-using namespace af;
 using std::vector;
+using af::array;
+using af::join;
+using af::randu;
+using af::tile;
 
 TEST(MatrixManipulation, SNIPPET_matrix_manipulation_tile)
 {
diff --git a/test/mean.cpp b/test/mean.cpp
index 4556fef690..2621f036f0 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -18,10 +18,15 @@
 #include <algorithm>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
 using af::cdouble;
 using af::cfloat;
+using af::constant;
+using af::dim4;
+using af::randu;
 
 template<typename T>
 class Mean : public ::testing::Test
@@ -78,50 +83,50 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<int> >        in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<int,float>(pFileName, numDims, in, tests);
 
     if (!isWeighted) {
-        af::dim4 dims = numDims[0];
+        dim4 dims = numDims[0];
         vector<T> input(in[0].begin(), in[0].end());
 
-        af::array inArray(dims, &(input.front()));
+        array inArray(dims, &(input.front()));
 
-        af::array outArray = af::mean(inArray, dim);
+        array outArray = mean(inArray, dim);
 
-        std::vector<outType> outData(dims.elements());
+        vector<outType> outData(dims.elements());
 
         outArray.host((void*)outData.data());
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
-            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
+            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
         }
     } else {
-        af::dim4 dims  = numDims[0];
-        af::dim4 wdims = numDims[1];
+        dim4 dims  = numDims[0];
+        dim4 wdims = numDims[1];
         vector<T> input(in[0].begin(), in[0].end());
         vector<float> weights(in[1].begin(), in[1].end());
 
-        af::array inArray(dims, &(input.front()));
-        af::array wtsArray(wdims, &(weights.front()));
+        array inArray(dims, &(input.front()));
+        array wtsArray(wdims, &(weights.front()));
 
-        af::array outArray = af::mean(inArray, wtsArray, dim);
+        array outArray = mean(inArray, wtsArray, dim);
 
-        std::vector<outType> outData(dims.elements());
+        vector<outType> outData(dims.elements());
 
         outArray.host((void*)outData.data());
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
-            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
+            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
         }
     }
 }
@@ -167,7 +172,7 @@ TYPED_TEST(Mean, Wtd_Dim1Matrix)
 }
 
 template<typename T>
-void meanAllTest(T const_value, af::dim4 dims)
+void meanAllTest(T const_value, dim4 dims)
 {
     typedef typename meanOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
@@ -194,52 +199,52 @@ void meanAllTest(T const_value, af::dim4 dims)
 
 TEST(MeanAll, f64)
 {
-    meanAllTest<double>(2.1, af::dim4(10, 10, 1, 1));
+    meanAllTest<double>(2.1, dim4(10, 10, 1, 1));
 }
 
 TEST(MeanAll, f32)
 {
-    meanAllTest<float>(2.1f, af::dim4(10, 5, 2, 1));
+    meanAllTest<float>(2.1f, dim4(10, 5, 2, 1));
 }
 
 TEST(MeanAll, s32)
 {
-    meanAllTest<int>(2, af::dim4(5, 5, 2, 2));
+    meanAllTest<int>(2, dim4(5, 5, 2, 2));
 }
 
 TEST(MeanAll, u32)
 {
-    meanAllTest<unsigned>(2, af::dim4(100, 1, 1, 1));
+    meanAllTest<unsigned>(2, dim4(100, 1, 1, 1));
 }
 
 TEST(MeanAll, s8)
 {
-    meanAllTest<char>(2, af::dim4(5, 5, 2, 2));
+    meanAllTest<char>(2, dim4(5, 5, 2, 2));
 }
 
 TEST(MeanAll, u8)
 {
-    meanAllTest<uchar>(2, af::dim4(100, 1, 1, 1));
+    meanAllTest<uchar>(2, dim4(100, 1, 1, 1));
 }
 
 TEST(MeanAll, c32)
 {
-    meanAllTest<cfloat>(cfloat(2.1f), af::dim4(10, 5, 2, 1));
+    meanAllTest<cfloat>(cfloat(2.1f), dim4(10, 5, 2, 1));
 }
 
 TEST(MeanAll, s16)
 {
-    meanAllTest<short>(2, af::dim4(5, 5, 2, 2));
+    meanAllTest<short>(2, dim4(5, 5, 2, 2));
 }
 
 TEST(MeanAll, u16)
 {
-    meanAllTest<ushort>(2, af::dim4(100, 1, 1, 1));
+    meanAllTest<ushort>(2, dim4(100, 1, 1, 1));
 }
 
 TEST(MeanAll, c64)
 {
-    meanAllTest<cdouble>(cdouble(2.1), af::dim4(10, 10, 1, 1));
+    meanAllTest<cdouble>(cdouble(2.1), dim4(10, 10, 1, 1));
 }
 
 
@@ -261,7 +266,7 @@ class WeightedMean : public ::testing::Test
 TYPED_TEST_CASE(WeightedMean, TestTypes);
 
 template<typename T, typename wtsType>
-void weightedMeanAllTest(af::dim4 dims)
+void weightedMeanAllTest(dim4 dims)
 {
     typedef typename meanOutType<T>::type outType;
 
@@ -299,19 +304,19 @@ void weightedMeanAllTest(af::dim4 dims)
 
 TYPED_TEST(WeightedMean, Basic)
 {
-    weightedMeanAllTest<TypeParam, float>(af::dim4(32, 30, 33, 17));
+    weightedMeanAllTest<TypeParam, float>(dim4(32, 30, 33, 17));
 }
 
 TEST(WeightedMean, Broadacst)
 {
     float val = 0.5f;
-    af::array a = af::randu(4096, 32);
-    af::array w = af::constant(val, a.dims());
-    af::array c = af::mean(a);
-    af::array d = af::mean(a, w);
+    array a = randu(4096, 32);
+    array w = constant(val, a.dims());
+    array c = mean(a);
+    array d = mean(a, w);
 
-    std::vector<float> hc(c.elements());
-    std::vector<float> hd(d.elements());
+    vector<float> hc(c.elements());
+    vector<float> hd(d.elements());
 
     c.host(hc.data());
     d.host(hd.data());
@@ -324,8 +329,6 @@ TEST(WeightedMean, Broadacst)
 
 TEST(Mean, Issue2093)
 {
-  using namespace af;
-
   const int NELEMS = 512;
 
   array data = randu(1, NELEMS);
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index ae743699ec..ffbf45b0df 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -20,6 +20,7 @@ using std::string;
 using std::vector;
 using std::abs;
 using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Meanshift : public ::testing::Test
@@ -41,9 +42,9 @@ TYPED_TEST(Meanshift, InvalidArgs)
     af_array inArray   = 0;
     af_array outArray  = 0;
 
-    af::dim4 dims = af::dim4(100,1,1,1);
+    dim4 dims = dim4(100,1,1,1);
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<TypeParam>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<TypeParam>::af_type));
     ASSERT_EQ(AF_ERR_SIZE, af_mean_shift(&outArray, inArray, 0.12f, 0.34f, 5, true));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -84,10 +85,10 @@ void meanshiftTest(string pTestFile, const float ss)
 
         ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, ss, 30.f, 5, isColor));
 
-        std::vector<T> outData(nElems);
+        vector<T> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<T> goldData(nElems);
+        vector<T> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
@@ -121,6 +122,16 @@ IMAGE_TESTS(double)
 
 //////////////////////////////////////// CPP ///////////////////////////////
 //
+
+using af::array;
+using af::iota;
+using af::constant;
+using af::loadImage;
+using af::max;
+using af::meanShift;
+using af::span;
+using af::seq;
+
 TEST(Meanshift, Color_CPP)
 {
     if (noDoubleTests<float>()) return;
@@ -139,15 +150,15 @@ TEST(Meanshift, Color_CPP)
         inFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
         outFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
 
-        af::array img   = af::loadImage(inFiles[testId].c_str(), true);
-        af::array gold  = af::loadImage(outFiles[testId].c_str(), true);
+        array img   = loadImage(inFiles[testId].c_str(), true);
+        array gold  = loadImage(outFiles[testId].c_str(), true);
         dim_t nElems = gold.elements();
-        af::array output= af::meanShift(img, 3.5f, 30.f, 5, true);
+        array output= meanShift(img, 3.5f, 30.f, 5, true);
 
-        std::vector<float> outData(nElems);
+        vector<float> outData(nElems);
         output.host((void*)outData.data());
 
-        std::vector<float> goldData(nElems);
+        vector<float> goldData(nElems);
         gold.host((void*)goldData.data());
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
@@ -156,8 +167,6 @@ TEST(Meanshift, Color_CPP)
 
 TEST(Meanshift, GFOR)
 {
-    using namespace af;
-
     dim4 dims = dim4(10, 10, 3);
     array A = iota(dims);
     array B = constant(0, dims);
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index e41e7a45cb..107685abbe 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -15,9 +15,12 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class MedianFilter : public ::testing::Test
@@ -45,29 +48,29 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<T> >      in;
     vector<vector<T> >   tests;
 
     readTests<T,T,int>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims      = numDims[0];
+    dim4 dims      = numDims[0];
     af_array outArray  = 0;
     af_array inArray   = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, pad));
 
-    std::vector<T> outData(dims.elements());
+    vector<T> outData(dims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     // cleanup
@@ -101,29 +104,29 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<T> >      in;
     vector<vector<T> >   tests;
 
     readTests<T,T,int>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims      = numDims[0];
+    dim4 dims      = numDims[0];
     af_array outArray  = 0;
     af_array inArray   = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_medfilt1(&outArray, inArray, w_wid, pad));
 
-    std::vector<T> outData(dims.elements());
+    vector<T> outData(dims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
     // cleanup
@@ -157,8 +160,6 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    using af::dim4;
-
     vector<dim4>       inDims;
     vector<string>    inFiles;
     vector<dim_t> outSizes;
@@ -184,10 +185,10 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
 
         ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
 
-        std::vector<T> outData(nElems);
+        vector<T> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<T> goldData(nElems);
+        vector<T> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
@@ -209,10 +210,10 @@ void medfiltInputTest(void)
     vector<T>   in(100, 1);
 
     // Check for 1D inputs -> medfilt1
-    af::dim4 dims = af::dim4(100, 1, 1, 1);
+    dim4 dims = dim4(100, 1, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
 
@@ -241,10 +242,10 @@ void medfiltWindowTest(void)
     vector<T>   in(100, 1);
 
     // Check for 4D inputs
-    af::dim4 dims(10, 10, 1, 1);
+    dim4 dims(10, 10, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 5, AF_PAD_ZERO));
 
@@ -268,10 +269,10 @@ void medfilt1d_WindowTest(void)
     vector<T>   in(100, 1);
 
     // Check for 4D inputs
-    af::dim4 dims(10, 10, 1, 1);
+    dim4 dims(10, 10, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, -1, AF_PAD_ZERO));
 
@@ -294,10 +295,10 @@ void medfiltPadTest(void)
     vector<T>   in(100, 1);
 
     // Check for 4D inputs
-    af::dim4 dims(10, 10, 1, 1);
+    dim4 dims(10, 10, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(3)));
 
@@ -322,10 +323,10 @@ void medfilt1d_PadTest(void)
     vector<T>   in(100, 1);
 
     // Check for 4D inputs
-    af::dim4 dims(10, 10, 1, 1);
+    dim4 dims(10, 10, 1, 1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(3)));
 
@@ -341,6 +342,9 @@ TYPED_TEST(MedianFilter1d, InvalidPadType)
 
 //////////////////////////////////// CPP ////////////////////////////////////
 //
+
+using af::array;
+
 TEST(MedianFilter, CPP)
 {
     if (noDoubleTests<float>()) return;
@@ -348,24 +352,24 @@ TEST(MedianFilter, CPP)
     const dim_t w_len = 3;
     const dim_t w_wid = 3;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTests<float,float,int>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x3_window.test"),
                                numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::medfilt(input, w_len, w_wid, AF_PAD_SYM);
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = medfilt(input, w_len, w_wid, AF_PAD_SYM);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 }
 
@@ -375,32 +379,30 @@ TEST(MedianFilter1d, CPP)
 
     const dim_t w_wid = 3;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTests<float,float,int>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"),
                                numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::medfilt1(input, w_wid, AF_PAD_SYM);
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = medfilt1(input, w_wid, AF_PAD_SYM);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 }
 
 
 TEST(MedianFilter, Docs)
 {
-    using af::array;
-
     float input[] = {
         1.0000,  2.0000,  3.0000,  4.0000,
         5.0000,  6.0000,  7.0000,  8.0000,
@@ -422,7 +424,7 @@ TEST(MedianFilter, Docs)
     //    2.0000        6.0000       10.0000       14.0000
     //    3.0000        7.0000       11.0000       15.0000
     //    4.0000        8.0000       12.0000       16.0000
-    array b = af::medfilt(a, 3, 3, AF_PAD_ZERO);
+    array b = medfilt(a, 3, 3, AF_PAD_ZERO);
     //af_print(b);
     //b=  0.0000        2.0000        6.0000        0.0000
     //    2.0000        6.0000       10.0000       10.0000
@@ -434,11 +436,17 @@ TEST(MedianFilter, Docs)
     b.host((void*)output);
 
     for (int i=0; i<16; ++i) {
-        ASSERT_EQ(output[i], gold[i]) << "output mismatch at i = " << i << std::endl;
+        ASSERT_EQ(output[i], gold[i]) << "output mismatch at i = " << i << endl;
     }
 }
 
-using namespace af;
+using af::iota;
+using af::constant;
+using af::span;
+using af::seq;
+using af::medfilt;
+using af::medfilt1;
+using af::max;
 
 TEST(MedianFilter, GFOR)
 {
diff --git a/test/median.cpp b/test/median.cpp
index 29a91d9732..d50fb912d0 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -13,27 +13,34 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
-using namespace af;
 using std::vector;
+using af::array;
+using af::dtype;
+using af::dtype_traits;
+using af::median;
+using af::randu;
+using af::seq;
+using af::span;
+using af::sum;
 
 template<typename Ti>
-af::array generateArray(int nx, int ny, int nz, int nw)
+array generateArray(int nx, int ny, int nz, int nw)
 {
-    array a = randu(nx, ny, nz, nw, (af::dtype)dtype_traits<Ti>::af_type);
+    array a = randu(nx, ny, nz, nw, (dtype)dtype_traits<Ti>::af_type);
     return a;
 }
 
 template<>
-af::array generateArray<int>(int nx, int ny, int nz, int nw)
+array generateArray<int>(int nx, int ny, int nz, int nw)
 {
-    array a = (randu(nx, ny, nz, nw, (af::dtype)dtype_traits<float>::af_type) * 1e6).as(s32);
+    array a = (randu(nx, ny, nz, nw, (dtype)dtype_traits<float>::af_type) * 1e6).as(s32);
     return a;
 }
 
 template<>
-af::array generateArray<unsigned int>(int nx, int ny, int nz, int nw)
+array generateArray<unsigned int>(int nx, int ny, int nz, int nw)
 {
-    array a = (randu(nx, ny, nz, nw, (af::dtype)dtype_traits<float>::af_type) * 1e6).as(u32);
+    array a = (randu(nx, ny, nz, nw, (dtype)dtype_traits<float>::af_type) * 1e6).as(u32);
     return a;
 }
 
@@ -49,7 +56,7 @@ void median_flat(int nx, int ny=1, int nz=1, int nw=1)
 
     To verify;
 
-    To *h_sa = sa.as((af_dtype)af::dtype_traits<To>::af_type).host<To>();
+    To *h_sa = sa.as((af_dtype)dtype_traits<To>::af_type).host<To>();
     if(sa.dims(0) % 2 == 1) {
         verify = h_sa[mid - 1];
     } else {
@@ -81,8 +88,8 @@ void median_test(int nx, int ny=1, int nz=1, int nw=1)
     array sa = sort(a, dim);
 
     double mid = (a.dims(dim) + 1) / 2;
-    af::seq mSeq[4] = {span, span, span, span};
-    mSeq[dim] = af::seq(mid, mid, 1.0);
+    seq mSeq[4] = {span, span, span, span};
+    mSeq[dim] = seq(mid, mid, 1.0);
 
     if(sa.dims(dim) % 2 == 1) {
         mSeq[dim] = mSeq[dim] - 1.0;
@@ -90,7 +97,7 @@ void median_test(int nx, int ny=1, int nz=1, int nw=1)
     } else {
         dim_t sdim[4] = {0};
         sdim[dim] = 1;
-        sa = sa.as((af_dtype)af::dtype_traits<To>::af_type);
+        sa = sa.as((af_dtype)dtype_traits<To>::af_type);
         array sas = shift(sa, sdim[0], sdim[1], sdim[2], sdim[3]);
         verify = ((sa + sas) / 2)(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     }
@@ -99,7 +106,7 @@ void median_test(int nx, int ny=1, int nz=1, int nw=1)
     array out = median(a, dim);
 
     ASSERT_EQ(out.dims() == verify.dims(), true);
-    ASSERT_NEAR(0, sum<double>(af::abs(out - verify)), 1e-5);
+    ASSERT_NEAR(0, sum<double>(abs(out - verify)), 1e-5);
 }
 
 #define MEDIAN_FLAT(To, Ti)                     \
diff --git a/test/memory.cpp b/test/memory.cpp
index d5d15dd37b..b16805bae3 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -13,16 +13,20 @@
 #include <af/traits.hpp>
 #include <vector>
 #include <iostream>
-#include <string>
 #include <testHelpers.hpp>
 #include <af/internal.h>
 
 using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
+using af::alloc;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::deviceGC;
+using af::deviceMemInfo;
+using af::dtype_traits;
+using af::randu;
+using af::seq;
+using af::span;
 
 const size_t step_bytes = 1024;
 
@@ -34,10 +38,10 @@ TEST(Memory, Scope)
     cleanSlate(); // Clean up everything done so far
 
     {
-        af::array a = af::randu(5, 5);
+        array a = randu(5, 5);
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -47,8 +51,8 @@ TEST(Memory, Scope)
     }
 
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
@@ -96,10 +100,10 @@ void memAllocArrayScopeTest(int elements)
     cleanSlate(); // Clean up everything done so far
 
     {
-        af::array a = af::randu(elements, (af_dtype)af::dtype_traits<T>::af_type);
+        array a = randu(elements, (af_dtype)dtype_traits<T>::af_type);
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -108,8 +112,8 @@ void memAllocArrayScopeTest(int elements)
         ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
     }
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
@@ -129,10 +133,10 @@ void memAllocPtrScopeTest(int elements)
     cleanSlate(); // Clean up everything done so far
 
     {
-        T *ptr = af::alloc<T>(elements);
+        T *ptr = alloc<T>(elements);
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -143,8 +147,8 @@ void memAllocPtrScopeTest(int elements)
         af::free(ptr);
     }
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
@@ -156,10 +160,10 @@ void memAllocPtrScopeTest(int elements)
     cleanSlate(); // Clean up everything done so far
 
     {
-        void *ptr = af::alloc(elements, (af_dtype)af::dtype_traits<T>::af_type);
+        void *ptr = alloc(elements, (af_dtype)dtype_traits<T>::af_type);
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -170,8 +174,8 @@ void memAllocPtrScopeTest(int elements)
         af::free(ptr);
     }
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
@@ -218,9 +222,9 @@ TEST(Memory, SingleSizeLoop)
     cleanSlate(); // Clean up everything done so far
 
     {
-        af::array a = af::randu(5, 5);
+        array a = randu(5, 5);
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
                       &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
@@ -230,10 +234,10 @@ TEST(Memory, SingleSizeLoop)
 
         for (int i = 0; i < 100; i++) {
 
-            a = af::randu(5,5);
+            a = randu(5,5);
 
-            af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                              &lock_bytes, &lock_buffers);
+            deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                          &lock_bytes, &lock_buffers);
 
             ASSERT_EQ(alloc_buffers, 2u); //2 because a new one is created before a is destroyed
             ASSERT_EQ(lock_buffers, 1u);
@@ -253,21 +257,21 @@ TEST(Memory, LargeLoop)
     const int num = step_bytes / sizeof(float);
     size_t allocated = step_bytes;
 
-    af::array a = af::randu(num);
+    array a = randu(num);
 
-    std::vector<float> hA(num);
+    vector<float> hA(num);
 
     a.host(&hA[0]);
 
     // Run a large loop that allocates more and more memory at each step
     for (int i = 0; i < 250; i++) {
-        af::array b = af::randu(num * (i + 1));
+        array b = randu(num * (i + 1));
         size_t current = (i + 1) * step_bytes;
         allocated += current;
 
         // Verify that new buffers are being allocated
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         // Limit to 10 to check before garbage collection
         if (i < 10) {
@@ -282,8 +286,8 @@ TEST(Memory, LargeLoop)
     size_t old_alloc_bytes = alloc_bytes;
     size_t old_alloc_buffers = alloc_buffers;
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(old_alloc_bytes, alloc_bytes);
     ASSERT_EQ(old_alloc_buffers, alloc_buffers);
@@ -301,10 +305,10 @@ TEST(Memory, IndexingOffset)
 
     const int num = step_bytes / sizeof(float);
 
-    af::array a = af::randu(num);
+    array a = randu(num);
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -312,10 +316,10 @@ TEST(Memory, IndexingOffset)
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
 
     {
-        af::array b = a(af::seq(1, num/2)); // Should just be an offset
+        array b = a(seq(1, num/2)); // Should just be an offset
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -325,8 +329,8 @@ TEST(Memory, IndexingOffset)
 
 
     // b should not have deleted a
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -344,10 +348,10 @@ TEST(Memory, IndexingCopy)
 
     const int num = step_bytes / sizeof(float);
 
-    af::array a = af::randu(num);
+    array a = randu(num);
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -356,10 +360,10 @@ TEST(Memory, IndexingCopy)
 
     {
         // Should just a copy
-        af::array b = a(af::seq(0, num/2-1, 2));
+        array b = a(seq(0, num/2-1, 2));
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 2u);
         ASSERT_EQ(lock_buffers, 2u);
@@ -369,8 +373,8 @@ TEST(Memory, IndexingCopy)
 
 
     // b should not have deleted a
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 2u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -388,10 +392,10 @@ TEST(Memory, Assign)
 
     const int num = step_bytes / sizeof(float);
 
-    af::array a = af::randu(num);
+    array a = randu(num);
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -399,11 +403,11 @@ TEST(Memory, Assign)
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
 
     {
-        af::array b = af::randu(num / 2);
-        a(af::seq(num / 2)) = b;
+        array b = randu(num / 2);
+        a(seq(num / 2)) = b;
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 2u);
         ASSERT_EQ(lock_buffers, 2u);
@@ -413,8 +417,8 @@ TEST(Memory, Assign)
 
 
     // b should not have deleted a
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 2u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -433,10 +437,10 @@ TEST(Memory, AssignLoop)
     const int num = step_bytes / sizeof(float);
     const int cols = 100;
 
-    af::array a = af::randu(num, cols);
+    array a = randu(num, cols);
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -445,11 +449,11 @@ TEST(Memory, AssignLoop)
 
     for (int i = 0; i < cols; i++) {
 
-        af::array b = af::randu(num);
-        a(af::span, i) = b;
+        array b = randu(num);
+        a(span, i) = b;
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 2u); // 3 because you need another scratch space for b
         ASSERT_EQ(lock_buffers, 2u);
@@ -467,11 +471,11 @@ TEST(Memory, AssignRef)
 
     const int num = step_bytes / sizeof(float);
 
-    af::array a = af::randu(num);
-    af::array a_ref = a;
+    array a = randu(num);
+    array a_ref = a;
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -479,12 +483,12 @@ TEST(Memory, AssignRef)
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
 
     {
-        af::array b = af::randu(num / 2);
+        array b = randu(num / 2);
         // This should do a full copy of a
-        a(af::seq(num / 2)) = b;
+        a(seq(num / 2)) = b;
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 3u);
         ASSERT_EQ(lock_buffers, 3u);
@@ -494,8 +498,8 @@ TEST(Memory, AssignRef)
 
 
     // b should not have deleted a
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 3u);
     ASSERT_EQ(lock_buffers, 2u); // a_ref
@@ -514,11 +518,11 @@ TEST(Memory, AssignRefLoop)
     const int num = step_bytes / sizeof(float);
     const int cols = 100;
 
-    af::array a = af::randu(num, cols);
-    af::array a_ref = a;
+    array a = randu(num, cols);
+    array a_ref = a;
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -527,11 +531,11 @@ TEST(Memory, AssignRefLoop)
 
     for (int i = 0; i < cols; i++) {
 
-        af::array b = af::randu(num);
-        a(af::span, i) = b;
+        array b = randu(num);
+        a(span, i) = b;
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 3u);
         ASSERT_EQ(lock_buffers, 3u);
@@ -541,8 +545,8 @@ TEST(Memory, AssignRefLoop)
 
 
     // b should not have deleted a
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 3u);
     ASSERT_EQ(lock_buffers, 2u); // a_ref
@@ -559,10 +563,10 @@ TEST(Memory, device)
     cleanSlate(); // Clean up everything done so far
 
     {
-        af::array a = af::randu(5, 5);
+        array a = randu(5, 5);
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -571,8 +575,8 @@ TEST(Memory, device)
 
         a.device<float>();
 
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -582,8 +586,8 @@ TEST(Memory, device)
         a.unlock(); //to reset the lock flag
     }
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 0u);
@@ -601,13 +605,13 @@ TEST(Memory, unlock)
 
     const dim_t num = step_bytes / sizeof(float);
 
-    std::vector<float> in(num);
+    vector<float> in(num);
 
     af_array arr = 0;
     ASSERT_EQ(AF_SUCCESS, af_create_array(&arr, &in[0], 1, &num, f32));
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -616,12 +620,12 @@ TEST(Memory, unlock)
 
     // arr1 gets released by end of the following code block
     {
-        af::array a(arr);
+        array a(arr);
         a.lock();
 
         // No new memory should be allocated
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                      &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -632,10 +636,10 @@ TEST(Memory, unlock)
     }
 
     // Making sure all unlocked buffers are freed
-    af::deviceGC();
+    deviceGC();
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
+                  &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 0u);
     ASSERT_EQ(lock_buffers, 0u);
@@ -649,26 +653,26 @@ TEST(Memory, IndexedDevice)
     const int nx = 8;
     const int ny = 8;
 
-    af::array in = af::randu(nx, ny);
+    array in = randu(nx, ny);
 
-    std::vector<float> in1(in.elements());
+    vector<float> in1(in.elements());
     in.host(&in1[0]);
 
     int offx = nx / 4;
     int offy = ny / 4;
 
-    in = in(af::seq(offx, offx + nx/2 - 1),
-            af::seq(offy, offy + ny/2- 1));
+    in = in(seq(offx, offx + nx/2 - 1),
+            seq(offy, offy + ny/2- 1));
 
     int nxo = (int)in.dims(0);
     int nyo = (int)in.dims(1);
 
-    void *rawPtr = af::getRawPtr(in);
+    void *rawPtr = getRawPtr(in);
     void *devPtr = in.device<float>();
     ASSERT_NE(devPtr, rawPtr);
     in.unlock();
 
-    std::vector<float> in2(in.elements());
+    vector<float> in2(in.elements());
     in.host(&in2[0]);
 
     for (int y = 0; y < nyo; y++) {
diff --git a/test/memory_lock.cpp b/test/memory_lock.cpp
index 63331f0d03..0e1753196a 100644
--- a/test/memory_lock.cpp
+++ b/test/memory_lock.cpp
@@ -20,6 +20,9 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
+using af::deviceGC;
+using af::deviceMemInfo;
 
 const size_t step_bytes = 1024;
 
@@ -34,12 +37,12 @@ TEST(Memory, lock)
 
     const dim_t num = step_bytes / sizeof(float);
 
-    std::vector<float> in(num);
+    vector<float> in(num);
 
     af_array arr = 0;
     ASSERT_EQ(AF_SUCCESS, af_create_array(&arr, &in[0], 1, &num, f32));
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
                       &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
@@ -49,11 +52,11 @@ TEST(Memory, lock)
 
     // arr1 gets released by end of the following code block
     {
-        af::array a(arr);
+        array a(arr);
         a.lock();
 
         // No new memory should be allocated
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
                           &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
@@ -63,9 +66,9 @@ TEST(Memory, lock)
     }
 
     // Making sure all unlocked buffers are freed
-    af::deviceGC();
+    deviceGC();
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
                       &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
diff --git a/test/moddims.cpp b/test/moddims.cpp
index ca8029cafb..4ddef7c56d 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -18,10 +18,11 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Moddims : public ::testing::Test
@@ -46,12 +47,12 @@ void moddimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *se
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     T *outData;
 
@@ -60,11 +61,11 @@ void moddimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *se
         af_array subArray  = 0;
         af_array outArray  = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&subArray,inArray,seqv->size(),&seqv->front()));
 
-        af::dim4 newDims(1);
+        dim4 newDims(1);
         newDims[0] = 2;
         newDims[1] = 3;
         ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,subArray,newDims.ndims(),newDims.get()));
@@ -82,9 +83,9 @@ void moddimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *se
         af_array inArray   = 0;
         af_array outArray  = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        af::dim4 newDims(1);
+        dim4 newDims(1);
         newDims[0] = dims[1];
         newDims[1] = dims[0]*dims[2];
         ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
@@ -100,7 +101,7 @@ void moddimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *se
         vector<T> currGoldBar   = tests[testIter];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< std::endl;
+            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
         }
     }
     delete[] outData;
@@ -122,19 +123,19 @@ void moddimsArgsTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array inArray   = 0;
     af_array outArray  = 0;
     af_array outArray2  = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    af::dim4 newDims(1);
+    dim4 newDims(1);
     newDims[0] = dims[1];
     newDims[1] = dims[0]*dims[2];
     ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,inArray,0,newDims.get()));
@@ -154,18 +155,18 @@ void moddimsMismatchTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array inArray   = 0;
     af_array outArray  = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    af::dim4 newDims(1);
+    dim4 newDims(1);
     newDims[0] = dims[1]-1;
     newDims[1] = (dims[0]-1)*dims[2];
     ASSERT_EQ(AF_ERR_SIZE, af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
@@ -181,41 +182,44 @@ TYPED_TEST(Moddims,Mismatch)
 
 /////////////////////////////////// CPP ///////////////////////////////////
 //
+
+using af::array;
+
 template<typename T>
 void cppModdimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=NULL)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     T *outData;
 
     if (isSubRef) {
-        af::array input(dims, &(in[0].front()));
+        array input(dims, &(in[0].front()));
 
-        af::array subArray = input(seqv->at(0), seqv->at(1));
+        array subArray = input(seqv->at(0), seqv->at(1));
 
-        af::dim4 newDims(1);
+        dim4 newDims(1);
         newDims[0] = 2;
         newDims[1] = 3;
-        af::array output = af::moddims(subArray, newDims.ndims(), newDims.get());
+        array output = moddims(subArray, newDims.ndims(), newDims.get());
 
         dim_t nElems = output.elements();
         outData = new T[nElems];
         output.host((void*)outData);
     } else {
-        af::array input(dims, &(in[0].front()));
+        array input(dims, &(in[0].front()));
 
-        af::dim4 newDims(1);
+        dim4 newDims(1);
         newDims[0] = dims[1];
         newDims[1] = dims[0]*dims[2];
 
-        af::array output = af::moddims(input, newDims.ndims(), newDims.get());
+        array output = moddims(input, newDims.ndims(), newDims.get());
 
         outData = new T[dims.elements()];
         output.host((void*)outData);
@@ -225,7 +229,7 @@ void cppModdimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq>
         vector<T> currGoldBar   = tests[testIter];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< std::endl;
+            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
         }
     }
     delete[] outData;
diff --git a/test/moments.cpp b/test/moments.cpp
index 1d16369483..331996673e 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -19,10 +19,15 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::identity;
+using af::loadImage;
+using af::max;
+using af::min;
 
 template<typename T>
 class Image : public ::testing::Test
@@ -43,100 +48,100 @@ void momentsTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<float> >   tests;
     readTests<T, float, float>(pTestFile, numDims, in, tests);
 
-    af::array imgArray(numDims.front(), &in.front()[0]);
+    array imgArray(numDims.front(), &in.front()[0]);
 
-    af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
+    array momentsArray = moments(imgArray, AF_MOMENT_M00);
     vector<float> mData(momentsArray.elements());
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_M01);
+    momentsArray = moments(imgArray, AF_MOMENT_M01);
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_M10);
+    momentsArray = moments(imgArray, AF_MOMENT_M10);
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_M11);
+    momentsArray = moments(imgArray, AF_MOMENT_M11);
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_FIRST_ORDER);
+    momentsArray = moments(imgArray, AF_MOMENT_FIRST_ORDER);
     mData.resize(momentsArray.elements());
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements()/4;i+=4) {
-        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-3 * tests[0][i] ) << "at: " << i   << std::endl;
-        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-3 * tests[1][i] ) << "at: " << i+1 << std::endl;
-        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-3 * tests[2][i] ) << "at: " << i+2 << std::endl;
-        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-3 * tests[3][i] ) << "at: " << i+3 << std::endl;
+        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-3 * tests[0][i] ) << "at: " << i   << endl;
+        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-3 * tests[1][i] ) << "at: " << i+1 << endl;
+        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-3 * tests[2][i] ) << "at: " << i+2 << endl;
+        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-3 * tests[3][i] ) << "at: " << i+3 << endl;
     }
 }
 
 void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
 {
     if (noImageIOTests()) return;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(pTestFile, numDims, in, tests);
 
-    af::array imgArray = af::loadImage(pImageFile.c_str(), isColor);
+    array imgArray = loadImage(pImageFile.c_str(), isColor);
 
-    double maxVal = af::max<double>(imgArray);
-    double minVal = af::min<double>(imgArray);
+    double maxVal = max<double>(imgArray);
+    double minVal = min<double>(imgArray);
     imgArray -= minVal;
     imgArray /= maxVal - minVal;
 
-    af::array momentsArray = af::moments(imgArray, AF_MOMENT_M00);
+    array momentsArray = moments(imgArray, AF_MOMENT_M00);
 
     vector<float> mData(momentsArray.elements());
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[0][i], mData[i], 1e-2 * tests[0][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[0][i], mData[i], 1e-2 * tests[0][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_M01);
+    momentsArray = moments(imgArray, AF_MOMENT_M01);
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[1][i], mData[i], 1e-2 * tests[1][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[1][i], mData[i], 1e-2 * tests[1][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_M10);
+    momentsArray = moments(imgArray, AF_MOMENT_M10);
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[2][i], mData[i], 1e-2 * tests[2][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[2][i], mData[i], 1e-2 * tests[2][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_M11);
+    momentsArray = moments(imgArray, AF_MOMENT_M11);
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[3][i], mData[i], 1e-2 * tests[3][i] ) << "at: " << i << std::endl;
+        ASSERT_NEAR(tests[3][i], mData[i], 1e-2 * tests[3][i] ) << "at: " << i << endl;
     }
 
-    momentsArray = af::moments(imgArray, AF_MOMENT_FIRST_ORDER);
+    momentsArray = moments(imgArray, AF_MOMENT_FIRST_ORDER);
     mData.resize(momentsArray.elements());
     momentsArray.host(&mData[0]);
     for(int i=0; i<momentsArray.elements()/4;i+=4) {
-        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-2 * tests[0][i] ) << "at: " << i   << std::endl;
-        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-2 * tests[1][i] ) << "at: " << i+1 << std::endl;
-        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-2 * tests[2][i] ) << "at: " << i+2 << std::endl;
-        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-2 * tests[3][i] ) << "at: " << i+3 << std::endl;
+        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-2 * tests[0][i] ) << "at: " << i   << endl;
+        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-2 * tests[1][i] ) << "at: " << i+1 << endl;
+        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-2 * tests[2][i] ) << "at: " << i+2 << endl;
+        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-2 * tests[3][i] ) << "at: " << i+3 << endl;
     }
 }
 
@@ -162,9 +167,9 @@ TYPED_TEST(Image, MomentsSynthTypes)
 
 TEST(Image, Moment_Issue1957)
 {
-    af::array A = af::identity(3, 3, b8);
+    array A = identity(3, 3, b8);
 
     double m00;
-    af::moments(&m00, A, AF_MOMENT_M00);
+    moments(&m00, A, AF_MOMENT_M00);
     ASSERT_EQ(m00, 3);
 }
diff --git a/test/morph.cpp b/test/morph.cpp
index 025100fb55..d20e6143ab 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -16,9 +16,12 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Morph : public ::testing::Test
@@ -38,22 +41,22 @@ void morphTest(string pTestFile)
 {
     if (noDoubleTests<inType>()) return;
 
-    vector<af::dim4>       numDims;
+    vector<dim4>       numDims;
     vector<vector<inType> >      in;
     vector<vector<inType> >   tests;
 
     readTests<inType,inType,int>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims      = numDims[0];
-    af::dim4 maskDims  = numDims[1];
+    dim4 dims      = numDims[0];
+    dim4 maskDims  = numDims[1];
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array maskArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &(in[1].front()),
-                maskDims.ndims(), maskDims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                maskDims.ndims(), maskDims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if (isDilation) {
         if (isVolume)
@@ -68,7 +71,7 @@ void morphTest(string pTestFile)
             ASSERT_EQ(AF_SUCCESS, af_erode(&outArray, inArray, maskArray));
     }
 
-    std::vector<inType> outData(dims.elements());
+    vector<inType> outData(dims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
@@ -76,7 +79,7 @@ void morphTest(string pTestFile)
         vector<inType> currGoldBar = tests[testIter];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
         }
     }
 
@@ -147,8 +150,6 @@ void morphImageTest(string pTestFile)
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    using af::dim4;
-
     vector<dim4>       inDims;
     vector<string>    inFiles;
     vector<dim_t> outSizes;
@@ -171,7 +172,7 @@ void morphImageTest(string pTestFile)
 
         dim4 mdims(3,3,1,1);
         ASSERT_EQ(AF_SUCCESS, af_constant(&maskArray, 1.0,
-                    mdims.ndims(), mdims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                    mdims.ndims(), mdims.get(), (af_dtype)dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray, inFiles[testId].c_str(), isColor));
         ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
@@ -182,10 +183,10 @@ void morphImageTest(string pTestFile)
         else
             ASSERT_EQ(AF_SUCCESS, af_erode(&outArray, inArray, maskArray));
 
-        std::vector<T> outData(nElems);
+        vector<T> outData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
 
-        std::vector<T> goldData(nElems);
+        vector<T> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
@@ -220,14 +221,14 @@ void morphInputTest(void)
     vector<T>   mask(9,1);
 
     // Check for 1D inputs
-    af::dim4 dims = af::dim4(100,1,1,1);
-    af::dim4 mdims(3,3,1,1);
+    dim4 dims = dim4(100,1,1,1);
+    dim4 mdims(3,3,1,1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate(&outArray, inArray, maskArray));
@@ -262,14 +263,14 @@ void morphMaskTest(void)
     vector<T>   mask(16,1);
 
     // Check for 4D mask
-    af::dim4 dims(10,10,1,1);
-    af::dim4 mdims(2,2,2,2);
+    dim4 dims(10,10,1,1);
+    dim4 mdims(2,2,2,2);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate(&outArray, inArray, maskArray));
@@ -279,10 +280,10 @@ void morphMaskTest(void)
     ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
 
     // Check for 1D mask
-    mdims = af::dim4(16,1,1,1);
+    mdims = dim4(16,1,1,1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate(&outArray, inArray, maskArray));
@@ -317,14 +318,14 @@ void morph3DMaskTest(void)
     vector<T>   mask(81,1);
 
     // Check for 2D mask
-    af::dim4 dims(10,10,10,1);
-    af::dim4 mdims(9,9,1,1);
+    dim4 dims(10,10,10,1);
+    dim4 mdims(9,9,1,1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate3(&outArray, inArray, maskArray));
@@ -334,10 +335,10 @@ void morph3DMaskTest(void)
     ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
 
     // Check for 4D mask
-    mdims = af::dim4(3,3,3,3);
+    mdims = dim4(3,3,3,3);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate3(&outArray, inArray, maskArray));
@@ -362,14 +363,23 @@ TYPED_TEST(Morph, ErodeVolumeInvalidMask)
 
 ////////////////////////////////////// CPP //////////////////////////////////
 //
+
+using af::array;
+using af::constant;
+using af::loadImage;
+using af::erode;
+using af::iota;
+using af::max;
+using af::randu;
+using af::seq;
+using af::span;
+
 template<typename T, bool isDilation, bool isColor>
 void cppMorphImageTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    using af::dim4;
-
     vector<dim4>       inDims;
     vector<string>    inFiles;
     vector<dim_t> outSizes;
@@ -383,21 +393,21 @@ void cppMorphImageTest(string pTestFile)
         inFiles[testId].insert(0,string(TEST_DIR"/morph/"));
         outFiles[testId].insert(0,string(TEST_DIR"/morph/"));
 
-        af::array mask = af::constant(1.0, 3, 3);
-        af::array img = af::loadImage(inFiles[testId].c_str(), isColor);
-        af::array gold = af::loadImage(outFiles[testId].c_str(), isColor);
+        array mask = constant(1.0, 3, 3);
+        array img = loadImage(inFiles[testId].c_str(), isColor);
+        array gold = loadImage(outFiles[testId].c_str(), isColor);
         dim_t nElems   = gold.elements();
-        af::array output;
+        array output;
 
         if (isDilation)
             output = dilate(img, mask);
         else
             output = erode(img, mask);
 
-        std::vector<T> outData(nElems);
+        vector<T> outData(nElems);
         output.host((void*)outData.data());
 
-        std::vector<T> goldData(nElems);
+        vector<T> goldData(nElems);
         gold.host((void*)goldData.data());
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
@@ -414,7 +424,6 @@ TEST(Morph, ColorImage_CPP)
     cppMorphImageTest<float, false, true>(string(TEST_DIR"/morph/color.test"));
 }
 
-using namespace af;
 TEST(Morph, GFOR)
 {
     dim4 dims = dim4(10, 10, 3);
@@ -472,7 +481,7 @@ TEST(Morph, EdgeIssue1564)
     array dilated = dilate(input.as(b8), mask.as(b8));
 
     size_t nElems = dilated.elements();
-    std::vector<char> outData(nElems);
+    vector<char> outData(nElems);
     dilated.host((void*)outData.data());
 
     for (size_t i=0; i<nElems; ++i) {
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index ea10735be1..249fda8535 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -15,10 +15,14 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::vector;
 using std::string;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class NearestNeighbour : public ::testing::Test
@@ -64,8 +68,6 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
 
     typedef typename otype_t<T>::otype To;
 
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<T> >   in;
     vector<vector<uint> >  tests;
@@ -80,9 +82,9 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
     af_array dist  = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&query, &(in[0].front()),
-                qDims.ndims(), qDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                qDims.ndims(), qDims.get(), (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(AF_SUCCESS, af_create_array(&train, &(in[1].front()),
-                tDims.ndims(), tDims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_nearest_neighbour(&idx, &dist, query, train, feat_dim, 1, type));
 
@@ -96,7 +98,7 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDist, dist));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ((To)goldDist[elIter], outDist[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ((To)goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outIdx;
@@ -157,9 +159,6 @@ TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim1)
 //
 TEST(NearestNeighbourSSD, CPP)
 {
-    using af::array;
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<uint> >     in;
     vector<vector<uint> >  tests;
@@ -185,7 +184,7 @@ TEST(NearestNeighbourSSD, CPP)
     dist.host(outDist);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outIdx;
@@ -194,9 +193,6 @@ TEST(NearestNeighbourSSD, CPP)
 
 TEST(NearestNeighbourSAD, CPP)
 {
-    using af::array;
-    using af::dim4;
-
     vector<dim4>         numDims;
     vector<vector<uint> >     in;
     vector<vector<uint> >  tests;
@@ -222,7 +218,7 @@ TEST(NearestNeighbourSAD, CPP)
     dist.host(outDist);
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
     }
 
     delete[] outIdx;
@@ -245,11 +241,11 @@ TEST(NearestNeighbourSSD, small)
         6, 5,
         8, 6.5
     };
-    af::array t(nfeat, ntrain, train);
-    af::array q(nfeat, nquery, query);
-    af::array indices;
-    af::array distances;
-    af::nearestNeighbour(indices, distances, q, t, 0, 1, AF_SSD);
+    array t(nfeat, ntrain, train);
+    array q(nfeat, nquery, query);
+    array indices;
+    array distances;
+    nearestNeighbour(indices, distances, q, t, 0, 1, AF_SSD);
 
     float expectedDistances[nquery] = {
         (5 - 0) * (5 - 0) + (5 - 0) * (5 - 0),
@@ -259,7 +255,7 @@ TEST(NearestNeighbourSSD, small)
         (5 - 8) * (5 - 8) + (5 - 6.5) * (5 - 6.5)
     };
 
-    std::vector<float> actualDistances(nquery);
+    vector<float> actualDistances(nquery);
     distances.host(&actualDistances[0]);
     for (int i = 0; i < nquery; i++)
     {
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index cfb1c2f24d..c05661e572 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -13,11 +13,18 @@
 #include <af/opencl.h>
 #include <iostream>
 
+using std::endl;
 using std::vector;
+using af::array;
+using af::constant;
+using af::getDeviceCount;
+using af::info;
+using af::randu;
+using af::setDevice;
 
 inline void checkErr(cl_int err, const char * name) {
     if (err != CL_SUCCESS) {
-        std::cerr << "ERROR: " << name  << " (" << err << ")" << std::endl;
+        std::cerr << "ERROR: " << name  << " (" << err << ")" << endl;
         exit(EXIT_FAILURE);
     }
 }
@@ -60,18 +67,18 @@ TEST(OCLExtContext, PushAndPop)
     cl_command_queue queue = NULL;
 
     getExternals(deviceId, context, queue);
-    int dCount = af::getDeviceCount();
+    int dCount = getDeviceCount();
     printf("\n%d devices before afcl::addDevice\n\n", dCount);
-    af::info();
+    info();
 
     afcl::addDevice(deviceId, context, queue);
-    ASSERT_EQ(true, dCount+1==af::getDeviceCount());
-    printf("\n%d devices after afcl::addDevice\n", af::getDeviceCount());
+    ASSERT_EQ(true, dCount+1==getDeviceCount());
+    printf("\n%d devices after afcl::addDevice\n", getDeviceCount());
 
     afcl::deleteDevice(deviceId, context);
-    ASSERT_EQ(true, dCount==af::getDeviceCount());
-    printf("\n%d devices after afcl::deleteDevice\n\n", af::getDeviceCount());
-    af::info();
+    ASSERT_EQ(true, dCount==getDeviceCount());
+    printf("\n%d devices after afcl::deleteDevice\n\n", getDeviceCount());
+    info();
 }
 
 TEST(OCLExtContext, set)
@@ -80,33 +87,33 @@ TEST(OCLExtContext, set)
     cl_context context = NULL;
     cl_command_queue queue = NULL;
 
-    int dCount = af::getDeviceCount(); //Before user device addition
-    af::setDevice(0);
-    af::info();
-    af::array t = af::randu(5,5);
+    int dCount = getDeviceCount(); //Before user device addition
+    setDevice(0);
+    info();
+    array t = randu(5,5);
     af_print(t);
 
     getExternals(deviceId, context, queue);
     afcl::addDevice(deviceId, context, queue);
     printf("\nBefore setting device to newly added one\n\n");
-    af::info();
+    info();
 
     printf("\n\nBefore setting device to newly added one\n\n");
-    af::setDevice(dCount); //In 0-based index, dCount is index of newly added device
-    af::info();
+    setDevice(dCount); //In 0-based index, dCount is index of newly added device
+    info();
 
     const int x = 5;
     const int y = 5;
     const int s = x * y;
-    af::array a = af::constant(1, x, y);
+    array a = constant(1, x, y);
     vector<float> host(s);
     a.host((void*)host.data());
     for (int i=0; i<s; ++i)
         ASSERT_EQ(host[i], 1.0f);
 
     printf("\n\nAfter reset to default set of devices\n\n");
-    af::setDevice(0);
-    af::info();
+    setDevice(0);
+    info();
     af_print(t);
 }
 
diff --git a/test/orb.cpp b/test/orb.cpp
index 1548b83d2d..0a6ca134e1 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -18,10 +18,15 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::abs;
+using std::cout;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::array;
 using af::dim4;
+using af::features;
+using af::loadImage;
 
 typedef struct
 {
@@ -109,9 +114,9 @@ bool compareHamming(int data_size, unsigned *cpu, unsigned *gpu, unsigned thr =
         unsigned x = (cpu[i] ^ gpu[i]);
         if(popcount(x) > thr) {
             ret = false;
-            std::cout<<std::endl<<"@compareHamming: first mismatch."<<std::endl;
-            std::cout<<"(cpu,gpu,cpu-gpu)["<<i<<"] : {"<<cpu[i]<<","<<gpu[i]<<","<<cpu[i]-gpu[i]<<"}"<<std::endl;
-            std::cout<<std::endl;
+            cout<<endl<<"@compareHamming: first mismatch."<<endl;
+            cout<<"(cpu,gpu,cpu-gpu)["<<i<<"] : {"<<cpu[i]<<","<<gpu[i]<<","<<cpu[i]-gpu[i]<<"}"<<endl;
+            cout<<endl;
             break;
         }
     }
@@ -200,11 +205,11 @@ void orbTest(string pTestFile)
         split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
         for (int elIter = 0; elIter < (int)n; elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
         }
 
         // TODO: improve distance for single/double-precision interchangeability
@@ -250,11 +255,11 @@ TEST(ORB, CPP)
     readImageFeaturesDescriptors<unsigned>(string(TEST_DIR"/orb/square.test"), inDims, inFiles, goldFeat, goldDesc);
     inFiles[0].insert(0,string(TEST_DIR"/orb/"));
 
-    af::array in = af::loadImage(inFiles[0].c_str(), false);
+    array in = loadImage(inFiles[0].c_str(), false);
 
-    af::features feat;
-    af::array desc;
-    af::orb(feat, desc, in, 20.0f, 400, 1.2f, 8, true);
+    features feat;
+    array desc;
+    orb(feat, desc, in, 20.0f, 400, 1.2f, 8, true);
 
     float * outX           = new float[feat.getNumFeatures()];
     float * outY           = new float[feat.getNumFeatures()];
@@ -287,11 +292,11 @@ TEST(ORB, CPP)
     split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
     for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
-        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
+        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
     }
 
     // TODO: improve distance for single/double-precision interchangeability
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 4d79a67da9..16d5b20585 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -23,8 +23,15 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::exception;
+using af::identity;
+using af::matmul;
+using af::max;
+
 
 ///////////////////////////////// CPP ////////////////////////////////////
 TEST(QRFactorized, CPP)
@@ -34,19 +41,19 @@ TEST(QRFactorized, CPP)
 
     int resultIdx = 0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, float>(string(TEST_DIR"/lapack/qrfactorized.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
 
-    af::array q, r, tau;
-    af::qr(q, r, tau, input);
+    array q, r, tau;
+    qr(q, r, tau, input);
 
-    af::dim4 qdims = q.dims();
-    af::dim4 rdims = r.dims();
+    dim4 qdims = q.dims();
+    dim4 rdims = r.dims();
 
     // Get result
     float* qData = new float[qdims.elements()];
@@ -58,7 +65,7 @@ TEST(QRFactorized, CPP)
     for (int y = 0; y < (int)qdims[1]; ++y) {
         for (int x = 0; x < (int)qdims[0]; ++x) {
             int elIter = y * qdims[0] + x;
-            ASSERT_NEAR(tests[resultIdx][elIter], qData[elIter], 0.001) << "at: " << elIter << std::endl;
+            ASSERT_NEAR(tests[resultIdx][elIter], qData[elIter], 0.001) << "at: " << elIter << endl;
         }
     }
 
@@ -69,7 +76,7 @@ TEST(QRFactorized, CPP)
             // Test only upper half
             if(x <= y) {
                 int elIter = y * rdims[0] + x;
-                ASSERT_NEAR(tests[resultIdx][elIter], rData[elIter], 0.001) << "at: " << elIter << std::endl;
+                ASSERT_NEAR(tests[resultIdx][elIter], rData[elIter], 0.001) << "at: " << elIter << endl;
             }
         }
     }
@@ -87,46 +94,46 @@ void qrTester(const int m, const int n, double eps)
         if (noLAPACKTests()) return;
 
 #if 1
-        af::array in = cpu_randu<T>(af::dim4(m, n));
+        array in = cpu_randu<T>(dim4(m, n));
 #else
-        af::array in = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+        array in = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
         //! [ex_qr_unpacked]
-        af::array q, r, tau;
-        af::qr(q, r, tau, in);
+        array q, r, tau;
+        qr(q, r, tau, in);
         //! [ex_qr_unpacked]
 
-        af::array qq = af::matmul(q, q.H());
-        af::array ii = af::identity(qq.dims(), qq.type());
+        array qq = matmul(q, q.H());
+        array ii = identity(qq.dims(), qq.type());
 
-        ASSERT_NEAR(0, af::max<double>(af::abs(real(qq - ii))), eps);
-        ASSERT_NEAR(0, af::max<double>(af::abs(imag(qq - ii))), eps);
+        ASSERT_NEAR(0, max<double>(abs(real(qq - ii))), eps);
+        ASSERT_NEAR(0, max<double>(abs(imag(qq - ii))), eps);
 
         //! [ex_qr_recon]
-        af::array re = af::matmul(q, r);
+        array re = matmul(q, r);
         //! [ex_qr_recon]
 
-        ASSERT_NEAR(0, af::max<double>(af::abs(real(re - in))), eps);
-        ASSERT_NEAR(0, af::max<double>(af::abs(imag(re - in))), eps);
+        ASSERT_NEAR(0, max<double>(abs(real(re - in))), eps);
+        ASSERT_NEAR(0, max<double>(abs(imag(re - in))), eps);
 
         //! [ex_qr_packed]
-        af::array out = in.copy();
-        af::array tau2;
+        array out = in.copy();
+        array tau2;
         qrInPlace(tau2, out);
         //! [ex_qr_packed]
 
-        af::array r2 = upper(out);
+        array r2 = upper(out);
 
-        ASSERT_NEAR(0, af::max<double>(af::abs(real(tau - tau2))), eps);
-        ASSERT_NEAR(0, af::max<double>(af::abs(imag(tau - tau2))), eps);
+        ASSERT_NEAR(0, max<double>(abs(real(tau - tau2))), eps);
+        ASSERT_NEAR(0, max<double>(abs(imag(tau - tau2))), eps);
 
-        ASSERT_NEAR(0, af::max<double>(af::abs(real(r2 - r))), eps);
-        ASSERT_NEAR(0, af::max<double>(af::abs(imag(r2 - r))), eps);
+        ASSERT_NEAR(0, max<double>(abs(real(r2 - r))), eps);
+        ASSERT_NEAR(0, max<double>(abs(imag(r2 - r))), eps);
 
 
-    } catch(af::exception &ex) {
-        std::cout << ex.what() << std::endl;
+    } catch(exception &ex) {
+        cout << ex.what() << endl;
         throw;
     }
 }
diff --git a/test/random.cpp b/test/random.cpp
index 272d3f248d..7932f0d699 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -21,13 +21,12 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
-using af::cfloat;
-using af::cdouble;
 using af::array;
-using af::randomEngine;
-using af::randomEngineType;
-using af::mean;
-using af::stdev;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
 
 template<typename T>
 class Random : public ::testing::Test
@@ -95,38 +94,38 @@ typedef ::testing::Types<unsigned> TestTypesSeed;
 TYPED_TEST_CASE(RandomSeed, TestTypesSeed);
 
 template<typename T>
-void randuTest(af::dim4 & dims)
+void randuTest(dim4 & dims)
 {
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
 
 template<typename T>
-void randnTest(af::dim4 &dims)
+void randnTest(dim4 &dims)
 {
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_randn(&outArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_randn(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
 
-#define RAND(d0, d1, d2, d3)                                    \
-    TYPED_TEST(Random,randu_##d0##_##d1##_##d2##_##d3)          \
-    {                                                           \
-        af::dim4 dims(d0, d1, d2, d3);                          \
-        randuTest<TypeParam>(dims);                             \
-    }                                                           \
-    TYPED_TEST(Random_norm,randn_##d0##_##d1##_##d2##_##d3)     \
-    {                                                           \
-        af::dim4 dims(d0, d1, d2, d3);                          \
-        randnTest<TypeParam>(dims);                             \
-    }                                                           \
+#define RAND(d0, d1, d2, d3)                                \
+    TYPED_TEST(Random,randu_##d0##_##d1##_##d2##_##d3)      \
+    {                                                       \
+        dim4 dims(d0, d1, d2, d3);                          \
+        randuTest<TypeParam>(dims);                         \
+    }                                                       \
+    TYPED_TEST(Random_norm,randn_##d0##_##d1##_##d2##_##d3) \
+    {                                                       \
+        dim4 dims(d0, d1, d2, d3);                          \
+        randnTest<TypeParam>(dims);                         \
+    }                                                       \
 
 RAND(1024, 1024,    1,    1);
 RAND( 512,  512,    1,    1);
@@ -167,7 +166,7 @@ void randuArgsTest()
     dim_t ndims = 4;
     dim_t dims[] = {1, 2, 3, 0};
     af_array outArray = 0;
-    ASSERT_EQ(AF_ERR_SIZE, af_randu(&outArray, ndims, dims, (af_dtype) af::dtype_traits<char>::af_type));
+    ASSERT_EQ(AF_ERR_SIZE, af_randu(&outArray, ndims, dims, (af_dtype) dtype_traits<char>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
@@ -182,16 +181,16 @@ void randuDimsTest()
 {
     if (noDoubleTests<T>()) return;
 
-    af::dim4 dims(1, 65535*32, 1, 1);
-    af::array large_rand = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    dim4 dims(1, 65535*32, 1, 1);
+    array large_rand = randu(dims, (af_dtype) dtype_traits<T>::af_type);
     ASSERT_EQ(large_rand.dims()[1], 65535*32);
 
-    dims = af::dim4(1, 1, 65535*32, 1);
-    large_rand = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    dims = dim4(1, 1, 65535*32, 1);
+    large_rand = randu(dims, (af_dtype) dtype_traits<T>::af_type);
     ASSERT_EQ(large_rand.dims()[2], 65535*32);
 
-    dims = af::dim4(1, 1, 1, 65535*32);
-    large_rand = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    dims = dim4(1, 1, 1, 65535*32);
+    large_rand = randu(dims, (af_dtype) dtype_traits<T>::af_type);
     ASSERT_EQ(large_rand.dims()[3], 65535*32);
 }
 
@@ -202,13 +201,27 @@ TYPED_TEST(Random,InvalidDims)
 
 ////////////////////////////////////// CPP /////////////////////////////////////
 //
+
+using af::allTrue;
+using af::constant;
+using af::getDefaultRandomEngine;
+using af::getSeed;
+using af::mean;
+using af::randomEngine;
+using af::randomEngineType;
+using af::randu;
+using af::setDefaultRandomEngineType;
+using af::setSeed;
+using af::stdev;
+using af::sum;
+
 TEST(RandomEngine, Default)
 {
     // Using default Random engine will cause segfaults
     // without setting one. This test should be before
     // setting it to test if default engine setup is working
     // as expected, otherwise the test will fail.
-    af::randomEngine engine = af::getDefaultRandomEngine();
+    randomEngine engine = getDefaultRandomEngine();
 }
 
 TEST(Random, CPP)
@@ -217,18 +230,18 @@ TEST(Random, CPP)
 
     // TEST will fail if exception is thrown, which are thrown
     // when only wrong inputs are thrown on bad access happens
-    af::dim4 dims(1, 2, 3, 1);
-    af::array out1 = af::randu(dims);
-    af::array out2 = af::randn(dims);
-    af::setDefaultRandomEngineType(AF_RANDOM_ENGINE_PHILOX);
-    af::array out3 = af::randu(dims);
-    af::array out4 = af::randn(dims);
-    af::setDefaultRandomEngineType(AF_RANDOM_ENGINE_THREEFRY);
-    af::array out5 = af::randu(dims);
-    af::array out6 = af::randn(dims);
-    af::setDefaultRandomEngineType(AF_RANDOM_ENGINE_MERSENNE);
-    af::array out7 = af::randu(dims);
-    af::array out8 = af::randn(dims);
+    dim4 dims(1, 2, 3, 1);
+    array out1 = randu(dims);
+    array out2 = randn(dims);
+    setDefaultRandomEngineType(AF_RANDOM_ENGINE_PHILOX);
+    array out3 = randu(dims);
+    array out4 = randn(dims);
+    setDefaultRandomEngineType(AF_RANDOM_ENGINE_THREEFRY);
+    array out5 = randu(dims);
+    array out6 = randn(dims);
+    setDefaultRandomEngineType(AF_RANDOM_ENGINE_MERSENNE);
+    array out7 = randu(dims);
+    array out8 = randn(dims);
     af::sync();
 }
 
@@ -238,25 +251,25 @@ void testSetSeed(const uintl seed0, const uintl seed1)
 
     if (noDoubleTests<T>()) return;
 
-    uintl orig_seed = af::getSeed();
+    uintl orig_seed = getSeed();
 
     const int num = 1024 * 1024;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    af::setSeed(seed0);
-    af::array in0 = af::randu(num, ty);
+    setSeed(seed0);
+    array in0 = randu(num, ty);
 
-    af::setSeed(seed1);
-    af::array in1 = af::randu(num, ty);
+    setSeed(seed1);
+    array in1 = randu(num, ty);
 
-    af::setSeed(seed0);
-    af::array in2 = af::randu(num, ty);
-    af::array in3 = af::randu(num, ty);
+    setSeed(seed0);
+    array in2 = randu(num, ty);
+    array in3 = randu(num, ty);
 
-    std::vector<T> h_in0(num);
-    std::vector<T> h_in1(num);
-    std::vector<T> h_in2(num);
-    std::vector<T> h_in3(num);
+    vector<T> h_in0(num);
+    vector<T> h_in1(num);
+    vector<T> h_in2(num);
+    vector<T> h_in3(num);
 
     in0.host((void *)&h_in0[0]);
     in1.host((void *)&h_in1[0]);
@@ -280,7 +293,7 @@ void testSetSeed(const uintl seed0, const uintl seed1)
         }
     }
 
-    af::setSeed(orig_seed); // Reset the seed
+    setSeed(orig_seed); // Reset the seed
 }
 
 TYPED_TEST(RandomSeed, setSeed)
@@ -293,24 +306,24 @@ void testGetSeed(const uintl seed0, const uintl seed1)
 {
     if (noDoubleTests<T>()) return;
 
-    uintl orig_seed = af::getSeed();
+    uintl orig_seed = getSeed();
 
     const int num = 1024;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    af::setSeed(seed0);
-    af::array in0 = af::randu(num, ty);
-    ASSERT_EQ(af::getSeed(), seed0);
+    setSeed(seed0);
+    array in0 = randu(num, ty);
+    ASSERT_EQ(getSeed(), seed0);
 
-    af::setSeed(seed1);
-    af::array in1 = af::randu(num, ty);
-    ASSERT_EQ(af::getSeed(), seed1);
+    setSeed(seed1);
+    array in1 = randu(num, ty);
+    ASSERT_EQ(getSeed(), seed1);
 
-    af::setSeed(seed0);
-    af::array in2 = af::randu(num, ty);
-    ASSERT_EQ(af::getSeed(), seed0);
+    setSeed(seed0);
+    array in2 = randu(num, ty);
+    ASSERT_EQ(getSeed(), seed0);
 
-    af::setSeed(orig_seed); // Reset the seed
+    setSeed(orig_seed); // Reset the seed
 }
 
 TYPED_TEST(Random, getSeed)
@@ -322,10 +335,10 @@ template <typename T>
 void testRandomEngineUniform(randomEngineType type)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem = 16*1024*1024;
-    af::randomEngine r(type, 0);
+    randomEngine r(type, 0);
     array A = randu(elem, ty, r);
     T m = mean<T>(A);
     T s = stdev<T>(A);
@@ -337,10 +350,10 @@ template <typename T>
 void testRandomEngineNormal(randomEngineType type)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem = 16*1024*1024;
-    af::randomEngine r(type, 0);
+    randomEngine r(type, 0);
     array A = randn(elem, ty, r);
     T m = mean<T>(A);
     T s = stdev<T>(A);
@@ -384,9 +397,9 @@ void testRandomEngineSeed(randomEngineType type)
     int elem = 4*32*1024;
     uintl orig_seed = 0;
     uintl new_seed = 1;
-    af::randomEngine e(type, orig_seed);
+    randomEngine e(type, orig_seed);
 
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
     array d1 = randu(elem, ty, e);
     e.setSeed(new_seed);
     array d2 = randu(elem, ty, e);
@@ -394,10 +407,10 @@ void testRandomEngineSeed(randomEngineType type)
     array d3 = randu(elem, ty, e);
     array d4 = randu(elem, ty, e);
 
-    std::vector<T> h1(elem);
-    std::vector<T> h2(elem);
-    std::vector<T> h3(elem);
-    std::vector<T> h4(elem);
+    vector<T> h1(elem);
+    vector<T> h2(elem);
+    vector<T> h3(elem);
+    vector<T> h4(elem);
 
     d1.host((void*)h1.data());
     d2.host((void*)h2.data());
@@ -432,17 +445,17 @@ template <typename T>
 void testRandomEnginePeriod(randomEngineType type)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     uint elem = 1024*1024;
     uint steps = 4*1024;
-    af::randomEngine r(type, 0);
+    randomEngine r(type, 0);
 
-    af::array first = af::randu(elem, ty, r);
+    array first = randu(elem, ty, r);
 
     for (int i = 0; i < steps; ++i) {
-        af::array step = af::randu(elem, ty, r);
-        bool different = !af::allTrue<bool>(first == step);
+        array step = randu(elem, ty, r);
+        bool different = !allTrue<bool>(first == step);
         ASSERT_TRUE(different);
     }
 }
@@ -464,25 +477,25 @@ TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEnginePeriod)
 
 template <typename T>
 T chi2_statistic(array input, array expected) {
-    expected *= af::sum<T>(input) / af::sum<T>(expected);
+    expected *= sum<T>(input) / sum<T>(expected);
     array diff = input - expected;
-    return af::sum<T>((diff * diff) / expected);
+    return sum<T>((diff * diff) / expected);
 }
 
 template <typename T>
 void testRandomEngineUniformChi2(randomEngineType type)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem = 256*1024*1024;
     int steps = 32;
     int bins = 100;
 
-    array total_hist = af::constant(0.0, bins, ty);
-    array expected = af::constant(1.0/bins, bins, ty);
+    array total_hist = constant(0.0, bins, ty);
+    array expected = constant(1.0/bins, bins, ty);
 
-    af::randomEngine r(type, 0);
+    randomEngine r(type, 0);
 
     // R> qchisq(c(5e-6, 1 - 5e-6), 99)
     // [1]  48.68125 173.87456
@@ -492,7 +505,7 @@ void testRandomEngineUniformChi2(randomEngineType type)
     bool prev_step = true;
     bool prev_total = true;
     for (int i = 0; i < steps; ++i) {
-        array step_hist = af::histogram(af::randu(elem, ty, r), bins, 0.0, 1.0);
+        array step_hist = histogram(randu(elem, ty, r), bins, 0.0, 1.0);
         T step_chi2 = chi2_statistic<T>(step_hist, expected);
         if (!prev_step) {
           EXPECT_GT(step_chi2, lower) << "at step: " << i;
diff --git a/test/random_practrand.cpp b/test/random_practrand.cpp
index 806c9a4693..fb3ecf62bf 100644
--- a/test/random_practrand.cpp
+++ b/test/random_practrand.cpp
@@ -10,18 +10,18 @@
 
 int main(int argc, char ** argv) {
   int backend = argc > 1 ? atoi(argv[1]) : 0;
-  af::setBackend(static_cast<af::Backend>(backend));
+  setBackend(static_cast<Backend>(backend));
   int device = argc > 2 ? atoi(argv[2]) : 0;
-  af::setDevice(device);
+  setDevice(device);
   int rng = argc > 3 ? atoi(argv[3]) : 100;
-  af::setDefaultRandomEngineType(static_cast<af::randomEngineType>(rng)); 	
-  
-  af::setSeed(0xfe47fe0cc078ec30ULL);
+  setDefaultRandomEngineType(static_cast<randomEngineType>(rng));
+
+  setSeed(0xfe47fe0cc078ec30ULL);
   int samples = 1024 * 1024;
   while (1) {
-    af::array values = af::randu(samples, u32);
+    array values = randu(samples, u32);
     uint32_t *pvalues = values.host<uint32_t>();
     fwrite((void*) pvalues, samples * sizeof(*pvalues), 1, stdout);
-    free(pvalues);
+    freeHost(pvalues);
   }
 }
diff --git a/test/range.cpp b/test/range.cpp
index c763c0241c..87e183beb4 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -22,8 +22,12 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::range;
 
 template<typename T>
 class Range : public ::testing::Test
@@ -48,11 +52,11 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w, const uin
 {
     if (noDoubleTests<T>()) return;
 
-    af::dim4 idims(x, y, z, w);
+    dim4 idims(x, y, z, w);
 
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_range(&outArray, idims.ndims(), idims.get(), dim, (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_range(&outArray, idims.ndims(), idims.get(), dim, (af_dtype) dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
@@ -77,7 +81,7 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w, const uin
                                  + z * idims[0] * idims[1]
                                  + y * idims[0] + x;
 
-                    ASSERT_EQ(val, outData[idx]) << "at: " << idx << std::endl;
+                    ASSERT_EQ(val, outData[idx]) << "at: " << idx << endl;
                 }
             }
         }
@@ -89,10 +93,10 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w, const uin
     if(outArray  != 0) af_release_array(outArray);
 }
 
-#define RANGE_INIT(desc, x, y, z, w, rep)                                                    \
-    TYPED_TEST(Range, desc)                                                                  \
-    {                                                                                       \
-        rangeTest<TypeParam>(x, y, z, w, rep);                                               \
+#define RANGE_INIT(desc, x, y, z, w, rep)                               \
+    TYPED_TEST(Range, desc)                                             \
+    {                                                                   \
+        rangeTest<TypeParam>(x, y, z, w, rep);                          \
     }
 
     RANGE_INIT(Range1D0, 100,  1, 1, 1, 0);
@@ -126,8 +130,8 @@ TEST(Range, CPP)
     const unsigned w = 2;
     const unsigned dim = 2;
 
-    af::dim4 idims(x, y, z, w);
-    af::array output = af::range(x, y, z, w, dim, f32);
+    dim4 idims(x, y, z, w);
+    array output = range(x, y, z, w, dim, f32);
 
     // Get result
     float* outData = new float[idims.elements()];
@@ -151,7 +155,7 @@ TEST(Range, CPP)
                     dim_t idx = (w * idims[0] * idims[1] * idims[2]) +
                                    (z * idims[0] * idims[1]) +
                                    (y * idims[0]) + x;
-                    ASSERT_EQ(val, outData[idx]) << "at: " << idx << std::endl;
+                    ASSERT_EQ(val, outData[idx]) << "at: " << idx << endl;
                 }
             }
         }
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index 7f2e76db0d..f859745d3b 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -23,8 +23,15 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::det;
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::join;
+using af::randu;
 
 template<typename T>
 class Rank : public ::testing::Test
@@ -36,7 +43,7 @@ class Det : public ::testing::Test
 {
 };
 
-typedef ::testing::Types<float, double, af::cfloat, af::cdouble> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(Rank, TestTypes);
 TYPED_TEST_CASE(Det, TestTypes);
 
@@ -47,9 +54,9 @@ void rankSmall()
     if (noLAPACKTests()) return;
 
     T ha[] = {1, 4, 7, 2, 5, 8, 3, 6, 20};
-    af::array a(3, 3, ha);
+    array a(3, 3, ha);
 
-    ASSERT_EQ(3, (int)af::rank(a));
+    ASSERT_EQ(3, (int)rank(a));
 }
 
 template<typename T>
@@ -58,13 +65,13 @@ void rankBig(const int num)
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
-    af::dtype dt = (af::dtype)af::dtype_traits<T>::af_type;
-    af::array a = af::randu(num, num, dt);
-    ASSERT_EQ(num, (int)af::rank(a));
+    dtype dt = (dtype)dtype_traits<T>::af_type;
+    array a = randu(num, num, dt);
+    ASSERT_EQ(num, (int)rank(a));
 
-    af::array b = af::randu(num, num/2, dt);
-    ASSERT_EQ(num/2, (int)af::rank(b));
-    ASSERT_EQ(num/2, (int)af::rank(transpose(b)));
+    array b = randu(num, num/2, dt);
+    ASSERT_EQ(num/2, (int)rank(b));
+    ASSERT_EQ(num/2, (int)rank(transpose(b)));
 }
 
 template<typename T>
@@ -73,15 +80,15 @@ void rankLow(const int num)
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
-    af::dtype dt = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype dt = (dtype)dtype_traits<T>::af_type;
 
-    af::array a = af::randu(3 * num, num, dt);
-    af::array b = af::randu(3 * num, num, dt);
-    af::array c = a + 0.2 * b;
-    af::array in = join(1, a, b, c);
+    array a = randu(3 * num, num, dt);
+    array b = randu(3 * num, num, dt);
+    array c = a + 0.2 * b;
+    array in = join(1, a, b, c);
 
     // The last third is just a linear combination of first and second thirds
-    ASSERT_EQ(2 * num, (int)af::rank(in));
+    ASSERT_EQ(2 * num, (int)rank(in));
 }
 
 TYPED_TEST(Rank, small)
@@ -105,17 +112,17 @@ void detTest()
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
-    af::dtype dt = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype dt = (dtype)dtype_traits<T>::af_type;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float,float,float>(string(TEST_DIR"/lapack/detSmall.test"),numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
-    af::array input = af::array(dims, &(in[0].front())).as(dt);
-    T output = af::det<T>(input);
+    array input = array(dims, &(in[0].front())).as(dt);
+    T output = det<T>(input);
 
     ASSERT_NEAR(abs((T)tests[0][0]), abs(output), 1e-6);
 }
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 49b8de86ce..a67f8815e9 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -79,17 +79,16 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
         af_get_type(&t, outArray);
 
         // Get result
-        To *outData;
-        outData = new To[dims.elements()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        vector<To> outData(dims.elements());
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
-        if(std::equal(currGoldBar.begin(), currGoldBar.end(), outData) == false)
+        if(std::equal(currGoldBar.begin(), currGoldBar.end(), outData.begin()) == false)
         {
             for (size_t elIter = 0; elIter < nElems; ++elIter) {
 
                 EXPECT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                                << " for dim " << d + off << std::endl;
+                                                                << " for dim " << d + off << endl;
             }
             af_print_array(outArray);
             for(int i = 0; i < (int)nElems; i++) {
@@ -103,9 +102,6 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
             FAIL();
         }
 
-
-        // Delete
-        freeHost(outData);
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
     }
 
@@ -221,17 +217,14 @@ void cppReduceTest(string pTestFile)
         array output = reduce(input, d);
 
         // Get result
-        To *outData = new To[dims.elements()];
-        output.host((void*)outData);
+        vector<To> outData(dims.elements());
+        output.host((void*)&outData.front());
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
             ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                            << " for dim " << d << std::endl;
+                                                            << " for dim " << d << endl;
         }
-
-        // Delete
-        freeHost(outData);
     }
 }
 
diff --git a/test/regions.cpp b/test/regions.cpp
index a7ae1d9ef9..a7d394f7de 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -22,8 +22,12 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::regions;
 
 template<typename T>
 class Regions : public ::testing::Test
@@ -43,26 +47,26 @@ void regionsTest(string pTestFile, af_connectivity connectivity, bool isSubRef =
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<uchar> > in;
     vector<vector<T> > tests;
     readTests<uchar, T, unsigned>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array tempArray = 0;
     af_array outArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<char>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<char>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_regions(&outArray, inArray, connectivity, (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_regions(&outArray, inArray, connectivity, (af_dtype) dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
@@ -73,7 +77,7 @@ void regionsTest(string pTestFile, af_connectivity connectivity, bool isSubRef =
         vector<T> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
         }
     }
 
@@ -103,14 +107,14 @@ TEST(Regions, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, unsigned>(string(TEST_DIR"/regions/regions_8x8_4.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, (float*)&(in[0].front()));
-    af::array output = af::regions(input.as(b8));
+    dim4 idims = numDims[0];
+    array input(idims, (float*)&(in[0].front()));
+    array output = regions(input.as(b8));
 
     // Get result
     float* outData = new float[idims.elements()];
@@ -121,7 +125,7 @@ TEST(Regions, CPP)
         vector<float> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
         }
     }
 
@@ -156,7 +160,7 @@ TEST(Regions, Docs_8)
     };
 
     //![ex_image_regions]
-    af::array in(8, 8, input);
+    array in(8, 8, input);
     //af_print(in);
     // in =
     // 0   0   0   0   1   0   1   0
@@ -169,7 +173,7 @@ TEST(Regions, Docs_8)
     // 0   1   0   0   0   1   0   0
 
     // Compute the label matrix using 8-way connectivity
-    af::array out = regions(in.as(b8), AF_CONNECTIVITY_8);
+    array out = regions(in.as(b8), AF_CONNECTIVITY_8);
     //af_print(out);
     // 0   0   0   0   4   0   5   0
     // 0   0   0   0   0   0   5   5
@@ -186,7 +190,7 @@ TEST(Regions, Docs_8)
     out.host((void*)output);
 
     for (int i=0; i<64; ++i) {
-        ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
+        ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
     }
 }
 
@@ -217,7 +221,7 @@ TEST(Regions, Docs_4)
 
 
     //![ex_image_regions_4conn]
-    af::array in(8, 8, input);
+    array in(8, 8, input);
     //af_print(in.T());
     //in
     //0  0  0  0  1  0  1  0
@@ -229,7 +233,7 @@ TEST(Regions, Docs_4)
     //0  0  0  0  1  0  0  1
     //0  1  0  0  0  1  0  0
     // Compute the label matrix using 4-way connectivity
-    af::array out = regions(in.as(b8), AF_CONNECTIVITY_4);
+    array out = regions(in.as(b8), AF_CONNECTIVITY_4);
     //af_print(out.T());
     //out
     //0  0  0  0  7  0 11  0
@@ -247,7 +251,7 @@ TEST(Regions, Docs_4)
     out.host((void*)output);
 
     for (int i=0; i<64; ++i) {
-        ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
+        ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
     }
 }
 
@@ -255,32 +259,32 @@ TEST(Regions, WholeImageComponent)
 {
     const int dim = 101;
     const int sz  = dim*dim;
-    std::vector<char> input(sz, 1);
-    std::vector<float> gold(sz, 1.0f);
+    vector<char> input(sz, 1);
+    vector<float> gold(sz, 1.0f);
 
-    af::array in  = af::array(dim, dim, input.data());
-    af::array out = af::regions(in, AF_CONNECTIVITY_4);
+    array in  = array(dim, dim, input.data());
+    array out = regions(in, AF_CONNECTIVITY_4);
 
-    std::vector<float> output(sz);
+    vector<float> output(sz);
     out.host((void*)output.data());
 
     for (int i=0; i<sz; ++i)
-        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
+        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
 }
 
 TEST(Regions, NoComponentImage)
 {
     const int dim = 101;
     const int sz  = dim*dim;
-    std::vector<char> input(sz, 0);
-    std::vector<float> gold(sz, 0.0f);
+    vector<char> input(sz, 0);
+    vector<float> gold(sz, 0.0f);
 
-    af::array in  = af::array(dim, dim, input.data());
-    af::array out = af::regions(in, AF_CONNECTIVITY_4);
+    array in  = array(dim, dim, input.data());
+    array out = regions(in, AF_CONNECTIVITY_4);
 
-    std::vector<float> output(sz);
+    vector<float> output(sz);
     out.host((void*)output.data());
 
     for (int i=0; i<sz; ++i)
-        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<std::endl;
+        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
 }
diff --git a/test/reorder.cpp b/test/reorder.cpp
index cada49c387..1c04e7f6d0 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -22,8 +22,15 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::allTrue;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::reorder;
+using af::tile;
+
 
 template<typename T>
 class Reorder : public ::testing::Test
@@ -50,23 +57,23 @@ void reorderTest(string pTestFile, const unsigned resultIdx,
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_reorder(&outArray, inArray, x, y, z, w));
@@ -78,7 +85,7 @@ void reorderTest(string pTestFile, const unsigned resultIdx,
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -140,15 +147,15 @@ TEST(Reorder, CPP)
     const unsigned z = 2;
     const unsigned w = 3;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/reorder/reorder4d.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
-    af::array input(idims, &(in[0].front()));
-    af::array output = af::reorder(input, x, y, z, w);
+    array input(idims, &(in[0].front()));
+    array output = reorder(input, x, y, z, w);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -157,7 +164,7 @@ TEST(Reorder, CPP)
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -175,9 +182,9 @@ TEST(Reorder, ISSUE_1777)
         h_input[i] = (float)(i);
     }
 
-    af::array a(m, n, &h_input[0]);
-    af::array a_t = af::tile(a, 1, 1, 3);
-    af::array a_r = af::reorder(a_t, 0, 2, 1);
+    array a(m, n, &h_input[0]);
+    array a_t = tile(a, 1, 1, 3);
+    array a_r = reorder(a_t, 0, 2, 1);
 
     vector<float> h_output(m * n * k);
     a_r.host((void *)&h_output[0]);
@@ -196,10 +203,10 @@ TEST(Reorder, MaxDim)
 
     const size_t largeDim = 65535 * 32 + 1 ;
 
-    af::array input  = af::range(af::dim4(2, largeDim, 2), 2);
-    af::array output = af::reorder(input, 2, 1, 0);
+    array input  = range(dim4(2, largeDim, 2), 2);
+    array output = reorder(input, 2, 1, 0);
 
-    af::array gold = af::range(af::dim4(2, largeDim, 2));
+    array gold = range(dim4(2, largeDim, 2));
 
-    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+    ASSERT_TRUE(allTrue<bool>(output == gold));
 }
diff --git a/test/replace.cpp b/test/replace.cpp
index 679299c664..cb6779f0de 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -17,14 +17,23 @@
 #include <testHelpers.hpp>
 
 using std::vector;
-using namespace af;
+using af::NaN;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::randu;
+using af::seq;
+using af::span;
 
 template<typename T>
 class Replace : public ::testing::Test
 {
 };
 
-typedef ::testing::Types<float, double, af::cfloat, af::cdouble, uint, int, intl, uintl, uchar, char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl, uchar, char, short, ushort> TestTypes;
 
 TYPED_TEST_CASE(Replace, TestTypes);
 
@@ -32,7 +41,7 @@ template<typename T>
 void replaceTest(const dim4 &dims)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
     array b = randu(dims, ty);
@@ -50,10 +59,10 @@ void replaceTest(const dim4 &dims)
 
     int num = (int)a.elements();
 
-    std::vector<T> ha(num);
-    std::vector<T> hb(num);
-    std::vector<T> hc(num);
-    std::vector<char> hcond(num);
+    vector<T> ha(num);
+    vector<T> hb(num);
+    vector<T> hc(num);
+    vector<char> hcond(num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -69,7 +78,7 @@ template<typename T>
 void replaceScalarTest(const dim4 &dims)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
 
@@ -84,9 +93,9 @@ void replaceScalarTest(const dim4 &dims)
     replace(c, cond, b);
     int num = (int)a.elements();
 
-    std::vector<T> ha(num);
-    std::vector<T> hc(num);
-    std::vector<char> hcond(num);
+    vector<T> ha(num);
+    vector<T> hc(num);
+    vector<char> hcond(num);
 
     a.host(&ha[0]);
     c.host(&hc[0]);
@@ -110,18 +119,18 @@ TYPED_TEST(Replace, Scalar)
 TEST(Replace, NaN)
 {
     dim4 dims(1000, 1250);
-    af::dtype ty = f32;
+    dtype ty = f32;
 
     array a = randu(dims, ty);
-    a(seq(a.dims(0) / 2), span, span, span) = af::NaN;
+    a(seq(a.dims(0) / 2), span, span, span) = NaN;
     array c = a.copy();
     float b = 0;
     replace(c, !isNaN(c), b);
 
     int num = (int)a.elements();
 
-    std::vector<float> ha(num);
-    std::vector<float> hc(num);
+    vector<float> ha(num);
+    vector<float> hc(num);
 
     a.host(&ha[0]);
     c.host(&hc[0]);
@@ -134,15 +143,15 @@ TEST(Replace, NaN)
 TEST(Replace, ISSUE_1249)
 {
     dim4 dims(2, 3, 4);
-    array cond = af::randu(dims) > 0.5;
-    array a = af::randu(dims);
+    array cond = randu(dims) > 0.5;
+    array a = randu(dims);
     array b = a.copy();
     replace(b, !cond, a - a * 0.9);
     array c = a - a * cond * 0.9;
 
     int num = (int)dims.elements();
-    std::vector<float> hb(num);
-    std::vector<float> hc(num);
+    vector<float> hb(num);
+    vector<float> hc(num);
 
     b.host(&hb[0]);
     c.host(&hc[0]);
@@ -156,15 +165,15 @@ TEST(Replace, ISSUE_1249)
 TEST(Replace, 4D)
 {
     dim4 dims(2, 3, 4, 2);
-    array cond = af::randu(dims) > 0.5;
-    array a = af::randu(dims);
+    array cond = randu(dims) > 0.5;
+    array a = randu(dims);
     array b = a.copy();
     replace(b, !cond, a - a * 0.9);
     array c = a - a * cond * 0.9;
 
     int num = (int)dims.elements();
-    std::vector<float> hb(num);
-    std::vector<float> hc(num);
+    vector<float> hb(num);
+    vector<float> hc(num);
 
     b.host(&hb[0]);
     c.host(&hc[0]);
@@ -177,16 +186,16 @@ TEST(Replace, 4D)
 TEST(Replace, ISSUE_1683)
 {
     array A = randu(10, 20, f32);
-    std::vector<float> ha1(A.elements());
+    vector<float> ha1(A.elements());
     A.host(ha1.data());
 
     array B = A(0, span);
     replace(B, A(0, span) > 0.5, 0);
 
-    std::vector<float> ha2(A.elements());
+    vector<float> ha2(A.elements());
     A.host(ha2.data());
 
-    std::vector<float> hb(B.elements());
+    vector<float> hb(B.elements());
     B.host(hb.data());
 
     // Ensures A is not modified by replace
diff --git a/test/resize.cpp b/test/resize.cpp
index 6c29e61cc6..20ff55821d 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -23,6 +23,8 @@ using std::endl;
 using std::abs;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Resize : public ::testing::Test
@@ -70,10 +72,10 @@ TYPED_TEST(Resize, InvalidDims)
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    af::dim4 dims = af::dim4(8,8,1,1);
+    dim4 dims = dim4(8,8,1,1);
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(),
-                                          (af_dtype) af::dtype_traits<TypeParam>::af_type));
+                                          (af_dtype) dtype_traits<TypeParam>::af_type));
     ASSERT_EQ(AF_ERR_SIZE, af_resize(&outArray, inArray, 0, 0, AF_INTERP_NEAREST));
     ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
 }
@@ -81,33 +83,33 @@ TYPED_TEST(Resize, InvalidDims)
 template<typename T>
 void compare(T test, T out, double err, size_t i)
 {
-    ASSERT_EQ(abs(test - out) < 0.0001, true) << "at: " << i << std::endl
-             << "for test = : " << test << std::endl
-             << "out data = : " << out << std::endl;
+    ASSERT_EQ(abs(test - out) < 0.0001, true) << "at: " << i << endl
+             << "for test = : " << test << endl
+             << "out data = : " << out << endl;
 }
 
 template<>
 void compare<uintl>(uintl test, uintl out, double err, size_t i)
 {
-    ASSERT_EQ(((intl)test - (intl)out) < 0.0001, true) << "at: " << i << std::endl
-             << "for test = : " << test << std::endl
-             << "out data = : " << out << std::endl;
+    ASSERT_EQ(((intl)test - (intl)out) < 0.0001, true) << "at: " << i << endl
+             << "for test = : " << test << endl
+             << "out data = : " << out << endl;
 }
 
 template<>
 void compare<uint>(uint test, uint out, double err, size_t i)
 {
-    ASSERT_EQ(((int)test - (int)out) < 0.0001, true) << "at: " << i << std::endl
-             << "for test = : " << test << std::endl
-             << "out data = : " << out << std::endl;
+    ASSERT_EQ(((int)test - (int)out) < 0.0001, true) << "at: " << i << endl
+             << "for test = : " << test << endl
+             << "out data = : " << out << endl;
 }
 
 template<>
 void compare<uchar>(uchar test, uchar out, double err, size_t i)
 {
-    ASSERT_EQ(((int)test - (int)out) < 0.0001, true) << "at: " << i << std::endl
-             << "for test = : " << test << std::endl
-             << "out data = : " << out << std::endl;
+    ASSERT_EQ(((int)test - (int)out) < 0.0001, true) << "at: " << i << endl
+             << "for test = : " << test << endl
+             << "out data = : " << out << endl;
 }
 
 template<typename T>
@@ -115,29 +117,29 @@ void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0, c
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
     af_array tempArray = 0;
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_resize(&outArray, inArray, odim0, odim1, method));
 
     // Get result
-    af::dim4 odims(odim0, odim1, dims[2], dims[3]);
+    dim4 odims(odim0, odim1, dims[2], dims[3]);
     T* outData = new T[odims.elements()];
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
 
@@ -316,20 +318,20 @@ TYPED_TEST(ResizeI, Resize1CLargeDownLinear)
 }
 
 template<typename T>
-void resizeArgsTest(af_err err, string pTestFile, const af::dim4 odims, const af_interp_type method)
+void resizeArgsTest(af_err err, string pTestFile, const dim4 odims, const af_interp_type method)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(err, af_resize(&outArray, inArray, odims[0], odims[1], method));
 
@@ -339,40 +341,47 @@ void resizeArgsTest(af_err err, string pTestFile, const af::dim4 odims, const af
 
 TYPED_TEST(Resize,InvalidArgsDims0)
 {
-    af::dim4 dims(0, 5, 2, 1);
+    dim4 dims(0, 5, 2, 1);
     resizeArgsTest<TypeParam>(AF_ERR_SIZE, string(TEST_DIR"/resize/square.test"), dims, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(Resize,InvalidArgsMethod)
 {
-    af::dim4 dims(10, 10, 1, 1);
+    dim4 dims(10, 10, 1, 1);
     resizeArgsTest<TypeParam>(AF_ERR_ARG, string(TEST_DIR"/resize/square.test"), dims, AF_INTERP_CUBIC);
 }
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
+
+using af::array;
+using af::constant;
+using af::max;
+using af::seq;
+using af::span;
+
 TEST(Resize, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/resize/square.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::resize(input, 16, 16);
+    dim4 dims = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = resize(input, 16, 16);
 
     // Get result
-    af::dim4 odims(16, 16, dims[2], dims[3]);
+    dim4 odims(16, 16, dims[2], dims[3]);
     float* outData = new float[odims.elements()];
     output.host((void*)outData);
 
     // Compare result
     size_t nElems = tests[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << std::endl;
+        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -383,24 +392,24 @@ TEST(ResizeScale1, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/resize/square.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::resize(2.f, input);
+    dim4 dims = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = resize(2.f, input);
 
     // Get result
-    af::dim4 odims(16, 16, dims[2], dims[3]);
+    dim4 odims(16, 16, dims[2], dims[3]);
     float* outData = new float[odims.elements()];
     output.host((void*)outData);
 
     // Compare result
     size_t nElems = tests[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << std::endl;
+        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -411,35 +420,32 @@ TEST(ResizeScale2, CPP)
 {
     if (noDoubleTests<float>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/resize/square.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::resize(2.f, 2.f, input);
+    dim4 dims = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = resize(2.f, 2.f, input);
 
     // Get result
-    af::dim4 odims(16, 16, dims[2], dims[3]);
+    dim4 odims(16, 16, dims[2], dims[3]);
     float* outData = new float[odims.elements()];
     output.host((void*)outData);
 
     // Compare result
     size_t nElems = tests[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << std::endl;
+        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] outData;
 }
 
-
-
 TEST(Resize, ExtractGFOR)
 {
-    using namespace af;
     dim4 dims = dim4(100, 100, 3);
     array A = round(100 * randu(dims));
     array B = constant(0, 200, 200, 3);
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 0d4b460033..3adb71db4f 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -21,8 +21,11 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Rotate : public ::testing::Test
@@ -45,12 +48,12 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
@@ -58,7 +61,7 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 
     float theta = angle * PI / 180.0f;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_rotate(&outArray, inArray, theta, crop, AF_INTERP_NEAREST));
 
@@ -84,7 +87,7 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
     ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.005));
 
     //for (size_t elIter = 0; elIter < nElems; ++elIter) {
-    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     //}
 
 
@@ -162,16 +165,16 @@ TEST(Rotate, CPP)
     const float angle = 180;
     const bool crop = false;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/rotate/rotate1.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
     float theta = angle * PI / 180.0f;
 
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::rotate(input, theta, crop, AF_INTERP_NEAREST);
+    array input(dims, &(in[0].front()));
+    array output = rotate(input, theta, crop, AF_INTERP_NEAREST);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 15734a3cc2..22e4727b33 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -21,8 +21,11 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class RotateLinear : public ::testing::Test
@@ -49,12 +52,12 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T, T, float>(pTestFile,numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
@@ -64,11 +67,11 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_rotate(&outArray, inArray, theta, crop, AF_INTERP_BILINEAR));
@@ -93,10 +96,10 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
             fail_count++;
         }
     }
-    ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.02)) << "where count = " << fail_count << std::endl;
+    ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.02)) << "where count = " << fail_count << endl;
 
     //for (size_t elIter = 0; elIter < nElems; ++elIter) {
-    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     //}
 
 
@@ -174,16 +177,16 @@ TEST(RotateLinear, CPP)
     const float angle = 180;
     const bool crop = false;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> >   in;
     vector<vector<float> >   tests;
     readTests<float, float, float>(string(TEST_DIR"/rotate/rotatelinear1.test"),numDims,in,tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
     float theta = angle * PI / 180.0f;
 
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::rotate(input, theta, crop, AF_INTERP_BILINEAR);
+    array input(dims, &(in[0].front()));
+    array output = rotate(input, theta, crop, AF_INTERP_BILINEAR);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
diff --git a/test/sat.cpp b/test/sat.cpp
index 4cfb582e71..239ab63b59 100644
--- a/test/sat.cpp
+++ b/test/sat.cpp
@@ -17,6 +17,12 @@
 
 using std::string;
 using std::vector;
+using af::accum;
+using af::allTrue;
+using af::array;
+using af::dtype_traits;
+using af::randu;
+using af::sat;
 
 template<typename T>
 class SAT : public ::testing::Test
@@ -35,11 +41,11 @@ TYPED_TEST(SAT, IntegralImage)
 {
     if(noDoubleTests<TypeParam>()) return;
 
-    af::array a = af::randu(530, 671, (af_dtype)af::dtype_traits<TypeParam>::af_type);
-    af::array b = af::accum(a, 0);
-    af::array c = af::accum(b, 1);
+    array a = randu(530, 671, (af_dtype)dtype_traits<TypeParam>::af_type);
+    array b = accum(a, 0);
+    array c = accum(b, 1);
 
-    af::array s = af::sat(a);
+    array s = sat(a);
 
-    EXPECT_EQ(true, af::allTrue<float>(c==s));
+    EXPECT_EQ(true, allTrue<float>(c==s));
 }
diff --git a/test/scan.cpp b/test/scan.cpp
index b63ac8e5e2..20145f2809 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -23,8 +23,16 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::allTrue;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::constant;
+using af::dim4;
+using af::dtype_traits;
+using af::range;
+using af::span;
+using af::seq;
 
 typedef af_err (*scanFunc)(af_array *, const af_array, const int);
 
@@ -33,12 +41,12 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
 {
     if (noDoubleTests<Ti>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
     readTests<int,int,int> (pTestFile,numDims,data,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     vector<Ti> in(data[0].begin(), data[0].end());
 
@@ -48,12 +56,12 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
 
     // Get input array
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
         ASSERT_EQ(AF_SUCCESS, af_release_array(tempArray));
     } else {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
     }
 
     // Compare result
@@ -72,7 +80,7 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
             ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
                 << " for dim " << d +off
-                << std::endl;
+                << endl;
         }
 
         // Delete
@@ -122,25 +130,25 @@ TEST(Scan,Test_Scan_Big1)
 ///////////////////////////////// CPP ////////////////////////////////////
 TEST(Accum, CPP)
 {
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
     readTests<int,int,int> (string(TEST_DIR"/scan/accum.test"),numDims,data,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     vector<float> in(data[0].begin(), data[0].end());
 
     if (noDoubleTests<float>()) return;
 
-    af::array input(dims, &(in.front()));
+    array input(dims, &(in.front()));
 
     // Compare result
     for (int d = 0; d < (int)tests.size(); ++d) {
         vector<float> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        af::array output = af::accum(input, d);
+        array output = accum(input, d);
 
         // Get result
         float *outData;
@@ -151,7 +159,7 @@ TEST(Accum, CPP)
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
             ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
                 << " for dim " << d
-                << std::endl;
+                << endl;
         }
 
         // Delete
@@ -164,44 +172,44 @@ TEST(Accum, MaxDim)
     const size_t largeDim = 65535 * 32 + 1;
 
     //first dimension kernel tests
-    af::array input = af::constant(0, 2, largeDim, 2, 2);
-    input(af::span, af::seq(0, 9999), af::span, af::span) = 1;
+    array input = constant(0, 2, largeDim, 2, 2);
+    input(span, seq(0, 9999), span, span) = 1;
 
-    af::array gold_first = af::constant(0, 2, largeDim, 2, 2);
-    gold_first(af::span, af::seq(0, 9999), af::span, af::span) = af::range(2, 10000, 2, 2) + 1;
+    array gold_first = constant(0, 2, largeDim, 2, 2);
+    gold_first(span, seq(0, 9999), span, span) = range(2, 10000, 2, 2) + 1;
 
-    af::array output_first = af::accum(input, 0);
-    ASSERT_TRUE(af::allTrue<bool>(output_first == gold_first));
+    array output_first = accum(input, 0);
+    ASSERT_TRUE(allTrue<bool>(output_first == gold_first));
 
 
-    input = af::constant(0, 2, 2, 2, largeDim);
-    input(af::span, af::span, af::span, af::seq(0, 9999)) = 1;
+    input = constant(0, 2, 2, 2, largeDim);
+    input(span, span, span, seq(0, 9999)) = 1;
 
-    gold_first = af::constant(0, 2, 2, 2, largeDim);
-    gold_first(af::span, af::span, af::span, af::seq(0, 9999)) = af::range(2, 2, 2, 10000) + 1;
+    gold_first = constant(0, 2, 2, 2, largeDim);
+    gold_first(span, span, span, seq(0, 9999)) = range(2, 2, 2, 10000) + 1;
 
-    output_first = af::accum(input, 0);
-    ASSERT_TRUE(af::allTrue<bool>(output_first == gold_first));
+    output_first = accum(input, 0);
+    ASSERT_TRUE(allTrue<bool>(output_first == gold_first));
 
 
     //other dimension kernel tests
-    input = af::constant(0, 2, largeDim, 2, 2);
-    input(af::span, af::seq(0, 9999), af::span, af::span) = 1;
+    input = constant(0, 2, largeDim, 2, 2);
+    input(span, seq(0, 9999), span, span) = 1;
 
-    af::array gold_dim = af::constant(10000, 2, largeDim, 2, 2);
-    gold_dim(af::span, af::seq(0, 9999), af::span, af::span) = af::range(af::dim4(2, 10000, 2, 2), 1) + 1;
+    array gold_dim = constant(10000, 2, largeDim, 2, 2);
+    gold_dim(span, seq(0, 9999), span, span) = range(dim4(2, 10000, 2, 2), 1) + 1;
 
-    af::array output_dim = af::accum(input, 1);
-    ASSERT_TRUE(af::allTrue<bool>(output_dim == gold_dim));
+    array output_dim = accum(input, 1);
+    ASSERT_TRUE(allTrue<bool>(output_dim == gold_dim));
 
 
-    input = af::constant(0, 2, 2, 2, largeDim);
-    input(af::span, af::span, af::span, af::seq(0, 9999)) = 1;
+    input = constant(0, 2, 2, 2, largeDim);
+    input(span, span, span, seq(0, 9999)) = 1;
 
-    gold_dim = af::constant(0, 2, 2, 2, largeDim);
-    gold_dim(af::span, af::span, af::span, af::seq(0, 9999)) = af::range(af::dim4(2, 2, 2, 10000), 1) + 1;
+    gold_dim = constant(0, 2, 2, 2, largeDim);
+    gold_dim(span, span, span, seq(0, 9999)) = range(dim4(2, 2, 2, 10000), 1) + 1;
 
-    output_dim = af::accum(input, 1);
-    ASSERT_TRUE(af::allTrue<bool>(output_dim == gold_dim));
+    output_dim = accum(input, 1);
+    ASSERT_TRUE(allTrue<bool>(output_dim == gold_dim));
 
 }
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
index 91149bdd99..f3ef75edd6 100644
--- a/test/scan_by_key.cpp
+++ b/test/scan_by_key.cpp
@@ -24,8 +24,10 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
 
 float randomInterval(float start, float end)
 {
@@ -38,13 +40,13 @@ int randomInterval(int start, int end)
 }
 
 template <typename T>
-std::vector<T> createScanKey(af::dim4 dims, int scanDim,
-        const std::vector<int> &nodeLengths,
+vector<T> createScanKey(dim4 dims, int scanDim,
+        const vector<int> &nodeLengths,
         T keyStart, T keyEnd)
 {
     std::srand(0);
     int elemCount = dims.elements();
-    std::vector<T> key(elemCount);
+    vector<T> key(elemCount);
 
     int stride = 1;
     for (int i = 0; i < scanDim; ++i) { stride *= dims[i]; }
@@ -70,10 +72,10 @@ std::vector<T> createScanKey(af::dim4 dims, int scanDim,
 }
 
 template <typename T>
-std::vector<T> createScanData(af::dim4 dims, T dataStart, T dataEnd)
+vector<T> createScanData(dim4 dims, T dataStart, T dataEnd)
 {
     int elemCount = dims.elements();
-    std::vector<T> in(elemCount);
+    vector<T> in(elemCount);
     for (int i = 0; i < elemCount; ++i) {
         in[i] = randomInterval(dataStart, dataEnd);
     }
@@ -81,10 +83,10 @@ std::vector<T> createScanData(af::dim4 dims, T dataStart, T dataEnd)
 }
 
 template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive_scan>
-void verify(af::dim4 dims,
-        const std::vector<Ti> &in,
-        const std::vector<Tk> &key,
-        const std::vector<To> &out,
+void verify(dim4 dims,
+        const vector<Ti> &in,
+        const vector<Tk> &key,
+        const vector<To> &out,
         int scanDim, double eps)
 {
     std::srand(1);
@@ -118,16 +120,16 @@ void verify(af::dim4 dims,
 }
 
 template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
-void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
+void scanByKeyTest(dim4 dims, int scanDim, vector<int> nodeLengths,
         int keyStart, int keyEnd, Ti dataStart, Ti dataEnd, double eps)
 {
-    std::vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
-    std::vector<Ti> in = createScanData<Ti>(dims, dataStart, dataEnd);
+    vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
+    vector<Ti> in = createScanData<Ti>(dims, dataStart, dataEnd);
 
-    af::array afkey(dims, key.data());
-    af::array afin(dims, in.data());
-    af::array afout = af::scanByKey(afkey, afin, scanDim, op, inclusive_scan);
-    std::vector<To> out(afout.elements());
+    array afkey(dims, key.data());
+    array afin(dims, in.data());
+    array afout = scanByKey(afkey, afin, scanDim, op, inclusive_scan);
+    vector<To> out(afout.elements());
     afout.host(out.data());
 
     verify<Ti, int, To, op, inclusive_scan>(dims, in, key, out, scanDim, eps);
@@ -136,10 +138,10 @@ void scanByKeyTest(af::dim4 dims, int scanDim, std::vector<int> nodeLengths,
 #define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS)   \
 TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                    \
 {                                                                               \
-    af::dim4 dims(X, Y, Z, W);                                                  \
+    dim4 dims(X, Y, Z, W);                                                      \
     int scanDim = DIM;                                                          \
     int nodel[] = {37, 256};                                                    \
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));       \
+    vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));            \
     int keyStart = 0;                                                           \
     int keyEnd = 15;                                                            \
     int dataStart = DSTART;                                                     \
@@ -180,10 +182,10 @@ SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float, false, 1,   -
 
 TEST(ScanByKey,Test_Scan_By_key_Simple_0)
 {
-    af::dim4 dims(16, 8, 2, 1);
+    dim4 dims(16, 8, 2, 1);
     int scanDim = 0;
     int nodel[] = {4, 8};
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+    vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
     int keyStart = 0;
     int keyEnd = 15;
     int dataStart = 2;
@@ -194,10 +196,10 @@ TEST(ScanByKey,Test_Scan_By_key_Simple_0)
 
 TEST(ScanByKey,Test_Scan_By_key_Simple_1)
 {
-    af::dim4 dims(8, 256+128, 1, 1);
+    dim4 dims(8, 256+128, 1, 1);
     int scanDim = 1;
     int nodel[] = {4, 8};
-    std::vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
+    vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
     int keyStart = 0;
     int keyEnd = 15;
     int dataStart = 2;
diff --git a/test/select.cpp b/test/select.cpp
index 265e47007b..37afa845b7 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -17,21 +17,35 @@
 #include <testHelpers.hpp>
 
 using std::vector;
-using namespace af;
+using af::NaN;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::constant;
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::eval;
+using af::randu;
+using af::select;
+using af::seq;
+using af::span;
+using af::sum;
+
 
 template<typename T>
 class Select : public ::testing::Test
 {
 };
 
-typedef ::testing::Types<float, double, af::cfloat, af::cdouble, uint, int, intl, uintl, uchar, char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl, uchar, char, short, ushort> TestTypes;
 TYPED_TEST_CASE(Select, TestTypes);
 
 template<typename T>
 void selectTest(const dim4 &dims)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
     array b = randu(dims, ty);
@@ -47,10 +61,10 @@ void selectTest(const dim4 &dims)
 
     int num = (int)a.elements();
 
-    std::vector<T> ha(num);
-    std::vector<T> hb(num);
-    std::vector<T> hc(num);
-    std::vector<char> hcond(num);
+    vector<T> ha(num);
+    vector<T> hb(num);
+    vector<T> hc(num);
+    vector<char> hcond(num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
@@ -66,7 +80,7 @@ template<typename T, bool is_right>
 void selectScalarTest(const dim4 &dims)
 {
     if (noDoubleTests<T>()) return;
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
     array cond = randu(dims, ty) > a;
@@ -80,9 +94,9 @@ void selectScalarTest(const dim4 &dims)
 
     int num = (int)a.elements();
 
-    std::vector<T> ha(num);
-    std::vector<T> hc(num);
-    std::vector<char> hcond(num);
+    vector<T> ha(num);
+    vector<T> hc(num);
+    vector<char> hcond(num);
 
     a.host(&ha[0]);
     c.host(&hc[0]);
@@ -117,17 +131,17 @@ TYPED_TEST(Select, LeftScalar)
 TEST(Select, NaN)
 {
     dim4 dims(1000, 1250);
-    af::dtype ty = f32;
+    dtype ty = f32;
 
     array a = randu(dims, ty);
-    a(seq(a.dims(0) / 2), span, span, span) = af::NaN;
+    a(seq(a.dims(0) / 2), span, span, span) = NaN;
     float b = 0;
     array c = select(isNaN(a), b, a);
 
     int num = (int)a.elements();
 
-    std::vector<float> ha(num);
-    std::vector<float> hc(num);
+    vector<float> ha(num);
+    vector<float> hc(num);
 
     a.host(&ha[0]);
     c.host(&hc[0]);
@@ -140,14 +154,14 @@ TEST(Select, NaN)
 TEST(Select, ISSUE_1249)
 {
     dim4 dims(2, 3, 4);
-    array cond = af::randu(dims) > 0.5;
-    array a = af::randu(dims);
+    array cond = randu(dims) > 0.5;
+    array a = randu(dims);
     array b = select(cond, a - a * 0.9, a);
     array c = a - a * cond * 0.9;
 
     int num = (int)dims.elements();
-    std::vector<float> hb(num);
-    std::vector<float> hc(num);
+    vector<float> hb(num);
+    vector<float> hc(num);
 
     b.host(&hb[0]);
     c.host(&hc[0]);
@@ -160,14 +174,14 @@ TEST(Select, ISSUE_1249)
 TEST(Select, 4D)
 {
     dim4 dims(2, 3, 4, 2);
-    array cond = af::randu(dims) > 0.5;
-    array a = af::randu(dims);
+    array cond = randu(dims) > 0.5;
+    array a = randu(dims);
     array b = select(cond, a - a * 0.9, a);
     array c = a - a * cond * 0.9;
 
     int num = (int)dims.elements();
-    std::vector<float> hb(num);
-    std::vector<float> hc(num);
+    vector<float> hb(num);
+    vector<float> hc(num);
 
     b.host(&hb[0]);
     c.host(&hc[0]);
@@ -181,21 +195,21 @@ TEST(Select, Issue_1730)
 {
     const int n = 1000;
     const int m = 200;
-    af::array a = af::randu(n, m) - 0.5;
-    af::eval(a);
+    array a = randu(n, m) - 0.5;
+    eval(a);
 
-    std::vector<float> ha1(a.elements());
+    vector<float> ha1(a.elements());
     a.host(&ha1[0]);
 
     const int n1 = n / 2;
     const int n2 = n1 + n / 4;
 
-    a(af::seq(n1, n2), af::span) =
-        af::select(a(af::seq(n1, n2), af::span) >= 0,
-                   a(af::seq(n1, n2), af::span),
-                   a(af::seq(n1, n2), af::span) * -1);
+    a(seq(n1, n2), span) =
+        select(a(seq(n1, n2), span) >= 0,
+                   a(seq(n1, n2), span),
+                   a(seq(n1, n2), span) * -1);
 
-    std::vector<float> ha2(a.elements());
+    vector<float> ha2(a.elements());
     a.host(&ha2[0]);
 
     for (int j = 0; j < m; j++) {
@@ -213,22 +227,22 @@ TEST(Select, Issue_1730_scalar)
 {
     const int n = 1000;
     const int m = 200;
-    af::array a = af::randu(n, m) - 0.5;
-    af::eval(a);
+    array a = randu(n, m) - 0.5;
+    eval(a);
 
-    std::vector<float> ha1(a.elements());
+    vector<float> ha1(a.elements());
     a.host(&ha1[0]);
 
     const int n1 = n / 2;
     const int n2 = n1 + n / 4;
 
     float val = 0;
-    a(af::seq(n1, n2), af::span) =
-        af::select(a(af::seq(n1, n2), af::span) >= 0,
-                   a(af::seq(n1, n2), af::span),
+    a(seq(n1, n2), span) =
+        select(a(seq(n1, n2), span) >= 0,
+                   a(seq(n1, n2), span),
                    val);
 
-    std::vector<float> ha2(a.elements());
+    vector<float> ha2(a.elements());
     a.host(&ha2[0]);
 
     for (int j = 0; j < m; j++) {
@@ -246,38 +260,38 @@ TEST(Select, MaxDim)
 {
     const size_t largeDim = 65535 * 32 + 1;
 
-    af::array a    = af::constant(1, largeDim);
-    af::array b    = af::constant(0, largeDim);
-    af::array cond = af::constant(0, largeDim, b8);
+    array a    = constant(1, largeDim);
+    array b    = constant(0, largeDim);
+    array cond = constant(0, largeDim, b8);
 
-    af::array sel  = af::select(cond, a, b);
+    array sel  = select(cond, a, b);
     float sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
 
-    a    = af::constant(1, 1, largeDim);
-    b    = af::constant(0, 1, largeDim);
-    cond = af::constant(0, 1, largeDim, b8);
+    a    = constant(1, 1, largeDim);
+    b    = constant(0, 1, largeDim);
+    cond = constant(0, 1, largeDim, b8);
 
-    sel  = af::select(cond, a, b);
+    sel  = select(cond, a, b);
     sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
 
-    a    = af::constant(1, 1, 1, largeDim);
-    b    = af::constant(0, 1, 1, largeDim);
-    cond = af::constant(0, 1, 1, largeDim, b8);
+    a    = constant(1, 1, 1, largeDim);
+    b    = constant(0, 1, 1, largeDim);
+    cond = constant(0, 1, 1, largeDim, b8);
 
-    sel  = af::select(cond, a, b);
+    sel  = select(cond, a, b);
     sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
 
-    a    = af::constant(1, 1, 1, 1, largeDim);
-    b    = af::constant(0, 1, 1, 1, largeDim);
-    cond = af::constant(0, 1, 1, 1, largeDim, b8);
+    a    = constant(1, 1, 1, 1, largeDim);
+    b    = constant(0, 1, 1, 1, largeDim);
+    cond = constant(0, 1, 1, 1, largeDim, b8);
 
-    sel  = af::select(cond, a, b);
+    sel  = select(cond, a, b);
     sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
diff --git a/test/set.cpp b/test/set.cpp
index 003b6e0dc7..b0b5d32185 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -23,13 +23,15 @@ using std::cout;
 using std::endl;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 void uniqueTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
@@ -39,7 +41,7 @@ void uniqueTest(string pTestFile)
     // Compare result
     for (int d = 0; d < (int)tests.size(); ++d) {
 
-        af::dim4 dims       = numDims[d];
+        dim4 dims       = numDims[d];
         vector<T> in(data[d].begin(), data[d].end());
 
         af_array inArray   = 0;
@@ -47,7 +49,7 @@ void uniqueTest(string pTestFile)
 
         // Get input array
         ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(),
-                                              dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                                              dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
 
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
@@ -62,7 +64,7 @@ void uniqueTest(string pTestFile)
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
             ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                            << " for test: " << d << std::endl;
+                                                            << " for test: " << d << endl;
         }
 
         if(inArray   != 0) af_release_array(inArray);
@@ -93,7 +95,7 @@ void setTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
@@ -103,10 +105,10 @@ void setTest(string pTestFile)
     // Compare result
     for (int d = 0; d < (int)tests.size(); d += 2) {
 
-        af::dim4 dims0       = numDims[d + 0];
+        dim4 dims0       = numDims[d + 0];
         vector<T> in0(data[d + 0].begin(), data[d + 0].end());
 
-        af::dim4 dims1       = numDims[d + 1];
+        dim4 dims1       = numDims[d + 1];
         vector<T> in1(data[d + 1].begin(), data[d + 1].end());
 
         af_array inArray0   = 0;
@@ -114,11 +116,11 @@ void setTest(string pTestFile)
         af_array outArray  = 0;
 
         ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray0, &in0.front(), dims0.ndims(),
-                                              dims0.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                                              dims0.get(), (af_dtype) dtype_traits<T>::af_type));
 
 
         ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray1, &in1.front(), dims1.ndims(),
-                                              dims1.get(), (af_dtype) af::dtype_traits<T>::af_type));
+                                              dims1.get(), (af_dtype) dtype_traits<T>::af_type));
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
@@ -131,7 +133,7 @@ void setTest(string pTestFile)
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
             ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                            << " for test: " << d << std::endl;
+                                                            << " for test: " << d << endl;
         }
 
         if(inArray0   != 0) af_release_array(inArray0);
diff --git a/test/shift.cpp b/test/shift.cpp
index a6cfc9e34c..6c04c604f7 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -22,8 +22,12 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::product;
 
 template<typename T>
 class Shift : public ::testing::Test
@@ -49,23 +53,23 @@ void shiftTest(string pTestFile, const unsigned resultIdx,
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_shift(&outArray, inArray, x, y, z, w));
@@ -77,7 +81,7 @@ void shiftTest(string pTestFile, const unsigned resultIdx,
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -123,14 +127,14 @@ TEST(Shift, CPP)
     const unsigned z = 0;
     const unsigned w = 0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/shift/shift4d.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
-    af::array output = af::shift(input, x, y, z, w);
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
+    array output = shift(input, x, y, z, w);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -139,7 +143,7 @@ TEST(Shift, CPP)
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -153,15 +157,15 @@ TEST(Shift, MaxDim)
     const size_t largeDim = 65535 * 32 + 1 ;
     const unsigned shift_x = 1;
 
-    af::array input = af::range(af::dim4(2, largeDim));
-    af::array output = af::shift(input, shift_x);
+    array input = range(dim4(2, largeDim));
+    array output = shift(input, shift_x);
 
-    output = af::abs(input - output);
-    ASSERT_EQ(1.f, af::product<float>(output));
+    output = abs(input - output);
+    ASSERT_EQ(1.f, product<float>(output));
 
-    input = af::range(af::dim4(2, 1, 1, largeDim));
-    output = af::shift(input, shift_x);
+    input = range(dim4(2, 1, 1, largeDim));
+    output = shift(input, shift_x);
 
-    output = af::abs(input - output);
-    ASSERT_EQ(1.f, af::product<float>(output));
+    output = abs(input - output);
+    ASSERT_EQ(1.f, product<float>(output));
 }
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index ecd9269e81..3b51809b80 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -18,10 +18,15 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::abs;
+using std::cout;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::array;
 using af::dim4;
+using af::features;
+using af::loadImage;
 
 typedef struct
 {
@@ -104,16 +109,16 @@ static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu
             sum += x*x;
             if (abs(x) > (float)unit_thr) {
                 ret = false;
-                std::cout<<std::endl<<"@compareEuclidean: unit mismatch."<<std::endl;
-                std::cout<<"(cpu,gpu,cpu-gpu)["<<i<<","<<l<<"] : {"<<cpu[idx]<<","<<gpu[idx]<<","<<cpu[idx]-gpu[idx]<<"}"<<std::endl;
-                std::cout<<std::endl;
+                cout<<endl<<"@compareEuclidean: unit mismatch."<<endl;
+                cout<<"(cpu,gpu,cpu-gpu)["<<i<<","<<l<<"] : {"<<cpu[idx]<<","<<gpu[idx]<<","<<cpu[idx]-gpu[idx]<<"}"<<endl;
+                cout<<endl;
                 break;
             }
         }
         if (sqrt(sum) > euc_thr) {
             ret = false;
-            std::cout<<std::endl<<"@compareEuclidean: distance mismatch."<<std::endl;
-            std::cout<<"Euclidean distance: "<<sqrt(sum)<<std::endl;
+            cout<<endl<<"@compareEuclidean: distance mismatch."<<endl;
+            cout<<"Euclidean distance: "<<sqrt(sum)<<endl;
         }
         if (ret == false)
             return ret;
@@ -208,11 +213,11 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
         split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
         for (int elIter = 0; elIter < (int)n; elIter++) {
-            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
         }
 
         EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 4.5f));
@@ -238,10 +243,10 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 }
 
 #define SIFT_INIT(desc, image, nLayers, contrastThr, edgeThr, initSigma, doubleInput) \
-    TYPED_TEST(SIFT, desc) \
-    {   \
-        for (int i = 0; i < 1; i++) \
-        siftTest<TypeParam>(string(TEST_DIR"/sift/"#image".test"), nLayers, contrastThr, edgeThr, initSigma, doubleInput);   \
+    TYPED_TEST(SIFT, desc)                                              \
+    {                                                                   \
+        for (int i = 0; i < 1; i++)                                     \
+            siftTest<TypeParam>(string(TEST_DIR"/sift/"#image".test"), nLayers, contrastThr, edgeThr, initSigma, doubleInput); \
     }
 
     SIFT_INIT(Man_Default, man, 3, 0.04f, 10.0f, 1.6f, true);
@@ -267,11 +272,11 @@ TEST(SIFT, CPP)
     readImageFeaturesDescriptors<float>(string(TEST_DIR"/sift/man.test"), inDims, inFiles, goldFeat, goldDesc);
     inFiles[0].insert(0,string(TEST_DIR"/sift/"));
 
-    af::array in = af::loadImage(inFiles[0].c_str(), false);
+    array in = loadImage(inFiles[0].c_str(), false);
 
-    af::features feat;
-    af::array desc;
-    af::sift(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f);
+    features feat;
+    array desc;
+    sift(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f);
 
     float * outX           = new float[feat.getNumFeatures()];
     float * outY           = new float[feat.getNumFeatures()];
@@ -279,7 +284,7 @@ TEST(SIFT, CPP)
     float * outOrientation = new float[feat.getNumFeatures()];
     float * outSize        = new float[feat.getNumFeatures()];
     float * outDesc        = new float[desc.elements()];
-    af::dim4 descDims = desc.dims();
+    dim4 descDims = desc.dims();
     feat.getX().host(outX);
     feat.getY().host(outY);
     feat.getScore().host(outScore);
@@ -305,11 +310,11 @@ TEST(SIFT, CPP)
     split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
     for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
-        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << std::endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << std::endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
     }
 
     EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 4.5f));
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 69a6250a8c..e9f1343173 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -15,8 +15,11 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Sobel : public ::testing::Test
@@ -45,24 +48,24 @@ void testSobelDerivatives(string pTestFile)
 {
     if (noDoubleTests<Ti>()) return;
 
-    vector<af::dim4>  numDims;
+    vector<dim4>  numDims;
     vector<vector<Ti> >      in;
     vector<vector<To> >   tests;
 
     readTests<Ti,To,int>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
+    dim4 dims    = numDims[0];
     af_array dxArray = 0;
     af_array dyArray = 0;
     af_array inArray = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<Ti>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<Ti>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_sobel_operator(&dxArray, &dyArray, inArray, 3));
 
-    std::vector<To> dxData(dims.elements());
-    std::vector<To> dyData(dims.elements());
+    vector<To> dxData(dims.elements());
+    vector<To> dyData(dims.elements());
 
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dxData.data(), dxArray));
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dyData.data(), dyArray));
@@ -71,11 +74,11 @@ void testSobelDerivatives(string pTestFile)
     vector<To> currDYGoldBar = tests[1];
     size_t nElems = currDXGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currDXGoldBar[elIter], dxData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currDXGoldBar[elIter], dxData[elIter])<< "at: " << elIter<< endl;
     }
     nElems = currDYGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currDYGoldBar[elIter], dyData[elIter])<< "at: " << elIter<< std::endl;
+        ASSERT_EQ(currDYGoldBar[elIter], dyData[elIter])<< "at: " << elIter<< endl;
     }
 
     // cleanup
diff --git a/test/sort.cpp b/test/sort.cpp
index 9a496f3236..dff8b64f11 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -22,8 +22,11 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Sort : public ::testing::Test
@@ -48,23 +51,23 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<float> > tests;
     readTests<T, float, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array tempArray = 0;
     af_array sxArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_sort(&sxArray, inArray, 0, dir));
@@ -77,7 +80,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -122,15 +125,15 @@ TEST(Sort, CPPDim0)
     const bool dir = true;
     const unsigned resultIdx0 = 0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
 
-    af::array output = af::sort(input, 0, dir);
+    array output = sort(input, 0, dir);
 
     size_t nElems = tests[resultIdx0].size();
 
@@ -140,7 +143,7 @@ TEST(Sort, CPPDim0)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -154,17 +157,17 @@ TEST(Sort, CPPDim1)
     const bool dir = true;
     const unsigned resultIdx0 = 0;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
 
-    af::array input_ = reorder(input, 1, 0, 2, 3);
+    array input_ = reorder(input, 1, 0, 2, 3);
 
-    af::array output = af::sort(input_, 1, dir);
+    array output = sort(input_, 1, dir);
 
     output = reorder(output, 1, 0, 2, 3); // Required for checking with test data
 
@@ -176,7 +179,7 @@ TEST(Sort, CPPDim1)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -190,17 +193,17 @@ TEST(Sort, CPPDim2)
     const bool dir = false;
     const unsigned resultIdx0 = 2;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
 
-    af::array input_ = reorder(input, 1, 2, 0, 3);
+    array input_ = reorder(input, 1, 2, 0, 3);
 
-    af::array output = af::sort(input_, 2, dir);
+    array output = sort(input_, 2, dir);
 
     output = reorder(output, 2, 0, 1, 3); // Required for checking with test data
 
@@ -212,7 +215,7 @@ TEST(Sort, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index dae46bef54..405174b90f 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -22,8 +22,11 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class SortByKey : public ::testing::Test
@@ -48,12 +51,12 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<float> > tests;
     readTests<T, float, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array ikeyArray = 0;
     af_array ivalArray = 0;
@@ -62,12 +65,12 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     af_array ovalArray = 0;
 
     if (isSubRef) {
-        //ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        //ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         //ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&ikeyArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&ivalArray, &(in[1].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&ikeyArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&ivalArray, &(in[1].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_sort_by_key(&okeyArray, &ovalArray, ikeyArray, ivalArray, 0, dir));
@@ -80,7 +83,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
     }
 
     T* valData = new T[tests[resultIdx1].size()];
@@ -89,7 +92,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 #ifndef AF_OPENCL
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
     }
 #endif
 
@@ -135,16 +138,16 @@ TEST(SortByKey, CPPDim0)
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_tiny.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array keys(idims, &(in[0].front()));
-    af::array vals(idims, &(in[1].front()));
-    af::array out_keys, out_vals;
-    af::sort(out_keys, out_vals, keys, vals, 0, dir);
+    dim4 idims = numDims[0];
+    array keys(idims, &(in[0].front()));
+    array vals(idims, &(in[1].front()));
+    array out_keys, out_vals;
+    sort(out_keys, out_vals, keys, vals, 0, dir);
 
     size_t nElems = tests[resultIdx0].size();
     // Get result
@@ -153,7 +156,7 @@ TEST(SortByKey, CPPDim0)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
     }
 
     float* valData = new float[tests[resultIdx1].size()];
@@ -161,7 +164,7 @@ TEST(SortByKey, CPPDim0)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -177,20 +180,20 @@ TEST(SortByKey, CPPDim1)
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array keys(idims, &(in[0].front()));
-    af::array vals(idims, &(in[1].front()));
+    dim4 idims = numDims[0];
+    array keys(idims, &(in[0].front()));
+    array vals(idims, &(in[1].front()));
 
-    af::array keys_ = reorder(keys, 1, 0, 2, 3);
-    af::array vals_ = reorder(vals, 1, 0, 2, 3);
+    array keys_ = reorder(keys, 1, 0, 2, 3);
+    array vals_ = reorder(vals, 1, 0, 2, 3);
 
-    af::array out_keys, out_vals;
-    af::sort(out_keys, out_vals, keys_, vals_, 1, dir);
+    array out_keys, out_vals;
+    sort(out_keys, out_vals, keys_, vals_, 1, dir);
 
     out_keys = reorder(out_keys, 1, 0, 2, 3);
     out_vals = reorder(out_vals, 1, 0, 2, 3);
@@ -202,7 +205,7 @@ TEST(SortByKey, CPPDim1)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
     }
 
     float* valData = new float[tests[resultIdx1].size()];
@@ -210,7 +213,7 @@ TEST(SortByKey, CPPDim1)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -226,20 +229,20 @@ TEST(SortByKey, CPPDim2)
     const unsigned resultIdx0 = 2;
     const unsigned resultIdx1 = 3;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array keys(idims, &(in[0].front()));
-    af::array vals(idims, &(in[1].front()));
+    dim4 idims = numDims[0];
+    array keys(idims, &(in[0].front()));
+    array vals(idims, &(in[1].front()));
 
-    af::array keys_ = reorder(keys, 1, 2, 0, 3);
-    af::array vals_ = reorder(vals, 1, 2, 0, 3);
+    array keys_ = reorder(keys, 1, 2, 0, 3);
+    array vals_ = reorder(vals, 1, 2, 0, 3);
 
-    af::array out_keys, out_vals;
-    af::sort(out_keys, out_vals, keys_, vals_, 2, dir);
+    array out_keys, out_vals;
+    sort(out_keys, out_vals, keys_, vals_, 2, dir);
 
     out_keys = reorder(out_keys, 2, 0, 1, 3);
     out_vals = reorder(out_vals, 2, 0, 1, 3);
@@ -251,7 +254,7 @@ TEST(SortByKey, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
     }
 
     float* valData = new float[tests[resultIdx1].size()];
@@ -259,7 +262,7 @@ TEST(SortByKey, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 0df4744c02..2a7b39ea66 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -22,8 +22,11 @@ using std::vector;
 using std::string;
 using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class SortIndex : public ::testing::Test
@@ -48,12 +51,12 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<float> > tests;
     readTests<T, float, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array tempArray = 0;
@@ -61,11 +64,11 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     af_array ixArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_sort_index(&sxArray, &ixArray, inArray, 0, dir));
@@ -78,7 +81,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Get result
@@ -88,7 +91,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 #ifndef AF_OPENCL
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
     }
 #endif
 
@@ -136,15 +139,15 @@ TEST(SortIndex, CPPDim0)
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
-    af::array outValues, outIndices;
-    af::sort(outValues, outIndices, input, 0, dir);
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
+    array outValues, outIndices;
+    sort(outValues, outIndices, input, 0, dir);
 
     size_t nElems = tests[resultIdx0].size();
 
@@ -154,7 +157,7 @@ TEST(SortIndex, CPPDim0)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Get result
@@ -163,7 +166,7 @@ TEST(SortIndex, CPPDim0)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -179,17 +182,17 @@ TEST(SortIndex, CPPDim1)
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input_(idims, &(in[0].front()));
-    af::array input = reorder(input_, 1, 0, 2, 3);
+    dim4 idims = numDims[0];
+    array input_(idims, &(in[0].front()));
+    array input = reorder(input_, 1, 0, 2, 3);
 
-    af::array outValues, outIndices;
-    af::sort(outValues, outIndices, input, 1, dir);
+    array outValues, outIndices;
+    sort(outValues, outIndices, input, 1, dir);
 
     outValues  = reorder(outValues,  1, 0, 2, 3);
     outIndices = reorder(outIndices, 1, 0, 2, 3);
@@ -202,7 +205,7 @@ TEST(SortIndex, CPPDim1)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Get result
@@ -211,7 +214,7 @@ TEST(SortIndex, CPPDim1)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -227,17 +230,17 @@ TEST(SortIndex, CPPDim2)
     const unsigned resultIdx0 = 2;
     const unsigned resultIdx1 = 3;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input_(idims, &(in[0].front()));
-    af::array input = reorder(input_, 1, 2, 0, 3);
+    dim4 idims = numDims[0];
+    array input_(idims, &(in[0].front()));
+    array input = reorder(input_, 1, 2, 0, 3);
 
-    af::array outValues, outIndices;
-    af::sort(outValues, outIndices, input, 2, dir);
+    array outValues, outIndices;
+    sort(outValues, outIndices, input, 2, dir);
 
     outValues  = reorder(outValues,  2, 0, 1, 3);
     outIndices = reorder(outIndices, 2, 0, 1, 3);
@@ -249,7 +252,7 @@ TEST(SortIndex, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
     }
 
     // Get result
@@ -258,7 +261,7 @@ TEST(SortIndex, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
diff --git a/test/sparse.cpp b/test/sparse.cpp
index f003bf45ef..6eb0f5a7d6 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -11,6 +11,14 @@
 #include <testHelpers.hpp>
 #include <sparse_common.hpp>
 
+using af::allTrue;
+using af::array;
+using af::deviceMemInfo;
+using af::dtype_traits;
+using af::identity;
+using af::randu;
+using af::span;
+
 #define SPARSE_TESTS(T, eps)                                \
     TEST(Sparse, T##Square)                                 \
     {                                                       \
@@ -69,7 +77,7 @@ CREATE_TESTS(AF_STORAGE_COO)
 
 TEST(Sparse, Create_AF_STORAGE_CSC)
 {
-    af::array d = af::identity(3, 3);
+    array d = identity(3, 3);
 
     af_array out = 0;
     ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array_from_dense(&out, d.get(), AF_STORAGE_CSC));
@@ -104,16 +112,19 @@ CAST_TESTS(cdouble, cfloat  )
 CAST_TESTS(cdouble, cdouble )
 
 
+
 TEST(Sparse, ISSUE_1745)
 {
-  af::array A = af::randu(4, 4);
-  A(1, af::span) = 0;
-  A(2, af::span) = 0;
+  using af::where;
+
+  array A = randu(4, 4);
+  A(1, span) = 0;
+  A(2, span) = 0;
 
-  af::array idx = where(A);
-  af::array data = A(idx);
-  af::array row_idx = (idx / A.dims()[0]).as(s64);
-  af::array col_idx = (idx % A.dims()[0]).as(s64);
+  array idx = where(A);
+  array data = A(idx);
+  array row_idx = (idx / A.dims()[0]).as(s64);
+  array col_idx = (idx % A.dims()[0]).as(s64);
 
   af_array A_sparse;
   ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array(&A_sparse, A.dims(0), A.dims(1), data.get(), row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
@@ -124,9 +135,9 @@ TEST(Sparse, ISSUE_2134_COO)
   int rows[] = {0,0,0,1,1,2,2};
   int cols[] = {0,1,2,0,1,0,2};
   float values[] = {3,3,4,3,10,4,3};
-  af::array row(7, rows);
-  af::array col(7, cols);
-  af::array value(7, values);
+  array row(7, rows);
+  array col(7, cols);
+  array value(7, values);
   af_array A = 0;
   EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
   if(A != 0) af_release_array(A);
@@ -143,9 +154,9 @@ TEST(Sparse, ISSUE_2134_CSR)
   int rows[] = {0,3,5,7};
   int cols[] = {0,1,2,0,1,0,2};
   float values[] = {3,3,4,3,10,4,3};
-  af::array row(4, rows);
-  af::array col(7, cols);
-  af::array value(7, values);
+  array row(4, rows);
+  array col(7, cols);
+  array value(7, values);
   af_array A = 0;
   EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
   if(A != 0) af_release_array(A);
@@ -162,9 +173,9 @@ TEST(Sparse, ISSUE_2134_CSC)
   int rows[] = {0,0,0,1,1,2,2};
   int cols[] = {0,3,5,7};
   float values[] = {3,3,4,3,10,4,3};
-  af::array row(7, rows);
-  af::array col(4, cols);
-  af::array value(7, values);
+  array row(7, rows);
+  array col(4, cols);
+  array value(7, values);
   af_array A = 0;
   EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
   if(A != 0) af_release_array(A);
@@ -179,12 +190,12 @@ TEST(Sparse, ISSUE_2134_CSC)
 template<typename T>
 class Sparse : public ::testing::Test {};
 
-typedef ::testing::Types<float, af::cfloat, double, af::cdouble> SparseTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble> SparseTypes;
 TYPED_TEST_CASE(Sparse, SparseTypes);
 
 TYPED_TEST(Sparse, DeepCopy) {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace af;
+
     cleanSlate();
 
     array s;
@@ -201,7 +212,7 @@ TYPED_TEST(Sparse, DeepCopy) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
                       &lock_bytes, &lock_buffers);
     size_t size_of_alloc = lock_bytes;
     size_t buffers_per_sparse = lock_buffers;
@@ -211,7 +222,7 @@ TYPED_TEST(Sparse, DeepCopy) {
         s2.eval();
 
         // Make sure that the deep copy allocated additional memory
-        af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+        deviceMemInfo(&alloc_bytes, &alloc_buffers,
                           &lock_bytes, &lock_buffers);
 
         EXPECT_NE(s.get(), s2.get()) << "The sparse arrays point to the same "
@@ -231,7 +242,7 @@ TYPED_TEST(Sparse, DeepCopy) {
 
 TYPED_TEST(Sparse, Empty) {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace af;
+
     af_array ret = 0;
     dim_t rows = 0, cols = 0, nnz = 0;
     EXPECT_EQ(AF_SUCCESS,
@@ -249,9 +260,9 @@ TYPED_TEST(Sparse, Empty) {
 
 TYPED_TEST(Sparse, EmptyDeepCopy) {
     if (noDoubleTests<TypeParam>()) return;
-    using namespace af;
+
     array a = sparse(0, 0,
-                     array(0, (af_dtype)af::dtype_traits<TypeParam>::af_type),
+                     array(0, (af_dtype)dtype_traits<TypeParam>::af_type),
                      array(1, s32), array(0, s32));
     EXPECT_TRUE(a.issparse());
     EXPECT_EQ(0, sparseGetNNZ(a));
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 16d1ae12b7..cd8e98d857 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -20,14 +20,18 @@
 
 using std::vector;
 using std::string;
-using std::cout;
-using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::deviceGC;
+using af::dim4;
+using af::freeHost;
+using af::max;
+using af::sum;
 
 template<typename T>
-af::array makeSparse(af::array A, int factor)
+array makeSparse(array A, int factor)
 {
     A = floor(A * 1000);
     A = A * ((A % factor) == 0) / 1000;
@@ -35,28 +39,28 @@ af::array makeSparse(af::array A, int factor)
 }
 
 template<>
-af::array makeSparse<cfloat>(af::array A, int factor)
+array makeSparse<cfloat>(array A, int factor)
 {
-    af::array r = real(A);
+    array r = real(A);
     r = floor(r * 1000);
     r = r * ((r % factor) == 0) / 1000;
 
-    af::array i = r / 2;
+    array i = r / 2;
 
-    A = af::complex(r, i);
+    A = complex(r, i);
     return A;
 }
 
 template<>
-af::array makeSparse<cdouble>(af::array A, int factor)
+array makeSparse<cdouble>(array A, int factor)
 {
-    af::array r = real(A);
+    array r = real(A);
     r = floor(r * 1000);
     r = r * ((r % factor) == 0) / 1000;
 
-    af::array i = r / 2;
+    array i = r / 2;
 
-    A = af::complex(r, i);
+    A = complex(r, i);
     return A;
 }
 
@@ -70,7 +74,7 @@ typedef enum {
 template<af_op_t op>
 struct arith_op
 {
-    af::array operator()(af::array v1, af::array v2)
+    array operator()(array v1, array v2)
     {
         return v1;
     }
@@ -79,7 +83,7 @@ struct arith_op
 template<>
 struct arith_op<af_add_t>
 {
-    af::array operator()(af::array v1, af::array v2)
+    array operator()(array v1, array v2)
     {
         return v1 + v2;
     }
@@ -88,7 +92,7 @@ struct arith_op<af_add_t>
 template<>
 struct arith_op<af_sub_t>
 {
-    af::array operator()(af::array v1, af::array v2)
+    array operator()(array v1, array v2)
     {
         return v1 - v2;
     }
@@ -97,7 +101,7 @@ struct arith_op<af_sub_t>
 template<>
 struct arith_op<af_mul_t>
 {
-    af::array operator()(af::array v1, af::array v2)
+    array operator()(array v1, array v2)
     {
         return v1 * v2;
     }
@@ -106,14 +110,14 @@ struct arith_op<af_mul_t>
 template<>
 struct arith_op<af_div_t>
 {
-    af::array operator()(af::array v1, af::array v2)
+    array operator()(array v1, array v2)
     {
         return v1 / v2;
     }
 };
 
 template<typename T>
-void sparseCompare(af::array A, af::array B, const double eps)
+void sparseCompare(array A, array B, const double eps)
 {
 // This macro is used to check if either value is finite and then call assert
 // If neither value is finite, then they can be assumed to be equal to either inf or nan
@@ -122,17 +126,17 @@ void sparseCompare(af::array A, af::array B, const double eps)
         ASSERT_NEAR(V1, V2, eps) << "at : " << i;   \
     }                                               \
 
-    af::array AValues = sparseGetValues(A);
-    af::array ARowIdx = sparseGetRowIdx(A);
-    af::array AColIdx = sparseGetColIdx(A);
+    array AValues = sparseGetValues(A);
+    array ARowIdx = sparseGetRowIdx(A);
+    array AColIdx = sparseGetColIdx(A);
 
-    af::array BValues = sparseGetValues(B);
-    af::array BRowIdx = sparseGetRowIdx(B);
-    af::array BColIdx = sparseGetColIdx(B);
+    array BValues = sparseGetValues(B);
+    array BRowIdx = sparseGetRowIdx(B);
+    array BColIdx = sparseGetColIdx(B);
 
     // Verify row and col indices
-    ASSERT_EQ(0, af::max<int>(ARowIdx - BRowIdx));
-    ASSERT_EQ(0, af::max<int>(AColIdx - BColIdx));
+    ASSERT_EQ(0, max<int>(ARowIdx - BRowIdx));
+    ASSERT_EQ(0, max<int>(AColIdx - BColIdx));
 
     T *ptrA = AValues.host<T>();
     T *ptrB = BValues.host<T>();
@@ -143,8 +147,8 @@ void sparseCompare(af::array A, af::array B, const double eps)
             ASSERT_FINITE_EQ(imag(ptrA[i]), imag(ptrB[i]));
         }
     }
-    af::freeHost(ptrA);
-    af::freeHost(ptrB);
+    freeHost(ptrA);
+    freeHost(ptrB);
 
 #undef ASSERT_FINITE_EQ
 }
@@ -152,101 +156,101 @@ void sparseCompare(af::array A, af::array B, const double eps)
 template<typename T, af_op_t op>
 void sparseArithTester(const int m, const int n, int factor, const double eps)
 {
-    af::deviceGC();
+    deviceGC();
 
     if (noDoubleTests<T>()) return;
 
 #if 1
-    af::array A = cpu_randu<T>(af::dim4(m, n));
-    af::array B = cpu_randu<T>(af::dim4(m, n));
+    array A = cpu_randu<T>(dim4(m, n));
+    array B = cpu_randu<T>(dim4(m, n));
 #else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array B = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    array A = randu(m, n, (dtype)dtype_traits<T>::af_type);
+    array B = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
     A = makeSparse<T>(A, factor);
 
-    af::array RA = af::sparse(A, AF_STORAGE_CSR);
-    af::array OA = af::sparse(A, AF_STORAGE_COO);
+    array RA = sparse(A, AF_STORAGE_CSR);
+    array OA = sparse(A, AF_STORAGE_COO);
 
     // Arith Op
-    af::array resR = arith_op<op>()(RA, B);
-    af::array resO = arith_op<op>()(OA, B);
-    af::array resD = arith_op<op>()( A, B);
+    array resR = arith_op<op>()(RA, B);
+    array resO = arith_op<op>()(OA, B);
+    array resD = arith_op<op>()( A, B);
 
-    af::array revR = arith_op<op>()(B, RA);
-    af::array revO = arith_op<op>()(B, OA);
-    af::array revD = arith_op<op>()(B,  A);
+    array revR = arith_op<op>()(B, RA);
+    array revO = arith_op<op>()(B, OA);
+    array revD = arith_op<op>()(B,  A);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(resR - resD))) / (m * n), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resR - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(real(resR - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(imag(resR - resD))) / (m * n), eps);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(resO - resD))) / (m * n), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(resO - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(real(resO - resD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(imag(resO - resD))) / (m * n), eps);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(revR - revD))) / (m * n), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revR - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(real(revR - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(imag(revR - revD))) / (m * n), eps);
 
-    ASSERT_NEAR(0, af::sum<double>(af::abs(real(revO - revD))) / (m * n), eps);
-    ASSERT_NEAR(0, af::sum<double>(af::abs(imag(revO - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(real(revO - revD))) / (m * n), eps);
+    ASSERT_NEAR(0, sum<double>(abs(imag(revO - revD))) / (m * n), eps);
 }
 
 // Mul
 template<typename T>
 void sparseArithTesterMul(const int m, const int n, int factor, const double eps)
 {
-    af::deviceGC();
+    deviceGC();
 
     if (noDoubleTests<T>()) return;
 
 #if 1
-    af::array A = cpu_randu<T>(af::dim4(m, n));
-    af::array B = cpu_randu<T>(af::dim4(m, n));
+    array A = cpu_randu<T>(dim4(m, n));
+    array B = cpu_randu<T>(dim4(m, n));
 #else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array B = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    array A = randu(m, n, (dtype)dtype_traits<T>::af_type);
+    array B = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
     A = makeSparse<T>(A, factor);
 
-    af::array RA = af::sparse(A, AF_STORAGE_CSR);
-    af::array OA = af::sparse(A, AF_STORAGE_COO);
+    array RA = sparse(A, AF_STORAGE_CSR);
+    array OA = sparse(A, AF_STORAGE_COO);
 
     // Forward
     {
         // Arith Op
-        af::array resR = arith_op<af_mul_t>()(RA, B);
-        af::array resO = arith_op<af_mul_t>()(OA, B);
+        array resR = arith_op<af_mul_t>()(RA, B);
+        array resO = arith_op<af_mul_t>()(OA, B);
 
         // We will test this by converting the COO to CSR and CSR to COO and
         // comparing them. In essense, we are comparing the resR and resO
         // TODO: Make a better comparison using dense
 
         // Check resR against conR
-        af::array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
+        array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
         sparseCompare<T>(resR, conR, eps);
 
         // Check resO against conO
-        af::array conO = sparseConvertTo(resR, AF_STORAGE_COO);
+        array conO = sparseConvertTo(resR, AF_STORAGE_COO);
         sparseCompare<T>(resO, conO, eps);
     }
 
     // Reverse
     {
         // Arith Op
-        af::array resR = arith_op<af_mul_t>()(B, RA);
-        af::array resO = arith_op<af_mul_t>()(B, OA);
+        array resR = arith_op<af_mul_t>()(B, RA);
+        array resO = arith_op<af_mul_t>()(B, OA);
 
         // We will test this by converting the COO to CSR and CSR to COO and
         // comparing them. In essense, we are comparing the resR and resO
         // TODO: Make a better comparison using dense
 
         // Check resR against conR
-        af::array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
+        array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
         sparseCompare<T>(resR, conR, eps);
 
         // Check resO against conO
-        af::array conO = sparseConvertTo(resR, AF_STORAGE_COO);
+        array conO = sparseConvertTo(resR, AF_STORAGE_COO);
         sparseCompare<T>(resO, conO, eps);
     }
 }
@@ -255,26 +259,26 @@ void sparseArithTesterMul(const int m, const int n, int factor, const double eps
 template<typename T>
 void sparseArithTesterDiv(const int m, const int n, int factor, const double eps)
 {
-    af::deviceGC();
+    deviceGC();
 
     if (noDoubleTests<T>()) return;
 
 #if 1
-    af::array A = cpu_randu<T>(af::dim4(m, n));
-    af::array B = cpu_randu<T>(af::dim4(m, n));
+    array A = cpu_randu<T>(dim4(m, n));
+    array B = cpu_randu<T>(dim4(m, n));
 #else
-    af::array A = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
-    af::array B = af::randu(m, n, (af::dtype)af::dtype_traits<T>::af_type);
+    array A = randu(m, n, (dtype)dtype_traits<T>::af_type);
+    array B = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
     A = makeSparse<T>(A, factor);
 
-    af::array RA = af::sparse(A, AF_STORAGE_CSR);
-    af::array OA = af::sparse(A, AF_STORAGE_COO);
+    array RA = sparse(A, AF_STORAGE_CSR);
+    array OA = sparse(A, AF_STORAGE_COO);
 
     // Arith Op
-    af::array resR = arith_op<af_div_t>()(RA, B);
-    af::array resO = arith_op<af_div_t>()(OA, B);
+    array resR = arith_op<af_div_t>()(RA, B);
+    array resO = arith_op<af_div_t>()(OA, B);
 
     // Assert division by sparse is not allowed
     af_array out_temp = 0;
@@ -287,11 +291,11 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     // TODO: Make a better comparison using dense
 
     // Check resR against conR
-    af::array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
+    array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
     sparseCompare<T>(resR, conR, eps);
 
     // Check resO against conO
-    af::array conO = sparseConvertTo(resR, AF_STORAGE_COO);
+    array conO = sparseConvertTo(resR, AF_STORAGE_COO);
     sparseCompare<T>(resO, conO, eps);
 }
 
diff --git a/test/sparse_convert.cpp b/test/sparse_convert.cpp
index 810a39b5a1..c2ad68296f 100644
--- a/test/sparse_convert.cpp
+++ b/test/sparse_convert.cpp
@@ -20,17 +20,18 @@
 
 using std::vector;
 using std::string;
-using std::cout;
-using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::max;
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 
 template<typename T>
-af::array makeSparse(af::array A, int factor)
+array makeSparse(array A, int factor)
 {
     A = floor(A * 1000);
     A = A * ((A % factor) == 0) / 1000;
@@ -38,28 +39,28 @@ af::array makeSparse(af::array A, int factor)
 }
 
 template<>
-af::array makeSparse<cfloat>(af::array A, int factor)
+array makeSparse<cfloat>(array A, int factor)
 {
-    af::array r = real(A);
+    array r = real(A);
     r = floor(r * 1000);
     r = r * ((r % factor) == 0) / 1000;
 
-    af::array i = r / 2;
+    array i = r / 2;
 
-    A = af::complex(r, i);
+    A = complex(r, i);
     return A;
 }
 
 template<>
-af::array makeSparse<cdouble>(af::array A, int factor)
+array makeSparse<cdouble>(array A, int factor)
 {
-    af::array r = real(A);
+    array r = real(A);
     r = floor(r * 1000);
     r = r * ((r % factor) == 0) / 1000;
 
-    af::array i = r / 2;
+    array i = r / 2;
 
-    A = af::complex(r, i);
+    A = complex(r, i);
     return A;
 }
 
@@ -68,18 +69,18 @@ void sparseConvertTester(const int m, const int n, int factor)
 {
     if (noDoubleTests<T>()) return;
 
-    af::array A = cpu_randu<T>(af::dim4(m, n));
+    array A = cpu_randu<T>(dim4(m, n));
 
     A = makeSparse<T>(A, factor);
 
     // Create Sparse Array of type src and dest From Dense
-    af::array sA = af::sparse(A, src);
+    array sA = sparse(A, src);
 
     // Convert src to dest format and dest to src
-    af::array s2d = sparseConvertTo(sA, dest);
+    array s2d = sparseConvertTo(sA, dest);
 
     // Create the dest type from dense - gold
-    af::array dA = af::sparse(A, dest);
+    array dA = sparse(A, dest);
 
     // Verify nnZ
     dim_t dNNZ   = sparseGetNNZ(dA);
@@ -94,21 +95,21 @@ void sparseConvertTester(const int m, const int n, int factor)
     ASSERT_EQ(dType, s2dType);
 
     // Get the individual arrays and verify equality
-    af::array dValues = sparseGetValues(dA);
-    af::array dRowIdx = sparseGetRowIdx(dA);
-    af::array dColIdx = sparseGetColIdx(dA);
+    array dValues = sparseGetValues(dA);
+    array dRowIdx = sparseGetRowIdx(dA);
+    array dColIdx = sparseGetColIdx(dA);
 
-    af::array s2dValues = sparseGetValues(s2d);
-    af::array s2dRowIdx = sparseGetRowIdx(s2d);
-    af::array s2dColIdx = sparseGetColIdx(s2d);
+    array s2dValues = sparseGetValues(s2d);
+    array s2dRowIdx = sparseGetRowIdx(s2d);
+    array s2dColIdx = sparseGetColIdx(s2d);
 
     // Verify values
-    ASSERT_EQ(0, af::max<double>(af::real(dValues - s2dValues)));
-    ASSERT_EQ(0, af::max<double>(af::imag(dValues - s2dValues)));
+    ASSERT_EQ(0, max<double>(real(dValues - s2dValues)));
+    ASSERT_EQ(0, max<double>(imag(dValues - s2dValues)));
 
     // Verify row and col indices
-    ASSERT_EQ(0, af::max<int   >(dRowIdx - s2dRowIdx));
-    ASSERT_EQ(0, af::max<int   >(dColIdx - s2dColIdx));
+    ASSERT_EQ(0, max<int   >(dRowIdx - s2dRowIdx));
+    ASSERT_EQ(0, max<int   >(dColIdx - s2dColIdx));
 }
 
 #define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F)                   \
@@ -141,12 +142,12 @@ TEST(SPARSE_CONVERT, CSC_ARG_ERROR)
 {
     const int m = 100, n = 28, factor = 5;
 
-    af::array A = cpu_randu<float>(af::dim4(m, n));
+    array A = cpu_randu<float>(dim4(m, n));
 
     A = makeSparse<float>(A, factor);
 
     // Create Sparse Array of type src and dest From Dense
-    af::array sA = af::sparse(A, AF_STORAGE_CSR);
+    array sA = sparse(A, AF_STORAGE_CSR);
 
     // Convert src to dest format and dest to src
     // Use C-API to catch error
diff --git a/test/stdev.cpp b/test/stdev.cpp
index c8f61e4364..714f64c6e7 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -18,9 +18,17 @@
 #include <algorithm>
 #include <testHelpers.hpp>
 
-using namespace af;
+using std::cout;
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::exception;
+using af::seq;
+using af::stdev;
 
 template<typename T>
 class StandardDev : public ::testing::Test
@@ -77,29 +85,29 @@ void stdevDimTest(string pFileName, dim_t dim=-1)
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<int> >       in;
     vector<vector<float> >  tests;
 
     readTestsFromFile<int,float>(pFileName, numDims, in, tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
     vector<T> input(in[0].begin(), in[0].end());
 
-    af::array a(dims, &(input.front()));
+    array a(dims, &(input.front()));
 
-    af::array b = stdev(a, dim);
+    array b = stdev(a, dim);
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
     size_t nElems    = currGoldBar.size();
-    std::vector<outType> outData(nElems);
+    vector<outType> outData(nElems);
 
     b.host((void*)outData.data());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
-        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
     }
 }
 
@@ -125,12 +133,12 @@ TYPED_TEST(StandardDev, Dim3)
 
 TEST(StandardDev, InvalidDim)
 {
-    ASSERT_THROW(af::stdev(af::array(), 5), af::exception);
+    ASSERT_THROW(stdev(array(), 5), exception);
 }
 
 TEST(StandardDev, InvalidType)
 {
-    ASSERT_THROW(af::stdev(constant(cdouble(1.0, -1.0), 10)), af::exception);
+    ASSERT_THROW(stdev(constant(cdouble(1.0, -1.0), 10)), exception);
 }
 
 template<typename T>
@@ -140,30 +148,30 @@ void stdevDimIndexTest(string pFileName, dim_t dim=-1)
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<int> >       in;
     vector<vector<float> >  tests;
 
     readTestsFromFile<int,float>(pFileName, numDims, in, tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
     vector<T> input(in[0].begin(), in[0].end());
 
-    af::array a(dims, &(input.front()));
-    af::array b = a(seq(2,6), seq(1,7));
+    array a(dims, &(input.front()));
+    array b = a(seq(2,6), seq(1,7));
 
-    af::array c = stdev(b, dim);
+    array c = stdev(b, dim);
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
     size_t nElems    = currGoldBar.size();
-    std::vector<outType> outData(nElems);
+    vector<outType> outData(nElems);
 
     c.host((void*)outData.data());
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
-        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
     }
 }
 
@@ -183,17 +191,17 @@ TYPED_TEST(StandardDev, All)
     if (noDoubleTests<TypeParam>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<int> >       in;
     vector<vector<float> >  tests;
 
     readTestsFromFile<int,float>(string(TEST_DIR "/stdev/mat_10x10_scalar.test"),
                                  numDims, in, tests);
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
     vector<TypeParam> input(in[0].begin(), in[0].end());
 
-    af::array a(dims, &(input.front()));
+    array a(dims, &(input.front()));
     outType b = stdev<outType>(a);
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
diff --git a/test/susan.cpp b/test/susan.cpp
index ee846d1c4d..258a3303b0 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -18,10 +18,16 @@
 #include <testHelpers.hpp>
 #include <typeinfo>
 
+using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::array;
 using af::dim4;
+using af::exception;
+using af::features;
+using af::loadImage;
+using af::randu;
 
 typedef struct
 {
@@ -77,15 +83,15 @@ void susanTest(string pTestFile, float t, float g)
     for (size_t testId=0; testId<testCount; ++testId) {
         inFiles[testId].insert(0, string(TEST_DIR "/susan/"));
 
-        af::array in = af::loadImage(inFiles[testId].c_str(), false);
+        array in = loadImage(inFiles[testId].c_str(), false);
 
-        af::features out = af::susan(in, 3, t, g, 0.05f, 3);
+        features out = susan(in, 3, t, g, 0.05f, 3);
 
-        std::vector<float> outX          (gold[0].size());
-        std::vector<float> outY          (gold[1].size());
-        std::vector<float> outScore      (gold[2].size());
-        std::vector<float> outOrientation(gold[3].size());
-        std::vector<float> outSize       (gold[4].size());
+        vector<float> outX          (gold[0].size());
+        vector<float> outY          (gold[1].size());
+        vector<float> outScore      (gold[2].size());
+        vector<float> outOrientation(gold[3].size());
+        vector<float> outSize       (gold[4].size());
         out.getX().host(outX.data());
         out.getY().host(outY.data());
         out.getScore().host(outScore.data());
@@ -103,19 +109,19 @@ void susanTest(string pTestFile, float t, float g)
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
         for (int elIter = 0; elIter < (int)out.getNumFeatures(); elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << std::endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << std::endl;
-            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << std::endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
         }
     }
 }
 
-#define SUSAN_TEST(image, tval, gval) \
-    TYPED_TEST(Susan, image) \
-    {   \
-        susanTest<TypeParam>(string(TEST_DIR "/susan/"#image".test"), tval, gval);\
+#define SUSAN_TEST(image, tval, gval)                                   \
+    TYPED_TEST(Susan, image)                                            \
+    {                                                                   \
+        susanTest<TypeParam>(string(TEST_DIR "/susan/"#image".test"), tval, gval); \
     }
 
 SUSAN_TEST(man_t32_g10, 32, 10);
@@ -125,10 +131,10 @@ SUSAN_TEST(square_t32_g20, 32, 20);
 TEST(Susan, InvalidDims)
 {
     try {
-        af::array a = af::randu(256);
-        af::features out = af::susan(a);
+        array a = randu(256);
+        features out = susan(a);
         EXPECT_TRUE(false);
-    } catch (af::exception &e) {
+    } catch (exception &e) {
         EXPECT_TRUE(true);
     }
 }
@@ -136,10 +142,10 @@ TEST(Susan, InvalidDims)
 TEST(Susan, InvalidRadius)
 {
     try {
-        af::array a = af::randu(256);
-        af::features out = af::susan(a, 10);
+        array a = randu(256);
+        features out = susan(a, 10);
         EXPECT_TRUE(false);
-    } catch (af::exception &e) {
+    } catch (exception &e) {
         EXPECT_TRUE(true);
     }
 }
@@ -147,10 +153,10 @@ TEST(Susan, InvalidRadius)
 TEST(Susan, InvalidThreshold)
 {
     try {
-        af::array a = af::randu(256);
-        af::features out = af::susan(a, 3, -32, 10, 0.05f, 3);
+        array a = randu(256);
+        features out = susan(a, 3, -32, 10, 0.05f, 3);
         EXPECT_TRUE(false);
-    } catch (af::exception &e) {
+    } catch (exception &e) {
         EXPECT_TRUE(true);
     }
 }
@@ -158,10 +164,10 @@ TEST(Susan, InvalidThreshold)
 TEST(Susan, InvalidFeatureRatio)
 {
     try {
-        af::array a = af::randu(256);
-        af::features out = af::susan(a, 3, 32, 10, 1.3f, 3);
+        array a = randu(256);
+        features out = susan(a, 3, 32, 10, 1.3f, 3);
         EXPECT_TRUE(false);
-    } catch (af::exception &e) {
+    } catch (exception &e) {
         EXPECT_TRUE(true);
     }
 }
@@ -169,10 +175,10 @@ TEST(Susan, InvalidFeatureRatio)
 TEST(Susan, InvalidEdge)
 {
     try {
-        af::array a = af::randu(128, 128);
-        af::features out = af::susan(a, 3, 32, 10, 1.3f, 129);
+        array a = randu(128, 128);
+        features out = susan(a, 3, 32, 10, 1.3f, 129);
         EXPECT_TRUE(false);
-    } catch (af::exception &e) {
+    } catch (exception &e) {
         EXPECT_TRUE(true);
     }
 }
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index 5d94ab2625..aab3c34b6b 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -23,8 +23,14 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dtype;
+using af::dtype_traits;
+using af::randu;
+using af::seq;
+using af::span;
 
 template<typename T>
 class svd : public ::testing::Test
@@ -57,25 +63,25 @@ void svdTest(const int M, const int N)
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+    dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    af::array A = af::randu(M, N, ty);
+    array A = randu(M, N, ty);
 
     //! [ex_svd_reg]
-    af::array U, S, Vt;
+    array U, S, Vt;
     af::svd(U, S, Vt, A);
 
     const int MN = std::min(M, N);
 
-    af::array UU = U(af::span, af::seq(MN));
-    af::array SS = af::diag(S, 0, false).as(ty);
-    af::array VV = Vt(af::seq(MN), af::span);
+    array UU = U(span, seq(MN));
+    array SS = diag(S, 0, false).as(ty);
+    array VV = Vt(seq(MN), span);
 
-    af::array AA = matmul(UU, SS, VV);
+    array AA = matmul(UU, SS, VV);
     //! [ex_svd_reg]
 
-    std::vector<T> hA(M * N);
-    std::vector<T> hAA(M * N);
+    vector<T> hA(M * N);
+    vector<T> hAA(M * N);
 
     A.host(&hA[0]);
     AA.host(&hAA[0]);
diff --git a/test/threading.cpp b/test/threading.cpp
index 766366a534..658e598516 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -23,6 +23,8 @@
 
 using namespace af;
 
+using std::cout;
+using std::endl;
 using std::vector;
 using std::string;
 
@@ -43,14 +45,14 @@ int nextTargetDeviceId()
 void morphTest(const array input, const array mask, const bool isDilation,
                const array gold, int targetDevice)
 {
-    af::setDevice(targetDevice);
+    setDevice(targetDevice);
 
     vector<float> goldData(gold.elements());
     vector<float> outData(gold.elements());
 
     gold.host((void*)goldData.data());
 
-    af::array out;
+    array out;
 
     for (unsigned i=0; i<ITERATION_COUNT; ++i)
         out = isDilation ? dilate(input, mask) : erode(input, mask);
@@ -97,13 +99,13 @@ TEST(Threading, SetPerThreadActiveDevice)
 
         for (size_t testId=0; testId<testCount; ++testId)
         {
-            int trgtDeviceId = totalTestCount % af::getDeviceCount();
+            int trgtDeviceId = totalTestCount % getDeviceCount();
 
             //prefix full path to image file names
             inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
             outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
 
-            af::setDevice(trgtDeviceId);
+            setDevice(trgtDeviceId);
 
             const array mask = constant(1.0, maskdims);
 
@@ -129,7 +131,7 @@ enum ArithOp
 
 void calc(ArithOp opcode, array op1, array op2, float outValue)
 {
-    af::setDevice(0);
+    setDevice(0);
     array res;
     for (unsigned i=0; i<ITERATION_COUNT; ++i)
     {
@@ -141,7 +143,7 @@ void calc(ArithOp opcode, array op1, array op2, float outValue)
         }
     }
 
-    std::vector<float> out(res.elements());
+    vector<float> out(res.elements());
     res.host((void*)out.data());
 
     for (unsigned i=0; i <out.size(); ++i)
@@ -150,9 +152,9 @@ void calc(ArithOp opcode, array op1, array op2, float outValue)
 
 TEST(Threading, SimultaneousRead)
 {
-    af::setDevice(0);
-    af::array A = af::constant(1.0, 100, 100);
-    af::array B = af::constant(1.0, 100, 100);
+    setDevice(0);
+    array A = constant(1.0, 100, 100);
+    array B = constant(1.0, 100, 100);
 
     vector<std::thread> tests;
 
@@ -182,7 +184,7 @@ size_t counter = THREAD_COUNT;
 
 void doubleAllocationTest()
 {
-    af::setDevice(0);
+    setDevice(0);
 
     //Block until all threads are launched and the
     //counter variable hits zero
@@ -196,7 +198,7 @@ void doubleAllocationTest()
         cv.wait(lock, [] {return counter==0;});
     lock.unlock();
 
-    af::array a = randu(5, 5);
+    array a = randu(5, 5);
 
     // Wait for for other threads to hit randu call
     // while this thread's variable a is still in scope.
@@ -219,7 +221,7 @@ TEST(Threading, MemoryManagementScope)
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
             &lock_bytes, &lock_buffers);
 
     ASSERT_EQ( lock_buffers,     0u);
@@ -230,10 +232,10 @@ TEST(Threading, MemoryManagementScope)
 
 void jitAllocationTest()
 {
-    af::setDevice(0);
+    setDevice(0);
 
     for (int i = 0; i < 100; ++i)
-        af::array a = af::constant(1, 5, 5);
+        array a = constant(1, 5, 5);
 }
 
 TEST(Threading, MemoryManagement_JIT_Node)
@@ -252,7 +254,7 @@ TEST(Threading, MemoryManagement_JIT_Node)
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
+    deviceMemInfo(&alloc_bytes, &alloc_buffers,
             &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers,     0u);
@@ -267,20 +269,20 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4>        numDims;
+    vector<dim4>        numDims;
     vector<vector<inType> >       in;
     vector<vector<outType> >   tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
     af_array outArray   = 0;
     af_array inArray    = 0;
 
     ASSERT_EQ(AF_SUCCESS, af_set_device(targetDevice));
 
     ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)af::dtype_traits<inType>::af_type));
+                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if (isInverse){
         switch (dims.ndims()) {
@@ -317,7 +319,7 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
         ASSERT_EQ(true, isUnderTolerance)<<
             "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
             (output_scale*outData[elIter]) << " at: " << elIter <<
-            " from thread: "<< std::this_thread::get_id() << std::endl;
+            " from thread: "<< std::this_thread::get_id() << endl;
     }
 
     // cleanup
@@ -505,46 +507,46 @@ void cppMatMulCheck(int targetDevice, string TestFile)
     if (noDoubleTests<T>()) return;
 
     using std::vector;
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
     readTests<T,T,int>(TestFile, numDims, hData, tests);
 
-    af::setDevice(targetDevice);
+    setDevice(targetDevice);
 
-    af::array a(numDims[0], &hData[0].front());
-    af::array b(numDims[1], &hData[1].front());
+    array a(numDims[0], &hData[0].front());
+    array b(numDims[1], &hData[1].front());
 
-    af::dim4 atdims = numDims[0];
+    dim4 atdims = numDims[0];
     {
         dim_t f  =    atdims[0];
         atdims[0]   =    atdims[1];
         atdims[1]   =    f;
     }
-    af::dim4 btdims = numDims[1];
+    dim4 btdims = numDims[1];
     {
         dim_t f = btdims[0];
         btdims[0] = btdims[1];
         btdims[1] = f;
     }
 
-    af::array aT = moddims(a, atdims.ndims(), atdims.get());
-    af::array bT = moddims(b, btdims.ndims(), btdims.get());
+    array aT = moddims(a, atdims.ndims(), atdims.get());
+    array bT = moddims(b, btdims.ndims(), btdims.get());
 
-    vector<af::array> out(tests.size());
+    vector<array> out(tests.size());
     if(isBVector) {
-        out[0] = af::matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
-        out[1] = af::matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
-        out[2] = af::matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
-        out[3] = af::matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
-        out[4] = af::matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
+        out[0] = matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
+        out[1] = matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
+        out[2] = matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
+        out[3] = matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
+        out[4] = matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
     }
     else {
-        out[0] = af::matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
-        out[1] = af::matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
-        out[2] = af::matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
-        out[3] = af::matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
+        out[0] = matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
+        out[1] = matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
+        out[2] = matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
+        out[3] = matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
     }
 
     for(size_t i = 0; i < tests.size(); i++) {
@@ -554,16 +556,16 @@ void cppMatMulCheck(int targetDevice, string TestFile)
 
         if (false == equal(h_out.begin(), h_out.end(), tests[i].begin())) {
 
-            std::cout << "Failed test " << i << "\nCalculated: " << std::endl;
-            std::copy(h_out.begin(), h_out.end(), std::ostream_iterator<T>(std::cout, ", "));
-            std::cout << "Expected: " << std::endl;
-            std::copy(tests[i].begin(), tests[i].end(), std::ostream_iterator<T>(std::cout, ", "));
+            cout << "Failed test " << i << "\nCalculated: " << endl;
+            std::copy(h_out.begin(), h_out.end(), std::ostream_iterator<T>(cout, ", "));
+            cout << "Expected: " << endl;
+            std::copy(tests[i].begin(), tests[i].end(), std::ostream_iterator<T>(cout, ", "));
             FAIL();
         }
     }
 }
 
-#define TEST_BLAS_FOR_TYPE(TypeName)                                         \
+#define TEST_BLAS_FOR_TYPE(TypeName)                                                \
     tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
             nextTargetDeviceId()%numDevices, TEST_DIR "/blas/Basic.test");          \
     tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
@@ -584,11 +586,11 @@ TEST(Threading, BLAS)
     ASSERT_EQ(true, numDevices>0);
 
     TEST_BLAS_FOR_TYPE(      float);
-    TEST_BLAS_FOR_TYPE( af::cfloat);
+    TEST_BLAS_FOR_TYPE( cfloat);
 
     if (noDoubleTests<double>()) {
         TEST_BLAS_FOR_TYPE(     double);
-        TEST_BLAS_FOR_TYPE(af::cdouble);
+        TEST_BLAS_FOR_TYPE(cdouble);
     }
 
     for (size_t testId=0; testId<tests.size(); ++testId)
@@ -631,7 +633,7 @@ TEST(Threading, DISABLED_MemoryManagerStressTest)
   vector<std::thread> threads;
   for (int i = 0; i < THREAD_COUNT; i++) {
     threads.emplace_back([] {
-        vector<af::array> arrg;
+        vector<array> arrg;
         int size = 100;
         int ex_count = 0;
 
@@ -640,13 +642,13 @@ TEST(Threading, DISABLED_MemoryManagerStressTest)
           try {
             // constantly change size of the array allocated
             size+=10;
-            arrg.push_back(af::randu(size));
+            arrg.push_back(randu(size));
 
             // delete some values intermittently
             if (!(size%200)) {
               arrg.erase(std::begin(arrg), std::begin(arrg)+5);
             }
-          } catch( const af::exception &ex ) {
+          } catch( const exception &ex ) {
             if (ex_count++ > 3) {
               break;
             }
diff --git a/test/tile.cpp b/test/tile.cpp
index 3830978983..5ef4570a03 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -20,10 +20,14 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::constant;
+using af::dim4;
+using af::dtype_traits;
+using af::product;
 
 template<typename T>
 class Tile : public ::testing::Test
@@ -49,23 +53,23 @@ void tileTest(string pTestFile, const unsigned resultIdx, const uint x, const ui
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     ASSERT_EQ(AF_SUCCESS, af_tile(&outArray, inArray, x, y, z, w));
@@ -77,7 +81,7 @@ void tileTest(string pTestFile, const unsigned resultIdx, const uint x, const ui
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -125,14 +129,14 @@ TEST(Tile, CPP)
     const unsigned z = 2;
     const unsigned w = 1;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/tile/tile_large3D.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
-    af::array output = af::tile(input, x, y, z, w);
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
+    array output = tile(input, x, y, z, w);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -141,7 +145,7 @@ TEST(Tile, CPP)
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -158,27 +162,27 @@ TEST(Tile, MaxDim)
     unsigned y = 2;
     unsigned w = 1;
 
-    af::array input = af::constant(1, 1, largeDim);
-    af::array output = af::tile(input, x, y, z, w);
+    array input = constant(1, 1, largeDim);
+    array output = tile(input, x, y, z, w);
 
     ASSERT_EQ(1, output.dims(0));
     ASSERT_EQ(2 * largeDim, output.dims(1));
     ASSERT_EQ(1, output.dims(2));
     ASSERT_EQ(1, output.dims(3));
 
-    ASSERT_EQ(1.f, af::product<float>(output));
+    ASSERT_EQ(1.f, product<float>(output));
 
     y = 1;
     w = 2;
 
-    input  = af::constant(1, 1, 1, 1, largeDim);
-    output = af::tile(input, x, y, z, w);
+    input  = constant(1, 1, 1, 1, largeDim);
+    output = tile(input, x, y, z, w);
 
     ASSERT_EQ(1, output.dims(0));
     ASSERT_EQ(1, output.dims(1));
     ASSERT_EQ(1, output.dims(2));
     ASSERT_EQ(2 * largeDim, output.dims(3));
 
-    ASSERT_EQ(1.f, af::product<float>(output));
+    ASSERT_EQ(1.f, product<float>(output));
 
 }
diff --git a/test/topk.cpp b/test/topk.cpp
index d98c5f1820..e3f206eef4 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -23,14 +23,12 @@
 #include <string>
 #include <utility>
 
-using af::allTrue;
 using af::array;
-using af::randu;
-using af::seq;
-using af::sort;
-using af::span;
-using af::sum;
+using af::dim4;
+using af::dtype_traits;
+using af::iota;
 using af::topk;
+using af::topkFunction;
 
 using std::iota;
 using std::make_pair;
@@ -55,7 +53,7 @@ void topkTest(const unsigned ndims, const dim_t* dims,
                    const int k, const int dim,
                    const af_topk_function order)
 {
-    af_dtype dtype = (af_dtype)af::dtype_traits<T>::af_type;
+    af_dtype dtype = (af_dtype)dtype_traits<T>::af_type;
 
     af_array input, output, outindex;
 
@@ -211,7 +209,7 @@ struct topk_params {
   int d1;
   int k;
   int dim;
-  af::topkFunction order;
+  topkFunction order;
 };
 
 ostream& operator<<(ostream& os, const topk_params &param) {
@@ -272,8 +270,6 @@ string print_context(int idx0, int idx1, const vector<float> &val, const vector<
 }
 
 TEST_P(TopKParams, CPP) {
-    using namespace af;
-
     topk_params params = GetParam();
     int d0 = params.d0;
     int d1 = params.d1;
diff --git a/test/transform.cpp b/test/transform.cpp
index 1e34b6ad15..ce4eac1233 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -19,8 +19,10 @@
 using std::vector;
 using std::string;
 using std::abs;
-using std::cout;
 using std::endl;
+using af::array;
+using af::dim4;
+using af::loadImage;
 
 template<typename T>
 class Transform : public ::testing::Test
@@ -49,7 +51,7 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<af::dim4> inNumDims;
+    vector<dim4> inNumDims;
     vector<string>   inFiles;
     vector<dim_t>    goldNumDims;
     vector<string>   goldFiles;
@@ -60,14 +62,14 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     inFiles[1].insert(0,string(TEST_DIR"/transform/"));
     goldFiles[0].insert(0,string(TEST_DIR"/transform/"));
 
-    af::dim4 objDims = inNumDims[0];
+    dim4 objDims = inNumDims[0];
 
-    vector<af::dim4>       HNumDims;
+    vector<dim4>       HNumDims;
     vector<vector<float> > HIn;
     vector<vector<float> > HTests;
     readTests<float, float, float>(pHomographyFile, HNumDims, HIn, HTests);
 
-    af::dim4 HDims = HNumDims[0];
+    dim4 HDims = HNumDims[0];
 
     af_array sceneArray_f32 = 0;
     af_array goldArray_f32 = 0;
@@ -110,7 +112,7 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     for (dim_t elIter = 0; elIter < goldEl; elIter++) {
         err += fabs((float)floor(outData[elIter]) - (float)floor(goldData[elIter])) > thr;
         if (err > maxErr) {
-            ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
+            ASSERT_LE(err, maxErr) << "at: " << elIter << endl;
         }
     }
 
@@ -214,12 +216,12 @@ TEST(Transform, CPP)
 {
     if (noImageIOTests()) return;
 
-    vector<af::dim4>   inDims;
+    vector<dim4>   inDims;
     vector<string> inFiles;
     vector<dim_t>  goldDim;
     vector<string> goldFiles;
 
-    vector<af::dim4> HDims;
+    vector<dim4> HDims;
     vector<vector<float> >   HIn;
     vector<vector<float> >   HTests;
     readTests<float, float, float>(TEST_DIR"/transform/tux_tmat.test",HDims,HIn,HTests);
@@ -231,17 +233,17 @@ TEST(Transform, CPP)
 
     goldFiles[0].insert(0,string(TEST_DIR"/transform/"));
 
-    af::array H = af::array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
-    af::array IH = af::array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
+    array H = array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
+    array IH = array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
 
-    af::array scene_img = af::loadImage(inFiles[1].c_str(), false);
+    array scene_img = loadImage(inFiles[1].c_str(), false);
 
-    af::array gold_img = af::loadImage(goldFiles[0].c_str(), false);
+    array gold_img = loadImage(goldFiles[0].c_str(), false);
 
-    af::array out_img = af::transform(scene_img, IH, inDims[0][0], inDims[0][1], AF_INTERP_NEAREST, false);
+    array out_img = transform(scene_img, IH, inDims[0][0], inDims[0][1], AF_INTERP_NEAREST, false);
 
-    af::dim4 outDims = out_img.dims();
-    af::dim4 goldDims = gold_img.dims();
+    dim4 outDims = out_img.dims();
+    dim4 goldDims = gold_img.dims();
 
     vector<float> h_out_img(outDims[0] * outDims[1]);
     out_img.host(&h_out_img.front());
@@ -260,7 +262,7 @@ TEST(Transform, CPP)
     for (dim_t elIter = 0; elIter < n; elIter++) {
         err += fabs((int)h_out_img[elIter] - h_gold_img[elIter]) > thr;
         if (err > maxErr) {
-            ASSERT_LE(err, maxErr) << "at: " << elIter << std::endl;
+            ASSERT_LE(err, maxErr) << "at: " << elIter << endl;
         }
     }
 }
@@ -271,33 +273,33 @@ TEST(Transform, CPP)
 // This test simply makes sure the batching is working correctly
 TEST(TransformBatching, CPP)
 {
-    vector<af::dim4>        vDims;
+    vector<dim4>        vDims;
     vector<vector<float> >  in;
     vector<vector<float> >  gold;
 
     readTests<float, float, int>(string(TEST_DIR"/transform/transform_batching.test"), vDims, in, gold);
 
-    af::array img0     (vDims[0], &(in[0].front()));
-    af::array img1     (vDims[1], &(in[1].front()));
-    af::array ip_tile  (vDims[2], &(in[2].front()));
-    af::array ip_quad  (vDims[3], &(in[3].front()));
-    af::array ip_mult  (vDims[4], &(in[4].front()));
-    af::array ip_tile3 (vDims[5], &(in[5].front()));
-    af::array ip_quad3 (vDims[6], &(in[6].front()));
-
-    af::array tf0      (vDims[7 + 0], &(in[7 + 0].front()));
-    af::array tf1      (vDims[7 + 1], &(in[7 + 1].front()));
-    af::array tf_tile  (vDims[7 + 2], &(in[7 + 2].front()));
-    af::array tf_quad  (vDims[7 + 3], &(in[7 + 3].front()));
-    af::array tf_mult  (vDims[7 + 4], &(in[7 + 4].front()));
-    af::array tf_mult3 (vDims[7 + 5], &(in[7 + 5].front()));
-    af::array tf_mult3x(vDims[7 + 6], &(in[7 + 6].front()));
+    array img0     (vDims[0], &(in[0].front()));
+    array img1     (vDims[1], &(in[1].front()));
+    array ip_tile  (vDims[2], &(in[2].front()));
+    array ip_quad  (vDims[3], &(in[3].front()));
+    array ip_mult  (vDims[4], &(in[4].front()));
+    array ip_tile3 (vDims[5], &(in[5].front()));
+    array ip_quad3 (vDims[6], &(in[6].front()));
+
+    array tf0      (vDims[7 + 0], &(in[7 + 0].front()));
+    array tf1      (vDims[7 + 1], &(in[7 + 1].front()));
+    array tf_tile  (vDims[7 + 2], &(in[7 + 2].front()));
+    array tf_quad  (vDims[7 + 3], &(in[7 + 3].front()));
+    array tf_mult  (vDims[7 + 4], &(in[7 + 4].front()));
+    array tf_mult3 (vDims[7 + 5], &(in[7 + 5].front()));
+    array tf_mult3x(vDims[7 + 6], &(in[7 + 6].front()));
 
     const int X = img0.dims(0);
     const int Y = img0.dims(1);
 
     ASSERT_EQ(gold.size(), 21u);
-    vector<af::array> out(gold.size());
+    vector<array> out(gold.size());
     out[0 ] = transform(img0    , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
     out[1 ] = transform(img0    , tf1      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
     out[2 ] = transform(img1    , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
@@ -325,7 +327,7 @@ TEST(TransformBatching, CPP)
     out[19] = transform(ip_tile3, tf_mult3 , Y, X, AF_INTERP_NEAREST);  // N,1 x N,N
     out[20] = transform(ip_quad3, tf_mult3x, Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
 
-    af::array x_(af::dim4(35, 40, 1, 1), &(gold[1].front()));
+    array x_(dim4(35, 40, 1, 1), &(gold[1].front()));
 
     for(int i = 0; i < (int)gold.size(); i++) {
         // Get result
@@ -333,8 +335,8 @@ TEST(TransformBatching, CPP)
         out[i].host((void*)&outData.front());
 
         for(int iter = 0; iter < (int)gold[i].size(); iter++) {
-            ASSERT_EQ(gold[i][iter], outData[iter]) << "at: " << iter << std::endl
-                    << "for " << i << "-th operation"<< std::endl;
+            ASSERT_EQ(gold[i][iter], outData[iter]) << "at: " << iter << endl
+                    << "for " << i << "-th operation"<< endl;
         }
     }
 }
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 2ab9f0c0bb..a0046268eb 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -18,8 +18,10 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::array;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class TransformCoordinates : public ::testing::Test
@@ -37,7 +39,7 @@ void transformCoordinatesTest(string pTestFile)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4>       inDims;
+    vector<dim4>       inDims;
     vector<vector<T> >     in;
     vector<vector<float> > gold;
 
@@ -45,7 +47,7 @@ void transformCoordinatesTest(string pTestFile)
 
     af_array tfArray = 0;
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)dtype_traits<T>::af_type));
 
     int nTests = in.size();
 
@@ -65,7 +67,7 @@ void transformCoordinatesTest(string pTestFile)
         const float thr = 1.f;
 
         for (dim_t elIter = 0; elIter < outEl; elIter++) {
-            ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << std::endl;
+            ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << endl;
         }
     }
 
@@ -86,19 +88,19 @@ TYPED_TEST(TransformCoordinates, 3DMatrix)
 //
 TEST(TransformCoordinates, CPP)
 {
-    vector<af::dim4>       inDims;
+    vector<dim4>       inDims;
     vector<vector<float> > in;
     vector<vector<float> > gold;
 
     readTests<float, float, float>(TEST_DIR"/transformCoordinates/3d_matrix.test",inDims,in,gold);
 
-    af::array tf = af::array(inDims[0][0], inDims[0][1], &(in[0].front()));
+    array tf = array(inDims[0][0], inDims[0][1], &(in[0].front()));
 
     float d0 = in[1][0];
     float d1 = in[1][1];
 
-    af::array out = af::transformCoordinates(tf, d0, d1);
-    af::dim4 outDims = out.dims();
+    array out = transformCoordinates(tf, d0, d1);
+    dim4 outDims = out.dims();
 
     vector<float> h_out(outDims[0] * outDims[1]);
     out.host(&h_out.front());
@@ -107,6 +109,6 @@ TEST(TransformCoordinates, CPP)
     const float thr = 1.f;
 
     for (size_t elIter = 0; elIter < n; elIter++) {
-        ASSERT_LE(fabs(h_out[elIter] - gold[0][elIter]), thr) << "at: " << elIter << std::endl;
+        ASSERT_LE(fabs(h_out[elIter] - gold[0][elIter]), thr) << "at: " << elIter << endl;
     }
 }
diff --git a/test/translate.cpp b/test/translate.cpp
index 355d30a553..c9c5012748 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -18,11 +18,12 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using std::abs;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Translate : public ::testing::Test
@@ -49,11 +50,11 @@ TYPED_TEST_CASE(Translate, TestTypes);
 TYPED_TEST_CASE(TranslateInt, TestTypesInt);
 
 template<typename T>
-void translateTest(string pTestFile, const unsigned resultIdx, af::dim4 odims, const float tx, const float ty, const af_interp_type method, const float max_fail_count = 0.0001)
+void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims, const float tx, const float ty, const af_interp_type method, const float max_fail_count = 0.0001)
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> >   in;
     vector<vector<float> >   tests;
     readTests<T, float, float>(pTestFile,numDims,in,tests);
@@ -61,9 +62,9 @@ void translateTest(string pTestFile, const unsigned resultIdx, af::dim4 odims, c
     af_array inArray = 0;
     af_array outArray = 0;
 
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_translate(&outArray, inArray, tx, ty, odims[0], odims[1], method));
 
@@ -81,7 +82,7 @@ void translateTest(string pTestFile, const unsigned resultIdx, af::dim4 odims, c
         }
     }
     ASSERT_EQ(true, (((float)fail_count / (float)(nElems)) <= max_fail_count))
-             << "Fail Count  = " << fail_count << std::endl;
+             << "Fail Count  = " << fail_count << endl;
 
     // Delete
     delete[] outData;
@@ -93,95 +94,95 @@ void translateTest(string pTestFile, const unsigned resultIdx, af::dim4 odims, c
 TYPED_TEST(Translate, Small1)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 0,
-                             af::dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
+                             dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(Translate, Small2)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 1,
-                             af::dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
+                             dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(Translate, Small3)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 2,
-                             af::dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
+                             dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(Translate, Small4)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 3,
-                             af::dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
+                             dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(Translate, Large1)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 0,
-                             af::dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
+                             dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(Translate, Large2)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 1,
-                             af::dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
+                             dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(Translate, Large3)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 2,
-                             af::dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR);
+                             dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(Translate, Large4)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 3,
-                             af::dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR);
+                             dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(TranslateInt, Small1)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 0,
-                             af::dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
+                             dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(TranslateInt, Small2)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 1,
-                             af::dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
+                             dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(TranslateInt, Small3)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 2,
-                             af::dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
+                             dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(TranslateInt, Small4)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 3,
-                             af::dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
+                             dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
 }
 
 TYPED_TEST(TranslateInt, Large1)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 0,
-                             af::dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
+                             dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(TranslateInt, Large2)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 1,
-                             af::dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
+                             dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
 }
 
 TYPED_TEST(TranslateInt, Large3)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 2,
-                             af::dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR, 0.001);
+                             dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR, 0.001);
 }
 
 TYPED_TEST(TranslateInt, Large4)
 {
     translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 3,
-                             af::dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR, 0.001);
+                             dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR, 0.001);
 }
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 60d0fc1a13..302623f46b 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -15,11 +15,16 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::abs;
+using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
+using af::allTrue;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Transpose : public ::testing::Test
@@ -49,21 +54,21 @@ void trsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=N
     if (noDoubleTests<T>())
         return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T,T,int>(pTestFile,numDims,in,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     af_array outArray   = 0;
     af_array inArray    = 0;
     T *outData;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     // check if the test is for indexed Array
     if (isSubRef) {
-        af::dim4 newDims(dims[1]-4,dims[0]-4,dims[2],dims[3]);
+        dim4 newDims(dims[1]-4,dims[0]-4,dims[2],dims[3]);
         af_array subArray = 0;
         ASSERT_EQ(AF_SUCCESS, af_index(&subArray,inArray,seqv->size(),&seqv->front()));
         ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray,subArray, false));
@@ -84,7 +89,7 @@ void trsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=N
         vector<T> currGoldBar   = tests[testIter];
         size_t nElems        = currGoldBar.size();
         for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< std::endl;
+            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
         }
     }
 
@@ -155,17 +160,17 @@ TYPED_TEST(Transpose,SubRefBatch)
 template<typename T>
 void trsCPPTest(string pFileName)
 {
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<T> >   in;
     vector<vector<T> >   tests;
     readTests<T, T, int>(pFileName, numDims, in, tests);
-    af::dim4 dims = numDims[0];
+    dim4 dims = numDims[0];
 
     if (noDoubleTests<T>()) return;
 
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::transpose(input);
+    array input(dims, &(in[0].front()));
+    array output = transpose(input);
 
     T *outData = new T[dims.elements()];
     output.host((void*)outData);
@@ -174,7 +179,7 @@ void trsCPPTest(string pFileName)
         vector<T> currGoldBar = tests[testIter];
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter << std::endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter << endl;
         }
     }
 
@@ -195,15 +200,15 @@ TEST(Transpose, CPP_f32)
 template<typename T>
 void trsCPPConjTest(dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1)
 {
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
-    af::dim4 dims(d0, d1, d2, d3);
+    dim4 dims(d0, d1, d2, d3);
 
     if (noDoubleTests<T>()) return;
 
-    af::array input = randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
-    af::array output_t = af::transpose(input, false);
-    af::array output_c = af::transpose(input, true);
+    array input = randu(dims, (af_dtype) dtype_traits<T>::af_type);
+    array output_t = transpose(input, false);
+    array output_c = transpose(input, true);
 
     T *tData  = new T[dims.elements()];
     T *cData = new T[dims.elements()];
@@ -212,8 +217,8 @@ void trsCPPConjTest(dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1)
 
     size_t nElems = dims.elements();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(real(tData[elIter]), real(cData[elIter]), 1e-6)<< "at: " << elIter << std::endl;
-        ASSERT_NEAR(-imag(tData[elIter]), imag(cData[elIter]), 1e-6)<< "at: " << elIter << std::endl;
+        ASSERT_NEAR(real(tData[elIter]), real(cData[elIter]), 1e-6)<< "at: " << elIter << endl;
+        ASSERT_NEAR(-imag(tData[elIter]), imag(cData[elIter]), 1e-6)<< "at: " << elIter << endl;
     }
 
     // cleanup
@@ -240,25 +245,29 @@ TEST(Transpose, MaxDim)
 {
     const size_t largeDim = 65535 * 33 + 1;
 
-    af::array input  = af::range(af::dim4(2, largeDim, 1, 1));
-    af::array gold   = af::range(af::dim4(largeDim, 2, 1, 1), 1);
-    af::array output = af::transpose(input);
+    array input  = range(dim4(2, largeDim, 1, 1));
+    array gold   = range(dim4(largeDim, 2, 1, 1), 1);
+    array output = transpose(input);
 
     ASSERT_EQ(output.dims(0), (int)largeDim);
     ASSERT_EQ(output.dims(1), 2);
-    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+    ASSERT_TRUE(allTrue<bool>(output == gold));
 
-    input  = af::range(af::dim4(2, 5, 1, largeDim));
-    gold   = af::range(af::dim4(5, 2, 1, largeDim), 1);
-    output = af::transpose(input);
+    input  = range(dim4(2, 5, 1, largeDim));
+    gold   = range(dim4(5, 2, 1, largeDim), 1);
+    output = transpose(input);
 
-    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+    ASSERT_TRUE(allTrue<bool>(output == gold));
 }
 
 
 TEST(Transpose, GFOR)
 {
-    using namespace af;
+    using af::constant;
+    using af::max;
+    using af::seq;
+    using af::span;
+
     dim4 dims = dim4(100, 100, 3);
     array A = round(100 * randu(dims));
     array B = constant(0, 100, 100, 3);
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index 14b4af8964..308d5f62a7 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -15,10 +15,13 @@
 #include <vector>
 #include <testHelpers.hpp>
 
-using std::string;
+using std::endl;
 using std::vector;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Transpose : public ::testing::Test
@@ -35,7 +38,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
 TYPED_TEST_CASE(Transpose, TestTypes);
 
 template<typename T>
-void transposeip_test(af::dim4 dims)
+void transposeip_test(dim4 dims)
 {
     if (noDoubleTests<T>())
         return;
@@ -43,7 +46,7 @@ void transposeip_test(af::dim4 dims)
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_randu(&inArray, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_randu(&inArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray, inArray, false));
     ASSERT_EQ(AF_SUCCESS, af_transpose_inplace(inArray, false));
@@ -56,7 +59,7 @@ void transposeip_test(af::dim4 dims)
 
     dim_t nElems = dims.elements();
     for (int elIter = 0; elIter < (int)nElems; ++elIter) {
-        ASSERT_EQ(trsData[elIter] , outData[elIter])<< "at: " << elIter << std::endl;
+        ASSERT_EQ(trsData[elIter] , outData[elIter])<< "at: " << elIter << endl;
     }
 
     // cleanup
@@ -67,7 +70,7 @@ void transposeip_test(af::dim4 dims)
 #define INIT_TEST(Side, D3, D4)                                                     \
     TYPED_TEST(Transpose, TranposeIP_##Side)                                        \
     {                                                                               \
-        transposeip_test<TypeParam>(af::dim4(Side, Side, D3, D4));                  \
+        transposeip_test<TypeParam>(dim4(Side, Side, D3, D4));                      \
     }
 
 INIT_TEST(10, 1, 1);
@@ -83,10 +86,10 @@ void transposeInPlaceCPPTest()
 {
     if (noDoubleTests<float>()) return;
 
-    af::dim4 dims(64, 64, 1,1);
+    dim4 dims(64, 64, 1,1);
 
-    af::array input = randu(dims);
-    af::array output = af::transpose(input);
+    array input = randu(dims);
+    array output = transpose(input);
     transposeInPlace(input);
 
     vector<float> outData(dims.elements());
@@ -97,6 +100,6 @@ void transposeInPlaceCPPTest()
 
     dim_t nElems = dims.elements();
     for (int elIter = 0; elIter < (int)nElems; ++elIter) {
-        ASSERT_EQ(trsData[elIter], outData[elIter])<< "at: " << elIter << std::endl;
+        ASSERT_EQ(trsData[elIter], outData[elIter])<< "at: " << elIter << endl;
     }
 }
diff --git a/test/triangle.cpp b/test/triangle.cpp
index 630ffdd638..349d4110d9 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -21,10 +21,9 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
 using std::abs;
-
+using af::array;
 using af::cfloat;
 using af::cdouble;
 using af::dim4;
@@ -33,7 +32,7 @@ using af::freeHost;
 template<typename T>
 class Triangle : public ::testing::Test { };
 
-typedef ::testing::Types<float, af::cfloat, double, af::cdouble, int, unsigned, char, uchar, uintl, intl, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char, uchar, uintl, intl, short, ushort> TestTypes;
 TYPED_TEST_CASE(Triangle, TestTypes);
 
 template<typename T>
@@ -41,13 +40,13 @@ void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag=false)
 {
     if (noDoubleTests<T>()) return;
 #if 1
-    af::array in = cpu_randu<T>(dims);
+    array in = cpu_randu<T>(dims);
 #else
-    af::array in = af::randu(dims, (af::dtype)af::dtype_traits<T>::af_type);
+    array in = randu(dims, (dtype)dtype_traits<T>::af_type);
 #endif
 
     T *h_in = in.host<T>();
-    af::array out = is_upper ?  upper(in, is_unit_diag) : lower(in, is_unit_diag);
+    array out = is_upper ?  upper(in, is_unit_diag) : lower(in, is_unit_diag);
     T *h_out = out.host<T>();
 
     int m = dims[0];
@@ -163,7 +162,13 @@ TYPED_TEST(Triangle, MaxDim)
 
 TEST(Lower, ExtractGFOR)
 {
-    using namespace af;
+    using af::constant;
+    using af::lower;
+    using af::max;
+    using af::round;
+    using af::seq;
+    using af::span;
+
     dim4 dims = dim4(100, 100, 3);
     array A = round(100 * randu(dims));
     array B = constant(0, 100, 100, 3);
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 0ee03ed3e6..25c1a25f84 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -20,10 +20,14 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::allTrue;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype_traits;
+using af::range;
 
 template<typename T>
 class Unwrap : public ::testing::Test
@@ -45,37 +49,37 @@ void unwrapTest(string pTestFile, const unsigned resultIdx,
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
     readTests<T, T, int>(pTestFile,numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
+    dim4 idims = numDims[0];
 
     af_array inArray = 0;
     af_array outArray = 0;
     af_array outArrayT = 0;
     af_array outArray2 = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_SUCCESS, af_unwrap(&outArray , inArray, wx, wy, sx, sy, px, py, true ));
     ASSERT_EQ(AF_SUCCESS, af_unwrap(&outArrayT, inArray, wx, wy, sx, sy, px, py, false));
     ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray2, outArrayT, false));
 
     size_t nElems = tests[resultIdx].size();
-    std::vector<T> outData(nElems);
+    vector<T> outData(nElems);
 
     // Compare is_column == true results
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData[0], outArray));
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Compare is_column == false results
     ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData[0], outArray2));
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     if(inArray   != 0) af_release_array(inArray);
@@ -154,14 +158,14 @@ TEST(Unwrap, CPP)
     const unsigned px = 3;
     const unsigned py = 3;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
     readTests<float, float, int>(string(TEST_DIR"/unwrap/unwrap_small.test"),numDims,in,tests);
 
-    af::dim4 idims = numDims[0];
-    af::array input(idims, &(in[0].front()));
-    af::array output = af::unwrap(input, wx, wy, sx, sy, px, py);
+    dim4 idims = numDims[0];
+    array input(idims, &(in[0].front()));
+    array output = unwrap(input, wx, wy, sx, sy, px, py);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -170,7 +174,7 @@ TEST(Unwrap, CPP)
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << std::endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Delete
@@ -180,7 +184,7 @@ TEST(Unwrap, CPP)
 TEST(Unwrap, MaxDim)
 {
     const size_t largeDim = 65535 + 1;
-    af::array input = af::range(5, 5, largeDim);
+    array input = range(5, 5, largeDim);
 
     const unsigned wx = 5;
     const unsigned wy = 5;
@@ -189,10 +193,10 @@ TEST(Unwrap, MaxDim)
     const unsigned px = 0;
     const unsigned py = 0;
 
-    af::array output = af::unwrap(input, wx, wy, sx, sy, px, py);
+    array output = unwrap(input, wx, wy, sx, sy, px, py);
 
-    af::array gold = af::range(af::dim4(5, 5, 1, largeDim));
-    gold = af::moddims(gold, af::dim4(25, 1, largeDim));
+    array gold = range(dim4(5, 5, 1, largeDim));
+    gold = moddims(gold, dim4(25, 1, largeDim));
 
-    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+    ASSERT_TRUE(allTrue<bool>(output == gold));
 }
diff --git a/test/var.cpp b/test/var.cpp
index 885fa716f0..6b6c38b547 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -20,6 +20,7 @@ using std::vector;
 using af::cdouble;
 using af::cfloat;
 using af::array;
+using af::dim4;
 
 template<typename T>
 class Var : public ::testing::Test
@@ -57,7 +58,7 @@ struct varOutType {
 // test var_all interface using cpp api
 
 template<typename T>
-void testCPPVar(T const_value, af::dim4 dims)
+void testCPPVar(T const_value, dim4 dims)
 {
     typedef typename varOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
@@ -99,17 +100,17 @@ void testCPPVar(T const_value, af::dim4 dims)
 
 TYPED_TEST(Var, AllCPPSmall)
 {
-    testCPPVar<TypeParam>(2, af::dim4(10, 10, 1, 1));
+    testCPPVar<TypeParam>(2, dim4(10, 10, 1, 1));
 }
 
 TYPED_TEST(Var, AllCPPMedium)
 {
-    testCPPVar<TypeParam>(2, af::dim4(100, 100, 1, 1));
+    testCPPVar<TypeParam>(2, dim4(100, 100, 1, 1));
 }
 
 TYPED_TEST(Var, AllCPPLarge)
 {
-    testCPPVar<TypeParam>(2, af::dim4(1000, 1000, 1, 1));
+    testCPPVar<TypeParam>(2, dim4(1000, 1000, 1, 1));
 }
 
 TYPED_TEST(Var, DimCPPSmall)
@@ -119,7 +120,7 @@ TYPED_TEST(Var, DimCPPSmall)
     if (noDoubleTests<TypeParam>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
     vector<vector<TypeParam> > in;
     vector<vector<outType> > tests;
 
@@ -164,10 +165,12 @@ TYPED_TEST(Var, DimCPPSmall)
 }
 
 TEST(Var, ISSUE2117) {
-  using namespace af;
+    using af::constant;
+    using af::var;
+    using af::sum;
 
-  array myArray = constant(1, 1000, 3000);
-  myArray = af::var(myArray, true, 1);
+    array myArray = constant(1, 1000, 3000);
+    myArray = var(myArray, true, 1);
 
-  ASSERT_NEAR(0.0f, sum<float>(myArray), 0.000001);
+    ASSERT_NEAR(0.0f, sum<float>(myArray), 0.000001);
 }
diff --git a/test/where.cpp b/test/where.cpp
index f8537b564d..7944e01e78 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -19,10 +19,16 @@
 
 using std::vector;
 using std::string;
-using std::cout;
 using std::endl;
+using af::allTrue;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::randu;
+using af::range;
 
 template<typename T>
 class Where : public ::testing::Test { };
@@ -35,12 +41,12 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
 {
     if (noDoubleTests<T>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
     readTests<int,int,int> (pTestFile,numDims,data,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     vector<T> in(data[0].begin(), data[0].end());
 
@@ -50,11 +56,11 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
 
     // Get input array
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
         ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
     } else {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<T>::af_type));
+        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     // Compare result
@@ -70,7 +76,7 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                        << std::endl;
+                                                        << endl;
     }
 
     if(inArray   != 0) af_release_array(inArray);
@@ -97,16 +103,16 @@ TYPED_TEST(Where, CPP)
 {
     if (noDoubleTests<TypeParam>()) return;
 
-    vector<af::dim4> numDims;
+    vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
     readTests<int,int,int> (string(TEST_DIR"/where/where.test"),numDims,data,tests);
-    af::dim4 dims       = numDims[0];
+    dim4 dims       = numDims[0];
 
     vector<float> in(data[0].begin(), data[0].end());
-    af::array input(dims, &in.front(), afHost);
-    af::array output = where(input);
+    array input(dims, &in.front(), afHost);
+    array output = where(input);
 
     // Compare result
     vector<uint> currGoldBar(tests[0].begin(), tests[0].end());
@@ -118,7 +124,7 @@ TYPED_TEST(Where, CPP)
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                        << std::endl;
+                                                        << endl;
     }
 }
 
@@ -126,19 +132,19 @@ TEST(Where, MaxDim)
 {
     const size_t largeDim = 65535 * 32 + 2;
 
-    af::array input = af::range(af::dim4(1, largeDim), 1);
-    af::array output = where(input % 2 == 0);
-    af::array gold = 2 * af::range(largeDim/2);
-    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+    array input = range(dim4(1, largeDim), 1);
+    array output = where(input % 2 == 0);
+    array gold = 2 * range(largeDim/2);
+    ASSERT_TRUE(allTrue<bool>(output == gold));
 
-    input = af::range(af::dim4(1, 1, 1, largeDim), 3);
+    input = range(dim4(1, 1, 1, largeDim), 3);
     output = where(input % 2 == 0);
-    ASSERT_TRUE(af::allTrue<bool>(output == gold));
+    ASSERT_TRUE(allTrue<bool>(output == gold));
 }
 
 TEST(Where, ISSUE_1259)
 {
-    af::array a = af::randu(10, 10, 10);
-    af::array indices = af::where(a > 2);
+    array a = randu(10, 10, 10);
+    array indices = where(a > 2);
     ASSERT_EQ(indices.elements(), 0);
 }
diff --git a/test/wrap.cpp b/test/wrap.cpp
index f3e2b55780..12dedc9ed1 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -24,8 +24,14 @@ using std::string;
 using std::cout;
 using std::endl;
 using std::abs;
+using af::allTrue;
+using af::array;
 using af::cfloat;
 using af::cdouble;
+using af::dtype;
+using af::dtype_traits;
+using af::randu;
+using af::range;
 
 template<typename T>
 class Wrap : public ::testing::Test
@@ -80,13 +86,13 @@ void wrapTest(const dim_t ix, const dim_t iy,
 
     int lim = std::max((dim_t)2, (dim_t)(250) / (wx * wy));
 
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
-    af::array in = af::round(lim * af::randu(ix, iy, nc, f32)).as(ty);
+    dtype ty = (dtype)dtype_traits<T>::af_type;
+    array in = round(lim * randu(ix, iy, nc, f32)).as(ty);
 
-    std::vector<T> h_in(in.elements());
+    vector<T> h_in(in.elements());
     in.host(&h_in[0]);
 
-    std::vector<int> h_factor(ix * iy);
+    vector<int> h_factor(ix * iy);
 
     dim_t ny = (iy + 2 * py - wy) / sy + 1;
     dim_t nx = (ix + 2 * px - wx) / sx + 1;
@@ -110,14 +116,14 @@ void wrapTest(const dim_t ix, const dim_t iy,
         }
     }
 
-    af::array factor(ix, iy, &h_factor[0]);
+    array factor(ix, iy, &h_factor[0]);
 
-    af::array in_dim = af::unwrap(in, wx, wy, sx, sy, px, py, cond);
-    af::array res_dim = af::wrap(in_dim, ix, iy, wx, wy, sx, sy, px, py, cond);
+    array in_dim = unwrap(in, wx, wy, sx, sy, px, py, cond);
+    array res_dim = wrap(in_dim, ix, iy, wx, wy, sx, sy, px, py, cond);
 
     ASSERT_EQ(in.elements(), res_dim.elements());
 
-    std::vector<T> h_res(ix * iy);
+    vector<T> h_res(ix * iy);
     res_dim.host(&h_res[0]);
 
     for (int n = 0; n < nc; n++) {
@@ -135,7 +141,7 @@ void wrapTest(const dim_t ix, const dim_t iy,
                 if (get_val(ival) == 0) continue;
 
                 ASSERT_NEAR(get_val<T>(ival * factor), get_val<T>(rval), 1E-5)
-                    << "at " << x << "," << y <<  " for cond  == " << cond << std::endl;
+                    << "at " << x << "," << y <<  " for cond  == " << cond << endl;
             }
         }
 
@@ -182,7 +188,7 @@ void wrapTest(const dim_t ix, const dim_t iy,
 TEST(Wrap, MaxDim)
 {
     const size_t largeDim = 65535 + 1;
-    af::array input = af::range(5, 5, 1, largeDim);
+    array input = range(5, 5, 1, largeDim);
 
     const unsigned wx = 5;
     const unsigned wy = 5;
@@ -191,8 +197,8 @@ TEST(Wrap, MaxDim)
     const unsigned px = 0;
     const unsigned py = 0;
 
-    af::array unwrapped = af::unwrap(input, wx, wy, sx, sy, px, py);
-    af::array output = af::wrap(unwrapped, 5, 5, wx, wy, sx, sy, px, py);
+    array unwrapped = unwrap(input, wx, wy, sx, sy, px, py);
+    array output = wrap(unwrapped, 5, 5, wx, wy, sx, sy, px, py);
 
-    ASSERT_TRUE(af::allTrue<bool>(output == input));
+    ASSERT_TRUE(allTrue<bool>(output == input));
 }
diff --git a/test/write.cpp b/test/write.cpp
index 1fc8a373ad..5dd26dda87 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -16,13 +16,15 @@
 #include <string>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
-using std::cout;
 using std::endl;
+using std::string;
+using std::vector;
+using af::array;
 using af::cfloat;
 using af::cdouble;
 using af::freeHost;
+using af::dim4;
+using af::dtype_traits;
 
 template<typename T>
 class Write : public ::testing::Test
@@ -39,15 +41,15 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char, un
 TYPED_TEST_CASE(Write, TestTypes);
 
 template<typename T>
-void writeTest(af::dim4 dims)
+void writeTest(dim4 dims)
 {
     if (noDoubleTests<T>()) return;
 
-    af::array A = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
-    af::array B = af::randu(dims, (af_dtype) af::dtype_traits<T>::af_type);
+    array A = randu(dims, (af_dtype) dtype_traits<T>::af_type);
+    array B = randu(dims, (af_dtype) dtype_traits<T>::af_type);
 
-    af::array A_copy = A.copy();
-    af::array B_copy = B.copy();
+    array A_copy = A.copy();
+    array B_copy = B.copy();
 
     T *a_host = A.host<T>();
     T *b_dev  = B.device<T>();
@@ -55,15 +57,15 @@ void writeTest(af::dim4 dims)
     A.write(b_dev, dims.elements() * sizeof(T), afDevice);
     B.write(a_host, dims.elements() * sizeof(T), afHost);
 
-    af::array check1 = A != B_copy;     // False so check1 is all 0s
-    af::array check2 = B != A_copy;     // False so check2 is all 0s
+    array check1 = A != B_copy;     // False so check1 is all 0s
+    array check2 = B != A_copy;     // False so check2 is all 0s
 
     char *h_check1 = check1.host<char>();
     char *h_check2 = check2.host<char>();
 
     for(int i = 0; i < (int)dims.elements(); i++) {
-        ASSERT_EQ(h_check1[i], 0) << "at: " << i << std::endl;
-        ASSERT_EQ(h_check2[i], 0) << "at: " << i << std::endl;
+        ASSERT_EQ(h_check1[i], 0) << "at: " << i << endl;
+        ASSERT_EQ(h_check2[i], 0) << "at: " << i << endl;
     }
 
     freeHost(a_host);
@@ -73,30 +75,30 @@ void writeTest(af::dim4 dims)
 
 TYPED_TEST(Write, Vector0)
 {
-    writeTest<TypeParam>(af::dim4(10));
+    writeTest<TypeParam>(dim4(10));
 }
 
 TYPED_TEST(Write, Vector1)
 {
-    writeTest<TypeParam>(af::dim4(1000));
+    writeTest<TypeParam>(dim4(1000));
 }
 
 TYPED_TEST(Write, Matrix0)
 {
-    writeTest<TypeParam>(af::dim4(64, 8));
+    writeTest<TypeParam>(dim4(64, 8));
 }
 
 TYPED_TEST(Write, Matrix1)
 {
-    writeTest<TypeParam>(af::dim4(256, 256));
+    writeTest<TypeParam>(dim4(256, 256));
 }
 
 TYPED_TEST(Write, Volume0)
 {
-    writeTest<TypeParam>(af::dim4(10, 10, 10));
+    writeTest<TypeParam>(dim4(10, 10, 10));
 }
 
 TYPED_TEST(Write, Volume1)
 {
-    writeTest<TypeParam>(af::dim4(32, 64, 16));
+    writeTest<TypeParam>(dim4(32, 64, 16));
 }
diff --git a/test/ycbcr_rgb.cpp b/test/ycbcr_rgb.cpp
index acb248c045..b3e239d391 100644
--- a/test/ycbcr_rgb.cpp
+++ b/test/ycbcr_rgb.cpp
@@ -14,18 +14,21 @@
 #include <vector>
 #include <testHelpers.hpp>
 
+using std::endl;
 using std::string;
 using std::vector;
+using af::array;
+using af::dim4;
 
 TEST(ycbcr_rgb, InvalidArray)
 {
     vector<float> in(100, 1);
 
-    af::dim4 dims(100);
-    af::array input(dims, &(in.front()));
+    dim4 dims(100);
+    array input(dims, &(in.front()));
 
     try {
-        af::array output = af::hsv2rgb(input);
+        array output = hsv2rgb(input);
         ASSERT_EQ(true, false);
     } catch(af::exception) {
         ASSERT_EQ(true, true);
@@ -35,42 +38,42 @@ TEST(ycbcr_rgb, InvalidArray)
 
 TEST(ycbcr2rgb, CPP)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::ycbcr2rgb(input);
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = ycbcr2rgb(input);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     output.host(outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 }
 
 TEST(ycbcr2rgb, MaxDim)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
     unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = af::tile(input, 1, ntile);
-    af::array output = af::ycbcr2rgb(input);
-    af::dim4 outDims = output.dims();
+    input = tile(input, 1, ntile);
+    array output = ycbcr2rgb(input);
+    dim4 outDims = output.dims();
 
     float *outData = new float[outDims.elements()];
     output.host((void*)outData);
@@ -81,7 +84,7 @@ TEST(ycbcr2rgb, MaxDim)
             for(int x=0; x<outDims[0]; ++x) {
                 int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
                 int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
             }
         }
     }
@@ -92,43 +95,43 @@ TEST(ycbcr2rgb, MaxDim)
 
 TEST(rgb2ycbcr, CPP)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
-    af::array output = af::rgb2ycbcr(input);
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
+    array output = rgb2ycbcr(input);
 
-    std::vector<float> outData(dims.elements());
+    vector<float> outData(dims.elements());
     output.host(outData.data());
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< std::endl;
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
     }
 
 }
 
 TEST(rgb2ycbcr, MaxDim)
 {
-    vector<af::dim4>      numDims;
+    vector<dim4>      numDims;
     vector<vector<float> >      in;
     vector<vector<float> >   tests;
 
     readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
 
-    af::dim4 dims    = numDims[0];
-    af::array input(dims, &(in[0].front()));
+    dim4 dims    = numDims[0];
+    array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
     unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = af::tile(input, 1, ntile);
-    af::array output = af::rgb2ycbcr(input);
-    af::dim4 outDims = output.dims();
+    input = tile(input, 1, ntile);
+    array output = rgb2ycbcr(input);
+    dim4 outDims = output.dims();
 
     float *outData = new float[outDims.elements()];
     output.host((void*)outData);
@@ -139,7 +142,7 @@ TEST(rgb2ycbcr, MaxDim)
             for(int x=0; x<outDims[0]; ++x) {
                 int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
                 int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << std::endl;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
             }
         }
     }

From fb52a7d41a12c4b258c5f0d7896ac1ba11b87dff Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 Jun 2018 01:47:42 -0400
Subject: [PATCH 1454/2677] Fix leak in cuda JIT by deleting link state and
 nvrtc program

We werent deleting the CUDA driver API link state and the nvrtc
program when generating kernels.
---
 src/backend/cuda/jit.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index cd5901cd61..cc361763f5 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -293,6 +293,7 @@ std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
     NVRTC_CHECK(nvrtcGetPTXSize(prog, &ptx_size));
     ptx.resize(ptx_size);
     NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));
+    NVRTC_CHECK(nvrtcDestroyProgram(&prog));
     return ptx;
 }
 
@@ -333,6 +334,7 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
     CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
     CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
     CU_CHECK(cuModuleGetFunction(&kernel, module, ker_name));
+    CU_LINK_CHECK(cuLinkDestroy(linkState));
     kc_entry_t entry = {module, kernel};
     return entry;
 }
@@ -351,7 +353,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
     int device      = getActiveDeviceId();
 
     kc_t::iterator idx = kernelCaches[device].find(funcName);
-    kc_entry_t entry = {NULL, NULL};
+    kc_entry_t entry{nullptr, nullptr};
 
     if (idx == kernelCaches[device].end()) {
         string jit_ker = getKernelString(funcName, full_nodes, full_ids, output_ids, is_linear);

From 256d1b9b359a0d2d2c1999af79ca5f90750d8a1c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 21 Jun 2018 18:36:35 +0530
Subject: [PATCH 1455/2677] Fix window cell indexing in graphics (#2207)

* Improve params documentation in graphics header
* Fix window cell indexing in grid based rendering
---
 include/af/graphics.h      | 7 +++++--
 src/api/c/image.cpp        | 3 ++-
 src/api/c/plot.cpp         | 9 ++++++---
 src/api/c/surface.cpp      | 3 ++-
 src/api/c/vector_field.cpp | 9 ++++++---
 5 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 1c44d0d360..100d88b753 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -488,8 +488,8 @@ class AFAPI Window {
         /**
            Setup grid layout for multiview mode in a window
 
-           \param[in]   rows is number of rows you want to show in a window
-           \param[in]   cols is number of coloumns you want to show in a window
+           \param[in]   rows is number of rows you want to divide the display area
+           \param[in]   cols is number of coloumns you want to divide the display area
 
            \ingroup gfx_func_window
         */
@@ -532,6 +532,9 @@ class AFAPI Window {
            called upon this function. This reference can be used later to issue
            draw calls using rendering functions.
 
+           \param[in] r is row identifier where current object has to be rendered
+           \param[in] c is column identifier where current object has to be rendered
+
            \return a reference to the object pointed by this
            to enable cascading this call with rendering functions.
 
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index b8996f948c..23ac91cdab 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -108,7 +108,8 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         window->setColorMap((forge::ColorMap)props->cmap);
         if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *image, props->title);
         else
             window->draw(*image);
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 85b4c69030..6252c88164 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -155,7 +155,8 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);
@@ -217,7 +218,8 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);
@@ -273,7 +275,8 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index ef461a4a78..fcbefbcb39 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -170,7 +170,8 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
 
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 2b1f1fe5b1..9297a47345 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -162,7 +162,8 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);
@@ -252,7 +253,8 @@ af_err vectorFieldWrapper(const af_window wind,
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);
@@ -330,7 +332,8 @@ af_err vectorFieldWrapper(const af_window wind,
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);

From 8ecbe6147347c05e2fab6983c4b8258ad940b56a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 21 Jun 2018 15:02:25 -0400
Subject: [PATCH 1456/2677] Update release notes for 3.6.1 (#2208)

* Update release notes for 3.6.1
---
 docs/pages/release_notes.md | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 8196414ec0..7bd99f2349 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,28 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.6.1
+======
+
+Improvements
+------------
+- FreeImage is now a run-time dependency [#2164]
+- Reduced binary size by setting the symbol visibility to hidden [#2168]
+- Add memory manager logging using the AF_TRACE=mem environment variable [#2169]
+- Improved CPU Anisotropic Diffusion performance [#2174]
+- Perform normalization after FFT for improved accuracy [#2185][#2192]
+- Updated CLBlast to v1.4.0 [#2178]
+- Added additional validation when using af::seq for indexing [#2153]
+- Perform checks for unsupported cards by the CUDA implementation [#2182]
+
+Bug Fixes
+---------
+- Fixed region when all pixels were the foreground or background [#2152]
+- Fixed several memory leaks [#2202][#2201][#2180][#2179][#2177][#2175]
+- Fixed bug in setDevice which didn't allow you to select the last device [#2189]
+- Fixed bug in min/max where the first element of the array was a NaN value [#2155]
+- Fixed window cell indexing for graphics [#2207]
+
 v3.6.0
 ======
 

From 4b450a7aac2c1a70dd90494ea4ebc878f2665095 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 27 Jun 2018 01:52:51 -0400
Subject: [PATCH 1457/2677] Fix logging issue when selecting specific module

---
 src/backend/common/Logger.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index aa48e8c84e..d82dcc523f 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -38,9 +38,10 @@ loggerFactory(string name) {
 
         // Log mode
         string env_var = getEnvVar("AF_TRACE");
-        if(env_var.find_first_of("all") != string::npos ||
-          env_var.find_first_of(name) != string::npos)
-              logger->set_level(trace);
+        if(env_var.find("all") != string::npos ||
+           env_var.find(name) != string::npos) {
+          logger->set_level(trace);
+        }
     }
     return logger;
 }

From 452f8f2786ea5ae37bec7e5cb2adb7cb15ce3898 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 27 Jun 2018 00:47:22 -0400
Subject: [PATCH 1458/2677] Refactor symbol manager to use module_loading
 functions

---
 src/api/unified/CMakeLists.txt                |  10 +
 src/api/unified/symbol_manager.cpp            | 203 ++++++++----------
 src/api/unified/symbol_manager.hpp            |  24 +--
 src/backend/common/DependencyModule.cpp       |  10 -
 src/backend/common/DependencyModule.hpp       |  44 ++--
 src/backend/common/module_loading.hpp         |   3 +-
 src/backend/common/module_loading_unix.cpp    |   3 +
 src/backend/common/module_loading_windows.cpp |   4 +
 test/backend.cpp                              |   4 +-
 9 files changed, 139 insertions(+), 166 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 924574b5fa..693f9b542b 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -41,6 +41,16 @@ target_sources(af
     ${CMAKE_SOURCE_DIR}/src/backend/common/util.cpp
   )
 
+if(WIN32)
+  target_sources(af
+    PRIVATE
+      ${CMAKE_SOURCE_DIR}/src/backend/common/module_loading_windows.cpp)
+else()
+  target_sources(af
+    PRIVATE
+      ${CMAKE_SOURCE_DIR}/src/backend/common/module_loading_unix.cpp)
+endif()
+
 target_compile_definitions(af
   PRIVATE
     AFDLL
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index d5b0da8658..cb8d9b6be7 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -8,133 +8,120 @@
  ********************************************************/
 
 #include "symbol_manager.hpp"
-#include <algorithm>
-#include <vector>
-#include <string>
-#include <cmath>
 #include <af/version.h>
+#include <common/module_loading.hpp>
+
+#include <cmath>
+#include <string>
+#include <type_traits>
+
+
+#ifndef WIN_OS
+#include <dlfcn.h>
+#else
+#include <Windows.h>
+#endif
+
+using common::loadLibrary;
+using common::unloadLibrary;
 
+using std::extent;
 using std::string;
-using std::replace;
 
 namespace unified
 {
 
-static const char* LIB_AF_BKND_NAME[NUM_BACKENDS] = {"cpu", "cuda", "opencl"};
 #if defined(OS_WIN)
-static const char* LIB_AF_BKND_PREFIX = "af";
+static const char* LIB_AF_BKND_PREFIX = "";
 static const char* LIB_AF_BKND_SUFFIX = ".dll";
+#define PATH_SEPARATOR "\\"
 #define RTLD_LAZY 0
 #else
+
 #if defined(__APPLE__)
-#define SO_SUFFIX_HELPER(VER) "." #VER ".dylib"
+#  define SO_SUFFIX_HELPER(VER) "." #VER ".dylib"
 #else
-#define SO_SUFFIX_HELPER(VER) ".so." #VER
-#endif // APPLE
-static const char* LIB_AF_BKND_PREFIX = "libaf";
+#  define SO_SUFFIX_HELPER(VER) ".so." #VER
+#endif
+   static const char* LIB_AF_BKND_PREFIX = "lib";
+#  define PATH_SEPARATOR "/"
 
-#define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
-static const char* LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
+#  define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
+   static const char* LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
 #endif
 
-static const char* LIB_AF_ENVARS[NUM_ENV_VARS] = {"AF_PATH", "AF_BUILD_PATH"};
-static const char* LIB_AF_RPATHS[NUM_ENV_VARS] = {"/lib/", "/src/backend/"};
-static const bool LIB_AF_RPATH_SUFFIX[NUM_ENV_VARS] = {false, true};
+string getBkndLibName(const af_backend backend) {
+    string ret;
+    switch (backend) {
+        case AF_BACKEND_CUDA: ret = string(LIB_AF_BKND_PREFIX) + "afcuda" + LIB_AF_BKND_SUFFIX; break;
+        case AF_BACKEND_OPENCL: ret = string(LIB_AF_BKND_PREFIX) + "afopencl" + LIB_AF_BKND_SUFFIX; break;
+        case AF_BACKEND_CPU: ret = string(LIB_AF_BKND_PREFIX) + "afcpu" + LIB_AF_BKND_SUFFIX; break;
+        default: assert(1!=1 && "Invalid backend");
+    }
+    return ret;
+}
+string getBackendDirectoryName(const af_backend backend) {
+    string ret;
+    switch (backend) {
+        case AF_BACKEND_CUDA: ret = "cuda"; break;
+        case AF_BACKEND_OPENCL: ret = "opencl"; break;
+        case AF_BACKEND_CPU: ret = "cpu"; break;
+        default: assert(1!=1 && "Invalid backend");
+      }
+    return ret;
+}
 
-inline string getBkndLibName(const int backend_index)
-{
-    int i = backend_index >=0 && backend_index<NUM_BACKENDS ? backend_index : 0;
-    return string(LIB_AF_BKND_PREFIX) + LIB_AF_BKND_NAME[i] + LIB_AF_BKND_SUFFIX;
+string join_path(string first) {
+    return first;
+}
+
+template<typename... ARGS>
+string join_path(string first, ARGS... args) {
+    if(first.empty()) { return join_path(args...); }
+    else              { return first + PATH_SEPARATOR + join_path(args...); }
 }
 
 /*flag parameter is not used on windows platform */
-LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
+LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
 {
-    /*
-     * The default search path is the colon separated list of
-     * paths stored in the environment variables:
-     * * LD_LIBRARY_PATH(Linux/Unix/Apple)
-     * * DYLD_LIBRARY_PATH (Apple)
-     * * PATH (Windows)
-    */
+    // The default search path is the colon separated list of paths stored in
+    // the environment variables:
     string bkndLibName = getBkndLibName(bknd_idx);
     string show_flag = getEnvVar("AF_SHOW_LOAD_PATH");
     bool show_load_path = show_flag=="1";
 
-#if defined(OS_WIN)
-    HMODULE retVal = LoadLibrary(bkndLibName.c_str());
-#else
-    LibHandle retVal = dlopen(bkndLibName.c_str(), flag);
-#endif
-    if(retVal != NULL) { // Success
-        if (show_load_path)
-            printf("Using %s from system path\n", bkndLibName.c_str());
-    } else {
-        /*
-         * In the event that dlopen returns NULL, search for the lib
-         * in hard coded paths based on the environment variables
-         * defined in the constant string array LIB_AF_PATHS
-         * * AF_PATH
-         * * AF_BUILD_PATH
-         *
-         * Note: This does not guarantee successful loading as the dependent
-         * libraries may still not load
-        */
-
-        for (int i=0; i<NUM_ENV_VARS; ++i) {
-            string abs_path = getEnvVar(LIB_AF_ENVARS[i])
-                                 + LIB_AF_RPATHS[i]
-                                 + (LIB_AF_RPATH_SUFFIX[i] ? LIB_AF_BKND_NAME[bknd_idx]+ string("/") : "")
-                                 + bkndLibName;
-#if defined(OS_WIN)
-            replace(abs_path.begin(), abs_path.end(), '/', '\\');
-            retVal = LoadLibrary(abs_path.c_str());
+    string paths[] = {
+        "",  // Default paths
+        ".", // Shared libraries in current directory
+        // Running from the CMake Build directory
+        join_path(".", "src", "backend", getBackendDirectoryName(bknd_idx)),
+        // Running from the test directory
+        join_path("..", "src", "backend", getBackendDirectoryName(bknd_idx)),
+        // Environment variable PATHS
+        join_path(getEnvVar("AF_BUILD_PATH"), "src", "backend", getBackendDirectoryName(bknd_idx)),
+        join_path(getEnvVar("AF_PATH"), "lib"),
+        join_path(getEnvVar("AF_PATH"), "lib64"),
+
+        // Common install paths
+#if !defined(OS_WIN)
+        "/opt/arrayfire-3/lib/",
+        "/opt/arrayfire/lib/",
+        "/usr/local/lib/",
+        "/usr/local/arrayfire/lib/"
 #else
-            retVal = dlopen(abs_path.c_str(), flag);
+        join_path(getEnvVar("ProgramFiles"), "ArrayFire", "lib"),
+        join_path(getEnvVar("ProgramFiles"), "ArrayFire", "v3", "lib")
 #endif
-            if (retVal!=NULL) {
-                if (show_load_path)
-                    printf("Using %s\n", abs_path.c_str());
-                // if the current absolute path based dlopen
-                // search is a success, then abandon search
-                // and proceed for compute
-                break;
-            }
-        }
+    };
 
-#if !defined(OS_WIN)
-        /*
-         * If Linux/OSX, then the following are also checked
-         * (only if lib is not found)
-         * /opt/arrayfire/lib
-         * /opt/arrayfire-3/lib
-         * /usr/local/lib
-         * /usr/local/arrayfire/lib
-         * /usr/local/arrayfire-3/lib
-        */
-        if (retVal == NULL) {
-            static const
-            std::vector<std::string> extraLibPaths {"/opt/arrayfire-3/lib/",
-                                                    "/opt/arrayfire/lib/",
-                                                    "/usr/local/lib/",
-                                                    "/usr/local/arrayfire-3/lib/",
-                                                    "/usr/local/arrayfire/lib/",
-                                                   };
-
-            for (auto libPath: extraLibPaths) {
-                string abs_path = libPath + bkndLibName;
-                retVal = dlopen(abs_path.c_str(), flag);
-                if (retVal != NULL) {
-                    if (show_load_path)
-                        printf("Using %s\n", abs_path.c_str());
-                    // if the current absolute path based dlopen
-                    // search is a success, then abandon search
-                    // and proceed for compute
-                    break;
-                }
+    LibHandle retVal = nullptr;
+    for (int i = 0; i < extent<decltype(paths)>::value; i++) {
+        if (retVal = common::loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
+            if (show_load_path) {
+                printf("Using %s\n", bkndLibName.c_str());
             }
+            break;
         }
-#endif
     }
 
     return retVal;
@@ -142,11 +129,7 @@ LibHandle openDynLibrary(const int bknd_idx, int flag=RTLD_LAZY)
 
 void closeDynLibrary(LibHandle handle)
 {
-#if defined(OS_WIN)
-    FreeLibrary(handle);
-#else
-    dlclose(handle);
-#endif
+    unloadLibrary(handle);
 }
 
 AFSymbolManager& AFSymbolManager::getInstance()
@@ -159,14 +142,14 @@ AFSymbolManager::AFSymbolManager()
     : activeHandle(NULL), defaultHandle(NULL), numBackends(0), backendsAvailable(0)
 {
     // In order of priority.
-    static const int order[] = {AF_BACKEND_CUDA,        // 1 -> Most Preferred
-                                AF_BACKEND_OPENCL,      // 4 -> Preferred if CUDA unavailable
-                                AF_BACKEND_CPU};        // 2 -> Preferred if CUDA and OpenCL unavailable
+    static const af_backend order[] = { AF_BACKEND_CUDA,
+                                        AF_BACKEND_OPENCL,
+                                        AF_BACKEND_CPU};
 
     // Decremeting loop. The last successful backend loaded will be the most prefered one.
     for(int i = NUM_BACKENDS - 1; i >= 0; i--) {
-        int backend = order[i] >> 1;    // Convert order[1, 4, 2] -> backend[0, 2, 1]
-        bkndHandles[backend] = openDynLibrary(backend);
+        int backend = order[i] >> 1; // 2 4 1 -> 1 2 0
+        bkndHandles[backend] = openDynLibrary(order[i]);
         if (bkndHandles[backend]) {
             activeHandle = bkndHandles[backend];
             activeBackend = (af_backend)order[i];
@@ -174,9 +157,9 @@ AFSymbolManager::AFSymbolManager()
             backendsAvailable += order[i];
         }
     }
-    // Keep a copy of default order handle
-    // inorder to use it in ::setBackend when
-    // the user passes AF_BACKEND_DEFAULT
+
+    // Keep a copy of default order handle inorder to use it in ::setBackend
+    // when the user passes AF_BACKEND_DEFAULT
     defaultHandle = activeHandle;
     defaultBackend = activeBackend;
 }
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index fcd3c16d2e..c6a7377d5f 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -9,16 +9,9 @@
 #pragma once
 
 #include <af/defines.h>
-#include <common/util.hpp>
 #include <common/err_common.hpp>
-
-#if defined(OS_WIN)
-#include <Windows.h>
-typedef HMODULE LibHandle;
-#else
-#include <dlfcn.h>
-typedef void* LibHandle;
-#endif
+#include <common/module_loading.hpp>
+#include <common/util.hpp>
 
 #include <array>
 #include <cstdlib>
@@ -29,7 +22,6 @@ namespace unified
 {
 
 const int NUM_BACKENDS = 3;
-const int NUM_ENV_VARS = 2;
 
 #define UNIFIED_ERROR_LOAD_LIB()                                        \
     AF_RETURN_ERROR("Failed to load dynamic library. "                  \
@@ -72,11 +64,7 @@ class AFSymbolManager {
             af_func& funcHandle = funcHandles[index][symbolName];
 
             if (!funcHandle) {
-#if defined(OS_WIN)
-                funcHandle = (af_func)GetProcAddress(activeHandle, symbolName);
-#else
-                funcHandle = (af_func)dlsym(activeHandle, symbolName);
-#endif
+                funcHandle = (af_func)common::getFunctionPointer(activeHandle, symbolName);
             }
             if (!funcHandle) {
                 std::string str = "Failed to load symbol: ";
@@ -141,8 +129,4 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg)
 #define CALL_NO_PARAMS() unified::AFSymbolManager::getInstance().call(__func__)
 #endif
 
-#if defined(OS_WIN)
-#define LOAD_SYMBOL() GetProcAddress(unified::AFSymbolManager::getInstance().getHandle(), __FUNCTION__)
-#else
-#define LOAD_SYMBOL() dlsym(unified::AFSymbolManager::getInstance().getHandle(), __func__)
-#endif
+#define LOAD_SYMBOL() common::getFunctionPointer(unified::AFSymbolManager::getInstance().getHandle(), __FUNCTION__)
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index 3d2f05d882..8bb5e8902b 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -29,16 +29,6 @@ namespace {
 
 namespace common {
 
-#ifdef OS_WIN
-void* DependencyModule::getFunctionPointer(LibHandle handle, const char* symbolName) {
-    return GetProcAddress(handle, symbolName);
-}
-#else
-void* DependencyModule::getFunctionPointer(LibHandle handle, const char* symbolName) {
-    return dlsym(handle, symbolName);
-}
-#endif
-
 DependencyModule::DependencyModule(const char* plugin_file_name, const char** paths)
     : handle(nullptr) {
     // TODO(umar): Implement handling of non-standard paths
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 986ed2e12c..3e577f4116 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -9,11 +9,10 @@
 
 #pragma once
 #include <common/defines.hpp>
+#include <common/module_loading.hpp>
 
 #include <utility>
-#include <type_traits>
 #include <vector>
-#include <string>
 
 namespace common {
 
@@ -24,39 +23,38 @@ namespace common {
 /// module class which will have member functions for each of the functions
 /// we use in ArrayFire
 class DependencyModule {
-  LibHandle handle;
-  std::vector<void*> functions;
-  void* getFunctionPointer(LibHandle handle, const char* name);
+    LibHandle handle;
+    std::vector<void*> functions;
 
-public:
-  DependencyModule(const char* plugin_file_name, const char** paths = nullptr);
+  public:
+    DependencyModule(const char* plugin_file_name, const char** paths = nullptr);
 
-  ~DependencyModule();
+    ~DependencyModule();
 
-  /// Returns a function pointer to the function with the name symbol_name
-  template<typename T>
-  T getSymbol(const char* symbol_name) {
-      functions.push_back(getFunctionPointer(handle, symbol_name));
-      return (T)functions.back();
-  }
+    /// Returns a function pointer to the function with the name symbol_name
+    template<typename T>
+    T getSymbol(const char* symbol_name) {
+        functions.push_back(getFunctionPointer(handle, symbol_name));
+        return (T)functions.back();
+    }
 
-  /// Returns true if the module was successfully loaded
-  bool isLoaded();
+    /// Returns true if the module was successfully loaded
+    bool isLoaded();
 
-  /// Returns true if the module was successfully loaded
-  bool symbolsLoaded();
+    /// Returns true if the module was successfully loaded
+    bool symbolsLoaded();
 
-  /// Returns the last error message that occurred because of loading the
-  /// library
-  std::string getErrorMessage();
+    /// Returns the last error message that occurred because of loading the
+    /// library
+    std::string getErrorMessage();
 };
 
 }
 
 /// Creates a function pointer
 #define MODULE_MEMBER(NAME)                     \
-  decltype(&::NAME) NAME
+    decltype(&::NAME) NAME
 
 /// Dynamically loads the function pointer at runtime
 #define MODULE_FUNCTION_INIT(NAME)                \
-  NAME = module.getSymbol<decltype(&::NAME)>(#NAME)
+    NAME = module.getSymbol<decltype(&::NAME)>(#NAME)
diff --git a/src/backend/common/module_loading.hpp b/src/backend/common/module_loading.hpp
index 13eab8ff48..83ced96b84 100644
--- a/src/backend/common/module_loading.hpp
+++ b/src/backend/common/module_loading.hpp
@@ -8,10 +8,11 @@
  ********************************************************/
 
 #include <common/defines.hpp>
-#include <string>
 
 namespace common {
 
+void* getFunctionPointer(LibHandle handle, const char* symbolName);
+
 LibHandle loadLibrary(const char* library_name);
 
 void unloadLibrary(LibHandle handle);
diff --git a/src/backend/common/module_loading_unix.cpp b/src/backend/common/module_loading_unix.cpp
index e2ab421183..b2ddb3fe61 100644
--- a/src/backend/common/module_loading_unix.cpp
+++ b/src/backend/common/module_loading_unix.cpp
@@ -17,6 +17,9 @@ using std::string;
 
 namespace common {
 
+void* getFunctionPointer(LibHandle handle, const char* symbolName) {
+  return dlsym(handle, symbolName);
+}
 
 LibHandle loadLibrary(const char* library_name) {
     return dlopen(library_name, RTLD_LAZY);
diff --git a/src/backend/common/module_loading_windows.cpp b/src/backend/common/module_loading_windows.cpp
index 712ef4aa89..d331118716 100644
--- a/src/backend/common/module_loading_windows.cpp
+++ b/src/backend/common/module_loading_windows.cpp
@@ -17,6 +17,10 @@ using std::string;
 
 namespace common {
 
+void* getFunctionPointer(LibHandle handle, const char* symbolName) {
+  return GetProcAddress(handle, symbolName);
+}
+
 LibHandle loadLibrary(const char* library_name) {
     return LoadLibrary(library_name);
 }
diff --git a/test/backend.cpp b/test/backend.cpp
index e59c4541a6..b08159c182 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -46,12 +46,12 @@ void testFunction()
 
     af_array outArray = 0;
     dim_t dims[] = {32, 32};
-    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) dtype_traits<T>::af_type));
+    EXPECT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) dtype_traits<T>::af_type));
 
     // Verify backends returned by array and by function are the same
     af_backend arrayBackend = (af_backend)0;
     af_get_backend_id(&arrayBackend, outArray);
-    ASSERT_EQ(arrayBackend, activeBackend);
+    EXPECT_EQ(arrayBackend, activeBackend);
 
     // cleanup
     if(outArray != 0) {

From 722f2236fb7bfc4cb94964eaeea90bb249ebe25e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 27 Jun 2018 01:49:19 -0400
Subject: [PATCH 1459/2677] Add trace logging to the unified backend

---
 docs/pages/configuring_arrayfire_environment.md |  4 +++-
 src/api/unified/CMakeLists.txt                  | 16 +++++++++++++---
 src/api/unified/symbol_manager.cpp              | 14 +++++++++++++-
 src/api/unified/symbol_manager.hpp              |  5 +++++
 4 files changed, 34 insertions(+), 5 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index fd8a4ba007..f8d082f71c 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -163,12 +163,14 @@ enable tracing of various modules within ArrayFire. This is a comma separated
 list of modules to trace. If enabled, ArrayFire will print relevant information
 to stdout. Currently the following modules are supported:
 
+- all: All trace outputs
 - mem: Memory management allocation, free and garbage collection information
+- unified: Unified backend dynamic loading information
 
 Tracing displays the information that could be useful when debugging or
 optimizing your application. Here is how you would use this variable:
 
-    AF_TRACE=mem ./myprogram
+    AF_TRACE=mem:unified ./myprogram
 
 This will print information about memory operations such as allocations,
 deallocations, and garbage collection.
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 693f9b542b..308bedcc24 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -32,13 +32,15 @@ target_sources(af
 
 target_sources(af
   PRIVATE
-    ${CMAKE_SOURCE_DIR}/src/api/c/version.cpp
     ${CMAKE_SOURCE_DIR}/src/api/c/type_util.cpp
+    ${CMAKE_SOURCE_DIR}/src/api/c/version.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/Logger.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/Logger.hpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/constants.cpp
     ${CMAKE_SOURCE_DIR}/src/backend/common/dim4.cpp
     ${CMAKE_SOURCE_DIR}/src/backend/common/err_common.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/constants.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/util.hpp
     ${CMAKE_SOURCE_DIR}/src/backend/common/util.cpp
+    ${CMAKE_SOURCE_DIR}/src/backend/common/util.hpp
   )
 
 if(WIN32)
@@ -67,6 +69,14 @@ target_include_directories(af
     ${CMAKE_BINARY_DIR}
   )
 
+if(AF_WITH_LOGGING)
+  dependency_check(spdlog_FOUND "spdlog not found.")
+  target_compile_definitions(af
+    PRIVATE AF_WITH_LOGGING)
+  target_link_libraries(af
+    PRIVATE
+      spdlog::spdlog)
+endif()
 
 target_link_libraries(af
   PRIVATE
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index cb8d9b6be7..989891de63 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -24,6 +24,7 @@
 
 using common::loadLibrary;
 using common::unloadLibrary;
+using common::loggerFactory;
 
 using std::extent;
 using std::string;
@@ -90,6 +91,9 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
     string show_flag = getEnvVar("AF_SHOW_LOAD_PATH");
     bool show_load_path = show_flag=="1";
 
+    // FIXME(umar): avoid this if at all possible
+    auto getLogger = [&]{ return spdlog::get("unified"); };
+
     string paths[] = {
         "",  // Default paths
         ".", // Shared libraries in current directory
@@ -116,7 +120,9 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
 
     LibHandle retVal = nullptr;
     for (int i = 0; i < extent<decltype(paths)>::value; i++) {
+        AF_TRACE("Attempting: {}", paths[i]);
         if (retVal = common::loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
+            AF_TRACE("FOUND: {}", join_path(paths[i], bkndLibName));
             if (show_load_path) {
                 printf("Using %s\n", bkndLibName.c_str());
             }
@@ -138,8 +144,13 @@ AFSymbolManager& AFSymbolManager::getInstance()
     return symbolManager;
 }
 
+spdlog::logger* AFSymbolManager::getLogger() {
+    return logger.get();
+}
+
 AFSymbolManager::AFSymbolManager()
-    : activeHandle(NULL), defaultHandle(NULL), numBackends(0), backendsAvailable(0)
+    : activeHandle(nullptr), defaultHandle(nullptr), numBackends(0),
+      backendsAvailable(0), logger(loggerFactory("unified"))
 {
     // In order of priority.
     static const af_backend order[] = { AF_BACKEND_CUDA,
@@ -157,6 +168,7 @@ AFSymbolManager::AFSymbolManager()
             backendsAvailable += order[i];
         }
     }
+    AF_TRACE("AF_DEFAULT_BACKEND: {}", getBackendDirectoryName(activeBackend));
 
     // Keep a copy of default order handle inorder to use it in ::setBackend
     // when the user passes AF_BACKEND_DEFAULT
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index c6a7377d5f..d688b1235a 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -10,6 +10,7 @@
 
 #include <af/defines.h>
 #include <common/err_common.hpp>
+#include <common/Logger.hpp>
 #include <common/module_loading.hpp>
 #include <common/util.hpp>
 
@@ -64,9 +65,11 @@ class AFSymbolManager {
             af_func& funcHandle = funcHandles[index][symbolName];
 
             if (!funcHandle) {
+                AF_TRACE("Loading: {}", symbolName);
                 funcHandle = (af_func)common::getFunctionPointer(activeHandle, symbolName);
             }
             if (!funcHandle) {
+                AF_TRACE("Failed to load symbol: {}", symbolName);
                 std::string str = "Failed to load symbol: ";
                 str += symbolName;
                 AF_RETURN_ERROR(str.c_str(),
@@ -77,6 +80,7 @@ class AFSymbolManager {
         }
 
         LibHandle getHandle() { return activeHandle; }
+        spdlog::logger* getLogger();
 
     protected:
         AFSymbolManager();
@@ -98,6 +102,7 @@ class AFSymbolManager {
         int backendsAvailable;
         af_backend activeBackend;
         af_backend defaultBackend;
+        std::shared_ptr<spdlog::logger> logger;
 };
 
 // Helper functions to ensure all the input arrays are on the active backend

From 75183506a0b21c37340c1a64af54e26dcc960264 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Janko=20Marohni=C4=87?= <janko.marohnic@gmail.com>
Date: Sat, 23 Jun 2018 22:30:08 +0200
Subject: [PATCH 1460/2677] Link to arrayfire-rb in the README

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index aa2aa200b7..6552ace4db 100644
--- a/README.md
+++ b/README.md
@@ -121,7 +121,8 @@ Wrappers for other languages are a work-in-progress:
   [Java](https://github.com/arrayfire/arrayfire-java),
   [Lua](https://github.com/arrayfire/arrayfire-lua),
   [NodeJS](https://github.com/arrayfire/arrayfire-js),
-  [R](https://github.com/arrayfire/arrayfire-r)
+  [R](https://github.com/arrayfire/arrayfire-r),
+  [Ruby](https://github.com/arrayfire/arrayfire-rb)
 
 __Third-party wrappers__
 

From 1c4524d3c3377140a48d4b1a96fa28bea7ef9061 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 27 Jun 2018 02:23:16 -0400
Subject: [PATCH 1461/2677] Avoid loading backends with no devices in the
 unified backend

Avoid loading a backend by the unified backend if there are no
devices available. This appears on platforms where OpenCL
runtime has not been installed and the unified backend is used.
The OpenCL backend will be selected by default but will fail to
work because no devices are available.
---
 src/api/unified/symbol_manager.cpp | 24 +++++++++++++++++++++---
 1 file changed, 21 insertions(+), 3 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 989891de63..009752276e 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -12,6 +12,7 @@
 #include <common/module_loading.hpp>
 
 #include <cmath>
+#include <functional>
 #include <string>
 #include <type_traits>
 
@@ -22,11 +23,13 @@
 #include <Windows.h>
 #endif
 
+using common::getFunctionPointer;
 using common::loadLibrary;
-using common::unloadLibrary;
 using common::loggerFactory;
+using common::unloadLibrary;
 
 using std::extent;
+using std::function;
 using std::string;
 
 namespace unified
@@ -117,12 +120,27 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
         join_path(getEnvVar("ProgramFiles"), "ArrayFire", "v3", "lib")
 #endif
     };
+    typedef af_err(*func)(int*);
 
     LibHandle retVal = nullptr;
     for (int i = 0; i < extent<decltype(paths)>::value; i++) {
         AF_TRACE("Attempting: {}", paths[i]);
-        if (retVal = common::loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
-            AF_TRACE("FOUND: {}", join_path(paths[i], bkndLibName));
+        if (retVal =
+            common::loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
+            AF_TRACE("Found: {}", join_path(paths[i], bkndLibName));
+
+            func count_func = (func)getFunctionPointer(retVal,
+                                                       "af_get_device_count");
+            if(count_func) {
+                int count = 0;
+                count_func(&count);
+                AF_TRACE("Device Count: {}.", count);
+                if(count == 0) {
+                    retVal = nullptr;
+                    continue;
+                }
+            }
+
             if (show_load_path) {
                 printf("Using %s\n", bkndLibName.c_str());
             }

From 79fee45f1483f2bcff6fe0113da933efb77ebe70 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 27 Jun 2018 12:52:08 -0400
Subject: [PATCH 1462/2677] Fix compilation issues with logging symbol manager
 on Windows builds

---
 src/api/unified/symbol_manager.cpp | 9 ++++-----
 src/backend/common/Logger.hpp      | 2 +-
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 009752276e..ba21a5af6b 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -17,10 +17,10 @@
 #include <type_traits>
 
 
-#ifndef WIN_OS
-#include <dlfcn.h>
-#else
+#ifdef OS_WIN
 #include <Windows.h>
+#else
+#include <dlfcn.h>
 #endif
 
 using common::getFunctionPointer;
@@ -125,8 +125,7 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
     LibHandle retVal = nullptr;
     for (int i = 0; i < extent<decltype(paths)>::value; i++) {
         AF_TRACE("Attempting: {}", paths[i]);
-        if (retVal =
-            common::loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
+        if (retVal = loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
             AF_TRACE("Found: {}", join_path(paths[i], bkndLibName));
 
             func count_func = (func)getFunctionPointer(retVal,
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index 08194f25e7..fb6eecaa3d 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -21,7 +21,7 @@
 /// functions will need to be implemented later.
 namespace spdlog {
     class logger { public: logger() {} };
-    std::shared_ptr<spdlog::logger> get(std::string &name);
+    std::shared_ptr<spdlog::logger> get(const std::string &name);
     std::shared_ptr<spdlog::logger> stdout_logger_mt(std::string&);
     namespace level {
         enum enum_level { trace };

From 38a80efb37382ee0024b448fdb1663e244896770 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Thu, 28 Jun 2018 21:09:24 -0500
Subject: [PATCH 1463/2677] Fixed CUDA lib filenames for macOS installers.

---
 src/backend/cuda/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 1de810edff..13a01e093c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -533,7 +533,7 @@ macro(afcu_collect_libs libname)
     get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
     install(FILES       "${outpath}"
             DESTINATION ${AF_INSTALL_BIN_DIR}
-            RENAME      "${PX}${libname}${SX}.${CUDA_VERSION}"
+            RENAME      "${PX}${libname}.${CUDA_VERSION}${SX}"
             COMPONENT   cuda_dependencies)
   else () #UNIX
     get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}${SX}" REALPATH)

From 6e11b67344f1d25354a8faece7a5bb2cba7edb84 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 4 Jul 2018 16:13:09 +0530
Subject: [PATCH 1464/2677] Fix NSIS dll path used for modifying PATH

---
 CMakeModules/nsis/NSIS.template.in | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
index 4923789c89..f45b01127a 100644
--- a/CMakeModules/nsis/NSIS.template.in
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -741,7 +741,7 @@ Section "-Core installation"
 SectionEnd
 
 Section "-Add to path"
-  Push $INSTDIR\bin
+  Push $INSTDIR\lib
   StrCmp "@CPACK_NSIS_MODIFY_PATH@" "ON" 0 doNotAddToPath
   StrCmp $DO_NOT_ADD_TO_PATH "1" doNotAddToPath 0
     Call AddToPath
@@ -913,7 +913,7 @@ Section "Uninstall"
 
   DeleteRegKey /ifempty SHCTX "Software\@CPACK_PACKAGE_VENDOR@\@CPACK_PACKAGE_INSTALL_REGISTRY_KEY@"
 
-  Push $INSTDIR\bin
+  Push $INSTDIR\lib
   StrCmp $DO_NOT_ADD_TO_PATH_ "1" doNotRemoveFromPath 0
     Call un.RemoveFromPath
   doNotRemoveFromPath:

From fe5a5d949caf18bec6f8f38269ad7b61851f8e90 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 3 Jul 2018 21:17:42 -0400
Subject: [PATCH 1465/2677] Update CLBlast tag to incorporate Windows fixes

---
 CMakeModules/build_CLBlast.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index acc6365675..4fa20ddd85 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -13,7 +13,7 @@ set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 1.4.0
+    GIT_TAG 43e3f27254c4f7e4a0b332f5b88965c53c20bdd1 # v1.4.0 plus CLBlast #295
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From c26152fc46678f59fa516802e26f8fb4532ee451 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 5 Jul 2018 15:49:58 +0530
Subject: [PATCH 1466/2677] Fix include dirs command in unified cmake script

---
 src/api/unified/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 308bedcc24..9427f90fa1 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -65,7 +65,7 @@ target_include_directories(af
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c
-    $<JOIN:$<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>, >
+    $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
     ${CMAKE_BINARY_DIR}
   )
 

From 58a8dbec661925059ebc329395fe30335b6c69c7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 6 Jul 2018 01:58:19 -0400
Subject: [PATCH 1467/2677] Fix the name of the environment variable defined by
 MKL

MKL_ROOT -> MKLROOT
---
 CMakeModules/FindMKL.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 1af07a103e..b4e3510e60 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -38,7 +38,7 @@ find_path(MKL_INCLUDE_DIR
   PATHS
     /opt/intel
     /opt/intel/mkl
-    $ENV{MKL_ROOT}
+    $ENV{MKLROOT}
     /opt/intel/compilers_and_libraries/linux/mkl
   PATH_SUFFIXES
     include
@@ -99,7 +99,7 @@ function(find_mkl_library)
       /opt/intel/mkl/lib
       /opt/intel/tbb/lib
       /opt/intel/lib
-      $ENV{MKL_ROOT}/lib
+      $ENV{MKLROOT}/lib
       /opt/intel/compilers_and_libraries/linux/mkl/lib
     PATH_SUFFIXES
       IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64
@@ -121,7 +121,7 @@ function(find_mkl_library)
         /opt/intel/mkl/lib
         /opt/intel/tbb/lib
         /opt/intel/lib
-        $ENV{MKL_ROOT}/lib
+        $ENV{MKLROOT}/lib
         /opt/intel/compilers_and_libraries/linux/mkl/lib
       PATH_SUFFIXES
         ""

From 89c12b977c262c8f04b26e17cbc7a2d1d22d8a71 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 6 Jul 2018 02:39:13 -0400
Subject: [PATCH 1468/2677] Add detailed documentation for the FindMKL module

---
 CMakeModules/FindMKL.cmake | 59 ++++++++++++++++++++++++++++++++++----
 1 file changed, 53 insertions(+), 6 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index b4e3510e60..c34cafd2c9 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -1,15 +1,62 @@
 # Copyright (c) 2018, ArrayFire
 # All rights reserved.
 #
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
+# This file is distributed under 3-clause BSD license. The complete license
+# agreement can be obtained at: http://arrayfire.com/licenses/BSD-3-Clause
 #
 # A FindMKL script based on the recommendations by the Intel's Link Line
 # Advisor. It currently only tested on the 2018 version of MKL on Windows,
-# Linux, and OSX but it should work on older versions. It creates an MKL::MKL
-# library which has the required dependencies to for a dynamic link based
-# on the advisor's output.
+# Linux, and OSX but it should work on older versions.
+#
+# To use this module call the mklvars.(sh,bat) script before you call cmake. This
+# script is located in the bin folder of your mkl installation. This will set the
+# MKLROOT environment variable which will be used to find the libraries on your system.
+#
+# Example:
+# set(MKL_THREAD_LAYER "TBB")
+# find_package(MKL)
+#
+# add_executable(myapp main.cpp)
+# target_link_libraries(myapp PRIVATE MKL::MKL)
+#
+# This module bases its behavior based on the following variables:
+#
+# ``MKL_THREAD_LAYER``
+#   The threading layer that needs to be used by the MKL library. This
+#   Defines which library will be used to parallelize the MKL kernels. Possible
+#   options are TBB(Default), GNU OpenMP, Intel OpenMP, Sequential
+#
+# This module provides the following :prop_tgt:'IMPORTED' targets:
+#
+# ``MKL::MKL``
+#   Target used to define and link all MKL libraries required by Intel's Link
+#   Line Advisor. This usually the only thing you need to link against unless
+#   you want to link against the single dynamic library version of MKL
+#   (libmkl_rt.so)
+#
+#  Optional:
+#
+# ``MKL::ThreadLayer{_STATIC}``
+#   Target used to define the threading layer(TBB, OpenMP, etc.) based on
+#   MKL_THREAD_LAYER variable.
+#
+# ``MKL::ThreadingLibrary``
+#   Target used to define the threading library(libtbb, libomp, etc) that the
+#   application will need to link against.
+#
+# ``MKL::Interface``
+#   Target used to determine which interface library to use(32bit int or 64bit
+#   int).
+#
+# ``MKL::Core``
+#   Target for the dynamic library dispatcher
+#
+# ``MKL::RT``
+#   Target for the single dynamic library
+#
+# ``MKL::{mkl_def;mkl_mc;mkl_mc3;mkl_avx;mkl_avx2;mkl_avx512}{_STATIC}``
+#   Targets for MKL kernel libraries.
+
 include(CheckTypeSize)
 
 check_type_size("int" INT_SIZE

From 542c074d697f0819cc17b2b8254ac19d0667c17d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Jul 2018 12:34:27 +0530
Subject: [PATCH 1469/2677] Fix grid based indexing calculation for histogram

---
 src/api/c/hist.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 81c8fab0d5..aca215baef 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -111,7 +111,8 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
         // Window's draw function requires either image or chart
         if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second, props->col * gridDims.first + props->row,
+            window->draw(gridDims.first, gridDims.second,
+                         props->row * gridDims.second + props->col,
                          *chart, props->title);
         else
             window->draw(*chart);

From 046719479ce665f6f545dbe86b63b5444ca4cc71 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 6 Jul 2018 03:11:49 -0400
Subject: [PATCH 1470/2677] Fix compile errors in gcc v8.1.

---
 src/backend/cpu/sparse_blas.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 417b6bec6f..db2ce59288 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -223,7 +223,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     static const int rColDim = 1;
 
     dim4 lDims = lhs.dims();
-    dim4 rDims = rhs.dims();
+    const dim4 rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
     //int K = lDims[lColDim];
@@ -453,7 +453,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     static const int rColDim = 1;
 
     auto lDims = lhs.dims();
-    auto rDims = rhs.dims();
+    const auto rDims = rhs.dims();
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
 

From 1e5f746b4ecd93ae2cdd16e3784c6801334bbc5b Mon Sep 17 00:00:00 2001
From: mlloreda <miguel@arrayfire.com>
Date: Fri, 8 Jun 2018 18:15:28 -0400
Subject: [PATCH 1471/2677] Improved RPM installer.

- Fixed requires list
- Minor fixes to CPackConfig
---
 CMakeModules/CPackConfig.cmake | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 70356b01af..dbcf4cb5ee 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -3,7 +3,7 @@
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
+# https://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.5)
 
@@ -19,7 +19,7 @@ mark_as_advanced(CPACK_GENERATOR)
 set(VENDOR_NAME "ArrayFire")
 set(LIBRARY_NAME ${PROJECT_NAME})
 string(TOLOWER "${LIBRARY_NAME}" APP_LOW_NAME)
-set(SITE_URL "www.arrayfire.com")
+set(SITE_URL "https://arrayfire.com")
 
 # Long description of the package
 set(CPACK_PACKAGE_DESCRIPTION
@@ -312,10 +312,15 @@ set(CPACK_DEBIAN_PACKAGE_HOMEPAGE http://www.arrayfire.com)
 ##
 # RPM package
 ##
-set(CPACK_RPM_PACKAGE_LICENSE "BSD")
+set(CPACK_RPM_PACKAGE_ARCHITECTURE "x86_64")
 set(CPACK_RPM_PACKAGE_AUTOREQPROV " no")
+set(CPACK_RPM_PACKAGE_GROUP "Development/Libraries")
+set(CPACK_RPM_PACKAGE_LICENSE "BSD")
+set(CPACK_RPM_PACKAGE_URL "${SITE_URL}")
+if(AF_WITH_GRAPHICS)
+    set(CPACK_RPM_PACKAGE_REQUIRES "fontconfig-devel, libX11, libXrandr, libXinerama, libXxf86vm, libXcursor, mesa-libGL-devel")
+endif()
 
-set(CPACK_PACKAGE_GROUP "Development/Libraries")
 ##
 # Source package
 ##

From eb35ecbf279893f54dfe2f94a7eef2404fcea710 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Tue, 10 Jul 2018 11:53:05 -0500
Subject: [PATCH 1472/2677] Modified lib destinations for installers:

- Now installing AF libs to `lib64` instead of `lib`.
- Installing dependencies to the same location as AF libs -
  $AF_INSTALL_LIB_DIR (lib64).
---
 CMakeLists.txt                    | 8 ++++----
 CMakeModules/AFInstallDirs.cmake  | 8 +++++++-
 src/backend/common/CMakeLists.txt | 2 +-
 src/backend/cuda/CMakeLists.txt   | 4 ++--
 src/backend/opencl/CMakeLists.txt | 2 +-
 5 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7975a3c1c1..cbd7892dab 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -231,7 +231,7 @@ if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
       set(fg_dlib_px "lib")
     endif ()
     install(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/${fg_dlib_px}/"
-      DESTINATION "${AF_INSTALL_BIN_DIR}"
+      DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
 endif()
 
@@ -293,14 +293,14 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL AND AF_INSTALL_STANDALONE
   if(TARGET MKL::ThreadingLibrary)
     install(FILES
       $<TARGET_FILE:MKL::ThreadingLibrary>
-      DESTINATION ${AF_INSTALL_BIN_DIR}
+      DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT mkl_dependencies)
   endif()
 
   if(NOT WIN32)
     install(FILES
       $<TARGET_FILE:MKL::Interface>
-      DESTINATION ${AF_INSTALL_BIN_DIR}
+      DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT mkl_dependencies)
   endif()
 
@@ -313,7 +313,7 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL AND AF_INSTALL_STANDALONE
     # is a linker script and not a symlink so it cant be resolved using
     # get_filename_component
     ${AF_ADDITIONAL_MKL_LIBRARIES}
-    DESTINATION ${AF_INSTALL_BIN_DIR}
+    DESTINATION ${AF_INSTALL_LIB_DIR}
     COMPONENT mkl_dependencies)
 endif()
 
diff --git a/CMakeModules/AFInstallDirs.cmake b/CMakeModules/AFInstallDirs.cmake
index c2e4e4fec8..2c7b96eaf8 100644
--- a/CMakeModules/AFInstallDirs.cmake
+++ b/CMakeModules/AFInstallDirs.cmake
@@ -2,6 +2,8 @@
 # Sets ArrayFire installation paths.
 #
 
+include(GNUInstallDirs)
+
 # NOTE: These paths are all relative to the project installation prefix.
 
 # Executables
@@ -11,7 +13,11 @@ endif()
 
 # Libraries
 if(NOT DEFINED AF_INSTALL_LIB_DIR)
-  set(AF_INSTALL_LIB_DIR "lib" CACHE PATH "Installation path for libraries")
+  if(WIN32)
+    set(AF_INSTALL_LIB_DIR "lib" CACHE PATH "Installation path for libraries")
+  else()
+    set(AF_INSTALL_LIB_DIR "${CMAKE_INSTALL_LIBDIR}" CACHE PATH "Installation path for libraries")
+  endif()
 endif()
 
 # Header files
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index dce6460821..cbd378e2d4 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -103,7 +103,7 @@ if(AF_WITH_GRAPHICS)
         $<TARGET_FILE:glbinding::glbinding>
         $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
         $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
-      DESTINATION ${AF_INSTALL_BIN_DIR}
+      DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT common_backend_dependencies)
   endif()
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 13a01e093c..48a6470880 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -538,7 +538,7 @@ macro(afcu_collect_libs libname)
   else () #UNIX
     get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}${SX}" REALPATH)
     install(FILES       ${outpath}
-            DESTINATION ${AF_INSTALL_BIN_DIR}
+            DESTINATION ${AF_INSTALL_LIB_DIR}
             RENAME      "${PX}${libname}${SX}.${CUDA_VERSION}"
             COMPONENT   cuda_dependencies)
   endif ()
@@ -562,7 +562,7 @@ if(AF_INSTALL_STANDALONE)
   elseif(UNIX)
     get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
     install(FILES       ${nvrtc_outpath}
-            DESTINATION ${AF_INSTALL_BIN_DIR}
+            DESTINATION ${AF_INSTALL_LIB_DIR}
             RENAME      "${PX}nvrtc-builtins${SX}"
             COMPONENT   cuda_dependencies)
   else()
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 741765974e..9c7d4ac44c 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -540,7 +540,7 @@ if(NOT APPLE AND AF_INSTALL_STANDALONE)
   if(UNIX)
     get_filename_component(opencl_outpath "${OpenCL_LIBRARIES}" REALPATH)
     install(FILES ${opencl_outpath}
-        DESTINATION ${AF_INSTALL_BIN_DIR}
+        DESTINATION ${AF_INSTALL_LIB_DIR}
         RENAME "${CMAKE_SHARED_LIBRARY_PREFIX}OpenCL${CMAKE_SHARED_LIBRARY_SUFFIX}.1"
         COMPONENT opencl_dependencies)
   else()

From e6bbbcf047778c2088abea2b7e825a7000599888 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Jul 2018 11:34:58 -0400
Subject: [PATCH 1473/2677] Remove buffer alloc calls from sparse. Pass params
 by value

---
 src/backend/opencl/kernel/scan_dim.hpp        | 12 +++---
 src/backend/opencl/kernel/scan_dim_by_key.hpp |  2 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    | 30 ++++++-------
 src/backend/opencl/kernel/sort.hpp            | 30 +++----------
 src/backend/opencl/kernel/sparse.hpp          | 43 ++++---------------
 5 files changed, 35 insertions(+), 82 deletions(-)

diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 9ebcbd02dc..7b65f2feaf 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -97,9 +97,9 @@ namespace kernel
     }
 
     template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_launcher(Param &out,
-                                  Param &tmp,
-                                  const Param &in,
+    static void scan_dim_launcher(Param out,
+                                  Param tmp,
+                                  const Param in,
                                   int dim, bool isFinalPass, uint threads_y,
                                   const uint groups_all[4])
     {
@@ -126,8 +126,8 @@ namespace kernel
     }
 
     template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_dim_launcher(Param &out,
-                                   Param &tmp,
+    static void bcast_dim_launcher(Param out,
+                                   Param tmp,
                                    int dim, bool isFinalPass, uint threads_y,
                                    const uint groups_all[4])
     {
@@ -152,7 +152,7 @@ namespace kernel
     }
 
     template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_dim(Param &out, const Param &in, int dim)
+    static void scan_dim(Param out, const Param in, int dim)
     {
         uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
         uint threads_x = THREADS_X;
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
index b77cd434f3..2f84509d1c 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -17,6 +17,6 @@ namespace opencl
 namespace kernel
 {
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_dim(Param &out, const Param &in, const Param &key, int dim);
+    void scan_dim(Param out, const Param in, const Param key, int dim);
 }
 }
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 858c6a3a2f..5075685b01 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -101,12 +101,12 @@ namespace kernel
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_nonfinal_launcher(Param &out,
-                                  Param &tmp,
-                                  Param &tmpflg,
-                                  Param &tmpid,
-                                  const Param &in,
-                                  const Param &key,
+    static void scan_dim_nonfinal_launcher(Param out,
+                                  Param tmp,
+                                  Param tmpflg,
+                                  Param tmpid,
+                                  const Param in,
+                                  const Param key,
                                   int dim, uint threads_y,
                                   const uint groups_all[4])
     {
@@ -139,9 +139,9 @@ namespace kernel
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_final_launcher(Param &out,
-                                  const Param &in,
-                                  const Param &key,
+    static void scan_dim_final_launcher(Param out,
+                                  const Param in,
+                                  const Param key,
                                   int dim, const bool calculateFlags, uint threads_y,
                                   const uint groups_all[4])
     {
@@ -167,9 +167,9 @@ namespace kernel
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_dim_launcher(Param &out,
-                                   Param &tmp,
-                                   Param &tmpid,
+    static void bcast_dim_launcher(Param out,
+                                   Param tmp,
+                                   Param tmpid,
                                    int dim, uint threads_y,
                                    const uint groups_all[4])
     {
@@ -195,7 +195,7 @@ namespace kernel
     }
 
     template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_dim(Param &out, const Param &in, const Param &key, int dim)
+    void scan_dim(Param out, const Param in, const Param key, int dim)
     {
         uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
         uint threads_x = THREADS_X;
@@ -264,8 +264,8 @@ namespace kernel
 }
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)\
-    template void scan_dim<Ti, Tk, To, ROp,  true>(Param &out, const Param &in, const Param &key, int dim);\
-    template void scan_dim<Ti, Tk, To, ROp, false>(Param &out, const Param &in, const Param &key, int dim);
+    template void scan_dim<Ti, Tk, To, ROp,  true>(Param out, const Param in, const Param key, int dim);\
+    template void scan_dim<Ti, Tk, To, ROp, false>(Param out, const Param in, const Param key, int dim);
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 3ced4e1e23..1c422acfe2 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -89,30 +89,15 @@ namespace opencl
             seqDims[dim] = 1;
 
             // Create/call iota
-            // Array<uint> key = iota<uint>(seqDims, tileDims);
-            dim4 keydims = inDims;
-            cl::Buffer* key = bufferAlloc(keydims.elements() * sizeof(uint));
-            Param pKey;
-            pKey.data = key;
-            pKey.info.offset = 0;
-            pKey.info.dims[0] = keydims[0];
-            pKey.info.strides[0] = 1;
-            for(int i = 1; i < 4; i++) {
-                pKey.info.dims[i] = keydims[i];
-                pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
-            }
+            Array<uint> pKey = createEmptyArray<uint>(inDims);
             kernel::iota<uint>(pKey, seqDims, tileDims);
 
+            pKey.setDataDims(inDims.elements());
+
             // Flat
-            //val.modDims(inDims.elements());
-            //key.modDims(inDims.elements());
-            pKey.info.dims[0] = inDims.elements();
-            pKey.info.strides[0] = 1;
             pVal.info.dims[0] = inDims.elements();
             pVal.info.strides[0] = 1;
             for(int i = 1; i < 4; i++) {
-                pKey.info.dims[i] = 1;
-                pKey.info.strides[i] = pKey.info.strides[i - 1] * pKey.info.dims[i - 1];
                 pVal.info.dims[i] = 1;
                 pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
             }
@@ -122,13 +107,13 @@ namespace opencl
             //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
             compute::command_queue c_queue(getQueue()());
 
-            compute::buffer pKey_buf((*pKey.data)());
+            compute::buffer pKey_buf((*pKey.get())());
             compute::buffer pVal_buf((*pVal.data)());
 
             compute::buffer_iterator<type_t<T> > val0 = compute::make_buffer_iterator<type_t<T> >(pVal_buf, 0);
             compute::buffer_iterator<type_t<T> > valN = compute::make_buffer_iterator<type_t<T> >(pVal_buf,+ pVal.info.dims[0]);
             compute::buffer_iterator<uint      > key0 = compute::make_buffer_iterator<uint>(pKey_buf, 0);
-            compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.info.dims[0]);
+            compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.dims()[0]);
             if(isAscending) {
                 compute::sort_by_key(val0, valN, key0, c_queue);
             } else {
@@ -139,12 +124,7 @@ namespace opencl
             //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
             compute::sort_by_key(key0, keyN, val0, c_queue);
 
-            // No need of doing moddims here because the original Array<T>
-            // dimensions have not been changed
-            //val.modDims(inDims);
-
             CL_DEBUG_FINISH(getQueue());
-            bufferFree(key);
         }
 
         template<typename T>
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 5f1efe076b..c098fe0067 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -149,43 +149,19 @@ namespace opencl
             int num_rows = dense.info.dims[0];
             int num_cols = dense.info.dims[1];
             int dense_elements = num_rows * num_cols;
-            Param sd1, rd1, sd0;
+
             // sd1 contains output of scan along dim 1 of dense
-            sd1.data = bufferAlloc(dense_elements * sizeof(int));
+            Array<int> sd1 = createEmptyArray<int>(dim4(num_rows, num_cols));
             // rd1 contains output of nonzero count along dim 1 along dense
-            rd1.data = bufferAlloc(num_rows * sizeof(int));
-            // sd0 contains output of exclusive scan rd1
-            sd0 = rowIdx;
-
-            sd1.info.offset = 0;
-            rd1.info.offset = 0;
-
-            sd1.info.dims[0] = num_rows;
-            rd1.info.dims[0] = num_rows;
-
-            sd1.info.dims[1] = num_cols;
-            rd1.info.dims[1] = 1;
-
-            sd1.info.dims[2] = 1;
-            rd1.info.dims[2] = 1;
-
-            sd1.info.dims[3] = 1;
-            rd1.info.dims[3] = 1;
-
-            sd1.info.strides[0] = 1;
-            rd1.info.strides[0] = 1;
-            for (int i = 1; i < 4; i++) {
-                sd1.info.strides[i] = sd1.info.dims[i - 1] * sd1.info.strides[i - 1];
-                rd1.info.strides[i] = rd1.info.dims[i - 1] * rd1.info.strides[i - 1];
-            }
+            Array<int> rd1 = createEmptyArray<int>(num_rows);
 
             scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
             reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
-            scan_first<int, int, af_add_t, false>(sd0, rd1);
+            scan_first<int, int, af_add_t, false>(rowIdx, rd1);
 
             int nnz = values.info.dims[0];
-            getQueue().enqueueWriteBuffer(*sd0.data, CL_TRUE,
-                                          sd0.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
+            getQueue().enqueueWriteBuffer(*rowIdx.data, CL_TRUE,
+                                          rowIdx.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
                                           sizeof(int),
                                           (void *)&nnz);
 
@@ -234,13 +210,10 @@ namespace opencl
             dense2csr_split(EnqueueArgs(getQueue(), global, local),
                             *values.data, *colIdx.data,
                             *dense.data, dense.info,
-                            *sd1.data, sd1.info,
-                            *sd0.data);
+                            *sd1.get(), sd1,
+                            *rowIdx.data);
 
             CL_DEBUG_FINISH(getQueue());
-
-            bufferFree(rd1.data);
-            bufferFree(sd1.data);
         }
 
         template<typename T>

From 1015a526babe3c8e62804718a72920fe9f746261 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 12 Jul 2018 12:59:37 -0400
Subject: [PATCH 1474/2677] Add the option to run a test by itself. Useful for
 parallel tests

---
 test/CMakeLists.txt | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 51e1a98fa0..eb80491b35 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -74,7 +74,7 @@ include(CMakeParseArguments)
 # 'BACKENDS'    Backends to target for this test. If not set then the test will
 #               compiled againat all backends
 function(make_test)
-  set(options CXX11)
+  set(options CXX11 SERIAL)
   set(single_args SRC)
   set(multi_args LIBRARIES DEFINITIONS BACKENDS)
   cmake_parse_arguments(mt_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
@@ -140,7 +140,13 @@ function(make_test)
     # TODO(umar): Create this executable separately
     if(NOT ${backend} STREQUAL "unified" OR ${target} STREQUAL "backend_unified")
       add_test(NAME ${target} COMMAND ${target})
+	    if(${mt_args_SERIAL})
+        set_tests_properties(${target}
+          PROPERTIES
+            RUN_SERIAL ON)
+	    endif(${mt_args_SERIAL})
     endif()
+
   endforeach()
 endfunction(make_test)
 
@@ -259,7 +265,7 @@ make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
 make_test(SRC susan.cpp)
 make_test(SRC svd_dense.cpp)
-make_test(SRC threading.cpp         CXX11)
+make_test(SRC threading.cpp         CXX11 SERIAL)
 make_test(SRC tile.cpp)
 make_test(SRC topk.cpp              CXX11)
 make_test(SRC transform.cpp)

From d88f626d2d0550d0bb42d90b8f9e4f09fa5b9b3b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 26 Jul 2017 20:36:40 +0530
Subject: [PATCH 1475/2677] FEAT: Iterative & Inverse Deconvolution Fns

The following iterative algorithms have been added
* Landweber - Tests PASS
* RichardsonLucy - Tests PASS

The following linear inverse algorithms have been added
* Tikhonov - Tests PASS

Wiener Filter  - Tests FAIL - Disabled temporarily, The function
is also not exposed at the moment.

An example demonstrating both iterative and inverse deconvolution
has been added as well.

Unit tests using grayscale images have also been added.
---
 docs/details/image.dox                        | 105 +++++-
 examples/image_processing/CMakeLists.txt      |  12 +
 examples/image_processing/deconvolution.cpp   |  71 ++++
 include/af/defines.h                          |  20 +-
 include/af/image.h                            |  88 +++++
 src/api/c/CMakeLists.txt                      |   1 +
 src/api/c/deconvolution.cpp                   | 321 ++++++++++++++++++
 src/api/cpp/CMakeLists.txt                    |   1 +
 src/api/cpp/deconvolution.cpp                 |  32 ++
 src/api/unified/image.cpp                     |  17 +-
 src/backend/common/dispatch.hpp               |  36 ++
 src/backend/cpu/CMakeLists.txt                |   1 +
 src/backend/cpu/copy.hpp                      |  31 +-
 src/backend/cpu/kernel/pad_array_borders.hpp  | 149 ++++++++
 src/backend/cpu/padarray.cpp                  |  33 +-
 src/backend/cuda/CMakeLists.txt               |   2 +
 src/backend/cuda/copy.hpp                     |   7 +-
 src/backend/cuda/kernel/pad_array_borders.hpp | 137 ++++++++
 src/backend/cuda/pad_array_borders.cu         |  54 +++
 src/backend/opencl/CMakeLists.txt             |   1 +
 src/backend/opencl/copy.hpp                   |  35 +-
 .../opencl/kernel/pad_array_borders.cl        |  93 +++++
 .../opencl/kernel/pad_array_borders.hpp       |  83 +++++
 test/CMakeLists.txt                           |   2 +
 test/inverse_deconv.cpp                       | 135 ++++++++
 test/iterative_deconv.cpp                     | 135 ++++++++
 test/testHelpers.hpp                          |   3 +-
 27 files changed, 1579 insertions(+), 26 deletions(-)
 create mode 100644 examples/image_processing/deconvolution.cpp
 create mode 100644 src/api/c/deconvolution.cpp
 create mode 100644 src/api/cpp/deconvolution.cpp
 create mode 100644 src/backend/cpu/kernel/pad_array_borders.hpp
 create mode 100644 src/backend/cuda/kernel/pad_array_borders.hpp
 create mode 100644 src/backend/cuda/pad_array_borders.cu
 create mode 100644 src/backend/opencl/kernel/pad_array_borders.cl
 create mode 100644 src/backend/opencl/kernel/pad_array_borders.hpp
 create mode 100644 test/inverse_deconv.cpp
 create mode 100644 test/iterative_deconv.cpp

diff --git a/docs/details/image.dox b/docs/details/image.dox
index d4d629ff66..1ad64c149b 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -908,6 +908,109 @@ double y_center = m01 / m00;
 The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a
 wide range of edges in images. A more in depth discussion on it can be found [here](https://en.wikipedia.org/wiki/Canny_edge_detector).
 
+=======================================================================
+
+\defgroup image_func_iterative_deconv Iterative Deconvolutions
+\ingroup imageflt_mat
+
+Iterative Deconvolution Algorithms
+
+The following table shows the iteration update equations of the respective
+deconvolution algorithms.
+
+<table>
+<tr><th>Algorithm</th><th>Update Equation</th></tr>
+<tr>
+  <td>VanCittert</td>
+  <td>
+    \f$ \hat{I}_{n} = \hat{I}_{n-1} + \alpha * (I - P \otimes \hat{I}_{n-1}) \f$
+  </td>
+</tr>
+<tr>
+  <td>Jansson-VanCittert</td>
+  <td>
+    \f$ \hat{I}_{n} = \hat{I}_{n-1} + \alpha * (1 - \frac{2*| \hat{I}_{n-1}-\frac{B}{2} |}{B}) * (I - P \otimes \hat{I}_{n-1}) \f$
+  </td>
+</tr>
+<tr>
+    <td>LandWeber</td>
+    <td>
+        \f$ \hat{I}_{n} = \hat{I}_{n-1} + \alpha * P^T \otimes (I - P \otimes \hat{I}_{n-1}) \f$
+    </td>
+</tr>
+</table>
+
+where
+    - \f$ I \f$ is the observed(input/blurred) image
+    - \f$ P \f$ is the point spread function
+    - \f$ P^T \f$ is the transpose of point spread function
+    - \f$ \hat{I}_{n} \f$ is the current iteration's updated image estimate
+    - \f$ \hat{I}_{n-1} \f$ is the previous iteration's image estimate
+    - \f$ \alpha \f$ is the relaxation factor
+    - \f$ \otimes \f$ indicates the convolution operator
+
+Iterative deconvolution function excepts \ref af::array of the following types only:
+    - \ref f32
+    - \ref s16
+    - \ref u16
+    - \ref u8
+
+\note The type of output \ref af::array from deconvolution will be double if
+the input array type is double. For other types, output type will be float.
+Should the caller want to save the image to disk or require the values of output
+to be in a fixed range, that should be done by the caller explicitly.
+
+=======================================================================
+
+\defgroup image_func_inverse_deconv Inverse Deconvolution
+\ingroup imageflt_mat
+
+Inverse deconvolution is an linear algorithm i.e. they are non-iterative in
+nature and usually faster than iterative deconvolution algorithms.
+
+Depending on the values passed on to the enum \ref af_inverse_deconv_algo,
+different equations are used to compute the final result.
+
+#### Tikhonov's Deconvolution Method:
+
+The update equation for this algorithm is as follows:
+
+\f[
+\hat{I}_{\omega} = \frac{ I_{\omega} * P^{*}_{\omega} } { |P_{\omega}|^2 + \gamma }
+\f]
+
+where
+    - \f$ I_{\omega} \f$ is the observed(input/blurred) image in frequency domain
+    - \f$ P_{\omega} \f$ is the point spread function in frequency domain
+    - \f$ \gamma \f$ is a user defined regularization constant
+
+#### Weiner's Deconvolution Method:
+
+The update equation for this algorithm is as follows:
+
+\f[
+\hat{I}_{\omega} = \frac{ I_{\omega} * P^{*}_{\omega} } { |P_{\omega}|^2 + \frac{\gamma}{|I_{\omega}|^2 - \gamma} }
+\f]
+
+where
+    - \f$ I_{\omega} \f$ is the input/blurred image in frequency domain
+    - \f$ P_{\omega} \f$ is the point spread function in frequency domain
+    - \f$ \gamma \f$ is a user defined noise variance constant
+
+
+Inverse deconvolution function excepts \ref af::array of the following types only:
+    - \ref f32
+    - \ref s16
+    - \ref u16
+    - \ref u8
+
+\note The type of output \ref af::array from deconvolution will be double
+if the input array type is double. Otherwise, it will be float in rest of
+the cases. Should the caller want to save the image to disk or require the
+values of output to be in a fixed range, that should be done by the caller
+explicitly.
+
+=======================================================================
+
 @}
 */
-
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index d6d921ad19..3e450911dd 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -58,6 +58,10 @@ if(ArrayFire_CPU_FOUND)
   # Gradient anisotropic diffusion example
   add_executable(gradient_diffusion_cpu gradient_diffusion.cpp)
   target_link_libraries(gradient_diffusion_cpu ArrayFire::afcpu)
+
+  #Image Deconvolution Example
+  add_executable(deconvolution_cpu deconvolution.cpp)
+  target_link_libraries(deconvolution_cpu ArrayFire::afcpu)
 endif()
 
 if(ArrayFire_CUDA_FOUND)
@@ -94,6 +98,10 @@ if(ArrayFire_CUDA_FOUND)
   # Gradient anisotropic diffusion example
   add_executable(gradient_diffusion_cuda gradient_diffusion.cpp)
   target_link_libraries(gradient_diffusion_cuda ArrayFire::afcuda)
+
+  #Image Deconvolution Example
+  add_executable(deconvolution_cuda deconvolution.cpp)
+  target_link_libraries(deconvolution_cuda ArrayFire::afcuda)
 endif()
 
 if(ArrayFire_OpenCL_FOUND)
@@ -130,4 +138,8 @@ if(ArrayFire_OpenCL_FOUND)
   # Gradient anisotropic diffusion example
   add_executable(gradient_diffusion_opencl gradient_diffusion.cpp)
   target_link_libraries(gradient_diffusion_opencl ArrayFire::afopencl)
+
+  #Image Deconvolution Example
+  add_executable(deconvolution_opencl deconvolution.cpp)
+  target_link_libraries(deconvolution_opencl ArrayFire::afopencl)
 endif()
diff --git a/examples/image_processing/deconvolution.cpp b/examples/image_processing/deconvolution.cpp
new file mode 100644
index 0000000000..5479cbac6c
--- /dev/null
+++ b/examples/image_processing/deconvolution.cpp
@@ -0,0 +1,71 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <stdio.h>
+#include <arrayfire.h>
+#include <af/util.h>
+#include <cstdlib>
+
+using namespace af;
+
+const unsigned ITERATIONS = 96;
+const float RELAXATION_FACTOR = 0.05f;
+
+array normalize(const array &in)
+{
+    float mx = max<float>(in.as(f32));
+    float mn = min<float>(in.as(f32));
+    return (in-mn)/(mx-mn);
+}
+
+int main(int argc, char* argv[])
+{
+    int device = argc > 1 ? atoi(argv[1]) : 0;
+
+    try {
+        af::setDevice(device);
+        af::info();
+
+        printf("** ArrayFire Image Deconvolution Demo **\n");
+        af::Window myWindow("Image Deconvolution");
+
+        array in       = loadImage(ASSETS_DIR "/examples/images/house.jpg",
+                                   false);
+        array kernel   = gaussianKernel(13, 13, 2.25, 2.25);
+        array blurred  = convolve(in, kernel);
+        array tikhonov = inverseDeconv(blurred, kernel, 0.05,
+                                       AF_INVERSE_DECONV_TIKHONOV);
+
+        array landweber  = iterativeDeconv(blurred, kernel,
+                ITERATIONS, RELAXATION_FACTOR,
+                AF_ITERATIVE_DECONV_LANDWEBER);
+
+        array richlucy   = iterativeDeconv(blurred, kernel,
+                ITERATIONS, RELAXATION_FACTOR,
+                AF_ITERATIVE_DECONV_RICHARDSONLUCY);
+
+        while(!myWindow.close()) {
+            myWindow.grid(2, 3);
+
+            myWindow(0, 0).image(normalize(in       ), "Input Image"    );
+            myWindow(1, 0).image(normalize(blurred  ), "Blurred Image"  );
+            myWindow(0, 1).image(normalize(tikhonov ), "Tikhonov"       );
+            myWindow(1, 1).image(normalize(landweber), "Landweber"      );
+            myWindow(0, 2).image(normalize(richlucy ), "Richardson-Lucy");
+
+            myWindow.show();
+        }
+
+    } catch (af::exception &e) {
+        fprintf(stderr, "%s\n", e.what());
+        throw;
+    }
+
+    return 0;
+}
diff --git a/include/af/defines.h b/include/af/defines.h
index c3b9296a9e..587a3788bc 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -254,7 +254,12 @@ typedef enum {
     ///
     /// Out of bound values are symmetric over the edge
     ///
-    AF_PAD_SYM
+    AF_PAD_SYM,
+
+    ///
+    /// Out of bound values are clamped to the edge
+    ///
+    AF_PAD_CLAMP_TO_EDGE,
 } af_border_type;
 
 typedef enum {
@@ -484,6 +489,17 @@ typedef enum {
     AF_TOPK_MAX     = 2,  ///< Top k max values
     AF_TOPK_DEFAULT = 0   ///< Default option (max)
 } af_topk_function;
+
+typedef enum {
+    AF_ITERATIVE_DECONV_LANDWEBER       = 1,        ///< Landweber Deconvolution
+    AF_ITERATIVE_DECONV_RICHARDSONLUCY  = 2,        ///< Richardson-Lucy Deconvolution
+    AF_ITERATIVE_DECONV_DEFAULT         = 0,        ///< Default is Landweber deconvolution
+} af_iterative_deconv_algo;
+
+typedef enum {
+    AF_INVERSE_DECONV_TIKHONOV       = 1,        ///< Tikhonov Inverse deconvolution
+    AF_INVERSE_DECONV_DEFAULT        = 0,        ///< Default is Tikhonov deconvolution
+} af_inverse_deconv_algo;
 #endif
 
 #ifdef __cplusplus
@@ -534,6 +550,8 @@ namespace af
     typedef af_flux_function fluxFunction;
     typedef af_diffusion_eq diffusionEq;
     typedef af_topk_function topkFunction;
+    typedef af_iterative_deconv_algo iterativeDeconvAlgo;
+    typedef af_inverse_deconv_algo inverseDeconvAlgo;
 #endif
 }
 
diff --git a/include/af/image.h b/include/af/image.h
index 05dd4fe81d..6acdd33d7c 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -732,6 +732,45 @@ AFAPI array anisotropicDiffusion(const af::array& in, const float timestep,
                                  const float conductance, const unsigned iterations,
                                  const fluxFunction fftype=AF_FLUX_EXPONENTIAL,
                                  const diffusionEq diffusionKind=AF_DIFFUSION_GRAD);
+
+/**
+  C++ Interface for Iterative deconvolution algorithm
+
+  \param[in] in is the blurred input image
+  \param[in] ker is the kernel(point spread function) known to have caused
+             the blur in the system
+  \param[in] iterations is the number of iterations the algorithm will run
+  \param[in] relaxFactor is the relaxation factor multiplied with distance
+             of estimate from observed image.
+  \param[in] algo takes value of type enum \ref af_iterative_deconv_algo
+             indicating the iterative deconvolution algorithm to be used
+  \return sharp image estimate generated from the blurred input
+
+  \note \p relax_factor argument is ignore when it
+  \ref AF_ITERATIVE_DECONV_RICHARDSONLUCY algorithm is used.
+
+  \ingroup image_func_iterative_deconv
+ */
+AFAPI array iterativeDeconv(const array& in, const array& ker,
+                            const unsigned iterations, const float relaxFactor,
+                            const iterativeDeconvAlgo algo);
+
+/**
+   C++ Interface for Tikhonov deconvolution algorithm
+
+   \param[in] in is the blurred input image
+   \param[in] psf is the kernel(point spread function) known to have caused
+              the blur in the system
+   \param[in] gamma is a user defined regularization constant
+   \param[in] algo takes different meaning depending on the algorithm chosen.
+              If \p algo is AF_INVERSE_DECONV_TIKHONOV, then \p gamma is
+              a user defined regularization constant.
+   \return sharp image estimate generated from the blurred input
+
+   \ingroup image_func_inverse_deconv
+ */
+AFAPI array inverseDeconv(const array& in, const array& psf,
+                          const float gamma, const inverseDeconvAlgo algo);
 #endif
 }
 #endif
@@ -1477,6 +1516,55 @@ extern "C" {
                                           const af_diffusion_eq diffusion_kind);
 #endif
 
+#if AF_API_VERSION >= 36
+    /**
+       C Interface for Iterative deconvolution algorithm
+
+       \param[out] out is the sharp estimate generated from the blurred input
+       \param[in] in is the blurred input image
+       \param[in] ker is the kernel(point spread function) known to have caused
+                  the blur in the system
+       \param[in] iterations is the number of iterations the algorithm will run
+       \param[in] relax_factor is the relaxation factor multiplied with
+                  distance of estimate from observed image.
+       \param[in] algo takes value of type enum \ref af_iterative_deconv_algo
+                  indicating the iterative deconvolution algorithm to be used
+       \return \ref AF_SUCCESS if the deconvolution is successful,
+       otherwise an appropriate error code is returned.
+
+       \note \p relax_factor argument is ignore when it
+       \ref AF_ITERATIVE_DECONV_RICHARDSONLUCY algorithm is used.
+
+       \ingroup image_func_iterative_deconv
+     */
+    AFAPI af_err af_iterative_deconv(af_array* out,
+                                     const af_array in, const af_array ker,
+                                     const unsigned iterations,
+                                     const float relax_factor,
+                                     const af_iterative_deconv_algo algo);
+
+    /**
+       C Interface for Tikhonov deconvolution algorithm
+
+       \param[out] out is the sharp estimate generated from the blurred input
+       \param[in] in is the blurred input image
+       \param[in] psf is the kernel(point spread function) known to have caused
+                  the blur in the system
+       \param[in] gamma takes different meaning depending on the algorithm
+                  chosen. If \p algo is AF_INVERSE_DECONV_TIKHONOV, then
+                  \p gamma is a user defined regularization constant.
+       \param[in] algo takes value of type enum \ref af_inverse_deconv_algo
+                  indicating the inverse deconvolution algorithm to be used
+       \return \ref AF_SUCCESS if the deconvolution is successful,
+       otherwise an appropriate error code is returned.
+
+       \ingroup image_func_inverse_deconv
+     */
+    AFAPI af_err af_inverse_deconv(af_array* out, const af_array in,
+                                   const af_array psf, const float gamma,
+                                   const af_inverse_deconv_algo algo);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 9c7d0a3bfd..7ef32c0e84 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -64,6 +64,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/corrcoef.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/covariance.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/deconvolution.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/det.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/diff.cpp
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
new file mode 100644
index 0000000000..90fd5f65ed
--- /dev/null
+++ b/src/api/c/deconvolution.cpp
@@ -0,0 +1,321 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/image.h>
+#include <common/err_common.hpp>
+#include <backend.hpp>
+#include <Array.hpp>
+#include <arith.hpp>
+#include <cast.hpp>
+#include <complex.hpp>
+#include <common/dispatch.hpp>
+#include <fft_common.hpp>
+#include <fftconvolve.hpp>
+#include <handle.hpp>
+#include <reduce.hpp>
+#include <shift.hpp>
+#include <select.hpp>
+#include <logic.hpp>
+#include <unary.hpp>
+#include <copy.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <type_traits>
+#include <vector>
+
+using af::dim4;
+using namespace detail;
+
+const int BASE_DIM = 2;
+
+#if defined(AF_CPU)
+// CPU backend uses FFTW or MKL
+// FFTW works with any data size, but is optimized for
+// size decomposition with prime factors up to
+// 13.
+const dim_t GREATEST_PRIME_FACTOR = 13;
+#else
+// cuFFT/clFFT works with any data size, but is optimized
+// for size decomposition with prime factors up to
+// 7.
+const dim_t GREATEST_PRIME_FACTOR = 7;
+#endif
+
+template<typename T, typename CT>
+Array<T> complexNorm(const Array<CT>& input)
+{
+    auto mag   = abs<T, CT>(input);
+    auto TWOS  = createValueArray(input.dims(), scalar<T>(2));
+    return arithOp<T, af_pow_t>(mag, TWOS, input.dims());
+}
+
+std::vector<af_seq>
+calcPadInfo(dim4& inLPad, dim4& psfLPad,
+            dim4& inUPad, dim4& psfUPad,
+            dim4& odims, dim_t nElems,
+            const dim4& idims, const dim4& fdims)
+{
+    std::vector<af_seq> index(4);
+
+    for (int d=0; d<4; ++d) {
+        if (d<BASE_DIM) {
+            dim_t pad = idims[d] + fdims[d];
+
+            while (greatestPrimeFactor(pad) > GREATEST_PRIME_FACTOR) pad++;
+
+            dim_t diffLen  = pad - idims[d];
+            inLPad[d]      = diffLen/2;
+            inUPad[d]      = diffLen/2 + diffLen%2;
+            psfLPad[d]     = 0;
+            psfUPad[d]     = pad - fdims[d];
+            odims[d]       = pad;
+            index[d].begin = inLPad[d];
+            index[d].end   = index[d].begin + idims[d]-1;
+            index[d].step  = 1;
+
+            nElems *= odims[d];
+        } else {
+            inUPad[d]  = 0;
+            psfUPad[d] = 0;
+            odims[d]   = std::max(idims[d], fdims[d]);
+            index[d]   = af_span;
+        }
+    }
+    return index;
+}
+
+template<typename T, typename CT>
+void richardsonLucy(Array<T>& currentEstimate, const Array<T>& in,
+                    const Array<CT>& P, const Array<CT>& Pc,
+                    const unsigned iters, const float normFactor,
+                    const dim4 odims)
+{
+    for (unsigned i=0; i<iters; ++i) {
+        auto fft1  = fft_r2c<CT, T, BASE_DIM>(currentEstimate);
+        auto cmul1 = arithOp<CT, af_mul_t>(fft1, P, P.dims());
+        auto ifft1 = fft_c2r<CT, T, BASE_DIM>(cmul1, normFactor, odims);
+        auto div1  = arithOp<T, af_div_t>(in, ifft1, in.dims());
+        auto fft2  = fft_r2c<CT, T, BASE_DIM>(div1);
+        auto cmul2 = arithOp<CT, af_mul_t>(fft2, Pc, Pc.dims());
+        auto ifft2 = fft_c2r<CT, T, BASE_DIM>(cmul2, normFactor, odims);
+
+        currentEstimate = arithOp<T, af_mul_t>(currentEstimate, ifft2,
+                                               ifft2.dims());
+    }
+}
+
+template<typename T, typename CT>
+void landweber(Array<T>& currentEstimate, const Array<T>& in,
+               const Array<CT>& P, const Array<CT>& Pc,
+               const unsigned iters, const float relaxFactor,
+               const float normFactor, const dim4 odims)
+{
+    const dim4& dims = P.dims();
+
+    auto I        = fft_r2c<CT, T, BASE_DIM>(in);
+    auto Pn       = complexNorm<T, CT>(P);
+    auto ONE      = createValueArray(dims, scalar<T>(1.0));
+    auto alpha    = createValueArray(dims, scalar<T>(relaxFactor));
+    auto alphaC   = cast<CT>(alpha);
+    auto prod     = arithOp<T, af_mul_t>(alpha, Pn, dims);
+    auto lhsFac   = arithOp<T, af_sub_t>(ONE, prod, dims);
+    auto lhs      = cast<CT>(lhsFac);
+    auto rhsFac   = arithOp<CT, af_mul_t>(Pc, I, dims);
+    auto rhs      = arithOp<CT, af_mul_t>(rhsFac, alphaC, dims);
+    auto iterTemp = I;
+
+    for (unsigned i=0; i<iters; ++i) {
+        auto mul  = arithOp<CT, af_mul_t>(iterTemp, lhs, dims);
+        iterTemp  = arithOp<CT, af_add_t>(mul, rhs, dims);
+    }
+    currentEstimate = fft_c2r<CT, T, BASE_DIM>(iterTemp, normFactor, odims);
+}
+
+template<typename InputType, typename RealType=float>
+af_array iterDeconv(const af_array in, const af_array ker,
+                    const uint iters, const float rfactor,
+                    const af_iterative_deconv_algo algo)
+{
+    typedef RealType T;
+    using CT = typename std::conditional< std::is_same<T, double>::value,
+                                          cdouble,
+                                          cfloat
+                                        >::type;
+    auto input   = castArray<T>(in);
+    auto psf     = castArray<T>(ker);
+    const dim4& idims  = input.dims();
+    const dim4& fdims  = psf.dims();
+    dim_t nElems = 1;
+
+    dim4 inUPad, psfUPad, inLPad, psfLPad, odims(1);
+
+    auto index     = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad,
+                                 odims, nElems, idims, fdims);
+    auto paddedIn  = padArrayBorders<T>(input,  inLPad,  inUPad,
+                                        AF_PAD_CLAMP_TO_EDGE);
+    auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad, AF_PAD_ZERO);
+
+    const int shiftDims[4] = { -int(fdims[0]/2), -int(fdims[1]/2), 0, 0 };
+    auto shiftedPsf   = shift(paddedPsf, shiftDims);
+
+    auto P  = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
+    auto Pc = conj(P);
+
+    Array<T> currentEstimate = paddedIn;
+    const double normFactor  = 1/(double)nElems;
+
+    switch(algo) {
+        case AF_ITERATIVE_DECONV_RICHARDSONLUCY:
+            richardsonLucy(currentEstimate, paddedIn, P, Pc,
+                           iters, normFactor, odims); break;
+        default:
+            landweber(currentEstimate, paddedIn, P, Pc,
+                      iters, rfactor, normFactor, odims); break;
+    }
+    return getHandle(createSubArray<T>(currentEstimate, index));
+}
+
+af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
+                           const unsigned iterations, const float relax_factor,
+                           const af_iterative_deconv_algo algo)
+{
+    try {
+        const ArrayInfo& inputInfo  = getInfo(in);
+        const dim4& inputDims  = inputInfo.dims();
+        const ArrayInfo& kernelInfo = getInfo(ker);
+        const dim4& kernelDims = kernelInfo.dims();
+
+        DIM_ASSERT(2, (inputDims.ndims() == 2));
+        DIM_ASSERT(3, (kernelDims.ndims() == 2));
+        ARG_ASSERT(4, (iterations > 0));
+        ARG_ASSERT(5, std::isfinite(relax_factor));
+        ARG_ASSERT(5, (relax_factor > 0));
+        ARG_ASSERT(6, (algo==AF_ITERATIVE_DECONV_DEFAULT ||
+                       algo==AF_ITERATIVE_DECONV_LANDWEBER ||
+                       algo==AF_ITERATIVE_DECONV_RICHARDSONLUCY));
+        af_array res   = 0;
+        unsigned iters = iterations;
+        float rfac     = relax_factor;
+
+        af_dtype inputType  = inputInfo.getType();
+        switch(inputType) {
+            case f32: res = iterDeconv<float >(in,ker,iters,rfac,algo); break;
+            case s16: res = iterDeconv<short >(in,ker,iters,rfac,algo); break;
+            case u16: res = iterDeconv<ushort>(in,ker,iters,rfac,algo); break;
+            case u8:  res = iterDeconv<uchar >(in,ker,iters,rfac,algo); break;
+            default : TYPE_ERROR(1, inputType);
+        }
+        std::swap(res, *out);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+template<typename CT>
+Array<CT> denominator(const Array<CT>& I, const Array<CT>& P, const float gamma,
+                      const af_inverse_deconv_algo algo)
+{
+    typedef typename af::dtype_traits<CT>::base_type T;
+
+    auto RCNST  = createValueArray(I.dims(), scalar<T>(gamma));
+
+    if (algo==AF_INVERSE_DECONV_TIKHONOV) {
+        auto normP  = complexNorm<T, CT>(P);
+        auto denom  = arithOp<T, af_add_t>(normP, RCNST, normP.dims());
+
+        return cast<CT, T>(denom);
+    } else {
+        //TODO(pradeep) Wiener Filter code path is disabled.
+        // This code path doesn't is not exposed using current API
+        auto normI  = complexNorm<T, CT>(I);
+        auto sRes   = arithOp<T, af_sub_t>(normI, RCNST, normI.dims());
+        auto dRes   = arithOp<T, af_div_t>(RCNST,  sRes, RCNST.dims());
+        auto normP  = complexNorm<T, CT>(P);
+        auto denom  = arithOp<T, af_add_t>(normP,  dRes, normP.dims());
+
+        return cast<CT, T>(denom);
+    }
+}
+
+template<typename InputType, typename RealType=float>
+af_array invDeconv(const af_array in, const af_array ker, const float gamma,
+                   const af_inverse_deconv_algo algo)
+{
+    typedef RealType T;
+    using CT = typename std::conditional< std::is_same<T, double>::value,
+                                          cdouble,
+                                          cfloat
+                                        >::type;
+    auto input = castArray<T>(in);
+    auto psf   = castArray<T>(ker);
+    const dim4& idims  = input.dims();
+    const dim4& fdims  = psf.dims();
+    dim_t nElems = 1;
+
+    dim4 inUPad, psfUPad, inLPad, psfLPad, odims(1);
+
+    auto index     = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad,
+                                 odims, nElems, idims, fdims);
+
+    auto paddedIn  = padArrayBorders<T>(input,  inLPad,  inUPad,
+                                        AF_PAD_CLAMP_TO_EDGE);
+    auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad,
+                                        AF_PAD_ZERO);
+    const int shiftDims[4] = { -int(fdims[0]/2), -int(fdims[1]/2), 0, 0};
+
+    auto shiftedPsf = shift(paddedPsf, shiftDims);
+
+    auto I      = fft_r2c<CT, T, BASE_DIM>(paddedIn);
+    auto P      = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
+    auto Pc     = conj(P);
+    auto numer  = arithOp<CT, af_mul_t>(I, Pc, I.dims());
+    auto denom  = denominator(I, P, gamma, algo);
+    auto absVal = abs<T, CT>(denom);
+    auto THRESH = createValueArray(I.dims(), scalar<T>(gamma));
+    auto cond   = logicOp<T, af_ge_t>(absVal, THRESH, absVal.dims());
+    auto val    = arithOp<CT, af_div_t>(numer, denom, numer.dims());
+
+    select_scalar<CT, false>(val, cond, val, 0);
+
+    auto ival   = fft_c2r<CT, T, BASE_DIM>(val, 1/(double)nElems, odims);
+
+    return getHandle(createSubArray<T>(ival, index));
+}
+
+af_err af_inverse_deconv(af_array* out, const af_array in, const af_array psf,
+                         const float gamma, const af_inverse_deconv_algo algo)
+{
+    try {
+        const ArrayInfo& inputInfo = getInfo(in);
+        const dim4& inputDims = inputInfo.dims();
+        const ArrayInfo& psfInfo   = getInfo(psf);
+        const dim4& psfDims   = psfInfo.dims();
+
+        DIM_ASSERT(2, (inputDims.ndims() == 2));
+        DIM_ASSERT(3, (psfDims.ndims() == 2));
+        ARG_ASSERT(4, std::isfinite(gamma));
+        ARG_ASSERT(4, (gamma > 0));
+        ARG_ASSERT(5, (algo==AF_INVERSE_DECONV_DEFAULT ||
+                       algo==AF_INVERSE_DECONV_TIKHONOV));
+        af_array res = 0;
+
+        af_dtype inputType  = inputInfo.getType();
+        switch(inputType) {
+            case f32: res = invDeconv<float >(in, psf, gamma, algo); break;
+            case s16: res = invDeconv<short >(in, psf, gamma, algo); break;
+            case u16: res = invDeconv<ushort>(in, psf, gamma, algo); break;
+            case u8:  res = invDeconv<uchar >(in, psf, gamma, algo); break;
+            default : TYPE_ERROR(1, inputType);
+        }
+        std::swap(res, *out);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 14a310b7d5..53d3aa97c6 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -19,6 +19,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/corrcoef.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/covariance.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/deconvolution.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/diff.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dog.cpp
diff --git a/src/api/cpp/deconvolution.cpp b/src/api/cpp/deconvolution.cpp
new file mode 100644
index 0000000000..923e0b271c
--- /dev/null
+++ b/src/api/cpp/deconvolution.cpp
@@ -0,0 +1,32 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/image.h>
+#include <af/array.h>
+#include "error.hpp"
+
+namespace af
+{
+array iterativeDeconv(const array& in, const array& ker,
+                      const unsigned iterations, const float relaxFactor,
+                      const iterativeDeconvAlgo algo)
+{
+    af_array temp = 0;
+    AF_THROW(af_iterative_deconv(&temp, in.get(), ker.get(), iterations, relaxFactor, algo));
+    return array(temp);
+}
+
+array inverseDeconv(const array& in, const array& psf,
+                    const float gamma, const inverseDeconvAlgo algo)
+{
+    af_array temp = 0;
+    AF_THROW(af_inverse_deconv(&temp, in.get(), psf.get(), gamma, algo));
+    return array(temp);
+}
+}
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 259e1b4aed..0979b0610c 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2018, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -273,3 +273,18 @@ af_err af_anisotropic_diffusion(af_array* out, const af_array in, const float dt
     CHECK_ARRAYS(in);
     return CALL(out, in, dt, K, iterations, fftype, eq);
 }
+
+af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
+                           const unsigned iterations, const float relax_factor,
+                           const af_iterative_deconv_algo algo)
+{
+    CHECK_ARRAYS(in, ker);
+    return CALL(out, in, ker, iterations, relax_factor, algo);
+}
+
+af_err af_inverse_deconv(af_array* out, const af_array in, const af_array psf,
+                         const float gamma,const af_inverse_deconv_algo algo)
+{
+    CHECK_ARRAYS(in, psf);
+    return CALL(out, in, psf, gamma, algo);
+}
diff --git a/src/backend/common/dispatch.hpp b/src/backend/common/dispatch.hpp
index 19343280b8..359fa61b59 100644
--- a/src/backend/common/dispatch.hpp
+++ b/src/backend/common/dispatch.hpp
@@ -9,6 +9,42 @@
 
 #pragma once
 
+#include <cmath>
+
 #define divup(a, b) (((a)+(b)-1)/(b))
 
 unsigned nextpow2(unsigned x);
+
+// isPrime & greatestPrimeFactor are tailored after
+// itk::Math::{IsPrimt, GreatestPrimeFactor}
+template <typename T>
+inline bool isPrime(T n)
+{
+    if( n <= 1 )
+        return false;
+
+    const T last = (T)std::sqrt( (double)n );
+    for (T x=2; x<=last; ++x)
+    {
+        if (n%x == 0)
+            return false;
+    }
+
+    return true;
+}
+
+template <typename T>
+inline T greatestPrimeFactor(T n)
+{
+    T v = 2;
+
+    while (v <= n)
+    {
+        if (n % v == 0 && isPrime(v))
+            n /= v;
+        else
+            v += 1;
+    }
+
+    return v;
+}
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 693ee9e912..89e265ab94 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -213,6 +213,7 @@ target_sources(afcpu
     kernel/morph.hpp
     kernel/nearest_neighbour.hpp
     kernel/orb.hpp
+    kernel/pad_array_borders.hpp
     kernel/random_engine.hpp
     kernel/random_engine_mersenne.hpp
     kernel/random_engine_philox.hpp
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 982b8a0a6c..d7781640b9 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -9,6 +9,9 @@
 #pragma once
 
 #include <Array.hpp>
+#include <queue.hpp>
+#include <kernel/pad_array_borders.hpp>
+#include <math.hpp>
 
 namespace af { class dim4; }
 
@@ -25,8 +28,32 @@ namespace cpu
     void copyArray(Array<outType> &out, const Array<inType> &in);
 
     template<typename inType, typename outType>
-    Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                            outType default_value=outType(0), double factor=1.0);
+    Array<outType> padArray(const Array<inType>& in, const dim4& dims,
+                            outType default_value=outType(0),
+                            double factor=1.0);
+
+    template<typename T>
+    Array<T> padArrayBorders(const Array<T>& in,
+                             const dim4& lowerBoundPadding,
+                             const dim4& upperBoundPadding,
+                             const af::borderType btype)
+    {
+        const dim4& iDims = in.dims();
+
+        dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
+                   lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
+                   lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
+                   lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
+
+        auto ret = (btype == AF_PAD_ZERO ?
+                    createValueArray<T>(oDims, scalar<T>(0)) :
+                    createEmptyArray<T>(oDims));
+        ret.eval();
+
+        getQueue().enqueue(kernel::padBorders<T>, ret, in,
+                           lowerBoundPadding, upperBoundPadding, btype);
+        return ret;
+    }
 
     template<typename T>
     void multiply_inplace(Array<T> &in, double val);
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
new file mode 100644
index 0000000000..c30761575c
--- /dev/null
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -0,0 +1,149 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+
+#include <algorithm>
+
+namespace cpu
+{
+namespace kernel
+{
+static dim_t
+idxByndEdge(const dim_t i, const dim_t lb,
+            const dim_t len, const af::borderType btype)
+{
+    dim_t retVal;
+    switch(btype) {
+        case AF_PAD_SYM:
+            retVal = ((i < lb || i>= (lb+len)) ? ((len-1)-((i-lb)%len)) : i-lb);
+            break;
+        case AF_PAD_CLAMP_TO_EDGE:
+            retVal = std::max(dim_t(0), std::min(i-lb, len-1));
+            break;
+        default:
+            retVal = 0;
+            break;
+    }
+    return retVal;
+}
+
+template<typename T>
+void padBorders(Param<T> out, CParam<T> in,
+                const dim4 lBoundPadSize,
+                const dim4 uBoundPadSize,
+                const af::borderType btype)
+{
+    const dim4& oDims = out.dims();
+    const dim4& oStrs = out.strides();
+    const dim4& iDims = in.dims();
+    const dim4& iStrs = in.strides();
+
+    T const * const src = in.get();
+    T * dst             = out.get();
+
+    const dim4 validRegEnds(
+            oDims[0] - uBoundPadSize[0],
+            oDims[1] - uBoundPadSize[1],
+            oDims[2] - uBoundPadSize[2],
+            oDims[3] - uBoundPadSize[3]);
+    const bool isInputLinear = iStrs[0]==1;
+
+    /*
+     * VALID REGION COPYING DOES
+     * NOT NEED ANY BOUND CHECKS
+     * */
+    for (dim_t l=lBoundPadSize[3]; l<validRegEnds[3]; ++l) {
+        dim_t oLOff   = oStrs[3] * l;
+        dim_t iLOff   = iStrs[3] * (l - lBoundPadSize[3]);
+
+        for (dim_t k=lBoundPadSize[2]; k<validRegEnds[2]; ++k) {
+            dim_t oKOff   = oStrs[2] * k;
+            dim_t iKOff   = iStrs[2] * (k - lBoundPadSize[2]);
+
+            for (dim_t j=lBoundPadSize[1]; j<validRegEnds[1]; ++j) {
+                dim_t oJOff   = oStrs[1] * j;
+                dim_t iJOff   = iStrs[1] * (j - lBoundPadSize[1]);
+
+                if (isInputLinear) {
+                    T const * const sptr = src + iLOff + iKOff + iJOff;
+                    T * dptr = dst + oLOff + oKOff + oJOff + lBoundPadSize[0];
+
+                    std::copy(sptr, sptr+iDims[0], dptr);
+                } else {
+                    for (dim_t i=lBoundPadSize[0]; i<validRegEnds[0]; ++i) {
+                        dim_t oIOff   = oStrs[0] * i;
+                        dim_t iIOff   = iStrs[0] * (i - lBoundPadSize[0]);
+
+                        dst[oLOff+oKOff+oJOff+oIOff] =
+                            src[iLOff + iKOff + iJOff + iIOff];
+                    }
+                }
+            } // second dimension loop
+        }// third dimension loop
+    } // fourth dimension loop
+
+    // If we have to do zero padding,
+    // just return as the output is filled with
+    // zeros during allocation
+    if (btype == AF_PAD_ZERO)
+        return;
+
+    /*
+     * PADDED REGIONS NEED BOUND
+     * CHECKS; FOLLOWING NESTED
+     * LOOPS SHALL ONLY PROCESS
+     * PADDED REGIONS AND SKIP REST
+     * */
+    for (dim_t l=0; l<oDims[3]; ++l) {
+        bool skipL = (l>=lBoundPadSize[3] && l<validRegEnds[3]);
+
+        if (skipL) continue;
+
+        dim_t oLOff = oStrs[3] * l;
+        dim_t iLOff = iStrs[3] *
+            idxByndEdge(l, lBoundPadSize[3], iDims[3], btype);
+
+        for (dim_t k=0; k<oDims[2]; ++k) {
+            bool skipK = (k>=lBoundPadSize[2] && k<validRegEnds[2]);
+
+            if (skipK) continue;
+
+            dim_t oKOff = oStrs[2] * k;
+            dim_t iKOff = iStrs[2] *
+                idxByndEdge(k, lBoundPadSize[2], iDims[2], btype);
+
+            for (dim_t j=0; j<oDims[1]; ++j) {
+                bool skipJ = (j>=lBoundPadSize[1] && j<validRegEnds[1]);
+
+                if (skipJ) continue;
+
+                dim_t oJOff = oStrs[1] * j;
+                dim_t iJOff = iStrs[1] *
+                    idxByndEdge(j, lBoundPadSize[1], iDims[1], btype);
+
+                for (dim_t i=0; i<oDims[0]; ++i) {
+                    bool skipI = (i>=lBoundPadSize[0] && i<validRegEnds[0]);
+
+                    if (skipI) continue;
+
+                    dim_t oIOff = oStrs[0] * i;
+                    dim_t iIOff = iStrs[0] *
+                        idxByndEdge(i, lBoundPadSize[0], iDims[0], btype);
+
+                    dst[oLOff+oKOff+oJOff+oIOff] = src[iLOff+iKOff+iJOff+iIOff];
+
+                } // first dimension loop
+            } // second dimension loop
+        } // third dimension loop
+    } // fourth dimension loop
+}
+}
+}
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
index b190cb45ec..f48ec0cdc0 100644
--- a/src/backend/cpu/padarray.cpp
+++ b/src/backend/cpu/padarray.cpp
@@ -23,7 +23,6 @@
 
 namespace cpu
 {
-
 template<typename T>
 void multiply_inplace(Array<T> &in, double val)
 {
@@ -32,7 +31,7 @@ void multiply_inplace(Array<T> &in, double val)
 }
 
 template<typename inType, typename outType>
-Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+Array<outType> padArray(const Array<inType>& in, const dim4& dims,
                         outType default_value, double factor)
 {
     Array<outType> ret = createValueArray<outType>(dims, default_value);
@@ -61,18 +60,18 @@ INSTANTIATE(ushort )
 
 
 #define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
-    template Array<float  > padArray<SRC_T, float  >(Array<SRC_T> const &src, dim4 const &dims, float   default_value, double factor); \
-    template Array<double > padArray<SRC_T, double >(Array<SRC_T> const &src, dim4 const &dims, double  default_value, double factor); \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
-    template Array<int    > padArray<SRC_T, int    >(Array<SRC_T> const &src, dim4 const &dims, int     default_value, double factor); \
-    template Array<uint   > padArray<SRC_T, uint   >(Array<SRC_T> const &src, dim4 const &dims, uint    default_value, double factor); \
-    template Array<intl   > padArray<SRC_T, intl   >(Array<SRC_T> const &src, dim4 const &dims, intl    default_value, double factor); \
-    template Array<uintl  > padArray<SRC_T, uintl  >(Array<SRC_T> const &src, dim4 const &dims, uintl   default_value, double factor); \
-    template Array<short  > padArray<SRC_T, short  >(Array<SRC_T> const &src, dim4 const &dims, short   default_value, double factor); \
-    template Array<ushort > padArray<SRC_T, ushort >(Array<SRC_T> const &src, dim4 const &dims, ushort  default_value, double factor); \
-    template Array<uchar  > padArray<SRC_T, uchar  >(Array<SRC_T> const &src, dim4 const &dims, uchar   default_value, double factor); \
-    template Array<char   > padArray<SRC_T, char   >(Array<SRC_T> const &src, dim4 const &dims, char    default_value, double factor); \
+    template Array<float  > padArray<SRC_T, float  >(const Array<SRC_T>& src, const dim4& dims, float   default_value, double factor); \
+    template Array<double > padArray<SRC_T, double >(const Array<SRC_T>& src, const dim4& dims, double  default_value, double factor); \
+    template Array<cfloat > padArray<SRC_T, cfloat >(const Array<SRC_T>& src, const dim4& dims, cfloat  default_value, double factor); \
+    template Array<cdouble> padArray<SRC_T, cdouble>(const Array<SRC_T>& src, const dim4& dims, cdouble default_value, double factor); \
+    template Array<int    > padArray<SRC_T, int    >(const Array<SRC_T>& src, const dim4& dims, int     default_value, double factor); \
+    template Array<uint   > padArray<SRC_T, uint   >(const Array<SRC_T>& src, const dim4& dims, uint    default_value, double factor); \
+    template Array<intl   > padArray<SRC_T, intl   >(const Array<SRC_T>& src, const dim4& dims, intl    default_value, double factor); \
+    template Array<uintl  > padArray<SRC_T, uintl  >(const Array<SRC_T>& src, const dim4& dims, uintl   default_value, double factor); \
+    template Array<short  > padArray<SRC_T, short  >(const Array<SRC_T>& src, const dim4& dims, short   default_value, double factor); \
+    template Array<ushort > padArray<SRC_T, ushort >(const Array<SRC_T>& src, const dim4& dims, ushort  default_value, double factor); \
+    template Array<uchar  > padArray<SRC_T, uchar  >(const Array<SRC_T>& src, const dim4& dims, uchar   default_value, double factor); \
+    template Array<char   > padArray<SRC_T, char   >(const Array<SRC_T>& src, const dim4& dims, char    default_value, double factor); \
 
 INSTANTIATE_PAD_ARRAY(float )
 INSTANTIATE_PAD_ARRAY(double)
@@ -86,11 +85,9 @@ INSTANTIATE_PAD_ARRAY(ushort)
 INSTANTIATE_PAD_ARRAY(short )
 
 #define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
+    template Array<cfloat > padArray<SRC_T, cfloat >(const Array<SRC_T>& src, const dim4& dims, cfloat  default_value, double factor); \
+    template Array<cdouble> padArray<SRC_T, cdouble>(const Array<SRC_T>& src, const dim4& dims, cdouble default_value, double factor); \
 
 INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
 INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
-
 }
-
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 48a6470880..53f0fc3837 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -174,6 +174,7 @@ cuda_add_library(afcuda
     moments.cu
     nearest_neighbour.cu
     orb.cu
+    pad_array_borders.cu
     product.cu
     qr.cu
     random_engine.cu
@@ -248,6 +249,7 @@ cuda_add_library(afcuda
     kernel/nearest_neighbour.hpp
     kernel/orb.hpp
     kernel/orb_patch.hpp
+    kernel/pad_array_borders.hpp
     kernel/random_engine.hpp
     kernel/random_engine_mersenne.hpp
     kernel/random_engine_philox.hpp
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index c25f084876..58279ff5d2 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -12,7 +12,6 @@
 
 namespace cuda
 {
-
     template<typename T>
     void copyData(T *data, const Array<T> &A);
 
@@ -26,6 +25,12 @@ namespace cuda
     Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
                             outType default_value, double factor=1.0);
 
+    template<typename T>
+    Array<T> padArrayBorders(Array<T> const& in,
+                             dim4 const& lowerBoundPadding,
+                             dim4 const& upperBoundPadding,
+                             const af::borderType btype);
+
     template<typename T>
     void multiply_inplace(Array<T> &in, double val);
 
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
new file mode 100644
index 0000000000..d11ae987dc
--- /dev/null
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -0,0 +1,137 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <platform.hpp>
+#include <backend.hpp>
+#include <common/dispatch.hpp>
+#include <Param.hpp>
+#include <debug_cuda.hpp>
+#include <math.hpp>
+
+namespace cuda
+{
+namespace kernel
+{
+static const int PADB_THREADS_X = 32;
+static const int PADB_THREADS_Y =  8;
+
+template<af_border_type BType>
+__device__
+int idxByndEdge(const int i, const int lb, const int len)
+{
+    uint retVal;
+    switch(BType) {
+        case AF_PAD_SYM:
+            retVal = ((i<lb || i>=(lb+len)) ? ((len-1) - ((i-lb)%len)) : i-lb);
+            break;
+        case AF_PAD_CLAMP_TO_EDGE:
+            retVal = clamp(i-lb, 0, len-1);
+            break;
+        default: //AF_PAD_ZERO
+            retVal = 0;
+            break;
+    }
+    return retVal;
+}
+
+template<typename T, af_border_type BType>
+__global__
+void padBordersKernel(Param<T> out, CParam<T> in,
+                      const int l0, const int l1,
+                      const int l2, const int l3,
+                      unsigned blk_x, unsigned blk_y)
+{
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+    const int k  = blockIdx.x / blk_x;
+    const int l  = blockIdx.y / blk_y;
+
+    const int blockIdx_x = blockIdx.x - (blk_x) * k;
+    const int blockIdx_y = blockIdx.y - (blk_y) * l;
+    const int i = blockIdx_x * blockDim.x + lx;
+    const int j = blockIdx_y * blockDim.y + ly;
+
+    const int d0 = in.dims[0];
+    const int d1 = in.dims[1];
+    const int d2 = in.dims[2];
+    const int d3 = in.dims[3];
+    const int s0 = in.strides[0];
+    const int s1 = in.strides[1];
+    const int s2 = in.strides[2];
+    const int s3 = in.strides[3];
+
+    const T * src = in.ptr ;
+          T * dst = out.ptr;
+
+    bool isNotPadding = ( l>=l3 && l<(d3+l3) ) &&
+                        ( k>=l2 && k<(d2+l2) ) &&
+                        ( j>=l1 && j<(d1+l1) ) &&
+                        ( i>=l0 && i<(d0+l0) );
+    T value = scalar<T>(0);
+
+    if (isNotPadding) {
+        unsigned iLOff = (l-l3) * s3;
+        unsigned iKOff = (k-l2) * s2;
+        unsigned iJOff = (j-l1) * s1;
+        unsigned iIOff = (i-l0) * s0;
+
+        value = src[ iLOff + iKOff + iJOff + iIOff ];
+    } else if (BType!=AF_PAD_ZERO) {
+        unsigned iLOff = idxByndEdge<BType>(l, l3, d3) * s3;
+        unsigned iKOff = idxByndEdge<BType>(k, l2, d2) * s2;
+        unsigned iJOff = idxByndEdge<BType>(j, l1, d1) * s1;
+        unsigned iIOff = idxByndEdge<BType>(i, l0, d0) * s0;
+
+        value = src[ iLOff + iKOff + iJOff + iIOff ];
+    }
+
+    if (i<out.dims[0] && j<out.dims[1] &&
+        k<out.dims[2] && l<out.dims[3]) {
+        unsigned off = (l*out.strides[3] + k*out.strides[2] +
+                        j*out.strides[1] + i*out.strides[0]);
+        dst[ off ] = value;
+    }
+}
+
+template<typename T>
+void padBorders(Param<T> out, CParam<T> in,
+                dim4 const lBoundPadding, const af::borderType btype)
+{
+    dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
+
+    int blk_x = divup(out.dims[0], PADB_THREADS_X);
+    int blk_y = divup(out.dims[1], PADB_THREADS_Y);
+
+    dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
+
+    switch(btype) {
+        case AF_PAD_SYM:
+            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_SYM>),
+                    blocks, threads, out, in,
+                    lBoundPadding[0], lBoundPadding[1],
+                    lBoundPadding[2], lBoundPadding[3],
+                    blk_x, blk_y); break;
+        case AF_PAD_CLAMP_TO_EDGE:
+            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_CLAMP_TO_EDGE>),
+                    blocks, threads, out, in,
+                    lBoundPadding[0], lBoundPadding[1],
+                    lBoundPadding[2], lBoundPadding[3],
+                    blk_x, blk_y); break;
+        default:
+            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_ZERO>),
+                    blocks, threads, out, in,
+                    lBoundPadding[0], lBoundPadding[1],
+                    lBoundPadding[2], lBoundPadding[3],
+                    blk_x, blk_y); break;
+    }
+    POST_LAUNCH_CHECK();
+}
+}
+}
diff --git a/src/backend/cuda/pad_array_borders.cu b/src/backend/cuda/pad_array_borders.cu
new file mode 100644
index 0000000000..7df417a73b
--- /dev/null
+++ b/src/backend/cuda/pad_array_borders.cu
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/dim4.hpp>
+#include <Array.hpp>
+#include <copy.hpp>
+#include <err_cuda.hpp>
+#include <kernel/pad_array_borders.hpp>
+
+namespace cuda
+{
+template<typename T>
+Array<T> padArrayBorders(Array<T> const& in,
+                         dim4 const& lowerBoundPadding,
+                         dim4 const& upperBoundPadding,
+                         const af::borderType btype)
+{
+    const dim4& iDims = in.dims();
+
+    dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
+               lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
+               lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
+               lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
+
+    auto ret = createEmptyArray<T>(oDims);
+
+    kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
+
+    return ret;
+}
+
+#define INSTANTIATE_PAD_ARRAY_BORDERS(T)                    \
+    template Array<T> padArrayBorders<T>(Array<T> const&,   \
+            dim4 const &, dim4 const &, const af::borderType);
+
+INSTANTIATE_PAD_ARRAY_BORDERS(cfloat )
+INSTANTIATE_PAD_ARRAY_BORDERS(cdouble)
+INSTANTIATE_PAD_ARRAY_BORDERS(float  )
+INSTANTIATE_PAD_ARRAY_BORDERS(double )
+INSTANTIATE_PAD_ARRAY_BORDERS(int    )
+INSTANTIATE_PAD_ARRAY_BORDERS(uint   )
+INSTANTIATE_PAD_ARRAY_BORDERS(intl   )
+INSTANTIATE_PAD_ARRAY_BORDERS(uintl  )
+INSTANTIATE_PAD_ARRAY_BORDERS(uchar  )
+INSTANTIATE_PAD_ARRAY_BORDERS(char   )
+INSTANTIATE_PAD_ARRAY_BORDERS(ushort )
+INSTANTIATE_PAD_ARRAY_BORDERS(short  )
+}
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 9c7d4ac44c..3a8e856f5c 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -284,6 +284,7 @@ target_sources(afopencl
     kernel/names.hpp
     kernel/nearest_neighbour.hpp
     kernel/orb.hpp
+    kernel/pad_array_borders.hpp
     kernel/random_engine.hpp
     kernel/range.hpp
     kernel/reduce.hpp
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index c1f5d9d670..4a4e0dffa2 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -9,10 +9,10 @@
 #pragma once
 
 #include <Array.hpp>
+#include <kernel/pad_array_borders.hpp>
 
 namespace opencl
 {
-
     template<typename T>
     void copyData(T *data, const Array<T> &A);
 
@@ -26,6 +26,39 @@ namespace opencl
     Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
                             outType default_value, double factor=1.0);
 
+    template<typename T>
+    Array<T> padArrayBorders(Array<T> const& in,
+                             dim4 const& lowerBoundPadding,
+                             dim4 const& upperBoundPadding,
+                             const af::borderType btype)
+    {
+        auto iDims = in.dims();
+
+        dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
+                lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
+                lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
+                lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
+
+        auto ret = createEmptyArray<T>(oDims);
+
+        switch(btype)
+        {
+            case AF_PAD_SYM:
+                kernel::padBorders<T, AF_PAD_SYM>(ret, in, lowerBoundPadding);
+                break;
+            case AF_PAD_CLAMP_TO_EDGE:
+                kernel::padBorders<T, AF_PAD_CLAMP_TO_EDGE>(ret, in,
+                        lowerBoundPadding);
+                break;
+            default:
+                kernel::padBorders<T, AF_PAD_ZERO>(ret, in, lowerBoundPadding);
+                break;
+        }
+
+        return ret;
+    }
+
+
     template<typename T>
     void multiply_inplace(Array<T> &in, double val);
 
diff --git a/src/backend/opencl/kernel/pad_array_borders.cl b/src/backend/opencl/kernel/pad_array_borders.cl
new file mode 100644
index 0000000000..e1c28c0700
--- /dev/null
+++ b/src/backend/opencl/kernel/pad_array_borders.cl
@@ -0,0 +1,93 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if AF_BORDER_TYPE==AF_PAD_SYM
+
+int idxByndEdge(const int i, const int lb, const int len)
+{
+    if (i < lb || i>= (lb+len)) {
+        return (len-1) - ((i-lb)%len);
+    } else
+        return i - lb;
+}
+
+#elif AF_BORDER_TYPE==AF_PAD_CLAMP_TO_EDGE
+
+int idxByndEdge(const int i, const int lb, const int len)
+{
+    return clamp(i-lb, 0, len-1);
+}
+
+#else
+
+#define DEFAULT_BORDER
+
+#endif
+
+__kernel
+void padBorders(__global T *       out,
+                KParam             oInfo,
+                __global const T * in,
+                KParam             iInfo,
+                int l0, int l1, int l2, int l3,
+                unsigned blk_x, unsigned blk_y)
+{
+    const int lx = get_local_id(0);
+    const int ly = get_local_id(1);
+    const int k  = get_group_id(0) / blk_x;
+    const int l  = get_group_id(1) / blk_y;
+
+    const int blockIdx_x = get_group_id(0) - (blk_x) * k;
+    const int blockIdx_y = get_group_id(1) - (blk_y) * l;
+    const int i = blockIdx_x * get_local_size(0) + lx;
+    const int j = blockIdx_y * get_local_size(1) + ly;
+
+    const int d0 = iInfo.dims[0];
+    const int d1 = iInfo.dims[1];
+    const int d2 = iInfo.dims[2];
+    const int d3 = iInfo.dims[3];
+    const int s0 = iInfo.strides[0];
+    const int s1 = iInfo.strides[1];
+    const int s2 = iInfo.strides[2];
+    const int s3 = iInfo.strides[3];
+
+    __global const T * src = in + iInfo.offset;
+    __global       T * dst = out;
+
+    bool isNotPadding = ( l>=l3 && l<(d3+l3) ) &&
+                        ( k>=l2 && k<(d2+l2) ) &&
+                        ( j>=l1 && j<(d1+l1) ) &&
+                        ( i>=l0 && i<(d0+l0) );
+    T value = (T)0;
+
+    if (isNotPadding) {
+        unsigned iLOff = (l - l3) * s3;
+        unsigned iKOff = (k - l2) * s2;
+        unsigned iJOff = (j - l1) * s1;
+        unsigned iIOff = (i - l0) * s0;
+
+        value = src[ iLOff + iKOff + iJOff + iIOff ];
+    } else {
+#if !defined(DEFAULT_BORDER)
+        unsigned iLOff = idxByndEdge(l, l3, d3) * s3;
+        unsigned iKOff = idxByndEdge(k, l2, d2) * s2;
+        unsigned iJOff = idxByndEdge(j, l1, d1) * s1;
+        unsigned iIOff = idxByndEdge(i, l0, d0) * s0;
+
+        value = src[ iLOff + iKOff + iJOff + iIOff ];
+#endif
+    }
+
+    if (i<oInfo.dims[0] && j<oInfo.dims[1] &&
+        k<oInfo.dims[2] && l<oInfo.dims[3]) {
+        unsigned offset = l*oInfo.strides[3] + k*oInfo.strides[2] +
+                          j*oInfo.strides[1] + i*oInfo.strides[0];
+        dst[ offset ] = value;
+    }
+}
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
new file mode 100644
index 0000000000..e710fe0cc4
--- /dev/null
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -0,0 +1,83 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <kernel_headers/pad_array_borders.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <string>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <Param.hpp>
+#include <debug_opencl.hpp>
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+
+namespace opencl
+{
+namespace kernel
+{
+static const int PADB_THREADS_X = 16;
+static const int PADB_THREADS_Y = 16;
+
+template<typename T, af_border_type BType>
+void padBorders(Param out, const Param in, dim4 const& lBPadding)
+{
+    std::string refName = std::string("padBorders_") +
+        std::string(dtype_traits<T>::getName()) +
+        std::to_string(BType);
+
+    int device = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog==0 && entry.ker==0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D AF_BORDER_TYPE="<< BType
+                << " -D AF_PAD_SYM="<< AF_PAD_SYM
+                << " -D AF_PAD_CLAMP_TO_EDGE="<< AF_PAD_CLAMP_TO_EDGE;
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
+            options << " -D USE_DOUBLE";
+
+        const char* ker_strs[] = {pad_array_borders_cl};
+        const int   ker_lens[] = {pad_array_borders_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "padBorders");
+
+        addKernelToCache(device, refName, entry);
+    }
+
+    NDRange local(PADB_THREADS_X, PADB_THREADS_Y);
+
+    int blk_x = divup(out.info.dims[0], local[0]);
+    int blk_y = divup(out.info.dims[1], local[1]);
+
+    NDRange global(blk_x * out.info.dims[2] * local[0],
+                   blk_y * out.info.dims[3] * local[1]);
+
+    auto padOP = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                               unsigned, unsigned, unsigned,
+                               unsigned, int, int> (*entry.ker);
+
+    padOP(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *in.data, in.info, lBPadding[0], lBPadding[1],
+            lBPadding[2], lBPadding[3], blk_x, blk_y);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+}
+}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index eb80491b35..57627ffe59 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -280,3 +280,5 @@ make_test(SRC where.cpp)
 make_test(SRC wrap.cpp)
 make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
+make_test(SRC inverse_deconv.cpp)
+make_test(SRC iterative_deconv.cpp)
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
new file mode 100644
index 0000000000..d2f09a6f48
--- /dev/null
+++ b/test/inverse_deconv.cpp
@@ -0,0 +1,135 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+using std::string;
+using std::vector;
+using std::abs;
+using namespace af;
+
+template<typename T>
+class InverseDeconvolution : public ::testing::Test
+{
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
+
+// register the type list
+TYPED_TEST_CASE(InverseDeconvolution, TestTypes);
+
+template<typename T, bool isColor>
+void imageTest(string pTestFile, const float gamma, const af_inverse_deconv_algo algo)
+{
+    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
+
+    if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
+
+    using af::dim4;
+
+    vector<dim4>       inDims;
+    vector<string>    inFiles;
+    vector<dim_t> outSizes;
+    vector<string>   outFiles;
+
+    readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
+
+    size_t testCount = inDims.size();
+
+    for (size_t testId=0; testId<testCount; ++testId)
+    {
+        inFiles[testId].insert(0, string(TEST_DIR "/inverse_deconv/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/inverse_deconv/"));
+
+        af_array _inArray   = 0;
+        af_array inArray    = 0;
+        af_array kerArray   = 0;
+        af_array _outArray  = 0;
+        af_array cstArray   = 0;
+        af_array minArray   = 0;
+        af_array numArray   = 0;
+        af_array denArray   = 0;
+        af_array divArray   = 0;
+        af_array outArray   = 0;
+        af_array goldArray  = 0;
+        af_array _goldArray = 0;
+        dim_t nElems        = 0;
+
+        ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
+
+        af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, _inArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, conv_image<OutType>(&goldArray, _goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+
+        unsigned ndims;
+        dim_t dims[4];
+        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_inverse_deconv(&_outArray, inArray, kerArray, gamma, algo));
+
+        double maxima, minima, imag;
+        ASSERT_EQ(AF_SUCCESS, af_min_all(&minima, &imag, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_max_all(&maxima, &imag, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&minArray, minima, ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_sub(&numArray, _outArray, minArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
+
+        std::vector<OutType> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+
+        std::vector<OutType> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
+
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+    }
+}
+
+TYPED_TEST(InverseDeconvolution, TikhonovOnGrayscale)
+{
+    // Test file name format: <colorspace>_<gamma with dots replaced by "_">_<inverse deconv algo>.test
+    imageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_00_1_tikhonov.test"),
+                                00.1f, AF_INVERSE_DECONV_TIKHONOV);
+}
+
+TYPED_TEST(InverseDeconvolution, DISABLED_WienerOnGrayscale)
+{
+    // Test file name format: <colorspace>_<gamma with dots replaced by "_">_<inverse deconv algo>.test
+    imageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_1_wiener.test"),
+                                1.0, AF_INVERSE_DECONV_DEFAULT);
+    //TODO(pradeep) change to wiener enum value
+}
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
new file mode 100644
index 0000000000..50546e1c08
--- /dev/null
+++ b/test/iterative_deconv.cpp
@@ -0,0 +1,135 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+#include <string>
+#include <vector>
+#include <testHelpers.hpp>
+
+using std::string;
+using std::vector;
+using std::abs;
+using namespace af;
+
+template<typename T>
+class IterativeDeconvolution : public ::testing::Test
+{
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
+
+// register the type list
+TYPED_TEST_CASE(IterativeDeconvolution, TestTypes);
+
+template<typename T, bool isColor>
+void imageTest(string pTestFile, const unsigned iters, const float rf, const af::iterativeDeconvAlgo algo)
+{
+    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
+
+    if (noDoubleTests<T>()) return;
+    if (noImageIOTests()) return;
+
+    using af::dim4;
+
+    vector<dim4>       inDims;
+    vector<string>    inFiles;
+    vector<dim_t> outSizes;
+    vector<string>   outFiles;
+
+    readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
+
+    size_t testCount = inDims.size();
+
+    for (size_t testId=0; testId<testCount; ++testId)
+    {
+        inFiles[testId].insert(0, string(TEST_DIR "/iterative_deconv/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/iterative_deconv/"));
+
+        af_array _inArray   = 0;
+        af_array inArray    = 0;
+        af_array kerArray   = 0;
+        af_array _outArray  = 0;
+        af_array cstArray   = 0;
+        af_array minArray   = 0;
+        af_array numArray   = 0;
+        af_array denArray   = 0;
+        af_array divArray   = 0;
+        af_array outArray   = 0;
+        af_array goldArray  = 0;
+        af_array _goldArray = 0;
+        dim_t nElems        = 0;
+
+        ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
+
+        af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, _inArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_EQ(AF_SUCCESS, conv_image<OutType>(&goldArray, _goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+
+        unsigned ndims;
+        dim_t dims[4];
+        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
+
+        ASSERT_EQ(AF_SUCCESS, af_iterative_deconv(&_outArray, inArray, kerArray, iters, rf, algo));
+
+        double maxima, minima, imag;
+        ASSERT_EQ(AF_SUCCESS, af_min_all(&minima, &imag, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_max_all(&maxima, &imag, _outArray));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_constant(&minArray, minima, ndims, dims, otype));
+        ASSERT_EQ(AF_SUCCESS, af_sub(&numArray, _outArray, minArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
+        ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
+
+        std::vector<OutType> outData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+
+        std::vector<OutType> goldData(nElems);
+        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
+
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+    }
+}
+
+TYPED_TEST(IterativeDeconvolution, LandweberOnGrayscale)
+{
+    // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation factor>_<algo>.test
+    imageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_landweber.test"),
+            100, 0.05, AF_ITERATIVE_DECONV_LANDWEBER);
+}
+
+TYPED_TEST(IterativeDeconvolution, RichardsonLucyOnGrayscale)
+{
+    // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation factor>_<algo>.test
+    // For RichardsonLucy algorithm, relaxation factor is not used.
+    imageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_lucy.test"),
+            100, 0.05, AF_ITERATIVE_DECONV_RICHARDSONLUCY);
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 87ff37872b..b35a85d8a8 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -322,8 +322,9 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
     accum /= data_size;
     double NRMSD = std::sqrt(accum)/(maxion-minion);
 
-    if (std::isnan(NRMSD) || NRMSD > tolerance)
+    if (std::isnan(NRMSD) || NRMSD > tolerance) {
         return false;
+    }
 
     return true;
 }

From 5337191aff4bbf58145bf47d08478f578e731ee1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 14 Jul 2018 22:38:52 -0400
Subject: [PATCH 1476/2677] Fix LAPACK functions with MKL

---
 src/backend/common/CMakeLists.txt | 23 ++++-------------------
 src/backend/cpu/CMakeLists.txt    | 17 ++++++++++++++---
 src/backend/opencl/CMakeLists.txt | 21 +++++++--------------
 3 files changed, 25 insertions(+), 36 deletions(-)

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index cbd378e2d4..4800e79446 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -32,6 +32,8 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
@@ -40,9 +42,9 @@ target_sources(afcommon_interface
   )
 
 if(WIN32)
-target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_windows.cpp)
+  target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_windows.cpp)
 else()
-target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_unix.cpp)
+  target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_unix.cpp)
 endif()
 
 target_include_directories(afcommon_interface
@@ -51,9 +53,6 @@ target_include_directories(afcommon_interface
     ${PROJECT_BINARY_DIR}
   )
 
-add_library(afcommon_lapack_interface INTERFACE)
-
-
 if(AF_WITH_LOGGING)
   dependency_check(spdlog_FOUND "spdlog not found.")
   target_compile_definitions(afcommon_interface
@@ -111,17 +110,3 @@ if(AF_WITH_GRAPHICS)
     add_dependencies(afcommon_interface forge-ext)
   endif()
 endif()
-
-if(LAPACK_FOUND)
-  target_sources(afcommon_lapack_interface
-    INTERFACE
-      ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.cpp
-      ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.hpp
-    )
-
-  target_include_directories(afcommon_lapack_interface
-    INTERFACE ${LAPACK_INCLUDE_DIR})
-
-  target_compile_definitions(afcommon_lapack_interface
-    INTERFACE WITH_LINEAR_ALGEBRA)
-endif()
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 89e265ab94..23bc87b75b 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -322,7 +322,6 @@ if(USE_CPU_MKL)
       c_api_interface
       cpp_api_interface
       afcommon_interface
-      afcommon_lapack_interface
       cpu_sort_by_key
       MKL::MKL
       Threads::Threads
@@ -336,14 +335,26 @@ else()
       c_api_interface
       cpp_api_interface
       afcommon_interface
-      afcommon_lapack_interface
       cpu_sort_by_key
       ${CBLAS_LIBRARIES}
-      ${LAPACK_LIBRARIES}
       FFTW::FFTW
       FFTW::FFTWF
       Threads::Threads
     )
+  if(LAPACK_FOUND)
+    target_link_libraries(afcpu
+      PRIVATE
+        ${LAPACK_LIBRARIES})
+    target_include_directories(afcpu
+      PRIVATE
+        ${LAPACK_INCLUDE_DIR})
+  endif()
+endif()
+
+if(LAPACK_FOUND OR MKL_FOUND)
+  target_compile_definitions(afcpu
+    PRIVATE
+      WITH_LINEAR_ALGEBRA)
 endif()
 
 install(TARGETS afcpu
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 3a8e856f5c..42ebc3ec47 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -454,7 +454,7 @@ if(AF_WITH_GRAPHICS)
     )
 endif()
 
-if(LAPACK_FOUND)
+if(LAPACK_FOUND OR MKL_FOUND)
   target_sources(afopencl
     PRIVATE
       magma/gebrd.cpp
@@ -486,8 +486,7 @@ if(LAPACK_FOUND)
       magma/ungqr.cpp
       magma/unmqr.cpp
       #magma/unmqr2.cpp
-
-    )
+      )
 
   if(USE_OPENCL_MKL)
     dependency_check(MKL_FOUND "MKL not found")
@@ -495,9 +494,7 @@ if(LAPACK_FOUND)
 
     target_link_libraries(afopencl
       PRIVATE
-        MKL::MKL
-      )
-
+        MKL::MKL)
   else()
     dependency_check(OpenCL_FOUND "OpenCL not found.")
 
@@ -508,7 +505,8 @@ if(LAPACK_FOUND)
     dependency_check(CBLAS_LIBRARIES "CBLAS not found.")
     target_include_directories(afopencl
       PRIVATE
-        ${CBLAS_INCLUDE_DIR})
+        ${CBLAS_INCLUDE_DIR}
+        ${LAPACK_INCLUDE_DIR})
     target_link_libraries(afopencl
       PRIVATE
         ${CBLAS_LIBRARIES}
@@ -518,13 +516,8 @@ if(LAPACK_FOUND)
   target_compile_definitions(
     afopencl
     PRIVATE
-      WITH_OPENCL_LINEAR_ALGEBRA
-    )
-
-  target_link_libraries(afopencl
-    PRIVATE
-      afcommon_lapack_interface)
-endif(LAPACK_FOUND)
+      WITH_LINEAR_ALGEBRA)
+endif(LAPACK_FOUND OR MKL_FOUND)
 
 install(TARGETS afopencl
   EXPORT ArrayFireOpenCLTargets

From facb1f153e7d5739c4bd90abacf67dc9549f6a3f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 14 Jul 2018 22:39:56 -0400
Subject: [PATCH 1477/2677] Remove reference to INTEL_MKL_ROOT_DIR env
 variable. Use MKLROOT

---
 CMakeModules/FindLAPACKE.cmake | 23 +++++++++--------------
 1 file changed, 9 insertions(+), 14 deletions(-)

diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 1bb75ce15d..84e20fe7e9 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -12,24 +12,19 @@
 SET(LAPACKE_ROOT_DIR CACHE STRING
   "Root directory for custom LAPACK implementation")
 
-IF (NOT INTEL_MKL_ROOT_DIR)
-  SET(INTEL_MKL_ROOT_DIR $ENV{INTEL_MKL_ROOT})
-ENDIF()
-
-IF(NOT LAPACKE_ROOT_DIR)
-
-  IF (ENV{LAPACKEDIR})
+if(NOT LAPACKE_ROOT_DIR)
+  if (ENV{LAPACKEDIR})
     SET(LAPACKE_ROOT_DIR $ENV{LAPACKEDIR})
-  ENDIF()
+  endif()
 
-  IF (ENV{LAPACKE_ROOT_DIR})
+  if (ENV{LAPACKE_ROOT_DIR})
     SET(LAPACKE_ROOT_DIR $ENV{LAPACKE_ROOT_DIR})
-  ENDIF()
+  endif()
 
-  IF (INTEL_MKL_ROOT_DIR)
-    SET(LAPACKE_ROOT_DIR ${INTEL_MKL_ROOT_DIR})
-  ENDIF()
-ENDIF()
+  if (ENV{MKLROOT})
+    SET(LAPACKE_ROOT_DIR $ENV{MKLROOT})
+  endif()
+endif()
 
 # Check if we can use PkgConfig
 FIND_PACKAGE(PkgConfig)

From a2fad1da300196343f06bcde722360fbed585f0c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 15 Jul 2018 18:12:02 -0400
Subject: [PATCH 1478/2677] Refactor how flags are set in CMake

---
 CMakeLists.txt                                |  3 --
 CMakeModules/InternalUtils.cmake              | 36 ++++++++++++++++++-
 CMakeModules/platform.cmake                   |  2 --
 src/api/unified/CMakeLists.txt                |  6 +---
 src/backend/cpu/CMakeLists.txt                |  4 +--
 .../cpu/kernel/sort_by_key/CMakeLists.txt     |  1 +
 src/backend/cuda/CMakeLists.txt               | 33 +++++++----------
 .../cuda/kernel/scan_by_key/CMakeLists.txt    |  5 ++-
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |  5 ++-
 src/backend/opencl/CMakeLists.txt             | 11 ++----
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  2 +-
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  3 +-
 12 files changed, 64 insertions(+), 47 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cbd7892dab..32770dd55a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -88,9 +88,6 @@ mark_as_advanced(
   CUDA_USE_STATIC_CUDA_RUNTIME
   CUDA_rt_LIBRARY)
 
-# TODO(umar): Add definitions should not be used. Instead use
-arrayfire_get_platform_definitions(platform_definitions)
-add_definitions(${platform_definitions})
 
 if(AF_WITH_GRAPHICS AND NOT AF_USE_SYSTEM_FORGE)
   include(build_forge)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 06871038b2..0372240e82 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -30,10 +30,44 @@ endfunction()
 
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
   if(NOT MSVC)
-    set(${cuda_flags} "-std=c++11"  PARENT_SCOPE)
+    set(${cuda_flags} "-std=c++11 -Xcompiler -fPIC  -Xcompiler=${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden"  PARENT_SCOPE)
   else()
     set(${cuda_flags} "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275"  PARENT_SCOPE)
   endif()
+
+  if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
+    if(${CUDA_VERSION_MAJOR} LESS 8)
+      set(cuda_flags "${cuda_flags} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED")
+    endif()
+  endif()
+endfunction()
+
+include(CheckCXXCompilerFlag)
+
+function(arrayfire_set_default_cxx_flags target)
+  arrayfire_get_platform_definitions(defs)
+  target_compile_definitions(${target} PRIVATE ${defs})
+
+  if(MSVC)
+    target_compile_options(${target}
+      PRIVATE
+        /wd4251 /wd4068 /wd4275 /bigobj)
+
+    if(CMAKE_GENERATOR MATCHES "Ninja")
+      target_compile_options(${target}
+        PRIVATE
+          /FS)
+    endif()
+  else()
+    check_cxx_compiler_flag(-Wno-ignored-attributes has_ignored_attributes_flag)
+
+    # OpenCL targets need this flag to avoid ignored attribute warnings in the
+    # OpenCL headers
+    if(has_ignored_attributes_flag)
+        target_compile_options(${target}
+          PRIVATE -Wno-ignored-attributes)
+    endif()
+  endif()
 endfunction()
 
 function(__af_deprecate_var var access value)
diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
index 8d1d21ec86..9f49de0b9b 100644
--- a/CMakeModules/platform.cmake
+++ b/CMakeModules/platform.cmake
@@ -30,6 +30,4 @@ if(WIN32)
   # C4275: Warnings about using non-exported classes as base class of an
   #        exported class
   add_compile_options(/wd4251 /wd4068 /wd4275)
-
-  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH}")
 endif()
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 9427f90fa1..38f887d576 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -43,6 +43,7 @@ target_sources(af
     ${CMAKE_SOURCE_DIR}/src/backend/common/util.hpp
   )
 
+arrayfire_set_default_cxx_flags(af)
 if(WIN32)
   target_sources(af
     PRIVATE
@@ -53,11 +54,6 @@ else()
       ${CMAKE_SOURCE_DIR}/src/backend/common/module_loading_unix.cpp)
 endif()
 
-target_compile_definitions(af
-  PRIVATE
-    AFDLL
-  )
-
 target_include_directories(af
   PUBLIC
     $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 23bc87b75b..a2f451734a 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -252,9 +252,7 @@ target_sources(afcpu
     ${CMAKE_CURRENT_SOURCE_DIR}/threads/async_queue.hpp
   )
 
-if(MSVC)
-  target_compile_options(afcpu PRIVATE /bigobj)
-endif()
+arrayfire_set_default_cxx_flags(afcpu)
 
 include("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
index fecc143f24..fd71ce54b7 100644
--- a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -25,6 +25,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
       COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL"
       FOLDER "Generated Targets")
 
+  arrayfire_set_default_cxx_flags(cpu_sort_by_key_${SBK_TYPE})
   # TODO(umar): This should just use the include directories from the
   # afcpu_static target
   target_include_directories(cpu_sort_by_key_${SBK_TYPE}
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 53f0fc3837..e410b57c91 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -10,6 +10,13 @@ include(select_compute_arch)
 
 dependency_check(CUDA_FOUND "CUDA not found.")
 
+find_cuda_helper_libs(nvrtc)
+find_cuda_helper_libs(nvrtc-builtins)
+
+get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
+
+include(CLKernelToH)
+
 if(NOT CUDA_architecture_build_targets)
   cuda_detect_installed_gpus(detected_gpus)
 endif()
@@ -18,29 +25,16 @@ set(CUDA_architecture_build_targets ${detected_gpus} CACHE
   STRING "The compute architectures targeted by this build. (Options: 3.0;Maxwell;All;Common)")
 
 cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
-message(STATUS "CUDA Architectures: ${CUDA_architecture_build_targets}")
+message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets}")
 
-find_cuda_helper_libs(nvrtc)
-find_cuda_helper_libs(nvrtc-builtins)
+set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};
+  ${cuda_architecture_flags}
+  )
 
-get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 mark_as_advanced(
     CUDA_LIBRARIES_PATH
     CUDA_architecture_build_targets)
 
-# TODO(umar): Move these flags to a separate function/target
-if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
-  if(${CUDA_VERSION_MAJOR} LESS 8)
-    add_definitions(-D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED)
-  endif()
-endif()
-
-include(CLKernelToH)
-
-set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};
-  ${cuda_architecture_flags}
-  )
-
 cuda_include_directories(
   ${CMAKE_CURRENT_BINARY_DIR}
   ${CMAKE_CURRENT_SOURCE_DIR}
@@ -114,9 +108,6 @@ function(cuda_add_library cuda_target)
 endfunction()
 
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
-if(NOT MSVC)
-  set(cuda_cxx_flags "${cuda_cxx_flags} -Xcompiler -fPIC  -Xcompiler=${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
-endif()
 
 if(AF_WITH_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")
   # This definition is required in addition to the definition below because in
@@ -412,6 +403,8 @@ cuda_add_library(afcuda
     OPTIONS "${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
   )
 
+arrayfire_set_default_cxx_flags(afcuda)
+
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
 if(AF_WITH_NONFREE)
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index bfd9ab4abd..78dd2b6341 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -16,6 +16,9 @@ endforeach()
 
 cuda_add_cuda_include_once()
 
+arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
+arrayfire_get_platform_definitions(platform_flags)
+
 foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     # When using cuda_compile with older versions of FindCUDA. The generated targets
     # have the same names as the source file. Since we are using the same file for
@@ -30,7 +33,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     cuda_compile(scan_by_key_gen_files "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
-          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "${cuda_cxx_flags} -DAFDLL"
+          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
       )
 
     list(APPEND SCAN_OBJ ${scan_by_key_gen_files})
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 573c487ad3..2860c0db99 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -17,6 +17,9 @@ foreach(STR ${FILESTRINGS})
     endif()
 endforeach()
 
+arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
+arrayfire_get_platform_definitions(platform_flags)
+
 foreach(SBK_TYPE ${SBK_TYPES})
     foreach(SBK_INST ${SBK_INSTS})
 
@@ -36,7 +39,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
           OPTIONS
             -DSBK_TYPE=${SBK_TYPE}
             -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
-            "${cuda_cxx_flags} -DAFDLL"
+            "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
       )
 
       list(APPEND SORT_OBJ ${scan_by_key_gen_files})
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 42ebc3ec47..9001713fba 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -12,13 +12,6 @@ set_property(CACHE AF_OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
 
 af_deprecate(OPENCL_BLAS_LIBRARY AF_OPENCL_BLAS_LIBRARY)
 
-include(CheckCXXCompilerFlag)
-check_cxx_compiler_flag(-Wno-ignored-attributes has_ignored_attributes_flag)
-
-if(has_ignored_attributes_flag)
-  set(opencl_cxx_flags -Wno-ignored-attributes)
-endif()
-
 include(build_clFFT)
 
 file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
@@ -384,14 +377,14 @@ target_include_directories(afopencl
     ../../../include
   )
 
+arrayfire_set_default_cxx_flags(afopencl)
+
 add_dependencies(afopencl ${cl_kernel_targets})
 add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 
 set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
-target_compile_options(afopencl PRIVATE ${opencl_cxx_flags})
-
 target_compile_definitions(afopencl
   PRIVATE
     CL_USE_DEPRECATED_OPENCL_1_2_APIS
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index bd7f3d2460..84bd1e7b23 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -46,7 +46,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         POSITION_INDEPENDENT_CODE ON
         FOLDER "Generated Targets")
 
-    target_compile_options(opencl_scan_by_key_${SBK_BINARY_OP} PRIVATE ${opencl_cxx_flags})
+    arrayfire_set_default_cxx_flags(opencl_scan_by_key_${SBK_BINARY_OP})
     target_compile_definitions(opencl_scan_by_key_${SBK_BINARY_OP}
       PRIVATE
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 8e0eecd6aa..7e65b8b3c1 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -41,7 +41,8 @@ foreach(SBK_TYPE ${SBK_TYPES})
         POSITION_INDEPENDENT_CODE ON
         FOLDER "Generated Targets")
 
-    target_compile_options(opencl_sort_by_key_${SBK_TYPE} PRIVATE ${opencl_cxx_flags})
+    arrayfire_set_default_cxx_flags(opencl_sort_by_key_${SBK_TYPE})
+
     target_compile_definitions(opencl_sort_by_key_${SBK_TYPE}
       PRIVATE
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>

From 6992e9fb16505eab1e2bc70229743a54b80fc5e8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Jul 2018 03:12:23 -0400
Subject: [PATCH 1479/2677] Add CTestCustom. Print info after tests. Mark
 sparse test SERIAL

---
 CMakeLists.txt                   |  4 ++++
 CMakeModules/ASANSuppression.txt |  5 +++++
 CMakeModules/CTestCustom.cmake   | 16 ++++++++++++++++
 test/CMakeLists.txt              | 14 +++++++++++++-
 test/print_info.cpp              | 26 ++++++++++++++++++++++++++
 5 files changed, 64 insertions(+), 1 deletion(-)
 create mode 100644 CMakeModules/ASANSuppression.txt
 create mode 100644 CMakeModules/CTestCustom.cmake
 create mode 100644 test/print_info.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 32770dd55a..7ce2521be9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -339,6 +339,10 @@ unset(CMAKE_CXX_VISIBILITY_PRESET)
 
 include(CTest)
 
+configure_file(
+  ${CMAKE_MODULE_PATH}/CTestCustom.cmake
+  ${PROJECT_BINARY_DIR}/CTestCustom.cmake)
+
 # Handle depricated BUILD_TEST variable if found.
 if(BUILD_TEST)
   set(BUILD_TESTING ${BUILD_TEST})
diff --git a/CMakeModules/ASANSuppression.txt b/CMakeModules/ASANSuppression.txt
new file mode 100644
index 0000000000..f5f58ad789
--- /dev/null
+++ b/CMakeModules/ASANSuppression.txt
@@ -0,0 +1,5 @@
+# This is a known leak.
+leak:getKernel
+#leak:libOpenCL
+leak:libnvidia-ptxjitcompile
+leak:tbb::internal::task_stream
diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
new file mode 100644
index 0000000000..45f4d25888
--- /dev/null
+++ b/CMakeModules/CTestCustom.cmake
@@ -0,0 +1,16 @@
+
+
+set(CTEST_CUSTOM_ERROR_POST_CONTEXT 20)
+set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 20)
+set(CTEST_CUSTOM_POST_TEST ./test/print_info)
+
+list(APPEND CTEST_CUSTOM_COVERAGE_EXCLUDE
+	"test/gtest/*"
+
+  # All external and third_party libraries
+	"src/backend/cpu/threads/*"
+	"src/backend/cuda/cub/*"
+	"cl2.hpp"
+
+  # Remove bin2cpp from coverage
+	"CMakeModules/*")
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 57627ffe59..577216757e 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -259,7 +259,7 @@ make_test(SRC solve_dense.cpp       CXX11)
 make_test(SRC sort.cpp)
 make_test(SRC sort_by_key.cpp)
 make_test(SRC sort_index.cpp)
-make_test(SRC sparse.cpp)
+make_test(SRC sparse.cpp            SERIAL)
 make_test(SRC sparse_arith.cpp)
 make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
@@ -282,3 +282,15 @@ make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
 make_test(SRC inverse_deconv.cpp)
 make_test(SRC iterative_deconv.cpp)
+
+
+add_executable(print_info print_info.cpp)
+if(AF_BUILD_UNIFIED)
+  target_link_libraries(print_info ArrayFire::af)
+elseif(AF_BUILD_OPENCL)
+  target_link_libraries(print_info ArrayFire::afopencl)
+elseif(AF_BUILD_CUDA)
+  target_link_libraries(print_info ArrayFire::afcuda)
+elseif(AF_BUILD_CPU)
+  target_link_libraries(print_info ArrayFire::afcpu)
+endif()
diff --git a/test/print_info.cpp b/test/print_info.cpp
new file mode 100644
index 0000000000..8bbb80be36
--- /dev/null
+++ b/test/print_info.cpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+
+using namespace af;
+
+int main(int argc, const char** argv) {
+    int backend = getAvailableBackends();
+    if (backend & AF_BACKEND_OPENCL) {
+        setBackend(AF_BACKEND_OPENCL);
+    } else if (backend & AF_BACKEND_CUDA) {
+        setBackend(AF_BACKEND_CUDA);
+    } else if (backend & AF_BACKEND_CPU) {
+        setBackend(AF_BACKEND_CPU);
+    }
+
+    info();
+    return 0;
+}

From 85650d8028e1766cf4ba6b87714c752c46079229 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 17 Jul 2018 15:56:03 -0400
Subject: [PATCH 1480/2677] Fix LAPACK linking errors

---
 src/backend/common/CMakeLists.txt | 9 +++++++--
 src/backend/common/lapacke.hpp    | 2 +-
 src/backend/cuda/CMakeLists.txt   | 1 +
 src/backend/opencl/CMakeLists.txt | 1 +
 4 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 4800e79446..4c7f7f0612 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -32,8 +32,6 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
@@ -62,6 +60,13 @@ if(AF_WITH_LOGGING)
       spdlog::spdlog)
 endif()
 
+if(APPLE AND NOT USE_MKL)
+  target_sources(afcommon_interface
+    INTERFACE
+      ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.cpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.hpp)
+endif()
+
 if(AF_WITH_GRAPHICS)
   dependency_check(glbinding_FOUND "glbinding not found.")
 
diff --git a/src/backend/common/lapacke.hpp b/src/backend/common/lapacke.hpp
index e13c6c113e..1fa3eabb89 100644
--- a/src/backend/common/lapacke.hpp
+++ b/src/backend/common/lapacke.hpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
 ********************************************************/
 
-#if defined(__APPLE__)
+#if defined(__APPLE__) && !defined(AF_CUDA)
 #include <types.hpp>
 #include <backend.hpp>
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e410b57c91..bdd90ed192 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -404,6 +404,7 @@ cuda_add_library(afcuda
   )
 
 arrayfire_set_default_cxx_flags(afcuda)
+target_compile_definitions(afcuda PRIVATE AF_CUDA)
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 9001713fba..bf3d1cd340 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -389,6 +389,7 @@ target_compile_definitions(afopencl
   PRIVATE
     CL_USE_DEPRECATED_OPENCL_1_2_APIS
     __CL_ENABLE_EXCEPTIONS
+    AF_OPENCL
   )
 
 target_link_libraries(afopencl

From d25ab305517adfd59297808af510f5104c435e96 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 17 Jul 2018 15:56:34 -0400
Subject: [PATCH 1481/2677] Fix redefinition and template initilization
 warnings

---
 src/api/c/imageio.cpp |  1 +
 src/api/cpp/data.cpp  | 88 +++++++++++++++++++++++--------------------
 2 files changed, 48 insertions(+), 41 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 5f4355dded..89d29c0db6 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -90,6 +90,7 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
     // NOTE: Redefine the MODULE_FUNCTION_INIT macro to call the static functions
     // instead of dynamically loaded symbols in case we are building with a static
     // FreeImage library
+    #undef MODULE_FUNCTION_INIT
     #define MODULE_FUNCTION_INIT(NAME)                  \
       NAME = &::NAME
 
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index b13c2395a1..e6945eaf9a 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -16,12 +16,21 @@
 #include "error.hpp"
 #include <af/defines.h>
 
-namespace af
-{
+#include <type_traits>
+
+using std::enable_if;
+using af::array;
+using af::dim4;
+using af::dtype;
+
+namespace {
+    template<typename T> struct is_complex          { static const bool value = false;  };
+    template<> struct           is_complex<af::cfloat>  { static const bool value = true;   };
+    template<> struct           is_complex<af::cdouble> { static const bool value = true;   };
 
     template<typename T>
-    array
-    constant(T val, const dim4 &dims, const af::dtype type)
+    typename enable_if<is_complex<T>::value == false, array>::type
+    constant(T val, const dim4& dims, const dtype type)
     {
         af_array res;
         if (type != s64 && type != u64) {
@@ -40,11 +49,12 @@ namespace af
         return array(res);
     }
 
-    template<>
-    AFAPI array constant(cfloat val, const dim4 &dims, const af::dtype type)
+    template<typename T>
+    typename enable_if<is_complex<T>::value == true, array>::type
+    constant(T val, const dim4& dims, const dtype type)
     {
         if (type != c32 && type != c64) {
-            return constant(real(val), dims, type);
+            return ::constant(real(val), dims, type);
         }
         af_array res;
         AF_THROW(af_constant_complex(&res,
@@ -54,57 +64,53 @@ namespace af
                                      dims.get(), type));
         return array(res);
     }
+}
 
-    template<>
-    AFAPI array constant(cdouble val, const dim4 &dims, const af::dtype type)
-    {
-        if (type != c32 && type != c64) {
-            return constant(real(val), dims, type);
-        }
-        af_array res;
-        AF_THROW(af_constant_complex(&res,
-                                     real(val),
-                                     imag(val),
-                                     dims.ndims(),
-                                     dims.get(), type));
-        return array(res);
-    }
+namespace af
+{
     template<typename T>
-    array constant(T val, const dim_t d0, const af::dtype ty)
-    {
-        return constant(val, dim4(d0), ty);
+    array constant(T val, const dim4& dims, const af::dtype type) {
+         return ::constant(val, dims, type);
     }
 
+     template<typename T>
+     array constant(T val, const dim_t d0, const af::dtype ty)
+     {
+         return ::constant(val, dim4(d0), ty);
+     }
+
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const af::dtype ty)
     {
-        return constant(val, dim4(d0, d1), ty);
+        return ::constant(val, dim4(d0, d1), ty);
     }
 
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const af::dtype ty)
     {
-        return constant(val, dim4(d0, d1, d2), ty);
+        return ::constant(val, dim4(d0, d1, d2), ty);
     }
 
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3, const af::dtype ty)
     {
-        return constant(val, dim4(d0, d1, d2, d3), ty);
-    }
-
-#define CONSTANT(TYPE)                                                              \
-    template AFAPI array constant<TYPE>(TYPE val, const dim4 &dims, const af::dtype ty);  \
-    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0, const af::dtype ty);    \
-    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,                         \
-                                            const dim_t d1, const af::dtype ty);    \
-    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,                         \
-                                            const dim_t d1,                         \
-                                            const dim_t d2, const af::dtype ty);    \
-    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,                         \
-                                            const dim_t d1,                         \
-                                            const dim_t d2,                         \
-                                            const dim_t d3, const af::dtype ty);
+        return ::constant(val, dim4(d0, d1, d2, d3), ty);
+    }
+
+#define CONSTANT(TYPE)                                                       \
+    template AFAPI array constant<TYPE>(TYPE val, const dim4&  dims,         \
+                                        const af::dtype ty);                 \
+    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,            \
+                                        const af::dtype ty);                 \
+    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,            \
+                                        const dim_t d1, const af::dtype ty); \
+    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,            \
+                                        const dim_t d1, const dim_t d2,      \
+                                        const af::dtype ty);                 \
+    template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,            \
+                                        const dim_t d1,                      \
+                                        const dim_t d2,                      \
+                                        const dim_t d3, const af::dtype ty);
     CONSTANT(double);
     CONSTANT(float);
     CONSTANT(int);

From d34745104e51f47d68b5c303c88f4da8901630db Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 18 Jul 2018 16:51:40 -0400
Subject: [PATCH 1482/2677] fix memAlloc for opencl backend, correct use case
 in morph

---
 src/backend/opencl/kernel/morph.hpp |  4 +---
 src/backend/opencl/memory.cpp       | 16 ++++++++--------
 src/backend/opencl/memory.hpp       |  3 ++-
 3 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index dd1dbf6f01..fb201e7155 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -90,7 +90,7 @@ void morph(Param out, const Param in, const Param mask, int windLen=0)
 
     // copy mask/filter to constant memory
     cl_int se_size   = sizeof(T)*windLen*windLen;
-    cl::Buffer *mBuff = bufferAlloc(se_size);
+    auto mBuff = memAlloc<T>(windLen*windLen);
     getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
 
     // calculate shared memory size
@@ -102,8 +102,6 @@ void morph(Param out, const Param in, const Param mask, int windLen=0)
             *out.data, out.info, *in.data, in.info, *mBuff,
             cl::Local(locSize*sizeof(T)), blk_x, blk_y, windLen);
 
-    bufferFree(mBuff);
-
     CL_DEBUG_FINISH(getQueue());
 }
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 4d2ff16dfe..4eb5892b46 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -64,11 +64,11 @@ void printMemInfo(const char *msg, const int device)
 }
 
 template<typename T>
-unique_ptr<T[], function<void(T *)>>
+unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>
 memAlloc(const size_t &elements)
 {
-    T* ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
-    return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
+    cl::Buffer* ptr = static_cast<cl::Buffer *>(memoryManager().alloc(elements * sizeof(T), false));
+    return unique_ptr<cl::Buffer, function<void(cl::Buffer* )>>(ptr, bufferFree);
 }
 
 void* memAllocUser(const size_t &bytes)
@@ -135,11 +135,11 @@ bool checkMemoryLimit()
     return memoryManager().checkMemoryLimit();
 }
 
-#define INSTANTIATE(T)                                                              \
-    template unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements); \
-    template void memFree(T* ptr);                                                  \
-    template T* pinnedAlloc(const size_t &elements);                                \
-    template void pinnedFree(T* ptr);                                               \
+#define INSTANTIATE(T)                                                                                 \
+    template unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc<T>(const size_t &elements); \
+    template void memFree(T* ptr);                                                                     \
+    template T* pinnedAlloc(const size_t &elements);                                                   \
+    template void pinnedFree(T* ptr);                                                                  \
 
     INSTANTIATE(float)
     INSTANTIATE(cfloat)
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index a5aa11bdcd..10b25b73e5 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -26,7 +26,8 @@ namespace opencl
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
 
-template<typename T>  std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
+template<typename T> std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>>
+      memAlloc(const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument

From f4be8ef0e3fb91b29f7ea9ea2c1fb59e90fb0852 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jul 2018 01:53:47 -0400
Subject: [PATCH 1483/2677] Add batching support for cond argument in select;
 Batching Tests (#2243)

* Add batching support for cond argument in select; Batching Tests
* Remove c++11 dependency on select tests
---
 src/api/c/select.cpp              |  78 +++++-----
 src/backend/cpu/kernel/select.hpp |  25 ++-
 test/select.cpp                   | 245 +++++++++++++++++++++++++++++-
 3 files changed, 298 insertions(+), 50 deletions(-)

diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 788f016706..0725625760 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -33,26 +33,24 @@ af_err af_select(af_array *out, const af_array cond, const af_array a, const af_
     try {
         const ArrayInfo& ainfo = getInfo(a);
         const ArrayInfo& binfo = getInfo(b);
-        const ArrayInfo& cinfo = getInfo(cond);
+        const ArrayInfo& cond_info = getInfo(cond);
 
-        if(cinfo.ndims() == 0) {
+        if(cond_info.ndims() == 0) {
             return af_retain_array(out, cond);
         }
 
         ARG_ASSERT(2, ainfo.getType() == binfo.getType());
-        ARG_ASSERT(1, cinfo.getType() == b8);
-
-        DIM_ASSERT(1, cinfo.ndims() == std::min(ainfo.ndims(), binfo.ndims()));
+        ARG_ASSERT(1, cond_info.getType() == b8);
 
         dim4 adims = ainfo.dims();
         dim4 bdims = binfo.dims();
-        dim4 cdims = cinfo.dims();
+        dim4 cond_dims = cond_info.dims();
         dim4 odims(1, 1, 1, 1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cdims[i] == std::min(adims[i], bdims[i]));
-            DIM_ASSERT(2, adims[i] == bdims[i] || adims[i] == 1 || bdims[i] == 1);
-            odims[i] = std::max(adims[i], bdims[i]);
+            DIM_ASSERT(2, (adims[i] == bdims[i] && adims[i] == cond_dims[i])
+                        || adims[i] == 1 || bdims[i] == 1 || cond_dims[i] == 1);
+            odims[i] = std::max(std::max(adims[i], bdims[i]), cond_dims[i]);
         }
 
         af_array res;
@@ -92,30 +90,31 @@ af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a,
         const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
-        DIM_ASSERT(1, cinfo.ndims() == ainfo.ndims());
 
         dim4 adims = ainfo.dims();
-        dim4 cdims = cinfo.dims();
+        dim4 cond_dims = cinfo.dims();
+        dim4 odims(1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cdims[i] == adims[i]);
+            DIM_ASSERT(1, cond_dims[i] == adims[i] | cond_dims[i] == 1 | adims[i] == 1);
+            odims[i] = std::max(cond_dims[i], adims[i]);
         }
 
         af_array res;
 
         switch (ainfo.getType()) {
-        case f32: res = select_scalar<float  , false>(cond, a, b, adims); break;
-        case f64: res = select_scalar<double , false>(cond, a, b, adims); break;
-        case c32: res = select_scalar<cfloat , false>(cond, a, b, adims); break;
-        case c64: res = select_scalar<cdouble, false>(cond, a, b, adims); break;
-        case s32: res = select_scalar<int    , false>(cond, a, b, adims); break;
-        case u32: res = select_scalar<uint   , false>(cond, a, b, adims); break;
-        case s16: res = select_scalar<short  , false>(cond, a, b, adims); break;
-        case u16: res = select_scalar<ushort , false>(cond, a, b, adims); break;
-        case s64: res = select_scalar<intl   , false>(cond, a, b, adims); break;
-        case u64: res = select_scalar<uintl  , false>(cond, a, b, adims); break;
-        case u8:  res = select_scalar<uchar  , false>(cond, a, b, adims); break;
-        case b8:  res = select_scalar<char   , false>(cond, a, b, adims); break;
+        case f32: res = select_scalar<float  , false>(cond, a, b, odims); break;
+        case f64: res = select_scalar<double , false>(cond, a, b, odims); break;
+        case c32: res = select_scalar<cfloat , false>(cond, a, b, odims); break;
+        case c64: res = select_scalar<cdouble, false>(cond, a, b, odims); break;
+        case s32: res = select_scalar<int    , false>(cond, a, b, odims); break;
+        case u32: res = select_scalar<uint   , false>(cond, a, b, odims); break;
+        case s16: res = select_scalar<short  , false>(cond, a, b, odims); break;
+        case u16: res = select_scalar<ushort , false>(cond, a, b, odims); break;
+        case s64: res = select_scalar<intl   , false>(cond, a, b, odims); break;
+        case u64: res = select_scalar<uintl  , false>(cond, a, b, odims); break;
+        case u8:  res = select_scalar<uchar  , false>(cond, a, b, odims); break;
+        case b8:  res = select_scalar<char   , false>(cond, a, b, odims); break;
         default:  TYPE_ERROR(2, ainfo.getType());
         }
 
@@ -131,30 +130,31 @@ af_err af_select_scalar_l(af_array *out, const af_array cond, const double a, co
         const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
-        DIM_ASSERT(1, cinfo.ndims() == binfo.ndims());
 
         dim4 bdims = binfo.dims();
-        dim4 cdims = cinfo.dims();
+        dim4 cond_dims = cinfo.dims();
+        dim4 odims(1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cdims[i] == bdims[i]);
+            DIM_ASSERT(1, cond_dims[i] == bdims[i] || cond_dims[i] == 1 || bdims[i] == 1);
+            odims[i] = std::max(cond_dims[i], bdims[i]);
         }
 
         af_array res;
 
         switch (binfo.getType()) {
-        case f32: res = select_scalar<float  , true >(cond, b, a, bdims); break;
-        case f64: res = select_scalar<double , true >(cond, b, a, bdims); break;
-        case c32: res = select_scalar<cfloat , true >(cond, b, a, bdims); break;
-        case c64: res = select_scalar<cdouble, true >(cond, b, a, bdims); break;
-        case s32: res = select_scalar<int    , true >(cond, b, a, bdims); break;
-        case u32: res = select_scalar<uint   , true >(cond, b, a, bdims); break;
-        case s16: res = select_scalar<short  , true >(cond, b, a, bdims); break;
-        case u16: res = select_scalar<ushort , true >(cond, b, a, bdims); break;
-        case s64: res = select_scalar<intl   , true >(cond, b, a, bdims); break;
-        case u64: res = select_scalar<uintl  , true >(cond, b, a, bdims); break;
-        case u8:  res = select_scalar<uchar  , true >(cond, b, a, bdims); break;
-        case b8:  res = select_scalar<char   , true >(cond, b, a, bdims); break;
+        case f32: res = select_scalar<float  , true >(cond, b, a, odims); break;
+        case f64: res = select_scalar<double , true >(cond, b, a, odims); break;
+        case c32: res = select_scalar<cfloat , true >(cond, b, a, odims); break;
+        case c64: res = select_scalar<cdouble, true >(cond, b, a, odims); break;
+        case s32: res = select_scalar<int    , true >(cond, b, a, odims); break;
+        case u32: res = select_scalar<uint   , true >(cond, b, a, odims); break;
+        case s16: res = select_scalar<short  , true >(cond, b, a, odims); break;
+        case u16: res = select_scalar<ushort , true >(cond, b, a, odims); break;
+        case s64: res = select_scalar<intl   , true >(cond, b, a, odims); break;
+        case u64: res = select_scalar<uintl  , true >(cond, b, a, odims); break;
+        case u8:  res = select_scalar<uchar  , true >(cond, b, a, odims); break;
+        case b8:  res = select_scalar<char   , true >(cond, b, a, odims); break;
         default:  TYPE_ERROR(2, binfo.getType());
         }
 
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index d88bae4fea..468fba0aac 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -81,7 +81,9 @@ template<typename T, bool flip>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b)
 {
     af::dim4 astrides = a.strides();
+    af::dim4 adims = a.dims();
     af::dim4 cstrides = cond.strides();
+    af::dim4 cdims = cond.dims();
 
     af::dim4 odims = out.dims();
     af::dim4 ostrides = out.strides();
@@ -90,27 +92,36 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b)
     T *optr = out.get();
     const char *cptr = cond.get();
 
+
+    bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
+                        adims[2] == odims[2], adims[3] == odims[3]};
+
+    bool is_c_same[] = {cdims[0] == odims[0], cdims[1] == odims[1],
+                        cdims[2] == odims[2], cdims[3] == odims[3]};
+
     for (int l = 0; l < odims[3]; l++) {
 
         int o_off3 = ostrides[3] * l;
-        int a_off3 = astrides[3] * l;
-        int c_off3 = cstrides[3] * l;
+        int a_off3 = astrides[3] * is_a_same[3] * l;
+        int c_off3 = cstrides[3] * is_c_same[3] * l;
 
         for (int k = 0; k < odims[2]; k++) {
 
             int o_off2 = ostrides[2] * k + o_off3;
-            int a_off2 = astrides[2] * k + a_off3;
-            int c_off2 = cstrides[2] * k + c_off3;
+            int a_off2 = astrides[2] * is_a_same[2] * k + a_off3;
+            int c_off2 = cstrides[2] * is_c_same[2] * k + c_off3;
 
             for (int j = 0; j < odims[1]; j++) {
 
                 int o_off1 = ostrides[1] * j + o_off2;
-                int a_off1 = astrides[1] * j + a_off2;
-                int c_off1 = cstrides[1] * j + c_off2;
+                int a_off1 = astrides[1] * is_a_same[1] * j + a_off2;
+                int c_off1 = cstrides[1] * is_c_same[1] * j + c_off2;
 
                 for (int i = 0; i < odims[0]; i++) {
 
-                    optr[o_off1 + i] = (flip ^ cptr[c_off1 + i]) ? aptr[a_off1 + i] : b;
+                    bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
+                    T    aval = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
+                    optr[o_off1 + i] = (flip ^ cval) ? aval : b;
                 }
             }
         }
diff --git a/test/select.cpp b/test/select.cpp
index 37afa845b7..3c7c4e90f8 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -7,16 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
+
+#include <cstdio>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
 using af::NaN;
 using af::array;
 using af::cdouble;
@@ -31,6 +34,9 @@ using af::select;
 using af::seq;
 using af::span;
 using af::sum;
+using std::string;
+using std::stringstream;
+using std::vector;
 
 
 template<typename T>
@@ -296,3 +302,234 @@ TEST(Select, MaxDim)
 
     ASSERT_FLOAT_EQ(sum, 0.f);
 }
+
+struct select_params {
+    dim4 out;
+    dim4 cond;
+    dim4 a;
+    dim4 b;
+  select_params(dim4 out_, dim4 cond_, dim4 a_, dim4 b_)
+    : out(out_), cond(cond_), a(a_), b(b_)
+  {}
+};
+
+class Select_ : public ::testing::TestWithParam<select_params> {};
+
+string pd4(dim4 dims) {
+    string out(32, '\0');
+    int len = snprintf(const_cast<char*>(out.data()), 32,
+                       "%d_%d_%d_%d", dims[0], dims[1], dims[2], dims[3]);
+    out.resize(len);
+    return out;
+}
+
+string testNameGenerator(const ::testing::TestParamInfo<Select_::ParamType> info) {
+    stringstream ss;
+    ss << "out_" << pd4(info.param.out)
+       << "_cond_" << pd4(info.param.cond)
+       << "_a_" << pd4(info.param.a)
+       << "_b_" << pd4(info.param.b);
+    return ss.str();
+}
+
+vector<select_params> getSelectTestParams(int M, int N) {
+  const select_params _[] = {select_params(dim4(M), dim4(M), dim4(M), dim4(M)),
+          select_params(dim4(M, N), dim4(M, N), dim4(M, N), dim4(M, N)),
+          select_params(dim4(M, N, N), dim4(M, N, N), dim4(M, N, N), dim4(M, N, N)),
+          select_params(dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N)),
+          select_params(dim4(M, N), dim4(M, 1), dim4(M, 1), dim4(M, N)),
+          select_params(dim4(M, N), dim4(M, 1), dim4(M, N), dim4(M, 1)),
+          select_params(dim4(M, N), dim4(M, 1), dim4(M, N), dim4(M, N)),
+          select_params(dim4(M, N), dim4(M, N), dim4(M, 1), dim4(M, N)),
+          select_params(dim4(M, N), dim4(M, N), dim4(M, N), dim4(M, 1)),
+          select_params(dim4(M, N), dim4(M, N), dim4(M, 1), dim4(M, 1))};
+  return vector<select_params>(_, _ + sizeof(_) / sizeof(_[0]));
+}
+
+INSTANTIATE_TEST_CASE_P(
+  SmallDims,
+  Select_,
+  ::testing::ValuesIn(getSelectTestParams(10, 5)),
+  testNameGenerator);
+
+INSTANTIATE_TEST_CASE_P(
+  Dims33_9,
+  Select_,
+  ::testing::ValuesIn(getSelectTestParams(33, 9)),
+  testNameGenerator);
+
+INSTANTIATE_TEST_CASE_P(
+  DimsLg,
+  Select_,
+  ::testing::ValuesIn(getSelectTestParams(512, 32)),
+  testNameGenerator);
+
+TEST_P(Select_, Batch) {
+    select_params params = GetParam();
+
+    float aval = 5.0f;
+    float bval = 10.0f;
+    array a = constant(aval, params.a);
+    array b = constant(bval, params.b);
+    array cond = (iota(params.cond) % 2).as(b8);
+
+    array out = select(cond, a, b);
+
+    EXPECT_EQ(out.dims(), params.out);
+
+    vector<float> h_out(out.elements()); out.host(h_out.data());
+    vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
+
+    vector<float> gold(params.out.elements());
+    for(int i = 0; i < gold.size(); i++) {
+        gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
+        ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
+    }
+}
+
+struct selectlr_params {
+  dim4 out;
+  dim4 cond;
+  dim4 ab;
+  selectlr_params(dim4 out_, dim4 cond_, dim4 ab_)
+    : out(out_), cond(cond_), ab(ab_) {}
+};
+
+class SelectLR_ : public ::testing::TestWithParam<selectlr_params> {};
+
+vector<selectlr_params> getSelectLRTestParams(int M, int N) {
+  const selectlr_params _[] = {
+              selectlr_params(dim4(M), dim4(M), dim4(M)),
+              selectlr_params(dim4(M, N), dim4(M, N), dim4(M, N)),
+              selectlr_params(dim4(M, N, N), dim4(M, N, N), dim4(M, N, N)),
+              selectlr_params(dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N)),
+              selectlr_params(dim4(M, N), dim4(M, 1), dim4(M, N)),
+              selectlr_params(dim4(M, N), dim4(M, N), dim4(M, 1))};
+
+  return vector<selectlr_params> (_, _+sizeof(_)/sizeof(_[0]));
+}
+
+string testNameGeneratorLR(const ::testing::TestParamInfo<SelectLR_::ParamType> info) {
+  stringstream ss;
+  ss << "out_" << pd4(info.param.out)
+     << "_cond_" << pd4(info.param.cond)
+     << "_ab_" << pd4(info.param.ab);
+  return ss.str();
+}
+
+INSTANTIATE_TEST_CASE_P(
+                        SmallDims,
+                        SelectLR_,
+                        ::testing::ValuesIn(getSelectLRTestParams(10, 5)),
+                        testNameGeneratorLR);
+
+INSTANTIATE_TEST_CASE_P(
+                        Dims33_9,
+                        SelectLR_,
+                        ::testing::ValuesIn(getSelectLRTestParams(33, 9)),
+                        testNameGeneratorLR);
+
+INSTANTIATE_TEST_CASE_P(
+                        DimsLg,
+                        SelectLR_,
+                        ::testing::ValuesIn(getSelectLRTestParams(512, 32)),
+                        testNameGeneratorLR);
+
+
+TEST_P(SelectLR_, BatchL) {
+    selectlr_params params = GetParam();
+
+    float aval = 5.0f;
+    float bval = 10.0f;
+    array b = constant(bval, params.ab);
+    array cond = (iota(params.cond) % 2).as(b8);
+
+    array out = select(cond, static_cast<double>(aval), b);
+
+    EXPECT_EQ(out.dims(), params.out);
+
+    vector<float> h_out(out.elements()); out.host(h_out.data());
+    vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
+
+    vector<float> gold(params.out.elements());
+    for(int i = 0; i < gold.size(); i++) {
+        gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
+        ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
+    }
+}
+
+TEST_P(SelectLR_, BatchR) {
+    selectlr_params params = GetParam();
+
+    float aval = 5.0f;
+    float bval = 10.0f;
+    array a = constant(aval, params.ab);
+    array cond = (iota(params.cond) % 2).as(b8);
+
+    array out = select(cond, a, static_cast<double>(bval));
+
+    EXPECT_EQ(out.dims(), params.out);
+
+    vector<float> h_out(out.elements()); out.host(h_out.data());
+    vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
+
+    vector<float> gold(params.out.elements());
+    for(int i = 0; i < gold.size(); i++) {
+        gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
+        ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
+    }
+}
+
+TEST(Select, InvalidSizeOfAB) {
+    af_array a = 0;
+    af_array b = 0;
+    af_array cond = 0;
+    af_array out = 0;
+
+    double val = 0;
+    dim_t dims = 10;
+    ASSERT_EQ(AF_SUCCESS, af_constant(&a, val, 1, &dims, f32));
+
+    dims = 9;
+    ASSERT_EQ(AF_SUCCESS, af_constant(&b, val, 1, &dims, f32));
+
+    dims = 10;
+    ASSERT_EQ(AF_SUCCESS, af_constant(&cond, val, 1, &dims, b8));
+
+    ASSERT_EQ(AF_ERR_SIZE, af_select(&out, cond, a, b));
+
+    char* msg = NULL;
+    dim_t len = 0;
+    af_get_last_error(&msg, &len);
+    af_free_host(msg);
+    af_release_array(a);
+    af_release_array(b);
+    af_release_array(cond);
+}
+
+TEST(Select, InvalidSizeOfCond) {
+    af_array a = 0;
+    af_array b = 0;
+    af_array cond = 0;
+    af_array out = 0;
+
+    double val = 0;
+    dim_t dims = 10;
+    ASSERT_EQ(AF_SUCCESS, af_constant(&a, val, 1, &dims, f32));
+
+    dims = 10;
+    ASSERT_EQ(AF_SUCCESS, af_constant(&b, val, 1, &dims, f32));
+
+    dims = 9;
+    ASSERT_EQ(AF_SUCCESS, af_constant(&cond, val, 1, &dims, b8));
+
+    ASSERT_EQ(AF_ERR_SIZE, af_select(&out, cond, a, b));
+
+    char* msg = NULL;
+    dim_t len = 0;
+    af_get_last_error(&msg, &len);
+    af_free_host(msg);
+    af_release_array(a);
+    af_release_array(b);
+    af_release_array(cond);
+}

From 35d9ceaf862d8b48926829a85cec9a53caab12fa Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Jul 2018 10:57:17 +0530
Subject: [PATCH 1484/2677] Fix Array padding kernel in cpu backend

---
 src/backend/cpu/copy.hpp                     |  1 +
 src/backend/cpu/kernel/pad_array_borders.hpp | 22 +++++---------------
 2 files changed, 6 insertions(+), 17 deletions(-)

diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index d7781640b9..fbccc7fbbd 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -38,6 +38,7 @@ namespace cpu
                              const dim4& upperBoundPadding,
                              const af::borderType btype)
     {
+        in.eval();
         const dim4& iDims = in.dims();
 
         dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
index c30761575c..2c5a03fa64 100644
--- a/src/backend/cpu/kernel/pad_array_borders.hpp
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -103,36 +103,24 @@ void padBorders(Param<T> out, CParam<T> in,
      * PADDED REGIONS AND SKIP REST
      * */
     for (dim_t l=0; l<oDims[3]; ++l) {
-        bool skipL = (l>=lBoundPadSize[3] && l<validRegEnds[3]);
-
-        if (skipL) continue;
-
+        bool skipL  = (l>=lBoundPadSize[3] && l<validRegEnds[3]);
         dim_t oLOff = oStrs[3] * l;
         dim_t iLOff = iStrs[3] *
             idxByndEdge(l, lBoundPadSize[3], iDims[3], btype);
-
         for (dim_t k=0; k<oDims[2]; ++k) {
-            bool skipK = (k>=lBoundPadSize[2] && k<validRegEnds[2]);
-
-            if (skipK) continue;
-
+            bool skipK  = (k>=lBoundPadSize[2] && k<validRegEnds[2]);
             dim_t oKOff = oStrs[2] * k;
             dim_t iKOff = iStrs[2] *
                 idxByndEdge(k, lBoundPadSize[2], iDims[2], btype);
-
             for (dim_t j=0; j<oDims[1]; ++j) {
-                bool skipJ = (j>=lBoundPadSize[1] && j<validRegEnds[1]);
-
-                if (skipJ) continue;
-
+                bool skipJ  = (j>=lBoundPadSize[1] && j<validRegEnds[1]);
                 dim_t oJOff = oStrs[1] * j;
                 dim_t iJOff = iStrs[1] *
                     idxByndEdge(j, lBoundPadSize[1], iDims[1], btype);
-
                 for (dim_t i=0; i<oDims[0]; ++i) {
                     bool skipI = (i>=lBoundPadSize[0] && i<validRegEnds[0]);
-
-                    if (skipI) continue;
+                    if (skipI && skipJ && skipK && skipL)
+                        continue;
 
                     dim_t oIOff = oStrs[0] * i;
                     dim_t iIOff = iStrs[0] *

From 32109f65e8ec6550a1fe6d3cba12930c8ba9a2c7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Jul 2018 10:57:58 +0530
Subject: [PATCH 1485/2677] Fix mem leaks in deconvolution tests

---
 src/api/c/deconvolution.cpp |  3 ++-
 src/backend/cpu/copy.hpp    |  1 -
 test/inverse_deconv.cpp     | 13 ++++++++-----
 test/iterative_deconv.cpp   | 14 +++++++++-----
 4 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index 90fd5f65ed..d33d877c37 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -82,6 +82,8 @@ calcPadInfo(dim4& inLPad, dim4& psfLPad,
 
             nElems *= odims[d];
         } else {
+            inLPad[d]  = 0;
+            psfLPad[d] = 0;
             inUPad[d]  = 0;
             psfUPad[d] = 0;
             odims[d]   = std::max(idims[d], fdims[d]);
@@ -263,7 +265,6 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
 
     auto index     = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad,
                                  odims, nElems, idims, fdims);
-
     auto paddedIn  = padArrayBorders<T>(input,  inLPad,  inUPad,
                                         AF_PAD_CLAMP_TO_EDGE);
     auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad,
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index fbccc7fbbd..d7781640b9 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -38,7 +38,6 @@ namespace cpu
                              const dim4& upperBoundPadding,
                              const af::borderType btype)
     {
-        in.eval();
         const dim4& iDims = in.dims();
 
         dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index d2f09a6f48..bf8061dd1c 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -33,7 +33,7 @@ typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
 TYPED_TEST_CASE(InverseDeconvolution, TestTypes);
 
 template<typename T, bool isColor>
-void imageTest(string pTestFile, const float gamma, const af_inverse_deconv_algo algo)
+void invDeconvImageTest(string pTestFile, const float gamma, const af_inverse_deconv_algo algo)
 {
     typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
 
@@ -72,6 +72,7 @@ void imageTest(string pTestFile, const float gamma, const af_inverse_deconv_algo
 
         ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
 
+        af_dtype itype = (af_dtype)af::dtype_traits<T>::af_type;
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
         ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
@@ -104,32 +105,34 @@ void imageTest(string pTestFile, const float gamma, const af_inverse_deconv_algo
         std::vector<OutType> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
-
         ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(kerArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
     }
 }
 
 TYPED_TEST(InverseDeconvolution, TikhonovOnGrayscale)
 {
     // Test file name format: <colorspace>_<gamma with dots replaced by "_">_<inverse deconv algo>.test
-    imageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_00_1_tikhonov.test"),
+    invDeconvImageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_00_1_tikhonov.test"),
                                 00.1f, AF_INVERSE_DECONV_TIKHONOV);
 }
 
 TYPED_TEST(InverseDeconvolution, DISABLED_WienerOnGrayscale)
 {
     // Test file name format: <colorspace>_<gamma with dots replaced by "_">_<inverse deconv algo>.test
-    imageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_1_wiener.test"),
+    invDeconvImageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_1_wiener.test"),
                                 1.0, AF_INVERSE_DECONV_DEFAULT);
     //TODO(pradeep) change to wiener enum value
 }
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 50546e1c08..c6f67c50af 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -33,7 +33,8 @@ typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
 TYPED_TEST_CASE(IterativeDeconvolution, TestTypes);
 
 template<typename T, bool isColor>
-void imageTest(string pTestFile, const unsigned iters, const float rf, const af::iterativeDeconvAlgo algo)
+void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
+                         const af::iterativeDeconvAlgo algo)
 {
     typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
 
@@ -72,6 +73,7 @@ void imageTest(string pTestFile, const unsigned iters, const float rf, const af:
 
         ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
 
+        af_dtype itype = (af_dtype)af::dtype_traits<T>::af_type;
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
         ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
@@ -104,25 +106,27 @@ void imageTest(string pTestFile, const unsigned iters, const float rf, const af:
         std::vector<OutType> goldData(nElems);
         ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
-
         ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(kerArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
         ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
     }
 }
 
 TYPED_TEST(IterativeDeconvolution, LandweberOnGrayscale)
 {
     // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation factor>_<algo>.test
-    imageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_landweber.test"),
+    iterDeconvImageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_landweber.test"),
             100, 0.05, AF_ITERATIVE_DECONV_LANDWEBER);
 }
 
@@ -130,6 +134,6 @@ TYPED_TEST(IterativeDeconvolution, RichardsonLucyOnGrayscale)
 {
     // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation factor>_<algo>.test
     // For RichardsonLucy algorithm, relaxation factor is not used.
-    imageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_lucy.test"),
+    iterDeconvImageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_lucy.test"),
             100, 0.05, AF_ITERATIVE_DECONV_RICHARDSONLUCY);
 }

From 64f35e40bcc0dff369d51fc3da1efad5dd7d3de1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jul 2018 01:14:32 -0400
Subject: [PATCH 1486/2677] Fix missing libdl when not building with graphics
 or freeimage

---
 src/api/c/CMakeLists.txt          | 1 -
 src/api/unified/CMakeLists.txt    | 1 -
 src/backend/common/CMakeLists.txt | 3 +++
 test/CMakeLists.txt               | 9 +++------
 4 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 7ef32c0e84..354005f5c3 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -166,7 +166,6 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage_STATIC)
   else ()
     target_include_directories(c_api_interface INTERFACE $<TARGET_PROPERTY:FreeImage::FreeImage,INTERFACE_INCLUDE_DIRECTORIES>)
-    target_link_libraries(c_api_interface INTERFACE ${CMAKE_DL_LIBS})
     if (WIN32 AND AF_INSTALL_STANDALONE)
       install(FILES $<TARGET_FILE:FreeImage::FreeImage>
         DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 38f887d576..bca6691749 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -29,7 +29,6 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/vision.cpp
   )
 
-
 target_sources(af
   PRIVATE
     ${CMAKE_SOURCE_DIR}/src/api/c/type_util.cpp
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 4c7f7f0612..97b225fc9f 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -45,6 +45,9 @@ else()
   target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_unix.cpp)
 endif()
 
+target_link_libraries(afcommon_interface
+  INTERFACE ${CMAKE_DL_LIBS})
+
 target_include_directories(afcommon_interface
   INTERFACE
     ${CMAKE_SOURCE_DIR}/src/backend
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 577216757e..72eb68cfef 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -30,7 +30,7 @@ if(NOT TARGET gtest)
 endif()
 
 # Reset the CXX flags for tests
-unset(CMAKE_CXX_STANDARD)
+set(CMAKE_CXX_STANDARD 98)
 set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
 
 if(${AF_USE_RELATIVE_TEST_DIR})
@@ -102,9 +102,7 @@ function(make_test)
     if(${backend} STREQUAL "unified")
       target_link_libraries(${target}
         PRIVATE
-          af
-          ${CMAKE_DL_LIBS}
-          )
+          af)
     else()
       target_link_libraries(${target}
         PRIVATE
@@ -133,8 +131,7 @@ function(make_test)
       target_compile_definitions(${target}
         PRIVATE
           WIN32_LEAN_AND_MEAN
-          NOMINMAX
-        )
+          NOMINMAX)
     endif()
 
     # TODO(umar): Create this executable separately

From 84e966169335a50638f6bad5039bd7e93f0b5013 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jul 2018 02:10:04 -0400
Subject: [PATCH 1487/2677] Remove allocation of raw pointers from various
 functions

---
 src/api/c/moments.cpp                | 15 ++++-----
 src/backend/cpu/homography.cpp       | 47 ++++++++++++++--------------
 src/backend/cuda/kernel/harris.hpp   | 10 +++---
 src/backend/cuda/kernel/reduce.hpp   | 19 +++++------
 src/backend/opencl/kernel/harris.hpp |  9 +++---
 test/CMakeLists.txt                  |  4 +--
 test/ocl_ext_context.cpp             |  6 ++++
 7 files changed, 58 insertions(+), 52 deletions(-)

diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 4232f29c0c..5345afd233 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -24,8 +24,11 @@
 #include <arith.hpp>
 
 #include <limits>
+#include <vector>
 
 using af::dim4;
+
+using std::vector;
 using namespace detail;
 
 template<typename T>
@@ -60,14 +63,12 @@ af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
 template<typename T>
 static inline void moment_copy(double* out, const af_array moments)
 {
-    dim_t elems;
-    af_get_elements(&elems, moments);
-    T *h_ptr = new T[elems];
-    af_get_data_ptr((void *)h_ptr, moments);
+    auto info = getInfo(moments);
+    vector<T> h_moments(info.elements());
+    copyData(h_moments.data(), moments);
 
-    for(unsigned i=0; i<elems; ++i)
-        out[i] = (double)h_ptr[i];
-    delete[] h_ptr;
+    // convert to double
+    copy(begin(h_moments), end(h_moments), out);
 }
 
 af_err af_moments_all(double* out, const af_array in, const af_moment_type moment)
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index fc0d75edeb..fa970f3953 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -17,7 +17,10 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+#include <array>
+
 using af::dim4;
+using std::array;
 
 namespace cpu
 {
@@ -52,38 +55,38 @@ struct EPS<double>
     static double eps() { return DBL_EPSILON; }
 };
 
-template<typename T>
-void JacobiSVD(T* S, T* V, int m, int n)
+template<typename T, int M, int N>
+void JacobiSVD(T* S, T* V)
 {
     const int iterations = 30;
-    T* d = new T[n];
+    array<T, N> d;
 
-    for (int i = 0; i < n; i++) {
+    for (int i = 0; i < N; i++) {
         T sd = 0;
-        for (int j = 0; j < m; j++) {
-            T t = S[i*m + j];
+        for (int j = 0; j < M; j++) {
+            T t = S[i*M + j];
             sd += t*t;
         }
         d[i] = sd;
 
-        V[i*n + i] = 1;
+        V[i*N + i] = 1;
     }
 
     for (int it = 0; it < iterations; it++) {
         bool converged = false;
 
-        for (int i = 0; i < n-1; i++) {
-            for (int j = i+1; j < n; j++) {
-                T* Si = S + i*m;
-                T* Sj = S + j*m;
-                T* Vi = V + i*n;
-                T* Vj = V + j*n;
+        for (int i = 0; i < N-1; i++) {
+            for (int j = i+1; j < N; j++) {
+                T* Si = S + i*M;
+                T* Sj = S + j*M;
+                T* Vi = V + i*N;
+                T* Vj = V + j*N;
 
                 T p = (T)0;
-                for (int k = 0; k < m; k++)
+                for (int k = 0; k < M; k++)
                     p += Si[k]*Sj[k];
 
-                if (std::abs(p) <= m*EPS<T>::eps()*std::sqrt(d[i]*d[j]))
+                if (std::abs(p) <= M*EPS<T>::eps()*std::sqrt(d[i]*d[j]))
                     continue;
 
                 T y = d[i] - d[j];
@@ -100,7 +103,7 @@ void JacobiSVD(T* S, T* V, int m, int n)
                 }
 
                 T a = 0, b = 0;
-                for (int k = 0; k < m; k++) {
+                for (int k = 0; k < M; k++) {
                     T t0 = c*Si[k] + s*Sj[k];
                     T t1 = c*Sj[k] - s*Si[k];
                     Si[k] = t0;
@@ -112,7 +115,7 @@ void JacobiSVD(T* S, T* V, int m, int n)
                 d[i] = a;
                 d[j] = b;
 
-                for (int l = 0; l < n; l++) {
+                for (int l = 0; l < N; l++) {
                     T t0 = Vi[l] * c + Vj[l] * s;
                     T t1 = Vj[l] * c - Vi[l] * s;
 
@@ -126,8 +129,6 @@ void JacobiSVD(T* S, T* V, int m, int n)
                 break;
         }
     }
-
-    delete[] d;
 }
 
 unsigned updateIterations(float inlier_ratio, unsigned iter)
@@ -212,14 +213,14 @@ int computeHomography(T* H_ptr, const float* rnd_ptr,
     Array<T> V = createValueArray<T>(af::dim4(Adims[1], Adims[1]), (T)0);
     V.eval();
     getQueue().sync();
-    JacobiSVD<T>(A.get(), V.get(), 9, 9);
+    JacobiSVD<T, 9, 9>(A.get(), V.get());
 
-    af::dim4 Vdims = V.dims();
+    dim4 Vdims = V.dims();
     T* V_ptr = V.get();
 
-    std::vector<T> vH;
+    array<T, 9> vH;
     for (unsigned j = 0; j < 9; j++)
-        vH.push_back(V_ptr[8 * Vdims[0] + j]);
+        vH[j] = V_ptr[8 * Vdims[0] + j];
 
     H_ptr[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
     H_ptr[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 15fc2d6033..58fbec280b 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -19,6 +19,8 @@
 #include "sort_by_key.hpp"
 #include "range.hpp"
 
+#include <vector>
+
 namespace cuda
 {
 
@@ -193,14 +195,14 @@ void harris(unsigned* corners_out,
             const float k_thr)
 {
     // Window filter
-    convAccT *h_filter = new convAccT[filter_len];
+    std::vector<convAccT> h_filter(filter_len);
     // Decide between rectangular or circular filter
     if (sigma < 0.5f) {
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
     }
     else {
-        gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
+        gaussian1D<convAccT>(h_filter.data(), (int)filter_len, sigma);
     }
 
     // Copy filter to device object
@@ -216,11 +218,9 @@ void harris(unsigned* corners_out,
     int filter_elem = filter.strides[3] * filter.dims[3];
     auto filter_alloc = memAlloc<convAccT>(filter_elem);
     filter.ptr = filter_alloc.get();
-    CUDA_CHECK(cudaMemcpyAsync(filter.ptr, h_filter, filter_elem * sizeof(convAccT),
+    CUDA_CHECK(cudaMemcpyAsync(filter.ptr, h_filter.data(), filter_elem * sizeof(convAccT),
                 cudaMemcpyHostToDevice, cuda::getActiveStream()));
 
-    delete[] h_filter;
-
     const unsigned border_len = filter_len / 2 + 1;
 
     Param<T> ix, iy;
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 6c54b058ba..e5c6c28ea8 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -19,6 +19,8 @@
 
 #include <cub/warp/warp_reduce.cuh>
 
+#include <vector>
+
 using std::unique_ptr;
 
 namespace cuda
@@ -379,26 +381,21 @@ namespace kernel
             reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
                                               change_nan, nanval);
 
-            unique_ptr<To[]> h_ptr(new To[tmp_elements]);
-            To* h_ptr_raw = h_ptr.get();
-
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(To),
+            std::vector<To> h_data(tmp_elements);
+            CUDA_CHECK(cudaMemcpyAsync(h_data.data(), tmp.ptr, tmp_elements * sizeof(To),
                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
             Binary<To, op> reduce;
             To out = Binary<To, op>::init();
             for (int i = 0; i < tmp_elements; i++) {
-                out = reduce(out, h_ptr_raw[i]);
+                out = reduce(out, h_data[i]);
             }
 
             return out;
-
         } else {
-
-            unique_ptr<Ti[]> h_ptr(new Ti[in_elements]);
-            Ti* h_ptr_raw = h_ptr.get();
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(Ti),
+            std::vector<Ti> h_data(in_elements);
+            CUDA_CHECK(cudaMemcpyAsync(h_data.data(), in.ptr, in_elements * sizeof(Ti),
                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
@@ -408,7 +405,7 @@ namespace kernel
             To nanval_to = scalar<To>(nanval);
 
             for (int i = 0; i < in_elements; i++) {
-                To in_val = transform(h_ptr_raw[i]);
+                To in_val = transform(h_data[i]);
                 if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval_to;
                 out = reduce(out, in_val);
             }
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index f170d67226..a92463d532 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -20,7 +20,9 @@
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
 #include <cache.hpp>
+
 #include <tuple>
+#include <vector>
 
 namespace opencl
 {
@@ -132,21 +134,20 @@ harris(unsigned* corners_out,
     using cl::EnqueueArgs;
     using cl::NDRange;
 
-
     // Window filter
-    convAccT* h_filter = new convAccT[filter_len];
+    std::vector<convAccT> h_filter(filter_len);
     // Decide between rectangular or circular filter
     if (sigma < 0.5f) {
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
     } else {
-        gaussian1D<convAccT>(h_filter, (int)filter_len, sigma);
+        gaussian1D<convAccT>(h_filter.data(), (int)filter_len, sigma);
     }
 
     const unsigned border_len = filter_len / 2 + 1;
 
     // Copy filter to device object
-    Array<convAccT> filter = createHostDataArray<convAccT>(filter_len, h_filter);
+    Array<convAccT> filter = createHostDataArray<convAccT>(filter_len, h_filter.data());
     Array<T> ix = createEmptyArray<T>(dim4(4, in.info.dims));
     Array<T> iy = createEmptyArray<T>(dim4(4, in.info.dims));
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 72eb68cfef..2666ccfc32 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -198,9 +198,11 @@ make_test(SRC imageio.cpp)
 make_test(SRC index.cpp)
 make_test(SRC info.cpp)
 make_test(SRC internal.cpp)
+make_test(SRC inverse_deconv.cpp)
 make_test(SRC inverse_dense.cpp)
 make_test(SRC iota.cpp)
 make_test(SRC ireduce.cpp)
+make_test(SRC iterative_deconv.cpp)
 make_test(SRC jit.cpp)
 make_test(SRC join.cpp)
 make_test(SRC lu_dense.cpp)
@@ -277,8 +279,6 @@ make_test(SRC where.cpp)
 make_test(SRC wrap.cpp)
 make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
-make_test(SRC inverse_deconv.cpp)
-make_test(SRC iterative_deconv.cpp)
 
 
 add_executable(print_info print_info.cpp)
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index c05661e572..3b747a924c 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -51,7 +51,13 @@ void getExternals(cl_device_id &deviceId, cl_context &context, cl_command_queue
         cId = clCreateContext(NULL, 1, &dId, NULL, NULL, &errorCode);
         checkErr(errorCode, "Context creation failed");
 
+        #ifdef CL_VERSION_2_0
+        qId = clCreateCommandQueueWithProperties(cId, dId, 0, &errorCode);
+        #else
         qId = clCreateCommandQueue(cId, dId, 0, &errorCode);
+        #endif
+
+
         checkErr(errorCode, "Command queue creation failed");
         call_once = false;
     }

From a01afe99c7af6c45b35ea00d4ebfd6eaee681cf9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jul 2018 03:35:39 -0400
Subject: [PATCH 1488/2677] Add tests for array io operations. Remove
 new/delete

---
 src/api/c/stream.cpp |  24 +++++----
 test/CMakeLists.txt  |   1 +
 test/arrayio.cpp     | 123 +++++++++++++++++++++++++++++++++++++++++++
 test/data            |   2 +-
 4 files changed, 138 insertions(+), 12 deletions(-)
 create mode 100644 test/arrayio.cpp

diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index f74fe31720..2725ee6e5a 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -7,20 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <fstream>
-#include <iomanip>
-#include <vector>
 
-#include <af/array.h>
-#include <common/ArrayInfo.hpp>
-#include <handle.hpp>
+
 #include <backend.hpp>
+#include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <handle.hpp>
 #include <type_util.hpp>
 
+#include <af/array.h>
 #include <af/index.h>
 
+#include <fstream>
+#include <iomanip>
+#include <vector>
+
 using namespace detail;
+using std::string;
+using std::vector;
 
 #define STREAM_FORMAT_VERSION 0x1
 static const char sfv_char = STREAM_FORMAT_VERSION;
@@ -297,21 +301,19 @@ int checkVersionAndFindIndex(const char *filename, const char *k)
         for(int i = 0; i < n_arrays; i++) {
             int klen = -1;
             fs.read((char*)&klen, sizeof(int));
-            char *readKey = new char[klen + 1];
-            fs.read(readKey, klen);
-            readKey[klen] = '\0';
+            string readKey;
+            readKey.resize(klen);
+            fs.read(&readKey.front(), klen);
 
             if(key == readKey) {
                 // Ket matches, break
                 index = i;
-                delete [] readKey;
                 break;
             } else {
                 // Key doesn't match. Skip the data
                 intl offset = -1;
                 fs.read((char*)&offset, sizeof(intl));
                 fs.seekg(offset, std::ios_base::cur);
-                delete [] readKey;
             }
         }
     } else {
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 2666ccfc32..8806a8966d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -151,6 +151,7 @@ make_test(SRC anisotropic_diffusion.cpp)
 make_test(SRC approx1.cpp)
 make_test(SRC approx2.cpp)
 make_test(SRC array.cpp)
+make_test(SRC arrayio.cpp)
 make_test(SRC assign.cpp)
 make_test(SRC backend.cpp)
 make_test(SRC basic.cpp)
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
new file mode 100644
index 0000000000..eb08dd0265
--- /dev/null
+++ b/test/arrayio.cpp
@@ -0,0 +1,123 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+
+#include <testHelpers.hpp>
+
+#include <complex>
+#include <string>
+#include <vector>
+
+using af::allTrue;
+using af::array;
+using af::constant;
+using af::dim4;
+using af::readArray;
+using af::saveArray;
+using std::complex;
+using std::string;
+using std::vector;
+
+struct type_params {
+  string name;
+  af_dtype type;
+  double real;
+  double imag;
+  type_params(string n, af_dtype t, double r, double i = 0.) : name(n), type(t), real(r), imag(i) {}
+};
+
+class ArrayIOType : public ::testing::TestWithParam<type_params> {};
+
+string getTypeName( const ::testing::TestParamInfo<ArrayIOType::ParamType> info) {
+    return info.param.name;
+}
+
+INSTANTIATE_TEST_CASE_P(Types,
+                        ArrayIOType,
+                        ::testing::Values(
+                                          type_params("f32", f32, 3.14f, 0),
+                                          type_params("f64", f64, 3.14, 0),
+                                          type_params("c32", c32, 3.0f, 4.5f),
+                                          type_params("c64", c64, 3.0, 4.5),
+                                          type_params("s32", s32, 11),
+                                          type_params("u32", u32, 12),
+                                          type_params("u8",  u8, 13),
+                                          type_params("b8",  b8, 1),
+                                          type_params("s64", s64, 15),
+                                          type_params("u64", u64, 16),
+                                          type_params("s16", s16, 17),
+                                          type_params("u16", u16, 18)),
+                        getTypeName);
+
+TEST_P(ArrayIOType, ReadType) {
+    type_params p = GetParam();
+    array arr = readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(), p.name.c_str());
+
+    ASSERT_EQ(arr.type(), p.type);
+}
+
+TEST_P(ArrayIOType, ReadSize) {
+    type_params p = GetParam();
+    array arr = readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(), p.name.c_str());
+
+    ASSERT_EQ(arr.dims(), dim4(10, 10));
+}
+
+template<typename T>
+void checkVals(array arr, double r, double i, af_dtype t) {
+  vector<T> d(arr.elements());
+  arr.host(d.data());
+  int elements = arr.elements();
+  for(int ii = 0; ii < elements; ii++) {
+      if(t == c32 || t == c64) {
+          ASSERT_EQ(r, real<T>(d[ii])) << "at: " << ii;
+          ASSERT_EQ(i, imag<T>(d[ii])) << "at: " << ii;
+      } else {
+          ASSERT_EQ(real(r), real(d[ii])) << "at: " << ii;
+      }
+  }
+}
+
+TEST_P(ArrayIOType, ReadContent) {
+    type_params p = GetParam();
+    array arr = readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(), p.name.c_str());
+
+    switch(arr.type()) {
+    case f32: checkVals<float>(arr, p.real, p.imag, p.type); break;
+    case f64: checkVals<double>(arr, p.real, p.imag, p.type); break;
+    case c32: checkVals<af::cfloat>(arr, p.real, p.imag, p.type); break;
+    case c64: checkVals<af::cdouble>(arr, p.real, p.imag, p.type); break;
+    case s32: checkVals<int>(arr, p.real, p.imag, p.type); break;
+    case u32: checkVals<unsigned>(arr, p.real, p.imag, p.type); break;
+    case u8: checkVals<unsigned char>(arr, p.real, p.imag, p.type); break;
+    case b8: checkVals<char>(arr, p.real, p.imag, p.type); break;
+    case s64: checkVals<long long>(arr, p.real, p.imag, p.type); break;
+    case u64: checkVals<unsigned long long>(arr, p.real, p.imag, p.type); break;
+    case s16: checkVals<short>(arr, p.real, p.imag, p.type); break;
+    case u16: checkVals<unsigned short>(arr, p.real, p.imag, p.type); break;
+    default: FAIL() << "Invalid type";
+    }
+}
+
+TEST(ArrayIO, Save) {
+    array a = constant(1, 10, 10);
+    array b = constant(2, 10, 10);
+
+    saveArray("a", a, "arr.af");
+    saveArray("b", b, "arr.af", true);
+
+    array aread = readArray("arr.af", "a");
+    array bread = readArray("arr.af", "b");
+
+    ASSERT_TRUE(allTrue<bool>(aread == a));
+    ASSERT_TRUE(allTrue<bool>(bread == b));
+}
diff --git a/test/data b/test/data
index f5aca1b32c..2b59e5af8c 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit f5aca1b32c1ba6c7ffbcccb883193ef74200b595
+Subproject commit 2b59e5af8ce801252f6f9b9c3e06f796b6203ed3

From 13fbf0c8418b11d9678295c744c45c456e4c23d4 Mon Sep 17 00:00:00 2001
From: Vardan Akopian <vakopian@sustain.net>
Date: Fri, 19 Aug 2016 18:45:43 -0700
Subject: [PATCH 1489/2677] optimize 2D morph by pre-computing the mask linear
 offsets

---
 src/backend/cpu/kernel/morph.hpp | 90 ++++++++++++++++----------------
 src/backend/cpu/morph.cpp        | 21 ++++++--
 test/testHelpers.hpp             |  3 ++
 3 files changed, 66 insertions(+), 48 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 79486cea17..c9b976a425 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -17,59 +17,59 @@ namespace cpu
 {
 namespace kernel
 {
-template<typename T, bool IsDilation>
-void morph(Param<T> out, CParam<T> in, CParam<T> mask)
+template<typename T>
+void getOffsets(std::vector<dim_t>& offsets,
+                const af::dim4& strides, const CParam<T>& mask)
 {
-    const af::dim4 ostrides = out.strides();
-    const af::dim4 istrides = in.strides();
     const af::dim4 fstrides = mask.strides();
-    const af::dim4 dims     = in.dims();
-    const af::dim4 window   = mask.dims();
-    const T*   filter   = mask.get();
-    const dim_t R0      = window[0]/2;
-    const dim_t R1      = window[1]/2;
+    const T * filter = mask.get();
+    const dim_t dim0 = mask.dims()[0], dim1 = mask.dims()[1];
+    const dim_t R0 = dim0/2;
+    const dim_t R1 = dim1/2;
+
+    offsets.reserve(mask.dims().elements());
+    for (dim_t j = 0; j < dim1; ++j) {
+        for (dim_t i = 0; i < dim0; ++i) {
+            if (filter[ getIdx(fstrides, i, j) ] > (T)0) {
+                dim_t offset = (j - R1) * strides[1] + (i - R0) * strides[0];
+                offsets.push_back(offset);
+            }
+        }
+    }
+}
 
+template<typename T, bool IsDilation>
+void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask)
+{
     T init = IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
+    const af::dim4 ostrides = paddedOut.strides();
+    T * outData             = paddedOut.get();
+    const af::dim4 istrides = paddedIn.strides();
+    const af::dim4 dims     = paddedIn.dims();
+    const T * inData        = paddedIn.get();
 
-        T* outData          = out.get() + b3 * ostrides[3];
-        const T*   inData   = in.get() + b3 * istrides[3];
+    std::vector<dim_t> offsets;
+    getOffsets(offsets, istrides, mask);
 
+    dim_t batchNumElements = dims[0] * dims[1];
+    for(dim_t b3=0; b3<dims[3]; ++b3) {
         for(dim_t b2=0; b2<dims[2]; ++b2) {
-            // either channels or batch is handled by outer most loop
-            for(dim_t j=0; j<dims[1]; ++j) {
-                // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
-                    // i steps along 1st dimension
-                    T filterResult = init;
-
-                    // wj,wi steps along 2nd & 1st dimensions of filter window respectively
-                    for(dim_t wj=0; wj<window[1]; wj++) {
-                        for(dim_t wi=0; wi<window[0]; wi++) {
-
-                            dim_t offj = j+wj-R1;
-                            dim_t offi = i+wi-R0;
-
-                            T maskValue = filter[ getIdx(fstrides, wi, wj) ];
-
-                            if ((maskValue > (T)0) && offi>=0 && offj>=0 && offi<dims[0] && offj<dims[1]) {
-
-                                T inValue   = inData[ getIdx(istrides, offi, offj) ];
-
-                                if (IsDilation)
-                                    filterResult = std::max(filterResult, inValue);
-                                else
-                                    filterResult = std::min(filterResult, inValue);
-                            }
-
-                        } // window 1st dimension loop ends here
-                    } // filter window loop ends here
-
-                    outData[ getIdx(ostrides, i, j) ] = filterResult;
-                } //1st dimension loop ends here
-            } // 2nd dimension loop ends here
-
+            for (dim_t n = 0; n < batchNumElements; ++n) {
+                T filterResult = init;
+                for (size_t oi = 0; oi < offsets.size(); ++oi) {
+                    dim_t x = n + offsets[oi];
+                    if (x >= 0 && x < batchNumElements) {
+                        T inValue = inData[x];
+                        if (IsDilation) {
+                            filterResult = std::max(filterResult, inValue);
+                        } else {
+                            filterResult = std::min(filterResult, inValue);
+                        }
+                    }
+                }
+                outData[n] = filterResult;
+            }
             // next iteration will be next batch if any
             outData += ostrides[2];
             inData  += istrides[2];
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index fa5f6c2477..723b36759a 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -9,6 +9,7 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
+#include <copy.hpp>
 #include <morph.hpp>
 #include <algorithm>
 #include <platform.hpp>
@@ -25,11 +26,25 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
     in.eval();
     mask.eval();
 
-    Array<T> out = createEmptyArray<T>(in.dims());
+    const af::dim4 idims = in.dims();
+    const af::dim4 mdims = mask.dims();
 
-    getQueue().enqueue(kernel::morph<T, isDilation>, out, in, mask);
+    const af::dim4 lpad(mdims[0]/2, mdims[1]/2, 0, 0);
+    const af::dim4 upad(lpad);
+    const af::dim4 odims(lpad[0] + idims[0] + upad[0],
+                         lpad[1] + idims[1] + upad[1],
+                         idims[2], idims[3]);
 
-    return out;
+    auto out = createEmptyArray<T>(odims);
+    auto inp = padArrayBorders(in, lpad, upad, AF_PAD_ZERO);
+
+    getQueue().enqueue(kernel::morph<T, isDilation>, out, inp, mask);
+
+    std::vector<af_seq> idxs(4, af_span);
+    idxs[0] = af_seq{double(lpad[0]), double(lpad[0]+idims[0]-1), 1.0};
+    idxs[1] = af_seq{double(lpad[1]), double(lpad[1]+idims[1]-1), 1.0};
+
+    return createSubArray(out, idxs);
 }
 
 template<typename T, bool isDilation>
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index b35a85d8a8..877f8b0a88 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -323,6 +323,9 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
     double NRMSD = std::sqrt(accum)/(maxion-minion);
 
     if (std::isnan(NRMSD) || NRMSD > tolerance) {
+#ifndef NDEBUG
+        printf("Comparison failed, NRMSD value: %lf\n", NRMSD);
+#endif
         return false;
     }
 

From acabaf39794b446a8b3f73b4fba55e4f7aaec70b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 21 Jul 2018 13:58:11 +0530
Subject: [PATCH 1490/2677] Fix padding value used in cpu backend 2D morph

---
 src/backend/cpu/kernel/morph.hpp | 40 ++++++++++++++++----------------
 src/backend/cpu/morph.cpp        |  4 +++-
 2 files changed, 23 insertions(+), 21 deletions(-)

diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index c9b976a425..a54200fc14 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -38,9 +38,17 @@ void getOffsets(std::vector<dim_t>& offsets,
     }
 }
 
+template<typename T, bool IsDilation>
+struct MorphFilterOp {
+    T operator()(const T& a, const T& b) {
+        return IsDilation ? std::max(a, b) : std::min(a, b);
+    }
+};
+
 template<typename T, bool IsDilation>
 void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask)
 {
+    MorphFilterOp<T, IsDilation> filterOp;
     T init = IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 
     const af::dim4 ostrides = paddedOut.strides();
@@ -52,28 +60,20 @@ void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask)
     std::vector<dim_t> offsets;
     getOffsets(offsets, istrides, mask);
 
-    dim_t batchNumElements = dims[0] * dims[1];
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        for(dim_t b2=0; b2<dims[2]; ++b2) {
-            for (dim_t n = 0; n < batchNumElements; ++n) {
-                T filterResult = init;
-                for (size_t oi = 0; oi < offsets.size(); ++oi) {
-                    dim_t x = n + offsets[oi];
-                    if (x >= 0 && x < batchNumElements) {
-                        T inValue = inData[x];
-                        if (IsDilation) {
-                            filterResult = std::max(filterResult, inValue);
-                        } else {
-                            filterResult = std::min(filterResult, inValue);
-                        }
-                    }
-                }
-                outData[n] = filterResult;
+    const dim_t batchSize = dims[0] * dims[1];
+    const int  batchCount = dims[2] * dims[3];
+    for (int b = 0; b < batchCount; ++b) {
+        for (dim_t n = 0; n < batchSize; ++n) {
+            T filterResult = init;
+            for (size_t oi = 0; oi < offsets.size(); ++oi) {
+                dim_t x = n + offsets[oi];
+                if (x >= 0 && x < batchSize)
+                    filterResult = filterOp(filterResult, inData[x]);
             }
-            // next iteration will be next batch if any
-            outData += ostrides[2];
-            inData  += istrides[2];
+            outData[n] = filterResult;
         }
+        outData += ostrides[2];
+        inData  += istrides[2];
     }
 }
 
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 723b36759a..ecb6681882 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -23,6 +23,8 @@ namespace cpu
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask)
 {
+    af::borderType padType = isDilation ? AF_PAD_ZERO : AF_PAD_CLAMP_TO_EDGE;
+
     in.eval();
     mask.eval();
 
@@ -36,7 +38,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
                          idims[2], idims[3]);
 
     auto out = createEmptyArray<T>(odims);
-    auto inp = padArrayBorders(in, lpad, upad, AF_PAD_ZERO);
+    auto inp = padArrayBorders(in, lpad, upad, padType);
 
     getQueue().enqueue(kernel::morph<T, isDilation>, out, inp, mask);
 

From 906f881e936fb2f3a427408b0f47ba25786bc2ba Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 25 Jul 2018 12:47:25 +0530
Subject: [PATCH 1491/2677] Fix typo in Susan detector example

---
 examples/computer_vision/susan.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/computer_vision/susan.cpp b/examples/computer_vision/susan.cpp
index 0ca947d4af..db107812a4 100644
--- a/examples/computer_vision/susan.cpp
+++ b/examples/computer_vision/susan.cpp
@@ -77,7 +77,7 @@ int main(int argc, char** argv)
     try {
         af::setDevice(device);
         af::info();
-        printf("** ArrayFire FAST Feature Detector Demo **\n\n");
+        printf("** ArrayFire SUSAN Feature Detector Demo **\n\n");
         susan_demo(console);
 
     } catch (af::exception& ae) {

From fbdd7990fd85eee09c59b7f86679966e5c176a3e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 25 Jul 2018 12:47:41 +0530
Subject: [PATCH 1492/2677] Bump up forge (gfx upstream) to v1.0.2

---
 CMakeModules/build_forge.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 2f964e0326..71cefe998a 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -7,7 +7,7 @@
 
 include(ExternalProject)
 
-set(FORGE_VERSION af3.6.0)
+set(FORGE_VERSION v1.0.2)
 set(prefix "${ArrayFire_BINARY_DIR}/third_party/forge")
 set(PX ${CMAKE_SHARED_LIBRARY_PREFIX})
 set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX})

From 2c8fb67ce5d07e573396eb8764470fb79086c797 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 25 Jul 2018 15:25:52 +0530
Subject: [PATCH 1493/2677] Refactor sign to signbit internally

The operation being performance is equivalent of std::signbit
thus, using signbit is more apt and removes unnecessary redefine
of sign function in opencl jit kernel.
---
 src/api/c/optypes.hpp            |  2 +-
 src/api/c/unary.cpp              | 10 ++++++----
 src/backend/cpu/unary.hpp        | 10 ++--------
 src/backend/cuda/kernel/jit.cuh  |  1 -
 src/backend/cuda/unary.hpp       |  5 ++++-
 src/backend/opencl/kernel/jit.cl |  1 -
 src/backend/opencl/unary.hpp     |  4 +++-
 7 files changed, 16 insertions(+), 17 deletions(-)

diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index 17e55d9944..cecf3bd8a0 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -75,7 +75,7 @@ typedef enum {
     af_ceil_t,
     af_round_t,
     af_trunc_t,
-    af_sign_t,
+    af_signbit_t,
 
     af_rem_t,
     af_mod_t,
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 396efac8bd..5028a9fd49 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -108,12 +108,14 @@ static af_err af_unary_complex(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-#define UNARY(fn)                                       \
-    af_err af_##fn(af_array *out, const af_array in)    \
+#define UNARY_FN(name, opcode)                          \
+    af_err af_##name(af_array *out, const af_array in)  \
     {                                                   \
-        return af_unary<af_##fn##_t>(out, in);          \
+        return af_unary<af_##opcode##_t>(out, in);          \
     }
 
+#define UNARY(fn) UNARY_FN(fn, fn)
+
 #define UNARY_COMPLEX(fn)                               \
     af_err af_##fn(af_array *out, const af_array in)    \
     {                                                   \
@@ -121,7 +123,7 @@ static af_err af_unary_complex(af_array *out, const af_array in)
     }
 
 UNARY(trunc)
-UNARY(sign)
+UNARY_FN(sign, signbit)
 UNARY(round)
 UNARY(floor)
 UNARY(ceil)
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 03410e2502..fab26cda21 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -16,12 +16,6 @@
 namespace cpu
 {
 
-template<typename T>
-T sign(T in)
-{
-    return T(std::signbit(in));
-}
-
 template<typename T>
 T sigmoid(T in)
 {
@@ -61,7 +55,7 @@ UNARY_OP(atanh)
 
 UNARY_OP(round)
 UNARY_OP(trunc)
-UNARY_OP_FN(sign, sign)
+UNARY_OP(signbit)
 UNARY_OP(floor)
 UNARY_OP(ceil)
 
@@ -83,7 +77,7 @@ UNARY_OP(tgamma)
 UNARY_OP(lgamma)
 
 #undef UNARY_OP
-#undef sign
+#undef UNARY_OP_FN
 
     template<typename T, af_op_t op>
     Array<T> unaryOp(const Array<T> &in)
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 3a5133f2dc..f59fd3e0bd 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -24,7 +24,6 @@ typedef cuDoubleComplex cdouble;
 // ----------------------------------------------
 // REAL NUMBER OPERATIONS
 // ----------------------------------------------
-#define sign(in) signbit((in))
 #define __noop(a) (a)
 #define __add(lhs, rhs) (lhs) + (rhs)
 #define __sub(lhs, rhs) (lhs) - (rhs)
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index ed3d81944a..bc0ae64450 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -62,7 +62,7 @@ UNARY_FN(cbrt)
 
 UNARY_FN(trunc)
 UNARY_FN(round)
-UNARY_FN(sign)
+UNARY_FN(signbit)
 UNARY_FN(ceil)
 UNARY_FN(floor)
 
@@ -70,6 +70,9 @@ UNARY_FN(isinf)
 UNARY_FN(isnan)
 UNARY_FN(iszero)
 
+#undef UNARY_DECL
+#undef UNARY_FN
+
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in)
 {
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index d846f86c13..b3462f3e54 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -11,7 +11,6 @@
 #define __not_select(cond, a, b) (cond) ? (b) : (a)
 #define __circular_mod(a, b) ((a) < (b)) ? (a) : (a - b)
 
-#define sign(in) signbit((in))
 #define __noop(a) (a)
 #define __add(lhs, rhs) (lhs) + (rhs)
 #define __sub(lhs, rhs) (lhs) - (rhs)
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 66f775da5d..78ff32e8c7 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -62,7 +62,7 @@ UNARY_FN(cbrt)
 
 UNARY_FN(trunc)
 UNARY_FN(round)
-UNARY_FN(sign)
+UNARY_FN(signbit)
 UNARY_FN(ceil)
 UNARY_FN(floor)
 
@@ -70,6 +70,8 @@ UNARY_FN(isinf)
 UNARY_FN(isnan)
 UNARY_FN(iszero)
 
+#undef UNARY_FN
+
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in)
 {

From 4c037239405c0cb4470a51f6529d9bf0c95d5f9b Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Tue, 17 Jul 2018 18:10:44 -0400
Subject: [PATCH 1494/2677] Create a set of testing macros for af::array and
 af_array objects

- Create the ASSERT_ARRAY_EQ macro to compare two af::array or af_array
- Tests types, dims, and values.
- Prints the index of the values that are different.
- Prints a more readable error message
---
 test/basic.cpp       |  64 +++++++++++++-
 test/testHelpers.hpp | 202 +++++++++++++++++++++++++++++++++++++++++--
 2 files changed, 257 insertions(+), 9 deletions(-)

diff --git a/test/basic.cpp b/test/basic.cpp
index 23b25307cb..c74a46d4e0 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -10,9 +10,10 @@
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <af/data.h>
-#include <vector>
 #include <testHelpers.hpp>
 
+#include <vector>
+
 using std::vector;
 using af::array;
 using af::constant;
@@ -320,3 +321,64 @@ TEST(BasicTests, Additionf32f64_CPP)
     }
     ASSERT_NEAR(0.0f, err, 1e-8);
 }
+
+TEST(Assert, TestEqualsCpp) {
+    array A = constant(1, 10, 10);
+    array B = constant(1, 10, 10);
+
+    // Testing this macro
+    // ASSERT_ARRAY_EQ(A, B);
+    ASSERT_TRUE(assertArrayEq("A", "B", A, B));
+}
+
+TEST(Assert, TestEqualsC) {
+    af_array A = 0;
+    af_array B = 0;
+    dim_t dims[] = {10, 10, 1, 1};
+    af_constant(&A, 1.0, 4, dims, f32);
+    af_constant(&B, 1.0, 4, dims, f32);
+
+    // Testing this macro
+    //ASSERT_ARRAY_EQ(a, b);
+    ASSERT_TRUE(assertArrayEq("A", "B", A, B));
+}
+
+TEST(Assert, TestEqualsDiffTypes) {
+    array A = constant(1, 10, 10, f64);
+    array B = constant(1, 10, 10);
+
+    // Testing this macro
+    // ASSERT_ARRAY_EQ(A, B);
+    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+}
+
+TEST(Assert, TestEqualsDiffSizes) {
+    array A = constant(1, 10, 9);
+    array B = constant(1, 10, 10);
+
+    // Testing this macro
+    // ASSERT_ARRAY_EQ(A, B);
+    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+}
+
+TEST(Assert, TestEqualsDiffValue) {
+    // array A = af::randu(3, 3, 3);
+    array A = constant(1, 3, 3);
+    array B = A;
+    B(2, 2) = 2;
+
+    // Testing this macro
+    //ASSERT_ARRAY_EQ(A, B);
+    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+}
+
+TEST(Assert, TestEqualsDiffComplexValue) {
+    // array A = af::randu(3, 3, 3);
+    array A = constant(af::cfloat(3.1f, 3.1f), 3, 3, c32);
+    array B = A;
+    B(2, 2) = 2.2;
+
+    // Testing this macro
+    // ASSERT_ARRAY_EQ(A, B);
+    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 877f8b0a88..ea7d92b4df 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -10,17 +10,21 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 
-#include <string>
+#include <af/array.h>
+#include <af/dim4.hpp>
+#include <af/internal.h>
+#include <arrayfire.h>
+#include <cfloat>
+#include <gtest/gtest.h>
+
+#include <algorithm>
 #include <fstream>
 #include <iterator>
-#include <vector>
-#include <algorithm>
 #include <limits>
 #include <stdexcept>
-#include <cfloat>
-#include <arrayfire.h>
-#include <af/dim4.hpp>
-#include <af/array.h>
+#include <string>
+#include <utility>
+#include <vector>
 
 typedef unsigned char  uchar;
 typedef unsigned int   uint;
@@ -333,7 +337,7 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
 }
 
 template<typename T, typename Other>
-struct is_same_type{
+struct is_same_type {
     static const bool value = false;
 };
 
@@ -355,6 +359,12 @@ struct cond_type<false, T, Other> {
     typedef Other type;
 };
 
+template<bool B, class T = void>
+struct enable_if {};
+
+template<class T>
+struct enable_if<true, T> { typedef T type; };
+
 template<typename T>
 inline double real(T val) { return (double)val; }
 template<>
@@ -369,6 +379,14 @@ inline double imag<af::cdouble>(af::cdouble val) { return imag(val); }
 template<>
 inline double imag<af::cfloat> (af::cfloat val) { return imag(val); }
 
+
+template< class T >
+struct is_floating_point {
+    static const bool value = is_same_type<float, T>::value  ||
+        is_same_type<double, T>::value  ||
+        is_same_type<long double, T>::value;
+};
+
 template<typename T>
 bool noDoubleTests()
 {
@@ -465,4 +483,172 @@ void cleanSlate()
   ASSERT_EQ(af::getMemStepSize(), step_bytes);
 }
 
+std::string dTypeToStr(af::dtype type) {
+    switch (type) {
+    case f32: return "f32"; break;
+    case c32: return "c32"; break;
+    case f64: return "f64"; break;
+    case c64: return "c64"; break;
+    case b8:  return "b8";  break;
+    case s32: return "s32"; break;
+    case u32: return "u32"; break;
+    case u8:  return "u8";  break;
+    case s64: return "s64"; break;
+    case u64: return "u64"; break;
+    case s16: return "s16"; break;
+    case u16: return "u16"; break;
+    default: return "";
+    }
+}
+
+af::dim4 unravel_idx(uint idx, af::array arr) {
+    af::dim4 coords;
+    af::dim4 dims = arr.dims();
+    af::dim4 st = af::getStrides(arr);
+
+    coords[3] = idx / (st[3]);
+    coords[2] = idx / (st[2]) % dims[2];
+    coords[1] = idx / (st[1]) % dims[1];
+    coords[0] = idx % dims[0];
+
+    return coords;
+}
+
+/// Compares two af::array or af_arrays for their type, dims, and values.
+///
+/// \param[in] EXPECTED This is the expected value of the assertion
+/// \param[in] ACTUAL This is the actual value of the calculation
+///
+/// \NOTE: This macro will deallocated the af_arrays after the call
+#define ASSERT_ARRAY_EQ(EXPECTED, ACTUAL) \
+    EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
+
+struct float_tag {};
+struct integer_tag {};
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      af::array a, af::array b,
+                                      integer_tag) {
+    uint nElems = a.elements();
+    af::dim4 arrDims = a.dims();
+
+    std::vector<T> hA(nElems);
+    a.host(hA.data());
+
+    std::vector<T> hB(nElems);
+    b.host(hB.data());
+
+    typedef typename std::vector<T>::iterator iter;
+    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
+
+    if (aItr == hA.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        int idx = std::distance(hA.begin(), aItr);
+        af::dim4 coords = unravel_idx(idx, a);
+
+        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
+                                             << coords[0] << ", " << coords[1] << ", "
+                                             << coords[2] << ", " << coords[3] << "): "
+                                             << aName << "(" << *aItr << "), "
+                                             << bName << "(" << *bItr << ")";
+    }
+
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      af::array a, af::array b,
+                                      float_tag) {
+    uint nElems = a.elements();
+    af::dim4 arrDims = a.dims();
+
+    std::vector<T> hA(nElems);
+    a.host(hA.data());
+
+    std::vector<T> hB(nElems);
+    b.host(hB.data());
+
+    typedef typename std::vector<T>::iterator iter;
+    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
+
+    if (aItr == hA.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        int idx = std::distance(hA.begin(), aItr);
+        af::dim4 coords = unravel_idx(idx, a);
+
+        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
+                                             << coords[0] << ", " << coords[1] << ", "
+                                             << coords[2] << ", " << coords[3] << "): "
+                                             << aName << "(" << *aItr << "), "
+                                             << bName << "(" << *bItr << ")";
+    }
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      af::array a, af::array b) {
+    typedef typename cond_type<
+        is_floating_point<typename af::dtype_traits<T>::base_type>::value,
+              float_tag, integer_tag>::type tag_type;
+    tag_type tag;
+
+    return elemWiseEq<T>(aName, bName, a, b, tag);
+}
+
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         af::array a, af::array b) {
+
+    af::dtype aType = a.type();
+    af::dtype bType = b.type();
+    if (aType != bType)
+        return ::testing::AssertionFailure() << "TYPE MISMATCH: "
+                                             << aName << "(" <<  dTypeToStr(a.type()) << ") and "
+                                             << bName << "(" << dTypeToStr(b.type()) << ")";
+
+    af::dtype arrDtype = aType;
+
+    const uint ndimIds = 4;
+    for (uint i = 0; i < ndimIds; ++i) {
+        if (a.dims()[i] != b.dims()[i])
+            return ::testing::AssertionFailure() << "SIZE MISMATCH on dim " << i << ": "
+                                                 << aName << "[" << a.dims() << "], "
+                                                 << bName << "[" << b.dims() << "]";
+    }
+
+    switch (arrDtype) {
+    case f32: return elemWiseEq<float>(aName, bName, a, b);              break;
+    case c32: return elemWiseEq<af::cfloat>(aName, bName, a, b);         break;
+    case f64: return elemWiseEq<double>(aName, bName, a, b);             break;
+    case c64: return elemWiseEq<af::cdouble>(aName, bName, a, b);        break;
+    case b8:  return elemWiseEq<char>(aName, bName, a, b);               break;
+    case s32: return elemWiseEq<int>(aName, bName, a, b);                break;
+    case u32: return elemWiseEq<uint>(aName, bName, a, b);               break;
+    case u8:  return elemWiseEq<uchar>(aName, bName, a, b);              break;
+    case s64: return elemWiseEq<long long>(aName, bName, a, b);          break;
+    case u64: return elemWiseEq<unsigned long long>(aName, bName, a, b); break;
+    case s16: return elemWiseEq<short>(aName, bName, a, b);              break;
+    case u16: return elemWiseEq<unsigned short>(aName, bName, a, b);     break;
+    default:  return ::testing::AssertionFailure()
+            << "INVALID TYPE, see enum numbers: "
+            << aName << "(" << a.type() << ") and "
+            << bName << "(" << b.type() << ")";
+    }
+
+    return ::testing::AssertionSuccess();
+}
+
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         af_array a, af_array b) {
+    af::array aa(a);
+    af::array bb(b);
+    return assertArrayEq(aName, bName, aa, bb);
+}
+
 #pragma GCC diagnostic pop

From dddbe628da946340e28375215df40ad05f73ae51 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Mon, 23 Jul 2018 17:28:02 -0400
Subject: [PATCH 1495/2677] Add operator<< for test and type enums in
 testHelpers

---
 test/testHelpers.hpp | 65 +++++++++++++++++++++++++-------------------
 1 file changed, 37 insertions(+), 28 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index ea7d92b4df..f7c9740d10 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -380,8 +380,8 @@ template<>
 inline double imag<af::cfloat> (af::cfloat val) { return imag(val); }
 
 
-template< class T >
-struct is_floating_point {
+template<class T>
+struct IsFloatingPoint {
     static const bool value = is_same_type<float, T>::value  ||
         is_same_type<double, T>::value  ||
         is_same_type<long double, T>::value;
@@ -483,25 +483,31 @@ void cleanSlate()
   ASSERT_EQ(af::getMemStepSize(), step_bytes);
 }
 
-std::string dTypeToStr(af::dtype type) {
+std::ostream& operator<<(std::ostream& os, af_err e) {
+    return os << af_err_to_string(e);
+}
+
+std::ostream& operator<<(std::ostream& os, af::dtype type) {
+    std::string name;
     switch (type) {
-    case f32: return "f32"; break;
-    case c32: return "c32"; break;
-    case f64: return "f64"; break;
-    case c64: return "c64"; break;
-    case b8:  return "b8";  break;
-    case s32: return "s32"; break;
-    case u32: return "u32"; break;
-    case u8:  return "u8";  break;
-    case s64: return "s64"; break;
-    case u64: return "u64"; break;
-    case s16: return "s16"; break;
-    case u16: return "u16"; break;
-    default: return "";
+    case f32: name = "f32"; break;
+    case c32: name = "c32"; break;
+    case f64: name = "f64"; break;
+    case c64: name = "c64"; break;
+    case b8:  name = "b8";  break;
+    case s32: name = "s32"; break;
+    case u32: name = "u32"; break;
+    case u8:  name = "u8";  break;
+    case s64: name = "s64"; break;
+    case u64: name = "u64"; break;
+    case s16: name = "s16"; break;
+    case u16: name = "u16"; break;
+    default: assert(false && "Invalid type");
     }
+    return os << name;
 }
 
-af::dim4 unravel_idx(uint idx, af::array arr) {
+af::dim4 unravelIdx(uint idx, af::array arr) {
     af::dim4 coords;
     af::dim4 dims = arr.dims();
     af::dim4 st = af::getStrides(arr);
@@ -523,13 +529,13 @@ af::dim4 unravel_idx(uint idx, af::array arr) {
 #define ASSERT_ARRAY_EQ(EXPECTED, ACTUAL) \
     EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
 
-struct float_tag {};
-struct integer_tag {};
+struct FloatTag {};
+struct IntegerTag {};
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       af::array a, af::array b,
-                                      integer_tag) {
+                                      IntegerTag) {
     uint nElems = a.elements();
     af::dim4 arrDims = a.dims();
 
@@ -548,7 +554,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         return ::testing::AssertionSuccess();
     } else {
         int idx = std::distance(hA.begin(), aItr);
-        af::dim4 coords = unravel_idx(idx, a);
+        af::dim4 coords = unravelIdx(idx, a);
 
         return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
                                              << coords[0] << ", " << coords[1] << ", "
@@ -562,7 +568,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       af::array a, af::array b,
-                                      float_tag) {
+                                      FloatTag) {
     uint nElems = a.elements();
     af::dim4 arrDims = a.dims();
 
@@ -581,7 +587,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         return ::testing::AssertionSuccess();
     } else {
         int idx = std::distance(hA.begin(), aItr);
-        af::dim4 coords = unravel_idx(idx, a);
+        af::dim4 coords = unravelIdx(idx, a);
 
         return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
                                              << coords[0] << ", " << coords[1] << ", "
@@ -595,9 +601,9 @@ template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       af::array a, af::array b) {
     typedef typename cond_type<
-        is_floating_point<typename af::dtype_traits<T>::base_type>::value,
-              float_tag, integer_tag>::type tag_type;
-    tag_type tag;
+        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
+              FloatTag, IntegerTag>::type TagType;
+    TagType tag;
 
     return elemWiseEq<T>(aName, bName, a, b, tag);
 }
@@ -609,8 +615,8 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     af::dtype bType = b.type();
     if (aType != bType)
         return ::testing::AssertionFailure() << "TYPE MISMATCH: "
-                                             << aName << "(" <<  dTypeToStr(a.type()) << ") and "
-                                             << bName << "(" << dTypeToStr(b.type()) << ")";
+                                             << aName << "(" <<  a.type() << ") and "
+                                             << bName << "(" << b.type() << ")";
 
     af::dtype arrDtype = aType;
 
@@ -651,4 +657,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     return assertArrayEq(aName, bName, aa, bb);
 }
 
+#define ASSERT_SUCCESS(CALL) \
+    ASSERT_EQ(AF_SUCCESS, CALL)
+
 #pragma GCC diagnostic pop

From eaf58a7881d3be1869a49d9a557e346ad5eee3bc Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Wed, 25 Jul 2018 15:56:53 -0400
Subject: [PATCH 1496/2677] Added assert for comparing af::array with
 std::vector

- Added type comparison in array-vector assert compare.
- Changed array and vector names for more clarity.
- Changed C API assert to not free C af_array after assert
---
 test/basic.cpp       | 111 ++++++++++++++++++++-------
 test/testHelpers.hpp | 177 ++++++++++++++++++++++++++++++++++++-------
 2 files changed, 231 insertions(+), 57 deletions(-)

diff --git a/test/basic.cpp b/test/basic.cpp
index c74a46d4e0..7c0a145751 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -323,62 +323,117 @@ TEST(BasicTests, Additionf32f64_CPP)
 }
 
 TEST(Assert, TestEqualsCpp) {
-    array A = constant(1, 10, 10);
-    array B = constant(1, 10, 10);
+    array gold = constant(1, 10, 10);
+    array out = constant(1, 10, 10);
 
     // Testing this macro
-    // ASSERT_ARRAY_EQ(A, B);
-    ASSERT_TRUE(assertArrayEq("A", "B", A, B));
+    // ASSERT_ARRAYS_EQ(gold, out);
+    ASSERT_TRUE(assertArrayEq("gold", "out", gold, out));
 }
 
 TEST(Assert, TestEqualsC) {
-    af_array A = 0;
-    af_array B = 0;
+    af_array gold = 0;
+    af_array out = 0;
     dim_t dims[] = {10, 10, 1, 1};
-    af_constant(&A, 1.0, 4, dims, f32);
-    af_constant(&B, 1.0, 4, dims, f32);
+    af_constant(&gold, 1.0, 4, dims, f32);
+    af_constant(&out, 1.0, 4, dims, f32);
 
     // Testing this macro
-    //ASSERT_ARRAY_EQ(a, b);
-    ASSERT_TRUE(assertArrayEq("A", "B", A, B));
+    // ASSERT_ARRAYS_EQ(gold, out);
+    ASSERT_TRUE(assertArrayEq("gold", "out", gold, out));
+
+    ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(gold));
 }
 
 TEST(Assert, TestEqualsDiffTypes) {
-    array A = constant(1, 10, 10, f64);
-    array B = constant(1, 10, 10);
+    array gold = constant(1, 10, 10, f64);
+    array out = constant(1, 10, 10);
 
     // Testing this macro
-    // ASSERT_ARRAY_EQ(A, B);
-    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+    // ASSERT_ARRAYS_EQ(gold, out);
+    ASSERT_FALSE(assertArrayEq("gold", "out", gold, out));
 }
 
 TEST(Assert, TestEqualsDiffSizes) {
-    array A = constant(1, 10, 9);
-    array B = constant(1, 10, 10);
+    array gold = constant(1, 10, 9);
+    array out = constant(1, 10, 10);
 
     // Testing this macro
-    // ASSERT_ARRAY_EQ(A, B);
-    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+    // ASSERT_ARRAYS_EQ(gold, out);
+    ASSERT_FALSE(assertArrayEq("gold", "out", gold, out));
 }
 
 TEST(Assert, TestEqualsDiffValue) {
     // array A = af::randu(3, 3, 3);
-    array A = constant(1, 3, 3);
-    array B = A;
-    B(2, 2) = 2;
+    array gold = constant(1, 3, 3);
+    array out = gold;
+    out(2, 2) = 2;
 
     // Testing this macro
-    //ASSERT_ARRAY_EQ(A, B);
-    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+    // ASSERT_ARRAYS_EQ(gold, out);
+    ASSERT_FALSE(assertArrayEq("gold", "out", gold, out));
 }
 
 TEST(Assert, TestEqualsDiffComplexValue) {
     // array A = af::randu(3, 3, 3);
-    array A = constant(af::cfloat(3.1f, 3.1f), 3, 3, c32);
-    array B = A;
-    B(2, 2) = 2.2;
+    array gold = constant(af::cfloat(3.1f, 3.1f), 3, 3, c32);
+    array out = gold;
+    out(2, 2) = 2.2;
+
+    // Testing this macro
+    // ASSERT_ARRAYS_EQ(gold, out);
+    ASSERT_FALSE(assertArrayEq("gold", "out", gold, out));
+}
+
+TEST(Assert, TestVectorEquals) {
+    array out = constant(3.1f, 3, 3);
+
+    vector<float> gold(out.elements());
+    dim4 goldDims(3, 3);
+    fill(gold.begin(), gold.end(), 3.1f);
+
+    // Testing this macro
+    // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
+    ASSERT_TRUE(assertArrayEq("gold", "goldDims", "out",
+                              gold, goldDims, out));
+}
+
+TEST(Assert, TestVectorDiffVecType) {
+    array out = constant(3.1f, 3, 3);
+
+    vector<int> gold(out.elements());
+    dim4 goldDims(3, 3);
+    fill(gold.begin(), gold.end(), 3.1f);
+
+    // Testing this macro
+    // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
+                              gold, goldDims, out));
+}
+
+TEST(Assert, TestVectorDiffDim4) {
+    array out = constant(3.1f, 3, 3);
+
+    vector<float> gold(out.elements());
+    dim4 goldDims(3, 2);
+    fill(gold.begin(), gold.end(), 3.1f);
+
+    // Testing this macro
+    // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
+                               gold, goldDims, out));
+}
+
+TEST(Assert, TestVectorDiffVecSize) {
+    array out = constant(3.1f, 3, 3);
+
+    vector<float> gold(out.elements() - 1);
+    dim4 goldDims(3, 3);
+    fill(gold.begin(), gold.end(), 3.1f);
 
     // Testing this macro
-    // ASSERT_ARRAY_EQ(A, B);
-    ASSERT_FALSE(assertArrayEq("A", "B", A, B));
+    // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
+                               gold, goldDims, out));
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index f7c9740d10..da8766c3a0 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -12,6 +12,7 @@
 
 #include <af/array.h>
 #include <af/dim4.hpp>
+#include <af/traits.hpp>
 #include <af/internal.h>
 #include <arrayfire.h>
 #include <cfloat>
@@ -23,6 +24,7 @@
 #include <limits>
 #include <stdexcept>
 #include <string>
+#include <typeinfo>
 #include <utility>
 #include <vector>
 
@@ -507,28 +509,45 @@ std::ostream& operator<<(std::ostream& os, af::dtype type) {
     return os << name;
 }
 
-af::dim4 unravelIdx(uint idx, af::array arr) {
+af::dim4 unravelIdx(uint idx, af::dim4 dims, af::dim4 strides) {
     af::dim4 coords;
-    af::dim4 dims = arr.dims();
-    af::dim4 st = af::getStrides(arr);
-
-    coords[3] = idx / (st[3]);
-    coords[2] = idx / (st[2]) % dims[2];
-    coords[1] = idx / (st[1]) % dims[1];
+    coords[3] = idx / (strides[3]);
+    coords[2] = idx / (strides[2]) % dims[2];
+    coords[1] = idx / (strides[1]) % dims[1];
     coords[0] = idx % dims[0];
 
     return coords;
 }
 
+af::dim4 unravelIdx(uint idx, af::array arr) {
+    af::dim4 dims = arr.dims();
+    af::dim4 st = af::getStrides(arr);
+    return unravelIdx(idx, dims, st);
+}
+
+/// Checks if the C-API arrayfire function returns successfully
+///
+/// \param[in] CALL This is the arrayfire C function
+#define ASSERT_SUCCESS(CALL)                    \
+    ASSERT_EQ(AF_SUCCESS, CALL)
+
 /// Compares two af::array or af_arrays for their type, dims, and values.
 ///
 /// \param[in] EXPECTED This is the expected value of the assertion
 /// \param[in] ACTUAL This is the actual value of the calculation
 ///
-/// \NOTE: This macro will deallocated the af_arrays after the call
-#define ASSERT_ARRAY_EQ(EXPECTED, ACTUAL) \
+/// \NOTE: This macro will deallocate the af_arrays after the call
+#define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
     EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
 
+/// Compares a std::vector with an af::array for their dims and values.
+///
+/// \param[in] EXPECTED_VEC The vector that represents the expected array
+/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
+/// \param[in] ACTUAL_ARR The actual array from the calculation
+#define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
+    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR)
+
 struct FloatTag {};
 struct IntegerTag {};
 
@@ -554,13 +573,13 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         return ::testing::AssertionSuccess();
     } else {
         int idx = std::distance(hA.begin(), aItr);
-        af::dim4 coords = unravelIdx(idx, a);
+        af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
 
         return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
                                              << coords[0] << ", " << coords[1] << ", "
                                              << coords[2] << ", " << coords[3] << "): "
-                                             << aName << "(" << *aItr << "), "
-                                             << bName << "(" << *bItr << ")";
+                                             << aName << "(" << hA[idx] << "), "
+                                             << bName << "(" << hB[idx] << ")";
     }
 
 }
@@ -587,13 +606,65 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         return ::testing::AssertionSuccess();
     } else {
         int idx = std::distance(hA.begin(), aItr);
-        af::dim4 coords = unravelIdx(idx, a);
+        af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
+
+        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
+                                             << coords[0] << ", " << coords[1] << ", "
+                                             << coords[2] << ", " << coords[3] << "): "
+                                             << aName << "(" << hA[idx] << "), "
+                                             << bName << "(" << hB[idx] << ")";
+    }
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
+                                      std::vector<T>& hA, af::dim4 aDims, af::array b,
+                                      IntegerTag) {
+    std::vector<T> hB(b.elements());
+    b.host(hB.data());
+
+    typedef typename std::vector<T>::iterator iter;
+    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
+
+    if (bItr == hB.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        int idx = std::distance(hB.begin(), bItr);
+        af::dim4 coords = unravelIdx(idx, b.dims(), af::getStrides(b));
 
         return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
                                              << coords[0] << ", " << coords[1] << ", "
                                              << coords[2] << ", " << coords[3] << "): "
-                                             << aName << "(" << *aItr << "), "
-                                             << bName << "(" << *bItr << ")";
+                                             << hA_name << "(" << hA[idx] << "), "
+                                             << bName << "(" << hB[idx] << ")";
+    }
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
+                                      std::vector<T>& hA, af::dim4 aDims, af::array b,
+                                      FloatTag) {
+    std::vector<T> hB(b.elements());
+    b.host(hB.data());
+
+    typedef typename std::vector<T>::iterator iter;
+    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
+
+    if (bItr == hB.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        int idx = std::distance(hB.begin(), bItr);
+        af::dim4 coords = unravelIdx(idx, b.dims(), af::getStrides(b));
+
+        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
+                                             << coords[0] << ", " << coords[1] << ", "
+                                             << coords[2] << ", " << coords[3] << "): "
+                                             << hA_name << "(" << hA[idx] << "), "
+                                             << bName << "(" << hB[idx] << ")";
     }
 }
 
@@ -602,31 +673,39 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       af::array a, af::array b) {
     typedef typename cond_type<
         IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
-              FloatTag, IntegerTag>::type TagType;
+        FloatTag, IntegerTag>::type TagType;
     TagType tag;
 
     return elemWiseEq<T>(aName, bName, a, b, tag);
 }
 
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
+                                      std::vector<T>& hA, af::dim4 aDims, af::array b) {
+    typedef typename cond_type<
+        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
+              FloatTag, IntegerTag>::type TagType;
+    TagType tag;
+
+    return elemWiseEq<T>(hA_name, bName, hA, aDims, b, tag);
+}
+
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          af::array a, af::array b) {
-
     af::dtype aType = a.type();
     af::dtype bType = b.type();
     if (aType != bType)
         return ::testing::AssertionFailure() << "TYPE MISMATCH: "
-                                             << aName << "(" <<  a.type() << ") and "
+                                             << aName << "(" << a.type() << ") and "
                                              << bName << "(" << b.type() << ")";
 
     af::dtype arrDtype = aType;
 
     const uint ndimIds = 4;
-    for (uint i = 0; i < ndimIds; ++i) {
-        if (a.dims()[i] != b.dims()[i])
-            return ::testing::AssertionFailure() << "SIZE MISMATCH on dim " << i << ": "
-                                                 << aName << "[" << a.dims() << "], "
-                                                 << bName << "[" << b.dims() << "]";
-    }
+    if (a.dims() != b.dims())
+        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
+                                             << aName << "([" << a.dims() << "]), "
+                                             << bName << "([" << b.dims() << "])";
 
     switch (arrDtype) {
     case f32: return elemWiseEq<float>(aName, bName, a, b);              break;
@@ -650,14 +729,54 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     return ::testing::AssertionSuccess();
 }
 
+// To support C API
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          af_array a, af_array b) {
-    af::array aa(a);
-    af::array bb(b);
-    return assertArrayEq(aName, bName, aa, bb);
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertArrayEq(aName, bName, aaa, bbb);
 }
 
-#define ASSERT_SUCCESS(CALL) \
-    ASSERT_EQ(AF_SUCCESS, CALL)
+template<typename T>
+::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsName,
+                                         std::string bName,
+                                         std::vector<T>& hA, af::dim4 aDims, af::array b) {
+    af::dtype aDtype = (af::dtype) af::dtype_traits<T>::af_type;
+    if (aDtype != b.type()) {
+        return ::testing::AssertionFailure() << "TYPE MISMATCH: "
+                                             << hA_name << "(" << aDtype << ") and "
+                                             << bName << "(" << b.type() << ")";
+    }
+
+    const uint ndimIds = 4;
+    if(aDims != b.dims()) {
+        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
+                                             << hA_name << "[" << aDims << "], "
+                                             << bName << "[" << b.dims() << "]";
+    }
+
+    // In case vector<T> a.size() != aDims.elements()
+    if (hA.size() != aDims.elements())
+        return ::testing::AssertionFailure() << "Gold af::array and std::vector SIZE MISMATCH: "
+                                             << hA_name << ".size()(" << hA.size() << "), "
+                                             << bName << "([" << aDims << "] = "
+                                             << aDims.elements() << ")";
+
+    return elemWiseEq<T>(hA_name, bName, hA, aDims, b);
+}
+
+// To support C API
+template<typename T>
+::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsName,
+                                         std::string bName,
+                                         std::vector<T>& hA, af::dim4 aDims, af_array b) {
+    af_array bb = 0;
+    af_retain_array(&bb, b);
+    af::array bbb(bb);
+    return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, bbb);
+}
 
 #pragma GCC diagnostic pop

From ca61e3481de9e29f43a08bd0df4070ddda4d5d90 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Tue, 24 Jul 2018 15:18:06 -0400
Subject: [PATCH 1497/2677] Using the new ASSERT*_ARRAY_EQ macros for tests

- Changed ASSERT_EQ(AF_SUCCESS, ...) to ASSERT_SUCCESS(...)
- Changed ASSERT_TRUE(allTrue<bool>...) to ASSERT_ARRAY_EQ() in tests
---
 test/anisotropic_diffusion.cpp |  60 ++++++++--------
 test/approx1.cpp               |  34 ++++-----
 test/approx2.cpp               |  28 ++++----
 test/arrayio.cpp               |   4 +-
 test/assign.cpp                |  28 ++++----
 test/backend.cpp               |   2 +-
 test/basic.cpp                 | 108 ++++++++++++++++------------
 test/bilateral.cpp             |  32 ++++-----
 test/blas.cpp                  |  40 +++++------
 test/canny.cpp                 |  60 ++++++++--------
 test/cast.cpp                  |   4 +-
 test/constant.cpp              |   8 +--
 test/convolve.cpp              |  60 ++++++++--------
 test/diff1.cpp                 |  47 +++++-------
 test/diff2.cpp                 |  46 +++++-------
 test/dot.cpp                   |  44 +++++-------
 test/empty.cpp                 |   6 +-
 test/fast.cpp                  |  36 +++++-----
 test/fft.cpp                   | 127 ++++++++++-----------------------
 test/fftconvolve.cpp           |  20 +++---
 test/flat.cpp                  |  31 ++++----
 test/gaussiankernel.cpp        |   8 +--
 test/gen_assign.cpp            |  62 ++++++++--------
 test/gen_index.cpp             |  32 ++++-----
 test/gfor.cpp                  |  10 +--
 test/gloh_nonfree.cpp          |  50 ++++++-------
 test/gradient.cpp              |  12 ++--
 test/hamming.cpp               |  18 ++---
 test/harris.cpp                |  36 +++++-----
 test/histogram.cpp             |  26 +++----
 test/homography.cpp            |  84 +++++++++++-----------
 test/imageio.cpp               |   4 +-
 test/index.cpp                 | 105 ++++++++++-----------------
 test/info.cpp                  |   6 +-
 test/inverse_deconv.cpp        |  64 ++++++++---------
 test/iota.cpp                  |  30 ++------
 test/ireduce.cpp               |   4 +-
 test/iterative_deconv.cpp      |  64 ++++++++---------
 test/jit.cpp                   |  22 +++---
 test/join.cpp                  |  42 ++++-------
 test/match_template.cpp        |  22 +++---
 test/meanshift.cpp             |  30 ++++----
 test/medfilt.cpp               |  64 ++++++++---------
 test/memory.cpp                |   2 +-
 test/memory_lock.cpp           |   2 +-
 test/moddims.cpp               |  38 +++++-----
 test/morph.cpp                 |  95 ++++++++++++------------
 test/nearest_neighbour.cpp     |  18 ++---
 test/orb.cpp                   |  40 +++++------
 test/random.cpp                |   4 +-
 test/range.cpp                 |   4 +-
 test/reduce.cpp                |  16 ++---
 test/regions.cpp               |  10 +--
 test/reorder.cpp               |  38 +++-------
 test/resize.cpp                |  16 ++---
 test/rotate.cpp                |   6 +-
 test/rotate_linear.cpp         |  10 +--
 test/scan.cpp                  |  24 +++----
 test/select.cpp                |  12 ++--
 test/set.cpp                   |  14 ++--
 test/shift.cpp                 |  34 ++-------
 test/sift_nonfree.cpp          |  50 ++++++-------
 test/sobel.cpp                 |  26 +++----
 test/solve_dense.cpp           |   2 +-
 test/sort.cpp                  |  10 +--
 test/sort_by_key.cpp           | 102 ++++----------------------
 test/sort_index.cpp            | 103 +++++---------------------
 test/sparse.cpp                |   2 +-
 test/testHelpers.hpp           |  22 +++---
 test/threading.cpp             |  34 ++++-----
 test/tile.cpp                  |  42 ++++-------
 test/topk.cpp                  |  28 ++++----
 test/transform.cpp             |  20 +++---
 test/transform_coordinates.cpp |  10 +--
 test/translate.cpp             |   6 +-
 test/transpose.cpp             |  22 +++---
 test/transpose_inplace.cpp     |  32 ++-------
 test/unwrap.cpp                |  15 ++--
 test/where.cpp                 |  32 +++------
 test/wrap.cpp                  |   2 +-
 80 files changed, 1090 insertions(+), 1473 deletions(-)

diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 2235fa6fca..979178e94f 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -86,58 +86,58 @@ void imageTest(string pTestFile, const float dt, const float K, const uint iters
         af_array _goldArray  = 0;
         dim_t nElems         = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, _inArray));
+        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
         // af_load_image always returns float array, so convert to output type
-        ASSERT_EQ(AF_SUCCESS, conv_image<OutType>(&goldArray, _goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(conv_image<OutType>(&goldArray, _goldArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         if (isCurvatureDiffusion) {
-            ASSERT_EQ(AF_SUCCESS, af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
+            ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
                                                            fluxKind, AF_DIFFUSION_MCDE));
         } else {
-            ASSERT_EQ(AF_SUCCESS, af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
+            ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
                                                            fluxKind, AF_DIFFUSION_GRAD));
         }
 
         double maxima, minima, imag;
-        ASSERT_EQ(AF_SUCCESS, af_min_all(&minima, &imag, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_max_all(&maxima, &imag, _outArray));
+        ASSERT_SUCCESS(af_min_all(&minima, &imag, _outArray));
+        ASSERT_SUCCESS(af_max_all(&maxima, &imag, _outArray));
 
         unsigned ndims;
         dim_t dims[4];
-        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
+        ASSERT_SUCCESS(af_get_numdims(&ndims, _outArray));
+        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
 
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
-        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&denArray, (maxima-minima), ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&minArray, minima, ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_sub(&numArray, _outArray, minArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
+        ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, otype));
+        ASSERT_SUCCESS(af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_SUCCESS(af_constant(&minArray, minima, ndims, dims, otype));
+        ASSERT_SUCCESS(af_sub(&numArray, _outArray, minArray, false));
+        ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
+        ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
         vector<OutType> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         vector<OutType> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.025f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_release_array(_inArray));
+        ASSERT_SUCCESS(af_release_array(_outArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(cstArray));
+        ASSERT_SUCCESS(af_release_array(minArray));
+        ASSERT_SUCCESS(af_release_array(denArray));
+        ASSERT_SUCCESS(af_release_array(numArray));
+        ASSERT_SUCCESS(af_release_array(divArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(_goldArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
     }
 }
 
diff --git a/test/approx1.cpp b/test/approx1.cpp
index d4ea7cb361..1c1ec364f0 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -78,20 +78,20 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
+    ASSERT_SUCCESS(af_approx1(&outArray, inArray, posArray, method, 0));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
@@ -143,18 +143,18 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_approx1(&outArray, inArray, posArray, method, 0));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
@@ -217,9 +217,9 @@ void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
     ASSERT_EQ(err, af_approx1(&outArray, inArray, posArray, method, 0));
 
@@ -259,15 +259,15 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if((af_dtype) dtype_traits<T>::af_type == c32 ||
        (af_dtype) dtype_traits<T>::af_type == c64) {
         ASSERT_EQ(AF_ERR_ARG, af_approx1(&outArray, inArray, posArray, method, 0));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_approx1(&outArray, inArray, posArray, method, 0));
+        ASSERT_SUCCESS(af_approx1(&outArray, inArray, posArray, method, 0));
     }
 
     if(inArray   != 0) af_release_array(inArray);
diff --git a/test/approx2.cpp b/test/approx2.cpp
index b7d233c126..10cd658fa2 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -76,21 +76,21 @@ void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
+    ASSERT_SUCCESS(af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
@@ -152,10 +152,10 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
 
     ASSERT_EQ(err, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
 
@@ -200,17 +200,17 @@ void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
 
     if((af_dtype) dtype_traits<T>::af_type == c32 ||
        (af_dtype) dtype_traits<T>::af_type == c64) {
         ASSERT_EQ(AF_ERR_ARG, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
+        ASSERT_SUCCESS(af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
     }
 
     if(inArray   != 0) af_release_array(inArray);
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
index eb08dd0265..e5a9d9609e 100644
--- a/test/arrayio.cpp
+++ b/test/arrayio.cpp
@@ -118,6 +118,6 @@ TEST(ArrayIO, Save) {
     array aread = readArray("arr.af", "a");
     array bread = readArray("arr.af", "b");
 
-    ASSERT_TRUE(allTrue<bool>(aread == a));
-    ASSERT_TRUE(allTrue<bool>(bread == b));
+    ASSERT_ARRAYS_EQ(a, aread);
+    ASSERT_ARRAYS_EQ(b, bread);
 }
diff --git a/test/assign.cpp b/test/assign.cpp
index 3da3dc7600..35bac70300 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -116,17 +116,17 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv)
     af_array rhsArray  = 0;
     af_array outArray  = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<inType>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<outType>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_assign_seq(&outArray, lhsArray, seqv->size(), &seqv->front(), rhsArray));
+    ASSERT_SUCCESS(af_assign_seq(&outArray, lhsArray, seqv->size(), &seqv->front(), rhsArray));
 
     outType *outData = new outType[dims1.elements()];
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     vector<outType> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -135,9 +135,9 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv)
     }
 
     delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(rhsArray));
+    ASSERT_SUCCESS(af_release_array(lhsArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 template<typename T>
@@ -500,13 +500,13 @@ TEST(ArrayAssign, InvalidArgs)
     ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray,
                                     lhsArray, seqv.size(), &seqv.front(), rhsArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in.front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<cfloat>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray,
                                     lhsArray, seqv.size(), &seqv.front(), rhsArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in.front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray, lhsArray, 0, &seqv.front(), rhsArray));
@@ -514,8 +514,8 @@ TEST(ArrayAssign, InvalidArgs)
     ASSERT_EQ(AF_ERR_TYPE, af_assign_seq(&outArray,
                                          lhsArray, seqv.size(), &seqv.front(), rhsArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
+    ASSERT_SUCCESS(af_release_array(rhsArray));
+    ASSERT_SUCCESS(af_release_array(lhsArray));
 }
 
 TEST(ArrayAssign, CPP_ASSIGN_TO_INDEXED)
@@ -891,7 +891,7 @@ TEST(Asssign, LinearAssignSeq)
     af_array rhs_arr = rhs.get();
     af_array out_arr;
 
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
               af_assign_seq(&out_arr, in_arr, 1, &ii.idx.seq, rhs_arr));
 
     array out(out_arr);
@@ -931,7 +931,7 @@ TEST(Asssign, LinearAssignGenSeq)
     af_array rhs_arr = rhs.get();
     af_array out_arr;
 
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
               af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
 
     array out(out_arr);
@@ -971,7 +971,7 @@ TEST(Asssign, LinearAssignGenArr)
     af_array rhs_arr = rhs.get();
     af_array out_arr;
 
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
               af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
 
     array out(out_arr);
diff --git a/test/backend.cpp b/test/backend.cpp
index b08159c182..78e2ff1f0b 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -55,7 +55,7 @@ void testFunction()
 
     // cleanup
     if(outArray != 0) {
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
     }
 }
 
diff --git a/test/basic.cpp b/test/basic.cpp
index 7c0a145751..3e81257bf7 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -28,17 +28,17 @@ TEST(BasicTests, constant1000x1000)
 
     double valA = 3.9;
     af_array a;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, valA, ndims, d, f32));
+    ASSERT_SUCCESS(af_constant(&a, valA, ndims, d, f32));
 
     vector<float> h_a(dim_size * dim_size, 100);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_a[0], a));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_a[0], a));
 
     size_t elements = dim_size * dim_size;
     for(size_t i = 0; i < elements; i++) {
         ASSERT_FLOAT_EQ(valA, h_a[i]);
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(a));
 }
 
 TEST(BasicTests, constant10x10)
@@ -51,17 +51,17 @@ TEST(BasicTests, constant10x10)
 
     double valA = 3.9;
     af_array a;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, valA, ndims, d, f32));
+    ASSERT_SUCCESS(af_constant(&a, valA, ndims, d, f32));
 
     vector<float> h_a(dim_size * dim_size, 0);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_a[0], a));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_a[0], a));
 
     size_t elements = dim_size * dim_size;
     for(size_t i = 0; i < elements; i++) {
         ASSERT_FLOAT_EQ(valA, h_a[i]);
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(a));
 }
 
 TEST(BasicTests, constant100x100)
@@ -74,17 +74,17 @@ TEST(BasicTests, constant100x100)
 
     double valA = 4.9;
     af_array a;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, valA, ndims, d, f32));
+    ASSERT_SUCCESS(af_constant(&a, valA, ndims, d, f32));
 
     vector<float> h_a(dim_size * dim_size, 0);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_a[0], a));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_a[0], a));
 
     size_t elements = dim_size * dim_size;
     for(size_t i = 0; i < elements; i++) {
         ASSERT_FLOAT_EQ(valA, h_a[i]);
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(a));
 }
 
 //TODO: Test All The Types \o/
@@ -104,19 +104,19 @@ TEST(BasicTests, AdditionSameType)
     af_array af32, bf32, cf32;
     af_array af64, bf64, cf64;
 
-    ASSERT_EQ(AF_SUCCESS, af_constant(&af32, valA, ndims, d, f32));
-    ASSERT_EQ(AF_SUCCESS, af_constant(&af64, valA, ndims, d, f64));
+    ASSERT_SUCCESS(af_constant(&af32, valA, ndims, d, f32));
+    ASSERT_SUCCESS(af_constant(&af64, valA, ndims, d, f64));
 
-    ASSERT_EQ(AF_SUCCESS, af_constant(&bf32, valB, ndims, d, f32));
-    ASSERT_EQ(AF_SUCCESS, af_constant(&bf64, valB, ndims, d, f64));
+    ASSERT_SUCCESS(af_constant(&bf32, valB, ndims, d, f32));
+    ASSERT_SUCCESS(af_constant(&bf64, valB, ndims, d, f64));
 
-    ASSERT_EQ(AF_SUCCESS, af_add(&cf32, af32, bf32, false));
-    ASSERT_EQ(AF_SUCCESS, af_add(&cf64, af64, bf64, false));
+    ASSERT_SUCCESS(af_add(&cf32, af32, bf32, false));
+    ASSERT_SUCCESS(af_add(&cf64, af64, bf64, false));
 
     vector<float>  h_cf32 (dim_size * dim_size);
     vector<double> h_cf64 (dim_size * dim_size);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_cf32[0], cf32));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_cf64[0], cf64));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_cf32[0], cf32));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_cf64[0], cf64));
 
     double err = 0;
 
@@ -129,12 +129,12 @@ TEST(BasicTests, AdditionSameType)
     }
     ASSERT_NEAR(0.0f, err, 1e-8);
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(af32));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(af64));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(bf32));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(bf64));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(cf32));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(cf64));
+    ASSERT_SUCCESS(af_release_array(af32));
+    ASSERT_SUCCESS(af_release_array(af64));
+    ASSERT_SUCCESS(af_release_array(bf32));
+    ASSERT_SUCCESS(af_release_array(bf64));
+    ASSERT_SUCCESS(af_release_array(cf32));
+    ASSERT_SUCCESS(af_release_array(cf64));
 }
 
 TEST(BasicTests, Additionf64f64)
@@ -151,12 +151,12 @@ TEST(BasicTests, Additionf64f64)
 
     af_array a, b, c;
 
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, valA, ndims, d, f64));
-    ASSERT_EQ(AF_SUCCESS, af_constant(&b, valB, ndims, d, f64));
-    ASSERT_EQ(AF_SUCCESS, af_add(&c, a, b, false));
+    ASSERT_SUCCESS(af_constant(&a, valA, ndims, d, f64));
+    ASSERT_SUCCESS(af_constant(&b, valB, ndims, d, f64));
+    ASSERT_SUCCESS(af_add(&c, a, b, false));
 
     vector<double> h_c(dim_size * dim_size, 0);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_c[0], c));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_c[0], c));
 
     double err = 0;
 
@@ -168,9 +168,9 @@ TEST(BasicTests, Additionf64f64)
     }
     ASSERT_NEAR(0.0f, err, 1e-8);
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(b));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(c));
+    ASSERT_SUCCESS(af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(b));
+    ASSERT_SUCCESS(af_release_array(c));
 
 }
 
@@ -189,12 +189,12 @@ TEST(BasicTests, Additionf32f64)
 
     af_array a, b, c;
 
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, valA, ndims, d, f32));
-    ASSERT_EQ(AF_SUCCESS, af_constant(&b, valB, ndims, d, f64));
-    ASSERT_EQ(AF_SUCCESS, af_add(&c, a, b, false));
+    ASSERT_SUCCESS(af_constant(&a, valA, ndims, d, f32));
+    ASSERT_SUCCESS(af_constant(&b, valB, ndims, d, f64));
+    ASSERT_SUCCESS(af_add(&c, a, b, false));
 
     vector<double> h_c(dim_size * dim_size);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void **)&h_c[0], c));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&h_c[0], c));
 
     double err = 0;
 
@@ -206,9 +206,9 @@ TEST(BasicTests, Additionf32f64)
     }
     ASSERT_NEAR(0.0f, err, 1e-8);
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(b));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(c));
+    ASSERT_SUCCESS(af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(b));
+    ASSERT_SUCCESS(af_release_array(c));
 }
 
 TEST(BasicArrayTests, constant10x10)
@@ -365,7 +365,6 @@ TEST(Assert, TestEqualsDiffSizes) {
 }
 
 TEST(Assert, TestEqualsDiffValue) {
-    // array A = af::randu(3, 3, 3);
     array gold = constant(1, 3, 3);
     array out = gold;
     out(2, 2) = 2;
@@ -376,7 +375,6 @@ TEST(Assert, TestEqualsDiffValue) {
 }
 
 TEST(Assert, TestEqualsDiffComplexValue) {
-    // array A = af::randu(3, 3, 3);
     array gold = constant(af::cfloat(3.1f, 3.1f), 3, 3, c32);
     array out = gold;
     out(2, 2) = 2.2;
@@ -412,10 +410,10 @@ TEST(Assert, TestVectorDiffVecType) {
                               gold, goldDims, out));
 }
 
-TEST(Assert, TestVectorDiffDim4) {
+TEST(Assert, TestVectorDiffGoldSizeDims) {
     array out = constant(3.1f, 3, 3);
 
-    vector<float> gold(out.elements());
+    vector<float> gold(3 * 3);
     dim4 goldDims(3, 2);
     fill(gold.begin(), gold.end(), 3.1f);
 
@@ -425,11 +423,11 @@ TEST(Assert, TestVectorDiffDim4) {
                                gold, goldDims, out));
 }
 
-TEST(Assert, TestVectorDiffVecSize) {
+TEST(Assert, TestVectorDiffOutSizeGoldSize) {
     array out = constant(3.1f, 3, 3);
 
-    vector<float> gold(out.elements() - 1);
-    dim4 goldDims(3, 3);
+    vector<float> gold(3 * 2);
+    dim4 goldDims(3, 2);
     fill(gold.begin(), gold.end(), 3.1f);
 
     // Testing this macro
@@ -437,3 +435,25 @@ TEST(Assert, TestVectorDiffVecSize) {
     ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
                                gold, goldDims, out));
 }
+
+TEST(Assert, TestVectorDiffDim4) {
+    array A = constant(3.1f, 3, 3);
+    vector<float> hA(A.elements());
+    dim4 adims(3, 2);
+    fill(hA.begin(), hA.end(), 3.1f);
+
+    // Testing this macro
+    // ASSERT_ARRAYS_EQ(A, B);
+    ASSERT_FALSE(assertArrayEq("hA", "adims", "A", hA, adims, A));
+}
+
+TEST(Assert, TestVectorDiffVecSize) {
+    array A = constant(3.1f, 3, 3);
+    vector<float> hA(A.elements()-1);
+    dim4 adims(3, 3);
+    fill(hA.begin(), hA.end(), 3.1f);
+
+    // Testing this macro
+    // ASSERT_ARRAYS_EQ(A, B);
+    ASSERT_FALSE(assertArrayEq("hA", "adims", "A", hA, adims, A));
+}
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 47f23ced68..775aaf1ea2 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -47,23 +47,23 @@ void bilateralTest(string pTestFile)
         inFiles[testId].insert(0,string(TEST_DIR"/bilateral/"));
         outFiles[testId].insert(0,string(TEST_DIR"/bilateral/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
+        ASSERT_SUCCESS(af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
 
         vector<T> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         vector<T> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
     }
 }
 
@@ -105,14 +105,14 @@ void bilateralDataTest(string pTestFile)
     af_array outArray  = 0;
     af_array inArray   = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_bilateral(&outArray, inArray, 2.25f, 25.56f, false));
+    ASSERT_SUCCESS(af_bilateral(&outArray, inArray, 2.25f, 25.56f, false));
 
     vector<outType> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<outType> currGoldBar = tests[testIter];
@@ -121,8 +121,8 @@ void bilateralDataTest(string pTestFile)
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(BilateralOnData, Rectangle)
@@ -146,10 +146,10 @@ TYPED_TEST(BilateralOnData, InvalidArgs)
 
     // check for color image bilateral
     dim4 dims = dim4(100,1,1,1);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<TypeParam>::af_type));
     ASSERT_EQ(AF_ERR_SIZE, af_bilateral(&outArray, inArray, 0.12f, 0.34f, true));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 // C++ unit tests
diff --git a/test/blas.cpp b/test/blas.cpp
index f606b1f4ed..bf89608b1e 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -56,7 +56,7 @@ void MatMulCheck(string TestFile)
     readTests<T,T,int>(TestFile, numDims, hData, tests);
 
     af_array a, aT, b, bT;
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
             af_create_array(&a, &hData[0].front(), numDims[0].ndims(), numDims[0].get(), (af_dtype) dtype_traits<T>::af_type));
     dim4 atdims = numDims[0];
     {
@@ -64,9 +64,9 @@ void MatMulCheck(string TestFile)
         atdims[0]   =    atdims[1];
         atdims[1]   =    f;
     }
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
             af_moddims(&aT, a, atdims.ndims(), atdims.get()));
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
             af_create_array(&b, &hData[1].front(), numDims[1].ndims(), numDims[1].get(), (af_dtype) dtype_traits<T>::af_type));
     dim4 btdims = numDims[1];
     {
@@ -74,29 +74,29 @@ void MatMulCheck(string TestFile)
         btdims[0] = btdims[1];
         btdims[1] = f;
     }
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
             af_moddims(&bT, b, btdims.ndims(), btdims.get()));
 
     vector<af_array> out(tests.size(), 0);
     if(isBVector) {
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[0] , aT, b,    AF_MAT_NONE,    AF_MAT_NONE));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[1] , bT, a,   AF_MAT_NONE,    AF_MAT_NONE));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[2] , b, a,    AF_MAT_TRANS,       AF_MAT_NONE));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[3] , bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[4] , b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS));
+        ASSERT_SUCCESS(af_matmul( &out[0] , aT, b,    AF_MAT_NONE,    AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul( &out[1] , bT, a,   AF_MAT_NONE,    AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul( &out[2] , b, a,    AF_MAT_TRANS,       AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul( &out[3] , bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS));
+        ASSERT_SUCCESS(af_matmul( &out[4] , b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS));
     }
     else {
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[0] , a, b, AF_MAT_NONE,   AF_MAT_NONE));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[1] , a, bT, AF_MAT_NONE,   AF_MAT_TRANS));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[2] , a, bT, AF_MAT_TRANS,      AF_MAT_NONE));
-        ASSERT_EQ(AF_SUCCESS, af_matmul( &out[3] , aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS));
+        ASSERT_SUCCESS(af_matmul( &out[0] , a, b, AF_MAT_NONE,   AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul( &out[1] , a, bT, AF_MAT_NONE,   AF_MAT_TRANS));
+        ASSERT_SUCCESS(af_matmul( &out[2] , a, bT, AF_MAT_TRANS,      AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul( &out[3] , aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS));
     }
 
     for(size_t i = 0; i < tests.size(); i++) {
         dim_t elems;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&elems, out[i]));
+        ASSERT_SUCCESS(af_get_elements(&elems, out[i]));
         vector<T> h_out(elems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void *)&h_out.front(), out[i]));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&h_out.front(), out[i]));
 
         if( false == equal(h_out.begin(), h_out.end(), tests[i].begin()) ) {
 
@@ -108,13 +108,13 @@ void MatMulCheck(string TestFile)
         }
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(aT));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(b));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(bT));
+    ASSERT_SUCCESS(af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(aT));
+    ASSERT_SUCCESS(af_release_array(b));
+    ASSERT_SUCCESS(af_release_array(bT));
 
     for (size_t i = 0; i <  out.size(); i++) {
-        ASSERT_EQ(AF_SUCCESS, af_release_array(out[i]));
+        ASSERT_SUCCESS(af_release_array(out[i]));
     }
 }
 
diff --git a/test/canny.cpp b/test/canny.cpp
index e84c3f0a1a..7be356a5eb 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -49,14 +49,14 @@ void cannyTest(string pTestFile)
     af_array outArray = 0;
     af_array sArray   = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&sArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&sArray, &(in[0].front()),
                 sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL, 0.4147f, 0.8454f, 3, true));
+    ASSERT_SUCCESS(af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL, 0.4147f, 0.8454f, 3, true));
 
     vector<char> outData(sDims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<char> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -65,8 +65,8 @@ void cannyTest(string pTestFile)
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(sArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(sArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(CannyEdgeDetector, ArraySizeLessThanBlockSize10x10)
@@ -112,42 +112,42 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
 
         af_dtype type = (af_dtype)dtype_traits<T>::af_type;
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
 
-        ASSERT_EQ(AF_SUCCESS, af_cast(&inArray, _inArray, type));
+        ASSERT_SUCCESS(af_cast(&inArray, _inArray, type));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image_native(&goldArray, outFiles[testId].c_str()));
+        ASSERT_SUCCESS(af_load_image_native(&goldArray, outFiles[testId].c_str()));
 
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_canny(&_outArray, inArray, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false));
+        ASSERT_SUCCESS(af_canny(&_outArray, inArray, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false));
 
         unsigned ndims = 0;
         dim_t dims[4];
 
-        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
+        ASSERT_SUCCESS(af_get_numdims(&ndims, _outArray));
+        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, f32));
+        ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_mul(&mulArray, cstArray, _outArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_cast(&outArray, mulArray, u8));
+        ASSERT_SUCCESS(af_mul(&mulArray, cstArray, _outArray, false));
+        ASSERT_SUCCESS(af_cast(&outArray, mulArray, u8));
 
         vector<unsigned char> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         vector<unsigned char> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 1.0e-3));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(mulArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_release_array(_inArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(cstArray));
+        ASSERT_SUCCESS(af_release_array(mulArray));
+        ASSERT_SUCCESS(af_release_array(_outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
     }
 }
 
@@ -165,12 +165,12 @@ TEST(CannyEdgeDetector, InvalidSizeArray)
 
     dim4 sDims(100, 1, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TEST(CannyEdgeDetector, Array4x4_Invalid)
@@ -182,12 +182,12 @@ TEST(CannyEdgeDetector, Array4x4_Invalid)
 
     dim4 sDims(4, 4, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TEST(CannyEdgeDetector, Sobel5x5_Invalid)
@@ -199,10 +199,10 @@ TEST(CannyEdgeDetector, Sobel5x5_Invalid)
 
     dim4 sDims(5, 5, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 5, true));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
diff --git a/test/cast.cpp b/test/cast.cpp
index 5327b134ff..5fe4728eb1 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -34,7 +34,7 @@ void cast_test()
     af_err err = af_cast(&b, a, tb);
     af_release_array(a);
     af_release_array(b);
-    ASSERT_EQ(err, AF_SUCCESS);
+    ASSERT_SUCCESS(err);
 }
 
 #define REAL_TO_TESTS(Ti, To)                   \
@@ -91,7 +91,7 @@ void cast_test_complex_real()
     af_randu(&a, dims.ndims(), dims.get(), ta);
     af_err err = af_cast(&b, a, tb);
     ASSERT_EQ(err, AF_ERR_TYPE);
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(a));
 }
 
 #define COMPLEX_REAL_TESTS(Ti, To)                      \
diff --git a/test/constant.cpp b/test/constant.cpp
index 629e3345db..eb09137eb4 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -57,7 +57,7 @@ void ConstantCCheck(T value) {
     dtype dty = (dtype) dtype_traits<T>::af_type;
     af_array out;
     dim_t dim[] = {(dim_t)num};
-    ASSERT_EQ(AF_SUCCESS, af_constant(&out, val, 1, dim, dty));
+    ASSERT_SUCCESS(af_constant(&out, val, 1, dim, dty));
 
     vector<T> h_in(num);
     af_get_data_ptr(&h_in.front(), out);
@@ -65,7 +65,7 @@ void ConstantCCheck(T value) {
     for (int i = 0; i < num; i++) {
         ASSERT_EQ(::real(h_in[i]), val);
     }
-    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(out));
 }
 
 template<typename T>
@@ -134,7 +134,7 @@ void IdentityCCheck() {
     dtype dty = (dtype) dtype_traits<T>::af_type;
     af_array out;
     dim_t dim[] = {(dim_t)num, (dim_t)num};
-    ASSERT_EQ(AF_SUCCESS, af_identity(&out, 2, dim, dty));
+    ASSERT_SUCCESS(af_identity(&out, 2, dim, dty));
 
     vector<T> h_in(num*num);
     af_get_data_ptr(&h_in.front(), out);
@@ -147,7 +147,7 @@ void IdentityCCheck() {
                 ASSERT_EQ(h_in[i * num + j], T(0));
         }
     }
-    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(out));
 }
 
 template<typename T>
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 9f0c6e9c12..0e87c5841b 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -56,31 +56,31 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
     af_array filter   = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()),
                 sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&filter, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()),
                 fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
     switch(baseDim) {
-    case 1: ASSERT_EQ(AF_SUCCESS, af_convolve1(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
-    case 2: ASSERT_EQ(AF_SUCCESS, af_convolve2(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
-    case 3: ASSERT_EQ(AF_SUCCESS, af_convolve3(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
+    case 1: ASSERT_SUCCESS(af_convolve1(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
+    case 2: ASSERT_SUCCESS(af_convolve2(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
+    case 3: ASSERT_SUCCESS(af_convolve3(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
     }
 
     vector<T> currGoldBar = tests[0];
     size_t nElems         = currGoldBar.size();
     vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(filter));
+    ASSERT_SUCCESS(af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(filter));
 }
 
 TYPED_TEST(Convolve, Vector)
@@ -222,30 +222,30 @@ void sepConvolveTest(string pTestFile, bool expand)
     af_array r_filter = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()),
                 sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&c_filter, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&c_filter, &(in[1].front()),
                 cfDims.ndims(), cfDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&r_filter, &(in[2].front()),
+    ASSERT_SUCCESS(af_create_array(&r_filter, &(in[2].front()),
                 rfDims.ndims(), rfDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     af_conv_mode  mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
-    ASSERT_EQ(AF_SUCCESS, af_convolve2_sep(&outArray, c_filter, r_filter, signal, mode));
+    ASSERT_SUCCESS(af_convolve2_sep(&outArray, c_filter, r_filter, signal, mode));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems         = currGoldBar.size();
     vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(c_filter));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(r_filter));
+    ASSERT_SUCCESS(af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(c_filter));
+    ASSERT_SUCCESS(af_release_array(r_filter));
 }
 
 TYPED_TEST(Convolve, Separable2D_Full)
@@ -304,18 +304,18 @@ TEST(Convolve, Separable_TypeCheck)
     af_array r_filter = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&signal, &(in.front()),
                 sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<float>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&c_filter, &(filt.front()),
+    ASSERT_SUCCESS(af_create_array(&c_filter, &(filt.front()),
                 fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&r_filter, &(filt.front()),
+    ASSERT_SUCCESS(af_create_array(&r_filter, &(filt.front()),
                 fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter, signal, AF_CONV_EXPAND));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(c_filter));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(r_filter));
+    ASSERT_SUCCESS(af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(c_filter));
+    ASSERT_SUCCESS(af_release_array(r_filter));
 }
 
 TEST(Convolve, Separable_DimCheck)
@@ -334,18 +334,18 @@ TEST(Convolve, Separable_DimCheck)
     af_array r_filter = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&signal, &(in.front()),
                 sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<float>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&c_filter, &(filt.front()),
+    ASSERT_SUCCESS(af_create_array(&c_filter, &(filt.front()),
                 fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&r_filter, &(filt.front()),
+    ASSERT_SUCCESS(af_create_array(&r_filter, &(filt.front()),
                 fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter, signal, AF_CONV_EXPAND));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(c_filter));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(r_filter));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(c_filter));
+    ASSERT_SUCCESS(af_release_array(r_filter));
+    ASSERT_SUCCESS(af_release_array(signal));
 }
 
 ///////////////////////////////////// CPP ////////////////////////////////
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 4fe71a2f51..10a22adb9c 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -63,40 +63,36 @@ void diff1Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
     readTests<T,T,int>(pTestFile,numDims,in,tests);
     dim4 dims       = numDims[0];
 
-    T *outData;
-
     af_array inArray   = 0;
     af_array outArray  = 0;
     af_array tempArray = 0;
     // Get input array
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     // Run diff1
-    ASSERT_EQ(AF_SUCCESS, af_diff1(&outArray, inArray, dim));
-
-    // Get result
-    outData = new T[dims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_diff1(&outArray, inArray, dim));
 
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<T> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
-        }
+        dim4 goldDims;
+        ASSERT_SUCCESS(af_get_dims(&goldDims[0],
+                               &goldDims[1],
+                               &goldDims[2],
+                               &goldDims[3],
+                               inArray));
+        goldDims[dim]--;
+
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
     }
 
-    // Delete
-    delete[] outData;
-
     if(inArray   != 0) af_release_array(inArray);
     if(outArray  != 0) af_release_array(outArray);
     if(tempArray != 0) af_release_array(tempArray);
@@ -168,7 +164,7 @@ void diff1ArgsTest(string pTestFile)
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray, -1));
     ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray,  5));
@@ -234,20 +230,13 @@ TEST(Diff1, CPP)
     array input(dims, &(in[0].front()));
     array output = diff1(input, dim);
 
-    // Get result
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
-
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
-        }
-    }
+        dim4 goldDims = dims;
+        goldDims[dim]--;
 
-    // Delete
-    delete[] outData;
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
+    }
 }
 
diff --git a/test/diff2.cpp b/test/diff2.cpp
index c337522a66..a5acafd04c 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -68,40 +68,36 @@ void diff2Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
     readTests<T,T,int>(pTestFile,numDims,in,tests);
     dim4 dims       = numDims[0];
 
-    T *outData;
-
     af_array inArray   = 0;
     af_array outArray  = 0;
     af_array tempArray = 0;
     // Get input array
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     // Run diff2
-    ASSERT_EQ(AF_SUCCESS, af_diff2(&outArray, inArray, dim));
-
-    // Get result
-    outData = new T[dims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_diff2(&outArray, inArray, dim));
 
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<T> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
-        }
+        dim4 goldDims;
+        ASSERT_SUCCESS(af_get_dims(&goldDims[0],
+                                   &goldDims[1],
+                                   &goldDims[2],
+                                   &goldDims[3],
+                                   inArray));
+        goldDims[dim] -= 2;
+
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
     }
 
-    // Delete
-    delete[] outData;
-
     if(inArray   != 0) af_release_array(inArray);
     if(outArray  != 0) af_release_array(outArray);
     if(tempArray != 0) af_release_array(tempArray);
@@ -170,7 +166,7 @@ void diff2ArgsTest(string pTestFile)
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray, -1));
     ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray,  5));
@@ -228,19 +224,13 @@ TEST(Diff2, CPP)
     array input(dims, &(in[0].front()));
     array output = diff2(input, dim);
 
-    float *outData = new float[dims.elements()];
-    output.host((void*)outData);
-
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
-        }
-    }
+        dim4 goldDims = input.dims();
+        goldDims[dim] -= 2;
 
-    // Delete
-    delete[] outData;
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
+    }
 }
 
diff --git a/test/dot.cpp b/test/dot.cpp
index ca6637462b..f9f84c6bc5 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -68,26 +68,26 @@ void dotTest(string pTestFile, const int resultIdx,
     af_array b = 0;
     af_array out = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&a, &(in[0].front()),
                 aDims.ndims(), aDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&b, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&b, &(in[1].front()),
                 bDims.ndims(), bDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_dot(&out, a, b, optLhs, optRhs));
+    ASSERT_SUCCESS(af_dot(&out, a, b, optLhs, optRhs));
 
     vector<T> goldData = tests[resultIdx];
     size_t nElems      = goldData.size();
     vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), out));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), out));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(b));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(b));
+    ASSERT_SUCCESS(af_release_array(out));
 }
 
 template<typename T>
@@ -128,20 +128,20 @@ void dotAllTest(string pTestFile, const int resultIdx,
     af_array a = 0;
     af_array b = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&a, &(in[0].front()),
                 aDims.ndims(), aDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&b, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&b, &(in[1].front()),
                 bDims.ndims(), bDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     double rval = 0, ival = 0;
-    ASSERT_EQ(AF_SUCCESS, af_dot_all(&rval, &ival, a, b, optLhs, optRhs));
+    ASSERT_SUCCESS(af_dot_all(&rval, &ival, a, b, optLhs, optRhs));
 
     vector<T> goldData = tests[resultIdx];
 
     compare<T>(rval, ival, goldData[0]);
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(b));
+    ASSERT_SUCCESS(af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(b));
 }
 
 
@@ -202,14 +202,8 @@ TEST(DotF, CPP)
     array out = dot(a, b, AF_MAT_CONJ, AF_MAT_NONE);
 
     vector<float> goldData = tests[0];
-    size_t nElems = goldData.size();
-    vector<float> outData(nElems);
-
-    out.host(&outData.front());
-
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< endl;
-    }
+    dim4 goldDims(1);
+    ASSERT_VEC_ARRAY_EQ(goldData, goldDims, out);
 }
 
 TEST(DotCCU, CPP)
@@ -229,14 +223,8 @@ TEST(DotCCU, CPP)
     array out = dot(a, b, AF_MAT_CONJ, AF_MAT_NONE);
 
     vector<cfloat> goldData = tests[2];
-    size_t nElems         = goldData.size();
-    vector<cfloat> outData(nElems);
-
-    out.host(&outData.front());
-
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldData[elIter], outData[elIter]) << "at: " << elIter<< endl;
-    }
+    dim4 goldDims(1);
+    ASSERT_VEC_ARRAY_EQ(goldData, goldDims, out);
 }
 
 TEST(DotAllF, CPP)
diff --git a/test/empty.cpp b/test/empty.cpp
index 0864280235..f07bca2346 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -8,6 +8,8 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
 #include <arrayfire.h>
 #include <cstdio>
 #include <cstdlib>
@@ -283,7 +285,7 @@ TEST(Array, TestEmptyImage) {
     ASSERT_EQ(nd, 0u);
     af_get_numdims(&nd, hout);
     ASSERT_EQ(nd, 0u);
-    ASSERT_EQ(AF_SUCCESS, af_release_array(h));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(hout));
+    ASSERT_SUCCESS(af_release_array(h));
+    ASSERT_SUCCESS(af_release_array(hout));
 }
 
diff --git a/test/fast.cpp b/test/fast.cpp
index 899c01e62e..cb5bca2e9c 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -92,35 +92,35 @@ void fastTest(string pTestFile, bool nonmax)
 
         inFiles[testId].insert(0,string(TEST_DIR"/fast/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
 
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_fast(&out, inArray, 20.0f, 9, nonmax, 0.05f, 3));
+        ASSERT_SUCCESS(af_fast(&out, inArray, 20.0f, 9, nonmax, 0.05f, 3));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
 
-        ASSERT_EQ(AF_SUCCESS, af_get_features_num(&n, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&x, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&y, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_score(&score, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_orientation(&orientation, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_size(&size, out));
+        ASSERT_SUCCESS(af_get_features_num(&n, out));
+        ASSERT_SUCCESS(af_get_features_xpos(&x, out));
+        ASSERT_SUCCESS(af_get_features_ypos(&y, out));
+        ASSERT_SUCCESS(af_get_features_score(&score, out));
+        ASSERT_SUCCESS(af_get_features_orientation(&orientation, out));
+        ASSERT_SUCCESS(af_get_features_size(&size, out));
 
 
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, x));
+        ASSERT_SUCCESS(af_get_elements(&nElems, x));
 
         float * outX           = new float[gold[0].size()];
         float * outY           = new float[gold[1].size()];
         float * outScore       = new float[gold[2].size()];
         float * outOrientation = new float[gold[3].size()];
         float * outSize        = new float[gold[4].size()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outX, x));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outY, y));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outScore, score));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outOrientation, orientation));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outSize, size));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outOrientation, orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outSize, size));
 
         vector<feat_t> out_feat;
         array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize, n);
@@ -139,10 +139,10 @@ void fastTest(string pTestFile, bool nonmax)
             ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
         }
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_features(out));
+        ASSERT_SUCCESS(af_release_features(out));
 
         delete [] outX;
         delete [] outY;
diff --git a/test/fft.cpp b/test/fft.cpp
index e3562d4180..6d18d8c8fe 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -51,11 +51,11 @@ TEST(fft, Invalid_Type)
     af_array outArray  = 0;
 
     dim4 dims(5 * 5 * 2 * 2);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<char>::af_type));
 
     ASSERT_EQ(AF_ERR_TYPE, af_fft(&outArray, inArray, 1.0, 0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TEST(fft2, Invalid_Array)
@@ -68,11 +68,11 @@ TEST(fft2, Invalid_Array)
     af_array outArray  = 0;
 
     dim4 dims(5 * 5 * 2 * 2);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_fft2(&outArray, inArray, 1.0, 0, 0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TEST(fft3, Invalid_Array)
@@ -85,11 +85,11 @@ TEST(fft3, Invalid_Array)
     af_array outArray  = 0;
 
     dim4 dims(10,10,1,1);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_fft3(&outArray, inArray, 1.0, 0, 0, 0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TEST(ifft2, Invalid_Array)
@@ -102,11 +102,11 @@ TEST(ifft2, Invalid_Array)
     af_array outArray  = 0;
 
     dim4 dims(100,1,1,1);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_ifft2(&outArray, inArray, 0.01, 0, 0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TEST(ifft3, Invalid_Array)
@@ -119,11 +119,11 @@ TEST(ifft3, Invalid_Array)
     af_array outArray  = 0;
 
     dim4 dims(10,10,1,1);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in.front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_ifft3(&outArray, inArray, 0.01, 0, 0, 0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename inType, typename outType, bool isInverse>
@@ -142,28 +142,28 @@ void fftTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     af_array outArray   = 0;
     af_array inArray    = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if (isInverse){
         switch (dims.ndims()) {
-            case 1 : ASSERT_EQ(AF_SUCCESS, af_ifft (&outArray, inArray, 1.0, pad0));              break;
-            case 2 : ASSERT_EQ(AF_SUCCESS, af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
-            case 3 : ASSERT_EQ(AF_SUCCESS, af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
+            case 1 : ASSERT_SUCCESS(af_ifft (&outArray, inArray, 1.0, pad0));              break;
+            case 2 : ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
+            case 3 : ASSERT_SUCCESS(af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
             default: throw std::runtime_error("This error shouldn't happen, pls check");
         }
     } else {
         switch(dims.ndims()) {
-            case 1 : ASSERT_EQ(AF_SUCCESS, af_fft (&outArray, inArray, 1.0, pad0));               break;
-            case 2 : ASSERT_EQ(AF_SUCCESS, af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
-            case 3 : ASSERT_EQ(AF_SUCCESS, af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
+            case 1 : ASSERT_SUCCESS(af_fft (&outArray, inArray, 1.0, pad0));               break;
+            case 2 : ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
+            case 3 : ASSERT_SUCCESS(af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
             default: throw std::runtime_error("This error shouldn't happen, pls check");
         }
     }
 
     size_t out_size = tests[0].size();
     outType *outData= new outType[out_size];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     vector<outType> goldBar(tests[0].begin(), tests[0].end());
 
@@ -184,8 +184,8 @@ void fftTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
 
     // cleanup
     delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 #define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, ...)  \
@@ -258,28 +258,28 @@ void fftBatchTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     af_array outArray   = 0;
     af_array inArray    = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if(isInverse) {
         switch(rank) {
-            case 1 : ASSERT_EQ(AF_SUCCESS, af_ifft (&outArray, inArray, 1.0, pad0));              break;
-            case 2 : ASSERT_EQ(AF_SUCCESS, af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
-            case 3 : ASSERT_EQ(AF_SUCCESS, af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
+            case 1 : ASSERT_SUCCESS(af_ifft (&outArray, inArray, 1.0, pad0));              break;
+            case 2 : ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
+            case 3 : ASSERT_SUCCESS(af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
             default: throw std::runtime_error("This error shouldn't happen, pls check");
         }
     } else {
         switch(rank) {
-            case 1 : ASSERT_EQ(AF_SUCCESS, af_fft (&outArray, inArray, 1.0, pad0));               break;
-            case 2 : ASSERT_EQ(AF_SUCCESS, af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
-            case 3 : ASSERT_EQ(AF_SUCCESS, af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
+            case 1 : ASSERT_SUCCESS(af_fft (&outArray, inArray, 1.0, pad0));               break;
+            case 2 : ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
+            case 3 : ASSERT_SUCCESS(af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
             default: throw std::runtime_error("This error shouldn't happen, pls check");
         }
     }
 
     size_t out_size = tests[0].size();
     outType *outData= new outType[out_size];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     vector<outType> goldBar(tests[0].begin(), tests[0].end());
 
@@ -308,8 +308,8 @@ void fftBatchTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
 
     // cleanup
     delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 #define INSTANTIATE_BATCH_TEST(func, name, rank, is_inverse, in_t, out_t, ...) \
@@ -624,15 +624,7 @@ TEST(fft, InPlace)
     array b = fft(a);
     fftInPlace(a);
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
-
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(ifft, InPlace)
@@ -644,12 +636,7 @@ TEST(ifft, InPlace)
     vector<cfloat> ha(a.elements());
     vector<cfloat> hb(b.elements());
 
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(fft2, InPlace)
@@ -658,15 +645,7 @@ TEST(fft2, InPlace)
     array b = fft2(a);
     fft2InPlace(a);
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
-
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(ifft2, InPlace)
@@ -675,15 +654,7 @@ TEST(ifft2, InPlace)
     array b = ifft2(a);
     ifft2InPlace(a);
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
-
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(fft3, InPlace)
@@ -692,15 +663,7 @@ TEST(fft3, InPlace)
     array b = fft3(a);
     fft3InPlace(a);
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
-
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(ifft3, InPlace)
@@ -709,15 +672,7 @@ TEST(ifft3, InPlace)
     array b = ifft3(a);
     ifft3InPlace(a);
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
-
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 void fft2InPlaceFunc()
@@ -726,15 +681,7 @@ void fft2InPlaceFunc()
     array b = fft2(a);
     fft2InPlace(a);
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
-
-    a.host(&ha[0]);
-    b.host(&hb[0]);
-
-    for (int i = 0; i < (int)a.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 using af::setDevice;
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index 9370d47eb1..e335715eef 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -66,28 +66,28 @@ void fftconvolveTest(string pTestFile, bool expand)
     af_array outArray = 0;
     af_dtype in_type =(af_dtype)dtype_traits<T>::af_type;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&signal, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()),
                                           sDims.ndims(), sDims.get(), in_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&filter, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()),
                                           fDims.ndims(), fDims.get(), in_type));
 
     af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
     switch(baseDim) {
-        case 1: ASSERT_EQ(AF_SUCCESS, af_fft_convolve1(&outArray, signal, filter, mode)); break;
-        case 2: ASSERT_EQ(AF_SUCCESS, af_fft_convolve2(&outArray, signal, filter, mode)); break;
-        case 3: ASSERT_EQ(AF_SUCCESS, af_fft_convolve3(&outArray, signal, filter, mode)); break;
+        case 1: ASSERT_SUCCESS(af_fft_convolve1(&outArray, signal, filter, mode)); break;
+        case 2: ASSERT_SUCCESS(af_fft_convolve2(&outArray, signal, filter, mode)); break;
+        case 3: ASSERT_SUCCESS(af_fft_convolve3(&outArray, signal, filter, mode)); break;
     }
 
     vector<T> currGoldBar = tests[0];
     size_t nElems         = currGoldBar.size();
 
     dim_t out_elems = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_elements(&out_elems, outArray));
+    ASSERT_SUCCESS(af_get_elements(&out_elems, outArray));
     ASSERT_EQ(nElems, (size_t)out_elems);
 
     vector<T> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_NEAR(
@@ -96,9 +96,9 @@ void fftconvolveTest(string pTestFile, bool expand)
             , 1e-2)<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(signal));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(filter));
+    ASSERT_SUCCESS(af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(filter));
 }
 
 template<typename T, int baseDim>
diff --git a/test/flat.cpp b/test/flat.cpp
index 4e518a3855..f66cc96f04 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -13,28 +13,25 @@
 #include <af/data.h>
 #include <testHelpers.hpp>
 
+#include <vector>
+
 using af::array;
+using af::dim4;
 using af::flat;
 using af::freeHost;
 using af::randu;
 using af::seq;
 using af::span;
 
+using std::vector;
+
 TEST(FlatTests, Test_flat_1D)
 {
     const int num = 10000;
     array in = randu(num);
     array out = flat(in);
 
-    float *h_in = in.host<float>();
-    float *h_out = out.host<float>();
-
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(h_in[i], h_out[i]);
-    }
-
-    freeHost(h_in);
-    freeHost(h_out);
+    ASSERT_ARRAYS_EQ(in, out);
 }
 
 TEST(FlatTests, Test_flat_2D)
@@ -46,15 +43,10 @@ TEST(FlatTests, Test_flat_2D)
     array in = randu(nx, ny);
     array out = flat(in);
 
-    float *h_in = in.host<float>();
-    float *h_out = out.host<float>();
-
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(h_in[i], h_out[i]);
-    }
-
-    freeHost(h_in);
-    freeHost(h_out);
+    vector<float> h_in_flat(in.elements());
+    in.host(h_in_flat.data());
+    dim4 h_in_flat_dims = dim4(nx*ny);
+    ASSERT_VEC_ARRAY_EQ(h_in_flat, h_in_flat_dims, out);
 }
 
 TEST(FlatTests, Test_flat_1D_index)
@@ -70,6 +62,7 @@ TEST(FlatTests, Test_flat_1D_index)
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
 
+    // TODO: Use ASSERT_ARRAYS_EQUAL
     for (int i = st; i <= en; i++) {
         ASSERT_EQ(h_in[i], h_out[i - st]);
     }
@@ -93,6 +86,7 @@ TEST(FlatTests, Test_flat_2D_index0)
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
 
+    // TODO: Use ASSERT_ARRAYS_EQUAL
     for (int j = 0; j < ny; j++) {
         const int in_off = j * nx;
         const int out_off =j * nxo;
@@ -120,6 +114,7 @@ TEST(FlatTests, Test_flat_2D_index1)
     float *h_in = in.host<float>();
     float *h_out = out.host<float>();
 
+    // TODO: Use ASSERT_ARRAYS_EQUAL
     for (int j = st; j <= en; j++) {
 
         const int in_off = j * nx;
diff --git a/test/gaussiankernel.cpp b/test/gaussiankernel.cpp
index 45d4575d42..183ef77942 100644
--- a/test/gaussiankernel.cpp
+++ b/test/gaussiankernel.cpp
@@ -48,13 +48,13 @@ void gaussianKernelTest(string pFileName, double sigma)
 
     vector<int> input(in[0].begin(), in[0].end());
 
-    ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&outArray, input[0], input[1], sigma, sigma));
+    ASSERT_SUCCESS(af_gaussian_kernel(&outArray, input[0], input[1], sigma, sigma));
 
     dim_t outElems = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_elements(&outElems, outArray));
+    ASSERT_SUCCESS(af_get_elements(&outElems, outArray));
     T *outData = new T[outElems];
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     vector<T> currGoldBar(tests[0].begin(), tests[0].end());
     size_t nElems = currGoldBar.size();
@@ -66,7 +66,7 @@ void gaussianKernelTest(string pFileName, double sigma)
     }
 
     delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(GaussianKernel, Small1D)
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 9d2776e6d2..254ef5381f 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -51,32 +51,32 @@ void testGeneralAssignOneArray(string pTestFile, const dim_t ndims, af_index_t*
     af_array lhsArray  = 0;
     af_array idxArray  = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray, &(in[2].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[2].front()),
                 dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[arrayDim].idx.arr = idxArray;
 
-    ASSERT_EQ(AF_SUCCESS, af_assign_gen(&outArray, lhsArray, ndims, indexs, rhsArray));
+    ASSERT_SUCCESS(af_assign_gen(&outArray, lhsArray, ndims, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(rhsArray));
+    ASSERT_SUCCESS(af_release_array(lhsArray));
+    ASSERT_SUCCESS(af_release_array(idxArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TEST(GeneralAssign, ASSS)
@@ -119,27 +119,27 @@ TEST(GeneralAssign, SSSS)
     indexs[0].isSeq = true;
     indexs[1].isSeq = true;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_assign_gen(&outArray, lhsArray, 2, indexs, rhsArray));
+    ASSERT_SUCCESS(af_assign_gen(&outArray, lhsArray, 2, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(rhsArray));
+    ASSERT_SUCCESS(af_release_array(lhsArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TEST(GeneralAssign, AAAA)
@@ -170,47 +170,47 @@ TEST(GeneralAssign, AAAA)
     indexs[2].isSeq = false;
     indexs[3].isSeq = false;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&lhsArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&rhsArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray0, &(in[2].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[2].front()),
                 dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[0].idx.arr = idxArray0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray1, &(in[3].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray1, &(in[3].front()),
                 dims3.ndims(), dims3.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[1].idx.arr = idxArray1;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray2, &(in[4].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray2, &(in[4].front()),
                 dims4.ndims(), dims4.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[2].idx.arr = idxArray2;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray3, &(in[5].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray3, &(in[5].front()),
                 dims5.ndims(), dims5.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[3].idx.arr = idxArray3;
 
-    ASSERT_EQ(AF_SUCCESS, af_assign_gen(&outArray, lhsArray, 4, indexs, rhsArray));
+    ASSERT_SUCCESS(af_assign_gen(&outArray, lhsArray, 4, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(rhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(lhsArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray1));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray2));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray3));
+    ASSERT_SUCCESS(af_release_array(rhsArray));
+    ASSERT_SUCCESS(af_release_array(lhsArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(idxArray0));
+    ASSERT_SUCCESS(af_release_array(idxArray1));
+    ASSERT_SUCCESS(af_release_array(idxArray2));
+    ASSERT_SUCCESS(af_release_array(idxArray3));
 }
 
 
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index efa9b5b88c..cffcf52835 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -42,28 +42,28 @@ void testGeneralIndexOneArray(string pTestFile, const dim_t ndims, af_index_t* i
     af_array inArray   = 0;
     af_array idxArray  = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[arrayDim].idx.arr = idxArray;
 
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&outArray, inArray, ndims, indexs));
+    ASSERT_SUCCESS(af_index_gen(&outArray, inArray, ndims, indexs));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(idxArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TEST(GeneralIndex, SSSA)
@@ -122,35 +122,35 @@ TEST(GeneralIndex, AASS)
 
     af_index_t indexs[2];
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray0, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[0].isSeq = false;
     indexs[0].idx.arr = idxArray0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray1, &(in[2].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray1, &(in[2].front()),
                 dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
     indexs[1].isSeq = false;
     indexs[1].idx.arr = idxArray1;
 
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&outArray, inArray, 2, indexs));
+    ASSERT_SUCCESS(af_index_gen(&outArray, inArray, 2, indexs));
 
     vector<float> currGoldBar = tests[0];
     size_t nElems = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray0));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray1));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(idxArray0));
+    ASSERT_SUCCESS(af_release_array(idxArray1));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 using af::array;
diff --git a/test/gfor.cpp b/test/gfor.cpp
index 6d7dbcfa9a..428cb37332 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -523,13 +523,5 @@ TEST(ASSIGN, ISSUE_1127)
     out1(seq(0,rows-1,2), seq(1,cols-1,2), span) = horiz;
     out1(seq(1,rows-1,2), seq(1,cols-1,2), span) = diag;
 
-    vector<float> hout0(out0.elements());
-    vector<float> hout1(out1.elements());
-
-    out0.host(&hout0[0]);
-    out1.host(&hout1[0]);
-
-    for (int i = 0; i < out0.elements(); i++) {
-        ASSERT_EQ(hout0[i], hout1[i]);
-    }
+    ASSERT_ARRAYS_EQ(out0, out1);
 }
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 5e6444eea3..50b705e76e 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -164,20 +164,20 @@ void glohTest(string pTestFile)
 
         inFiles[testId].insert(0,string(TEST_DIR"/gloh/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
+        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_gloh(&feat, &desc, inArray, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f));
+        ASSERT_SUCCESS(af_gloh(&feat, &desc, inArray, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
 
-        ASSERT_EQ(AF_SUCCESS, af_get_features_num(&n, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&x, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&y, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_score(&score, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_orientation(&orientation, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_size(&size, feat));
+        ASSERT_SUCCESS(af_get_features_num(&n, feat));
+        ASSERT_SUCCESS(af_get_features_xpos(&x, feat));
+        ASSERT_SUCCESS(af_get_features_ypos(&y, feat));
+        ASSERT_SUCCESS(af_get_features_score(&score, feat));
+        ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
+        ASSERT_SUCCESS(af_get_features_size(&size, feat));
 
         float * outX           = new float[n];
         float * outY           = new float[n];
@@ -186,15 +186,15 @@ void glohTest(string pTestFile)
         float * outSize        = new float[n];
         dim_t descSize;
         dim_t descDims[4];
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&descSize, desc));
-        ASSERT_EQ(AF_SUCCESS, af_get_dims(&descDims[0], &descDims[1], &descDims[2], &descDims[3], desc));
+        ASSERT_SUCCESS(af_get_elements(&descSize, desc));
+        ASSERT_SUCCESS(af_get_dims(&descDims[0], &descDims[1], &descDims[2], &descDims[3], desc));
         float * outDesc     = new float[descSize];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outX, x));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outY, y));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outScore, score));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outOrientation, orientation));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outSize, size));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDesc, desc));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outOrientation, orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outSize, size));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outDesc, desc));
 
         vector<feat_desc_t> out_feat_desc;
         array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, n);
@@ -223,15 +223,15 @@ void glohTest(string pTestFile)
 
         EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 5.5f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(x));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(y));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(score));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(orientation));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(size));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(desc));
+        ASSERT_SUCCESS(af_release_array(x));
+        ASSERT_SUCCESS(af_release_array(y));
+        ASSERT_SUCCESS(af_release_array(score));
+        ASSERT_SUCCESS(af_release_array(orientation));
+        ASSERT_SUCCESS(af_release_array(size));
+        ASSERT_SUCCESS(af_release_array(desc));
 
         delete[] outX;
         delete[] outY;
diff --git a/test/gradient.cpp b/test/gradient.cpp
index 58b1efbbbb..3e74a1f0ed 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -62,19 +62,19 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
     af_array g1Array = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_gradient(&g0Array, &g1Array, inArray));
+    ASSERT_SUCCESS(af_gradient(&g0Array, &g1Array, inArray));
 
     size_t nElems = tests[resultIdx0].size();
     // Get result
     T* grad0Data = new T[tests[resultIdx0].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)grad0Data, g0Array));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)grad0Data, g0Array));
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
@@ -83,7 +83,7 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 
     // Get result
     T* grad1Data = new T[tests[resultIdx1].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)grad1Data, g1Array));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)grad1Data, g1Array));
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 528a484442..131d68c0a4 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -68,12 +68,12 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     af_array idx   = 0;
     af_array dist  = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&query, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&query, &(in[0].front()),
                 qDims.ndims(), qDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&train, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&train, &(in[1].front()),
                 tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_hamming_matcher(&idx, &dist, query, train, feat_dim, 1));
+    ASSERT_SUCCESS(af_hamming_matcher(&idx, &dist, query, train, feat_dim, 1));
 
     vector<uint> goldIdx  = tests[0];
     vector<uint> goldDist = tests[1];
@@ -81,8 +81,8 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     uint *outIdx          = new uint[nElems];
     uint *outDist         = new uint[nElems];
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outIdx,  idx));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDist, dist));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outIdx,  idx));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outDist, dist));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
@@ -90,10 +90,10 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
 
     delete[] outIdx;
     delete[] outDist;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(dist));
+    ASSERT_SUCCESS(af_release_array(query));
+    ASSERT_SUCCESS(af_release_array(train));
+    ASSERT_SUCCESS(af_release_array(idx));
+    ASSERT_SUCCESS(af_release_array(dist));
 }
 
 TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim0)
diff --git a/test/harris.cpp b/test/harris.cpp
index 46152d4591..754da00408 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -83,35 +83,35 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
 
         inFiles[testId].insert(0,string(TEST_DIR"/harris/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
 
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_harris(&out, inArray, 500, 1e5f, sigma, block_size, 0.04f));
+        ASSERT_SUCCESS(af_harris(&out, inArray, 500, 1e5f, sigma, block_size, 0.04f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
 
-        ASSERT_EQ(AF_SUCCESS, af_get_features_num(&n, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&x, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&y, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_score(&score, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_orientation(&orientation, out));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_size(&size, out));
+        ASSERT_SUCCESS(af_get_features_num(&n, out));
+        ASSERT_SUCCESS(af_get_features_xpos(&x, out));
+        ASSERT_SUCCESS(af_get_features_ypos(&y, out));
+        ASSERT_SUCCESS(af_get_features_score(&score, out));
+        ASSERT_SUCCESS(af_get_features_orientation(&orientation, out));
+        ASSERT_SUCCESS(af_get_features_size(&size, out));
 
 
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, x));
+        ASSERT_SUCCESS(af_get_elements(&nElems, x));
 
         vector<float> outX           (gold[0].size());
         vector<float> outY           (gold[1].size());
         vector<float> outScore       (gold[2].size());
         vector<float> outOrientation (gold[3].size());
         vector<float> outSize        (gold[4].size());
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outX.front(), x));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outY.front(), y));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outScore.front(), score));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outOrientation.front(), orientation));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outSize.front(), size));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outX.front(), x));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outY.front(), y));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outScore.front(), score));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outOrientation.front(), orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outSize.front(), size));
 
         vector<feat_t> out_feat;
         array_to_feat(out_feat, &outX.front(), &outY.front(),
@@ -132,10 +132,10 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
             ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
         }
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_features(out));
+        ASSERT_SUCCESS(af_release_features(out));
     }
 }
 
diff --git a/test/histogram.cpp b/test/histogram.cpp
index a74b0fa6be..0d9258cfaf 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -54,25 +54,24 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
     af_array outArray   = 0;
     af_array inArray    = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<inType>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS,af_histogram(&outArray,inArray,nbins,minval,maxval));
+    ASSERT_SUCCESS(af_histogram(&outArray,inArray,nbins,minval,maxval));
 
     vector<outType> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<outType> currGoldBar = tests[testIter];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
-        }
+
+        dim4 goldDims(nbins, 1, dims[2], dims[3]);
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(Histogram,256Bins0min255max_ones)
@@ -137,10 +136,11 @@ TEST(Histogram, CPP)
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<uint> currGoldBar = tests[testIter];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
-        }
+
+        dim4 goldDims = numDims[0];
+        goldDims[0] = nbins;
+        goldDims[1] = 1;
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
     }
 }
 
diff --git a/test/homography.cpp b/test/homography.cpp
index 7023e849b7..b149fed18a 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -69,13 +69,13 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     af_array train_feat_y     = 0;
     af_features train_feat;
 
-    ASSERT_EQ(AF_SUCCESS, af_load_image(&trainArray_f32, inFiles[0].c_str(), false));
-    ASSERT_EQ(AF_SUCCESS, conv_image<T>(&trainArray, trainArray_f32));
+    ASSERT_SUCCESS(af_load_image(&trainArray_f32, inFiles[0].c_str(), false));
+    ASSERT_SUCCESS(conv_image<T>(&trainArray, trainArray_f32));
 
-    ASSERT_EQ(AF_SUCCESS, af_orb(&train_feat, &train_desc, trainArray, 20.0f, 2000, 1.2f, 8, true));
+    ASSERT_SUCCESS(af_orb(&train_feat, &train_desc, trainArray, 20.0f, 2000, 1.2f, 8, true));
 
-    ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&train_feat_x, train_feat));
-    ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&train_feat_y, train_feat));
+    ASSERT_SUCCESS(af_get_features_xpos(&train_feat_x, train_feat));
+    ASSERT_SUCCESS(af_get_features_ypos(&train_feat_y, train_feat));
 
     af_array queryArray       = 0;
     af_array query_desc       = 0;
@@ -99,46 +99,46 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     const dim_t test_d1 = inDims[0][1] * size_ratio;
     const dim_t tDims[] = {test_d0, test_d1};
     if (rotate)
-        ASSERT_EQ(AF_SUCCESS, af_rotate(&queryArray, trainArray, theta, false, AF_INTERP_NEAREST));
+        ASSERT_SUCCESS(af_rotate(&queryArray, trainArray, theta, false, AF_INTERP_NEAREST));
     else
-        ASSERT_EQ(AF_SUCCESS, af_resize(&queryArray, trainArray, test_d0, test_d1, AF_INTERP_BILINEAR));
+        ASSERT_SUCCESS(af_resize(&queryArray, trainArray, test_d0, test_d1, AF_INTERP_BILINEAR));
 
-    ASSERT_EQ(AF_SUCCESS, af_orb(&query_feat, &query_desc, queryArray, 20.0f, 2000, 1.2f, 8, true));
+    ASSERT_SUCCESS(af_orb(&query_feat, &query_desc, queryArray, 20.0f, 2000, 1.2f, 8, true));
 
-    ASSERT_EQ(AF_SUCCESS, af_hamming_matcher(&idx, &dist, train_desc, query_desc, 0, 1));
+    ASSERT_SUCCESS(af_hamming_matcher(&idx, &dist, train_desc, query_desc, 0, 1));
 
     dim_t distDims[4];
-    ASSERT_EQ(AF_SUCCESS, af_get_dims(&distDims[0], &distDims[1], &distDims[2], &distDims[3], dist));
+    ASSERT_SUCCESS(af_get_dims(&distDims[0], &distDims[1], &distDims[2], &distDims[3], dist));
 
-    ASSERT_EQ(AF_SUCCESS, af_constant(&const_50, 50, 2, distDims, u32));
-    ASSERT_EQ(AF_SUCCESS, af_lt(&dist_thr, dist, const_50, false));
-    ASSERT_EQ(AF_SUCCESS, af_where(&train_idx, dist_thr));
+    ASSERT_SUCCESS(af_constant(&const_50, 50, 2, distDims, u32));
+    ASSERT_SUCCESS(af_lt(&dist_thr, dist, const_50, false));
+    ASSERT_SUCCESS(af_where(&train_idx, dist_thr));
 
     dim_t tidxDims[4];
-    ASSERT_EQ(AF_SUCCESS, af_get_dims(&tidxDims[0], &tidxDims[1], &tidxDims[2], &tidxDims[3], train_idx));
+    ASSERT_SUCCESS(af_get_dims(&tidxDims[0], &tidxDims[1], &tidxDims[2], &tidxDims[3], train_idx));
     af_index_t tindexs;
     tindexs.isSeq = false;
     tindexs.idx.seq = af_make_seq(0, tidxDims[0]-1, 1);
     tindexs.idx.arr = train_idx;
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&query_idx, idx, 1, &tindexs));
+    ASSERT_SUCCESS(af_index_gen(&query_idx, idx, 1, &tindexs));
 
-    ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&query_feat_x, query_feat));
-    ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&query_feat_y, query_feat));
+    ASSERT_SUCCESS(af_get_features_xpos(&query_feat_x, query_feat));
+    ASSERT_SUCCESS(af_get_features_ypos(&query_feat_y, query_feat));
 
     dim_t qidxDims[4];
-    ASSERT_EQ(AF_SUCCESS, af_get_dims(&qidxDims[0], &qidxDims[1], &qidxDims[2], &qidxDims[3], query_idx));
+    ASSERT_SUCCESS(af_get_dims(&qidxDims[0], &qidxDims[1], &qidxDims[2], &qidxDims[3], query_idx));
     af_index_t qindexs;
     qindexs.isSeq = false;
     qindexs.idx.seq = af_make_seq(0, qidxDims[0]-1, 1);
     qindexs.idx.arr = query_idx;
 
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&train_feat_x_idx, train_feat_x, 1, &tindexs));
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&train_feat_y_idx, train_feat_y, 1, &tindexs));
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&query_feat_x_idx, query_feat_x, 1, &qindexs));
-    ASSERT_EQ(AF_SUCCESS, af_index_gen(&query_feat_y_idx, query_feat_y, 1, &qindexs));
+    ASSERT_SUCCESS(af_index_gen(&train_feat_x_idx, train_feat_x, 1, &tindexs));
+    ASSERT_SUCCESS(af_index_gen(&train_feat_y_idx, train_feat_y, 1, &tindexs));
+    ASSERT_SUCCESS(af_index_gen(&query_feat_x_idx, query_feat_x, 1, &qindexs));
+    ASSERT_SUCCESS(af_index_gen(&query_feat_y_idx, query_feat_y, 1, &qindexs));
 
     int inliers = 0;
-    ASSERT_EQ(AF_SUCCESS, af_homography(&H, &inliers, train_feat_x_idx, train_feat_y_idx,
+    ASSERT_SUCCESS(af_homography(&H, &inliers, train_feat_x_idx, train_feat_y_idx,
                                         query_feat_x_idx, query_feat_y_idx, htype,
                                         3.0f, 1000, (af_dtype) dtype_traits<T>::af_type));
 
@@ -172,25 +172,25 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     delete[] gold_t;
     delete[] out_t;
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(queryArray));
-
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query_desc));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(dist));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(const_50));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(dist_thr));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train_idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query_idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_features(query_feat));
-    ASSERT_EQ(AF_SUCCESS, af_release_features(train_feat));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_x_idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train_feat_y_idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_x_idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query_feat_y_idx));
-
-    ASSERT_EQ(AF_SUCCESS, af_release_array(trainArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(trainArray_f32));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train_desc));
+    ASSERT_SUCCESS(af_release_array(queryArray));
+
+    ASSERT_SUCCESS(af_release_array(query_desc));
+    ASSERT_SUCCESS(af_release_array(idx));
+    ASSERT_SUCCESS(af_release_array(dist));
+    ASSERT_SUCCESS(af_release_array(const_50));
+    ASSERT_SUCCESS(af_release_array(dist_thr));
+    ASSERT_SUCCESS(af_release_array(train_idx));
+    ASSERT_SUCCESS(af_release_array(query_idx));
+    ASSERT_SUCCESS(af_release_features(query_feat));
+    ASSERT_SUCCESS(af_release_features(train_feat));
+    ASSERT_SUCCESS(af_release_array(train_feat_x_idx));
+    ASSERT_SUCCESS(af_release_array(train_feat_y_idx));
+    ASSERT_SUCCESS(af_release_array(query_feat_x_idx));
+    ASSERT_SUCCESS(af_release_array(query_feat_y_idx));
+
+    ASSERT_SUCCESS(af_release_array(trainArray));
+    ASSERT_SUCCESS(af_release_array(trainArray_f32));
+    ASSERT_SUCCESS(af_release_array(train_desc));
 }
 
 #define HOMOGRAPHY_INIT(desc, image, htype, rotate, size_ratio)                 \
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 27b8668f6f..e3e1168eee 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -50,11 +50,11 @@ void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
     dim4 dims       = numDims[0];
 
     af_array imgArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_load_image(&imgArray, pImageFile.c_str(), isColor));
+    ASSERT_SUCCESS(af_load_image(&imgArray, pImageFile.c_str(), isColor));
 
     // Get result
     float *imgData = new float[dims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*) imgData, imgArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*) imgData, imgArray));
 
     bool isJPEG = false;
     if(pImageFile.find(".jpg") != string::npos) {
diff --git a/test/index.cpp b/test/index.cpp
index 5ca08d9d0d..fa4ff2ef4d 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -54,11 +54,11 @@ DimCheck(const vector<af_seq> &seqs) {
     for(int i = 0; i < (int)dims; i++) { hData[i] = i; }
 
     af_array a = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &hData.front(), ndims, d, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &hData.front(), ndims, d, (af_dtype) dtype_traits<T>::af_type));
 
     vector<af_array> indexed_array(seqs.size(), 0);
     for(size_t i = 0; i < seqs.size(); i++) {
-        ASSERT_EQ(AF_SUCCESS, af_index(&(indexed_array[i]), a, ndims, &seqs[i]))
+        ASSERT_SUCCESS(af_index(&(indexed_array[i]), a, ndims, &seqs[i]))
             << "where seqs[i].begin == "    << seqs[i].begin
             << " seqs[i].step == "          << seqs[i].step
             << " seqs[i].end == "           << seqs[i].end;
@@ -67,9 +67,9 @@ DimCheck(const vector<af_seq> &seqs) {
     vector<T*> h_indexed(seqs.size());
     for(size_t i = 0; i < seqs.size(); i++) {
         dim_t elems;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&elems, indexed_array[i]));
+        ASSERT_SUCCESS(af_get_elements(&elems, indexed_array[i]));
         h_indexed[i] = new T[elems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void *)(h_indexed[i]), indexed_array[i]));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)(h_indexed[i]), indexed_array[i]));
     }
 
     for(size_t k = 0; k < seqs.size(); k++) {
@@ -86,9 +86,9 @@ DimCheck(const vector<af_seq> &seqs) {
         delete[] h_indexed[k];
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(a));
     for (size_t i = 0; i < indexed_array.size(); i++) {
-        ASSERT_EQ(AF_SUCCESS, af_release_array(indexed_array[i]));
+        ASSERT_SUCCESS(af_release_array(indexed_array[i]));
     }
 }
 
@@ -267,19 +267,19 @@ DimCheck2D(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDims)
     dim4 dimensions = numDims[0];
 
     af_array a = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&a, &(hData[0].front()), NDims, dimensions.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &(hData[0].front()), NDims, dimensions.get(), (af_dtype) dtype_traits<T>::af_type));
 
     vector<af_array> indexed_arrays(seqs.size(), 0);
     for(size_t i = 0; i < seqs.size(); i++) {
-        ASSERT_EQ(AF_SUCCESS, af_index(&(indexed_arrays[i]), a, NDims, seqs[i].data()));
+        ASSERT_SUCCESS(af_index(&(indexed_arrays[i]), a, NDims, seqs[i].data()));
     }
 
     vector<T*> h_indexed(seqs.size(), NULL);
     for(size_t i = 0; i < seqs.size(); i++) {
         dim_t elems;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&elems, indexed_arrays[i]));
+        ASSERT_SUCCESS(af_get_elements(&elems, indexed_arrays[i]));
         h_indexed[i] = new T[elems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void *)h_indexed[i], indexed_arrays[i]));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)h_indexed[i], indexed_arrays[i]));
 
         T* ptr = h_indexed[i];
         if(false == equal(ptr, ptr + tests[i].size(), tests[i].begin())) {
@@ -292,9 +292,9 @@ DimCheck2D(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDims)
         delete[] h_indexed[i];
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(a));
+    ASSERT_SUCCESS(af_release_array(a));
     for (size_t i = 0; i < indexed_arrays.size(); i++) {
-        ASSERT_EQ(AF_SUCCESS, af_release_array(indexed_arrays[i]));
+        ASSERT_SUCCESS(af_release_array(indexed_arrays[i]));
     }
 }
 
@@ -640,28 +640,23 @@ void arrayIndexTest(string pTestFile, int dim)
     af_array inArray   = 0;
     af_array idxArray  = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&idxArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[1].front()),
                 dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_lookup(&outArray, inArray, idxArray, dim));
+    ASSERT_SUCCESS(af_lookup(&outArray, inArray, idxArray, dim));
 
     vector<T> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    T *outData = new T[nElems];
+    dim4 goldDims = dims0;
+    goldDims[dim] = dims1[0];
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
-    }
-
-    delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idxArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(idxArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(lookup, Dim0)
@@ -700,16 +695,10 @@ TEST(lookup, CPP)
     array output = af::lookup(input, indices, 0);
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
-
-    output.host((void*)outData);
+    dim4 goldDims = dims0;
+    goldDims[0] = dims1[0];
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
-    }
-
-    delete[] outData;
+    ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
 }
 
 TEST(lookup, largeDim)
@@ -729,7 +718,7 @@ TEST(lookup, Issue2009)
     array idx = constant(0, 1, u32);
     array b   = af::lookup(a, idx, 1);
 
-    ASSERT_EQ(true, allTrue<bool>(a==b));
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(SeqIndex, CPP_END)
@@ -835,16 +824,10 @@ TEST(SeqIndex, CPPLarge)
     array output = af::lookup(input, indices, 0);
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    float *outData = new float[nElems];
-
-    output.host((void*)outData);
+    dim4 goldDims = dims0;
+    goldDims[0] = dims1[0];
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
-    }
-
-    delete[] outData;
+    ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
 }
 
 TEST(SeqIndex, Cascade00)
@@ -1334,7 +1317,7 @@ TEST(Assign, LinearIndexSeq)
     af_index_t ii = idx.get();
     af_array out_arr;
 
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
               af_index(&out_arr, in_arr, 1, &ii.idx.seq));
 
     array out(out_arr);
@@ -1369,7 +1352,7 @@ TEST(Assign, LinearIndexGenSeq)
     af_index_t ii = idx.get();
     af_array out_arr;
 
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
               af_index_gen(&out_arr, in_arr, 1, &ii));
 
     array out(out_arr);
@@ -1404,7 +1387,7 @@ TEST(Assign, LinearIndexGenArr)
     af_index_t ii = idx.get();
     af_array out_arr;
 
-    ASSERT_EQ(AF_SUCCESS,
+    ASSERT_SUCCESS(
               af_index_gen(&out_arr, in_arr, 1, &ii));
 
     array out(out_arr);
@@ -1442,8 +1425,6 @@ TEST(Index, ISSUE_1101_FULL)
 {
     deviceGC();
     array a = randu(5,5);
-    vector<float> ha(a.elements());
-    a.host(&ha[0]);
 
     size_t aby, abu, lby, lbu;
     deviceMemInfo(&aby, &abu, &lby, &lbu);
@@ -1458,11 +1439,7 @@ TEST(Index, ISSUE_1101_FULL)
     ASSERT_EQ(lby, lby1);
     ASSERT_EQ(lbu, lbu1);
 
-    vector<float> hb(b.elements());
-    b.host(&hb[0]);
-    for (int i = 0; i < b.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_ARRAYS_EQ(a, b);
 }
 
 TEST(Index, ISSUE_1101_COL0)
@@ -1470,7 +1447,8 @@ TEST(Index, ISSUE_1101_COL0)
     deviceGC();
     array a = randu(5,5);
     vector<float> ha(a.elements());
-    a.host(&ha[0]);
+    a.host(ha.data());
+    vector<float> gold(ha.begin(), ha.begin()+5);
 
     size_t aby, abu, lby, lbu;
     deviceMemInfo(&aby, &abu, &lby, &lbu);
@@ -1480,17 +1458,12 @@ TEST(Index, ISSUE_1101_COL0)
     size_t aby1, abu1, lby1, lbu1;
     deviceMemInfo(&aby1, &abu1, &lby1, &lbu1);
 
-    ASSERT_EQ(aby, aby1);
-    ASSERT_EQ(abu, abu1);
-    ASSERT_EQ(lby, lby1);
-    ASSERT_EQ(lbu, lbu1);
-
-    vector<float> hb(b.elements());
-    b.host(&hb[0]);
-    for (int i = 0; i < b.elements(); i++) {
-        ASSERT_EQ(ha[i], hb[i]);
-    }
+    ASSERT_EQ(aby, aby1) << "Number of bytes different";
+    ASSERT_EQ(abu, abu1) << "Number of buffers different";
+    ASSERT_EQ(lby, lby1) << "Number of bytes different";
+    ASSERT_EQ(lbu, lbu1) << "Number of buffers different";
 
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(a.dims()[0]), b);
 }
 
 TEST(Index, ISSUE_1101_MODDIMS)
diff --git a/test/info.cpp b/test/info.cpp
index e999e32ff6..51570f7a67 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -33,17 +33,17 @@ void testFunction()
 
     af_array outArray = 0;
     dim4 dims(32, 32, 1, 1);
-    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     // cleanup
     if(outArray != 0) {
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
     }
 }
 
 void infoTest()
 {
     int nDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&nDevices));
+    ASSERT_SUCCESS(af_get_device_count(&nDevices));
     ASSERT_EQ(true, nDevices>0);
 
     const char* ENV = getenv("AF_MULTI_GPU_TESTS");
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index bf8061dd1c..d438de7aba 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -70,53 +70,53 @@ void invDeconvImageTest(string pTestFile, const float gamma, const af_inverse_de
         af_array _goldArray = 0;
         dim_t nElems        = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
+        ASSERT_SUCCESS(af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
 
         af_dtype itype = (af_dtype)af::dtype_traits<T>::af_type;
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, _inArray));
+        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<OutType>(&goldArray, _goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<OutType>(&goldArray, _goldArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         unsigned ndims;
         dim_t dims[4];
-        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
+        ASSERT_SUCCESS(af_get_numdims(&ndims, goldArray));
+        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_inverse_deconv(&_outArray, inArray, kerArray, gamma, algo));
+        ASSERT_SUCCESS(af_inverse_deconv(&_outArray, inArray, kerArray, gamma, algo));
 
         double maxima, minima, imag;
-        ASSERT_EQ(AF_SUCCESS, af_min_all(&minima, &imag, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_max_all(&maxima, &imag, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&denArray, (maxima-minima), ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&minArray, minima, ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_sub(&numArray, _outArray, minArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
+        ASSERT_SUCCESS(af_min_all(&minima, &imag, _outArray));
+        ASSERT_SUCCESS(af_max_all(&maxima, &imag, _outArray));
+        ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, otype));
+        ASSERT_SUCCESS(af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_SUCCESS(af_constant(&minArray, minima, ndims, dims, otype));
+        ASSERT_SUCCESS(af_sub(&numArray, _outArray, minArray, false));
+        ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
+        ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
         std::vector<OutType> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         std::vector<OutType> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(kerArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
+
+        ASSERT_SUCCESS(af_release_array(_inArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(kerArray));
+        ASSERT_SUCCESS(af_release_array(cstArray));
+        ASSERT_SUCCESS(af_release_array(minArray));
+        ASSERT_SUCCESS(af_release_array(denArray));
+        ASSERT_SUCCESS(af_release_array(numArray));
+        ASSERT_SUCCESS(af_release_array(divArray));
+        ASSERT_SUCCESS(af_release_array(_outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(_goldArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
     }
diff --git a/test/iota.cpp b/test/iota.cpp
index ed9ed5e5ef..f12ddff406 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -51,7 +51,7 @@ void iotaTest(const dim4 idims, const dim4 tdims)
 
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_iota(&outArray, idims.ndims(), idims.get(),
+    ASSERT_SUCCESS(af_iota(&outArray, idims.ndims(), idims.get(),
                tdims.ndims(), tdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     af_array temp0 = 0, temp1 = 0, temp2 = 0;
@@ -60,20 +60,11 @@ void iotaTest(const dim4 idims, const dim4 tdims)
     for(unsigned i = 0; i < 4; i++) {
         fulldims[i] = idims[i] * tdims[i];
     }
-    ASSERT_EQ(AF_SUCCESS, af_range(&temp2, tempdims.ndims(), tempdims.get(), 0, (af_dtype) dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_moddims(&temp1, temp2, idims.ndims(), idims.get()));
-    ASSERT_EQ(AF_SUCCESS, af_tile(&temp0, temp1, tdims[0], tdims[1], tdims[2], tdims[3]));
+    ASSERT_SUCCESS(af_range(&temp2, tempdims.ndims(), tempdims.get(), 0, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_moddims(&temp1, temp2, idims.ndims(), idims.get()));
+    ASSERT_SUCCESS(af_tile(&temp0, temp1, tdims[0], tdims[1], tdims[2], tdims[3]));
 
-    // Get result
-    vector<T> outData(fulldims.elements());
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
-
-    vector<T> tileData(fulldims.elements());
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&tileData.front(), temp0));
-
-    // Compare result
-    for(int i = 0; i < (int) fulldims.elements(); i++)
-        ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << endl;
+    ASSERT_ARRAYS_EQ(temp0, outArray);
 
     if(outArray  != 0) af_release_array(outArray);
     if(temp0     != 0) af_release_array(temp0);
@@ -125,14 +116,5 @@ TEST(Iota, CPP)
     array output = iota(idims, tdims);
     array tileArray = tile(moddims(range(dim4(idims.elements()), 0), idims), tdims);
 
-    // Get result
-    vector<float> outData (fulldims.elements());
-    output.host((void*)&outData.front());
-
-    vector<float> tileData (fulldims.elements());
-    tileArray.host((void*)&tileData.front());
-
-    // Compare result
-    for(int i = 0; i < (int)fulldims.elements(); i++)
-        ASSERT_EQ(tileData[i], outData[i]) << "at: " << i << endl;
+    ASSERT_ARRAYS_EQ(tileArray, output);
 }
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index c1a15b343f..1b152cbfea 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -182,7 +182,7 @@ TEST(IndexedReduce, MinReduceDimensionHasSingleValue)
     array mm, indx;
     min(mm, indx, data, 2);
 
-    ASSERT_TRUE(allTrue<bool>(mm == data));
+    ASSERT_ARRAYS_EQ(data, mm);
     ASSERT_TRUE(allTrue<bool>(indx == 0));
 }
 
@@ -193,7 +193,7 @@ TEST(IndexedReduce, MaxReduceDimensionHasSingleValue)
     array mm, indx;
     max(mm, indx, data, 2);
 
-    ASSERT_TRUE(allTrue<bool>(mm == data));
+    ASSERT_ARRAYS_EQ(data, mm);
     ASSERT_TRUE(allTrue<bool>(indx == 0));
 }
 
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index c6f67c50af..1e2072ce71 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -71,53 +71,53 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
         af_array _goldArray = 0;
         dim_t nElems        = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
+        ASSERT_SUCCESS(af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
 
         af_dtype itype = (af_dtype)af::dtype_traits<T>::af_type;
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, _inArray));
+        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<OutType>(&goldArray, _goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<OutType>(&goldArray, _goldArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         unsigned ndims;
         dim_t dims[4];
-        ASSERT_EQ(AF_SUCCESS, af_get_numdims(&ndims, goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
+        ASSERT_SUCCESS(af_get_numdims(&ndims, goldArray));
+        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_iterative_deconv(&_outArray, inArray, kerArray, iters, rf, algo));
+        ASSERT_SUCCESS(af_iterative_deconv(&_outArray, inArray, kerArray, iters, rf, algo));
 
         double maxima, minima, imag;
-        ASSERT_EQ(AF_SUCCESS, af_min_all(&minima, &imag, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_max_all(&maxima, &imag, _outArray));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&cstArray, 255.0, ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&denArray, (maxima-minima), ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_constant(&minArray, minima, ndims, dims, otype));
-        ASSERT_EQ(AF_SUCCESS, af_sub(&numArray, _outArray, minArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_div(&divArray, numArray, denArray, false));
-        ASSERT_EQ(AF_SUCCESS, af_mul(&outArray, divArray, cstArray, false));
+        ASSERT_SUCCESS(af_min_all(&minima, &imag, _outArray));
+        ASSERT_SUCCESS(af_max_all(&maxima, &imag, _outArray));
+        ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, otype));
+        ASSERT_SUCCESS(af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_SUCCESS(af_constant(&minArray, minima, ndims, dims, otype));
+        ASSERT_SUCCESS(af_sub(&numArray, _outArray, minArray, false));
+        ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
+        ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
         std::vector<OutType> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         std::vector<OutType> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(kerArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(cstArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(minArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(denArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(numArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(divArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(_goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
+
+        ASSERT_SUCCESS(af_release_array(_inArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(kerArray));
+        ASSERT_SUCCESS(af_release_array(cstArray));
+        ASSERT_SUCCESS(af_release_array(minArray));
+        ASSERT_SUCCESS(af_release_array(denArray));
+        ASSERT_SUCCESS(af_release_array(numArray));
+        ASSERT_SUCCESS(af_release_array(divArray));
+        ASSERT_SUCCESS(af_release_array(_outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(_goldArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
     }
diff --git a/test/jit.cpp b/test/jit.cpp
index 69bbd87e6c..dd5a5045ce 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -311,11 +311,11 @@ TEST(JIT, NonLinearLargeX)
     dim_t sdims[] = {1, 1, 1};
     dim_t ndims = 3;
 
-    ASSERT_EQ(AF_SUCCESS, af_randu(&r, ndims, rdims, f32));
-    ASSERT_EQ(AF_SUCCESS, af_constant(&c, 1, ndims, cdims, f32));
-    ASSERT_EQ(AF_SUCCESS, af_eval(c));
-    ASSERT_EQ(AF_SUCCESS, af_sub(&s, r, c, true));
-    ASSERT_EQ(AF_SUCCESS, af_eval(s));
+    ASSERT_SUCCESS(af_randu(&r, ndims, rdims, f32));
+    ASSERT_SUCCESS(af_constant(&c, 1, ndims, cdims, f32));
+    ASSERT_SUCCESS(af_eval(c));
+    ASSERT_SUCCESS(af_sub(&s, r, c, true));
+    ASSERT_SUCCESS(af_eval(s));
 
     dim_t relem = 1;
     dim_t celem = 1;
@@ -331,9 +331,9 @@ TEST(JIT, NonLinearLargeX)
     vector<float> hc(celem);
     vector<float> hs(selem);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hr.data(), r));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hc.data(), c));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(hs.data(), s));
+    ASSERT_SUCCESS(af_get_data_ptr(hr.data(), r));
+    ASSERT_SUCCESS(af_get_data_ptr(hc.data(), c));
+    ASSERT_SUCCESS(af_get_data_ptr(hs.data(), s));
 
     for (int k = 0; k < sdims[2]; k++) {
         for (int j = 0; j < sdims[1]; j++) {
@@ -356,9 +356,9 @@ TEST(JIT, NonLinearLargeX)
         }
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(r));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(c));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(s));
+    ASSERT_SUCCESS(af_release_array(r));
+    ASSERT_SUCCESS(af_release_array(c));
+    ASSERT_SUCCESS(af_release_array(s));
 }
 
 TEST(JIT, ISSUE_1894)
diff --git a/test/join.cpp b/test/join.cpp
index e2bacc1cf8..200d4d576d 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -69,35 +69,27 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&in0Array, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&in0Array, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&in0Array, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in0Array, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&in1Array, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&in1Array, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&in1Array, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in1Array, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_join(&outArray, dim, in0Array, in1Array));
+    ASSERT_SUCCESS(af_join(&outArray, dim, in0Array, in1Array));
 
-    // Get result
-    T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    dim4 goldDims = i0dims;
+    goldDims[dim] = i0dims[dim] + i1dims[dim];
 
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, outArray);
 
     if(in0Array  != 0) af_release_array(in0Array);
     if(in1Array  != 0) af_release_array(in1Array);
@@ -170,18 +162,10 @@ TEST(Join, CPP)
 
     array output = join(dim, input0, input1);
 
-    // Get result
-    float* outData = new float[tests[resultIdx].size()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
+    dim4 goldDims = i0dims;
+    goldDims[dim] = i0dims[dim] + i1dims[dim];
 
-    // Delete
-    delete[] outData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, output);
 }
 
 TEST(JoinMany0, CPP)
diff --git a/test/match_template.cpp b/test/match_template.cpp
index e911522e79..f9174b4a26 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -55,17 +55,17 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType)
     af_array sArray   = 0;
     af_array tArray   = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&sArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&sArray, &(in[0].front()),
                 sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&tArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&tArray, &(in[1].front()),
                 tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_match_template(&outArray, sArray, tArray, pMatchType));
+    ASSERT_SUCCESS(af_match_template(&outArray, sArray, tArray, pMatchType));
 
     vector<outType> outData(sDims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<outType> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -74,9 +74,9 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType)
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(sArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(tArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(sArray));
+    ASSERT_SUCCESS(af_release_array(tArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(MatchTemplate, Matrix_SAD)
@@ -105,16 +105,16 @@ TEST(MatchTemplate, InvalidMatchType)
     dim4 sDims(10, 10, 1, 1);
     dim4 tDims(4, 4, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&tArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&tArray, &in.front(),
                 tDims.ndims(), tDims.get(), (af_dtype) dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_match_template(&outArray, inArray, tArray, (af_match_type)-1));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(tArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(tArray));
 }
 
 ///////////////////////////////// CPP TESTS /////////////////////////////
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index ffbf45b0df..0222eb52be 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -43,10 +43,10 @@ TYPED_TEST(Meanshift, InvalidArgs)
     af_array outArray  = 0;
 
     dim4 dims = dim4(100,1,1,1);
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<TypeParam>::af_type));
     ASSERT_EQ(AF_ERR_SIZE, af_mean_shift(&outArray, inArray, 0.12f, 0.34f, 5, true));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename T, bool isColor>
@@ -76,28 +76,28 @@ void meanshiftTest(string pTestFile, const float ss)
         inFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
         outFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
+        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray_f32, outFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&goldArray, goldArray_f32)); // af_load_image always returns float array
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_load_image(&goldArray_f32, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<T>(&goldArray, goldArray_f32)); // af_load_image always returns float array
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_mean_shift(&outArray, inArray, ss, 30.f, 5, isColor));
+        ASSERT_SUCCESS(af_mean_shift(&outArray, inArray, ss, 30.f, 5, isColor));
 
         vector<T> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         vector<T> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray_f32));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(inArray_f32));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
+        ASSERT_SUCCESS(af_release_array(goldArray_f32));
     }
 }
 
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 107685abbe..169f673003 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -58,14 +58,14 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
     af_array outArray  = 0;
     af_array inArray   = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, pad));
+    ASSERT_SUCCESS(af_medfilt2(&outArray, inArray, w_len, w_wid, pad));
 
     vector<T> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -74,8 +74,8 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(MedianFilter, ZERO_PAD_3x3)
@@ -114,14 +114,14 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
     af_array outArray  = 0;
     af_array inArray   = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_medfilt1(&outArray, inArray, w_wid, pad));
+    ASSERT_SUCCESS(af_medfilt1(&outArray, inArray, w_wid, pad));
 
     vector<T> outData(dims.elements());
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems        = currGoldBar.size();
@@ -130,8 +130,8 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(MedianFilter1d, ZERO_PAD_3)
@@ -179,23 +179,23 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
         inFiles[testId].insert(0,string(TEST_DIR"/medianfilter/"));
         outFiles[testId].insert(0,string(TEST_DIR"/medianfilter/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
+        ASSERT_SUCCESS(af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
 
         vector<T> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         vector<T> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
     }
 }
 
@@ -212,18 +212,18 @@ void medfiltInputTest(void)
     // Check for 1D inputs -> medfilt1
     dim4 dims = dim4(100, 1, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
+    ASSERT_SUCCESS(af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
 
     bool medfilt1;
-    ASSERT_EQ(AF_SUCCESS, af_is_vector(&medfilt1, outArray));
+    ASSERT_SUCCESS(af_is_vector(&medfilt1, outArray));
 
     ASSERT_EQ(true, medfilt1);
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(MedianFilter, InvalidArray)
@@ -244,12 +244,12 @@ void medfiltWindowTest(void)
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 5, AF_PAD_ZERO));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(MedianFilter, InvalidWindow)
@@ -271,12 +271,12 @@ void medfilt1d_WindowTest(void)
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, -1, AF_PAD_ZERO));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(MedianFilter1d, InvalidWindow)
@@ -297,14 +297,14 @@ void medfiltPadTest(void)
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(3)));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(-1)));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(MedianFilter, InvalidPadType)
@@ -325,14 +325,14 @@ void medfilt1d_PadTest(void)
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(3)));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(-1)));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(MedianFilter1d, InvalidPadType)
diff --git a/test/memory.cpp b/test/memory.cpp
index b16805bae3..91fd41e2f6 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -608,7 +608,7 @@ TEST(Memory, unlock)
     vector<float> in(num);
 
     af_array arr = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&arr, &in[0], 1, &num, f32));
+    ASSERT_SUCCESS(af_create_array(&arr, &in[0], 1, &num, f32));
 
     deviceMemInfo(&alloc_bytes, &alloc_buffers,
                   &lock_bytes, &lock_buffers);
diff --git a/test/memory_lock.cpp b/test/memory_lock.cpp
index 0e1753196a..dd6bb3d5f2 100644
--- a/test/memory_lock.cpp
+++ b/test/memory_lock.cpp
@@ -40,7 +40,7 @@ TEST(Memory, lock)
     vector<float> in(num);
 
     af_array arr = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&arr, &in[0], 1, &num, f32));
+    ASSERT_SUCCESS(af_create_array(&arr, &in[0], 1, &num, f32));
 
     deviceMemInfo(&alloc_bytes, &alloc_buffers,
                       &lock_bytes, &lock_buffers);
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 4ddef7c56d..9fe79bf3a9 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -61,40 +61,40 @@ void moddimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *se
         af_array subArray  = 0;
         af_array outArray  = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&subArray,inArray,seqv->size(),&seqv->front()));
+        ASSERT_SUCCESS(af_index(&subArray,inArray,seqv->size(),&seqv->front()));
 
         dim4 newDims(1);
         newDims[0] = 2;
         newDims[1] = 3;
-        ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,subArray,newDims.ndims(),newDims.get()));
+        ASSERT_SUCCESS(af_moddims(&outArray,subArray,newDims.ndims(),newDims.get()));
 
         dim_t nElems;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems,outArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems,outArray));
 
         outData          = new T[nElems];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(subArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(subArray));
     } else {
         af_array inArray   = 0;
         af_array outArray  = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
         dim4 newDims(1);
         newDims[0] = dims[1];
         newDims[1] = dims[0]*dims[2];
-        ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
+        ASSERT_SUCCESS(af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
 
         outData          = new T[dims.elements()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
     }
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
@@ -133,16 +133,16 @@ void moddimsArgsTest(string pTestFile)
     af_array inArray   = 0;
     af_array outArray  = 0;
     af_array outArray2  = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     dim4 newDims(1);
     newDims[0] = dims[1];
     newDims[1] = dims[0]*dims[2];
-    ASSERT_EQ(AF_SUCCESS, af_moddims(&outArray,inArray,0,newDims.get()));
+    ASSERT_SUCCESS(af_moddims(&outArray,inArray,0,newDims.get()));
     ASSERT_EQ(AF_ERR_ARG, af_moddims(&outArray2,inArray,newDims.ndims(),NULL));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(Moddims,InvalidArgs)
@@ -164,14 +164,14 @@ void moddimsMismatchTest(string pTestFile)
 
     af_array inArray   = 0;
     af_array outArray  = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     dim4 newDims(1);
     newDims[0] = dims[1]-1;
     newDims[1] = (dims[0]-1)*dims[2];
     ASSERT_EQ(AF_ERR_SIZE, af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(Moddims,Mismatch)
diff --git a/test/morph.cpp b/test/morph.cpp
index d20e6143ab..c55eaec60e 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -53,40 +53,33 @@ void morphTest(string pTestFile)
     af_array inArray   = 0;
     af_array maskArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&maskArray, &(in[1].front()),
                 maskDims.ndims(), maskDims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if (isDilation) {
         if (isVolume)
-            ASSERT_EQ(AF_SUCCESS, af_dilate3(&outArray, inArray, maskArray));
+            ASSERT_SUCCESS(af_dilate3(&outArray, inArray, maskArray));
         else
-            ASSERT_EQ(AF_SUCCESS, af_dilate(&outArray, inArray, maskArray));
+            ASSERT_SUCCESS(af_dilate(&outArray, inArray, maskArray));
     }
     else {
         if (isVolume)
-            ASSERT_EQ(AF_SUCCESS, af_erode3(&outArray, inArray, maskArray));
+            ASSERT_SUCCESS(af_erode3(&outArray, inArray, maskArray));
         else
-            ASSERT_EQ(AF_SUCCESS, af_erode(&outArray, inArray, maskArray));
+            ASSERT_SUCCESS(af_erode(&outArray, inArray, maskArray));
     }
 
-    vector<inType> outData(dims.elements());
-
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
-
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<inType> currGoldBar = tests[testIter];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
-        }
+        ASSERT_VEC_ARRAY_EQ(currGoldBar, dims, outArray);
     }
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(maskArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(Morph, Dilate3x3)
@@ -171,30 +164,30 @@ void morphImageTest(string pTestFile)
         outFiles[testId].insert(0,string(TEST_DIR"/morph/"));
 
         dim4 mdims(3,3,1,1);
-        ASSERT_EQ(AF_SUCCESS, af_constant(&maskArray, 1.0,
+        ASSERT_SUCCESS(af_constant(&maskArray, 1.0,
                     mdims.ndims(), mdims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems, goldArray));
+        ASSERT_SUCCESS(af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         if (isDilation)
-            ASSERT_EQ(AF_SUCCESS, af_dilate(&outArray, inArray, maskArray));
+            ASSERT_SUCCESS(af_dilate(&outArray, inArray, maskArray));
         else
-            ASSERT_EQ(AF_SUCCESS, af_erode(&outArray, inArray, maskArray));
+            ASSERT_SUCCESS(af_erode(&outArray, inArray, maskArray));
 
         vector<T> outData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
         vector<T> goldData(nElems);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(goldArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(maskArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
     }
 }
 
@@ -224,10 +217,10 @@ void morphInputTest(void)
     dim4 dims = dim4(100,1,1,1);
     dim4 mdims(3,3,1,1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
                 mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
@@ -235,9 +228,9 @@ void morphInputTest(void)
     else
         ASSERT_EQ(AF_ERR_SIZE, af_erode(&outArray, inArray, maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
+    ASSERT_SUCCESS(af_release_array(maskArray));
 }
 
 TYPED_TEST(Morph, DilateInvalidInput)
@@ -266,10 +259,10 @@ void morphMaskTest(void)
     dim4 dims(10,10,1,1);
     dim4 mdims(2,2,2,2);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
                 mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
@@ -277,12 +270,12 @@ void morphMaskTest(void)
     else
         ASSERT_EQ(AF_ERR_SIZE, af_erode(&outArray, inArray, maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
+    ASSERT_SUCCESS(af_release_array(maskArray));
 
     // Check for 1D mask
     mdims = dim4(16,1,1,1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
                 mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
@@ -290,9 +283,9 @@ void morphMaskTest(void)
     else
         ASSERT_EQ(AF_ERR_SIZE, af_erode(&outArray, inArray, maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
+    ASSERT_SUCCESS(af_release_array(maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(Morph, DilateInvalidMask)
@@ -321,10 +314,10 @@ void morph3DMaskTest(void)
     dim4 dims(10,10,10,1);
     dim4 mdims(9,9,1,1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
                 dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
                 mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
@@ -332,12 +325,12 @@ void morph3DMaskTest(void)
     else
         ASSERT_EQ(AF_ERR_SIZE, af_erode3(&outArray, inArray, maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
+    ASSERT_SUCCESS(af_release_array(maskArray));
 
     // Check for 4D mask
     mdims = dim4(3,3,3,3);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&maskArray, &mask.front(),
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
                 mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     if (isDilation)
@@ -345,9 +338,9 @@ void morph3DMaskTest(void)
     else
         ASSERT_EQ(AF_ERR_SIZE, af_erode3(&outArray, inArray, maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(maskArray));
+    ASSERT_SUCCESS(af_release_array(maskArray));
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 TYPED_TEST(Morph, DilateVolumeInvalidMask)
@@ -497,15 +490,15 @@ TEST(Morph, UnsupportedKernel2D)
 
     af_array in, mask, out;
 
-    ASSERT_EQ(AF_SUCCESS, af_constant(&mask, 1.0, ndims, kdims, f32));
-    ASSERT_EQ(AF_SUCCESS, af_randu(&in, ndims, dims, f32));
+    ASSERT_SUCCESS(af_constant(&mask, 1.0, ndims, kdims, f32));
+    ASSERT_SUCCESS(af_randu(&in, ndims, dims, f32));
 
 #if defined(AF_CPU)
-    ASSERT_EQ(AF_SUCCESS, af_dilate(&out, in, mask));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
+    ASSERT_SUCCESS(af_dilate(&out, in, mask));
+    ASSERT_SUCCESS(af_release_array(out));
 #else
     ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_dilate(&out, in, mask));
 #endif
-    ASSERT_EQ(AF_SUCCESS, af_release_array(in));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(mask));
+    ASSERT_SUCCESS(af_release_array(in));
+    ASSERT_SUCCESS(af_release_array(mask));
 }
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 249fda8535..5b3e90b631 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -81,12 +81,12 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
     af_array idx   = 0;
     af_array dist  = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&query, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&query, &(in[0].front()),
                 qDims.ndims(), qDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&train, &(in[1].front()),
+    ASSERT_SUCCESS(af_create_array(&train, &(in[1].front()),
                 tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_nearest_neighbour(&idx, &dist, query, train, feat_dim, 1, type));
+    ASSERT_SUCCESS(af_nearest_neighbour(&idx, &dist, query, train, feat_dim, 1, type));
 
     vector<uint> goldIdx  = tests[0];
     vector<uint> goldDist = tests[1];
@@ -94,8 +94,8 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
     uint *outIdx          = new uint[nElems];
     To *outDist           = new To[nElems];
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outIdx,  idx));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDist, dist));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outIdx,  idx));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outDist, dist));
 
     for (size_t elIter=0; elIter<nElems; ++elIter) {
         ASSERT_EQ((To)goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
@@ -103,10 +103,10 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
 
     delete[] outIdx;
     delete[] outDist;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(query));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(train));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idx));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(dist));
+    ASSERT_SUCCESS(af_release_array(query));
+    ASSERT_SUCCESS(af_release_array(train));
+    ASSERT_SUCCESS(af_release_array(idx));
+    ASSERT_SUCCESS(af_release_array(dist));
 }
 
 /////////////////////////////////////////////////
diff --git a/test/orb.cpp b/test/orb.cpp
index 0a6ca134e1..521e6d7510 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -157,20 +157,20 @@ void orbTest(string pTestFile)
 
         inFiles[testId].insert(0,string(TEST_DIR"/orb/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
+        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_orb(&feat, &desc, inArray, 20.0f, 400, 1.2f, 8, true));
+        ASSERT_SUCCESS(af_orb(&feat, &desc, inArray, 20.0f, 400, 1.2f, 8, true));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
 
-        ASSERT_EQ(AF_SUCCESS, af_get_features_num(&n, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&x, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&y, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_score(&score, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_orientation(&orientation, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_size(&size, feat));
+        ASSERT_SUCCESS(af_get_features_num(&n, feat));
+        ASSERT_SUCCESS(af_get_features_xpos(&x, feat));
+        ASSERT_SUCCESS(af_get_features_ypos(&y, feat));
+        ASSERT_SUCCESS(af_get_features_score(&score, feat));
+        ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
+        ASSERT_SUCCESS(af_get_features_size(&size, feat));
 
         float * outX           = new float[n];
         float * outY           = new float[n];
@@ -178,14 +178,14 @@ void orbTest(string pTestFile)
         float * outOrientation = new float[n];
         float * outSize        = new float[n];
         dim_t descSize;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&descSize, desc));
+        ASSERT_SUCCESS(af_get_elements(&descSize, desc));
         unsigned * outDesc     = new unsigned[descSize];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outX, x));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outY, y));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outScore, score));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outOrientation, orientation));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outSize, size));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDesc, desc));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outOrientation, orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outSize, size));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outDesc, desc));
 
         vector<feat_desc_t> out_feat_desc;
         array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, n);
@@ -215,11 +215,11 @@ void orbTest(string pTestFile)
         // TODO: improve distance for single/double-precision interchangeability
         EXPECT_TRUE(compareHamming(descSize, (unsigned*)&v_out_desc[0], (unsigned*)&v_gold_desc[0], 3));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_features(feat));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(desc));
+        ASSERT_SUCCESS(af_release_features(feat));
+        ASSERT_SUCCESS(af_release_array(desc));
 
         delete[] outX;
         delete[] outY;
diff --git a/test/random.cpp b/test/random.cpp
index 7932f0d699..c585f19c95 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -99,7 +99,7 @@ void randuTest(dim4 & dims)
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
@@ -110,7 +110,7 @@ void randnTest(dim4 &dims)
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_randn(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randn(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
     if(outArray != 0) af_release_array(outArray);
 }
diff --git a/test/range.cpp b/test/range.cpp
index 87e183beb4..9246084ffd 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -56,11 +56,11 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w, const uin
 
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_range(&outArray, idims.ndims(), idims.get(), dim, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_range(&outArray, idims.ndims(), idims.get(), dim, (af_dtype) dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     for(int w = 0; w < (int)idims[3]; w++) {
diff --git a/test/reduce.cpp b/test/reduce.cpp
index a67f8815e9..8f797034cd 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -60,11 +60,11 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
 
     // Get input array
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(tempArray));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
+        ASSERT_SUCCESS(af_release_array(tempArray));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
     }
 
     // Compare result
@@ -73,14 +73,14 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
         vector<To> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        ASSERT_EQ(AF_SUCCESS, af_reduce(&outArray, inArray, d + off));
+        ASSERT_SUCCESS(af_reduce(&outArray, inArray, d + off));
 
         af_dtype t;
         af_get_type(&t, outArray);
 
         // Get result
         vector<To> outData(dims.elements());
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         if(std::equal(currGoldBar.begin(), currGoldBar.end(), outData.begin()) == false)
@@ -102,10 +102,10 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
             FAIL();
         }
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename T,reduceFunc OP>
diff --git a/test/regions.cpp b/test/regions.cpp
index a7d394f7de..b451b5224f 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -59,18 +59,18 @@ void regionsTest(string pTestFile, af_connectivity connectivity, bool isSubRef =
     af_array outArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_regions(&outArray, inArray, connectivity, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_regions(&outArray, inArray, connectivity, (af_dtype) dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
diff --git a/test/reorder.cpp b/test/reorder.cpp
index 1c04e7f6d0..85220a2449 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -69,27 +69,17 @@ void reorderTest(string pTestFile, const unsigned resultIdx,
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_reorder(&outArray, inArray, x, y, z, w));
+    ASSERT_SUCCESS(af_reorder(&outArray, inArray, x, y, z, w));
 
-    // Get result
-    T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(idims[x], idims[y], idims[z], idims[w]);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, outArray);
 
     if(inArray   != 0) af_release_array(inArray);
     if(outArray  != 0) af_release_array(outArray);
@@ -157,18 +147,8 @@ TEST(Reorder, CPP)
     array input(idims, &(in[0].front()));
     array output = reorder(input, x, y, z, w);
 
-    // Get result
-    float* outData = new float[tests[resultIdx].size()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(idims[x], idims[y], idims[z], idims[w]);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, output);
 }
 
 TEST(Reorder, ISSUE_1777)
@@ -208,5 +188,5 @@ TEST(Reorder, MaxDim)
 
     array gold = range(dim4(2, largeDim, 2));
 
-    ASSERT_TRUE(allTrue<bool>(output == gold));
+    ASSERT_ARRAYS_EQ(gold, output);
 }
diff --git a/test/resize.cpp b/test/resize.cpp
index 20ff55821d..7d0a009bbb 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -74,10 +74,10 @@ TYPED_TEST(Resize, InvalidDims)
 
     dim4 dims = dim4(8,8,1,1);
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(),
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(),
                                           (af_dtype) dtype_traits<TypeParam>::af_type));
     ASSERT_EQ(AF_ERR_SIZE, af_resize(&outArray, inArray, 0, 0, AF_INTERP_NEAREST));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename T>
@@ -129,19 +129,19 @@ void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0, c
     af_array tempArray = 0;
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_resize(&outArray, inArray, odim0, odim1, method));
+    ASSERT_SUCCESS(af_resize(&outArray, inArray, odim0, odim1, method));
 
     // Get result
     dim4 odims(odim0, odim1, dims[2], dims[3]);
     T* outData = new T[odims.elements()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
@@ -331,7 +331,7 @@ void resizeArgsTest(af_err err, string pTestFile, const dim4 odims, const af_int
 
     af_array inArray = 0;
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     ASSERT_EQ(err, af_resize(&outArray, inArray, odims[0], odims[1], method));
 
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 3adb71db4f..2dd021eefd 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -61,13 +61,13 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 
     float theta = angle * PI / 180.0f;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_rotate(&outArray, inArray, theta, crop, AF_INTERP_NEAREST));
+    ASSERT_SUCCESS(af_rotate(&outArray, inArray, theta, crop, AF_INTERP_NEAREST));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 22e4727b33..916ec61955 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -67,18 +67,18 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
 
     if (isSubRef) {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_rotate(&outArray, inArray, theta, crop, AF_INTERP_BILINEAR));
+    ASSERT_SUCCESS(af_rotate(&outArray, inArray, theta, crop, AF_INTERP_BILINEAR));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
diff --git a/test/scan.cpp b/test/scan.cpp
index 20145f2809..a84a12e515 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -56,12 +56,12 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
 
     // Get input array
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(tempArray));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
+        ASSERT_SUCCESS(af_release_array(tempArray));
     } else {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
     }
 
     // Compare result
@@ -69,12 +69,12 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
         vector<To> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        ASSERT_EQ(AF_SUCCESS, af_scan(&outArray, inArray, d + off));
+        ASSERT_SUCCESS(af_scan(&outArray, inArray, d + off));
 
         // Get result
         To *outData;
         outData = new To[dims.elements()];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
@@ -85,10 +85,10 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
 
         // Delete
         delete[] outData;
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 #define SCAN_TESTS(FN, TAG, Ti, To)             \
@@ -179,7 +179,7 @@ TEST(Accum, MaxDim)
     gold_first(span, seq(0, 9999), span, span) = range(2, 10000, 2, 2) + 1;
 
     array output_first = accum(input, 0);
-    ASSERT_TRUE(allTrue<bool>(output_first == gold_first));
+    ASSERT_ARRAYS_EQ(gold_first, output_first);
 
 
     input = constant(0, 2, 2, 2, largeDim);
@@ -189,7 +189,7 @@ TEST(Accum, MaxDim)
     gold_first(span, span, span, seq(0, 9999)) = range(2, 2, 2, 10000) + 1;
 
     output_first = accum(input, 0);
-    ASSERT_TRUE(allTrue<bool>(output_first == gold_first));
+    ASSERT_ARRAYS_EQ(gold_first, output_first);
 
 
     //other dimension kernel tests
@@ -200,7 +200,7 @@ TEST(Accum, MaxDim)
     gold_dim(span, seq(0, 9999), span, span) = range(dim4(2, 10000, 2, 2), 1) + 1;
 
     array output_dim = accum(input, 1);
-    ASSERT_TRUE(allTrue<bool>(output_dim == gold_dim));
+    ASSERT_ARRAYS_EQ(gold_dim, output_dim);
 
 
     input = constant(0, 2, 2, 2, largeDim);
@@ -210,6 +210,6 @@ TEST(Accum, MaxDim)
     gold_dim(span, span, span, seq(0, 9999)) = range(dim4(2, 2, 2, 10000), 1) + 1;
 
     output_dim = accum(input, 1);
-    ASSERT_TRUE(allTrue<bool>(output_dim == gold_dim));
+    ASSERT_ARRAYS_EQ(gold_dim, output_dim);
 
 }
diff --git a/test/select.cpp b/test/select.cpp
index 3c7c4e90f8..e6064d80f8 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -488,13 +488,13 @@ TEST(Select, InvalidSizeOfAB) {
 
     double val = 0;
     dim_t dims = 10;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, val, 1, &dims, f32));
+    ASSERT_SUCCESS(af_constant(&a, val, 1, &dims, f32));
 
     dims = 9;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&b, val, 1, &dims, f32));
+    ASSERT_SUCCESS(af_constant(&b, val, 1, &dims, f32));
 
     dims = 10;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&cond, val, 1, &dims, b8));
+    ASSERT_SUCCESS(af_constant(&cond, val, 1, &dims, b8));
 
     ASSERT_EQ(AF_ERR_SIZE, af_select(&out, cond, a, b));
 
@@ -515,13 +515,13 @@ TEST(Select, InvalidSizeOfCond) {
 
     double val = 0;
     dim_t dims = 10;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&a, val, 1, &dims, f32));
+    ASSERT_SUCCESS(af_constant(&a, val, 1, &dims, f32));
 
     dims = 10;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&b, val, 1, &dims, f32));
+    ASSERT_SUCCESS(af_constant(&b, val, 1, &dims, f32));
 
     dims = 9;
-    ASSERT_EQ(AF_SUCCESS, af_constant(&cond, val, 1, &dims, b8));
+    ASSERT_SUCCESS(af_constant(&cond, val, 1, &dims, b8));
 
     ASSERT_EQ(AF_ERR_SIZE, af_select(&out, cond, a, b));
 
diff --git a/test/set.cpp b/test/set.cpp
index b0b5d32185..a26ec096e2 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -48,18 +48,18 @@ void uniqueTest(string pTestFile)
         af_array outArray  = 0;
 
         // Get input array
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(),
+        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
                                               dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
 
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        ASSERT_EQ(AF_SUCCESS, af_set_unique(&outArray, inArray, d == 0 ? false : true));
+        ASSERT_SUCCESS(af_set_unique(&outArray, inArray, d == 0 ? false : true));
 
         // Get result
         vector<T >outData (currGoldBar.size());
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
@@ -115,20 +115,20 @@ void setTest(string pTestFile)
         af_array inArray1   = 0;
         af_array outArray  = 0;
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray0, &in0.front(), dims0.ndims(),
+        ASSERT_SUCCESS(af_create_array(&inArray0, &in0.front(), dims0.ndims(),
                                               dims0.get(), (af_dtype) dtype_traits<T>::af_type));
 
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray1, &in1.front(), dims1.ndims(),
+        ASSERT_SUCCESS(af_create_array(&inArray1, &in1.front(), dims1.ndims(),
                                               dims1.get(), (af_dtype) dtype_traits<T>::af_type));
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        ASSERT_EQ(AF_SUCCESS, af_set_func(&outArray, inArray0, inArray1, d == 0 ? false : true));
+        ASSERT_SUCCESS(af_set_func(&outArray, inArray0, inArray1, d == 0 ? false : true));
 
         // Get result
         vector<T> outData(currGoldBar.size());
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
diff --git a/test/shift.cpp b/test/shift.cpp
index 6c04c604f7..2cc4b1c4ef 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -65,27 +65,16 @@ void shiftTest(string pTestFile, const unsigned resultIdx,
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_shift(&outArray, inArray, x, y, z, w));
+    ASSERT_SUCCESS(af_shift(&outArray, inArray, x, y, z, w));
 
-    // Get result
-    T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], idims, outArray);
 
     if(inArray   != 0) af_release_array(inArray);
     if(outArray  != 0) af_release_array(outArray);
@@ -136,18 +125,7 @@ TEST(Shift, CPP)
     array input(idims, &(in[0].front()));
     array output = shift(input, x, y, z, w);
 
-    // Get result
-    float* outData = new float[tests[resultIdx].size()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], idims, output);
 }
 
 TEST(Shift, MaxDim)
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index 3b51809b80..c5a403d90a 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -163,20 +163,20 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 
         inFiles[testId].insert(0,string(TEST_DIR"/sift/"));
 
-        ASSERT_EQ(AF_SUCCESS, af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
-        ASSERT_EQ(AF_SUCCESS, conv_image<T>(&inArray, inArray_f32));
+        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_sift(&feat, &desc, inArray, nLayers, contrastThr, edgeThr, initSigma, doubleInput, 1.f/256.f, 0.05f));
+        ASSERT_SUCCESS(af_sift(&feat, &desc, inArray, nLayers, contrastThr, edgeThr, initSigma, doubleInput, 1.f/256.f, 0.05f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
 
-        ASSERT_EQ(AF_SUCCESS, af_get_features_num(&n, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_xpos(&x, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_ypos(&y, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_score(&score, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_orientation(&orientation, feat));
-        ASSERT_EQ(AF_SUCCESS, af_get_features_size(&size, feat));
+        ASSERT_SUCCESS(af_get_features_num(&n, feat));
+        ASSERT_SUCCESS(af_get_features_xpos(&x, feat));
+        ASSERT_SUCCESS(af_get_features_ypos(&y, feat));
+        ASSERT_SUCCESS(af_get_features_score(&score, feat));
+        ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
+        ASSERT_SUCCESS(af_get_features_size(&size, feat));
 
         float * outX           = new float[n];
         float * outY           = new float[n];
@@ -185,15 +185,15 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
         float * outSize        = new float[n];
         dim_t descSize;
         dim_t descDims[4];
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&descSize, desc));
-        ASSERT_EQ(AF_SUCCESS, af_get_dims(&descDims[0], &descDims[1], &descDims[2], &descDims[3], desc));
+        ASSERT_SUCCESS(af_get_elements(&descSize, desc));
+        ASSERT_SUCCESS(af_get_dims(&descDims[0], &descDims[1], &descDims[2], &descDims[3], desc));
         float * outDesc     = new float[descSize];
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outX, x));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outY, y));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outScore, score));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outOrientation, orientation));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outSize, size));
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outDesc, desc));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outOrientation, orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outSize, size));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)outDesc, desc));
 
         vector<feat_desc_t> out_feat_desc;
         array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, n);
@@ -222,15 +222,15 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 
         EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 4.5f));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(inArray_f32));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(x));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(y));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(score));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(orientation));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(size));
-        ASSERT_EQ(AF_SUCCESS, af_release_array(desc));
+        ASSERT_SUCCESS(af_release_array(x));
+        ASSERT_SUCCESS(af_release_array(y));
+        ASSERT_SUCCESS(af_release_array(score));
+        ASSERT_SUCCESS(af_release_array(orientation));
+        ASSERT_SUCCESS(af_release_array(size));
+        ASSERT_SUCCESS(af_release_array(desc));
 
         delete[] outX;
         delete[] outY;
diff --git a/test/sobel.cpp b/test/sobel.cpp
index e9f1343173..70f2f25679 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -59,32 +59,24 @@ void testSobelDerivatives(string pTestFile)
     af_array dyArray = 0;
     af_array inArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<Ti>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_sobel_operator(&dxArray, &dyArray, inArray, 3));
-
-    vector<To> dxData(dims.elements());
-    vector<To> dyData(dims.elements());
-
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dxData.data(), dxArray));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)dyData.data(), dyArray));
+    ASSERT_SUCCESS(af_sobel_operator(&dxArray, &dyArray, inArray, 3));
 
     vector<To> currDXGoldBar = tests[0];
     vector<To> currDYGoldBar = tests[1];
+
     size_t nElems = currDXGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currDXGoldBar[elIter], dxData[elIter])<< "at: " << elIter<< endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(currDXGoldBar, dims, dxArray);
+
     nElems = currDYGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currDYGoldBar[elIter], dyData[elIter])<< "at: " << elIter<< endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(currDYGoldBar, dims, dyArray);
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(dxArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(dyArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(dxArray));
+    ASSERT_SUCCESS(af_release_array(dyArray));
 }
 
 TYPED_TEST(Sobel, Rectangle)
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 650ef84250..0581544088 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -166,7 +166,7 @@ TEST(Solve, Threading)
     vector<std::thread> tests;
 
     int numDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_SUCCESS(af_get_device_count(&numDevices));
     ASSERT_EQ(true, numDevices>0);
 
     SOLVE_LU_TESTS_THREADING(float, 0.01);
diff --git a/test/sort.cpp b/test/sort.cpp
index dff8b64f11..12f93b7ea0 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -63,20 +63,20 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
     af_array sxArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_sort(&sxArray, inArray, 0, dir));
+    ASSERT_SUCCESS(af_sort(&sxArray, inArray, 0, dir));
 
     size_t nElems = tests[resultIdx0].size();
 
     // Get result
     T* sxData = new T[tests[resultIdx0].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)sxData, sxArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)sxData, sxArray));
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index 405174b90f..981e432e55 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -53,8 +53,8 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 
     vector<dim4> numDims;
     vector<vector<T> > in;
-    vector<vector<float> > tests;
-    readTests<T, float, int>(pTestFile,numDims,in,tests);
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile,numDims,in,tests);
 
     dim4 idims = numDims[0];
 
@@ -65,41 +65,26 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     af_array ovalArray = 0;
 
     if (isSubRef) {
-        //ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        //ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        //ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        //ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&ikeyArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&ivalArray, &(in[1].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&ikeyArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&ivalArray, &(in[1].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_sort_by_key(&okeyArray, &ovalArray, ikeyArray, ivalArray, 0, dir));
+    ASSERT_SUCCESS(af_sort_by_key(&okeyArray, &ovalArray, ikeyArray, ivalArray, 0, dir));
 
     size_t nElems = tests[resultIdx0].size();
 
-    // Get result
-    T* keyData = new T[tests[resultIdx0].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)keyData, okeyArray));
-
     // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
-    }
-
-    T* valData = new T[tests[resultIdx1].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)valData, ovalArray));
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, okeyArray);
 
 #ifndef AF_OPENCL
     // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, ovalArray);
 #endif
 
-    // Delete
-    delete[] keyData;
-    delete[] valData;
-
     if(ikeyArray != 0) af_release_array(ikeyArray);
     if(ivalArray != 0) af_release_array(ivalArray);
     if(okeyArray != 0) af_release_array(okeyArray);
@@ -149,27 +134,8 @@ TEST(SortByKey, CPPDim0)
     array out_keys, out_vals;
     sort(out_keys, out_vals, keys, vals, 0, dir);
 
-    size_t nElems = tests[resultIdx0].size();
-    // Get result
-    float* keyData = new float[tests[resultIdx0].size()];
-    out_keys.host((void*)keyData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
-    }
-
-    float* valData = new float[tests[resultIdx1].size()];
-    out_vals.host((void*)valData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] keyData;
-    delete[] valData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, out_keys);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, out_vals);
 }
 
 TEST(SortByKey, CPPDim1)
@@ -198,27 +164,8 @@ TEST(SortByKey, CPPDim1)
     out_keys = reorder(out_keys, 1, 0, 2, 3);
     out_vals = reorder(out_vals, 1, 0, 2, 3);
 
-    size_t nElems = tests[resultIdx0].size();
-    // Get result
-    float* keyData = new float[tests[resultIdx0].size()];
-    out_keys.host((void*)keyData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
-    }
-
-    float* valData = new float[tests[resultIdx1].size()];
-    out_vals.host((void*)valData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] keyData;
-    delete[] valData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, out_keys);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, out_vals);
 }
 
 TEST(SortByKey, CPPDim2)
@@ -247,25 +194,6 @@ TEST(SortByKey, CPPDim2)
     out_keys = reorder(out_keys, 2, 0, 1, 3);
     out_vals = reorder(out_vals, 2, 0, 1, 3);
 
-    size_t nElems = tests[resultIdx0].size();
-    // Get result
-    float* keyData = new float[tests[resultIdx0].size()];
-    out_keys.host((void*)keyData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], keyData[elIter]) << "at: " << elIter << endl;
-    }
-
-    float* valData = new float[tests[resultIdx1].size()];
-    out_vals.host((void*)valData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], valData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] keyData;
-    delete[] valData;
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, out_keys);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, out_vals);
 }
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 2a7b39ea66..03b7a71518 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -64,41 +64,23 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     af_array ixArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_sort_index(&sxArray, &ixArray, inArray, 0, dir));
+    ASSERT_SUCCESS(af_sort_index(&sxArray, &ixArray, inArray, 0, dir));
 
-    size_t nElems = tests[resultIdx0].size();
-
-    // Get result
-    T* sxData = new T[tests[resultIdx0].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)sxData, sxArray));
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Get result
-    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)ixData, ixArray));
+    vector<T> sxTest(tests[resultIdx0].begin(), tests[resultIdx0].end());
+    ASSERT_VEC_ARRAY_EQ(sxTest, idims, sxArray);
 
 #ifndef AF_OPENCL
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
-    }
+    vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
+    ASSERT_VEC_ARRAY_EQ(ixTest, idims, ixArray);
 #endif
 
-    // Delete
-    delete[] sxData;
-    delete[] ixData;
-
     if(inArray   != 0) af_release_array(inArray);
     if(sxArray   != 0) af_release_array(sxArray);
     if(ixArray   != 0) af_release_array(ixArray);
@@ -151,27 +133,10 @@ TEST(SortIndex, CPPDim0)
 
     size_t nElems = tests[resultIdx0].size();
 
-    // Get result
-    float* sxData = new float[tests[resultIdx0].size()];
-    outValues.host((void*)sxData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Get result
-    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
-    outIndices.host((void*)ixData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
-    // Delete
-    delete[] sxData;
-    delete[] ixData;
+    vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
+    ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
 
 TEST(SortIndex, CPPDim1)
@@ -199,27 +164,10 @@ TEST(SortIndex, CPPDim1)
 
     size_t nElems = tests[resultIdx0].size();
 
-    // Get result
-    float* sxData = new float[tests[resultIdx0].size()];
-    outValues.host((void*)sxData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Get result
-    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
-    outIndices.host((void*)ixData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
-    // Delete
-    delete[] sxData;
-    delete[] ixData;
+    vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
+    ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
 
 TEST(SortIndex, CPPDim2)
@@ -246,25 +194,8 @@ TEST(SortIndex, CPPDim2)
     outIndices = reorder(outIndices, 2, 0, 1, 3);
     size_t nElems = tests[resultIdx0].size();
 
-    // Get result
-    float* sxData = new float[tests[resultIdx0].size()];
-    outValues.host((void*)sxData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Get result
-    unsigned* ixData = new unsigned[tests[resultIdx1].size()];
-    outIndices.host((void*)ixData);
-
-    // Compare result
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], ixData[elIter]) << "at: " << elIter << endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
-    // Delete
-    delete[] sxData;
-    delete[] ixData;
+    vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
+    ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 6eb0f5a7d6..6086a3a5e4 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -236,7 +236,7 @@ TYPED_TEST(Sparse, DeepCopy) {
                                   "copy do not match the original array";
         array d = dense(s);
         array d2 = dense(s2);
-        ASSERT_TRUE(allTrue<bool>(d == d2));
+        ASSERT_ARRAYS_EQ(d, d2);
     }
 }
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index da8766c3a0..e3f6b7fe9d 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -509,6 +509,8 @@ std::ostream& operator<<(std::ostream& os, af::dtype type) {
     return os << name;
 }
 
+// Calculate a linearized index's multi-dimensonal coordinates in an af::array,
+//  given its dimension sizes and strides
 af::dim4 unravelIdx(uint idx, af::dim4 dims, af::dim4 strides) {
     af::dim4 coords;
     coords[3] = idx / (strides[3]);
@@ -531,20 +533,18 @@ af::dim4 unravelIdx(uint idx, af::array arr) {
 #define ASSERT_SUCCESS(CALL)                    \
     ASSERT_EQ(AF_SUCCESS, CALL)
 
-/// Compares two af::array or af_arrays for their type, dims, and values.
+/// Compares two af::array or af_arrays for their types, dims, and values.
 ///
-/// \param[in] EXPECTED This is the expected value of the assertion
-/// \param[in] ACTUAL This is the actual value of the calculation
-///
-/// \NOTE: This macro will deallocate the af_arrays after the call
+/// \param[in] EXPECTED The expected array of the assertion
+/// \param[in] ACTUAL The actual resulting array from the calculation
 #define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
     EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
 
-/// Compares a std::vector with an af::array for their dims and values.
+/// Compares a std::vector with an af::/af_array for their types, dims, and values.
 ///
 /// \param[in] EXPECTED_VEC The vector that represents the expected array
 /// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
-/// \param[in] ACTUAL_ARR The actual array from the calculation
+/// \param[in] ACTUAL_ARR The actual resulting array from the calculation
 #define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
     EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR)
 
@@ -754,15 +754,15 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsN
     const uint ndimIds = 4;
     if(aDims != b.dims()) {
         return ::testing::AssertionFailure() << "SIZE MISMATCH: "
-                                             << hA_name << "[" << aDims << "], "
-                                             << bName << "[" << b.dims() << "]";
+                                             << aDimsName << "([" << aDims << "]), "
+                                             << bName << "([" << b.dims() << "])";
     }
 
     // In case vector<T> a.size() != aDims.elements()
     if (hA.size() != aDims.elements())
-        return ::testing::AssertionFailure() << "Gold af::array and std::vector SIZE MISMATCH: "
+        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
                                              << hA_name << ".size()(" << hA.size() << "), "
-                                             << bName << "([" << aDims << "] = "
+                                             << aDimsName << "([" << aDims << "] = "
                                              << aDims.elements() << ")";
 
     return elemWiseEq<T>(hA_name, bName, hA, aDims, b);
diff --git a/test/threading.cpp b/test/threading.cpp
index 658e598516..b8cfa51a96 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -279,30 +279,30 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
     af_array outArray   = 0;
     af_array inArray    = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_set_device(targetDevice));
+    ASSERT_SUCCESS(af_set_device(targetDevice));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()),
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
                 dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
 
     if (isInverse){
         switch (dims.ndims()) {
-            case 1 : ASSERT_EQ(AF_SUCCESS, af_ifft (&outArray, inArray, 1.0, pad0));              break;
-            case 2 : ASSERT_EQ(AF_SUCCESS, af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
-            case 3 : ASSERT_EQ(AF_SUCCESS, af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
+            case 1 : ASSERT_SUCCESS(af_ifft (&outArray, inArray, 1.0, pad0));              break;
+            case 2 : ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
+            case 3 : ASSERT_SUCCESS(af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
             default: throw std::runtime_error("This error shouldn't happen, pls check");
         }
     } else {
         switch(dims.ndims()) {
-            case 1 : ASSERT_EQ(AF_SUCCESS, af_fft (&outArray, inArray, 1.0, pad0));               break;
-            case 2 : ASSERT_EQ(AF_SUCCESS, af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
-            case 3 : ASSERT_EQ(AF_SUCCESS, af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
+            case 1 : ASSERT_SUCCESS(af_fft (&outArray, inArray, 1.0, pad0));               break;
+            case 2 : ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
+            case 3 : ASSERT_SUCCESS(af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
             default: throw std::runtime_error("This error shouldn't happen, pls check");
         }
     }
 
     size_t out_size = tests[0].size();
     outType *outData= new outType[out_size];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     vector<outType> goldBar(tests[0].begin(), tests[0].end());
 
@@ -324,8 +324,8 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
 
     // cleanup
     delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 #define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, file)                         \
@@ -347,7 +347,7 @@ TEST(Threading, FFT_R2C)
     vector<std::thread> tests;
 
     int numDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_SUCCESS(af_get_device_count(&numDevices));
     ASSERT_EQ(true, numDevices>0);
 
     // Real to complex transforms
@@ -392,7 +392,7 @@ TEST(Threading, FFT_C2C)
     vector<std::thread> tests;
 
     int numDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_SUCCESS(af_get_device_count(&numDevices));
     ASSERT_EQ(true, numDevices>0);
 
     // complex to complex transforms
@@ -442,7 +442,7 @@ TEST(Threading, FFT_ALL)
     vector<std::thread> tests;
 
     int numDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_SUCCESS(af_get_device_count(&numDevices));
     ASSERT_EQ(true, numDevices>0);
 
     // Real to complex transforms
@@ -582,7 +582,7 @@ TEST(Threading, BLAS)
     vector<std::thread> tests;
 
     int numDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_SUCCESS(af_get_device_count(&numDevices));
     ASSERT_EQ(true, numDevices>0);
 
     TEST_BLAS_FOR_TYPE(      float);
@@ -613,7 +613,7 @@ TEST(Threading, Sparse)
     vector<std::thread> tests;
 
     int numDevices = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_device_count(&numDevices));
+    ASSERT_SUCCESS(af_get_device_count(&numDevices));
     ASSERT_EQ(true, numDevices>0);
 
     SPARSE_TESTS(  float, 1E-3);
@@ -667,7 +667,7 @@ TEST(Threading, DISABLED_Sort)
 
     vector<std::thread> tests;
 
-    ASSERT_EQ(AF_SUCCESS, af_set_device(0));
+    ASSERT_SUCCESS(af_set_device(0));
 
     for (int i=0; i<THREAD_COUNT; ++i) {
         tests.emplace_back([] {
diff --git a/test/tile.cpp b/test/tile.cpp
index 5ef4570a03..982f217a14 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -65,27 +65,20 @@ void tileTest(string pTestFile, const unsigned resultIdx, const uint x, const ui
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_tile(&outArray, inArray, x, y, z, w));
+    ASSERT_SUCCESS(af_tile(&outArray, inArray, x, y, z, w));
 
-    // Get result
-    T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(idims[0] * x,
+                  idims[1] * y,
+                  idims[2] * z,
+                  idims[3] * w);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, outArray);
 
     if(inArray   != 0) af_release_array(inArray);
     if(outArray  != 0) af_release_array(outArray);
@@ -138,18 +131,11 @@ TEST(Tile, CPP)
     array input(idims, &(in[0].front()));
     array output = tile(input, x, y, z, w);
 
-    // Get result
-    float* outData = new float[tests[resultIdx].size()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[resultIdx].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(idims[0] * x,
+                  idims[1] * y,
+                  idims[2] * z,
+                  idims[3] * w);
+    ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, output);
 }
 
 TEST(Tile, MaxDim)
diff --git a/test/topk.cpp b/test/topk.cpp
index e3f206eef4..9dbe1b8df5 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -110,14 +110,14 @@ void topkTest(const unsigned ndims, const dim_t* dims,
         }
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&input, inData.data(), ndims, dims, dtype));
-    ASSERT_EQ(AF_SUCCESS, af_topk(&output, &outindex, input, k, dim, order));
+    ASSERT_SUCCESS(af_create_array(&input, inData.data(), ndims, dims, dtype));
+    ASSERT_SUCCESS(af_topk(&output, &outindex, input, k, dim, order));
 
     vector<T> hovals(oelems);
     vector<uint> hoidxs(oelems);
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)hovals.data(), output));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)hoidxs.data(), outindex));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)hovals.data(), output));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)hoidxs.data(), outindex));
 
     for (int i=0; i<oelems; ++i)
     {
@@ -130,9 +130,9 @@ void topkTest(const unsigned ndims, const dim_t* dims,
         ASSERT_EQ(outIdxs[i], hoidxs[i]) << "at: " << i;
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_release_array(input    ));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(output   ));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outindex ));
+    ASSERT_SUCCESS(af_release_array(input    ));
+    ASSERT_SUCCESS(af_release_array(output   ));
+    ASSERT_SUCCESS(af_release_array(outindex ));
 }
 
 TYPED_TEST(TopK, Max1D0)
@@ -187,20 +187,20 @@ TEST(TopK, ValidationCheck_DimN)
 {
     dim_t dims[4] = {10, 10, 1, 1};
     af_array out, idx, in;
-    ASSERT_EQ(AF_SUCCESS, af_randu(&in, 2, dims, f32));
+    ASSERT_SUCCESS(af_randu(&in, 2, dims, f32));
     ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_topk(&out, &idx, in, 10, 1, AF_TOPK_MAX));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(in));
+    ASSERT_SUCCESS(af_release_array(in));
 }
 
 TEST(TopK, ValidationCheck_DefaultDim)
 {
     dim_t dims[4] = {10, 10, 1, 1};
     af_array out, idx, in;
-    ASSERT_EQ(AF_SUCCESS, af_randu(&in, 4, dims, f32));
-    ASSERT_EQ(AF_SUCCESS, af_topk(&out, &idx, in, 10, -1, AF_TOPK_MAX));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(in));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(out));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(idx));
+    ASSERT_SUCCESS(af_randu(&in, 4, dims, f32));
+    ASSERT_SUCCESS(af_topk(&out, &idx, in, 10, -1, AF_TOPK_MAX));
+    ASSERT_SUCCESS(af_release_array(in));
+    ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(idx));
 }
 
 
diff --git a/test/transform.cpp b/test/transform.cpp
index ce4eac1233..b1068b1baf 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -79,27 +79,27 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     af_array outArray = 0;
     af_array HArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_load_image(&sceneArray_f32, inFiles[1].c_str(), false));
-    ASSERT_EQ(AF_SUCCESS, af_load_image(&goldArray_f32, goldFiles[0].c_str(), false));
+    ASSERT_SUCCESS(af_load_image(&sceneArray_f32, inFiles[1].c_str(), false));
+    ASSERT_SUCCESS(af_load_image(&goldArray_f32, goldFiles[0].c_str(), false));
 
-    ASSERT_EQ(AF_SUCCESS, conv_image<T>(&sceneArray, sceneArray_f32));
-    ASSERT_EQ(AF_SUCCESS, conv_image<T>(&goldArray, goldArray_f32));
+    ASSERT_SUCCESS(conv_image<T>(&sceneArray, sceneArray_f32));
+    ASSERT_SUCCESS(conv_image<T>(&goldArray, goldArray_f32));
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&HArray, &(HIn[0].front()), HDims.ndims(), HDims.get(), f32));
+    ASSERT_SUCCESS(af_create_array(&HArray, &(HIn[0].front()), HDims.ndims(), HDims.get(), f32));
 
-    ASSERT_EQ(AF_SUCCESS, af_transform(&outArray, sceneArray, HArray, objDims[0], objDims[1], method, invert));
+    ASSERT_SUCCESS(af_transform(&outArray, sceneArray, HArray, objDims[0], objDims[1], method, invert));
 
     // Get gold data
     dim_t goldEl = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_elements(&goldEl, goldArray));
+    ASSERT_SUCCESS(af_get_elements(&goldEl, goldArray));
     vector<T> goldData(goldEl);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&goldData.front(), goldArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&goldData.front(), goldArray));
 
     // Get result
     dim_t outEl = 0;
-    ASSERT_EQ(AF_SUCCESS, af_get_elements(&outEl, outArray));
+    ASSERT_SUCCESS(af_get_elements(&outEl, outArray));
     vector<T> outData(outEl);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
     const float thr = 1.1f;
 
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index a0046268eb..f959455f25 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -47,7 +47,7 @@ void transformCoordinatesTest(string pTestFile)
 
     af_array tfArray = 0;
     af_array outArray = 0;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)dtype_traits<T>::af_type));
 
     int nTests = in.size();
 
@@ -55,15 +55,15 @@ void transformCoordinatesTest(string pTestFile)
         dim_t d0 = (dim_t)in[test][0];
         dim_t d1 = (dim_t)in[test][1];
 
-        ASSERT_EQ(AF_SUCCESS, af_transform_coordinates(&outArray, tfArray, d0, d1));
+        ASSERT_SUCCESS(af_transform_coordinates(&outArray, tfArray, d0, d1));
 
         // Get result
         dim_t outEl = 0;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&outEl, outArray));
+        ASSERT_SUCCESS(af_get_elements(&outEl, outArray));
         vector<T> outData(outEl);
-        ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
-        ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
         const float thr = 1.f;
 
         for (dim_t elIter = 0; elIter < outEl; elIter++) {
diff --git a/test/translate.cpp b/test/translate.cpp
index c9c5012748..9cf8991ae2 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -64,13 +64,13 @@ void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims, const
 
     dim4 dims = numDims[0];
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_translate(&outArray, inArray, tx, ty, odims[0], odims[1], method));
+    ASSERT_SUCCESS(af_translate(&outArray, inArray, tx, ty, odims[0], odims[1], method));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 302623f46b..89a002e00e 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -64,26 +64,26 @@ void trsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=N
     af_array outArray   = 0;
     af_array inArray    = 0;
     T *outData;
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
     // check if the test is for indexed Array
     if (isSubRef) {
         dim4 newDims(dims[1]-4,dims[0]-4,dims[2],dims[3]);
         af_array subArray = 0;
-        ASSERT_EQ(AF_SUCCESS, af_index(&subArray,inArray,seqv->size(),&seqv->front()));
-        ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray,subArray, false));
+        ASSERT_SUCCESS(af_index(&subArray,inArray,seqv->size(),&seqv->front()));
+        ASSERT_SUCCESS(af_transpose(&outArray,subArray, false));
         // destroy the temporary indexed Array
-        ASSERT_EQ(AF_SUCCESS, af_release_array(subArray));
+        ASSERT_SUCCESS(af_release_array(subArray));
 
         dim_t nElems;
-        ASSERT_EQ(AF_SUCCESS, af_get_elements(&nElems,outArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems,outArray));
         outData = new T[nElems];
     } else {
-        ASSERT_EQ(AF_SUCCESS,af_transpose(&outArray,inArray, false));
+        ASSERT_SUCCESS(af_transpose(&outArray,inArray, false));
         outData = new T[dims.elements()];
     }
 
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     for (size_t testIter=0; testIter<tests.size(); ++testIter) {
         vector<T> currGoldBar   = tests[testIter];
@@ -95,8 +95,8 @@ void trsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=N
 
     // cleanup
     delete[] outData;
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 TYPED_TEST(Transpose,Vector)
@@ -251,13 +251,13 @@ TEST(Transpose, MaxDim)
 
     ASSERT_EQ(output.dims(0), (int)largeDim);
     ASSERT_EQ(output.dims(1), 2);
-    ASSERT_TRUE(allTrue<bool>(output == gold));
+    ASSERT_ARRAYS_EQ(gold, output);
 
     input  = range(dim4(2, 5, 1, largeDim));
     gold   = range(dim4(5, 2, 1, largeDim), 1);
     output = transpose(input);
 
-    ASSERT_TRUE(allTrue<bool>(output == gold));
+    ASSERT_ARRAYS_EQ(gold, output);
 }
 
 
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index 308d5f62a7..5b01a7682a 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -46,25 +46,16 @@ void transposeip_test(dim4 dims)
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_randu(&inArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randu(&inArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray, inArray, false));
-    ASSERT_EQ(AF_SUCCESS, af_transpose_inplace(inArray, false));
+    ASSERT_SUCCESS(af_transpose(&outArray, inArray, false));
+    ASSERT_SUCCESS(af_transpose_inplace(inArray, false));
 
-    vector<T> outData(dims.elements());
-    vector<T> trsData(dims.elements());
-
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData.front(), outArray));
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&trsData.front(), inArray));
-
-    dim_t nElems = dims.elements();
-    for (int elIter = 0; elIter < (int)nElems; ++elIter) {
-        ASSERT_EQ(trsData[elIter] , outData[elIter])<< "at: " << elIter << endl;
-    }
+    ASSERT_ARRAYS_EQ(inArray, outArray);
 
     // cleanup
-    ASSERT_EQ(AF_SUCCESS, af_release_array(inArray));
-    ASSERT_EQ(AF_SUCCESS, af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
 }
 
 #define INIT_TEST(Side, D3, D4)                                                     \
@@ -92,14 +83,5 @@ void transposeInPlaceCPPTest()
     array output = transpose(input);
     transposeInPlace(input);
 
-    vector<float> outData(dims.elements());
-    vector<float> trsData(dims.elements());
-
-    output.host((void*)&outData.front());
-    input.host((void*)&trsData.front());
-
-    dim_t nElems = dims.elements();
-    for (int elIter = 0; elIter < (int)nElems; ++elIter) {
-        ASSERT_EQ(trsData[elIter], outData[elIter])<< "at: " << elIter << endl;
-    }
+    ASSERT_ARRAYS_EQ(input, output);
 }
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 25c1a25f84..c73318f7c6 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -61,23 +61,24 @@ void unwrapTest(string pTestFile, const unsigned resultIdx,
     af_array outArrayT = 0;
     af_array outArray2 = 0;
 
-    ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_SUCCESS, af_unwrap(&outArray , inArray, wx, wy, sx, sy, px, py, true ));
-    ASSERT_EQ(AF_SUCCESS, af_unwrap(&outArrayT, inArray, wx, wy, sx, sy, px, py, false));
-    ASSERT_EQ(AF_SUCCESS, af_transpose(&outArray2, outArrayT, false));
+    ASSERT_SUCCESS(af_unwrap(&outArray , inArray, wx, wy, sx, sy, px, py, true ));
+    ASSERT_SUCCESS(af_unwrap(&outArrayT, inArray, wx, wy, sx, sy, px, py, false));
+    ASSERT_SUCCESS(af_transpose(&outArray2, outArrayT, false));
 
     size_t nElems = tests[resultIdx].size();
     vector<T> outData(nElems);
 
+    // TODO: Change to ASSERT_VEC_ARRAY_EQ
     // Compare is_column == true results
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData[0], outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData[0], outArray));
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
 
     // Compare is_column == false results
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr((void*)&outData[0], outArray2));
+    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData[0], outArray2));
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
     }
@@ -198,5 +199,5 @@ TEST(Unwrap, MaxDim)
     array gold = range(dim4(5, 5, 1, largeDim));
     gold = moddims(gold, dim4(25, 1, largeDim));
 
-    ASSERT_TRUE(allTrue<bool>(output == gold));
+    ASSERT_ARRAYS_EQ(gold, output);
 }
diff --git a/test/where.cpp b/test/where.cpp
index 7944e01e78..875ce5f505 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -56,28 +56,20 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
 
     // Get input array
     if (isSubRef) {
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
-        ASSERT_EQ(AF_SUCCESS, af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
     } else {
 
-        ASSERT_EQ(AF_SUCCESS, af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
     }
 
     // Compare result
     vector<uint> currGoldBar(tests[0].begin(), tests[0].end());
 
     // Run sum
-    ASSERT_EQ(AF_SUCCESS, af_where(&outArray, inArray));
+    ASSERT_SUCCESS(af_where(&outArray, inArray));
 
-    // Get result
-    size_t nElems = currGoldBar.size();
-    vector<uint> outData(nElems);
-    ASSERT_EQ(AF_SUCCESS, af_get_data_ptr(&outData.front(), outArray));
-
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                        << endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(currGoldBar, dim4(tests[0].size()), outArray);
 
     if(inArray   != 0) af_release_array(inArray);
     if(outArray  != 0) af_release_array(outArray);
@@ -117,15 +109,7 @@ TYPED_TEST(Where, CPP)
     // Compare result
     vector<uint> currGoldBar(tests[0].begin(), tests[0].end());
 
-    // Get result
-    size_t nElems = currGoldBar.size();
-    vector<uint> outData(nElems);
-    output.host((void*)&(outData.front()));
-
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                        << endl;
-    }
+    ASSERT_VEC_ARRAY_EQ(currGoldBar, dim4(tests[0].size()), output);
 }
 
 TEST(Where, MaxDim)
@@ -135,11 +119,11 @@ TEST(Where, MaxDim)
     array input = range(dim4(1, largeDim), 1);
     array output = where(input % 2 == 0);
     array gold = 2 * range(largeDim/2);
-    ASSERT_TRUE(allTrue<bool>(output == gold));
+    ASSERT_ARRAYS_EQ(gold.as(u32), output);
 
     input = range(dim4(1, 1, 1, largeDim), 3);
     output = where(input % 2 == 0);
-    ASSERT_TRUE(allTrue<bool>(output == gold));
+    ASSERT_ARRAYS_EQ(gold.as(u32), output);
 }
 
 TEST(Where, ISSUE_1259)
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 12dedc9ed1..7384b31293 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -200,5 +200,5 @@ TEST(Wrap, MaxDim)
     array unwrapped = unwrap(input, wx, wy, sx, sy, px, py);
     array output = wrap(unwrapped, 5, 5, wx, wy, sx, sy, px, py);
 
-    ASSERT_TRUE(allTrue<bool>(output == input));
+    ASSERT_ARRAYS_EQ(output, input);
 }

From 0e50a10f94397ccf2e897ec330b9494fc8e0f937 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Wed, 1 Aug 2018 12:25:22 -0400
Subject: [PATCH 1498/2677] Implemented ASSERT_ARRAYS_NEAR

---
 test/basic.cpp       |  16 +++
 test/testHelpers.hpp | 229 ++++++++++++++++++++++++++++---------------
 2 files changed, 164 insertions(+), 81 deletions(-)

diff --git a/test/basic.cpp b/test/basic.cpp
index 3e81257bf7..84a2f591ee 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -457,3 +457,19 @@ TEST(Assert, TestVectorDiffVecSize) {
     // ASSERT_ARRAYS_EQ(A, B);
     ASSERT_FALSE(assertArrayEq("hA", "adims", "A", hA, adims, A));
 }
+
+TEST(Assert, TestArraysNear) {
+    array gold = constant(1, 3, 3);
+    array out = constant(1, 3, 3);
+    gold(2, 2) = 2.2345;
+    out(2, 2) = 2.2445;
+    float maxDiff = 0.001;
+
+    // Testing this macro
+    // ASSERT_ARRAYS_NEAR(gold, out, maxDiff);
+    ASSERT_FALSE(assertArrayEq("gold", "out", gold, out, maxDiff));
+}
+
+TEST(Assert, TestVecArrayNear) {
+    ASSERT_TRUE(true);
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index e3f6b7fe9d..0f02743e2c 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -12,13 +12,13 @@
 
 #include <af/array.h>
 #include <af/dim4.hpp>
-#include <af/traits.hpp>
 #include <af/internal.h>
+#include <af/traits.hpp>
 #include <arrayfire.h>
-#include <cfloat>
 #include <gtest/gtest.h>
 
 #include <algorithm>
+#include <cfloat>
 #include <fstream>
 #include <iterator>
 #include <limits>
@@ -533,14 +533,14 @@ af::dim4 unravelIdx(uint idx, af::array arr) {
 #define ASSERT_SUCCESS(CALL)                    \
     ASSERT_EQ(AF_SUCCESS, CALL)
 
-/// Compares two af::array or af_arrays for their types, dims, and values.
+/// Compares two af::array or af_arrays for their types, dims, and values (strict equality).
 ///
 /// \param[in] EXPECTED The expected array of the assertion
 /// \param[in] ACTUAL The actual resulting array from the calculation
 #define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
     EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
 
-/// Compares a std::vector with an af::/af_array for their types, dims, and values.
+/// Compares a std::vector with an af::/af_array for their types, dims, and values (strict equality).
 ///
 /// \param[in] EXPECTED_VEC The vector that represents the expected array
 /// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
@@ -548,13 +548,39 @@ af::dim4 unravelIdx(uint idx, af::array arr) {
 #define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
     EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR)
 
+/// Compares two af::array or af_arrays for their type, dims, and values (with a given tolerance).
+///
+/// \param[in] EXPECTED Expected value of the assertion
+/// \param[in] ACTUAL Actual value of the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+///
+/// \NOTE: This macro will deallocate the af_arrays after the call
+#define ASSERT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
+    EXPECT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
+
+/// Compares a std::vector with an af::array for their dims and values (with a given tolerance).
+///
+/// \param[in] EXPECTED_VEC The vector that represents the expected array
+/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
+/// \param[in] ACTUAL_ARR The actual array from the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+#define ASSERT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
+    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
+                            MAX_ABSDIFF)
+
 struct FloatTag {};
 struct IntegerTag {};
 
+//********** af::array to af::array **********//
+
+// Argument maxAbsDiff is not used in this integer version of elemWiseEq
+//  but it is needed for compilation
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       af::array a, af::array b,
-                                      IntegerTag) {
+                                      IntegerTag, float maxAbsDiff) {
     uint nElems = a.elements();
     af::dim4 arrDims = a.dims();
 
@@ -587,7 +613,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       af::array a, af::array b,
-                                      FloatTag) {
+                                      FloatTag, float maxAbsDiff) {
     uint nElems = a.elements();
     af::dim4 arrDims = a.dims();
 
@@ -597,25 +623,117 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     std::vector<T> hB(nElems);
     b.host(hB.data());
 
-    typedef typename std::vector<T>::iterator iter;
-    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
-    iter aItr = mismatches.first;
-    iter bItr = mismatches.second;
-
-    if (aItr == hA.end()) {
+    using std::abs;
+    using af::abs;
+
+    // ASSERT_NEAR
+    if (maxAbsDiff != 0) {
+        for (int idx = 0; idx < hA.size(); ++idx) {
+            double absdiff = abs(hA[idx] - hB[idx]);
+            if (absdiff > maxAbsDiff) {
+                af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
+                return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
+                                                     << coords[0] << ", " << coords[1] << ", "
+                                                     << coords[2] << ", " << coords[3] << "): "
+                                                     << aName << "(" << hA[idx] << "), "
+                                                     << bName << "(" << hB[idx] << "). "
+                                                     << "Absolute difference: " << absdiff;
+            }
+        }
         return ::testing::AssertionSuccess();
-    } else {
-        int idx = std::distance(hA.begin(), aItr);
-        af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
+    }
+    // ASSERT_EQ
+    else {
+        typedef typename std::vector<T>::iterator iter;
+        std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
+        iter aItr = mismatches.first;
+        iter bItr = mismatches.second;
+
+        if (aItr == hA.end()) {
+            return ::testing::AssertionSuccess();
+        } else {
+            int idx = std::distance(hA.begin(), aItr);
+            af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
+
+            return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
+                                                 << coords[0] << ", " << coords[1] << ", "
+                                                 << coords[2] << ", " << coords[3] << "): "
+                                                 << aName << "(" << hA[idx] << "), "
+                                                 << bName << "(" << hB[idx] << ")";
+        }
+    }
+}
 
-        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
-                                             << coords[0] << ", " << coords[1] << ", "
-                                             << coords[2] << ", " << coords[3] << "): "
-                                             << aName << "(" << hA[idx] << "), "
-                                             << bName << "(" << hB[idx] << ")";
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      af::array a, af::array b, float maxAbsDiff = 0.f) {
+    typedef typename cond_type<
+        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
+        FloatTag, IntegerTag>::type TagType;
+    TagType tag;
+
+    return elemWiseEq<T>(aName, bName, a, b, tag, maxAbsDiff);
+}
+
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         af::array a, af::array b,
+                                         float maxAbsDiff = 0.f) {
+    af::dtype aType = a.type();
+    af::dtype bType = b.type();
+    if (aType != bType)
+        return ::testing::AssertionFailure() << "TYPE MISMATCH: "
+                                             << aName << "(" << a.type() << ") and "
+                                             << bName << "(" << b.type() << ")";
+    af::dtype arrDtype = aType;
+
+
+    const uint ndimIds = 4;
+    if (a.dims() != b.dims())
+        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
+                                             << aName << "([" << a.dims() << "]), "
+                                             << bName << "([" << b.dims() << "])";
+
+    switch (arrDtype) {
+    case f32: return elemWiseEq<float>(aName, bName, a, b, maxAbsDiff);              break;
+    case c32: return elemWiseEq<af::cfloat>(aName, bName, a, b, maxAbsDiff);         break;
+    case f64: return elemWiseEq<double>(aName, bName, a, b, maxAbsDiff);             break;
+    case c64: return elemWiseEq<af::cdouble>(aName, bName, a, b, maxAbsDiff);        break;
+    case b8:  return elemWiseEq<char>(aName, bName, a, b, maxAbsDiff);               break;
+    case s32: return elemWiseEq<int>(aName, bName, a, b, maxAbsDiff);                break;
+    case u32: return elemWiseEq<uint>(aName, bName, a, b, maxAbsDiff);               break;
+    case u8:  return elemWiseEq<uchar>(aName, bName, a, b, maxAbsDiff);              break;
+    case s64: return elemWiseEq<long long>(aName, bName, a, b, maxAbsDiff);          break;
+    case u64: return elemWiseEq<unsigned long long>(aName, bName, a, b, maxAbsDiff); break;
+    case s16: return elemWiseEq<short>(aName, bName, a, b, maxAbsDiff);              break;
+    case u16: return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);     break;
+    default:  return ::testing::AssertionFailure()
+            << "INVALID TYPE, see enum numbers: "
+            << aName << "(" << a.type() << ") and "
+            << bName << "(" << b.type() << ")";
     }
+
+    return ::testing::AssertionSuccess();
+}
+
+// To support C API
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         af_array a, af_array b, float maxAbsDiff = 0.f) {
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertArrayEq(aName, bName, aaa, bbb, maxAbsDiff);
+}
+
+::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           af::array a, af::array b, float maxAbsDiff) {
+    return assertArrayEq(aName, bName, a, b, maxAbsDiff);
 }
 
+//********** std::vector to af::array **********//
+
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
                                       std::vector<T>& hA, af::dim4 aDims, af::array b,
@@ -668,17 +786,6 @@ ::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
     }
 }
 
-template<typename T>
-::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      af::array a, af::array b) {
-    typedef typename cond_type<
-        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
-        FloatTag, IntegerTag>::type TagType;
-    TagType tag;
-
-    return elemWiseEq<T>(aName, bName, a, b, tag);
-}
-
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
                                       std::vector<T>& hA, af::dim4 aDims, af::array b) {
@@ -690,56 +797,6 @@ ::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
     return elemWiseEq<T>(hA_name, bName, hA, aDims, b, tag);
 }
 
-::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         af::array a, af::array b) {
-    af::dtype aType = a.type();
-    af::dtype bType = b.type();
-    if (aType != bType)
-        return ::testing::AssertionFailure() << "TYPE MISMATCH: "
-                                             << aName << "(" << a.type() << ") and "
-                                             << bName << "(" << b.type() << ")";
-
-    af::dtype arrDtype = aType;
-
-    const uint ndimIds = 4;
-    if (a.dims() != b.dims())
-        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
-                                             << aName << "([" << a.dims() << "]), "
-                                             << bName << "([" << b.dims() << "])";
-
-    switch (arrDtype) {
-    case f32: return elemWiseEq<float>(aName, bName, a, b);              break;
-    case c32: return elemWiseEq<af::cfloat>(aName, bName, a, b);         break;
-    case f64: return elemWiseEq<double>(aName, bName, a, b);             break;
-    case c64: return elemWiseEq<af::cdouble>(aName, bName, a, b);        break;
-    case b8:  return elemWiseEq<char>(aName, bName, a, b);               break;
-    case s32: return elemWiseEq<int>(aName, bName, a, b);                break;
-    case u32: return elemWiseEq<uint>(aName, bName, a, b);               break;
-    case u8:  return elemWiseEq<uchar>(aName, bName, a, b);              break;
-    case s64: return elemWiseEq<long long>(aName, bName, a, b);          break;
-    case u64: return elemWiseEq<unsigned long long>(aName, bName, a, b); break;
-    case s16: return elemWiseEq<short>(aName, bName, a, b);              break;
-    case u16: return elemWiseEq<unsigned short>(aName, bName, a, b);     break;
-    default:  return ::testing::AssertionFailure()
-            << "INVALID TYPE, see enum numbers: "
-            << aName << "(" << a.type() << ") and "
-            << bName << "(" << b.type() << ")";
-    }
-
-    return ::testing::AssertionSuccess();
-}
-
-// To support C API
-::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         af_array a, af_array b) {
-    af_array aa = 0, bb = 0;
-    af_retain_array(&aa, a);
-    af_retain_array(&bb, b);
-    af::array aaa(aa);
-    af::array bbb(bb);
-    return assertArrayEq(aName, bName, aaa, bbb);
-}
-
 template<typename T>
 ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsName,
                                          std::string bName,
@@ -779,4 +836,14 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsN
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, bbb);
 }
 
+template<typename T>
+::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
+                                           std::string bName,
+                                           std::string maxAbsDiffName,
+                                           std::vector<T>& hA, af::dim4 aDims,
+                                           af::array b,
+                                           float maxAbsDiff) {
+    return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
+}
+
 #pragma GCC diagnostic pop

From 72b3a429c20b0a03dd320be7979ad7e86c2e3258 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Wed, 1 Aug 2018 15:22:38 -0400
Subject: [PATCH 1499/2677] Implemented ASSERT_VEC_ARRAY_NEAR and refactored
 assert functions

---
 test/basic.cpp       | 119 ++++++++++---
 test/testHelpers.hpp | 409 ++++++++++++++++++++-----------------------
 2 files changed, 285 insertions(+), 243 deletions(-)

diff --git a/test/basic.cpp b/test/basic.cpp
index 84a2f591ee..6379398f1c 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
-#include <arrayfire.h>
 #include <af/data.h>
+#include <arrayfire.h>
+#include <gtest/gtest.h>
 #include <testHelpers.hpp>
 
 #include <vector>
@@ -17,6 +17,7 @@
 using std::vector;
 using af::array;
 using af::constant;
+using af::dim4;
 
 TEST(BasicTests, constant1000x1000)
 {
@@ -437,39 +438,111 @@ TEST(Assert, TestVectorDiffOutSizeGoldSize) {
 }
 
 TEST(Assert, TestVectorDiffDim4) {
-    array A = constant(3.1f, 3, 3);
-    vector<float> hA(A.elements());
-    dim4 adims(3, 2);
-    fill(hA.begin(), hA.end(), 3.1f);
+    array out = constant(3.1f, 3, 3);
+    vector<float> gold(out.elements());
+    dim4 goldDims(3, 2);
+    fill(gold.begin(), gold.end(), 3.1f);
 
     // Testing this macro
-    // ASSERT_ARRAYS_EQ(A, B);
-    ASSERT_FALSE(assertArrayEq("hA", "adims", "A", hA, adims, A));
+    // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out", gold, goldDims, out));
 }
 
 TEST(Assert, TestVectorDiffVecSize) {
-    array A = constant(3.1f, 3, 3);
-    vector<float> hA(A.elements()-1);
-    dim4 adims(3, 3);
-    fill(hA.begin(), hA.end(), 3.1f);
+    array out = constant(3.1f, 3, 3);
+    vector<float> gold(out.elements() - 1);
+    dim4 goldDims(3, 3);
+    fill(gold.begin(), gold.end(), 3.1f);
 
     // Testing this macro
-    // ASSERT_ARRAYS_EQ(A, B);
-    ASSERT_FALSE(assertArrayEq("hA", "adims", "A", hA, adims, A));
+    // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out", gold, goldDims, out));
 }
 
-TEST(Assert, TestArraysNear) {
-    array gold = constant(1, 3, 3);
-    array out = constant(1, 3, 3);
-    gold(2, 2) = 2.2345;
-    out(2, 2) = 2.2445;
-    float maxDiff = 0.001;
+TEST(Assert, TestArraysNearC) {
+    af_array gold = 0;
+    af_array out = 0;
+    dim_t dims[] = {10, 10, 1, 1};
+    af_constant(&gold, 2.2345f, 4, dims, f32);
+    af_constant(&out, 2.2346f, 4, dims, f32);
+
+    float maxDiff = 0.001f;
 
     // Testing this macro
     // ASSERT_ARRAYS_NEAR(gold, out, maxDiff);
-    ASSERT_FALSE(assertArrayEq("gold", "out", gold, out, maxDiff));
+    ASSERT_TRUE(assertArrayNear("gold", "out", "maxDiff", gold, out, maxDiff));
+
+    ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(gold));
 }
 
-TEST(Assert, TestVecArrayNear) {
-    ASSERT_TRUE(true);
+TEST(Assert, TestVecArrayNearC) {
+    vector<float> gold(3 * 3);
+    fill(gold.begin(), gold.end(), 2.2345f);
+    dim4 goldDims(3, 3);
+
+    af_array out = 0;
+    dim_t dims[] = {3, 3, 1, 1};
+    af_constant(&out, 2.2346f, 4, dims, f32);
+
+    float maxDiff = 0.001f;
+
+    // Testing this macro
+    // ASSERT_VEC_ARRAY_NEAR(gold, goldDims, out, maxDiff);
+    ASSERT_TRUE(assertArrayNear("gold", "goldDims", "out", "maxDiff",
+                                gold, goldDims, out, maxDiff));
+
+    ASSERT_SUCCESS(af_release_array(out));
+}
+
+TEST(Assert, TestArraysNearWithinThresh) {
+    array gold = constant(2.2345f, 3, 3);
+    array out = gold;
+    out(2, 2) += 0.0001f;
+    float maxDiff = 0.001f;
+
+    // Testing this macro
+    // ASSERT_ARRAYS_NEAR(gold, out, maxDiff);
+    ASSERT_TRUE(assertArrayNear("gold", "out", "maxDiff", gold, out, maxDiff));
+}
+
+TEST(Assert, TestArraysNearExceedThresh) {
+    array gold = constant(2.2345f, 3, 3);
+    array out = gold;
+    out(2, 2) += 0.002f;
+    float maxDiff = 0.001f;
+
+    // Testing this macro
+    // ASSERT_ARRAYS_NEAR(gold, out, maxDiff);
+    ASSERT_FALSE(assertArrayNear("gold", "out", "maxDiff", gold, out, maxDiff));
+}
+
+TEST(Assert, TestVecArrayNearWithinThresh) {
+    vector<float> gold(3 * 3);
+    fill(gold.begin(), gold.end(), 2.2345f);
+    dim4 goldDims(3, 3);
+
+    array out = constant(2.2345f, goldDims);
+    out(2, 2) += 0.0001f;
+    float maxDiff = 0.001f;
+
+    // Testing this macro
+    // ASSERT_VEC_ARRAY_NEAR(gold, goldDims, out, maxDiff);
+    ASSERT_TRUE(assertArrayNear("gold", "goldDims", "out", "maxAbsDiff",
+                                 gold, goldDims, out, maxDiff));
+}
+
+TEST(Assert, TestVecArrayNearExceedThresh) {
+    vector<float> gold(3 * 3);
+    fill(gold.begin(), gold.end(), 2.2345f);
+    dim4 goldDims(3, 3);
+
+    array out = constant(2.2345f, goldDims);
+    out(2, 2) += 0.002f;
+    float maxDiff = 0.001f;
+
+    // Testing this macro
+    // ASSERT_VEC_ARRAY_NEAR(gold, goldDims, out, maxDiff);
+    ASSERT_FALSE(assertArrayNear("gold", "goldDims", "out", "maxAbsDiff",
+                                 gold, goldDims, out, maxDiff));
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 0f02743e2c..fc5b8c0b2e 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -527,152 +527,105 @@ af::dim4 unravelIdx(uint idx, af::array arr) {
     return unravelIdx(idx, dims, st);
 }
 
-/// Checks if the C-API arrayfire function returns successfully
-///
-/// \param[in] CALL This is the arrayfire C function
-#define ASSERT_SUCCESS(CALL)                    \
-    ASSERT_EQ(AF_SUCCESS, CALL)
-
-/// Compares two af::array or af_arrays for their types, dims, and values (strict equality).
-///
-/// \param[in] EXPECTED The expected array of the assertion
-/// \param[in] ACTUAL The actual resulting array from the calculation
-#define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
-    EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
-
-/// Compares a std::vector with an af::/af_array for their types, dims, and values (strict equality).
-///
-/// \param[in] EXPECTED_VEC The vector that represents the expected array
-/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
-/// \param[in] ACTUAL_ARR The actual resulting array from the calculation
-#define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
-    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR)
-
-/// Compares two af::array or af_arrays for their type, dims, and values (with a given tolerance).
-///
-/// \param[in] EXPECTED Expected value of the assertion
-/// \param[in] ACTUAL Actual value of the calculation
-/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
-///            elements of EXPECTED and ACTUAL
-///
-/// \NOTE: This macro will deallocate the af_arrays after the call
-#define ASSERT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
-    EXPECT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
-
-/// Compares a std::vector with an af::array for their dims and values (with a given tolerance).
-///
-/// \param[in] EXPECTED_VEC The vector that represents the expected array
-/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
-/// \param[in] ACTUAL_ARR The actual array from the calculation
-/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
-///            elements of EXPECTED and ACTUAL
-#define ASSERT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
-    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
-                            MAX_ABSDIFF)
-
 struct FloatTag {};
 struct IntegerTag {};
 
-//********** af::array to af::array **********//
-
-// Argument maxAbsDiff is not used in this integer version of elemWiseEq
-//  but it is needed for compilation
-template<typename T>
-::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      af::array a, af::array b,
-                                      IntegerTag, float maxAbsDiff) {
-    uint nElems = a.elements();
-    af::dim4 arrDims = a.dims();
+af::dim4 calcStrides(const af::dim4 &parentDim)
+{
+    af::dim4 out(1, 1, 1, 1);
+    dim_t *out_dims = out.get();
+    const dim_t *parent_dims =  parentDim.get();
 
-    std::vector<T> hA(nElems);
-    a.host(hA.data());
+    for (dim_t i=1; i < 4; i++) {
+        out_dims[i] = out_dims[i - 1] * parent_dims[i-1];
+    }
 
-    std::vector<T> hB(nElems);
-    b.host(hB.data());
+    return out;
+}
 
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      std::vector<T>& a, af::dim4 aDims,
+                                      std::vector<T>& b, af::dim4 bDims,
+                                      float maxAbsDiff, IntegerTag) {
     typedef typename std::vector<T>::iterator iter;
-    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
+    std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());
     iter aItr = mismatches.first;
     iter bItr = mismatches.second;
 
-    if (aItr == hA.end()) {
+    if (bItr == b.end()) {
         return ::testing::AssertionSuccess();
     } else {
-        int idx = std::distance(hA.begin(), aItr);
-        af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
-
-        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
-                                             << coords[0] << ", " << coords[1] << ", "
-                                             << coords[2] << ", " << coords[3] << "): "
-                                             << aName << "(" << hA[idx] << "), "
-                                             << bName << "(" << hB[idx] << ")";
+        int idx = std::distance(b.begin(), bItr);
+        af::dim4 bStrides = calcStrides(bDims);
+        af::dim4 coords = unravelIdx(idx, bDims, bStrides);
+
+        return ::testing::AssertionFailure() << "VALUE DIFFERS:\n"
+                                             << " at ([" << coords << "]):\n"
+                                             << aName << "(" << a[idx] << ")\n"
+                                             << bName << "(" << b[idx] << ")";
     }
-
 }
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      af::array a, af::array b,
-                                      FloatTag, float maxAbsDiff) {
-    uint nElems = a.elements();
-    af::dim4 arrDims = a.dims();
-
-    std::vector<T> hA(nElems);
-    a.host(hA.data());
-
-    std::vector<T> hB(nElems);
-    b.host(hB.data());
-
-    using std::abs;
-    using af::abs;
+                                      std::vector<T>& a, af::dim4 aDims,
+                                      std::vector<T>& b, af::dim4 bDims,
+                                      float maxAbsDiff, FloatTag) {
+    typedef typename std::vector<T>::iterator iter;
+    // TODO(mark): Modify equality for float
+    std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
 
     // ASSERT_NEAR
     if (maxAbsDiff != 0) {
-        for (int idx = 0; idx < hA.size(); ++idx) {
-            double absdiff = abs(hA[idx] - hB[idx]);
+        using std::abs;
+        using af::abs;
+        for (int idx = 0; idx < a.size(); ++idx) {
+            double absdiff = abs(a[idx] - b[idx]);
             if (absdiff > maxAbsDiff) {
-                af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
-                return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
-                                                     << coords[0] << ", " << coords[1] << ", "
-                                                     << coords[2] << ", " << coords[3] << "): "
-                                                     << aName << "(" << hA[idx] << "), "
-                                                     << bName << "(" << hB[idx] << "). "
-                                                     << "Absolute difference: " << absdiff;
+                af::dim4 coords = unravelIdx(idx, aDims, calcStrides(aDims));
+                return ::testing::AssertionFailure() << "ABS DIFF EXCEEDS THRESHOLD:\n"
+                                                     << " at ([" << coords << "]):\n"
+                                                     << aName << "(" << a[idx] << ")\n"
+                                                     << bName << "(" << b[idx] << ")\n"
+                                                     << "Expected abs diff: " << maxAbsDiff << "\n"
+                                                     << "Actual abs diff  : " << absdiff;
             }
         }
         return ::testing::AssertionSuccess();
     }
     // ASSERT_EQ
     else {
-        typedef typename std::vector<T>::iterator iter;
-        std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
-        iter aItr = mismatches.first;
-        iter bItr = mismatches.second;
-
-        if (aItr == hA.end()) {
+        if (bItr == b.end()) {
             return ::testing::AssertionSuccess();
         } else {
-            int idx = std::distance(hA.begin(), aItr);
-            af::dim4 coords = unravelIdx(idx, a.dims(), af::getStrides(a));
-
-            return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
-                                                 << coords[0] << ", " << coords[1] << ", "
-                                                 << coords[2] << ", " << coords[3] << "): "
-                                                 << aName << "(" << hA[idx] << "), "
-                                                 << bName << "(" << hB[idx] << ")";
+            int idx = std::distance(b.begin(), bItr);
+            af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
+
+            return ::testing::AssertionFailure() << "VALUE DIFFERS:\n"
+                                                 << " at ([" << coords << "]):\n"
+                                                 << aName << "(" << a[idx] << ")\n"
+                                                 << bName << "(" << b[idx] << ")";
         }
     }
 }
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      af::array a, af::array b, float maxAbsDiff = 0.f) {
+                                      af::array a, af::array b, float maxAbsDiff) {
     typedef typename cond_type<
         IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
         FloatTag, IntegerTag>::type TagType;
     TagType tag;
 
-    return elemWiseEq<T>(aName, bName, a, b, tag, maxAbsDiff);
+    std::vector<T> hA(a.elements());
+    a.host(hA.data());
+
+    std::vector<T> hB(b.elements());
+    b.host(hB.data());
+    return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff, tag);
 }
 
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
@@ -681,31 +634,32 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     af::dtype aType = a.type();
     af::dtype bType = b.type();
     if (aType != bType)
-        return ::testing::AssertionFailure() << "TYPE MISMATCH: "
-                                             << aName << "(" << a.type() << ") and "
-                                             << bName << "(" << b.type() << ")";
+        return ::testing::AssertionFailure() << "TYPE MISMATCH: \n"
+                                             << "Expected: " << aName << "(" << a.type() << ")\n"
+                                             << "Actual:   " << bName << "(" << b.type() << ")";
     af::dtype arrDtype = aType;
-
-
     const uint ndimIds = 4;
     if (a.dims() != b.dims())
-        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
-                                             << aName << "([" << a.dims() << "]), "
-                                             << bName << "([" << b.dims() << "])";
+        return ::testing::AssertionFailure() << "SIZE MISMATCH: \n"
+                                             << "Expected: " << aName << "([" << a.dims() << "]),\n"
+                                             << "Actual:   " << bName << "([" << b.dims() << "])";
+
+    uint nElems = a.elements();
+    af::dim4 arrDims = a.dims();
 
     switch (arrDtype) {
-    case f32: return elemWiseEq<float>(aName, bName, a, b, maxAbsDiff);              break;
-    case c32: return elemWiseEq<af::cfloat>(aName, bName, a, b, maxAbsDiff);         break;
-    case f64: return elemWiseEq<double>(aName, bName, a, b, maxAbsDiff);             break;
-    case c64: return elemWiseEq<af::cdouble>(aName, bName, a, b, maxAbsDiff);        break;
-    case b8:  return elemWiseEq<char>(aName, bName, a, b, maxAbsDiff);               break;
-    case s32: return elemWiseEq<int>(aName, bName, a, b, maxAbsDiff);                break;
-    case u32: return elemWiseEq<uint>(aName, bName, a, b, maxAbsDiff);               break;
-    case u8:  return elemWiseEq<uchar>(aName, bName, a, b, maxAbsDiff);              break;
-    case s64: return elemWiseEq<long long>(aName, bName, a, b, maxAbsDiff);          break;
-    case u64: return elemWiseEq<unsigned long long>(aName, bName, a, b, maxAbsDiff); break;
-    case s16: return elemWiseEq<short>(aName, bName, a, b, maxAbsDiff);              break;
-    case u16: return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);     break;
+    case f32: return elemWiseEq<float>              (aName, bName, a, b, maxAbsDiff); break;
+    case c32: return elemWiseEq<af::cfloat>         (aName, bName, a, b, maxAbsDiff); break;
+    case f64: return elemWiseEq<double>             (aName, bName, a, b, maxAbsDiff); break;
+    case c64: return elemWiseEq<af::cdouble>        (aName, bName, a, b, maxAbsDiff); break;
+    case b8:  return elemWiseEq<char>               (aName, bName, a, b, maxAbsDiff); break;
+    case s32: return elemWiseEq<int>                (aName, bName, a, b, maxAbsDiff); break;
+    case u32: return elemWiseEq<uint>               (aName, bName, a, b, maxAbsDiff); break;
+    case u8:  return elemWiseEq<uchar>              (aName, bName, a, b, maxAbsDiff); break;
+    case s64: return elemWiseEq<long long>          (aName, bName, a, b, maxAbsDiff); break;
+    case u64: return elemWiseEq<unsigned long long> (aName, bName, a, b, maxAbsDiff); break;
+    case s16: return elemWiseEq<short>              (aName, bName, a, b, maxAbsDiff); break;
+    case u16: return elemWiseEq<unsigned short>     (aName, bName, a, b, maxAbsDiff); break;
     default:  return ::testing::AssertionFailure()
             << "INVALID TYPE, see enum numbers: "
             << aName << "(" << a.type() << ") and "
@@ -715,114 +669,52 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     return ::testing::AssertionSuccess();
 }
 
-// To support C API
-::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         af_array a, af_array b, float maxAbsDiff = 0.f) {
-    af_array aa = 0, bb = 0;
-    af_retain_array(&aa, a);
-    af_retain_array(&bb, b);
-    af::array aaa(aa);
-    af::array bbb(bb);
-    return assertArrayEq(aName, bName, aaa, bbb, maxAbsDiff);
-}
-
-::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
-                                           std::string maxAbsDiffName,
-                                           af::array a, af::array b, float maxAbsDiff) {
-    return assertArrayEq(aName, bName, a, b, maxAbsDiff);
-}
-
-//********** std::vector to af::array **********//
-
-template<typename T>
-::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
-                                      std::vector<T>& hA, af::dim4 aDims, af::array b,
-                                      IntegerTag) {
-    std::vector<T> hB(b.elements());
-    b.host(hB.data());
-
-    typedef typename std::vector<T>::iterator iter;
-    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
-    iter aItr = mismatches.first;
-    iter bItr = mismatches.second;
-
-    if (bItr == hB.end()) {
-        return ::testing::AssertionSuccess();
-    } else {
-        int idx = std::distance(hB.begin(), bItr);
-        af::dim4 coords = unravelIdx(idx, b.dims(), af::getStrides(b));
-
-        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
-                                             << coords[0] << ", " << coords[1] << ", "
-                                             << coords[2] << ", " << coords[3] << "): "
-                                             << hA_name << "(" << hA[idx] << "), "
-                                             << bName << "(" << hB[idx] << ")";
-    }
-}
-
-template<typename T>
-::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
-                                      std::vector<T>& hA, af::dim4 aDims, af::array b,
-                                      FloatTag) {
-    std::vector<T> hB(b.elements());
-    b.host(hB.data());
-
-    typedef typename std::vector<T>::iterator iter;
-    std::pair<iter, iter> mismatches = std::mismatch(hA.begin(), hA.end(), hB.begin());
-    iter aItr = mismatches.first;
-    iter bItr = mismatches.second;
-
-    if (bItr == hB.end()) {
-        return ::testing::AssertionSuccess();
-    } else {
-        int idx = std::distance(hB.begin(), bItr);
-        af::dim4 coords = unravelIdx(idx, b.dims(), af::getStrides(b));
-
-        return ::testing::AssertionFailure() << "VALUE DIFFERS at ("
-                                             << coords[0] << ", " << coords[1] << ", "
-                                             << coords[2] << ", " << coords[3] << "): "
-                                             << hA_name << "(" << hA[idx] << "), "
-                                             << bName << "(" << hB[idx] << ")";
-    }
-}
-
 template<typename T>
-::testing::AssertionResult elemWiseEq(std::string hA_name, std::string bName,
-                                      std::vector<T>& hA, af::dim4 aDims, af::array b) {
-    typedef typename cond_type<
-        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
-              FloatTag, IntegerTag>::type TagType;
-    TagType tag;
-
-    return elemWiseEq<T>(hA_name, bName, hA, aDims, b, tag);
-}
-
-template<typename T>
-::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsName,
+::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsName,
                                          std::string bName,
-                                         std::vector<T>& hA, af::dim4 aDims, af::array b) {
+                                         std::vector<T>& hA, af::dim4 aDims,
+                                         af::array b,
+                                         float maxAbsDiff = 0.0f) {
     af::dtype aDtype = (af::dtype) af::dtype_traits<T>::af_type;
     if (aDtype != b.type()) {
-        return ::testing::AssertionFailure() << "TYPE MISMATCH: "
-                                             << hA_name << "(" << aDtype << ") and "
-                                             << bName << "(" << b.type() << ")";
+        return ::testing::AssertionFailure() << "TYPE MISMATCH:\n"
+                                             << "Expected: " << aName << "(" << aDtype << ")\n"
+                                             << "Actual:   " << bName << "(" << b.type() << ")";
     }
 
     const uint ndimIds = 4;
     if(aDims != b.dims()) {
-        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
-                                             << aDimsName << "([" << aDims << "]), "
-                                             << bName << "([" << b.dims() << "])";
+        return ::testing::AssertionFailure() << "SIZE MISMATCH:\n"
+                                             << "Expected: " << aDimsName << "([" << aDims << "])\n"
+                                             << "Actual:   " << bName << "([" << b.dims() << "])";
     }
 
     // In case vector<T> a.size() != aDims.elements()
     if (hA.size() != aDims.elements())
-        return ::testing::AssertionFailure() << "SIZE MISMATCH: "
-                                             << hA_name << ".size()(" << hA.size() << "), "
-                                             << aDimsName << "([" << aDims << "] = "
+        return ::testing::AssertionFailure() << "SIZE MISMATCH:\n"
+                                             << "Expected: " << aName << ".size()(" << hA.size() << ")\n"
+                                             << "Actual:   " << aDimsName << "([" << aDims << "] => "
                                              << aDims.elements() << ")";
 
-    return elemWiseEq<T>(hA_name, bName, hA, aDims, b);
+    typedef typename cond_type<
+        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
+        FloatTag, IntegerTag>::type TagType;
+    TagType tag;
+
+    std::vector<T> hB(b.elements());
+    b.host(&hB.front());
+    return elemWiseEq<T>(aName, bName, hA, aDims, hB, b.dims(), maxAbsDiff, tag);
+}
+
+// To support C API
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         af_array a, af_array b) {
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertArrayEq(aName, bName, aaa, bbb, 0.0f);
 }
 
 // To support C API
@@ -836,6 +728,12 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsN
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, bbb);
 }
 
+::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           af::array a, af::array b, float maxAbsDiff) {
+    return assertArrayEq(aName, bName, a, b, maxAbsDiff);
+}
+
 template<typename T>
 ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
                                            std::string bName,
@@ -846,4 +744,75 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
 }
 
+// To support C API
+::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           af_array a, af_array b, float maxAbsDiff) {
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertArrayNear(aName, bName, maxAbsDiffName, aaa, bbb, maxAbsDiff);
+}
+
+// To support C API
+template<typename T>
+::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
+                                           std::string bName,
+                                           std::string maxAbsDiffName,
+                                           std::vector<T>& hA, af::dim4 aDims,
+                                           af_array b,
+                                           float maxAbsDiff) {
+    af_array bb = 0;
+    af_retain_array(&bb, b);
+    af::array bbb(bb);
+    return assertArrayNear(hA_name, aDimsName, maxAbsDiffName, bName, hA, aDims,
+                         bbb, maxAbsDiff);
+}
+
+/// Checks if the C-API arrayfire function returns successfully
+///
+/// \param[in] CALL This is the arrayfire C function
+#define ASSERT_SUCCESS(CALL)                    \
+    ASSERT_EQ(AF_SUCCESS, CALL)
+
+/// Compares two af::array or af_arrays for their types, dims, and values (strict equality).
+///
+/// \param[in] EXPECTED The expected array of the assertion
+/// \param[in] ACTUAL The actual resulting array from the calculation
+#define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
+    EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
+
+/// Compares a std::vector with an af::/af_array for their types, dims, and values (strict equality).
+///
+/// \param[in] EXPECTED_VEC The vector that represents the expected array
+/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
+/// \param[in] ACTUAL_ARR The actual resulting array from the calculation
+#define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
+    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR)
+
+/// Compares two af::array or af_arrays for their type, dims, and values (with a given tolerance).
+///
+/// \param[in] EXPECTED Expected value of the assertion
+/// \param[in] ACTUAL Actual value of the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+///
+/// \NOTE: This macro will deallocate the af_arrays after the call
+#define ASSERT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
+    EXPECT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
+
+/// Compares a std::vector with an af::array for their dims and values (with a given tolerance).
+///
+/// \param[in] EXPECTED_VEC The vector that represents the expected array
+/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
+/// \param[in] ACTUAL_ARR The actual array from the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+#define ASSERT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, MAX_ABSDIFF) \
+    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
+                            MAX_ABSDIFF)
+
+
 #pragma GCC diagnostic pop

From 5705a538304448992a5ccdf65d9dc4b3453388a3 Mon Sep 17 00:00:00 2001
From: mark-poscablo <mark@arrayfire.com>
Date: Thu, 2 Aug 2018 01:48:13 -0400
Subject: [PATCH 1500/2677] Incorporated the ASSERT_*_NEAR macros to some of
 the tests

- blas
- fftconvolve
- hsv_rgb
- resize
---
 test/blas.cpp        | 12 +-----------
 test/fftconvolve.cpp | 15 +--------------
 test/hsv_rgb.cpp     | 16 ++--------------
 test/resize.cpp      | 45 ++++++--------------------------------------
 4 files changed, 10 insertions(+), 78 deletions(-)

diff --git a/test/blas.cpp b/test/blas.cpp
index bf89608b1e..bece1b6280 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -295,15 +295,5 @@ TEST(MatrixMultiply, ISSUE_1882)
     array res1 = matmul(A.T(), B.T());
     array res2 = matmulTT(A, B);
 
-    vector<float> hres1(res1.elements());
-    vector<float> hres2(res2.elements());
-
-    res1.host(&hres1.front());
-    res2.host(&hres2.front());
-
-    ASSERT_EQ(hres1.size(), hres2.size());
-
-    for (size_t i = 0; i < hres1.size(); i++) {
-        ASSERT_NEAR(hres1[i], hres2[i], 1E-5);
-    }
+    ASSERT_ARRAYS_NEAR(res1, res2, 1E-5);
 }
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index e335715eef..64eb3a79c0 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -172,20 +172,7 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
         break;
     }
 
-    size_t outElems  = out.elements();
-    size_t goldElems = gold.elements();
-
-    ASSERT_EQ(goldElems, outElems);
-
-    vector<T> goldData(goldElems);
-    gold.host(&goldData.front());
-
-    vector<T> outData(outElems);
-    out.host(&outData.front());
-
-    for (size_t elIter=0; elIter<outElems; ++elIter) {
-        ASSERT_NEAR(goldData[elIter], outData[elIter], 5e-2) << "at: " << elIter << endl;
-    }
+    ASSERT_ARRAYS_NEAR(gold, out, 5e-2);
 }
 
 TYPED_TEST(FFTConvolveLarge, VectorLargeSignalSmallFilter)
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index 258c64bb46..92f8292307 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -50,14 +50,8 @@ TEST(hsv2rgb, CPP)
     array input(dims, &(in[0].front()));
     array output = hsv2rgb(input);
 
-    vector<float> outData(dims.elements());
-    output.host((void*)outData.data());
-
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
-    }
+    ASSERT_VEC_ARRAY_NEAR(currGoldBar, dims, output, 1.0e-3);
 }
 
 TEST(rgb2hsv, CPP)
@@ -72,14 +66,8 @@ TEST(rgb2hsv, CPP)
     array input(dims, &(in[0].front()));
     array output = rgb2hsv(input);
 
-    vector<float> outData(dims.elements());
-    output.host((void*)outData.data());
-
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
-    }
+    ASSERT_VEC_ARRAY_NEAR(currGoldBar, dims, output, 1.0e-3);
 }
 
 TEST(rgb2hsv, MaxDim)
diff --git a/test/resize.cpp b/test/resize.cpp
index 7d0a009bbb..1155383a20 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -373,19 +373,8 @@ TEST(Resize, CPP)
     array input(dims, &(in[0].front()));
     array output = resize(input, 16, 16);
 
-    // Get result
-    dim4 odims(16, 16, dims[2], dims[3]);
-    float* outData = new float[odims.elements()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[0].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(16, 16, dims[2], dims[3]);
+    ASSERT_VEC_ARRAY_NEAR(tests[0], goldDims, output, 0.0001);
 }
 
 TEST(ResizeScale1, CPP)
@@ -401,19 +390,8 @@ TEST(ResizeScale1, CPP)
     array input(dims, &(in[0].front()));
     array output = resize(2.f, input);
 
-    // Get result
-    dim4 odims(16, 16, dims[2], dims[3]);
-    float* outData = new float[odims.elements()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[0].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(16, 16, dims[2], dims[3]);
+    ASSERT_VEC_ARRAY_NEAR(tests[0], goldDims, output, 0.0001);
 }
 
 TEST(ResizeScale2, CPP)
@@ -429,19 +407,8 @@ TEST(ResizeScale2, CPP)
     array input(dims, &(in[0].front()));
     array output = resize(2.f, 2.f, input);
 
-    // Get result
-    dim4 odims(16, 16, dims[2], dims[3]);
-    float* outData = new float[odims.elements()];
-    output.host((void*)outData);
-
-    // Compare result
-    size_t nElems = tests[0].size();
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(tests[0][elIter], outData[elIter], 0.0001) << "at: " << elIter << endl;
-    }
-
-    // Delete
-    delete[] outData;
+    dim4 goldDims(16, 16, dims[2], dims[3]);
+    ASSERT_VEC_ARRAY_NEAR(tests[0], goldDims, output, 0.0001);
 }
 
 TEST(Resize, ExtractGFOR)

From a6ddc860b3e9a5562d976cd3c010c3a1f20bb058 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 25 Jul 2018 17:34:45 -0400
Subject: [PATCH 1501/2677] Add bigobj EHsc and FS flags to CUDA_NVCC_FLAGS and
 test executables

---
 CMakeModules/InternalUtils.cmake | 18 +++++++++++-------
 CMakeModules/build_forge.cmake   |  2 +-
 test/CMakeLists.txt              |  4 ++++
 3 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 0372240e82..2cf734b420 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -30,16 +30,20 @@ endfunction()
 
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
   if(NOT MSVC)
-    set(${cuda_flags} "-std=c++11 -Xcompiler -fPIC  -Xcompiler=${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden"  PARENT_SCOPE)
+    set(flags "-std=c++11 -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
   else()
-    set(${cuda_flags} "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275"  PARENT_SCOPE)
+    set(flags "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275 -Xcompiler /bigobj -Xcompiler /EHsc")
+    if(CMAKE_GENERATOR MATCHES "Ninja")
+      set(flags "${flags} -Xcompiler /FS")
+    endif()
   endif()
 
-  if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0")
-    if(${CUDA_VERSION_MAJOR} LESS 8)
-      set(cuda_flags "${cuda_flags} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED")
-    endif()
+  if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
+      CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0" AND
+      ${CUDA_VERSION_MAJOR} LESS 8)
+    set(flags "${flags} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED")
   endif()
+  set(${cuda_flags} "${flags}" PARENT_SCOPE)
 endfunction()
 
 include(CheckCXXCompilerFlag)
@@ -51,7 +55,7 @@ function(arrayfire_set_default_cxx_flags target)
   if(MSVC)
     target_compile_options(${target}
       PRIVATE
-        /wd4251 /wd4068 /wd4275 /bigobj)
+        /wd4251 /wd4068 /wd4275 /bigobj /EHsc)
 
     if(CMAKE_GENERATOR MATCHES "Ninja")
       target_compile_options(${target}
diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
index 71cefe998a..7ae5a165f9 100644
--- a/CMakeModules/build_forge.cmake
+++ b/CMakeModules/build_forge.cmake
@@ -13,7 +13,7 @@ set(PX ${CMAKE_SHARED_LIBRARY_PREFIX})
 set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX})
 
 if(MSVC)
-  set(disable_warning_flags "/wd4251")
+  set(disable_warning_flags "/wd4251 /EHsc")
   set(SX ${CMAKE_LINK_LIBRARY_SUFFIX})
 endif()
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 8806a8966d..65292e5107 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -128,6 +128,10 @@ function(make_test)
         ${mt_args_DEFINITIONS}
       )
     if(WIN32)
+      target_compile_options(${target}
+        PRIVATE
+          /bigobj
+          /EHsc)
       target_compile_definitions(${target}
         PRIVATE
           WIN32_LEAN_AND_MEAN

From 6e19ef0891362b82b3c2d47c1f5a1c86ef1891a7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 2 Aug 2018 22:53:34 -0400
Subject: [PATCH 1502/2677] Rename DEPRECATED macro to avoid redefinition
 warnings

---
 include/af/compatible.h      | 38 ++++++++++++++++++------------------
 include/af/defines.h         |  6 +++---
 include/af/device.h          |  4 ++--
 include/af/graphics.h        | 12 ++++++------
 src/backend/opencl/Param.hpp |  6 +++---
 5 files changed, 33 insertions(+), 33 deletions(-)

diff --git a/include/af/compatible.h b/include/af/compatible.h
index f922b93aae..cecf3dc0e4 100644
--- a/include/af/compatible.h
+++ b/include/af/compatible.h
@@ -18,93 +18,93 @@ class array;
 /// \ingroup device_func_count
 /// \copydoc getDeviceCount()
 /// \deprecated Use getDeviceCount() instead
-DEPRECATED("Use getDeviceCount instead")
+AF_DEPRECATED("Use getDeviceCount instead")
 AFAPI int devicecount();
 
 /// \ingroup device_func_get
 /// \copydoc getDevice()
 /// \deprecated Use getDevice() instead
-DEPRECATED("Use getDevice instead")
+AF_DEPRECATED("Use getDevice instead")
 AFAPI int deviceget();
 
 /// \ingroup device_func_set
 /// \copydoc setDevice()
 /// \deprecated Use setDevice() instead
-DEPRECATED("Use setDevice instead")
+AF_DEPRECATED("Use setDevice instead")
 AFAPI void deviceset(const int device);
 
 /// \ingroup imageio_func_load
 /// \copydoc loadImage
 /// \deprecated Use \ref loadImage instead
-DEPRECATED("Use loadImage instead")
+AF_DEPRECATED("Use loadImage instead")
 AFAPI array loadimage(const char* filename, const bool is_color=false);
 
 /// \ingroup imageio_func_save
 /// \copydoc saveImage
 /// \deprecated Use \ref saveImage instead
-DEPRECATED("Use saveImage instead")
+AF_DEPRECATED("Use saveImage instead")
 AFAPI void saveimage(const char* filename, const array& in);
 
 /// \ingroup image_func_gauss
 /// \copydoc image_func_gauss
 /// \deprecated Use \ref gaussianKernel instead
-DEPRECATED("Use gaussianKernel instead")
+AF_DEPRECATED("Use gaussianKernel instead")
 AFAPI array gaussiankernel(const int rows, const int cols, const double sig_r = 0, const double sig_c = 0);
 
 /// \ingroup reduce_func_all_true
 /// \copydoc af::allTrue(const array&)
 /// \deprecated Use \ref af::allTrue(const array&) instead
 template<typename T>
-DEPRECATED("Use allTrue instead")
+AF_DEPRECATED("Use allTrue instead")
 T alltrue(const array &in);
 
 /// \ingroup reduce_func_any_true
 /// \copydoc af::allTrue(const array&)
 /// \deprecated Use \ref af::anyTrue(const array&) instead
 template<typename T>
-DEPRECATED("Use anyTrue instead")
+AF_DEPRECATED("Use anyTrue instead")
 T anytrue(const array &in);
 
 /// \ingroup reduce_func_all_true
 /// \copydoc allTrue
 /// \deprecated Use \ref af::allTrue instead
-DEPRECATED("Use allTrue instead")
+AF_DEPRECATED("Use allTrue instead")
 AFAPI array alltrue(const array &in, const int dim = -1);
 
 /// \ingroup reduce_func_any_true
 /// \copydoc anyTrue
 /// \deprecated Use \ref af::anyTrue instead
-DEPRECATED("Use anyTrue instead")
+AF_DEPRECATED("Use anyTrue instead")
 AFAPI array anytrue(const array &in, const int dim = -1);
 
 /// \ingroup set_func_unique
 /// \copydoc setUnique
 /// \deprecated Use \ref setUnique instead
-DEPRECATED("Use setUnique instead")
+AF_DEPRECATED("Use setUnique instead")
 AFAPI array setunique(const array &in, const bool is_sorted=false);
 
 /// \ingroup set_func_union
 /// \copydoc setUnion
 /// \deprecated Use \ref setUnion instead
-DEPRECATED("Use setUnion instead")
+AF_DEPRECATED("Use setUnion instead")
 AFAPI array setunion(const array &first, const array &second, const bool is_unique=false);
 
 /// \ingroup set_func_intersect
 /// \copydoc setIntersect
 /// \deprecated Use \ref setIntersect instead
-DEPRECATED("Use setIntersect instead")
+AF_DEPRECATED("Use setIntersect instead")
 AFAPI array setintersect(const array &first, const array &second, const bool is_unique=false);
 
 /// \ingroup image_func_histequal
 /// \copydoc histEqual
 /// \deprecated Use \ref histEqual instead
-DEPRECATED("Use histEqual instead")
+AF_DEPRECATED("Use histEqual instead")
 AFAPI array histequal(const array& in, const array& hist);
 
 /// \ingroup image_func_colorspace
 /// \copydoc colorSpace
 /// \deprecated Use \ref colorSpace instead
-DEPRECATED("Use colorSpace instead")
+AF_DEPRECATED("Use colorSpace instead")
 AFAPI array colorspace(const array& image, const CSpace to, const CSpace from);
 
 /// Image Filtering
@@ -124,26 +124,26 @@ AFAPI array colorspace(const array& image, const CSpace to, const CSpace from);
 /// \note Filtering done using correlation. Array values outside bounds are assumed to have zero value (0).
 /// \ingroup image_func_filter
 /// \deprecated Use \ref af::convolve instead
-DEPRECATED("Use af::convolve instead")
+AF_DEPRECATED("Use af::convolve instead")
 AFAPI array filter(const array& image, const array& kernel);
 
 /// \ingroup reduce_func_product
 /// \copydoc product(const array&, const int);
 /// \deprecated Use \ref product instead
-DEPRECATED("Use af::product instead")
+AF_DEPRECATED("Use af::product instead")
 AFAPI array mul(const array& in, const int dim = -1);
 
 /// \ingroup reduce_func_product
 /// \copydoc product(const array&)
 /// \deprecated Use \ref product instead
 template<typename T>
-DEPRECATED("Use af::product instead")
+AF_DEPRECATED("Use af::product instead")
 T mul(const array& in);
 
 /// \ingroup device_func_prop
 /// \copydoc deviceInfo
 /// \deprecated Use \ref deviceInfo instead
-DEPRECATED("Use deviceInfo instead")
+AF_DEPRECATED("Use deviceInfo instead")
 AFAPI void deviceprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
 
 }
diff --git a/include/af/defines.h b/include/af/defines.h
index 587a3788bc..e82809472e 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -26,15 +26,15 @@
     #endif
     #define __PRETTY_FUNCTION__ __FUNCSIG__
     #define SIZE_T_FRMT_SPECIFIER "%Iu"
-    #define DEPRECATED(msg) __declspec(deprecated( msg ))
+    #define AF_DEPRECATED(msg) __declspec(deprecated( msg ))
 #else
     #define AFAPI   __attribute__((visibility("default")))
     #include <stdbool.h>
     #define SIZE_T_FRMT_SPECIFIER "%zu"
 #if __GNUC__ >= 4 && __GNUC_MINOR > 4
-    #define DEPRECATED(msg) __attribute__((deprecated( msg )))
+    #define AF_DEPRECATED(msg) __attribute__((deprecated( msg )))
 #else
-    #define DEPRECATED(msg) __attribute__((deprecated))
+    #define AF_DEPRECATED(msg) __attribute__((deprecated))
 #endif
 
 #endif
diff --git a/include/af/device.h b/include/af/device.h
index c6f2750374..2c36fb4b9b 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -385,7 +385,7 @@ extern "C" {
        \ingroup device_func_mem
     */
 #if AF_API_VERSION >= 33
-    DEPRECATED("Use af_lock_array instead")
+    AF_DEPRECATED("Use af_lock_array instead")
 #endif
     AFAPI af_err af_lock_device_ptr(const af_array arr);
 #endif
@@ -398,7 +398,7 @@ extern "C" {
        \ingroup device_func_mem
     */
 #if AF_API_VERSION >= 33
-    DEPRECATED("Use af_unlock_array instead")
+    AF_DEPRECATED("Use af_unlock_array instead")
 #endif
     AFAPI af_err af_unlock_device_ptr(const af_array arr);
 #endif
diff --git a/include/af/graphics.h b/include/af/graphics.h
index 100d88b753..59faab127d 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -169,7 +169,7 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-        DEPRECATED("Use plot instead")
+        AF_DEPRECATED("Use plot instead")
         void plot3(const array& in, const char* title=NULL);
 #endif
 
@@ -279,7 +279,7 @@ class AFAPI Window {
 
            \ingroup gfx_func_draw
          */
-        DEPRECATED("Use scatter instead")
+        AF_DEPRECATED("Use scatter instead")
         void scatter3(const array& P, const af::markerType marker = AF_MARKER_POINT,
                       const char* const title = NULL);
 #endif
@@ -644,7 +644,7 @@ AFAPI af_err af_draw_image(const af_window wind, const af_array in, const af_cel
 
    \ingroup gfx_func_draw
 */
-DEPRECATED("Use af_draw_plot_nd or af_draw_plot_2d instead")
+AF_DEPRECATED("Use af_draw_plot_nd or af_draw_plot_2d instead")
 AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props);
 
 #if AF_API_VERSION >= 32
@@ -663,7 +663,7 @@ AFAPI af_err af_draw_plot(const af_window wind, const af_array X, const af_array
 
    \ingroup gfx_func_draw
 */
-DEPRECATED("Use af_draw_plot_nd or af_draw_plot_3d instead")
+AF_DEPRECATED("Use af_draw_plot_nd or af_draw_plot_3d instead")
 AFAPI af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props);
 #endif
 
@@ -749,7 +749,7 @@ AFAPI af_err af_draw_plot_3d(const af_window wind,
 
    \ingroup gfx_func_draw
 */
-DEPRECATED("Use af_draw_scatter_nd or af_draw_scatter_2d instead")
+AF_DEPRECATED("Use af_draw_scatter_nd or af_draw_scatter_2d instead")
 AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
                              const af_marker_type marker, const af_cell* const props);
 #endif
@@ -769,7 +769,7 @@ AFAPI af_err af_draw_scatter(const af_window wind, const af_array X, const af_ar
 
    \ingroup gfx_func_draw
 */
-DEPRECATED("Use af_draw_scatter_nd or af_draw_scatter_3d instead")
+AF_DEPRECATED("Use af_draw_scatter_nd or af_draw_scatter_3d instead")
 AFAPI af_err af_draw_scatter3(const af_window wind, const af_array P,
                               const af_marker_type marker, const af_cell* const props);
 #endif
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 9f690ea6d5..0a671a42b8 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -22,13 +22,13 @@ namespace opencl
         Param(const Param& other) = default;
         Param(Param&& other) = default;
 
-        // DEPRECATED("Use Array<T>")
+        // AF_DEPRECATED("Use Array<T>")
         Param();
-        // DEPRECATED("Use Array<T>")
+        // AF_DEPRECATED("Use Array<T>")
         Param(cl::Buffer *data_, KParam info_);
         ~Param() = default;
     };
 
-    // DEPRECATED("Use Array<T>")
+    // AF_DEPRECATED("Use Array<T>")
     Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]);
 }

From 680899ed74ee132ce4f7f71ccf994adbd663cef0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 2 Aug 2018 23:09:41 -0400
Subject: [PATCH 1503/2677] Fix Formatting warnings

---
 examples/computer_vision/fast.cpp        |  2 +-
 examples/computer_vision/susan.cpp       |  2 +-
 examples/graphics/fractal.cpp            |  2 +-
 examples/image_processing/image_demo.cpp | 10 +++++-----
 test/select.cpp                          |  2 +-
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/computer_vision/fast.cpp b/examples/computer_vision/fast.cpp
index 85b2e5907d..ecedd87144 100644
--- a/examples/computer_vision/fast.cpp
+++ b/examples/computer_vision/fast.cpp
@@ -50,7 +50,7 @@ static void fast_demo(bool console)
     freeHost(h_x);
     freeHost(h_y);
 
-    printf("Features found: %lu\n", feat.getNumFeatures());
+    printf("Features found: %zu\n", feat.getNumFeatures());
 
     if (!console) {
         af::Window wnd("FAST Feature Detector");
diff --git a/examples/computer_vision/susan.cpp b/examples/computer_vision/susan.cpp
index db107812a4..93d4157216 100644
--- a/examples/computer_vision/susan.cpp
+++ b/examples/computer_vision/susan.cpp
@@ -54,7 +54,7 @@ static void susan_demo(bool console)
     freeHost(h_x);
     freeHost(h_y);
 
-    printf("Features found: %lu\n", feat.getNumFeatures());
+    printf("Features found: %zu\n", feat.getNumFeatures());
 
     if (!console) {
         af::Window wnd("FAST Feature Detector");
diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index 717a9ccee5..2e1f4e6579 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -77,7 +77,7 @@ int main(int argc, char **argv)
         af::Window wnd(WIDTH, HEIGHT, "Fractal Demo");
         wnd.setColorMap(AF_COLORMAP_SPECTRUM);
 
-        float center[] = {-0.75, 0.1};
+        float center[] = {-0.75f, 0.1f};
         // Keep zomming out for each frame
         for (int i = 10; i < 400; i++) {
             int zoom = i * i;
diff --git a/examples/image_processing/image_demo.cpp b/examples/image_processing/image_demo.cpp
index 477594efe3..4d41c3dbe5 100644
--- a/examples/image_processing/image_demo.cpp
+++ b/examples/image_processing/image_demo.cpp
@@ -23,11 +23,11 @@ static void channel_split(array& rgb, array& outr, array& outg, array& outb) {
 
 // 5x5 sigma-3 gaussian blur weights
 static const float h_gauss[] = {
-    0.0318,  0.0375,  0.0397,  0.0375,  0.0318,
-    0.0375,  0.0443,  0.0469,  0.0443,  0.0375,
-    0.0397,  0.0469,  0.0495,  0.0469,  0.0397,
-    0.0375,  0.0443,  0.0469,  0.0443,  0.0375,
-    0.0318,  0.0375,  0.0397,  0.0375,  0.0318,
+    0.0318f,  0.0375f,  0.0397f,  0.0375f,  0.0318f,
+    0.0375f,  0.0443f,  0.0469f,  0.0443f,  0.0375f,
+    0.0397f,  0.0469f,  0.0495f,  0.0469f,  0.0397f,
+    0.0375f,  0.0443f,  0.0469f,  0.0443f,  0.0375f,
+    0.0318f,  0.0375f,  0.0397f,  0.0375f,  0.0318f,
 };
 
 // 3x3 sobel weights
diff --git a/test/select.cpp b/test/select.cpp
index e6064d80f8..b831acdeff 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -318,7 +318,7 @@ class Select_ : public ::testing::TestWithParam<select_params> {};
 string pd4(dim4 dims) {
     string out(32, '\0');
     int len = snprintf(const_cast<char*>(out.data()), 32,
-                       "%d_%d_%d_%d", dims[0], dims[1], dims[2], dims[3]);
+                       "%lld_%lld_%lld_%lld", dims[0], dims[1], dims[2], dims[3]);
     out.resize(len);
     return out;
 }

From 022b3abc9ca67707df2027fa1511ee8c220081f0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 30 Apr 2018 23:33:51 -0400
Subject: [PATCH 1504/2677] Implement meanvar. Returns the mean and variance of
 an array

This function performs the same number of operations as regular
var but returns the intermediate mean array as well
---
 include/af/defines.h           |   9 ++
 include/af/statistics.h        |  35 ++++-
 src/api/c/var.cpp              | 190 +++++++++++++++-------
 src/api/unified/statistics.cpp |   7 +
 test/CMakeLists.txt            |   1 +
 test/data                      |   2 +-
 test/meanvar.cpp               | 280 +++++++++++++++++++++++++++++++++
 7 files changed, 466 insertions(+), 58 deletions(-)
 create mode 100644 test/meanvar.cpp

diff --git a/include/af/defines.h b/include/af/defines.h
index e82809472e..0a09962bab 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -500,6 +500,15 @@ typedef enum {
     AF_INVERSE_DECONV_TIKHONOV       = 1,        ///< Tikhonov Inverse deconvolution
     AF_INVERSE_DECONV_DEFAULT        = 0,        ///< Default is Tikhonov deconvolution
 } af_inverse_deconv_algo;
+
+#endif
+
+#if AF_API_VERSION >= 37
+typedef enum {
+              AF_VARIANCE_DEFAULT    = 0, ///< Default (Population) variance
+              AF_VARIANCE_SAMPLE     = 1, ///< Sample variance
+              AF_VARIANCE_POPULATION = 2, ///< Population variance
+} af_var_bias;
 #endif
 
 #ifdef __cplusplus
diff --git a/include/af/statistics.h b/include/af/statistics.h
index ecfbd8cfeb..6bd7685233 100644
--- a/include/af/statistics.h
+++ b/include/af/statistics.h
@@ -70,6 +70,23 @@ AFAPI array var(const array& in, const bool isbiased=false, const dim_t dim=-1);
 */
 AFAPI array var(const array& in, const array &weights, const dim_t dim=-1);
 
+#if AF_API_VERSION >= 37
+/**
+   C++ Interface for mean and variance
+
+   \param[out] mean     The mean of the input array along \p dim dimension
+   \param[out] var      The variance of the input array along the \p dim dimension
+   \param[in]  in       The input array
+   \param[in]  weights  The weights to scale the input array before calculating
+                        the mean and varience. If empty, the input is not scaled
+   \param[in] bias      The type of bias used for variance calculation
+   \param[in] dim       The dimension along which the variance and mean are
+                        calculated. Default is -1 meaning the first non-zero dim
+  */
+AFAPI void meanvar(array& mean, array& var, const array& in, const array& weights,
+                   const af_var_bias bias = AF_VARIANCE_POPULATION, const dim_t dim=-1);
+#endif
+
 /**
    C++ Interface for standard deviation
 
@@ -279,6 +296,23 @@ AFAPI af_err af_var(af_array *out, const af_array in, const bool isbiased, const
 */
 AFAPI af_err af_var_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim);
 
+#if AF_API_VERSION >= 37
+/**
+   C Interface for mean and variance
+
+   \param[out] mean     The mean of the input array along \p dim dimension
+   \param[out] var      The variance of the input array along the \p dim dimension
+   \param[in]  in       The input array
+   \param[in]  weights  The weights to scale the input array before calculating
+                        the mean and varience. If empty, the input is not scaled
+   \param[in]  bias     The type of bias used for variance calculation
+   \param[in]  dim      The dimension along which the variance and mean are
+                        calculated. Default is -1 meaning the first non-zero dim
+  */
+AFAPI af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
+                        const af_array weights, const af_var_bias bias, const dim_t dim);
+#endif
+
 /**
    C Interface for standard deviation
 
@@ -416,7 +450,6 @@ AFAPI af_err af_median_all(double *realVal, double *imagVal, const af_array in);
 
    \ingroup stat_func_corrcoef
 */
-
 AFAPI af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y);
 
 #if AF_API_VERSION >= 36
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 16ddde829e..fba13f84c8 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -22,8 +22,15 @@
 
 #include "stats.h"
 
+#include <tuple>
+
 using namespace detail;
 
+using std::ignore;
+using std::make_tuple;
+using std::tie;
+using std::tuple;
+
 template<typename inType, typename outType>
 static outType varAll(const af_array& in, const bool isbiased)
 {
@@ -66,14 +73,30 @@ static outType varAll(const af_array& in, const af_array weights)
 }
 
 template<typename inType, typename outType>
-static af_array var(const af_array& in, const bool isbiased, int dim)
-{
+static
+tuple<Array<outType>, Array<outType>>
+meanvar(const Array<inType> &in, const Array<typename baseOutType<outType>::type>& weights,
+        const af_var_bias bias, const dim_t dim) {
+
     typedef typename baseOutType<outType>::type weightType;
-    Array<inType> _in    = getArray<inType>(in);
-    Array<outType> input = cast<outType>(_in);
+    Array<outType> input = cast<outType>(in);
     dim4 iDims = input.dims();
 
-    Array<outType> meanArr = mean<inType, weightType, outType>(_in, dim);
+    Array<outType> meanArr = *initArray<outType>();
+    Array<outType> normArr = *initArray<outType>();
+    if(weights.isEmpty()) {
+        meanArr = mean<outType, weightType, outType>(input, dim);
+        auto val = 1.0 / (bias == AF_VARIANCE_POPULATION ? iDims[dim] : iDims[dim]-1);
+        normArr = createValueArray<outType>(meanArr.dims(), scalar<outType>(val));
+    } else {
+        meanArr = mean<outType, weightType>(input, weights, dim);
+        Array<outType> wtsSum = cast<outType>(reduce<af_add_t, weightType, weightType>(weights, dim));
+        Array<outType> ones = createValueArray<outType>(wtsSum.dims(), scalar<outType>(1));
+        if(bias == AF_VARIANCE_SAMPLE) {
+          wtsSum = arithOp<outType, af_sub_t>(wtsSum, ones, ones.dims());
+        }
+        normArr  = arithOp<outType, af_div_t>(ones, wtsSum, meanArr.dims());
+    }
 
     /* now tile meanArr along dim and use it for variance computation */
     dim4 tileDims(1);
@@ -84,62 +107,84 @@ static af_array var(const af_array& in, const bool isbiased, int dim)
     Array<outType> diff    = arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
     Array<outType> diffSq  = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     Array<outType> redDiff = reduce<af_add_t, outType, outType>(diffSq, dim);
-    dim4 oDims = redDiff.dims();
 
-    Array<outType> divArr = createValueArray<outType>(oDims, scalar<outType>(isbiased ? iDims[dim] : iDims[dim]-1));
-    Array<outType> result = arithOp<outType, af_div_t>(redDiff, divArr, redDiff.dims());
+    Array<outType> variance = arithOp<outType, af_mul_t>(normArr, redDiff, redDiff.dims());
 
-    return getHandle<outType>(result);
+    return make_tuple(meanArr, variance);
 }
 
-template<typename inType, typename outType>
-static af_array var(const af_array& in, const af_array& weights, int dim)
-{
-    typedef typename baseOutType<outType>::type bType;
 
-    Array<outType> input = cast<outType>(getArray<inType>(in));
-    dim4 iDims = input.dims();
+template<typename inType, typename outType>
+static
+tuple<af_array, af_array>
+meanvar(const af_array &in, const af_array &weights,
+        const af_var_bias bias, const dim_t dim) {
+
+  typedef typename baseOutType<outType>::type weightType;
+  Array<outType> mean = *initArray<outType>(), var = *initArray<outType>();
+
+  Array<weightType> w = *initArray<weightType>();
+  if(weights != 0) {
+    w = getArray<weightType>(weights);
+  }
+  tie(mean, var) = meanvar<inType, outType>(getArray<inType>(in), w,
+                           bias, dim);
+  return make_tuple(getHandle(mean), getHandle(var));
 
-    Array<outType> meanArr = mean<outType, bType>(input, getArray<bType>(weights), dim);
+}
 
-    /* now tile meanArr along dim and use it for variance computation */
-    dim4 tileDims(1);
-    tileDims[dim] = iDims[dim];
-    Array<outType> tMeanArr = tile<outType>(meanArr, tileDims);
-    /* now mean array is ready */
+/// Calculates the variance
+///
+/// \note Only calculates the weighted variance if the weights array is non-empty
+template<typename inType, typename outType>
+static Array<outType>
+var(const Array<inType>& in,
+    const Array<typename baseOutType<outType>::type>& weights,
+    const af_var_bias bias, int dim)
+{
+    typedef typename baseOutType<outType>::type weightType;
 
-    Array<outType> wts     = cast<outType>(getArray<bType>(weights));
-    Array<outType> diff    = arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
-    Array<outType> diffSq  = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
-    Array<outType> wDiffSq = arithOp<outType, af_mul_t>(diffSq, wts, diffSq.dims());
-    Array<outType> accWDS  = reduce<af_add_t, outType, outType>(wDiffSq, dim);
-    Array<outType> divArr  = reduce<af_add_t, outType, outType>(wts, dim);
-    Array<outType> result  = arithOp<outType, af_div_t>(accWDS, divArr, accWDS.dims());
+    Array<outType> variance = *initArray<outType>();
+    tie(ignore, variance) = meanvar<inType, outType>(in, weights, bias, dim);
+    return variance;
+}
 
-    return getHandle<outType>(result);
+template<typename inType, typename outType>
+static af_array var_(const af_array& in, const af_array& weights,
+                     const af_var_bias bias, int dim) {
+  using bType = typename baseOutType<outType>::type;
+  if(weights == 0) {
+    Array<bType> empty = *initArray<bType>();
+    return getHandle(var<inType, outType>(getArray<inType>(in), empty, bias, dim));
+  } else {
+    return getHandle(var<inType, outType>(getArray<inType>(in), getArray<bType>(weights), bias, dim));
+  }
 }
 
 af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t dim)
 {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        ARG_ASSERT(3, (dim>=0 && dim<=3));
 
         af_array output = 0;
         const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
+
+        af_array no_weights = 0;
+        af_var_bias bias = (isbiased) ? AF_VARIANCE_POPULATION : AF_VARIANCE_SAMPLE;
         switch(type) {
-            case f64: output = var<double,  double>(in, isbiased, dim); break;
-            case f32: output = var<float ,  float >(in, isbiased, dim); break;
-            case s32: output = var<int   ,  float >(in, isbiased, dim); break;
-            case u32: output = var<uint  ,  float >(in, isbiased, dim); break;
-            case s16: output = var<short ,  float >(in, isbiased, dim); break;
-            case u16: output = var<ushort,  float >(in, isbiased, dim); break;
-            case s64: output = var<intl  ,  double>(in, isbiased, dim); break;
-            case u64: output = var<uintl ,  double>(in, isbiased, dim); break;
-            case  u8: output = var<uchar ,  float >(in, isbiased, dim); break;
-            case  b8: output = var<char  ,  float >(in, isbiased, dim); break;
-            case c32: output = var<cfloat,  cfloat>(in, isbiased, dim); break;
-            case c64: output = var<cdouble,cdouble>(in, isbiased, dim); break;
+            case f32: output = var_<float ,  float >(in, no_weights, bias, dim); break;
+            case f64: output = var_<double,  double>(in, no_weights, bias, dim); break;
+            case s32: output = var_<int   ,  float >(in, no_weights, bias, dim); break;
+            case u32: output = var_<uint  ,  float >(in, no_weights, bias, dim); break;
+            case s16: output = var_<short ,  float >(in, no_weights, bias, dim); break;
+            case u16: output = var_<ushort,  float >(in, no_weights, bias, dim); break;
+            case s64: output = var_<intl  ,  double>(in, no_weights, bias, dim); break;
+            case u64: output = var_<uintl ,  double>(in, no_weights, bias, dim); break;
+            case  u8: output = var_<uchar ,  float >(in, no_weights, bias, dim); break;
+            case  b8: output = var_<char  ,  float >(in, no_weights, bias, dim); break;
+            case c32: output = var_<cfloat,  cfloat>(in, no_weights, bias, dim); break;
+            case c64: output = var_<cdouble,cdouble>(in, no_weights, bias, dim); break;
             default : TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -151,7 +196,7 @@ af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t
 af_err af_var_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim)
 {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        ARG_ASSERT(3, (dim>=0 && dim<=3));
 
         af_array output = 0;
         const ArrayInfo& iInfo = getInfo(in);
@@ -159,21 +204,21 @@ af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
         af_dtype iType  = iInfo.getType();
         af_dtype wType  = wInfo.getType();
 
-        ARG_ASSERT(3, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
+        ARG_ASSERT(2, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
 
         switch(iType) {
-            case f64: output = var<double,  double>(in, weights, dim); break;
-            case f32: output = var<float ,  float >(in, weights, dim); break;
-            case s32: output = var<int   ,  float >(in, weights, dim); break;
-            case u32: output = var<uint  ,  float >(in, weights, dim); break;
-            case s16: output = var<short ,  float >(in, weights, dim); break;
-            case u16: output = var<ushort,  float >(in, weights, dim); break;
-            case s64: output = var<intl  ,  double>(in, weights, dim); break;
-            case u64: output = var<uintl ,  double>(in, weights, dim); break;
-            case  u8: output = var<uchar ,  float >(in, weights, dim); break;
-            case  b8: output = var<char  ,  float >(in, weights, dim); break;
-            case c32: output = var<cfloat,  cfloat>(in, weights, dim); break;
-            case c64: output = var<cdouble,cdouble>(in, weights, dim); break;
+            case f64: output = var_<double,  double>(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case f32: output = var_<float ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case s32: output = var_<int   ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case u32: output = var_<uint  ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case s16: output = var_<short ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case u16: output = var_<ushort,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case s64: output = var_<intl  ,  double>(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case u64: output = var_<uintl ,  double>(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case  u8: output = var_<uchar ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case  b8: output = var_<char  ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case c32: output = var_<cfloat,  cfloat>(in, weights, AF_VARIANCE_POPULATION, dim); break;
+            case c64: output = var_<cdouble,cdouble>(in, weights, AF_VARIANCE_POPULATION, dim); break;
             default : TYPE_ERROR(1, iType);
         }
         std::swap(*out, output);
@@ -252,3 +297,36 @@ af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in,
     CATCHALL;
     return AF_SUCCESS;
 }
+
+af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
+                  const af_array weights, const af_var_bias bias, const dim_t dim) {
+
+  try {
+      af_array output = 0;
+      const ArrayInfo& iInfo = getInfo(in);
+      if(weights != 0) {
+        const ArrayInfo& wInfo = getInfo(weights);
+        af_dtype wType  = wInfo.getType();
+        ARG_ASSERT(3, (wType==f32 || wType==f64));
+      }
+      af_dtype iType  = iInfo.getType();
+
+      switch(iType) {
+        case f32: tie(*mean, *var) = meanvar<float,   float>(in, weights, bias, dim); break;
+        case f64: tie(*mean, *var) = meanvar<double, double>(in, weights, bias, dim); break;
+        case s32: tie(*mean, *var) = meanvar<int,     float>(in, weights, bias, dim); break;
+        case u32: tie(*mean, *var) = meanvar<uint,    float>(in, weights, bias, dim); break;
+        case s16: tie(*mean, *var) = meanvar<short ,  float >(in, weights, bias, dim); break;
+        case u16: tie(*mean, *var) = meanvar<ushort,  float >(in, weights, bias, dim); break;
+        case s64: tie(*mean, *var) = meanvar<intl  ,  double>(in, weights, bias, dim); break;
+        case u64: tie(*mean, *var) = meanvar<uintl ,  double>(in, weights, bias, dim); break;
+        case  u8: tie(*mean, *var) = meanvar<uchar ,  float >(in, weights, bias, dim); break;
+        case  b8: tie(*mean, *var) = meanvar<char  ,  float >(in, weights, bias, dim); break;
+        case c32: tie(*mean, *var) = meanvar<cfloat,  cfloat>(in, weights, bias, dim); break;
+        case c64: tie(*mean, *var) = meanvar<cdouble,cdouble>(in, weights, bias, dim); break;
+        default : TYPE_ERROR(1, iType);
+      }
+  }
+  CATCHALL;
+  return AF_SUCCESS;
+}
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index 130daaed3d..a6ba5e93c7 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -35,6 +35,13 @@ af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
     return CALL(out, in, weights, dim);
 }
 
+af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
+                  const af_array weights, const af_var_bias bias, const dim_t dim)
+{
+    CHECK_ARRAYS(in, weights);
+    return CALL(mean, var, in, weights, bias, dim);
+}
+
 af_err af_stdev(af_array *out, const af_array in, const dim_t dim)
 {
     CHECK_ARRAYS(in);
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 65292e5107..b358778673 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -218,6 +218,7 @@ make_test(SRC math.cpp)
 make_test(SRC matrix_manipulation.cpp)
 make_test(SRC mean.cpp)
 make_test(SRC meanshift.cpp)
+make_test(SRC meanvar.cpp CXX11)
 make_test(SRC medfilt.cpp)
 make_test(SRC median.cpp)
 make_test(SRC memory.cpp)
diff --git a/test/data b/test/data
index 2b59e5af8c..40a7c36ca3 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 2b59e5af8ce801252f6f9b9c3e06f796b6203ed3
+Subproject commit 40a7c36ca39e78cc44325933920da8bfb2ad4e21
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
new file mode 100644
index 0000000000..fd2007a816
--- /dev/null
+++ b/test/meanvar.cpp
@@ -0,0 +1,280 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+
+#include <testHelpers.hpp>
+
+#include <iterator>
+#include <string>
+#include <vector>
+
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using af::dtype_traits;
+using std::back_inserter;
+using std::string;
+using std::vector;
+
+template<typename T>
+struct elseType {
+  typedef typename cond_type< is_same_type<T, uintl>::value ||
+                              is_same_type<T, intl> ::value,
+                              double,
+                              T>::type type;
+};
+
+template<typename T>
+struct varOutType {
+  typedef typename cond_type< is_same_type<T, float >::value ||
+                              is_same_type<T, int   >::value ||
+                              is_same_type<T, uint  >::value ||
+                              is_same_type<T, short >::value ||
+                              is_same_type<T, ushort>::value ||
+                              is_same_type<T, uchar >::value ||
+                              is_same_type<T, char  >::value,
+                              float,
+                              typename elseType<T>::type>::type type;
+};
+
+
+template<typename T>
+using outType = typename varOutType<T>::type;
+
+template<typename T>
+struct meanvar_test {
+  static af_dtype af_type;
+  string test_description_;
+  af_array in_;
+  af_array weights_;
+  af_var_bias bias_;
+  int dim_;
+  vector<outType<T>> mean_;
+  vector<outType<T>> variance_;
+  meanvar_test(string description, af_array in, af_array weights,
+               af_var_bias bias, int dim,
+               vector<double> &mean, vector<double> &variance)
+    : test_description_(description)
+    , in_(0)
+    , weights_(0)
+    , bias_(bias)
+    , dim_(dim) {
+    af_retain_array(&in_, in);
+    if(weights) {
+        af_retain_array(&weights_, weights);
+    }
+
+    mean_.reserve(mean.size());
+    variance_.reserve(variance.size());
+    std::copy(begin(mean), end(mean), back_inserter(mean_));
+    std::copy(begin(variance), end(variance), back_inserter(variance_));
+  }
+};
+
+template<typename T>
+af_dtype meanvar_test<T>::af_type = dtype_traits<T>::af_type;
+
+template<typename T>
+class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
+public:
+  void meanvar_test_function(meanvar_test<T> test) {
+      af_array mean, var;
+
+      // Cast to the expected type
+      af_array in = 0;
+      af_cast(&in, test.in_, (af_dtype) dtype_traits<T>::af_type);
+
+      EXPECT_EQ(AF_SUCCESS, af_meanvar(&mean, &var, in, test.weights_, test.bias_, test.dim_));
+
+      vector<outType<T>> h_mean(test.mean_.size()), h_var(test.variance_.size());
+
+      dim4 outDim(1);
+      af_get_dims(&outDim[0], &outDim[1], &outDim[2], &outDim[3], in);
+      outDim[test.dim_] = 1;
+
+      if (is_same_type<float, outType<T>>::value ||
+          is_same_type<cfloat, outType<T>>::value) {
+          ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
+          ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
+      } else {
+          ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.00001f);
+          ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.0001f);
+      }
+
+      ASSERT_SUCCESS(af_release_array(in));
+      ASSERT_SUCCESS(af_release_array(mean));
+      ASSERT_SUCCESS(af_release_array(var));
+  }
+};
+
+af_array empty = 0;
+
+enum test_size {
+  MEANVAR_SMALL,
+  MEANVAR_LARGE
+};
+
+template<typename T>
+meanvar_test<T>
+meanvar_test_gen(string name, int in_index, int weight_index, af_var_bias bias, int dim, int mean_index, int var_index, test_size size) {
+
+    vector<af_array> inputs;
+    vector<vector<double>> outputs;
+    if(size == MEANVAR_SMALL) {
+        vector<af::dim4> numDims_;
+        vector<vector<double> > in_;
+        vector<vector<double> > tests_;
+        readTests<double, typename varOutType<double>::type, double> (TEST_DIR"/meanvar/meanvar.data", numDims_, in_, tests_);
+
+        inputs.resize(in_.size());
+        for(int i = 0; i < in_.size(); i++) {
+          af_create_array(&inputs[i], &in_[i].front(),
+                          numDims_[i].ndims(), numDims_[i].get(), f64);
+        }
+
+        outputs.resize(tests_.size());
+        for(int i = 0; i < tests_.size(); i++) {
+            copy(tests_[i].begin(), tests_[i].end(), back_inserter(outputs[i]));
+        }
+    } else {
+
+      dim_t full_array_size = 2000;
+      vector<vector<dim_t> > dimensions = {
+                                          {2000, 1, 1, 1},  // 0
+                                          {1, 2000, 1, 1},  // 1
+                                          {1, 1, 2000, 1},  // 2
+
+                                          {500, 4, 1, 1},  // 3
+                                          {4, 500, 1, 1},  // 4
+                                          {50, 40, 1, 1}   // 5
+      };
+
+      vector<double> large_(full_array_size);
+      for(int i = 0; i < large_.size(); i++) {
+          large_[i] = static_cast<double>(i);
+      }
+
+      inputs.resize(dimensions.size());
+      for(int i = 0; i < dimensions.size(); i++) {
+          af_array large_array = 0;
+          af_create_array(&large_array, &large_.front(), 4, dimensions[i].data(), f64);
+          inputs[i] = large_array;
+      }
+
+      outputs.push_back(vector<double>(1, 999.5));
+      outputs.push_back(vector<double>(1, 333500));
+      outputs.push_back({249.50, 749.50, 1249.50, 1749.50});
+      outputs.push_back(vector<double>(4, 20875));
+  }
+  if(weight_index == -1) {
+      return meanvar_test<T> (name,
+                              inputs[in_index],
+                              empty,
+                              bias,
+                              dim,
+                              outputs[mean_index],
+                              outputs[var_index]);
+  } else {
+      return meanvar_test<T>(name,
+                             inputs[in_index],
+                             inputs[weight_index],
+                             bias,
+                             dim,
+                             outputs[mean_index],
+                             outputs[var_index]);
+  }
+}
+
+
+template<typename T>
+vector<meanvar_test<T> >
+small_test_values() {
+  return {
+                    // |           Name |   in_index | weight_index |                  bias |  dim | mean_index | var_index |
+    meanvar_test_gen<T>(    "Sample1Ddim0",           0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_SMALL),
+    meanvar_test_gen<T>(    "Sample1Ddim1",           1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_SMALL),
+    meanvar_test_gen<T>(    "Sample2Ddim0",           2,            -1,     AF_VARIANCE_SAMPLE,     0,           3,          4, MEANVAR_SMALL),
+    meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,     AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_SMALL),
+
+    meanvar_test_gen<T>("Population1Ddim0",           0,            -1, AF_VARIANCE_POPULATION,     0,           0,          2, MEANVAR_SMALL),
+    meanvar_test_gen<T>("Population1Ddim1",           1,            -1, AF_VARIANCE_POPULATION,     1,           0,          2, MEANVAR_SMALL),
+    meanvar_test_gen<T>("Population2Ddim0",           2,            -1, AF_VARIANCE_POPULATION,     0,           3,          5, MEANVAR_SMALL),
+    meanvar_test_gen<T>("Population2Ddim1",           2,            -1, AF_VARIANCE_POPULATION,     1,           6,          8, MEANVAR_SMALL)
+    };
+}
+
+template<typename T>
+vector<meanvar_test<T> >
+large_test_values() {
+  return {
+                      // |           Name |   in_index | weight_index |                  bias |  dim | mean_index | var_index |
+    meanvar_test_gen<T>(    "Sample1Ddim0",           0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_LARGE),
+    meanvar_test_gen<T>(    "Sample1Ddim1",           1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_LARGE),
+    meanvar_test_gen<T>(    "Sample1Ddim2",           2,            -1,     AF_VARIANCE_SAMPLE,     2,           0,          1, MEANVAR_LARGE),
+    meanvar_test_gen<T>(    "Sample2Ddim0",           3,            -1,     AF_VARIANCE_SAMPLE,     0,           2,          3, MEANVAR_LARGE),
+    // TODO(uamr) Add additional large tests
+    //meanvar_test_gen<T>(    "Sample2Ddim1",           3,            -1,     AF_VARIANCE_SAMPLE,     1,           2,          3, MEANVAR_LARGE),
+    //meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,     AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_LARGE),
+      };
+}
+
+#define MEANVAR_TEST(NAME, TYPE)                                        \
+  using MeanVar##NAME = MeanVarTyped<TYPE>;                             \
+  INSTANTIATE_TEST_CASE_P(Small,                                        \
+                          MeanVar##NAME,                                \
+                          ::testing::ValuesIn(small_test_values<TYPE>()), \
+                          []( const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) { \
+                            return info.param.test_description_;        \
+                          });                                           \
+  INSTANTIATE_TEST_CASE_P(Large,                                        \
+                          MeanVar##NAME,                                \
+                          ::testing::ValuesIn(large_test_values<TYPE>()), \
+                          []( const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) { \
+                            return info.param.test_description_;        \
+                          });                                           \
+                                                                        \
+  TEST_P(MeanVar##NAME, Testing) {                                      \
+    meanvar_test<TYPE> test = GetParam();                               \
+    meanvar_test_function(test);                                        \
+  }                                                                     \
+
+MEANVAR_TEST(Float, float)
+MEANVAR_TEST(Double, double)
+MEANVAR_TEST(Int, int)
+MEANVAR_TEST(UnsignedInt, unsigned int)
+MEANVAR_TEST(Short, short)
+MEANVAR_TEST(UnsignedShort, unsigned short)
+MEANVAR_TEST(Long, long long)
+MEANVAR_TEST(UnsignedLong, unsigned long long)
+MEANVAR_TEST(ComplexFloat, af::af_cfloat)
+MEANVAR_TEST(ComplexDouble, af::af_cdouble)
+
+#undef MEANVAR_TEST
+
+#define MEANVAR_TEST(NAME, TYPE)                                        \
+  using MeanVar##NAME = MeanVarTyped<TYPE>;                             \
+  INSTANTIATE_TEST_CASE_P(Small,                                        \
+                          MeanVar##NAME,                                \
+                          ::testing::ValuesIn(small_test_values<TYPE>()), \
+                          []( const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) { \
+                            return info.param.test_description_;        \
+                          });                                           \
+                                                                        \
+  TEST_P(MeanVar##NAME, Testing) {                                      \
+    meanvar_test<TYPE> test = GetParam();                               \
+    meanvar_test_function(test);                                        \
+  }                                                                     \
+
+// Only test small sizes because the range of the large arrays go out of bounds
+MEANVAR_TEST(UnsignedChar, unsigned char)
+//MEANVAR_TEST(Bool, unsigned char) // TODO(umar): test this type

From 1828be1112de1dc1357a10bbbd94b7010bc49d33 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 4 Aug 2018 04:24:24 -0400
Subject: [PATCH 1505/2677] Fix missing trycatch in
 af_get_default_random_engine

---
 src/api/c/random.cpp     | 11 +++++++----
 test/getting_started.cpp |  2 +-
 test/median.cpp          |  2 +-
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 30503673ed..8dbec0a3d5 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -108,11 +108,14 @@ static void validateRandomType(const af_random_engine_type type)
 
 af_err af_get_default_random_engine(af_random_engine *r)
 {
-    AF_CHECK(af_init());
+    try {
+        AF_CHECK(af_init());
 
-    thread_local RandomEngine re;
-    *r = static_cast<af_random_engine> (&re);
-    return AF_SUCCESS;
+        thread_local RandomEngine re;
+        *r = static_cast<af_random_engine> (&re);
+        return AF_SUCCESS;
+    }
+    CATCHALL;
 }
 
 af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_type rtype, uintl seed)
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index 964dff5e92..35ad878364 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -163,7 +163,7 @@ TEST(GettingStarted, SNIPPET_getting_started_dims)
 
     // and whether or not the array is empty and how much memory it takes on
     // the device:
-    printf("empty? %d  total elements: %lld  bytes: %lu\n", a.isempty(), a.elements(), a.bytes());
+    printf("empty? %d  total elements: %lld  bytes: %zu\n", a.isempty(), a.elements(), a.bytes());
     //! [ex_getting_started_prop]
 
     ASSERT_EQ(f32, a.type());
diff --git a/test/median.cpp b/test/median.cpp
index d50fb912d0..6668a39ca3 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -167,7 +167,7 @@ MEDIAN_FLAT(double, double)
     TEST(Median, Ti##_4D_##dim##_odd)           \
     {                                           \
         median_test<To, Ti, dim>(123, 25, 3, 3);\
-    }                                           \
+    }
 
 
 #define MEDIAN(To, Ti)      \

From 68c4ad05d7f65818b9348edcd15fc551dd55e2e4 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Sun, 5 Aug 2018 15:04:25 -0400
Subject: [PATCH 1506/2677] Assert macros: display surrounding values around
 value mismatch (#2257)

* ASSERT_*_EQ and NEAR: Display surrounding values around value mismatch
* Removed duplicate code for looking for near mismatch
* ASSERT_*_EQ and NEAR: Changed vector and array args to const ref, reduced code line lengths
* Polished context, show dim0 positions of values in context, fixed ravelIdx()
---
 test/testHelpers.hpp | 276 +++++++++++++++++++++++++++++++------------
 1 file changed, 202 insertions(+), 74 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index fc5b8c0b2e..779de52c0b 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -22,6 +22,7 @@
 #include <fstream>
 #include <iterator>
 #include <limits>
+#include <sstream>
 #include <stdexcept>
 #include <string>
 #include <typeinfo>
@@ -32,6 +33,8 @@ typedef unsigned char  uchar;
 typedef unsigned int   uint;
 typedef unsigned short ushort;
 
+namespace {
+
 std::string readNextNonEmptyLine(std::ifstream &file)
 {
     std::string result = "";
@@ -509,6 +512,14 @@ std::ostream& operator<<(std::ostream& os, af::dtype type) {
     return os << name;
 }
 
+// Calculate a multi-dimensional coordinates' linearized index
+int ravelIdx(af::dim4 coords, af::dim4 strides) {
+    return coords[3] * strides[3]
+        + coords[2] * strides[2]
+        + coords[1] * strides[1]
+        + coords[0];
+}
+
 // Calculate a linearized index's multi-dimensonal coordinates in an af::array,
 //  given its dimension sizes and strides
 af::dim4 unravelIdx(uint idx, af::dim4 dims, af::dim4 strides) {
@@ -527,9 +538,6 @@ af::dim4 unravelIdx(uint idx, af::array arr) {
     return unravelIdx(idx, dims, st);
 }
 
-struct FloatTag {};
-struct IntegerTag {};
-
 af::dim4 calcStrides(const af::dim4 &parentDim)
 {
     af::dim4 out(1, 1, 1, 1);
@@ -543,12 +551,113 @@ af::dim4 calcStrides(const af::dim4 &parentDim)
     return out;
 }
 
+std::string minimalDim4(af::dim4 coords, af::dim4 dims) {
+    std::ostringstream os;
+    os << "(" << coords[0];
+    if (dims[1] > 1)
+        os << ", " << coords[1];
+    if (dims[2] > 1)
+        os << ", " << coords[2];
+    if (dims[3] > 1)
+        os << ", " << coords[3];
+    os << ")";
+
+    return os.str();
+}
+
+template<typename T>
+std::string printContext(const std::vector<T>& hGold, std::string goldName,
+                         const std::vector<T>& hOut, std::string outName,
+                         af::dim4 arrDims,
+                         af::dim4 arrStrides,
+                         int idx) {
+    std::ostringstream os;
+
+    af::dim4 coords = unravelIdx(idx, arrDims, arrStrides);
+    int ctxWidth = 5;
+
+    // Coordinates that span dim0
+    af::dim4 coordsMinBound = coords;
+    coordsMinBound[0] = 0;
+    af::dim4 coordsMaxBound = coords;
+    coordsMaxBound[0] = arrDims[0] - 1;
+
+    // dim0 positions that can be displayed
+    int dim0Start = std::max<int>(0, coords[0] - ctxWidth);
+    int dim0End = std::min<int>(coords[0] + ctxWidth + 1, arrDims[0]);
+
+    // Linearized indices of values in vectors that can be displayed
+    int vecStartIdx = std::max<int>(ravelIdx(coordsMinBound, arrStrides),
+                                     idx - ctxWidth);
+    int vecEndIdx = std::min<int>(idx + ctxWidth + 1,
+                                   ravelIdx(coordsMaxBound, arrStrides) + 1);
+
+    // Display as minimal coordinates as needed
+    // First value is the range of dim0 positions that will be displayed
+    os << "Viewing slice (" << dim0Start << ":" << dim0End - 1;
+    if (arrDims[1] > 1)
+        os << ", " << coords[1];
+    if (arrDims[2] > 1)
+        os << ", " << coords[2];
+    if (arrDims[3] > 1)
+        os << ", " << coords[3];
+    os << "), dims are " << minimalDim4(arrDims, arrDims) << "\n";
+
+    int varNameWidth = std::max<int>(goldName.length(), outName.length());
+    int valsWidth = 10;
+
+    // Display dim0 positions
+    os << std::setw(varNameWidth) << "" << "    ";
+    for (uint i = dim0Start; i < dim0End; ++i) {
+        if (i == coords[0]) {
+            std::ostringstream tmpOs;
+            tmpOs << "[" << i << "]";
+            os << std::setw(valsWidth) << std::left << tmpOs.str() << " ";
+        } else
+            os << std::setw(valsWidth) << std::left << i << " ";
+    }
+    os << "\n";
+
+    // Display output values
+    os << std::setw(varNameWidth) << outName << ": { ";
+    for (uint i = vecStartIdx; i < vecEndIdx; ++i) {
+        if (i == idx) {
+            std::ostringstream tmpOs;
+            tmpOs << "[" << hOut[i] << "]";
+            os << std::setw(valsWidth) << tmpOs.str() << " ";
+        }
+        else {
+            os << std::setw(valsWidth) << hOut[i] << " ";
+        }
+    }
+    os << "}\n";
+
+    // Display reference values
+    os << std::setw(varNameWidth) << goldName << ": { ";
+    for (uint i = vecStartIdx; i < vecEndIdx; ++i) {
+        if (i == idx) {
+            std::ostringstream tmpOs;
+            tmpOs << "[" << hGold[i] << "]";
+            os << std::setw(valsWidth) << tmpOs.str() << " ";
+        }
+        else {
+            os << std::setw(valsWidth) << hGold[i] << " ";
+        }
+    }
+    os << "}";
+
+    return os.str();
+}
+
+struct FloatTag {};
+struct IntegerTag {};
+
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      std::vector<T>& a, af::dim4 aDims,
-                                      std::vector<T>& b, af::dim4 bDims,
+                                      const std::vector<T>& a, af::dim4 aDims,
+                                      const std::vector<T>& b, af::dim4 bDims,
                                       float maxAbsDiff, IntegerTag) {
-    typedef typename std::vector<T>::iterator iter;
+    typedef typename std::vector<T>::const_iterator iter;
     std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());
     iter aItr = mismatches.first;
     iter bItr = mismatches.second;
@@ -557,64 +666,74 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         return ::testing::AssertionSuccess();
     } else {
         int idx = std::distance(b.begin(), bItr);
+        af::dim4 aStrides = calcStrides(aDims);
         af::dim4 bStrides = calcStrides(bDims);
         af::dim4 coords = unravelIdx(idx, bDims, bStrides);
 
-        return ::testing::AssertionFailure() << "VALUE DIFFERS:\n"
-                                             << " at ([" << coords << "]):\n"
-                                             << aName << "(" << a[idx] << ")\n"
-                                             << bName << "(" << b[idx] << ")";
+        return ::testing::AssertionFailure()
+            << "VALUE DIFFERS at "
+            << minimalDim4(coords, aDims) << ":\n"
+            << printContext(a, aName, b, bName, aDims, aStrides, idx);
     }
 }
 
+struct absMatch{
+    float diff_;
+    absMatch(float diff) : diff_(diff) {}
+
+    template<typename T>
+    bool operator() (T lhs, T rhs) {
+        using std::abs;
+        using af::abs;
+        return abs(rhs - lhs) <= diff_;
+    }
+};
+
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      std::vector<T>& a, af::dim4 aDims,
-                                      std::vector<T>& b, af::dim4 bDims,
+                                      const std::vector<T>& a, af::dim4 aDims,
+                                      const std::vector<T>& b, af::dim4 bDims,
                                       float maxAbsDiff, FloatTag) {
-    typedef typename std::vector<T>::iterator iter;
+    typedef typename std::vector<T>::const_iterator iter;
     // TODO(mark): Modify equality for float
-    std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());
+    std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(),
+                                                     b.begin(),
+                                                     absMatch(maxAbsDiff));
+
     iter aItr = mismatches.first;
     iter bItr = mismatches.second;
 
-    // ASSERT_NEAR
-    if (maxAbsDiff != 0) {
-        using std::abs;
-        using af::abs;
-        for (int idx = 0; idx < a.size(); ++idx) {
-            double absdiff = abs(a[idx] - b[idx]);
-            if (absdiff > maxAbsDiff) {
-                af::dim4 coords = unravelIdx(idx, aDims, calcStrides(aDims));
-                return ::testing::AssertionFailure() << "ABS DIFF EXCEEDS THRESHOLD:\n"
-                                                     << " at ([" << coords << "]):\n"
-                                                     << aName << "(" << a[idx] << ")\n"
-                                                     << bName << "(" << b[idx] << ")\n"
-                                                     << "Expected abs diff: " << maxAbsDiff << "\n"
-                                                     << "Actual abs diff  : " << absdiff;
-            }
-        }
+    if (aItr == a.end()) {
         return ::testing::AssertionSuccess();
-    }
-    // ASSERT_EQ
-    else {
-        if (bItr == b.end()) {
-            return ::testing::AssertionSuccess();
-        } else {
-            int idx = std::distance(b.begin(), bItr);
-            af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
-
-            return ::testing::AssertionFailure() << "VALUE DIFFERS:\n"
-                                                 << " at ([" << coords << "]):\n"
-                                                 << aName << "(" << a[idx] << ")\n"
-                                                 << bName << "(" << b[idx] << ")";
+    } else {
+        int idx = std::distance(b.begin(), bItr);
+        af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
+
+        af::dim4 aStrides = calcStrides(aDims);
+        af::dim4 bStrides = calcStrides(bDims);
+
+        ::testing::AssertionResult result =
+        ::testing::AssertionFailure()
+              << "VALUE DIFFERS at "
+              << minimalDim4(coords, aDims) << ":\n"
+              << printContext(a, aName, b, bName, aDims, aStrides, idx);
+
+        if(maxAbsDiff > 0) {
+            using std::abs;
+            using af::abs;
+            double absdiff = abs(*aItr - *bItr);
+            result << "\n  Actual diff: " << absdiff << "\n"
+                   << "Expected diff: " << maxAbsDiff;
         }
+
+        return result;
     }
 }
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      af::array a, af::array b, float maxAbsDiff) {
+                                      const af::array& a, const af::array& b,
+                                      float maxAbsDiff) {
     typedef typename cond_type<
         IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
         FloatTag, IntegerTag>::type TagType;
@@ -629,20 +748,23 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
 }
 
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         af::array a, af::array b,
+                                         const af::array& a, const af::array& b,
                                          float maxAbsDiff = 0.f) {
     af::dtype aType = a.type();
     af::dtype bType = b.type();
     if (aType != bType)
-        return ::testing::AssertionFailure() << "TYPE MISMATCH: \n"
-                                             << "Expected: " << aName << "(" << a.type() << ")\n"
-                                             << "Actual:   " << bName << "(" << b.type() << ")";
+        return ::testing::AssertionFailure()
+            << "TYPE MISMATCH: \n"
+            << "  Actual: " << bName << "(" << b.type() << ")\n"
+            << "Expected: " << aName << "(" << a.type() << ")";
+
     af::dtype arrDtype = aType;
     const uint ndimIds = 4;
     if (a.dims() != b.dims())
-        return ::testing::AssertionFailure() << "SIZE MISMATCH: \n"
-                                             << "Expected: " << aName << "([" << a.dims() << "]),\n"
-                                             << "Actual:   " << bName << "([" << b.dims() << "])";
+        return ::testing::AssertionFailure()
+            << "SIZE MISMATCH: \n"
+            << "  Actual: " << bName << "([" << b.dims() << "])\n"
+            << "Expected: " << aName << "([" << a.dims() << "])";
 
     uint nElems = a.elements();
     af::dim4 arrDims = a.dims();
@@ -662,8 +784,8 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     case u16: return elemWiseEq<unsigned short>     (aName, bName, a, b, maxAbsDiff); break;
     default:  return ::testing::AssertionFailure()
             << "INVALID TYPE, see enum numbers: "
-            << aName << "(" << a.type() << ") and "
-            << bName << "(" << b.type() << ")";
+            << bName << "(" << b.type() << ") and "
+            << aName << "(" << a.type() << ")";
     }
 
     return ::testing::AssertionSuccess();
@@ -672,29 +794,32 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
 template<typename T>
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsName,
                                          std::string bName,
-                                         std::vector<T>& hA, af::dim4 aDims,
-                                         af::array b,
+                                         const std::vector<T>& hA, af::dim4 aDims,
+                                         const af::array& b,
                                          float maxAbsDiff = 0.0f) {
     af::dtype aDtype = (af::dtype) af::dtype_traits<T>::af_type;
     if (aDtype != b.type()) {
-        return ::testing::AssertionFailure() << "TYPE MISMATCH:\n"
-                                             << "Expected: " << aName << "(" << aDtype << ")\n"
-                                             << "Actual:   " << bName << "(" << b.type() << ")";
+        return ::testing::AssertionFailure()
+            << "TYPE MISMATCH:\n"
+            << "  Actual: " << bName << "(" << b.type() << ")\n"
+            << "Expected: " << aName << "(" << aDtype << ")";
     }
 
     const uint ndimIds = 4;
     if(aDims != b.dims()) {
-        return ::testing::AssertionFailure() << "SIZE MISMATCH:\n"
-                                             << "Expected: " << aDimsName << "([" << aDims << "])\n"
-                                             << "Actual:   " << bName << "([" << b.dims() << "])";
+        return ::testing::AssertionFailure()
+            << "SIZE MISMATCH:\n"
+            << "  Actual: " << bName << "([" << b.dims() << "])\n"
+            << "Expected: " << aDimsName << "([" << aDims << "])";
     }
 
     // In case vector<T> a.size() != aDims.elements()
     if (hA.size() != aDims.elements())
-        return ::testing::AssertionFailure() << "SIZE MISMATCH:\n"
-                                             << "Expected: " << aName << ".size()(" << hA.size() << ")\n"
-                                             << "Actual:   " << aDimsName << "([" << aDims << "] => "
-                                             << aDims.elements() << ")";
+        return ::testing::AssertionFailure()
+            << "SIZE MISMATCH:\n"
+            << "  Actual: " << aDimsName << "([" << aDims << "] => "
+            << aDims.elements() << ")\n"
+            << "Expected: " << aName << ".size()(" << hA.size() << ")";
 
     typedef typename cond_type<
         IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
@@ -708,7 +833,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsNam
 
 // To support C API
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         af_array a, af_array b) {
+                                         const af_array a, const af_array b) {
     af_array aa = 0, bb = 0;
     af_retain_array(&aa, a);
     af_retain_array(&bb, b);
@@ -721,7 +846,8 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
 template<typename T>
 ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsName,
                                          std::string bName,
-                                         std::vector<T>& hA, af::dim4 aDims, af_array b) {
+                                         const std::vector<T>& hA, af::dim4 aDims,
+                                         const af_array b) {
     af_array bb = 0;
     af_retain_array(&bb, b);
     af::array bbb(bb);
@@ -730,7 +856,8 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsN
 
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
-                                           af::array a, af::array b, float maxAbsDiff) {
+                                           const af::array& a, const af::array& b,
+                                           float maxAbsDiff) {
     return assertArrayEq(aName, bName, a, b, maxAbsDiff);
 }
 
@@ -738,8 +865,8 @@ template<typename T>
 ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
                                            std::string bName,
                                            std::string maxAbsDiffName,
-                                           std::vector<T>& hA, af::dim4 aDims,
-                                           af::array b,
+                                           const std::vector<T>& hA, af::dim4 aDims,
+                                           const af::array& b,
                                            float maxAbsDiff) {
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
 }
@@ -747,7 +874,8 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
 // To support C API
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
-                                           af_array a, af_array b, float maxAbsDiff) {
+                                           const af_array a, const af_array b,
+                                           float maxAbsDiff) {
     af_array aa = 0, bb = 0;
     af_retain_array(&aa, a);
     af_retain_array(&bb, b);
@@ -761,8 +889,8 @@ template<typename T>
 ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
                                            std::string bName,
                                            std::string maxAbsDiffName,
-                                           std::vector<T>& hA, af::dim4 aDims,
-                                           af_array b,
+                                           const std::vector<T>& hA, af::dim4 aDims,
+                                           const af_array b,
                                            float maxAbsDiff) {
     af_array bb = 0;
     af_retain_array(&bb, b);
@@ -814,5 +942,5 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
     EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
                             MAX_ABSDIFF)
 
-
+}
 #pragma GCC diagnostic pop

From daa8e8bc1c834e6a1d7fe272994616e5899b1f92 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 6 Aug 2018 11:45:30 +0530
Subject: [PATCH 1507/2677] Correct deconv API guards to have minimum 3.7
 version

---
 include/af/image.h | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/include/af/image.h b/include/af/image.h
index 6acdd33d7c..5c701429f2 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -732,7 +732,9 @@ AFAPI array anisotropicDiffusion(const af::array& in, const float timestep,
                                  const float conductance, const unsigned iterations,
                                  const fluxFunction fftype=AF_FLUX_EXPONENTIAL,
                                  const diffusionEq diffusionKind=AF_DIFFUSION_GRAD);
+#endif
 
+#if AF_API_VERSION >= 37
 /**
   C++ Interface for Iterative deconvolution algorithm
 
@@ -1516,7 +1518,7 @@ extern "C" {
                                           const af_diffusion_eq diffusion_kind);
 #endif
 
-#if AF_API_VERSION >= 36
+#if AF_API_VERSION >= 37
     /**
        C Interface for Iterative deconvolution algorithm
 

From 5d022593b49d5741df26e4875fd2b2d2e2a95dd9 Mon Sep 17 00:00:00 2001
From: mark-poscablo <mark@arrayfire.com>
Date: Mon, 6 Aug 2018 13:51:53 -0400
Subject: [PATCH 1508/2677] Assert macros: minimize spacings between values in
 context window

---
 test/testHelpers.hpp | 121 ++++++++++++++++++++++++++++---------------
 1 file changed, 80 insertions(+), 41 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 779de52c0b..d9e9fbcb46 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -22,6 +22,7 @@
 #include <fstream>
 #include <iterator>
 #include <limits>
+#include <numeric>
 #include <sstream>
 #include <stdexcept>
 #include <string>
@@ -512,12 +513,20 @@ std::ostream& operator<<(std::ostream& os, af::dtype type) {
     return os << name;
 }
 
+// Overloading unary + op is needed to make unsigned char values printable
+//  as numbers
+
+const af::cfloat& operator+(const af::cfloat& val) {
+    return val;
+}
+
+const af::cdouble& operator+(const af::cdouble& val) {
+    return val;
+}
+
 // Calculate a multi-dimensional coordinates' linearized index
 int ravelIdx(af::dim4 coords, af::dim4 strides) {
-    return coords[3] * strides[3]
-        + coords[2] * strides[2]
-        + coords[1] * strides[1]
-        + coords[0];
+    return std::inner_product(coords.get(), coords.get()+4, strides.get(), 0);
 }
 
 // Calculate a linearized index's multi-dimensonal coordinates in an af::array,
@@ -589,8 +598,6 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
     // Linearized indices of values in vectors that can be displayed
     int vecStartIdx = std::max<int>(ravelIdx(coordsMinBound, arrStrides),
                                      idx - ctxWidth);
-    int vecEndIdx = std::min<int>(idx + ctxWidth + 1,
-                                   ravelIdx(coordsMaxBound, arrStrides) + 1);
 
     // Display as minimal coordinates as needed
     // First value is the range of dim0 positions that will be displayed
@@ -603,48 +610,78 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
         os << ", " << coords[3];
     os << "), dims are " << minimalDim4(arrDims, arrDims) << "\n";
 
-    int varNameWidth = std::max<int>(goldName.length(), outName.length());
-    int valsWidth = 10;
-
-    // Display dim0 positions
-    os << std::setw(varNameWidth) << "" << "    ";
-    for (uint i = dim0Start; i < dim0End; ++i) {
-        if (i == coords[0]) {
-            std::ostringstream tmpOs;
-            tmpOs << "[" << i << "]";
-            os << std::setw(valsWidth) << std::left << tmpOs.str() << " ";
-        } else
-            os << std::setw(valsWidth) << std::left << i << " ";
-    }
-    os << "\n";
-
-    // Display output values
-    os << std::setw(varNameWidth) << outName << ": { ";
-    for (uint i = vecStartIdx; i < vecEndIdx; ++i) {
-        if (i == idx) {
-            std::ostringstream tmpOs;
-            tmpOs << "[" << hOut[i] << "]";
-            os << std::setw(valsWidth) << tmpOs.str() << " ";
+    uint ctxElems = dim0End - dim0Start;
+    std::vector<int> valFieldWidths(ctxElems);
+    std::vector<std::string> ctxDim0(ctxElems);
+    std::vector<std::string> ctxOutVals(ctxElems);
+    std::vector<std::string> ctxGoldVals(ctxElems);
+
+    // Get dim0 positions and out/reference values for the context window
+    //
+    // Also get the max string length between the position and out/ref values
+    // per item so that it can be used later as the field width for
+    // displaying each item in the context window
+    for (uint i = 0; i < ctxElems; ++i) {
+        std::ostringstream tmpOs;
+
+        uint dim0 = dim0Start + i;
+        if (dim0 == coords[0])
+            tmpOs << "[" << dim0 << "]";
+        else
+            tmpOs << dim0;
+        ctxDim0[i] = tmpOs.str();
+        int dim0Len = tmpOs.str().length();
+        tmpOs.str(std::string());
+
+        uint valIdx = vecStartIdx + i;
+
+        T outVal = hOut[valIdx];
+        if (valIdx == idx) {
+            tmpOs << "[" << +hOut[valIdx] << "]";
         }
         else {
-            os << std::setw(valsWidth) << hOut[i] << " ";
+            tmpOs << +hOut[valIdx];
         }
-    }
-    os << "}\n";
-
-    // Display reference values
-    os << std::setw(varNameWidth) << goldName << ": { ";
-    for (uint i = vecStartIdx; i < vecEndIdx; ++i) {
-        if (i == idx) {
-            std::ostringstream tmpOs;
-            tmpOs << "[" << hGold[i] << "]";
-            os << std::setw(valsWidth) << tmpOs.str() << " ";
+        ctxOutVals[i] = tmpOs.str();
+        int outLen = tmpOs.str().length();
+        tmpOs.str(std::string());
+
+        T goldVal = hGold[valIdx];
+        if (valIdx == idx) {
+            tmpOs << "[" << +hGold[valIdx] << "]";
         }
         else {
-            os << std::setw(valsWidth) << hGold[i] << " ";
+            tmpOs << +hGold[valIdx];
         }
+        ctxGoldVals[i] = tmpOs.str();
+        int goldLen = tmpOs.str().length();
+        tmpOs.str(std::string());
+
+        int maxWidth = std::max<int>(dim0Len, outLen);
+        maxWidth = std::max<int>(maxWidth, goldLen);
+        valFieldWidths[i] = maxWidth;
     }
-    os << "}";
+
+    int varNameWidth = std::max<int>(goldName.length(), outName.length());
+
+    // Display dim0 positions, output values, and reference values
+    os << std::right << std::setw(varNameWidth) << "" << "   ";
+    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
+        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxDim0[i];
+    }
+    os << "\n";
+
+    os << std::right << std::setw(varNameWidth) << outName << ": {";
+    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
+        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxOutVals[i];
+    }
+    os << " }\n";
+
+    os << std::right << std::setw(varNameWidth) << goldName << ": {";
+    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
+        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxGoldVals[i];
+    }
+    os << " }";
 
     return os.str();
 }
@@ -656,6 +693,8 @@ template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T>& a, af::dim4 aDims,
                                       const std::vector<T>& b, af::dim4 bDims,
+
+
                                       float maxAbsDiff, IntegerTag) {
     typedef typename std::vector<T>::const_iterator iter;
     std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());

From 72f0c109f4f49232d81d0813b148545ea243bdb7 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Mon, 6 Aug 2018 21:21:19 -0500
Subject: [PATCH 1509/2677] spdlog v1.0.0 added as part of ArrayFire by
 default.

---
 .gitmodules                        |  3 +++
 CMakeLists.txt                     | 11 ++++++++---
 CMakeModules/CTestCustom.cmake     |  1 +
 extern/spdlog                      |  1 +
 src/api/unified/CMakeLists.txt     | 10 +---------
 src/api/unified/symbol_manager.cpp |  1 +
 src/backend/common/CMakeLists.txt  | 13 +++----------
 src/backend/common/Logger.cpp      | 21 +++++++--------------
 src/backend/common/Logger.hpp      | 16 +---------------
 9 files changed, 26 insertions(+), 51 deletions(-)
 create mode 160000 extern/spdlog

diff --git a/.gitmodules b/.gitmodules
index 74b855c3a8..1126dbd05e 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -13,3 +13,6 @@
 [submodule "src/backend/cuda/cub"]
 	path = src/backend/cuda/cub
 	url = https://github.com/NVlabs/cub.git
+[submodule "extern/spdlog"]
+	path = extern/spdlog
+	url = https://github.com/gabime/spdlog.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7ce2521be9..cf2c4d8515 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -33,7 +33,6 @@ find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
 find_package(MKL)
-find_package(spdlog QUIET)
 
 # Graphics dependencies
 find_package(glbinding QUIET)
@@ -49,7 +48,7 @@ option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
 
 option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
-option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ${spdlog_FOUND})
+option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
 
 option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
 
@@ -86,7 +85,10 @@ mark_as_advanced(
   AF_WITH_CPUID
   CUDA_HOST_COMPILER
   CUDA_USE_STATIC_CUDA_RUNTIME
-  CUDA_rt_LIBRARY)
+  CUDA_rt_LIBRARY
+  SPDLOG_BUILD_EXAMPLES
+  SPDLOG_BUILD_TESTING)
+
 
 
 if(AF_WITH_GRAPHICS AND NOT AF_USE_SYSTEM_FORGE)
@@ -136,6 +138,8 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
+set(SPDLOG_BUILD_TESTING OFF)
+add_subdirectory(extern/spdlog EXCLUDE_FROM_ALL)
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
 add_subdirectory(src/api/cpp)
@@ -356,3 +360,4 @@ conditional_directory(AF_BUILD_DOCS docs)
 
 include(CPackConfig)
 
+ 
diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index 45f4d25888..d92a809721 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -8,6 +8,7 @@ list(APPEND CTEST_CUSTOM_COVERAGE_EXCLUDE
 	"test/gtest/*"
 
   # All external and third_party libraries
+	"extern/spdlog/*"
 	"src/backend/cpu/threads/*"
 	"src/backend/cuda/cub/*"
 	"cl2.hpp"
diff --git a/extern/spdlog b/extern/spdlog
new file mode 160000
index 0000000000..caff7296b1
--- /dev/null
+++ b/extern/spdlog
@@ -0,0 +1 @@
+Subproject commit caff7296b162d97e44d6a1cc039adf689cfc02b3
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index bca6691749..9812643c5e 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -64,18 +64,10 @@ target_include_directories(af
     ${CMAKE_BINARY_DIR}
   )
 
-if(AF_WITH_LOGGING)
-  dependency_check(spdlog_FOUND "spdlog not found.")
-  target_compile_definitions(af
-    PRIVATE AF_WITH_LOGGING)
-  target_link_libraries(af
-    PRIVATE
-      spdlog::spdlog)
-endif()
-
 target_link_libraries(af
   PRIVATE
     cpp_api_interface
+    spdlog
     Threads::Threads
     ${CMAKE_DL_LIBS}
   )
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index ba21a5af6b..044f2e1365 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -15,6 +15,7 @@
 #include <functional>
 #include <string>
 #include <type_traits>
+#include <spdlog/spdlog.h>
 
 
 #ifdef OS_WIN
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 97b225fc9f..2d7c82ac98 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -46,7 +46,9 @@ else()
 endif()
 
 target_link_libraries(afcommon_interface
-  INTERFACE ${CMAKE_DL_LIBS})
+  INTERFACE
+    spdlog
+    ${CMAKE_DL_LIBS})
 
 target_include_directories(afcommon_interface
   INTERFACE
@@ -54,15 +56,6 @@ target_include_directories(afcommon_interface
     ${PROJECT_BINARY_DIR}
   )
 
-if(AF_WITH_LOGGING)
-  dependency_check(spdlog_FOUND "spdlog not found.")
-  target_compile_definitions(afcommon_interface
-    INTERFACE AF_WITH_LOGGING)
-  target_link_libraries(afcommon_interface
-    INTERFACE
-      spdlog::spdlog)
-endif()
-
 if(APPLE AND NOT USE_MKL)
   target_sources(afcommon_interface
     INTERFACE
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index d82dcc523f..17a419ffba 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -7,18 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#ifdef _WIN32
+#include <windows.h> // spdlog needs this
+#endif
+
 #include <common/Logger.hpp>
 #include <common/util.hpp>
 
 #include <array>
 #include <cstdlib>
-#include <string>
 #include <memory>
+#include <string>
+#include <spdlog/sinks/stdout_sinks.h>
 
 using std::array;
 using std::make_shared;
-using std::string;
 using std::shared_ptr;
+using std::string;
 using std::to_string;
 
 using spdlog::get;
@@ -27,8 +32,6 @@ using spdlog::logger;
 using spdlog::stdout_logger_mt;
 
 namespace common {
-
-#ifdef AF_WITH_LOGGING
 shared_ptr<logger>
 loggerFactory(string name) {
     shared_ptr<logger> logger;
@@ -55,14 +58,4 @@ string bytesToString(size_t bytes) {
     }
     return fmt::format("{:.3g} {}", fbytes, units[count]);
 }
-#else
-  shared_ptr<logger>
-  loggerFactory(string name) {
-    return make_shared<logger>();
-  }
-
-  string bytesToString(size_t bytes) {
-    return "";
-  }
-#endif
 }
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index fb6eecaa3d..5776fe9c3c 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -12,23 +12,9 @@
 #include <memory>
 #include <string>
 
-#ifdef AF_WITH_LOGGING
-#include <spdlog/spdlog.h>
-#else
-
-/// This is a stub class to match the spdlog API in case it is not installed on
-/// the users system. Only the functions we used are implemented here. Other
-/// functions will need to be implemented later.
 namespace spdlog {
-    class logger { public: logger() {} };
-    std::shared_ptr<spdlog::logger> get(const std::string &name);
-    std::shared_ptr<spdlog::logger> stdout_logger_mt(std::string&);
-    namespace level {
-        enum enum_level { trace };
-    }
+    class logger;
 }
-#endif
-
 namespace common {
     std::shared_ptr<spdlog::logger> loggerFactory(std::string name);
     std::string bytesToString(size_t bytes);

From ce4cbc95a8632efb584ce78d176628b673fe164a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 16 Jul 2018 19:43:33 +0530
Subject: [PATCH 1510/2677] Fix template param order in sparse conv decl

Prior to this change, the backend/sparse.hpp headers had the source
and destination storage template parameters out of order. This didn't
cause any issue w.r.t code because `src/api/c/` level functions were
assuming the correct order and the template function implementations
were also defined with correct order. I found the problem when I looked
into sparse headers for internal usage.
---
 src/backend/cpu/sparse.hpp    | 4 +++-
 src/backend/cuda/sparse.hpp   | 4 +++-
 src/backend/opencl/sparse.hpp | 4 +++-
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 8399c566b8..6535eddd65 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
@@ -31,7 +33,7 @@ common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
-template<typename T, af_storage src, af_storage dest>
+template<typename T, af_storage dest, af_storage src>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
index 1ff7d8972c..23f2d9d6a8 100644
--- a/src/backend/cuda/sparse.hpp
+++ b/src/backend/cuda/sparse.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
@@ -19,7 +21,7 @@ common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
-template<typename T, af_storage src, af_storage dest>
+template<typename T, af_storage dest, af_storage src>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index 805afd6c26..d50141ac22 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
@@ -19,7 +21,7 @@ common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
-template<typename T, af_storage src, af_storage dest>
+template<typename T, af_storage dest, af_storage src>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
 
 }

From c44b3c06f672eed9f897bc545c2a3599de99f75b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 8 Aug 2018 13:22:06 +0530
Subject: [PATCH 1511/2677] Fix SparseArray args in createDeviceDataSparseArray
 fn

---
 src/backend/common/SparseArray.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index 668e1f621f..d0fc64918d 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -135,7 +135,7 @@ SparseArray<T> createDeviceDataSparseArray(
         const int * const _rowIdx, const int * const _colIdx,
         const af::storage _storage, const bool _copy)
 {
-    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, _copy);
+    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, true, _copy);
 }
 
 template<typename T>

From 58308c9366338fb529044a4eba5306ed71862c98 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Aug 2018 16:49:30 -0400
Subject: [PATCH 1512/2677] Add volta and remove fermi from CUDA Toolkit 9.0
 builds

---
 CMakeModules/select_compute_arch.cmake | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/select_compute_arch.cmake b/CMakeModules/select_compute_arch.cmake
index 8fb44d80a8..d0ace2aab6 100644
--- a/CMakeModules/select_compute_arch.cmake
+++ b/CMakeModules/select_compute_arch.cmake
@@ -30,12 +30,18 @@ endif ()
 
 if (CUDA_VERSION VERSION_GREATER "7.5")
   list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1" "6.1+PTX")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1")
 else()
   list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
 endif ()
 
-
+if (CUDA_VERSION VERSION_GREATER "8.5")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta")
+  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Fermi")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0" "7.0+PTX")
+else()
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.1+PTX")
+endif()
 
 ################################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
@@ -141,6 +147,9 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
       elseif(${arch_name} STREQUAL "Pascal")
         set(arch_bin 6.0 6.1)
         set(arch_ptx 6.1)
+      elseif(${arch_name} STREQUAL "Volta")
+        set(arch_bin 7.0 7.0)
+        set(arch_ptx 7.0)
       else()
         message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
       endif()

From c0c30f5a9e5028af0650e0a8231ac6be68b7b31b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 12 Aug 2018 05:29:32 -0400
Subject: [PATCH 1513/2677] Add missing AF_WITH_LOGGING definiton

---
 CMakeLists.txt                     | 4 ++++
 src/api/unified/symbol_manager.cpp | 5 ++++-
 src/api/unified/symbol_manager.hpp | 1 +
 src/backend/cpu/memory.cpp         | 4 ++--
 src/backend/cuda/memory.cpp        | 3 ++-
 src/backend/opencl/memory.cpp      | 1 +
 6 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cf2c4d8515..3344cdeffe 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -180,6 +180,10 @@ endif()
 
 foreach(backend ${built_backends})
   target_compile_definitions(${backend} PRIVATE AFDLL)
+  if(AF_WITH_LOGGING)
+    target_compile_definitions(${backend}
+      PRIVATE AF_WITH_LOGGING)
+  endif()
 endforeach()
 
 if(AF_BUILD_FRAMEWORK)
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 044f2e1365..22a09dd56d 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -8,14 +8,17 @@
  ********************************************************/
 
 #include "symbol_manager.hpp"
+
 #include <af/version.h>
+
+#include <common/Logger.hpp>
 #include <common/module_loading.hpp>
+#include <spdlog/spdlog.h>
 
 #include <cmath>
 #include <functional>
 #include <string>
 #include <type_traits>
-#include <spdlog/spdlog.h>
 
 
 #ifdef OS_WIN
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index d688b1235a..d17f9a5ee8 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -18,6 +18,7 @@
 #include <cstdlib>
 #include <string>
 #include <unordered_map>
+#include <spdlog/spdlog.h>
 
 namespace unified
 {
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 660ee94df9..2a658e9eb4 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -10,13 +10,13 @@
 #include <memory.hpp>
 
 #include <common/Logger.hpp>
+#include <common/MemoryManagerImpl.hpp>
 #include <err_cpu.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <spdlog/spdlog.h>
 #include <types.hpp>
 
-#include <common/MemoryManagerImpl.hpp>
-
 template class common::MemoryManager<cpu::MemoryManager>;
 
 #ifndef AF_MEM_DEBUG
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 3f1e317827..175a8721e0 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -10,6 +10,7 @@
 #include <memory.hpp>
 
 #include <common/Logger.hpp>
+#include <common/MemoryManagerImpl.hpp>
 #include <common/dispatch.hpp>
 #include <common/util.hpp>
 #include <cuda.h>
@@ -17,11 +18,11 @@
 #include <cuda_runtime_api.h>
 #include <err_cuda.hpp>
 #include <platform.hpp>
+#include <spdlog/spdlog.h>
 #include <types.hpp>
 
 #include <mutex>
 
-#include <common/MemoryManagerImpl.hpp>
 template class common::MemoryManager<cuda::MemoryManager>;
 template class common::MemoryManager<cuda::MemoryManagerPinned>;
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 4eb5892b46..01b1bffdb6 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -13,6 +13,7 @@
 #include <err_opencl.hpp>
 
 #include <common/Logger.hpp>
+#include <spdlog/spdlog.h>
 
 #include <common/MemoryManagerImpl.hpp>
 template class common::MemoryManager<opencl::MemoryManager>;

From b562e974264f5338a14db37a5800e96580205586 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 13 Aug 2018 19:52:53 -0400
Subject: [PATCH 1514/2677] Fix grammar in select. Add example to select's
 detailed documentation (#2277)

---
 docs/details/data.dox | 32 +++++++++++++++++++++++++++-----
 test/select.cpp       | 27 +++++++++++++++++++++++++++
 2 files changed, 54 insertions(+), 5 deletions(-)

diff --git a/docs/details/data.dox b/docs/details/data.dox
index a5edfb3e9d..9fe1d54ec2 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -277,11 +277,33 @@ Mirrors the array along the specified dimensions.
 
 \defgroup data_func_select select
 
-\brief Select elements from two arrays based on an conditional array
+\brief Selects elements from two arrays based on the values of a binary
+       conditional array.
 
-If the condition array has an element as true, then the element from
-the lhs array/value is selected, otherwise the element from the rhs
-array/value is selected.
+Creates a new array that is composed of values either from array \p a or array
+\p b, based on a third conditional array. For all non-zero elements in the
+conditional array, the output array will contain values from \p a. Otherwise the
+output will contain values from \p b.
+
+\snippet test/select.cpp ex_data_select
+
+is equivalent to:
+
+\snippet test/select.cpp ex_data_select_c
+
+The conditional array must be a b8 typed array.
+
+The select function can perform batched operations based on the size of each of
+the inputs. The following table describes the input and output sizes for
+supported batched configurations.
+
+| Output | Condition Array | Array A | Array B |
+|--------|-----------------|---------|---------|
+| (M, N) | (M, 1)          | (M, 1)  | (M, N)  |
+| (M, N) | (M, 1)          | (M, N)  | (M, 1)  |
+| (M, N) | (M, 1)          | (M, N)  | (M, N)  |
+| (M, N) | (M, N)          | (M, 1)  | (M, N)  |
+| (M, N) | (M, N)          | (M, 1)  | (M, N)  |
 
 \ingroup manip_mat
 \ingroup arrayfire_func
@@ -290,7 +312,7 @@ array/value is selected.
 
 \defgroup data_func_replace replace
 
-\brief Replace elements of an array based on an conditional array
+\brief Replace elements of an array based on a conditional array
 
 - Input values are retained when corresponding elements from condition array are true.
 - Input values are replaced when corresponding elements from condition array are false.
diff --git a/test/select.cpp b/test/select.cpp
index b831acdeff..4328dbf666 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -533,3 +533,30 @@ TEST(Select, InvalidSizeOfCond) {
     af_release_array(b);
     af_release_array(cond);
 }
+
+
+TEST(Select, SNIPPET_select) {
+    //! [ex_data_select]
+    int elements = 9;
+    char hCond[] = {1, 0, 1, 0, 1, 0, 1, 0, 1};
+    float hA[]  = {2, 2, 2, 2, 2, 2, 2, 2, 2};
+    float hB[]  = {3, 3, 3, 3, 3, 3, 3, 3, 3};
+
+    array cond(elements, hCond);
+    array a(elements, hA);
+    array b(elements, hB);
+
+    array out = select(cond, a, b);
+    //out = {2, 3, 2, 3, 2, 3, 2, 3, 2};
+    //! [ex_data_select]
+
+    //! [ex_data_select_c]
+    vector<float> hOut(elements);
+    for(size_t i = 0; i < hOut.size(); i++) {
+        if(hCond[i]) { hOut[i] = hA[i]; }
+        else         { hOut[i] = hB[i]; }
+    }
+    //! [ex_data_select_c]
+
+    ASSERT_VEC_ARRAY_EQ(hOut, dim4(9), out);
+}

From d0e23acd12b3891a005cac67a2727bee1710a6d8 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 5 Sep 2018 18:34:42 -0400
Subject: [PATCH 1515/2677] clarify approx indexing in documentation (#2287)

* clarify approx indexing in documentation
* add test snippets, move documentation to common signal.dox
---
 docs/details/signal.dox | 12 ++++++++++++
 include/af/signal.h     |  8 ++++----
 test/approx1.cpp        | 22 ++++++++++++++++++++++
 test/approx2.cpp        | 37 +++++++++++++++++++++++++++++++++++++
 4 files changed, 75 insertions(+), 4 deletions(-)

diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index ddb7508fb3..67b1bfa2f8 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -218,6 +218,12 @@ It has three options for the type of interpolation to perform:
 - Bilinear interpolation - \ref AF_INTERP_BILINEAR
 - Cubic interpolation - \ref AF_INTERP_CUBIC
 
+Interpolation is performed assuming input data is equally spaced with indices
+in the range [0, n). The positions are sampled with respect to data at these
+locations.
+
+\snippet test/approx1.cpp ex_signal_approx1
+
 \defgroup signal_func_approx2 approx2
 \ingroup approx_mat
 
@@ -228,6 +234,12 @@ It has three options for the type of interpolation to perform:
 - Bilinear interpolation - \ref AF_INTERP_BILINEAR
 - Cubic interpolation - \ref AF_INTERP_CUBIC
 
+Interpolation is performed assuming input data is equally spaced with indices
+in the range [0, n) along each dimension.
+The positions are sampled with respect to data at these locations.
+
+\snippet test/approx2.cpp ex_signal_approx2
+
 \defgroup signal_func_fir fir
 \ingroup sigfilt_mat
 
diff --git a/include/af/signal.h b/include/af/signal.h
index 44994324bf..50846b5926 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -18,7 +18,7 @@ class array;
 class dim4;
 
 /**
-   C++ Interface for data interpolation on one dimensional signals
+   C++ Interface for data interpolation on one dimensional signals.
 
    \param[in]  in is the input array
    \param[in]  pos array contains the interpolation locations
@@ -33,7 +33,7 @@ AFAPI array approx1(const array &in, const array &pos,
                     const interpType method = AF_INTERP_LINEAR, const float offGrid = 0.0f);
 
 /**
-   C++ Interface for data interpolation on two dimensional signals
+   C++ Interface for data interpolation on two dimensional signals.
 
    \param[in]  in is the input array
    \param[in]  pos0 array contains the interpolation locations for first dimension
@@ -674,7 +674,7 @@ extern "C" {
 #endif
 
 /**
-   C Interface for signals interpolation on one dimensional signals
+   C Interface for signals interpolation on one dimensional signals.
 
    \param[out] out is the array with interpolated values
    \param[in]  in is the input array
@@ -691,7 +691,7 @@ AFAPI af_err af_approx1(af_array *out, const af_array in, const af_array pos,
                         const af_interp_type method, const float offGrid);
 
 /**
-   C Interface for signals interpolation on two dimensional signals
+   C Interface for signals interpolation on two dimensional signals.
 
    \param[out] out is the array with interpolated values
    \param[in]  in is the input array
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 1c1ec364f0..e737616540 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -463,3 +463,25 @@ TEST(Approx1, CPPCubicMaxDims)
 
     SUCCEED();
 }
+
+TEST(Approx1, SNIPPET_approx1) {
+
+    //! [ex_signal_approx1]
+
+    // input data
+    float inv[3] = {10, 20, 30};
+    af::array in(3, inv);
+
+    // positions of interpolated values
+    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    af::array pos(5, pv);
+
+    af::array interpolated = approx1(in, pos);
+    // interpolated == { 10, 15, 20, 25, 30 };
+
+    //! [ex_signal_approx1]
+
+    float iv[5] = {10, 15, 20, 25, 30 };
+    af::array interp_gold(5, iv);
+    ASSERT_ARRAYS_NEAR(interpolated, interp_gold, 1e-5);
+}
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 10cd658fa2..8f28278a9b 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -435,3 +435,40 @@ TEST(Approx2, CPPCubicMaxDims)
 
     SUCCEED();
 }
+
+TEST(Approx2, SNIPPET_approx2) {
+
+    //! [ex_signal_approx2]
+
+    // constant input data
+    // {{1 2 3},
+    //  {1 2 3},
+    //  {1 2 3}},
+    float input_vals[9] = {1, 1, 1,
+                           2, 2, 2,
+                           3, 3, 3};
+    array input(3, 3, input_vals);
+
+    // generate grid of interpolation locations
+    // interpolation locations along dim0
+    float p0[4] = {0.5, 1.5,
+                   0.5, 1.5};
+    array pos0(2, 2, p0);
+    // interpolation locations along dim1
+    float p1[4] = {0.5, 0.5,
+                   1.5, 1.5};
+    array pos1(2, 2, p1);
+
+    array interpolated = approx2(input, pos0, pos1);
+    // interpolated == {{1.5 2.5},
+    //                  {1.5 2.5}};
+
+    //! [ex_signal_approx2]
+
+    float expected_interp[4] = {1.5, 1.5,
+                                2.5, 2.5};
+
+    array interpolated_gold(2, 2, expected_interp);
+    ASSERT_ARRAYS_NEAR(interpolated, interpolated_gold, 1e-5);
+
+}

From 986310355bc686d3ff5bd7eb73e905ac111358cd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Sep 2018 00:18:06 -0400
Subject: [PATCH 1516/2677] Fix overflow in dim4::ndims.

---
 src/backend/common/dim4.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/common/dim4.cpp b/src/backend/common/dim4.cpp
index 90408f62a4..46ddab69ae 100644
--- a/src/backend/common/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -64,7 +64,7 @@ dim4::elements()
 dim_t
 dim4::ndims() const
 {
-    int num = elements();
+    dim_t num = elements();
     if (num == 0) return 0;
     if (num == 1) return 1;
 

From 14f0d4f26d0c70f8cd7607aef1b920885f61e541 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Sep 2018 00:23:41 -0400
Subject: [PATCH 1517/2677] Fixes to improve the usage of ArrayFire as a
 subproject

---
 src/api/unified/CMakeLists.txt    | 22 +++++++++++-----------
 src/backend/common/CMakeLists.txt |  6 +++---
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 9812643c5e..f6fb2404de 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -31,26 +31,26 @@ target_sources(af
 
 target_sources(af
   PRIVATE
-    ${CMAKE_SOURCE_DIR}/src/api/c/type_util.cpp
-    ${CMAKE_SOURCE_DIR}/src/api/c/version.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/Logger.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/Logger.hpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/constants.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/dim4.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/err_common.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/util.cpp
-    ${CMAKE_SOURCE_DIR}/src/backend/common/util.hpp
+    ${ArrayFire_SOURCE_DIR}/src/api/c/type_util.cpp
+    ${ArrayFire_SOURCE_DIR}/src/api/c/version.cpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/Logger.cpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/Logger.hpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/constants.cpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/dim4.cpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/err_common.cpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/util.cpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/util.hpp
   )
 
 arrayfire_set_default_cxx_flags(af)
 if(WIN32)
   target_sources(af
     PRIVATE
-      ${CMAKE_SOURCE_DIR}/src/backend/common/module_loading_windows.cpp)
+      ${ArrayFire_SOURCE_DIR}/src/backend/common/module_loading_windows.cpp)
 else()
   target_sources(af
     PRIVATE
-      ${CMAKE_SOURCE_DIR}/src/backend/common/module_loading_unix.cpp)
+      ${ArrayFire_SOURCE_DIR}/src/backend/common/module_loading_unix.cpp)
 endif()
 
 target_include_directories(af
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 2d7c82ac98..339a46fc18 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -36,7 +36,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.hpp
-    ${PROJECT_BINARY_DIR}/version.hpp
+    ${ArrayFire_BINARY_DIR}/version.hpp
   )
 
 if(WIN32)
@@ -52,8 +52,8 @@ target_link_libraries(afcommon_interface
 
 target_include_directories(afcommon_interface
   INTERFACE
-    ${CMAKE_SOURCE_DIR}/src/backend
-    ${PROJECT_BINARY_DIR}
+    ${ArrayFire_SOURCE_DIR}/src/backend
+    ${ArrayFire_BINARY_DIR}
   )
 
 if(APPLE AND NOT USE_MKL)

From d72bd832677e70ac07db094f9e17650877e73bbc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Sep 2018 20:42:44 -0400
Subject: [PATCH 1518/2677] Fix error when no backends are available.

---
 src/api/unified/symbol_manager.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 22a09dd56d..b3352bfcba 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -189,7 +189,9 @@ AFSymbolManager::AFSymbolManager()
             backendsAvailable += order[i];
         }
     }
-    AF_TRACE("AF_DEFAULT_BACKEND: {}", getBackendDirectoryName(activeBackend));
+    if(activeBackend) {
+        AF_TRACE("AF_DEFAULT_BACKEND: {}", getBackendDirectoryName(activeBackend));
+    }
 
     // Keep a copy of default order handle inorder to use it in ::setBackend
     // when the user passes AF_BACKEND_DEFAULT

From 7bd6ff38abf2aacb37cc723e433d8d8ae0bfef1b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Sep 2018 20:43:06 -0400
Subject: [PATCH 1519/2677] Clearify getAvailableBackends documentation

---
 include/af/backend.h | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/include/af/backend.h b/include/af/backend.h
index 94c4951d45..ddf38d17aa 100644
--- a/include/af/backend.h
+++ b/include/af/backend.h
@@ -36,7 +36,20 @@ AFAPI af_err af_get_backend_count(unsigned* num_backends);
 
 #if AF_API_VERSION >= 32
 /**
-   \param[out] backends is the OR sum of the backends available.
+   Returns a flag of all available backends
+
+   \code{.cpp}
+   int backends = 0;
+   af_get_available_backends(&backends);
+
+   if(backends & AF_BACKEND_CUDA) {
+       // The CUDA backend is available
+   }
+   \endcode
+
+   \param[out] backends A flag of all available backends. Use the &(and)
+   operator to check if a particular backend is available
+
    \returns \ref af_err error code
 
    \ingroup unified_func_getavailbackends
@@ -107,7 +120,17 @@ AFAPI unsigned getBackendCount();
 
 #if AF_API_VERSION >= 32
 /**
-   \returns OR sum of the backends available
+   Returns a flag of all available backends
+
+   \code{.cpp}
+   int backends = getAvailableBackends();
+
+   if(backends & AF_BACKEND_CUDA) {
+   // The CUDA backend is available
+   }
+   \endcode
+
+   \returns A flag of available backends
 
    \ingroup unified_func_getavailbackends
  */

From defcac49c6751fc63dcd3505353d341e55ced9d5 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Thu, 13 Sep 2018 10:32:01 -0400
Subject: [PATCH 1520/2677] Improve tile documentation (#2293)

---
 docs/details/data.dox | 26 +++++++++++++++++++++--
 include/af/data.h     | 36 ++++++++++++++++++++-----------
 test/tile.cpp         | 49 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 97 insertions(+), 14 deletions(-)

diff --git a/docs/details/data.dox b/docs/details/data.dox
index 9fe1d54ec2..9e35e3c1e7 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -159,9 +159,31 @@ Requires that all dimensions except the join dimension must be the same for all
 
 \defgroup manip_func_tile tile
 
-\brief Tile the input array along specified dimensions
+\brief Repeat the contents of the input array along the specified dimensions
 
-Creates copys of the array a specified number of times within the output array
+Creates copies of the input array and concatenates them with each other, such
+that the output array will have as many copies of the input array as the user
+specifies, along each dimension. In this sense, the output array is essentially
+a set of "tiles", where each copy of the input array (including the original) is
+a "tile" (hence the name of this function).
+
+Given below are some examples. The input array looks like this:
+
+\snippet test/tile.cpp ex_tile_input
+
+Here, the input array is tiled along the first dimenson, 2 times:
+
+\snippet test/tile.cpp ex_tile_0_2
+
+Here, the input is tiled along the second dimension, 3 times:
+
+\snippet test/tile.cpp ex_tile_1_3
+
+Lastly, one can also tile along multiple dimensions simultaneously. Here, the
+input is tiled 2 times in the first dimension and 3 times in the second
+dimension:
+
+\snippet test/tile.cpp ex_tile_0_2_and_1_3
 
 \ingroup manip_mat
 \ingroup arrayfire_func
diff --git a/include/af/data.h b/include/af/data.h
index 53993ebbe7..66bb023036 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -234,11 +234,15 @@ namespace af
 
     /**
         \param[in] in is the input array
-        \param[in] x is the number of times \p in is tiled along first dimension
-        \param[in] y is the number of times \p in is tiled along second dimension
-        \param[in] z is the number of times \p in is tiled along third dimension
-        \param[in] w is the number of times \p in is tiled along fourth dimension
-        \return the tiled output
+        \param[in] x is the number of times \p in is copied along the first dimension
+        \param[in] y is the number of times \p in is copied along the the second dimension
+        \param[in] z is the number of times \p in is copied along the third dimension
+        \param[in] w is the number of times \p in is copied along the fourth dimension
+        \return The tiled version of the input array
+
+        \note \p x, \p y, \p z, and \p w includes the original in the count as
+              well. Thus, if no duplicates are needed in a certain dimension,
+              leave it as 1 (the default value for just one copy)
 
         \ingroup manip_func_tile
     */
@@ -247,8 +251,12 @@ namespace af
 
     /**
         \param[in] in is the input array
-        \param[in] dims dim4 of tile dimensions
-        \return the tiled output
+        \param[in] dims specifies the number of times \p in is copied along each dimension
+        \return The tiled version of the input array
+
+        \note Each component of \p dims includes the original in the count as
+              well. Thus, if no duplicates are needed in a certain dimension,
+              leave it as 1 (the default value for just one copy)
 
         \ingroup manip_func_tile
     */
@@ -550,12 +558,16 @@ extern "C" {
     AFAPI af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs);
 
     /**
-        \param[out] out is the generated array
+        \param[out] out is the tiled version of the input array
         \param[in] in is the input matrix
-        \param[in] x is the number of times \p in is tiled along first dimension
-        \param[in] y is the number of times \p in is tiled along second dimension
-        \param[in] z is the number of times \p in is tiled along third dimension
-        \param[in] w is the number of times \p in is tiled along fourth dimension
+        \param[in] x is the number of times \p in is copied along the first dimension
+        \param[in] y is the number of times \p in is copied along the the second dimension
+        \param[in] z is the number of times \p in is copied along the third dimension
+        \param[in] w is the number of times \p in is copied along the fourth dimension
+
+        \note \p x, \p y, \p z, and \p w includes the original in the count as
+              well. Thus, if no duplicates are needed in a certain dimension,
+              leave it as 1 (the default value for just one copy)
 
         \ingroup manip_func_tile
     */
diff --git a/test/tile.cpp b/test/tile.cpp
index 982f217a14..1cc8533db7 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -28,6 +28,8 @@ using af::constant;
 using af::dim4;
 using af::dtype_traits;
 using af::product;
+using af::seq;
+using af::span;
 
 template<typename T>
 class Tile : public ::testing::Test
@@ -172,3 +174,50 @@ TEST(Tile, MaxDim)
     ASSERT_EQ(1.f, product<float>(output));
 
 }
+
+TEST(Tile, DocSnippet) {
+    //! [ex_tile_input]
+    float hA[] = {0, 1, 2, 3, 4, 5};
+    array A(3, 2, hA);
+    //  0.  3.
+    //  1.  4.
+    //  2.  5.
+    //! [ex_tile_input]
+
+    //! [ex_tile_0_2]
+    array B = tile(A, 2, 1);
+    //  0.  3.
+    //  1.  4.
+    //  2.  5.
+    //  0.  3.
+    //  1.  4.
+    //  2.  5.
+    //! [ex_tile_0_2]
+
+    ASSERT_ARRAYS_EQ(A, B(seq(0, 2), span));
+    ASSERT_ARRAYS_EQ(A, B(seq(3, 5), span));
+
+    //! [ex_tile_1_3]
+    array C = tile(A, 1, 3);
+    //  0.  3.  0.  3.  0.  3.
+    //  1.  4.  1.  4.  1.  4.
+    //  2.  5.  2.  5.  2.  5.
+    //! [ex_tile_1_3]
+
+    ASSERT_ARRAYS_EQ(A, C(span, seq(0, 1)));
+    ASSERT_ARRAYS_EQ(A, C(span, seq(2, 3)));
+    ASSERT_ARRAYS_EQ(A, C(span, seq(4, 5)));
+
+    //! [ex_tile_0_2_and_1_3]
+    array D = tile(A, 2, 3);
+    //  0.  3.  0.  3.  0.  3.
+    //  1.  4.  1.  4.  1.  4.
+    //  2.  5.  2.  5.  2.  5.
+    //  0.  3.  0.  3.  0.  3.
+    //  1.  4.  1.  4.  1.  4.
+    //  2.  5.  2.  5.  2.  5.
+    //! [ex_tile_0_2_and_1_3]
+
+    ASSERT_ARRAYS_EQ(C, D(seq(0, 2), span));
+    ASSERT_ARRAYS_EQ(C, D(seq(3, 5), span));
+}

From bfbd6846969a691b89bdb85f59637d3397daaf91 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 18 Sep 2018 02:13:07 -0400
Subject: [PATCH 1521/2677] Fix indexing formulae and lookup documentation

* fix negative indexing not including last element of array

* update lookup documentation with new examples

* splits example snippets, minor wording fixes
---
 docs/details/index.dox              |  56 +++++----------
 include/af/index.h                  |  18 ++---
 src/backend/cpu/utility.hpp         |   5 +-
 src/backend/cuda/utility.hpp        |   5 +-
 src/backend/opencl/kernel/assign.cl |   5 +-
 src/backend/opencl/kernel/index.cl  |   4 +-
 src/backend/opencl/kernel/lookup.cl |   5 +-
 test/index.cpp                      | 108 ++++++++++++++++++++++++++++
 8 files changed, 151 insertions(+), 55 deletions(-)

diff --git a/docs/details/index.dox b/docs/details/index.dox
index dcf278efa9..72a95da048 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -5,49 +5,31 @@
 \defgroup index_func_index index
 \ingroup index_mat
 
-\brief Lookup values on array based on sequences and/or arrays
+\brief Lookup values of an array based on sequences and/or arrays
 
 
 
 \defgroup index_func_lookup Lookup
 \ingroup index_mat
 
-\brief Index an array using another array.
-
-Lets look at an example of how \ref af::lookup function does indexing.
-\code
-array a = range(dim4(5));
-af_print(a);
-//  0
-//  1
-//  2
-//  3
-//  4
-
-array b = range(dim4(2)) + 2; // Create an array with values [0,2] range and add 2 to them
-af_print(b);
-// 2
-// 3
-
-array c = lookup(a, b, 0);
-af_print(c);
-//  2
-//  3
-
-
-array d = lookup(a, b, 1);
-af_print(d);
-//  0   0
-//  1   1
-//  2   2
-//  3   3
-//  4   4
-
-// Since the second(1) dimension has only single element, all indices map to first & single element
-// along that dimension. Thus, the output array has two columns with elements repeatd twice because
-// the index array b has 2 elements.
-
-\endcode
+\brief Lookup values of an array by indexing with another array.
+
+Will return an array with the values in the \p in array from the locations specified in the \p idx array.
+The resulting array contains values corresponding to each of the provided indices.
+Locations of the input data are assumed to be in the range [0, n). Indexing outside of this range will result in mirrored wrap-around behavior.
+
+A simple example of one-dimension indexing can be seen in the following example.
+
+\snippet test/index.cpp ex_index_lookup1d
+
+Index locations can also be out of bounds.
+
+\snippet test/index.cpp ex_index_lookup_oob
+
+The dimensiong along which to query the indices can also be specified. The resulting array will be of the same size as the input, except for the queried dimension which will match the number of elements in the index array.
+
+\snippet test/index.cpp ex_index_lookup2d
+
 
 
 
diff --git a/include/af/index.h b/include/af/index.h
index 8d37f7f517..e91735da94 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -157,12 +157,12 @@ class AFAPI index {
 };
 
 ///
-/// Lookup the values of input array based on index
+/// Lookup the values of an input array by indexing with another array
 ///
-/// \param[in] in is input lookup array
-/// \param[in] idx is lookup indices
+/// \param[in] in is the input array that will be queried
+/// \param[in] idx are the lookup indices
 /// \param[in] dim specifies the dimension for indexing
-/// \returns an array containing values at locations specified by \p index
+/// \returns an array containing values of \p in at locations specified by \p index
 ///
 /// \ingroup index_func_lookup
 ///
@@ -213,12 +213,12 @@ extern "C" {
 
 
     ///
-    /// Lookup the values of input array based on index
+    /// Lookup the values of an input array by indexing with another array
     ///
-    /// \param[out] out      output array containing values at locations
-    ///                      specified by \p index
-    /// \param[in] in        is input lookup array
-    /// \param[in] indices   is lookup indices
+    /// \param[out] out      output array containing values of \p in at locations
+    ///                      specified by \p indices
+    /// \param[in] in        is the input array that will be queried
+    /// \param[in] indices   are the lookup indices
     /// \param[in] dim       specifies the dimension for indexing
     ///
     /// \ingroup index_func_lookup
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index c1a8a86d04..25bfacb7b5 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -19,10 +19,11 @@ static inline
 dim_t trimIndex(int const & idx, dim_t const & len)
 {
     int ret_val = idx;
-    int offset  = abs(ret_val)%len;
     if (ret_val<0) {
-        ret_val = offset-1;
+        int offset  = (abs(ret_val)-1)%len;
+        ret_val = offset;
     } else if (ret_val>=(int)len) {
+        int offset  = abs(ret_val)%len;
         ret_val = len-offset-1;
     }
     return ret_val;
diff --git a/src/backend/cuda/utility.hpp b/src/backend/cuda/utility.hpp
index bae4cc78b3..1f01b6ddf7 100644
--- a/src/backend/cuda/utility.hpp
+++ b/src/backend/cuda/utility.hpp
@@ -17,10 +17,11 @@ namespace cuda
 static __DH__ dim_t trimIndex(const int &idx, const dim_t &len)
 {
     int ret_val = idx;
-    int offset  = abs(ret_val)%len;
     if (ret_val<0) {
-        ret_val = offset-1;
+        int offset  = (abs(ret_val)-1)%len;
+        ret_val = offset;
     } else if (ret_val>=len) {
+        int offset  = abs(ret_val)%len;
         ret_val = len-offset-1;
     }
     return ret_val;
diff --git a/src/backend/opencl/kernel/assign.cl b/src/backend/opencl/kernel/assign.cl
index 927ccdd4c1..e24c258efa 100644
--- a/src/backend/opencl/kernel/assign.cl
+++ b/src/backend/opencl/kernel/assign.cl
@@ -16,10 +16,11 @@ typedef struct {
 int trimIndex(int idx, const int len)
 {
     int ret_val = idx;
-    int offset  = abs(ret_val)%len;
     if (ret_val<0) {
-        ret_val = offset-1;
+        int offset  = (abs(ret_val)-1)%len;
+        ret_val = offset;
     } else if (ret_val>=len) {
+        int offset  = abs(ret_val)%len;
         ret_val = len-offset-1;
     }
     return ret_val;
diff --git a/src/backend/opencl/kernel/index.cl b/src/backend/opencl/kernel/index.cl
index 0d2839d588..6b44938dd1 100644
--- a/src/backend/opencl/kernel/index.cl
+++ b/src/backend/opencl/kernel/index.cl
@@ -18,8 +18,10 @@ int trimIndex(int idx, const int len)
     int ret_val = idx;
     int offset  = abs(ret_val)%len;
     if (ret_val<0) {
-        ret_val = offset-1;
+        int offset  = (abs(ret_val)-1)%len;
+        ret_val = offset;
     } else if (ret_val>=len) {
+        int offset  = abs(ret_val)%len;
         ret_val = len-offset-1;
     }
     return ret_val;
diff --git a/src/backend/opencl/kernel/lookup.cl b/src/backend/opencl/kernel/lookup.cl
index d24572fa8d..686cf48f7a 100644
--- a/src/backend/opencl/kernel/lookup.cl
+++ b/src/backend/opencl/kernel/lookup.cl
@@ -10,10 +10,11 @@
 int trimIndex(int idx, const int len)
 {
     int ret_val = idx;
-    int offset  = abs(ret_val)%len;
     if (ret_val<0) {
-        ret_val = offset-1;
+        int offset  = (abs(ret_val)-1)%len;
+        ret_val = offset;
     } else if (ret_val>=len) {
+        int offset  = abs(ret_val)%len;
         ret_val = len-offset-1;
     }
     return ret_val;
diff --git a/test/index.cpp b/test/index.cpp
index fa4ff2ef4d..289e402a9c 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -721,6 +721,114 @@ TEST(lookup, Issue2009)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
+TEST(lookup, SNIPPET_lookup1d)
+{
+    //! [ex_index_lookup1d]
+
+    // input array
+    float in_[5] = {10, 20, 30, 40, 50};
+    af::array in(5, in_);
+
+    // indices to lookup
+    int idx_[3] = {1, 3, 2};
+    af::array idx(3, idx_);
+
+    af::array indexed = af::lookup(in, idx);
+    // indexed == { 20, 40, 30 };
+
+    //! [ex_index_lookup1d]
+
+    //indexing tests
+    float in_g[3] = {20, 40, 30 };
+    af::array indexed_gold(3, in_g);
+    ASSERT_ARRAYS_NEAR(indexed, indexed_gold, 1e-5);
+}
+
+TEST(lookup, SNIPPET_lookup_oob)
+{
+    //! [ex_index_lookup_oob]
+
+    // input array
+    float in_[5] = {10, 20, 30, 40, 50};
+    af::array in(5, in_);
+
+    // indexing past end of array
+    int idx_outofbounds_p_[8] = {4,  5,  6,  7,  8,  9, 10, 11};
+    af::array idx_outofbounds_p(8, idx_outofbounds_p_);
+
+    // and indexing before beginning of array
+    int idx_outofbounds_n_[8] = {0, -1, -2, -3, -4, -5, -6, -7};
+    af::array idx_outofbounds_n(8, idx_outofbounds_n_);
+
+    af::array indexed_out_of_bounds_pos = af::lookup(in, idx_outofbounds_p);
+    af::array indexed_out_of_bounds_neg = af::lookup(in, idx_outofbounds_n);
+    // indexed_out_of_bounds_pos == { 50, 50, 40, 30, 20, 10, 50, 40 }
+    // indexed_out_of_bounds_neg == { 10, 10, 20, 30, 40, 50, 10, 20 }
+
+    //! [ex_index_lookup_oob]
+
+    // out of bounds tests
+    float oob_p_g_[8] = { 50, 50, 40, 30, 20, 10, 50, 40 };
+    af::array oob_p_g(8, oob_p_g_);
+    ASSERT_ARRAYS_NEAR(indexed_out_of_bounds_pos, oob_p_g, 1e-5);
+    float oob_n_g_[8] = { 10, 10, 20, 30, 40, 50, 10, 20 };
+    af::array oob_n_g(8, oob_n_g_);
+    ASSERT_ARRAYS_NEAR(indexed_out_of_bounds_neg, oob_n_g, 1e-5);
+}
+
+TEST(lookup, SNIPPET_lookup2d)
+{
+    //! [ex_index_lookup2d]
+
+    // constant input data
+    float input_vals[9] = {10, 20, 30,
+                           11, 21, 31,
+                           12, 22, 32};
+    array input(3, 3, input_vals);
+    // {{10 11 12},
+    //  {20 21 22},
+    //  {30 31 32}},
+
+
+    // indices to lookup
+    int idx_[6] = {0, 0, 1, 1, 2, 2};
+    af::array idx(6, idx_);
+
+    //will look up all indices along specified dimension
+    af::array indexed = af::lookup(input, idx); //(dim = 0)
+    // indexed == { 10, 11, 12,
+    //              10, 11, 12,
+    //              20, 21, 22,
+    //              20, 21, 22,
+    //              30, 31, 32,
+    //              30, 31, 32 };
+
+    af::array indexed_dim1 = af::lookup(input, idx, 1);
+    // indexed_dim1 == { 10, 10, 11, 11, 12, 12,
+    //                   20, 20, 21, 21, 22, 22,
+    //                   30, 30, 31, 31, 32, 32 };
+
+    //! [ex_index_lookup2d]
+
+    float expected_indexed[18] = { 10, 10, 20, 20, 30, 30,
+                                   11, 11, 21, 21, 31, 31,
+                                   12, 12, 22, 22, 32, 32 };
+
+    array indexed_gold(6, 3, expected_indexed);
+    ASSERT_ARRAYS_NEAR(indexed, indexed_gold, 1e-5);
+
+    float expected_indexed_dim1[18] = { 10, 20, 30,
+                                        10, 20, 30,
+                                        11, 21, 31,
+                                        11, 21, 31,
+                                        12, 22, 32,
+                                        12, 22, 32 };
+
+    array indexed_gold_dim1(3, 6, expected_indexed_dim1);
+    ASSERT_ARRAYS_NEAR(indexed_dim1, indexed_gold_dim1, 1e-5);
+
+}
+
 TEST(SeqIndex, CPP_END)
 {
     const int n = 5;

From 0641ff46815f22f004f41b981d0e2e1e3efcac58 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Sep 2018 20:38:42 -0400
Subject: [PATCH 1522/2677] Add assertions for input sizes and k in the topk
 function

---
 src/api/c/topk.cpp | 5 ++++-
 test/topk.cpp      | 3 ++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index 9ac81402b0..4aa85d9af1 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -43,7 +43,7 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
 
         ArrayInfo inInfo = getInfo(in);
 
-        ARG_ASSERT(1, (inInfo.ndims()>0));
+        ARG_ASSERT(2, (inInfo.ndims()>0));
 
         if (inInfo.elements() == 1) {
             dim_t dims[1] = {1};
@@ -63,6 +63,9 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
             }
         }
 
+        ARG_ASSERT(2, (inInfo.dims()[rdim] >= k));
+        ARG_ASSERT(4, (k <= 256)); // TODO(umar): Remove this limitation
+
         if (rdim!=0)
             AF_ERROR("topk is supported along dimenion 0 only.", AF_ERR_NOT_SUPPORTED);
 
diff --git a/test/topk.cpp b/test/topk.cpp
index 9dbe1b8df5..f7ec4d5db2 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -241,7 +241,8 @@ INSTANTIATE_TEST_CASE_P(InstantiationName,
                                           topk_params{10, 10,     5, 0,  AF_TOPK_MAX},
                                           topk_params{10, 100,    5, 0,  AF_TOPK_MAX},
                                           topk_params{10, 1000,   5, 0,  AF_TOPK_MAX},
-                                          topk_params{10, 10000,  5, 0,  AF_TOPK_MAX}
+                                          topk_params{10, 10000,  5, 0,  AF_TOPK_MAX},
+                                          topk_params{1000, 10,   256, 0,  AF_TOPK_MAX}
                                           ),
                         []( const ::testing::TestParamInfo<TopKParams::ParamType> info) {
                           stringstream ss;

From 2d576d062bff0d84b577bd3dd8b9152b7c999ce8 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 20 Sep 2018 02:09:55 -0400
Subject: [PATCH 1523/2677] Adds ability to return multiple values from nearest
 neighbor

* Returns multiple values from nearest neighbor using topk (removes obsolete code/kernels)
* Adds assertions and tests for maximum number of neighbors, including tests all nearest-neighbors API parameters
* Updates documentation to reflect new behavior
* Fixes out of bounds shared memory indexing
* Fixes gtest linking issues
---
 include/af/vision.h                           |   8 +-
 src/api/c/nearest_neighbour.cpp               |   1 +
 src/backend/cpu/kernel/nearest_neighbour.hpp  |  15 +-
 src/backend/cpu/nearest_neighbour.cpp         |  20 +-
 src/backend/cpu/topk.cpp                      |   2 +
 src/backend/cuda/kernel/nearest_neighbour.hpp | 399 ++----------------
 src/backend/cuda/nearest_neighbour.cu         |  14 +-
 src/backend/cuda/topk.cu                      |   2 +
 .../opencl/kernel/nearest_neighbour.cl        | 284 +------------
 .../opencl/kernel/nearest_neighbour.hpp       |  77 +---
 src/backend/opencl/nearest_neighbour.cpp      |   9 +-
 src/backend/opencl/topk.cpp                   |   2 +
 test/CMakeLists.txt                           |   2 +-
 test/nearest_neighbour.cpp                    | 251 ++++++++++-
 14 files changed, 363 insertions(+), 723 deletions(-)

diff --git a/include/af/vision.h b/include/af/vision.h
index 78cc107ac5..d40fed7970 100644
--- a/include/af/vision.h
+++ b/include/af/vision.h
@@ -223,8 +223,8 @@ AFAPI void hammingMatcher(array& idx, array& dist,
    \param[in]  train is the array containing the data used as training data
    \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
                indicated here must be of equal length for both query and train arrays)
-   \param[in]  n_dist is the number of smallest distances to return (currently, only 1
-               is supported)
+   \param[in]  n_dist is the number of smallest distances to return (currently only 
+               values <= 256 are supported)
    \param[in]  dist_type is the distance computation type. Currently \ref AF_SAD (sum
                of absolute differences), \ref AF_SSD (sum of squared differences), and
                \ref AF_SHD (hamming distances) are supported.
@@ -535,8 +535,8 @@ extern "C" {
         \param[in]  train is the array containing the data used as training data
         \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
                     indicated here must be of equal length for both query and train arrays)
-        \param[in]  n_dist is the number of smallest distances to return (currently, only 1
-                    is supported)
+        \param[in]  n_dist is the number of smallest distances to return (currently, only
+                    values <= 256 are supported)
         \param[in]  dist_type is the distance computation type. Currently \ref AF_SAD (sum
                     of absolute differences), \ref AF_SSD (sum of squared differences), and
                     \ref AF_SHD (hamming distances) are supported.
diff --git a/src/api/c/nearest_neighbour.cpp b/src/api/c/nearest_neighbour.cpp
index a224bf474b..6d53f5feed 100644
--- a/src/api/c/nearest_neighbour.cpp
+++ b/src/api/c/nearest_neighbour.cpp
@@ -54,6 +54,7 @@ af_err af_nearest_neighbour(af_array* idx, af_array* dist,
         DIM_ASSERT(3, tDims[2] == 1 && tDims[3] == 1);
         DIM_ASSERT(4, (dist_dim == 0 || dist_dim == 1));
         DIM_ASSERT(5, n_dist > 0 && n_dist <= (uint)tDims[train_samples]);
+        ARG_ASSERT(5, n_dist > 0 && n_dist <= 256);
         ARG_ASSERT(6, dist_type == AF_SAD || dist_type == AF_SSD || dist_type == AF_SHD);
         TYPE_ASSERT(qType == tType);
 
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 7f515966b0..ae0d670a57 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -86,7 +86,7 @@ struct dist_op<ushort, To, AF_SHD>
 };
 
 template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour(Param<uint> idx, Param<To> dist,
+void nearest_neighbour(Param<To> dists,
                        CParam<T> query, CParam<T> train,
                        const uint dist_dim, const uint n_dist)
 {
@@ -100,8 +100,7 @@ void nearest_neighbour(Param<uint> idx, Param<To> dist,
 
     const T* qPtr = query.get();
     const T* tPtr = train.get();
-    uint* iPtr = idx.get();
-    To* dPtr = dist.get();
+    To* dPtr = dists.get();
 
     dist_op<T, To, dist_type> op;
 
@@ -125,16 +124,8 @@ void nearest_neighbour(Param<uint> idx, Param<To> dist,
                 local_dist += op(qPtr[qIdx], tPtr[tIdx]);
             }
 
-            if (local_dist < best_dist) {
-                best_dist = local_dist;
-                best_idx  = j;
-            }
+            dPtr[i*nTrain + j] = local_dist;
         }
-
-        size_t oIdx;
-        oIdx = i;
-        iPtr[oIdx] = best_idx;
-        dPtr[oIdx] = best_dist;
     }
 }
 
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index e0294fda27..1bc33403d9 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -12,6 +12,7 @@
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <topk.hpp>
 #include <err_cpu.hpp>
 #include <kernel/nearest_neighbour.hpp>
 
@@ -26,10 +27,6 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
                        const uint dist_dim, const uint n_dist,
                        const af_match_type dist_type)
 {
-    if (n_dist > 1) {
-        CPU_NOT_SUPPORTED("\nNumber of smallest distances can't be <1\n");
-    }
-
     idx.eval();
     dist.eval();
     query.eval();
@@ -37,24 +34,31 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
 
     uint sample_dim  = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
-    const dim4 outDims(n_dist, qDims[sample_dim]);
+    const dim4 tDims = train.dims();
+    const dim4 outDims (n_dist, qDims[sample_dim]);
+    const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
+
+    Array<To> tmp_dists = createEmptyArray<To>(distDims);
 
     idx  = createEmptyArray<uint>(outDims);
     dist = createEmptyArray<To  >(outDims);
 
     switch(dist_type) {
         case AF_SAD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, tmp_dists, query, train, dist_dim, n_dist);
             break;
         case AF_SSD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, tmp_dists, query, train, dist_dim, n_dist);
             break;
         case AF_SHD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, idx, dist, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, tmp_dists, query, train, dist_dim, n_dist);
             break;
         default:
             AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
+
+    cpu::topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
+
 }
 
 #define INSTANTIATE(T, To)                                                              \
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index c921782739..24446bb00e 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -95,4 +95,6 @@ INSTANTIATE(float )
 INSTANTIATE(double)
 INSTANTIATE(int   )
 INSTANTIATE(uint  )
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
 }
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index fd386af63e..55010a9ec7 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -94,154 +94,15 @@ struct dist_op<uchar, To, AF_SHD>
     }
 };
 
-
-template<typename T, typename To, af_match_type dist_type, unsigned feat_len, bool use_shmem>
-__global__ void nearest_neighbour_unroll(
-    unsigned* out_idx,
-    To* out_dist,
-    CParam<T> query,
-    CParam<T> train,
-    const To max_dist)
-{
-    unsigned nquery = query.dims[0];
-    unsigned ntrain = train.dims[0];
-
-    unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
-    unsigned tid = threadIdx.x;
-
-    __shared__ To       s_dist[THREADS];
-    __shared__ unsigned s_idx[THREADS];
-
-    extern __shared__ char smem[];
-    T* s_query = (T*)smem;
-    T* s_train = (T*)smem + feat_len;
-
-    s_dist[tid] = max_dist;
-    s_idx[tid]  = 0xffffffff;
-
-    bool valid_feat = (f < ntrain);
-
-    if (valid_feat) {
-        // Copy blockDim.x training features to shared memory
-        if (use_shmem) {
-            #pragma unroll
-            for (unsigned i = 0; i < feat_len; i++) {
-                s_train[i * blockDim.x + tid] = train.ptr[i * ntrain + f];
-            }
-        }
-    }
-    __syncthreads();
-
-    dist_op<T, To, dist_type> op;
-
-    for (unsigned j = 0; j < nquery; j++) {
-        s_dist[tid] = max_dist;
-
-        // Load one query feature that will be tested against all training
-        // features in current block
-        if (tid < feat_len) {
-            s_query[tid] = query.ptr[tid * nquery + j];
-        }
-        __syncthreads();
-
-        To dist = 0;
-        if (valid_feat) {
-            #pragma unroll
-            for (unsigned k = 0; k < feat_len; k++) {
-                // Calculate Hamming distance for 32-bits of descriptor and
-                // accumulates to dist
-                if (use_shmem) {
-                    dist += op(s_train[k * blockDim.x + tid], s_query[k]);
-                }
-                else {
-                    dist += op(train.ptr[k * ntrain + f], s_query[k]);
-                }
-            }
-
-            // Only stores the feature index and distance if it's smaller
-            // than the best match found so far
-            s_dist[tid] = dist;
-            s_idx[tid]  = f;
-        }
-        __syncthreads();
-
-        // Find best match in training features from block to the current
-        // query feature
-        if (tid < 128) {
-            if (s_dist[tid + 128] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 128];
-                s_idx[tid]  = s_idx[tid + 128];
-            }
-        }
-        __syncthreads();
-        if (tid < 64) {
-            if (s_dist[tid + 64] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 64];
-                s_idx[tid]  = s_idx[tid + 64];
-            }
-        }
-        __syncthreads();
-        if (tid < 32) {
-            if (s_dist[tid + 32] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 32];
-                s_idx[tid]  = s_idx[tid + 32];
-            }
-        }
-        __syncthreads();
-        if (tid < 16) {
-            if (s_dist[tid + 16] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 16];
-                s_idx[tid]  = s_idx[tid + 16];
-            }
-        }
-        __syncthreads();
-        if (tid < 8) {
-            if (s_dist[tid + 8] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 8];
-                s_idx[tid]  = s_idx[tid + 8];
-            }
-        }
-        __syncthreads();
-        if (tid < 4) {
-            if (s_dist[tid + 4] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 4];
-                s_idx[tid]  = s_idx[tid + 4];
-            }
-        }
-        __syncthreads();
-        if (tid < 2) {
-            if (s_dist[tid + 2] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 2];
-                s_idx[tid]  = s_idx[tid + 2];
-            }
-        }
-        __syncthreads();
-        if (tid < 1) {
-            if (s_dist[tid + 1] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 1];
-                s_idx[tid]  = s_idx[tid + 1];
-            }
-        }
-        __syncthreads();
-
-        // Store best match in training features from block to the current
-        // query feature
-        if (valid_feat) {
-            out_dist[j * gridDim.x + blockIdx.x] = s_dist[0];
-            out_idx[j * gridDim.x + blockIdx.x]  = s_idx[0];
-        }
-        __syncthreads();
-    }
-}
-
 template<typename T, typename To, af_match_type dist_type, bool use_shmem>
-__global__ void nearest_neighbour(
-    unsigned* out_idx,
+__global__ void all_distances(
     To* out_dist,
     CParam<T> query,
     CParam<T> train,
     const To max_dist,
-    const unsigned feat_len)
+    const unsigned feat_len,
+    const unsigned max_feat_len,
+    const unsigned feat_offset)
 {
     unsigned nquery = query.dims[0];
     unsigned ntrain = train.dims[0];
@@ -250,22 +111,21 @@ __global__ void nearest_neighbour(
     unsigned tid = threadIdx.x;
 
     __shared__ To s_dist[THREADS];
-    __shared__ unsigned s_idx[THREADS];
 
     extern __shared__ char smem[];
     T* s_query = (T*)smem;
-    T* s_train = (T*)smem + feat_len;
+    T* s_train = (T*)smem + max_feat_len;
 
     s_dist[tid] = max_dist;
-    s_idx[tid]  = 0xffffffff;
 
     bool valid_feat = (f < ntrain);
 
     if (valid_feat) {
         // Copy blockDim.x training features to shared memory
         if (use_shmem) {
-            for (unsigned i = 0; i < feat_len; i++) {
-                s_train[i * blockDim.x + tid] = train.ptr[i * ntrain + f];
+            unsigned end_feat = min(feat_offset + max_feat_len, feat_len);
+            for (unsigned i = feat_offset; i < end_feat; i++) {
+                s_train[(i - feat_offset) * blockDim.x + tid] = train.ptr[i * ntrain + f];
             }
         }
     }
@@ -278,275 +138,82 @@ __global__ void nearest_neighbour(
 
         // Load one query feature that will be tested against all training
         // features in current block
-        if (tid < feat_len) {
-            s_query[tid] = query.ptr[tid * nquery + j];
+        if (tid < max_feat_len) {
+            s_query[tid] = query.ptr[(tid + feat_offset) * nquery + j];
         }
         __syncthreads();
 
         To dist = 0;
         if (valid_feat) {
-            for (unsigned k = 0; k < feat_len; k++) {
+            unsigned feat_end = min(feat_offset + max_feat_len, feat_len);
+            for (unsigned k = feat_offset; k < feat_end; k++) {
                 // Calculate Hamming distance for 32-bits of descriptor and
                 // accumulates to dist
                 if (use_shmem) {
-                    dist += op(s_train[k * blockDim.x + tid], s_query[k]);
+                    dist += op(s_train[(k - feat_offset) * blockDim.x + tid], s_query[k - feat_offset]);
                 }
                 else {
-                    dist += op(train.ptr[k * ntrain + f], s_query[k]);
+                    dist += op(train.ptr[k * ntrain + f], s_query[k - feat_offset]);
                 }
             }
 
             // Only stores the feature index and distance if it's smaller
             // than the best match found so far
             s_dist[tid] = dist;
-            s_idx[tid]  = f;
         }
-        __syncthreads();
 
-        // Find best match in training features from block to the current
-        // query feature
-        if (tid < 128) {
-            if (s_dist[tid + 128] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 128];
-                s_idx[tid]  = s_idx[tid + 128];
-            }
-        }
-        __syncthreads();
-        if (tid < 64) {
-            if (s_dist[tid + 64] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 64];
-                s_idx[tid]  = s_idx[tid + 64];
-            }
-        }
-        __syncthreads();
-        if (tid < 32) {
-            if (s_dist[tid + 32] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 32];
-                s_idx[tid]  = s_idx[tid + 32];
-            }
-        }
-        __syncthreads();
-        if (tid < 16) {
-            if (s_dist[tid + 16] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 16];
-                s_idx[tid]  = s_idx[tid + 16];
-            }
-        }
-        __syncthreads();
-        if (tid < 8) {
-            if (s_dist[tid + 8] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 8];
-                s_idx[tid]  = s_idx[tid + 8];
-            }
-        }
-        __syncthreads();
-        if (tid < 4) {
-            if (s_dist[tid + 4] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 4];
-                s_idx[tid]  = s_idx[tid + 4];
-            }
-        }
         __syncthreads();
-        if (tid < 2) {
-            if (s_dist[tid + 2] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 2];
-                s_idx[tid]  = s_idx[tid + 2];
-            }
-        }
-        __syncthreads();
-        if (tid < 1) {
-            if (s_dist[tid + 1] < s_dist[tid]) {
-                s_dist[tid] = s_dist[tid + 1];
-                s_idx[tid]  = s_idx[tid + 1];
-            }
-        }
-        __syncthreads();
-
         // Store best match in training features from block to the current
         // query feature
         if (valid_feat) {
-            out_dist[j * gridDim.x + blockIdx.x] = s_dist[0];
-            out_idx[j * gridDim.x + blockIdx.x]  = s_idx[0];
+            if(feat_offset == 0)
+                out_dist[j * ntrain + f] = s_dist[tid];
+            else
+                out_dist[j * ntrain + f] += s_dist[tid];
         }
         __syncthreads();
     }
 }
 
-template<typename To>
-__global__ void select_matches(
-    Param<unsigned> idx,
-    Param<To> dist,
-    const unsigned* in_idx,
-    const To* in_dist,
-    const unsigned nfeat,
-    const unsigned nelem,
-    const To max_dist)
-{
-    unsigned f = blockIdx.x * blockDim.x + threadIdx.x;
-    unsigned sid = threadIdx.x * blockDim.y + threadIdx.y;
-
-    __shared__ To s_dist[THREADS];
-    __shared__ unsigned s_idx[THREADS];
-
-    s_dist[sid] = max_dist;
-    if (f < nfeat) {
-        for (unsigned i = threadIdx.y; i < nelem; i += blockDim.y) {
-            To dist = in_dist[f * nelem + i];
-
-            // Copy all best matches previously found in nearest_neighbour() to
-            // shared memory
-            if (dist < s_dist[sid]) {
-                s_dist[sid] = dist;
-                s_idx[sid]  = in_idx[f * nelem + i];
-            }
-        }
-    }
-    __syncthreads();
-
-    // Reduce best matches and find the best of them all
-    for (unsigned i = blockDim.y / 2; i > 0; i >>= 1) {
-        if (threadIdx.y < i) {
-            To dist = s_dist[sid + i];
-            if (dist < s_dist[sid]) {
-                s_dist[sid] = dist;
-                s_idx[sid]  = s_idx[sid + i];
-            }
-        }
-        __syncthreads();
-    }
-
-    // Store best matches and indexes to training dataset
-    if (threadIdx.y == 0 && f < nfeat) {
-        dist.ptr[f] = s_dist[threadIdx.x * blockDim.y];
-        idx.ptr[f]  = s_idx[threadIdx.x * blockDim.y];
-    }
-}
-
 template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour(Param<uint> idx,
-                       Param<To> dist,
+void all_distances(Param<To> dist,
                        CParam<T> query,
                        CParam<T> train,
                        const dim_t dist_dim,
                        const unsigned n_dist)
 {
     const unsigned feat_len = query.dims[dist_dim];
+    const unsigned max_kern_feat_len = min(THREADS, feat_len);
     const To max_dist = maxval<To>();
 
-    if (feat_len > THREADS) {
-        char errMessage[256];
-        snprintf(errMessage, sizeof(errMessage),
-                 "CUDA Maximum number of features supported in nearest_neighbor is %d\n", THREADS);
-        CUDA_NOT_SUPPORTED(errMessage);
-    }
-
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
-    const unsigned nquery = query.dims[sample_dim];
     const unsigned ntrain = train.dims[sample_dim];
 
     dim3 threads(THREADS, 1);
     dim3 blocks(divup(ntrain, threads.x), 1);
 
     // Determine maximum feat_len capable of using shared memory (faster)
-    int device = getActiveDeviceId();
+    int device          = getActiveDeviceId();
     cudaDeviceProp prop = getDeviceProp(device);
-    size_t avail_smem = prop.sharedMemPerBlock;
-    size_t smem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
-    size_t strain_sz = threads.x * feat_len * sizeof(T);
-    bool use_shmem = (avail_smem >= (smem_predef + strain_sz)) ? true : false;
-    unsigned smem_sz = (use_shmem) ? smem_predef + strain_sz : smem_predef;
-
-    unsigned nblk = blocks.x;
-
-    auto d_blk_idx  = memAlloc<unsigned>(nblk * nquery);
-    auto d_blk_dist = memAlloc<To>(nblk * nquery);
+    size_t avail_smem   = prop.sharedMemPerBlock;
+    size_t smem_predef  = 2 * THREADS * sizeof(unsigned) + max_kern_feat_len * sizeof(T);
+    size_t strain_sz    = threads.x * max_kern_feat_len * sizeof(T);
+    bool use_shmem      = (avail_smem >= (smem_predef + strain_sz)) ? true : false;
+    unsigned smem_sz    = (use_shmem) ? smem_predef + strain_sz : smem_predef;
 
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
-    if (use_shmem) {
-        switch(feat_len) {
-        // Optimized lengths (faster due to loop unrolling)
-        case 1:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,1,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 2:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,2,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 4:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,4,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 8:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,8,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 16:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,16,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 32:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,32,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 64:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,64,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        default:
-            CUDA_LAUNCH_SMEM((nearest_neighbour<T,To,dist_type,true>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist, feat_len);
-        }
-    }
-    else {
-        switch(feat_len) {
-        // Optimized lengths (faster due to loop unrolling)
-        case 1:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,1,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 2:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,2,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 4:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,4,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 8:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,8,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 16:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,16,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 32:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,32,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        case 64:
-            CUDA_LAUNCH_SMEM((nearest_neighbour_unroll<T,To,dist_type,64,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist);
-            break;
-        default:
-            CUDA_LAUNCH_SMEM((nearest_neighbour<T,To,dist_type,false>), blocks, threads, smem_sz,
-                             d_blk_idx.get(), d_blk_dist.get(), query, train, max_dist, feat_len);
+    for(int feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
+        if (use_shmem) {
+            CUDA_LAUNCH_SMEM((all_distances<T,To,dist_type,true>), blocks, threads, smem_sz,
+                             dist.ptr, query, train, max_dist, feat_len, max_kern_feat_len, feat_offset);
+        } else {
+            CUDA_LAUNCH_SMEM((all_distances<T,To,dist_type,false>), blocks, threads, smem_sz,
+                             dist.ptr, query, train, max_dist, feat_len, max_kern_feat_len, feat_offset);
         }
     }
     POST_LAUNCH_CHECK();
-
-    threads = dim3(32, 8);
-    blocks = dim3(nquery, 1);
-
-    // Reduce all smallest Hamming distances from each block and store final
-    // best match
-    CUDA_LAUNCH(select_matches, blocks, threads,
-                idx, dist, d_blk_idx.get(), d_blk_dist.get(), nquery, nblk, max_dist);
-    POST_LAUNCH_CHECK();
-
 }
 
 } // namespace kernel
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index eb1781fe4c..a5c2b0fcf6 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -12,6 +12,7 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
+#include <topk.hpp>
 #include <transpose.hpp>
 
 using af::dim4;
@@ -25,11 +26,14 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
                      const uint dist_dim, const uint n_dist,
                      const af_match_type dist_type)
 {
-    uint sample_dim = (dist_dim == 0) ? 1 : 0;
+    uint  sample_dim = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
 
     const dim4 outDims(n_dist, qDims[sample_dim]);
+    const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
+
+    Array<To> tmp_dists = createEmptyArray<To>(distDims);
 
     idx  = createEmptyArray<uint>(outDims);
     dist = createEmptyArray<To>(outDims);
@@ -38,14 +42,16 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
     Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
     switch(dist_type) {
-        case AF_SAD: kernel::nearest_neighbour<T, To, AF_SAD>(idx, dist, queryT, trainT, 1, n_dist);
+        case AF_SAD: kernel::all_distances<T, To, AF_SAD>(tmp_dists, queryT, trainT, 1, n_dist);
                      break;
-        case AF_SSD: kernel::nearest_neighbour<T, To, AF_SSD>(idx, dist, queryT, trainT, 1, n_dist);
+        case AF_SSD: kernel::all_distances<T, To, AF_SSD>(tmp_dists, queryT, trainT, 1, n_dist);
                      break;
-        case AF_SHD: kernel::nearest_neighbour<T, To, AF_SHD>(idx, dist, queryT, trainT, 1, n_dist);
+        case AF_SHD: kernel::all_distances<T, To, AF_SHD>(tmp_dists, queryT, trainT, 1, n_dist);
                      break;
         default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
+
+    topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
 }
 
 #define INSTANTIATE(T, To)                                                              \
diff --git a/src/backend/cuda/topk.cu b/src/backend/cuda/topk.cu
index 0b6129cc7c..8d44076516 100644
--- a/src/backend/cuda/topk.cu
+++ b/src/backend/cuda/topk.cu
@@ -34,4 +34,6 @@ INSTANTIATE(float )
 INSTANTIATE(double)
 INSTANTIATE(int   )
 INSTANTIATE(uint  )
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
 }
diff --git a/src/backend/opencl/kernel/nearest_neighbour.cl b/src/backend/opencl/kernel/nearest_neighbour.cl
index dd9197c582..a8b039490d 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.cl
+++ b/src/backend/opencl/kernel/nearest_neighbour.cl
@@ -45,148 +45,7 @@ unsigned _shd_(T v1, T v2)
 #endif
 
 __kernel
-void nearest_neighbour_unroll(
-    __global unsigned* out_idx,
-    __global To* out_dist,
-    __global const T* query,
-    KParam qInfo,
-    __global const T* train,
-    KParam tInfo,
-    const To max_dist,
-    __local T* lmem)
-{
-    unsigned nquery = qInfo.dims[0];
-    unsigned ntrain = tInfo.dims[0];
-
-    unsigned f = get_global_id(0);
-    unsigned tid = get_local_id(0);
-
-    __local To l_dist[THREADS];
-    __local unsigned l_idx[THREADS];
-
-    __local T* l_query = lmem;
-    __local T* l_train = lmem + FEAT_LEN;
-
-    l_dist[tid] = max_dist;
-    l_idx[tid]  = 0xffffffff;
-
-    bool valid_feat = (f < ntrain);
-
-#ifdef USE_LOCAL_MEM
-    if (valid_feat) {
-        // Copy local_size(0) training features to shared memory
-        #pragma unroll
-        for (unsigned i = 0; i < FEAT_LEN; i++) {
-            l_train[i * get_local_size(0) + tid] = train[i * ntrain + f + tInfo.offset];
-        }
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-#endif
-
-    for (int j = 0; j < (int)nquery; j++) {
-        l_dist[tid] = max_dist;
-
-        // Load one query feature that will be tested against all training
-        // features in current block
-        if (tid < FEAT_LEN) {
-            l_query[tid] = query[tid * nquery + j + qInfo.offset];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-
-        To dist = 0;
-        if (valid_feat) {
-            #pragma unroll
-            for (int k = 0; k < (int)FEAT_LEN; k++) {
-                // Calculate Hamming distance for 32-bits of descriptor and
-                // accumulates to dist
-#ifdef USE_LOCAL_MEM
-                dist += DISTOP(l_train[k * get_local_size(0) + tid], l_query[k]);
-#else
-                dist += DISTOP(train[k * ntrain + f + tInfo.offset], l_query[k]);
-#endif
-            }
-        }
-
-        // Only stores the feature index and distance if it's smaller
-        // than the best match found so far
-        if (valid_feat) {
-            l_dist[tid] = dist;
-            l_idx[tid]  = f;
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-
-        // Find best match in training features from block to the current
-        // query feature
-        if (tid < 128) {
-            if (l_dist[tid + 128] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 128];
-                l_idx[tid]  = l_idx[tid + 128];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 64) {
-            if (l_dist[tid + 64] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 64];
-                l_idx[tid]  = l_idx[tid + 64];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 32) {
-            if (l_dist[tid + 32] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 32];
-                l_idx[tid]  = l_idx[tid + 32];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 16) {
-            if (l_dist[tid + 16] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 16];
-                l_idx[tid]  = l_idx[tid + 16];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 8) {
-            if (l_dist[tid + 8] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 8];
-                l_idx[tid]  = l_idx[tid + 8];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 4) {
-            if (l_dist[tid + 4] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 4];
-                l_idx[tid]  = l_idx[tid + 4];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 2) {
-            if (l_dist[tid + 2] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 2];
-                l_idx[tid]  = l_idx[tid + 2];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 1) {
-            if (l_dist[tid + 1] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 1];
-                l_idx[tid]  = l_idx[tid + 1];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-
-        // Store best match in training features from block to the current
-        // query feature
-        if (valid_feat) {
-            out_dist[j * get_num_groups(0) + get_group_id(0)] = l_dist[0];
-            out_idx[j * get_num_groups(0) + get_group_id(0)]  = l_idx[0];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-}
-
-__kernel
-void nearest_neighbour(
-    __global unsigned* out_idx,
+void all_distances(
     __global To* out_dist,
     __global const T* query,
     KParam qInfo,
@@ -194,6 +53,8 @@ void nearest_neighbour(
     KParam tInfo,
     const To max_dist,
     const unsigned feat_len,
+    const unsigned max_feat_len,
+    const unsigned feat_offset,
     __local T* lmem)
 {
     unsigned nquery = qInfo.dims[0];
@@ -203,21 +64,20 @@ void nearest_neighbour(
     unsigned tid = get_local_id(0);
 
     __local To l_dist[THREADS];
-    __local unsigned l_idx[THREADS];
 
     __local T* l_query = lmem;
-    __local T* l_train = lmem + feat_len;
+    __local T* l_train = lmem + max_feat_len;
 
     l_dist[tid] = max_dist;
-    l_idx[tid]  = 0xffffffff;
 
     bool valid_feat = (f < ntrain);
 
 #ifdef USE_LOCAL_MEM
     if (valid_feat) {
         // Copy local_size(0) training features to shared memory
-        for (unsigned i = 0; i < feat_len; i++) {
-            l_train[i * get_local_size(0) + tid] = train[i * ntrain + f + tInfo.offset];
+        unsigned end_feat = min(feat_offset + max_feat_len, feat_len);
+        for (unsigned i = feat_offset; i < feat_len; i++) {
+            l_train[(i - feat_offset) * get_local_size(0) + tid] = train[i * ntrain + f + tInfo.offset];
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
@@ -228,20 +88,21 @@ void nearest_neighbour(
 
         // Load one query feature that will be tested against all training
         // features in current block
-        if (tid < feat_len) {
-            l_query[tid] = query[tid * nquery + j + qInfo.offset];
+        if (tid < max_feat_len) {
+            l_query[tid] = query[(tid + feat_offset) * nquery + j + qInfo.offset];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
 
         To dist = 0;
         if (valid_feat) {
-            for (int k = 0; k < (int)feat_len; k++) {
+            unsigned feat_end = min(feat_offset + max_feat_len, feat_len);
+            for (unsigned k = feat_offset; k < feat_end; k++) {
                 // Calculate Hamming distance for 32-bits of descriptor and
                 // accumulates to dist
 #ifdef USE_LOCAL_MEM
-                dist += DISTOP(l_train[k * get_local_size(0) + tid], l_query[k]);
+                dist += DISTOP(l_train[(k - feat_offset) * get_local_size(0) + tid], l_query[k - feat_offset]);
 #else
-                dist += DISTOP(train[k * ntrain + f + tInfo.offset], l_query[k]);
+                dist += DISTOP(train[k * ntrain + f + tInfo.offset], l_query[k - feat_offset]);
 #endif
             }
         }
@@ -250,129 +111,18 @@ void nearest_neighbour(
         // than the best match found so far
         if (valid_feat) {
             l_dist[tid] = dist;
-            l_idx[tid]  = f;
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-
-        // Find best match in training features from block to the current
-        // query feature
-        if (tid < 128) {
-            if (l_dist[tid + 128] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 128];
-                l_idx[tid]  = l_idx[tid + 128];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 64) {
-            if (l_dist[tid + 64] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 64];
-                l_idx[tid]  = l_idx[tid + 64];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 32) {
-            if (l_dist[tid + 32] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 32];
-                l_idx[tid]  = l_idx[tid + 32];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 16) {
-            if (l_dist[tid + 16] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 16];
-                l_idx[tid]  = l_idx[tid + 16];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 8) {
-            if (l_dist[tid + 8] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 8];
-                l_idx[tid]  = l_idx[tid + 8];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 4) {
-            if (l_dist[tid + 4] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 4];
-                l_idx[tid]  = l_idx[tid + 4];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 2) {
-            if (l_dist[tid + 2] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 2];
-                l_idx[tid]  = l_idx[tid + 2];
-            }
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-        if (tid < 1) {
-            if (l_dist[tid + 1] < l_dist[tid]) {
-                l_dist[tid] = l_dist[tid + 1];
-                l_idx[tid]  = l_idx[tid + 1];
-            }
         }
         barrier(CLK_LOCAL_MEM_FENCE);
 
         // Store best match in training features from block to the current
         // query feature
         if (valid_feat) {
-            out_dist[j * get_num_groups(0) + get_group_id(0)] = l_dist[0];
-            out_idx[j * get_num_groups(0) + get_group_id(0)]  = l_idx[0];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-}
+            if(feat_offset == 0)
+                out_dist[j * ntrain + f] = l_dist[tid];
+            else
+                out_dist[j * ntrain + f] += l_dist[tid];
 
-__kernel
-void select_matches(
-    __global unsigned* idx,
-    __global To* dist,
-    __global const unsigned* in_idx,
-    __global const To* in_dist,
-    const unsigned nfeat,
-    const unsigned nelem,
-    const To max_dist)
-{
-    unsigned f = get_global_id(0);
-    unsigned lsz1 = get_local_size(1);
-    unsigned sid = get_local_id(0) * lsz1 + get_local_id(1);
-
-    __local To l_dist[THREADS];
-    __local unsigned l_idx[THREADS];
-
-    bool valid_feat = (f < nfeat);
-
-    l_dist[sid] = max_dist;
-    if (valid_feat) {
-        for (unsigned i = get_local_id(1); i < nelem; i += get_local_size(1)) {
-            To dist = in_dist[f * nelem + i];
-
-            // Copy all best matches previously found in nearest_neighbour() to
-            // shared memory
-            if (dist < l_dist[sid]) {
-                l_dist[sid] = dist;
-                l_idx[sid]  = in_idx[f * nelem + i];
-            }
-        }
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    for (unsigned i = get_local_size(1) / 2; i > 0; i >>= 1) {
-        if (get_local_id(1) < i) {
-            if (valid_feat) {
-                To dist = l_dist[sid + i];
-                if (dist < l_dist[sid]) {
-                    l_dist[sid] = dist;
-                    l_idx[sid]  = l_idx[sid + i];
-                }
-            }
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
-
-    // Store best matches and indexes to training dataset
-    if (get_local_id(1) == 0 && valid_feat) {
-        dist[f] = l_dist[get_local_id(0) * get_local_size(1)];
-        idx[f]  = l_idx[get_local_id(0) * get_local_size(1)];
-    }
 }
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 8d4853d057..714b3f9c21 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -35,20 +35,20 @@ namespace kernel
 static const unsigned THREADS = 256;
 
 template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour(Param idx,
-                       Param dist,
-                       Param query,
-                       Param train,
-                       const dim_t dist_dim,
-                       const unsigned n_dist)
+void all_distances(Param dist,
+                   Param query,
+                   Param train,
+                   const dim_t dist_dim,
+                   const unsigned n_dist)
 {
     const unsigned feat_len = query.info.dims[dist_dim];
+    const unsigned max_kern_feat_len = min(THREADS, feat_len);
     const To max_dist = maxval<To>();
 
     // Determine maximum feat_len capable of using shared memory (faster)
     cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
-    size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
-    size_t ltrain_sz = THREADS * feat_len * sizeof(T);
+    size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + max_kern_feat_len * sizeof(T);
+    size_t ltrain_sz = THREADS * max_kern_feat_len * sizeof(T);
     bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
     size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
 
@@ -100,72 +100,37 @@ void nearest_neighbour(Param idx,
                 options.str());
 
         entry.prog = new Program(prog);
-        entry.ker = new Kernel[3];
+        entry.ker = new Kernel;
 
-        entry.ker[0] = Kernel(*entry.prog, "nearest_neighbour_unroll");
-        entry.ker[1] = Kernel(*entry.prog, "nearest_neighbour");
-        entry.ker[2] = Kernel(*entry.prog, "select_matches");
+        *entry.ker = Kernel(*entry.prog, "all_distances");
 
         addKernelToCache(device, ref_name, entry);
     }
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
-    const unsigned nquery = query.info.dims[sample_dim];
     const unsigned ntrain = train.info.dims[sample_dim];
 
     unsigned nblk = divup(ntrain, THREADS);
     const NDRange local(THREADS, 1);
     const NDRange global(nblk * THREADS, 1);
 
-    cl::Buffer *d_blk_idx  = bufferAlloc(nblk * nquery * sizeof(unsigned));
-    cl::Buffer *d_blk_dist = bufferAlloc(nblk * nquery * sizeof(To));
-
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
-    if (unroll_len > 0) {
-        auto huOp = KernelFunctor<Buffer, Buffer,
-                                Buffer, KParam,
-                                Buffer, KParam,
-                                const To,
-                                LocalSpaceArg> (entry.ker[0]);
-
-        huOp(EnqueueArgs(getQueue(), global, local),
-              *d_blk_idx, *d_blk_dist,
-              *query.data, query.info, *train.data, train.info,
-              max_dist, cl::Local(lmem_sz));
-    }
-    else {
-        auto hmOp = KernelFunctor<Buffer, Buffer,
-                                Buffer, KParam,
-                                Buffer, KParam,
-                                const To, const unsigned,
-                                LocalSpaceArg> (entry.ker[1]);
-
+    auto hmOp = KernelFunctor<Buffer,
+                              Buffer, KParam,
+                              Buffer, KParam,
+                              const To, const unsigned,
+                              const unsigned,const unsigned,
+                              LocalSpaceArg> (*entry.ker);
+
+    for(int feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
         hmOp(EnqueueArgs(getQueue(), global, local),
-              *d_blk_idx, *d_blk_dist,
+              *dist.data,
               *query.data, query.info, *train.data, train.info,
-              max_dist, feat_len, cl::Local(lmem_sz));
+              max_dist, feat_len, max_kern_feat_len, feat_offset, cl::Local(lmem_sz));
+        CL_DEBUG_FINISH(getQueue());
     }
-    CL_DEBUG_FINISH(getQueue());
-
-    const NDRange local_sm(32, 8);
-    const NDRange global_sm(divup(nquery, 32) * 32, 8);
-
-    // Reduce all smallest Hamming distances from each block and store final
-    // best match
-    auto smOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                            const unsigned, const unsigned,
-                            const To> (entry.ker[2]);
-
-    smOp(EnqueueArgs(getQueue(), global_sm, local_sm),
-          *idx.data, *dist.data,
-          *d_blk_idx, *d_blk_dist,
-          nquery, nblk, max_dist);
-    CL_DEBUG_FINISH(getQueue());
-
-    bufferFree(d_blk_idx);
-    bufferFree(d_blk_dist);
 }
 
 } // namespace kernel
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 4c9344030a..da3ed641ee 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -12,6 +12,7 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
+#include <topk.hpp>
 #include <transpose.hpp>
 
 using af::dim4;
@@ -27,7 +28,12 @@ void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
 {
     uint sample_dim = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
+    const dim4 tDims = train.dims();
+
     const dim4 outDims(n_dist, qDims[sample_dim]);
+    const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
+
+    Array<To> tmp_dists = createEmptyArray<To>(distDims);
 
     idx  = createEmptyArray<uint>(outDims);
     dist = createEmptyArray<To>(outDims);
@@ -35,8 +41,9 @@ void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
     Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
     Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
-    kernel::nearest_neighbour<T, To, dist_type>(idx, dist, queryT, trainT, 1, n_dist);
+    kernel::all_distances<T, To, dist_type>(tmp_dists, queryT, trainT, 1, n_dist);
 
+    topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
 }
 
 template<typename T, typename To>
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 22ec69ea98..5936b7613d 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -152,4 +152,6 @@ INSTANTIATE(float )
 INSTANTIATE(double)
 INSTANTIATE(int   )
 INSTANTIATE(uint  )
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
 }
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b358778673..0967c3e4b1 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -227,7 +227,7 @@ make_test(SRC missing.cpp)
 make_test(SRC moddims.cpp)
 make_test(SRC moments.cpp)
 make_test(SRC morph.cpp)
-make_test(SRC nearest_neighbour.cpp)
+make_test(SRC nearest_neighbour.cpp CXX11)
 
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 5b3e90b631..c0f8cbf146 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <arrayfire.h>
 #include <af/dim4.hpp>
@@ -15,14 +16,17 @@
 #include <vector>
 #include <testHelpers.hpp>
 
-using std::endl;
-using std::vector;
-using std::string;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
+using af::constant;
 using af::dim4;
 using af::dtype_traits;
+using af::randu;
+using af::range;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
 class NearestNeighbour : public ::testing::Test
@@ -262,3 +266,242 @@ TEST(NearestNeighbourSSD, small)
         EXPECT_NEAR(expectedDistances[i], actualDistances[i], 1E-8);
     }
 }
+
+TEST(KNearestNeighbourSSD, small)
+{
+    const int ntrain = 5;
+    const int nquery = 3;
+    const int nfeat  = 2;
+
+    float query[nquery * nfeat] = {
+        5,  5,
+        0,  0,
+       10, 10,
+    };
+
+    float train[ntrain * nfeat] = {
+        0,   0,
+        3.5, 4,
+        5,   5,
+        6,   5,
+        8,   6.5
+    };
+
+    array t(nfeat, ntrain, train);
+    array q(nfeat, nquery, query);
+    array indices;
+    array distances;
+    const int k = 2;
+    nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD);
+
+    float expectedDistances[nquery * ntrain] = {
+        (5 - 5) * (5 - 5) + (5 - 5) * (5 - 5),
+        (5 - 6) * (5 - 6) + (5 - 5) * (5 - 5),
+
+        (0 - 0)   * (0 - 0) + (0 - 0)   * (0 - 0),
+        (0 - 3.5) * (0 - 4) + (0 - 3.5) * (0 - 4),
+
+        (10 - 8)   * (10 - 8)   + (10 - 6.5) * (10 - 6.5),
+        (10 - 6)   * (10 - 5)   + (10 - 6)   * (10 - 5)
+    };
+
+    vector<float> actualDistances(nquery);
+    distances.host(&actualDistances[0]);
+    for (int i = 0; i < nquery; i++) {
+        EXPECT_NEAR(expectedDistances[i], actualDistances[i], 1E-8);
+    }
+}
+
+struct nearest_neighbors_params {
+    string testname_;
+    int    k_, nfeat_, ntrain_, nquery_;
+    int    feat_dim_;
+    dim4 qdims_, tdims_, idims_, ddims_;
+    vector<float> query_;
+    vector<float> train_;
+    vector<unsigned int> indices_;
+    vector<float> dists_;
+
+    nearest_neighbors_params(string testname, int k, int feat_dim, array query, array train, array indices, array dists)
+    : testname_(testname), k_(k), feat_dim_(feat_dim), query_(query.elements()), train_(train.elements()), indices_(indices.elements()), dists_(dists.elements())
+    {
+        qdims_ = query.dims();
+        tdims_ = train.dims();
+        idims_ = indices.dims();
+        ddims_ = dists.dims();
+
+        query.host(query_.data());
+        train.host(train_.data());
+        indices.host(indices_.data());
+        dists.host(dists_.data());
+    }
+};
+
+template<typename TestClass>
+string testNameGenerator(const ::testing::TestParamInfo<typename TestClass::ParamType> info) {
+    return info.param.testname_;
+}
+
+class NearestNeighborsTest  : public ::testing::TestWithParam<nearest_neighbors_params> { };
+class KNearestNeighborsTest : public ::testing::TestWithParam<nearest_neighbors_params> { };
+
+nearest_neighbors_params
+single_knn_data(const string testname, const int nquery, const int ntrain, const int nfeat, const int k, const int feat_dim) {
+    array indices, dists;
+    array query, train;
+    if(feat_dim == 0) {
+        query = constant(0, nfeat, nquery);
+        train = constant(1, nfeat, ntrain);
+    } else {
+        query = constant(0, nquery, nfeat);
+        train = constant(1, ntrain, nfeat);
+    }
+
+    indices = constant(0, k, nquery, u32);
+    dists   = constant(nfeat, k, nquery);
+
+    return nearest_neighbors_params(testname, k, feat_dim, query, train, indices, dists);
+}
+
+nearest_neighbors_params
+knn_data(const string testname, const int nquery, const int ntrain, const int nfeat, const int k, const int feat_dim) {
+    array indices, dists;
+    array query, train;
+    if(feat_dim == 0) {
+        query = constant(0, nfeat, nquery);
+        train = range(dim4(nfeat, ntrain), 1);
+    } else {
+        query = constant(0, nquery, nfeat);
+        train = range(dim4(ntrain, nfeat), 0);
+    }
+
+    indices = range(dim4(k, nquery), 0, u32);
+    dists   = range(dim4(k, nquery));
+    dists  *= dists;
+
+    return nearest_neighbors_params(testname, k, feat_dim, query, train, indices, dists);
+}
+
+vector<nearest_neighbors_params> genNNTests() {
+    return {single_knn_data("1q1t",      1,     1, 10, 1, 0),
+            single_knn_data("1q10t",     1,    10, 10, 1, 0),
+            single_knn_data("1q100t",    1,   100, 10, 1, 0),
+            single_knn_data("1q1000t",   1,  1000, 10, 1, 0),
+            single_knn_data("1q100000t", 1, 10000, 10, 1, 0),
+            single_knn_data("10q1t",        10, 1, 10, 1, 0),
+            single_knn_data("100q1t",      100, 1, 10, 1, 0),
+            single_knn_data("1000q1t",    1000, 1, 10, 1, 0),
+            single_knn_data("10000q1t",  10000, 1, 10, 1, 0),
+            single_knn_data("100000q1t", 10000, 1, 10, 1, 0),
+            single_knn_data("1q1tfl1",      10, 1,  1, 1, 0),
+            single_knn_data("1q1tfl2",      10, 1,  2, 1, 0),
+            single_knn_data("1q1tfl4",      10, 1,  4, 1, 0),
+            single_knn_data("1q1tfl8",      10, 1,  8, 1, 0),
+            single_knn_data("1q1tfl16",     10, 1, 16, 1, 0),
+            single_knn_data("1q1tfl32",     10, 1, 32, 1, 0),
+            single_knn_data("1q1tfl64",     10, 1, 64, 1, 0),
+            single_knn_data("1q1tfl128",    10, 1,128, 1, 0),
+            single_knn_data("1q1tfl256",    10, 1,256, 1, 0),
+            single_knn_data("1q1tfl10000",  10, 1,10000, 1, 0),
+            single_knn_data("10q1t1d",        10, 1, 10, 1, 1),
+            single_knn_data("100q1t1d",      100, 1, 10, 1, 1),
+            single_knn_data("1000q1t1d",    1000, 1, 10, 1, 1),
+            single_knn_data("10000q1t1d",  10000, 1, 10, 1, 1),
+            single_knn_data("100000q1t1d", 10000, 1, 10, 1, 1),
+           };
+}
+
+vector<nearest_neighbors_params> genKNNTests() {
+    return { knn_data("1q1000t1k",   1,  1000, 1,   1, 0),
+             knn_data("1q1000t2k",   1,  1000, 1,   2, 0),
+             knn_data("1q1000t4k",   1,  1000, 1,   4, 0),
+             knn_data("1q1000t8k",   1,  1000, 1,   8, 0),
+             knn_data("1q1000t16k",  1,  1000, 1,  16, 0),
+             knn_data("1q1000t32k",  1,  1000, 1,  32, 0),
+             knn_data("1q1000t64k",  1,  1000, 1,  64, 0),
+             knn_data("1q1000t128k", 1,  1000, 1, 128, 0),
+             knn_data("1q1000t256k", 1,  1000, 1, 256, 0)
+            };
+}
+
+INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD,
+                        NearestNeighborsTest,
+                        ::testing::ValuesIn(genNNTests()),
+                        testNameGenerator<NearestNeighborsTest>
+                        );
+
+INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD,
+                        KNearestNeighborsTest,
+                        ::testing::ValuesIn(genKNNTests()),
+                        testNameGenerator<KNearestNeighborsTest>
+                        );
+
+TEST_P(NearestNeighborsTest, SingleQTests) {
+    nearest_neighbors_params params = GetParam();
+    array query = array(params.qdims_, params.query_.data());
+    array train = array(params.tdims_, params.train_.data());
+
+    const int k = params.k_;
+    const int feat_dim = params.feat_dim_;
+
+    array indices, distances;
+
+    nearestNeighbour(indices, distances, query, train, feat_dim, k, AF_SSD);
+
+    array indices_gold(params.idims_, params.indices_.data());
+    array distances_gold(params.ddims_, params.dists_.data());
+
+    ASSERT_ARRAYS_EQ(indices_gold, indices);
+    ASSERT_ARRAYS_NEAR(distances_gold, distances, 1e-5);
+}
+
+TEST_P(KNearestNeighborsTest, SingleQTests) {
+    nearest_neighbors_params params = GetParam();
+
+    array query = array(params.qdims_, params.query_.data());
+    array train = array(params.tdims_, params.train_.data());
+
+    const int k = params.k_;
+    const int feat_dim = params.feat_dim_;
+
+    array indices, distances;
+
+    nearestNeighbour(indices, distances, query, train, feat_dim, k, AF_SSD);
+
+    array indices_gold(params.idims_, params.indices_.data());
+    array distances_gold(params.ddims_, params.dists_.data());
+
+    ASSERT_ARRAYS_EQ(indices_gold, indices);
+    ASSERT_ARRAYS_NEAR(distances_gold, distances, 1e-5);
+}
+
+TEST(KNearestNeighbours, InvalidNegativeK)
+{
+    const int ntrain = 500;
+    const int nquery = 1;
+    const int nfeat  = 2;
+
+    array t = randu(nfeat, ntrain);
+    array q = randu(nfeat, nquery);
+
+    array indices;
+    array distances;
+    int k = -1;
+    ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD), af::exception);
+}
+
+TEST(KNearestNeighbours, InvalidLargeK)
+{
+    const int ntrain = 500;
+    const int nquery = 1;
+    const int nfeat  = 2;
+
+    array t = randu(nfeat, ntrain);
+    array q = randu(nfeat, nquery);
+
+    array indices;
+    array distances;
+    int k = 257;
+    ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD), af::exception);
+}
+

From b4f92309e602c380334193196a016042e939d516 Mon Sep 17 00:00:00 2001
From: mark-poscablo <mark@arrayfire.com>
Date: Thu, 9 Aug 2018 22:32:17 -0400
Subject: [PATCH 1524/2677] Added pinverse (#2279)

---
 docs/details/lapack.dox    |  25 +++
 include/af/lapack.h        |  46 +++++
 src/api/c/CMakeLists.txt   |   1 +
 src/api/c/pinverse.cpp     | 199 +++++++++++++++++++++
 src/api/cpp/lapack.cpp     |   7 +
 src/api/unified/lapack.cpp |   7 +
 test/CMakeLists.txt        |   1 +
 test/data                  |   2 +-
 test/pinverse.cpp          | 346 +++++++++++++++++++++++++++++++++++++
 9 files changed, 633 insertions(+), 1 deletion(-)
 create mode 100644 src/api/c/pinverse.cpp
 create mode 100644 test/pinverse.cpp

diff --git a/docs/details/lapack.dox b/docs/details/lapack.dox
index 13232e5209..2ce6c85e72 100644
--- a/docs/details/lapack.dox
+++ b/docs/details/lapack.dox
@@ -252,6 +252,31 @@ I [3 3 1 1]
 
 \endcode
 
+=======================================================================
+
+\defgroup lapack_ops_func_pinv pinverse
+
+\ingroup lapack_ops_mat
+
+\brief Pseudo-invert a matrix
+
+This function calculates the Moore-Penrose pseudoinverse of a matrix \f$A\f$,
+using \ref af::svd at its core. If \f$A\f$ is of size \f$M \times N\f$, then its
+pseudoinverse \f$A^+\f$ will be of size \f$N \times M\f$.
+
+This calculation can be batched if the input array is three or four-dimensional
+\f$(M \times N \times P \times Q\f$, with \f$Q=1\f$ for only three dimensions
+\f$)\f$. Each \f$M \times N\f$ slice along the third dimension will have its own
+pseudoinverse, for a total of \f$P \times Q\f$ pseudoinverses in the output array
+\f$(N \times M \times P \times Q)\f$.
+
+Here's an example snippet of its usage. In this example, we have a matrix \f$A\f$
+and we compute its pseudoinverse \f$A^+\f$. This condition must hold:
+\f$AA^+A=A\f$, given that the two matrices are pseudoinverses of each other (in
+fact, this is one of the Moore-Penrose conditions):
+
+\snippet test/pinverse.cpp ex_pinverse
+
 ==================================================================================
 
 \defgroup lapack_ops_func_rank rank
diff --git a/include/af/lapack.h b/include/af/lapack.h
index bb54069550..5c43a22f36 100644
--- a/include/af/lapack.h
+++ b/include/af/lapack.h
@@ -200,6 +200,28 @@ namespace af
     */
     AFAPI array inverse(const array &in, const matProp options = AF_MAT_NONE);
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for pseudo-inverting (Moore-Penrose) a matrix.
+       Currently uses the SVD-based approach.
+
+       \param[in] in is the input matrix
+       \param[in] tol defines the lower threshold for singular values from SVD
+       \param[in] options must be AF_MAT_NONE (more options might be supported
+                  in the future)
+       \returns the pseudo-inverse of the input matrix
+
+       \note \p tol is not the actual lower threshold, but it is passed in as
+             a parameter to the calculation of the actual threshold relative to
+             the shape and contents of \p in.
+       \note This function is not supported in GFOR
+
+       \ingroup lapack_ops_func_pinv
+    */
+    AFAPI array pinverse(const array &in, const double tol=1E-6,
+                         const matProp options = AF_MAT_NONE);
+#endif
+
     /**
        C++ Interface for finding the rank of a matrix
 
@@ -401,6 +423,30 @@ extern "C" {
     */
     AFAPI af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for pseudo-inverting (Moore-Penrose) a matrix.
+       Currently uses the SVD-based approach.
+
+       \param[out] out will contain the pseudo-inverse of matrix \p in
+       \param[in] in is the input matrix
+       \param[in] tol defines the lower threshold for singular values from SVD
+       \param[in] options must be AF_MAT_NONE (more options might be supported
+       in the future)
+
+       \note \p tol is not the actual lower threshold, but it is passed in as a
+             parameter to the calculation of the actual threshold relative to the
+             shape and contents of \p in.
+       \note At first, try setting \p tol to 1e-6 for single precision and 1e-12
+             for double.
+       \note This function is not supported in GFOR
+
+       \ingroup lapack_ops_func_pinv
+    */
+    AFAPI af_err af_pinverse(af_array *out, const af_array in, const double tol,
+                             const af_mat_prop options);
+#endif
+
     /**
        C Interface for finding the rank of a matrix
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 354005f5c3..0060c0d4d4 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -113,6 +113,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/ops.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/optypes.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/orb.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/pinverse.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/plot.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/print.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/qr.cpp
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
new file mode 100644
index 0000000000..dd875852e0
--- /dev/null
+++ b/src/api/c/pinverse.cpp
@@ -0,0 +1,199 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <backend.hpp>
+#include <Array.hpp>
+
+#include <af/array.h>
+#include <af/complex.h>
+#include <af/defines.h>
+#include <af/lapack.h>
+#include <arith.hpp>
+#include <blas.hpp>
+#include <cast.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <diagonal.hpp>
+#include <handle.hpp>
+#include <logic.hpp>
+#include <math.hpp>
+#include <reduce.hpp>
+#include <select.hpp>
+#include <svd.hpp>
+#include <tile.hpp>
+#include <transpose.hpp>
+
+using af::dim4;
+using af::dtype_traits;
+using std::vector;
+using std::swap;
+
+using namespace detail;
+
+const double dfltTol = 1e-6;
+
+template<typename T>
+Array<T> getSubArray(const Array<T> &in, const bool copy,
+                       uint dim0begin = 0, uint dim0end = 0,
+                       uint dim1begin = 0, uint dim1end = 0,
+                       uint dim2begin = 0, uint dim2end = 0,
+                       uint dim3begin = 0, uint dim3end = 0) {
+    vector<af_seq> seqs = {
+        {static_cast<double>(dim0begin), static_cast<double>(dim0end), 1.},
+        {static_cast<double>(dim1begin), static_cast<double>(dim1end), 1.},
+        {static_cast<double>(dim2begin), static_cast<double>(dim2end), 1.},
+        {static_cast<double>(dim3begin), static_cast<double>(dim3end), 1.}
+    };
+    return createSubArray<T>(in, seqs, copy);
+}
+
+// Moore-Penrose Pseudoinverse
+template<typename T>
+Array<T> pinverseSvd(const Array<T> &in, const double tol)
+{
+    in.eval();
+    int M = in.dims()[0];
+    int N = in.dims()[1];
+    int P = in.dims()[2];
+    int Q = in.dims()[3];
+
+    // Compute SVD
+    typedef typename dtype_traits<T>::base_type Tr;
+    // Ideally, these initializations should use createEmptyArray(), but for some
+    // reason, linux-opencl-k80 will produce wrong results for large arrays
+    Array<T> u = createValueArray<T>(dim4(M, M, P, Q), scalar<T>(0));
+    Array<T> vT = createValueArray<T>(dim4(N, N, P, Q), scalar<T>(0));
+    Array<Tr> sVec = createValueArray<Tr>(dim4(min(M, N), 1, P, Q), scalar<Tr>(0));
+    for (uint j = 0; j < Q; ++j) {
+        for (uint i = 0; i < P; ++i) {
+            Array<T> inSlice = getSubArray(in, false,
+                                           0, M - 1,
+                                           0, N - 1,
+                                           i, i,
+                                           j, j);
+            Array<Tr> sVecSlice = getSubArray(sVec, false,
+                                              0, sVec.dims()[0] - 1,
+                                              0, 0,
+                                              i, i,
+                                              j, j);
+            Array<T> uSlice = getSubArray(u, false,
+                                          0, u.dims()[0] - 1,
+                                          0, u.dims()[1] - 1,
+                                          i, i,
+                                          j, j);
+            Array<T> vTSlice = getSubArray(vT, false,
+                                           0, vT.dims()[0] - 1,
+                                           0, vT.dims()[1] - 1,
+                                           i, i,
+                                           j, j);
+            svd<T, Tr>(sVecSlice, uSlice, vTSlice, inSlice);
+        }
+    }
+
+    // Cast s back to original data type for matmul later
+    // (since svd() makes s' type the base type of T)
+    Array<T> sVecCast = cast<T, Tr>(sVec);
+
+    Array<T> v = transpose(vT, true);
+
+    // Build relative tolerance array
+    Array<Tr> sVecMax = reduce<af_max_t, Tr, Tr>(sVec, 0);
+    Array<T> sVecMaxCast = cast<T, Tr>(sVecMax);
+    double tolMulShape = tol * static_cast<double>(max(M, N));
+    Array<T> tolMulShapeArr = createValueArray<T>(sVecMaxCast.dims(),
+                                                  scalar<T>(tolMulShape));
+    Array<T> relTol = arithOp<T, af_mul_t>(tolMulShapeArr, sVecMaxCast,
+                                           sVecMaxCast.dims());
+    Array<T> relTolArr = tile<T>(relTol, dim4(sVecCast.dims()[0]));
+
+    // Get reciprocal of sVec's non-zero values for s pinverse, except for
+    // very small non-zero values though (< relTol), in order to avoid very
+    // large reciprocals
+    Array<T> ones = createValueArray<T>(sVecCast.dims(), scalar<T>(1.));
+    Array<T> sVecRecip = arithOp<T, af_div_t>(ones, sVecCast, sVecCast.dims());
+    Array<char> cond = logicOp<T, af_ge_t>(sVecCast, relTolArr, sVecCast.dims());
+    Array<T> zeros = createValueArray<T>(sVecCast.dims(), scalar<T>(0.));
+    sVecRecip = createSelectNode<T>(cond, sVecRecip, zeros, sVecRecip.dims());
+
+    // Make s vector into s pinverse array
+    Array<T> sVecRecipMod = modDims<T>(sVecRecip, dim4(sVecRecip.dims()[0],
+                                                       (sVecRecip.dims()[2]
+                                                        * sVecRecip.dims()[3])));
+    Array<T> sPinv = diagCreate<T>(sVecRecipMod, 0);
+    sPinv = modDims<T>(sPinv, dim4(sPinv.dims()[0], sPinv.dims()[1],
+                                   sVecRecip.dims()[2], sVecRecip.dims()[3]));
+
+    Array<T> uT = transpose(u, true);
+
+    // Crop v and u* for final matmul later based on s+'s size, because
+    // sVec produced by svd() has minimal dim length (no extra zeroes).
+    // Thus s+ produced by diagCreate() will have minimal dims as well,
+    // and v could have an extra dim0 or u* could have an extra dim1
+    if (v.dims()[1] > sPinv.dims()[0]) {
+        v = getSubArray(v, false,
+                        0, v.dims()[0] - 1,
+                        0, sPinv.dims()[0] - 1,
+                        0, v.dims()[2] - 1,
+                        0, v.dims()[3] - 1);
+    }
+    if (uT.dims()[0] > sPinv.dims()[1]) {
+        uT = getSubArray(uT, false,
+                         0, sPinv.dims()[1] - 1,
+                         0, uT.dims()[1] - 1,
+                         0, uT.dims()[2] - 1,
+                         0, uT.dims()[3] - 1);
+    }
+
+    Array<T> out = matmul<T>(matmul<T>(v, sPinv, AF_MAT_NONE, AF_MAT_NONE),
+                             uT, AF_MAT_NONE, AF_MAT_NONE);
+
+    return out;
+}
+
+template<typename T>
+static inline af_array pinverse(const af_array in, const double tol)
+{
+    return getHandle(pinverseSvd<T>(getArray<T>(in), tol));
+}
+
+af_err af_pinverse(af_array *out, const af_array in, const double tol,
+                   const af_mat_prop options)
+{
+    try {
+        const ArrayInfo& i_info = getInfo(in);
+
+        af_dtype type = i_info.getType();
+
+        if (options != AF_MAT_NONE) {
+            AF_ERROR("Using this property is not yet supported in inverse", AF_ERR_NOT_SUPPORTED);
+        }
+
+        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
+        ARG_ASSERT(2, tol >= 0.); // Ensure tolerance is not negative
+
+        af_array output;
+
+        if(i_info.ndims() == 0) {
+            return af_retain_array(out, in);
+        }
+
+        switch(type) {
+            case f32: output = pinverse<float  >(in, tol);  break;
+            case f64: output = pinverse<double >(in, tol);  break;
+            case c32: output = pinverse<cfloat >(in, tol);  break;
+            case c64: output = pinverse<cdouble>(in, tol);  break;
+            default:  TYPE_ERROR(1, type);
+        }
+        swap(*out, output);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
diff --git a/src/api/cpp/lapack.cpp b/src/api/cpp/lapack.cpp
index 091c807612..83a3163078 100644
--- a/src/api/cpp/lapack.cpp
+++ b/src/api/cpp/lapack.cpp
@@ -117,6 +117,13 @@ namespace af
         return array(out);
     }
 
+    array pinverse(const array &in, const double tol, const matProp options)
+    {
+        af_array out;
+        AF_THROW(af_pinverse(&out, in.get(), tol, options));
+        return array(out);
+    }
+
     unsigned rank(const array &in, const double tol)
     {
         unsigned r = 0;
diff --git a/src/api/unified/lapack.cpp b/src/api/unified/lapack.cpp
index 8a367017cf..7c0927d2d9 100644
--- a/src/api/unified/lapack.cpp
+++ b/src/api/unified/lapack.cpp
@@ -79,6 +79,13 @@ af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options)
     return CALL(out, in, options);
 }
 
+af_err af_pinverse(af_array *out, const af_array in, const double tol,
+                   const af_mat_prop options)
+{
+    CHECK_ARRAYS(in);
+    return CALL(out, in, options);
+}
+
 af_err af_rank(unsigned *rank, const af_array in, const double tol)
 {
     CHECK_ARRAYS(in);
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 0967c3e4b1..693c4ab1b6 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -236,6 +236,7 @@ if(OpenCL_FOUND)
 endif()
 
 make_test(SRC orb.cpp)
+make_test(SRC pinverse.cpp)
 make_test(SRC qr_dense.cpp)
 make_test(SRC random.cpp)
 make_test(SRC range.cpp)
diff --git a/test/data b/test/data
index 40a7c36ca3..f8270901bc 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 40a7c36ca39e78cc44325933920da8bfb2ad4e21
+Subproject commit f8270901bc80e1ceec2ce33ba151b1acf3db4f16
diff --git a/test/pinverse.cpp b/test/pinverse.cpp
new file mode 100644
index 0000000000..85fb278797
--- /dev/null
+++ b/test/pinverse.cpp
@@ -0,0 +1,346 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <arrayfire.h>
+#include <af/dim4.hpp>
+#include <af/defines.h>
+#include <af/traits.hpp>
+#include <iostream>
+#include <complex>
+#include <testHelpers.hpp>
+#include <limits>
+
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::constant;
+using af::dim4;
+using af::dtype;
+using af::dtype_traits;
+using af::exception;
+using af::identity;
+using af::matmul;
+using af::max;
+using af::pinverse;
+using af::randu;
+using af::span;
+using std::abs;
+using std::string;
+using std::vector;
+
+template<typename T>
+array makeComplex(dim4 dims, const vector<T>& real, const vector<T>& imag) {
+    array realArr(dims, &real.front());
+    array imagArr(dims, &imag.front());
+    return af::complex(realArr, imagArr);
+}
+
+template<typename T>
+array readTestInput(string testFilePath) {
+    typedef typename dtype_traits<T>::base_type InBaseType;
+    dtype outAfType = (dtype) dtype_traits<T>::af_type;
+
+    vector<dim4> dimsVec;
+    vector<vector<InBaseType> > inVec;
+    vector<vector<InBaseType> > goldVec;
+    readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec, goldVec);
+    dim4 inDims = dimsVec[0];
+
+    if (outAfType == c32 || outAfType == c64) {
+        return makeComplex(inDims, inVec[1], inVec[2]);
+    }
+    else {
+        return array(inDims, &inVec[0].front());
+    }
+}
+
+template<typename T>
+array readTestGold(string testFilePath) {
+    typedef typename dtype_traits<T>::base_type InBaseType;
+    dtype outAfType = (dtype) dtype_traits<T>::af_type;
+
+    vector<dim4> dimsVec;
+    vector<vector<InBaseType> > inVec;
+    vector<vector<InBaseType> > goldVec;
+    readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec, goldVec);
+    dim4 goldDims(dimsVec[0][1], dimsVec[0][0]);
+
+    if (outAfType == c32 || outAfType == c64) {
+        return makeComplex(goldDims, goldVec[1], goldVec[2]);
+    }
+    else {
+        return array(goldDims, &goldVec[0].front());
+    }
+}
+
+template<typename T>
+class Pinverse : public ::testing::Test
+{
+
+};
+
+// Epsilons taken from test/inverse.cpp
+template<typename T>
+double eps();
+
+template<>
+double eps<float>() {
+    return 0.01f;
+}
+
+template<>
+double eps<double>() {
+    return 1e-5;
+}
+
+template<>
+double eps<cfloat>() {
+    return 0.01f;
+}
+
+template<>
+double eps<cdouble>() {
+    return 1e-5;
+}
+
+template<typename T>
+double relEps(array in) {
+    typedef typename af::dtype_traits<T>::base_type InBaseType;
+    return std::numeric_limits<InBaseType>::epsilon()
+        * std::max(in.dims(0), in.dims(1)) * af::max<double>(in);
+}
+
+typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+TYPED_TEST_CASE(Pinverse, TestTypes);
+
+// Test Moore-Penrose conditions in the following first 4 tests
+// See https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Definition
+TYPED_TEST(Pinverse, AApinvA_A) {
+    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array inpinv = pinverse(in);
+    array out = matmul(in, inpinv, in);
+    ASSERT_ARRAYS_NEAR(in, out, eps<TypeParam>());
+}
+
+TYPED_TEST(Pinverse, ApinvAApinv_Apinv) {
+    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array inpinv = pinverse(in);
+    array out = matmul(inpinv, in, inpinv);
+    ASSERT_ARRAYS_NEAR(inpinv, out, eps<TypeParam>());
+}
+
+TYPED_TEST(Pinverse, AApinv_IsHermitian) {
+    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array inpinv = pinverse(in);
+    array aapinv = matmul(in, inpinv);
+    array out = matmul(in, inpinv).H();
+    ASSERT_ARRAYS_NEAR(aapinv, out, eps<TypeParam>());
+}
+
+TYPED_TEST(Pinverse, ApinvA_IsHermitian) {
+    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array inpinv = pinverse(in);
+    array apinva = af::matmul(inpinv, in);
+    array out = af::matmul(inpinv, in).H();
+    ASSERT_ARRAYS_NEAR(apinva, out, eps<TypeParam>());
+}
+
+TYPED_TEST(Pinverse, Large) {
+    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse640x480.test"));
+    array inpinv = pinverse(in);
+    array out = matmul(in, inpinv, in);
+    ASSERT_ARRAYS_NEAR(in, out, relEps<TypeParam>(in));
+}
+
+TYPED_TEST(Pinverse, LargeTall) {
+    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse640x480.test")).T();
+    array inpinv = pinverse(in);
+    array out = matmul(in, inpinv, in);
+    ASSERT_ARRAYS_NEAR(in, out, relEps<TypeParam>(in));
+}
+
+TEST(Pinverse, Square) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x10.test"));
+    array inpinv = pinverse(in);
+    array out = matmul(in, inpinv, in);
+    ASSERT_ARRAYS_NEAR(in, out, eps<float>());
+}
+
+TEST(Pinverse, Dim1GtDim0) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse8x10.test"));
+    array inpinv = pinverse(in);
+    array out = matmul(in, inpinv, in);
+    ASSERT_ARRAYS_NEAR(in, out, eps<float>());
+}
+
+TEST(Pinverse, CompareWithNumpy) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array gold = readTestGold<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array out = pinverse(in);
+    ASSERT_ARRAYS_NEAR(gold, out, relEps<float>(gold));
+}
+
+TEST(Pinverse, SmallSigValExistsFloat) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    const dim_t dim0 = in.dims(0);
+    const dim_t dim1 = in.dims(1);
+
+    // Generate sigma with small non-zero value
+    af::array u;
+    af::array vT;
+    af::array sVec;
+    af::svd(u, sVec, vT, in);
+    dim_t sSize = sVec.elements();
+
+    sVec(2) = 1e-12;
+    af::array s = af::diag(sVec, 0, false);
+    af::array zeros = af::constant(0,
+                                   dim0 > sSize ? dim0 - sSize : sSize,
+                                   dim1 > sSize ? dim1 - sSize : sSize);
+    s = af::join(dim0 > dim1 ? 0 : 1, s, zeros);
+
+    // Make new input array that has a small non-zero value in its SVD sigma
+    in = af::matmul(u, s, vT);
+    array inpinv = pinverse(in);
+    array out = matmul(in, inpinv, in);
+
+    ASSERT_ARRAYS_NEAR(in, out, eps<float>());
+}
+
+TEST(Pinverse, SmallSigValExistsDouble) {
+    array in = readTestInput<double>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    const dim_t dim0 = in.dims(0);
+    const dim_t dim1 = in.dims(1);
+
+    // Generate sigma with small non-zero value
+    array u;
+    array vT;
+    array sVec;
+    svd(u, sVec, vT, in);
+    dim_t sSize = sVec.elements();
+
+    sVec(2) = (double) 1e-16;
+    array s = diag(sVec, 0, false);
+    array zeros = constant(0,
+                           dim0 > sSize ? dim0 - sSize : sSize,
+                           dim1 > sSize ? dim1 - sSize : sSize,
+                           f64);
+    s = join(dim0 > dim1 ? 0 : 1, s, zeros);
+
+    // Make new input array that has a small non-zero value in its SVD sigma
+    in = matmul(u, s, vT);
+    array inpinv = pinverse(in, 1e-15);
+    array out = matmul(in, inpinv, in);
+
+    ASSERT_ARRAYS_NEAR(in, out, eps<double>());
+}
+
+TEST(Pinverse, Batching3D) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8x2.test"));
+    array inpinv0 = pinverse(in(span, span, 0));
+    array inpinv1 = pinverse(in(span, span, 1));
+
+    array out = pinverse(in);
+    array out0 = out(span, span, 0);
+    array out1 = out(span, span, 1);
+
+    ASSERT_ARRAYS_NEAR(inpinv0, out0, relEps<float>(inpinv0));
+    ASSERT_ARRAYS_NEAR(inpinv1, out1, relEps<float>(inpinv1));
+}
+
+TEST(Pinverse, Batching4D) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8x2x2.test"));
+    array inpinv00 = pinverse(in(span, span, 0, 0));
+    array inpinv01 = pinverse(in(span, span, 0, 1));
+    array inpinv10 = pinverse(in(span, span, 1, 0));
+    array inpinv11 = pinverse(in(span, span, 1, 1));
+
+    array out = pinverse(in);
+    array out00 = out(span, span, 0, 0);
+    array out01 = out(span, span, 0, 1);
+    array out10 = out(span, span, 1, 0);
+    array out11 = out(span, span, 1, 1);
+
+    ASSERT_ARRAYS_NEAR(inpinv00, out00, relEps<float>(inpinv00));
+    ASSERT_ARRAYS_NEAR(inpinv01, out01, relEps<float>(inpinv01));
+    ASSERT_ARRAYS_NEAR(inpinv10, out10, relEps<float>(inpinv10));
+    ASSERT_ARRAYS_NEAR(inpinv11, out11, relEps<float>(inpinv11));
+}
+
+TEST(Pinverse, CustomTol) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array inpinv = pinverse(in, 1e-12);
+    array out = matmul(in, inpinv, in);
+    ASSERT_ARRAYS_NEAR(in, out, eps<float>());
+}
+
+TEST(Pinverse, C) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    af_array inpinv = 0, out = 0;
+    ASSERT_SUCCESS(af_pinverse(&inpinv, in.get(), 1e-6, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&out, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&out, out, in.get(), AF_MAT_NONE, AF_MAT_NONE));
+
+    ASSERT_ARRAYS_NEAR(in.get(), out, eps<float>());
+
+    ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(inpinv));
+}
+
+TEST(Pinverse, C_CustomTol) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    af_array inpinv = 0, out = 0;
+    ASSERT_SUCCESS(af_pinverse(&inpinv, in.get(), 1e-12, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&out, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&out, out, in.get(), AF_MAT_NONE, AF_MAT_NONE));
+
+    ASSERT_ARRAYS_NEAR(in.get(), out, eps<float>());
+
+    ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(inpinv));
+}
+
+TEST(Pinverse, NegativeTol) {
+    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array out;
+    ASSERT_THROW(out = pinverse(in, -1.f), exception);
+}
+
+TEST(Pinverse, InvalidType) {
+    array in = constant(0, 10, 8, u8);
+    array out;
+    ASSERT_THROW(out = pinverse(in, -1.f), exception);
+}
+
+TEST(Pinverse, InvalidMatProp) {
+    array in = constant(0.f, 10, 8, f32);
+    array out;
+    ASSERT_THROW(out = pinverse(in, -1.f, AF_MAT_SYM), exception);
+}
+
+TEST(Pinverse, DocSnippet) {
+    //! [ex_pinverse]
+    float hA[] = {0, 1, 2, 3, 4, 5};
+    array A(3, 2, hA);
+    //  0.0000     3.0000
+    //  1.0000     4.0000
+    //  2.0000     5.0000
+
+    array Apinv = pinverse(A);
+    // -0.7778    -0.1111     0.5556
+    //  0.2778     0.1111    -0.0556
+
+    array MustBeA = matmul(A, Apinv, A);
+    //  0.0000     3.0000
+    //  1.0000     4.0000
+    //  2.0000     5.0000
+    //! [ex_pinverse]
+    ASSERT_ARRAYS_NEAR(A, MustBeA, eps<float>());
+}

From 6fc326f38de0ae81c80099203c66f56168586827 Mon Sep 17 00:00:00 2001
From: mark-poscablo <mark@arrayfire.com>
Date: Fri, 31 Aug 2018 15:52:41 -0400
Subject: [PATCH 1525/2677] svd OpenCL: Use buffer map/unmap instead of
 read/write in order to correctly write into subarrays (#2279)

---
 src/backend/opencl/svd.cpp | 53 +++++++++++++++++++++++++-------------
 1 file changed, 35 insertions(+), 18 deletions(-)

diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 811aa91b36..bc5c980686 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -116,55 +116,71 @@ void svd(Array<T > &arrU,
     int nru = 0;
     int ncvt = 0;
 
-    std::vector<T> A(m * n);
+    // Instead of copying U, S, VT, and A to the host and copying the results
+    // back to the device, create a pointer that's mapped to device memory where
+    // the computation can directly happen
+    T *mappedA = (T*) getQueue().enqueueMapBuffer(*arrA.get(), CL_FALSE,
+                                                  CL_MAP_READ,
+                                                  sizeof(T) * arrA.getOffset(),
+                                                  sizeof(T) * arrA.elements());
     std::vector<T> tauq(min_mn), taup(min_mn);
     std::vector<T> work(lwork);
-    std::vector<Tr> s0(min_mn), s1(min_mn - 1);
+    Tr *mappedS0 = (Tr*) getQueue().enqueueMapBuffer(*arrS.get(), CL_TRUE,
+                                                     CL_MAP_WRITE,
+                                                     sizeof(Tr) * arrS.getOffset(),
+                                                     sizeof(Tr) * arrS.elements());
+    std::vector<Tr> s1(min_mn - 1);
     std::vector<Tr> rwork(5 * min_mn);
 
     int info = 0;
 
-    copyData(&A[0], arrA);
-
-
     // Bidiagonalize A
     // (CWorkspace: need 2*N + M, prefer 2*N + (M + N)*NB)
     // (RWorkspace: need N)
     magma_gebrd_hybrid<T>(m, n,
-                          &A[0], lda,
+                          mappedA, lda,
                           (*arrA.get())(), arrA.getOffset(), ldda,
-                          (void *)&s0[0], (void *)&s1[0],
+                          (void *)mappedS0, (void *)&s1[0],
                           &tauq[0], &taup[0],
                           &work[0], lwork,
                           getQueue()(), &info, false);
 
-    std::vector<T> U(1), VT(1);
+    T *mappedU = nullptr, *mappedVT = nullptr;
     std::vector<T> cdummy(1);
 
     if (want_vectors) {
 
-        U = std::vector<T>(m * m);
-        VT = std::vector<T>(n * n);
+        mappedU = (T*) getQueue().enqueueMapBuffer(*arrU.get(), CL_FALSE,
+                                                   CL_MAP_WRITE,
+                                                   sizeof(T) * arrU.getOffset(),
+                                                   sizeof(T) * arrU.elements());
+        mappedVT = (T*) getQueue().enqueueMapBuffer(*arrVT.get(), CL_TRUE,
+                                                    CL_MAP_WRITE,
+                                                    sizeof(T) * arrVT.getOffset(),
+                                                    sizeof(T) * arrVT.elements());
 
         // If left singular vectors desired in U, copy result to U
         // and generate left bidiagonalizing vectors in U
         // (CWorkspace: need 2*N + NCU, prefer 2*N + NCU*NB)
         // (RWorkspace: 0)
-        LAPACKE_CHECK(cpu_lapack_lacpy('L', m, n, &A[0], lda, &U[0], ldu));
+        LAPACKE_CHECK(cpu_lapack_lacpy('L', m, n, mappedA, lda, mappedU, ldu));
 
         int ncu = m;
-        LAPACKE_CHECK(cpu_lapack_ungbr_work('Q', m, ncu, n, &U[0], ldu, &tauq[0], &work[0], lwork));
+        LAPACKE_CHECK(cpu_lapack_ungbr_work('Q', m, ncu, n, mappedU, ldu,
+                                            &tauq[0], &work[0], lwork));
 
         // If right singular vectors desired in VT, copy result to
         // VT and generate right bidiagonalizing vectors in VT
         // (CWorkspace: need 3*N-1, prefer 2*N + (N-1)*NB)
         // (RWorkspace: 0)
-        LAPACKE_CHECK(cpu_lapack_lacpy('U', n, n, &A[0], lda, &VT[0], ldvt));
-        LAPACKE_CHECK(cpu_lapack_ungbr_work('P', n, n, n, &VT[0], ldvt, &taup[0], &work[0], lwork));
+        LAPACKE_CHECK(cpu_lapack_lacpy('U', n, n, mappedA, lda, mappedVT, ldvt));
+        LAPACKE_CHECK(cpu_lapack_ungbr_work('P', n, n, n, mappedVT, ldvt,
+                                            &taup[0], &work[0], lwork));
 
         nru = m;
         ncvt = n;
     }
+    getQueue().enqueueUnmapMemObject(*arrA.get(), mappedA);
 
     // Perform bidiagonal QR iteration, if desired, computing
     // left singular vectors in U and computing right singular
@@ -172,16 +188,17 @@ void svd(Array<T > &arrU,
     // (CWorkspace: need 0)
     // (RWorkspace: need BDSPAC)
     LAPACKE_CHECK(cpu_lapack_bdsqr_work('U', n, ncvt, nru, izero,
-                                        &s0[0], &s1[0], &VT[0], ldvt, &U[0], ldu,
+                                        mappedS0, &s1[0], mappedVT,
+                                        ldvt, mappedU, ldu,
                                         &cdummy[0], ione, &rwork[0]));
 
 
     if (want_vectors) {
-        writeHostDataArray(arrU, &U[0], arrU.elements() * sizeof(T));
-        writeHostDataArray(arrVT, &VT[0], arrVT.elements() * sizeof(T));
+          getQueue().enqueueUnmapMemObject(*arrU.get(), mappedU);
+          getQueue().enqueueUnmapMemObject(*arrVT.get(), mappedVT);
     }
 
-    writeHostDataArray(arrS, &s0[0], arrS.elements() * sizeof(Tr));
+    getQueue().enqueueUnmapMemObject(*arrS.get(), mappedS0);
 
     if (iscl == 1) {
         Tr rscale = scalar<Tr>(1);

From d11154307572a25f96b31bbae59f9e6d833523d0 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Mon, 24 Sep 2018 13:36:12 -0400
Subject: [PATCH 1526/2677] Fix and improve accum documentation

---
 docs/details/algorithm.dox | 21 ++++++++++++++--
 include/af/algorithm.h     | 12 +++++-----
 test/scan.cpp              | 49 ++++++++++++++++++++++++++++++++++++++
 3 files changed, 74 insertions(+), 8 deletions(-)

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index 6e6f3b8cde..4918978b40 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -107,9 +107,26 @@ Return type is u32 for all input types
 
 \ingroup scan_mat
 
-Perform inclusive sum along specified dimension
+Calculate the cumulative sum (inclusive) along the specified dimension
 
-This table defines the return value types for the corresponding input types
+For a 1D array \f$X\f$, the inclusive cumulative sum calculates \f$x_i =
+\sum_{p=0}^{i}x_p\f$ for every \f$x \in X\f$. Here is a simple example for the
+1D case:
+
+\snippet test/scan.cpp ex_accum_1D
+
+For 2D arrays (and higher dimensions), you can specify the dimension along which
+the cumulative sum will be calculated. Thus, the formula above will be
+calculated for all array slices along the specified dimension (in the 2D case
+for example, this looks like \f$x_{i,j} = \sum_{p=0}^{j}x_{i,p}\f$ if the second
+dimension (dim1) was chosen). If no dimension is specified, then the first
+dimension (dim0) is used by default (only in the C++ API; the dimension is
+required to be specified in the C API):
+
+\snippet test/scan.cpp ex_accum_2D
+
+The output array type may be different from the input array type. The following
+table defines the corresponding output types for each input type:
 
 Input Type          | Output Type
 --------------------|---------------------
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 737ef1a3d3..89a0774418 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -306,11 +306,11 @@ namespace af
     template<typename T> void max(T *val, unsigned *idx, const array &in);
 
     /**
-       C++ Interface inclusive sum (cumulative sum) of an array
+       C++ Interface for computing the cumulative sum (inclusive) of an array
 
        \param[in] in is the input array
-       \param[in] dim The dimension along which exclusive sum is performed
-       \return the output containing exclusive sums of the input
+       \param[in] dim is the dimension along which the inclusive sum is calculated
+       \return the output containing inclusive sums of the input
 
        \ingroup scan_func_accum
     */
@@ -761,11 +761,11 @@ extern "C" {
     AFAPI af_err af_imax_all(double *real, double *imag, unsigned *idx, const af_array in);
 
     /**
-       C Interface inclusive sum (cumulative sum) of an array
+       C Interface for computing the cumulative sum (inclusive) of an array
 
-       \param[out] out will contain exclusive sums of the input
+       \param[out] out will contain inclusive sums of the input
        \param[in] in is the input array
-       \param[in] dim The dimension along which exclusive sum is performed
+       \param[in] dim is the dimension along which the inclusive sum is calculated
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup scan_func_accum
diff --git a/test/scan.cpp b/test/scan.cpp
index a84a12e515..5865d241c5 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -213,3 +213,52 @@ TEST(Accum, MaxDim)
     ASSERT_ARRAYS_EQ(gold_dim, output_dim);
 
 }
+
+TEST(Accum, DocSnippet) {
+    //! [ex_accum_1D]
+    float hA[] = {0, 1, 2, 3, 4};
+    array A(5, hA);
+    //  0.
+    //  1.
+    //  2.
+    //  3.
+    //  4.
+
+    array accumA = accum(A);
+    //  0.
+    //  1.
+    //  3.
+    //  6.
+    //  10.
+    //! [ex_accum_1D]
+
+    float h_gold_accumA[] = {0, 1, 3, 6, 10};
+    array gold_accumA(5, h_gold_accumA);
+    ASSERT_ARRAYS_EQ(gold_accumA, accumA);
+
+    //! [ex_accum_2D]
+    float hB[] = {0, 1, 2, 3, 4, 5, 6, 7, 8};
+    array B(3, 3, hB);
+    //  0.     3.     6.
+    //  1.     4.     7.
+    //  2.     5.     8.
+
+    array accumB_dim0 = accum(B);
+    //  0.     3.     6.
+    //  1.     7.     13.
+    //  3.     12.    21.
+
+    array accumB_dim1 = accum(B, 1);
+    //  0.     3.     9.
+    //  1.     5.     12.
+    //  2.     7.     15.
+    //! [ex_accum_2D]
+
+    float h_gold_accumB_dim0[] = {0, 1, 3, 3, 7, 12, 6, 13, 21};
+    array gold_accumB_dim0(3, 3, h_gold_accumB_dim0);
+    ASSERT_ARRAYS_EQ(gold_accumB_dim0, accumB_dim0);
+
+    float h_gold_accumB_dim1[] = {0, 1, 2, 3, 5, 7, 9, 12, 15};
+    array gold_accumB_dim1(3, 3, h_gold_accumB_dim1);
+    ASSERT_ARRAYS_EQ(gold_accumB_dim1, accumB_dim1);
+}

From 11a4c9fc2beb51ceefe319f49f38906fa30902c6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Sat, 29 Sep 2018 08:12:19 -0400
Subject: [PATCH 1527/2677] Improve docs and tests for SET fns

* add set operations examples to documentation
* change ARRAYS_EQ to VEC_ARRAY_EQ in unit tests
* add note regarding batching to documentation
---
 docs/details/algorithm.dox |  38 +++++++++-
 include/af/algorithm.h     |  28 +++----
 test/CMakeLists.txt        |   2 +-
 test/set.cpp               | 152 +++++++++++++++++++++++++++++++++++++
 4 files changed, 202 insertions(+), 18 deletions(-)

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index 4918978b40..15ddff9927 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -234,7 +234,22 @@ Sort a multi dimensional array based on keys
 
 \ingroup set_mat
 
-Find unique values from an input
+Finds unique values from an input set. The input must be a one-dimensional array. Batching is not currently supported.
+
+A simple example of finding the unique values of a set using setUnique() can be seen below:
+
+\snippet test/set.cpp ex_set_unique_simple
+
+The function can be sped up if it is known that the inputs are sorted.
+
+\snippet test/set.cpp ex_set_unique_sorted
+
+The inputs can be sorted in ascending or descending order.
+
+\snippet test/set.cpp ex_set_unique_desc
+
+
+
 
 
 
@@ -242,7 +257,16 @@ Find unique values from an input
 
 \ingroup set_mat
 
-Find union of two inputs
+Find the union of two sets. The inputs must be one-dimensional arrays. Batching is not currently supported.
+
+A simple example of finding the union of two sets using setUnion() can be seen below:
+
+\snippet test/set.cpp ex_set_union_simple
+
+The function can be sped up if it is known that each input is sorted in increasing order and its values are unique.
+
+\snippet test/set.cpp ex_set_union
+
 
 
 
@@ -250,7 +274,15 @@ Find union of two inputs
 
 \ingroup set_mat
 
-Find intersection of two inputs
+Find the intersection of two sets. The inputs must be one-dimensional arrays. Batching is not currently supported.
+
+A simple example of finding the intersection of two sets using setIntersect() can be seen below:
+
+\snippet test/set.cpp ex_set_intersect_simple
+
+The function can be sped up if it is known that each input is sorted in increasing order and its values are unique.
+
+\snippet test/set.cpp ex_set_intersect
 
 
 @}
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 89a0774418..517d65dcff 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -429,24 +429,24 @@ namespace af
     AFAPI array setUnique(const array &in, const bool is_sorted=false);
 
     /**
-       C++ Interface for performing union of two arrays
+       C++ Interface for finding the union of two arrays
 
-       \param[in] first is the first array
-       \param[in] second is the second array
+       \param[in] first is the first input array
+       \param[in] second is the second input array
        \param[in] is_unique if true, skips calling unique internally
-       \return the union of \p first and \p second
+       \return all unique values present in \p first and \p second (union) in increasing order
 
        \ingroup set_func_union
     */
     AFAPI array setUnion(const array &first, const array &second, const bool is_unique=false);
 
     /**
-       C++ Interface for performing intersect of two arrays
+       C++ Interface for finding the intersection of two arrays
 
-       \param[in] first is the first array
-       \param[in] second is the second array
+       \param[in] first is the first input array
+       \param[in] second is the second input array
        \param[in] is_unique if true, skips calling unique internally
-       \return the intersection of \p first and \p second
+       \return unique values that are present in both \p first and \p second(intersection) in increasing order
 
        \ingroup set_func_intersect
     */
@@ -895,11 +895,11 @@ extern "C" {
     AFAPI af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted);
 
     /**
-       C Interface for performing union of two arrays
+       C Interface for finding the union of two arrays
 
        \param[out] out will contain the union of \p first and \p second
-       \param[in] first is the first array
-       \param[in] second is the second array
+       \param[in] first is the first input array
+       \param[in] second is the second input array
        \param[in] is_unique if true, skips calling unique internally
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -908,11 +908,11 @@ extern "C" {
     AFAPI af_err af_set_union(af_array *out, const af_array first, const af_array second, const bool is_unique);
 
     /**
-       C Interface for performing intersect of two arrays
+       C Interface for finding the intersection of two arrays
 
        \param[out] out will contain the intersection of \p first and \p second
-       \param[in] first is the first array
-       \param[in] second is the second array
+       \param[in] first is the first input array
+       \param[in] second is the second input array
        \param[in] is_unique if true, skips calling unique internally
        \return \ref AF_SUCCESS if the execution completes properly
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 693c4ab1b6..6540e20482 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -252,7 +252,7 @@ make_test(SRC sat.cpp)
 make_test(SRC scan.cpp)
 make_test(SRC scan_by_key.cpp)
 make_test(SRC select.cpp)
-make_test(SRC set.cpp)
+make_test(SRC set.cpp CXX11)
 make_test(SRC shift.cpp)
 
 if(AF_WITH_NONFREE)
diff --git a/test/set.cpp b/test/set.cpp
index a26ec096e2..23a994a5db 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -161,3 +161,155 @@ SET_TESTS(short)
 SET_TESTS(ushort)
 SET_TESTS(intl)
 SET_TESTS(uintl)
+
+// Documentation examples for setUnique
+TEST(Set, SNIPPET_setUniqueSorted) {
+
+    //! [ex_set_unique_sorted]
+
+    // input data
+    int h_set[6] = {1, 2, 2, 3, 3, 3};
+    af::array set(6, h_set);
+
+    // is_sorted flag specifies if input is sorted,
+    // allows algorithm to skip internal sorting step
+    const bool is_sorted = true;
+    af::array unique = setUnique(set, is_sorted);
+    // unique == { 1, 2, 3 };
+
+    //! [ex_set_unique_sorted]
+
+    vector<int> unique_gold = { 1, 2, 3 };
+    dim4 gold_dim(3, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(unique_gold, gold_dim, unique);
+}
+
+TEST(Set, SNIPPET_setUniqueSortedDesc) {
+
+    //! [ex_set_unique_desc]
+
+    // input data
+    int h_set[6] = {3, 3, 3, 2, 2, 1};
+    af::array set(6, h_set);
+
+    // is_sorted flag specifies if input is sorted,
+    // allows algorithm to skip internal sorting step
+    // input can be sorted in ascending or descending order
+    const bool is_sorted = true;
+    af::array unique = setUnique(set, is_sorted);
+    // unique == { 3, 2, 1 };
+
+    //! [ex_set_unique_desc]
+
+    vector<int> unique_gold = { 3, 2, 1 };
+    dim4 gold_dim(3, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(unique_gold, gold_dim, unique);
+}
+
+TEST(Set, SNIPPET_setUniqueSimple) {
+
+    //! [ex_set_unique_simple]
+
+    // input data
+    int h_set[6] = {3, 2, 3, 3, 2, 1};
+    af::array set(6, h_set);
+
+    af::array unique = setUnique(set);
+    // unique == { 1, 2, 3 };
+
+    //! [ex_set_unique_simple]
+
+    vector<int> unique_gold = { 1, 2, 3 };
+    dim4 gold_dim(3, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(unique_gold, gold_dim, unique);
+}
+
+// Documentation examples for setUnion
+TEST(Set, SNIPPET_setUnion) {
+
+    //! [ex_set_union]
+
+    // input data
+    int h_setA[4] = {1, 2, 3, 4};
+    int h_setB[4] = {2, 3, 4, 5};
+    af::array setA(4, h_setA);
+    af::array setB(4, h_setB);
+
+    const bool is_unique = true;
+    // is_unique flag specifies if inputs are unique,
+    // allows algorithm to skip internal calls to setUnique
+    // inputs must be unique and sorted in increasing order
+    af::array setAB = setUnion(setA, setB, is_unique);
+    // setAB == { 1, 2, 3, 4, 5 };
+
+    //! [ex_set_union]
+
+    vector<int> union_gold = { 1, 2, 3, 4, 5 };
+    dim4 gold_dim(5, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(union_gold, gold_dim, setAB);
+}
+
+TEST(Set, SNIPPET_setUnionSimple) {
+
+    //! [ex_set_union_simple]
+
+    // input data
+    int h_setA[4] = {1, 2, 3, 3};
+    int h_setB[4] = {3, 4, 5, 5};
+    af::array setA(4, h_setA);
+    af::array setB(4, h_setB);
+
+    af::array setAB = setUnion(setA, setB);
+    // setAB == { 1, 2, 3, 4, 5 };
+
+    //! [ex_set_union_simple]
+
+    vector<int> union_gold = { 1, 2, 3, 4, 5 };
+    dim4 gold_dim(5, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(union_gold, gold_dim, setAB);
+}
+
+// Documentation examples for setIntersect()
+TEST(Set, SNIPPET_setIntersect) {
+
+    //! [ex_set_intersect]
+
+    // input data
+    int h_setA[4] = {1, 2, 3, 4};
+    int h_setB[4] = {2, 3, 4, 5};
+    af::array setA(4, h_setA);
+    af::array setB(4, h_setB);
+
+    const bool is_unique = true;
+    // is_unique flag specifies if inputs are unique,
+    // allows algorithm to skip internal calls to setUnique
+    // inputs must be unique and sorted in increasing order
+    af::array setA_B = setIntersect(setA, setB, is_unique);
+    // setA_B == { 2, 3, 4 };
+
+    //! [ex_set_intersect]
+
+    vector<int> intersect_gold = { 2, 3, 4 };
+    dim4 gold_dim(3, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(intersect_gold, gold_dim, setA_B);
+}
+
+TEST(Set, SNIPPET_setIntersectSimple) {
+
+    //! [ex_set_intersect_simple]
+
+    // input data
+    int h_setA[4] = {1, 2, 3, 3};
+    int h_setB[4] = {3, 3, 4, 5};
+    af::array setA(4, h_setA);
+    af::array setB(4, h_setB);
+
+    af::array setA_B = setIntersect(setA, setB);
+    // setA_B == { 3 };
+
+    //! [ex_set_intersect_simple]
+
+    vector<int> intersect_gold = { 3 };
+    dim4 gold_dim(1, 1, 1, 1);
+    ASSERT_VEC_ARRAY_EQ(intersect_gold, gold_dim, setA_B);
+}

From 9796d10cb275b86e3b60aa7c549831e1befb9ae5 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Sun, 30 Sep 2018 20:39:08 -0500
Subject: [PATCH 1528/2677] Improve unwrap documentation

---
 assets                 |   2 +-
 docs/details/image.dox | 145 +++++++++++++++++------------------------
 include/af/image.h     |  63 +++++++++++-------
 test/unwrap.cpp        |  45 +++++++++++++
 4 files changed, 148 insertions(+), 107 deletions(-)

diff --git a/assets b/assets
index 64be18117a..12f486d049 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit 64be18117a43460a050257d288953490cc00848b
+Subproject commit 12f486d049903a823e836e2ac95d9e48ce6cbcf3
diff --git a/docs/details/image.dox b/docs/details/image.dox
index 1ad64c149b..7a8dcae3fe 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -770,96 +770,73 @@ The output array of this function will have \f$ S(x, y) \f$ values at their corr
 \defgroup image_func_unwrap unwrap
 \ingroup image_mod_mat
 
-Generate an array with image windows as columns
+\brief Rearrange windowed sections of an array into columns (or rows)
+
+The figure below illustrates how unwrap works. A moving window (marked by
+orange boxes in the figure) of size `wx` \f$\times \f$ `wy` captures sections of
+the input array, and flattens them into columns (or rows if `is_column` is false)
+of the output array (illustrated in the right image). It starts at the top-left
+section of the input array and moves in column-major order, each time moving in
+strides of `sx` units along the column and `sy` units along the row, whenever it
+exhausts a column (stride size illustrated as the white arrows in the left image,
+and window movement illustrated as the progression of the small yellow numbers on
+the corner of each window). When the remainder of the column or row is not big
+enough to accomodate the window, that remainder is skipped and the window moves
+on (in the figure, the last row is not captured in any of the windows).
+
+Optionally, one can specify that the input image's border be padded (with zeros,
+represented as the gray boxes in the figure) before the moving window starts
+capturing sections. The width of the padding is defined by `px` for the top and
+bottom and `py` for the left and right sides, with maximum values of `wx`-1 and
+`wy`-1, respectively. The moving window then captures sections as if the padding
+is part of the input image, and thus the padding also becomes part of the output
+array's columns (illustrated in the bottom of the right image).
+
+\image html unwrap_640.png "Unwrap on a 3x4 input array, using a 2x2 window, 2x2 stride, 1x1 padding"
+
+In the figure, the stride is set to be equally large as the window size (both
+2x2), and thus the sections that the window captures are distinct. However, when
+the stride is set to the minimum (1x1) and is smaller than the window size, the
+sections overlap (which in turn makes the output's columns overlap as well). The
+window then acts as a perfect "sliding window" in this case (see the first code
+example below). In general, there will be some overlap as long as the stride is
+smaller than the window size (though the overlap decreases as the stride
+approaches the window size), and when the stride is equal or greater than the
+window size, each section (and output column) will be distinct.
+
+For inputs that have more than two dimensions, the unwrap operation will be
+applied to each 2D slice of the input. This is especially useful for
+independently processing each channel of an image (or set of images) - each
+channel (along the third dimension) on the input corresponds to the same channel
+on the output, and each image (along the fourth dimension) on the input
+corresponds to the same image on the output.
+
+The size of the output is shown below. `nsections_dim0` and `nsections_dim1`
+denote how many windows can fit along the column and row, given the padded image
+size, window size, strides, and skips (if any):
 
-unwrap takes in an input image along with the window sizes \p wx and \p
-wy, strides \p sx and \p sy, and padding \p px and \p py. This function then
-generates a matrix where each windows is an independent column.
-
-The number of columns (rows if is_column is true) in the output array are govenered by the number of
-windows that can be fit along x and y directions. Padding is applied along all
-4 sides of the matrix with \p px defining the height of the padding along dim
-0 and \p py defining the width of the padding along dim 1.
-
-The first column window is always at the top left corner of the input including
-padding. If a window cannot fit before the end of the matrix + padding, it is
-skipped from the generated matrix.
-
-Padding can take a maximum value of window - 1 repectively for x and y.
-
-For multiple channels (3rd and 4th dimension), the generated matrix contains
-the same number of channels as the input matrix. Each channel of the output
-matrix corresponds to the same channel of the input.
-
-So the dimensions of the output matrix are:
 \code
-[(wx * wy),         // Column height
- (No. of windows along dim 0 of input * No. of windows along dim 1 of input), // No. of columns per channel
- input.dims()[2],   // Channels
- input.dims()[3]]   // Volumns
+dim4(
+    wx * wy,                               // No. of rows (column height)
+    nsections_dim0 * nsections_dim1,       // No. of columns per channel
+    input.dims(2),                         // No. of channels
+    input.dims(3)                          // No. of images
+)
 \endcode
 
-When strides are 1, the operation is sliding window. When strides are equal to
-the respective window sizes, the option is distinct window. Other stride
-values are also allowed.
+Here are some code examples that demonstrate unwrap's usage:
 
-\code
-A [5 5 1 1]
-10 15 20 25 30
-11 16 21 26 31
-12 17 22 27 32
-13 18 23 28 33
-14 19 24 29 34
-
-// Window 3x3, strides 1x1, padding 0x0
-unwrap(A, 3, 3, 1, 1, 0, 0) [9 9 1 1]
-10 11 12 15 16 17 20 21 22
-11 12 13 16 17 18 21 22 23
-12 13 14 17 18 19 22 23 24
-15 16 17 20 21 22 25 26 27
-16 17 18 21 22 23 26 27 28
-17 18 19 22 23 24 27 28 29
-20 21 22 25 26 27 30 31 32
-21 22 23 26 27 28 31 32 33
-22 23 24 27 28 29 32 33 34
-
-// Window 3x3, strides 1x1, padding 1x1
-unwrap(A, 3, 3, 1, 1, 1, 1) [9 25 1 1]
- 0  0  0  0  0  0 10 11 12 13  0 15 16 17 18  0 20 21 22 23  0 25 26 27 28
- 0  0  0  0  0 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
- 0  0  0  0  0 11 12 13 14  0 16 17 18 19  0 21 22 23 24  0 26 27 28 29  0
- 0 10 11 12 13  0 15 16 17 18  0 20 21 22 23  0 25 26 27 28  0 30 31 32 33
-10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
-11 12 13 14  0 16 17 18 19  0 21 22 23 24  0 26 27 28 29  0 31 32 33 34  0
- 0 15 16 17 18  0 20 21 22 23  0 25 26 27 28  0 30 31 32 33  0  0  0  0  0
-15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  0  0  0  0  0
-16 17 18 19  0 21 22 23 24  0 26 27 28 29  0 31 32 33 34  0  0  0  0  0  0
-
-// Window 3x3, strides 3x3 ("distinct"), padding 0x0
-unwrap(A, 3, 3, 3, 3, 0, 0) [9 1 1 1]
-   10
-   11
-   12
-   15
-   16
-   17
-   20
-   21
-   22
-
-// Window 3x3, strides 3x3 ("distinct"), padding 2x2
-unwrap(A, 3, 3, 3, 3, 2, 2) [9 9 1 1]
-    0     0     0     0    16    19     0    31    34
-    0     0     0     0    17     0     0    32     0
-    0     0     0    15    18     0    30    33     0
-    0     0     0     0    21    24     0     0     0
-    0     0     0     0    22     0     0     0     0
-    0     0     0    20    23     0     0     0     0
-    0    11    14     0    26    29     0     0     0
-    0    12     0     0    27     0     0     0     0
-   10    13     0    25    28     0     0     0     0
-\endcode
+\snippet test/unwrap.cpp ex_unwrap
+
+One context where unwrap can be used is pre-processing an array or image for
+making window operations efficient (i.e. convolutions, computing the average
+pixel intensity around a point in an image, etc). Since each window capture is
+laid out as a column in an unwrapped array, vectorized operations can be executed
+efficiently on it (as opposed to strided access of each row in a window in the original
+array).
 
+Note that the actual implementation of unwrap may not match the way the operation
+is described above, but the effect should be the same.
 
 =======================================================================
 
diff --git a/include/af/image.h b/include/af/image.h
index 5c701429f2..195e8c63b8 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -572,17 +572,26 @@ AFAPI array colorSpace(const array& image, const CSpace to, const CSpace from);
 
 #if AF_API_VERSION >= 31
 /**
-   C++ Interface wrapper for unwrap
+   C++ Interface for rearranging windowed sections of an input into columns
+   (or rows)
 
-   \param[in]  in is the input image (or set of images)
-   \param[in]  wx is the block window size along 0th-dimension between [1, input.dims[0] + px]
-   \param[in]  wy is the block window size along 1st-dimension between [1, input.dims[1] + py]
-   \param[in]  sx is the stride along 0th-dimension
-   \param[in]  sy is the stride along 1st-dimension
-   \param[in]  px is the padding along 0th-dimension between [0, wx). Padding is applied both before and after.
-   \param[in]  py is the padding along 1st-dimension between [0, wy). Padding is applied both before and after.
-   \param[in]  is_column specifies the layout for the unwrapped patch. If is_column is false, the unrapped patch is laid out as a row.
-   \returns    an array with the image blocks as rows or columns
+   \param[in]  in is the input array
+   \param[in]  wx is the window size along dimension 0
+   \param[in]  wy is the window size along dimension 1
+   \param[in]  sx is the stride along dimension 0
+   \param[in]  sy is the stride along dimension 1
+   \param[in]  px is the padding along dimension 0
+   \param[in]  py is the padding along dimension 1
+   \param[in]  is_column determines whether the section becomes a column (if
+               true) or a row (if false)
+   \returns    an array with the input's sections rearraged as columns (or rows)
+
+   \note \p in can hold multiple images for processing if it is three or
+         four-dimensional
+   \note \p wx and \p wy must be between [1, input.dims(0 (1)) + px (py)]
+   \note \p sx and \p sy must be greater than 1
+   \note \p px and \p py must be between [0, wx (wy) - 1]. Padding becomes part of
+         the input image prior to the windowing
 
    \ingroup image_func_unwrap
 */
@@ -1328,19 +1337,29 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface wrapper for unwrap
+       C Interface for rearranging windowed sections of an input into columns
+       (or rows)
 
-       \param[out] out is an array with image blocks as rows or columns.
-       \param[in]  in is the input image (or set of images)
-       \param[in]  wx is the block window size along 0th-dimension between [1, input.dims[0] + px]
-       \param[in]  wy is the block window size along 1st-dimension between [1, input.dims[1] + py]
-       \param[in]  sx is the stride along 0th-dimension
-       \param[in]  sy is the stride along 1st-dimension
-       \param[in]  px is the padding along 0th-dimension between [0, wx). Padding is applied both before and after.
-       \param[in]  py is the padding along 1st-dimension between [0, wy). Padding is applied both before and after.
-       \param[in]  is_column specifies the layout for the unwrapped patch. If is_column is false, the unrapped patch is laid out as a row.
-       \return     \ref AF_SUCCESS if the color transformation is successful,
-       otherwise an appropriate error code is returned.
+       \param[out] out is an array with the input's sections rearraged as columns
+                   (or rows)
+       \param[in]  in is the input array
+       \param[in]  wx is the window size along dimension 0
+       \param[in]  wy is the window size along dimension 1
+       \param[in]  sx is the stride along dimension 0
+       \param[in]  sy is the stride along dimension 1
+       \param[in]  px is the padding along dimension 0
+       \param[in]  py is the padding along dimension 1
+       \param[in]  is_column determines whether the section becomes a column (if
+                   true) or a row (if false)
+       \return     \ref AF_SUCCESS if unwrap is successful,
+                   otherwise an appropriate error code is returned.
+
+       \note \p in can hold multiple images for processing if it is three or
+             four-dimensional
+       \note \p wx and \p wy must be between [1, input.dims(0 (1)) + px (py)]
+       \note \p sx and \p sy must be greater than 1
+       \note \p px and \p py must be between [0, wx (wy) - 1]. Padding becomes
+             part of the input image prior to the windowing
 
        \ingroup image_func_unwrap
     */
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index c73318f7c6..9510dd7112 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -201,3 +201,48 @@ TEST(Unwrap, MaxDim)
 
     ASSERT_ARRAYS_EQ(gold, output);
 }
+
+TEST(Unwrap, DocSnippet) {
+    //! [ex_unwrap]
+    float hA[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
+    array A(dim4(3, 3), hA);
+    //  1.     4.     7.
+    //  2.     5.     8.
+    //  3.     6.     9.
+
+    array A_simple = unwrap(A,
+                            2, 2,  // window size
+                            1, 1); // stride (sliding window)
+    //  1.     2.     4.     5.
+    //  2.     3.     5.     6.
+    //  4.     5.     7.     8.
+    //  5.     6.     8.     9.
+
+    array A_padded = unwrap(A,
+                            2, 2,  // window size
+                            2, 2,  // stride (distinct)
+                            1, 1); // padding
+    //  0.     0.     0.     5.
+    //  0.     0.     4.     6.
+    //  0.     2.     0.     8.
+    //  1.     3.     7.     9.
+    //! [ex_unwrap]
+
+    float gold_hA_simple[] = {
+        1, 2, 4, 5,
+        2, 3, 5, 6,
+        4, 5, 7, 8,
+        5, 6, 8, 9
+    };
+    array gold_A_simple(dim4(4, 4), gold_hA_simple);
+    ASSERT_ARRAYS_EQ(gold_A_simple, A_simple);
+
+    float gold_hA_padded[] = {
+        0, 0, 0, 1,
+        0, 0, 2, 3,
+        0, 4, 0, 7,
+        5, 6, 8, 9
+    };
+    array gold_A_padded(dim4(4, 4), gold_hA_padded);
+    ASSERT_ARRAYS_EQ(gold_A_padded, A_padded);
+}

From d57ae2ceee553d0bae46731200debca02b53a4d8 Mon Sep 17 00:00:00 2001
From: Jacob Kahn <jacobkahn1@gmail.com>
Date: Tue, 2 Oct 2018 23:31:22 -0700
Subject: [PATCH 1529/2677] Allow custom path for loading dynamic backend libs

Adds a check to the `AF_BUILD_LIB_CUSTOM_PATH` environment variable as one of the paths searched when attempting to dynamically open backends.

Important for specialized build configurations in which shared backend libraries may be built in a non-standard directory.

Updates arrayfire environment documentation for AF_BUILD_LIB_CUSTOM_PATH
---
 docs/pages/configuring_arrayfire_environment.md | 10 ++++++++++
 src/api/unified/symbol_manager.cpp              |  1 +
 2 files changed, 11 insertions(+)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index f8d082f71c..30f1d2c011 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -212,3 +212,13 @@ When set, this environment variable specifies the maximum length of the CPU JIT
 tree after which evaluation is forced.
 
 The default value, as of v3.4, 100. This value was 20 for older versions.
+
+AF_BUILD_LIB_CUSTOM_PATH {#af_build_lib_custom_path}
+-------------------------------------------------------------------------------
+
+When set, this environment variable specifies a custom path along which the
+symbol manager will search for dynamic (shared library) backends to load. This 
+is useful for specialized build configurations that use the unified backend and
+build shared libraries separately.
+
+By default, no additional path will be searched for an empty value.
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index b3352bfcba..67ef3c83e5 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -112,6 +112,7 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
         join_path(getEnvVar("AF_BUILD_PATH"), "src", "backend", getBackendDirectoryName(bknd_idx)),
         join_path(getEnvVar("AF_PATH"), "lib"),
         join_path(getEnvVar("AF_PATH"), "lib64"),
+        getEnvVar("AF_BUILD_LIB_CUSTOM_PATH"),
 
         // Common install paths
 #if !defined(OS_WIN)

From 76a71bc79897bfb75714668cda290441dec7338f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 Oct 2018 01:41:44 -0400
Subject: [PATCH 1530/2677] Fix issues with tile with a large repeat dimension

---
 src/backend/cuda/jit.cpp      | 36 ++++++++++++++++++-----------------
 src/backend/cuda/math.hpp     |  2 +-
 src/backend/cuda/platform.cpp |  2 +-
 test/tile.cpp                 | 23 ++++++++++++++++++++++
 4 files changed, 44 insertions(+), 19 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index cc361763f5..23d8915d9b 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -113,12 +113,12 @@ struct Param
 
     static const char *linearIndex = R"JIT(
         uint threadId = threadIdx.x;
-        int idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
+        long long idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
         if (idx >= outref.dims[3] * outref.strides[3]) return;
         )JIT";
 
     static const char *generalIndex = R"JIT(
-        uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        long long id0 = 0, id1 = 0, id2 = 0, id3 = 0;
         long blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
         if (num_odims > 2) {
             id2 = blockIdx_x / blocks_x;
@@ -142,11 +142,12 @@ struct Param
                     id1 < outref.dims[1] &&
                     id2 < outref.dims[2] &&
                     id3 < outref.dims[3];
-        if (!cond) return;
 
-        int idx = outref.strides[3] * id3 +
-                  outref.strides[2] * id2 +
-                  outref.strides[1] * id1 + id0;
+        if (!cond) { continue; }
+
+        long long idx = outref.strides[3] * id3 +
+                        outref.strides[2] * id2 +
+                        outref.strides[1] * id1 + id0;
         )JIT";
 
     stringstream inParamStream;
@@ -167,8 +168,8 @@ struct Param
         node->genFuncs(opsStream, ids_curr);
     }
 
-    outrefstream << "Param<" << full_nodes[output_ids[0]]->getTypeStr()
-                 << "> outref = out" << output_ids[0] << ";\n";
+    outrefstream << "const Param<" << full_nodes[output_ids[0]]->getTypeStr()
+                 << "> &outref = out" << output_ids[0] << ";\n";
 
     for (int i = 0; i < (int)output_ids.size(); i++) {
         int id = output_ids[i];
@@ -370,6 +371,7 @@ template<typename T>
 void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
 {
     int num_outputs = (int)outputs.size();
+    int device      = getActiveDeviceId();
 
     if (num_outputs == 0) return;
 
@@ -404,17 +406,18 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
     int threads_x = 1, threads_y = 1;
     int blocks_x_ = 1, blocks_y_ = 1;
     int blocks_x  = 1, blocks_y = 1, blocks_z = 1, blocks_x_total;
-    const int max_blocks = 65535;
 
-    int num_odims = 4;
+    cudaDeviceProp properties = getDeviceProp(device);
+    const long long max_blocks_x = properties.maxGridSize[0];
+    const long long max_blocks_y = properties.maxGridSize[1];
 
+    int num_odims = 4;
     while (num_odims >= 1) {
         if (outputs[0].dims[num_odims - 1] == 1) num_odims--;
         else break;
     }
 
     if (is_linear) {
-
         threads_x = 256;
         threads_y =  1;
 
@@ -423,12 +426,11 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
                                 outputs[0].dims[2] *
                                 outputs[0].dims[3]), threads_x);
 
-        int repeat_x = divup(blocks_x_total, max_blocks);
+        int repeat_x = divup(blocks_x_total, max_blocks_x);
         blocks_x = divup(blocks_x_total, repeat_x);
     } else {
-
         threads_x = 32;
-        threads_y =  8;
+        threads_y = 8;
 
         blocks_x_ = divup(outputs[0].dims[0], threads_x);
         blocks_y_ = divup(outputs[0].dims[1], threads_y);
@@ -436,11 +438,11 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
         blocks_x = blocks_x_ * outputs[0].dims[2];
         blocks_y = blocks_y_ * outputs[0].dims[3];
 
-        blocks_z = divup(blocks_y, max_blocks);
+        blocks_z = divup(blocks_y, max_blocks_y);
         blocks_y = divup(blocks_y, blocks_z);
 
         blocks_x_total = blocks_x;
-        int repeat_x = divup(blocks_x_total, max_blocks);
+        int repeat_x = divup(blocks_x_total, max_blocks_x);
         blocks_x = divup(blocks_x_total, repeat_x);
     }
 
@@ -468,7 +470,7 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
                             1,
                             0,
                             getActiveStream(),
-                            &args.front(),
+                            args.data(),
                             NULL));
 
     // Reset the thread local vectors
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 298f374f8c..f549b8a063 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -14,7 +14,7 @@
 #include "types.hpp"
 
 #ifdef __CUDACC__
-#include <math_functions.h>
+#include <cuda_runtime_api.h>
 #include <math_constants.h>
 #endif
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 6b61574010..4e36eec7ca 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -518,7 +518,7 @@ DeviceManager::DeviceManager()
     for(int i = 0; i < nDevices; i++) {
         cudaDevice_t dev;
         cudaGetDeviceProperties(&dev.prop, i);
-        if (dev.prop.major<getMinSupportedCompute(cudaMajorVer)) {
+        if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
             continue;
         } else {
             dev.flops = dev.prop.multiProcessorCount *
diff --git a/test/tile.cpp b/test/tile.cpp
index 1cc8533db7..d1855b397f 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -221,3 +221,26 @@ TEST(Tile, DocSnippet) {
     ASSERT_ARRAYS_EQ(C, D(seq(0, 2), span));
     ASSERT_ARRAYS_EQ(C, D(seq(3, 5), span));
 }
+
+// The tile was failing for larger sizes because of JIT kernels were not able to
+// handle repeated x blocks. The kernels were exiting early which caused the
+// next iteration to fail
+TEST(Tile, LargeRepeatDim) {
+    long long dim0 = 33;
+    long long largeDim = 40001;
+    array temp_ones = af::iota(largeDim, dim4(1), u8);
+    temp_ones = af::moddims(temp_ones, 1, 1, largeDim);
+    temp_ones.eval();
+
+    array temp = tile(temp_ones,  dim0, 1, 1);
+    temp.eval();
+    vector<unsigned char> empty(dim0 * largeDim);
+    for(long long ii = 0; ii < largeDim; ii++) {
+        int offset = ii * dim0;
+        for(int i = 0; i < dim0; i++) {
+            empty[offset + i] = ii;
+        }
+    }
+
+    ASSERT_VEC_ARRAY_EQ(empty, dim4(dim0, 1, largeDim), temp);
+}

From 37b7da45aaf62ef1f340a5d72fd3c87a6375ed31 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 Oct 2018 02:24:51 -0400
Subject: [PATCH 1531/2677] Improve error messages in JIT.

---
 src/backend/cuda/jit.cpp | 46 +++++++++++++++++++++++-----------------
 1 file changed, 26 insertions(+), 20 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 23d8915d9b..1735036393 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -213,29 +213,35 @@ typedef struct {
     CUfunction ker;
 } kc_entry_t;
 
-#define CU_CHECK(fn) do {                       \
-        CUresult res = fn;                      \
-        if (res == CUDA_SUCCESS) break;         \
-        char cu_err_msg[1024];                  \
-        snprintf(cu_err_msg,                    \
-                 sizeof(cu_err_msg),            \
-                 "CU Error (%d)\n",             \
-                 (int)(res));                   \
-        AF_ERROR(cu_err_msg,                    \
-                 AF_ERR_INTERNAL);              \
+#define CU_CHECK(fn) do {                                 \
+        CUresult res = fn;                                \
+        if (res == CUDA_SUCCESS) break;                   \
+        char cu_err_msg[1024];                            \
+        const char *cu_err_name;                          \
+        const char *cu_err_string;                        \
+        cuGetErrorName(res, &cu_err_name);                \
+        cuGetErrorString(res, &cu_err_string);            \
+        snprintf(cu_err_msg,                              \
+                 sizeof(cu_err_msg),                      \
+                 "CU Error %s(%d): %s\n",                 \
+                 cu_err_name, (int)(res), cu_err_string); \
+        AF_ERROR(cu_err_msg,                              \
+                 AF_ERR_INTERNAL);                        \
     } while(0)
 
 #ifndef NDEBUG
-#define CU_LINK_CHECK(fn) do {                  \
-        CUresult res = fn;                      \
-        if (res == CUDA_SUCCESS) break;         \
-        char cu_err_msg[1024];                  \
-        snprintf(cu_err_msg,                    \
-                 sizeof(cu_err_msg),            \
-                 "CU Error (%d)\n%s\n",         \
-                 (int)(res), linkError);        \
-        AF_ERROR(cu_err_msg,                    \
-                 AF_ERR_INTERNAL);              \
+#define CU_LINK_CHECK(fn) do {                            \
+        CUresult res = fn;                                \
+        if (res == CUDA_SUCCESS) break;                   \
+        char cu_err_msg[1024];                            \
+        const char *cu_err_name;                          \
+        cuGetErrorName(res, &cu_err_name);                \
+        snprintf(cu_err_msg,                              \
+                 sizeof(cu_err_msg),                      \
+                 "CU Error %s(%d): %s\n",                 \
+                 cu_err_name, (int)(res), linkError);     \
+        AF_ERROR(cu_err_msg,                              \
+                 AF_ERR_INTERNAL);                        \
     } while(0)
 #else
 #define CU_LINK_CHECK(fn) CU_CHECK(fn)

From fcf184366daf5d4b9b1fb90382cb9f982c15213e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 Oct 2018 01:52:20 -0400
Subject: [PATCH 1532/2677] Correct the display of dimension in the tests.

---
 test/testHelpers.hpp | 39 +++++++++++++++++++++------------------
 1 file changed, 21 insertions(+), 18 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index d9e9fbcb46..7ac4a32f82 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -525,13 +525,13 @@ const af::cdouble& operator+(const af::cdouble& val) {
 }
 
 // Calculate a multi-dimensional coordinates' linearized index
-int ravelIdx(af::dim4 coords, af::dim4 strides) {
+dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
     return std::inner_product(coords.get(), coords.get()+4, strides.get(), 0);
 }
 
 // Calculate a linearized index's multi-dimensonal coordinates in an af::array,
 //  given its dimension sizes and strides
-af::dim4 unravelIdx(uint idx, af::dim4 dims, af::dim4 strides) {
+af::dim4 unravelIdx(dim_t idx, af::dim4 dims, af::dim4 strides) {
     af::dim4 coords;
     coords[3] = idx / (strides[3]);
     coords[2] = idx / (strides[2]) % dims[2];
@@ -541,7 +541,7 @@ af::dim4 unravelIdx(uint idx, af::dim4 dims, af::dim4 strides) {
     return coords;
 }
 
-af::dim4 unravelIdx(uint idx, af::array arr) {
+af::dim4 unravelIdx(dim_t idx, af::array arr) {
     af::dim4 dims = arr.dims();
     af::dim4 st = af::getStrides(arr);
     return unravelIdx(idx, dims, st);
@@ -563,12 +563,15 @@ af::dim4 calcStrides(const af::dim4 &parentDim)
 std::string minimalDim4(af::dim4 coords, af::dim4 dims) {
     std::ostringstream os;
     os << "(" << coords[0];
-    if (dims[1] > 1)
+    if (dims[1] > 1 || dims[2] > 1 || dims[3] > 1) {
         os << ", " << coords[1];
-    if (dims[2] > 1)
+    }
+    if (dims[2] > 1 || dims[3] > 1) {
         os << ", " << coords[2];
-    if (dims[3] > 1)
+    }
+    if (dims[3] > 1) {
         os << ", " << coords[3];
+    }
     os << ")";
 
     return os.str();
@@ -579,11 +582,11 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
                          const std::vector<T>& hOut, std::string outName,
                          af::dim4 arrDims,
                          af::dim4 arrStrides,
-                         int idx) {
+                         dim_t idx) {
     std::ostringstream os;
 
     af::dim4 coords = unravelIdx(idx, arrDims, arrStrides);
-    int ctxWidth = 5;
+    dim_t ctxWidth = 5;
 
     // Coordinates that span dim0
     af::dim4 coordsMinBound = coords;
@@ -592,23 +595,23 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
     coordsMaxBound[0] = arrDims[0] - 1;
 
     // dim0 positions that can be displayed
-    int dim0Start = std::max<int>(0, coords[0] - ctxWidth);
-    int dim0End = std::min<int>(coords[0] + ctxWidth + 1, arrDims[0]);
+    dim_t dim0Start = std::max<int>(0, coords[0] - ctxWidth);
+    dim_t dim0End = std::min<int>(coords[0] + ctxWidth + 1, arrDims[0]);
 
     // Linearized indices of values in vectors that can be displayed
-    int vecStartIdx = std::max<int>(ravelIdx(coordsMinBound, arrStrides),
-                                     idx - ctxWidth);
+    dim_t vecStartIdx = std::max<int>(ravelIdx(coordsMinBound, arrStrides),
+                                      idx - ctxWidth);
 
     // Display as minimal coordinates as needed
     // First value is the range of dim0 positions that will be displayed
     os << "Viewing slice (" << dim0Start << ":" << dim0End - 1;
-    if (arrDims[1] > 1)
+    if (arrDims[1] > 1 || arrDims[2] > 1 || arrDims[3] > 1)
         os << ", " << coords[1];
-    if (arrDims[2] > 1)
+    if (arrDims[2] > 1 || arrDims[3] > 1)
         os << ", " << coords[2];
     if (arrDims[3] > 1)
         os << ", " << coords[3];
-    os << "), dims are " << minimalDim4(arrDims, arrDims) << "\n";
+    os << "), dims are (" << arrDims << ") strides: (" << arrStrides << ")\n";
 
     uint ctxElems = dim0End - dim0Start;
     std::vector<int> valFieldWidths(ctxElems);
@@ -704,7 +707,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     if (bItr == b.end()) {
         return ::testing::AssertionSuccess();
     } else {
-        int idx = std::distance(b.begin(), bItr);
+        dim_t idx = std::distance(b.begin(), bItr);
         af::dim4 aStrides = calcStrides(aDims);
         af::dim4 bStrides = calcStrides(bDims);
         af::dim4 coords = unravelIdx(idx, bDims, bStrides);
@@ -745,7 +748,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     if (aItr == a.end()) {
         return ::testing::AssertionSuccess();
     } else {
-        int idx = std::distance(b.begin(), bItr);
+        dim_t idx = std::distance(b.begin(), bItr);
         af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
 
         af::dim4 aStrides = calcStrides(aDims);
@@ -805,7 +808,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
             << "  Actual: " << bName << "([" << b.dims() << "])\n"
             << "Expected: " << aName << "([" << a.dims() << "])";
 
-    uint nElems = a.elements();
+    dim_t nElems = a.elements();
     af::dim4 arrDims = a.dims();
 
     switch (arrDtype) {

From 9ea3a7c5fe6e37a90cf1177e7a76c189aba29aeb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 13 Oct 2018 11:23:42 +0530
Subject: [PATCH 1533/2677] Enable double in jit kernels for supported devices

Prior to this change, double was enabled based on JIT kernel
name string encoding i.e. if string has any one of the characters
d, D, z, and Z.
---
 src/backend/opencl/jit.cpp | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 040cbc23dc..60069f8a0b 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -41,7 +41,7 @@ using std::vector;
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<Node *> &full_nodes,
                           const vector<Node_ids> &full_ids,
-                          bool is_linear, bool *is_double)
+                          bool is_linear)
 {
     stringstream hashName;
     stringstream funcName;
@@ -60,11 +60,6 @@ static string getFuncName(const vector<Node *> &output_nodes,
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
-    string nameStr = funcName.str();
-    string dblChars = "dDzZ";
-    size_t loc = nameStr.find_first_of(dblChars);
-    *is_double = (loc != std::string::npos);
-
     std::hash<std::string> hash_fn;
     hashName << "KER" << hash_fn(funcName.str());
     return hashName.str();
@@ -176,8 +171,7 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
                         const vector<Node_ids> &full_ids,
                         const bool is_linear)
 {
-    bool is_dbl = false;
-    string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear, &is_dbl);
+    string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear);
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, funcName);
@@ -189,7 +183,8 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
 
         cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, is_dbl ? string(" -D USE_DOUBLE") :  string(""));
+        buildProgram(prog, 2, ker_strs, ker_lens,
+                     isDoubleSupported(device) ? string(" -D USE_DOUBLE") :  string(""));
 
         entry.prog = new cl::Program(prog);
         entry.ker = new Kernel(*entry.prog, funcName.c_str());

From 44340236c79522d53ff70ea788ea0591bfc58f22 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 13 Oct 2018 12:13:29 +0530
Subject: [PATCH 1534/2677] Fix pow fn precision for integral types

Adds unit tests for the corresponding issue
---
 docs/details/arith.dox           | 16 ++++++++++
 src/backend/cuda/binary.hpp      | 21 ++++++++++++-
 src/backend/cuda/kernel/jit.cuh  |  8 +++--
 src/backend/opencl/binary.hpp    | 21 ++++++++++++-
 src/backend/opencl/kernel/jit.cl | 15 +++++++--
 test/CMakeLists.txt              |  2 +-
 test/binary.cpp                  | 54 ++++++++++++++++++++++++++++++++
 7 files changed, 129 insertions(+), 8 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index a75c3a2cc4..056c126d53 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -438,6 +438,22 @@ Find root of an input
 
 Raise an array to a power
 
+If the input array has values beyond what a floating point type can represent, then there is no
+guarantee that the results will be accurate. The exact type mapping from integral types to floating
+point types used to compute power is given below.
+
+| Input Type         | Compute Type   |
+| :------------------| :--------------|
+| unsigned long long | double         |
+| long long          | double         |
+| unsigned int       | double         |
+| int                | double         |
+| unsigned short     | float          |
+| short              | float          |
+| unsigned char      | float          |
+
+The output array will be of the same type as input.
+
 \copydoc arith_real_only
 
 
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index 58d6254453..0b072c157f 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -119,10 +119,29 @@ BINARY_TYPE_1(bitshiftr)
 
 BINARY_TYPE_2(min)
 BINARY_TYPE_2(max)
-BINARY_TYPE_2(pow)
 BINARY_TYPE_2(rem)
 BINARY_TYPE_2(mod)
 
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_pow_t> {
+    const char *name() { return "__pow"; }
+};
+
+#define POW_BINARY_OP(INTYPE, OPNAME)       \
+template<typename To>                       \
+struct BinOp<To, INTYPE, af_pow_t> {        \
+    const char *name() { return OPNAME; }   \
+};
+
+POW_BINARY_OP(double, "pow"    )
+POW_BINARY_OP( float, "powf"   )
+POW_BINARY_OP(  intl, "__powll")
+POW_BINARY_OP( uintl, "__powul")
+POW_BINARY_OP(  uint, "__powui")
+POW_BINARY_OP(   int, "__powsi")
+
+#undef POW_BINARY_OP
+
 template<typename Ti>
 struct BinOp<cfloat, Ti, af_cplx2_t>
 {
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index f59fd3e0bd..d8e6b741dc 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -55,10 +55,14 @@ typedef cuDoubleComplex cdouble;
 #define __max(lhs, rhs) ((lhs) > (rhs)) ? (lhs) : (rhs)
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
-#define __pow(lhs, rhs) fpow((float)lhs, (float)rhs)
+
+#define __pow(lhs, rhs) __float2int_rn(pow(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
+#define __powll(lhs, rhs) __double2ll_rn(pow(__ll2double_rn(lhs), __ll2double_rn(rhs)))
+#define __powul(lhs, rhs) __double2ull_rn(pow(__ull2double_rn(lhs), __ull2double_rn(rhs)))
+#define __powui(lhs, rhs) __double2uint_rn(pow(__uint2double_rn(lhs), __uint2double_rn(rhs)))
+#define __powsi(lhs, rhs) __double2int_rn(pow(__int2double_rn(lhs), __int2double_rn(rhs)))
 
 #define __convert_char(val) (char)((val) != 0)
-#define fpow(lhs, rhs) pow((lhs), (rhs))
 #define frem(lhs, rhs) remainder((lhs), (rhs))
 #define iszero(a) ((a) == 0)
 
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 9a653a8302..7226d4e382 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -121,10 +121,29 @@ BINARY_TYPE_1(bitshiftr)
 
 BINARY_TYPE_2(min)
 BINARY_TYPE_2(max)
-BINARY_TYPE_2(pow)
 BINARY_TYPE_2(rem)
 BINARY_TYPE_2(mod)
 
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_pow_t> {
+    const char *name() { return "__pow"; }
+};
+
+#define POW_BINARY_OP(INTYPE, OPNAME)       \
+template<typename To>                       \
+struct BinOp<To, INTYPE, af_pow_t> {        \
+    const char *name() { return OPNAME; }   \
+};
+
+POW_BINARY_OP(double, "pow"    )
+POW_BINARY_OP( float, "pow"    )
+POW_BINARY_OP(  intl, "__powll")
+POW_BINARY_OP( uintl, "__powul")
+POW_BINARY_OP(  uint, "__powui")
+POW_BINARY_OP(   int, "__powsi")
+
+#undef POW_BINARY_OP
+
 template<typename Ti>
 struct BinOp<cfloat, Ti, af_cplx2_t>
 {
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index b3462f3e54..1ab81f16ac 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -111,7 +111,18 @@ float2 __cdivf(float2 lhs, float2 rhs)
 #define __max(lhs, rhs) ((lhs) > (rhs)) ? (lhs) : (rhs)
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
-#define __pow(lhs, rhs) fpow((float)lhs, (float)rhs)
+
+#define __pow(lhs, rhs) convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#define __powll(lhs, rhs) convert_long_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#define __powul(lhs, rhs) convert_ulong_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+
+#ifdef USE_DOUBLE
+#define __powui(lhs, rhs) convert_uint_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#define __powsi(lhs, rhs) convert_int_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#else
+#define __powui(lhs, rhs) convert_uint_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#define __powsi(lhs, rhs) convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#endif
 
 float2 __cminf(float2 lhs, float2 rhs)
 {
@@ -137,8 +148,6 @@ float2 __convert_cfloat(float in)
 
 #define __convert_char(val) (char)(convert_char((val)) != 0)
 
-#define fpow(lhs, rhs) pow((lhs), (rhs))
-
 #define frem(lhs, rhs) remainder((lhs), (rhs))
 
 #define iszero(a) ((a) == 0)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 6540e20482..ede1c2c53e 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -161,7 +161,7 @@ make_test(SRC backend.cpp)
 make_test(SRC basic.cpp)
 make_test(SRC basic_c.c)
 make_test(SRC bilateral.cpp)
-make_test(SRC binary.cpp)
+make_test(SRC binary.cpp CXX11)
 make_test(SRC blas.cpp)
 make_test(SRC canny.cpp)
 make_test(SRC cast.cpp)
diff --git a/test/binary.cpp b/test/binary.cpp
index 43b131b2ee..3bf483036d 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -7,12 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <af/array.h>
 #include <af/arith.h>
 #include <af/data.h>
 #include <testHelpers.hpp>
 
+#include <cfenv>
+#include <cmath>
+
 using namespace std;
 using namespace af;
 
@@ -346,3 +350,53 @@ TEST(BinaryTests, ISSUE_1762)
         ASSERT_EQ(imag(hres[i]), 0);
     }
 }
+
+template<typename T>
+class PowPrecisionTest : public ::testing::TestWithParam<T> {};
+
+#define DEF_TEST(Sx, T)                                 \
+using PowPrecisionTest##Sx = PowPrecisionTest< T >;     \
+TEST_P(PowPrecisionTest##Sx, Issue2304)                 \
+{                                                       \
+    T param     = GetParam();                           \
+    auto dtype  = (af_dtype)dtype_traits< T >::af_type; \
+    af::array A = af::constant(param, 1, dtype);        \
+    af::array B = af::pow(A, 2);                        \
+    vector<T> hres(1, 0);                               \
+    B.host(&hres[0]);                                   \
+    std::fesetround(FE_TONEAREST);                      \
+    T gold = (T)std::rint(std::pow((double)param, 2.0));\
+    ASSERT_EQ(hres[0], gold);                           \
+}
+
+DEF_TEST(ULong , unsigned long long)
+DEF_TEST(Long  , long long)
+DEF_TEST(UInt  , unsigned int)
+DEF_TEST(Int   , int)
+DEF_TEST(UShort, unsigned short)
+DEF_TEST(Short , short)
+DEF_TEST(UChar , unsigned char)
+
+#undef DEF_TEST
+
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestULong,
+                        testing::Range<unsigned long long>(1, 1e7, 1e6));
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestLong,
+                        testing::Range<long long>(1, 1e7, 1e6));
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUInt,
+                        testing::Range<unsigned int>(1, 65000, 15e3));
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestInt,
+                        testing::Range<int>(1, 46340, 10e3));
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUShort,
+                        testing::Range<unsigned short>(1, 255, 100));
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestShort,
+                        testing::Range<short>(1, 180, 50));
+INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUChar,
+                        testing::Range<unsigned char>(1, 12, 5));
+
+INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestLong,
+                        testing::Range<long long>(-1e7, 0, 1e6));
+INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestInt,
+                        testing::Range<int  >(-46340, 0, 10e3));
+INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestShort,
+                        testing::Range<short>(-180, 0, 50));

From 6dfd02fe745d053110e215e4e42fffd47bfdd61e Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Tue, 2 Oct 2018 18:35:14 -0500
Subject: [PATCH 1535/2677] Indexing: Use Param/Array::strides instead of
 toStrides

---
 src/api/c/reorder.cpp            |  1 +
 src/backend/cpu/kernel/index.hpp |  2 +-
 src/backend/cuda/index.cu        |  2 +-
 src/backend/opencl/index.cpp     |  2 +-
 test/index.cpp                   | 39 ++++++++++++++++++++++++++++++++
 test/reorder.cpp                 | 20 ++++++++++++----
 6 files changed, 58 insertions(+), 8 deletions(-)

diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 3a06b4c4c8..2fb0731e69 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -50,6 +50,7 @@ static inline af_array reorder(const af_array in, const af::dim4 &rdims0)
             ostrides[i] = istrides[rdims[i]];
         }
         Array<T> Out = In;
+        // Use modDims instead of setDataDims to only modify the ArrayInfo
         Out.modDims(odims);
         Out.modStrides(ostrides);
         out = getHandle(Out);
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index 065bc310ab..fa7b18d4d2 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -24,7 +24,7 @@ void index(Param<T> out, CParam<T> in, const af::dim4 dDims,
 {
     const af::dim4 iDims    = in.dims();
     const af::dim4 iOffs    = toOffset(seqs, dDims);
-    const af::dim4 iStrds   = toStride(seqs, dDims);
+    const af::dim4 iStrds   = in.strides();
     const af::dim4 oDims    = out.dims();
     const af::dim4 oStrides = out.strides();
     const T *src        = in.get();
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cu
index 7ebc6f1f97..962d1fc486 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cu
@@ -37,7 +37,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     dim4 dDims = in.getDataDims();
     dim4 oDims = toDims  (seqs, iDims);
     dim4 iOffs = toOffset(seqs, dDims);
-    dim4 iStrds= toStride(seqs, dDims);
+    dim4 iStrds= in.strides();
 
     for (dim_t i=0; i<4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 978b6f30b2..acc715aa78 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -36,7 +36,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     dim4 dDims = in.getDataDims();
     dim4 oDims = toDims  (seqs, iDims);
     dim4 iOffs = toOffset(seqs, dDims);
-    dim4 iStrds= toStride(seqs, dDims);
+    dim4 iStrds= in.strides();
 
     for (dim_t i=0; i<4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
diff --git a/test/index.cpp b/test/index.cpp
index 289e402a9c..de7512c698 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1657,3 +1657,42 @@ TEST(Index, InvalidSequence_NegativeRangePositiveStep)
 {
     EXPECT_THROW(af::seq(-1,-5,1), af::exception);
 }
+
+TEST(Index, ISSUE_2273) {
+    int h_idx[2] = {1, 1};
+    array idx(2, h_idx);
+
+    float h_input[12] = {0.f, 1.f, 2.f, 3.f, 4.f, 5.f,
+                         6.f, 7.f, 8.f, 9.f, 10.f, 11.f};
+    array input(2, 3, 2, h_input);
+    array input_reord = reorder(input, 0, 2, 1);
+    array output = input_reord(span, idx, span);
+
+    float h_gold[12] = {6.f, 7.f, 6.f, 7.f,
+                        8.f, 9.f, 8.f, 9.f,
+                        10.f, 11.f, 10.f, 11.f};
+    array gold(2, 2, 3, h_gold);
+
+    ASSERT_ARRAYS_EQ(gold, output);
+}
+
+TEST(Index, ISSUE_2273_Flipped) {
+    int h_idx[2] = {1, 1};
+    array idx(2, h_idx);
+
+    float h_input[12] = {0.f, 1.f, 6.f, 7.f,
+                         2.f, 3.f, 8.f, 9.f,
+                         4.f, 5.f, 10.f, 11.f};
+    array input(2, 2, 3, h_input);
+    array input_reord = reorder(input, 0, 2, 1);
+    array input_slice = input_reord(span, span, idx);
+
+    array input_ref = iota(dim4(2, 3, 2));
+    array input_ref_slice = input_ref(span, span, idx);
+
+    float h_gold[12] = {6.f, 7.f, 8.f, 9.f, 10.f, 11.f,
+                        6.f, 7.f, 8.f, 9.f, 10.f, 11.f};
+    array input_slice_gold(2, 3, 2, h_gold);
+
+    ASSERT_ARRAYS_EQ(input_slice_gold, input_slice);
+}
diff --git a/test/reorder.cpp b/test/reorder.cpp
index 85220a2449..464589f56d 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -18,18 +18,18 @@
 #include <string>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::allTrue;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::reorder;
+using af::seq;
+using af::span;
 using af::tile;
+using std::string;
+using std::vector;
 
 
 template<typename T>
@@ -190,3 +190,13 @@ TEST(Reorder, MaxDim)
 
     ASSERT_ARRAYS_EQ(gold, output);
 }
+
+TEST(Reorder, InputArrayUnchanged) {
+    float h_input[12] = {0.f, 1.f, 2.f, 3.f, 4.f, 5.f,
+                         6.f, 7.f, 8.f, 9.f, 10.f, 11.f};
+    array input(2, 3, 2, h_input);
+    array input_reord = reorder(input, 0, 2, 1);
+
+    array input_gold(2, 3, 2, h_input);
+    ASSERT_ARRAYS_EQ(input_gold, input);
+}

From 42d64f515a41748b6de4c511e29910474651a1d0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 16 Oct 2018 11:22:49 +0530
Subject: [PATCH 1536/2677] Add broadcast batching for matmul

---
 docs/details/blas.dox       | 16 ++++++++++-
 src/api/c/blas.cpp          |  2 +-
 src/backend/cpu/blas.cpp    |  4 +--
 src/backend/opencl/blas.cpp |  4 +--
 test/blas.cpp               | 54 +++++++++++++++++++++++++++++++++++++
 5 files changed, 74 insertions(+), 6 deletions(-)

diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index 275efe9121..24950e18b5 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -28,7 +28,21 @@ operations specified in the options. The operations are done while reading the
 data from memory. This results in no additional memory being used for temporary
 buffers.
 
-\note Sparse support was added to ArrayFire in v3.4.0. This function can be use
+Batched matrix multiplications are supported. Given below are the supported
+formats for given matrices A and B.
+
+| Input Matrix A             | Input Matrix B             | Output Matrix Size          |
+|:--------------------------:|:--------------------------:|:---------------------------:|
+| \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ K, N,  1,  1 \} \f$ |
+| \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ K, N, b2, b3 \} \f$ |
+| \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ K, N, b2, b3 \} \f$ |
+| \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ K, N, b2, b3 \} \f$ |
+
+For the last two entries in the above table, the 2D matrix is broadcasted to
+match the dimensions of 3D/4D array. This broadcast doesn't involve any additional
+memory allocations either on host or device.
+
+\note Sparse support was added to ArrayFire in v3.4.0. This function can be used
 for Sparse-Dense matrix multiplication. See the notes of the function for usage
 and restrictions.
 
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 7e005b3950..58cb92bb85 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -127,7 +127,7 @@ af_err af_matmul(af_array *out,
         dim4 lDims = lhsInfo.dims();
         dim4 rDims = rhsInfo.dims();
 
-        if (lDims.ndims() > 2 || rDims.ndims() > 2) {
+        if (lDims.ndims() > 2 && rDims.ndims() > 2) {
             DIM_ASSERT(1, lDims.ndims() == rDims.ndims());
             if (lDims[2] != rDims[2] && lDims[2] != 1 && rDims[2] != 1) {
                 AF_ERROR("Batch size mismatch along dimension 2", AF_ERR_BATCH);
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index ca2e194644..ada51d1eea 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -193,8 +193,8 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
         bool is_r_d3_batched = oDims[3] == rDims[3];
 
         for (int n = 0; n < batchSize; n++) {
-            int w = n / rDims[2];
-            int z = n - w * rDims[2];
+            int w = n / oDims[2];
+            int z = n - w * oDims[2];
 
             int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
             int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index bec8cfbc83..d8a6f73a57 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -92,8 +92,8 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     bool is_r_d3_batched = oDims[3] == rDims[3];
 
     for (int n = 0; n < batchSize; n++) {
-        int w = n / rDims[2];
-        int z = n - w * rDims[2];
+        int w = n / oDims[2];
+        int z = n - w * oDims[2];
 
         int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
         int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
diff --git a/test/blas.cpp b/test/blas.cpp
index bece1b6280..ab666f12aa 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -297,3 +297,57 @@ TEST(MatrixMultiply, ISSUE_1882)
 
     ASSERT_ARRAYS_NEAR(res1, res2, 1E-5);
 }
+
+TEST(MatrixMultiply, LhsBroadcastBatched)
+{
+    const int M = 512;
+    const int K = 512;
+    const int N = 10;
+    const int D2 = 2;
+    const int D3 = 3;
+
+    for (int d3 = 1; d3 <= D3; d3 *= D3) {
+        for (int d2 = 1; d2 <= D2; d2 *= D2) {
+            array a = randu(M, K);
+            array b = randu(K, N, d2, d3);
+            array c = matmul(a, b);
+
+            for (int j = 0; j < d3; j++) {
+                for (int i = 0; i < d2; i++) {
+                    array b_ij = b(span, span, i, j);
+                    array c_ij = c(span, span, i, j);
+                    array res = matmul(a, b_ij);
+                    EXPECT_LT(max<float>(abs(c_ij - res)), 1E-3)
+                        << " for d2 = " << d2 << " for d3 = " << d3;
+                }
+            }
+        }
+    }
+}
+
+TEST(MatrixMultiply, RhsBroadcastBatched)
+{
+    const int M = 512;
+    const int K = 512;
+    const int N = 10;
+    const int D2 = 2;
+    const int D3 = 3;
+
+    for (int d3 = 1; d3 <= D3; d3 *= D3) {
+        for (int d2 = 1; d2 <= D2; d2 *= D2) {
+            array a = randu(M, K, d2, d3);
+            array b = randu(K, N);
+            array c = matmul(a, b);
+
+            for (int j = 0; j < d3; j++) {
+                for (int i = 0; i < d2; i++) {
+                    array a_ij = a(span, span, i, j);
+                    array c_ij = c(span, span, i, j);
+                    array res = matmul(a_ij, b);
+                    EXPECT_LT(max<float>(abs(c_ij - res)), 1E-3)
+                        << " for d2 = " << d2 << " for d3 = " << d3;
+                }
+            }
+        }
+    }
+}

From d577f6dbf547690bee3f6c6d5176706245205678 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 17 Oct 2018 15:26:52 +0530
Subject: [PATCH 1537/2677] Reduce iteration count in anisotropic smoothing
 tests

---
 test/anisotropic_diffusion.cpp | 16 ++++++++--------
 test/data                      |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 979178e94f..4afbe8c16f 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -148,14 +148,14 @@ TYPED_TEST(AnisotropicDiffusion, GradientGrayscale)
     // Divide second value by 100 to get time step `K`
     // Divide third value stays as it is since it is iteration count
     // Fourth value is a 4-character string indicating the flux kind
-    imageTest<TypeParam, false>(string(TEST_DIR "/gradient_diffusion/gray_00125_100_64_exp.test"),
-                                0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL);
+    imageTest<TypeParam, false>(string(TEST_DIR "/gradient_diffusion/gray_00125_100_2_exp.test"),
+                                0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
 }
 
 TYPED_TEST(AnisotropicDiffusion, GradientColorImage)
 {
-    imageTest<TypeParam, true>(string(TEST_DIR "/gradient_diffusion/color_00125_100_64_exp.test"),
-                               0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL);
+    imageTest<TypeParam, true>(string(TEST_DIR "/gradient_diffusion/color_00125_100_2_exp.test"),
+                               0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
 }
 
 TEST(AnisotropicDiffusion, GradientInvalidInputArray)
@@ -174,14 +174,14 @@ TYPED_TEST(AnisotropicDiffusion, CurvatureGrayscale)
     // Divide second value by 100 to get time step `K`
     // Divide third value stays as it is since it is iteration count
     // Fourth value is a 4-character string indicating the flux kind
-    imageTest<TypeParam, false>(string(TEST_DIR "/curvature_diffusion/gray_00125_100_64_mcde.test"),
-                                0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL, true);
+    imageTest<TypeParam, false>(string(TEST_DIR "/curvature_diffusion/gray_00125_100_2_mcde.test"),
+                                0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
 }
 
 TYPED_TEST(AnisotropicDiffusion, CurvatureColorImage)
 {
-    imageTest<TypeParam, true>(string(TEST_DIR "/curvature_diffusion/color_00125_100_64_mcde.test"),
-                               0.125f, 1.0, 64, AF_FLUX_EXPONENTIAL, true);
+    imageTest<TypeParam, true>(string(TEST_DIR "/curvature_diffusion/color_00125_100_2_mcde.test"),
+                               0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
 }
 
 TEST(AnisotropicDiffusion, CurvatureInvalidInputArray)
diff --git a/test/data b/test/data
index f8270901bc..ada7fe1a41 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit f8270901bc80e1ceec2ce33ba151b1acf3db4f16
+Subproject commit ada7fe1a41851c18e3cda44df1ac827b9b3b39a9

From 0cd90492a8001f1071d2e182ffc15d6dc01f9c32 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 Oct 2018 17:37:46 -0400
Subject: [PATCH 1538/2677] Remove setDevice from af::array destructor

The setDevice call is unnecessary because the memory manager does not
free the array. Also, it seems that cudaFree doesn't require that
the device is set based on my experiments.
---
 src/api/c/array.cpp | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 0563a02f6e..54aba91ad7 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -194,9 +194,6 @@ af_err af_release_array(af_array arr)
                 default : TYPE_ERROR(0, type);
             }
         } else {
-
-            setDevice(info.getDevId());
-
             switch(type) {
             case f32:   releaseHandle<float   >(arr); break;
             case c32:   releaseHandle<cfloat  >(arr); break;
@@ -212,8 +209,6 @@ af_err af_release_array(af_array arr)
             case u16:   releaseHandle<ushort  >(arr); break;
             default:    TYPE_ERROR(0, type);
             }
-
-            setDevice(dev);
         }
     }
     CATCHALL

From 541195ee4ec363d7c8f130d10eae4446fa5eb323 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 Oct 2018 15:34:49 -0400
Subject: [PATCH 1539/2677] Rename cpu::TNJ namespace and directory to JIT

---
 src/backend/cpu/Array.cpp                   | 31 ++++++-------
 src/backend/cpu/Array.hpp                   | 20 ++++-----
 src/backend/cpu/{TNJ => JIT}/BinaryNode.hpp | 14 +++---
 src/backend/cpu/{TNJ => JIT}/BufferNode.hpp | 16 ++++---
 src/backend/cpu/{TNJ => JIT}/Node.hpp       | 22 ++++++---
 src/backend/cpu/{TNJ => JIT}/ScalarNode.hpp |  2 +-
 src/backend/cpu/{TNJ => JIT}/UnaryNode.hpp  | 14 +++---
 src/backend/cpu/arith.hpp                   | 26 +++++------
 src/backend/cpu/cast.hpp                    | 34 +++++++-------
 src/backend/cpu/complex.hpp                 | 50 ++++++++++-----------
 src/backend/cpu/kernel/Array.hpp            | 26 +++++------
 src/backend/cpu/logic.hpp                   | 40 ++++++++---------
 src/backend/cpu/unary.hpp                   | 22 ++++-----
 13 files changed, 165 insertions(+), 152 deletions(-)
 rename src/backend/cpu/{TNJ => JIT}/BinaryNode.hpp (82%)
 rename src/backend/cpu/{TNJ => JIT}/BufferNode.hpp (89%)
 rename src/backend/cpu/{TNJ => JIT}/Node.hpp (83%)
 rename src/backend/cpu/{TNJ => JIT}/ScalarNode.hpp (97%)
 rename src/backend/cpu/{TNJ => JIT}/UnaryNode.hpp (75%)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 4a24ac9c68..664db43047 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -11,10 +11,10 @@
 #include <Array.hpp>
 #include <kernel/Array.hpp>
 
+#include <JIT/BufferNode.hpp>
+#include <JIT/Node.hpp>
+#include <JIT/ScalarNode.hpp>
 #include <Param.hpp>
-#include <TNJ/BufferNode.hpp>
-#include <TNJ/Node.hpp>
-#include <TNJ/ScalarNode.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <copy.hpp>
@@ -36,9 +36,10 @@
 namespace cpu
 {
 
-using TNJ::BufferNode;
-using TNJ::Node;
-using TNJ::Node_ptr;
+using JIT::BufferNode;
+using JIT::Node;
+using JIT::Node_ptr;
+using JIT::Node_map_t;
 
 using af::dim4;
 using std::vector;
@@ -74,7 +75,7 @@ Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_de
 }
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, TNJ::Node_ptr n) :
+Array<T>::Array(af::dim4 dims, JIT::Node_ptr n) :
     info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data(), data_dims(dims),
     node(n), ready(false), owner(true)
@@ -143,7 +144,7 @@ template<typename T>
 void evalMultiple(vector<Array<T>*> array_ptrs)
 {
     vector<Array<T>> arrays;
-    vector<TNJ::Node_ptr> nodes;
+    vector<JIT::Node_ptr> nodes;
     bool isWorker = getQueue().is_worker();
     for (auto &array : array_ptrs) {
         if (array->ready) continue;
@@ -200,9 +201,9 @@ template<typename T>
 Array<T>
 createValueArray(const dim4 &size, const T& value)
 {
-    TNJ::ScalarNode<T> *node = new TNJ::ScalarNode<T>(value);
-    return createNodeArray<T>(size, TNJ::Node_ptr(
-                                  reinterpret_cast<TNJ::Node *>(node)));
+    JIT::ScalarNode<T> *node = new JIT::ScalarNode<T>(value);
+    return createNodeArray<T>(size, JIT::Node_ptr(
+                                  reinterpret_cast<JIT::Node *>(node)));
 }
 
 template<typename T>
@@ -237,8 +238,8 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 
                 Node *n = node.get();
 
-                TNJ::Node_map_t nodes_map;
-                vector<TNJ::Node *> full_nodes;
+                Node_map_t nodes_map;
+                vector<Node *> full_nodes;
                 n->getNodesMap(nodes_map, full_nodes);
                 unsigned length =0, buf_count = 0, bytes = 0;
                 for(auto &entry : nodes_map) {
@@ -348,7 +349,7 @@ Array<T>::setDataDims(const dim4 &new_dims)
                                                        const vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, TNJ::Node_ptr node); \
+    template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       T*   Array<T>::device();                             \
@@ -357,7 +358,7 @@ Array<T>::setDataDims(const dim4 &new_dims)
     template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
                                    const T * const in_data,             \
                                    bool is_device);                     \
-    template       TNJ::Node_ptr Array<T>::getNode() const;             \
+    template       JIT::Node_ptr Array<T>::getNode() const;             \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
     template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index b8f3ba2b7f..c90bc258c6 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -10,7 +10,7 @@
 //This is the array implementation class.
 #pragma once
 #include <Param.hpp>
-#include <TNJ/Node.hpp>
+#include <JIT/Node.hpp>
 #include <common/ArrayInfo.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
@@ -28,10 +28,10 @@ namespace cpu
 {
     namespace kernel
     {
-        template<typename T> void evalArray(Param<T> in, TNJ::Node_ptr node);
+        template<typename T> void evalArray(Param<T> in, JIT::Node_ptr node);
 
         template<typename T>
-        void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> nodes);
+        void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> nodes);
 
     }
 
@@ -45,7 +45,7 @@ namespace cpu
 
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, TNJ::Node_ptr node);
+    Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
 
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
@@ -112,7 +112,7 @@ namespace cpu
         //data if parent. empty if child
         std::shared_ptr<T> data;
         af::dim4 data_dims;
-        TNJ::Node_ptr node;
+        JIT::Node_ptr node;
 
         bool ready;
         bool owner;
@@ -122,7 +122,7 @@ namespace cpu
 
         explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        explicit Array(af::dim4 dims, TNJ::Node_ptr n);
+        explicit Array(af::dim4 dims, JIT::Node_ptr n);
 
     public:
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,
@@ -231,7 +231,7 @@ namespace cpu
             return CParam<T>(this->get(), this->dims(), this->strides());
         }
 
-        TNJ::Node_ptr getNode() const;
+        JIT::Node_ptr getNode() const;
 
         friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
@@ -241,15 +241,15 @@ namespace cpu
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, TNJ::Node_ptr node);
+        friend Array<T> createNodeArray<T>(const af::dim4 &dims, JIT::Node_ptr node);
 
         friend Array<T> createSubArray<T>(const Array<T>& parent,
                                           const std::vector<af_seq> &index,
                                           bool copy);
 
-        friend void kernel::evalArray<T>(Param<T> in, TNJ::Node_ptr node);
+        friend void kernel::evalArray<T>(Param<T> in, JIT::Node_ptr node);
         friend void kernel::evalMultiple<T>(std::vector<Param<T>> arrays,
-                                            std::vector<TNJ::Node_ptr> nodes);
+                                            std::vector<JIT::Node_ptr> nodes);
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
diff --git a/src/backend/cpu/TNJ/BinaryNode.hpp b/src/backend/cpu/JIT/BinaryNode.hpp
similarity index 82%
rename from src/backend/cpu/TNJ/BinaryNode.hpp
rename to src/backend/cpu/JIT/BinaryNode.hpp
index 37d053363f..f1d356da0e 100644
--- a/src/backend/cpu/TNJ/BinaryNode.hpp
+++ b/src/backend/cpu/JIT/BinaryNode.hpp
@@ -20,10 +20,10 @@ namespace cpu
     template<typename To, typename Ti, af_op_t op>
     struct BinOp
     {
-        void eval(TNJ::array<To> &out,
-                  const TNJ::array<Ti> &lhs,
-                  const TNJ::array<Ti> &rhs,
-                  int lim)
+        void eval(JIT::array<To> &out,
+                  const JIT::array<Ti> &lhs,
+                  const JIT::array<Ti> &rhs,
+                  int lim) const
         {
             for (int i = 0; i < lim; i++) {
                 out[i] = scalar<To>(0);
@@ -31,7 +31,7 @@ namespace cpu
         }
     };
 
-namespace TNJ
+namespace JIT
 {
 
     template<typename To, typename Ti, af_op_t op>
@@ -50,12 +50,12 @@ namespace TNJ
         {
         }
 
-        void calc(int x, int y, int z, int w, int lim)
+        void calc(int x, int y, int z, int w, int lim) final
         {
             m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
         }
 
-        void calc(int idx, int lim)
+        void calc(int idx, int lim) final
         {
             m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
         }
diff --git a/src/backend/cpu/TNJ/BufferNode.hpp b/src/backend/cpu/JIT/BufferNode.hpp
similarity index 89%
rename from src/backend/cpu/TNJ/BufferNode.hpp
rename to src/backend/cpu/JIT/BufferNode.hpp
index 214afce673..b49b38a36a 100644
--- a/src/backend/cpu/TNJ/BufferNode.hpp
+++ b/src/backend/cpu/JIT/BufferNode.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
 
-namespace TNJ
+namespace JIT
 {
 
     using std::shared_ptr;
@@ -58,7 +58,7 @@ namespace TNJ
                            });
         }
 
-        void calc(int x, int y, int z, int w, int lim)
+        void calc(int x, int y, int z, int w, int lim) final
         {
             dim_t l_off = 0;
             l_off += (w < (int)m_dims[3]) * w * m_strides[3];
@@ -71,7 +71,7 @@ namespace TNJ
             }
         }
 
-        void calc(int idx, int lim)
+        void calc(int idx, int lim) final
         {
             T *in_ptr = m_ptr + idx;
             T *out_ptr = this->m_val.data();
@@ -80,7 +80,7 @@ namespace TNJ
             }
         }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
             len++;
             buf_count++;
@@ -88,7 +88,11 @@ namespace TNJ
             return;
         }
 
-        bool isLinear(const dim_t *dims)
+        size_t getBytes() const final {
+            return m_bytes;
+        }
+
+        bool isLinear(const dim_t *dims) const final
         {
             return m_linear_buffer &&
                 dims[0] == m_dims[0] &&
@@ -97,7 +101,7 @@ namespace TNJ
                 dims[3] == m_dims[3];
         }
 
-        bool isBuffer() { return true; }
+        bool isBuffer() const final { return true; }
 
     };
 
diff --git a/src/backend/cpu/TNJ/Node.hpp b/src/backend/cpu/JIT/Node.hpp
similarity index 83%
rename from src/backend/cpu/TNJ/Node.hpp
rename to src/backend/cpu/JIT/Node.hpp
index 1b89b7812f..98d076bcb7 100644
--- a/src/backend/cpu/TNJ/Node.hpp
+++ b/src/backend/cpu/JIT/Node.hpp
@@ -14,14 +14,18 @@
 #include <memory>
 #include <unordered_map>
 
+namespace common {
+    class NodeIterator;
+}
+
 namespace cpu
 {
 
-namespace TNJ
+namespace JIT
 {
 
     static const int VECTOR_LENGTH = 256;
-    static const int MAX_CHILDREN = 2;
+    static const int MAX_CHILDREN = 3;
 
     class Node;
     using std::shared_ptr;
@@ -42,6 +46,7 @@ namespace TNJ
 
         const int m_height;
         const std::array<Node_ptr, MAX_CHILDREN> m_children;
+        friend common::NodeIterator;
 
     public:
         Node(const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
@@ -53,7 +58,7 @@ namespace TNJ
         {
             auto iter = node_map.find(this);
             if (iter == node_map.end()) {
-                for (const auto &child : m_children) {
+                for (auto &child : m_children) {
                     if (child == nullptr) break;
                     child->getNodesMap(node_map, full_nodes);
                 }
@@ -75,22 +80,25 @@ namespace TNJ
         {
         }
 
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const
         {
             len++;
         }
 
-        virtual bool isLinear(const dim_t *dims) { return true; }
-        virtual bool isBuffer() { return false; }
+        virtual bool isLinear(const dim_t *dims) const { return true; }
+        virtual bool isBuffer() const { return false; }
         virtual ~Node() {}
 
+        virtual size_t getBytes() const {
+          return 0;
+        }
     };
 
     template<typename T>
     class TNode : public Node
     {
     public:
-        alignas(16) TNJ::array<T> m_val;
+        alignas(16) JIT::array<T> m_val;
     public:
         TNode(T val, const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
             Node(height, children)
diff --git a/src/backend/cpu/TNJ/ScalarNode.hpp b/src/backend/cpu/JIT/ScalarNode.hpp
similarity index 97%
rename from src/backend/cpu/TNJ/ScalarNode.hpp
rename to src/backend/cpu/JIT/ScalarNode.hpp
index 716c5964a9..00e2dc23c2 100644
--- a/src/backend/cpu/TNJ/ScalarNode.hpp
+++ b/src/backend/cpu/JIT/ScalarNode.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
 
-namespace TNJ
+namespace JIT
 {
 
     template<typename T>
diff --git a/src/backend/cpu/TNJ/UnaryNode.hpp b/src/backend/cpu/JIT/UnaryNode.hpp
similarity index 75%
rename from src/backend/cpu/TNJ/UnaryNode.hpp
rename to src/backend/cpu/JIT/UnaryNode.hpp
index 270054e193..ebbf3c32da 100644
--- a/src/backend/cpu/TNJ/UnaryNode.hpp
+++ b/src/backend/cpu/JIT/UnaryNode.hpp
@@ -18,8 +18,8 @@ namespace cpu
     template<typename To, typename Ti, af_op_t op>
     struct UnOp
     {
-        void eval(TNJ::array<To> &out,
-                  const TNJ::array<Ti> &in, int lim)
+        void eval(JIT::array<To> &out,
+                  const JIT::array<Ti> &in, int lim) const
         {
             for (int i = 0; i < lim; i++) {
                 out[i] = To(in[i]);
@@ -27,7 +27,7 @@ namespace cpu
         }
     };
 
-namespace TNJ
+namespace JIT
 {
 
     template<typename To, typename Ti, af_op_t op>
@@ -45,14 +45,14 @@ namespace TNJ
         {
         }
 
-        void calc(int x, int y, int z, int w, int lim)
+        void calc(int x, int y, int z, int w, int lim) final
         {
-            m_op.eval(this->m_val, m_child->m_val, lim);
+            m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
         }
 
-        void calc(int idx, int lim)
+        void calc(int idx, int lim) final
         {
-            m_op.eval(this->m_val, m_child->m_val, lim);
+            m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
         }
 
     };
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 87c8bd5eb3..84b15de176 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -12,7 +12,7 @@
 #include <optypes.hpp>
 #include <err_cpu.hpp>
 #include <cmath>
-#include <TNJ/BinaryNode.hpp>
+#include <JIT/BinaryNode.hpp>
 
 namespace cpu
 {
@@ -21,10 +21,10 @@ namespace cpu
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        void eval(TNJ::array<T> &out,           \
-                  const TNJ::array<T> &lhs,     \
-                  const TNJ::array<T> &rhs,     \
-                  int lim)                      \
+        void eval(JIT::array<T> &out,           \
+                  const JIT::array<T> &lhs,     \
+                  const JIT::array<T> &rhs,     \
+                  int lim) const                \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
                 out[i] = lhs[i] op rhs[i];      \
@@ -58,9 +58,9 @@ template<> STATIC_ double __rem<double>(double lhs, double rhs) { return remaind
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        void eval(TNJ::array<T> &out,           \
-                  const TNJ::array<T> &lhs,     \
-                  const TNJ::array<T> &rhs,     \
+        void eval(JIT::array<T> &out,           \
+                  const JIT::array<T> &lhs,     \
+                  const JIT::array<T> &rhs,     \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -80,13 +80,13 @@ NUMERIC_FN(af_hypot_t, hypot)
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
 {
-    TNJ::Node_ptr lhs_node = lhs.getNode();
-    TNJ::Node_ptr rhs_node = rhs.getNode();
+    JIT::Node_ptr lhs_node = lhs.getNode();
+    JIT::Node_ptr rhs_node = rhs.getNode();
 
-    TNJ::BinaryNode<T, T, op> *node = new TNJ::BinaryNode<T, T, op>(lhs_node, rhs_node);
+    JIT::BinaryNode<T, T, op> *node = new JIT::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-    return createNodeArray<T>(odims, TNJ::Node_ptr(
-                                  reinterpret_cast<TNJ::Node *>(node)));
+    return createNodeArray<T>(odims,
+                              JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
 }
 
 }
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 0bc0ef8cb8..2e98c12b76 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -14,7 +14,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 #include <types.hpp>
-#include <TNJ/UnaryNode.hpp>
+#include <JIT/UnaryNode.hpp>
 #include <Array.hpp>
 
 namespace cpu
@@ -23,8 +23,8 @@ namespace cpu
 template<typename To, typename Ti>
 struct UnOp<To, Ti, af_cast_t>
 {
-    void eval(TNJ::array<To> &out,
-              const TNJ::array<Ti> &in, int lim)
+    void eval(JIT::array<To> &out,
+              const JIT::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(in[i]);
@@ -36,8 +36,8 @@ template<typename To>
 struct UnOp<To, std::complex<float>, af_cast_t>
 {
     typedef std::complex<float> Ti;
-    void eval(TNJ::array<To> &out,
-              const TNJ::array<Ti> &in, int lim)
+    void eval(JIT::array<To> &out,
+              const JIT::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(std::abs(in[i]));
@@ -49,8 +49,8 @@ template<typename To>
 struct UnOp<To, std::complex<double>, af_cast_t>
 {
     typedef std::complex<double> Ti;
-    void eval(TNJ::array<To> &out,
-              const TNJ::array<Ti> &in, int lim)
+    void eval(JIT::array<To> &out,
+              const JIT::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(std::abs(in[i]));
@@ -68,8 +68,8 @@ struct UnOp<std::complex<float>, std::complex<double>, af_cast_t>
 {
     typedef std::complex<double> Ti;
     typedef std::complex<float> To;
-    void eval(TNJ::array<To> &out,
-              const TNJ::array<Ti> &in, int lim)
+    void eval(JIT::array<To> &out,
+              const JIT::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(in[i]);
@@ -82,8 +82,8 @@ struct UnOp<std::complex<double>, std::complex<float>, af_cast_t>
 {
     typedef std::complex<float> Ti;
     typedef std::complex<double> To;
-    void eval(TNJ::array<To> &out,
-              const TNJ::array<Ti> &in, int lim)
+    void eval(JIT::array<To> &out,
+              const JIT::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(in[i]);
@@ -95,8 +95,8 @@ struct UnOp<std::complex<double>, std::complex<float>, af_cast_t>
     template<>                                          \
     struct UnOp<char, T, af_cast_t>                     \
     {                                                   \
-        void eval(TNJ::array<char> &out,                \
-                  const TNJ::array<T> &in, int lim)     \
+        void eval(JIT::array<char> &out,                \
+                  const JIT::array<T> &in, int lim)     \
         {                                               \
             for (int i = 0; i < lim; i++) {             \
                 out[i] = char(in[i] != 0);              \
@@ -115,10 +115,10 @@ struct CastWrapper
 {
     Array<To> operator()(const Array<Ti> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<To, Ti, af_cast_t> *node = new TNJ::UnaryNode<To, Ti, af_cast_t>(in_node);
-        return createNodeArray<To>(in.dims(), TNJ::Node_ptr(
-                                       reinterpret_cast<TNJ::Node *>(node)));
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<To, Ti, af_cast_t> *node = new JIT::UnaryNode<To, Ti, af_cast_t>(in_node);
+        return createNodeArray<To>(in.dims(), JIT::Node_ptr(
+                                       reinterpret_cast<JIT::Node *>(node)));
     }
 };
 
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index bd4219b7b5..a20c02825a 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -12,8 +12,8 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <err_cpu.hpp>
-#include <TNJ/BinaryNode.hpp>
-#include <TNJ/UnaryNode.hpp>
+#include <JIT/BinaryNode.hpp>
+#include <JIT/UnaryNode.hpp>
 
 namespace cpu
 {
@@ -21,9 +21,9 @@ namespace cpu
     template<typename To, typename Ti>
     struct BinOp<To, Ti, af_cplx2_t>
     {
-        void eval(TNJ::array<To> &out,
-                  const TNJ::array<Ti> &lhs,
-                  const TNJ::array<Ti> &rhs,
+        void eval(JIT::array<To> &out,
+                  const JIT::array<Ti> &lhs,
+                  const JIT::array<Ti> &rhs,
                   int lim)
         {
             for (int i = 0; i < lim; i++) {
@@ -35,22 +35,22 @@ namespace cpu
     template<typename To, typename Ti>
     Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
     {
-        TNJ::Node_ptr lhs_node = lhs.getNode();
-        TNJ::Node_ptr rhs_node = rhs.getNode();
+        JIT::Node_ptr lhs_node = lhs.getNode();
+        JIT::Node_ptr rhs_node = rhs.getNode();
 
-        TNJ::BinaryNode<To, Ti, af_cplx2_t> *node =
-            new TNJ::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
+        JIT::BinaryNode<To, Ti, af_cplx2_t> *node =
+            new JIT::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
 
-        return createNodeArray<To>(odims, TNJ::Node_ptr(
-                                       reinterpret_cast<TNJ::Node *>(node)));
+        return createNodeArray<To>(odims,
+                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
 #define CPLX_UNARY_FN(op)                               \
     template<typename To, typename Ti>                  \
     struct UnOp<To, Ti, af_##op##_t>                    \
     {                                                   \
-        void eval(TNJ::array<To> &out,                  \
-                  const TNJ::array<Ti> &in, int lim)    \
+        void eval(JIT::array<To> &out,                  \
+                  const JIT::array<Ti> &in, int lim)    \
         {                                               \
             for (int i = 0; i < lim; i++) {             \
                 out[i] = std::op(in[i]);                \
@@ -66,40 +66,40 @@ namespace cpu
     template<typename To, typename Ti>
     Array<To> real(const Array<Ti> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<To, Ti, af_real_t> *node = new TNJ::UnaryNode<To, Ti, af_real_t>(in_node);
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<To, Ti, af_real_t> *node = new JIT::UnaryNode<To, Ti, af_real_t>(in_node);
 
         return createNodeArray<To>(in.dims(),
-                                   TNJ::Node_ptr(reinterpret_cast<TNJ::Node *>(node)));
+                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
     template<typename To, typename Ti>
     Array<To> imag(const Array<Ti> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<To, Ti, af_imag_t> *node = new TNJ::UnaryNode<To, Ti, af_imag_t>(in_node);
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<To, Ti, af_imag_t> *node = new JIT::UnaryNode<To, Ti, af_imag_t>(in_node);
 
         return createNodeArray<To>(in.dims(),
-                                   TNJ::Node_ptr(reinterpret_cast<TNJ::Node *>(node)));
+                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
     template<typename To, typename Ti>
     Array<To> abs(const Array<Ti> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<To, Ti, af_abs_t> *node = new TNJ::UnaryNode<To, Ti, af_abs_t>(in_node);
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<To, Ti, af_abs_t> *node = new JIT::UnaryNode<To, Ti, af_abs_t>(in_node);
 
         return createNodeArray<To>(in.dims(),
-                                   TNJ::Node_ptr(reinterpret_cast<TNJ::Node *>(node)));
+                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
     template<typename T>
     Array<T> conj(const Array<T> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<T, T, af_conj_t> *node = new TNJ::UnaryNode<T, T, af_conj_t>(in_node);
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<T, T, af_conj_t> *node = new JIT::UnaryNode<T, T, af_conj_t>(in_node);
 
         return createNodeArray<T>(in.dims(),
-                                  TNJ::Node_ptr(reinterpret_cast<TNJ::Node *>(node)));
+                                  JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 }
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 63094154f0..884beeab43 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <Param.hpp>
 #include <platform.hpp>
-#include <TNJ/Node.hpp>
+#include <JIT/Node.hpp>
 #include <vector>
 
 namespace cpu
@@ -19,20 +19,20 @@ namespace kernel
 {
 
 template<typename T>
-void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> output_nodes_)
+void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> output_nodes_)
 {
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
-    TNJ::Node_map_t nodes;
+    JIT::Node_map_t nodes;
     std::vector<T *> ptrs;
-    std::vector<TNJ::TNode<T> *> output_nodes;
-    std::vector<TNJ::Node *> full_nodes;
+    std::vector<JIT::TNode<T> *> output_nodes;
+    std::vector<JIT::Node *> full_nodes;
 
     int narrays = static_cast<int>(arrays.size());
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
-        output_nodes.push_back(reinterpret_cast<TNJ::TNode<T> *>(output_nodes_[i].get()));
+        output_nodes.push_back(reinterpret_cast<JIT::TNode<T> *>(output_nodes_[i].get()));
         output_nodes_[i]->getNodesMap(nodes, full_nodes);
     }
 
@@ -43,9 +43,9 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
 
     if (is_linear) {
         int num = arrays[0].dims().elements();
-        int cnum = TNJ::VECTOR_LENGTH * std::ceil(double(num) / TNJ::VECTOR_LENGTH);
-        for (int i = 0; i < cnum; i += TNJ::VECTOR_LENGTH) {
-            int lim = std::min(TNJ::VECTOR_LENGTH, num - i);
+        int cnum = JIT::VECTOR_LENGTH * std::ceil(double(num) / JIT::VECTOR_LENGTH);
+        for (int i = 0; i < cnum; i += JIT::VECTOR_LENGTH) {
+            int lim = std::min(JIT::VECTOR_LENGTH, num - i);
             for (int n = 0; n < (int)full_nodes.size(); n++) {
                 full_nodes[n]->calc(i, lim);
             }
@@ -67,9 +67,9 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
                     dim_t offy = y * ostrs[1] + offz;
 
                     int dim0 = odims[0];
-                    int cdim0 = TNJ::VECTOR_LENGTH * std::ceil(double(dim0) / TNJ::VECTOR_LENGTH);
-                    for (int x = 0; x < (int)cdim0; x += TNJ::VECTOR_LENGTH) {
-                        int lim = std::min(TNJ::VECTOR_LENGTH, dim0 - x);
+                    int cdim0 = JIT::VECTOR_LENGTH * std::ceil(double(dim0) / JIT::VECTOR_LENGTH);
+                    for (int x = 0; x < (int)cdim0; x += JIT::VECTOR_LENGTH) {
+                        int lim = std::min(JIT::VECTOR_LENGTH, dim0 - x);
                         dim_t id = x + offy;
 
                         for (int n = 0; n < (int)full_nodes.size(); n++) {
@@ -88,7 +88,7 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<TNJ::Node_ptr> outpu
 }
 
 template<typename T>
-void evalArray(Param<T> arr, TNJ::Node_ptr node)
+void evalArray(Param<T> arr, JIT::Node_ptr node)
 {
     evalMultiple<T>({arr}, {node});
 }
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index 331d6ddb91..07ed3955ca 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -12,7 +12,7 @@
 #include <optypes.hpp>
 #include <err_cpu.hpp>
 #include <types.hpp>
-#include <TNJ/BinaryNode.hpp>
+#include <JIT/BinaryNode.hpp>
 
 namespace cpu
 {
@@ -21,9 +21,9 @@ namespace cpu
     template<typename T>                        \
     struct BinOp<char, T, OP>                   \
     {                                           \
-        void eval(TNJ::array<char> &out,        \
-                  const TNJ::array<T> &lhs,     \
-                  const TNJ::array<T> &rhs,     \
+        void eval(JIT::array<char> &out,        \
+                  const JIT::array<T> &lhs,     \
+                  const JIT::array<T> &rhs,     \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -49,9 +49,9 @@ namespace cpu
     struct BinOp<char, std::complex<T>, OP>     \
     {                                           \
         typedef std::complex<T> Ti;             \
-        void eval(TNJ::array<char> &out,        \
-                  const TNJ::array<Ti> &lhs,    \
-                  const TNJ::array<Ti> &rhs,    \
+        void eval(JIT::array<char> &out,        \
+                  const JIT::array<Ti> &lhs,    \
+                  const JIT::array<Ti> &rhs,    \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -82,13 +82,13 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T, af_op_t op>
     Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
     {
-        TNJ::Node_ptr lhs_node = lhs.getNode();
-        TNJ::Node_ptr rhs_node = rhs.getNode();
+        JIT::Node_ptr lhs_node = lhs.getNode();
+        JIT::Node_ptr rhs_node = rhs.getNode();
 
-        TNJ::BinaryNode<char, T, op> *node = new TNJ::BinaryNode<char, T, op>(lhs_node, rhs_node);
+        JIT::BinaryNode<char, T, op> *node = new JIT::BinaryNode<char, T, op>(lhs_node, rhs_node);
 
-        return createNodeArray<char>(odims, TNJ::Node_ptr(
-                                          reinterpret_cast<TNJ::Node *>(node)));
+        return createNodeArray<char>(odims,
+                                     JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
 
@@ -97,9 +97,9 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        void eval(TNJ::array<T> &out,           \
-                  const TNJ::array<T> &lhs,     \
-                  const TNJ::array<T> &rhs,     \
+        void eval(JIT::array<T> &out,           \
+                  const JIT::array<T> &lhs,     \
+                  const JIT::array<T> &rhs,     \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -119,12 +119,12 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T, af_op_t op>
     Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
     {
-        TNJ::Node_ptr lhs_node = lhs.getNode();
-        TNJ::Node_ptr rhs_node = rhs.getNode();
+        JIT::Node_ptr lhs_node = lhs.getNode();
+        JIT::Node_ptr rhs_node = rhs.getNode();
 
-        TNJ::BinaryNode<T, T, op> *node = new TNJ::BinaryNode<T, T, op>(lhs_node, rhs_node);
+        JIT::BinaryNode<T, T, op> *node = new JIT::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-        return createNodeArray<T>(odims, TNJ::Node_ptr(
-                                      reinterpret_cast<TNJ::Node *>(node)));
+        return createNodeArray<T>(odims,
+                                  JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 }
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index fab26cda21..b6b2f8010e 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <err_cpu.hpp>
-#include <TNJ/UnaryNode.hpp>
+#include <JIT/UnaryNode.hpp>
 #include <cmath>
 
 namespace cpu
@@ -26,8 +26,8 @@ T sigmoid(T in)
     template<typename T>                            \
     struct UnOp<T, T, af_##op##_t>                  \
     {                                               \
-        void eval(TNJ::array<T> &out,               \
-                  const TNJ::array<T> &in, int lim) \
+        void eval(JIT::array<T> &out,               \
+                  const JIT::array<T> &in, int lim) \
         {                                           \
             for (int i = 0; i < lim; i++) {         \
                 out[i] = fn(in[i]);                 \
@@ -82,11 +82,11 @@ UNARY_OP(lgamma)
     template<typename T, af_op_t op>
     Array<T> unaryOp(const Array<T> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<T, T, op> *node = new TNJ::UnaryNode<T, T, op>(in_node);
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<T, T, op> *node = new JIT::UnaryNode<T, T, op>(in_node);
 
         return createNodeArray<T>(in.dims(),
-                                  TNJ::Node_ptr(reinterpret_cast<TNJ::Node *>(node)));
+                                  JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
 #define iszero(a) ((a) == 0)
@@ -95,8 +95,8 @@ UNARY_OP(lgamma)
     template<typename T>                            \
     struct UnOp<char, T, af_##name##_t>             \
     {                                               \
-        void eval(TNJ::array<char> &out,            \
-                  const TNJ::array<T> &in, int lim) \
+        void eval(JIT::array<char> &out,            \
+                  const JIT::array<T> &in, int lim) \
         {                                           \
             for (int i = 0; i < lim; i++) {         \
                 out[i] = op(in[i]);                 \
@@ -111,11 +111,11 @@ UNARY_OP(lgamma)
     template<typename T, af_op_t op>
     Array<char> checkOp(const Array<T> &in)
     {
-        TNJ::Node_ptr in_node = in.getNode();
-        TNJ::UnaryNode<char, T, op> *node = new TNJ::UnaryNode<char, T, op>(in_node);
+        JIT::Node_ptr in_node = in.getNode();
+        JIT::UnaryNode<char, T, op> *node = new JIT::UnaryNode<char, T, op>(in_node);
 
         return createNodeArray<char>(in.dims(),
-                                     TNJ::Node_ptr(reinterpret_cast<TNJ::Node *>(node)));
+                                     JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
     }
 
 }

From b136f20b6e085c1d0a3eed8b2d0437e38f054bbb Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Fri, 19 Oct 2018 23:30:02 -0500
Subject: [PATCH 1540/2677] Implement approx1 and approx2 along specified
 dimensions

- FEAT: Adding the approx1 and approx2 along any given dimension for all backends.
- PERF, OpenCL: Fixes the launch configuration for approx1
- Throw exception for invalid step size of 0.
- Cleanup API entry source files and tests
- Improved documentation for approx1/2().
- New approx1 tests:
    - Check for non-monotonic positions.
    - Check for approx behavior with af::Inf values.
---
 assets                               |   2 +-
 docs/details/signal.dox              |  69 +++--
 include/af/signal.h                  | 204 +++++++++++---
 src/api/c/approx.cpp                 | 188 ++++++++-----
 src/api/cpp/approx.cpp               |  39 ++-
 src/api/unified/signal.cpp           |  33 ++-
 src/backend/cpu/approx.cpp           |  86 +++---
 src/backend/cpu/approx.hpp           |   8 +-
 src/backend/cpu/kernel/approx.hpp    | 124 +++++----
 src/backend/cpu/kernel/interp.hpp    | 123 ++++++---
 src/backend/cuda/approx.cu           |  75 +++--
 src/backend/cuda/approx.hpp          |   8 +-
 src/backend/cuda/kernel/approx.hpp   | 111 +++++---
 src/backend/cuda/kernel/interp.hpp   | 127 +++++----
 src/backend/opencl/approx.cpp        |  73 +++--
 src/backend/opencl/approx.hpp        |   8 +-
 src/backend/opencl/kernel/approx.hpp |  46 ++--
 src/backend/opencl/kernel/approx1.cl |  50 ++--
 src/backend/opencl/kernel/approx2.cl |  53 ++--
 src/backend/opencl/kernel/interp.cl  | 182 ++++++++----
 test/approx1.cpp                     | 395 +++++++++++++++++++++++++--
 test/approx2.cpp                     | 357 +++++++++++++++++++++---
 test/testHelpers.hpp                 |   2 -
 23 files changed, 1775 insertions(+), 588 deletions(-)

diff --git a/assets b/assets
index 12f486d049..fac641d359 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit 12f486d049903a823e836e2ac95d9e48ce6cbcf3
+Subproject commit fac641d359db1881ec801f0ef0fddb5673817699
diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index 67b1bfa2f8..34482aa9da 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -211,34 +211,59 @@ respectively, then the possible batch operations are as follows.
 \defgroup signal_func_approx1 approx1
 \ingroup approx_mat
 
-approx1 interpolates data along the first dimensions.
-It has three options for the type of interpolation to perform:
-- Nearest neighbor  - \ref AF_INTERP_NEAREST
-- Linear interpolation  - \ref AF_INTERP_LINEAR
-- Bilinear interpolation - \ref AF_INTERP_BILINEAR
-- Cubic interpolation - \ref AF_INTERP_CUBIC
+Performs interpolation on data along a single dimension.
 
-Interpolation is performed assuming input data is equally spaced with indices
-in the range [0, n). The positions are sampled with respect to data at these
-locations.
+Interpolation is the process of computing for unknown values within a
+continuous range described by a discrete set of known values. These
+known values (`in`) correspond to a uniformly-spaced range of indices
+determined by start and step values, whose defaults are 0.0 and 1.0,
+respectively.
 
-\snippet test/approx1.cpp ex_signal_approx1
+The positions array (`pos`) contains the interpolating points (indices
+whose values we want to find) along a given dimension. Values of **known indices**
+will be looked up in the input array, while values of **unknown indices**
+will be found via interpolation. Indices outside of the index range
+are not extrapolated. Instead, those values are set `off_grid`, whose
+default value is 0.0.
 
-\defgroup signal_func_approx2 approx2
-\ingroup approx_mat
+The following image illustrates a simple example (known values
+represented by blue dots, unknown values represented by red dots):
+
+\image html approx1_default_idx.png "approx1() using idx_start=0.0, idx_step=1.0"
+
+Several interpolation methods are supported by approx1:
 
-approx2 performs interpolation on data along the first and second dimensions.
-It has three options for the type of interpolation to perform:
-- Nearest neighbor  - \ref AF_INTERP_NEAREST
-- Linear interpolation  - \ref AF_INTERP_LINEAR
-- Bilinear interpolation - \ref AF_INTERP_BILINEAR
-- Cubic interpolation - \ref AF_INTERP_CUBIC
+- Nearest neighbor interpolation - \ref AF_INTERP_NEAREST
+- Linear interpolation (default) - \ref AF_INTERP_LINEAR, \ref AF_INTERP_LINEAR_COSINE
+- Cubic interpolation - \ref AF_INTERP_CUBIC, \ref AF_INTERP_CUBIC_SPLINE
+- Lower interpolation - \ref AF_INTERP_LOWER
 
-Interpolation is performed assuming input data is equally spaced with indices
-in the range [0, n) along each dimension.
-The positions are sampled with respect to data at these locations.
+Unless specified, linear interpolation is performed by default. Refer
+to \ref af_interp_type for more information about ArrayFire's
+interpolation types.
+
+\defgroup signal_func_approx2 approx2
+\ingroup approx_mat
 
-\snippet test/approx2.cpp ex_signal_approx2
+Performs interpolation on data along two dimensions.
+
+Interpolation is the process of computing for unknown values within a
+continuous range described by a discrete set of known values. These
+known values correspond to a uniformly-spaced range of indices
+determined by start and step values, whose defaults are 0.0 and 1.0,
+respectively.
+
+The positions arrays (`pos0` and `pos1`) contain the interpolating
+points (indices whose values we want to find) along two given
+dimensions. Values of **known indices** will be looked up in the input
+array, while values of **unknown indices** will be found via
+interpolation. Indices outside of the index range are not
+extrapolated. Instead, those values are set to `off_grid`, whose
+default value is 0.0.
+
+All of the interpolation methods defined in \ref af_interp_type are
+supported by approx2. Unless specified, linear interpolation is
+performed by default.
 
 \defgroup signal_func_fir fir
 \ingroup sigfilt_mat
diff --git a/include/af/signal.h b/include/af/signal.h
index 50846b5926..f85c7045d3 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -18,35 +18,112 @@ class array;
 class dim4;
 
 /**
-   C++ Interface for data interpolation on one dimensional signals.
+   C++ Interface for data interpolation on one-dimensional signals.
 
-   \param[in]  in is the input array
-   \param[in]  pos array contains the interpolation locations
-   \param[in]  method is the interpolation type, it can take one of the values defined by the
-               enum \ref af_interp_type
-   \param[in]  offGrid is the value that will set in the output array when certain index is out of bounds
-   \return     the array with interpolated values
+   \param[in]  in is the multidimensional input array. Values assumed to lie uniformly spaced indices in the range of `[0, n)`, where `n` is the number of elements in the array.
+   \param[in]  pos positions of the interpolation points along the first dimension.
+   \param[in]  method is the interpolation method to be used. The following types (defined in enum \ref af_interp_type) are supported: nearest neighbor, linear, and cubic.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
+   \returns    the interpolated array.
+
+   The code sample below demonstrates approx1()'s usage:
+
+   \snippet test/approx1.cpp ex_signal_approx1
 
    \ingroup signal_func_approx1
  */
 AFAPI array approx1(const array &in, const array &pos,
-                    const interpType method = AF_INTERP_LINEAR, const float offGrid = 0.0f);
+                    const interpType method = AF_INTERP_LINEAR, const float off_grid = 0.0f);
 
 /**
-   C++ Interface for data interpolation on two dimensional signals.
+   C++ Interface for data interpolation on two-dimensional signals.
 
-   \param[in]  in is the input array
-   \param[in]  pos0 array contains the interpolation locations for first dimension
-   \param[in]  pos1 array contains the interpolation locations for second dimension
-   \param[in]  method is the interpolation type, it can take one of the values defined by the
-               enum \ref af_interp_type
-   \param[in]  offGrid is the value that will set in the output array when certain index is out of bounds
-   \return     the array with interpolated values
+   \param[in]  in is the multidimensional input array. Values assumed to lie uniformly spaced indices in the range of `[0, n)` along both interpolation dimensions. `n` is the number of elements in the array.
+   \param[in]  pos0 positions of the interpolation points along the first dimension.
+   \param[in]  pos1 positions of the interpolation points along the second dimension.
+   \param[in]  method is the interpolation method to be used. All interpolation types defined in \ref af_interp_type are supported.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
+   \returns    the interpolated array.
+
+   The code sample below demonstrates approx2()'s usage:
+
+   \snippet test/approx2.cpp ex_signal_approx2
 
    \ingroup signal_func_approx2
  */
 AFAPI array approx2(const array &in, const array &pos0, const array &pos1,
-                    const interpType method = AF_INTERP_LINEAR, const float offGrid = 0.0f);
+                    const interpType method = AF_INTERP_LINEAR, const float off_grid = 0.0f);
+
+
+#if AF_API_VERSION >= 37
+/**
+   C++ Interface for data interpolation on one-dimensional signals.
+
+   The following version of approx1() accepts the dimension to perform
+   the interpolation along the input. It also accepts start and step
+   values which define the uniform range of corresponding indices.
+
+   The following image illustrates what the range of indices
+   corresponding to the input values look like if `idx_start` and
+   `idx_step` are set to an arbitrary value of 10,
+
+   \image html approx1_arbitrary_idx.png "approx1() using idx_start=10.0, idx_step=10.0"
+
+   The blue dots represent indices whose values are known. The red dots
+   represent indices whose values are unknown.
+
+   \param[in]  in is the multidimensional input array. Values lie on uniformly spaced indices determined by `idx_start` and `idx_step`.
+   \param[in]  pos positions of the interpolation points along `interp_dim`.
+   \param[in]  interp_dim is the dimension to perform interpolation across.
+   \param[in]  idx_start is the first index value along `interp_dim`.
+   \param[in]  idx_step is the uniform spacing value between subsequent indices along `interp_dim`.
+   \param[in]  method is the interpolation method to be used. The following types (defined in enum \ref af_interp_type) are supported: nearest neighbor, linear, and cubic.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
+   \returns    the interpolated array.
+
+   The code sample below demonstrates usage:
+
+   \snippet test/approx1.cpp ex_signal_approx1_uniform
+
+   \ingroup signal_func_approx1
+ */
+AFAPI array approx1(const array &in,
+                    const array &pos, const int interp_dim,
+                    const double idx_start, const double idx_step,
+                    const interpType method = AF_INTERP_LINEAR, const float off_grid = 0.0f);
+
+/**
+   C++ Interface for data interpolation on two-dimensional signals.
+
+   The following version of the approx2() accepts the two dimensions
+   to perform the interpolation along the input. It also accepts start
+   and step values which define the uniform range of corresponding
+   indices.
+
+   \param[in]  in is the multidimensional input array.
+   \param[in]  pos0 positions of the interpolation points along `interp_dim0`.
+   \param[in]  interp_dim0 is the first dimension to perform interpolation across.
+   \param[in]  idx_start_dim0 is the first index value along `interp_dim0`.
+   \param[in]  idx_step_dim0 is the uniform spacing value between subsequent indices along `interp_dim0`.
+   \param[in]  pos1 positions of the interpolation points along `interp_dim1`.
+   \param[in]  interp_dim1 is the second dimension to perform interpolation across.
+   \param[in]  idx_start_dim1 is the first index value along `interp_dim1`.
+   \param[in]  idx_step_dim1 is the uniform spacing value between subsequent indices along `interp_dim1`.
+   \param[in]  method is the interpolation method to be used. All interpolation types defined in \ref af_interp_type are supported.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
+   \returns    the interpolated array.
+
+   The code sample below demonstrates usage:
+
+   \snippet test/approx2.cpp ex_signal_approx2_uniform
+
+   \ingroup signal_func_approx2
+ */
+AFAPI array approx2(const array &in,
+                    const array &pos0, const int interp_dim0, const double idx_start_dim0, const double idx_step_dim0,
+                    const array &pos1, const int interp_dim1, const double idx_start_dim1, const double idx_step_dim1,
+                    const interpType method = AF_INTERP_LINEAR, const float off_grid = 0.0f);
+#endif
 
 /**
    C++ Interface for fast fourier transform on one dimensional signals
@@ -676,37 +753,100 @@ extern "C" {
 /**
    C Interface for signals interpolation on one dimensional signals.
 
-   \param[out] out is the array with interpolated values
-   \param[in]  in is the input array
-   \param[in]  pos array contains the interpolation locations
-   \param[in]  method is the interpolation type, it can take one of the values defined by the
-               enum \ref af_interp_type
-   \param[in]  offGrid is the value that will set in the output array when certain index is out of bounds
+   \param[out] out the interpolated array.
+   \param[in]  in is the multidimensional input array. Values assumed to lie uniformly spaced indices in the range of `[0, n)`, where `n` is the number of elements in the array.
+   \param[in]  pos positions of the interpolation points along the first dimension.
+   \param[in]  method is the interpolation method to be used. The following types (defined in enum \ref af_interp_type) are supported: nearest neighbor, linear, and cubic.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
    \return     \ref AF_SUCCESS if the interpolation operation is successful,
                otherwise an appropriate error code is returned.
 
    \ingroup signal_func_approx1
  */
 AFAPI af_err af_approx1(af_array *out, const af_array in, const af_array pos,
-                        const af_interp_type method, const float offGrid);
+                        const af_interp_type method, const float off_grid);
 
 /**
    C Interface for signals interpolation on two dimensional signals.
 
-   \param[out] out is the array with interpolated values
-   \param[in]  in is the input array
-   \param[in]  pos0 array contains the interpolation locations for first dimension
-   \param[in]  pos1 array contains the interpolation locations for second dimension
-   \param[in]  method is the interpolation type, it can take one of the values defined by the
-               enum \ref af_interp_type
-   \param[in]  offGrid is the value that will set in the output array when certain index is out of bounds
+   \param[out] out the interpolated array.
+   \param[in]  in is the multidimensional input array. Values assumed to lie uniformly spaced indices in the range of `[0, n)` along both interpolation dimensions. `n` is the number of elements in the array.
+   \param[in]  pos0 positions of the interpolation points along the first dimension.
+   \param[in]  pos1 positions of the interpolation points along the second dimension.
+   \param[in]  method is the interpolation method to be used. All interpolation types defined in \ref af_interp_type are supported.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
    \return     \ref AF_SUCCESS if the interpolation operation is successful,
                otherwise an appropriate error code is returned.
 
    \ingroup signal_func_approx2
  */
 AFAPI af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const af_array pos1,
-                        const af_interp_type method, const float offGrid);
+                        const af_interp_type method, const float off_grid);
+
+#if AF_API_VERSION >= 37
+/**
+   C Interface for signals interpolation on one dimensional signals along specified dimension.
+
+   af_approx1_uniform() accepts the dimension to perform the
+   interpolation along the input. It also accepts start and step
+   values which define the uniform range of corresponding indices.
+
+   The following image illustrates what the range of indices
+   corresponding to the input values look like if `idx_start` and
+   `idx_step` are set to an arbitrary value of 10,
+
+   \image html approx1_arbitrary_idx.png "approx1() using idx_start=10.0, idx_step=10.0"
+
+   The blue dots represent indices whose values are known. The red dots
+   represent indices whose values are unknown.
+
+   \param[out] out the interpolated array.
+   \param[in]  in is the multidimensional input array. Values lie on uniformly spaced indices determined by `idx_start` and `idx_step`.
+   \param[in]  pos positions of the interpolation points along `interp_dim`.
+   \param[in]  interp_dim is the dimension to perform interpolation across.
+   \param[in]  idx_start is the first index value along `interp_dim`.
+   \param[in]  idx_step is the uniform spacing value between subsequent indices along `interp_dim`.
+   \param[in]  method is the interpolation method to be used. The following types (defined in enum \ref af_interp_type) are supported: nearest neighbor, linear, and cubic.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
+   \return     \ref AF_SUCCESS if the interpolation operation is successful,
+               otherwise an appropriate error code is returned.
+
+   \ingroup signal_func_approx1
+ */
+AFAPI af_err af_approx1_uniform(af_array *out, const af_array in,
+                                const af_array pos, const int interp_dim,
+                                const double idx_start, const double idx_step,
+                                const af_interp_type method, const float off_grid);
+
+/**
+   C Interface for signals interpolation on two dimensional signals alog specified dimensions.
+
+   af_approx2_uniform() accepts two dimensions to perform the
+   interpolation along the input. It also accepts start and step
+   values which define the uniform range of corresponding indices.
+
+   \param[out] out the interpolated array.
+   \param[in]  in is the multidimensional input array.
+   \param[in]  pos0 positions of the interpolation points along `interp_dim0`.
+   \param[in]  interp_dim0 is the first dimension to perform interpolation across.
+   \param[in]  idx_start_dim0 is the first index value along `interp_dim0`.
+   \param[in]  idx_step_dim0 is the uniform spacing value between subsequent indices along `interp_dim0`.
+   \param[in]  pos1 positions of the interpolation points along `interp_dim1`.
+   \param[in]  interp_dim1 is the second dimension to perform interpolation across.
+   \param[in]  idx_start_dim1 is the first index value along `interp_dim1`.
+   \param[in]  idx_step_dim1 is the uniform spacing value between subsequent indices along `interp_dim1`.
+   \param[in]  method is the interpolation method to be used. All interpolation types defined in \ref af_interp_type are supported.
+   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
+   \return     \ref AF_SUCCESS if the interpolation operation is successful,
+               otherwise an appropriate error code is returned.
+
+   \ingroup signal_func_approx2
+ */
+AFAPI af_err af_approx2_uniform(af_array *out, const af_array in,
+                                const af_array pos0, const int interp_dim0, const double idx_start_dim0, const double idx_step_dim0,
+                                const af_array pos1, const int interp_dim1, const double idx_start_dim1, const double idx_step_dim1,
+                                const af_interp_type method, const float off_grid);
+#endif
 
 /**
    C Interface for fast fourier transform on one dimensional signals
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index daf45f7eb9..7dafc0182a 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -20,106 +20,162 @@ using af::dim4;
 using namespace detail;
 
 template<typename Ty, typename Tp>
-static inline af_array approx1(const af_array in, const af_array pos,
+static inline af_array approx1(const af_array yi,
+                               const af_array xo, const int xdim,
+                               const Tp &xi_beg, const Tp &xi_step,
                                const af_interp_type method, const float offGrid)
 {
-    return getHandle(approx1<Ty>(getArray<Ty>(in), getArray<Tp>(pos), method, offGrid));
+    return getHandle(approx1<Ty>(getArray<Ty>(yi),
+                                 getArray<Tp>(xo), xdim,
+                                 xi_beg, xi_step,
+                                 method, offGrid));
 }
 
 template<typename Ty, typename Tp>
-static inline af_array approx2(const af_array in, const af_array pos0, const af_array pos1,
+static inline af_array approx2(const af_array zi,
+                               const af_array xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                               const af_array yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                                const af_interp_type method, const float offGrid)
 {
-    return getHandle(approx2<Ty>(getArray<Ty>(in), getArray<Tp>(pos0), getArray<Tp>(pos1),
+    return getHandle(approx2<Ty>(getArray<Ty>(zi),
+                                 getArray<Tp>(xo), xdim, xi_beg, xi_step,
+                                 getArray<Tp>(yo), ydim, yi_beg, yi_step,
                                  method, offGrid));
 }
 
-af_err af_approx1(af_array *out, const af_array in, const af_array pos,
+af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
                   const af_interp_type method, const float offGrid)
+{
+    return af_approx1_uniform(yo, yi, xo, 0, 0.0, 1.0, method, offGrid);
+}
+
+af_err af_approx1_uniform(af_array *yo, const af_array yi,
+                          const af_array xo, const int xdim,
+                          const double xi_beg, const double xi_step,
+                          const af_interp_type method, const float offGrid)
 {
     try {
-        const ArrayInfo& i_info = getInfo(in);
-        const ArrayInfo& p_info = getInfo(pos);
-
-        dim4 idims = i_info.dims();
-        dim4 pdims = p_info.dims();
-
-        af_dtype itype = i_info.getType();
-
-        ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
-        ARG_ASSERT(2, p_info.isRealFloating());                   // Only floating types
-        ARG_ASSERT(1, i_info.isSingle() == p_info.isSingle());    // Must have same precision
-        ARG_ASSERT(1, i_info.isDouble() == p_info.isDouble());    // Must have same precision
-        // POS should either be (x, 1, 1, 1) or (1, idims[1], idims[2], idims[3])
-        DIM_ASSERT(2, p_info.isColumn() ||
-                      (pdims[1] == idims[1] && pdims[2] == idims[2] && pdims[3] == idims[3]));
-        ARG_ASSERT(3, (method == AF_INTERP_LINEAR  ||
-                       method == AF_INTERP_NEAREST ||
-                       method == AF_INTERP_CUBIC   ||
-                       method == AF_INTERP_CUBIC_SPLINE ||
+        const ArrayInfo& yi_info = getInfo(yi);
+        const ArrayInfo& xo_info = getInfo(xo);
+
+        dim4 yi_dims = yi_info.dims();
+        dim4 xo_dims = xo_info.dims();
+
+        ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
+        ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
+        ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle());    // Must have same precision
+        ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble());    // Must have same precision
+        ARG_ASSERT(3, xdim >= 0 && xdim < 4);
+
+        // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
+        if (xo_dims[xdim] != xo_dims.elements()) {
+            for (int i = 0; i < 4; i++) {
+                if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
+            }
+        }
+
+        ARG_ASSERT(5, xi_step != 0);
+        ARG_ASSERT(6, (method == AF_INTERP_CUBIC         ||
+                       method == AF_INTERP_CUBIC_SPLINE  ||
+                       method == AF_INTERP_LINEAR        ||
                        method == AF_INTERP_LINEAR_COSINE ||
-                       method == AF_INTERP_LOWER));
+                       method == AF_INTERP_LOWER         ||
+                       method == AF_INTERP_NEAREST));
 
-        if(idims.ndims() == 0 || pdims.ndims() ==  0) {
-            return af_create_handle(out, 0, nullptr, itype);
+        if (yi_dims.ndims() == 0 || xo_dims.ndims() ==  0) {
+            return af_create_handle(yo, 0, nullptr, yi_info.getType());
         }
 
         af_array output;
 
-        switch(itype) {
-            case f32: output = approx1<float  , float >(in, pos, method, offGrid);  break;
-            case f64: output = approx1<double , double>(in, pos, method, offGrid);  break;
-            case c32: output = approx1<cfloat , float >(in, pos, method, offGrid);  break;
-            case c64: output = approx1<cdouble, double>(in, pos, method, offGrid);  break;
-            default:  TYPE_ERROR(1, itype);
+        switch(yi_info.getType()) {
+        case f32: output = approx1<float  , float >(yi, xo, xdim,
+                                                    xi_beg, xi_step,
+                                                    method, offGrid);  break;
+        case f64: output = approx1<double , double>(yi, xo, xdim,
+                                                    xi_beg, xi_step,
+                                                    method, offGrid);  break;
+        case c32: output = approx1<cfloat , float >(yi, xo, xdim,
+                                                    xi_beg, xi_step,
+                                                    method, offGrid);  break;
+        case c64: output = approx1<cdouble, double>(yi, xo, xdim,
+                                                    xi_beg, xi_step,
+                                                    method, offGrid);  break;
+        default:  TYPE_ERROR(1, yi_info.getType());
         }
-        std::swap(*out,output);
+        std::swap(*yo,output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const af_array pos1,
+af_err af_approx2(af_array *zo, const af_array zi, const af_array xo, const af_array yo,
                   const af_interp_type method, const float offGrid)
+{
+    return af_approx2_uniform(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method, offGrid);
+}
+
+af_err af_approx2_uniform(af_array *zo, const af_array zi,
+                          const af_array xo, const int xdim, const double xi_beg, const double xi_step,
+                          const af_array yo, const int ydim, const double yi_beg, const double yi_step,
+                          const af_interp_type method, const float offGrid)
 {
     try {
-        const ArrayInfo& i_info = getInfo(in);
-        const ArrayInfo& p_info = getInfo(pos0);
-        const ArrayInfo& q_info = getInfo(pos1);
-
-        dim4 idims = i_info.dims();
-        dim4 pdims = p_info.dims();
-        dim4 qdims = q_info.dims();
-
-        af_dtype itype = i_info.getType();
-
-        ARG_ASSERT(1, i_info.isFloating());                     // Only floating and complex types
-        ARG_ASSERT(2, p_info.isRealFloating());                 // Only floating types
-        ARG_ASSERT(3, q_info.isRealFloating());                 // Only floating types
-        ARG_ASSERT(1, p_info.getType() == q_info.getType());    // Must have same type
-        ARG_ASSERT(1, i_info.isSingle() == p_info.isSingle());  // Must have same precision
-        ARG_ASSERT(1, i_info.isDouble() == p_info.isDouble());  // Must have same precision
-        DIM_ASSERT(2, pdims == qdims);                          // POS0 and POS1 must have same dims
-
-        // POS should either be (x, y, 1, 1) or (x, y, idims[2], idims[3])
-        DIM_ASSERT(2, (pdims[2] == 1        && pdims[3] == 1) ||
-                      (pdims[2] == idims[2] && pdims[3] == idims[3]));
-
-        if(idims.ndims() == 0 || pdims.ndims() ==  0 || qdims.ndims() == 0) {
-            return af_create_handle(out, 0, nullptr, itype);
+        const ArrayInfo& zi_info = getInfo(zi);
+        const ArrayInfo& xo_info = getInfo(xo);
+        const ArrayInfo& yo_info = getInfo(yo);
+
+        dim4 zi_dims = zi_info.dims();
+        dim4 xo_dims = xo_info.dims();
+        dim4 yo_dims = yo_info.dims();
+
+        ARG_ASSERT(1, zi_info.isFloating());                     // Only floating and complex types
+        ARG_ASSERT(2, xo_info.isRealFloating());                 // Only floating types
+        ARG_ASSERT(4, yo_info.isRealFloating());                 // Only floating types
+        ARG_ASSERT(2, xo_info.getType() == yo_info.getType());    // Must have same type
+        ARG_ASSERT(1, zi_info.isSingle() == xo_info.isSingle());  // Must have same precision
+        ARG_ASSERT(1, zi_info.isDouble() == xo_info.isDouble());  // Must have same precision
+        DIM_ASSERT(2, xo_dims == yo_dims);                        // POS0 and POS1 must have same dims
+
+        ARG_ASSERT(3, xdim >= 0 && xdim < 4);
+        ARG_ASSERT(5, ydim >= 0 && ydim < 4);
+        ARG_ASSERT(7, xi_step != 0);
+        ARG_ASSERT(9, yi_step != 0);
+
+        // POS should either be (x, y, 1, 1) or (x, y, zi_dims[2], zi_dims[3])
+        if (xo_dims[xdim] * xo_dims[ydim] != xo_dims.elements()) {
+            for (int i = 0; i < 4; i++) {
+                if (xdim != i && ydim != i) DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
+            }
+        }
+
+        if (zi_dims.ndims() == 0 || xo_dims.ndims() ==  0 || yo_dims.ndims() == 0) {
+            return af_create_handle(zo, 0, nullptr, zi_info.getType());
         }
 
         af_array output;
 
-        switch(itype) {
-            case f32: output = approx2<float  , float >(in, pos0, pos1, method, offGrid);  break;
-            case f64: output = approx2<double , double>(in, pos0, pos1, method, offGrid);  break;
-            case c32: output = approx2<cfloat , float >(in, pos0, pos1, method, offGrid);  break;
-            case c64: output = approx2<cdouble, double>(in, pos0, pos1, method, offGrid);  break;
-            default:  TYPE_ERROR(1, itype);
+        switch(zi_info.getType()) {
+        case f32: output = approx2<float  , float >(zi,
+                                                    xo, xdim, xi_beg, xi_step,
+                                                    yo, ydim, yi_beg, yi_step,
+                                                    method, offGrid);  break;
+        case f64: output = approx2<double , double>(zi,
+                                                    xo, xdim, xi_beg, xi_step,
+                                                    yo, ydim, yi_beg, yi_step,
+                                                    method, offGrid);  break;
+        case c32: output = approx2<cfloat , float >(zi,
+                                                    xo, xdim, xi_beg, xi_step,
+                                                    yo, ydim, yi_beg, yi_step,
+                                                    method, offGrid);  break;
+        case c64: output = approx2<cdouble, double>(zi,
+                                                    xo, xdim, xi_beg, xi_step,
+                                                    yo, ydim, yi_beg, yi_step,
+                                                    method, offGrid);  break;
+        default:  TYPE_ERROR(1, zi_info.getType());
         }
-        std::swap(*out,output);
+        std::swap(*zo, output);
     }
     CATCHALL;
 
diff --git a/src/api/cpp/approx.cpp b/src/api/cpp/approx.cpp
index 3d02a84b05..fe840cd7ba 100644
--- a/src/api/cpp/approx.cpp
+++ b/src/api/cpp/approx.cpp
@@ -13,18 +13,41 @@
 
 namespace af
 {
-    array approx1(const array& in, const array &pos, const interpType method, const float offGrid)
+    array approx1(const array& yi, const array &xo, const interpType method, const float offGrid)
     {
-        af_array out = 0;
-        AF_THROW(af_approx1(&out, in.get(), pos.get(), method, offGrid));
-        return array(out);
+        af_array yo = 0;
+        AF_THROW(af_approx1(&yo, yi.get(), xo.get(), method, offGrid));
+        return array(yo);
     }
 
-    array approx2(const array& in, const array &pos0, const array &pos1,
+    array approx2(const array& zi, const array &xo, const array &yo,
                   const interpType method, const float offGrid)
     {
-        af_array out = 0;
-        AF_THROW(af_approx2(&out, in.get(), pos0.get(), pos1.get(), method, offGrid));
-        return array(out);
+        af_array zo = 0;
+        AF_THROW(af_approx2(&zo, zi.get(), xo.get(), yo.get(), method, offGrid));
+        return array(zo);
+    }
+
+     array approx1(const array &yi,
+                   const array &xo, const int xdim,
+                   const double xi_beg, const double xi_step,
+                   const interpType method, const float offGrid)
+     {
+        af_array yo = 0;
+        AF_THROW(af_approx1_uniform(&yo, yi.get(), xo.get(), xdim, xi_beg, xi_step, method, offGrid));
+        return array(yo);
+     }
+
+    array approx2(const array &zi,
+                  const array &xo, const int xdim, const double xi_beg, const double xi_step,
+                  const array &yo, const int ydim, const double yi_beg, const double yi_step,
+                  const interpType method, const float offGrid)
+    {
+        af_array zo = 0;
+        AF_THROW(af_approx2_uniform(&zo, zi.get(),
+                                    xo.get(), xdim, xi_beg, xi_step,
+                                    yo.get(), ydim, yi_beg, yi_step,
+                                    method, offGrid));
+        return array(zo);
     }
 }
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 22dff492bf..2fc73649e5 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -11,16 +11,37 @@
 #include <af/signal.h>
 #include "symbol_manager.hpp"
 
-af_err af_approx1(af_array *out, const af_array in, const af_array pos, const af_interp_type method, const float offGrid)
+af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
+                  const af_interp_type method, const float offGrid)
 {
-    CHECK_ARRAYS(in, pos);
-    return CALL(out, in, pos, method, offGrid);
+    CHECK_ARRAYS(yi, xo);
+    return CALL(yo, yi, xo, method, offGrid);
 }
 
-af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const af_array pos1, const af_interp_type method, const float offGrid)
+af_err af_approx2(af_array *zo, const af_array zi,
+                  const af_array xo, const af_array yo,
+                  const af_interp_type method, const float offGrid)
 {
-    CHECK_ARRAYS(in, pos0, pos1);
-    return CALL(out, in, pos0, pos1, method, offGrid);
+    CHECK_ARRAYS(zi, xo, yo);
+    return CALL(zo, zi, xo, yo, method, offGrid);
+}
+
+af_err af_approx1_uniform(af_array *yo, const af_array yi,
+                          const af_array xo, const int xdim,
+                          const double xi_beg, const double xi_step,
+                          const af_interp_type method, const float offGrid)
+{
+    CHECK_ARRAYS(yi, xo);
+    return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
+}
+
+af_err af_approx2_uniform(af_array *zo, const af_array zi,
+                          const af_array xo, const int xdim, const double xi_beg, const double xi_step,
+                          const af_array yo, const int ydim, const double yi_beg, const double yi_step,
+                          const af_interp_type method, const float offGrid)
+{
+    CHECK_ARRAYS(zi, xo, yo);
+    return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step, method, offGrid);
 }
 
 af_err af_set_fft_plan_cache_size(size_t cache_size)
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index ef82be05e0..e6c8def15a 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -8,98 +8,118 @@
  ********************************************************/
 
 #include <approx.hpp>
-
-#include <Array.hpp>
 #include <kernel/approx.hpp>
 #include <platform.hpp>
-
-#include <af/defines.h>
 #include <af/dim4.hpp>
 
 namespace cpu
 {
 
 template<typename Ty, typename Tp>
-Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+Array<Ty> approx1(const Array<Ty> &yi,
+                  const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step,
                   const af_interp_type method, const float offGrid)
 {
-    in.eval();
-    pos.eval();
+    yi.eval();
+    xo.eval();
 
-    af::dim4 odims = in.dims();
-    odims[0] = pos.dims()[0];
+    dim4 odims = yi.dims();
+    odims[xdim] = xo.dims()[xdim];
 
-    Array<Ty> out = createEmptyArray<Ty>(odims);
+    Array<Ty> yo = createEmptyArray<Ty>(odims);
 
     switch(method) {
     case AF_INTERP_NEAREST:
     case AF_INTERP_LOWER:
         getQueue().enqueue(kernel::approx1<Ty, Tp, 1>,
-                           out, in, pos, offGrid, method);
+                           yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
         break;
     case AF_INTERP_LINEAR:
     case AF_INTERP_LINEAR_COSINE:
         getQueue().enqueue(kernel::approx1<Ty, Tp, 2>,
-                           out, in, pos, offGrid, method);
+                           yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
         break;
     case AF_INTERP_CUBIC:
     case AF_INTERP_CUBIC_SPLINE:
         getQueue().enqueue(kernel::approx1<Ty, Tp, 3>,
-                           out, in, pos, offGrid, method);
+                           yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
         break;
     default:
         break;
     }
-    return out;
+    return yo;
 }
 
-
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+Array<Ty> approx2(const Array<Ty> &zi,
+                  const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                  const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                   const af_interp_type method, const float offGrid)
 {
-    in.eval();
-    pos0.eval();
-    pos1.eval();
+    zi.eval();
+    xo.eval();
+    yo.eval();
 
-    af::dim4 odims = in.dims();
-    odims[0] = pos0.dims()[0];
-    odims[1] = pos0.dims()[1];
+    dim4 odims = zi.dims();
+    odims[xdim] = xo.dims()[xdim];
+    odims[ydim] = xo.dims()[ydim];
 
-    Array<Ty> out = createEmptyArray<Ty>(odims);
+    Array<Ty> zo = createEmptyArray<Ty>(odims);
 
     switch(method) {
     case AF_INTERP_NEAREST:
     case AF_INTERP_LOWER:
         getQueue().enqueue(kernel::approx2<Ty, Tp, 1>,
-                           out, in, pos0, pos1, offGrid, method);
+                           zo, zi,
+                           xo, xdim, xi_beg, xi_step,
+                           yo, ydim, yi_beg, yi_step, offGrid, method);
         break;
     case AF_INTERP_LINEAR:
     case AF_INTERP_BILINEAR:
     case AF_INTERP_LINEAR_COSINE:
     case AF_INTERP_BILINEAR_COSINE:
         getQueue().enqueue(kernel::approx2<Ty, Tp, 2>,
-                           out, in, pos0, pos1, offGrid, method);
+                           zo, zi,
+                           xo, xdim, xi_beg, xi_step,
+                           yo, ydim, yi_beg, yi_step,
+                           offGrid, method);
         break;
     case AF_INTERP_CUBIC:
     case AF_INTERP_BICUBIC:
     case AF_INTERP_CUBIC_SPLINE:
     case AF_INTERP_BICUBIC_SPLINE:
         getQueue().enqueue(kernel::approx2<Ty, Tp, 3>,
-                           out, in, pos0, pos1, offGrid, method);
+                           zo, zi,
+                           xo, xdim, xi_beg, xi_step,
+                           yo, ydim, yi_beg, yi_step,
+                           offGrid, method);
         break;
     default:
         break;
     }
-    return out;
+    return zo;
 }
 
-#define INSTANTIATE(Ty, Tp)                                                                    \
-    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos,              \
-                                       const af_interp_type method, const float offGrid);      \
-    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos0,             \
-                                       const Array<Tp> &pos1, const af_interp_type method,     \
-                                       const float offGrid);                                   \
+#define INSTANTIATE(Ty, Tp)                                         \
+    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &yi,         \
+                                       const Array<Tp> &xo,         \
+                                       const int xdim,              \
+                                       const Tp &xi_beg,            \
+                                       const Tp &xi_step,           \
+                                       const af_interp_type method, \
+                                       const float offGrid);        \
+    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
+                                       const Array<Tp> &xo,         \
+                                       const int xdim,              \
+                                       const Tp &xi_beg,            \
+                                       const Tp &xi_step,           \
+                                       const Array<Tp> &yo,         \
+                                       const int ydim,              \
+                                       const Tp &yi_beg,            \
+                                       const Tp &yi_step,           \
+                                       const af_interp_type method, \
+                                       const float offGrid);        \
 
 INSTANTIATE(float  , float )
 INSTANTIATE(double , double)
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index b300294c5d..e55a013665 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -13,10 +13,14 @@
 namespace cpu
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+    Array<Ty> approx1(const Array<Ty> &yi,
+                      const Array<Tp> &xo, const int xdim,
+                      const Tp &xi_beg, const Tp &xi_step,
                       const af_interp_type method, const float offGrid);
 
     template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+    Array<Ty> approx2(const Array<Ty> &zi,
+                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                       const af_interp_type method, const float offGrid);
 }
diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index c8d1137476..f0b001836b 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -18,48 +18,53 @@ namespace kernel
 {
 
 template<typename InT, typename LocT, int order>
-void approx1(Param<InT> output, CParam<InT> input,
-             CParam<LocT> xposition, const float offGrid, af_interp_type method)
+void approx1(Param<InT> yo, CParam<InT> yi,
+             CParam<LocT> xo, const int xdim,
+             const LocT &xi_beg, const LocT &xi_step,
+             const float offGrid, af_interp_type method)
 {
-    InT * out = output.get();
-    const LocT *xpos = xposition.get();
+    InT *yo_ptr = yo.get();
+    const LocT *xo_ptr = xo.get();
 
-    const af::dim4 odims     = output.dims();
-    const af::dim4 idims     = input.dims();
-    const af::dim4 xdims     = xposition.dims();
+    const af::dim4 yo_dims     = yo.dims();
+    const af::dim4 yi_dims     = yi.dims();
+    const af::dim4 xo_dims     = xo.dims();
 
-    const af::dim4 ostrides  = output.strides();
-    const af::dim4 istrides  = input.strides();
-    const af::dim4 xstrides  = xposition.strides();
+    const af::dim4 yo_strides  = yo.strides();
+    const af::dim4 yi_strides  = yi.strides();
+    const af::dim4 xo_strides  = xo.strides();
 
     Interp1<InT, LocT, order> interp;
-    bool batch = !(xdims[1] == 1 && xdims[2] == 1 && xdims[3] == 1);
+    bool is_xo_off[] = {xo_dims[0] > 1, xo_dims[1] > 1, xo_dims[2] > 1, xo_dims[3] > 1};
+    bool is_yi_off[] = {true, true, true, true};
+    is_yi_off[xdim] = false;
 
-    for(dim_t idw = 0; idw < odims[3]; idw++) {
-        for(dim_t idz = 0; idz < odims[2]; idz++) {
-            dim_t ooffzw = idw * ostrides[3] + idz * ostrides[2];
-            dim_t ioffzw = idw * istrides[3] + idz * istrides[2];
-            dim_t xoffzw = idw * xstrides[3] + idz * xstrides[2];
+    for(dim_t idw = 0; idw < yo_dims[3]; idw++) {
+        for(dim_t idz = 0; idz < yo_dims[2]; idz++) {
+            dim_t yo_off_zw = idw * yo_strides[3] + idz * yo_strides[2];
+            dim_t yi_off_zw = idw * yi_strides[3] * is_yi_off[3] + idz * yi_strides[2] * is_yi_off[2];
+            dim_t xo_off_zw = idw * xo_strides[3] * is_xo_off[3] + idz * xo_strides[2] * is_xo_off[2];
 
-            for(dim_t idy = 0; idy < odims[1]; idy++) {
+            for(dim_t idy = 0; idy < yo_dims[1]; idy++) {
 
-                dim_t ooff = ooffzw + idy * ostrides[1];
-                dim_t ioff = ioffzw + idy * istrides[1];
-                dim_t xoff = xoffzw + idy * xstrides[1];
+                dim_t yo_off = yo_off_zw + idy * yo_strides[1];
+                dim_t yi_off = yi_off_zw + idy * yi_strides[1] * is_yi_off[1];
+                dim_t xo_off = xo_off_zw + idy * xo_strides[1] * is_xo_off[1];
 
-                for(dim_t idx = 0; idx < odims[0]; idx++) {
+                for(dim_t idx = 0; idx < yo_dims[0]; idx++) {
 
-                    const LocT x = xpos[batch * xoff + idx];
+                    dim_t yi_idx = idx * is_yi_off[0];
+                    const LocT x = (xo_ptr[xo_off + idx * is_xo_off[0]] - xi_beg) / xi_step;
 
                     // FIXME: Only cubic interpolation is doing clamping
                     // We need to make it consistent across all methods
                     // Not changing the behavior because tests will fail
                     bool clamp = order == 3;
 
-                    if (x < 0 || idims[0] < x + 1) {
-                        out[ooff + idx] = scalar<InT>(offGrid);
+                    if (x < 0 || yi_dims[xdim] < x + 1) {
+                        yo_ptr[yo_off + idx] = scalar<InT>(offGrid);
                     } else {
-                        interp(output, ooff + idx, input, ioff, x, method, 1, clamp);
+                        interp(yo, yo_off + idx, yi, yi_off + yi_idx, x, method, 1, clamp, xdim);
                     }
                 }
             }
@@ -68,53 +73,60 @@ void approx1(Param<InT> output, CParam<InT> input,
 }
 
 template<typename InT, typename LocT, int order>
-void approx2(Param<InT> output, CParam<InT> input,
-             CParam<LocT> xposition, CParam<LocT> yposition,
+void approx2(Param<InT> zo, CParam<InT> zi,
+             CParam<LocT> xo, const int xdim, const LocT &xi_beg, const LocT &xi_step,
+             CParam<LocT> yo, const int ydim, const LocT &yi_beg, const LocT &yi_step,
              float const offGrid, af_interp_type method)
 {
-    InT * out = output.get();
-    const LocT *xpos = xposition.get();
-    const LocT *ypos = yposition.get();
-
-    af::dim4 const odims     = output.dims();
-    af::dim4 const idims     = input.dims();
-    af::dim4 const xdims     = xposition.dims();
-    af::dim4 const ostrides  = output.strides();
-    af::dim4 const istrides  = input.strides();
-    af::dim4 const xstrides  = xposition.strides();
-    af::dim4 const ystrides  = yposition.strides();
+    InT *zo_ptr = zo.get();
+    const LocT *xo_ptr = xo.get();
+    const LocT *yo_ptr = yo.get();
+
+    af::dim4 const zo_dims     = zo.dims();
+    af::dim4 const zi_dims     = zi.dims();
+    af::dim4 const xo_dims     = xo.dims();
+    af::dim4 const zo_strides  = zo.strides();
+    af::dim4 const zi_strides  = zi.strides();
+    af::dim4 const xo_strides  = xo.strides();
+    af::dim4 const yo_strides  = yo.strides();
 
     Interp2<InT, LocT, order> interp;
-    bool batch = !(xdims[2] == 1 && xdims[3] == 1);
+    bool is_xo_off[] = {xo_dims[0] > 1, xo_dims[1] > 1, xo_dims[2] > 1, xo_dims[3] > 1};
+    bool is_zi_off[] = {true, true, true, true};
+    is_zi_off[xdim] = false;
+    is_zi_off[ydim] = false;
 
-    for(dim_t idw = 0; idw < odims[3]; idw++) {
-        for(dim_t idz = 0; idz < odims[2]; idz++) {
+    for(dim_t idw = 0; idw < zo_dims[3]; idw++) {
+        for(dim_t idz = 0; idz < zo_dims[2]; idz++) {
 
-            dim_t xoffzw = idw * xstrides[3] + idz * xstrides[2];
-            dim_t yoffzw = idw * ystrides[3] + idz * ystrides[2];
-            dim_t ooffzw = idw * ostrides[3] + idz * ostrides[2];
-            dim_t ioffzw = idw * istrides[3] + idz * istrides[2];
+            dim_t zo_off_zw = idw * zo_strides[3] + idz * zo_strides[2];
+            dim_t zi_off_zw = idw * zi_strides[3] * is_zi_off[3] + idz * zi_strides[2] * is_zi_off[2];
+            dim_t xo_off_zw = idw * xo_strides[3] * is_xo_off[3] + idz * xo_strides[2] * is_xo_off[2];
+            dim_t yo_off_zw = idw * yo_strides[3] * is_xo_off[3] + idz * yo_strides[2] * is_xo_off[2];
 
-            for(dim_t idy = 0; idy < odims[1]; idy++) {
-                dim_t xoff = xoffzw * batch + idy * xstrides[1];
-                dim_t yoff = yoffzw * batch + idy * ystrides[1];
-                dim_t ooff = ooffzw         + idy * ostrides[1];
+            for(dim_t idy = 0; idy < zo_dims[1]; idy++) {
+                dim_t xo_off = xo_off_zw + idy * xo_strides[1] * is_xo_off[1];
+                dim_t yo_off = yo_off_zw + idy * yo_strides[1] * is_xo_off[1];
+                dim_t zi_off = zi_off_zw + idy * zi_strides[1] * is_zi_off[1];
+                dim_t zo_off = zo_off_zw + idy * zo_strides[1];
 
-                for(dim_t idx = 0; idx < odims[0]; idx++) {
+                for(dim_t idx = 0; idx < zo_dims[0]; idx++) {
 
-                    const LocT x = xpos[xoff + idx];
-                    const LocT y = ypos[yoff + idx];
+                    const LocT x = (xo_ptr[xo_off + idx] - xi_beg) / xi_step;
+                    const LocT y = (yo_ptr[yo_off + idx] - yi_beg) / yi_step;
+
+                    dim_t zi_idx = idx * zi_strides[0] * is_zi_off[0];
 
                     // FIXME: Only cubic interpolation is doing clamping
                     // We need to make it consistent across all methods
                     // Not changing the behavior because tests will fail
                     bool clamp = order == 3;
 
-                    if (x < 0 || idims[0] < x + 1 ||
-                        y < 0 || idims[1] < y + 1 ) {
-                        out[ooff + idx] = scalar<InT>(offGrid);
+                    if (x < 0 || zi_dims[xdim] < x + 1 ||
+                        y < 0 || zi_dims[ydim] < y + 1 ) {
+                        zo_ptr[zo_off + idx] = scalar<InT>(offGrid);
                     } else {
-                        interp(output, ooff + idx, input, ioffzw, x, y, method, 1, clamp);
+                        interp(zo, zo_off + idx, zi, zi_off + zi_idx, x, y, method, 1, clamp, xdim, ydim);
                     }
                 }
             }
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index a4fffa5802..90cce006ce 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -93,22 +93,28 @@ struct Interp1<InT, LocT, 1>
 {
     void operator()(Param<InT> &out, int ooff,
                     CParam<InT> &in, int ioff, LocT x,
-                    af_interp_type  method, int batch, bool clamp)
+                    af_interp_type  method, int batch, bool clamp,
+                    int xdim = 0, int batch_dim = 1)
     {
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
-        int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
-        bool cond = xid >= 0 && xid < idims[0];
-        if (clamp) xid = std::max(0, std::min(xid, (int)idims[0]));
 
         InT *outptr = out.get();
         const dim4 ostrides = out.strides();
-        int idx = ioff + xid;
+
+        const int x_lim = idims[xdim];
+        const int x_stride = istrides[xdim];
+
+        int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
+        bool cond = xid >= 0 && xid < x_lim;
+        if (clamp) xid = std::max(0, std::min(xid, x_lim));
+
+        const int idx = ioff + xid * x_stride;
 
         for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * istrides[1];
-            outptr[ooff + n * ostrides[1]] = (cond || clamp) ? inptr[idx_n] : scalar<InT>(0);
+            int idx_n = idx + n * istrides[batch_dim];
+            outptr[ooff + n * ostrides[batch_dim]] = (cond || clamp) ? inptr[idx_n] : scalar<InT>(0);
         }
     }
 };
@@ -118,20 +124,25 @@ struct Interp1<InT, LocT, 2>
 {
     void operator()(Param<InT> &out, int ooff,
                     CParam<InT> &in, int ioff, LocT x,
-                    af_interp_type  method, int batch, bool clamp)
+                    af_interp_type  method, int batch, bool clamp,
+                    int xdim = 0, int batch_dim = 1)
     {
         typedef vtype_t<InT> VT;
 
         const int grid_x = floor(x);    // nearest grid
-        const LocT off_x = x - grid_x;    // fractional offset
-        const int idx = ioff + grid_x;
+        const LocT off_x = x - grid_x;  // fractional offset
+
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
         InT *outptr = out.get();
         const dim4 ostrides = out.strides();
 
-        bool cond[2] = {true, grid_x + 1 < idims[0]};
+        const int x_lim = idims[xdim];
+        const int x_stride = istrides[xdim];
+        const int idx = ioff + grid_x * x_stride;
+
+        bool cond[2] = {true, grid_x + 1 < x_lim};
         int  offx[2] = {0 , cond[1] ? 1 : 0};
 
         LocT ratio = off_x;
@@ -142,12 +153,12 @@ struct Interp1<InT, LocT, 2>
 
         const VT zero = scalar<VT>(0);
         for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * istrides[1];
+            int idx_n = idx + n * istrides[batch_dim];
             VT val[2] = {zero, zero};
             for (int i = 0; i < 2; i++) {
-                if (clamp || cond[i]) val[i] = inptr[idx_n + offx[i]];
+                if (clamp || cond[i]) val[i] = inptr[idx_n + offx[i] * x_stride];
             }
-            outptr[ooff + n * ostrides[1]] = linearInterpFunc(val, ratio);
+            outptr[ooff + n * ostrides[batch_dim]] = linearInterpFunc(val, ratio);
         }
     }
 };
@@ -157,31 +168,37 @@ struct Interp1<InT, LocT, 3>
 {
     void operator()(Param<InT> &out, int ooff,
                     CParam<InT> &in, int ioff, LocT x,
-                    af_interp_type  method, int batch, bool clamp)
+                    af_interp_type  method, int batch, bool clamp,
+                    int xdim = 0, int batch_dim = 1)
     {
         typedef vtype_t<InT> VT;
 
         const int grid_x = floor(x);    // nearest grid
-        const LocT off_x = x - grid_x;    // fractional offset
-        const int idx = ioff + grid_x;
+        const LocT off_x = x - grid_x;  // fractional offset
+
         const InT *inptr = in.get();
         const dim4 idims = in.dims();
         const dim4 istrides = in.strides();
         InT *outptr = out.get();
         const dim4 ostrides = out.strides();
 
-        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
+
+        const int x_lim = idims[xdim];
+        const int x_stride = istrides[xdim];
+        const int idx = ioff + grid_x * x_stride;
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
         int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
         const VT zero = scalar<VT>(0);
         for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * istrides[1];
+            int idx_n = idx + n * istrides[batch_dim];
             VT val[4] = {zero, zero, zero, zero};
             for (int i = 0; i < 4; i++) {
-                if (clamp || cond[i]) val[i] = inptr[idx_n + off[i]];
+                if (clamp || cond[i]) val[i] = inptr[idx_n + off[i] * x_stride];
             }
             bool spline = method == AF_INTERP_CUBIC_SPLINE;
-            outptr[ooff + n * ostrides[2]] =  cubicInterpFunc(val, off_x, spline);
+            outptr[ooff + n * ostrides[batch_dim]] =  cubicInterpFunc(val, off_x, spline);
         }
     }
 };
@@ -196,7 +213,8 @@ struct Interp2<InT, LocT, 1>
 {
     void operator()(Param<InT> &out, int ooff,
                     CParam<InT> &in, int ioff, LocT x, LocT y,
-                    af_interp_type  method, int nimages, bool clamp)
+                    af_interp_type  method, int nimages, bool clamp,
+                    int xdim = 0, int ydim = 1, int batch_dim = 2)
     {
         const InT *inptr = in.get();
         const dim4 istrides = in.strides();
@@ -208,19 +226,24 @@ struct Interp2<InT, LocT, 1>
         int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
         int yid = (method == AF_INTERP_LOWER ? std::floor(y) : std::round(y));
 
-        bool condX = xid >= 0 && xid < idims[0];
-        bool condY = yid >= 0 && yid < idims[1];
+        const int x_lim = idims[xdim];
+        const int y_lim = idims[ydim];
+        const int x_stride = istrides[xdim];
+        const int y_stride = istrides[ydim];
+        const int idx = ioff + yid * y_stride + xid * x_stride;
+
+        bool condX = xid >= 0 && xid < x_lim;
+        bool condY = yid >= 0 && yid < y_lim;
 
         if (clamp) {
-            xid = std::max(0, std::min(xid, (int)idims[0]));
-            yid = std::max(0, std::min(yid, (int)idims[1]));
+            xid = std::max(0, std::min(xid, x_lim));
+            yid = std::max(0, std::min(yid, y_lim));
         }
 
         bool cond = condX && condY;
-        int idx = ioff + yid * istrides[1] + xid;
         for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * istrides[2];
-            outptr[ooff + n * ostrides[2]] = (clamp || cond) ? inptr[idx_n] : scalar<InT>(0);
+            int idx_n = idx + n * istrides[batch_dim];
+            outptr[ooff + n * ostrides[batch_dim]] = (clamp || cond) ? inptr[idx_n] : scalar<InT>(0);
         }
     }
 };
@@ -230,7 +253,8 @@ struct Interp2<InT, LocT, 2>
 {
     void operator()(Param<InT> &out, int ooff,
                     CParam<InT> &in, int ioff, LocT x, LocT y,
-                    af_interp_type  method, int nimages, bool clamp)
+                    af_interp_type  method, int nimages, bool clamp,
+                    int xdim = 0, int ydim = 1, int batch_dim = 2)
     {
         typedef vtype_t<InT> VT;
 
@@ -247,10 +271,14 @@ struct Interp2<InT, LocT, 2>
         const int grid_y = floor(y);
         const LocT off_y = y - grid_y;
 
-        const int idx = ioff + grid_y * istrides[1] + grid_x;
+        const int x_lim = idims[xdim];
+        const int y_lim = idims[ydim];
+        const int x_stride = istrides[xdim];
+        const int y_stride = istrides[ydim];
+        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
 
-        bool condX[2] = {true, x + 1 < idims[0]};
-        bool condY[2] = {true, y + 1 < idims[1]};
+        bool condX[2] = {true, x + 1 < x_lim};
+        bool condY[2] = {true, y + 1 < y_lim};
 
         int offX[2] = {0, condX[1] ? 1 : 0};
         int offY[2] = {0, condY[1] ? 1 : 0};
@@ -266,16 +294,16 @@ struct Interp2<InT, LocT, 2>
         }
 
         for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * istrides[2];
+            int idx_n = idx + n * istrides[batch_dim];
             VT val[2][2];
             for (int j = 0; j < 2; j++) {
-                int off_y = idx_n + offY[j] * istrides[1];
+                int off_y = idx_n + offY[j] * y_stride;
                 for (int i = 0; i < 2; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = cond ? inptr[off_y + offX[i]] : zero;
+                    val[j][i] = cond ? inptr[off_y + offX[i] * x_stride] : zero;
                 }
             }
-            outptr[ooff + n * ostrides[2]] = bilinearInterpFunc(val, off_x, off_y);
+            outptr[ooff + n * ostrides[batch_dim]] = bilinearInterpFunc(val, off_x, off_y);
         }
     }
 };
@@ -285,7 +313,8 @@ struct Interp2<InT, LocT, 3>
 {
     void operator()(Param<InT> &out, int ooff,
                     CParam<InT> &in, int ioff, LocT x, LocT y,
-                    af_interp_type  method, int nimages, bool clamp)
+                    af_interp_type  method, int nimages, bool clamp,
+                    int xdim = 0, int ydim = 1, int batch_dim = 2)
     {
         typedef vtype_t<InT> VT;
 
@@ -302,29 +331,33 @@ struct Interp2<InT, LocT, 3>
         const int grid_y = floor(y);
         const LocT off_y = y - grid_y;
 
-        const int idx = ioff + grid_y * istrides[1] + grid_x;
+        const int x_lim = idims[xdim];
+        const int y_lim = idims[ydim];
+        const int x_stride = istrides[xdim];
+        const int y_stride = istrides[ydim];
+        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
 
         // used for setting values at boundaries
-        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < idims[0], grid_x + 2 < idims[0]};
-        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < idims[1], grid_y + 2 < idims[1]};
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim, grid_y + 2 < y_lim};
         int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
         int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
         bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
         VT zero = scalar<VT>(0);
         for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * istrides[2];
+            int idx_n = idx + n * istrides[batch_dim];
 
             //for bicubic interpolation, work with 4x4 val at a time
             VT val[4][4];
             for (int j = 0; j < 4; j++) {
-                int ioff_j = idx_n + offY[j] * istrides[1];
+                int ioff_j = idx_n + offY[j] * y_stride;
                 for (int i = 0; i < 4; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = cond ? inptr[ioff_j + offX[i]] : zero;
+                    val[j][i] = cond ? inptr[ioff_j + offX[i] * x_stride] : zero;
                 }
             }
-            outptr[ooff + n * ostrides[2]] = bicubicInterpFunc(val, off_x, off_y, spline);
+            outptr[ooff + n * ostrides[batch_dim]] = bicubicInterpFunc(val, off_x, off_y, spline);
         }
     }
 };
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cu
index 8854cb8964..13e5d2340f 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cu
@@ -16,79 +16,104 @@
 namespace cuda
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+    Array<Ty> approx1(const Array<Ty> &yi,
+                      const Array<Tp> &xo, const int xdim,
+                      const Tp &xi_beg, const Tp &xi_step,
                       const af_interp_type method, const float offGrid)
     {
-        af::dim4 idims = in.dims();
-        af::dim4 odims = in.dims();
-        odims[0] = pos.dims()[0];
+        af::dim4 odims = yi.dims();
+        odims[xdim] = xo.dims()[xdim];
 
         // Create output placeholder
-        Array<Ty> out = createEmptyArray<Ty>(odims);
+        Array<Ty> yo = createEmptyArray<Ty>(odims);
 
         switch(method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp, 1> (out, in, pos, offGrid, method);
+            kernel::approx1<Ty, Tp, 1> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp, 2> (out, in, pos, offGrid, method);
+            kernel::approx1<Ty, Tp, 2> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp, 3> (out, in, pos, offGrid, method);
+            kernel::approx1<Ty, Tp, 3> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
             break;
         default:
             break;
         }
-        return out;
+        return yo;
     }
 
     template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+    Array<Ty> approx2(const Array<Ty> &zi,
+                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                       const af_interp_type method, const float offGrid)
     {
-        af::dim4 idims = in.dims();
-        af::dim4 odims = pos0.dims();
-        odims[2] = in.dims()[2];
-        odims[3] = in.dims()[3];
+        af::dim4 odims = zi.dims();
+        odims[xdim] = xo.dims()[xdim];
+        odims[ydim] = xo.dims()[ydim];
 
         // Create output placeholder
-        Array<Ty> out = createEmptyArray<Ty>(odims);
+        Array<Ty> zo = createEmptyArray<Ty>(odims);
 
         switch(method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp, 1> (out, in, pos0, pos1, offGrid, method);
+            kernel::approx2<Ty, Tp, 1> (zo, zi,
+                                        xo, xdim, xi_beg, xi_step,
+                                        yo, ydim, yi_beg, yi_step,
+                                        offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp, 2> (out, in, pos0, pos1, offGrid, method);
+            kernel::approx2<Ty, Tp, 2> (zo, zi,
+                                        xo, xdim, xi_beg, xi_step,
+                                        yo, ydim, yi_beg, yi_step,
+                                        offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp, 3> (out, in, pos0, pos1, offGrid, method);
+            kernel::approx2<Ty, Tp, 3> (zo, zi,
+                                        xo, xdim, xi_beg, xi_step,
+                                        yo, ydim, yi_beg, yi_step,
+                                        offGrid, method);
             break;
         default:
             break;
         }
-        return out;
+        return zo;
     }
 
-#define INSTANTIATE(Ty, Tp)                                             \
-    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos, \
-                                       const af_interp_type method, const float offGrid); \
-    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos0, \
-                                       const Array<Tp> &pos1, const af_interp_type method, \
-                                       const float offGrid);            \
+#define INSTANTIATE(Ty, Tp)                                         \
+    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &yi,         \
+                                       const Array<Tp> &xo,         \
+                                       const int xdim,              \
+                                       const Tp &xi_beg,            \
+                                       const Tp &xi_step,           \
+                                       const af_interp_type method, \
+                                       const float offGrid);        \
+    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
+                                       const Array<Tp> &xo,         \
+                                       const int xdim,              \
+                                       const Tp &xi_beg,            \
+                                       const Tp &xi_step,           \
+                                       const Array<Tp> &yo,         \
+                                       const int ydim,              \
+                                       const Tp &yi_beg,            \
+                                       const Tp &yi_step,           \
+                                       const af_interp_type method, \
+                                       const float offGrid);        \
 
     INSTANTIATE(float  , float )
     INSTANTIATE(double , double)
     INSTANTIATE(cfloat , float )
     INSTANTIATE(cdouble, double)
+
 }
diff --git a/src/backend/cuda/approx.hpp b/src/backend/cuda/approx.hpp
index 902e1dbd3b..91d3228a5b 100644
--- a/src/backend/cuda/approx.hpp
+++ b/src/backend/cuda/approx.hpp
@@ -12,10 +12,14 @@
 namespace cuda
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+    Array<Ty> approx1(const Array<Ty> &yi,
+                      const Array<Tp> &xo, const int xdim,
+                      const Tp &xi_beg, const Tp &xi_step,
                       const af_interp_type method, const float offGrid);
 
     template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+    Array<Ty> approx2(const Array<Ty> &zi,
+                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                       const af_interp_type method, const float offGrid);
 }
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 9f7cc1eeb8..c9db048f40 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -25,7 +25,9 @@ namespace cuda
 
         template<typename Ty, typename Tp, int order>
         __global__
-        void approx1_kernel(Param<Ty> out, CParam<Ty> in, CParam<Tp> xpos,
+        void approx1_kernel(Param<Ty> yo, CParam<Ty> yi,
+                            CParam<Tp> xo, const int xdim,
+                            const Tp xi_beg, const Tp xi_step,
                             const float offGrid, const int blocksMatX, const bool batch,
                             af_interp_type method)
         {
@@ -33,25 +35,33 @@ namespace cuda
             const int blockIdx_x = blockIdx.x - idy * blocksMatX;
             const int idx = blockIdx_x * blockDim.x + threadIdx.x;
 
-            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / out.dims[2];
-            const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * out.dims[2];
+            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / yo.dims[2];
+            const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * yo.dims[2];
 
-            if (idx >= out.dims[0] || idy >= out.dims[1] ||
-                idz >= out.dims[2] || idw >= out.dims[3])
+            if (idx >= yo.dims[0] || idy >= yo.dims[1] ||
+                idz >= yo.dims[2] || idw >= yo.dims[3])
                 return;
 
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                                               + idy * out.strides[1] + idx;
-            int xmid = idx;
-            if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
+            bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
+            bool is_yi_off[] = {true, true, true, true};
+            is_yi_off[xdim] = false;
 
-            const Tp x = xpos.ptr[xmid];
-            if (x < 0 || in.dims[0] < x+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
+            const int yo_idx = idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx;
+            int xo_idx = idx * is_xo_off[0];
+            xo_idx += idw * xo.strides[3] * is_xo_off[3];
+            xo_idx += idz * xo.strides[2] * is_xo_off[2];
+            xo_idx += idy * xo.strides[1] * is_xo_off[1];
+
+            const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+            if (x < 0 || yi.dims[xdim] < x+1) {
+                yo.ptr[yo_idx] = scalar<Ty>(offGrid);
                 return;
             }
 
-            int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1];
+            int yi_idx = idx * is_yi_off[0];
+            yi_idx += idw * yi.strides[3] * is_yi_off[3];
+            yi_idx += idz * yi.strides[2] * is_yi_off[2];
+            yi_idx += idy * yi.strides[1] * is_yi_off[1];
 
             // FIXME: Only cubic interpolation is doing clamping
             // We need to make it consistent across all methods
@@ -59,13 +69,15 @@ namespace cuda
             bool clamp = order == 3;
 
             Interp1<Ty, Tp, order> interp;
-            interp(out, omId, in, ioff, x, method, 1, clamp);
+            interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp, xdim);
         }
 
         template<typename Ty, typename Tp, int order>
         __global__
-        void approx2_kernel(Param<Ty> out, CParam<Ty> in,
-                            CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
+        void approx2_kernel(Param<Ty> zo, CParam<Ty> zi,
+                            CParam<Tp> xo, const int xdim, const Tp xi_beg, const Tp xi_step,
+                            CParam<Tp> yo, const int ydim, const Tp yi_beg, const Tp yi_step,
+                            const float offGrid,
                             const int blocksMatX, const int blocksMatY, const bool batch,
                             af_interp_type method)
         {
@@ -77,26 +89,30 @@ namespace cuda
             const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksMatY;
             const int idy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            if (idx >= out.dims[0] || idy >= out.dims[1] ||
-                idz >= out.dims[2] || idw >= out.dims[3])
+            if (idx >= zo.dims[0] || idy >= zo.dims[1] ||
+                idz >= zo.dims[2] || idw >= zo.dims[3])
                 return;
 
-            const int omId = idw * out.strides[3] + idz * out.strides[2]
-                + idy * out.strides[1] + idx;
-            int xmid = idy * xpos.strides[1] + idx;
-            int ymid = idy * ypos.strides[1] + idx;
-            if(batch) {
-                xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
-                ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
-            }
-
-            const Tp x = xpos.ptr[xmid], y = ypos.ptr[ymid];
-            if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-                out.ptr[omId] = scalar<Ty>(offGrid);
+            bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
+            bool is_zi_off[] = {true, true, true, true};
+            is_zi_off[xdim] = false;
+            is_zi_off[ydim] = false;
+
+            const int zo_idx = idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx;
+            int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
+            int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
+            xo_idx += idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2]  * is_xo_off[2];
+            yo_idx += idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2]  * is_xo_off[2];
+
+            const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+            const Tp y = (yo.ptr[yo_idx] - yi_beg) / yi_step;
+            if (x < 0 || y < 0 || zi.dims[xdim] < x+1 || zi.dims[ydim] < y+1) {
+                zo.ptr[zo_idx] = scalar<Ty>(offGrid);
                 return;
             }
 
-            int ioff = idw * in.strides[3] + idz * in.strides[2];
+            int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0];
+            zi_idx += idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
 
             // FIXME: Only cubic interpolation is doing clamping
             // We need to make it consistent across all methods
@@ -104,50 +120,55 @@ namespace cuda
             bool clamp = order == 3;
 
             Interp2<Ty, Tp, order> interp;
-            interp(out, omId, in, ioff, x, y, method, 1, clamp);
+            interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp, xdim, ydim);
         }
 
         ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template <typename Ty, typename Tp, int order>
-        void approx1(Param<Ty> out, CParam<Ty> in,
-                     CParam<Tp> xpos, const float offGrid,
+        void approx1(Param<Ty> yo, CParam<Ty> yi,
+                     CParam<Tp> xo, const int xdim,
+                     const Tp &xi_beg, const Tp &xi_step,
+                     const float offGrid,
                      af_interp_type method)
         {
             dim3 threads(THREADS, 1, 1);
-            int blocksPerMat = divup(out.dims[0], threads.x);
-            dim3 blocks(blocksPerMat * out.dims[1], out.dims[2] * out.dims[3]);
+            int blocksPerMat = divup(yo.dims[0], threads.x);
+            dim3 blocks(blocksPerMat * yo.dims[1], yo.dims[2] * yo.dims[3]);
 
-            bool batch = !(xpos.dims[1] == 1 && xpos.dims[2] == 1 && xpos.dims[3] == 1);
+            bool batch = !(xo.dims[1] == 1 && xo.dims[2] == 1 && xo.dims[3] == 1);
 
             const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
             blocks.z = divup(blocks.y, maxBlocksY);
             blocks.y = divup(blocks.y, blocks.z);
 
             CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads,
-                            out, in, xpos, offGrid, blocksPerMat, batch, method);
+                        yo, yi, xo, xdim, xi_beg, xi_step, offGrid, blocksPerMat, batch, method);
             POST_LAUNCH_CHECK();
         }
 
         template <typename Ty, typename Tp, int order>
-        void approx2(Param<Ty> out, CParam<Ty> in,
-                     CParam<Tp> xpos, CParam<Tp> ypos, const float offGrid,
+        void approx2(Param<Ty> zo, CParam<Ty> zi,
+                     CParam<Tp> xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                     CParam<Tp> yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                     const float offGrid,
                      af_interp_type method)
         {
             dim3 threads(TX, TY, 1);
-            int blocksPerMatX = divup(out.dims[0], threads.x);
-            int blocksPerMatY = divup(out.dims[1], threads.y);
-            dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3]);
+            int blocksPerMatX = divup(zo.dims[0], threads.x);
+            int blocksPerMatY = divup(zo.dims[1], threads.y);
+            dim3 blocks(blocksPerMatX * zo.dims[2], blocksPerMatY * zo.dims[3]);
 
-            bool batch = !(xpos.dims[2] == 1 && xpos.dims[3] == 1);
+            bool batch = !(xo.dims[2] == 1 && xo.dims[3] == 1);
 
             const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
             blocks.z = divup(blocks.y, maxBlocksY);
             blocks.y = divup(blocks.y, blocks.z);
 
             CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads,
-                        out, in, xpos, ypos, offGrid, blocksPerMatX, blocksPerMatY, batch, method);
+                        zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+                        offGrid, blocksPerMatX, blocksPerMatY, batch, method);
             POST_LAUNCH_CHECK();
         }
     }
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index 4f3bb6e785..cc1b05eef9 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -111,18 +111,23 @@ struct Interp1<Ty, Tp, 1>
 {
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch, bool clamp)
+                               af_interp_type method, int batch, bool clamp,
+                               int xdim = 0, int batch_dim = 1)
     {
-        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         Ty zero = scalar<Ty>(0);
-        bool cond = xid >= 0 && xid < in.dims[0];
-        if (clamp) xid = max(0, min(xid, in.dims[0]));
 
-        const int idx = ioff + xid;
+        const int x_lim = in.dims[xdim];
+        const int x_stride = in.strides[xdim];
+
+        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        bool cond = xid >= 0 && xid < x_lim;
+        if (clamp) xid = max(0, min(xid, x_lim));
+
+        const int idx = ioff + xid * x_stride;
 
         for (int n = 0; n < batch; n++) {
-            Ty outval = (cond || clamp) ? in.ptr[idx + n * in.strides[1]] : zero;
-            out.ptr[ooff + n * out.strides[1]] = outval;
+            Ty outval = (cond || clamp) ? in.ptr[idx + n * in.strides[batch_dim]] : zero;
+            out.ptr[ooff + n * out.strides[batch_dim]] = outval;
         }
     }
 };
@@ -132,16 +137,20 @@ struct Interp1<Ty, Tp, 2>
 {
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch, bool clamp)
+                               af_interp_type method, int batch, bool clamp,
+                               int xdim = 0, int batch_dim = 1)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
         const int grid_x = floor(x);    // nearest grid
         const WT off_x = x - grid_x;    // fractional offset
-        const int idx = ioff + grid_x;
 
-        bool cond[2] = {true, grid_x + 1 < in.dims[0]};
+        const int x_lim = in.dims[xdim];
+        const int x_stride = in.strides[xdim];
+        const int idx = ioff + grid_x * x_stride;
+
+        bool cond[2] = {true, grid_x + 1 < x_lim};
         int  offx[2]  = {0, cond[1] ? 1 : 0};
         WT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
@@ -152,10 +161,10 @@ struct Interp1<Ty, Tp, 2>
         Ty zero = scalar<Ty>(0);
 
         for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[1];
-            VT val[2] = {(clamp || cond[0]) ? in.ptr[idx_n + offx[0]] : zero,
-                         (clamp || cond[1]) ? in.ptr[idx_n + offx[1]] : zero};
-            out.ptr[ooff + n * out.strides[1]] = linearInterpFunc(val, ratio);
+            int idx_n = idx + n * in.strides[batch_dim];
+            VT val[2] = {(clamp || cond[0]) ? in.ptr[idx_n + offx[0] * x_stride] : zero,
+                         (clamp || cond[1]) ? in.ptr[idx_n + offx[1] * x_stride] : zero};
+            out.ptr[ooff + n * out.strides[batch_dim]] = linearInterpFunc(val, ratio);
         }
     }
 };
@@ -165,27 +174,31 @@ struct Interp1<Ty, Tp, 3>
 {
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch, bool clamp)
+                               af_interp_type method, int batch, bool clamp,
+                               int xdim = 0, int batch_dim = 1)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
         const int grid_x = floor(x);    // nearest grid
         const WT off_x = x - grid_x;    // fractional offset
-        const int idx = ioff + grid_x;
 
-        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+        const int x_lim = in.dims[xdim];
+        const int x_stride = in.strides[xdim];
+        const int idx = ioff + grid_x * x_stride;
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
         int  offx[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
         bool spline = method == AF_INTERP_CUBIC_SPLINE;
         Ty zero = scalar<Ty>(0);
         for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[1];
+            int idx_n = idx + n * in.strides[batch_dim];
             VT val[4];
             for (int i = 0; i < 4; i++) {
-                val[i] = (clamp || cond[i]) ? in.ptr[idx_n + offx[i]] : zero;
+                val[i] = (clamp || cond[i]) ? in.ptr[idx_n + offx[i] * x_stride] : zero;
             }
-            out.ptr[ooff + n * out.strides[1]] = cubicInterpFunc(val, off_x, spline);
+            out.ptr[ooff + n * out.strides[batch_dim]] = cubicInterpFunc(val, off_x, spline);
         }
     }
 };
@@ -201,27 +214,35 @@ struct Interp2<Ty, Tp, 1>
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x, Tp y,
                                af_interp_type method,
-                               int nimages, bool clamp)
+                               int batch, bool clamp,
+                               int xdim = 0, int ydim = 1,
+                               int batch_dim = 2)
     {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
 
+        const int x_lim = in.dims[xdim];
+        const int y_lim = in.dims[ydim];
+        const int x_stride = in.strides[xdim];
+        const int y_stride = in.strides[ydim];
+
         if (clamp) {
-            xid = max(0, min(xid, in.dims[0]));
-            yid = max(0, min(yid, in.dims[1]));
+            xid = max(0, min(xid, in.dims[xdim]));
+            yid = max(0, min(yid, in.dims[ydim]));
         }
-        int idx = ioff + yid * in.strides[1] + xid;
 
-        bool condX = xid >= 0 && xid < in.dims[0];
-        bool condY = yid >= 0 && yid < in.dims[1];
+        const int idx = ioff + yid * y_stride + xid * x_stride;
+
+        bool condX = xid >= 0 && xid < x_lim;
+        bool condY = yid >= 0 && yid < y_lim;
 
         Ty zero = scalar<Ty>(0);
         bool cond = condX && condY;
 
-        for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * in.strides[2];
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
             Ty val = (clamp || cond) ? in.ptr[idx_n] : zero;
-            out.ptr[ooff + n * out.strides[2]] = val;
+            out.ptr[ooff + n * out.strides[batch_dim]] = val;
         }
     }
 };
@@ -232,7 +253,9 @@ struct Interp2<Ty, Tp, 2>
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x, Tp y,
                                af_interp_type method,
-                               int nimages, bool clamp)
+                               int batch, bool clamp,
+                               int xdim = 0, int ydim = 1,
+                               int batch_dim = 2)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -243,10 +266,14 @@ struct Interp2<Ty, Tp, 2>
         const int grid_y = floor(y);
         const WT off_y = y - grid_y;
 
-        const int idx = ioff + grid_y * in.strides[1] + grid_x;
+        const int x_lim = in.dims[xdim];
+        const int y_lim = in.dims[ydim];
+        const int x_stride = in.strides[xdim];
+        const int y_stride = in.strides[ydim];
+        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
 
-        bool condX[2] = {true, x + 1 < in.dims[0]};
-        bool condY[2] = {true, y + 1 < in.dims[1]};
+        bool condX[2] = {true, x + 1 < x_lim};
+        bool condY[2] = {true, y + 1 < y_lim};
         int  offx[2]  = {0, condX[1] ? 1 : 0};
         int  offy[2]  = {0, condY[1] ? 1 : 0};
 
@@ -260,17 +287,17 @@ struct Interp2<Ty, Tp, 2>
 
         Ty zero = scalar<Ty>(0);
 
-        for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * in.strides[2];
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
             VT val[2][2];
             for (int j = 0; j < 2; j++) {
-                int ioff_j = idx_n + offy[j] * in.strides[1];
+                int ioff_j = idx_n + offy[j] * y_stride;
                 for (int i = 0; i < 2; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = (cond) ? in.ptr[ioff_j + offx[i]] : zero;
+                    val[j][i] = (cond) ? in.ptr[ioff_j + offx[i] * x_stride] : zero;
                 }
             }
-            out.ptr[ooff + n * out.strides[2]] = bilinearInterpFunc(val, xratio, yratio);
+            out.ptr[ooff + n * out.strides[batch_dim]] = bilinearInterpFunc(val, xratio, yratio);
         }
     }
 };
@@ -281,7 +308,9 @@ struct Interp2<Ty, Tp, 3>
     __device__ void operator()(Param<Ty> out, int ooff,
                                CParam<Ty> in, int ioff, Tp x, Tp y,
                                af_interp_type method,
-                               int nimages, bool clamp)
+                               int batch, bool clamp,
+                               int xdim = 0, int ydim = 1,
+                               int batch_dim = 2)
     {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -292,31 +321,35 @@ struct Interp2<Ty, Tp, 3>
         const int grid_y = floor(y);
         const WT off_y = y - grid_y;
 
-        const int idx = ioff + grid_y * in.strides[1] + grid_x;
+        const int x_lim = in.dims[xdim];
+        const int y_lim = in.dims[ydim];
+        const int x_stride = in.strides[xdim];
+        const int y_stride = in.strides[ydim];
+        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
 
         // used for setting values at boundaries
-        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim, grid_y + 2 < y_lim};
         int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
         int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
         //for bicubic interpolation, work with 4x4 val at a time
         Ty zero = scalar<Ty>(0);
         bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
-        for (int n = 0; n < nimages; n++) {
-            int idx_n = idx + n * in.strides[2];
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
             VT val[4][4];
 #pragma unroll
             for (int j = 0; j < 4; j++) {
-                int ioff_j = idx_n + offY[j] * in.strides[1];
+                int ioff_j = idx_n + offY[j] * y_stride;
 #pragma unroll
                 for (int i = 0; i < 4; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = (cond) ? in.ptr[ioff_j + offX[i]] : zero;
+                    val[j][i] = (cond) ? in.ptr[ioff_j + offX[i] * x_stride] : zero;
                 }
             }
 
-            out.ptr[ooff + n * out.strides[2]] = bicubicInterpFunc(val, off_x, off_y, spline);
+            out.ptr[ooff + n * out.strides[batch_dim]] = bicubicInterpFunc(val, off_x, off_y, spline);
         }
     }
 };
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index 5b4e6acef9..957bf73bfb 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -16,77 +16,104 @@
 namespace opencl
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+    Array<Ty> approx1(const Array<Ty> &yi,
+                      const Array<Tp> &xo, const int xdim,
+                      const Tp &xi_beg, const Tp &xi_step,
                       const af_interp_type method, const float offGrid)
     {
-        af::dim4 odims = in.dims();
-        odims[0] = pos.dims()[0];
+        af::dim4 odims = yi.dims();
+        odims[xdim] = xo.dims()[xdim];
 
         // Create output placeholder
-        Array<Ty> out = createEmptyArray<Ty>(odims);
+        Array<Ty> yo = createEmptyArray<Ty>(odims);
         switch(method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp, 1> (out, in, pos, offGrid, method);
+            kernel::approx1<Ty, Tp, 1> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp, 2> (out, in, pos, offGrid, method);
+            kernel::approx1<Ty, Tp, 2> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp, 3> (out, in, pos, offGrid, method);
+            kernel::approx1<Ty, Tp, 3> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
             break;
         default:
             break;
         }
-        return out;
+        return yo;
     }
 
     template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+    Array<Ty> approx2(const Array<Ty> &zi,
+                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                       const af_interp_type method, const float offGrid)
     {
-        af::dim4 odims = pos0.dims();
-        odims[2] = in.dims()[2];
-        odims[3] = in.dims()[3];
+        af::dim4 odims = zi.dims();
+        odims[xdim] = xo.dims()[xdim];
+        odims[ydim] = xo.dims()[ydim];
 
         // Create output placeholder
-        Array<Ty> out = createEmptyArray<Ty>(odims);
+        Array<Ty> zo = createEmptyArray<Ty>(odims);
 
         switch(method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp, 1> (out, in, pos0, pos1, offGrid, method);
+            kernel::approx2<Ty, Tp, 1> (zo, zi,
+                                        xo, xdim, xi_beg, xi_step,
+                                        yo, ydim, yi_beg, yi_step,
+                                        offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp, 2> (out, in, pos0, pos1, offGrid, method);
+            kernel::approx2<Ty, Tp, 2> (zo, zi,
+                                        xo, xdim, xi_beg, xi_step,
+                                        yo, ydim, yi_beg, yi_step,
+                                        offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp, 3> (out, in, pos0, pos1, offGrid, method);
+            kernel::approx2<Ty, Tp, 3> (zo, zi,
+                                        xo, xdim, xi_beg, xi_step,
+                                        yo, ydim, yi_beg, yi_step,
+                                        offGrid, method);
             break;
         default:
             break;
         }
 
-        return out;
+        return zo;
     }
 
-#define INSTANTIATE(Ty, Tp)                                             \
-    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos, \
-                                       const af_interp_type method, const float offGrid); \
-    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &in, const Array<Tp> &pos0, \
-                                       const Array<Tp> &pos1, const af_interp_type method, \
-                                       const float offGrid);            \
+#define INSTANTIATE(Ty, Tp)                                         \
+    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &yi,         \
+                                       const Array<Tp> &xo,         \
+                                       const int xdim,              \
+                                       const Tp &xi_beg,            \
+                                       const Tp &xi_step,           \
+                                       const af_interp_type method, \
+                                       const float offGrid);        \
+    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
+                                       const Array<Tp> &xo,         \
+                                       const int xdim,              \
+                                       const Tp &xi_beg,            \
+                                       const Tp &xi_step,           \
+                                       const Array<Tp> &yo,         \
+                                       const int ydim,              \
+                                       const Tp &yi_beg,            \
+                                       const Tp &yi_step,           \
+                                       const af_interp_type method, \
+                                       const float offGrid);        \
 
     INSTANTIATE(float  , float )
     INSTANTIATE(double , double)
     INSTANTIATE(cfloat , float )
     INSTANTIATE(cdouble, double)
+
 }
diff --git a/src/backend/opencl/approx.hpp b/src/backend/opencl/approx.hpp
index 108dcedb94..7e14696c1d 100644
--- a/src/backend/opencl/approx.hpp
+++ b/src/backend/opencl/approx.hpp
@@ -12,10 +12,14 @@
 namespace opencl
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &in, const Array<Tp> &pos,
+    Array<Ty> approx1(const Array<Ty> &yi,
+                      const Array<Tp> &xo, const int xdim,
+                      const Tp &xi_beg, const Tp &xi_step,
                       const af_interp_type method, const float offGrid);
 
     template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &in, const Array<Tp> &pos0, const Array<Tp> &pos1,
+    Array<Ty> approx2(const Array<Ty> &zi,
+                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
                       const af_interp_type method, const float offGrid);
 }
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index f1bec8a1c0..0cf29fc780 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -73,7 +73,8 @@ std::string generateOptionsString()
 // Wrapper functions
 ///////////////////////////////////////////////////////////////////////////
 template <typename Ty, typename Tp, int order>
-void approx1(Param out, const Param in, const Param xpos, const float offGrid,
+void approx1(Param yo, const Param yi, const Param xo, const int xdim,
+             const Tp xi_beg, const Tp xi_step, const float offGrid,
              af_interp_type method)
 {
     std::string refName = std::string("approx1_kernel_") +
@@ -98,27 +99,31 @@ void approx1(Param out, const Param in, const Param xpos, const float offGrid,
     }
 
     auto approx1Op = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
-                                    const Buffer, const KParam, const Ty,
+                                    const Buffer, const KParam, const int,
+                                    const Tp, const Tp, const Ty,
                                     const int, const int, const int >(*entry.ker);
 
     NDRange local(THREADS, 1, 1);
-    dim_t blocksPerMat = divup(out.info.dims[0], local[0]);
-    NDRange global(blocksPerMat * local[0] * out.info.dims[1],
-                   out.info.dims[2] * out.info.dims[3] * local[1], 1);
+    dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
+    NDRange global(blocksPerMat * local[0] * yo.info.dims[1],
+                   yo.info.dims[2] * yo.info.dims[3] * local[1]);
 
     // Passing bools to opencl kernels is not allowed
-    bool batch = !(xpos.info.dims[1] == 1 && xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
+    bool batch = !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
     approx1Op(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info, *in.data, in.info,
-              *xpos.data, xpos.info, scalar<Ty>(offGrid),
+              *yo.data, yo.info, *yi.data, yi.info,
+              *xo.data, xo.info, xdim, xi_beg, xi_step,
+              scalar<Ty>(offGrid),
               blocksPerMat, (int)batch, (int)method);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template <typename Ty, typename Tp, int order>
-void approx2(Param out, const Param in, const Param xpos, const Param ypos,
+void approx2(Param zo, const Param zi,
+             const Param xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Param yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
              const float offGrid, af_interp_type method)
 {
     std::string refName = std::string("approx2_kernel_") +
@@ -142,22 +147,27 @@ void approx2(Param out, const Param in, const Param xpos, const Param ypos,
         addKernelToCache(device, refName, entry);
     }
 
-    auto approx2Op = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
-                                    const Buffer, const KParam, const Buffer, const KParam,
+    auto approx2Op = KernelFunctor< Buffer, const KParam,
+                                    const Buffer, const KParam,
+                                    const Buffer, const KParam, const int,
+                                    const Buffer, const KParam, const int,
+                                    const Tp, const Tp, const Tp, const Tp,
                                     const Ty, const int, const int, const int, const int >(*entry.ker);
 
     NDRange local(TX, TY, 1);
-    dim_t blocksPerMatX = divup(out.info.dims[0], local[0]);
-    dim_t blocksPerMatY = divup(out.info.dims[1], local[1]);
-    NDRange global(blocksPerMatX * local[0] * out.info.dims[2],
-                   blocksPerMatY * local[1] * out.info.dims[3], 1);
+    dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
+    dim_t blocksPerMatY = divup(zo.info.dims[1], local[1]);
+    NDRange global(blocksPerMatX * local[0] * zo.info.dims[2],
+                   blocksPerMatY * local[1] * zo.info.dims[3], 1);
 
     // Passing bools to opencl kernels is not allowed
-    bool batch = !(xpos.info.dims[2] == 1 && xpos.info.dims[3] == 1);
+    bool batch = !(xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
     approx2Op(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info, *in.data, in.info,
-              *xpos.data, xpos.info, *ypos.data, ypos.info,
+              *zo.data, zo.info, *zi.data, zi.info,
+              *xo.data, xo.info, xdim,
+              *yo.data, yo.info, ydim,
+              xi_beg, xi_step, yi_beg, yi_step,
               scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
 
     CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index 89c4bccc37..be95771dce 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -8,43 +8,53 @@
  ********************************************************/
 
 __kernel
-void approx1_kernel(__global       Ty *d_out, const KParam out,
-                    __global const Ty *d_in,  const KParam in,
-                    __global const Tp *d_xpos, const KParam xpos,
+void approx1_kernel(__global       Ty *d_yo, const KParam yo,
+                    __global const Ty *d_yi,  const KParam yi,
+                    __global const Tp *d_xo, const KParam xo, const int xdim,
+                    const Tp xi_beg, const Tp xi_step,
                     const Ty offGrid, const int blocksMatX, const int batch, const int method)
 {
-    const int idw = get_group_id(1) / out.dims[2];
-    const int idz = get_group_id(1)  - idw * out.dims[2];
+    const int idw = get_group_id(1) / yo.dims[2];
+    const int idz = get_group_id(1)  - idw * yo.dims[2];
 
     const int idy = get_group_id(0) / blocksMatX;
     const int blockIdx_x = get_group_id(0) - idy * blocksMatX;
     const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
 
-    if(idx >= out.dims[0] ||
-       idy >= out.dims[1] ||
-       idz >= out.dims[2] ||
-       idw >= out.dims[3])
+    if(idx >= yo.dims[0] ||
+       idy >= yo.dims[1] ||
+       idz >= yo.dims[2] ||
+       idw >= yo.dims[3])
         return;
 
-    const int omId = idw * out.strides[3] + idz * out.strides[2]
-        + idy * out.strides[1] + idx + out.offset;
-    int xmid = idx + xpos.offset;
-    if(batch) xmid += idw * xpos.strides[3] + idz * xpos.strides[2] + idy * xpos.strides[1];
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
+    bool is_yi_off[] = {true, true, true, true};
+    is_yi_off[xdim] = false;
 
-    const Tp x = d_xpos[xmid];
-    if (x < 0 || in.dims[0] < x+1) {
-        d_out[omId] = offGrid;
+    const int yo_idx = idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx + yo.offset;
+
+    int xo_idx = idx * is_xo_off[0] + xo.offset;
+    xo_idx += idw * xo.strides[3] * is_xo_off[3];
+    xo_idx += idz * xo.strides[2] * is_xo_off[2];
+    xo_idx += idy * xo.strides[1] * is_xo_off[1];
+
+    const Tp x = (d_xo[xo_idx] - xi_beg) / xi_step;
+    if (x < 0 || yi.dims[xdim] < x+1) {
+        d_yo[yo_idx] = offGrid;
         return;
     }
 
-    int ioff = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + in.offset;
+    int yi_idx = idx * is_yi_off[0] + yi.offset;
+    yi_idx += idw * yi.strides[3] * is_yi_off[3];
+    yi_idx += idz * yi.strides[2] * is_yi_off[2];
+    yi_idx += idy * yi.strides[1] * is_yi_off[1];
 
     // FIXME: Only cubic interpolation is doing clamping
     // We need to make it consistent across all methods
     // Not changing the behavior because tests will fail
     bool clamp = INTERP_ORDER == 3;
 
-    interp1(d_out, out, omId,
-             d_in,  in, ioff,
-            x, method, 1, clamp);
+    interp1_dim(d_yo, yo, yo_idx,
+                d_yi, yi, yi_idx,
+                x, method, 1, clamp, xdim);
 }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index def691216b..0da4e2bda7 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -8,10 +8,12 @@
  ********************************************************/
 
 __kernel
-void approx2_kernel(__global       Ty *d_out, const KParam out,
-                    __global const Ty *d_in,  const KParam in,
-                    __global const Tp *d_xpos, const KParam xpos,
-                    __global const Tp *d_ypos, const KParam ypos,
+void approx2_kernel(__global       Ty *d_zo, const KParam zo,
+                    __global const Ty *d_zi,  const KParam zi,
+                    __global const Tp *d_xo, const KParam xo, const int xdim,
+                    __global const Tp *d_yo, const KParam yo, const int ydim,
+                    const Tp xi_beg, const Tp xi_step,
+                    const Tp yi_beg, const Tp yi_step,
                     const Ty offGrid, const int blocksMatX, const int blocksMatY,
                     const int batch, int method)
 {
@@ -24,35 +26,40 @@ void approx2_kernel(__global       Ty *d_out, const KParam out,
     const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int idy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(idx >= out.dims[0] ||
-       idy >= out.dims[1] ||
-       idz >= out.dims[2] ||
-       idw >= out.dims[3])
+    if(idx >= zo.dims[0] ||
+       idy >= zo.dims[1] ||
+       idz >= zo.dims[2] ||
+       idw >= zo.dims[3])
         return;
 
-    const int omId = idw * out.strides[3] + idz * out.strides[2]
-        + idy * out.strides[1] + idx + out.offset;
-    int xmid = idy * xpos.strides[1] + idx + xpos.offset;
-    int ymid = idy * ypos.strides[1] + idx + ypos.offset;
-    if(batch) {
-        xmid += idw * xpos.strides[3] + idz * xpos.strides[2];
-        ymid += idw * ypos.strides[3] + idz * ypos.strides[2];
-    }
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
+    bool is_zi_off[] = {true, true, true, true};
+    is_zi_off[xdim] = false;
+    is_zi_off[ydim] = false;
+
+    const int zo_idx = idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx + zo.offset;
+    int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + xo.offset;
+    int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + yo.offset;
+    xo_idx += idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2]  * is_xo_off[2];
+    yo_idx += idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2]  * is_xo_off[2];
 
-    const Tp x = d_xpos[xmid], y = d_ypos[ymid];
-    if (x < 0 || y < 0 || in.dims[0] < x+1 || in.dims[1] < y+1) {
-        d_out[omId] = offGrid;
+    const Tp x = (d_xo[xo_idx] - xi_beg) / xi_step;
+    const Tp y = (d_yo[yo_idx] - yi_beg) / yi_step;
+    if (x < 0 || y < 0 || zi.dims[xdim] < x+1 || zi.dims[ydim] < y+1) {
+        d_zo[zo_idx] = offGrid;
         return;
     }
 
-    int ioff = idw * in.strides[3] + idz * in.strides[2] + in.offset;
+    int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0] + zi.offset;
+    zi_idx += idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
 
     // FIXME: Only cubic interpolation is doing clamping
     // We need to make it consistent across all methods
     // Not changing the behavior because tests will fail
     bool clamp = INTERP_ORDER == 3;
 
-    interp2(d_out, out, omId,
-             d_in,  in, ioff,
-            x, y, method, 1, clamp);
+    interp2_dim(d_zo, zo, zo_idx,
+                d_zi,  zi, zi_idx,
+                x, y, method, 1, clamp,
+                xdim, ydim);
 }
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index 713f44f86b..9cb435adc8 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -83,114 +83,136 @@ InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio, InterpP
 }
 
 #if INTERP_ORDER == 1
-void interp1(
+void interp1_general(
     __global InterpInTy *d_out,
     KParam out, int ooff,
     __global const InterpInTy *d_in,
     KParam in, int ioff, InterpPosTy x,
-    int method, int batch, bool clamp)
+    int method, int batch, bool clamp,
+    int xdim, int batch_dim)
 {
-    int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
     InterpInTy zero = ZERO;
-    bool cond = xid >= 0 && xid < in.dims[0];
-    if (clamp) xid = max(0, min(xid, (int)in.dims[0]));
-    const int idx = ioff + xid;
+
+    const int x_lim = in.dims[xdim];
+    const int x_stride = in.strides[xdim];
+
+    int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+    bool cond = xid >= 0 && xid < x_lim;
+    if (clamp) xid = max(0, min(xid, x_lim));
+
+    const int idx = ioff + xid * x_stride;
+
     for (int n = 0; n < batch; n++) {
-        int idx_n = idx + n * in.strides[1];
-        d_out[ooff + n * out.strides[1]] = (clamp || cond) ? d_in[idx_n] : zero;
+        int idx_n = idx + n * in.strides[batch_dim];
+        d_out[ooff + n * out.strides[batch_dim]] = (clamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-void interp1(
+void interp1_general(
     __global InterpInTy *d_out,
     KParam out, int ooff,
     __global const InterpInTy *d_in,
     KParam in, int ioff, InterpPosTy x,
-    int method, int batch, bool clamp)
+    int method, int batch, bool clamp,
+    int xdim, int batch_dim)
 {
     const int grid_x = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;    // fractional offset
-    const int idx = ioff + grid_x;
+
+    const int x_lim = in.dims[xdim];
+    const int x_stride = in.strides[xdim];
+    const int idx = ioff + grid_x * x_stride;
+
     InterpValTy zero = ZERO;
-    bool cond[2] = {true, grid_x + 1 < in.dims[0]};
+    bool cond[2] = {true, grid_x + 1 < x_lim};
     int  offx[2]  = {0, cond[1] ? 1 : 0};
-
     InterpPosTy ratio = off_x;
     if (method == AF_INTERP_LINEAR_COSINE) {
         ratio = (1 - cos(ratio * (InterpPosTy)M_PI))/2;
     }
 
     for (int n = 0; n < batch; n++) {
-        int idx_n = idx + n * in.strides[1];
-        InterpValTy val[2] = {(clamp || cond[0]) ? d_in[idx_n + offx[0]] : zero,
-                              (clamp || cond[1]) ? d_in[idx_n + offx[1]] : zero};
+        int idx_n = idx + n * in.strides[batch_dim];
+        InterpValTy val[2] = {(clamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
+                              (clamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
 
-        d_out[ooff + n * out.strides[1]] = linearInterpFunc(val, ratio);
+        d_out[ooff + n * out.strides[batch_dim]] = linearInterpFunc(val, ratio);
     }
 }
 #elif INTERP_ORDER == 3
-void interp1(
+void interp1_general(
     __global InterpInTy *d_out,
     KParam out, int ooff,
     __global const InterpInTy *d_in,
     KParam in, int ioff, InterpPosTy x,
-    int method, int batch, bool clamp)
+    int method, int batch, bool clamp,
+    int xdim, int batch_dim)
 {
     const int grid_x = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;    // fractional offset
-    const int idx = ioff + grid_x;
 
-    bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
+    const int x_lim = in.dims[xdim];
+    const int x_stride = in.strides[xdim];
+    const int idx = ioff + grid_x * x_stride;
+
+    bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
     int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
     InterpValTy zero = ZERO;
 
     for (int n = 0; n < batch; n++) {
         InterpValTy val[4];
-        int idx_n = idx + n * in.strides[1];
+        int idx_n = idx + n * in.strides[batch_dim];
         for (int i = 0; i < 4; i++) {
-            val[i] = (clamp || cond[i]) ? d_in[idx_n + off[i]] : zero;
+            val[i] = (clamp || cond[i]) ? d_in[idx_n + off[i] * x_stride] : zero;
         }
         bool spline = method == AF_INTERP_CUBIC_SPLINE;
-        d_out[ooff + n * out.strides[1]] = cubicInterpFunc(val, off_x, spline);;
+        d_out[ooff + n * out.strides[batch_dim]] = cubicInterpFunc(val, off_x, spline);;
     }
 }
 #endif
 
 #if INTERP_ORDER == 1
-void interp2(
+void interp2_general(
     __global InterpInTy *d_out,
     KParam out, int ooff,
     __global const InterpInTy *d_in,
     KParam in, int ioff, InterpPosTy x, InterpPosTy y,
-    int method, int nimages, bool clamp)
+    int method, int batch, bool clamp,
+    int xdim, int ydim, int batch_dim)
 {
     int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
     int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
 
+    const int x_lim = in.dims[xdim];
+    const int y_lim = in.dims[ydim];
+    const int x_stride = in.strides[xdim];
+    const int y_stride = in.strides[ydim];
+
     if (clamp) {
-        xid = max(0, min(xid, (int)in.dims[0]));
-        yid = max(0, min(yid, (int)in.dims[1]));
+        xid = max(0, min(xid, x_lim));
+        yid = max(0, min(yid, y_lim));
     }
-    int idx = ioff + yid * in.strides[1] + xid;
+    const int idx = ioff + yid * y_stride + xid * x_stride;
 
-    bool condX = xid >= 0 && xid < in.dims[0];
-    bool condY = yid >= 0 && yid < in.dims[1];
+    bool condX = xid >= 0 && xid < x_lim;
+    bool condY = yid >= 0 && yid < y_lim;
 
     InterpInTy zero = ZERO;
     bool cond = condX && condY;
-    for (int n = 0; n < nimages; n++) {
-        int idx_n = idx + n * in.strides[2];
-        d_out[ooff + n * out.strides[2]] =  (clamp || cond) ? d_in[idx_n] : zero;
+    for (int n = 0; n < batch; n++) {
+        int idx_n = idx + n * in.strides[batch_dim];
+        d_out[ooff + n * out.strides[batch_dim]] =  (clamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-void interp2(
+void interp2_general(
     __global InterpInTy *d_out,
     KParam out, int ooff,
     __global const InterpInTy *d_in,
     KParam in, int ioff, InterpPosTy x, InterpPosTy y,
-    int method, int nimages, bool clamp)
+    int method, int batch, bool clamp,
+    int xdim, int ydim, int batch_dim)
 {
     const int grid_x = floor(x);
     const InterpPosTy off_x = x - grid_x;
@@ -198,10 +220,14 @@ void interp2(
     const int grid_y = floor(y);
     const InterpPosTy off_y = y - grid_y;
 
-    const int idx = ioff + grid_y * in.strides[1] + grid_x;
+    const int x_lim = in.dims[xdim];
+    const int y_lim = in.dims[ydim];
+    const int x_stride = in.strides[xdim];
+    const int y_stride = in.strides[ydim];
+    const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
 
-    bool condX[2] = {true, x + 1 < in.dims[0]};
-    bool condY[2] = {true, y + 1 < in.dims[1]};
+    bool condX[2] = {true, x + 1 < x_lim};
+    bool condY[2] = {true, y + 1 < y_lim};
     int  offx[2]  = {0, condX[1] ? 1 : 0};
     int  offy[2]  = {0, condY[1] ? 1 : 0};
 
@@ -212,26 +238,27 @@ void interp2(
     }
 
     InterpValTy zero = ZERO;
-    for (int n = 0; n < nimages; n++) {
-        int idx_n = idx + n * in.strides[2];
+    for (int n = 0; n < batch; n++) {
+        int idx_n = idx + n * in.strides[batch_dim];
         InterpValTy val[2][2];
         for (int j = 0; j < 2; j++) {
-            int off_y = idx_n + offy[j] * in.strides[1];
+            int off_y = idx_n + offy[j] * y_stride;
             for (int i = 0; i < 2; i++) {
                 bool cond = (clamp || (condX[i] && condY[j]));
-                val[j][i] = cond ? d_in[off_y + offx[i]] : zero;
+                val[j][i] = cond ? d_in[off_y + offx[i] * x_stride] : zero;
             }
         }
-        d_out[ooff + n * out.strides[2]] =  bilinearInterpFunc(val, xratio, yratio);
+        d_out[ooff + n * out.strides[batch_dim]] =  bilinearInterpFunc(val, xratio, yratio);
     }
 }
 #elif INTERP_ORDER == 3
-void interp2(
+void interp2_general(
     __global InterpInTy *d_out,
     KParam out, int ooff,
     __global const InterpInTy *d_in,
     KParam in, int ioff, InterpPosTy x, InterpPosTy y,
-    int method, int nimages, bool clamp)
+    int method, int batch, bool clamp,
+    int xdim, int ydim, int batch_dim)
 {
     const int grid_x = floor(x);
     const InterpPosTy off_x = x - grid_x;
@@ -239,31 +266,76 @@ void interp2(
     const int grid_y = floor(y);
     const InterpPosTy off_y = y - grid_y;
 
-    const int idx = ioff + grid_y * in.strides[1] + grid_x;
-
+    const int x_lim = in.dims[xdim];
+    const int y_lim = in.dims[ydim];
+    const int x_stride = in.strides[xdim];
+    const int y_stride = in.strides[ydim];
+    const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
 
     // used for setting values at boundaries
-    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < in.dims[0], grid_x + 2 < in.dims[0]};
-    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < in.dims[1], grid_y + 2 < in.dims[1]};
+    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
+    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim, grid_y + 2 < y_lim};
     int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
     int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
     InterpValTy zero = ZERO;
-    for (int n = 0; n < nimages; n++) {
-        int idx_n = idx + n * in.strides[2];
+    for (int n = 0; n < batch; n++) {
+        int idx_n = idx + n * in.strides[batch_dim];
         //for bicubic interpolation, work with 4x4 val at a time
         InterpValTy val[4][4];
 #pragma unroll
         for (int j = 0; j < 4; j++) {
-            int ioff_j = idx_n + offY[j] * in.strides[1];
+            int ioff_j = idx_n + offY[j] * y_stride;
 #pragma unroll
             for (int i = 0; i < 4; i++) {
                 bool cond = (clamp || (condX[i] && condY[j]));
-                val[j][i] = cond ? d_in[ioff_j + offX[i]] : zero;
+                val[j][i] = cond ? d_in[ioff_j + offX[i] * x_stride] : zero;
             }
         }
         bool spline  = method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE;
-        d_out[ooff +  n * out.strides[2]] = bicubicInterpFunc(val, off_x, off_y, spline);
+        d_out[ooff +  n * out.strides[batch_dim]] = bicubicInterpFunc(val, off_x, off_y, spline);
     }
 }
 #endif
+
+#define interp1_dim(d_out,                      \
+                    out, ooff, d_in,            \
+                    in, ioff, x,                \
+                    method, batch, clamp,       \
+                    xdim)                       \
+    interp1_general(d_out,                      \
+                    out, ooff, d_in,            \
+                    in, ioff, x,                \
+                    method, batch, clamp,       \
+                    xdim, 1)                    \
+
+#define interp1(d_out,                          \
+                out, ooff, d_in,                \
+                in, ioff, x,                    \
+                method, batch, clamp)           \
+    interp1_dim(d_out,                          \
+                out, ooff, d_in,                \
+                in, ioff, x,                    \
+                method, batch, clamp,           \
+                0)                              \
+
+#define interp2_dim(d_out,                      \
+                    out, ooff, d_in,            \
+                    in, ioff, x, y,             \
+                    method, batch, clamp,       \
+                    xdim, ydim)                 \
+    interp2_general(d_out,                      \
+                    out, ooff, d_in,            \
+                    in, ioff, x, y,             \
+                    method, batch, clamp,       \
+                    xdim, ydim, 2)              \
+
+#define interp2(d_out,                          \
+                out, ooff, d_in,                \
+                in, ioff, x, y,                 \
+                method, batch, clamp)           \
+    interp2_dim(d_out,                          \
+                out, ooff, d_in,                \
+                in, ioff, x, y,                 \
+                method, batch, clamp,           \
+                0, 1)                           \
diff --git a/test/approx1.cpp b/test/approx1.cpp
index e737616540..8da14a03b0 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -29,6 +29,7 @@ using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::randu;
+using af::reorder;
 using af::span;
 using af::seq;
 using af::sum;
@@ -296,7 +297,6 @@ TYPED_TEST(Approx1, Approx1CubicArgsPrecision)
 TEST(Approx1, CPP)
 {
     const unsigned resultIdx = 1;
-    const af_interp_type method = AF_INTERP_LINEAR;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
     vector<vector<BT> > in;
@@ -308,7 +308,7 @@ TEST(Approx1, CPP)
 
     array input(idims, &(in[0].front()));
     array pos(pdims, &(in[1].front()));
-
+    const af_interp_type method = AF_INTERP_LINEAR;
     array output = approx1(input, pos, method, 0);
 
     // Get result
@@ -339,15 +339,15 @@ TEST(Approx1, CPPNearestBatch)
     array outSerial(pos.dims());
     for (int i = 0; i < pos.dims(1); i++) {
         outSerial(span, i) = approx1(input(span, i),
-                                         pos(span, i),
-                                         AF_INTERP_NEAREST);
+                                     pos(span, i),
+                                     AF_INTERP_NEAREST);
     }
 
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(1)) {
         outGFOR(span, i) = approx1(input(span, i),
-                                       pos(span, i),
-                                       AF_INTERP_NEAREST);
+                                   pos(span, i),
+                                   AF_INTERP_NEAREST);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
@@ -396,8 +396,8 @@ TEST(Approx1, CPPCubicBatch)
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(1)) {
         outGFOR(span, i) = approx1(input(span, i),
-                                           pos(span, i),
-                                           AF_INTERP_CUBIC_SPLINE);
+                                   pos(span, i),
+                                   AF_INTERP_CUBIC_SPLINE);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
@@ -464,24 +464,379 @@ TEST(Approx1, CPPCubicMaxDims)
     SUCCEED();
 }
 
-TEST(Approx1, SNIPPET_approx1) {
+TEST(Approx1, OtherDimLinear)
+{
+    int start = 0;
+    int stop = 10000;
+    int step = 100;
+    int num = 1000;
+    array xi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array yi = 4 * xi - 3;
+    array xo = af::round(step * randu(num, 2, 2, 2));
+    array yo = 4 * xo - 3;
+    for (int d = 1; d < 4; d++) {
+        dim4 rdims(0,1,2,3);
+        rdims[0] = d;
+        rdims[d] = 0;
+
+        array yi_reordered = reorder(yi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered = approx1(yi_reordered, xo_reordered,
+                                     d, start, step, AF_INTERP_LINEAR);
+        array res = reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
+    }
+}
+
+TEST(Approx1, OtherDimCubic)
+{
+    float start = 0;
+    float stop = 100;
+    float step = 0.01;
+    int num = 1000;
+    array xi = af::tile(af::seq(start, stop, step), 1, 2, 2, 2);
+    array yi = af::sin(xi);
+    array xo = af::round(step * af::randu(num, 2, 2, 2));
+    array yo = af::sin(xo);
+    for (int d = 1; d < 4; d++) {
+        dim4 rdims(0,1,2,3);
+        rdims[0] = d;
+        rdims[d] = 0;
+
+        array yi_reordered = reorder(yi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered = approx1(yi_reordered, xo_reordered,
+                                     d, start, step, AF_INTERP_CUBIC);
+        array res = reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
+    }
+}
 
+TEST(Approx1, CPPUsage)
+{
     //! [ex_signal_approx1]
 
-    // input data
-    float inv[3] = {10, 20, 30};
-    af::array in(3, inv);
+    // Input data array.
+    float input_vals[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3, 1), input_vals);
+    // [3 1 1 1]
+    //     10.0000
+    //     20.0000
+    //     30.0000
 
-    // positions of interpolated values
+    // Array of positions to be found along the first dimension.
     float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
-    af::array pos(5, pv);
-
-    af::array interpolated = approx1(in, pos);
-    // interpolated == { 10, 15, 20, 25, 30 };
+    array pos(dim4(5,1), pv);
+    // [5 1 1 1]
+    //     0.0000
+    //     0.5000
+    //     1.0000
+    //     1.5000
+    //     2.0000
+
+    // Perform interpolation across dimension 0.
+    array interp = approx1(in, pos);
+    // [5 1 1 1]
+    //     10.0000
+    //     15.0000
+    //     20.0000
+    //     25.0000
+    //     30.0000
 
     //! [ex_signal_approx1]
 
-    float iv[5] = {10, 15, 20, 25, 30 };
-    af::array interp_gold(5, iv);
-    ASSERT_ARRAYS_NEAR(interpolated, interp_gold, 1e-5);
+    float civ[5] = {10.0, 15.0, 20.0, 25.0, 30.0};
+    array interp_gold(dim4(5,1), civ);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+
+}
+
+
+TEST(Approx1, CPPUniformUsage)
+{
+    //! [ex_signal_approx1_uniform]
+
+    float input_vals[9] = {10.0, 20.0, 30.0,
+                           40.0, 50.0, 60.0,
+                           70.0, 80.0, 90.0};
+    array in(dim4(3, 3), input_vals);
+    // [3 3 1 1]
+    //     10.0000    40.0000    70.0000
+    //     20.0000    50.0000    80.0000
+    //     30.0000    60.0000    90.0000
+
+    // Array of positions to be found along the interpolation
+    // dimension, `interp_dim`.
+    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    array pos(dim4(5,1), pv);
+    // [5 1 1 1]
+    //     0.0000
+    //     0.5000
+    //     1.0000
+    //     1.5000
+    //     2.0000
+
+    // Define range of indices with which the input values will
+    // correspond along the interpolation dimension.
+    const double idx_start = 0.0;
+    const double idx_step = 1.0;
+
+    // Perform interpolation across dimension 0.
+    int interp_dim = 0;
+    array col_major_interp = approx1(in, pos, interp_dim, idx_start, idx_step);
+    // [5 3 1 1]
+    //     10.0000    40.0000    70.0000
+    //     15.0000    45.0000    75.0000
+    //     20.0000    50.0000    80.0000
+    //     25.0000    55.0000    85.0000
+    //     30.0000    60.0000    90.0000
+
+    // Perform interpolation across dimension 1.
+    interp_dim = 1;
+    array row_major_interp = approx1(in, transpose(pos), interp_dim, idx_start, idx_step);
+    // [3 5 1 1]
+    //     10.0000    25.0000    40.0000    55.0000    70.0000
+    //     20.0000    35.0000    50.0000    65.0000    80.0000
+    //     30.0000    45.0000    60.0000    75.0000    90.0000
+
+    //! [ex_signal_approx1_uniform]
+
+    float civ[15] = {10.0, 15.0, 20.0, 25.0, 30.0,
+                     40.0, 45.0, 50.0, 55.0, 60.0,
+                     70.0, 75.0, 80.0, 85.0, 90.0};
+    array interp_gold_col(dim4(5,3), civ);
+    ASSERT_ARRAYS_EQ(col_major_interp, interp_gold_col);
+
+
+    float riv[15] = {10.0, 20.0, 30.0,
+                     25.0, 35.0, 45.0,
+                     40.0, 50.0, 60.0,
+                     55.0, 65.0, 75.0,
+                     70.0, 80.0, 90.0};
+    array interp_gold_row(dim4(3,5), riv);
+    ASSERT_ARRAYS_EQ(row_major_interp, interp_gold_row);
+}
+
+TEST(Approx1, CPPDecimalStepRescaleGrid)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    float pv[5] = {0, 0.25, 0.5, 0.75, 1.0};
+    array pos(dim4(5,1), pv);
+
+    const int interp_grid_start = 0;
+    const double interp_grid_step = 0.5;
+    const int interp_dim = 0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[5] = {10.0, 15.0, 20.0, 25.0, 30.0};
+    array interp_gold(dim4(5,1), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+TEST(Approx1, CPPRepeatPos)
+{
+    float inv[9] = {10.0, 20.0, 30.0,
+                    40.0, 50.0, 60.0,
+                    70.0, 80.0, 90.0};
+    array in(dim4(3, 3), inv);
+    float pv[5] = {0.0, 0.5, 0.5, 1.5, 1.5};
+    array pos(dim4(5,1), pv);
+
+    const int interp_grid_start = 0;
+    const double interp_grid_step = 1.0;
+    const int interp_dim = 0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[15] = {10.0, 15.0, 15.0, 25.0, 25.0,
+                    40.0, 45.0, 45.0, 55.0, 55.0,
+                    70.0, 75.0, 75.0, 85.0, 85.0};
+    array interp_gold(dim4(5,3), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+
+TEST(Approx1, CPPNonMonotonicPos)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    float pv[5] = {0.5, 1.0, 1.5, 0.0, 2.0};
+    array pos(dim4(5,1), pv);
+
+    const int interp_grid_start = 0;
+    const double interp_grid_step = 1.0;
+    const int interp_dim = 0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[5] = {15.0, 20.0, 25.0, 10.0, 30.0};
+    array interp_gold(dim4(5,1), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+TEST(Approx1, CPPMismatchingIndexingDim)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    float pv[4] = {0.0, 0.5, 1.0, 2.0};
+    array pos(dim4(1,4), pv);
+
+    const int interp_grid_start = 0;
+    const double interp_grid_step = 1.0;
+    const int interp_dim = 1;
+    const float off_grid = -1.0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step,
+                           AF_INTERP_LINEAR, off_grid);
+
+    float iv[12] = {10.0, 20.0, 30.0,
+                   -1.0, -1.0, -1.0,
+                   -1.0, -1.0, -1.0,
+                   -1.0, -1.0, -1.0};
+    array interp_gold(dim4(3,4), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+TEST(Approx1, CPPNegativeGridStart)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    array pos(dim4(5,1), pv);
+
+    const int interp_grid_start = -1;
+    const double interp_grid_step = 1;
+    const int interp_dim = 0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[5] = {20.0, 25.0, 30.0, 0.0, 0.0};
+    array interp_gold(dim4(5,1), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+
+}
+
+TEST(Approx1, CPPInterpolateBackwards)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    array pos(dim4(3,1), pv);
+
+    const int interp_grid_start = in.elements()-1;
+    const double interp_grid_step = -1;
+    const int interp_dim = 0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[5] = {30.0, 25.0, 20.0, 15.0, 10.0};
+    array interp_gold(dim4(3,1), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+
+TEST(Approx1, CPPStartOffGridAndNegativeStep)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    float pv[5] = {0.0, -0.5, -1.0, -1.5, -2.0};
+    array pos(dim4(5,1), pv);
+
+    const int interp_grid_start = -1;
+    const double interp_grid_step = -1;
+    const int interp_dim = 0;
+    array interp = approx1(in,
+                           pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[5] = {0.0, 0.0, 10.0, 15.0, 20.0};
+    array interp_gold(dim4(5,1), iv);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+TEST(Approx1, CPPUniformInvalidStepSize)
+{
+    try
+    {
+        float inv[3] = {10.0, 20.0, 30.0};
+        array in(dim4(3,1), inv);
+        float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+        array pos(dim4(5,1), pv);
+
+        const int interp_grid_start = 0;
+        const double interp_grid_step = 0;
+        const int interp_dim = 0;
+        array interp = approx1(in,
+                               pos, interp_dim, interp_grid_start, interp_grid_step);
+        FAIL() << "Expected af::exception\n";
+    } catch (af::exception &ex) {
+        SUCCEED();
+    } catch(...) {
+        FAIL() << "Expected af::exception\n";
+    }
+}
+
+// Unless the sampling grid specifications - begin, step - are
+// specified by the user, ArrayFire will assume a regular grid with a
+// starting index of 0 and a step value of 1.
+TEST(Approx1, CPPInfCheck)
+{
+    array sampled(seq(0.0, 5.0, 0.5));
+    sampled(0) = af::Inf;
+    seq xo(0.0, 2.0, 0.25);
+    array interp = approx1(sampled, xo);
+    array interp_augmented = join(1, xo, interp);
+
+    float goldv[9] = {af::Inf, af::Inf, af::Inf, af::Inf, 0.5, 0.625, 0.75, 0.875, 1.0};
+    array gold(dim4(9,1), goldv);
+    interp(af::isInf(interp)) = 0;
+    gold(af::isInf(gold)) = 0;
+    ASSERT_ARRAYS_EQ(interp, gold);
+}
+
+TEST(Approx1, CPPUniformInfCheck)
+{
+    array sampled(seq(10.0, 50.0, 10.0));
+    sampled(0) = af::Inf;
+    seq xo(0.0, 8.0, 2.0);
+    array interp = approx1(sampled,
+                           xo, 0,
+                           0, 2);
+    float goldv[5] = {af::Inf, 20.0, 30.0, 40.0, 50.0};
+    array gold(dim4(5,1), goldv);
+    interp(af::isInf(interp)) = 0;
+    gold(af::isInf(gold)) = 0;
+    ASSERT_ARRAYS_EQ(interp, gold);
+}
+
+TEST(Approx1, CPPEmptyPos)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    array pos;
+    array interp = approx1(in, pos);
+    ASSERT_TRUE(pos.isempty());
+    ASSERT_TRUE(interp.isempty());
+}
+
+TEST(Approx1, CPPEmptyInput)
+{
+    array in;
+    float pv[3] = {0.0, 1.0, 2.0};
+    array pos(dim4(3,1), pv);
+
+    array interp = approx1(in, pos);
+    ASSERT_TRUE(in.isempty());
+    ASSERT_TRUE(interp.isempty());
+}
+
+TEST(Approx1, CPPEmptyPosAndInput)
+{
+    array in;
+    array pos;
+    array interp = approx1(in, pos);
+    ASSERT_TRUE(in.isempty());
+    ASSERT_TRUE(pos.isempty());
+    ASSERT_TRUE(interp.isempty());
 }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 8f28278a9b..2ea2652108 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -165,20 +165,20 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
     if(outArray  != 0) af_release_array(outArray);
 }
 
-    TYPED_TEST(Approx2, Approx2NearestArgsPos3D)
-    {
-        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
-    }
+TYPED_TEST(Approx2, Approx2NearestArgsPos3D)
+{
+    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+}
 
-    TYPED_TEST(Approx2, Approx2LinearArgsPos3D)
-    {
-        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
-    }
+TYPED_TEST(Approx2, Approx2LinearArgsPos3D)
+{
+    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
+}
 
-    TYPED_TEST(Approx2, Approx2NearestArgsPosUnequal)
-    {
-        approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_unequal.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
-    }
+TYPED_TEST(Approx2, Approx2NearestArgsPosUnequal)
+{
+    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_unequal.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+}
 
 template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
@@ -436,39 +436,326 @@ TEST(Approx2, CPPCubicMaxDims)
     SUCCEED();
 }
 
-TEST(Approx2, SNIPPET_approx2) {
+TEST(Approx2, OtherDimLinear)
+{
+    int start = 0;
+    int stop = 10000;
+    int step = 100;
+    int num = 1000;
+    array xi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array yi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array zi = 4 * xi * yi - 3 * xi;
+    array xo = af::round(step * randu(num, 2, 2, 2));
+    array yo = af::round(step * randu(num, 2, 2, 2));
+    array zo = 4 * xo * yo - 3 * xo;
+    for (int d = 1; d < 3; d++) {
+        dim4 rdims(0,1,2,3);
+        rdims[0] = d;
+        rdims[d] = 0;
+
+        array zi_reordered = reorder(zi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered = reorder(yo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array zo_reordered = approx2(zi_reordered,
+                                     xo_reordered, d, start, step,
+                                     yo_reordered, d + 1, start, step,
+                                     AF_INTERP_LINEAR);
+        rdims[d] = 0;
+        rdims[0] = d;
+        array res = af::reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
+    }
+}
+
+TEST(Approx2, OtherDimCubic)
+{
+    float start = 0;
+    float stop = 100;
+    float step = 0.01;
+    int num = 1000;
+    array xi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array yi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array zi = 4 * sin(xi) * cos(yi);
+    array xo = af::round(step * randu(num, 2, 2, 2));
+    array yo = af::round(step * randu(num, 2, 2, 2));
+    array zo = 4 * sin(xo) * cos(yo);
+    for (int d = 1; d < 3; d++) {
+        dim4 rdims(0,1,2,3);
+        rdims[0] = d;
+        rdims[d] = 0;
+
+        array zi_reordered = reorder(zi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered = reorder(yo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array zo_reordered = approx2(zi_reordered,
+                                     xo_reordered, d, start, step,
+                                     yo_reordered, d + 1, start, step,
+                                     AF_INTERP_CUBIC);
+        rdims[d] = 0;
+        rdims[0] = d;
+        array res = reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
+    }
+}
 
+TEST(Approx2, CPPUsage)
+{
     //! [ex_signal_approx2]
 
-    // constant input data
-    // {{1 2 3},
-    //  {1 2 3},
-    //  {1 2 3}},
-    float input_vals[9] = {1, 1, 1,
-                           2, 2, 2,
-                           3, 3, 3};
+    // Input data array.
+    float input_vals[9] = {1.0, 1.0, 1.0,
+                           2.0, 2.0, 2.0,
+                           3.0, 3.0, 3.0};
     array input(3, 3, input_vals);
+    // [3 3 1 1]
+    //     1.0000     2.0000     3.0000
+    //     1.0000     2.0000     3.0000
+    //     1.0000     2.0000     3.0000
+
+    // First array of positions to be found along the first dimension.
+    float pv0[4] = {0.5, 1.5, 0.5, 1.5};
+    array pos0(2, 2, pv0);
+    // [2 2 1 1]
+    //     0.5000     0.5000
+    //     1.5000     1.5000
+
+    // Second array of positions to be found along the second
+    // dimension.
+    float pv1[4] = {0.5, 0.5, 1.5, 1.5};
+    array pos1(2, 2, pv1);
+    // [2 2 1 1]
+    //     0.5000     1.5000
+    //     0.5000     1.5000
+
+    array interp = approx2(input, pos0, pos1);
+    // [2 2 1 1]
+    //     1.5000     2.5000
+    //     1.5000     2.5000
 
-    // generate grid of interpolation locations
-    // interpolation locations along dim0
-    float p0[4] = {0.5, 1.5,
-                   0.5, 1.5};
-    array pos0(2, 2, p0);
-    // interpolation locations along dim1
-    float p1[4] = {0.5, 0.5,
-                   1.5, 1.5};
-    array pos1(2, 2, p1);
+    //! [ex_signal_approx2]
 
-    array interpolated = approx2(input, pos0, pos1);
-    // interpolated == {{1.5 2.5},
-    //                  {1.5 2.5}};
+    float expected_interp[4] = {1.5, 1.5,
+                                2.5, 2.5};
 
-    //! [ex_signal_approx2]
+    array interp_gold(2, 2, expected_interp);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+TEST(Approx2, CPPUniformUsage)
+{
+    //! [ex_signal_approx2_uniform]
+
+    // Input data array.
+    float input_vals[9] = {1.0, 1.0, 1.0,
+                           2.0, 2.0, 2.0,
+                           3.0, 3.0, 3.0};
+    array input(3, 3, input_vals);
+    // [3 3 1 1]
+    //     1.0000     2.0000     3.0000
+    //     1.0000     2.0000     3.0000
+    //     1.0000     2.0000     3.0000
+
+    // First array of positions to be found along the interpolation
+    // dimension, `interp_dim0`.
+    float pv0[4] = {0.5, 1.5, 0.5, 1.5};
+    array pos0(2, 2, pv0);
+    // [2 2 1 1]
+    //     0.5000     0.5000
+    //     1.5000     1.5000
+
+    // Second array of positions to be found along the interpolation
+    // dimension, `interp_dim1`.
+    float pv1[4] = {0.5, 0.5, 1.5, 1.5};
+    array pos1(2, 2, pv1);
+    // [2 2 1 1]
+    //     0.5000     1.5000
+    //     0.5000     1.5000
+
+    // Define range of indices with which the input values will
+    // correspond along both dimensions to be interpolated.
+    const double idx_start_dim0 = 0.0;
+    const double idx_step_dim0 = 1.0;
+    const int interp_dim0 = 0;
+    const int interp_dim1 = 1;
+    array interp = approx2(input,
+                           pos0, interp_dim0, idx_start_dim0, idx_step_dim0,
+                           pos1, interp_dim1, idx_start_dim0, idx_step_dim0);
+    // [2 2 1 1]
+    //     1.5000     2.5000
+    //     1.5000     2.5000
+
+    //! [ex_signal_approx2_uniform]
 
     float expected_interp[4] = {1.5, 1.5,
                                 2.5, 2.5};
 
-    array interpolated_gold(2, 2, expected_interp);
-    ASSERT_ARRAYS_NEAR(interpolated, interpolated_gold, 1e-5);
+    array interp_gold(2, 2, expected_interp);
+    ASSERT_ARRAYS_EQ(interp, interp_gold);
+}
+
+TEST(Approx2, CPPUniformOneDimIndices)
+{
+    float inv[9] = {10.0, 20.0, 30.0,
+                    40.0, 50.0, 60.0,
+                    70.0, 80.0, 90.0};
+    array input(dim4(3,3), inv);
+
+    float p0[3] = {0.0, 1.0, 2.0};
+    float p1[3] = {0.0, 1.0, 2.0};
+    array pos0(dim4(3,1), p0);
+    array pos1(dim4(3,1), p1);
+
+    const int pos0_interp_grid_start = 0;
+    const double pos0_interp_grid_step = 1;
+    array interpolated = approx2(input,
+                                 pos0, 0, pos0_interp_grid_start, pos0_interp_grid_step,
+                                 pos1, 1, pos0_interp_grid_start, pos0_interp_grid_step);
+
+    float expected_interp[3] = {10.0, 50.0, 90.0};
+
+
+    array interpolated_gold(dim4(3,1), expected_interp);
+    ASSERT_ARRAYS_EQ(interpolated, interpolated_gold);
+}
+
+TEST(Approx2, CPPUniformTwoDimIndices)
+{
+    float inv[9] = {10.0, 20.0, 30.0,
+                    40.0, 50.0, 60.0,
+                    70.0, 80.0, 90.0};
+    array input(dim4(3,3), inv);
+
+    float p0[4] = {0, 2, 0, 2};
+    float p1[4] = {0, 0, 2, 2};
+    array pos0(dim4(2,2), p0);
+    array pos1(dim4(2,2), p1);
+    const int pos0_interp_grid_start = 0;
+    const double pos0_interp_grid_step = 1;
+    const int pos0_interp_dim = 0;
+    const int pos1_interp_dim = 1;
+
+    array interpolated = approx2(input,
+                                 pos0, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
+                                 pos1, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+
+    float expected_interp[4] = {10.0, 30.0, 70.0, 90.0};
+    array interpolated_gold(dim4(2,2), expected_interp);
+    ASSERT_ARRAYS_EQ(interpolated, interpolated_gold);
+}
+
+TEST(Approx2, CPPUniformInvalidStepSize)
+{
+    try
+    {
+        float inv[9] = {10.0, 20.0, 30.0,
+                        40.0, 50.0, 60.0,
+                        70.0, 80.0, 90.0};
+        array in(dim4(3,3), inv);
+        float pv[3] = {0.0, -1.0, -2.0};
+        array pos(dim4(3,1), pv);
+        const int pos0_interp_grid_start = -1;
+        const double pos0_interp_grid_step = 0;
+        const int pos0_interp_dim = 0;
+        const int pos1_interp_dim = 1;
+
+        array interpolated = approx2(in,
+                                     pos, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
+                                     pos, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+        FAIL() << "Expected af::exception\n";
+    } catch (af::exception &ex) {
+        SUCCEED();
+    } catch(...) {
+        FAIL() << "Expected af::exception\n";
+    }
+}
+
+TEST(Approx2, CPPUniformColumnMajorInterpolation)
+{
+    float inv[9] = {10.0, 20.0, 30.0,
+                    40.0, 50.0, 60.0,
+                    70.0, 80.0, 90.0};
+    array input(dim4(3,3), inv);
+
+    float p0[4] = {0, 2, 0, 2};
+    float p1[4] = {0, 0, 2, 2};
+    array pos0(dim4(2,2), p0);
+    array pos1(dim4(2,2), p1);
+    const int pos0_interp_dim = 0;
+    const int pos1_interp_dim = 1;
+    const int pos0_interp_grid_start = 0;
+    const double pos0_interp_grid_step = 1;
+
+    array first = approx2(input,
+                          pos0, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
+                          pos1, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+
+    array second = approx2(input,
+                           pos1, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
+                           pos0, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+
+    // Verify.
+    float expected_interp[4] = {10.0, 30.0, 70.0, 90.0};
+    array interpolated_gold(dim4(2,2), expected_interp);
+    ASSERT_ARRAYS_EQ(first, interpolated_gold);
+    ASSERT_ARRAYS_EQ(first, second);
+}
+
+TEST(Approx2, CPPUniformRowMajorInterpolation)
+{
+    float inv[9] = {10.0, 20.0, 30.0,
+                    40.0, 50.0, 60.0,
+                    70.0, 80.0, 90.0};
+    array input(dim4(3,3), inv);
+
+    float p0[4] = {0, 2, 0, 2};
+    float p1[4] = {0, 0, 2, 2};
+    array pos0(dim4(2,2), p0);
+    array pos1(dim4(2,2), p1);
+    const int pos0_interp_grid_start = 0;
+    const double pos0_interp_grid_step = 1;
+
+    array first = approx2(input,
+                          pos0, 1, pos0_interp_grid_start, pos0_interp_grid_step,
+                          pos1, 0, pos0_interp_grid_start, pos0_interp_grid_step);
+
+    array second = approx2(input,
+                           pos1, 0, pos0_interp_grid_start, pos0_interp_grid_step,
+                           pos0, 1, pos0_interp_grid_start, pos0_interp_grid_step);
+
+    // Verify.
+    float expected_interp[4] = {10.0, 70.0, 30.0, 90.0};
+    array interpolated_gold(dim4(2,2), expected_interp);
+    ASSERT_ARRAYS_EQ(first, interpolated_gold);
+    ASSERT_ARRAYS_EQ(first, second);
+}
 
+TEST(Approx2, CPPEmptyPos)
+{
+    float inv[3] = {10.0, 20.0, 30.0};
+    array in(dim4(3,1), inv);
+    array pos;
+    array interpolated = approx2(in, pos, pos);
+    ASSERT_TRUE(pos.isempty());
+    ASSERT_TRUE(interpolated.isempty());
+}
+
+TEST(Approx2, CPPEmptyInput)
+{
+    array in;
+    float pv[3] = {0.0, 1.0, 2.0};
+    array pos(dim4(3,1), pv);
+
+    array interpolated = approx2(in, pos, pos);
+    ASSERT_TRUE(in.isempty());
+    ASSERT_TRUE(interpolated.isempty());
+}
+
+TEST(Approx2, CPPEmptyPosAndInput)
+{
+    array in;
+    array pos;
+    array interpolated = approx2(in, pos, pos);
+    ASSERT_TRUE(in.isempty());
+    ASSERT_TRUE(pos.isempty());
+    ASSERT_TRUE(interpolated.isempty());
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 7ac4a32f82..0aaf5ba96d 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -696,8 +696,6 @@ template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T>& a, af::dim4 aDims,
                                       const std::vector<T>& b, af::dim4 bDims,
-
-
                                       float maxAbsDiff, IntegerTag) {
     typedef typename std::vector<T>::const_iterator iter;
     std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());

From 97001c7061ff0c9466d4a17c757af0619929aaca Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Sat, 20 Oct 2018 14:38:43 +0530
Subject: [PATCH 1541/2677] Improve wrap documentation

---
 assets                 |  2 +-
 docs/details/image.dox | 75 +++++++++++++++++++++++++++++++++++++--
 include/af/image.h     | 79 +++++++++++++++++++++++++++++-------------
 test/wrap.cpp          | 69 ++++++++++++++++++++++++++++++++----
 4 files changed, 191 insertions(+), 34 deletions(-)

diff --git a/assets b/assets
index fac641d359..6b13342b97 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit fac641d359db1881ec801f0ef0fddb5673817699
+Subproject commit 6b13342b97ab8d0157e75f3fa5ee4ef2fa1b1078
diff --git a/docs/details/image.dox b/docs/details/image.dox
index 7a8dcae3fe..ef1f56affc 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -843,9 +843,80 @@ is described above, but the effect should be the same.
 \defgroup image_func_wrap wrap
 \ingroup image_mod_mat
 
-Wrap takes an unwrapped image (see \ref unwrap()) and converts it back to an image.
+Performs the opposite of \ref unwrap().
+
+More specifically, wrap takes each column (or row if `is_column` is false) of the
+\f$m \times n\f$ input array and reshapes them into `wx` \f$\times\f$ `wy`
+patches (where \f$m =\f$ `wx` \f$\times\f$ `wy`) of the `ox` \f$\times\f$ `oy`
+output array. Wrap is typically used on an array that has been previously
+unwrapped - for example, in the case of image processing, one can unwrap an
+image, process the unwrapped array, and then compose it back into an image using
+wrap. 
+
+The figure below illustrates how wrap works. The process can be visualized as a
+moving window (orange boxes in the figure) taking a column from the input
+(top-left), reshaping it into a patch (bottom-left), and then placing that patch
+on its corresponding position in the output array (right; numbers in yellow show
+correspondence). It starts placing a patch on the output's top-left corner, then
+moves `sx` units along the column, and `sy` units along the row whenever it
+exhausts a column. If padding exists in the input array (gray-filled boxes),
+which typically happens when padding was applied on the previous unwrap, then
+`px` and `py` must be specified in order for the padding to be removed on the
+output array (in the figure, the output array on the right will actually only
+contain the inner boxes, size `ox` \f$\times\f$ `oy`).
+
+\image html wrap_distinct.png "Wrap on a 4x6 input array, using a 2x2 window, 2x2 stride, 1x1 padding. The output array is 3x4"
+
+There are some things that must be considered when wrapping a previously
+unwrapped array. First, wrap must use the same parameters that unwrap used, and
+must use the original array's size (before unwrap) as `ox` and `oy`. This is
+necessary to correctly elicit wrap's behavior as the opposite of unwrap. Second,
+one must consider whether the previous unwrap used a distinct or sliding window
+configuration, since the element-wise mapping from the input array to the output
+depends on the configuration. If the distinct window configuration (the stride is
+at least as large as the window size) was used, then the mapping is
+straightforward - each column will map to a unique section in the output array,
+and therefore each element in the input will map to a unique position in the
+output (shown in the figure above). However, in the case of the sliding window
+configuration (the stride is smaller than the window size), some of the columns
+will map to overlapping sections in the output array, and so elements from
+multiple columns will map to the same position on the output array. Recomposing
+the array then requires some way to choose between competing elements to place in
+that position. To address this contention, wrap simply sums all of the competing
+elements and places the sum in that position. The figure below illustrates this
+behavior: the fourth element of the first column and the third element of the
+second column in the input array both map to the same position on the output
+array, and thus their sum is placed on that position (this happens on the second
+and third column of the input as well - they both map to the third element of the
+second column in the output). Given this behavior, it is up to the user to
+pre-process the input (unwrapped) array (or post-process the output (wrapped)
+array) in a way that somehow takes all of the competing elements into
+consideration.
+
+\image html wrap_sliding.png "Wrap on the same array as above, but with 1x1 stride (sliding window)"
+
+For inputs that have more than two dimensions, the wrap operation will be
+applied to each 2D slice of the input. This is especially useful for
+independently processing each channel of an image (or set of images) - each
+channel (along the third dimension) on the input corresponds to the same channel
+on the output, and each image (along the fourth dimension) on the input
+corresponds to the same image on the output.
 
-The inputs to this function should be the same as the inputs used to generate the unwrapped image.
+Here are some code examples that demonstrate wrap's usage. The first one shows
+wrapping a previously unwrapped array that used a 1x1 padding and a distinct
+window configuration. Notice how the arguments used in unwrap are the same as
+those used in wrap:
+
+\snippet test/wrap.cpp ex_wrap_1
+
+The next one shows what happens when both unwrap and wrap uses the sliding window
+configuration. Notice how the original array is not recovered through wrap;
+instead, overlapping elements are summed, just as described above:
+
+\snippet test/wrap.cpp ex_wrap_2
+
+Note that the actual implementation of unwrap may not match the way the operation
+is visualized above, but the effect should be the same.
 
 =======================================================================
 
diff --git a/include/af/image.h b/include/af/image.h
index 195e8c63b8..f69e3c004d 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -602,19 +602,34 @@ AFAPI array unwrap(const array& in, const dim_t wx, const dim_t wy,
 
 #if AF_API_VERSION >= 31
 /**
-   C++ Interface wrapper for wrap
-
-   \param[in]  in is the input image (or set of images)
-   \param[in]  ox is the 0th-dimension of output
-   \param[in]  oy is the ist-dimension of output
-   \param[in]  wx is the block window size along 0th-dimension between
-   \param[in]  wy is the block window size along 1st-dimension between
-   \param[in]  sx is the stride along 0th-dimension
-   \param[in]  sy is the stride along 1st-dimension
-   \param[in]  px is the padding used along 0th-dimension between [0, wx).
-   \param[in]  py is the padding used along 1st-dimension between [0, wy).
-   \param[in]  is_column specifies the layout for the unwrapped patch. If is_column is false, the rows are treated as patches
-   \returns    an array of images after converting rows or columns into image windows
+   C++ Interface for performing the opposite of \ref unwrap()
+
+   \param[in]  in is the input array
+   \param[in]  ox is the output's dimension 0 size
+   \param[in]  oy is the output's dimension 1 size
+   \param[in]  wx is the window size along dimension 0
+   \param[in]  wy is the window size along dimension 1
+   \param[in]  sx is the stride along dimension 0
+   \param[in]  sy is the stride along dimension 1
+   \param[in]  px is the padding along dimension 0
+   \param[in]  py is the padding along dimension 1
+   \param[in]  is_column determines whether an output patch is formed from a
+               column (if true) or a row (if false)
+   \returns    an array with the input's columns (or rows) reshaped as patches
+
+   \note Wrap is typically used to recompose an unwrapped image. If this is the
+         case, use the same parameters that were used in \ref unwrap(). Also
+         use the original image size (before unwrap) for \p ox and \p oy.
+   \note The window/patch size, \p wx \f$\times\f$ \p wy, must equal
+         `input.dims(0)` (or `input.dims(1)` if \p is_column is false).
+   \note \p sx and \p sy must be at least 1
+   \note \p px and \p py must be between [0, wx) and [0, wy), respectively
+   \note The number of patches, `input.dims(1)` (or `input.dims(0)` if
+         \p is_column is false), must equal \f$nx \times\ ny\f$, where
+         \f$\displaystyle nx = \frac{ox + 2px - wx}{sx} + 1\f$ and
+         \f$\displaystyle ny = \frac{oy + 2py - wy}{sy} + 1\f$
+   \note Batched wrap can be performed on multiple 2D slices at once if \p in
+         is three or four-dimensional
 
    \ingroup image_func_wrap
 */
@@ -1370,23 +1385,37 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface wrapper for wrap
+       C Interface for performing the opposite of \ref unwrap()
 
-       \param[out] out is an array after converting
+       \param[out] out is an array with the input's columns (or rows) reshaped as
+                   patches
        \param[in]  in is the input array
-       \param[in]  ox is the 0th-dimension of \p out
-       \param[in]  oy is the ist-dimension of \p out
-       \param[in]  wx is the block window size along 0th-dimension between
-       \param[in]  wy is the block window size along 1st-dimension between
-       \param[in]  sx is the stride along 0th-dimension
-       \param[in]  sy is the stride along 1st-dimension
-       \param[in]  px is the padding used along 0th-dimension between [0, wx).
-       \param[in]  py is the padding used along 1st-dimension between [0, wy).
-       \param[in]  is_column specifies the layout for the unwrapped patch. If is_column is false, the rows are treated as the patches
+       \param[in]  ox is the output's dimension 0 size
+       \param[in]  oy is the output's dimension 1 size
+       \param[in]  wx is the window size along dimension 0
+       \param[in]  wy is the window size along dimension 1
+       \param[in]  sx is the stride along dimension 0
+       \param[in]  sy is the stride along dimension 1
+       \param[in]  px is the padding along dimension 0
+       \param[in]  py is the padding along dimension 1
+       \param[in]  is_column determines whether an output patch is formed from a
+                   column (if true) or a row (if false)
        \return     \ref AF_SUCCESS if the color transformation is successful,
        otherwise an appropriate error code is returned.
 
-       \note The padding used in \ref af_unwrap is calculated from the provided parameters
+       \note Wrap is typically used to recompose an unwrapped image. If this is the
+             case, use the same parameters that were used in \ref unwrap(). Also
+             use the original image size (before unwrap) for \p ox and \p oy.
+       \note The window/patch size, \p wx \f$\times\f$ \p wy, must equal
+             `input.dims(0)` (or `input.dims(1)` if \p is_column is false).
+       \note \p sx and \p sy must be at least 1
+       \note \p px and \p py must be between [0, wx) and [0, wy), respectively
+       \note The number of patches, `input.dims(1)` (or `input.dims(0)` if
+             \p is_column is false), must equal \f$nx \times\ ny\f$, where
+             \f$\displaystyle nx = \frac{ox + 2px - wx}{sx} + 1\f$ and
+             \f$\displaystyle ny = \frac{oy + 2py - wy}{sy} + 1\f$
+       \note Batched wrap can be performed on multiple 2D slices at once if \p in
+             is three or four-dimensional
 
        \ingroup image_func_wrap
     */
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 7384b31293..d8446c7db3 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -19,19 +19,20 @@
 #include <testHelpers.hpp>
 #include <algorithm>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
 using af::allTrue;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
+using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::randu;
 using af::range;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
 class Wrap : public ::testing::Test
@@ -202,3 +203,59 @@ TEST(Wrap, MaxDim)
 
     ASSERT_ARRAYS_EQ(output, input);
 }
+
+TEST(Wrap, DocSnippet) {
+    //! [ex_wrap_1]
+    float hA[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
+    array A(dim4(3, 3), hA);
+    //  1.     4.     7.
+    //  2.     5.     8.
+    //  3.     6.     9.
+
+    array A_unwrapped = unwrap(A,
+                               2, 2,  // window size
+                               2, 2,  // stride (distinct)
+                               1, 1); // padding
+    //  0.     0.     0.     5.
+    //  0.     0.     4.     6.
+    //  0.     2.     0.     8.
+    //  1.     3.     7.     9.
+
+    array A_wrapped = wrap(A_unwrapped,
+                           3, 3,  // A's size
+                           2, 2,  // window size
+                           2, 2,  // stride (distinct)
+                           1, 1); // padding
+    //  1.     4.     7.
+    //  2.     5.     8.
+    //  3.     6.     9.
+    //! [ex_wrap_1]
+
+    ASSERT_ARRAYS_EQ(A, A_wrapped);
+
+    //! [ex_wrap_2]
+    float hB[] = {1, 1, 1, 1, 1, 1, 1, 1, 1};
+    array B(dim4(3, 3), hB);
+    //  1.     1.     1.
+    //  1.     1.     1.
+    //  1.     1.     1.
+    array B_unwrapped = unwrap(B,
+                               2, 2,  // window size
+                               1, 1); // stride (sliding)
+    //  1.     1.     1.     1.
+    //  1.     1.     1.     1.
+    //  1.     1.     1.     1.
+    //  1.     1.     1.     1.
+    array B_wrapped = wrap(B_unwrapped,
+                           3, 3,  // B's size
+                           2, 2,  // window size
+                           1, 1); // stride (sliding)
+    //  1.     2.     1.
+    //  2.     4.     2.
+    //  1.     2.     1.
+    //! [ex_wrap_2]
+
+    float gold_hB_wrapped[] = {1, 2, 1, 2, 4, 2, 1, 2, 1};
+    array gold_B_wrapped(dim4(3, 3), gold_hB_wrapped);
+    ASSERT_ARRAYS_EQ(gold_B_wrapped, B_wrapped);
+}

From a786112937e6121aef726f2dfff9876aeab0e29f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 15 Oct 2018 14:25:22 -0400
Subject: [PATCH 1542/2677] Use sizeof(Param<T>) to determine JIT eval. Add
 NodeIterator

Uses the the size of the Param<T> object to determine when the JIT node should
be evaluated. This is required because there is a limit to the size of the
parameters you can send to a CUDA kernel.

Also created a NodeIterator which traverses the node tree. This improves the
readability of the code.
---
 src/backend/common/CMakeLists.txt     |   1 +
 src/backend/common/NodeIterator.hpp   | 107 ++++++++++++++++++++++++++
 src/backend/cpu/Array.cpp             |  20 +++--
 src/backend/cuda/Array.cpp            | 102 ++++++++++++++----------
 src/backend/cuda/CMakeLists.txt       |   1 +
 src/backend/cuda/JIT/BufferNode.hpp   |  28 +++++--
 src/backend/cuda/JIT/NaryNode.hpp     |   5 +-
 src/backend/cuda/JIT/Node.cpp         |  40 ++++++++++
 src/backend/cuda/JIT/Node.hpp         |  76 +++++++++---------
 src/backend/cuda/JIT/ScalarNode.hpp   |  11 ++-
 src/backend/cuda/JIT/ShiftNode.hpp    |  16 ++--
 src/backend/cuda/jit.cpp              |  10 +--
 src/backend/opencl/Array.cpp          |  81 +++++++++----------
 src/backend/opencl/JIT/BufferNode.hpp |  26 +++++--
 src/backend/opencl/JIT/NaryNode.hpp   |  10 ++-
 src/backend/opencl/JIT/Node.hpp       |  58 +++++++++-----
 src/backend/opencl/JIT/ScalarNode.hpp |  11 ++-
 src/backend/opencl/JIT/ShiftNode.hpp  |  16 ++--
 src/backend/opencl/jit.cpp            |   8 +-
 19 files changed, 423 insertions(+), 204 deletions(-)
 create mode 100644 src/backend/common/NodeIterator.hpp
 create mode 100644 src/backend/cuda/JIT/Node.cpp

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 339a46fc18..3e746b0bdb 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -19,6 +19,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/MatrixAlgebraHandle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/NodeIterator.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
diff --git a/src/backend/common/NodeIterator.hpp b/src/backend/common/NodeIterator.hpp
new file mode 100644
index 0000000000..3345d381ea
--- /dev/null
+++ b/src/backend/common/NodeIterator.hpp
@@ -0,0 +1,107 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <JIT/Node.hpp>
+#include <backend.hpp>
+
+#include <cstddef>
+#include <iterator>
+#include <vector>
+
+namespace common {
+  // TODO: unify all definitions of MAX_CHILDREN
+  constexpr int MAX_CHILDREN = 3;
+
+/// A node iterator that performs a breadth first traversal of the node tree
+class NodeIterator : public std::iterator<std::input_iterator_tag, detail::JIT::Node> {
+    std::vector<pointer> tree;
+    int index;
+
+    /// Copies the children of the \p n Node to the end of the tree vector
+    void copy_children_to_end(detail::JIT::Node* n) {
+        for(int i = 0; n->m_children[i] != nullptr && i < MAX_CHILDREN; i++) {
+            auto ptr = n->m_children[i].get();
+            if(find(begin(tree), end(tree), ptr) == end(tree)) {
+                tree.push_back(ptr);
+            }
+        }
+    }
+
+  public:
+    using pointer   = detail::JIT::Node*;
+    using reference = detail::JIT::Node&;
+
+    /// NodeIterator Constructor
+    ///
+    /// \param[in] root The root node of the tree
+    NodeIterator(pointer root) : tree{root}, index(0) {
+        tree.reserve(root->getHeight()*8);
+    }
+
+    /// The equality operator
+    ///
+    /// \param[in] other the rhs of the node
+    bool operator==(const NodeIterator& other) const noexcept {
+        // If the tree vector is empty in the other iterator then this means that the other
+        // iterator is a sentinel(end) node.
+        if(other.tree.empty()) {
+            // If the index is the same as the tree size then the index is past the
+            // end of the tree
+            return index == tree.size();
+        }
+        return index == other.index && tree == other.tree;
+    }
+
+    bool operator!=(const NodeIterator& other) const noexcept {
+        return !operator==(other);
+    }
+
+    /// Advances the iterator by one node in the tree
+    NodeIterator& operator++() noexcept {
+        if(index < tree.size()) {
+            copy_children_to_end(tree[index]);
+        }
+        index++;
+        return *this;
+    }
+
+    /// @copydoc operator++()
+    NodeIterator operator++(int) noexcept {
+        NodeIterator before(*this);
+        operator++();
+        return before;
+    }
+
+    /// Advances the iterator by count nodes
+    NodeIterator& operator+=(std::size_t count) noexcept {
+        while (count-- > 0) {
+            operator++();
+        }
+        return *this;
+    }
+
+    reference operator*() const noexcept {
+        return *tree[index];
+    }
+
+    pointer operator->() const noexcept {
+        return tree[index];
+    }
+
+    /// Creates a sentinel iterator. This is equivalent to the end iterator
+    NodeIterator() = default;
+    NodeIterator(const NodeIterator& other) = default;
+    NodeIterator(NodeIterator&& other) noexcept = default;
+    ~NodeIterator() noexcept = default;
+    NodeIterator& operator=(const NodeIterator& other) noexcept = default;
+    NodeIterator& operator=(NodeIterator&& other) noexcept = default;
+};
+
+}
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 664db43047..7afdcdf3ea 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -16,6 +16,7 @@
 #include <JIT/ScalarNode.hpp>
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/NodeIterator.hpp>
 #include <common/err_common.hpp>
 #include <copy.hpp>
 #include <memory.hpp>
@@ -40,6 +41,7 @@ using JIT::BufferNode;
 using JIT::Node;
 using JIT::Node_ptr;
 using JIT::Node_map_t;
+using common::NodeIterator;
 
 using af::dim4;
 using std::vector;
@@ -238,14 +240,16 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 
                 Node *n = node.get();
 
-                Node_map_t nodes_map;
-                vector<Node *> full_nodes;
-                n->getNodesMap(nodes_map, full_nodes);
-                unsigned length =0, buf_count = 0, bytes = 0;
-                for(auto &entry : nodes_map) {
-                    Node *node = entry.first;
-                    node->getInfo(length, buf_count, bytes);
-                }
+                size_t buffer_size;
+                NodeIterator it(n);
+                NodeIterator end_node;
+                size_t bytes = accumulate(it, end_node,
+                                          size_t(0),
+                                          [=](const size_t prev, const Node& n) {
+                                              // getBytes returns the size of the data Array. Sub arrays will
+                                              // be represented by their parent size.
+                                              return prev + n.getBytes();
+                                          });
 
                 if (2 * bytes > lock_bytes) {
                     out.eval();
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 9d9d4595f2..69aa3b7530 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -7,28 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <JIT/BufferNode.hpp>
+#include <common/NodeIterator.hpp>
+#include <af/dim4.hpp>
 #include <copy.hpp>
 #include <err_cuda.hpp>
-#include <JIT/BufferNode.hpp>
-#include <scalar.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
+#include <scalar.hpp>
 
 #include <cstddef>
 #include <memory>
+#include <numeric>
 
 using af::dim4;
+using cuda::JIT::BufferNode;
+using cuda::JIT::Node;
+using common::NodeIterator;
+using cuda::JIT::Node_ptr;
+using std::accumulate;
 using std::shared_ptr;
 
 namespace cuda
 {
-
-    using JIT::BufferNode;
-    using JIT::Node;
-    using JIT::Node_ptr;
-
     template<typename T>
     Node_ptr bufferNodePtr()
     {
@@ -220,46 +222,66 @@ namespace cuda
                     lock_bytes > getMaxBytes() ||
                     lock_buffers > getMaxBuffers();
 
-
                 // We eval in the following cases.
-                // 1. Too many bytes are locked up by JIT causing memory pressure.
-                // Too many bytes is assumed to be half of all bytes allocated so far.
-                // 2. Too many buffers in a nonlinear kernel cause param space overflow.
-                // Too many buffers comes out to be about 50 (51 including output).
-                // Too many buffers can occur in a tree of size 25 in the worst case scenario.
+                //
+                // 1. Too many bytes are locked up by JIT causing memory
+                //    pressure. Too many bytes is assumed to be half of all bytes
+                //    allocated so far.
+                //
+                // 2. Too many buffers in a nonlinear kernel cause param space
+                //    overflow. This happens when the number of nodes reaches 50
+                //    (51 including output). Too many buffers can occur in a tree
+                //    of size 25 in the worst case.
+                //
                 // TODO: Find better solution than the following emperical solution.
                 if (node->getHeight() > 25 || isBufferLimit) {
-
+                    // This is the size of the params that are passed by default
+                    constexpr int param_base_size = sizeof(Param<T>) + (4 * sizeof(uint));
+
+                    // This is the maximum size of the params that can be allowed by CUDA
+                    // NOTE: This number should have been (4096 - some_buffer_size) BUT
+                    // kernels who's kernel sizes come close to this value are not passing
+                    // and cuModuleLoadDataEx is failing with CUDA_ERROR_INVALID_IMAGE(200).
+                    // 35*sizeof(int) seems to be the magic number that passes all tests.
+                    // I have no idea why this is the case.
+                    constexpr int max_param_size = (4096 - (sizeof(Param<T>) + 35*sizeof(uint)));
                     Node *n = node.get();
 
-                    // Use thread local to reuse the memory every time you are here.
-                    thread_local JIT::Node_map_t nodes_map;
-                    thread_local std::vector<Node *> full_nodes;
-                    thread_local std::vector<JIT::Node_ids> full_ids;
-
-                    // Reserve some memory
-                    if (nodes_map.size() == 0) {
-                        nodes_map.reserve(1024);
-                        full_nodes.reserve(1024);
-                        full_ids.reserve(1024);
-                    }
-
-                    n->getNodesMap(nodes_map, full_nodes, full_ids);
-
-                    unsigned length = 0, buf_count = 0, bytes = 0;
-                    bool is_linear = true;
-                    dim_t dims_[] = {dims[0], dims[1], dims[2], dims[3]};
-                    for(auto &jit_node : full_nodes) {
-                        jit_node->getInfo(length, buf_count, bytes);
-                        is_linear &= jit_node->isLinear(dims_);
+                    struct  tree_info {
+                        size_t buffer_size;
+                        int num_buffers;
+                        int param_scalar_size;
+                        bool is_linear;
+                    };
+                    NodeIterator end_node;
+                    dim4 outdim = out.dims();
+                    tree_info info = accumulate(NodeIterator(n), end_node,
+                                                tree_info{0, 0, 0, true},
+                                                [=](tree_info& prev, const Node& node) {
+                                                    if(node.isBuffer()) {
+                                                        const auto& buf_node = static_cast<const BufferNode<T>&>(node);
+                                                        prev.buffer_size += buf_node.getBytes();
+                                                        prev.num_buffers++;
+                                                        prev.is_linear &= buf_node.isLinear((dim_t*)outdim.get());
+                                                    } else {
+                                                        prev.param_scalar_size += node.getParamBytes();
+                                                    }
+                                                    // getBytes returns the size of the data Array. Sub arrays will
+                                                    // be represented by their parent size.
+                                                    return prev;
+                                                });
+                    int param_size = param_base_size + info.param_scalar_size;
+                    if(info.is_linear) {
+                        param_size += info.num_buffers * sizeof(T*);
+                    } else {
+                        param_size += info.num_buffers * sizeof(Param<T>);
                     }
 
-                    // Reset the thread local vectors
-                    nodes_map.clear();
-                    full_nodes.clear();
-                    full_ids.clear();
 
-                    if (2 * bytes > lock_bytes || (!is_linear && buf_count >= 50)) {
+                    // TODO: the buffer_size check here is very conservative. It will trigger
+                    // an evaluation of the node in most cases. We should be checking the
+                    // amount of memory available to guard this eval
+                    if (param_size >= max_param_size  || info.buffer_size * 2 > lock_bytes) {
                         out.eval();
                     }
                 }
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index bdd90ed192..6bab2313ab 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -394,6 +394,7 @@ cuda_add_library(afcuda
     JIT/BinaryNode.hpp
     JIT/BufferNode.hpp
     JIT/Node.hpp
+    JIT/Node.cpp
     JIT/ScalarNode.hpp
     JIT/UnaryNode.hpp
     JIT/NaryNode.hpp
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 0c7a3d21fe..0f8327a2ed 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -38,7 +38,7 @@ namespace JIT
         {
         }
 
-        bool isBuffer() { return true; }
+        bool isBuffer() const final { return true; }
 
         void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
         {
@@ -50,7 +50,7 @@ namespace JIT
                 });
         }
 
-        bool isLinear(dim_t dims[4])
+        bool isLinear(dim_t dims[4]) const final
         {
             bool same_dims = true;
             for (int i = 0; same_dims && i < 4; i++) {
@@ -59,13 +59,13 @@ namespace JIT
             return m_linear_buffer && same_dims;
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             if (is_linear) {
                 kerStream << m_type_str << " *in" << id << "_ptr,\n";
@@ -75,7 +75,7 @@ namespace JIT
             }
         }
 
-        void setArgs(std::vector<void *> &args, bool is_linear)
+        void setArgs(std::vector<void *> &args, bool is_linear) const final
         {
             if (is_linear) {
                 args.push_back((void *)&m_param.ptr);
@@ -84,7 +84,7 @@ namespace JIT
             }
         }
 
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             std::string idx_str = std::string("int idx") + std::to_string(id);
 
@@ -106,22 +106,34 @@ namespace JIT
             }
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = "
                       << "in" << ids.id << "_ptr[idx" << ids.id << "];"
                       << "\n";
         }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
             len++;
             buf_count++;
             bytes += m_bytes;
             return;
         }
+
+        // Return the size of the size of the buffer node in bytes. Zero otherwise
+        virtual size_t getBytes() const final {
+            return m_bytes;
+        }
+
+        // Return the size of the parameter in bytes that will be passed to the
+        // kernel
+        virtual short getParamBytes() const final {
+            return m_linear_buffer ? sizeof(T*) : sizeof(Param<T>);
+        }
     };
 
+
 }
 
 }
diff --git a/src/backend/cuda/JIT/NaryNode.hpp b/src/backend/cuda/JIT/NaryNode.hpp
index c0a499136a..d7f73c85bc 100644
--- a/src/backend/cuda/JIT/NaryNode.hpp
+++ b/src/backend/cuda/JIT/NaryNode.hpp
@@ -37,7 +37,8 @@ namespace JIT
               m_op_str(op_str)
         {
         }
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             // Make the dec representation of enum part of the Kernel name
             kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
@@ -50,7 +51,7 @@ namespace JIT
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
             for (int i = 0; i < m_num_children; i++) {
diff --git a/src/backend/cuda/JIT/Node.cpp b/src/backend/cuda/JIT/Node.cpp
new file mode 100644
index 0000000000..1b4f65e84d
--- /dev/null
+++ b/src/backend/cuda/JIT/Node.cpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Node.hpp>
+
+#include <string>
+#include <vector>
+using namespace std;
+
+namespace cuda {
+namespace JIT {
+
+  int Node::getNodesMap(Node_map_t &node_map,
+                vector<const Node *> &full_nodes,
+                vector<Node_ids> &full_ids) const {
+    auto iter = node_map.find(this);
+    if (iter == node_map.end()) {
+        Node_ids ids;
+
+        for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
+            ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes,
+                                                          full_ids);
+        }
+        ids.id = node_map.size();
+        node_map[this] = ids.id;
+        full_nodes.push_back(this);
+        full_ids.push_back(ids);
+        return ids.id;
+    }
+    return iter->second;
+}
+
+  }
+}
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/cuda/JIT/Node.hpp
index 6b5c2349d1..7a42834ecf 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/cuda/JIT/Node.hpp
@@ -16,17 +16,20 @@
 #include <memory>
 #include <unordered_map>
 
+namespace common {
+    class NodeIterator;
+}
+using std::shared_ptr;
+using std::vector;
+
 namespace cuda
 {
 
 namespace JIT
 {
 
-    static const int MAX_CHILDREN = 3;
+    constexpr int MAX_CHILDREN = 3;
     class Node;
-    using std::shared_ptr;
-    using std::vector;
-    typedef shared_ptr<Node> Node_ptr;
 
     typedef struct
     {
@@ -34,63 +37,56 @@ namespace JIT
         std::array<int, MAX_CHILDREN> child_ids;
     } Node_ids;
 
-    typedef std::unordered_map<Node *, int> Node_map_t;
-    typedef Node_map_t::iterator Node_map_iter;
+    using Node_ptr = shared_ptr<Node>;
+    using Node_map_t = std::unordered_map<const Node *, int> ;
+    using Node_map_iter = Node_map_t::iterator;
 
     class Node
     {
     protected:
-        const int m_height;
         const std::string m_type_str;
         const std::string m_name_str;
         const std::array<Node_ptr, MAX_CHILDREN> m_children;
+        const int m_height;
+        friend class common::NodeIterator;
 
     public:
 
         Node(const char *type_str, const char *name_str, const int height,
              const std::array<Node_ptr, MAX_CHILDREN> children)
-            : m_height(height),
-              m_type_str(type_str),
+            : m_type_str(type_str),
               m_name_str(name_str),
-              m_children(children)
-        {}
+              m_children(children),
+              m_height(height) {}
 
         int getNodesMap(Node_map_t &node_map,
-                        vector<Node *> &full_nodes,
-                        vector<Node_ids> &full_ids)
-        {
-            auto iter = node_map.find(this);
-            if (iter == node_map.end()) {
-                Node_ids ids;
-                for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
-                    ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
-                }
-                ids.id = node_map.size();
-                node_map[this] = ids.id;
-                full_nodes.push_back(this);
-                full_ids.push_back(ids);
-                return ids.id;
-            }
-            return iter->second;
-        }
+                        vector<const Node *> &full_nodes,
+                        vector<Node_ids> &full_ids) const;
 
-        virtual void genKerName (std::stringstream &kerStream, Node_ids ids) {}
-        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) {}
-        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) {}
-        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) {}
+        virtual void genKerName (std::stringstream &kerStream, Node_ids ids) const {}
+        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const {}
+        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const {}
+        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) const {}
 
-        virtual void setArgs (std::vector<void *> &args, bool is_linear) { }
+        virtual void setArgs (std::vector<void *> &args, bool is_linear) const { }
 
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
-        {
+        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const {
             len++;
         }
 
-        virtual bool isBuffer() { return false; }
-        virtual bool isLinear(dim_t dims[4]) { return true; }
-        std::string getTypeStr() { return m_type_str; }
-        int getHeight()  { return m_height; }
-        std::string getNameStr() { return m_name_str; }
+        // Return the size of the parameter in bytes that will be passed to the
+        // kernel
+        virtual short getParamBytes() const {
+            return 0;
+        }
+
+        // Return the size of the size of the buffer node in bytes. Zero otherwise
+        virtual size_t getBytes() const { return 0; }
+        virtual bool isBuffer() const { return false; }
+        virtual bool isLinear(dim_t dims[4]) const { return true; }
+        std::string getTypeStr() const { return m_type_str; }
+        int getHeight()  const { return m_height; }
+        std::string getNameStr() const { return m_name_str; }
 
         virtual ~Node() {}
     };
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index aae0496ec3..ed25fb0439 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -33,28 +33,31 @@ namespace JIT
         {
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             kerStream << m_type_str << " scalar" << id << ", " << "\n";
         }
 
-        void setArgs(std::vector<void *> &args, bool is_linear)
+        void setArgs(std::vector<void *> &args, bool is_linear) const final
         {
             args.push_back((void *)&m_val);
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = "
                       << "scalar" << ids.id << ";"
                       << "\n";
         }
+
+        // Return the info for the params and the size of the buffers
+        virtual short getParamBytes() const final { return static_cast<short>(sizeof(T)); }
     };
 
 }
diff --git a/src/backend/cuda/JIT/ShiftNode.hpp b/src/backend/cuda/JIT/ShiftNode.hpp
index 80f88179d5..33781b1cc8 100644
--- a/src/backend/cuda/JIT/ShiftNode.hpp
+++ b/src/backend/cuda/JIT/ShiftNode.hpp
@@ -38,26 +38,24 @@ namespace JIT
         {
         }
 
-        bool isBuffer() { return false; }
-
         void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode<T> *>(node_ptr)->setData(param, data, bytes, is_linear);
         }
 
-        bool isLinear(dim_t dims[4])
+        bool isLinear(dim_t dims[4]) const final
         {
             return false;
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode<T> *>(node_ptr)->genParams(kerStream, id, is_linear);
@@ -66,7 +64,7 @@ namespace JIT
             }
         }
 
-        void setArgs(std::vector<void *> &args, bool is_linear)
+        void setArgs(std::vector<void *> &args, bool is_linear) const final
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode<T> *>(node_ptr)->setArgs(args, is_linear);
@@ -76,7 +74,7 @@ namespace JIT
             }
         }
 
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             std::string idx_str = std::string("idx") + std::to_string(id);
             std::string info_str = std::string("in") + std::to_string(id);
@@ -107,14 +105,14 @@ namespace JIT
             kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = "
                       << "in" << ids.id << "_ptr[idx" << ids.id << "];"
                       << "\n";
         }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode<T> *>(node_ptr)->getInfo(len, buf_count, bytes);
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 1735036393..9de916865a 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -48,7 +48,7 @@ using std::unique_ptr;
 using std::vector;
 
 static string getFuncName(const vector<Node *> &output_nodes,
-                          const vector<Node *> &full_nodes,
+                          const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids,
                           bool is_linear)
 {
@@ -74,7 +74,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
 }
 
 static string getKernelString(const string funcName,
-                              const vector<Node *> &full_nodes,
+                              const vector<const Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
                               const vector<int> &output_ids,
                               bool is_linear)
@@ -333,7 +333,7 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
     CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.data(),
                                 ptx.size(), ker_name, 0, NULL, NULL));
 
-    void *cubin;
+    void *cubin = nullptr;
     size_t cubinSize;
 
     CUmodule module;
@@ -348,7 +348,7 @@ static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
 
 static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<int> &output_ids,
-                            const vector<Node *> &full_nodes,
+                            const vector<const Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear)
 {
@@ -383,7 +383,7 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
-    thread_local vector<Node *> full_nodes;
+    thread_local vector<const Node *> full_nodes;
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 8d1b0e11cf..1537392add 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -7,31 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <copy.hpp>
-#include <scalar.hpp>
 #include <JIT/BufferNode.hpp>
+#include <af/dim4.hpp>
+#include <af/opencl.h>
+#include <common/NodeIterator.hpp>
+#include <common/util.hpp>
+#include <copy.hpp>
 #include <err_opencl.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
+#include <scalar.hpp>
+
 #include <cstddef>
-#include <af/opencl.h>
-#include <common/util.hpp>
+#include <numeric>
 
 using af::dim4;
+using common::NodeIterator;
+using opencl::JIT::BufferNode;
+using opencl::JIT::Node;
+using opencl::JIT::Node_ptr;
+using std::accumulate;
 
 namespace opencl
 {
-    using JIT::BufferNode;
-    using JIT::Node;
-    using JIT::Node_ptr;
-
     template<typename T>
     Node_ptr bufferNodePtr()
     {
-        return Node_ptr(new BufferNode(dtype_traits<T>::getName(),
-                                       shortname<T>(true)));
+        return std::make_shared<BufferNode>(dtype_traits<T>::getName(), shortname<T>(true));
     }
 
     template<typename T>
@@ -255,38 +258,38 @@ namespace opencl
                 // TODO: Find better solution than the following emperical solution.
                 bool isParamLimit = (isNvidia && node->getHeight() > 24);
                 if (isParamLimit || isBufferLimit) {
+                    // This is the maximum non-linear buffers that are allowed in
+                    // the parameter list
+                    constexpr int max_nonlinear_buffer_count = 48;
 
                     Node *n = node.get();
 
-                    // Use thread local to reuse the memory every time you are here.
-                    thread_local JIT::Node_map_t nodes_map;
-                    thread_local std::vector<Node *> full_nodes;
-                    thread_local std::vector<JIT::Node_ids> full_ids;
-
-                    // Reserve some memory
-                    if (nodes_map.size() == 0) {
-                        nodes_map.reserve(1024);
-                        full_nodes.reserve(1024);
-                        full_ids.reserve(1024);
-                    }
-
-                    n->getNodesMap(nodes_map, full_nodes, full_ids);
-
-                    unsigned length = 0, buf_count = 0, bytes = 0;
-                    bool is_linear = true;
-                    dim_t dims_[] = {dims[0], dims[1], dims[2], dims[3]};
-                    for(auto &jit_node : full_nodes) {
-                        jit_node->getInfo(length, buf_count, bytes);
-                        is_linear &= jit_node->isLinear(dims_);
-                    }
-
-                    // Reset the thread local vectors
-                    nodes_map.clear();
-                    full_nodes.clear();
-                    full_ids.clear();
+                    struct  tree_info {
+                        size_t buffer_size;
+                        int num_buffers;
+                        bool is_linear;
+                    };
+                    NodeIterator it(n);
+                    NodeIterator end_node;
+                    dim4 outdim = out.dims();
+                    tree_info info = accumulate(it, end_node,
+                                                tree_info{0, 0, true},
+                                                [=](tree_info& prev, Node& n) {
+                                                    if(n.isBuffer()) {
+                                                        auto& buf_node = static_cast<BufferNode&>(n);
+                                                        prev.buffer_size += buf_node.getBytes();
+                                                        prev.num_buffers++;
+                                                        prev.is_linear &= buf_node.isLinear((dim_t*)outdim.get());
+                                                    }
+                                                    // getBytes returns the size of the data Array. Sub arrays will
+                                                    // be represented by their parent size.
+                                                    return prev;
+                                                });
+                    isBufferLimit = 2 * info.buffer_size > lock_bytes;
+                    isParamLimit = isNvidia &&
+                                   !info.is_linear &&
+                                   info.num_buffers >= max_nonlinear_buffer_count;
 
-                    isBufferLimit = 2 * bytes > lock_bytes;
-                    isParamLimit = isNvidia && !is_linear && buf_count >= 48;
                     if (isBufferLimit || isParamLimit) {
                         out.eval();
                     }
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index 9e856312e6..c44524df84 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -38,7 +38,9 @@ namespace JIT
         {
         }
 
-        bool isBuffer() { return true; }
+        bool isBuffer() const final {
+            return true;
+        }
 
         void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
         {
@@ -50,7 +52,7 @@ namespace JIT
                 });
         }
 
-        bool isLinear(dim_t dims[4])
+        bool isLinear(dim_t dims[4]) const final
         {
             bool same_dims = true;
             for (int i = 0; same_dims && i < 4; i++) {
@@ -59,13 +61,13 @@ namespace JIT
             return m_linear_buffer && same_dims;
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             if (!is_linear) {
                 kerStream << "__global " << m_type_str << " *in" << id
@@ -76,7 +78,7 @@ namespace JIT
             }
         }
 
-        int setArgs(cl::Kernel &ker, int id, bool is_linear)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
         {
             ker.setArg(id + 0, *m_data);
             if (!is_linear) {
@@ -87,7 +89,7 @@ namespace JIT
             return id + 2;
         }
 
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             std::string idx_str = std::string("int idx") + std::to_string(id);
             std::string info_str = std::string("iInfo") + std::to_string(id);
@@ -108,19 +110,27 @@ namespace JIT
             }
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        // Return the size of the parameter in bytes that will be passed to the
+        // kernel
+        virtual short getParamBytes() const final {
+            return m_linear_buffer ? sizeof(void*) : sizeof(KParam);
+        }
+
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = "
                       << "in" << ids.id << "[idx" << ids.id << "];"
                       << "\n";
         }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
             len++;
             buf_count++;
             bytes += m_bytes;
         }
+
+        size_t getBytes() const final { return m_bytes; }
     };
 
 }
diff --git a/src/backend/opencl/JIT/NaryNode.hpp b/src/backend/opencl/JIT/NaryNode.hpp
index 3cc8765a40..544e438b1d 100644
--- a/src/backend/opencl/JIT/NaryNode.hpp
+++ b/src/backend/opencl/JIT/NaryNode.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include "Node.hpp"
 #include <iomanip>
+#include <utility>
 
 namespace opencl
 {
@@ -29,15 +30,16 @@ namespace JIT
                  const char *name_str,
                  const char *op_str,
                  const int num_children,
-                 const std::array<Node_ptr, MAX_CHILDREN> &children,
+                 const std::array<Node_ptr, MAX_CHILDREN>&& children,
                  const int op, const int height)
-            : Node(out_type_str, name_str, height, children),
+          : Node(out_type_str, name_str, height,
+                 std::forward<const std::array<Node_ptr, MAX_CHILDREN>>(children)),
               m_num_children(num_children),
               m_op(op),
               m_op_str(op_str)
         {
         }
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             // Make the dec representation of enum part of the Kernel name
             kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
@@ -50,7 +52,7 @@ namespace JIT
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
             for (int i = 0; i < m_num_children; i++) {
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index 8f69a4452a..22006f7c9b 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -16,17 +16,21 @@
 #include <memory>
 #include <unordered_map>
 
+using std::shared_ptr;
+using std::vector;
+
+namespace common {
+    class NodeIterator;
+}
+
 namespace opencl
 {
 
 namespace JIT
 {
 
-    static const int MAX_CHILDREN = 3;
+    constexpr int MAX_CHILDREN = 3;
     class Node;
-    using std::shared_ptr;
-    using std::vector;
-    typedef shared_ptr<Node> Node_ptr;
 
     typedef struct
     {
@@ -34,8 +38,9 @@ namespace JIT
         std::array<int, MAX_CHILDREN> child_ids;
     } Node_ids;
 
-    typedef std::unordered_map<Node *, int> Node_map_t;
-    typedef Node_map_t::iterator Node_map_iter;
+    using Node_ptr = shared_ptr<Node>;
+    using Node_map_t = std::unordered_map<const Node *, int>;
+    using Node_map_iter = Node_map_t::iterator;
 
     class Node
     {
@@ -44,11 +49,14 @@ namespace JIT
         const std::string m_name_str;
         const int m_height;
         const std::array<Node_ptr, MAX_CHILDREN> m_children;
+        friend common::NodeIterator;
 
     public:
 
+        virtual bool isBuffer() const { return false; }
+
         Node(const char *type_str, const char *name_str, const int height,
-             const std::array<Node_ptr, MAX_CHILDREN> children)
+             const std::array<Node_ptr, MAX_CHILDREN>&& children)
             : m_type_str(type_str),
               m_name_str(name_str),
               m_height(height),
@@ -56,8 +64,8 @@ namespace JIT
         {}
 
         int getNodesMap(Node_map_t &node_map,
-                        vector<Node *> &full_nodes,
-                        vector<Node_ids> &full_ids)
+                        vector<const Node *> &full_nodes,
+                        vector<Node_ids> &full_ids) const
         {
             auto iter = node_map.find(this);
             if (iter == node_map.end()) {
@@ -74,25 +82,35 @@ namespace JIT
             return iter->second;
         }
 
-        virtual void genKerName(std::stringstream &kerStream, Node_ids ids) {}
-        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) {}
-        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) {}
-        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) {}
+        virtual void genKerName(std::stringstream &kerStream, Node_ids ids) const {}
+        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const {}
+        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const {}
+        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) const {}
 
-        virtual int setArgs (cl::Kernel &ker, int id, bool is_linear) { return id; }
+        virtual int setArgs (cl::Kernel &ker, int id, bool is_linear) const { return id; }
 
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const
         {
             len++;
         }
 
-        virtual bool isBuffer() { return false; }
-        virtual bool isLinear(dim_t dims[4]) { return true; }
-        std::string getTypeStr() { return m_type_str; }
-        int getHeight()  { return m_height; }
-        std::string getNameStr() { return m_name_str; }
+        // Return the size of the parameter in bytes that will be passed to the
+        // kernel
+        virtual short getParamBytes() const {
+            return 0;
+        }
+
+        virtual bool isLinear(dim_t dims[4]) const { return true; }
+        std::string getTypeStr() const { return m_type_str; }
+        int getHeight() const  { return m_height; }
+        virtual size_t getBytes() const { return 0; }
+        std::string getNameStr() const { return m_name_str; }
 
         virtual ~Node() {}
+        Node(const Node& other) = delete;
+        Node(const Node&& other) = delete;
+        Node& operator=(const Node& other) = delete;
+        Node& operator=(const Node&& other) = delete;
     };
 }
 
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
index e3e269e1fd..34ddd4c2a2 100644
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ b/src/backend/opencl/JIT/ScalarNode.hpp
@@ -33,29 +33,32 @@ namespace JIT
         {
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             kerStream << m_type_str << " scalar" << id << ", " << "\n";
         }
 
-        int setArgs(cl::Kernel &ker, int id, bool is_linear)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
         {
             ker.setArg(id, m_val);
             return id + 1;
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = "
                       << "scalar" << ids.id << ";"
                       << "\n";
         }
+
+        // Return the info for the params and the size of the buffers
+        virtual short getParamBytes() const final { return static_cast<short>(sizeof(T)); }
     };
 
 }
diff --git a/src/backend/opencl/JIT/ShiftNode.hpp b/src/backend/opencl/JIT/ShiftNode.hpp
index bcba20c01d..9e92c190da 100644
--- a/src/backend/opencl/JIT/ShiftNode.hpp
+++ b/src/backend/opencl/JIT/ShiftNode.hpp
@@ -37,26 +37,24 @@ namespace JIT
         {
         }
 
-        bool isBuffer() { return false; }
-
         void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode *>(node_ptr)->setData(info, data, bytes, is_linear);
         }
 
-        bool isLinear(dim_t dims[4])
+        bool isLinear(dim_t dims[4]) const final
         {
             return false;
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids)
+        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear)
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode *>(node_ptr)->genParams(kerStream, id, is_linear);
@@ -65,7 +63,7 @@ namespace JIT
             }
         }
 
-        int setArgs(cl::Kernel &ker, int id, bool is_linear)
+        int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
         {
             auto node_ptr = m_buffer_node.get();
             int curr_id = dynamic_cast<BufferNode *>(node_ptr)->setArgs(ker, id, is_linear);
@@ -75,7 +73,7 @@ namespace JIT
             return curr_id + 4;
         }
 
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear)
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
         {
             std::string idx_str = std::string("idx") + std::to_string(id);
             std::string info_str = std::string("iInfo") + std::to_string(id);
@@ -105,14 +103,14 @@ namespace JIT
                       << "\n";
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids)
+        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = "
                       << "in" << ids.id << "[idx" << ids.id << "];"
                       << "\n";
         }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes)
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
             auto node_ptr = m_buffer_node.get();
             dynamic_cast<BufferNode *>(node_ptr)->getInfo(len, buf_count, bytes);
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 60069f8a0b..d13342fae3 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -39,7 +39,7 @@ using std::stringstream;
 using std::vector;
 
 static string getFuncName(const vector<Node *> &output_nodes,
-                          const vector<Node *> &full_nodes,
+                          const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids,
                           bool is_linear)
 {
@@ -66,7 +66,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
 }
 
 static string getKernelString(const string funcName,
-                              const vector<Node *> &full_nodes,
+                              const vector<const Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
                               const vector<int> &output_ids,
                               bool is_linear)
@@ -167,7 +167,7 @@ static string getKernelString(const string funcName,
 
 static Kernel getKernel(const vector<Node *> &output_nodes,
                         const vector<int> &output_ids,
-                        const vector<Node *> &full_nodes,
+                        const vector<const Node *> &full_nodes,
                         const vector<Node_ids> &full_ids,
                         const bool is_linear)
 {
@@ -205,7 +205,7 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
-    thread_local vector<Node *> full_nodes;
+    thread_local vector<const Node *> full_nodes;
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 

From 9444a8b3d97ba7f57aa4215db21ae7d7c02f1106 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 21 Oct 2018 03:05:45 -0400
Subject: [PATCH 1543/2677] Cleanup shift and select nodes

---
 src/backend/cuda/JIT/BinaryNode.hpp   |   2 -
 src/backend/cuda/JIT/BufferNode.hpp   |  24 ++----
 src/backend/cuda/JIT/NaryNode.hpp     |   4 -
 src/backend/cuda/JIT/ScalarNode.hpp   |   7 +-
 src/backend/cuda/JIT/ShiftNode.hpp    |  42 +++------
 src/backend/cuda/JIT/UnaryNode.hpp    |   2 -
 src/backend/cuda/select.cu            |  91 ++++++++++++++++----
 src/backend/cuda/select.hpp           |  42 ++-------
 src/backend/cuda/shift.cpp            |  30 +++++--
 src/backend/opencl/JIT/BufferNode.hpp |   4 +-
 src/backend/opencl/JIT/Node.cpp       |  47 ++++++++++
 src/backend/opencl/JIT/Node.hpp       |   6 +-
 src/backend/opencl/JIT/ShiftNode.hpp  |  41 ++++-----
 src/backend/opencl/select.cpp         | 119 ++++++++++++++++++++------
 src/backend/opencl/select.hpp         |  44 ++--------
 src/backend/opencl/shift.cpp          |  33 +++++--
 16 files changed, 317 insertions(+), 221 deletions(-)
 create mode 100644 src/backend/opencl/JIT/Node.cpp

diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
index fd897cc809..f58b84e945 100644
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ b/src/backend/cuda/JIT/BinaryNode.hpp
@@ -16,7 +16,6 @@ namespace cuda
 
 namespace JIT
 {
-
     class BinaryNode : public NaryNode
     {
     public:
@@ -28,7 +27,6 @@ namespace JIT
         {
         }
     };
-
 }
 
 }
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
index 0f8327a2ed..1850e59839 100644
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ b/src/backend/cuda/JIT/BufferNode.hpp
@@ -18,8 +18,6 @@ namespace cuda
 
 namespace JIT
 {
-
-
     template<typename T>
     class BufferNode : public Node
     {
@@ -70,8 +68,7 @@ namespace JIT
             if (is_linear) {
                 kerStream << m_type_str << " *in" << id << "_ptr,\n";
             } else {
-                kerStream << "Param<" << m_type_str << "> in" << id
-                          << ",\n";
+                kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
             }
         }
 
@@ -92,25 +89,18 @@ namespace JIT
                 kerStream << idx_str << " = idx;\n";
             } else {
                 std::string info_str = std::string("in") + std::to_string(id);
-                kerStream << idx_str << " = "
-                          << "(id3 < " << info_str << ".dims[3]) * "
-                          << info_str << ".strides[3] * id3 + "
-                          << "(id2 < " << info_str << ".dims[2]) * "
-                          << info_str << ".strides[2] * id2 + "
-                          << "(id1 < " << info_str << ".dims[1]) * "
-                          << info_str << ".strides[1] * id1 + "
-                          << "(id0 < " << info_str << ".dims[0]) * "
-                          << "id0;"
-                          << "\n";
+                kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                          << info_str << ".strides[3] * id3 + (id2 < " << info_str << ".dims[2]) * "
+                          << info_str << ".strides[2] * id2 + (id1 < " << info_str << ".dims[1]) * "
+                          << info_str << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * id0;\n";
                 kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
             }
         }
 
         void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << "in" << ids.id << "_ptr[idx" << ids.id << "];"
-                      << "\n";
+            kerStream << m_type_str << " val" << ids.id
+                      << " = in" << ids.id << "_ptr[idx" << ids.id << "];\n";
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
diff --git a/src/backend/cuda/JIT/NaryNode.hpp b/src/backend/cuda/JIT/NaryNode.hpp
index d7f73c85bc..a3a650796d 100644
--- a/src/backend/cuda/JIT/NaryNode.hpp
+++ b/src/backend/cuda/JIT/NaryNode.hpp
@@ -13,10 +13,8 @@
 
 namespace cuda
 {
-
 namespace JIT
 {
-
     class NaryNode : public Node
     {
     private:
@@ -61,7 +59,5 @@ namespace JIT
             kerStream << ");\n";
         }
     };
-
 }
-
 }
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/cuda/JIT/ScalarNode.hpp
index ed25fb0439..bb249b9fec 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/cuda/JIT/ScalarNode.hpp
@@ -41,7 +41,7 @@ namespace JIT
 
         void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
-            kerStream << m_type_str << " scalar" << id << ", " << "\n";
+            kerStream << m_type_str << " scalar" << id << ", \n";
         }
 
         void setArgs(std::vector<void *> &args, bool is_linear) const final
@@ -51,9 +51,8 @@ namespace JIT
 
         void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << "scalar" << ids.id << ";"
-                      << "\n";
+            kerStream << m_type_str << " val" << ids.id
+                      << " = scalar" << ids.id << ";\n";
         }
 
         // Return the info for the params and the size of the buffers
diff --git a/src/backend/cuda/JIT/ShiftNode.hpp b/src/backend/cuda/JIT/ShiftNode.hpp
index 33781b1cc8..d52cf7da8f 100644
--- a/src/backend/cuda/JIT/ShiftNode.hpp
+++ b/src/backend/cuda/JIT/ShiftNode.hpp
@@ -15,22 +15,20 @@
 
 namespace cuda
 {
-
 namespace JIT
 {
     template<typename T>
     class ShiftNode : public Node
     {
     private:
-
-        Node_ptr m_buffer_node;
+        std::shared_ptr<BufferNode<T>> m_buffer_node;
         const std::array<int, 4> m_shifts;
 
     public:
 
         ShiftNode(const char *type_str,
                   const char *name_str,
-                  Node_ptr buffer_node,
+                  std::shared_ptr<BufferNode<T>> buffer_node,
                   const std::array<int, 4> shifts)
             : Node(type_str, name_str, 0, {}),
               m_buffer_node(buffer_node),
@@ -40,8 +38,7 @@ namespace JIT
 
         void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode<T> *>(node_ptr)->setData(param, data, bytes, is_linear);
+            m_buffer_node->setData(param, data, bytes, is_linear);
         }
 
         bool isLinear(dim_t dims[4]) const final
@@ -57,8 +54,7 @@ namespace JIT
 
         void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode<T> *>(node_ptr)->genParams(kerStream, id, is_linear);
+            m_buffer_node->genParams(kerStream, id, is_linear);
             for (int i = 0; i < 4; i++) {
                 kerStream << "int shift" << id << "_" << i << ",\n";
             }
@@ -66,8 +62,7 @@ namespace JIT
 
         void setArgs(std::vector<void *> &args, bool is_linear) const final
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode<T> *>(node_ptr)->setArgs(args, is_linear);
+            m_buffer_node->setArgs(args, is_linear);
             for (int i = 0; i < 4; i++) {
                 const int &d = m_shifts[i];
                 args.push_back((void *)&d);
@@ -85,39 +80,30 @@ namespace JIT
                 kerStream << "int " << id_str << i
                           << " = __circular_mod(id" << i
                           << " + " << shift_str << i
-                          << ", " << info_str << ".dims[" << i << "]"
-                          << ");\n";
+                          << ", " << info_str << ".dims[" << i << "]);\n";
             }
 
-            kerStream << "int " << idx_str << " = "
-                      << "(" << id_str << "3 < " << info_str << ".dims[3]) * "
+            kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str << ".dims[3]) * "
                       << info_str << ".strides[3] * " << id_str << "3;\n";
-            kerStream << idx_str  << " += "
-                      << "(" << id_str << "2 < " << info_str << ".dims[2]) * "
+            kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
                       << info_str << ".strides[2] * " << id_str << "2;\n";
-            kerStream << idx_str  << " += "
-                      << "(" << id_str << "1 < " << info_str << ".dims[1]) * "
+            kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
                       << info_str << ".strides[1] * " << id_str << "1;\n";
-            kerStream << idx_str  << " += "
-                      << "(" << id_str << "0 < " << info_str << ".dims[0]) * "
-                      << id_str << "0;"
-                      << "\n";
+            kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
+                      << id_str << "0;\n";
             kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
         }
 
         void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << "in" << ids.id << "_ptr[idx" << ids.id << "];"
-                      << "\n";
+            kerStream << m_type_str << " val" << ids.id
+                      << " = in" << ids.id << "_ptr[idx" << ids.id << "];\n";
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode<T> *>(node_ptr)->getInfo(len, buf_count, bytes);
+            m_buffer_node->getInfo(len, buf_count, bytes);
         }
     };
 }
-
 }
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
index 9d814ac4bf..6bdecc0f0f 100644
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ b/src/backend/cuda/JIT/UnaryNode.hpp
@@ -16,7 +16,6 @@ namespace cuda
 
 namespace JIT
 {
-
     class UnaryNode : public NaryNode
     {
     public:
@@ -28,7 +27,6 @@ namespace JIT
         {
         }
     };
-
 }
 
 }
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index 41741284e9..a5d6e6fe2a 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -8,24 +8,83 @@
  ********************************************************/
 #include <Array.hpp>
 #include <select.hpp>
+#include <scalar.hpp>
 #include <err_cuda.hpp>
 #include <kernel/select.hpp>
+#include <JIT/NaryNode.hpp>
 
 namespace cuda
 {
     template<typename T>
-    void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b)
+    void select(Array<T> &out,
+                const Array<char> &cond,
+                const Array<T> &a, const Array<T> &b)
     {
         kernel::select<T>(out, cond, a, b, out.ndims());
     }
 
     template<typename T, bool flip>
-    void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b)
+    void select_scalar(Array<T> &out,
+                       const Array<char> &cond,
+                       const Array<T> &a, const double &b)
     {
         kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
     }
 
-#define INSTANTIATE(T)                                              \
+    template<typename T>
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const Array<T> &b,
+                              const af::dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        auto b_node = b.getNode();
+        int height = std::max(a_node->getHeight(), b_node->getHeight());
+        height = std::max(height, cond_node->getHeight()) + 1;
+
+        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
+                                                "__select", 3, {{cond_node, a_node, b_node}},
+                                                (int)af_select_t, height);
+
+        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        return out;
+    }
+
+    template<typename T, bool flip>
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const double &b_val,
+                              const af::dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
+        auto b_node = b.getNode();
+        int height = std::max(a_node->getHeight(), b_node->getHeight());
+        height = std::max(height, cond_node->getHeight()) + 1;
+
+        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
+                                                flip ? "__not_select" : "__select",
+                                                3, {{cond_node, a_node, b_node}},
+                                                (int)(flip ? af_not_select_t : af_select_t),
+                                                height);
+
+        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        return out;
+    }
+
+#define INSTANTIATE(T)                                                    \
+    template                                                              \
+    Array<T> createSelectNode<T>(const Array<char> &cond,                 \
+                                 const Array<T> &a, const Array<T> &b,    \
+                                 const af::dim4 &odims);                  \
+    template                                                              \
+    Array<T> createSelectNode<T, true>(const Array<char> &cond,           \
+                                       const Array<T> &a, const double &b_val, \
+                                       const af::dim4 &odims);            \
+    template                                                              \
+    Array<T> createSelectNode<T, false>(const Array<char> &cond,          \
+                                        const Array<T> &a, const double &b_val, \
+                                        const af::dim4 &odims);           \
     template void select<T>(Array<T> &out, const Array<char> &cond, \
                             const Array<T> &a, const Array<T> &b);  \
     template void select_scalar<T, true >(Array<T> &out,            \
@@ -35,18 +94,18 @@ namespace cuda
     template void select_scalar<T, false>(Array<T> &out, const      \
                                           Array<char> &cond,        \
                                           const Array<T> &a,        \
-                                          const double &b);         \
+                                          const double &b)
 
-    INSTANTIATE(float  )
-    INSTANTIATE(double )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(char   )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+    INSTANTIATE(float  );
+    INSTANTIATE(double );
+    INSTANTIATE(cfloat );
+    INSTANTIATE(cdouble);
+    INSTANTIATE(int    );
+    INSTANTIATE(uint   );
+    INSTANTIATE(intl   );
+    INSTANTIATE(uintl  );
+    INSTANTIATE(char   );
+    INSTANTIATE(uchar  );
+    INSTANTIATE(short  );
+    INSTANTIATE(ushort );
 }
diff --git a/src/backend/cuda/select.hpp b/src/backend/cuda/select.hpp
index 1184dfe3ae..cd15509d51 100644
--- a/src/backend/cuda/select.hpp
+++ b/src/backend/cuda/select.hpp
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <JIT/NaryNode.hpp>
-#include <scalar.hpp>
+#include <af/dim4.hpp>
 #include <Array.hpp>
 
 namespace cuda
@@ -20,39 +19,12 @@ namespace cuda
     void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
 
     template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const Array<T> &b, const af::dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        auto b_node = b.getNode();
-        int height = std::max(a_node->getHeight(), b_node->getHeight());
-        height = std::max(height, cond_node->getHeight()) + 1;
-
-        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
-                                                "__select", 3, {{cond_node, a_node, b_node}},
-                                                (int)af_select_t, height);
-
-        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
-        return out;
-    }
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const Array<T> &b,
+                              const af::dim4 &odims);
 
     template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const double &b_val, const af::dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
-        auto b_node = b.getNode();
-        int height = std::max(a_node->getHeight(), b_node->getHeight());
-        height = std::max(height, cond_node->getHeight()) + 1;
-
-        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
-                                                flip ? "__not_select" : "__select",
-                                                3, {{cond_node, a_node, b_node}},
-                                                (int)(flip ? af_not_select_t : af_select_t),
-                                                height);
-
-        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
-        return out;
-    }
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const double &b_val,
+                              const af::dim4 &odims);
 }
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index f8785f4edd..65a2cfebed 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -7,12 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <JIT/BufferNode.hpp>
 #include <JIT/ShiftNode.hpp>
 #include <Array.hpp>
 #include <shift.hpp>
-#include <stdexcept>
 #include <err_cuda.hpp>
 
+#include <memory>
+
+using af::dim4;
+
+using cuda::JIT::BufferNode;
+using cuda::JIT::Node_ptr;
+using cuda::JIT::ShiftNode;
+
+using std::array;
+using std::make_shared;
+using std::static_pointer_cast;
+using std::string;
+
 namespace cuda
 {
     template<typename T>
@@ -23,12 +36,12 @@ namespace cuda
         // Force input to be evaluated so that in is always a buffer.
         in.eval();
 
-        std::string name_str("Sh");
+        string name_str("Sh");
         name_str += shortname<T>(true);
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
+        const dim4 iDims = in.dims();
+        dim4 oDims = iDims;
 
-        std::array<int, 4> shifts;
+        array<int, 4> shifts;
         for(int i = 0; i < 4; i++) {
             // sdims_[i] will always be positive and always [0, oDims[i]].
             // Negative shifts are converted to position by going the other way round
@@ -36,9 +49,10 @@ namespace cuda
             assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
         }
 
-        auto node = new JIT::ShiftNode<T>(getFullName<T>(), name_str.c_str(),
-                                          in.getNode(), shifts);
-        return createNodeArray<T>(oDims, JIT::Node_ptr(node));
+        auto node = make_shared<ShiftNode<T>>(getFullName<T>(), name_str.c_str(),
+                                              static_pointer_cast<BufferNode<T>>(in.getNode()),
+                                              shifts);
+        return createNodeArray<T>(oDims, Node_ptr(node));
     }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
index c44524df84..118280f521 100644
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ b/src/backend/opencl/JIT/BufferNode.hpp
@@ -71,10 +71,10 @@ namespace JIT
         {
             if (!is_linear) {
                 kerStream << "__global " << m_type_str << " *in" << id
-                          << ", KParam iInfo" << id << ", " << "\n";
+                          << ", KParam iInfo" << id << ", \n";
             } else {
                 kerStream << "__global " << m_type_str << " *in" << id
-                          << ", dim_t iInfo" << id << "_offset, " << "\n";
+                          << ", dim_t iInfo" << id << "_offset, \n";
             }
         }
 
diff --git a/src/backend/opencl/JIT/Node.cpp b/src/backend/opencl/JIT/Node.cpp
new file mode 100644
index 0000000000..ca193e70ed
--- /dev/null
+++ b/src/backend/opencl/JIT/Node.cpp
@@ -0,0 +1,47 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <JIT/Node.hpp>
+
+#include <spdlog/fmt/ostr.h>
+
+#include <string>
+#include <vector>
+
+using std::vector;
+
+namespace opencl {
+namespace JIT {
+
+int Node::getNodesMap(Node_map_t &node_map,
+                      vector<const Node *> &full_nodes,
+                      vector<Node_ids> &full_ids) const
+{
+    auto iter = node_map.find(this);
+    if (iter == node_map.end()) {
+        Node_ids ids;
+        for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
+            ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
+        }
+        ids.id = node_map.size();
+        node_map[this] = ids.id;
+        full_nodes.push_back(this);
+        full_ids.push_back(ids);
+        return ids.id;
+    }
+    return iter->second;
+}
+
+void Node::genKerName(std::stringstream &kerStream, Node_ids ids) const
+{
+    fmt::print(kerStream, "_{0}{1:0<3}", m_name_str, ids.id);
+}
+
+}  // JIT
+}  // opencl
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
index 22006f7c9b..3df8a1c85a 100644
--- a/src/backend/opencl/JIT/Node.hpp
+++ b/src/backend/opencl/JIT/Node.hpp
@@ -106,11 +106,7 @@ namespace JIT
         virtual size_t getBytes() const { return 0; }
         std::string getNameStr() const { return m_name_str; }
 
-        virtual ~Node() {}
-        Node(const Node& other) = delete;
-        Node(const Node&& other) = delete;
-        Node& operator=(const Node& other) = delete;
-        Node& operator=(const Node&& other) = delete;
+        virtual ~Node() = default;
     };
 }
 
diff --git a/src/backend/opencl/JIT/ShiftNode.hpp b/src/backend/opencl/JIT/ShiftNode.hpp
index 9e92c190da..bbfe3db8a4 100644
--- a/src/backend/opencl/JIT/ShiftNode.hpp
+++ b/src/backend/opencl/JIT/ShiftNode.hpp
@@ -15,21 +15,20 @@
 
 namespace opencl
 {
-
 namespace JIT
 {
     class ShiftNode : public Node
     {
     private:
 
-        Node_ptr m_buffer_node;
+        std::shared_ptr<BufferNode> m_buffer_node;
         const std::array<int, 4> m_shifts;
 
     public:
 
         ShiftNode(const char *type_str,
                   const char *name_str,
-                  Node_ptr buffer_node,
+                  std::shared_ptr<BufferNode> buffer_node,
                   const std::array<int, 4> shifts)
             : Node(type_str, name_str, 0, {}),
               m_buffer_node(buffer_node),
@@ -39,8 +38,7 @@ namespace JIT
 
         void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode *>(node_ptr)->setData(info, data, bytes, is_linear);
+            m_buffer_node->setData(info, data, bytes, is_linear);
         }
 
         bool isLinear(dim_t dims[4]) const final
@@ -56,8 +54,7 @@ namespace JIT
 
         void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode *>(node_ptr)->genParams(kerStream, id, is_linear);
+            m_buffer_node->genParams(kerStream, id, is_linear);
             for (int i = 0; i < 4; i++) {
                 kerStream << "int shift" << id << "_" << i << ",\n";
             }
@@ -65,8 +62,7 @@ namespace JIT
 
         int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
         {
-            auto node_ptr = m_buffer_node.get();
-            int curr_id = dynamic_cast<BufferNode *>(node_ptr)->setArgs(ker, id, is_linear);
+            int curr_id = m_buffer_node->setArgs(ker, id, is_linear);
             for (int i = 0; i < 4; i++) {
                 ker.setArg(curr_id + i, m_shifts[i]);
             }
@@ -84,36 +80,29 @@ namespace JIT
                 kerStream << "int " << id_str << i
                           << " = __circular_mod(id" << i
                           << " + " << shift_str << i
-                          << ", " << info_str << ".dims[" << i << "]"
-                          << ");\n";
+                          << ", " << info_str << ".dims[" << i << "]);\n";
             }
 
-            kerStream << "int " << idx_str << " = "
-                      << "(" << id_str << "3 < " << info_str << ".dims[3]) * "
+            kerStream << "int " << idx_str << " = (" << id_str << "3 < "
+                      << info_str << ".dims[3]) * "
                       << info_str << ".strides[3] * " << id_str << "3;\n";
-            kerStream << idx_str  << " += "
-                      << "(" << id_str << "2 < " << info_str << ".dims[2]) * "
+            kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
                       << info_str << ".strides[2] * " << id_str << "2;\n";
-            kerStream << idx_str  << " += "
-                      << "(" << id_str << "1 < " << info_str << ".dims[1]) * "
+            kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
                       << info_str << ".strides[1] * " << id_str << "1;\n";
-            kerStream << idx_str  << " += "
-                      << "(" << id_str << "0 < " << info_str << ".dims[0]) * "
-                      << id_str << "0 + " << info_str << ".offset;"
-                      << "\n";
+            kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
+                      << id_str << "0 + " << info_str << ".offset;\n";
         }
 
         void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
         {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << "in" << ids.id << "[idx" << ids.id << "];"
-                      << "\n";
+            kerStream << m_type_str << " val" << ids.id
+                      << " = in" << ids.id << "[idx" << ids.id << "];\n";
         }
 
         void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
         {
-            auto node_ptr = m_buffer_node.get();
-            dynamic_cast<BufferNode *>(node_ptr)->getInfo(len, buf_count, bytes);
+            m_buffer_node->getInfo(len, buf_count, bytes);
         }
     };
 }
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 333e8fa0e8..16451ebb11 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -6,13 +6,67 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <Array.hpp>
 #include <select.hpp>
-#include <err_opencl.hpp>
 #include <kernel/select.hpp>
 
+#include <Array.hpp>
+#include <JIT/NaryNode.hpp>
+#include <err_opencl.hpp>
+#include <scalar.hpp>
+
+#include <memory>
+
+using af::dim4;
+
+using opencl::JIT::NaryNode;
+
+using std::make_shared;
+using std::max;
+
 namespace opencl
 {
+    template<typename T>
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const Array<T> &b,
+                              const dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        auto b_node = b.getNode();
+        int height = max(a_node->getHeight(), b_node->getHeight());
+        height = max(height, cond_node->getHeight()) + 1;
+        auto node = make_shared<NaryNode>(NaryNode(dtype_traits<T>::getName(),
+                                                   shortname<T>(true), "__select",
+                                                   3, {{cond_node, a_node, b_node}},
+                                                   (int)af_select_t, height));
+
+        Array<T> out = createNodeArray<T>(odims, node);
+        return out;
+    }
+
+    template<typename T, bool flip>
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const double &b_val,
+                              const dim4 &odims)
+    {
+        auto cond_node = cond.getNode();
+        auto a_node = a.getNode();
+        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
+        auto b_node = b.getNode();
+        int height = max(a_node->getHeight(), b_node->getHeight());
+        height = max(height, cond_node->getHeight()) + 1;
+
+        auto node = make_shared<NaryNode>(NaryNode(dtype_traits<T>::getName(),
+                                                   shortname<T>(true),
+                                                   (flip ? "__not_select" : "__select"),
+                                                   3, {{cond_node, a_node, b_node}},
+                                                   (int)(flip ? af_not_select_t : af_select_t),
+                                                   height));
+
+        Array<T> out = createNodeArray<T>(odims, node);
+        return out;
+    }
+
     template<typename T>
     void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b)
     {
@@ -25,29 +79,42 @@ namespace opencl
         kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
     }
 
+#define INSTANTIATE(T)                                                  \
+  template                                                              \
+  Array<T> createSelectNode<T>(const Array<char> &cond,                 \
+                               const Array<T> &a, const Array<T> &b,    \
+                               const af::dim4 &odims);                  \
+  template                                                              \
+  Array<T> createSelectNode<T, true>(const Array<char> &cond,           \
+                                     const Array<T> &a, const double &b_val, \
+                                     const af::dim4 &odims);            \
+  template                                                              \
+  Array<T> createSelectNode<T, false>(const Array<char> &cond,          \
+                                      const Array<T> &a, const double &b_val, \
+                                      const af::dim4 &odims);           \
+  template void select<T>(Array<T> &out, const Array<char> &cond,       \
+                          const Array<T> &a, const Array<T> &b);        \
+  template void select_scalar<T, true >(Array<T> &out,                  \
+                                        const Array<char> &cond,        \
+                                        const Array<T> &a,              \
+                                        const double &b);               \
+  template void select_scalar<T, false>(Array<T> &out, const            \
+                                        Array<char> &cond,              \
+                                        const Array<T> &a,              \
+                                        const double &b)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(cfloat);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
+INSTANTIATE(uint);
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(char);
+INSTANTIATE(uchar);
+INSTANTIATE(short);
+INSTANTIATE(ushort);
 
-#define INSTANTIATE(T)                                              \
-    template void select<T>(Array<T> &out, const Array<char> &cond, \
-                            const Array<T> &a, const Array<T> &b);  \
-    template void select_scalar<T, true >(Array<T> &out,            \
-                                          const Array<char> &cond,  \
-                                          const Array<T> &a,        \
-                                          const double &b);         \
-    template void select_scalar<T, false>(Array<T> &out, const      \
-                                          Array<char> &cond,        \
-                                          const Array<T> &a,        \
-                                          const double &b);         \
-
-    INSTANTIATE(float  )
-    INSTANTIATE(double )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(char   )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+#undef INSTANTIATE
 }
diff --git a/src/backend/opencl/select.hpp b/src/backend/opencl/select.hpp
index e9119e9ca4..66614cdc8f 100644
--- a/src/backend/opencl/select.hpp
+++ b/src/backend/opencl/select.hpp
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <JIT/NaryNode.hpp>
-#include <scalar.hpp>
+#include <af/dim4.hpp>
 #include <Array.hpp>
 
 namespace opencl
@@ -20,41 +19,12 @@ namespace opencl
     void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
 
     template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const Array<T> &b, const af::dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        auto b_node = b.getNode();
-        int height = std::max(a_node->getHeight(), b_node->getHeight());
-        height = std::max(height, cond_node->getHeight()) + 1;
-
-        JIT::NaryNode *node = new JIT::NaryNode(dtype_traits<T>::getName(),
-                                                shortname<T>(true),
-                                                "__select", 3, {{cond_node, a_node, b_node}},
-                                                (int)af_select_t, height);
-
-        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
-        return out;
-    }
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const Array<T> &b,
+                              const af::dim4 &odims);
 
     template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const double &b_val, const af::dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
-        auto b_node = b.getNode();
-        int height = std::max(a_node->getHeight(), b_node->getHeight());
-        height = std::max(height, cond_node->getHeight()) + 1;
-
-        JIT::NaryNode *node = new JIT::NaryNode(dtype_traits<T>::getName(),
-                                                shortname<T>(true),
-                                                flip ? "__not_select" : "__select",
-                                                3, {{cond_node, a_node, b_node}},
-                                                (int)(flip ? af_not_select_t : af_select_t),
-                                                height);
-
-        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
-        return out;
-    }
+    Array<T> createSelectNode(const Array<char> &cond,
+                              const Array<T> &a, const double &b_val,
+                              const af::dim4 &odims);
 }
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index 9c6598ef4f..dc8bf813e0 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -7,11 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <JIT/ShiftNode.hpp>
 #include <Array.hpp>
 #include <shift.hpp>
-#include <stdexcept>
 #include <err_opencl.hpp>
+#include <JIT/ShiftNode.hpp>
+#include <JIT/ShiftNode.hpp>
+
+#include <memory>
+#include <stdexcept>
+
+using af::dim4;
+
+using opencl::JIT::BufferNode;
+using opencl::JIT::Node_ptr;
+using opencl::JIT::ShiftNode;
+
+using std::array;
+using std::make_shared;
+using std::static_pointer_cast;
+using std::string;
 
 namespace opencl
 {
@@ -22,12 +36,12 @@ namespace opencl
         // Force input to be evaluated so that in is always a buffer.
         in.eval();
 
-        std::string name_str("Sh");
+        string name_str("Sh");
         name_str += shortname<T>(true);
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
+        const dim4 iDims = in.dims();
+        dim4 oDims = iDims;
 
-        std::array<int, 4> shifts;
+        array<int, 4> shifts;
         for(int i = 0; i < 4; i++) {
             // sdims_[i] will always be positive and always [0, oDims[i]].
             // Negative shifts are converted to position by going the other way round
@@ -35,9 +49,10 @@ namespace opencl
             assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
         }
 
-        auto node = new JIT::ShiftNode(dtype_traits<T>::getName(), name_str.c_str(),
-                                       in.getNode(), shifts);
-        return createNodeArray<T>(oDims, JIT::Node_ptr(node));
+        auto node = make_shared<ShiftNode>(dtype_traits<T>::getName(), name_str.c_str(),
+                                           static_pointer_cast<BufferNode>(in.getNode()),
+                                           shifts);
+        return createNodeArray<T>(oDims, Node_ptr(node));
     }
 
 #define INSTANTIATE(T)                                                  \

From 737609bc4c7d3a00bcd06cccbd7d319bb3cbcae9 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Tue, 23 Oct 2018 11:30:16 -0500
Subject: [PATCH 1544/2677] Fix compilation issues on macOS.

Apple LLVM version 9.0.0 (clang-900.0.39.2)
---
 src/backend/common/NodeIterator.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/common/NodeIterator.hpp b/src/backend/common/NodeIterator.hpp
index 3345d381ea..92113eb1a8 100644
--- a/src/backend/common/NodeIterator.hpp
+++ b/src/backend/common/NodeIterator.hpp
@@ -100,7 +100,7 @@ class NodeIterator : public std::iterator<std::input_iterator_tag, detail::JIT::
     NodeIterator(const NodeIterator& other) = default;
     NodeIterator(NodeIterator&& other) noexcept = default;
     ~NodeIterator() noexcept = default;
-    NodeIterator& operator=(const NodeIterator& other) noexcept = default;
+    NodeIterator& operator=(const NodeIterator& other) = default;
     NodeIterator& operator=(NodeIterator&& other) noexcept = default;
 };
 

From 8b11fffb236bb2396aed7349cae54ff8a2fd1c19 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 26 Oct 2018 00:21:44 -0400
Subject: [PATCH 1545/2677] Move JIT classes into the common namespace for
 OpenCL and CUDA

Moves JIT classes for OpenCL and CUDA into the common interface.
This does modify the CPU backend. Most of the specilization has
been moved into the JIT/kernel_generators.hpp header.
---
 src/backend/common/CMakeLists.txt             |  12 +-
 src/backend/common/jit/BinaryNode.hpp         |  26 ++++
 src/backend/common/jit/BufferNodeBase.hpp     |  94 ++++++++++++
 .../{opencl/JIT => common/jit}/NaryNode.hpp   |  29 ++--
 src/backend/{cuda/JIT => common/jit}/Node.cpp |   8 +-
 src/backend/{cuda/JIT => common/jit}/Node.hpp |  68 +++++----
 src/backend/common/{ => jit}/NodeIterator.hpp |  18 ++-
 .../{cuda/JIT => common/jit}/ScalarNode.hpp   |  26 ++--
 src/backend/common/jit/ShiftNodeBase.hpp      |  87 +++++++++++
 src/backend/common/jit/UnaryNode.hpp          |  27 ++++
 src/backend/cpu/Array.cpp                     |  33 ++---
 src/backend/cpu/Array.hpp                     |  20 +--
 src/backend/cpu/arith.hpp                     |  23 ++-
 src/backend/cpu/cast.hpp                      |  34 ++---
 src/backend/cpu/complex.hpp                   |  49 +++----
 src/backend/cpu/{JIT => jit}/BinaryNode.hpp   |   8 +-
 src/backend/cpu/{JIT => jit}/BufferNode.hpp   |   2 +-
 src/backend/cpu/{JIT => jit}/Node.hpp         |  33 ++---
 src/backend/cpu/{JIT => jit}/ScalarNode.hpp   |   2 +-
 src/backend/cpu/{JIT => jit}/UnaryNode.hpp    |   6 +-
 src/backend/cpu/kernel/Array.hpp              |  26 ++--
 src/backend/cpu/logic.hpp                     |  38 +++--
 src/backend/cpu/unary.hpp                     |  24 ++-
 src/backend/cuda/Array.cpp                    |  20 +--
 src/backend/cuda/Array.hpp                    |  18 +--
 src/backend/cuda/CMakeLists.txt               |  22 +--
 src/backend/cuda/JIT/BinaryNode.hpp           |  32 ----
 src/backend/cuda/JIT/BufferNode.hpp           | 129 ----------------
 src/backend/cuda/JIT/NaryNode.hpp             |  63 --------
 src/backend/cuda/JIT/ShiftNode.hpp            | 109 --------------
 src/backend/cuda/JIT/UnaryNode.hpp            |  32 ----
 src/backend/cuda/JIT/types.h                  |  21 ---
 src/backend/cuda/backend.hpp                  |   2 +-
 src/backend/cuda/binary.hpp                   |  18 +--
 src/backend/cuda/cast.hpp                     |  14 +-
 src/backend/cuda/complex.hpp                  |  50 +++----
 src/backend/cuda/jit.cpp                      |  12 +-
 src/backend/cuda/jit/BufferNode.hpp           |  21 +++
 src/backend/cuda/jit/kernel_generators.hpp    | 102 +++++++++++++
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |  10 ++
 src/backend/cuda/scalar.hpp                   |   5 +-
 src/backend/cuda/select.cu                    |  25 ++--
 src/backend/cuda/shift.cpp                    |  14 +-
 src/backend/cuda/types.cpp                    |  47 ------
 src/backend/cuda/types.hpp                    |  51 ++++++-
 src/backend/cuda/unary.hpp                    |  26 ++--
 src/backend/opencl/Array.cpp                  |  78 +++++-----
 src/backend/opencl/Array.hpp                  |  18 +--
 src/backend/opencl/CMakeLists.txt             |   8 +-
 src/backend/opencl/JIT/BinaryNode.hpp         |  34 -----
 src/backend/opencl/JIT/BufferNode.hpp         | 138 ------------------
 src/backend/opencl/JIT/Node.cpp               |  47 ------
 src/backend/opencl/JIT/Node.hpp               | 113 --------------
 src/backend/opencl/JIT/ScalarNode.hpp         |  66 ---------
 src/backend/opencl/JIT/ShiftNode.hpp          | 110 --------------
 src/backend/opencl/binary.hpp                 |  18 +--
 src/backend/opencl/cast.hpp                   |  14 +-
 src/backend/opencl/complex.hpp                |  50 +++----
 src/backend/opencl/jit.cpp                    |  43 +++---
 .../{JIT/UnaryNode.hpp => jit/BufferNode.hpp} |  22 +--
 src/backend/opencl/jit/kernel_generators.hpp  |  98 +++++++++++++
 src/backend/opencl/scalar.hpp                 |   4 +-
 src/backend/opencl/select.cpp                 |   4 +-
 src/backend/opencl/shift.cpp                  |  13 +-
 src/backend/opencl/types.cpp                  |  31 ----
 src/backend/opencl/types.hpp                  |  27 +++-
 src/backend/opencl/unary.hpp                  |  26 ++--
 67 files changed, 1021 insertions(+), 1477 deletions(-)
 create mode 100644 src/backend/common/jit/BinaryNode.hpp
 create mode 100644 src/backend/common/jit/BufferNodeBase.hpp
 rename src/backend/{opencl/JIT => common/jit}/NaryNode.hpp (74%)
 rename src/backend/{cuda/JIT => common/jit}/Node.cpp (89%)
 rename src/backend/{cuda/JIT => common/jit}/Node.hpp (57%)
 rename src/backend/common/{ => jit}/NodeIterator.hpp (89%)
 rename src/backend/{cuda/JIT => common/jit}/ScalarNode.hpp (64%)
 create mode 100644 src/backend/common/jit/ShiftNodeBase.hpp
 create mode 100644 src/backend/common/jit/UnaryNode.hpp
 rename src/backend/cpu/{JIT => jit}/BinaryNode.hpp (91%)
 rename src/backend/cpu/{JIT => jit}/BufferNode.hpp (99%)
 rename src/backend/cpu/{JIT => jit}/Node.hpp (77%)
 rename src/backend/cpu/{JIT => jit}/ScalarNode.hpp (97%)
 rename src/backend/cpu/{JIT => jit}/UnaryNode.hpp (92%)
 delete mode 100644 src/backend/cuda/JIT/BinaryNode.hpp
 delete mode 100644 src/backend/cuda/JIT/BufferNode.hpp
 delete mode 100644 src/backend/cuda/JIT/NaryNode.hpp
 delete mode 100644 src/backend/cuda/JIT/ShiftNode.hpp
 delete mode 100644 src/backend/cuda/JIT/UnaryNode.hpp
 delete mode 100644 src/backend/cuda/JIT/types.h
 create mode 100644 src/backend/cuda/jit/BufferNode.hpp
 create mode 100644 src/backend/cuda/jit/kernel_generators.hpp
 delete mode 100644 src/backend/cuda/types.cpp
 delete mode 100644 src/backend/opencl/JIT/BinaryNode.hpp
 delete mode 100644 src/backend/opencl/JIT/BufferNode.hpp
 delete mode 100644 src/backend/opencl/JIT/Node.cpp
 delete mode 100644 src/backend/opencl/JIT/Node.hpp
 delete mode 100644 src/backend/opencl/JIT/ScalarNode.hpp
 delete mode 100644 src/backend/opencl/JIT/ShiftNode.hpp
 rename src/backend/opencl/{JIT/UnaryNode.hpp => jit/BufferNode.hpp} (50%)
 create mode 100644 src/backend/opencl/jit/kernel_generators.hpp
 delete mode 100644 src/backend/opencl/types.cpp

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 3e746b0bdb..07b2e8654b 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -7,6 +7,17 @@
 
 add_library(afcommon_interface INTERFACE)
 
+target_sources(afcommon_interface
+  INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/BinaryNode.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/NaryNode.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/NodeIterator.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/ScalarNode.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/UnaryNode.hpp
+    )
+
 target_sources(afcommon_interface
   INTERFACE
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.cpp
@@ -19,7 +30,6 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/MatrixAlgebraHandle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/NodeIterator.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
diff --git a/src/backend/common/jit/BinaryNode.hpp b/src/backend/common/jit/BinaryNode.hpp
new file mode 100644
index 0000000000..e3e5860db6
--- /dev/null
+++ b/src/backend/common/jit/BinaryNode.hpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/jit/NaryNode.hpp>
+
+#include <cmath>
+
+namespace common {
+class BinaryNode : public NaryNode
+{
+  public:
+    BinaryNode(const char *out_type_str, const char *name_str,
+              const char *op_str,
+              common::Node_ptr lhs, common::Node_ptr rhs, int op)
+      : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}},
+                op, std::max(lhs->getHeight(), rhs->getHeight()) + 1)
+    {
+    }
+};
+}
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
new file mode 100644
index 0000000000..12bfad060c
--- /dev/null
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -0,0 +1,94 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <jit/kernel_generators.hpp>
+#include <backend.hpp>
+
+#include <sstream>
+
+namespace common {
+
+template<typename DataType, typename ParamType>
+class BufferNodeBase : public common::Node
+{
+private:
+    DataType m_data;
+    ParamType m_param;
+    unsigned m_bytes;
+    std::once_flag m_set_data_flag;
+    bool m_linear_buffer;
+
+public:
+
+    BufferNodeBase(const char *type_str,
+                  const char *name_str)
+        : Node(type_str, name_str, 0, {})
+    {
+    }
+
+    bool isBuffer() const final { return true; }
+
+
+    void setData(ParamType param, DataType data, const unsigned bytes, bool is_linear)
+    {
+        std::call_once(m_set_data_flag, [this, param, data, bytes, is_linear]() {
+                                          m_param = param;
+                                          m_data = data;
+                                          m_bytes = bytes;
+                                          m_linear_buffer = is_linear;
+                                        });
+    }
+
+    bool isLinear(dim_t dims[4]) const final
+    {
+        bool same_dims = true;
+        for (int i = 0; same_dims && i < 4; i++) {
+            same_dims &= (dims[i] == m_param.dims[i]);
+        }
+        return m_linear_buffer && same_dims;
+    }
+
+    void genKerName(std::stringstream &kerStream, const common::Node_ids& ids) const final
+    {
+        kerStream << "_" << m_name_str;
+        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+    }
+
+
+    void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
+    {
+        detail::generateParamDeclaration(kerStream, id, is_linear, m_type_str);
+    }
+
+    int setArgs(int start_id, bool is_linear,
+                std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const override {
+        return detail::setKernelArguments(start_id, is_linear, setArg, m_data, m_param);
+    }
+
+    void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
+    {
+        detail::generateBufferOffsets(kerStream, id, is_linear, m_type_str);
+    }
+
+    void genFuncs(std::stringstream &kerStream, const common::Node_ids& ids) const final
+    {
+        detail::generateBufferRead(kerStream, ids.id, m_type_str);
+    }
+
+    void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final {
+        len++;
+        buf_count++;
+        bytes += m_bytes;
+    }
+
+    size_t getBytes() const final { return m_bytes; }
+};
+
+}
diff --git a/src/backend/opencl/JIT/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
similarity index 74%
rename from src/backend/opencl/JIT/NaryNode.hpp
rename to src/backend/common/jit/NaryNode.hpp
index 544e438b1d..e3c04caedd 100644
--- a/src/backend/opencl/JIT/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -8,18 +8,17 @@
  ********************************************************/
 
 #pragma once
-#include "Node.hpp"
+#include <common/jit/Node.hpp>
+
+#include <array>
 #include <iomanip>
+#include <string>
+#include <sstream>
 #include <utility>
 
-namespace opencl
-{
-
-namespace JIT
-{
+namespace common {
 
-    class NaryNode : public Node
-    {
+    class NaryNode : public Node {
     private:
         const int m_num_children;
         const int m_op;
@@ -30,16 +29,17 @@ namespace JIT
                  const char *name_str,
                  const char *op_str,
                  const int num_children,
-                 const std::array<Node_ptr, MAX_CHILDREN>&& children,
+                 const std::array<common::Node_ptr, Node::kMaxChildren> &&children,
                  const int op, const int height)
-          : Node(out_type_str, name_str, height,
-                 std::forward<const std::array<Node_ptr, MAX_CHILDREN>>(children)),
+            : common::Node(out_type_str, name_str, height,
+                           std::forward<const std::array<common::Node_ptr, Node::kMaxChildren>>(children)),
               m_num_children(num_children),
               m_op(op),
               m_op_str(op_str)
         {
         }
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
+
+        void genKerName(std::stringstream &kerStream, const common::Node_ids& ids) const final
         {
             // Make the dec representation of enum part of the Kernel name
             kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
@@ -52,7 +52,7 @@ namespace JIT
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
+        void genFuncs(std::stringstream &kerStream, const common::Node_ids& ids) const final
         {
             kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
             for (int i = 0; i < m_num_children; i++) {
@@ -62,7 +62,4 @@ namespace JIT
             kerStream << ");\n";
         }
     };
-
-}
-
 }
diff --git a/src/backend/cuda/JIT/Node.cpp b/src/backend/common/jit/Node.cpp
similarity index 89%
rename from src/backend/cuda/JIT/Node.cpp
rename to src/backend/common/jit/Node.cpp
index 1b4f65e84d..4ac215a86e 100644
--- a/src/backend/cuda/JIT/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Node.hpp>
+#include <common/jit/Node.hpp>
 
 #include <string>
 #include <vector>
 using namespace std;
 
-namespace cuda {
-namespace JIT {
+namespace common {
 
   int Node::getNodesMap(Node_map_t &node_map,
                 vector<const Node *> &full_nodes,
@@ -23,7 +22,7 @@ namespace JIT {
     if (iter == node_map.end()) {
         Node_ids ids;
 
-        for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
+        for (int i = 0; i < kMaxChildren && m_children[i] != nullptr; i++) {
             ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes,
                                                           full_ids);
         }
@@ -36,5 +35,4 @@ namespace JIT {
     return iter->second;
 }
 
-  }
 }
diff --git a/src/backend/cuda/JIT/Node.hpp b/src/backend/common/jit/Node.hpp
similarity index 57%
rename from src/backend/cuda/JIT/Node.hpp
rename to src/backend/common/jit/Node.hpp
index 7a42834ecf..6d5c702c08 100644
--- a/src/backend/cuda/JIT/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -10,65 +10,60 @@
 #pragma once
 #include <platform.hpp>
 #include <optypes.hpp>
+
 #include <array>
-#include <string>
-#include <vector>
+#include <functional>
 #include <memory>
+#include <string>
 #include <unordered_map>
+#include <vector>
 
 namespace common {
-    class NodeIterator;
-}
-using std::shared_ptr;
-using std::vector;
-
-namespace cuda
-{
-
-namespace JIT
-{
-
-    constexpr int MAX_CHILDREN = 3;
     class Node;
+    struct Node_ids;
 
-    typedef struct
-    {
-        int id;
-        std::array<int, MAX_CHILDREN> child_ids;
-    } Node_ids;
-
-    using Node_ptr = shared_ptr<Node>;
+    using Node_ptr = std::shared_ptr<Node>;
     using Node_map_t = std::unordered_map<const Node *, int> ;
     using Node_map_iter = Node_map_t::iterator;
 
     class Node
     {
+    public:
+        static const int kMaxChildren = 3;
     protected:
+        const std::array<Node_ptr, kMaxChildren> m_children;
         const std::string m_type_str;
         const std::string m_name_str;
-        const std::array<Node_ptr, MAX_CHILDREN> m_children;
         const int m_height;
-        friend class common::NodeIterator;
+        template<typename T> friend class NodeIterator;
 
     public:
-
         Node(const char *type_str, const char *name_str, const int height,
-             const std::array<Node_ptr, MAX_CHILDREN> children)
+             const std::array<Node_ptr, kMaxChildren> children)
             : m_type_str(type_str),
               m_name_str(name_str),
               m_children(children),
               m_height(height) {}
 
         int getNodesMap(Node_map_t &node_map,
-                        vector<const Node *> &full_nodes,
-                        vector<Node_ids> &full_ids) const;
-
-        virtual void genKerName (std::stringstream &kerStream, Node_ids ids) const {}
-        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const {}
-        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const {}
-        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) const {}
-
-        virtual void setArgs (std::vector<void *> &args, bool is_linear) const { }
+                        std::vector<const Node *> &full_nodes,
+                        std::vector<Node_ids> &full_ids) const;
+
+        virtual void genKerName (std::stringstream &kerStream, const Node_ids& ids) const { }
+        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const { }
+        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const { }
+        virtual void genFuncs   (std::stringstream &kerStream, const Node_ids& ids) const { }
+
+        /// Calls the setArg function on each of the arguments passed into the kernel
+        ///
+        /// \param[in] start_id The index of the staring argument
+        /// \param[in] is_linear determines if the kernel should be linear or not
+        /// \param[in] setArg the function that will be called for each argument
+        ///
+        /// \returns the next index that will need to be set in the kernl. This
+        ///          is usually start_id + the number of times setArg is called
+        virtual int setArgs(int start_id, bool is_linear,
+                            std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const { return start_id; }
 
         virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const {
             len++;
@@ -90,6 +85,9 @@ namespace JIT
 
         virtual ~Node() {}
     };
-}
 
+    struct Node_ids {
+        std::array<int, Node::kMaxChildren> child_ids;
+        int id;
+    };
 }
diff --git a/src/backend/common/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
similarity index 89%
rename from src/backend/common/NodeIterator.hpp
rename to src/backend/common/jit/NodeIterator.hpp
index 92113eb1a8..f5ea4a5c87 100644
--- a/src/backend/common/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <JIT/Node.hpp>
 #include <backend.hpp>
 
 #include <cstddef>
@@ -16,17 +15,22 @@
 #include <vector>
 
 namespace common {
-  // TODO: unify all definitions of MAX_CHILDREN
-  constexpr int MAX_CHILDREN = 3;
+class Node; // TODO(umar): Remove when CPU backend Node class is moved from JIT to common
 
 /// A node iterator that performs a breadth first traversal of the node tree
-class NodeIterator : public std::iterator<std::input_iterator_tag, detail::JIT::Node> {
+template<typename Node = common::Node>
+class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
+  public:
+    using pointer   = Node*;
+    using reference = Node&;
+
+  private:
     std::vector<pointer> tree;
     int index;
 
     /// Copies the children of the \p n Node to the end of the tree vector
-    void copy_children_to_end(detail::JIT::Node* n) {
-        for(int i = 0; n->m_children[i] != nullptr && i < MAX_CHILDREN; i++) {
+    void copy_children_to_end(Node* n) {
+        for(int i = 0; n->m_children[i] != nullptr && i < Node::kMaxChildren; i++) {
             auto ptr = n->m_children[i].get();
             if(find(begin(tree), end(tree), ptr) == end(tree)) {
                 tree.push_back(ptr);
@@ -35,8 +39,6 @@ class NodeIterator : public std::iterator<std::input_iterator_tag, detail::JIT::
     }
 
   public:
-    using pointer   = detail::JIT::Node*;
-    using reference = detail::JIT::Node&;
 
     /// NodeIterator Constructor
     ///
diff --git a/src/backend/cuda/JIT/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
similarity index 64%
rename from src/backend/cuda/JIT/ScalarNode.hpp
rename to src/backend/common/jit/ScalarNode.hpp
index bb249b9fec..33f00cc326 100644
--- a/src/backend/cuda/JIT/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -8,19 +8,17 @@
  ********************************************************/
 
 #pragma once
-#include "Node.hpp"
+#include <common/jit/Node.hpp>
+
 #include <math.hpp>
 #include <types.hpp>
 #include <iomanip>
 
-namespace cuda
-{
-
-namespace JIT
+namespace common
 {
 
     template <typename T>
-    class ScalarNode : public Node
+    class ScalarNode : public common::Node
     {
     private:
         const T m_val;
@@ -28,12 +26,13 @@ namespace JIT
     public:
 
         ScalarNode(T val)
-            : Node(getFullName<T>(), shortname<T>(false), 0, {}),
+            : Node(detail::getFullName<T>(), detail::shortname<T>(false), 0, {}),
               m_val(val)
         {
         }
 
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
+        void genKerName(std::stringstream &kerStream,
+                        const common::Node_ids& ids) const final
         {
             kerStream << "_" << m_name_str;
             kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
@@ -44,12 +43,15 @@ namespace JIT
             kerStream << m_type_str << " scalar" << id << ", \n";
         }
 
-        void setArgs(std::vector<void *> &args, bool is_linear) const final
+        int setArgs(int start_id, bool is_linear,
+                    std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const final
         {
-            args.push_back((void *)&m_val);
+            setArg(start_id, static_cast<const void*>(&m_val), sizeof(T));
+            return start_id + 1;
         }
 
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
+        void genFuncs(std::stringstream &kerStream,
+                      const common::Node_ids& ids) const final
         {
             kerStream << m_type_str << " val" << ids.id
                       << " = scalar" << ids.id << ";\n";
@@ -60,5 +62,3 @@ namespace JIT
     };
 
 }
-
-}
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
new file mode 100644
index 0000000000..381499b231
--- /dev/null
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -0,0 +1,87 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <jit/BufferNode.hpp>
+#include <jit/kernel_generators.hpp>
+
+#include <iomanip>
+#include <backend.hpp>
+
+#include <array>
+#include <memory>
+#include <sstream>
+#include <string>
+
+namespace common {
+
+    template<typename BufferNode>
+    class ShiftNodeBase : public Node
+    {
+    private:
+        std::shared_ptr<BufferNode> m_buffer_node;
+        const std::array<int, 4> m_shifts;
+
+    public:
+        ShiftNodeBase(const char *type_str,
+                  const char *name_str,
+                  std::shared_ptr<BufferNode> buffer_node,
+                  const std::array<int, 4> shifts)
+            : Node(type_str, name_str, 0, {}),
+              m_buffer_node(buffer_node),
+              m_shifts(shifts)
+        {
+        }
+
+        bool isLinear(dim_t dims[4]) const final
+        {
+            return false;
+        }
+
+        void genKerName(std::stringstream &kerStream, const common::Node_ids& ids) const final
+        {
+            kerStream << "_" << m_name_str;
+            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+        }
+
+        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
+        {
+            m_buffer_node->genParams(kerStream, id, is_linear);
+            for (int i = 0; i < 4; i++) {
+                kerStream << "int shift" << id << "_" << i << ",\n";
+            }
+        }
+
+        int setArgs(int start_id, bool is_linear,
+                    std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const {
+            int curr_id = m_buffer_node->setArgs(start_id, is_linear, setArg);
+            for (int i = 0; i < 4; i++) {
+                const int &d = m_shifts[i];
+                setArg(curr_id+i, static_cast<const void*>(&d), sizeof(int));
+            }
+            return curr_id + 4;
+        }
+
+        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
+        {
+            detail::generateShiftNodeOffsets(kerStream, id, is_linear, m_type_str);
+        }
+
+        void genFuncs(std::stringstream &kerStream, const common::Node_ids& ids) const final
+        {
+            detail::generateShiftNodeRead(kerStream, ids.id, m_type_str);
+        }
+
+        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
+        {
+            m_buffer_node->getInfo(len, buf_count, bytes);
+        }
+    };
+}
diff --git a/src/backend/common/jit/UnaryNode.hpp b/src/backend/common/jit/UnaryNode.hpp
new file mode 100644
index 0000000000..9df843ec27
--- /dev/null
+++ b/src/backend/common/jit/UnaryNode.hpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <common/jit/NaryNode.hpp>
+
+
+namespace common {
+
+class UnaryNode : public NaryNode
+{
+public:
+  UnaryNode(const char *out_type_str, const char *name_str,
+            const char *op_str,
+            Node_ptr child, int op)
+    : NaryNode(out_type_str, name_str, op_str,
+               1, {{child}}, op, child->getHeight() + 1)
+  {
+  }
+};
+}
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 7afdcdf3ea..a5df8fc42e 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -11,12 +11,12 @@
 #include <Array.hpp>
 #include <kernel/Array.hpp>
 
-#include <JIT/BufferNode.hpp>
-#include <JIT/Node.hpp>
-#include <JIT/ScalarNode.hpp>
+#include <jit/BufferNode.hpp>
+#include <jit/Node.hpp>
+#include <jit/ScalarNode.hpp>
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/NodeIterator.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <common/err_common.hpp>
 #include <copy.hpp>
 #include <memory.hpp>
@@ -37,10 +37,10 @@
 namespace cpu
 {
 
-using JIT::BufferNode;
-using JIT::Node;
-using JIT::Node_ptr;
-using JIT::Node_map_t;
+using jit::BufferNode;
+using jit::Node;
+using jit::Node_ptr;
+using jit::Node_map_t;
 using common::NodeIterator;
 
 using af::dim4;
@@ -77,7 +77,7 @@ Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_de
 }
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, JIT::Node_ptr n) :
+Array<T>::Array(af::dim4 dims, Node_ptr n) :
     info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
     data(), data_dims(dims),
     node(n), ready(false), owner(true)
@@ -146,7 +146,7 @@ template<typename T>
 void evalMultiple(vector<Array<T>*> array_ptrs)
 {
     vector<Array<T>> arrays;
-    vector<JIT::Node_ptr> nodes;
+    vector<Node_ptr> nodes;
     bool isWorker = getQueue().is_worker();
     for (auto &array : array_ptrs) {
         if (array->ready) continue;
@@ -203,9 +203,8 @@ template<typename T>
 Array<T>
 createValueArray(const dim4 &size, const T& value)
 {
-    JIT::ScalarNode<T> *node = new JIT::ScalarNode<T>(value);
-    return createNodeArray<T>(size, JIT::Node_ptr(
-                                  reinterpret_cast<JIT::Node *>(node)));
+    jit::ScalarNode<T> *node = new jit::ScalarNode<T>(value);
+    return createNodeArray<T>(size, Node_ptr(node));
 }
 
 template<typename T>
@@ -241,8 +240,8 @@ createNodeArray(const dim4 &dims, Node_ptr node)
                 Node *n = node.get();
 
                 size_t buffer_size;
-                NodeIterator it(n);
-                NodeIterator end_node;
+                NodeIterator<jit::Node> it(n);
+                NodeIterator<jit::Node> end_node;
                 size_t bytes = accumulate(it, end_node,
                                           size_t(0),
                                           [=](const size_t prev, const Node& n) {
@@ -353,7 +352,7 @@ Array<T>::setDataDims(const dim4 &new_dims)
                                                        const vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
+    template       Array<T>  createNodeArray<T>       (const dim4 &size, Node_ptr node); \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
     template       T*   Array<T>::device();                             \
@@ -362,7 +361,7 @@ Array<T>::setDataDims(const dim4 &new_dims)
     template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
                                    const T * const in_data,             \
                                    bool is_device);                     \
-    template       JIT::Node_ptr Array<T>::getNode() const;             \
+    template       Node_ptr Array<T>::getNode() const;                  \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
     template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index c90bc258c6..1464ee7315 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -10,7 +10,7 @@
 //This is the array implementation class.
 #pragma once
 #include <Param.hpp>
-#include <JIT/Node.hpp>
+#include <jit/Node.hpp>
 #include <common/ArrayInfo.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
@@ -28,10 +28,10 @@ namespace cpu
 {
     namespace kernel
     {
-        template<typename T> void evalArray(Param<T> in, JIT::Node_ptr node);
+        template<typename T> void evalArray(Param<T> in, jit::Node_ptr node);
 
         template<typename T>
-        void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> nodes);
+        void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> nodes);
 
     }
 
@@ -45,7 +45,7 @@ namespace cpu
 
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
+    Array<T> createNodeArray(const af::dim4 &size, jit::Node_ptr node);
 
     // Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
@@ -112,7 +112,7 @@ namespace cpu
         //data if parent. empty if child
         std::shared_ptr<T> data;
         af::dim4 data_dims;
-        JIT::Node_ptr node;
+        jit::Node_ptr node;
 
         bool ready;
         bool owner;
@@ -122,7 +122,7 @@ namespace cpu
 
         explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        explicit Array(af::dim4 dims, JIT::Node_ptr n);
+        explicit Array(af::dim4 dims, jit::Node_ptr n);
 
     public:
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,
@@ -231,7 +231,7 @@ namespace cpu
             return CParam<T>(this->get(), this->dims(), this->strides());
         }
 
-        JIT::Node_ptr getNode() const;
+        jit::Node_ptr getNode() const;
 
         friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
@@ -241,15 +241,15 @@ namespace cpu
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, JIT::Node_ptr node);
+        friend Array<T> createNodeArray<T>(const af::dim4 &dims, jit::Node_ptr node);
 
         friend Array<T> createSubArray<T>(const Array<T>& parent,
                                           const std::vector<af_seq> &index,
                                           bool copy);
 
-        friend void kernel::evalArray<T>(Param<T> in, JIT::Node_ptr node);
+        friend void kernel::evalArray<T>(Param<T> in, jit::Node_ptr node);
         friend void kernel::evalMultiple<T>(std::vector<Param<T>> arrays,
-                                            std::vector<JIT::Node_ptr> nodes);
+                                            std::vector<jit::Node_ptr> nodes);
 
         friend void destroyArray<T>(Array<T> *arr);
         friend void *getDevicePtr<T>(const Array<T>& arr);
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 84b15de176..780a776955 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -12,7 +12,7 @@
 #include <optypes.hpp>
 #include <err_cpu.hpp>
 #include <cmath>
-#include <JIT/BinaryNode.hpp>
+#include <jit/BinaryNode.hpp>
 
 namespace cpu
 {
@@ -21,9 +21,9 @@ namespace cpu
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        void eval(JIT::array<T> &out,           \
-                  const JIT::array<T> &lhs,     \
-                  const JIT::array<T> &rhs,     \
+        void eval(jit::array<T> &out,           \
+                  const jit::array<T> &lhs,     \
+                  const jit::array<T> &rhs,     \
                   int lim) const                \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -58,9 +58,9 @@ template<> STATIC_ double __rem<double>(double lhs, double rhs) { return remaind
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        void eval(JIT::array<T> &out,           \
-                  const JIT::array<T> &lhs,     \
-                  const JIT::array<T> &rhs,     \
+        void eval(jit::array<T> &out,           \
+                  const jit::array<T> &lhs,     \
+                  const jit::array<T> &rhs,     \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -80,13 +80,12 @@ NUMERIC_FN(af_hypot_t, hypot)
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
 {
-    JIT::Node_ptr lhs_node = lhs.getNode();
-    JIT::Node_ptr rhs_node = rhs.getNode();
+    jit::Node_ptr lhs_node = lhs.getNode();
+    jit::Node_ptr rhs_node = rhs.getNode();
 
-    JIT::BinaryNode<T, T, op> *node = new JIT::BinaryNode<T, T, op>(lhs_node, rhs_node);
+    jit::BinaryNode<T, T, op> *node = new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-    return createNodeArray<T>(odims,
-                              JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+    return createNodeArray<T>(odims, jit::Node_ptr(node));
 }
 
 }
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 2e98c12b76..7b8aea2715 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -14,7 +14,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 #include <types.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <jit/UnaryNode.hpp>
 #include <Array.hpp>
 
 namespace cpu
@@ -23,8 +23,8 @@ namespace cpu
 template<typename To, typename Ti>
 struct UnOp<To, Ti, af_cast_t>
 {
-    void eval(JIT::array<To> &out,
-              const JIT::array<Ti> &in, int lim)
+    void eval(jit::array<To> &out,
+              const jit::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(in[i]);
@@ -36,8 +36,8 @@ template<typename To>
 struct UnOp<To, std::complex<float>, af_cast_t>
 {
     typedef std::complex<float> Ti;
-    void eval(JIT::array<To> &out,
-              const JIT::array<Ti> &in, int lim)
+    void eval(jit::array<To> &out,
+              const jit::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(std::abs(in[i]));
@@ -49,8 +49,8 @@ template<typename To>
 struct UnOp<To, std::complex<double>, af_cast_t>
 {
     typedef std::complex<double> Ti;
-    void eval(JIT::array<To> &out,
-              const JIT::array<Ti> &in, int lim)
+    void eval(jit::array<To> &out,
+              const jit::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(std::abs(in[i]));
@@ -68,8 +68,8 @@ struct UnOp<std::complex<float>, std::complex<double>, af_cast_t>
 {
     typedef std::complex<double> Ti;
     typedef std::complex<float> To;
-    void eval(JIT::array<To> &out,
-              const JIT::array<Ti> &in, int lim)
+    void eval(jit::array<To> &out,
+              const jit::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(in[i]);
@@ -82,8 +82,8 @@ struct UnOp<std::complex<double>, std::complex<float>, af_cast_t>
 {
     typedef std::complex<float> Ti;
     typedef std::complex<double> To;
-    void eval(JIT::array<To> &out,
-              const JIT::array<Ti> &in, int lim)
+    void eval(jit::array<To> &out,
+              const jit::array<Ti> &in, int lim)
     {
         for (int i = 0; i < lim; i++) {
             out[i] = To(in[i]);
@@ -95,8 +95,8 @@ struct UnOp<std::complex<double>, std::complex<float>, af_cast_t>
     template<>                                          \
     struct UnOp<char, T, af_cast_t>                     \
     {                                                   \
-        void eval(JIT::array<char> &out,                \
-                  const JIT::array<T> &in, int lim)     \
+        void eval(jit::array<char> &out,                \
+                  const jit::array<T> &in, int lim)     \
         {                                               \
             for (int i = 0; i < lim; i++) {             \
                 out[i] = char(in[i] != 0);              \
@@ -115,10 +115,10 @@ struct CastWrapper
 {
     Array<To> operator()(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<To, Ti, af_cast_t> *node = new JIT::UnaryNode<To, Ti, af_cast_t>(in_node);
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(
-                                       reinterpret_cast<JIT::Node *>(node)));
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<To, Ti, af_cast_t> *node = new jit::UnaryNode<To, Ti, af_cast_t>(in_node);
+        return createNodeArray<To>(in.dims(), jit::Node_ptr(
+                                       reinterpret_cast<jit::Node *>(node)));
     }
 };
 
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index a20c02825a..a68e8ffbea 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -12,8 +12,8 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <err_cpu.hpp>
-#include <JIT/BinaryNode.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <jit/BinaryNode.hpp>
+#include <jit/UnaryNode.hpp>
 
 namespace cpu
 {
@@ -21,9 +21,9 @@ namespace cpu
     template<typename To, typename Ti>
     struct BinOp<To, Ti, af_cplx2_t>
     {
-        void eval(JIT::array<To> &out,
-                  const JIT::array<Ti> &lhs,
-                  const JIT::array<Ti> &rhs,
+        void eval(jit::array<To> &out,
+                  const jit::array<Ti> &lhs,
+                  const jit::array<Ti> &rhs,
                   int lim)
         {
             for (int i = 0; i < lim; i++) {
@@ -35,22 +35,21 @@ namespace cpu
     template<typename To, typename Ti>
     Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
     {
-        JIT::Node_ptr lhs_node = lhs.getNode();
-        JIT::Node_ptr rhs_node = rhs.getNode();
+        jit::Node_ptr lhs_node = lhs.getNode();
+        jit::Node_ptr rhs_node = rhs.getNode();
 
-        JIT::BinaryNode<To, Ti, af_cplx2_t> *node =
-            new JIT::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
+        jit::BinaryNode<To, Ti, af_cplx2_t> *node =
+            new jit::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
 
-        return createNodeArray<To>(odims,
-                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<To>(odims, jit::Node_ptr(node));
     }
 
 #define CPLX_UNARY_FN(op)                               \
     template<typename To, typename Ti>                  \
     struct UnOp<To, Ti, af_##op##_t>                    \
     {                                                   \
-        void eval(JIT::array<To> &out,                  \
-                  const JIT::array<Ti> &in, int lim)    \
+        void eval(jit::array<To> &out,                  \
+                  const jit::array<Ti> &in, int lim)    \
         {                                               \
             for (int i = 0; i < lim; i++) {             \
                 out[i] = std::op(in[i]);                \
@@ -66,40 +65,40 @@ namespace cpu
     template<typename To, typename Ti>
     Array<To> real(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<To, Ti, af_real_t> *node = new JIT::UnaryNode<To, Ti, af_real_t>(in_node);
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<To, Ti, af_real_t> *node = new jit::UnaryNode<To, Ti, af_real_t>(in_node);
 
         return createNodeArray<To>(in.dims(),
-                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+                                   jit::Node_ptr(static_cast<jit::Node *>(node)));
     }
 
     template<typename To, typename Ti>
     Array<To> imag(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<To, Ti, af_imag_t> *node = new JIT::UnaryNode<To, Ti, af_imag_t>(in_node);
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<To, Ti, af_imag_t> *node = new jit::UnaryNode<To, Ti, af_imag_t>(in_node);
 
         return createNodeArray<To>(in.dims(),
-                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+                                   jit::Node_ptr(static_cast<jit::Node *>(node)));
     }
 
     template<typename To, typename Ti>
     Array<To> abs(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<To, Ti, af_abs_t> *node = new JIT::UnaryNode<To, Ti, af_abs_t>(in_node);
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<To, Ti, af_abs_t> *node = new jit::UnaryNode<To, Ti, af_abs_t>(in_node);
 
         return createNodeArray<To>(in.dims(),
-                                   JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+                                   jit::Node_ptr(static_cast<jit::Node *>(node)));
     }
 
     template<typename T>
     Array<T> conj(const Array<T> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<T, T, af_conj_t> *node = new JIT::UnaryNode<T, T, af_conj_t>(in_node);
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<T, T, af_conj_t> *node = new jit::UnaryNode<T, T, af_conj_t>(in_node);
 
         return createNodeArray<T>(in.dims(),
-                                  JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+                                  jit::Node_ptr(static_cast<jit::Node *>(node)));
     }
 }
diff --git a/src/backend/cpu/JIT/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
similarity index 91%
rename from src/backend/cpu/JIT/BinaryNode.hpp
rename to src/backend/cpu/jit/BinaryNode.hpp
index f1d356da0e..3a2564637b 100644
--- a/src/backend/cpu/JIT/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -20,9 +20,9 @@ namespace cpu
     template<typename To, typename Ti, af_op_t op>
     struct BinOp
     {
-        void eval(JIT::array<To> &out,
-                  const JIT::array<Ti> &lhs,
-                  const JIT::array<Ti> &rhs,
+        void eval(jit::array<To> &out,
+                  const jit::array<Ti> &lhs,
+                  const jit::array<Ti> &rhs,
                   int lim) const
         {
             for (int i = 0; i < lim; i++) {
@@ -31,7 +31,7 @@ namespace cpu
         }
     };
 
-namespace JIT
+namespace jit
 {
 
     template<typename To, typename Ti, af_op_t op>
diff --git a/src/backend/cpu/JIT/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
similarity index 99%
rename from src/backend/cpu/JIT/BufferNode.hpp
rename to src/backend/cpu/jit/BufferNode.hpp
index b49b38a36a..f81cc49902 100644
--- a/src/backend/cpu/JIT/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
 
-namespace JIT
+namespace jit
 {
 
     using std::shared_ptr;
diff --git a/src/backend/cpu/JIT/Node.hpp b/src/backend/cpu/jit/Node.hpp
similarity index 77%
rename from src/backend/cpu/JIT/Node.hpp
rename to src/backend/cpu/jit/Node.hpp
index 98d076bcb7..f7ffc2eced 100644
--- a/src/backend/cpu/JIT/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -15,46 +15,41 @@
 #include <unordered_map>
 
 namespace common {
+    template<typename T>
     class NodeIterator;
 }
 
 namespace cpu
 {
 
-namespace JIT
+namespace jit
 {
-
-    static const int VECTOR_LENGTH = 256;
-    static const int MAX_CHILDREN = 3;
-
     class Node;
-    using std::shared_ptr;
-    using std::vector;
-    typedef shared_ptr<Node> Node_ptr;
+    constexpr int VECTOR_LENGTH = 256;
 
-    typedef std::unordered_map<Node *, int> Node_map_t;
-    typedef Node_map_t::iterator Node_map_iter;
+    using Node_ptr = std::shared_ptr<Node>;
+    using Node_map_t = std::unordered_map<Node *, int>;
+    using Node_map_iter = Node_map_t::iterator;
 
     template<typename T>
     using array = std::array<T, VECTOR_LENGTH>;
 
-
     class Node
     {
-
+    public:
+        static const int kMaxChildren = 2;
     protected:
-
         const int m_height;
-        const std::array<Node_ptr, MAX_CHILDREN> m_children;
-        friend common::NodeIterator;
+        const std::array<Node_ptr, kMaxChildren> m_children;
+        template<typename T> friend class common::NodeIterator;
 
     public:
-        Node(const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
+        Node(const int height, const std::array<Node_ptr, kMaxChildren> children) :
             m_height(height),
             m_children(children)
         {}
 
-        int getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes)
+        int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes)
         {
             auto iter = node_map.find(this);
             if (iter == node_map.end()) {
@@ -98,9 +93,9 @@ namespace JIT
     class TNode : public Node
     {
     public:
-        alignas(16) JIT::array<T> m_val;
+        alignas(16) jit::array<T> m_val;
     public:
-        TNode(T val, const int height, const std::array<Node_ptr, MAX_CHILDREN> children) :
+        TNode(T val, const int height, const std::array<Node_ptr, kMaxChildren> children) :
             Node(height, children)
             {
                 m_val.fill(val);
diff --git a/src/backend/cpu/JIT/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
similarity index 97%
rename from src/backend/cpu/JIT/ScalarNode.hpp
rename to src/backend/cpu/jit/ScalarNode.hpp
index 00e2dc23c2..5adae29f22 100644
--- a/src/backend/cpu/JIT/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -15,7 +15,7 @@
 namespace cpu
 {
 
-namespace JIT
+namespace jit
 {
 
     template<typename T>
diff --git a/src/backend/cpu/JIT/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
similarity index 92%
rename from src/backend/cpu/JIT/UnaryNode.hpp
rename to src/backend/cpu/jit/UnaryNode.hpp
index ebbf3c32da..85eed07055 100644
--- a/src/backend/cpu/JIT/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -18,8 +18,8 @@ namespace cpu
     template<typename To, typename Ti, af_op_t op>
     struct UnOp
     {
-        void eval(JIT::array<To> &out,
-                  const JIT::array<Ti> &in, int lim) const
+        void eval(jit::array<To> &out,
+                  const jit::array<Ti> &in, int lim) const
         {
             for (int i = 0; i < lim; i++) {
                 out[i] = To(in[i]);
@@ -27,7 +27,7 @@ namespace cpu
         }
     };
 
-namespace JIT
+namespace jit
 {
 
     template<typename To, typename Ti, af_op_t op>
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 884beeab43..85a50a8879 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <Param.hpp>
 #include <platform.hpp>
-#include <JIT/Node.hpp>
+#include <jit/Node.hpp>
 #include <vector>
 
 namespace cpu
@@ -19,20 +19,20 @@ namespace kernel
 {
 
 template<typename T>
-void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> output_nodes_)
+void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> output_nodes_)
 {
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
-    JIT::Node_map_t nodes;
+    jit::Node_map_t nodes;
     std::vector<T *> ptrs;
-    std::vector<JIT::TNode<T> *> output_nodes;
-    std::vector<JIT::Node *> full_nodes;
+    std::vector<jit::TNode<T> *> output_nodes;
+    std::vector<jit::Node *> full_nodes;
 
     int narrays = static_cast<int>(arrays.size());
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
-        output_nodes.push_back(reinterpret_cast<JIT::TNode<T> *>(output_nodes_[i].get()));
+        output_nodes.push_back(reinterpret_cast<jit::TNode<T> *>(output_nodes_[i].get()));
         output_nodes_[i]->getNodesMap(nodes, full_nodes);
     }
 
@@ -43,9 +43,9 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> outpu
 
     if (is_linear) {
         int num = arrays[0].dims().elements();
-        int cnum = JIT::VECTOR_LENGTH * std::ceil(double(num) / JIT::VECTOR_LENGTH);
-        for (int i = 0; i < cnum; i += JIT::VECTOR_LENGTH) {
-            int lim = std::min(JIT::VECTOR_LENGTH, num - i);
+        int cnum = jit::VECTOR_LENGTH * std::ceil(double(num) / jit::VECTOR_LENGTH);
+        for (int i = 0; i < cnum; i += jit::VECTOR_LENGTH) {
+            int lim = std::min(jit::VECTOR_LENGTH, num - i);
             for (int n = 0; n < (int)full_nodes.size(); n++) {
                 full_nodes[n]->calc(i, lim);
             }
@@ -67,9 +67,9 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> outpu
                     dim_t offy = y * ostrs[1] + offz;
 
                     int dim0 = odims[0];
-                    int cdim0 = JIT::VECTOR_LENGTH * std::ceil(double(dim0) / JIT::VECTOR_LENGTH);
-                    for (int x = 0; x < (int)cdim0; x += JIT::VECTOR_LENGTH) {
-                        int lim = std::min(JIT::VECTOR_LENGTH, dim0 - x);
+                    int cdim0 = jit::VECTOR_LENGTH * std::ceil(double(dim0) / jit::VECTOR_LENGTH);
+                    for (int x = 0; x < (int)cdim0; x += jit::VECTOR_LENGTH) {
+                        int lim = std::min(jit::VECTOR_LENGTH, dim0 - x);
                         dim_t id = x + offy;
 
                         for (int n = 0; n < (int)full_nodes.size(); n++) {
@@ -88,7 +88,7 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<JIT::Node_ptr> outpu
 }
 
 template<typename T>
-void evalArray(Param<T> arr, JIT::Node_ptr node)
+void evalArray(Param<T> arr, jit::Node_ptr node)
 {
     evalMultiple<T>({arr}, {node});
 }
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index 07ed3955ca..726643fd6a 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -12,7 +12,7 @@
 #include <optypes.hpp>
 #include <err_cpu.hpp>
 #include <types.hpp>
-#include <JIT/BinaryNode.hpp>
+#include <jit/BinaryNode.hpp>
 
 namespace cpu
 {
@@ -21,9 +21,9 @@ namespace cpu
     template<typename T>                        \
     struct BinOp<char, T, OP>                   \
     {                                           \
-        void eval(JIT::array<char> &out,        \
-                  const JIT::array<T> &lhs,     \
-                  const JIT::array<T> &rhs,     \
+        void eval(jit::array<char> &out,        \
+                  const jit::array<T> &lhs,     \
+                  const jit::array<T> &rhs,     \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -49,9 +49,9 @@ namespace cpu
     struct BinOp<char, std::complex<T>, OP>     \
     {                                           \
         typedef std::complex<T> Ti;             \
-        void eval(JIT::array<char> &out,        \
-                  const JIT::array<Ti> &lhs,    \
-                  const JIT::array<Ti> &rhs,    \
+        void eval(jit::array<char> &out,        \
+                  const jit::array<Ti> &lhs,    \
+                  const jit::array<Ti> &rhs,    \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -82,13 +82,12 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T, af_op_t op>
     Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
     {
-        JIT::Node_ptr lhs_node = lhs.getNode();
-        JIT::Node_ptr rhs_node = rhs.getNode();
+        jit::Node_ptr lhs_node = lhs.getNode();
+        jit::Node_ptr rhs_node = rhs.getNode();
 
-        JIT::BinaryNode<char, T, op> *node = new JIT::BinaryNode<char, T, op>(lhs_node, rhs_node);
+        jit::BinaryNode<char, T, op> *node = new jit::BinaryNode<char, T, op>(lhs_node, rhs_node);
 
-        return createNodeArray<char>(odims,
-                                     JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<char>(odims, jit::Node_ptr(node));
     }
 
 
@@ -97,9 +96,9 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T>                        \
     struct BinOp<T, T, OP>                      \
     {                                           \
-        void eval(JIT::array<T> &out,           \
-                  const JIT::array<T> &lhs,     \
-                  const JIT::array<T> &rhs,     \
+        void eval(jit::array<T> &out,           \
+                  const jit::array<T> &lhs,     \
+                  const jit::array<T> &rhs,     \
                   int lim)                      \
         {                                       \
             for (int i = 0; i < lim; i++) {     \
@@ -119,12 +118,11 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
     template<typename T, af_op_t op>
     Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
     {
-        JIT::Node_ptr lhs_node = lhs.getNode();
-        JIT::Node_ptr rhs_node = rhs.getNode();
+        jit::Node_ptr lhs_node = lhs.getNode();
+        jit::Node_ptr rhs_node = rhs.getNode();
 
-        JIT::BinaryNode<T, T, op> *node = new JIT::BinaryNode<T, T, op>(lhs_node, rhs_node);
+        jit::BinaryNode<T, T, op> *node = new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-        return createNodeArray<T>(odims,
-                                  JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<T>(odims, jit::Node_ptr(node));
     }
 }
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index b6b2f8010e..f81ad78442 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <err_cpu.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <jit/UnaryNode.hpp>
 #include <cmath>
 
 namespace cpu
@@ -26,8 +26,8 @@ T sigmoid(T in)
     template<typename T>                            \
     struct UnOp<T, T, af_##op##_t>                  \
     {                                               \
-        void eval(JIT::array<T> &out,               \
-                  const JIT::array<T> &in, int lim) \
+        void eval(jit::array<T> &out,               \
+                  const jit::array<T> &in, int lim) \
         {                                           \
             for (int i = 0; i < lim; i++) {         \
                 out[i] = fn(in[i]);                 \
@@ -82,11 +82,10 @@ UNARY_OP(lgamma)
     template<typename T, af_op_t op>
     Array<T> unaryOp(const Array<T> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<T, T, op> *node = new JIT::UnaryNode<T, T, op>(in_node);
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<T, T, op> *node = new jit::UnaryNode<T, T, op>(in_node);
 
-        return createNodeArray<T>(in.dims(),
-                                  JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<T>(in.dims(), jit::Node_ptr(node));
     }
 
 #define iszero(a) ((a) == 0)
@@ -95,8 +94,8 @@ UNARY_OP(lgamma)
     template<typename T>                            \
     struct UnOp<char, T, af_##name##_t>             \
     {                                               \
-        void eval(JIT::array<char> &out,            \
-                  const JIT::array<T> &in, int lim) \
+        void eval(jit::array<char> &out,            \
+                  const jit::array<T> &in, int lim) \
         {                                           \
             for (int i = 0; i < lim; i++) {         \
                 out[i] = op(in[i]);                 \
@@ -111,11 +110,10 @@ UNARY_OP(lgamma)
     template<typename T, af_op_t op>
     Array<char> checkOp(const Array<T> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode<char, T, op> *node = new JIT::UnaryNode<char, T, op>(in_node);
+        jit::Node_ptr in_node = in.getNode();
+        jit::UnaryNode<char, T, op> *node = new jit::UnaryNode<char, T, op>(in_node);
 
-        return createNodeArray<char>(in.dims(),
-                                     JIT::Node_ptr(reinterpret_cast<JIT::Node *>(node)));
+        return createNodeArray<char>(in.dims(), jit::Node_ptr(node));
     }
 
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 69aa3b7530..87663bad16 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <JIT/BufferNode.hpp>
-#include <common/NodeIterator.hpp>
+#include <jit/BufferNode.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <af/dim4.hpp>
 #include <copy.hpp>
 #include <err_cuda.hpp>
@@ -22,10 +22,10 @@
 #include <numeric>
 
 using af::dim4;
-using cuda::JIT::BufferNode;
-using cuda::JIT::Node;
+using cuda::jit::BufferNode;
+using common::Node;
 using common::NodeIterator;
-using cuda::JIT::Node_ptr;
+using common::Node_ptr;
 using std::accumulate;
 using std::shared_ptr;
 
@@ -89,7 +89,7 @@ namespace cuda
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, JIT::Node_ptr n) :
+    Array<T>::Array(af::dim4 dims, common::Node_ptr n) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(), data_dims(dims),
         node(n), ready(false), owner(true)
@@ -148,7 +148,7 @@ namespace cuda
     void evalMultiple(std::vector<Array<T>*> arrays)
     {
         std::vector<Param<T> > outputs;
-        std::vector<JIT::Node *> nodes;
+        std::vector<common::Node *> nodes;
 
         for (int i = 0; i < (int)arrays.size(); i++) {
             Array<T> *array = arrays[i];
@@ -253,9 +253,9 @@ namespace cuda
                         int param_scalar_size;
                         bool is_linear;
                     };
-                    NodeIterator end_node;
+                    NodeIterator<> end_node;
                     dim4 outdim = out.dims();
-                    tree_info info = accumulate(NodeIterator(n), end_node,
+                    tree_info info = accumulate(NodeIterator<>(n), end_node,
                                                 tree_info{0, 0, 0, true},
                                                 [=](tree_info& prev, const Node& node) {
                                                     if(node.isBuffer()) {
@@ -427,7 +427,7 @@ namespace cuda
                                                        const std::vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
+    template       Array<T>  createNodeArray<T>       (const dim4 &size, common::Node_ptr node); \
     template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
                                    const T * const in_data,             \
                                    bool is_device);                     \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index dfce67ea16..e22f4a1b03 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -17,7 +17,7 @@
 #include <cuda.h>
 #include <cuda_runtime_api.h>
 #include <Param.hpp>
-#include <JIT/Node.hpp>
+#include <common/jit/Node.hpp>
 #include <vector>
 #include <memory.hpp>
 
@@ -28,16 +28,16 @@ namespace cuda
     template<typename T> class Array;
 
     template<typename T>
-    void evalNodes(Param<T> out, JIT::Node *node);
+    void evalNodes(Param<T> out, common::Node *node);
 
     template<typename T>
-    void evalNodes(std::vector<Param<T> > &out, std::vector<JIT::Node *> nodes);
+    void evalNodes(std::vector<Param<T> > &out, std::vector<common::Node *> nodes);
 
     template<typename T>
     void evalMultiple(std::vector<Array<T> *> arrays);
 
     template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
+    Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
 
     template<typename T>
     Array<T> createValueArray(const af::dim4 &size, const T& value);
@@ -105,7 +105,7 @@ namespace cuda
         std::shared_ptr<T> data;
         af::dim4 data_dims;
 
-        JIT::Node_ptr node;
+        common::Node_ptr node;
         bool ready;
         bool owner;
 
@@ -114,7 +114,7 @@ namespace cuda
         explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false, bool copy_device = false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
         Array(Param<T> &tmp, bool owner);
-        Array(af::dim4 dims, JIT::Node_ptr n);
+        Array(af::dim4 dims, common::Node_ptr n);
     public:
 
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,
@@ -224,8 +224,8 @@ namespace cuda
             return CParam<T>(this->get(), this->dims().get(), this->strides().get());
         }
 
-        JIT::Node_ptr getNode();
-        JIT::Node_ptr getNode() const;
+        common::Node_ptr getNode();
+        common::Node_ptr getNode() const;
 
         friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
@@ -235,7 +235,7 @@ namespace cuda
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
         friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, JIT::Node_ptr node);
+        friend Array<T> createNodeArray<T>(const af::dim4 &dims, common::Node_ptr node);
 
         friend Array<T> createSubArray<T>(const Array<T>& parent,
                                           const std::vector<af_seq> &index,
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6bab2313ab..d52569b9ac 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -41,7 +41,7 @@ cuda_include_directories(
   ${ArrayFire_SOURCE_DIR}/include
   ${ArrayFire_BINARY_DIR}/include
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel
-  ${CMAKE_CURRENT_SOURCE_DIR}/JIT
+  ${CMAKE_CURRENT_SOURCE_DIR}/jit
   ${CMAKE_CURRENT_SOURCE_DIR}/cub
   ${ArrayFire_SOURCE_DIR}/src/api/c
   ${ArrayFire_SOURCE_DIR}/src/backend
@@ -122,6 +122,9 @@ include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
 cuda_add_library(afcuda
     scan.cu
+    kernel/convolve.cu
+    kernel/convolve_separable.cu
+
     all.cu
     anisotropic_diffusion.cu
     any.cu
@@ -198,9 +201,6 @@ cuda_add_library(afcuda
     where.cu
     wrap.cu
 
-    kernel/convolve.cu
-    kernel/convolve_separable.cu
-
     kernel/anisotropic_diffusion.hpp
     kernel/approx.hpp
     kernel/assign.hpp
@@ -383,7 +383,6 @@ cuda_add_library(afcuda
     transform.hpp
     transpose.hpp
     triangle.hpp
-    types.cpp
     types.hpp
     unary.hpp
     unwrap.hpp
@@ -391,15 +390,8 @@ cuda_add_library(afcuda
     where.hpp
     wrap.hpp
 
-    JIT/BinaryNode.hpp
-    JIT/BufferNode.hpp
-    JIT/Node.hpp
-    JIT/Node.cpp
-    JIT/ScalarNode.hpp
-    JIT/UnaryNode.hpp
-    JIT/NaryNode.hpp
-    JIT/ShiftNode.hpp
-    JIT/types.h
+    jit/BufferNode.hpp
+    jit/kernel_generators.hpp
 
     OPTIONS "${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
   )
@@ -445,7 +437,7 @@ target_include_directories (afcuda
     ${ArrayFire_SOURCE_DIR}/src/api/c
     ${CMAKE_CURRENT_SOURCE_DIR}
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel
-    ${CMAKE_CURRENT_SOURCE_DIR}/JIT
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit
     ${CMAKE_CURRENT_BINARY_DIR}
 )
 
diff --git a/src/backend/cuda/JIT/BinaryNode.hpp b/src/backend/cuda/JIT/BinaryNode.hpp
deleted file mode 100644
index f58b84e945..0000000000
--- a/src/backend/cuda/JIT/BinaryNode.hpp
+++ /dev/null
@@ -1,32 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "NaryNode.hpp"
-#include <iomanip>
-
-namespace cuda
-{
-
-namespace JIT
-{
-    class BinaryNode : public NaryNode
-    {
-    public:
-        BinaryNode(const char *out_type_str, const char *name_str,
-                   const char *op_str,
-                   Node_ptr lhs, Node_ptr rhs, int op)
-            : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}},
-                       op, std::max(lhs->getHeight(), rhs->getHeight()) + 1)
-        {
-        }
-    };
-}
-
-}
diff --git a/src/backend/cuda/JIT/BufferNode.hpp b/src/backend/cuda/JIT/BufferNode.hpp
deleted file mode 100644
index 1850e59839..0000000000
--- a/src/backend/cuda/JIT/BufferNode.hpp
+++ /dev/null
@@ -1,129 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "../Param.hpp"
-#include "Node.hpp"
-#include <iomanip>
-#include <mutex>
-
-namespace cuda
-{
-
-namespace JIT
-{
-    template<typename T>
-    class BufferNode : public Node
-    {
-    private:
-        std::shared_ptr<T> m_data;
-        Param<T> m_param;
-        unsigned m_bytes;
-        std::once_flag m_set_data_flag;
-        bool m_linear_buffer;
-
-    public:
-
-        BufferNode(const char *type_str,
-                   const char *name_str)
-            : Node(type_str, name_str, 0, {})
-        {
-        }
-
-        bool isBuffer() const final { return true; }
-
-        void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
-        {
-            std::call_once(m_set_data_flag, [this, param, data, bytes, is_linear]() {
-                    m_param = param;
-                    m_data = data;
-                    m_bytes = bytes;
-                    m_linear_buffer = is_linear;
-                });
-        }
-
-        bool isLinear(dim_t dims[4]) const final
-        {
-            bool same_dims = true;
-            for (int i = 0; same_dims && i < 4; i++) {
-                same_dims &= (dims[i] == m_param.dims[i]);
-            }
-            return m_linear_buffer && same_dims;
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            if (is_linear) {
-                kerStream << m_type_str << " *in" << id << "_ptr,\n";
-            } else {
-                kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
-            }
-        }
-
-        void setArgs(std::vector<void *> &args, bool is_linear) const final
-        {
-            if (is_linear) {
-                args.push_back((void *)&m_param.ptr);
-            } else {
-                args.push_back((void *)&m_param);
-            }
-        }
-
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            std::string idx_str = std::string("int idx") + std::to_string(id);
-
-            if (is_linear) {
-                kerStream << idx_str << " = idx;\n";
-            } else {
-                std::string info_str = std::string("in") + std::to_string(id);
-                kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                          << info_str << ".strides[3] * id3 + (id2 < " << info_str << ".dims[2]) * "
-                          << info_str << ".strides[2] * id2 + (id1 < " << info_str << ".dims[1]) * "
-                          << info_str << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * id0;\n";
-                kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
-            }
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id
-                      << " = in" << ids.id << "_ptr[idx" << ids.id << "];\n";
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
-        {
-            len++;
-            buf_count++;
-            bytes += m_bytes;
-            return;
-        }
-
-        // Return the size of the size of the buffer node in bytes. Zero otherwise
-        virtual size_t getBytes() const final {
-            return m_bytes;
-        }
-
-        // Return the size of the parameter in bytes that will be passed to the
-        // kernel
-        virtual short getParamBytes() const final {
-            return m_linear_buffer ? sizeof(T*) : sizeof(Param<T>);
-        }
-    };
-
-
-}
-
-}
diff --git a/src/backend/cuda/JIT/NaryNode.hpp b/src/backend/cuda/JIT/NaryNode.hpp
deleted file mode 100644
index a3a650796d..0000000000
--- a/src/backend/cuda/JIT/NaryNode.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*******************************************************
- * Copyright (c) 2018, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "Node.hpp"
-#include <iomanip>
-
-namespace cuda
-{
-namespace JIT
-{
-    class NaryNode : public Node
-    {
-    private:
-        const int m_num_children;
-        const int m_op;
-        const std::string m_op_str;
-
-    public:
-        NaryNode(const char *out_type_str,
-                 const char *name_str,
-                 const char *op_str,
-                 const int num_children,
-                 const std::array<Node_ptr, MAX_CHILDREN> &children,
-                 const int op, const int height)
-            : Node(out_type_str, name_str, height, children),
-              m_num_children(num_children),
-              m_op(op),
-              m_op_str(op_str)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            // Make the dec representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            for (int i = 0; i < m_num_children; i++) {
-                kerStream << std::setw(3)
-                          << std::setfill('0')
-                          << std::dec
-                          << ids.child_ids[i];
-            }
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
-            for (int i = 0; i < m_num_children; i++) {
-                if (i > 0) kerStream << ", ";
-                kerStream << "val" << ids.child_ids[i];
-            }
-            kerStream << ");\n";
-        }
-    };
-}
-}
diff --git a/src/backend/cuda/JIT/ShiftNode.hpp b/src/backend/cuda/JIT/ShiftNode.hpp
deleted file mode 100644
index d52cf7da8f..0000000000
--- a/src/backend/cuda/JIT/ShiftNode.hpp
+++ /dev/null
@@ -1,109 +0,0 @@
-/*******************************************************
- * Copyright (c) 2018, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "BufferNode.hpp"
-#include "Node.hpp"
-#include <iomanip>
-#include <mutex>
-
-namespace cuda
-{
-namespace JIT
-{
-    template<typename T>
-    class ShiftNode : public Node
-    {
-    private:
-        std::shared_ptr<BufferNode<T>> m_buffer_node;
-        const std::array<int, 4> m_shifts;
-
-    public:
-
-        ShiftNode(const char *type_str,
-                  const char *name_str,
-                  std::shared_ptr<BufferNode<T>> buffer_node,
-                  const std::array<int, 4> shifts)
-            : Node(type_str, name_str, 0, {}),
-              m_buffer_node(buffer_node),
-              m_shifts(shifts)
-        {
-        }
-
-        void setData(Param<T> param, std::shared_ptr<T> data, const unsigned bytes, bool is_linear)
-        {
-            m_buffer_node->setData(param, data, bytes, is_linear);
-        }
-
-        bool isLinear(dim_t dims[4]) const final
-        {
-            return false;
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            m_buffer_node->genParams(kerStream, id, is_linear);
-            for (int i = 0; i < 4; i++) {
-                kerStream << "int shift" << id << "_" << i << ",\n";
-            }
-        }
-
-        void setArgs(std::vector<void *> &args, bool is_linear) const final
-        {
-            m_buffer_node->setArgs(args, is_linear);
-            for (int i = 0; i < 4; i++) {
-                const int &d = m_shifts[i];
-                args.push_back((void *)&d);
-            }
-        }
-
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            std::string idx_str = std::string("idx") + std::to_string(id);
-            std::string info_str = std::string("in") + std::to_string(id);
-            std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
-            std::string shift_str = std::string("shift") + std::to_string(id) + "_";
-
-            for (int i = 0; i < 4; i++) {
-                kerStream << "int " << id_str << i
-                          << " = __circular_mod(id" << i
-                          << " + " << shift_str << i
-                          << ", " << info_str << ".dims[" << i << "]);\n";
-            }
-
-            kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str << ".dims[3]) * "
-                      << info_str << ".strides[3] * " << id_str << "3;\n";
-            kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
-                      << info_str << ".strides[2] * " << id_str << "2;\n";
-            kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
-                      << info_str << ".strides[1] * " << id_str << "1;\n";
-            kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
-                      << id_str << "0;\n";
-            kerStream << m_type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id
-                      << " = in" << ids.id << "_ptr[idx" << ids.id << "];\n";
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
-        {
-            m_buffer_node->getInfo(len, buf_count, bytes);
-        }
-    };
-}
-}
diff --git a/src/backend/cuda/JIT/UnaryNode.hpp b/src/backend/cuda/JIT/UnaryNode.hpp
deleted file mode 100644
index 6bdecc0f0f..0000000000
--- a/src/backend/cuda/JIT/UnaryNode.hpp
+++ /dev/null
@@ -1,32 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "NaryNode.hpp"
-#include <iomanip>
-
-namespace cuda
-{
-
-namespace JIT
-{
-    class UnaryNode : public NaryNode
-    {
-    public:
-        UnaryNode(const char *out_type_str, const char *name_str,
-                  const char *op_str,
-                  Node_ptr child, int op)
-            : NaryNode(out_type_str, name_str, op_str,
-                       1, {{child}}, op, child->getHeight() + 1)
-        {
-        }
-    };
-}
-
-}
diff --git a/src/backend/cuda/JIT/types.h b/src/backend/cuda/JIT/types.h
deleted file mode 100644
index 4a97ef3842..0000000000
--- a/src/backend/cuda/JIT/types.h
+++ /dev/null
@@ -1,21 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <cuComplex.h>
-#include <math_functions.h>
-typedef unsigned char uchar;
-typedef unsigned int uint;
-typedef unsigned short ushort;
-typedef cuFloatComplex cfloat;
-typedef cuDoubleComplex cdouble;
-typedef long long intl;
-typedef unsigned long long uintl;
-
-__device__ __inline__ float cabs2(cfloat in) { return in.x * in.x + in.y * in.y;}
-__device__ __inline__ double cabs2(cdouble in) { return in.x * in.x + in.y * in.y; }
diff --git a/src/backend/cuda/backend.hpp b/src/backend/cuda/backend.hpp
index 475a343a0a..d785844dfd 100644
--- a/src/backend/cuda/backend.hpp
+++ b/src/backend/cuda/backend.hpp
@@ -19,6 +19,6 @@
 #define __DH__
 #endif
 
-#include "types.hpp"
+namespace cuda {}
 
 namespace detail = cuda;
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index 0b072c157f..f07199b478 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <JIT/BinaryNode.hpp>
+#include <common/jit/BinaryNode.hpp>
 
 namespace cuda
 {
@@ -192,15 +192,15 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af:
 {
     BinOp<To, Ti, op> bop;
 
-    JIT::Node_ptr lhs_node = lhs.getNode();
-    JIT::Node_ptr rhs_node = rhs.getNode();
-    JIT::BinaryNode *node = new JIT::BinaryNode(getFullName<To>(),
-                                                shortname<To>(true),
-                                                bop.name(),
-                                                lhs_node,
-                                                rhs_node, (int)(op));
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
+    common::BinaryNode *node = new common::BinaryNode(getFullName<To>(),
+                                                      shortname<To>(true),
+                                                      bop.name(),
+                                                      lhs_node,
+                                                      rhs_node, (int)(op));
 
-    return createNodeArray<To>(odims, JIT::Node_ptr(node));
+    return createNodeArray<To>(odims, common::Node_ptr(node));
 }
 
 }
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 5b1ffe3a96..2c219dbcce 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -13,7 +13,7 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
 #include <types.hpp>
 #include <Array.hpp>
 
@@ -106,12 +106,12 @@ struct CastWrapper
     Array<To> operator()(const Array<Ti> &in)
     {
         CastOp<To, Ti> cop;
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
-                                                  shortname<To>(true),
-                                                  cop.name(),
-                                                  in_node, af_cast_t);
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
+                                                        shortname<To>(true),
+                                                        cop.name(),
+                                                        in_node, af_cast_t);
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 };
 
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 578d982087..945c545df5 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -11,7 +11,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <binary.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
 
 namespace cuda
 {
@@ -24,25 +24,25 @@ namespace cuda
     template<typename To, typename Ti>
     Array<To> real(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
-                                                  shortname<To>(true),
-                                                  "__creal",
-                                                  in_node, af_real_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
+                                                        shortname<To>(true),
+                                                        "__creal",
+                                                        in_node, af_real_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 
     template<typename To, typename Ti>
     Array<To> imag(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
-                                                  shortname<To>(true),
-                                                  "__cimag",
-                                                  in_node, af_imag_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
+                                                        shortname<To>(true),
+                                                        "__cimag",
+                                                        in_node, af_imag_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 
     template<typename T> static const char *abs_name() { return "fabs"; }
@@ -52,13 +52,13 @@ namespace cuda
     template<typename To, typename Ti>
     Array<To> abs(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<To>(),
-                                                  shortname<To>(true),
-                                                  abs_name<Ti>(),
-                                                  in_node, af_abs_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
+                                                        shortname<To>(true),
+                                                        abs_name<Ti>(),
+                                                        in_node, af_abs_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 
     template<typename T> static const char *conj_name() { return "__noop"; }
@@ -68,12 +68,12 @@ namespace cuda
     template<typename T>
     Array<T> conj(const Array<T> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<T>(),
-                                                  shortname<T>(true),
-                                                  conj_name<T>(),
-                                                  in_node, af_conj_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(getFullName<T>(),
+                                                        shortname<T>(true),
+                                                        conj_name<T>(),
+                                                        in_node, af_conj_t);
 
-        return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<T>(in.dims(), common::Node_ptr(node));
     }
 }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 9de916865a..427dec5910 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -13,7 +13,7 @@
 
 #include <Array.hpp>
 #include <copy.hpp>
-#include <JIT/Node.hpp>
+#include <common/jit/Node.hpp>
 #include <platform.hpp>
 #include <math.hpp>
 
@@ -32,9 +32,9 @@
 namespace cuda
 {
 
-using JIT::Node;
-using JIT::Node_ids;
-using JIT::Node_map_t;
+using common::Node;
+using common::Node_ids;
+using common::Node_map_t;
 
 using std::array;
 using std::hash;
@@ -455,7 +455,9 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
     vector<void *> args;
 
     for (const auto &node : full_nodes) {
-        node->setArgs(args, is_linear);
+        node->setArgs(0, is_linear, [&] (int id, const void* ptr, size_t size){
+                                        args.push_back(const_cast<void*>(ptr));
+                                    });
     }
 
     for (int i = 0; i < num_outputs; i++) {
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
new file mode 100644
index 0000000000..3d27022881
--- /dev/null
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include "../Param.hpp"
+#include <common/jit/BufferNodeBase.hpp>
+
+namespace cuda
+{
+namespace jit
+{
+  template<typename T>
+  using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
+}
+}
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
new file mode 100644
index 0000000000..de3d933cfc
--- /dev/null
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -0,0 +1,102 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <common/jit/Node.hpp>
+
+#include <functional>
+#include <iomanip>
+#include <memory>
+#include <sstream>
+#include <string>
+
+namespace cuda {
+
+namespace {
+
+  /// Creates a string that will be used to declare the parameter of kernel
+  void generateParamDeclaration(std::stringstream& kerStream, int id, bool is_linear,
+                                const std::string& m_type_str) {
+      if (is_linear) {
+          kerStream << m_type_str << " *in" << id << "_ptr,\n";
+      } else {
+          kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
+      }
+  }
+
+
+  /// Calls the setArg function to set the arguments for a kernel call
+  template<typename T>
+  int setKernelArguments(int start_id, bool is_linear,
+                         std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+                         const std::shared_ptr<T>& ptr, const Param<T>& info) {
+      if (is_linear) {
+          setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
+      } else {
+          setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
+      }
+      return start_id + 1;
+  }
+
+  /// Generates the code to calculate the offsets for a buffer
+  void generateBufferOffsets(std::stringstream &kerStream, int id,
+                             bool is_linear, const std::string& type_str) {
+      std::string idx_str = std::string("int idx") + std::to_string(id);
+
+      if (is_linear) {
+          kerStream << idx_str << " = idx;\n";
+      } else {
+          std::string info_str = std::string("in") + std::to_string(id);
+          kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                    << info_str << ".strides[3] * id3 + (id2 < " << info_str << ".dims[2]) * "
+                    << info_str << ".strides[2] * id2 + (id1 < " << info_str << ".dims[1]) * "
+                    << info_str << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * id0;\n";
+          kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
+      }
+  }
+
+  /// Generates the code to read a buffer and store it in a local variable
+  void generateBufferRead(std::stringstream &kerStream, int id,
+                          const std::string& type_str) {
+      kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id << "];\n";
+  }
+
+  void generateShiftNodeOffsets(std::stringstream &kerStream, int id,
+                                bool is_linear, const std::string& type_str) {
+      std::string idx_str = std::string("idx") + std::to_string(id);
+      std::string info_str = std::string("in") + std::to_string(id);
+      std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
+      std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+
+      for (int i = 0; i < 4; i++) {
+          kerStream << "int " << id_str << i
+                    << " = __circular_mod(id" << i
+                    << " + " << shift_str << i
+                    << ", " << info_str << ".dims[" << i << "]);\n";
+      }
+
+      kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str << ".dims[3]) * "
+                << info_str << ".strides[3] * " << id_str << "3;\n";
+      kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
+                << info_str << ".strides[2] * " << id_str << "2;\n";
+      kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
+                << info_str << ".strides[1] * " << id_str << "1;\n";
+      kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
+                << id_str << "0;\n";
+      kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
+  }
+
+  void generateShiftNodeRead(std::stringstream &kerStream, int id,
+                             const std::string& type_str) {
+      kerStream << type_str << " val" << id
+                << " = in" << id << "_ptr[idx" << id << "];\n";
+  }
+
+}
+}
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 7d5cf7beba..8f578abf92 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -1,6 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
 #include <debug_cuda.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
 #include <thrust/sort.h>
+#include <types.hpp>
 
 namespace cuda
 {
diff --git a/src/backend/cuda/scalar.hpp b/src/backend/cuda/scalar.hpp
index 46fca748a8..4a23315679 100644
--- a/src/backend/cuda/scalar.hpp
+++ b/src/backend/cuda/scalar.hpp
@@ -10,7 +10,8 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <JIT/ScalarNode.hpp>
+#include <common/jit/ScalarNode.hpp>
+#include <memory>
 
 namespace cuda
 {
@@ -18,7 +19,7 @@ namespace cuda
 template<typename T>
 Array<T> createScalarNode(const dim4 &size, const T val)
 {
-    return createNodeArray<T>(size, JIT::Node_ptr(new JIT::ScalarNode<T>(val)));
+    return createNodeArray<T>(size, std::make_shared<common::ScalarNode<T>>(val));
 }
 
 }
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index a5d6e6fe2a..aca530d5bd 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -11,7 +11,10 @@
 #include <scalar.hpp>
 #include <err_cuda.hpp>
 #include <kernel/select.hpp>
-#include <JIT/NaryNode.hpp>
+#include <common/jit/NaryNode.hpp>
+
+using common::NaryNode;
+using common::Node_ptr;
 
 namespace cuda
 {
@@ -42,11 +45,11 @@ namespace cuda
         int height = std::max(a_node->getHeight(), b_node->getHeight());
         height = std::max(height, cond_node->getHeight()) + 1;
 
-        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
-                                                "__select", 3, {{cond_node, a_node, b_node}},
-                                                (int)af_select_t, height);
+        NaryNode *node = new NaryNode(getFullName<T>(), shortname<T>(true),
+                                      "__select", 3, {{cond_node, a_node, b_node}},
+                                      (int)af_select_t, height);
 
-        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
         return out;
     }
 
@@ -62,13 +65,13 @@ namespace cuda
         int height = std::max(a_node->getHeight(), b_node->getHeight());
         height = std::max(height, cond_node->getHeight()) + 1;
 
-        JIT::NaryNode *node = new JIT::NaryNode(getFullName<T>(), shortname<T>(true),
-                                                flip ? "__not_select" : "__select",
-                                                3, {{cond_node, a_node, b_node}},
-                                                (int)(flip ? af_not_select_t : af_select_t),
-                                                height);
+        NaryNode *node = new NaryNode(getFullName<T>(), shortname<T>(true),
+                                      flip ? "__not_select" : "__select",
+                                      3, {{cond_node, a_node, b_node}},
+                                      (int)(flip ? af_not_select_t : af_select_t),
+                                      height);
 
-        Array<T> out = createNodeArray<T>(odims, JIT::Node_ptr(node));
+        Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
         return out;
     }
 
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index 65a2cfebed..59a5ff73af 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <JIT/BufferNode.hpp>
-#include <JIT/ShiftNode.hpp>
+#include <jit/BufferNode.hpp>
+#include <common/jit/ShiftNodeBase.hpp>
 #include <Array.hpp>
 #include <shift.hpp>
 #include <err_cuda.hpp>
@@ -17,9 +17,10 @@
 
 using af::dim4;
 
-using cuda::JIT::BufferNode;
-using cuda::JIT::Node_ptr;
-using cuda::JIT::ShiftNode;
+using common::Node_ptr;
+using common::ShiftNodeBase;
+
+using cuda::jit::BufferNode;
 
 using std::array;
 using std::make_shared;
@@ -28,6 +29,9 @@ using std::string;
 
 namespace cuda
 {
+    template<typename T>
+    using ShiftNode = ShiftNodeBase<BufferNode<T>>;
+
     template<typename T>
     Array<T> shift(const Array<T> &in, const int sdims[4])
     {
diff --git a/src/backend/cuda/types.cpp b/src/backend/cuda/types.cpp
deleted file mode 100644
index 370f0515d4..0000000000
--- a/src/backend/cuda/types.cpp
+++ /dev/null
@@ -1,47 +0,0 @@
-/*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
-
-#include <af/defines.h>
-#include "types.hpp"
-#include <sstream>
-#include <af/traits.hpp>
-
-namespace cuda
-{
-
-    template<typename T> const char *shortname(bool caps) { return caps ?  "Q" : "q"; }
-    template<> const char *shortname<float   >(bool caps) { return caps ?  "S" : "s"; }
-    template<> const char *shortname<double  >(bool caps) { return caps ?  "D" : "d"; }
-    template<> const char *shortname<cfloat  >(bool caps) { return caps ?  "C" : "c"; }
-    template<> const char *shortname<cdouble >(bool caps) { return caps ?  "Z" : "z"; }
-    template<> const char *shortname<int     >(bool caps) { return caps ?  "I" : "i"; }
-    template<> const char *shortname<uint    >(bool caps) { return caps ?  "U" : "u"; }
-    template<> const char *shortname<char    >(bool caps) { return caps ?  "J" : "j"; }
-    template<> const char *shortname<uchar   >(bool caps) { return caps ?  "V" : "v"; }
-    template<> const char *shortname<intl    >(bool caps) { return caps ?  "X" : "x"; }
-    template<> const char *shortname<uintl   >(bool caps) { return caps ?  "Y" : "y"; }
-    template<> const char *shortname<short   >(bool caps) { return caps ?  "P" : "p"; }
-    template<> const char *shortname<ushort  >(bool caps) { return caps ?  "Q" : "q"; }
-
-#define INSTANTIATE(T)                                      \
-    template<> const char *getFullName<T>() { return #T; }  \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(char)
-    INSTANTIATE(unsigned char)
-    INSTANTIATE(short)
-    INSTANTIATE(unsigned short)
-    INSTANTIATE(int)
-    INSTANTIATE(unsigned int)
-    INSTANTIATE(unsigned long long)
-    INSTANTIATE(long long)
-}
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 3376e84bb5..afdd959c8a 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -8,22 +8,57 @@
  ********************************************************/
 
 #pragma once
-#include <cuComplex.h>
-#include <string>
 #include <af/traits.hpp>
+#include <cuComplex.h>
 
 namespace cuda
 {
-typedef cuFloatComplex   cfloat;
-typedef cuDoubleComplex cdouble;
-typedef unsigned int   uint;
-typedef unsigned char  uchar;
-typedef unsigned short ushort;
+using cdouble = cuDoubleComplex;
+using cfloat = cuFloatComplex;
+using uchar = unsigned char;
+using uint = unsigned int;
+// using intl = long long ;                // defined in af/defines.h
+// using uintl = unsigned long long;       // defined in af/defines.h
+using ushort = unsigned short;
 
 template<typename T> struct is_complex          { static const bool value = false;  };
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
 template<> struct           is_complex<cdouble> { static const bool value = true;   };
 
-template<typename T> const char *shortname(bool caps = true);
+namespace {
+template<typename T> const char *shortname(bool caps = false) { return caps ?  "Q" : "q"; }
+template<> const char *shortname<float   >(bool caps) { return caps ?  "S" : "s"; }
+template<> const char *shortname<double  >(bool caps) { return caps ?  "D" : "d"; }
+template<> const char *shortname<cfloat  >(bool caps) { return caps ?  "C" : "c"; }
+template<> const char *shortname<cdouble >(bool caps) { return caps ?  "Z" : "z"; }
+template<> const char *shortname<int     >(bool caps) { return caps ?  "I" : "i"; }
+template<> const char *shortname<uint    >(bool caps) { return caps ?  "U" : "u"; }
+template<> const char *shortname<char    >(bool caps) { return caps ?  "J" : "j"; }
+template<> const char *shortname<uchar   >(bool caps) { return caps ?  "V" : "v"; }
+template<> const char *shortname<intl    >(bool caps) { return caps ?  "X" : "x"; }
+template<> const char *shortname<uintl   >(bool caps) { return caps ?  "Y" : "y"; }
+template<> const char *shortname<short   >(bool caps) { return caps ?  "P" : "p"; }
+template<> const char *shortname<ushort  >(bool caps) { return caps ?  "Q" : "q"; }
+
 template<typename T> const char *getFullName();
+
+#define SPECIALIZE(T)                                      \
+    template<> const char *getFullName<T>() { return #T; }
+
+    SPECIALIZE(float)
+    SPECIALIZE(double)
+    SPECIALIZE(cfloat)
+    SPECIALIZE(cdouble)
+    SPECIALIZE(char)
+    SPECIALIZE(unsigned char)
+    SPECIALIZE(short)
+    SPECIALIZE(unsigned short)
+    SPECIALIZE(int)
+    SPECIALIZE(unsigned int)
+    SPECIALIZE(unsigned long long)
+    SPECIALIZE(long long)
+
+#undef SPECIALIZE
+}
+
 }
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index bc0ae64450..0081f99019 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
 
 namespace cuda
 {
@@ -76,27 +76,27 @@ UNARY_FN(iszero)
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in)
 {
-    JIT::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
 
-    JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<T>(),
-                                              shortname<T>(true),
-                                              unaryName<op>(),
-                                              in_node, op);
+    common::UnaryNode *node = new common::UnaryNode(getFullName<T>(),
+                                                    shortname<T>(true),
+                                                    unaryName<op>(),
+                                                    in_node, op);
 
-    return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
+    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in)
 {
-    JIT::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
 
-    JIT::UnaryNode *node = new JIT::UnaryNode(getFullName<char>(),
-                                              shortname<char>(true),
-                                              unaryName<op>(),
-                                              in_node, op);
+    common::UnaryNode *node = new common::UnaryNode(getFullName<char>(),
+                                                    shortname<char>(true),
+                                                    unaryName<op>(),
+                                                    in_node, op);
 
-    return createNodeArray<char>(in.dims(), JIT::Node_ptr(node));
+    return createNodeArray<char>(in.dims(), common::Node_ptr(node));
 }
 
 }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 1537392add..f32a35596f 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <JIT/BufferNode.hpp>
+#include <jit/BufferNode.hpp>
 #include <af/dim4.hpp>
 #include <af/opencl.h>
-#include <common/NodeIterator.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
@@ -23,22 +23,29 @@
 #include <numeric>
 
 using af::dim4;
+
+using cl::Buffer;
+
 using common::NodeIterator;
-using opencl::JIT::BufferNode;
-using opencl::JIT::Node;
-using opencl::JIT::Node_ptr;
+using opencl::jit::BufferNode;
+using common::Node;
+using common::Node_ptr;
+
 using std::accumulate;
+using std::is_standard_layout;
+using std::make_shared;
+using std::vector;
 
 namespace opencl
 {
     template<typename T>
     Node_ptr bufferNodePtr()
     {
-        return std::make_shared<BufferNode>(dtype_traits<T>::getName(), shortname<T>(true));
+        return make_shared<BufferNode>(dtype_traits<T>::getName(), shortname<T>(true));
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims) :
+    Array<T>::Array(dim4 dims) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(bufferAlloc(info.elements() * sizeof(T)), bufferFree),
         data_dims(dims),
@@ -47,7 +54,7 @@ namespace opencl
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, JIT::Node_ptr n) :
+    Array<T>::Array(dim4 dims, Node_ptr n) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(),
         data_dims(dims),
@@ -56,27 +63,27 @@ namespace opencl
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, const T * const in_data) :
+    Array<T>::Array(dim4 dims, const T * const in_data) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
         data(bufferAlloc(info.elements()*sizeof(T)), bufferFree),
         data_dims(dims),
         node(bufferNodePtr<T>()), ready(true), owner(true)
     {
-        static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
+        static_assert(is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
         static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
         getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T)*info.elements(), in_data);
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy) :
+    Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy) :
         info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new cl::Buffer(mem), bufferFree),
+        data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new Buffer(mem), bufferFree),
         data_dims(dims),
         node(bufferNodePtr<T>()), ready(true), owner(true)
     {
         if (copy) {
             clRetainMemObject(mem);
-            cl::Buffer src_buf = cl::Buffer((cl_mem)(mem));
+            Buffer src_buf = Buffer((cl_mem)(mem));
             getQueue().enqueueCopyBuffer(src_buf, *data.get(),
                                          src_offset, 0,
                                          sizeof(T) * info.elements());
@@ -98,23 +105,23 @@ namespace opencl
     template<typename T>
     Array<T>::Array(Param &tmp, bool owner_) :
         info(getActiveDeviceId(),
-             af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3]),
+             dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3]),
              0,
-             af::dim4(tmp.info.strides[0], tmp.info.strides[1],
+             dim4(tmp.info.strides[0], tmp.info.strides[1],
                       tmp.info.strides[2], tmp.info.strides[3]),
              (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.data, owner_ ? bufferFree : [] (cl::Buffer* ptr) {}),
-        data_dims(af::dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
+        data(tmp.data, owner_ ? bufferFree : [] (Buffer* ptr) {}),
+        data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
         node(bufferNodePtr<T>()), ready(true), owner(owner_)
     {
     }
 
     template<typename T>
-    Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
+    Array<T>::Array(dim4 dims, dim4 strides, dim_t offset_,
                     const T * const in_data, bool is_device) :
         info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
         data(is_device ?
-             (new cl::Buffer((cl_mem)in_data)) :
+             (new Buffer((cl_mem)in_data)) :
              (bufferAlloc(info.total() * sizeof(T))), bufferFree),
         data_dims(dims),
         node(bufferNodePtr<T>()),
@@ -154,7 +161,7 @@ namespace opencl
     }
 
     template<typename T>
-    cl::Buffer* Array<T>::device()
+    Buffer* Array<T>::device()
     {
         if (!isOwner() || getOffset() || data.use_count() > 1) {
             *this = copyArray<T>(*this);
@@ -163,10 +170,10 @@ namespace opencl
     }
 
     template<typename T>
-    void evalMultiple(std::vector<Array<T>*> arrays)
+    void evalMultiple(vector<Array<T>*> arrays)
     {
-        std::vector<Param> outputs;
-        std::vector<Node *> nodes;
+        vector<Param> outputs;
+        vector<Node *> nodes;
 
         for (auto array : arrays) {
             if (array->isReady()) {
@@ -222,8 +229,6 @@ namespace opencl
         return node;
     }
 
-    using af::dim4;
-
     template<typename T>
     Array<T> createNodeArray(const dim4 &dims, Node_ptr node)
     {
@@ -269,10 +274,9 @@ namespace opencl
                         int num_buffers;
                         bool is_linear;
                     };
-                    NodeIterator it(n);
-                    NodeIterator end_node;
+                    NodeIterator<> it(n);
                     dim4 outdim = out.dims();
-                    tree_info info = accumulate(it, end_node,
+                    tree_info info = accumulate(it, NodeIterator<>(),
                                                 tree_info{0, 0, true},
                                                 [=](tree_info& prev, Node& n) {
                                                     if(n.isBuffer()) {
@@ -302,7 +306,7 @@ namespace opencl
 
     template<typename T>
     Array<T> createSubArray(const Array<T>& parent,
-                            const std::vector<af_seq> &index,
+                            const vector<af_seq> &index,
                             bool copy)
     {
         parent.eval();
@@ -419,10 +423,10 @@ namespace opencl
             arr = copyArray<T>(arr);
         }
 
-        cl::Buffer& buf = *arr.get();
+        Buffer& buf = *arr.get();
 
         clRetainMemObject((cl_mem)(data));
-        cl::Buffer data_buf = cl::Buffer((cl_mem)(data));
+        Buffer data_buf = Buffer((cl_mem)(data));
 
         getQueue().enqueueCopyBuffer(data_buf, buf,
                                      0, (size_t)arr.getOffset(),
@@ -450,24 +454,24 @@ namespace opencl
     template       Array<T>  *initArray<T      >      ();               \
     template       Array<T>  createParamArray<T>      (Param &tmp, bool owner);    \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
-                                                       const std::vector<af_seq> &index, \
+                                                       const vector<af_seq> &index, \
                                                        bool copy);      \
     template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, JIT::Node_ptr node); \
-    template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
+    template       Array<T>  createNodeArray<T>       (const dim4 &size, Node_ptr node); \
+    template       Array<T>::Array(dim4 dims, dim4 strides, dim_t offset, \
                                    const T * const in_data,             \
                                    bool is_device);                     \
-    template       Array<T>::Array(af::dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
+    template       Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
     template       Array<T>::~Array        ();                          \
     template       Node_ptr Array<T>::getNode() const;                  \
     template       void Array<T>::eval();                               \
     template       void Array<T>::eval() const;                         \
-    template       cl::Buffer* Array<T>::device();                      \
+    template       Buffer* Array<T>::device();                      \
     template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, \
                                                        const size_t bytes); \
     template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, \
                                                        const size_t bytes); \
-    template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
+    template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
     template       void Array<T>::setDataDims(const dim4 &new_dims);    \
 
     INSTANTIATE(float)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 0dff1be5d6..f0b0939205 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -16,7 +16,7 @@
 #include <types.hpp>
 #include <traits.hpp>
 #include <Param.hpp>
-#include <JIT/Node.hpp>
+#include <common/jit/Node.hpp>
 #include <memory.hpp>
 #include <memory>
 #include <err_opencl.hpp>
@@ -30,12 +30,12 @@ namespace opencl
     template<typename T>
     void evalMultiple(std::vector<Array<T> *> arrays);
 
-    void evalNodes(Param &out, JIT::Node *node);
-    void evalNodes(std::vector<Param> &outputs, std::vector<JIT::Node *> nodes);
+    void evalNodes(Param &out, common::Node *node);
+    void evalNodes(std::vector<Param> &outputs, std::vector<common::Node *> nodes);
 
     /// Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, JIT::Node_ptr node);
+    Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
 
     /// Creates a new Array object on the heap and returns a reference to it.
     template<typename T>
@@ -110,7 +110,7 @@ namespace opencl
         Buffer_ptr  data;
         af::dim4 data_dims;
 
-        JIT::Node_ptr node;
+        common::Node_ptr node;
         bool ready;
         bool owner;
 
@@ -118,7 +118,7 @@ namespace opencl
 
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
         Array(Param &tmp, bool owner);
-        explicit Array(af::dim4 dims, JIT::Node_ptr n);
+        explicit Array(af::dim4 dims, common::Node_ptr n);
         explicit Array(af::dim4 dims, const T * const in_data);
         explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
 
@@ -245,8 +245,8 @@ namespace opencl
             return kinfo;
         }
 
-        JIT::Node_ptr getNode() const;
-        JIT::Node_ptr getNode();
+        common::Node_ptr getNode() const;
+        common::Node_ptr getNode();
 
     public:
         std::shared_ptr<T> getMappedPtr() const
@@ -280,7 +280,7 @@ namespace opencl
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
         friend Array<T> createParamArray<T>(Param &tmp, bool owner);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, JIT::Node_ptr node);
+        friend Array<T> createNodeArray<T>(const af::dim4 &dims, common::Node_ptr node);
 
         friend Array<T> createSubArray<T>(const Array<T>& parent,
                                           const std::vector<af_seq> &index,
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index bf3d1cd340..47d8da0193 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -216,7 +216,6 @@ target_sources(afopencl
     transpose_inplace.cpp
     triangle.cpp
     triangle.hpp
-    types.cpp
     types.hpp
     unary.hpp
     unwrap.cpp
@@ -330,11 +329,8 @@ target_sources(afopencl
 
 target_sources(afopencl
   PRIVATE
-    JIT/BinaryNode.hpp
-    JIT/BufferNode.hpp
-    JIT/Node.hpp
-    JIT/ScalarNode.hpp
-    JIT/UnaryNode.hpp
+    jit/BufferNode.hpp
+    jit/kernel_generators.hpp
   )
 
 target_sources(afopencl
diff --git a/src/backend/opencl/JIT/BinaryNode.hpp b/src/backend/opencl/JIT/BinaryNode.hpp
deleted file mode 100644
index 3b0f923e3b..0000000000
--- a/src/backend/opencl/JIT/BinaryNode.hpp
+++ /dev/null
@@ -1,34 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "NaryNode.hpp"
-#include <iomanip>
-
-namespace opencl
-{
-
-namespace JIT
-{
-
-    class BinaryNode : public NaryNode
-    {
-    public:
-        BinaryNode(const char *out_type_str, const char *name_str,
-                   const char *op_str,
-                   Node_ptr lhs, Node_ptr rhs, int op)
-            : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}},
-                       op, std::max(lhs->getHeight(), rhs->getHeight()) + 1)
-        {
-        }
-    };
-
-}
-
-}
diff --git a/src/backend/opencl/JIT/BufferNode.hpp b/src/backend/opencl/JIT/BufferNode.hpp
deleted file mode 100644
index 118280f521..0000000000
--- a/src/backend/opencl/JIT/BufferNode.hpp
+++ /dev/null
@@ -1,138 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <af/defines.h>
-#include "../kernel/KParam.hpp"
-#include "Node.hpp"
-#include <iomanip>
-#include <mutex>
-
-namespace opencl
-{
-
-namespace JIT
-{
-
-
-    class BufferNode : public Node
-    {
-    private:
-        std::shared_ptr<cl::Buffer> m_data;
-        KParam m_info;
-        unsigned m_bytes;
-        std::once_flag m_set_data_flag;
-        bool m_linear_buffer;
-
-    public:
-
-        BufferNode(const char *type_str,
-                   const char *name_str)
-            : Node(type_str, name_str, 0, {})
-        {
-        }
-
-        bool isBuffer() const final {
-            return true;
-        }
-
-        void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
-        {
-            std::call_once(m_set_data_flag, [this, info, data, bytes, is_linear]() {
-                    m_info = info;
-                    m_data = data;
-                    m_bytes = bytes;
-                    m_linear_buffer = is_linear;
-                });
-        }
-
-        bool isLinear(dim_t dims[4]) const final
-        {
-            bool same_dims = true;
-            for (int i = 0; same_dims && i < 4; i++) {
-                same_dims &= (dims[i] == m_info.dims[i]);
-            }
-            return m_linear_buffer && same_dims;
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            if (!is_linear) {
-                kerStream << "__global " << m_type_str << " *in" << id
-                          << ", KParam iInfo" << id << ", \n";
-            } else {
-                kerStream << "__global " << m_type_str << " *in" << id
-                          << ", dim_t iInfo" << id << "_offset, \n";
-            }
-        }
-
-        int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
-        {
-            ker.setArg(id + 0, *m_data);
-            if (!is_linear) {
-                ker.setArg(id + 1,  m_info);
-            } else {
-                ker.setArg(id + 1, m_info.offset);
-            }
-            return id + 2;
-        }
-
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            std::string idx_str = std::string("int idx") + std::to_string(id);
-            std::string info_str = std::string("iInfo") + std::to_string(id);
-
-            if (!is_linear) {
-                kerStream << idx_str << " = "
-                          << "(id3 < " << info_str << ".dims[3]) * "
-                          << info_str << ".strides[3] * id3 + "
-                          << "(id2 < " << info_str << ".dims[2]) * "
-                          << info_str << ".strides[2] * id2 + "
-                          << "(id1 < " << info_str << ".dims[1]) * "
-                          << info_str << ".strides[1] * id1 + "
-                          << "(id0 < " << info_str << ".dims[0]) * "
-                          << "id0 + " << info_str << ".offset;"
-                          << "\n";
-            } else {
-                kerStream << idx_str << " = idx + " << info_str << "_offset;" << "\n";
-            }
-        }
-
-        // Return the size of the parameter in bytes that will be passed to the
-        // kernel
-        virtual short getParamBytes() const final {
-            return m_linear_buffer ? sizeof(void*) : sizeof(KParam);
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << "in" << ids.id << "[idx" << ids.id << "];"
-                      << "\n";
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
-        {
-            len++;
-            buf_count++;
-            bytes += m_bytes;
-        }
-
-        size_t getBytes() const final { return m_bytes; }
-    };
-
-}
-
-}
diff --git a/src/backend/opencl/JIT/Node.cpp b/src/backend/opencl/JIT/Node.cpp
deleted file mode 100644
index ca193e70ed..0000000000
--- a/src/backend/opencl/JIT/Node.cpp
+++ /dev/null
@@ -1,47 +0,0 @@
-/*******************************************************
- * Copyright (c) 2018, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <JIT/Node.hpp>
-
-#include <spdlog/fmt/ostr.h>
-
-#include <string>
-#include <vector>
-
-using std::vector;
-
-namespace opencl {
-namespace JIT {
-
-int Node::getNodesMap(Node_map_t &node_map,
-                      vector<const Node *> &full_nodes,
-                      vector<Node_ids> &full_ids) const
-{
-    auto iter = node_map.find(this);
-    if (iter == node_map.end()) {
-        Node_ids ids;
-        for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
-            ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
-        }
-        ids.id = node_map.size();
-        node_map[this] = ids.id;
-        full_nodes.push_back(this);
-        full_ids.push_back(ids);
-        return ids.id;
-    }
-    return iter->second;
-}
-
-void Node::genKerName(std::stringstream &kerStream, Node_ids ids) const
-{
-    fmt::print(kerStream, "_{0}{1:0<3}", m_name_str, ids.id);
-}
-
-}  // JIT
-}  // opencl
diff --git a/src/backend/opencl/JIT/Node.hpp b/src/backend/opencl/JIT/Node.hpp
deleted file mode 100644
index 3df8a1c85a..0000000000
--- a/src/backend/opencl/JIT/Node.hpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <platform.hpp>
-#include <optypes.hpp>
-#include <array>
-#include <string>
-#include <vector>
-#include <memory>
-#include <unordered_map>
-
-using std::shared_ptr;
-using std::vector;
-
-namespace common {
-    class NodeIterator;
-}
-
-namespace opencl
-{
-
-namespace JIT
-{
-
-    constexpr int MAX_CHILDREN = 3;
-    class Node;
-
-    typedef struct
-    {
-        int id;
-        std::array<int, MAX_CHILDREN> child_ids;
-    } Node_ids;
-
-    using Node_ptr = shared_ptr<Node>;
-    using Node_map_t = std::unordered_map<const Node *, int>;
-    using Node_map_iter = Node_map_t::iterator;
-
-    class Node
-    {
-    protected:
-        const std::string m_type_str;
-        const std::string m_name_str;
-        const int m_height;
-        const std::array<Node_ptr, MAX_CHILDREN> m_children;
-        friend common::NodeIterator;
-
-    public:
-
-        virtual bool isBuffer() const { return false; }
-
-        Node(const char *type_str, const char *name_str, const int height,
-             const std::array<Node_ptr, MAX_CHILDREN>&& children)
-            : m_type_str(type_str),
-              m_name_str(name_str),
-              m_height(height),
-              m_children(children)
-        {}
-
-        int getNodesMap(Node_map_t &node_map,
-                        vector<const Node *> &full_nodes,
-                        vector<Node_ids> &full_ids) const
-        {
-            auto iter = node_map.find(this);
-            if (iter == node_map.end()) {
-                Node_ids ids;
-                for (int i = 0; i < MAX_CHILDREN && m_children[i] != nullptr; i++) {
-                    ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
-                }
-                ids.id = node_map.size();
-                node_map[this] = ids.id;
-                full_nodes.push_back(this);
-                full_ids.push_back(ids);
-                return ids.id;
-            }
-            return iter->second;
-        }
-
-        virtual void genKerName(std::stringstream &kerStream, Node_ids ids) const {}
-        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const {}
-        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const {}
-        virtual void genFuncs   (std::stringstream &kerStream, Node_ids) const {}
-
-        virtual int setArgs (cl::Kernel &ker, int id, bool is_linear) const { return id; }
-
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const
-        {
-            len++;
-        }
-
-        // Return the size of the parameter in bytes that will be passed to the
-        // kernel
-        virtual short getParamBytes() const {
-            return 0;
-        }
-
-        virtual bool isLinear(dim_t dims[4]) const { return true; }
-        std::string getTypeStr() const { return m_type_str; }
-        int getHeight() const  { return m_height; }
-        virtual size_t getBytes() const { return 0; }
-        std::string getNameStr() const { return m_name_str; }
-
-        virtual ~Node() = default;
-    };
-}
-
-}
diff --git a/src/backend/opencl/JIT/ScalarNode.hpp b/src/backend/opencl/JIT/ScalarNode.hpp
deleted file mode 100644
index 34ddd4c2a2..0000000000
--- a/src/backend/opencl/JIT/ScalarNode.hpp
+++ /dev/null
@@ -1,66 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "Node.hpp"
-#include <math.hpp>
-#include <types.hpp>
-#include <iomanip>
-
-namespace opencl
-{
-
-namespace JIT
-{
-
-    template <typename T>
-    class ScalarNode : public Node
-    {
-    private:
-        const T m_val;
-
-    public:
-
-        ScalarNode(T val)
-            : Node(dtype_traits<T>::getName(), shortname<T>(false), 0, {}),
-              m_val(val)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            kerStream << m_type_str << " scalar" << id << ", " << "\n";
-        }
-
-        int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
-        {
-            ker.setArg(id, m_val);
-            return id + 1;
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id << " = "
-                      << "scalar" << ids.id << ";"
-                      << "\n";
-        }
-
-        // Return the info for the params and the size of the buffers
-        virtual short getParamBytes() const final { return static_cast<short>(sizeof(T)); }
-    };
-
-}
-
-}
diff --git a/src/backend/opencl/JIT/ShiftNode.hpp b/src/backend/opencl/JIT/ShiftNode.hpp
deleted file mode 100644
index bbfe3db8a4..0000000000
--- a/src/backend/opencl/JIT/ShiftNode.hpp
+++ /dev/null
@@ -1,110 +0,0 @@
-/*******************************************************
- * Copyright (c) 2018, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include "BufferNode.hpp"
-#include "Node.hpp"
-#include <iomanip>
-#include <mutex>
-
-namespace opencl
-{
-namespace JIT
-{
-    class ShiftNode : public Node
-    {
-    private:
-
-        std::shared_ptr<BufferNode> m_buffer_node;
-        const std::array<int, 4> m_shifts;
-
-    public:
-
-        ShiftNode(const char *type_str,
-                  const char *name_str,
-                  std::shared_ptr<BufferNode> buffer_node,
-                  const std::array<int, 4> shifts)
-            : Node(type_str, name_str, 0, {}),
-              m_buffer_node(buffer_node),
-              m_shifts(shifts)
-        {
-        }
-
-        void setData(KParam info, std::shared_ptr<cl::Buffer> data, const unsigned bytes, bool is_linear)
-        {
-            m_buffer_node->setData(info, data, bytes, is_linear);
-        }
-
-        bool isLinear(dim_t dims[4]) const final
-        {
-            return false;
-        }
-
-        void genKerName(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            m_buffer_node->genParams(kerStream, id, is_linear);
-            for (int i = 0; i < 4; i++) {
-                kerStream << "int shift" << id << "_" << i << ",\n";
-            }
-        }
-
-        int setArgs(cl::Kernel &ker, int id, bool is_linear) const final
-        {
-            int curr_id = m_buffer_node->setArgs(ker, id, is_linear);
-            for (int i = 0; i < 4; i++) {
-                ker.setArg(curr_id + i, m_shifts[i]);
-            }
-            return curr_id + 4;
-        }
-
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            std::string idx_str = std::string("idx") + std::to_string(id);
-            std::string info_str = std::string("iInfo") + std::to_string(id);
-            std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
-            std::string shift_str = std::string("shift") + std::to_string(id) + "_";
-
-            for (int i = 0; i < 4; i++) {
-                kerStream << "int " << id_str << i
-                          << " = __circular_mod(id" << i
-                          << " + " << shift_str << i
-                          << ", " << info_str << ".dims[" << i << "]);\n";
-            }
-
-            kerStream << "int " << idx_str << " = (" << id_str << "3 < "
-                      << info_str << ".dims[3]) * "
-                      << info_str << ".strides[3] * " << id_str << "3;\n";
-            kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
-                      << info_str << ".strides[2] * " << id_str << "2;\n";
-            kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
-                      << info_str << ".strides[1] * " << id_str << "1;\n";
-            kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
-                      << id_str << "0 + " << info_str << ".offset;\n";
-        }
-
-        void genFuncs(std::stringstream &kerStream, Node_ids ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id
-                      << " = in" << ids.id << "[idx" << ids.id << "];\n";
-        }
-
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
-        {
-            m_buffer_node->getInfo(len, buf_count, bytes);
-        }
-    };
-}
-
-}
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 7226d4e382..b3ff5b5b5a 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <JIT/BinaryNode.hpp>
+#include <common/jit/BinaryNode.hpp>
 
 namespace opencl
 {
@@ -194,15 +194,15 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af:
 {
     BinOp<To, Ti, op> bop;
 
-    JIT::Node_ptr lhs_node = lhs.getNode();
-    JIT::Node_ptr rhs_node = rhs.getNode();
-    JIT::BinaryNode *node = new JIT::BinaryNode(dtype_traits<To>::getName(),
-                                                shortname<To>(true),
-                                                bop.name(),
-                                                lhs_node,
-                                                rhs_node, (int)(op));
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
+    common::BinaryNode *node = new common::BinaryNode(dtype_traits<To>::getName(),
+                                                      shortname<To>(true),
+                                                      bop.name(),
+                                                      lhs_node,
+                                                      rhs_node, (int)(op));
 
-    return createNodeArray<To>(odims, JIT::Node_ptr(node));
+    return createNodeArray<To>(odims, common::Node_ptr(node));
 }
 
 }
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index 3df062053f..1adf84fddc 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -13,7 +13,7 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
 #include <types.hpp>
 #include <Array.hpp>
 
@@ -104,12 +104,12 @@ struct CastWrapper
     Array<To> operator()(const Array<Ti> &in)
     {
         CastOp<To, Ti> cop;
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<To>::getName(),
-                                                  shortname<To>(true),
-                                                  cop.name(),
-                                                  in_node, af_cast_t);
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
+                                                        shortname<To>(true),
+                                                        cop.name(),
+                                                        in_node, af_cast_t);
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 };
 
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index e72850675d..0d92b1cd74 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -11,7 +11,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <binary.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
 
 namespace opencl
 {
@@ -24,25 +24,25 @@ namespace opencl
     template<typename To, typename Ti>
     Array<To> real(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<To>::getName(),
-                                                  shortname<To>(true),
-                                                  "__creal",
-                                                  in_node, af_real_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
+                                                        shortname<To>(true),
+                                                        "__creal",
+                                                        in_node, af_real_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 
     template<typename To, typename Ti>
     Array<To> imag(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<To>::getName(),
-                                                  shortname<To>(true),
-                                                  "__cimag",
-                                                  in_node, af_imag_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
+                                                        shortname<To>(true),
+                                                        "__cimag",
+                                                        in_node, af_imag_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 
     template<typename T> static const char *abs_name() { return "fabs"; }
@@ -52,13 +52,13 @@ namespace opencl
     template<typename To, typename Ti>
     Array<To> abs(const Array<Ti> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<To>::getName(),
-                                                  shortname<To>(true),
-                                                  abs_name<Ti>(),
-                                                  in_node, af_abs_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
+                                                        shortname<To>(true),
+                                                        abs_name<Ti>(),
+                                                        in_node, af_abs_t);
 
-        return createNodeArray<To>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 
     template<typename T> static const char *conj_name() { return "__noop"; }
@@ -68,12 +68,12 @@ namespace opencl
     template<typename T>
     Array<T> conj(const Array<T> &in)
     {
-        JIT::Node_ptr in_node = in.getNode();
-        JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<T>::getName(),
-                                                  shortname<T>(true),
-                                                  conj_name<T>(),
-                                                  in_node, af_conj_t);
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node = new common::UnaryNode(dtype_traits<T>::getName(),
+                                                        shortname<T>(true),
+                                                        conj_name<T>(),
+                                                        in_node, af_conj_t);
 
-        return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
+        return createNodeArray<T>(in.dims(), common::Node_ptr(node));
     }
 }
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index d13342fae3..2a3a4eb9e0 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -9,35 +9,37 @@
 
 #include <af/dim4.hpp>
 #include <Array.hpp>
-#include <map>
-#include <vector>
-#include <stdexcept>
 #include <copy.hpp>
-#include <JIT/Node.hpp>
+#include <common/jit/Node.hpp>
 #include <kernel_headers/jit.hpp>
 #include <program.hpp>
 #include <common/dispatch.hpp>
 #include <err_opencl.hpp>
-#include <functional>
 #include <af/opencl.h>
 
-namespace opencl
-{
+#include <functional>
+#include <stdexcept>
+#include <vector>
 
-using JIT::Node;
-using JIT::Node_ids;
-using JIT::Node_map_t;
+using common::Node;
+using common::Node_ids;
+using common::Node_map_t;
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
-using std::string;
-using std::stringstream;
+using cl::NullRange;
+using cl::Program;
+
+using std::hash;
+using std::string; using std::stringstream;
 using std::vector;
 
+namespace opencl
+{
+
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids,
@@ -60,7 +62,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
-    std::hash<std::string> hash_fn;
+    hash<string> hash_fn;
     hashName << "KER" << hash_fn(funcName.str());
     return hashName.str();
 }
@@ -182,11 +184,11 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
 
-        cl::Program prog;
+        Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens,
                      isDoubleSupported(device) ? string(" -D USE_DOUBLE") :  string(""));
 
-        entry.prog = new cl::Program(prog);
+        entry.prog = new Program(prog);
         entry.ker = new Kernel(*entry.prog, funcName.c_str());
 
         addKernelToCache(device, funcName, entry);
@@ -271,7 +273,10 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
 
     int nargs = 0;
     for (const auto &node : full_nodes) {
-        nargs = node->setArgs(ker, nargs, is_linear);
+        nargs = node->setArgs(nargs, is_linear,
+                              [&] (int id, const void* ptr, size_t arg_size) {
+                                  ker.setArg(id, arg_size, ptr);
+                              });
     }
 
     // Set output parameters
@@ -288,7 +293,7 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     ker.setArg(nargs + 2,  groups_1);
     ker.setArg(nargs + 3,  num_odims);
 
-    getQueue().enqueueNDRangeKernel(ker, cl::NullRange, global, local);
+    getQueue().enqueueNDRangeKernel(ker, NullRange, global, local);
 
     // Reset the thread local vectors
     nodes.clear();
diff --git a/src/backend/opencl/JIT/UnaryNode.hpp b/src/backend/opencl/jit/BufferNode.hpp
similarity index 50%
rename from src/backend/opencl/JIT/UnaryNode.hpp
rename to src/backend/opencl/jit/BufferNode.hpp
index d2eb373b5f..7eb164a600 100644
--- a/src/backend/opencl/JIT/UnaryNode.hpp
+++ b/src/backend/opencl/jit/BufferNode.hpp
@@ -8,25 +8,17 @@
  ********************************************************/
 
 #pragma once
-#include "NaryNode.hpp"
+#include <af/defines.h>
+#include "../kernel/KParam.hpp"
+#include <common/jit/Node.hpp>
+#include <common/jit/BufferNodeBase.hpp>
 #include <iomanip>
+#include <mutex>
 
 namespace opencl
 {
-
-namespace JIT
+namespace jit
 {
-    class UnaryNode : public NaryNode
-    {
-    public:
-        UnaryNode(const char *out_type_str, const char *name_str,
-                  const char *op_str,
-                  Node_ptr child, int op)
-            : NaryNode(out_type_str, name_str, op_str,
-                       1, {{child}}, op, child->getHeight() + 1)
-        {
-        }
-    };
+    using BufferNode = common::BufferNodeBase<std::shared_ptr<cl::Buffer>, KParam>;
 }
-
 }
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
new file mode 100644
index 0000000000..8754ef8582
--- /dev/null
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -0,0 +1,98 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+#include <sstream>
+
+namespace opencl {
+
+namespace {
+
+  /// Creates a string that will be used to declare the parameter of kernel
+  void generateParamDeclaration(std::stringstream& kerStream, int id, bool is_linear,
+                                const std::string& m_type_str) {
+    if (is_linear) {
+        kerStream << "__global " << m_type_str << " *in" << id
+                  << ", dim_t iInfo" << id << "_offset, \n";
+    } else {
+        kerStream << "__global " << m_type_str << " *in" << id
+                  << ", KParam iInfo" << id << ", \n";
+    }
+  }
+
+  /// Calls the setArg function to set the arguments for a kernel call
+  int setKernelArguments(int start_id, bool is_linear,
+                         std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+                         const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
+      setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()), sizeof(cl_mem));
+      if (is_linear) {
+          setArg(start_id + 1, static_cast<const void*>(&info.offset), sizeof(dim_t));
+      } else {
+          setArg(start_id + 1,  static_cast<const void*>(&info), sizeof(KParam));
+      }
+      return start_id + 2;
+  }
+
+
+  /// Generates the code to calculate the offsets for a buffer
+  void generateBufferOffsets(std::stringstream &kerStream, int id, bool is_linear, const std::string& type_str) {
+      std::string idx_str = std::string("int idx") + std::to_string(id);
+      std::string info_str = std::string("iInfo") + std::to_string(id);
+
+      if (is_linear) {
+          kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
+      } else {
+          kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                    << info_str << ".strides[3] * id3 + (id2 < " << info_str << ".dims[2]) * "
+                    << info_str << ".strides[2] * id2 + (id1 < " << info_str << ".dims[1]) * "
+                    << info_str << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * id0 + "
+                    << info_str << ".offset;\n";
+      }
+  }
+
+  /// Generates the code to read a buffer and store it in a local variable
+  void generateBufferRead(std::stringstream &kerStream, int id, const std::string& type_str)
+  {
+      kerStream << type_str << " val" << id << " = in" << id << "[idx" << id << "];\n";
+  }
+
+
+  void generateShiftNodeOffsets(std::stringstream &kerStream, int id,
+                                bool is_linear, const std::string& type_str) {
+      std::string idx_str = std::string("idx") + std::to_string(id);
+      std::string info_str = std::string("iInfo") + std::to_string(id);
+      std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
+      std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+
+      for (int i = 0; i < 4; i++) {
+          kerStream << "int " << id_str << i
+                    << " = __circular_mod(id" << i
+                    << " + " << shift_str << i
+                    << ", " << info_str << ".dims[" << i << "]);\n";
+      }
+
+      kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str << ".dims[3]) * "
+                << info_str << ".strides[3] * " << id_str << "3;\n";
+      kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
+                << info_str << ".strides[2] * " << id_str << "2;\n";
+      kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
+                << info_str << ".strides[1] * " << id_str << "1;\n";
+      kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
+                << id_str << "0 + " << info_str << ".offset;\n";
+  }
+
+  void generateShiftNodeRead(std::stringstream &kerStream, int id,
+                             const std::string& type_str) {
+      kerStream << type_str << " val" << id
+                << " = in" << id << "[idx" << id << "];\n";
+  }
+
+}
+}
diff --git a/src/backend/opencl/scalar.hpp b/src/backend/opencl/scalar.hpp
index fbd96b3ecc..720da5e969 100644
--- a/src/backend/opencl/scalar.hpp
+++ b/src/backend/opencl/scalar.hpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <JIT/ScalarNode.hpp>
+#include <common/jit/ScalarNode.hpp>
 
 namespace opencl
 {
@@ -18,7 +18,7 @@ namespace opencl
 template<typename T>
 Array<T> createScalarNode(const dim4 &size, const T val)
 {
-    return createNodeArray<T>(size, JIT::Node_ptr(new JIT::ScalarNode<T>(val)));
+    return createNodeArray<T>(size, common::Node_ptr(new common::ScalarNode<T>(val)));
 }
 
 }
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 16451ebb11..f8c3294033 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -10,7 +10,7 @@
 #include <kernel/select.hpp>
 
 #include <Array.hpp>
-#include <JIT/NaryNode.hpp>
+#include <common/jit/NaryNode.hpp>
 #include <err_opencl.hpp>
 #include <scalar.hpp>
 
@@ -18,7 +18,7 @@
 
 using af::dim4;
 
-using opencl::JIT::NaryNode;
+using common::NaryNode;
 
 using std::make_shared;
 using std::max;
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index dc8bf813e0..6531c3e130 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -10,17 +10,16 @@
 #include <Array.hpp>
 #include <shift.hpp>
 #include <err_opencl.hpp>
-#include <JIT/ShiftNode.hpp>
-#include <JIT/ShiftNode.hpp>
+#include <common/jit/ShiftNodeBase.hpp>
 
 #include <memory>
 #include <stdexcept>
 
 using af::dim4;
 
-using opencl::JIT::BufferNode;
-using opencl::JIT::Node_ptr;
-using opencl::JIT::ShiftNode;
+using opencl::jit::BufferNode;
+using common::Node_ptr;
+using common::ShiftNodeBase;
 
 using std::array;
 using std::make_shared;
@@ -29,6 +28,8 @@ using std::string;
 
 namespace opencl
 {
+    using ShiftNode = ShiftNodeBase<BufferNode>;
+
     template<typename T>
     Array<T> shift(const Array<T> &in, const int sdims[4])
     {
@@ -52,7 +53,7 @@ namespace opencl
         auto node = make_shared<ShiftNode>(dtype_traits<T>::getName(), name_str.c_str(),
                                            static_pointer_cast<BufferNode>(in.getNode()),
                                            shifts);
-        return createNodeArray<T>(oDims, Node_ptr(node));
+        return createNodeArray<T>(oDims, common::Node_ptr(node));
     }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
deleted file mode 100644
index 13744e444b..0000000000
--- a/src/backend/opencl/types.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-/*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
-
-#include <af/defines.h>
-#include "types.hpp"
-
-namespace opencl
-{
-
-    template<typename T> const char *shortname(bool caps) { return caps ? "X" : "x"; }
-
-    template<> const char *shortname<float   >(bool caps) { return caps ? "S" : "s"; }
-    template<> const char *shortname<double  >(bool caps) { return caps ? "D" : "d"; }
-    template<> const char *shortname<cfloat  >(bool caps) { return caps ? "C" : "c"; }
-    template<> const char *shortname<cdouble >(bool caps) { return caps ? "Z" : "z"; }
-    template<> const char *shortname<int     >(bool caps) { return caps ? "I" : "i"; }
-    template<> const char *shortname<uint    >(bool caps) { return caps ? "U" : "u"; }
-    template<> const char *shortname<char    >(bool caps) { return caps ? "J" : "j"; }
-    template<> const char *shortname<uchar   >(bool caps) { return caps ? "V" : "v"; }
-    template<> const char *shortname<intl    >(bool caps) { return caps ? "L" : "l"; }
-    template<> const char *shortname<uintl   >(bool caps) { return caps ? "K" : "k"; }
-    template<> const char *shortname<short   >(bool caps) { return caps ? "P" : "p"; }
-    template<> const char *shortname<ushort  >(bool caps) { return caps ? "Q" : "q"; }
-
-}
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index df829c7c42..fb4eb6ca52 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <cmath>
 #include <type_util.hpp>
+#include <af/traits.hpp>
 
 using std::string;
 
@@ -35,8 +36,6 @@ template<typename T> struct is_complex          { static const bool value = fals
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
 template<> struct           is_complex<cdouble> { static const bool value = true;   };
 
-template<typename T> const char *shortname(bool caps=false);
-
 template<typename T>
 struct ToNumStr
 {
@@ -106,4 +105,28 @@ struct ToNumStr<cdouble>
         return s.str();
     }
 };
+
+namespace {
+template<typename T> const char *shortname(bool caps) { return caps ? "X" : "x"; }
+
+template<> const char *shortname<float   >(bool caps) { return caps ? "S" : "s"; }
+template<> const char *shortname<double  >(bool caps) { return caps ? "D" : "d"; }
+template<> const char *shortname<cfloat  >(bool caps) { return caps ? "C" : "c"; }
+template<> const char *shortname<cdouble >(bool caps) { return caps ? "Z" : "z"; }
+template<> const char *shortname<int     >(bool caps) { return caps ? "I" : "i"; }
+template<> const char *shortname<uint    >(bool caps) { return caps ? "U" : "u"; }
+template<> const char *shortname<char    >(bool caps) { return caps ? "J" : "j"; }
+template<> const char *shortname<uchar   >(bool caps) { return caps ? "V" : "v"; }
+template<> const char *shortname<intl    >(bool caps) { return caps ? "L" : "l"; }
+template<> const char *shortname<uintl   >(bool caps) { return caps ? "K" : "k"; }
+template<> const char *shortname<short   >(bool caps) { return caps ? "P" : "p"; }
+template<> const char *shortname<ushort  >(bool caps) { return caps ? "Q" : "q"; }
+
+template<typename T>
+const char *getFullName() {
+    return af::dtype_traits<T>::getName();
+}
+
+}
+
 }
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 78ff32e8c7..84c35cc566 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 #include <math.hpp>
-#include <JIT/UnaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
 
 namespace opencl
 {
@@ -75,27 +75,27 @@ UNARY_FN(iszero)
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in)
 {
-    JIT::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
 
-    JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<T>::getName(),
-                                              shortname<T>(true),
-                                              unaryName<op>(),
-                                              in_node, op);
+    common::UnaryNode *node = new common::UnaryNode(dtype_traits<T>::getName(),
+                                                    shortname<T>(true),
+                                                    unaryName<op>(),
+                                                    in_node, op);
 
-    return createNodeArray<T>(in.dims(), JIT::Node_ptr(node));
+    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in)
 {
-    JIT::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
 
-    JIT::UnaryNode *node = new JIT::UnaryNode(dtype_traits<char>::getName(),
-                                              shortname<char>(true),
-                                              unaryName<op>(),
-                                              in_node, op);
+    common::UnaryNode *node = new common::UnaryNode(dtype_traits<char>::getName(),
+                                                    shortname<char>(true),
+                                                    unaryName<op>(),
+                                                    in_node, op);
 
-    return createNodeArray<char>(in.dims(), JIT::Node_ptr(node));
+    return createNodeArray<char>(in.dims(), common::Node_ptr(node));
 }
 
 }

From 572ab7caa9bf03305347d0cf1f59404a491cae8c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 31 Oct 2018 12:59:10 -0400
Subject: [PATCH 1546/2677] Added ParamIterator for the cpu backend.

---
 src/backend/cpu/CMakeLists.txt    |   1 +
 src/backend/cpu/ParamIterator.hpp | 149 ++++++++++++++++++++++++++++++
 2 files changed, 150 insertions(+)
 create mode 100644 src/backend/cpu/ParamIterator.hpp

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index a2f451734a..cc190aa09e 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -101,6 +101,7 @@ target_sources(afcpu
     orb.cpp
     orb.hpp
     padarray.cpp
+    ParamIterator.hpp
     platform.cpp
     platform.hpp
     print.hpp
diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
new file mode 100644
index 0000000000..de430aff56
--- /dev/null
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -0,0 +1,149 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/dim4.hpp>
+#include <Param.hpp>
+
+#include <array>
+#include <cstddef>
+#include <iterator>
+
+namespace cpu {
+
+/// A Param iterator that iterates through a Param object
+template<typename T>
+class ParamIterator {
+    T* ptr;
+
+    // NOTE: This is not really the true coordinate of the iteration. It's
+    // values will go down as you move through the array.
+    std::array<dim_t, AF_MAX_DIMS> dim_index;
+
+    // The dimension of the array
+    const af::dim4 dims;
+
+    // The iterator's stride
+    const af::dim4 stride;
+
+    /// Calculates the iterator offsets. These are different from the original offsets
+    /// because they define the stride from the end of the last element in the previous
+    /// dimension to the first element on the next dimension.
+    static dim4 calculate_iterator_stride(const dim4 &dims, const dim4 &stride) noexcept {
+        dim4 out(stride[0],
+                 stride[1] - (stride[0] * dims[0]),
+                 stride[2] - (stride[1] * dims[1]),
+                 stride[3] - (stride[2] * dims[2]));
+
+        return out;
+    }
+
+  public:
+    using difference_type = ptrdiff_t;
+    using value_type = T;
+    using pointer = T*;
+    using reference = T&;
+    using iterator_category = std::forward_iterator_tag;
+
+    /// Creates a sentinel iterator. This is equivalent to the end iterator
+    ParamIterator() noexcept
+        : ptr(nullptr)
+        , dim_index{dims[0], dims[1], dims[2], dims[3]}
+        , dims(1)
+        , stride(1) {}
+
+    /// ParamIterator Constructor
+    ParamIterator(cpu::Param<T>& in) noexcept
+        : ptr(in.get())
+        , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]}
+        , dims(in.dims())
+        , stride(calculate_iterator_stride(dims, in.strides())) {}
+
+    ParamIterator(cpu::CParam<typename std::remove_const<T>::type>& in) noexcept
+        : ptr(in.get())
+        , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]}
+        , dims(in.dims())
+        , stride(calculate_iterator_stride(dims, in.strides())) {
+    }
+
+    /// The equality operator
+    bool operator==(const ParamIterator& other) const noexcept {
+        return ptr == other.ptr;
+    }
+
+    /// The inequality operator
+    bool operator!=(const ParamIterator& other) const noexcept {
+        return ptr != other.ptr;
+    }
+
+    /// Advances the iterator
+    ParamIterator& operator++() noexcept {
+        for(int i = 0; i < AF_MAX_DIMS; i++) {
+            dim_index[i]--;
+            ptr += stride[i];
+            if(dim_index[i]) {
+                return *this;
+            }
+            dim_index[i] = dims[i];
+        }
+        ptr = nullptr;
+        return *this;
+    }
+
+    /// @copydoc operator++()
+    ParamIterator& operator++(int) noexcept {
+        ParamIterator before(*this);
+        operator++();
+        return before;
+    }
+
+    /// Advances the iterator by count elements
+    ParamIterator& operator+=(std::size_t count) noexcept {
+        while (count-- > 0) {
+            operator++();
+        }
+        return *this;
+    }
+
+    const reference operator*() const noexcept {
+        return *ptr;
+    }
+
+    const pointer operator->() const noexcept {
+        return ptr;
+    }
+
+    ParamIterator(const ParamIterator<T>& other) = default;
+    ParamIterator(ParamIterator<T>&& other) = default;
+    ~ParamIterator() noexcept = default;
+    ParamIterator<T>& operator=(const ParamIterator<T>& other) noexcept = default;
+    ParamIterator<T>& operator=(ParamIterator<T>&& other) noexcept = default;
+};
+
+  template<typename T>
+  ParamIterator<T> begin(Param<T>& param) {
+      return ParamIterator<T>(param);
+  }
+
+  template<typename T>
+  ParamIterator<T> end(Param<T>& param) {
+      return ParamIterator<T>();
+  }
+
+  template<typename T>
+  ParamIterator<const T> begin(CParam<T>& param) {
+      return ParamIterator<const T>(param);
+  }
+
+  template<typename T>
+  ParamIterator<const T> end(CParam<T>& param) {
+      return ParamIterator<const T>();
+  }
+
+}

From 6fb213988c5f210e140626b442425d7c49149fe1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 29 Oct 2018 20:44:45 -0400
Subject: [PATCH 1547/2677] Mark more tests as serial tests. Update CDash drop
 site

---
 CTestConfig.cmake   | 2 +-
 test/CMakeLists.txt | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CTestConfig.cmake b/CTestConfig.cmake
index e9ed850094..ae3a6b355c 100644
--- a/CTestConfig.cmake
+++ b/CTestConfig.cmake
@@ -8,6 +8,6 @@ set(CTEST_PROJECT_NAME "ArrayFire")
 set(CTEST_NIGHTLY_START_TIME "01:00:00 UTC")
 
 set(CTEST_DROP_METHOD "http")
-set(CTEST_DROP_SITE "67.207.87.39")
+set(CTEST_DROP_SITE "ci.arrayfire.org")
 set(CTEST_DROP_LOCATION "/submit.php?project=ArrayFire")
 set(CTEST_DROP_SITE_CDASH TRUE)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index ede1c2c53e..5c16ee88df 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -237,10 +237,10 @@ endif()
 
 make_test(SRC orb.cpp)
 make_test(SRC pinverse.cpp)
-make_test(SRC qr_dense.cpp)
+make_test(SRC qr_dense.cpp SERIAL)
 make_test(SRC random.cpp)
 make_test(SRC range.cpp)
-make_test(SRC rank_dense.cpp)
+make_test(SRC rank_dense.cpp SERIAL)
 make_test(SRC reduce.cpp)
 make_test(SRC regions.cpp)
 make_test(SRC reorder.cpp)
@@ -261,7 +261,7 @@ if(AF_WITH_NONFREE)
 endif()
 
 make_test(SRC sobel.cpp)
-make_test(SRC solve_dense.cpp       CXX11)
+make_test(SRC solve_dense.cpp       CXX11 SERIAL)
 make_test(SRC sort.cpp)
 make_test(SRC sort_by_key.cpp)
 make_test(SRC sort_index.cpp)
@@ -270,7 +270,7 @@ make_test(SRC sparse_arith.cpp)
 make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
 make_test(SRC susan.cpp)
-make_test(SRC svd_dense.cpp)
+make_test(SRC svd_dense.cpp         SERIAL)
 make_test(SRC threading.cpp         CXX11 SERIAL)
 make_test(SRC tile.cpp)
 make_test(SRC topk.cpp              CXX11)

From a0ee7bcad458cbfca04c84e8b78263c54dfa6a32 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Sun, 4 Nov 2018 10:33:28 -0500
Subject: [PATCH 1548/2677] Allow af_approx1 to write to an existing af_array
 as output (#2324)

* Allow af_approx1 to use an existing af_array as its output
* Documented in/out behavior af_approx1 output
* Removed unnecessary consts from fft_inplace and getWritableArrayt
* Fixed typos in src/api/c/approx1.cpp and reformatted parameter docs
---
 include/af/signal.h           | 56 ++++++++++++++++-------
 src/api/c/approx.cpp          | 52 ++++++++++++----------
 src/api/c/fft.cpp             |  2 +-
 src/api/c/handle.hpp          |  4 +-
 src/backend/cpu/approx.cpp    | 29 +++++-------
 src/backend/cpu/approx.hpp    |  8 ++--
 src/backend/cuda/approx.cu    | 30 +++++--------
 src/backend/cuda/approx.hpp   |  8 ++--
 src/backend/opencl/approx.cpp | 29 +++++-------
 src/backend/opencl/approx.hpp |  8 ++--
 test/approx1.cpp              | 84 +++++++++++++++++++++++++++++++++++
 11 files changed, 203 insertions(+), 107 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index f85c7045d3..75408c3deb 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -753,13 +753,23 @@ extern "C" {
 /**
    C Interface for signals interpolation on one dimensional signals.
 
-   \param[out] out the interpolated array.
-   \param[in]  in is the multidimensional input array. Values assumed to lie uniformly spaced indices in the range of `[0, n)`, where `n` is the number of elements in the array.
-   \param[in]  pos positions of the interpolation points along the first dimension.
-   \param[in]  method is the interpolation method to be used. The following types (defined in enum \ref af_interp_type) are supported: nearest neighbor, linear, and cubic.
-   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
-   \return     \ref AF_SUCCESS if the interpolation operation is successful,
-               otherwise an appropriate error code is returned.
+   \param[in,out] out      is the interpolated array.
+   \param[in]     in       is the multidimensional input array. Values assumed to
+                           lie uniformly spaced indices in the range of `[0, n)`,
+                           where `n` is the number of elements in the array.
+   \param[in]     pos      positions of the interpolation points along the first
+                           dimension.
+   \param[in]     method   is the interpolation method to be used. The following
+                           types (defined in enum \ref af_interp_type)
+                           are supported: nearest neighbor, linear, and cubic.
+   \param[in]     off_grid is the default value for any indices outside the
+                           valid range of indices.
+   \return        \ref AF_SUCCESS if the interpolation operation is successful,
+                  otherwise an appropriate error code is returned.
+
+   \note \p out can either be a null or existing `af_array` object. If it is a
+         sub-array of an existing `af_array`, only the corresponding portion of
+         the `af_array` will be overwritten
 
    \ingroup signal_func_approx1
  */
@@ -800,16 +810,28 @@ AFAPI af_err af_approx2(af_array *out, const af_array in, const af_array pos0, c
    The blue dots represent indices whose values are known. The red dots
    represent indices whose values are unknown.
 
-   \param[out] out the interpolated array.
-   \param[in]  in is the multidimensional input array. Values lie on uniformly spaced indices determined by `idx_start` and `idx_step`.
-   \param[in]  pos positions of the interpolation points along `interp_dim`.
-   \param[in]  interp_dim is the dimension to perform interpolation across.
-   \param[in]  idx_start is the first index value along `interp_dim`.
-   \param[in]  idx_step is the uniform spacing value between subsequent indices along `interp_dim`.
-   \param[in]  method is the interpolation method to be used. The following types (defined in enum \ref af_interp_type) are supported: nearest neighbor, linear, and cubic.
-   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
-   \return     \ref AF_SUCCESS if the interpolation operation is successful,
-               otherwise an appropriate error code is returned.
+   \param[in,out] out        the interpolated array.
+   \param[in]     in         is the multidimensional input array. Values lie on
+                             uniformly spaced indices determined by `idx_start`
+                             and `idx_step`.
+   \param[in]     pos        positions of the interpolation points along
+                             `interp_dim`.
+   \param[in]     interp_dim is the dimension to perform interpolation across.
+   \param[in]     idx_start  is the first index value along `interp_dim`.
+   \param[in]     idx_step   is the uniform spacing value between subsequent
+                             indices along `interp_dim`.
+   \param[in]     method     is the interpolation method to be used. The
+                             following types (defined in enum
+                             \ref af_interp_type) are supported: nearest
+                             neighbor, linear, and cubic.
+   \param[in]     off_grid   is the default value for any indices outside the
+                             valid range of indices.
+   \return        \ref AF_SUCCESS if the interpolation operation is successful,
+                  otherwise an appropriate error code is returned.
+
+   \note \p out can either be a null or existing `af_array` object. If it is a
+         sub-array of an existing `af_array`, only the corresponding portion of
+         the `af_array` will be overwritten
 
    \ingroup signal_func_approx1
  */
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 7dafc0182a..24bd671c8a 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -20,15 +20,15 @@ using af::dim4;
 using namespace detail;
 
 template<typename Ty, typename Tp>
-static inline af_array approx1(const af_array yi,
-                               const af_array xo, const int xdim,
-                               const Tp &xi_beg, const Tp &xi_step,
-                               const af_interp_type method, const float offGrid)
+static inline void approx1(af_array *yo, const af_array yi,
+                           const af_array xo, const int xdim,
+                           const Tp &xi_beg, const Tp &xi_step,
+                           const af_interp_type method, const float offGrid)
 {
-    return getHandle(approx1<Ty>(getArray<Ty>(yi),
-                                 getArray<Tp>(xo), xdim,
-                                 xi_beg, xi_step,
-                                 method, offGrid));
+    approx1<Ty>(getWritableArray<Ty>(*yo), getArray<Ty>(yi),
+                getArray<Tp>(xo), xdim,
+                xi_beg, xi_step,
+                method, offGrid);
 }
 
 template<typename Ty, typename Tp>
@@ -58,8 +58,8 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi,
         const ArrayInfo& yi_info = getInfo(yi);
         const ArrayInfo& xo_info = getInfo(xo);
 
-        dim4 yi_dims = yi_info.dims();
-        dim4 xo_dims = xo_info.dims();
+        const dim4 yi_dims = yi_info.dims();
+        const dim4 xo_dims = xo_info.dims();
 
         ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
         ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
@@ -83,27 +83,33 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi,
                        method == AF_INTERP_NEAREST));
 
         if (yi_dims.ndims() == 0 || xo_dims.ndims() ==  0) {
-            return af_create_handle(yo, 0, nullptr, yi_info.getType());
+            *yo = createHandle(dim4(0,0,0,0), yi_info.getType());
+            return AF_SUCCESS;
         }
 
-        af_array output;
+        dim4 yo_dims = yi_dims;
+        yo_dims[xdim] = xo_dims[xdim];
+        if (*yo == 0) {
+            *yo = createHandle(yo_dims, yi_info.getType());
+        }
+
+        DIM_ASSERT(1, getInfo(*yo).dims() == yo_dims);
 
         switch(yi_info.getType()) {
-        case f32: output = approx1<float  , float >(yi, xo, xdim,
-                                                    xi_beg, xi_step,
-                                                    method, offGrid);  break;
-        case f64: output = approx1<double , double>(yi, xo, xdim,
-                                                    xi_beg, xi_step,
-                                                    method, offGrid);  break;
-        case c32: output = approx1<cfloat , float >(yi, xo, xdim,
-                                                    xi_beg, xi_step,
-                                                    method, offGrid);  break;
-        case c64: output = approx1<cdouble, double>(yi, xo, xdim,
+        case f32: approx1<float  , float >(yo, yi, xo, xdim,
+                                           xi_beg, xi_step,
+                                           method, offGrid);  break;
+        case f64: approx1<double , double>(yo, yi, xo, xdim,
+                                           xi_beg, xi_step,
+                                           method, offGrid);  break;
+        case c32: approx1<cfloat , float >(yo, yi, xo, xdim,
+                                           xi_beg, xi_step,
+                                           method, offGrid);  break;
+        case c64: approx1<cdouble, double>(yo, yi, xo, xdim,
                                                     xi_beg, xi_step,
                                                     method, offGrid);  break;
         default:  TYPE_ERROR(1, yi_info.getType());
         }
-        std::swap(*yo,output);
     }
     CATCHALL;
 
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index a04529ae0f..78c7c51c94 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -91,7 +91,7 @@ af_err af_ifft3(af_array *out, const af_array in, const double norm_factor, cons
 }
 
 template<typename T, int rank, bool direction>
-static void fft_inplace(const af_array in, const double norm_factor)
+static void fft_inplace(af_array in, const double norm_factor)
 {
     Array<T> &input = getWritableArray<T>(in);
     fft_inplace<T, rank, direction>(input);
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index aaf19930ee..8d30f54889 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -81,9 +81,9 @@ detail::Array<To> castArray(const af_array &in)
 
 template<typename T>
 static detail::Array<T> &
-getWritableArray(const af_array &arr)
+getWritableArray(af_array &arr)
 {
-    const detail::Array<T> &A = getArray<T>(arr);
+    const detail::Array<T> &A = getArray<T>((const af_array) arr);
     ARG_ASSERT(0, A.isSparse() == false);
     return const_cast<detail::Array<T>&>(A);
 }
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index e6c8def15a..5ff4038f1a 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -16,19 +16,14 @@ namespace cpu
 {
 
 template<typename Ty, typename Tp>
-Array<Ty> approx1(const Array<Ty> &yi,
-                  const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step,
-                  const af_interp_type method, const float offGrid)
+void approx1(Array<Ty> &yo, const Array<Ty> &yi,
+             const Array<Tp> &xo, const int xdim,
+             const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid)
 {
     yi.eval();
     xo.eval();
 
-    dim4 odims = yi.dims();
-    odims[xdim] = xo.dims()[xdim];
-
-    Array<Ty> yo = createEmptyArray<Ty>(odims);
-
     switch(method) {
     case AF_INTERP_NEAREST:
     case AF_INTERP_LOWER:
@@ -48,7 +43,6 @@ Array<Ty> approx1(const Array<Ty> &yi,
     default:
         break;
     }
-    return yo;
 }
 
 template<typename Ty, typename Tp>
@@ -102,13 +96,14 @@ Array<Ty> approx2(const Array<Ty> &zi,
 }
 
 #define INSTANTIATE(Ty, Tp)                                         \
-    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &yi,         \
-                                       const Array<Tp> &xo,         \
-                                       const int xdim,              \
-                                       const Tp &xi_beg,            \
-                                       const Tp &xi_step,           \
-                                       const af_interp_type method, \
-                                       const float offGrid);        \
+    template void approx1<Ty, Tp>(Array<Ty> &yo,                    \
+                                  const Array<Ty> &yi,              \
+                                  const Array<Tp> &xo,              \
+                                  const int xdim,                   \
+                                  const Tp &xi_beg,                 \
+                                  const Tp &xi_step,                \
+                                  const af_interp_type method,      \
+                                  const float offGrid);             \
     template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
                                        const Array<Tp> &xo,         \
                                        const int xdim,              \
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index e55a013665..1bc134463b 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -13,10 +13,10 @@
 namespace cpu
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &yi,
-                      const Array<Tp> &xo, const int xdim,
-                      const Tp &xi_beg, const Tp &xi_step,
-                      const af_interp_type method, const float offGrid);
+    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
+                 const Array<Tp> &xo, const int xdim,
+                 const Tp &xi_beg, const Tp &xi_step,
+                 const af_interp_type method, const float offGrid);
 
     template<typename Ty, typename Tp>
     Array<Ty> approx2(const Array<Ty> &zi,
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cu
index 13e5d2340f..1ddffdf73b 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cu
@@ -16,17 +16,11 @@
 namespace cuda
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &yi,
-                      const Array<Tp> &xo, const int xdim,
-                      const Tp &xi_beg, const Tp &xi_step,
-                      const af_interp_type method, const float offGrid)
+    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
+                 const Array<Tp> &xo, const int xdim,
+                 const Tp &xi_beg, const Tp &xi_step,
+                 const af_interp_type method, const float offGrid)
     {
-        af::dim4 odims = yi.dims();
-        odims[xdim] = xo.dims()[xdim];
-
-        // Create output placeholder
-        Array<Ty> yo = createEmptyArray<Ty>(odims);
-
         switch(method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -43,7 +37,6 @@ namespace cuda
         default:
             break;
         }
-        return yo;
     }
 
     template<typename Ty, typename Tp>
@@ -92,13 +85,14 @@ namespace cuda
     }
 
 #define INSTANTIATE(Ty, Tp)                                         \
-    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &yi,         \
-                                       const Array<Tp> &xo,         \
-                                       const int xdim,              \
-                                       const Tp &xi_beg,            \
-                                       const Tp &xi_step,           \
-                                       const af_interp_type method, \
-                                       const float offGrid);        \
+    template void approx1<Ty, Tp>(Array<Ty> &yo,                    \
+                                  const Array<Ty> &yi,              \
+                                  const Array<Tp> &xo,              \
+                                  const int xdim,                   \
+                                  const Tp &xi_beg,                 \
+                                  const Tp &xi_step,                \
+                                  const af_interp_type method,      \
+                                  const float offGrid);             \
     template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
                                        const Array<Tp> &xo,         \
                                        const int xdim,              \
diff --git a/src/backend/cuda/approx.hpp b/src/backend/cuda/approx.hpp
index 91d3228a5b..02289136fb 100644
--- a/src/backend/cuda/approx.hpp
+++ b/src/backend/cuda/approx.hpp
@@ -12,10 +12,10 @@
 namespace cuda
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &yi,
-                      const Array<Tp> &xo, const int xdim,
-                      const Tp &xi_beg, const Tp &xi_step,
-                      const af_interp_type method, const float offGrid);
+    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
+                 const Array<Tp> &xo, const int xdim,
+                 const Tp &xi_beg, const Tp &xi_step,
+                 const af_interp_type method, const float offGrid);
 
     template<typename Ty, typename Tp>
     Array<Ty> approx2(const Array<Ty> &zi,
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index 957bf73bfb..edaffd6245 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -16,16 +16,11 @@
 namespace opencl
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &yi,
-                      const Array<Tp> &xo, const int xdim,
-                      const Tp &xi_beg, const Tp &xi_step,
-                      const af_interp_type method, const float offGrid)
+    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
+                 const Array<Tp> &xo, const int xdim,
+                 const Tp &xi_beg, const Tp &xi_step,
+                 const af_interp_type method, const float offGrid)
     {
-        af::dim4 odims = yi.dims();
-        odims[xdim] = xo.dims()[xdim];
-
-        // Create output placeholder
-        Array<Ty> yo = createEmptyArray<Ty>(odims);
         switch(method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -42,7 +37,6 @@ namespace opencl
         default:
             break;
         }
-        return yo;
     }
 
     template<typename Ty, typename Tp>
@@ -92,13 +86,14 @@ namespace opencl
     }
 
 #define INSTANTIATE(Ty, Tp)                                         \
-    template Array<Ty> approx1<Ty, Tp>(const Array<Ty> &yi,         \
-                                       const Array<Tp> &xo,         \
-                                       const int xdim,              \
-                                       const Tp &xi_beg,            \
-                                       const Tp &xi_step,           \
-                                       const af_interp_type method, \
-                                       const float offGrid);        \
+    template void approx1<Ty, Tp>(Array<Ty> &yo,                    \
+                                  const Array<Ty> &yi,              \
+                                  const Array<Tp> &xo,              \
+                                  const int xdim,                   \
+                                  const Tp &xi_beg,                 \
+                                  const Tp &xi_step,                \
+                                  const af_interp_type method,      \
+                                  const float offGrid);             \
     template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
                                        const Array<Tp> &xo,         \
                                        const int xdim,              \
diff --git a/src/backend/opencl/approx.hpp b/src/backend/opencl/approx.hpp
index 7e14696c1d..db26c4151a 100644
--- a/src/backend/opencl/approx.hpp
+++ b/src/backend/opencl/approx.hpp
@@ -12,10 +12,10 @@
 namespace opencl
 {
     template<typename Ty, typename Tp>
-    Array<Ty> approx1(const Array<Ty> &yi,
-                      const Array<Tp> &xo, const int xdim,
-                      const Tp &xi_beg, const Tp &xi_step,
-                      const af_interp_type method, const float offGrid);
+    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
+                 const Array<Tp> &xo, const int xdim,
+                 const Tp &xi_beg, const Tp &xi_step,
+                 const af_interp_type method, const float offGrid);
 
     template<typename Ty, typename Tp>
     Array<Ty> approx2(const Array<Ty> &zi,
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 8da14a03b0..7ff3ac9801 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -840,3 +840,87 @@ TEST(Approx1, CPPEmptyPosAndInput)
     ASSERT_TRUE(pos.isempty());
     ASSERT_TRUE(interp.isempty());
 }
+
+TEST(Approx1, UseNullInitialOutput) {
+    float h_in[3] = {10, 20, 30};
+    dim_t h_in_dims = 3;
+
+    af_array in = 0;
+    ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
+
+    float h_pos[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    dim_t h_pos_dims = 5;
+    af_array pos = 0;
+    ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
+
+    af_array out = 0;
+    ASSERT_SUCCESS(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0));
+
+    ASSERT_FALSE(out == 0);
+}
+
+TEST(Approx1, UseExistingOutputArray) {
+    float h_in[3] = {10, 20, 30};
+    dim_t h_in_dims = 3;
+
+    af_array in = 0;
+    ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
+
+    float h_pos[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    dim_t h_pos_dims = 5;
+    af_array pos = 0;
+    ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
+
+    dim_t h_out_dims = 5;
+    af_array out_ptr = 0;
+    ASSERT_SUCCESS(af_create_handle(&out_ptr, 1, &h_out_dims, f32));
+    af_array out_ptr_copy = out_ptr;
+    ASSERT_SUCCESS(af_approx1(&out_ptr, in, pos, AF_INTERP_LINEAR, 0));
+
+    // Verify that the original output af_array memory was used
+    ASSERT_EQ(out_ptr_copy, out_ptr);
+
+    af_array out_no_alloc = 0;
+    ASSERT_SUCCESS(af_approx1(&out_no_alloc, in, pos, AF_INTERP_LINEAR, 0));
+
+    // Verify that the contents of an approx with a previously allocated output
+    // and that of a non-allocated output match
+    ASSERT_ARRAYS_EQ(out_ptr, out_no_alloc);
+}
+
+TEST(Approx1, UseExistingOutputSlice) {
+    float h_in[3] = {10, 20, 30};
+    dim_t h_in_dims = 3;
+
+    af_array in = 0;
+    ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
+
+    float h_pos[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    dim_t h_pos_dims = 5;
+    af_array pos = 0;
+    ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
+
+    float h_out[15] = {1.0, 1.5, 2.0, 2.5, 3.0,
+                       4.0, 4.5, 5.0, 5.5, 6.0,
+                       7.0, 7.5, 8.0, 8.5, 9.0};
+    dim_t h_out_dims[2] = {5, 3};
+    af_array out = 0;
+    ASSERT_SUCCESS(af_create_array(&out, &h_out[0], 2, &h_out_dims[0], f32));
+    af_seq idx_dim1 = {1, 1, 1}; // get slice 1 of dim1
+    af_seq idx[2] = {af_span, idx_dim1};
+    af_array out_slice = 0;
+    ASSERT_SUCCESS(af_index(&out_slice, out, 2, &idx[0]));
+    ASSERT_SUCCESS(af_approx1(&out_slice, in, pos, AF_INTERP_LINEAR, 0));
+
+    dim_t nelems = 0;
+    ASSERT_SUCCESS(af_get_elements(&nelems, out));
+    vector<float> h_out_approx(nelems);
+    ASSERT_SUCCESS(af_get_data_ptr(&h_out_approx.front(), out));
+
+    float h_gold[15] = {1.0, 1.5, 2.0, 2.5, 3.0,
+                        10.0, 15.0, 20.0, 25.0, 30.0,
+                        7.0, 7.5, 8.0, 8.5, 9.0};
+    af_array gold = 0;
+    ASSERT_SUCCESS(af_create_array(&gold, &h_gold[0], 2, &h_out_dims[0], f32));
+    ASSERT_ARRAYS_EQ(gold, out);
+}

From d6126d28eb9b2416dda9740ab26e0b3f647f1623 Mon Sep 17 00:00:00 2001
From: jcai1 <jcai@fb.com>
Date: Wed, 7 Nov 2018 14:57:26 -0800
Subject: [PATCH 1549/2677] Fix int overflow in flops calculation

---
 src/backend/cuda/platform.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 4e36eec7ca..8408f7c8da 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -521,7 +521,7 @@ DeviceManager::DeviceManager()
         if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
             continue;
         } else {
-            dev.flops = dev.prop.multiProcessorCount *
+            dev.flops = static_cast<size_t>(dev.prop.multiProcessorCount) *
                         compute2cores(dev.prop.major, dev.prop.minor) *
                         dev.prop.clockRate;
             dev.nativeId = i;

From 2bac5df9f8e46cda6a9110ef84efca1d6afa02c3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 16 Nov 2018 09:44:40 -0500
Subject: [PATCH 1550/2677] Fix medfilt1 bug related to switch fallthrough

---
 src/backend/cuda/kernel/medfilt.hpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index ff3780ba58..fc9a07fb5b 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -349,16 +349,16 @@ void medfilt1(Param<T> out, CParam<T> in, int w_wid)
     const size_t shrdMemBytes = sizeof(T) * (THREADS_X + w_wid - 1);
 
     switch(w_wid) {
-        case  3: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 3>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case  5: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 4>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case  7: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 5>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case  9: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 6>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case 11: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 7>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case 13: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 8>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case 15: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 9>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case 17: CUDA_LAUNCH_SMEM((medfilt1<T,pad,10>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        case 19: CUDA_LAUNCH_SMEM((medfilt1<T,pad,11>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
-        default: CUDA_LAUNCH_SMEM((medfilt1<T,pad,62>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x);
+        case  3: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 3>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case  5: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 4>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case  7: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 5>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case  9: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 6>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case 11: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 7>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case 13: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 8>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case 15: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 9>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case 17: CUDA_LAUNCH_SMEM((medfilt1<T,pad,10>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        case 19: CUDA_LAUNCH_SMEM((medfilt1<T,pad,11>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+        default: CUDA_LAUNCH_SMEM((medfilt1<T,pad,62>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
     }
 
     POST_LAUNCH_CHECK();

From 95615a3c75493983a45ff92d3e776afdfc427a0d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 16 Nov 2018 09:59:01 -0500
Subject: [PATCH 1551/2677] Fix FindMKL script for GNU OpenMP threading layer

---
 CMakeModules/FindMKL.cmake | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index c34cafd2c9..5baec89995 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -218,7 +218,11 @@ if(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
 elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
   find_package(OpenMP REQUIRED)
   find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_gnu_thread)
-  set(MKL::ThreadingLibrary OpenMP::OpenMP_CXX CACHE STRING "The OpenMP Threading Library")
+  add_library(MKL::ThreadingLibrary SHARED IMPORTED)
+  set_target_properties(MKL::ThreadingLibrary
+    PROPERTIES
+      IMPORTED_LOCATION "${OpenMP_gomp_LIBRARY}"
+      INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_CXX)
 elseif(MKL_THREAD_LAYER STREQUAL "TBB")
   find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_tbb_thread)
   find_mkl_library(NAME ThreadingLibrary LIBRARY_NAME tbb)

From e97e7fa8f5deb4880e89c2f6a1fd61ca38602bed Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 12 Nov 2018 12:15:06 -0500
Subject: [PATCH 1552/2677] Fix several -Wall -Wextra warnings

---
 examples/benchmarks/cg.cpp                    |   2 +-
 examples/getting_started/vectorize.cpp        |   2 +-
 examples/graphics/conway.cpp                  |   2 +-
 examples/graphics/conway_pretty.cpp           |   2 +-
 examples/graphics/field.cpp                   |   2 +-
 examples/graphics/gravity_sim.cpp             |   3 +-
 examples/graphics/histogram.cpp               |   2 +-
 examples/graphics/plot2d.cpp                  |   2 +-
 examples/graphics/plot3.cpp                   |   2 +-
 examples/graphics/surface.cpp                 |   2 +-
 examples/machine_learning/rbm.cpp             |   2 +-
 examples/unified/basic.cpp                    |   2 +-
 src/api/c/anisotropic_diffusion.cpp           |   7 +-
 src/api/c/array.cpp                           |  23 ++-
 src/api/c/assign.cpp                          |  27 ++--
 src/api/c/canny.cpp                           |   2 +-
 src/api/c/cast.cpp                            |  19 ---
 src/api/c/colorspace.cpp                      |   3 +
 src/api/c/corrcoef.cpp                        |   1 +
 src/api/c/data.cpp                            |   1 -
 src/api/c/device.cpp                          |   1 +
 src/api/c/fft.cpp                             |  10 ++
 src/api/c/fft_common.hpp                      |  13 +-
 src/api/c/handle.hpp                          |  62 +++-----
 src/api/c/hist.cpp                            |   5 +
 src/api/c/image.cpp                           |   3 +
 src/api/c/imageio_helper.h                    |   1 +
 src/api/c/index.cpp                           |   2 +-
 src/api/c/median.cpp                          |   1 +
 src/api/c/pinverse.cpp                        |  12 +-
 src/api/c/plot.cpp                            |  43 ++++++
 src/api/c/select.cpp                          |   2 +-
 src/api/c/sift.cpp                            |  20 +++
 src/api/c/sparse.cpp                          |  16 +--
 src/api/c/stdev.cpp                           |   1 +
 src/api/c/surface.cpp                         |   5 +
 src/api/c/var.cpp                             |   3 -
 src/api/c/vector_field.cpp                    |  18 +++
 src/api/c/window.cpp                          |  48 +++++++
 src/api/unified/symbol_manager.cpp            |   5 +-
 src/backend/common/DependencyModule.cpp       |  15 ++
 src/backend/common/Logger.cpp                 |   7 +-
 src/backend/common/MemoryManagerImpl.hpp      |   6 +-
 src/backend/common/defines.hpp                |   8 +-
 src/backend/common/jit/Node.hpp               |  39 +++--
 src/backend/common/jit/NodeIterator.hpp       |   2 +-
 src/backend/common/jit/ScalarNode.hpp         |   2 +
 src/backend/common/jit/ShiftNodeBase.hpp      |   1 +
 src/backend/cpu/Array.cpp                     |   1 -
 src/backend/cpu/copy.cpp                      |  32 +----
 src/backend/cpu/fft.cpp                       |   1 +
 src/backend/cpu/iota.cpp                      |   2 +-
 src/backend/cpu/jit/BinaryNode.hpp            |   5 +
 src/backend/cpu/jit/Node.hpp                  |  25 +++-
 src/backend/cpu/jit/UnaryNode.hpp             |   5 +
 .../cpu/kernel/anisotropic_diffusion.hpp      |   4 +-
 src/backend/cpu/kernel/convolve.hpp           |   5 +-
 src/backend/cpu/kernel/dot.hpp                |   2 +
 src/backend/cpu/kernel/exampleFunction.hpp    |   1 +
 src/backend/cpu/kernel/fftconvolve.hpp        |   1 +
 src/backend/cpu/kernel/iota.hpp               |   2 +-
 src/backend/cpu/kernel/join.hpp               |  36 +++--
 src/backend/cpu/kernel/nearest_neighbour.hpp  |   5 +-
 src/backend/cpu/nearest_neighbour.cpp         |   6 +-
 src/backend/cpu/platform.cpp                  |   2 +
 src/backend/cpu/sift.cpp                      |  14 ++
 src/backend/cpu/sobel.cpp                     |   1 +
 src/backend/cpu/solve.cpp                     |   1 +
 src/backend/cpu/sparse_blas.cpp               |   4 +
 src/backend/cuda/Param.hpp                    |  10 ++
 src/backend/cuda/ThrustAllocator.cuh          |   1 +
 src/backend/cuda/copy.cu                      |  36 +----
 src/backend/cuda/fast.cu                      |   2 -
 src/backend/cuda/fftconvolve.cu               |  15 +-
 src/backend/cuda/harris.cu                    |   2 -
 src/backend/cuda/iota.cu                      |   2 +-
 src/backend/cuda/jit.cpp                      |   2 +-
 src/backend/cuda/jit/kernel_generators.hpp    |   2 +
 src/backend/cuda/kernel/fftconvolve.hpp       |  12 +-
 src/backend/cuda/kernel/histogram.hpp         |   6 +-
 src/backend/cuda/kernel/iota.hpp              |   5 +-
 src/backend/cuda/kernel/medfilt.hpp           |   1 +
 src/backend/cuda/kernel/nearest_neighbour.hpp |   7 +-
 src/backend/cuda/kernel/orb.hpp               |   4 +
 .../cuda/kernel/random_engine_mersenne.hpp    |   8 +-
 src/backend/cuda/kernel/rotate.hpp            |   6 +-
 src/backend/cuda/kernel/sort.hpp              |  51 ++-----
 src/backend/cuda/kernel/sort_by_key.hpp       |  26 +---
 .../cuda/kernel/thrust_sort_by_key.hpp        |  11 +-
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |   2 -
 src/backend/cuda/kernel/topk.hpp              |   1 +
 src/backend/cuda/kernel/transpose.hpp         |   2 +-
 src/backend/cuda/memory.cpp                   |   1 +
 src/backend/cuda/nearest_neighbour.cu         |   6 +-
 src/backend/cuda/orb.cu                       |   2 -
 src/backend/cuda/platform.cpp                 |  11 +-
 src/backend/cuda/sift.cu                      |  14 ++
 src/backend/cuda/solve.cu                     |   1 +
 src/backend/cuda/sort.cu                      |   6 +-
 src/backend/cuda/sparse_blas.cpp              |   1 +
 src/backend/cuda/transpose.cu                 |   5 +-
 src/backend/opencl/Array.cpp                  |   2 +-
 src/backend/opencl/Array.hpp                  |   2 +-
 src/backend/opencl/copy.cpp                   |  33 +----
 src/backend/opencl/cpu/cpu_solve.cpp          |   1 +
 src/backend/opencl/cpu/cpu_sparse_blas.cpp    |   4 +
 src/backend/opencl/err_clblast.hpp            |   1 +
 src/backend/opencl/iota.cpp                   |   2 +-
 src/backend/opencl/jit/kernel_generators.hpp  |   6 +-
 src/backend/opencl/kernel/histogram.hpp       |   6 +-
 src/backend/opencl/kernel/iota.cl             |   1 -
 src/backend/opencl/kernel/iota.hpp            |   5 +-
 src/backend/opencl/kernel/laset.hpp           |   5 +-
 src/backend/opencl/kernel/laswp.hpp           |   4 +-
 src/backend/opencl/kernel/lookup.hpp          |   2 +-
 .../opencl/kernel/nearest_neighbour.hpp       |   9 +-
 src/backend/opencl/kernel/sort.hpp            |  15 +-
 .../opencl/kernel/sort_by_key_impl.hpp        |   5 +-
 src/backend/opencl/kernel/sparse.hpp          |   1 -
 src/backend/opencl/kernel/swapdblk.hpp        |   6 +-
 src/backend/opencl/kernel/transpose.hpp       |   6 +-
 .../opencl/kernel/transpose_inplace.hpp       |   6 +-
 src/backend/opencl/lookup.cpp                 |  10 +-
 src/backend/opencl/magma/laset.cpp            |   6 +-
 src/backend/opencl/magma/laswp.cpp            |   5 +-
 src/backend/opencl/magma/magma_blas_clblast.h |  18 +--
 src/backend/opencl/magma/magma_cpu_lapack.h   |   8 +-
 src/backend/opencl/magma/magma_helper.cpp     |   6 +-
 src/backend/opencl/magma/magma_helper.h       |   2 +-
 src/backend/opencl/magma/swapdblk.cpp         |   2 +-
 src/backend/opencl/magma/transpose.cpp        |   7 +-
 .../opencl/magma/transpose_inplace.cpp        |   9 +-
 src/backend/opencl/magma/ungqr.cpp            |   4 +-
 src/backend/opencl/medfilt.cpp                |   1 +
 src/backend/opencl/nearest_neighbour.cpp      |   2 +-
 src/backend/opencl/platform.cpp               |   5 +
 src/backend/opencl/platform.hpp               |   2 +
 src/backend/opencl/sift.cpp                   |  14 ++
 src/backend/opencl/solve.cpp                  |   5 +-
 src/backend/opencl/sort.cpp                   |   6 +-
 src/backend/opencl/topk.cpp                   |   1 -
 src/backend/opencl/transpose.cpp              |   9 +-
 src/backend/opencl/transpose_inplace.cpp      |   8 +-
 src/backend/opencl/types.hpp                  |  26 ++--
 test/approx1.cpp                              | 136 +++++++++---------
 test/approx2.cpp                              |  11 +-
 test/dot.cpp                                  |   2 +-
 test/fft.cpp                                  |   4 +-
 test/flat.cpp                                 |   1 -
 test/inverse_deconv.cpp                       |   1 -
 test/inverse_dense.cpp                        |   6 +-
 test/iterative_deconv.cpp                     |   1 -
 test/meanvar.cpp                              |  10 +-
 test/print_info.cpp                           |   2 +-
 test/random.cpp                               |   4 +-
 test/reduce.cpp                               |   3 +
 test/resize.cpp                               |   8 +-
 test/rotate.cpp                               | 104 +++++++-------
 test/rotate_linear.cpp                        | 104 +++++++-------
 test/select.cpp                               |   6 +-
 test/sobel.cpp                                |   3 -
 test/sort_by_key.cpp                          |   6 +-
 test/sort_index.cpp                           |   9 +-
 test/testHelpers.hpp                          |  16 +--
 test/topk.cpp                                 |  12 +-
 165 files changed, 869 insertions(+), 733 deletions(-)

diff --git a/examples/benchmarks/cg.cpp b/examples/benchmarks/cg.cpp
index 57ee972661..47c35af8c4 100644
--- a/examples/benchmarks/cg.cpp
+++ b/examples/benchmarks/cg.cpp
@@ -114,7 +114,7 @@ void checkConjugateGradient(const af::array in)
     af_print(dot(res, res));
 }
 
-int main(int argc, char *argv[])
+int main(int , char **)
 {
     af::info();
     setupInputs();
diff --git a/examples/getting_started/vectorize.cpp b/examples/getting_started/vectorize.cpp
index 55f5e05ebc..673520c148 100644
--- a/examples/getting_started/vectorize.cpp
+++ b/examples/getting_started/vectorize.cpp
@@ -179,7 +179,7 @@ static void bench_tile2()
     dist_tile2(A, B);
 }
 
-int main(int argc, char **argv)
+int main(int, char **)
 {
     try {
 
diff --git a/examples/graphics/conway.cpp b/examples/graphics/conway.cpp
index c3e4696ee8..9a91240001 100644
--- a/examples/graphics/conway.cpp
+++ b/examples/graphics/conway.cpp
@@ -13,7 +13,7 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
diff --git a/examples/graphics/conway_pretty.cpp b/examples/graphics/conway_pretty.cpp
index 6980f7b27d..6ba8ca62c0 100644
--- a/examples/graphics/conway_pretty.cpp
+++ b/examples/graphics/conway_pretty.cpp
@@ -13,7 +13,7 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
index 9c94a0a493..3d177ae9ea 100644
--- a/examples/graphics/field.cpp
+++ b/examples/graphics/field.cpp
@@ -17,7 +17,7 @@ const static float MINIMUM = -3.0f;
 const static float MAXIMUM =  3.0f;
 const static float STEP = 0.18f;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         af::info();
diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 1bbb00cdbe..6e55406038 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -168,10 +168,9 @@ void collisions(vector<af::array> &pos, vector<af::array> &vels, bool is3D) {
 }
 
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
-
         af::info();
 
         af::Window myWindow(width, height, "Gravity Simulation using ArrayFire");
diff --git a/examples/graphics/histogram.cpp b/examples/graphics/histogram.cpp
index ded0d085a7..3365a373ea 100644
--- a/examples/graphics/histogram.cpp
+++ b/examples/graphics/histogram.cpp
@@ -13,7 +13,7 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         // Initialize the kernel array just once
diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index a1e871d030..e8e48389f5 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -16,7 +16,7 @@ using namespace af;
 static const int ITERATIONS = 50;
 static const float PRECISION = 1.0f/ITERATIONS;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         // Initialize the kernel array just once
diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index 7122498ec7..28932e8103 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -16,7 +16,7 @@ using namespace af;
 static const int ITERATIONS = 200;
 static const float PRECISION = 1.0f/ITERATIONS;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         // Initialize the kernel array just once
diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index b48cb8fa47..44e0eb7ccd 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -16,7 +16,7 @@ using namespace af;
 static const int M = 30;
 static const int N = 2 * M;
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     try {
         // Initialize the kernel array just once
diff --git a/examples/machine_learning/rbm.cpp b/examples/machine_learning/rbm.cpp
index 8c832c11ec..e0b996267b 100644
--- a/examples/machine_learning/rbm.cpp
+++ b/examples/machine_learning/rbm.cpp
@@ -158,7 +158,7 @@ class rbm {
     }
 };
 
-int rbm_demo(bool console, int perc)
+int rbm_demo(bool /*console*/, int perc)
 {
     printf("** ArrayFire RBM Demo **\n\n");
 
diff --git a/examples/unified/basic.cpp b/examples/unified/basic.cpp
index 89364777df..d573251777 100644
--- a/examples/unified/basic.cpp
+++ b/examples/unified/basic.cpp
@@ -36,7 +36,7 @@ void testBackend()
     af_print(B);
 }
 
-int main(int argc, char *argv[])
+int main(int, char **)
 {
     std::generate(input.begin(), input.end(), unifRand);
 
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index a4e432c484..50098ba38f 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -67,22 +67,19 @@ af_err af_anisotropic_diffusion(af_array* out, const af_array in, const float dt
         ARG_ASSERT(3, (K>0 || K<0));
         ARG_ASSERT(4, (iterations>0));
 
-        float DT = dt;
-        float maxDt = 1.0f/std::pow(2.0f, static_cast<float>(2)+1);
-
         const af_flux_function F = (fftype==AF_FLUX_DEFAULT ? AF_FLUX_EXPONENTIAL : fftype);
 
         auto input = castArray<float>(in);
 
         af_array output = 0;
         switch(inputType) {
-            case f64: output = diffusion<double>(input, DT, K, iterations, F, eq); break;
+            case f64: output = diffusion<double>(input, dt, K, iterations, F, eq); break;
             case f32:
             case s32:
             case u32:
             case s16:
             case u16:
-            case u8 : output = diffusion<float>(input, DT, K, iterations, F, eq); break;
+            case u8 : output = diffusion<float>(input, dt, K, iterations, F, eq); break;
             default : TYPE_ERROR(1, inputType);
         }
         std::swap(*out, output);
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 54aba91ad7..965a7d25f5 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -19,6 +19,27 @@
 using namespace detail;
 using common::SparseArrayBase;
 
+af_array createHandle(af::dim4 d, af_dtype dtype)
+{
+    using namespace detail;
+
+    switch(dtype) {
+        case f32: return createHandle<float  >(d);
+        case c32: return createHandle<cfloat >(d);
+        case f64: return createHandle<double >(d);
+        case c64: return createHandle<cdouble>(d);
+        case b8:  return createHandle<char   >(d);
+        case s32: return createHandle<int    >(d);
+        case u32: return createHandle<uint   >(d);
+        case u8:  return createHandle<uchar  >(d);
+        case s64: return createHandle<intl   >(d);
+        case u64: return createHandle<uintl  >(d);
+        case s16: return createHandle<short  >(d);
+        case u16: return createHandle<ushort >(d);
+        default:    TYPE_ERROR(3, dtype);
+    }
+}
+
 af_err af_get_data_ptr(void *data, const af_array arr)
 {
     try {
@@ -180,8 +201,6 @@ af_err af_get_data_ref_count(int *use_count, const af_array in)
 af_err af_release_array(af_array arr)
 {
     try {
-        int dev = getActiveDeviceId();
-
         const ArrayInfo& info = getInfo(arr, false, false);
         af_dtype type = info.getType();
 
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 82cc923480..4efeb74ea2 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -81,20 +81,28 @@ void assign(Array<Tout> &out, const vector<af_seq> seqs,
 
 template<typename T>
 static
-void assign(Array<T> &out, const vector<af_seq> iv,
-            const af_array &in)
-{
+typename std::enable_if<is_complex<T>::value, void>::type
+assign(Array<T> &out, const vector<af_seq> iv,
+       const af_array &in) {
     const ArrayInfo& iInfo = getInfo(in);
     af_dtype iType = iInfo.getType();
-
-    if(out.getType() == c64 || out.getType() == c32) {
-        switch(iType) {
+    switch(iType) {
         case c64: assign<T, cdouble>(out, iv, getArray<cdouble>(in));  break;
         case c32: assign<T, cfloat >(out, iv, getArray<cfloat >(in));  break;
         default : TYPE_ERROR(1, iType); break;
-        }
-    } else {
-        switch(iType) {
+    }
+}
+
+template<typename T>
+static
+typename std::enable_if<is_complex<T>::value == false, void>::type
+assign(Array<T> &out, const vector<af_seq> iv,
+       const af_array &in)
+{
+    const ArrayInfo& iInfo = getInfo(in);
+    af_dtype iType = iInfo.getType();
+
+    switch(iType) {
         case f64: assign<T, double >(out, iv, getArray<double >(in));  break;
         case f32: assign<T, float  >(out, iv, getArray<float  >(in));  break;
         case s32: assign<T, int    >(out, iv, getArray<int    >(in));  break;
@@ -106,7 +114,6 @@ void assign(Array<T> &out, const vector<af_seq> iv,
         case u8 : assign<T, uchar  >(out, iv, getArray<uchar  >(in));  break;
         case b8 : assign<T, char   >(out, iv, getArray<char   >(in));  break;
         default : TYPE_ERROR(1, iType); break;
-        }
     }
 }
 
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 3ab8a5184b..144bc72e23 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -113,7 +113,7 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
 
         auto binRes = createSubArray<float>(sigmas, sliceIndex, false);
 
-        copyArray<float>(binRes, sigma);
+        copyArray(binRes, sigma);
     }
 
     dim4 odims = sigmas.dims();
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index ba4f52940c..30a062c2a6 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -83,22 +83,3 @@ af_err af_cast(af_array *out, const af_array in, const af_dtype type)
 
     return AF_SUCCESS;
 }
-
-af_err af_cplx(af_array *out, const af_array in, const af_dtype type)
-{
-    try {
-        af_array res;
-        const ArrayInfo& in_info = getInfo(in);
-
-        if (in_info.isDouble()) {
-            res = cast(in, c64);
-        } else {
-            res = cast(in, c32);
-        }
-
-        std::swap(*out, res);
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
diff --git a/src/api/c/colorspace.cpp b/src/api/c/colorspace.cpp
index 7f04ef0fba..947c24e36d 100644
--- a/src/api/c/colorspace.cpp
+++ b/src/api/c/colorspace.cpp
@@ -15,6 +15,8 @@
 template<af_cspace_t FROM, af_cspace_t TO>
 void color_space(af_array *out, const af_array image)
 {
+    UNUSED(out);
+    UNUSED(image);
     AF_ERROR("Color Space: Conversion from source type to output type not supported",
              AF_ERR_NOT_SUPPORTED);
 }
@@ -35,6 +37,7 @@ void color_space<F, T>(af_array *out, const af_array image)     \
 
 INSTANTIATE_CSPACE_DEFS1(AF_HSV  , AF_RGB  , af_hsv2rgb  );
 INSTANTIATE_CSPACE_DEFS1(AF_RGB  , AF_HSV  , af_rgb2hsv  );
+
 INSTANTIATE_CSPACE_DEFS2(AF_RGB  , AF_GRAY , af_rgb2gray , 0.2126f, 0.7152f, 0.0722f);
 INSTANTIATE_CSPACE_DEFS2(AF_GRAY , AF_RGB  , af_gray2rgb , 1.0f, 1.0f, 1.0f);
 INSTANTIATE_CSPACE_DEFS2(AF_YCbCr, AF_RGB  , af_ycbcr2rgb, AF_YCC_601);
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index a41d9a07dc..c6b6a776c3 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -50,6 +50,7 @@ static To corrcoef(const af_array& X, const af_array& Y)
 
 af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y)
 {
+    UNUSED(imagVal); // TODO: implement for complex types
     try {
         const ArrayInfo& xInfo = getInfo(X);
         const ArrayInfo& yInfo = getInfo(Y);
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index f8c4ad80f6..14f75dcc9b 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -30,7 +30,6 @@ using namespace detail;
 
 dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
 {
-
     DIM_ASSERT(1, ndims >= 1);
 
     dim4 d(1, 1, 1, 1);
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index ddf69b3f93..c3513b73e6 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -92,6 +92,7 @@ af_err af_info()
 
 af_err af_info_string(char **str, const bool verbose)
 {
+    UNUSED(verbose); // TODO(umar): Add something useful
     try {
         std::string infoStr = getDeviceInfo();
         af_alloc_host((void**)str, sizeof(char) * (infoStr.size() + 1));
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 78c7c51c94..96647e62ef 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -17,6 +17,16 @@
 using af::dim4;
 using namespace detail;
 
+void computePaddedDims(dim4 &pdims,
+                       const dim4 &idims,
+                       const dim_t npad,
+                       dim_t const * const pad)
+{
+    for (int i = 0; i < 4; i++) {
+        pdims[i] = (i < (int)npad) ? pad[i] : idims[i];
+    }
+}
+
 template<typename inType, typename outType, int rank, bool direction>
 static af_array fft(const af_array in, const double norm_factor,
                     const dim_t npad, const dim_t  * const pad)
diff --git a/src/api/c/fft_common.hpp b/src/api/c/fft_common.hpp
index cbe4ec143d..7675ad4c8d 100644
--- a/src/api/c/fft_common.hpp
+++ b/src/api/c/fft_common.hpp
@@ -12,15 +12,10 @@
 
 using namespace detail;
 
-static void computePaddedDims(dim4 &pdims,
-                              const dim4 &idims,
-                              const dim_t npad,
-                              dim_t const * const pad)
-{
-    for (int i = 0; i < 4; i++) {
-        pdims[i] = (i < (int)npad) ? pad[i] : idims[i];
-    }
-}
+void computePaddedDims(dim4 &pdims,
+                       const dim4 &idims,
+                       const dim_t npad,
+                       dim_t const * const pad);
 
 template<typename inType, typename outType, int rank, bool direction>
 Array<outType> fft(const Array<inType> input, const double norm_factor,
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 8d30f54889..e1c6de84c8 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -20,8 +20,15 @@
 
 const ArrayInfo& getInfo(const af_array arr, bool sparse_check = true, bool device_check = true);
 
+af_array retain(const af_array in);
+
+af::dim4 verifyDims(const unsigned ndims, const dim_t * const dims);
+
+af_array createHandle(af::dim4 d, af_dtype dtype);
+
+namespace {
+
 template<typename T>
-static
 detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims)
 {
     in.eval(); //FIXME: Figure out a better way
@@ -34,7 +41,6 @@ detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims)
 }
 
 template<typename T>
-static
 detail::Array<T> flat(const detail::Array<T>& in)
 {
     const af::dim4 newDims(in.elements());
@@ -42,7 +48,7 @@ detail::Array<T> flat(const detail::Array<T>& in)
 }
 
 template<typename T>
-static const detail::Array<T> &
+const detail::Array<T> &
 getArray(const af_array &arr)
 {
     detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
@@ -80,16 +86,16 @@ detail::Array<To> castArray(const af_array &in)
 }
 
 template<typename T>
-static detail::Array<T> &
+detail::Array<T> &
 getWritableArray(af_array &arr)
 {
-    const detail::Array<T> &A = getArray<T>((const af_array) arr);
+    const detail::Array<T> &A = getArray<T>(arr);
     ARG_ASSERT(0, A.isSparse() == false);
     return const_cast<detail::Array<T>&>(A);
 }
 
 template<typename T>
-static af_array
+af_array
 getHandle(const detail::Array<T> &A)
 {
     detail::Array<T> *ret = detail::initArray<T>();
@@ -99,7 +105,7 @@ getHandle(const detail::Array<T> &A)
 }
 
 template<typename T>
-static af_array retainHandle(const af_array in)
+af_array retainHandle(const af_array in)
 {
     detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
     detail::Array<T> *out = detail::initArray<T>();
@@ -108,70 +114,44 @@ static af_array retainHandle(const af_array in)
 }
 
 template<typename T>
-static af_array createHandle(af::dim4 d)
+af_array createHandle(af::dim4 d)
 {
     return getHandle(detail::createEmptyArray<T>(d));
 }
 
-static af_array createHandle(af::dim4 d, af_dtype dtype)
-{
-    using namespace detail;
-
-    switch(dtype) {
-        case f32: return createHandle<float  >(d);
-        case c32: return createHandle<cfloat >(d);
-        case f64: return createHandle<double >(d);
-        case c64: return createHandle<cdouble>(d);
-        case b8:  return createHandle<char   >(d);
-        case s32: return createHandle<int    >(d);
-        case u32: return createHandle<uint   >(d);
-        case u8:  return createHandle<uchar  >(d);
-        case s64: return createHandle<intl   >(d);
-        case u64: return createHandle<uintl  >(d);
-        case s16: return createHandle<short  >(d);
-        case u16: return createHandle<ushort >(d);
-        default:    TYPE_ERROR(3, dtype);
-    }
-}
-
 template<typename T>
-static af_array createHandleFromValue(af::dim4 d, double val)
+af_array createHandleFromValue(af::dim4 d, double val)
 {
     return getHandle(detail::createValueArray<T>(d, detail::scalar<T>(val)));
 }
 
 template<typename T>
-static af_array createHandleFromData(af::dim4 d, const T * const data)
+af_array createHandleFromData(af::dim4 d, const T * const data)
 {
     return getHandle(detail::createHostDataArray<T>(d, data));
 }
 
 template<typename T>
-static void copyData(T *data, const af_array &arr)
+void copyData(T *data, const af_array &arr)
 {
     return detail::copyData(data, getArray<T>(arr));
 }
 
 template<typename T>
-static af_array copyArray(const af_array in)
+af_array copyArray(const af_array in)
 {
     const detail::Array<T> &inArray = getArray<T>(in);
     return getHandle<T>(detail::copyArray<T>(inArray));
 }
 
 template<typename T>
-static void releaseHandle(const af_array arr)
+void releaseHandle(const af_array arr)
 {
     detail::destroyArray(reinterpret_cast<detail::Array<T>*>(arr));
 }
 
-af_array retain(const af_array in);
-
-af::dim4 verifyDims(const unsigned ndims, const dim_t * const dims);
-
-
 template<typename T>
-static detail::Array<T> &
+detail::Array<T> &
 getCopyOnWriteArray(const af_array &arr)
 {
     detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
@@ -187,3 +167,5 @@ getCopyOnWriteArray(const af_array &arr)
 
     return *A;
 }
+
+}
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index aca215baef..684f6c23e1 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -120,6 +120,11 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(minval);
+    UNUSED(maxval);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 23ac91cdab..4200f91a4e 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -118,6 +118,9 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
 
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(in);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/imageio_helper.h b/src/api/c/imageio_helper.h
index 034eb219c9..a073cd0150 100644
--- a/src/api/c/imageio_helper.h
+++ b/src/api/c/imageio_helper.h
@@ -69,6 +69,7 @@ typedef enum {
 // In case this handler is invoked, it throws an af exception.
 static void FreeImageErrorHandler(FREE_IMAGE_FORMAT oFif, const char* zMessage)
 {
+    UNUSED(oFif);
     printf("FreeImage Error Handler: %s\n", zMessage);
 }
 
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index c656845fba..af4ff7d687 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -155,7 +155,7 @@ af_err af_lookup(af_array *out, const af_array in,
             return AF_SUCCESS;
         }
 
-        ARG_ASSERT(3, (dim >= 0 && dim <= 3));
+        ARG_ASSERT(3, (dim <= 3));
         ARG_ASSERT(2, idxInfo.isVector() || idxInfo.isScalar());
 
         af_dtype idxType = idxInfo.getType();
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 3c62f755f2..70f2eefec7 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -155,6 +155,7 @@ static af_array median(const af_array& in, const dim_t dim)
 
 af_err af_median_all(double *realVal, double *imagVal, const af_array in)
 {
+    UNUSED(imagVal);
     try {
         const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index dd875852e0..99fc856391 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -58,10 +58,10 @@ template<typename T>
 Array<T> pinverseSvd(const Array<T> &in, const double tol)
 {
     in.eval();
-    int M = in.dims()[0];
-    int N = in.dims()[1];
-    int P = in.dims()[2];
-    int Q = in.dims()[3];
+    dim_t M = in.dims()[0];
+    dim_t N = in.dims()[1];
+    dim_t P = in.dims()[2];
+    dim_t Q = in.dims()[3];
 
     // Compute SVD
     typedef typename dtype_traits<T>::base_type Tr;
@@ -70,8 +70,8 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol)
     Array<T> u = createValueArray<T>(dim4(M, M, P, Q), scalar<T>(0));
     Array<T> vT = createValueArray<T>(dim4(N, N, P, Q), scalar<T>(0));
     Array<Tr> sVec = createValueArray<Tr>(dim4(min(M, N), 1, P, Q), scalar<Tr>(0));
-    for (uint j = 0; j < Q; ++j) {
-        for (uint i = 0; i < P; ++i) {
+    for (dim_t j = 0; j < Q; ++j) {
+        for (dim_t i = 0; i < P; ++i) {
             Array<T> inSlice = getSubArray(in, false,
                                            0, M - 1,
                                            0, N - 1,
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 6252c88164..7858db1373 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -349,6 +349,9 @@ af_err af_draw_plot_nd(const af_window wind, const af_array in,
 #if defined(WITH_GRAPHICS)
     return plotWrapper(wind, in, 1, props);
 #else
+    UNUSED(wind);
+    UNUSED(in);
+    UNUSED(props);
     return AF_ERR_NO_GFX;
 #endif
 }
@@ -359,6 +362,10 @@ af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
 #if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, props);
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(Y);
+    UNUSED(props);
     return AF_ERR_NO_GFX;
 #endif
 }
@@ -370,6 +377,11 @@ af_err af_draw_plot_3d(const af_window wind,
 #if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, Z, props);
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(Y);
+    UNUSED(Z);
+    UNUSED(props);
     return AF_ERR_NO_GFX;
 #endif
 }
@@ -382,6 +394,10 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
 #if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, props);
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(Y);
+    UNUSED(props);
     return AF_ERR_NO_GFX;
 #endif
 }
@@ -413,6 +429,9 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
 
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(P);
+    UNUSED(props);
     return AF_ERR_NO_GFX;
 #endif
 }
@@ -427,6 +446,10 @@ af_err af_draw_scatter_nd(const af_window wind, const af_array in,
     forge::MarkerType fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
 #else
+    UNUSED(wind);
+    UNUSED(in);
+    UNUSED(af_marker);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -438,6 +461,11 @@ af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array
     forge::MarkerType fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(Y);
+    UNUSED(af_marker);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -450,6 +478,12 @@ af_err af_draw_scatter_3d(const af_window wind,
     forge::MarkerType fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(Y);
+    UNUSED(Z);
+    UNUSED(af_marker);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -463,6 +497,11 @@ af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
     forge::MarkerType fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 #else
+    UNUSED(wind);
+    UNUSED(X);
+    UNUSED(Y);
+    UNUSED(af_marker);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -495,6 +534,10 @@ af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_
 
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(P);
+    UNUSED(af_marker);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 0725625760..c0ac42d031 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -96,7 +96,7 @@ af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a,
         dim4 odims(1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cond_dims[i] == adims[i] | cond_dims[i] == 1 | adims[i] == 1);
+            DIM_ASSERT(1, cond_dims[i] == adims[i] || cond_dims[i] == 1 || adims[i] == 1);
             odims[i] = std::max(cond_dims[i], adims[i]);
         }
 
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index afeabaef89..a70ba534f5 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -82,6 +82,16 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
         }
         std::swap(*desc, tmp_desc);
 #else
+        UNUSED(feat);
+        UNUSED(desc);
+        UNUSED(in);
+        UNUSED(n_layers);
+        UNUSED(contrast_thr);
+        UNUSED(edge_thr);
+        UNUSED(init_sigma);
+        UNUSED(double_input);
+        UNUSED(img_scale);
+        UNUSED(feature_ratio);
         AF_ERROR("ArrayFire was not built with nonfree support, SIFT disabled\n", AF_ERR_NONFREE);
 #endif
     }
@@ -123,6 +133,16 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsig
         }
         std::swap(*desc, tmp_desc);
 #else
+        UNUSED(feat);
+        UNUSED(desc);
+        UNUSED(in);
+        UNUSED(n_layers);
+        UNUSED(contrast_thr);
+        UNUSED(edge_thr);
+        UNUSED(init_sigma);
+        UNUSED(double_input);
+        UNUSED(img_scale);
+        UNUSED(feature_ratio);
         AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
 #endif
     }
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 4ef5e966f3..db09946f40 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -92,11 +92,11 @@ af_err af_create_sparse_array(
           DIM_ASSERT(4, rInfo.elements() == nNZ);
           DIM_ASSERT(5, cInfo.elements() == nNZ);
         } else if(stype == AF_STORAGE_CSR) {
-          DIM_ASSERT(4, rInfo.elements() == nRows + 1);
+          DIM_ASSERT(4, (dim_t)rInfo.elements() == nRows + 1);
           DIM_ASSERT(5, cInfo.elements() == nNZ);
         } else if(stype == AF_STORAGE_CSC) {
           DIM_ASSERT(4, rInfo.elements() == nNZ);
-          DIM_ASSERT(5, cInfo.elements() == nCols + 1);
+          DIM_ASSERT(5, (dim_t)cInfo.elements() == nCols + 1);
         }
 
         af_array output = 0;
@@ -191,7 +191,7 @@ af_err af_create_sparse_array_from_ptr(
 
 template<typename T>
 af_array createSparseArrayFromDense(
-        const af::dim4 &dims, const af_array _in,
+        const af_array _in,
         const af_storage stype)
 {
     const Array<T> in = getArray<T>(_in);
@@ -228,15 +228,13 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
 
         TYPE_ASSERT(info.isFloating());
 
-        af::dim4 dims(info.dims()[0], info.dims()[1]);
-
         af_array output = 0;
 
         switch(info.getType()) {
-            case f32: output = createSparseArrayFromDense<float  >(dims, in, stype); break;
-            case f64: output = createSparseArrayFromDense<double >(dims, in, stype); break;
-            case c32: output = createSparseArrayFromDense<cfloat >(dims, in, stype); break;
-            case c64: output = createSparseArrayFromDense<cdouble>(dims, in, stype); break;
+            case f32: output = createSparseArrayFromDense<float  >(in, stype); break;
+            case f64: output = createSparseArrayFromDense<double >(in, stype); break;
+            case c32: output = createSparseArrayFromDense<cfloat >(in, stype); break;
+            case c64: output = createSparseArrayFromDense<cdouble>(in, stype); break;
             default: TYPE_ERROR(1, info.getType());
         }
         std::swap(*out, output);
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index bd2a705016..43afd1313c 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -70,6 +70,7 @@ static af_array stdev(const af_array& in, int dim)
 
 af_err af_stdev_all(double *realVal, double *imagVal, const af_array in)
 {
+    UNUSED(imagVal); //TODO implement for complex values
     try {
         const ArrayInfo& info = getInfo(in);
         af_dtype type = info.getType();
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index fcbefbcb39..c3f863d62e 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -179,6 +179,11 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(xVals);
+    UNUSED(yVals);
+    UNUSED(S);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index fba13f84c8..ff5a60ee40 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -142,8 +142,6 @@ var(const Array<inType>& in,
     const Array<typename baseOutType<outType>::type>& weights,
     const af_var_bias bias, int dim)
 {
-    typedef typename baseOutType<outType>::type weightType;
-
     Array<outType> variance = *initArray<outType>();
     tie(ignore, variance) = meanvar<inType, outType>(in, weights, bias, dim);
     return variance;
@@ -302,7 +300,6 @@ af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
                   const af_array weights, const af_var_bias bias, const dim_t dim) {
 
   try {
-      af_array output = 0;
       const ArrayInfo& iInfo = getInfo(in);
       if(weights != 0) {
         const ArrayInfo& wInfo = getInfo(weights);
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 9297a47345..d3e9577a69 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -352,6 +352,10 @@ af_err af_draw_vector_field_nd(const af_window wind,
 #if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, points, directions, props);
 #else
+    UNUSED(wind);
+    UNUSED(points);
+    UNUSED(directions);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -365,6 +369,14 @@ af_err af_draw_vector_field_3d(
 #if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
 #else
+    UNUSED(wind);
+    UNUSED(xPoints);
+    UNUSED(yPoints);
+    UNUSED(zPoints);
+    UNUSED(xDirs);
+    UNUSED(yDirs);
+    UNUSED(zDirs);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -378,6 +390,12 @@ af_err af_draw_vector_field_2d(
 #if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, xPoints, yPoints, xDirs, yDirs, props);
 #else
+    UNUSED(wind);
+    UNUSED(xPoints);
+    UNUSED(yPoints);
+    UNUSED(xDirs);
+    UNUSED(yDirs);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 897dd4546f..78b80222f1 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -53,6 +53,10 @@ af_err af_create_window(af_window *out, const int width, const int height, const
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(out);
+    UNUSED(width);
+    UNUSED(height);
+    UNUSED(title);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -72,6 +76,9 @@ af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(x);
+    UNUSED(y);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -91,6 +98,8 @@ af_err af_set_title(const af_window wind, const char* const title)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(title);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -110,6 +119,9 @@ af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(w);
+    UNUSED(h);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -132,6 +144,9 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(rows);
+    UNUSED(cols);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -189,6 +204,12 @@ af_err af_set_axes_limits_compute(const af_window wind,
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(z);
+    UNUSED(exact);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -238,6 +259,13 @@ af_err af_set_axes_limits_2d(const af_window wind,
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(xmin);
+    UNUSED(xmax);
+    UNUSED(ymin);
+    UNUSED(ymax);
+    UNUSED(exact);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -292,6 +320,15 @@ af_err af_set_axes_limits_3d(const af_window wind,
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(xmin);
+    UNUSED(xmax);
+    UNUSED(ymin);
+    UNUSED(ymax);
+    UNUSED(zmin);
+    UNUSED(zmax);
+    UNUSED(exact);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -328,6 +365,11 @@ af_err af_set_axes_titles(const af_window wind,
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(xtitle);
+    UNUSED(ytitle);
+    UNUSED(ztitle);
+    UNUSED(props);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -347,6 +389,7 @@ af_err af_show(const af_window wind)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -366,6 +409,8 @@ af_err af_is_window_closed(bool *out, const af_window wind)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(out);
+    UNUSED(wind);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -388,6 +433,8 @@ af_err af_set_visibility(const af_window wind, const bool is_visible)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
+    UNUSED(is_visible);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
@@ -412,6 +459,7 @@ af_err af_destroy_window(const af_window wind)
     CATCHALL;
     return AF_SUCCESS;
 #else
+    UNUSED(wind);
     AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 67ef3c83e5..273850d79d 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -128,9 +128,9 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
     typedef af_err(*func)(int*);
 
     LibHandle retVal = nullptr;
-    for (int i = 0; i < extent<decltype(paths)>::value; i++) {
+    for (size_t i = 0; i < extent<decltype(paths)>::value; i++) {
         AF_TRACE("Attempting: {}", paths[i]);
-        if (retVal = loadLibrary(join_path(paths[i], bkndLibName).c_str())) {
+        if ((retVal = loadLibrary(join_path(paths[i], bkndLibName).c_str()))) {
             AF_TRACE("Found: {}", join_path(paths[i], bkndLibName));
 
             func count_func = (func)getFunctionPointer(retVal,
@@ -258,6 +258,7 @@ bool checkArray(af_backend activeBackend, af_array a)
 
 bool checkArrays(af_backend activeBackend)
 {
+    UNUSED(activeBackend);
     // Dummy
     return true;
 }
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index 8bb5e8902b..daa0d1141f 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -18,6 +18,20 @@
 #include <dlfcn.h>
 #endif
 
+#ifdef OS_WIN
+#include <Windows.h>
+static const char* librarySuffix = ".dll";
+static const char* libraryPrefix = "";
+#elif defined(OS_MAC)
+static const char* librarySuffix = ".dylib";
+static const char* libraryPrefix = "lib";
+#elif defined(OS_LNX)
+static const char* librarySuffix = ".so";
+static const char* libraryPrefix = "lib";
+#else
+#error "Unsupported platform"
+#endif
+
 using std::string;
 
 namespace {
@@ -32,6 +46,7 @@ namespace common {
 DependencyModule::DependencyModule(const char* plugin_file_name, const char** paths)
     : handle(nullptr) {
     // TODO(umar): Implement handling of non-standard paths
+    UNUSED(paths);
     if(plugin_file_name) {
         handle = loadLibrary(libName(plugin_file_name).c_str());
     }
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index 17a419ffba..4521e15422 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -51,10 +51,11 @@ loggerFactory(string name) {
 
 string bytesToString(size_t bytes) {
     static array<const char *, 5> units{"B", "KB", "MB", "GB", "TB"};
-    int count = 0;
+    size_t count = 0;
     double fbytes = static_cast<double>(bytes);
-    for(count = 0; count < units.size() && fbytes > 1000.0; count++) {
-        fbytes *= (1.0 / 1024.0);
+    size_t num_units = units.size();
+    for(count = 0; count < num_units && fbytes > 1000.0f; count++) {
+        fbytes *= (1.0f / 1024.0f);
     }
     return fmt::format("{:.3g} {}", fbytes, units[count]);
 }
diff --git a/src/backend/common/MemoryManagerImpl.hpp b/src/backend/common/MemoryManagerImpl.hpp
index 859c47d574..dc9fa8ea1c 100644
--- a/src/backend/common/MemoryManagerImpl.hpp
+++ b/src/backend/common/MemoryManagerImpl.hpp
@@ -82,8 +82,8 @@ MemoryManager<T>::MemoryManager(int num_devices,
     : mem_step_size(1024),
       max_buffers(max_buffers),
       memory(num_devices),
-      debug_mode(debug),
-      logger (loggerFactory("mem")) {
+      logger (loggerFactory("mem")),
+      debug_mode(debug) {
     // Check for environment variables
 
     // Debug mode
@@ -252,7 +252,7 @@ void MemoryManager<T>::garbageCollect() {
 
 template<typename T>
 void MemoryManager<T>::printInfo(const char *msg, const int device) {
-    const memory_info& current = this->getCurrentMemoryInfo();
+    const memory_info& current = memory[device];
 
     printf("%s\n", msg);
     printf("---------------------------------------------------------\n"
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 4f71e94d23..394520e23d 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -26,6 +26,8 @@ clipFilePath(std::string path, std::string str)
     }
 }
 
+#define UNUSED(expr) do { (void)(expr); } while (0)
+
 #if defined(_WIN32) || defined(_MSC_VER)
     #define __PRETTY_FUNCTION__ __FUNCSIG__
     #if _MSC_VER < 1900
@@ -53,15 +55,9 @@ typedef enum {
 #ifdef OS_WIN
 #include <Windows.h>
 using LibHandle = HMODULE;
-static const char* librarySuffix = ".dll";
-static const char* libraryPrefix = "";
 #elif defined(OS_MAC)
-static const char* librarySuffix = ".dylib";
-static const char* libraryPrefix = "lib";
 using LibHandle = void*;
 #elif defined(OS_LNX)
-static const char* librarySuffix = ".so";
-static const char* libraryPrefix = "lib";
 using LibHandle = void*;
 #else
 #error "Unsupported platform"
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 6d5c702c08..b7088b8c86 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -40,19 +40,33 @@ namespace common {
     public:
         Node(const char *type_str, const char *name_str, const int height,
              const std::array<Node_ptr, kMaxChildren> children)
-            : m_type_str(type_str),
+            : m_children(children),
+              m_type_str(type_str),
               m_name_str(name_str),
-              m_children(children),
               m_height(height) {}
 
         int getNodesMap(Node_map_t &node_map,
                         std::vector<const Node *> &full_nodes,
                         std::vector<Node_ids> &full_ids) const;
 
-        virtual void genKerName (std::stringstream &kerStream, const Node_ids& ids) const { }
-        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const { }
-        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const { }
-        virtual void genFuncs   (std::stringstream &kerStream, const Node_ids& ids) const { }
+        virtual void genKerName (std::stringstream &kerStream, const Node_ids& ids) const {
+            UNUSED(kerStream);
+            UNUSED(ids);
+        }
+        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const {
+            UNUSED(kerStream);
+            UNUSED(id);
+            UNUSED(is_linear);
+        }
+        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const {
+            UNUSED(kerStream);
+            UNUSED(id);
+            UNUSED(is_linear);
+        }
+        virtual void genFuncs   (std::stringstream &kerStream, const Node_ids& ids) const {
+            UNUSED(kerStream);
+            UNUSED(ids);
+        }
 
         /// Calls the setArg function on each of the arguments passed into the kernel
         ///
@@ -63,9 +77,15 @@ namespace common {
         /// \returns the next index that will need to be set in the kernl. This
         ///          is usually start_id + the number of times setArg is called
         virtual int setArgs(int start_id, bool is_linear,
-                            std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const { return start_id; }
+                            std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const {
+            UNUSED(is_linear);
+            UNUSED(setArg);
+            return start_id;
+        }
 
         virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const {
+            UNUSED(buf_count);
+            UNUSED(bytes);
             len++;
         }
 
@@ -78,7 +98,10 @@ namespace common {
         // Return the size of the size of the buffer node in bytes. Zero otherwise
         virtual size_t getBytes() const { return 0; }
         virtual bool isBuffer() const { return false; }
-        virtual bool isLinear(dim_t dims[4]) const { return true; }
+        virtual bool isLinear(dim_t dims[4]) const {
+            UNUSED(dims);
+            return true;
+        }
         std::string getTypeStr() const { return m_type_str; }
         int getHeight()  const { return m_height; }
         std::string getNameStr() const { return m_name_str; }
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index f5ea4a5c87..c7bb8cb8a1 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -26,7 +26,7 @@ class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
 
   private:
     std::vector<pointer> tree;
-    int index;
+    size_t index;
 
     /// Copies the children of the \p n Node to the end of the tree vector
     void copy_children_to_end(Node* n) {
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 33f00cc326..fcd8bc8dff 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -40,12 +40,14 @@ namespace common
 
         void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
         {
+            UNUSED(is_linear);
             kerStream << m_type_str << " scalar" << id << ", \n";
         }
 
         int setArgs(int start_id, bool is_linear,
                     std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const final
         {
+            UNUSED(is_linear);
             setArg(start_id, static_cast<const void*>(&m_val), sizeof(T));
             return start_id + 1;
         }
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 381499b231..211c1831f5 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -42,6 +42,7 @@ namespace common {
 
         bool isLinear(dim_t dims[4]) const final
         {
+            UNUSED(dims);
             return false;
         }
 
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index a5df8fc42e..2fc6e8c49b 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -239,7 +239,6 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 
                 Node *n = node.get();
 
-                size_t buffer_size;
                 NodeIterator<jit::Node> it(n);
                 NodeIterator<jit::Node> end_node;
                 size_t bytes = accumulate(it, end_node,
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index db157b69e9..a4eac8935a 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -52,6 +52,8 @@ Array<T> copyArray(const Array<T> &A)
 template<typename inType, typename outType>
 void copyArray(Array<outType> &out, Array<inType> const &in)
 {
+    static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
+                  "Cannot copy from complex value to a non complex value");
     out.eval();
     in.eval();
     getQueue().enqueue(kernel::copy<outType, inType>, out, in);
@@ -106,36 +108,6 @@ INSTANTIATE_COPY_ARRAY(short )
 INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat )
 INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
-#define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
-    template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
-    {\
-        char errMessage[1024];                                              \
-        snprintf(errMessage, sizeof(errMessage),                            \
-                "CPU copyArray<"#SRC_T","#DST_T"> is not supported\n");    \
-        CPU_NOT_SUPPORTED(errMessage);                                      \
-    }
-
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , double)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , float)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , uchar)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , char)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , uint)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , int)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , intl)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , uintl)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , short)
-SPECILIAZE_UNUSED_COPYARRAY(cfloat , ushort)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, double)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, float)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, uchar)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, char)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, uint)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, int)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, intl)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
-SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
-
 template<typename T>
 T getScalar(const Array<T> &in)
 {
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index d4975177aa..2b93af14f1 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -22,6 +22,7 @@ namespace cpu
 
 void setFFTPlanCacheSize(size_t numPlans)
 {
+    UNUSED(numPlans);
 }
 
 template<typename T, int rank, bool direction>
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index db19708b46..2a2abce0a6 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -26,7 +26,7 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
 
     Array<T> out = createEmptyArray<T>(outdims);
 
-    getQueue().enqueue(kernel::iota<T>, out, dims, tile_dims);
+    getQueue().enqueue(kernel::iota<T>, out, dims);
 
     return out;
 }
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 3a2564637b..25d48a4db5 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -52,11 +52,16 @@ namespace jit
 
         void calc(int x, int y, int z, int w, int lim) final
         {
+            UNUSED(x);
+            UNUSED(y);
+            UNUSED(z);
+            UNUSED(w);
             m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
         }
 
         void calc(int idx, int lim) final
         {
+            UNUSED(idx);
             m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
         }
     };
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index f7ffc2eced..ddb913c682 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -8,7 +8,9 @@
  ********************************************************/
 
 #pragma once
+#include <common/defines.hpp>
 #include <optypes.hpp>
+
 #include <array>
 #include <vector>
 #include <memory>
@@ -67,20 +69,29 @@ namespace jit
 
         int getHeight() { return m_height; }
 
-        virtual void calc(int x, int y, int z, int w, int lim)
-        {
+        virtual void calc(int x, int y, int z, int w, int lim) {
+            UNUSED(x);
+            UNUSED(y);
+            UNUSED(z);
+            UNUSED(w);
+            UNUSED(lim);
         }
 
-        virtual void calc(int idx, int lim)
-        {
+        virtual void calc(int idx, int lim) {
+            UNUSED(idx);
+            UNUSED(lim);
         }
 
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const
-        {
+        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const {
+            UNUSED(buf_count);
+            UNUSED(bytes);
             len++;
         }
 
-        virtual bool isLinear(const dim_t *dims) const { return true; }
+        virtual bool isLinear(const dim_t *dims) const {
+            UNUSED(dims);
+            return true;
+        }
         virtual bool isBuffer() const { return false; }
         virtual ~Node() {}
 
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 85eed07055..0948f4ea4a 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -47,11 +47,16 @@ namespace jit
 
         void calc(int x, int y, int z, int w, int lim) final
         {
+            UNUSED(x);
+            UNUSED(y);
+            UNUSED(z);
+            UNUSED(w);
             m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
         }
 
         void calc(int idx, int lim) final
         {
+            UNUSED(idx);
             m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
         }
 
diff --git a/src/backend/cpu/kernel/anisotropic_diffusion.hpp b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
index 0980f1e8c7..e7798c3b9f 100644
--- a/src/backend/cpu/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
@@ -84,7 +84,7 @@ float computeGradientBasedUpdate(const float mct,
 float computeCurvatureBasedUpdate(const float mct,
                                   const float NW, const float N, const float NE,
                                   const float  W, const float C, const float  E,
-                                  const float SW, const float S, const float SE, const af_flux_function fftype)
+                                  const float SW, const float S, const float SE)
 {
     float delta = 0.f;
     float prop_grad = 0.f;
@@ -173,7 +173,7 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const
                                 img[ index(ip1, j  , d1stride) ],
                                 img[ index(im1, jp1, d1stride) ],
                                 img[ index(i  , jp1, d1stride) ],
-                                img[ index(ip1, jp1, d1stride) ], fftype);
+                                img[ index(ip1, jp1, d1stride) ]);
 
                     } else {
                         delta = computeGradientBasedUpdate(
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 25523f6e43..0a8a927adf 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -189,13 +189,14 @@ void convolve2_separable(InT *optr, InT const * const iptr, AccT const * const f
                         af::dim4 const & oDims, af::dim4 const & sDims, af::dim4 const & orgDims, dim_t fDim,
                         af::dim4 const & oStrides, af::dim4 const & sStrides, dim_t fStride)
 {
+    UNUSED(orgDims);
+    UNUSED(sStrides);
+    UNUSED(fStride);
     for(dim_t j=0; j<oDims[1]; ++j) {
-
         dim_t jOff = j*oStrides[1];
         dim_t cj = j + (conv_dim==1)*(Expand ? 0: fDim>>1);
 
         for(dim_t i=0; i<oDims[0]; ++i) {
-
             dim_t iOff = i*oStrides[0];
             dim_t ci = i + (conv_dim==0)*(Expand ? 0 : fDim>>1);
 
diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
index 2d9a85be7e..6d80a488e8 100644
--- a/src/backend/cpu/kernel/dot.hpp
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -26,6 +26,8 @@ template<typename T, bool conjugate, bool both_conjugate>
 void dot(Param<T> output, CParam<T> lhs, CParam<T> rhs,
          af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    UNUSED(optLhs);
+    UNUSED(optRhs);
     int N = lhs.dims(0);
 
     T out = 0;
diff --git a/src/backend/cpu/kernel/exampleFunction.hpp b/src/backend/cpu/kernel/exampleFunction.hpp
index 6bf58cef4b..a9e01916ee 100644
--- a/src/backend/cpu/kernel/exampleFunction.hpp
+++ b/src/backend/cpu/kernel/exampleFunction.hpp
@@ -19,6 +19,7 @@ namespace kernel
 template<typename T>
 void exampleFunction(Param<T> out, CParam<T> a, CParam<T> b, const af_someenum_t method)
 {
+    UNUSED(method);
     dim4 oDims    = out.dims();
 
     dim4 aStrides = a.strides();        // you can retrieve strides
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 5825f25ca3..419c060a5b 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -159,6 +159,7 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                    const af::dim4& fd, const int half_di0, const int baseDim,
                    const int fftScale, const bool expand)
 {
+    UNUSED(id);
     for (int d3 = 0; d3 < (int)od[3]; d3++) {
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
             for (int d1 = 0; d1 < (int)od[1]; d1++) {
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index 873ab56036..4769cf6318 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -16,7 +16,7 @@ namespace kernel
 {
 
 template<typename T>
-void iota(Param<T> output, const af::dim4 &sdims, const af::dim4 &tdims)
+void iota(Param<T> output, const af::dim4 &sdims)
 {
     const af::dim4 dims    = output.dims();
     T* out             = output.get();
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index 13830799a9..0ffdc851fe 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -28,8 +28,7 @@ af::dim4 calcOffset(const af::dim4 dims)
 
 template<typename To, typename Tx, int dim>
 void join_append(To *out, const Tx *X, const af::dim4 &offset,
-           const af::dim4 &odims, const af::dim4 &xdims,
-           const af::dim4 &ost, const af::dim4 &xst)
+           const af::dim4 &xdims, const af::dim4 &ost, const af::dim4 &xst)
 {
     for(dim_t ow = 0; ow < xdims[3]; ow++) {
         const dim_t xW = ow * xst[3];
@@ -61,34 +60,33 @@ void join(Param<Tx> out, const int dim, CParam<Tx> first, CParam<Ty> second)
     const Ty* sptr = second.get();
 
     af::dim4 zero(0,0,0,0);
-    const af::dim4 odims = out.dims();
     const af::dim4 fdims = first.dims();
     const af::dim4 sdims = second.dims();
 
     switch(dim) {
         case 0:
             join_append<Tx, Tx, 0>(outPtr, fptr, zero,
-                                   odims, fdims, out.strides(), first.strides());
+                                   fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims),
-                                   odims, sdims, out.strides(), second.strides());
+                                   sdims, out.strides(), second.strides());
             break;
         case 1:
             join_append<Tx, Tx, 1>(outPtr, fptr, zero,
-                                   odims, fdims, out.strides(), first.strides());
+                                   fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims),
-                                   odims, sdims, out.strides(), second.strides());
+                                   sdims, out.strides(), second.strides());
             break;
         case 2:
             join_append<Tx, Tx, 2>(outPtr, fptr, zero,
-                                   odims, fdims, out.strides(), first.strides());
+                                   fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims),
-                                   odims, sdims, out.strides(), second.strides());
+                                   sdims, out.strides(), second.strides());
             break;
         case 3:
             join_append<Tx, Tx, 3>(outPtr, fptr, zero,
-                                   odims, fdims, out.strides(), first.strides());
+                                   fdims, out.strides(), first.strides());
             join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims),
-                                   odims, sdims, out.strides(), second.strides());
+                                   sdims, out.strides(), second.strides());
             break;
     }
 }
@@ -101,38 +99,38 @@ void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs)
     switch(dim) {
         case 0:
             join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+                                 inputs[0].dims(), out.strides(), inputs[0].strides());
             for(int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
                 join_append<T, T, 0>(out.get(), inputs[i].get(), calcOffset<0>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+                                     inputs[i].dims(), out.strides(), inputs[i].strides());
             }
             break;
         case 1:
             join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+                                 inputs[0].dims(), out.strides(), inputs[0].strides());
             for(int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
                 join_append<T, T, 1>(out.get(), inputs[i].get(), calcOffset<1>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+                                     inputs[i].dims(), out.strides(), inputs[i].strides());
             }
             break;
         case 2:
             join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+                                 inputs[0].dims(), out.strides(), inputs[0].strides());
             for(int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
                 join_append<T, T, 2>(out.get(), inputs[i].get(), calcOffset<2>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+                        inputs[i].dims(), out.strides(), inputs[i].strides());
             }
             break;
         case 3:
             join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
-                        out.dims(), inputs[0].dims(), out.strides(), inputs[0].strides());
+                                 inputs[0].dims(), out.strides(), inputs[0].strides());
             for(int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
                 join_append<T, T, 3>(out.get(), inputs[i].get(), calcOffset<3>(d),
-                        out.dims(), inputs[i].dims(), out.strides(), inputs[i].strides());
+                                     inputs[i].dims(), out.strides(), inputs[i].strides());
             }
             break;
     }
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index ae0d670a57..0a23bee43a 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -88,7 +88,7 @@ struct dist_op<ushort, To, AF_SHD>
 template<typename T, typename To, af_match_type dist_type>
 void nearest_neighbour(Param<To> dists,
                        CParam<T> query, CParam<T> train,
-                       const uint dist_dim, const uint n_dist)
+                       const uint dist_dim)
 {
     uint sample_dim = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
@@ -105,9 +105,6 @@ void nearest_neighbour(Param<To> dists,
     dist_op<T, To, dist_type> op;
 
     for (unsigned i = 0; i < nQuery; i++) {
-        To best_dist = maxval<To>();
-        unsigned best_idx  = 0;
-
         for (unsigned j = 0; j < nTrain; j++) {
             To local_dist = 0;
             for (unsigned k = 0; k < distLength; k++) {
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 1bc33403d9..e917c84287 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -45,13 +45,13 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
 
     switch(dist_type) {
         case AF_SAD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, tmp_dists, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, tmp_dists, query, train, dist_dim);
             break;
         case AF_SSD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, tmp_dists, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, tmp_dists, query, train, dist_dim);
             break;
         case AF_SHD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, tmp_dists, query, train, dist_dim, n_dist);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, tmp_dists, query, train, dist_dim);
             break;
         default:
             AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index ab5b226538..8817ba0b3c 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -174,6 +174,7 @@ std::string getDeviceInfo()
 
 bool isDoubleSupported(int device)
 {
+    UNUSED(device);
     return DeviceManager::IS_DOUBLE_SUPPORTED;
 }
 
@@ -216,6 +217,7 @@ int getActiveDeviceId()
 
 size_t getDeviceMemorySize(int device)
 {
+    UNUSED(device);
     return common::getHostMemorySize();
 }
 
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 021a4648f0..db2d630dbe 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -42,6 +42,20 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
                                   contrast_thr, edge_thr, init_sigma, double_input,
                                   img_scale, feature_ratio, compute_GLOH);
 #else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(score);
+    UNUSED(ori);
+    UNUSED(size);
+    UNUSED(desc);
+    UNUSED(in);
+    UNUSED(n_layers);
+    UNUSED(contrast_thr);
+    UNUSED(edge_thr);
+    UNUSED(init_sigma);
+    UNUSED(double_input);
+    UNUSED(img_scale);
+    UNUSED(feature_ratio);
     if (compute_GLOH)
         AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
     else
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index f01e670ef8..8d0baa446e 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -24,6 +24,7 @@ template<typename Ti, typename To>
 std::pair< Array<To>, Array<To> >
 sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
 {
+    UNUSED(ker_size);
     img.eval();
     // ket_size is for future proofing, this argument is not used
     // currently
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 2cc3806939..75276601d5 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -76,6 +76,7 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
+    UNUSED(options);
     A.eval();
     pivot.eval();
     b.eval();
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index db2ce59288..26c157a16e 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -209,6 +209,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
     // MKL: CSRMM Does not support optRhs
+    UNUSED(optRhs);
 
     lhs.eval();
     rhs.eval();
@@ -321,6 +322,7 @@ void mv(Param<T> output,
         CParam<T> right,
         int M)
 {
+    UNUSED(M);
     const T   *valPtr = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
@@ -380,6 +382,7 @@ void mm(Param<T> output,
         int M, int N,
         int ldb, int ldc)
 {
+    UNUSED(M);
     const T *valPtr = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
@@ -442,6 +445,7 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    UNUSED(optRhs);
     lhs.eval();
     rhs.eval();
 
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 6bee8a5106..b2c17832f9 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -34,8 +34,18 @@ class Param
             strides[i] = istrides[i];
         }
     }
+    size_t elements() const noexcept { return dims[0] * dims[1] * dims[2] * dims[3]; }
 };
 
+template<typename T>
+Param<T> flat(Param<T> in) {
+    in.dims[0] = in.elements();
+    in.dims[1] = 1;
+    in.dims[2] = 1;
+    in.dims[3] = 1;
+    return in;
+}
+
 template<typename T>
 class CParam
 {
diff --git a/src/backend/cuda/ThrustAllocator.cuh b/src/backend/cuda/ThrustAllocator.cuh
index 756f568a5c..04af03565a 100644
--- a/src/backend/cuda/ThrustAllocator.cuh
+++ b/src/backend/cuda/ThrustAllocator.cuh
@@ -38,6 +38,7 @@ struct ThrustAllocator : thrust::device_malloc_allocator<T>
 
     void deallocate(pointer p, size_type n)
     {
+        UNUSED(n);
         memFree<T>(p.get());// delegate to ArrayFire allocator
     }
 };
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index b69f650b31..e8f0d6e412 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -66,7 +66,7 @@ namespace cuda
     template<typename inType, typename outType>
     Array<outType> padArray(Array<inType> const &in, dim4 const &dims, outType default_value, double factor)
     {
-        ARG_ASSERT(1, (in.ndims() == dims.ndims()));
+        ARG_ASSERT(1, (in.ndims() == (size_t)dims.ndims()));
         Array<outType> ret = createEmptyArray<outType>(dims);
         kernel::copy<inType, outType>(ret, in, in.ndims(), default_value, factor);
         return ret;
@@ -107,7 +107,9 @@ namespace cuda
     template<typename inType, typename outType>
     void copyArray(Array<outType> &out, Array<inType> const &in)
     {
-        ARG_ASSERT(1, (in.ndims() == out.dims().ndims()));
+        static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
+                      "Cannot copy from complex value to a non complex value");
+        ARG_ASSERT(1, (in.ndims() == (size_t)out.dims().ndims()));
         copyWrapper<inType, outType> copyFn;
         copyFn(out, in);
     }
@@ -176,36 +178,6 @@ namespace cuda
     INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
     INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
 
-#define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
-    template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
-    {\
-        char errMessage[1024];                                              \
-        snprintf(errMessage, sizeof(errMessage),                            \
-                "CUDA copyArray<"#SRC_T","#DST_T"> is not supported\n");    \
-        CUDA_NOT_SUPPORTED(errMessage);                                     \
-    }
-
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, double)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, float)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, uchar)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, char)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, uint)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, int)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, intl)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, uintl)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, short)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, ushort)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, double)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, float)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uchar)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, char)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uint)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, int)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, intl)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
-
     template<typename T>
     T getScalar(const Array<T> &in)
     {
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index e3de11f691..41f3705610 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -24,8 +24,6 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
               const bool non_max, const float feature_ratio, const unsigned edge)
 {
-    const dim4 dims = in.dims();
-
     unsigned nfeat;
     float *d_x_out;
     float *d_y_out;
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cu
index 3c18e9401e..cda209c72e 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cu
@@ -74,30 +74,27 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     Array<cT> signal_packed = createEmptyArray<cT>(spDims);
     Array<cT> filter_packed = createEmptyArray<cT>(fpDims);
 
-    kernel::packDataHelper<cT, T>(signal_packed, filter_packed, signal, filter, baseDim);
+    kernel::packDataHelper<cT, T>(signal_packed, filter_packed, signal, filter);
 
     fft_inplace<cT, baseDim, true>(signal_packed);
     fft_inplace<cT, baseDim, true>(filter_packed);
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    if (expand)
-        kernel::complexMultiplyHelper<T, cT>(out, signal_packed, filter_packed, signal, filter, kind);
-    else
-        kernel::complexMultiplyHelper<T, cT>(out, signal_packed, filter_packed, signal, filter, kind);
+    kernel::complexMultiplyHelper<T, cT>(signal_packed, filter_packed, kind);
 
     if (kind == AF_BATCH_RHS) {
         fft_inplace<cT, baseDim, false>(filter_packed);
         if (expand)
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, filter_packed, signal, filter, kind);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, filter_packed, signal, filter);
         else
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(out, filter_packed, signal, filter, kind);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(out, filter_packed, signal, filter);
     } else {
         fft_inplace<cT, baseDim, false>(signal_packed);
         if (expand)
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, signal_packed, signal, filter, kind);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, signal_packed, signal, filter);
         else
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(out, signal_packed, signal, filter, kind);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(out, signal_packed, signal, filter);
     }
 
     return out;
diff --git a/src/backend/cuda/harris.cu b/src/backend/cuda/harris.cu
index d5b83c45ee..6116182f3c 100644
--- a/src/backend/cuda/harris.cu
+++ b/src/backend/cuda/harris.cu
@@ -24,8 +24,6 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &score_ou
                 const Array<T> &in, const unsigned max_corners, const float min_response,
                 const float sigma, const unsigned filter_len, const float k_thr)
 {
-    const dim4 dims = in.dims();
-
     unsigned nfeat;
     float *d_x_out;
     float *d_y_out;
diff --git a/src/backend/cuda/iota.cu b/src/backend/cuda/iota.cu
index eee4344d4d..cd06e63770 100644
--- a/src/backend/cuda/iota.cu
+++ b/src/backend/cuda/iota.cu
@@ -22,7 +22,7 @@ namespace cuda
         dim4 outdims = dims * tile_dims;
 
         Array<T> out = createEmptyArray<T>(outdims);
-        kernel::iota<T>(out, dims, tile_dims);
+        kernel::iota<T>(out, dims);
 
         return out;
     }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 427dec5910..9b1cb17249 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -455,7 +455,7 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
     vector<void *> args;
 
     for (const auto &node : full_nodes) {
-        node->setArgs(0, is_linear, [&] (int id, const void* ptr, size_t size){
+        node->setArgs(0, is_linear, [&] (int /*id*/, const void* ptr, size_t /*size*/){
                                         args.push_back(const_cast<void*>(ptr));
                                     });
     }
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index de3d933cfc..e165cecd7d 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -36,6 +36,7 @@ namespace {
   int setKernelArguments(int start_id, bool is_linear,
                          std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
                          const std::shared_ptr<T>& ptr, const Param<T>& info) {
+      UNUSED(ptr);
       if (is_linear) {
           setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
       } else {
@@ -69,6 +70,7 @@ namespace {
 
   void generateShiftNodeOffsets(std::stringstream &kerStream, int id,
                                 bool is_linear, const std::string& type_str) {
+      UNUSED(is_linear);
       std::string idx_str = std::string("idx") + std::to_string(id);
       std::string info_str = std::string("in") + std::to_string(id);
       std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 9abedc9504..f030f7ee3e 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -254,8 +254,7 @@ template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed,
                     Param<convT> filter_packed,
                     CParam<T> sig,
-                    CParam<T> filter,
-                    const int baseDim)
+                    CParam<T> filter)
 {
     dim_t *sd = sig.dims;
 
@@ -286,12 +285,10 @@ void packDataHelper(Param<convT> sig_packed,
     POST_LAUNCH_CHECK();
 }
 
+// TODO(umar): This needs a better name
 template<typename T, typename convT>
-void complexMultiplyHelper(Param<T> out,
-                           Param<convT> sig_packed,
+void complexMultiplyHelper(Param<convT> sig_packed,
                            Param<convT> filter_packed,
-                           CParam<T> sig,
-                           CParam<T> filter,
                            AF_BATCH_KIND kind)
 {
     int sig_packed_elem = 1;
@@ -338,8 +335,7 @@ template<typename T, typename convT, bool roundOut, int baseDim, bool expand>
 void reorderOutputHelper(Param<T> out,
                          Param<convT> packed,
                          CParam<T> sig,
-                         CParam<T> filter,
-                         AF_BATCH_KIND kind)
+                         CParam<T> filter)
 {
     dim_t *sd = sig.dims;
     int fftScale = 1;
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index cf4f567cee..8d2a919558 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -18,9 +18,9 @@ namespace cuda
 namespace kernel
 {
 
-static const unsigned MAX_BINS  = 4000;
-static const int THREADS_X =  256;
-static const int THRD_LOAD =   16;
+constexpr int MAX_BINS  = 4000;
+constexpr int THREADS_X =  256;
+constexpr int THRD_LOAD =   16;
 
 __forceinline__ __device__ int minimum(int a, int b)
 {
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 4984bd8e35..bded6043ef 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -28,7 +28,6 @@ namespace cuda
         __global__
         void iota_kernel(Param<T> out,
                          const int s0, const int s1, const int s2, const int s3,
-                         const int t0, const int t1, const int t2, const int t3,
                          const int blocksPerMatX, const int blocksPerMatY)
         {
             const int oz = blockIdx.x / blocksPerMatX;
@@ -69,7 +68,7 @@ namespace cuda
         // Wrapper functions
         ///////////////////////////////////////////////////////////////////////////
         template<typename T>
-        void iota(Param<T> out, const af::dim4 &sdims, const af::dim4 &tdims)
+        void iota(Param<T> out, const af::dim4 &sdims)
         {
             dim3 threads(IOTA_TX, IOTA_TY, 1);
 
@@ -86,7 +85,7 @@ namespace cuda
 
             CUDA_LAUNCH((iota_kernel<T>), blocks, threads,
                     out, sdims[0], sdims[1], sdims[2], sdims[3],
-                    tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
+                    blocksPerMatX, blocksPerMatY);
 
             POST_LAUNCH_CHECK();
         }
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index fc9a07fb5b..84743981ff 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -317,6 +317,7 @@ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0)
 template<typename T, af_border_type pad>
 void medfilt2(Param<T> out, CParam<T> in, int w_len, int w_wid)
 {
+    UNUSED(w_wid);
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 55010a9ec7..15d0d004d7 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -179,10 +179,9 @@ template<typename T, typename To, af_match_type dist_type>
 void all_distances(Param<To> dist,
                        CParam<T> query,
                        CParam<T> train,
-                       const dim_t dist_dim,
-                       const unsigned n_dist)
+                       const dim_t dist_dim)
 {
-    const unsigned feat_len = query.dims[dist_dim];
+    const dim_t feat_len = query.dims[dist_dim];
     const unsigned max_kern_feat_len = min(THREADS, feat_len);
     const To max_dist = maxval<To>();
 
@@ -204,7 +203,7 @@ void all_distances(Param<To> dist,
 
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
-    for(int feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
+    for(dim_t feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
         if (use_shmem) {
             CUDA_LAUNCH_SMEM((all_distances<T,To,dist_type,true>), blocks, threads, smem_sz,
                              dist.ptr, query, train, max_dist, feat_len, max_kern_feat_len, feat_offset);
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index b0fc95b343..526cc077f1 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -321,6 +321,10 @@ void orb(unsigned* out_feat,
          const unsigned levels,
          const bool blur_img)
 {
+    UNUSED(fast_thr);
+    UNUSED(max_feat);
+    UNUSED(scl_fctr);
+    UNUSED(levels);
     unsigned patch_size = REF_PAT_SIZE;
 
     unsigned max_levels = feat_pyr.size();
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index c70f82a643..41cf57ef41 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -47,10 +47,10 @@ namespace cuda
 namespace kernel
 {
 
-    static const uint N = 351;
-    static const uint BLOCKS = 32;
-    static const uint STATE_SIZE = (256*3);
-    static const uint TABLE_SIZE = 16;
+    constexpr int N = 351;
+    constexpr int BLOCKS = 32;
+    constexpr int STATE_SIZE = (256*3);
+    constexpr int TABLE_SIZE = 16;
 
     //Utils
     static inline __device__ void read_table(uint * const sharedTable, const uint * const table)
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index f38c7160ff..c7a2df3219 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -19,10 +19,10 @@ namespace cuda
     namespace kernel
     {
         // Kernel Launch Config Values
-        static const unsigned TX = 16;
-        static const unsigned TY = 16;
+        constexpr unsigned TX = 16;
+        constexpr unsigned TY = 16;
         // Used for batching images
-        static const unsigned TI = 4;
+        constexpr int TI = 4;
 
         typedef struct {
             float tmat[6];
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index 9554b7a8d3..aa0dcbc924 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -10,19 +10,18 @@
 #include <math.hpp>
 #include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <kernel/iota.hpp>
+#include <iota.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
 #include <thrust/sort.h>
 #include <kernel/thrust_sort_by_key.hpp>
+#include <handle.hpp>
 
 namespace cuda
 {
     namespace kernel
     {
-        ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename T>
         void sort0Iterative(Param<T> val, bool isAscending)
         {
@@ -50,8 +49,8 @@ namespace cuda
             POST_LAUNCH_CHECK();
         }
 
-        template<typename T, int dim>
-        void sortBatched(Param<T> pVal, bool isAscending)
+        template<typename T>
+        void sortBatched(Param<T> pVal, int dim, bool isAscending)
         {
             af::dim4 inDims;
             for(int i = 0; i < 4; i++)
@@ -65,46 +64,16 @@ namespace cuda
             seqDims[dim] = 1;
 
             // Create/call iota
-            // Array<uint> key = iota<uint>(seqDims, tileDims);
-            dim4 keydims = inDims;
-            auto key = memAlloc<uint>(keydims.elements());
-            Param<uint> pKey;
-            pKey.ptr = key.get();
-            pKey.strides[0] = 1;
-            pKey.dims[0] = keydims[0];
-            for(int i = 1; i < 4; i++) {
-                pKey.dims[i] = keydims[i];
-                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
-            }
-            kernel::iota<uint>(pKey, seqDims, tileDims);
+            Array<uint> pKey = iota<uint>(seqDims, tileDims);
 
-            // Flat
-            //val.modDims(inDims.elements());
-            //key.modDims(inDims.elements());
-            pKey.dims[0] = inDims.elements();
-            pKey.strides[0] = 1;
-            pVal.dims[0] = inDims.elements();
-            pVal.strides[0] = 1;
-            for(int i = 1; i < 4; i++) {
-                pKey.dims[i] = 1;
-                pKey.strides[i] = pKey.strides[i - 1] * pKey.dims[i - 1];
-                pVal.dims[i] = 1;
-                pVal.strides[i] = pVal.strides[i - 1] * pVal.dims[i - 1];
-            }
+            pVal = flat(pVal);
 
             // Sort indices
             // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-            thrustSortByKey(pVal.ptr, pKey.ptr, pVal.dims[0], isAscending);
+            thrustSortByKey(pVal.ptr, pKey.get(), pVal.dims[0], isAscending);
 
             // Needs to be ascending (true) in order to maintain the indices properly
-            thrustSortByKey(pKey.ptr, pVal.ptr, pVal.dims[0], true);
-
-            // No need of doing moddims here because the original Array<T>
-            // dimensions have not been changed
-            //val.modDims(inDims);
-
-            // Not really necessary
-            // CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+            thrustSortByKey(pKey.get(), pVal.ptr, pVal.dims[0], true);
         }
 
         template<typename T>
@@ -113,9 +82,9 @@ namespace cuda
             int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
 
             if(higherDims > 10)
-              sortBatched<T, 0>(val, isAscending);
+                sortBatched<T>(val, 0, isAscending);
             else
-              kernel::sort0Iterative<T>(val, isAscending);
+                kernel::sort0Iterative<T>(val, isAscending);
         }
     }
 }
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 4d4eaa37e9..3f45326aea 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -14,17 +14,14 @@
 #include <Param.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <kernel/iota.hpp>
+#include <iota.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
 
 namespace cuda
 {
     namespace kernel
     {
-
-        ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv>
         void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
         {
@@ -66,17 +63,7 @@ namespace cuda
             seqDims[dim] = 1;
 
             // Create/call iota
-            // Array<uint> key = iota<uint>(seqDims, tileDims);
-            auto Seq = memAlloc<uint>(elements);
-            Param<uint> pSeq;
-            pSeq.ptr = Seq.get();
-            pSeq.strides[0] = 1;
-            pSeq.dims[0] = inDims[0];
-            for(int i = 1; i < 4; i++) {
-                pSeq.dims[i] = inDims[i];
-                pSeq.strides[i] = pSeq.strides[i - 1] * pSeq.dims[i - 1];
-            }
-            cuda::kernel::iota<uint>(pSeq, seqDims, tileDims);
+            Array<uint> Seq = iota<uint>(seqDims, tileDims);
 
             Tk *Key = pKey.ptr;
             auto cKey = memAlloc<Tk>(elements);
@@ -100,17 +87,16 @@ namespace cuda
             // No need of doing moddims here because the original Array<T>
             // dimensions have not been changed
             //val.modDims(inDims);
-
         }
 
         template<typename Tk, typename Tv>
         void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
         {
             int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
-            // Batced sort performs 4x sort by keys
-            // But this is only useful before GPU is saturated
-            // The GPU is saturated at around 100,000 integers
-            // Call batched sort only if both conditions are met
+
+            // Batced sort performs 4x sort by keys But this is only useful
+            // before GPU is saturated The GPU is saturated at around 100,000
+            // integers Call batched sort only if both conditions are met
             if(higherDims > 4 && okey.dims[0] < 100000)
                 kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
             else
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key.hpp b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
index 0fcb013a34..17476ef0a6 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
@@ -1,12 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
 #pragma once
 #include <Param.hpp>
 namespace cuda
 {
     namespace kernel
     {
-        ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv>
         void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending);
     }
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 8f578abf92..905b3e9bee 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -16,9 +16,7 @@ namespace cuda
 {
     namespace kernel
     {
-        ///////////////////////////////////////////////////////////////////////////
         // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
         template<typename Tk, typename Tv>
         void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending)
         {
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 792c5f601e..7ad01dad94 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -160,6 +160,7 @@ inline
 void topk(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
           const int k, const int dim, const af::topkFunction order)
 {
+    assert(dim == 0);
     //TODO Add switch statement when support for other dims is added
     topkDim0<T>(ovals, oidxs, ivals, k, order);
 }
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 0c259c7a55..7e80d049aa 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -91,7 +91,7 @@ namespace kernel
     }
 
     template<typename T, bool conjugate>
-    void transpose(Param<T> out, CParam<T> in, const int ndims)
+    void transpose(Param<T> out, CParam<T> in)
     {
         // dimensions passed to this function should be input dimensions
         // any necessary transformations and dimension related calculations are
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 175a8721e0..8bcafb9597 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -221,6 +221,7 @@ int MemoryManagerPinned::getActiveDeviceId()
 
 size_t MemoryManagerPinned::getMaxMemorySize(int id)
 {
+    UNUSED(id);
     return cuda::getHostMemorySize();
 }
 
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index a5c2b0fcf6..2aebba471d 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -42,11 +42,11 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
     Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
     switch(dist_type) {
-        case AF_SAD: kernel::all_distances<T, To, AF_SAD>(tmp_dists, queryT, trainT, 1, n_dist);
+        case AF_SAD: kernel::all_distances<T, To, AF_SAD>(tmp_dists, queryT, trainT, 1);
                      break;
-        case AF_SSD: kernel::all_distances<T, To, AF_SSD>(tmp_dists, queryT, trainT, 1, n_dist);
+        case AF_SSD: kernel::all_distances<T, To, AF_SSD>(tmp_dists, queryT, trainT, 1);
                      break;
-        case AF_SHD: kernel::all_distances<T, To, AF_SHD>(tmp_dists, queryT, trainT, 1, n_dist);
+        case AF_SHD: kernel::all_distances<T, To, AF_SHD>(tmp_dists, queryT, trainT, 1);
                      break;
         default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index 8e9b3f5a01..8479da443c 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -28,8 +28,6 @@ unsigned orb(Array<float> &x, Array<float> &y,
              const float scl_fctr, const unsigned levels,
              const bool blur_img)
 {
-    const dim4 dims = image.dims();
-
     std::vector<unsigned> feat_pyr, lvl_best;
     std::vector<float> lvl_scl;
     std::vector<float*> d_x_pyr, d_y_pyr;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 8408f7c8da..97ec3d2161 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -153,10 +153,10 @@ int getMinSupportedCompute(int cudaMajorVer)
     // Vector of minimum supported compute versions
     // for CUDA toolkit (i+1).* where i is the index
     // of the vector
-    static const std::array<int,10> minSV{1, 1, 1, 1, 1, 1, 2, 2, 3, 3};
+    static const std::array<int, 10> minSV{1, 1, 1, 1, 1, 1, 2, 2, 3, 3};
 
-    auto CVSize = minSV.size();
-    return (cudaMajorVer>CVSize ? minSV[CVSize-1] : minSV[cudaMajorVer-1]);
+    int CVSize = static_cast<int>(minSV.size());
+    return (cudaMajorVer > CVSize ? minSV[CVSize-1] : minSV[cudaMajorVer-1]);
 }
 
 ///////////////////////////////////////////////////////////////////////////
@@ -218,6 +218,7 @@ string getPlatformInfo()
 
 bool isDoubleSupported(int device)
 {
+    UNUSED(device);
     return true;
 }
 
@@ -231,7 +232,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     cudaDeviceProp dev = getDeviceProp(getActiveDeviceId());
 
     // Name
-    snprintf(d_name, 64, "%s", dev.name);
+    snprintf(d_name, 256, "%s", dev.name);
 
     //Platform
     std::string cudaRuntime = getCUDARuntimeVersion();
@@ -242,7 +243,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     snprintf(d_compute, 10, "%d.%d", dev.major, dev.minor);
 
     // Sanitize input
-    for (int i = 0; i < 63; i++) {
+    for (int i = 0; i < 256; i++) {
         if (d_name[i] == ' ') {
             if (d_name[i + 1] == 0 || d_name[i + 1] == ' ') d_name[i] = 0;
             else d_name[i] = '_';
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index b5e6634b05..8aec3ebfe3 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -69,6 +69,20 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
 
     return nfeat_out;
 #else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(score);
+    UNUSED(ori);
+    UNUSED(size);
+    UNUSED(desc);
+    UNUSED(in);
+    UNUSED(n_layers);
+    UNUSED(contrast_thr);
+    UNUSED(edge_thr);
+    UNUSED(init_sigma);
+    UNUSED(double_input);
+    UNUSED(img_scale);
+    UNUSED(feature_ratio);
     if (compute_GLOH)
         AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
     else
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index e65a918dc4..5a69c2c84d 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -166,6 +166,7 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
+    UNUSED(options);
     int N = A.dims()[0];
     int NRHS = b.dims()[1];
 
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index f42ea37da5..f49dc45dcd 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -24,9 +24,9 @@ namespace cuda
         Array<T> out = copyArray<T>(in);
         switch(dim) {
             case 0: kernel::sort0<T>(out, isAscending); break;
-            case 1: kernel::sortBatched<T, 1>(out, isAscending); break;
-            case 2: kernel::sortBatched<T, 2>(out, isAscending); break;
-            case 3: kernel::sortBatched<T, 3>(out, isAscending); break;
+            case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+            case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+            case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
             default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         }
 
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 136ccb9faf..588776c732 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -123,6 +123,7 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    UNUSED(optRhs);
     // Similar Operations to GEMM
     cusparseOperation_t lOpts = toCusparseTranspose(optLhs);
 
diff --git a/src/backend/cuda/transpose.cu b/src/backend/cuda/transpose.cu
index fff167a86d..ff9fa4b9fd 100644
--- a/src/backend/cuda/transpose.cu
+++ b/src/backend/cuda/transpose.cu
@@ -21,14 +21,13 @@ template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate)
 {
     const dim4 inDims   = in.dims();
-    const dim4 inStrides= in.strides();
 
     dim4 outDims  = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
 
     Array<T> out  = createEmptyArray<T>(outDims);
 
-    if(conjugate)   { kernel::transpose<T, true>(out, in, inDims.ndims()); }
-    else            { kernel::transpose<T, false>(out, in, inDims.ndims());}
+    if(conjugate)   { kernel::transpose<T, true>(out, in); }
+    else            { kernel::transpose<T, false>(out, in);}
 
     return out;
 }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index f32a35596f..d16ef27c3c 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -110,7 +110,7 @@ namespace opencl
              dim4(tmp.info.strides[0], tmp.info.strides[1],
                       tmp.info.strides[2], tmp.info.strides[3]),
              (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.data, owner_ ? bufferFree : [] (Buffer* ptr) {}),
+        data(tmp.data, owner_ ? bufferFree : [] (Buffer*) {}),
         data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
         node(bufferNodePtr<T>()), ready(true), owner(owner_)
     {
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index f0b0939205..2e73df9390 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -198,7 +198,7 @@ namespace opencl
             return data.use_count();
         }
 
-        const dim_t getOffset() const
+        dim_t getOffset() const
         {
             return info.getOffset();
         }
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 203e6dfb98..b828bcc3b5 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -19,7 +19,6 @@ namespace opencl
     template<typename T>
     void copyData(T *data, const Array<T> &A)
     {
-
         // FIXME: Merge this with copyArray
         A.eval();
 
@@ -120,6 +119,8 @@ namespace opencl
     template<typename inType, typename outType>
     void copyArray(Array<outType> &out, Array<inType> const &in)
     {
+        static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
+                      "Cannot copy from complex value to a non complex value");
         copyWrapper<inType, outType> copyFn;
         copyFn(out, in);
     }
@@ -188,36 +189,6 @@ namespace opencl
     INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
     INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
 
-#define SPECILIAZE_UNUSED_COPYARRAY(SRC_T, DST_T) \
-    template<> void copyArray<SRC_T, DST_T>(Array<DST_T> &out, Array<SRC_T> const &in) \
-    {\
-        char errMessage[1024];                                              \
-        snprintf(errMessage, sizeof(errMessage),                            \
-                "OpenCL copyArray<"#SRC_T","#DST_T"> is not supported\n");  \
-        OPENCL_NOT_SUPPORTED(errMessage);                                   \
-    }
-
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, double)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, float)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, uchar)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, char)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, uint)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, int)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, intl)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, uintl)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, short)
-    SPECILIAZE_UNUSED_COPYARRAY(cfloat, ushort)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, double)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, float)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uchar)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, char)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uint)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, int)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, intl)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, uintl)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, short)
-    SPECILIAZE_UNUSED_COPYARRAY(cdouble, ushort)
-
     template<typename T>
     T getScalar(const Array<T> &in)
     {
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index b3616d083c..1886b4cbc9 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -81,6 +81,7 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
                  const Array<T> &b, const af_mat_prop options)
 {
+    UNUSED(options);
     int N = A.dims()[0];
     int NRHS = b.dims()[1];
 
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 3d3b46e8e8..22e04155e5 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -214,6 +214,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
     // MKL: CSRMM Does not support optRhs
+    UNUSED(optRhs);
 
     lhs.eval();
     rhs.eval();
@@ -325,6 +326,7 @@ void mv(Array<T> output,
         const Array<T> right,
         int M)
 {
+    UNUSED(M);
     auto oPtr   = output.getMappedPtr();
     auto rhtPtr = right .getMappedPtr();
     auto vPtr   = values.getMappedPtr();
@@ -395,6 +397,7 @@ void mm(Array<T> output,
         int M, int N,
         int ldb, int ldc)
 {
+    UNUSED(M);
     auto oPtr   = output.getMappedPtr();
     auto rhtPtr = right .getMappedPtr();
     auto vPtr   = values.getMappedPtr();
@@ -468,6 +471,7 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
 {
+    UNUSED(optRhs);
     lhs.eval();
     rhs.eval();
 
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
index 577be84f7f..522935499e 100644
--- a/src/backend/opencl/err_clblast.hpp
+++ b/src/backend/opencl/err_clblast.hpp
@@ -69,6 +69,7 @@ static const char * _clblastGetResultString(clblast::StatusCode st)
     case clblast::StatusCode::kInsufficientMemoryY:        return "Vector Y's OpenCL buffer is too small";
 
     // Custom additional status codes for CLBlast
+    case clblast::StatusCode::kInsufficientMemoryTemp:     return "Temporary buffer provided to GEMM routine is too small";
     case clblast::StatusCode::kInvalidBatchCount:          return "The batch count needs to be positive";
     case clblast::StatusCode::kInvalidOverrideKernel:      return "Trying to override parameters for an invalid kernel";
     case clblast::StatusCode::kMissingOverrideParameter:   return "Missing override parameter(s) for the target kernel";
diff --git a/src/backend/opencl/iota.cpp b/src/backend/opencl/iota.cpp
index ac4408c8b4..c570856fa5 100644
--- a/src/backend/opencl/iota.cpp
+++ b/src/backend/opencl/iota.cpp
@@ -22,7 +22,7 @@ namespace opencl
         dim4 outdims = dims * tile_dims;
 
         Array<T> out = createEmptyArray<T>(outdims);
-        kernel::iota<T>(out, dims, tile_dims);
+        kernel::iota<T>(out, dims);
 
         return out;
     }
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 8754ef8582..853293011a 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -43,6 +43,7 @@ namespace {
 
   /// Generates the code to calculate the offsets for a buffer
   void generateBufferOffsets(std::stringstream &kerStream, int id, bool is_linear, const std::string& type_str) {
+      UNUSED(type_str);
       std::string idx_str = std::string("int idx") + std::to_string(id);
       std::string info_str = std::string("iInfo") + std::to_string(id);
 
@@ -64,8 +65,11 @@ namespace {
   }
 
 
+  inline
   void generateShiftNodeOffsets(std::stringstream &kerStream, int id,
                                 bool is_linear, const std::string& type_str) {
+      UNUSED(is_linear);
+      UNUSED(type_str);
       std::string idx_str = std::string("idx") + std::to_string(id);
       std::string info_str = std::string("iInfo") + std::to_string(id);
       std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
@@ -88,11 +92,11 @@ namespace {
                 << id_str << "0 + " << info_str << ".offset;\n";
   }
 
+  inline
   void generateShiftNodeRead(std::stringstream &kerStream, int id,
                              const std::string& type_str) {
       kerStream << type_str << " val" << id
                 << " = in" << id << "[idx" << id << "];\n";
   }
-
 }
 }
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 2a4b9c98e2..f0d56d8273 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -28,9 +28,9 @@ namespace opencl
 {
 namespace kernel
 {
-static const unsigned MAX_BINS  = 4000;
-static const int THREADS_X =  256;
-static const int THRD_LOAD =   16;
+constexpr int MAX_BINS  = 4000;
+constexpr int THREADS_X =  256;
+constexpr int THRD_LOAD =   16;
 
 template<typename inType, typename outType, bool isLinear>
 void histogram(Param out, const Param in, int nbins, float minval, float maxval)
diff --git a/src/backend/opencl/kernel/iota.cl b/src/backend/opencl/kernel/iota.cl
index 75d055d1a1..f00335e0af 100644
--- a/src/backend/opencl/kernel/iota.cl
+++ b/src/backend/opencl/kernel/iota.cl
@@ -10,7 +10,6 @@
 __kernel
 void iota_kernel(__global T *out, const KParam op,
                  const int s0, const int s1, const int s2, const int s3,
-                 const int t0, const int t1, const int t2, const int t3,
                  const int blocksPerMatX, const int blocksPerMatY)
 {
     const int oz = get_group_id(0) / blocksPerMatX;
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index bc97629850..ee01154da8 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -37,7 +37,7 @@ static const int TILEX = 512;
 static const int TILEY = 32;
 
 template<typename T>
-void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
+void iota(Param out, const af::dim4 &sdims)
 {
     std::string refName = std::string("iota_kernel_") + std::string(dtype_traits<T>::getName());
 
@@ -62,7 +62,6 @@ void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
     }
 
     auto iotaOp = KernelFunctor<Buffer, const KParam,
-                                const int, const int, const int, const int,
                                 const int, const int, const int, const int,
                                 const int, const int> (*entry.ker);
 
@@ -75,7 +74,7 @@ void iota(Param out, const af::dim4 &sdims, const af::dim4 &tdims)
 
     iotaOp(EnqueueArgs(getQueue(), global, local),
            *out.data, out.info, sdims[0], sdims[1], sdims[2], sdims[3],
-           tdims[0], tdims[1], tdims[2], tdims[3], blocksPerMatX, blocksPerMatY);
+           blocksPerMatX, blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 4a1bf4ce5b..d209598c13 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -43,7 +43,7 @@ template<> const char *laset_name<2>() { return "laset_upper"; }
 template<typename T, int uplo>
 void laset(int m, int  n,
            T offdiag, T diag,
-           cl_mem dA, size_t dA_offset, magma_int_t ldda)
+           cl_mem dA, size_t dA_offset, magma_int_t ldda, cl_command_queue queue)
 {
     std::string refName = laset_name<uplo>() + std::string("_") +
         std::string(dtype_traits<T>::getName()) +
@@ -83,7 +83,8 @@ void laset(int m, int  n,
 
     auto lasetOp = KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(*entry.ker);
 
-    lasetOp(EnqueueArgs(getQueue(), global, local), m, n, offdiag, diag, dAObj, dA_offset, ldda);
+    cl::CommandQueue q(queue);
+    lasetOp(EnqueueArgs(q, global, local), m, n, offdiag, diag, dAObj, dA_offset, ldda);
 }
 }
 }
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 7e8a9d0733..77f70238a7 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -39,7 +39,7 @@ typedef struct {
 } zlaswp_params_t;
 
 template<typename T>
-void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int *ipiv, int inci)
+void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int *ipiv, int inci, cl::CommandQueue &queue)
 {
     std::string refName = std::string("laswp_") + std::string(dtype_traits<T>::getName());
 
@@ -84,7 +84,7 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int
 
         unsigned long long k_offset = offset + k*ldda;
 
-        laswpOp(EnqueueArgs(getQueue(), global, local), n, inObj, k_offset, ldda, params);
+        laswpOp(EnqueueArgs(queue, global, local), n, inObj, k_offset, ldda, params);
     }
 }
 }
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 47b11a5eaf..263c4ed571 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -33,7 +33,7 @@ static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
 
 template<typename in_t, typename idx_t, unsigned dim>
-void lookup(Param out, const Param in, const Param indices, int nDims)
+void lookup(Param out, const Param in, const Param indices)
 {
     std::string refName = std::string("lookupND_") +
         std::string(dtype_traits<in_t>::getName()) +
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 714b3f9c21..3d913f0b98 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -38,11 +38,10 @@ template<typename T, typename To, af_match_type dist_type>
 void all_distances(Param dist,
                    Param query,
                    Param train,
-                   const dim_t dist_dim,
-                   const unsigned n_dist)
+                   const dim_t dist_dim)
 {
-    const unsigned feat_len = query.info.dims[dist_dim];
-    const unsigned max_kern_feat_len = min(THREADS, feat_len);
+    const dim_t feat_len = query.info.dims[dist_dim];
+    const unsigned max_kern_feat_len = min(THREADS, static_cast<unsigned>(feat_len));
     const To max_dist = maxval<To>();
 
     // Determine maximum feat_len capable of using shared memory (faster)
@@ -124,7 +123,7 @@ void all_distances(Param dist,
                               const unsigned,const unsigned,
                               LocalSpaceArg> (*entry.ker);
 
-    for(int feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
+    for(dim_t feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
         hmOp(EnqueueArgs(getQueue(), global, local),
               *dist.data,
               *query.data, query.info, *train.data, train.info,
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 1c422acfe2..87730ea35c 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -10,13 +10,11 @@
 #pragma once
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
 #include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/sort_helper.hpp>
-#include <kernel/iota.hpp>
+#include <iota.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -35,7 +33,6 @@ using cl::Kernel;
 using cl::KernelFunctor;
 using cl::EnqueueArgs;
 using cl::NDRange;
-using std::string;
 
 namespace opencl
 {
@@ -74,8 +71,8 @@ namespace opencl
             CL_DEBUG_FINISH(getQueue());
         }
 
-        template<typename T, int dim>
-        void sortBatched(Param pVal, bool isAscending)
+        template<typename T>
+        void sortBatched(Param pVal, int dim, bool isAscending)
         {
             af::dim4 inDims;
             for(int i = 0; i < 4; i++)
@@ -89,8 +86,8 @@ namespace opencl
             seqDims[dim] = 1;
 
             // Create/call iota
-            Array<uint> pKey = createEmptyArray<uint>(inDims);
-            kernel::iota<uint>(pKey, seqDims, tileDims);
+            //Array<uint> pKey = createEmptyArray<uint>(inDims);
+            Array<uint> pKey = iota<uint>(seqDims, tileDims);
 
             pKey.setDataDims(inDims.elements());
 
@@ -133,7 +130,7 @@ namespace opencl
             int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
             // TODO Make a better heurisitic
             if(higherDims > 10)
-                sortBatched<T, 0>(val, isAscending);
+                sortBatched<T>(val, 0, isAscending);
             else
                 kernel::sort0Iterative<T>(val, isAscending);
         }
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 56cc5e9dfd..7f5cd9f73f 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -20,7 +20,7 @@
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <math.hpp>
-#include <kernel/iota.hpp>
+#include <iota.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -161,8 +161,7 @@ namespace opencl
             seqDims[dim] = 1;
 
             // Create/call iota
-            Array<unsigned> pSeq = createEmptyArray<unsigned>(inDims);
-            kernel::iota<unsigned>(pSeq, seqDims, tileDims);
+            Array<unsigned> pSeq = iota<unsigned>(seqDims, tileDims);
 
             int elements = inDims.elements();
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index c098fe0067..4ec35eceb9 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -148,7 +148,6 @@ namespace opencl
         {
             int num_rows = dense.info.dims[0];
             int num_cols = dense.info.dims[1];
-            int dense_elements = num_rows * num_cols;
 
             // sd1 contains output of scan along dim 1 of dense
             Array<int> sd1 = createEmptyArray<int>(dim4(num_rows, num_cols));
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 55761dff0b..a653b47b17 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -33,7 +33,8 @@ namespace kernel
 template<typename T>
 void swapdblk(int n, int nb,
               cl_mem dA, size_t dA_offset, int ldda, int inca,
-              cl_mem dB, size_t dB_offset, int lddb, int incb)
+              cl_mem dB, size_t dB_offset, int lddb, int incb,
+              cl_command_queue queue)
 {
     std::string refName = std::string("swapdblk_") + std::string(dtype_traits<T>::getName());
 
@@ -91,7 +92,8 @@ void swapdblk(int n, int nb,
     auto swapdOp = KernelFunctor<int, Buffer, unsigned long long, int, int,
                                Buffer, unsigned long long, int, int>(*entry.ker);
 
-    swapdOp(EnqueueArgs(getQueue(), global, local),
+    cl::CommandQueue q(queue);
+    swapdOp(EnqueueArgs(q, global, local),
             nb, dAObj, dA_offset, ldda, inca, dBObj, dB_offset, lddb, incb);
 }
 }
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 6142af863a..f69738aad6 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -35,7 +35,7 @@ static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
 template<typename T, bool conjugate, bool IS32MULTIPLE>
-void transpose(Param out, const Param in)
+void transpose(Param out, const Param in, cl::CommandQueue queue)
 {
     std::string refName = std::string("transpose_") + std::string(dtype_traits<T>::getName()) +
         std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
@@ -76,10 +76,10 @@ void transpose(Param out, const Param in)
     auto transposeOp = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
                                       const int, const int> (*entry.ker);
 
-    transposeOp(EnqueueArgs(getQueue(), global, local),
+    transposeOp(EnqueueArgs(queue, global, local),
                 *out.data, out.info, *in.data, in.info, blk_x, blk_y);
 
-    CL_DEBUG_FINISH(getQueue());
+    CL_DEBUG_FINISH(queue);
 }
 }
 }
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 4b0dd8b2cb..c1ebb71a6c 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -35,7 +35,7 @@ static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
 template<typename T, bool conjugate, bool IS32MULTIPLE>
-void transpose_inplace(Param in)
+void transpose_inplace(Param in, cl::CommandQueue &queue)
 {
     std::string refName = std::string("transpose_inplace_") + std::string(dtype_traits<T>::getName()) +
         std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
@@ -74,9 +74,9 @@ void transpose_inplace(Param in)
 
     auto transposeOp = KernelFunctor<Buffer, const KParam, const int, const int> (*entry.ker);
 
-    transposeOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, blk_x, blk_y);
+    transposeOp(EnqueueArgs(queue, global, local), *in.data, in.info, blk_x, blk_y);
 
-    CL_DEBUG_FINISH(getQueue());
+    CL_DEBUG_FINISH(queue);
 }
 }
 }
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 08bade3594..a3354e16b7 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -27,13 +27,11 @@ Array<in_t> lookup(const Array<in_t> &input,
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    dim_t nDims = iDims.ndims();
-
     switch(dim) {
-        case 0: kernel::lookup<in_t, idx_t, 0>(out, input, indices, nDims); break;
-        case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices, nDims); break;
-        case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices, nDims); break;
-        case 3: kernel::lookup<in_t, idx_t, 3>(out, input, indices, nDims); break;
+        case 0: kernel::lookup<in_t, idx_t, 0>(out, input, indices); break;
+        case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices); break;
+        case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices); break;
+        case 3: kernel::lookup<in_t, idx_t, 3>(out, input, indices); break;
     }
 
     return out;
diff --git a/src/backend/opencl/magma/laset.cpp b/src/backend/opencl/magma/laset.cpp
index 26b618a592..bcbf5e2ec3 100644
--- a/src/backend/opencl/magma/laset.cpp
+++ b/src/backend/opencl/magma/laset.cpp
@@ -82,9 +82,9 @@ magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n,
 
 
     switch (uplo) {
-    case MagmaFull : return opencl::kernel::laset<T, 0>(m, n, offdiag, diag, dA, dA_offset, ldda);
-    case MagmaLower: return opencl::kernel::laset<T, 1>(m, n, offdiag, diag, dA, dA_offset, ldda);
-    case MagmaUpper: return opencl::kernel::laset<T, 2>(m, n, offdiag, diag, dA, dA_offset, ldda);
+    case MagmaFull : return opencl::kernel::laset<T, 0>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
+    case MagmaLower: return opencl::kernel::laset<T, 1>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
+    case MagmaUpper: return opencl::kernel::laset<T, 2>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
     default: return;
     }
 
diff --git a/src/backend/opencl/magma/laswp.cpp b/src/backend/opencl/magma/laswp.cpp
index 32b2e67d90..b6bf3b6a9a 100644
--- a/src/backend/opencl/magma/laswp.cpp
+++ b/src/backend/opencl/magma/laswp.cpp
@@ -79,7 +79,8 @@ magmablas_laswp(
         return;  //info;
     }
 
-    opencl::kernel::laswp<T>(n, dAT, dAT_offset, ldda, k1, k2, ipiv, inci);
+    cl::CommandQueue q(queue, true);
+    opencl::kernel::laswp<T>(n, dAT, dAT_offset, ldda, k1, k2, ipiv, inci, q);
 }
 
 
@@ -89,7 +90,7 @@ magmablas_laswp(
         cl_mem dAT, size_t dAT_offset, magma_int_t ldda,    \
         magma_int_t k1, magma_int_t k2,                     \
         const magma_int_t *ipiv, magma_int_t inci,          \
-        magma_queue_t queue);                               \
+        magma_queue_t queue);
 
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 992b4bbdc2..e978eda3ae 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -91,7 +91,7 @@ struct gpu_blas_gemm_func
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         const cl_mem b_buffer, const size_t b_offset, const size_t b_ld, const T beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -112,7 +112,7 @@ struct gpu_blas_gemv_func
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         const cl_mem x_buffer, const size_t x_offset, const size_t x_inc, const T beta,
         cl_mem y_buffer, const size_t y_offset, const size_t y_inc,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -132,7 +132,7 @@ struct gpu_blas_trmm_func
         const size_t m, const size_t n, const T alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -151,7 +151,7 @@ struct gpu_blas_trsm_func
         const size_t m, const size_t n, const T alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -170,7 +170,7 @@ struct gpu_blas_trsv_func
         const size_t n,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         cl_mem x_buffer, const size_t x_offset, const size_t x_inc,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -191,7 +191,7 @@ struct gpu_blas_herk_func
         const size_t n, const size_t k, const BasicType alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const BasicType beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -212,7 +212,7 @@ struct gpu_blas_herk_func<float>
         const size_t n, const size_t k, const float alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const float beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -233,7 +233,7 @@ struct gpu_blas_herk_func<double>
         const size_t n, const size_t k, const double alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const double beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
@@ -253,7 +253,7 @@ struct gpu_blas_syrk_func
         const size_t n, const size_t k, const T alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const T beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
     {
         assert(num_queues == 1);
         assert(num_wait_events == 0);
diff --git a/src/backend/opencl/magma/magma_cpu_lapack.h b/src/backend/opencl/magma/magma_cpu_lapack.h
index fdcf2a7136..2529ad5291 100644
--- a/src/backend/opencl/magma/magma_cpu_lapack.h
+++ b/src/backend/opencl/magma/magma_cpu_lapack.h
@@ -22,16 +22,16 @@
 #define LAPACKE_dungbr_work(...) LAPACKE_dorgbr_work(__VA_ARGS__)
 
 template<typename... Args>
-int LAPACKE_slacgv(Args... args) { return 0; }
+int LAPACKE_slacgv(Args... /*args*/) { return 0; }
 
 template<typename... Args>
-int LAPACKE_dlacgv(Args... args) { return 0; }
+int LAPACKE_dlacgv(Args... /*args*/) { return 0; }
 
 template<typename... Args>
-int LAPACKE_slacgv_work(Args... args) { return 0; }
+int LAPACKE_slacgv_work(Args... /*args*/) { return 0; }
 
 template<typename... Args>
-int LAPACKE_dlacgv_work(Args... args) { return 0; }
+int LAPACKE_dlacgv_work(Args... /*args*/) { return 0; }
 
 #define lapack_complex_float magmaFloatComplex
 #define lapack_complex_double magmaDoubleComplex
diff --git a/src/backend/opencl/magma/magma_helper.cpp b/src/backend/opencl/magma/magma_helper.cpp
index 481f08c346..116df3933a 100644
--- a/src/backend/opencl/magma/magma_helper.cpp
+++ b/src/backend/opencl/magma/magma_helper.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include "magma_common.h"
+#include "common/defines.hpp"
 
 template<typename T> T magma_one() { return (T)1.0; }
 template<typename T> T magma_neg_one() { return (T)-1.0; }
@@ -119,18 +120,21 @@ magma_int_t magma_get_potrf_nb<double>(magma_int_t m)
 template<>
 magma_int_t magma_get_potrf_nb<magmaFloatComplex>(magma_int_t m)
 {
+    UNUSED(m);
     return 128;
 }
 
 template<>
 magma_int_t magma_get_potrf_nb<magmaDoubleComplex>(magma_int_t m)
 {
+    UNUSED(m);
     return  64;
 }
 
 template<typename T>
 magma_int_t magma_get_geqrf_nb(magma_int_t m )
 {
+    UNUSED(m);
     return 128;
 }
 
@@ -167,7 +171,7 @@ magma_int_t magma_get_geqrf_nb<magmaDoubleComplex>( magma_int_t m )
     /* Other */
 #endif
 
-template<typename T> T magma_make(double r, double i) { return (T) r; }
+template<typename T> T magma_make(double r, double i) { UNUSED(i); return (T) r; }
 template float magma_make<float>(double r, double i);
 template double magma_make<double>(double r, double i);
 template<> magmaFloatComplex magma_make<magmaFloatComplex>(double r, double i)
diff --git a/src/backend/opencl/magma/magma_helper.h b/src/backend/opencl/magma/magma_helper.h
index f073335196..74b2d5ee19 100644
--- a/src/backend/opencl/magma/magma_helper.h
+++ b/src/backend/opencl/magma/magma_helper.h
@@ -22,6 +22,6 @@ template<typename T> bool magma_is_real();
 template<typename T> magma_int_t magma_get_getrf_nb(int num);
 template<typename T> magma_int_t magma_get_potrf_nb(int num);
 template<typename T> magma_int_t magma_get_geqrf_nb(int num);
-template<typename T> magma_int_t magma_get_gebrd_nb(int num) { return 32; }
+template<typename T> magma_int_t magma_get_gebrd_nb(int /*num*/) { return 32; }
 
 #endif
diff --git a/src/backend/opencl/magma/swapdblk.cpp b/src/backend/opencl/magma/swapdblk.cpp
index 412138727e..a33eea4304 100644
--- a/src/backend/opencl/magma/swapdblk.cpp
+++ b/src/backend/opencl/magma/swapdblk.cpp
@@ -18,7 +18,7 @@ magmablas_swapdblk(magma_int_t n, magma_int_t nb,
 {
     opencl::kernel::swapdblk<T>(n, nb,
                                 dA, dA_offset, ldda, inca,
-                                dB, dB_offset, lddb, incb);
+                                dB, dB_offset, lddb, incb, queue);
 }
 
 
diff --git a/src/backend/opencl/magma/transpose.cpp b/src/backend/opencl/magma/transpose.cpp
index 24e2e9c159..ce5fbf3edb 100644
--- a/src/backend/opencl/magma/transpose.cpp
+++ b/src/backend/opencl/magma/transpose.cpp
@@ -87,12 +87,13 @@ magmablas_transpose(
 
     using namespace opencl;
 
+    cl::CommandQueue q(queue, true);
     if (m % 32 == 0 && n % 32 == 0) {
         kernel::transpose<T, false, true >(makeParam(dAT, dAT_offset, odims, ostrides),
-                                           makeParam(dA , dA_offset , idims, istrides));
+                                           makeParam(dA , dA_offset , idims, istrides), q);
     } else {
         kernel::transpose<T, false, false>(makeParam(dAT, dAT_offset, odims, ostrides),
-                                           makeParam(dA , dA_offset , idims, istrides));
+                                           makeParam(dA , dA_offset , idims, istrides), q);
     }
 }
 
@@ -101,7 +102,7 @@ magmablas_transpose(
         magma_int_t m, magma_int_t n,                       \
         cl_mem dA,  size_t dA_offset,  magma_int_t ldda,    \
         cl_mem dAT, size_t dAT_offset, magma_int_t lddat,   \
-        magma_queue_t queue);                               \
+        magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/transpose_inplace.cpp b/src/backend/opencl/magma/transpose_inplace.cpp
index 6dad153143..8dc9cabc79 100644
--- a/src/backend/opencl/magma/transpose_inplace.cpp
+++ b/src/backend/opencl/magma/transpose_inplace.cpp
@@ -78,18 +78,19 @@ magmablas_transpose_inplace(
 
     using namespace opencl;
 
+    cl::CommandQueue q(queue, true);
     if (n % 32 == 0) {
-        kernel::transpose_inplace<T, false, true >(makeParam(dA , dA_offset , dims, strides));
+        kernel::transpose_inplace<T, false, true >(makeParam(dA , dA_offset , dims, strides), q);
     } else {
-        kernel::transpose_inplace<T, false, false>(makeParam(dA , dA_offset , dims, strides));
+        kernel::transpose_inplace<T, false, false>(makeParam(dA , dA_offset , dims, strides), q);
     }
 }
 
 #define INSTANTIATE(T)                                  \
     template void magmablas_transpose_inplace<T>(       \
         magma_int_t n,                                  \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
-        magma_queue_t queue);                           \
+        cl_mem dA, size_t dA_offset,                    \
+        magma_int_t ldda, magma_queue_t queue);
 
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/magma/ungqr.cpp b/src/backend/opencl/magma/ungqr.cpp
index 5ea05acebb..88fd9a5c5f 100644
--- a/src/backend/opencl/magma/ungqr.cpp
+++ b/src/backend/opencl/magma/ungqr.cpp
@@ -68,8 +68,8 @@ magma_ungqr_gpu(
     magma_queue_t queue,
     magma_int_t *info)
 {
-#define dA(i,j) (dA),  ((i) + (j)*ldda)
-#define dT(j)   (dT),  ((j)*nb)
+#define dA(i,j) (dA),  (dA_offset + ((i) + (j)*ldda))
+#define dT(j)   (dT),  (dT_offset + ((j)*nb))
 
     static const Ty c_zero = magma_zero<Ty>();
     static const Ty c_one  = magma_one<Ty>();
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 43de95f066..f16f9cd564 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -36,6 +36,7 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 template<typename T, af_border_type pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
 {
+    UNUSED(w_wid);
     ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER2_LEN));
     ARG_ASSERT(2, (w_len % 2 != 0));
 
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index da3ed641ee..18ec62133e 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -41,7 +41,7 @@ void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
     Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
     Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
-    kernel::all_distances<T, To, dist_type>(tmp_dists, queryT, trainT, 1, n_dist);
+    kernel::all_distances<T, To, dist_type>(tmp_dists, queryT, trainT, 1);
 
     topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index e407be27bb..226d5967c9 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -63,11 +63,14 @@ using cl::Device;
 
 namespace opencl
 {
+
+#if defined(WITH_GRAPHICS)
 #if defined (OS_MAC)
 static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
 static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
 #endif
+#endif
 
 static const std::string get_system(void)
 {
@@ -433,6 +436,8 @@ bool OpenCLCPUOffload(bool forceOffloadOSX)
     bool osx_offload = isHostUnifiedMemory(getDevice());
     // Force condition
     offload = osx_offload && (offload || forceOffloadOSX);
+#else
+    UNUSED(forceOffloadOSX);
 #endif
     return offload;
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 80654d4bef..d83969e48d 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -18,6 +18,8 @@
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Wignored-qualifiers"
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index b9ef9ad6a2..f79e0ba7fd 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -61,6 +61,20 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
 
     return nfeat_out;
 #else
+    UNUSED(x_out);
+    UNUSED(y_out);
+    UNUSED(score_out);
+    UNUSED(ori_out);
+    UNUSED(size_out);
+    UNUSED(desc_out);
+    UNUSED(in);
+    UNUSED(n_layers);
+    UNUSED(contrast_thr);
+    UNUSED(edge_thr);
+    UNUSED(init_sigma);
+    UNUSED(double_input);
+    UNUSED(img_scale);
+    UNUSED(feature_ratio);
     if (compute_GLOH)
         AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
     else
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index a427ed28e0..13d1101d44 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -75,8 +75,9 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
 
     cl::Buffer *A_buf = A.get();
     int info = 0;
+    cl_command_queue q = getQueue()();
     magma_getrf_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
-                       &ipiv[0], getQueue()(), &info);
+                       &ipiv[0], q, &info);
 
     cl::Buffer *B_buf = B.get();
     int K = B.dims()[1];
@@ -84,7 +85,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
                        (*A_buf)(), A.getOffset(), A.strides()[1],
                        &ipiv[0],
                        (*B_buf)(), B.getOffset(), B.strides()[1],
-                       getQueue()(), &info);
+                       q, &info);
     return B;
 }
 
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index b20b9fb15c..9a2288a8b1 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -25,9 +25,9 @@ namespace opencl
             Array<T> out = copyArray<T>(in);
             switch(dim) {
                 case 0: kernel::sort0<T>(out, isAscending); break;
-                case 1: kernel::sortBatched<T, 1>(out, isAscending); break;
-                case 2: kernel::sortBatched<T, 2>(out, isAscending); break;
-                case 3: kernel::sortBatched<T, 3>(out, isAscending); break;
+                case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+                case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+                case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
                 default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
             }
 
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 5936b7613d..de0527e299 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -73,7 +73,6 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         Buffer *ibuf = indices.get();
         Buffer *vbuf = values.get();
 
-        cl_int err;
         Event ev_in, ev_val, ev_ind;
 
         T* ptr =
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index cbc2345ccd..be2832cb77 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -26,16 +26,15 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
 
     if(conjugate) {
         if(inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0)
-            kernel::transpose<T, true, true>(out, in);
+            kernel::transpose<T, true, true>(out, in, getQueue());
         else
-            kernel::transpose<T, true, false>(out, in);
+            kernel::transpose<T, true, false>(out, in, getQueue());
     } else {
         if(inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0)
-            kernel::transpose<T, false, true>(out, in);
+            kernel::transpose<T, false, true>(out, in, getQueue());
         else
-            kernel::transpose<T, false, false>(out, in);
+            kernel::transpose<T, false, false>(out, in, getQueue());
     }
-
     return out;
 }
 
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index 0cf758e64a..1f1e008f24 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -24,14 +24,14 @@ void transpose_inplace(Array<T> &in, const bool conjugate)
 
     if(conjugate) {
         if(iDims[0] % kernel::TILE_DIM == 0 && iDims[1] % kernel::TILE_DIM == 0)
-            kernel::transpose_inplace<T, true, true>(in);
+            kernel::transpose_inplace<T, true, true>(in, getQueue());
         else
-            kernel::transpose_inplace<T, true, false>(in);
+            kernel::transpose_inplace<T, true, false>(in, getQueue());
     } else {
         if(iDims[0] % kernel::TILE_DIM == 0 && iDims[1] % kernel::TILE_DIM == 0)
-            kernel::transpose_inplace<T, false, true>(in);
+            kernel::transpose_inplace<T, false, true>(in, getQueue());
         else
-            kernel::transpose_inplace<T, false, false>(in);
+            kernel::transpose_inplace<T, false, false>(in, getQueue());
     }
 }
 
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index fb4eb6ca52..01730c4b49 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -107,20 +107,20 @@ struct ToNumStr<cdouble>
 };
 
 namespace {
-template<typename T> const char *shortname(bool caps) { return caps ? "X" : "x"; }
+template<typename T> inline const char *shortname(bool caps) { return caps ? "X" : "x"; }
 
-template<> const char *shortname<float   >(bool caps) { return caps ? "S" : "s"; }
-template<> const char *shortname<double  >(bool caps) { return caps ? "D" : "d"; }
-template<> const char *shortname<cfloat  >(bool caps) { return caps ? "C" : "c"; }
-template<> const char *shortname<cdouble >(bool caps) { return caps ? "Z" : "z"; }
-template<> const char *shortname<int     >(bool caps) { return caps ? "I" : "i"; }
-template<> const char *shortname<uint    >(bool caps) { return caps ? "U" : "u"; }
-template<> const char *shortname<char    >(bool caps) { return caps ? "J" : "j"; }
-template<> const char *shortname<uchar   >(bool caps) { return caps ? "V" : "v"; }
-template<> const char *shortname<intl    >(bool caps) { return caps ? "L" : "l"; }
-template<> const char *shortname<uintl   >(bool caps) { return caps ? "K" : "k"; }
-template<> const char *shortname<short   >(bool caps) { return caps ? "P" : "p"; }
-template<> const char *shortname<ushort  >(bool caps) { return caps ? "Q" : "q"; }
+template<> inline const char *shortname<float   >(bool caps) { return caps ? "S" : "s"; }
+template<> inline const char *shortname<double  >(bool caps) { return caps ? "D" : "d"; }
+template<> inline const char *shortname<cfloat  >(bool caps) { return caps ? "C" : "c"; }
+template<> inline const char *shortname<cdouble >(bool caps) { return caps ? "Z" : "z"; }
+template<> inline const char *shortname<int     >(bool caps) { return caps ? "I" : "i"; }
+template<> inline const char *shortname<uint    >(bool caps) { return caps ? "U" : "u"; }
+template<> inline const char *shortname<char    >(bool caps) { return caps ? "J" : "j"; }
+template<> inline const char *shortname<uchar   >(bool caps) { return caps ? "V" : "v"; }
+template<> inline const char *shortname<intl    >(bool caps) { return caps ? "L" : "l"; }
+template<> inline const char *shortname<uintl   >(bool caps) { return caps ? "K" : "k"; }
+template<> inline const char *shortname<short   >(bool caps) { return caps ? "P" : "p"; }
+template<> inline const char *shortname<ushort  >(bool caps) { return caps ? "Q" : "q"; }
 
 template<typename T>
 const char *getFullName() {
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 7ff3ac9801..7c798ddc5a 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -200,7 +200,7 @@ TYPED_TEST(Approx1, Approx1Cubic)
 // Test Argument Failure Cases
 ///////////////////////////////////////////////////////////////////////////////
 template<typename T>
-void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, const af_err err)
+void approx1ArgsTest(string pTestFile, const af_interp_type method, const af_err err)
 {
     if (noDoubleTests<T>()) return;
     typedef typename dtype_traits<T>::base_type BT;
@@ -231,19 +231,19 @@ void approx1ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
 
 TYPED_TEST(Approx1, Approx1NearestArgsPos2D)
 {
-    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), AF_INTERP_NEAREST, AF_ERR_SIZE);
 }
 TYPED_TEST(Approx1, Approx1LinearArgsPos2D)
 {
-    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
+    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), AF_INTERP_LINEAR, AF_ERR_SIZE);
 }
 TYPED_TEST(Approx1, Approx1ArgsInterpBilinear)
 {
-    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 0, AF_INTERP_BILINEAR, AF_ERR_ARG);
+    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1.test"), AF_INTERP_BILINEAR, AF_ERR_ARG);
 }
 
 template<typename T>
-void approx1ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
+void approx1ArgsTestPrecision(string pTestFile, const unsigned , const af_interp_type method)
 {
     if (noDoubleTests<T>()) return;
     vector<dim4> numDims;
@@ -517,7 +517,7 @@ TEST(Approx1, CPPUsage)
     //! [ex_signal_approx1]
 
     // Input data array.
-    float input_vals[3] = {10.0, 20.0, 30.0};
+    float input_vals[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3, 1), input_vals);
     // [3 1 1 1]
     //     10.0000
@@ -525,7 +525,7 @@ TEST(Approx1, CPPUsage)
     //     30.0000
 
     // Array of positions to be found along the first dimension.
-    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float pv[5] = {0.0f, 0.5, 1.0f, 1.5, 2.0f};
     array pos(dim4(5,1), pv);
     // [5 1 1 1]
     //     0.0000
@@ -545,7 +545,7 @@ TEST(Approx1, CPPUsage)
 
     //! [ex_signal_approx1]
 
-    float civ[5] = {10.0, 15.0, 20.0, 25.0, 30.0};
+    float civ[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
     array interp_gold(dim4(5,1), civ);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 
@@ -556,9 +556,9 @@ TEST(Approx1, CPPUniformUsage)
 {
     //! [ex_signal_approx1_uniform]
 
-    float input_vals[9] = {10.0, 20.0, 30.0,
-                           40.0, 50.0, 60.0,
-                           70.0, 80.0, 90.0};
+    float input_vals[9] = {10.0f, 20.0f, 30.0f,
+                           40.0f, 50.0f, 60.0f,
+                           70.0f, 80.0f, 90.0f};
     array in(dim4(3, 3), input_vals);
     // [3 3 1 1]
     //     10.0000    40.0000    70.0000
@@ -567,7 +567,7 @@ TEST(Approx1, CPPUniformUsage)
 
     // Array of positions to be found along the interpolation
     // dimension, `interp_dim`.
-    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float pv[5] = {0.0f, 0.5, 1.0f, 1.5f, 2.0f};
     array pos(dim4(5,1), pv);
     // [5 1 1 1]
     //     0.0000
@@ -601,27 +601,27 @@ TEST(Approx1, CPPUniformUsage)
 
     //! [ex_signal_approx1_uniform]
 
-    float civ[15] = {10.0, 15.0, 20.0, 25.0, 30.0,
-                     40.0, 45.0, 50.0, 55.0, 60.0,
-                     70.0, 75.0, 80.0, 85.0, 90.0};
+    float civ[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
+                     40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
+                     70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
     array interp_gold_col(dim4(5,3), civ);
     ASSERT_ARRAYS_EQ(col_major_interp, interp_gold_col);
 
 
-    float riv[15] = {10.0, 20.0, 30.0,
-                     25.0, 35.0, 45.0,
-                     40.0, 50.0, 60.0,
-                     55.0, 65.0, 75.0,
-                     70.0, 80.0, 90.0};
+    float riv[15] = {10.0f, 20.0f, 30.0f,
+                     25.0f, 35.0f, 45.0f,
+                     40.0f, 50.0f, 60.0f,
+                     55.0f, 65.0f, 75.0f,
+                     70.0f, 80.0f, 90.0f};
     array interp_gold_row(dim4(3,5), riv);
     ASSERT_ARRAYS_EQ(row_major_interp, interp_gold_row);
 }
 
 TEST(Approx1, CPPDecimalStepRescaleGrid)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
-    float pv[5] = {0, 0.25, 0.5, 0.75, 1.0};
+    float pv[5] = {0.f, 0.25f, 0.5f, 0.75f, 1.0f};
     array pos(dim4(5,1), pv);
 
     const int interp_grid_start = 0;
@@ -630,18 +630,18 @@ TEST(Approx1, CPPDecimalStepRescaleGrid)
     array interp = approx1(in,
                            pos, interp_dim, interp_grid_start, interp_grid_step);
 
-    float iv[5] = {10.0, 15.0, 20.0, 25.0, 30.0};
+    float iv[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
     array interp_gold(dim4(5,1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
 TEST(Approx1, CPPRepeatPos)
 {
-    float inv[9] = {10.0, 20.0, 30.0,
-                    40.0, 50.0, 60.0,
-                    70.0, 80.0, 90.0};
+    float inv[9] = {10.0f, 20.0f, 30.0f,
+                    40.0f, 50.0f, 60.0f,
+                    70.0f, 80.0f, 90.0f};
     array in(dim4(3, 3), inv);
-    float pv[5] = {0.0, 0.5, 0.5, 1.5, 1.5};
+    float pv[5] = {0.0f, 0.5f, 0.5f, 1.5f, 1.5f};
     array pos(dim4(5,1), pv);
 
     const int interp_grid_start = 0;
@@ -650,9 +650,9 @@ TEST(Approx1, CPPRepeatPos)
     array interp = approx1(in,
                            pos, interp_dim, interp_grid_start, interp_grid_step);
 
-    float iv[15] = {10.0, 15.0, 15.0, 25.0, 25.0,
-                    40.0, 45.0, 45.0, 55.0, 55.0,
-                    70.0, 75.0, 75.0, 85.0, 85.0};
+    float iv[15] = {10.0f, 15.0f, 15.0f, 25.0f, 25.0f,
+                    40.0f, 45.0f, 45.0f, 55.0f, 55.0f,
+                    70.0f, 75.0f, 75.0f, 85.0f, 85.0f};
     array interp_gold(dim4(5,3), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
@@ -660,9 +660,9 @@ TEST(Approx1, CPPRepeatPos)
 
 TEST(Approx1, CPPNonMonotonicPos)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
-    float pv[5] = {0.5, 1.0, 1.5, 0.0, 2.0};
+    float pv[5] = {0.5f, 1.0f, 1.5f, 0.0f, 2.0f};
     array pos(dim4(5,1), pv);
 
     const int interp_grid_start = 0;
@@ -671,16 +671,16 @@ TEST(Approx1, CPPNonMonotonicPos)
     array interp = approx1(in,
                            pos, interp_dim, interp_grid_start, interp_grid_step);
 
-    float iv[5] = {15.0, 20.0, 25.0, 10.0, 30.0};
+    float iv[5] = {15.0f, 20.0f, 25.0f, 10.0f, 30.0f};
     array interp_gold(dim4(5,1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
 TEST(Approx1, CPPMismatchingIndexingDim)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
-    float pv[4] = {0.0, 0.5, 1.0, 2.0};
+    float pv[4] = {0.0f, 0.5f, 1.0f, 2.0f};
     array pos(dim4(1,4), pv);
 
     const int interp_grid_start = 0;
@@ -691,19 +691,19 @@ TEST(Approx1, CPPMismatchingIndexingDim)
                            pos, interp_dim, interp_grid_start, interp_grid_step,
                            AF_INTERP_LINEAR, off_grid);
 
-    float iv[12] = {10.0, 20.0, 30.0,
-                   -1.0, -1.0, -1.0,
-                   -1.0, -1.0, -1.0,
-                   -1.0, -1.0, -1.0};
+    float iv[12] = {10.0f, 20.0f, 30.0f,
+                   -1.0f, -1.0f, -1.0f,
+                   -1.0f, -1.0f, -1.0f,
+                   -1.0f, -1.0f, -1.0f};
     array interp_gold(dim4(3,4), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
 TEST(Approx1, CPPNegativeGridStart)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
-    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float pv[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
     array pos(dim4(5,1), pv);
 
     const int interp_grid_start = -1;
@@ -712,7 +712,7 @@ TEST(Approx1, CPPNegativeGridStart)
     array interp = approx1(in,
                            pos, interp_dim, interp_grid_start, interp_grid_step);
 
-    float iv[5] = {20.0, 25.0, 30.0, 0.0, 0.0};
+    float iv[5] = {20.0f, 25.0f, 30.0f, 0.0f, 0.0f};
     array interp_gold(dim4(5,1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 
@@ -720,9 +720,9 @@ TEST(Approx1, CPPNegativeGridStart)
 
 TEST(Approx1, CPPInterpolateBackwards)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
-    float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float pv[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
     array pos(dim4(3,1), pv);
 
     const int interp_grid_start = in.elements()-1;
@@ -731,7 +731,7 @@ TEST(Approx1, CPPInterpolateBackwards)
     array interp = approx1(in,
                            pos, interp_dim, interp_grid_start, interp_grid_step);
 
-    float iv[5] = {30.0, 25.0, 20.0, 15.0, 10.0};
+    float iv[5] = {30.0f, 25.0f, 20.0f, 15.0f, 10.0f};
     array interp_gold(dim4(3,1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
@@ -739,9 +739,9 @@ TEST(Approx1, CPPInterpolateBackwards)
 
 TEST(Approx1, CPPStartOffGridAndNegativeStep)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
-    float pv[5] = {0.0, -0.5, -1.0, -1.5, -2.0};
+    float pv[5] = {0.0f, -0.5f, -1.0f, -1.5f, -2.0f};
     array pos(dim4(5,1), pv);
 
     const int interp_grid_start = -1;
@@ -750,7 +750,7 @@ TEST(Approx1, CPPStartOffGridAndNegativeStep)
     array interp = approx1(in,
                            pos, interp_dim, interp_grid_start, interp_grid_step);
 
-    float iv[5] = {0.0, 0.0, 10.0, 15.0, 20.0};
+    float iv[5] = {0.0f, 0.0f, 10.0f, 15.0f, 20.0f};
     array interp_gold(dim4(5,1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
@@ -759,9 +759,9 @@ TEST(Approx1, CPPUniformInvalidStepSize)
 {
     try
     {
-        float inv[3] = {10.0, 20.0, 30.0};
+        float inv[3] = {10.0f, 20.0f, 30.0f};
         array in(dim4(3,1), inv);
-        float pv[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+        float pv[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
         array pos(dim4(5,1), pv);
 
         const int interp_grid_start = 0;
@@ -788,7 +788,11 @@ TEST(Approx1, CPPInfCheck)
     array interp = approx1(sampled, xo);
     array interp_augmented = join(1, xo, interp);
 
-    float goldv[9] = {af::Inf, af::Inf, af::Inf, af::Inf, 0.5, 0.625, 0.75, 0.875, 1.0};
+    float goldv[9] = {static_cast<float>(af::Inf),
+                      static_cast<float>(af::Inf),
+                      static_cast<float>(af::Inf),
+                      static_cast<float>(af::Inf),
+                      0.5f, 0.625f, 0.75f, 0.875f, 1.0f};
     array gold(dim4(9,1), goldv);
     interp(af::isInf(interp)) = 0;
     gold(af::isInf(gold)) = 0;
@@ -803,7 +807,7 @@ TEST(Approx1, CPPUniformInfCheck)
     array interp = approx1(sampled,
                            xo, 0,
                            0, 2);
-    float goldv[5] = {af::Inf, 20.0, 30.0, 40.0, 50.0};
+    float goldv[5] = {static_cast<float>(af::Inf), 20.0f, 30.0f, 40.0f, 50.0f};
     array gold(dim4(5,1), goldv);
     interp(af::isInf(interp)) = 0;
     gold(af::isInf(gold)) = 0;
@@ -812,7 +816,7 @@ TEST(Approx1, CPPUniformInfCheck)
 
 TEST(Approx1, CPPEmptyPos)
 {
-    float inv[3] = {10.0, 20.0, 30.0};
+    float inv[3] = {10.0f, 20.0f, 30.0f};
     array in(dim4(3,1), inv);
     array pos;
     array interp = approx1(in, pos);
@@ -823,7 +827,7 @@ TEST(Approx1, CPPEmptyPos)
 TEST(Approx1, CPPEmptyInput)
 {
     array in;
-    float pv[3] = {0.0, 1.0, 2.0};
+    float pv[3] = {0.0f, 1.0f, 2.0f};
     array pos(dim4(3,1), pv);
 
     array interp = approx1(in, pos);
@@ -842,13 +846,13 @@ TEST(Approx1, CPPEmptyPosAndInput)
 }
 
 TEST(Approx1, UseNullInitialOutput) {
-    float h_in[3] = {10, 20, 30};
+    float h_in[3] = {10.f, 20.f, 30.f};
     dim_t h_in_dims = 3;
 
     af_array in = 0;
     ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
 
-    float h_pos[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
     dim_t h_pos_dims = 5;
     af_array pos = 0;
     ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
@@ -860,13 +864,13 @@ TEST(Approx1, UseNullInitialOutput) {
 }
 
 TEST(Approx1, UseExistingOutputArray) {
-    float h_in[3] = {10, 20, 30};
+    float h_in[3] = {10.f, 20.f, 30.f};
     dim_t h_in_dims = 3;
 
     af_array in = 0;
     ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
 
-    float h_pos[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
     dim_t h_pos_dims = 5;
     af_array pos = 0;
     ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
@@ -889,20 +893,20 @@ TEST(Approx1, UseExistingOutputArray) {
 }
 
 TEST(Approx1, UseExistingOutputSlice) {
-    float h_in[3] = {10, 20, 30};
+    float h_in[3] = {10.f, 20.f, 30.f};
     dim_t h_in_dims = 3;
 
     af_array in = 0;
     ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
 
-    float h_pos[5] = {0.0, 0.5, 1.0, 1.5, 2.0};
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
     dim_t h_pos_dims = 5;
     af_array pos = 0;
     ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
 
-    float h_out[15] = {1.0, 1.5, 2.0, 2.5, 3.0,
-                       4.0, 4.5, 5.0, 5.5, 6.0,
-                       7.0, 7.5, 8.0, 8.5, 9.0};
+    float h_out[15] = {1.0f, 1.5f, 2.0f, 2.5f, 3.0f,
+                       4.0f, 4.5f, 5.0f, 5.5f, 6.0f,
+                       7.0f, 7.5f, 8.0f, 8.5f, 9.0f};
     dim_t h_out_dims[2] = {5, 3};
     af_array out = 0;
     ASSERT_SUCCESS(af_create_array(&out, &h_out[0], 2, &h_out_dims[0], f32));
@@ -917,9 +921,9 @@ TEST(Approx1, UseExistingOutputSlice) {
     vector<float> h_out_approx(nelems);
     ASSERT_SUCCESS(af_get_data_ptr(&h_out_approx.front(), out));
 
-    float h_gold[15] = {1.0, 1.5, 2.0, 2.5, 3.0,
-                        10.0, 15.0, 20.0, 25.0, 30.0,
-                        7.0, 7.5, 8.0, 8.5, 9.0};
+    float h_gold[15] = {1.0f, 1.5f, 2.0f, 2.5f, 3.0f,
+                        10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
+                        7.0f, 7.5f, 8.0f, 8.5f, 9.0f};
     af_array gold = 0;
     ASSERT_SUCCESS(af_create_array(&gold, &h_gold[0], 2, &h_out_dims[0], f32));
     ASSERT_ARRAYS_EQ(gold, out);
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 2ea2652108..53b46b947e 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -128,11 +128,9 @@ TYPED_TEST(Approx2, LinearBatch)
     approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2_batch.test"), 1, AF_INTERP_LINEAR);
 }
 
-///////////////////////////////////////////////////////////////////////////////
 // Test Argument Failure Cases
-///////////////////////////////////////////////////////////////////////////////
 template<typename T>
-void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, const af_err err)
+void approx2ArgsTest(string pTestFile, const af_interp_type method, const af_err err)
 {
     if (noDoubleTests<T>()) return;
     typedef typename dtype_traits<T>::base_type BT;
@@ -167,22 +165,23 @@ void approx2ArgsTest(string pTestFile, const unsigned resultIdx, const af_interp
 
 TYPED_TEST(Approx2, Approx2NearestArgsPos3D)
 {
-    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), AF_INTERP_NEAREST, AF_ERR_SIZE);
 }
 
 TYPED_TEST(Approx2, Approx2LinearArgsPos3D)
 {
-    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), 1, AF_INTERP_LINEAR, AF_ERR_SIZE);
+    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), AF_INTERP_LINEAR, AF_ERR_SIZE);
 }
 
 TYPED_TEST(Approx2, Approx2NearestArgsPosUnequal)
 {
-    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_unequal.test"), 0, AF_INTERP_NEAREST, AF_ERR_SIZE);
+    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_unequal.test"), AF_INTERP_NEAREST, AF_ERR_SIZE);
 }
 
 template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
 {
+    UNUSED(resultIdx);
     if (noDoubleTests<T>()) return;
     vector<dim4> numDims;
     vector<vector<T> > in;
diff --git a/test/dot.cpp b/test/dot.cpp
index f9f84c6bc5..742ba35cb6 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -91,7 +91,7 @@ void dotTest(string pTestFile, const int resultIdx,
 }
 
 template<typename T>
-void compare(double rval, double ival, T gold)
+void compare(double rval, double /*ival*/, T gold)
 {
     ASSERT_NEAR(gold, rval, 0.03);
 }
diff --git a/test/fft.cpp b/test/fft.cpp
index 6d18d8c8fe..fbffc7646b 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -344,7 +344,7 @@ INSTANTIATE_BATCH_TEST(fft2, C2C_Double_Pad, 2, false, cdouble, cdouble, string(
 /////////////////////////////////////// CPP ////////////////////////////////////
 //
 template<typename inType, typename outType, bool isInverse>
-void cppFFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
+void cppFFTTest(string pTestFile)
 {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
@@ -390,7 +390,7 @@ void cppFFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
 }
 
 template<typename inType, typename outType, bool isInverse>
-void cppDFTTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
+void cppDFTTest(string pTestFile)
 {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
diff --git a/test/flat.cpp b/test/flat.cpp
index f66cc96f04..7f622943b0 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -38,7 +38,6 @@ TEST(FlatTests, Test_flat_2D)
 {
     const int nx = 200;
     const int ny = 200;
-    const int num =  nx * ny;
 
     array in = randu(nx, ny);
     array out = flat(in);
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index d438de7aba..87094a8892 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -72,7 +72,6 @@ void invDeconvImageTest(string pTestFile, const float gamma, const af_inverse_de
 
         ASSERT_SUCCESS(af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
 
-        af_dtype itype = (af_dtype)af::dtype_traits<T>::af_type;
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
         ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index 8c8f9040ea..0a78752a34 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -32,7 +32,7 @@ using af::matmul;
 using af::max;
 
 template<typename T>
-void inverseTester(const int m, const int n, const int k, double eps)
+void inverseTester(const int m, const int n, double eps)
 {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
@@ -87,9 +87,9 @@ typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(Inverse, TestTypes);
 
 TYPED_TEST(Inverse, Square) {
-    inverseTester<TypeParam>(1000, 1000, 100, eps<TypeParam>());
+    inverseTester<TypeParam>(1000, 1000, eps<TypeParam>());
 }
 
 TYPED_TEST(Inverse, SquareMultiplePowerOfTwo) {
-    inverseTester<TypeParam>(2048, 2048, 512, eps<TypeParam>());
+    inverseTester<TypeParam>(2048, 2048, eps<TypeParam>());
 }
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 1e2072ce71..95472baeec 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -73,7 +73,6 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
 
         ASSERT_SUCCESS(af_gaussian_kernel(&kerArray, 13, 13, 2.25, 2.25));
 
-        af_dtype itype = (af_dtype)af::dtype_traits<T>::af_type;
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
         ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index fd2007a816..18b26ae7ea 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -137,13 +137,13 @@ meanvar_test_gen(string name, int in_index, int weight_index, af_var_bias bias,
         readTests<double, typename varOutType<double>::type, double> (TEST_DIR"/meanvar/meanvar.data", numDims_, in_, tests_);
 
         inputs.resize(in_.size());
-        for(int i = 0; i < in_.size(); i++) {
+        for(size_t i = 0; i < in_.size(); i++) {
           af_create_array(&inputs[i], &in_[i].front(),
                           numDims_[i].ndims(), numDims_[i].get(), f64);
         }
 
         outputs.resize(tests_.size());
-        for(int i = 0; i < tests_.size(); i++) {
+        for(size_t i = 0; i < tests_.size(); i++) {
             copy(tests_[i].begin(), tests_[i].end(), back_inserter(outputs[i]));
         }
     } else {
@@ -160,12 +160,12 @@ meanvar_test_gen(string name, int in_index, int weight_index, af_var_bias bias,
       };
 
       vector<double> large_(full_array_size);
-      for(int i = 0; i < large_.size(); i++) {
+      for(size_t i = 0; i < large_.size(); i++) {
           large_[i] = static_cast<double>(i);
       }
 
       inputs.resize(dimensions.size());
-      for(int i = 0; i < dimensions.size(); i++) {
+      for(size_t i = 0; i < dimensions.size(); i++) {
           af_array large_array = 0;
           af_create_array(&large_array, &large_.front(), 4, dimensions[i].data(), f64);
           inputs[i] = large_array;
@@ -222,7 +222,7 @@ large_test_values() {
     meanvar_test_gen<T>(    "Sample1Ddim1",           1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_LARGE),
     meanvar_test_gen<T>(    "Sample1Ddim2",           2,            -1,     AF_VARIANCE_SAMPLE,     2,           0,          1, MEANVAR_LARGE),
     meanvar_test_gen<T>(    "Sample2Ddim0",           3,            -1,     AF_VARIANCE_SAMPLE,     0,           2,          3, MEANVAR_LARGE),
-    // TODO(uamr) Add additional large tests
+    // TODO(umar) Add additional large tests
     //meanvar_test_gen<T>(    "Sample2Ddim1",           3,            -1,     AF_VARIANCE_SAMPLE,     1,           2,          3, MEANVAR_LARGE),
     //meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,     AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_LARGE),
       };
diff --git a/test/print_info.cpp b/test/print_info.cpp
index 8bbb80be36..0154ca9d95 100644
--- a/test/print_info.cpp
+++ b/test/print_info.cpp
@@ -11,7 +11,7 @@
 
 using namespace af;
 
-int main(int argc, const char** argv) {
+int main(int, const char**) {
     int backend = getAvailableBackends();
     if (backend & AF_BACKEND_OPENCL) {
         setBackend(AF_BACKEND_OPENCL);
diff --git a/test/random.cpp b/test/random.cpp
index c585f19c95..9f7f66dba0 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -447,8 +447,8 @@ void testRandomEnginePeriod(randomEngineType type)
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    uint elem = 1024*1024;
-    uint steps = 4*1024;
+    int elem = 1024*1024;
+    int steps = 4*1024;
     randomEngine r(type, 0);
 
     array first = randu(elem, ty, r);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 8f797034cd..1debe89f92 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -401,18 +401,21 @@ TEST(Reduce, Test_max_Global)
 template<typename T>
 void typed_assert_eq(T lhs, T rhs, bool both = true)
 {
+    UNUSED(both);
     ASSERT_EQ(lhs, rhs);
 }
 
 template<>
 void typed_assert_eq<float>(float lhs, float rhs, bool both)
 {
+    UNUSED(both);
     ASSERT_FLOAT_EQ(lhs, rhs);
 }
 
 template<>
 void typed_assert_eq<double>(double lhs, double rhs, bool both)
 {
+    UNUSED(both);
     ASSERT_DOUBLE_EQ(lhs, rhs);
 }
 
diff --git a/test/resize.cpp b/test/resize.cpp
index 1155383a20..9990df2290 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -83,7 +83,7 @@ TYPED_TEST(Resize, InvalidDims)
 template<typename T>
 void compare(T test, T out, double err, size_t i)
 {
-    ASSERT_EQ(abs(test - out) < 0.0001, true) << "at: " << i << endl
+    ASSERT_EQ(abs(test - out) < err, true) << "at: " << i << endl
              << "for test = : " << test << endl
              << "out data = : " << out << endl;
 }
@@ -91,7 +91,7 @@ void compare(T test, T out, double err, size_t i)
 template<>
 void compare<uintl>(uintl test, uintl out, double err, size_t i)
 {
-    ASSERT_EQ(((intl)test - (intl)out) < 0.0001, true) << "at: " << i << endl
+    ASSERT_EQ(((intl)test - (intl)out) < err, true) << "at: " << i << endl
              << "for test = : " << test << endl
              << "out data = : " << out << endl;
 }
@@ -99,7 +99,7 @@ void compare<uintl>(uintl test, uintl out, double err, size_t i)
 template<>
 void compare<uint>(uint test, uint out, double err, size_t i)
 {
-    ASSERT_EQ(((int)test - (int)out) < 0.0001, true) << "at: " << i << endl
+    ASSERT_EQ(((int)test - (int)out) < err, true) << "at: " << i << endl
              << "for test = : " << test << endl
              << "out data = : " << out << endl;
 }
@@ -107,7 +107,7 @@ void compare<uint>(uint test, uint out, double err, size_t i)
 template<>
 void compare<uchar>(uchar test, uchar out, double err, size_t i)
 {
-    ASSERT_EQ(((int)test - (int)out) < 0.0001, true) << "at: " << i << endl
+    ASSERT_EQ(((int)test - (int)out) < err, true) << "at: " << i << endl
              << "for test = : " << test << endl
              << "out data = : " << out << endl;
 }
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 2dd021eefd..2625e9c523 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -44,7 +44,7 @@ TYPED_TEST_CASE(Rotate, TestTypes);
 #define PI 3.1415926535897931f
 
 template<typename T>
-void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, const bool crop, const bool recenter, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
+void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, const bool crop, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
 {
     if (noDoubleTests<T>()) return;
 
@@ -99,61 +99,61 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define ROTATE_INIT(desc, file, resultIdx, angle, crop, recenter)                               \
+#define ROTATE_INIT(desc, file, resultIdx, angle, crop)                                         \
     TYPED_TEST(Rotate, desc)                                                                    \
     {                                                                                           \
-        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop, recenter);\
+        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop);  \
     }
 
-    ROTATE_INIT(Square180NoCropRecenter     , rotate1,  0, 180, false, true);
-    ROTATE_INIT(Square180CropRecenter       , rotate1,  1, 180, true , true);
-    ROTATE_INIT(Square90NoCropRecenter      , rotate1,  2, 90 , false, true);
-    ROTATE_INIT(Square90CropRecenter        , rotate1,  3, 90 , true , true);
-    ROTATE_INIT(Square45NoCropRecenter      , rotate1,  4, 45 , false, true);
-    ROTATE_INIT(Square45CropRecenter        , rotate1,  5, 45 , true , true);
-    ROTATE_INIT(Squarem45NoCropRecenter     , rotate1,  6,-45 , false, true);
-    ROTATE_INIT(Squarem45CropRecenter       , rotate1,  7,-45 , true , true);
-    ROTATE_INIT(Square60NoCropRecenter      , rotate1,  8, 60 , false, true);
-    ROTATE_INIT(Square60CropRecenter        , rotate1,  9, 60 , true , true);
-    ROTATE_INIT(Square30NoCropRecenter      , rotate1, 10, 30 , false, true);
-    ROTATE_INIT(Square30CropRecenter        , rotate1, 11, 30 , true , true);
-    ROTATE_INIT(Square15NoCropRecenter      , rotate1, 12, 15 , false, true);
-    ROTATE_INIT(Square15CropRecenter        , rotate1, 13, 15 , true , true);
-    ROTATE_INIT(Square10NoCropRecenter      , rotate1, 14, 10 , false, true);
-    ROTATE_INIT(Square10CropRecenter        , rotate1, 15, 10 , true , true);
-    ROTATE_INIT(Square01NoCropRecenter      , rotate1, 16,  1 , false, true);
-    ROTATE_INIT(Square01CropRecenter        , rotate1, 17,  1 , true , true);
-    ROTATE_INIT(Square360NoCropRecenter     , rotate1, 18, 360, false, true);
-    ROTATE_INIT(Square360CropRecenter       , rotate1, 19, 360, true , true);
-    ROTATE_INIT(Squarem180NoCropRecenter    , rotate1, 20,-180, false, true);
-    ROTATE_INIT(Squarem180CropRecenter      , rotate1, 21,-180, false, true);
-    ROTATE_INIT(Square00NoCropRecenter      , rotate1, 22,  0 , false, true);
-    ROTATE_INIT(Square00CropRecenter        , rotate1, 23,  0 , true , true);
-
-    ROTATE_INIT(Rectangle180NoCropRecenter     , rotate2,  0, 180, false, true);
-    ROTATE_INIT(Rectangle180CropRecenter       , rotate2,  1, 180, true , true);
-    ROTATE_INIT(Rectangle90NoCropRecenter      , rotate2,  2, 90 , false, true);
-    ROTATE_INIT(Rectangle90CropRecenter        , rotate2,  3, 90 , true , true);
-    ROTATE_INIT(Rectangle45NoCropRecenter      , rotate2,  4, 45 , false, true);
-    ROTATE_INIT(Rectangle45CropRecenter        , rotate2,  5, 45 , true , true);
-    ROTATE_INIT(Rectanglem45NoCropRecenter     , rotate2,  6,-45 , false, true);
-    ROTATE_INIT(Rectanglem45CropRecenter       , rotate2,  7,-45 , true , true);
-    ROTATE_INIT(Rectangle60NoCropRecenter      , rotate2,  8, 60 , false, true);
-    ROTATE_INIT(Rectangle60CropRecenter        , rotate2,  9, 60 , true , true);
-    ROTATE_INIT(Rectangle30NoCropRecenter      , rotate2, 10, 30 , false, true);
-    ROTATE_INIT(Rectangle30CropRecenter        , rotate2, 11, 30 , true , true);
-    ROTATE_INIT(Rectangle15NoCropRecenter      , rotate2, 12, 15 , false, true);
-    ROTATE_INIT(Rectangle15CropRecenter        , rotate2, 13, 15 , true , true);
-    ROTATE_INIT(Rectangle10NoCropRecenter      , rotate2, 14, 10 , false, true);
-    ROTATE_INIT(Rectangle10CropRecenter        , rotate2, 15, 10 , true , true);
-    ROTATE_INIT(Rectangle01NoCropRecenter      , rotate2, 16,  1 , false, true);
-    ROTATE_INIT(Rectangle01CropRecenter        , rotate2, 17,  1 , true , true);
-    ROTATE_INIT(Rectangle360NoCropRecenter     , rotate2, 18, 360, false, true);
-    ROTATE_INIT(Rectangle360CropRecenter       , rotate2, 19, 360, true , true);
-    ROTATE_INIT(Rectanglem180NoCropRecenter    , rotate2, 20,-180, false, true);
-    ROTATE_INIT(Rectanglem180CropRecenter      , rotate2, 21,-180, false, true);
-    ROTATE_INIT(Rectangle00NoCropRecenter      , rotate2, 22,  0 , false, true);
-    ROTATE_INIT(Rectangle00CropRecenter        , rotate2, 23,  0 , true , true);
+    ROTATE_INIT(Square180NoCropRecenter     , rotate1,  0, 180, false);
+    ROTATE_INIT(Square180CropRecenter       , rotate1,  1, 180, true );
+    ROTATE_INIT(Square90NoCropRecenter      , rotate1,  2, 90 , false);
+    ROTATE_INIT(Square90CropRecenter        , rotate1,  3, 90 , true );
+    ROTATE_INIT(Square45NoCropRecenter      , rotate1,  4, 45 , false);
+    ROTATE_INIT(Square45CropRecenter        , rotate1,  5, 45 , true );
+    ROTATE_INIT(Squarem45NoCropRecenter     , rotate1,  6,-45 , false);
+    ROTATE_INIT(Squarem45CropRecenter       , rotate1,  7,-45 , true );
+    ROTATE_INIT(Square60NoCropRecenter      , rotate1,  8, 60 , false);
+    ROTATE_INIT(Square60CropRecenter        , rotate1,  9, 60 , true );
+    ROTATE_INIT(Square30NoCropRecenter      , rotate1, 10, 30 , false);
+    ROTATE_INIT(Square30CropRecenter        , rotate1, 11, 30 , true );
+    ROTATE_INIT(Square15NoCropRecenter      , rotate1, 12, 15 , false);
+    ROTATE_INIT(Square15CropRecenter        , rotate1, 13, 15 , true );
+    ROTATE_INIT(Square10NoCropRecenter      , rotate1, 14, 10 , false);
+    ROTATE_INIT(Square10CropRecenter        , rotate1, 15, 10 , true );
+    ROTATE_INIT(Square01NoCropRecenter      , rotate1, 16,  1 , false);
+    ROTATE_INIT(Square01CropRecenter        , rotate1, 17,  1 , true );
+    ROTATE_INIT(Square360NoCropRecenter     , rotate1, 18, 360, false);
+    ROTATE_INIT(Square360CropRecenter       , rotate1, 19, 360, true );
+    ROTATE_INIT(Squarem180NoCropRecenter    , rotate1, 20,-180, false);
+    ROTATE_INIT(Squarem180CropRecenter      , rotate1, 21,-180, false);
+    ROTATE_INIT(Square00NoCropRecenter      , rotate1, 22,  0 , false);
+    ROTATE_INIT(Square00CropRecenter        , rotate1, 23,  0 , true );
+
+    ROTATE_INIT(Rectangle180NoCropRecenter     , rotate2,  0, 180, false);
+    ROTATE_INIT(Rectangle180CropRecenter       , rotate2,  1, 180, true );
+    ROTATE_INIT(Rectangle90NoCropRecenter      , rotate2,  2, 90 , false);
+    ROTATE_INIT(Rectangle90CropRecenter        , rotate2,  3, 90 , true );
+    ROTATE_INIT(Rectangle45NoCropRecenter      , rotate2,  4, 45 , false);
+    ROTATE_INIT(Rectangle45CropRecenter        , rotate2,  5, 45 , true );
+    ROTATE_INIT(Rectanglem45NoCropRecenter     , rotate2,  6,-45 , false);
+    ROTATE_INIT(Rectanglem45CropRecenter       , rotate2,  7,-45 , true );
+    ROTATE_INIT(Rectangle60NoCropRecenter      , rotate2,  8, 60 , false);
+    ROTATE_INIT(Rectangle60CropRecenter        , rotate2,  9, 60 , true );
+    ROTATE_INIT(Rectangle30NoCropRecenter      , rotate2, 10, 30 , false);
+    ROTATE_INIT(Rectangle30CropRecenter        , rotate2, 11, 30 , true );
+    ROTATE_INIT(Rectangle15NoCropRecenter      , rotate2, 12, 15 , false);
+    ROTATE_INIT(Rectangle15CropRecenter        , rotate2, 13, 15 , true );
+    ROTATE_INIT(Rectangle10NoCropRecenter      , rotate2, 14, 10 , false);
+    ROTATE_INIT(Rectangle10CropRecenter        , rotate2, 15, 10 , true );
+    ROTATE_INIT(Rectangle01NoCropRecenter      , rotate2, 16,  1 , false);
+    ROTATE_INIT(Rectangle01CropRecenter        , rotate2, 17,  1 , true );
+    ROTATE_INIT(Rectangle360NoCropRecenter     , rotate2, 18, 360, false);
+    ROTATE_INIT(Rectangle360CropRecenter       , rotate2, 19, 360, true );
+    ROTATE_INIT(Rectanglem180NoCropRecenter    , rotate2, 20,-180, false);
+    ROTATE_INIT(Rectanglem180CropRecenter      , rotate2, 21,-180, false);
+    ROTATE_INIT(Rectangle00NoCropRecenter      , rotate2, 22,  0 , false);
+    ROTATE_INIT(Rectangle00CropRecenter        , rotate2, 23,  0 , true );
 
 ////////////////////////////////// CPP //////////////////////////////////////
 //
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 916ec61955..0300ef72b3 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -48,7 +48,7 @@ TYPED_TEST_CASE(RotateLinear, TestTypes);
 #define PI 3.1415926535897931f
 
 template<typename T>
-void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, const bool crop, const bool recenter, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
+void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, const bool crop, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
 {
     if (noDoubleTests<T>()) return;
 
@@ -111,61 +111,61 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
     if(tempArray != 0) af_release_array(tempArray);
 }
 
-#define ROTATE_INIT(desc, file, resultIdx, angle, crop, recenter)       \
+#define ROTATE_INIT(desc, file, resultIdx, angle, crop)                 \
     TYPED_TEST(RotateLinear, desc)                                      \
     {                                                                   \
-        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop, recenter); \
+        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop); \
     }
 
-    ROTATE_INIT(Square180NoCropRecenter     , rotatelinear1,  0, 180, false, true);
-    ROTATE_INIT(Square180CropRecenter       , rotatelinear1,  1, 180, true , true);
-    ROTATE_INIT(Square90NoCropRecenter      , rotatelinear1,  2, 90 , false, true);
-    ROTATE_INIT(Square90CropRecenter        , rotatelinear1,  3, 90 , true , true);
-    ROTATE_INIT(Square45NoCropRecenter      , rotatelinear1,  4, 45 , false, true);
-    ROTATE_INIT(Square45CropRecenter        , rotatelinear1,  5, 45 , true , true);
-    ROTATE_INIT(Squarem45NoCropRecenter     , rotatelinear1,  6,-45 , false, true);
-    ROTATE_INIT(Squarem45CropRecenter       , rotatelinear1,  7,-45 , true , true);
-    ROTATE_INIT(Square60NoCropRecenter      , rotatelinear1,  8, 60 , false, true);
-    ROTATE_INIT(Square60CropRecenter        , rotatelinear1,  9, 60 , true , true);
-    ROTATE_INIT(Square30NoCropRecenter      , rotatelinear1, 10, 30 , false, true);
-    ROTATE_INIT(Square30CropRecenter        , rotatelinear1, 11, 30 , true , true);
-    ROTATE_INIT(Square15NoCropRecenter      , rotatelinear1, 12, 15 , false, true);
-    ROTATE_INIT(Square15CropRecenter        , rotatelinear1, 13, 15 , true , true);
-    ROTATE_INIT(Square10NoCropRecenter      , rotatelinear1, 14, 10 , false, true);
-    ROTATE_INIT(Square10CropRecenter        , rotatelinear1, 15, 10 , true , true);
-    ROTATE_INIT(Square01NoCropRecenter      , rotatelinear1, 16,  1 , false, true);
-    ROTATE_INIT(Square01CropRecenter        , rotatelinear1, 17,  1 , true , true);
-    ROTATE_INIT(Square360NoCropRecenter     , rotatelinear1, 18, 360, false, true);
-    ROTATE_INIT(Square360CropRecenter       , rotatelinear1, 19, 360, true , true);
-    ROTATE_INIT(Squarem180NoCropRecenter    , rotatelinear1, 20,-180, false, true);
-    ROTATE_INIT(Squarem180CropRecenter      , rotatelinear1, 21,-180, false, true);
-    ROTATE_INIT(Square00NoCropRecenter      , rotatelinear1, 22,  0 , false, true);
-    ROTATE_INIT(Square00CropRecenter        , rotatelinear1, 23,  0 , true , true);
-
-    ROTATE_INIT(Rectangle180NoCropRecenter     , rotatelinear2,  0, 180, false, true);
-    ROTATE_INIT(Rectangle180CropRecenter       , rotatelinear2,  1, 180, true , true);
-    ROTATE_INIT(Rectangle90NoCropRecenter      , rotatelinear2,  2, 90 , false, true);
-    ROTATE_INIT(Rectangle90CropRecenter        , rotatelinear2,  3, 90 , true , true);
-    ROTATE_INIT(Rectangle45NoCropRecenter      , rotatelinear2,  4, 45 , false, true);
-    ROTATE_INIT(Rectangle45CropRecenter        , rotatelinear2,  5, 45 , true , true);
-    ROTATE_INIT(Rectanglem45NoCropRecenter     , rotatelinear2,  6,-45 , false, true);
-    ROTATE_INIT(Rectanglem45CropRecenter       , rotatelinear2,  7,-45 , true , true);
-    ROTATE_INIT(Rectangle60NoCropRecenter      , rotatelinear2,  8, 60 , false, true);
-    ROTATE_INIT(Rectangle60CropRecenter        , rotatelinear2,  9, 60 , true , true);
-    ROTATE_INIT(Rectangle30NoCropRecenter      , rotatelinear2, 10, 30 , false, true);
-    ROTATE_INIT(Rectangle30CropRecenter        , rotatelinear2, 11, 30 , true , true);
-    ROTATE_INIT(Rectangle15NoCropRecenter      , rotatelinear2, 12, 15 , false, true);
-    ROTATE_INIT(Rectangle15CropRecenter        , rotatelinear2, 13, 15 , true , true);
-    ROTATE_INIT(Rectangle10NoCropRecenter      , rotatelinear2, 14, 10 , false, true);
-    ROTATE_INIT(Rectangle10CropRecenter        , rotatelinear2, 15, 10 , true , true);
-    ROTATE_INIT(Rectangle01NoCropRecenter      , rotatelinear2, 16,  1 , false, true);
-    ROTATE_INIT(Rectangle01CropRecenter        , rotatelinear2, 17,  1 , true , true);
-    ROTATE_INIT(Rectangle360NoCropRecenter     , rotatelinear2, 18, 360, false, true);
-    ROTATE_INIT(Rectangle360CropRecenter       , rotatelinear2, 19, 360, true , true);
-    ROTATE_INIT(Rectanglem180NoCropRecenter    , rotatelinear2, 20,-180, false, true);
-    ROTATE_INIT(Rectanglem180CropRecenter      , rotatelinear2, 21,-180, false, true);
-    ROTATE_INIT(Rectangle00NoCropRecenter      , rotatelinear2, 22,  0 , false, true);
-    ROTATE_INIT(Rectangle00CropRecenter        , rotatelinear2, 23,  0 , true , true);
+    ROTATE_INIT(Square180NoCropRecenter     , rotatelinear1,  0, 180, false);
+    ROTATE_INIT(Square180CropRecenter       , rotatelinear1,  1, 180, true );
+    ROTATE_INIT(Square90NoCropRecenter      , rotatelinear1,  2, 90 , false);
+    ROTATE_INIT(Square90CropRecenter        , rotatelinear1,  3, 90 , true );
+    ROTATE_INIT(Square45NoCropRecenter      , rotatelinear1,  4, 45 , false);
+    ROTATE_INIT(Square45CropRecenter        , rotatelinear1,  5, 45 , true );
+    ROTATE_INIT(Squarem45NoCropRecenter     , rotatelinear1,  6,-45 , false);
+    ROTATE_INIT(Squarem45CropRecenter       , rotatelinear1,  7,-45 , true );
+    ROTATE_INIT(Square60NoCropRecenter      , rotatelinear1,  8, 60 , false);
+    ROTATE_INIT(Square60CropRecenter        , rotatelinear1,  9, 60 , true );
+    ROTATE_INIT(Square30NoCropRecenter      , rotatelinear1, 10, 30 , false);
+    ROTATE_INIT(Square30CropRecenter        , rotatelinear1, 11, 30 , true );
+    ROTATE_INIT(Square15NoCropRecenter      , rotatelinear1, 12, 15 , false);
+    ROTATE_INIT(Square15CropRecenter        , rotatelinear1, 13, 15 , true );
+    ROTATE_INIT(Square10NoCropRecenter      , rotatelinear1, 14, 10 , false);
+    ROTATE_INIT(Square10CropRecenter        , rotatelinear1, 15, 10 , true );
+    ROTATE_INIT(Square01NoCropRecenter      , rotatelinear1, 16,  1 , false);
+    ROTATE_INIT(Square01CropRecenter        , rotatelinear1, 17,  1 , true );
+    ROTATE_INIT(Square360NoCropRecenter     , rotatelinear1, 18, 360, false);
+    ROTATE_INIT(Square360CropRecenter       , rotatelinear1, 19, 360, true );
+    ROTATE_INIT(Squarem180NoCropRecenter    , rotatelinear1, 20,-180, false);
+    ROTATE_INIT(Squarem180CropRecenter      , rotatelinear1, 21,-180, false);
+    ROTATE_INIT(Square00NoCropRecenter      , rotatelinear1, 22,  0 , false);
+    ROTATE_INIT(Square00CropRecenter        , rotatelinear1, 23,  0 , true );
+
+    ROTATE_INIT(Rectangle180NoCropRecenter     , rotatelinear2,  0, 180, false);
+    ROTATE_INIT(Rectangle180CropRecenter       , rotatelinear2,  1, 180, true );
+    ROTATE_INIT(Rectangle90NoCropRecenter      , rotatelinear2,  2, 90 , false);
+    ROTATE_INIT(Rectangle90CropRecenter        , rotatelinear2,  3, 90 , true );
+    ROTATE_INIT(Rectangle45NoCropRecenter      , rotatelinear2,  4, 45 , false);
+    ROTATE_INIT(Rectangle45CropRecenter        , rotatelinear2,  5, 45 , true );
+    ROTATE_INIT(Rectanglem45NoCropRecenter     , rotatelinear2,  6,-45 , false);
+    ROTATE_INIT(Rectanglem45CropRecenter       , rotatelinear2,  7,-45 , true );
+    ROTATE_INIT(Rectangle60NoCropRecenter      , rotatelinear2,  8, 60 , false);
+    ROTATE_INIT(Rectangle60CropRecenter        , rotatelinear2,  9, 60 , true );
+    ROTATE_INIT(Rectangle30NoCropRecenter      , rotatelinear2, 10, 30 , false);
+    ROTATE_INIT(Rectangle30CropRecenter        , rotatelinear2, 11, 30 , true );
+    ROTATE_INIT(Rectangle15NoCropRecenter      , rotatelinear2, 12, 15 , false);
+    ROTATE_INIT(Rectangle15CropRecenter        , rotatelinear2, 13, 15 , true );
+    ROTATE_INIT(Rectangle10NoCropRecenter      , rotatelinear2, 14, 10 , false);
+    ROTATE_INIT(Rectangle10CropRecenter        , rotatelinear2, 15, 10 , true );
+    ROTATE_INIT(Rectangle01NoCropRecenter      , rotatelinear2, 16,  1 , false);
+    ROTATE_INIT(Rectangle01CropRecenter        , rotatelinear2, 17,  1 , true );
+    ROTATE_INIT(Rectangle360NoCropRecenter     , rotatelinear2, 18, 360, false);
+    ROTATE_INIT(Rectangle360CropRecenter       , rotatelinear2, 19, 360, true );
+    ROTATE_INIT(Rectanglem180NoCropRecenter    , rotatelinear2, 20,-180, false);
+    ROTATE_INIT(Rectanglem180CropRecenter      , rotatelinear2, 21,-180, false);
+    ROTATE_INIT(Rectangle00NoCropRecenter      , rotatelinear2, 22,  0 , false);
+    ROTATE_INIT(Rectangle00CropRecenter        , rotatelinear2, 23,  0 , true );
 
 ////////////////////////////////// CPP //////////////////////////////////////
 
diff --git a/test/select.cpp b/test/select.cpp
index 4328dbf666..9c85a18432 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -381,7 +381,7 @@ TEST_P(Select_, Batch) {
     vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
 
     vector<float> gold(params.out.elements());
-    for(int i = 0; i < gold.size(); i++) {
+    for(size_t i = 0; i < gold.size(); i++) {
         gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
         ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
     }
@@ -452,7 +452,7 @@ TEST_P(SelectLR_, BatchL) {
     vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
 
     vector<float> gold(params.out.elements());
-    for(int i = 0; i < gold.size(); i++) {
+    for(size_t i = 0; i < gold.size(); i++) {
         gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
         ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
     }
@@ -474,7 +474,7 @@ TEST_P(SelectLR_, BatchR) {
     vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
 
     vector<float> gold(params.out.elements());
-    for(int i = 0; i < gold.size(); i++) {
+    for(size_t i = 0; i < gold.size(); i++) {
         gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
         ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
     }
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 70f2f25679..b144b464f6 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -67,10 +67,7 @@ void testSobelDerivatives(string pTestFile)
     vector<To> currDXGoldBar = tests[0];
     vector<To> currDYGoldBar = tests[1];
 
-    size_t nElems = currDXGoldBar.size();
     ASSERT_VEC_ARRAY_EQ(currDXGoldBar, dims, dxArray);
-
-    nElems = currDYGoldBar.size();
     ASSERT_VEC_ARRAY_EQ(currDYGoldBar, dims, dyArray);
 
     // cleanup
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index 981e432e55..a8102095a3 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -75,12 +75,12 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
 
     ASSERT_SUCCESS(af_sort_by_key(&okeyArray, &ovalArray, ikeyArray, ivalArray, 0, dir));
 
-    size_t nElems = tests[resultIdx0].size();
-
     // Compare result
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, okeyArray);
 
-#ifndef AF_OPENCL
+#ifdef AF_OPENCL
+    UNUSED(resultIdx1);
+#else
     // Compare result
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, ovalArray);
 #endif
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 03b7a71518..8811bcf53b 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -76,7 +76,9 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     vector<T> sxTest(tests[resultIdx0].begin(), tests[resultIdx0].end());
     ASSERT_VEC_ARRAY_EQ(sxTest, idims, sxArray);
 
-#ifndef AF_OPENCL
+#ifdef AF_OPENCL
+    UNUSED(resultIdx1);
+#else
     vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
     ASSERT_VEC_ARRAY_EQ(ixTest, idims, ixArray);
 #endif
@@ -131,8 +133,6 @@ TEST(SortIndex, CPPDim0)
     array outValues, outIndices;
     sort(outValues, outIndices, input, 0, dir);
 
-    size_t nElems = tests[resultIdx0].size();
-
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
     vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
@@ -162,8 +162,6 @@ TEST(SortIndex, CPPDim1)
     outValues  = reorder(outValues,  1, 0, 2, 3);
     outIndices = reorder(outIndices, 1, 0, 2, 3);
 
-    size_t nElems = tests[resultIdx0].size();
-
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
     vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
@@ -192,7 +190,6 @@ TEST(SortIndex, CPPDim2)
 
     outValues  = reorder(outValues,  2, 0, 1, 3);
     outIndices = reorder(outIndices, 2, 0, 1, 3);
-    size_t nElems = tests[resultIdx0].size();
 
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 0aaf5ba96d..718ae32c52 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -34,6 +34,8 @@ typedef unsigned char  uchar;
 typedef unsigned int   uint;
 typedef unsigned short ushort;
 
+#define UNUSED(expr) do { (void)(expr); } while (0)
+
 namespace {
 
 std::string readNextNonEmptyLine(std::ifstream &file)
@@ -638,7 +640,6 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
 
         uint valIdx = vecStartIdx + i;
 
-        T outVal = hOut[valIdx];
         if (valIdx == idx) {
             tmpOs << "[" << +hOut[valIdx] << "]";
         }
@@ -649,7 +650,6 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
         int outLen = tmpOs.str().length();
         tmpOs.str(std::string());
 
-        T goldVal = hGold[valIdx];
         if (valIdx == idx) {
             tmpOs << "[" << +hGold[valIdx] << "]";
         }
@@ -697,9 +697,9 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T>& a, af::dim4 aDims,
                                       const std::vector<T>& b, af::dim4 bDims,
                                       float maxAbsDiff, IntegerTag) {
+    UNUSED(maxAbsDiff);
     typedef typename std::vector<T>::const_iterator iter;
     std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());
-    iter aItr = mismatches.first;
     iter bItr = mismatches.second;
 
     if (bItr == b.end()) {
@@ -750,7 +750,6 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
 
         af::dim4 aStrides = calcStrides(aDims);
-        af::dim4 bStrides = calcStrides(bDims);
 
         ::testing::AssertionResult result =
         ::testing::AssertionFailure()
@@ -799,16 +798,12 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
             << "Expected: " << aName << "(" << a.type() << ")";
 
     af::dtype arrDtype = aType;
-    const uint ndimIds = 4;
     if (a.dims() != b.dims())
         return ::testing::AssertionFailure()
             << "SIZE MISMATCH: \n"
             << "  Actual: " << bName << "([" << b.dims() << "])\n"
             << "Expected: " << aName << "([" << a.dims() << "])";
 
-    dim_t nElems = a.elements();
-    af::dim4 arrDims = a.dims();
-
     switch (arrDtype) {
     case f32: return elemWiseEq<float>              (aName, bName, a, b, maxAbsDiff); break;
     case c32: return elemWiseEq<af::cfloat>         (aName, bName, a, b, maxAbsDiff); break;
@@ -845,7 +840,6 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsNam
             << "Expected: " << aName << "(" << aDtype << ")";
     }
 
-    const uint ndimIds = 4;
     if(aDims != b.dims()) {
         return ::testing::AssertionFailure()
             << "SIZE MISMATCH:\n"
@@ -854,7 +848,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsNam
     }
 
     // In case vector<T> a.size() != aDims.elements()
-    if (hA.size() != aDims.elements())
+    if (hA.size() != static_cast<size_t>(aDims.elements()))
         return ::testing::AssertionFailure()
             << "SIZE MISMATCH:\n"
             << "  Actual: " << aDimsName << "([" << aDims << "] => "
@@ -898,6 +892,7 @@ ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
                                            const af::array& a, const af::array& b,
                                            float maxAbsDiff) {
+    UNUSED(maxAbsDiffName);
     return assertArrayEq(aName, bName, a, b, maxAbsDiff);
 }
 
@@ -908,6 +903,7 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
                                            const std::vector<T>& hA, af::dim4 aDims,
                                            const af::array& b,
                                            float maxAbsDiff) {
+    UNUSED(maxAbsDiffName);
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
 }
 
diff --git a/test/topk.cpp b/test/topk.cpp
index f7ec4d5db2..9088bbcb37 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -49,9 +49,9 @@ typedef ::testing::Types<float, double, int, uint> TestTypes;
 TYPED_TEST_CASE(TopK, TestTypes);
 
 template<typename T>
-void topkTest(const unsigned ndims, const dim_t* dims,
-                   const int k, const int dim,
-                   const af_topk_function order)
+void topkTest(const int ndims, const dim_t* dims,
+              const unsigned k, const int dim,
+              const af_topk_function order)
 {
     af_dtype dtype = (af_dtype)dtype_traits<T>::af_type;
 
@@ -63,7 +63,7 @@ void topkTest(const unsigned ndims, const dim_t* dims,
     for (int i=0; i<ndims; i++)
     {
         ielems *= dims[i];
-        oelems *= (i==dim ? k : dims[i]);
+        oelems *= (i == dim ? k : dims[i]);
     }
 
     size_t bCount = ielems/dims[dim];
@@ -119,7 +119,7 @@ void topkTest(const unsigned ndims, const dim_t* dims,
     ASSERT_SUCCESS(af_get_data_ptr((void*)hovals.data(), output));
     ASSERT_SUCCESS(af_get_data_ptr((void*)hoidxs.data(), outindex));
 
-    for (int i=0; i<oelems; ++i)
+    for (size_t i=0; i<oelems; ++i)
     {
         switch(dtype)
         {
@@ -285,7 +285,7 @@ TEST_P(TopKParams, CPP) {
       in = -in + (d0 * d1-1);
     }
     array val, idx;
-    topk(val, idx, in, k, 0, order);
+    topk(val, idx, in, k, dim, order);
 
     vector<float>    hval(k * d1);
     vector<unsigned> hidx(k * d1);

From b7e25632d65a811203063c2751efeaf99406176d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 12 Nov 2018 14:29:15 -0500
Subject: [PATCH 1553/2677] Fix pinverse call for the unified backend

---
 src/api/unified/lapack.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/unified/lapack.cpp b/src/api/unified/lapack.cpp
index 7c0927d2d9..5f6a736204 100644
--- a/src/api/unified/lapack.cpp
+++ b/src/api/unified/lapack.cpp
@@ -83,7 +83,7 @@ af_err af_pinverse(af_array *out, const af_array in, const double tol,
                    const af_mat_prop options)
 {
     CHECK_ARRAYS(in);
-    return CALL(out, in, options);
+    return CALL(out, in, tol, options);
 }
 
 af_err af_rank(unsigned *rank, const af_array in, const double tol)

From 4c6eba787e88170495e6ff4b16592d0f8302c7b2 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Mon, 19 Nov 2018 14:18:51 -0500
Subject: [PATCH 1554/2677] Fix memory leaks on pinverse C tests

---
 test/pinverse.cpp | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 85fb278797..0059508c5f 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -283,27 +283,29 @@ TEST(Pinverse, CustomTol) {
 
 TEST(Pinverse, C) {
     array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
-    af_array inpinv = 0, out = 0;
+    af_array inpinv = 0, identity = 0, out = 0;
     ASSERT_SUCCESS(af_pinverse(&inpinv, in.get(), 1e-6, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&out, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&out, out, in.get(), AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&identity, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&out, identity, in.get(), AF_MAT_NONE, AF_MAT_NONE));
 
     ASSERT_ARRAYS_NEAR(in.get(), out, eps<float>());
 
     ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(identity));
     ASSERT_SUCCESS(af_release_array(inpinv));
 }
 
 TEST(Pinverse, C_CustomTol) {
     array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
-    af_array inpinv = 0, out = 0;
+    af_array inpinv = 0, identity = 0, out = 0;
     ASSERT_SUCCESS(af_pinverse(&inpinv, in.get(), 1e-12, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&out, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&out, out, in.get(), AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&identity, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(af_matmul(&out, identity, in.get(), AF_MAT_NONE, AF_MAT_NONE));
 
     ASSERT_ARRAYS_NEAR(in.get(), out, eps<float>());
 
     ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(identity));
     ASSERT_SUCCESS(af_release_array(inpinv));
 }
 

From 2c6b5c04af2e1fdcdeabd7127208da38de0260db Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Tue, 20 Nov 2018 03:24:36 -0500
Subject: [PATCH 1555/2677] svdInPlace: Flip the inequality on the dims
 restriction (#2331)

* Remove dims restriction from svdInPlace
* Restore dims assertion, but flipped the direction of the inequality. Updated docs.
---
 docs/details/lapack.dox |  22 ++++----
 include/af/lapack.h     |  32 ++++++++----
 src/api/c/svd.cpp       |   3 +-
 test/svd_dense.cpp      | 111 ++++++++++++++++++++++++++++++++++------
 4 files changed, 132 insertions(+), 36 deletions(-)

diff --git a/docs/details/lapack.dox b/docs/details/lapack.dox
index 2ce6c85e72..8bf5d5a5ea 100644
--- a/docs/details/lapack.dox
+++ b/docs/details/lapack.dox
@@ -127,21 +127,23 @@ When memory is a concern, users can perform Cholesky decomposition in place as s
 
 \ingroup lapack_factor_mat
 
-\brief Perform Singular Value Decomposition
+\brief Computes the singular value decomposition of a matrix
 
-This function factorizes a  matrix **A** into two unitary matrices **U** and **Vt**, and a diagonal matrix **S** such that
-
-     \f$A = U * S * Vt\f$
-
-If **A** has **M** rows and **N** columns, **U** is of the size **M x M** , **V** is of size **N x N**, and **S** is of size **M x N**
-
-The arrayfire function only returns the non zero diagonal elements of **S**. To reconstruct the original matrix **A** from the individual factors, the following code snuppet can be used:
+This function factorizes a matrix \f$A\f$ into two unitary matrices, \f$U\f$ and
+\f$V^T\f$, and a diagonal matrix \f$S\f$, such that \f$A = USV^T\f$. If \f$A\f$
+has \f$M\f$ rows and \f$N\f$ columns (\f$M \times N\f$), then \f$U\f$ will be
+\f$M \times M\f$, \f$V\f$ will be \f$N \times N\f$, and \f$S\f$ will be
+\f$M \times N\f$. However, for \f$S\f$, this function only returns the non-zero
+diagonal elements as a sorted (in descending order) 1D array.
 
+To reconstruct the original matrix \f$A\f$ from the individual factors, the
+following code snippet can be used:
 
 \snippet test/svd_dense.cpp ex_svd_reg
 
-When memory is a concern, and **A** is dispensible, \ref svdInPlace() can be used
-
+When memory is a concern, and \f$A\f$ is dispensable, \ref svdInPlace() can be
+used. However, this in-place version is currently limited to input arrays where
+\f$M \geq N\f$.
 
 =======================================================================
 
diff --git a/include/af/lapack.h b/include/af/lapack.h
index 5c43a22f36..53386f7277 100644
--- a/include/af/lapack.h
+++ b/include/af/lapack.h
@@ -30,12 +30,18 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for SVD decomposition
+       C++ Interface for SVD decomposition (in-place)
 
-       \param[out] u is the output array containing U
-       \param[out] s is the output array containing the diagonal values of sigma, (singular values of the input matrix))
-       \param[out] vt is the output array containing V^H
-       \param[inout] in is the input matrix and will contain random data after this operation
+       \param[out]    u is the output array containing U
+       \param[out]    s is the output array containing the diagonal values of sigma,
+                        (singular values of the input matrix))
+       \param[out]    vt is the output array containing V^H
+       \param[in,out] in is the input matrix and will contain random data after
+                         this operation
+
+       \note Currently, \p in is limited to arrays where `dim0` \f$\geq\f$ `dim1`
+       \note This is best used when minimizing memory usage and \p in is
+             dispensable
 
        \ingroup lapack_factor_func_svd
     */
@@ -294,12 +300,18 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for SVD decomposition
+       C Interface for SVD decomposition (in-place)
 
-       \param[out] u is the output array containing U
-       \param[out] s is the output array containing the diagonal values of sigma, (singular values of the input matrix))
-       \param[out] vt is the output array containing V^H
-       \param[inout] in is the input matrix that will contain random data after this operation
+       \param[out]    u  is the output array containing U
+       \param[out]    s  is the output array containing the diagonal values of
+                         sigma, (singular values of the input matrix))
+       \param[out]    vt is the output array containing V^H
+       \param[in,out] in is the input matrix that will contain random data after
+                         this operation
+
+       \note Currently, \p in is limited to arrays where `dim0` \f$\geq\f$ `dim1`
+       \note This is best used when minimizing memory usage and \p in is
+             dispensable
 
        \ingroup lapack_factor_func_svd
     */
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index 43346a90b1..e0e9423c48 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -107,7 +107,6 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
         const ArrayInfo& info = getInfo(in);
         af::dim4 dims = info.dims();
 
-        DIM_ASSERT(3, dims[0] <= dims[1]);
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
         af_dtype type = info.getType();
 
@@ -118,6 +117,7 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
             return AF_SUCCESS;
         }
 
+        DIM_ASSERT(3, dims[0] >= dims[1]);
 
         switch (type) {
         case f64:
@@ -131,6 +131,7 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
             break;
         case c32:
             svdInPlace<cfloat>(s, u, vt, in);
+
             break;
         default:
             TYPE_ERROR(1, type);
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index aab3c34b6b..cb6368b8d8 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -18,19 +18,21 @@
 #include <string>
 #include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
+using af::dim4;
 using af::dtype;
 using af::dtype_traits;
+using af::iota;
 using af::randu;
 using af::seq;
 using af::span;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
 class svd : public ::testing::Test
@@ -59,7 +61,6 @@ template<> double get_val<cdouble>(cdouble val)
 template<typename T>
 void svdTest(const int M, const int N)
 {
-
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -80,19 +81,60 @@ void svdTest(const int M, const int N)
     array AA = matmul(UU, SS, VV);
     //! [ex_svd_reg]
 
-    vector<T> hA(M * N);
-    vector<T> hAA(M * N);
+#if defined(OS_MAC)
+    ASSERT_ARRAYS_NEAR(A, AA, 3E-3);
+#else
+    ASSERT_ARRAYS_NEAR(A, AA, 1E-3);
+#endif
+}
+
+template<typename T>
+void svdInPlaceTest(const int M, const int N)
+{
+    if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
+    dtype ty = (dtype)dtype_traits<T>::af_type;
+
+    array A = randu(M, N, ty);
+    array A_copy = A.copy();
+
+    array U, S, Vt;
+    af::svdInPlace(U, S, Vt, A);
 
-    A.host(&hA[0]);
-    AA.host(&hAA[0]);
+    const int MN = std::min(M, N);
+
+    array UU = U(span, seq(MN));
+    array SS = diag(S, 0, false).as(ty);
+    array VV = Vt(seq(MN), span);
+
+    array AA = matmul(UU, SS, VV);
 
-    for (int i = 0; i < M * N; i++) {
 #if defined(OS_MAC)
-        ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 3E-3);
+    ASSERT_ARRAYS_NEAR(A_copy, AA, 3E-3);
 #else
-        ASSERT_NEAR(get_val(hA[i]), get_val(hAA[i]), 1E-3);
+    ASSERT_ARRAYS_NEAR(A_copy, AA, 1E-3);
 #endif
-    }
+}
+
+template<typename T>
+void checkInPlaceSameResults(const int M, const int N)
+{
+    if (noDoubleTests<T>()) return;
+    if (noLAPACKTests()) return;
+
+    dtype ty = (dtype)dtype_traits<T>::af_type;
+
+    array in = randu(dim4(M, N), ty);
+    array u, s, v;
+    af::svd(u, s, v, in);
+
+    array uu, ss, vv;
+    af::svdInPlace(uu, ss, vv, in);
+
+    ASSERT_ARRAYS_EQ(u, uu);
+    ASSERT_ARRAYS_EQ(s, ss);
+    ASSERT_ARRAYS_EQ(v, vv);
 }
 
 TYPED_TEST(svd, Square)
@@ -109,3 +151,42 @@ TYPED_TEST(svd, Rect1)
 {
     svdTest<TypeParam>(300, 500);
 }
+
+TYPED_TEST(svd, InPlaceSquare)
+{
+    svdInPlaceTest<TypeParam>(500, 500);
+}
+
+TYPED_TEST(svd, InPlaceRect0)
+{
+    svdInPlaceTest<TypeParam>(500, 300);
+}
+
+// dim0 < dim1 case not supported for now
+// TYPED_TEST(svd, InPlaceRect1)
+// {
+//     svdInPlaceTest<TypeParam>(300, 500);
+// }
+
+TYPED_TEST(svd, InPlaceSameResultsSquare)
+{
+    checkInPlaceSameResults<TypeParam>(10, 10);
+}
+
+TYPED_TEST(svd, InPlaceSameResultsRect0)
+{
+    checkInPlaceSameResults<TypeParam>(10, 8);
+}
+
+// dim0 < dim1 case not supported for now
+// TYPED_TEST(svd, InPlaceSameResultsRect1)
+// {
+//     checkInPlaceSameResults<TypeParam>(8, 10);
+// }
+
+TEST(svd, InPlaceRect0_Exception) {
+    array in = randu(3, 5);
+    array u, s, v;
+    EXPECT_THROW(svdInPlace(u, s, v, in), af::exception);
+}
+

From e5d763854a57b1823b7c78a342df297ba6830d49 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 21 Nov 2018 12:32:07 +0530
Subject: [PATCH 1556/2677] Fix af_window typedef in graphics header

af_window was earlier a typedef to normal integral type instead of
using either intptr_t/uintptr_t or void*. This is not portable to
other architectures.
---
 include/af/graphics.h      |  2 +-
 src/api/c/hist.cpp         |  2 +-
 src/api/c/image.cpp        |  2 +-
 src/api/c/plot.cpp         |  6 +++---
 src/api/c/surface.cpp      |  2 +-
 src/api/c/vector_field.cpp |  6 +++---
 src/api/c/window.cpp       | 26 +++++++++++++-------------
 7 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index 59faab127d..a1dbf5d3da 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -12,7 +12,7 @@
 #include <af/defines.h>
 #include <af/array.h>
 
-typedef unsigned long long af_window;
+typedef void* af_window;
 
 typedef struct {
     int row;
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 684f6c23e1..3b0977c6ed 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -93,7 +93,7 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
 
         ARG_ASSERT(0, Xinfo.isVector());
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 4200f91a4e..f681e9bdbe 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -90,7 +90,7 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
         DIM_ASSERT(0, in_dims[2] == 1 || in_dims[2] == 3 || in_dims[2] == 4);
         DIM_ASSERT(0, in_dims[3] == 1);
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
         forge::Image* image = NULL;
 
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 7858db1373..170efdd1c6 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -137,7 +137,7 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
         DIM_ASSERT(0, dims.ndims() == 2);
         DIM_ASSERT(0, dims[order_dim] == 2 || dims[order_dim] == 3);
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
@@ -200,7 +200,7 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
         af_array pIn[] = {X, Y, Z};
         AF_CHECK(af_join_many(&in, 1, 3, pIn));
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
@@ -257,7 +257,7 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
         af_array in = 0;
         AF_CHECK(af_join(&in, 1, X, Y));
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index c3f863d62e..8714954fc0 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -153,7 +153,7 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
             DIM_ASSERT(3, ( X_dims[0] * Y_dims[0] == (dim_t)Sinfo.elements()));
         }
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index d3e9577a69..b3eb29e832 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -138,7 +138,7 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
 
         TYPE_ASSERT(pType == dType);
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
@@ -225,7 +225,7 @@ af_err vectorFieldWrapper(const af_window wind,
         DIM_ASSERT(1, xpType == ypType);
         DIM_ASSERT(1, xpType == zpType);
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
@@ -306,7 +306,7 @@ af_err vectorFieldWrapper(const af_window wind,
 
         DIM_ASSERT(1, xpType == ypType);
 
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
         forge::Chart* chart = NULL;
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 78b80222f1..6437538bf9 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -48,7 +48,7 @@ af_err af_create_window(af_window *out, const int width, const int height, const
         // Create a chart map
         fgMngr.setWindowChartGrid(wnd, 1, 1);
 
-        *out = reinterpret_cast<af_window>(wnd);
+        *out = static_cast<af_window>(wnd);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -70,7 +70,7 @@ af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
         wnd->setPos(x, y);
     }
     CATCHALL;
@@ -92,7 +92,7 @@ af_err af_set_title(const af_window wind, const char* const title)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
         wnd->setTitle(title);
     }
     CATCHALL;
@@ -113,7 +113,7 @@ af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
         wnd->setSize(w, h);
     }
     CATCHALL;
@@ -135,7 +135,7 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
 
         // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
@@ -162,7 +162,7 @@ af_err af_set_axes_limits_compute(const af_window wind,
     }
 
     try {
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
 
         // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
@@ -226,7 +226,7 @@ af_err af_set_axes_limits_2d(const af_window wind,
     }
 
     try {
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
 
         // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
@@ -283,7 +283,7 @@ af_err af_set_axes_limits_3d(const af_window wind,
     }
 
     try {
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
 
         // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
@@ -346,7 +346,7 @@ af_err af_set_axes_titles(const af_window wind,
     }
 
     try {
-        forge::Window* window = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* window = static_cast<forge::Window*>(wind);
 
         // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
@@ -383,7 +383,7 @@ af_err af_show(const af_window wind)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
         wnd->swapBuffers();
     }
     CATCHALL;
@@ -403,7 +403,7 @@ af_err af_is_window_closed(bool *out, const af_window wind)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
         *out = wnd->close();
     }
     CATCHALL;
@@ -424,7 +424,7 @@ af_err af_set_visibility(const af_window wind, const bool is_visible)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
         if (is_visible)
             wnd->show();
         else
@@ -448,7 +448,7 @@ af_err af_destroy_window(const af_window wind)
     }
 
     try {
-        forge::Window* wnd = reinterpret_cast<forge::Window*>(wind);
+        forge::Window* wnd = static_cast<forge::Window*>(wind);
 
         // Delete chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();

From 65b675b32ba3c8236ea70c0855209e128ee7f751 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 22 Nov 2018 03:13:05 -0500
Subject: [PATCH 1557/2677] Deprecate [u]intl

intl and uintl are not in the global namespace and are not prefixed
by af_ and therefore could conflict with user code. This will deprecate
their usage in user code but maintains those types in the backend.
---
 include/af/data.h                             |  4 ++--
 include/af/defines.h                          |  8 ++++---
 include/af/random.h                           | 20 +++++++++---------
 src/api/c/corrcoef.cpp                        | 21 ++++++++++++-------
 src/api/c/handle.hpp                          |  3 +++
 src/api/c/stream.cpp                          | 13 ++++++------
 src/api/c/type_util.cpp                       |  7 ++++---
 src/api/cpp/array.cpp                         | 12 +++++------
 src/api/cpp/corrcoef.cpp                      |  4 ++--
 src/api/cpp/data.cpp                          |  4 ++--
 src/api/cpp/device.cpp                        |  4 ++--
 src/api/cpp/random.cpp                        | 14 ++++++-------
 src/api/cpp/stdev.cpp                         |  4 ++--
 src/api/cpp/var.cpp                           |  4 ++--
 src/api/unified/data.cpp                      |  4 ++--
 src/api/unified/random.cpp                    |  8 +++----
 src/backend/cpu/types.hpp                     | 12 ++++++-----
 src/backend/cuda/types.hpp                    | 12 +++++------
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  1 +
 src/backend/opencl/types.hpp                  | 12 ++++++-----
 test/testHelpers.hpp                          | 19 +++++++++++++----
 21 files changed, 108 insertions(+), 82 deletions(-)

diff --git a/include/af/data.h b/include/af/data.h
index 66bb023036..18961b8760 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -461,7 +461,7 @@ extern "C" {
        \ingroup data_func_constant
     */
 
-    AFAPI af_err af_constant_long (af_array *arr, const  intl val, const unsigned ndims, const dim_t * const dims);
+    AFAPI af_err af_constant_long (af_array *arr, const long long val, const unsigned ndims, const dim_t * const dims);
 
     /**
         \param[out] arr is the generated array of type \ref u64
@@ -472,7 +472,7 @@ extern "C" {
        \ingroup data_func_constant
     */
 
-    AFAPI af_err af_constant_ulong(af_array *arr, const uintl val, const unsigned ndims, const dim_t * const dims);
+    AFAPI af_err af_constant_ulong(af_array *arr, const unsigned long long val, const unsigned ndims, const dim_t * const dims);
     /**
        @}
     */
diff --git a/include/af/defines.h b/include/af/defines.h
index 0a09962bab..92c1e47b4a 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -18,7 +18,7 @@
         #define AFAPI  __declspec(dllimport)
     #endif
 
-// bool
+    // bool
     #ifndef __cplusplus
         #define bool unsigned char
         #define false 0
@@ -52,8 +52,10 @@
 
 #include <stdlib.h>
 
-typedef long long intl;
-typedef unsigned long long uintl;
+#ifndef AFDLL  // prevents the use of these types internally
+typedef AF_DEPRECATED("intl is deprecated. Use long long instead.") long long intl;
+typedef AF_DEPRECATED("uintl is deprecated. Use unsigned long long instead.") unsigned long long uintl;
+#endif
 
 #include <af/version.h>
 #ifndef AF_API_VERSION
diff --git a/include/af/random.h b/include/af/random.h
index f606cb3511..4940378709 100644
--- a/include/af/random.h
+++ b/include/af/random.h
@@ -48,7 +48,7 @@ namespace af
                 \ingroup random_engine_func_constructor
             */
             explicit
-            randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT, uintl seedIn = 0);
+            randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT, unsigned long long seedIn = 0);
 
             /**
                 Copy constructor for \ref af::randomEngine.
@@ -121,7 +121,7 @@ namespace af
 
                 \ingroup random_engine_class
             */
-            void setSeed(const uintl seed);
+            void setSeed(const unsigned long long seed);
 
             /**
                 \defgroup random_engine_get_seed getSeed
@@ -132,7 +132,7 @@ namespace af
 
                 \ingroup random_engine_class
             */
-            uintl getSeed(void) const;
+            unsigned long long getSeed(void) const;
 
             /**
                 \defgroup random_engine_get_handle get
@@ -314,14 +314,14 @@ namespace af
 
         \ingroup random_func_set_seed
     */
-    AFAPI void setSeed(const uintl seed);
+    AFAPI void setSeed(const unsigned long long seed);
 
     /**
         \returns seed A 64 bit unsigned integer
 
         \ingroup random_func_get_seed
     */
-    AFAPI uintl getSeed();
+    AFAPI unsigned long long getSeed();
 
 }
 #endif
@@ -342,7 +342,7 @@ extern "C" {
 
        \ingroup random_engine_func_constructor
     */
-    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_engine_type rtype, uintl seed);
+    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_engine_type rtype, unsigned long long seed);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -432,7 +432,7 @@ extern "C" {
 
        \ingroup random_engine_set_seed
     */
-    AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed);
+    AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const unsigned long long seed);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -472,7 +472,7 @@ extern "C" {
 
        \ingroup random_engine_get_type
     */
-    AFAPI af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine);
+    AFAPI af_err af_random_engine_get_seed(unsigned long long * const seed, af_random_engine engine);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -512,14 +512,14 @@ extern "C" {
 
         \ingroup random_func_set_seed
     */
-    AFAPI af_err af_set_seed(const uintl seed);
+    AFAPI af_err af_set_seed(const unsigned long long seed);
 
     /**
         \param[out] seed A 64 bit unsigned integer
 
         \ingroup random_func_get_seed
     */
-    AFAPI af_err af_get_seed(uintl *seed);
+    AFAPI af_err af_get_seed(unsigned long long *seed);
 
 #ifdef __cplusplus
 }
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index c6b6a776c3..ced05c5945 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -7,21 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+
+#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/statistics.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
+#include <cast.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
-#include <reduce.hpp>
-#include <arith.hpp>
 #include <math.hpp>
-#include <cast.hpp>
-#include <cmath>
+#include <reduce.hpp>
+#include <stats.h>
+#include <types.hpp>
 
-#include "stats.h"
+#include <cmath>
 
-using namespace detail;
+using detail::arithOp;
+using detail::reduce_all;
+using detail::intl;
+using detail::uintl;
 
 template<typename Ti, typename To>
 static To corrcoef(const af_array& X, const af_array& Y)
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index e1c6de84c8..04aa55eaa3 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -17,6 +17,7 @@
 #include <copy.hpp>
 #include <cast.hpp>
 #include <af/dim4.hpp>
+#include <types.hpp>
 
 const ArrayInfo& getInfo(const af_array arr, bool sparse_check = true, bool device_check = true);
 
@@ -63,7 +64,9 @@ detail::Array<To> castArray(const af_array &in)
 {
     using detail::cfloat;
     using detail::cdouble;
+    using detail::intl;
     using detail::uint;
+    using detail::uintl;
     using detail::uchar;
     using detail::ushort;
 
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index 2725ee6e5a..4db9d6e588 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -34,13 +34,12 @@ static int save(const char *key, const af_array arr, const char *filename, const
 {
     // (char     )   Version (Once)
     // (int      )   No. of Arrays (Once)
-        // (int    )   Length of the key
-        // (cstring)   Key
-        // (intl   )   Offset bytes to next array (type + dims + data)
-        // (char   )   Type
-        // (intl   )   dim4 (x 4)
-        // (T      )   data (x elements)
-
+    // (int    )   Length of the key
+    // (cstring)   Key
+    // (intl   )   Offset bytes to next array (type + dims + data)
+    // (char   )   Type
+    // (intl   )   dim4 (x 4)
+    // (T      )   data (x elements)
     // Setup all the data structures that need to be written to file
     ///////////////////////////////////////////////////////////////////////////
     std::string k(key);
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index f8926797b0..f79cc72737 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/util.h>
 #include <type_util.hpp>
+
+#include <af/util.h>
 #include <common/err_common.hpp>
 
 size_t size_of(af_dtype type)
@@ -25,8 +26,8 @@ size_t size_of(af_dtype type)
             case c64: return sizeof(double) * 2;
             case s16: return sizeof(short);
             case u16: return sizeof(unsigned short);
-            case s64: return sizeof(intl);
-            case u64: return sizeof(uintl);
+            case s64: return sizeof(long long);
+            case u64: return sizeof(unsigned long long);
             default : TYPE_ERROR(1, type);
         }
     } CATCHALL;
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index dd84bf2729..e5e6ed00c6 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -221,8 +221,8 @@ namespace af
     INSTANTIATE(int)
     INSTANTIATE(unsigned char)
     INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+    INSTANTIATE(long long)
+    INSTANTIATE(unsigned long long)
     INSTANTIATE(short)
     INSTANTIATE(unsigned short)
 
@@ -1014,8 +1014,8 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     INSTANTIATE(int)
     INSTANTIATE(unsigned char)
     INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+    INSTANTIATE(long long)
+    INSTANTIATE(unsigned long long)
     INSTANTIATE(short)
     INSTANTIATE(unsigned short)
 
@@ -1051,8 +1051,8 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     INSTANTIATE(int)
     INSTANTIATE(unsigned char)
     INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+    INSTANTIATE(long long)
+    INSTANTIATE(unsigned long long)
     INSTANTIATE(short)
     INSTANTIATE(unsigned short)
 
diff --git a/src/api/cpp/corrcoef.cpp b/src/api/cpp/corrcoef.cpp
index ed78a684e3..01023b11ed 100644
--- a/src/api/cpp/corrcoef.cpp
+++ b/src/api/cpp/corrcoef.cpp
@@ -28,8 +28,8 @@ INSTANTIATE_CORRCOEF(int);
 INSTANTIATE_CORRCOEF(unsigned int);
 INSTANTIATE_CORRCOEF(char);
 INSTANTIATE_CORRCOEF(unsigned char);
-INSTANTIATE_CORRCOEF(intl);
-INSTANTIATE_CORRCOEF(uintl);
+INSTANTIATE_CORRCOEF(long long);
+INSTANTIATE_CORRCOEF(unsigned long long);
 INSTANTIATE_CORRCOEF(short);
 INSTANTIATE_CORRCOEF(unsigned short);
 
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index e6945eaf9a..150a124fc5 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -38,11 +38,11 @@ namespace {
                                  dims.ndims(), dims.get(), type));
         }
         else if (type == s64) {
-                AF_THROW(af_constant_long (&res, ( intl)val,
+                AF_THROW(af_constant_long (&res, (long long)val,
                                            dims.ndims(),
                                            dims.get()));
         } else {
-            AF_THROW(af_constant_ulong(&res, (uintl)val,
+            AF_THROW(af_constant_ulong(&res, (unsigned long long)val,
                                        dims.ndims(),
                                        dims.get()));
         }
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 1b68c4b8ce..9499a3d644 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -213,7 +213,7 @@ namespace af
     INSTANTIATE(char)
     INSTANTIATE(short)
     INSTANTIATE(unsigned short)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+    INSTANTIATE(long long)
+    INSTANTIATE(unsigned long long)
 
 }
diff --git a/src/api/cpp/random.cpp b/src/api/cpp/random.cpp
index f4706c5874..b312b7fd45 100644
--- a/src/api/cpp/random.cpp
+++ b/src/api/cpp/random.cpp
@@ -15,7 +15,7 @@
 
 namespace af
 {
-    randomEngine::randomEngine(randomEngineType type, uintl seed) : engine(0)
+    randomEngine::randomEngine(randomEngineType type, unsigned long long seed) : engine(0)
     {
         AF_THROW(af_create_random_engine(&engine, type, seed));
     }
@@ -59,14 +59,14 @@ namespace af
         AF_THROW(af_random_engine_set_type(&engine, type));
     }
 
-    void randomEngine::setSeed(const uintl seed)
+    void randomEngine::setSeed(const unsigned long long seed)
     {
         AF_THROW(af_random_engine_set_seed(&engine, seed));
     }
 
-    uintl randomEngine::getSeed(void) const
+    unsigned long long randomEngine::getSeed(void) const
     {
-        uintl seed;
+        unsigned long long seed;
         AF_THROW(af_random_engine_get_seed(&seed, engine));
         return seed;
     }
@@ -166,14 +166,14 @@ namespace af
         return randomEngine(handle);
     }
 
-    void setSeed(const uintl seed)
+    void setSeed(const unsigned long long seed)
     {
         AF_THROW(af_set_seed(seed));
     }
 
-    uintl getSeed()
+    unsigned long long getSeed()
     {
-        uintl seed = 0;
+        unsigned long long seed = 0;
         AF_THROW(af_get_seed(&seed));
         return seed;
     }
diff --git a/src/api/cpp/stdev.cpp b/src/api/cpp/stdev.cpp
index 5a050570a4..4812267bfc 100644
--- a/src/api/cpp/stdev.cpp
+++ b/src/api/cpp/stdev.cpp
@@ -42,8 +42,8 @@ INSTANTIATE_STDEV(float);
 INSTANTIATE_STDEV(double);
 INSTANTIATE_STDEV(int);
 INSTANTIATE_STDEV(unsigned int);
-INSTANTIATE_STDEV(intl);
-INSTANTIATE_STDEV(uintl);
+INSTANTIATE_STDEV(long long);
+INSTANTIATE_STDEV(unsigned long long);
 INSTANTIATE_STDEV(short);
 INSTANTIATE_STDEV(unsigned short);
 INSTANTIATE_STDEV(char);
diff --git a/src/api/cpp/var.cpp b/src/api/cpp/var.cpp
index bcff1dcf99..e5c778d269 100644
--- a/src/api/cpp/var.cpp
+++ b/src/api/cpp/var.cpp
@@ -78,8 +78,8 @@ INSTANTIATE_VAR(float);
 INSTANTIATE_VAR(double);
 INSTANTIATE_VAR(int);
 INSTANTIATE_VAR(unsigned int);
-INSTANTIATE_VAR(intl);
-INSTANTIATE_VAR(uintl);
+INSTANTIATE_VAR(long long);
+INSTANTIATE_VAR(unsigned long long);
 INSTANTIATE_VAR(short);
 INSTANTIATE_VAR(unsigned short);
 INSTANTIATE_VAR(char);
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index 256dab27ca..a87a691ded 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -26,13 +26,13 @@ af_err af_constant_complex(af_array *arr, const double real, const double imag,
 }
 
 
-af_err af_constant_long (af_array *arr, const  intl val, const unsigned ndims, const dim_t * const dims)
+af_err af_constant_long (af_array *arr, const long long val, const unsigned ndims, const dim_t * const dims)
 {
     return CALL(arr, val, ndims, dims);
 }
 
 
-af_err af_constant_ulong(af_array *arr, const uintl val, const unsigned ndims, const dim_t * const dims)
+af_err af_constant_ulong(af_array *arr, const unsigned long long val, const unsigned ndims, const dim_t * const dims)
 {
     return CALL(arr, val, ndims, dims);
 }
diff --git a/src/api/unified/random.cpp b/src/api/unified/random.cpp
index dd8871efc3..0abea9a522 100644
--- a/src/api/unified/random.cpp
+++ b/src/api/unified/random.cpp
@@ -56,12 +56,12 @@ af_err af_release_random_engine(af_random_engine engineHandle)
     return CALL(engineHandle);
 }
 
-af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
+af_err af_random_engine_set_seed(af_random_engine *engine, const unsigned long long seed)
 {
     return CALL(engine, seed);
 }
 
-af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
+af_err af_random_engine_get_seed(unsigned long long * const seed, af_random_engine engine)
 {
     return CALL(seed, engine);
 }
@@ -76,12 +76,12 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
     return CALL(out, ndims, dims, type);
 }
 
-af_err af_set_seed(const uintl seed)
+af_err af_set_seed(const unsigned long long seed)
 {
     return CALL(seed);
 }
 
-af_err af_get_seed(uintl *seed)
+af_err af_get_seed(unsigned long long *seed)
 {
     return CALL(seed);
 }
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 84e62f8286..565f8a463d 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -12,11 +12,13 @@
 
 namespace cpu
 {
-typedef std::complex<float>     cfloat;
-typedef std::complex<double>    cdouble;
-typedef unsigned int   uint;
-typedef unsigned char  uchar;
-typedef unsigned short ushort;
+using cfloat  = std::complex<float>;
+using cdouble = std::complex<double>;
+using uint    = unsigned int;
+using uchar   = unsigned char;
+using ushort  = unsigned short;
+using intl    = long long;
+using uintl   = unsigned long long;
 
 template<typename T> struct is_complex          { static const bool value = false;  };
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index afdd959c8a..c515086d81 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -14,12 +14,12 @@
 namespace cuda
 {
 using cdouble = cuDoubleComplex;
-using cfloat = cuFloatComplex;
-using uchar = unsigned char;
-using uint = unsigned int;
-// using intl = long long ;                // defined in af/defines.h
-// using uintl = unsigned long long;       // defined in af/defines.h
-using ushort = unsigned short;
+using cfloat  = cuFloatComplex;
+using uchar   = unsigned char;
+using uint    = unsigned int;
+using intl    = long long;
+using uintl   = unsigned long long;
+using ushort  = unsigned short;
 
 template<typename T> struct is_complex          { static const bool value = false;  };
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 5075685b01..f46815a606 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -22,6 +22,7 @@
 #include <cache.hpp>
 #include "names.hpp"
 #include "config.hpp"
+#include <types.hpp>
 
 using cl::Buffer;
 using cl::Program;
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 01730c4b49..92ed49d142 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -26,11 +26,13 @@ using std::string;
 
 namespace opencl
 {
-typedef cl_float2   cfloat;
-typedef cl_double2 cdouble;
-typedef cl_uchar     uchar;
-typedef cl_uint       uint;
-typedef cl_ushort   ushort;
+using cfloat  = cl_float2;
+using cdouble = cl_double2;
+using uchar   = cl_uchar;
+using uint    = cl_uint;
+using ushort  = cl_ushort;
+using intl    = long long;
+using uintl   = unsigned long long;
 
 template<typename T> struct is_complex          { static const bool value = false;  };
 template<> struct           is_complex<cfloat>  { static const bool value = true;   };
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 718ae32c52..74db5f096a 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -30,14 +30,25 @@
 #include <utility>
 #include <vector>
 
-typedef unsigned char  uchar;
-typedef unsigned int   uint;
-typedef unsigned short ushort;
-
 #define UNUSED(expr) do { (void)(expr); } while (0)
 
+namespace aft {
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+typedef intl intl;
+typedef uintl uintl;
+#pragma GCC diagnostic pop
+}
+
+using aft::uintl;
+using aft::intl;
+
 namespace {
 
+typedef unsigned char  uchar;
+typedef unsigned int   uint;
+typedef unsigned short ushort;
+
 std::string readNextNonEmptyLine(std::ifstream &file)
 {
     std::string result = "";

From 2d1b9f41c7855820ebad201ebfa1bbab5b0a3b77 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 20 Nov 2018 02:56:51 -0500
Subject: [PATCH 1558/2677] Move is_complex to common.

---
 src/api/c/assign.cpp                       | 18 ++++---
 src/api/cpp/data.cpp                       | 10 ++--
 src/backend/common/CMakeLists.txt          |  1 +
 src/backend/common/complex.hpp             | 31 ++++++++++++
 src/backend/cpu/blas.cpp                   |  3 ++
 src/backend/cpu/copy.cpp                   |  6 ++-
 src/backend/cpu/kernel/interp.hpp          |  3 +-
 src/backend/cpu/kernel/resize.hpp          |  3 +-
 src/backend/cpu/sparse.cpp                 | 15 +++---
 src/backend/cpu/sparse_blas.cpp            | 12 ++---
 src/backend/cpu/types.hpp                  |  4 --
 src/backend/cuda/copy.cu                   |  4 ++
 src/backend/cuda/types.hpp                 |  4 --
 src/backend/opencl/copy.cpp                |  3 ++
 src/backend/opencl/cpu/cpu_blas.cpp        | 11 ++--
 src/backend/opencl/cpu/cpu_sparse_blas.cpp | 16 +++---
 src/backend/opencl/cpu/cpu_sparse_blas.hpp |  8 +--
 src/backend/opencl/kernel/resize.hpp       | 33 +++++-------
 src/backend/opencl/kernel/rotate.hpp       | 33 +++++-------
 src/backend/opencl/kernel/transform.hpp    | 59 ++++++++++------------
 src/backend/opencl/magma/labrd.cpp         |  4 +-
 src/backend/opencl/types.hpp               | 13 ++---
 22 files changed, 158 insertions(+), 136 deletions(-)
 create mode 100644 src/backend/common/complex.hpp

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 4efeb74ea2..b2c657a2c0 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -13,6 +13,7 @@
 #include <af/data.h>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/complex.hpp>
 #include <handle.hpp>
 #include <backend.hpp>
 #include <Array.hpp>
@@ -23,11 +24,16 @@
 #include <indexing_common.hpp>
 
 using namespace detail;
-using std::vector;
-using std::swap;
+
+using std::enable_if;
 using std::signbit;
+using std::swap;
+using std::vector;
+
 using common::convert2Canonical;
 using common::createSpanIndex;
+using common::if_complex;
+using common::if_real;
 
 template<typename Tout, typename Tin>
 static
@@ -81,9 +87,9 @@ void assign(Array<Tout> &out, const vector<af_seq> seqs,
 
 template<typename T>
 static
-typename std::enable_if<is_complex<T>::value, void>::type
+if_complex<T>
 assign(Array<T> &out, const vector<af_seq> iv,
-       const af_array &in) {
+            const af_array &in) {
     const ArrayInfo& iInfo = getInfo(in);
     af_dtype iType = iInfo.getType();
     switch(iType) {
@@ -95,9 +101,9 @@ assign(Array<T> &out, const vector<af_seq> iv,
 
 template<typename T>
 static
-typename std::enable_if<is_complex<T>::value == false, void>::type
+if_real<T>
 assign(Array<T> &out, const vector<af_seq> iv,
-       const af_array &in)
+            const af_array &in)
 {
     const ArrayInfo& iInfo = getInfo(in);
     af_dtype iType = iInfo.getType();
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 150a124fc5..cbd9f5dcb0 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -24,12 +24,16 @@ using af::dim4;
 using af::dtype;
 
 namespace {
-    template<typename T> struct is_complex          { static const bool value = false;  };
+    // NOTE: we are repeating this here so that we don't need to access the is_complex
+    // types in backend/common. This is done to isolate the C++ API from the internal
+    // API
+    template<typename T> struct is_complex              { static const bool value = false;  };
     template<> struct           is_complex<af::cfloat>  { static const bool value = true;   };
     template<> struct           is_complex<af::cdouble> { static const bool value = true;   };
 
-    template<typename T>
-    typename enable_if<is_complex<T>::value == false, array>::type
+    template<typename T,
+             typename = typename enable_if<is_complex<T>::value == false, T>::type>
+    array
     constant(T val, const dim4& dims, const dtype type)
     {
         af_array res;
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 07b2e8654b..4aa0b49491 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -34,6 +34,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cblas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/complex.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/constants.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/defines.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dim4.cpp
diff --git a/src/backend/common/complex.hpp b/src/backend/common/complex.hpp
new file mode 100644
index 0000000000..20692414fb
--- /dev/null
+++ b/src/backend/common/complex.hpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <types.hpp>
+#include <backend.hpp>
+#include <Array.hpp>
+
+#include <type_traits>
+
+namespace common {
+
+// The value returns true if the type is a complex type. False otherwise
+template<typename T> struct is_complex          { static const bool value = false;  };
+template<> struct           is_complex<detail::cfloat>  { static const bool value = true;   };
+template<> struct           is_complex<detail::cdouble> { static const bool value = true;   };
+
+/// This is an enable_if for complex types.
+template<typename T, typename TYPE = void>
+using if_complex = typename std::enable_if<is_complex<T>::value, TYPE>::type;
+
+/// This is an enable_if for real types.
+template<typename T, typename TYPE = void>
+using if_real = typename std::enable_if<is_complex<T>::value == false, TYPE>::type;
+
+}
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index ada51d1eea..517405aa12 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -17,6 +17,7 @@
 #include <Param.hpp>
 #include <common/blas_headers.hpp>
 #include <common/err_common.hpp>
+#include <common/complex.hpp>
 #include <kernel/dot.hpp>
 #include <platform.hpp>
 #include <types.hpp>
@@ -41,6 +42,8 @@ using std::is_floating_point;
 using std::remove_const;
 using std::conditional;
 
+using common::is_complex;
+
 // Some implementations of BLAS require void* for complex pointers while others use float*/double*
 //
 // Sample cgemm API
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index a4eac8935a..d2651fb291 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -14,7 +14,7 @@
 #include <kernel/copy.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <types.hpp>
+#include <common/complex.hpp>
 
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -22,6 +22,8 @@
 #include <cstdio>
 #include <cstring>
 
+using common::is_complex;
+
 namespace cpu
 {
 
@@ -53,7 +55,7 @@ template<typename inType, typename outType>
 void copyArray(Array<outType> &out, Array<inType> const &in)
 {
     static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
-                  "Cannot copy from complex value to a non complex value");
+                  "Cannot copy from complex Array<T> to a non complex Array<T>");
     out.eval();
     in.eval();
     getQueue().enqueue(kernel::copy<outType, inType>, out, in);
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index 90cce006ce..1553bf21fe 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -11,6 +11,7 @@
 #include <math.hpp>
 #include <af/constants.h>
 #include <type_traits>
+#include <common/complex.hpp>
 
 namespace cpu
 {
@@ -24,7 +25,7 @@ template<typename T>
 using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
 
 template<typename T>
-using vtype_t = typename conditional<is_complex<T>::value,
+using vtype_t = typename conditional<common::is_complex<T>::value,
                                      T, wtype_t<T>
                                      >::type;
 
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index d83a52c5dc..1ab0025b67 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 #include <af/traits.hpp>
+#include <common/complex.hpp>
 
 namespace cpu
 {
@@ -37,7 +38,7 @@ template<typename T>
 using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
 
 template<typename T>
-using vtype_t = typename conditional<is_complex<T>::value,
+using vtype_t = typename conditional<common::is_complex<T>::value,
                                      T, wtype_t<T>
                                     >::type;
 
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index d6032ade4a..0073cc78ae 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -18,6 +18,7 @@
 #include <complex.hpp>
 #include <copy.hpp>
 #include <common/err_common.hpp>
+#include <common/complex.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -25,10 +26,10 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
-namespace cpu
-{
-
-using namespace common;
+using common::is_complex;
+using common::SparseArray;
+using common::createArrayDataSparseArray;
+using common::createEmptySparseArray;
 
 using std::add_const;
 using std::add_pointer;
@@ -38,6 +39,9 @@ using std::remove_const;
 using std::conditional;
 using std::is_same;
 
+namespace cpu
+{
+
 template<typename T, class Enable = void>
 struct blas_base {
     using type = T;
@@ -46,8 +50,7 @@ struct blas_base {
 template<typename T>
 struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
     using type = typename conditional<is_same<T, cdouble>::value,
-                                      sp_cdouble, sp_cfloat>
-                                     ::type;
+                                      sp_cdouble, sp_cfloat>::type;
 };
 
 template<typename T>
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 26c157a16e..470c93ff6b 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -12,6 +12,7 @@
 #include <af/dim4.hpp>
 #include <complex.hpp>
 #include <common/err_common.hpp>
+#include <common/complex.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
@@ -20,10 +21,7 @@
 #include <string>
 #include <cassert>
 
-namespace cpu
-{
-
-using namespace common;
+using common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
@@ -33,6 +31,9 @@ using std::remove_const;
 using std::conditional;
 using std::is_same;
 
+namespace cpu
+{
+
 template<typename T, class Enable = void>
 struct blas_base {
     using type = T;
@@ -41,8 +42,7 @@ struct blas_base {
 template<typename T>
 struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
     using type = typename conditional<is_same<T, cdouble>::value,
-                                      sp_cdouble, sp_cfloat>
-                                     ::type;
+                                      sp_cdouble, sp_cfloat>::type;
 };
 
 template<typename T>
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 565f8a463d..782fabda44 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -19,8 +19,4 @@ using uchar   = unsigned char;
 using ushort  = unsigned short;
 using intl    = long long;
 using uintl   = unsigned long long;
-
-template<typename T> struct is_complex          { static const bool value = false;  };
-template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-template<> struct           is_complex<cdouble> { static const bool value = true;   };
 }
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index e8f0d6e412..3ccb770295 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -14,6 +14,10 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 
+#include <common/complex.hpp>
+
+using common::is_complex;
+
 namespace cuda
 {
 
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index c515086d81..45e8338cc3 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -21,10 +21,6 @@ using intl    = long long;
 using uintl   = unsigned long long;
 using ushort  = unsigned short;
 
-template<typename T> struct is_complex          { static const bool value = false;  };
-template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-template<> struct           is_complex<cdouble> { static const bool value = true;   };
-
 namespace {
 template<typename T> const char *shortname(bool caps = false) { return caps ?  "Q" : "q"; }
 template<> const char *shortname<float   >(bool caps) { return caps ?  "S" : "s"; }
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index b828bcc3b5..a1f66d35f3 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -12,6 +12,9 @@
 #include <kernel/memcopy.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
+#include <common/complex.hpp>
+
+using common::is_complex;
 
 namespace opencl
 {
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 38679cc8c4..51b5123ee3 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -12,11 +12,9 @@
 #include <cpu/cpu_blas.hpp>
 #include <math.hpp>
 #include <common/blas_headers.hpp>
+#include <common/complex.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+using common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
@@ -25,6 +23,11 @@ using std::is_floating_point;
 using std::remove_const;
 using std::conditional;
 
+namespace opencl
+{
+namespace cpu
+{
+
 // Some implementations of BLAS require void* for complex pointers while others use float*/double*
 //
 // Sample cgemm API
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 22e04155e5..626f48dd0e 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -10,22 +10,17 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_sparse_blas.hpp>
 
-#include <stdexcept>
-#include <string>
-#include <cassert>
-
 #include <af/dim4.hpp>
+#include <common/complex.hpp>
 #include <complex.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+#include <stdexcept>
+#include <string>
 
-using namespace common;
+using common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
@@ -35,6 +30,9 @@ using std::remove_const;
 using std::conditional;
 using std::is_same;
 
+namespace opencl {
+namespace cpu {
+
 template<typename T, class Enable = void>
 struct blas_base {
     using type = T;
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.hpp b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
index 2837d5c02b..01df836839 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
@@ -15,11 +15,11 @@
 #endif
 
 #ifdef USE_MKL
-typedef MKL_Complex8    sp_cfloat;
-typedef MKL_Complex16   sp_cdouble;
+using sp_cfloat = MKL_Complex8;
+using sp_cdouble = MKL_Complex16;
 #else
-typedef opencl::cfloat  sp_cfloat;
-typedef opencl::cdouble sp_cdouble;
+using sp_cfloat = opencl::cfloat;
+using sp_cdouble = opencl::cdouble;
 #endif
 
 namespace opencl
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index b016f2d7f3..83fa03a388 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -14,17 +14,10 @@
 #include <string>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/complex.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
 namespace opencl
 {
 namespace kernel
@@ -32,13 +25,11 @@ namespace kernel
 static const int RESIZE_TX = 16;
 static const int RESIZE_TY = 16;
 
-using std::conditional;
-using std::is_same;
 template<typename T>
-using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+using wtype_t = typename std::conditional<std::is_same<T, double>::value, double, float>::type;
 
 template<typename T>
-using vtype_t = typename conditional< is_complex<T>::value, T, wtype_t<T> >::type;
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T, wtype_t<T> >::type;
 
 template<typename T, af_interp_type method>
 void resize(Param out, const Param in)
@@ -78,30 +69,30 @@ void resize(Param out, const Param in)
 
         const char* ker_strs[] = {resize_cl};
         const int   ker_lens[] = {resize_cl_len};
-        Program prog;
+        cl::Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "resize_kernel");
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "resize_kernel");
 
         addKernelToCache(device, refName, entry);
     }
 
-    auto resizeOp = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
-                                   const int, const int, const float, const float > (*entry.ker);
+    auto resizeOp = cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer, const KParam,
+                                      const int, const int, const float, const float > (*entry.ker);
 
-    NDRange local(RESIZE_TX, RESIZE_TY, 1);
+    cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], local[0]);
     int blocksPerMatY = divup(out.info.dims[1], local[1]);
-    NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                   local[1] * blocksPerMatY * in.info.dims[3], 1);
+    cl::NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                       local[1] * blocksPerMatY * in.info.dims[3], 1);
 
     double xd = (double)in.info.dims[0] / (double)out.info.dims[0];
     double yd = (double)in.info.dims[1] / (double)out.info.dims[1];
 
     float xf = (float)xd, yf = (float)yd;
 
-    resizeOp(EnqueueArgs(getQueue(), global, local),
+    resizeOp(cl::EnqueueArgs(getQueue(), global, local),
              *out.data, out.info, *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
 
     CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 0b08b02d87..4d947d012a 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -15,6 +15,7 @@
 #include <string>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/complex.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <type_util.hpp>
@@ -22,14 +23,6 @@
 #include "config.hpp"
 #include "interp.hpp"
 
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
-
 namespace opencl
 {
 namespace kernel
@@ -43,13 +36,11 @@ typedef struct {
     float tmat[6];
 } tmat_t;
 
-using std::conditional;
-using std::is_same;
 template<typename T>
-using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+using wtype_t = typename std::conditional<std::is_same<T, double>::value, double, float>::type;
 
 template<typename T>
-using vtype_t = typename conditional< is_complex<T>::value, T, wtype_t<T> >::type;
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T, wtype_t<T> >::type;
 
 template<typename T, int order>
 void rotate(Param out, const Param in, const float theta, af_interp_type method)
@@ -87,17 +78,17 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method)
 
         const char *ker_strs[] = {interp_cl, rotate_cl};
         const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
-        Program prog;
+        cl::Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "rotate_kernel");
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "rotate_kernel");
 
         addKernelToCache(device, refName, entry);
     }
 
-    auto rotateOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                  const tmat_t, const int, const int,
-                                  const int, const int, const int>(*entry.ker);
+    auto rotateOp = cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer, const KParam,
+                                      const tmat_t, const int, const int,
+                                      const int, const int, const int>(*entry.ker);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
@@ -122,7 +113,7 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method)
     t.tmat[5] = round(ty * 1000) / 1000.0f;
 
 
-    NDRange local(TX, TY, 1);
+    cl::NDRange local(TX, TY, 1);
 
     int nimages  = in.info.dims[2];
     int nbatches = in.info.dims[3];
@@ -138,9 +129,9 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method)
     }
     global_y *= nbatches;
 
-    NDRange global(global_x, global_y, 1);
+    cl::NDRange global(global_x, global_y, 1);
 
-    rotateOp(EnqueueArgs(getQueue(), global, local),
+    rotateOp(cl::EnqueueArgs(getQueue(), global, local),
              *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
              blocksXPerImage, blocksYPerImage, (int)method);
 
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index cf649c4b24..92821f0940 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -8,27 +8,22 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/interp.hpp>
 #include <kernel_headers/transform.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
-#include <common/dispatch.hpp>
+#include <kernel_headers/interp.hpp>
+
+#include "config.hpp"
+#include "interp.hpp"
 #include <Param.hpp>
 #include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <common/complex.hpp>
 #include <debug_opencl.hpp>
-#include <type_util.hpp>
 #include <math.hpp>
-#include "config.hpp"
-#include "interp.hpp"
+#include <program.hpp>
+#include <traits.hpp>
+#include <type_util.hpp>
 
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
+#include <string>
 
 namespace opencl
 {
@@ -39,15 +34,13 @@ namespace opencl
         // Used for batching images
         static const int TI = 4;
 
-        using std::conditional;
-        using std::is_same;
         template<typename T>
-        using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+        using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                                  double, float>::type;
 
         template<typename T>
-        using vtype_t = typename conditional<is_complex<T>::value,
-                                             T, wtype_t<T>
-                                            >::type;
+        using vtype_t = typename std::conditional<common::is_complex<T>::value,
+                                                  T, wtype_t<T>>::type;
 
 
         template<typename T, int order>
@@ -56,7 +49,7 @@ namespace opencl
                        bool isPerspective, af_interp_type method)
         {
 
-            typedef typename dtype_traits<T>::base_type BT;
+            using BT = typename dtype_traits<T>::base_type;
 
             std::string ref_name =
                 std::string("transform_") +
@@ -99,26 +92,26 @@ namespace opencl
 
                 const char *ker_strs[] = {interp_cl, transform_cl};
                 const int   ker_lens[] = {interp_cl_len, transform_cl_len};
-                Program prog;
+                cl::Program prog;
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "transform_kernel");
+                entry.prog = new cl::Program(prog);
+                entry.ker = new cl::Kernel(*entry.prog, "transform_kernel");
 
                 addKernelToCache(device, ref_name, entry);
             }
 
-            auto transformOp = KernelFunctor<Buffer, const KParam,
-                                              const Buffer, const KParam,
-                                              const Buffer, const KParam,
-                                              const int, const int, const int, const int,
-                                              const int, const int, const int, const int>(*entry.ker);
+            auto transformOp = cl::KernelFunctor<cl::Buffer, const KParam,
+                                                 const cl::Buffer, const KParam,
+                                                 const cl::Buffer, const KParam,
+                                                 const int, const int, const int, const int,
+                                                 const int, const int, const int, const int>(*entry.ker);
 
             const int nImg2 = in.info.dims[2];
             const int nImg3 = in.info.dims[3];
             const int nTfs2 = tf.info.dims[2];
             const int nTfs3 = tf.info.dims[3];
 
-            NDRange local(TX, TY, 1);
+            cl::NDRange local(TX, TY, 1);
 
             int batchImg2 = 1;
             if(nImg2 != nTfs2)
@@ -137,9 +130,9 @@ namespace opencl
                           * max((nTfs2 / nImg2), 1)
                           * max((nTfs3 / nImg3), 1);
 
-            NDRange global(global_x, global_y, global_z);
+            cl::NDRange global(global_x, global_y, global_z);
 
-            transformOp(EnqueueArgs(getQueue(), global, local),
+            transformOp(cl::EnqueueArgs(getQueue(), global, local),
                         *out.data, out.info, *in.data, in.info, *tf.data, tf.info,
                         nImg2, nImg3, nTfs2, nTfs3, batchImg2,
                         blocksXPerImage, blocksYPerImage, (int)method);
diff --git a/src/backend/opencl/magma/labrd.cpp b/src/backend/opencl/magma/labrd.cpp
index bde9fdb4eb..61bc58b84a 100644
--- a/src/backend/opencl/magma/labrd.cpp
+++ b/src/backend/opencl/magma/labrd.cpp
@@ -60,7 +60,7 @@
 #include "magma_helper.h"
 #include "magma_sync.h"
 #include <traits.hpp>
-#include <types.hpp>
+#include <common/complex.hpp>
 
 #include <algorithm>
 
@@ -207,7 +207,7 @@ magma_labrd_gpu(
 
     typedef typename af::dtype_traits<Ty>::base_type Tr;
 
-    const bool is_cplx = opencl::is_complex<Ty>::value;
+    constexpr bool is_cplx = common::is_complex<Ty>::value;
 
     Tr *d = (Tr *)_d;
     Tr *e = (Tr *)_e;
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 92ed49d142..aa2a449cf7 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -16,13 +16,12 @@
 #include <CL/cl.h>
 #endif
 #pragma GCC diagnostic pop
-#include <sstream>
-#include <string>
-#include <cmath>
 #include <type_util.hpp>
 #include <af/traits.hpp>
 
-using std::string;
+#include <sstream>
+#include <string>
+#include <cmath>
 
 namespace opencl
 {
@@ -34,10 +33,6 @@ using ushort  = cl_ushort;
 using intl    = long long;
 using uintl   = unsigned long long;
 
-template<typename T> struct is_complex          { static const bool value = false;  };
-template<> struct           is_complex<cfloat>  { static const bool value = true;   };
-template<> struct           is_complex<cdouble> { static const bool value = true;   };
-
 template<typename T>
 struct ToNumStr
 {
@@ -70,7 +65,7 @@ struct ToNumStr<double>
         static const char* PINF = "+INFINITY";
         static const char* NINF = "-INFINITY";
         if (std::isinf(val)) {
-            return string(val < 0 ? NINF : PINF);
+            return val < 0 ? NINF : PINF;
         }
         return std::to_string(val);
     }

From bcbbec923f0578f141dba81b4ac5a988bc7c15df Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 20 Nov 2018 04:16:32 -0500
Subject: [PATCH 1559/2677] getWriteableArray -> getArray. reinterpret_cast ->
 static_cast

---
 src/api/c/approx.cpp                    |  2 +-
 src/api/c/array.cpp                     |  4 +--
 src/api/c/assign.cpp                    | 26 ++++++++--------
 src/api/c/cholesky.cpp                  |  2 +-
 src/api/c/fft.cpp                       |  2 +-
 src/api/c/gradient.cpp                  |  2 +-
 src/api/c/handle.hpp                    | 40 +++++++++++--------------
 src/api/c/lu.cpp                        |  2 +-
 src/api/c/qr.cpp                        |  2 +-
 src/api/c/sparse_handle.hpp             | 25 +++++++---------
 src/api/c/stream.cpp                    | 13 ++++++--
 src/api/c/svd.cpp                       |  2 +-
 src/api/c/transpose.cpp                 |  2 +-
 src/api/c/type_util.hpp                 |  1 -
 src/backend/cpu/kernel/sift_nonfree.hpp |  2 ++
 src/backend/cpu/platform.cpp            |  1 +
 src/backend/cuda/sift.cu                |  2 --
 17 files changed, 65 insertions(+), 65 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 24bd671c8a..6a7bd4d3f3 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -25,7 +25,7 @@ static inline void approx1(af_array *yo, const af_array yi,
                            const Tp &xi_beg, const Tp &xi_step,
                            const af_interp_type method, const float offGrid)
 {
-    approx1<Ty>(getWritableArray<Ty>(*yo), getArray<Ty>(yi),
+    approx1<Ty>(getArray<Ty>(*yo), getArray<Ty>(yi),
                 getArray<Tp>(xo), xdim,
                 xi_beg, xi_step,
                 method, offGrid);
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 965a7d25f5..0124928dda 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -280,9 +280,9 @@ template<typename T>
 void write_array(af_array arr, const T * const data, const size_t bytes, af_source src)
 {
     if(src == afHost) {
-        writeHostDataArray(getWritableArray<T>(arr), data, bytes);
+        writeHostDataArray(getArray<T>(arr), data, bytes);
     } else {
-        writeDeviceDataArray(getWritableArray<T>(arr), data, bytes);
+        writeDeviceDataArray(getArray<T>(arr), data, bytes);
     }
     return;
 }
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index b2c657a2c0..bdcfaee2b9 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -181,18 +181,18 @@ af_err af_assign_seq(af_array *out,
                 const ArrayInfo& oInfo = getInfo(res);
                 af_dtype oType = oInfo.getType();
                 switch(oType) {
-                case c64: assign(getWritableArray<cdouble>(res), inSeqs, rhs); break;
-                case c32: assign(getWritableArray<cfloat >(res), inSeqs, rhs); break;
-                case f64: assign(getWritableArray<double >(res), inSeqs, rhs); break;
-                case f32: assign(getWritableArray<float  >(res), inSeqs, rhs); break;
-                case s32: assign(getWritableArray<int    >(res), inSeqs, rhs); break;
-                case u32: assign(getWritableArray<uint   >(res), inSeqs, rhs); break;
-                case s64: assign(getWritableArray<intl   >(res), inSeqs, rhs); break;
-                case u64: assign(getWritableArray<uintl  >(res), inSeqs, rhs); break;
-                case s16: assign(getWritableArray<short  >(res), inSeqs, rhs); break;
-                case u16: assign(getWritableArray<ushort >(res), inSeqs, rhs); break;
-                case u8 : assign(getWritableArray<uchar  >(res), inSeqs, rhs); break;
-                case b8 : assign(getWritableArray<char   >(res), inSeqs, rhs); break;
+                case c64: assign(getArray<cdouble>(res), inSeqs, rhs); break;
+                case c32: assign(getArray<cfloat >(res), inSeqs, rhs); break;
+                case f64: assign(getArray<double >(res), inSeqs, rhs); break;
+                case f32: assign(getArray<float  >(res), inSeqs, rhs); break;
+                case s32: assign(getArray<int    >(res), inSeqs, rhs); break;
+                case u32: assign(getArray<uint   >(res), inSeqs, rhs); break;
+                case s64: assign(getArray<intl   >(res), inSeqs, rhs); break;
+                case u64: assign(getArray<uintl  >(res), inSeqs, rhs); break;
+                case s16: assign(getArray<short  >(res), inSeqs, rhs); break;
+                case u16: assign(getArray<ushort >(res), inSeqs, rhs); break;
+                case u8 : assign(getArray<uchar  >(res), inSeqs, rhs); break;
+                case b8 : assign(getArray<char   >(res), inSeqs, rhs); break;
                 default : TYPE_ERROR(1, oType); break;
                 }
             }
@@ -210,7 +210,7 @@ template<typename T>
 inline
 void genAssign(af_array& out, const af_index_t* indexs, const af_array& rhs)
 {
-    detail::assign<T>(getWritableArray<T>(out), indexs, getArray<T>(rhs));
+    detail::assign<T>(getArray<T>(out), indexs, getArray<T>(rhs));
 }
 
 af_err af_assign_gen(af_array *out, const af_array lhs,
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index 94421a45d1..3605a30b50 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -28,7 +28,7 @@ static inline af_array cholesky(int *info, const af_array in, const bool is_uppe
 template<typename T>
 static inline int cholesky_inplace(af_array in, const bool is_upper)
 {
-     return cholesky_inplace<T>(getWritableArray<T>(in), is_upper);
+     return cholesky_inplace<T>(getArray<T>(in), is_upper);
 }
 
 af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_upper)
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 96647e62ef..696c0ec848 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -103,7 +103,7 @@ af_err af_ifft3(af_array *out, const af_array in, const double norm_factor, cons
 template<typename T, int rank, bool direction>
 static void fft_inplace(af_array in, const double norm_factor)
 {
-    Array<T> &input = getWritableArray<T>(in);
+    Array<T> &input = getArray<T>(in);
     fft_inplace<T, rank, direction>(input);
     if (norm_factor != 1) {
         multiply_inplace<T>(input, norm_factor);
diff --git a/src/api/c/gradient.cpp b/src/api/c/gradient.cpp
index 9a679dcf0c..d313d86686 100644
--- a/src/api/c/gradient.cpp
+++ b/src/api/c/gradient.cpp
@@ -21,7 +21,7 @@ using namespace detail;
 template<typename T>
 static inline void gradient(af_array *grad0, af_array *grad1, const af_array in)
 {
-    gradient<T>(getWritableArray<T>(*grad0), getWritableArray<T>(*grad1), getArray<T>(in));
+    gradient<T>(getArray<T>(*grad0), getArray<T>(*grad1), getArray<T>(in));
 }
 
 af_err af_gradient(af_array *grows, af_array *gcols, const af_array in)
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 04aa55eaa3..fad190d0dc 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -49,13 +49,21 @@ detail::Array<T> flat(const detail::Array<T>& in)
 }
 
 template<typename T>
-const detail::Array<T> &
+const detail::Array<T>&
 getArray(const af_array &arr)
 {
-    detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
+    const detail::Array<T> *A = static_cast<const detail::Array<T>*>(arr);
+    if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
+        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+    return *A;
+}
+
+template<typename T>
+detail::Array<T>& getArray(af_array &arr)
+{
+    detail::Array<T> *A = static_cast<detail::Array<T>*>(arr);
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
-    ARG_ASSERT(0, A->isSparse() == false);
     return *A;
 }
 
@@ -88,32 +96,20 @@ detail::Array<To> castArray(const af_array &in)
     }
 }
 
-template<typename T>
-detail::Array<T> &
-getWritableArray(af_array &arr)
-{
-    const detail::Array<T> &A = getArray<T>(arr);
-    ARG_ASSERT(0, A.isSparse() == false);
-    return const_cast<detail::Array<T>&>(A);
-}
-
 template<typename T>
 af_array
 getHandle(const detail::Array<T> &A)
 {
-    detail::Array<T> *ret = detail::initArray<T>();
-    *ret = A;
-    af_array arr = reinterpret_cast<af_array>(ret);
-    return arr;
+    detail::Array<T> *ret = new detail::Array<T>(A);
+    return static_cast<af_array>(ret);
 }
 
 template<typename T>
 af_array retainHandle(const af_array in)
 {
-    detail::Array<T> *A = reinterpret_cast<detail::Array<T> *>(in);
-    detail::Array<T> *out = detail::initArray<T>();
-    *out= *A;
-    return reinterpret_cast<af_array>(out);
+    detail::Array<T> *A = static_cast<detail::Array<T> *>(in);
+    detail::Array<T> *out = new detail::Array<T>(*A);
+    return static_cast<af_array>(out);
 }
 
 template<typename T>
@@ -150,14 +146,14 @@ af_array copyArray(const af_array in)
 template<typename T>
 void releaseHandle(const af_array arr)
 {
-    detail::destroyArray(reinterpret_cast<detail::Array<T>*>(arr));
+    detail::destroyArray(static_cast<detail::Array<T>*>(arr));
 }
 
 template<typename T>
 detail::Array<T> &
 getCopyOnWriteArray(const af_array &arr)
 {
-    detail::Array<T> *A = reinterpret_cast<detail::Array<T>*>(arr);
+    detail::Array<T> *A = static_cast<detail::Array<T>*>(arr);
 
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index 8f73a30ce4..82ef8c5fb1 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -37,7 +37,7 @@ static inline void lu(af_array *lower, af_array *upper, af_array *pivot,
 template<typename T>
 static inline af_array lu_inplace(af_array in, bool is_lapack_piv)
 {
-    return getHandle(lu_inplace<T>(getWritableArray<T>(in), !is_lapack_piv));
+    return getHandle(lu_inplace<T>(getArray<T>(in), !is_lapack_piv));
 }
 
 af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array in)
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index 78252130a8..e6477f420d 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -36,7 +36,7 @@ static inline void qr(af_array *q, af_array *r, af_array *tau, const af_array in
 template<typename T>
 static inline af_array qr_inplace(af_array in)
 {
-    return getHandle(qr_inplace<T>(getWritableArray<T>(in)));
+    return getHandle(qr_inplace<T>(getArray<T>(in)));
 }
 
 af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 19dedb3ba2..453e4b085b 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -25,42 +25,39 @@ const common::SparseArrayBase& getSparseArrayBase(const af_array arr, bool devic
 template<typename T>
 const common::SparseArray<T>& getSparseArray(const af_array &arr)
 {
-    common::SparseArray<T> *A = reinterpret_cast<common::SparseArray<T>*>(arr);
+    const common::SparseArray<T> *A = static_cast<const common::SparseArray<T>*>(arr);
     ARG_ASSERT(0, A->isSparse() == true);
     return *A;
 }
 
 template<typename T>
-common::SparseArray<T>& getWritableSparseArray(const af_array &arr)
+common::SparseArray<T>& getSparseArray(af_array &arr)
 {
-    const common::SparseArray<T> &A = getSparseArray<T>(arr);
-    ARG_ASSERT(0, A.isSparse() == true);
-    return const_cast<common::SparseArray<T>&>(A);
+    common::SparseArray<T> *A =  static_cast<common::SparseArray<T>*>(arr);
+    ARG_ASSERT(0, A->isSparse() == true);
+    return *A;
 }
 
 template<typename T>
 static af_array
 getHandle(const common::SparseArray<T> &A)
 {
-    common::SparseArray<T> *ret = common::initSparseArray<T>();
-    *ret = A;
-    af_array arr = reinterpret_cast<af_array>(ret);
-    return arr;
+    common::SparseArray<T> *ret = new common::SparseArray<T>(A);
+    return static_cast<af_array>(ret);
 }
 
 template<typename T>
 static void releaseSparseHandle(const af_array arr)
 {
-    common::destroySparseArray(reinterpret_cast<common::SparseArray<T>*>(arr));
+    common::destroySparseArray(static_cast<common::SparseArray<T>*>(arr));
 }
 
 template<typename T>
 af_array retainSparseHandle(const af_array in)
 {
-    common::SparseArray<T> *sparse = reinterpret_cast<common::SparseArray<T> *>(in);
-    common::SparseArray<T> *out = common::initSparseArray<T>();
-    *out = *sparse;
-    return reinterpret_cast<af_array>(out);
+    const common::SparseArray<T> *sparse = static_cast<const common::SparseArray<T> *>(in);
+    common::SparseArray<T> *out = new common::SparseArray<T>(*sparse);
+    return static_cast<af_array>(out);
 }
 
 // based on castArray in handle.hpp
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index 4db9d6e588..ea3232cfa8 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-
-
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
@@ -22,10 +20,19 @@
 #include <iomanip>
 #include <vector>
 
-using namespace detail;
 using std::string;
 using std::vector;
 
+using af::dim4;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createHostDataArray;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+
 #define STREAM_FORMAT_VERSION 0x1
 static const char sfv_char = STREAM_FORMAT_VERSION;
 
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index e0e9423c48..d12b4a9144 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -57,7 +57,7 @@ static inline void svdInPlace(af_array *s, af_array *u, af_array *vt, af_array i
     Array<T > uA  = createEmptyArray<T >(af::dim4(M, M));
     Array<T > vtA = createEmptyArray<T >(af::dim4(N, N));
 
-    svdInPlace<T, Tr>(sA, uA, vtA, getWritableArray<T>(in));
+    svdInPlace<T, Tr>(sA, uA, vtA, getArray<T>(in));
 
     *s = getHandle(sA);
     *u = getHandle(uA);
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index b75f5628fd..d77f5257f1 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -79,7 +79,7 @@ af_err af_transpose(af_array *out, af_array in, const bool conjugate)
 template<typename T>
 static inline void transpose_inplace(af_array in, const bool conjugate)
 {
-    return detail::transpose_inplace<T>(getWritableArray<T>(in), conjugate);
+    return detail::transpose_inplace<T>(getArray<T>(in), conjugate);
 }
 
 af_err af_transpose_inplace(af_array in, const bool conjugate)
diff --git a/src/api/c/type_util.hpp b/src/api/c/type_util.hpp
index 5fd37fd8fe..881a9f8c44 100644
--- a/src/api/c/type_util.hpp
+++ b/src/api/c/type_util.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <string>
 #include <af/defines.h>
 
 const char *getName(af_dtype type);
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index 0f19522239..c436ac6b3e 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -628,6 +628,7 @@ void computeDescriptor(
     const unsigned octave,
     const unsigned n_layers)
 {
+    UNUSED(response_in);
     float desc[128];
 
     for (unsigned f = 0; f < total_feat; f++) {
@@ -747,6 +748,7 @@ void computeGLOHDescriptor(
     const unsigned octave,
     const unsigned n_layers)
 {
+    UNUSED(response_in);
     float desc[272];
 
     for (unsigned f = 0; f < total_feat; f++) {
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 8817ba0b3c..a54aa79436 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -210,6 +210,7 @@ int getDeviceCount()
     return DeviceManager::NUM_DEVICES;
 }
 
+// Get the currently active device id
 int getActiveDeviceId()
 {
     return DeviceManager::ACTIVE_DEVICE_ID;
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index 8aec3ebfe3..ebcae8e1e8 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -32,8 +32,6 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const bool compute_GLOH)
 {
 #ifdef AF_WITH_NONFREE_SIFT
-    const dim4 dims = in.dims();
-
     unsigned nfeat_out;
     unsigned desc_len;
     float* x_out;

From 675d610e0ab4ad076977f864cfe661a85f4dcae8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 20 Nov 2018 04:19:43 -0500
Subject: [PATCH 1560/2677] Hide Array<T> constructor. Use createStridedArray
 instead.

---
 src/api/c/internal.cpp       | 34 +++++++++++++++++++++-------------
 src/backend/cpu/Array.hpp    | 14 +++++++++++---
 src/backend/cpu/types.hpp    |  6 +++---
 src/backend/cuda/Array.hpp   |  8 ++++++++
 src/backend/cuda/types.hpp   |  2 +-
 src/backend/opencl/Array.hpp |  8 ++++++++
 src/backend/opencl/types.hpp |  6 +++---
 7 files changed, 55 insertions(+), 23 deletions(-)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 60aa31f346..e9f13cfa51 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -18,7 +18,15 @@
 #include <common/err_common.hpp>
 #include <cstring>
 
-using namespace detail;
+using af::dim4;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createStridedArray;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 af_err af_create_strided_array(af_array *arr,
                                const void *data,
@@ -54,18 +62,18 @@ af_err af_create_strided_array(af_array *arr,
         AF_CHECK(af_init());
 
         switch (ty) {
-        case f32: res = getHandle(Array<float  >(dims, strides, offset, (float   *)data, isdev)); break;
-        case f64: res = getHandle(Array<double >(dims, strides, offset, (double  *)data, isdev)); break;
-        case c32: res = getHandle(Array<cfloat >(dims, strides, offset, (cfloat  *)data, isdev)); break;
-        case c64: res = getHandle(Array<cdouble>(dims, strides, offset, (cdouble *)data, isdev)); break;
-        case u32: res = getHandle(Array<uint   >(dims, strides, offset, (uint    *)data, isdev)); break;
-        case s32: res = getHandle(Array<int    >(dims, strides, offset, (int     *)data, isdev)); break;
-        case u64: res = getHandle(Array<uintl  >(dims, strides, offset, (uintl   *)data, isdev)); break;
-        case s64: res = getHandle(Array<intl   >(dims, strides, offset, (intl    *)data, isdev)); break;
-        case u16: res = getHandle(Array<ushort >(dims, strides, offset, (ushort  *)data, isdev)); break;
-        case s16: res = getHandle(Array<short  >(dims, strides, offset, (short   *)data, isdev)); break;
-        case b8 : res = getHandle(Array<char   >(dims, strides, offset, (char    *)data, isdev)); break;
-        case u8 : res = getHandle(Array<uchar  >(dims, strides, offset, (uchar   *)data, isdev)); break;
+        case f32: res = getHandle(createStridedArray<float  >(dims, strides, offset, (float   *)data, isdev)); break;
+        case f64: res = getHandle(createStridedArray<double >(dims, strides, offset, (double  *)data, isdev)); break;
+        case c32: res = getHandle(createStridedArray<cfloat >(dims, strides, offset, (cfloat  *)data, isdev)); break;
+        case c64: res = getHandle(createStridedArray<cdouble>(dims, strides, offset, (cdouble *)data, isdev)); break;
+        case u32: res = getHandle(createStridedArray<uint   >(dims, strides, offset, (uint    *)data, isdev)); break;
+        case s32: res = getHandle(createStridedArray<int    >(dims, strides, offset, (int     *)data, isdev)); break;
+        case u64: res = getHandle(createStridedArray<uintl  >(dims, strides, offset, (uintl   *)data, isdev)); break;
+        case s64: res = getHandle(createStridedArray<intl   >(dims, strides, offset, (intl    *)data, isdev)); break;
+        case u16: res = getHandle(createStridedArray<ushort >(dims, strides, offset, (ushort  *)data, isdev)); break;
+        case s16: res = getHandle(createStridedArray<short  >(dims, strides, offset, (short   *)data, isdev)); break;
+        case b8 : res = getHandle(createStridedArray<char   >(dims, strides, offset, (char    *)data, isdev)); break;
+        case u8 : res = getHandle(createStridedArray<uchar  >(dims, strides, offset, (uchar   *)data, isdev)); break;
         default: TYPE_ERROR(6, ty);
         }
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 1464ee7315..075cfe60a0 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -58,6 +58,12 @@ namespace cpu
     template<typename T>
     Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
 
+    template<typename T>
+    Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
+                                const T * const in_data, bool is_device) {
+        return Array<T>(dims, strides, offset, in_data, is_device);
+    }
+
     /// Copies data to an existing Array object from a host pointer
     template<typename T>
     void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
@@ -107,7 +113,6 @@ namespace cpu
     class Array
     {
         ArrayInfo info; // Must be the first element of Array<T>
-        //TODO: Generator based array
 
         //data if parent. empty if child
         std::shared_ptr<T> data;
@@ -123,11 +128,11 @@ namespace cpu
         explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
         Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
         explicit Array(af::dim4 dims, jit::Node_ptr n);
-
-    public:
         Array(af::dim4 dims, af::dim4 strides, dim_t offset,
               const T * const in_data, bool is_device = false);
 
+    public:
+
         void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
         void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
         void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }
@@ -238,6 +243,9 @@ namespace cpu
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
+        friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
+                                              const T * const in_data, bool is_device);
+
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 782fabda44..073f2f258d 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -12,11 +12,11 @@
 
 namespace cpu
 {
-using cfloat  = std::complex<float>;
 using cdouble = std::complex<double>;
+using cfloat  = std::complex<float>;
+using intl    = long long;
 using uint    = unsigned int;
 using uchar   = unsigned char;
-using ushort  = unsigned short;
-using intl    = long long;
 using uintl   = unsigned long long;
+using ushort  = unsigned short;
 }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index e22f4a1b03..157ceb2616 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -48,6 +48,12 @@ namespace cuda
     template<typename T>
     Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
 
+    template<typename T>
+    Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
+                                const T * const in_data, bool is_device) {
+        return Array<T>(dims, strides, offset, in_data, is_device);
+    }
+
     /// Copies data to an existing Array object from a host pointer
     template<typename T>
     void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
@@ -231,6 +237,8 @@ namespace cuda
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
+        friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
+                                              const T * const in_data, bool is_device);
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 45e8338cc3..3b5321702f 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -15,9 +15,9 @@ namespace cuda
 {
 using cdouble = cuDoubleComplex;
 using cfloat  = cuFloatComplex;
+using intl    = long long;
 using uchar   = unsigned char;
 using uint    = unsigned int;
-using intl    = long long;
 using uintl   = unsigned long long;
 using ushort  = unsigned short;
 
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 2e73df9390..5d9546ba49 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -48,6 +48,12 @@ namespace opencl
     template<typename T>
     Array<T> createDeviceDataArray(const af::dim4 &size, const void *data, bool copy = false);
 
+    template<typename T>
+    Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
+                                const T * const in_data, bool is_device) {
+        return Array<T>(dims, strides, offset, in_data, is_device);
+    }
+
     /// Copies data to an existing Array object from a host pointer
     template<typename T>
     void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
@@ -276,6 +282,8 @@ namespace opencl
         friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
         friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
         friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data, bool copy);
+        friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
+                                              const T * const in_data, bool is_device);
 
         friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index aa2a449cf7..d958c36aee 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -25,13 +25,13 @@
 
 namespace opencl
 {
-using cfloat  = cl_float2;
 using cdouble = cl_double2;
+using cfloat  = cl_float2;
+using intl    = long long;
 using uchar   = cl_uchar;
 using uint    = cl_uint;
-using ushort  = cl_ushort;
-using intl    = long long;
 using uintl   = unsigned long long;
+using ushort  = cl_ushort;
 
 template<typename T>
 struct ToNumStr

From fc7a7f7afc46184de23f4b063bf315729555da39 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 23 Nov 2018 12:12:23 -0500
Subject: [PATCH 1561/2677] Fix additional warnings on Windows

---
 src/backend/cpu/kernel/susan.hpp              | 16 +++++------
 src/backend/cpu/padarray.cpp                  |  6 +++--
 src/backend/opencl/magma/magma_blas_clblast.h | 27 ++++++++++++-------
 test/testHelpers.hpp                          |  2 +-
 4 files changed, 31 insertions(+), 20 deletions(-)

diff --git a/src/backend/cpu/kernel/susan.hpp b/src/backend/cpu/kernel/susan.hpp
index 3d9c098c9e..9d5d2e0009 100644
--- a/src/backend/cpu/kernel/susan.hpp
+++ b/src/backend/cpu/kernel/susan.hpp
@@ -17,7 +17,7 @@ namespace kernel
 
 template<typename T>
 void susan_responses(Param<T> output, CParam<T> input,
-                     const unsigned idim0, const unsigned idim1,
+                     const dim_t idim0, const dim_t idim1,
                      const int radius, const float t, const float g,
                      const unsigned border_len)
 {
@@ -27,9 +27,9 @@ void susan_responses(Param<T> output, CParam<T> input,
     const unsigned r = border_len;
     const int rSqrd = radius*radius;
 
-    for (unsigned y = r; y < idim1 - r; ++y) {
-        for (unsigned x = r; x < idim0 - r; ++x) {
-            const unsigned idx = y * idim0 + x;
+    for (dim_t y = r; y < idim1 - r; ++y) {
+        for (dim_t x = r; x < idim0 - r; ++x) {
+            const dim_t idx = y * idim0 + x;
             T m_0 = in[idx];
             float nM = 0.0f;
 
@@ -53,7 +53,7 @@ void susan_responses(Param<T> output, CParam<T> input,
 
 template<typename T>
 void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> response,
-                 shared_ptr<unsigned> counter, const unsigned idim0, const unsigned idim1,
+                 shared_ptr<unsigned> counter, const dim_t idim0, const dim_t idim1,
                  CParam<T> input, const unsigned border_len, const unsigned max_corners)
 {
     float* x_out    = xcoords.get();
@@ -65,8 +65,8 @@ void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> respon
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
 
-    for (unsigned y = r; y < idim1 - r; y++) {
-        for (unsigned x = r; x < idim0 - r; x++) {
+    for (dim_t y = r; y < idim1 - r; y++) {
+        for (dim_t x = r; x < idim0 - r; x++) {
             const T v = resp_in[y * idim0 + x];
 
             // Find maximum neighborhood response
@@ -82,7 +82,7 @@ void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> respon
             // Stores corner to {x,y,resp}_out if it's response is maximum compared
             // to its 8-neighborhood and greater or equal minimum response
             if (v > max_v) {
-                const unsigned idx = *count;
+                const dim_t idx = *count;
                 *count += 1;
                 if (idx < max_corners) {
                     x_out[idx]    = (float)x;
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
index f48ec0cdc0..4f7b611b34 100644
--- a/src/backend/cpu/padarray.cpp
+++ b/src/backend/cpu/padarray.cpp
@@ -27,7 +27,8 @@ template<typename T>
 void multiply_inplace(Array<T> &in, double val)
 {
     in.eval();
-    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, 0, val);
+    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in,
+                       static_cast<T>(0), val);
 }
 
 template<typename inType, typename outType>
@@ -37,7 +38,8 @@ Array<outType> padArray(const Array<inType>& in, const dim4& dims,
     Array<outType> ret = createValueArray<outType>(dims, default_value);
     ret.eval();
     in.eval();
-    getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in, outType(default_value), factor);
+    getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in,
+                       static_cast<outType>(default_value), factor);
 
     return ret;
 }
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index e978eda3ae..4ba8dac927 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -91,8 +91,9 @@ struct gpu_blas_gemm_func
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         const cl_mem b_buffer, const size_t b_offset, const size_t b_ld, const T beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -112,8 +113,9 @@ struct gpu_blas_gemv_func
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         const cl_mem x_buffer, const size_t x_offset, const size_t x_inc, const T beta,
         cl_mem y_buffer, const size_t y_offset, const size_t y_inc,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -132,8 +134,9 @@ struct gpu_blas_trmm_func
         const size_t m, const size_t n, const T alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -151,8 +154,9 @@ struct gpu_blas_trsm_func
         const size_t m, const size_t n, const T alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -170,8 +174,9 @@ struct gpu_blas_trsv_func
         const size_t n,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
         cl_mem x_buffer, const size_t x_offset, const size_t x_inc,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         return clblast::Trsv<typename CLBlastType<T>::Type>(
@@ -191,8 +196,9 @@ struct gpu_blas_herk_func
         const size_t n, const size_t k, const BasicType alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const BasicType beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -212,8 +218,9 @@ struct gpu_blas_herk_func<float>
         const size_t n, const size_t k, const float alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const float beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -233,8 +240,9 @@ struct gpu_blas_herk_func<double>
         const size_t n, const size_t k, const double alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const double beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
@@ -253,8 +261,9 @@ struct gpu_blas_syrk_func
         const size_t n, const size_t k, const T alpha,
         const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const T beta,
         cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event */*wait_events*/, cl_event *events)
+        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
     {
+        UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 74db5f096a..63082fc5f9 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -539,7 +539,7 @@ const af::cdouble& operator+(const af::cdouble& val) {
 
 // Calculate a multi-dimensional coordinates' linearized index
 dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
-    return std::inner_product(coords.get(), coords.get()+4, strides.get(), 0);
+    return std::inner_product(coords.get(), coords.get()+4, strides.get(), 0LL);
 }
 
 // Calculate a linearized index's multi-dimensonal coordinates in an af::array,

From c98d2e0413d9279f394d1ff51ed31566c8910378 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 21 Nov 2018 18:28:20 +0530
Subject: [PATCH 1562/2677] Fix typo in matmul batch info table

---
 docs/details/blas.dox | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index 24950e18b5..ccbe6649e7 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -29,14 +29,17 @@ data from memory. This results in no additional memory being used for temporary
 buffers.
 
 Batched matrix multiplications are supported. Given below are the supported
-formats for given matrices A and B.
+types of batch operations for any given set of two matrices A and B.
 
-| Input Matrix A             | Input Matrix B             | Output Matrix Size          |
+| Size of Input Matrix A     | Size of Input Matrix B     | Output Matrix Size          |
 |:--------------------------:|:--------------------------:|:---------------------------:|
-| \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ K, N,  1,  1 \} \f$ |
-| \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ K, N, b2, b3 \} \f$ |
-| \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ K, N, b2, b3 \} \f$ |
-| \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ K, N, b2, b3 \} \f$ |
+| \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ M, N,  1,  1 \} \f$ |
+| \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ M, N, b2, b3 \} \f$ |
+| \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ M, N, b2, b3 \} \f$ |
+| \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ M, N, b2, b3 \} \f$ |
+
+where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the
+respective dimension.
 
 For the last two entries in the above table, the 2D matrix is broadcasted to
 match the dimensions of 3D/4D array. This broadcast doesn't involve any additional

From 4bf81e6cdacfa4f135a934a761b46603cf6fae91 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Nov 2018 00:58:00 -0500
Subject: [PATCH 1563/2677] Add missing c++ typedef for af_var_bias in 3.7

---
 include/af/defines.h | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/include/af/defines.h b/include/af/defines.h
index 92c1e47b4a..db88b90d55 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -564,6 +564,9 @@ namespace af
     typedef af_iterative_deconv_algo iterativeDeconvAlgo;
     typedef af_inverse_deconv_algo inverseDeconvAlgo;
 #endif
+#if AF_API_VERSION >= 37
+    typedef af_var_bias varBias;
+#endif
 }
 
 #endif

From 3c0bc69a67fee9cae15289df61b28d8d750cb3b6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Nov 2018 00:58:42 -0500
Subject: [PATCH 1564/2677] Fix error on osx clang wrt the order of functions
 in approx

---
 src/api/c/approx.cpp | 40 ++++++++++++++++++++++------------------
 1 file changed, 22 insertions(+), 18 deletions(-)

diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 6a7bd4d3f3..f06995b617 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -19,16 +19,18 @@
 using af::dim4;
 using namespace detail;
 
-template<typename Ty, typename Tp>
-static inline void approx1(af_array *yo, const af_array yi,
-                           const af_array xo, const int xdim,
-                           const Tp &xi_beg, const Tp &xi_step,
-                           const af_interp_type method, const float offGrid)
-{
-    approx1<Ty>(getArray<Ty>(*yo), getArray<Ty>(yi),
-                getArray<Tp>(xo), xdim,
-                xi_beg, xi_step,
-                method, offGrid);
+namespace {
+    template<typename Ty, typename Tp>
+    inline void approx1(af_array *yo, const af_array yi,
+                        const af_array xo, const int xdim,
+                        const Tp &xi_beg, const Tp &xi_step,
+                        const af_interp_type method, const float offGrid)
+    {
+        approx1<Ty>(getArray<Ty>(*yo), getArray<Ty>(yi),
+                    getArray<Tp>(xo), xdim,
+                    xi_beg, xi_step,
+                    method, offGrid);
+    }
 }
 
 template<typename Ty, typename Tp>
@@ -43,12 +45,6 @@ static inline af_array approx2(const af_array zi,
                                  method, offGrid));
 }
 
-af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
-                  const af_interp_type method, const float offGrid)
-{
-    return af_approx1_uniform(yo, yi, xo, 0, 0.0, 1.0, method, offGrid);
-}
-
 af_err af_approx1_uniform(af_array *yo, const af_array yi,
                           const af_array xo, const int xdim,
                           const double xi_beg, const double xi_step,
@@ -116,10 +112,11 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi,
     return AF_SUCCESS;
 }
 
-af_err af_approx2(af_array *zo, const af_array zi, const af_array xo, const af_array yo,
+
+af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
                   const af_interp_type method, const float offGrid)
 {
-    return af_approx2_uniform(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method, offGrid);
+  return af_approx1_uniform(yo, yi, xo, 0, 0.0, 1.0, method, offGrid);
 }
 
 af_err af_approx2_uniform(af_array *zo, const af_array zi,
@@ -187,3 +184,10 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi,
 
     return AF_SUCCESS;
 }
+
+af_err af_approx2(af_array *zo, const af_array zi, const af_array xo, const af_array yo,
+                  const af_interp_type method, const float offGrid)
+{
+  return af_approx2_uniform(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method, offGrid);
+}
+

From 618d3b13d0a302f57a69718be3c6ff71f4934ae6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 25 Nov 2018 22:23:16 -0500
Subject: [PATCH 1565/2677] Remove the initArray function.

---
 src/api/c/var.cpp                 | 16 ++++++++--------
 src/backend/cpu/Array.cpp         |  4 ----
 src/backend/cpu/Array.hpp         |  6 ------
 src/backend/cpu/kernel/resize.hpp |  8 ++++++++
 src/backend/cuda/Array.cpp        |  7 -------
 src/backend/cuda/Array.hpp        |  6 ------
 src/backend/opencl/Array.cpp      |  7 -------
 src/backend/opencl/Array.hpp      |  6 ------
 8 files changed, 16 insertions(+), 44 deletions(-)

diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index ff5a60ee40..ac18b8a639 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -82,8 +82,8 @@ meanvar(const Array<inType> &in, const Array<typename baseOutType<outType>::type
     Array<outType> input = cast<outType>(in);
     dim4 iDims = input.dims();
 
-    Array<outType> meanArr = *initArray<outType>();
-    Array<outType> normArr = *initArray<outType>();
+    Array<outType> meanArr = createEmptyArray<outType>({0});
+    Array<outType> normArr = createEmptyArray<outType>({0});
     if(weights.isEmpty()) {
         meanArr = mean<outType, weightType, outType>(input, dim);
         auto val = 1.0 / (bias == AF_VARIANCE_POPULATION ? iDims[dim] : iDims[dim]-1);
@@ -121,9 +121,9 @@ meanvar(const af_array &in, const af_array &weights,
         const af_var_bias bias, const dim_t dim) {
 
   typedef typename baseOutType<outType>::type weightType;
-  Array<outType> mean = *initArray<outType>(), var = *initArray<outType>();
+  Array<outType> mean = createEmptyArray<outType>({0}), var = createEmptyArray<outType>({0});
 
-  Array<weightType> w = *initArray<weightType>();
+  Array<weightType> w = createEmptyArray<weightType>({0});
   if(weights != 0) {
     w = getArray<weightType>(weights);
   }
@@ -142,7 +142,7 @@ var(const Array<inType>& in,
     const Array<typename baseOutType<outType>::type>& weights,
     const af_var_bias bias, int dim)
 {
-    Array<outType> variance = *initArray<outType>();
+    Array<outType> variance = createEmptyArray<outType>({0});
     tie(ignore, variance) = meanvar<inType, outType>(in, weights, bias, dim);
     return variance;
 }
@@ -152,10 +152,10 @@ static af_array var_(const af_array& in, const af_array& weights,
                      const af_var_bias bias, int dim) {
   using bType = typename baseOutType<outType>::type;
   if(weights == 0) {
-    Array<bType> empty = *initArray<bType>();
-    return getHandle(var<inType, outType>(getArray<inType>(in), empty, bias, dim));
+      Array<bType> empty = createEmptyArray<bType>({0});
+      return getHandle(var<inType, outType>(getArray<inType>(in), empty, bias, dim));
   } else {
-    return getHandle(var<inType, outType>(getArray<inType>(in), getArray<bType>(weights), bias, dim));
+      return getHandle(var<inType, outType>(getArray<inType>(in), getArray<bType>(weights), bias, dim));
   }
 }
 
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 2fc6e8c49b..0fdcb05177 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -214,9 +214,6 @@ createEmptyArray(const dim4 &size)
     return Array<T>(size);
 }
 
-template<typename T>
-Array<T> *initArray() { return new Array<T>(dim4()); }
-
 template<typename T>
 Array<T>
 createNodeArray(const dim4 &dims, Node_ptr node)
@@ -346,7 +343,6 @@ Array<T>::setDataDims(const dim4 &new_dims)
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
-    template       Array<T>  *initArray<T      >      ();               \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
                                                        const vector<af_seq> &index, \
                                                        bool copy);      \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 075cfe60a0..667507a0e9 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -72,11 +72,6 @@ namespace cpu
     template<typename T>
     void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
 
-    /// Create an Array object and do not assign any values to it.
-    /// \NOTE: This object should not be used to initalize an array. Use
-    ///       createEmptyArray instead
-    template<typename T> Array<T> *initArray();
-
     /// Creates an empty array of a given size. No data is initialized
     ///
     /// \param[in] size The dimension of the output array
@@ -247,7 +242,6 @@ namespace cpu
                                               const T * const in_data, bool is_device);
 
 
-        friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
         friend Array<T> createNodeArray<T>(const af::dim4 &dims, jit::Node_ptr node);
 
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index 1ab0025b67..0dc171688f 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -49,6 +49,14 @@ struct resize_op
               const af::dim4 &ostrides, const af::dim4 &istrides,
               const dim_t x, const dim_t y)
     {
+        UNUSED(outPtr);
+        UNUSED(inPtr);
+        UNUSED(odims);
+        UNUSED(idims);
+        UNUSED(ostrides);
+        UNUSED(istrides);
+        UNUSED(x);
+        UNUSED(y);
         return;
     }
 };
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 87663bad16..f44e7efd95 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -315,12 +315,6 @@ namespace cuda
         return Array<T>(size);
     }
 
-    template<typename T>
-    Array<T> *initArray()
-    {
-        return new Array<T>(dim4());
-    }
-
     template<typename T>
     Array<T> createSubArray(const Array<T>& parent,
                             const std::vector<af_seq> &index,
@@ -421,7 +415,6 @@ namespace cuda
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
-    template       Array<T>  *initArray<T      >      ();               \
     template       Array<T>  createParamArray<T>      (Param<T> &tmp, bool owner); \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
                                                        const std::vector<af_seq> &index, \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 157ceb2616..f74fd27715 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -62,11 +62,6 @@ namespace cuda
     template<typename T>
     void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
 
-    /// Create an Array object and do not assign any values to it.
-    /// \NOTE: This object should not be used to initalize an array. Use
-    ///       createEmptyArray instead
-    template<typename T> Array<T> *initArray();
-
     /// Creates an empty array of a given size. No data is initialized
     ///
     /// \param[in] size The dimension of the output array
@@ -240,7 +235,6 @@ namespace cuda
         friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
                                               const T * const in_data, bool is_device);
 
-        friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
         friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
         friend Array<T> createNodeArray<T>(const af::dim4 &dims, common::Node_ptr node);
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index d16ef27c3c..a2e74b3867 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -378,12 +378,6 @@ namespace opencl
         return Array<T>(size);
     }
 
-    template<typename T>
-    Array<T> *initArray()
-    {
-        return new Array<T>(dim4());
-    }
-
     template<typename T>
     Array<T>
     createParamArray(Param &tmp, bool owner)
@@ -451,7 +445,6 @@ namespace opencl
     template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data, bool copy); \
     template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
     template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
-    template       Array<T>  *initArray<T      >      ();               \
     template       Array<T>  createParamArray<T>      (Param &tmp, bool owner);    \
     template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
                                                        const vector<af_seq> &index, \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 5d9546ba49..a8a27271be 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -62,11 +62,6 @@ namespace opencl
     template<typename T>
     void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
 
-    /// Create an Array object and do not assign any values to it.
-    /// \NOTE: This object should not be used to initalize an array. Use
-    ///       createEmptyArray instead
-    template<typename T> Array<T> *initArray();
-
     /// Creates an empty array of a given size. No data is initialized
     ///
     /// \param[in] size The dimension of the output array
@@ -285,7 +280,6 @@ namespace opencl
         friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
                                               const T * const in_data, bool is_device);
 
-        friend Array<T> *initArray<T>();
         friend Array<T> createEmptyArray<T>(const af::dim4 &size);
         friend Array<T> createParamArray<T>(Param &tmp, bool owner);
         friend Array<T> createNodeArray<T>(const af::dim4 &dims, common::Node_ptr node);

From b8daf96a618617513122da253a024c1a6909d910 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Nov 2018 10:48:02 -0500
Subject: [PATCH 1566/2677] Fix additional warnings

---
 include/af/defines.h                         | 88 ++++++++++----------
 include/af/vision.h                          |  2 +-
 src/api/c/pinverse.cpp                       |  2 -
 src/backend/common/Logger.cpp                |  2 +-
 src/backend/common/lapacke.cpp               | 22 +++++
 src/backend/cpu/Array.hpp                    |  2 +-
 src/backend/cpu/jit/BinaryNode.hpp           |  2 +
 src/backend/cpu/kernel/meanshift.hpp         |  6 +-
 src/backend/cpu/kernel/pad_array_borders.hpp |  4 +-
 src/backend/cpu/kernel/sparse_arith.hpp      |  2 +
 test/testHelpers.hpp                         | 36 ++++----
 11 files changed, 97 insertions(+), 71 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index db88b90d55..efc553e902 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -66,63 +66,63 @@ typedef enum {
     ///
     /// The function returned successfully
     ///
-    AF_SUCCESS            =   0,
+    AF_SUCCESS            =   0
 
     // 100-199 Errors in environment
 
     ///
     /// The system or device ran out of memory
     ///
-    AF_ERR_NO_MEM         = 101,
+    , AF_ERR_NO_MEM         = 101
 
     ///
     /// There was an error in the device driver
     ///
-    AF_ERR_DRIVER         = 102,
+    , AF_ERR_DRIVER         = 102
 
     ///
     /// There was an error with the runtime environment
     ///
-    AF_ERR_RUNTIME        = 103,
+    , AF_ERR_RUNTIME        = 103
 
     // 200-299 Errors in input parameters
 
     ///
     /// The input array is not a valid af_array object
     ///
-    AF_ERR_INVALID_ARRAY  = 201,
+    , AF_ERR_INVALID_ARRAY  = 201
 
     ///
     /// One of the function arguments is incorrect
     ///
-    AF_ERR_ARG            = 202,
+    , AF_ERR_ARG            = 202
 
     ///
     /// The size is incorrect
     ///
-    AF_ERR_SIZE           = 203,
+    , AF_ERR_SIZE           = 203
 
     ///
     /// The type is not suppported by this function
     ///
-    AF_ERR_TYPE           = 204,
+    , AF_ERR_TYPE           = 204
 
     ///
     /// The type of the input arrays are not compatible
     ///
-    AF_ERR_DIFF_TYPE      = 205,
+    , AF_ERR_DIFF_TYPE      = 205
 
     ///
     /// Function does not support GFOR / batch mode
     ///
-    AF_ERR_BATCH          = 207,
+    , AF_ERR_BATCH          = 207
 
 
 #if AF_API_VERSION >= 33
     ///
     /// Input does not belong to the current device.
     ///
-    AF_ERR_DEVICE         = 208,
+    , AF_ERR_DEVICE         = 208
 #endif
 
     // 300-399 Errors for missing software features
@@ -130,18 +130,18 @@ typedef enum {
     ///
     /// The option is not supported
     ///
-    AF_ERR_NOT_SUPPORTED  = 301,
+    , AF_ERR_NOT_SUPPORTED  = 301
 
     ///
     /// This build of ArrayFire does not support this feature
     ///
-    AF_ERR_NOT_CONFIGURED = 302,
+    , AF_ERR_NOT_CONFIGURED = 302
 
 #if AF_API_VERSION >= 32
     ///
     /// This build of ArrayFire is not compiled with "nonfree" algorithms
     ///
-    AF_ERR_NONFREE        = 303,
+    , AF_ERR_NONFREE        = 303
 #endif
 
     // 400-499 Errors for missing hardware features
@@ -149,13 +149,13 @@ typedef enum {
     ///
     /// This device does not support double
     ///
-    AF_ERR_NO_DBL         = 401,
+    , AF_ERR_NO_DBL         = 401
 
     ///
     /// This build of ArrayFire was not built with graphics or this device does
     /// not support graphics
     ///
-    AF_ERR_NO_GFX         = 402,
+    , AF_ERR_NO_GFX         = 402
 
     // 500-599 Errors specific to heterogenous API
 
@@ -163,21 +163,21 @@ typedef enum {
     ///
     /// There was an error when loading the libraries
     ///
-    AF_ERR_LOAD_LIB       = 501,
+    , AF_ERR_LOAD_LIB       = 501
 #endif
 
 #if AF_API_VERSION >= 32
     ///
     /// There was an error when loading the symbols
     ///
-    AF_ERR_LOAD_SYM       = 502,
+    , AF_ERR_LOAD_SYM       = 502
 #endif
 
 #if AF_API_VERSION >= 32
     ///
     /// There was a mismatch between the input array and the active backend
     ///
-    AF_ERR_ARR_BKND_MISMATCH    = 503,
+    , AF_ERR_ARR_BKND_MISMATCH    = 503
 #endif
 
     // 900-999 Errors from upstream libraries and runtimes
@@ -186,12 +186,12 @@ typedef enum {
     /// There was an internal error either in ArrayFire or in a project
     /// upstream
     ///
-    AF_ERR_INTERNAL       = 998,
+    , AF_ERR_INTERNAL       = 998
 
     ///
     /// Unknown Error
     ///
-    AF_ERR_UNKNOWN        = 999
+    , AF_ERR_UNKNOWN        = 999
 } af_err;
 
 typedef enum {
@@ -204,18 +204,18 @@ typedef enum {
     u32,    ///< 32-bit unsigned integral values
     u8 ,    ///< 8-bit unsigned integral values
     s64,    ///< 64-bit signed integral values
-    u64,    ///< 64-bit unsigned integral values
+    u64    ///< 64-bit unsigned integral values
 #if AF_API_VERSION >= 32
-    s16,    ///< 16-bit signed integral values
+    , s16    ///< 16-bit signed integral values
 #endif
 #if AF_API_VERSION >= 32
-    u16,    ///< 16-bit unsigned integral values
+    , u16    ///< 16-bit unsigned integral values
 #endif
 } af_dtype;
 
 typedef enum {
     afDevice,   ///< Device pointer
-    afHost,     ///< Host pointer
+    afHost     ///< Host pointer
 } af_source;
 
 #define AF_MAX_DIMS 4
@@ -228,21 +228,21 @@ typedef enum {
     AF_INTERP_LINEAR,          ///< Linear Interpolation
     AF_INTERP_BILINEAR,        ///< Bilinear Interpolation
     AF_INTERP_CUBIC,           ///< Cubic Interpolation
-    AF_INTERP_LOWER,           ///< Floor Indexed
+    AF_INTERP_LOWER           ///< Floor Indexed
 #if AF_API_VERSION >= 34
-    AF_INTERP_LINEAR_COSINE,   ///< Linear Interpolation with cosine smoothing
+    , AF_INTERP_LINEAR_COSINE   ///< Linear Interpolation with cosine smoothing
 #endif
 #if AF_API_VERSION >= 34
-    AF_INTERP_BILINEAR_COSINE, ///< Bilinear Interpolation with cosine smoothing
+    , AF_INTERP_BILINEAR_COSINE ///< Bilinear Interpolation with cosine smoothing
 #endif
 #if AF_API_VERSION >= 34
-    AF_INTERP_BICUBIC,         ///< Bicubic Interpolation
+    , AF_INTERP_BICUBIC         ///< Bicubic Interpolation
 #endif
 #if AF_API_VERSION >= 34
-    AF_INTERP_CUBIC_SPLINE,    ///< Cubic Interpolation with Catmull-Rom splines
+    , AF_INTERP_CUBIC_SPLINE    ///< Cubic Interpolation with Catmull-Rom splines
 #endif
 #if AF_API_VERSION >= 34
-    AF_INTERP_BICUBIC_SPLINE,  ///< Bicubic Interpolation with Catmull-Rom splines
+    , AF_INTERP_BICUBIC_SPLINE  ///< Bicubic Interpolation with Catmull-Rom splines
 #endif
 
 } af_interp_type;
@@ -261,7 +261,7 @@ typedef enum {
     ///
     /// Out of bound values are clamped to the edge
     ///
-    AF_PAD_CLAMP_TO_EDGE,
+    AF_PAD_CLAMP_TO_EDGE
 } af_border_type;
 
 typedef enum {
@@ -286,13 +286,13 @@ typedef enum {
     ///
     /// Output of the convolution is signal_len + filter_len - 1
     ///
-    AF_CONV_EXPAND,
+    AF_CONV_EXPAND
 } af_conv_mode;
 
 typedef enum {
     AF_CONV_AUTO,    ///< ArrayFire automatically picks the right convolution algorithm
     AF_CONV_SPATIAL, ///< Perform convolution in spatial domain
-    AF_CONV_FREQ,    ///< Perform convolution in frequency domain
+    AF_CONV_FREQ     ///< Perform convolution in frequency domain
 } af_conv_domain;
 
 typedef enum {
@@ -311,16 +311,16 @@ typedef enum {
 typedef enum {
     AF_YCC_601 = 601,  ///< ITU-R BT.601 (formerly CCIR 601) standard
     AF_YCC_709 = 709,  ///< ITU-R BT.709 standard
-    AF_YCC_2020 = 2020  ///< ITU-R BT.2020 standard
+    AF_YCC_2020 = 2020 ///< ITU-R BT.2020 standard
 } af_ycc_std;
 #endif
 
 typedef enum {
     AF_GRAY = 0, ///< Grayscale
     AF_RGB,      ///< 3-channel RGB
-    AF_HSV,      ///< 3-channel HSV
+    AF_HSV       ///< 3-channel HSV
 #if AF_API_VERSION >= 31
-    AF_YCbCr     ///< 3-channel YCbCr
+    , AF_YCbCr     ///< 3-channel YCbCr
 #endif
 } af_cspace_t;
 
@@ -349,7 +349,7 @@ typedef enum {
     AF_NORM_MATRIX_2,      ///< returns the max singular value). Currently NOT SUPPORTED
     AF_NORM_MATRIX_L_PQ,   ///< returns Lpq-norm
 
-    AF_NORM_EUCLID = AF_NORM_VECTOR_2, ///< The default. Same as AF_NORM_VECTOR_2
+    AF_NORM_EUCLID = AF_NORM_VECTOR_2 ///< The default. Same as AF_NORM_VECTOR_2
 } af_norm_type;
 
 #if AF_API_VERSION >= 31
@@ -393,7 +393,7 @@ typedef enum {
     AF_BACKEND_DEFAULT = 0,  ///< Default backend order: OpenCL -> CUDA -> CPU
     AF_BACKEND_CPU     = 1,  ///< CPU a.k.a sequential algorithms
     AF_BACKEND_CUDA    = 2,  ///< CUDA Compute Backend
-    AF_BACKEND_OPENCL  = 4,  ///< OpenCL Compute Backend
+    AF_BACKEND_OPENCL  = 4   ///< OpenCL Compute Backend
 } af_backend;
 #endif
 
@@ -460,7 +460,7 @@ typedef enum {
 #if AF_API_VERSION >= 35
 typedef enum {
     AF_CANNY_THRESHOLD_MANUAL    = 0, ///< User has to define canny thresholds manually
-    AF_CANNY_THRESHOLD_AUTO_OTSU = 1, ///< Determine canny algorithm thresholds using Otsu algorithm
+    AF_CANNY_THRESHOLD_AUTO_OTSU = 1  ///< Determine canny algorithm thresholds using Otsu algorithm
 } af_canny_threshold;
 #endif
 
@@ -469,7 +469,7 @@ typedef enum {
     AF_STORAGE_DENSE     = 0,   ///< Storage type is dense
     AF_STORAGE_CSR       = 1,   ///< Storage type is CSR
     AF_STORAGE_CSC       = 2,   ///< Storage type is CSC
-    AF_STORAGE_COO       = 3,   ///< Storage type is COO
+    AF_STORAGE_COO       = 3    ///< Storage type is COO
 } af_storage;
 #endif
 
@@ -495,12 +495,12 @@ typedef enum {
 typedef enum {
     AF_ITERATIVE_DECONV_LANDWEBER       = 1,        ///< Landweber Deconvolution
     AF_ITERATIVE_DECONV_RICHARDSONLUCY  = 2,        ///< Richardson-Lucy Deconvolution
-    AF_ITERATIVE_DECONV_DEFAULT         = 0,        ///< Default is Landweber deconvolution
+    AF_ITERATIVE_DECONV_DEFAULT         = 0         ///< Default is Landweber deconvolution
 } af_iterative_deconv_algo;
 
 typedef enum {
     AF_INVERSE_DECONV_TIKHONOV       = 1,        ///< Tikhonov Inverse deconvolution
-    AF_INVERSE_DECONV_DEFAULT        = 0,        ///< Default is Tikhonov deconvolution
+    AF_INVERSE_DECONV_DEFAULT        = 0         ///< Default is Tikhonov deconvolution
 } af_inverse_deconv_algo;
 
 #endif
@@ -509,7 +509,7 @@ typedef enum {
 typedef enum {
               AF_VARIANCE_DEFAULT    = 0, ///< Default (Population) variance
               AF_VARIANCE_SAMPLE     = 1, ///< Sample variance
-              AF_VARIANCE_POPULATION = 2, ///< Population variance
+              AF_VARIANCE_POPULATION = 2  ///< Population variance
 } af_var_bias;
 #endif
 
diff --git a/include/af/vision.h b/include/af/vision.h
index d40fed7970..8376912ad2 100644
--- a/include/af/vision.h
+++ b/include/af/vision.h
@@ -40,7 +40,7 @@ class array;
     \ingroup cv_func_fast
  */
 AFAPI features fast(const array& in, const float thr=20.0f, const unsigned arc_length=9,
-                    const bool non_max=true, const float feature_ratio=0.05,
+                    const bool non_max=true, const float feature_ratio=0.05f,
                     const unsigned edge=3);
 
 #if AF_API_VERSION >= 31
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 99fc856391..42ecb167d8 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -36,8 +36,6 @@ using std::swap;
 
 using namespace detail;
 
-const double dfltTol = 1e-6;
-
 template<typename T>
 Array<T> getSubArray(const Array<T> &in, const bool copy,
                        uint dim0begin = 0, uint dim0end = 0,
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index 4521e15422..905ec81bc2 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -50,7 +50,7 @@ loggerFactory(string name) {
 }
 
 string bytesToString(size_t bytes) {
-    static array<const char *, 5> units{"B", "KB", "MB", "GB", "TB"};
+    static array<const char *, 5> units{{"B", "KB", "MB", "GB", "TB"}};
     size_t count = 0;
     double fbytes = static_cast<double>(bytes);
     size_t num_units = units.size();
diff --git a/src/backend/common/lapacke.cpp b/src/backend/common/lapacke.cpp
index a381831deb..adfec9a0e1 100644
--- a/src/backend/common/lapacke.cpp
+++ b/src/backend/common/lapacke.cpp
@@ -9,6 +9,7 @@
 
 #if defined(__APPLE__) && !defined(AF_CUDA)
 #include <common/lapacke.hpp>
+#include <common/defines.hpp>
 #include <Accelerate/Accelerate.h>
 #include <traits.hpp>
 
@@ -29,6 +30,7 @@
 #define LAPACK_FUNC(X, T, TO)                                                       \
 int LAPACKE_##X##geqrf(int layout, int M, int N, T *A, int lda, T *tau)             \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int lwork = N * BS;                                                             \
     T *work = new T[lwork];                                                         \
     int info = 0;                                                                   \
@@ -39,12 +41,14 @@ int LAPACKE_##X##geqrf(int layout, int M, int N, T *A, int lda, T *tau)
 int LAPACKE_##X##geqrf_work(int layout, int M, int N, T *A, int lda,                \
                             T *tau, T *work, int lwork)                             \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##geqrf_(&M, &N, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);               \
     return info;                                                                    \
 }                                                                                   \
 int LAPACKE_##X##getrf(int layout, int M, int N, T *A, int lda, int *pivot)         \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##getrf_(&M, &N, (TO)A, &lda, pivot, &info);                                   \
     return info;                                                                    \
@@ -52,12 +56,14 @@ int LAPACKE_##X##getrf(int layout, int M, int N, T *A, int lda, int *pivot)
 int LAPACKE_##X##getrs(int layout, char trans, int M, int N, const T *A,            \
                        int lda, const int *pivot, T *B, int ldb)                    \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##getrs_(&trans, &M, &N, (TO)A, &lda, (int *)pivot, (TO)B, &ldb, &info);       \
     return info;                                                                    \
 }                                                                                   \
 int LAPACKE_##X##potrf(int layout, char uplo, int N, T *A, int lda)                 \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##potrf_(&uplo, &N, (TO)A, &lda, &info);                                       \
     return info;                                                                    \
@@ -65,6 +71,7 @@ int LAPACKE_##X##potrf(int layout, char uplo, int N, T *A, int lda)
 int LAPACKE_##X##gesv(int layout, int N, int nrhs, T *A, int lda,                   \
                       int *pivot, T *B, int ldb)                                    \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##gesv_(&N, &nrhs, (TO)A, &lda, pivot, (TO)B, &ldb, &info);                    \
     return info;                                                                    \
@@ -72,6 +79,7 @@ int LAPACKE_##X##gesv(int layout, int N, int nrhs, T *A, int lda,
 int LAPACKE_##X##gels(int layout, char trans, int M, int N, int nrhs,               \
                       T *A, int lda, T *B, int ldb)                                 \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int lwork = std::min(M, N) + std::max(M, std::max(N, nrhs)) * BS;               \
     T *work = new T[lwork];                                                         \
     int info = 0;                                                                   \
@@ -82,6 +90,7 @@ int LAPACKE_##X##gels(int layout, char trans, int M, int N, int nrhs,
 }                                                                                   \
 int LAPACKE_##X##getri(int layout, int N, T *A, int lda, const int *pivot)          \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int lwork = N * BS;                                                             \
     T *work = new T[lwork];                                                         \
     int info = 0;                                                                   \
@@ -92,6 +101,7 @@ int LAPACKE_##X##getri(int layout, int N, T *A, int lda, const int *pivot)
 }                                                                                   \
 int LAPACKE_##X##trtri(int layout, char uplo, char diag, int N, T *A, int lda)      \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##trtri_(&uplo, &diag, &N, (TO)A, &lda, &info);                                \
     return info;                                                                    \
@@ -99,6 +109,7 @@ int LAPACKE_##X##trtri(int layout, char uplo, char diag, int N, T *A, int lda)
 int LAPACKE_##X##trtrs(int layout, char uplo, char trans, char diag,                \
                        int N, int NRHS, const T *A, int lda, T *B, int ldb)         \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##trtrs_(&uplo, &trans, &diag, &N, &NRHS, (TO)A, &lda, (TO)B, &ldb, &info);    \
     return info;                                                                    \
@@ -106,6 +117,7 @@ int LAPACKE_##X##trtrs(int layout, char uplo, char trans, char diag,
 int LAPACKE_##X##larft(int layout, char direct, char storev, int N, int K,          \
                        const T *v, int ldv, const T *tau, T *t, int ldt)            \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     X##larft_(&direct, &storev, &N, &K, (TO)v, &ldv,                                \
                         (TO)const_cast<T*>(tau), (TO)t, &ldt);                      \
     return 0;                                                                       \
@@ -113,6 +125,7 @@ int LAPACKE_##X##larft(int layout, char direct, char storev, int N, int K,
 int LAPACKE_##X##laswp(int layout, int N, T *A, int lda,                            \
                        int k1, int k2, const int *pivot, int incx)                  \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     X##laswp_(&N, (TO)A, &lda, &k1, &k2, const_cast<int*>(pivot), &incx);           \
     return 0;                                                                       \
 }                                                                                   \
@@ -125,6 +138,7 @@ LAPACK_FUNC(z, cdouble, __CLPK_doublecomplex*)
 #define LAPACK_GQR(P, X, T, TO)                                                     \
 int LAPACKE_##X##P(int layout, int M, int N, int K, T *A, int lda, const T *tau)    \
 {                                                                                   \
+    UNUSED(layout);                                                       \
     int lwork = N * 32;                                                             \
     T *work = new T[lwork];                                                         \
     int info = 0;                                                                   \
@@ -142,6 +156,7 @@ LAPACK_GQR(ungqr, z, cdouble, __CLPK_doublecomplex*)
 int LAPACKE_##X##P##_work(int layout, int M, int N, int K, T *A, int lda,           \
                           const T *tau, T *work, int lwork)                         \
 {                                                                                   \
+    UNUSED(layout);                                                                 \
     int info = 0;                                                                   \
     X##P##_(&M, &N, &K, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);             \
     return info;                                                                    \
@@ -157,6 +172,7 @@ int LAPACKE_##X##P##_work(int layout, char side, char trans, int M, int N, int K
                           const T *A, int lda, const T *tau, T *c, int ldc,         \
                           T *work, int lwork)                                       \
 {                                                                                   \
+    UNUSED(layout);                                                        \
     int info = 0;                                                                   \
     X##P##_(&side, &trans, &M, &N, &K, (TO)A, &lda, (TO)tau, (TO)c, &ldc,           \
                       (TO)work, &lwork, &info);                                     \
@@ -178,6 +194,7 @@ LAPACK_MQR_WORK(unmqr, z, cdouble, __CLPK_doublecomplex*)
                        T* u, int ldu,               \
                        T* vt, int ldvt)             \
     {                                               \
+        UNUSED(layout);                             \
         int info = 0;                               \
         int lwork = -1;                             \
         T work_param = 0;                           \
@@ -204,6 +221,7 @@ LAPACK_MQR_WORK(unmqr, z, cdouble, __CLPK_doublecomplex*)
                        T* u, int ldu,               \
                        T* vt, int ldvt)             \
     {                                               \
+        UNUSED(layout);                             \
         int info = 0;                               \
         int max_mn = std::max(m, n);                \
         int min_mn = std::max(m, n);                \
@@ -240,6 +258,7 @@ LAPACK_LAMCH(d, double)
                            int lda, T* b,                       \
                            int ldb )                            \
     {                                                           \
+        UNUSED(matrix_order);                                   \
         int info = 0;                                           \
         X##lacpy_(&uplo, &m, &n, (TO)a, &lda, (TO)b, &ldb);     \
         return info;                                            \
@@ -256,6 +275,7 @@ LAPACK_LACPY(z, cdouble,__CLPK_doublecomplex*)
                               int lda, const T* tau, T* work,       \
                               int lwork )                           \
     {                                                               \
+        UNUSED(matrix_order);                                       \
         int info = 0;                                               \
         X##P##_(&vect, &m, &n, &k, (TO)a, &lda,                     \
                 (TO)tau, (TO)work, &lwork, &info);                  \
@@ -275,6 +295,7 @@ LAPACK_GBR_WORK(ungbr, z, cdouble,__CLPK_doublecomplex*)
                                  int ldu, T* c,                         \
                                  int ldc, Tr* work)                     \
     {                                                                   \
+        UNUSED(matrix_order);                                           \
         int info = 0;                                                   \
         X##bdsqr_(&uplo, &n, &ncvt, &nru, &ncc, d, e,                   \
                   (TO)vt, &ldvt, (TO)u, &ldu,                           \
@@ -296,6 +317,7 @@ LAPACK_BDSQR_WORK(z, cdouble, double,__CLPK_doublecomplex*)
                                  T* taup,                           \
                                  T* work, int lwork )               \
     {                                                               \
+        UNUSED(matrix_order);                                       \
         int info = 0;                                               \
         X##gebrd_(&m, &n, (TO)a, &lda, d, e, (TO)tauq, (TO)taup,    \
                   (TO)work, &lwork, &info);                         \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 667507a0e9..251f2ee5e3 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -218,7 +218,7 @@ namespace cpu
         int useCount() const
         {
             if (!data.get()) eval();
-            return data.use_count();
+            return static_cast<int>(data.use_count());
         }
 
         operator Param<T>()
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 25d48a4db5..41a61cbe37 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -25,6 +25,8 @@ namespace cpu
                   const jit::array<Ti> &rhs,
                   int lim) const
         {
+            UNUSED(lhs);
+            UNUSED(rhs);
             for (int i = 0; i < lim; i++) {
                 out[i] = scalar<To>(0);
             }
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 7847136b20..f7e696de0e 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -32,9 +32,9 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
     const AccType cvar      = chromaticSigma * chromaticSigma;
 
 
-    std::array<AccType, 4> currentCenterColors{0};
-    std::array<AccType, 4> currentMeanColors{0};
-    std::array<AccType, 4> tempColors{0};
+    std::array<AccType, 4> currentCenterColors{{ 0 }};
+    std::array<AccType, 4> currentMeanColors{{ 0 }};
+    std::array<AccType, 4> tempColors{{ 0 }};
     for (dim_t b3=0; b3<dims[3]; ++b3) {
         for (unsigned b2=0; b2<bCount; ++b2) {
 
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
index 2c5a03fa64..61e99c95a2 100644
--- a/src/backend/cpu/kernel/pad_array_borders.hpp
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -16,7 +16,8 @@ namespace cpu
 {
 namespace kernel
 {
-static dim_t
+namespace {
+static inline dim_t
 idxByndEdge(const dim_t i, const dim_t lb,
             const dim_t len, const af::borderType btype)
 {
@@ -34,6 +35,7 @@ idxByndEdge(const dim_t i, const dim_t lb,
     }
     return retVal;
 }
+}
 
 template<typename T>
 void padBorders(Param<T> out, CParam<T> in,
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index 9b5a7cc4f1..4d81a44935 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -21,6 +21,8 @@ struct arith_op
 {
     T operator()(T v1, T v2)
     {
+        UNUSED(v1);
+        UNUSED(v2);
         return scalar<T>(0);
     }
 };
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 63082fc5f9..cc23a7ac29 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -91,7 +91,7 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
 
         testInputs.resize(inputCount,vector<inType>(0));
         for(unsigned k=0; k<inputCount; k++) {
-            unsigned nElems = inputDims[k].elements();
+            dim_t nElems = inputDims[k].elements();
             testInputs[k].resize(nElems);
             FileElementType tmp;
             for(unsigned i = 0; i < nElems; i++) {
@@ -143,7 +143,7 @@ void readTestsFromFile(const std::string &FileName, std::vector<af::dim4> &input
 
         testInputs.resize(inputCount,vector<inType>(0));
         for(unsigned k=0; k<inputCount; k++) {
-            unsigned nElems = inputDims[k].elements();
+            dim_t nElems = inputDims[k].elements();
             testInputs[k].resize(nElems);
             inType tmp;
             for(unsigned i = 0; i < nElems; i++) {
@@ -470,10 +470,10 @@ af::array cpu_randu(const af::dim4 dims)
     bool isTypeCplx = is_same_type<T, af::cfloat>::value || is_same_type<T, af::cdouble>::value;
     bool isTypeFloat = is_same_type<BT, float>::value || is_same_type<BT, double>::value;
 
-    dim_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
+    size_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
 
     std::vector<BT> out(elements);
-    for(int i = 0; i < (int)elements; i++) {
+    for(size_t i = 0; i < elements; i++) {
         out[i] = isTypeFloat ? (BT)(rand())/RAND_MAX : rand() % 100;
     }
 
@@ -608,12 +608,12 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
     coordsMaxBound[0] = arrDims[0] - 1;
 
     // dim0 positions that can be displayed
-    dim_t dim0Start = std::max<int>(0, coords[0] - ctxWidth);
-    dim_t dim0End = std::min<int>(coords[0] + ctxWidth + 1, arrDims[0]);
+    dim_t dim0Start = std::max<dim_t>(0LL, coords[0] - ctxWidth);
+    dim_t dim0End = std::min<dim_t>(coords[0] + ctxWidth + 1LL, arrDims[0]);
 
     // Linearized indices of values in vectors that can be displayed
-    dim_t vecStartIdx = std::max<int>(ravelIdx(coordsMinBound, arrStrides),
-                                      idx - ctxWidth);
+    dim_t vecStartIdx = std::max<dim_t>(ravelIdx(coordsMinBound, arrStrides),
+                                        idx - ctxWidth);
 
     // Display as minimal coordinates as needed
     // First value is the range of dim0 positions that will be displayed
@@ -626,7 +626,7 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
         os << ", " << coords[3];
     os << "), dims are (" << arrDims << ") strides: (" << arrStrides << ")\n";
 
-    uint ctxElems = dim0End - dim0Start;
+    dim_t ctxElems = dim0End - dim0Start;
     std::vector<int> valFieldWidths(ctxElems);
     std::vector<std::string> ctxDim0(ctxElems);
     std::vector<std::string> ctxOutVals(ctxElems);
@@ -637,19 +637,19 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
     // Also get the max string length between the position and out/ref values
     // per item so that it can be used later as the field width for
     // displaying each item in the context window
-    for (uint i = 0; i < ctxElems; ++i) {
+    for (dim_t i = 0; i < ctxElems; ++i) {
         std::ostringstream tmpOs;
 
-        uint dim0 = dim0Start + i;
+        dim_t dim0 = dim0Start + i;
         if (dim0 == coords[0])
             tmpOs << "[" << dim0 << "]";
         else
             tmpOs << dim0;
         ctxDim0[i] = tmpOs.str();
-        int dim0Len = tmpOs.str().length();
+        size_t dim0Len = tmpOs.str().length();
         tmpOs.str(std::string());
 
-        uint valIdx = vecStartIdx + i;
+        dim_t valIdx = vecStartIdx + i;
 
         if (valIdx == idx) {
             tmpOs << "[" << +hOut[valIdx] << "]";
@@ -658,7 +658,7 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
             tmpOs << +hOut[valIdx];
         }
         ctxOutVals[i] = tmpOs.str();
-        int outLen = tmpOs.str().length();
+        size_t outLen = tmpOs.str().length();
         tmpOs.str(std::string());
 
         if (valIdx == idx) {
@@ -668,7 +668,7 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
             tmpOs << +hGold[valIdx];
         }
         ctxGoldVals[i] = tmpOs.str();
-        int goldLen = tmpOs.str().length();
+        size_t goldLen = tmpOs.str().length();
         tmpOs.str(std::string());
 
         int maxWidth = std::max<int>(dim0Len, outLen);
@@ -676,7 +676,7 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
         valFieldWidths[i] = maxWidth;
     }
 
-    int varNameWidth = std::max<int>(goldName.length(), outName.length());
+    size_t varNameWidth = std::max<size_t>(goldName.length(), outName.length());
 
     // Display dim0 positions, output values, and reference values
     os << std::right << std::setw(varNameWidth) << "" << "   ";
@@ -789,10 +789,10 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         FloatTag, IntegerTag>::type TagType;
     TagType tag;
 
-    std::vector<T> hA(a.elements());
+    std::vector<T> hA(static_cast<size_t>(a.elements()));
     a.host(hA.data());
 
-    std::vector<T> hB(b.elements());
+    std::vector<T> hB(static_cast<size_t>(b.elements()));
     b.host(hB.data());
     return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff, tag);
 }

From 2cb2b7f89f33f4519154c00f2c2db8e1782da1fa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Nov 2018 15:39:54 -0500
Subject: [PATCH 1567/2677] Remove cudaMemcpys to the default streams in the
 CUDA backend

There were some calls being made to the default CUDA stream. This is
problematic because the default cuda stream is blocks and blocks
all other streams when executing. We should only perform calls on
the current stream.
---
 src/backend/cuda/copy.cu           | 44 ++++++++++++++++--------------
 src/backend/cuda/copy.hpp          | 17 ++++++++++--
 src/backend/cuda/hist_graphics.cpp |  4 ++-
 src/backend/cuda/image.cpp         |  4 ++-
 src/backend/cuda/plot.cpp          |  5 +++-
 src/backend/cuda/surface.cpp       |  5 +++-
 src/backend/cuda/vector_field.cpp  |  5 +++-
 7 files changed, 55 insertions(+), 29 deletions(-)

diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 3ccb770295..2cc50b4aea 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -22,47 +22,49 @@ namespace cuda
 {
 
     template<typename T>
-    void copyData(T *data, const Array<T> &A)
+    void copyData(T *dst, const Array<T> &src)
     {
         // FIXME: Merge this with copyArray
-        A.eval();
+        src.eval();
 
-        Array<T> out = A;
+        Array<T> out = src;
         const T *ptr = NULL;
 
-        if (A.isLinear() || // No offsets, No strides
-            A.ndims() == 1 // Simple offset, no strides.
+        if (src.isLinear() || // No offsets, No strides
+            src.ndims() == 1 // Simple offset, no strides.
             ) {
 
             //A.get() gets data with offsets
-            ptr = A.get();
+            ptr = src.get();
         } else {
             //FIXME: Think about implementing eval
-            out = copyArray(A);
+            out = copyArray(src);
             ptr = out.get();
         }
 
-        CUDA_CHECK(cudaMemcpy(data, ptr,
-                              A.elements() * sizeof(T),
-                              cudaMemcpyDeviceToHost));
-
+        auto stream = cuda::getActiveStream();
+        CUDA_CHECK(cudaMemcpyAsync(dst, ptr,
+                                   src.elements() * sizeof(T),
+                                   cudaMemcpyDeviceToHost,
+                                   stream));
+        CUDA_CHECK(cudaStreamSynchronize(stream));
         return;
     }
 
     template<typename T>
-    Array<T> copyArray(const Array<T> &A)
+    Array<T> copyArray(const Array<T> &src)
     {
-        Array<T> out = createEmptyArray<T>(A.dims());
+        Array<T> out = createEmptyArray<T>(src.dims());
 
-        if (A.isLinear()) {
-            CUDA_CHECK(cudaMemcpyAsync(out.get(), A.get(),
-                                       A.elements() * sizeof(T),
+        if (src.isLinear()) {
+            CUDA_CHECK(cudaMemcpyAsync(out.get(), src.get(),
+                                       src.elements() * sizeof(T),
                                        cudaMemcpyDeviceToDevice,
                                        cuda::getActiveStream()));
         } else {
             // FIXME: Seems to fail when using Param<T>
-            kernel::memcopy(out.get(), out.strides().get(), A.get(), A.dims().get(),
-                            A.strides().get(), (uint)A.ndims());
+            kernel::memcopy(out.get(), out.strides().get(), src.get(), src.dims().get(),
+                            src.strides().get(), (uint)src.ndims());
         }
         return out;
     }
@@ -118,9 +120,9 @@ namespace cuda
         copyFn(out, in);
     }
 
-#define INSTANTIATE(T)                                              \
-    template void      copyData<T> (T *data, const Array<T> &from); \
-    template Array<T> copyArray<T>(const Array<T> &A);              \
+#define INSTANTIATE(T)                                                  \
+    template void      copyData<T> (T *dst, const Array<T> &src);       \
+    template Array<T>  copyArray<T>(const Array<T> &src);               \
     template void      multiply_inplace<T> (Array<T> &in, double norm); \
 
     INSTANTIATE(float  )
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index 58279ff5d2..7d2b316fb0 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -12,11 +12,22 @@
 
 namespace cuda
 {
+    // Copies(blocking) data from an Array<T> object to a contiguous host side
+    // pointer.
+    //
+    // \param dst The destination pointer on the host system.
+    // \param src    The source array
     template<typename T>
-    void copyData(T *data, const Array<T> &A);
-
+    void copyData(T *dst, const Array<T> &src);
+
+    // Create a deep copy of the \p src Array with the same size and shape. The new
+    // Array will not maintain the subarray metadata of the \p src array.
+    //
+    // \param   src  The source Array<T> object.
+    // \returns      A new Array<T> object with the same shape and data as the
+    //               \p src Array<T>
     template<typename T>
-    Array<T> copyArray(const Array<T> &A);
+    Array<T> copyArray(const Array<T> &src);
 
     template<typename inType, typename outType>
     void copyArray(Array<outType> &out, const Array<inType> &in);
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index d43355a123..d95080daa0 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -43,7 +43,9 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, hist->vertices());
         gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, data.get(), hist->verticesSize(), cudaMemcpyDeviceToHost));
+            auto stream = cuda::getActiveStream();
+            CUDA_CHECK(cudaMemcpyAsync(ptr, data.get(), hist->verticesSize(), cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index ef177dc28f..76f624a21b 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -47,7 +47,9 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         glBufferData((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->size(), 0, (gl::GLenum)GL_STREAM_DRAW);
         gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, in.get(), image->size(), cudaMemcpyDeviceToHost));
+            CUDA_CHECK(cudaMemcpyAsync(ptr, in.get(), image->size(),
+                                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
             glUnmapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER);
         }
         glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, 0);
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index e9d7bea738..9a6ac8013a 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -48,7 +48,10 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, plot->vertices());
         gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, P.get(), plot->verticesSize(), cudaMemcpyDeviceToHost));
+            auto stream = cuda::getActiveStream();
+            CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), plot->verticesSize(),
+                                       cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index cc7ac29e73..196a61bbd1 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -48,7 +48,10 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, surface->vertices());
         gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, P.get(), surface->verticesSize(), cudaMemcpyDeviceToHost));
+            auto stream = cuda::getActiveStream();
+            CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), surface->verticesSize(),
+                                       cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 043d0ba591..7102218f47 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -59,7 +59,10 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, vector_field->vertices());
         gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpy(ptr, points.get(), vector_field->verticesSize(), cudaMemcpyDeviceToHost));
+            auto stream = cuda::getActiveStream();
+            CUDA_CHECK(cudaMemcpyAsync(ptr, points.get(), vector_field->verticesSize(),
+                                       cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
             glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
         }
         glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);

From 935b37084cb8dcd30adfbeee4ab9adfc7fa290a5 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Fri, 30 Nov 2018 00:13:45 -0500
Subject: [PATCH 1568/2677] Test assert macros for using existing output arrays
 (#2347)

Added new assert macros for testing cases when output array is either null,
already existing, a subarray, or reordered array. This will make it easier to test
functions to write to an existing af_array.
---
 test/approx1.cpp     | 131 ++++++++++++-----------
 test/testHelpers.hpp | 250 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 320 insertions(+), 61 deletions(-)

diff --git a/test/approx1.cpp b/test/approx1.cpp
index 7c798ddc5a..48a35b2f9c 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -845,86 +845,95 @@ TEST(Approx1, CPPEmptyPosAndInput)
     ASSERT_TRUE(interp.isempty());
 }
 
-TEST(Approx1, UseNullInitialOutput) {
-    float h_in[3] = {10.f, 20.f, 30.f};
-    dim_t h_in_dims = 3;
-
+void testSpclOutArray(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
+                      float* h_pos, dim4 pos_dims,
+                      TestOutputArrayType out_array_type)
+{
     af_array in = 0;
-    ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
-
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim_t h_pos_dims = 5;
     af_array pos = 0;
-    ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
+    ASSERT_SUCCESS(
+        af_create_array(&in, h_in, in_dims.ndims(), in_dims.get(), f32));
+    ASSERT_SUCCESS(
+        af_create_array(&pos, h_pos, pos_dims.ndims(), pos_dims.get(), f32));
 
     af_array out = 0;
+    TestOutputArrayInfo metadata(out_array_type);
+    genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(), f32,
+                       &metadata);
     ASSERT_SUCCESS(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0));
 
-    ASSERT_FALSE(out == 0);
+    af_array gold = 0;
+    ASSERT_SUCCESS(
+        af_create_array(&gold, h_gold, gold_dims.ndims(), gold_dims.get(), f32));
+
+    ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+
+    if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
+    if (pos != 0)  { ASSERT_SUCCESS(af_release_array(pos)); }
+    if (in != 0)   { ASSERT_SUCCESS(af_release_array(in)); }
 }
 
-TEST(Approx1, UseExistingOutputArray) {
-    float h_in[3] = {10.f, 20.f, 30.f};
-    dim_t h_in_dims = 3;
+TEST(Approx1, UseNullOutputArray) {
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
 
-    af_array in = 0;
-    ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims(5);
+
+    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
+    dim4 gold_dims(5);
+
+    SCOPED_TRACE("UseNullOutputArray");
+    testSpclOutArray(h_gold, gold_dims, h_in, in_dims,
+                     h_pos, pos_dims, NULL_ARRAY);
+}
+
+TEST(Approx1, UseFullExistingOutputArray) {
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
 
     float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim_t h_pos_dims = 5;
-    af_array pos = 0;
-    ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
+    dim4 pos_dims(5);
+
+    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
+    dim4 gold_dims(5);
+
+    SCOPED_TRACE("UseFullExistingOutputArray");
+    testSpclOutArray(h_gold, gold_dims, h_in, in_dims,
+                     h_pos, pos_dims, FULL_ARRAY);
+}
 
-    dim_t h_out_dims = 5;
-    af_array out_ptr = 0;
-    ASSERT_SUCCESS(af_create_handle(&out_ptr, 1, &h_out_dims, f32));
-    af_array out_ptr_copy = out_ptr;
-    ASSERT_SUCCESS(af_approx1(&out_ptr, in, pos, AF_INTERP_LINEAR, 0));
+TEST(Approx1, UseExistingOutputSubArray) {
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
 
-    // Verify that the original output af_array memory was used
-    ASSERT_EQ(out_ptr_copy, out_ptr);
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims (5);
 
-    af_array out_no_alloc = 0;
-    ASSERT_SUCCESS(af_approx1(&out_no_alloc, in, pos, AF_INTERP_LINEAR, 0));
+    float h_gold_subarr[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
+    dim4 gold_subarr_dims(5);
 
-    // Verify that the contents of an approx with a previously allocated output
-    // and that of a non-allocated output match
-    ASSERT_ARRAYS_EQ(out_ptr, out_no_alloc);
+    SCOPED_TRACE("UseExistingOutputSubArray");
+    testSpclOutArray(h_gold_subarr, gold_subarr_dims, h_in, in_dims,
+                     h_pos, pos_dims, SUB_ARRAY);
 }
 
-TEST(Approx1, UseExistingOutputSlice) {
-    float h_in[3] = {10.f, 20.f, 30.f};
-    dim_t h_in_dims = 3;
+TEST(Approx1, UseReorderedOutputArray) {
 
-    af_array in = 0;
-    ASSERT_SUCCESS(af_create_array(&in, &h_in[0], 1, &h_in_dims, f32));
+    float h_in[9] = {10.0f, 20.0f, 30.0f,
+                     40.0f, 50.0f, 60.0f,
+                     70.0f, 80.0f, 90.0f};
+    dim4 in_dims(3, 3);
 
     float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim_t h_pos_dims = 5;
-    af_array pos = 0;
-    ASSERT_SUCCESS(af_create_array(&pos, &h_pos[0], 1, &h_pos_dims, f32));
+    dim4 pos_dims(5);
 
-    float h_out[15] = {1.0f, 1.5f, 2.0f, 2.5f, 3.0f,
-                       4.0f, 4.5f, 5.0f, 5.5f, 6.0f,
-                       7.0f, 7.5f, 8.0f, 8.5f, 9.0f};
-    dim_t h_out_dims[2] = {5, 3};
-    af_array out = 0;
-    ASSERT_SUCCESS(af_create_array(&out, &h_out[0], 2, &h_out_dims[0], f32));
-    af_seq idx_dim1 = {1, 1, 1}; // get slice 1 of dim1
-    af_seq idx[2] = {af_span, idx_dim1};
-    af_array out_slice = 0;
-    ASSERT_SUCCESS(af_index(&out_slice, out, 2, &idx[0]));
-    ASSERT_SUCCESS(af_approx1(&out_slice, in, pos, AF_INTERP_LINEAR, 0));
-
-    dim_t nelems = 0;
-    ASSERT_SUCCESS(af_get_elements(&nelems, out));
-    vector<float> h_out_approx(nelems);
-    ASSERT_SUCCESS(af_get_data_ptr(&h_out_approx.front(), out));
-
-    float h_gold[15] = {1.0f, 1.5f, 2.0f, 2.5f, 3.0f,
-                        10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
-                        7.0f, 7.5f, 8.0f, 8.5f, 9.0f};
-    af_array gold = 0;
-    ASSERT_SUCCESS(af_create_array(&gold, &h_gold[0], 2, &h_out_dims[0], f32));
-    ASSERT_ARRAYS_EQ(gold, out);
+    float h_gold[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
+                        40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
+                        70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
+    dim4 gold_dims(5, 3);
+
+    SCOPED_TRACE("UseReorderedOutputArray");
+    testSpclOutArray(h_gold, gold_dims, h_in, in_dims,
+                     h_pos, pos_dims, REORDERED_ARRAY);
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index cc23a7ac29..ba144d8159 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -502,6 +502,8 @@ void cleanSlate()
   ASSERT_EQ(af::getMemStepSize(), step_bytes);
 }
 
+//********** arrayfire custom test asserts ***********
+
 std::ostream& operator<<(std::ostream& os, af_err e) {
     return os << af_err_to_string(e);
 }
@@ -797,6 +799,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff, tag);
 }
 
+// Called by ASSERT_ARRAYS_EQ
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          const af::array& a, const af::array& b,
                                          float maxAbsDiff = 0.f) {
@@ -837,6 +840,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     return ::testing::AssertionSuccess();
 }
 
+// Called by ASSERT_VEC_ARRAY_EQ
 template<typename T>
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsName,
                                          std::string bName,
@@ -899,6 +903,7 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsN
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, bbb);
 }
 
+// Called by ASSERT_ARRAYS_NEAR
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
                                            const af::array& a, const af::array& b,
@@ -907,6 +912,7 @@ ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
     return assertArrayEq(aName, bName, a, b, maxAbsDiff);
 }
 
+// Called by ASSERT_VEC_ARRAY_NEAR
 template<typename T>
 ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
                                            std::string bName,
@@ -959,6 +965,16 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
 #define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
     EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
 
+/// Same as ASSERT_ARRAYS_EQ, but for cases when a "special" output array is
+/// given to the function.
+/// The special array can be null, a full-sized array, a subarray, or reordered
+/// Can only be used for testing C-API functions currently
+///
+/// \param[in] EXPECTED The expected array of the assertion
+/// \param[in] ACTUAL The actual resulting array from the calculation
+#define ASSERT_SPECIAL_ARRAYS_EQ(EXPECTED, ACTUAL, META)               \
+    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED, ACTUAL, META)
+
 /// Compares a std::vector with an af::/af_array for their types, dims, and values (strict equality).
 ///
 /// \param[in] EXPECTED_VEC The vector that represents the expected array
@@ -990,4 +1006,238 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
                             MAX_ABSDIFF)
 
 }
+
+enum TestOutputArrayType {
+    // Test af_* function when given a null array as its output
+    NULL_ARRAY,
+
+    // Test af_* function when given an output array that is the same size as
+    // the expected output
+    FULL_ARRAY,
+
+    // Test af_* function when given an output array that is a sub-array of a
+    // larger array (the sub-array size is still the same size as the expected
+    // output). Only the sub-array must be modified by the af_* function
+    SUB_ARRAY,
+
+    // Test af_* function when given an output array that was previously
+    // reordered (but after the reorder, has still the same shape as the expected
+    // output). This specifically uses the reorder behavior when dim0 is kept,
+    // and thus no data movement is done - only the dims and strides are modified
+    REORDERED_ARRAY
+};
+
+class TestOutputArrayInfo {
+    af_array out_arr;
+    af_array out_arr_cpy;
+    af_array out_subarr;
+    dim_t out_subarr_ndims;
+    af_seq out_subarr_idxs[4];
+    TestOutputArrayType out_arr_type;
+
+public:
+
+    TestOutputArrayInfo(TestOutputArrayType arr_type)
+        :out_arr(0),
+         out_arr_cpy(0),
+         out_subarr(0),
+         out_subarr_ndims(0),
+         out_arr_type(arr_type)
+    {
+        for (uint i = 0; i < 4; ++i) {
+            out_subarr_idxs[i] = af_span;
+        }
+    }
+
+    ~TestOutputArrayInfo() {
+        if (out_subarr)  af_release_array(out_subarr);
+        if (out_arr_cpy) af_release_array(out_arr_cpy);
+        if (out_arr)     af_release_array(out_arr);
+    }
+
+    void init(const unsigned ndims, const dim_t *const dims,
+                    const af_dtype ty) {
+        ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
+    }
+
+    void init(const unsigned ndims, const dim_t *const dims,
+                    const af_dtype ty,
+                    const af_seq *const subarr_idxs) {
+        ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
+        ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
+        for (uint i = 0; i < ndims; ++i) {
+            out_subarr_idxs[i] = subarr_idxs[i];
+        }
+        out_subarr_ndims = ndims;
+
+        ASSERT_SUCCESS(af_index(&out_subarr, out_arr, ndims, subarr_idxs));
+    }
+
+    af_array getOutput() {
+        if (out_arr_type == SUB_ARRAY) {
+            return out_subarr;
+        }
+        else {
+            return out_arr;
+        }
+    }
+
+    void setOutput(af_array array) {
+        if (out_arr != 0) {
+            ASSERT_SUCCESS(af_release_array(out_arr));
+        }
+        out_arr = array;
+    }
+
+    af_array getFullOutput()                 { return out_arr; }
+    af_array getFullOutputCopy()             { return out_arr_cpy; }
+    af_seq   *getSubArrayIdxs()              { return &out_subarr_idxs[0]; }
+    dim_t    getSubArrayNumDims()            { return out_subarr_ndims; }
+    TestOutputArrayType getOutputArrayType() { return out_arr_type; }
+};
+
+// Generates a random array. testWriteToOutputArray expects that it will receive
+// the same af_array that this generates after the af_* function is called
+void genRegularArray(TestOutputArrayInfo *metadata,
+                     const unsigned ndims, const dim_t *const dims, const af_dtype ty) {
+    metadata->init(ndims, dims, ty);
+}
+
+// Generates a large, random array, and extracts a subarray for the af_* function
+// to use. testWriteToOutputArray expects that the large array that it receives is
+// equal to the same large array with the gold array injected on the same subarray location
+void genSubArray(TestOutputArrayInfo *metadata,
+                 const unsigned ndims, const dim_t *const dims, const af_dtype ty) {
+    const dim_t pad_size = 2;
+
+    // The large array is padded on both sides of each dimension
+    // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
+    dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
+    for (uint i = 0; i < ndims; ++i) {
+        full_arr_dims[i] = dims[i] + 2*pad_size;
+    }
+
+    // Calculate index of sub-array. These will be used also by
+    // testWriteToOutputArray so that the gold sub array will be placed in the
+    // same location. Currently, this location is the center of the large array
+    af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
+    for (uint i = 0; i < ndims; ++i) {
+        af_seq idx = {pad_size, pad_size + dims[i] - 1.0, 1.0};
+        subarr_idxs[i] = idx;
+    }
+
+    metadata->init(ndims, full_arr_dims, ty, &subarr_idxs[0]);
+}
+
+// Generates a reordered array. testWriteToOutputArray expects that this array
+// will still have the correct output values from the af_* function, even though
+// the array was initially reordered.
+void genReorderedArray(TestOutputArrayInfo *metadata,
+                       const unsigned ndims, const dim_t *const dims, const af_dtype ty) {
+    // The rest of this function assumes that dims has 4 elements. Just in case
+    // dims has < 4 elements, use another dims array that is filled with 1s
+    dim_t all_dims[4] = {1, 1, 1, 1};
+    for (uint i = 0; i < ndims; ++i) {
+        all_dims[i] = dims[i];
+    }
+
+    // This reorder combination will not move data around, but will simply
+    // call modDims and modStrides (see src/api/c/reorder.cpp).
+    // The output will be checked if it is still correct even with the
+    // modified dims and strides "hack" with no data movement
+    uint reorder_idxs[4] = {0, 2, 1, 3};
+
+    // Shape the output array such that the reordered output array will have
+    // the correct dimensions that the test asks for (i.e. must match dims arg)
+    dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
+    for (uint i = 0; i < 4; ++i) {
+        init_dims[i] = all_dims[reorder_idxs[i]];
+    }
+    metadata->init(4, init_dims, ty);
+
+    af_array reordered = 0;
+    ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
+                              reorder_idxs[0], reorder_idxs[1],
+                              reorder_idxs[2], reorder_idxs[3]));
+    metadata->setOutput(reordered);
+}
+
+// Partner function of testWriteToOutputArray. This generates the "special"
+// array that testWriteToOutputArray will use to check if the af_* function
+// correctly uses an existing array as its output
+void genTestOutputArray(af_array *out_ptr,
+                        const unsigned ndims, const dim_t *const dims,
+                        const af_dtype ty,
+                        TestOutputArrayInfo* metadata) {
+    switch (metadata->getOutputArrayType()) {
+    case FULL_ARRAY:
+        genRegularArray(metadata, ndims, dims, ty);
+        break;
+    case SUB_ARRAY:
+        genSubArray(metadata, ndims, dims, ty);
+        break;
+    case REORDERED_ARRAY:
+        genReorderedArray(metadata, ndims, dims, ty);
+        break;
+    default:
+        break;
+    }
+    *out_ptr = metadata->getOutput();
+}
+
+// Partner function of genTestOutputArray. This uses the same "special"
+// array that genTestOutputArray generates, and checks whether the
+// af_* function wrote to that array correctly
+::testing::AssertionResult
+testWriteToOutputArray(std::string gold_name, std::string result_name,
+                       const af_array gold, const af_array out,
+                       TestOutputArrayInfo *metadata) {
+    // In the case of NULL_ARRAY, the output array starts out as null.
+    // After the af_* function is called, it shouldn't be null anymore
+    if (metadata->getOutputArrayType() == NULL_ARRAY) {
+        if (out == 0) {
+            return ::testing::AssertionFailure()
+                << "Output af_array " << result_name << " is null";
+        }
+        metadata->setOutput(out);
+    }
+    // For every other case, must check if the af_array generated by
+    // genTestOutputArray was used by the af_* function as its output array
+    else {
+        if (metadata->getOutput() != out) {
+            return ::testing::AssertionFailure()
+                << "af_array POINTER MISMATCH:\n"
+                << "  Actual: " << out << "\n"
+                << "Expected: " << metadata->getOutput();
+        }
+    }
+
+    if (metadata->getOutputArrayType() == SUB_ARRAY) {
+        // There are two full arrays. One will be injected with the gold
+        // subarray, the other should have already been injected with the af_*
+        // function's output. Then we compare the two full arrays
+        af_array gold_full_array = metadata->getFullOutputCopy();
+        af_assign_seq(&gold_full_array,
+                      gold_full_array,
+                      metadata->getSubArrayNumDims(),
+                      metadata->getSubArrayIdxs(),
+                      gold);
+
+        return assertArrayEq(gold_name, result_name,
+                             metadata->getFullOutputCopy(),
+                             metadata->getFullOutput());
+    }
+    else {
+        return assertArrayEq(gold_name, result_name, gold, out);
+    }
+}
+
+// Called by ASSERT_SPECIAL_ARRAYS_EQ
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         std::string metadataName,
+                                         const af_array a, const af_array b,
+                                         TestOutputArrayInfo *metadata) {
+    return testWriteToOutputArray(aName, bName, a, b, metadata);
+}
+
 #pragma GCC diagnostic pop

From 1b792bc3c12b6014079646d99c4f733c998d98af Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Nov 2018 01:49:12 -0500
Subject: [PATCH 1569/2677] Fix leaks in meanvar tests

Fix several leaks in the meanvar tests.
---
 test/meanvar.cpp | 116 +++++++++++++++++++++++++++--------------------
 1 file changed, 68 insertions(+), 48 deletions(-)

diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 18b26ae7ea..243cd73ec1 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -23,6 +23,7 @@ using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using std::back_inserter;
+using std::move;
 using std::string;
 using std::vector;
 
@@ -53,32 +54,58 @@ using outType = typename varOutType<T>::type;
 
 template<typename T>
 struct meanvar_test {
-  static af_dtype af_type;
-  string test_description_;
-  af_array in_;
-  af_array weights_;
-  af_var_bias bias_;
-  int dim_;
-  vector<outType<T>> mean_;
-  vector<outType<T>> variance_;
-  meanvar_test(string description, af_array in, af_array weights,
-               af_var_bias bias, int dim,
-               vector<double> &mean, vector<double> &variance)
-    : test_description_(description)
-    , in_(0)
-    , weights_(0)
-    , bias_(bias)
-    , dim_(dim) {
-    af_retain_array(&in_, in);
-    if(weights) {
-        af_retain_array(&weights_, weights);
+    static af_dtype af_type;
+    string test_description_;
+    af_array in_;
+    af_array weights_;
+    af_var_bias bias_;
+    int dim_;
+    vector<outType<T>> mean_;
+    vector<outType<T>> variance_;
+
+    meanvar_test(string description, af_array in, af_array weights,
+                af_var_bias bias, int dim,
+                vector<double> &&mean, vector<double> &&variance)
+      : test_description_(description)
+      , in_(0)
+      , weights_(0)
+      , bias_(bias)
+      , dim_(dim) {
+        af_retain_array(&in_, in);
+        if (weights) {
+            af_retain_array(&weights_, weights);
+        }
+        mean_.reserve(mean.size());
+        variance_.reserve(variance.size());
+        std::copy(begin(mean), end(mean), back_inserter(mean_));
+        std::copy(begin(variance), end(variance), back_inserter(variance_));
     }
 
-    mean_.reserve(mean.size());
-    variance_.reserve(variance.size());
-    std::copy(begin(mean), end(mean), back_inserter(mean_));
-    std::copy(begin(variance), end(variance), back_inserter(variance_));
-  }
+    meanvar_test(const meanvar_test<T> &other)
+      : test_description_(other.test_description_)
+      , in_(0)
+      , weights_(0)
+      , bias_(other.bias_)
+      , dim_(other.dim_)
+      , mean_(other.mean_)
+      , variance_(other.variance_) {
+        af_retain_array(&in_, other.in_);
+        if (other.weights_) {
+            af_retain_array(&weights_, other.weights_);
+        }
+    }
+
+    ~meanvar_test() {
+        af_release_array(in_);
+        if (weights_) {
+            af_release_array(weights_);
+            weights_ = 0;
+        }
+    }
+
+    meanvar_test() = default;
+    meanvar_test(meanvar_test<T> &&other) = default;
+    meanvar_test& operator=(meanvar_test<T> &&other) = default;
 };
 
 template<typename T>
@@ -87,7 +114,7 @@ af_dtype meanvar_test<T>::af_type = dtype_traits<T>::af_type;
 template<typename T>
 class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
 public:
-  void meanvar_test_function(meanvar_test<T> test) {
+  void meanvar_test_function(meanvar_test<T>& test) {
       af_array mean, var;
 
       // Cast to the expected type
@@ -138,8 +165,8 @@ meanvar_test_gen(string name, int in_index, int weight_index, af_var_bias bias,
 
         inputs.resize(in_.size());
         for(size_t i = 0; i < in_.size(); i++) {
-          af_create_array(&inputs[i], &in_[i].front(),
-                          numDims_[i].ndims(), numDims_[i].get(), f64);
+            af_create_array(&inputs[i], &in_[i].front(),
+                            numDims_[i].ndims(), numDims_[i].get(), f64);
         }
 
         outputs.resize(tests_.size());
@@ -166,33 +193,26 @@ meanvar_test_gen(string name, int in_index, int weight_index, af_var_bias bias,
 
       inputs.resize(dimensions.size());
       for(size_t i = 0; i < dimensions.size(); i++) {
-          af_array large_array = 0;
-          af_create_array(&large_array, &large_.front(), 4, dimensions[i].data(), f64);
-          inputs[i] = large_array;
+          af_create_array(&inputs[i], &large_.front(), 4, dimensions[i].data(), f64);
       }
 
       outputs.push_back(vector<double>(1, 999.5));
       outputs.push_back(vector<double>(1, 333500));
       outputs.push_back({249.50, 749.50, 1249.50, 1749.50});
       outputs.push_back(vector<double>(4, 20875));
-  }
-  if(weight_index == -1) {
-      return meanvar_test<T> (name,
-                              inputs[in_index],
-                              empty,
-                              bias,
-                              dim,
-                              outputs[mean_index],
-                              outputs[var_index]);
-  } else {
-      return meanvar_test<T>(name,
-                             inputs[in_index],
-                             inputs[weight_index],
-                             bias,
-                             dim,
-                             outputs[mean_index],
-                             outputs[var_index]);
-  }
+    }
+    meanvar_test<T> out = meanvar_test<T> (name,
+                                           inputs[in_index],
+                                           (weight_index == -1) ? empty : inputs[weight_index],
+                                           bias,
+                                           dim,
+                                           move(outputs[mean_index]),
+                                           move(outputs[var_index]));
+
+    for(auto input : inputs) {
+      af_release_array(input);
+    }
+    return out;
 }
 
 

From 89f07e27ac11f797a7633cd09556b5884861c3d1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 12 Dec 2018 00:47:20 -0500
Subject: [PATCH 1570/2677] Update order of compilation for cuda files. Update
 gitignore

---
 .gitignore                      | 2 +-
 src/backend/cuda/CMakeLists.txt | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index e9254cf240..b7e83d2e9a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,7 +3,6 @@ CMakeFiles/
 build*/
 Makefile
 cmake_install.cmake
-**~
 GTAGS
 GRTAGS
 GPATH
@@ -11,3 +10,4 @@ GPATH
 docs/details/examples.dox
 /TAGS
 external/
+compile_commands.json
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index d52569b9ac..9998b375fd 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -122,6 +122,8 @@ include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
 cuda_add_library(afcuda
     scan.cu
+    sort.hpp
+    scan_by_key.cu
     kernel/convolve.cu
     kernel/convolve_separable.cu
 
@@ -177,7 +179,6 @@ cuda_add_library(afcuda
     reorder.cu
     resize.cu
     rotate.cu
-    scan_by_key.cu
     select.cu
     set.cu
     sift.cu
@@ -368,7 +369,6 @@ cuda_add_library(afcuda
     sift.hpp
     sobel.hpp
     solve.hpp
-    sort.hpp
     sort_by_key.hpp
     sort_index.hpp
     sparse.hpp

From 4d003fc1768dd693b1f9ea32030bccc3690a88fa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 12 Dec 2018 01:20:03 -0500
Subject: [PATCH 1571/2677] Update error tolerances for batched matmul.
 Required to pass on 6.x

Batched matmul do not produce identical results as their serial
counterparts on some cards.
---
 test/blas.cpp | 26 +++++++-------------------
 1 file changed, 7 insertions(+), 19 deletions(-)

diff --git a/test/blas.cpp b/test/blas.cpp
index ab666f12aa..a0a5422f05 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -93,19 +93,10 @@ void MatMulCheck(string TestFile)
     }
 
     for(size_t i = 0; i < tests.size(); i++) {
-        dim_t elems;
-        ASSERT_SUCCESS(af_get_elements(&elems, out[i]));
-        vector<T> h_out(elems);
-        ASSERT_SUCCESS(af_get_data_ptr((void *)&h_out.front(), out[i]));
-
-        if( false == equal(h_out.begin(), h_out.end(), tests[i].begin()) ) {
-
-            cout << "Failed test " << i << "\nCalculated: " << endl;
-            copy(h_out.begin(), h_out.end(), ostream_iterator<T>(cout, ", "));
-            cout << "Expected: " << endl;
-            copy(tests[i].begin(), tests[i].end(), ostream_iterator<T>(cout, ", "));
-            FAIL();
-        }
+        dim4 dd;
+        dim_t *d = dd.get();
+        af_get_dims(&d[0], &d[1], &d[2], &d[3], out[i]);
+        ASSERT_VEC_ARRAY_EQ(tests[i], dd, out[i]);
     }
 
     ASSERT_SUCCESS(af_release_array(a));
@@ -274,8 +265,7 @@ TEST(MatrixMultiply, Batched)
                     array b_ij = b(span, span, i, j);
                     array c_ij = c(span, span, i, j);
                     array res = matmul(a_ij, b_ij);
-                    EXPECT_LT(max<float>(abs(c_ij - res)), 1E-5)
-                        << " for d2 = " << d2 << " for d3 = " << d3;
+                    ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
                 }
             }
         }
@@ -317,8 +307,7 @@ TEST(MatrixMultiply, LhsBroadcastBatched)
                     array b_ij = b(span, span, i, j);
                     array c_ij = c(span, span, i, j);
                     array res = matmul(a, b_ij);
-                    EXPECT_LT(max<float>(abs(c_ij - res)), 1E-3)
-                        << " for d2 = " << d2 << " for d3 = " << d3;
+                    ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
                 }
             }
         }
@@ -344,8 +333,7 @@ TEST(MatrixMultiply, RhsBroadcastBatched)
                     array a_ij = a(span, span, i, j);
                     array c_ij = c(span, span, i, j);
                     array res = matmul(a_ij, b);
-                    EXPECT_LT(max<float>(abs(c_ij - res)), 1E-3)
-                        << " for d2 = " << d2 << " for d3 = " << d3;
+                    ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
                 }
             }
         }

From 8542abfd3f5ffca469f0cc74859963d09f9d857a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 12 Dec 2018 01:17:07 -0500
Subject: [PATCH 1572/2677] Create an instance of the af::array::write for
 void* pointers

Currently you can only passed supported type pointers to the array.
void* can also be used to pass data since we are passing bytes
as the second argument. Type information is not necessary with this
function
---
 src/api/cpp/array.cpp |  7 +++++++
 test/write.cpp        | 26 ++++++++++++++------------
 2 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index e5e6ed00c6..450403cc9b 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -1019,6 +1019,13 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     INSTANTIATE(short)
     INSTANTIATE(unsigned short)
 
+    template<> AFAPI void array::write(const void *ptr,
+                                       const size_t bytes,
+                                       af::source src)
+    {
+        AF_THROW(af_write_array(get(), ptr, bytes, src));
+    }
+
 #undef INSTANTIATE
 
     template<> AFAPI void* array::device() const
diff --git a/test/write.cpp b/test/write.cpp
index 5dd26dda87..f1a148ac94 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -57,20 +57,11 @@ void writeTest(dim4 dims)
     A.write(b_dev, dims.elements() * sizeof(T), afDevice);
     B.write(a_host, dims.elements() * sizeof(T), afHost);
 
-    array check1 = A != B_copy;     // False so check1 is all 0s
-    array check2 = B != A_copy;     // False so check2 is all 0s
-
-    char *h_check1 = check1.host<char>();
-    char *h_check2 = check2.host<char>();
-
-    for(int i = 0; i < (int)dims.elements(); i++) {
-        ASSERT_EQ(h_check1[i], 0) << "at: " << i << endl;
-        ASSERT_EQ(h_check2[i], 0) << "at: " << i << endl;
-    }
+    ASSERT_ARRAYS_EQ(B_copy, A);
+    ASSERT_ARRAYS_EQ(A_copy, B);
 
+    af_free_device(b_dev);
     freeHost(a_host);
-    freeHost(h_check1);
-    freeHost(h_check2);
 }
 
 TYPED_TEST(Write, Vector0)
@@ -102,3 +93,14 @@ TYPED_TEST(Write, Volume1)
 {
     writeTest<TypeParam>(dim4(32, 64, 16));
 }
+
+TEST(Write, VoidPointer) {
+  vector<float> gold(100, 5);
+
+  array a(100);
+
+  void* h_gold = (void*)&gold.front();
+  a.write(h_gold, 100 * sizeof(float), afHost);
+
+  ASSERT_VEC_ARRAY_EQ(gold, dim4(100), a);
+}

From fad8d38b49eedb9bd283f1d7ca8a0cd28d0c7584 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 29 Nov 2018 13:08:43 +0530
Subject: [PATCH 1573/2677] Remove MKL usage from sparse to sparse/dense
 conversions

Latest MKL API, doesn't handle the dense data to sparse
storage conversion. It expects pointers to rows, cols and
values arrays. It also returns sparse_matrix_t opaque handles
that we don't use inside ArrayFire. Hence, deprecated MKL API
has been removed in favor of our in-house kernels for conversions.
---
 src/backend/cpu/kernel/sparse.hpp |  10 +-
 src/backend/cpu/sparse.cpp        | 371 +++++-------------------------
 src/backend/cpu/sparse.hpp        |  14 --
 src/backend/cpu/sparse_blas.cpp   | 189 +++++++--------
 test/data                         |   2 +-
 5 files changed, 161 insertions(+), 425 deletions(-)

diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index 36992b97b5..87521ce564 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -45,7 +45,7 @@ void coo2dense(Param<T> output,
 }
 
 template<typename T>
-void dense_csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+void dense2csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                CParam<T> in)
 {
     const T * iPtr = in.get();
@@ -70,8 +70,8 @@ void dense_csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 }
 
 template<typename T>
-void csr_dense(Param<T> out,
-                CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
+void csr2dense(Param<T> out,
+               CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
 {
     T   *oPtr = out.get();
     const T   *vPtr = values.get();
@@ -107,7 +107,7 @@ struct SpKIPCompareK
 };
 
 template<typename T>
-void csr_coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
+void csr2coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
              CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx)
 {
     // First calculate the linear index
@@ -143,7 +143,7 @@ void csr_coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
 }
 
 template<typename T>
-void coo_csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
+void coo2csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
              CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx)
 {
     T   * ovPtr = ovalues.get();
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 0073cc78ae..dbea4d8d08 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -26,366 +26,114 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
-using common::is_complex;
+namespace cpu {
+
 using common::SparseArray;
 using common::createArrayDataSparseArray;
 using common::createEmptySparseArray;
 
-using std::add_const;
-using std::add_pointer;
-using std::enable_if;
-using std::is_floating_point;
-using std::remove_const;
-using std::conditional;
-using std::is_same;
-
-namespace cpu
-{
-
-template<typename T, class Enable = void>
-struct blas_base {
-    using type = T;
-};
-
-template<typename T>
-struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
-    using type = typename conditional<is_same<T, cdouble>::value,
-                                      sp_cdouble, sp_cfloat>::type;
-};
-
-template<typename T>
-using cptr_type     =   typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T*>::type;
-template<typename T>
-using ptr_type     =    typename conditional<   is_complex<T>::value,
-                                                typename blas_base<T>::type *,
-                                                T*>::type;
-template<typename T>
-using scale_type   =    typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T *>::type;
-
-#ifdef USE_MKL
-
-// void mkl_zdnscsr (const MKL_INT *job ,
-//                   const MKL_INT *m , const MKL_INT *n ,
-//                   MKL_Complex16 *adns , const MKL_INT *lda ,
-//                   MKL_Complex16 *acsr ,
-//                   MKL_INT *ja , MKL_INT *ia ,
-//                   MKL_INT *info );
-template<typename T>
-using dnscsr_func_def = void (*)(const int *,
-                                 const int *, const int *,
-                                 ptr_type<T>, const int *,
-                                 ptr_type<T>,
-                                 int *, int *,
-                                 int *);
-
-//void mkl_zcsrcsc (const MKL_INT *job ,
-//                  const MKL_INT *n ,
-//                  MKL_Complex16 *acsr ,
-//                  MKL_INT *ja , MKL_INT *ia ,
-//                  MKL_Complex16 *acsc ,
-//                  MKL_INT *ja1 , MKL_INT *ia1 ,
-//                  MKL_INT *info );
-template<typename T>
-using csrcsc_func_def = void (*)(const int *,
-                                 const int *,
-                                 ptr_type<T>, int *, int *,
-                                 ptr_type<T>,
-                                 int *, int *,
-                                 int *);
-
-#define SPARSE_FUNC_DEF( FUNC )                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
-  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &mkl_##PREFIX##FUNC; }
-
-SPARSE_FUNC_DEF(dnscsr)
-SPARSE_FUNC(dnscsr, float,  s)
-SPARSE_FUNC(dnscsr, double, d)
-SPARSE_FUNC(dnscsr, cfloat, c)
-SPARSE_FUNC(dnscsr, cdouble,z)
-
-SPARSE_FUNC_DEF(csrcsc)
-SPARSE_FUNC(csrcsc, float,  s)
-SPARSE_FUNC(csrcsc, double, d)
-SPARSE_FUNC(csrcsc, cfloat, c)
-SPARSE_FUNC(csrcsc, cdouble,z)
-
-#undef SPARSE_FUNC
-#undef SPARSE_FUNC_DEF
-
-#endif // USE_MKL
-
-////////////////////////////////////////////////////////////////////////////////
-// Common Funcs for MKL and Non-MKL Code Paths
-////////////////////////////////////////////////////////////////////////////////
-
-// Partial template specialization of sparseConvertDenseToStorage for COO
-// However, template specialization is not allowed
-template<typename T>
-SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
-{
-    in.eval();
-
-    Array<uint> nonZeroIdx_ = where<T>(in);
-    Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
-
-    dim_t nNZ = nonZeroIdx.elements();
-
-    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
-    constDim.eval();
-
-    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
-    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
-
-    Array<T> values = copyArray<T>(in);
-    values.modDims(dim4(values.elements()));
-    values = lookup<T, int>(values, nonZeroIdx, 0);
-
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
-}
-
-// Partial template specialization of sparseConvertStorageToDense for COO
-// However, template specialization is not allowed
-template<typename T>
-Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
-{
-    in.eval();
-
-    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
-    dense.eval();
-
-    const Array<T>   values = in.getValues();
-    const Array<int> rowIdx = in.getRowIdx();
-    const Array<int> colIdx = in.getColIdx();
-
-    getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
-
-    return dense;
-}
-
-////////////////////////////////////////////////////////////////////////////////
-#ifdef USE_MKL // Implementation using MKL
-////////////////////////////////////////////////////////////////////////////////
-
-template<typename T, af_storage stype>
-SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
-{
-    in_.eval();
-
-    // MKL only has dns->csr.
-    // CSR <-> CSC is only supported if input is square
-    uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
-
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
-    sparse_.eval();
-
-    auto func = [=] (Param<T> values, Param<int> rowIdx, Param<int> colIdx, int num, CParam<T> in) {
-        // Read: https://software.intel.com/en-us/node/520848
-        // But job description is incorrect with regards to job[1]
-        // 0 implies row major and 1 implies column major
-        int j1 = 1, j2 = 0;
-        const int job[] = {0, j1, j2, 2, num, 1};
-
-        const int M = in.dims(0);
-        const int N = in.dims(1);
-
-        int ldd = in.strides(1);
-
-        int info = 0;
-
-        // Have to mess up all const correctness because MKL dnscsr function
-        // is bidirectional and has input/output on all pointers
-        dnscsr_func<T>()(
-                job, &M, &N,
-                reinterpret_cast<ptr_type<T>>(const_cast<T*>(in.get())), &ldd,
-                reinterpret_cast<ptr_type<T>>(values.get()),
-                colIdx.get(),
-                rowIdx.get(),
-                &info);
-    };
-
-
-    Array<T  > &values = sparse_.getValues();
-    Array<int> &rowIdx = sparse_.getRowIdx();
-    Array<int> &colIdx = sparse_.getColIdx();
-
-    getQueue().enqueue(func, values, rowIdx, colIdx, (int)sparse_.elements(), in_);
-
-    if(stype == AF_STORAGE_CSR)
-        return sparse_;
-    else
-        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
-
-    return sparse_;
-}
-
 template<typename T, af_storage stype>
-Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
 {
-    // MKL only has dns<->csr.
-    // CSR <-> CSC is only supported if input is square
-
-    if(stype == AF_STORAGE_CSC)
-        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
-
-    in_.eval();
-
-    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
-    dense_.eval();
-
-    auto func = [=] (Param<T> dense,
-                     CParam<T> values, CParam<int> rowIdx,
-                     CParam<int> colIdx, int num) {
-        // Read: https://software.intel.com/en-us/node/520848
-        // But job description is incorrect with regards to job[1]
-        // 0 implies row major and 1 implies column major
-        int j1 = 1, j2 = 0;
-        const int job[] = {1, j1, j2, 2, num, 1};
+    in.eval();
 
-        const int M = dense.dims(0);
-        const int N = dense.dims(1);
+    if (stype == AF_STORAGE_CSR) {
+        uint nNZ = reduce_all<af_notzero_t, T, uint>(in);
 
-        int ldd = dense.strides(1);
+        auto sparse = createEmptySparseArray<T>(in.dims(), nNZ, stype);
+        sparse.eval();
 
-        int info = 0;
+        Array<T  > values = sparse.getValues();
+        Array<int> rowIdx = sparse.getRowIdx();
+        Array<int> colIdx = sparse.getColIdx();
 
-        // Have to mess up all const correctness because MKL dnscsr function
-        // is bidirectional and has input/output on all pointers
-        dnscsr_func<T>()(
-                job, &M, &N,
-                reinterpret_cast<ptr_type<T>>(dense.get()), &ldd,
-                reinterpret_cast<ptr_type<T>>(const_cast<T*>(values.get())),
-                const_cast<int*>(colIdx.get()),
-                const_cast<int*>(rowIdx.get()),
-                &info);
-    };
+        getQueue().enqueue(kernel::dense2csr<T>, values, rowIdx, colIdx, in);
 
-    Array<T  > values = in_.getValues();
-    Array<int> rowIdx = in_.getRowIdx();
-    Array<int> colIdx = in_.getColIdx();
+        return sparse;
+    } else if (stype == AF_STORAGE_COO) {
+        auto nonZeroIdx = cast<int, uint>(where<T>(in));
 
-    getQueue().enqueue(func, dense_, values, rowIdx, colIdx, (int)in_.elements());
+        dim_t nNZ = nonZeroIdx.elements();
 
-    if(stype == AF_STORAGE_CSR)
-        return dense_;
-    else
-        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+        auto cnst = createValueArray<int>(dim4(nNZ), in.dims()[0]);
+        cnst.eval();
 
-    return dense_;
-}
+        auto rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
+        auto colIdx = arithOp<int, af_div_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
 
-////////////////////////////////////////////////////////////////////////////////
-#else // Implementation without using MKL
-////////////////////////////////////////////////////////////////////////////////
+        Array<T> values = copyArray<T>(in);
+        values.modDims(dim4(values.elements()));
+        values = lookup<T, int>(values, nonZeroIdx, 0);
 
-template<typename T, af_storage stype>
-SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
-{
-    in_.eval();
-
-    uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
-
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, AF_STORAGE_CSR);
-    sparse_.eval();
-
-    Array<T  > values = sparse_.getValues();
-    Array<int> rowIdx = sparse_.getRowIdx();
-    Array<int> colIdx = sparse_.getColIdx();
-
-    if(stype == AF_STORAGE_CSR)
-        getQueue().enqueue(kernel::dense_csr<T>, values, rowIdx, colIdx, in_);
-    else
+        return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, stype);
+    } else {
         AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
-
-
-    return sparse_;
+    }
 }
 
 template<typename T, af_storage stype>
-Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 {
-    in_.eval();
+    in.eval();
 
-    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
-    dense_.eval();
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    dense.eval();
 
-    Array<T  > values = in_.getValues();
-    Array<int> rowIdx = in_.getRowIdx();
-    Array<int> colIdx = in_.getColIdx();
+    Array<T  > values = in.getValues();
+    Array<int> rowIdx = in.getRowIdx();
+    Array<int> colIdx = in.getColIdx();
 
     if(stype == AF_STORAGE_CSR)
-        getQueue().enqueue(kernel::csr_dense<T>, dense_, values, rowIdx, colIdx);
+        getQueue().enqueue(kernel::csr2dense<T>, dense, values, rowIdx, colIdx);
+    else if (stype == AF_STORAGE_COO)
+        getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
     else
         AF_ERROR("CPU Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
 
-    return dense_;
+    return dense;
 }
 
-////////////////////////////////////////////////////////////////////////////////
-#endif //USE_MKL
-////////////////////////////////////////////////////////////////////////////////
-
-////////////////////////////////////////////////////////////////////////////////
-// Common Funcs for MKL and Non-MKL Code Paths
-////////////////////////////////////////////////////////////////////////////////
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
 {
     in.eval();
 
-    SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    auto converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
     converted.eval();
 
     function<void (Param<T>, Param<int>, Param<int>,
-                   CParam<T>, CParam<int>, CParam<int>)
-            > converter;
+                   CParam<T>, CParam<int>, CParam<int>)> converter;
 
     if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
-        converter = kernel::csr_coo<T>;
+        converter = kernel::csr2coo<T>;
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-        converter = kernel::coo_csr<T>;
+        converter = kernel::coo2csr<T>;
     } else {
         // Should never come here
         AF_ERROR("CPU Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
     }
-
-    getQueue().enqueue(converter,
-                       converted.getValues(), converted.getRowIdx(), converted.getColIdx(),
+    getQueue().enqueue(converter, converted.getValues(),
+                       converted.getRowIdx(), converted.getColIdx(),
                        in.getValues(), in.getRowIdx(), in.getColIdx());
-
     return converted;
 }
 
-#define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T> &in);  \
-
-#define INSTANTIATE_COO_SPECIAL(T)                                                                      \
-    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in)        \
-    { return sparseConvertDenseToCOO<T>(in); }                                                          \
-    template<> Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in)        \
-    { return sparseConvertCOOToDense<T>(in); }                                                          \
-
-#define INSTANTIATE_SPARSE(T)                                                                           \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in);         \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in);         \
-                                                                                                        \
-    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in);         \
-    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in);         \
-                                                                                                        \
-    INSTANTIATE_COO_SPECIAL(T)                                                                          \
-                                                                                                        \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                           \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                           \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                           \
-
+#define INSTANTIATE_TO_STORAGE(T, S)                                                                \
+template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T>&); \
+template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T>&); \
+template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T>&); \
+
+#define INSTANTIATE_SPARSE(T)                                                               \
+template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in); \
+template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in); \
+template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in); \
+template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in); \
+template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in); \
+template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in); \
+                                                                                            \
+INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                   \
+INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                   \
+INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                   \
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
@@ -393,7 +141,6 @@ INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
 #undef INSTANTIATE_TO_STORAGE
-#undef INSTANTIATE_COO_SPECIAL
 #undef INSTANTIATE_SPARSE
 
 }
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 6535eddd65..1b132e63de 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -12,21 +12,8 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
-#ifdef USE_MKL
-#include <mkl_spblas.h>
-#endif
-
 namespace cpu
 {
-
-#ifdef USE_MKL
-typedef MKL_Complex8  sp_cfloat;
-typedef MKL_Complex16 sp_cdouble;
-#else
-typedef cfloat        sp_cfloat;
-typedef cdouble       sp_cdouble;
-#endif
-
 template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 
@@ -35,5 +22,4 @@ Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
 template<typename T, af_storage dest, af_storage src>
 common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
-
 }
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 470c93ff6b..7bd14e03d1 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -9,30 +9,32 @@
 
 #include <sparse_blas.hpp>
 
+#ifdef USE_MKL
+#include <mkl_spblas.h>
+#endif
+
 #include <af/dim4.hpp>
 #include <complex.hpp>
-#include <common/err_common.hpp>
 #include <common/complex.hpp>
+#include <common/err_common.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <types.hpp>
 
 #include <stdexcept>
 #include <string>
 #include <cassert>
 
-using common::is_complex;
+namespace cpu {
 
-using std::add_const;
-using std::add_pointer;
-using std::enable_if;
-using std::is_floating_point;
-using std::remove_const;
-using std::conditional;
-using std::is_same;
-
-namespace cpu
-{
+#ifdef USE_MKL
+using sp_cfloat = MKL_Complex8;
+using sp_cdouble = MKL_Complex16;
+#else
+using sp_cfloat = cfloat;
+using sp_cdouble = cdouble;
+#endif
 
 template<typename T, class Enable = void>
 struct blas_base {
@@ -40,21 +42,22 @@ struct blas_base {
 };
 
 template<typename T>
-struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
-    using type = typename conditional<is_same<T, cdouble>::value,
-                                      sp_cdouble, sp_cfloat>::type;
+struct blas_base <T, typename std::enable_if< common::is_complex<T>::value>::type> {
+    using type = typename std::conditional<std::is_same<T, cdouble>::value,
+                                      sp_cdouble, sp_cfloat>
+                                     ::type;
 };
 
 template<typename T>
-using cptr_type     =   typename conditional<   is_complex<T>::value,
+using cptr_type    = typename std::conditional< common::is_complex<T>::value,
                                                 const typename blas_base<T>::type *,
                                                 const T*>::type;
 template<typename T>
-using ptr_type     =    typename conditional<   is_complex<T>::value,
+using ptr_type     = typename std::conditional< common::is_complex<T>::value,
                                                 typename blas_base<T>::type *,
                                                 T*>::type;
 template<typename T>
-using scale_type   =    typename conditional<   is_complex<T>::value,
+using scale_type   = typename std::conditional< common::is_complex<T>::value,
                                                 const typename blas_base<T>::type,
                                                 const T>::type;
 
@@ -64,9 +67,46 @@ To getScaleValue(Ti val)
     return (To)(val);
 }
 
+template<typename T, int value>
+scale_type<T> getScale()
+{
+    static T val(value);
+    return getScaleValue<scale_type<T>, T>(val);
+}
+
+sparse_operation_t
+toSparseTranspose(af_mat_prop opt)
+{
+    sparse_operation_t out = SPARSE_OPERATION_NON_TRANSPOSE;
+    switch(opt) {
+        case AF_MAT_NONE        : out = SPARSE_OPERATION_NON_TRANSPOSE;         break;
+        case AF_MAT_TRANS       : out = SPARSE_OPERATION_TRANSPOSE;             break;
+        case AF_MAT_CTRANS      : out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE;   break;
+        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+    return out;
+}
+
 #ifdef USE_MKL
 
-// MKL
+template<>
+const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
+{
+    sp_cfloat ret;
+    ret.real = val.real();
+    ret.imag = val.imag();
+    return ret;
+}
+
+template<>
+const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
+{
+    sp_cdouble ret;
+    ret.real = val.real();
+    ret.imag = val.imag();
+    return ret;
+}
+
 // sparse_status_t mkl_sparse_z_create_csr (
 //                 sparse_matrix_t *A,
 //                 sparse_index_base_t indexing,
@@ -74,7 +114,33 @@ To getScaleValue(Ti val)
 //                 MKL_INT *rows_start, MKL_INT *rows_end,
 //                 MKL_INT *col_indx,
 //                 MKL_Complex16 *values);
-//
+
+template<typename T>
+using create_csr_func_def = sparse_status_t (*)
+                           (sparse_matrix_t *,
+                            sparse_index_base_t,
+                            int, int,
+                            int *, int *, int*,
+                            ptr_type<T>);
+
+#define SPARSE_FUNC_DEF( FUNC )                         \
+template<typename T> FUNC##_func_def<T> FUNC##_func();
+
+SPARSE_FUNC_DEF( create_csr )
+
+#undef SPARSE_FUNC_DEF
+
+#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
+  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
+{ return &mkl_sparse_##PREFIX##_##FUNC; }
+
+SPARSE_FUNC(create_csr , float   , s)
+SPARSE_FUNC(create_csr , double  , d)
+SPARSE_FUNC(create_csr , cfloat  , c)
+SPARSE_FUNC(create_csr , cdouble , z)
+
+#undef SPARSE_FUNC
+
 // sparse_status_t mkl_sparse_z_mv (
 //                 sparse_operation_t operation,
 //                 MKL_Complex16 alpha,
@@ -96,14 +162,6 @@ To getScaleValue(Ti val)
 //                 MKL_Complex16 *y,
 //                 MKL_INT ldy);
 
-template<typename T>
-using create_csr_func_def = sparse_status_t (*)
-                           (sparse_matrix_t *,
-                            sparse_index_base_t,
-                            int, int,
-                            int *, int *, int*,
-                            ptr_type<T>);
-
 template<typename T>
 using mv_func_def         = sparse_status_t (*)
                            (sparse_operation_t,
@@ -133,12 +191,6 @@ template<typename T> FUNC##_func_def<T> FUNC##_func();
   template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
 { return &mkl_sparse_##PREFIX##_##FUNC; }
 
-SPARSE_FUNC_DEF( create_csr )
-SPARSE_FUNC(create_csr , float   , s)
-SPARSE_FUNC(create_csr , double  , d)
-SPARSE_FUNC(create_csr , cfloat  , c)
-SPARSE_FUNC(create_csr , cdouble , z)
-
 SPARSE_FUNC_DEF( mv )
 SPARSE_FUNC(mv , float   , s)
 SPARSE_FUNC(mv , double  , d)
@@ -151,59 +203,6 @@ SPARSE_FUNC(mm , double  , d)
 SPARSE_FUNC(mm , cfloat  , c)
 SPARSE_FUNC(mm , cdouble , z)
 
-template<>
-const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
-{
-    sp_cfloat ret;
-    ret.real = val.real();
-    ret.imag = val.imag();
-    return ret;
-}
-
-template<>
-const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
-{
-    sp_cdouble ret;
-    ret.real = val.real();
-    ret.imag = val.imag();
-    return ret;
-}
-
-#else   // USE_MKL
-
-// From mkl_spblas.h
-typedef enum
-{
-    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
-    SPARSE_OPERATION_TRANSPOSE              = 11,
-    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
-} sparse_operation_t;
-
-#endif  // USE_MKL
-
-sparse_operation_t
-toSparseTranspose(af_mat_prop opt)
-{
-    sparse_operation_t out = SPARSE_OPERATION_NON_TRANSPOSE;
-    switch(opt) {
-        case AF_MAT_NONE        : out = SPARSE_OPERATION_NON_TRANSPOSE;         break;
-        case AF_MAT_TRANS       : out = SPARSE_OPERATION_TRANSPOSE;             break;
-        case AF_MAT_CTRANS      : out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE;   break;
-        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
-    }
-    return out;
-}
-
-template<typename T, int value>
-scale_type<T> getScale()
-{
-    static T val(value);
-    return getScaleValue<scale_type<T>, T>(val);
-}
-
-////////////////////////////////////////////////////////////////////////////////
-#ifdef USE_MKL // Implementation using MKL
-////////////////////////////////////////////////////////////////////////////////
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs)
@@ -291,9 +290,15 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     return out;
 }
 
-////////////////////////////////////////////////////////////////////////////////
-#else // Implementation without using MKL
-////////////////////////////////////////////////////////////////////////////////
+#else // #if USE_MKL
+
+// From mkl_spblas.h
+typedef enum
+{
+    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
+    SPARSE_OPERATION_TRANSPOSE              = 11,
+    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
+} sparse_operation_t;
 
 template<typename T>
 T getConjugate(const T &in)
@@ -500,9 +505,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     return out;
 }
 
-////////////////////////////////////////////////////////////////////////////////
-#endif
-////////////////////////////////////////////////////////////////////////////////
+#endif // #if USE_MKL
 
 #define INSTANTIATE_SPARSE(T)                                                           \
     template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
diff --git a/test/data b/test/data
index ada7fe1a41..2ef3476e57 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit ada7fe1a41851c18e3cda44df1ac827b9b3b39a9
+Subproject commit 2ef3476e5798ed2396219b9189e4dde90e37f531

From 450340b240b84b0740254cc836e0a067a693d190 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 29 Nov 2018 14:54:17 +0530
Subject: [PATCH 1574/2677] FEAT: sparse-sparse add/sub support

CPU and OpenCL backends have support for mul/div but they
are disabled to have feature parity with CUDA which doesn't
have support for mul/div.

The output of sub/div/mul is not guaranteed to have only
non-zero results of the arithmetic operation. The user has
to take care of pruning the zero results from the output.
---
 src/api/c/binary.cpp                          |  86 +++++----
 src/backend/cpu/kernel/sparse_arith.hpp       | 101 ++++++++++
 src/backend/cpu/sparse_arith.cpp              |  56 +++++-
 src/backend/cpu/sparse_arith.hpp              |   6 +-
 src/backend/cuda/sparse_arith.cu              |  91 +++++++++
 src/backend/cuda/sparse_arith.hpp             |   6 +-
 src/backend/opencl/kernel/sp_sp_arith_csr.cl  |  62 ++++++
 src/backend/opencl/kernel/sparse_arith.hpp    | 180 ++++++++++++++----
 .../opencl/kernel/ssarith_calc_out_nnz.cl     |  58 ++++++
 src/backend/opencl/sparse_arith.cpp           |  45 ++++-
 src/backend/opencl/sparse_arith.hpp           |   7 +-
 test/sparse_arith.cpp                         |  56 ++++++
 12 files changed, 665 insertions(+), 89 deletions(-)
 create mode 100644 src/backend/opencl/kernel/sp_sp_arith_csr.cl
 create mode 100644 src/backend/opencl/kernel/ssarith_calc_out_nnz.cl

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index f0efc1ba51..50f0a942a0 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -35,6 +35,14 @@ static inline af_array arithOp(const af_array lhs, const af_array rhs,
     return res;
 }
 
+template<typename T, af_op_t op>
+static inline
+af_array sparseArithOp(const af_array lhs, const af_array rhs)
+{
+    auto res = arithOp<T, op>(getSparseArray<T>(lhs), getSparseArray<T>(rhs));
+    return getHandle(res);
+}
+
 template<typename T, af_op_t op>
 static inline af_array arithSparseDenseOp(const af_array lhs, const af_array rhs,
                                           const bool reverse)
@@ -80,10 +88,11 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, co
 }
 
 template<af_op_t op>
-static af_err af_arith_real(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
+static
+af_err af_arith_real(af_array *out, const af_array lhs, const af_array rhs,
+                     const bool batchMode)
 {
     try {
-
         const ArrayInfo& linfo = getInfo(lhs);
         const ArrayInfo& rinfo = getInfo(rhs);
 
@@ -111,30 +120,33 @@ static af_err af_arith_real(af_array *out, const af_array lhs, const af_array rh
     return AF_SUCCESS;
 }
 
-//template<af_op_t op>
-//static af_err af_arith_sparse(af_array *out, const af_array lhs, const af_array rhs)
-//{
-//    try {
-//        SparseArrayBase linfo = getSparseArrayBase(lhs);
-//        SparseArrayBase rinfo = getSparseArrayBase(rhs);
-//
-//        dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
-//
-//        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
-//        af_array res;
-//        switch (otype) {
-//        case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
-//        case f64: res = arithOp<double , op>(lhs, rhs, odims); break;
-//        case c32: res = arithOp<cfloat , op>(lhs, rhs, odims); break;
-//        case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
-//        default: TYPE_ERROR(0, otype);
-//        }
-//
-//        std::swap(*out, res);
-//    }
-//    CATCHALL;
-//    return AF_SUCCESS;
-//}
+template<af_op_t op>
+static af_err
+af_arith_sparse(af_array *out, const af_array lhs, const af_array rhs)
+{
+    try {
+        common::SparseArrayBase linfo = getSparseArrayBase(lhs);
+        common::SparseArrayBase rinfo = getSparseArrayBase(rhs);
+
+        ARG_ASSERT(1, (linfo.getStorage()==rinfo.getStorage()));
+        ARG_ASSERT(1, (linfo.dims()==rinfo.dims()));
+        ARG_ASSERT(1, (linfo.getStorage()==AF_STORAGE_CSR));
+
+        const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
+        af_array res;
+        switch (otype) {
+            case f32: res = sparseArithOp<float  , op>(lhs, rhs); break;
+            case f64: res = sparseArithOp<double , op>(lhs, rhs); break;
+            case c32: res = sparseArithOp<cfloat , op>(lhs, rhs); break;
+            case c64: res = sparseArithOp<cdouble, op>(lhs, rhs); break;
+            default: TYPE_ERROR(0, otype);
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
 
 template<af_op_t op>
 static af_err af_arith_sparse_dense(af_array *out, const af_array lhs, const af_array rhs,
@@ -142,7 +154,7 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs, const af_
 {
     using namespace common;
     try {
-        SparseArrayBase linfo = getSparseArrayBase(lhs);
+        common::SparseArrayBase linfo = getSparseArrayBase(lhs);
         ArrayInfo       rinfo = getInfo(rhs);
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
@@ -161,18 +173,20 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs, const af_
     return AF_SUCCESS;
 }
 
-af_err af_add(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
+af_err af_add(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode)
 {
     // Check if inputs are sparse
     ArrayInfo linfo = getInfo(lhs, false, true);
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
     if(linfo.isSparse() && rinfo.isSparse()) {
-        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_add_t>(out, lhs, rhs);
+        return af_arith_sparse<af_add_t>(out, lhs, rhs);
     } else if(linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_add_t>(out, lhs, rhs);
     } else if(!linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true); // dense should be rhs
+        // second operand(Array) of af_arith call should be dense
+        return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true);
     } else {
         return af_arith<af_add_t>(out, lhs, rhs, batchMode);
     }
@@ -185,7 +199,10 @@ af_err af_mul(af_array *out, const af_array lhs, const af_array rhs, const bool
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
     if(linfo.isSparse() && rinfo.isSparse()) {
-        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_mul_t>(out, lhs, rhs);
+        //return af_arith_sparse<af_mul_t>(out, lhs, rhs);
+        //MKL doesn't have mul or div support yet, hence
+        //this is commented out although alternative cpu code exists
+        return AF_ERR_NOT_SUPPORTED;
     } else if(linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_mul_t>(out, lhs, rhs);
     } else if(!linfo.isSparse() && rinfo.isSparse()) {
@@ -202,7 +219,7 @@ af_err af_sub(af_array *out, const af_array lhs, const af_array rhs, const bool
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
     if(linfo.isSparse() && rinfo.isSparse()) {
-        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_sub_t>(out, lhs, rhs);
+        return af_arith_sparse<af_sub_t>(out, lhs, rhs);
     } else if(linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_sub_t>(out, lhs, rhs);
     } else if(!linfo.isSparse() && rinfo.isSparse()) {
@@ -219,7 +236,10 @@ af_err af_div(af_array *out, const af_array lhs, const af_array rhs, const bool
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
     if(linfo.isSparse() && rinfo.isSparse()) {
-        return AF_ERR_NOT_SUPPORTED; //af_arith_sparse<af_div_t>(out, lhs, rhs);
+        //return af_arith_sparse<af_div_t>(out, lhs, rhs);
+        //MKL doesn't have mul or div support yet, hence
+        //this is commented out although alternative cpu code exists
+        return AF_ERR_NOT_SUPPORTED;
     } else if(linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
     } else if(!linfo.isSparse() && rinfo.isSparse()) {
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index 4d81a44935..9eef3e98f0 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -11,6 +11,8 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+#include <cmath>
+
 namespace cpu
 {
 namespace kernel
@@ -143,5 +145,104 @@ void sparseArithOpS(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
     }
 }
 
+// The following functions can handle CSR
+// storage format only as of now.
+static
+void calcOutNNZ(Param<int> outRowIdx,
+                const uint M, const uint N,
+                CParam<int> lRowIdx, CParam<int> lColIdx,
+                CParam<int> rRowIdx, CParam<int> rColIdx)
+{
+          int *orPtr = outRowIdx.get();
+    const int *lrPtr = lRowIdx.get();
+    const int *lcPtr = lColIdx.get();
+    const int *rrPtr = rRowIdx.get();
+    const int *rcPtr = rColIdx.get();
+
+    unsigned csrOutCount = 0;
+    for (uint row=0; row<M; ++row) {
+        const int lEnd = lrPtr[row+1];
+        const int rEnd = rrPtr[row+1];
+
+        uint rowNNZ = 0;
+        int l = lrPtr[row];
+        int r = rrPtr[row];
+        while (l < lEnd && r < rEnd) {
+            int lci = lcPtr[l];
+            int rci = rcPtr[r];
+
+            l += (lci <= rci);
+            r += (lci >= rci);
+            rowNNZ++;
+        }
+        // Elements from lhs or rhs are exhausted.
+        // Just count left over elements
+        rowNNZ += (lEnd-l);
+        rowNNZ += (rEnd-r);
+
+        orPtr[row] = csrOutCount;
+        csrOutCount += rowNNZ;
+    }
+    //Write out the Rows+1 entry
+    orPtr[M] = csrOutCount;
+}
+
+template<typename T, af_op_t op>
+void sparseArithOp(Param<T> oVals, Param<int> oColIdx,
+                   CParam<int> oRowIdx, const uint Rows,
+                   CParam<T> lvals, CParam<int> lRowIdx, CParam<int> lColIdx,
+                   CParam<T> rvals, CParam<int> rRowIdx, CParam<int> rColIdx)
+{
+    const int *orPtr = oRowIdx.get();
+    const   T *lvPtr = lvals.get();
+    const int *lrPtr = lRowIdx.get();
+    const int *lcPtr = lColIdx.get();
+    const   T *rvPtr = rvals.get();
+    const int *rrPtr = rRowIdx.get();
+    const int *rcPtr = rColIdx.get();
+
+    arith_op<T, op> binOp;
+
+    auto ZERO = scalar<T>(0);
+
+    for (uint row=0; row<Rows; ++row) {
+        const int lEnd = lrPtr[row+1];
+        const int rEnd = rrPtr[row+1];
+        const int offs = orPtr[row];
+
+          T *ovPtr = oVals.get() + offs;
+        int *ocPtr = oColIdx.get() + offs;
+
+        uint rowNNZ = 0;
+        int l = lrPtr[row];
+        int r = rrPtr[row];
+        while (l < lEnd && r < rEnd) {
+            int lci = lcPtr[l];
+            int rci = rcPtr[r];
+
+            T lhs = (lci <= rci ? lvPtr[l] : ZERO);
+            T rhs = (lci >= rci ? rvPtr[r] : ZERO);
+
+            ovPtr[ rowNNZ ] = binOp(lhs, rhs);
+            ocPtr[ rowNNZ ] = (lci <= rci) ? lci : rci;
+
+            l += (lci <= rci);
+            r += (lci >= rci);
+            rowNNZ++;
+        }
+        while (l < lEnd) {
+            ovPtr[ rowNNZ ] = binOp(lvPtr[l], ZERO);
+            ocPtr[ rowNNZ ] = lcPtr[l];
+            l++;
+            rowNNZ++;
+        }
+        while (r < rEnd) {
+            ovPtr[ rowNNZ ] = binOp(ZERO, rvPtr[r]);
+            ocPtr[ rowNNZ ] = rcPtr[r];
+            r++;
+            rowNNZ++;
+        }
+    }
+}
 }
 }
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index 09ede431b0..298d906f1e 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -9,14 +9,8 @@
 
 #include <sparse_arith.hpp>
 #include <common/SparseArray.hpp>
-#include <optypes.hpp>
 #include <sparse.hpp>
-
-#include <kernel/sparse_arith.hpp>
-
-#include <stdexcept>
-#include <string>
-
+#include <optypes.hpp>
 #include <af/dim4.hpp>
 #include <arith.hpp>
 #include <complex.hpp>
@@ -26,6 +20,13 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+#include <kernel/sparse_arith.hpp>
+
+#include <algorithm>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
 namespace cpu
 {
 
@@ -115,6 +116,39 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
     return out;
 }
 
+template<typename T, af_op_t op>
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
+{
+    af::storage sfmt = lhs.getStorage();
+
+    lhs.eval();
+    rhs.eval();
+
+    const dim4 dims = lhs.dims();
+    const uint M = dims[0];
+    const uint N = dims[1];
+
+    auto rowArr = createEmptyArray<int>(dim4(M+1));
+
+    getQueue().enqueue(kernel::calcOutNNZ, rowArr, M, N,
+                       lhs.getRowIdx(), lhs.getColIdx(),
+                       rhs.getRowIdx(), rhs.getColIdx());
+    getQueue().sync();
+
+    uint nnz = rowArr.get()[M];
+    auto out = createEmptySparseArray<T>(dims, nnz, sfmt);
+    out.eval();
+
+    copyArray(out.getRowIdx(), rowArr);
+
+    getQueue().enqueue(kernel::sparseArithOp<T, op>,
+                       out.getValues(), out.getColIdx(),
+                       out.getRowIdx(), M,
+                       lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                       rhs.getValues(), rhs.getRowIdx(), rhs.getColIdx());
+    return out;
+}
+
 #define INSTANTIATE(T)                                                                              \
     template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
@@ -132,6 +166,14 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
                                                   const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
                                                  const bool reverse);                               \
+    template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_sub_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_mul_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_div_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
index db55154814..1cd1a6911c 100644
--- a/src/backend/cpu/sparse_arith.hpp
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 #include <sparse.hpp>
@@ -14,7 +16,6 @@
 
 namespace cpu
 {
-
 // These two functions cannot be overloaded by return type.
 // So have to give them separate names.
 template<typename T, af_op_t op>
@@ -25,4 +26,7 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                                 const bool reverse = false);
 
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const common::SparseArray<T> &rhs);
 }
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 2126234f66..9754213838 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -103,6 +103,89 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
     return out;
 }
 
+template<typename T>
+using csrgeam_def = cusparseStatus_t (*)(cusparseHandle_t, int, int,
+        const T*, const cusparseMatDescr_t, int, const T*, const int*, const int*,
+        const T*, const cusparseMatDescr_t, int, const T*, const int*, const int*,
+        const cusparseMatDescr_t, T*, int*, int*);
+
+#define SPARSE_ARITH_OP_FUNC_DEF( FUNC )            \
+template<typename T> FUNC##_def<T> FUNC##_func();
+
+SPARSE_ARITH_OP_FUNC_DEF( csrgeam );
+
+#define SPARSE_ARITH_OP_FUNC( FUNC, TYPE, INFIX )   \
+template<> FUNC##_def<TYPE> FUNC##_func<TYPE>()     \
+{ return cusparse##INFIX##FUNC; }
+
+SPARSE_ARITH_OP_FUNC(csrgeam, float  , S);
+SPARSE_ARITH_OP_FUNC(csrgeam, double , D);
+SPARSE_ARITH_OP_FUNC(csrgeam, cfloat , C);
+SPARSE_ARITH_OP_FUNC(csrgeam, cdouble, Z);
+
+template<typename T, af_op_t op>
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
+{
+    lhs.eval();
+    rhs.eval();
+    af::storage sfmt = lhs.getStorage();
+
+    cusparseMatDescr_t desc;
+    cusparseCreateMatDescr(&desc);
+
+    const dim4 ldims = lhs.dims();
+
+    const int M = ldims[0];
+    const int N = ldims[1];
+
+    const dim_t nnzA = lhs.getNNZ();
+    const dim_t nnzB = rhs.getNNZ();
+
+    const int* csrRowPtrA = lhs.getRowIdx().get();
+    const int* csrColPtrA = lhs.getColIdx().get();
+    const int* csrRowPtrB = rhs.getRowIdx().get();
+    const int* csrColPtrB = rhs.getColIdx().get();
+
+    auto outRowIdx = createEmptyArray<int>(dim4(M+1));
+
+    int* csrRowPtrC = outRowIdx.get();
+    int baseC, nnzC;
+    int* nnzcDevHostPtr = &nnzC;
+
+    cusparseXcsrgeamNnz(sparseHandle(), M, N,
+                        desc, nnzA, csrRowPtrA, csrColPtrA,
+                        desc, nnzB, csrRowPtrB, csrColPtrB,
+                        desc, csrRowPtrC, nnzcDevHostPtr);
+    if (NULL != nnzcDevHostPtr) {
+        nnzC = *nnzcDevHostPtr;
+    } else {
+        cudaMemcpyAsync(&nnzC, csrRowPtrC+M, sizeof(int),
+                        cudaMemcpyDeviceToHost, cuda::getActiveStream());
+        cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int),
+                        cudaMemcpyDeviceToHost, cuda::getActiveStream());
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+        nnzC -= baseC;
+    }
+
+    auto outColIdx = createEmptyArray<int>(dim4(nnzC));
+    auto outValues = createEmptyArray<T>(dim4(nnzC));
+
+    T alpha = scalar<T>(1);
+    T beta  = op == af_sub_t ? scalar<T>(-1) : alpha;
+
+    csrgeam_func<T>()(sparseHandle(), M, N,
+                      &alpha, desc, nnzA,
+                      lhs.getValues().get(), csrRowPtrA, csrColPtrA,
+                      &beta,  desc, nnzB,
+                      rhs.getValues().get(), csrRowPtrB, csrColPtrB,
+                      desc, outValues.get(), csrRowPtrC, outColIdx.get());
+
+    SparseArray<T> retVal = createArrayDataSparseArray(ldims,
+                                 outValues, outRowIdx, outColIdx,
+                                 sfmt);
+    return retVal;
+}
+
 #define INSTANTIATE(T)                                                                              \
     template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
@@ -120,6 +203,14 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
                                                   const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
                                                   const bool reverse);                              \
+    template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_sub_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_mul_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_div_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
index 5ea1e68059..f9ee528ae5 100644
--- a/src/backend/cuda/sparse_arith.hpp
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -25,5 +25,7 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                                 const bool reverse = false);
 
-}
-
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const common::SparseArray<T> &rhs);
+}
\ No newline at end of file
diff --git a/src/backend/opencl/kernel/sp_sp_arith_csr.cl b/src/backend/opencl/kernel/sp_sp_arith_csr.cl
new file mode 100644
index 0000000000..684beef7bf
--- /dev/null
+++ b/src/backend/opencl/kernel/sp_sp_arith_csr.cl
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+//TODO_PERF(pradeep) More performance improvements are possible
+__attribute__((reqd_work_group_size(256, 1, 1)))
+kernel
+void ssarith_csr_kernel(global T* oVals, global int* oColIdx,
+                        global const int* oRowIdx,
+                        uint M, uint N,
+                        uint nnza, global const T *lVals,
+                        global const int *lRowIdx, global const int *lColIdx,
+                        uint nnzb, global const T *rVals,
+                        global const int *rRowIdx, global const int *rColIdx)
+{
+    const uint row     = get_global_id(0);
+
+    const bool valid = row < M;
+
+    const uint lEnd    = (valid ? lRowIdx[row+1] : 0);
+    const uint rEnd    = (valid ? rRowIdx[row+1] : 0);
+    const uint offset  = (valid ? oRowIdx[row]   : 0);
+
+    global   T *ovPtr = oVals + offset;
+    global int *ocPtr = oColIdx + offset;
+
+    uint l = (valid ? lRowIdx[row] : 0);
+    uint r = (valid ? rRowIdx[row] : 0);
+
+    uint nnz = 0;
+    while (l < lEnd && r < rEnd) {
+        uint lci = lColIdx[l];
+        uint rci = rColIdx[r];
+
+        T lhs = (lci <= rci ? lVals[l] : IDENTITY_VALUE);
+        T rhs = (lci >= rci ? rVals[r] : IDENTITY_VALUE);
+
+        ovPtr[ nnz ] = OP(lhs, rhs);
+        ocPtr[ nnz ] = (lci <= rci) ? lci : rci;
+
+        l += (lci <= rci);
+        r += (lci >= rci);
+        nnz++;
+    }
+    while (l < lEnd) {
+        ovPtr[nnz] = OP(lVals[l], IDENTITY_VALUE);
+        ocPtr[nnz] = lColIdx[l];
+        l++;
+        nnz++;
+    }
+    while (r < rEnd) {
+        ovPtr[nnz] = OP(IDENTITY_VALUE, rVals[r]);
+        ocPtr[nnz] = rColIdx[r];
+        r++;
+        nnz++;
+    }
+}
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 3cef0fdcab..6539d4e73f 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -11,24 +11,21 @@
 #include <kernel_headers/sparse_arith_csr.hpp>
 #include <kernel_headers/sparse_arith_coo.hpp>
 #include <kernel_headers/sparse_arith_common.hpp>
+#include <kernel_headers/ssarith_calc_out_nnz.hpp>
+#include <kernel_headers/sp_sp_arith_csr.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
 #include <mutex>
 #include <map>
+#include <common/complex.hpp>
 #include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <debug_opencl.hpp>
 #include <cache.hpp>
 #include <type_util.hpp>
-
-using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::EnqueueArgs;
-using cl::NDRange;
-using std::string;
+#include <types.hpp>
+#include <math.hpp>
 
 namespace opencl
 {
@@ -83,24 +80,24 @@ namespace opencl
                 const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
                 const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
 
-                Program prog;
+                cl::Program prog;
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel");
+                entry.prog = new cl::Program(prog);
+                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel");
 
                 addKernelToCache(device, ref_name, entry);
             }
 
-            auto sparseArithCSROp = KernelFunctor<Buffer, const KParam,
-                 const Buffer, const Buffer, const Buffer,
+            auto sparseArithCSROp = cl::KernelFunctor<cl::Buffer, const KParam,
+                 const cl::Buffer, const cl::Buffer, const cl::Buffer,
                  const int,
-                 const Buffer, const KParam,
+                 const cl::Buffer, const KParam,
                  const int>(*entry.ker);
 
-            NDRange local(TX, TY, 1);
-            NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
+            cl::NDRange local(TX, TY, 1);
+            cl::NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
 
-            sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
+            sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local),
                     *out.data, out.info,
                     *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
                     *rhs.data, rhs.info, reverse);
@@ -139,24 +136,24 @@ namespace opencl
                 const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
                 const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
 
-                Program prog;
+                cl::Program prog;
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel");
+                entry.prog = new cl::Program(prog);
+                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel");
 
                 addKernelToCache(device, ref_name, entry);
             }
 
-            auto sparseArithCOOOp = KernelFunctor<Buffer, const KParam,
-                 const Buffer, const Buffer, const Buffer,
+            auto sparseArithCOOOp = cl::KernelFunctor<cl::Buffer, const KParam,
+                 const cl::Buffer, const cl::Buffer, const cl::Buffer,
                  const int,
-                 const Buffer, const KParam,
+                 const cl::Buffer, const KParam,
                  const int>(*entry.ker);
 
-            NDRange local(THREADS, 1, 1);
-            NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+            cl::NDRange local(THREADS, 1, 1);
+            cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
 
-            sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
+            sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local),
                     *out.data, out.info,
                     *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
                     *rhs.data, rhs.info, reverse);
@@ -196,23 +193,23 @@ namespace opencl
                 const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
                 const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
 
-                Program prog;
+                cl::Program prog;
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
+                entry.prog = new cl::Program(prog);
+                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
 
                 addKernelToCache(device, ref_name, entry);
             }
 
-            auto sparseArithCSROp = KernelFunctor<const Buffer, const Buffer, const Buffer,
+            auto sparseArithCSROp = cl::KernelFunctor<const cl::Buffer, const cl::Buffer, const cl::Buffer,
                  const int,
-                 const Buffer, const KParam,
+                 const cl::Buffer, const KParam,
                  const int>(*entry.ker);
 
-            NDRange local(TX, TY, 1);
-            NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
+            cl::NDRange local(TX, TY, 1);
+            cl::NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
 
-            sparseArithCSROp(EnqueueArgs(getQueue(), global, local),
+            sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local),
                     *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
                     *rhs.data, rhs.info, reverse);
 
@@ -250,27 +247,126 @@ namespace opencl
                 const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
                 const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
 
-                Program prog;
+                cl::Program prog;
                 buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
+                entry.prog = new cl::Program(prog);
+                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
 
                 addKernelToCache(device, ref_name, entry);
             }
 
-            auto sparseArithCOOOp = KernelFunctor<Buffer, Buffer, Buffer,
+            auto sparseArithCOOOp = cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer,
                  const int,
-                 const Buffer, const KParam,
+                 const cl::Buffer, const KParam,
                  const int>(*entry.ker);
 
-            NDRange local(THREADS, 1, 1);
-            NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+            cl::NDRange local(THREADS, 1, 1);
+            cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
 
-            sparseArithCOOOp(EnqueueArgs(getQueue(), global, local),
+            sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local),
                     *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
                     *rhs.data, rhs.info, reverse);
 
             CL_DEBUG_FINISH(getQueue());
         }
+
+        static
+        void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC,
+                const uint M, const uint N,
+                uint nnzA, const Param lrowIdx, const Param lcolIdx,
+                uint nnzB, const Param rrowIdx, const Param rcolIdx)
+        {
+            std::string refName = std::string("csr_calc_output_NNZ");
+            int device = getActiveDeviceId();
+            kc_entry_t entry = kernelCache(device, refName);
+
+            if (entry.prog==0 && entry.ker==0) {
+                const char *kerStrs[] = { ssarith_calc_out_nnz_cl };
+                const int kerLens[] = { ssarith_calc_out_nnz_cl_len };
+
+                cl::Program prog;
+                buildProgram(prog, 1, kerStrs, kerLens, std::string(""));
+                entry.prog = new cl::Program(prog);
+                entry.ker = new cl::Kernel(*entry.prog, "csr_calc_out_nnz");
+
+                addKernelToCache(device, refName, entry);
+            }
+            auto calcNNZop = cl::KernelFunctor<cl::Buffer, cl::Buffer, unsigned,
+                 const cl::Buffer, const cl::Buffer,
+                 const cl::Buffer, const cl::Buffer,
+                 cl::LocalSpaceArg>(*entry.ker);
+
+            cl::NDRange local(256, 1);
+            cl::NDRange global(divup(M, local[0])*local[0], 1, 1);
+
+            nnzC = 0;
+            cl::Buffer* out = bufferAlloc(sizeof(unsigned));
+            getQueue().enqueueWriteBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
+
+            calcNNZop(cl::EnqueueArgs(getQueue(), global, local),
+                      *out, *outRowIdx.data, M,
+                      *lrowIdx.data, *lcolIdx.data,
+                      *rrowIdx.data, *rcolIdx.data,
+                      cl::Local(local[0]*sizeof(unsigned int)));
+            getQueue().enqueueReadBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
+
+            CL_DEBUG_FINISH(getQueue());
+        }
+
+        template<typename T, af_op_t op>
+        void ssArithCSR(Param oVals, Param oColIdx,
+                const Param oRowIdx, const uint M, const uint N,
+                unsigned nnzA, const Param lVals, const Param lRowIdx, const Param lColIdx,
+                unsigned nnzB, const Param rVals, const Param rRowIdx, const Param rColIdx)
+        {
+            std::string refName = std::string("ss_arith_csr_") +
+                                  getOpString<op>() + "_" +
+                                  std::string(dtype_traits<T>::getName());
+            int device = getActiveDeviceId();
+            kc_entry_t entry = kernelCache(device, refName);
+
+            if (entry.prog==0 && entry.ker==0) {
+                const T iden_val = (op == af_mul_t || op == af_div_t ?
+                                      scalar<T>(1) : scalar<T>(0));
+                ToNumStr<T> toNumStr;
+                std::ostringstream options;
+                options << " -D T="  << dtype_traits<T>::getName()
+                        << " -D OP=" << getOpString<op>()
+                        << " -D IDENTITY_VALUE=(T)(" << af::scalar_to_option(iden_val) << ")";
+
+                options << " -D IS_CPLX=" << common::is_complex<T>::value;
+                if (std::is_same<T, double>::value ||
+                        std::is_same<T, cdouble>::value) {
+                    options << " -D USE_DOUBLE";
+                }
+
+                const char *kerStrs[] = { sparse_arith_common_cl, sp_sp_arith_csr_cl };
+                const int kerLens[] = { sparse_arith_common_cl_len, sp_sp_arith_csr_cl_len };
+
+                cl::Program prog;
+                buildProgram(prog, 2, kerStrs, kerLens, options.str());
+                entry.prog = new cl::Program(prog);
+                entry.ker = new cl::Kernel(*entry.prog, "ssarith_csr_kernel");
+
+                addKernelToCache(device, refName, entry);
+            }
+            auto arithOp = cl::KernelFunctor<cl::Buffer, cl::Buffer,
+                 cl::Buffer, unsigned, unsigned,
+                 unsigned, const cl::Buffer,
+                 const cl::Buffer, const cl::Buffer,
+                 unsigned, const cl::Buffer,
+                 const cl::Buffer, const cl::Buffer>(*entry.ker);
+
+            cl::NDRange local(256, 1);
+            cl::NDRange global(divup(M, local[0])*local[0], 1, 1);
+
+            arithOp(cl::EnqueueArgs(getQueue(), global, local),
+                      *oVals.data, *oColIdx.data,
+                      *oRowIdx.data, M, N,
+                      nnzA, *lVals.data, *lRowIdx.data, *lColIdx.data,
+                      nnzB, *rVals.data, *rRowIdx.data, *rColIdx.data);
+
+            CL_DEBUG_FINISH(getQueue());
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl b/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl
new file mode 100644
index 0000000000..6b162cb239
--- /dev/null
+++ b/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+kernel
+void csr_calc_out_nnz(global unsigned* nnzc,
+                      global int* oRowIdx, uint M,
+                      global const int *lRowIdx, global const int *lColIdx,
+                      global const int *rRowIdx, global const int *rColIdx,
+                      local uint* blkNnz)
+{
+    const uint row  = get_global_id(0);
+    const uint tid  = get_local_id(0);
+
+    const bool valid = row < M;
+
+    const uint lEnd = (valid ? lRowIdx[row+1] : 0);
+    const uint rEnd = (valid ? rRowIdx[row+1] : 0);
+
+    blkNnz[tid] = 0;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    uint l = (valid ? lRowIdx[row] : 0);
+    uint r = (valid ? rRowIdx[row] : 0);
+    uint nnz = 0;
+    while (l < lEnd && r < rEnd) {
+        uint lci = lColIdx[l];
+        uint rci = rColIdx[r];
+        l += (lci <= rci);
+        r += (lci >= rci);
+        nnz++;
+    }
+    nnz += (lEnd-l);
+    nnz += (rEnd-r);
+
+    blkNnz[tid] = nnz;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (valid)
+        oRowIdx[row+1] = nnz;
+
+    for(uint s=get_local_size(0)/2; s>0; s>>=1) {
+        if (tid < s) {
+            blkNnz[tid] += blkNnz[tid + s];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (tid == 0) {
+        nnz = blkNnz[0];
+        atomic_add(nnzc, nnz);
+    }
+}
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index a5e269ea2a..40f5c19dac 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -21,6 +21,7 @@
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <scan.hpp>
 #include <where.hpp>
 
 namespace opencl
@@ -103,6 +104,45 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
     return out;
 }
 
+template<typename T, af_op_t op>
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
+{
+    lhs.eval();
+    rhs.eval();
+    af::storage sfmt = lhs.getStorage();
+
+    const dim4 ldims = lhs.dims();
+
+    const uint M = ldims[0];
+    const uint N = ldims[1];
+
+    const dim_t nnzA = lhs.getNNZ();
+    const dim_t nnzB = rhs.getNNZ();
+
+    auto temp = createValueArray<int>(dim4(M+1), scalar<int>(0));
+    temp.eval();
+
+    unsigned nnzC = 0;
+    kernel::csrCalcOutNNZ(temp, nnzC, M, N,
+            nnzA, lhs.getRowIdx(), lhs.getColIdx(),
+            nnzB, rhs.getRowIdx(), rhs.getColIdx());
+
+    auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
+
+    auto outColIdx = createEmptyArray<int>(dim4(nnzC));
+    auto outValues = createEmptyArray<T>(dim4(nnzC));
+
+    kernel::ssArithCSR<T, op>(outValues, outColIdx,
+            outRowIdx, M, N,
+            nnzA, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+            nnzB, rhs.getValues(), rhs.getRowIdx(), rhs.getColIdx());
+
+    SparseArray<T> retVal = createArrayDataSparseArray(ldims,
+                                 outValues, outRowIdx, outColIdx,
+                                 sfmt);
+    return retVal;
+}
+
 #define INSTANTIATE(T)                                                                              \
     template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
@@ -120,6 +160,10 @@ SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bo
                                                   const bool reverse);                              \
     template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
                                                   const bool reverse);                              \
+    template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
+    template SparseArray<T> arithOp<T, af_sub_t>(const common::SparseArray<T> &lhs,                 \
+                                                 const common::SparseArray<T> &rhs);                \
 
 INSTANTIATE(float  )
 INSTANTIATE(double )
@@ -127,4 +171,3 @@ INSTANTIATE(cfloat )
 INSTANTIATE(cdouble)
 
 }
-
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
index 4afc799cad..3a54a674d6 100644
--- a/src/backend/opencl/sparse_arith.hpp
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -25,6 +25,7 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                                 const bool reverse = false);
 
-}
-
-
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const common::SparseArray<T> &rhs);
+}
\ No newline at end of file
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index cd8e98d857..8b7c35b8e7 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -328,3 +328,59 @@ ARITH_TESTS(float  , 1e-6)
 ARITH_TESTS(double , 1e-6)
 ARITH_TESTS(cfloat , 1e-4) // This is mostly for complex division in OpenCL
 ARITH_TESTS(cdouble, 1e-6)
+
+// Sparse-Sparse Arithmetic testing function
+template<typename T, af_op_t op>
+void ssArithmetic(const int m, const int n, int factor, const double eps)
+{
+    deviceGC();
+
+    if (noDoubleTests<T>()) return;
+
+#if 1
+    array A = cpu_randu<T>(dim4(m, n));
+    array B = cpu_randu<T>(dim4(m, n));
+#else
+    array A = randu(m, n, (dtype)dtype_traits<T>::af_type);
+    array B = randu(m, n, (dtype)dtype_traits<T>::af_type);
+#endif
+
+    A = makeSparse<T>(A, factor);
+    B = makeSparse<T>(B, factor);
+
+    array spA = sparse(A, AF_STORAGE_CSR);
+    array spB = sparse(B, AF_STORAGE_CSR);
+
+    arith_op<op> binOp;
+
+    // Arith Op
+    array resS = binOp(spA, spB);
+    array resD = binOp(A, B);
+    array revS = binOp(spB, spA);
+    array revD = binOp(B,  A);
+
+    ASSERT_ARRAYS_NEAR(resD, dense(resS), eps);
+    ASSERT_ARRAYS_NEAR(revD, dense(revS), eps);
+}
+
+#define SP_SP_ARITH_TEST(type, m, n, factor, eps)       \
+TEST(SparseSparseArith, type##_Addition_##m##_##n)      \
+{                                                       \
+    ssArithmetic<type, af_add_t>(m, n, factor, eps);    \
+}                                                       \
+TEST(SparseSparseArith, type##_Subtraction_##m##_##n)   \
+{                                                       \
+    ssArithmetic<type, af_sub_t>(m, n, factor, eps);    \
+}
+
+#define SP_SP_ARITH_TESTS(T, eps)                       \
+    SP_SP_ARITH_TEST(T, 10  , 10  , 5, eps)             \
+    SP_SP_ARITH_TEST(T, 1024, 1024, 5, eps)             \
+    SP_SP_ARITH_TEST(T, 100 , 100 , 1, eps)             \
+    SP_SP_ARITH_TEST(T, 2048, 1000, 6, eps)             \
+    SP_SP_ARITH_TEST(T, 123 , 278 , 5, eps)             \
+
+SP_SP_ARITH_TESTS(float  , 1e-6)
+SP_SP_ARITH_TESTS(double , 1e-6)
+SP_SP_ARITH_TESTS(cfloat , 1e-4) // This is mostly for complex division in OpenCL
+SP_SP_ARITH_TESTS(cdouble, 1e-6)

From 2d088b85c7f6c6435f5c6103340e3eb1d104a3d2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 30 Nov 2018 18:57:16 +0530
Subject: [PATCH 1575/2677] Changes to download and use mtx files

- Includes tests to read sparse matrix from mtx file

cmake configure downloads the compressed mtx files, uncompresses
the files and places them under the source tree location
`test/data/matrixmarket` so that clean builds don't redownload
entire data set. Hence, a new change has been added to test/data
git repository to ignore the matrixmarket folder.

If for any reason download fails, MTX tests are disabled.
---
 test/CMakeLists.txt                           |  22 +-
 .../download_sparse_datasets.cmake            |  74 +++
 test/matrixmarket.cpp                         |  32 ++
 test/mmio/CMakeLists.txt                      |  19 +
 test/mmio/mmio.c                              | 510 ++++++++++++++++++
 test/mmio/mmio.h                              | 139 +++++
 test/testHelpers.hpp                          |  95 ++++
 7 files changed, 889 insertions(+), 2 deletions(-)
 create mode 100644 test/CMakeModules/download_sparse_datasets.cmake
 create mode 100644 test/matrixmarket.cpp
 create mode 100644 test/mmio/CMakeLists.txt
 create mode 100644 test/mmio/mmio.c
 create mode 100644 test/mmio/mmio.h

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5c16ee88df..eb072133b6 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -5,6 +5,15 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
+set(AF_TEST_WITH_MTX_FILES
+    ON CACHE BOOL
+    "Download and run tests on large matrices form sparse.tamu.edu")
+
+set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
+if (AF_TEST_WITH_MTX_FILES)
+  include(download_sparse_datasets)
+endif ()
+
 if(NOT TARGET gtest)
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
   # newer cmakes. This sets the default global setting for that policy.
@@ -29,6 +38,10 @@ if(NOT TARGET gtest)
     gtest_hide_internal_symbols)
 endif()
 
+if(AF_TEST_WITH_MTX_FILES AND NOT TARGET mmio)
+  add_subdirectory(mmio)
+endif()
+
 # Reset the CXX flags for tests
 set(CMAKE_CXX_STANDARD 98)
 set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
@@ -265,8 +278,10 @@ make_test(SRC solve_dense.cpp       CXX11 SERIAL)
 make_test(SRC sort.cpp)
 make_test(SRC sort_by_key.cpp)
 make_test(SRC sort_index.cpp)
-make_test(SRC sparse.cpp            SERIAL)
-make_test(SRC sparse_arith.cpp)
+make_test(SRC sparse.cpp            SERIAL
+          LIBRARIES mmio)
+make_test(SRC sparse_arith.cpp
+          LIBRARIES mmio)
 make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
 make_test(SRC susan.cpp)
@@ -287,6 +302,9 @@ make_test(SRC wrap.cpp)
 make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
 
+if(AF_TEST_WITH_MTX_FILES)
+    make_test(SRC matrixmarket.cpp LIBRARIES mmio)
+endif()
 
 add_executable(print_info print_info.cpp)
 if(AF_BUILD_UNIFIED)
diff --git a/test/CMakeModules/download_sparse_datasets.cmake b/test/CMakeModules/download_sparse_datasets.cmake
new file mode 100644
index 0000000000..fbf099a42e
--- /dev/null
+++ b/test/CMakeModules/download_sparse_datasets.cmake
@@ -0,0 +1,74 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+set(URL "https://sparse.tamu.edu")
+
+function(download_mtx name group)
+  set(file_name "${group}/${name}.tar.gz")
+  if (NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
+    file(DOWNLOAD
+        "${URL}/MM/${file_name}"
+        ${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}
+        INACTIVITY_TIMEOUT 600
+        SHOW_PROGRESS
+        STATUS out_status
+        TLS_VERIFY ON
+        )
+    list(GET out_status 0 error_code)
+    list(GET out_status 1 error_string)
+    if (${error_code} EQUAL 0)
+      message("Downloaded ${name} file from sparse.tamu.edu")
+      file(MAKE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}")
+      execute_process(
+        COMMAND ${CMAKE_COMMAND} -E tar xzf "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}"
+        WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}"
+        )
+      message("Extracted mtx files to test data directory")
+    else ()
+      if (${error_code} EQUAL 503)
+          message(FATAL_ERROR "${URL} service unavailable")
+      elseif (${error_code} EQUAL 504)
+          message(FATAL_ERROR "Request to ${URL} timedout")
+      elseif (${error_code} EQUAL 521)
+        # CLOUDFLARE error code
+        message(FATAL_ERROR "Request to ${URL} has been refused")
+      elseif (${error_code} EQUAL 523)
+        # CLOUDFLARE error code
+        message(FATAL_ERROR "${URL} is unreachable")
+      else ()
+        message("Failed to download ${name} file from sparse.tamu.edu")
+        message("Failure message: ${error_string}")
+      endif ()
+      file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
+
+      #Force test with mtx files to be turned since one of the downloads failed
+      set(AF_TEST_WITH_MTX_FILES OFF)
+    endif ()
+  endif ()
+endfunction()
+
+# Following files are used for testing mtx read fn
+# integer data
+download_mtx("Trec4" "JGD_Kocay")
+# real data
+download_mtx("bcsstm02" "HB")
+# complex data
+download_mtx("young4c" "HB")
+
+#Following files are used for sparse-sparse arith
+# real data
+#linear programming problem
+download_mtx("lpi_vol1" "LPnetlib")
+download_mtx("lpi_qual" "LPnetlib")
+#Subsequent Circuit Simulation problem
+download_mtx("oscil_dcop_12" "Sandia")
+download_mtx("oscil_dcop_42" "Sandia")
+
+# complex data
+#Quantum Chemistry problem
+download_mtx("conf6_0-4x4-20" "QCD")
+download_mtx("conf6_0-4x4-30" "QCD")
diff --git a/test/matrixmarket.cpp b/test/matrixmarket.cpp
new file mode 100644
index 0000000000..6ae6a8acef
--- /dev/null
+++ b/test/matrixmarket.cpp
@@ -0,0 +1,32 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
+TEST(Sparse, ReadRealMTXFile)
+{
+    af::array out;
+    std::string file(TEST_DIR "/matrixmarket/HB/bcsstm02/bcsstm02.mtx");
+    ASSERT_TRUE(mtxReadSparseMatrix(out, file.c_str()));
+}
+
+TEST(Sparse, ReadComplexMTXFile)
+{
+    af::array out;
+    std::string file(TEST_DIR "/matrixmarket/HB/young4c/young4c.mtx");
+    ASSERT_TRUE(mtxReadSparseMatrix(out, file.c_str()));
+}
+
+TEST(Sparse, FailIntegerMTXRead)
+{
+    af::array out;
+    std::string file(TEST_DIR "/matrixmarket/JGD_Kocay/Trec4/Trec4.mtx");
+    ASSERT_FALSE(mtxReadSparseMatrix(out, file.c_str()));
+}
diff --git a/test/mmio/CMakeLists.txt b/test/mmio/CMakeLists.txt
new file mode 100644
index 0000000000..3ce5d57e17
--- /dev/null
+++ b/test/mmio/CMakeLists.txt
@@ -0,0 +1,19 @@
+# Copyright (c) 2018, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+cmake_minimum_required(VERSION 3.5)
+
+project(MatrixMarketIO LANGUAGES C)
+
+add_library(mmio STATIC mmio.c)
+
+target_include_directories(mmio
+    PUBLIC
+      $<BUILD_INTERFACE:${MatrixMarketIO_SOURCE_DIR}>
+    )
+
+target_compile_definitions(mmio PUBLIC USE_MTX)
diff --git a/test/mmio/mmio.c b/test/mmio/mmio.c
new file mode 100644
index 0000000000..4a1640791b
--- /dev/null
+++ b/test/mmio/mmio.c
@@ -0,0 +1,510 @@
+/*
+*   Matrix Market I/O library for ANSI C
+*
+*   See http://math.nist.gov/MatrixMarket for details.
+*
+*
+*/
+
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+#include <ctype.h>
+
+#include "mmio.h"
+
+int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
+                double **val_, int **I_, int **J_)
+{
+    FILE *f;
+    MM_typecode matcode;
+    int M, N, nz;
+    int i;
+    double *val;
+    int *I, *J;
+
+    if ((f = fopen(fname, "r")) == NULL)
+            return -1;
+
+
+    if (mm_read_banner(f, &matcode) != 0)
+    {
+        printf("mm_read_unsymetric: Could not process Matrix Market banner ");
+        printf(" in file [%s]\n", fname);
+        return -1;
+    }
+
+
+
+    if ( !(mm_is_real(matcode) && mm_is_matrix(matcode) &&
+            mm_is_sparse(matcode)))
+    {
+        fprintf(stderr, "Sorry, this application does not support ");
+        fprintf(stderr, "Market Market type: [%s]\n",
+                mm_typecode_to_str(matcode));
+        return -1;
+    }
+
+    /* find out size of sparse matrix: M, N, nz .... */
+
+    if (mm_read_mtx_crd_size(f, &M, &N, &nz) !=0)
+    {
+        fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n");
+        return -1;
+    }
+
+    *M_ = M;
+    *N_ = N;
+    *nz_ = nz;
+
+    /* reseve memory for matrices */
+
+    I = (int *) malloc(nz * sizeof(int));
+    J = (int *) malloc(nz * sizeof(int));
+    val = (double *) malloc(nz * sizeof(double));
+
+    *val_ = val;
+    *I_ = I;
+    *J_ = J;
+
+    /* NOTE: when reading in doubles, ANSI C requires the use of the "l"  */
+    /*   specifier as in "%lg", "%lf", "%le", otherwise errors will occur */
+    /*  (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15)            */
+
+    for (i=0; i<nz; i++)
+    {
+        fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]);
+        I[i]--;  /* adjust from 1-based to 0-based */
+        J[i]--;
+    }
+    fclose(f);
+
+    return 0;
+}
+
+int mm_is_valid(MM_typecode matcode)
+{
+    if (!mm_is_matrix(matcode)) return 0;
+    if (mm_is_dense(matcode) && mm_is_pattern(matcode)) return 0;
+    if (mm_is_real(matcode) && mm_is_hermitian(matcode)) return 0;
+    if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) ||
+                mm_is_skew(matcode))) return 0;
+    return 1;
+}
+
+int mm_read_banner(FILE *f, MM_typecode *matcode)
+{
+    char line[MM_MAX_LINE_LENGTH];
+    char banner[MM_MAX_TOKEN_LENGTH];
+    char mtx[MM_MAX_TOKEN_LENGTH];
+    char crd[MM_MAX_TOKEN_LENGTH];
+    char data_type[MM_MAX_TOKEN_LENGTH];
+    char storage_scheme[MM_MAX_TOKEN_LENGTH];
+    char *p;
+
+
+    mm_clear_typecode(matcode);
+
+    if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL)
+        return MM_PREMATURE_EOF;
+
+    if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type,
+        storage_scheme) != 5)
+        return MM_PREMATURE_EOF;
+
+    for (p=mtx; *p!='\0'; *p=tolower(*p),p++);  /* convert to lower case */
+    for (p=crd; *p!='\0'; *p=tolower(*p),p++);
+    for (p=data_type; *p!='\0'; *p=tolower(*p),p++);
+    for (p=storage_scheme; *p!='\0'; *p=tolower(*p),p++);
+
+    /* check for banner */
+    if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0)
+        return MM_NO_HEADER;
+
+    /* first field should be "mtx" */
+    if (strcmp(mtx, MM_MTX_STR) != 0)
+        return  MM_UNSUPPORTED_TYPE;
+    mm_set_matrix(matcode);
+
+
+    /* second field describes whether this is a sparse matrix (in coordinate
+            storgae) or a dense array */
+
+
+    if (strcmp(crd, MM_SPARSE_STR) == 0)
+        mm_set_sparse(matcode);
+    else
+    if (strcmp(crd, MM_DENSE_STR) == 0)
+            mm_set_dense(matcode);
+    else
+        return MM_UNSUPPORTED_TYPE;
+
+
+    /* third field */
+
+    if (strcmp(data_type, MM_REAL_STR) == 0)
+        mm_set_real(matcode);
+    else
+    if (strcmp(data_type, MM_COMPLEX_STR) == 0)
+        mm_set_complex(matcode);
+    else
+    if (strcmp(data_type, MM_PATTERN_STR) == 0)
+        mm_set_pattern(matcode);
+    else
+    if (strcmp(data_type, MM_INT_STR) == 0)
+        mm_set_integer(matcode);
+    else
+        return MM_UNSUPPORTED_TYPE;
+
+
+    /* fourth field */
+
+    if (strcmp(storage_scheme, MM_GENERAL_STR) == 0)
+        mm_set_general(matcode);
+    else
+    if (strcmp(storage_scheme, MM_SYMM_STR) == 0)
+        mm_set_symmetric(matcode);
+    else
+    if (strcmp(storage_scheme, MM_HERM_STR) == 0)
+        mm_set_hermitian(matcode);
+    else
+    if (strcmp(storage_scheme, MM_SKEW_STR) == 0)
+        mm_set_skew(matcode);
+    else
+        return MM_UNSUPPORTED_TYPE;
+
+
+    return 0;
+}
+
+int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz)
+{
+    if (fprintf(f, "%d %d %d\n", M, N, nz) != 3)
+        return MM_COULD_NOT_WRITE_FILE;
+    else
+        return 0;
+}
+
+int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz )
+{
+    char line[MM_MAX_LINE_LENGTH];
+    int num_items_read;
+
+    /* set return null parameter values, in case we exit with errors */
+    *M = *N = *nz = 0;
+
+    /* now continue scanning until you reach the end-of-comments */
+    do
+    {
+        if (fgets(line,MM_MAX_LINE_LENGTH,f) == NULL)
+            return MM_PREMATURE_EOF;
+    }while (line[0] == '%');
+
+    /* line[] is either blank or has M,N, nz */
+    if (sscanf(line, "%d %d %d", M, N, nz) == 3)
+        return 0;
+
+    else
+    do
+    {
+        num_items_read = fscanf(f, "%d %d %d", M, N, nz);
+        if (num_items_read == EOF) return MM_PREMATURE_EOF;
+    }
+    while (num_items_read != 3);
+
+    return 0;
+}
+
+
+int mm_read_mtx_array_size(FILE *f, int *M, int *N)
+{
+    char line[MM_MAX_LINE_LENGTH];
+    int num_items_read;
+    /* set return null parameter values, in case we exit with errors */
+    *M = *N = 0;
+
+    /* now continue scanning until you reach the end-of-comments */
+    do
+    {
+        if (fgets(line,MM_MAX_LINE_LENGTH,f) == NULL)
+            return MM_PREMATURE_EOF;
+    }while (line[0] == '%');
+
+    /* line[] is either blank or has M,N, nz */
+    if (sscanf(line, "%d %d", M, N) == 2)
+        return 0;
+
+    else /* we have a blank line */
+    do
+    {
+        num_items_read = fscanf(f, "%d %d", M, N);
+        if (num_items_read == EOF) return MM_PREMATURE_EOF;
+    }
+    while (num_items_read != 2);
+
+    return 0;
+}
+
+int mm_write_mtx_array_size(FILE *f, int M, int N)
+{
+    if (fprintf(f, "%d %d\n", M, N) != 2)
+        return MM_COULD_NOT_WRITE_FILE;
+    else
+        return 0;
+}
+
+
+
+/*-------------------------------------------------------------------------*/
+
+/******************************************************************/
+/* use when I[], J[], and val[]J, and val[] are already allocated */
+/******************************************************************/
+
+int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[],
+        double val[], MM_typecode matcode)
+{
+    int i;
+    if (mm_is_complex(matcode))
+    {
+        for (i=0; i<nz; i++)
+            if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2*i], &val[2*i+1])
+                != 4) return MM_PREMATURE_EOF;
+    }
+    else if (mm_is_real(matcode))
+    {
+        for (i=0; i<nz; i++)
+        {
+            if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i])
+                != 3) return MM_PREMATURE_EOF;
+
+        }
+    }
+
+    else if (mm_is_pattern(matcode))
+    {
+        for (i=0; i<nz; i++)
+            if (fscanf(f, "%d %d", &I[i], &J[i])
+                != 2) return MM_PREMATURE_EOF;
+    }
+    else
+        return MM_UNSUPPORTED_TYPE;
+
+    return 0;
+
+}
+
+int mm_read_mtx_crd_entry(FILE *f, int *I, int *J,
+        double *real, double *imag, MM_typecode matcode)
+{
+    if (mm_is_complex(matcode))
+    {
+            if (fscanf(f, "%d %d %lg %lg", I, J, real, imag)
+                != 4) return MM_PREMATURE_EOF;
+    }
+    else if (mm_is_real(matcode))
+    {
+            if (fscanf(f, "%d %d %lg\n", I, J, real)
+                != 3) return MM_PREMATURE_EOF;
+
+    }
+
+    else if (mm_is_pattern(matcode))
+    {
+            if (fscanf(f, "%d %d", I, J) != 2) return MM_PREMATURE_EOF;
+    }
+    else
+        return MM_UNSUPPORTED_TYPE;
+
+    return 0;
+
+}
+
+
+/************************************************************************
+    mm_read_mtx_crd()  fills M, N, nz, array of values, and return
+                        type code, e.g. 'MCRS'
+
+                        if matrix is complex, values[] is of size 2*nz,
+                            (nz pairs of real/imaginary values)
+************************************************************************/
+
+int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J,
+        double **val, MM_typecode *matcode)
+{
+    int ret_code;
+    FILE *f;
+
+    if (strcmp(fname, "stdin") == 0) f=stdin;
+    else
+    if ((f = fopen(fname, "r")) == NULL)
+        return MM_COULD_NOT_READ_FILE;
+
+
+    if ((ret_code = mm_read_banner(f, matcode)) != 0)
+        return ret_code;
+
+    if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) &&
+            mm_is_matrix(*matcode)))
+        return MM_UNSUPPORTED_TYPE;
+
+    if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0)
+        return ret_code;
+
+
+    *I = (int *)  malloc(*nz * sizeof(int));
+    *J = (int *)  malloc(*nz * sizeof(int));
+    *val = NULL;
+
+    if (mm_is_complex(*matcode))
+    {
+        *val = (double *) malloc(*nz * 2 * sizeof(double));
+        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
+                *matcode);
+        if (ret_code != 0) return ret_code;
+    }
+    else if (mm_is_real(*matcode))
+    {
+        *val = (double *) malloc(*nz * sizeof(double));
+        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
+                *matcode);
+        if (ret_code != 0) return ret_code;
+    }
+
+    else if (mm_is_pattern(*matcode))
+    {
+        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
+                *matcode);
+        if (ret_code != 0) return ret_code;
+    }
+
+    if (f != stdin) fclose(f);
+    return 0;
+}
+
+int mm_write_banner(FILE *f, MM_typecode matcode)
+{
+    char *str = mm_typecode_to_str(matcode);
+    int ret_code;
+
+    ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str);
+    free(str);
+    if (ret_code !=2 )
+        return MM_COULD_NOT_WRITE_FILE;
+    else
+        return 0;
+}
+
+int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[],
+        double val[], MM_typecode matcode)
+{
+    FILE *f;
+    int i;
+
+    if (strcmp(fname, "stdout") == 0)
+        f = stdout;
+    else
+    if ((f = fopen(fname, "w")) == NULL)
+        return MM_COULD_NOT_WRITE_FILE;
+
+    /* print banner followed by typecode */
+    fprintf(f, "%s ", MatrixMarketBanner);
+    fprintf(f, "%s\n", mm_typecode_to_str(matcode));
+
+    /* print matrix sizes and nonzeros */
+    fprintf(f, "%d %d %d\n", M, N, nz);
+
+    /* print values */
+    if (mm_is_pattern(matcode))
+        for (i=0; i<nz; i++)
+            fprintf(f, "%d %d\n", I[i], J[i]);
+    else
+    if (mm_is_real(matcode))
+        for (i=0; i<nz; i++)
+            fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]);
+    else
+    if (mm_is_complex(matcode))
+        for (i=0; i<nz; i++)
+            fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2*i],
+                        val[2*i+1]);
+    else
+    {
+        if (f != stdout) fclose(f);
+        return MM_UNSUPPORTED_TYPE;
+    }
+
+    if (f !=stdout) fclose(f);
+
+    return 0;
+}
+
+
+/**
+*  Create a new copy of a string s.  mm_strdup() is a common routine, but
+*  not part of ANSI C, so it is included here.  Used by mm_typecode_to_str().
+*
+*/
+char *mm_strdup(const char *s)
+{
+	int len = strlen(s);
+	char *s2 = (char *) malloc((len+1)*sizeof(char));
+	return strcpy(s2, s);
+}
+
+char  *mm_typecode_to_str(MM_typecode matcode)
+{
+    char buffer[MM_MAX_LINE_LENGTH];
+    char *types[4];
+	char *mm_strdup(const char *);
+    int error =0;
+
+    /* check for MTX type */
+    if (mm_is_matrix(matcode))
+        types[0] = MM_MTX_STR;
+    else
+        error=1;
+
+    /* check for CRD or ARR matrix */
+    if (mm_is_sparse(matcode))
+        types[1] = MM_SPARSE_STR;
+    else
+    if (mm_is_dense(matcode))
+        types[1] = MM_DENSE_STR;
+    else
+        return NULL;
+
+    /* check for element data type */
+    if (mm_is_real(matcode))
+        types[2] = MM_REAL_STR;
+    else
+    if (mm_is_complex(matcode))
+        types[2] = MM_COMPLEX_STR;
+    else
+    if (mm_is_pattern(matcode))
+        types[2] = MM_PATTERN_STR;
+    else
+    if (mm_is_integer(matcode))
+        types[2] = MM_INT_STR;
+    else
+        return NULL;
+
+
+    /* check for symmetry type */
+    if (mm_is_general(matcode))
+        types[3] = MM_GENERAL_STR;
+    else
+    if (mm_is_symmetric(matcode))
+        types[3] = MM_SYMM_STR;
+    else
+    if (mm_is_hermitian(matcode))
+        types[3] = MM_HERM_STR;
+    else
+    if (mm_is_skew(matcode))
+        types[3] = MM_SKEW_STR;
+    else
+        return NULL;
+
+    sprintf(buffer,"%s %s %s %s", types[0], types[1], types[2], types[3]);
+    return mm_strdup(buffer);
+
+}
diff --git a/test/mmio/mmio.h b/test/mmio/mmio.h
new file mode 100644
index 0000000000..51016caa30
--- /dev/null
+++ b/test/mmio/mmio.h
@@ -0,0 +1,139 @@
+/*
+*   Matrix Market I/O library for ANSI C
+*
+*   See http://math.nist.gov/MatrixMarket for details.
+*
+*
+*/
+
+#ifndef MM_IO_H
+#define MM_IO_H
+
+#define MM_MAX_LINE_LENGTH 1025
+#define MatrixMarketBanner "%%MatrixMarket"
+#define MM_MAX_TOKEN_LENGTH 64
+
+typedef char MM_typecode[4];
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+char *mm_typecode_to_str(MM_typecode matcode);
+
+int mm_read_banner(FILE *f, MM_typecode *matcode);
+int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz);
+int mm_read_mtx_array_size(FILE *f, int *M, int *N);
+
+int mm_write_banner(FILE *f, MM_typecode matcode);
+int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz);
+int mm_write_mtx_array_size(FILE *f, int M, int N);
+
+
+/********************* MM_typecode query fucntions ***************************/
+
+#define mm_is_matrix(typecode)	((typecode)[0]=='M')
+
+#define mm_is_sparse(typecode)	((typecode)[1]=='C')
+#define mm_is_coordinate(typecode)((typecode)[1]=='C')
+#define mm_is_dense(typecode)	((typecode)[1]=='A')
+#define mm_is_array(typecode)	((typecode)[1]=='A')
+
+#define mm_is_complex(typecode)	((typecode)[2]=='C')
+#define mm_is_real(typecode)		((typecode)[2]=='R')
+#define mm_is_pattern(typecode)	((typecode)[2]=='P')
+#define mm_is_integer(typecode) ((typecode)[2]=='I')
+
+#define mm_is_symmetric(typecode)((typecode)[3]=='S')
+#define mm_is_general(typecode)	((typecode)[3]=='G')
+#define mm_is_skew(typecode)	((typecode)[3]=='K')
+#define mm_is_hermitian(typecode)((typecode)[3]=='H')
+
+int mm_is_valid(MM_typecode matcode);		/* too complex for a macro */
+
+
+/********************* MM_typecode modify fucntions ***************************/
+
+#define mm_set_matrix(typecode)	((*typecode)[0]='M')
+#define mm_set_coordinate(typecode)	((*typecode)[1]='C')
+#define mm_set_array(typecode)	((*typecode)[1]='A')
+#define mm_set_dense(typecode)	mm_set_array(typecode)
+#define mm_set_sparse(typecode)	mm_set_coordinate(typecode)
+
+#define mm_set_complex(typecode)((*typecode)[2]='C')
+#define mm_set_real(typecode)	((*typecode)[2]='R')
+#define mm_set_pattern(typecode)((*typecode)[2]='P')
+#define mm_set_integer(typecode)((*typecode)[2]='I')
+
+
+#define mm_set_symmetric(typecode)((*typecode)[3]='S')
+#define mm_set_general(typecode)((*typecode)[3]='G')
+#define mm_set_skew(typecode)	((*typecode)[3]='K')
+#define mm_set_hermitian(typecode)((*typecode)[3]='H')
+
+#define mm_clear_typecode(typecode) ((*typecode)[0]=(*typecode)[1]= \
+									(*typecode)[2]=' ',(*typecode)[3]='G')
+
+#define mm_initialize_typecode(typecode) mm_clear_typecode(typecode)
+
+
+/********************* Matrix Market error codes ***************************/
+
+
+#define MM_COULD_NOT_READ_FILE	11
+#define MM_PREMATURE_EOF		12
+#define MM_NOT_MTX				13
+#define MM_NO_HEADER			14
+#define MM_UNSUPPORTED_TYPE		15
+#define MM_LINE_TOO_LONG		16
+#define MM_COULD_NOT_WRITE_FILE	17
+
+
+/******************** Matrix Market internal definitions ********************
+
+   MM_matrix_typecode: 4-character sequence
+
+				    ojbect 		sparse/   	data        storage
+						  		dense     	type        scheme
+
+   string position:	 [0]        [1]			[2]         [3]
+
+   Matrix typecode:  M(atrix)  C(oord)		R(eal)   	G(eneral)
+						        A(array)	C(omplex)   H(ermitian)
+											P(attern)   S(ymmetric)
+								    		I(nteger)	K(kew)
+
+ ***********************************************************************/
+
+#define MM_MTX_STR		"matrix"
+#define MM_ARRAY_STR	"array"
+#define MM_DENSE_STR	"array"
+#define MM_COORDINATE_STR "coordinate"
+#define MM_SPARSE_STR	"coordinate"
+#define MM_COMPLEX_STR	"complex"
+#define MM_REAL_STR		"real"
+#define MM_INT_STR		"integer"
+#define MM_GENERAL_STR  "general"
+#define MM_SYMM_STR		"symmetric"
+#define MM_HERM_STR		"hermitian"
+#define MM_SKEW_STR		"skew-symmetric"
+#define MM_PATTERN_STR  "pattern"
+
+
+/*  high level routines */
+
+int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[],
+		 double val[], MM_typecode matcode);
+int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[],
+		double val[], MM_typecode matcode);
+int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img,
+			MM_typecode matcode);
+
+int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
+                double **val_, int **I_, int **J_);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index ba144d8159..49207e0ca1 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -30,6 +30,10 @@
 #include <utility>
 #include <vector>
 
+#if defined(USE_MTX)
+#include <mmio.h>
+#endif
+
 #define UNUSED(expr) do { (void)(expr); } while (0)
 
 namespace aft {
@@ -1005,6 +1009,97 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
     EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
                             MAX_ABSDIFF)
 
+#if defined(USE_MTX)
+::testing::AssertionResult
+mtxReadSparseMatrix(af::array &out, const char* fileName)
+{
+    FILE *fileHandle;
+
+    if ((fileHandle = fopen(fileName, "r")) == NULL) {
+        return ::testing::AssertionFailure()
+            << "Failed to open mtx file: " << fileName <<"\n";
+    }
+
+    MM_typecode matcode;
+    if (mm_read_banner(fileHandle, &matcode)) {
+        return ::testing::AssertionFailure()
+            << "Could not process Matrix Market banner.\n";
+    }
+
+    if (!(mm_is_matrix(matcode) && mm_is_sparse(matcode))) {
+        return ::testing::AssertionFailure()
+            << "Input mtx doesn't have a sparse matrix.\n";
+    }
+
+    if(mm_is_integer(matcode)) {
+        return ::testing::AssertionFailure()
+            << "MTX file has integer data. \
+                Integer sparse matrices are not supported in ArrayFire yet.\n";
+    }
+
+    int M=0, N=0, nz=0;
+    if (mm_read_mtx_crd_size(fileHandle, &M, &N, &nz)) {
+        return ::testing::AssertionFailure()
+            << "Failed to read matrix dimensions.\n";
+    }
+
+    if (mm_is_real(matcode)) {
+        std::vector<int> I(nz);
+        std::vector<int> J(nz);
+        std::vector<float> V(nz);
+
+        for (unsigned i=0; i<nz; ++i) {
+            int c, r;
+            double v;
+            int readCount = fscanf(fileHandle, "%d %d %lg\n", &r, &c, &v);
+            if (readCount != 3) {
+                fclose(fileHandle);
+                return ::testing::AssertionFailure()
+                    << "\nEnd of file reached, expected more data, "
+                    << "following are some reasons this happens.\n"
+                    << "\t - use of template type that doesn't match data type\n"
+                    << "\t - the mtx file itself doesn't have enough data\n";
+            }
+            I[i] = r-1;
+            J[i] = c-1;
+            V[i] = (float)v;
+        }
+
+        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), f32, AF_STORAGE_COO);
+    } else if (mm_is_complex(matcode)) {
+        std::vector<int> I(nz);
+        std::vector<int> J(nz);
+        std::vector<af::cfloat> V(nz);
+
+        for (unsigned i=0; i<nz; ++i) {
+            int c, r;
+            double real, imag;
+            int readCount = fscanf(fileHandle, "%d %d %lg %lg\n", &r, &c, &real, &imag);
+            if (readCount != 4) {
+                fclose(fileHandle);
+                return ::testing::AssertionFailure()
+                    << "\nEnd of file reached, expected more data, "
+                    << "following are some reasons this happens.\n"
+                    << "\t - use of template type that doesn't match data type\n"
+                    << "\t - the mtx file itself doesn't have enough data\n";
+            }
+            I[i] = r-1;
+            J[i] = c-1;
+            V[i] = af::cfloat(float(real), float(imag));
+        }
+
+        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), c32, AF_STORAGE_COO);
+    } else {
+        return ::testing::AssertionFailure()
+            << "Unknown matcode from MTX FILE\n";
+    }
+
+
+    fclose(fileHandle);
+    return ::testing::AssertionSuccess();
+}
+#endif //USE_MTX
+
 }
 
 enum TestOutputArrayType {

From d0da171ce8e297cc93d21bc47715d1cc0b61f1bf Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 30 Nov 2018 18:58:05 +0530
Subject: [PATCH 1576/2677] Sparse-Sparse arithmetic tests using MTX files

---
 test/sparse_arith.cpp | 54 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)

diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 8b7c35b8e7..a29a644a8b 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -384,3 +384,57 @@ SP_SP_ARITH_TESTS(float  , 1e-6)
 SP_SP_ARITH_TESTS(double , 1e-6)
 SP_SP_ARITH_TESTS(cfloat , 1e-4) // This is mostly for complex division in OpenCL
 SP_SP_ARITH_TESTS(cdouble, 1e-6)
+
+#if defined(USE_MTX)
+
+// Sparse-Sparse Arithmetic testing function using mtx files
+template<af_op_t op>
+void ssArithmeticMTX(const char* op1, const char* op2)
+{
+    deviceGC();
+
+    //Re-enable when double is enabled if (noDoubleTests<T>()) return;
+
+    array cooA, cooB;
+    ASSERT_TRUE(mtxReadSparseMatrix(cooA, op1));
+    ASSERT_TRUE(mtxReadSparseMatrix(cooB, op2));
+
+    array spA = sparseConvertTo(cooA, AF_STORAGE_CSR);
+    array spB = sparseConvertTo(cooB, AF_STORAGE_CSR);
+
+    array A = dense(spA);
+    array B = dense(spB);
+
+    arith_op<op> binOp;
+
+    // Arith Op
+    array resS = binOp(spA, spB);
+    array resD = binOp(A, B);
+    array revS = binOp(spB, spA);
+    array revD = binOp(B,  A);
+
+    ASSERT_ARRAYS_NEAR(resD, dense(resS), 1e-4);
+    ASSERT_ARRAYS_NEAR(revD, dense(revS), 1e-4);
+}
+
+TEST(SparseSparseArith, LinearProgrammingData)
+{
+    std::string file1(TEST_DIR "/matrixmarket/LPnetlib/lpi_vol1/lpi_vol1.mtx");
+    std::string file2(TEST_DIR "/matrixmarket/LPnetlib/lpi_qual/lpi_qual.mtx");
+    ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
+}
+
+TEST(SparseSparseArith, SubsequentCircuitSimData)
+{
+    std::string file1(TEST_DIR "/matrixmarket/Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
+    std::string file2(TEST_DIR "/matrixmarket/Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
+    ssArithmeticMTX<af_sub_t>(file1.c_str(), file2.c_str());
+}
+
+TEST(SparseSparseArith, QuantumChemistryData)
+{
+    std::string file1(TEST_DIR "/matrixmarket/QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
+    std::string file2(TEST_DIR "/matrixmarket/QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
+    ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
+}
+#endif

From 48d10defab6d7723a07dbd63a48752841c1f02f8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 18 Dec 2018 00:46:48 +0530
Subject: [PATCH 1577/2677] Load graphics dependencies at runtime

- Forge is no longer a linktime dependency
- glbinding is not a dependency going forward.
- glad GL loader is a compile time dependency, but not required during
link-time or run-time.

If forge and it's dependencies (glfw, fontconfig, freetype) are
available at runtime, graphics functionality will automatically
work as it should, otherwise a runtime exception is thrown.

In order to make this runtime loading of graphics dependencies happen,
all the graphics(forge) C++-API calls have been refactored to use C-API.

An additional, miscallaneous change is that interop manager class and
it's derived classes are cleaned up a bit.
---
 .gitmodules                                   |   6 +
 CMakeLists.txt                                |  43 +--
 .../AFconfigure_forge_submodule.cmake         |  28 ++
 CMakeModules/InternalUtils.cmake              |   6 +
 CMakeModules/build_forge.cmake                |  66 ----
 CMakeModules/platform.cmake                   |   7 +-
 extern/forge                                  |   1 +
 extern/glad                                   |   1 +
 src/api/c/CMakeLists.txt                      |   3 -
 src/api/c/hist.cpp                            |  53 +--
 src/api/c/image.cpp                           |  39 +-
 src/api/c/plot.cpp                            | 220 +++++------
 src/api/c/surface.cpp                         |  51 ++-
 src/api/c/vector_field.cpp                    | 125 +++---
 src/api/c/window.cpp                          | 143 +++----
 src/backend/common/CMakeLists.txt             |  39 +-
 src/backend/common/InteropManager.cpp         | 133 -------
 src/backend/common/InteropManager.hpp         |  84 +++-
 src/backend/common/err_common.cpp             |   6 -
 src/backend/common/forge_loader.hpp           | 102 +++++
 src/backend/common/graphics_common.cpp        | 363 +++++++++++-------
 src/backend/common/graphics_common.hpp        |  95 ++---
 src/backend/cpu/CMakeLists.txt                |   3 -
 src/backend/cpu/hist_graphics.cpp             |  16 +-
 src/backend/cpu/hist_graphics.hpp             |   6 +-
 src/backend/cpu/image.cpp                     |  15 +-
 src/backend/cpu/image.hpp                     |   9 +-
 src/backend/cpu/plot.cpp                      |  16 +-
 src/backend/cpu/plot.hpp                      |   9 +-
 src/backend/cpu/surface.cpp                   |  16 +-
 src/backend/cpu/surface.hpp                   |  10 +-
 src/backend/cpu/vector_field.cpp              |  28 +-
 src/backend/cpu/vector_field.hpp              |  11 +-
 src/backend/cuda/CMakeLists.txt               |   3 -
 src/backend/cuda/GraphicsResourceManager.cpp  |  25 +-
 src/backend/cuda/GraphicsResourceManager.hpp  |  25 +-
 src/backend/cuda/hist_graphics.cpp            |  40 +-
 src/backend/cuda/hist_graphics.hpp            |   6 +-
 src/backend/cuda/image.cpp                    |  44 ++-
 src/backend/cuda/image.hpp                    |   9 +-
 src/backend/cuda/platform.cpp                 |   8 +
 src/backend/cuda/plot.cpp                     |  42 +-
 src/backend/cuda/plot.hpp                     |  10 +-
 src/backend/cuda/surface.cpp                  |  42 +-
 src/backend/cuda/surface.hpp                  |  10 +-
 src/backend/cuda/vector_field.cpp             |  71 ++--
 src/backend/cuda/vector_field.hpp             |  11 +-
 src/backend/opencl/CMakeLists.txt             |   3 -
 .../opencl/GraphicsResourceManager.cpp        |   6 +-
 .../opencl/GraphicsResourceManager.hpp        |  12 +-
 src/backend/opencl/hist_graphics.cpp          |  22 +-
 src/backend/opencl/hist_graphics.hpp          |   5 +-
 src/backend/opencl/image.cpp                  |  27 +-
 src/backend/opencl/image.hpp                  |   9 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |   3 +
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |   3 +
 src/backend/opencl/platform.cpp               |  34 +-
 src/backend/opencl/platform.hpp               |   2 +-
 src/backend/opencl/plot.cpp                   |  25 +-
 src/backend/opencl/plot.hpp                   |   9 +-
 src/backend/opencl/surface.cpp                |  22 +-
 src/backend/opencl/surface.hpp                |  11 +-
 src/backend/opencl/vector_field.cpp           |  39 +-
 src/backend/opencl/vector_field.hpp           |  13 +-
 64 files changed, 1184 insertions(+), 1160 deletions(-)
 create mode 100644 CMakeModules/AFconfigure_forge_submodule.cmake
 delete mode 100644 CMakeModules/build_forge.cmake
 create mode 160000 extern/forge
 create mode 160000 extern/glad
 delete mode 100644 src/backend/common/InteropManager.cpp
 create mode 100644 src/backend/common/forge_loader.hpp

diff --git a/.gitmodules b/.gitmodules
index 1126dbd05e..40a0000571 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -16,3 +16,9 @@
 [submodule "extern/spdlog"]
 	path = extern/spdlog
 	url = https://github.com/gabime/spdlog.git
+[submodule "extern/forge"]
+	path = extern/forge
+	url = https://github.com/arrayfire/forge.git
+[submodule "extern/glad"]
+	path = extern/glad
+	url = https://github.com/arrayfire/glad.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3344cdeffe..423248c226 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -34,9 +34,6 @@ find_package(LAPACKE)
 find_package(Doxygen)
 find_package(MKL)
 
-# Graphics dependencies
-find_package(glbinding QUIET)
-find_package(Forge QUIET)
 include(boost_package)
 
 option(AF_BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
@@ -46,7 +43,7 @@ option(AF_BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
 option(AF_BUILD_DOCS     "Create ArrayFire Documentation"            ${DOXYGEN_FOUND})
 option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
 
-option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  $<AND:${OPENGL_FOUND},${Forge_FOUND},${glbinding_FOUND}>)
+option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  ${OPENGL_FOUND})
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
 
@@ -55,8 +52,6 @@ option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OF
 cmake_dependent_option(AF_WITH_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
   "BUILD_TESTING" OFF)
 
-cmake_dependent_option(AF_USE_SYSTEM_FORGE "Use system Forge" OFF
-                       "AF_WITH_GRAPHICS" OFF)
 cmake_dependent_option(AF_WITH_IMAGEIO "Build ArrayFire with Image IO support" ${FreeImage_FOUND}
                        "FreeImage_FOUND" OFF)
 cmake_dependent_option(AF_BUILD_FRAMEWORK "Build an ArrayFire framework for Apple platforms.(Experimental)" OFF
@@ -81,7 +76,6 @@ af_deprecate(USE_CPUID             AF_WITH_CPUID)
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
   AF_INSTALL_STANDALONE
-  AF_USE_SYSTEM_FORGE
   AF_WITH_CPUID
   CUDA_HOST_COMPILER
   CUDA_USE_STATIC_CUDA_RUNTIME
@@ -91,8 +85,8 @@ mark_as_advanced(
 
 
 
-if(AF_WITH_GRAPHICS AND NOT AF_USE_SYSTEM_FORGE)
-  include(build_forge)
+if(AF_WITH_GRAPHICS)
+  include(AFconfigure_forge_submodule)
 endif()
 
 configure_file(
@@ -140,6 +134,7 @@ endif()
 
 set(SPDLOG_BUILD_TESTING OFF)
 add_subdirectory(extern/spdlog EXCLUDE_FROM_ALL)
+add_subdirectory(extern/glad)
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
 add_subdirectory(src/api/cpp)
@@ -212,33 +207,7 @@ install(FILES ${ArrayFire_BINARY_DIR}/include/af/version.h
         DESTINATION "${AF_INSTALL_INC_DIR}/af/"
         COMPONENT headers)
 
-if(Forge_FOUND AND NOT AF_USE_SYSTEM_FORGE)
-    option(AF_INSTALL_FORGE_DEV "Install Forge Header and Share Files with ArrayFire" OFF)
-    mark_as_advanced(AF_INSTALL_FORGE_DEV)
-    af_deprecate(INSTALL_FORGE_DEV AF_INSTALL_FORGE_DEV)
-    if(AF_INSTALL_FORGE_DEV)
-        install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/include/"
-                DESTINATION "${AF_INSTALL_INC_DIR}"
-                COMPONENT headers
-        )
-        install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/share/Forge/"
-                DESTINATION "${AF_INSTALL_DATA_DIR}/../Forge"
-                COMPONENT share
-        )
-        install(DIRECTORY "${ArrayFire_BINARY_DIR}/third_party/forge/lib/"
-                DESTINATION "${AF_INSTALL_LIB_DIR}"
-                COMPONENT forge
-        )
-    endif()
-    #install forge library & dependencies
-    set(fg_dlib_px "bin")
-    if (UNIX)
-      set(fg_dlib_px "lib")
-    endif ()
-    install(DIRECTORY "${PROJECT_BINARY_DIR}/third_party/forge/${fg_dlib_px}/"
-      DESTINATION "${AF_INSTALL_LIB_DIR}"
-      COMPONENT common_backend_dependencies)
-endif()
+#TODO(pradeep) install forge dependency for packaging - not required for builds
 
 # install the examples irrespective of the AF_BUILD_EXAMPLES value
 # only the examples source files are installed, so the installation of these
@@ -363,5 +332,3 @@ conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
 
 include(CPackConfig)
-
- 
diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
new file mode 100644
index 0000000000..be6fd40434
--- /dev/null
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -0,0 +1,28 @@
+# Copyright (c) 2019, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
+set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
+set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
+set(CMAKE_BUILD_TYPE Release)
+set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
+set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
+set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
+add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
+mark_as_advanced(
+    FG_BUILD_EXAMPLES
+    FG_BUILD_DOCS
+    FG_WITH_FREEIMAGE
+    FG_USE_WINDOW_TOOLKIT
+    FG_USE_SYSTEM_CL2HPP
+    FG_ENABLE_HUNTER
+    glfw3_DIR
+    glm_DIR
+    glbinding_DIR
+    )
+set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
+set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 2cf734b420..fc4a1beb6e 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -173,3 +173,9 @@ macro(arrayfire_set_cmake_default_variables)
     set(CMAKE_INSTALL_RPATH "/opt/arrayfire/lib")
   endif()
 endmacro()
+
+mark_as_advanced(
+    pkgcfg_lib_PC_CBLAS_cblas
+    pkgcfg_lib_PC_LAPACKE_lapacke
+    pkgcfg_lib_PKG_FFTW_fftw3
+    )
diff --git a/CMakeModules/build_forge.cmake b/CMakeModules/build_forge.cmake
deleted file mode 100644
index 7ae5a165f9..0000000000
--- a/CMakeModules/build_forge.cmake
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright (c) 2017, ArrayFire
-# All rights reserved.
-#
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
-
-include(ExternalProject)
-
-set(FORGE_VERSION v1.0.2)
-set(prefix "${ArrayFire_BINARY_DIR}/third_party/forge")
-set(PX ${CMAKE_SHARED_LIBRARY_PREFIX})
-set(SX ${CMAKE_SHARED_LIBRARY_SUFFIX})
-
-if(MSVC)
-  set(disable_warning_flags "/wd4251 /EHsc")
-  set(SX ${CMAKE_LINK_LIBRARY_SUFFIX})
-endif()
-
-set(forge_lib "${PROJECT_BINARY_DIR}/third_party/forge/lib/${PX}forge${SX}")
-
-# Create a list with an alternate separator e.g. pipe symbol
-string(REPLACE ";" "|" CMAKE_PREFIX_PATH_ALT_SEP "${CMAKE_PREFIX_PATH}")
-
-# FIXME Tag forge correctly during release
-ExternalProject_Add(
-    forge-ext
-    GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG ${FORGE_VERSION}
-    PREFIX "${prefix}"
-    UPDATE_COMMAND ""
-    BUILD_BYPRODUCTS ${forge_lib}
-    CMAKE_GENERATOR "${CMAKE_GENERATOR}"
-	LIST_SEPARATOR | # Use the alternate list separator
-    CMAKE_ARGS
-      -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH_ALT_SEP}"
-      -DBUILD_SHARED_LIBS:BOOL=ON
-      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-      -DCMAKE_BUILD_TYPE:STRING=Release
-      -DCMAKE_CXX_FLAGS:STRING=${disable_warning_flags}
-      -DFG_BUILD_EXAMPLES:BOOL=OFF
-      -DFG_BUILD_DOCS:BOOL=OFF
-      -DFG_WITH_FREEIMAGE:BOOL=OFF
-      -DCMAKE_SHARED_LINKER_FLAGS:STRING=${CMAKE_SHARED_LINKER_FLAGS}
-    )
-
-# NOTE: This approach doesn't work because the ExternalProject_Add outputs are
-# created at build time. The targets are created at configuration time.
-#
-# make_directory("${prefix}/include")
-# make_directory("${ArrayFire_BINARY_DIR}/third_party/forge/lib")
-# execute_process(COMMAND ${CMAKE_COMMAND} -E touch "${forge_lib}")
-
-# add_library(Forge::Forge SHARED IMPORTED GLOBAL)
-# set_target_properties(Forge::Forge PROPERTIES
-#   INTERFACE_LINK_LIBRARIES "${forge_lib}"
-#   INTERFACE_INCLUDE_DIRECTORIES "${prefix}/include"
-#   )
-#
-# add_dependencies(Forge::Forge forge-ext)
-
-set(Forge_INCLUDE_DIR "${prefix}/include")
-set(Forge_LIBRARIES "${forge_lib}")
-
-find_package_handle_standard_args(Forge DEFAULT_MSG
-    Forge_INCLUDE_DIR Forge_LIBRARIES)
diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
index 9f49de0b9b..da2c851d95 100644
--- a/CMakeModules/platform.cmake
+++ b/CMakeModules/platform.cmake
@@ -10,6 +10,9 @@
 # Add paths and flags specific platforms. This can inc
 
 if(APPLE)
+  # IMP NOTE: After removing link time dependency of gfx libs, glbinding is
+  #           still needed in cmake's prefix path so that forge doesn't fail
+  #           in cmake generation phase because of no glbinding.
   # Some homebrew libraries(glbinding) are not installed in directories that
   # CMake searches by default.
   set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/usr/local/opt")
@@ -24,10 +27,8 @@ if(UNIX AND NOT APPLE)
 endif()
 
 if(WIN32)
-  # C4251: Warnings about dll interfaces. Thrown by glbinding, may be fixed in
-  #        the future
   # C4068: Warnings about unknown pragmas
   # C4275: Warnings about using non-exported classes as base class of an
   #        exported class
-  add_compile_options(/wd4251 /wd4068 /wd4275)
+  add_compile_options(/wd4068 /wd4275)
 endif()
diff --git a/extern/forge b/extern/forge
new file mode 160000
index 0000000000..64f0a7409d
--- /dev/null
+++ b/extern/forge
@@ -0,0 +1 @@
+Subproject commit 64f0a7409d407ee7ec405f5018a4feb158e6e9e8
diff --git a/extern/glad b/extern/glad
new file mode 160000
index 0000000000..6e58ccdfa8
--- /dev/null
+++ b/extern/glad
@@ -0,0 +1 @@
+Subproject commit 6e58ccdfa8e65e1dc5d04a0b9c752c6508ef80b5
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 0060c0d4d4..fcbda27fbd 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -176,9 +176,6 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
 endif()
 
 if(AF_WITH_GRAPHICS)
-    if(NOT AF_USE_SYSTEM_FORGE)
-      add_dependencies(c_api_interface forge-ext)
-    endif()
     target_compile_definitions(c_api_interface INTERFACE WITH_GRAPHICS)
 endif()
 
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 3b0977c6ed..b4ef22f8f0 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -24,9 +24,10 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Chart* setup_histogram(const forge::Window* const window,
-                              const af_array in, const double minval, const double maxval,
-                              const af_cell* const props)
+fg_chart setup_histogram(fg_window const window,
+                         const af_array in,
+                         const double minval, const double maxval,
+                         const af_cell* const props)
 {
     Array<T> histogramInput = getArray<T>(in);
     dim_t nBins = histogramInput.elements();
@@ -35,23 +36,26 @@ forge::Chart* setup_histogram(const forge::Window* const window,
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
-    forge::Chart* chart = NULL;
+    fg_chart chart = NULL;
     if (props->col>-1 && props->row>-1)
         chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
     else
         chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
 
     // Create a histogram for the chart
-    forge::Histogram* hist = fgMngr.getHistogram(chart, nBins, getGLType<T>());
+    fg_histogram hist = fgMngr.getHistogram(chart, nBins, getGLType<T>());
 
-    // Set histogram bar colors to orange
-    hist->setColor(0.929f, 0.486f, 0.2745f, 1.0f);
+    // Set histogram bar colors to ArrayFire's orange
+    FG_CHECK(fg_set_histogram_color(hist, 0.929f, 0.486f, 0.2745f, 1.0f));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
-        float xMin, xMax, yMin, yMax;
-        chart->getAxesLimits(&xMin, &xMax, &yMin, &yMax);
+        float xMin, xMax, yMin, yMax, zMin, zMax;
+        FG_CHECK(fg_get_chart_axes_limits(&xMin, &xMax,
+                                          &yMin, &yMax,
+                                          &zMin, &zMax,
+                                          chart));
         T freqMax = detail::reduce_all<af_max_t, T, T>(histogramInput);
 
         if(xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
@@ -68,8 +72,7 @@ forge::Chart* setup_histogram(const forge::Window* const window,
             // For histogram, always set yMin to 0.
             yMin = 0;
         }
-
-        chart->setAxesLimits(xMin, xMax, yMin, yMax);
+        FG_CHECK(fg_set_chart_axes_limits(chart, xMin, xMax, yMin, yMax, zMin, zMax));
     }
 
     copy_histogram<T>(histogramInput, hist);
@@ -78,11 +81,13 @@ forge::Chart* setup_histogram(const forge::Window* const window,
 }
 #endif
 
-af_err af_draw_hist(const af_window wind, const af_array X, const double minval, const double maxval,
+af_err af_draw_hist(const af_window window,
+                    const af_array X,
+                    const double minval, const double maxval,
                     const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
@@ -93,10 +98,9 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
 
         ARG_ASSERT(0, Xinfo.isVector());
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         switch(Xtype) {
             case f32: chart = setup_histogram<float  >(window, X, minval, maxval, props); break;
@@ -107,20 +111,21 @@ af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
             case u8 : chart = setup_histogram<uchar  >(window, X, minval, maxval, props); break;
             default:  TYPE_ERROR(1, Xtype);
         }
-
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(X);
     UNUSED(minval);
     UNUSED(maxval);
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index f681e9bdbe..b61fef5ba6 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -53,7 +53,7 @@ Array<float> normalizePerType<float>(const Array<float>& in)
 }
 
 template<typename T>
-static forge::Image* convert_and_copy_image(const af_array in)
+static fg_image convert_and_copy_image(const af_array in)
 {
     const Array<T> _in  = getArray<T>(in);
     dim4 inDims = _in.dims();
@@ -64,24 +64,25 @@ static forge::Image* convert_and_copy_image(const af_array in)
 
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
-    // The inDims[2] * 100 is a hack to convert to forge::ChannelFormat
+    // The inDims[2] * 100 is a hack to convert to fg_channel_format
     // TODO Write a proper conversion function
-    forge::Image* ret_val = fgMngr.getImage(inDims[1], inDims[0], (forge::ChannelFormat)(inDims[2] * 100), getGLType<T>());
-
+    fg_image ret_val = fgMngr.getImage(inDims[1], inDims[0],
+                                       (fg_channel_format)(inDims[2] * 100),
+                                       getGLType<T>());
     copy_image<T>(normalizePerType<T>(imgData), ret_val);
 
     return ret_val;
 }
 #endif
 
-af_err af_draw_image(const af_window wind, const af_array in, const af_cell* const props)
+af_err af_draw_image(const af_window window,
+                     const af_array in, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         fprintf(stderr, "Not a valid window\n");
         return AF_SUCCESS;
     }
-
     try {
         const ArrayInfo& info = getInfo(in);
 
@@ -90,9 +91,8 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
         DIM_ASSERT(0, in_dims[2] == 1 || in_dims[2] == 3 || in_dims[2] == 4);
         DIM_ASSERT(0, in_dims[3] == 1);
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
-        forge::Image* image = NULL;
+        fg_image image = NULL;
 
         switch(type) {
             case f32: image = convert_and_copy_image<float >(in); break;
@@ -106,21 +106,24 @@ af_err af_draw_image(const af_window wind, const af_array in, const af_cell* con
         }
 
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        window->setColorMap((forge::ColorMap)props->cmap);
-        if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *image, props->title);
-        else
-            window->draw(*image);
+        FG_CHECK(fg_set_window_colormap(window, (fg_color_map)props->cmap));
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_image_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           image, props->title, true));
+        } else {
+            FG_CHECK(fg_draw_image(window, image, true));
+        }
     }
     CATCHALL;
 
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(in);
     UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
+    AF_RETURN_ERROR("ArrayFire compiled without graphics support",
+                    AF_ERR_NO_GFX);
 #endif
 }
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 170efdd1c6..e464f0dcd9 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -30,9 +30,9 @@ using namespace graphics;
 
 // Requires in_ to be in either [order, n] or [n, order] format
 template<typename T, int order>
-forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
-                         const af_cell* const props,
-                         forge::PlotType ptype, forge::MarkerType mtype)
+fg_chart setup_plot(fg_window window, const af_array in_,
+                    const af_cell* const props,
+                    fg_plot_type ptype, fg_marker_type mtype)
 {
     Array<T> in = getArray<T>(in_);
 
@@ -51,7 +51,7 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
-    forge::Chart* chart = NULL;
+    fg_chart chart = NULL;
     fg_chart_type ctype = order == 2 ? FG_CHART_2D : FG_CHART_3D;
 
     if (props->col > -1 && props->row > -1)
@@ -59,17 +59,20 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
     else
         chart = fgMngr.getChart(window, 0, 0, ctype);
 
-    forge::Plot* plot = fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
+    fg_plot plot = fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
 
     // ArrayFire LOGO Orange shade
-    plot->setColor(0.929f, 0.529f, 0.212f, 1.0);
+    FG_CHECK(fg_set_plot_color(plot, 0.929f, 0.529f, 0.212f, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
         T     dmin[3], dmax[3];
-        chart->getAxesLimits(&cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2]);
+        FG_CHECK(fg_get_chart_axes_limits(&cmin[0], &cmax[0],
+                                          &cmin[1], &cmax[1],
+                                          &cmin[2], &cmax[2],
+                                          chart));
         copyData(dmin, reduce<af_min_t, T, T>(in, 1));
         copyData(dmax, reduce<af_max_t, T, T>(in, 1));
 
@@ -93,38 +96,36 @@ forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
                 if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
             }
         }
-
-        if(order == 2) {
-            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1]);
-        } else if(order == 3) {
-            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1], cmin[2], cmax[2]);
-        }
+        FG_CHECK(fg_set_chart_axes_limits(chart,
+                                          cmin[0], cmax[0],
+                                          cmin[1], cmax[1],
+                                          cmin[2], cmax[2]));
     }
-
     copy_plot<T>(in, plot);
 
     return chart;
 }
 
 template<typename T>
-forge::Chart* setup_plot(const forge::Window* const window, const af_array in_,
-                         const int order, const af_cell* const props,
-                         forge::PlotType ptype, forge::MarkerType mtype)
+fg_chart setup_plot(fg_window window, const af_array in_,
+                    const int order, const af_cell* const props,
+                    fg_plot_type ptype, fg_marker_type mtype)
 {
     if(order == 2)
         return setup_plot<T, 2>(window, in_, props, ptype, mtype);
     else if(order == 3)
         return setup_plot<T, 3>(window, in_, props, ptype, mtype);
-
     // Dummy to avoid warnings
     return NULL;
 }
 
-af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
+af_err plotWrapper(const af_window window,
+                   const af_array in, const int order_dim,
                    const af_cell* const props,
-                   forge::PlotType ptype = FG_PLOT_LINE, forge::MarkerType marker = FG_MARKER_NONE)
+                   fg_plot_type ptype = FG_PLOT_LINE,
+                   fg_marker_type marker = FG_MARKER_NONE)
 {
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
@@ -137,10 +138,9 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
         DIM_ASSERT(0, dims.ndims() == 2);
         DIM_ASSERT(0, dims[order_dim] == 2 || dims[order_dim] == 3);
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         switch(type) {
             case f32: chart = setup_plot<float  >(window, in, dims[order_dim], props, ptype, marker); break;
@@ -153,24 +153,27 @@ af_err plotWrapper(const af_window wind, const af_array in, const int order_dim,
         }
 
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, const af_array Z,
+af_err plotWrapper(const af_window window,
+                   const af_array X, const af_array Y, const af_array Z,
                    const af_cell* const props,
-                   forge::PlotType ptype = FG_PLOT_LINE,
-                   forge::MarkerType marker = FG_MARKER_NONE)
+                   fg_plot_type ptype = FG_PLOT_LINE,
+                   fg_marker_type marker = FG_MARKER_NONE)
 {
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
@@ -200,10 +203,9 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
         af_array pIn[] = {X, Y, Z};
         AF_CHECK(af_join_many(&in, 1, 3, pIn));
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         switch(xType) {
             case f32: chart = setup_plot<float  >(window, in, 3, props, ptype, marker); break;
@@ -214,15 +216,16 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
             case u8 : chart = setup_plot<uchar  >(window, in, 3, props, ptype, marker); break;
             default:  TYPE_ERROR(1, xType);
         }
-
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
 
         AF_CHECK(af_release_array(in));
     }
@@ -230,11 +233,13 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y, con
     return AF_SUCCESS;
 }
 
-af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
+af_err plotWrapper(const af_window window,
+                   const af_array X, const af_array Y,
                    const af_cell* const props,
-                   forge::PlotType ptype = FG_PLOT_LINE, forge::MarkerType marker = FG_MARKER_NONE)
+                   fg_plot_type ptype = FG_PLOT_LINE,
+                   fg_marker_type marker = FG_MARKER_NONE)
 {
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
@@ -257,10 +262,9 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
         af_array in = 0;
         AF_CHECK(af_join(&in, 1, X, Y));
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         switch(xType) {
             case f32: chart = setup_plot<float  >(window, in, 2, props, ptype, marker); break;
@@ -271,15 +275,16 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
             case u8 : chart = setup_plot<uchar  >(window, in, 2, props, ptype, marker); break;
             default:  TYPE_ERROR(1, xType);
         }
-
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
 
         AF_CHECK(af_release_array(in));
     }
@@ -289,60 +294,8 @@ af_err plotWrapper(const af_window wind, const af_array X, const af_array Y,
 
 #endif // WITH_GRAPHICS
 
-//
-//template<typename T>
-//forge::Chart* setup_plot(const forge::Window* const window,
-//                         const af_array X, const af_array Y,
-//                         const af_cell* const props,
-//                         forge::PlotType type, forge::MarkerType marker)
-//{
-//    Array<T> xIn = getArray<T>(X);
-//    Array<T> yIn = getArray<T>(Y);
-//
-//    T xmax = reduce_all<af_max_t, T, T>(xIn);
-//    T xmin = reduce_all<af_min_t, T, T>(xIn);
-//    T ymax = reduce_all<af_max_t, T, T>(yIn);
-//    T ymin = reduce_all<af_min_t, T, T>(yIn);
-//
-//    dim4 rdims(1, 0, 2, 3);
-//
-//    dim_t elements = xIn.elements();
-//    dim4 rowDims = dim4(1, elements, 1, 1);
-//
-//    // Force the vectors to be row vectors
-//    // This ensures we can use join(0,..) and skip reorder
-//    xIn = modDims(xIn, rowDims);
-//    yIn = modDims(yIn, rowDims);
-//
-//    // join along first dimension, skip reorder
-//    Array<T> P = join(0, xIn, yIn);
-//
-//    ForgeManager& fgMngr = ForgeManager::getInstance();
-//
-//    // Get the chart for the current grid position (if any)
-//    forge::Chart* chart = NULL;
-//    if (props->col>-1 && props->row>-1)
-//        chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
-//    else
-//        chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
-//
-//    forge::Plot* plot = fgMngr.getPlot(chart, elements, getGLType<T>(), type, marker);
-//
-//    plot->setColor(1.0, 0.0, 0.0, 1.0);
-//
-//    chart->setAxesLimits(xmin, xmax, ymin, ymax);
-//
-//    chart->setAxesTitles("X Axis", "Y Axis");
-//
-//    copy_plot<T>(P, plot);
-//
-//    return chart;
-//}
-
-
-////////////////////////////////////////////////////////////////////////////////
+
 // Plot API
-////////////////////////////////////////////////////////////////////////////////
 af_err af_draw_plot_nd(const af_window wind, const af_array in,
                        const af_cell* const props)
 {
@@ -356,7 +309,8 @@ af_err af_draw_plot_nd(const af_window wind, const af_array in,
 #endif
 }
 
-af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
+af_err af_draw_plot_2d(const af_window wind,
+                       const af_array X, const af_array Y,
                        const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
@@ -386,10 +340,10 @@ af_err af_draw_plot_3d(const af_window wind,
 #endif
 }
 
-////////////////////////////////////////////////////////////////////////////////
 // Deprecated Plot API
-////////////////////////////////////////////////////////////////////////////////
-af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
+af_err af_draw_plot(const af_window wind,
+                    const af_array X, const af_array Y,
+                    const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, props);
@@ -402,7 +356,8 @@ af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, co
 #endif
 }
 
-af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
+af_err af_draw_plot3(const af_window wind,
+                     const af_array P, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
     try {
@@ -436,14 +391,13 @@ af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* cons
 #endif
 }
 
-////////////////////////////////////////////////////////////////////////////////
 // Scatter API
-////////////////////////////////////////////////////////////////////////////////
 af_err af_draw_scatter_nd(const af_window wind, const af_array in,
-                          const af_marker_type af_marker, const af_cell* const props)
+                          const af_marker_type af_marker,
+                          const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
 #else
     UNUSED(wind);
@@ -454,11 +408,13 @@ af_err af_draw_scatter_nd(const af_window wind, const af_array in,
 #endif
 }
 
-af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array Y,
-                          const af_marker_type af_marker, const af_cell* const props)
+af_err af_draw_scatter_2d(const af_window wind,
+                          const af_array X, const af_array Y,
+                          const af_marker_type af_marker,
+                          const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 #else
     UNUSED(wind);
@@ -472,10 +428,11 @@ af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array
 
 af_err af_draw_scatter_3d(const af_window wind,
                           const af_array X, const af_array Y, const af_array Z,
-                          const af_marker_type af_marker, const af_cell* const props)
+                          const af_marker_type af_marker,
+                          const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
 #else
     UNUSED(wind);
@@ -488,13 +445,14 @@ af_err af_draw_scatter_3d(const af_window wind,
 #endif
 }
 
-////////////////////////////////////////////////////////////////////////////////
 // Deprecated Scatter API
-////////////////////////////////////////////////////////////////////////////////
-af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type af_marker, const af_cell* const props)
+af_err af_draw_scatter(const af_window wind,
+                       const af_array X, const af_array Y,
+                       const af_marker_type af_marker,
+                       const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 #else
     UNUSED(wind);
@@ -506,10 +464,12 @@ af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
 #endif
 }
 
-af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type af_marker, const af_cell* const props)
+af_err af_draw_scatter3(const af_window wind,
+                        const af_array P, const af_marker_type af_marker,
+                        const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    forge::MarkerType fg_marker = getFGMarker(af_marker);
+    fg_marker_type fg_marker = getFGMarker(af_marker);
     try {
         const ArrayInfo& info = getInfo(P);
         af::dim4  dims = info.dims();
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 8714954fc0..6a2e3ade04 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -28,9 +28,10 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Chart* setup_surface(const forge::Window* const window,
-                            const af_array xVals, const af_array yVals, const af_array zVals,
-                            const af_cell* const props)
+fg_chart setup_surface(fg_window window,
+                       const af_array xVals, const af_array yVals,
+                       const af_array zVals,
+                       const af_cell* const props)
 {
     Array<T> xIn = getArray<T>(xVals);
     Array<T> yIn = getArray<T>(yVals);
@@ -71,22 +72,25 @@ forge::Chart* setup_surface(const forge::Window* const window,
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
-    forge::Chart* chart = NULL;
+    fg_chart chart = NULL;
     if (props->col>-1 && props->row>-1)
         chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
     else
         chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
 
-    forge::Surface* surface = fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
+    fg_surface surface = fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
 
-    surface->setColor(0.0, 1.0, 0.0, 1.0);
+    FG_CHECK(fg_set_surface_color(surface, 0.0, 1.0, 0.0, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
         T     dmin[3], dmax[3];
-        chart->getAxesLimits(&cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2]);
+        FG_CHECK(fg_get_chart_axes_limits(&cmin[0], &cmax[0],
+                                          &cmin[1], &cmax[1],
+                                          &cmin[2], &cmax[2],
+                                          chart));
         dmin[0] = reduce_all<af_min_t, T, T>(xIn);
         dmax[0] = reduce_all<af_max_t, T, T>(xIn);
         dmin[1] = reduce_all<af_min_t, T, T>(yIn);
@@ -113,19 +117,23 @@ forge::Chart* setup_surface(const forge::Window* const window,
             if(cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
         }
 
-        chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1], cmin[2], cmax[2]);
+        FG_CHECK(fg_set_chart_axes_limits(chart,
+                                          cmin[0], cmax[0],
+                                          cmin[1], cmax[1],
+                                          cmin[2], cmax[2]));
     }
-
     copy_surface<T>(Z, surface);
 
     return chart;
 }
 #endif
 
-af_err af_draw_surface(const af_window wind, const af_array xVals, const af_array yVals, const af_array S, const af_cell* const props)
+af_err af_draw_surface(const af_window window,
+                       const af_array xVals, const af_array yVals,
+                       const af_array S, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
@@ -153,10 +161,9 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
             DIM_ASSERT(3, ( X_dims[0] * Y_dims[0] == (dim_t)Sinfo.elements()));
         }
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         switch(Xtype) {
             case f32: chart = setup_surface<float  >(window, xVals, yVals , S, props); break;
@@ -167,19 +174,21 @@ af_err af_draw_surface(const af_window wind, const af_array xVals, const af_arra
             case u8 : chart = setup_surface<uchar  >(window, xVals, yVals , S, props); break;
             default:  TYPE_ERROR(1, Xtype);
         }
-
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        if (props->col>-1 && props->row>-1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(xVals);
     UNUSED(yVals);
     UNUSED(S);
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index b3eb29e832..49ae93d881 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -30,9 +30,11 @@ using namespace detail;
 using namespace graphics;
 
 template<typename T>
-forge::Chart* setup_vector_field(const forge::Window* const window,
-                                 const vector<af_array>& points, const vector<af_array>& directions,
-                                 const af_cell* const props, const bool transpose_ = true)
+fg_chart setup_vector_field(fg_window window,
+                            const vector<af_array>& points,
+                            const vector<af_array>& directions,
+                            const af_cell* const props,
+                            const bool transpose_ = true)
 {
     vector< Array<T> > pnts;
     vector< Array<T> > dirs;
@@ -55,7 +57,7 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
     ForgeManager& fgMngr = ForgeManager::getInstance();
 
     // Get the chart for the current grid position (if any)
-    forge::Chart* chart = NULL;
+    fg_chart chart = NULL;
 
     if(pIn.dims()[0] == 2) {
         if (props->col>-1 && props->row>-1)
@@ -69,17 +71,20 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
             chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
     }
 
-    forge::VectorField* vectorfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
+    fg_vector_field vfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
 
     // ArrayFire LOGO dark blue shade
-    vectorfield->setColor(0.130f, 0.173f, 0.263f, 1.0);
+    FG_CHECK(fg_set_vector_field_color(vfield, 0.130f, 0.173f, 0.263f, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
         T     dmin[3], dmax[3];
-        chart->getAxesLimits(&cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2]);
+        FG_CHECK(fg_get_chart_axes_limits(&cmin[0], &cmax[0],
+                                          &cmin[1], &cmax[1],
+                                          &cmin[2], &cmax[2],
+                                          chart));
         copyData(dmin, reduce<af_min_t, T, T>(pIn, 1));
         copyData(dmax, reduce<af_max_t, T, T>(pIn, 1));
 
@@ -103,23 +108,21 @@ forge::Chart* setup_vector_field(const forge::Window* const window,
                 if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
             }
         }
-
-        if(pIn.dims()[0] == 2) {
-            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1]);
-        } else if(pIn.dims()[0] == 3) {
-            chart->setAxesLimits(cmin[0], cmax[0], cmin[1], cmax[1], cmin[2], cmax[2]);
-        }
+        FG_CHECK(fg_set_chart_axes_limits(chart,
+                                          cmin[0], cmax[0],
+                                          cmin[1], cmax[1],
+                                          cmin[2], cmax[2]));
     }
-
-    copy_vector_field<T>(pIn, dIn, vectorfield);
+    copy_vector_field<T>(pIn, dIn, vfield);
 
     return chart;
 }
 
-af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_array directions,
+af_err vectorFieldWrapper(const af_window window,
+                          const af_array points, const af_array directions,
                           const af_cell* const props)
 {
-    if(wind==0) {
+    if(window == 0) {
         AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
     }
 
@@ -138,10 +141,9 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
 
         TYPE_ASSERT(pType == dType);
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         vector<af_array> pnts;
         pnts.push_back(points);
@@ -158,29 +160,33 @@ af_err vectorFieldWrapper(const af_window wind, const af_array points, const af_
             case u8 : chart = setup_vector_field<uchar  >(window, pnts, dirs, props); break;
             default:  TYPE_ERROR(1, pType);
         }
-
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err vectorFieldWrapper(const af_window wind,
-                          const af_array xPoints, const af_array yPoints, const af_array zPoints,
-                          const af_array xDirs, const af_array yDirs, const af_array zDirs,
+af_err vectorFieldWrapper(const af_window window,
+                          const af_array xPoints,
+                          const af_array yPoints,
+                          const af_array zPoints,
+                          const af_array xDirs,
+                          const af_array yDirs,
+                          const af_array zDirs,
                           const af_cell* const props)
 {
-    if(wind==0) {
+    if(window == 0) {
         AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
     }
-
     try {
         const ArrayInfo& xpInfo = getInfo(xPoints);
         const ArrayInfo& ypInfo = getInfo(yPoints);
@@ -225,10 +231,9 @@ af_err vectorFieldWrapper(const af_window wind,
         DIM_ASSERT(1, xpType == ypType);
         DIM_ASSERT(1, xpType == zpType);
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         vector<af_array> points;
         points.push_back(xPoints);
@@ -249,26 +254,27 @@ af_err vectorFieldWrapper(const af_window wind,
             case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
             default:  TYPE_ERROR(1, xpType);
         }
-
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err vectorFieldWrapper(const af_window wind,
+af_err vectorFieldWrapper(const af_window window,
                           const af_array xPoints, const af_array yPoints,
                           const af_array xDirs, const af_array yDirs,
                           const af_cell* const props)
 {
-    if(wind==0) {
+    if(window == 0) {
         AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
     }
 
@@ -306,10 +312,9 @@ af_err vectorFieldWrapper(const af_window wind,
 
         DIM_ASSERT(1, xpType == ypType);
 
-        forge::Window* window = static_cast<forge::Window*>(wind);
         makeContextCurrent(window);
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         vector<af_array> points;
         points.push_back(xPoints);
@@ -330,13 +335,15 @@ af_err vectorFieldWrapper(const af_window wind,
         }
 
         auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        // Window's draw function requires either image or chart
-        if (props->col > -1 && props->row > -1)
-            window->draw(gridDims.first, gridDims.second,
-                         props->row * gridDims.second + props->col,
-                         *chart, props->title);
-        else
-            window->draw(*chart);
+
+        if (props->col>-1 && props->row>-1) {
+            FG_CHECK(fg_draw_chart_to_cell(window,
+                                           gridDims.first, gridDims.second,
+                                           props->row * gridDims.second + props->col,
+                                           chart, props->title));
+        } else {
+            FG_CHECK(fg_draw_chart(window, chart));
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -344,10 +351,10 @@ af_err vectorFieldWrapper(const af_window wind,
 
 #endif // WITH_GRAPHICS
 
-// ADD THIS TO UNIFIED
 af_err af_draw_vector_field_nd(const af_window wind,
-                const af_array points, const af_array directions,
-                const af_cell* const props)
+                               const af_array points,
+                               const af_array directions,
+                               const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, points, directions, props);
@@ -362,8 +369,10 @@ af_err af_draw_vector_field_nd(const af_window wind,
 
 af_err af_draw_vector_field_3d(
                 const af_window wind,
-                const af_array xPoints, const af_array yPoints, const af_array zPoints,
-                const af_array xDirs, const af_array yDirs, const af_array zDirs,
+                const af_array xPoints, const af_array yPoints,
+                const af_array zPoints,
+                const af_array xDirs, const af_array yDirs,
+                const af_array zDirs,
                 const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 6437538bf9..c766f54541 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -26,10 +26,9 @@ using namespace graphics;
 af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
 {
 #if defined(WITH_GRAPHICS)
-    forge::Window* wnd;
     try {
         graphics::ForgeManager& fgMngr = graphics::ForgeManager::getInstance();
-        forge::Window* mainWnd = NULL;
+        fg_window mainWnd = NULL;
 
         try {
             mainWnd = fgMngr.getMainWindow();
@@ -37,18 +36,18 @@ af_err af_create_window(af_window *out, const int width, const int height, const
             std::cerr<<"OpenGL context creation failed"<<std::endl;
         }
 
-        if(mainWnd==0) {
+        if (mainWnd == 0) {
             std::cerr<<"Not a valid window"<<std::endl;
             return AF_SUCCESS;
         }
 
-        wnd = new forge::Window(width, height, title, mainWnd);
-        wnd->setFont(fgMngr.getFont());
+        fg_window temp = nullptr;
 
-        // Create a chart map
-        fgMngr.setWindowChartGrid(wnd, 1, 1);
+        FG_CHECK(fg_create_window(&temp, width, height, title, mainWnd, false));
 
-        *out = static_cast<af_window>(wnd);
+        fgMngr.setWindowChartGrid(temp, 1, 1);
+
+        std::swap(*out, temp);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -64,16 +63,12 @@ af_err af_create_window(af_window *out, const int width, const int height, const
 af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
-    try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-        wnd->setPos(x, y);
-    }
-    CATCHALL;
+    FG_CHECK(fg_set_window_position(wind, x, y));
     return AF_SUCCESS;
 #else
     UNUSED(wind);
@@ -86,16 +81,12 @@ af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
 af_err af_set_title(const af_window wind, const char* const title)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
-    try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-        wnd->setTitle(title);
-    }
-    CATCHALL;
+    FG_CHECK(fg_set_window_title(wind, title));
     return AF_SUCCESS;
 #else
     UNUSED(wind);
@@ -107,16 +98,12 @@ af_err af_set_title(const af_window wind, const char* const title)
 af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
-    try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-        wnd->setSize(w, h);
-    }
-    CATCHALL;
+    FG_CHECK(fg_set_window_size(wind, w, h));
     return AF_SUCCESS;
 #else
     UNUSED(wind);
@@ -129,17 +116,13 @@ af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
 af_err af_grid(const af_window wind, const int rows, const int cols)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
     try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-
-        // Recreate a chart map
-        ForgeManager& fgMngr = ForgeManager::getInstance();
-        fgMngr.setWindowChartGrid(wnd, rows, cols);
+        ForgeManager::getInstance().setWindowChartGrid(wind, rows, cols);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -151,23 +134,20 @@ af_err af_grid(const af_window wind, const int rows, const int cols)
 #endif
 }
 
-af_err af_set_axes_limits_compute(const af_window wind,
+af_err af_set_axes_limits_compute(const af_window window,
                                   const af_array x, const af_array y, const af_array z,
                                   const bool exact, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
     try {
-        forge::Window* window = static_cast<forge::Window*>(wind);
-
-        // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         fg_chart_type ctype = (z ? FG_CHART_3D : FG_CHART_2D);
 
@@ -199,12 +179,13 @@ af_err af_set_axes_limits_compute(const af_window wind,
         }
 
         fgMngr.setChartAxesOverride(chart);
-        chart->setAxesLimits(xmin, xmax, ymin, ymax, zmin, zmax);
+        FG_CHECK(fg_set_chart_axes_limits(chart, xmin, xmax,
+                                          ymin, ymax, zmin, zmax));
     }
     CATCHALL;
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(x);
     UNUSED(y);
     UNUSED(z);
@@ -214,24 +195,21 @@ af_err af_set_axes_limits_compute(const af_window wind,
 #endif
 }
 
-af_err af_set_axes_limits_2d(const af_window wind,
+af_err af_set_axes_limits_2d(const af_window window,
                              const float xmin, const float xmax,
                              const float ymin, const float ymax,
                              const bool exact, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
     try {
-        forge::Window* window = static_cast<forge::Window*>(wind);
-
-        // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
         // The ctype here below doesn't really matter as it is only fetching
         // the chart. It will not set it.
         // If this is actually being done, then it is extremely bad.
@@ -254,12 +232,13 @@ af_err af_set_axes_limits_2d(const af_window wind,
         }
 
         fgMngr.setChartAxesOverride(chart);
-        chart->setAxesLimits(_xmin, _xmax, _ymin, _ymax);
+        FG_CHECK(fg_set_chart_axes_limits(chart, _xmin, _xmax,
+                                          _ymin, _ymax, 0.0f, 0.0f));
     }
     CATCHALL;
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(xmin);
     UNUSED(xmax);
     UNUSED(ymin);
@@ -270,25 +249,22 @@ af_err af_set_axes_limits_2d(const af_window wind,
 #endif
 }
 
-af_err af_set_axes_limits_3d(const af_window wind,
+af_err af_set_axes_limits_3d(const af_window window,
                              const float xmin, const float xmax,
                              const float ymin, const float ymax,
                              const float zmin, const float zmax,
                              const bool exact, const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
     try {
-        forge::Window* window = static_cast<forge::Window*>(wind);
-
-        // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
         // The ctype here below doesn't really matter as it is only fetching
         // the chart. It will not set it.
         // If this is actually being done, then it is extremely bad.
@@ -315,12 +291,13 @@ af_err af_set_axes_limits_3d(const af_window wind,
         }
 
         fgMngr.setChartAxesOverride(chart);
-        chart->setAxesLimits(_xmin, _xmax, _ymin, _ymax, _zmin, _zmax);
+        FG_CHECK(fg_set_chart_axes_limits(chart, _xmin, _xmax,
+                                          _ymin, _ymax, _zmin, _zmax));
     }
     CATCHALL;
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(xmin);
     UNUSED(xmax);
     UNUSED(ymin);
@@ -333,25 +310,22 @@ af_err af_set_axes_limits_3d(const af_window wind,
 #endif
 }
 
-af_err af_set_axes_titles(const af_window wind,
+af_err af_set_axes_titles(const af_window window,
                           const char * const xtitle,
                           const char * const ytitle,
                           const char * const ztitle,
                           const af_cell* const props)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
     try {
-        forge::Window* window = static_cast<forge::Window*>(wind);
-
-        // Recreate a chart map
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
-        forge::Chart* chart = NULL;
+        fg_chart chart = NULL;
 
         fg_chart_type ctype = (ztitle ? FG_CHART_3D : FG_CHART_2D);
 
@@ -360,12 +334,12 @@ af_err af_set_axes_titles(const af_window wind,
         else
             chart = fgMngr.getChart(window, 0, 0, ctype);
 
-        chart->setAxesTitles(xtitle, ytitle, ztitle);
+        FG_CHECK(fg_set_chart_axes_titles(chart, xtitle, ytitle, ztitle));
     }
     CATCHALL;
     return AF_SUCCESS;
 #else
-    UNUSED(wind);
+    UNUSED(window);
     UNUSED(xtitle);
     UNUSED(ytitle);
     UNUSED(ztitle);
@@ -377,16 +351,12 @@ af_err af_set_axes_titles(const af_window wind,
 af_err af_show(const af_window wind)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
-    try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-        wnd->swapBuffers();
-    }
-    CATCHALL;
+    FG_CHECK(fg_swap_window_buffers(wind));
     return AF_SUCCESS;
 #else
     UNUSED(wind);
@@ -397,16 +367,12 @@ af_err af_show(const af_window wind)
 af_err af_is_window_closed(bool *out, const af_window wind)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
-    try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-        *out = wnd->close();
-    }
-    CATCHALL;
+    FG_CHECK(fg_close_window(out, wind));
     return AF_SUCCESS;
 #else
     UNUSED(out);
@@ -418,19 +384,15 @@ af_err af_is_window_closed(bool *out, const af_window wind)
 af_err af_set_visibility(const af_window wind, const bool is_visible)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
-    try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-        if (is_visible)
-            wnd->show();
-        else
-            wnd->hide();
+    if (is_visible) {
+        FG_CHECK(fg_show_window(wind));
+    } else {
+        FG_CHECK(fg_hide_window(wind));
     }
-    CATCHALL;
     return AF_SUCCESS;
 #else
     UNUSED(wind);
@@ -442,21 +404,16 @@ af_err af_set_visibility(const af_window wind, const bool is_visible)
 af_err af_destroy_window(const af_window wind)
 {
 #if defined(WITH_GRAPHICS)
-    if(wind==0) {
+    if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
 
     try {
-        forge::Window* wnd = static_cast<forge::Window*>(wind);
-
-        // Delete chart map
-        ForgeManager& fgMngr = ForgeManager::getInstance();
-        fgMngr.setWindowChartGrid(wnd, 0, 0);
-
-        delete wnd;
+        ForgeManager::getInstance().setWindowChartGrid(wind, 0, 0);
     }
     CATCHALL;
+    FG_CHECK(fg_release_window(wind));
     return AF_SUCCESS;
 #else
     UNUSED(wind);
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 4aa0b49491..bc8147dafa 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -60,6 +60,7 @@ endif()
 target_link_libraries(afcommon_interface
   INTERFACE
     spdlog
+    af_glad_interface
     ${CMAKE_DL_LIBS})
 
 target_include_directories(afcommon_interface
@@ -76,50 +77,18 @@ if(APPLE AND NOT USE_MKL)
 endif()
 
 if(AF_WITH_GRAPHICS)
-  dependency_check(glbinding_FOUND "glbinding not found.")
-
   target_include_directories(afcommon_interface
     INTERFACE
-      ${Forge_INCLUDE_DIR}
+    ${OPENGL_INCLUDE_DIR}
+    ${ArrayFire_SOURCE_DIR}/extern/forge/include
+    ${ArrayFire_BINARY_DIR}/extern/forge/include
     )
   target_sources(afcommon_interface
     INTERFACE
-      ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.cpp
       ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
       ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.cpp
       ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.hpp
       )
 
-  target_link_libraries(afcommon_interface
-    INTERFACE
-      OpenGL::GL
-      ${Forge_LIBRARIES})
-
   target_compile_definitions(afcommon_interface INTERFACE WITH_GRAPHICS)
-
-  if(APPLE)
-    # TODO: On APPLE platform linking directly against glbinding brings in flags
-    # that causes issues when building ArrayFire with LAPACK and Graphics. This
-    # was due to the way glbinding was brining in some Framework flags which
-    # cause issues with the Accelerate Framework. This is probably a bug in
-    # glbindings cmake file
-    target_link_libraries(afcommon_interface
-      INTERFACE
-        $<TARGET_PROPERTY:glbinding::glbinding,IMPORTED_LOCATION_RELEASE>)
-  else()
-    target_link_libraries(afcommon_interface INTERFACE glbinding::glbinding)
-  endif()
-
-  if(AF_INSTALL_STANDALONE)
-    install(FILES
-        $<TARGET_FILE:glbinding::glbinding>
-        $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
-        $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:glbinding::glbinding>>
-      DESTINATION ${AF_INSTALL_LIB_DIR}
-      COMPONENT common_backend_dependencies)
-  endif()
-
-  if(NOT AF_USE_SYSTEM_FORGE)
-    add_dependencies(afcommon_interface forge-ext)
-  endif()
 endif()
diff --git a/src/backend/common/InteropManager.cpp b/src/backend/common/InteropManager.cpp
deleted file mode 100644
index a0375b1a4d..0000000000
--- a/src/backend/common/InteropManager.cpp
+++ /dev/null
@@ -1,133 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#if defined(WITH_GRAPHICS)
-//FIXME CPU backend doesn't required the following class implementation
-//InteropManager.hpp is not used while building CPU backend.
-#ifndef AF_CPU
-#include <common/InteropManager.hpp>
-#include <cstdio>
-#include <common/err_common.hpp>
-#include <common/util.hpp>
-#include <backend.hpp>
-#include <GraphicsResourceManager.hpp>
-#include <platform.hpp>
-
-template<typename R>
-using RVector = std::vector<std::shared_ptr<R>>;
-
-namespace common
-{
-template<class T, typename R>
-InteropManager<T, R>::~InteropManager()
-{
-    try {
-        destroyResources();
-    } catch (AfError &ex) {
-
-        std::string perr = getEnvVar("AF_PRINT_ERRORS");
-        if(!perr.empty()) {
-            if(perr != "0") fprintf(stderr, "%s\n", ex.what());
-        }
-    }
-}
-
-template<class T, typename R>
-RVector<R> InteropManager<T, R>::getBufferResource(const forge::Image* image)
-{
-    void * key = (void*)image;
-
-    if (mInteropMap.find(key) == mInteropMap.end()) {
-        std::vector<uint32_t> handles;
-        handles.push_back(image->pixels());
-        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
-
-        mInteropMap[key] = output;
-    }
-
-    return mInteropMap[key];
-}
-
-template<class T, typename R>
-RVector<R> InteropManager<T, R>::getBufferResource(const forge::Plot* plot)
-{
-    void * key = (void*)plot;
-
-    if (mInteropMap.find(key) == mInteropMap.end()) {
-        std::vector<uint32_t> handles;
-        handles.push_back(plot->vertices());
-        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
-
-        mInteropMap[key] = output;
-    }
-
-    return mInteropMap[key];
-}
-
-template<class T, typename R>
-RVector<R> InteropManager<T, R>::getBufferResource(const forge::Histogram* histogram)
-{
-    void * key = (void*)histogram;
-
-    if (mInteropMap.find(key) == mInteropMap.end()) {
-        std::vector<uint32_t> handles;
-        handles.push_back(histogram->vertices());
-        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
-
-        mInteropMap[key] = output;
-    }
-
-    return mInteropMap[key];
-}
-
-template<class T, typename R>
-RVector<R> InteropManager<T, R>::getBufferResource(const forge::Surface* surface)
-{
-    void * key = (void*)surface;
-
-    if (mInteropMap.find(key) == mInteropMap.end()) {
-        std::vector<uint32_t> handles;
-        handles.push_back(surface->vertices());
-        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
-
-        mInteropMap[key] = output;
-    }
-
-    return mInteropMap[key];
-}
-
-template<class T, typename R>
-RVector<R> InteropManager<T, R>::getBufferResource(const forge::VectorField* field)
-{
-    void * key = (void*)field;
-
-    if (mInteropMap.find(key) == mInteropMap.end()) {
-        std::vector<uint32_t> handles;
-        handles.push_back(field->vertices());
-        handles.push_back(field->directions());
-        std::vector<resource_t> output = static_cast<T*>(this)->registerResources(handles);
-
-        mInteropMap[key] = output;
-    }
-
-    return mInteropMap[key];
-}
-
-template<class T, typename R>
-void InteropManager<T, R>::destroyResources()
-{
-    for(auto iter : mInteropMap) {
-        iter.second.clear();
-    }
-}
-
-template class InteropManager<detail::GraphicsResourceManager, detail::CGR_t>;
-}
-#endif
-#endif
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index a026340afd..5038af846c 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -10,10 +10,14 @@
 #pragma once
 
 #if defined(WITH_GRAPHICS)
-#include <forge.h>
+#include <common/err_common.hpp>
+#include <common/forge_loader.hpp>
+#include <common/util.hpp>
+
+#include <cstdio>
 #include <map>
-#include <vector>
 #include <memory>
+#include <vector>
 
 namespace common
 {
@@ -21,23 +25,81 @@ template<class T, typename R>
 class InteropManager
 {
     using resource_t = typename std::shared_ptr<R>;
-    using res_vec_t = typename std::vector<resource_t>;
-    using res_map_t = typename std::map<void*, res_vec_t>;
+    using  res_vec_t = typename std::vector<resource_t>;
+    using  res_map_t = typename std::map<void*, res_vec_t>;
 
     public:
         InteropManager() {}
-        ~InteropManager();
 
-        res_vec_t getBufferResource(const forge::Image* image);
-        res_vec_t getBufferResource(const forge::Plot* plot);
-        res_vec_t getBufferResource(const forge::Histogram* histogram);
-        res_vec_t getBufferResource(const forge::Surface* surface);
-        res_vec_t getBufferResource(const forge::VectorField* field);
+        ~InteropManager() {
+            try {
+                destroyResources();
+            } catch (AfError &ex) {
+                std::string perr = getEnvVar("AF_PRINT_ERRORS");
+                if(!perr.empty()) {
+                    if(perr != "0") fprintf(stderr, "%s\n", ex.what());
+                }
+            }
+        }
+
+        res_vec_t getImageResources(const fg_window image) {
+            if (mInteropMap.find(image) == mInteropMap.end()) {
+                uint32_t buffer;
+                FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+                mInteropMap[image] =
+                    static_cast<T*>(this)->registerResources({buffer});
+            }
+            return mInteropMap[image];
+        }
+
+        res_vec_t getPlotResources(const fg_plot plot) {
+            if (mInteropMap.find(plot) == mInteropMap.end()) {
+                uint32_t buffer;
+                FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
+                mInteropMap[plot] =
+                    static_cast<T*>(this)->registerResources({buffer});
+            }
+            return mInteropMap[plot];
+        }
+
+        res_vec_t getHistogramResources(const fg_histogram histogram) {
+            if (mInteropMap.find(histogram) == mInteropMap.end()) {
+                uint32_t buffer;
+                FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, histogram));
+                mInteropMap[histogram] =
+                    static_cast<T*>(this)->registerResources({buffer});
+            }
+            return mInteropMap[histogram];
+        }
+
+        res_vec_t getSurfaceResources(const fg_surface surface) {
+            if (mInteropMap.find(surface) == mInteropMap.end()) {
+                uint32_t buffer;
+                FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
+                mInteropMap[surface] =
+                    static_cast<T*>(this)->registerResources({buffer});
+            }
+            return mInteropMap[surface];
+        }
+
+        res_vec_t getVectorFieldResources(const fg_vector_field field) {
+            if (mInteropMap.find(field) == mInteropMap.end()) {
+                uint32_t verts, dirs;
+                FG_CHECK(fg_get_vector_field_vertex_buffer(&verts, field));
+                FG_CHECK(fg_get_vector_field_direction_buffer(&dirs, field));
+                mInteropMap[field] =
+                    static_cast<T*>(this)->registerResources({verts, dirs});
+            }
+            return mInteropMap[field];
+        }
 
     protected:
         InteropManager(InteropManager const&);
         void operator=(InteropManager const&);
-        void destroyResources();
+
+        void destroyResources() {
+            for(auto iter : mInteropMap) iter.second.clear();
+        }
 
         res_map_t mInteropMap;
 };
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 3c40dcd0f5..4f4bb2fcf6 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -211,12 +211,6 @@ af_err processException()
 
         print_error(ss.str());
         err = ex.getError();
-#if defined(WITH_GRAPHICS) && !defined(AF_UNIFIED)
-    } catch (const forge::Error &ex) {
-        ss << ex << "\n";
-        print_error(ss.str());
-        err = AF_ERR_INTERNAL;
-#endif
 #ifdef AF_OPENCL
     } catch(const cl::Error &ex) {
       char opencl_err_msg[1024];
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
new file mode 100644
index 0000000000..04376eb23c
--- /dev/null
+++ b/src/backend/common/forge_loader.hpp
@@ -0,0 +1,102 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/DependencyModule.hpp>
+
+#include <glad/glad.h>
+
+#include <forge.h>
+
+class ForgeModule {
+    common::DependencyModule module;
+
+    public:
+    MODULE_MEMBER(fg_create_window);
+    MODULE_MEMBER(fg_get_window_context_handle);
+    MODULE_MEMBER(fg_get_window_display_handle);
+    MODULE_MEMBER(fg_make_window_current);
+    MODULE_MEMBER(fg_set_window_font);
+    MODULE_MEMBER(fg_set_window_position);
+    MODULE_MEMBER(fg_set_window_title);
+    MODULE_MEMBER(fg_set_window_size);
+    MODULE_MEMBER(fg_set_window_colormap);
+    MODULE_MEMBER(fg_draw_chart_to_cell);
+    MODULE_MEMBER(fg_draw_chart);
+    MODULE_MEMBER(fg_draw_image_to_cell);
+    MODULE_MEMBER(fg_draw_image);
+    MODULE_MEMBER(fg_swap_window_buffers);
+    MODULE_MEMBER(fg_close_window);
+    MODULE_MEMBER(fg_show_window);
+    MODULE_MEMBER(fg_hide_window);
+    MODULE_MEMBER(fg_release_window);
+
+    MODULE_MEMBER(fg_create_font);
+    MODULE_MEMBER(fg_load_system_font);
+    MODULE_MEMBER(fg_release_font);
+
+    MODULE_MEMBER(fg_create_image);
+    MODULE_MEMBER(fg_get_pixel_buffer);
+    MODULE_MEMBER(fg_get_image_size);
+    MODULE_MEMBER(fg_release_image);
+
+    MODULE_MEMBER(fg_create_plot);
+    MODULE_MEMBER(fg_set_plot_color);
+    MODULE_MEMBER(fg_get_plot_vertex_buffer);
+    MODULE_MEMBER(fg_get_plot_vertex_buffer_size);
+    MODULE_MEMBER(fg_release_plot);
+
+    MODULE_MEMBER(fg_create_histogram);
+    MODULE_MEMBER(fg_set_histogram_color);
+    MODULE_MEMBER(fg_get_histogram_vertex_buffer);
+    MODULE_MEMBER(fg_get_histogram_vertex_buffer_size);
+    MODULE_MEMBER(fg_release_histogram);
+
+    MODULE_MEMBER(fg_create_surface);
+    MODULE_MEMBER(fg_set_surface_color);
+    MODULE_MEMBER(fg_get_surface_vertex_buffer);
+    MODULE_MEMBER(fg_get_surface_vertex_buffer_size);
+    MODULE_MEMBER(fg_release_surface);
+
+    MODULE_MEMBER(fg_create_vector_field);
+    MODULE_MEMBER(fg_set_vector_field_color);
+    MODULE_MEMBER(fg_get_vector_field_vertex_buffer_size);
+    MODULE_MEMBER(fg_get_vector_field_direction_buffer_size);
+    MODULE_MEMBER(fg_get_vector_field_vertex_buffer);
+    MODULE_MEMBER(fg_get_vector_field_direction_buffer);
+    MODULE_MEMBER(fg_release_vector_field);
+
+    MODULE_MEMBER(fg_create_chart);
+    MODULE_MEMBER(fg_get_chart_type);
+    MODULE_MEMBER(fg_get_chart_axes_limits);
+    MODULE_MEMBER(fg_set_chart_axes_limits);
+    MODULE_MEMBER(fg_set_chart_axes_titles);
+    MODULE_MEMBER(fg_append_image_to_chart);
+    MODULE_MEMBER(fg_append_plot_to_chart);
+    MODULE_MEMBER(fg_append_histogram_to_chart);
+    MODULE_MEMBER(fg_append_surface_to_chart);
+    MODULE_MEMBER(fg_append_vector_field_to_chart);
+    MODULE_MEMBER(fg_release_chart);
+
+    ForgeModule();
+};
+
+namespace graphics {
+ForgeModule& forgePlugin();
+}
+
+#define FG_CHECK(fn)                           \
+    do {                                       \
+        fg_err e = graphics::forgePlugin().fn; \
+        if (e != FG_ERR_NONE) {                \
+            AF_ERROR("forge call failed",      \
+                    AF_ERR_INTERNAL);          \
+        }                                      \
+    } while(0);
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 3cbc83b7a2..ecfd603baf 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -10,7 +10,6 @@
 #if defined(WITH_GRAPHICS)
 
 #include <common/graphics_common.hpp>
-#include <glbinding/Meta.h>
 #include <common/err_common.hpp>
 #include <backend.hpp>
 #include <platform.hpp>
@@ -19,13 +18,90 @@
 #include <utility>
 
 using namespace std;
-using namespace gl;
+
+ForgeModule::ForgeModule()
+    : module("forge", nullptr)
+{
+    if (!module.isLoaded()) {
+        string error_message = "Error loading Forge: "
+            + module.getErrorMessage()
+            + "\nForge or one of it's dependencies failed to "
+            "load. Try installing Forge or check if Forge is in the "
+            "search path.";
+        AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+    }
+    MODULE_FUNCTION_INIT(fg_create_window);
+    MODULE_FUNCTION_INIT(fg_get_window_context_handle);
+    MODULE_FUNCTION_INIT(fg_get_window_display_handle);
+    MODULE_FUNCTION_INIT(fg_make_window_current);
+    MODULE_FUNCTION_INIT(fg_set_window_font);
+    MODULE_FUNCTION_INIT(fg_set_window_position);
+    MODULE_FUNCTION_INIT(fg_set_window_title);
+    MODULE_FUNCTION_INIT(fg_set_window_size);
+    MODULE_FUNCTION_INIT(fg_set_window_colormap);
+    MODULE_FUNCTION_INIT(fg_draw_chart_to_cell);
+    MODULE_FUNCTION_INIT(fg_draw_chart);
+    MODULE_FUNCTION_INIT(fg_draw_image_to_cell);
+    MODULE_FUNCTION_INIT(fg_draw_image);
+    MODULE_FUNCTION_INIT(fg_swap_window_buffers);
+    MODULE_FUNCTION_INIT(fg_close_window);
+    MODULE_FUNCTION_INIT(fg_show_window);
+    MODULE_FUNCTION_INIT(fg_hide_window);
+    MODULE_FUNCTION_INIT(fg_release_window);
+
+    MODULE_FUNCTION_INIT(fg_create_font);
+    MODULE_FUNCTION_INIT(fg_load_system_font);
+    MODULE_FUNCTION_INIT(fg_release_font);
+
+    MODULE_FUNCTION_INIT(fg_create_image);
+    MODULE_FUNCTION_INIT(fg_get_pixel_buffer);
+    MODULE_FUNCTION_INIT(fg_get_image_size);
+    MODULE_FUNCTION_INIT(fg_release_image);
+
+    MODULE_FUNCTION_INIT(fg_create_plot);
+    MODULE_FUNCTION_INIT(fg_set_plot_color);
+    MODULE_FUNCTION_INIT(fg_get_plot_vertex_buffer);
+    MODULE_FUNCTION_INIT(fg_get_plot_vertex_buffer_size);
+    MODULE_FUNCTION_INIT(fg_release_plot);
+
+    MODULE_FUNCTION_INIT(fg_create_histogram);
+    MODULE_FUNCTION_INIT(fg_set_histogram_color);
+    MODULE_FUNCTION_INIT(fg_get_histogram_vertex_buffer);
+    MODULE_FUNCTION_INIT(fg_get_histogram_vertex_buffer_size);
+    MODULE_FUNCTION_INIT(fg_release_histogram);
+
+    MODULE_FUNCTION_INIT(fg_create_surface);
+    MODULE_FUNCTION_INIT(fg_set_surface_color);
+    MODULE_FUNCTION_INIT(fg_get_surface_vertex_buffer);
+    MODULE_FUNCTION_INIT(fg_get_surface_vertex_buffer_size);
+    MODULE_FUNCTION_INIT(fg_release_surface);
+
+    MODULE_FUNCTION_INIT(fg_create_vector_field);
+    MODULE_FUNCTION_INIT(fg_set_vector_field_color);
+    MODULE_FUNCTION_INIT(fg_get_vector_field_vertex_buffer_size);
+    MODULE_FUNCTION_INIT(fg_get_vector_field_direction_buffer_size);
+    MODULE_FUNCTION_INIT(fg_get_vector_field_vertex_buffer);
+    MODULE_FUNCTION_INIT(fg_get_vector_field_direction_buffer);
+    MODULE_FUNCTION_INIT(fg_release_vector_field);
+
+    MODULE_FUNCTION_INIT(fg_create_chart);
+    MODULE_FUNCTION_INIT(fg_get_chart_type);
+    MODULE_FUNCTION_INIT(fg_get_chart_axes_limits);
+    MODULE_FUNCTION_INIT(fg_set_chart_axes_limits);
+    MODULE_FUNCTION_INIT(fg_set_chart_axes_titles);
+    MODULE_FUNCTION_INIT(fg_append_image_to_chart);
+    MODULE_FUNCTION_INIT(fg_append_plot_to_chart);
+    MODULE_FUNCTION_INIT(fg_append_histogram_to_chart);
+    MODULE_FUNCTION_INIT(fg_append_surface_to_chart);
+    MODULE_FUNCTION_INIT(fg_append_vector_field_to_chart);
+    MODULE_FUNCTION_INIT(fg_release_chart);
+}
 
 template<typename T>
-gl::GLenum getGLType() { return GL_FLOAT; }
+fg_dtype getGLType() { return FG_FLOAT32; }
 
-forge::MarkerType getFGMarker(const af_marker_type af_marker) {
-    forge::MarkerType fg_marker;
+fg_marker_type getFGMarker(const af_marker_type af_marker) {
+    fg_marker_type fg_marker;
     switch (af_marker) {
         case AF_MARKER_NONE     : fg_marker = FG_MARKER_NONE;        break;
         case AF_MARKER_POINT    : fg_marker = FG_MARKER_POINT;       break;
@@ -41,61 +117,35 @@ forge::MarkerType getFGMarker(const af_marker_type af_marker) {
 }
 
 #define INSTANTIATE_GET_FG_TYPE(T, ForgeEnum)\
-    template<> forge::dtype getGLType<T>() { return ForgeEnum; }
+    template<> fg_dtype getGLType<T>() { return ForgeEnum; }
 
-INSTANTIATE_GET_FG_TYPE(float           , forge::f32);
-INSTANTIATE_GET_FG_TYPE(int             , forge::s32);
-INSTANTIATE_GET_FG_TYPE(unsigned        , forge::u32);
-INSTANTIATE_GET_FG_TYPE(char            , forge::s8);
-INSTANTIATE_GET_FG_TYPE(unsigned char   , forge::u8);
-INSTANTIATE_GET_FG_TYPE(unsigned short  , forge::u16);
-INSTANTIATE_GET_FG_TYPE(short           , forge::s16);
-
-gl::GLenum glErrorSkip(const char *msg, const char* file, int line)
-{
-#ifndef NDEBUG
-    gl::GLenum x = gl::glGetError();
-    if (x != GL_NO_ERROR) {
-        char buf[1024];
-        sprintf(buf, "GL Error Skipped at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
-        AF_ERROR(buf, AF_ERR_INTERNAL);
-    }
-    return x;
-#else
-    return (gl::GLenum)0;
-#endif
-}
+INSTANTIATE_GET_FG_TYPE(float           , FG_FLOAT32);
+INSTANTIATE_GET_FG_TYPE(int             , FG_INT32  );
+INSTANTIATE_GET_FG_TYPE(unsigned        , FG_UINT32 );
+INSTANTIATE_GET_FG_TYPE(char            , FG_INT8   );
+INSTANTIATE_GET_FG_TYPE(unsigned char   , FG_UINT8  );
+INSTANTIATE_GET_FG_TYPE(unsigned short  , FG_UINT16 );
+INSTANTIATE_GET_FG_TYPE(short           , FG_INT16  );
 
-gl::GLenum glErrorCheck(const char *msg, const char* file, int line)
+GLenum glErrorCheck(const char *msg, const char* file, int line)
 {
 // Skipped in release mode
 #ifndef NDEBUG
-    gl::GLenum x = gl::glGetError();
+    GLenum x = glGetError();
 
     if (x != GL_NO_ERROR) {
         char buf[1024];
-        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
+        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n",
+                file, line, msg, (int)x, glGetString(x));
         AF_ERROR(buf, AF_ERR_INTERNAL);
     }
     return x;
 #else
-    return (gl::GLenum)0;
+    return (GLenum)0;
 #endif
 }
 
-gl::GLenum glForceErrorCheck(const char *msg, const char* file, int line)
-{
-    gl::GLenum x = gl::glGetError();
-
-    if (x != GL_NO_ERROR) {
-        char buf[1024];
-        sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n", file, line, msg, (int)x, glbinding::Meta::getString(x).c_str());
-        AF_ERROR(buf, AF_ERR_INTERNAL);
-    }
-    return x;
-}
-
-size_t getTypeSize(gl::GLenum type)
+size_t getTypeSize(GLenum type)
 {
     switch(type) {
         case GL_FLOAT:          return sizeof(float);
@@ -109,10 +159,9 @@ size_t getTypeSize(gl::GLenum type)
     }
 }
 
-void makeContextCurrent(forge::Window *window)
+void makeContextCurrent(fg_window window)
 {
-    window->makeCurrent();
-    glbinding::Binding::useCurrentContext();
+    FG_CHECK(fg_make_window_current(window));
     CheckGL("End makeContextCurrent");
 }
 
@@ -171,79 +220,86 @@ double step_round(const double in, const bool dir)
     return mag * mult;
 }
 
-namespace graphics
+namespace graphics {
+
+ForgeModule& forgePlugin()
 {
+    return *(ForgeManager::getInstance().mPlugin);
+}
+
 ForgeManager& ForgeManager::getInstance()
 {
     static ForgeManager my_instance;
     return my_instance;
 }
 
+ForgeManager::ForgeManager()
+    : mPlugin(new ForgeModule()) {}
+
 ForgeManager::~ForgeManager()
 {
+    ForgeModule& _ = forgePlugin();
     /* clear all OpenGL resource objects (images, plots, histograms etc) first
      * and then delete the windows */
     for(ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
-        delete (iter->second);
+        _.fg_release_image(iter->second);
 
     for(PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
-        delete (iter->second);
+        _.fg_release_plot(iter->second);
 
     for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
-        delete (iter->second);
+        _.fg_release_histogram(iter->second);
+
+    for(SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
+        _.fg_release_surface(iter->second);
+
+    for(VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
+        _.fg_release_vector_field(iter->second);
 
     for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++) {
         for(int i = 0; i < (int)(iter->second).size(); i++) {
-            if((iter->second)[i] != NULL) {
-                delete (iter->second)[i];
-                mChartAxesOverrideMap.erase((iter->second)[i]);
+            fg_chart chrt = (iter->second)[i];
+            if (chrt) {
+                mChartAxesOverrideMap.erase((chrt));
+                _.fg_release_chart(chrt);
             }
         }
     }
 }
 
-forge::Font* ForgeManager::getFont()
+fg_window ForgeManager::getMainWindow()
 {
-    static std::once_flag flag;
-    static std::unique_ptr<forge::Font> fnt;
-
-    CheckGL("Begin ForgeManager::getFont");
-    std::call_once(flag,
-            [] {
-                fnt.reset(new forge::Font());
-#if defined(_WIN32) || defined(_MSC_VER)
-                fnt->loadSystemFont("Arial");
-#else
-                fnt->loadSystemFont("Vera");
-#endif
-            });
-    CheckGL("End ForgeManager::getFont");
-
-    return fnt.get();
-}
+    class Window {
+        public:
+        Window(fg_window h) : handle(h) {}
+        ~Window() { forgePlugin().fg_release_window(handle); }
+        fg_window handle;
+    };
 
-forge::Window* ForgeManager::getMainWindow()
-{
     static std::once_flag flag;
-    static std::unique_ptr<forge::Window> wnd;
+    static std::unique_ptr<Window> wnd;
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
 
     if (noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         std::call_once(flag,
-                [] {
-                    wnd.reset(new forge::Window(WIDTH, HEIGHT, "ArrayFire", NULL, true));
-                    makeContextCurrent(wnd.get());
-
-                    ForgeManager::getInstance().setWindowChartGrid(wnd.get(), 1, 1);
-                });
+            [] {
+                fg_window w = nullptr;
+                FG_CHECK(fg_create_window(&w, WIDTH, HEIGHT, "ArrayFire", NULL, true));
+                makeContextCurrent(w);
+                ForgeManager::getInstance().setWindowChartGrid(w, 1, 1);
+                wnd.reset(new Window(w));
+                if (!gladLoadGL()) {
+                AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB);
+                }
+            });
     }
 
-    return wnd.get();
+    return wnd->handle;
 }
 
-void ForgeManager::setWindowChartGrid(const forge::Window* window,
+void ForgeManager::setWindowChartGrid(const fg_window window,
                                       const int r, const int c)
 {
     ChartMapIter iter = mChartMap.find(window);
@@ -254,9 +310,10 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
         // This has to be cleared as there is no guarantee that existing
         // chart types(2D/3D) match the future grid requirements
         for(int i = 0; i < (int)(iter->second).size(); i++) {
-            if ((iter->second)[i] != NULL) {
-                delete (iter->second)[i];
-                mChartAxesOverrideMap.erase((iter->second)[i]);
+            fg_chart chrt = (iter->second)[i];
+            if (chrt) {
+                mChartAxesOverrideMap.erase(chrt);
+                FG_CHECK(fg_release_chart(chrt));
             }
         }
         (iter->second).clear();
@@ -267,12 +324,12 @@ void ForgeManager::setWindowChartGrid(const forge::Window* window,
         mChartMap.erase(window);
         mWndGridMap.erase(window);
     } else {
-        mChartMap[window] = std::vector<forge::Chart*>(r * c);
+        mChartMap[window] = std::vector<fg_chart>(r * c);
         mWndGridMap[window] = std::make_pair(r, c);
     }
 }
 
-WindGridDims_t ForgeManager::getWindowGrid(const forge::Window* window)
+WindGridDims_t ForgeManager::getWindowGrid(const fg_window window)
 {
     GridMapIter gIter = mWndGridMap.find(window);
 
@@ -283,10 +340,11 @@ WindGridDims_t ForgeManager::getWindowGrid(const forge::Window* window)
     return mWndGridMap[window];
 }
 
-forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, const int c,
-                                     const forge::ChartType ctype)
+fg_chart ForgeManager::getChart(const fg_window window,
+                                const int r, const int c,
+                                const fg_chart_type ctype)
 {
-    forge::Chart* chart = NULL;
+    fg_chart chart = NULL;
     ChartMapIter iter = mChartMap.find(window);
     GridMapIter gIter = mWndGridMap.find(window);
 
@@ -303,17 +361,22 @@ forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, c
 
         if (chart == NULL) {
             // Chart has not been created
-            chart = new forge::Chart(ctype);
-            (iter->second)[c * gRows + r] = chart;
-            // Set Axes override to false
-            mChartAxesOverrideMap[chart] = false;
-        } else if (chart->getChartType()!=ctype) {
-            // Existing chart is of incompatible type
-            delete (iter->second)[c * gRows + r];
-            chart = new forge::Chart(ctype);
+            FG_CHECK(fg_create_chart(&chart, ctype));
             (iter->second)[c * gRows + r] = chart;
             // Set Axes override to false
             mChartAxesOverrideMap[chart] = false;
+        } else {
+            fg_chart_type chart_type;
+            FG_CHECK(fg_get_chart_type(&chart_type, chart));
+            if (chart_type != ctype) {
+                // Existing chart is of incompatible type
+                mChartAxesOverrideMap.erase(chart);
+                FG_CHECK(fg_release_chart(chart));
+                FG_CHECK(fg_create_chart(&chart, ctype));
+                (iter->second)[c * gRows + r] = chart;
+                // Set Axes override to false
+                mChartAxesOverrideMap[chart] = false;
+            }
         }
     } else {
         // The chart map for this was never created
@@ -323,7 +386,7 @@ forge::Chart* ForgeManager::getChart(const forge::Window* window, const int r, c
     return chart;
 }
 
-forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, forge::dtype type)
+fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode, fg_dtype type)
 {
     /* w, h needs to fall in the range of [0, 2^16]
      * for the ForgeManager to correctly retrieve
@@ -340,16 +403,16 @@ forge::Image* ForgeManager::getImage(int w, int h, forge::ChannelFormat mode, fo
     ImgMapIter iter = mImgMap.find(keypair);
 
     if (iter==mImgMap.end()) {
-        forge::Image* temp = new forge::Image(w, h, mode, type);
-
-        mImgMap[keypair] = temp;
+        fg_image img = nullptr;
+        FG_CHECK(fg_create_image(&img, w, h, mode, type));
+        mImgMap[keypair] = img;
     }
 
     return mImgMap[keypair];
 }
 
-forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
-                                     forge::ChannelFormat mode, forge::dtype type)
+fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
+                                fg_channel_format mode, fg_dtype type)
 {
     /* w, h needs to fall in the range of [0, 2^16]
      * for the ForgeManager to correctly retrieve
@@ -366,21 +429,24 @@ forge::Image* ForgeManager::getImage(forge::Chart* chart, int w, int h,
     ImgMapIter iter = mImgMap.find(keypair);
 
     if (iter==mImgMap.end()) {
-        if(chart->getChartType() != FG_CHART_2D)
-            AF_ERROR("Image can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
-
-        forge::Image* temp = new forge::Image(w, h, mode, type);
+        fg_chart_type chart_type;
+        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        if(chart_type != FG_CHART_2D)
+            AF_ERROR("Image can only be added to chart of type FG_CHART_2D",
+                     AF_ERR_TYPE);
 
-        mImgMap[keypair] = temp;
+        fg_image img = nullptr;
+        FG_CHECK(fg_create_image(&img, w, h, mode, type));
+        mImgMap[keypair] = img;
 
-        chart->add(*mImgMap[keypair]);
+        FG_CHECK(fg_append_image_to_chart(chart, img));
     }
 
     return mImgMap[keypair];
 }
 
-forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtype dtype,
-                                   forge::PlotType ptype, forge::MarkerType mtype)
+fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
+                              fg_plot_type ptype, fg_marker_type mtype)
 {
     long long key = ((nPoints & _48BIT) << 48);
     key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
@@ -390,17 +456,20 @@ forge::Plot* ForgeManager::getPlot(forge::Chart* chart, int nPoints, forge::dtyp
     PltMapIter iter = mPltMap.find(keypair);
 
     if (iter==mPltMap.end()) {
-        forge::Plot* temp = new forge::Plot(nPoints, dtype, chart->getChartType(), ptype, mtype);
+        fg_chart_type chart_type;
+        FG_CHECK(fg_get_chart_type(&chart_type, chart));
 
-        mPltMap[keypair] = temp;
+        fg_plot plt = nullptr;
+        FG_CHECK(fg_create_plot(&plt, nPoints, dtype, chart_type, ptype, mtype));
+        mPltMap[keypair] = plt;
 
-        chart->add(*mPltMap[keypair]);
+        FG_CHECK(fg_append_plot_to_chart(chart, plt));
     }
 
     return mPltMap[keypair];
 }
 
-forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, forge::dtype type)
+fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins, fg_dtype type)
 {
     long long key = ((nBins & _48BIT) << 48) | (type & _16BIT);
 
@@ -409,20 +478,23 @@ forge::Histogram* ForgeManager::getHistogram(forge::Chart* chart, int nBins, for
     HstMapIter iter = mHstMap.find(keypair);
 
     if (iter==mHstMap.end()) {
-        if(chart->getChartType() != FG_CHART_2D)
-            AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D", AF_ERR_TYPE);
+        fg_chart_type chart_type;
+        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        if(chart_type != FG_CHART_2D)
+            AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D",
+                     AF_ERR_TYPE);
 
-        forge::Histogram* temp = new forge::Histogram(nBins, type);
+        fg_histogram hst = nullptr;
+        FG_CHECK(fg_create_histogram(&hst, nBins, type));
+        mHstMap[keypair] = hst;
 
-        mHstMap[keypair] = temp;
-
-        chart->add(*mHstMap[keypair]);
+        FG_CHECK(fg_append_histogram_to_chart(chart, hst));
     }
 
     return mHstMap[keypair];
 }
 
-forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, forge::dtype type)
+fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY, fg_dtype type)
 {
     /* nX * nY needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
@@ -437,20 +509,24 @@ forge::Surface* ForgeManager::getSurface(forge::Chart* chart, int nX, int nY, fo
     SfcMapIter iter = mSfcMap.find(keypair);
 
     if (iter==mSfcMap.end()) {
-        if(chart->getChartType() != FG_CHART_3D)
-            AF_ERROR("Surface can only be added to chart of type FG_CHART_3D", AF_ERR_TYPE);
-
-        forge::Surface* temp = new forge::Surface(nX, nY, type);
-
-        mSfcMap[keypair] = temp;
-
-        chart->add(*mSfcMap[keypair]);
+        fg_chart_type chart_type;
+        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        if(chart_type != FG_CHART_3D)
+            AF_ERROR("Surface can only be added to chart of type FG_CHART_3D",
+                     AF_ERR_TYPE);
+
+        fg_surface surf = nullptr;
+        FG_CHECK(fg_create_surface(&surf, nX, nY, type,
+                                   FG_PLOT_SURFACE, FG_MARKER_NONE));
+        mSfcMap[keypair] = surf;
+
+        FG_CHECK(fg_append_surface_to_chart(chart, surf));
     }
 
     return mSfcMap[keypair];
 }
 
-forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoints, forge::dtype type)
+fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints, fg_dtype type)
 {
     long long key = (((nPoints) & _48BIT) << 48) | (type & _16BIT);
 
@@ -459,17 +535,20 @@ forge::VectorField* ForgeManager::getVectorField(forge::Chart* chart, int nPoint
     VcfMapIter iter = mVcfMap.find(keypair);
 
     if (iter==mVcfMap.end()) {
-        forge::VectorField* temp = new forge::VectorField(nPoints, type, chart->getChartType());
+        fg_chart_type chart_type;
+        FG_CHECK(fg_get_chart_type(&chart_type, chart));
 
-        mVcfMap[keypair] = temp;
+        fg_vector_field vfield = nullptr;
+        FG_CHECK(fg_create_vector_field(&vfield, nPoints, type, chart_type));
+        mVcfMap[keypair] = vfield;
 
-        chart->add(*mVcfMap[keypair]);
+        FG_CHECK(fg_append_vector_field_to_chart(chart, vfield));
     }
 
     return mVcfMap[keypair];
 }
 
-bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
+bool ForgeManager::getChartAxesOverride(fg_chart chart)
 {
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
@@ -478,7 +557,7 @@ bool ForgeManager::getChartAxesOverride(forge::Chart* chart)
     return mChartAxesOverrideMap[chart];
 }
 
-void ForgeManager::setChartAxesOverride(forge::Chart* chart, bool flag)
+void ForgeManager::setChartAxesOverride(fg_chart chart, bool flag)
 {
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
@@ -488,4 +567,10 @@ void ForgeManager::setChartAxesOverride(forge::Chart* chart, bool flag)
 }
 }
 
+#else
+
+ForgeModule::ForgeModule()
+    : module(nullptr, nullptr), glmodule(nullptr, nullptr)
+{ }
+
 #endif
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index e895f5c90f..652ac4a39b 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -12,10 +12,7 @@
 #if defined(WITH_GRAPHICS)
 
 #include <af/graphics.h>
-
-#include <forge.h>
-#include <glbinding/gl/gl.h>
-#include <glbinding/Binding.h>
+#include <common/forge_loader.hpp>
 
 #include <vector>
 #include <map>
@@ -23,26 +20,21 @@
 
 // default to f32(float) type
 template<typename T>
-forge::dtype getGLType();
+fg_dtype getGLType();
 
 // Print for OpenGL errors
 // Returns 1 if an OpenGL error occurred, 0 otherwise.
-gl::GLenum glErrorSkip(const char *msg, const char* file, int line);
-gl::GLenum glErrorCheck(const char *msg, const char* file, int line);
-gl::GLenum glForceErrorCheck(const char *msg, const char* file, int line);
+GLenum glErrorCheck(const char *msg, const char* file, int line);
 
 #define CheckGL(msg)      glErrorCheck     (msg, __AF_FILENAME__, __LINE__)
-#define ForceCheckGL(msg) glForceErrorCheck(msg, __AF_FILENAME__, __LINE__)
-#define CheckGLSkip(msg)  glErrorSkip      (msg, __AF_FILENAME__, __LINE__)
 
-forge::MarkerType getFGMarker(const af_marker_type af_marker);
+fg_marker_type getFGMarker(const af_marker_type af_marker);
 
-void makeContextCurrent(forge::Window *window);
+void makeContextCurrent(fg_window window);
 
 double step_round(const double in, const bool dir);
 
-namespace graphics
-{
+namespace graphics {
 enum Defaults {
     WIDTH = 1280,
     HEIGHT= 720
@@ -52,13 +44,13 @@ static const long long _16BIT = 0x000000000000FFFF;
 static const long long _32BIT = 0x00000000FFFFFFFF;
 static const long long _48BIT = 0x0000FFFFFFFFFFFF;
 
-typedef std::pair<long long, forge::Chart*> ChartKey_t;
+typedef std::pair<long long, fg_chart> ChartKey_t;
 
-typedef std::map<ChartKey_t, forge::Image*      > ImageMap_t;
-typedef std::map<ChartKey_t, forge::Plot*       > PlotMap_t;
-typedef std::map<ChartKey_t, forge::Histogram*  > HistogramMap_t;
-typedef std::map<ChartKey_t, forge::Surface*    > SurfaceMap_t;
-typedef std::map<ChartKey_t, forge::VectorField*> VectorFieldMap_t;
+typedef std::map<ChartKey_t, fg_image> ImageMap_t;
+typedef std::map<ChartKey_t, fg_plot> PlotMap_t;
+typedef std::map<ChartKey_t, fg_histogram> HistogramMap_t;
+typedef std::map<ChartKey_t, fg_surface> SurfaceMap_t;
+typedef std::map<ChartKey_t, fg_vector_field> VectorFieldMap_t;
 
 typedef ImageMap_t::iterator ImgMapIter;
 typedef PlotMap_t::iterator PltMapIter;
@@ -66,16 +58,16 @@ typedef HistogramMap_t::iterator HstMapIter;
 typedef SurfaceMap_t::iterator SfcMapIter;
 typedef VectorFieldMap_t::iterator VcfMapIter;
 
-typedef std::vector<forge::Chart*> ChartVec_t;
-typedef std::map<const forge::Window*, ChartVec_t> ChartMap_t;
+typedef std::vector<fg_chart> ChartVec_t;
+typedef std::map<const fg_window, ChartVec_t> ChartMap_t;
 typedef std::pair<int, int> WindGridDims_t;
-typedef std::map<const forge::Window*, WindGridDims_t> WindGridMap_t;
+typedef std::map<const fg_window, WindGridDims_t> WindGridMap_t;
 typedef ChartVec_t::iterator ChartVecIter;
 typedef ChartMap_t::iterator ChartMapIter;
 typedef WindGridMap_t::iterator GridMapIter;
 
 // Keeps track of which charts have manually assigned axes limits
-typedef std::map<forge::Chart*, bool> ChartAxesOverride_t;
+typedef std::map<fg_chart, bool> ChartAxesOverride_t;
 typedef ChartAxesOverride_t::iterator ChartAxesOverrideIter;
 
 /**
@@ -84,11 +76,11 @@ typedef ChartAxesOverride_t::iterator ChartAxesOverrideIter;
  * It manages the windows, and other renderables (given below) that are drawed
  * onto chosen window.
  * Renderables:
- *             forge::Image
- *             forge::Plot
- *             forge::Histogram
- *             forge::Surface
- *             forge::VectorField
+ *             fg_image
+ *             fg_plot
+ *             fg_histogram
+ *             fg_surface
+ *             fg_vector_field
  * */
 class ForgeManager
 {
@@ -103,35 +95,44 @@ class ForgeManager
         WindGridMap_t       mWndGridMap;
         ChartAxesOverride_t mChartAxesOverrideMap;
 
+        ForgeModule* mPlugin;
+
     public:
         static ForgeManager& getInstance();
         ~ForgeManager();
 
-        forge::Font*    getFont();
-        forge::Window*  getMainWindow();
+        friend ForgeModule& forgePlugin();
 
-        void            setWindowChartGrid(const forge::Window* window,
+        fg_window getMainWindow();
+
+        void            setWindowChartGrid(const fg_window window,
                                            const int r, const int c);
 
-        WindGridDims_t getWindowGrid(const forge::Window* window);
-        forge::Chart*   getChart(const forge::Window* window, const int r, const int c,
-                                 const forge::ChartType ctype);
+        WindGridDims_t getWindowGrid(const fg_window window);
+
+        fg_chart getChart(const fg_window window, const int r, const int c,
+                          const fg_chart_type ctype);
+
+        fg_image getImage(int w, int h, fg_channel_format mode,
+                          fg_dtype type);
+
+        fg_image getImage(fg_chart chart, int w, int h,
+                          fg_channel_format mode, fg_dtype type);
+
+        fg_plot getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
+                        fg_plot_type ptype, fg_marker_type mtype);
+
+        fg_histogram getHistogram(fg_chart chart, int nBins, fg_dtype type);
+
+        fg_surface getSurface(fg_chart chart, int nX, int nY, fg_dtype type);
 
-        forge::Image*       getImage        (int w, int h, forge::ChannelFormat mode,
-                                             forge::dtype type);
-        forge::Image*       getImage        (forge::Chart* chart, int w, int h,
-                                             forge::ChannelFormat mode, forge::dtype type);
-        forge::Plot *       getPlot         (forge::Chart* chart, int nPoints, forge::dtype dtype,
-                                             forge::PlotType ptype, forge::MarkerType mtype);
-        forge::Histogram*   getHistogram    (forge::Chart* chart, int nBins, forge::dtype type);
-        forge::Surface*     getSurface      (forge::Chart* chart, int nX, int nY, forge::dtype type);
-        forge::VectorField* getVectorField  (forge::Chart* chart, int nPoints, forge::dtype type);
+        fg_vector_field getVectorField(fg_chart chart, int nPoints, fg_dtype type);
 
-        bool getChartAxesOverride(forge::Chart* chart);
-        void setChartAxesOverride(forge::Chart* chart, bool flag = true);
+        bool getChartAxesOverride(fg_chart chart);
+        void setChartAxesOverride(fg_chart chart, bool flag = true);
 
     protected:
-        ForgeManager() {}
+        ForgeManager();
         ForgeManager(ForgeManager const&);
         void operator=(ForgeManager const&);
 };
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index cc190aa09e..840a3abc3c 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -263,9 +263,6 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(AF_WITH_GRAPHICS)
-  if(NOT AF_USE_SYSTEM_FORGE)
-    add_dependencies(afcpu forge-ext)
-  endif()
   target_sources(afcpu
     PRIVATE
       hist_graphics.cpp
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 21ca622ec8..5fc8cb33e4 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -14,27 +14,29 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-namespace cpu
-{
-using namespace gl;
+namespace cpu {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
+void copy_histogram(const Array<T> &data, fg_histogram hist)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     data.eval();
     getQueue().sync();
 
     CheckGL("Begin copy_histogram");
+    unsigned bytes = 0, buffer = 0;
+    FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, hist));
+    FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
-    glBindBuffer(GL_ARRAY_BUFFER, hist->vertices());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, hist->verticesSize(), data.get());
+    glBindBuffer(GL_ARRAY_BUFFER, buffer);
+    glBufferSubData(GL_ARRAY_BUFFER, 0, bytes, data.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("End copy_histogram");
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_histogram<T>(const Array<T> &data, const forge::Histogram* hist);
+template void copy_histogram<T>(const Array<T> &, fg_histogram);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
diff --git a/src/backend/cpu/hist_graphics.hpp b/src/backend/cpu/hist_graphics.hpp
index 2c83e225bf..46a8d5be55 100644
--- a/src/backend/cpu/hist_graphics.hpp
+++ b/src/backend/cpu/hist_graphics.hpp
@@ -14,13 +14,11 @@
 #include <common/graphics_common.hpp>
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const forge::Histogram* hist);
+void copy_histogram(const Array<T> &data, fg_histogram hist);
 
 }
 
 #endif
-
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 1ce9896946..f8ed9f07fd 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -21,21 +21,22 @@
 
 using af::dim4;
 
-namespace cpu
-{
-using namespace gl;
+namespace cpu {
 
 template<typename T>
-void copy_image(const Array<T> &in, const forge::Image* image)
+void copy_image(const Array<T> &in, fg_image image)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     in.eval();
     getQueue().sync();
 
     CheckGL("Before CopyArrayToImage");
     const T *d_X = in.get();
-    size_t data_size = image->size();
+    unsigned data_size = 0, buffer = 0;
+    FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+    FG_CHECK(fg_get_image_size(&data_size, image));
 
-    glBindBuffer(gl::GL_PIXEL_UNPACK_BUFFER, image->pixels());
+    glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
     glBufferSubData(GL_PIXEL_UNPACK_BUFFER, 0, data_size, d_X);
     glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
 
@@ -43,7 +44,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_image<T>(const Array<T> &in, const forge::Image* image);
+template void copy_image<T>(const Array<T> &, fg_image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index 7fea631d84..21daeec7f0 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -12,10 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void copy_image(const Array<T> &in, const forge::Image* image);
+namespace cpu {
+
+template<typename T>
+void copy_image(const Array<T> &in, fg_image image);
+
 }
 
 #endif
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 4152152bbb..75f085aa20 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -18,27 +18,29 @@
 
 using af::dim4;
 
-namespace cpu
-{
-using namespace gl;
+namespace cpu {
 
 template<typename T>
-void copy_plot(const Array<T> &P, forge::Plot* plot)
+void copy_plot(const Array<T> &P, fg_plot plot)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     P.eval();
     getQueue().sync();
 
     CheckGL("Before CopyArrayToVBO");
+    unsigned bytes = 0, buffer = 0;
+    FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
+    FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
 
-    glBindBuffer(GL_ARRAY_BUFFER, plot->vertices());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, plot->verticesSize(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, buffer);
+    glBufferSubData(GL_ARRAY_BUFFER, 0, bytes, P.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("In CopyArrayToVBO");
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_plot<T>(const Array<T> &P, forge::Plot* plot);
+template void copy_plot<T>(const Array<T> &, fg_plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/plot.hpp b/src/backend/cpu/plot.hpp
index f6f9a3fde0..170c9cbd30 100644
--- a/src/backend/cpu/plot.hpp
+++ b/src/backend/cpu/plot.hpp
@@ -12,10 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void copy_plot(const Array<T> &P, forge::Plot* plot);
+namespace cpu {
+
+template<typename T>
+void copy_plot(const Array<T> &P, fg_plot plot);
+
 }
 
 #endif
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index b5ffb67113..eca8261100 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -18,27 +18,29 @@
 
 using af::dim4;
 
-namespace cpu
-{
-using namespace gl;
+namespace cpu {
 
 template<typename T>
-void copy_surface(const Array<T> &P, forge::Surface* surface)
+void copy_surface(const Array<T> &P, fg_surface surface)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     P.eval();
     getQueue().sync();
 
     CheckGL("Before CopyArrayToVBO");
+    unsigned bytes = 0, buffer = 0;
+    FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
+    FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
 
-    glBindBuffer(GL_ARRAY_BUFFER, surface->vertices());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, surface->verticesSize(), P.get());
+    glBindBuffer(GL_ARRAY_BUFFER, buffer);
+    glBufferSubData(GL_ARRAY_BUFFER, 0, bytes, P.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("In CopyArrayToVBO");
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_surface<T>(const Array<T> &P, forge::Surface* surface);
+template void copy_surface<T>(const Array<T> &, fg_surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/surface.hpp b/src/backend/cpu/surface.hpp
index 6f8b247496..d6953634f8 100644
--- a/src/backend/cpu/surface.hpp
+++ b/src/backend/cpu/surface.hpp
@@ -12,11 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void copy_surface(const Array<T> &P, forge::Surface* surface);
+namespace cpu {
+
+template<typename T>
+void copy_surface(const Array<T> &P, fg_surface surface);
+
 }
 
 #endif
-
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index 56ad8287e3..dcbb34ccfb 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -18,34 +18,40 @@
 
 using af::dim4;
 
-namespace cpu
-{
-using namespace gl;
+namespace cpu {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       forge::VectorField* vector_field)
+                       fg_vector_field vfield)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     points.eval();
     directions.eval();
     getQueue().sync();
 
     CheckGL("Before CopyArrayToVBO");
 
-    glBindBuffer(GL_ARRAY_BUFFER, vector_field->vertices());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, vector_field->verticesSize(), points.get());
+    unsigned size1 = 0, size2 = 0;
+    unsigned buff1 = 0, buff2 = 0;
+    FG_CHECK(fg_get_vector_field_vertex_buffer_size(&size1, vfield));
+    FG_CHECK(fg_get_vector_field_direction_buffer_size(&size2, vfield));
+    FG_CHECK(fg_get_vector_field_vertex_buffer(&buff1, vfield));
+    FG_CHECK(fg_get_vector_field_direction_buffer(&buff2, vfield));
+
+    glBindBuffer(GL_ARRAY_BUFFER, buff1);
+    glBufferSubData(GL_ARRAY_BUFFER, 0, size1, points.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
-    glBindBuffer(GL_ARRAY_BUFFER, vector_field->directions());
-    glBufferSubData(GL_ARRAY_BUFFER, 0, vector_field->directionsSize(), directions.get());
+    glBindBuffer(GL_ARRAY_BUFFER, buff2);
+    glBufferSubData(GL_ARRAY_BUFFER, 0, size2, directions.get());
     glBindBuffer(GL_ARRAY_BUFFER, 0);
 
     CheckGL("In CopyArrayToVBO");
 }
 
-#define INSTANTIATE(T)                                                                      \
-    template void copy_vector_field<T>(const Array<T> &points, const Array<T> &directions,  \
-                                       forge::VectorField* vector_field);
+#define INSTANTIATE(T)                                                  \
+template void copy_vector_field<T>(const Array<T> &, const Array<T> &,  \
+                                   fg_vector_field);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/vector_field.hpp b/src/backend/cpu/vector_field.hpp
index 78e1e8f747..13535556da 100644
--- a/src/backend/cpu/vector_field.hpp
+++ b/src/backend/cpu/vector_field.hpp
@@ -12,11 +12,12 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                           forge::VectorField* vector_field);
+namespace cpu {
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       fg_vector_field vector_field);
+
 }
 
 #endif
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9998b375fd..8ab2602a03 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -406,9 +406,6 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(AF_WITH_GRAPHICS)
-  if(NOT AF_USE_SYSTEM_FORGE)
-    add_dependencies(afcuda forge-ext)
-  endif()
   target_sources(afcuda
     PRIVATE
       GraphicsResourceManager.cpp
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index 8f89f706ea..df9c1b3833 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -8,16 +8,26 @@
  ********************************************************/
 
 #if defined(WITH_GRAPHICS)
+
+#if defined(OS_WIN)
+#include <windows.h>
+#endif
+
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+#include <common/graphics_common.hpp>
+#define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#include <cuda_gl_interop.h>
+#include <platform.hpp>
 #include <err_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
 
-namespace cuda
-{
-ShrdResVector GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
+namespace cuda {
+GraphicsResourceManager::ShrdResVector
+GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
 {
     ShrdResVector output;
 
-    auto deleter = [](CGR_t* handle) {
+    auto deleter = [](cudaGraphicsResource_t* handle) {
         //FIXME Having a CUDA_CHECK around unregister
         //call is causing invalid GL context.
         //Moving ForgeManager class singleton as data
@@ -29,9 +39,10 @@ ShrdResVector GraphicsResourceManager::registerResources(std::vector<uint32_t> r
     };
 
     for (auto id: resources) {
-        CGR_t r;
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&r, id, cudaGraphicsMapFlagsWriteDiscard));
-        output.emplace_back(new CGR_t(r), deleter);
+        cudaGraphicsResource_t r;
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&r, id,
+                    cudaGraphicsMapFlagsWriteDiscard));
+        output.emplace_back(new cudaGraphicsResource_t(r), deleter);
     }
 
     return output;
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index ad86e22157..5cab5e4031 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -10,31 +10,18 @@
 #pragma once
 
 #if defined(WITH_GRAPHICS)
-#if defined(OS_WIN)
-#include <windows.h>
-#endif
-
-// cuda_gl_interop.h does not include OpenGL headers for ARM
-#include <common/graphics_common.hpp>
-using namespace gl;
-#define GL_VERSION gl::GL_VERSION
-#define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cuda_gl_interop.h>
 #include <common/InteropManager.hpp>
+
 #include <map>
 #include <vector>
 
-namespace cuda
-{
-typedef cudaGraphicsResource_t CGR_t;
-typedef std::shared_ptr<CGR_t> SharedResource;
-typedef std::vector<SharedResource> ShrdResVector;
-
-class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, CGR_t>
+namespace cuda {
+class GraphicsResourceManager :
+    public common::InteropManager<GraphicsResourceManager, cudaGraphicsResource_t>
 {
     public:
+        using ShrdResVector = std::vector< std::shared_ptr<cudaGraphicsResource_t> >;
+
         GraphicsResourceManager() {}
         ShrdResVector registerResources(std::vector<uint32_t> resources);
 
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index d95080daa0..52e0a596d7 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -15,46 +15,50 @@
 #include <debug_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
 
-namespace cuda
-{
-using namespace gl;
+namespace cuda {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
+void copy_histogram(const Array<T> &data, fg_histogram hist)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+    auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
-        ShrdResVector res = interopManager().getBufferResource(hist);
+        auto res = interopManager().getHistogramResources(hist);
 
-        // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = hist->verticesSize();
+        size_t bytes = 0;
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, *(res[0].get()));
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), stream);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo,
+                                             &bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_vbo, d_P, bytes, cudaMemcpyDeviceToDevice, stream);
+        cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
         CheckGL("After cuda resource copy");
 
         POST_LAUNCH_CHECK();
     } else {
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, hist));
+        FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, hist->vertices());
-        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        glBindBuffer(GL_ARRAY_BUFFER, buffer);
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            auto stream = cuda::getActiveStream();
-            CUDA_CHECK(cudaMemcpyAsync(ptr, data.get(), hist->verticesSize(), cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaMemcpyAsync(ptr, data.get(), bytes,
+                                       cudaMemcpyDeviceToHost, stream));
             CUDA_CHECK(cudaStreamSynchronize(stream));
-            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
+            glUnmapBuffer(GL_ARRAY_BUFFER);
         }
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_histogram<T>(const Array<T> &data, const forge::Histogram* hist);
+template void copy_histogram<T>(const Array<T> &, fg_histogram);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
diff --git a/src/backend/cuda/hist_graphics.hpp b/src/backend/cuda/hist_graphics.hpp
index 0c7b163796..b331301ca0 100644
--- a/src/backend/cuda/hist_graphics.hpp
+++ b/src/backend/cuda/hist_graphics.hpp
@@ -14,13 +14,11 @@
 #include <common/graphics_common.hpp>
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const forge::Histogram* hist);
+void copy_histogram(const Array<T> &data, fg_histogram hist);
 
 }
 
 #endif
-
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 76f624a21b..42840ad661 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -20,45 +20,49 @@
 
 using af::dim4;
 
-namespace cuda
-{
-using namespace gl;
+namespace cuda {
 
 template<typename T>
-void copy_image(const Array<T> &in, const forge::Image* image)
+void copy_image(const Array<T> &in, fg_image image)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+    auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        ShrdResVector res = interopManager().getBufferResource(image);
+        auto res = interopManager().getImageResources(image);
 
         const T *d_X = in.get();
-        // Map resource. Copy data to pixels. Unmap resource.
-        size_t num_bytes;
+        size_t bytes = 0;
         T* d_pixels = NULL;
-        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels, &num_bytes, *(res[0].get()));
-        cudaMemcpyAsync(d_pixels, d_X, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), stream);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels,
+                                             &bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_pixels, d_X, bytes, cudaMemcpyDeviceToDevice, stream);
+        cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
     } else {
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->pixels());
-        glBufferData((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, image->size(), 0, (gl::GLenum)GL_STREAM_DRAW);
-        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        unsigned data_size = 0, buffer = 0;
+        FG_CHECK(fg_get_image_size(&data_size, image));
+        FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+
+        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
+        glBufferData(GL_PIXEL_UNPACK_BUFFER, data_size, 0, GL_STREAM_DRAW);
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            CUDA_CHECK(cudaMemcpyAsync(ptr, in.get(), image->size(),
-                                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-            glUnmapBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER);
+            CUDA_CHECK(cudaMemcpyAsync(ptr, in.get(), data_size,
+                                       cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
+            glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
         }
-        glBindBuffer((gl::GLenum)GL_PIXEL_UNPACK_BUFFER, 0);
+        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)      \
-    template void copy_image<T>(const Array<T> &in, const forge::Image* image);
+template void copy_image<T>(const Array<T> &, fg_image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/image.hpp b/src/backend/cuda/image.hpp
index 5667a4ed45..fd0fd519e2 100644
--- a/src/backend/cuda/image.hpp
+++ b/src/backend/cuda/image.hpp
@@ -12,10 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void copy_image(const Array<T> &in, const forge::Image* image);
+namespace cuda {
+
+template<typename T>
+void copy_image(const Array<T> &in, fg_image image);
+
 }
 
 #endif
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 97ec3d2161..3e29963728 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -7,6 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(OS_WIN)
+#include <windows.h>
+#endif
+
 #include <af/version.h>
 #include <af/cuda.h>
 #include <platform.hpp>
@@ -17,6 +21,10 @@
 #include <err_cuda.hpp>
 #include <common/util.hpp>
 #include <common/host_memory.hpp>
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+#include <common/graphics_common.hpp>
+#define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#include <cuda_gl_interop.h>
 
 #include <algorithm>
 #include <array>
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 9a6ac8013a..48dcdd270f 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -13,54 +13,54 @@
 #include <plot.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <join.hpp>
-#include <reduce.hpp>
-#include <reorder.hpp>
 #include <GraphicsResourceManager.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
-using namespace gl;
+namespace cuda {
 
 template<typename T>
-void copy_plot(const Array<T> &P, forge::Plot* plot)
+void copy_plot(const Array<T> &P, fg_plot plot)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+    auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        ShrdResVector res = interopManager().getBufferResource(plot);
+        auto res = interopManager().getPlotResources(plot);
 
-        // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = plot->verticesSize();
+        size_t bytes = 0;
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, *(res[0].get()));
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), stream);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo,
+                                             &bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_vbo, d_P, bytes, cudaMemcpyDeviceToDevice, stream);
+        cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
         CheckGL("After cuda resource copy");
 
         POST_LAUNCH_CHECK();
     } else {
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
+        FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, plot->vertices());
-        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        glBindBuffer(GL_ARRAY_BUFFER, buffer);
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            auto stream = cuda::getActiveStream();
-            CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), plot->verticesSize(),
+            CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
             CUDA_CHECK(cudaStreamSynchronize(stream));
-            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
+            glUnmapBuffer(GL_ARRAY_BUFFER);
         }
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_plot<T>(const Array<T> &P, forge::Plot* plot);
+template void copy_plot<T>(const Array<T> &, fg_plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/plot.hpp b/src/backend/cuda/plot.hpp
index 7a2ced069c..6d87853014 100644
--- a/src/backend/cuda/plot.hpp
+++ b/src/backend/cuda/plot.hpp
@@ -12,11 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void copy_plot(const Array<T> &P, forge::Plot* plot);
+namespace cuda {
+
+template<typename T>
+void copy_plot(const Array<T> &P, fg_plot plot);
+
 }
 
 #endif
-
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 196a61bbd1..b2777a3fa4 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -13,54 +13,54 @@
 #include <surface.hpp>
 #include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <join.hpp>
-#include <reduce.hpp>
-#include <reorder.hpp>
 #include <GraphicsResourceManager.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
-using namespace gl;
+namespace cuda {
 
 template<typename T>
-void copy_surface(const Array<T> &P, forge::Surface* surface)
+void copy_surface(const Array<T> &P, fg_surface surface)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+    auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
-        ShrdResVector res = interopManager().getBufferResource(surface);
+        auto res = interopManager().getSurfaceResources(surface);
 
-        // Map resource. Copy data to VBO. Unmap resource.
-        size_t num_bytes = surface->verticesSize();
+        size_t bytes = 0;
         T* d_vbo = NULL;
-        cudaGraphicsMapResources(1, res[0].get(), cuda::getActiveStream());
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, *(res[0].get()));
-        cudaMemcpyAsync(d_vbo, d_P, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
-        cudaGraphicsUnmapResources(1, res[0].get(), cuda::getActiveStream());
+        cudaGraphicsMapResources(1, res[0].get(), stream);
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo,
+                                             &bytes, *(res[0].get()));
+        cudaMemcpyAsync(d_vbo, d_P, bytes, cudaMemcpyDeviceToDevice, stream);
+        cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
         CheckGL("After cuda resource copy");
 
         POST_LAUNCH_CHECK();
     } else {
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
+        FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
+
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, surface->vertices());
-        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        glBindBuffer(GL_ARRAY_BUFFER, buffer);
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            auto stream = cuda::getActiveStream();
-            CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), surface->verticesSize(),
+            CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
             CUDA_CHECK(cudaStreamSynchronize(stream));
-            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
+            glUnmapBuffer(GL_ARRAY_BUFFER);
         }
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_surface<T>(const Array<T> &P, forge::Surface* surface);
+template void copy_surface<T>(const Array<T> &, fg_surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/surface.hpp b/src/backend/cuda/surface.hpp
index 42342454ac..84da47708b 100644
--- a/src/backend/cuda/surface.hpp
+++ b/src/backend/cuda/surface.hpp
@@ -12,11 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void copy_surface(const Array<T> &P, forge::Surface* surface);
+namespace cuda {
+
+template<typename T>
+void copy_surface(const Array<T> &P, fg_surface surface);
+
 }
 
 #endif
-
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 7102218f47..3e53208ab4 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -17,62 +17,81 @@
 
 using af::dim4;
 
-namespace cuda
-{
-using namespace gl;
+namespace cuda {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       forge::VectorField* vector_field)
+                       fg_vector_field vfield)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+    auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
-        ShrdResVector res = interopManager().getBufferResource(vector_field);
-        CGR_t resources[2] = {*res[0].get(), *res[1].get()};
+        auto res = interopManager().getVectorFieldResources(vfield);
+        cudaGraphicsResource_t resources[2] = {*res[0].get(), *res[1].get()};
 
-        // Map resource. Copy data to VBO. Unmap resource.
-        // Map all resources at once.
-        cudaGraphicsMapResources(2, resources, cuda::getActiveStream());
+        cudaGraphicsMapResources(2, resources, stream);
 
         // Points
         {
-            const T *ptr     = points.get();
-            size_t num_bytes = vector_field->verticesSize();
+            const T *ptr = points.get();
+            size_t bytes = 0;
             T* d_vbo = NULL;
-            cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[0]);
-            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
+            cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
+                                                 resources[0]);
+            cudaMemcpyAsync(d_vbo, ptr, bytes, cudaMemcpyDeviceToDevice, stream);
         }
         // Directions
         {
             const T *ptr = directions.get();
-            size_t num_bytes = vector_field->directionsSize();
+            size_t bytes = 0;
             T* d_vbo = NULL;
-            cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &num_bytes, resources[1]);
-            cudaMemcpyAsync(d_vbo, ptr, num_bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream());
+            cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
+                                                 resources[1]);
+            cudaMemcpyAsync(d_vbo, ptr, bytes, cudaMemcpyDeviceToDevice, stream);
         }
-        cudaGraphicsUnmapResources(2, resources, cuda::getActiveStream());
+        cudaGraphicsUnmapResources(2, resources, stream);
 
         CheckGL("After cuda resource copy");
 
         POST_LAUNCH_CHECK();
     } else {
         CheckGL("Begin CUDA fallback-resource copy");
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, vector_field->vertices());
-        gl::GLubyte* ptr = (gl::GLubyte*)glMapBuffer((gl::GLenum)GL_ARRAY_BUFFER, (gl::GLenum)GL_WRITE_ONLY);
+        unsigned size1 = 0, size2 = 0;
+        unsigned buff1 = 0, buff2 = 0;
+        FG_CHECK(fg_get_vector_field_vertex_buffer_size(&size1, vfield));
+        FG_CHECK(fg_get_vector_field_direction_buffer_size(&size2, vfield));
+        FG_CHECK(fg_get_vector_field_vertex_buffer(&buff1, vfield));
+        FG_CHECK(fg_get_vector_field_direction_buffer(&buff2, vfield));
+
+        // Points
+        glBindBuffer(GL_ARRAY_BUFFER, buff1);
+        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        if (ptr) {
+            CUDA_CHECK(cudaMemcpyAsync(ptr, points.get(), size1,
+                                       cudaMemcpyDeviceToHost, stream));
+            CUDA_CHECK(cudaStreamSynchronize(stream));
+            glUnmapBuffer(GL_ARRAY_BUFFER);
+        }
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+        // Directions
+        glBindBuffer(GL_ARRAY_BUFFER, buff2);
+        ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            auto stream = cuda::getActiveStream();
-            CUDA_CHECK(cudaMemcpyAsync(ptr, points.get(), vector_field->verticesSize(),
+            CUDA_CHECK(cudaMemcpyAsync(ptr, directions.get(), size2,
                                        cudaMemcpyDeviceToHost, stream));
             CUDA_CHECK(cudaStreamSynchronize(stream));
-            glUnmapBuffer((gl::GLenum)GL_ARRAY_BUFFER);
+            glUnmapBuffer(GL_ARRAY_BUFFER);
         }
-        glBindBuffer((gl::GLenum)GL_ARRAY_BUFFER, 0);
+        glBindBuffer(GL_ARRAY_BUFFER, 0);
+
         CheckGL("End CUDA fallback-resource copy");
     }
 }
 
-#define INSTANTIATE(T)                                                                      \
-    template void copy_vector_field<T>(const Array<T> &points, const Array<T> &directions,  \
-                                       forge::VectorField* vector_field);
+#define INSTANTIATE(T)                                                  \
+template void copy_vector_field<T>(const Array<T> &, const Array<T> &,  \
+                                   fg_vector_field);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/vector_field.hpp b/src/backend/cuda/vector_field.hpp
index 48006f3d36..80009b75f0 100644
--- a/src/backend/cuda/vector_field.hpp
+++ b/src/backend/cuda/vector_field.hpp
@@ -12,11 +12,12 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                           forge::VectorField* vector_field);
+namespace cuda {
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       fg_vector_field vector_field);
+
 }
 
 #endif
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 47d8da0193..a5b4a6e47d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -424,9 +424,6 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(AF_WITH_GRAPHICS)
-  if(NOT AF_USE_SYSTEM_FORGE)
-    add_dependencies(afopencl forge-ext)
-  endif()
   target_sources(afopencl
     PRIVATE
       GraphicsResourceManager.hpp
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
index 77d20ee9b9..79914cf686 100644
--- a/src/backend/opencl/GraphicsResourceManager.cpp
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -11,9 +11,9 @@
 #include <platform.hpp>
 #include <GraphicsResourceManager.hpp>
 
-namespace opencl
-{
-ShrdResVector GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
+namespace opencl {
+GraphicsResourceManager::ShrdResVector
+GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
 {
     ShrdResVector output;
 
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index 4ffd361b69..e13aacf814 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -20,15 +20,13 @@ namespace cl
 class Buffer;
 }
 
-namespace opencl
-{
-typedef cl::Buffer CGR_t;
-typedef std::shared_ptr<CGR_t> SharedResource;
-typedef std::vector<SharedResource> ShrdResVector;
-
-class GraphicsResourceManager : public common::InteropManager<GraphicsResourceManager, cl::Buffer>
+namespace opencl {
+class GraphicsResourceManager :
+    public common::InteropManager<GraphicsResourceManager, cl::Buffer>
 {
     public:
+        using ShrdResVector = std::vector< std::shared_ptr<cl::Buffer> >;
+
         GraphicsResourceManager() {}
         ShrdResVector registerResources(std::vector<uint32_t> resources);
 
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 798363769e..33ea91494c 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -15,19 +15,19 @@
 #include <hist_graphics.hpp>
 #include <GraphicsResourceManager.hpp>
 
-namespace opencl
-{
-using namespace gl;
+namespace opencl {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
+void copy_histogram(const Array<T> &data, fg_histogram hist)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = data.get();
-        size_t bytes = hist->verticesSize();
+        unsigned bytes = 0;
+        FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
-        ShrdResVector res = interopManager().getBufferResource(hist);
+        auto res = interopManager().getHistogramResources(hist);
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
@@ -47,11 +47,15 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
     } else {
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, hist));
+        FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
+
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, hist->vertices());
+        glBindBuffer(GL_ARRAY_BUFFER, buffer);
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*data.get(), CL_TRUE, 0, hist->verticesSize(), ptr);
+            getQueue().enqueueReadBuffer(*data.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -60,7 +64,7 @@ void copy_histogram(const Array<T> &data, const forge::Histogram* hist)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_histogram<T>(const Array<T> &data, const forge::Histogram* hist);
+template void copy_histogram<T>(const Array<T> &, fg_histogram);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
diff --git a/src/backend/opencl/hist_graphics.hpp b/src/backend/opencl/hist_graphics.hpp
index 2e6c980027..1c42736b3b 100644
--- a/src/backend/opencl/hist_graphics.hpp
+++ b/src/backend/opencl/hist_graphics.hpp
@@ -12,11 +12,10 @@
 #include <common/graphics_common.hpp>
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, const forge::Histogram* hist);
+void copy_histogram(const Array<T> &data, fg_histogram hist);
 
 }
 
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 7acf5a66c9..db85214fca 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -18,20 +18,21 @@
 #include <stdexcept>
 #include <vector>
 
-namespace opencl
-{
-using namespace gl;
+namespace opencl {
 
 template<typename T>
-void copy_image(const Array<T> &in, const forge::Image* image)
+void copy_image(const Array<T> &in, fg_image image)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
 
-        ShrdResVector res = interopManager().getBufferResource(image);
+        auto res = interopManager().getImageResources(image);
 
         const cl::Buffer *d_X = in.get();
-        size_t num_bytes = image->size();
+
+        unsigned bytes = 0;
+        FG_CHECK(fg_get_image_size(&bytes, image));
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
@@ -44,7 +45,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_X, *(res[0].get()), 0, 0, num_bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_X, *(res[0].get()), 0, 0, bytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
@@ -52,11 +53,15 @@ void copy_image(const Array<T> &in, const forge::Image* image)
         CheckGL("End opencl resource copy");
     } else {
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, image->pixels());
-        glBufferData(GL_PIXEL_UNPACK_BUFFER, image->size(), 0, GL_STREAM_DRAW);
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_image_size(&bytes, image));
+        FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+
+        glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
+        glBufferData(GL_PIXEL_UNPACK_BUFFER, bytes, 0, GL_STREAM_DRAW);
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, 0, image->size(), ptr);
+            getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
         }
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
@@ -65,7 +70,7 @@ void copy_image(const Array<T> &in, const forge::Image* image)
 }
 
 #define INSTANTIATE(T)      \
-    template void copy_image<T>(const Array<T> &in, const forge::Image* image);
+template void copy_image<T>(const Array<T> &, fg_image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/image.hpp b/src/backend/opencl/image.hpp
index ee0ee87583..5c2841664d 100644
--- a/src/backend/opencl/image.hpp
+++ b/src/backend/opencl/image.hpp
@@ -12,10 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void copy_image(const Array<T> &in, const forge::Image* image);
+namespace opencl {
+
+template<typename T>
+void copy_image(const Array<T> &in, fg_image image);
+
 }
 
 #endif
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 84bd1e7b23..f717a31ec5 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -39,6 +39,9 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:af_glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
+        ${ArrayFire_SOURCE_DIR}/extern/forge/include
+        ${ArrayFire_BINARY_DIR}/extern/forge/include
       )
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 7e65b8b3c1..aad5b2b6f6 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -34,6 +34,9 @@ foreach(SBK_TYPE ${SBK_TYPES})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:af_glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
+        ${ArrayFire_SOURCE_DIR}/extern/forge/include
+        ${ArrayFire_BINARY_DIR}/extern/forge/include
       )
 
     set_target_properties(opencl_sort_by_key_${SBK_TYPE}
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 226d5967c9..85b2033a32 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -10,28 +10,19 @@
 // Include this before af/opencl.h
 // Causes conflict between system cl.hpp and opencl/cl.hpp
 #if defined(WITH_GRAPHICS)
-
 #include <common/graphics_common.hpp>
-
-#if defined(OS_MAC)
-#include <OpenGL/OpenGL.h>
-#include <libkern/OSAtomic.h>
-#else
-#include <glbinding/gl/gl.h>
-#endif // !__APPLE__
-
 #endif
 
 #include <af/version.h>
 #include <af/opencl.h>
 #include <common/defines.hpp>
+#include <common/host_memory.hpp>
+#include <common/util.hpp>
+#include <blas.hpp>
+#include <clfft.hpp>
 #include <errorcodes.hpp>
 #include <err_opencl.hpp>
-#include <blas.hpp>
-#include <common/host_memory.hpp>
-#include <common/InteropManager.hpp>
 #include <platform.hpp>
-#include <common/util.hpp>
 #include <version.hpp>
 
 #include <boost/compute/context.hpp>
@@ -49,7 +40,6 @@
 #include <thread>
 #include <utility>
 #include <vector>
-#include <clfft.hpp>
 
 using std::string;
 using std::vector;
@@ -940,7 +930,7 @@ DeviceManager::DeviceManager()
             /* loop over devices and replace contexts with
              * OpenGL shared contexts whereever applicable */
             int devCount = mDevices.size();
-            forge::Window* wHandle = graphics::ForgeManager::getInstance().getMainWindow();
+            fg_window wHandle = graphics::ForgeManager::getInstance().getMainWindow();
             for(int i=0; i<devCount; ++i)
                 markDeviceForInterop(i, wHandle);
         } catch (...) {
@@ -965,7 +955,7 @@ DeviceManager::DeviceManager()
 }
 
 #if defined(WITH_GRAPHICS)
-void DeviceManager::markDeviceForInterop(const int device, const forge::Window* wHandle)
+void DeviceManager::markDeviceForInterop(const int device, const fg_window wHandle)
 {
     try {
         if (device >= (int)mQueues.size() ||
@@ -985,6 +975,10 @@ void DeviceManager::markDeviceForInterop(const int device, const forge::Window*
             // call forge to get OpenGL sharing context and details
             cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
 
+            long long wnd_ctx, wnd_dsp;
+            FG_CHECK(fg_get_window_context_handle(&wnd_ctx, wHandle));
+            FG_CHECK(fg_get_window_display_handle(&wnd_dsp, wHandle));
+
 #ifdef OS_MAC
             CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
             CGLShareGroupObj cgl_share_group = CGLGetShareGroup(cgl_current_ctx);
@@ -995,11 +989,11 @@ void DeviceManager::markDeviceForInterop(const int device, const forge::Window*
             };
 #else
             cl_context_properties cps[] = {
-                CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
+                CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
 #if defined(_WIN32) || defined(_MSC_VER)
-                CL_WGL_HDC_KHR, (cl_context_properties)wHandle->display(),
+                CL_WGL_HDC_KHR, (cl_context_properties)wnd_dsp,
 #else
-                CL_GLX_DISPLAY_KHR, (cl_context_properties)wHandle->display(),
+                CL_GLX_DISPLAY_KHR, (cl_context_properties)wnd_dsp,
 #endif
                 CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
                 0
@@ -1008,7 +1002,7 @@ void DeviceManager::markDeviceForInterop(const int device, const forge::Window*
             // Check if current OpenCL device is belongs to the OpenGL context
             {
                 cl_context_properties test_cps[] = {
-                    CL_GL_CONTEXT_KHR, (cl_context_properties)wHandle->context(),
+                    CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
                     CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
                     0
                 };
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index d83969e48d..acc7cf5f72 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -187,7 +187,7 @@ class DeviceManager
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
 #if defined(WITH_GRAPHICS)
-        void markDeviceForInterop(const int device, const forge::Window* wHandle);
+        void markDeviceForInterop(const int device, const fg_window wHandle);
 #endif
 
     private:
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 4651d60737..8c2dba3426 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -13,26 +13,23 @@
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <GraphicsResourceManager.hpp>
-#include <join.hpp>
 #include <plot.hpp>
-#include <reduce.hpp>
-#include <reorder.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
-using namespace gl;
+namespace opencl {
 
 template<typename T>
-void copy_plot(const Array<T> &P, forge::Plot* plot)
+void copy_plot(const Array<T> &P, fg_plot plot)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
-        size_t bytes = plot->verticesSize();
+        unsigned bytes = 0;
+        FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
 
-        ShrdResVector res = interopManager().getBufferResource(plot);
+        auto res = interopManager().getPlotResources(plot);
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
@@ -52,11 +49,15 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
     } else {
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
+        FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
+
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, plot->vertices());
+        glBindBuffer(GL_ARRAY_BUFFER, buffer);
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, plot->verticesSize(), ptr);
+            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -65,7 +66,7 @@ void copy_plot(const Array<T> &P, forge::Plot* plot)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_plot<T>(const Array<T> &P, forge::Plot* plot);
+template void copy_plot<T>(const Array<T> &, fg_plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/plot.hpp b/src/backend/opencl/plot.hpp
index 9f50a06656..8ca29cd07a 100644
--- a/src/backend/opencl/plot.hpp
+++ b/src/backend/opencl/plot.hpp
@@ -12,10 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void copy_plot(const Array<T> &P, forge::Plot* plot);
+namespace opencl {
+
+template<typename T>
+void copy_plot(const Array<T> &P, fg_plot plot);
+
 }
 
 #endif
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index ef7782c561..6d50ffe787 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -20,19 +20,19 @@
 
 using af::dim4;
 
-namespace opencl
-{
-using namespace gl;
+namespace opencl {
 
 template<typename T>
-void copy_surface(const Array<T> &P, forge::Surface* surface)
+void copy_surface(const Array<T> &P, fg_surface surface)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
-        size_t bytes = surface->verticesSize();
+        unsigned bytes = 0;
+        FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
 
-        ShrdResVector res = interopManager().getBufferResource(surface);
+        auto res = interopManager().getSurfaceResources(surface);
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
@@ -52,11 +52,15 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
     } else {
+        unsigned bytes = 0, buffer = 0;
+        FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
+        FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
+
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, surface->vertices());
+        glBindBuffer(GL_ARRAY_BUFFER, buffer);
         GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
-            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, surface->verticesSize(), ptr);
+            getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -65,7 +69,7 @@ void copy_surface(const Array<T> &P, forge::Surface* surface)
 }
 
 #define INSTANTIATE(T)  \
-    template void copy_surface<T>(const Array<T> &P, forge::Surface* surface);
+template void copy_surface<T>(const Array<T> &, fg_surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/surface.hpp b/src/backend/opencl/surface.hpp
index a87471c79b..a22ea20585 100644
--- a/src/backend/opencl/surface.hpp
+++ b/src/backend/opencl/surface.hpp
@@ -12,12 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void copy_surface(const Array<T> &P, forge::Surface* surface);
-}
+namespace opencl {
 
-#endif
+template<typename T>
+void copy_surface(const Array<T> &P, fg_surface surface);
 
+}
 
+#endif
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index c53279aa0c..145f67a6f4 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -17,22 +17,23 @@
 
 using af::dim4;
 
-namespace opencl
-{
-using namespace gl;
+namespace opencl {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       forge::VectorField* vector_field)
+                       fg_vector_field vfield)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_points      = points.get();
         const cl::Buffer *d_directions  = directions.get();
-        size_t pBytes = vector_field->verticesSize();
-        size_t dBytes = vector_field->directionsSize();
+        unsigned pBytes = 0;
+        unsigned dBytes = 0;
+        FG_CHECK(fg_get_vector_field_vertex_buffer_size(&pBytes, vfield));
+        FG_CHECK(fg_get_vector_field_direction_buffer_size(&dBytes, vfield));
 
-        ShrdResVector res = interopManager().getBufferResource(vector_field);
+        auto res = interopManager().getVectorFieldResources(vfield);
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
@@ -54,19 +55,29 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         CL_DEBUG_FINISH(getQueue());
         CheckGL("End OpenCL resource copy");
     } else {
+        unsigned size1 = 0, size2 = 0;
+        unsigned buff1 = 0, buff2 = 0;
+        FG_CHECK(fg_get_vector_field_vertex_buffer_size(&size1, vfield));
+        FG_CHECK(fg_get_vector_field_direction_buffer_size(&size2, vfield));
+        FG_CHECK(fg_get_vector_field_vertex_buffer(&buff1, vfield));
+        FG_CHECK(fg_get_vector_field_direction_buffer(&buff2, vfield));
+
         CheckGL("Begin OpenCL fallback-resource copy");
-        glBindBuffer(GL_ARRAY_BUFFER, vector_field->vertices());
+
+        // Points
+        glBindBuffer(GL_ARRAY_BUFFER, buff1);
         GLubyte* pPtr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (pPtr) {
-            getQueue().enqueueReadBuffer(*points.get(), CL_TRUE, 0, vector_field->verticesSize(), pPtr);
+            getQueue().enqueueReadBuffer(*points.get(), CL_TRUE, 0, size1, pPtr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
 
-        glBindBuffer(GL_ARRAY_BUFFER, vector_field->directions());
+        // Directions
+        glBindBuffer(GL_ARRAY_BUFFER, buff2);
         GLubyte* dPtr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (dPtr) {
-            getQueue().enqueueReadBuffer(*directions.get(), CL_TRUE, 0, vector_field->directionsSize(), dPtr);
+            getQueue().enqueueReadBuffer(*directions.get(), CL_TRUE, 0, size2, dPtr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -74,9 +85,9 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
     }
 }
 
-#define INSTANTIATE(T)                                                                      \
-    template void copy_vector_field<T>(const Array<T> &points, const Array<T> &directions,  \
-                                       forge::VectorField* vector_field);
+#define INSTANTIATE(T)                                                  \
+template void copy_vector_field<T>(const Array<T> &, const Array<T> &,  \
+                                   fg_vector_field);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/vector_field.hpp b/src/backend/opencl/vector_field.hpp
index 9685e28e76..c0a3fc2849 100644
--- a/src/backend/opencl/vector_field.hpp
+++ b/src/backend/opencl/vector_field.hpp
@@ -12,13 +12,12 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                           forge::VectorField* vector_field);
-}
+namespace opencl {
 
-#endif
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       fg_vector_field vector_field);
 
+}
 
+#endif

From 5df10936e7762cc3bb2af06de30f922e381fc52f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Dec 2018 02:03:38 +0530
Subject: [PATCH 1578/2677] Remove conditional graphics build support

Going forward from commit where graphics dependencies are loaded
at runtime, all builds can be built with graphics code enabled since it is
not a link time dependency.

If graphics library Forge & OpenGL, and their respective dependencies
are loaded successfully at runtime, then the user will be able to use
graphics. Hence, the option to build with or without graphics is not
needed from now on.

However, a new cmake option `AF_BUILD_FORGE`, which is disabled by
default, is provided to enable building forge(submodule) along with
arrayfire code to help in cases where a developer chooses to
use graphics functionality either for testing, debugging or packaging.
---
 CMakeLists.txt                                |  13 +-
 .../AFconfigure_forge_submodule.cmake         |  14 +-
 CMakeModules/CPackConfig.cmake                |   8 +-
 CMakeModules/osx_install/InstallTool.cmake    |   8 -
 CMakeModules/osx_install/OSXInstaller.cmake   | 343 ------------------
 .../osx_install/distribution-no-gl.dist       |  78 ----
 CMakeModules/osx_install/distribution.dist    | 148 --------
 .../osx_install/forge_scripts/postinstall     |  66 ----
 src/api/c/CMakeLists.txt                      |   4 -
 src/api/c/hist.cpp                            |  12 -
 src/api/c/image.cpp                           |  11 -
 src/api/c/plot.cpp                            |  88 -----
 src/api/c/surface.cpp                         |  12 -
 src/api/c/vector_field.cpp                    |  34 --
 src/api/c/window.cpp                          | 116 ------
 src/backend/common/CMakeLists.txt             |  23 +-
 src/backend/common/InteropManager.hpp         |   2 -
 src/backend/common/err_common.cpp             |   5 +-
 src/backend/common/graphics_common.cpp        |  10 -
 src/backend/common/graphics_common.hpp        |   4 -
 src/backend/cpu/CMakeLists.txt                |  26 +-
 src/backend/cpu/hist_graphics.cpp             |   4 -
 src/backend/cpu/hist_graphics.hpp             |   4 -
 src/backend/cpu/image.cpp                     |   4 -
 src/backend/cpu/image.hpp                     |   4 -
 src/backend/cpu/plot.cpp                      |   4 -
 src/backend/cpu/plot.hpp                      |   5 -
 src/backend/cpu/surface.cpp                   |   4 -
 src/backend/cpu/surface.hpp                   |   4 -
 src/backend/cpu/vector_field.cpp              |   4 -
 src/backend/cpu/vector_field.hpp              |   5 -
 src/backend/cuda/CMakeLists.txt               |  29 +-
 src/backend/cuda/GraphicsResourceManager.cpp  |   3 -
 src/backend/cuda/GraphicsResourceManager.hpp  |   2 -
 src/backend/cuda/hist_graphics.cpp            |   4 -
 src/backend/cuda/hist_graphics.hpp            |   4 -
 src/backend/cuda/image.cpp                    |   4 -
 src/backend/cuda/image.hpp                    |   4 -
 src/backend/cuda/platform.cpp                 |   7 -
 src/backend/cuda/platform.hpp                 |   9 +-
 src/backend/cuda/plot.cpp                     |   4 -
 src/backend/cuda/plot.hpp                     |   4 -
 src/backend/cuda/surface.cpp                  |   4 -
 src/backend/cuda/surface.hpp                  |   4 -
 src/backend/cuda/vector_field.cpp             |   4 -
 src/backend/cuda/vector_field.hpp             |   5 -
 src/backend/opencl/CMakeLists.txt             |  30 +-
 .../opencl/GraphicsResourceManager.cpp        |   2 -
 .../opencl/GraphicsResourceManager.hpp        |   2 -
 src/backend/opencl/hist_graphics.cpp          |   4 -
 src/backend/opencl/hist_graphics.hpp          |   5 -
 src/backend/opencl/image.cpp                  |   4 -
 src/backend/opencl/image.hpp                  |   4 -
 src/backend/opencl/platform.cpp               |  19 +-
 src/backend/opencl/platform.hpp               |  15 +-
 src/backend/opencl/plot.cpp                   |   4 -
 src/backend/opencl/plot.hpp                   |   5 -
 src/backend/opencl/surface.cpp                |   4 -
 src/backend/opencl/surface.hpp                |   4 -
 src/backend/opencl/vector_field.cpp           |   4 -
 src/backend/opencl/vector_field.hpp           |   4 -
 61 files changed, 68 insertions(+), 1200 deletions(-)
 delete mode 100755 CMakeModules/osx_install/InstallTool.cmake
 delete mode 100644 CMakeModules/osx_install/OSXInstaller.cmake
 delete mode 100644 CMakeModules/osx_install/distribution-no-gl.dist
 delete mode 100644 CMakeModules/osx_install/distribution.dist
 delete mode 100755 CMakeModules/osx_install/forge_scripts/postinstall

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 423248c226..2cfe3b2ba4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,8 +42,9 @@ option(AF_BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FO
 option(AF_BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
 option(AF_BUILD_DOCS     "Create ArrayFire Documentation"            ${DOXYGEN_FOUND})
 option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
+option(AF_BUILD_FORGE
+    "Forge libs are not built by default as it is not link time dependency" OFF)
 
-option(AF_WITH_GRAPHICS "Build ArrayFire with Forge Graphics"  ${OPENGL_FOUND})
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
 
@@ -65,7 +66,6 @@ af_deprecate(BUILD_CPU             AF_BUILD_CPU)
 af_deprecate(BUILD_CUDA            AF_BUILD_CUDA)
 af_deprecate(BUILD_OPENCL          AF_BUILD_OPENCL)
 af_deprecate(BUILD_UNIFIED         AF_BUILD_UNIFIED)
-af_deprecate(BUILD_GRAPHICS        AF_WITH_GRAPHICS)
 af_deprecate(BUILD_DOCS            AF_BUILD_DOCS)
 af_deprecate(BUILD_NONFREE         AF_WITH_NONFREE)
 af_deprecate(BUILD_EXAMPLES        AF_BUILD_EXAMPLES)
@@ -83,11 +83,10 @@ mark_as_advanced(
   SPDLOG_BUILD_EXAMPLES
   SPDLOG_BUILD_TESTING)
 
-
-
-if(AF_WITH_GRAPHICS)
-  include(AFconfigure_forge_submodule)
-endif()
+#Configure forge submodule
+#forge is included in ALL target if AF_BUILD_FORGE is ON
+#otherwise, forge is not built at all
+include(AFconfigure_forge_submodule)
 
 configure_file(
     ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index be6fd40434..fe000e4124 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -12,7 +12,11 @@ set(CMAKE_BUILD_TYPE Release)
 set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
 set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
 set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
-add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
+if (AF_BUILD_FORGE)
+  add_subdirectory(extern/forge)
+else (AF_BUILD_FORGE)
+  add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
+endif (AF_BUILD_FORGE)
 mark_as_advanced(
     FG_BUILD_EXAMPLES
     FG_BUILD_DOCS
@@ -26,3 +30,11 @@ mark_as_advanced(
     )
 set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
 set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
+
+if (AF_BUILD_FORGE)
+  install(FILES
+      $<TARGET_FILE:forge>
+      $<TARGET_SONAME_FILE:forge>
+      DESTINATION "${AF_INSTALL_LIB_DIR}"
+      COMPONENT common_backend_dependencies)
+endif (AF_BUILD_FORGE)
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index dbcf4cb5ee..5101b9eff4 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -62,11 +62,7 @@ if (WIN32)
   set(inst_pkg_hash "-${GIT_COMMIT_HASH}")
 endif ()
 
-if(AF_WITH_GRAPHICS)
-  set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}${inst_pkg_hash}")
-else()
-  set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}-no-gl${inst_pkg_hash}")
-endif()
+set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}${inst_pkg_hash}")
 
 # Platform specific settings for CPACK generators
 # - OSX specific
@@ -317,7 +313,7 @@ set(CPACK_RPM_PACKAGE_AUTOREQPROV " no")
 set(CPACK_RPM_PACKAGE_GROUP "Development/Libraries")
 set(CPACK_RPM_PACKAGE_LICENSE "BSD")
 set(CPACK_RPM_PACKAGE_URL "${SITE_URL}")
-if(AF_WITH_GRAPHICS)
+if(AF_BUILD_FORGE)
     set(CPACK_RPM_PACKAGE_REQUIRES "fontconfig-devel, libX11, libXrandr, libXinerama, libXxf86vm, libXcursor, mesa-libGL-devel")
 endif()
 
diff --git a/CMakeModules/osx_install/InstallTool.cmake b/CMakeModules/osx_install/InstallTool.cmake
deleted file mode 100755
index dbb1e45c2a..0000000000
--- a/CMakeModules/osx_install/InstallTool.cmake
+++ /dev/null
@@ -1,8 +0,0 @@
-
-EXECUTE_PROCESS( COMMAND otool -L ${CMAKE_CURRENT_BINARY_DIR}/package/lib/libforge.dylib
-                 COMMAND grep glfw
-                 COMMAND cut -d\  -f1
-                 COMMAND xargs -Jglfwlib install_name_tool -change glfwlib /usr/local/lib/libglfw.dylib ${CMAKE_CURRENT_BINARY_DIR}/package/lib/libforge.dylib
-                 OUTPUT_FILE /tmp/af.out
-                 ERROR_FILE /tmp/af.err
-)
diff --git a/CMakeModules/osx_install/OSXInstaller.cmake b/CMakeModules/osx_install/OSXInstaller.cmake
deleted file mode 100644
index ea9b616519..0000000000
--- a/CMakeModules/osx_install/OSXInstaller.cmake
+++ /dev/null
@@ -1,343 +0,0 @@
-#
-# Builds ArrayFire Installers for OSX
-#
-INCLUDE(CMakeParseArguments)
-INCLUDE(Version)
-
-SET(BIN2CPP_PROGRAM "bin2cpp")
-
-SET(OSX_INSTALL_SOURCE ${PROJECT_SOURCE_DIR}/CMakeModules/osx_install)
-
-################################################################################
-## Create Directory Structure
-################################################################################
-SET(OSX_TEMP "${PROJECT_BINARY_DIR}/osx_install_files")
-
-# Common files - ArrayFireConfig*.cmake
-FILE(GLOB COMMONCMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFireConfig*.cmake")
-
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_COMMON)
-FOREACH(SRC ${COMMONLIB} ${COMMONCMAKE})
-    FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
-    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_COMMON PRE_BUILD
-                       COMMAND ${CMAKE_COMMAND} -E copy
-                       ${SRC} "${OSX_TEMP}/common/${SRC_REL}"
-                       WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                       COMMENT "Copying Common files to temporary OSX Install Dir"
-                       )
-ENDFOREACH()
-
-# Backends - CPU, CUDA, OpenCL, Unified
-MACRO(OSX_INSTALL_SETUP BACKEND LIB)
-    FILE(GLOB ${BACKEND}LIB "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_LIB_DIR}/lib${LIB}.${AF_VERSION}.dylib")
-    FILE(GLOB ${BACKEND}CMAKE "${CMAKE_INSTALL_PREFIX}/${AF_INSTALL_CMAKE_DIR}/ArrayFire${BACKEND}*.cmake")
-
-    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_${BACKEND})
-    FOREACH(SRC ${${BACKEND}LIB} ${${BACKEND}CMAKE})
-        FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
-        ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
-                           COMMAND ${CMAKE_COMMAND} -E copy
-                           ${SRC} "${OSX_TEMP}/${BACKEND}/${SRC_REL}"
-                           WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                           COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir - File: ${SRC_REL}"
-                           )
-    ENDFOREACH()
-    # Create symlinks separately. Copying them in above command will do a deep copy
-    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
-                       COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "lib${LIB}.${ArrayFire_VERSION}.dylib"
-                       "lib${LIB}.${ArrayFire_VERSION_MAJOR}.dylib"
-                       WORKING_DIRECTORY "${OSX_TEMP}/${BACKEND}/${AF_INSTALL_LIB_DIR}"
-                       COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir (Symlink)"
-                       )
-    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_${BACKEND} PRE_BUILD
-                       COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "lib${LIB}.${AF_VERSION_MAJOR}.dylib"
-                       "lib${LIB}.dylib"
-                       WORKING_DIRECTORY "${OSX_TEMP}/${BACKEND}/${AF_INSTALL_LIB_DIR}"
-                       COMMENT "Copying ${BACKEND} files to temporary OSX Install Dir (Symlink)"
-                       )
-ENDMACRO(OSX_INSTALL_SETUP)
-
-OSX_INSTALL_SETUP(CPU afcpu)
-OSX_INSTALL_SETUP(CUDA afcuda)
-OSX_INSTALL_SETUP(OpenCL afopencl)
-OSX_INSTALL_SETUP(Unified af)
-
-# Headers
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_INCLUDE
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  ${CMAKE_INSTALL_PREFIX}/include/af "${OSX_TEMP}/include/af"
-                  COMMAND ${CMAKE_COMMAND} -E copy
-                  ${CMAKE_INSTALL_PREFIX}/include/arrayfire.h "${OSX_TEMP}/include/arrayfire.h"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying header files to temporary OSX Install Dir"
-                  )
-
-# Examples
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_EXAMPLES
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/examples" "${OSX_TEMP}/examples"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying examples files to temporary OSX Install Dir"
-                  )
-
-# Documentation
-ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_DOC
-                  COMMAND ${CMAKE_COMMAND} -E copy_directory
-                  "${CMAKE_INSTALL_PREFIX}/share/ArrayFire/doc" "${OSX_TEMP}/doc"
-                  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                  COMMENT "Copying documentation files to temporary OSX Install Dir"
-                  )
-
-IF(AF_WITH_GRAPHICS)
-    MAKE_DIRECTORY("${OSX_TEMP}/Forge")
-
-    # Forge library versions for setting up symlinks
-    STRING(SUBSTRING ${FORGE_VERSION} 0 1 FORGE_VERSION_MAJOR) # Will return x
-
-    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_LIB)
-    SET(FORGE_LIB "${CMAKE_INSTALL_PREFIX}/lib/libforge.${FORGE_VERSION}.dylib")
-    FOREACH(SRC ${FORGE_LIB})
-        FILE(RELATIVE_PATH SRC_REL ${CMAKE_INSTALL_PREFIX} ${SRC})
-        ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
-                           COMMAND ${CMAKE_COMMAND} -E copy
-                           ${SRC} "${OSX_TEMP}/Forge/${SRC_REL}"
-                           WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                           COMMENT "Copying libforge files to temporary OSX Install Dir - File: ${SRC_REL}"
-        )
-    ENDFOREACH()
-    # Create symlinks separately. Copying them in above command will do a deep copy
-    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
-                       COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "libforge.${FORGE_VERSION}.dylib"
-                       "libforge.${FORGE_VERSION_MAJOR}.dylib"
-                       WORKING_DIRECTORY "${OSX_TEMP}/Forge/${AF_INSTALL_LIB_DIR}"
-                       COMMENT "Copying libforge files to temporary OSX Install Dir (Symlink)"
-                       )
-    ADD_CUSTOM_COMMAND(TARGET OSX_INSTALL_SETUP_FORGE_LIB PRE_BUILD
-                       COMMAND ${CMAKE_COMMAND} -E create_symlink
-                       "libforge.${FORGE_VERSION_MAJOR}.dylib"
-                       "libforge.dylib"
-                       WORKING_DIRECTORY "${OSX_TEMP}/Forge/${AF_INSTALL_LIB_DIR}"
-                       COMMENT "Copying libforge files to temporary OSX Install Dir (Symlink)"
-                       )
-
-    # Forge Headers
-    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_INCLUDE
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory
-                      "${CMAKE_INSTALL_PREFIX}/include/fg" "${OSX_TEMP}/Forge/include/fg"
-                      COMMAND ${CMAKE_COMMAND} -E copy
-                      "${CMAKE_INSTALL_PREFIX}/include/forge.h" "${OSX_TEMP}/Forge/include/forge.h"
-                      COMMAND ${CMAKE_COMMAND} -E copy
-                      "${CMAKE_INSTALL_PREFIX}/include/ComputeCopy.h" "${OSX_TEMP}/Forge/include/ComputeCopy.h"
-                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                      COMMENT "Copying examples files to temporary OSX Install Dir"
-                      )
-    # Forge Examples
-    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_EXAMPLES
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory
-                      "${CMAKE_INSTALL_PREFIX}/share/Forge/examples" "${OSX_TEMP}/Forge/examples"
-                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                      COMMENT "Copying examples files to temporary OSX Install Dir"
-                      )
-
-    # Documentation
-    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_DOC
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory
-                      "${CMAKE_INSTALL_PREFIX}/share/Forge/doc" "${OSX_TEMP}/Forge/doc"
-                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                      COMMENT "Copying documentation files to temporary OSX Install Dir"
-                      )
-
-    # Forge CMake
-    ADD_CUSTOM_TARGET(OSX_INSTALL_SETUP_FORGE_CMAKE
-                      COMMAND ${CMAKE_COMMAND} -E copy_directory
-                      "${CMAKE_INSTALL_PREFIX}/share/Forge/cmake" "${OSX_TEMP}/Forge/cmake"
-                      WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
-                      COMMENT "Copying documentation files to temporary OSX Install Dir"
-                      )
-ENDIF(AF_WITH_GRAPHICS)
-################################################################################
-
-FUNCTION(PKG_BUILD)
-    CMAKE_PARSE_ARGUMENTS(ARGS "" "DEPENDS;INSTALL_LOCATION;IDENTIFIER;PATH_TO_FILES;PKG_NAME;TARGETS;SCRIPT_DIR" "FILTERS" ${ARGN})
-
-    FOREACH(filter ${ARGS_FILTERS})
-        LIST(APPEND  FILTER_LIST --filter ${filter})
-    ENDFOREACH()
-
-    IF(ARGS_SCRIPT_DIR)
-        LIST(APPEND SCRPT_DIR --scripts ${ARGS_SCRIPT_DIR})
-    ENDIF(ARGS_SCRIPT_DIR)
-
-    SET(PACKAGE_NAME "${ARGS_PKG_NAME}.pkg")
-    ADD_CUSTOM_COMMAND( OUTPUT ${PACKAGE_NAME}
-                        DEPENDS ${ARGS_DEPENDS}
-                        COMMAND pkgbuild    --install-location  ${ARGS_INSTALL_LOCATION}
-                                            --identifier        ${ARGS_IDENTIFIER}
-                                            --root              ${ARGS_PATH_TO_FILES}
-                                            ${SCRPT_DIR}
-                                            ${FILTER_LIST}
-                                            ${ARGS_PKG_NAME}.pkg
-                        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
-                        COMMENT "Building ${ARGS_PKG_NAME} package"
-                )
-    ADD_CUSTOM_TARGET(${ARGS_PKG_NAME}_installer DEPENDS ${PACKAGE_NAME})
-
-    SET("${ARGS_TARGETS}" ${ARGS_PKG_NAME}_installer PARENT_SCOPE)
-ENDFUNCTION(PKG_BUILD)
-
-FUNCTION(PRODUCT_BUILD)
-    CMAKE_PARSE_ARGUMENTS(ARGS "" "" "DEPENDS" ${ARGN})
-    IF(AF_WITH_GRAPHICS)
-        SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution.dist")
-    ELSE(AF_WITH_GRAPHICS)
-        SET(DISTRIBUTION_FILE       "${OSX_INSTALL_SOURCE}/distribution-no-gl.dist")
-    ENDIF(AF_WITH_GRAPHICS)
-
-    SET(DISTRIBUTION_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/distribution.dist.out")
-
-    SET(WELCOME_FILE       "${OSX_INSTALL_SOURCE}/welcome.html")
-    SET(WELCOME_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/welcome.html.out")
-
-    SET(README_FILE       "${OSX_INSTALL_SOURCE}/readme.html")
-    SET(README_FILE_OUT   "${CMAKE_CURRENT_BINARY_DIR}/readme.html.out")
-
-    SET(AF_TITLE    "ArrayFire ${AF_VERSION}")
-    CONFIGURE_FILE(${DISTRIBUTION_FILE} ${DISTRIBUTION_FILE_OUT})
-    CONFIGURE_FILE(${WELCOME_FILE} ${WELCOME_FILE_OUT})
-    CONFIGURE_FILE(${README_FILE} ${README_FILE_OUT})
-
-    IF(AF_WITH_GRAPHICS)
-        SET(PACKAGE_NAME "arrayfire-${AF_VERSION}.pkg")
-    ELSE(AF_WITH_GRAPHICS)
-        SET(PACKAGE_NAME "arrayfire-no-gl-${AF_VERSION}.pkg")
-    ENDIF(AF_WITH_GRAPHICS)
-
-    ADD_CUSTOM_COMMAND( OUTPUT ${PACKAGE_NAME}
-                        DEPENDS ${ARGS_DEPENDS}
-                        COMMAND pwd
-                        COMMAND productbuild    --distribution  ${DISTRIBUTION_FILE_OUT}
-                                                ${PACKAGE_NAME}
-                        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
-                        COMMENT "Creating ArrayFire.pkg OSX Installer")
-    ADD_CUSTOM_TARGET(osx_installer DEPENDS ${PACKAGE_NAME})
-ENDFUNCTION(PRODUCT_BUILD)
-
-
-PKG_BUILD(  PKG_NAME        ArrayFireCPU
-            DEPENDS         OSX_INSTALL_SETUP_CPU
-            TARGETS         cpu_package
-            INSTALL_LOCATION /usr/local
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.cpu.lib
-            PATH_TO_FILES   ${OSX_TEMP}/CPU
-            FILTERS         opencl cuda unified)
-
-PKG_BUILD(  PKG_NAME        ArrayFireCUDA
-            DEPENDS         OSX_INSTALL_SETUP_CUDA
-            TARGETS         cuda_package
-            INSTALL_LOCATION /usr/local
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.cuda.lib
-            PATH_TO_FILES   ${OSX_TEMP}/CUDA
-            FILTERS         cpu opencl unified)
-
-PKG_BUILD(  PKG_NAME        ArrayFireOPENCL
-            DEPENDS         OSX_INSTALL_SETUP_OpenCL
-            TARGETS         opencl_package
-            INSTALL_LOCATION /usr/local
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.opencl.lib
-            PATH_TO_FILES   ${OSX_TEMP}/OpenCL
-            FILTERS         cpu cuda unified)
-
-PKG_BUILD(  PKG_NAME        ArrayFireUNIFIED
-            DEPENDS         OSX_INSTALL_SETUP_Unified
-            TARGETS         unified_package
-            INSTALL_LOCATION /usr/local
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.unified.lib
-            PATH_TO_FILES   ${OSX_TEMP}/Unified
-            FILTERS         cpu cuda opencl)
-
-PKG_BUILD(  PKG_NAME        ArrayFireCommon
-            DEPENDS         OSX_INSTALL_SETUP_COMMON
-            TARGETS         common_package
-            INSTALL_LOCATION /usr/local
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.libcommon
-            PATH_TO_FILES   ${OSX_TEMP}/common
-            FILTERS         cpu cuda opencl unified)
-
-PKG_BUILD(  PKG_NAME        ArrayFireHeaders
-            DEPENDS         OSX_INSTALL_SETUP_INCLUDE
-            TARGETS         header_package
-            INSTALL_LOCATION /usr/local/include
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.inc
-            PATH_TO_FILES   ${OSX_TEMP}/include)
-
-PKG_BUILD(  PKG_NAME        ArrayFireExamples
-            DEPENDS         OSX_INSTALL_SETUP_EXAMPLES
-            TARGETS         examples_package
-            INSTALL_LOCATION /usr/local/share/ArrayFire/examples
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.examples
-            PATH_TO_FILES   ${OSX_TEMP}/examples
-            FILTERS         cmake)
-
-PKG_BUILD(  PKG_NAME        ArrayFireDoc
-            DEPENDS         OSX_INSTALL_SETUP_DOC
-            TARGETS         doc_package
-            INSTALL_LOCATION /usr/local/share/ArrayFire/doc
-            IDENTIFIER      com.arrayfire.pkg.arrayfire.doc
-            PATH_TO_FILES   ${OSX_TEMP}/doc
-            FILTERS         cmake)
-
-IF(AF_WITH_GRAPHICS)
-    PKG_BUILD(  PKG_NAME        ForgeLibrary
-                DEPENDS         OSX_INSTALL_SETUP_FORGE_LIB
-                TARGETS         forge_lib_package
-                INSTALL_LOCATION /usr/local/lib
-                SCRIPT_DIR      ${OSX_INSTALL_SOURCE}/forge_scripts
-                IDENTIFIER      com.arrayfire.pkg.forge.lib
-                PATH_TO_FILES   ${OSX_TEMP}/Forge/lib)
-
-    PKG_BUILD(  PKG_NAME        ForgeHeaders
-                DEPENDS         OSX_INSTALL_SETUP_FORGE_INCLUDE
-                TARGETS         forge_header_package
-                INSTALL_LOCATION /usr/local/include
-                IDENTIFIER      com.arrayfire.pkg.forge.inc
-                PATH_TO_FILES   ${OSX_TEMP}/Forge/include)
-
-    PKG_BUILD(  PKG_NAME        ForgeExamples
-                DEPENDS         OSX_INSTALL_SETUP_FORGE_EXAMPLES
-                TARGETS         forge_examples_package
-                INSTALL_LOCATION /usr/local/share/Forge/examples
-                IDENTIFIER      com.arrayfire.pkg.forge.examples
-                PATH_TO_FILES   ${OSX_TEMP}/Forge/examples
-                )
-
-    PKG_BUILD(  PKG_NAME        ForgeDoc
-                DEPENDS         OSX_INSTALL_SETUP_FORGE_DOC
-                TARGETS         forge_doc_package
-                INSTALL_LOCATION /usr/local/share/Forge/doc
-                IDENTIFIER      com.arrayfire.pkg.forge.doc
-                PATH_TO_FILES   ${OSX_TEMP}/Forge/doc
-                )
-
-    PKG_BUILD(  PKG_NAME        ForgeCMake
-                DEPENDS         OSX_INSTALL_SETUP_FORGE_CMAKE
-                TARGETS         forge_cmake_package
-                INSTALL_LOCATION /usr/local/share/Forge/cmake
-                IDENTIFIER      com.arrayfire.pkg.forge.cmake
-                PATH_TO_FILES   ${OSX_TEMP}/Forge/cmake
-                )
-ENDIF(AF_WITH_GRAPHICS)
-
-IF(AF_WITH_GRAPHICS)
-    PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
-                          ${common_package} ${header_package} ${examples_package} ${doc_package}
-                          ${forge_lib_package} ${forge_header_package} ${forge_examples_package} ${forge_doc_package} ${forge_cmake_package}
-                          )
-ELSE(AF_WITH_GRAPHICS)
-    PRODUCT_BUILD(DEPENDS ${cpu_package} ${cuda_package} ${opencl_package} ${unified_package}
-                          ${common_package} ${header_package} ${examples_package} ${doc_package}
-                          )
-ENDIF(AF_WITH_GRAPHICS)
-
diff --git a/CMakeModules/osx_install/distribution-no-gl.dist b/CMakeModules/osx_install/distribution-no-gl.dist
deleted file mode 100644
index 81734358c7..0000000000
--- a/CMakeModules/osx_install/distribution-no-gl.dist
+++ /dev/null
@@ -1,78 +0,0 @@
-<?xml version="1.0" encoding="utf-8" standalone="no"?>
-<installer-gui-script minSpecVersion="1">
-    <title>${AF_TITLE}</title>
-    <welcome    file="${WELCOME_FILE_OUT}" />
-    <readme     file="${README_FILE_OUT}" mime-type="test/html" />
-    <license    file="${PROJECT_SOURCE_DIR}/LICENSE" mime-type="test/plain" />
-    <script>
-      function CheckBackendSelected() {
-      return choices.opencl_lib.selected ||
-            choices.cuda_lib.selected ||
-            choices.cpu_lib.selected;
-      }
-    </script>
-
-    <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"       version="${AF_VERSION}" onConclusion="none">ArrayFireCPU.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"      version="${AF_VERSION}" onConclusion="none">ArrayFireCUDA.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"    version="${AF_VERSION}" onConclusion="none">ArrayFireOPENCL.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.unified.lib"   version="${AF_VERSION}" onConclusion="none">ArrayFireUNIFIED.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.inc"           version="${AF_VERSION}" onConclusion="none">ArrayFireHeaders.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
-    <options customize="always" require-scripts="false"/>
-    <choices-outline>
-        <line choice="libs">
-            <line choice="cpu_lib"/>
-            <line choice="cuda_lib"/>
-            <line choice="opencl_lib"/>
-            <line choice="com.arrayfire.arrayfire.unified.lib"/>
-            <line choice="com.arrayfire.arrayfire.libcommon"/>
-        </line>
-        <line choice="com.arrayfire.arrayfire.inc"/>
-        <line choice="com.arrayfire.arrayfire.examples"/>
-        <line choice="com.arrayfire.arrayfire.doc"/>
-    </choices-outline>
-    <choice id="libs" title="ArrayFire Libraries" visible="true" />
-    <choice title="CPU Libraries"
-            description="ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
-            id="cpu_lib" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"/>
-    </choice>
-    <choice title="CUDA Libraries"
-            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the corresponding CMake config files."
-            id="cuda_lib" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"/>
-    </choice>
-    <choice title="OpenCL Libraries"
-            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. Also installs the corresponding CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
-            id="opencl_lib" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"/>
-    </choice>
-    <choice title="Unified Library"
-            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. Also installs the corresponding CMake config files. NOTE: This option requires the other platforms to work properly"
-            id="com.arrayfire.arrayfire.unified.lib"
-            selected="CheckBackendSelected()"
-            visible="true"
-            enabled="CheckBackendSelected()">
-        <pkg-ref id="com.arrayfire.arrayfire.unified.lib"/>
-    </choice>
-    <choice title="Library Common"
-            description="Installs ArrayFireConfig.cmake files"
-            id="com.arrayfire.arrayfire.libcommon"
-            selected="CheckBackendSelected()"
-            visible="false"
-            enabled="CheckBackendSelected()">
-        <pkg-ref id="com.arrayfire.arrayfire.libcommon"/>
-    </choice>
-    <choice title="ArrayFire Headers" description="ArrayFire Headers" id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.inc"/>
-    </choice>
-    <choice title="Examples" description="ArrayFire Examples" id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.examples"/>
-    </choice>
-    <choice title="Documentation" description="ArrayFire Documentation" id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.doc"/>
-    </choice>
-
-</installer-gui-script>
diff --git a/CMakeModules/osx_install/distribution.dist b/CMakeModules/osx_install/distribution.dist
deleted file mode 100644
index 9117d250db..0000000000
--- a/CMakeModules/osx_install/distribution.dist
+++ /dev/null
@@ -1,148 +0,0 @@
-<?xml version="1.0" encoding="utf-8" standalone="no"?>
-<installer-gui-script minSpecVersion="1">
-    <title>${AF_TITLE}</title>
-    <welcome    file="${WELCOME_FILE_OUT}" />
-    <readme     file="${README_FILE_OUT}" mime-type="test/html" />
-    <license    file="${PROJECT_SOURCE_DIR}/LICENSE" mime-type="test/plain" />
-    <script>
-      function CheckBackendSelected() {
-      return choices.opencl_lib.selected ||
-            choices.cuda_lib.selected ||
-            choices.cpu_lib.selected;
-      }
-      function CheckBackendOrForgeSelected() {
-      return choices.opencl_lib.selected    ||
-            choices.cuda_lib.selected       ||
-            choices.cpu_lib.selected        ||
-            choices.forge_inc.selected      ||
-            choices.forge_examples.selected;
-      }
-      function EnableForgeLibOption() {
-      return !(choices.opencl_lib.selected     ||
-               choices.cuda_lib.selected       ||
-               choices.cpu_lib.selected        ||
-               choices.forge_inc.selected);
-      }
-      function CheckForgeSelected() {
-      return choices.forge_lib.selected;
-      }
-    </script>
-
-    <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"       version="${AF_VERSION}" onConclusion="none">ArrayFireCPU.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"      version="${AF_VERSION}" onConclusion="none">ArrayFireCUDA.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"    version="${AF_VERSION}" onConclusion="none">ArrayFireOPENCL.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.unified.lib"   version="${AF_VERSION}" onConclusion="none">ArrayFireUNIFIED.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.inc"           version="${AF_VERSION}" onConclusion="none">ArrayFireHeaders.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.examples"      version="${AF_VERSION}" onConclusion="none">ArrayFireExamples.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.doc"           version="${AF_VERSION}" onConclusion="none">ArrayFireDoc.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.arrayfire.libcommon"     version="${AF_VERSION}" onConclusion="none">ArrayFireCommon.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.forge.inc"               version="${AF_VERSION}" onConclusion="none">ForgeHeaders.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.forge.lib"               version="${AF_VERSION}" onConclusion="none">ForgeLibrary.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.forge.examples"          version="${AF_VERSION}" onConclusion="none">ForgeExamples.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.forge.doc"               version="${AF_VERSION}" onConclusion="none">ForgeDoc.pkg</pkg-ref>
-    <pkg-ref id="com.arrayfire.forge.cmake"             version="${AF_VERSION}" onConclusion="none">ForgeCMake.pkg</pkg-ref>
-    <options customize="always" require-scripts="false"/>
-    <choices-outline>
-        <line choice="libs">
-            <line choice="cpu_lib"/>
-            <line choice="cuda_lib"/>
-            <line choice="opencl_lib"/>
-            <line choice="com.arrayfire.arrayfire.unified.lib"/>
-            <line choice="com.arrayfire.arrayfire.libcommon"/>
-        </line>
-        <line choice="com.arrayfire.arrayfire.inc"/>
-        <line choice="com.arrayfire.arrayfire.examples"/>
-        <line choice="com.arrayfire.arrayfire.doc"/>
-        <line choice="forge">
-            <line choice="forge_inc"/>
-            <line choice="forge_lib"/>
-            <line choice="forge_examples"/>
-            <line choice="com.arrayfire.forge.doc"/>
-            <line choice="com.arrayfire.forge.cmake"/>
-        </line>
-    </choices-outline>
-    <choice id="libs" title="ArrayFire Libraries" visible="true" enabled="CheckForgeSelected()" />
-    <choice title="CPU Libraries"
-            description="ArrayFire targeting CPUs. Also installs the corresponding CMake config files."
-            id="cpu_lib" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.cpu.lib"/>
-    </choice>
-    <choice title="CUDA Libraries"
-            description="ArrayFire which targets the CUDA platform. This platform allows you to to take advantage of the CUDA enabled GPUs to run ArrayFire code. Also installs the corresponding CMake config files."
-            id="cuda_lib" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.cuda.lib"/>
-    </choice>
-    <choice title="OpenCL Libraries"
-            description="ArrayFire which targets the OpenCL platform. This platform allows you to use the ArrayFire library which targets OpenCL devices. Also installs the corresponding CMake config files. NOTE: Currently ArrayFire does not support OpenCL for the Intel CPU on Apple."
-            id="opencl_lib" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.opencl.lib"/>
-    </choice>
-    <choice title="Unified Library"
-            description="This library will allow you to choose the platform(cpu, cuda, opencl) at runtime. Also installs the corresponding CMake config files. NOTE: This option requires the other platforms to work properly"
-            id="com.arrayfire.arrayfire.unified.lib"
-            selected="CheckBackendSelected()"
-            visible="true"
-            enabled="CheckBackendSelected()">
-        <pkg-ref id="com.arrayfire.arrayfire.unified.lib"/>
-    </choice>
-    <choice title="Library Common"
-            description="Installs ArrayFireConfig.cmake files"
-            id="com.arrayfire.arrayfire.libcommon"
-            selected="CheckBackendSelected()"
-            visible="false"
-            enabled="CheckBackendSelected()">
-        <pkg-ref id="com.arrayfire.arrayfire.libcommon"/>
-    </choice>
-    <choice title="ArrayFire Headers"
-        description="ArrayFire Headers. Installed to /usr/local/include."
-        id="com.arrayfire.arrayfire.inc" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.inc"/>
-    </choice>
-    <choice title="ArrayFire Examples"
-        description="ArrayFire Examples. Installed to /usr/local/share/ArrayFire/examples."
-        id="com.arrayfire.arrayfire.examples" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.examples"/>
-    </choice>
-    <choice title="ArrayFire Documentation"
-        description="ArrayFire Documentation. Installed to /usr/local/share/ArrayFire/doc."
-        id="com.arrayfire.arrayfire.doc" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.arrayfire.doc"/>
-    </choice>
-
-    <choice id="forge" title="Forge Installation" visible="true" />
-    <choice title="Forge Headers"
-        description="Forge Headers Files. Installed to /usr/local/include."
-        id="forge_inc" visible="true" enabled="true">
-        <pkg-ref id="com.arrayfire.forge.inc"/>
-    </choice>
-    <choice title="Forge Library"
-        description="Installs libforge. Installs to /usr/local/lib/libforge.dylib. This option is required for ArrayFire libs. To unset this, disable ArrayFire Libraries and Forge Headers."
-        id="forge_lib"
-        selected="CheckBackendOrForgeSelected()"
-        visible="true"
-        enabled="EnableForgeLibOption()">
-        <pkg-ref id="com.arrayfire.forge.lib"/>
-    </choice>
-    <choice title="Forge Examples"
-        description="Forge Examples. Installed to /usr/local/share/Forge/examples."
-        id="forge_examples"
-        visible="true"
-        enabled="true">
-        <pkg-ref id="com.arrayfire.forge.examples"/>
-    </choice>
-    <choice title="Forge Documentation"
-        description="Forge Documentation. Installed to /usr/local/share/Forge/doc."
-        id="com.arrayfire.forge.doc"
-        visible="true"
-        enabled="true">
-        <pkg-ref id="com.arrayfire.forge.doc"/>
-    </choice>
-    <choice title="Forge CMake"
-            description="Installs ForgeConfig.cmake files"
-            id="com.arrayfire.forge.cmake"
-            selected="CheckForgeSelected()"
-            visible="false"
-            enabled="CheckForgeSelected()">
-        <pkg-ref id="com.arrayfire.forge.cmake"/>
-    </choice>
-</installer-gui-script>
diff --git a/CMakeModules/osx_install/forge_scripts/postinstall b/CMakeModules/osx_install/forge_scripts/postinstall
deleted file mode 100755
index 6ff54687b2..0000000000
--- a/CMakeModules/osx_install/forge_scripts/postinstall
+++ /dev/null
@@ -1,66 +0,0 @@
-#!/bin/bash
-
-set -e
-set -o pipefail
-
-err_file=/tmp/AFInstallerForge.err
-brew=/usr/local/bin/brew
-
-echo $(date) > $err_file
-
-if [ ! -f $brew ]; then
-    osascript -e 'tell app "Installer" to display dialog "Brew not installed. Please install brew at brew.sh"'
-    echo "Brew not found" >> $err_file
-    exit 1
-fi
-
-#user=$(ps aux | grep console | grep -v 'grep\|root' | cut -d' ' -f1 | head -n1)
-user=$(stat -f '%Su' $HOME)
-
-if [ -z $user ]; then
-    echo "User not found" >> $err_file
-    exit 1
-fi
-
-echo "User: ${user}" >> $err_file
-
-function deps_err
-{
-    osascript -e 'tell app "Installer" to display dialog "ArrayFire files installed but failed to install ArrayFire/Forge dependencies using Brew."'
-    osascript -e 'tell app "Installer" to display dialog "Visit https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures to fix errors manually."'
-    open https://github.com/arrayfire/arrayfire/wiki/Fixing-Common-OS-X-Installer-Failures
-    echo "Dependencies failed to install" >> $err_file
-    exit 1
-}
-
-echo "Output of brew tap:" >> $err_file
-echo "-------------------" >> $err_file
-su $user -c "$brew tap" >> $err_file
-echo "-------------------" >> $err_file
-
-BREW_VERSIONS=$(su $user -c "$brew tap" | grep "homebrew/versions") || true
-if [[ -z "${BREW_VERSIONS}" ]]; then
-    su $user -c "$brew tap homebrew/versions" >> $err_file 2>&1
-else
-    echo "Homebrew/Versions already present in brew tap." >> $err_file
-fi
-
-GLFW_INSTALLED=$(su $user -c "$brew ls --versions glfw" | grep "glfw") || true
-if [[ -z "${GLFW_INSTALLED}" ]]; then
-    echo "Installing GLFW" >> $err_file
-    echo "-------------------" >> $err_file
-    su $user -c "$brew install glfw" >> $err_file 2>&1 || deps_err
-    echo "-------------------" >> $err_file
-else
-    echo "GLFW Version ${GLFW_INSTALLED} is already installed." >> $err_file
-fi
-
-FTCG_INSTALLED=$(su $user -c "$brew ls --versions fontconfig" | grep "fontconfig") || true
-if [[ -z "${FTCG_INSTALLED}" ]]; then
-    echo "Installing FontConfig" >> $err_file
-    echo "-------------------" >> $err_file
-    su $user -c "$brew install fontconfig" >> $err_file 2>&1 || deps_err
-    echo "-------------------" >> $err_file
-else
-    echo "FontConfig Version ${FTCG_INSTALLED} is already installed." >> $err_file
-fi
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index fcbda27fbd..6f7bbd4c0f 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -175,10 +175,6 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
   endif ()
 endif()
 
-if(AF_WITH_GRAPHICS)
-    target_compile_definitions(c_api_interface INTERFACE WITH_GRAPHICS)
-endif()
-
 target_include_directories(c_api_interface
   INTERFACE
     ${CMAKE_CURRENT_SOURCE_DIR}
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index b4ef22f8f0..eecb0e8235 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -19,8 +19,6 @@
 
 using af::dim4;
 using namespace detail;
-
-#if defined(WITH_GRAPHICS)
 using namespace graphics;
 
 template<typename T>
@@ -79,14 +77,12 @@ fg_chart setup_histogram(fg_window const window,
 
     return chart;
 }
-#endif
 
 af_err af_draw_hist(const af_window window,
                     const af_array X,
                     const double minval, const double maxval,
                     const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
@@ -124,12 +120,4 @@ af_err af_draw_hist(const af_window window,
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(X);
-    UNUSED(minval);
-    UNUSED(maxval);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index b61fef5ba6..86c0bc41ae 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -29,8 +29,6 @@
 
 using af::dim4;
 using namespace detail;
-
-#if defined(WITH_GRAPHICS)
 using namespace graphics;
 
 
@@ -73,12 +71,10 @@ static fg_image convert_and_copy_image(const af_array in)
 
     return ret_val;
 }
-#endif
 
 af_err af_draw_image(const af_window window,
                      const af_array in, const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         fprintf(stderr, "Not a valid window\n");
         return AF_SUCCESS;
@@ -119,11 +115,4 @@ af_err af_draw_image(const af_window window,
     CATCHALL;
 
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(in);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support",
-                    AF_ERR_NO_GFX);
-#endif
 }
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index e464f0dcd9..b96f6b7d08 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -24,8 +24,6 @@
 
 using af::dim4;
 using namespace detail;
-
-#if defined(WITH_GRAPHICS)
 using namespace graphics;
 
 // Requires in_ to be in either [order, n] or [n, order] format
@@ -292,52 +290,25 @@ af_err plotWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-#endif // WITH_GRAPHICS
-
-
 // Plot API
 af_err af_draw_plot_nd(const af_window wind, const af_array in,
                        const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return plotWrapper(wind, in, 1, props);
-#else
-    UNUSED(wind);
-    UNUSED(in);
-    UNUSED(props);
-    return AF_ERR_NO_GFX;
-#endif
 }
 
 af_err af_draw_plot_2d(const af_window wind,
                        const af_array X, const af_array Y,
                        const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, props);
-#else
-    UNUSED(wind);
-    UNUSED(X);
-    UNUSED(Y);
-    UNUSED(props);
-    return AF_ERR_NO_GFX;
-#endif
 }
 
 af_err af_draw_plot_3d(const af_window wind,
                        const af_array X, const af_array Y, const af_array Z,
                        const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, Z, props);
-#else
-    UNUSED(wind);
-    UNUSED(X);
-    UNUSED(Y);
-    UNUSED(Z);
-    UNUSED(props);
-    return AF_ERR_NO_GFX;
-#endif
 }
 
 // Deprecated Plot API
@@ -345,21 +316,12 @@ af_err af_draw_plot(const af_window wind,
                     const af_array X, const af_array Y,
                     const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return plotWrapper(wind, X, Y, props);
-#else
-    UNUSED(wind);
-    UNUSED(X);
-    UNUSED(Y);
-    UNUSED(props);
-    return AF_ERR_NO_GFX;
-#endif
 }
 
 af_err af_draw_plot3(const af_window wind,
                      const af_array P, const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     try {
         const ArrayInfo& info = getInfo(P);
         af::dim4  dims = info.dims();
@@ -383,12 +345,6 @@ af_err af_draw_plot3(const af_window wind,
     CATCHALL;
 
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(P);
-    UNUSED(props);
-    return AF_ERR_NO_GFX;
-#endif
 }
 
 // Scatter API
@@ -396,16 +352,8 @@ af_err af_draw_scatter_nd(const af_window wind, const af_array in,
                           const af_marker_type af_marker,
                           const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
-#else
-    UNUSED(wind);
-    UNUSED(in);
-    UNUSED(af_marker);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_draw_scatter_2d(const af_window wind,
@@ -413,17 +361,8 @@ af_err af_draw_scatter_2d(const af_window wind,
                           const af_marker_type af_marker,
                           const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
-#else
-    UNUSED(wind);
-    UNUSED(X);
-    UNUSED(Y);
-    UNUSED(af_marker);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_draw_scatter_3d(const af_window wind,
@@ -431,18 +370,8 @@ af_err af_draw_scatter_3d(const af_window wind,
                           const af_marker_type af_marker,
                           const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
-#else
-    UNUSED(wind);
-    UNUSED(X);
-    UNUSED(Y);
-    UNUSED(Z);
-    UNUSED(af_marker);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 // Deprecated Scatter API
@@ -451,24 +380,14 @@ af_err af_draw_scatter(const af_window wind,
                        const af_marker_type af_marker,
                        const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
-#else
-    UNUSED(wind);
-    UNUSED(X);
-    UNUSED(Y);
-    UNUSED(af_marker);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_draw_scatter3(const af_window wind,
                         const af_array P, const af_marker_type af_marker,
                         const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     fg_marker_type fg_marker = getFGMarker(af_marker);
     try {
         const ArrayInfo& info = getInfo(P);
@@ -493,11 +412,4 @@ af_err af_draw_scatter3(const af_window wind,
     CATCHALL;
 
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(P);
-    UNUSED(af_marker);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 6a2e3ade04..c9fb426c48 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -23,8 +23,6 @@
 
 using af::dim4;
 using namespace detail;
-
-#if defined(WITH_GRAPHICS)
 using namespace graphics;
 
 template<typename T>
@@ -126,13 +124,11 @@ fg_chart setup_surface(fg_window window,
 
     return chart;
 }
-#endif
 
 af_err af_draw_surface(const af_window window,
                        const af_array xVals, const af_array yVals,
                        const af_array S, const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
@@ -187,12 +183,4 @@ af_err af_draw_surface(const af_window window,
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(xVals);
-    UNUSED(yVals);
-    UNUSED(S);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 49ae93d881..702a60ccdb 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -25,8 +25,6 @@
 using std::vector;
 using af::dim4;
 using namespace detail;
-
-#if defined(WITH_GRAPHICS)
 using namespace graphics;
 
 template<typename T>
@@ -349,22 +347,12 @@ af_err vectorFieldWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-#endif // WITH_GRAPHICS
-
 af_err af_draw_vector_field_nd(const af_window wind,
                                const af_array points,
                                const af_array directions,
                                const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, points, directions, props);
-#else
-    UNUSED(wind);
-    UNUSED(points);
-    UNUSED(directions);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_draw_vector_field_3d(
@@ -375,19 +363,7 @@ af_err af_draw_vector_field_3d(
                 const af_array zDirs,
                 const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
-#else
-    UNUSED(wind);
-    UNUSED(xPoints);
-    UNUSED(yPoints);
-    UNUSED(zPoints);
-    UNUSED(xDirs);
-    UNUSED(yDirs);
-    UNUSED(zDirs);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_draw_vector_field_2d(
@@ -396,15 +372,5 @@ af_err af_draw_vector_field_2d(
                 const af_array xDirs, const af_array yDirs,
                 const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     return vectorFieldWrapper(wind, xPoints, yPoints, xDirs, yDirs, props);
-#else
-    UNUSED(wind);
-    UNUSED(xPoints);
-    UNUSED(yPoints);
-    UNUSED(xDirs);
-    UNUSED(yDirs);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index c766f54541..5f371e27ba 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -17,15 +17,10 @@
 
 using af::dim4;
 using namespace detail;
-
-#if defined(WITH_GRAPHICS)
 using namespace graphics;
-#endif
-
 
 af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
 {
-#if defined(WITH_GRAPHICS)
     try {
         graphics::ForgeManager& fgMngr = graphics::ForgeManager::getInstance();
         fg_window mainWnd = NULL;
@@ -35,7 +30,6 @@ af_err af_create_window(af_window *out, const int width, const int height, const
         } catch(...) {
             std::cerr<<"OpenGL context creation failed"<<std::endl;
         }
-
         if (mainWnd == 0) {
             std::cerr<<"Not a valid window"<<std::endl;
             return AF_SUCCESS;
@@ -51,99 +45,59 @@ af_err af_create_window(af_window *out, const int width, const int height, const
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(out);
-    UNUSED(width);
-    UNUSED(height);
-    UNUSED(title);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     FG_CHECK(fg_set_window_position(wind, x, y));
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(x);
-    UNUSED(y);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_title(const af_window wind, const char* const title)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     FG_CHECK(fg_set_window_title(wind, title));
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(title);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     FG_CHECK(fg_set_window_size(wind, w, h));
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(w);
-    UNUSED(h);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_grid(const af_window wind, const int rows, const int cols)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     try {
         ForgeManager::getInstance().setWindowChartGrid(wind, rows, cols);
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(rows);
-    UNUSED(cols);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_axes_limits_compute(const af_window window,
                                   const af_array x, const af_array y, const af_array z,
                                   const bool exact, const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     try {
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
@@ -184,15 +138,6 @@ af_err af_set_axes_limits_compute(const af_window window,
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(z);
-    UNUSED(exact);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_axes_limits_2d(const af_window window,
@@ -200,12 +145,10 @@ af_err af_set_axes_limits_2d(const af_window window,
                              const float ymin, const float ymax,
                              const bool exact, const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     try {
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
@@ -237,16 +180,6 @@ af_err af_set_axes_limits_2d(const af_window window,
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(xmin);
-    UNUSED(xmax);
-    UNUSED(ymin);
-    UNUSED(ymax);
-    UNUSED(exact);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_axes_limits_3d(const af_window window,
@@ -255,12 +188,10 @@ af_err af_set_axes_limits_3d(const af_window window,
                              const float zmin, const float zmax,
                              const bool exact, const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     try {
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
@@ -296,18 +227,6 @@ af_err af_set_axes_limits_3d(const af_window window,
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(xmin);
-    UNUSED(xmax);
-    UNUSED(ymin);
-    UNUSED(ymax);
-    UNUSED(zmin);
-    UNUSED(zmax);
-    UNUSED(exact);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_axes_titles(const af_window window,
@@ -316,12 +235,10 @@ af_err af_set_axes_titles(const af_window window,
                           const char * const ztitle,
                           const af_cell* const props)
 {
-#if defined(WITH_GRAPHICS)
     if(window == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     try {
         ForgeManager& fgMngr = ForgeManager::getInstance();
 
@@ -338,52 +255,30 @@ af_err af_set_axes_titles(const af_window window,
     }
     CATCHALL;
     return AF_SUCCESS;
-#else
-    UNUSED(window);
-    UNUSED(xtitle);
-    UNUSED(ytitle);
-    UNUSED(ztitle);
-    UNUSED(props);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_show(const af_window wind)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     FG_CHECK(fg_swap_window_buffers(wind));
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_is_window_closed(bool *out, const af_window wind)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     FG_CHECK(fg_close_window(out, wind));
     return AF_SUCCESS;
-#else
-    UNUSED(out);
-    UNUSED(wind);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_set_visibility(const af_window wind, const bool is_visible)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
@@ -394,30 +289,19 @@ af_err af_set_visibility(const af_window wind, const bool is_visible)
         FG_CHECK(fg_hide_window(wind));
     }
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    UNUSED(is_visible);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
 af_err af_destroy_window(const af_window wind)
 {
-#if defined(WITH_GRAPHICS)
     if(wind == 0) {
         std::cerr<<"Not a valid window"<<std::endl;
         return AF_SUCCESS;
     }
-
     try {
         ForgeManager::getInstance().setWindowChartGrid(wind, 0, 0);
     }
     CATCHALL;
     FG_CHECK(fg_release_window(wind));
     return AF_SUCCESS;
-#else
-    UNUSED(wind);
-    AF_RETURN_ERROR("ArrayFire compiled without graphics support", AF_ERR_NO_GFX);
-#endif
 }
 
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index bc8147dafa..e78b2aeee6 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -42,8 +42,11 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/dispatch.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
@@ -67,6 +70,9 @@ target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
     ${ArrayFire_BINARY_DIR}
+    ${OPENGL_INCLUDE_DIR}
+    ${ArrayFire_SOURCE_DIR}/extern/forge/include
+    ${ArrayFire_BINARY_DIR}/extern/forge/include
   )
 
 if(APPLE AND NOT USE_MKL)
@@ -75,20 +81,3 @@ if(APPLE AND NOT USE_MKL)
       ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.cpp
       ${CMAKE_CURRENT_SOURCE_DIR}/lapacke.hpp)
 endif()
-
-if(AF_WITH_GRAPHICS)
-  target_include_directories(afcommon_interface
-    INTERFACE
-    ${OPENGL_INCLUDE_DIR}
-    ${ArrayFire_SOURCE_DIR}/extern/forge/include
-    ${ArrayFire_BINARY_DIR}/extern/forge/include
-    )
-  target_sources(afcommon_interface
-    INTERFACE
-      ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
-      ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.cpp
-      ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.hpp
-      )
-
-  target_compile_definitions(afcommon_interface INTERFACE WITH_GRAPHICS)
-endif()
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index 5038af846c..1f70d0c111 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#if defined(WITH_GRAPHICS)
 #include <common/err_common.hpp>
 #include <common/forge_loader.hpp>
 #include <common/util.hpp>
@@ -104,4 +103,3 @@ class InteropManager
         res_map_t mInteropMap;
 };
 }
-#endif
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 4f4bb2fcf6..2aa9a39921 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -12,6 +12,7 @@
 #include <common/err_common.hpp>
 #include <type_util.hpp>
 #include <common/util.hpp>
+#include <common/graphics_common.hpp>
 
 #include <algorithm>
 #include <cstdio>
@@ -19,10 +20,6 @@
 #include <sstream>
 #include <string>
 
-#if defined(WITH_GRAPHICS) && !defined(AF_UNIFIED)
-#include <common/graphics_common.hpp>
-#endif
-
 #ifdef AF_OPENCL
 #include <platform.hpp>
 #include <errorcodes.hpp>
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index ecfd603baf..2bb554a312 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
-
 #include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
 #include <backend.hpp>
@@ -566,11 +564,3 @@ void ForgeManager::setChartAxesOverride(fg_chart chart, bool flag)
     mChartAxesOverrideMap[chart] = flag;
 }
 }
-
-#else
-
-ForgeModule::ForgeModule()
-    : module(nullptr, nullptr), glmodule(nullptr, nullptr)
-{ }
-
-#endif
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 652ac4a39b..16d03b8678 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -9,8 +9,6 @@
 
 #pragma once
 
-#if defined(WITH_GRAPHICS)
-
 #include <af/graphics.h>
 #include <common/forge_loader.hpp>
 
@@ -139,5 +137,3 @@ class ForgeManager
 }
 
 #define MAIN_WINDOW graphics::ForgeManager::getInstance().getMainWindow()
-
-#endif
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 840a3abc3c..abbfe47b77 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -54,6 +54,8 @@ target_sources(afcpu
     gradient.hpp
     harris.cpp
     harris.hpp
+    hist_graphics.cpp
+    hist_graphics.hpp
     histogram.cpp
     histogram.hpp
     homography.cpp
@@ -64,6 +66,8 @@ target_sources(afcpu
     identity.hpp
     iir.cpp
     iir.hpp
+    image.cpp
+    image.hpp
     index.cpp
     index.hpp
     inverse.cpp
@@ -104,6 +108,8 @@ target_sources(afcpu
     ParamIterator.hpp
     platform.cpp
     platform.hpp
+    plot.cpp
+    plot.hpp
     print.hpp
     qr.cpp
     qr.hpp
@@ -150,6 +156,8 @@ target_sources(afcpu
     sparse_arith.hpp
     sparse_blas.cpp
     sparse_blas.hpp
+    surface.cpp
+    surface.hpp
     susan.cpp
     susan.hpp
     svd.cpp
@@ -170,6 +178,8 @@ target_sources(afcpu
     unwrap.cpp
     unwrap.hpp
     utility.hpp
+    vector_field.cpp
+    vector_field.hpp
     where.cpp
     where.hpp
     wrap.cpp
@@ -262,22 +272,6 @@ if(AF_WITH_NONFREE)
   target_compile_definitions(afcpu PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-if(AF_WITH_GRAPHICS)
-  target_sources(afcpu
-    PRIVATE
-      hist_graphics.cpp
-      hist_graphics.hpp
-      image.cpp
-      image.hpp
-      plot.cpp
-      plot.hpp
-      surface.cpp
-      surface.hpp
-      vector_field.cpp
-      vector_field.hpp
-    )
-endif()
-
 target_include_directories(afcpu
   PUBLIC
     $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 5fc8cb33e4..d1e8d8baf7 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <hist_graphics.hpp>
 #include <err_cpu.hpp>
 #include <platform.hpp>
@@ -46,5 +44,3 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/hist_graphics.hpp b/src/backend/cpu/hist_graphics.hpp
index 46a8d5be55..be397ffcfb 100644
--- a/src/backend/cpu/hist_graphics.hpp
+++ b/src/backend/cpu/hist_graphics.hpp
@@ -9,8 +9,6 @@
 
 #pragma once
 
-#if defined (WITH_GRAPHICS)
-
 #include <common/graphics_common.hpp>
 #include <Array.hpp>
 
@@ -20,5 +18,3 @@ template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
 }
-
-#endif
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index f8ed9f07fd..43bdb46da2 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -10,8 +10,6 @@
 // Parts of this code sourced from SnopyDogy
 // https://gist.github.com/SnopyDogy/a9a22497a893ec86aa3e
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <image.hpp>
 #include <err_cpu.hpp>
@@ -56,5 +54,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index 21daeec7f0..06493f6850 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
 
 }
-
-#endif
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 75f085aa20..549bf1f36f 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <plot.hpp>
 #include <err_cpu.hpp>
@@ -51,5 +49,3 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/plot.hpp b/src/backend/cpu/plot.hpp
index 170c9cbd30..f64ec8966c 100644
--- a/src/backend/cpu/plot.hpp
+++ b/src/backend/cpu/plot.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,6 +16,3 @@ template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
 }
-
-#endif
-
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index eca8261100..80a80ab7e1 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <surface.hpp>
 #include <err_cpu.hpp>
@@ -51,5 +49,3 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/surface.hpp b/src/backend/cpu/surface.hpp
index d6953634f8..8437d45e18 100644
--- a/src/backend/cpu/surface.hpp
+++ b/src/backend/cpu/surface.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
 }
-
-#endif
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index dcbb34ccfb..93914fe6bc 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <vector_field.hpp>
 #include <err_cpu.hpp>
@@ -62,5 +60,3 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cpu/vector_field.hpp b/src/backend/cpu/vector_field.hpp
index 13535556da..45f5bb5929 100644
--- a/src/backend/cpu/vector_field.hpp
+++ b/src/backend/cpu/vector_field.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -19,6 +17,3 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vector_field);
 
 }
-
-#endif
-
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8ab2602a03..c1d3022170 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -317,13 +317,19 @@ cuda_add_library(afcuda
     fft.cpp
     fft.hpp
     fftconvolve.hpp
+    GraphicsResourceManager.cpp
+    GraphicsResourceManager.hpp
     gradient.hpp
     harris.hpp
+    hist_graphics.cpp
+    hist_graphics.hpp
     histogram.hpp
     homography.hpp
     hsv_rgb.hpp
     identity.hpp
     iir.hpp
+    image.cpp
+    image.hpp
     index.hpp
     inverse.hpp
     iota.hpp
@@ -349,6 +355,8 @@ cuda_add_library(afcuda
     orb.hpp
     platform.cpp
     platform.hpp
+    plot.cpp
+    plot.hpp
     print.hpp
     qr.hpp
     random_engine.hpp
@@ -375,6 +383,8 @@ cuda_add_library(afcuda
     sparse_arith.hpp
     sparse_blas.cpp
     sparse_blas.hpp
+    surface.cpp
+    surface.hpp
     susan.hpp
     svd.hpp
     tile.hpp
@@ -387,6 +397,8 @@ cuda_add_library(afcuda
     unary.hpp
     unwrap.hpp
     utility.hpp
+    vector_field.cpp
+    vector_field.hpp
     where.hpp
     wrap.hpp
 
@@ -405,23 +417,6 @@ if(AF_WITH_NONFREE)
   target_compile_definitions(afcuda PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-if(AF_WITH_GRAPHICS)
-  target_sources(afcuda
-    PRIVATE
-      GraphicsResourceManager.cpp
-      GraphicsResourceManager.hpp
-      hist_graphics.cpp
-      hist_graphics.hpp
-      image.cpp
-      image.hpp
-      plot.cpp
-      plot.hpp
-      surface.cpp
-      surface.hpp
-      vector_field.cpp
-      vector_field.hpp)
-endif()
-
 add_dependencies(afcuda ${jit_kernel_targets})
 
 target_include_directories (afcuda
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index df9c1b3833..a66ec039b4 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
-
 #if defined(OS_WIN)
 #include <windows.h>
 #endif
@@ -48,4 +46,3 @@ GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
     return output;
 }
 }
-#endif
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index 5cab5e4031..109770a08e 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#if defined(WITH_GRAPHICS)
 #include <common/InteropManager.hpp>
 
 #include <map>
@@ -30,4 +29,3 @@ class GraphicsResourceManager :
         void operator=(GraphicsResourceManager const&);
 };
 }
-#endif
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 52e0a596d7..c6749bf1ef 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <hist_graphics.hpp>
 #include <err_cuda.hpp>
@@ -68,5 +66,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cuda/hist_graphics.hpp b/src/backend/cuda/hist_graphics.hpp
index b331301ca0..eca5c2f57d 100644
--- a/src/backend/cuda/hist_graphics.hpp
+++ b/src/backend/cuda/hist_graphics.hpp
@@ -9,8 +9,6 @@
 
 #pragma once
 
-#if defined (WITH_GRAPHICS)
-
 #include <common/graphics_common.hpp>
 #include <Array.hpp>
 
@@ -20,5 +18,3 @@ template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
 }
-
-#endif
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 42840ad661..104e1dcde6 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -10,8 +10,6 @@
 // Parts of this code sourced from SnopyDogy
 // https://gist.github.com/SnopyDogy/a9a22497a893ec86aa3e
 
-#if defined(WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <image.hpp>
 #include <err_cuda.hpp>
@@ -74,5 +72,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }
-
-#endif
diff --git a/src/backend/cuda/image.hpp b/src/backend/cuda/image.hpp
index fd0fd519e2..e97d78aaa7 100644
--- a/src/backend/cuda/image.hpp
+++ b/src/backend/cuda/image.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
 
 }
-
-#endif
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 3e29963728..eed023999e 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -380,10 +380,6 @@ cudaDeviceProp getDeviceProp(int device)
     return DeviceManager::getInstance().cuDevices[0].prop;
 }
 
-///////////////////////////////////////////////////////////////////////////
-// DeviceManager Class Functions
-///////////////////////////////////////////////////////////////////////////
-#if defined(WITH_GRAPHICS)
 bool DeviceManager::checkGraphicsInteropCapability()
 {
     static std::once_flag checkInteropFlag;
@@ -405,7 +401,6 @@ bool DeviceManager::checkGraphicsInteropCapability()
 
     return capable;
 }
-#endif
 
 DeviceManager& DeviceManager::getInstance()
 {
@@ -435,7 +430,6 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
-#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager()
 {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -448,7 +442,6 @@ GraphicsResourceManager& interopManager()
 
     return *(inst.gfxManagers[id].get());
 }
-#endif
 
 PlanCache& fftManager()
 {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index bcf2b433dd..2502a89ffe 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -84,9 +84,7 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
-#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager();
-#endif
 
 PlanCache& fftManager();
 
@@ -103,9 +101,7 @@ class DeviceManager
     public:
         static const unsigned MAX_DEVICES = 16;
 
-#if defined(WITH_GRAPHICS)
         static bool checkGraphicsInteropCapability();
-#endif
 
         static DeviceManager& getInstance();
 
@@ -113,9 +109,7 @@ class DeviceManager
 
         friend MemoryManagerPinned& pinnedMemoryManager();
 
-#if defined(WITH_GRAPHICS)
         friend GraphicsResourceManager& interopManager();
-#endif
 
         friend std::string getDeviceInfo(int device);
 
@@ -164,8 +158,7 @@ class DeviceManager
         std::unique_ptr<MemoryManager> memManager;
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
-#if defined(WITH_GRAPHICS)
+
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
-#endif
 };
 }
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 48dcdd270f..ecfa6267dc 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <plot.hpp>
 #include <err_cuda.hpp>
@@ -71,5 +69,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cuda/plot.hpp b/src/backend/cuda/plot.hpp
index 6d87853014..7b0a7473f3 100644
--- a/src/backend/cuda/plot.hpp
+++ b/src/backend/cuda/plot.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
 }
-
-#endif
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index b2777a3fa4..880d03ecc1 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <surface.hpp>
 #include <err_cuda.hpp>
@@ -71,5 +69,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cuda/surface.hpp b/src/backend/cuda/surface.hpp
index 84da47708b..a9fef84fb6 100644
--- a/src/backend/cuda/surface.hpp
+++ b/src/backend/cuda/surface.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
 }
-
-#endif
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 3e53208ab4..5a5359843b 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <vector_field.hpp>
 #include <err_cuda.hpp>
@@ -102,5 +100,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/cuda/vector_field.hpp b/src/backend/cuda/vector_field.hpp
index 80009b75f0..f42a241b86 100644
--- a/src/backend/cuda/vector_field.hpp
+++ b/src/backend/cuda/vector_field.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -19,6 +17,3 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vector_field);
 
 }
-
-#endif
-
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a5b4a6e47d..fcd2f41452 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -95,10 +95,14 @@ target_sources(afopencl
     fft.hpp
     fftconvolve.cpp
     fftconvolve.hpp
+    GraphicsResourceManager.cpp
+    GraphicsResourceManager.hpp
     gradient.cpp
     gradient.hpp
     harris.cpp
     harris.hpp
+    hist_graphics.cpp
+    hist_graphics.hpp
     histogram.cpp
     histogram.hpp
     homography.cpp
@@ -109,6 +113,8 @@ target_sources(afopencl
     identity.hpp
     iir.cpp
     iir.hpp
+    image.cpp
+    image.hpp
     index.cpp
     index.hpp
     inverse.cpp
@@ -150,6 +156,8 @@ target_sources(afopencl
     orb.hpp
     platform.cpp
     platform.hpp
+    plot.cpp
+    plot.hpp
     print.hpp
     product.cpp
     program.cpp
@@ -200,6 +208,8 @@ target_sources(afopencl
     sparse_blas.cpp
     sparse_blas.hpp
     sum.cpp
+    surface.cpp
+    surface.hpp
     susan.cpp
     susan.hpp
     svd.cpp
@@ -220,6 +230,8 @@ target_sources(afopencl
     unary.hpp
     unwrap.cpp
     unwrap.hpp
+    vector_field.cpp
+    vector_field.hpp
     where.cpp
     where.hpp
     wrap.cpp
@@ -423,24 +435,6 @@ if(AF_WITH_NONFREE)
   target_compile_definitions(afopencl PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-if(AF_WITH_GRAPHICS)
-  target_sources(afopencl
-    PRIVATE
-      GraphicsResourceManager.hpp
-      GraphicsResourceManager.cpp
-      hist_graphics.cpp
-      hist_graphics.hpp
-      image.cpp
-      image.hpp
-      plot.cpp
-      plot.hpp
-      surface.cpp
-      surface.hpp
-      vector_field.cpp
-      vector_field.hpp
-    )
-endif()
-
 if(LAPACK_FOUND OR MKL_FOUND)
   target_sources(afopencl
     PRIVATE
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
index 79914cf686..fe20fcf210 100644
--- a/src/backend/opencl/GraphicsResourceManager.cpp
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
 #include <platform.hpp>
 #include <GraphicsResourceManager.hpp>
 
@@ -23,4 +22,3 @@ GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
     return output;
 }
 }
-#endif
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index e13aacf814..fdf5dce3b4 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#if defined(WITH_GRAPHICS)
 #include <common/InteropManager.hpp>
 
 #include <map>
@@ -35,4 +34,3 @@ class GraphicsResourceManager :
         void operator=(GraphicsResourceManager const&);
 };
 }
-#endif
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 33ea91494c..c42efe21bc 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
@@ -74,5 +72,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/opencl/hist_graphics.hpp b/src/backend/opencl/hist_graphics.hpp
index 1c42736b3b..d891aa7a2e 100644
--- a/src/backend/opencl/hist_graphics.hpp
+++ b/src/backend/opencl/hist_graphics.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <common/graphics_common.hpp>
 #include <Array.hpp>
 
@@ -18,6 +16,3 @@ template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
 }
-
-#endif
-
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index db85214fca..41ad3fc8e4 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
@@ -82,5 +80,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }
-
-#endif
diff --git a/src/backend/opencl/image.hpp b/src/backend/opencl/image.hpp
index 5c2841664d..7f4d37efa5 100644
--- a/src/backend/opencl/image.hpp
+++ b/src/backend/opencl/image.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
 
 }
-
-#endif
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 85b2033a32..a18a81cb25 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -9,9 +9,7 @@
 
 // Include this before af/opencl.h
 // Causes conflict between system cl.hpp and opencl/cl.hpp
-#if defined(WITH_GRAPHICS)
 #include <common/graphics_common.hpp>
-#endif
 
 #include <af/version.h>
 #include <af/opencl.h>
@@ -54,13 +52,11 @@ using cl::Device;
 namespace opencl
 {
 
-#if defined(WITH_GRAPHICS)
 #if defined (OS_MAC)
 static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
 static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
 #endif
-#endif
 
 static const std::string get_system(void)
 {
@@ -721,7 +717,6 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
-#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager()
 {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -734,7 +729,6 @@ GraphicsResourceManager& interopManager()
 
     return *(inst.gfxManagers[id].get());
 }
-#endif
 
 PlanCache& fftManager()
 {
@@ -777,11 +771,9 @@ DeviceManager& DeviceManager::getInstance()
 
 DeviceManager::~DeviceManager()
 {
-#if defined(WITH_GRAPHICS)
     for (int i=0; i<getDeviceCount(); ++i) {
         delete gfxManagers[i].release();
     }
-#endif
 #ifndef OS_WIN
     //TODO: FIXME:
     // clfftTeardown() causes a "Pure Virtual Function Called" crash on
@@ -922,7 +914,6 @@ DeviceManager::DeviceManager()
         }
     }
 
-#if defined(WITH_GRAPHICS)
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
     if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
@@ -936,7 +927,7 @@ DeviceManager::DeviceManager()
         } catch (...) {
         }
     }
-#endif
+
     mUserDeviceOffset = mDevices.size();
     //Initialize FFT setup data structure
     CLFFT_CHECK(clfftInitSetupData(mFFTSetup.get()));
@@ -954,8 +945,7 @@ DeviceManager::DeviceManager()
     }
 }
 
-#if defined(WITH_GRAPHICS)
-void DeviceManager::markDeviceForInterop(const int device, const fg_window wHandle)
+void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
 {
     try {
         if (device >= (int)mQueues.size() ||
@@ -976,8 +966,8 @@ void DeviceManager::markDeviceForInterop(const int device, const fg_window wHand
             cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
 
             long long wnd_ctx, wnd_dsp;
-            FG_CHECK(fg_get_window_context_handle(&wnd_ctx, wHandle));
-            FG_CHECK(fg_get_window_display_handle(&wnd_dsp, wHandle));
+            FG_CHECK(fg_get_window_context_handle(&wnd_ctx, const_cast<fg_window>(wHandle)));
+            FG_CHECK(fg_get_window_display_handle(&wnd_dsp, const_cast<fg_window>(wHandle)));
 
 #ifdef OS_MAC
             CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
@@ -1059,7 +1049,6 @@ void DeviceManager::markDeviceForInterop(const int device, const fg_window wHand
          * on that particular OpenCL device. So mark it as no GL sharing */
     }
 }
-#endif
 }
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index acc7cf5f72..80b1da12b6 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -8,10 +8,6 @@
  ********************************************************/
 
 #pragma once
-#if defined(WITH_GRAPHICS)
-#include <fg/window.h>
-#endif
-
 #define CL_HPP_ENABLE_EXCEPTIONS
 #define CL_HPP_MINIMUM_OPENCL_VERSION 120
 #define CL_HPP_TARGET_OPENCL_VERSION 120
@@ -106,9 +102,7 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
-#if defined(WITH_GRAPHICS)
 GraphicsResourceManager& interopManager();
-#endif
 
 PlanCache& fftManager();
 
@@ -126,9 +120,7 @@ class DeviceManager
 
     friend MemoryManagerPinned& pinnedMemoryManager();
 
-#if defined(WITH_GRAPHICS)
     friend GraphicsResourceManager& interopManager();
-#endif
 
     friend PlanCache& fftManager();
 
@@ -186,9 +178,7 @@ class DeviceManager
         // variables
         DeviceManager(DeviceManager const&);
         void operator=(DeviceManager const&);
-#if defined(WITH_GRAPHICS)
-        void markDeviceForInterop(const int device, const fg_window wHandle);
-#endif
+        void markDeviceForInterop(const int device, const void* wHandle);
 
     private:
         // Attributes
@@ -203,10 +193,7 @@ class DeviceManager
 
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
-
-#if defined(WITH_GRAPHICS)
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
-#endif
         std::unique_ptr<clfftSetupData> mFFTSetup;
 
         using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 8c2dba3426..ad68818f6b 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
@@ -77,5 +75,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/opencl/plot.hpp b/src/backend/opencl/plot.hpp
index 8ca29cd07a..1d8c2e9f10 100644
--- a/src/backend/opencl/plot.hpp
+++ b/src/backend/opencl/plot.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,6 +16,3 @@ template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
 }
-
-#endif
-
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 6d50ffe787..5575974040 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
@@ -80,5 +78,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/opencl/surface.hpp b/src/backend/opencl/surface.hpp
index a22ea20585..6eedbfec66 100644
--- a/src/backend/opencl/surface.hpp
+++ b/src/backend/opencl/surface.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -18,5 +16,3 @@ template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
 }
-
-#endif
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 145f67a6f4..87eb12ae02 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
@@ -98,5 +96,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }
-
-#endif  // WITH_GRAPHICS
diff --git a/src/backend/opencl/vector_field.hpp b/src/backend/opencl/vector_field.hpp
index c0a3fc2849..62b5db39c0 100644
--- a/src/backend/opencl/vector_field.hpp
+++ b/src/backend/opencl/vector_field.hpp
@@ -7,8 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined (WITH_GRAPHICS)
-
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
@@ -19,5 +17,3 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vector_field);
 
 }
-
-#endif

From 738b87c5ceb32bce64faee3d2631a304cf0925fe Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Dec 2018 09:24:03 +0530
Subject: [PATCH 1579/2677] Mark gfx dependencies in RPM cpack as optional

---
 CMakeModules/AFconfigure_forge_submodule.cmake | 1 -
 CMakeModules/CPackConfig.cmake                 | 2 +-
 CMakeModules/platform.cmake                    | 7 -------
 3 files changed, 1 insertion(+), 9 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index fe000e4124..bde7b18728 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -26,7 +26,6 @@ mark_as_advanced(
     FG_ENABLE_HUNTER
     glfw3_DIR
     glm_DIR
-    glbinding_DIR
     )
 set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
 set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 5101b9eff4..8086314196 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -314,7 +314,7 @@ set(CPACK_RPM_PACKAGE_GROUP "Development/Libraries")
 set(CPACK_RPM_PACKAGE_LICENSE "BSD")
 set(CPACK_RPM_PACKAGE_URL "${SITE_URL}")
 if(AF_BUILD_FORGE)
-    set(CPACK_RPM_PACKAGE_REQUIRES "fontconfig-devel, libX11, libXrandr, libXinerama, libXxf86vm, libXcursor, mesa-libGL-devel")
+    set(CPACK_RPM_PACKAGE_SUGGESTS "fontconfig-devel, libX11, libXrandr, libXinerama, libXxf86vm, libXcursor, mesa-libGL-devel")
 endif()
 
 ##
diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
index da2c851d95..68c66d9b2d 100644
--- a/CMakeModules/platform.cmake
+++ b/CMakeModules/platform.cmake
@@ -10,13 +10,6 @@
 # Add paths and flags specific platforms. This can inc
 
 if(APPLE)
-  # IMP NOTE: After removing link time dependency of gfx libs, glbinding is
-  #           still needed in cmake's prefix path so that forge doesn't fail
-  #           in cmake generation phase because of no glbinding.
-  # Some homebrew libraries(glbinding) are not installed in directories that
-  # CMake searches by default.
-  set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/usr/local/opt")
-
   # Default path for Intel MKL libraries
   set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/opt/intel/mkl/lib")
 endif()

From a1d86ae29219af26b00c6a6441bd21e7358b5001 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Dec 2018 09:34:14 +0530
Subject: [PATCH 1580/2677] Fix deconvolution enums version guard

---
 include/af/defines.h | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index efc553e902..c38a9390a7 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -491,6 +491,14 @@ typedef enum {
     AF_TOPK_MAX     = 2,  ///< Top k max values
     AF_TOPK_DEFAULT = 0   ///< Default option (max)
 } af_topk_function;
+#endif
+
+#if AF_API_VERSION >= 37
+typedef enum {
+              AF_VARIANCE_DEFAULT    = 0, ///< Default (Population) variance
+              AF_VARIANCE_SAMPLE     = 1, ///< Sample variance
+              AF_VARIANCE_POPULATION = 2  ///< Population variance
+} af_var_bias;
 
 typedef enum {
     AF_ITERATIVE_DECONV_LANDWEBER       = 1,        ///< Landweber Deconvolution
@@ -502,15 +510,6 @@ typedef enum {
     AF_INVERSE_DECONV_TIKHONOV       = 1,        ///< Tikhonov Inverse deconvolution
     AF_INVERSE_DECONV_DEFAULT        = 0         ///< Default is Tikhonov deconvolution
 } af_inverse_deconv_algo;
-
-#endif
-
-#if AF_API_VERSION >= 37
-typedef enum {
-              AF_VARIANCE_DEFAULT    = 0, ///< Default (Population) variance
-              AF_VARIANCE_SAMPLE     = 1, ///< Sample variance
-              AF_VARIANCE_POPULATION = 2  ///< Population variance
-} af_var_bias;
 #endif
 
 #ifdef __cplusplus
@@ -561,11 +560,11 @@ namespace af
     typedef af_flux_function fluxFunction;
     typedef af_diffusion_eq diffusionEq;
     typedef af_topk_function topkFunction;
-    typedef af_iterative_deconv_algo iterativeDeconvAlgo;
-    typedef af_inverse_deconv_algo inverseDeconvAlgo;
 #endif
 #if AF_API_VERSION >= 37
     typedef af_var_bias varBias;
+    typedef af_iterative_deconv_algo iterativeDeconvAlgo;
+    typedef af_inverse_deconv_algo inverseDeconvAlgo;
 #endif
 }
 

From f1556e6cc1d7b0031ed49a77d37be6f1505304b0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Dec 2018 16:09:59 +0530
Subject: [PATCH 1581/2677] Fix enum declaration order in cpu/sparse_blas
 translation unit

This seems to be happening on gcc 8.2.1. Doesn't happen on 7 or lower.
---
 src/backend/cpu/sparse_blas.cpp | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 7bd14e03d1..77b82704ad 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -34,6 +34,14 @@ using sp_cdouble = MKL_Complex16;
 #else
 using sp_cfloat = cfloat;
 using sp_cdouble = cdouble;
+
+// From mkl_spblas.h
+typedef enum
+{
+    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
+    SPARSE_OPERATION_TRANSPOSE              = 11,
+    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
+} sparse_operation_t;
 #endif
 
 template<typename T, class Enable = void>
@@ -292,14 +300,6 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
 #else // #if USE_MKL
 
-// From mkl_spblas.h
-typedef enum
-{
-    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
-    SPARSE_OPERATION_TRANSPOSE              = 11,
-    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
-} sparse_operation_t;
-
 template<typename T>
 T getConjugate(const T &in)
 {

From 0ac8e0ddb3902f46c6046315845cbf192a44943e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Dec 2018 16:17:45 +0530
Subject: [PATCH 1582/2677] Refactor sparse-dense arith fn name in backend API

---
 src/api/c/binary.cpp                |  2 +-
 src/backend/cpu/sparse_arith.cpp    | 10 +++++-----
 src/backend/cpu/sparse_arith.hpp    |  4 ++--
 src/backend/cuda/sparse_arith.cu    | 10 +++++-----
 src/backend/cuda/sparse_arith.hpp   |  6 +++---
 src/backend/opencl/sparse_arith.cpp | 16 ++++++++--------
 src/backend/opencl/sparse_arith.hpp |  6 +++---
 7 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 50f0a942a0..21d4c81fb9 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -50,7 +50,7 @@ static inline af_array arithSparseDenseOp(const af_array lhs, const af_array rhs
     if(op == af_add_t || op == af_sub_t)
         return getHandle(arithOpD<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
     else if(op == af_mul_t || op == af_div_t)
-        return getHandle(arithOpS<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
+        return getHandle(arithOp<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
 
 }
 
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index 298d906f1e..5fd96cef5a 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -89,7 +89,7 @@ Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool rev
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
 {
     lhs.eval();
     rhs.eval();
@@ -158,13 +158,13 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
                                             const bool reverse);                                    \
     template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
-    template SparseArray<T> arithOpS<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                  const bool reverse);                               \
     template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
                                                  const common::SparseArray<T> &rhs);                \
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
index 1cd1a6911c..364dbb18ea 100644
--- a/src/backend/cpu/sparse_arith.hpp
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -23,8 +23,8 @@ Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                  const bool reverse = false);
 
 template<typename T, af_op_t op>
-common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                                const bool reverse = false);
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                               const bool reverse = false);
 
 template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 9754213838..76ec4e9333 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -78,7 +78,7 @@ Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool rev
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
 {
     lhs.eval();
     rhs.eval();
@@ -195,13 +195,13 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
                                             const bool reverse);                                    \
     template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
-    template SparseArray<T> arithOpS<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
     template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
                                                  const common::SparseArray<T> &rhs);                \
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
index f9ee528ae5..bbdf18e541 100644
--- a/src/backend/cuda/sparse_arith.hpp
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -22,10 +22,10 @@ Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                  const bool reverse = false);
 
 template<typename T, af_op_t op>
-common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                                const bool reverse = false);
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                               const bool reverse = false);
 
 template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
-}
\ No newline at end of file
+}
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index 40f5c19dac..ea36b384fa 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -79,7 +79,7 @@ Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool rev
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOpS(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
 {
     lhs.eval();
     rhs.eval();
@@ -152,13 +152,13 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
                                             const bool reverse);                                    \
     template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
                                             const bool reverse);                                    \
-    template SparseArray<T> arithOpS<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOpS<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,   \
+    template SparseArray<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
+                                                 const bool reverse);                               \
+    template SparseArray<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
+                                                 const bool reverse);                               \
+    template SparseArray<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
+                                                 const bool reverse);                               \
+    template SparseArray<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
                                                   const bool reverse);                              \
     template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
                                                  const common::SparseArray<T> &rhs);                \
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
index 3a54a674d6..a794f1e69d 100644
--- a/src/backend/opencl/sparse_arith.hpp
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -22,10 +22,10 @@ Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                  const bool reverse = false);
 
 template<typename T, af_op_t op>
-common::SparseArray<T> arithOpS(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                                const bool reverse = false);
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                               const bool reverse = false);
 
 template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
-}
\ No newline at end of file
+}

From fe20629a602b1ff96907e1f7870ecaac5595a489 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <miguel@arrayfire.com>
Date: Wed, 19 Dec 2018 13:02:41 -0600
Subject: [PATCH 1583/2677] Fixed failing `write` tests.

Tests were failing due to af_free_device() call which is only necessary when
calling unlock().
---
 test/write.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/test/write.cpp b/test/write.cpp
index f1a148ac94..f04ddd113c 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -60,7 +60,6 @@ void writeTest(dim4 dims)
     ASSERT_ARRAYS_EQ(B_copy, A);
     ASSERT_ARRAYS_EQ(A_copy, B);
 
-    af_free_device(b_dev);
     freeHost(a_host);
 }
 

From 70be3d46c8ba850c45f13c99d2c6174dede51c5e Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Thu, 20 Dec 2018 10:30:47 -0500
Subject: [PATCH 1584/2677] Fix output indices on broadcast step of exclusive
 scan (#2366)

Fix output indices on broadcast step of 1D exclusive scan in CUDA
---
 src/backend/cuda/kernel/scan_dim.hpp    |  16 +++-
 src/backend/cuda/kernel/scan_first.hpp  |  29 +++---
 src/backend/opencl/kernel/scan_dim.cl   |   6 +-
 src/backend/opencl/kernel/scan_first.cl |   4 +-
 test/scan.cpp                           | 116 +++++++++++++++++++++---
 5 files changed, 139 insertions(+), 32 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 22bad27cc8..8412b6df9c 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -135,7 +135,8 @@ namespace kernel
                                  uint blocks_x,
                                  uint blocks_y,
                                  uint blocks_dim,
-                                 uint lim)
+                                 uint lim,
+                                 bool inclusive_scan)
     {
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
@@ -159,10 +160,14 @@ namespace kernel
         const int blockIdx_dim = ids[dim];
 
         ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+        optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
         const int id_dim = ids[dim];
         const int out_dim = out.dims[dim];
 
+        // Shift broadcast one step to the right for exclusive scan (#2366)
+        int offset = inclusive_scan ? 0 : out.strides[dim];
+        optr += offset;
+
         bool is_valid =
             (ids[0] < out.dims[0]) &&
             (ids[1] < out.dims[1]) &&
@@ -228,7 +233,8 @@ namespace kernel
     static void bcast_dim_launcher(Param<To> out,
                                    CParam<To> tmp,
                                    const uint threads_y,
-                                   const dim_t blocks_all[4])
+                                   const dim_t blocks_all[4],
+                                   bool inclusive_scan)
     {
 
         dim3 threads(THREADS_X, threads_y);
@@ -243,7 +249,7 @@ namespace kernel
         uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
         CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads,
-            out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
+                    out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim], lim, inclusive_scan);
 
         POST_LAUNCH_CHECK();
     }
@@ -296,7 +302,7 @@ namespace kernel
             }
 
             blocks_all[dim] = bdim;
-            bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all);
+            bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all, inclusive_scan);
         }
     }
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index bbd16f2c00..5b4fee09e1 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -113,14 +113,13 @@ namespace kernel
         }
     }
 
-    template<typename To, af_op_t op>
-    __global__
-    static void bcast_first_kernel(Param<To> out,
-                                   CParam<To> tmp,
-                                   uint blocks_x,
-                                   uint blocks_y,
-                                   uint lim)
-    {
+    template <typename To, af_op_t op>
+    __global__ static void bcast_first_kernel(Param<To> out,
+                                              CParam<To> tmp,
+                                              uint blocks_x,
+                                              uint blocks_y,
+                                              uint lim,
+                                              bool inclusive_scan) {
         const int tidx = threadIdx.x;
         const int tidy = threadIdx.y;
 
@@ -145,13 +144,13 @@ namespace kernel
         Binary<To, op> binop;
         To accum = tptr[blockIdx_x - 1];
 
-        for (int k = 0, id = xid;
+        // Shift broadcast one step to the right for exclusive scan (#2366)
+        int offset = !inclusive_scan;
+        for (int k = 0, id = xid + offset;
              k < lim && id < out.dims[0];
              k++, id += blockDim.x) {
-
             optr[id] = binop(accum, optr[id]);
         }
-
     }
 
     template<typename Ti, typename To, af_op_t op, bool isFinalPass, bool inclusive_scan>
@@ -198,7 +197,8 @@ namespace kernel
                                      CParam<To> tmp,
                                      const uint blocks_x,
                                      const uint blocks_y,
-                                     const uint threads_x)
+                                     const uint threads_x,
+                                     bool inclusive_scan)
     {
 
         dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
@@ -211,7 +211,8 @@ namespace kernel
 
         uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-        CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp, blocks_x, blocks_y, lim);
+        CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads,
+                    out, tmp, blocks_x, blocks_y, lim, inclusive_scan);
 
         POST_LAUNCH_CHECK();
     }
@@ -259,7 +260,7 @@ namespace kernel
                                                             threads_x);
             }
 
-            bcast_first_launcher<To, op>(out, tmp, blocks_x, blocks_y, threads_x);
+            bcast_first_launcher<To, op>(out, tmp, blocks_x, blocks_y, threads_x, inclusive_scan);
 
         }
     }
diff --git a/src/backend/opencl/kernel/scan_dim.cl b/src/backend/opencl/kernel/scan_dim.cl
index 625f14800a..8b379407d7 100644
--- a/src/backend/opencl/kernel/scan_dim.cl
+++ b/src/backend/opencl/kernel/scan_dim.cl
@@ -141,7 +141,11 @@ void bcast_dim_kernel(__global To *oData, KParam oInfo,
         tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] + ids[1] * tInfo.strides[1] + ids[0];
 
         ids[dim] = ids[dim] * DIMY * lim + lidy;
-        oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+        oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+
+        // Shift broadcast one step to the right for exclusive scan (#2366)
+        int offset = inclusive_scan ? 0 : oInfo.strides[dim];
+        oData += offset;
 
         const int id_dim = ids[dim];
         const int out_dim = oInfo.dims[dim];
diff --git a/src/backend/opencl/kernel/scan_first.cl b/src/backend/opencl/kernel/scan_first.cl
index d245b9c1a0..48bd975eea 100644
--- a/src/backend/opencl/kernel/scan_first.cl
+++ b/src/backend/opencl/kernel/scan_first.cl
@@ -118,7 +118,9 @@ void bcast_first_kernel(__global To *oData, KParam oInfo,
 
             To accum = tData[groupId_x - 1];
 
-            for (int k = 0, id = xid;
+            // Shift broadcast one step to the right for exclusive scan (#2366)
+            int offset = !inclusive_scan;
+            for (int k = 0, id = xid + offset;
                  k < lim && id < oInfo.dims[0];
                  k++, id += DIMX) {
 
diff --git a/test/scan.cpp b/test/scan.cpp
index 5865d241c5..9c2a1bf7da 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -7,32 +7,37 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
-#include <arrayfire.h>
+#include <af/array.h>
+#include <af/device.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/array.h>
-#include <vector>
+#include <algorithm>
+#include <arrayfire.h>
+#include <gtest/gtest.h>
 #include <iostream>
+#include <iterator>
 #include <string>
 #include <testHelpers.hpp>
-#include <af/device.h>
 #include <utility>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::allTrue;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::constant;
 using af::dim4;
 using af::dtype_traits;
 using af::range;
-using af::span;
+using af::scan;
 using af::seq;
+using af::span;
+using af::sum;
+using std::cout;
+using std::copy;
+using std::endl;
+using std::string;
+using std::vector;
 
 typedef af_err (*scanFunc)(af_array *, const af_array, const int);
 
@@ -262,3 +267,92 @@ TEST(Accum, DocSnippet) {
     array gold_accumB_dim1(3, 3, h_gold_accumB_dim1);
     ASSERT_ARRAYS_EQ(gold_accumB_dim1, accumB_dim1);
 }
+
+TEST(Scan, ExclusiveSum1D) {
+    const int in_size = 80000;
+    vector<int> h_in(in_size, 1);
+    vector<int> h_gold(in_size, 0);
+    for (int i = 1; i < h_gold.size(); ++i) {
+        h_gold[i] = h_in[i] + h_gold[i-1];
+    }
+
+    array in(in_size, &h_in.front());
+    array out = scan(in, 0, AF_BINARY_ADD, false);
+
+    ASSERT_VEC_ARRAY_EQ(h_gold, dim4(in_size), out);
+}
+
+TEST(Scan, ExclusiveSum2D_Dim0) {
+    const int in_size = 80000 * 2;
+    vector<int> h_in(in_size, 1);
+    vector<int> h_gold(in_size, 0);
+    for (int i = 1; i < h_gold.size() / 2; ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+
+    array in(in_size / 2, 2, &h_in.front());
+    array out = scan(in, 0, AF_BINARY_ADD, false);
+    array gold(in_size / 2, 2, &h_gold.front());
+
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+TEST(Scan, ExclusiveSum2D_Dim1) {
+    const int in_size = 80000 * 2;
+    vector<int> h_in(in_size, 1);
+    vector<int> h_gold(in_size, 0);
+    for (int i = 1; i < h_gold.size() / 2; ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+
+    array in(2, in_size / 2, &h_in.front());
+    array out = scan(in, 1, AF_BINARY_ADD, false);
+    array gold(in_size / 2, 2, &h_gold.front());
+    gold = gold.T();
+
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+TEST(Scan, ExclusiveSum2D_Dim2) {
+    const int in_size = 80000 * 2;
+    vector<int> h_in(in_size, 1);
+    vector<int> h_gold(in_size, 0);
+    for (int i = 1; i < h_gold.size() / 2; ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+
+    array in(1, 2, in_size / 2, &h_in.front());
+    array out = scan(in, 2, AF_BINARY_ADD, false);
+    array gold(in_size / 2, 2, &h_gold.front());
+    gold = af::reorder(gold, 2, 1, 0);
+
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+TEST(Scan, ExclusiveSum2D_Dim3) {
+  const int in_size = 80000 * 2;
+  vector<int> h_in(in_size, 1);
+  vector<int> h_gold(in_size, 0);
+  for (int i = 1; i < h_gold.size() / 2; ++i) {
+    h_gold[i] = h_in[i] + h_gold[i - 1];
+  }
+  for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+    h_gold[i] = h_in[i] + h_gold[i - 1];
+  }
+
+  array in(1, 1, 2, in_size / 2, &h_in.front());
+  array out = scan(in, 3, AF_BINARY_ADD, false);
+  array gold(in_size / 2, 2, &h_gold.front());
+  gold = af::reorder(gold, 2, 3, 1, 0);
+
+  ASSERT_ARRAYS_EQ(gold, out);
+}

From 2a3591564cdca06e7568afb19dca8f6ffcc000bf Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Thu, 20 Dec 2018 08:02:01 -0500
Subject: [PATCH 1585/2677] Moved testHelpers << operator overload to global
 namespace

---
 test/testHelpers.hpp | 50 ++++++++++++++++++++++----------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 49207e0ca1..8f115345cd 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -47,6 +47,30 @@ typedef uintl uintl;
 using aft::uintl;
 using aft::intl;
 
+std::ostream &operator<<(std::ostream &os, af_err e) {
+    return os << af_err_to_string(e);
+}
+
+std::ostream &operator<<(std::ostream &os, af::dtype type) {
+    std::string name;
+    switch (type) {
+    case f32: name = "f32"; break;
+    case c32: name = "c32"; break;
+    case f64: name = "f64"; break;
+    case c64: name = "c64"; break;
+    case b8 : name = "b8" ; break;
+    case s32: name = "s32"; break;
+    case u32: name = "u32"; break;
+    case u8 : name = "u8" ; break;
+    case s64: name = "s64"; break;
+    case u64: name = "u64"; break;
+    case s16: name = "s16"; break;
+    case u16: name = "u16"; break;
+    default: assert(false && "Invalid type");
+    }
+    return os << name;
+}
+
 namespace {
 
 typedef unsigned char  uchar;
@@ -508,30 +532,6 @@ void cleanSlate()
 
 //********** arrayfire custom test asserts ***********
 
-std::ostream& operator<<(std::ostream& os, af_err e) {
-    return os << af_err_to_string(e);
-}
-
-std::ostream& operator<<(std::ostream& os, af::dtype type) {
-    std::string name;
-    switch (type) {
-    case f32: name = "f32"; break;
-    case c32: name = "c32"; break;
-    case f64: name = "f64"; break;
-    case c64: name = "c64"; break;
-    case b8:  name = "b8";  break;
-    case s32: name = "s32"; break;
-    case u32: name = "u32"; break;
-    case u8:  name = "u8";  break;
-    case s64: name = "s64"; break;
-    case u64: name = "u64"; break;
-    case s16: name = "s16"; break;
-    case u16: name = "u16"; break;
-    default: assert(false && "Invalid type");
-    }
-    return os << name;
-}
-
 // Overloading unary + op is needed to make unsigned char values printable
 //  as numbers
 
@@ -1100,7 +1100,7 @@ mtxReadSparseMatrix(af::array &out, const char* fileName)
 }
 #endif //USE_MTX
 
-}
+} // namespace
 
 enum TestOutputArrayType {
     // Test af_* function when given a null array as its output

From 8736732d6838a1121b400ca3e98e069a56207be2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Dec 2018 16:57:06 -0500
Subject: [PATCH 1586/2677] Fix OpenGL errors when building on osx.

---
 src/backend/opencl/CMakeLists.txt | 5 +++++
 src/backend/opencl/platform.cpp   | 4 ++++
 test/testHelpers.hpp              | 5 +++--
 3 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index fcd2f41452..8c33b85d1d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -435,6 +435,11 @@ if(AF_WITH_NONFREE)
   target_compile_definitions(afopencl PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
+if(APPLE)
+  target_link_libraries(afopencl
+    PRIVATE OpenGL::GL)
+endif()
+
 if(LAPACK_FOUND OR MKL_FOUND)
   target_sources(afopencl
     PRIVATE
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index a18a81cb25..dd14b54837 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -23,6 +23,10 @@
 #include <platform.hpp>
 #include <version.hpp>
 
+#ifdef OS_MAC
+#include <OpenGL/CGLCurrent.h>
+#endif
+
 #include <boost/compute/context.hpp>
 #include <boost/compute/utility/program_cache.hpp>
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 8f115345cd..d0c5892ad6 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -1048,7 +1048,7 @@ mtxReadSparseMatrix(af::array &out, const char* fileName)
         std::vector<int> J(nz);
         std::vector<float> V(nz);
 
-        for (unsigned i=0; i<nz; ++i) {
+        for (int i=0; i < nz; ++i) {
             int c, r;
             double v;
             int readCount = fscanf(fileHandle, "%d %d %lg\n", &r, &c, &v);
@@ -1071,7 +1071,7 @@ mtxReadSparseMatrix(af::array &out, const char* fileName)
         std::vector<int> J(nz);
         std::vector<af::cfloat> V(nz);
 
-        for (unsigned i=0; i<nz; ++i) {
+        for (int i=0; i < nz; ++i) {
             int c, r;
             double real, imag;
             int readCount = fscanf(fileHandle, "%d %d %lg %lg\n", &r, &c, &real, &imag);
@@ -1332,6 +1332,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          std::string metadataName,
                                          const af_array a, const af_array b,
                                          TestOutputArrayInfo *metadata) {
+    UNUSED(metadataName);
     return testWriteToOutputArray(aName, bName, a, b, metadata);
 }
 

From dfa4559e36089c43dff27ded09be3597600b2fd9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Dec 2018 22:19:04 -0500
Subject: [PATCH 1587/2677] Fix multiple array eval

The multi-array eval function in the CUDA backend was not setting the Array's
Node pointer after the Array<T> object was evaluated. This caused the CUDA
jit to evaluate the same expression multiple times and add overhead due to
multiple kernel instantiations.
---
 src/backend/cpu/Array.cpp    | 19 ++++++++++---------
 src/backend/cuda/Array.cpp   | 20 +++++++++-----------
 src/backend/opencl/Array.cpp | 10 ++++++----
 3 files changed, 25 insertions(+), 24 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 0fdcb05177..f0847e2b3d 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -145,23 +145,24 @@ T* Array<T>::device()
 template<typename T>
 void evalMultiple(vector<Array<T>*> array_ptrs)
 {
-    vector<Array<T>> arrays;
+    vector<Array<T>*> output_arrays;
     vector<Node_ptr> nodes;
-    bool isWorker = getQueue().is_worker();
-    for (auto &array : array_ptrs) {
+    vector<Param<T>> params;
+    if (getQueue().is_worker()) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+    for (Array<T>* array : array_ptrs) {
         if (array->ready) continue;
-        if (isWorker) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+
         array->setId(getActiveDeviceId());
         array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
-        arrays.push_back(*array);
+
+        output_arrays.push_back(array);
+        params.push_back(*array);
         nodes.push_back(array->node);
     }
 
-    vector<Param<T>> params(arrays.begin(), arrays.end());
-    if (arrays.size() > 0) {
+    if (output_arrays.size() > 0) {
         getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
-        for (auto &array : array_ptrs) {
-            if (array->ready) continue;
+        for (Array<T>* array : output_arrays) {
             array->ready = true;
             array->node = bufferNodePtr<T>();
         }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index f44e7efd95..ed6b27085a 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -26,8 +26,10 @@ using cuda::jit::BufferNode;
 using common::Node;
 using common::NodeIterator;
 using common::Node_ptr;
+
 using std::accumulate;
 using std::shared_ptr;
+using std::vector;
 
 namespace cuda
 {
@@ -147,12 +149,11 @@ namespace cuda
     template<typename T>
     void evalMultiple(std::vector<Array<T>*> arrays)
     {
-        std::vector<Param<T> > outputs;
-        std::vector<common::Node *> nodes;
-
-        for (int i = 0; i < (int)arrays.size(); i++) {
-            Array<T> *array = arrays[i];
+        vector<Param<T> > outputs;
+        vector<Array<T> *> output_arrays;
+        vector<Node*> nodes;
 
+        for (Array<T>* array : arrays) {
             if (array->isReady()) {
                 continue;
             }
@@ -162,18 +163,15 @@ namespace cuda
             array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
             outputs.push_back(*array);
+            output_arrays.push_back(array);
             nodes.push_back(array->node.get());
         }
 
         evalNodes(outputs, nodes);
 
-        for (int i = 0; i < (int)arrays.size(); i++) {
-            Array<T> *array = arrays[i];
-
-            if (array->isReady()) continue;
-            // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
+        for(Array<T>* array : output_arrays)
             array->node = bufferNodePtr<T>();
-        }
+
         return;
     }
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index a2e74b3867..e104edc654 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -173,15 +173,17 @@ namespace opencl
     void evalMultiple(vector<Array<T>*> arrays)
     {
         vector<Param> outputs;
+        vector<Array<T> *> output_arrays;
         vector<Node *> nodes;
 
-        for (auto array : arrays) {
+        for (Array<T>* array : arrays) {
             if (array->isReady()) {
                 continue;
             }
 
             const ArrayInfo info = array->info;
 
+            array->ready = true;
             array->setId(getActiveDeviceId());
             array->data = Buffer_ptr(bufferAlloc(info.elements() * sizeof(T)), bufferFree);
 
@@ -192,13 +194,13 @@ namespace opencl
                             0};
 
             Param res = {array->data.get(), kInfo};
+
             outputs.push_back(res);
+            output_arrays.push_back(array);
             nodes.push_back(array->node.get());
         }
         evalNodes(outputs, nodes);
-        for (auto array : arrays) {
-            if (array->isReady()) continue;
-            array->ready = true;
+        for (Array<T>* array : output_arrays) {
             array->node = bufferNodePtr<T>();
         }
     }

From 79a75b992720f1e82f5ac0b0976034bc22777acf Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 22 Dec 2018 20:33:32 +0530
Subject: [PATCH 1588/2677] Move forge manager instance inside device manager
 (#2381)

* Move forge manager instance inside device manager

This helps making forge manager resources to be released after
interop manager and other memory managers are cleared out.

* Fix ForgeManager instantiations in all backends
* Mark ForgeManager copy/move cstr(s) delete
* Remove obfuscation of forgeplugin use in FG_CHECK macro
---
 src/api/c/hist.cpp                     |  32 ++--
 src/api/c/image.cpp                    |  25 +--
 src/api/c/plot.cpp                     |  70 +++----
 src/api/c/surface.cpp                  |  31 +--
 src/api/c/vector_field.cpp             |  71 +++----
 src/api/c/window.cpp                   | 152 ++++++++-------
 src/backend/common/InteropManager.hpp  |  12 +-
 src/backend/common/forge_loader.hpp    |  10 +-
 src/backend/common/graphics_common.cpp | 255 +++++++++++++------------
 src/backend/common/graphics_common.hpp |  45 ++---
 src/backend/cpu/hist_graphics.cpp      |   4 +-
 src/backend/cpu/image.cpp              |   4 +-
 src/backend/cpu/platform.cpp           |  11 +-
 src/backend/cpu/platform.hpp           |  12 +-
 src/backend/cpu/plot.cpp               |   4 +-
 src/backend/cpu/surface.cpp            |   4 +-
 src/backend/cpu/vector_field.cpp       |   8 +-
 src/backend/cuda/hist_graphics.cpp     |   6 +-
 src/backend/cuda/image.cpp             |   6 +-
 src/backend/cuda/platform.cpp          |   7 +-
 src/backend/cuda/platform.hpp          |  11 ++
 src/backend/cuda/plot.cpp              |   6 +-
 src/backend/cuda/surface.cpp           |   6 +-
 src/backend/cuda/vector_field.cpp      |  10 +-
 src/backend/opencl/hist_graphics.cpp   |   6 +-
 src/backend/opencl/image.cpp           |   6 +-
 src/backend/opencl/platform.cpp        |  23 ++-
 src/backend/opencl/platform.hpp        |   9 +
 src/backend/opencl/plot.cpp            |   6 +-
 src/backend/opencl/surface.cpp         |   6 +-
 src/backend/opencl/vector_field.cpp    |  12 +-
 31 files changed, 470 insertions(+), 400 deletions(-)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index eecb0e8235..19ded99726 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -27,11 +27,13 @@ fg_chart setup_histogram(fg_window const window,
                          const double minval, const double maxval,
                          const af_cell* const props)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+
     Array<T> histogramInput = getArray<T>(in);
     dim_t nBins = histogramInput.elements();
 
     // Retrieve Forge Histogram with nBins and array type
-    ForgeManager& fgMngr = ForgeManager::getInstance();
+    ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
@@ -44,13 +46,13 @@ fg_chart setup_histogram(fg_window const window,
     fg_histogram hist = fgMngr.getHistogram(chart, nBins, getGLType<T>());
 
     // Set histogram bar colors to ArrayFire's orange
-    FG_CHECK(fg_set_histogram_color(hist, 0.929f, 0.486f, 0.2745f, 1.0f));
+    FG_CHECK(_.fg_set_histogram_color(hist, 0.929f, 0.486f, 0.2745f, 1.0f));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float xMin, xMax, yMin, yMax, zMin, zMax;
-        FG_CHECK(fg_get_chart_axes_limits(&xMin, &xMax,
+        FG_CHECK(_.fg_get_chart_axes_limits(&xMin, &xMax,
                                           &yMin, &yMax,
                                           &zMin, &zMax,
                                           chart));
@@ -70,7 +72,7 @@ fg_chart setup_histogram(fg_window const window,
             // For histogram, always set yMin to 0.
             yMin = 0;
         }
-        FG_CHECK(fg_set_chart_axes_limits(chart, xMin, xMax, yMin, yMax, zMin, zMax));
+        FG_CHECK(_.fg_set_chart_axes_limits(chart, xMin, xMax, yMin, yMax, zMin, zMax));
     }
 
     copy_histogram<T>(histogramInput, hist);
@@ -83,12 +85,11 @@ af_err af_draw_hist(const af_window window,
                     const double minval, const double maxval,
                     const af_cell* const props)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& Xinfo = getInfo(X);
         af_dtype Xtype  = Xinfo.getType();
 
@@ -107,15 +108,16 @@ af_err af_draw_hist(const af_window window,
             case u8 : chart = setup_histogram<uchar  >(window, X, minval, maxval, props); break;
             default:  TYPE_ERROR(1, Xtype);
         }
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
     }
     CATCHALL;
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 86c0bc41ae..b7c7565b48 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -60,7 +60,7 @@ static fg_image convert_and_copy_image(const af_array in)
 
     Array<T> imgData = reorder(_in, rdims);
 
-    ForgeManager& fgMngr = ForgeManager::getInstance();
+    ForgeManager& fgMngr = forgeManager();
 
     // The inDims[2] * 100 is a hack to convert to fg_channel_format
     // TODO Write a proper conversion function
@@ -75,11 +75,11 @@ static fg_image convert_and_copy_image(const af_array in)
 af_err af_draw_image(const af_window window,
                      const af_array in, const af_cell* const props)
 {
-    if(window == 0) {
-        fprintf(stderr, "Not a valid window\n");
-        return AF_SUCCESS;
-    }
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& info = getInfo(in);
 
         af::dim4 in_dims = info.dims();
@@ -101,15 +101,16 @@ af_err af_draw_image(const af_window window,
             default:  TYPE_ERROR(1, type);
         }
 
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
-        FG_CHECK(fg_set_window_colormap(window, (fg_color_map)props->cmap));
+        ForgeModule& _ = graphics::forgePlugin();
+        auto gridDims = forgeManager().getWindowGrid(window);
+        FG_CHECK(_.fg_set_window_colormap(window, (fg_color_map)props->cmap));
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_image_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           image, props->title, true));
+            FG_CHECK(_.fg_draw_image_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             image, props->title, true));
         } else {
-            FG_CHECK(fg_draw_image(window, image, true));
+            FG_CHECK(_.fg_draw_image(window, image, true));
         }
     }
     CATCHALL;
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index b96f6b7d08..26b2cccec2 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -32,6 +32,8 @@ fg_chart setup_plot(fg_window window, const af_array in_,
                     const af_cell* const props,
                     fg_plot_type ptype, fg_marker_type mtype)
 {
+    ForgeModule& _ = graphics::forgePlugin();
+
     Array<T> in = getArray<T>(in_);
 
     af::dim4 dims = in.dims();
@@ -46,7 +48,7 @@ fg_chart setup_plot(fg_window window, const af_array in_,
 
     af::dim4 tdims = in.dims(); //transposed dimensions
 
-    ForgeManager& fgMngr = ForgeManager::getInstance();
+    ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
@@ -60,14 +62,14 @@ fg_chart setup_plot(fg_window window, const af_array in_,
     fg_plot plot = fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
 
     // ArrayFire LOGO Orange shade
-    FG_CHECK(fg_set_plot_color(plot, 0.929f, 0.529f, 0.212f, 1.0));
+    FG_CHECK(_.fg_set_plot_color(plot, 0.929f, 0.529f, 0.212f, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
         T     dmin[3], dmax[3];
-        FG_CHECK(fg_get_chart_axes_limits(&cmin[0], &cmax[0],
+        FG_CHECK(_.fg_get_chart_axes_limits(&cmin[0], &cmax[0],
                                           &cmin[1], &cmax[1],
                                           &cmin[2], &cmax[2],
                                           chart));
@@ -94,7 +96,7 @@ fg_chart setup_plot(fg_window window, const af_array in_,
                 if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
             }
         }
-        FG_CHECK(fg_set_chart_axes_limits(chart,
+        FG_CHECK(_.fg_set_chart_axes_limits(chart,
                                           cmin[0], cmax[0],
                                           cmin[1], cmax[1],
                                           cmin[2], cmax[2]));
@@ -123,12 +125,11 @@ af_err plotWrapper(const af_window window,
                    fg_plot_type ptype = FG_PLOT_LINE,
                    fg_marker_type marker = FG_MARKER_NONE)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& info = getInfo(in);
         af::dim4  dims = info.dims();
         af_dtype  type = info.getType();
@@ -150,15 +151,16 @@ af_err plotWrapper(const af_window window,
             default:  TYPE_ERROR(1, type);
         }
 
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
                                            gridDims.first, gridDims.second,
                                            props->row * gridDims.second + props->col,
                                            chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
     }
     CATCHALL;
@@ -171,12 +173,11 @@ af_err plotWrapper(const af_window window,
                    fg_plot_type ptype = FG_PLOT_LINE,
                    fg_marker_type marker = FG_MARKER_NONE)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& xInfo = getInfo(X);
         af::dim4  xDims = xInfo.dims();
         af_dtype  xType = xInfo.getType();
@@ -214,15 +215,16 @@ af_err plotWrapper(const af_window window,
             case u8 : chart = setup_plot<uchar  >(window, in, 3, props, ptype, marker); break;
             default:  TYPE_ERROR(1, xType);
         }
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
 
         AF_CHECK(af_release_array(in));
@@ -237,12 +239,11 @@ af_err plotWrapper(const af_window window,
                    fg_plot_type ptype = FG_PLOT_LINE,
                    fg_marker_type marker = FG_MARKER_NONE)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& xInfo = getInfo(X);
         af::dim4  xDims = xInfo.dims();
         af_dtype  xType = xInfo.getType();
@@ -273,15 +274,16 @@ af_err plotWrapper(const af_window window,
             case u8 : chart = setup_plot<uchar  >(window, in, 2, props, ptype, marker); break;
             default:  TYPE_ERROR(1, xType);
         }
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
 
         AF_CHECK(af_release_array(in));
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index c9fb426c48..ab3f77baf8 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -31,6 +31,7 @@ fg_chart setup_surface(fg_window window,
                        const af_array zVals,
                        const af_cell* const props)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     Array<T> xIn = getArray<T>(xVals);
     Array<T> yIn = getArray<T>(yVals);
     Array<T> zIn = getArray<T>(zVals);
@@ -67,7 +68,7 @@ fg_chart setup_surface(fg_window window,
     std::vector<Array<T> > inputs{xIn, yIn, zIn};
     Array<T> Z = join(0, inputs);
 
-    ForgeManager& fgMngr = ForgeManager::getInstance();
+    ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
@@ -78,14 +79,14 @@ fg_chart setup_surface(fg_window window,
 
     fg_surface surface = fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
 
-    FG_CHECK(fg_set_surface_color(surface, 0.0, 1.0, 0.0, 1.0));
+    FG_CHECK(_.fg_set_surface_color(surface, 0.0, 1.0, 0.0, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
         T     dmin[3], dmax[3];
-        FG_CHECK(fg_get_chart_axes_limits(&cmin[0], &cmax[0],
+        FG_CHECK(_.fg_get_chart_axes_limits(&cmin[0], &cmax[0],
                                           &cmin[1], &cmax[1],
                                           &cmin[2], &cmax[2],
                                           chart));
@@ -115,7 +116,7 @@ fg_chart setup_surface(fg_window window,
             if(cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
         }
 
-        FG_CHECK(fg_set_chart_axes_limits(chart,
+        FG_CHECK(_.fg_set_chart_axes_limits(chart,
                                           cmin[0], cmax[0],
                                           cmin[1], cmax[1],
                                           cmin[2], cmax[2]));
@@ -129,12 +130,11 @@ af_err af_draw_surface(const af_window window,
                        const af_array xVals, const af_array yVals,
                        const af_array S, const af_cell* const props)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& Xinfo = getInfo(xVals);
         af::dim4 X_dims = Xinfo.dims();
         af_dtype Xtype  = Xinfo.getType();
@@ -170,15 +170,16 @@ af_err af_draw_surface(const af_window window,
             case u8 : chart = setup_surface<uchar  >(window, xVals, yVals , S, props); break;
             default:  TYPE_ERROR(1, Xtype);
         }
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
     }
     CATCHALL;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 702a60ccdb..70a0f47c9d 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -34,6 +34,7 @@ fg_chart setup_vector_field(fg_window window,
                             const af_cell* const props,
                             const bool transpose_ = true)
 {
+    ForgeModule& _ = graphics::forgePlugin();
     vector< Array<T> > pnts;
     vector< Array<T> > dirs;
 
@@ -52,7 +53,7 @@ fg_chart setup_vector_field(fg_window window,
         dIn = transpose<T>(dIn, false);
     }
 
-    ForgeManager& fgMngr = ForgeManager::getInstance();
+    ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
@@ -72,14 +73,14 @@ fg_chart setup_vector_field(fg_window window,
     fg_vector_field vfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
 
     // ArrayFire LOGO dark blue shade
-    FG_CHECK(fg_set_vector_field_color(vfield, 0.130f, 0.173f, 0.263f, 1.0));
+    FG_CHECK(_.fg_set_vector_field_color(vfield, 0.130f, 0.173f, 0.263f, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
     if(!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
         T     dmin[3], dmax[3];
-        FG_CHECK(fg_get_chart_axes_limits(&cmin[0], &cmax[0],
+        FG_CHECK(_.fg_get_chart_axes_limits(&cmin[0], &cmax[0],
                                           &cmin[1], &cmax[1],
                                           &cmin[2], &cmax[2],
                                           chart));
@@ -106,7 +107,7 @@ fg_chart setup_vector_field(fg_window window,
                 if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
             }
         }
-        FG_CHECK(fg_set_chart_axes_limits(chart,
+        FG_CHECK(_.fg_set_chart_axes_limits(chart,
                                           cmin[0], cmax[0],
                                           cmin[1], cmax[1],
                                           cmin[2], cmax[2]));
@@ -120,11 +121,11 @@ af_err vectorFieldWrapper(const af_window window,
                           const af_array points, const af_array directions,
                           const af_cell* const props)
 {
-    if(window == 0) {
-        AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
         const ArrayInfo& pInfo = getInfo(points);
         af::dim4 pDims  = pInfo.dims();
         af_dtype pType  = pInfo.getType();
@@ -158,15 +159,16 @@ af_err vectorFieldWrapper(const af_window window,
             case u8 : chart = setup_vector_field<uchar  >(window, pnts, dirs, props); break;
             default:  TYPE_ERROR(1, pType);
         }
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
     }
     CATCHALL;
@@ -182,10 +184,11 @@ af_err vectorFieldWrapper(const af_window window,
                           const af_array zDirs,
                           const af_cell* const props)
 {
-    if(window == 0) {
-        AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
-    }
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_SUCCESS);
+        }
+
         const ArrayInfo& xpInfo = getInfo(xPoints);
         const ArrayInfo& ypInfo = getInfo(yPoints);
         const ArrayInfo& zpInfo = getInfo(zPoints);
@@ -252,15 +255,16 @@ af_err vectorFieldWrapper(const af_window window,
             case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
             default:  TYPE_ERROR(1, xpType);
         }
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
     }
     CATCHALL;
@@ -272,11 +276,11 @@ af_err vectorFieldWrapper(const af_window window,
                           const af_array xDirs, const af_array yDirs,
                           const af_cell* const props)
 {
-    if(window == 0) {
-        AF_RETURN_ERROR("Not a valid window", AF_SUCCESS);
-    }
-
     try {
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_SUCCESS);
+        }
+
         const ArrayInfo& xpInfo = getInfo(xPoints);
         const ArrayInfo& ypInfo = getInfo(yPoints);
 
@@ -332,15 +336,16 @@ af_err vectorFieldWrapper(const af_window window,
             default:  TYPE_ERROR(1, xpType);
         }
 
-        auto gridDims = ForgeManager::getInstance().getWindowGrid(window);
+        auto gridDims = forgeManager().getWindowGrid(window);
 
+        ForgeModule& _ = graphics::forgePlugin();
         if (props->col>-1 && props->row>-1) {
-            FG_CHECK(fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+            FG_CHECK(_.fg_draw_chart_to_cell(window,
+                                             gridDims.first, gridDims.second,
+                                             props->row * gridDims.second + props->col,
+                                             chart, props->title));
         } else {
-            FG_CHECK(fg_draw_chart(window, chart));
+            FG_CHECK(_.fg_draw_chart(window, chart));
         }
     }
     CATCHALL;
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 5f371e27ba..e496ac7f08 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -14,6 +14,7 @@
 #include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
 #include <backend.hpp>
+#include <platform.hpp>
 
 using af::dim4;
 using namespace detail;
@@ -22,22 +23,16 @@ using namespace graphics;
 af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
 {
     try {
-        graphics::ForgeManager& fgMngr = graphics::ForgeManager::getInstance();
-        fg_window mainWnd = NULL;
+        ForgeManager& fgMngr = forgeManager();
+        fg_window mainWnd    = fgMngr.getMainWindow();
 
-        try {
-            mainWnd = fgMngr.getMainWindow();
-        } catch(...) {
-            std::cerr<<"OpenGL context creation failed"<<std::endl;
-        }
         if (mainWnd == 0) {
-            std::cerr<<"Not a valid window"<<std::endl;
-            return AF_SUCCESS;
+            AF_ERROR("OpenGL context creation failed", AF_ERR_INTERNAL);
         }
 
         fg_window temp = nullptr;
 
-        FG_CHECK(fg_create_window(&temp, width, height, title, mainWnd, false));
+        FG_CHECK(forgePlugin().fg_create_window(&temp, width, height, title, mainWnd, false));
 
         fgMngr.setWindowChartGrid(temp, 1, 1);
 
@@ -49,42 +44,47 @@ af_err af_create_window(af_window *out, const int width, const int height, const
 
 af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
+    try {
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        FG_CHECK(forgePlugin().fg_set_window_position(wind, x, y));
     }
-    FG_CHECK(fg_set_window_position(wind, x, y));
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_set_title(const af_window wind, const char* const title)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
+    try {
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        FG_CHECK(forgePlugin().fg_set_window_title(wind, title));
     }
-    FG_CHECK(fg_set_window_title(wind, title));
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
+    try {
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        FG_CHECK(forgePlugin().fg_set_window_size(wind, w, h));
     }
-    FG_CHECK(fg_set_window_size(wind, w, h));
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_grid(const af_window wind, const int rows, const int cols)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
     try {
-        ForgeManager::getInstance().setWindowChartGrid(wind, rows, cols);
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        forgeManager().setWindowChartGrid(wind, rows, cols);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -94,12 +94,12 @@ af_err af_set_axes_limits_compute(const af_window window,
                                   const af_array x, const af_array y, const af_array z,
                                   const bool exact, const af_cell* const props)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
     try {
-        ForgeManager& fgMngr = ForgeManager::getInstance();
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
+        ForgeManager& fgMngr = forgeManager();
 
         fg_chart chart = NULL;
 
@@ -133,7 +133,7 @@ af_err af_set_axes_limits_compute(const af_window window,
         }
 
         fgMngr.setChartAxesOverride(chart);
-        FG_CHECK(fg_set_chart_axes_limits(chart, xmin, xmax,
+        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, xmin, xmax,
                                           ymin, ymax, zmin, zmax));
     }
     CATCHALL;
@@ -145,12 +145,12 @@ af_err af_set_axes_limits_2d(const af_window window,
                              const float ymin, const float ymax,
                              const bool exact, const af_cell* const props)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
     try {
-        ForgeManager& fgMngr = ForgeManager::getInstance();
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
+        ForgeManager& fgMngr = forgeManager();
 
         fg_chart chart = NULL;
         // The ctype here below doesn't really matter as it is only fetching
@@ -175,7 +175,7 @@ af_err af_set_axes_limits_2d(const af_window window,
         }
 
         fgMngr.setChartAxesOverride(chart);
-        FG_CHECK(fg_set_chart_axes_limits(chart, _xmin, _xmax,
+        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, _xmin, _xmax,
                                           _ymin, _ymax, 0.0f, 0.0f));
     }
     CATCHALL;
@@ -188,12 +188,12 @@ af_err af_set_axes_limits_3d(const af_window window,
                              const float zmin, const float zmax,
                              const bool exact, const af_cell* const props)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
     try {
-        ForgeManager& fgMngr = ForgeManager::getInstance();
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
+        ForgeManager& fgMngr = forgeManager();
 
         fg_chart chart = NULL;
         // The ctype here below doesn't really matter as it is only fetching
@@ -222,7 +222,7 @@ af_err af_set_axes_limits_3d(const af_window window,
         }
 
         fgMngr.setChartAxesOverride(chart);
-        FG_CHECK(fg_set_chart_axes_limits(chart, _xmin, _xmax,
+        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, _xmin, _xmax,
                                           _ymin, _ymax, _zmin, _zmax));
     }
     CATCHALL;
@@ -235,12 +235,12 @@ af_err af_set_axes_titles(const af_window window,
                           const char * const ztitle,
                           const af_cell* const props)
 {
-    if(window == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
     try {
-        ForgeManager& fgMngr = ForgeManager::getInstance();
+        if(window == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+
+        ForgeManager& fgMngr = forgeManager();
 
         fg_chart chart = NULL;
 
@@ -251,7 +251,7 @@ af_err af_set_axes_titles(const af_window window,
         else
             chart = fgMngr.getChart(window, 0, 0, ctype);
 
-        FG_CHECK(fg_set_chart_axes_titles(chart, xtitle, ytitle, ztitle));
+        FG_CHECK(forgePlugin().fg_set_chart_axes_titles(chart, xtitle, ytitle, ztitle));
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -259,49 +259,53 @@ af_err af_set_axes_titles(const af_window window,
 
 af_err af_show(const af_window wind)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
+    try {
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        FG_CHECK(forgePlugin().fg_swap_window_buffers(wind));
     }
-    FG_CHECK(fg_swap_window_buffers(wind));
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_is_window_closed(bool *out, const af_window wind)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
+    try {
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        FG_CHECK(forgePlugin().fg_close_window(out, wind));
     }
-    FG_CHECK(fg_close_window(out, wind));
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_set_visibility(const af_window wind, const bool is_visible)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
-    if (is_visible) {
-        FG_CHECK(fg_show_window(wind));
-    } else {
-        FG_CHECK(fg_hide_window(wind));
+    try {
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        if (is_visible) {
+            FG_CHECK(forgePlugin().fg_show_window(wind));
+        } else {
+            FG_CHECK(forgePlugin().fg_hide_window(wind));
+        }
     }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_destroy_window(const af_window wind)
 {
-    if(wind == 0) {
-        std::cerr<<"Not a valid window"<<std::endl;
-        return AF_SUCCESS;
-    }
     try {
-        ForgeManager::getInstance().setWindowChartGrid(wind, 0, 0);
+        if(wind == 0) {
+            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
+        }
+        forgeManager().setWindowChartGrid(wind, 0, 0);
+        FG_CHECK(forgePlugin().fg_release_window(wind));
     }
     CATCHALL;
-    FG_CHECK(fg_release_window(wind));
     return AF_SUCCESS;
 }
-
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index 1f70d0c111..d94ccb6fd9 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -44,7 +44,7 @@ class InteropManager
         res_vec_t getImageResources(const fg_window image) {
             if (mInteropMap.find(image) == mInteropMap.end()) {
                 uint32_t buffer;
-                FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+                FG_CHECK(graphics::forgePlugin().fg_get_pixel_buffer(&buffer, image));
                 mInteropMap[image] =
                     static_cast<T*>(this)->registerResources({buffer});
             }
@@ -54,7 +54,7 @@ class InteropManager
         res_vec_t getPlotResources(const fg_plot plot) {
             if (mInteropMap.find(plot) == mInteropMap.end()) {
                 uint32_t buffer;
-                FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
+                FG_CHECK(graphics::forgePlugin().fg_get_plot_vertex_buffer(&buffer, plot));
                 mInteropMap[plot] =
                     static_cast<T*>(this)->registerResources({buffer});
             }
@@ -64,7 +64,7 @@ class InteropManager
         res_vec_t getHistogramResources(const fg_histogram histogram) {
             if (mInteropMap.find(histogram) == mInteropMap.end()) {
                 uint32_t buffer;
-                FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, histogram));
+                FG_CHECK(graphics::forgePlugin().fg_get_histogram_vertex_buffer(&buffer, histogram));
                 mInteropMap[histogram] =
                     static_cast<T*>(this)->registerResources({buffer});
             }
@@ -74,7 +74,7 @@ class InteropManager
         res_vec_t getSurfaceResources(const fg_surface surface) {
             if (mInteropMap.find(surface) == mInteropMap.end()) {
                 uint32_t buffer;
-                FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
+                FG_CHECK(graphics::forgePlugin().fg_get_surface_vertex_buffer(&buffer, surface));
                 mInteropMap[surface] =
                     static_cast<T*>(this)->registerResources({buffer});
             }
@@ -84,8 +84,8 @@ class InteropManager
         res_vec_t getVectorFieldResources(const fg_vector_field field) {
             if (mInteropMap.find(field) == mInteropMap.end()) {
                 uint32_t verts, dirs;
-                FG_CHECK(fg_get_vector_field_vertex_buffer(&verts, field));
-                FG_CHECK(fg_get_vector_field_direction_buffer(&dirs, field));
+                FG_CHECK(graphics::forgePlugin().fg_get_vector_field_vertex_buffer(&verts, field));
+                FG_CHECK(graphics::forgePlugin().fg_get_vector_field_direction_buffer(&dirs, field));
                 mInteropMap[field] =
                     static_cast<T*>(this)->registerResources({verts, dirs});
             }
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index 04376eb23c..02d2b83e98 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -15,10 +15,10 @@
 
 #include <forge.h>
 
-class ForgeModule {
-    common::DependencyModule module;
-
+class ForgeModule : public common::DependencyModule {
     public:
+    ForgeModule();
+
     MODULE_MEMBER(fg_create_window);
     MODULE_MEMBER(fg_get_window_context_handle);
     MODULE_MEMBER(fg_get_window_display_handle);
@@ -84,8 +84,6 @@ class ForgeModule {
     MODULE_MEMBER(fg_append_surface_to_chart);
     MODULE_MEMBER(fg_append_vector_field_to_chart);
     MODULE_MEMBER(fg_release_chart);
-
-    ForgeModule();
 };
 
 namespace graphics {
@@ -94,7 +92,7 @@ ForgeModule& forgePlugin();
 
 #define FG_CHECK(fn)                           \
     do {                                       \
-        fg_err e = graphics::forgePlugin().fn; \
+        fg_err e = (fn);                       \
         if (e != FG_ERR_NONE) {                \
             AF_ERROR("forge call failed",      \
                     AF_ERR_INTERNAL);          \
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 2bb554a312..7281f53a88 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -17,82 +17,89 @@
 
 using namespace std;
 
+/// Dynamically loads forge function pointer at runtime
+#define FG_MODULE_FUNCTION_INIT(NAME)                \
+    NAME = DependencyModule::getSymbol<decltype(&::NAME)>(#NAME)
+
 ForgeModule::ForgeModule()
-    : module("forge", nullptr)
+    : DependencyModule("forge", nullptr)
 {
-    if (!module.isLoaded()) {
-        string error_message = "Error loading Forge: "
-            + module.getErrorMessage()
-            + "\nForge or one of it's dependencies failed to "
-            "load. Try installing Forge or check if Forge is in the "
-            "search path.";
-        AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+    if (DependencyModule::isLoaded()) {
+        FG_MODULE_FUNCTION_INIT(fg_create_window);
+        FG_MODULE_FUNCTION_INIT(fg_get_window_context_handle);
+        FG_MODULE_FUNCTION_INIT(fg_get_window_display_handle);
+        FG_MODULE_FUNCTION_INIT(fg_make_window_current);
+        FG_MODULE_FUNCTION_INIT(fg_set_window_font);
+        FG_MODULE_FUNCTION_INIT(fg_set_window_position);
+        FG_MODULE_FUNCTION_INIT(fg_set_window_title);
+        FG_MODULE_FUNCTION_INIT(fg_set_window_size);
+        FG_MODULE_FUNCTION_INIT(fg_set_window_colormap);
+        FG_MODULE_FUNCTION_INIT(fg_draw_chart_to_cell);
+        FG_MODULE_FUNCTION_INIT(fg_draw_chart);
+        FG_MODULE_FUNCTION_INIT(fg_draw_image_to_cell);
+        FG_MODULE_FUNCTION_INIT(fg_draw_image);
+        FG_MODULE_FUNCTION_INIT(fg_swap_window_buffers);
+        FG_MODULE_FUNCTION_INIT(fg_close_window);
+        FG_MODULE_FUNCTION_INIT(fg_show_window);
+        FG_MODULE_FUNCTION_INIT(fg_hide_window);
+        FG_MODULE_FUNCTION_INIT(fg_release_window);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_font);
+        FG_MODULE_FUNCTION_INIT(fg_load_system_font);
+        FG_MODULE_FUNCTION_INIT(fg_release_font);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_image);
+        FG_MODULE_FUNCTION_INIT(fg_get_pixel_buffer);
+        FG_MODULE_FUNCTION_INIT(fg_get_image_size);
+        FG_MODULE_FUNCTION_INIT(fg_release_image);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_plot);
+        FG_MODULE_FUNCTION_INIT(fg_set_plot_color);
+        FG_MODULE_FUNCTION_INIT(fg_get_plot_vertex_buffer);
+        FG_MODULE_FUNCTION_INIT(fg_get_plot_vertex_buffer_size);
+        FG_MODULE_FUNCTION_INIT(fg_release_plot);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_histogram);
+        FG_MODULE_FUNCTION_INIT(fg_set_histogram_color);
+        FG_MODULE_FUNCTION_INIT(fg_get_histogram_vertex_buffer);
+        FG_MODULE_FUNCTION_INIT(fg_get_histogram_vertex_buffer_size);
+        FG_MODULE_FUNCTION_INIT(fg_release_histogram);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_surface);
+        FG_MODULE_FUNCTION_INIT(fg_set_surface_color);
+        FG_MODULE_FUNCTION_INIT(fg_get_surface_vertex_buffer);
+        FG_MODULE_FUNCTION_INIT(fg_get_surface_vertex_buffer_size);
+        FG_MODULE_FUNCTION_INIT(fg_release_surface);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_vector_field);
+        FG_MODULE_FUNCTION_INIT(fg_set_vector_field_color);
+        FG_MODULE_FUNCTION_INIT(fg_get_vector_field_vertex_buffer_size);
+        FG_MODULE_FUNCTION_INIT(fg_get_vector_field_direction_buffer_size);
+        FG_MODULE_FUNCTION_INIT(fg_get_vector_field_vertex_buffer);
+        FG_MODULE_FUNCTION_INIT(fg_get_vector_field_direction_buffer);
+        FG_MODULE_FUNCTION_INIT(fg_release_vector_field);
+
+        FG_MODULE_FUNCTION_INIT(fg_create_chart);
+        FG_MODULE_FUNCTION_INIT(fg_get_chart_type);
+        FG_MODULE_FUNCTION_INIT(fg_get_chart_axes_limits);
+        FG_MODULE_FUNCTION_INIT(fg_set_chart_axes_limits);
+        FG_MODULE_FUNCTION_INIT(fg_set_chart_axes_titles);
+        FG_MODULE_FUNCTION_INIT(fg_append_image_to_chart);
+        FG_MODULE_FUNCTION_INIT(fg_append_plot_to_chart);
+        FG_MODULE_FUNCTION_INIT(fg_append_histogram_to_chart);
+        FG_MODULE_FUNCTION_INIT(fg_append_surface_to_chart);
+        FG_MODULE_FUNCTION_INIT(fg_append_vector_field_to_chart);
+        FG_MODULE_FUNCTION_INIT(fg_release_chart);
+
+        if (!DependencyModule::symbolsLoaded()) {
+            string error_message = "Error loading Forge: "
+                + DependencyModule::getErrorMessage()
+                + "\nForge or one of it's dependencies failed to "
+                "load. Try installing Forge or check if Forge is in the "
+                "search path.";
+            AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+        }
     }
-    MODULE_FUNCTION_INIT(fg_create_window);
-    MODULE_FUNCTION_INIT(fg_get_window_context_handle);
-    MODULE_FUNCTION_INIT(fg_get_window_display_handle);
-    MODULE_FUNCTION_INIT(fg_make_window_current);
-    MODULE_FUNCTION_INIT(fg_set_window_font);
-    MODULE_FUNCTION_INIT(fg_set_window_position);
-    MODULE_FUNCTION_INIT(fg_set_window_title);
-    MODULE_FUNCTION_INIT(fg_set_window_size);
-    MODULE_FUNCTION_INIT(fg_set_window_colormap);
-    MODULE_FUNCTION_INIT(fg_draw_chart_to_cell);
-    MODULE_FUNCTION_INIT(fg_draw_chart);
-    MODULE_FUNCTION_INIT(fg_draw_image_to_cell);
-    MODULE_FUNCTION_INIT(fg_draw_image);
-    MODULE_FUNCTION_INIT(fg_swap_window_buffers);
-    MODULE_FUNCTION_INIT(fg_close_window);
-    MODULE_FUNCTION_INIT(fg_show_window);
-    MODULE_FUNCTION_INIT(fg_hide_window);
-    MODULE_FUNCTION_INIT(fg_release_window);
-
-    MODULE_FUNCTION_INIT(fg_create_font);
-    MODULE_FUNCTION_INIT(fg_load_system_font);
-    MODULE_FUNCTION_INIT(fg_release_font);
-
-    MODULE_FUNCTION_INIT(fg_create_image);
-    MODULE_FUNCTION_INIT(fg_get_pixel_buffer);
-    MODULE_FUNCTION_INIT(fg_get_image_size);
-    MODULE_FUNCTION_INIT(fg_release_image);
-
-    MODULE_FUNCTION_INIT(fg_create_plot);
-    MODULE_FUNCTION_INIT(fg_set_plot_color);
-    MODULE_FUNCTION_INIT(fg_get_plot_vertex_buffer);
-    MODULE_FUNCTION_INIT(fg_get_plot_vertex_buffer_size);
-    MODULE_FUNCTION_INIT(fg_release_plot);
-
-    MODULE_FUNCTION_INIT(fg_create_histogram);
-    MODULE_FUNCTION_INIT(fg_set_histogram_color);
-    MODULE_FUNCTION_INIT(fg_get_histogram_vertex_buffer);
-    MODULE_FUNCTION_INIT(fg_get_histogram_vertex_buffer_size);
-    MODULE_FUNCTION_INIT(fg_release_histogram);
-
-    MODULE_FUNCTION_INIT(fg_create_surface);
-    MODULE_FUNCTION_INIT(fg_set_surface_color);
-    MODULE_FUNCTION_INIT(fg_get_surface_vertex_buffer);
-    MODULE_FUNCTION_INIT(fg_get_surface_vertex_buffer_size);
-    MODULE_FUNCTION_INIT(fg_release_surface);
-
-    MODULE_FUNCTION_INIT(fg_create_vector_field);
-    MODULE_FUNCTION_INIT(fg_set_vector_field_color);
-    MODULE_FUNCTION_INIT(fg_get_vector_field_vertex_buffer_size);
-    MODULE_FUNCTION_INIT(fg_get_vector_field_direction_buffer_size);
-    MODULE_FUNCTION_INIT(fg_get_vector_field_vertex_buffer);
-    MODULE_FUNCTION_INIT(fg_get_vector_field_direction_buffer);
-    MODULE_FUNCTION_INIT(fg_release_vector_field);
-
-    MODULE_FUNCTION_INIT(fg_create_chart);
-    MODULE_FUNCTION_INIT(fg_get_chart_type);
-    MODULE_FUNCTION_INIT(fg_get_chart_axes_limits);
-    MODULE_FUNCTION_INIT(fg_set_chart_axes_limits);
-    MODULE_FUNCTION_INIT(fg_set_chart_axes_titles);
-    MODULE_FUNCTION_INIT(fg_append_image_to_chart);
-    MODULE_FUNCTION_INIT(fg_append_plot_to_chart);
-    MODULE_FUNCTION_INIT(fg_append_histogram_to_chart);
-    MODULE_FUNCTION_INIT(fg_append_surface_to_chart);
-    MODULE_FUNCTION_INIT(fg_append_vector_field_to_chart);
-    MODULE_FUNCTION_INIT(fg_release_chart);
 }
 
 template<typename T>
@@ -159,7 +166,7 @@ size_t getTypeSize(GLenum type)
 
 void makeContextCurrent(fg_window window)
 {
-    FG_CHECK(fg_make_window_current(window));
+    FG_CHECK(graphics::forgePlugin().fg_make_window_current(window));
     CheckGL("End makeContextCurrent");
 }
 
@@ -222,13 +229,7 @@ namespace graphics {
 
 ForgeModule& forgePlugin()
 {
-    return *(ForgeManager::getInstance().mPlugin);
-}
-
-ForgeManager& ForgeManager::getInstance()
-{
-    static ForgeManager my_instance;
-    return my_instance;
+    return detail::forgeManager().plugin();
 }
 
 ForgeManager::ForgeManager()
@@ -236,60 +237,68 @@ ForgeManager::ForgeManager()
 
 ForgeManager::~ForgeManager()
 {
-    ForgeModule& _ = forgePlugin();
     /* clear all OpenGL resource objects (images, plots, histograms etc) first
      * and then delete the windows */
     for(ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
-        _.fg_release_image(iter->second);
+        mPlugin->fg_release_image(iter->second);
 
     for(PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
-        _.fg_release_plot(iter->second);
+        mPlugin->fg_release_plot(iter->second);
 
     for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
-        _.fg_release_histogram(iter->second);
+        mPlugin->fg_release_histogram(iter->second);
 
     for(SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
-        _.fg_release_surface(iter->second);
+        mPlugin->fg_release_surface(iter->second);
 
     for(VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
-        _.fg_release_vector_field(iter->second);
+        mPlugin->fg_release_vector_field(iter->second);
 
     for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++) {
         for(int i = 0; i < (int)(iter->second).size(); i++) {
             fg_chart chrt = (iter->second)[i];
             if (chrt) {
                 mChartAxesOverrideMap.erase((chrt));
-                _.fg_release_chart(chrt);
+                mPlugin->fg_release_chart(chrt);
             }
         }
     }
+    mPlugin->fg_release_window(wnd->handle);
+}
+
+ForgeModule& ForgeManager::plugin() {
+    return *mPlugin;
 }
 
 fg_window ForgeManager::getMainWindow()
 {
-    class Window {
-        public:
-        Window(fg_window h) : handle(h) {}
-        ~Window() { forgePlugin().fg_release_window(handle); }
-        fg_window handle;
-    };
-
     static std::once_flag flag;
-    static std::unique_ptr<Window> wnd;
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
 
     if (noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
         std::call_once(flag,
-            [] {
+            [this] {
+                if (!this->mPlugin->isLoaded()) {
+                    string error_message = "Error loading Forge: "
+                        + this->mPlugin->getErrorMessage()
+                        + "\nForge or one of it's dependencies failed to "
+                        "load. Try installing Forge or check if Forge is in the "
+                        "search path.";
+                    AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+                }
                 fg_window w = nullptr;
-                FG_CHECK(fg_create_window(&w, WIDTH, HEIGHT, "ArrayFire", NULL, true));
-                makeContextCurrent(w);
-                ForgeManager::getInstance().setWindowChartGrid(w, 1, 1);
-                wnd.reset(new Window(w));
+                fg_err e = this->mPlugin->fg_create_window(&w, WIDTH, HEIGHT,
+                                                           "ArrayFire", NULL, true);
+                if (e != FG_ERR_NONE) {
+                    AF_ERROR("Graphics Window creation failed", AF_ERR_INTERNAL);
+                }
+                this->mPlugin->fg_make_window_current(w);
+                this->setWindowChartGrid(w, 1, 1);
+                this->wnd.reset(new Window({w}));
                 if (!gladLoadGL()) {
-                AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB);
+                    AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB);
                 }
             });
     }
@@ -311,7 +320,7 @@ void ForgeManager::setWindowChartGrid(const fg_window window,
             fg_chart chrt = (iter->second)[i];
             if (chrt) {
                 mChartAxesOverrideMap.erase(chrt);
-                FG_CHECK(fg_release_chart(chrt));
+                FG_CHECK(mPlugin->fg_release_chart(chrt));
             }
         }
         (iter->second).clear();
@@ -359,18 +368,18 @@ fg_chart ForgeManager::getChart(const fg_window window,
 
         if (chart == NULL) {
             // Chart has not been created
-            FG_CHECK(fg_create_chart(&chart, ctype));
+            FG_CHECK(mPlugin->fg_create_chart(&chart, ctype));
             (iter->second)[c * gRows + r] = chart;
             // Set Axes override to false
             mChartAxesOverrideMap[chart] = false;
         } else {
             fg_chart_type chart_type;
-            FG_CHECK(fg_get_chart_type(&chart_type, chart));
+            FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
             if (chart_type != ctype) {
                 // Existing chart is of incompatible type
                 mChartAxesOverrideMap.erase(chart);
-                FG_CHECK(fg_release_chart(chart));
-                FG_CHECK(fg_create_chart(&chart, ctype));
+                FG_CHECK(mPlugin->fg_release_chart(chart));
+                FG_CHECK(mPlugin->fg_create_chart(&chart, ctype));
                 (iter->second)[c * gRows + r] = chart;
                 // Set Axes override to false
                 mChartAxesOverrideMap[chart] = false;
@@ -402,7 +411,7 @@ fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode, fg_dtype t
 
     if (iter==mImgMap.end()) {
         fg_image img = nullptr;
-        FG_CHECK(fg_create_image(&img, w, h, mode, type));
+        FG_CHECK(mPlugin->fg_create_image(&img, w, h, mode, type));
         mImgMap[keypair] = img;
     }
 
@@ -428,16 +437,16 @@ fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
 
     if (iter==mImgMap.end()) {
         fg_chart_type chart_type;
-        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
         if(chart_type != FG_CHART_2D)
             AF_ERROR("Image can only be added to chart of type FG_CHART_2D",
                      AF_ERR_TYPE);
 
         fg_image img = nullptr;
-        FG_CHECK(fg_create_image(&img, w, h, mode, type));
+        FG_CHECK(mPlugin->fg_create_image(&img, w, h, mode, type));
         mImgMap[keypair] = img;
 
-        FG_CHECK(fg_append_image_to_chart(chart, img));
+        FG_CHECK(mPlugin->fg_append_image_to_chart(chart, img));
     }
 
     return mImgMap[keypair];
@@ -455,13 +464,13 @@ fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
 
     if (iter==mPltMap.end()) {
         fg_chart_type chart_type;
-        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
 
         fg_plot plt = nullptr;
-        FG_CHECK(fg_create_plot(&plt, nPoints, dtype, chart_type, ptype, mtype));
+        FG_CHECK(mPlugin->fg_create_plot(&plt, nPoints, dtype, chart_type, ptype, mtype));
         mPltMap[keypair] = plt;
 
-        FG_CHECK(fg_append_plot_to_chart(chart, plt));
+        FG_CHECK(mPlugin->fg_append_plot_to_chart(chart, plt));
     }
 
     return mPltMap[keypair];
@@ -477,16 +486,16 @@ fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins, fg_dtype type
 
     if (iter==mHstMap.end()) {
         fg_chart_type chart_type;
-        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
         if(chart_type != FG_CHART_2D)
             AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D",
                      AF_ERR_TYPE);
 
         fg_histogram hst = nullptr;
-        FG_CHECK(fg_create_histogram(&hst, nBins, type));
+        FG_CHECK(mPlugin->fg_create_histogram(&hst, nBins, type));
         mHstMap[keypair] = hst;
 
-        FG_CHECK(fg_append_histogram_to_chart(chart, hst));
+        FG_CHECK(mPlugin->fg_append_histogram_to_chart(chart, hst));
     }
 
     return mHstMap[keypair];
@@ -508,17 +517,17 @@ fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY, fg_dtype typ
 
     if (iter==mSfcMap.end()) {
         fg_chart_type chart_type;
-        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
         if(chart_type != FG_CHART_3D)
             AF_ERROR("Surface can only be added to chart of type FG_CHART_3D",
                      AF_ERR_TYPE);
 
         fg_surface surf = nullptr;
-        FG_CHECK(fg_create_surface(&surf, nX, nY, type,
+        FG_CHECK(mPlugin->fg_create_surface(&surf, nX, nY, type,
                                    FG_PLOT_SURFACE, FG_MARKER_NONE));
         mSfcMap[keypair] = surf;
 
-        FG_CHECK(fg_append_surface_to_chart(chart, surf));
+        FG_CHECK(mPlugin->fg_append_surface_to_chart(chart, surf));
     }
 
     return mSfcMap[keypair];
@@ -534,13 +543,13 @@ fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints, fg_dty
 
     if (iter==mVcfMap.end()) {
         fg_chart_type chart_type;
-        FG_CHECK(fg_get_chart_type(&chart_type, chart));
+        FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
 
         fg_vector_field vfield = nullptr;
-        FG_CHECK(fg_create_vector_field(&vfield, nPoints, type, chart_type));
+        FG_CHECK(mPlugin->fg_create_vector_field(&vfield, nPoints, type, chart_type));
         mVcfMap[keypair] = vfield;
 
-        FG_CHECK(fg_append_vector_field_to_chart(chart, vfield));
+        FG_CHECK(mPlugin->fg_append_vector_field_to_chart(chart, vfield));
     }
 
     return mVcfMap[keypair];
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 16d03b8678..5ffa3e82ef 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -14,6 +14,7 @@
 
 #include <vector>
 #include <map>
+#include <memory>
 #include <utility>
 
 // default to f32(float) type
@@ -69,20 +70,27 @@ typedef std::map<fg_chart, bool> ChartAxesOverride_t;
 typedef ChartAxesOverride_t::iterator ChartAxesOverrideIter;
 
 /**
- * ForgeManager class follows a single pattern. Any user of this class, has
- * to call ForgeManager::getInstance inorder to use Forge resources for rendering.
- * It manages the windows, and other renderables (given below) that are drawed
- * onto chosen window.
+ * Only device manager class can create objects of this class.
+ * You have to call forgeManager() defined in platform.hpp to
+ * access the object. It manages the windows, and other
+ * renderables (given below) that are drawed onto chosen window.
  * Renderables:
- *             fg_image
- *             fg_plot
- *             fg_histogram
- *             fg_surface
- *             fg_vector_field
+ *      fg_image
+ *      fg_plot
+ *      fg_histogram
+ *      fg_surface
+ *      fg_vector_field
  * */
 class ForgeManager
 {
+    struct Window {
+        fg_window handle;
+    };
+
     private:
+        ForgeModule* mPlugin;
+        std::unique_ptr<Window> wnd;
+
         ImageMap_t          mImgMap;
         PlotMap_t           mPltMap;
         HistogramMap_t      mHstMap;
@@ -93,14 +101,14 @@ class ForgeManager
         WindGridMap_t       mWndGridMap;
         ChartAxesOverride_t mChartAxesOverrideMap;
 
-        ForgeModule* mPlugin;
-
     public:
-        static ForgeManager& getInstance();
+        ForgeManager();
+        ForgeManager(ForgeManager const&) = delete;
+        ForgeManager& operator=(ForgeManager const&) = delete;
+        ForgeManager(ForgeManager &&) = delete;
+        ForgeManager& operator=(ForgeManager &&) = delete;
         ~ForgeManager();
-
-        friend ForgeModule& forgePlugin();
-
+        ForgeModule& plugin();
         fg_window getMainWindow();
 
         void            setWindowChartGrid(const fg_window window,
@@ -128,12 +136,5 @@ class ForgeManager
 
         bool getChartAxesOverride(fg_chart chart);
         void setChartAxesOverride(fg_chart chart, bool flag = true);
-
-    protected:
-        ForgeManager();
-        ForgeManager(ForgeManager const&);
-        void operator=(ForgeManager const&);
 };
 }
-
-#define MAIN_WINDOW graphics::ForgeManager::getInstance().getMainWindow()
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index d1e8d8baf7..c269e87874 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -23,8 +23,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
 
     CheckGL("Begin copy_histogram");
     unsigned bytes = 0, buffer = 0;
-    FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, hist));
-    FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
+    FG_CHECK(_.fg_get_histogram_vertex_buffer(&buffer, hist));
+    FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
     glBindBuffer(GL_ARRAY_BUFFER, buffer);
     glBufferSubData(GL_ARRAY_BUFFER, 0, bytes, data.get());
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 43bdb46da2..be4be570b8 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -31,8 +31,8 @@ void copy_image(const Array<T> &in, fg_image image)
     CheckGL("Before CopyArrayToImage");
     const T *d_X = in.get();
     unsigned data_size = 0, buffer = 0;
-    FG_CHECK(fg_get_pixel_buffer(&buffer, image));
-    FG_CHECK(fg_get_image_size(&data_size, image));
+    FG_CHECK(_.fg_get_pixel_buffer(&buffer, image));
+    FG_CHECK(_.fg_get_image_size(&data_size, image));
 
     glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
     glBufferSubData(GL_PIXEL_UNPACK_BUFFER, 0, data_size, d_X);
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index a54aa79436..7a5421f75b 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -9,8 +9,9 @@
 
 #include <af/version.h>
 #include <platform.hpp>
-#include <common/defines.hpp>
 #include <version.hpp>
+#include <common/defines.hpp>
+#include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
 
 #include <cctype>
@@ -262,7 +263,8 @@ bool& evalFlag()
 
 DeviceManager::DeviceManager()
     : queues(MAX_QUEUES)
-    , memManager(new MemoryManager()) {}
+    , memManager(new MemoryManager()),
+      fgMngr(new graphics::ForgeManager()){}
 
 
 MemoryManager& memoryManager()
@@ -271,6 +273,11 @@ MemoryManager& memoryManager()
     return *(inst.memManager);
 }
 
+graphics::ForgeManager& forgeManager()
+{
+    return *(DeviceManager::getInstance().fgMngr);
+}
+
 DeviceManager& DeviceManager::getInstance()
 {
     static DeviceManager* my_instance = new DeviceManager();
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index f07b4effb4..6530d0d914 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -78,6 +78,10 @@ class CPUInfo {
         bool   mIsHTT;
 };
 
+namespace graphics {
+  class ForgeManager;
+}
+
 namespace cpu
 {
 int getBackend();
@@ -108,6 +112,8 @@ bool& evalFlag();
 
 MemoryManager& memoryManager();
 
+graphics::ForgeManager& forgeManager();
+
 class DeviceManager
 {
     public:
@@ -122,6 +128,8 @@ class DeviceManager
 
         friend MemoryManager& memoryManager();
 
+        friend graphics::ForgeManager& forgeManager();
+
         CPUInfo getCPUInfo() const;
 
     private:
@@ -134,8 +142,10 @@ class DeviceManager
         void operator=(DeviceManager const&) = delete;
 
         // Attributes
+        std::unique_ptr<graphics::ForgeManager> fgMngr;
+        std::unique_ptr<MemoryManager> memManager;
         std::vector<queue> queues;
         const CPUInfo cinfo;
-        std::unique_ptr<MemoryManager> memManager;
+
 };
 }
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 549bf1f36f..4e196167a9 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -27,8 +27,8 @@ void copy_plot(const Array<T> &P, fg_plot plot)
 
     CheckGL("Before CopyArrayToVBO");
     unsigned bytes = 0, buffer = 0;
-    FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
-    FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
+    FG_CHECK(_.fg_get_plot_vertex_buffer(&buffer, plot));
+    FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
 
     glBindBuffer(GL_ARRAY_BUFFER, buffer);
     glBufferSubData(GL_ARRAY_BUFFER, 0, bytes, P.get());
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 80a80ab7e1..46e01c7dbf 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -27,8 +27,8 @@ void copy_surface(const Array<T> &P, fg_surface surface)
 
     CheckGL("Before CopyArrayToVBO");
     unsigned bytes = 0, buffer = 0;
-    FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
-    FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
+    FG_CHECK(_.fg_get_surface_vertex_buffer(&buffer, surface));
+    FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
 
     glBindBuffer(GL_ARRAY_BUFFER, buffer);
     glBufferSubData(GL_ARRAY_BUFFER, 0, bytes, P.get());
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index 93914fe6bc..f5064179a7 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -31,10 +31,10 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
     unsigned size1 = 0, size2 = 0;
     unsigned buff1 = 0, buff2 = 0;
-    FG_CHECK(fg_get_vector_field_vertex_buffer_size(&size1, vfield));
-    FG_CHECK(fg_get_vector_field_direction_buffer_size(&size2, vfield));
-    FG_CHECK(fg_get_vector_field_vertex_buffer(&buff1, vfield));
-    FG_CHECK(fg_get_vector_field_direction_buffer(&buff2, vfield));
+    FG_CHECK(_.fg_get_vector_field_vertex_buffer_size(&size1, vfield));
+    FG_CHECK(_.fg_get_vector_field_direction_buffer_size(&size2, vfield));
+    FG_CHECK(_.fg_get_vector_field_vertex_buffer(&buff1, vfield));
+    FG_CHECK(_.fg_get_vector_field_direction_buffer(&buff2, vfield));
 
     glBindBuffer(GL_ARRAY_BUFFER, buff1);
     glBufferSubData(GL_ARRAY_BUFFER, 0, size1, points.get());
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index c6749bf1ef..b6a1e8ec1b 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -18,7 +18,6 @@ namespace cuda {
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist)
 {
-    ForgeModule& _ = graphics::forgePlugin();
     auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
@@ -37,9 +36,10 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
 
         POST_LAUNCH_CHECK();
     } else {
+        ForgeModule& _ = graphics::forgePlugin();
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, hist));
-        FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
+        FG_CHECK(_.fg_get_histogram_vertex_buffer(&buffer, hist));
+        FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 104e1dcde6..dba78928f2 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -23,7 +23,6 @@ namespace cuda {
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image)
 {
-    ForgeModule& _ = graphics::forgePlugin();
     auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         auto res = interopManager().getImageResources(image);
@@ -40,10 +39,11 @@ void copy_image(const Array<T> &in, fg_image image)
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
     } else {
+        ForgeModule& _ = graphics::forgePlugin();
         CheckGL("Begin CUDA fallback-resource copy");
         unsigned data_size = 0, buffer = 0;
-        FG_CHECK(fg_get_image_size(&data_size, image));
-        FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+        FG_CHECK(_.fg_get_image_size(&data_size, image));
+        FG_CHECK(_.fg_get_pixel_buffer(&buffer, image));
 
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
         glBufferData(GL_PIXEL_UNPACK_BUFFER, data_size, 0, GL_STREAM_DRAW);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index eed023999e..5bd93c90f5 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -430,6 +430,11 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
+graphics::ForgeManager& forgeManager()
+{
+    return *(DeviceManager::getInstance().fgMngr);
+}
+
 GraphicsResourceManager& interopManager()
 {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -506,7 +511,7 @@ SparseHandle sparseHandle()
 }
 
 DeviceManager::DeviceManager()
-    : cuDevices(0), nDevices(0)
+    : cuDevices(0), nDevices(0), fgMngr(new graphics::ForgeManager())
 {
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
     if (nDevices == 0)
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 2502a89ffe..f3b38440a1 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -26,6 +26,11 @@
 namespace spdlog {
   class logger;
 }
+
+namespace graphics {
+  class ForgeManager;
+}
+
 namespace cuda
 {
 int getBackend();
@@ -84,6 +89,8 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
+graphics::ForgeManager& forgeManager();
+
 GraphicsResourceManager& interopManager();
 
 PlanCache& fftManager();
@@ -109,6 +116,8 @@ class DeviceManager
 
         friend MemoryManagerPinned& pinnedMemoryManager();
 
+        friend graphics::ForgeManager& forgeManager();
+
         friend GraphicsResourceManager& interopManager();
 
         friend std::string getDeviceInfo(int device);
@@ -155,6 +164,8 @@ class DeviceManager
         int nDevices;
         cudaStream_t streams[MAX_DEVICES];
 
+        std::unique_ptr<graphics::ForgeManager> fgMngr;
+
         std::unique_ptr<MemoryManager> memManager;
 
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index ecfa6267dc..d77f7dbf2b 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -20,7 +20,6 @@ namespace cuda {
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot)
 {
-    ForgeModule& _ = graphics::forgePlugin();
     auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
@@ -39,9 +38,10 @@ void copy_plot(const Array<T> &P, fg_plot plot)
 
         POST_LAUNCH_CHECK();
     } else {
+        ForgeModule& _ = graphics::forgePlugin();
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
-        FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
+        FG_CHECK(_.fg_get_plot_vertex_buffer(&buffer, plot));
+        FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 880d03ecc1..c7c52bd9c3 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -20,7 +20,6 @@ namespace cuda {
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface)
 {
-    ForgeModule& _ = graphics::forgePlugin();
     auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
@@ -39,9 +38,10 @@ void copy_surface(const Array<T> &P, fg_surface surface)
 
         POST_LAUNCH_CHECK();
     } else {
+        ForgeModule& _ = graphics::forgePlugin();
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
-        FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
+        FG_CHECK(_.fg_get_surface_vertex_buffer(&buffer, surface));
+        FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 5a5359843b..fc2ac458da 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -21,7 +21,6 @@ template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield)
 {
-    ForgeModule& _ = graphics::forgePlugin();
     auto stream = cuda::getActiveStream();
     if(DeviceManager::checkGraphicsInteropCapability()) {
         auto res = interopManager().getVectorFieldResources(vfield);
@@ -53,13 +52,14 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         POST_LAUNCH_CHECK();
     } else {
+        ForgeModule& _ = graphics::forgePlugin();
         CheckGL("Begin CUDA fallback-resource copy");
         unsigned size1 = 0, size2 = 0;
         unsigned buff1 = 0, buff2 = 0;
-        FG_CHECK(fg_get_vector_field_vertex_buffer_size(&size1, vfield));
-        FG_CHECK(fg_get_vector_field_direction_buffer_size(&size2, vfield));
-        FG_CHECK(fg_get_vector_field_vertex_buffer(&buff1, vfield));
-        FG_CHECK(fg_get_vector_field_direction_buffer(&buff2, vfield));
+        FG_CHECK(_.fg_get_vector_field_vertex_buffer_size(&size1, vfield));
+        FG_CHECK(_.fg_get_vector_field_direction_buffer_size(&size2, vfield));
+        FG_CHECK(_.fg_get_vector_field_vertex_buffer(&buff1, vfield));
+        FG_CHECK(_.fg_get_vector_field_direction_buffer(&buff2, vfield));
 
         // Points
         glBindBuffer(GL_ARRAY_BUFFER, buff1);
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index c42efe21bc..c0874e570c 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -23,7 +23,7 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = data.get();
         unsigned bytes = 0;
-        FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
+        FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
         auto res = interopManager().getHistogramResources(hist);
 
@@ -46,8 +46,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
         CheckGL("End OpenCL resource copy");
     } else {
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_histogram_vertex_buffer(&buffer, hist));
-        FG_CHECK(fg_get_histogram_vertex_buffer_size(&bytes, hist));
+        FG_CHECK(_.fg_get_histogram_vertex_buffer(&buffer, hist));
+        FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 41ad3fc8e4..86418db1e9 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -30,7 +30,7 @@ void copy_image(const Array<T> &in, fg_image image)
         const cl::Buffer *d_X = in.get();
 
         unsigned bytes = 0;
-        FG_CHECK(fg_get_image_size(&bytes, image));
+        FG_CHECK(_.fg_get_image_size(&bytes, image));
 
         std::vector<cl::Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
@@ -52,8 +52,8 @@ void copy_image(const Array<T> &in, fg_image image)
     } else {
         CheckGL("Begin OpenCL fallback-resource copy");
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_image_size(&bytes, image));
-        FG_CHECK(fg_get_pixel_buffer(&buffer, image));
+        FG_CHECK(_.fg_get_image_size(&bytes, image));
+        FG_CHECK(_.fg_get_pixel_buffer(&buffer, image));
 
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
         glBufferData(GL_PIXEL_UNPACK_BUFFER, bytes, 0, GL_STREAM_DRAW);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index dd14b54837..32a8f7fc17 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -721,6 +721,11 @@ MemoryManagerPinned& pinnedMemoryManager()
     return *(inst.pinnedMemManager.get());
 }
 
+graphics::ForgeManager& forgeManager()
+{
+    return *(DeviceManager::getInstance().fgMngr);
+}
+
 GraphicsResourceManager& interopManager()
 {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -813,6 +818,7 @@ DeviceManager::~DeviceManager()
 
 DeviceManager::DeviceManager()
     : mUserDeviceOffset(0),
+      fgMngr(new graphics::ForgeManager()),
       mFFTSetup(new clfftSetupData)
 {
     std::vector<cl::Platform>   platforms;
@@ -835,8 +841,6 @@ DeviceManager::DeviceManager()
         DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
     }
 
-
-
     // Iterate through platforms, get all available devices and store them
     for (auto &platform : platforms) {
         std::vector<Device> current_devices;
@@ -848,7 +852,6 @@ DeviceManager::DeviceManager()
                 throw;
             }
         }
-
         for (auto dev : current_devices) {
             mDevices.push_back(new Device(dev));
         }
@@ -910,7 +913,6 @@ DeviceManager::DeviceManager()
                 break;
             }
         }
-
         if (!default_device_set) {
             printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
                     deviceENV.c_str());
@@ -920,12 +922,14 @@ DeviceManager::DeviceManager()
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
-    if(noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
+    if (fgMngr->plugin().isLoaded() && noGraphicsENV.empty()) {
+        // If forge library was successfully loaded and
+        // AF_DISABLE_GRAPHICS is not defined
         try {
             /* loop over devices and replace contexts with
              * OpenGL shared contexts whereever applicable */
             int devCount = mDevices.size();
-            fg_window wHandle = graphics::ForgeManager::getInstance().getMainWindow();
+            fg_window wHandle = fgMngr->getMainWindow();
             for(int i=0; i<devCount; ++i)
                 markDeviceForInterop(i, wHandle);
         } catch (...) {
@@ -970,9 +974,10 @@ void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
             cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
 
             long long wnd_ctx, wnd_dsp;
-            FG_CHECK(fg_get_window_context_handle(&wnd_ctx, const_cast<fg_window>(wHandle)));
-            FG_CHECK(fg_get_window_display_handle(&wnd_dsp, const_cast<fg_window>(wHandle)));
-
+            fgMngr->plugin().fg_get_window_context_handle(&wnd_ctx,
+                                                          const_cast<fg_window>(wHandle));
+            fgMngr->plugin().fg_get_window_display_handle(&wnd_dsp,
+                                                          const_cast<fg_window>(wHandle));
 #ifdef OS_MAC
             CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
             CGLShareGroupObj cgl_share_group = CGLGetShareGroup(cgl_current_ctx);
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 80b1da12b6..79bd82b6b6 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -36,6 +36,10 @@ namespace boost {
   }
 }
 
+namespace graphics {
+  class ForgeManager;
+}
+
 // Forward declaration from clFFT.h
 struct clfftSetupData_;
 typedef clfftSetupData_ clfftSetupData;
@@ -102,6 +106,8 @@ MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
 
+graphics::ForgeManager& forgeManager();
+
 GraphicsResourceManager& interopManager();
 
 PlanCache& fftManager();
@@ -120,6 +126,8 @@ class DeviceManager
 
     friend MemoryManagerPinned& pinnedMemoryManager();
 
+    friend graphics::ForgeManager& forgeManager();
+
     friend GraphicsResourceManager& interopManager();
 
     friend PlanCache& fftManager();
@@ -191,6 +199,7 @@ class DeviceManager
         std::vector<int>         mPlatforms;
         unsigned mUserDeviceOffset;
 
+        std::unique_ptr<graphics::ForgeManager> fgMngr;
         std::unique_ptr<MemoryManager> memManager;
         std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
         std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index ad68818f6b..6f8db54664 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -25,7 +25,7 @@ void copy_plot(const Array<T> &P, fg_plot plot)
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
         unsigned bytes = 0;
-        FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
+        FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
 
         auto res = interopManager().getPlotResources(plot);
 
@@ -48,8 +48,8 @@ void copy_plot(const Array<T> &P, fg_plot plot)
         CheckGL("End OpenCL resource copy");
     } else {
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_plot_vertex_buffer(&buffer, plot));
-        FG_CHECK(fg_get_plot_vertex_buffer_size(&bytes, plot));
+        FG_CHECK(_.fg_get_plot_vertex_buffer(&buffer, plot));
+        FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 5575974040..58ba8063ae 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -28,7 +28,7 @@ void copy_surface(const Array<T> &P, fg_surface surface)
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
         unsigned bytes = 0;
-        FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
+        FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
 
         auto res = interopManager().getSurfaceResources(surface);
 
@@ -51,8 +51,8 @@ void copy_surface(const Array<T> &P, fg_surface surface)
         CheckGL("End OpenCL resource copy");
     } else {
         unsigned bytes = 0, buffer = 0;
-        FG_CHECK(fg_get_surface_vertex_buffer(&buffer, surface));
-        FG_CHECK(fg_get_surface_vertex_buffer_size(&bytes, surface));
+        FG_CHECK(_.fg_get_surface_vertex_buffer(&buffer, surface));
+        FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 87eb12ae02..8264fa33ee 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -28,8 +28,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         const cl::Buffer *d_directions  = directions.get();
         unsigned pBytes = 0;
         unsigned dBytes = 0;
-        FG_CHECK(fg_get_vector_field_vertex_buffer_size(&pBytes, vfield));
-        FG_CHECK(fg_get_vector_field_direction_buffer_size(&dBytes, vfield));
+        FG_CHECK(_.fg_get_vector_field_vertex_buffer_size(&pBytes, vfield));
+        FG_CHECK(_.fg_get_vector_field_direction_buffer_size(&dBytes, vfield));
 
         auto res = interopManager().getVectorFieldResources(vfield);
 
@@ -55,10 +55,10 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
     } else {
         unsigned size1 = 0, size2 = 0;
         unsigned buff1 = 0, buff2 = 0;
-        FG_CHECK(fg_get_vector_field_vertex_buffer_size(&size1, vfield));
-        FG_CHECK(fg_get_vector_field_direction_buffer_size(&size2, vfield));
-        FG_CHECK(fg_get_vector_field_vertex_buffer(&buff1, vfield));
-        FG_CHECK(fg_get_vector_field_direction_buffer(&buff2, vfield));
+        FG_CHECK(_.fg_get_vector_field_vertex_buffer_size(&size1, vfield));
+        FG_CHECK(_.fg_get_vector_field_direction_buffer_size(&size2, vfield));
+        FG_CHECK(_.fg_get_vector_field_vertex_buffer(&buff1, vfield));
+        FG_CHECK(_.fg_get_vector_field_direction_buffer(&buff2, vfield));
 
         CheckGL("Begin OpenCL fallback-resource copy");
 

From 5cc99675d2f514228a1b734f8456eb5c847339fa Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 30 Jul 2018 09:48:58 +0530
Subject: [PATCH 1589/2677] Use mkl gemm batch in CPU backend when appropriate

Default to Intel OpenMP on Linux, OSX and Windows

Intel TBB is not giving speed up as expected as of now. Hence, switching
to OpenMP as thread layer.

* GNU OpenMP causing some opencl tests to fail on certain debian
  configurations. Hence, choosing Intel OpenMP.
* GNU OpenMP related mkl_gnu_thread library is not installed
  on OSX's Intel MKL Installation, so on OSX only option is Intel OpenMP.
* Windows OpenMP support is lacking behind by so many versions, so we
  are using Intel OpenMP on Windows too.
---
 CMakeLists.txt                  |   3 +
 src/backend/cpu/blas.cpp        | 171 ++++++++++++++++++++++----------
 src/backend/cpu/sparse_blas.cpp |  10 +-
 test/blas.cpp                   |   1 -
 4 files changed, 129 insertions(+), 56 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2cfe3b2ba4..f62299b836 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -22,6 +22,9 @@ include(CheckCXXCompilerFlag)
 
 arrayfire_set_cmake_default_variables()
 
+#Set Intel OpenMP as default MKL thread layer
+set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
+
 find_package(CUDA 7.0)
 find_package(OpenCL 1.2)
 find_package(OpenGL)
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 517405aa12..31ba8ffa7e 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -29,6 +29,9 @@
 
 #include <algorithm>
 #include <type_traits>
+#include <vector>
+
+using std::vector;
 
 namespace cpu
 {
@@ -48,28 +51,48 @@ using common::is_complex;
 //
 // Sample cgemm API
 // OpenBLAS
-// void cblas_cgemm(OPENBLAS_CONST enum CBLAS_ORDER Order, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransA, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransB,
-//                  OPENBLAS_CONST blasint M, OPENBLAS_CONST blasint N, OPENBLAS_CONST blasint K,
-//                  OPENBLAS_CONST float *alpha, OPENBLAS_CONST float *A, OPENBLAS_CONST blasint lda,
-//                  OPENBLAS_CONST float *B, OPENBLAS_CONST blasint ldb, OPENBLAS_CONST float *beta,
+// void cblas_cgemm(OPENBLAS_CONST enum CBLAS_ORDER Order,
+//                  OPENBLAS_CONST enum CBLAS_TRANSPOSE TransA,
+//                  OPENBLAS_CONST enum CBLAS_TRANSPOSE TransB,
+//                  OPENBLAS_CONST blasint M,
+//                  OPENBLAS_CONST blasint N,
+//                  OPENBLAS_CONST blasint K,
+//                  OPENBLAS_CONST float *alpha, OPENBLAS_CONST float *A,
+//                  OPENBLAS_CONST blasint lda,
+//                  OPENBLAS_CONST float *B, OPENBLAS_CONST blasint ldb,
+//                  OPENBLAS_CONST float *beta,
 //                  float *C, OPENBLAS_CONST blasint ldc);
 //
 // MKL
-// void cblas_cgemm(const  CBLAS_LAYOUT Layout, const CBLAS_TRANSPOSE TransA, const  CBLAS_TRANSPOSE TransB,
+// void cblas_cgemm(const  CBLAS_LAYOUT Layout,
+//                  const CBLAS_TRANSPOSE TransA, const  CBLAS_TRANSPOSE TransB,
 //                  const MKL_INT M, const MKL_INT N, const MKL_INT K,
 //                  const void *alpha, const void *A, const MKL_INT lda,
 //                  const void *B, const MKL_INT ldb, const void *beta,
 //                  void *C, const MKL_INT ldc);
+// void cblas_cgemm_batch(const  CBLAS_LAYOUT Layout,
+//                        const CBLAS_TRANSPOSE* TransA,
+//                        const CBLAS_TRANSPOSE* TransB,
+//                        const MKL_INT* M, const MKL_INT* N, const MKL_INT* K,
+//                        const void *alpha, const void **A, const MKL_INT* lda,
+//                        const void **B, const MKL_INT* ldb, const void *beta,
+//                        void **C, const MKL_INT* ldc,
+//                        const MKL_INT group_count, const MKL_INT* group_size);
+//
 // atlas cblas
-// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE TransA,
-//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+// void cblas_cgemm(const enum CBLAS_ORDER Order,
+//                  const enum CBLAS_TRANSPOSE TransA,
+//                  const enum CBLAS_TRANSPOSE TransB,
+//                  const int M, const int N, const int K,
 //                  const void *alpha, const void *A, const int lda,
 //                  const void *B, const int ldb, const void *beta,
 //                  void *C, const int ldc);
 //
 // LAPACKE
-// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE TransA,
-//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+// void cblas_cgemm(const enum CBLAS_ORDER Order,
+//                  const enum CBLAS_TRANSPOSE TransA,
+//                  const enum CBLAS_TRANSPOSE TransB,
+//                  const int M, const int N, const int K,
 //                  const void *alpha, const void *A, const int lda,
 //                  const void *B, const int ldb, const void *beta,
 //                  void *C, const int ldc);
@@ -82,20 +105,26 @@ struct blas_base {
 };
 
 template<typename T>
-using cptr_type     =   typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T*>::type;
+using cptr_type   =   typename conditional<is_complex<T>::value,
+                                           const typename blas_base<T>::type *,
+                                           const T*>::type;
 template<typename T>
-using ptr_type     =    typename conditional<   is_complex<T>::value,
-                                                typename blas_base<T>::type *,
-                                                T*>::type;
+using ptr_type   =    typename conditional<is_complex<T>::value,
+                                           typename blas_base<T>::type *,
+                                           T*>::type;
 template<typename T>
-using scale_type   =    typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T>::type;
+using scale_type =    typename conditional<is_complex<T>::value,
+                                           const typename blas_base<T>::type *,
+                                           const T>::type;
 
 template<typename T>
-using gemm_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE, const CBLAS_TRANSPOSE,
+using batch_scale_type = typename conditional<is_complex<T>::value,
+                                              const typename blas_base<T>::type*,
+                                              const T*>::type;
+
+template<typename T>
+using gemm_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE,
+                                const CBLAS_TRANSPOSE,
                                 const blasint, const blasint, const blasint,
                                 scale_type<T>, cptr_type<T>, const blasint,
                                 cptr_type<T>, const blasint,
@@ -108,6 +137,18 @@ using gemv_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE,
                                 cptr_type<T>, const blasint,
                                 scale_type<T>, ptr_type<T>, const blasint);
 
+#ifdef USE_MKL
+template<typename T>
+using gemm_batch_func_def = void (*)( const CBLAS_LAYOUT,
+                                      const CBLAS_TRANSPOSE*,
+                                      const CBLAS_TRANSPOSE*,
+                                      const MKL_INT*, const MKL_INT*, const MKL_INT*,
+                                      batch_scale_type<T>, cptr_type<T>*, const MKL_INT*,
+                                      cptr_type<T>*, const MKL_INT*, batch_scale_type<T>,
+                                      ptr_type<T>*, const MKL_INT*,
+                                      const MKL_INT, const MKL_INT*);
+#endif
+
 #define BLAS_FUNC_DEF( FUNC )                           \
 template<typename T> FUNC##_func_def<T> FUNC##_func();
 
@@ -127,6 +168,14 @@ BLAS_FUNC(gemv , double  , d)
 BLAS_FUNC(gemv , cfloat  , c)
 BLAS_FUNC(gemv , cdouble , z)
 
+#ifdef USE_MKL
+BLAS_FUNC_DEF( gemm_batch )
+BLAS_FUNC(gemm_batch , float   , s)
+BLAS_FUNC(gemm_batch , double  , d)
+BLAS_FUNC(gemm_batch , cfloat  , c)
+BLAS_FUNC(gemm_batch , cdouble , z)
+#endif
+
 template<typename T, int value>
 typename enable_if<is_floating_point<T>::value, scale_type<T>>::type
 getScale() { return T(value); }
@@ -188,44 +237,64 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
         dim4 rStrides = right.strides();
         dim4 oStrides = output.strides();
 
-        int batchSize = oDims[2] * oDims[3];
+        if (oDims.ndims() <= 2) {
+            if (rDims[bColDim] == 1) {
+                dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
+                        left.get(), lStrides[1], right.get(), incr, beta,
+                        output.get(), 1);
+            } else {
+                gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
+                        left.get(), lStrides[1], right.get(), rStrides[1], beta,
+                        output.get(), output.dims(0));
+            }
+        } else {
+            int batchSize = oDims[2] * oDims[3];
 
-        bool is_l_d2_batched = oDims[2] == lDims[2];
-        bool is_l_d3_batched = oDims[3] == lDims[3];
-        bool is_r_d2_batched = oDims[2] == rDims[2];
-        bool is_r_d3_batched = oDims[3] == rDims[3];
+            const bool is_l_d2_batched = oDims[2] == lDims[2];
+            const bool is_l_d3_batched = oDims[3] == lDims[3];
+            const bool is_r_d2_batched = oDims[2] == rDims[2];
+            const bool is_r_d3_batched = oDims[3] == rDims[3];
 
-        for (int n = 0; n < batchSize; n++) {
-            int w = n / oDims[2];
-            int z = n - w * oDims[2];
+            vector< CBT* > lptrs(batchSize);
+            vector< CBT* > rptrs(batchSize);
+            vector<  BT* > optrs(batchSize);
 
-            int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
-            int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+            for (int n = 0; n < batchSize; n++) {
+                int w = n / oDims[2];
+                int z = n - w * oDims[2];
 
-            CBT *lptr = reinterpret_cast<CBT*>(left.get() + loff);
-            CBT *rptr = reinterpret_cast<CBT*>(right.get() + roff);
-            BT *optr = reinterpret_cast<BT*>(output.get() + z * oStrides[2] + w * oStrides[3]);
+                int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
+                int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
 
-            if(rDims[bColDim] == 1) {
-                dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                gemv_func<T>()(
-                    CblasColMajor, lOpts,
-                    lDims[0], lDims[1],
-                    alpha,
-                    lptr, lStrides[1],
-                    rptr, incr,
-                    beta,
-                    optr, 1);
-            } else {
-                gemm_func<T>()(
-                    CblasColMajor, lOpts, rOpts,
-                    M, N, K,
-                    alpha,
-                    lptr, lStrides[1],
-                    rptr, rStrides[1],
-                    beta,
-                    optr, output.dims(0));
+                lptrs[n] = reinterpret_cast<CBT*>(left.get() + loff);
+                rptrs[n] = reinterpret_cast<CBT*>(right.get() + roff);
+                optrs[n] = reinterpret_cast<BT*>(output.get() + z * oStrides[2] + w * oStrides[3]);
             }
+
+#ifdef USE_MKL
+            // MKL can handle multiple groups of batches
+            // However, for ArrayFire's use case, the group_count=1
+            const MKL_INT lda = lStrides[1];
+            const MKL_INT ldb = rStrides[1];
+            const MKL_INT ldc = oStrides[1];
+
+            gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts, &M, &N, &K,
+                    &alpha, lptrs.data(), &lda, rptrs.data(), &ldb, &beta,
+                    optrs.data(), &ldc, 1, &batchSize);
+#else
+            for (int n = 0; n < batchSize; n++) {
+                if(rDims[bColDim] == 1) {
+                    dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                    gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
+                            lptrs[n], lStrides[1], rptrs[n], incr, beta, optrs[n], 1);
+                } else {
+                    gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
+                            lptrs[n], lStrides[1], rptrs[n], rStrides[1], beta,
+                            optrs[n], output.dims(0));
+                }
+            }
+#endif
         }
     };
     getQueue().enqueue(func, out, lhs, rhs);
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 77b82704ad..18d8f59590 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -230,8 +230,9 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
     static const int rColDim = 1;
 
-    dim4 lDims = lhs.dims();
-    const dim4 rDims = rhs.dims();
+    const dim4& lDims = lhs.dims();
+    const dim4& rDims = rhs.dims();
+
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
     //int K = lDims[lColDim];
@@ -461,8 +462,9 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     static const int rColDim = 1;
 
-    auto lDims = lhs.dims();
-    const auto rDims = rhs.dims();
+    const dim4& lDims = lhs.dims();
+    const dim4& rDims = rhs.dims();
+
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
 
diff --git a/test/blas.cpp b/test/blas.cpp
index a0a5422f05..17c0911bbf 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -252,7 +252,6 @@ TEST(MatrixMultiply, Batched)
     const int N = 10;
     const int D2 = 2;
     const int D3 = 3;
-
     for (int d3 = 1; d3 <= D3; d3 *= D3) {
         for (int d2 = 1; d2 <= D2; d2 *= D2) {
             array a = randu(M, K, d2, d3);

From c2d047546d263fae5b4466b552bc9a8706d71468 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 26 Dec 2018 22:33:12 -0500
Subject: [PATCH 1590/2677] Add .clang-format files to enforce formatting.

* Use clang-format to enforce formatting rules.
* We need multiple clang format files because we need to maintain
  c++03 compatibility for tests and examples.
---
 examples/.clang-format | 144 +++++++++++++++++++++++++++++++++++++++++
 src/.clang-format      | 144 +++++++++++++++++++++++++++++++++++++++++
 test/.clang-format     | 144 +++++++++++++++++++++++++++++++++++++++++
 3 files changed, 432 insertions(+)
 create mode 100644 examples/.clang-format
 create mode 100644 src/.clang-format
 create mode 100644 test/.clang-format

diff --git a/examples/.clang-format b/examples/.clang-format
new file mode 100644
index 0000000000..692cbc2f40
--- /dev/null
+++ b/examples/.clang-format
@@ -0,0 +1,144 @@
+---
+Language:        Cpp
+# BasedOnStyle:  Google
+AccessModifierOffset: -1
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: true
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: Left
+AlignOperands:   true
+AlignTrailingComments: true
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortBlocksOnASingleLine: true
+AllowShortCaseLabelsOnASingleLine: true
+AllowShortFunctionsOnASingleLine: All
+AllowShortIfStatementsOnASingleLine: true
+AllowShortLoopsOnASingleLine: true
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments: true
+BinPackParameters: true
+BraceWrapping:   
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  AfterExternBlock: false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+  SplitEmptyFunction: false
+  SplitEmptyRecord: false
+  SplitEmptyNamespace: false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Custom
+BreakInheritanceList: BeforeComma
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializers: BeforeComma
+BreakStringLiterals: true
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: true
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks:   Preserve
+IncludeCategories: 
+  - Regex:           '^<af/.*\.h.*>'
+    Priority:        2
+  - Regex:           '^<.*\.h.*>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        3
+  - Regex:           '.*'
+    Priority:        4
+IncludeIsMainRegex: '([-_](test|unittest))?$'
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth:     4
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Never
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Right
+RawStringFormats: 
+  - Language:        Cpp
+    Delimiters:      
+      - cc
+      - CC
+      - cpp
+      - Cpp
+      - CPP
+      - 'c++'
+      - 'C++'
+      - R
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+  - Language:        TextProto
+    Delimiters:      
+      - pb
+      - PB
+      - proto
+      - PROTO
+    EnclosingFunctions: 
+      - EqualsProto
+      - EquivToProto
+      - PARSE_PARTIAL_TEXT_PROTO
+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+ReflowComments:  true
+SortIncludes:    true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: false
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: false
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 2
+SpacesInAngles:  false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp03
+TabWidth:        4
+UseTab:          Never
+
diff --git a/src/.clang-format b/src/.clang-format
new file mode 100644
index 0000000000..47afdf3208
--- /dev/null
+++ b/src/.clang-format
@@ -0,0 +1,144 @@
+---
+Language:        Cpp
+# BasedOnStyle:  Google
+AccessModifierOffset: -1
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: true
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: Left
+AlignOperands:   true
+AlignTrailingComments: true
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortBlocksOnASingleLine: true
+AllowShortCaseLabelsOnASingleLine: true
+AllowShortFunctionsOnASingleLine: All
+AllowShortIfStatementsOnASingleLine: true
+AllowShortLoopsOnASingleLine: true
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments: true
+BinPackParameters: true
+BraceWrapping:   
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  AfterExternBlock: false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+  SplitEmptyFunction: false
+  SplitEmptyRecord: false
+  SplitEmptyNamespace: false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Custom
+BreakInheritanceList: BeforeComma
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializers: BeforeComma
+BreakStringLiterals: true
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: true
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks:   Preserve
+IncludeCategories: 
+  - Regex:           '^<af/.*\.h.*>'
+    Priority:        2
+  - Regex:           '^<.*\.h.*>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        3
+  - Regex:           '.*'
+    Priority:        4
+IncludeIsMainRegex: '([-_](test|unittest))?$'
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth:     4
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Never
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Right
+RawStringFormats: 
+  - Language:        Cpp
+    Delimiters:      
+      - cc
+      - CC
+      - cpp
+      - Cpp
+      - CPP
+      - 'c++'
+      - 'C++'
+      - R
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+  - Language:        TextProto
+    Delimiters:      
+      - pb
+      - PB
+      - proto
+      - PROTO
+    EnclosingFunctions: 
+      - EqualsProto
+      - EquivToProto
+      - PARSE_PARTIAL_TEXT_PROTO
+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+ReflowComments:  true
+SortIncludes:    true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: false
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: false
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 2
+SpacesInAngles:  false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        4
+UseTab:          Never
+
diff --git a/test/.clang-format b/test/.clang-format
new file mode 100644
index 0000000000..692cbc2f40
--- /dev/null
+++ b/test/.clang-format
@@ -0,0 +1,144 @@
+---
+Language:        Cpp
+# BasedOnStyle:  Google
+AccessModifierOffset: -1
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: true
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: Left
+AlignOperands:   true
+AlignTrailingComments: true
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortBlocksOnASingleLine: true
+AllowShortCaseLabelsOnASingleLine: true
+AllowShortFunctionsOnASingleLine: All
+AllowShortIfStatementsOnASingleLine: true
+AllowShortLoopsOnASingleLine: true
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments: true
+BinPackParameters: true
+BraceWrapping:   
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  AfterExternBlock: false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+  SplitEmptyFunction: false
+  SplitEmptyRecord: false
+  SplitEmptyNamespace: false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Custom
+BreakInheritanceList: BeforeComma
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializers: BeforeComma
+BreakStringLiterals: true
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: true
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks:   Preserve
+IncludeCategories: 
+  - Regex:           '^<af/.*\.h.*>'
+    Priority:        2
+  - Regex:           '^<.*\.h.*>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        3
+  - Regex:           '.*'
+    Priority:        4
+IncludeIsMainRegex: '([-_](test|unittest))?$'
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth:     4
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Never
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Right
+RawStringFormats: 
+  - Language:        Cpp
+    Delimiters:      
+      - cc
+      - CC
+      - cpp
+      - Cpp
+      - CPP
+      - 'c++'
+      - 'C++'
+      - R
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+  - Language:        TextProto
+    Delimiters:      
+      - pb
+      - PB
+      - proto
+      - PROTO
+    EnclosingFunctions: 
+      - EqualsProto
+      - EquivToProto
+      - PARSE_PARTIAL_TEXT_PROTO
+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+ReflowComments:  true
+SortIncludes:    true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: false
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: false
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 2
+SpacesInAngles:  false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp03
+TabWidth:        4
+UseTab:          Never
+

From 71286231349bb8ca18c956c2a6e67bf6c296e5fb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 26 Dec 2018 02:24:15 -0500
Subject: [PATCH 1591/2677] Update all source files based on clang-format rules

---
 examples/benchmarks/blas.cpp                  |    21 +-
 examples/benchmarks/cg.cpp                    |    80 +-
 examples/benchmarks/fft.cpp                   |    18 +-
 examples/benchmarks/pi.cpp                    |    29 +-
 examples/common/idxio.h                       |    27 +-
 examples/common/progress.h                    |    14 +-
 examples/computer_vision/fast.cpp             |    36 +-
 examples/computer_vision/harris.cpp           |    48 +-
 examples/computer_vision/matching.cpp         |   111 +-
 examples/computer_vision/susan.cpp            |    36 +-
 examples/financial/black_scholes_options.cpp  |    49 +-
 examples/financial/heston_model.cpp           |    82 +-
 examples/financial/input.h                    |  5324 +++++---
 examples/financial/monte_carlo_options.cpp    |    71 +-
 examples/getting_started/convolve.cpp         |    29 +-
 examples/getting_started/integer.cpp          |    19 +-
 examples/getting_started/rainfall.cpp         |    33 +-
 examples/getting_started/vectorize.cpp        |    71 +-
 examples/graphics/conway.cpp                  |    40 +-
 examples/graphics/conway_pretty.cpp           |    84 +-
 examples/graphics/field.cpp                   |    24 +-
 examples/graphics/fractal.cpp                 |    51 +-
 examples/graphics/gravity_sim.cpp             |   201 +-
 examples/graphics/gravity_sim_init.h          | 11004 ++++++++++------
 examples/graphics/histogram.cpp               |     7 +-
 examples/graphics/plot2d.cpp                  |    26 +-
 examples/graphics/plot3.cpp                   |    29 +-
 examples/graphics/surface.cpp                 |    14 +-
 examples/helloworld/helloworld.cpp            |    10 +-
 .../adaptive_thresholding.cpp                 |    83 +-
 .../image_processing/binary_thresholding.cpp  |    74 +-
 .../image_processing/brain_segmentation.cpp   |    67 +-
 examples/image_processing/deconvolution.cpp   |    47 +-
 examples/image_processing/edge.cpp            |    59 +-
 examples/image_processing/filters.cpp         |   214 +-
 .../image_processing/gradient_diffusion.cpp   |    45 +-
 examples/image_processing/image_demo.cpp      |    45 +-
 examples/image_processing/image_editing.cpp   |   125 +-
 examples/image_processing/morphing.cpp        |    78 +-
 examples/image_processing/optical_flow.cpp    |    81 +-
 examples/image_processing/pyramids.cpp        |    56 +-
 examples/lin_algebra/cholesky.cpp             |     8 +-
 examples/lin_algebra/lu.cpp                   |     3 +-
 examples/lin_algebra/qr.cpp                   |     4 +-
 examples/lin_algebra/svd.cpp                  |     9 +-
 examples/machine_learning/bagging.cpp         |    68 +-
 examples/machine_learning/deep_belief_net.cpp |   206 +-
 .../machine_learning/geneticalgorithm.cpp     |   122 +-
 examples/machine_learning/kmeans.cpp          |    77 +-
 examples/machine_learning/knn.cpp             |    51 +-
 .../machine_learning/logistic_regression.cpp  |    84 +-
 examples/machine_learning/mnist_common.h      |    70 +-
 examples/machine_learning/naive_bayes.cpp     |    79 +-
 examples/machine_learning/neural_network.cpp  |   132 +-
 examples/machine_learning/perceptron.cpp      |    60 +-
 examples/machine_learning/rbm.cpp             |   114 +-
 .../machine_learning/softmax_regression.cpp   |    93 +-
 examples/pde/swe.cpp                          |    92 +-
 examples/unified/basic.cpp                    |    13 +-
 src/api/c/anisotropic_diffusion.cpp           |    66 +-
 src/api/c/approx.cpp                          |   227 +-
 src/api/c/array.cpp                           |   569 +-
 src/api/c/assign.cpp                          |   244 +-
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 test/jit.cpp                                  |   177 +-
 test/join.cpp                                 |   144 +-
 test/lu_dense.cpp                             |    93 +-
 test/manual_memory_test.cpp                   |    23 +-
 test/match_template.cpp                       |   105 +-
 test/math.cpp                                 |   154 +-
 test/matrix_manipulation.cpp                  |    74 +-
 test/matrixmarket.cpp                         |     9 +-
 test/mean.cpp                                 |   262 +-
 test/meanshift.cpp                            |   141 +-
 test/meanvar.cpp                              |   351 +-
 test/medfilt.cpp                              |   399 +-
 test/median.cpp                               |   131 +-
 test/memory.cpp                               |   311 +-
 test/memory_lock.cpp                          |    29 +-
 test/missing.cpp                              |    13 +-
 test/mmio/mmio.c                              |   366 +-
 test/mmio/mmio.h                              |   134 +-
 test/moddims.cpp                              |   218 +-
 test/moments.cpp                              |   130 +-
 test/morph.cpp                                |   377 +-
 test/nearest_neighbour.cpp                    |   358 +-
 test/ocl_ext_context.cpp                      |    85 +-
 test/orb.cpp                                  |   214 +-
 test/pinverse.cpp                             |   151 +-
 test/qr_dense.cpp                             |    48 +-
 test/random.cpp                               |   355 +-
 test/random_practrand.cpp                     |    34 +-
 test/range.cpp                                |   136 +-
 test/rank_dense.cpp                           |    87 +-
 test/reduce.cpp                               |   474 +-
 test/regions.cpp                              |   230 +-
 test/reorder.cpp                              |   166 +-
 test/replace.cpp                              |    89 +-
 test/resize.cpp                               |   404 +-
 test/rotate.cpp                               |   195 +-
 test/rotate_linear.cpp                        |   214 +-
 test/sat.cpp                                  |    26 +-
 test/scan.cpp                                 |   166 +-
 test/scan_by_key.cpp                          |   229 +-
 test/select.cpp                               |   285 +-
 test/set.cpp                                  |   140 +-
 test/shift.cpp                                |   133 +-
 test/sift_nonfree.cpp                         |   249 +-
 test/sobel.cpp                                |    55 +-
 test/solve_common.hpp                         |    73 +-
 test/solve_dense.cpp                          |    56 +-
 test/sort.cpp                                 |   147 +-
 test/sort_by_key.cpp                          |   130 +-
 test/sort_index.cpp                           |   136 +-
 test/sparse.cpp                               |   307 +-
 test/sparse_arith.cpp                         |   244 +-
 test/sparse_common.hpp                        |   102 +-
 test/sparse_convert.cpp                       |    85 +-
 test/stdev.cpp                                |   160 +-
 test/susan.cpp                                |   134 +-
 test/svd_dense.cpp                            |    68 +-
 test/testHelpers.hpp                          |   791 +-
 test/threading.cpp                            |   742 +-
 test/tile.cpp                                 |   159 +-
 test/topk.cpp                                 |   270 +-
 test/transform.cpp                            |   338 +-
 test/transform_coordinates.cpp                |    64 +-
 test/translate.cpp                            |   188 +-
 test/transpose.cpp                            |   221 +-
 test/transpose_inplace.cpp                    |    45 +-
 test/triangle.cpp                             |   107 +-
 test/unwrap.cpp                               |   226 +-
 test/var.cpp                                  |    90 +-
 test/where.cpp                                |    88 +-
 test/wrap.cpp                                 |   162 +-
 test/write.cpp                                |    75 +-
 test/ycbcr_rgb.cpp                            |   134 +-
 1394 files changed, 84876 insertions(+), 84573 deletions(-)

diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index e1e3f0db60..fac3368e49 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -8,22 +8,20 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <stdio.h>
 #include <math.h>
+#include <stdio.h>
 #include <cstdlib>
 
 using namespace af;
 
 // create a small wrapper to benchmark
-static array A; // populated before each timing
-static void fn()
-{
+static array A;  // populated before each timing
+static void fn() {
     array B = matmul(A, A);  // matrix multiply
     B.eval();                // ensure evaluated
 }
 
-int main(int argc, char ** argv)
-{
+int main(int argc, char** argv) {
     double peak = 0;
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
@@ -32,13 +30,11 @@ int main(int argc, char ** argv)
 
         printf("Benchmark N-by-N matrix multiply\n");
         for (int n = 128; n <= 2048; n += 128) {
-
             printf("%4d x %4d: ", n, n);
-            A = constant(1,n,n);
-            double time = timeit(fn); // time in seconds
-            double gflops = 2.0 * powf(n,3) / (time * 1e9);
-            if (gflops > peak)
-                peak = gflops;
+            A             = constant(1, n, n);
+            double time   = timeit(fn);  // time in seconds
+            double gflops = 2.0 * powf(n, 3) / (time * 1e9);
+            if (gflops > peak) peak = gflops;
 
             printf(" %4.0f Gflops\n", gflops);
             fflush(stdout);
@@ -48,7 +44,6 @@ int main(int argc, char ** argv)
         throw;
     }
 
-
     printf(" ### peak %g GFLOPS\n", peak);
 
     return 0;
diff --git a/examples/benchmarks/cg.cpp b/examples/benchmarks/cg.cpp
index 47c35af8c4..cda79cec24 100644
--- a/examples/benchmarks/cg.cpp
+++ b/examples/benchmarks/cg.cpp
@@ -11,8 +11,8 @@
 
 using namespace af;
 
-static size_t dimension = 4 * 1024;
-static const int maxIter = 10;
+static size_t dimension         = 4 * 1024;
+static const int maxIter        = 10;
 static const int sparsityFactor = 7;
 
 static array A;
@@ -20,8 +20,7 @@ static array spA;  // Sparse A
 static array x0;
 static array b;
 
-void setupInputs()
-{
+void setupInputs() {
     // Generate a random input: A
     array T = randu(dimension, dimension, f32);
     // Create 0s in input.
@@ -29,7 +28,7 @@ void setupInputs()
     A = floor(T * 1000);
     A = A * ((A % sparsityFactor) == 0) / 1000;
     // Make it positive definite
-    A = transpose(A) + A + A.dims(0)*identity(A.dims(0), A.dims(0), f32);
+    A = transpose(A) + A + A.dims(0) * identity(A.dims(0), A.dims(0), f32);
 
     // Make A sparse as spA
     spA = sparse(A);
@@ -37,85 +36,80 @@ void setupInputs()
     // Generate x0: Random guess
     x0 = randu(A.dims(0), f32);
 
-    //Generate b
+    // Generate b
     b = matmul(A, x0);
 
     std::cout << "Sparsity of A = "
-              << 100.f * (float)sparseGetNNZ(spA) / (float)spA.elements()
-              << "%" << std::endl;
-    std::cout << "Memory Usage of A = "
-              << A.bytes() / (1024.f * 1024.f)
+              << 100.f * (float)sparseGetNNZ(spA) / (float)spA.elements() << "%"
+              << std::endl;
+    std::cout << "Memory Usage of A = " << A.bytes() / (1024.f * 1024.f)
               << " MB" << std::endl;
     std::cout << "Memory Usage of spA = "
-              <<(sparseGetValues(spA).bytes()
-               + sparseGetRowIdx(spA).bytes()
-               + sparseGetColIdx(spA).bytes()) / (1024.f * 1024.f)
+              << (sparseGetValues(spA).bytes() + sparseGetRowIdx(spA).bytes() +
+                  sparseGetColIdx(spA).bytes()) /
+                     (1024.f * 1024.f)
               << " MB" << std::endl;
 }
 
-void sparseConjugateGradient(void)
-{
+void sparseConjugateGradient(void) {
     array x = constant(0, b.dims(), f32);
     array r = b - matmul(spA, x);
     array p = r;
 
     for (int i = 0; i < maxIter; ++i) {
-        array Ap = matmul(spA, p);
+        array Ap        = matmul(spA, p);
         array alpha_num = dot(r, r);
         array alpha_den = dot(p, Ap);
-        array alpha = alpha_num/alpha_den;
-        r -= tile(alpha, Ap.dims())*Ap;
-        x += tile(alpha, Ap.dims())*p;
+        array alpha     = alpha_num / alpha_den;
+        r -= tile(alpha, Ap.dims()) * Ap;
+        x += tile(alpha, Ap.dims()) * p;
         array beta_num = dot(r, r);
-        array beta = beta_num/alpha_num;
-        p = r + tile(beta, p.dims()) * p;
+        array beta     = beta_num / alpha_num;
+        p              = r + tile(beta, p.dims()) * p;
     }
 }
 
-void denseConjugateGradient(void)
-{
+void denseConjugateGradient(void) {
     array x = constant(0, b.dims(), f32);
     array r = b - matmul(A, x);
     array p = r;
 
     for (int i = 0; i < maxIter; ++i) {
-        array Ap = matmul(A, p);
+        array Ap        = matmul(A, p);
         array alpha_num = dot(r, r);
         array alpha_den = dot(p, Ap);
-        array alpha = alpha_num/alpha_den;
-        r -= tile(alpha, Ap.dims())*Ap;
-        x += tile(alpha, Ap.dims())*p;
+        array alpha     = alpha_num / alpha_den;
+        r -= tile(alpha, Ap.dims()) * Ap;
+        x += tile(alpha, Ap.dims()) * p;
         array beta_num = dot(r, r);
-        array beta = beta_num/alpha_num;
-        p = r + tile(beta, p.dims()) * p;
+        array beta     = beta_num / alpha_num;
+        p              = r + tile(beta, p.dims()) * p;
     }
 }
 
-void checkConjugateGradient(const af::array in)
-{
+void checkConjugateGradient(const af::array in) {
     array x = constant(0, b.dims(), f32);
     array r = b - matmul(in, x);
     array p = r;
 
     for (int i = 0; i < maxIter; ++i) {
-        array Ap = matmul(in, p);
+        array Ap        = matmul(in, p);
         array alpha_num = dot(r, r);
         array alpha_den = dot(p, Ap);
-        array alpha = alpha_num/alpha_den;
-        r -= tile(alpha, Ap.dims())*Ap;
-        x += tile(alpha, Ap.dims())*p;
+        array alpha     = alpha_num / alpha_den;
+        r -= tile(alpha, Ap.dims()) * Ap;
+        x += tile(alpha, Ap.dims()) * p;
         array beta_num = dot(r, r);
-        array beta = beta_num/alpha_num;
-        p = r + tile(beta, p.dims()) * p;
+        array beta     = beta_num / alpha_num;
+        p              = r + tile(beta, p.dims()) * p;
     }
     array res = x0 - x;
 
-    std::cout<<"Final difference in solutions:\n";
+    std::cout << "Final difference in solutions:\n";
     af_print(dot(res, res));
 }
 
-int main(int , char **)
-{
+int main(int, char **) {
     af::info();
     setupInputs();
 
@@ -128,12 +122,10 @@ int main(int , char **)
     af::sync();
 
     std::cout << "Dense Conjugate Gradient Time: "
-              << timeit(denseConjugateGradient) * 1000
-              << "ms" << std::endl;
+              << timeit(denseConjugateGradient) * 1000 << "ms" << std::endl;
 
     std::cout << "Sparse Conjugate Gradient Time: "
-              << timeit(sparseConjugateGradient) * 1000
-              << "ms" << std::endl;
+              << timeit(sparseConjugateGradient) * 1000 << "ms" << std::endl;
 
     return 0;
 }
diff --git a/examples/benchmarks/fft.cpp b/examples/benchmarks/fft.cpp
index 5b196c8877..490a1fa18e 100644
--- a/examples/benchmarks/fft.cpp
+++ b/examples/benchmarks/fft.cpp
@@ -8,22 +8,20 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <stdio.h>
 #include <math.h>
+#include <stdio.h>
 #include <cstdlib>
 
 using namespace af;
 
 // create a small wrapper to benchmark
-static array A; // populated before each timing
-static void fn()
-{
+static array A;  // populated before each timing
+static void fn() {
     array B = fft2(A);  // matrix multiply
     B.eval();           // ensure evaluated
 }
 
-int main(int argc, char ** argv)
-{
+int main(int argc, char** argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         setDevice(device);
@@ -34,16 +32,14 @@ int main(int argc, char ** argv)
             int N = (1 << M);
 
             printf("%4d x %4d: ", N, N);
-            A = randu(N,N);
-            double time = timeit(fn); // time in seconds
+            A             = randu(N, N);
+            double time   = timeit(fn);  // time in seconds
             double gflops = 10.0 * N * N * M / (time * 1e9);
 
             printf(" %4.0f Gflops\n", gflops);
             fflush(stdout);
         }
-    } catch (af::exception& e) {
-        fprintf(stderr, "%s\n", e.what());
-    }
+    } catch (af::exception& e) { fprintf(stderr, "%s\n", e.what()); }
 
     return 0;
 }
diff --git a/examples/benchmarks/pi.cpp b/examples/benchmarks/pi.cpp
index 9ad5ad41b7..8913f36bc1 100644
--- a/examples/benchmarks/pi.cpp
+++ b/examples/benchmarks/pi.cpp
@@ -15,10 +15,10 @@
    - count what percent fell inside (top quarter) of unit circle
 */
 
-#include <stdio.h>
+#include <arrayfire.h>
 #include <math.h>
+#include <stdio.h>
 #include <cstdlib>
-#include <arrayfire.h>
 using namespace af;
 
 // generate millions of random samples
@@ -27,40 +27,35 @@ static int samples = 20e6;
 /* Self-contained code to run host and device estimates of PI.  Note that
    each is generating its own random values, so the estimates of PI
    will differ. */
-static double pi_device()
-{
-    array x = randu(samples,f32), y = randu(samples,f32);
-    return 4.0 * sum<float>(sqrt(x*x + y*y) < 1) / samples;
+static double pi_device() {
+    array x = randu(samples, f32), y = randu(samples, f32);
+    return 4.0 * sum<float>(sqrt(x * x + y * y) < 1) / samples;
 }
 
-static double pi_host()
-{
+static double pi_host() {
     int count = 0;
     for (int i = 0; i < samples; ++i) {
         float x = float(rand()) / RAND_MAX;
         float y = float(rand()) / RAND_MAX;
-        if (sqrt(x*x + y*y) < 1)
-            count++;
+        if (sqrt(x * x + y * y) < 1) count++;
     }
     return 4.0 * count / samples;
 }
 
-
-
 // void wrappers for timeit()
 static void device_wrapper() { pi_device(); }
 static void host_wrapper() { pi_host(); }
 
-
-int main(int argc, char ** argv)
-{
+int main(int argc, char** argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         setDevice(device);
         info();
 
-        printf("device:  %.5f seconds to estimate  pi = %.5f\n", timeit(device_wrapper), pi_device());
-        printf("  host:  %.5f seconds to estimate  pi = %.5f\n", timeit(host_wrapper), pi_host());
+        printf("device:  %.5f seconds to estimate  pi = %.5f\n",
+               timeit(device_wrapper), pi_device());
+        printf("  host:  %.5f seconds to estimate  pi = %.5f\n",
+               timeit(host_wrapper), pi_host());
     } catch (exception& e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
diff --git a/examples/common/idxio.h b/examples/common/idxio.h
index 16b710a42b..cc80b6e125 100644
--- a/examples/common/idxio.h
+++ b/examples/common/idxio.h
@@ -9,21 +9,19 @@
 
 #pragma once
 
-#include <iostream>
+#include <arrayfire.h>
+#include <algorithm>
 #include <fstream>
+#include <iostream>
 #include <stdexcept>
 #include <vector>
-#include <algorithm>
-#include <arrayfire.h>
 
-union Data
-{
+union Data {
     unsigned dim;
     char bytes[4];
 };
 
-unsigned char reverse_char(unsigned char b)
-{
+unsigned char reverse_char(unsigned char b) {
     b = (b & 0xF0) >> 4 | (b & 0x0F) << 4;
     b = (b & 0xCC) >> 2 | (b & 0x33) << 2;
     b = (b & 0xAA) >> 1 | (b & 0x55) << 1;
@@ -31,8 +29,7 @@ unsigned char reverse_char(unsigned char b)
 }
 
 // http://stackoverflow.com/a/9144870/2192361
-unsigned reverse(unsigned x)
-{
+unsigned reverse(unsigned x) {
     x = ((x >> 1) & 0x55555555u) | ((x & 0x55555555u) << 1);
     x = ((x >> 2) & 0x33333333u) | ((x & 0x33333333u) << 2);
     x = ((x >> 4) & 0x0f0f0f0fu) | ((x & 0x0f0f0f0fu) << 4);
@@ -42,24 +39,22 @@ unsigned reverse(unsigned x)
 }
 
 template<class ty>
-void read_idx(std::vector<dim_t> &dims, std::vector<ty> &data, const char *name)
-{
+void read_idx(std::vector<dim_t> &dims, std::vector<ty> &data,
+              const char *name) {
     std::ifstream f(name, std::ios::in | std::ios::binary);
     if (!f.is_open()) throw std::runtime_error("Unable to open file");
 
     Data d;
     f.read(d.bytes, sizeof(d.bytes));
 
-    if (d.bytes[2] != 8) {
-        throw std::runtime_error("Unsupported data type");
-    }
+    if (d.bytes[2] != 8) { throw std::runtime_error("Unsupported data type"); }
 
-    unsigned numdims = d.bytes[3];
+    unsigned numdims  = d.bytes[3];
     unsigned elemsize = 1;
 
     // Read the dimensions
     size_t elem = 1;
-    dims = std::vector<dim_t>(numdims);
+    dims        = std::vector<dim_t>(numdims);
     for (unsigned i = 0; i < numdims; i++) {
         f.read(d.bytes, sizeof(d.bytes));
 
diff --git a/examples/common/progress.h b/examples/common/progress.h
index 6452aa2a5b..90ccdd0abc 100644
--- a/examples/common/progress.h
+++ b/examples/common/progress.h
@@ -10,14 +10,13 @@
 #ifndef __PROGRESS_H
 #define __PROGRESS_H
 
-#include <cmath>
 #include <algorithm>
+#include <cmath>
 
-static bool progress(unsigned iter_curr, af::timer t, double time_total)
-{
+static bool progress(unsigned iter_curr, af::timer t, double time_total) {
     static unsigned iter_prev = 0;
-    static double time_prev = 0;
-    static double max_rate = 0;
+    static double time_prev   = 0;
+    static double max_rate    = 0;
 
     af::sync();
     double time_curr = af::timer::stop(t);
@@ -25,15 +24,14 @@ static bool progress(unsigned iter_curr, af::timer t, double time_total)
     if ((time_curr - time_prev) < 1) return true;
 
     double rate = (iter_curr - iter_prev) / (time_curr - time_prev);
-    printf("  iterations per second: %.0f   (progress %.0f%%)\n",
-            rate, 100.0f * time_curr / time_total);
+    printf("  iterations per second: %.0f   (progress %.0f%%)\n", rate,
+           100.0f * time_curr / time_total);
 
     max_rate = std::max(max_rate, rate);
 
     iter_prev = iter_curr;
     time_prev = time_curr;
 
-
     if (time_curr < time_total) return true;
 
     printf(" ### %f iterations per second (max)\n", max_rate);
diff --git a/examples/computer_vision/fast.cpp b/examples/computer_vision/fast.cpp
index ecedd87144..0dbc12b3b7 100644
--- a/examples/computer_vision/fast.cpp
+++ b/examples/computer_vision/fast.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cstdio>
 #include <arrayfire.h>
+#include <cstdio>
 #include <cstdlib>
 
 using namespace af;
 
-static void fast_demo(bool console)
-{
+static void fast_demo(bool console) {
     // Load image
     array img_color;
     if (console)
@@ -23,7 +22,8 @@ static void fast_demo(bool console)
         img_color = loadImage(ASSETS_DIR "/examples/images/man.jpg", true);
     // Convert the image from RGB to gray-scale
     array img = colorSpace(img_color, AF_GRAY, AF_RGB);
-    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f] interval
+    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f]
+    // interval
     img_color /= 255.f;
 
     features feat = fast(img, 20.0f, 9, true, 0.05);
@@ -34,17 +34,17 @@ static void fast_demo(bool console)
     // Draw draw_len x draw_len crosshairs where the corners are
     const int draw_len = 3;
     for (size_t f = 0; f < feat.getNumFeatures(); f++) {
-        int x = h_x[f];
-        int y = h_y[f];
-        img_color(y, seq(x-draw_len, x+draw_len), 0) = 0.f;
-        img_color(y, seq(x-draw_len, x+draw_len), 1) = 1.f;
-        img_color(y, seq(x-draw_len, x+draw_len), 2) = 0.f;
+        int x                                            = h_x[f];
+        int y                                            = h_y[f];
+        img_color(y, seq(x - draw_len, x + draw_len), 0) = 0.f;
+        img_color(y, seq(x - draw_len, x + draw_len), 1) = 1.f;
+        img_color(y, seq(x - draw_len, x + draw_len), 2) = 0.f;
 
-        // Draw vertical line of (draw_len * 2 + 1) pixels centered on  the corner
-        // Set only the first channel to 1 (green lines)
-        img_color(seq(y-draw_len, y+draw_len), x, 0) = 0.f;
-        img_color(seq(y-draw_len, y+draw_len), x, 1) = 1.f;
-        img_color(seq(y-draw_len, y+draw_len), x, 2) = 0.f;
+        // Draw vertical line of (draw_len * 2 + 1) pixels centered on  the
+        // corner Set only the first channel to 1 (green lines)
+        img_color(seq(y - draw_len, y + draw_len), x, 0) = 0.f;
+        img_color(seq(y - draw_len, y + draw_len), x, 1) = 1.f;
+        img_color(seq(y - draw_len, y + draw_len), x, 2) = 0.f;
     }
 
     freeHost(h_x);
@@ -56,17 +56,15 @@ static void fast_demo(bool console)
         af::Window wnd("FAST Feature Detector");
 
         // Previews color image with green crosshairs
-        while(!wnd.close())
-            wnd.image(img_color);
+        while (!wnd.close()) wnd.image(img_color);
     } else {
         af_print(feat.getX());
         af_print(feat.getY());
     }
 }
 
-int main(int argc, char** argv)
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char** argv) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
diff --git a/examples/computer_vision/harris.cpp b/examples/computer_vision/harris.cpp
index c1571fa2ed..d97a30d803 100644
--- a/examples/computer_vision/harris.cpp
+++ b/examples/computer_vision/harris.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cstdio>
 #include <arrayfire.h>
+#include <cstdio>
 #include <cstdlib>
 
 using namespace af;
 
-static void harris_demo(bool console)
-{
+static void harris_demo(bool console) {
     af::Window wnd("Harris Corner Detector");
 
     // Load image
@@ -25,7 +24,8 @@ static void harris_demo(bool console)
         img_color = loadImage(ASSETS_DIR "/examples/images/man.jpg", true);
     // Convert the image from RGB to gray-scale
     array img = colorSpace(img_color, AF_GRAY, AF_RGB);
-    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f] interval
+    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f]
+    // interval
     img_color /= 255.f;
 
     // Calculate image gradients
@@ -37,8 +37,8 @@ static void harris_demo(bool console)
     array ixy = ix * iy;
     array iyy = iy * iy;
 
-    // Compute a Gaussian kernel with standard deviation of 1.0 and length of 5 pixels
-    // These values can be changed to use a smaller or larger window
+    // Compute a Gaussian kernel with standard deviation of 1.0 and length of 5
+    // pixels These values can be changed to use a smaller or larger window
     array gauss_filt = gaussianKernel(5, 5, 1.0, 1.0);
 
     // Filter second-order derivatives with Gaussian kernel computed previously
@@ -55,13 +55,13 @@ static void harris_demo(bool console)
     array response = idet - 0.04f * (itr * itr);
 
     // Gets maximum response for each 3x3 neighborhood
-    //array max_resp = maxfilt(response, 3, 3);
-    array mask = constant(1,3,3);
+    // array max_resp = maxfilt(response, 3, 3);
+    array mask     = constant(1, 3, 3);
     array max_resp = dilate(response, mask);
 
     // Discard responses that are not greater than threshold
     array corners = response > 1e5f;
-    corners = corners * response;
+    corners       = corners * response;
 
     // Discard responses that are not equal to maximum neighborhood response,
     // scale them to original response value
@@ -78,17 +78,17 @@ static void harris_demo(bool console)
         for (int x = draw_len; x < img_color.dims(1) - draw_len; x++) {
             // Only draws crosshair if is a corner
             if (h_corners[x * corners.dims(0) + y] > 1e5f) {
-                // Draw horizontal line of (draw_len * 2 + 1) pixels centered on the corner
-                // Set only the first channel to 1 (green lines)
-                img_color(y, seq(x-draw_len, x+draw_len), 0) = 0.f;
-                img_color(y, seq(x-draw_len, x+draw_len), 1) = 1.f;
-                img_color(y, seq(x-draw_len, x+draw_len), 2) = 0.f;
-
-                // Draw vertical line of (draw_len * 2 + 1) pixels centered on  the corner
-                // Set only the first channel to 1 (green lines)
-                img_color(seq(y-draw_len, y+draw_len), x, 0) = 0.f;
-                img_color(seq(y-draw_len, y+draw_len), x, 1) = 1.f;
-                img_color(seq(y-draw_len, y+draw_len), x, 2) = 0.f;
+                // Draw horizontal line of (draw_len * 2 + 1) pixels centered on
+                // the corner Set only the first channel to 1 (green lines)
+                img_color(y, seq(x - draw_len, x + draw_len), 0) = 0.f;
+                img_color(y, seq(x - draw_len, x + draw_len), 1) = 1.f;
+                img_color(y, seq(x - draw_len, x + draw_len), 2) = 0.f;
+
+                // Draw vertical line of (draw_len * 2 + 1) pixels centered on
+                // the corner Set only the first channel to 1 (green lines)
+                img_color(seq(y - draw_len, y + draw_len), x, 0) = 0.f;
+                img_color(seq(y - draw_len, y + draw_len), x, 1) = 1.f;
+                img_color(seq(y - draw_len, y + draw_len), x, 2) = 0.f;
 
                 good_corners++;
             }
@@ -100,8 +100,7 @@ static void harris_demo(bool console)
 
     if (!console) {
         // Previews color image with green crosshairs
-        while(!wnd.close())
-            wnd.image(img_color);
+        while (!wnd.close()) wnd.image(img_color);
     } else {
         // Find corner indexes in the image as 1D indexes
         array idx = where(corners);
@@ -118,9 +117,8 @@ static void harris_demo(bool console)
     }
 }
 
-int main(int argc, char** argv)
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char** argv) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
diff --git a/examples/computer_vision/matching.cpp b/examples/computer_vision/matching.cpp
index 2c42f9578b..80cc9a80b5 100644
--- a/examples/computer_vision/matching.cpp
+++ b/examples/computer_vision/matching.cpp
@@ -8,44 +8,42 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <cstdlib>
 #include <cstdio>
+#include <cstdlib>
 #include <iostream>
 
 using namespace af;
 
-array normalize(array a)
-{
+array normalize(array a) {
     float mx = af::max<float>(a);
     float mn = af::min<float>(a);
-    return (a-mn)/(mx-mn);
+    return (a - mn) / (mx - mn);
 }
 
-void drawRectangle(array &out, unsigned x, unsigned y, unsigned dim0, unsigned dim1)
-{
+void drawRectangle(array& out, unsigned x, unsigned y, unsigned dim0,
+                   unsigned dim1) {
     printf("\nMatching patch origin = (%u, %u)\n\n", x, y);
-    seq col_span(x, x+dim0, 1);
-    seq row_span(y, y+dim1, 1);
-    //edge on left
-    out(col_span, y       , 0) = 0.f;
-    out(col_span, y       , 1) = 0.f;
-    out(col_span, y       , 2) = 1.f;
-    //edge on right
-    out(col_span, y+dim1  , 0) = 0.f;
-    out(col_span, y+dim1  , 1) = 0.f;
-    out(col_span, y+dim1  , 2) = 1.f;
-    //edge on top
-    out(x       , row_span, 0) = 0.f;
-    out(x       , row_span, 1) = 0.f;
-    out(x       , row_span, 2) = 1.f;
-    //edge on bottom
-    out(x+dim0  , row_span, 0) = 0.f;
-    out(x+dim0  , row_span, 1) = 0.f;
-    out(x+dim0  , row_span, 2) = 1.f;
+    seq col_span(x, x + dim0, 1);
+    seq row_span(y, y + dim1, 1);
+    // edge on left
+    out(col_span, y, 0) = 0.f;
+    out(col_span, y, 1) = 0.f;
+    out(col_span, y, 2) = 1.f;
+    // edge on right
+    out(col_span, y + dim1, 0) = 0.f;
+    out(col_span, y + dim1, 1) = 0.f;
+    out(col_span, y + dim1, 2) = 1.f;
+    // edge on top
+    out(x, row_span, 0) = 0.f;
+    out(x, row_span, 1) = 0.f;
+    out(x, row_span, 2) = 1.f;
+    // edge on bottom
+    out(x + dim0, row_span, 0) = 0.f;
+    out(x + dim0, row_span, 1) = 0.f;
+    out(x + dim0, row_span, 2) = 1.f;
 }
 
-static void templateMatchingDemo(bool console)
-{
+static void templateMatchingDemo(bool console) {
     // Load image
     array img_color;
     if (console)
@@ -54,62 +52,71 @@ static void templateMatchingDemo(bool console)
         img_color = loadImage(ASSETS_DIR "/examples/images/man.jpg", true);
 
     // Convert the image from RGB to gray-scale
-    array img = colorSpace(img_color, AF_GRAY, AF_RGB);
+    array img  = colorSpace(img_color, AF_GRAY, AF_RGB);
     dim4 iDims = img.dims();
-    std::cout<<"Input image dimensions: " << iDims << std::endl << std::endl;
-    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f] interval
+    std::cout << "Input image dimensions: " << iDims << std::endl << std::endl;
+    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f]
+    // interval
 
     // extract a patch from input image
     unsigned patch_size = 100;
-    array tmp_img = img(seq(100, 100+patch_size, 1.0), seq(100, 100+patch_size, 1.0));
-    array result  = matchTemplate(img, tmp_img); // Default disparity metric is
-                                                 // Sum of Absolute differences (SAD)
-                                                 // Currently supported metrics are
-                                                 // AF_SAD, AF_ZSAD, AF_LSAD, AF_SSD,
-                                                 // AF_ZSSD, ASF_LSSD
-    array disp_img = img/255.0f;
-    array disp_tmp = tmp_img/255.0f;
+    array tmp_img =
+        img(seq(100, 100 + patch_size, 1.0), seq(100, 100 + patch_size, 1.0));
+    array result =
+        matchTemplate(img, tmp_img);  // Default disparity metric is
+                                      // Sum of Absolute differences (SAD)
+                                      // Currently supported metrics are
+                                      // AF_SAD, AF_ZSAD, AF_LSAD, AF_SSD,
+                                      // AF_ZSSD, ASF_LSSD
+    array disp_img = img / 255.0f;
+    array disp_tmp = tmp_img / 255.0f;
     array disp_res = normalize(result);
 
     unsigned minLoc;
-    float    minVal;
+    float minVal;
     min<float>(&minVal, &minLoc, disp_res);
-    std::cout<< "Location(linear index) of minimum disparity value = " << minLoc << std::endl;
+    std::cout << "Location(linear index) of minimum disparity value = "
+              << minLoc << std::endl;
 
     if (!console) {
         // Draw a rectangle on input image where the template matches
         array marked_res = tile(disp_img, 1, 1, 3);
-        drawRectangle(marked_res, minLoc%iDims[0], minLoc/iDims[0], patch_size, patch_size);
+        drawRectangle(marked_res, minLoc % iDims[0], minLoc / iDims[0],
+                      patch_size, patch_size);
 
-        std::cout<<"Note: Based on the disparity metric option provided to matchTemplate function\n"
-            "either minimum or maximum disparity location is the starting corner\n"
-            "of our best matching patch to template image in the search image"<< std::endl;
+        std::cout << "Note: Based on the disparity metric option provided to "
+                     "matchTemplate function\n"
+                     "either minimum or maximum disparity location is the "
+                     "starting corner\n"
+                     "of our best matching patch to template image in the "
+                     "search image"
+                  << std::endl;
 
         af::Window wnd("Template Matching Demo");
 
         // Previews color image with green crosshairs
-        while(!wnd.close()) {
+        while (!wnd.close()) {
             wnd.setColorMap(AF_COLORMAP_DEFAULT);
             wnd.grid(2, 2);
-            wnd(0, 0).image(disp_img  , "Search Image"    );
-            wnd(0, 1).image(disp_tmp  , "Template Patch"  );
-            wnd(1, 0).image(marked_res, "Best Match"      );
+            wnd(0, 0).image(disp_img, "Search Image");
+            wnd(0, 1).image(disp_tmp, "Template Patch");
+            wnd(1, 0).image(marked_res, "Best Match");
             wnd.setColorMap(AF_COLORMAP_HEAT);
-            wnd(1, 1).image(disp_res  , "Disparity values");
+            wnd(1, 1).image(disp_res, "Disparity values");
             wnd.show();
         }
     }
 }
 
-int main(int argc, char** argv)
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char** argv) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
         af::setDevice(device);
         af::info();
-        std::cout << "** ArrayFire template matching Demo **" << std::endl << std::endl;
+        std::cout << "** ArrayFire template matching Demo **" << std::endl
+                  << std::endl;
         templateMatchingDemo(console);
 
     } catch (af::exception& ae) {
diff --git a/examples/computer_vision/susan.cpp b/examples/computer_vision/susan.cpp
index 93d4157216..417213de7c 100644
--- a/examples/computer_vision/susan.cpp
+++ b/examples/computer_vision/susan.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cstdio>
 #include <arrayfire.h>
+#include <cstdio>
 #include <cstdlib>
 
 using namespace af;
 
-static void susan_demo(bool console)
-{
+static void susan_demo(bool console) {
     // Load image
     array img_color;
     if (console)
@@ -23,7 +22,8 @@ static void susan_demo(bool console)
         img_color = loadImage(ASSETS_DIR "/examples/images/man.jpg", true);
     // Convert the image from RGB to gray-scale
     array img = colorSpace(img_color, AF_GRAY, AF_RGB);
-    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f] interval
+    // For visualization in ArrayFire, color images must be in the [0.0f-1.0f]
+    // interval
     img_color /= 255.f;
 
     features feat = susan(img, 3, 32.0f, 10, 0.05f, 3);
@@ -39,17 +39,17 @@ static void susan_demo(bool console)
     // Draw draw_len x draw_len crosshairs where the corners are
     const int draw_len = 3;
     for (size_t f = 0; f < feat.getNumFeatures(); f++) {
-        int x = h_x[f];
-        int y = h_y[f];
-        img_color(x, seq(y-draw_len, y+draw_len), 0) = 0.f;
-        img_color(x, seq(y-draw_len, y+draw_len), 1) = 1.f;
-        img_color(x, seq(y-draw_len, y+draw_len), 2) = 0.f;
+        int x                                            = h_x[f];
+        int y                                            = h_y[f];
+        img_color(x, seq(y - draw_len, y + draw_len), 0) = 0.f;
+        img_color(x, seq(y - draw_len, y + draw_len), 1) = 1.f;
+        img_color(x, seq(y - draw_len, y + draw_len), 2) = 0.f;
 
-        // Draw vertical line of (draw_len * 2 + 1) pixels centered on  the corner
-        // Set only the first channel to 1 (green lines)
-        img_color(seq(x-draw_len, x+draw_len), y, 0) = 0.f;
-        img_color(seq(x-draw_len, x+draw_len), y, 1) = 1.f;
-        img_color(seq(x-draw_len, x+draw_len), y, 2) = 0.f;
+        // Draw vertical line of (draw_len * 2 + 1) pixels centered on  the
+        // corner Set only the first channel to 1 (green lines)
+        img_color(seq(x - draw_len, x + draw_len), y, 0) = 0.f;
+        img_color(seq(x - draw_len, x + draw_len), y, 1) = 1.f;
+        img_color(seq(x - draw_len, x + draw_len), y, 2) = 0.f;
     }
     freeHost(h_x);
     freeHost(h_y);
@@ -60,8 +60,7 @@ static void susan_demo(bool console)
         af::Window wnd("FAST Feature Detector");
 
         // Previews color image with green crosshairs
-        while(!wnd.close())
-            wnd.image(img_color);
+        while (!wnd.close()) wnd.image(img_color);
     } else {
         af_print(feat.getX());
         af_print(feat.getY());
@@ -69,9 +68,8 @@ static void susan_demo(bool console)
     }
 }
 
-int main(int argc, char** argv)
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char** argv) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
diff --git a/examples/financial/black_scholes_options.cpp b/examples/financial/black_scholes_options.cpp
index f1af40aec5..3bc1347d93 100644
--- a/examples/financial/black_scholes_options.cpp
+++ b/examples/financial/black_scholes_options.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <iostream>
-#include <stdio.h>
-#include <math.h>
 #include <arrayfire.h>
+#include <math.h>
+#include <stdio.h>
 #include <cstdlib>
+#include <iostream>
 
 #include "input.h"
 using namespace af;
@@ -19,17 +19,13 @@ using namespace af;
 // Use the relationship between the cumulative normal distribution and the
 // (complementary) error function:
 // https://en.wikipedia.org/wiki/Error_function#Cumulative_distribution_function
-array cnd(array x)
-{
+array cnd(array x) {
     const float sqrt05 = sqrt(0.5f);
-    return 0.5f * erfc(- x * sqrt05);
+    return 0.5f * erfc(-x * sqrt05);
 }
 
-static void black_scholes(array& C, array& P,
-                          const array& S, const array& X,
-                          const array& R, const array& V,
-                          const array& T)
-{
+static void black_scholes(array& C, array& P, const array& S, const array& X,
+                          const array& R, const array& V, const array& T) {
     // This function computes the call and put option prices based on
     // Black-Scholes Model
 
@@ -40,28 +36,27 @@ static void black_scholes(array& C, array& P,
     // T = Time to maturity
 
     array d1 = log(S / X);
-    d1 = d1 + (R + (V*V)*0.5) * T;
-    d1 = d1 / (V*sqrt(T));
+    d1       = d1 + (R + (V * V) * 0.5) * T;
+    d1       = d1 / (V * sqrt(T));
 
-    array d2 = d1 - (V*sqrt(T));
+    array d2 = d1 - (V * sqrt(T));
 
     array cnd_d1 = cnd(d1);
     array cnd_d2 = cnd(d2);
 
-    C = S * cnd_d1  - (X * exp((-R)*T) * cnd_d2);
-    P = X * exp((-R)*T) * (1 - cnd_d2) - (S * (1 - cnd_d1));
+    C = S * cnd_d1 - (X * exp((-R) * T) * cnd_d2);
+    P = X * exp((-R) * T) * (1 - cnd_d2) - (S * (1 - cnd_d1));
 }
 
-int main(int argc, char **argv)
-{
-
+int main(int argc, char** argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         setDevice(device);
         info();
 
-        printf("** ArrayFire Black-Scholes Example **\n"
-               "**          by AccelerEyes         **\n\n");
+        printf(
+            "** ArrayFire Black-Scholes Example **\n"
+            "**          by AccelerEyes         **\n\n");
 
         array GC1(4000, 1, C1);
         array GC2(4000, 1, C2);
@@ -69,7 +64,6 @@ int main(int argc, char **argv)
         array GC4(4000, 1, C4);
         array GC5(4000, 1, C5);
 
-
         // Compile kernels
         // Create GPU copies of the data
         array Sg = GC1;
@@ -80,15 +74,13 @@ int main(int argc, char **argv)
         array Cg, Pg;
 
         // Warm up black scholes example
-        black_scholes(Cg, Pg, Sg,Xg,Rg,Vg,Tg);
+        black_scholes(Cg, Pg, Sg, Xg, Rg, Vg, Tg);
         eval(Cg, Pg);
         printf("Warming up done\n");
         af::sync();
 
-
         int iter = 1000;
         for (int n = 50; n <= 500; n += 50) {
-
             // Create GPU copies of the data
             Sg = tile(GC1, n, 1);
             Xg = tile(GC2, n, 1);
@@ -103,15 +95,16 @@ int main(int argc, char **argv)
 
             timer::start();
             for (int i = 0; i < iter; i++) {
-                black_scholes(Cg, Pg, Sg,Xg,Rg,Vg,Tg);
+                black_scholes(Cg, Pg, Sg, Xg, Rg, Vg, Tg);
                 eval(Cg, Pg);
             }
             af::sync();
 
             double t = timer::stop() / iter;
-            printf("Input Data Size = %8d. Mean GPU Time: %0.6f ms\n", (int)dims[0], 1000 * t);
+            printf("Input Data Size = %8d. Mean GPU Time: %0.6f ms\n",
+                   (int)dims[0], 1000 * t);
         }
-    } catch (af::exception& e){
+    } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/financial/heston_model.cpp b/examples/financial/heston_model.cpp
index 0be51fcfec..79e7ff9dfe 100644
--- a/examples/financial/heston_model.cpp
+++ b/examples/financial/heston_model.cpp
@@ -2,19 +2,19 @@
  * Copyright (c) 2015, Michael Nowotny
  * All rights reserved.
  *
- * Redistribution and use in source and binary forms, with or without modification,
- * are permitted provided that the following conditions are met:
+ * Redistribution and use in source and binary forms, with or without
+ *modification, are permitted provided that the following conditions are met:
  *
  * 1. Redistributions of source code must retain the above copyright notice,
  * this list of conditions and the following disclaimer.
  *
  * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation and/or other
- * materials provided with the distribution.
+ * this list of conditions and the following disclaimer in the documentation
+ *and/or other materials provided with the distribution.
  *
- * 3. Neither the name of the copyright holder nor the names of its contributors may be used
- * to endorse or promote products derived from this software without specific
- * prior written permission.
+ * 3. Neither the name of the copyright holder nor the names of its contributors
+ *may be used to endorse or promote products derived from this software without
+ *specific prior written permission.
  *
  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
@@ -27,19 +27,19 @@
  * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
  * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-***********************************************************************************************/
+ ***********************************************************************************************/
 
+#include <arrayfire.h>
 #include <stdio.h>
 #include <iostream>
-#include <arrayfire.h>
 
 using namespace std;
 using namespace af;
 
-void simulateHestonModel(af::array &xres, af::array &vres,
-                         float T, unsigned int N, unsigned int R, float mu, float kappa,
-                         float vBar, float sigmaV, float rho, float x0, float v0)
-{
+void simulateHestonModel(af::array &xres, af::array &vres, float T,
+                         unsigned int N, unsigned int R, float mu, float kappa,
+                         float vBar, float sigmaV, float rho, float x0,
+                         float v0) {
     float deltaT = T / (float)(N - 1);
 
     af::array x[] = {af::constant(x0, R), af::constant(0, R)};
@@ -47,7 +47,7 @@ void simulateHestonModel(af::array &xres, af::array &vres,
 
     float sqrtDeltaT = sqrt(deltaT);
 
-    float sqrtOneMinusRhoSquare = sqrt(1 - rho*rho);
+    float sqrtOneMinusRhoSquare = sqrt(1 - rho * rho);
 
     float mArray[] = {rho, sqrtOneMinusRhoSquare};
     af::array m(2, 1, mArray);
@@ -56,14 +56,16 @@ void simulateHestonModel(af::array &xres, af::array &vres,
     af::array zeroConstant = constant(0, R);
 
     for (unsigned int t = 1; t < N; t++) {
-        tPrevious = (t+1) % 2;
-        tCurrent = t % 2;
+        tPrevious = (t + 1) % 2;
+        tCurrent  = t % 2;
 
-        af::array dBt = randn(R, 2) * sqrtDeltaT;
+        af::array dBt      = randn(R, 2) * sqrtDeltaT;
         af::array sqrtVLag = af::sqrt(v[tPrevious]);
 
-        x[tCurrent]= x[tPrevious] + (mu - 0.5 * v[tPrevious]) * deltaT + (sqrtVLag * dBt(span, 0));
-        af::array vTmp = v[tPrevious] + kappa * (vBar - v[tPrevious]) * deltaT + sigmaV * (sqrtVLag * matmul(dBt, m));
+        x[tCurrent] = x[tPrevious] + (mu - 0.5 * v[tPrevious]) * deltaT +
+                      (sqrtVLag * dBt(span, 0));
+        af::array vTmp = v[tPrevious] + kappa * (vBar - v[tPrevious]) * deltaT +
+                         sigmaV * (sqrtVLag * matmul(dBt, m));
         v[tCurrent] = max(vTmp, zeroConstant);
     }
 
@@ -71,22 +73,20 @@ void simulateHestonModel(af::array &xres, af::array &vres,
     vres = v[tCurrent];
 }
 
-int main()
-{
-    float T = 1;
-    unsigned int nT = 10 * T;
+int main() {
+    float T                  = 1;
+    unsigned int nT          = 10 * T;
     unsigned int R_first_run = 1000;
-    unsigned int R = 20000000;
-
-    float x0 = 0; // initial log stock price
-    float v0 = pow(0.087, 2); // initial volatility
-    float r = log(1.0319); // risk-free rate
-    float rho = -0.82; // instantaneous correlation between Brownian motions
-    float sigmaV = 0.14; // variance of volatility
-    float kappa = 3.46; // mean reversion speed
-    float vBar = 0.008; // mean variance
-    float k = log(0.95); // strike price
+    unsigned int R           = 20000000;
 
+    float x0     = 0;              // initial log stock price
+    float v0     = pow(0.087, 2);  // initial volatility
+    float r      = log(1.0319);    // risk-free rate
+    float rho    = -0.82;  // instantaneous correlation between Brownian motions
+    float sigmaV = 0.14;   // variance of volatility
+    float kappa  = 3.46;   // mean reversion speed
+    float vBar   = 0.008;  // mean variance
+    float k      = log(0.95);  // strike price
 
     // Price European call option
     try {
@@ -94,22 +94,24 @@ int main()
         af::array v;
 
         // first run
-        simulateHestonModel(x, v, T, nT, R_first_run, r, kappa, vBar, sigmaV, rho, x0, v0);
-        af::sync(); // Ensure the first run is finished
+        simulateHestonModel(x, v, T, nT, R_first_run, r, kappa, vBar, sigmaV,
+                            rho, x0, v0);
+        af::sync();  // Ensure the first run is finished
 
         timer::start();
-        simulateHestonModel(x, v, T, nT, R, r, kappa, vBar, sigmaV, rho, x0, v0);
+        simulateHestonModel(x, v, T, nT, R, r, kappa, vBar, sigmaV, rho, x0,
+                            v0);
         af::sync();
         cout << "Time in simulation: " << timer::stop() << endl;
 
-        af::array K = exp(constant(k, x.dims()));
+        af::array K            = exp(constant(k, x.dims()));
         af::array zeroConstant = constant(0, x.dims());
-        af::array C_CPU = exp(-r * T) * mean(af::max(af::exp(x) - K, zeroConstant));
+        af::array C_CPU =
+            exp(-r * T) * mean(af::max(af::exp(x) - K, zeroConstant));
 
         af_print(C_CPU);
         return 0;
-    } catch (af::exception& e) {
-
+    } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
         return 1;
     }
diff --git a/examples/financial/input.h b/examples/financial/input.h
index 4b44e96f0c..220969ceee 100644
--- a/examples/financial/input.h
+++ b/examples/financial/input.h
@@ -7,2018 +7,3326 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+float C1[] = {5.000000f,   10.000000f,  100.000000f, 100.000000f, 60.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              5.000000f,   10.000000f,  100.000000f, 100.000000f, 60.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
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+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000};
 
-float C1[] = {
-    5.000000f, 10.000000f, 100.000000f, 100.000000f, 60.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    5.000000f, 10.000000f, 100.000000f, 100.000000f, 60.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
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-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    5.000000f, 10.000000f, 100.000000f, 100.000000f, 60.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000
-};
-
-float C2[] = {
-    5.000000f, 12.000000f, 100.000000f, 100.000000f, 65.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f,
-    100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f,
-    110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
-    110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
-    110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f,
-    41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f,
-    41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    60.750000f, 60.750000f, 60.750000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
-    110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
-    110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
-    90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f, 41.250000f, 41.250000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f,
-    60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f,
-    60.750000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
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-    41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    60.750000f, 60.750000f, 60.750000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
-    110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
-    110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
-    90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f, 41.250000f, 41.250000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f,
-    60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f,
-    60.750000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
-    90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f,
-    100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f,
-    100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f,
-    60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f,
-    60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f,
-    41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 90.000000f,
-    90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f,
-    100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f,
-    100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f,
-    100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f,
-    110.000000f, 110.000000f, 110.000000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f,
-    60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f,
-    60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f,
-    41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f,
-    41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f,
-    41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 90.000000f, 90.000000f, 90.000000f,
-    100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f,
-    100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f,
-    100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f,
-    110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
-    110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
-    110.000000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f,
-    41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f,
-    41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f,
-    41.250000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
-    110.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
-    90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
-    110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    110.000000f, 110.000000f, 110.000000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f,
-    60.750000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f,
-    41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f,
-    41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f,
-    60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f,
-    41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f,
-    60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f,
-    60.750000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
-    90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f,
-    90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
-    110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f,
-    90.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
-    110.000000f, 110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
-    110.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f,
-    100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f,
-    41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f,
-    41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f,
-    60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f,
-    41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f,
-    60.750000f, 60.750000f, 60.750000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f,
-    60.750000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 50.000000f, 50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 41.250000f,
-    41.250000f, 41.250000f, 41.250000f, 41.250000f, 41.250000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f, 50.000000f,
-    50.000000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f, 60.750000f,
-    110.000000f, 110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
-    110.000000f, 90.000000f, 90.000000f, 90.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000
-};
+float C2[] = {5.000000f,   12.000000f,  100.000000f, 100.000000f, 65.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
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+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              5.000000f,   12.000000f,  100.000000f, 100.000000f, 65.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 90.000000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  90.000000f,  100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 100.000000f, 100.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  90.000000f,
+              90.000000f,  90.000000f,  100.000000f, 100.000000f, 100.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 110.000000f, 110.000000f, 110.000000f, 41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              41.250000f,  41.250000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  50.000000f,  50.000000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  41.250000f,
+              41.250000f,  41.250000f,  41.250000f,  41.250000f,  41.250000f,
+              50.000000f,  50.000000f,  50.000000f,  50.000000f,  50.000000f,
+              50.000000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              60.750000f,  60.750000f,  60.750000f,  60.750000f,  60.750000f,
+              110.000000f, 110.000000f, 90.000000f,  90.000000f,  90.000000f,
+              100.000000f, 100.000000f, 100.000000f, 110.000000f, 110.000000f,
+              110.000000f, 90.000000f,  90.000000f,  90.000000f,  100.000000f,
+              100.000000f, 100.000000f, 110.000000f, 110.000000f, 110.000000};
 
 float C3[] = {
-    0.100000f, 0.100000f, 0.050000f, 0.050000f, 0.080000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
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+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.027500f,
+    0.027500f, 0.027500f, 0.027500f, 0.027500f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f};
 
 float C4[] = {
-    0.200000f, 0.200000f, 0.150000f, 0.150000f, 0.300000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
-    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
-    0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
-    0.500000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
-    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
-    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
-    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
-    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
-    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
-    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
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+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f,
+    0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.250000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.500000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
+    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
+    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
+    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
+    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
+    0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.450000f, 0.650000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.450000f,
+    0.650000f, 0.250000f, 0.350000f, 0.450000f, 0.250000f, 0.250000f, 0.500000f,
+    0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f, 0.500000f,
+    0.500000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f, 0.100000f,
+    0.100000f, 0.100000f, 0.100000f};
 
 float C5[] = {
-    0.500000f, 0.500000f, 1.000000f, 1.000000f, 0.250000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
-    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
-    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
-    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
-    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
-    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
-    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
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+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f,
+    0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f,
+    0.150000f, 0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f,
+    0.250000f, 0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f,
+    0.350000f, 0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f,
+    0.400000f, 0.750000f, 0.050000f, 0.150000f, 0.250000f, 0.350000f, 0.400000f,
+    0.750000f, 0.250000f, 0.350000f, 0.400000f, 0.500000f, 1.000000f, 0.100000f,
+    0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f,
+    1.000000f, 0.100000f, 0.500000f, 1.000000f, 0.100000f, 0.500000f, 1.000000f,
+    0.100000f, 0.500000f, 1.000000f};
diff --git a/examples/financial/monte_carlo_options.cpp b/examples/financial/monte_carlo_options.cpp
index 8f733ceb7e..321b3e966d 100644
--- a/examples/financial/monte_carlo_options.cpp
+++ b/examples/financial/monte_carlo_options.cpp
@@ -7,25 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <iostream>
-#include <stdio.h>
-#include <math.h>
 #include <arrayfire.h>
+#include <math.h>
+#include <stdio.h>
 #include <af/util.h>
+#include <iostream>
 
 using namespace af;
-template<class ty> dtype get_dtype();
+template<class ty>
+dtype get_dtype();
 
-template<> dtype get_dtype<float>() { return f32; }
-template<> dtype get_dtype<double>() { return f64; }
+template<>
+dtype get_dtype<float>() {
+    return f32;
+}
+template<>
+dtype get_dtype<double>() {
+    return f64;
+}
 
 template<class ty, bool use_barrier>
-static ty monte_carlo_barrier(int N, ty K, ty t, ty vol, ty r, ty strike, int steps, ty B)
-{
-    dtype pres = get_dtype<ty>();
+static ty monte_carlo_barrier(int N, ty K, ty t, ty vol, ty r, ty strike,
+                              int steps, ty B) {
+    dtype pres   = get_dtype<ty>();
     array payoff = constant(0, N, 1, pres);
 
-    ty dt = t / (ty)(steps - 1);
+    ty dt   = t / (ty)(steps - 1);
     array s = constant(strike, N, 1, pres);
 
     array randmat = randn(N, steps - 1, pres);
@@ -33,52 +40,46 @@ static ty monte_carlo_barrier(int N, ty K, ty t, ty vol, ty r, ty strike, int st
 
     array S = product(join(1, s, randmat), 1);
 
-    if (use_barrier) {
-        S = S * allTrue(S < B, 1);
-    }
+    if (use_barrier) { S = S * allTrue(S < B, 1); }
 
     payoff = max(0.0, S - K);
-    ty P = mean<ty>(payoff) * exp(-r * t);
+    ty P   = mean<ty>(payoff) * exp(-r * t);
     return P;
 }
 
 template<class ty, bool use_barrier>
-double monte_carlo_bench(int N)
-{
-    int steps = 180;
+double monte_carlo_bench(int N) {
+    int steps      = 180;
     ty stock_price = 100.0;
-    ty maturity = 0.5;
-    ty volatility = .30;
-    ty rate = .01;
-    ty strike = 100;
-    ty barrier = 115.0;
+    ty maturity    = 0.5;
+    ty volatility  = .30;
+    ty rate        = .01;
+    ty strike      = 100;
+    ty barrier     = 115.0;
 
     timer::start();
     for (int i = 0; i < 10; i++) {
-        monte_carlo_barrier<ty, use_barrier>(N, stock_price, maturity, volatility,
-                                             rate, strike, steps, barrier);
+        monte_carlo_barrier<ty, use_barrier>(
+            N, stock_price, maturity, volatility, rate, strike, steps, barrier);
     }
     return timer::stop() / 10;
 }
 
-int main()
-{
+int main() {
     try {
-
         // Warm up and caching
         monte_carlo_bench<float, false>(1000);
         monte_carlo_bench<float, true>(1000);
 
         for (int n = 10000; n <= 100000; n += 10000) {
-            printf("Time for %7d paths - "
-                   "vanilla method: %4.3f ms,  "
-                   "barrier method: %4.3f ms\n", n,
-                   1000 * monte_carlo_bench<float, false>(n),
-                   1000 * monte_carlo_bench<float, true>(n));
+            printf(
+                "Time for %7d paths - "
+                "vanilla method: %4.3f ms,  "
+                "barrier method: %4.3f ms\n",
+                n, 1000 * monte_carlo_bench<float, false>(n),
+                1000 * monte_carlo_bench<float, true>(n));
         }
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/getting_started/convolve.cpp b/examples/getting_started/convolve.cpp
index 8a9cfd38db..c07cedfc3c 100644
--- a/examples/getting_started/convolve.cpp
+++ b/examples/getting_started/convolve.cpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <arrayfire.h>
 #include <stdio.h>
 #include <cstdlib>
-#include <arrayfire.h>
 using namespace af;
 
 // use static variables at file scope so timeit() wrapper functions
@@ -19,41 +19,38 @@ using namespace af;
 static array img;
 
 // 5x5 derivative with separable kernels
-static float h_dx[] = {1.f / 12, -8.f / 12, 0, 8.f / 12, -1.f / 12}; // five point stencil
+static float h_dx[]     = {1.f / 12, -8.f / 12, 0, 8.f / 12,
+                       -1.f / 12};  // five point stencil
 static float h_spread[] = {1.f / 5, 1.f / 5, 1.f / 5, 1.f / 5, 1.f / 5};
-static array dx, spread, kernel; // device kernels
+static array dx, spread, kernel;  // device kernels
 
-static array full_out, dsep_out, hsep_out; // save output for value checks
+static array full_out, dsep_out, hsep_out;  // save output for value checks
 // wrapper functions for timeit() below
-static void full() { full_out = convolve2(img, kernel);}
+static void full() { full_out = convolve2(img, kernel); }
 static void dsep() { dsep_out = convolve(dx, spread, img); }
 
-static bool fail(array &left, array &right)
-{
+static bool fail(array &left, array &right) {
     return (max<float>(abs(left - right)) > 1e-6);
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
         // setup image and device copies of kernels
-        img = randu(640, 480);
-        dx = array(5, 1, h_dx); // 5x1 kernel
-        spread = array(1, 5, h_spread); // 1x5 kernel
-        kernel = matmul(dx, spread); // 5x5 kernel
+        img    = randu(640, 480);
+        dx     = array(5, 1, h_dx);      // 5x1 kernel
+        spread = array(1, 5, h_spread);  // 1x5 kernel
+        kernel = matmul(dx, spread);     // 5x5 kernel
 
         printf("full 2D convolution:         %.5f seconds\n", timeit(full));
         printf("separable, device pointers:  %.5f seconds\n", timeit(dsep));
 
         // ensure values are all the same across versions
         if (fail(full_out, dsep_out)) { throw af::exception("full != dsep"); }
-    } catch (af::exception& e) {
-        fprintf(stderr, "%s\n", e.what());
-    }
+    } catch (af::exception &e) { fprintf(stderr, "%s\n", e.what()); }
 
     return 0;
 }
diff --git a/examples/getting_started/integer.cpp b/examples/getting_started/integer.cpp
index 1d1c2cab4c..b508e4d711 100644
--- a/examples/getting_started/integer.cpp
+++ b/examples/getting_started/integer.cpp
@@ -7,26 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 #include <cstdlib>
 
 using namespace af;
 
-int main(int argc, char ** argv)
-{
+int main(int argc, char** argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        printf("\n=== ArrayFire signed(s32) / unsigned(u32) Integer Example ===\n");
+        printf(
+            "\n=== ArrayFire signed(s32) / unsigned(u32) Integer Example "
+            "===\n");
 
         int h_A[] = {1, 2, 4, -1, 2, 0, 4, 2, 3};
         int h_B[] = {2, 3, -5, 6, 0, 10, -12, 0, 1};
-        array A = array(3, 3, h_A);
-        array B = array(3, 3, h_B);
+        array A   = array(3, 3, h_A);
+        array B   = array(3, 3, h_B);
 
         printf("--\nSub-refencing and Sub-assignment\n");
         af_print(A);
@@ -36,7 +37,7 @@ int main(int argc, char ** argv)
         A(1) = 100;
         af_print(A);
         af_print(B);
-        A(1,span) = B(2,span);
+        A(1, span) = B(2, span);
         af_print(A);
 
         printf("--Bit-wise operations\n");
@@ -56,8 +57,8 @@ int main(int argc, char ** argv)
 
         printf("\n--Flip Vertically / Horizontally\n");
         af_print(A);
-        af_print(flip(A,0));
-        af_print(flip(A,1));
+        af_print(flip(A, 0));
+        af_print(flip(A, 1));
 
         printf("\n--Sum along columns\n");
         af_print(A);
diff --git a/examples/getting_started/rainfall.cpp b/examples/getting_started/rainfall.cpp
index 98c97d30ea..04e39303f8 100644
--- a/examples/getting_started/rainfall.cpp
+++ b/examples/getting_started/rainfall.cpp
@@ -22,44 +22,41 @@
 //  "Rapid Problem Solving Using Thrust", Nathan Bell, NVIDIA
 
 #include <arrayfire.h>
-#include <af/util.h>
 #include <stdio.h>
+#include <af/util.h>
 #include <cstdlib>
 using namespace af;
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
         int days = 9, sites = 4;
-        int n = 10; // measurements
-        float day_[] =         {0, 0, 1, 2, 5, 5, 6, 6, 7, 8 }; // ascending
-        float site_[] =        {2, 3, 0, 1, 1, 2, 0, 1, 2, 1 };
-        float measurement_[] = {9, 5, 6, 3, 3, 8, 2, 6, 5, 10}; // inches
-        array day(n,day_);
-        array site(n,site_);
-        array measurement(n,measurement_);
+        int n                = 10;                              // measurements
+        float day_[]         = {0, 0, 1, 2, 5, 5, 6, 6, 7, 8};  // ascending
+        float site_[]        = {2, 3, 0, 1, 1, 2, 0, 1, 2, 1};
+        float measurement_[] = {9, 5, 6, 3, 3, 8, 2, 6, 5, 10};  // inches
+        array day(n, day_);
+        array site(n, site_);
+        array measurement(n, measurement_);
 
         array rainfall = constant(0, sites);
-        gfor (seq s, sites) {
-            rainfall(s) = sum(measurement * (site == s));
-        }
+        gfor(seq s, sites) { rainfall(s) = sum(measurement * (site == s)); }
 
         printf("total rainfall at each site:\n");
         af_print(rainfall);
 
-        array is_between = 1 <= day && day <= 5; // days 1 and 5
+        array is_between   = 1 <= day && day <= 5;  // days 1 and 5
         float rain_between = sum<float>(measurement * is_between);
         printf("rain between days: %g\n", rain_between);
 
-        printf("number of days with rain: %g\n", sum<float>(diff1(day) > 0) + 1);
+        printf("number of days with rain: %g\n",
+               sum<float>(diff1(day) > 0) + 1);
 
-        array per_day = constant(0, days);
-        gfor (seq d, days)
-            per_day(d) = sum(measurement * (day == d));
+        array per_day                = constant(0, days);
+        gfor(seq d, days) per_day(d) = sum(measurement * (day == d));
 
         printf("total rainfall each day:\n");
         af_print(per_day);
diff --git a/examples/getting_started/vectorize.cpp b/examples/getting_started/vectorize.cpp
index 673520c148..c94adba257 100644
--- a/examples/getting_started/vectorize.cpp
+++ b/examples/getting_started/vectorize.cpp
@@ -7,24 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 
 using namespace af;
 
 array A, B;
 
-static array dist_naive(array a, array b)
-{
+static array dist_naive(array a, array b) {
     array dist_mat = constant(0, a.dims(1), (int)b.dims(1));
 
     // Iterate through columns a
     for (int ii = 0; ii < (int)a.dims(1); ii++) {
-
         // Iterate through columns of b
         for (int jj = 0; jj < (int)b.dims(1); jj++) {
-
             // Get the sum of absolute differences
             for (int kk = 0; kk < (int)a.dims(0); kk++) {
                 dist_mat(ii, jj) += abs(a(kk, ii) - b(kk, jj));
@@ -35,8 +32,7 @@ static array dist_naive(array a, array b)
     return dist_mat;
 }
 
-static array dist_vec(array a, array b)
-{
+static array dist_vec(array a, array b) {
     array dist_mat = constant(0, (int)a.dims(1), (int)b.dims(1));
 
     // Iterate through columns a
@@ -55,12 +51,11 @@ static array dist_vec(array a, array b)
     return dist_mat;
 }
 
-static array dist_gfor1(array a, array b)
-{
+static array dist_gfor1(array a, array b) {
     array dist_mat = constant(0, (int)a.dims(1), (int)b.dims(1));
 
     // GFOR along columns of a
-    gfor (seq ii, (int)a.dims(1)) {
+    gfor(seq ii, (int)a.dims(1)) {
         array avec = a(span, ii);
 
         // Itere through columns of b
@@ -75,12 +70,11 @@ static array dist_gfor1(array a, array b)
     return dist_mat;
 }
 
-static array dist_gfor2(array a, array b)
-{
+static array dist_gfor2(array a, array b) {
     array dist_mat = constant(0, (int)a.dims(1), (int)b.dims(1));
 
     // GFOR along columns of b
-    gfor (seq jj, (int)b.dims(1)) {
+    gfor(seq jj, (int)b.dims(1)) {
         array bvec = b(span, jj);
 
         // Iterate through columns of A
@@ -95,8 +89,7 @@ static array dist_gfor2(array a, array b)
     return dist_mat;
 }
 
-static array dist_tile1(array a, array b)
-{
+static array dist_tile1(array a, array b) {
     // int feat_len = (int)a.dims(0); // Same as (int)b.dims(0);
     int alen = (int)a.dims(1);
     int blen = (int)b.dims(1);
@@ -105,7 +98,6 @@ static array dist_tile1(array a, array b)
 
     // Iterate through columns of b
     for (int jj = 0; jj < blen; jj++) {
-
         // Get the column vector of b
         // shape of bvec is (feat_len, 1)
         array bvec = b(span, jj);
@@ -125,11 +117,10 @@ static array dist_tile1(array a, array b)
     return dist_mat;
 }
 
-static array dist_tile2(array a, array b)
-{
+static array dist_tile2(array a, array b) {
     int feat_len = (int)a.dims(0);
-    int alen = (int)a.dims(1);
-    int blen = (int)b.dims(1);
+    int alen     = (int)a.dims(1);
+    int blen     = (int)b.dims(1);
 
     // Shape of a is (feat_len, alen, 1)
     array a_mod = a;
@@ -149,40 +140,20 @@ static array dist_tile2(array a, array b)
     return dist_mat;
 }
 
-static void bench_naive()
-{
-    dist_naive(A, B);
-}
+static void bench_naive() { dist_naive(A, B); }
 
-static void bench_vec()
-{
-    dist_vec(A, B);
-}
+static void bench_vec() { dist_vec(A, B); }
 
-static void bench_gfor1()
-{
-    dist_gfor1(A, B);
-}
+static void bench_gfor1() { dist_gfor1(A, B); }
 
-static void bench_gfor2()
-{
-    dist_gfor2(A, B);
-}
+static void bench_gfor2() { dist_gfor2(A, B); }
 
-static void bench_tile1()
-{
-    dist_tile1(A, B);
-}
+static void bench_tile1() { dist_tile1(A, B); }
 
-static void bench_tile2()
-{
-    dist_tile2(A, B);
-}
+static void bench_tile2() { dist_tile2(A, B); }
 
-int main(int, char **)
-{
+int main(int, char **) {
     try {
-
         af::info();
 
         // Do not increase the sizes
@@ -191,7 +162,7 @@ int main(int, char **)
         B = randu(3, 300);
 
         array d1 = dist_naive(A, B);
-        array d2 = dist_vec  (A, B);
+        array d2 = dist_vec(A, B);
         array d3 = dist_gfor1(A, B);
         array d4 = dist_gfor2(A, B);
         array d5 = dist_tile1(A, B);
@@ -206,13 +177,13 @@ int main(int, char **)
         printf("\n");
 
         printf("Time for dist_naive: %2.2fms\n", 1000 * timeit(bench_naive));
-        printf("Time for dist_vec  : %2.2fms\n", 1000 * timeit(bench_vec  ));
+        printf("Time for dist_vec  : %2.2fms\n", 1000 * timeit(bench_vec));
         printf("Time for dist_gfor1: %2.2fms\n", 1000 * timeit(bench_gfor1));
         printf("Time for dist_gfor2: %2.2fms\n", 1000 * timeit(bench_gfor2));
         printf("Time for dist_tile1: %2.2fms\n", 1000 * timeit(bench_tile1));
         printf("Time for dist_tile2: %2.2fms\n", 1000 * timeit(bench_tile2));
 
-    } catch(af::exception ex) {
+    } catch (af::exception ex) {
         fprintf(stderr, "%s\n", ex.what());
         throw;
     }
diff --git a/examples/graphics/conway.cpp b/examples/graphics/conway.cpp
index 9a91240001..745187f75e 100644
--- a/examples/graphics/conway.cpp
+++ b/examples/graphics/conway.cpp
@@ -8,28 +8,39 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <iostream>
 #include <cstdio>
+#include <iostream>
 
 using namespace af;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
-        static const int reset = 500;
+        static const int reset        = 500;
         static const int game_w = 128, game_h = 128;
 
         af::info();
 
-        std::cout << "This example demonstrates the Conway's Game of Life using ArrayFire" << std::endl
-                  << "There are 4 simple rules of Conways's Game of Life" << std::endl
-                  << "1. Any live cell with fewer than two live neighbours dies, as if caused by under-population." << std::endl
-                  << "2. Any live cell with two or three live neighbours lives on to the next generation." << std::endl
-                  << "3. Any live cell with more than three live neighbours dies, as if by overcrowding." << std::endl
-                  << "4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction." << std::endl
-                  << "Each white block in the visualization represents 1 alive cell, black space represents dead cells" << std::endl
-                  ;
+        std::cout << "This example demonstrates the Conway's Game of Life "
+                     "using ArrayFire"
+                  << std::endl
+                  << "There are 4 simple rules of Conways's Game of Life"
+                  << std::endl
+                  << "1. Any live cell with fewer than two live neighbours "
+                     "dies, as if caused by under-population."
+                  << std::endl
+                  << "2. Any live cell with two or three live neighbours lives "
+                     "on to the next generation."
+                  << std::endl
+                  << "3. Any live cell with more than three live neighbours "
+                     "dies, as if by overcrowding."
+                  << std::endl
+                  << "4. Any dead cell with exactly three live neighbours "
+                     "becomes a live cell, as if by reproduction."
+                  << std::endl
+                  << "Each white block in the visualization represents 1 alive "
+                     "cell, black space represents dead cells"
+                  << std::endl;
 
         af::Window myWindow(512, 512, "Conway's Game of Life using ArrayFire");
 
@@ -40,13 +51,12 @@ int main(int, char **)
         array state;
         state = (af::randu(game_h, game_w, f32) > 0.5).as(f32);
 
-        while(!myWindow.close()) {
-
+        while (!myWindow.close()) {
             myWindow.image(state);
             frame_count++;
 
             // Generate a random starting state
-            if(frame_count % reset == 0)
+            if (frame_count % reset == 0)
                 state = (af::randu(game_h, game_w, f32) > 0.5).as(f32);
 
             // Convolve gets neighbors
diff --git a/examples/graphics/conway_pretty.cpp b/examples/graphics/conway_pretty.cpp
index 6ba8ca62c0..4d8de380bd 100644
--- a/examples/graphics/conway_pretty.cpp
+++ b/examples/graphics/conway_pretty.cpp
@@ -8,40 +8,62 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <iostream>
 #include <cstdio>
+#include <iostream>
 
 using namespace af;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
-        static const int reset = 500;
+        static const int reset        = 500;
         static const int game_w = 128, game_h = 128;
 
         af::info();
 
-        std::cout << "This example demonstrates the Conway's Game of Life using ArrayFire" << std::endl
-                  << "There are 4 simple rules of Conways's Game of Life" << std::endl
-                  << "1. Any live cell with fewer than two live neighbours dies, as if caused by under-population." << std::endl
-                  << "2. Any live cell with two or three live neighbours lives on to the next generation." << std::endl
-                  << "3. Any live cell with more than three live neighbours dies, as if by overcrowding." << std::endl
-                  << "4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction." << std::endl
-                  << "Each white block in the visualization represents 1 alive cell, black space represents dead cells" << std::endl
+        std::cout << "This example demonstrates the Conway's Game of Life "
+                     "using ArrayFire"
+                  << std::endl
+                  << "There are 4 simple rules of Conways's Game of Life"
+                  << std::endl
+                  << "1. Any live cell with fewer than two live neighbours "
+                     "dies, as if caused by under-population."
+                  << std::endl
+                  << "2. Any live cell with two or three live neighbours lives "
+                     "on to the next generation."
+                  << std::endl
+                  << "3. Any live cell with more than three live neighbours "
+                     "dies, as if by overcrowding."
+                  << std::endl
+                  << "4. Any dead cell with exactly three live neighbours "
+                     "becomes a live cell, as if by reproduction."
+                  << std::endl
+                  << "Each white block in the visualization represents 1 alive "
+                     "cell, black space represents dead cells"
+                  << std::endl
                   << std::endl;
 
-        std::cout << "The conway_pretty example visualizes all the states in Conway" << std::endl
-                  << "Red   : Cells that have died due to under population"          << std::endl
-                  << "Yellow: Cells that continue to live from previous state"       << std::endl
-                  << "Green : Cells that are new as a result of reproduction"        << std::endl
-                  << "Blue  : Cells that have died due to over population"           << std::endl
-                  << std::endl;
-
-        std::cout << "This examples is throttled so as to be a better visualization" << std::endl;
-
-        af::Window simpleWindow(512, 512, "Conway's Game Of Life - Current State");
-        af::Window prettyWindow(512, 512, "Conway's Game Of Life - Visualizing States");
+        std::cout
+            << "The conway_pretty example visualizes all the states in Conway"
+            << std::endl
+            << "Red   : Cells that have died due to under population"
+            << std::endl
+            << "Yellow: Cells that continue to live from previous state"
+            << std::endl
+            << "Green : Cells that are new as a result of reproduction"
+            << std::endl
+            << "Blue  : Cells that have died due to over population"
+            << std::endl
+            << std::endl;
+
+        std::cout
+            << "This examples is throttled so as to be a better visualization"
+            << std::endl;
+
+        af::Window simpleWindow(512, 512,
+                                "Conway's Game Of Life - Current State");
+        af::Window prettyWindow(512, 512,
+                                "Conway's Game Of Life - Visualizing States");
         simpleWindow.setPos(32, 32);
         prettyWindow.setPos(512 + 32, 32);
 
@@ -54,15 +76,15 @@ int main(int, char **)
 
         array display = tile(state, 1, 1, 3, 1);
 
-        while(!simpleWindow.close() && !prettyWindow.close()) {
+        while (!simpleWindow.close() && !prettyWindow.close()) {
             af::timer delay = timer::start();
 
-            if(!simpleWindow.close())   simpleWindow.image(state);
-            if(!prettyWindow.close())   prettyWindow.image(display);
+            if (!simpleWindow.close()) simpleWindow.image(state);
+            if (!prettyWindow.close()) prettyWindow.image(display);
             frame_count++;
 
             // Generate a random starting state
-            if(frame_count % reset == 0)
+            if (frame_count % reset == 0)
                 state = (af::randu(game_h, game_w, f32) > 0.5).as(f32);
 
             // Convolve gets neighbors
@@ -76,10 +98,10 @@ int main(int, char **)
             af::array C0 = (nHood == 2);
             af::array C1 = (nHood == 3);
 
-            array a0 = (state == 1) && (nHood < 2); // Die of under population
-            array a1 = (state != 0) && (C0 || C1);  // Continue to live
-            array a2 = (state == 0) && C1;          // Reproduction
-            array a3 = (state == 1) && (nHood > 3); // Over-population
+            array a0 = (state == 1) && (nHood < 2);  // Die of under population
+            array a1 = (state != 0) && (C0 || C1);   // Continue to live
+            array a2 = (state == 0) && C1;           // Reproduction
+            array a3 = (state == 1) && (nHood > 3);  // Over-population
 
             display = join(2, a0 + a1, a1 + a2, a3).as(f32);
 
@@ -87,7 +109,7 @@ int main(int, char **)
             state = state * C0 + C1;
 
             double fps = 30;
-            while(timer::stop(delay) < (1 / fps)) { }
+            while (timer::stop(delay) < (1 / fps)) {}
         }
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
index 3d177ae9ea..a723791fc8 100644
--- a/examples/graphics/field.cpp
+++ b/examples/graphics/field.cpp
@@ -8,17 +8,16 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <cstdio>
 #include <math.h>
+#include <cstdio>
 
 using namespace af;
 
 const static float MINIMUM = -3.0f;
-const static float MAXIMUM =  3.0f;
-const static float STEP = 0.18f;
+const static float MAXIMUM = 3.0f;
+const static float STEP    = 0.18f;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         af::info();
         af::Window myWindow(1024, 1024, "2D Vector Field example: ArrayFire");
@@ -36,21 +35,20 @@ int main(int, char **)
         do {
             array points = join(1, flat(x), flat(y));
 
-            array saddle = join(1, flat(x), -1.0f*flat(y));
+            array saddle = join(1, flat(x), -1.0f * flat(y));
 
-            array bvals  = sin(scale*(x*x + y*y));
-            array hbowl  = join(1, constant(1, x.elements()), flat(bvals));
+            array bvals = sin(scale * (x * x + y * y));
+            array hbowl = join(1, constant(1, x.elements()), flat(bvals));
             hbowl.eval();
 
             myWindow(0, 0).vectorField(points, saddle, "Saddle point");
-            myWindow(0, 1).vectorField(points, hbowl, "hilly bowl (in a loop with varying amplitude)");
+            myWindow(0, 1).vectorField(
+                points, hbowl, "hilly bowl (in a loop with varying amplitude)");
             myWindow.show();
 
             scale -= 0.0010f;
-            if (scale < -0.01f) {
-                scale = 2.0f;
-            }
-        } while(!myWindow.close());
+            if (scale < -0.01f) { scale = 2.0f; }
+        } while (!myWindow.close());
 
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/graphics/fractal.cpp b/examples/graphics/fractal.cpp
index 2e1f4e6579..a86dd32805 100644
--- a/examples/graphics/fractal.cpp
+++ b/examples/graphics/fractal.cpp
@@ -7,43 +7,43 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
-#include <iostream>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <cmath>
 #include <cstdlib>
+#include <iostream>
 
-#define WIDTH 400 // Width of image
-#define HEIGHT 400 // Width of image
+#define WIDTH 400   // Width of image
+#define HEIGHT 400  // Width of image
 
 using namespace af;
 using std::abs;
 
-array complex_grid(int width, int height, float zoom, float center[2])
-{
-
+array complex_grid(int width, int height, float zoom, float center[2]) {
     // Generate sequences of length width, height
-    array X = (iota(dim4(1, height), dim4(width , 1)) -  (float)height / 2.0) / zoom + center[0];
-    array Y = (iota(dim4(width , 1), dim4(1, height)) -  (float)width  / 2.0) / zoom + center[1];
+    array X =
+        (iota(dim4(1, height), dim4(width, 1)) - (float)height / 2.0) / zoom +
+        center[0];
+    array Y =
+        (iota(dim4(width, 1), dim4(1, height)) - (float)width / 2.0) / zoom +
+        center[1];
 
     // Return the locations as a complex grid
     return complex(X, Y);
 }
 
-array mandelbrot(const array &in, int iter, float maxval)
-{
-    array C = in;
-    array Z = C;
+array mandelbrot(const array &in, int iter, float maxval) {
+    array C   = in;
+    array Z   = C;
     array mag = constant(0, C.dims());
 
     for (int ii = 1; ii < iter; ii++) {
-
         // Do the calculation
         Z = Z * Z + C;
 
         // Get indices where abs(Z) crosses maxval
         array cond = (abs(Z) > maxval).as(f32);
-        mag = af::max(mag, cond * ii);
+        mag        = af::max(mag, cond * ii);
 
         // If abs(Z) cross maxval, turn off those locations
         C = C * (1 - cond);
@@ -58,17 +58,15 @@ array mandelbrot(const array &in, int iter, float maxval)
     return mag / maxval;
 }
 
-array normalize(array a)
-{
+array normalize(array a) {
     float mx = af::max<float>(a);
     float mn = af::min<float>(a);
-    return (a-mn)/(mx-mn);
+    return (a - mn) / (mx - mn);
 }
 
-int main(int argc, char **argv)
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
-    int iter = argc > 2 ? atoi(argv[2]) : 100;
+int main(int argc, char **argv) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
+    int iter     = argc > 2 ? atoi(argv[2]) : 100;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     try {
         af::setDevice(device);
@@ -81,18 +79,19 @@ int main(int argc, char **argv)
         // Keep zomming out for each frame
         for (int i = 10; i < 400; i++) {
             int zoom = i * i;
-            if(!(i % 10)) {
-                printf("iteration: %d zoom: %d\n", i, zoom); fflush(stdout);
+            if (!(i % 10)) {
+                printf("iteration: %d zoom: %d\n", i, zoom);
+                fflush(stdout);
             }
 
             // Generate the grid at the current zoom factor
             array c = complex_grid(WIDTH, HEIGHT, zoom, center);
 
-            iter =sqrt(abs(2*sqrt(abs(1-sqrt(5*zoom)))))*100;
+            iter = sqrt(abs(2 * sqrt(abs(1 - sqrt(5 * zoom))))) * 100;
             // Generate the mandelbrot image
             array mag = mandelbrot(c, iter, 1000);
 
-            if(!console) {
+            if (!console) {
                 if (wnd.close()) break;
                 array mag_norm = normalize(mag);
                 wnd.image(mag_norm);
diff --git a/examples/graphics/gravity_sim.cpp b/examples/graphics/gravity_sim.cpp
index 6e55406038..3de7424c1d 100644
--- a/examples/graphics/gravity_sim.cpp
+++ b/examples/graphics/gravity_sim.cpp
@@ -8,25 +8,28 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <iostream>
 #include <cstdio>
+#include <iostream>
 #include <vector>
 #include "gravity_sim_init.h"
 
 using namespace af;
 using namespace std;
 
-static const bool is3D = true; const static int total_particles = 4000;
-static const int reset = 3000;
-static const float min_dist = 3;
+static const bool is3D           = true;
+const static int total_particles = 4000;
+static const int reset           = 3000;
+static const float min_dist      = 3;
 static const int width = 768, height = 768, depth = 768;
 static const int gravity_constant = 20000;
 
 float mass_range = 0;
 float min_mass   = 0;
 
-void initial_conditions_rand(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
-    for(int i=0; i< (int)pos.size(); ++i) {
+void initial_conditions_rand(af::array &mass, vector<af::array> &pos,
+                             vector<af::array> &vels,
+                             vector<af::array> &forces) {
+    for (int i = 0; i < (int)pos.size(); ++i) {
         pos[i]    = af::randn(total_particles) * width + width;
         vels[i]   = 0 * af::randu(total_particles) - 0.5;
         forces[i] = af::constant(0, total_particles);
@@ -34,29 +37,31 @@ void initial_conditions_rand(af::array &mass, vector<af::array> &pos, vector<af:
     mass = af::constant(gravity_constant, total_particles);
 }
 
-void initial_conditions_galaxy(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces) {
+void initial_conditions_galaxy(af::array &mass, vector<af::array> &pos,
+                               vector<af::array> &vels,
+                               vector<af::array> &forces) {
     af::array initial_cond_consts(af::dim4(7, total_particles), hbd);
     initial_cond_consts = initial_cond_consts.T();
 
-    for(int i=0; i< (int)pos.size(); ++i) {
+    for (int i = 0; i < (int)pos.size(); ++i) {
         pos[i]    = af::randn(total_particles) * width + width;
         vels[i]   = 0 * (af::randu(total_particles) - 0.5);
         forces[i] = af::constant(0, total_particles);
     }
 
-    mass    =  initial_cond_consts(span, 0);
-    pos[0]  = (initial_cond_consts(span, 1)/32 + 0.6) * width;
-    pos[1]  = (initial_cond_consts(span, 2)/32 + 0.3) * height;
-    pos[2]  = (initial_cond_consts(span, 3)/32 + 0.5) * depth;
-    vels[0] = (initial_cond_consts(span, 4)/32) * width;
-    vels[1] = (initial_cond_consts(span, 5)/32) * height;
-    vels[2] = (initial_cond_consts(span, 6)/32) * depth;
+    mass    = initial_cond_consts(span, 0);
+    pos[0]  = (initial_cond_consts(span, 1) / 32 + 0.6) * width;
+    pos[1]  = (initial_cond_consts(span, 2) / 32 + 0.3) * height;
+    pos[2]  = (initial_cond_consts(span, 3) / 32 + 0.5) * depth;
+    vels[0] = (initial_cond_consts(span, 4) / 32) * width;
+    vels[1] = (initial_cond_consts(span, 5) / 32) * height;
+    vels[2] = (initial_cond_consts(span, 6) / 32) * depth;
 
-    pos[0](seq(0, pos[0].dims(0)-1, 2)) -=  0.4 * width;
-    pos[1](seq(0, pos[0].dims(0)-1, 2)) +=  0.4 * height;
-    vels[0](seq(0, pos[0].dims(0)-1, 2)) +=  4;
+    pos[0](seq(0, pos[0].dims(0) - 1, 2)) -= 0.4 * width;
+    pos[1](seq(0, pos[0].dims(0) - 1, 2)) += 0.4 * height;
+    vels[0](seq(0, pos[0].dims(0) - 1, 2)) += 4;
 
-    min_mass = min<float>(mass);
+    min_mass   = min<float>(mass);
     mass_range = max<float>(mass) - min<float>(mass);
 }
 
@@ -65,115 +70,120 @@ af::array ids_from_pos(vector<af::array> &pos) {
 }
 
 af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz) {
-    af::array x0  = (pos[0] - width/2);
-    af::array y0  = (pos[1] - height/2) * cos(Rx) + (pos[2] - depth/2) * sin(Rx);
-    af::array z0  = (pos[2] - depth/2)  * cos(Rx) - (pos[2] - depth/2) * sin(Rx);
+    af::array x0 = (pos[0] - width / 2);
+    af::array y0 =
+        (pos[1] - height / 2) * cos(Rx) + (pos[2] - depth / 2) * sin(Rx);
+    af::array z0 =
+        (pos[2] - depth / 2) * cos(Rx) - (pos[2] - depth / 2) * sin(Rx);
 
-    af::array x1  =  x0*cos(Ry) - z0*sin(Ry);
-    af::array y1  =  y0;
+    af::array x1 = x0 * cos(Ry) - z0 * sin(Ry);
+    af::array y1 = y0;
 
-    af::array x2  =  x1*cos(Rz) + y1*sin(Rz);
-    af::array y2  =  y1*cos(Rz) - x1*sin(Rz);
+    af::array x2 = x1 * cos(Rz) + y1 * sin(Rz);
+    af::array y2 = y1 * cos(Rz) - x1 * sin(Rz);
 
-    x2 += width/2;
-    y2 += height/2;
+    x2 += width / 2;
+    y2 += height / 2;
 
     return (x2.as(u32) * height) + y2.as(u32);
 }
 
-af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz, af::array filter) {
-    af::array x0  = (pos[0](filter) - width/2);
-    af::array y0  = (pos[1](filter) - height/2) * cos(Rx) + (pos[2](filter) - depth/2) * sin(Rx);
-    af::array z0  = (pos[2](filter) - depth/2)  * cos(Rx) - (pos[2](filter) - depth/2) * sin(Rx);
+af::array ids_from_3D(vector<af::array> &pos, float Rx, float Ry, float Rz,
+                      af::array filter) {
+    af::array x0 = (pos[0](filter) - width / 2);
+    af::array y0 = (pos[1](filter) - height / 2) * cos(Rx) +
+                   (pos[2](filter) - depth / 2) * sin(Rx);
+    af::array z0 = (pos[2](filter) - depth / 2) * cos(Rx) -
+                   (pos[2](filter) - depth / 2) * sin(Rx);
 
-    af::array x1  =  x0*cos(Ry) - z0*sin(Ry);
-    af::array y1  =  y0;
+    af::array x1 = x0 * cos(Ry) - z0 * sin(Ry);
+    af::array y1 = y0;
 
-    af::array x2  =  x1*cos(Rz) + y1*sin(Rz);
-    af::array y2  =  y1*cos(Rz) - x1*sin(Rz);
+    af::array x2 = x1 * cos(Rz) + y1 * sin(Rz);
+    af::array y2 = y1 * cos(Rz) - x1 * sin(Rz);
 
-    x2 += width/2;
-    y2 += height/2;
+    x2 += width / 2;
+    y2 += height / 2;
 
     return (x2.as(u32) * height) + y2.as(u32);
 }
 
-
-void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels, vector<af::array> &forces, float dt) {
-    for(int i=0; i< (int)pos.size(); ++i) {
+void simulate(af::array &mass, vector<af::array> &pos, vector<af::array> &vels,
+              vector<af::array> &forces, float dt) {
+    for (int i = 0; i < (int)pos.size(); ++i) {
         pos[i] += vels[i] * dt;
         pos[i].eval();
     }
 
-    //calculate forces to each particle
+    // calculate forces to each particle
     vector<af::array> diff(pos.size());
-    af::array dist = af::constant(0, pos[0].dims(0),pos[0].dims(0));
+    af::array dist = af::constant(0, pos[0].dims(0), pos[0].dims(0));
 
-    for(int i=0; i< (int)pos.size(); ++i) {
-        diff[i] = tile(pos[i], 1, pos[i].dims(0)) - transpose(tile(pos[i], 1, pos[i].dims(0)));
-        dist += (diff[i]*diff[i]);
+    for (int i = 0; i < (int)pos.size(); ++i) {
+        diff[i] = tile(pos[i], 1, pos[i].dims(0)) -
+                  transpose(tile(pos[i], 1, pos[i].dims(0)));
+        dist += (diff[i] * diff[i]);
     }
 
     dist = sqrt(dist);
     dist = af::max(min_dist, dist);
     dist *= dist * dist;
 
-    for(int i=0; i< (int)pos.size(); ++i) {
-        //calculate force vectors
+    for (int i = 0; i < (int)pos.size(); ++i) {
+        // calculate force vectors
         forces[i] = diff[i] / dist;
         forces[i].eval();
 
-        //af::array idx = af::where(af::isNaN(forces[i]));
-        //if(idx.elements() > 0)
+        // af::array idx = af::where(af::isNaN(forces[i]));
+        // if(idx.elements() > 0)
         //    forces[i](idx) = 0;
-        //forces[i] = sum(forces[i]).T();
+        // forces[i] = sum(forces[i]).T();
         forces[i] = matmul(forces[i].T(), mass);
 
-        //update force scaled to time, magnitude constant
+        // update force scaled to time, magnitude constant
         forces[i] *= (gravity_constant);
         forces[i].eval();
 
-        //update velocities from forces
+        // update velocities from forces
         vels[i] += forces[i] * dt;
         vels[i].eval();
 
-        //noise
-        //forces[i] += 0.1 * af::randn(forces[i].dims(0));
+        // noise
+        // forces[i] += 0.1 * af::randn(forces[i].dims(0));
 
-        //dampening
-        //vels[i] *= 1 - (0.005*dt);
+        // dampening
+        // vels[i] *= 1 - (0.005*dt);
     }
 }
 
 void collisions(vector<af::array> &pos, vector<af::array> &vels, bool is3D) {
-    //clamp particles inside screen border
-    af::array invalid_x = -2 * (pos[0] > width-1 || pos[0] < 0) + 1;
-    af::array invalid_y = -2 * (pos[1] > height-1 || pos[1] < 0) + 1;
-    //af::array invalid_x = (pos[0] < width-1 || pos[0] > 0);
-    //af::array invalid_y = (pos[1] < height-1 || pos[1] > 0);
-    vels[0]= invalid_x * vels[0] ;
-    vels[1]= invalid_y * vels[1] ;
-
-    af::array projected_px = min(width-1, max(0, pos[0]));
+    // clamp particles inside screen border
+    af::array invalid_x = -2 * (pos[0] > width - 1 || pos[0] < 0) + 1;
+    af::array invalid_y = -2 * (pos[1] > height - 1 || pos[1] < 0) + 1;
+    // af::array invalid_x = (pos[0] < width-1 || pos[0] > 0);
+    // af::array invalid_y = (pos[1] < height-1 || pos[1] > 0);
+    vels[0] = invalid_x * vels[0];
+    vels[1] = invalid_y * vels[1];
+
+    af::array projected_px = min(width - 1, max(0, pos[0]));
     af::array projected_py = min(height - 1, max(0, pos[1]));
-    pos[0] = projected_px;
-    pos[1] = projected_py;
+    pos[0]                 = projected_px;
+    pos[1]                 = projected_py;
 
-    if(is3D){
-        af::array invalid_z = -2 * (pos[2] > depth-1 || pos[2] < 0) + 1;
-        vels[2]= invalid_z * vels[2] ;
+    if (is3D) {
+        af::array invalid_z    = -2 * (pos[2] > depth - 1 || pos[2] < 0) + 1;
+        vels[2]                = invalid_z * vels[2];
         af::array projected_pz = min(depth - 1, max(0, pos[2]));
-        pos[2] = projected_pz;
+        pos[2]                 = projected_pz;
     }
 }
 
-
-int main(int, char **)
-{
+int main(int, char **) {
     try {
         af::info();
 
-        af::Window myWindow(width, height, "Gravity Simulation using ArrayFire");
+        af::Window myWindow(width, height,
+                            "Gravity Simulation using ArrayFire");
         myWindow.setColorMap(AF_COLORMAP_HEAT);
 
         int frame_count = 0;
@@ -181,12 +191,12 @@ int main(int, char **)
         // Initialize the kernel array just once
         const af::array draw_kernel = gaussianKernel(7, 7);
 
-        const int dims = (is3D)? 3 : 2;
+        const int dims = (is3D) ? 3 : 2;
 
         vector<af::array> pos(dims);
         vector<af::array> vels(dims);
         vector<af::array> forces(dims);
-        af::array         mass;
+        af::array mass;
 
         // Generate a random starting state
         initial_conditions_galaxy(mass, pos, vels, forces);
@@ -195,22 +205,28 @@ int main(int, char **)
         af::array ids(total_particles, u32);
 
         af::timer timer = af::timer::start();
-        while(!myWindow.close()) {
+        while (!myWindow.close()) {
             float dt = af::timer::stop(timer);
-            timer = af::timer::start();
+            timer    = af::timer::start();
 
-            af::array mid = mass(span) > (min_mass + mass_range/3);
-            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0, mid) :  ids_from_pos(pos);
-            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            af::array mid = mass(span) > (min_mass + mass_range / 3);
+            ids = (is3D) ? ids_from_3D(pos, 0, 0 + frame_count / 150.f, 0, mid)
+                         : ids_from_pos(pos);
+            // ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) : ids_from_pos(pos);
+            // //uncomment for no 3d rotation
             image(ids) += 4.f;
 
-            mid = mass(span) > (min_mass + 2*mass_range/3);
-            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0, mid) :  ids_from_pos(pos);
-            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            mid = mass(span) > (min_mass + 2 * mass_range / 3);
+            ids = (is3D) ? ids_from_3D(pos, 0, 0 + frame_count / 150.f, 0, mid)
+                         : ids_from_pos(pos);
+            // ids = (is3D)? ids_from_3D(pos, 0, 0, 0, mid) : ids_from_pos(pos);
+            // //uncomment for no 3d rotation
             image(ids) += 4.f;
 
-            ids = (is3D)? ids_from_3D(pos, 0, 0+frame_count/150.f, 0) :  ids_from_pos(pos);
-            //ids = (is3D)? ids_from_3D(pos, 0, 0, 0) :  ids_from_pos(pos); //uncomment for no 3d rotation
+            ids = (is3D) ? ids_from_3D(pos, 0, 0 + frame_count / 150.f, 0)
+                         : ids_from_pos(pos);
+            // ids = (is3D)? ids_from_3D(pos, 0, 0, 0) :  ids_from_pos(pos);
+            // //uncomment for no 3d rotation
             image(ids) += 4.f;
 
             image = convolve(image, draw_kernel);
@@ -220,18 +236,17 @@ int main(int, char **)
             frame_count++;
 
             // Generate a random starting state
-            if(frame_count % reset == 0) {
+            if (frame_count % reset == 0) {
                 initial_conditions_galaxy(mass, pos, vels, forces);
             }
 
-            //simulate
+            // simulate
             simulate(mass, pos, vels, forces, dt);
 
-            //check for collisions and adjust positions/velocities accordingly
+            // check for collisions and adjust positions/velocities accordingly
             collisions(pos, vels, is3D);
-
         }
-    } catch (af::exception& e) {
+    } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/graphics/gravity_sim_init.h b/examples/graphics/gravity_sim_init.h
index 6596a2615a..afa6abd538 100644
--- a/examples/graphics/gravity_sim_init.h
+++ b/examples/graphics/gravity_sim_init.h
@@ -1,4006 +1,7004 @@
 const int HBD_NUM_ELEMENTS = 4000 * 7;
-//halo, bulge, and disk particles
-float hbd[] = {
- 4.9161855e-03, -1.5334119e+00, -8.3381424e+00,  4.4288845e+00, -2.3778248e-01,  4.2592272e-02, -4.4895774e-01,
- 4.9161855e-03,  1.9886702e-02,  6.0085773e+00,  3.1188631e-01,  8.1422836e-01, -1.4591325e-02,  7.5382882e-01,
- 4.9161855e-03,  1.1676190e+00, -4.6193779e-01, -5.0477743e-01, -1.4803666e+00,  5.6056118e-01, -2.9858449e-02,
- 4.9161855e-03, -1.4250363e+00,  1.0891747e+01,  2.5225203e+00, -6.5798134e-02, -3.5946497e-01,  1.7471495e-01,
- 4.9161855e-03, -3.7135857e-01,  4.8796633e-01, -3.7898597e-01,  8.5347527e-01,  2.2493289e-01, -2.7678892e-01,
- 4.9161855e-03,  2.2072470e+00, -2.5046587e+00,  2.6029270e+00,  3.0826443e-01,  5.8606583e-01,  2.0105042e-01,
- 4.9161855e-03,  1.0779227e+00, -4.0834007e+00, -3.3965745e+00, -4.8430148e-01, -7.1573091e-01,  1.2384786e-01,
- 4.9161855e-03, -3.8722844e+00, -4.2357988e+00, -1.9723746e+00,  3.5759529e-01,  4.8990592e-01, -4.3040028e-01,
- 4.9161855e-03, -1.3005282e-01, -2.3483203e-01,  1.3832784e-01,  1.3746375e+00, -1.2947829e+00,  6.1215276e-01,
- 4.9161855e-03,  3.6822948e-01,  4.2760900e-01,  1.1544695e+00, -2.3177411e-02, -6.9136995e-01, -6.6200425e-03,
- 4.9161855e-03, -1.2485707e+00,  2.0474775e-01, -2.1652168e-01,  2.7034196e-01,  1.6398503e+00, -7.8224945e-01,
- 4.9161855e-03, -3.3862705e+00,  1.2049110e+00,  1.0672448e+00, -1.6531572e-01, -2.4370559e-01,  8.7125647e-01,
- 4.9161855e-03,  3.4262960e+00,  3.9102471e+00,  6.6162848e-01,  7.8005123e-01, -1.0415094e-01,  5.0161743e-01,
- 4.9161855e-03,  1.5740298e-01,  1.3008093e+00,  7.8130345e+00, -1.6444305e-01,  3.3037327e-03,  1.9713788e-01,
- 4.9161855e-03,  5.6700945e-01,  1.8889900e-01,  2.7523971e+00, -3.4313673e-01, -6.4287108e-01, -1.8927544e-01,
- 4.9161855e-03,  1.8354661e+00,  1.3209668e+00,  1.6966065e+00,  5.3318393e-01,  3.4129089e-01, -8.0587679e-01,
- 4.9161855e-03, -7.8488460e+00,  3.2376931e+00,  2.6638079e+00,  3.4405673e-01, -2.1986680e-01,  1.6776933e-01,
- 4.9161855e-03,  3.2422847e-01, -1.2311785e+00,  9.0597588e-01,  3.6714745e-01, -1.3913552e-01,  9.0002306e-02,
- 4.9161855e-03, -1.9477528e-01, -2.3987198e+00, -4.2354431e+00, -2.1188869e-01, -6.4195746e-01,  1.5219630e-01,
- 4.9161855e-03,  3.2330542e+00,  1.1787817e+00, -1.3654234e+00,  1.9920348e-01, -1.0560199e+00, -4.0022919e-01,
- 4.9161855e-03, -2.2656450e+00,  2.3343153e+00,  3.0343585e+00,  1.3909769e-01, -5.8018422e-01,  7.7305830e-01,
- 4.9161855e-03,  1.0106117e+01,  8.4062157e+00, -5.3659506e+00, -3.3819172e-01, -5.7871189e-02, -5.2655820e-02,
- 4.9161855e-03, -8.4759682e-02, -2.4386784e-01,  2.2389056e-01, -8.3496273e-01,  1.1504352e+00,  3.2196254e-03,
- 4.9161855e-03, -4.8354459e+00, -1.1709679e+01, -4.4684467e+00, -3.7076837e-01,  2.6136923e-01, -1.4268482e-01,
- 4.9161855e-03, -1.3268198e+00, -2.3238692e+00,  6.7897618e-01,  3.0518329e-01,  6.8463421e-01, -7.1791840e-01,
- 4.9161855e-03, -5.2054877e+00,  2.0948052e+00,  1.9656231e+00,  7.4416548e-01,  4.4825464e-01, -3.2727838e-01,
- 4.9161855e-03, -8.2616639e-01,  1.0700088e+00,  3.5586545e+00,  4.8024514e-01,  1.1944018e-01,  3.0837712e-01,
- 4.9161855e-03, -2.9101398e+00, -3.6366568e+00,  8.7982547e-01,  3.6643305e-01, -3.8197124e-01, -1.1440479e-01,
- 4.9161855e-03,  3.5198438e-01,  4.9096385e-01, -6.6494130e-02, -1.0383745e-01,  3.9406076e-01,  7.3723292e-01,
- 4.9161855e-03, -6.9214082e+00, -5.5405111e+00, -2.3041859e+00,  3.3985880e-01,  1.0167535e-02,  1.0593475e-01,
- 4.9161855e-03,  1.0908546e+00, -5.3155913e+00, -4.5045247e+00,  1.8077201e-01, -4.4904891e-01,  4.7391072e-01,
- 4.9161855e-03, -1.0766581e-01,  6.7338924e+00,  6.1174130e+00, -2.3362583e-01,  7.6430768e-02, -2.4832390e-01,
- 4.9161855e-03, -4.9775305e-01,  1.6378751e+00, -2.6263945e+00, -3.0084690e-01, -5.1551086e-01, -6.6373748e-01,
- 4.9161855e-03, -3.8946674e+00, -1.4725525e+00,  2.4148097e+00, -1.7075756e-01,  5.3592271e-01,  7.2393781e-01,
- 4.9161855e-03,  6.8583161e-02, -1.5991354e+00, -3.0150402e-01,  1.5219669e-01, -5.6440836e-01,  1.5284424e+00,
- 4.9161855e-03, -4.2822695e+00,  4.0367408e+00, -2.2387395e+00,  1.0239060e-01,  3.2810995e-01, -1.4511149e-01,
- 4.9161855e-03,  5.3348875e-01, -3.6950427e-01,  1.0364149e+00,  7.8612208e-02, -2.7073494e-01,  1.9663854e-01,
- 4.9161855e-03, -3.3353384e+00,  4.3220544e+00, -1.5343003e+00,  6.7457032e-01, -1.8098858e-01,  7.6241505e-01,
- 4.9161855e-03, -8.8430309e+00,  6.6101489e+00,  2.2365890e+00, -2.9622875e-03, -5.7892501e-01,  2.3848678e-01,
- 4.9161855e-03, -2.7121809e+00, -3.7584829e+00,  2.4702384e+00,  3.9350358e-01, -6.7748266e-01, -5.7142133e-01,
- 4.9161855e-03,  1.7517463e+00, -5.2237463e-01,  1.2052536e+00,  2.6133826e-01, -4.3084338e-01, -2.8758329e-01,
- 4.9161855e-03, -4.4221100e-01,  2.4987850e-01, -9.0834004e-01, -1.6435069e+00, -3.5537782e-01, -5.6679737e-02,
- 4.9161855e-03,  9.5630264e+00,  7.2472978e-01, -2.7188256e+00,  4.1388586e-01, -2.7986884e-01,  9.9171564e-02,
- 4.9161855e-03, -2.5304942e+00, -1.9891304e-01, -1.3565568e+00,  1.6445565e-01,  6.5720814e-01,  8.8133616e-04,
- 4.9161855e-03, -6.8739529e+00,  6.0871582e+00,  4.0246663e+00, -1.1313155e-01,  2.6078510e-01,  1.1052500e-02,
- 4.9161855e-03,  1.8411478e-01,  6.3666153e-01, -1.7665352e+00,  7.3893017e-01,  8.2843482e-02,  1.3584135e-01,
- 4.9161855e-03,  1.2281631e-01, -4.8358020e-01, -4.2862403e-01, -1.4062686e+00,  2.6675841e-01, -5.2812093e-01,
- 4.9161855e-03, -1.8010849e+00,  2.5018549e+00, -1.1007906e+00, -3.0198583e-01, -2.5083411e-01, -9.4572407e-01,
- 4.9161855e-03,  2.9228494e-02,  2.8824418e+00, -7.7373713e-01, -8.9457905e-01, -3.9830649e-01, -8.2690775e-01,
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- 4.9161855e-03,  5.8095527e+00,  4.5163748e-01, -1.3569316e+00, -7.1711606e-01,  4.6302426e-01, -1.2976727e-01,
- 4.9161855e-03,  1.2101072e+01, -3.3772957e+00, -5.3192800e-01, -4.1993264e-02, -1.0637641e-01, -1.1508505e-01,
- 4.9161855e-03,  2.6165378e+00,  1.8762544e+00, -6.6478405e+00,  4.9833903e-01,  5.6820488e-01,  9.6074417e-03,
- 4.9161855e-03, -2.7133231e+00, -5.9103000e-01,  4.9870867e-02, -2.2181080e-01, -1.8415939e-02,  5.7156056e-01,
- 4.9161855e-03,  1.0539672e+00, -7.1663280e+00,  4.3730845e+00, -2.0142028e-01,  4.7404751e-01, -2.7490994e-01,
- 4.9161855e-03, -1.1627064e+01, -3.0775794e-01, -5.9770060e+00, -7.5886458e-02,  4.0517724e-01, -1.3981339e-01,
- 4.9161855e-03,  1.0866967e+00, -7.9000783e-01,  2.5184824e+00,  1.1489426e-01, -5.5397308e-01, -9.2689073e-01,
- 4.9161855e-03, -1.8292384e-01,  3.2646315e+00, -1.6746950e+00,  5.0538975e-01, -8.1804043e-01,  7.3222065e-01,
- 4.9161855e-03,  1.4929719e+00,  9.4005907e-01,  1.8587011e+00,  4.4272500e-01, -5.7933551e-01,  1.1078842e-02,
- 4.9161855e-03,  4.0897088e+00, -8.3170910e+00, -7.7612681e+00, -1.3118382e-01,  2.2805281e-01, -5.7812393e-01,
- 4.9161855e-03,  8.6598027e-01, -1.0456352e+00,  3.8437498e-01,  1.6694506e+00, -6.2009120e-01,  5.3192055e-01,
- 4.9161855e-03, -4.8537847e-01,  9.1856569e-01, -1.3051009e+00,  6.5430939e-01, -5.9828395e-01,  1.1575594e+00,
- 4.9161855e-03, -4.2665830e+00, -3.0704074e+00, -1.0525151e+00, -4.6153173e-01,  3.5057652e-01,  2.7432105e-01,
- 4.9161855e-03,  5.1324239e+00, -3.9258289e-01,  2.4644251e+00,  7.1393543e-01,  5.6272078e-02,  5.0331020e-01,
- 4.9161855e-03,  2.1729605e+00, -2.9398150e+00,  3.8983128e+00, -5.7526851e-01, -5.4395968e-01,  2.6677924e-01,
- 4.9161855e-03, -4.6834240e+00, -7.1150680e+00,  5.3980551e+00,  2.3003122e-01, -9.5528945e-02,  1.0089890e-01,
- 4.9161855e-03, -6.5583615e+00,  6.1323514e+00,  3.4290126e-01,  5.6338448e-02, -3.6545107e-01,  6.3475060e-01,
- 4.9161855e-03, -4.7143194e-01, -5.2725344e+00,  1.0759580e+00,  2.6186921e-02,  2.0417234e-01,  3.1454092e-01,
- 4.9161855e-03,  1.4883240e+00, -2.8093128e+00,  3.0265145e+00, -4.0938655e-01, -8.7190077e-02,  3.6416546e-01,
- 4.9161855e-03,  2.1199739e+00, -5.4996886e+00,  3.2656703e+00, -1.9891968e-01, -1.9218311e-01,  4.7576624e-01,
- 4.9161855e-03,  5.6682081e+00,  9.3008503e-02,  3.7969866e+00, -4.5014992e-01, -5.4205108e-01, -1.7190477e-01,
- 4.9161855e-03,  2.9768403e+00, -4.0278282e+00,  6.8811315e-01, -1.3242954e-01, -2.6241624e-01,  2.3300681e-01,
- 4.9161855e-03,  3.2816823e+00, -1.5965747e+00, -4.6481495e+00, -7.3801905e-01,  2.7248913e-01, -4.6172965e-02,
- 4.9161855e-03, -1.2009241e+01, -3.1461194e+00,  6.5948210e+00,  2.2816226e-02,  1.7971846e-01, -7.1230225e-02,
- 4.9161855e-03,  1.0664890e+00, -4.2399839e-02, -1.1740028e+00, -2.5743067e-01, -1.9595818e-01, -4.6895766e-01,
- 4.9161855e-03, -4.4604793e-01, -4.1761667e-01, -5.9358352e-01, -1.4772195e-01,  3.2849824e-01,  9.1546112e-01,
- 4.9161855e-03, -1.0685309e+00, -8.3202881e-01,  1.9027503e+00,  3.7143436e-01,  1.0500257e+00,  7.3510087e-01,
- 4.9161855e-03,  2.6647577e-01,  5.7187647e-01, -5.4631060e-01, -7.7697217e-01,  5.5341065e-01,  8.8884197e-02,
- 4.9161855e-03, -2.4092264e+00, -2.3437815e+00, -5.6990242e+00,  4.0246669e-02, -6.9021386e-01,  4.8528168e-01,
- 4.9161855e-03, -2.9229283e-01,  2.7454209e+00, -1.2440990e+00,  5.0732434e-01,  1.6615523e-01, -5.7657963e-01,
- 4.9161855e-03, -3.1489432e+00,  1.2680652e+00, -5.7047668e+00, -2.0682169e-01, -5.2342772e-01,  3.2621157e-01,
- 4.9161855e-03, -4.2064637e-01,  8.1609935e-01,  6.2681526e-01,  3.5374090e-01,  6.2999052e-01, -5.8346725e-01,
- 4.9161855e-03,  7.1308404e-02,  1.8311420e-01,  4.0706435e-01,  3.4199366e-01,  9.3160830e-03,  4.1215700e-01,
- 4.9161855e-03,  5.6278663e+00,  3.3636853e-01, -6.4618564e-01,  1.4624824e-01,  2.6545855e-01, -2.6047999e-01,
- 4.9161855e-03,  2.1086318e+00,  1.4405881e+00,  1.9607490e+00,  4.1016015e-01, -1.0820497e+00,  5.2126324e-01,
- 4.9161855e-03,  2.2687659e+00, -3.8944154e+00, -3.5740595e+00,  5.5470216e-01,  1.0869193e-01,  1.2446215e-01,
- 4.9161855e-03, -3.6911979e+00, -1.6825495e-02,  2.7175789e+00,  3.3319286e-01,  4.5574255e-02, -2.9945102e-01,
- 4.9161855e-03, -9.1713123e+00, -1.1326112e+01,  8.7793245e+00,  3.2807869e-01,  3.1993087e-02,  6.5704375e-03,
- 4.9161855e-03, -6.3241405e+00,  4.5917640e+00,  5.2446551e+00,  8.6806208e-02, -1.1900769e-01,  3.7303127e-02,
- 4.9161855e-03,  1.8690332e+00,  5.1850295e-01, -4.2205045e-01,  5.1754210e-02,  1.0277729e+00, -9.3673009e-01,
- 4.9161855e-03,  1.1749099e+00,  1.8220998e+00,  3.7768686e+00,  3.2626029e-02,  1.9230081e-01, -6.1840069e-01,
- 4.9161855e-03, -6.4281154e+00, -3.2852066e+00, -3.6263623e+00,  4.3581065e-02, -9.3072295e-02,  2.2059004e-01,
- 4.9161855e-03, -2.8914037e+00, -8.9913285e-01, -6.0291066e+00, -7.3334366e-02, -1.7908965e-01,  2.4383314e-01,
- 4.9161855e-03,  3.5674961e+00, -1.9904513e+00, -2.8840287e+00, -2.1585038e-01,  2.6890549e-01,  5.7695067e-01,
- 4.9161855e-03, -4.5172372e+00, -1.2764982e+01, -6.5555286e+00, -8.7975547e-02, -2.8868642e-02, -2.4445239e-01,
- 4.9161855e-03,  1.1917623e+00,  2.7240102e+00, -5.6969924e+00,  1.5443534e-01,  8.0268896e-01,  7.6069735e-02,
- 4.9161855e-03,  1.8703443e+00, -1.6433734e+00, -3.6527286e+00,  9.3277645e-01, -2.1267043e-01,  1.9547650e-01,
- 4.9161855e-03,  3.5234538e-01, -3.5503694e-01, -3.5764150e-02, -2.7299783e-01,  2.0867128e+00, -4.0437704e-01,
- 4.9161855e-03,  7.0537286e+00,  4.2256870e+00, -2.3376143e+00,  1.0489196e-01, -2.2336484e-01, -2.2279005e-01,
- 4.9161855e-03,  1.2876858e+00,  7.2569623e+00, -2.2856178e+00, -3.6533204e-01, -2.2654597e-01, -3.9202511e-01,
- 4.9161855e-03, -2.9575005e+00,  4.0046115e+00,  1.9336003e+00,  7.7007276e-01,  1.8195377e-01,  5.0428671e-01,
- 4.9161855e-03,  3.6017182e+00,  9.1012402e+00, -6.7456603e+00, -1.3861659e-01, -2.6884264e-01, -3.9056700e-01,
- 4.9161855e-03, -1.1627531e+00,  1.7062700e+00, -7.1475458e-01, -1.5973236e-02, -5.2192539e-01,  9.2492419e-01,
- 4.9161855e-03,  7.0983272e+00,  4.3586853e-01, -3.5620954e+00,  3.9555708e-01,  5.6896615e-01, -3.9723828e-01,
- 4.9161855e-03,  1.4865612e+00, -1.0475974e+00, -8.4833641e+00, -3.7397227e-01,  1.3291334e-01,  3.3054215e-01,
- 4.9161855e-03,  3.3097060e+00, -4.0853152e+00,  2.3023739e+00, -7.3129189e-01,  4.1393802e-01,  2.4469729e-01,
- 4.9161855e-03, -6.4677873e+00, -1.6074709e+00,  2.2694349e+00,  2.4836297e-01, -4.7907314e-01, -1.2783307e-02,
- 4.9161855e-03,  7.6441946e+00, -6.5884595e+00,  8.2836065e+00, -6.5808132e-02, -1.2891619e-01, -1.0536889e-01,
- 4.9161855e-03, -6.1940775e+00, -7.0686564e+00,  2.8182077e+00,  4.6267312e-02,  2.1834882e-01, -2.8412163e-01,
- 4.9161855e-03,  7.5322211e-01,  4.4226575e-01,  8.6104780e-01, -4.5959395e-01, -1.2565438e+00,  1.0619931e+00,
- 4.9161855e-03, -3.1116338e+00,  5.5792129e-01,  5.3073101e+00,  3.0462223e-01,  7.5853378e-02, -1.9224058e-01,
- 4.9161855e-03,  2.2643218e+00,  2.0357387e+00,  4.4502897e+00, -2.8496760e-01,  1.2047067e-01,  6.4417034e-01,
- 4.9161855e-03, -1.4413284e+00,  3.5867362e+00, -2.4204571e+00,  4.2380524e-01, -2.1113880e-01, -1.7703670e-01,
- 4.9161855e-03, -6.8668759e-01, -9.5317203e-01,  1.5330289e-01,  5.7356155e-01,  6.3638610e-01,  7.7120703e-01,
- 4.9161855e-03, -1.0682197e+00, -6.9213104e+00, -5.8608122e+00,  1.0352087e-01, -3.3730379e-01,  1.9342881e-01,
- 4.9161855e-03, -2.4783916e+00,  1.2663845e+00,  1.5080407e+00,  3.5923757e-03,  5.0929576e-01,  3.1987467e-01,
- 4.9161855e-03,  6.2106740e-01, -8.0850184e-01,  6.0432136e-01,  1.0544959e+00,  3.5460990e-02,  7.1798617e-01,
- 4.9161855e-03,  5.7629764e-01, -4.1872951e-01,  2.6883879e-01, -5.7401496e-01, -5.2689475e-01, -2.9298371e-01,
- 4.9161855e-03, -6.0079894e+00, -3.0357261e+00,  1.1362796e+00,  1.8514165e-01, -1.0868914e-02, -2.6686630e-01,
- 4.9161855e-03, -6.4743943e+00,  5.0929122e+00,  4.5632439e+00, -8.3602853e-03,  1.3735165e-01, -3.0539981e-01,
- 4.9161855e-03, -1.1718397e+00, -4.3745694e+00,  4.1264515e+00,  3.4016520e-01, -2.4106152e-01, -6.2656836e-03,
- 4.9161855e-03,  4.5977187e+00,  9.2932510e-01,  1.8005730e+00,  7.5450696e-02,  2.5778416e-01, -1.0443735e-01,
- 4.9161855e-03, -1.2225604e+00,  3.8227065e+00, -4.0077796e+00,  3.7918901e-01, -3.4038458e-02, -2.2999659e-01,
- 4.9161855e-03, -1.6463979e+00,  3.3725232e-01, -2.3585579e+00, -7.5838506e-02,  7.1057733e-03,  2.9407086e-02,
- 4.9161855e-03,  5.4664793e+00, -3.7369993e-01,  1.8591646e+00,  6.9752198e-01,  5.2111161e-01, -5.1446843e-01,
- 4.9161855e-03, -2.0373304e+00,  2.6609144e+00, -1.8289629e+00,  5.7756305e-01, -3.7016757e-03, -1.2520009e-01,
- 4.9161855e-03, -4.3900475e-01,  1.6747446e+00,  4.9002385e+00,  2.5009772e-01, -1.8630438e-01,  3.6023688e-01,
- 4.9161855e-03, -6.4800224e+00,  1.0171971e+00,  2.6008205e+00,  7.6939821e-02,  3.9370355e-01,  1.5263109e-02,
- 4.9161855e-03,  7.7535975e-01, -6.5957302e-01, -1.4328420e-01,  1.3423905e-01, -1.1076678e+00,  2.9757038e-01,
+// halo, bulge, and disk particles
+float hbd[] = {4.9161855e-03,  -1.5334119e+00, -8.3381424e+00, 4.4288845e+00,
+               -2.3778248e-01, 4.2592272e-02,  -4.4895774e-01, 4.9161855e-03,
+               1.9886702e-02,  6.0085773e+00,  3.1188631e-01,  8.1422836e-01,
+               -1.4591325e-02, 7.5382882e-01,  4.9161855e-03,  1.1676190e+00,
+               -4.6193779e-01, -5.0477743e-01, -1.4803666e+00, 5.6056118e-01,
+               -2.9858449e-02, 4.9161855e-03,  -1.4250363e+00, 1.0891747e+01,
+               2.5225203e+00,  -6.5798134e-02, -3.5946497e-01, 1.7471495e-01,
+               4.9161855e-03,  -3.7135857e-01, 4.8796633e-01,  -3.7898597e-01,
+               8.5347527e-01,  2.2493289e-01,  -2.7678892e-01, 4.9161855e-03,
+               2.2072470e+00,  -2.5046587e+00, 2.6029270e+00,  3.0826443e-01,
+               5.8606583e-01,  2.0105042e-01,  4.9161855e-03,  1.0779227e+00,
+               -4.0834007e+00, -3.3965745e+00, -4.8430148e-01, -7.1573091e-01,
+               1.2384786e-01,  4.9161855e-03,  -3.8722844e+00, -4.2357988e+00,
+               -1.9723746e+00, 3.5759529e-01,  4.8990592e-01,  -4.3040028e-01,
+               4.9161855e-03,  -1.3005282e-01, -2.3483203e-01, 1.3832784e-01,
+               1.3746375e+00,  -1.2947829e+00, 6.1215276e-01,  4.9161855e-03,
+               3.6822948e-01,  4.2760900e-01,  1.1544695e+00,  -2.3177411e-02,
+               -6.9136995e-01, -6.6200425e-03, 4.9161855e-03,  -1.2485707e+00,
+               2.0474775e-01,  -2.1652168e-01, 2.7034196e-01,  1.6398503e+00,
+               -7.8224945e-01, 4.9161855e-03,  -3.3862705e+00, 1.2049110e+00,
+               1.0672448e+00,  -1.6531572e-01, -2.4370559e-01, 8.7125647e-01,
+               4.9161855e-03,  3.4262960e+00,  3.9102471e+00,  6.6162848e-01,
+               7.8005123e-01,  -1.0415094e-01, 5.0161743e-01,  4.9161855e-03,
+               1.5740298e-01,  1.3008093e+00,  7.8130345e+00,  -1.6444305e-01,
+               3.3037327e-03,  1.9713788e-01,  4.9161855e-03,  5.6700945e-01,
+               1.8889900e-01,  2.7523971e+00,  -3.4313673e-01, -6.4287108e-01,
+               -1.8927544e-01, 4.9161855e-03,  1.8354661e+00,  1.3209668e+00,
+               1.6966065e+00,  5.3318393e-01,  3.4129089e-01,  -8.0587679e-01,
+               4.9161855e-03,  -7.8488460e+00, 3.2376931e+00,  2.6638079e+00,
+               3.4405673e-01,  -2.1986680e-01, 1.6776933e-01,  4.9161855e-03,
+               3.2422847e-01,  -1.2311785e+00, 9.0597588e-01,  3.6714745e-01,
+               -1.3913552e-01, 9.0002306e-02,  4.9161855e-03,  -1.9477528e-01,
+               -2.3987198e+00, -4.2354431e+00, -2.1188869e-01, -6.4195746e-01,
+               1.5219630e-01,  4.9161855e-03,  3.2330542e+00,  1.1787817e+00,
+               -1.3654234e+00, 1.9920348e-01,  -1.0560199e+00, -4.0022919e-01,
+               4.9161855e-03,  -2.2656450e+00, 2.3343153e+00,  3.0343585e+00,
+               1.3909769e-01,  -5.8018422e-01, 7.7305830e-01,  4.9161855e-03,
+               1.0106117e+01,  8.4062157e+00,  -5.3659506e+00, -3.3819172e-01,
+               -5.7871189e-02, -5.2655820e-02, 4.9161855e-03,  -8.4759682e-02,
+               -2.4386784e-01, 2.2389056e-01,  -8.3496273e-01, 1.1504352e+00,
+               3.2196254e-03,  4.9161855e-03,  -4.8354459e+00, -1.1709679e+01,
+               -4.4684467e+00, -3.7076837e-01, 2.6136923e-01,  -1.4268482e-01,
+               4.9161855e-03,  -1.3268198e+00, -2.3238692e+00, 6.7897618e-01,
+               3.0518329e-01,  6.8463421e-01,  -7.1791840e-01, 4.9161855e-03,
+               -5.2054877e+00, 2.0948052e+00,  1.9656231e+00,  7.4416548e-01,
+               4.4825464e-01,  -3.2727838e-01, 4.9161855e-03,  -8.2616639e-01,
+               1.0700088e+00,  3.5586545e+00,  4.8024514e-01,  1.1944018e-01,
+               3.0837712e-01,  4.9161855e-03,  -2.9101398e+00, -3.6366568e+00,
+               8.7982547e-01,  3.6643305e-01,  -3.8197124e-01, -1.1440479e-01,
+               4.9161855e-03,  3.5198438e-01,  4.9096385e-01,  -6.6494130e-02,
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+               1.7740901e-01,  -2.2428825e-01, -7.9772305e-01, 2.2299300e-01,
+               4.2524221e-04,  1.0266142e-01,  -3.9200943e-02, -1.6250725e-01,
+               -2.1084811e-01, 4.7313869e-01,  7.5736183e-01,  4.2524221e-04,
+               -5.2503270e-01, -2.5550249e-01, 2.4210323e-01,  4.2290211e-01,
+               -1.1937749e-03, -2.8803447e-01, 4.2524221e-04,  6.8656705e-02,
+               2.3230983e-01,  -1.0208790e-02, -1.9244626e-01, 8.1877112e-01,
+               -2.5449389e-01, 4.2524221e-04,  -5.4129776e-02, 2.9140076e-01,
+               -4.6895444e-01, -2.3883762e-02, -1.9746602e-01, -1.4508346e-02,
+               4.2524221e-04,  -3.0830520e-01, -2.6217067e-01, -2.6785174e-01,
+               6.7281228e-01,  3.7336886e-01,  -1.4304060e-01, 4.2524221e-04,
+               1.5217099e-01,  2.0078890e-01,  7.7753231e-02,  -3.3346283e-01,
+               -1.2821050e-01, -4.3130264e-01, 4.2524221e-04,  3.8476987e-04,
+               -7.6562621e-02, -4.8909627e-02, -1.1036193e-01, 2.4940021e-01,
+               2.4720046e-01,  4.2524221e-04,  1.9815315e-01,  1.9162391e-01,
+               6.0125452e-02,  -7.7126014e-01, 4.2003978e-02,  6.3951693e-02,
+               4.2524221e-04,  9.2402853e-02,  -1.9484653e-01, -1.4663309e-01,
+               1.7251915e-01,  -1.6592954e-01, -3.1574631e-01, 4.2524221e-04,
+               1.4493692e-01,  -3.1712703e-02, -1.5764284e-01, -1.6178896e-01,
+               3.3917201e-01,  -4.9173659e-01, 4.2524221e-04,  2.1914667e-01,
+               -7.4241884e-02, -9.9493600e-02, -1.7168714e-01, 1.7520438e-01,
+               1.1748855e+00,  4.2524221e-04,  -1.6493322e-01, 2.1094975e-01,
+               2.6855225e-02,  8.0839500e-02,  6.4471591e-01,  2.5444278e-01,
+               4.2524221e-04,  -1.0818439e-01, 5.0222378e-02,  1.0443858e-01,
+               7.3543733e-01,  -5.2923161e-01, 2.3857592e-02,  4.2524221e-04,
+               -1.3066588e-01, 3.3706114e-01,  -6.5367684e-02, -1.9584729e-01,
+               -9.6636809e-02, 5.7062846e-01,  4.2524221e-04,  8.9271449e-02,
+               -1.5417366e-02, -8.2307503e-02, -5.0039625e-01, 2.5350851e-01,
+               -2.4847549e-01, 4.2524221e-04,  -2.8799692e-01, -1.0268785e-01,
+               -6.9768213e-02, 1.9839688e-01,  -9.6014850e-02, 1.1959620e-02,
+               4.2524221e-04,  -7.6331727e-02, 1.0289106e-01,  2.5628258e-02,
+               -9.5651820e-02, -3.1599486e-01, 3.4648609e-01,  4.2524221e-04,
+               -4.9910601e-02, 8.5599929e-02,  -3.1449606e-03, -1.6781870e-01,
+               1.0333546e+00,  -6.6645592e-01, 4.2524221e-04,  8.2493991e-02,
+               -9.5790043e-02, 4.3036491e-02,  1.8140252e-01,  5.4385066e-01,
+               3.2726720e-02,  4.2524221e-04,  2.2156011e-01,  3.1133004e-02,
+               -1.4379646e-01, -5.9910184e-01, 1.0038698e+00,  -3.0557862e-01,
+               4.2524221e-04,  3.7525645e-01,  7.0815518e-02,  2.8620017e-01,
+               6.9975668e-01,  1.0616329e-01,  1.8318458e-01,  4.2524221e-04,
+               9.5496923e-02,  -3.8357295e-02, 7.5472467e-02,  1.4580189e-02,
+               1.3419588e-01,  -2.0312097e-02, 4.2524221e-04,  4.9029529e-02,
+               1.7314212e-01,  -4.9041037e-02, -2.6927444e-01, -2.4882385e-01,
+               -2.5494534e-01, 4.2524221e-04,  -6.4100541e-02, 2.6978979e-01,
+               2.4858065e-02,  -8.1361562e-01, -3.7216064e-01, 4.3392561e-02,
+               4.2524221e-04,  6.9799364e-02,  -1.3860419e-01, 1.0984455e-01,
+               4.8301801e-01,  5.5070144e-01,  -3.3188796e-01, 4.2524221e-04,
+               -8.2801402e-02, -6.8652697e-02, -1.9647431e-02, 1.8623030e-01,
+               -1.3855183e-01, 3.1506360e-01,  4.2524221e-04,  3.6300448e-01,
+               -8.0298670e-02, -3.1002939e-01, -3.3787906e-01, -3.0862695e-01,
+               2.7613443e-01,  4.2524221e-04,  3.7739474e-01,  1.1907437e-01,
+               -3.9434172e-02, 5.8045042e-01,  4.5934165e-01,  2.9962903e-01,
+               4.2524221e-04,  2.9385680e-02,  1.1072745e-01,  5.8579307e-02,
+               -2.8264758e-01, -1.0784884e-01, 1.2321078e+00,  4.2524221e-04,
+               7.9958871e-02,  1.2411897e-01,  9.8061837e-02,  3.3262360e-01,
+               -8.3796644e-01, 4.0548918e-01,  4.2524221e-04,  7.8290664e-02,
+               4.5500584e-02,  9.9731199e-02,  -4.6239632e-01, 3.0574635e-01,
+               -4.3212789e-01, 4.2524221e-04,  3.6696273e-01,  5.7200775e-03,
+               5.3992327e-02,  -1.6632666e-01, -3.1065517e-03, -1.1606836e-01,
+               4.2524221e-04,  2.3191632e-01,  3.3108935e-01,  2.0009531e-02,
+               4.3141481e-01,  7.1523404e-01,  -4.0791895e-02, 4.2524221e-04,
+               -2.0644982e-01, 3.2929885e-01,  -2.1481182e-01, 3.4483513e-01,
+               8.7951744e-01,  2.2883956e-01,  4.2524221e-04,  -2.4269024e-02,
+               8.0496661e-02,  -2.2875665e-02, -4.7301382e-02, -1.2039685e-01,
+               -4.8519605e-01, 4.2524221e-04,  -3.5178763e-01, -1.1468551e-01,
+               -7.2022155e-02, 7.1914357e-01,  -1.8774068e-01, 2.9152307e-01,
+               4.2524221e-04,  1.5231021e-01,  2.1161540e-01,  -1.1754553e-01,
+               -7.1294534e-01, -6.2154621e-01, -1.9393834e-01, 4.2524221e-04,
+               -7.8070223e-02, 1.7216440e-01,  1.7939833e-01,  4.8407644e-01,
+               -1.7517121e-01, 4.1451525e-02,  4.2524221e-04,  1.9436933e-02,
+               4.3368284e-02,  -3.5639319e-03, 6.7544144e-01,  5.4782498e-01,
+               3.4879735e-01,  4.2524221e-04,  -1.3366042e-01, -8.3979061e-03,
+               -8.7891303e-02, -9.8265654e-01, -4.2677250e-02, -1.1890029e-01,
+               4.2524221e-04,  1.2091810e-01,  -1.8473221e-01, 3.7591079e-01,
+               1.7912203e-01,  7.1378611e-03,  5.6433028e-01,  4.2524221e-04,
+               -3.0588778e-02, -8.0224700e-02, 2.0911565e-01,  1.7871276e-01,
+               -4.5090526e-01, 1.7313591e-01,  4.2524221e-04,  2.1592773e-01,
+               -1.0682704e-01, -1.4687291e-01, -2.1309285e-01, 3.2003528e-01,
+               9.6824163e-01,  4.2524221e-04,  -7.1326107e-02, -1.8375346e-01,
+               1.6073698e-01,  6.6706583e-02,  -2.2058874e-01, -1.6864805e-01,
+               4.2524221e-04,  -4.4198960e-02, -1.1312663e-01, 1.0822348e-01,
+               1.3487945e-01,  -7.0401341e-01, -1.2007080e+00, 4.2524221e-04,
+               -2.9746767e-02, -1.3425194e-01, -2.5086749e-01, -1.1511848e-01,
+               -8.7276441e-01, 1.6036594e-01,  4.2524221e-04,  1.7037044e-01,
+               1.7299759e-01,  4.6205060e-03,  5.1056665e-01,  1.0041865e+00,
+               2.3419438e-01,  4.2524221e-04,  1.6252996e-01,  1.1271755e-01,
+               4.6216175e-02,  5.6226152e-01,  6.6637951e-01,  5.3371119e-01,
+               4.2524221e-04,  -1.9546813e-01, 1.3906172e-01,  -5.5975009e-02,
+               -1.0969467e-01, -1.2633232e+00, -4.3421894e-02, 4.2524221e-04,
+               -1.4044075e-01, -2.6630515e-01, 6.1962787e-02,  4.6771467e-01,
+               -6.9051319e-01, 2.6465434e-01,  4.2524221e-04,  1.7195286e-01,
+               -5.2851868e-01, -1.6422449e-01, 1.1703679e-01,  7.2824037e-01,
+               -3.6378372e-01, 4.2524221e-04,  1.0194746e-01,  -9.7751893e-02,
+               1.6529745e-01,  2.4984296e-01,  3.8181201e-02,  2.7078211e-01,
+               4.2524221e-04,  2.0533490e-01,  1.9480339e-01,  -6.6993818e-02,
+               3.9745870e-01,  -7.9133675e-02, -1.1942380e-01, 4.2524221e-04,
+               -3.9208923e-02, 9.8150961e-02,  1.0030308e-01,  -5.7831265e-02,
+               -6.4350224e-01, 8.4775603e-01,  4.2524221e-04,  1.3816082e-01,
+               -1.4092979e-02, -1.0894109e-01, 2.8519067e-01,  5.8030725e-01,
+               6.5652287e-01,  4.2524221e-04,  3.1362314e-02,  -6.5740333e-03,
+               6.7480214e-02,  4.2265895e-01,  -5.1995921e-01, -2.8980300e-02,
+               4.2524221e-04,  -1.1953717e-01, 1.5453845e-01,  1.3720915e-01,
+               -1.5399654e-01, -1.2724885e-01, 6.4902240e-01,  4.2524221e-04,
+               -2.4549389e-01, -7.9987049e-02, 8.9279823e-02,  -9.2930816e-02,
+               -6.1336237e-01, 4.7973198e-01,  4.2524221e-04,  2.5360553e-02,
+               -2.6513871e-02, 5.4526389e-02,  -9.8100655e-02, 6.5327984e-01,
+               -5.2721924e-01, 4.2524221e-04,  -1.0606319e-01, -6.9447577e-02,
+               4.3061398e-02,  -1.0653659e+00, 6.2340677e-01,  4.6419606e-02};
diff --git a/examples/graphics/histogram.cpp b/examples/graphics/histogram.cpp
index 3365a373ea..7986e44dd5 100644
--- a/examples/graphics/histogram.cpp
+++ b/examples/graphics/histogram.cpp
@@ -8,20 +8,19 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <cstdio>
 #include <math.h>
+#include <cstdio>
 
 using namespace af;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         // Initialize the kernel array just once
         af::info();
         af::Window myWindow(512, 512, "Histogram example using ArrayFire");
         af::Window imgWnd(480, 640, "Input Image");
 
-        array img = loadImage(ASSETS_DIR"/examples/images/arrow.jpg", false);
+        array img = loadImage(ASSETS_DIR "/examples/images/arrow.jpg", false);
         array hist_out = histogram(img, 256, 0, 255);
 
         myWindow.setAxesTitles("Bins", "Frequency");
diff --git a/examples/graphics/plot2d.cpp b/examples/graphics/plot2d.cpp
index e8e48389f5..1bc72ac1e5 100644
--- a/examples/graphics/plot2d.cpp
+++ b/examples/graphics/plot2d.cpp
@@ -8,43 +8,41 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <cstdio>
 #include <math.h>
+#include <cstdio>
 
 using namespace af;
 
-static const int ITERATIONS = 50;
-static const float PRECISION = 1.0f/ITERATIONS;
+static const int ITERATIONS  = 50;
+static const float PRECISION = 1.0f / ITERATIONS;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         // Initialize the kernel array just once
         af::info();
         af::Window myWindow(800, 800, "2D Plot example: ArrayFire");
 
         array Y;
-        int sign = 1;
-        array X = seq(-af::Pi, af::Pi, PRECISION);
-        array noise = randn(X.dims(0))/5.f;
+        int sign    = 1;
+        array X     = seq(-af::Pi, af::Pi, PRECISION);
+        array noise = randn(X.dims(0)) / 5.f;
 
         myWindow.grid(2, 1);
 
-        for (double val=0; !myWindow.close(); ) {
-
+        for (double val = 0; !myWindow.close();) {
             Y = sin(X);
 
-            myWindow(0,0).plot(X, Y);
-            myWindow(1,0).scatter(X, Y + noise, AF_MARKER_POINT);
+            myWindow(0, 0).plot(X, Y);
+            myWindow(1, 0).scatter(X, Y + noise, AF_MARKER_POINT);
 
             myWindow.show();
 
             X = X + PRECISION * float(sign);
             val += PRECISION * float(sign);
 
-            if (val>af::Pi) {
+            if (val > af::Pi) {
                 sign = -1;
-            } else if (val<-af::Pi) {
+            } else if (val < -af::Pi) {
                 sign = 1;
             }
         }
diff --git a/examples/graphics/plot3.cpp b/examples/graphics/plot3.cpp
index 28932e8103..9be0d4f308 100644
--- a/examples/graphics/plot3.cpp
+++ b/examples/graphics/plot3.cpp
@@ -8,36 +8,35 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <cstdio>
 #include <math.h>
+#include <cstdio>
 
 using namespace af;
 
-static const int ITERATIONS = 200;
-static const float PRECISION = 1.0f/ITERATIONS;
+static const int ITERATIONS  = 200;
+static const float PRECISION = 1.0f / ITERATIONS;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         // Initialize the kernel array just once
         af::info();
         af::Window myWindow(800, 800, "3D Line Plot example: ArrayFire");
 
-        static float t=0.1;
-        array Z = seq( 0.1f, 10.f, PRECISION);
+        static float t = 0.1;
+        array Z        = seq(0.1f, 10.f, PRECISION);
 
         do {
-            array Y = sin((Z*t) + t) / Z;
-            array X = cos((Z*t) + t) / Z;
-            X = max(min(X, 1.0), -1.0);
-            Y = max(min(Y, 1.0), -1.0);
+            array Y = sin((Z * t) + t) / Z;
+            array X = cos((Z * t) + t) / Z;
+            X       = max(min(X, 1.0), -1.0);
+            Y       = max(min(Y, 1.0), -1.0);
 
-            //Pts can be passed in as a matrix in the form n x 3, 3 x n
-            //or in the flattened xyz-triplet array with size 3n x 1
+            // Pts can be passed in as a matrix in the form n x 3, 3 x n
+            // or in the flattened xyz-triplet array with size 3n x 1
             myWindow.plot(X, Y, Z);
 
-            t+=0.01;
-        } while(!myWindow.close());
+            t += 0.01;
+        } while (!myWindow.close());
 
     } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/graphics/surface.cpp b/examples/graphics/surface.cpp
index 44e0eb7ccd..f9e89ed835 100644
--- a/examples/graphics/surface.cpp
+++ b/examples/graphics/surface.cpp
@@ -8,16 +8,15 @@
  ********************************************************/
 
 #include <arrayfire.h>
-#include <cstdio>
 #include <math.h>
+#include <cstdio>
 
 using namespace af;
 
 static const int M = 30;
 static const int N = 2 * M;
 
-int main(int, char **)
-{
+int main(int, char**) {
     try {
         // Initialize the kernel array just once
         af::info();
@@ -27,10 +26,11 @@ int main(int, char **)
         const array x = iota(dim4(N, 1), dim4(1, N)) / M - 1;
         const array y = iota(dim4(1, N), dim4(N, 1)) / M - 1;
 
-        static float t=0;
-        while(!myWindow.close()) {
-            t+=0.07;
-            array z = 10*x*-abs(y) * cos(x*x*(y+t))+sin(y*(x+t))-1.5;
+        static float t = 0;
+        while (!myWindow.close()) {
+            t += 0.07;
+            array z = 10 * x * -abs(y) * cos(x * x * (y + t)) +
+                      sin(y * (x + t)) - 1.5;
             myWindow.surface(x, y, z);
         }
 
diff --git a/examples/helloworld/helloworld.cpp b/examples/helloworld/helloworld.cpp
index b4958e920d..d0b8ca20a5 100644
--- a/examples/helloworld/helloworld.cpp
+++ b/examples/helloworld/helloworld.cpp
@@ -13,18 +13,15 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
-{
+int main(int argc, char* argv[]) {
     try {
-
-
         // Select a device and display arrayfire info
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
         printf("Create a 5-by-3 matrix of random floats on the GPU\n");
-        array A = randu(5,3, f32);
+        array A = randu(5, 3, f32);
         af_print(A);
 
         printf("Element-wise arithmetic\n");
@@ -53,7 +50,7 @@ int main(int argc, char *argv[])
         af_print(S);
 
         printf("Create 2-by-3 matrix from host data\n");
-        float d[] = { 1, 2, 3, 4, 5, 6 };
+        float d[] = {1, 2, 3, 4, 5, 6};
         array D(2, 3, d, afHost);
         af_print(D);
 
@@ -69,7 +66,6 @@ int main(int argc, char *argv[])
         af_print(inds);
 
     } catch (af::exception& e) {
-
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/image_processing/adaptive_thresholding.cpp b/examples/image_processing/adaptive_thresholding.cpp
index 8a5ffc6621..db2a1d1697 100644
--- a/examples/image_processing/adaptive_thresholding.cpp
+++ b/examples/image_processing/adaptive_thresholding.cpp
@@ -1,89 +1,79 @@
 /*******************************************************
-* Copyright (c) 2015, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
+#include <arrayfire.h>
 #include <cmath>
 #include <cstdio>
 #include <cstdlib>
-#include <arrayfire.h>
 
 using namespace af;
 using std::abs;
 
-typedef enum {
-    MEAN = 0,
-    MEDIAN,
-    MINMAX_AVG
-} LocalThresholdType;
+typedef enum { MEAN = 0, MEDIAN, MINMAX_AVG } LocalThresholdType;
 
-array threshold(const array &in, float thresholdValue)
-{
-    int channels = in.dims(2);
+array threshold(const array &in, float thresholdValue) {
+    int channels  = in.dims(2);
     array ret_val = in.copy();
-    if (channels>1)
-        ret_val = colorSpace(in, AF_GRAY, AF_RGB);
-    ret_val = (ret_val<thresholdValue)*0.0f + 255.0f*(ret_val>thresholdValue);
+    if (channels > 1) ret_val = colorSpace(in, AF_GRAY, AF_RGB);
+    ret_val =
+        (ret_val < thresholdValue) * 0.0f + 255.0f * (ret_val > thresholdValue);
     return ret_val;
 }
 
-array adaptiveThreshold(const array &in, LocalThresholdType kind, int window_size, int constnt)
-{
-    int wr = window_size;
+array adaptiveThreshold(const array &in, LocalThresholdType kind,
+                        int window_size, int constnt) {
+    int wr        = window_size;
     array ret_val = colorSpace(in, AF_GRAY, AF_RGB);
     if (kind == MEAN) {
-        array wind = constant(1, wr, wr) / (wr*wr);
+        array wind = constant(1, wr, wr) / (wr * wr);
         array mean = convolve(ret_val, wind);
         array diff = mean - ret_val;
-        ret_val = (diff<constnt)*0.f + 255.f*(diff>constnt);
-    }
-    else if (kind == MEDIAN) {
+        ret_val    = (diff < constnt) * 0.f + 255.f * (diff > constnt);
+    } else if (kind == MEDIAN) {
         array medf = medfilt(ret_val, wr, wr);
         array diff = medf - ret_val;
-        ret_val = (diff<constnt)*0.f + 255.f*(diff>constnt);
-    }
-    else if (kind == MINMAX_AVG) {
+        ret_val    = (diff < constnt) * 0.f + 255.f * (diff > constnt);
+    } else if (kind == MINMAX_AVG) {
         array minf = minfilt(ret_val, wr, wr);
         array maxf = maxfilt(ret_val, wr, wr);
         array mean = (minf + maxf) / 2.0f;
         array diff = mean - ret_val;
-        ret_val = (diff<constnt)*0.f + 255.f*(diff>constnt);
+        ret_val    = (diff < constnt) * 0.f + 255.f * (diff > constnt);
     }
     ret_val = 255.f - ret_val;
     return ret_val;
 }
 
-array iterativeThreshold(const array &in)
-{
-    array ret_val = colorSpace(in, AF_GRAY, AF_RGB);
-    float T = mean<float>(ret_val);
+array iterativeThreshold(const array &in) {
+    array ret_val   = colorSpace(in, AF_GRAY, AF_RGB);
+    float T         = mean<float>(ret_val);
     bool isContinue = true;
     while (isContinue) {
-        array region1 = (ret_val > T)*ret_val;
-        array region2 = (ret_val <= T)*ret_val;
-        float r1_avg = mean<float>(region1);
-        float r2_avg = mean<float>(region2);
-        float tempT = (r1_avg + r2_avg) / 2.0f;
-        if (abs(tempT - T)<0.01f) {
-            break;
-        }
+        array region1 = (ret_val > T) * ret_val;
+        array region2 = (ret_val <= T) * ret_val;
+        float r1_avg  = mean<float>(region1);
+        float r2_avg  = mean<float>(region2);
+        float tempT   = (r1_avg + r2_avg) / 2.0f;
+        if (abs(tempT - T) < 0.01f) { break; }
         T = tempT;
     }
     return threshold(ret_val, T);
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        array sudoku = loadImage(ASSETS_DIR "/examples/images/sudoku.jpg", true);
+        array sudoku =
+            loadImage(ASSETS_DIR "/examples/images/sudoku.jpg", true);
 
         array mnt = adaptiveThreshold(sudoku, MEAN, 37, 10);
         array mdt = adaptiveThreshold(sudoku, MEDIAN, 7, 4);
@@ -101,8 +91,7 @@ int main(int argc, char **argv)
             wnd(0, 2).image(itt, "Iterative Threshold");
             wnd.show();
         }
-    }
-    catch (af::exception& e) {
+    } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/image_processing/binary_thresholding.cpp b/examples/image_processing/binary_thresholding.cpp
index bb2ffa88af..73a376b982 100644
--- a/examples/image_processing/binary_thresholding.cpp
+++ b/examples/image_processing/binary_thresholding.cpp
@@ -1,49 +1,47 @@
 /*******************************************************
-* Copyright (c) 2015, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
+#include <arrayfire.h>
 #include <cmath>
 #include <cstdio>
 #include <cstdlib>
-#include <arrayfire.h>
 
 using namespace af;
 
-array threshold(const array &in, float thresholdValue)
-{
-    int channels = in.dims(2);
+array threshold(const array& in, float thresholdValue) {
+    int channels  = in.dims(2);
     array ret_val = in.copy();
-    if (channels>1)
-        ret_val = colorSpace(in, AF_GRAY, AF_RGB);
-    ret_val = (ret_val<thresholdValue)*0.0f + 255.0f*(ret_val>thresholdValue);
+    if (channels > 1) ret_val = colorSpace(in, AF_GRAY, AF_RGB);
+    ret_val =
+        (ret_val < thresholdValue) * 0.0f + 255.0f * (ret_val > thresholdValue);
     return ret_val;
 }
 
 /**
-* Note:
-* suffix B indicates subset of all graylevels before current gray level
-* suffix F indicates subset of all graylevels after current gray level
-*/
-array otsu(const array& in)
-{
+ * Note:
+ * suffix B indicates subset of all graylevels before current gray level
+ * suffix F indicates subset of all graylevels after current gray level
+ */
+array otsu(const array& in) {
     array gray;
     int channels = in.dims(2);
-    if (channels>1)
+    if (channels > 1)
         gray = colorSpace(in, AF_GRAY, AF_RGB);
     else
         gray = in;
     unsigned total = gray.elements();
-    array hist = histogram(gray, 256, 0.0f, 255.0f);
-    array wts = range(256);
+    array hist     = histogram(gray, 256, 0.0f, 255.0f);
+    array wts      = range(256);
 
-    array wtB = accum(hist);
-    array wtF = total - wtB;
-    array sumB = accum(wts*hist);
+    array wtB   = accum(hist);
+    array wtF   = total - wtB;
+    array sumB  = accum(wts * hist);
     array meanB = sumB / wtB;
     float lastElemInSumB;
     sumB(seq(255, 255, 1)).host((void*)&lastElemInSumB);
@@ -52,28 +50,29 @@ array otsu(const array& in)
 
     array interClsVar = wtB * wtF * mDiff * mDiff;
 
-    float max = af::max<float>(interClsVar);
+    float max        = af::max<float>(interClsVar);
     float threshold2 = where(interClsVar == max).scalar<unsigned>();
-    array threshIdx = where(interClsVar >= max);
-    float threshold1 = threshIdx.elements()>0 ? threshIdx.scalar<unsigned>() : 0.0f;
+    array threshIdx  = where(interClsVar >= max);
+    float threshold1 =
+        threshIdx.elements() > 0 ? threshIdx.scalar<unsigned>() : 0.0f;
 
     return threshold(gray, (threshold1 + threshold2) / 2.0f);
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char** argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        array bimodal = loadImage(ASSETS_DIR "/examples/images/noisy_square.png", false);
+        array bimodal =
+            loadImage(ASSETS_DIR "/examples/images/noisy_square.png", false);
         bimodal = resize(0.75f, bimodal);
 
-        array bt = threshold(bimodal, 180.0f);
-        array ot = otsu(bimodal);
-        array bimodHist = histogram(bimodal, 256, 0, 255);
-        array smooth = convolve(bimodal, gaussianKernel(5, 5));
+        array bt         = threshold(bimodal, 180.0f);
+        array ot         = otsu(bimodal);
+        array bimodHist  = histogram(bimodal, 256, 0, 255);
+        array smooth     = convolve(bimodal, gaussianKernel(5, 5));
         array smoothHist = histogram(smooth, 256, 0, 255);
 
         af::Window wnd(1536, 1024, "Binary Thresholding Algorithms");
@@ -95,8 +94,7 @@ int main(int argc, char **argv)
             wnd(2, 2).image(otsu(smooth), "Otsu's Threshold on Smoothed Image");
             wnd.show();
         }
-    }
-    catch (af::exception& e) {
+    } catch (af::exception& e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/image_processing/brain_segmentation.cpp b/examples/image_processing/brain_segmentation.cpp
index 253d37e5f1..316d1508a2 100644
--- a/examples/image_processing/brain_segmentation.cpp
+++ b/examples/image_processing/brain_segmentation.cpp
@@ -7,47 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <string.h>
-#include <stdio.h>
-#include <math.h>
 #include <arrayfire.h>
+#include <math.h>
+#include <stdio.h>
+#include <string.h>
 #include "../common/progress.h"
 
 using namespace af;
 
-const float h_sx_kernel[] = {  1,  2,  1,
-    0,  0,  0,
-    -1, -2, -1
-};
-const float h_sy_kernel[] = { -1, 0, 1,
-    -2, 0, 2,
-    -1, 0, 1
-};
+const float h_sx_kernel[] = {1, 2, 1, 0, 0, 0, -1, -2, -1};
+const float h_sy_kernel[] = {-1, 0, 1, -2, 0, 2, -1, 0, 1};
 
 // Unused
-//const float h_lp_kernel[] = { -0.5f, -1.0f, -0.5f,
+// const float h_lp_kernel[] = { -0.5f, -1.0f, -0.5f,
 //    -1.0f,  6.0f, -1.0f,
 //    -0.5f, -1.0f, -0.5f
 //};
 
-array edges_slice(array x)
-{
+array edges_slice(array x) {
     array ret;
     static array kernelx = array(dim4(3, 3), h_sx_kernel);
     static array kernely = array(dim4(3, 3), h_sy_kernel);
-    ret = convolve(x, kernelx) + convolve(x, kernely);
+    ret                  = convolve(x, kernelx) + convolve(x, kernely);
     return abs(ret);
 }
 
-array gauss(array x, float u, float s)
-{
+array gauss(array x, float u, float s) {
     double f = 1 / sqrt(2 * af::Pi * s * s);
-    array e = exp(-pow((x - u), 2) / (2 * s * s));
+    array e  = exp(-pow((x - u), 2) / (2 * s * s));
     return f * e;
 }
 
-array segment_volume(array A, int k)
-{
+array segment_volume(array A, int k) {
     array I1 = A(span, span, k);
 
     float mx = max<float>(I1);
@@ -86,26 +77,25 @@ array segment_volume(array A, int k)
     L12_old = L12;
 
     array L1 = (L10 + L11 + L12) / 3;
-    array S = (L0 > L1);
+    array S  = (L0 > L1);
     return S.as(A.type());
 }
 
-void brain_seg(bool console)
-{
+void brain_seg(bool console) {
     af::Window wnd("Brain Segmentation Demo");
     wnd.setColorMap(AF_COLORMAP_HEAT);
 
-    double time_total = 30; // run for N seconds
+    double time_total = 30;  // run for N seconds
 
-    array B = loadImage(ASSETS_DIR "/examples/images/brain.png");
+    array B    = loadImage(ASSETS_DIR "/examples/images/brain.png");
     int slices = 256;
 
-    B = moddims(B, dim4(B.dims(0), B.dims(1)/slices, slices));
+    B = moddims(B, dim4(B.dims(0), B.dims(1) / slices, slices));
     af::sync();
 
     int N = 2 * slices - 1;
 
-    timer t = timer::start();
+    timer t  = timer::start();
     int iter = 0;
 
     /* loop forward and backward for 100 frames
@@ -115,8 +105,8 @@ void brain_seg(bool console)
     for (int i = 0; !wnd.close(); i++) {
         iter++;
 
-        int j = i % N;
-        int k = std::min(j, N - j);
+        int j    = i % N;
+        int k    = std::min(j, N - j);
         array Bi = B(span, span, k);
 
         /* process */
@@ -128,9 +118,9 @@ void brain_seg(bool console)
         if (!console) {
             wnd.grid(2, 2);
 
-            wnd(0, 0).image(Bi/255.f, "Input");
+            wnd(0, 0).image(Bi / 255.f, "Input");
             wnd(1, 0).image(Ei, "Edges");
-            wnd(0, 1).image(Mi/255.f, "Meanshift");
+            wnd(0, 1).image(Mi / 255.f, "Meanshift");
             wnd(1, 1).image(Si, "Segmented");
 
             wnd.show();
@@ -143,16 +133,13 @@ void brain_seg(bool console)
 
         /* we have had ran throuh simlation results
          * exit the rendering loop */
-        if (!progress(iter, t, time_total))
-            break;
-        if (!(i<100*N))
-            break;
+        if (!progress(iter, t, time_total)) break;
+        if (!(i < 100 * N)) break;
     }
 }
 
-int main(int argc, char* argv[])
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char* argv[]) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
@@ -162,9 +149,7 @@ int main(int argc, char* argv[])
         printf("Brain segmentation example\n");
         brain_seg(console);
 
-    } catch (af::exception& e) {
-        fprintf(stderr, "%s\n", e.what());
-    }
+    } catch (af::exception& e) { fprintf(stderr, "%s\n", e.what()); }
 
     return 0;
 }
diff --git a/examples/image_processing/deconvolution.cpp b/examples/image_processing/deconvolution.cpp
index 5479cbac6c..201d3a8a43 100644
--- a/examples/image_processing/deconvolution.cpp
+++ b/examples/image_processing/deconvolution.cpp
@@ -7,25 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 #include <cstdlib>
 
 using namespace af;
 
-const unsigned ITERATIONS = 96;
+const unsigned ITERATIONS     = 96;
 const float RELAXATION_FACTOR = 0.05f;
 
-array normalize(const array &in)
-{
+array normalize(const array &in) {
     float mx = max<float>(in.as(f32));
     float mn = min<float>(in.as(f32));
-    return (in-mn)/(mx-mn);
+    return (in - mn) / (mx - mn);
 }
 
-int main(int argc, char* argv[])
-{
+int main(int argc, char *argv[]) {
     int device = argc > 1 ? atoi(argv[1]) : 0;
 
     try {
@@ -35,29 +33,28 @@ int main(int argc, char* argv[])
         printf("** ArrayFire Image Deconvolution Demo **\n");
         af::Window myWindow("Image Deconvolution");
 
-        array in       = loadImage(ASSETS_DIR "/examples/images/house.jpg",
-                                   false);
-        array kernel   = gaussianKernel(13, 13, 2.25, 2.25);
-        array blurred  = convolve(in, kernel);
-        array tikhonov = inverseDeconv(blurred, kernel, 0.05,
-                                       AF_INVERSE_DECONV_TIKHONOV);
+        array in = loadImage(ASSETS_DIR "/examples/images/house.jpg", false);
+        array kernel  = gaussianKernel(13, 13, 2.25, 2.25);
+        array blurred = convolve(in, kernel);
+        array tikhonov =
+            inverseDeconv(blurred, kernel, 0.05, AF_INVERSE_DECONV_TIKHONOV);
 
-        array landweber  = iterativeDeconv(blurred, kernel,
-                ITERATIONS, RELAXATION_FACTOR,
-                AF_ITERATIVE_DECONV_LANDWEBER);
+        array landweber =
+            iterativeDeconv(blurred, kernel, ITERATIONS, RELAXATION_FACTOR,
+                            AF_ITERATIVE_DECONV_LANDWEBER);
 
-        array richlucy   = iterativeDeconv(blurred, kernel,
-                ITERATIONS, RELAXATION_FACTOR,
-                AF_ITERATIVE_DECONV_RICHARDSONLUCY);
+        array richlucy =
+            iterativeDeconv(blurred, kernel, ITERATIONS, RELAXATION_FACTOR,
+                            AF_ITERATIVE_DECONV_RICHARDSONLUCY);
 
-        while(!myWindow.close()) {
+        while (!myWindow.close()) {
             myWindow.grid(2, 3);
 
-            myWindow(0, 0).image(normalize(in       ), "Input Image"    );
-            myWindow(1, 0).image(normalize(blurred  ), "Blurred Image"  );
-            myWindow(0, 1).image(normalize(tikhonov ), "Tikhonov"       );
-            myWindow(1, 1).image(normalize(landweber), "Landweber"      );
-            myWindow(0, 2).image(normalize(richlucy ), "Richardson-Lucy");
+            myWindow(0, 0).image(normalize(in), "Input Image");
+            myWindow(1, 0).image(normalize(blurred), "Blurred Image");
+            myWindow(0, 1).image(normalize(tikhonov), "Tikhonov");
+            myWindow(1, 1).image(normalize(landweber), "Landweber");
+            myWindow(0, 2).image(normalize(richlucy), "Richardson-Lucy");
 
             myWindow.show();
         }
diff --git a/examples/image_processing/edge.cpp b/examples/image_processing/edge.cpp
index a145e83058..a8c29a26df 100644
--- a/examples/image_processing/edge.cpp
+++ b/examples/image_processing/edge.cpp
@@ -7,16 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 #include <cstdlib>
 
 using namespace af;
 
-void prewitt(array &mag, array &dir, const array &in)
-{
-    static float h1[] = { 1, 1, 1};
+void prewitt(array &mag, array &dir, const array &in) {
+    static float h1[] = {1, 1, 1};
     static float h2[] = {-1, 0, 1};
     static array colf(3, 1, h1);
     static array rowf(3, 1, h2);
@@ -30,8 +29,7 @@ void prewitt(array &mag, array &dir, const array &in)
     dir = atan2(Gy, Gx);
 }
 
-void sobelFilter(array &mag, array &dir, const array &in)
-{
+void sobelFilter(array &mag, array &dir, const array &in) {
     array Gx, Gy;
     sobel(Gx, Gy, in, 3);
     // Find magnitude and direction
@@ -39,60 +37,58 @@ void sobelFilter(array &mag, array &dir, const array &in)
     dir = atan2(Gy, Gx);
 }
 
-array normalize(const array &in)
-{
+array normalize(const array &in) {
     float mx = max<float>(in);
     float mn = min<float>(in);
-    return (in-mn)/(mx-mn);
+    return (in - mn) / (mx - mn);
 }
 
-array edge(const array &in, int method = 0)
-{
+array edge(const array &in, int method = 0) {
     int w = 5;
-    if (in.dims(0) <  512) w = 3;
+    if (in.dims(0) < 512) w = 3;
     if (in.dims(0) > 2048) w = 7;
 
     int h = 5;
-    if (in.dims(0) <  512) h = 3;
+    if (in.dims(0) < 512) h = 3;
     if (in.dims(0) > 2048) h = 7;
 
-    array ker = gaussianKernel(w, h);
+    array ker    = gaussianKernel(w, h);
     array smooth = convolve(in, ker);
     array mag, dir;
 
-    switch(method) {
-        case  1: prewitt(mag, dir, smooth);     break;
-        case  2: sobelFilter(mag, dir, smooth); break;
-        case  3: mag = canny(in, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.18, 0.54).as(f32);   break;
+    switch (method) {
+        case 1: prewitt(mag, dir, smooth); break;
+        case 2: sobelFilter(mag, dir, smooth); break;
+        case 3:
+            mag = canny(in, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.18, 0.54).as(f32);
+            break;
         default: throw af::exception("Unsupported type");
     }
 
     return normalize(mag);
 }
 
-void edge()
-{
+void edge() {
     af::Window myWindow("Edge Dectectors");
     af::Window myWindow2(512, 512, "Histogram");
 
     array in = loadImage(ASSETS_DIR "/examples/images/trees_ctm.jpg", false);
 
-    array prewitt = edge(in, 1);
-    array sobelFilter   = edge(in, 2);
-    array hst = histogram(in, 256, 0, 255);
-    array cny = edge(in, 3);
+    array prewitt     = edge(in, 1);
+    array sobelFilter = edge(in, 2);
+    array hst         = histogram(in, 256, 0, 255);
+    array cny         = edge(in, 3);
 
     myWindow2.setAxesTitles("Bins", "Frequency");
 
-    while(!myWindow.close() && !myWindow2.close()) {
-
+    while (!myWindow.close() && !myWindow2.close()) {
         /* show input, prewitt and sobel edge detectors in a grid */
         myWindow.grid(2, 2);
 
-        myWindow(0,0).image(in/255     , "Input Image");
-        myWindow(0,1).image(prewitt    , "Prewitt"    );
-        myWindow(1,0).image(sobelFilter, "Sobel"      );
-        myWindow(1,1).image(cny        , "Canny"      );
+        myWindow(0, 0).image(in / 255, "Input Image");
+        myWindow(0, 1).image(prewitt, "Prewitt");
+        myWindow(1, 0).image(sobelFilter, "Sobel");
+        myWindow(1, 1).image(cny, "Canny");
 
         myWindow.show();
 
@@ -101,8 +97,7 @@ void edge()
     }
 }
 
-int main(int argc, char* argv[])
-{
+int main(int argc, char *argv[]) {
     int device = argc > 1 ? atoi(argv[1]) : 0;
 
     try {
diff --git a/examples/image_processing/filters.cpp b/examples/image_processing/filters.cpp
index 221081b117..0c8ba11fd2 100644
--- a/examples/image_processing/filters.cpp
+++ b/examples/image_processing/filters.cpp
@@ -1,37 +1,36 @@
 /*******************************************************
-* Copyright (c) 2015, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
+#include <arrayfire.h>
 #include <cmath>
 #include <cstdio>
 #include <cstdlib>
-#include <arrayfire.h>
 
 using namespace af;
 
 /**
-* randomization - controls % of total number of pixels in the image
-* that will be effected by random noise
-* repeat - # of times the process is carried out on the previous steps output
-*/
-array hurl(const array &in, int randomization, int repeat)
-{
-    int w = in.dims(0);
-    int h = in.dims(1);
-    float f = randomization / 100.0f;
-    int dim = (int)(f*w*h);
+ * randomization - controls % of total number of pixels in the image
+ * that will be effected by random noise
+ * repeat - # of times the process is carried out on the previous steps output
+ */
+array hurl(const array &in, int randomization, int repeat) {
+    int w         = in.dims(0);
+    int h         = in.dims(1);
+    float f       = randomization / 100.0f;
+    int dim       = (int)(f * w * h);
     array ret_val = in.copy();
-    array temp = moddims(ret_val, w*h, 3);
-    for (int i = 0; i<repeat; ++i) {
-        array idxs = (w*h)  * randu(dim);
-        array rndR = 255.0f * randu(dim);
-        array rndG = 255.0f * randu(dim);
-        array rndB = 255.0f * randu(dim);
+    array temp    = moddims(ret_val, w * h, 3);
+    for (int i = 0; i < repeat; ++i) {
+        array idxs    = (w * h) * randu(dim);
+        array rndR    = 255.0f * randu(dim);
+        array rndG    = 255.0f * randu(dim);
+        array rndB    = 255.0f * randu(dim);
         temp(idxs, 0) = rndR;
         temp(idxs, 1) = rndG;
         temp(idxs, 2) = rndB;
@@ -41,60 +40,56 @@ array hurl(const array &in, int randomization, int repeat)
 }
 
 /**
-* Retrieve a new image of same dimensions of the original image
-* where each original image's pixel is replaced by randomly picked
-* neighbor in the provided local neighborhood window
-*/
-array getRandomNeighbor(const array &in, int windW, int windH)
-{
-    array rnd = 2.0f*randu(in.dims(0), in.dims(1)) - 1.0f;
-    array sx = seq(in.dims(0));
-    array sy = seq(in.dims(1));
-    array vx = tile(sx, 1, in.dims(1)) + floor(rnd*windW);
-    array vy = tile(sy.T(), in.dims(0), 1) + floor(rnd*windH);
+ * Retrieve a new image of same dimensions of the original image
+ * where each original image's pixel is replaced by randomly picked
+ * neighbor in the provided local neighborhood window
+ */
+array getRandomNeighbor(const array &in, int windW, int windH) {
+    array rnd = 2.0f * randu(in.dims(0), in.dims(1)) - 1.0f;
+    array sx  = seq(in.dims(0));
+    array sy  = seq(in.dims(1));
+    array vx  = tile(sx, 1, in.dims(1)) + floor(rnd * windW);
+    array vy  = tile(sy.T(), in.dims(0), 1) + floor(rnd * windH);
     array vxx = clamp(vx, 0, in.dims(0));
     array vyy = clamp(vy, 0, in.dims(1));
     array in2 = moddims(in, vx.elements(), 3);
-    return moddims(in2(vyy*in.dims(0) + vxx, span), in.dims());
+    return moddims(in2(vyy * in.dims(0) + vxx, span), in.dims());
 }
 
 /**
-* randomly pick neighbor from given window size and replace the
-* current pixel with the randomly chosen color.
-* No new colors are introduced, unlike hurl.
-*/
-array spread(const array &in, int window_width, int window_height)
-{
+ * randomly pick neighbor from given window size and replace the
+ * current pixel with the randomly chosen color.
+ * No new colors are introduced, unlike hurl.
+ */
+array spread(const array &in, int window_width, int window_height) {
     return getRandomNeighbor(in, window_width, window_height);
 }
 
 /**
-* randomization - controls % of total number of pixels in the image
-* that will be effected by random noise
-* repeat - # of times the process is carried out on the previous steps output
-*/
-array pick(const array &in, int randomization, int repeat)
-{
-    int w = in.dims(0);
-    int h = in.dims(1);
-    float f = randomization / 100.0f;
-    int dim = (int)(f*w*h);
+ * randomization - controls % of total number of pixels in the image
+ * that will be effected by random noise
+ * repeat - # of times the process is carried out on the previous steps output
+ */
+array pick(const array &in, int randomization, int repeat) {
+    int w         = in.dims(0);
+    int h         = in.dims(1);
+    float f       = randomization / 100.0f;
+    int dim       = (int)(f * w * h);
     array ret_val = in.copy();
-    for (int i = 0; i<repeat; ++i) {
-        array idxs = (w*h)  * randu(dim);
-        array rnd = getRandomNeighbor(ret_val, 1, 1);
-        array temp_src = moddims(rnd, w*h, 3);
-        array temp_dst = moddims(ret_val, w*h, 3);
+    for (int i = 0; i < repeat; ++i) {
+        array idxs           = (w * h) * randu(dim);
+        array rnd            = getRandomNeighbor(ret_val, 1, 1);
+        array temp_src       = moddims(rnd, w * h, 3);
+        array temp_dst       = moddims(ret_val, w * h, 3);
         temp_dst(idxs, span) = temp_src(idxs, span);
-        ret_val = moddims(temp_dst, in.dims());
+        ret_val              = moddims(temp_dst, in.dims());
     }
     return ret_val;
 }
 
-void prewitt(array &mag, array &dir, const array &in)
-{
-    static float h1[] = { 1, 1, 1 };
-    static float h2[] = { -1, 0, 1 };
+void prewitt(array &mag, array &dir, const array &in) {
+    static float h1[] = {1, 1, 1};
+    static float h2[] = {-1, 0, 1};
     static array h1d(3, h1);
     static array h2d(3, h2);
 
@@ -107,8 +102,7 @@ void prewitt(array &mag, array &dir, const array &in)
     dir = atan2(Gy, Gx);
 }
 
-void sobelFilter(array &mag, array &dir, const array &in)
-{
+void sobelFilter(array &mag, array &dir, const array &in) {
     // Find the gradients
     array Gy, Gx;
     af::sobel(Gx, Gy, in);
@@ -118,112 +112,112 @@ void sobelFilter(array &mag, array &dir, const array &in)
     dir = atan2(Gy, Gx);
 }
 
-void normalizeImage(array &in)
-{
+void normalizeImage(array &in) {
     float min = af::min<float>(in);
     float max = af::max<float>(in);
-    in = 255.0f*((in - min) / (max - min));
+    in        = 255.0f * ((in - min) / (max - min));
 }
 
-array DifferenceOfGaussian(const array &in, int window_radius1, int window_radius2)
-{
+array DifferenceOfGaussian(const array &in, int window_radius1,
+                           int window_radius2) {
     array ret_val;
-    int w1 = 2 * window_radius1 + 1;
-    int w2 = 2 * window_radius2 + 1;
+    int w1   = 2 * window_radius1 + 1;
+    int w2   = 2 * window_radius2 + 1;
     array g1 = gaussianKernel(w1, w1);
     array g2 = gaussianKernel(w2, w2);
-    ret_val = (convolve(in, g1) - convolve(in, g2));
+    ret_val  = (convolve(in, g1) - convolve(in, g2));
     normalizeImage(ret_val);
     return ret_val;
 }
 
-array medianfilter(const array &in, int window_width, int window_height)
-{
+array medianfilter(const array &in, int window_width, int window_height) {
     array ret_val(in.dims());
-    ret_val(span, span, 0) = medfilt(in(span, span, 0), window_width, window_height);
-    ret_val(span, span, 1) = medfilt(in(span, span, 1), window_width, window_height);
-    ret_val(span, span, 2) = medfilt(in(span, span, 2), window_width, window_height);
+    ret_val(span, span, 0) =
+        medfilt(in(span, span, 0), window_width, window_height);
+    ret_val(span, span, 1) =
+        medfilt(in(span, span, 1), window_width, window_height);
+    ret_val(span, span, 2) =
+        medfilt(in(span, span, 2), window_width, window_height);
     return ret_val;
 }
 
-array gaussianblur(const array &in, int window_width, int window_height, double sigma)
-{
+array gaussianblur(const array &in, int window_width, int window_height,
+                   double sigma) {
     array g = gaussianKernel(window_width, window_height, sigma, sigma);
     return convolve(in, g);
 }
 
 /**
-* azimuth range is [0-360]
-* elevation range is [0-180]
-* depth range is [1-100]
-* Note: this function has been tailored after
-* the emboss implementation in GIMP editor
-**/
-array emboss(const array &input, float azimuth, float elevation, float depth)
-{
-    if (depth<1 || depth>100) {
+ * azimuth range is [0-360]
+ * elevation range is [0-180]
+ * depth range is [1-100]
+ * Note: this function has been tailored after
+ * the emboss implementation in GIMP editor
+ **/
+array emboss(const array &input, float azimuth, float elevation, float depth) {
+    if (depth < 1 || depth > 100) {
         printf("Depth should be in the range of 1-100");
         return input;
     }
-    static float x[3] = { -1, 0, 1 };
+    static float x[3] = {-1, 0, 1};
     static array hg(3, x);
     static array vg = hg.T();
 
     array in = input;
-    if (in.dims(2)>1)
+    if (in.dims(2) > 1)
         in = colorSpace(input, AF_GRAY, AF_RGB);
     else
         in = input;
 
     // convert angles to radians
-    float phi = elevation*af::Pi / 180.0f;
-    float theta = azimuth*af::Pi / 180.0f;
+    float phi   = elevation * af::Pi / 180.0f;
+    float theta = azimuth * af::Pi / 180.0f;
 
     // compute light pos in cartesian coordinates
     // and scale with maximum intensity
     // phi will effect the amount of we intend to put
     // on a pixel
     float pos[3];
-    pos[0] = 255.99f * cos(phi)*cos(theta);
-    pos[1] = 255.99f * cos(phi)*sin(theta);
+    pos[0] = 255.99f * cos(phi) * cos(theta);
+    pos[1] = 255.99f * cos(phi) * sin(theta);
     pos[2] = 255.99f * sin(phi);
 
     // compute gradient vector
     array gx = convolve(in, vg);
     array gy = convolve(in, hg);
 
-    float pxlz = (6 * 255.0f) / depth;
+    float pxlz   = (6 * 255.0f) / depth;
     array zdepth = constant(pxlz, gx.dims());
-    array vdot = gx*pos[0] + gy*pos[1] + pxlz*pos[2];
-    array outwd = vdot < 0.0f;
-    array norm = vdot / sqrt(gx*gx + gy*gy + zdepth*zdepth);
+    array vdot   = gx * pos[0] + gy * pos[1] + pxlz * pos[2];
+    array outwd  = vdot < 0.0f;
+    array norm   = vdot / sqrt(gx * gx + gy * gy + zdepth * zdepth);
 
     array color = outwd * 0.0f + (1 - outwd) * norm;
     return color;
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        array img = loadImage(ASSETS_DIR "/examples/images/vegetable-woman.jpg", true);
+        array img =
+            loadImage(ASSETS_DIR "/examples/images/vegetable-woman.jpg", true);
 
         array prew_mag, prew_dir;
         array sob_mag, sob_dir;
         array img1ch = colorSpace(img, AF_GRAY, AF_RGB);
         prewitt(prew_mag, prew_dir, img1ch);
         sobelFilter(sob_mag, sob_dir, img1ch);
-        array sprd = spread(img, 3, 3);
-        array hrl = hurl(img, 10, 1);
+        array sprd  = spread(img, 3, 3);
+        array hrl   = hurl(img, 10, 1);
         array pckng = pick(img, 40, 2);
         array difog = DifferenceOfGaussian(img, 1, 2);
-        array bil = bilateral(hrl, 3.0f, 40.0f);
-        array mf = medianfilter(hrl, 5, 5);
-        array gb = gaussianblur(hrl, 3, 3, 0.8);
-        array emb = emboss(img, 45, 20, 10);
+        array bil   = bilateral(hrl, 3.0f, 40.0f);
+        array mf    = medianfilter(hrl, 5, 5);
+        array gb    = gaussianblur(hrl, 3, 3, 0.8);
+        array emb   = emboss(img, 45, 20, 10);
 
         af::Window wnd("Image Filters Demo");
         printf("Press ESC while the window is in focus to exit\n");
@@ -237,13 +231,13 @@ int main(int argc, char **argv)
             wnd(1, 2).image(sob_mag / 255, "Sobel edge filter");
             wnd(0, 3).image(sprd / 255, "Spread filter");
             wnd(1, 3).image(pckng / 255, "Pick filter");
-            wnd(0, 4).image(difog / 255, "Difference of gaussians(3x3 and 5x5)");
+            wnd(0, 4).image(difog / 255,
+                            "Difference of gaussians(3x3 and 5x5)");
             wnd(1, 4).image(emb / 255, "Emboss effect");
             wnd.show();
         }
 
-    }
-    catch (af::exception& e) {
+    } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/image_processing/gradient_diffusion.cpp b/examples/image_processing/gradient_diffusion.cpp
index 2bbaf921bd..cf4cc402e0 100644
--- a/examples/image_processing/gradient_diffusion.cpp
+++ b/examples/image_processing/gradient_diffusion.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 #include <cstdlib>
 
@@ -16,28 +16,25 @@ using namespace af;
 
 static const unsigned ITERS = 64;
 
-array normalize(const array &p_in)
-{
+array normalize(const array &p_in) {
     float mx = max<float>(p_in);
     float mn = min<float>(p_in);
-    return (p_in-mn)/(mx-mn);
+    return (p_in - mn) / (mx - mn);
 }
 
-array sobelFilter(const array &p_in)
-{
+array sobelFilter(const array &p_in) {
     int w = 5;
-    if (p_in.dims(0) <  512) w = 3;
+    if (p_in.dims(0) < 512) w = 3;
     if (p_in.dims(0) > 2048) w = 7;
 
     int h = 5;
-    if (p_in.dims(0) <  512) h = 3;
+    if (p_in.dims(0) < 512) h = 3;
     if (p_in.dims(0) > 2048) h = 7;
 
-    array ker = gaussianKernel(w, h);
+    array ker    = gaussianKernel(w, h);
     array smooth = convolve(p_in, ker);
 
-    for (unsigned i=1; i<ITERS; ++i)
-        smooth = convolve(smooth, ker);
+    for (unsigned i = 1; i < ITERS; ++i) smooth = convolve(smooth, ker);
 
     array Gx, Gy;
     sobel(Gx, Gy, smooth, 3);
@@ -47,13 +44,11 @@ array sobelFilter(const array &p_in)
 
 array in, edges, smoothed;
 
-void anisotropicSmoothing()
-{
+void anisotropicSmoothing() {
     smoothed = anisotropicDiffusion(in, 0.125, 0.35f, ITERS);
 }
 
-int main(int argc, char* argv[])
-{
+int main(int argc, char *argv[]) {
     int device = argc > 1 ? atoi(argv[1]) : 0;
 
     try {
@@ -75,20 +70,24 @@ int main(int argc, char* argv[])
 
         edges = normalize(hypot(Gx, Gy));
 
-        while(!myWindow.close()) {
-
+        while (!myWindow.close()) {
             myWindow.grid(2, 2);
 
-            myWindow(0, 0) .image(in/255.0f          , "Input Image"                       );
-            myWindow(0, 1) .image(normalize(smoothed), "Anisotropically smooted Input"     );
-            myWindow(1, 0) .image(normalize(sEdges)  , "Gradient Magnitude after gaussian blur t=64");
-            myWindow(1, 1) .image(normalize(edges)   , "Gradient Magnitude after diffusion t=64");
+            myWindow(0, 0).image(in / 255.0f, "Input Image");
+            myWindow(0, 1).image(normalize(smoothed),
+                                 "Anisotropically smooted Input");
+            myWindow(1, 0).image(normalize(sEdges),
+                                 "Gradient Magnitude after gaussian blur t=64");
+            myWindow(1, 1).image(normalize(edges),
+                                 "Gradient Magnitude after diffusion t=64");
 
             myWindow.show();
         }
 
-        printf("\nAnisotropic Diffusion avg runtime for current image in Seconds: %g\n",
-                timeit(anisotropicSmoothing));
+        printf(
+            "\nAnisotropic Diffusion avg runtime for current image in Seconds: "
+            "%g\n",
+            timeit(anisotropicSmoothing));
 
     } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
diff --git a/examples/image_processing/image_demo.cpp b/examples/image_processing/image_demo.cpp
index 4d41c3dbe5..6a73c2cb91 100644
--- a/examples/image_processing/image_demo.cpp
+++ b/examples/image_processing/image_demo.cpp
@@ -7,13 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <cstdlib>
 
 using namespace af;
 
-
 // Split a MxNx3 image into 3 separate channel matrices.
 static void channel_split(array& rgb, array& outr, array& outg, array& outb) {
     outr = rgb(span, span, 0);
@@ -23,23 +22,17 @@ static void channel_split(array& rgb, array& outr, array& outg, array& outb) {
 
 // 5x5 sigma-3 gaussian blur weights
 static const float h_gauss[] = {
-    0.0318f,  0.0375f,  0.0397f,  0.0375f,  0.0318f,
-    0.0375f,  0.0443f,  0.0469f,  0.0443f,  0.0375f,
-    0.0397f,  0.0469f,  0.0495f,  0.0469f,  0.0397f,
-    0.0375f,  0.0443f,  0.0469f,  0.0443f,  0.0375f,
-    0.0318f,  0.0375f,  0.0397f,  0.0375f,  0.0318f,
+    0.0318f, 0.0375f, 0.0397f, 0.0375f, 0.0318f, 0.0375f, 0.0443f,
+    0.0469f, 0.0443f, 0.0375f, 0.0397f, 0.0469f, 0.0495f, 0.0469f,
+    0.0397f, 0.0375f, 0.0443f, 0.0469f, 0.0443f, 0.0375f, 0.0318f,
+    0.0375f, 0.0397f, 0.0375f, 0.0318f,
 };
 
 // 3x3 sobel weights
-static const float h_sobel[] = {
-    -2.0, -1.0,  0.0,
-    -1.0,  0.0,  1.0,
-    0.0,  1.0,  2.0
-};
+static const float h_sobel[] = {-2.0, -1.0, 0.0, -1.0, 0.0, 1.0, 0.0, 1.0, 2.0};
 
 // Demonstrates various image manipulations.
-static void img_test_demo()
-{
+static void img_test_demo() {
     af::Window wnd("Image Demo");
 
     // load convolution kernels
@@ -47,12 +40,15 @@ static void img_test_demo()
     array sobel_k = array(3, 3, h_sobel);
 
     // load images
-    array img_gray = loadImage(ASSETS_DIR "/examples/images/trees_ctm.jpg", false);         // 1 channel grayscale [0-255]
-    array img_rgb  = loadImage(ASSETS_DIR "/examples/images/sunset_emp.jpg", true) / 255.f; // 3 channel RGB       [0-1]
+    array img_gray = loadImage(ASSETS_DIR "/examples/images/trees_ctm.jpg",
+                               false);  // 1 channel grayscale [0-255]
+    array img_rgb =
+        loadImage(ASSETS_DIR "/examples/images/sunset_emp.jpg", true) /
+        255.f;  // 3 channel RGB       [0-1]
 
-    array rotatedImg = rotate(img_gray, Pi / 2, false)/255.f;
-    //array thrs_img = (img_gray < 130.f).as(s32);
-    array thrs_img = (img_gray<130.f).as(f32);
+    array rotatedImg = rotate(img_gray, Pi / 2, false) / 255.f;
+    // array thrs_img = (img_gray < 130.f).as(s32);
+    array thrs_img = (img_gray < 130.f).as(f32);
 
     // rgb channels
     array rr, gg, bb;
@@ -65,10 +61,10 @@ static void img_test_demo()
 
     // image histogram equalization
     array ihist = histogram(img_gray, 256, 0, 255);
-    array inorm = histEqual(img_gray, ihist)/255.f;
+    array inorm = histEqual(img_gray, ihist) / 255.f;
 
-    array edge_det = abs(convolve(img_gray, sobel_k))/255.f;
-    array smt = convolve(img_gray, gauss_k)/255.f;
+    array edge_det = abs(convolve(img_gray, sobel_k)) / 255.f;
+    array smt      = convolve(img_gray, gauss_k) / 255.f;
 
     while (!wnd.close()) {
         wnd.grid(2, 4);
@@ -90,10 +86,7 @@ static void img_test_demo()
     }
 }
 
-
-
-int main(int argc, char** argv)
-{
+int main(int argc, char** argv) {
     int device = argc > 1 ? atoi(argv[1]) : 0;
 
     try {
diff --git a/examples/image_processing/image_editing.cpp b/examples/image_processing/image_editing.cpp
index 54001ab438..41c9390656 100644
--- a/examples/image_processing/image_editing.cpp
+++ b/examples/image_processing/image_editing.cpp
@@ -1,118 +1,112 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
+#include <arrayfire.h>
 #include <cmath>
 #include <cstdio>
 #include <cstdlib>
-#include <arrayfire.h>
 
 using namespace af;
 
 /**
-* contrast value should be in the rnage [-1,1]
-* */
-array changeContrast(const array &in, const float contrast)
-{
-    float scale = tan((contrast + 1)*Pi / 4);
+ * contrast value should be in the rnage [-1,1]
+ * */
+array changeContrast(const array &in, const float contrast) {
+    float scale = tan((contrast + 1) * Pi / 4);
     return (((in / 255.0f - 0.5f) * scale + 0.5f) * 255.0f);
 }
 
 /**
-* brightness value should be in the rnage [0,1]
-* */
-array changeBrightness(const array &in, const float brightness, const float channelMax = 255.0f)
-{
-    float factor = brightness*channelMax;
+ * brightness value should be in the rnage [0,1]
+ * */
+array changeBrightness(const array &in, const float brightness,
+                       const float channelMax = 255.0f) {
+    float factor = brightness * channelMax;
     return (in + factor);
 }
 
-array clamp(const array &in, float min = 0.0f, float max = 255.0f)
-{
-    return ((in<min)*0.0f + (in>max)*255.0f + (in >= min && in <= max)*in);
+array clamp(const array &in, float min = 0.0f, float max = 255.0f) {
+    return ((in < min) * 0.0f + (in > max) * 255.0f +
+            (in >= min && in <= max) * in);
 }
 
 /**
-* radius effects the level of details that will effected during sharpening process
-* amount value should be in the range [0,1] or [1,]
-* note: value of 1.0 for amount results unsharp masking
-*       values > 1.0 results in highboost filter effect
-* */
-array usm(const array &in, float radius, float amount)
-{
-    int gKernelLen = 2 * radius + 1;
+ * radius effects the level of details that will effected during sharpening
+ * process amount value should be in the range [0,1] or [1,] note: value of 1.0
+ * for amount results unsharp masking values > 1.0 results in highboost filter
+ * effect
+ * */
+array usm(const array &in, float radius, float amount) {
+    int gKernelLen   = 2 * radius + 1;
     array blurKernel = gaussianKernel(gKernelLen, gKernelLen);
-    array blur = convolve(in, blurKernel);
-    return (in + amount*(in - blur));
+    array blur       = convolve(in, blurKernel);
+    return (in + amount * (in - blur));
 }
 
 /**
-* x,y - starting position of zoom
-* width, height - dimensions of the rectangle to where we have to zoom in
-* */
-array digZoom(const array &in, int x, int y, int width, int height)
-{
+ * x,y - starting position of zoom
+ * width, height - dimensions of the rectangle to where we have to zoom in
+ * */
+array digZoom(const array &in, int x, int y, int width, int height) {
     array cropped = in(seq(x, width - 1), seq(y, height - 1), span);
     return resize(cropped, (unsigned)in.dims(0), (unsigned)in.dims(1));
 }
 
 /**
-* a - foregound image
-* b - background image
-* mask - mask map
-* */
-array alphaBlend(const array &a, const array &b, const array &mask)
-{
+ * a - foregound image
+ * b - background image
+ * mask - mask map
+ * */
+array alphaBlend(const array &a, const array &b, const array &mask) {
     array tiledMask;
-    if (mask.dims(2) != a.dims(2))
-        tiledMask = tile(mask, 1, 1, a.dims(2));
-    return a*tiledMask + (1.0f - tiledMask)*b;
+    if (mask.dims(2) != a.dims(2)) tiledMask = tile(mask, 1, 1, a.dims(2));
+    return a * tiledMask + (1.0f - tiledMask) * b;
 }
 
-void normalizeImage(array &in)
-{
+void normalizeImage(array &in) {
     float min = af::min<float>(in);
     float max = af::max<float>(in);
-    in = 255.0f*((in - min) / (max - min));
+    in        = 255.0f * ((in - min) / (max - min));
 }
 
 /**
-* dimensions of the mask control the thickness of the boundary that
-* will be extracted by the following function
-*/
-array boundary(const array &in, const array &mask)
-{
+ * dimensions of the mask control the thickness of the boundary that
+ * will be extracted by the following function
+ */
+array boundary(const array &in, const array &mask) {
     array ret_val = in - erode(in, mask);
     normalizeImage(ret_val);
     return ret_val;
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char **argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        array man = loadImage(ASSETS_DIR "/examples/images/man.jpg", true);
+        array man   = loadImage(ASSETS_DIR "/examples/images/man.jpg", true);
         array fight = loadImage(ASSETS_DIR "/examples/images/fight.jpg", true);
-        array nature = resize(loadImage(ASSETS_DIR "/examples/images/nature.jpg", true), fight.dims(0), fight.dims(1));
+        array nature =
+            resize(loadImage(ASSETS_DIR "/examples/images/nature.jpg", true),
+                   fight.dims(0), fight.dims(1));
 
-        array intensity = colorSpace(fight, AF_GRAY, AF_RGB);
-        array mask = clamp(intensity, 10.0f, 255.0f)>0.0f;
-        array blend = alphaBlend(fight, nature, mask);
-        array highcon = changeContrast(man, 0.3);
+        array intensity  = colorSpace(fight, AF_GRAY, AF_RGB);
+        array mask       = clamp(intensity, 10.0f, 255.0f) > 0.0f;
+        array blend      = alphaBlend(fight, nature, mask);
+        array highcon    = changeContrast(man, 0.3);
         array highbright = changeBrightness(man, 0.2);
         array translated = translate(man, 100, 100, 200, 126);
-        array sharp = usm(man, 3, 1.2);
-        array zoom = digZoom(man, 28, 10, 192, 192);
+        array sharp      = usm(man, 3, 1.2);
+        array zoom       = digZoom(man, 28, 10, 192, 192);
         array morph_mask = constant(1, 3, 3);
-        array bdry = boundary(man, morph_mask);
+        array bdry       = boundary(man, morph_mask);
 
         af::Window wnd("Image Editing Operations");
         printf("Press ESC while the window is in focus to exit\n");
@@ -130,8 +124,7 @@ int main(int argc, char **argv)
             wnd(1, 4).image(bdry / 255, "Boundary extraction");
             wnd.show();
         }
-    }
-    catch (af::exception& e) {
+    } catch (af::exception &e) {
         fprintf(stderr, "%s\n", e.what());
         throw;
     }
diff --git a/examples/image_processing/morphing.cpp b/examples/image_processing/morphing.cpp
index 685069d7ab..51108490c2 100644
--- a/examples/image_processing/morphing.cpp
+++ b/examples/image_processing/morphing.cpp
@@ -7,80 +7,71 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 #include <cstdlib>
 
 using namespace af;
 
-array morphopen(const array& img, const array& mask)
-{
+array morphopen(const array& img, const array& mask) {
     return dilate(erode(img, mask), mask);
 }
 
-array morphclose(const array& img, const array& mask)
-{
+array morphclose(const array& img, const array& mask) {
     return erode(dilate(img, mask), mask);
 }
 
-array morphgrad(const array& img, const array& mask)
-{
+array morphgrad(const array& img, const array& mask) {
     return (dilate(img, mask) - erode(img, mask));
 }
 
-array tophat(const array& img, const array& mask)
-{
+array tophat(const array& img, const array& mask) {
     return (img - morphopen(img, mask));
 }
 
-array bottomhat(const array& img, const array& mask)
-{
+array bottomhat(const array& img, const array& mask) {
     return (morphclose(img, mask) - img);
 }
 
-array border(const array& img, const int left, const int right,
-        const int top, const int bottom,
-        const float value = 0.0)
-{
-    if((int)img.dims(0) < (top + bottom))
+array border(const array& img, const int left, const int right, const int top,
+             const int bottom, const float value = 0.0) {
+    if ((int)img.dims(0) < (top + bottom))
         printf("input does not have enough rows\n");
-    if((int)img.dims(1) < (left + right))
+    if ((int)img.dims(1) < (left + right))
         fprintf(stderr, "input does not have enough columns\n");
 
     dim4 imgDims = img.dims();
-    array ret = constant(value, imgDims);
-    ret(seq(top, imgDims[0]-bottom), seq(left, imgDims[1]-right), span, span) =
-        img(seq(top, imgDims[0]-bottom), seq(left, imgDims[1]-right), span, span);
+    array ret    = constant(value, imgDims);
+    ret(seq(top, imgDims[0] - bottom), seq(left, imgDims[1] - right), span,
+        span)    = img(seq(top, imgDims[0] - bottom),
+                    seq(left, imgDims[1] - right), span, span);
 
     return ret;
 }
 
 array border(const array& img, const int w, const int h,
-        const float value = 0.0)
-{
+             const float value = 0.0) {
     return border(img, w, w, h, h, value);
 }
 
-array border(const array& img, const int size, const float value = 0.0)
-{
+array border(const array& img, const int size, const float value = 0.0) {
     return border(img, size, size, size, size, value);
 }
 
-array blur(const array& img, const array mask = gaussianKernel(3,3))
-{
+array blur(const array& img, const array mask = gaussianKernel(3, 3)) {
     array blurred = array(img.dims(), img.type());
-    for(int i = 0; i < (int)blurred.dims(2); i++)
+    for (int i = 0; i < (int)blurred.dims(2); i++)
         blurred(span, span, i) = convolve(img(span, span, i), mask);
     return blurred;
 }
 
 // Demonstrates various image morphing manipulations.
-static void morphing_demo()
-{
+static void morphing_demo() {
     af::Window wnd(1280, 720, "Morphological Operations");
     // load images
-    array img_rgb = loadImage(ASSETS_DIR "/examples/images/man.jpg", true) / 255.f; // 3 channel RGB       [0-1]
+    array img_rgb = loadImage(ASSETS_DIR "/examples/images/man.jpg", true) /
+                    255.f;  // 3 channel RGB       [0-1]
 
     array mask = constant(1, 5, 5);
 
@@ -91,34 +82,33 @@ static void morphing_demo()
     array gr = morphgrad(img_rgb, mask);
     array th = tophat(img_rgb, mask);
     array bh = bottomhat(img_rgb, mask);
-    array bl = blur(img_rgb, gaussianKernel(5,5));
+    array bl = blur(img_rgb, gaussianKernel(5, 5));
     array bp = border(img_rgb, 20, 30, 40, 50, 0.5);
     array bo = border(img_rgb, 20);
 
     while (!wnd.close()) {
         wnd.grid(3, 4);
 
-        wnd(0, 0).image(img_rgb, "Input"          );
-        wnd(1, 0).image(er     , "Erosion"        );
-        wnd(2, 0).image(di     , "Dilation"       );
+        wnd(0, 0).image(img_rgb, "Input");
+        wnd(1, 0).image(er, "Erosion");
+        wnd(2, 0).image(di, "Dilation");
 
-        wnd(0, 1).image(op     , "Opening"        );
-        wnd(1, 1).image(cl     , "Closing"        );
-        wnd(2, 1).image(gr     , "Gradient"       );
+        wnd(0, 1).image(op, "Opening");
+        wnd(1, 1).image(cl, "Closing");
+        wnd(2, 1).image(gr, "Gradient");
 
-        wnd(0, 2).image(th     , "TopHat"         );
-        wnd(1, 2).image(bh     , "BottomHat"      );
-        wnd(2, 2).image(bl     , "Blur"           );
+        wnd(0, 2).image(th, "TopHat");
+        wnd(1, 2).image(bh, "BottomHat");
+        wnd(2, 2).image(bl, "Blur");
 
-        wnd(0, 3).image(bp     , "Border to Gray" );
-        wnd(1, 3).image(bo     , "Border to black");
+        wnd(0, 3).image(bp, "Border to Gray");
+        wnd(1, 3).image(bo, "Border to black");
 
         wnd.show();
     }
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char** argv) {
     int device = argc > 1 ? atoi(argv[1]) : 0;
 
     try {
diff --git a/examples/image_processing/optical_flow.cpp b/examples/image_processing/optical_flow.cpp
index ae77d4e478..6d859068a7 100644
--- a/examples/image_processing/optical_flow.cpp
+++ b/examples/image_processing/optical_flow.cpp
@@ -7,52 +7,49 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <string.h>
-#include <stdio.h>
+#include <arrayfire.h>
 #include <math.h>
+#include <stdio.h>
+#include <string.h>
 #include <algorithm>
-#include <arrayfire.h>
 
 using namespace af;
 
-static void diffs(array& Ix, array& Iy, array& It, array I1, array I2)
-{
-        //  3x3 derivative kernels
-    float dx_kernel[] = { -1.0f / 6.0f, -1.0f / 6.0f, -1.0f / 6.0f,
-        0.0f / 6.0f,  0.0f / 6.0f,  0.0f / 6.0f,
-        1.0f / 6.0f,  1.0f / 6.0f,  1.0f / 6.0f
-    };
-    float dy_kernel[] = { -1.0f / 6.0f,  0.0f / 6.0f,  1.0f / 6.0f,
-        -1.0f / 6.0f,  0.0f / 6.0f,  1.0f / 6.0f,
-        -1.0f / 6.0f,  0.0f / 6.0f,  1.0f / 6.0f
-    };
-    array dx = array(dim4(3, 3), dx_kernel);
-    array dy = array(dim4(3, 3), dy_kernel);
-    array dt = constant(1,1, 2) / 4.0;
+static void diffs(array& Ix, array& Iy, array& It, array I1, array I2) {
+    //  3x3 derivative kernels
+    float dx_kernel[] = {-1.0f / 6.0f, -1.0f / 6.0f, -1.0f / 6.0f,
+                         0.0f / 6.0f,  0.0f / 6.0f,  0.0f / 6.0f,
+                         1.0f / 6.0f,  1.0f / 6.0f,  1.0f / 6.0f};
+    float dy_kernel[] = {-1.0f / 6.0f, 0.0f / 6.0f, 1.0f / 6.0f,
+                         -1.0f / 6.0f, 0.0f / 6.0f, 1.0f / 6.0f,
+                         -1.0f / 6.0f, 0.0f / 6.0f, 1.0f / 6.0f};
+    array dx          = array(dim4(3, 3), dx_kernel);
+    array dy          = array(dim4(3, 3), dy_kernel);
+    array dt          = constant(1, 1, 2) / 4.0;
 
     Ix = convolve(I1, dx) + convolve(I2, dx);
     Iy = convolve(I1, dy) + convolve(I2, dy);
     It = convolve(I2, dt) - convolve(I1, dt);
 }
 
-static void optical_flow_demo(bool console)
-{
+static void optical_flow_demo(bool console) {
     af::Window wnd("Horn-Schunck Optical Flow Demo");
     wnd.setColorMap(AF_COLORMAP_COLORS);
 
-    double time_total = 10; // run for N seconds
+    double time_total = 10;  // run for N seconds
 
     const float h_mean_kernel[] = {1.0f / 12.0f, 2.0f / 12.0f, 1.0f / 12.0f,
-        2.0f / 12.0f,        0.0f,  2.0f / 12.0f,
-        1.0f / 12.0f, 2.0f / 12.0f, 1.1f / 12.0f
-    };
-    array mean_kernel = array(dim4(3, 3), h_mean_kernel, afHost);
+                                   2.0f / 12.0f, 0.0f,         2.0f / 12.0f,
+                                   1.0f / 12.0f, 2.0f / 12.0f, 1.1f / 12.0f};
+    array mean_kernel           = array(dim4(3, 3), h_mean_kernel, afHost);
 
-    array I1 = loadImage(ASSETS_DIR "/examples/images/circle_left.ppm"); // grayscale
+    array I1 =
+        loadImage(ASSETS_DIR "/examples/images/circle_left.ppm");  // grayscale
     array I2 = loadImage(ASSETS_DIR "/examples/images/circle_center.ppm");
 
-    array u = constant(0,I1.dims()), v = constant(0,I1.dims());
-    array Ix, Iy, It; diffs(Ix, Iy, It, I1, I2);
+    array u = constant(0, I1.dims()), v = constant(0, I1.dims());
+    array Ix, Iy, It;
+    diffs(Ix, Iy, It, I1, I2);
 
     timer time_start, time_last;
     time_start = time_last = timer::start();
@@ -65,15 +62,15 @@ static void optical_flow_demo(bool console)
         array v_ = convolve(v, mean_kernel);
 
         const float alphasq = 0.1f;
-        array num = Ix * u_ + Iy * v_ + It;
-        array den = alphasq + Ix * Ix + Iy * Iy;
+        array num           = Ix * u_ + Iy * v_ + It;
+        array den           = alphasq + Ix * Ix + Iy * Iy;
 
         array tmp = 0.01 * num;
-        u = u_ - (Ix * tmp) / den;
-        v = v_ - (Iy * tmp) / den;
+        u         = u_ - (Ix * tmp) / den;
+        v         = v_ - (Iy * tmp) / den;
 
         if (!console) {
-            wnd.grid(2,2);
+            wnd.grid(2, 2);
 
             wnd(0, 0).image(I1, "I1");
             wnd(1, 0).image(I2, "I2");
@@ -85,29 +82,25 @@ static void optical_flow_demo(bool console)
 
         double elapsed = timer::stop(time_last);
         if (elapsed > 1) {
-            double rate = (iter - iter_last) / elapsed;
+            double rate          = (iter - iter_last) / elapsed;
             double total_elapsed = timer::stop(time_start);
-            time_last = timer::start();
-            iter_last = iter;
-            max_rate = std::max(max_rate, rate);
-            if (total_elapsed >= time_total) {
-                break;
-            }
+            time_last            = timer::start();
+            iter_last            = iter;
+            max_rate             = std::max(max_rate, rate);
+            if (total_elapsed >= time_total) { break; }
             if (!console)
                 printf("  iterations per second: %.0f   (progress %.0f%%)\n",
-                        rate, 100.0f * total_elapsed / time_total);
+                       rate, 100.0f * total_elapsed / time_total);
         }
     }
 
     if (console) {
         printf(" ### optical_flow %f iterations per second (max)\n", max_rate);
     }
-
 }
 
-int main(int argc, char* argv[])
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char* argv[]) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
 
     try {
diff --git a/examples/image_processing/pyramids.cpp b/examples/image_processing/pyramids.cpp
index 18726d7d9f..b09e895d6a 100644
--- a/examples/image_processing/pyramids.cpp
+++ b/examples/image_processing/pyramids.cpp
@@ -7,36 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <cstdlib>
 
 using namespace af;
 
-static const float pyramid_kernel[] = {
-    1,  4,  6,  4, 1,
-    4, 16, 24, 16, 4,
-    6, 24, 36, 24, 6,
-    4, 16, 24, 16, 4,
-    1,  4,  6,  4, 1
-};
+static const float pyramid_kernel[] = {1,  4, 6,  4,  1,  4, 16, 24, 16,
+                                       4,  6, 24, 36, 24, 6, 4,  16, 24,
+                                       16, 4, 1,  4,  6,  4, 1};
 
-array pyramid(const array& img, const int level, const bool sampling)
-{
+array pyramid(const array& img, const int level, const bool sampling) {
     array pyr = img.copy();
     array kernel(5, 5, pyramid_kernel);
     kernel = kernel / 256.f;
-    if(sampling) {                              //Downsample
-        for(int i = 0; i < level; i++) {
-            for(int j = 0; j < pyr.dims(2); j++)
+    if (sampling) {  // Downsample
+        for (int i = 0; i < level; i++) {
+            for (int j = 0; j < pyr.dims(2); j++)
                 pyr(span, span, j) = convolve(pyr(span, span, j), kernel);
-            pyr = pyr(seq(0, pyr.dims(0)-1, 2), seq(0, pyr.dims(1)-1, 2), span);
+            pyr = pyr(seq(0, pyr.dims(0) - 1, 2), seq(0, pyr.dims(1) - 1, 2),
+                      span);
         }
-    } else {                                    // Up sample
-        for(int i = 0; i < level; i++) {
-            array tmp = constant(0, pyr.dims(0) * 2, pyr.dims(1) * 2, pyr.dims(2));
-            tmp(seq(0, 2*pyr.dims(0)-1, 2), seq(0, 2*pyr.dims(1)-1, 2), span) = pyr;
-            for(int j = 0; j < pyr.dims(2); j++)
+    } else {  // Up sample
+        for (int i = 0; i < level; i++) {
+            array tmp =
+                constant(0, pyr.dims(0) * 2, pyr.dims(1) * 2, pyr.dims(2));
+            tmp(seq(0, 2 * pyr.dims(0) - 1, 2), seq(0, 2 * pyr.dims(1) - 1, 2),
+                span) = pyr;
+            for (int j = 0; j < pyr.dims(2); j++)
                 tmp(span, span, j) = convolve(tmp(span, span, j), kernel * 4.f);
             pyr = tmp;
         }
@@ -44,20 +42,21 @@ array pyramid(const array& img, const int level, const bool sampling)
     return pyr;
 }
 
-void pyramids_demo()
-{
+void pyramids_demo() {
     af::Window wnd_rgb("Image Pyramids - RGB Images");
     af::Window wnd_gray("Image Pyramids - Grayscale Images");
     wnd_rgb.setPos(25, 25);
     wnd_gray.setPos(150, 150);
 
-    array img_rgb = loadImage(ASSETS_DIR "/examples/images/atlantis.png", true) / 255.f; // 3 channel RGB       [0-1]
+    array img_rgb =
+        loadImage(ASSETS_DIR "/examples/images/atlantis.png", true) /
+        255.f;  // 3 channel RGB       [0-1]
     array img_gray = colorSpace(img_rgb, AF_GRAY, AF_RGB);
 
-    array downc1 = pyramid(img_rgb,  1, true);
-    array downc2 = pyramid(img_rgb,  2, true);
-    array upc1   = pyramid(img_rgb,  1, false);
-    array upc2   = pyramid(img_rgb,  2, false);
+    array downc1 = pyramid(img_rgb, 1, true);
+    array downc2 = pyramid(img_rgb, 2, true);
+    array upc1   = pyramid(img_rgb, 1, false);
+    array upc2   = pyramid(img_rgb, 2, false);
 
     array downg1 = pyramid(img_gray, 1, true);
     array downg2 = pyramid(img_gray, 2, true);
@@ -65,7 +64,6 @@ void pyramids_demo()
     array upg2   = pyramid(img_gray, 2, false);
 
     while (!wnd_rgb.close() && !wnd_gray.close()) {
-
         wnd_rgb.grid(2, 3);
         wnd_rgb(0, 0).image(img_rgb, "color image");
         wnd_rgb(1, 0).image(downc1, "downsample 1 level");
@@ -74,7 +72,6 @@ void pyramids_demo()
         wnd_rgb(0, 2).image(upc2, "upsample 2 level");
         wnd_rgb.show();
 
-
         wnd_gray.grid(2, 3);
         wnd_gray(0, 0).image(img_gray, "grayscale image");
         wnd_gray(1, 0).image(downg1, "downsample 1 level");
@@ -85,8 +82,7 @@ void pyramids_demo()
     }
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char** argv) {
     int device = argc > 1 ? atoi(argv[1]) : 0;
 
     try {
diff --git a/examples/lin_algebra/cholesky.cpp b/examples/lin_algebra/cholesky.cpp
index 617ba1b74b..56524314ce 100644
--- a/examples/lin_algebra/cholesky.cpp
+++ b/examples/lin_algebra/cholesky.cpp
@@ -13,17 +13,15 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
-{
+int main(int argc, char* argv[]) {
     try {
-
         // Select a device and display arrayfire info
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        int n = 5;
-        array t = randu(n, n);
+        int n    = 5;
+        array t  = randu(n, n);
         array in = matmulNT(t, t) + identity(n, n) * n;
         af_print(in);
 
diff --git a/examples/lin_algebra/lu.cpp b/examples/lin_algebra/lu.cpp
index afdd2e8952..a162ce1962 100644
--- a/examples/lin_algebra/lu.cpp
+++ b/examples/lin_algebra/lu.cpp
@@ -13,8 +13,7 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
-{
+int main(int argc, char* argv[]) {
     try {
         // Select a device and display arrayfire info
         int device = argc > 1 ? atoi(argv[1]) : 0;
diff --git a/examples/lin_algebra/qr.cpp b/examples/lin_algebra/qr.cpp
index e4c954e378..334e53b876 100644
--- a/examples/lin_algebra/qr.cpp
+++ b/examples/lin_algebra/qr.cpp
@@ -13,10 +13,8 @@
 
 using namespace af;
 
-int main(int argc, char *argv[])
-{
+int main(int argc, char* argv[]) {
     try {
-
         // Select a device and display arrayfire info
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
diff --git a/examples/lin_algebra/svd.cpp b/examples/lin_algebra/svd.cpp
index ab0fe9e4fd..e731532d1d 100644
--- a/examples/lin_algebra/svd.cpp
+++ b/examples/lin_algebra/svd.cpp
@@ -13,23 +13,22 @@
 
 using namespace af;
 
-int main(int argc, char* argv[])
-{
+int main(int argc, char* argv[]) {
     try {
         // Select a device and display arrayfire info
         int device = argc > 1 ? atoi(argv[1]) : 0;
         af::setDevice(device);
         af::info();
 
-        float h_buffer[] = {1, 4, 2, 5, 3, 6 };  // host array
-        array in(2, 3, h_buffer);                        // copy host data to device
+        float h_buffer[] = {1, 4, 2, 5, 3, 6};  // host array
+        array in(2, 3, h_buffer);               // copy host data to device
 
         array u;
         array s_vec;
         array vt;
         svd(u, s_vec, vt, in);
 
-        array s_mat = diag(s_vec, 0, false);
+        array s_mat    = diag(s_vec, 0, false);
         array in_recon = matmul(u, s_mat, vt(seq(2), span));
 
         af_print(in);
diff --git a/examples/machine_learning/bagging.cpp b/examples/machine_learning/bagging.cpp
index 7c9895053d..2a52cc554e 100644
--- a/examples/machine_learning/bagging.cpp
+++ b/examples/machine_learning/bagging.cpp
@@ -8,50 +8,46 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 
 // Get accuracy of the predicted results
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     return 100 * count<float>(predicted == target) / target.elements();
 }
 
 // Calculate all the distances from testing set to training set
-array distance(array train, array test)
-{
-    const int feat_len = train.dims(1);
+array distance(array train, array test) {
+    const int feat_len  = train.dims(1);
     const int num_train = train.dims(0);
-    const int num_test  =  test.dims(0);
-    array dist = constant(0, num_train, num_test);
+    const int num_test  = test.dims(0);
+    array dist          = constant(0, num_train, num_test);
 
     // Iterate over each attribute
     for (int ii = 0; ii < feat_len; ii++) {
-
         // Get a attribute vectors
         array train_i = train(span, ii);
-        array test_i  = test (span, ii).T();
+        array test_i  = test(span, ii).T();
 
         // Tile the vectors to generate matrices
-        array train_tiled = tile(train_i, 1,   num_test);
-        array test_tiled  = tile( test_i, num_train, 1 );
+        array train_tiled = tile(train_i, 1, num_test);
+        array test_tiled  = tile(test_i, num_train, 1);
 
         // Add the distance for this attribute
         dist = dist + abs(train_tiled - test_tiled);
-        dist.eval(); // Necessary to free up train_i, test_i
+        dist.eval();  // Necessary to free up train_i, test_i
     }
 
     return dist;
 }
 
-array knn(array &train_feats, array &test_feats, array &train_labels)
-{
+array knn(array &train_feats, array &test_feats, array &train_labels) {
     // Find distances between training and testing sets
     array dist = distance(train_feats, test_feats);
 
@@ -63,16 +59,14 @@ array knn(array &train_feats, array &test_feats, array &train_labels)
     return train_labels(idx);
 }
 
-
 array bagging(array &train_feats, array &test_feats, array &train_labels,
-              int num_classes, int num_models, int sample_size)
-{
+              int num_classes, int num_models, int sample_size) {
     int num_train = train_feats.dims(0);
-    int num_test  =  test_feats.dims(0);
+    int num_test  = test_feats.dims(0);
 
-    array idx = floor(randu(sample_size, num_models) * num_train);
+    array idx        = floor(randu(sample_size, num_models) * num_train);
     array labels_all = constant(0, num_test, num_classes);
-    array off = seq(num_test);
+    array off        = seq(num_test);
 
     for (int i = 0; i < num_models; i++) {
         array ii = idx(span, i);
@@ -82,7 +76,7 @@ array bagging(array &train_feats, array &test_feats, array &train_labels,
 
         // Get the predicted results
         array labels_ii = knn(train_feats_ii, test_feats, train_labels_ii);
-        array lidx = labels_ii * num_test + off;
+        array lidx      = labels_ii * num_test + off;
 
         labels_all(lidx) = labels_all(lidx) + 1;
     }
@@ -93,24 +87,21 @@ array bagging(array &train_feats, array &test_feats, array &train_labels,
     return labels;
 }
 
-
-void bagging_demo(bool console, int perc)
-{
+void bagging_demo(bool console, int perc) {
     array train_images, train_labels;
     array test_images, test_labels;
     int num_train, num_test, num_classes;
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<false>(&num_classes, &num_train, &num_test,
-                       train_images, test_images,
-                       train_labels, test_labels, frac);
+    setup_mnist<false>(&num_classes, &num_train, &num_test, train_images,
+                       test_images, train_labels, test_labels, frac);
 
     int feature_length = train_images.elements() / num_train;
-    array train_feats = moddims(train_images, feature_length, num_train).T();
-    array test_feats  = moddims(test_images , feature_length, num_test ).T();
+    array train_feats  = moddims(train_images, feature_length, num_train).T();
+    array test_feats   = moddims(test_images, feature_length, num_test).T();
 
-    int num_models = 10;
+    int num_models  = 10;
     int sample_size = 1000;
 
     timer::start();
@@ -121,31 +112,26 @@ void bagging_demo(bool console, int perc)
 
     // Results
     printf("Accuracy on testing  data: %2.2f\n",
-           accuracy(res_labels , test_labels));
+           accuracy(res_labels, test_labels));
 
     printf("Prediction time: %4.4f\n", test_time);
 
     if (false && !console) {
         display_results<false>(test_images, res_labels, test_labels.T(), 20);
     }
-
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         setDevice(device);
         af::info();
         bagging_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/deep_belief_net.cpp b/examples/machine_learning/deep_belief_net.cpp
index 75e982115e..a9f3296c1c 100644
--- a/examples/machine_learning/deep_belief_net.cpp
+++ b/examples/machine_learning/deep_belief_net.cpp
@@ -8,18 +8,17 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 using std::vector;
 
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
     max(val, plabels, predicted, 1);
@@ -27,58 +26,46 @@ float accuracy(const array& predicted, const array& target)
 }
 
 // Derivative of the activation function
-array deriv(const array &out)
-{
-    return out * (1 - out);
-}
+array deriv(const array &out) { return out * (1 - out); }
 
 // Cost function
-double error(const array &out,
-             const array &pred)
-{
+double error(const array &out, const array &pred) {
     array dif = (out - pred);
     return sqrt((double)(sum<float>(dif * dif)));
 }
 
-array sigmoid_binary(const array in)
-{
+array sigmoid_binary(const array in) {
     // Choosing "1" with probability sigmoid(in)
     return (sigmoid(in) > randu(in.dims())).as(f32);
 }
 
 class rbm {
-
-private:
+   private:
     array weights;
     array h_bias;
     array v_bias;
 
-public:
-    rbm(int v_size, int h_size) :
-        weights(randu(h_size, v_size)/100.f),
-        h_bias(constant(0, 1, h_size)),
-        v_bias(constant(0, 1, v_size))
-    {
-    }
+   public:
+    rbm(int v_size, int h_size)
+        : weights(randu(h_size, v_size) / 100.f)
+        , h_bias(constant(0, 1, h_size))
+        , v_bias(constant(0, 1, v_size)) {}
 
-    array get_weights()
-    {
+    array get_weights() {
         return transpose(join(1, weights, transpose(h_bias)));
     }
 
-    void train(const array &in, double lr, int num_epochs, int batch_size, bool verbose)
-    {
+    void train(const array &in, double lr, int num_epochs, int batch_size,
+               bool verbose) {
         const int num_samples = in.dims(0);
         const int num_batches = num_samples / batch_size;
 
-        for (int i = 0; i <  num_epochs; i++) {
-
+        for (int i = 0; i < num_epochs; i++) {
             double err = 0;
 
             for (int j = 0; j < num_batches - 1; j++) {
-
-                int st = j * batch_size;
-                int en = std::min(num_samples - 1, st + batch_size - 1);
+                int st  = j * batch_size;
+                int en  = std::min(num_samples - 1, st + batch_size - 1);
                 int num = en - st + 1;
 
                 array v_pos = in(seq(st, en), span);
@@ -86,17 +73,16 @@ class rbm {
                 array h_pos = sigmoid_binary(tile(h_bias, num) +
                                              matmulNT(v_pos, weights));
 
-                array v_neg = sigmoid_binary(tile(v_bias, num) +
-                                             matmul(h_pos, weights));
+                array v_neg =
+                    sigmoid_binary(tile(v_bias, num) + matmul(h_pos, weights));
 
                 array h_neg = sigmoid_binary(tile(h_bias, num) +
                                              matmulNT(v_neg, weights));
 
-
                 array c_pos = matmulTN(h_pos, v_pos);
                 array c_neg = matmulTN(h_neg, v_neg);
 
-                array delta_w = lr * (c_pos - c_neg) / num;
+                array delta_w  = lr * (c_pos - c_neg) / num;
                 array delta_vb = lr * sum(v_pos - v_neg) / num;
                 array delta_hb = lr * sum(h_pos - h_neg) / num;
 
@@ -104,27 +90,23 @@ class rbm {
                 v_bias += delta_vb;
                 h_bias += delta_hb;
 
-                if (verbose) {
-                    err += error(v_pos, v_neg);
-                }
+                if (verbose) { err += error(v_pos, v_neg); }
             }
 
             if (verbose) {
-                printf("Epoch %d: Reconstruction error: %0.4f\n", i + 1, err / num_batches);
+                printf("Epoch %d: Reconstruction error: %0.4f\n", i + 1,
+                       err / num_batches);
             }
         }
     }
 
-    array prop_up(const array &in)
-    {
-        return sigmoid(tile(h_bias, in.dims(0)) +
-                       matmulNT(in, weights));
+    array prop_up(const array &in) {
+        return sigmoid(tile(h_bias, in.dims(0)) + matmulNT(in, weights));
     }
 };
 
 class dbn {
-
-private:
+   private:
     const int in_size;
     const int out_size;
     const int num_hidden;
@@ -132,39 +114,34 @@ class dbn {
     std::vector<array> weights;
     std::vector<int> hidden;
 
-    array add_bias(const array &in)
-    {
+    array add_bias(const array &in) {
         // Bias input is added on top of given input
         return join(1, constant(1, in.dims(0), 1), in);
     }
 
-    vector<array> forward_propagate(const array& input)
-    {
+    vector<array> forward_propagate(const array &input) {
         // Get activations at each layer
         vector<array> signal(num_total);
         signal[0] = input;
 
         for (int i = 0; i < num_total - 1; i++) {
-            array in = add_bias(signal[i]);
-            array out = matmul(in, weights[i]);
+            array in      = add_bias(signal[i]);
+            array out     = matmul(in, weights[i]);
             signal[i + 1] = sigmoid(out);
         }
 
         return signal;
     }
 
-    void back_propagate(const vector<array> signal,
-                        const array &target,
-                        const double &alpha)
-    {
-
+    void back_propagate(const vector<array> signal, const array &target,
+                        const double &alpha) {
         // Get error for output layer
-        array out = signal[num_total  - 1];
+        array out = signal[num_total - 1];
         array err = (out - target);
-        int m = target.dims(0);
+        int m     = target.dims(0);
 
         for (int i = num_total - 2; i >= 0; i--) {
-            array in = add_bias(signal[i]);
+            array in    = add_bias(signal[i]);
             array delta = (deriv(out) * err).T();
 
             // Adjust weights
@@ -180,59 +157,44 @@ class dbn {
         }
     }
 
-public:
-
-    dbn(const int in_sz, const int out_sz,
-        const std::vector<int> hidden_layers) :
-        in_size(in_sz),
-        out_size(out_sz),
-        num_hidden(hidden_layers.size()),
-        num_total(hidden_layers.size() + 2),
-        weights(hidden_layers.size() + 1),
-        hidden(hidden_layers)
-    {
-    }
-
-    void train(const array &input, const array &target,
-               double lr_rbm = 1.0,
-               double lr_nn  = 1.0,
-               const int epochs_rbm = 15,
-               const int epochs_nn = 300,
-               const int batch_size = 100,
-               double maxerr = 1.0, bool verbose=false)
-    {
-
+   public:
+    dbn(const int in_sz, const int out_sz, const std::vector<int> hidden_layers)
+        : in_size(in_sz)
+        , out_size(out_sz)
+        , num_hidden(hidden_layers.size())
+        , num_total(hidden_layers.size() + 2)
+        , weights(hidden_layers.size() + 1)
+        , hidden(hidden_layers) {}
+
+    void train(const array &input, const array &target, double lr_rbm = 1.0,
+               double lr_nn = 1.0, const int epochs_rbm = 15,
+               const int epochs_nn = 300, const int batch_size = 100,
+               double maxerr = 1.0, bool verbose = false) {
         // Pre-training hidden layers
         array X = input;
         for (int i = 0; i < num_hidden; i++) {
-
-            if (verbose) {
-                printf("Training Hidden Layer %d\n", i);
-            }
+            if (verbose) { printf("Training Hidden Layer %d\n", i); }
 
             int visible = (i == 0) ? in_size : hidden[i - 1];
 
             rbm r(visible, hidden[i]);
             r.train(X, lr_rbm, epochs_rbm, batch_size, verbose);
 
-            X = r.prop_up(X);
+            X          = r.prop_up(X);
             weights[i] = r.get_weights();
 
-            if (verbose) {
-                printf("\n");
-            }
+            if (verbose) { printf("\n"); }
         }
 
-        weights[num_hidden] = 0.05 * randu(hidden[num_hidden - 1] + 1, out_size) - 0.0025;
+        weights[num_hidden] =
+            0.05 * randu(hidden[num_hidden - 1] + 1, out_size) - 0.0025;
 
         const int num_samples = input.dims(0);
         const int num_batches = num_samples / batch_size;
 
         // Training the entire network
         for (int i = 0; i < epochs_nn; i++) {
-
             for (int j = 0; j < num_batches; j++) {
-
                 int st = j * batch_size;
                 int en = std::min(num_samples - 1, st + batch_size - 1);
 
@@ -241,17 +203,16 @@ class dbn {
 
                 // Propagate the inputs forward
                 vector<array> signals = forward_propagate(x);
-                array out = signals[num_total - 1];
+                array out             = signals[num_total - 1];
 
                 // Propagate the error backward
                 back_propagate(signals, y, lr_nn);
             }
 
-
             // Validate with last batch
-            int st = (num_batches - 1) * batch_size;
-            int en = num_samples - 1;
-            array out = predict(input(seq(st, en), span));
+            int st     = (num_batches - 1) * batch_size;
+            int en     = num_samples - 1;
+            array out  = predict(input(seq(st, en), span));
             double err = error(out, target(seq(st, en), span));
 
             // Check if convergence criteria has been met
@@ -261,22 +222,20 @@ class dbn {
             }
 
             if (verbose) {
-                if ((i + 1) % 10 == 0) printf("Epoch: %4d, Error: %0.4f\n", i+1, err);
+                if ((i + 1) % 10 == 0)
+                    printf("Epoch: %4d, Error: %0.4f\n", i + 1, err);
             }
         }
     }
 
-    array predict(const array &input)
-    {
+    array predict(const array &input) {
         vector<array> signal = forward_propagate(input);
-        array out = signal[num_total - 1];
+        array out            = signal[num_total - 1];
         return out;
     }
-
 };
 
-int dbn_demo(bool console, int perc)
-{
+int dbn_demo(bool console, int perc) {
     printf("** ArrayFire DBN Demo **\n\n");
 
     array train_images, test_images;
@@ -285,14 +244,14 @@ int dbn_demo(bool console, int perc)
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<true>(&num_classes, &num_train, &num_test,
-                      train_images, test_images, train_target, test_target, frac);
+    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
+                      test_images, train_target, test_target, frac);
 
     int feature_size = train_images.elements() / num_train;
 
     // Reshape images into feature vectors
     array train_feats = moddims(train_images, feature_size, num_train).T();
-    array test_feats  = moddims(test_images , feature_size, num_test ).T();
+    array test_feats  = moddims(test_images, feature_size, num_test).T();
 
     train_target = train_target.T();
     test_target  = test_target.T();
@@ -308,27 +267,24 @@ int dbn_demo(bool console, int perc)
     // Train network
     timer::start();
     network.train(train_feats, train_target,
-                  0.2,  // rbm learning rate
-                  4.0,  // nn learning rate
-                  15,   // rbm epochs
-                  250,  // nn epochs
-                  100,  // batch_size
-                  0.5,  // max error
-                  true);// verbose
+                  0.2,    // rbm learning rate
+                  4.0,    // nn learning rate
+                  15,     // rbm epochs
+                  250,    // nn epochs
+                  100,    // batch_size
+                  0.5,    // max error
+                  true);  // verbose
     af::sync();
     double train_time = timer::stop();
 
     // Run the trained network and test accuracy.
     array train_output = network.predict(train_feats);
-    array test_output  = network.predict(test_feats );
-
+    array test_output  = network.predict(test_feats);
 
     // Benchmark prediction
     af::sync();
     timer::start();
-    for (int i = 0; i < 100; i++) {
-        network.predict(test_feats);
-    }
+    for (int i = 0; i < 100; i++) { network.predict(test_feats); }
     af::sync();
     double test_time = timer::stop() / 100;
 
@@ -338,7 +294,7 @@ int dbn_demo(bool console, int perc)
 
     printf("\nTest set:\n");
     printf("Accuracy on testing  data: %2.2f\n",
-           accuracy(test_output , test_target ));
+           accuracy(test_output, test_target));
 
     printf("\nTraining time: %4.4lf s\n", train_time);
     printf("Prediction time: %4.4lf s\n\n", test_time);
@@ -352,21 +308,17 @@ int dbn_demo(bool console, int perc)
     return 0;
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         return dbn_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/geneticalgorithm.cpp b/examples/machine_learning/geneticalgorithm.cpp
index db43bf0e67..d930a9cd44 100644
--- a/examples/machine_learning/geneticalgorithm.cpp
+++ b/examples/machine_learning/geneticalgorithm.cpp
@@ -9,119 +9,120 @@
 
 #include <arrayfire.h>
 #include <climits>
+#include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <cstdio>
 
 using namespace af;
 static const float DefaultTopFittest = 0.5;
 
-array update(const array& searchSpace, const array& sampleX, const array& sampleY, const int n)
-{
-    return searchSpace(sampleY*n + sampleX);
+array update(const array& searchSpace, const array& sampleX,
+             const array& sampleY, const int n) {
+    return searchSpace(sampleY * n + sampleX);
 }
 
 array selectFittest(const array& sampleZ, const int nSamples,
-        const float topFit = DefaultTopFittest)
-{
-    //pick top fittest
+                    const float topFit = DefaultTopFittest) {
+    // pick top fittest
     array indices, values;
     sort(values, indices, sampleZ);
-    int topFitElem = topFit*nSamples;
-    int n = indices.elements();
-    return (n > topFitElem) ? indices(seq(n - topFitElem, n-1)) : indices;
+    int topFitElem = topFit * nSamples;
+    int n          = indices.elements();
+    return (n > topFitElem) ? indices(seq(n - topFitElem, n - 1)) : indices;
 }
 
-void reproduce(array& searchSpace, array& sampleX, array& sampleY, array& sampleZ, const int nSamples, const int n)
-{
-    //Get fittest parents
+void reproduce(array& searchSpace, array& sampleX, array& sampleY,
+               array& sampleZ, const int nSamples, const int n) {
+    // Get fittest parents
     array selection = selectFittest(sampleZ, nSamples);
-    array parentsX = sampleX(selection);
-    array parentsY = sampleY(selection);
-    int bits = (int)log2(n);
+    array parentsX  = sampleX(selection);
+    array parentsY  = sampleY(selection);
+    int bits        = (int)log2(n);
 
-    //Divide selection in two
+    // Divide selection in two
     array parentsX1 = parentsX.rows(0, parentsX.elements() / 2 - 1);
-    array parentsX2 = parentsX.rows(parentsX.elements() / 2, parentsX.elements() - 1);
+    array parentsX2 =
+        parentsX.rows(parentsX.elements() / 2, parentsX.elements() - 1);
     array parentsY1 = parentsY.rows(0, parentsY.elements() / 2 - 1);
-    array parentsY2 = parentsY.rows(parentsY.elements() / 2, parentsY.elements() - 1);
+    array parentsY2 =
+        parentsY.rows(parentsY.elements() / 2, parentsY.elements() - 1);
 
-    //Get crossover points (at which bit to crossover) and construct bit masks from them
+    // Get crossover points (at which bit to crossover) and construct bit masks
+    // from them
     array crossover = randu(nSamples / 4, u32) % bits;
     array lowermask = (1 << crossover) - 1;
     array uppermask = INT_MAX - lowermask;
 
-    //Create children as the cross between two parents
+    // Create children as the cross between two parents
     array childrenX1 = (parentsX1 & uppermask) + (parentsX2 & lowermask);
     array childrenY1 = (parentsY1 & uppermask) + (parentsY2 & lowermask);
 
     array childrenX2 = (parentsX2 & uppermask) + (parentsX1 & lowermask);
     array childrenY2 = (parentsY2 & uppermask) + (parentsY1 & lowermask);
 
-    //Join two new sets
+    // Join two new sets
     sampleX = join(0, childrenX1, childrenX2);
     sampleY = join(0, childrenY1, childrenY2);
 
-    //Create mutant children
+    // Create mutant children
     array mutantX = sampleX;
     array mutantY = sampleY;
 
-    //Flip a random bit to vary the gene pool
+    // Flip a random bit to vary the gene pool
     mutantX = mutantX ^ (1 << (randu(nSamples / 2, u32) % bits));
     mutantY = mutantY ^ (1 << (randu(nSamples / 2, u32) % bits));
 
     sampleX = join(0, sampleX, mutantX);
     sampleY = join(0, sampleY, mutantY);
 
-    //Update the value of each sample with the new coordinates
+    // Update the value of each sample with the new coordinates
     sampleZ = update(searchSpace, sampleX, sampleY, n);
 }
 
-void initSamples(array& searchSpace, array& sampleX, array& sampleY, array& sampleZ, const int nSamples, const int n)
-{
+void initSamples(array& searchSpace, array& sampleX, array& sampleY,
+                 array& sampleZ, const int nSamples, const int n) {
     setSeed(time(NULL));
     sampleX = randu(nSamples, u32) % n;
     sampleY = randu(nSamples, u32) % n;
     sampleZ = update(searchSpace, sampleX, sampleY, n);
 }
 
-void init(array& searchSpace, array& searchSpaceXDisplay, array& searchSpaceYDisplay, array& sampleX, array& sampleY, array& sampleZ, const int nSamples, const int n)
-{
-    //initialize space
-    searchSpace = range(dim4(n/2, n/2), 0) + range(dim4(n/2, n/2), 1);
+void init(array& searchSpace, array& searchSpaceXDisplay,
+          array& searchSpaceYDisplay, array& sampleX, array& sampleY,
+          array& sampleZ, const int nSamples, const int n) {
+    // initialize space
+    searchSpace = range(dim4(n / 2, n / 2), 0) + range(dim4(n / 2, n / 2), 1);
     searchSpace = join(0, searchSpace, flip(searchSpace, 0));
     searchSpace = join(1, searchSpace, flip(searchSpace, 1));
 
-    //initialize display data
+    // initialize display data
     searchSpaceXDisplay = iota(dim4(n, 1), dim4(1, n));
     searchSpaceYDisplay = iota(dim4(1, n), dim4(n, 1));
 
-    //initalize searchers
+    // initalize searchers
     initSamples(searchSpace, sampleX, sampleY, sampleZ, nSamples, n);
 }
 
-void reproducePrint(float& currentMax,
-        array& searchSpace, array& sampleX, array& sampleY, array& sampleZ,
-        const float trueMax, const int nSamples, const int n)
-{
+void reproducePrint(float& currentMax, array& searchSpace, array& sampleX,
+                    array& sampleY, array& sampleZ, const float trueMax,
+                    const int nSamples, const int n) {
     if (currentMax < trueMax * 0.99) {
         float maximum = max<float>(sampleZ);
-        array whereM = where(sampleZ == maximum);
+        array whereM  = where(sampleZ == maximum);
         if (maximum < trueMax * 0.99) {
             printf("Current max at ");
         } else {
             printf("\nMax found at ");
         }
         printf("(%d,%d): %f (trueMax %f)\n",
-                sampleX(whereM).scalar<unsigned int>(),
-                sampleY(whereM).scalar<unsigned int>(), maximum, trueMax);
+               sampleX(whereM).scalar<unsigned int>(),
+               sampleY(whereM).scalar<unsigned int>(), maximum, trueMax);
         currentMax = maximum;
         reproduce(searchSpace, sampleX, sampleY, sampleZ, nSamples, n);
     }
 }
 
-void geneticSearch(bool console, const int nSamples, const int n)
-{
+void geneticSearch(bool console, const int nSamples, const int n) {
     array searchSpaceXDisplay = 0;
     array searchSpaceYDisplay = 0;
     array searchSpace;
@@ -129,8 +130,8 @@ void geneticSearch(bool console, const int nSamples, const int n)
     array sampleY;
     array sampleZ;
 
-    init(searchSpace, searchSpaceXDisplay, searchSpaceYDisplay,
-            sampleX, sampleY, sampleZ, nSamples, n);
+    init(searchSpace, searchSpaceXDisplay, searchSpaceYDisplay, sampleX,
+         sampleY, sampleZ, nSamples, n);
     float trueMax = max<float>(searchSpace);
     float maximum = -trueMax;
 
@@ -139,25 +140,26 @@ void geneticSearch(bool console, const int nSamples, const int n)
         win.grid(1, 2);
         do {
             reproducePrint(maximum, searchSpace, sampleX, sampleY, sampleZ,
-                    trueMax, nSamples, n);
-            win(0,0).setAxesTitles("IdX", "IdY", "Search Space");
-            win(0,1).setAxesTitles("IdX", "IdY", "Search Space");
-            win(0,0).surface(searchSpaceXDisplay, searchSpaceYDisplay, searchSpace);
-            win(0,1).scatter(sampleX.as(f32), sampleY.as(f32), sampleZ.as(f32), AF_MARKER_CIRCLE);
+                           trueMax, nSamples, n);
+            win(0, 0).setAxesTitles("IdX", "IdY", "Search Space");
+            win(0, 1).setAxesTitles("IdX", "IdY", "Search Space");
+            win(0, 0).surface(searchSpaceXDisplay, searchSpaceYDisplay,
+                              searchSpace);
+            win(0, 1).scatter(sampleX.as(f32), sampleY.as(f32), sampleZ.as(f32),
+                              AF_MARKER_CIRCLE);
             win.show();
         } while (!win.close());
     } else {
         do {
             reproducePrint(maximum, searchSpace, sampleX, sampleY, sampleZ,
-                    trueMax, nSamples, n);
+                           trueMax, nSamples, n);
         } while (maximum < trueMax * 0.99);
     }
 }
 
-int main(int argc, char** argv)
-{
-    bool console = false;
-    const int n = 32;
+int main(int argc, char** argv) {
+    bool console       = false;
+    const int n        = 32;
     const int nSamples = 16;
     if (argc > 2 || (argc == 2 && strcmp(argv[1], "-"))) {
         printf("usage: %s [-]\n", argv[0]);
@@ -169,17 +171,19 @@ int main(int argc, char** argv)
     try {
         af::info();
         printf("** ArrayFire Genetic Algorithm Search Demo **\n\n");
-        printf("Search for trueMax in a search space where the objective function is defined as :\n\n");
+        printf(
+            "Search for trueMax in a search space where the objective function "
+            "is defined as :\n\n");
         printf("SS(x ,y) = min(x, n - (x + 1)) + min(y, n - (y + 1))\n\n");
         printf("(x, y) belongs to RxR; R = [0, n); n = %d\n\n", n);
         if (!console) {
             printf("The left figure shows the objective function.\n");
-            printf("The figure on the right shows current generation's parameters and function values.\n\n");
+            printf(
+                "The figure on the right shows current generation's parameters "
+                "and function values.\n\n");
         }
         geneticSearch(console, nSamples, n);
-    } catch (af::exception& e) {
-        fprintf(stderr, "%s\n", e.what());
-    }
+    } catch (af::exception& e) { fprintf(stderr, "%s\n", e.what()); }
 
     return 0;
 }
diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index 8a5e3da917..65369f671e 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -7,20 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <iostream>
-#include <stdio.h>
 #include <arrayfire.h>
+#include <stdio.h>
 #include <af/util.h>
 #include <cstdlib>
+#include <iostream>
 
 using namespace af;
 
-array distance(array data, array means)
-{
-    int n = data.dims(0); // Number of features
-    int k = means.dims(1); // Number of means
+array distance(array data, array means) {
+    int n = data.dims(0);   // Number of features
+    int k = means.dims(1);  // Number of means
 
-    array data2  = tile(data , 1, k, 1);
+    array data2  = tile(data, 1, k, 1);
     array means2 = tile(means, n, 1, 1);
 
     // Currently using manhattan distance
@@ -29,8 +28,7 @@ array distance(array data, array means)
 }
 
 // Get cluster id of each location in data
-array clusterize(const array data, const array means)
-{
+array clusterize(const array data, const array means) {
     // Get manhattan distance
     array dists = distance(data, means);
 
@@ -42,14 +40,14 @@ array clusterize(const array data, const array means)
     return idx;
 }
 
-array new_means(array data, array clusters, int k)
-{
-    int d = data.dims(2);
-    array means = constant(0, 1, k, d);
+array new_means(array data, array clusters, int k) {
+    int d           = data.dims(2);
+    array means     = constant(0, 1, k, d);
     array clustersd = tile(clusters, 1, 1, d);
 
-    gfor (seq ii, k) {
-        means(span, ii, span) = sum(data * (clustersd == ii)) / (sum(clusters == ii) + 1e-5);
+    gfor(seq ii, k) {
+        means(span, ii, span) =
+            sum(data * (clustersd == ii)) / (sum(clusters == ii) + 1e-5);
     }
 
     return means;
@@ -59,10 +57,10 @@ array new_means(array data, array clusters, int k)
 // data:  input,  1D or 2D (range > [0-1])
 // k:     input,  # desired means (k > 1)
 // means: output, vector of means
-void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
-{
-    unsigned n = in.dims(0); // Num features
-    unsigned d = in.dims(2); // feature length
+void kmeans(array &means, array &clusters, const array in, int k,
+            int iter = 100) {
+    unsigned n = in.dims(0);  // Num features
+    unsigned d = in.dims(2);  // feature length
 
     // reshape input
     array data = in * 0;
@@ -72,11 +70,13 @@ void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
     array maximum = max(in);
 
     gfor(seq ii, d) {
-        data(span, span, ii) = (in(span, span, ii) - minimum(ii).scalar<float>()) / maximum(ii).scalar<float>();
+        data(span, span, ii) =
+            (in(span, span, ii) - minimum(ii).scalar<float>()) /
+            maximum(ii).scalar<float>();
     }
 
     // Initial guess of means
-    means = randu(1, k, d);
+    means               = randu(1, k, d);
     array curr_clusters = constant(0, data.dims(0)) - 1;
     array prev_clusters;
 
@@ -91,7 +91,7 @@ void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
         // Break early if clusters not changing
         unsigned num_changed = count<unsigned>(prev_clusters != curr_clusters);
 
-        if (num_changed < (n/1000) + 1) break;
+        if (num_changed < (n / 1000) + 1) break;
 
         // Update current means for new clusters
         means = new_means(data, curr_clusters, k);
@@ -99,20 +99,21 @@ void kmeans(array &means, array &clusters, const array in, int k, int iter=100)
 
     // Scale up means
     gfor(seq ii, d) {
-        means(span, span, ii) = maximum(ii) * means(span, span, ii) + minimum(ii);
+        means(span, span, ii) =
+            maximum(ii) * means(span, span, ii) + minimum(ii);
     }
 
     clusters = prev_clusters;
-
 }
 
 // K-Means image recoloring.
 // Shifts the hues of an image to the k mean hues.
-int kmeans_demo(int k, bool console)
-{
+int kmeans_demo(int k, bool console) {
     printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);
 
-    array img = loadImage(ASSETS_DIR"/examples/images/vegetable-woman.jpg", true) / 255; // [0-255]
+    array img =
+        loadImage(ASSETS_DIR "/examples/images/vegetable-woman.jpg", true) /
+        255;  // [0-255]
 
     int w = img.dims(0), h = img.dims(1), c = img.dims(2);
     array vec = moddims(img, w * h, 1, c);
@@ -148,33 +149,31 @@ int kmeans_demo(int k, bool console)
         printf("Graphics not implemented yet\n");
 #endif
     } else {
-        means_full = moddims(means_full, means_full.dims(1), means_full.dims(2));
-        means_half = moddims(means_half, means_half.dims(1), means_half.dims(2));
-        means_dbl  = moddims(means_dbl , means_dbl.dims(1) , means_dbl.dims(2) );
+        means_full =
+            moddims(means_full, means_full.dims(1), means_full.dims(2));
+        means_half =
+            moddims(means_half, means_half.dims(1), means_half.dims(2));
+        means_dbl = moddims(means_dbl, means_dbl.dims(1), means_dbl.dims(2));
 
         af_print(means_full);
         af_print(means_half);
-        af_print(means_dbl );
+        af_print(means_dbl);
     }
 
     return 0;
 }
 
-int main(int argc, char** argv)
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char **argv) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
-    int k = argc > 3 ? atoi(argv[3]) : 16;
+    int k        = argc > 3 ? atoi(argv[3]) : 16;
 
     try {
-
         af::setDevice(device);
         af::info();
         return kmeans_demo(k, console);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/knn.cpp b/examples/machine_learning/knn.cpp
index e07c8536b1..a23a62db53 100644
--- a/examples/machine_learning/knn.cpp
+++ b/examples/machine_learning/knn.cpp
@@ -8,52 +8,47 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 
 // Get accuracy of the predicted results
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     return 100 * count<float>(predicted == target) / target.elements();
 }
 
 // Calculate all the distances from testing set to training set
-array distance(array train, array test)
-{
-
-    const int feat_len = train.dims(1);
+array distance(array train, array test) {
+    const int feat_len  = train.dims(1);
     const int num_train = train.dims(0);
-    const int num_test  =  test.dims(0);
+    const int num_test  = test.dims(0);
 
     array dist = constant(0, num_train, num_test);
 
     // Iterate over each attribute
     for (int ii = 0; ii < feat_len; ii++) {
-
         // Get a attribute vectors
         array train_i = train(span, ii);
-        array test_i  = test (span, ii).T();
+        array test_i  = test(span, ii).T();
 
         // Tile the vectors to generate matrices
-        array train_tiled = tile(train_i, 1,   num_test);
-        array test_tiled  = tile( test_i, num_train, 1 );
+        array train_tiled = tile(train_i, 1, num_test);
+        array test_tiled  = tile(test_i, num_train, 1);
 
         // Add the distance for this attribute
         dist = dist + abs(train_tiled - test_tiled);
-        dist.eval(); // Necessary to free up train_i, test_i
+        dist.eval();  // Necessary to free up train_i, test_i
     }
 
     return dist;
 }
 
-array knn(array &train_feats, array &test_feats, array &train_labels)
-{
+array knn(array &train_feats, array &test_feats, array &train_labels) {
     // Find distances between training and testing sets
     array dist = distance(train_feats, test_feats);
 
@@ -65,21 +60,19 @@ array knn(array &train_feats, array &test_feats, array &train_labels)
     return train_labels(idx);
 }
 
-void knn_demo(bool console, int perc)
-{
+void knn_demo(bool console, int perc) {
     array train_images, train_labels;
     array test_images, test_labels;
     int num_train, num_test, num_classes;
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<false>(&num_classes, &num_train, &num_test,
-                       train_images, test_images,
-                       train_labels, test_labels, frac);
+    setup_mnist<false>(&num_classes, &num_train, &num_test, train_images,
+                       test_images, train_labels, test_labels, frac);
 
     int feature_length = train_images.elements() / num_train;
-    array train_feats = moddims(train_images, feature_length, num_train).T();
-    array test_feats  = moddims(test_images , feature_length, num_test ).T();
+    array train_feats  = moddims(train_images, feature_length, num_train).T();
+    array test_feats   = moddims(test_images, feature_length, num_test).T();
 
     timer::start();
     // Get the predicted results
@@ -88,7 +81,7 @@ void knn_demo(bool console, int perc)
 
     // Results
     printf("Accuracy on testing  data: %2.2f\n",
-           accuracy(res_labels , test_labels));
+           accuracy(res_labels, test_labels));
 
     printf("Prediction time: %4.4f\n", test_time);
 
@@ -97,21 +90,17 @@ void knn_demo(bool console, int perc)
     }
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         knn_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/logistic_regression.cpp b/examples/machine_learning/logistic_regression.cpp
index 00b9eaad40..c77f251474 100644
--- a/examples/machine_learning/logistic_regression.cpp
+++ b/examples/machine_learning/logistic_regression.cpp
@@ -8,17 +8,16 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
     max(val, plabels, predicted, 1);
@@ -26,21 +25,18 @@ float accuracy(const array& predicted, const array& target)
     return 100 * count<float>(plabels == tlabels) / tlabels.elements();
 }
 
-float abserr(const array& predicted, const array& target)
-{
+float abserr(const array &predicted, const array &target) {
     return 100 * sum<float>(abs(predicted - target)) / predicted.elements();
 }
 
 // Predict based on given parameters
-array predict(const array &X, const array &Weights)
-{
+array predict(const array &X, const array &Weights) {
     array Z = matmul(X, Weights);
     return sigmoid(Z);
 }
 
-void cost(array &J, array &dJ, const array &Weights,
-          const array &X, const array &Y, double lambda = 1.0)
-{
+void cost(array &J, array &dJ, const array &Weights, const array &X,
+          const array &Y, double lambda = 1.0) {
     // Number of samples
     int m = Y.dims(0);
 
@@ -54,7 +50,7 @@ void cost(array &J, array &dJ, const array &Weights,
     array H = predict(X, Weights);
 
     // Cost of misprediction
-    array Jerr =  -sum(Y * log(H) + (1 - Y) * log(1 - H));
+    array Jerr = -sum(Y * log(H) + (1 - Y) * log(1 - H));
 
     // Regularization cost
     array Jreg = 0.5 * sum(lambdat * Weights * Weights);
@@ -64,17 +60,12 @@ void cost(array &J, array &dJ, const array &Weights,
 
     // Find the gradient of cost
     array D = (H - Y);
-    dJ = (matmulTN(X, D) + lambdat * Weights) / m;
+    dJ      = (matmulTN(X, D) + lambdat * Weights) / m;
 }
 
-array train(const array &X, const array &Y,
-            double alpha = 0.1,
-            double lambda = 1.0,
-            double maxerr = 0.01,
-            int maxiter = 1000,
-            bool verbose = false)
-{
-
+array train(const array &X, const array &Y, double alpha = 0.1,
+            double lambda = 1.0, double maxerr = 0.01, int maxiter = 1000,
+            bool verbose = false) {
     // Initialize parameters to 0
     array Weights = constant(0, X.dims(1), Y.dims(1));
 
@@ -82,7 +73,6 @@ array train(const array &X, const array &Y,
     float err = 0;
 
     for (int i = 0; i < maxiter; i++) {
-
         // Get the cost and gradient
         cost(J, dJ, Weights, X, Y, lambda);
 
@@ -107,8 +97,7 @@ array train(const array &X, const array &Y,
 
 void benchmark_logistic_regression(const array &train_feats,
                                    const array &train_targets,
-                                   const array test_feats)
-{
+                                   const array test_feats) {
     timer::start();
     array Weights = train(train_feats, train_targets, 0.1, 1.0, 0.01, 1000);
     af::sync();
@@ -117,7 +106,7 @@ void benchmark_logistic_regression(const array &train_feats,
     timer::start();
     const int iter = 100;
     for (int i = 0; i < iter; i++) {
-        array test_outputs  = predict(test_feats , Weights);
+        array test_outputs = predict(test_feats, Weights);
         test_outputs.eval();
     }
     af::sync();
@@ -125,50 +114,49 @@ void benchmark_logistic_regression(const array &train_feats,
 }
 
 // Demo of one vs all logistic regression
-int logit_demo(bool console, int perc)
-{
+int logit_demo(bool console, int perc) {
     array train_images, train_targets;
     array test_images, test_targets;
     int num_train, num_test, num_classes;
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<true>(&num_classes, &num_train, &num_test,
-                      train_images, test_images,
-                      train_targets, test_targets, frac);
+    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
+                      test_images, train_targets, test_targets, frac);
 
     // Reshape images into feature vectors
     int feature_length = train_images.elements() / num_train;
-    array train_feats = moddims(train_images, feature_length, num_train).T();
-    array test_feats  = moddims(test_images , feature_length, num_test ).T();
+    array train_feats  = moddims(train_images, feature_length, num_train).T();
+    array test_feats   = moddims(test_images, feature_length, num_test).T();
 
     train_targets = train_targets.T();
     test_targets  = test_targets.T();
 
     // Add a bias that is always 1
     train_feats = join(1, constant(1, num_train, 1), train_feats);
-    test_feats  = join(1, constant(1, num_test , 1), test_feats );
+    test_feats  = join(1, constant(1, num_test, 1), test_feats);
 
     // Train logistic regression parameters
-    array Weights = train(train_feats, train_targets,
-                          0.1,  // learning rate (aka alpha)
-                          1.0,  // regularization constant (aka weight decay, aka lamdba)
-                          0.01, // maximum error
-                          1000, // maximum iterations
-                          true);// verbose
+    array Weights =
+        train(train_feats, train_targets,
+              0.1,    // learning rate (aka alpha)
+              1.0,    // regularization constant (aka weight decay, aka lamdba)
+              0.01,   // maximum error
+              1000,   // maximum iterations
+              true);  // verbose
 
     // Predict the results
     array train_outputs = predict(train_feats, Weights);
-    array test_outputs  = predict(test_feats , Weights);
+    array test_outputs  = predict(test_feats, Weights);
 
     printf("Accuracy on training data: %2.2f\n",
-           accuracy(train_outputs, train_targets ));
+           accuracy(train_outputs, train_targets));
 
     printf("Accuracy on testing data: %2.2f\n",
-           accuracy(test_outputs , test_targets ));
+           accuracy(test_outputs, test_targets));
 
     printf("Maximum error on testing data: %2.2f\n",
-           abserr(test_outputs , test_targets ));
+           abserr(test_outputs, test_targets));
 
     benchmark_logistic_regression(train_feats, train_targets, test_feats);
 
@@ -181,21 +169,17 @@ int logit_demo(bool console, int perc)
     return 0;
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         return logit_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/mnist_common.h b/examples/machine_learning/mnist_common.h
index e6ece0de80..a32d21932c 100644
--- a/examples/machine_learning/mnist_common.h
+++ b/examples/machine_learning/mnist_common.h
@@ -7,26 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sstream>
 #include <algorithm>
+#include <sstream>
 #include <utility>
 #include "../common/idxio.h"
 
-bool compare(const std::pair<float, int> l,
-             const std::pair<float, int> r)
-{
+bool compare(const std::pair<float, int> l, const std::pair<float, int> r) {
     return l.first >= r.first;
 }
 
 typedef std::pair<float, int> sort_type;
 
 template<bool expand_labels>
-std::string classify(af::array arr, int k)
-{
+std::string classify(af::array arr, int k) {
     std::stringstream ss;
     if (expand_labels) {
         af::array vec = arr(af::span, k).as(f32);
-        float *h_vec = vec.host<float>();
+        float *h_vec  = vec.host<float>();
         std::vector<sort_type> data;
 
         for (int i = 0; i < (int)vec.elements(); i++)
@@ -45,53 +42,53 @@ std::string classify(af::array arr, int k)
 template<bool expand_labels>
 static void setup_mnist(int *num_classes, int *num_train, int *num_test,
                         af::array &train_images, af::array &test_images,
-                        af::array &train_labels, af::array &test_labels, float frac)
-{
+                        af::array &train_labels, af::array &test_labels,
+                        float frac) {
     std::vector<dim_t> idims;
-    std::vector<float   > idata;
-    read_idx(idims, idata, ASSETS_DIR"/examples/data/mnist/images-subset");
+    std::vector<float> idata;
+    read_idx(idims, idata, ASSETS_DIR "/examples/data/mnist/images-subset");
 
     std::vector<dim_t> ldims;
     std::vector<unsigned> ldata;
-    read_idx(ldims, ldata, ASSETS_DIR"/examples/data/mnist/labels-subset");
+    read_idx(ldims, ldata, ASSETS_DIR "/examples/data/mnist/labels-subset");
 
     std::reverse(idims.begin(), idims.end());
     unsigned numdims = idims.size();
     af::array images = af::array(af::dim4(numdims, &idims[0]), &idata[0]);
 
-    af::array R = af::randu(10000, 1);
-    af::array cond = R < std::min(frac, 0.8f);
-    af::array train_indices = where( cond);
+    af::array R             = af::randu(10000, 1);
+    af::array cond          = R < std::min(frac, 0.8f);
+    af::array train_indices = where(cond);
     af::array test_indices  = where(!cond);
 
     train_images = lookup(images, train_indices, 2) / 255;
-    test_images  = lookup(images, test_indices , 2) / 255;
+    test_images  = lookup(images, test_indices, 2) / 255;
 
     *num_classes = 10;
-    *num_train = train_images.dims(2);
-    *num_test  = test_images.dims(2);
+    *num_train   = train_images.dims(2);
+    *num_test    = test_images.dims(2);
 
     if (expand_labels) {
         train_labels = af::constant(0, *num_classes, *num_train);
-        test_labels  = af::constant(0, *num_classes, *num_test );
+        test_labels  = af::constant(0, *num_classes, *num_test);
 
-        unsigned  *h_train_idx = train_indices.host<unsigned>();
-        unsigned  *h_test_idx  =  test_indices.host<unsigned>();
+        unsigned *h_train_idx = train_indices.host<unsigned>();
+        unsigned *h_test_idx  = test_indices.host<unsigned>();
 
         for (int ii = 0; ii < *num_train; ii++) {
             train_labels(ldata[h_train_idx[ii]], ii) = 1;
         }
 
         for (int ii = 0; ii < *num_test; ii++) {
-            test_labels(ldata[ h_test_idx[ii]], ii) = 1;
+            test_labels(ldata[h_test_idx[ii]], ii) = 1;
         }
 
         af::freeHost(h_train_idx);
         af::freeHost(h_test_idx);
     } else {
         af::array labels = af::array(ldims[0], &ldata[0]);
-        train_labels = labels(train_indices);
-        test_labels  = labels( test_indices);
+        train_labels     = labels(train_indices);
+        test_labels      = labels(test_indices);
     }
 
     return;
@@ -109,14 +106,10 @@ static af::array randidx(int num, int total)
 }
 #endif
 
-
-
 template<bool expand_labels>
 static void display_results(const af::array &test_images,
                             const af::array &test_output,
-                            const af::array &test_actual,
-                            int num_display)
-{
+                            const af::array &test_actual, int num_display) {
 #if 0
     af::array locs = randidx(num_display, test_images.dims(2));
 
@@ -142,14 +135,17 @@ static void display_results(const af::array &test_images,
     }
 #else
     using namespace af;
-    for(int i = 0; i < num_display; i++) {
-        std::cout << "Predicted: " << classify<expand_labels>(test_output, i) << std::endl;
-        std::cout << "Actual: " << classify<expand_labels>(test_actual, i) << std::endl;
-
-        unsigned char* img = (test_images(span, span, i) > 0.1f).as(u8).host<unsigned char>();
-        for(int j = 0; j < 28; j++) {
-            for(int k = 0; k < 28; k++) {
-                std::cout << (img[j*28+k] ? "\u2588" : " ") << " ";
+    for (int i = 0; i < num_display; i++) {
+        std::cout << "Predicted: " << classify<expand_labels>(test_output, i)
+                  << std::endl;
+        std::cout << "Actual: " << classify<expand_labels>(test_actual, i)
+                  << std::endl;
+
+        unsigned char *img =
+            (test_images(span, span, i) > 0.1f).as(u8).host<unsigned char>();
+        for (int j = 0; j < 28; j++) {
+            for (int k = 0; k < 28; k++) {
+                std::cout << (img[j * 28 + k] ? "\u2588" : " ") << " ";
             }
             std::cout << std::endl;
         }
diff --git a/examples/machine_learning/naive_bayes.cpp b/examples/machine_learning/naive_bayes.cpp
index 788570bcd3..1ea0d45afa 100644
--- a/examples/machine_learning/naive_bayes.cpp
+++ b/examples/machine_learning/naive_bayes.cpp
@@ -8,42 +8,38 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 
 // Get accuracy of the predicted results
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     return 100 * count<float>(predicted == target) / target.elements();
 }
 
-void naive_bayes_train(float *priors,
-                       array &mu, array &sig2,
-                       const array &train_feats,
-                       const array &train_classes,
-                       int num_classes)
-{
-    const int feat_len = train_feats.dims(0);
+void naive_bayes_train(float *priors, array &mu, array &sig2,
+                       const array &train_feats, const array &train_classes,
+                       int num_classes) {
+    const int feat_len    = train_feats.dims(0);
     const int num_samples = train_classes.elements();
 
     // Get mean and variance from trianing data
-    mu  = constant(0, feat_len, num_classes);
+    mu   = constant(0, feat_len, num_classes);
     sig2 = constant(0, feat_len, num_classes);
 
     for (int ii = 0; ii < num_classes; ii++) {
-        array idx = where(train_classes == ii);
+        array idx            = where(train_classes == ii);
         array train_feats_ii = lookup(train_feats, idx, 1);
 
-        mu(span, ii)  = mean(train_feats_ii, 1);
+        mu(span, ii) = mean(train_feats_ii, 1);
 
         // Some pixels are always 0. Add a small variance.
-        sig2(span,ii) = var(train_feats_ii, 0, 1) + 0.01;
+        sig2(span, ii) = var(train_feats_ii, 0, 1) + 0.01;
 
         // Calculate priors
         priors[ii] = (float)idx.elements() / (float)num_samples;
@@ -53,10 +49,8 @@ void naive_bayes_train(float *priors,
     sig2.eval();
 }
 
-array naive_bayes_predict(float *priors,
-                          const array &mu, const array &sig2,
-                          const array &test_feats, int num_classes)
-{
+array naive_bayes_predict(float *priors, const array &mu, const array &sig2,
+                          const array &test_feats, int num_classes) {
     int num_test = test_feats.dims(1);
 
     // Predict the probabilities for testing data
@@ -64,16 +58,16 @@ array naive_bayes_predict(float *priors,
     array log_probs = constant(1, num_test, num_classes);
 
     for (int ii = 0; ii < num_classes; ii++) {
-
         // Tile the current mean and variance to the testing data size
-        array Mu  = tile(mu (span, ii), 1, num_test);
+        array Mu   = tile(mu(span, ii), 1, num_test);
         array Sig2 = tile(sig2(span, ii), 1, num_test);
 
         // This is the same as log of the CDF of the normal distribution
-        array Df = test_feats - Mu;
-        array log_P =  (-(Df * Df) / (2 * Sig2))  - log(sqrt(2 * af::Pi * Sig2));
+        array Df    = test_feats - Mu;
+        array log_P = (-(Df * Df) / (2 * Sig2)) - log(sqrt(2 * af::Pi * Sig2));
 
-        // Accumulate the probabilities, multiply with priors (add log of priors)
+        // Accumulate the probabilities, multiply with priors (add log of
+        // priors)
         log_probs(span, ii) = log(priors[ii]) + sum(log_P).T();
     }
 
@@ -84,15 +78,15 @@ array naive_bayes_predict(float *priors,
 }
 
 void benchmark_nb(const array &train_feats, const array test_feats,
-                  const array &train_labels, int num_classes)
-{
+                  const array &train_labels, int num_classes) {
     array mu, sig2;
-    int iter = 25;
+    int iter      = 25;
     float *priors = new float[num_classes];
 
     timer::start();
     for (int i = 0; i < iter; i++) {
-        naive_bayes_train(priors, mu, sig2, train_feats, train_labels, num_classes);
+        naive_bayes_train(priors, mu, sig2, train_feats, train_labels,
+                          num_classes);
     }
     af::sync();
     printf("Training time: %4.4lf s\n", timer::stop() / iter);
@@ -107,21 +101,19 @@ void benchmark_nb(const array &train_feats, const array test_feats,
     delete[] priors;
 }
 
-void naive_bayes_demo(bool console, int perc)
-{
+void naive_bayes_demo(bool console, int perc) {
     array train_images, train_labels;
     array test_images, test_labels;
     int num_train, num_test, num_classes;
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<false>(&num_classes, &num_train, &num_test,
-                       train_images, test_images,
-                       train_labels, test_labels, frac);
+    setup_mnist<false>(&num_classes, &num_train, &num_test, train_images,
+                       test_images, train_labels, test_labels, frac);
 
     int feature_length = train_images.elements() / num_train;
-    array train_feats = moddims(train_images, feature_length, num_train);
-    array test_feats  = moddims(test_images , feature_length, num_test );
+    array train_feats  = moddims(train_images, feature_length, num_train);
+    array test_feats   = moddims(test_images, feature_length, num_test);
 
     // Get training parameters
     array mu, sig2;
@@ -129,13 +121,14 @@ void naive_bayes_demo(bool console, int perc)
     naive_bayes_train(priors, mu, sig2, train_feats, train_labels, num_classes);
 
     // Predict the classes
-    array res_labels = naive_bayes_predict(priors, mu, sig2, test_feats, num_classes);
+    array res_labels =
+        naive_bayes_predict(priors, mu, sig2, test_feats, num_classes);
     delete[] priors;
 
     // Results
     printf("Trainng samples: %4d, Testing samples: %4d\n", num_train, num_test);
     printf("Accuracy on testing  data: %2.2f\n",
-           accuracy(res_labels , test_labels));
+           accuracy(res_labels, test_labels));
 
     benchmark_nb(train_feats, test_feats, train_labels, num_classes);
 
@@ -143,25 +136,21 @@ void naive_bayes_demo(bool console, int perc)
         test_images = test_images.T();
         test_labels = test_labels.T();
         // FIXME: Crashing in mnist_common.h::classify
-        //display_results<false>(test_images, res_labels, test_labels , 20);
+        // display_results<false>(test_images, res_labels, test_labels , 20);
     }
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         naive_bayes_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index f6effeb759..3c4996d971 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -8,18 +8,17 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 using std::vector;
 
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
     max(val, plabels, predicted, 1);
@@ -27,83 +26,68 @@ float accuracy(const array& predicted, const array& target)
 }
 
 // Derivative of the activation function
-array deriv(const array &out)
-{
-    return out * (1 - out);
-}
+array deriv(const array &out) { return out * (1 - out); }
 
 // Cost function
-double error(const array &out,
-             const array &pred)
-{
+double error(const array &out, const array &pred) {
     array dif = (out - pred);
     return sqrt((double)(sum<float>(dif * dif)));
 }
 
 class ann {
-
-private:
+   private:
     int num_layers;
     vector<array> weights;
 
     // Add bias input to the output from previous layer
     array add_bias(const array &in);
 
-    vector<array> forward_propagate(const array& input);
+    vector<array> forward_propagate(const array &input);
 
-    void back_propagate(const vector<array> signal,
-                        const array &pred,
+    void back_propagate(const vector<array> signal, const array &pred,
                         const double &alpha);
-public:
 
+   public:
     // Create a network with given parameters
-    ann(vector<int> layers, double range=0.05);
+    ann(vector<int> layers, double range = 0.05);
 
     // Output after single pass of forward propagation
     array predict(const array &input);
 
     // Method to trian the neural net
-    double train(const array &input, const array &target,
-                 double alpha = 1.0,
-                 int max_epochs = 300,
-                 int batch_size = 100,
-                 double maxerr = 1.0,
-                 bool verbose = false);
+    double train(const array &input, const array &target, double alpha = 1.0,
+                 int max_epochs = 300, int batch_size = 100,
+                 double maxerr = 1.0, bool verbose = false);
 };
 
-array ann::add_bias(const array &in)
-{
+array ann::add_bias(const array &in) {
     // Bias input is added on top of given input
     return join(1, constant(1, in.dims(0), 1), in);
 }
 
-vector<array> ann::forward_propagate(const array& input)
-{
+vector<array> ann::forward_propagate(const array &input) {
     // Get activations at each layer
     vector<array> signal(num_layers);
     signal[0] = input;
 
     for (int i = 0; i < num_layers - 1; i++) {
-        array in = add_bias(signal[i]);
-        array out = matmul(in, weights[i]);
+        array in      = add_bias(signal[i]);
+        array out     = matmul(in, weights[i]);
         signal[i + 1] = sigmoid(out);
     }
 
     return signal;
 }
 
-void ann::back_propagate(const vector<array> signal,
-                         const array &target,
-                         const double &alpha)
-{
-
+void ann::back_propagate(const vector<array> signal, const array &target,
+                         const double &alpha) {
     // Get error for output layer
-    array out = signal[num_layers  - 1];
+    array out = signal[num_layers - 1];
     array err = (out - target);
-    int m = target.dims(0);
+    int m     = target.dims(0);
 
     for (int i = num_layers - 2; i >= 0; i--) {
-        array in = add_bias(signal[i]);
+        array in    = add_bias(signal[i]);
         array delta = (deriv(out) * err).T();
 
         // Adjust weights
@@ -119,28 +103,22 @@ void ann::back_propagate(const vector<array> signal,
     }
 }
 
-ann::ann(vector<int> layers, double range) :
-    num_layers(layers.size()),
-    weights(layers.size() - 1)
-{
+ann::ann(vector<int> layers, double range)
+    : num_layers(layers.size()), weights(layers.size() - 1) {
     // Generate uniformly distributed random numbers between [-range/2,range/2]
     for (int i = 0; i < num_layers - 1; i++) {
-        weights[i] = range * randu(layers[i] + 1, layers[i + 1]) - range/2;
+        weights[i] = range * randu(layers[i] + 1, layers[i + 1]) - range / 2;
     }
 }
 
-array ann::predict(const array &input)
-{
+array ann::predict(const array &input) {
     vector<array> signal = forward_propagate(input);
-    array out = signal[num_layers - 1];
+    array out            = signal[num_layers - 1];
     return out;
 }
 
-double ann::train(const array &input, const array &target,
-                  double alpha, int max_epochs, int batch_size,
-                  double maxerr, bool verbose)
-{
-
+double ann::train(const array &input, const array &target, double alpha,
+                  int max_epochs, int batch_size, double maxerr, bool verbose) {
     const int num_samples = input.dims(0);
     const int num_batches = num_samples / batch_size;
 
@@ -148,9 +126,7 @@ double ann::train(const array &input, const array &target,
 
     // Training the entire network
     for (int i = 0; i < max_epochs; i++) {
-
         for (int j = 0; j < num_batches - 1; j++) {
-
             int st = j * batch_size;
             int en = st + batch_size - 1;
 
@@ -159,18 +135,17 @@ double ann::train(const array &input, const array &target,
 
             // Propagate the inputs forward
             vector<array> signals = forward_propagate(x);
-            array out = signals[num_layers - 1];
-
+            array out             = signals[num_layers - 1];
 
             // Propagate the error backward
             back_propagate(signals, y, alpha);
         }
 
         // Validate with last batch
-        int st = (num_batches - 1) * batch_size;
-        int en = num_samples - 1;
+        int st    = (num_batches - 1) * batch_size;
+        int en    = num_samples - 1;
         array out = predict(input(seq(st, en), span));
-        err = error(out, target(seq(st, en), span));
+        err       = error(out, target(seq(st, en), span));
 
         // Check if convergence criteria has been met
         if (err < maxerr) {
@@ -179,14 +154,14 @@ double ann::train(const array &input, const array &target,
         }
 
         if (verbose) {
-            if ((i + 1) % 10 == 0) printf("Epoch: %4d, Error: %0.4f\n", i+1, err);
+            if ((i + 1) % 10 == 0)
+                printf("Epoch: %4d, Error: %0.4f\n", i + 1, err);
         }
     }
     return err;
 }
 
-int ann_demo(bool console, int perc)
-{
+int ann_demo(bool console, int perc) {
     printf("** ArrayFire ANN Demo **\n\n");
 
     array train_images, test_images;
@@ -195,14 +170,14 @@ int ann_demo(bool console, int perc)
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<true>(&num_classes, &num_train, &num_test,
-                      train_images, test_images, train_target, test_target, frac);
+    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
+                      test_images, train_target, test_target, frac);
 
     int feature_size = train_images.elements() / num_train;
 
     // Reshape images into feature vectors
     array train_feats = moddims(train_images, feature_size, num_train).T();
-    array test_feats  = moddims(test_images , feature_size, num_test ).T();
+    array test_feats  = moddims(test_images, feature_size, num_test).T();
 
     train_target = train_target.T();
     test_target  = test_target.T();
@@ -220,25 +195,22 @@ int ann_demo(bool console, int perc)
     // Train network
     timer::start();
     network.train(train_feats, train_target,
-                  2.0, // learning rate / alpha
-                  250, // max epochs
-                  100, // batch size
-                  0.5, // max error
-                  true); // verbose
+                  2.0,    // learning rate / alpha
+                  250,    // max epochs
+                  100,    // batch size
+                  0.5,    // max error
+                  true);  // verbose
     af::sync();
     double train_time = timer::stop();
 
     // Run the trained network and test accuracy.
     array train_output = network.predict(train_feats);
-    array test_output  = network.predict(test_feats );
-
+    array test_output  = network.predict(test_feats);
 
     // Benchmark prediction
     af::sync();
     timer::start();
-    for (int i = 0; i < 100; i++) {
-        network.predict(test_feats);
-    }
+    for (int i = 0; i < 100; i++) { network.predict(test_feats); }
     af::sync();
     double test_time = timer::stop() / 100;
 
@@ -248,7 +220,7 @@ int ann_demo(bool console, int perc)
 
     printf("\nTest set:\n");
     printf("Accuracy on testing  data: %2.2f\n",
-           accuracy(test_output , test_target ));
+           accuracy(test_output, test_target));
 
     printf("\nTraining time: %4.4lf s\n", train_time);
     printf("Prediction time: %4.4lf s\n\n", test_time);
@@ -262,21 +234,17 @@ int ann_demo(bool console, int perc)
     return 0;
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         return ann_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/perceptron.cpp b/examples/machine_learning/perceptron.cpp
index f04e050e1b..49845461eb 100644
--- a/examples/machine_learning/perceptron.cpp
+++ b/examples/machine_learning/perceptron.cpp
@@ -8,17 +8,16 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
     max(val, plabels, predicted, 1);
@@ -27,26 +26,21 @@ float accuracy(const array& predicted, const array& target)
 }
 
 // Predict based on given parameters
-array predict(const array &X, const array &Weights)
-{
+array predict(const array &X, const array &Weights) {
     return sigmoid(matmul(X, Weights));
 }
 
-array train(const array &X, const array &Y,
-            double alpha = 0.1,
-            double maxerr = 0.05,
-            int maxiter = 1000, bool verbose = false)
-{
-
+array train(const array &X, const array &Y, double alpha = 0.1,
+            double maxerr = 0.05, int maxiter = 1000, bool verbose = false) {
     // Initialize parameters to 0
     array Weights = constant(0, X.dims(1), Y.dims(1));
 
     for (int i = 0; i < maxiter; i++) {
-        array P = predict(X, Weights);
+        array P   = predict(X, Weights);
         array err = Y - P;
 
         float mean_abs_err = mean<float>(abs(err));
-        if (mean_abs_err  < maxerr) break;
+        if (mean_abs_err < maxerr) break;
 
         if (verbose && (i + 1) % 25 == 0) {
             printf("Iter: %d, Err: %.4f\n", i + 1, mean_abs_err);
@@ -58,10 +52,8 @@ array train(const array &X, const array &Y,
     return Weights;
 }
 
-void benchmark_perceptron(const array &train_feats,
-                          const array &train_targets,
-                          const array test_feats)
-{
+void benchmark_perceptron(const array &train_feats, const array &train_targets,
+                          const array test_feats) {
     timer::start();
     array Weights = train(train_feats, train_targets, 0.1, 0.01, 1000);
     af::sync();
@@ -70,7 +62,7 @@ void benchmark_perceptron(const array &train_feats,
     timer::start();
     const int iter = 100;
     for (int i = 0; i < iter; i++) {
-        array test_outputs  = predict(test_feats , Weights);
+        array test_outputs = predict(test_feats, Weights);
         test_outputs.eval();
     }
     af::sync();
@@ -78,42 +70,40 @@ void benchmark_perceptron(const array &train_feats,
 }
 
 // Demo of one vs all logistic regression
-int perceptron_demo(bool console, int perc)
-{
+int perceptron_demo(bool console, int perc) {
     array train_images, train_targets;
     array test_images, test_targets;
     int num_train, num_test, num_classes;
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<true>(&num_classes, &num_train, &num_test,
-                      train_images, test_images,
-                      train_targets, test_targets, frac);
+    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
+                      test_images, train_targets, test_targets, frac);
 
     // Reshape images into feature vectors
     int feature_length = train_images.elements() / num_train;
-    array train_feats = moddims(train_images, feature_length, num_train).T();
-    array test_feats  = moddims(test_images , feature_length, num_test ).T();
+    array train_feats  = moddims(train_images, feature_length, num_train).T();
+    array test_feats   = moddims(test_images, feature_length, num_test).T();
 
     train_targets = train_targets.T();
     test_targets  = test_targets.T();
 
     // Add a bias that is always 1
     train_feats = join(1, constant(1, num_train, 1), train_feats);
-    test_feats  = join(1, constant(1, num_test , 1), test_feats );
+    test_feats  = join(1, constant(1, num_test, 1), test_feats);
 
     // Train logistic regression parameters
     array Weights = train(train_feats, train_targets, 0.1, 0.01, 1000, true);
 
     // Predict the results
     array train_outputs = predict(train_feats, Weights);
-    array test_outputs  = predict(test_feats , Weights);
+    array test_outputs  = predict(test_feats, Weights);
 
     printf("Accuracy on training data: %2.2f\n",
-           accuracy(train_outputs, train_targets ));
+           accuracy(train_outputs, train_targets));
 
     printf("Accuracy on testing data: %2.2f\n",
-           accuracy(test_outputs , test_targets ));
+           accuracy(test_outputs, test_targets));
 
     benchmark_perceptron(train_feats, train_targets, test_feats);
 
@@ -127,21 +117,17 @@ int perceptron_demo(bool console, int perc)
     return 0;
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         return perceptron_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/rbm.cpp b/examples/machine_learning/rbm.cpp
index e0b996267b..7da01cc7f3 100644
--- a/examples/machine_learning/rbm.cpp
+++ b/examples/machine_learning/rbm.cpp
@@ -8,18 +8,17 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 using std::vector;
 
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
     max(val, plabels, predicted, 1);
@@ -27,68 +26,49 @@ float accuracy(const array& predicted, const array& target)
 }
 
 // Derivative of the activation function
-array deriv(const array &out)
-{
-    return out * (1 - out);
-}
+array deriv(const array &out) { return out * (1 - out); }
 
 // Cost function
-double error(const array &out,
-             const array &pred)
-{
+double error(const array &out, const array &pred) {
     array dif = (out - pred);
     return sqrt((double)(sum<float>(dif * dif)));
 }
 
-array binary(const array in)
-{
+array binary(const array in) {
     // Choosing "1" with probability sigmoid(in)
     return (in > randu(in.dims())).as(f32);
 }
 
 class rbm {
-
-private:
+   private:
     array weights;
     array h_bias;
     array v_bias;
 
     // Add bias input to the output from previous layer
-    array vtoh(const array &v)
-    {
-        return binary(prop_up(v));
-    }
+    array vtoh(const array &v) { return binary(prop_up(v)); }
 
-    array htov(const array &h)
-    {
-        return binary(prop_down(h));
-    }
-
-public:
+    array htov(const array &h) { return binary(prop_down(h)); }
 
+   public:
     rbm() {}
 
-    rbm(int v_size, int h_size) :
-        weights(randu(h_size, v_size)/100 - 0.05),
-        h_bias(constant(0, 1, h_size)),
-        v_bias(constant(0, 1, v_size))
-    {
-    }
+    rbm(int v_size, int h_size)
+        : weights(randu(h_size, v_size) / 100 - 0.05)
+        , h_bias(constant(0, 1, h_size))
+        , v_bias(constant(0, 1, v_size)) {}
 
-    array prop_up(const array &v)
-    {
+    array prop_up(const array &v) {
         array h_bias_tile = tile(h_bias, v.dims(0));
         return sigmoid(h_bias_tile + matmulNT(v, weights));
     }
 
-    array prop_down(const array &h)
-    {
+    array prop_down(const array &h) {
         array v_bias_tile = tile(v_bias, h.dims(0));
         return sigmoid(v_bias_tile + matmul(h, weights));
     }
 
-    void gibbs_vhv(array &vt, array &ht, const array &v, int k = 1)
-    {
+    void gibbs_vhv(array &vt, array &ht, const array &v, int k = 1) {
         vt = v;
         for (int i = 0; i < k; i++) {
             ht = vtoh(vt);
@@ -96,8 +76,7 @@ class rbm {
         }
     }
 
-    void gibbs_hvh(array &vt, array &ht, const array &h, int k = 1)
-    {
+    void gibbs_hvh(array &vt, array &ht, const array &h, int k = 1) {
         ht = h;
         for (int i = 0; i < k; i++) {
             vt = htov(ht);
@@ -105,23 +84,17 @@ class rbm {
         }
     }
 
-    void train(const array &in,
-               double lr = 0.1,
-               int num_epochs = 15,
-               int batch_size = 100,
-               int k = 1, bool verbose = false)
-    {
+    void train(const array &in, double lr = 0.1, int num_epochs = 15,
+               int batch_size = 100, int k = 1, bool verbose = false) {
         const int num_samples = in.dims(0);
         const int num_batches = num_samples / batch_size;
 
-        for (int i = 0; i <  num_epochs; i++) {
-
+        for (int i = 0; i < num_epochs; i++) {
             double err = 0;
 
             for (int j = 0; j < num_batches - 1; j++) {
-
-                int st = j * batch_size;
-                int en = std::min(num_samples - 1, st + batch_size - 1);
+                int st  = j * batch_size;
+                int en  = std::min(num_samples - 1, st + batch_size - 1);
                 int num = en - st + 1;
 
                 array v_pos = in(seq(st, en), span);
@@ -136,7 +109,7 @@ class rbm {
                 array c_pos = matmulTN(h_pos, v_pos);
                 array c_neg = matmulTN(h_neg, v_neg);
 
-                array delta_w = lr * (c_pos - c_neg) / num;
+                array delta_w  = lr * (c_pos - c_neg) / num;
                 array delta_vb = lr * sum(v_pos - v_neg) / num;
                 array delta_hb = lr * sum(h_pos - h_neg) / num;
 
@@ -144,13 +117,12 @@ class rbm {
                 v_bias += delta_vb;
                 h_bias += delta_hb;
 
-                if (verbose) {
-                    err += error(v_pos, v_neg);
-                }
+                if (verbose) { err += error(v_pos, v_neg); }
             }
 
             if (verbose) {
-                printf("Epoch %d: Reconstruction error: %0.4f\n", i + 1, err / num_batches);
+                printf("Epoch %d: Reconstruction error: %0.4f\n", i + 1,
+                       err / num_batches);
             }
         }
 
@@ -158,8 +130,7 @@ class rbm {
     }
 };
 
-int rbm_demo(bool /*console*/, int perc)
-{
+int rbm_demo(bool /*console*/, int perc) {
     printf("** ArrayFire RBM Demo **\n\n");
 
     array train_images, test_images;
@@ -168,8 +139,8 @@ int rbm_demo(bool /*console*/, int perc)
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<true>(&num_classes, &num_train, &num_test,
-                      train_images, test_images, train_target, test_target, frac);
+    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
+                      test_images, train_target, test_target, frac);
 
     dim4 dims = train_images.dims();
 
@@ -177,7 +148,7 @@ int rbm_demo(bool /*console*/, int perc)
 
     // Reshape images into feature vectors
     array train_feats = moddims(train_images, feature_size, num_train).T();
-    array test_feats  = moddims(test_images , feature_size, num_test ).T();
+    array test_feats  = moddims(test_images, feature_size, num_test).T();
 
     train_target = train_target.T();
     test_target  = test_target.T();
@@ -185,24 +156,23 @@ int rbm_demo(bool /*console*/, int perc)
     rbm network(train_feats.dims(1), 2000);
 
     network.train(train_feats,
-                  0.1, // learning rate
-                  15,  // num epochs
-                  100, // batch size
-                  1,   // k
+                  0.1,  // learning rate
+                  15,   // num epochs
+                  100,  // batch size
+                  1,    // k
                   true);
 
     // Test reconstructed images
     for (int ii = 0; ii < 5; ii++) {
-
         array in = test_feats(ii, span);
         array res, tmp;
 
         network.gibbs_vhv(res, tmp, in);
 
-        in  = moddims(in , dims[0], dims[1]);
+        in  = moddims(in, dims[0], dims[1]);
         res = moddims(res, dims[0], dims[1]);
 
-        in = round(in);
+        in  = round(in);
         res = round(res);
 
         printf("Reconstructed Error for image %2d: %.4f\n", ii,
@@ -212,21 +182,17 @@ int rbm_demo(bool /*console*/, int perc)
     return 0;
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         return rbm_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/machine_learning/softmax_regression.cpp b/examples/machine_learning/softmax_regression.cpp
index 9d8e36d859..452ab8950c 100644
--- a/examples/machine_learning/softmax_regression.cpp
+++ b/examples/machine_learning/softmax_regression.cpp
@@ -8,17 +8,16 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <math.h>
 #include <stdio.h>
-#include <vector>
-#include <string>
 #include <af/util.h>
-#include <math.h>
+#include <string>
+#include <vector>
 #include "mnist_common.h"
 
 using namespace af;
 
-float accuracy(const array& predicted, const array& target)
-{
+float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
     max(val, plabels, predicted, 1);
@@ -26,28 +25,22 @@ float accuracy(const array& predicted, const array& target)
     return 100 * count<float>(plabels == tlabels) / tlabels.elements();
 }
 
-float abserr(const array& predicted, const array& target)
-{
+float abserr(const array &predicted, const array &target) {
     return 100 * sum<float>(abs(predicted - target)) / predicted.elements();
 }
 
-array divide(const array &a, const array &b)
-{
-    return a / b;
-}
+array divide(const array &a, const array &b) { return a / b; }
 
 // Predict based on given parameters
-array predict(const array &X, const array &Weights)
-{
-    array Z = matmul(X, Weights);
-    array EZ = exp(Z);
+array predict(const array &X, const array &Weights) {
+    array Z   = matmul(X, Weights);
+    array EZ  = exp(Z);
     array nrm = sum(EZ, 1);
     return batchFunc(EZ, nrm, divide);
 }
 
-void cost(array &J, array &dJ, const array &Weights,
-          const array &X, const array &Y, double lambda = 1.0)
-{
+void cost(array &J, array &dJ, const array &Weights, const array &X,
+          const array &Y, double lambda = 1.0) {
     // Number of samples
     int m = Y.dims(0);
 
@@ -61,7 +54,7 @@ void cost(array &J, array &dJ, const array &Weights,
     array H = predict(X, Weights);
 
     // Cost of misprediction
-    array Jerr =  -sum(Y * log(H));
+    array Jerr = -sum(Y * log(H));
 
     // Regularization cost
     array Jreg = 0.5 * sum(lambdat * Weights * Weights);
@@ -71,17 +64,12 @@ void cost(array &J, array &dJ, const array &Weights,
 
     // Find the gradient of cost
     array D = (H - Y);
-    dJ = (matmulTN(X, D) + lambdat * Weights) / m;
+    dJ      = (matmulTN(X, D) + lambdat * Weights) / m;
 }
 
-array train(const array &X, const array &Y,
-            double alpha = 0.1,
-            double lambda = 1.0,
-            double maxerr = 0.01,
-            int maxiter = 1000,
-            bool verbose = false)
-{
-
+array train(const array &X, const array &Y, double alpha = 0.1,
+            double lambda = 1.0, double maxerr = 0.01, int maxiter = 1000,
+            bool verbose = false) {
     // Initialize parameters to 0
     array Weights = constant(0, X.dims(1), Y.dims(1));
 
@@ -89,7 +77,6 @@ array train(const array &X, const array &Y,
     float err = 0;
 
     for (int i = 0; i < maxiter; i++) {
-
         // Get the cost and gradient
         cost(J, dJ, Weights, X, Y, lambda);
 
@@ -114,8 +101,7 @@ array train(const array &X, const array &Y,
 
 void benchmark_softmax_regression(const array &train_feats,
                                   const array &train_targets,
-                                  const array test_feats)
-{
+                                  const array test_feats) {
     timer::start();
     array Weights = train(train_feats, train_targets, 0.1, 1.0, 0.01, 1000);
     af::sync();
@@ -124,7 +110,7 @@ void benchmark_softmax_regression(const array &train_feats,
     timer::start();
     const int iter = 100;
     for (int i = 0; i < iter; i++) {
-        array test_outputs  = predict(test_feats , Weights);
+        array test_outputs = predict(test_feats, Weights);
         test_outputs.eval();
     }
     af::sync();
@@ -132,50 +118,49 @@ void benchmark_softmax_regression(const array &train_feats,
 }
 
 // Demo of one vs all logistic regression
-int logit_demo(bool console, int perc)
-{
+int logit_demo(bool console, int perc) {
     array train_images, train_targets;
     array test_images, test_targets;
     int num_train, num_test, num_classes;
 
     // Load mnist data
     float frac = (float)(perc) / 100.0;
-    setup_mnist<true>(&num_classes, &num_train, &num_test,
-                      train_images, test_images,
-                      train_targets, test_targets, frac);
+    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
+                      test_images, train_targets, test_targets, frac);
 
     // Reshape images into feature vectors
     int feature_length = train_images.elements() / num_train;
-    array train_feats = moddims(train_images, feature_length, num_train).T();
-    array test_feats  = moddims(test_images , feature_length, num_test ).T();
+    array train_feats  = moddims(train_images, feature_length, num_train).T();
+    array test_feats   = moddims(test_images, feature_length, num_test).T();
 
     train_targets = train_targets.T();
     test_targets  = test_targets.T();
 
     // Add a bias that is always 1
     train_feats = join(1, constant(1, num_train, 1), train_feats);
-    test_feats  = join(1, constant(1, num_test , 1), test_feats );
+    test_feats  = join(1, constant(1, num_test, 1), test_feats);
 
     // Train logistic regression parameters
-    array Weights = train(train_feats, train_targets,
-                          0.1,  // learning rate (aka alpha)
-                          1.0,  // regularization constant (aka weight decay, aka lamdba)
-                          0.01, // maximum error
-                          1000, // maximum iterations
-                          true);// verbose
+    array Weights =
+        train(train_feats, train_targets,
+              0.1,    // learning rate (aka alpha)
+              1.0,    // regularization constant (aka weight decay, aka lamdba)
+              0.01,   // maximum error
+              1000,   // maximum iterations
+              true);  // verbose
 
     // Predict the results
     array train_outputs = predict(train_feats, Weights);
-    array test_outputs  = predict(test_feats , Weights);
+    array test_outputs  = predict(test_feats, Weights);
 
     printf("Accuracy on training data: %2.2f\n",
-           accuracy(train_outputs, train_targets ));
+           accuracy(train_outputs, train_targets));
 
     printf("Accuracy on testing data: %2.2f\n",
-           accuracy(test_outputs , test_targets ));
+           accuracy(test_outputs, test_targets));
 
     printf("Maximum error on testing data: %2.2f\n",
-           abserr(test_outputs , test_targets ));
+           abserr(test_outputs, test_targets));
 
     benchmark_softmax_regression(train_feats, train_targets, test_feats);
 
@@ -188,21 +173,17 @@ int logit_demo(bool console, int perc)
     return 0;
 }
 
-int main(int argc, char** argv)
-{
+int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
 
     try {
-
         af::setDevice(device);
         af::info();
         return logit_demo(console, perc);
 
-    } catch (af::exception &ae) {
-        std::cerr << ae.what() << std::endl;
-    }
+    } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;
 }
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index bee396a155..c7f9d6ebda 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -1,23 +1,20 @@
+#include <arrayfire.h>
 #include <math.h>
 #include <stdio.h>
-#include <string.h>
 #include <stdlib.h>
-#include <math.h>
-#include <arrayfire.h>
+#include <string.h>
 
 using namespace af;
 
-Window *win;
+Window* win;
 
-array normalize(array a, float max)
-{
+array normalize(array a, float max) {
     float mx = max * 0.5;
     float mn = -max * 0.5;
-    return (a-mn)/(mx-mn);
+    return (a - mn) / (mx - mn);
 }
 
-static void swe(bool console)
-{
+static void swe(bool console) {
     // Grid length, number and spacing
     const unsigned Lx = 1600, nx = Lx + 1;
     const unsigned Ly = 1600, ny = Ly + 1;
@@ -26,81 +23,86 @@ static void swe(bool console)
 
     array ZERO = constant(0, nx, ny);
     array um = ZERO, vm = ZERO;
-    unsigned io = (unsigned)floor(Lx  / 6.0f),
-             jo = (unsigned)floor(Ly / 6.0f),
+    unsigned io = (unsigned)floor(Lx / 6.0f), jo = (unsigned)floor(Ly / 6.0f),
              k = 15;
-    array x = tile(range(nx), 1, ny);
-    array y = tile(range(dim4(1, ny), 1), nx, 1);
+    array x    = tile(range(nx), 1, ny);
+    array y    = tile(range(dim4(1, ny), 1), nx, 1);
 
-    //initial condition
-    array etam  = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+    // initial condition
+    array etam =
+        0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
     float m_eta = max<float>(etam);
     array eta   = etam;
-    float dt = 0.5;
+    float dt    = 0.5;
 
     // conv kernels
     float h_diff_kernel[] = {9.81f * (dt / dx), 0, -9.81f * (dt / dx)};
-    float h_lap_kernel[] = { 0,  1,  0,
-                             1, -4,  1,
-                             0,  1,  0 };
+    float h_lap_kernel[]  = {0, 1, 0, 1, -4, 1, 0, 1, 0};
 
     array h_diff_kernel_arr(3, h_diff_kernel);
     array h_lap_kernel_arr(3, 3, h_lap_kernel);
 
-    if(!console) {
-        win = new Window(1536, 768,"Shallow Water Equations");
+    if (!console) {
+        win = new Window(1536, 768, "Shallow Water Equations");
         win->grid(2, 2);
     }
 
-    unsigned iter = 0;
+    unsigned iter            = 0;
     unsigned random_interval = 30;
 
     while (!win->close()) {
-        if( iter>2000 ) {
+        if (iter > 2000) {
             // Initial condition
-            etam = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+            etam  = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) /
+                               (k * k));
             m_eta = max<float>(etam);
-            eta = etam;
-            iter = 0;
+            eta   = etam;
+            iter  = 0;
         }
 
-        //raindrops
-        if(iter % 100 == 0 || iter % 130 == 0 || iter % random_interval == 0) {
-            unsigned io = (unsigned)floor(rand() % Lx),
-                     jo = (unsigned)floor(rand() % Ly);
+        // raindrops
+        if (iter % 100 == 0 || iter % 130 == 0 || iter % random_interval == 0) {
+            unsigned io     = (unsigned)floor(rand() % Lx),
+                     jo     = (unsigned)floor(rand() % Ly);
             random_interval = rand() % 200 + 1;
-            eta += 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) / (k * k));
+            eta += 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) /
+                               (k * k));
         }
 
         // compute
-        array up = um + convolve(eta, h_diff_kernel_arr);
-        array vp = um + convolve(eta, h_diff_kernel_arr.T());
-        array e = convolve(eta, h_lap_kernel_arr);
+        array up   = um + convolve(eta, h_diff_kernel_arr);
+        array vp   = um + convolve(eta, h_diff_kernel_arr.T());
+        array e    = convolve(eta, h_lap_kernel_arr);
         array etap = 2 * eta - etam + (2 * dt * dt) / (dx * dy) * e;
 
         etam = eta;
-        eta = etap;
+        eta  = etap;
 
         m_eta = max<float>(etam);
         if (!console) {
             (*win)(0, 0).setColorMap(AF_COLORMAP_BLUE);
             array hist_out = histogram(normalize(eta, m_eta), 15);
 
-            (*win)(0, 1).setAxesLimits(0, hist_out.elements(), 0, max<float>(hist_out));
-
-            (*win)(0,0).image(normalize(eta, m_eta));
-            (*win)(0,1).hist(hist_out, 0, 1, "Normalized Pressure Distribution");
-            (*win)(1,0).plot(range(up.dims(1)), vp.col(0), "Pressure at left boundary");
-            (*win)(1,1).plot(flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)), "Gradients versus Magnitude at left boundary"); // viz
+            (*win)(0, 1).setAxesLimits(0, hist_out.elements(), 0,
+                                       max<float>(hist_out));
+
+            (*win)(0, 0).image(normalize(eta, m_eta));
+            (*win)(0, 1).hist(hist_out, 0, 1,
+                              "Normalized Pressure Distribution");
+            (*win)(1, 0).plot(range(up.dims(1)), vp.col(0),
+                              "Pressure at left boundary");
+            (*win)(1, 1).plot(
+                flat(eta.col(0)), flat(up.col(0)), flat(vp.col(0)),
+                "Gradients versus Magnitude at left boundary");  // viz
             win->show();
-        } else eval(eta, up, vp);
+        } else
+            eval(eta, up, vp);
         iter++;
     }
 }
 
-int main(int argc, char* argv[])
-{
-    int device = argc > 1 ? atoi(argv[1]) : 0;
+int main(int argc, char* argv[]) {
+    int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     try {
         af::setDevice(device);
diff --git a/examples/unified/basic.cpp b/examples/unified/basic.cpp
index d573251777..89d4eed1a0 100644
--- a/examples/unified/basic.cpp
+++ b/examples/unified/basic.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <arrayfire.h>
+#include <algorithm>
 #include <cstdio>
 #include <vector>
-#include <algorithm>
 
 using namespace af;
 
@@ -18,13 +18,9 @@ std::vector<float> input(100);
 
 // Generate a random number between 0 and 1
 // return a uniform number in [0,1].
-double unifRand()
-{
-    return rand() / double(RAND_MAX);
-}
+double unifRand() { return rand() / double(RAND_MAX); }
 
-void testBackend()
-{
+void testBackend() {
     af::info();
 
     af::dim4 dims(10, 10, 1, 1);
@@ -36,8 +32,7 @@ void testBackend()
     af_print(B);
 }
 
-int main(int, char **)
-{
+int main(int, char**) {
     std::generate(input.begin(), input.end(), unifRand);
 
     try {
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index 50098ba38f..9b560d28c0 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -7,17 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/image.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
+#include <anisotropic_diffusion.hpp>
+
 #include <arith.hpp>
+#include <backend.hpp>
 #include <cast.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
 #include <gradient.hpp>
+#include <handle.hpp>
 #include <reduce.hpp>
-#include <anisotropic_diffusion.hpp>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 #include <type_traits>
 
@@ -27,16 +28,14 @@ using namespace detail;
 template<typename T>
 af_array diffusion(const Array<float> in, const float dt, const float K,
                    const unsigned iterations, const af_flux_function fftype,
-                   const af::diffusionEq eq)
-{
-    auto out    = copyArray(in);
-    auto dims   = out.dims();
-    auto g0     = createEmptyArray<float>(dims);
-    auto g1     = createEmptyArray<float>(dims);
-    float cnst  = -2.0f*K*K/dims.elements();
-
-    for (unsigned i=0; i<iterations; ++i)
-    {
+                   const af::diffusionEq eq) {
+    auto out   = copyArray(in);
+    auto dims  = out.dims();
+    auto g0    = createEmptyArray<float>(dims);
+    auto g1    = createEmptyArray<float>(dims);
+    float cnst = -2.0f * K * K / dims.elements();
+
+    for (unsigned i = 0; i < iterations; ++i) {
         gradient<float>(g0, g1, out);
 
         auto g0Sqr = arithOp<float, af_mul_t>(g0, g0, dims);
@@ -44,43 +43,48 @@ af_array diffusion(const Array<float> in, const float dt, const float K,
         auto sumd  = arithOp<float, af_add_t>(g0Sqr, g1Sqr, dims);
         float avg  = reduce_all<af_add_t, float, float>(sumd, true, 0);
 
-        anisotropicDiffusion(out, dt, 1.0f/(cnst*avg), fftype, eq);
+        anisotropicDiffusion(out, dt, 1.0f / (cnst * avg), fftype, eq);
     }
 
     return getHandle(cast<T, float>(out));
 }
 
-af_err af_anisotropic_diffusion(af_array* out, const af_array in, const float dt,
-                                const float K, const unsigned iterations,
+af_err af_anisotropic_diffusion(af_array* out, const af_array in,
+                                const float dt, const float K,
+                                const unsigned iterations,
                                 const af_flux_function fftype,
-                                const af_diffusion_eq eq)
-{
+                                const af_diffusion_eq eq) {
     try {
         const ArrayInfo& info = getInfo(in);
 
         const af::dim4& inputDimensions = info.dims();
-        const af_dtype  inputType       = info.getType();
-        const unsigned  inputNumDims    = inputDimensions.ndims();
+        const af_dtype inputType        = info.getType();
+        const unsigned inputNumDims     = inputDimensions.ndims();
 
-        DIM_ASSERT(1, (inputNumDims>=2));
+        DIM_ASSERT(1, (inputNumDims >= 2));
 
-        ARG_ASSERT(3, (K>0 || K<0));
-        ARG_ASSERT(4, (iterations>0));
+        ARG_ASSERT(3, (K > 0 || K < 0));
+        ARG_ASSERT(4, (iterations > 0));
 
-        const af_flux_function F = (fftype==AF_FLUX_DEFAULT ? AF_FLUX_EXPONENTIAL : fftype);
+        const af_flux_function F =
+            (fftype == AF_FLUX_DEFAULT ? AF_FLUX_EXPONENTIAL : fftype);
 
         auto input = castArray<float>(in);
 
         af_array output = 0;
-        switch(inputType) {
-            case f64: output = diffusion<double>(input, dt, K, iterations, F, eq); break;
+        switch (inputType) {
+            case f64:
+                output = diffusion<double>(input, dt, K, iterations, F, eq);
+                break;
             case f32:
             case s32:
             case u32:
             case s16:
             case u16:
-            case u8 : output = diffusion<float>(input, dt, K, iterations, F, eq); break;
-            default : TYPE_ERROR(1, inputType);
+            case u8:
+                output = diffusion<float>(input, dt, K, iterations, F, eq);
+                break;
+            default: TYPE_ERROR(1, inputType);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index f06995b617..321048e5d8 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -7,63 +7,63 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/signal.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
+#include <approx.hpp>
+
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <approx.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
+
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/signal.h>
 
 using af::dim4;
 using namespace detail;
 
 namespace {
-    template<typename Ty, typename Tp>
-    inline void approx1(af_array *yo, const af_array yi,
-                        const af_array xo, const int xdim,
-                        const Tp &xi_beg, const Tp &xi_step,
-                        const af_interp_type method, const float offGrid)
-    {
-        approx1<Ty>(getArray<Ty>(*yo), getArray<Ty>(yi),
-                    getArray<Tp>(xo), xdim,
-                    xi_beg, xi_step,
-                    method, offGrid);
-    }
+template<typename Ty, typename Tp>
+inline void approx1(af_array *yo, const af_array yi, const af_array xo,
+                    const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                    const af_interp_type method, const float offGrid) {
+    approx1<Ty>(getArray<Ty>(*yo), getArray<Ty>(yi), getArray<Tp>(xo), xdim,
+                xi_beg, xi_step, method, offGrid);
 }
+}  // namespace
 
 template<typename Ty, typename Tp>
-static inline af_array approx2(const af_array zi,
-                               const af_array xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                               const af_array yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                               const af_interp_type method, const float offGrid)
-{
-    return getHandle(approx2<Ty>(getArray<Ty>(zi),
-                                 getArray<Tp>(xo), xdim, xi_beg, xi_step,
-                                 getArray<Tp>(yo), ydim, yi_beg, yi_step,
-                                 method, offGrid));
+static inline af_array approx2(const af_array zi, const af_array xo,
+                               const int xdim, const Tp &xi_beg,
+                               const Tp &xi_step, const af_array yo,
+                               const int ydim, const Tp &yi_beg,
+                               const Tp &yi_step, const af_interp_type method,
+                               const float offGrid) {
+    return getHandle(approx2<Ty>(getArray<Ty>(zi), getArray<Tp>(xo), xdim,
+                                 xi_beg, xi_step, getArray<Tp>(yo), ydim,
+                                 yi_beg, yi_step, method, offGrid));
 }
 
-af_err af_approx1_uniform(af_array *yo, const af_array yi,
-                          const af_array xo, const int xdim,
-                          const double xi_beg, const double xi_step,
-                          const af_interp_type method, const float offGrid)
-{
+af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
+                          const int xdim, const double xi_beg,
+                          const double xi_step, const af_interp_type method,
+                          const float offGrid) {
     try {
-        const ArrayInfo& yi_info = getInfo(yi);
-        const ArrayInfo& xo_info = getInfo(xo);
+        const ArrayInfo &yi_info = getInfo(yi);
+        const ArrayInfo &xo_info = getInfo(xo);
 
         const dim4 yi_dims = yi_info.dims();
         const dim4 xo_dims = xo_info.dims();
 
-        ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
-        ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
-        ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle());    // Must have same precision
-        ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble());    // Must have same precision
+        ARG_ASSERT(1, yi_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
+        ARG_ASSERT(1, yi_info.isSingle() ==
+                          xo_info.isSingle());  // Must have same precision
+        ARG_ASSERT(1, yi_info.isDouble() ==
+                          xo_info.isDouble());  // Must have same precision
         ARG_ASSERT(3, xdim >= 0 && xdim < 4);
 
-        // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
+        // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2],
+        // yi_dims[3])
         if (xo_dims[xdim] != xo_dims.elements()) {
             for (int i = 0; i < 4; i++) {
                 if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
@@ -71,40 +71,41 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi,
         }
 
         ARG_ASSERT(5, xi_step != 0);
-        ARG_ASSERT(6, (method == AF_INTERP_CUBIC         ||
-                       method == AF_INTERP_CUBIC_SPLINE  ||
-                       method == AF_INTERP_LINEAR        ||
-                       method == AF_INTERP_LINEAR_COSINE ||
-                       method == AF_INTERP_LOWER         ||
-                       method == AF_INTERP_NEAREST));
-
-        if (yi_dims.ndims() == 0 || xo_dims.ndims() ==  0) {
-            *yo = createHandle(dim4(0,0,0,0), yi_info.getType());
+        ARG_ASSERT(
+            6,
+            (method == AF_INTERP_CUBIC || method == AF_INTERP_CUBIC_SPLINE ||
+             method == AF_INTERP_LINEAR || method == AF_INTERP_LINEAR_COSINE ||
+             method == AF_INTERP_LOWER || method == AF_INTERP_NEAREST));
+
+        if (yi_dims.ndims() == 0 || xo_dims.ndims() == 0) {
+            *yo = createHandle(dim4(0, 0, 0, 0), yi_info.getType());
             return AF_SUCCESS;
         }
 
-        dim4 yo_dims = yi_dims;
+        dim4 yo_dims  = yi_dims;
         yo_dims[xdim] = xo_dims[xdim];
-        if (*yo == 0) {
-            *yo = createHandle(yo_dims, yi_info.getType());
-        }
+        if (*yo == 0) { *yo = createHandle(yo_dims, yi_info.getType()); }
 
         DIM_ASSERT(1, getInfo(*yo).dims() == yo_dims);
 
-        switch(yi_info.getType()) {
-        case f32: approx1<float  , float >(yo, yi, xo, xdim,
-                                           xi_beg, xi_step,
-                                           method, offGrid);  break;
-        case f64: approx1<double , double>(yo, yi, xo, xdim,
-                                           xi_beg, xi_step,
-                                           method, offGrid);  break;
-        case c32: approx1<cfloat , float >(yo, yi, xo, xdim,
-                                           xi_beg, xi_step,
-                                           method, offGrid);  break;
-        case c64: approx1<cdouble, double>(yo, yi, xo, xdim,
-                                                    xi_beg, xi_step,
-                                                    method, offGrid);  break;
-        default:  TYPE_ERROR(1, yi_info.getType());
+        switch (yi_info.getType()) {
+            case f32:
+                approx1<float, float>(yo, yi, xo, xdim, xi_beg, xi_step, method,
+                                      offGrid);
+                break;
+            case f64:
+                approx1<double, double>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                        method, offGrid);
+                break;
+            case c32:
+                approx1<cfloat, float>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       method, offGrid);
+                break;
+            case c64:
+                approx1<cdouble, double>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                         method, offGrid);
+                break;
+            default: TYPE_ERROR(1, yi_info.getType());
         }
     }
     CATCHALL;
@@ -112,34 +113,36 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi,
     return AF_SUCCESS;
 }
 
-
 af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
-                  const af_interp_type method, const float offGrid)
-{
-  return af_approx1_uniform(yo, yi, xo, 0, 0.0, 1.0, method, offGrid);
+                  const af_interp_type method, const float offGrid) {
+    return af_approx1_uniform(yo, yi, xo, 0, 0.0, 1.0, method, offGrid);
 }
 
-af_err af_approx2_uniform(af_array *zo, const af_array zi,
-                          const af_array xo, const int xdim, const double xi_beg, const double xi_step,
-                          const af_array yo, const int ydim, const double yi_beg, const double yi_step,
-                          const af_interp_type method, const float offGrid)
-{
+af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
+                          const int xdim, const double xi_beg,
+                          const double xi_step, const af_array yo,
+                          const int ydim, const double yi_beg,
+                          const double yi_step, const af_interp_type method,
+                          const float offGrid) {
     try {
-        const ArrayInfo& zi_info = getInfo(zi);
-        const ArrayInfo& xo_info = getInfo(xo);
-        const ArrayInfo& yo_info = getInfo(yo);
+        const ArrayInfo &zi_info = getInfo(zi);
+        const ArrayInfo &xo_info = getInfo(xo);
+        const ArrayInfo &yo_info = getInfo(yo);
 
         dim4 zi_dims = zi_info.dims();
         dim4 xo_dims = xo_info.dims();
         dim4 yo_dims = yo_info.dims();
 
-        ARG_ASSERT(1, zi_info.isFloating());                     // Only floating and complex types
-        ARG_ASSERT(2, xo_info.isRealFloating());                 // Only floating types
-        ARG_ASSERT(4, yo_info.isRealFloating());                 // Only floating types
-        ARG_ASSERT(2, xo_info.getType() == yo_info.getType());    // Must have same type
-        ARG_ASSERT(1, zi_info.isSingle() == xo_info.isSingle());  // Must have same precision
-        ARG_ASSERT(1, zi_info.isDouble() == xo_info.isDouble());  // Must have same precision
-        DIM_ASSERT(2, xo_dims == yo_dims);                        // POS0 and POS1 must have same dims
+        ARG_ASSERT(1, zi_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
+        ARG_ASSERT(4, yo_info.isRealFloating());  // Only floating types
+        ARG_ASSERT(
+            2, xo_info.getType() == yo_info.getType());  // Must have same type
+        ARG_ASSERT(1, zi_info.isSingle() ==
+                          xo_info.isSingle());  // Must have same precision
+        ARG_ASSERT(1, zi_info.isDouble() ==
+                          xo_info.isDouble());  // Must have same precision
+        DIM_ASSERT(2, xo_dims == yo_dims);  // POS0 and POS1 must have same dims
 
         ARG_ASSERT(3, xdim >= 0 && xdim < 4);
         ARG_ASSERT(5, ydim >= 0 && ydim < 4);
@@ -149,34 +152,40 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi,
         // POS should either be (x, y, 1, 1) or (x, y, zi_dims[2], zi_dims[3])
         if (xo_dims[xdim] * xo_dims[ydim] != xo_dims.elements()) {
             for (int i = 0; i < 4; i++) {
-                if (xdim != i && ydim != i) DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
+                if (xdim != i && ydim != i)
+                    DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
             }
         }
 
-        if (zi_dims.ndims() == 0 || xo_dims.ndims() ==  0 || yo_dims.ndims() == 0) {
+        if (zi_dims.ndims() == 0 || xo_dims.ndims() == 0 ||
+            yo_dims.ndims() == 0) {
             return af_create_handle(zo, 0, nullptr, zi_info.getType());
         }
 
         af_array output;
 
-        switch(zi_info.getType()) {
-        case f32: output = approx2<float  , float >(zi,
-                                                    xo, xdim, xi_beg, xi_step,
-                                                    yo, ydim, yi_beg, yi_step,
-                                                    method, offGrid);  break;
-        case f64: output = approx2<double , double>(zi,
-                                                    xo, xdim, xi_beg, xi_step,
-                                                    yo, ydim, yi_beg, yi_step,
-                                                    method, offGrid);  break;
-        case c32: output = approx2<cfloat , float >(zi,
-                                                    xo, xdim, xi_beg, xi_step,
-                                                    yo, ydim, yi_beg, yi_step,
-                                                    method, offGrid);  break;
-        case c64: output = approx2<cdouble, double>(zi,
-                                                    xo, xdim, xi_beg, xi_step,
-                                                    yo, ydim, yi_beg, yi_step,
-                                                    method, offGrid);  break;
-        default:  TYPE_ERROR(1, zi_info.getType());
+        switch (zi_info.getType()) {
+            case f32:
+                output = approx2<float, float>(zi, xo, xdim, xi_beg, xi_step,
+                                               yo, ydim, yi_beg, yi_step,
+                                               method, offGrid);
+                break;
+            case f64:
+                output = approx2<double, double>(zi, xo, xdim, xi_beg, xi_step,
+                                                 yo, ydim, yi_beg, yi_step,
+                                                 method, offGrid);
+                break;
+            case c32:
+                output = approx2<cfloat, float>(zi, xo, xdim, xi_beg, xi_step,
+                                                yo, ydim, yi_beg, yi_step,
+                                                method, offGrid);
+                break;
+            case c64:
+                output = approx2<cdouble, double>(zi, xo, xdim, xi_beg, xi_step,
+                                                  yo, ydim, yi_beg, yi_step,
+                                                  method, offGrid);
+                break;
+            default: TYPE_ERROR(1, zi_info.getType());
         }
         std::swap(*zo, output);
     }
@@ -185,9 +194,9 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi,
     return AF_SUCCESS;
 }
 
-af_err af_approx2(af_array *zo, const af_array zi, const af_array xo, const af_array yo,
-                  const af_interp_type method, const float offGrid)
-{
-  return af_approx2_uniform(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method, offGrid);
+af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
+                  const af_array yo, const af_interp_type method,
+                  const float offGrid) {
+    return af_approx2_uniform(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method,
+                              offGrid);
 }
-
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 0124928dda..c5d402004b 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -6,12 +6,11 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <handle.hpp>
-#include <common/ArrayInfo.hpp>
-#include <platform.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
+#include <common/ArrayInfo.hpp>
 #include <copy.hpp>
+#include <handle.hpp>
+#include <platform.hpp>
 #include <sparse.hpp>
 #include <sparse_handle.hpp>
 #include <af/sparse.h>
@@ -19,97 +18,118 @@
 using namespace detail;
 using common::SparseArrayBase;
 
-af_array createHandle(af::dim4 d, af_dtype dtype)
-{
+af_array createHandle(af::dim4 d, af_dtype dtype) {
     using namespace detail;
 
-    switch(dtype) {
-        case f32: return createHandle<float  >(d);
-        case c32: return createHandle<cfloat >(d);
-        case f64: return createHandle<double >(d);
+    switch (dtype) {
+        case f32: return createHandle<float>(d);
+        case c32: return createHandle<cfloat>(d);
+        case f64: return createHandle<double>(d);
         case c64: return createHandle<cdouble>(d);
-        case b8:  return createHandle<char   >(d);
-        case s32: return createHandle<int    >(d);
-        case u32: return createHandle<uint   >(d);
-        case u8:  return createHandle<uchar  >(d);
-        case s64: return createHandle<intl   >(d);
-        case u64: return createHandle<uintl  >(d);
-        case s16: return createHandle<short  >(d);
-        case u16: return createHandle<ushort >(d);
-        default:    TYPE_ERROR(3, dtype);
+        case b8: return createHandle<char>(d);
+        case s32: return createHandle<int>(d);
+        case u32: return createHandle<uint>(d);
+        case u8: return createHandle<uchar>(d);
+        case s64: return createHandle<intl>(d);
+        case u64: return createHandle<uintl>(d);
+        case s16: return createHandle<short>(d);
+        case u16: return createHandle<ushort>(d);
+        default: TYPE_ERROR(3, dtype);
     }
 }
 
-af_err af_get_data_ptr(void *data, const af_array arr)
-{
+af_err af_get_data_ptr(void *data, const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
-        switch(type) {
-        case f32:   copyData(static_cast<float    *>(data), arr);  break;
-        case c32:   copyData(static_cast<cfloat   *>(data), arr);  break;
-        case f64:   copyData(static_cast<double   *>(data), arr);  break;
-        case c64:   copyData(static_cast<cdouble  *>(data), arr);  break;
-        case b8:    copyData(static_cast<char     *>(data), arr);  break;
-        case s32:   copyData(static_cast<int      *>(data), arr);  break;
-        case u32:   copyData(static_cast<unsigned *>(data), arr);  break;
-        case u8:    copyData(static_cast<uchar    *>(data), arr);  break;
-        case s64:   copyData(static_cast<intl     *>(data), arr);  break;
-        case u64:   copyData(static_cast<uintl    *>(data), arr);  break;
-        case s16:   copyData(static_cast<short    *>(data), arr);  break;
-        case u16:   copyData(static_cast<ushort   *>(data), arr);  break;
-        default:    TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: copyData(static_cast<float *>(data), arr); break;
+            case c32: copyData(static_cast<cfloat *>(data), arr); break;
+            case f64: copyData(static_cast<double *>(data), arr); break;
+            case c64: copyData(static_cast<cdouble *>(data), arr); break;
+            case b8: copyData(static_cast<char *>(data), arr); break;
+            case s32: copyData(static_cast<int *>(data), arr); break;
+            case u32: copyData(static_cast<unsigned *>(data), arr); break;
+            case u8: copyData(static_cast<uchar *>(data), arr); break;
+            case s64: copyData(static_cast<intl *>(data), arr); break;
+            case u64: copyData(static_cast<uintl *>(data), arr); break;
+            case s16: copyData(static_cast<short *>(data), arr); break;
+            case u16: copyData(static_cast<ushort *>(data), arr); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
-//Strong Exception Guarantee
-af_err af_create_array(af_array *result, const void * const data,
-                       const unsigned ndims, const dim_t * const dims,
-                       const af_dtype type)
-{
+// Strong Exception Guarantee
+af_err af_create_array(af_array *result, const void *const data,
+                       const unsigned ndims, const dim_t *const dims,
+                       const af_dtype type) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
         dim4 d = verifyDims(ndims, dims);
 
-        switch(type) {
-        case f32:   out = createHandleFromData(d, static_cast<const float   *>(data)); break;
-        case c32:   out = createHandleFromData(d, static_cast<const cfloat  *>(data)); break;
-        case f64:   out = createHandleFromData(d, static_cast<const double  *>(data)); break;
-        case c64:   out = createHandleFromData(d, static_cast<const cdouble *>(data)); break;
-        case b8:    out = createHandleFromData(d, static_cast<const char    *>(data)); break;
-        case s32:   out = createHandleFromData(d, static_cast<const int     *>(data)); break;
-        case u32:   out = createHandleFromData(d, static_cast<const uint    *>(data)); break;
-        case u8:    out = createHandleFromData(d, static_cast<const uchar   *>(data)); break;
-        case s64:   out = createHandleFromData(d, static_cast<const intl    *>(data)); break;
-        case u64:   out = createHandleFromData(d, static_cast<const uintl   *>(data)); break;
-        case s16:   out = createHandleFromData(d, static_cast<const short   *>(data)); break;
-        case u16:   out = createHandleFromData(d, static_cast<const ushort  *>(data)); break;
-        default:    TYPE_ERROR(4, type);
+        switch (type) {
+            case f32:
+                out = createHandleFromData(d, static_cast<const float *>(data));
+                break;
+            case c32:
+                out =
+                    createHandleFromData(d, static_cast<const cfloat *>(data));
+                break;
+            case f64:
+                out =
+                    createHandleFromData(d, static_cast<const double *>(data));
+                break;
+            case c64:
+                out =
+                    createHandleFromData(d, static_cast<const cdouble *>(data));
+                break;
+            case b8:
+                out = createHandleFromData(d, static_cast<const char *>(data));
+                break;
+            case s32:
+                out = createHandleFromData(d, static_cast<const int *>(data));
+                break;
+            case u32:
+                out = createHandleFromData(d, static_cast<const uint *>(data));
+                break;
+            case u8:
+                out = createHandleFromData(d, static_cast<const uchar *>(data));
+                break;
+            case s64:
+                out = createHandleFromData(d, static_cast<const intl *>(data));
+                break;
+            case u64:
+                out = createHandleFromData(d, static_cast<const uintl *>(data));
+                break;
+            case s16:
+                out = createHandleFromData(d, static_cast<const short *>(data));
+                break;
+            case u16:
+                out =
+                    createHandleFromData(d, static_cast<const ushort *>(data));
+                break;
+            default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
-//Strong Exception Guarantee
-af_err af_create_handle(af_array *result,
-                        const unsigned ndims, const dim_t * const dims,
-                        const af_dtype type)
-{
+// Strong Exception Guarantee
+af_err af_create_handle(af_array *result, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype type) {
     try {
         AF_CHECK(af_init());
 
         if (ndims > 0) ARG_ASSERT(2, ndims > 0 && dims != NULL);
 
         dim4 d(0);
-        for(unsigned i = 0; i < ndims; i++) {
-            d[i] = dims[i];
-        }
+        for (unsigned i = 0; i < ndims; i++) { d[i] = dims[i]; }
 
         af_array out = createHandle(d, type);
         std::swap(*result, out);
@@ -118,48 +138,46 @@ af_err af_create_handle(af_array *result,
     return AF_SUCCESS;
 }
 
-//Strong Exception Guarantee
-af_err af_copy_array(af_array *out, const af_array in)
-{
+// Strong Exception Guarantee
+af_err af_copy_array(af_array *out, const af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in, false);
-        const af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in, false);
+        const af_dtype type   = info.getType();
 
         af_array res = 0;
-        if(info.isSparse()) {
+        if (info.isSparse()) {
             SparseArrayBase sbase = getSparseArrayBase(in);
-            if(info.ndims() == 0) {
-                return af_create_sparse_array_from_ptr(out,
-                                               info.dims()[0], info.dims()[1],
-                                                0, nullptr, nullptr, nullptr,
-                                                type, sbase.getStorage(), afDevice);
+            if (info.ndims() == 0) {
+                return af_create_sparse_array_from_ptr(
+                    out, info.dims()[0], info.dims()[1], 0, nullptr, nullptr,
+                    nullptr, type, sbase.getStorage(), afDevice);
             } else {
-                switch(type) {
-                case f32: res = copySparseArray<float  >(in); break;
-                case f64: res = copySparseArray<double >(in); break;
-                case c32: res = copySparseArray<cfloat >(in); break;
-                case c64: res = copySparseArray<cdouble>(in); break;
-                default : TYPE_ERROR(0, type);
+                switch (type) {
+                    case f32: res = copySparseArray<float>(in); break;
+                    case f64: res = copySparseArray<double>(in); break;
+                    case c32: res = copySparseArray<cfloat>(in); break;
+                    case c64: res = copySparseArray<cdouble>(in); break;
+                    default: TYPE_ERROR(0, type);
                 }
             }
         } else {
-            if(info.ndims() == 0) {
+            if (info.ndims() == 0) {
                 return af_create_handle(out, 0, nullptr, type);
             } else {
-                  switch(type) {
-                  case f32:   res = copyArray<float   >(in); break;
-                  case c32:   res = copyArray<cfloat  >(in); break;
-                  case f64:   res = copyArray<double  >(in); break;
-                  case c64:   res = copyArray<cdouble >(in); break;
-                  case b8:    res = copyArray<char    >(in); break;
-                  case s32:   res = copyArray<int     >(in); break;
-                  case u32:   res = copyArray<uint    >(in); break;
-                  case u8:    res = copyArray<uchar   >(in); break;
-                  case s64:   res = copyArray<intl    >(in); break;
-                  case u64:   res = copyArray<uintl   >(in); break;
-                  case s16:   res = copyArray<short   >(in); break;
-                  case u16:   res = copyArray<ushort  >(in); break;
-                  default:    TYPE_ERROR(1, type);
+                switch (type) {
+                    case f32: res = copyArray<float>(in); break;
+                    case c32: res = copyArray<cfloat>(in); break;
+                    case f64: res = copyArray<double>(in); break;
+                    case c64: res = copyArray<cdouble>(in); break;
+                    case b8: res = copyArray<char>(in); break;
+                    case s32: res = copyArray<int>(in); break;
+                    case u32: res = copyArray<uint>(in); break;
+                    case u8: res = copyArray<uchar>(in); break;
+                    case s64: res = copyArray<intl>(in); break;
+                    case u64: res = copyArray<uintl>(in); break;
+                    case s16: res = copyArray<short>(in); break;
+                    case u16: res = copyArray<ushort>(in); break;
+                    default: TYPE_ERROR(1, type);
                 }
             }
         }
@@ -169,28 +187,27 @@ af_err af_copy_array(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-//Strong Exception Guarantee
-af_err af_get_data_ref_count(int *use_count, const af_array in)
-{
+// Strong Exception Guarantee
+af_err af_get_data_ref_count(int *use_count, const af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in, false, false);
-        const af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in, false, false);
+        const af_dtype type   = info.getType();
 
         int res;
-        switch(type) {
-        case f32:   res = getArray<float   >(in).useCount(); break;
-        case c32:   res = getArray<cfloat  >(in).useCount(); break;
-        case f64:   res = getArray<double  >(in).useCount(); break;
-        case c64:   res = getArray<cdouble >(in).useCount(); break;
-        case b8:    res = getArray<char    >(in).useCount(); break;
-        case s32:   res = getArray<int     >(in).useCount(); break;
-        case u32:   res = getArray<uint    >(in).useCount(); break;
-        case u8:    res = getArray<uchar   >(in).useCount(); break;
-        case s64:   res = getArray<intl    >(in).useCount(); break;
-        case u64:   res = getArray<uintl   >(in).useCount(); break;
-        case s16:   res = getArray<short   >(in).useCount(); break;
-        case u16:   res = getArray<ushort  >(in).useCount(); break;
-        default:    TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: res = getArray<float>(in).useCount(); break;
+            case c32: res = getArray<cfloat>(in).useCount(); break;
+            case f64: res = getArray<double>(in).useCount(); break;
+            case c64: res = getArray<cdouble>(in).useCount(); break;
+            case b8: res = getArray<char>(in).useCount(); break;
+            case s32: res = getArray<int>(in).useCount(); break;
+            case u32: res = getArray<uint>(in).useCount(); break;
+            case u8: res = getArray<uchar>(in).useCount(); break;
+            case s64: res = getArray<intl>(in).useCount(); break;
+            case u64: res = getArray<uintl>(in).useCount(); break;
+            case s16: res = getArray<short>(in).useCount(); break;
+            case u16: res = getArray<ushort>(in).useCount(); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*use_count, res);
     }
@@ -198,35 +215,34 @@ af_err af_get_data_ref_count(int *use_count, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_release_array(af_array arr)
-{
+af_err af_release_array(af_array arr) {
     try {
-        const ArrayInfo& info = getInfo(arr, false, false);
-        af_dtype type = info.getType();
-
-        if(info.isSparse()) {
-            switch(type) {
-                case f32: releaseSparseHandle<float  >(arr); break;
-                case f64: releaseSparseHandle<double >(arr); break;
-                case c32: releaseSparseHandle<cfloat >(arr); break;
+        const ArrayInfo &info = getInfo(arr, false, false);
+        af_dtype type         = info.getType();
+
+        if (info.isSparse()) {
+            switch (type) {
+                case f32: releaseSparseHandle<float>(arr); break;
+                case f64: releaseSparseHandle<double>(arr); break;
+                case c32: releaseSparseHandle<cfloat>(arr); break;
                 case c64: releaseSparseHandle<cdouble>(arr); break;
-                default : TYPE_ERROR(0, type);
+                default: TYPE_ERROR(0, type);
             }
         } else {
-            switch(type) {
-            case f32:   releaseHandle<float   >(arr); break;
-            case c32:   releaseHandle<cfloat  >(arr); break;
-            case f64:   releaseHandle<double  >(arr); break;
-            case c64:   releaseHandle<cdouble >(arr); break;
-            case b8:    releaseHandle<char    >(arr); break;
-            case s32:   releaseHandle<int     >(arr); break;
-            case u32:   releaseHandle<uint    >(arr); break;
-            case u8:    releaseHandle<uchar   >(arr); break;
-            case s64:   releaseHandle<intl    >(arr); break;
-            case u64:   releaseHandle<uintl   >(arr); break;
-            case s16:   releaseHandle<short   >(arr); break;
-            case u16:   releaseHandle<ushort  >(arr); break;
-            default:    TYPE_ERROR(0, type);
+            switch (type) {
+                case f32: releaseHandle<float>(arr); break;
+                case c32: releaseHandle<cfloat>(arr); break;
+                case f64: releaseHandle<double>(arr); break;
+                case c64: releaseHandle<cdouble>(arr); break;
+                case b8: releaseHandle<char>(arr); break;
+                case s32: releaseHandle<int>(arr); break;
+                case u32: releaseHandle<uint>(arr); break;
+                case u8: releaseHandle<uchar>(arr); break;
+                case s64: releaseHandle<intl>(arr); break;
+                case u64: releaseHandle<uintl>(arr); break;
+                case s16: releaseHandle<short>(arr); break;
+                case u16: releaseHandle<ushort>(arr); break;
+                default: TYPE_ERROR(0, type);
             }
         }
     }
@@ -235,40 +251,38 @@ af_err af_release_array(af_array arr)
     return AF_SUCCESS;
 }
 
-af_array retain(const af_array in)
-{
-    const ArrayInfo& info = getInfo(in, false, false);
-    af_dtype ty = info.getType();
-
-    if(info.isSparse()) {
-        switch(ty) {
-        case f32: return retainSparseHandle<float          >(in);
-        case f64: return retainSparseHandle<double         >(in);
-        case c32: return retainSparseHandle<detail::cfloat >(in);
-        case c64: return retainSparseHandle<detail::cdouble>(in);
-        default: TYPE_ERROR(1, ty);
+af_array retain(const af_array in) {
+    const ArrayInfo &info = getInfo(in, false, false);
+    af_dtype ty           = info.getType();
+
+    if (info.isSparse()) {
+        switch (ty) {
+            case f32: return retainSparseHandle<float>(in);
+            case f64: return retainSparseHandle<double>(in);
+            case c32: return retainSparseHandle<detail::cfloat>(in);
+            case c64: return retainSparseHandle<detail::cdouble>(in);
+            default: TYPE_ERROR(1, ty);
         }
     } else {
-        switch(ty) {
-        case f32: return retainHandle<float           >(in);
-        case f64: return retainHandle<double          >(in);
-        case s32: return retainHandle<int             >(in);
-        case u32: return retainHandle<uint            >(in);
-        case u8:  return retainHandle<uchar           >(in);
-        case c32: return retainHandle<detail::cfloat  >(in);
-        case c64: return retainHandle<detail::cdouble >(in);
-        case b8:  return retainHandle<char            >(in);
-        case s64: return retainHandle<intl            >(in);
-        case u64: return retainHandle<uintl           >(in);
-        case s16: return retainHandle<short           >(in);
-        case u16: return retainHandle<ushort          >(in);
-        default: TYPE_ERROR(1, ty);
+        switch (ty) {
+            case f32: return retainHandle<float>(in);
+            case f64: return retainHandle<double>(in);
+            case s32: return retainHandle<int>(in);
+            case u32: return retainHandle<uint>(in);
+            case u8: return retainHandle<uchar>(in);
+            case c32: return retainHandle<detail::cfloat>(in);
+            case c64: return retainHandle<detail::cdouble>(in);
+            case b8: return retainHandle<char>(in);
+            case s64: return retainHandle<intl>(in);
+            case u64: return retainHandle<uintl>(in);
+            case s16: return retainHandle<short>(in);
+            case u16: return retainHandle<ushort>(in);
+            default: TYPE_ERROR(1, ty);
         }
     }
 }
 
-af_err af_retain_array(af_array *out, const af_array in)
-{
+af_err af_retain_array(af_array *out, const af_array in) {
     try {
         *out = retain(in);
     }
@@ -277,9 +291,9 @@ af_err af_retain_array(af_array *out, const af_array in)
 }
 
 template<typename T>
-void write_array(af_array arr, const T * const data, const size_t bytes, af_source src)
-{
-    if(src == afHost) {
+void write_array(af_array arr, const T *const data, const size_t bytes,
+                 af_source src) {
+    if (src == afHost) {
         writeHostDataArray(getArray<T>(arr), data, bytes);
     } else {
         writeDeviceDataArray(getArray<T>(arr), data, bytes);
@@ -287,134 +301,181 @@ void write_array(af_array arr, const T * const data, const size_t bytes, af_sour
     return;
 }
 
-af_err af_write_array(af_array arr, const void *data, const size_t bytes, af_source src)
-{
+af_err af_write_array(af_array arr, const void *data, const size_t bytes,
+                      af_source src) {
     try {
         af_dtype type = getInfo(arr).getType();
-        //DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
-
-        switch(type) {
-        case f32:   write_array(arr, static_cast<const float   *>(data), bytes, src); break;
-        case c32:   write_array(arr, static_cast<const cfloat  *>(data), bytes, src); break;
-        case f64:   write_array(arr, static_cast<const double  *>(data), bytes, src); break;
-        case c64:   write_array(arr, static_cast<const cdouble *>(data), bytes, src); break;
-        case b8:    write_array(arr, static_cast<const char    *>(data), bytes, src); break;
-        case s32:   write_array(arr, static_cast<const int     *>(data), bytes, src); break;
-        case u32:   write_array(arr, static_cast<const uint    *>(data), bytes, src); break;
-        case u8:    write_array(arr, static_cast<const uchar   *>(data), bytes, src); break;
-        case s64:   write_array(arr, static_cast<const intl    *>(data), bytes, src); break;
-        case u64:   write_array(arr, static_cast<const uintl   *>(data), bytes, src); break;
-        case s16:   write_array(arr, static_cast<const short   *>(data), bytes, src); break;
-        case u16:   write_array(arr, static_cast<const ushort  *>(data), bytes, src); break;
-        default:    TYPE_ERROR(4, type);
+        // DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
+
+        switch (type) {
+            case f32:
+                write_array(arr, static_cast<const float *>(data), bytes, src);
+                break;
+            case c32:
+                write_array(arr, static_cast<const cfloat *>(data), bytes, src);
+                break;
+            case f64:
+                write_array(arr, static_cast<const double *>(data), bytes, src);
+                break;
+            case c64:
+                write_array(arr, static_cast<const cdouble *>(data), bytes,
+                            src);
+                break;
+            case b8:
+                write_array(arr, static_cast<const char *>(data), bytes, src);
+                break;
+            case s32:
+                write_array(arr, static_cast<const int *>(data), bytes, src);
+                break;
+            case u32:
+                write_array(arr, static_cast<const uint *>(data), bytes, src);
+                break;
+            case u8:
+                write_array(arr, static_cast<const uchar *>(data), bytes, src);
+                break;
+            case s64:
+                write_array(arr, static_cast<const intl *>(data), bytes, src);
+                break;
+            case u64:
+                write_array(arr, static_cast<const uintl *>(data), bytes, src);
+                break;
+            case s16:
+                write_array(arr, static_cast<const short *>(data), bytes, src);
+                break;
+            case u16:
+                write_array(arr, static_cast<const ushort *>(data), bytes, src);
+                break;
+            default: TYPE_ERROR(4, type);
         }
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
-af_err af_get_elements(dim_t *elems, const af_array arr)
-{
+af_err af_get_elements(dim_t *elems, const af_array arr) {
     try {
         // Do not check for device mismatch
-        *elems =  getInfo(arr, false, false).elements();
-    } CATCHALL
+        *elems = getInfo(arr, false, false).elements();
+    }
+    CATCHALL
     return AF_SUCCESS;
 }
 
-af_err af_get_type(af_dtype *type, const af_array arr)
-{
+af_err af_get_type(af_dtype *type, const af_array arr) {
     try {
         // Do not check for device mismatch
         *type = getInfo(arr, false, false).getType();
-    } CATCHALL
+    }
+    CATCHALL
     return AF_SUCCESS;
 }
 
 af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
-                   const af_array in)
-{
+                   const af_array in) {
     try {
         // Do not check for device mismatch
-        const ArrayInfo& info = getInfo(in, false, false);
-        *d0 = info.dims()[0];
-        *d1 = info.dims()[1];
-        *d2 = info.dims()[2];
-        *d3 = info.dims()[3];
+        const ArrayInfo &info = getInfo(in, false, false);
+        *d0                   = info.dims()[0];
+        *d1                   = info.dims()[1];
+        *d2                   = info.dims()[2];
+        *d3                   = info.dims()[3];
     }
     CATCHALL
     return AF_SUCCESS;
 }
 
-af_err af_get_numdims(unsigned *nd, const af_array in)
-{
+af_err af_get_numdims(unsigned *nd, const af_array in) {
     try {
         // Do not check for device mismatch
-        const ArrayInfo& info = getInfo(in, false, false);
-        *nd = info.ndims();
+        const ArrayInfo &info = getInfo(in, false, false);
+        *nd                   = info.ndims();
     }
     CATCHALL
     return AF_SUCCESS;
 }
 
-
 #undef INSTANTIATE
-#define INSTANTIATE(fn1, fn2)                                   \
-    af_err fn1(bool *result, const af_array in)                 \
-    {                                                           \
-        try {                                                   \
-            const ArrayInfo& info = getInfo(in, false, false);  \
-            *result = info.fn2();                               \
-        }                                                       \
-        CATCHALL                                                \
-            return AF_SUCCESS;                                  \
+#define INSTANTIATE(fn1, fn2)                                  \
+    af_err fn1(bool *result, const af_array in) {              \
+        try {                                                  \
+            const ArrayInfo &info = getInfo(in, false, false); \
+            *result               = info.fn2();                \
+        }                                                      \
+        CATCHALL                                               \
+        return AF_SUCCESS;                                     \
     }
 
-INSTANTIATE(af_is_empty       , isEmpty       )
-INSTANTIATE(af_is_scalar      , isScalar      )
-INSTANTIATE(af_is_row         , isRow         )
-INSTANTIATE(af_is_column      , isColumn      )
-INSTANTIATE(af_is_vector      , isVector      )
-INSTANTIATE(af_is_complex     , isComplex     )
-INSTANTIATE(af_is_real        , isReal        )
-INSTANTIATE(af_is_double      , isDouble      )
-INSTANTIATE(af_is_single      , isSingle      )
+INSTANTIATE(af_is_empty, isEmpty)
+INSTANTIATE(af_is_scalar, isScalar)
+INSTANTIATE(af_is_row, isRow)
+INSTANTIATE(af_is_column, isColumn)
+INSTANTIATE(af_is_vector, isVector)
+INSTANTIATE(af_is_complex, isComplex)
+INSTANTIATE(af_is_real, isReal)
+INSTANTIATE(af_is_double, isDouble)
+INSTANTIATE(af_is_single, isSingle)
 INSTANTIATE(af_is_realfloating, isRealFloating)
-INSTANTIATE(af_is_floating    , isFloating    )
-INSTANTIATE(af_is_integer     , isInteger     )
-INSTANTIATE(af_is_bool        , isBool        )
-INSTANTIATE(af_is_sparse      , isSparse      )
+INSTANTIATE(af_is_floating, isFloating)
+INSTANTIATE(af_is_integer, isInteger)
+INSTANTIATE(af_is_bool, isBool)
+INSTANTIATE(af_is_sparse, isSparse)
 
 #undef INSTANTIATE
 
 template<typename T>
-inline void getScalar(T* out, const af_array& arr)
-{
+inline void getScalar(T *out, const af_array &arr) {
     out[0] = getScalar<T>(getArray<T>(arr));
 }
 
-af_err af_get_scalar(void* output_value, const af_array arr)
-{
+af_err af_get_scalar(void *output_value, const af_array arr) {
     try {
-        ARG_ASSERT(0, (output_value!=NULL));
-
-        const ArrayInfo& info   = getInfo(arr);
-        const af_dtype type     = info.getType();
-
-        switch(type) {
-        case f32: getScalar<float  >(reinterpret_cast<float*  >(output_value), arr); break;
-        case f64: getScalar<double >(reinterpret_cast<double* >(output_value), arr); break;
-        case  b8: getScalar<char   >(reinterpret_cast<char*   >(output_value), arr); break;
-        case s32: getScalar<int    >(reinterpret_cast<int*    >(output_value), arr); break;
-        case u32: getScalar<uint   >(reinterpret_cast<uint*   >(output_value), arr); break;
-        case  u8: getScalar<uchar  >(reinterpret_cast<uchar*  >(output_value), arr); break;
-        case s64: getScalar<intl   >(reinterpret_cast<intl*   >(output_value), arr); break;
-        case u64: getScalar<uintl  >(reinterpret_cast<uintl*  >(output_value), arr); break;
-        case s16: getScalar<short  >(reinterpret_cast<short*  >(output_value), arr); break;
-        case u16: getScalar<ushort >(reinterpret_cast<ushort* >(output_value), arr); break;
-        case c32: getScalar<cfloat >(reinterpret_cast<cfloat* >(output_value), arr); break;
-        case c64: getScalar<cdouble>(reinterpret_cast<cdouble*>(output_value), arr); break;
-        default:    TYPE_ERROR(4, type);
+        ARG_ASSERT(0, (output_value != NULL));
+
+        const ArrayInfo &info = getInfo(arr);
+        const af_dtype type   = info.getType();
+
+        switch (type) {
+            case f32:
+                getScalar<float>(reinterpret_cast<float *>(output_value), arr);
+                break;
+            case f64:
+                getScalar<double>(reinterpret_cast<double *>(output_value),
+                                  arr);
+                break;
+            case b8:
+                getScalar<char>(reinterpret_cast<char *>(output_value), arr);
+                break;
+            case s32:
+                getScalar<int>(reinterpret_cast<int *>(output_value), arr);
+                break;
+            case u32:
+                getScalar<uint>(reinterpret_cast<uint *>(output_value), arr);
+                break;
+            case u8:
+                getScalar<uchar>(reinterpret_cast<uchar *>(output_value), arr);
+                break;
+            case s64:
+                getScalar<intl>(reinterpret_cast<intl *>(output_value), arr);
+                break;
+            case u64:
+                getScalar<uintl>(reinterpret_cast<uintl *>(output_value), arr);
+                break;
+            case s16:
+                getScalar<short>(reinterpret_cast<short *>(output_value), arr);
+                break;
+            case u16:
+                getScalar<ushort>(reinterpret_cast<ushort *>(output_value),
+                                  arr);
+                break;
+            case c32:
+                getScalar<cfloat>(reinterpret_cast<cfloat *>(output_value),
+                                  arr);
+                break;
+            case c64:
+                getScalar<cdouble>(reinterpret_cast<cdouble *>(output_value),
+                                   arr);
+                break;
+            default: TYPE_ERROR(4, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index bdcfaee2b9..c844ed52ec 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -7,21 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/index.h>
-#include <af/data.h>
+#include <Array.hpp>
+#include <assign.hpp>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/err_common.hpp>
 #include <common/complex.hpp>
-#include <handle.hpp>
-#include <backend.hpp>
-#include <Array.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
-#include <assign.hpp>
+#include <handle.hpp>
+#include <indexing_common.hpp>
 #include <math.hpp>
 #include <tile.hpp>
-#include <indexing_common.hpp>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/index.h>
 
 using namespace detail;
 
@@ -36,11 +36,9 @@ using common::if_complex;
 using common::if_real;
 
 template<typename Tout, typename Tin>
-static
-void assign(Array<Tout> &out, const vector<af_seq> seqs,
-            const Array<Tin> &in)
-{
-    size_t ndims  = seqs.size();
+static void assign(Array<Tout>& out, const vector<af_seq> seqs,
+                   const Array<Tin>& in) {
+    size_t ndims      = seqs.size();
     const dim4& outDs = out.dims();
     const dim4& iDims = in.dims();
 
@@ -57,23 +55,20 @@ void assign(Array<Tout> &out, const vector<af_seq> seqs,
 
     isVec &= in.isVector() || in.isScalar();
 
-    for (dim_t i = ndims; i < (int)in.ndims(); i++) {
-        oDims[i] = 1;
-    }
+    for (dim_t i = ndims; i < (int)in.ndims(); i++) { oDims[i] = 1; }
 
     if (isVec) {
-        if (oDims.elements() != (dim_t)in.elements() &&
-            in.elements() != 1) {
+        if (oDims.elements() != (dim_t)in.elements() && in.elements() != 1) {
             AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
         }
 
         // If both out and in are vectors of equal elements,
         // reshape in to out dims
-        Array<Tin> in_ = in.elements() == 1 ? tile(in, oDims)
-                                           : modDims(in, oDims);
+        Array<Tin> in_ =
+            in.elements() == 1 ? tile(in, oDims) : modDims(in, oDims);
         auto dst = createSubArray<Tout>(out, seqs, false);
 
-        copyArray<Tin , Tout>(dst, in_);
+        copyArray<Tin, Tout>(dst, in_);
     } else {
         for (int i = 0; i < AF_MAX_DIMS; i++) {
             if (oDims[i] != iDims[i])
@@ -81,52 +76,45 @@ void assign(Array<Tout> &out, const vector<af_seq> seqs,
         }
         Array<Tout> dst = createSubArray<Tout>(out, seqs, false);
 
-        copyArray<Tin , Tout>(dst, in);
+        copyArray<Tin, Tout>(dst, in);
     }
 }
 
 template<typename T>
-static
-if_complex<T>
-assign(Array<T> &out, const vector<af_seq> iv,
-            const af_array &in) {
+static if_complex<T> assign(Array<T>& out, const vector<af_seq> iv,
+                            const af_array& in) {
     const ArrayInfo& iInfo = getInfo(in);
-    af_dtype iType = iInfo.getType();
-    switch(iType) {
-        case c64: assign<T, cdouble>(out, iv, getArray<cdouble>(in));  break;
-        case c32: assign<T, cfloat >(out, iv, getArray<cfloat >(in));  break;
-        default : TYPE_ERROR(1, iType); break;
+    af_dtype iType         = iInfo.getType();
+    switch (iType) {
+        case c64: assign<T, cdouble>(out, iv, getArray<cdouble>(in)); break;
+        case c32: assign<T, cfloat>(out, iv, getArray<cfloat>(in)); break;
+        default: TYPE_ERROR(1, iType); break;
     }
 }
 
 template<typename T>
-static
-if_real<T>
-assign(Array<T> &out, const vector<af_seq> iv,
-            const af_array &in)
-{
+static if_real<T> assign(Array<T>& out, const vector<af_seq> iv,
+                         const af_array& in) {
     const ArrayInfo& iInfo = getInfo(in);
-    af_dtype iType = iInfo.getType();
-
-    switch(iType) {
-        case f64: assign<T, double >(out, iv, getArray<double >(in));  break;
-        case f32: assign<T, float  >(out, iv, getArray<float  >(in));  break;
-        case s32: assign<T, int    >(out, iv, getArray<int    >(in));  break;
-        case u32: assign<T, uint   >(out, iv, getArray<uint   >(in));  break;
-        case s64: assign<T, intl   >(out, iv, getArray<intl   >(in));  break;
-        case u64: assign<T, uintl  >(out, iv, getArray<uintl  >(in));  break;
-        case s16: assign<T, short  >(out, iv, getArray<short  >(in));  break;
-        case u16: assign<T, ushort >(out, iv, getArray<ushort >(in));  break;
-        case u8 : assign<T, uchar  >(out, iv, getArray<uchar  >(in));  break;
-        case b8 : assign<T, char   >(out, iv, getArray<char   >(in));  break;
-        default : TYPE_ERROR(1, iType); break;
+    af_dtype iType         = iInfo.getType();
+
+    switch (iType) {
+        case f64: assign<T, double>(out, iv, getArray<double>(in)); break;
+        case f32: assign<T, float>(out, iv, getArray<float>(in)); break;
+        case s32: assign<T, int>(out, iv, getArray<int>(in)); break;
+        case u32: assign<T, uint>(out, iv, getArray<uint>(in)); break;
+        case s64: assign<T, intl>(out, iv, getArray<intl>(in)); break;
+        case u64: assign<T, uintl>(out, iv, getArray<uintl>(in)); break;
+        case s16: assign<T, short>(out, iv, getArray<short>(in)); break;
+        case u16: assign<T, ushort>(out, iv, getArray<ushort>(in)); break;
+        case u8: assign<T, uchar>(out, iv, getArray<uchar>(in)); break;
+        case b8: assign<T, char>(out, iv, getArray<char>(in)); break;
+        default: TYPE_ERROR(1, iType); break;
     }
 }
 
-af_err af_assign_seq(af_array *out,
-                     const af_array lhs, const unsigned ndims,
-                     const af_seq *index, const af_array rhs)
-{
+af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
+                     const af_seq* index, const af_array rhs) {
     try {
         ARG_ASSERT(0, (lhs != 0));
         ARG_ASSERT(1, (ndims > 0));
@@ -138,12 +126,13 @@ af_err af_assign_seq(af_array *out,
             af_array tmp_in, tmp_out;
             AF_CHECK(af_flat(&tmp_in, lhs));
             AF_CHECK(af_assign_seq(&tmp_out, tmp_in, ndims, index, rhs));
-            AF_CHECK(af_moddims(out, tmp_out, lInfo.ndims(), lInfo.dims().get()));
+            AF_CHECK(
+                af_moddims(out, tmp_out, lInfo.ndims(), lInfo.dims().get()));
             AF_CHECK(af_release_array(tmp_in));
             // This can run into a double free issue if tmp_in == tmp_out
             // The condition ensures release only if both are different
             // Issue found on Tegra X1
-            if(tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
+            if (tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
             return AF_SUCCESS;
         }
 
@@ -163,40 +152,43 @@ af_err af_assign_seq(af_array *out,
         try {
             if (lhs != rhs) {
                 const dim4& outDims = getInfo(res).dims();
-                const dim4& inDims = getInfo(rhs).dims();
+                const dim4& inDims  = getInfo(rhs).dims();
 
                 vector<af_seq> inSeqs(ndims, af_span);
-                for (unsigned i=0; i<ndims; ++i) {
+                for (unsigned i = 0; i < ndims; ++i) {
                     inSeqs[i] = convert2Canonical(index[i], outDims[i]);
-                    ARG_ASSERT(3, (inSeqs[i].begin >= 0. || inSeqs[i].end >= 0.));
+                    ARG_ASSERT(3,
+                               (inSeqs[i].begin >= 0. || inSeqs[i].end >= 0.));
                     if (signbit(inSeqs[i].step)) {
                         ARG_ASSERT(3, inSeqs[i].begin >= inSeqs[i].end);
                     } else {
                         ARG_ASSERT(3, inSeqs[i].begin <= inSeqs[i].end);
                     }
                 }
-                DIM_ASSERT(0, (outDims.ndims()>=inDims.ndims()));
-                DIM_ASSERT(0, (outDims.ndims()>=(dim_t)ndims));
+                DIM_ASSERT(0, (outDims.ndims() >= inDims.ndims()));
+                DIM_ASSERT(0, (outDims.ndims() >= (dim_t)ndims));
 
                 const ArrayInfo& oInfo = getInfo(res);
-                af_dtype oType = oInfo.getType();
-                switch(oType) {
-                case c64: assign(getArray<cdouble>(res), inSeqs, rhs); break;
-                case c32: assign(getArray<cfloat >(res), inSeqs, rhs); break;
-                case f64: assign(getArray<double >(res), inSeqs, rhs); break;
-                case f32: assign(getArray<float  >(res), inSeqs, rhs); break;
-                case s32: assign(getArray<int    >(res), inSeqs, rhs); break;
-                case u32: assign(getArray<uint   >(res), inSeqs, rhs); break;
-                case s64: assign(getArray<intl   >(res), inSeqs, rhs); break;
-                case u64: assign(getArray<uintl  >(res), inSeqs, rhs); break;
-                case s16: assign(getArray<short  >(res), inSeqs, rhs); break;
-                case u16: assign(getArray<ushort >(res), inSeqs, rhs); break;
-                case u8 : assign(getArray<uchar  >(res), inSeqs, rhs); break;
-                case b8 : assign(getArray<char   >(res), inSeqs, rhs); break;
-                default : TYPE_ERROR(1, oType); break;
+                af_dtype oType         = oInfo.getType();
+                switch (oType) {
+                    case c64:
+                        assign(getArray<cdouble>(res), inSeqs, rhs);
+                        break;
+                    case c32: assign(getArray<cfloat>(res), inSeqs, rhs); break;
+                    case f64: assign(getArray<double>(res), inSeqs, rhs); break;
+                    case f32: assign(getArray<float>(res), inSeqs, rhs); break;
+                    case s32: assign(getArray<int>(res), inSeqs, rhs); break;
+                    case u32: assign(getArray<uint>(res), inSeqs, rhs); break;
+                    case s64: assign(getArray<intl>(res), inSeqs, rhs); break;
+                    case u64: assign(getArray<uintl>(res), inSeqs, rhs); break;
+                    case s16: assign(getArray<short>(res), inSeqs, rhs); break;
+                    case u16: assign(getArray<ushort>(res), inSeqs, rhs); break;
+                    case u8: assign(getArray<uchar>(res), inSeqs, rhs); break;
+                    case b8: assign(getArray<char>(res), inSeqs, rhs); break;
+                    default: TYPE_ERROR(1, oType); break;
                 }
             }
-        } catch(...) {
+        } catch (...) {
             af_release_array(res);
             throw;
         }
@@ -207,18 +199,15 @@ af_err af_assign_seq(af_array *out,
 }
 
 template<typename T>
-inline
-void genAssign(af_array& out, const af_index_t* indexs, const af_array& rhs)
-{
+inline void genAssign(af_array& out, const af_index_t* indexs,
+                      const af_array& rhs) {
     detail::assign<T>(getArray<T>(out), indexs, getArray<T>(rhs));
 }
 
-af_err af_assign_gen(af_array *out, const af_array lhs,
-                    const dim_t ndims, const af_index_t* indexs,
-                    const af_array rhs_)
-{
+af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
+                     const af_index_t* indexs, const af_array rhs_) {
     try {
-        ARG_ASSERT(3, (indexs!=NULL));
+        ARG_ASSERT(3, (indexs != NULL));
 
         int track = 0;
         vector<af_seq> seqs(AF_MAX_DIMS, af_span);
@@ -230,13 +219,13 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
         }
 
         af_array rhs = rhs_;
-        if (track==(int)ndims) {
+        if (track == (int)ndims) {
             // all indexs are sequences, redirecting to af_assign
             return af_assign_seq(out, lhs, ndims, seqs.data(), rhs);
         }
 
-        ARG_ASSERT(1, (lhs!=0));
-        ARG_ASSERT(4, (rhs!=0));
+        ARG_ASSERT(1, (lhs != 0));
+        ARG_ASSERT(4, (rhs != 0));
 
         const ArrayInfo& lInfo = getInfo(lhs);
         const ArrayInfo& rInfo = getInfo(rhs);
@@ -245,10 +234,9 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
         af_dtype lhsType       = lInfo.getType();
         af_dtype rhsType       = rInfo.getType();
 
-        if(rhsDims.ndims() == 0)
-            return af_retain_array(out, lhs);
+        if (rhsDims.ndims() == 0) return af_retain_array(out, lhs);
 
-        if(lhsDims.ndims() == 0)
+        if (lhsDims.ndims() == 0)
             return af_create_handle(out, 0, nullptr, lhsType);
 
         ARG_ASSERT(2, (ndims == 1) || (ndims == (dim_t)lInfo.ndims()));
@@ -257,18 +245,19 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
             af_array tmp_in = 0, tmp_out = 0;
             AF_CHECK(af_flat(&tmp_in, lhs));
             AF_CHECK(af_assign_gen(&tmp_out, tmp_in, ndims, indexs, rhs_));
-            AF_CHECK(af_moddims(out, tmp_out, lInfo.ndims(), lInfo.dims().get()));
+            AF_CHECK(
+                af_moddims(out, tmp_out, lInfo.ndims(), lInfo.dims().get()));
             AF_CHECK(af_release_array(tmp_in));
             // This can run into a double free issue if tmp_in == tmp_out
             // The condition ensures release only if both are different
             // Issue found on Tegra X1
-            if(tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
+            if (tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
             return AF_SUCCESS;
         }
 
-        ARG_ASSERT(1, (lhsType==rhsType));
-        ARG_ASSERT(1, (lhsDims.ndims()>=rhsDims.ndims()));
-        ARG_ASSERT(2, (lhsDims.ndims()>=ndims));
+        ARG_ASSERT(1, (lhsType == rhsType));
+        ARG_ASSERT(1, (lhsDims.ndims() >= rhsDims.ndims()));
+        ARG_ASSERT(2, (lhsDims.ndims() >= ndims));
 
         af_array output = 0;
         if (*out != lhs) {
@@ -286,20 +275,19 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
         // if af_array are indexs along any
         // particular dimension, set the length of
         // that dimension accordingly before any checks
-        for (dim_t i=0; i<ndims; i++) {
+        for (dim_t i = 0; i < ndims; i++) {
             if (!indexs[i].isSeq)
                 oDims[i] = getInfo(indexs[i].idx.arr).elements();
         }
 
-        for (dim_t i = ndims; i < (dim_t)lInfo.ndims(); i++)
-            oDims[i] = 1;
+        for (dim_t i = ndims; i < (dim_t)lInfo.ndims(); i++) oDims[i] = 1;
 
         bool isVec = true;
         for (int i = 0; isVec && i < oDims.ndims() - 1; i++) {
             isVec &= oDims[i] == 1;
         }
 
-        //TODO: Move logic out of this
+        // TODO: Move logic out of this
         isVec &= rInfo.isVector() || rInfo.isScalar();
         if (isVec) {
             if (oDims.elements() != (dim_t)rInfo.elements() &&
@@ -307,8 +295,8 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
                 AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
             }
             if (rInfo.elements() == 1) {
-                AF_CHECK(af_tile(&rhs, rhs_, oDims[0],
-                                 oDims[1], oDims[2], oDims[3]));
+                AF_CHECK(af_tile(&rhs, rhs_, oDims[0], oDims[1], oDims[2],
+                                 oDims[3]));
             } else {
                 // If both out and rhs are vectors of equal
                 // elements, reshape rhs to out dims
@@ -323,23 +311,23 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
         }
 
         std::array<af_index_t, AF_MAX_DIMS> idxrs;
-        for (dim_t i=0; i<AF_MAX_DIMS; ++i) {
+        for (dim_t i = 0; i < AF_MAX_DIMS; ++i) {
             if (i < ndims) {
                 bool isSeq = indexs[i].isSeq;
                 if (!isSeq) {
                     // check if all af_arrays have atleast one value
                     // to enable indexing along that dimension
                     const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
-                    af_dtype idxType = idxInfo.getType();
+                    af_dtype idxType         = idxInfo.getType();
 
-                    ARG_ASSERT(3, (idxType!=c32));
-                    ARG_ASSERT(3, (idxType!=c64));
-                    ARG_ASSERT(3, (idxType!=b8 ));
+                    ARG_ASSERT(3, (idxType != c32));
+                    ARG_ASSERT(3, (idxType != c64));
+                    ARG_ASSERT(3, (idxType != b8));
 
-                    idxrs[i] = { { indexs[i].idx.arr },
-                                 isSeq, indexs[i].isBatch };
+                    idxrs[i] = {{indexs[i].idx.arr}, isSeq, indexs[i].isBatch};
                 } else {
-                    af_seq inSeq = convert2Canonical(indexs[i].idx.seq, lhsDims[i]);
+                    af_seq inSeq =
+                        convert2Canonical(indexs[i].idx.seq, lhsDims[i]);
                     ARG_ASSERT(3, (inSeq.begin >= 0 || inSeq.end >= 0));
                     if (signbit(inSeq.step)) {
                         ARG_ASSERT(3, inSeq.begin >= inSeq.end);
@@ -359,31 +347,29 @@ af_err af_assign_gen(af_array *out, const af_array lhs,
         af_index_t* ptr = idxrs.data();
 
         try {
-            switch(rhsType) {
+            switch (rhsType) {
                 case c64: genAssign<cdouble>(output, ptr, rhs); break;
-                case f64: genAssign<double >(output, ptr, rhs); break;
-                case c32: genAssign<cfloat >(output, ptr, rhs); break;
-                case f32: genAssign<float  >(output, ptr, rhs); break;
-                case u64: genAssign<uintl  >(output, ptr, rhs); break;
-                case u32: genAssign<uint   >(output, ptr, rhs); break;
-                case s64: genAssign<intl   >(output, ptr, rhs); break;
-                case s32: genAssign<int    >(output, ptr, rhs); break;
-                case s16: genAssign<short  >(output, ptr, rhs); break;
-                case u16: genAssign<ushort >(output, ptr, rhs); break;
-                case  u8: genAssign<uchar  >(output, ptr, rhs); break;
-                case  b8: genAssign<char   >(output, ptr, rhs); break;
+                case f64: genAssign<double>(output, ptr, rhs); break;
+                case c32: genAssign<cfloat>(output, ptr, rhs); break;
+                case f32: genAssign<float>(output, ptr, rhs); break;
+                case u64: genAssign<uintl>(output, ptr, rhs); break;
+                case u32: genAssign<uint>(output, ptr, rhs); break;
+                case s64: genAssign<intl>(output, ptr, rhs); break;
+                case s32: genAssign<int>(output, ptr, rhs); break;
+                case s16: genAssign<short>(output, ptr, rhs); break;
+                case u16: genAssign<ushort>(output, ptr, rhs); break;
+                case u8: genAssign<uchar>(output, ptr, rhs); break;
+                case b8: genAssign<char>(output, ptr, rhs); break;
                 default: TYPE_ERROR(1, rhsType);
             }
-        } catch(...) {
+        } catch (...) {
             if (*out != lhs) {
                 AF_CHECK(af_release_array(output));
-                if (isVec)
-                    AF_CHECK(af_release_array(rhs));
+                if (isVec) AF_CHECK(af_release_array(rhs));
             }
             throw;
         }
-        if (isVec)
-            AF_CHECK(af_release_array(rhs));
+        if (isVec) AF_CHECK(af_release_array(rhs));
         swap(*out, output);
     }
     CATCHALL;
diff --git a/src/api/c/bilateral.cpp b/src/api/c/bilateral.cpp
index 9bf70a7516..bb3beccb43 100644
--- a/src/api/c/bilateral.cpp
+++ b/src/api/c/bilateral.cpp
@@ -7,56 +7,75 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <bilateral.hpp>
 #include <common/err_common.hpp>
+#include <handle.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename inType, typename outType, bool isColor>
-static inline af_array bilateral(const af_array &in, const float &sp_sig, const float &chr_sig)
-{
-    return getHandle(bilateral<inType, outType, isColor>(getArray<inType>(in), sp_sig, chr_sig));
+static inline af_array bilateral(const af_array &in, const float &sp_sig,
+                                 const float &chr_sig) {
+    return getHandle(bilateral<inType, outType, isColor>(getArray<inType>(in),
+                                                         sp_sig, chr_sig));
 }
 
 template<bool isColor>
-static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma, const float &c_sigma)
-{
+static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma,
+                        const float &c_sigma) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
 
-        DIM_ASSERT(1, (dims.ndims()>=2));
+        DIM_ASSERT(1, (dims.ndims() >= 2));
 
         af_array output;
-        switch(type) {
-            case f64: output = bilateral<double, double, isColor> (in, s_sigma, c_sigma); break;
-            case f32: output = bilateral<float ,  float, isColor> (in, s_sigma, c_sigma); break;
-            case b8 : output = bilateral<char  ,  float, isColor> (in, s_sigma, c_sigma); break;
-            case s32: output = bilateral<int   ,  float, isColor> (in, s_sigma, c_sigma); break;
-            case u32: output = bilateral<uint  ,  float, isColor> (in, s_sigma, c_sigma); break;
-            case u8 : output = bilateral<uchar ,  float, isColor> (in, s_sigma, c_sigma); break;
-            case s16: output = bilateral<short ,  float, isColor> (in, s_sigma, c_sigma); break;
-            case u16: output = bilateral<ushort,  float, isColor> (in, s_sigma, c_sigma); break;
-            default : TYPE_ERROR(1, type);
+        switch (type) {
+            case f64:
+                output =
+                    bilateral<double, double, isColor>(in, s_sigma, c_sigma);
+                break;
+            case f32:
+                output = bilateral<float, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            case b8:
+                output = bilateral<char, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            case s32:
+                output = bilateral<int, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            case u32:
+                output = bilateral<uint, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            case u8:
+                output = bilateral<uchar, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            case s16:
+                output = bilateral<short, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            case u16:
+                output =
+                    bilateral<ushort, float, isColor>(in, s_sigma, c_sigma);
+                break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma, const float chromatic_sigma, const bool isColor)
-{
+af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma,
+                    const float chromatic_sigma, const bool isColor) {
     if (isColor)
-        return bilateral<true>(out,in,spatial_sigma,chromatic_sigma);
+        return bilateral<true>(out, in, spatial_sigma, chromatic_sigma);
     else
-        return bilateral<false>(out,in,spatial_sigma,chromatic_sigma);
+        return bilateral<false>(out, in, spatial_sigma, chromatic_sigma);
 }
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 21d4c81fb9..ce06041910 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <af/data.h>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
-#include <implicit.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
-#include <sparse_handle.hpp>
+#include <implicit.hpp>
+#include <optypes.hpp>
 #include <sparse.hpp>
+#include <sparse_handle.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
 
 #include <arith.hpp>
 #include <logic.hpp>
@@ -29,56 +29,55 @@ using af::dim4;
 
 template<typename T, af_op_t op>
 static inline af_array arithOp(const af_array lhs, const af_array rhs,
-                               const dim4 &odims)
-{
-    af_array res = getHandle(arithOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
+                               const dim4 &odims) {
+    af_array res =
+        getHandle(arithOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
     return res;
 }
 
 template<typename T, af_op_t op>
-static inline
-af_array sparseArithOp(const af_array lhs, const af_array rhs)
-{
+static inline af_array sparseArithOp(const af_array lhs, const af_array rhs) {
     auto res = arithOp<T, op>(getSparseArray<T>(lhs), getSparseArray<T>(rhs));
     return getHandle(res);
 }
 
 template<typename T, af_op_t op>
-static inline af_array arithSparseDenseOp(const af_array lhs, const af_array rhs,
-                                          const bool reverse)
-{
-    if(op == af_add_t || op == af_sub_t)
-        return getHandle(arithOpD<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
-    else if(op == af_mul_t || op == af_div_t)
-        return getHandle(arithOp<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
-
+static inline af_array arithSparseDenseOp(const af_array lhs,
+                                          const af_array rhs,
+                                          const bool reverse) {
+    if (op == af_add_t || op == af_sub_t)
+        return getHandle(
+            arithOpD<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
+    else if (op == af_mul_t || op == af_div_t)
+        return getHandle(
+            arithOp<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
 }
 
 template<af_op_t op>
-static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
+                       const bool batchMode) {
     try {
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
-        case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
-        case f64: res = arithOp<double , op>(lhs, rhs, odims); break;
-        case c32: res = arithOp<cfloat , op>(lhs, rhs, odims); break;
-        case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
-        case s32: res = arithOp<int    , op>(lhs, rhs, odims); break;
-        case u32: res = arithOp<uint   , op>(lhs, rhs, odims); break;
-        case u8 : res = arithOp<uchar  , op>(lhs, rhs, odims); break;
-        case b8 : res = arithOp<char   , op>(lhs, rhs, odims); break;
-        case s64: res = arithOp<intl   , op>(lhs, rhs, odims); break;
-        case u64: res = arithOp<uintl  , op>(lhs, rhs, odims); break;
-        case s16: res = arithOp<short  , op>(lhs, rhs, odims); break;
-        case u16: res = arithOp<ushort , op>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, otype);
+            case f32: res = arithOp<float, op>(lhs, rhs, odims); break;
+            case f64: res = arithOp<double, op>(lhs, rhs, odims); break;
+            case c32: res = arithOp<cfloat, op>(lhs, rhs, odims); break;
+            case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
+            case s32: res = arithOp<int, op>(lhs, rhs, odims); break;
+            case u32: res = arithOp<uint, op>(lhs, rhs, odims); break;
+            case u8: res = arithOp<uchar, op>(lhs, rhs, odims); break;
+            case b8: res = arithOp<char, op>(lhs, rhs, odims); break;
+            case s64: res = arithOp<intl, op>(lhs, rhs, odims); break;
+            case u64: res = arithOp<uintl, op>(lhs, rhs, odims); break;
+            case s16: res = arithOp<short, op>(lhs, rhs, odims); break;
+            case u16: res = arithOp<ushort, op>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, otype);
         }
 
         std::swap(*out, res);
@@ -88,30 +87,28 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs, co
 }
 
 template<af_op_t op>
-static
-af_err af_arith_real(af_array *out, const af_array lhs, const af_array rhs,
-                     const bool batchMode)
-{
+static af_err af_arith_real(af_array *out, const af_array lhs,
+                            const af_array rhs, const bool batchMode) {
     try {
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
-        case f32: res = arithOp<float  , op>(lhs, rhs, odims); break;
-        case f64: res = arithOp<double , op>(lhs, rhs, odims); break;
-        case s32: res = arithOp<int    , op>(lhs, rhs, odims); break;
-        case u32: res = arithOp<uint   , op>(lhs, rhs, odims); break;
-        case u8 : res = arithOp<uchar  , op>(lhs, rhs, odims); break;
-        case b8 : res = arithOp<char   , op>(lhs, rhs, odims); break;
-        case s64: res = arithOp<intl   , op>(lhs, rhs, odims); break;
-        case u64: res = arithOp<uintl  , op>(lhs, rhs, odims); break;
-        case s16: res = arithOp<short  , op>(lhs, rhs, odims); break;
-        case u16: res = arithOp<ushort , op>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, otype);
+            case f32: res = arithOp<float, op>(lhs, rhs, odims); break;
+            case f64: res = arithOp<double, op>(lhs, rhs, odims); break;
+            case s32: res = arithOp<int, op>(lhs, rhs, odims); break;
+            case u32: res = arithOp<uint, op>(lhs, rhs, odims); break;
+            case u8: res = arithOp<uchar, op>(lhs, rhs, odims); break;
+            case b8: res = arithOp<char, op>(lhs, rhs, odims); break;
+            case s64: res = arithOp<intl, op>(lhs, rhs, odims); break;
+            case u64: res = arithOp<uintl, op>(lhs, rhs, odims); break;
+            case s16: res = arithOp<short, op>(lhs, rhs, odims); break;
+            case u16: res = arithOp<ushort, op>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, otype);
         }
 
         std::swap(*out, res);
@@ -121,23 +118,22 @@ af_err af_arith_real(af_array *out, const af_array lhs, const af_array rhs,
 }
 
 template<af_op_t op>
-static af_err
-af_arith_sparse(af_array *out, const af_array lhs, const af_array rhs)
-{
+static af_err af_arith_sparse(af_array *out, const af_array lhs,
+                              const af_array rhs) {
     try {
         common::SparseArrayBase linfo = getSparseArrayBase(lhs);
         common::SparseArrayBase rinfo = getSparseArrayBase(rhs);
 
-        ARG_ASSERT(1, (linfo.getStorage()==rinfo.getStorage()));
-        ARG_ASSERT(1, (linfo.dims()==rinfo.dims()));
-        ARG_ASSERT(1, (linfo.getStorage()==AF_STORAGE_CSR));
+        ARG_ASSERT(1, (linfo.getStorage() == rinfo.getStorage()));
+        ARG_ASSERT(1, (linfo.dims() == rinfo.dims()));
+        ARG_ASSERT(1, (linfo.getStorage() == AF_STORAGE_CSR));
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
-            case f32: res = sparseArithOp<float  , op>(lhs, rhs); break;
-            case f64: res = sparseArithOp<double , op>(lhs, rhs); break;
-            case c32: res = sparseArithOp<cfloat , op>(lhs, rhs); break;
+            case f32: res = sparseArithOp<float, op>(lhs, rhs); break;
+            case f64: res = sparseArithOp<double, op>(lhs, rhs); break;
+            case c32: res = sparseArithOp<cfloat, op>(lhs, rhs); break;
             case c64: res = sparseArithOp<cdouble, op>(lhs, rhs); break;
             default: TYPE_ERROR(0, otype);
         }
@@ -149,22 +145,30 @@ af_arith_sparse(af_array *out, const af_array lhs, const af_array rhs)
 }
 
 template<af_op_t op>
-static af_err af_arith_sparse_dense(af_array *out, const af_array lhs, const af_array rhs,
-                                    const bool reverse = false)
-{
+static af_err af_arith_sparse_dense(af_array *out, const af_array lhs,
+                                    const af_array rhs,
+                                    const bool reverse = false) {
     using namespace common;
     try {
         common::SparseArrayBase linfo = getSparseArrayBase(lhs);
-        ArrayInfo       rinfo = getInfo(rhs);
+        ArrayInfo rinfo               = getInfo(rhs);
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
         switch (otype) {
-        case f32: res = arithSparseDenseOp<float  , op>(lhs, rhs, reverse); break;
-        case f64: res = arithSparseDenseOp<double , op>(lhs, rhs, reverse); break;
-        case c32: res = arithSparseDenseOp<cfloat , op>(lhs, rhs, reverse); break;
-        case c64: res = arithSparseDenseOp<cdouble, op>(lhs, rhs, reverse); break;
-        default: TYPE_ERROR(0, otype);
+            case f32:
+                res = arithSparseDenseOp<float, op>(lhs, rhs, reverse);
+                break;
+            case f64:
+                res = arithSparseDenseOp<double, op>(lhs, rhs, reverse);
+                break;
+            case c32:
+                res = arithSparseDenseOp<cfloat, op>(lhs, rhs, reverse);
+                break;
+            case c64:
+                res = arithSparseDenseOp<cdouble, op>(lhs, rhs, reverse);
+                break;
+            default: TYPE_ERROR(0, otype);
         }
 
         std::swap(*out, res);
@@ -174,17 +178,16 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs, const af_
 }
 
 af_err af_add(af_array *out, const af_array lhs, const af_array rhs,
-              const bool batchMode)
-{
+              const bool batchMode) {
     // Check if inputs are sparse
     ArrayInfo linfo = getInfo(lhs, false, true);
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
-    if(linfo.isSparse() && rinfo.isSparse()) {
+    if (linfo.isSparse() && rinfo.isSparse()) {
         return af_arith_sparse<af_add_t>(out, lhs, rhs);
-    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+    } else if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_add_t>(out, lhs, rhs);
-    } else if(!linfo.isSparse() && rinfo.isSparse()) {
+    } else if (!linfo.isSparse() && rinfo.isSparse()) {
         // second operand(Array) of af_arith call should be dense
         return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true);
     } else {
@@ -192,91 +195,94 @@ af_err af_add(af_array *out, const af_array lhs, const af_array rhs,
     }
 }
 
-af_err af_mul(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_mul(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     // Check if inputs are sparse
     ArrayInfo linfo = getInfo(lhs, false, true);
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
-    if(linfo.isSparse() && rinfo.isSparse()) {
-        //return af_arith_sparse<af_mul_t>(out, lhs, rhs);
-        //MKL doesn't have mul or div support yet, hence
-        //this is commented out although alternative cpu code exists
+    if (linfo.isSparse() && rinfo.isSparse()) {
+        // return af_arith_sparse<af_mul_t>(out, lhs, rhs);
+        // MKL doesn't have mul or div support yet, hence
+        // this is commented out although alternative cpu code exists
         return AF_ERR_NOT_SUPPORTED;
-    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+    } else if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_mul_t>(out, lhs, rhs);
-    } else if(!linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_mul_t>(out, rhs, lhs, true); // dense should be rhs
+    } else if (!linfo.isSparse() && rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_mul_t>(out, rhs, lhs,
+                                               true);  // dense should be rhs
     } else {
         return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
     }
 }
 
-af_err af_sub(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_sub(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     // Check if inputs are sparse
     ArrayInfo linfo = getInfo(lhs, false, true);
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
-    if(linfo.isSparse() && rinfo.isSparse()) {
+    if (linfo.isSparse() && rinfo.isSparse()) {
         return af_arith_sparse<af_sub_t>(out, lhs, rhs);
-    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+    } else if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_sub_t>(out, lhs, rhs);
-    } else if(!linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_sub_t>(out, rhs, lhs, true); // dense should be rhs
+    } else if (!linfo.isSparse() && rinfo.isSparse()) {
+        return af_arith_sparse_dense<af_sub_t>(out, rhs, lhs,
+                                               true);  // dense should be rhs
     } else {
         return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
     }
 }
 
-af_err af_div(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_div(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     // Check if inputs are sparse
     ArrayInfo linfo = getInfo(lhs, false, true);
     ArrayInfo rinfo = getInfo(rhs, false, true);
 
-    if(linfo.isSparse() && rinfo.isSparse()) {
-        //return af_arith_sparse<af_div_t>(out, lhs, rhs);
-        //MKL doesn't have mul or div support yet, hence
-        //this is commented out although alternative cpu code exists
+    if (linfo.isSparse() && rinfo.isSparse()) {
+        // return af_arith_sparse<af_div_t>(out, lhs, rhs);
+        // MKL doesn't have mul or div support yet, hence
+        // this is commented out although alternative cpu code exists
         return AF_ERR_NOT_SUPPORTED;
-    } else if(linfo.isSparse() && !rinfo.isSparse()) {
+    } else if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
-    } else if(!linfo.isSparse() && rinfo.isSparse()) {
+    } else if (!linfo.isSparse() && rinfo.isSparse()) {
         // Division by sparse is currently not allowed - for convinence of
         // dealing with division by 0
-        // return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense should be rhs
+        // return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense
+        // should be rhs
         return AF_ERR_NOT_SUPPORTED;
     } else {
         return af_arith<af_div_t>(out, lhs, rhs, batchMode);
     }
 }
 
-af_err af_maxof(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_maxof(af_array *out, const af_array lhs, const af_array rhs,
+                const bool batchMode) {
     return af_arith<af_max_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_minof(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_minof(af_array *out, const af_array lhs, const af_array rhs,
+                const bool batchMode) {
     return af_arith<af_min_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_rem(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_rem(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     return af_arith_real<af_rem_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_mod(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_mod(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     return af_arith_real<af_mod_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_pow(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_pow(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     try {
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
         if (rinfo.isComplex()) {
             af_array log_lhs, log_res;
             af_array res;
@@ -310,16 +316,17 @@ af_err af_pow(af_array *out, const af_array lhs, const af_array rhs, const bool
             std::swap(*out, res);
             return AF_SUCCESS;
         }
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return af_arith_real<af_pow_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_root(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_root(af_array *out, const af_array lhs, const af_array rhs,
+               const bool batchMode) {
     try {
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
         if (linfo.isComplex() || rinfo.isComplex()) {
             af_array log_lhs, log_res;
             af_array res;
@@ -331,7 +338,8 @@ af_err af_root(af_array *out, const af_array lhs, const af_array rhs, const bool
         }
 
         af_array one;
-        AF_CHECK(af_constant(&one, 1, linfo.ndims(), linfo.dims().get(), linfo.getType()));
+        AF_CHECK(af_constant(&one, 1, linfo.ndims(), linfo.dims().get(),
+                             linfo.getType()));
 
         af_array inv_lhs;
         AF_CHECK(af_div(&inv_lhs, one, lhs, batchMode));
@@ -340,16 +348,15 @@ af_err af_root(af_array *out, const af_array lhs, const af_array rhs, const bool
 
         AF_CHECK(af_release_array(one));
         AF_CHECK(af_release_array(inv_lhs));
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs,
+                const bool batchMode) {
     try {
-
         const af_dtype type = implicit(lhs, rhs);
 
         if (type != f32 && type != f64) {
@@ -357,16 +364,16 @@ af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs, const boo
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
         af_array res;
         switch (type) {
-        case f32: res = arithOp<float , af_atan2_t>(lhs, rhs, odims); break;
-        case f64: res = arithOp<double, af_atan2_t>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, type);
+            case f32: res = arithOp<float, af_atan2_t>(lhs, rhs, odims); break;
+            case f64: res = arithOp<double, af_atan2_t>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -375,10 +382,9 @@ af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs, const boo
     return AF_SUCCESS;
 }
 
-af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs,
+                const bool batchMode) {
     try {
-
         const af_dtype type = implicit(lhs, rhs);
 
         if (type != f32 && type != f64) {
@@ -386,16 +392,16 @@ af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs, const boo
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
         af_array res;
         switch (type) {
-        case f32: res = arithOp<float , af_hypot_t>(lhs, rhs, odims); break;
-        case f64: res = arithOp<double, af_hypot_t>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, type);
+            case f32: res = arithOp<float, af_hypot_t>(lhs, rhs, odims); break;
+            case f64: res = arithOp<double, af_hypot_t>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -405,38 +411,39 @@ af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs, const boo
 }
 
 template<typename T, af_op_t op>
-static inline af_array logicOp(const af_array lhs, const af_array rhs, const dim4 &odims)
-{
-    af_array res = getHandle(logicOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
+static inline af_array logicOp(const af_array lhs, const af_array rhs,
+                               const dim4 &odims) {
+    af_array res =
+        getHandle(logicOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
     return res;
 }
 
 template<af_op_t op>
-static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs,
+                       const bool batchMode) {
     try {
         const af_dtype type = implicit(lhs, rhs);
 
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
         af_array res;
         switch (type) {
-        case f32: res = logicOp<float  , op>(lhs, rhs, odims); break;
-        case f64: res = logicOp<double , op>(lhs, rhs, odims); break;
-        case c32: res = logicOp<cfloat , op>(lhs, rhs, odims); break;
-        case c64: res = logicOp<cdouble, op>(lhs, rhs, odims); break;
-        case s32: res = logicOp<int    , op>(lhs, rhs, odims); break;
-        case u32: res = logicOp<uint   , op>(lhs, rhs, odims); break;
-        case u8 : res = logicOp<uchar  , op>(lhs, rhs, odims); break;
-        case b8 : res = logicOp<char   , op>(lhs, rhs, odims); break;
-        case s64: res = logicOp<intl   , op>(lhs, rhs, odims); break;
-        case u64: res = logicOp<uintl  , op>(lhs, rhs, odims); break;
-        case s16: res = logicOp<short  , op>(lhs, rhs, odims); break;
-        case u16: res = logicOp<ushort , op>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, type);
+            case f32: res = logicOp<float, op>(lhs, rhs, odims); break;
+            case f64: res = logicOp<double, op>(lhs, rhs, odims); break;
+            case c32: res = logicOp<cfloat, op>(lhs, rhs, odims); break;
+            case c64: res = logicOp<cdouble, op>(lhs, rhs, odims); break;
+            case s32: res = logicOp<int, op>(lhs, rhs, odims); break;
+            case u32: res = logicOp<uint, op>(lhs, rhs, odims); break;
+            case u8: res = logicOp<uchar, op>(lhs, rhs, odims); break;
+            case b8: res = logicOp<char, op>(lhs, rhs, odims); break;
+            case s64: res = logicOp<intl, op>(lhs, rhs, odims); break;
+            case u64: res = logicOp<uintl, op>(lhs, rhs, odims); break;
+            case s16: res = logicOp<short, op>(lhs, rhs, odims); break;
+            case u16: res = logicOp<ushort, op>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -445,79 +452,80 @@ static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs, co
     return AF_SUCCESS;
 }
 
-af_err af_eq(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_eq(af_array *out, const af_array lhs, const af_array rhs,
+             const bool batchMode) {
     return af_logic<af_eq_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_neq(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_neq(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     return af_logic<af_neq_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_gt(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_gt(af_array *out, const af_array lhs, const af_array rhs,
+             const bool batchMode) {
     return af_logic<af_gt_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_ge(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_ge(af_array *out, const af_array lhs, const af_array rhs,
+             const bool batchMode) {
     return af_logic<af_ge_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_lt(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_lt(af_array *out, const af_array lhs, const af_array rhs,
+             const bool batchMode) {
     return af_logic<af_lt_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_le(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_le(af_array *out, const af_array lhs, const af_array rhs,
+             const bool batchMode) {
     return af_logic<af_le_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_and(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_and(af_array *out, const af_array lhs, const af_array rhs,
+              const bool batchMode) {
     return af_logic<af_and_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_or(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_or(af_array *out, const af_array lhs, const af_array rhs,
+             const bool batchMode) {
     return af_logic<af_or_t>(out, lhs, rhs, batchMode);
 }
 
 template<typename T, af_op_t op>
-static inline af_array bitOp(const af_array lhs, const af_array rhs, const dim4 &odims)
-{
-    af_array res = getHandle(bitOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
+static inline af_array bitOp(const af_array lhs, const af_array rhs,
+                             const dim4 &odims) {
+    af_array res =
+        getHandle(bitOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
     return res;
 }
 
 template<af_op_t op>
-static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs,
+                         const bool batchMode) {
     try {
         const af_dtype type = implicit(lhs, rhs);
 
-        const ArrayInfo& linfo = getInfo(lhs);
-        const ArrayInfo& rinfo = getInfo(rhs);
+        const ArrayInfo &linfo = getInfo(lhs);
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
-        if(odims.ndims() == 0) {
+        if (odims.ndims() == 0) {
             return af_create_handle(out, 0, nullptr, type);
         }
 
         af_array res;
         switch (type) {
-        case s32: res = bitOp<int    , op>(lhs, rhs, odims); break;
-        case u32: res = bitOp<uint   , op>(lhs, rhs, odims); break;
-        case u8 : res = bitOp<uchar  , op>(lhs, rhs, odims); break;
-        case b8 : res = bitOp<char   , op>(lhs, rhs, odims); break;
-        case s64: res = bitOp<intl   , op>(lhs, rhs, odims); break;
-        case u64: res = bitOp<uintl  , op>(lhs, rhs, odims); break;
-        case s16: res = bitOp<short  , op>(lhs, rhs, odims); break;
-        case u16: res = bitOp<ushort , op>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, type);
+            case s32: res = bitOp<int, op>(lhs, rhs, odims); break;
+            case u32: res = bitOp<uint, op>(lhs, rhs, odims); break;
+            case u8: res = bitOp<uchar, op>(lhs, rhs, odims); break;
+            case b8: res = bitOp<char, op>(lhs, rhs, odims); break;
+            case s64: res = bitOp<intl, op>(lhs, rhs, odims); break;
+            case u64: res = bitOp<uintl, op>(lhs, rhs, odims); break;
+            case s16: res = bitOp<short, op>(lhs, rhs, odims); break;
+            case u16: res = bitOp<ushort, op>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -526,27 +534,27 @@ static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs,
     return AF_SUCCESS;
 }
 
-af_err af_bitand(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_bitand(af_array *out, const af_array lhs, const af_array rhs,
+                 const bool batchMode) {
     return af_bitwise<af_bitand_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_bitor(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_bitor(af_array *out, const af_array lhs, const af_array rhs,
+                const bool batchMode) {
     return af_bitwise<af_bitor_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_bitxor(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_bitxor(af_array *out, const af_array lhs, const af_array rhs,
+                 const bool batchMode) {
     return af_bitwise<af_bitxor_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_bitshiftl(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_bitshiftl(af_array *out, const af_array lhs, const af_array rhs,
+                    const bool batchMode) {
     return af_bitwise<af_bitshiftl_t>(out, lhs, rhs, batchMode);
 }
 
-af_err af_bitshiftr(af_array *out, const af_array lhs, const af_array rhs, const bool batchMode)
-{
+af_err af_bitshiftr(af_array *out, const af_array lhs, const af_array rhs,
+                    const bool batchMode) {
     return af_bitwise<af_bitshiftr_t>(out, lhs, rhs, batchMode);
 }
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 58cb92bb85..1bde6589c6 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -7,121 +7,128 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/blas.h>
+#include <Array.hpp>
+#include <backend.hpp>
 #include <blas.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
-#include <Array.hpp>
+#include <sparse_blas.hpp>
+#include <sparse_handle.hpp>
 #include <af/array.h>
+#include <af/blas.h>
 #include <af/defines.h>
-#include <common/ArrayInfo.hpp>
-#include <sparse_handle.hpp>
-#include <sparse_blas.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
 
 template<typename T>
 static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
-                                    af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                                    af_mat_prop optLhs, af_mat_prop optRhs) {
     return getHandle(detail::matmul<T>(getSparseArray<T>(lhs), getArray<T>(rhs),
-                     optLhs, optRhs));
+                                       optLhs, optRhs));
 }
 
 template<typename T>
 static inline af_array matmul(const af_array lhs, const af_array rhs,
-                    af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    return getHandle(detail::matmul<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
+                              af_mat_prop optLhs, af_mat_prop optRhs) {
+    return getHandle(
+        detail::matmul<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
 template<typename T>
 static inline af_array dot(const af_array lhs, const af_array rhs,
-                    af_mat_prop optLhs, af_mat_prop optRhs)
-{
-    return getHandle(detail::dot<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
+                           af_mat_prop optLhs, af_mat_prop optRhs) {
+    return getHandle(
+        detail::dot<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
-af_err af_sparse_matmul(af_array *out,
-                        const af_array lhs, const af_array rhs,
-                        const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_sparse_matmul(af_array *out, const af_array lhs, const af_array rhs,
+                        const af_mat_prop optLhs, const af_mat_prop optRhs) {
     using namespace detail;
 
     try {
         common::SparseArrayBase lhsBase = getSparseArrayBase(lhs);
-        const ArrayInfo& rhsInfo = getInfo(rhs);
+        const ArrayInfo &rhsInfo        = getInfo(rhs);
 
-        ARG_ASSERT(2, lhsBase.isSparse() == true && rhsInfo.isSparse() == false);
+        ARG_ASSERT(2,
+                   lhsBase.isSparse() == true && rhsInfo.isSparse() == false);
 
         af_dtype lhs_type = lhsBase.getType();
         af_dtype rhs_type = rhsInfo.getType();
 
         ARG_ASSERT(1, lhsBase.getStorage() == AF_STORAGE_CSR);
 
-        if (!(optLhs == AF_MAT_NONE ||
-              optLhs == AF_MAT_TRANS ||
-              optLhs == AF_MAT_CTRANS)) {   // Note the ! operator.
-            AF_ERROR("Using this property is not yet supported in sparse matmul", AF_ERR_NOT_SUPPORTED);
+        if (!(optLhs == AF_MAT_NONE || optLhs == AF_MAT_TRANS ||
+              optLhs == AF_MAT_CTRANS)) {  // Note the ! operator.
+            AF_ERROR(
+                "Using this property is not yet supported in sparse matmul",
+                AF_ERR_NOT_SUPPORTED);
         }
 
         // No transpose options for RHS
         if (optRhs != AF_MAT_NONE) {
-            AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in matmul",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         if (rhsInfo.ndims() > 2) {
-            AF_ERROR("Sparse matmul can not be used in batch mode", AF_ERR_BATCH);
+            AF_ERROR("Sparse matmul can not be used in batch mode",
+                     AF_ERR_BATCH);
         }
 
         TYPE_ASSERT(lhs_type == rhs_type);
 
         af::dim4 ldims = lhsBase.dims();
-        int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
-        int rRowDim = (optRhs == AF_MAT_NONE) ? 0 : 1;
+        int lColDim    = (optLhs == AF_MAT_NONE) ? 1 : 0;
+        int rRowDim    = (optRhs == AF_MAT_NONE) ? 0 : 1;
 
         DIM_ASSERT(1, ldims[lColDim] == rhsInfo.dims()[rRowDim]);
 
         af_array output = 0;
-        switch(lhs_type) {
-            case f32: output = sparseMatmul<float  >(lhs, rhs, optLhs, optRhs);   break;
-            case c32: output = sparseMatmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
-            case f64: output = sparseMatmul<double >(lhs, rhs, optLhs, optRhs);   break;
-            case c64: output = sparseMatmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
-            default:  TYPE_ERROR(1, lhs_type);
+        switch (lhs_type) {
+            case f32:
+                output = sparseMatmul<float>(lhs, rhs, optLhs, optRhs);
+                break;
+            case c32:
+                output = sparseMatmul<cfloat>(lhs, rhs, optLhs, optRhs);
+                break;
+            case f64:
+                output = sparseMatmul<double>(lhs, rhs, optLhs, optRhs);
+                break;
+            case c64:
+                output = sparseMatmul<cdouble>(lhs, rhs, optLhs, optRhs);
+                break;
+            default: TYPE_ERROR(1, lhs_type);
         }
         std::swap(*out, output);
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_matmul(af_array *out,
-                 const af_array lhs, const af_array rhs,
-                 const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
+                 const af_mat_prop optLhs, const af_mat_prop optRhs) {
     using namespace detail;
 
     try {
-        const ArrayInfo& lhsInfo = getInfo(lhs, false, true);
-        const ArrayInfo& rhsInfo = getInfo(rhs, true, true);
+        const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
+        const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
 
-        if(lhsInfo.isSparse())
+        if (lhsInfo.isSparse())
             return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
 
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
 
-        if (!(optLhs == AF_MAT_NONE ||
-              optLhs == AF_MAT_TRANS ||
+        if (!(optLhs == AF_MAT_NONE || optLhs == AF_MAT_TRANS ||
               optLhs == AF_MAT_CTRANS)) {
-            AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in matmul",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
-        if (!(optRhs == AF_MAT_NONE ||
-              optRhs == AF_MAT_TRANS ||
+        if (!(optRhs == AF_MAT_NONE || optRhs == AF_MAT_TRANS ||
               optRhs == AF_MAT_CTRANS)) {
-            AF_ERROR("Using this property is not yet supported in matmul", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in matmul",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         dim4 lDims = lhsInfo.dims();
@@ -145,12 +152,12 @@ af_err af_matmul(af_array *out,
 
         DIM_ASSERT(1, lhsInfo.dims()[aColDim] == rhsInfo.dims()[bRowDim]);
 
-        switch(lhs_type) {
-            case f32: output = matmul<float  >(lhs, rhs, optLhs, optRhs);   break;
-            case c32: output = matmul<cfloat >(lhs, rhs, optLhs, optRhs);   break;
-            case f64: output = matmul<double >(lhs, rhs, optLhs, optRhs);   break;
-            case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs);   break;
-            default:  TYPE_ERROR(1, lhs_type);
+        switch (lhs_type) {
+            case f32: output = matmul<float>(lhs, rhs, optLhs, optRhs); break;
+            case c32: output = matmul<cfloat>(lhs, rhs, optLhs, optRhs); break;
+            case f64: output = matmul<double>(lhs, rhs, optLhs, optRhs); break;
+            case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs); break;
+            default: TYPE_ERROR(1, lhs_type);
         }
         std::swap(*out, output);
     }
@@ -158,33 +165,30 @@ af_err af_matmul(af_array *out,
     return AF_SUCCESS;
 }
 
-af_err af_dot(af_array *out,
-              const af_array lhs, const af_array rhs,
-              const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
+              const af_mat_prop optLhs, const af_mat_prop optRhs) {
     using namespace detail;
 
     try {
-        const ArrayInfo& lhsInfo = getInfo(lhs);
-        const ArrayInfo& rhsInfo = getInfo(rhs);
+        const ArrayInfo &lhsInfo = getInfo(lhs);
+        const ArrayInfo &rhsInfo = getInfo(rhs);
 
         if (optLhs != AF_MAT_NONE && optLhs != AF_MAT_CONJ) {
-            AF_ERROR("Using this property is not yet supported in dot", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in dot",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         if (optRhs != AF_MAT_NONE && optRhs != AF_MAT_CONJ) {
-            AF_ERROR("Using this property is not yet supported in dot", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in dot",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         DIM_ASSERT(1, lhsInfo.dims()[0] == rhsInfo.dims()[0]);
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
 
-        if(lhsInfo.ndims() == 0) {
-            return af_retain_array(out, lhs);
-        }
-        if (lhsInfo.ndims() > 1 ||
-            rhsInfo.ndims() > 1) {
+        if (lhsInfo.ndims() == 0) { return af_retain_array(out, lhs); }
+        if (lhsInfo.ndims() > 1 || rhsInfo.ndims() > 1) {
             AF_ERROR("dot can not be used in batch mode", AF_ERR_BATCH);
         }
 
@@ -192,12 +196,12 @@ af_err af_dot(af_array *out,
 
         af_array output = 0;
 
-        switch(lhs_type) {
-        case f32: output = dot<float  >(lhs, rhs, optLhs, optRhs);    break;
-        case c32: output = dot<cfloat >(lhs, rhs, optLhs, optRhs);    break;
-        case f64: output = dot<double >(lhs, rhs, optLhs, optRhs);    break;
-        case c64: output = dot<cdouble>(lhs, rhs, optLhs, optRhs);    break;
-        default:  TYPE_ERROR(1, lhs_type);
+        switch (lhs_type) {
+            case f32: output = dot<float>(lhs, rhs, optLhs, optRhs); break;
+            case c32: output = dot<cfloat>(lhs, rhs, optLhs, optRhs); break;
+            case f64: output = dot<double>(lhs, rhs, optLhs, optRhs); break;
+            case c64: output = dot<cdouble>(lhs, rhs, optLhs, optRhs); break;
+            default: TYPE_ERROR(1, lhs_type);
         }
         std::swap(*out, output);
     }
@@ -206,23 +210,20 @@ af_err af_dot(af_array *out,
 }
 
 template<typename T>
-static inline
-T dotAll(af_array out)
-{
+static inline T dotAll(af_array out) {
     T res;
     AF_CHECK(af_eval(out));
     AF_CHECK(af_get_data_ptr((void *)&res, out));
     return res;
 }
 
-af_err af_dot_all(double *rval, double *ival,
-                  const af_array lhs, const af_array rhs,
-                  const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_dot_all(double *rval, double *ival, const af_array lhs,
+                  const af_array rhs, const af_mat_prop optLhs,
+                  const af_mat_prop optRhs) {
     using namespace detail;
 
     try {
-                  *rval = 0;
+        *rval = 0;
         if (ival) *ival = 0;
 
         af_array out = 0;
@@ -231,25 +232,23 @@ af_err af_dot_all(double *rval, double *ival,
         ArrayInfo lhsInfo = getInfo(lhs);
         af_dtype lhs_type = lhsInfo.getType();
 
-        switch(lhs_type) {
-        case f32: *rval = dotAll<float >(out); break;
-        case f64: *rval = dotAll<double>(out); break;
-        case c32:
-        {
-            cfloat temp = dotAll<cfloat>(out);
-                      *rval = real(temp);
-            if (ival) *ival = imag(temp);
-        } break;
-        case c64:
-        {
-            cdouble temp = dotAll<cdouble>(out);
-                      *rval = real(temp);
-            if (ival) *ival = imag(temp);
-        } break;
-        default: TYPE_ERROR(1, lhs_type);
+        switch (lhs_type) {
+            case f32: *rval = dotAll<float>(out); break;
+            case f64: *rval = dotAll<double>(out); break;
+            case c32: {
+                cfloat temp = dotAll<cfloat>(out);
+                *rval       = real(temp);
+                if (ival) *ival = imag(temp);
+            } break;
+            case c64: {
+                cdouble temp = dotAll<cdouble>(out);
+                *rval        = real(temp);
+                if (ival) *ival = imag(temp);
+            } break;
+            default: TYPE_ERROR(1, lhs_type);
         }
 
-        if(out != 0) AF_CHECK(af_release_array(out));
+        if (out != 0) AF_CHECK(af_release_array(out));
     }
     CATCHALL
     return AF_SUCCESS;
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 144bc72e23..06d7a7c090 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -7,28 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <af/seq.h>
-#include <common/err_common.hpp>
-#include <backend.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
 #include <arith.hpp>
+#include <backend.hpp>
 #include <canny.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <convolve.hpp>
 #include <copy.hpp>
+#include <handle.hpp>
 #include <histogram.hpp>
+#include <iota.hpp>
+#include <ireduce.hpp>
 #include <logic.hpp>
+#include <reduce.hpp>
 #include <sobel.hpp>
+#include <tile.hpp>
 #include <transpose.hpp>
 #include <unary.hpp>
-#include <reduce.hpp>
-#include <ireduce.hpp>
-#include <tile.hpp>
-#include <iota.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
+#include <af/seq.h>
 #include <utility>
 #include <vector>
 
@@ -36,8 +36,8 @@ using af::dim4;
 using std::vector;
 using namespace detail;
 
-Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy, const bool& isf)
-{
+Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy,
+                               const bool& isf) {
     if (isf) {
         Array<float> gx2 = detail::abs<float, float>(gx);
         Array<float> gy2 = detail::abs<float, float>(gy);
@@ -45,41 +45,42 @@ Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy, c
     } else {
         Array<float> gx2 = detail::arithOp<float, af_mul_t>(gx, gx, gx.dims());
         Array<float> gy2 = detail::arithOp<float, af_mul_t>(gy, gy, gy.dims());
-        Array<float> sg  = detail::arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
+        Array<float> sg =
+            detail::arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
         return detail::unaryOp<float, af_sqrt_t>(sg);
     }
 }
 
 Array<float> otsuThreshold(const Array<float>& supEdges,
-                           const unsigned NUM_BINS, const float maxVal)
-{
-    Array<uint> hist = detail::histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
+                           const unsigned NUM_BINS, const float maxVal) {
+    Array<uint> hist =
+        detail::histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
 
     const af::dim4 hDims = hist.dims();
 
     // reduce along histogram dimension i.e. 0th dimension
-    auto totals  = reduce<af_add_t, uint, float>(hist, 0);
+    auto totals = reduce<af_add_t, uint, float>(hist, 0);
 
     // tile histogram total along 0th dimension
     auto ttotals = tile(totals, af::dim4(hDims[0]));
 
     // pixel frequency probabilities
-    auto probability = arithOp<float, af_div_t>(cast<float, uint>(hist), ttotals, hDims);
+    auto probability =
+        arithOp<float, af_div_t>(cast<float, uint>(hist), ttotals, hDims);
 
     std::vector<af_seq> seqBegin(4, af_span);
     std::vector<af_seq> seqRest(4, af_span);
 
-    seqBegin[0] = af_make_seq(0, hDims[0]-1, 1);
-    seqRest[0] = af_make_seq(0, hDims[0]-1, 1);
+    seqBegin[0] = af_make_seq(0, hDims[0] - 1, 1);
+    seqRest[0]  = af_make_seq(0, hDims[0] - 1, 1);
 
     const af::dim4& iDims = supEdges.dims();
 
     Array<float> sigmas = detail::createEmptyArray<float>(hDims);
 
-    for (unsigned b=0; b<(NUM_BINS-1); ++b)
-    {
+    for (unsigned b = 0; b < (NUM_BINS - 1); ++b) {
         seqBegin[0].end  = (double)b;
-        seqRest[0].begin = (double)(b+1);
+        seqRest[0].begin = (double)(b + 1);
 
         auto frontPartition = createSubArray(probability, seqBegin, false);
         auto endPartition   = createSubArray(probability, seqRest, false);
@@ -87,16 +88,17 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
         auto qL = reduce<af_add_t, float, float>(frontPartition, 0);
         auto qH = reduce<af_add_t, float, float>(endPartition, 0);
 
-        const dim4 fdims(b+1, hDims[1], hDims[2], hDims[3]);
-        const dim4 edims(NUM_BINS-1-b, hDims[1], hDims[2], hDims[3]);
+        const dim4 fdims(b + 1, hDims[1], hDims[2], hDims[3]);
+        const dim4 edims(NUM_BINS - 1 - b, hDims[1], hDims[2], hDims[3]);
 
         const dim4 tdims(1, hDims[1], hDims[2], hDims[3]);
-        auto frontWeights   = iota<float>(dim4(b+1), tdims);
-        auto endWeights     = iota<float>(dim4(NUM_BINS-1-b), tdims);
-        auto offsetValues   = createValueArray<float>(edims, b+1);
+        auto frontWeights = iota<float>(dim4(b + 1), tdims);
+        auto endWeights   = iota<float>(dim4(NUM_BINS - 1 - b), tdims);
+        auto offsetValues = createValueArray<float>(edims, b + 1);
 
         endWeights = arithOp<float, af_add_t>(endWeights, offsetValues, edims);
-        auto __muL = arithOp<float, af_mul_t>(frontPartition, frontWeights, fdims);
+        auto __muL =
+            arithOp<float, af_mul_t>(frontPartition, frontWeights, fdims);
         auto __muH = arithOp<float, af_mul_t>(endPartition, endWeights, edims);
         auto _muL  = reduce<af_add_t, float, float>(__muL, 0);
         auto _muH  = reduce<af_add_t, float, float>(__muH, 0);
@@ -116,8 +118,8 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
         copyArray(binRes, sigma);
     }
 
-    dim4 odims = sigmas.dims();
-    odims[0] = 1;
+    dim4 odims          = sigmas.dims();
+    odims[0]            = 1;
     Array<float> thresh = createEmptyArray<float>(odims);
     Array<uint> locs    = createEmptyArray<uint>(odims);
 
@@ -126,62 +128,70 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
     return cast<float, uint>(tile(locs, dim4(iDims[0], iDims[1], 1, 1)));
 }
 
-Array<float> normalize(const Array<float>& supEdges, const float minVal, const float maxVal)
-{
+Array<float> normalize(const Array<float>& supEdges, const float minVal,
+                       const float maxVal) {
     auto minArray = createValueArray<float>(supEdges.dims(), minVal);
-    auto diff     = arithOp<float, af_sub_t>(supEdges, minArray, supEdges.dims());
-    auto denom    = createValueArray<float>(supEdges.dims(), (maxVal-minVal));
+    auto diff  = arithOp<float, af_sub_t>(supEdges, minArray, supEdges.dims());
+    auto denom = createValueArray<float>(supEdges.dims(), (maxVal - minVal));
     return arithOp<float, af_div_t>(diff, denom, supEdges.dims());
 }
 
-std::pair< Array<char>, Array<char> >
-computeCandidates(const Array<float>& supEdges, const float t1,
-                  const af_canny_threshold ct, const float t2)
-{
+std::pair<Array<char>, Array<char>> computeCandidates(
+    const Array<float>& supEdges, const float t1, const af_canny_threshold ct,
+    const float t2) {
     float maxVal = detail::reduce_all<af_max_t, float, float>(supEdges);
     const unsigned NUM_BINS = static_cast<unsigned>(maxVal);
 
     auto lowRatio = createValueArray<float>(supEdges.dims(), t1);
 
-    switch(ct)
-    {
-        case AF_CANNY_THRESHOLD_AUTO_OTSU:
-            {
-                auto T2            = otsuThreshold(supEdges, NUM_BINS, maxVal);
-                auto T1            = arithOp<float, af_mul_t>(T2, lowRatio, T2.dims());
-                Array<char> weak1  = logicOp<float, af_ge_t >(supEdges,    T1, supEdges.dims());
-                Array<char> weak2  = logicOp<float, af_lt_t >(supEdges,    T2, supEdges.dims());
-                Array<char> weak   = logicOp<char , af_and_t>(   weak1, weak2,    weak1.dims());
-                Array<char> strong = logicOp<float, af_ge_t >(supEdges,    T2, supEdges.dims());
-                return std::make_pair(strong, weak);
-            };
-        default:
-            {
-                float minVal       = detail::reduce_all<af_min_t, float, float>(supEdges);
-                auto normG         = normalize(supEdges, minVal, maxVal);
-                auto T2            = createValueArray<float>(supEdges.dims(), t2);
-                auto T1            = createValueArray<float>(supEdges.dims(), t1);
-                Array<char> weak1  = logicOp<float, af_ge_t >(normG,    T1, normG.dims());
-                Array<char> weak2  = logicOp<float, af_lt_t >(normG,    T2, normG.dims());
-                Array<char> weak   = logicOp<char , af_and_t>(weak1, weak2, weak1.dims());
-                Array<char> strong = logicOp<float, af_ge_t >(normG,    T2, normG.dims());
-                return std::make_pair(strong, weak);
-            };
+    switch (ct) {
+        case AF_CANNY_THRESHOLD_AUTO_OTSU: {
+            auto T2 = otsuThreshold(supEdges, NUM_BINS, maxVal);
+            auto T1 = arithOp<float, af_mul_t>(T2, lowRatio, T2.dims());
+            Array<char> weak1 =
+                logicOp<float, af_ge_t>(supEdges, T1, supEdges.dims());
+            Array<char> weak2 =
+                logicOp<float, af_lt_t>(supEdges, T2, supEdges.dims());
+            Array<char> weak =
+                logicOp<char, af_and_t>(weak1, weak2, weak1.dims());
+            Array<char> strong =
+                logicOp<float, af_ge_t>(supEdges, T2, supEdges.dims());
+            return std::make_pair(strong, weak);
+        };
+        default: {
+            float minVal = detail::reduce_all<af_min_t, float, float>(supEdges);
+            auto normG   = normalize(supEdges, minVal, maxVal);
+            auto T2      = createValueArray<float>(supEdges.dims(), t2);
+            auto T1      = createValueArray<float>(supEdges.dims(), t1);
+            Array<char> weak1 =
+                logicOp<float, af_ge_t>(normG, T1, normG.dims());
+            Array<char> weak2 =
+                logicOp<float, af_lt_t>(normG, T2, normG.dims());
+            Array<char> weak =
+                logicOp<char, af_and_t>(weak1, weak2, weak1.dims());
+            Array<char> strong =
+                logicOp<float, af_ge_t>(normG, T2, normG.dims());
+            return std::make_pair(strong, weak);
+        };
     }
 }
 
 template<typename T>
-af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold ct,
-                     const float t2, const unsigned sw, const bool isf)
-{
-    static const vector<float> v{-0.11021f, -0.23691f, -0.30576f, -0.23691f, -0.11021f};
-    Array<float> cFilter= detail::createHostDataArray<float>(dim4(5, 1), v.data());
-    Array<float> rFilter= detail::createHostDataArray<float>(dim4(1, 5), v.data());
+af_array cannyHelper(const Array<T> in, const float t1,
+                     const af_canny_threshold ct, const float t2,
+                     const unsigned sw, const bool isf) {
+    static const vector<float> v{-0.11021f, -0.23691f, -0.30576f, -0.23691f,
+                                 -0.11021f};
+    Array<float> cFilter =
+        detail::createHostDataArray<float>(dim4(5, 1), v.data());
+    Array<float> rFilter =
+        detail::createHostDataArray<float>(dim4(1, 5), v.data());
 
     // Run separable convolution to smooth the input image
-    Array<float> smt = detail::convolve2<float, float, false>(cast<float, T>(in), cFilter, rFilter);
+    Array<float> smt = detail::convolve2<float, float, false>(
+        cast<float, T>(in), cFilter, rFilter);
 
-    auto g  = detail::sobelDerivatives<float, float>(smt, sw);
+    auto g          = detail::sobelDerivatives<float, float>(smt, sw);
     Array<float> gx = g.first;
     Array<float> gy = g.second;
 
@@ -191,36 +201,58 @@ af_array cannyHelper(const Array<T> in, const float t1, const af_canny_threshold
 
     auto swpair = computeCandidates(supEdges, t1, ct, t2);
 
-    return getHandle(detail::edgeTrackingByHysteresis(swpair.first, swpair.second));
+    return getHandle(
+        detail::edgeTrackingByHysteresis(swpair.first, swpair.second));
 }
 
 af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
-                const float t1, const float t2, const unsigned sw, const bool isf)
-{
+                const float t1, const float t2, const unsigned sw,
+                const bool isf) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        af::dim4 dims         = info.dims();
 
         DIM_ASSERT(2, (dims.ndims() >= 2));
         // Input should be a minimum of 5x5 image
         // since the gaussian filter used for smoothing
         // the input is of 5x5 size. It's not mandatory but
         // it is essentially of no use if image is less than 5x5
-        DIM_ASSERT(2, (dims[0]>=5 && dims[1]>=5));
-        ARG_ASSERT(5, (sw==3));
+        DIM_ASSERT(2, (dims[0] >= 5 && dims[1] >= 5));
+        ARG_ASSERT(5, (sw == 3));
 
         af_array output;
 
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: output = cannyHelper<float >(getArray<float >(in), t1, ct, t2, sw, isf); break;
-            case f64: output = cannyHelper<double>(getArray<double>(in), t1, ct, t2, sw, isf); break;
-            case s32: output = cannyHelper<int   >(getArray<int   >(in), t1, ct, t2, sw, isf); break;
-            case u32: output = cannyHelper<uint  >(getArray<uint  >(in), t1, ct, t2, sw, isf); break;
-            case s16: output = cannyHelper<short >(getArray<short >(in), t1, ct, t2, sw, isf); break;
-            case u16: output = cannyHelper<ushort>(getArray<ushort>(in), t1, ct, t2, sw, isf); break;
-            case u8:  output = cannyHelper<uchar >(getArray<uchar >(in), t1, ct, t2, sw, isf); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                output = cannyHelper<float>(getArray<float>(in), t1, ct, t2, sw,
+                                            isf);
+                break;
+            case f64:
+                output = cannyHelper<double>(getArray<double>(in), t1, ct, t2,
+                                             sw, isf);
+                break;
+            case s32:
+                output =
+                    cannyHelper<int>(getArray<int>(in), t1, ct, t2, sw, isf);
+                break;
+            case u32:
+                output =
+                    cannyHelper<uint>(getArray<uint>(in), t1, ct, t2, sw, isf);
+                break;
+            case s16:
+                output = cannyHelper<short>(getArray<short>(in), t1, ct, t2, sw,
+                                            isf);
+                break;
+            case u16:
+                output = cannyHelper<ushort>(getArray<ushort>(in), t1, ct, t2,
+                                             sw, isf);
+                break;
+            case u8:
+                output = cannyHelper<uchar>(getArray<uchar>(in), t1, ct, t2, sw,
+                                            isf);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         // output array is binary array
         std::swap(output, *out);
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 30a062c2a6..8309b4a834 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -7,71 +7,68 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <sparse_handle.hpp>
-#include <sparse.hpp>
 #include <common/ArrayInfo.hpp>
 #include <optypes.hpp>
+#include <sparse.hpp>
+#include <sparse_handle.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/defines.h>
 
+#include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
 
 using namespace detail;
 
-static af_array cast(const af_array in, const af_dtype type)
-{
+static af_array cast(const af_array in, const af_dtype type) {
     const ArrayInfo& info = getInfo(in, false, true);
 
-    if (info.getType() == type) {
-        return retain(in);
-    }
+    if (info.getType() == type) { return retain(in); }
 
-    if(info.isSparse()) {
+    if (info.isSparse()) {
         switch (type) {
-        case f32: return getHandle(castSparse<float  >(in));
-        case f64: return getHandle(castSparse<double >(in));
-        case c32: return getHandle(castSparse<cfloat >(in));
-        case c64: return getHandle(castSparse<cdouble>(in));
-        default: TYPE_ERROR(2, type);
+            case f32: return getHandle(castSparse<float>(in));
+            case f64: return getHandle(castSparse<double>(in));
+            case c32: return getHandle(castSparse<cfloat>(in));
+            case c64: return getHandle(castSparse<cdouble>(in));
+            default: TYPE_ERROR(2, type);
         }
     } else {
         switch (type) {
-        case f32: return getHandle(castArray<float   >(in));
-        case f64: return getHandle(castArray<double  >(in));
-        case c32: return getHandle(castArray<cfloat  >(in));
-        case c64: return getHandle(castArray<cdouble >(in));
-        case s32: return getHandle(castArray<int     >(in));
-        case u32: return getHandle(castArray<uint    >(in));
-        case u8 : return getHandle(castArray<uchar   >(in));
-        case b8 : return getHandle(castArray<char    >(in));
-        case s64: return getHandle(castArray<intl    >(in));
-        case u64: return getHandle(castArray<uintl   >(in));
-        case s16: return getHandle(castArray<short   >(in));
-        case u16: return getHandle(castArray<ushort  >(in));
-        default: TYPE_ERROR(2, type);
+            case f32: return getHandle(castArray<float>(in));
+            case f64: return getHandle(castArray<double>(in));
+            case c32: return getHandle(castArray<cfloat>(in));
+            case c64: return getHandle(castArray<cdouble>(in));
+            case s32: return getHandle(castArray<int>(in));
+            case u32: return getHandle(castArray<uint>(in));
+            case u8: return getHandle(castArray<uchar>(in));
+            case b8: return getHandle(castArray<char>(in));
+            case s64: return getHandle(castArray<intl>(in));
+            case u64: return getHandle(castArray<uintl>(in));
+            case s16: return getHandle(castArray<short>(in));
+            case u16: return getHandle(castArray<ushort>(in));
+            default: TYPE_ERROR(2, type);
         }
     }
 }
 
-af_err af_cast(af_array *out, const af_array in, const af_dtype type)
-{
+af_err af_cast(af_array* out, const af_array in, const af_dtype type) {
     try {
         const ArrayInfo& info = getInfo(in, false, true);
 
         af_dtype inType = info.getType();
-        if((inType == c32 || inType == c64)
-            && (type == f32 || type == f64)) {
-            AF_ERROR("Casting is not allowed from complex (c32/c64) to real (f32/f64) types.\n"
-                     "Use abs, real, imag etc to convert complex to floating type.",
-                     AF_ERR_TYPE);
+        if ((inType == c32 || inType == c64) && (type == f32 || type == f64)) {
+            AF_ERROR(
+                "Casting is not allowed from complex (c32/c64) to real "
+                "(f32/f64) types.\n"
+                "Use abs, real, imag etc to convert complex to floating type.",
+                AF_ERR_TYPE);
         }
 
         dim4 idims = info.dims();
-        if(idims.elements() == 0) {
+        if (idims.elements() == 0) {
             return af_create_handle(out, 0, nullptr, type);
         }
 
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index 3605a30b50..b83369d4dc 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -7,34 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
-#include <common/ArrayInfo.hpp>
 #include <cholesky.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array cholesky(int *info, const af_array in, const bool is_upper)
-{
+static inline af_array cholesky(int *info, const af_array in,
+                                const bool is_upper) {
     return getHandle(cholesky<T>(info, getArray<T>(in), is_upper));
 }
 
 template<typename T>
-static inline int cholesky_inplace(af_array in, const bool is_upper)
-{
-     return cholesky_inplace<T>(getArray<T>(in), is_upper);
+static inline int cholesky_inplace(af_array in, const bool is_upper) {
+    return cholesky_inplace<T>(getArray<T>(in), is_upper);
 }
 
-af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_upper)
-{
+af_err af_cholesky(af_array *out, int *info, const af_array in,
+                   const bool is_upper) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("cholesky can not be used in batch mode", AF_ERR_BATCH);
@@ -42,19 +41,20 @@ af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_up
 
         af_dtype type = i_info.getType();
 
-        if(i_info.ndims() == 0) {
+        if (i_info.ndims() == 0) {
             return af_create_handle(out, 0, nullptr, type);
         }
-        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
-        ARG_ASSERT(2, i_info.isFloating());                  // Only floating and complex types
+        DIM_ASSERT(
+            1, i_info.dims()[0] == i_info.dims()[1]);  // Only square matrices
+        ARG_ASSERT(2, i_info.isFloating());  // Only floating and complex types
 
         af_array output;
-        switch(type) {
-            case f32: output = cholesky<float  >(info, in, is_upper);  break;
-            case f64: output = cholesky<double >(info, in, is_upper);  break;
-            case c32: output = cholesky<cfloat >(info, in, is_upper);  break;
-            case c64: output = cholesky<cdouble>(info, in, is_upper);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = cholesky<float>(info, in, is_upper); break;
+            case f64: output = cholesky<double>(info, in, is_upper); break;
+            case c32: output = cholesky<cfloat>(info, in, is_upper); break;
+            case c64: output = cholesky<cdouble>(info, in, is_upper); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
@@ -63,31 +63,28 @@ af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_up
     return AF_SUCCESS;
 }
 
-af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper)
-{
+af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("cholesky can not be used in batch mode", AF_ERR_BATCH);
         }
 
         af_dtype type = i_info.getType();
-        if(i_info.ndims() == 0) {
-            return AF_SUCCESS;
-        }
-        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
-        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]); // Only square matrices
-
+        if (i_info.ndims() == 0) { return AF_SUCCESS; }
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
+        DIM_ASSERT(
+            1, i_info.dims()[0] == i_info.dims()[1]);  // Only square matrices
 
         int out;
 
-        switch(type) {
-            case f32: out = cholesky_inplace<float  >(in, is_upper);  break;
-            case f64: out = cholesky_inplace<double >(in, is_upper);  break;
-            case c32: out = cholesky_inplace<cfloat >(in, is_upper);  break;
-            case c64: out = cholesky_inplace<cdouble>(in, is_upper);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: out = cholesky_inplace<float>(in, is_upper); break;
+            case f64: out = cholesky_inplace<double>(in, is_upper); break;
+            case c32: out = cholesky_inplace<cfloat>(in, is_upper); break;
+            case c64: out = cholesky_inplace<cdouble>(in, is_upper); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*info, out);
     }
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index f6cef2e8e1..464383d05c 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <af/data.h>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
-#include <implicit.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
+#include <implicit.hpp>
+#include <optypes.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
 
 #include <arith.hpp>
 #include <logic.hpp>
@@ -25,20 +25,17 @@ using namespace detail;
 using af::dim4;
 
 template<typename T>
-static inline af_array clampOp(const af_array in,
-                               const af_array lo,
-                               const af_array hi,
-                               const dim4 &odims)
-{
+static inline af_array clampOp(const af_array in, const af_array lo,
+                               const af_array hi, const dim4& odims) {
     const Array<T> L = castArray<T>(lo);
     const Array<T> H = castArray<T>(hi);
     const Array<T> I = castArray<T>(in);
-    return getHandle(arithOp<T, af_min_t>(arithOp<T, af_max_t>(I, L, odims), H, odims));
+    return getHandle(
+        arithOp<T, af_min_t>(arithOp<T, af_max_t>(I, L, odims), H, odims));
 }
 
-af_err af_clamp(af_array *out, const af_array in,
-                const af_array lo, const af_array hi, const bool batch)
-{
+af_err af_clamp(af_array* out, const af_array in, const af_array lo,
+                const af_array hi, const bool batch) {
     try {
         const ArrayInfo& linfo = getInfo(lo);
         const ArrayInfo& hinfo = getInfo(hi);
@@ -47,24 +44,24 @@ af_err af_clamp(af_array *out, const af_array in,
         DIM_ASSERT(2, linfo.dims() == hinfo.dims());
         TYPE_ASSERT(linfo.getType() == hinfo.getType());
 
-        dim4 odims = getOutDims(iinfo.dims(), linfo.dims(), batch);
+        dim4 odims           = getOutDims(iinfo.dims(), linfo.dims(), batch);
         const af_dtype otype = implicit(iinfo.getType(), linfo.getType());
 
         af_array res;
         switch (otype) {
-        case f32: res = clampOp<float  >(in, lo, hi, odims); break;
-        case f64: res = clampOp<double >(in, lo, hi, odims); break;
-        case c32: res = clampOp<cfloat >(in, lo, hi, odims); break;
-        case c64: res = clampOp<cdouble>(in, lo, hi, odims); break;
-        case s32: res = clampOp<int    >(in, lo, hi, odims); break;
-        case u32: res = clampOp<uint   >(in, lo, hi, odims); break;
-        case u8 : res = clampOp<uchar  >(in, lo, hi, odims); break;
-        case b8 : res = clampOp<char   >(in, lo, hi, odims); break;
-        case s64: res = clampOp<intl   >(in, lo, hi, odims); break;
-        case u64: res = clampOp<uintl  >(in, lo, hi, odims); break;
-        case s16: res = clampOp<short  >(in, lo, hi, odims); break;
-        case u16: res = clampOp<ushort >(in, lo, hi, odims); break;
-        default: TYPE_ERROR(0, otype);
+            case f32: res = clampOp<float>(in, lo, hi, odims); break;
+            case f64: res = clampOp<double>(in, lo, hi, odims); break;
+            case c32: res = clampOp<cfloat>(in, lo, hi, odims); break;
+            case c64: res = clampOp<cdouble>(in, lo, hi, odims); break;
+            case s32: res = clampOp<int>(in, lo, hi, odims); break;
+            case u32: res = clampOp<uint>(in, lo, hi, odims); break;
+            case u8: res = clampOp<uchar>(in, lo, hi, odims); break;
+            case b8: res = clampOp<char>(in, lo, hi, odims); break;
+            case s64: res = clampOp<intl>(in, lo, hi, odims); break;
+            case u64: res = clampOp<uintl>(in, lo, hi, odims); break;
+            case s16: res = clampOp<short>(in, lo, hi, odims); break;
+            case u16: res = clampOp<ushort>(in, lo, hi, odims); break;
+            default: TYPE_ERROR(0, otype);
         }
 
         std::swap(*out, res);
diff --git a/src/api/c/colorspace.cpp b/src/api/c/colorspace.cpp
index 947c24e36d..8fe078d6a5 100644
--- a/src/api/c/colorspace.cpp
+++ b/src/api/c/colorspace.cpp
@@ -7,70 +7,72 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
+#include <common/err_common.hpp>
 #include <af/array.h>
+#include <af/defines.h>
 #include <af/image.h>
-#include <common/err_common.hpp>
 
 template<af_cspace_t FROM, af_cspace_t TO>
-void color_space(af_array *out, const af_array image)
-{
+void color_space(af_array *out, const af_array image) {
     UNUSED(out);
     UNUSED(image);
-    AF_ERROR("Color Space: Conversion from source type to output type not supported",
-             AF_ERR_NOT_SUPPORTED);
+    AF_ERROR(
+        "Color Space: Conversion from source type to output type not supported",
+        AF_ERR_NOT_SUPPORTED);
 }
 
-#define INSTANTIATE_CSPACE_DEFS1(F, T, FUNC)                    \
-template<>                                                      \
-void color_space<F, T>(af_array *out, const af_array image)     \
-{                                                               \
-    AF_CHECK(FUNC(out, image));                                 \
-}
+#define INSTANTIATE_CSPACE_DEFS1(F, T, FUNC)                       \
+    template<>                                                     \
+    void color_space<F, T>(af_array * out, const af_array image) { \
+        AF_CHECK(FUNC(out, image));                                \
+    }
 
-#define INSTANTIATE_CSPACE_DEFS2(F, T, FUNC, ...)               \
-template<>                                                      \
-void color_space<F, T>(af_array *out, const af_array image)     \
-{                                                               \
-    AF_CHECK(FUNC(out, image, __VA_ARGS__));                    \
-}
+#define INSTANTIATE_CSPACE_DEFS2(F, T, FUNC, ...)                  \
+    template<>                                                     \
+    void color_space<F, T>(af_array * out, const af_array image) { \
+        AF_CHECK(FUNC(out, image, __VA_ARGS__));                   \
+    }
 
-INSTANTIATE_CSPACE_DEFS1(AF_HSV  , AF_RGB  , af_hsv2rgb  );
-INSTANTIATE_CSPACE_DEFS1(AF_RGB  , AF_HSV  , af_rgb2hsv  );
+INSTANTIATE_CSPACE_DEFS1(AF_HSV, AF_RGB, af_hsv2rgb);
+INSTANTIATE_CSPACE_DEFS1(AF_RGB, AF_HSV, af_rgb2hsv);
 
-INSTANTIATE_CSPACE_DEFS2(AF_RGB  , AF_GRAY , af_rgb2gray , 0.2126f, 0.7152f, 0.0722f);
-INSTANTIATE_CSPACE_DEFS2(AF_GRAY , AF_RGB  , af_gray2rgb , 1.0f, 1.0f, 1.0f);
-INSTANTIATE_CSPACE_DEFS2(AF_YCbCr, AF_RGB  , af_ycbcr2rgb, AF_YCC_601);
-INSTANTIATE_CSPACE_DEFS2(AF_RGB  , AF_YCbCr, af_rgb2ycbcr, AF_YCC_601);
+INSTANTIATE_CSPACE_DEFS2(AF_RGB, AF_GRAY, af_rgb2gray, 0.2126f, 0.7152f,
+                         0.0722f);
+INSTANTIATE_CSPACE_DEFS2(AF_GRAY, AF_RGB, af_gray2rgb, 1.0f, 1.0f, 1.0f);
+INSTANTIATE_CSPACE_DEFS2(AF_YCbCr, AF_RGB, af_ycbcr2rgb, AF_YCC_601);
+INSTANTIATE_CSPACE_DEFS2(AF_RGB, AF_YCbCr, af_rgb2ycbcr, AF_YCC_601);
 
 template<af_cspace_t FROM>
-static void color_space(af_array *out, const af_array image, const af_cspace_t to)
-{
-    switch(to) {
-        case AF_GRAY : color_space<FROM, AF_GRAY >(out, image);     break;
-        case AF_RGB  : color_space<FROM, AF_RGB  >(out, image);     break;
-        case AF_HSV  : color_space<FROM, AF_HSV  >(out, image);     break;
-        case AF_YCbCr: color_space<FROM, AF_YCbCr>(out, image);     break;
-        default: AF_ERROR("Incorrect enum value for output color type", AF_ERR_ARG);
+static void color_space(af_array *out, const af_array image,
+                        const af_cspace_t to) {
+    switch (to) {
+        case AF_GRAY: color_space<FROM, AF_GRAY>(out, image); break;
+        case AF_RGB: color_space<FROM, AF_RGB>(out, image); break;
+        case AF_HSV: color_space<FROM, AF_HSV>(out, image); break;
+        case AF_YCbCr: color_space<FROM, AF_YCbCr>(out, image); break;
+        default:
+            AF_ERROR("Incorrect enum value for output color type", AF_ERR_ARG);
     }
 }
 
-af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to, const af_cspace_t from)
-{
+af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to,
+                      const af_cspace_t from) {
     try {
-        if (from == to) {
-            return af_retain_array(out, image);
-        }
+        if (from == to) { return af_retain_array(out, image); }
 
-        ARG_ASSERT(2, (to == AF_GRAY || to == AF_RGB || to == AF_HSV || to == AF_YCbCr));
-        ARG_ASSERT(2, (from == AF_GRAY || from == AF_RGB || from == AF_HSV || from == AF_YCbCr));
+        ARG_ASSERT(2, (to == AF_GRAY || to == AF_RGB || to == AF_HSV ||
+                       to == AF_YCbCr));
+        ARG_ASSERT(2, (from == AF_GRAY || from == AF_RGB || from == AF_HSV ||
+                       from == AF_YCbCr));
 
-        switch(from) {
-            case AF_GRAY : color_space<AF_GRAY >(out, image, to);   break;
-            case AF_RGB  : color_space<AF_RGB  >(out, image, to);   break;
-            case AF_HSV  : color_space<AF_HSV  >(out, image, to);   break;
-            case AF_YCbCr: color_space<AF_YCbCr>(out, image, to);   break;
-            default: AF_ERROR("Incorrect enum value for input color type", AF_ERR_ARG);
+        switch (from) {
+            case AF_GRAY: color_space<AF_GRAY>(out, image, to); break;
+            case AF_RGB: color_space<AF_RGB>(out, image, to); break;
+            case AF_HSV: color_space<AF_HSV>(out, image, to); break;
+            case AF_YCbCr: color_space<AF_YCbCr>(out, image, to); break;
+            default:
+                AF_ERROR("Incorrect enum value for input color type",
+                         AF_ERR_ARG);
         }
     }
     CATCHALL;
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index 063761aec1..969d3a4501 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <af/data.h>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
-#include <implicit.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
+#include <implicit.hpp>
+#include <optypes.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
 
 #include <complex.hpp>
 
@@ -25,16 +25,15 @@ using af::dim4;
 
 template<typename To, typename Ti>
 static inline af_array cplx(const af_array lhs, const af_array rhs,
-                            const dim4 &odims)
-{
-    af_array res = getHandle(cplx<To, Ti>(castArray<Ti>(lhs), castArray<Ti>(rhs), odims));
+                            const dim4 &odims) {
+    af_array res =
+        getHandle(cplx<To, Ti>(castArray<Ti>(lhs), castArray<Ti>(rhs), odims));
     return res;
 }
 
-af_err af_cplx2(af_array *out, const af_array lhs, const af_array rhs, bool batchMode)
-{
+af_err af_cplx2(af_array *out, const af_array lhs, const af_array rhs,
+                bool batchMode) {
     try {
-
         af_dtype type = implicit(lhs, rhs);
 
         if (type == c32 || type == c64) {
@@ -43,13 +42,14 @@ af_err af_cplx2(af_array *out, const af_array lhs, const af_array rhs, bool batc
 
         if (type != f64) type = f32;
 
-        dim4 odims = getOutDims(getInfo(lhs).dims(), getInfo(rhs).dims(), batchMode);
+        dim4 odims =
+            getOutDims(getInfo(lhs).dims(), getInfo(rhs).dims(), batchMode);
 
         af_array res;
         switch (type) {
-        case f32: res = cplx<cfloat , float >(lhs, rhs, odims); break;
-        case f64: res = cplx<cdouble, double>(lhs, rhs, odims); break;
-        default: TYPE_ERROR(0, type);
+            case f32: res = cplx<cfloat, float>(lhs, rhs, odims); break;
+            case f64: res = cplx<cdouble, double>(lhs, rhs, odims); break;
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -58,30 +58,24 @@ af_err af_cplx2(af_array *out, const af_array lhs, const af_array rhs, bool batc
     return AF_SUCCESS;
 }
 
-af_err af_cplx(af_array *out, const af_array in)
-{
+af_err af_cplx(af_array *out, const af_array in) {
     try {
-
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
         if (type == c32 || type == c64) {
             AF_ERROR("Inputs to cplx2 can not be of complex type", AF_ERR_ARG);
         }
 
         af_array tmp;
-        AF_CHECK(af_constant(&tmp,
-                             0, info.ndims(),
-                             info.dims().get(),
-                             type));
+        AF_CHECK(af_constant(&tmp, 0, info.ndims(), info.dims().get(), type));
 
         af_array res;
         switch (type) {
+            case f32: res = cplx<cfloat, float>(in, tmp, info.dims()); break;
+            case f64: res = cplx<cdouble, double>(in, tmp, info.dims()); break;
 
-        case f32: res = cplx<cfloat , float >(in, tmp, info.dims()); break;
-        case f64: res = cplx<cdouble, double>(in, tmp, info.dims()); break;
-
-        default: TYPE_ERROR(0, type);
+            default: TYPE_ERROR(0, type);
         }
 
         AF_CHECK(af_release_array(tmp));
@@ -92,24 +86,23 @@ af_err af_cplx(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_real(af_array *out, const af_array in)
-{
+af_err af_real(af_array *out, const af_array in) {
     try {
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-
-        if (type != c32 && type != c64) {
-            return af_retain_array(out, in);
-        }
+        if (type != c32 && type != c64) { return af_retain_array(out, in); }
 
         af_array res;
         switch (type) {
-
-        case c32: res = getHandle(real<float , cfloat >(getArray<cfloat >(in))); break;
-        case c64: res = getHandle(real<double, cdouble>(getArray<cdouble>(in))); break;
-
-        default: TYPE_ERROR(0, type);
+            case c32:
+                res = getHandle(real<float, cfloat>(getArray<cfloat>(in)));
+                break;
+            case c64:
+                res = getHandle(real<double, cdouble>(getArray<cdouble>(in)));
+                break;
+
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -118,12 +111,10 @@ af_err af_real(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_imag(af_array *out, const af_array in)
-{
+af_err af_imag(af_array *out, const af_array in) {
     try {
-
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
         if (type != c32 && type != c64) {
             return af_constant(out, 0, info.ndims(), info.dims().get(), type);
@@ -131,11 +122,14 @@ af_err af_imag(af_array *out, const af_array in)
 
         af_array res;
         switch (type) {
-
-        case c32: res = getHandle(imag<float , cfloat >(getArray<cfloat >(in))); break;
-        case c64: res = getHandle(imag<double, cdouble>(getArray<cdouble>(in))); break;
-
-        default: TYPE_ERROR(0, type);
+            case c32:
+                res = getHandle(imag<float, cfloat>(getArray<cfloat>(in)));
+                break;
+            case c64:
+                res = getHandle(imag<double, cdouble>(getArray<cdouble>(in)));
+                break;
+
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -144,24 +138,23 @@ af_err af_imag(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_conjg(af_array *out, const af_array in)
-{
+af_err af_conjg(af_array *out, const af_array in) {
     try {
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-
-        if (type != c32 && type != c64) {
-            return af_retain_array(out, in);
-        }
+        if (type != c32 && type != c64) { return af_retain_array(out, in); }
 
         af_array res;
         switch (type) {
-
-        case c32: res = getHandle(conj<cfloat >(getArray<cfloat >(in))); break;
-        case c64: res = getHandle(conj<cdouble>(getArray<cdouble>(in))); break;
-
-        default: TYPE_ERROR(0, type);
+            case c32:
+                res = getHandle(conj<cfloat>(getArray<cfloat>(in)));
+                break;
+            case c64:
+                res = getHandle(conj<cdouble>(getArray<cdouble>(in)));
+                break;
+
+            default: TYPE_ERROR(0, type);
         }
 
         std::swap(*out, res);
@@ -170,24 +163,29 @@ af_err af_conjg(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_abs(af_array *out, const af_array in)
-{
+af_err af_abs(af_array *out, const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
-        af_dtype in_type = in_info.getType();
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype in_type         = in_info.getType();
         af_array res;
 
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
 
         switch (type) {
-        case f32: res = getHandle(abs<float ,  float >(castArray<float  >(in))); break;
-        case f64: res = getHandle(abs<double,  double>(castArray<double >(in))); break;
-        case c32: res = getHandle(abs<float , cfloat >(castArray<cfloat >(in))); break;
-        case c64: res = getHandle(abs<double, cdouble>(castArray<cdouble>(in))); break;
-        default:
-            TYPE_ERROR(1, in_type); break;
+            case f32:
+                res = getHandle(abs<float, float>(castArray<float>(in)));
+                break;
+            case f64:
+                res = getHandle(abs<double, double>(castArray<double>(in)));
+                break;
+            case c32:
+                res = getHandle(abs<float, cfloat>(castArray<cfloat>(in)));
+                break;
+            case c64:
+                res = getHandle(abs<double, cdouble>(castArray<cdouble>(in)));
+                break;
+            default: TYPE_ERROR(1, in_type); break;
         }
 
         std::swap(*out, res);
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 7e5b56fc8c..9303583944 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -6,18 +6,18 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/signal.h>
-#include <af/data.h>
-#include <handle.hpp>
 #include <arith.hpp>
+#include <backend.hpp>
 #include <cast.hpp>
-#include <tile.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
 #include <convolve.hpp>
 #include <fftconvolve.hpp>
+#include <handle.hpp>
+#include <tile.hpp>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/signal.h>
 
 #include <cstdio>
 
@@ -25,14 +25,15 @@ using af::dim4;
 using namespace detail;
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-inline static af_array convolve(const af_array &s, const af_array &f, AF_BATCH_KIND kind)
-{
-    return getHandle(convolve<T, accT, baseDim, expand>(getArray<T>(s), castArray<accT>(f), kind));
+inline static af_array convolve(const af_array &s, const af_array &f,
+                                AF_BATCH_KIND kind) {
+    return getHandle(convolve<T, accT, baseDim, expand>(
+        getArray<T>(s), castArray<accT>(f), kind));
 }
 
 template<typename T, typename accT, bool expand>
-inline static af_array convolve2(const af_array &s, const af_array &c_f, const af_array &r_f)
-{
+inline static af_array convolve2(const af_array &s, const af_array &c_f,
+                                 const af_array &r_f) {
     const Array<accT> colFilter = castArray<accT>(c_f);
     const Array<accT> rowFilter = castArray<accT>(r_f);
     const Array<accT> signal    = castArray<accT>(s);
@@ -41,80 +42,118 @@ inline static af_array convolve2(const af_array &s, const af_array &c_f, const a
         Array<accT> colArray = detail::tile(colFilter, signal.dims());
         Array<accT> rowArray = detail::tile(rowFilter, signal.dims());
 
-        Array<accT> filter = arithOp<accT, af_mul_t>(colArray, rowArray, signal.dims());
+        Array<accT> filter =
+            arithOp<accT, af_mul_t>(colArray, rowArray, signal.dims());
 
-        return getHandle(cast<T, accT>(arithOp<accT, af_mul_t>(signal, filter, signal.dims())));
+        return getHandle(cast<T, accT>(
+            arithOp<accT, af_mul_t>(signal, filter, signal.dims())));
     }
 
     ARG_ASSERT(2, colFilter.isVector());
     ARG_ASSERT(3, rowFilter.isVector());
 
-    return getHandle(convolve2<T, accT, expand>(getArray<T>(s), colFilter, rowFilter));
+    return getHandle(
+        convolve2<T, accT, expand>(getArray<T>(s), colFilter, rowFilter));
 }
 
 template<dim_t baseDim>
-AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
-{
+AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
-    if (sn==baseDim && fn==baseDim)
+    if (sn == baseDim && fn == baseDim)
         return AF_BATCH_NONE;
-    else if (sn==baseDim && (fn>baseDim && fn<=4))
+    else if (sn == baseDim && (fn > baseDim && fn <= 4))
         return AF_BATCH_RHS;
-    else if ((sn>baseDim && sn<=4) && fn==baseDim)
+    else if ((sn > baseDim && sn <= 4) && fn == baseDim)
         return AF_BATCH_LHS;
-    else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
+    else if ((sn > baseDim && sn <= 4) && (fn > baseDim && fn <= 4)) {
         bool doesDimensionsMatch = true;
-        bool isInterleaved = true;
-        for (dim_t i=baseDim; i<4; i++) {
+        bool isInterleaved       = true;
+        for (dim_t i = baseDim; i < 4; i++) {
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
-            isInterleaved &= (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
+            isInterleaved &=
+                (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
         }
         if (doesDimensionsMatch) return AF_BATCH_SAME;
         return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
-    }
-    else
+    } else
         return AF_BATCH_UNSUPPORTED;
 }
 
 template<dim_t baseDim, bool expand>
-af_err convolve(af_array *out, const af_array signal, const af_array filter)
-{
+af_err convolve(af_array *out, const af_array signal, const af_array filter) {
     try {
-        const ArrayInfo& sInfo = getInfo(signal);
-        const ArrayInfo& fInfo = getInfo(filter);
+        const ArrayInfo &sInfo = getInfo(signal);
+        const ArrayInfo &fInfo = getInfo(filter);
 
-        af_dtype stype  = sInfo.getType();
+        af_dtype stype = sInfo.getType();
 
         dim4 sdims = sInfo.dims();
         dim4 fdims = fInfo.dims();
 
-        if(fdims.ndims() == 0 || sdims.ndims() ==  0) {
+        if (fdims.ndims() == 0 || sdims.ndims() == 0) {
             return af_retain_array(out, signal);
         }
 
         AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
 
-        ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED && convBT != AF_BATCH_DIFF));
+        ARG_ASSERT(1,
+                   (convBT != AF_BATCH_UNSUPPORTED && convBT != AF_BATCH_DIFF));
 
         af_array output;
-        switch(stype) {
-            case c32: output = convolve<cfloat ,  cfloat, baseDim, expand>(signal, filter, convBT); break;
-            case c64: output = convolve<cdouble, cdouble, baseDim, expand>(signal, filter, convBT); break;
-            case f32: output = convolve<float  ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case f64: output = convolve<double ,  double, baseDim, expand>(signal, filter, convBT); break;
-            case u32: output = convolve<uint   ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case s32: output = convolve<int    ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case u16: output = convolve<ushort ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case s16: output = convolve<short  ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case u64: output = convolve<uintl  ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case s64: output = convolve<intl   ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case u8:  output = convolve<uchar  ,   float, baseDim, expand>(signal, filter, convBT); break;
-            case b8:  output = convolve<char   ,   float, baseDim, expand>(signal, filter, convBT); break;
+        switch (stype) {
+            case c32:
+                output = convolve<cfloat, cfloat, baseDim, expand>(
+                    signal, filter, convBT);
+                break;
+            case c64:
+                output = convolve<cdouble, cdouble, baseDim, expand>(
+                    signal, filter, convBT);
+                break;
+            case f32:
+                output = convolve<float, float, baseDim, expand>(signal, filter,
+                                                                 convBT);
+                break;
+            case f64:
+                output = convolve<double, double, baseDim, expand>(
+                    signal, filter, convBT);
+                break;
+            case u32:
+                output = convolve<uint, float, baseDim, expand>(signal, filter,
+                                                                convBT);
+                break;
+            case s32:
+                output = convolve<int, float, baseDim, expand>(signal, filter,
+                                                               convBT);
+                break;
+            case u16:
+                output = convolve<ushort, float, baseDim, expand>(
+                    signal, filter, convBT);
+                break;
+            case s16:
+                output = convolve<short, float, baseDim, expand>(signal, filter,
+                                                                 convBT);
+                break;
+            case u64:
+                output = convolve<uintl, float, baseDim, expand>(signal, filter,
+                                                                 convBT);
+                break;
+            case s64:
+                output = convolve<intl, float, baseDim, expand>(signal, filter,
+                                                                convBT);
+                break;
+            case u8:
+                output = convolve<uchar, float, baseDim, expand>(signal, filter,
+                                                                 convBT);
+                break;
+            case b8:
+                output = convolve<char, float, baseDim, expand>(signal, filter,
+                                                                convBT);
+                break;
             default: TYPE_ERROR(1, stype);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
@@ -122,50 +161,85 @@ af_err convolve(af_array *out, const af_array signal, const af_array filter)
 }
 
 template<bool expand>
-af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter, const af_array signal)
-{
+af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter,
+                     const af_array signal) {
     try {
-        const ArrayInfo& sInfo = getInfo(signal);
+        const ArrayInfo &sInfo = getInfo(signal);
 
-        const dim4& sdims = sInfo.dims();
+        const dim4 &sdims = sInfo.dims();
 
-        const af_dtype signalType  = sInfo.getType();
+        const af_dtype signalType = sInfo.getType();
 
-        ARG_ASSERT(1, (sdims.ndims()>=2));
+        ARG_ASSERT(1, (sdims.ndims() >= 2));
 
         af_array output = 0;
 
-        switch(signalType) {
-            case c32: output = convolve2<cfloat ,  cfloat, expand>(signal, col_filter, row_filter); break;
-            case c64: output = convolve2<cdouble, cdouble, expand>(signal, col_filter, row_filter); break;
-            case f32: output = convolve2<float  ,   float, expand>(signal, col_filter, row_filter); break;
-            case f64: output = convolve2<double ,  double, expand>(signal, col_filter, row_filter); break;
-            case u32: output = convolve2<uint   ,   float, expand>(signal, col_filter, row_filter); break;
-            case s32: output = convolve2<int    ,   float, expand>(signal, col_filter, row_filter); break;
-            case u16: output = convolve2<ushort ,   float, expand>(signal, col_filter, row_filter); break;
-            case s16: output = convolve2<short  ,   float, expand>(signal, col_filter, row_filter); break;
-            case u64: output = convolve2<uintl  ,   float, expand>(signal, col_filter, row_filter); break;
-            case s64: output = convolve2<intl   ,   float, expand>(signal, col_filter, row_filter); break;
-            case u8:  output = convolve2<uchar  ,   float, expand>(signal, col_filter, row_filter); break;
-            case b8:  output = convolve2<char   ,   float, expand>(signal, col_filter, row_filter); break;
+        switch (signalType) {
+            case c32:
+                output = convolve2<cfloat, cfloat, expand>(signal, col_filter,
+                                                           row_filter);
+                break;
+            case c64:
+                output = convolve2<cdouble, cdouble, expand>(signal, col_filter,
+                                                             row_filter);
+                break;
+            case f32:
+                output = convolve2<float, float, expand>(signal, col_filter,
+                                                         row_filter);
+                break;
+            case f64:
+                output = convolve2<double, double, expand>(signal, col_filter,
+                                                           row_filter);
+                break;
+            case u32:
+                output = convolve2<uint, float, expand>(signal, col_filter,
+                                                        row_filter);
+                break;
+            case s32:
+                output = convolve2<int, float, expand>(signal, col_filter,
+                                                       row_filter);
+                break;
+            case u16:
+                output = convolve2<ushort, float, expand>(signal, col_filter,
+                                                          row_filter);
+                break;
+            case s16:
+                output = convolve2<short, float, expand>(signal, col_filter,
+                                                         row_filter);
+                break;
+            case u64:
+                output = convolve2<uintl, float, expand>(signal, col_filter,
+                                                         row_filter);
+                break;
+            case s64:
+                output = convolve2<intl, float, expand>(signal, col_filter,
+                                                        row_filter);
+                break;
+            case u8:
+                output = convolve2<uchar, float, expand>(signal, col_filter,
+                                                         row_filter);
+                break;
+            case b8:
+                output = convolve2<char, float, expand>(signal, col_filter,
+                                                        row_filter);
+                break;
             default: TYPE_ERROR(1, signalType);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-
 template<int baseDim>
-bool isFreqDomain(const af_array &signal, const af_array filter, af_conv_domain domain)
-{
+bool isFreqDomain(const af_array &signal, const af_array filter,
+                  af_conv_domain domain) {
     if (domain == AF_CONV_FREQ) return true;
     if (domain != AF_CONV_AUTO) return false;
 
-    const ArrayInfo& sInfo = getInfo(signal);
-    const ArrayInfo& fInfo = getInfo(filter);
+    const ArrayInfo &sInfo = getInfo(signal);
+    const ArrayInfo &fInfo = getInfo(filter);
 
     dim4 sdims = sInfo.dims();
     dim4 fdims = fInfo.dims();
@@ -173,9 +247,7 @@ bool isFreqDomain(const af_array &signal, const af_array filter, af_conv_domain
     if (identifyBatchKind<baseDim>(sdims, fdims) == AF_BATCH_DIFF) return true;
 
     int kbatch = 1;
-    for(int i = 3; i >= baseDim; i--) {
-        kbatch *= fdims[i];
-    }
+    for (int i = 3; i >= baseDim; i--) { kbatch *= fdims[i]; }
 
     if (kbatch >= 10) return true;
 
@@ -198,23 +270,25 @@ bool isFreqDomain(const af_array &signal, const af_array filter, af_conv_domain
     return false;
 }
 
-af_err af_convolve1(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
-{
+af_err af_convolve1(af_array *out, const af_array signal, const af_array filter,
+                    const af_conv_mode mode, af_conv_domain domain) {
     try {
         if (isFreqDomain<1>(signal, filter, domain))
             return af_fft_convolve1(out, signal, filter, mode);
 
         if (mode == AF_CONV_EXPAND)
-            return convolve<1, true >(out, signal, filter);
+            return convolve<1, true>(out, signal, filter);
         else
             return convolve<1, false>(out, signal, filter);
-    } CATCHALL;
+    }
+    CATCHALL;
 }
 
-af_err af_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
-{
+af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
+                    const af_conv_mode mode, af_conv_domain domain) {
     try {
-        if (getInfo(signal).dims().ndims()<2 || getInfo(filter).dims().ndims()<2) {
+        if (getInfo(signal).dims().ndims() < 2 ||
+            getInfo(filter).dims().ndims() < 2) {
             return af_convolve1(out, signal, filter, mode, domain);
         }
 
@@ -222,16 +296,18 @@ af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
             return af_fft_convolve2(out, signal, filter, mode);
 
         if (mode == AF_CONV_EXPAND)
-            return convolve<2, true >(out, signal, filter);
+            return convolve<2, true>(out, signal, filter);
         else
             return convolve<2, false>(out, signal, filter);
-    } CATCHALL;
+    }
+    CATCHALL;
 }
 
-af_err af_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)
-{
+af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
+                    const af_conv_mode mode, af_conv_domain domain) {
     try {
-        if (getInfo(signal).dims().ndims()<3 || getInfo(filter).dims().ndims()<3) {
+        if (getInfo(signal).dims().ndims() < 3 ||
+            getInfo(filter).dims().ndims() < 3) {
             return af_convolve2(out, signal, filter, mode, domain);
         }
 
@@ -239,18 +315,21 @@ af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
             return af_fft_convolve3(out, signal, filter, mode);
 
         if (mode == AF_CONV_EXPAND)
-            return convolve<3, true >(out, signal, filter);
+            return convolve<3, true>(out, signal, filter);
         else
             return convolve<3, false>(out, signal, filter);
-    } CATCHALL;
+    }
+    CATCHALL;
 }
 
-af_err af_convolve2_sep(af_array *out, const af_array signal, const af_array col_filter, const af_array row_filter, const af_conv_mode mode)
-{
+af_err af_convolve2_sep(af_array *out, const af_array signal,
+                        const af_array col_filter, const af_array row_filter,
+                        const af_conv_mode mode) {
     try {
         if (mode == AF_CONV_EXPAND)
-            return convolve2_sep<true >(out, signal, col_filter, row_filter);
+            return convolve2_sep<true>(out, signal, col_filter, row_filter);
         else
             return convolve2_sep<false>(out, signal, col_filter, row_filter);
-    } CATCHALL;
+    }
+    CATCHALL;
 }
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index ced05c5945..cb47e1d1df 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -7,10 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-
-#include <af/defines.h>
-#include <af/dim4.hpp>
-#include <af/statistics.h>
 #include <arith.hpp>
 #include <backend.hpp>
 #include <cast.hpp>
@@ -20,22 +16,24 @@
 #include <reduce.hpp>
 #include <stats.h>
 #include <types.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/statistics.h>
 
 #include <cmath>
 
 using detail::arithOp;
-using detail::reduce_all;
 using detail::intl;
+using detail::reduce_all;
 using detail::uintl;
 
 template<typename Ti, typename To>
-static To corrcoef(const af_array& X, const af_array& Y)
-{
+static To corrcoef(const af_array& X, const af_array& Y) {
     Array<To> xIn = cast<To>(getArray<Ti>(X));
     Array<To> yIn = cast<To>(getArray<Ti>(Y));
 
     dim4 dims = xIn.dims();
-    dim_t n= xIn.elements();
+    dim_t n   = xIn.elements();
 
     To xSum = detail::reduce_all<af_add_t, To, To>(xIn);
     To ySum = detail::reduce_all<af_add_t, To, To>(yIn);
@@ -48,40 +46,41 @@ static To corrcoef(const af_array& X, const af_array& Y)
     To ySqSum = detail::reduce_all<af_add_t, To, To>(ySq);
     To xySum  = detail::reduce_all<af_add_t, To, To>(xy);
 
-    To result = (n*xySum - xSum*ySum)/(sqrt(n*xSqSum-xSum*xSum)*sqrt(n*ySqSum-ySum*ySum));
+    To result = (n * xySum - xSum * ySum) / (sqrt(n * xSqSum - xSum * xSum) *
+                                             sqrt(n * ySqSum - ySum * ySum));
 
     return result;
 }
 
-af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y)
-{
-    UNUSED(imagVal); // TODO: implement for complex types
+af_err af_corrcoef(double* realVal, double* imagVal, const af_array X,
+                   const af_array Y) {
+    UNUSED(imagVal);  // TODO: implement for complex types
     try {
         const ArrayInfo& xInfo = getInfo(X);
         const ArrayInfo& yInfo = getInfo(Y);
-        dim4 xDims      = xInfo.dims();
-        dim4 yDims      = yInfo.dims();
-        af_dtype xType  = xInfo.getType();
-        af_dtype yType  = yInfo.getType();
+        dim4 xDims             = xInfo.dims();
+        dim4 yDims             = yInfo.dims();
+        af_dtype xType         = xInfo.getType();
+        af_dtype yType         = yInfo.getType();
 
-        ARG_ASSERT(2, (xType==yType));
-        ARG_ASSERT(2, (xDims.ndims()==yDims.ndims()));
+        ARG_ASSERT(2, (xType == yType));
+        ARG_ASSERT(2, (xDims.ndims() == yDims.ndims()));
 
-        for (dim_t i=0; i<xDims.ndims(); ++i)
-            ARG_ASSERT(2, (xDims[i]==yDims[i]));
+        for (dim_t i = 0; i < xDims.ndims(); ++i)
+            ARG_ASSERT(2, (xDims[i] == yDims[i]));
 
-        switch(xType) {
+        switch (xType) {
             case f64: *realVal = corrcoef<double, double>(X, Y); break;
-            case f32: *realVal = corrcoef<float , float >(X, Y); break;
-            case s32: *realVal = corrcoef<int   , float >(X, Y); break;
-            case u32: *realVal = corrcoef<uint  , float >(X, Y); break;
-            case s64: *realVal = corrcoef<intl  , double>(X, Y); break;
-            case u64: *realVal = corrcoef<uintl , double>(X, Y); break;
-            case s16: *realVal = corrcoef<short , float >(X, Y); break;
-            case u16: *realVal = corrcoef<ushort, float >(X, Y); break;
-            case  u8: *realVal = corrcoef<uchar , float >(X, Y); break;
-            case  b8: *realVal = corrcoef<char  , float >(X, Y); break;
-            default : TYPE_ERROR(1, xType);
+            case f32: *realVal = corrcoef<float, float>(X, Y); break;
+            case s32: *realVal = corrcoef<int, float>(X, Y); break;
+            case u32: *realVal = corrcoef<uint, float>(X, Y); break;
+            case s64: *realVal = corrcoef<intl, double>(X, Y); break;
+            case u64: *realVal = corrcoef<uintl, double>(X, Y); break;
+            case s16: *realVal = corrcoef<short, float>(X, Y); break;
+            case u16: *realVal = corrcoef<ushort, float>(X, Y); break;
+            case u8: *realVal = corrcoef<uchar, float>(X, Y); break;
+            case b8: *realVal = corrcoef<char, float>(X, Y); break;
+            default: TYPE_ERROR(1, xType);
         }
     }
     CATCHALL;
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index c19330baa6..b250743ad1 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/statistics.h>
-#include <af/defines.h>
+#include <arith.hpp>
 #include <backend.hpp>
-#include <reduce.hpp>
+#include <cast.hpp>
 #include <handle.hpp>
-#include <mean.hpp>
-#include <arith.hpp>
-#include <unary.hpp>
 #include <math.hpp>
-#include <cast.hpp>
+#include <mean.hpp>
+#include <reduce.hpp>
 #include <tile.hpp>
+#include <unary.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/statistics.h>
 
 #include "stats.h"
 
@@ -26,59 +26,60 @@ using af::dim4;
 using namespace detail;
 
 template<typename T, typename cType>
-static af_array cov(const af_array& X, const af_array& Y, const bool isbiased)
-{
+static af_array cov(const af_array& X, const af_array& Y, const bool isbiased) {
     typedef typename baseOutType<cType>::type weightType;
-    Array<T> _x = getArray<T>(X);
-    Array<T> _y = getArray<T>(Y);
+    Array<T> _x       = getArray<T>(X);
+    Array<T> _y       = getArray<T>(Y);
     Array<cType> xArr = cast<cType>(_x);
     Array<cType> yArr = cast<cType>(_y);
 
     dim4 xDims = xArr.dims();
-    dim_t N = isbiased ? xDims[0] : xDims[0]-1;
+    dim_t N    = isbiased ? xDims[0] : xDims[0] - 1;
 
-    Array<cType> xmArr = createValueArray<cType>(xDims, mean<T, weightType, cType>(_x));
-    Array<cType> ymArr = createValueArray<cType>(xDims, mean<T, weightType, cType>(_y));
-    Array<cType> nArr  = createValueArray<cType>(xDims, scalar<cType>(N));
+    Array<cType> xmArr =
+        createValueArray<cType>(xDims, mean<T, weightType, cType>(_x));
+    Array<cType> ymArr =
+        createValueArray<cType>(xDims, mean<T, weightType, cType>(_y));
+    Array<cType> nArr = createValueArray<cType>(xDims, scalar<cType>(N));
 
-    Array<cType> diffX = detail::arithOp<cType, af_sub_t>(xArr, xmArr, xDims);
-    Array<cType> diffY = detail::arithOp<cType, af_sub_t>(yArr, ymArr, xDims);
-    Array<cType> mulXY = detail::arithOp<cType, af_mul_t>(diffX, diffY, xDims);
-    Array<cType> redArr= detail::reduce<af_add_t, cType, cType>(mulXY, 0);
-    xDims[0] = 1;
-    Array<cType> result= detail::arithOp<cType, af_div_t>(redArr, nArr, xDims);
+    Array<cType> diffX  = detail::arithOp<cType, af_sub_t>(xArr, xmArr, xDims);
+    Array<cType> diffY  = detail::arithOp<cType, af_sub_t>(yArr, ymArr, xDims);
+    Array<cType> mulXY  = detail::arithOp<cType, af_mul_t>(diffX, diffY, xDims);
+    Array<cType> redArr = detail::reduce<af_add_t, cType, cType>(mulXY, 0);
+    xDims[0]            = 1;
+    Array<cType> result = detail::arithOp<cType, af_div_t>(redArr, nArr, xDims);
 
     return getHandle<cType>(result);
 }
 
-af_err af_cov(af_array* out, const af_array X, const af_array Y, const bool isbiased)
-{
+af_err af_cov(af_array* out, const af_array X, const af_array Y,
+              const bool isbiased) {
     try {
         const ArrayInfo& xInfo = getInfo(X);
         const ArrayInfo& yInfo = getInfo(Y);
-        dim4 xDims      = xInfo.dims();
-        dim4 yDims      = yInfo.dims();
-        af_dtype xType  = xInfo.getType();
-        af_dtype yType  = yInfo.getType();
+        dim4 xDims             = xInfo.dims();
+        dim4 yDims             = yInfo.dims();
+        af_dtype xType         = xInfo.getType();
+        af_dtype yType         = yInfo.getType();
 
-        ARG_ASSERT(1, (xDims.ndims()<=2));
-        ARG_ASSERT(2, (xDims.ndims()==yDims.ndims()));
-        ARG_ASSERT(2, (xDims[0]==yDims[0]));
-        ARG_ASSERT(2, (xDims[1]==yDims[1]));
-        ARG_ASSERT(2, (xType==yType));
+        ARG_ASSERT(1, (xDims.ndims() <= 2));
+        ARG_ASSERT(2, (xDims.ndims() == yDims.ndims()));
+        ARG_ASSERT(2, (xDims[0] == yDims[0]));
+        ARG_ASSERT(2, (xDims[1] == yDims[1]));
+        ARG_ASSERT(2, (xType == yType));
 
         af_array output = 0;
-        switch(xType) {
+        switch (xType) {
             case f64: output = cov<double, double>(X, Y, isbiased); break;
-            case f32: output = cov<float , float >(X, Y, isbiased); break;
-            case s32: output = cov<int   , float >(X, Y, isbiased); break;
-            case u32: output = cov<uint  , float >(X, Y, isbiased); break;
-            case s64: output = cov<intl  , double>(X, Y, isbiased); break;
-            case u64: output = cov<uintl , double>(X, Y, isbiased); break;
-            case s16: output = cov<short , float >(X, Y, isbiased); break;
-            case u16: output = cov<ushort, float >(X, Y, isbiased); break;
-            case  u8: output = cov<uchar , float >(X, Y, isbiased); break;
-            default : TYPE_ERROR(1, xType);
+            case f32: output = cov<float, float>(X, Y, isbiased); break;
+            case s32: output = cov<int, float>(X, Y, isbiased); break;
+            case u32: output = cov<uint, float>(X, Y, isbiased); break;
+            case s64: output = cov<intl, double>(X, Y, isbiased); break;
+            case u64: output = cov<uintl, double>(X, Y, isbiased); break;
+            case s16: output = cov<short, float>(X, Y, isbiased); break;
+            case u16: output = cov<ushort, float>(X, Y, isbiased); break;
+            case u8: output = cov<uchar, float>(X, Y, isbiased); break;
+            default: TYPE_ERROR(1, xType);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 14f75dcc9b..8aad509cbe 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -7,34 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
-#include <af/array.h>
-#include <af/data.h>
-#include <af/device.h>
-#include <af/util.h>
-#include <copy.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <copy.hpp>
+#include <diagonal.hpp>
 #include <handle.hpp>
+#include <identity.hpp>
+#include <iota.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 #include <range.hpp>
-#include <iota.hpp>
-#include <identity.hpp>
-#include <diagonal.hpp>
 #include <triangle.hpp>
-#include <platform.hpp>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/device.h>
+#include <af/dim4.hpp>
+#include <af/util.h>
+#include <complex>
 
 using af::dim4;
 using namespace detail;
 
-dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
-{
+dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
     DIM_ASSERT(1, ndims >= 1);
 
     dim4 d(1, 1, 1, 1);
 
-    for(unsigned i = 0; i < ndims; i++) {
+    for (unsigned i = 0; i < ndims; i++) {
         d[i] = dims[i];
         DIM_ASSERT(2, dims[i] >= 1);
     }
@@ -42,70 +41,66 @@ dim4 verifyDims(const unsigned ndims, const dim_t * const dims)
     return d;
 }
 
-//Strong Exception Guarantee
-af_err af_constant(af_array *result, const double value,
-                   const unsigned ndims, const dim_t * const dims,
-                   const af_dtype type)
-{
+// Strong Exception Guarantee
+af_err af_constant(af_array *result, const double value, const unsigned ndims,
+                   const dim_t *const dims, const af_dtype type) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
         dim4 d(1, 1, 1, 1);
-        if(ndims <= 0) {
+        if (ndims <= 0) {
             return af_create_handle(result, 0, nullptr, type);
         } else {
             d = verifyDims(ndims, dims);
         }
 
-
-        switch(type) {
-        case f32:   out = createHandleFromValue<float  >(d, value); break;
-        case c32:   out = createHandleFromValue<cfloat >(d, value); break;
-        case f64:   out = createHandleFromValue<double >(d, value); break;
-        case c64:   out = createHandleFromValue<cdouble>(d, value); break;
-        case b8:    out = createHandleFromValue<char   >(d, value); break;
-        case s32:   out = createHandleFromValue<int    >(d, value); break;
-        case u32:   out = createHandleFromValue<uint   >(d, value); break;
-        case u8:    out = createHandleFromValue<uchar  >(d, value); break;
-        case s64:   out = createHandleFromValue<intl   >(d, value); break;
-        case u64:   out = createHandleFromValue<uintl  >(d, value); break;
-        case s16:   out = createHandleFromValue<short  >(d, value); break;
-        case u16:   out = createHandleFromValue<ushort >(d, value); break;
-        default:    TYPE_ERROR(4, type);
+        switch (type) {
+            case f32: out = createHandleFromValue<float>(d, value); break;
+            case c32: out = createHandleFromValue<cfloat>(d, value); break;
+            case f64: out = createHandleFromValue<double>(d, value); break;
+            case c64: out = createHandleFromValue<cdouble>(d, value); break;
+            case b8: out = createHandleFromValue<char>(d, value); break;
+            case s32: out = createHandleFromValue<int>(d, value); break;
+            case u32: out = createHandleFromValue<uint>(d, value); break;
+            case u8: out = createHandleFromValue<uchar>(d, value); break;
+            case s64: out = createHandleFromValue<intl>(d, value); break;
+            case u64: out = createHandleFromValue<uintl>(d, value); break;
+            case s16: out = createHandleFromValue<short>(d, value); break;
+            case u16: out = createHandleFromValue<ushort>(d, value); break;
+            default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
 template<typename To, typename Ti>
-static inline af_array createCplx(dim4 dims, const Ti real, const Ti imag)
-{
-    To cval = scalar<To, Ti>(real, imag);
+static inline af_array createCplx(dim4 dims, const Ti real, const Ti imag) {
+    To cval      = scalar<To, Ti>(real, imag);
     af_array out = getHandle(createValueArray<To>(dims, cval));
     return out;
 }
 
-af_err af_constant_complex(af_array *result, const double real, const double imag,
-                           const unsigned ndims, const dim_t * const dims, af_dtype type)
-{
+af_err af_constant_complex(af_array *result, const double real,
+                           const double imag, const unsigned ndims,
+                           const dim_t *const dims, af_dtype type) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
         dim4 d(1, 1, 1, 1);
-        if(ndims <= 0) {
+        if (ndims <= 0) {
             return af_create_handle(result, 0, nullptr, type);
         } else {
             d = verifyDims(ndims, dims);
         }
 
         switch (type) {
-        case c32: out = createCplx<cfloat , float >(d, real, imag); break;
-        case c64: out = createCplx<cdouble, double>(d, real, imag); break;
-        default:   TYPE_ERROR(5, type);
+            case c32: out = createCplx<cfloat, float>(d, real, imag); break;
+            case c64: out = createCplx<cdouble, double>(d, real, imag); break;
+            default: TYPE_ERROR(5, type);
         }
 
         std::swap(*result, out);
@@ -114,15 +109,14 @@ af_err af_constant_complex(af_array *result, const double real, const double ima
     return AF_SUCCESS;
 }
 
-af_err af_constant_long(af_array *result, const intl val,
-                        const unsigned ndims, const dim_t * const dims)
-{
+af_err af_constant_long(af_array *result, const intl val, const unsigned ndims,
+                        const dim_t *const dims) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
         dim4 d(1, 1, 1, 1);
-        if(ndims <= 0) {
+        if (ndims <= 0) {
             return af_create_handle(result, 0, nullptr, s64);
         } else {
             d = verifyDims(ndims, dims);
@@ -131,20 +125,20 @@ af_err af_constant_long(af_array *result, const intl val,
         out = getHandle(createValueArray<intl>(d, val));
 
         std::swap(*result, out);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 af_err af_constant_ulong(af_array *result, const uintl val,
-                         const unsigned ndims, const dim_t * const dims)
-{
+                         const unsigned ndims, const dim_t *const dims) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
         dim4 d(1, 1, 1, 1);
-        if(ndims <= 0) {
+        if (ndims <= 0) {
             return af_create_handle(result, 0, nullptr, u64);
         } else {
             d = verifyDims(ndims, dims);
@@ -152,44 +146,45 @@ af_err af_constant_ulong(af_array *result, const uintl val,
         out = getHandle(createValueArray<uintl>(d, val));
 
         std::swap(*result, out);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-static inline af_array identity_(const af::dim4 &dims)
-{
+static inline af_array identity_(const af::dim4 &dims) {
     return getHandle(detail::identity<T>(dims));
 }
 
-af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_identity(af_array *out, const unsigned ndims, const dim_t *const dims,
+                   const af_dtype type) {
     try {
         af_array result;
         AF_CHECK(af_init());
 
-        if(ndims == 0) {
-            return af_create_handle(out, 0, nullptr, type);
-        }
+        if (ndims == 0) { return af_create_handle(out, 0, nullptr, type); }
 
         dim4 d = verifyDims(ndims, dims);
 
-        switch(type) {
-        case f32:   result = identity_<float  >(d);    break;
-        case c32:   result = identity_<cfloat >(d);    break;
-        case f64:   result = identity_<double >(d);    break;
-        case c64:   result = identity_<cdouble>(d);    break;
-        case s32:   result = identity_<int    >(d);    break;
-        case u32:   result = identity_<uint   >(d);    break;
-        case u8:    result = identity_<uchar  >(d);    break;
-        case u64:   result = identity_<uintl  >(d);    break;
-        case s64:   result = identity_<intl   >(d);    break;
-        case u16:   result = identity_<ushort >(d);    break;
-        case s16:   result = identity_<short  >(d);    break;
-            // Removed because of bool type. Functions implementations exist.
-        case b8:    result = identity_<char   >(d);    break;
-        default:    TYPE_ERROR(3, type);
+        switch (type) {
+            case f32: result = identity_<float>(d); break;
+            case c32: result = identity_<cfloat>(d); break;
+            case f64: result = identity_<double>(d); break;
+            case c64: result = identity_<cdouble>(d); break;
+            case s32: result = identity_<int>(d); break;
+            case u32: result = identity_<uint>(d); break;
+            case u8: result = identity_<uchar>(d); break;
+            case u64: result = identity_<uintl>(d); break;
+            case s64: result = identity_<intl>(d); break;
+            case u16: result = identity_<ushort>(d); break;
+            case s16:
+                result = identity_<short>(d);
+                break;
+                // Removed because of bool type. Functions implementations
+                // exist.
+            case b8: result = identity_<char>(d); break;
+            default: TYPE_ERROR(3, type);
         }
         std::swap(*out, result);
     }
@@ -198,37 +193,35 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims
 }
 
 template<typename T>
-static inline af_array range_(const dim4& d, const int seq_dim)
-{
+static inline af_array range_(const dim4 &d, const int seq_dim) {
     return getHandle(range<T>(d, seq_dim));
 }
 
-//Strong Exception Guarantee
-af_err af_range(af_array *result, const unsigned ndims, const dim_t * const dims,
-               const int seq_dim, const af_dtype type)
-{
+// Strong Exception Guarantee
+af_err af_range(af_array *result, const unsigned ndims, const dim_t *const dims,
+                const int seq_dim, const af_dtype type) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
         dim4 d(0);
-        if(ndims <= 0) {
+        if (ndims <= 0) {
             return af_create_handle(result, 0, nullptr, type);
         } else {
             d = verifyDims(ndims, dims);
         }
 
-        switch(type) {
-        case f32:   out = range_<float  >(d, seq_dim); break;
-        case f64:   out = range_<double >(d, seq_dim); break;
-        case s32:   out = range_<int    >(d, seq_dim); break;
-        case u32:   out = range_<uint   >(d, seq_dim); break;
-        case s64:   out = range_<intl   >(d, seq_dim); break;
-        case u64:   out = range_<uintl  >(d, seq_dim); break;
-        case s16:   out = range_<short  >(d, seq_dim); break;
-        case u16:   out = range_<ushort >(d, seq_dim); break;
-        case u8:    out = range_<uchar  >(d, seq_dim); break;
-        default:    TYPE_ERROR(4, type);
+        switch (type) {
+            case f32: out = range_<float>(d, seq_dim); break;
+            case f64: out = range_<double>(d, seq_dim); break;
+            case s32: out = range_<int>(d, seq_dim); break;
+            case u32: out = range_<uint>(d, seq_dim); break;
+            case s64: out = range_<intl>(d, seq_dim); break;
+            case u64: out = range_<uintl>(d, seq_dim); break;
+            case s16: out = range_<short>(d, seq_dim); break;
+            case u16: out = range_<ushort>(d, seq_dim); break;
+            case u8: out = range_<uchar>(d, seq_dim); break;
+            default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
     }
@@ -237,22 +230,19 @@ af_err af_range(af_array *result, const unsigned ndims, const dim_t * const dims
 }
 
 template<typename T>
-static inline af_array iota_(const dim4 &dims, const dim4 &tile_dims)
-{
+static inline af_array iota_(const dim4 &dims, const dim4 &tile_dims) {
     return getHandle(iota<T>(dims, tile_dims));
 }
 
-//Strong Exception Guarantee
-af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
-               const unsigned t_ndims, const dim_t * const tdims, const af_dtype type)
-{
+// Strong Exception Guarantee
+af_err af_iota(af_array *result, const unsigned ndims, const dim_t *const dims,
+               const unsigned t_ndims, const dim_t *const tdims,
+               const af_dtype type) {
     try {
         af_array out;
         AF_CHECK(af_init());
 
-        if(ndims == 0) {
-            return af_create_handle(result, 0, nullptr, type);
-        }
+        if (ndims == 0) { return af_create_handle(result, 0, nullptr, type); }
 
         DIM_ASSERT(1, ndims > 0 && ndims <= 4);
         DIM_ASSERT(3, t_ndims > 0 && t_ndims <= 4);
@@ -260,17 +250,17 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
         dim4 d = verifyDims(ndims, dims);
         dim4 t = verifyDims(t_ndims, tdims);
 
-        switch(type) {
-        case f32:   out = iota_<float  >(d, t); break;
-        case f64:   out = iota_<double >(d, t); break;
-        case s32:   out = iota_<int    >(d, t); break;
-        case u32:   out = iota_<uint   >(d, t); break;
-        case s64:   out = iota_<intl   >(d, t); break;
-        case u64:   out = iota_<uintl  >(d, t); break;
-        case s16:   out = iota_<short  >(d, t); break;
-        case u16:   out = iota_<ushort >(d, t); break;
-        case u8:    out = iota_<uchar  >(d, t); break;
-        default:    TYPE_ERROR(4, type);
+        switch (type) {
+            case f32: out = iota_<float>(d, t); break;
+            case f64: out = iota_<double>(d, t); break;
+            case s32: out = iota_<int>(d, t); break;
+            case u32: out = iota_<uint>(d, t); break;
+            case s64: out = iota_<intl>(d, t); break;
+            case u64: out = iota_<uintl>(d, t); break;
+            case s16: out = iota_<short>(d, t); break;
+            case u16: out = iota_<ushort>(d, t); break;
+            case u8: out = iota_<uchar>(d, t); break;
+            default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
     }
@@ -279,157 +269,152 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t * const dims,
 }
 
 template<typename T>
-static inline af_array diagCreate(const af_array in, const int num)
-{
+static inline af_array diagCreate(const af_array in, const int num) {
     return getHandle(diagCreate<T>(getArray<T>(in), num));
 }
 
 template<typename T>
-static inline af_array diagExtract(const af_array in, const int num)
-{
+static inline af_array diagExtract(const af_array in, const int num) {
     return getHandle(diagExtract<T>(getArray<T>(in), num));
 }
 
-af_err af_diag_create(af_array *out, const af_array in, const int num)
-{
+af_err af_diag_create(af_array *out, const af_array in, const int num) {
     try {
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
         DIM_ASSERT(1, in_info.ndims() <= 2);
         af_dtype type = in_info.getType();
 
         af_array result;
 
-        if(in_info.dims()[0] == 0) {
+        if (in_info.dims()[0] == 0) {
             return af_create_handle(out, 0, nullptr, type);
         }
 
-        switch(type) {
-        case f32:   result = diagCreate<float  >(in, num);    break;
-        case c32:   result = diagCreate<cfloat >(in, num);    break;
-        case f64:   result = diagCreate<double >(in, num);    break;
-        case c64:   result = diagCreate<cdouble>(in, num);    break;
-        case s32:   result = diagCreate<int    >(in, num);    break;
-        case u32:   result = diagCreate<uint   >(in, num);    break;
-        case s64:   result = diagCreate<intl   >(in, num);    break;
-        case u64:   result = diagCreate<uintl  >(in, num);    break;
-        case s16:   result = diagCreate<short  >(in, num);    break;
-        case u16:   result = diagCreate<ushort >(in, num);    break;
-        case u8:    result = diagCreate<uchar  >(in, num);    break;
-            // Removed because of bool type. Functions implementations exist.
-        case b8:    result = diagCreate<char   >(in, num);    break;
-        default:    TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: result = diagCreate<float>(in, num); break;
+            case c32: result = diagCreate<cfloat>(in, num); break;
+            case f64: result = diagCreate<double>(in, num); break;
+            case c64: result = diagCreate<cdouble>(in, num); break;
+            case s32: result = diagCreate<int>(in, num); break;
+            case u32: result = diagCreate<uint>(in, num); break;
+            case s64: result = diagCreate<intl>(in, num); break;
+            case u64: result = diagCreate<uintl>(in, num); break;
+            case s16: result = diagCreate<short>(in, num); break;
+            case u16: result = diagCreate<ushort>(in, num); break;
+            case u8:
+                result = diagCreate<uchar>(in, num);
+                break;
+                // Removed because of bool type. Functions implementations
+                // exist.
+            case b8: result = diagCreate<char>(in, num); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, result);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_diag_extract(af_array *out, const af_array in, const int num)
-{
-
+af_err af_diag_extract(af_array *out, const af_array in, const int num) {
     try {
-        const ArrayInfo& in_info = getInfo(in);
-        af_dtype type = in_info.getType();
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
 
-        if(in_info.ndims() == 0) {
+        if (in_info.ndims() == 0) {
             return af_create_handle(out, 0, nullptr, type);
         }
 
         DIM_ASSERT(1, in_info.ndims() >= 2);
 
         af_array result;
-        switch(type) {
-        case f32:   result = diagExtract<float  >(in, num);    break;
-        case c32:   result = diagExtract<cfloat >(in, num);    break;
-        case f64:   result = diagExtract<double >(in, num);    break;
-        case c64:   result = diagExtract<cdouble>(in, num);    break;
-        case s32:   result = diagExtract<int    >(in, num);    break;
-        case u32:   result = diagExtract<uint   >(in, num);    break;
-        case s64:   result = diagExtract<intl   >(in, num);    break;
-        case u64:   result = diagExtract<uintl  >(in, num);    break;
-        case s16:   result = diagExtract<short  >(in, num);    break;
-        case u16:   result = diagExtract<ushort >(in, num);    break;
-        case u8:    result = diagExtract<uchar  >(in, num);    break;
-            // Removed because of bool type. Functions implementations exist.
-        case b8:    result = diagExtract<char   >(in, num);    break;
-        default:    TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: result = diagExtract<float>(in, num); break;
+            case c32: result = diagExtract<cfloat>(in, num); break;
+            case f64: result = diagExtract<double>(in, num); break;
+            case c64: result = diagExtract<cdouble>(in, num); break;
+            case s32: result = diagExtract<int>(in, num); break;
+            case u32: result = diagExtract<uint>(in, num); break;
+            case s64: result = diagExtract<intl>(in, num); break;
+            case u64: result = diagExtract<uintl>(in, num); break;
+            case s16: result = diagExtract<short>(in, num); break;
+            case u16: result = diagExtract<ushort>(in, num); break;
+            case u8:
+                result = diagExtract<uchar>(in, num);
+                break;
+                // Removed because of bool type. Functions implementations
+                // exist.
+            case b8: result = diagExtract<char>(in, num); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, result);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T, bool is_upper>
-af_array triangle(const af_array in, bool is_unit_diag)
-{
+af_array triangle(const af_array in, bool is_unit_diag) {
     if (is_unit_diag)
-        return getHandle(triangle<T, is_upper,  true>(getArray<T>(in)));
+        return getHandle(triangle<T, is_upper, true>(getArray<T>(in)));
     else
         return getHandle(triangle<T, is_upper, false>(getArray<T>(in)));
 }
 
-af_err af_lower(af_array *out, const af_array in, bool is_unit_diag)
-{
+af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array res;
-        switch(type) {
-        case f32: res = triangle<float   , false>(in, is_unit_diag); break;
-        case f64: res = triangle<double  , false>(in, is_unit_diag); break;
-        case c32: res = triangle<cfloat  , false>(in, is_unit_diag); break;
-        case c64: res = triangle<cdouble , false>(in, is_unit_diag); break;
-        case s32: res = triangle<int     , false>(in, is_unit_diag); break;
-        case u32: res = triangle<uint    , false>(in, is_unit_diag); break;
-        case s64: res = triangle<intl    , false>(in, is_unit_diag); break;
-        case u64: res = triangle<uintl   , false>(in, is_unit_diag); break;
-        case s16: res = triangle<short   , false>(in, is_unit_diag); break;
-        case u16: res = triangle<ushort  , false>(in, is_unit_diag); break;
-        case u8 : res = triangle<uchar   , false>(in, is_unit_diag); break;
-        case b8 : res = triangle<char    , false>(in, is_unit_diag); break;
+        switch (type) {
+            case f32: res = triangle<float, false>(in, is_unit_diag); break;
+            case f64: res = triangle<double, false>(in, is_unit_diag); break;
+            case c32: res = triangle<cfloat, false>(in, is_unit_diag); break;
+            case c64: res = triangle<cdouble, false>(in, is_unit_diag); break;
+            case s32: res = triangle<int, false>(in, is_unit_diag); break;
+            case u32: res = triangle<uint, false>(in, is_unit_diag); break;
+            case s64: res = triangle<intl, false>(in, is_unit_diag); break;
+            case u64: res = triangle<uintl, false>(in, is_unit_diag); break;
+            case s16: res = triangle<short, false>(in, is_unit_diag); break;
+            case u16: res = triangle<ushort, false>(in, is_unit_diag); break;
+            case u8: res = triangle<uchar, false>(in, is_unit_diag); break;
+            case b8: res = triangle<char, false>(in, is_unit_diag); break;
         }
         std::swap(*out, res);
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
 
-
-af_err af_upper(af_array *out, const af_array in, bool is_unit_diag)
-{
+af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array res;
-        switch(type) {
-        case f32: res = triangle<float   , true>(in, is_unit_diag); break;
-        case f64: res = triangle<double  , true>(in, is_unit_diag); break;
-        case c32: res = triangle<cfloat  , true>(in, is_unit_diag); break;
-        case c64: res = triangle<cdouble , true>(in, is_unit_diag); break;
-        case s32: res = triangle<int     , true>(in, is_unit_diag); break;
-        case u32: res = triangle<uint    , true>(in, is_unit_diag); break;
-        case s64: res = triangle<intl    , true>(in, is_unit_diag); break;
-        case u64: res = triangle<uintl   , true>(in, is_unit_diag); break;
-        case s16: res = triangle<short   , true>(in, is_unit_diag); break;
-        case u16: res = triangle<ushort  , true>(in, is_unit_diag); break;
-        case u8 : res = triangle<uchar   , true>(in, is_unit_diag); break;
-        case b8 : res = triangle<char    , true>(in, is_unit_diag); break;
+        switch (type) {
+            case f32: res = triangle<float, true>(in, is_unit_diag); break;
+            case f64: res = triangle<double, true>(in, is_unit_diag); break;
+            case c32: res = triangle<cfloat, true>(in, is_unit_diag); break;
+            case c64: res = triangle<cdouble, true>(in, is_unit_diag); break;
+            case s32: res = triangle<int, true>(in, is_unit_diag); break;
+            case u32: res = triangle<uint, true>(in, is_unit_diag); break;
+            case s64: res = triangle<intl, true>(in, is_unit_diag); break;
+            case u64: res = triangle<uintl, true>(in, is_unit_diag); break;
+            case s16: res = triangle<short, true>(in, is_unit_diag); break;
+            case u16: res = triangle<ushort, true>(in, is_unit_diag); break;
+            case u8: res = triangle<uchar, true>(in, is_unit_diag); break;
+            case b8: res = triangle<char, true>(in, is_unit_diag); break;
         }
         std::swap(*out, res);
     }
     CATCHALL
-        return AF_SUCCESS;
+    return AF_SUCCESS;
 }
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index d33d877c37..174843c03c 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -7,23 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <common/err_common.hpp>
-#include <backend.hpp>
 #include <Array.hpp>
 #include <arith.hpp>
+#include <backend.hpp>
 #include <cast.hpp>
-#include <complex.hpp>
 #include <common/dispatch.hpp>
+#include <common/err_common.hpp>
+#include <complex.hpp>
+#include <copy.hpp>
 #include <fft_common.hpp>
 #include <fftconvolve.hpp>
 #include <handle.hpp>
+#include <logic.hpp>
 #include <reduce.hpp>
-#include <shift.hpp>
 #include <select.hpp>
-#include <logic.hpp>
+#include <shift.hpp>
 #include <unary.hpp>
-#include <copy.hpp>
+#include <af/image.h>
 
 #include <algorithm>
 #include <cmath>
@@ -49,35 +49,31 @@ const dim_t GREATEST_PRIME_FACTOR = 7;
 #endif
 
 template<typename T, typename CT>
-Array<T> complexNorm(const Array<CT>& input)
-{
-    auto mag   = abs<T, CT>(input);
-    auto TWOS  = createValueArray(input.dims(), scalar<T>(2));
+Array<T> complexNorm(const Array<CT>& input) {
+    auto mag  = abs<T, CT>(input);
+    auto TWOS = createValueArray(input.dims(), scalar<T>(2));
     return arithOp<T, af_pow_t>(mag, TWOS, input.dims());
 }
 
-std::vector<af_seq>
-calcPadInfo(dim4& inLPad, dim4& psfLPad,
-            dim4& inUPad, dim4& psfUPad,
-            dim4& odims, dim_t nElems,
-            const dim4& idims, const dim4& fdims)
-{
+std::vector<af_seq> calcPadInfo(dim4& inLPad, dim4& psfLPad, dim4& inUPad,
+                                dim4& psfUPad, dim4& odims, dim_t nElems,
+                                const dim4& idims, const dim4& fdims) {
     std::vector<af_seq> index(4);
 
-    for (int d=0; d<4; ++d) {
-        if (d<BASE_DIM) {
+    for (int d = 0; d < 4; ++d) {
+        if (d < BASE_DIM) {
             dim_t pad = idims[d] + fdims[d];
 
             while (greatestPrimeFactor(pad) > GREATEST_PRIME_FACTOR) pad++;
 
             dim_t diffLen  = pad - idims[d];
-            inLPad[d]      = diffLen/2;
-            inUPad[d]      = diffLen/2 + diffLen%2;
+            inLPad[d]      = diffLen / 2;
+            inUPad[d]      = diffLen / 2 + diffLen % 2;
             psfLPad[d]     = 0;
             psfUPad[d]     = pad - fdims[d];
             odims[d]       = pad;
             index[d].begin = inLPad[d];
-            index[d].end   = index[d].begin + idims[d]-1;
+            index[d].end   = index[d].begin + idims[d] - 1;
             index[d].step  = 1;
 
             nElems *= odims[d];
@@ -97,9 +93,8 @@ template<typename T, typename CT>
 void richardsonLucy(Array<T>& currentEstimate, const Array<T>& in,
                     const Array<CT>& P, const Array<CT>& Pc,
                     const unsigned iters, const float normFactor,
-                    const dim4 odims)
-{
-    for (unsigned i=0; i<iters; ++i) {
+                    const dim4 odims) {
+    for (unsigned i = 0; i < iters; ++i) {
         auto fft1  = fft_r2c<CT, T, BASE_DIM>(currentEstimate);
         auto cmul1 = arithOp<CT, af_mul_t>(fft1, P, P.dims());
         auto ifft1 = fft_c2r<CT, T, BASE_DIM>(cmul1, normFactor, odims);
@@ -108,17 +103,16 @@ void richardsonLucy(Array<T>& currentEstimate, const Array<T>& in,
         auto cmul2 = arithOp<CT, af_mul_t>(fft2, Pc, Pc.dims());
         auto ifft2 = fft_c2r<CT, T, BASE_DIM>(cmul2, normFactor, odims);
 
-        currentEstimate = arithOp<T, af_mul_t>(currentEstimate, ifft2,
-                                               ifft2.dims());
+        currentEstimate =
+            arithOp<T, af_mul_t>(currentEstimate, ifft2, ifft2.dims());
     }
 }
 
 template<typename T, typename CT>
 void landweber(Array<T>& currentEstimate, const Array<T>& in,
-               const Array<CT>& P, const Array<CT>& Pc,
-               const unsigned iters, const float relaxFactor,
-               const float normFactor, const dim4 odims)
-{
+               const Array<CT>& P, const Array<CT>& Pc, const unsigned iters,
+               const float relaxFactor, const float normFactor,
+               const dim4 odims) {
     const dim4& dims = P.dims();
 
     auto I        = fft_r2c<CT, T, BASE_DIM>(in);
@@ -133,86 +127,89 @@ void landweber(Array<T>& currentEstimate, const Array<T>& in,
     auto rhs      = arithOp<CT, af_mul_t>(rhsFac, alphaC, dims);
     auto iterTemp = I;
 
-    for (unsigned i=0; i<iters; ++i) {
-        auto mul  = arithOp<CT, af_mul_t>(iterTemp, lhs, dims);
-        iterTemp  = arithOp<CT, af_add_t>(mul, rhs, dims);
+    for (unsigned i = 0; i < iters; ++i) {
+        auto mul = arithOp<CT, af_mul_t>(iterTemp, lhs, dims);
+        iterTemp = arithOp<CT, af_add_t>(mul, rhs, dims);
     }
     currentEstimate = fft_c2r<CT, T, BASE_DIM>(iterTemp, normFactor, odims);
 }
 
-template<typename InputType, typename RealType=float>
-af_array iterDeconv(const af_array in, const af_array ker,
-                    const uint iters, const float rfactor,
-                    const af_iterative_deconv_algo algo)
-{
+template<typename InputType, typename RealType = float>
+af_array iterDeconv(const af_array in, const af_array ker, const uint iters,
+                    const float rfactor, const af_iterative_deconv_algo algo) {
     typedef RealType T;
-    using CT = typename std::conditional< std::is_same<T, double>::value,
-                                          cdouble,
-                                          cfloat
-                                        >::type;
-    auto input   = castArray<T>(in);
-    auto psf     = castArray<T>(ker);
-    const dim4& idims  = input.dims();
-    const dim4& fdims  = psf.dims();
-    dim_t nElems = 1;
+    using CT   = typename std::conditional<std::is_same<T, double>::value,
+                                         cdouble, cfloat>::type;
+    auto input = castArray<T>(in);
+    auto psf   = castArray<T>(ker);
+    const dim4& idims = input.dims();
+    const dim4& fdims = psf.dims();
+    dim_t nElems      = 1;
 
     dim4 inUPad, psfUPad, inLPad, psfLPad, odims(1);
 
-    auto index     = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad,
-                                 odims, nElems, idims, fdims);
-    auto paddedIn  = padArrayBorders<T>(input,  inLPad,  inUPad,
-                                        AF_PAD_CLAMP_TO_EDGE);
+    auto index = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad, odims, nElems,
+                             idims, fdims);
+    auto paddedIn =
+        padArrayBorders<T>(input, inLPad, inUPad, AF_PAD_CLAMP_TO_EDGE);
     auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad, AF_PAD_ZERO);
 
-    const int shiftDims[4] = { -int(fdims[0]/2), -int(fdims[1]/2), 0, 0 };
-    auto shiftedPsf   = shift(paddedPsf, shiftDims);
+    const int shiftDims[4] = {-int(fdims[0] / 2), -int(fdims[1] / 2), 0, 0};
+    auto shiftedPsf        = shift(paddedPsf, shiftDims);
 
     auto P  = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
     auto Pc = conj(P);
 
     Array<T> currentEstimate = paddedIn;
-    const double normFactor  = 1/(double)nElems;
+    const double normFactor  = 1 / (double)nElems;
 
-    switch(algo) {
+    switch (algo) {
         case AF_ITERATIVE_DECONV_RICHARDSONLUCY:
-            richardsonLucy(currentEstimate, paddedIn, P, Pc,
-                           iters, normFactor, odims); break;
+            richardsonLucy(currentEstimate, paddedIn, P, Pc, iters, normFactor,
+                           odims);
+            break;
         default:
-            landweber(currentEstimate, paddedIn, P, Pc,
-                      iters, rfactor, normFactor, odims); break;
+            landweber(currentEstimate, paddedIn, P, Pc, iters, rfactor,
+                      normFactor, odims);
+            break;
     }
     return getHandle(createSubArray<T>(currentEstimate, index));
 }
 
 af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
                            const unsigned iterations, const float relax_factor,
-                           const af_iterative_deconv_algo algo)
-{
+                           const af_iterative_deconv_algo algo) {
     try {
         const ArrayInfo& inputInfo  = getInfo(in);
-        const dim4& inputDims  = inputInfo.dims();
+        const dim4& inputDims       = inputInfo.dims();
         const ArrayInfo& kernelInfo = getInfo(ker);
-        const dim4& kernelDims = kernelInfo.dims();
+        const dim4& kernelDims      = kernelInfo.dims();
 
         DIM_ASSERT(2, (inputDims.ndims() == 2));
         DIM_ASSERT(3, (kernelDims.ndims() == 2));
         ARG_ASSERT(4, (iterations > 0));
         ARG_ASSERT(5, std::isfinite(relax_factor));
         ARG_ASSERT(5, (relax_factor > 0));
-        ARG_ASSERT(6, (algo==AF_ITERATIVE_DECONV_DEFAULT ||
-                       algo==AF_ITERATIVE_DECONV_LANDWEBER ||
-                       algo==AF_ITERATIVE_DECONV_RICHARDSONLUCY));
+        ARG_ASSERT(6, (algo == AF_ITERATIVE_DECONV_DEFAULT ||
+                       algo == AF_ITERATIVE_DECONV_LANDWEBER ||
+                       algo == AF_ITERATIVE_DECONV_RICHARDSONLUCY));
         af_array res   = 0;
         unsigned iters = iterations;
         float rfac     = relax_factor;
 
-        af_dtype inputType  = inputInfo.getType();
-        switch(inputType) {
-            case f32: res = iterDeconv<float >(in,ker,iters,rfac,algo); break;
-            case s16: res = iterDeconv<short >(in,ker,iters,rfac,algo); break;
-            case u16: res = iterDeconv<ushort>(in,ker,iters,rfac,algo); break;
-            case u8:  res = iterDeconv<uchar >(in,ker,iters,rfac,algo); break;
-            default : TYPE_ERROR(1, inputType);
+        af_dtype inputType = inputInfo.getType();
+        switch (inputType) {
+            case f32:
+                res = iterDeconv<float>(in, ker, iters, rfac, algo);
+                break;
+            case s16:
+                res = iterDeconv<short>(in, ker, iters, rfac, algo);
+                break;
+            case u16:
+                res = iterDeconv<ushort>(in, ker, iters, rfac, algo);
+                break;
+            case u8: res = iterDeconv<uchar>(in, ker, iters, rfac, algo); break;
+            default: TYPE_ERROR(1, inputType);
         }
         std::swap(res, *out);
     }
@@ -222,54 +219,49 @@ af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
 
 template<typename CT>
 Array<CT> denominator(const Array<CT>& I, const Array<CT>& P, const float gamma,
-                      const af_inverse_deconv_algo algo)
-{
+                      const af_inverse_deconv_algo algo) {
     typedef typename af::dtype_traits<CT>::base_type T;
 
-    auto RCNST  = createValueArray(I.dims(), scalar<T>(gamma));
+    auto RCNST = createValueArray(I.dims(), scalar<T>(gamma));
 
-    if (algo==AF_INVERSE_DECONV_TIKHONOV) {
-        auto normP  = complexNorm<T, CT>(P);
-        auto denom  = arithOp<T, af_add_t>(normP, RCNST, normP.dims());
+    if (algo == AF_INVERSE_DECONV_TIKHONOV) {
+        auto normP = complexNorm<T, CT>(P);
+        auto denom = arithOp<T, af_add_t>(normP, RCNST, normP.dims());
 
         return cast<CT, T>(denom);
     } else {
-        //TODO(pradeep) Wiener Filter code path is disabled.
+        // TODO(pradeep) Wiener Filter code path is disabled.
         // This code path doesn't is not exposed using current API
-        auto normI  = complexNorm<T, CT>(I);
-        auto sRes   = arithOp<T, af_sub_t>(normI, RCNST, normI.dims());
-        auto dRes   = arithOp<T, af_div_t>(RCNST,  sRes, RCNST.dims());
-        auto normP  = complexNorm<T, CT>(P);
-        auto denom  = arithOp<T, af_add_t>(normP,  dRes, normP.dims());
+        auto normI = complexNorm<T, CT>(I);
+        auto sRes  = arithOp<T, af_sub_t>(normI, RCNST, normI.dims());
+        auto dRes  = arithOp<T, af_div_t>(RCNST, sRes, RCNST.dims());
+        auto normP = complexNorm<T, CT>(P);
+        auto denom = arithOp<T, af_add_t>(normP, dRes, normP.dims());
 
         return cast<CT, T>(denom);
     }
 }
 
-template<typename InputType, typename RealType=float>
+template<typename InputType, typename RealType = float>
 af_array invDeconv(const af_array in, const af_array ker, const float gamma,
-                   const af_inverse_deconv_algo algo)
-{
+                   const af_inverse_deconv_algo algo) {
     typedef RealType T;
-    using CT = typename std::conditional< std::is_same<T, double>::value,
-                                          cdouble,
-                                          cfloat
-                                        >::type;
+    using CT   = typename std::conditional<std::is_same<T, double>::value,
+                                         cdouble, cfloat>::type;
     auto input = castArray<T>(in);
     auto psf   = castArray<T>(ker);
-    const dim4& idims  = input.dims();
-    const dim4& fdims  = psf.dims();
-    dim_t nElems = 1;
+    const dim4& idims = input.dims();
+    const dim4& fdims = psf.dims();
+    dim_t nElems      = 1;
 
     dim4 inUPad, psfUPad, inLPad, psfLPad, odims(1);
 
-    auto index     = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad,
-                                 odims, nElems, idims, fdims);
-    auto paddedIn  = padArrayBorders<T>(input,  inLPad,  inUPad,
-                                        AF_PAD_CLAMP_TO_EDGE);
-    auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad,
-                                        AF_PAD_ZERO);
-    const int shiftDims[4] = { -int(fdims[0]/2), -int(fdims[1]/2), 0, 0};
+    auto index = calcPadInfo(inLPad, psfLPad, inUPad, psfUPad, odims, nElems,
+                             idims, fdims);
+    auto paddedIn =
+        padArrayBorders<T>(input, inLPad, inUPad, AF_PAD_CLAMP_TO_EDGE);
+    auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad, AF_PAD_ZERO);
+    const int shiftDims[4] = {-int(fdims[0] / 2), -int(fdims[1] / 2), 0, 0};
 
     auto shiftedPsf = shift(paddedPsf, shiftDims);
 
@@ -285,35 +277,34 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
 
     select_scalar<CT, false>(val, cond, val, 0);
 
-    auto ival   = fft_c2r<CT, T, BASE_DIM>(val, 1/(double)nElems, odims);
+    auto ival = fft_c2r<CT, T, BASE_DIM>(val, 1 / (double)nElems, odims);
 
     return getHandle(createSubArray<T>(ival, index));
 }
 
 af_err af_inverse_deconv(af_array* out, const af_array in, const af_array psf,
-                         const float gamma, const af_inverse_deconv_algo algo)
-{
+                         const float gamma, const af_inverse_deconv_algo algo) {
     try {
         const ArrayInfo& inputInfo = getInfo(in);
-        const dim4& inputDims = inputInfo.dims();
+        const dim4& inputDims      = inputInfo.dims();
         const ArrayInfo& psfInfo   = getInfo(psf);
-        const dim4& psfDims   = psfInfo.dims();
+        const dim4& psfDims        = psfInfo.dims();
 
         DIM_ASSERT(2, (inputDims.ndims() == 2));
         DIM_ASSERT(3, (psfDims.ndims() == 2));
         ARG_ASSERT(4, std::isfinite(gamma));
         ARG_ASSERT(4, (gamma > 0));
-        ARG_ASSERT(5, (algo==AF_INVERSE_DECONV_DEFAULT ||
-                       algo==AF_INVERSE_DECONV_TIKHONOV));
+        ARG_ASSERT(5, (algo == AF_INVERSE_DECONV_DEFAULT ||
+                       algo == AF_INVERSE_DECONV_TIKHONOV));
         af_array res = 0;
 
-        af_dtype inputType  = inputInfo.getType();
-        switch(inputType) {
-            case f32: res = invDeconv<float >(in, psf, gamma, algo); break;
-            case s16: res = invDeconv<short >(in, psf, gamma, algo); break;
+        af_dtype inputType = inputInfo.getType();
+        switch (inputType) {
+            case f32: res = invDeconv<float>(in, psf, gamma, algo); break;
+            case s16: res = invDeconv<short>(in, psf, gamma, algo); break;
             case u16: res = invDeconv<ushort>(in, psf, gamma, algo); break;
-            case u8:  res = invDeconv<uchar >(in, psf, gamma, algo); break;
-            default : TYPE_ERROR(1, inputType);
+            case u8: res = invDeconv<uchar>(in, psf, gamma, algo); break;
+            default: TYPE_ERROR(1, inputType);
         }
         std::swap(res, *out);
     }
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index 72587b3d35..1cd6e76ac1 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -7,29 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <math.hpp>
-#include <lu.hpp>
-#include <diagonal.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
+#include <diagonal.hpp>
+#include <handle.hpp>
+#include <lu.hpp>
+#include <math.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-T det(const af_array a)
-{
+T det(const af_array a) {
     const Array<T> A = getArray<T>(a);
 
     const int num = A.dims()[0];
 
-    if(num == 0) {
+    if (num == 0) {
         T res = scalar<T>(1.0);
         return res;
     }
@@ -37,7 +36,7 @@ T det(const af_array a)
     std::vector<T> hD(num);
     std::vector<int> hP(num);
 
-    Array<T> D = createEmptyArray<T>(dim4());
+    Array<T> D       = createEmptyArray<T>(dim4());
     Array<int> pivot = createEmptyArray<int>(dim4());
 
     // Free memory as soon as possible
@@ -52,10 +51,10 @@ T det(const af_array a)
     }
 
     bool is_neg = false;
-    T res = scalar<T>(is_neg ? -1 : 1);
+    T res       = scalar<T>(is_neg ? -1 : 1);
     for (int i = 0; i < num; i++) {
         res = res * hD[i];
-        is_neg ^= (hP[i] != (i+1));
+        is_neg ^= (hP[i] != (i + 1));
     }
 
     if (is_neg) res = res * scalar<T>(-1);
@@ -63,11 +62,9 @@ T det(const af_array a)
     return res;
 }
 
-af_err af_det(double *real_val, double *imag_val, const af_array in)
-{
-
+af_err af_det(double *real_val, double *imag_val, const af_array in) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
@@ -75,9 +72,10 @@ af_err af_det(double *real_val, double *imag_val, const af_array in)
 
         af_dtype type = i_info.getType();
 
-        if(i_info.dims()[0])
-          DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]);      // Only square matrices
-        ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
+        if (i_info.dims()[0])
+            DIM_ASSERT(1, i_info.dims()[0] ==
+                              i_info.dims()[1]);  // Only square matrices
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
         *real_val = 0;
         *imag_val = 0;
@@ -85,20 +83,20 @@ af_err af_det(double *real_val, double *imag_val, const af_array in)
         cfloat cfval;
         cdouble cdval;
 
-        switch(type) {
-        case f32: *real_val = det<float  >(in);  break;
-        case f64: *real_val = det<double >(in);  break;
-        case c32:
-            cfval = det<cfloat >(in);
-            *real_val = real(cfval);
-            *imag_val = imag(cfval);
-            break;
-        case c64:
-            cdval = det<cdouble>(in);
-            *real_val = real(cdval);
-            *imag_val = imag(cdval);
-            break;
-        default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: *real_val = det<float>(in); break;
+            case f64: *real_val = det<double>(in); break;
+            case c32:
+                cfval     = det<cfloat>(in);
+                *real_val = real(cfval);
+                *imag_val = imag(cfval);
+                break;
+            case c64:
+                cdval     = det<cdouble>(in);
+                *real_val = real(cdval);
+                *imag_val = imag(cdval);
+                break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index c3513b73e6..47a3ba69fa 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -7,92 +7,86 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/device.h>
-#include <af/version.h>
-#include <af/backend.h>
-#include <backend.hpp>
-#include <platform.hpp>
 #include <Array.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
+#include <platform.hpp>
 #include <sparse_handle.hpp>
-#include <common/err_common.hpp>
+#include <af/backend.h>
+#include <af/device.h>
+#include <af/dim4.hpp>
+#include <af/version.h>
 #include <cstring>
 
 using namespace detail;
 
-af_err af_set_backend(const af_backend bknd)
-{
+af_err af_set_backend(const af_backend bknd) {
     try {
-        ARG_ASSERT(0, bknd==getBackend());
+        ARG_ASSERT(0, bknd == getBackend());
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_get_backend_count(unsigned* num_backends)
-{
+af_err af_get_backend_count(unsigned* num_backends) {
     *num_backends = 1;
     return AF_SUCCESS;
 }
 
-af_err af_get_available_backends(int* result)
-{
+af_err af_get_available_backends(int* result) {
     try {
         *result = getBackend();
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_backend_id(af_backend *result, const af_array in)
-{
+af_err af_get_backend_id(af_backend* result, const af_array in) {
     try {
         ARG_ASSERT(1, in != 0);
         const ArrayInfo& info = getInfo(in, false, false);
-        *result = info.getBackendId();
-    } CATCHALL;
+        *result               = info.getBackendId();
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_device_id(int *device, const af_array in)
-{
+af_err af_get_device_id(int* device, const af_array in) {
     try {
         ARG_ASSERT(1, in != 0);
         const ArrayInfo& info = getInfo(in, false, false);
-        *device = info.getDevId();
-    } CATCHALL;
+        *device               = info.getDevId();
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_active_backend(af_backend *result)
-{
+af_err af_get_active_backend(af_backend* result) {
     *result = (af_backend)getBackend();
     return AF_SUCCESS;
 }
 
-af_err af_init()
-{
+af_err af_init() {
     try {
         thread_local std::once_flag flag;
-        std::call_once(flag, []() {
-                getDeviceInfo();
-            });
-    } CATCHALL;
+        std::call_once(flag, []() { getDeviceInfo(); });
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_info()
-{
+af_err af_info() {
     try {
         printf("%s", getDeviceInfo().c_str());
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_info_string(char **str, const bool verbose)
-{
-    UNUSED(verbose); // TODO(umar): Add something useful
+af_err af_info_string(char** str, const bool verbose) {
+    UNUSED(verbose);  // TODO(umar): Add something useful
     try {
         std::string infoStr = getDeviceInfo();
         af_alloc_host((void**)str, sizeof(char) * (infoStr.size() + 1));
@@ -101,117 +95,115 @@ af_err af_info_string(char **str, const bool verbose)
         // str.c_str wont cut it
         infoStr.copy(*str, infoStr.size());
         (*str)[infoStr.size()] = '\0';
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_device_info(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-{
+af_err af_device_info(char* d_name, char* d_platform, char* d_toolkit,
+                      char* d_compute) {
     try {
         devprop(d_name, d_platform, d_toolkit, d_compute);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_dbl_support(bool* available, const int device)
-{
+af_err af_get_dbl_support(bool* available, const int device) {
     try {
         *available = isDoubleSupported(device);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_device_count(int *nDevices)
-{
+af_err af_get_device_count(int* nDevices) {
     try {
         *nDevices = getDeviceCount();
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_get_device(int *device)
-{
+af_err af_get_device(int* device) {
     try {
         *device = getActiveDeviceId();
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_device(const int device)
-{
+af_err af_set_device(const int device) {
     try {
         ARG_ASSERT(0, device >= 0);
         ARG_ASSERT(0, setDevice(device) >= 0);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_sync(const int device)
-{
+af_err af_sync(const int device) {
     try {
         int dev = device == -1 ? getActiveDeviceId() : device;
         detail::sync(dev);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-
 template<typename T>
-static inline void eval(af_array arr)
-{
+static inline void eval(af_array arr) {
     getArray<T>(arr).eval();
     return;
 }
 
 template<typename T>
-static inline void sparseEval(af_array arr)
-{
+static inline void sparseEval(af_array arr) {
     getSparseArray<T>(arr).eval();
     return;
 }
 
-af_err af_eval(af_array arr)
-{
+af_err af_eval(af_array arr) {
     try {
         const ArrayInfo& info = getInfo(arr, false);
-        af_dtype type = info.getType();
+        af_dtype type         = info.getType();
 
-        if(info.isSparse()) {
-            switch(type) {
-                case f32: sparseEval<float  >(arr); break;
-                case f64: sparseEval<double >(arr); break;
-                case c32: sparseEval<cfloat >(arr); break;
+        if (info.isSparse()) {
+            switch (type) {
+                case f32: sparseEval<float>(arr); break;
+                case f64: sparseEval<double>(arr); break;
+                case c32: sparseEval<cfloat>(arr); break;
                 case c64: sparseEval<cdouble>(arr); break;
-                default : TYPE_ERROR(0, type);
+                default: TYPE_ERROR(0, type);
             }
         } else {
             switch (type) {
-                case f32: eval<float  >(arr); break;
-                case f64: eval<double >(arr); break;
-                case c32: eval<cfloat >(arr); break;
+                case f32: eval<float>(arr); break;
+                case f64: eval<double>(arr); break;
+                case c32: eval<cfloat>(arr); break;
                 case c64: eval<cdouble>(arr); break;
-                case s32: eval<int    >(arr); break;
-                case u32: eval<uint   >(arr); break;
-                case u8 : eval<uchar  >(arr); break;
-                case b8 : eval<char   >(arr); break;
-                case s64: eval<intl   >(arr); break;
-                case u64: eval<uintl  >(arr); break;
-                case s16: eval<short  >(arr); break;
-                case u16: eval<ushort >(arr); break;
+                case s32: eval<int>(arr); break;
+                case u32: eval<uint>(arr); break;
+                case u8: eval<uchar>(arr); break;
+                case b8: eval<char>(arr); break;
+                case s64: eval<intl>(arr); break;
+                case u64: eval<uintl>(arr); break;
+                case s16: eval<short>(arr); break;
+                case u16: eval<ushort>(arr); break;
                 default: TYPE_ERROR(0, type);
             }
         }
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-static inline void evalMultiple(int num, af_array *arrayPtrs)
-{
+static inline void evalMultiple(int num, af_array* arrayPtrs) {
     Array<T> empty = createEmptyArray<T>(dim4());
     std::vector<Array<T>*> arrays(num, &empty);
 
@@ -223,12 +215,11 @@ static inline void evalMultiple(int num, af_array *arrayPtrs)
     return;
 }
 
-af_err af_eval_multiple(int num, af_array *arrays)
-{
+af_err af_eval_multiple(int num, af_array* arrays) {
     try {
         const ArrayInfo& info = getInfo(arrays[0]);
-        af_dtype type = info.getType();
-        dim4 dims = info.dims();
+        af_dtype type         = info.getType();
+        dim4 dims             = info.dims();
 
         for (int i = 1; i < num; i++) {
             const ArrayInfo& currInfo = getInfo(arrays[i]);
@@ -244,41 +235,40 @@ af_err af_eval_multiple(int num, af_array *arrays)
         }
 
         switch (type) {
-        case f32: evalMultiple<float  >(num, arrays); break;
-        case f64: evalMultiple<double >(num, arrays); break;
-        case c32: evalMultiple<cfloat >(num, arrays); break;
-        case c64: evalMultiple<cdouble>(num, arrays); break;
-        case s32: evalMultiple<int    >(num, arrays); break;
-        case u32: evalMultiple<uint   >(num, arrays); break;
-        case u8 : evalMultiple<uchar  >(num, arrays); break;
-        case b8 : evalMultiple<char   >(num, arrays); break;
-        case s64: evalMultiple<intl   >(num, arrays); break;
-        case u64: evalMultiple<uintl  >(num, arrays); break;
-        case s16: evalMultiple<short  >(num, arrays); break;
-        case u16: evalMultiple<ushort >(num, arrays); break;
-        default:
-            TYPE_ERROR(0, type);
+            case f32: evalMultiple<float>(num, arrays); break;
+            case f64: evalMultiple<double>(num, arrays); break;
+            case c32: evalMultiple<cfloat>(num, arrays); break;
+            case c64: evalMultiple<cdouble>(num, arrays); break;
+            case s32: evalMultiple<int>(num, arrays); break;
+            case u32: evalMultiple<uint>(num, arrays); break;
+            case u8: evalMultiple<uchar>(num, arrays); break;
+            case b8: evalMultiple<char>(num, arrays); break;
+            case s64: evalMultiple<intl>(num, arrays); break;
+            case u64: evalMultiple<uintl>(num, arrays); break;
+            case s16: evalMultiple<short>(num, arrays); break;
+            case u16: evalMultiple<ushort>(num, arrays); break;
+            default: TYPE_ERROR(0, type);
         }
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_set_manual_eval_flag(bool flag)
-{
+af_err af_set_manual_eval_flag(bool flag) {
     try {
         bool& backendFlag = evalFlag();
-        backendFlag = !flag;
-    } CATCHALL;
+        backendFlag       = !flag;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-
-af_err af_get_manual_eval_flag(bool *flag)
-{
+af_err af_get_manual_eval_flag(bool* flag) {
     try {
         bool backendFlag = evalFlag();
-        *flag = !backendFlag;
-    } CATCHALL;
+        *flag            = !backendFlag;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index e8575176c1..1e2c024afe 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -7,40 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/algorithm.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <diff.hpp>
+#include <handle.hpp>
+#include <af/algorithm.h>
+#include <af/defines.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array diff1(const af_array in, const int dim)
-{
+static inline af_array diff1(const af_array in, const int dim) {
     return getHandle(diff1<T>(getArray<T>(in), dim));
 }
 
 template<typename T>
-static inline af_array diff2(const af_array in, const int dim)
-{
+static inline af_array diff2(const af_array in, const int dim) {
     return getHandle(diff2<T>(getArray<T>(in), dim));
 }
 
-af_err af_diff1(af_array *out, const af_array in, const int dim)
-{
+af_err af_diff1(af_array* out, const af_array in, const int dim) {
     try {
-
         ARG_ASSERT(2, ((dim >= 0) && (dim < 4)));
 
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        af_dtype type         = info.getType();
 
         af::dim4 in_dims = info.dims();
-        if(in_dims[dim] < 2) {
+        if (in_dims[dim] < 2) {
             return af_create_handle(out, 0, nullptr, type);
         }
 
@@ -48,62 +44,59 @@ af_err af_diff1(af_array *out, const af_array in, const int dim)
 
         af_array output;
 
-        switch(type) {
-            case f32: output = diff1<float  >(in,dim);  break;
-            case c32: output = diff1<cfloat >(in,dim);  break;
-            case f64: output = diff1<double >(in,dim);  break;
-            case c64: output = diff1<cdouble>(in,dim);  break;
-            case b8:  output = diff1<char   >(in,dim);  break;
-            case s32: output = diff1<int    >(in,dim);  break;
-            case u32: output = diff1<uint   >(in,dim);  break;
-            case s64: output = diff1<intl   >(in,dim);  break;
-            case u64: output = diff1<uintl  >(in,dim);  break;
-            case s16: output = diff1<short  >(in,dim);  break;
-            case u16: output = diff1<ushort >(in,dim);  break;
-            case u8:  output = diff1<uchar  >(in,dim);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = diff1<float>(in, dim); break;
+            case c32: output = diff1<cfloat>(in, dim); break;
+            case f64: output = diff1<double>(in, dim); break;
+            case c64: output = diff1<cdouble>(in, dim); break;
+            case b8: output = diff1<char>(in, dim); break;
+            case s32: output = diff1<int>(in, dim); break;
+            case u32: output = diff1<uint>(in, dim); break;
+            case s64: output = diff1<intl>(in, dim); break;
+            case u64: output = diff1<uintl>(in, dim); break;
+            case s16: output = diff1<short>(in, dim); break;
+            case u16: output = diff1<ushort>(in, dim); break;
+            case u8: output = diff1<uchar>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_diff2(af_array *out, const af_array in, const int dim)
-{
-
+af_err af_diff2(af_array* out, const af_array in, const int dim) {
     try {
-
         ARG_ASSERT(2, ((dim >= 0) && (dim < 4)));
 
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        af_dtype type         = info.getType();
 
         af::dim4 in_dims = info.dims();
-        if(in_dims[dim] < 3) {
+        if (in_dims[dim] < 3) {
             return af_create_handle(out, 0, nullptr, type);
         }
         DIM_ASSERT(1, in_dims[dim] >= 3);
 
         af_array output;
 
-        switch(type) {
-            case f32: output = diff2<float  >(in,dim);  break;
-            case c32: output = diff2<cfloat >(in,dim);  break;
-            case f64: output = diff2<double >(in,dim);  break;
-            case c64: output = diff2<cdouble>(in,dim);  break;
-            case b8:  output = diff2<char   >(in,dim);  break;
-            case s32: output = diff2<int    >(in,dim);  break;
-            case u32: output = diff2<uint   >(in,dim);  break;
-            case s64: output = diff2<intl   >(in,dim);  break;
-            case u64: output = diff2<uintl  >(in,dim);  break;
-            case s16: output = diff2<short  >(in,dim);  break;
-            case u16: output = diff2<ushort >(in,dim);  break;
-            case u8:  output = diff2<uchar  >(in,dim);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = diff2<float>(in, dim); break;
+            case c32: output = diff2<cfloat>(in, dim); break;
+            case f64: output = diff2<double>(in, dim); break;
+            case c64: output = diff2<cdouble>(in, dim); break;
+            case b8: output = diff2<char>(in, dim); break;
+            case s32: output = diff2<int>(in, dim); break;
+            case u32: output = diff2<uint>(in, dim); break;
+            case s64: output = diff2<intl>(in, dim); break;
+            case u64: output = diff2<uintl>(in, dim); break;
+            case s16: output = diff2<short>(in, dim); break;
+            case u16: output = diff2<ushort>(in, dim); break;
+            case u8: output = diff2<uchar>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index fffdff7d0a..7b932817a7 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -7,35 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/vision.h>
-#include <af/image.h>
-#include <common/err_common.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
-#include <handle.hpp>
+#include <common/err_common.hpp>
 #include <convolve.hpp>
-#include <arith.hpp>
+#include <handle.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, typename accT>
-static af_array dog(const af_array& in, const int radius1, const int radius2)
-{
+static af_array dog(const af_array& in, const int radius1, const int radius2) {
     af_array g1, g2;
     g1 = g2 = 0;
-    AF_CHECK(af_gaussian_kernel(&g1, 2*radius1+1, 2*radius1+1, 0.0, 0.0));
-    AF_CHECK(af_gaussian_kernel(&g2, 2*radius2+1, 2*radius2+1, 0.0, 0.0));
+    AF_CHECK(
+        af_gaussian_kernel(&g1, 2 * radius1 + 1, 2 * radius1 + 1, 0.0, 0.0));
+    AF_CHECK(
+        af_gaussian_kernel(&g2, 2 * radius2 + 1, 2 * radius2 + 1, 0.0, 0.0));
 
-    Array<accT> input  = castArray<accT>(in);
-    dim4 iDims      = input.dims();
+    Array<accT> input = castArray<accT>(in);
+    dim4 iDims        = input.dims();
 
     AF_BATCH_KIND bkind = iDims[2] > 1 ? AF_BATCH_LHS : AF_BATCH_NONE;
 
-    Array<accT> smth1 = convolve<accT, accT, 2, false>(input, castArray<accT>(g1), bkind);
-    Array<accT> smth2 = convolve<accT, accT, 2, false>(input, castArray<accT>(g2), bkind);
-    Array<accT> retVal= arithOp<accT, af_sub_t>(smth1, smth2, iDims);
+    Array<accT> smth1 =
+        convolve<accT, accT, 2, false>(input, castArray<accT>(g1), bkind);
+    Array<accT> smth2 =
+        convolve<accT, accT, 2, false>(input, castArray<accT>(g2), bkind);
+    Array<accT> retVal = arithOp<accT, af_sub_t>(smth1, smth2, iDims);
 
     AF_CHECK(af_release_array(g1));
     AF_CHECK(af_release_array(g2));
@@ -43,26 +46,26 @@ static af_array dog(const af_array& in, const int radius1, const int radius2)
     return getHandle<accT>(retVal);
 }
 
-af_err af_dog(af_array *out, const af_array in, const int radius1, const int radius2)
-{
+af_err af_dog(af_array* out, const af_array in, const int radius1,
+              const int radius2) {
     try {
         const ArrayInfo& info = getInfo(in);
-        dim4 inDims = info.dims();
-        ARG_ASSERT(1, (inDims.ndims()>=2));
-        ARG_ASSERT(1, (inDims.ndims()<=3));
+        dim4 inDims           = info.dims();
+        ARG_ASSERT(1, (inDims.ndims() >= 2));
+        ARG_ASSERT(1, (inDims.ndims() <= 3));
 
         af_array output;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: output = dog<float , float>(in, radius1, radius2); break;
-            case f64: output = dog<double,double>(in, radius1, radius2); break;
-            case b8 : output = dog<char  , float>(in, radius1, radius2); break;
-            case s32: output = dog<int   , float>(in, radius1, radius2); break;
-            case u32: output = dog<uint  , float>(in, radius1, radius2); break;
-            case s16: output = dog<short , float>(in, radius1, radius2); break;
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32: output = dog<float, float>(in, radius1, radius2); break;
+            case f64: output = dog<double, double>(in, radius1, radius2); break;
+            case b8: output = dog<char, float>(in, radius1, radius2); break;
+            case s32: output = dog<int, float>(in, radius1, radius2); break;
+            case u32: output = dog<uint, float>(in, radius1, radius2); break;
+            case s16: output = dog<short, float>(in, radius1, radius2); break;
             case u16: output = dog<ushort, float>(in, radius1, radius2); break;
-            case u8 : output = dog<uchar , float>(in, radius1, radius2); break;
-            default : TYPE_ERROR(1, type);
+            case u8: output = dog<uchar, float>(in, radius1, radius2); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 8afb0f02d3..3747126a1f 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -7,14 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/exception.h>
-#include <af/device.h>
 #include <common/err_common.hpp>
-#include <string>
+#include <af/device.h>
+#include <af/exception.h>
 #include <algorithm>
+#include <string>
 
-void af_get_last_error(char **str, dim_t *len)
-{
+void af_get_last_error(char **str, dim_t *len) {
     std::string &global_error_string = get_global_error_string();
     dim_t slen = std::min(MAX_ERR_SIZE, (int)global_error_string.size());
 
@@ -24,11 +23,11 @@ void af_get_last_error(char **str, dim_t *len)
         return;
     }
 
-    af_alloc_host((void**)str, sizeof(char) * (slen + 1));
+    af_alloc_host((void **)str, sizeof(char) * (slen + 1));
     global_error_string.copy(*str, slen);
 
-    (*str)[slen] = '\0';
+    (*str)[slen]        = '\0';
     global_error_string = std::string("");
 
-    if(len) *len = slen;
+    if (len) *len = slen;
 }
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index 31940de1c1..b86186245e 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -7,36 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>          // Needed if you use dim4 class
+#include <af/dim4.hpp>  // Needed if you use dim4 class
 
-#include <af/util.h>            // Include header where function is delcared
+#include <af/util.h>  // Include header where function is delcared
 
-#include <af/defines.h>         // Include this header to access any enums,
-                                // #defines or constants declared
+#include <af/defines.h>  // Include this header to access any enums,
+                         // #defines or constants declared
 
-#include <common/err_common.hpp>       // Header with error checking functions & macros
+#include <common/err_common.hpp>  // Header with error checking functions & macros
 
-#include <backend.hpp>          // This header make sures appropriate backend
-                                // related namespace is being used
+#include <backend.hpp>  // This header make sures appropriate backend
+                        // related namespace is being used
 
-#include <Array.hpp>            // Header in which backend specific Array class
-                                // is defined
+#include <Array.hpp>  // Header in which backend specific Array class
+                      // is defined
 
-#include <handle.hpp>           // Header that helps you retrieve backend specific
-                                // Arrays based on the af_array
-                                // (typedef in defines.h) handle.
+#include <handle.hpp>  // Header that helps you retrieve backend specific
+                       // Arrays based on the af_array
+                       // (typedef in defines.h) handle.
 
 #include <exampleFunction.hpp>  // This is the backend specific header
                                 // where your new function declaration
                                 // is written
 
-using namespace detail;         // detail is an alias to appropriate backend
-                                // defined in backend.hpp. You don't need to
-                                // change this
+using namespace detail;  // detail is an alias to appropriate backend
+                         // defined in backend.hpp. You don't need to
+                         // change this
 
 template<typename T>
-af_array example(const af_array& a, const af_array& b, const af_someenum_t& param)
-{
+af_array example(const af_array& a, const af_array& b,
+                 const af_someenum_t& param) {
     // getArray<T> function is defined in handle.hpp
     // and it returns backend specific Array, namely one of the following
     //      * cpu::Array<T>
@@ -45,52 +45,56 @@ af_array example(const af_array& a, const af_array& b, const af_someenum_t& para
     // getHandle<T> function is defined in handle.hpp takes one of the
     // above backend specific detail::Array<T> and returns the
     // universal array handle af_array
-    return getHandle<T>( exampleFunction(getArray<T>(a), getArray<T>(b), param) );
+    return getHandle<T>(exampleFunction(getArray<T>(a), getArray<T>(b), param));
 }
 
-af_err af_example_function(af_array* out, const af_array a, const af_someenum_t param)
-{
+af_err af_example_function(af_array* out, const af_array a,
+                           const af_someenum_t param) {
     try {
         af_array output = 0;
-        const ArrayInfo& info = getInfo(a);        // ArrayInfo is the base class which
-                                            // each backend specific Array inherits
-                                            // This class stores the basic array meta-data
-                                            // such as type of data, dimensions,
-                                            // offsets and strides. This class is declared
-                                            // in src/backend/common/ArrayInfo.hpp
+        const ArrayInfo& info =
+            getInfo(a);  // ArrayInfo is the base class which
+                         // each backend specific Array inherits
+                         // This class stores the basic array meta-data
+                         // such as type of data, dimensions,
+                         // offsets and strides. This class is declared
+                         // in src/backend/common/ArrayInfo.hpp
         af::dim4 dims = info.dims();
 
-        ARG_ASSERT(2, (dims.ndims()>=0 && dims.ndims()<=3));
-                                            // defined in err_common.hpp
-                                            // there are other useful Macros
-                                            // for different purposes, feel free
-                                            // to look at the header
+        ARG_ASSERT(2, (dims.ndims() >= 0 && dims.ndims() <= 3));
+        // defined in err_common.hpp
+        // there are other useful Macros
+        // for different purposes, feel free
+        // to look at the header
 
         af_dtype type = info.getType();
 
-        switch(type) {                      // Based on the data type, call backend specific
-                                            // implementation
-            case f64: output = example<double >(a, a, param); break;
-            case f32: output = example<float  >(a, a, param); break;
-            case s32: output = example<int    >(a, a, param); break;
-            case u32: output = example<uint   >(a, a, param); break;
-            case  u8: output = example<uchar  >(a, a, param); break;
-            case  b8: output = example<char   >(a, a, param); break;
-            case c32: output = example<cfloat >(a, a, param); break;
+        switch (type) {  // Based on the data type, call backend specific
+                         // implementation
+            case f64: output = example<double>(a, a, param); break;
+            case f32: output = example<float>(a, a, param); break;
+            case s32: output = example<int>(a, a, param); break;
+            case u32: output = example<uint>(a, a, param); break;
+            case u8: output = example<uchar>(a, a, param); break;
+            case b8: output = example<char>(a, a, param); break;
+            case c32: output = example<cfloat>(a, a, param); break;
             case c64: output = example<cdouble>(a, a, param); break;
-            default : TYPE_ERROR(1, type);  // Another helpful macro from err_common.hpp
-                                            // that helps throw type based error messages
+            default:
+                TYPE_ERROR(1,
+                           type);  // Another helpful macro from err_common.hpp
+                                   // that helps throw type based error messages
         }
 
-        std::swap(*out, output);            // if the function has returned successfully,
-                                            // swap the temporary 'output' variable with
-                                            // '*out'
+        std::swap(*out, output);  // if the function has returned successfully,
+                                  // swap the temporary 'output' variable with
+                                  // '*out'
     }
-    CATCHALL;                               // All throws/exceptions from any internal
-                                            // implementations are caught by this CATCHALL
-                                            // macro and handled appropriately.
-
-    return AF_SUCCESS;                      // In case of successfull completion, return AF_SUCCESS
-                                            // There are set of error codes defined in defines.h
-                                            // which you are used by CATCHALL to return approriate code
+    CATCHALL;  // All throws/exceptions from any internal
+               // implementations are caught by this CATCHALL
+               // macro and handled appropriately.
+
+    return AF_SUCCESS;  // In case of successfull completion, return AF_SUCCESS
+                        // There are set of error codes defined in defines.h
+                        // which you are used by CATCHALL to return approriate
+                        // code
 }
diff --git a/src/api/c/fast.cpp b/src/api/c/fast.cpp
index 172349afd1..742d68e21f 100644
--- a/src/api/c/fast.cpp
+++ b/src/api/c/fast.cpp
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <fast.hpp>
+#include <features.hpp>
+#include <handle.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/features.h>
 #include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
-#include <features.hpp>
-#include <fast.hpp>
 
 using af::dim4;
 using namespace detail;
@@ -23,19 +23,17 @@ using namespace detail;
 template<typename T>
 static af_features fast(af_array const &in, const float thr,
                         const unsigned arc_length, const bool non_max,
-                        const float feature_ratio, const unsigned edge)
-{
-    Array<float> x = createEmptyArray<float>(dim4());
-    Array<float> y = createEmptyArray<float>(dim4());
+                        const float feature_ratio, const unsigned edge) {
+    Array<float> x     = createEmptyArray<float>(dim4());
+    Array<float> y     = createEmptyArray<float>(dim4());
     Array<float> score = createEmptyArray<float>(dim4());
 
     af_features_t feat;
-    feat.n = fast<T>(x, y, score,
-                     getArray<T>(in), thr,
-                     arc_length, non_max, feature_ratio, edge);
+    feat.n = fast<T>(x, y, score, getArray<T>(in), thr, arc_length, non_max,
+                     feature_ratio, edge);
 
     Array<float> orientation = createValueArray<float>(feat.n, 0.0);
-    Array<float> size = createValueArray<float>(feat.n, 1.0);
+    Array<float> size        = createValueArray<float>(feat.n, 1.0);
 
     feat.x           = getHandle(x);
     feat.y           = getHandle(y);
@@ -46,16 +44,15 @@ static af_features fast(af_array const &in, const float thr,
     return getFeaturesHandle(feat);
 }
 
-
 af_err af_fast(af_features *out, const af_array in, const float thr,
                const unsigned arc_length, const bool non_max,
-               const float feature_ratio, const unsigned edge)
-{
+               const float feature_ratio, const unsigned edge) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
-        ARG_ASSERT(2, (dims[0] >= (dim_t)(2*edge+1) || dims[1] >= (dim_t)(2*edge+1)));
+        ARG_ASSERT(2, (dims[0] >= (dim_t)(2 * edge + 1) ||
+                       dims[1] >= (dim_t)(2 * edge + 1)));
         ARG_ASSERT(3, thr > 0.0f);
         ARG_ASSERT(4, (arc_length >= 9 && arc_length <= 16));
         ARG_ASSERT(6, (feature_ratio > 0.0f && feature_ratio <= 1.0f));
@@ -63,17 +60,41 @@ af_err af_fast(af_features *out, const af_array in, const float thr,
         dim_t in_ndims = dims.ndims();
         DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
 
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: *out = fast<float >(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case f64: *out = fast<double>(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case b8 : *out = fast<char  >(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case s32: *out = fast<int   >(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case u32: *out = fast<uint  >(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case s16: *out = fast<short >(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case u16: *out = fast<ushort>(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            case u8 : *out = fast<uchar >(in, thr, arc_length, non_max, feature_ratio, edge); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                *out = fast<float>(in, thr, arc_length, non_max, feature_ratio,
+                                   edge);
+                break;
+            case f64:
+                *out = fast<double>(in, thr, arc_length, non_max, feature_ratio,
+                                    edge);
+                break;
+            case b8:
+                *out = fast<char>(in, thr, arc_length, non_max, feature_ratio,
+                                  edge);
+                break;
+            case s32:
+                *out = fast<int>(in, thr, arc_length, non_max, feature_ratio,
+                                 edge);
+                break;
+            case u32:
+                *out = fast<uint>(in, thr, arc_length, non_max, feature_ratio,
+                                  edge);
+                break;
+            case s16:
+                *out = fast<short>(in, thr, arc_length, non_max, feature_ratio,
+                                   edge);
+                break;
+            case u16:
+                *out = fast<ushort>(in, thr, arc_length, non_max, feature_ratio,
+                                    edge);
+                break;
+            case u8:
+                *out = fast<uchar>(in, thr, arc_length, non_max, feature_ratio,
+                                   edge);
+                break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/features.cpp b/src/api/c/features.cpp
index ffa7fe0c75..0c933aaa1c 100644
--- a/src/api/c/features.cpp
+++ b/src/api/c/features.cpp
@@ -7,22 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
-#include <af/array.h>
 #include <features.hpp>
 #include <handle.hpp>
+#include <af/array.h>
+#include <af/features.h>
 
-af_err af_release_features(af_features featHandle)
-{
-
+af_err af_release_features(af_features featHandle) {
     try {
         af_features_t feat = *(af_features_t *)featHandle;
         if (feat.n > 0) {
-            if (feat.x != 0)           AF_CHECK(af_release_array(feat.x));
-            if (feat.y != 0)           AF_CHECK(af_release_array(feat.y));
-            if (feat.score != 0)       AF_CHECK(af_release_array(feat.score));
-            if (feat.orientation != 0) AF_CHECK(af_release_array(feat.orientation));
-            if (feat.size != 0)        AF_CHECK(af_release_array(feat.size));
+            if (feat.x != 0) AF_CHECK(af_release_array(feat.x));
+            if (feat.y != 0) AF_CHECK(af_release_array(feat.y));
+            if (feat.score != 0) AF_CHECK(af_release_array(feat.score));
+            if (feat.orientation != 0)
+                AF_CHECK(af_release_array(feat.orientation));
+            if (feat.size != 0) AF_CHECK(af_release_array(feat.size));
             feat.n = 0;
         }
         delete (af_features_t *)featHandle;
@@ -31,15 +30,13 @@ af_err af_release_features(af_features featHandle)
     return AF_SUCCESS;
 }
 
-af_features getFeaturesHandle(const af_features_t feat)
-{
+af_features getFeaturesHandle(const af_features_t feat) {
     af_features_t *featHandle = new af_features_t;
-    *featHandle = feat;
+    *featHandle               = feat;
     return (af_features)featHandle;
 }
 
-af_err af_create_features(af_features *featHandle, dim_t num)
-{
+af_err af_create_features(af_features *featHandle, dim_t num) {
     try {
         af_features_t feat;
         feat.n = num;
@@ -54,20 +51,19 @@ af_err af_create_features(af_features *featHandle, dim_t num)
         }
 
         *featHandle = getFeaturesHandle(feat);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_features_t getFeatures(const af_features featHandle)
-{
+af_features_t getFeatures(const af_features featHandle) {
     return *(af_features_t *)featHandle;
 }
 
-af_err af_retain_features(af_features *outHandle, const af_features featHandle)
-{
+af_err af_retain_features(af_features *outHandle,
+                          const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
         af_features_t out;
 
@@ -79,68 +75,62 @@ af_err af_retain_features(af_features *outHandle, const af_features featHandle)
         AF_CHECK(af_retain_array(&out.size, feat.size));
 
         *outHandle = getFeaturesHandle(out);
-
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_features_num(dim_t *num, const af_features featHandle)
-{
+af_err af_get_features_num(dim_t *num, const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
-        *num = feat.n;
-
-    } CATCHALL;
+        *num               = feat.n;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_features_xpos(af_array *out, const af_features featHandle)
-{
+af_err af_get_features_xpos(af_array *out, const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
-        *out = feat.x;
-    } CATCHALL;
+        *out               = feat.x;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_features_ypos(af_array *out, const af_features featHandle)
-{
+af_err af_get_features_ypos(af_array *out, const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
-        *out = feat.y;
-    } CATCHALL;
+        *out               = feat.y;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_features_score(af_array *out, const af_features featHandle)
-{
+af_err af_get_features_score(af_array *out, const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
-        *out = feat.score;
-    } CATCHALL;
+        *out               = feat.score;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_features_orientation(af_array *out, const af_features featHandle)
-{
+af_err af_get_features_orientation(af_array *out,
+                                   const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
-        *out = feat.orientation;
-    } CATCHALL;
+        *out               = feat.orientation;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_features_size(af_array *out, const af_features featHandle)
-{
+af_err af_get_features_size(af_array *out, const af_features featHandle) {
     try {
-
         af_features_t feat = getFeatures(featHandle);
-        *out = feat.size;
-    } CATCHALL;
+        *out               = feat.size;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/features.hpp b/src/api/c/features.hpp
index 6152fa48e2..ab61cb5c8b 100644
--- a/src/api/c/features.hpp
+++ b/src/api/c/features.hpp
@@ -7,6 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
+#include <cstddef>
+#include <af/array.h>
+#include <af/features.h>
 
 typedef struct {
     size_t n;
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 696c0ec848..e5405ee47c 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -7,21 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/signal.h>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
 #include <fft_common.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/signal.h>
 
 using af::dim4;
 using namespace detail;
 
-void computePaddedDims(dim4 &pdims,
-                       const dim4 &idims,
-                       const dim_t npad,
-                       dim_t const * const pad)
-{
+void computePaddedDims(dim4 &pdims, const dim4 &idims, const dim_t npad,
+                       dim_t const *const pad) {
     for (int i = 0; i < 4; i++) {
         pdims[i] = (i < (int)npad) ? pad[i] : idims[i];
     }
@@ -29,103 +26,114 @@ void computePaddedDims(dim4 &pdims,
 
 template<typename inType, typename outType, int rank, bool direction>
 static af_array fft(const af_array in, const double norm_factor,
-                    const dim_t npad, const dim_t  * const pad)
-{
-    return getHandle(fft<inType, outType, rank, direction>(getArray<inType>(in),
-                                                           norm_factor, npad, pad));
+                    const dim_t npad, const dim_t *const pad) {
+    return getHandle(fft<inType, outType, rank, direction>(
+        getArray<inType>(in), norm_factor, npad, pad));
 }
 
 template<int rank, bool direction>
-static af_err fft(af_array *out, const af_array in, const double norm_factor, const dim_t npad, const dim_t * const pad)
-{
+static af_err fft(af_array *out, const af_array in, const double norm_factor,
+                  const dim_t npad, const dim_t *const pad) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
 
-        if(dims.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (dims.ndims() == 0) { return af_retain_array(out, in); }
 
-        DIM_ASSERT(1, (dims.ndims()>=rank));
+        DIM_ASSERT(1, (dims.ndims() >= rank));
 
         af_array output;
-        switch(type) {
-            case c32: output = fft<cfloat , cfloat , rank, direction>(in, norm_factor, npad, pad); break;
-            case c64: output = fft<cdouble, cdouble, rank, direction>(in, norm_factor, npad, pad); break;
-            case f32: output = fft<float , cfloat  , rank, direction>(in, norm_factor, npad, pad); break;
-            case f64: output = fft<double, cdouble , rank, direction>(in, norm_factor, npad, pad); break;
+        switch (type) {
+            case c32:
+                output = fft<cfloat, cfloat, rank, direction>(in, norm_factor,
+                                                              npad, pad);
+                break;
+            case c64:
+                output = fft<cdouble, cdouble, rank, direction>(in, norm_factor,
+                                                                npad, pad);
+                break;
+            case f32:
+                output = fft<float, cfloat, rank, direction>(in, norm_factor,
+                                                             npad, pad);
+                break;
+            case f64:
+                output = fft<double, cdouble, rank, direction>(in, norm_factor,
+                                                               npad, pad);
+                break;
             default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_fft(af_array *out, const af_array in, const double norm_factor, const dim_t pad0)
-{
+af_err af_fft(af_array *out, const af_array in, const double norm_factor,
+              const dim_t pad0) {
     const dim_t pad[1] = {pad0};
-    return fft<1, true>(out, in, norm_factor, (pad0>0?1:0), pad);
+    return fft<1, true>(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad);
 }
 
-af_err af_fft2(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1)
-{
+af_err af_fft2(af_array *out, const af_array in, const double norm_factor,
+               const dim_t pad0, const dim_t pad1) {
     const dim_t pad[2] = {pad0, pad1};
-    return fft<2, true>(out, in, norm_factor, (pad0>0&&pad1>0?2:0), pad);
+    return fft<2, true>(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0),
+                        pad);
 }
 
-af_err af_fft3(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1, const dim_t pad2)
-{
+af_err af_fft3(af_array *out, const af_array in, const double norm_factor,
+               const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     const dim_t pad[3] = {pad0, pad1, pad2};
-    return fft<3, true>(out, in, norm_factor, (pad0>0&&pad1>0&&pad2>0?3:0), pad);
+    return fft<3, true>(out, in, norm_factor,
+                        (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad);
 }
 
-af_err af_ifft(af_array *out, const af_array in, const double norm_factor, const dim_t pad0)
-{
+af_err af_ifft(af_array *out, const af_array in, const double norm_factor,
+               const dim_t pad0) {
     const dim_t pad[1] = {pad0};
-    return fft<1, false>(out, in, norm_factor, (pad0>0?1:0), pad);
+    return fft<1, false>(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad);
 }
 
-af_err af_ifft2(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1)
-{
+af_err af_ifft2(af_array *out, const af_array in, const double norm_factor,
+                const dim_t pad0, const dim_t pad1) {
     const dim_t pad[2] = {pad0, pad1};
-    return fft<2, false>(out, in, norm_factor, (pad0>0&&pad1>0?2:0), pad);
+    return fft<2, false>(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0),
+                         pad);
 }
 
-af_err af_ifft3(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1, const dim_t pad2)
-{
+af_err af_ifft3(af_array *out, const af_array in, const double norm_factor,
+                const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     const dim_t pad[3] = {pad0, pad1, pad2};
-    return fft<3, false>(out, in, norm_factor, (pad0>0&&pad1>0&&pad2>0?3:0), pad);
+    return fft<3, false>(out, in, norm_factor,
+                         (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad);
 }
 
 template<typename T, int rank, bool direction>
-static void fft_inplace(af_array in, const double norm_factor)
-{
+static void fft_inplace(af_array in, const double norm_factor) {
     Array<T> &input = getArray<T>(in);
     fft_inplace<T, rank, direction>(input);
-    if (norm_factor != 1) {
-        multiply_inplace<T>(input, norm_factor);
-    }
+    if (norm_factor != 1) { multiply_inplace<T>(input, norm_factor); }
 }
 
 template<int rank, bool direction>
-static af_err fft_inplace(af_array in, const double norm_factor)
-{
+static af_err fft_inplace(af_array in, const double norm_factor) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
-        af::dim4 dims  = info.dims();
-
-        if(dims.ndims() == 0) {
-            return AF_SUCCESS;
-        }
-        DIM_ASSERT(1, (dims.ndims()>=rank));
-
-        switch(type) {
-            case c32: fft_inplace<cfloat , rank, direction>(in, norm_factor); break;
-            case c64: fft_inplace<cdouble, rank, direction>(in, norm_factor); break;
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
+
+        if (dims.ndims() == 0) { return AF_SUCCESS; }
+        DIM_ASSERT(1, (dims.ndims() >= rank));
+
+        switch (type) {
+            case c32:
+                fft_inplace<cfloat, rank, direction>(in, norm_factor);
+                break;
+            case c64:
+                fft_inplace<cdouble, rank, direction>(in, norm_factor);
+                break;
             default: TYPE_ERROR(1, type);
         }
     }
@@ -134,145 +142,142 @@ static af_err fft_inplace(af_array in, const double norm_factor)
     return AF_SUCCESS;
 }
 
-af_err af_fft_inplace(af_array in, const double norm_factor)
-{
+af_err af_fft_inplace(af_array in, const double norm_factor) {
     return fft_inplace<1, true>(in, norm_factor);
 }
 
-af_err af_fft2_inplace(af_array in, const double norm_factor)
-{
+af_err af_fft2_inplace(af_array in, const double norm_factor) {
     return fft_inplace<2, true>(in, norm_factor);
 }
 
-af_err af_fft3_inplace(af_array in, const double norm_factor)
-{
+af_err af_fft3_inplace(af_array in, const double norm_factor) {
     return fft_inplace<3, true>(in, norm_factor);
 }
 
-af_err af_ifft_inplace(af_array in, const double norm_factor)
-{
+af_err af_ifft_inplace(af_array in, const double norm_factor) {
     return fft_inplace<1, false>(in, norm_factor);
 }
 
-af_err af_ifft2_inplace(af_array in, const double norm_factor)
-{
+af_err af_ifft2_inplace(af_array in, const double norm_factor) {
     return fft_inplace<2, false>(in, norm_factor);
 }
 
-af_err af_ifft3_inplace(af_array in, const double norm_factor)
-{
+af_err af_ifft3_inplace(af_array in, const double norm_factor) {
     return fft_inplace<3, false>(in, norm_factor);
 }
 
 template<typename inType, typename outType, int rank>
 static af_array fft_r2c(const af_array in, const double norm_factor,
-                        const dim_t npad, const dim_t  * const pad)
-{
+                        const dim_t npad, const dim_t *const pad) {
     return getHandle(fft_r2c<inType, outType, rank>(getArray<inType>(in),
                                                     norm_factor, npad, pad));
 }
 
 template<int rank>
-static af_err fft_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t npad, const dim_t * const pad)
-{
+static af_err fft_r2c(af_array *out, const af_array in,
+                      const double norm_factor, const dim_t npad,
+                      const dim_t *const pad) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
 
-        if(dims.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
-        DIM_ASSERT(1, (dims.ndims()>=rank));
+        if (dims.ndims() == 0) { return af_retain_array(out, in); }
+        DIM_ASSERT(1, (dims.ndims() >= rank));
 
         af_array output;
-        switch(type) {
-            case f32: output = fft_r2c<float , cfloat  , rank>(in, norm_factor, npad, pad); break;
-            case f64: output = fft_r2c<double, cdouble , rank>(in, norm_factor, npad, pad); break;
-        default: {
-            TYPE_ERROR(1, type);
-        }
+        switch (type) {
+            case f32:
+                output =
+                    fft_r2c<float, cfloat, rank>(in, norm_factor, npad, pad);
+                break;
+            case f64:
+                output =
+                    fft_r2c<double, cdouble, rank>(in, norm_factor, npad, pad);
+                break;
+            default: { TYPE_ERROR(1, type); }
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_fft_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t pad0)
-{
+af_err af_fft_r2c(af_array *out, const af_array in, const double norm_factor,
+                  const dim_t pad0) {
     const dim_t pad[1] = {pad0};
-    return fft_r2c<1>(out, in, norm_factor, (pad0>0?1:0), pad);
+    return fft_r2c<1>(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad);
 }
 
-af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1)
-{
+af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_factor,
+                   const dim_t pad0, const dim_t pad1) {
     const dim_t pad[2] = {pad0, pad1};
-    return fft_r2c<2>(out, in, norm_factor, (pad0>0&&pad1>0?2:0), pad);
+    return fft_r2c<2>(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0),
+                      pad);
 }
 
-af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1, const dim_t pad2)
-{
+af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_factor,
+                   const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     const dim_t pad[3] = {pad0, pad1, pad2};
-    return fft_r2c<3>(out, in, norm_factor, (pad0>0&&pad1>0&&pad2>0?3:0), pad);
+    return fft_r2c<3>(out, in, norm_factor,
+                      (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad);
 }
 
-
 template<typename inType, typename outType, int rank>
 static af_array fft_c2r(const af_array in, const double norm_factor,
-                        const dim4 &odims)
-{
+                        const dim4 &odims) {
     return getHandle(fft_c2r<inType, outType, rank>(getArray<inType>(in),
                                                     norm_factor, odims));
 }
 
 template<int rank>
-static af_err fft_c2r(af_array *out, const af_array in, const double norm_factor, const bool is_odd)
-{
+static af_err fft_c2r(af_array *out, const af_array in,
+                      const double norm_factor, const bool is_odd) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
-        af::dim4 idims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 idims        = info.dims();
 
-        if(idims.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
-        DIM_ASSERT(1, (idims.ndims()>=rank));
+        if (idims.ndims() == 0) { return af_retain_array(out, in); }
+        DIM_ASSERT(1, (idims.ndims() >= rank));
 
         dim4 odims = idims;
-        odims[0] = 2 * (odims[0] - 1) + (is_odd ? 1 : 0);
+        odims[0]   = 2 * (odims[0] - 1) + (is_odd ? 1 : 0);
 
         af_array output;
-        switch(type) {
-            case c32: output = fft_c2r<cfloat , float  , rank>(in, norm_factor, odims); break;
-            case c64: output = fft_c2r<cdouble, double , rank>(in, norm_factor, odims); break;
+        switch (type) {
+            case c32:
+                output = fft_c2r<cfloat, float, rank>(in, norm_factor, odims);
+                break;
+            case c64:
+                output = fft_c2r<cdouble, double, rank>(in, norm_factor, odims);
+                break;
             default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_fft_c2r(af_array *out, const af_array in, const double norm_factor, const bool is_odd)
-{
+af_err af_fft_c2r(af_array *out, const af_array in, const double norm_factor,
+                  const bool is_odd) {
     return fft_c2r<1>(out, in, norm_factor, is_odd);
 }
 
-af_err af_fft2_c2r(af_array *out, const af_array in, const double norm_factor, const bool is_odd)
-{
+af_err af_fft2_c2r(af_array *out, const af_array in, const double norm_factor,
+                   const bool is_odd) {
     return fft_c2r<2>(out, in, norm_factor, is_odd);
 }
 
-af_err af_fft3_c2r(af_array *out, const af_array in, const double norm_factor, const bool is_odd)
-{
+af_err af_fft3_c2r(af_array *out, const af_array in, const double norm_factor,
+                   const bool is_odd) {
     return fft_c2r<3>(out, in, norm_factor, is_odd);
 }
 
-af_err af_set_fft_plan_cache_size(size_t cache_size)
-{
+af_err af_set_fft_plan_cache_size(size_t cache_size) {
     try {
         detail::setFFTPlanCacheSize(cache_size);
     }
diff --git a/src/api/c/fft_common.hpp b/src/api/c/fft_common.hpp
index 7675ad4c8d..76e4dc777e 100644
--- a/src/api/c/fft_common.hpp
+++ b/src/api/c/fft_common.hpp
@@ -6,42 +6,35 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <handle.hpp>
-#include <fft.hpp>
 #include <copy.hpp>
+#include <fft.hpp>
+#include <handle.hpp>
 
 using namespace detail;
 
-void computePaddedDims(dim4 &pdims,
-                       const dim4 &idims,
-                       const dim_t npad,
-                       dim_t const * const pad);
+void computePaddedDims(dim4 &pdims, const dim4 &idims, const dim_t npad,
+                       dim_t const *const pad);
 
 template<typename inType, typename outType, int rank, bool direction>
 Array<outType> fft(const Array<inType> input, const double norm_factor,
-                   const dim_t npad, const dim_t  * const pad)
-{
+                   const dim_t npad, const dim_t *const pad) {
     dim4 pdims(1);
     computePaddedDims(pdims, input.dims(), npad, pad);
     auto res = padArray(input, pdims, scalar<outType>(0));
 
     fft_inplace<outType, rank, direction>(res);
-    if (norm_factor != 1.0)
-        multiply_inplace(res, norm_factor);
+    if (norm_factor != 1.0) multiply_inplace(res, norm_factor);
 
     return res;
 }
 
 template<typename inType, typename outType, int rank>
 Array<outType> fft_r2c(const Array<inType> input, const double norm_factor,
-                       const dim_t npad, const dim_t  * const pad)
-{
+                       const dim_t npad, const dim_t *const pad) {
     dim4 idims = input.dims();
 
     bool is_pad = false;
-    for (int i = 0; i < npad; i++) {
-        is_pad |= (pad[i] != idims[i]);
-    }
+    for (int i = 0; i < npad; i++) { is_pad |= (pad[i] != idims[i]); }
 
     Array<inType> tmp = input;
 
@@ -52,16 +45,14 @@ Array<outType> fft_r2c(const Array<inType> input, const double norm_factor,
     }
 
     auto res = fft_r2c<outType, inType, rank>(tmp);
-    if (norm_factor != 1.0)
-        multiply_inplace(res, norm_factor);
+    if (norm_factor != 1.0) multiply_inplace(res, norm_factor);
 
     return res;
 }
 
 template<typename inType, typename outType, int rank>
 Array<outType> fft_c2r(const Array<inType> input, const double norm_factor,
-                       const dim4 &odims)
-{
+                       const dim4 &odims) {
     Array<outType> output = fft_c2r<outType, inType, rank>(input, odims);
 
     if (norm_factor != 1) {
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 5418adbf7f..a26d0e41d7 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -6,29 +6,29 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/signal.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
 #include <arith.hpp>
-#include <fftconvolve.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <fft_common.hpp>
+#include <fftconvolve.hpp>
+#include <handle.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/signal.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, typename convT, typename cT, int baseDim>
-static inline
-af_array fftconvolve_fallback(const af_array signal, const af_array filter, bool expand)
-{
+static inline af_array fftconvolve_fallback(const af_array signal,
+                                            const af_array filter,
+                                            bool expand) {
     const Array<cT> S = castArray<cT>(signal);
     const Array<cT> F = castArray<cT>(filter);
-    const dim4 sdims = S.dims();
-    const dim4 fdims = F.dims();
+    const dim4 sdims  = S.dims();
+    const dim4 fdims  = F.dims();
     dim4 odims(1, 1, 1, 1);
     dim4 psdims(1, 1, 1, 1);
     dim4 pfdims(1, 1, 1, 1);
@@ -40,7 +40,7 @@ af_array fftconvolve_fallback(const af_array signal, const af_array filter, bool
         dim_t tdim_i = sdims[i] + fdims[i] - 1;
 
         // Pad temporary buffers to power of 2 for performance
-        odims[i] = nextpow2(tdim_i);
+        odims[i]  = nextpow2(tdim_i);
         psdims[i] = nextpow2(tdim_i);
         pfdims[i] = nextpow2(tdim_i);
 
@@ -50,19 +50,19 @@ af_array fftconvolve_fallback(const af_array signal, const af_array filter, bool
         // Get the indexing params for output
         if (expand) {
             index[i].begin = 0;
-            index[i].end = tdim_i - 1;
+            index[i].end   = tdim_i - 1;
         } else {
             index[i].begin = fdims[i] / 2;
-            index[i].end = index[i].begin + sdims[i] - 1;
+            index[i].end   = index[i].begin + sdims[i] - 1;
         }
         index[i].step = 1;
     }
 
     for (int i = baseDim; i < 4; i++) {
-        odims[i] = std::max(sdims[i], fdims[i]);
+        odims[i]  = std::max(sdims[i], fdims[i]);
         psdims[i] = sdims[i];
         pfdims[i] = fdims[i];
-        index[i] = af_span;
+        index[i]  = af_span;
     }
 
     // fft(signal)
@@ -75,7 +75,8 @@ af_array fftconvolve_fallback(const af_array signal, const af_array filter, bool
     T1 = arithOp<cT, af_mul_t>(T1, T2, odims);
 
     // ifft(ffit(signal) * fft(filter))
-    T1 = fft<cT, cT, baseDim, false>(T1, 1.0/(double)count, baseDim, odims.get());
+    T1 = fft<cT, cT, baseDim, false>(T1, 1.0 / (double)count, baseDim,
+                                     odims.get());
 
     // Index to proper offsets
     T1 = createSubArray<cT>(T1, index);
@@ -87,47 +88,50 @@ af_array fftconvolve_fallback(const af_array signal, const af_array filter, bool
     }
 }
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-inline static af_array fftconvolve(const af_array &s, const af_array &f, const bool expand, AF_BATCH_KIND kind)
-{
-    if (kind == AF_BATCH_DIFF) return fftconvolve_fallback<T, convT, cT, baseDim>(s, f, expand);
-    else return getHandle(fftconvolve<T, convT, cT, isDouble, roundOut, baseDim>(getArray<T>(s), castArray<T>(f), expand, kind));
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
+inline static af_array fftconvolve(const af_array &s, const af_array &f,
+                                   const bool expand, AF_BATCH_KIND kind) {
+    if (kind == AF_BATCH_DIFF)
+        return fftconvolve_fallback<T, convT, cT, baseDim>(s, f, expand);
+    else
+        return getHandle(fftconvolve<T, convT, cT, isDouble, roundOut, baseDim>(
+            getArray<T>(s), castArray<T>(f), expand, kind));
 }
 
 template<dim_t baseDim>
-AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
-{
+AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
-    if (sn==baseDim && fn==baseDim)
+    if (sn == baseDim && fn == baseDim)
         return AF_BATCH_NONE;
-    else if (sn==baseDim && (fn>baseDim && fn<=4))
+    else if (sn == baseDim && (fn > baseDim && fn <= 4))
         return AF_BATCH_RHS;
-    else if ((sn>baseDim && sn<=4) && fn==baseDim)
+    else if ((sn > baseDim && sn <= 4) && fn == baseDim)
         return AF_BATCH_LHS;
-    else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
-       bool doesDimensionsMatch = true;
-        bool isInterleaved = true;
-        for (dim_t i=baseDim; i<4; i++) {
+    else if ((sn > baseDim && sn <= 4) && (fn > baseDim && fn <= 4)) {
+        bool doesDimensionsMatch = true;
+        bool isInterleaved       = true;
+        for (dim_t i = baseDim; i < 4; i++) {
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
-            isInterleaved &= (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
+            isInterleaved &=
+                (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
         }
         if (doesDimensionsMatch) return AF_BATCH_SAME;
         return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
-    }
-    else
+    } else
         return AF_BATCH_UNSUPPORTED;
 }
 
 template<dim_t baseDim>
-af_err fft_convolve(af_array *out, const af_array signal, const af_array filter, const bool expand)
-{
+af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
+                    const bool expand) {
     try {
-        const ArrayInfo& sInfo = getInfo(signal);
-        const ArrayInfo& fInfo = getInfo(filter);
+        const ArrayInfo &sInfo = getInfo(signal);
+        const ArrayInfo &fInfo = getInfo(filter);
 
-        af_dtype stype  = sInfo.getType();
+        af_dtype stype = sInfo.getType();
 
         dim4 sdims = sInfo.dims();
         dim4 fdims = fInfo.dims();
@@ -137,45 +141,90 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
         ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED));
 
         af_array output;
-        switch(stype) {
-            case f64: output = fftconvolve<double, double, cdouble, true , false, baseDim>(signal, filter, expand, convBT); break;
-            case f32: output = fftconvolve<float , float,  cfloat,  false, false, baseDim>(signal, filter, expand, convBT); break;
-            case u32: output = fftconvolve<uint  , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case s32: output = fftconvolve<int   , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case u64: output = fftconvolve<uintl , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case s64: output = fftconvolve<intl  , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case u16: output = fftconvolve<ushort, float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case s16: output = fftconvolve<short , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case u8:  output = fftconvolve<uchar , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case b8:  output = fftconvolve<char  , float,  cfloat,  false, true,  baseDim>(signal, filter, expand, convBT); break;
-            case c32: output = fftconvolve_fallback<cfloat , cfloat , cfloat , baseDim>(signal, filter, expand); break;
-            case c64: output = fftconvolve_fallback<cdouble, cdouble, cdouble, baseDim>(signal, filter, expand); break;
+        switch (stype) {
+            case f64:
+                output =
+                    fftconvolve<double, double, cdouble, true, false, baseDim>(
+                        signal, filter, expand, convBT);
+                break;
+            case f32:
+                output =
+                    fftconvolve<float, float, cfloat, false, false, baseDim>(
+                        signal, filter, expand, convBT);
+                break;
+            case u32:
+                output = fftconvolve<uint, float, cfloat, false, true, baseDim>(
+                    signal, filter, expand, convBT);
+                break;
+            case s32:
+                output = fftconvolve<int, float, cfloat, false, true, baseDim>(
+                    signal, filter, expand, convBT);
+                break;
+            case u64:
+                output =
+                    fftconvolve<uintl, float, cfloat, false, true, baseDim>(
+                        signal, filter, expand, convBT);
+                break;
+            case s64:
+                output = fftconvolve<intl, float, cfloat, false, true, baseDim>(
+                    signal, filter, expand, convBT);
+                break;
+            case u16:
+                output =
+                    fftconvolve<ushort, float, cfloat, false, true, baseDim>(
+                        signal, filter, expand, convBT);
+                break;
+            case s16:
+                output =
+                    fftconvolve<short, float, cfloat, false, true, baseDim>(
+                        signal, filter, expand, convBT);
+                break;
+            case u8:
+                output =
+                    fftconvolve<uchar, float, cfloat, false, true, baseDim>(
+                        signal, filter, expand, convBT);
+                break;
+            case b8:
+                output = fftconvolve<char, float, cfloat, false, true, baseDim>(
+                    signal, filter, expand, convBT);
+                break;
+            case c32:
+                output = fftconvolve_fallback<cfloat, cfloat, cfloat, baseDim>(
+                    signal, filter, expand);
+                break;
+            case c64:
+                output =
+                    fftconvolve_fallback<cdouble, cdouble, cdouble, baseDim>(
+                        signal, filter, expand);
+                break;
             default: TYPE_ERROR(1, stype);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_fft_convolve1(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode)
-{
+af_err af_fft_convolve1(af_array *out, const af_array signal,
+                        const af_array filter, const af_conv_mode mode) {
     return fft_convolve<1>(out, signal, filter, mode == AF_CONV_EXPAND);
 }
 
-af_err af_fft_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode)
-{
-    if (getInfo(signal).dims().ndims()<2 && getInfo(filter).dims().ndims()<2) {
+af_err af_fft_convolve2(af_array *out, const af_array signal,
+                        const af_array filter, const af_conv_mode mode) {
+    if (getInfo(signal).dims().ndims() < 2 &&
+        getInfo(filter).dims().ndims() < 2) {
         return fft_convolve<1>(out, signal, filter, mode == AF_CONV_EXPAND);
     } else {
         return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
     }
 }
 
-af_err af_fft_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode)
-{
-    if (getInfo(signal).dims().ndims()<3 && getInfo(filter).dims().ndims()<3) {
+af_err af_fft_convolve3(af_array *out, const af_array signal,
+                        const af_array filter, const af_conv_mode mode) {
+    if (getInfo(signal).dims().ndims() < 3 &&
+        getInfo(filter).dims().ndims() < 3) {
         return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
     } else {
         return fft_convolve<3>(out, signal, filter, mode == AF_CONV_EXPAND);
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index b0435bb545..4ad1834904 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -7,61 +7,86 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
+#include <medfilt.hpp>
+#include <af/data.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/image.h>
 #include <af/signal.h>
-#include <af/data.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
-#include <medfilt.hpp>
 
 using af::dim4;
 using namespace detail;
 
-af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length,
+                  const dim_t wind_width, const af_border_type edge_pad) {
     return af_medfilt2(out, in, wind_length, wind_width, edge_pad);
 }
 
 template<typename T>
-static af_array medfilt1(af_array const &in, dim_t w_wid, af_border_type edge_pad)
-{
-    switch(edge_pad) {
-        case AF_PAD_ZERO : return getHandle<T>(medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
-        case AF_PAD_SYM  : return getHandle<T>(medfilt1<T, AF_PAD_SYM >(getArray<T>(in), w_wid)); break;
-        default          : return getHandle<T>(medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid)); break;
+static af_array medfilt1(af_array const &in, dim_t w_wid,
+                         af_border_type edge_pad) {
+    switch (edge_pad) {
+        case AF_PAD_ZERO:
+            return getHandle<T>(
+                medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid));
+            break;
+        case AF_PAD_SYM:
+            return getHandle<T>(
+                medfilt1<T, AF_PAD_SYM>(getArray<T>(in), w_wid));
+            break;
+        default:
+            return getHandle<T>(
+                medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid));
+            break;
     }
 }
 
-af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
+                   const af_border_type edge_pad) {
     try {
-        ARG_ASSERT(2, (wind_width>0));
-        ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
+        ARG_ASSERT(2, (wind_width > 0));
+        ARG_ASSERT(4, (edge_pad >= AF_PAD_ZERO && edge_pad <= AF_PAD_SYM));
 
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
         dim_t input_ndims = dims.ndims();
         DIM_ASSERT(1, (input_ndims >= 1));
 
-        if (wind_width==1) {
+        if (wind_width == 1) {
             *out = retain(in);
         } else {
             af_array output;
-            af_dtype type  = info.getType();
-            switch(type) {
-                case f32: output = medfilt1<float >(in, wind_width, edge_pad); break;
-                case f64: output = medfilt1<double>(in, wind_width, edge_pad); break;
-                case b8 : output = medfilt1<char  >(in, wind_width, edge_pad); break;
-                case s32: output = medfilt1<int   >(in, wind_width, edge_pad); break;
-                case u32: output = medfilt1<uint  >(in, wind_width, edge_pad); break;
-                case s16: output = medfilt1<short >(in, wind_width, edge_pad); break;
-                case u16: output = medfilt1<ushort>(in, wind_width, edge_pad); break;
-                case u8 : output = medfilt1<uchar >(in, wind_width, edge_pad); break;
-                default : TYPE_ERROR(1, type);
+            af_dtype type = info.getType();
+            switch (type) {
+                case f32:
+                    output = medfilt1<float>(in, wind_width, edge_pad);
+                    break;
+                case f64:
+                    output = medfilt1<double>(in, wind_width, edge_pad);
+                    break;
+                case b8:
+                    output = medfilt1<char>(in, wind_width, edge_pad);
+                    break;
+                case s32:
+                    output = medfilt1<int>(in, wind_width, edge_pad);
+                    break;
+                case u32:
+                    output = medfilt1<uint>(in, wind_width, edge_pad);
+                    break;
+                case s16:
+                    output = medfilt1<short>(in, wind_width, edge_pad);
+                    break;
+                case u16:
+                    output = medfilt1<ushort>(in, wind_width, edge_pad);
+                    break;
+                case u8:
+                    output = medfilt1<uchar>(in, wind_width, edge_pad);
+                    break;
+                default: TYPE_ERROR(1, type);
             }
             std::swap(*out, output);
         }
@@ -72,48 +97,81 @@ af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, con
 }
 
 template<typename T>
-static af_array medfilt2(af_array const &in, dim_t w_len, dim_t w_wid, af_border_type edge_pad)
-{
-    switch(edge_pad) {
-        case AF_PAD_ZERO : return getHandle<T>(medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid)); break;
-        case AF_PAD_SYM  : return getHandle<T>(medfilt2<T, AF_PAD_SYM >(getArray<T>(in), w_len, w_wid)); break;
-        default          : return getHandle<T>(medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid)); break;
+static af_array medfilt2(af_array const &in, dim_t w_len, dim_t w_wid,
+                         af_border_type edge_pad) {
+    switch (edge_pad) {
+        case AF_PAD_ZERO:
+            return getHandle<T>(
+                medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid));
+            break;
+        case AF_PAD_SYM:
+            return getHandle<T>(
+                medfilt2<T, AF_PAD_SYM>(getArray<T>(in), w_len, w_wid));
+            break;
+        default:
+            return getHandle<T>(
+                medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid));
+            break;
     }
 }
 
-af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length,
+                   const dim_t wind_width, const af_border_type edge_pad) {
     try {
-        ARG_ASSERT(2, (wind_length==wind_width));
-        ARG_ASSERT(2, (wind_length>0));
-        ARG_ASSERT(3, (wind_width>0));
-        ARG_ASSERT(4, (edge_pad>=AF_PAD_ZERO && edge_pad<=AF_PAD_SYM));
+        ARG_ASSERT(2, (wind_length == wind_width));
+        ARG_ASSERT(2, (wind_length > 0));
+        ARG_ASSERT(3, (wind_width > 0));
+        ARG_ASSERT(4, (edge_pad >= AF_PAD_ZERO && edge_pad <= AF_PAD_SYM));
 
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
-        if(info.isColumn()) {
+        if (info.isColumn()) {
             return af_medfilt1(out, in, wind_width, edge_pad);
         }
 
         dim_t input_ndims = dims.ndims();
         DIM_ASSERT(1, (input_ndims >= 2));
 
-        if (wind_length==1) {
+        if (wind_length == 1) {
             *out = retain(in);
         } else {
             af_array output;
-            af_dtype type  = info.getType();
-            switch(type) {
-                case f32: output = medfilt2<float >(in, wind_length, wind_width, edge_pad); break;
-                case f64: output = medfilt2<double>(in, wind_length, wind_width, edge_pad); break;
-                case b8 : output = medfilt2<char  >(in, wind_length, wind_width, edge_pad); break;
-                case s32: output = medfilt2<int   >(in, wind_length, wind_width, edge_pad); break;
-                case u32: output = medfilt2<uint  >(in, wind_length, wind_width, edge_pad); break;
-                case s16: output = medfilt2<short >(in, wind_length, wind_width, edge_pad); break;
-                case u16: output = medfilt2<ushort>(in, wind_length, wind_width, edge_pad); break;
-                case u8 : output = medfilt2<uchar >(in, wind_length, wind_width, edge_pad); break;
-                default : TYPE_ERROR(1, type);
+            af_dtype type = info.getType();
+            switch (type) {
+                case f32:
+                    output =
+                        medfilt2<float>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case f64:
+                    output =
+                        medfilt2<double>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case b8:
+                    output =
+                        medfilt2<char>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case s32:
+                    output =
+                        medfilt2<int>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case u32:
+                    output =
+                        medfilt2<uint>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case s16:
+                    output =
+                        medfilt2<short>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case u16:
+                    output =
+                        medfilt2<ushort>(in, wind_length, wind_width, edge_pad);
+                    break;
+                case u8:
+                    output =
+                        medfilt2<uchar>(in, wind_length, wind_width, edge_pad);
+                    break;
+                default: TYPE_ERROR(1, type);
             }
             std::swap(*out, output);
         }
@@ -124,16 +182,15 @@ af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, co
 }
 
 af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
-                  const dim_t wind_width, const af_border_type edge_pad)
-{
+                  const dim_t wind_width, const af_border_type edge_pad) {
     try {
-        ARG_ASSERT(2, (wind_length==wind_width));
-        ARG_ASSERT(2, (wind_length>0));
-        ARG_ASSERT(3, (wind_width>0));
-        ARG_ASSERT(4, (edge_pad==AF_PAD_ZERO));
+        ARG_ASSERT(2, (wind_length == wind_width));
+        ARG_ASSERT(2, (wind_length > 0));
+        ARG_ASSERT(3, (wind_width > 0));
+        ARG_ASSERT(4, (edge_pad == AF_PAD_ZERO));
 
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
         dim_t input_ndims = dims.ndims();
         DIM_ASSERT(1, (input_ndims >= 2));
@@ -152,16 +209,15 @@ af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
 }
 
 af_err af_maxfilt(af_array *out, const af_array in, const dim_t wind_length,
-                  const dim_t wind_width, const af_border_type edge_pad)
-{
+                  const dim_t wind_width, const af_border_type edge_pad) {
     try {
-        ARG_ASSERT(2, (wind_length==wind_width));
-        ARG_ASSERT(2, (wind_length>0));
-        ARG_ASSERT(3, (wind_width>0));
-        ARG_ASSERT(4, (edge_pad==AF_PAD_ZERO));
+        ARG_ASSERT(2, (wind_length == wind_width));
+        ARG_ASSERT(2, (wind_length > 0));
+        ARG_ASSERT(3, (wind_width > 0));
+        ARG_ASSERT(4, (edge_pad == AF_PAD_ZERO));
 
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
         dim_t input_ndims = dims.ndims();
         DIM_ASSERT(1, (input_ndims >= 2));
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 954fc714f1..7e1acb5cdb 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -7,50 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <vector>
 #include <cassert>
+#include <vector>
 
-#include <af/array.h>
-#include <af/data.h>
-#include <af/index.h>
-#include <af/seq.h>
+#include <Array.hpp>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
-#include <Array.hpp>
 #include <lookup.hpp>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/index.h>
+#include <af/seq.h>
 
 using namespace detail;
-using std::vector;
 using std::swap;
+using std::vector;
 
 template<typename T>
-static af_array flipArray(const af_array in, const unsigned dim)
-{
+static af_array flipArray(const af_array in, const unsigned dim) {
     const Array<T> &input = getArray<T>(in);
     vector<af_seq> index(4);
 
-    for (int i = 0; i < 4; i++) {
-        index[i] = af_span;
-    }
+    for (int i = 0; i < 4; i++) { index[i] = af_span; }
 
     // Reverse "dim"
     dim4 in_dims = input.dims();
-    af_seq s = {(double)(in_dims[dim] - 1), 0, -1};
+    af_seq s     = {(double)(in_dims[dim] - 1), 0, -1};
 
     index[dim] = s;
 
-    Array<T> dst =  createSubArray(input, index);
+    Array<T> dst = createSubArray(input, index);
 
     return getHandle(dst);
 }
 
-af_err af_flip(af_array *result, const af_array in, const unsigned dim)
-{
+af_err af_flip(af_array *result, const af_array in, const unsigned dim) {
     af_array out;
     try {
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
         if (in_info.ndims() <= dim) {
             *result = retain(in);
@@ -59,20 +55,20 @@ af_err af_flip(af_array *result, const af_array in, const unsigned dim)
 
         af_dtype in_type = in_info.getType();
 
-        switch(in_type) {
-        case f32:    out = flipArray<float>   (in, dim);  break;
-        case c32:    out = flipArray<cfloat>  (in, dim);  break;
-        case f64:    out = flipArray<double>  (in, dim);  break;
-        case c64:    out = flipArray<cdouble> (in, dim);  break;
-        case b8:     out = flipArray<char>    (in, dim);  break;
-        case s32:    out = flipArray<int>     (in, dim);  break;
-        case u32:    out = flipArray<unsigned>(in, dim);  break;
-        case s64:    out = flipArray<intl>    (in, dim);  break;
-        case u64:    out = flipArray<uintl>   (in, dim);  break;
-        case s16:    out = flipArray<short>   (in, dim);  break;
-        case u16:    out = flipArray<ushort>  (in, dim);  break;
-        case u8:     out = flipArray<uchar>   (in, dim);  break;
-        default:    TYPE_ERROR(1, in_type);
+        switch (in_type) {
+            case f32: out = flipArray<float>(in, dim); break;
+            case c32: out = flipArray<cfloat>(in, dim); break;
+            case f64: out = flipArray<double>(in, dim); break;
+            case c64: out = flipArray<cdouble>(in, dim); break;
+            case b8: out = flipArray<char>(in, dim); break;
+            case s32: out = flipArray<int>(in, dim); break;
+            case u32: out = flipArray<unsigned>(in, dim); break;
+            case s64: out = flipArray<intl>(in, dim); break;
+            case u64: out = flipArray<uintl>(in, dim); break;
+            case s16: out = flipArray<short>(in, dim); break;
+            case u16: out = flipArray<ushort>(in, dim); break;
+            case u8: out = flipArray<uchar>(in, dim); break;
+            default: TYPE_ERROR(1, in_type);
         }
         swap(*result, out);
     }
diff --git a/src/api/c/gaussian_kernel.cpp b/src/api/c/gaussian_kernel.cpp
index 52c410a031..0fb1bfefb6 100644
--- a/src/api/c/gaussian_kernel.cpp
+++ b/src/api/c/gaussian_kernel.cpp
@@ -7,27 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <common/err_common.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
-#include <reduce.hpp>
-#include <arith.hpp>
 #include <math.hpp>
-#include <unary.hpp>
 #include <range.hpp>
 #include <reduce.hpp>
 #include <transpose.hpp>
+#include <unary.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using namespace detail;
 
 template<typename T>
-Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r, const double sigma_c)
-{
+Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r,
+                        const double sigma_c) {
     const dim4 odims = dim4(rows, cols);
-    double sigma = 0;
+    double sigma     = 0;
 
     Array<T> tmp  = createValueArray<T>(odims, scalar<T>(0));
     Array<T> half = createValueArray<T>(odims, 0.5);
@@ -37,28 +36,30 @@ Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r, co
         Array<T> wt = range<T>(dim4(cols, rows), 0);
         Array<T> w  = transpose<T>(wt, false);
 
-        Array<T> c = createValueArray<T>(odims, scalar<T>((double)(cols - 1) / 2.0));
+        Array<T> c =
+            createValueArray<T>(odims, scalar<T>((double)(cols - 1) / 2.0));
         w = arithOp<T, af_sub_t>(w, c, odims);
 
-        sigma = sigma_c > 0 ? sigma_c : 0.25 * cols;
+        sigma        = sigma_c > 0 ? sigma_c : 0.25 * cols;
         Array<T> sig = createValueArray<T>(odims, sigma);
-        w = arithOp<T, af_div_t>(w, sig, odims);
+        w            = arithOp<T, af_div_t>(w, sig, odims);
 
-        w = arithOp<T, af_mul_t>(w, w, odims);
+        w   = arithOp<T, af_mul_t>(w, w, odims);
         tmp = arithOp<T, af_add_t>(w, tmp, odims);
     }
 
     if (rows > 1) {
         Array<T> w = range<T>(dim4(rows, cols), 0);
 
-        Array<T> r = createValueArray<T>(odims, scalar<T>((double)(rows - 1) / 2.0));
+        Array<T> r =
+            createValueArray<T>(odims, scalar<T>((double)(rows - 1) / 2.0));
         w = arithOp<T, af_sub_t>(w, r, odims);
 
-        sigma = sigma_r > 0 ? sigma_r : 0.25 * rows;
+        sigma        = sigma_r > 0 ? sigma_r : 0.25 * rows;
         Array<T> sig = createValueArray<T>(odims, sigma);
 
-        w = arithOp<T, af_div_t>(w, sig, odims);
-        w = arithOp<T, af_mul_t>(w, w, odims);
+        w   = arithOp<T, af_div_t>(w, sig, odims);
+        w   = arithOp<T, af_mul_t>(w, w, odims);
         tmp = arithOp<T, af_add_t>(w, tmp, odims);
     }
 
@@ -71,19 +72,19 @@ Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r, co
     T norm_factor = reduce_all<af_add_t, T, T>(tmp);
 
     Array<T> norm = createValueArray(odims, norm_factor);
-    Array<T> res = arithOp<T, af_div_t>(tmp, norm, odims);
+    Array<T> res  = arithOp<T, af_div_t>(tmp, norm, odims);
 
     return res;
 }
 
-af_err af_gaussian_kernel(af_array *out,
-                          const int rows, const int cols,
-                          const double sigma_r, const double sigma_c)
-{
+af_err af_gaussian_kernel(af_array *out, const int rows, const int cols,
+                          const double sigma_r, const double sigma_c) {
     try {
         af_array res;
-        res = getHandle<float>(gaussianKernel<float>(rows, cols, sigma_r, sigma_c));
+        res = getHandle<float>(
+            gaussianKernel<float>(rows, cols, sigma_r, sigma_c));
         std::swap(*out, res);
-    }CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/gradient.cpp b/src/api/c/gradient.cpp
index d313d86686..857ad2f2b3 100644
--- a/src/api/c/gradient.cpp
+++ b/src/api/c/gradient.cpp
@@ -7,29 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
 #include <gradient.hpp>
+#include <handle.hpp>
+#include <af/defines.h>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline void gradient(af_array *grad0, af_array *grad1, const af_array in)
-{
+static inline void gradient(af_array *grad0, af_array *grad1,
+                            const af_array in) {
     gradient<T>(getArray<T>(*grad0), getArray<T>(*grad1), getArray<T>(in));
 }
 
-af_err af_gradient(af_array *grows, af_array *gcols, const af_array in)
-{
+af_err af_gradient(af_array *grows, af_array *gcols, const af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        af::dim4 idims = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 idims        = info.dims();
 
         DIM_ASSERT(2, info.elements() > 0);
 
@@ -38,12 +37,12 @@ af_err af_gradient(af_array *grows, af_array *gcols, const af_array in)
         AF_CHECK(af_create_handle(&grad0, idims.ndims(), idims.get(), type));
         AF_CHECK(af_create_handle(&grad1, idims.ndims(), idims.get(), type));
 
-        switch(type) {
-            case f32: gradient<float  >(&grad0, &grad1, in);  break;
-            case c32: gradient<cfloat >(&grad0, &grad1, in);  break;
-            case f64: gradient<double >(&grad0, &grad1, in);  break;
-            case c64: gradient<cdouble>(&grad0, &grad1, in);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: gradient<float>(&grad0, &grad1, in); break;
+            case c32: gradient<cfloat>(&grad0, &grad1, in); break;
+            case f64: gradient<double>(&grad0, &grad1, in); break;
+            case c64: gradient<cdouble>(&grad0, &grad1, in); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*grows, grad0);
         std::swap(*gcols, grad1);
diff --git a/src/api/c/hamming.cpp b/src/api/c/hamming.cpp
index 9a57fef26c..d38a1f0bf1 100644
--- a/src/api/c/hamming.cpp
+++ b/src/api/c/hamming.cpp
@@ -10,8 +10,9 @@
 #include <af/defines.h>
 #include <af/vision.h>
 
-af_err af_hamming_matcher(af_array* idx, af_array* dist, const af_array query, const af_array train,
-        const dim_t dist_dim, const unsigned n_dist)
-{
-    return af_nearest_neighbour(idx, dist, query, train, dist_dim, n_dist, AF_SHD);
+af_err af_hamming_matcher(af_array* idx, af_array* dist, const af_array query,
+                          const af_array train, const dim_t dist_dim,
+                          const unsigned n_dist) {
+    return af_nearest_neighbour(idx, dist, query, train, dist_dim, n_dist,
+                                AF_SHD);
 }
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index fad190d0dc..66eb435b21 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -8,31 +8,31 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <backend.hpp>
+#include <cast.hpp>
 #include <common/err_common.hpp>
-#include <math.hpp>
 #include <copy.hpp>
-#include <cast.hpp>
-#include <af/dim4.hpp>
+#include <math.hpp>
 #include <types.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
-const ArrayInfo& getInfo(const af_array arr, bool sparse_check = true, bool device_check = true);
+const ArrayInfo &getInfo(const af_array arr, bool sparse_check = true,
+                         bool device_check = true);
 
 af_array retain(const af_array in);
 
-af::dim4 verifyDims(const unsigned ndims, const dim_t * const dims);
+af::dim4 verifyDims(const unsigned ndims, const dim_t *const dims);
 
 af_array createHandle(af::dim4 d, af_dtype dtype);
 
 namespace {
 
 template<typename T>
-detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims)
-{
-    in.eval(); //FIXME: Figure out a better way
+detail::Array<T> modDims(const detail::Array<T> &in, const af::dim4 &newDims) {
+    in.eval();  // FIXME: Figure out a better way
 
     detail::Array<T> Out = in;
     if (!in.isLinear()) Out = detail::copyArray<T>(in);
@@ -42,129 +42,111 @@ detail::Array<T> modDims(const detail::Array<T>& in, const af::dim4 &newDims)
 }
 
 template<typename T>
-detail::Array<T> flat(const detail::Array<T>& in)
-{
+detail::Array<T> flat(const detail::Array<T> &in) {
     const af::dim4 newDims(in.elements());
     return modDims<T>(in, newDims);
 }
 
 template<typename T>
-const detail::Array<T>&
-getArray(const af_array &arr)
-{
-    const detail::Array<T> *A = static_cast<const detail::Array<T>*>(arr);
+const detail::Array<T> &getArray(const af_array &arr) {
+    const detail::Array<T> *A = static_cast<const detail::Array<T> *>(arr);
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
     return *A;
 }
 
 template<typename T>
-detail::Array<T>& getArray(af_array &arr)
-{
-    detail::Array<T> *A = static_cast<detail::Array<T>*>(arr);
+detail::Array<T> &getArray(af_array &arr) {
+    detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
     return *A;
 }
 
 template<typename To>
-detail::Array<To> castArray(const af_array &in)
-{
-    using detail::cfloat;
+detail::Array<To> castArray(const af_array &in) {
     using detail::cdouble;
+    using detail::cfloat;
     using detail::intl;
+    using detail::uchar;
     using detail::uint;
     using detail::uintl;
-    using detail::uchar;
     using detail::ushort;
 
-    const ArrayInfo& info = getInfo(in);
+    const ArrayInfo &info = getInfo(in);
     switch (info.getType()) {
-        case f32: return detail::cast<To, float  >(getArray<float  >(in));
-        case f64: return detail::cast<To, double >(getArray<double >(in));
-        case c32: return detail::cast<To, cfloat >(getArray<cfloat >(in));
+        case f32: return detail::cast<To, float>(getArray<float>(in));
+        case f64: return detail::cast<To, double>(getArray<double>(in));
+        case c32: return detail::cast<To, cfloat>(getArray<cfloat>(in));
         case c64: return detail::cast<To, cdouble>(getArray<cdouble>(in));
-        case s32: return detail::cast<To, int    >(getArray<int    >(in));
-        case u32: return detail::cast<To, uint   >(getArray<uint   >(in));
-        case u8 : return detail::cast<To, uchar  >(getArray<uchar  >(in));
-        case b8 : return detail::cast<To, char   >(getArray<char   >(in));
-        case s64: return detail::cast<To, intl   >(getArray<intl   >(in));
-        case u64: return detail::cast<To, uintl  >(getArray<uintl  >(in));
-        case s16: return detail::cast<To, short  >(getArray<short  >(in));
-        case u16: return detail::cast<To, ushort >(getArray<ushort >(in));
+        case s32: return detail::cast<To, int>(getArray<int>(in));
+        case u32: return detail::cast<To, uint>(getArray<uint>(in));
+        case u8: return detail::cast<To, uchar>(getArray<uchar>(in));
+        case b8: return detail::cast<To, char>(getArray<char>(in));
+        case s64: return detail::cast<To, intl>(getArray<intl>(in));
+        case u64: return detail::cast<To, uintl>(getArray<uintl>(in));
+        case s16: return detail::cast<To, short>(getArray<short>(in));
+        case u16: return detail::cast<To, ushort>(getArray<ushort>(in));
         default: TYPE_ERROR(1, info.getType());
     }
 }
 
 template<typename T>
-af_array
-getHandle(const detail::Array<T> &A)
-{
+af_array getHandle(const detail::Array<T> &A) {
     detail::Array<T> *ret = new detail::Array<T>(A);
     return static_cast<af_array>(ret);
 }
 
 template<typename T>
-af_array retainHandle(const af_array in)
-{
-    detail::Array<T> *A = static_cast<detail::Array<T> *>(in);
+af_array retainHandle(const af_array in) {
+    detail::Array<T> *A   = static_cast<detail::Array<T> *>(in);
     detail::Array<T> *out = new detail::Array<T>(*A);
     return static_cast<af_array>(out);
 }
 
 template<typename T>
-af_array createHandle(af::dim4 d)
-{
+af_array createHandle(af::dim4 d) {
     return getHandle(detail::createEmptyArray<T>(d));
 }
 
 template<typename T>
-af_array createHandleFromValue(af::dim4 d, double val)
-{
+af_array createHandleFromValue(af::dim4 d, double val) {
     return getHandle(detail::createValueArray<T>(d, detail::scalar<T>(val)));
 }
 
 template<typename T>
-af_array createHandleFromData(af::dim4 d, const T * const data)
-{
+af_array createHandleFromData(af::dim4 d, const T *const data) {
     return getHandle(detail::createHostDataArray<T>(d, data));
 }
 
 template<typename T>
-void copyData(T *data, const af_array &arr)
-{
+void copyData(T *data, const af_array &arr) {
     return detail::copyData(data, getArray<T>(arr));
 }
 
 template<typename T>
-af_array copyArray(const af_array in)
-{
+af_array copyArray(const af_array in) {
     const detail::Array<T> &inArray = getArray<T>(in);
     return getHandle<T>(detail::copyArray<T>(inArray));
 }
 
 template<typename T>
-void releaseHandle(const af_array arr)
-{
-    detail::destroyArray(static_cast<detail::Array<T>*>(arr));
+void releaseHandle(const af_array arr) {
+    detail::destroyArray(static_cast<detail::Array<T> *>(arr));
 }
 
 template<typename T>
-detail::Array<T> &
-getCopyOnWriteArray(const af_array &arr)
-{
-    detail::Array<T> *A = static_cast<detail::Array<T>*>(arr);
+detail::Array<T> &getCopyOnWriteArray(const af_array &arr) {
+    detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
 
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
 
     ARG_ASSERT(0, A->isSparse() == false);
 
-    if (A->useCount() > 1) {
-        *A = copyArray(*A);
-    }
+    if (A->useCount() > 1) { *A = copyArray(*A); }
 
     return *A;
 }
 
-}
+}  // namespace
diff --git a/src/api/c/harris.cpp b/src/api/c/harris.cpp
index 1663ff84dc..ea2f00934f 100644
--- a/src/api/c/harris.cpp
+++ b/src/api/c/harris.cpp
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/features.h>
-#include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
 #include <features.hpp>
+#include <handle.hpp>
 #include <harris.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/features.h>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
@@ -23,19 +23,17 @@ using namespace detail;
 template<typename T, typename convAccT>
 static af_features harris(af_array const &in, const unsigned max_corners,
                           const float min_response, const float sigma,
-                          const unsigned filter_len, const float k_thr)
-{
-    Array<float> x = createEmptyArray<float>(dim4());
-    Array<float> y = createEmptyArray<float>(dim4());
+                          const unsigned filter_len, const float k_thr) {
+    Array<float> x     = createEmptyArray<float>(dim4());
+    Array<float> y     = createEmptyArray<float>(dim4());
     Array<float> score = createEmptyArray<float>(dim4());
 
     af_features_t feat;
-    feat.n = harris<T, convAccT>(x, y, score,
-                                 getArray<T>(in), max_corners, min_response,
-                                 sigma, filter_len, k_thr);
+    feat.n = harris<T, convAccT>(x, y, score, getArray<T>(in), max_corners,
+                                 min_response, sigma, filter_len, k_thr);
 
     Array<float> orientation = createValueArray<float>(feat.n, 0.0);
-    Array<float> size = createValueArray<float>(feat.n, 1.0);
+    Array<float> size        = createValueArray<float>(feat.n, 1.0);
 
     feat.x           = getHandle(x);
     feat.y           = getHandle(y);
@@ -46,24 +44,25 @@ static af_features harris(af_array const &in, const unsigned max_corners,
     return getFeaturesHandle(feat);
 }
 
-
-af_err af_harris(af_features *out, const af_array in, const unsigned max_corners,
-                 const float min_response, const float sigma,
-                 const unsigned block_size, const float k_thr)
-{
+af_err af_harris(af_features *out, const af_array in,
+                 const unsigned max_corners, const float min_response,
+                 const float sigma, const unsigned block_size,
+                 const float k_thr) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
-        dim_t in_ndims = dims.ndims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
+        dim_t in_ndims        = dims.ndims();
 
-        unsigned filter_len = (block_size == 0) ? floor(6.f * sigma) : block_size;
-        if (block_size == 0 && filter_len % 2 == 0)
-            filter_len--;
+        unsigned filter_len =
+            (block_size == 0) ? floor(6.f * sigma) : block_size;
+        if (block_size == 0 && filter_len % 2 == 0) filter_len--;
 
-        const unsigned edge = (block_size > 0) ? block_size / 2 : filter_len / 2;
+        const unsigned edge =
+            (block_size > 0) ? block_size / 2 : filter_len / 2;
 
         DIM_ASSERT(1, (in_ndims == 2));
-        ARG_ASSERT(1, (dims[0] >= (dim_t)(2*edge+1) || dims[1] >= (dim_t)(2*edge+1)));
+        ARG_ASSERT(1, (dims[0] >= (dim_t)(2 * edge + 1) ||
+                       dims[1] >= (dim_t)(2 * edge + 1)));
         ARG_ASSERT(3, (max_corners > 0) || (min_response > 0.0f));
         ARG_ASSERT(7, (k_thr >= 0.01f));
         // Upper limits for sigma and block_size are due to convolve2 template
@@ -71,11 +70,17 @@ af_err af_harris(af_features *out, const af_array in, const unsigned max_corners
         ARG_ASSERT(4, (block_size > 2) || (sigma >= 0.5f && sigma <= 5.f));
         ARG_ASSERT(5, (block_size <= 32));
 
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f64: *out = harris<double, double>(in, max_corners, min_response, sigma, filter_len, k_thr); break;
-            case f32: *out = harris<float , float >(in, max_corners, min_response, sigma, filter_len, k_thr); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f64:
+                *out = harris<double, double>(in, max_corners, min_response,
+                                              sigma, filter_len, k_thr);
+                break;
+            case f32:
+                *out = harris<float, float>(in, max_corners, min_response,
+                                            sigma, filter_len, k_thr);
+                break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 19ded99726..10d61963a0 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -7,37 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/graphics.h>
-#include <common/graphics_common.hpp>
-#include <common/ArrayInfo.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
-#include <reduce.hpp>
 #include <cast.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <common/graphics_common.hpp>
 #include <handle.hpp>
 #include <hist_graphics.hpp>
+#include <reduce.hpp>
+#include <af/graphics.h>
 
 using af::dim4;
 using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg_chart setup_histogram(fg_window const window,
-                         const af_array in,
+fg_chart setup_histogram(fg_window const window, const af_array in,
                          const double minval, const double maxval,
-                         const af_cell* const props)
-{
+                         const af_cell* const props) {
     ForgeModule& _ = graphics::forgePlugin();
 
     Array<T> histogramInput = getArray<T>(in);
-    dim_t nBins = histogramInput.elements();
+    dim_t nBins             = histogramInput.elements();
 
     // Retrieve Forge Histogram with nBins and array type
     ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
-    if (props->col>-1 && props->row>-1)
+    if (props->col > -1 && props->row > -1)
         chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
     else
         chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
@@ -50,15 +48,13 @@ fg_chart setup_histogram(fg_window const window,
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
-    if(!fgMngr.getChartAxesOverride(chart)) {
+    if (!fgMngr.getChartAxesOverride(chart)) {
         float xMin, xMax, yMin, yMax, zMin, zMax;
-        FG_CHECK(_.fg_get_chart_axes_limits(&xMin, &xMax,
-                                          &yMin, &yMax,
-                                          &zMin, &zMax,
-                                          chart));
+        FG_CHECK(_.fg_get_chart_axes_limits(&xMin, &xMax, &yMin, &yMax, &zMin,
+                                            &zMax, chart));
         T freqMax = detail::reduce_all<af_max_t, T, T>(histogramInput);
 
-        if(xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
+        if (xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
             // No previous limits. Set without checking
             xMin = step_round(minval, false);
             xMax = step_round(maxval, true);
@@ -66,13 +62,14 @@ fg_chart setup_histogram(fg_window const window,
             // For histogram, always set yMin to 0.
             yMin = 0;
         } else {
-            if(xMin > minval)  xMin = step_round(minval, false);
-            if(xMax < maxval)  xMax = step_round(maxval, true);
-            if(yMax < freqMax) yMax = step_round(freqMax, true);
+            if (xMin > minval) xMin = step_round(minval, false);
+            if (xMax < maxval) xMax = step_round(maxval, true);
+            if (yMax < freqMax) yMax = step_round(freqMax, true);
             // For histogram, always set yMin to 0.
             yMin = 0;
         }
-        FG_CHECK(_.fg_set_chart_axes_limits(chart, xMin, xMax, yMin, yMax, zMin, zMax));
+        FG_CHECK(_.fg_set_chart_axes_limits(chart, xMin, xMax, yMin, yMax, zMin,
+                                            zMax));
     }
 
     copy_histogram<T>(histogramInput, hist);
@@ -80,18 +77,14 @@ fg_chart setup_histogram(fg_window const window,
     return chart;
 }
 
-af_err af_draw_hist(const af_window window,
-                    const af_array X,
+af_err af_draw_hist(const af_window window, const af_array X,
                     const double minval, const double maxval,
-                    const af_cell* const props)
-{
+                    const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& Xinfo = getInfo(X);
-        af_dtype Xtype  = Xinfo.getType();
+        af_dtype Xtype         = Xinfo.getType();
 
         ARG_ASSERT(0, Xinfo.isVector());
 
@@ -99,23 +92,39 @@ af_err af_draw_hist(const af_window window,
 
         fg_chart chart = NULL;
 
-        switch(Xtype) {
-            case f32: chart = setup_histogram<float  >(window, X, minval, maxval, props); break;
-            case s32: chart = setup_histogram<int    >(window, X, minval, maxval, props); break;
-            case u32: chart = setup_histogram<uint   >(window, X, minval, maxval, props); break;
-            case s16: chart = setup_histogram<short  >(window, X, minval, maxval, props); break;
-            case u16: chart = setup_histogram<ushort >(window, X, minval, maxval, props); break;
-            case u8 : chart = setup_histogram<uchar  >(window, X, minval, maxval, props); break;
-            default:  TYPE_ERROR(1, Xtype);
+        switch (Xtype) {
+            case f32:
+                chart =
+                    setup_histogram<float>(window, X, minval, maxval, props);
+                break;
+            case s32:
+                chart = setup_histogram<int>(window, X, minval, maxval, props);
+                break;
+            case u32:
+                chart = setup_histogram<uint>(window, X, minval, maxval, props);
+                break;
+            case s16:
+                chart =
+                    setup_histogram<short>(window, X, minval, maxval, props);
+                break;
+            case u16:
+                chart =
+                    setup_histogram<ushort>(window, X, minval, maxval, props);
+                break;
+            case u8:
+                chart =
+                    setup_histogram<uchar>(window, X, minval, maxval, props);
+                break;
+            default: TYPE_ERROR(1, Xtype);
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index fc246ed5ab..a4447ac82e 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -7,84 +7,82 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <af/index.h>
-#include <af/data.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
 #include <cast.hpp>
-#include <scan.hpp>
-#include <arith.hpp>
-#include <reduce.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <lookup.hpp>
+#include <reduce.hpp>
+#include <scan.hpp>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/image.h>
+#include <af/index.h>
 
 using namespace detail;
 
 template<typename T, typename hType>
-static af_array hist_equal(const af_array& in, const af_array& hist)
-{
+static af_array hist_equal(const af_array& in, const af_array& hist) {
     const Array<T> input = getArray<T>(in);
 
     af_array vInput = 0;
     AF_CHECK(af_flat(&vInput, in));
 
-    Array<float> fHist  = cast<float>(getArray<hType>(hist));
+    Array<float> fHist = cast<float>(getArray<hType>(hist));
 
-    dim4 hDims = fHist.dims();
+    dim4 hDims       = fHist.dims();
     dim_t grayLevels = fHist.elements();
 
     Array<float> cdf = scan<af_add_t, float, float>(fHist, 0);
 
     float minCdf = reduce_all<af_min_t, float, float>(cdf);
     float maxCdf = reduce_all<af_max_t, float, float>(cdf);
-    float factor = (float)(grayLevels-1)/(maxCdf - minCdf);
+    float factor = (float)(grayLevels - 1) / (maxCdf - minCdf);
 
     // constant array of min value from cdf
     Array<float> minCnst = createValueArray<float>(hDims, minCdf);
     // constant array of factor variable
     Array<float> facCnst = createValueArray<float>(hDims, factor);
     // cdf(i) - min for all elements
-    Array<float> diff    = arithOp<float, af_sub_t>(cdf, minCnst, hDims);
+    Array<float> diff = arithOp<float, af_sub_t>(cdf, minCnst, hDims);
     // multiply factor with difference
     Array<float> normCdf = arithOp<float, af_mul_t>(diff, facCnst, hDims);
     // index input array with normalized cdf array
-    Array<float> idxArr  = lookup<float, T>(normCdf, getArray<T>(vInput), 0);
+    Array<float> idxArr = lookup<float, T>(normCdf, getArray<T>(vInput), 0);
 
     Array<T> result = cast<T>(idxArr);
-    result = modDims(result, input.dims());
+    result          = modDims(result, input.dims());
 
     AF_CHECK(af_release_array(vInput));
 
     return getHandle<T>(result);
 }
 
-af_err af_hist_equal(af_array *out, const af_array in, const af_array hist)
-{
+af_err af_hist_equal(af_array* out, const af_array in, const af_array hist) {
     try {
         const ArrayInfo& dataInfo = getInfo(in);
         const ArrayInfo& histInfo = getInfo(hist);
 
-        af_dtype dataType  = dataInfo.getType();
-        af::dim4 histDims  = histInfo.dims();
+        af_dtype dataType = dataInfo.getType();
+        af::dim4 histDims = histInfo.dims();
 
-        ARG_ASSERT(2, (histDims.ndims()==1));
+        ARG_ASSERT(2, (histDims.ndims() == 1));
 
         af_array output = 0;
-        switch(dataType) {
+        switch (dataType) {
             case f64: output = hist_equal<double, uint>(in, hist); break;
-            case f32: output = hist_equal<float , uint>(in, hist); break;
-            case s32: output = hist_equal<int   , uint>(in, hist); break;
-            case u32: output = hist_equal<uint  , uint>(in, hist); break;
-            case s16: output = hist_equal<short , uint>(in, hist); break;
+            case f32: output = hist_equal<float, uint>(in, hist); break;
+            case s32: output = hist_equal<int, uint>(in, hist); break;
+            case u32: output = hist_equal<uint, uint>(in, hist); break;
+            case s16: output = hist_equal<short, uint>(in, hist); break;
             case u16: output = hist_equal<ushort, uint>(in, hist); break;
-            case s64: output = hist_equal<intl  , uint>(in, hist); break;
-            case u64: output = hist_equal<uintl , uint>(in, hist); break;
-            case u8 : output = hist_equal<uchar , uint>(in, hist); break;
-            default : TYPE_ERROR(1, dataType);
+            case s64: output = hist_equal<intl, uint>(in, hist); break;
+            case u64: output = hist_equal<uintl, uint>(in, hist); break;
+            case u8: output = hist_equal<uchar, uint>(in, hist); break;
+            default: TYPE_ERROR(1, dataType);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index d9803314c4..ad18aa63c7 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -7,53 +7,81 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/image.h>
+#include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
 #include <histogram.hpp>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
-template<typename inType,typename outType>
+template<typename inType, typename outType>
 static inline af_array histogram(const af_array in, const unsigned &nbins,
                                  const double &minval, const double &maxval,
-                                 const bool islinear)
-{
+                                 const bool islinear) {
     if (islinear)
-        return getHandle(histogram<inType,outType, true>(getArray<inType>(in),nbins,minval,maxval));
+        return getHandle(histogram<inType, outType, true>(
+            getArray<inType>(in), nbins, minval, maxval));
     else
-        return getHandle(histogram<inType,outType, false>(getArray<inType>(in),nbins,minval,maxval));
+        return getHandle(histogram<inType, outType, false>(
+            getArray<inType>(in), nbins, minval, maxval));
 }
 
-af_err af_histogram(af_array *out, const af_array in,
-                    const unsigned nbins, const double minval, const double maxval)
-{
+af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
+                    const double minval, const double maxval) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array output;
-        switch(type) {
-            case f32: output = histogram<float , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case f64: output = histogram<double, uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case b8 : output = histogram<char  , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case s32: output = histogram<int   , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case u32: output = histogram<uint  , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case s16: output = histogram<short , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case u16: output = histogram<ushort, uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case s64: output = histogram<intl  , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case u64: output = histogram<uintl , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            case u8 : output = histogram<uchar , uint>(in, nbins, minval, maxval, info.isLinear()); break;
-            default : TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                output = histogram<float, uint>(in, nbins, minval, maxval,
+                                                info.isLinear());
+                break;
+            case f64:
+                output = histogram<double, uint>(in, nbins, minval, maxval,
+                                                 info.isLinear());
+                break;
+            case b8:
+                output = histogram<char, uint>(in, nbins, minval, maxval,
+                                               info.isLinear());
+                break;
+            case s32:
+                output = histogram<int, uint>(in, nbins, minval, maxval,
+                                              info.isLinear());
+                break;
+            case u32:
+                output = histogram<uint, uint>(in, nbins, minval, maxval,
+                                               info.isLinear());
+                break;
+            case s16:
+                output = histogram<short, uint>(in, nbins, minval, maxval,
+                                                info.isLinear());
+                break;
+            case u16:
+                output = histogram<ushort, uint>(in, nbins, minval, maxval,
+                                                 info.isLinear());
+                break;
+            case s64:
+                output = histogram<intl, uint>(in, nbins, minval, maxval,
+                                               info.isLinear());
+                break;
+            case u64:
+                output = histogram<uintl, uint>(in, nbins, minval, maxval,
+                                                info.isLinear());
+                break;
+            case u8:
+                output = histogram<uchar, uint>(in, nbins, minval, maxval,
+                                                info.isLinear());
+                break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index 022b1cf147..f888b4f92c 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -7,57 +7,55 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/vision.h>
-#include <af/random.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <homography.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/random.h>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline void homography(af_array &H, int &inliers,
-                              const af_array x_src, const af_array y_src,
-                              const af_array x_dst, const af_array y_dst,
-                              const af_homography_type htype, const float inlier_thr,
-                              const unsigned iterations)
-{
+static inline void homography(af_array& H, int& inliers, const af_array x_src,
+                              const af_array y_src, const af_array x_dst,
+                              const af_array y_dst,
+                              const af_homography_type htype,
+                              const float inlier_thr,
+                              const unsigned iterations) {
     Array<T> bestH = createEmptyArray<T>(af::dim4(3, 3));
     af_array initial;
-    unsigned d = (iterations + 256 - 1) / 256;
+    unsigned d    = (iterations + 256 - 1) / 256;
     dim_t rdims[] = {4, d * 256};
     AF_CHECK(af_randu(&initial, 2, rdims, f32));
-    inliers = homography<T>(bestH,
-                            getArray<float>(x_src), getArray<float>(y_src),
-                            getArray<float>(x_dst), getArray<float>(y_dst),
-                            getArray<float>(initial),
-                            htype, inlier_thr, iterations);
+    inliers =
+        homography<T>(bestH, getArray<float>(x_src), getArray<float>(y_src),
+                      getArray<float>(x_dst), getArray<float>(y_dst),
+                      getArray<float>(initial), htype, inlier_thr, iterations);
     AF_CHECK(af_release_array(initial));
 
     H = getHandle<T>(bestH);
 }
 
-af_err af_homography(af_array *H, int *inliers,
-                     const af_array x_src, const af_array y_src,
-                     const af_array x_dst, const af_array y_dst,
-                     const af_homography_type htype, const float inlier_thr,
-                     const unsigned iterations, const af_dtype otype)
-{
+af_err af_homography(af_array* H, int* inliers, const af_array x_src,
+                     const af_array y_src, const af_array x_dst,
+                     const af_array y_dst, const af_homography_type htype,
+                     const float inlier_thr, const unsigned iterations,
+                     const af_dtype otype) {
     try {
         const ArrayInfo& xsinfo = getInfo(x_src);
         const ArrayInfo& ysinfo = getInfo(y_src);
         const ArrayInfo& xdinfo = getInfo(x_dst);
         const ArrayInfo& ydinfo = getInfo(y_dst);
 
-        af::dim4 xsdims  = xsinfo.dims();
-        af::dim4 ysdims  = ysinfo.dims();
-        af::dim4 xddims  = xdinfo.dims();
-        af::dim4 yddims  = ydinfo.dims();
+        af::dim4 xsdims = xsinfo.dims();
+        af::dim4 ysdims = ysinfo.dims();
+        af::dim4 xddims = xdinfo.dims();
+        af::dim4 yddims = ydinfo.dims();
 
         af_dtype xstype = xsinfo.getType();
         af_dtype ystype = ysinfo.getType();
@@ -80,10 +78,16 @@ af_err af_homography(af_array *H, int *inliers,
         af_array outH;
         int outInl;
 
-        switch(otype) {
-            case f32: homography<float >(outH, outInl, x_src, y_src, x_dst, y_dst, htype, inlier_thr, iterations);  break;
-            case f64: homography<double>(outH, outInl, x_src, y_src, x_dst, y_dst, htype, inlier_thr, iterations);  break;
-            default:  TYPE_ERROR(1, otype);
+        switch (otype) {
+            case f32:
+                homography<float>(outH, outInl, x_src, y_src, x_dst, y_dst,
+                                  htype, inlier_thr, iterations);
+                break;
+            case f64:
+                homography<double>(outH, outInl, x_src, y_src, x_dst, y_dst,
+                                   htype, inlier_thr, iterations);
+                break;
+            default: TYPE_ERROR(1, otype);
         }
         std::swap(*H, outH);
         std::swap(*inliers, outInl);
diff --git a/src/api/c/hsv_rgb.cpp b/src/api/c/hsv_rgb.cpp
index 04385070d1..e321125bc9 100644
--- a/src/api/c/hsv_rgb.cpp
+++ b/src/api/c/hsv_rgb.cpp
@@ -1,44 +1,41 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
+#include <hsv_rgb.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/image.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
-#include <hsv_rgb.hpp>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, bool isHSV2RGB>
-static af_array convert(const af_array& in)
-{
+static af_array convert(const af_array& in) {
     const Array<T> input = getArray<T>(in);
     if (isHSV2RGB) {
         return getHandle<T>(hsv2rgb<T>(input));
-    }
-    else {
+    } else {
         return getHandle<T>(rgb2hsv<T>(input));
     }
 }
 
 template<bool isHSV2RGB>
-af_err convert(af_array* out, const af_array& in)
-{
+af_err convert(af_array* out, const af_array& in) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype iType = info.getType();
-        af::dim4 inputDims = info.dims();
+        af_dtype iType        = info.getType();
+        af::dim4 inputDims    = info.dims();
 
-        if(info.ndims() == 0) {
+        if (info.ndims() == 0) {
             return af_create_handle(out, 0, nullptr, iType);
         }
 
@@ -47,7 +44,7 @@ af_err convert(af_array* out, const af_array& in)
         af_array output = 0;
         switch (iType) {
             case f64: output = convert<double, isHSV2RGB>(in); break;
-            case f32: output = convert<float , isHSV2RGB>(in); break;
+            case f32: output = convert<float, isHSV2RGB>(in); break;
             default: TYPE_ERROR(1, iType); break;
         }
         std::swap(*out, output);
@@ -56,12 +53,10 @@ af_err convert(af_array* out, const af_array& in)
     return AF_SUCCESS;
 }
 
-af_err af_hsv2rgb(af_array* out, const af_array in)
-{
+af_err af_hsv2rgb(af_array* out, const af_array in) {
     return convert<true>(out, in);
 }
 
-af_err af_rgb2hsv(af_array* out, const af_array in)
-{
+af_err af_rgb2hsv(af_array* out, const af_array in) {
     return convert<false>(out, in);
 }
diff --git a/src/api/c/iir.cpp b/src/api/c/iir.cpp
index 826c14c1b0..96dfc2b187 100644
--- a/src/api/c/iir.cpp
+++ b/src/api/c/iir.cpp
@@ -6,51 +6,48 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/signal.h>
-#include <af/arith.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
 #include <convolve.hpp>
+#include <handle.hpp>
 #include <iir.hpp>
+#include <af/arith.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/signal.h>
 
 #include <cstdio>
 
 using af::dim4;
 using namespace detail;
 
-af_err af_fir(af_array *y, const af_array b, const af_array x)
-{
+af_err af_fir(af_array* y, const af_array b, const af_array x) {
     try {
         af_array out;
         AF_CHECK(af_convolve1(&out, x, b, AF_CONV_EXPAND, AF_CONV_AUTO));
 
-        dim4 xdims = getInfo(x).dims();
+        dim4 xdims    = getInfo(x).dims();
         af_seq seqs[] = {af_span, af_span, af_span, af_span};
         seqs[0].begin = 0;
-        seqs[0].end = xdims[0] - 1;
-        seqs[0].step = 1;
+        seqs[0].end   = xdims[0] - 1;
+        seqs[0].step  = 1;
         af_array res;
         AF_CHECK(af_index(&res, out, 4, seqs));
         AF_CHECK(af_release_array(out));
         std::swap(*y, res);
-
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 template<typename T>
-inline static af_array iir(const af_array b, const af_array a, const af_array x)
-{
-    return getHandle(iir<T>(getArray<T>(b),
-                            getArray<T>(a),
-                            getArray<T>(x)));
+inline static af_array iir(const af_array b, const af_array a,
+                           const af_array x) {
+    return getHandle(iir<T>(getArray<T>(b), getArray<T>(a), getArray<T>(x)));
 }
 
-af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
-{
+af_err af_iir(af_array* y, const af_array b, const af_array a,
+              const af_array x) {
     try {
         const ArrayInfo& ainfo = getInfo(a);
         const ArrayInfo& binfo = getInfo(b);
@@ -66,9 +63,7 @@ af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
         dim4 bdims = binfo.dims();
         dim4 xdims = xinfo.dims();
 
-        if(xinfo.ndims() == 0) {
-            return af_retain_array(y, x);
-        }
+        if (xinfo.ndims() == 0) { return af_retain_array(y, x); }
 
         if (xinfo.ndims() > 1) {
             if (binfo.ndims() > 1) {
@@ -89,14 +84,15 @@ af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
 
         af_array res;
         switch (xtype) {
-        case f32: res = iir<float  >(b, a, x); break;
-        case f64: res = iir<double >(b, a, x); break;
-        case c32: res = iir<cfloat >(b, a, x); break;
-        case c64: res = iir<cdouble>(b, a, x); break;
-        default: TYPE_ERROR(1, xtype);
+            case f32: res = iir<float>(b, a, x); break;
+            case f64: res = iir<double>(b, a, x); break;
+            case c32: res = iir<cfloat>(b, a, x); break;
+            case c64: res = iir<cdouble>(b, a, x); break;
+            default: TYPE_ERROR(1, xtype);
         }
 
         std::swap(*y, res);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index b7c7565b48..17505279b7 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -7,23 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-
+#include <af/data.h>
 #include <af/graphics.h>
 #include <af/image.h>
 #include <af/index.h>
-#include <af/data.h>
 
+#include <arith.hpp>
+#include <backend.hpp>
+#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
-#include <image.hpp>
+#include <common/graphics_common.hpp>
 #include <handle.hpp>
+#include <image.hpp>
+#include <join.hpp>
 #include <reorder.hpp>
 #include <tile.hpp>
-#include <join.hpp>
-#include <cast.hpp>
-#include <arith.hpp>
 
 #include <limits>
 
@@ -31,10 +30,8 @@ using af::dim4;
 using namespace detail;
 using namespace graphics;
 
-
 template<typename T>
-Array<T> normalizePerType(const Array<T>& in)
-{
+Array<T> normalizePerType(const Array<T>& in) {
     Array<float> inFloat = cast<float, T>(in);
 
     Array<float> cnst = createValueArray<float>(in.dims(), 1.0 - 1.0e-6f);
@@ -45,18 +42,16 @@ Array<T> normalizePerType(const Array<T>& in)
 }
 
 template<>
-Array<float> normalizePerType<float>(const Array<float>& in)
-{
+Array<float> normalizePerType<float>(const Array<float>& in) {
     return in;
 }
 
 template<typename T>
-static fg_image convert_and_copy_image(const af_array in)
-{
-    const Array<T> _in  = getArray<T>(in);
-    dim4 inDims = _in.dims();
+static fg_image convert_and_copy_image(const af_array in) {
+    const Array<T> _in = getArray<T>(in);
+    dim4 inDims        = _in.dims();
 
-    dim4 rdims = (inDims[2]>1 ? dim4(2, 1, 0, 3) : dim4(1, 0, 2, 3));
+    dim4 rdims = (inDims[2] > 1 ? dim4(2, 1, 0, 3) : dim4(1, 0, 2, 3));
 
     Array<T> imgData = reorder(_in, rdims);
 
@@ -64,21 +59,18 @@ static fg_image convert_and_copy_image(const af_array in)
 
     // The inDims[2] * 100 is a hack to convert to fg_channel_format
     // TODO Write a proper conversion function
-    fg_image ret_val = fgMngr.getImage(inDims[1], inDims[0],
-                                       (fg_channel_format)(inDims[2] * 100),
-                                       getGLType<T>());
+    fg_image ret_val =
+        fgMngr.getImage(inDims[1], inDims[0],
+                        (fg_channel_format)(inDims[2] * 100), getGLType<T>());
     copy_image<T>(normalizePerType<T>(imgData), ret_val);
 
     return ret_val;
 }
 
-af_err af_draw_image(const af_window window,
-                     const af_array in, const af_cell* const props)
-{
+af_err af_draw_image(const af_window window, const af_array in,
+                     const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& info = getInfo(in);
 
@@ -90,25 +82,25 @@ af_err af_draw_image(const af_window window,
         makeContextCurrent(window);
         fg_image image = NULL;
 
-        switch(type) {
-            case f32: image = convert_and_copy_image<float >(in); break;
-            case b8 : image = convert_and_copy_image<char  >(in); break;
-            case s32: image = convert_and_copy_image<int   >(in); break;
-            case u32: image = convert_and_copy_image<uint  >(in); break;
-            case s16: image = convert_and_copy_image<short >(in); break;
+        switch (type) {
+            case f32: image = convert_and_copy_image<float>(in); break;
+            case b8: image = convert_and_copy_image<char>(in); break;
+            case s32: image = convert_and_copy_image<int>(in); break;
+            case u32: image = convert_and_copy_image<uint>(in); break;
+            case s16: image = convert_and_copy_image<short>(in); break;
             case u16: image = convert_and_copy_image<ushort>(in); break;
-            case u8 : image = convert_and_copy_image<uchar >(in); break;
-            default:  TYPE_ERROR(1, type);
+            case u8: image = convert_and_copy_image<uchar>(in); break;
+            default: TYPE_ERROR(1, type);
         }
 
         ForgeModule& _ = graphics::forgePlugin();
-        auto gridDims = forgeManager().getWindowGrid(window);
+        auto gridDims  = forgeManager().getWindowGrid(window);
         FG_CHECK(_.fg_set_window_colormap(window, (fg_color_map)props->cmap));
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_image_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             image, props->title, true));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_image_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, image, props->title,
+                true));
         } else {
             FG_CHECK(_.fg_draw_image(window, image, true));
         }
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 89d29c0db6..c44da9d0f8 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -11,42 +11,41 @@
 
 #include "imageio_helper.h"
 
-#include <af/array.h>
-#include <af/index.h>
-#include <af/dim4.hpp>
-#include <af/arith.h>
-#include <af/algorithm.h>
-#include <af/blas.h>
-#include <af/data.h>
-#include <af/image.h>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <traits.hpp>
-#include <memory.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
+#include <memory.hpp>
+#include <traits.hpp>
+#include <af/algorithm.h>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/blas.h>
+#include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
+#include <af/index.h>
 
 #include <common/DependencyModule.hpp>
 
-#include <string>
-#include <cstring>
 #include <cstdio>
 #include <cstdlib>
+#include <cstring>
 #include <memory>
+#include <string>
 
 using af::dim4;
 using namespace detail;
-using std::unique_ptr;
 using std::string;
 using std::swap;
+using std::unique_ptr;
 
 template<typename T, FI_CHANNELS fi_color, FI_CHANNELS fo_color>
-static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcPitch,
-                        const uint fi_w, const uint fi_h)
-{
+static af_err readImage(af_array* rImage, const uchar* pSrcLine,
+                        const int nSrcPitch, const uint fi_w, const uint fi_h) {
     // create an array to receive the loaded image data.
     AF_CHECK(af_init());
-    float *pDst = pinnedAlloc<float>(fi_w * fi_h * 4); // 4 channels is max
+    float* pDst  = pinnedAlloc<float>(fi_w * fi_h * 4);  // 4 channels is max
     float* pDst0 = pDst;
     float* pDst1 = pDst + (fi_w * fi_h * 1);
     float* pDst2 = pDst + (fi_w * fi_h * 2);
@@ -57,22 +56,25 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
 
     for (uint x = 0; x < fi_w; ++x) {
         for (uint y = 0; y < fi_h; ++y) {
-            const T *src = (T*)(pSrcLine - y * nSrcPitch);
-            if(fo_color == 1) {
-                pDst0[indx] = (T) *(src + (x * step));
-            } else if(fo_color >= 3) {
-                if((af_dtype) af::dtype_traits<T>::af_type == u8) {
-                    pDst0[indx] = (float) *(src + (x * step + FI_RGBA_RED));
-                    pDst1[indx] = (float) *(src + (x * step + FI_RGBA_GREEN));
-                    pDst2[indx] = (float) *(src + (x * step + FI_RGBA_BLUE));
-                    if (fo_color == 4) pDst3[indx] = (float) *(src + (x * step + FI_RGBA_ALPHA));
+            const T* src = (T*)(pSrcLine - y * nSrcPitch);
+            if (fo_color == 1) {
+                pDst0[indx] = (T) * (src + (x * step));
+            } else if (fo_color >= 3) {
+                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
+                    pDst0[indx] = (float)*(src + (x * step + FI_RGBA_RED));
+                    pDst1[indx] = (float)*(src + (x * step + FI_RGBA_GREEN));
+                    pDst2[indx] = (float)*(src + (x * step + FI_RGBA_BLUE));
+                    if (fo_color == 4)
+                        pDst3[indx] =
+                            (float)*(src + (x * step + FI_RGBA_ALPHA));
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (float) *(src + (x * step + 0));
-                    pDst1[indx] = (float) *(src + (x * step + 1));
-                    pDst2[indx] = (float) *(src + (x * step + 2));
-                    if (fo_color == 4) pDst3[indx] = (float) *(src + (x * step + 3));
+                    pDst0[indx] = (float)*(src + (x * step + 0));
+                    pDst1[indx] = (float)*(src + (x * step + 1));
+                    pDst2[indx] = (float)*(src + (x * step + 2));
+                    if (fo_color == 4)
+                        pDst3[indx] = (float)*(src + (x * step + 3));
                 }
             }
             indx++;
@@ -81,32 +83,31 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
 
     // TODO
     af::dim4 dims(fi_h, fi_w, fo_color, 1);
-    af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type);
+    af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(),
+                                 (af_dtype)af::dtype_traits<float>::af_type);
     pinnedFree(pDst);
     return err;
 }
 
 #ifdef FREEIMAGE_STATIC
-    // NOTE: Redefine the MODULE_FUNCTION_INIT macro to call the static functions
-    // instead of dynamically loaded symbols in case we are building with a static
-    // FreeImage library
-    #undef MODULE_FUNCTION_INIT
-    #define MODULE_FUNCTION_INIT(NAME)                  \
-      NAME = &::NAME
-
-FreeImage_Module::FreeImage_Module()
-  : module(nullptr, nullptr) {
+// NOTE: Redefine the MODULE_FUNCTION_INIT macro to call the static functions
+// instead of dynamically loaded symbols in case we are building with a static
+// FreeImage library
+#undef MODULE_FUNCTION_INIT
+#define MODULE_FUNCTION_INIT(NAME) NAME = &::NAME
+
+FreeImage_Module::FreeImage_Module() : module(nullptr, nullptr) {
     // We don't care if the module loaded if we are staticly linking against
     // FreeImage
     ::FreeImage_Initialise(false);
 #else
-FreeImage_Module::FreeImage_Module()
-  : module("freeimage", nullptr) {
-    if(!module.isLoaded()) {
-        string error_message = "Error loading FreeImage: " + module.getErrorMessage()
-            + "\nFreeImage or one of it's dependencies failed to "
-              "load. Try installing FreeImage or check if FreeImage is in the "
-              "search path.";
+FreeImage_Module::FreeImage_Module() : module("freeimage", nullptr) {
+    if (!module.isLoaded()) {
+        string error_message =
+            "Error loading FreeImage: " + module.getErrorMessage() +
+            "\nFreeImage or one of it's dependencies failed to "
+            "load. Try installing FreeImage or check if FreeImage is in the "
+            "search path.";
         AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
     }
 #endif
@@ -136,10 +137,10 @@ FreeImage_Module::FreeImage_Module()
     MODULE_FUNCTION_INIT(FreeImage_Unload);
 
 #ifndef FREEIMAGE_STATIC
-    if(!module.symbolsLoaded()) {
-        string error_message = "Error loading FreeImage: "
-          + module.getErrorMessage()
-          + "\nThe installed version of FreeImage is not compatible with "
+    if (!module.symbolsLoaded()) {
+        string error_message =
+            "Error loading FreeImage: " + module.getErrorMessage() +
+            "\nThe installed version of FreeImage is not compatible with "
             "ArrayFire. Please create an issue on which this error message";
         AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
     }
@@ -153,41 +154,40 @@ FreeImage_Module::~FreeImage_Module() {
 }
 
 FreeImage_Module& getFreeImagePlugin() {
-    static FreeImage_Module *plugin = new FreeImage_Module();
+    static FreeImage_Module* plugin = new FreeImage_Module();
     return *plugin;
 }
 
 bitmap_ptr make_bitmap_ptr(FIBITMAP* ptr) {
-  return bitmap_ptr(ptr, getFreeImagePlugin().FreeImage_Unload);
+    return bitmap_ptr(ptr, getFreeImagePlugin().FreeImage_Unload);
 }
 
 template<typename T, FI_CHANNELS fo_color>
-static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcPitch,
-                        const uint fi_w, const uint fi_h)
-{
+static af_err readImage(af_array* rImage, const uchar* pSrcLine,
+                        const int nSrcPitch, const uint fi_w, const uint fi_h) {
     // create an array to receive the loaded image data.
     AF_CHECK(af_init());
-    float *pDst = pinnedAlloc<float>(fi_w * fi_h);
+    float* pDst = pinnedAlloc<float>(fi_w * fi_h);
 
     uint indx = 0;
     uint step = nSrcPitch / (fi_w * sizeof(T));
     T r, g, b;
     for (uint x = 0; x < fi_w; ++x) {
         for (uint y = 0; y < fi_h; ++y) {
-            const T *src = (T*)(pSrcLine - y * nSrcPitch);
-            if(fo_color == 1) {
-                pDst[indx] = (T) *(src + (x * step));
-            } else if(fo_color >= 3) {
-                if((af_dtype) af::dtype_traits<T>::af_type == u8) {
-                    r = (T) *(src + (x * step + FI_RGBA_RED));
-                    g = (T) *(src + (x * step + FI_RGBA_GREEN));
-                    b = (T) *(src + (x * step + FI_RGBA_BLUE));
+            const T* src = (T*)(pSrcLine - y * nSrcPitch);
+            if (fo_color == 1) {
+                pDst[indx] = (T) * (src + (x * step));
+            } else if (fo_color >= 3) {
+                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
+                    r = (T) * (src + (x * step + FI_RGBA_RED));
+                    g = (T) * (src + (x * step + FI_RGBA_GREEN));
+                    b = (T) * (src + (x * step + FI_RGBA_BLUE));
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    r = (T) *(src + (x * step + 0));
-                    g = (T) *(src + (x * step + 1));
-                    b = (T) *(src + (x * step + 2));
+                    r = (T) * (src + (x * step + 0));
+                    g = (T) * (src + (x * step + 1));
+                    b = (T) * (src + (x * step + 2));
                 }
                 pDst[indx] = r * 0.2989f + g * 0.5870f + b * 0.1140f;
             }
@@ -196,7 +196,8 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
     }
 
     af::dim4 dims(fi_h, fi_w, 1, 1);
-    af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<float>::af_type);
+    af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(),
+                                 (af_dtype)af::dtype_traits<float>::af_type);
     pinnedFree(pDst);
     return err;
 }
@@ -205,8 +206,7 @@ static af_err readImage(af_array *rImage, const uchar* pSrcLine, const int nSrcP
 // File IO
 ////////////////////////////////////////////////////////////////////////////////
 // Load image from disk.
-af_err af_load_image(af_array *out, const char* filename, const bool isColor)
-{
+af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
     try {
         ARG_ASSERT(1, filename != NULL);
 
@@ -221,14 +221,15 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
             fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
-        if(fif == FIF_UNKNOWN) {
-            AF_ERROR("FreeImage Error: Unknown File or Filetype", AF_ERR_NOT_SUPPORTED);
+        if (fif == FIF_UNKNOWN) {
+            AF_ERROR("FreeImage Error: Unknown File or Filetype",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         int flags = 0;
-        if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+        if (fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
 #ifdef JPEG_GREYSCALE
-        if(fif == FIF_JPEG && !isColor) flags = flags | JPEG_GREYSCALE;
+        if (fif == FIF_JPEG && !isColor) flags = flags | JPEG_GREYSCALE;
 #endif
 
         // check that the plugin has reading capabilities ...
@@ -237,32 +238,39 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
             pBitmap.reset(_.FreeImage_Load(fif, filename, flags));
         }
 
-        if(pBitmap == NULL) {
-            AF_ERROR("FreeImage Error: Error reading image or file does not exist", AF_ERR_RUNTIME);
+        if (pBitmap == NULL) {
+            AF_ERROR(
+                "FreeImage Error: Error reading image or file does not exist",
+                AF_ERR_RUNTIME);
         }
 
         // check image color type
-        uint color_type = _.FreeImage_GetColorType(pBitmap.get());
+        uint color_type   = _.FreeImage_GetColorType(pBitmap.get());
         const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
-        //int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
+        // int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
         int fi_color;
-        switch(color_type) {
-            case 0:                     // FIC_MINISBLACK
-            case 1:                     // FIC_MINISWHITE
-                fi_color = 1; break;
-            case 2:                     // FIC_PALETTE
-            case 3:                     // FIC_RGB
-                fi_color = 3; break;
-            case 4:                     // FIC_RGBALPHA
-            case 5:                     // FIC_CMYK
-                fi_color = 4; break;
-            default:                    // Should not come here
-                fi_color = 3; break;
+        switch (color_type) {
+            case 0:  // FIC_MINISBLACK
+            case 1:  // FIC_MINISWHITE
+                fi_color = 1;
+                break;
+            case 2:  // FIC_PALETTE
+            case 3:  // FIC_RGB
+                fi_color = 3;
+                break;
+            case 4:  // FIC_RGBALPHA
+            case 5:  // FIC_CMYK
+                fi_color = 4;
+                break;
+            default:  // Should not come here
+                fi_color = 3;
+                break;
         }
 
         const int fi_bpc = fi_bpp / fi_color;
-        if(fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
-            AF_ERROR("FreeImage Error: Bits per channel not supported", AF_ERR_NOT_SUPPORTED);
+        if (fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
+            AF_ERROR("FreeImage Error: Bits per channel not supported",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         // data type
@@ -274,87 +282,220 @@ af_err af_load_image(af_array *out, const char* filename, const bool isColor)
 
         // FI = row major | AF = column major
         uint nSrcPitch = _.FreeImage_GetPitch(pBitmap.get());
-        const uchar* pSrcLine = _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
+        const uchar* pSrcLine =
+            _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
 
         // result image
         af_array rImage;
         if (isColor) {
-            if(fi_color == 4) {     //4 channel image
-                if(fi_bpc == 8)
-                    AF_CHECK((readImage<uchar,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 16)
-                    AF_CHECK((readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 32)
-                    switch(image_type) {
-                        case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_INT32: AF_CHECK((readImage<int,   AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+            if (fi_color == 4) {  // 4 channel image
+                if (fi_bpc == 8)
+                    AF_CHECK((readImage<uchar, AFFI_RGBA, AFFI_RGBA>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                else if (fi_bpc == 16)
+                    AF_CHECK(
+                        (readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage,
+                                                                  pSrcLine,
+                                                                  nSrcPitch,
+                                                                  fi_w, fi_h));
+                else if (fi_bpc == 32)
+                    switch (image_type) {
+                        case FIT_UINT32:
+                            AF_CHECK((readImage<uint, AFFI_RGBA,
+                                                AFFI_RGBA>)(&rImage, pSrcLine,
+                                                            nSrcPitch, fi_w,
+                                                            fi_h));
+                            break;
+                        case FIT_INT32:
+                            AF_CHECK((
+                                readImage<int, AFFI_RGBA, AFFI_RGBA>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                            break;
+                        case FIT_FLOAT:
+                            AF_CHECK((readImage<float, AFFI_RGBA,
+                                                AFFI_RGBA>)(&rImage, pSrcLine,
+                                                            nSrcPitch, fi_w,
+                                                            fi_h));
+                            break;
+                        default:
+                            AF_ERROR("FreeImage Error: Unknown image type",
+                                     AF_ERR_NOT_SUPPORTED);
+                            break;
                     }
             } else if (fi_color == 1) {
-                if(fi_bpc == 8)
-                    AF_CHECK((readImage<uchar,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 16)
-                    AF_CHECK((readImage<ushort, AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 32)
-                    switch(image_type) {
-                        case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_INT32: AF_CHECK((readImage<int,   AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_GRAY, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                if (fi_bpc == 8)
+                    AF_CHECK((readImage<uchar, AFFI_GRAY, AFFI_RGB>)(&rImage,
+                                                                     pSrcLine,
+                                                                     nSrcPitch,
+                                                                     fi_w,
+                                                                     fi_h));
+                else if (fi_bpc == 16)
+                    AF_CHECK((readImage<ushort, AFFI_GRAY, AFFI_RGB>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                else if (fi_bpc == 32)
+                    switch (image_type) {
+                        case FIT_UINT32:
+                            AF_CHECK((
+                                readImage<uint, AFFI_GRAY, AFFI_RGB>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                            break;
+                        case FIT_INT32:
+                            AF_CHECK(
+                                (readImage<int, AFFI_GRAY, AFFI_RGB>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                            break;
+                        case FIT_FLOAT:
+                            AF_CHECK((readImage<float, AFFI_GRAY,
+                                                AFFI_RGB>)(&rImage, pSrcLine,
+                                                           nSrcPitch, fi_w,
+                                                           fi_h));
+                            break;
+                        default:
+                            AF_ERROR("FreeImage Error: Unknown image type",
+                                     AF_ERR_NOT_SUPPORTED);
+                            break;
                     }
-            } else {             //3 channel image
-                if(fi_bpc == 8)
-                    AF_CHECK((readImage<uchar,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 16)
-                    AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 32)
-                    switch(image_type) {
-                        case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_INT32: AF_CHECK((readImage<int,   AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+            } else {  // 3 channel image
+                if (fi_bpc == 8)
+                    AF_CHECK((
+                        readImage<uchar, AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine,
+                                                              nSrcPitch, fi_w,
+                                                              fi_h));
+                else if (fi_bpc == 16)
+                    AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                     pSrcLine,
+                                                                     nSrcPitch,
+                                                                     fi_w,
+                                                                     fi_h));
+                else if (fi_bpc == 32)
+                    switch (image_type) {
+                        case FIT_UINT32:
+                            AF_CHECK(
+                                (readImage<uint, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                            break;
+                        case FIT_INT32:
+                            AF_CHECK(
+                                (readImage<int, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                     pSrcLine,
+                                                                     nSrcPitch,
+                                                                     fi_w,
+                                                                     fi_h));
+                            break;
+                        case FIT_FLOAT:
+                            AF_CHECK((
+                                readImage<float, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                      pSrcLine,
+                                                                      nSrcPitch,
+                                                                      fi_w,
+                                                                      fi_h));
+                            break;
+                        default:
+                            AF_ERROR("FreeImage Error: Unknown image type",
+                                     AF_ERR_NOT_SUPPORTED);
+                            break;
                     }
             }
-        } else {                    //output gray irrespective
-            if(fi_color == 1) {     //4 channel image
-                if(fi_bpc == 8)
-                    AF_CHECK((readImage<uchar,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 16)
-                    AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 32)
-                    switch(image_type) {
-                        case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_INT32: AF_CHECK((readImage<int,   AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+        } else {                  // output gray irrespective
+            if (fi_color == 1) {  // 4 channel image
+                if (fi_bpc == 8)
+                    AF_CHECK((readImage<uchar, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                           nSrcPitch, fi_w,
+                                                           fi_h));
+                else if (fi_bpc == 16)
+                    AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                            nSrcPitch, fi_w,
+                                                            fi_h));
+                else if (fi_bpc == 32)
+                    switch (image_type) {
+                        case FIT_UINT32:
+                            AF_CHECK((readImage<uint, AFFI_GRAY>)(&rImage,
+                                                                  pSrcLine,
+                                                                  nSrcPitch,
+                                                                  fi_w, fi_h));
+                            break;
+                        case FIT_INT32:
+                            AF_CHECK((readImage<int, AFFI_GRAY>)(&rImage,
+                                                                 pSrcLine,
+                                                                 nSrcPitch,
+                                                                 fi_w, fi_h));
+                            break;
+                        case FIT_FLOAT:
+                            AF_CHECK((readImage<float, AFFI_GRAY>)(&rImage,
+                                                                   pSrcLine,
+                                                                   nSrcPitch,
+                                                                   fi_w, fi_h));
+                            break;
+                        default:
+                            AF_ERROR("FreeImage Error: Unknown image type",
+                                     AF_ERR_NOT_SUPPORTED);
+                            break;
                     }
             } else if (fi_color == 3 || fi_color == 4) {
-                if(fi_bpc == 8)
-                    AF_CHECK((readImage<uchar,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 16)
-                    AF_CHECK((readImage<ushort, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-                else if(fi_bpc == 32)
-                    switch(image_type) {
-                        case FIT_UINT32: AF_CHECK((readImage<uint,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_INT32: AF_CHECK((readImage<int,   AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        case FIT_FLOAT: AF_CHECK((readImage<float,  AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                        default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+                if (fi_bpc == 8)
+                    AF_CHECK((readImage<uchar, AFFI_RGB>)(&rImage, pSrcLine,
+                                                          nSrcPitch, fi_w,
+                                                          fi_h));
+                else if (fi_bpc == 16)
+                    AF_CHECK((readImage<ushort, AFFI_RGB>)(&rImage, pSrcLine,
+                                                           nSrcPitch, fi_w,
+                                                           fi_h));
+                else if (fi_bpc == 32)
+                    switch (image_type) {
+                        case FIT_UINT32:
+                            AF_CHECK((readImage<uint, AFFI_RGB>)(&rImage,
+                                                                 pSrcLine,
+                                                                 nSrcPitch,
+                                                                 fi_w, fi_h));
+                            break;
+                        case FIT_INT32:
+                            AF_CHECK((readImage<int, AFFI_RGB>)(&rImage,
+                                                                pSrcLine,
+                                                                nSrcPitch, fi_w,
+                                                                fi_h));
+                            break;
+                        case FIT_FLOAT:
+                            AF_CHECK((readImage<float, AFFI_RGB>)(&rImage,
+                                                                  pSrcLine,
+                                                                  nSrcPitch,
+                                                                  fi_w, fi_h));
+                            break;
+                        default:
+                            AF_ERROR("FreeImage Error: Unknown image type",
+                                     AF_ERR_NOT_SUPPORTED);
+                            break;
                     }
             }
         }
 
-        swap(*out,rImage);
-    } CATCHALL;
+        swap(*out, rImage);
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 // Save an image to disk.
-af_err af_save_image(const char* filename, const af_array in_)
-{
+af_err af_save_image(const char* filename, const af_array in_) {
     try {
-
         ARG_ASSERT(0, filename != NULL);
 
         FreeImage_Module& _ = getFreeImagePlugin();
@@ -368,7 +509,7 @@ af_err af_save_image(const char* filename, const af_array in_)
             fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
-        if(fif == FIF_UNKNOWN) {
+        if (fif == FIF_UNKNOWN) {
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
@@ -385,9 +526,11 @@ af_err af_save_image(const char* filename, const af_array in_)
         uint fi_h = info.dims()[0];
 
         // create the result image storage using FreeImage
-        bitmap_ptr pResultBitmap = make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
-        if(pResultBitmap == NULL) {
-            AF_ERROR("FreeImage Error: Error creating image or file", AF_ERR_RUNTIME);
+        bitmap_ptr pResultBitmap =
+            make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
+        if (pResultBitmap == NULL) {
+            AF_ERROR("FreeImage Error: Error creating image or file",
+                     AF_ERR_RUNTIME);
         }
 
         // FI assumes [0-255]
@@ -398,15 +541,17 @@ af_err af_save_image(const char* filename, const af_array in_)
         AF_CHECK(af_max_all(&max_real, &max_imag, in_));
         if (max_real <= 1) {
             af_array c255 = 0;
-            AF_CHECK(af_constant(&c255, 255.0, info.ndims(), info.dims().get(), f32));
+            AF_CHECK(af_constant(&c255, 255.0, info.ndims(), info.dims().get(),
+                                 f32));
             AF_CHECK(af_mul(&in, in_, c255, false));
             AF_CHECK(af_release_array(c255));
             free_in = true;
-        } else if(max_real < 256) {
+        } else if (max_real < 256) {
             in = in_;
         } else if (max_real < 65536) {
             af_array c255 = 0;
-            AF_CHECK(af_constant(&c255, 257.0, info.ndims(), info.dims().get(), f32));
+            AF_CHECK(af_constant(&c255, 257.0, info.ndims(), info.dims().get(),
+                                 f32));
             AF_CHECK(af_div(&in, in_, c255, false));
             AF_CHECK(af_release_array(c255));
             free_in = true;
@@ -416,26 +561,27 @@ af_err af_save_image(const char* filename, const af_array in_)
 
         // FI = row major | AF = column major
         uint nDstPitch = _.FreeImage_GetPitch(pResultBitmap.get());
-        uchar* pDstLine = _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
+        uchar* pDstLine =
+            _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
         af_array rr = 0, gg = 0, bb = 0, aa = 0;
-        AF_CHECK(channel_split(in, info.dims(), &rr, &gg, &bb, &aa)); // convert array to 3 channels if needed
+        AF_CHECK(channel_split(in, info.dims(), &rr, &gg, &bb,
+                               &aa));  // convert array to 3 channels if needed
 
-        uint step = channels; // force 3 channels saving
+        uint step = channels;  // force 3 channels saving
         uint indx = 0;
 
         af_array rrT = 0, ggT = 0, bbT = 0, aaT = 0;
-        if(channels == 4) {
-
+        if (channels == 4) {
             AF_CHECK(af_transpose(&rrT, rr, false));
             AF_CHECK(af_transpose(&ggT, gg, false));
             AF_CHECK(af_transpose(&bbT, bb, false));
             AF_CHECK(af_transpose(&aaT, aa, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc3 = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc3           = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -445,10 +591,14 @@ af_err af_save_image(const char* filename, const af_array in_)
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
-                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
-                    *(pDstLine + x * step + FI_RGBA_ALPHA) = (uchar) pSrc3[indx]; // a
+                    *(pDstLine + x * step + FI_RGBA_RED) =
+                        (uchar)pSrc0[indx];  // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) =
+                        (uchar)pSrc1[indx];  // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE) =
+                        (uchar)pSrc2[indx];  // b
+                    *(pDstLine + x * step + FI_RGBA_ALPHA) =
+                        (uchar)pSrc3[indx];  // a
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -457,15 +607,15 @@ af_err af_save_image(const char* filename, const af_array in_)
             pinnedFree(pSrc1);
             pinnedFree(pSrc2);
             pinnedFree(pSrc3);
-        } else if(channels == 3) {
+        } else if (channels == 3) {
             AF_CHECK(af_transpose(&rrT, rr, false));
             AF_CHECK(af_transpose(&ggT, gg, false));
             AF_CHECK(af_transpose(&bbT, bb, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -474,9 +624,12 @@ af_err af_save_image(const char* filename, const af_array in_)
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
-                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
+                    *(pDstLine + x * step + FI_RGBA_RED) =
+                        (uchar)pSrc0[indx];  // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) =
+                        (uchar)pSrc1[indx];  // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE) =
+                        (uchar)pSrc2[indx];  // b
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -487,12 +640,12 @@ af_err af_save_image(const char* filename, const af_array in_)
         } else {
             AF_CHECK(af_transpose(&rrT, rr, false));
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
 
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step) = (uchar) pSrc0[indx];
+                    *(pDstLine + x * step) = (uchar)pSrc0[indx];
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -501,24 +654,25 @@ af_err af_save_image(const char* filename, const af_array in_)
         }
 
         int flags = 0;
-        if(fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
+        if (fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
 
         // now save the result image
-        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) == TRUE)) {
+        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) ==
+              TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
-        if(free_in) AF_CHECK(af_release_array(in ));
-        if(rr != 0) AF_CHECK(af_release_array(rr ));
-        if(gg != 0) AF_CHECK(af_release_array(gg ));
-        if(bb != 0) AF_CHECK(af_release_array(bb ));
-        if(aa != 0) AF_CHECK(af_release_array(aa ));
-        if(rrT!= 0) AF_CHECK(af_release_array(rrT));
-        if(ggT!= 0) AF_CHECK(af_release_array(ggT));
-        if(bbT!= 0) AF_CHECK(af_release_array(bbT));
-        if(aaT!= 0) AF_CHECK(af_release_array(aaT));
-
-    } CATCHALL
+        if (free_in) AF_CHECK(af_release_array(in));
+        if (rr != 0) AF_CHECK(af_release_array(rr));
+        if (gg != 0) AF_CHECK(af_release_array(gg));
+        if (bb != 0) AF_CHECK(af_release_array(bb));
+        if (aa != 0) AF_CHECK(af_release_array(aa));
+        if (rrT != 0) AF_CHECK(af_release_array(rrT));
+        if (ggT != 0) AF_CHECK(af_release_array(ggT));
+        if (bbT != 0) AF_CHECK(af_release_array(bbT));
+        if (aaT != 0) AF_CHECK(af_release_array(aaT));
+    }
+    CATCHALL
 
     return AF_SUCCESS;
 }
@@ -527,8 +681,7 @@ af_err af_save_image(const char* filename, const af_array in_)
 // Memory IO
 ////////////////////////////////////////////////////////////////////////////////
 /// Load image from memory.
-af_err af_load_image_memory(af_array *out, const void* ptr)
-{
+af_err af_load_image_memory(af_array* out, const void* ptr) {
     try {
         ARG_ASSERT(1, ptr != NULL);
 
@@ -537,21 +690,22 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
         // set your own FreeImage error handler
         _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
-        FIMEMORY *stream = (FIMEMORY*)ptr;
+        FIMEMORY* stream = (FIMEMORY*)ptr;
         _.FreeImage_SeekMemory(stream, 0L, SEEK_SET);
 
         // try to guess the file format from the file extension
         FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileTypeFromMemory(stream, 0);
-        //if (fif == FIF_UNKNOWN) {
+        // if (fif == FIF_UNKNOWN) {
         //    fif = FreeImage_GetFIFFromFilenameFromMemory(filename);
         //}
 
-        if(fif == FIF_UNKNOWN) {
-            AF_ERROR("FreeImage Error: Unknown File or Filetype", AF_ERR_NOT_SUPPORTED);
+        if (fif == FIF_UNKNOWN) {
+            AF_ERROR("FreeImage Error: Unknown File or Filetype",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         int flags = 0;
-        if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+        if (fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
 
         // check that the plugin has reading capabilities ...
         bitmap_ptr pBitmap = make_bitmap_ptr(NULL);
@@ -559,31 +713,38 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
             pBitmap.reset(_.FreeImage_LoadFromMemory(fif, stream, flags));
         }
 
-        if(pBitmap == NULL) {
-            AF_ERROR("FreeImage Error: Error reading image or file does not exist", AF_ERR_RUNTIME);
+        if (pBitmap == NULL) {
+            AF_ERROR(
+                "FreeImage Error: Error reading image or file does not exist",
+                AF_ERR_RUNTIME);
         }
 
         // check image color type
-        uint color_type = _.FreeImage_GetColorType(pBitmap.get());
+        uint color_type   = _.FreeImage_GetColorType(pBitmap.get());
         const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
-        //int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
+        // int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
         int fi_color;
-        switch(color_type) {
-            case 0:                     // FIC_MINISBLACK
-            case 1:                     // FIC_MINISWHITE
-                fi_color = 1; break;
-            case 2:                     // FIC_PALETTE
-            case 3:                     // FIC_RGB
-                fi_color = 3; break;
-            case 4:                     // FIC_RGBALPHA
-            case 5:                     // FIC_CMYK
-                fi_color = 4; break;
-            default:                    // Should not come here
-                fi_color = 3; break;
+        switch (color_type) {
+            case 0:  // FIC_MINISBLACK
+            case 1:  // FIC_MINISWHITE
+                fi_color = 1;
+                break;
+            case 2:  // FIC_PALETTE
+            case 3:  // FIC_RGB
+                fi_color = 3;
+                break;
+            case 4:  // FIC_RGBALPHA
+            case 5:  // FIC_CMYK
+                fi_color = 4;
+                break;
+            default:  // Should not come here
+                fi_color = 3;
+                break;
         }
         const int fi_bpc = fi_bpp / fi_color;
-        if(fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
-            AF_ERROR("FreeImage Error: Bits per channel not supported", AF_ERR_NOT_SUPPORTED);
+        if (fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
+            AF_ERROR("FreeImage Error: Bits per channel not supported",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         // sizes
@@ -592,42 +753,65 @@ af_err af_load_image_memory(af_array *out, const void* ptr)
 
         // FI = row major | AF = column major
         uint nSrcPitch = _.FreeImage_GetPitch(pBitmap.get());
-        const uchar* pSrcLine = _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
+        const uchar* pSrcLine =
+            _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
 
         // result image
         af_array rImage;
-        if(fi_color == 4) {     //4 channel image
-            if(fi_bpc == 8)
-                AF_CHECK((readImage<uchar,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 16)
-                AF_CHECK((readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 32)
-                AF_CHECK((readImage<float,  AFFI_RGBA, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-        } else if (fi_color == 1) { // 1 channel image
-            if(fi_bpc == 8)
-                AF_CHECK((readImage<uchar,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 16)
-                AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 32)
-                AF_CHECK((readImage<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-        } else {             //3 channel image
-            if(fi_bpc == 8)
-                AF_CHECK((readImage<uchar,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 16)
-                AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 32)
-                AF_CHECK((readImage<float,  AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
+        if (fi_color == 4) {  // 4 channel image
+            if (fi_bpc == 8)
+                AF_CHECK((readImage<uchar, AFFI_RGBA, AFFI_RGBA>)(&rImage,
+                                                                  pSrcLine,
+                                                                  nSrcPitch,
+                                                                  fi_w, fi_h));
+            else if (fi_bpc == 16)
+                AF_CHECK((readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage,
+                                                                   pSrcLine,
+                                                                   nSrcPitch,
+                                                                   fi_w, fi_h));
+            else if (fi_bpc == 32)
+                AF_CHECK((readImage<float, AFFI_RGBA, AFFI_RGBA>)(&rImage,
+                                                                  pSrcLine,
+                                                                  nSrcPitch,
+                                                                  fi_w, fi_h));
+        } else if (fi_color == 1) {  // 1 channel image
+            if (fi_bpc == 8)
+                AF_CHECK((readImage<uchar, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                       nSrcPitch, fi_w, fi_h));
+            else if (fi_bpc == 16)
+                AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                        nSrcPitch, fi_w, fi_h));
+            else if (fi_bpc == 32)
+                AF_CHECK((readImage<float, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                       nSrcPitch, fi_w, fi_h));
+        } else {  // 3 channel image
+            if (fi_bpc == 8)
+                AF_CHECK((readImage<uchar, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                pSrcLine,
+                                                                nSrcPitch, fi_w,
+                                                                fi_h));
+            else if (fi_bpc == 16)
+                AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                 pSrcLine,
+                                                                 nSrcPitch,
+                                                                 fi_w, fi_h));
+            else if (fi_bpc == 32)
+                AF_CHECK((readImage<float, AFFI_RGB, AFFI_RGB>)(&rImage,
+                                                                pSrcLine,
+                                                                nSrcPitch, fi_w,
+                                                                fi_h));
         }
 
-        swap(*out,rImage);
-    } CATCHALL;
+        swap(*out, rImage);
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 // Save an image to memory.
-af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_format format)
-{
+af_err af_save_image_memory(void** ptr, const af_array in_,
+                            const af_image_format format) {
     try {
         FreeImage_Module& _ = getFreeImagePlugin();
 
@@ -637,7 +821,8 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
         // try to guess the file format from the file extension
         FREE_IMAGE_FORMAT fif = (FREE_IMAGE_FORMAT)format;
 
-        if(fif == FIF_UNKNOWN || fif > 34) { // FreeImage FREE_IMAGE_FORMAT has upto 34 enums as of 3.17
+        if (fif == FIF_UNKNOWN || fif > 34) {  // FreeImage FREE_IMAGE_FORMAT
+                                               // has upto 34 enums as of 3.17
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
@@ -654,9 +839,11 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
         uint fi_h = info.dims()[0];
 
         // create the result image storage using FreeImage
-        bitmap_ptr pResultBitmap = make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
-        if(pResultBitmap == NULL) {
-            AF_ERROR("FreeImage Error: Error creating image or file", AF_ERR_RUNTIME);
+        bitmap_ptr pResultBitmap =
+            make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
+        if (pResultBitmap == NULL) {
+            AF_ERROR("FreeImage Error: Error creating image or file",
+                     AF_ERR_RUNTIME);
         }
 
         // FI assumes [0-255]
@@ -667,7 +854,8 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
         AF_CHECK(af_max_all(&max_real, &max_imag, in_));
         if (max_real <= 1) {
             af_array c255;
-            AF_CHECK(af_constant(&c255, 255.0, info.ndims(), info.dims().get(), f32));
+            AF_CHECK(af_constant(&c255, 255.0, info.ndims(), info.dims().get(),
+                                 f32));
             AF_CHECK(af_mul(&in, in_, c255, false));
             AF_CHECK(af_release_array(c255));
             free_in = true;
@@ -677,26 +865,27 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
 
         // FI = row major | AF = column major
         uint nDstPitch = _.FreeImage_GetPitch(pResultBitmap.get());
-        uchar* pDstLine = _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
+        uchar* pDstLine =
+            _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
         af_array rr = 0, gg = 0, bb = 0, aa = 0;
-        AF_CHECK(channel_split(in, info.dims(), &rr, &gg, &bb, &aa)); // convert array to 3 channels if needed
+        AF_CHECK(channel_split(in, info.dims(), &rr, &gg, &bb,
+                               &aa));  // convert array to 3 channels if needed
 
-        uint step = channels; // force 3 channels saving
+        uint step = channels;  // force 3 channels saving
         uint indx = 0;
 
         af_array rrT = 0, ggT = 0, bbT = 0, aaT = 0;
-        if(channels == 4) {
-
+        if (channels == 4) {
             AF_CHECK(af_transpose(&rrT, rr, false));
             AF_CHECK(af_transpose(&ggT, gg, false));
             AF_CHECK(af_transpose(&bbT, bb, false));
             AF_CHECK(af_transpose(&aaT, aa, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc3 = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc3           = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -706,10 +895,14 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
-                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
-                    *(pDstLine + x * step + FI_RGBA_ALPHA) = (uchar) pSrc3[indx]; // a
+                    *(pDstLine + x * step + FI_RGBA_RED) =
+                        (uchar)pSrc0[indx];  // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) =
+                        (uchar)pSrc1[indx];  // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE) =
+                        (uchar)pSrc2[indx];  // b
+                    *(pDstLine + x * step + FI_RGBA_ALPHA) =
+                        (uchar)pSrc3[indx];  // a
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -718,15 +911,15 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             pinnedFree(pSrc1);
             pinnedFree(pSrc2);
             pinnedFree(pSrc3);
-        } else if(channels == 3) {
+        } else if (channels == 3) {
             AF_CHECK(af_transpose(&rrT, rr, false));
             AF_CHECK(af_transpose(&ggT, gg, false));
             AF_CHECK(af_transpose(&bbT, bb, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1 = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2 = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -735,9 +928,12 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             // Copy the array into FreeImage buffer
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step + FI_RGBA_RED  ) = (uchar) pSrc0[indx]; // r
-                    *(pDstLine + x * step + FI_RGBA_GREEN) = (uchar) pSrc1[indx]; // g
-                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (uchar) pSrc2[indx]; // b
+                    *(pDstLine + x * step + FI_RGBA_RED) =
+                        (uchar)pSrc0[indx];  // r
+                    *(pDstLine + x * step + FI_RGBA_GREEN) =
+                        (uchar)pSrc1[indx];  // g
+                    *(pDstLine + x * step + FI_RGBA_BLUE) =
+                        (uchar)pSrc2[indx];  // b
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -748,12 +944,12 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
         } else {
             AF_CHECK(af_transpose(&rrT, rr, false));
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0 = pinnedAlloc<float>(cinfo.elements());
+            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
 
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step) = (uchar) pSrc0[indx];
+                    *(pDstLine + x * step) = (uchar)pSrc0[indx];
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -761,39 +957,38 @@ af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_forma
             pinnedFree(pSrc0);
         }
 
-        uint8_t* data = nullptr;
+        uint8_t* data          = nullptr;
         uint32_t size_in_bytes = 0;
-        FIMEMORY *stream = _.FreeImage_OpenMemory(data, size_in_bytes);
+        FIMEMORY* stream       = _.FreeImage_OpenMemory(data, size_in_bytes);
 
         int flags = 0;
-        if(fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
+        if (fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
 
         // now save the result image
-        if (!(_.FreeImage_SaveToMemory(fif, pResultBitmap.get(), stream, flags) == TRUE)) {
+        if (!(_.FreeImage_SaveToMemory(fif, pResultBitmap.get(), stream,
+                                       flags) == TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
         *ptr = stream;
 
-        if(free_in) AF_CHECK(af_release_array(in ));
-        if(rr != 0) AF_CHECK(af_release_array(rr ));
-        if(gg != 0) AF_CHECK(af_release_array(gg ));
-        if(bb != 0) AF_CHECK(af_release_array(bb ));
-        if(aa != 0) AF_CHECK(af_release_array(aa ));
-        if(rrT!= 0) AF_CHECK(af_release_array(rrT));
-        if(ggT!= 0) AF_CHECK(af_release_array(ggT));
-        if(bbT!= 0) AF_CHECK(af_release_array(bbT));
-        if(aaT!= 0) AF_CHECK(af_release_array(aaT));
-
-    } CATCHALL
+        if (free_in) AF_CHECK(af_release_array(in));
+        if (rr != 0) AF_CHECK(af_release_array(rr));
+        if (gg != 0) AF_CHECK(af_release_array(gg));
+        if (bb != 0) AF_CHECK(af_release_array(bb));
+        if (aa != 0) AF_CHECK(af_release_array(aa));
+        if (rrT != 0) AF_CHECK(af_release_array(rrT));
+        if (ggT != 0) AF_CHECK(af_release_array(ggT));
+        if (bbT != 0) AF_CHECK(af_release_array(bbT));
+        if (aaT != 0) AF_CHECK(af_release_array(aaT));
+    }
+    CATCHALL
 
     return AF_SUCCESS;
 }
 
-af_err af_delete_image_memory(void *ptr)
-{
+af_err af_delete_image_memory(void* ptr) {
     try {
-
         ARG_ASSERT(0, ptr != NULL);
 
         FreeImage_Module& _ = getFreeImagePlugin();
@@ -801,48 +996,50 @@ af_err af_delete_image_memory(void *ptr)
         // set your own FreeImage error handler
         _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
-        FIMEMORY *stream = (FIMEMORY*)ptr;
+        FIMEMORY* stream = (FIMEMORY*)ptr;
         _.FreeImage_SeekMemory(stream, 0L, SEEK_SET);
 
         // Ensure data is freeimage compatible
-        FREE_IMAGE_FORMAT fif = _.FreeImage_GetFileTypeFromMemory((FIMEMORY*)ptr, 0);
-        if(fif == FIF_UNKNOWN) {
+        FREE_IMAGE_FORMAT fif =
+            _.FreeImage_GetFileTypeFromMemory((FIMEMORY*)ptr, 0);
+        if (fif == FIF_UNKNOWN) {
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
-        _.FreeImage_CloseMemory((FIMEMORY *)ptr);
-
-    } CATCHALL
+        _.FreeImage_CloseMemory((FIMEMORY*)ptr);
+    }
+    CATCHALL
 
     return AF_SUCCESS;
 }
 
-#else   // WITH_FREEIMAGE
-#include <af/image.h>
-#include <stdio.h>
+#else  // WITH_FREEIMAGE
 #include <common/err_common.hpp>
-af_err af_load_image(af_array *out, const char* filename, const bool isColor)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+#include <stdio.h>
+#include <af/image.h>
+af_err af_load_image(af_array *out, const char *filename, const bool isColor) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 
-af_err af_save_image(const char* filename, const af_array in_)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+af_err af_save_image(const char *filename, const af_array in_) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 
-af_err af_load_image_memory(af_array *out, const void* ptr)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+af_err af_load_image_memory(af_array *out, const void *ptr) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 
-af_err af_save_image_memory(void **ptr, const af_array in_, const af_image_format format)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+af_err af_save_image_memory(void **ptr, const af_array in_,
+                            const af_image_format format) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 
-af_err af_delete_image_memory(void *ptr)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+af_err af_delete_image_memory(void *ptr) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 #endif  // WITH_FREEIMAGE
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 3ed61d8ace..13b7d0a3b7 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -11,36 +11,36 @@
 
 #include "imageio_helper.h"
 
-#include <af/array.h>
-#include <af/index.h>
-#include <af/dim4.hpp>
-#include <af/arith.h>
-#include <af/algorithm.h>
-#include <af/blas.h>
-#include <af/data.h>
-#include <af/image.h>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <traits.hpp>
-#include <memory.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
+#include <memory.hpp>
+#include <traits.hpp>
+#include <af/algorithm.h>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/blas.h>
+#include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
+#include <af/index.h>
 
-#include <string>
-#include <cstring>
 #include <cstdio>
 #include <cstdlib>
+#include <cstring>
+#include <string>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, FI_CHANNELS fi_color>
-static af_err readImage_t(af_array *rImage, const uchar* pSrcLine, const int nSrcPitch,
-                            const uint fi_w, const uint fi_h)
-{
+static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
+                          const int nSrcPitch, const uint fi_w,
+                          const uint fi_h) {
     // create an array to receive the loaded image data.
     AF_CHECK(af_init());
-    T *pDst = pinnedAlloc<T>(fi_w * fi_h * 4); // 4 channels is max
+    T* pDst  = pinnedAlloc<T>(fi_w * fi_h * 4);  // 4 channels is max
     T* pDst0 = pDst;
     T* pDst1 = pDst + (fi_w * fi_h * 1);
     T* pDst2 = pDst + (fi_w * fi_h * 2);
@@ -51,22 +51,24 @@ static af_err readImage_t(af_array *rImage, const uchar* pSrcLine, const int nSr
 
     for (uint x = 0; x < fi_w; ++x) {
         for (uint y = 0; y < fi_h; ++y) {
-            const T *src = (T*)((uchar*)pSrcLine - y * nSrcPitch);
-            if(fi_color == 1) {
-                pDst0[indx] = (T) *(src + (x * step));
-            } else if(fi_color >= 3) {
-                if((af_dtype) af::dtype_traits<T>::af_type == u8) {
-                    pDst0[indx] = (T) *(src + (x * step + FI_RGBA_RED));
-                    pDst1[indx] = (T) *(src + (x * step + FI_RGBA_GREEN));
-                    pDst2[indx] = (T) *(src + (x * step + FI_RGBA_BLUE));
-                    if (fi_color == 4) pDst3[indx] = (T) *(src + (x * step + FI_RGBA_ALPHA));
+            const T* src = (T*)((uchar*)pSrcLine - y * nSrcPitch);
+            if (fi_color == 1) {
+                pDst0[indx] = (T) * (src + (x * step));
+            } else if (fi_color >= 3) {
+                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
+                    pDst0[indx] = (T) * (src + (x * step + FI_RGBA_RED));
+                    pDst1[indx] = (T) * (src + (x * step + FI_RGBA_GREEN));
+                    pDst2[indx] = (T) * (src + (x * step + FI_RGBA_BLUE));
+                    if (fi_color == 4)
+                        pDst3[indx] = (T) * (src + (x * step + FI_RGBA_ALPHA));
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (T) *(src + (x * step + 0));
-                    pDst1[indx] = (T) *(src + (x * step + 1));
-                    pDst2[indx] = (T) *(src + (x * step + 2));
-                    if (fi_color == 4) pDst3[indx] = (T) *(src + (x * step + 3));
+                    pDst0[indx] = (T) * (src + (x * step + 0));
+                    pDst1[indx] = (T) * (src + (x * step + 1));
+                    pDst2[indx] = (T) * (src + (x * step + 2));
+                    if (fi_color == 4)
+                        pDst3[indx] = (T) * (src + (x * step + 3));
                 }
             }
             indx++;
@@ -76,25 +78,33 @@ static af_err readImage_t(af_array *rImage, const uchar* pSrcLine, const int nSr
     // TODO
     af::dim4 dims(fi_h, fi_w, fi_color, 1);
     af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(),
-                                 (af_dtype) af::dtype_traits<T>::af_type);
+                                 (af_dtype)af::dtype_traits<T>::af_type);
     pinnedFree(pDst);
     return err;
 }
 
-FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type)
-{
-    if(channels == AFFI_GRAY) {
-             if(type == u8 ) return FIT_BITMAP;
-        else if(type == u16) return FIT_UINT16;
-        else if(type == f32) return FIT_FLOAT;
-    } else if(channels == AFFI_RGB) {
-             if(type == u8 ) return FIT_BITMAP;
-        else if(type == u16) return FIT_RGB16;
-        else if(type == f32) return FIT_RGBF;
-    } else if(channels == AFFI_RGBA) {
-             if(type == u8 ) return FIT_BITMAP;
-        else if(type == u16) return FIT_RGBA16;
-        else if(type == f32) return FIT_RGBAF;
+FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type) {
+    if (channels == AFFI_GRAY) {
+        if (type == u8)
+            return FIT_BITMAP;
+        else if (type == u16)
+            return FIT_UINT16;
+        else if (type == f32)
+            return FIT_FLOAT;
+    } else if (channels == AFFI_RGB) {
+        if (type == u8)
+            return FIT_BITMAP;
+        else if (type == u16)
+            return FIT_RGB16;
+        else if (type == f32)
+            return FIT_RGBF;
+    } else if (channels == AFFI_RGBA) {
+        if (type == u8)
+            return FIT_BITMAP;
+        else if (type == u16)
+            return FIT_RGBA16;
+        else if (type == f32)
+            return FIT_RGBAF;
     }
     return FIT_BITMAP;
 }
@@ -103,8 +113,7 @@ FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type)
 // File IO
 ////////////////////////////////////////////////////////////////////////////////
 // Load image from disk.
-af_err af_load_image_native(af_array *out, const char* filename)
-{
+af_err af_load_image_native(af_array* out, const char* filename) {
     try {
         ARG_ASSERT(1, filename != NULL);
 
@@ -119,12 +128,13 @@ af_err af_load_image_native(af_array *out, const char* filename)
             fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
-        if(fif == FIF_UNKNOWN) {
-            AF_ERROR("FreeImage Error: Unknown File or Filetype", AF_ERR_NOT_SUPPORTED);
+        if (fif == FIF_UNKNOWN) {
+            AF_ERROR("FreeImage Error: Unknown File or Filetype",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         int flags = 0;
-        if(fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+        if (fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
 
         // check that the plugin has reading capabilities ...
         bitmap_ptr pBitmap = make_bitmap_ptr(nullptr);
@@ -132,32 +142,39 @@ af_err af_load_image_native(af_array *out, const char* filename)
             pBitmap.reset(_.FreeImage_Load(fif, filename, flags));
         }
 
-        if(pBitmap == NULL) {
-            AF_ERROR("FreeImage Error: Error reading image or file does not exist", AF_ERR_RUNTIME);
+        if (pBitmap == NULL) {
+            AF_ERROR(
+                "FreeImage Error: Error reading image or file does not exist",
+                AF_ERR_RUNTIME);
         }
 
         // check image color type
-        uint color_type = _.FreeImage_GetColorType(pBitmap.get());
+        uint color_type   = _.FreeImage_GetColorType(pBitmap.get());
         const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
-        //int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
+        // int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
         int fi_color;
-        switch(color_type) {
-            case 0:                     // FIC_MINISBLACK
-            case 1:                     // FIC_MINISWHITE
-                fi_color = 1; break;
-            case 2:                     // FIC_PALETTE
-            case 3:                     // FIC_RGB
-                fi_color = 3; break;
-            case 4:                     // FIC_RGBALPHA
-            case 5:                     // FIC_CMYK
-                fi_color = 4; break;
-            default:                    // Should not come here
-                fi_color = 3; break;
+        switch (color_type) {
+            case 0:  // FIC_MINISBLACK
+            case 1:  // FIC_MINISWHITE
+                fi_color = 1;
+                break;
+            case 2:  // FIC_PALETTE
+            case 3:  // FIC_RGB
+                fi_color = 3;
+                break;
+            case 4:  // FIC_RGBALPHA
+            case 5:  // FIC_CMYK
+                fi_color = 4;
+                break;
+            default:  // Should not come here
+                fi_color = 3;
+                break;
         }
 
         const int fi_bpc = fi_bpp / fi_color;
-        if(fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
-            AF_ERROR("FreeImage Error: Bits per channel not supported", AF_ERR_NOT_SUPPORTED);
+        if (fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
+            AF_ERROR("FreeImage Error: Bits per channel not supported",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         // data type
@@ -169,81 +186,148 @@ af_err af_load_image_native(af_array *out, const char* filename)
 
         // FI = row major | AF = column major
         uint nSrcPitch = _.FreeImage_GetPitch(pBitmap.get());
-        const uchar* pSrcLine = _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
+        const uchar* pSrcLine =
+            _.FreeImage_GetBits(pBitmap.get()) + nSrcPitch * (fi_h - 1);
 
         // result image
         af_array rImage;
-        if(fi_color == 4) {     //4 channel image
-            if(fi_bpc == 8)
-                AF_CHECK((readImage_t<uchar,  AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 16)
-                AF_CHECK((readImage_t<ushort, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 32)
-                switch(image_type) {
-                    case FIT_UINT32: AF_CHECK((readImage_t<uint, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    case FIT_INT32: AF_CHECK((readImage_t<int, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    case FIT_FLOAT: AF_CHECK((readImage_t<float, AFFI_RGBA>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+        if (fi_color == 4) {  // 4 channel image
+            if (fi_bpc == 8)
+                AF_CHECK((readImage_t<uchar, AFFI_RGBA>)(&rImage, pSrcLine,
+                                                         nSrcPitch, fi_w,
+                                                         fi_h));
+            else if (fi_bpc == 16)
+                AF_CHECK((readImage_t<ushort, AFFI_RGBA>)(&rImage, pSrcLine,
+                                                          nSrcPitch, fi_w,
+                                                          fi_h));
+            else if (fi_bpc == 32)
+                switch (image_type) {
+                    case FIT_UINT32:
+                        AF_CHECK((readImage_t<uint, AFFI_RGBA>)(&rImage,
+                                                                pSrcLine,
+                                                                nSrcPitch, fi_w,
+                                                                fi_h));
+                        break;
+                    case FIT_INT32:
+                        AF_CHECK((readImage_t<int, AFFI_RGBA>)(&rImage,
+                                                               pSrcLine,
+                                                               nSrcPitch, fi_w,
+                                                               fi_h));
+                        break;
+                    case FIT_FLOAT:
+                        AF_CHECK((readImage_t<float, AFFI_RGBA>)(&rImage,
+                                                                 pSrcLine,
+                                                                 nSrcPitch,
+                                                                 fi_w, fi_h));
+                        break;
+                    default:
+                        AF_ERROR("FreeImage Error: Unknown image type",
+                                 AF_ERR_NOT_SUPPORTED);
+                        break;
                 }
         } else if (fi_color == 1) {
-            if(fi_bpc == 8)
-                AF_CHECK((readImage_t<uchar,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 16)
-                AF_CHECK((readImage_t<ushort, AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 32)
-                switch(image_type) {
-                    case FIT_UINT32: AF_CHECK((readImage_t<uint,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    case FIT_INT32: AF_CHECK((readImage_t<int,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    case FIT_FLOAT: AF_CHECK((readImage_t<float,  AFFI_GRAY>)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+            if (fi_bpc == 8)
+                AF_CHECK((readImage_t<uchar, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                         nSrcPitch, fi_w,
+                                                         fi_h));
+            else if (fi_bpc == 16)
+                AF_CHECK((readImage_t<ushort, AFFI_GRAY>)(&rImage, pSrcLine,
+                                                          nSrcPitch, fi_w,
+                                                          fi_h));
+            else if (fi_bpc == 32)
+                switch (image_type) {
+                    case FIT_UINT32:
+                        AF_CHECK((readImage_t<uint, AFFI_GRAY>)(&rImage,
+                                                                pSrcLine,
+                                                                nSrcPitch, fi_w,
+                                                                fi_h));
+                        break;
+                    case FIT_INT32:
+                        AF_CHECK((readImage_t<int, AFFI_GRAY>)(&rImage,
+                                                               pSrcLine,
+                                                               nSrcPitch, fi_w,
+                                                               fi_h));
+                        break;
+                    case FIT_FLOAT:
+                        AF_CHECK((readImage_t<float, AFFI_GRAY>)(&rImage,
+                                                                 pSrcLine,
+                                                                 nSrcPitch,
+                                                                 fi_w, fi_h));
+                        break;
+                    default:
+                        AF_ERROR("FreeImage Error: Unknown image type",
+                                 AF_ERR_NOT_SUPPORTED);
+                        break;
                 }
-        } else {             //3 channel imag
-            if(fi_bpc == 8)
-                AF_CHECK((readImage_t<uchar,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 16)
-                AF_CHECK((readImage_t<ushort, AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h));
-            else if(fi_bpc == 32)
-                switch(image_type) {
-                    case FIT_UINT32: AF_CHECK((readImage_t<uint,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    case FIT_INT32: AF_CHECK((readImage_t<int,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    case FIT_FLOAT: AF_CHECK((readImage_t<float,  AFFI_RGB >)(&rImage, pSrcLine, nSrcPitch, fi_w, fi_h)); break;
-                    default: AF_ERROR("FreeImage Error: Unknown image type", AF_ERR_NOT_SUPPORTED); break;
+        } else {  // 3 channel imag
+            if (fi_bpc == 8)
+                AF_CHECK((readImage_t<uchar, AFFI_RGB>)(&rImage, pSrcLine,
+                                                        nSrcPitch, fi_w, fi_h));
+            else if (fi_bpc == 16)
+                AF_CHECK((readImage_t<ushort, AFFI_RGB>)(&rImage, pSrcLine,
+                                                         nSrcPitch, fi_w,
+                                                         fi_h));
+            else if (fi_bpc == 32)
+                switch (image_type) {
+                    case FIT_UINT32:
+                        AF_CHECK((readImage_t<uint, AFFI_RGB>)(&rImage,
+                                                               pSrcLine,
+                                                               nSrcPitch, fi_w,
+                                                               fi_h));
+                        break;
+                    case FIT_INT32:
+                        AF_CHECK((readImage_t<int, AFFI_RGB>)(&rImage, pSrcLine,
+                                                              nSrcPitch, fi_w,
+                                                              fi_h));
+                        break;
+                    case FIT_FLOAT:
+                        AF_CHECK((readImage_t<float, AFFI_RGB>)(&rImage,
+                                                                pSrcLine,
+                                                                nSrcPitch, fi_w,
+                                                                fi_h));
+                        break;
+                    default:
+                        AF_ERROR("FreeImage Error: Unknown image type",
+                                 AF_ERR_NOT_SUPPORTED);
+                        break;
                 }
         }
 
-        std::swap(*out,rImage);
-    } CATCHALL;
+        std::swap(*out, rImage);
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T, FI_CHANNELS channels>
-static void save_t(T* pDstLine, const af_array in, const dim4 dims, uint nDstPitch)
-{
+static void save_t(T* pDstLine, const af_array in, const dim4 dims,
+                   uint nDstPitch) {
     af_array rr = 0, gg = 0, bb = 0, aa = 0;
-    AF_CHECK(channel_split(in, dims, &rr, &gg, &bb, &aa)); // convert array to 3 channels if needed
+    AF_CHECK(channel_split(in, dims, &rr, &gg, &bb,
+                           &aa));  // convert array to 3 channels if needed
 
     af_array rrT = 0, ggT = 0, bbT = 0, aaT = 0;
     T *pSrc0 = 0, *pSrc1 = 0, *pSrc2 = 0, *pSrc3 = 0;
 
-    uint step = channels; // force 3 channels saving
+    uint step = channels;  // force 3 channels saving
     uint indx = 0;
 
-                      AF_CHECK(af_transpose(&rrT, rr, false));
-    if(channels >= 3) AF_CHECK(af_transpose(&ggT, gg, false));
-    if(channels >= 3) AF_CHECK(af_transpose(&bbT, bb, false));
-    if(channels >= 4) AF_CHECK(af_transpose(&aaT, aa, false));
+    AF_CHECK(af_transpose(&rrT, rr, false));
+    if (channels >= 3) AF_CHECK(af_transpose(&ggT, gg, false));
+    if (channels >= 3) AF_CHECK(af_transpose(&bbT, bb, false));
+    if (channels >= 4) AF_CHECK(af_transpose(&aaT, aa, false));
 
     const ArrayInfo& cinfo = getInfo(rrT);
-                      pSrc0 = pinnedAlloc<T>(cinfo.elements());
-    if(channels >= 3) pSrc1 = pinnedAlloc<T>(cinfo.elements());
-    if(channels >= 3) pSrc2 = pinnedAlloc<T>(cinfo.elements());
-    if(channels >= 4) pSrc3 = pinnedAlloc<T>(cinfo.elements());
+    pSrc0                  = pinnedAlloc<T>(cinfo.elements());
+    if (channels >= 3) pSrc1 = pinnedAlloc<T>(cinfo.elements());
+    if (channels >= 3) pSrc2 = pinnedAlloc<T>(cinfo.elements());
+    if (channels >= 4) pSrc3 = pinnedAlloc<T>(cinfo.elements());
 
-                      AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
-    if(channels >= 3) AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
-    if(channels >= 3) AF_CHECK(af_get_data_ptr((void*)pSrc2, bbT));
-    if(channels >= 4) AF_CHECK(af_get_data_ptr((void*)pSrc3, aaT));
+    AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
+    if (channels >= 3) AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
+    if (channels >= 3) AF_CHECK(af_get_data_ptr((void*)pSrc2, bbT));
+    if (channels >= 4) AF_CHECK(af_get_data_ptr((void*)pSrc3, aaT));
 
     const uint fi_w = dims[1];
     const uint fi_h = dims[0];
@@ -251,47 +335,51 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims, uint nDstPit
     // Copy the array into FreeImage buffer
     for (uint y = 0; y < fi_h; ++y) {
         for (uint x = 0; x < fi_w; ++x) {
-            if(channels == 1) {
-                *(pDstLine + x * step) = (T) pSrc0[indx]; // r -> 0
-            } else if(channels >=3) {
-                if((af_dtype) af::dtype_traits<T>::af_type == u8) {
-                    *(pDstLine + x * step + FI_RGBA_RED  ) = (T) pSrc0[indx]; // r -> 0
-                    *(pDstLine + x * step + FI_RGBA_GREEN) = (T) pSrc1[indx]; // g -> 1
-                    *(pDstLine + x * step + FI_RGBA_BLUE ) = (T) pSrc2[indx]; // b -> 2
-                    if(channels >= 4) *(pDstLine + x * step + FI_RGBA_ALPHA) = (T) pSrc3[indx]; // a
+            if (channels == 1) {
+                *(pDstLine + x * step) = (T)pSrc0[indx];  // r -> 0
+            } else if (channels >= 3) {
+                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
+                    *(pDstLine + x * step + FI_RGBA_RED) =
+                        (T)pSrc0[indx];  // r -> 0
+                    *(pDstLine + x * step + FI_RGBA_GREEN) =
+                        (T)pSrc1[indx];  // g -> 1
+                    *(pDstLine + x * step + FI_RGBA_BLUE) =
+                        (T)pSrc2[indx];  // b -> 2
+                    if (channels >= 4)
+                        *(pDstLine + x * step + FI_RGBA_ALPHA) =
+                            (T)pSrc3[indx];  // a
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    *(pDstLine + x * step + 0) = (T) pSrc0[indx]; // r -> 0
-                    *(pDstLine + x * step + 1) = (T) pSrc1[indx]; // g -> 1
-                    *(pDstLine + x * step + 2) = (T) pSrc2[indx]; // b -> 2
-                    if(channels >= 4) *(pDstLine + x * step + 3) = (T) pSrc3[indx]; // a
+                    *(pDstLine + x * step + 0) = (T)pSrc0[indx];  // r -> 0
+                    *(pDstLine + x * step + 1) = (T)pSrc1[indx];  // g -> 1
+                    *(pDstLine + x * step + 2) = (T)pSrc2[indx];  // b -> 2
+                    if (channels >= 4)
+                        *(pDstLine + x * step + 3) = (T)pSrc3[indx];  // a
                 }
             }
             ++indx;
         }
         pDstLine = (T*)(((uchar*)pDstLine) - nDstPitch);
     }
-                      pinnedFree(pSrc0);
-    if(channels >= 3) pinnedFree(pSrc1);
-    if(channels >= 3) pinnedFree(pSrc2);
-    if(channels >= 4) pinnedFree(pSrc3);
-
-    if(rr != 0) AF_CHECK(af_release_array(rr ));
-    if(gg != 0) AF_CHECK(af_release_array(gg ));
-    if(bb != 0) AF_CHECK(af_release_array(bb ));
-    if(aa != 0) AF_CHECK(af_release_array(aa ));
-    if(rrT!= 0) AF_CHECK(af_release_array(rrT));
-    if(ggT!= 0) AF_CHECK(af_release_array(ggT));
-    if(bbT!= 0) AF_CHECK(af_release_array(bbT));
-    if(aaT!= 0) AF_CHECK(af_release_array(aaT));
+    pinnedFree(pSrc0);
+    if (channels >= 3) pinnedFree(pSrc1);
+    if (channels >= 3) pinnedFree(pSrc2);
+    if (channels >= 4) pinnedFree(pSrc3);
+
+    if (rr != 0) AF_CHECK(af_release_array(rr));
+    if (gg != 0) AF_CHECK(af_release_array(gg));
+    if (bb != 0) AF_CHECK(af_release_array(bb));
+    if (aa != 0) AF_CHECK(af_release_array(aa));
+    if (rrT != 0) AF_CHECK(af_release_array(rrT));
+    if (ggT != 0) AF_CHECK(af_release_array(ggT));
+    if (bbT != 0) AF_CHECK(af_release_array(bbT));
+    if (aaT != 0) AF_CHECK(af_release_array(aaT));
 }
 
 // Save an image to disk.
-af_err af_save_image_native(const char* filename, const af_array in)
-{
+af_err af_save_image_native(const char* filename, const af_array in) {
     try {
-
         ARG_ASSERT(0, filename != NULL);
 
         FreeImage_Module& _ = getFreeImagePlugin();
@@ -305,7 +393,7 @@ af_err af_save_image_native(const char* filename, const af_array in)
             fif = _.FreeImage_GetFIFFromFilename(filename);
         }
 
-        if(fif == FIF_UNKNOWN) {
+        if (fif == FIF_UNKNOWN) {
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
@@ -325,8 +413,8 @@ af_err af_save_image_native(const char* filename, const af_array in)
         // FI assumes [0-65k] for u16
         // FI assumes [0-1]   for f32
         int fi_bpp = 0;
-        switch(type) {
-            case u8:  fi_bpp = channels * 8; break;
+        switch (type) {
+            case u8: fi_bpp = channels * 8; break;
             case u16: fi_bpp = channels * 16; break;
             case f32: fi_bpp = channels * 32; break;
             default: TYPE_ERROR(1, type);
@@ -336,79 +424,116 @@ af_err af_save_image_native(const char* filename, const af_array in)
 
         // create the result image storage using FreeImage
         bitmap_ptr pResultBitmap = make_bitmap_ptr(nullptr);
-        switch(type) {
-            case u8:  pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp, 0, 0, 0)); break;
-            case u16: pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp, 0, 0, 0)); break;
-            case f32: pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h, fi_bpp, 0, 0, 0)); break;
+        switch (type) {
+            case u8:
+                pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h,
+                                                          fi_bpp, 0, 0, 0));
+                break;
+            case u16:
+                pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h,
+                                                          fi_bpp, 0, 0, 0));
+                break;
+            case f32:
+                pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h,
+                                                          fi_bpp, 0, 0, 0));
+                break;
             default: TYPE_ERROR(1, type);
         }
 
-        if(pResultBitmap == NULL) {
-            AF_ERROR("FreeImage Error: Error creating image or file", AF_ERR_RUNTIME);
+        if (pResultBitmap == NULL) {
+            AF_ERROR("FreeImage Error: Error creating image or file",
+                     AF_ERR_RUNTIME);
         }
 
         // FI = row major | AF = column major
         uint nDstPitch = _.FreeImage_GetPitch(pResultBitmap.get());
-        void* pDstLine = _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
-
-        if(channels == AFFI_GRAY) {
-            switch(type) {
-                case u8:  save_t<uchar , AFFI_GRAY>((uchar *)pDstLine, in, info.dims(), nDstPitch); break;
-                case u16: save_t<ushort, AFFI_GRAY>((ushort*)pDstLine, in, info.dims(), nDstPitch); break;
-                case f32: save_t<float , AFFI_GRAY>((float *)pDstLine, in, info.dims(), nDstPitch); break;
+        void* pDstLine =
+            _.FreeImage_GetBits(pResultBitmap.get()) + nDstPitch * (fi_h - 1);
+
+        if (channels == AFFI_GRAY) {
+            switch (type) {
+                case u8:
+                    save_t<uchar, AFFI_GRAY>((uchar*)pDstLine, in, info.dims(),
+                                             nDstPitch);
+                    break;
+                case u16:
+                    save_t<ushort, AFFI_GRAY>((ushort*)pDstLine, in,
+                                              info.dims(), nDstPitch);
+                    break;
+                case f32:
+                    save_t<float, AFFI_GRAY>((float*)pDstLine, in, info.dims(),
+                                             nDstPitch);
+                    break;
                 default: TYPE_ERROR(1, type);
             }
-        } else if(channels == AFFI_RGB) {
-            switch(type) {
-                case u8:  save_t<uchar , AFFI_RGB >((uchar *)pDstLine, in, info.dims(), nDstPitch); break;
-                case u16: save_t<ushort, AFFI_RGB >((ushort*)pDstLine, in, info.dims(), nDstPitch); break;
-                case f32: save_t<float , AFFI_RGB >((float *)pDstLine, in, info.dims(), nDstPitch); break;
+        } else if (channels == AFFI_RGB) {
+            switch (type) {
+                case u8:
+                    save_t<uchar, AFFI_RGB>((uchar*)pDstLine, in, info.dims(),
+                                            nDstPitch);
+                    break;
+                case u16:
+                    save_t<ushort, AFFI_RGB>((ushort*)pDstLine, in, info.dims(),
+                                             nDstPitch);
+                    break;
+                case f32:
+                    save_t<float, AFFI_RGB>((float*)pDstLine, in, info.dims(),
+                                            nDstPitch);
+                    break;
                 default: TYPE_ERROR(1, type);
             }
         } else {
-            switch(type) {
-                case u8:  save_t<uchar , AFFI_RGBA>((uchar *)pDstLine, in, info.dims(), nDstPitch); break;
-                case u16: save_t<ushort, AFFI_RGBA>((ushort*)pDstLine, in, info.dims(), nDstPitch); break;
-                case f32: save_t<float , AFFI_RGBA>((float *)pDstLine, in, info.dims(), nDstPitch); break;
+            switch (type) {
+                case u8:
+                    save_t<uchar, AFFI_RGBA>((uchar*)pDstLine, in, info.dims(),
+                                             nDstPitch);
+                    break;
+                case u16:
+                    save_t<ushort, AFFI_RGBA>((ushort*)pDstLine, in,
+                                              info.dims(), nDstPitch);
+                    break;
+                case f32:
+                    save_t<float, AFFI_RGBA>((float*)pDstLine, in, info.dims(),
+                                             nDstPitch);
+                    break;
                 default: TYPE_ERROR(1, type);
             }
         }
 
         int flags = 0;
-        if(fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
+        if (fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
 
         // now save the result image
-        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) == TRUE)) {
+        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) ==
+              TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
-
-    } CATCHALL
+    }
+    CATCHALL
 
     return AF_SUCCESS;
 }
 
-af_err af_is_image_io_available(bool *out)
-{
+af_err af_is_image_io_available(bool* out) {
     *out = true;
     return AF_SUCCESS;
 }
 
-#else   // WITH_FREEIMAGE
-#include <af/image.h>
-#include <stdio.h>
+#else  // WITH_FREEIMAGE
 #include <common/err_common.hpp>
-af_err af_load_image_native(af_array *out, const char* filename)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+#include <stdio.h>
+#include <af/image.h>
+af_err af_load_image_native(af_array* out, const char* filename) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 
-af_err af_save_image_native(const char* filename, const af_array in)
-{
-    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support", AF_ERR_NOT_CONFIGURED);
+af_err af_save_image_native(const char* filename, const af_array in) {
+    AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
+                    AF_ERR_NOT_CONFIGURED);
 }
 
-af_err af_is_image_io_available(bool *out)
-{
+af_err af_is_image_io_available(bool* out) {
     *out = false;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/imageio_helper.h b/src/api/c/imageio_helper.h
index a073cd0150..787a391e59 100644
--- a/src/api/c/imageio_helper.h
+++ b/src/api/c/imageio_helper.h
@@ -11,20 +11,20 @@
 #define IMAGEIO_HELPER_H
 
 #include <common/DependencyModule.hpp>
+#include <common/err_common.hpp>
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/index.h>
-#include <common/err_common.hpp>
 
 #include <FreeImage.h>
 
-#include <memory>
 #include <functional>
+#include <memory>
 
 class FreeImage_Module {
     common::DependencyModule module;
 
-public:
+   public:
     MODULE_MEMBER(FreeImage_Allocate);
     MODULE_MEMBER(FreeImage_AllocateT);
     MODULE_MEMBER(FreeImage_CloseMemory);
@@ -54,21 +54,21 @@ class FreeImage_Module {
     ~FreeImage_Module();
 };
 
-FreeImage_Module& getFreeImagePlugin();
+FreeImage_Module &getFreeImagePlugin();
 
-using bitmap_ptr = std::unique_ptr<FIBITMAP, std::function<void(FIBITMAP*)>>;
-bitmap_ptr make_bitmap_ptr(FIBITMAP*);
+using bitmap_ptr = std::unique_ptr<FIBITMAP, std::function<void(FIBITMAP *)>>;
+bitmap_ptr make_bitmap_ptr(FIBITMAP *);
 
 typedef enum {
-    AFFI_GRAY = 1,
-    AFFI_RGB  = 3,
-    AFFI_RGBA = 4
+    AFFI_GRAY = 1,  //< gray
+    AFFI_RGB  = 3,  //< rgb
+    AFFI_RGBA = 4   //< rgba
 } FI_CHANNELS;
 
 // Error handler for FreeImage library.
 // In case this handler is invoked, it throws an af exception.
-static void FreeImageErrorHandler(FREE_IMAGE_FORMAT oFif, const char* zMessage)
-{
+static void FreeImageErrorHandler(FREE_IMAGE_FORMAT oFif,
+                                  const char *zMessage) {
     UNUSED(oFif);
     printf("FreeImage Error Handler: %s\n", zMessage);
 }
@@ -76,14 +76,13 @@ static void FreeImageErrorHandler(FREE_IMAGE_FORMAT oFif, const char* zMessage)
 //  Split a MxNx3 image into 3 separate channel matrices.
 //  Produce 3 channels if needed
 static af_err channel_split(const af_array rgb, const af::dim4 &dims,
-                            af_array *outr, af_array *outg, af_array *outb, af_array *outa)
-{
+                            af_array *outr, af_array *outg, af_array *outb,
+                            af_array *outa) {
     try {
         af_seq idx[4][3] = {{af_span, af_span, {0, 0, 1}},
                             {af_span, af_span, {1, 1, 1}},
                             {af_span, af_span, {2, 2, 1}},
-                            {af_span, af_span, {3, 3, 1}}
-                           };
+                            {af_span, af_span, {3, 3, 1}}};
 
         if (dims[2] == 4) {
             AF_CHECK(af_index(outr, rgb, dims.ndims(), idx[0]));
@@ -97,7 +96,8 @@ static af_err channel_split(const af_array rgb, const af::dim4 &dims,
         } else {
             AF_CHECK(af_index(outr, rgb, dims.ndims(), idx[0]));
         }
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
diff --git a/src/api/c/implicit.cpp b/src/api/c/implicit.cpp
index c1e02dd6c4..b55834ced2 100644
--- a/src/api/c/implicit.cpp
+++ b/src/api/c/implicit.cpp
@@ -17,53 +17,39 @@ Order of precedence:
 - double > float > uintl > intl > uint > int > uchar > char
 */
 
-af_dtype implicit(const af_dtype lty, const af_dtype rty)
-{
-    if (lty == rty) {
-        return lty;
-    }
+af_dtype implicit(const af_dtype lty, const af_dtype rty) {
+    if (lty == rty) { return lty; }
 
-    if (lty == c64 || rty == c64) {
-           return c64;
-    }
+    if (lty == c64 || rty == c64) { return c64; }
 
     if (lty == c32 || rty == c32) {
-        if (lty == f64 || rty == f64)  return c64;
+        if (lty == f64 || rty == f64) return c64;
         return c32;
     }
 
     if (lty == f64 || rty == f64) return f64;
     if (lty == f32 || rty == f32) return f32;
 
-    if ((lty == u64) ||
-        (rty == u64)) return u64;
+    if ((lty == u64) || (rty == u64)) return u64;
 
-    if ((lty == s64) ||
-        (rty == s64)) return s64;
+    if ((lty == s64) || (rty == s64)) return s64;
 
-    if ((lty == u32) ||
-        (rty == u32)) return u32;
+    if ((lty == u32) || (rty == u32)) return u32;
 
-    if ((lty == s32) ||
-        (rty == s32)) return s32;
+    if ((lty == s32) || (rty == s32)) return s32;
 
-    if ((lty == u16) ||
-        (rty == u16)) return u16;
+    if ((lty == u16) || (rty == u16)) return u16;
 
-    if ((lty == s16) ||
-        (rty == s16)) return s16;
+    if ((lty == s16) || (rty == s16)) return s16;
 
-    if ((lty == u8 ) ||
-        (rty == u8 )) return u8;
+    if ((lty == u8) || (rty == u8)) return u8;
 
-    if ((lty == b8 ) &&
-        (rty == b8 )) return b8;
+    if ((lty == b8) && (rty == b8)) return b8;
 
     return f32;
 }
 
-af_dtype implicit(const af_array lhs, const af_array rhs)
-{
+af_dtype implicit(const af_array lhs, const af_array rhs) {
     const ArrayInfo& lInfo = getInfo(lhs);
     const ArrayInfo& rInfo = getInfo(rhs);
 
diff --git a/src/api/c/implicit.hpp b/src/api/c/implicit.hpp
index e9f2e806c3..d0bb51d62e 100644
--- a/src/api/c/implicit.hpp
+++ b/src/api/c/implicit.hpp
@@ -8,14 +8,14 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
-#include <af/defines.h>
+#include <backend.hpp>
+#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
+#include <optypes.hpp>
 #include <types.hpp>
-#include <cast.hpp>
+#include <af/array.h>
+#include <af/defines.h>
 
 using namespace detail;
 
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index af4ff7d687..45de2c14a0 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -7,19 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <indexing_common.hpp>
 #include <index.hpp>
+#include <indexing_common.hpp>
 
 #include <Array.hpp>
-#include <af/arith.h>
-#include <af/array.h>
-#include <af/data.h>
-#include <af/index.h>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <lookup.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/index.h>
 
 #include <array>
 #include <cassert>
@@ -27,40 +27,36 @@
 #include <vector>
 
 using namespace detail;
-using std::vector;
-using std::swap;
 using std::signbit;
+using std::swap;
+using std::vector;
 
-using common::createSpanIndex;
 using common::convert2Canonical;
+using common::createSpanIndex;
 
 namespace common {
-af_index_t createSpanIndex()
-{
-    static af_index_t s = []{
+af_index_t createSpanIndex() {
+    static af_index_t s = [] {
         af_index_t s;
         s.idx.seq = af_span;
-        s.isSeq = true;
+        s.isSeq   = true;
         s.isBatch = false;
         return s;
     }();
     return s;
 }
 
-af_seq convert2Canonical(const af_seq s, const dim_t len)
-{
+af_seq convert2Canonical(const af_seq s, const dim_t len) {
     double begin = signbit(s.begin) ? (len + s.begin) : s.begin;
-    double end   = signbit(s.end  ) ? (len + s.end)   : s.end;
+    double end   = signbit(s.end) ? (len + s.end) : s.end;
 
     return af_seq{begin, end, s.step};
 }
-}
+}  // namespace common
 
 template<typename T>
-static
-af_array indexBySeqs(const af_array &src,
-                     const vector<af_seq> indicesV)
-{
+static af_array indexBySeqs(const af_array& src,
+                            const vector<af_seq> indicesV) {
     size_t ndims = indicesV.size();
     auto input   = getArray<T>(src);
 
@@ -70,42 +66,41 @@ af_array indexBySeqs(const af_array &src,
         return getHandle(createSubArray(input, indicesV));
 }
 
-af_err af_index(af_array *result, const af_array in,
-                const unsigned ndims, const af_seq* indices)
-{
+af_err af_index(af_array* result, const af_array in, const unsigned ndims,
+                const af_seq* indices) {
     try {
         const ArrayInfo& inInfo = getInfo(in);
-        af_dtype type = inInfo.getType();
-        const dim4& iDims = inInfo.dims();
+        af_dtype type           = inInfo.getType();
+        const dim4& iDims       = inInfo.dims();
 
         vector<af_seq> indices_(ndims, af_span);
-        for (unsigned i=0; i<ndims; ++i) {
+        for (unsigned i = 0; i < ndims; ++i) {
             indices_[i] = convert2Canonical(indices[i], iDims[i]);
 
             ARG_ASSERT(3, (indices_[i].begin >= 0. && indices_[i].end >= 0.));
             if (signbit(indices_[i].step)) {
-              ARG_ASSERT(3, indices_[i].begin >= indices_[i].end);
+                ARG_ASSERT(3, indices_[i].begin >= indices_[i].end);
             } else {
-              ARG_ASSERT(3, indices_[i].begin <= indices_[i].end);
+                ARG_ASSERT(3, indices_[i].begin <= indices_[i].end);
             }
         }
 
         af_array out = 0;
 
-        switch(type) {
-            case f32: out = indexBySeqs<float>   (in, indices_);  break;
-            case c32: out = indexBySeqs<cfloat>  (in, indices_);  break;
-            case f64: out = indexBySeqs<double>  (in, indices_);  break;
-            case c64: out = indexBySeqs<cdouble> (in, indices_);  break;
-            case b8:  out = indexBySeqs<char>    (in, indices_);  break;
-            case s32: out = indexBySeqs<int>     (in, indices_);  break;
-            case u32: out = indexBySeqs<unsigned>(in, indices_);  break;
-            case s16: out = indexBySeqs<short>   (in, indices_);  break;
-            case u16: out = indexBySeqs<ushort>  (in, indices_);  break;
-            case s64: out = indexBySeqs<intl>    (in, indices_);  break;
-            case u64: out = indexBySeqs<uintl>   (in, indices_);  break;
-            case u8:  out = indexBySeqs<uchar>   (in, indices_);  break;
-            default:    TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: out = indexBySeqs<float>(in, indices_); break;
+            case c32: out = indexBySeqs<cfloat>(in, indices_); break;
+            case f64: out = indexBySeqs<double>(in, indices_); break;
+            case c64: out = indexBySeqs<cdouble>(in, indices_); break;
+            case b8: out = indexBySeqs<char>(in, indices_); break;
+            case s32: out = indexBySeqs<int>(in, indices_); break;
+            case u32: out = indexBySeqs<unsigned>(in, indices_); break;
+            case s16: out = indexBySeqs<short>(in, indices_); break;
+            case u16: out = indexBySeqs<ushort>(in, indices_); break;
+            case s64: out = indexBySeqs<intl>(in, indices_); break;
+            case u64: out = indexBySeqs<uintl>(in, indices_); break;
+            case u8: out = indexBySeqs<uchar>(in, indices_); break;
+            default: TYPE_ERROR(1, type);
         }
         swap(*result, out);
     }
@@ -114,39 +109,36 @@ af_err af_index(af_array *result, const af_array in,
 }
 
 template<typename T, typename idx_t>
-inline
-af_array lookup(const af_array& in, const af_array& idx, const unsigned dim)
-{
+inline af_array lookup(const af_array& in, const af_array& idx,
+                       const unsigned dim) {
     return getHandle(lookup(getArray<T>(in), getArray<idx_t>(idx), dim));
 }
 
 template<typename idx_t>
-static
-af_array lookup(const af_array& in, const af_array& idx, const unsigned dim)
-{
+static af_array lookup(const af_array& in, const af_array& idx,
+                       const unsigned dim) {
     const ArrayInfo& inInfo = getInfo(in);
-    af_dtype inType = inInfo.getType();
-
-    switch(inType) {
-        case f32: return lookup<float   , idx_t>(in, idx, dim);
-        case c32: return lookup<cfloat  , idx_t>(in, idx, dim);
-        case f64: return lookup<double  , idx_t>(in, idx, dim);
-        case c64: return lookup<cdouble , idx_t>(in, idx, dim);
-        case s32: return lookup<int     , idx_t>(in, idx, dim);
+    af_dtype inType         = inInfo.getType();
+
+    switch (inType) {
+        case f32: return lookup<float, idx_t>(in, idx, dim);
+        case c32: return lookup<cfloat, idx_t>(in, idx, dim);
+        case f64: return lookup<double, idx_t>(in, idx, dim);
+        case c64: return lookup<cdouble, idx_t>(in, idx, dim);
+        case s32: return lookup<int, idx_t>(in, idx, dim);
         case u32: return lookup<unsigned, idx_t>(in, idx, dim);
-        case s64: return lookup<intl    , idx_t>(in, idx, dim);
-        case u64: return lookup<uintl   , idx_t>(in, idx, dim);
-        case s16: return lookup<short   , idx_t>(in, idx, dim);
-        case u16: return lookup<ushort  , idx_t>(in, idx, dim);
-        case  u8: return lookup<uchar   , idx_t>(in, idx, dim);
-        case  b8: return lookup<char    , idx_t>(in, idx, dim);
-        default : TYPE_ERROR(1, inType);
+        case s64: return lookup<intl, idx_t>(in, idx, dim);
+        case u64: return lookup<uintl, idx_t>(in, idx, dim);
+        case s16: return lookup<short, idx_t>(in, idx, dim);
+        case u16: return lookup<ushort, idx_t>(in, idx, dim);
+        case u8: return lookup<uchar, idx_t>(in, idx, dim);
+        case b8: return lookup<char, idx_t>(in, idx, dim);
+        default: TYPE_ERROR(1, inType);
     }
 }
 
-af_err af_lookup(af_array *out, const af_array in,
-                 const af_array indices, const unsigned dim)
-{
+af_err af_lookup(af_array* out, const af_array in, const af_array indices,
+                 const unsigned dim) {
     try {
         const ArrayInfo& idxInfo = getInfo(indices);
 
@@ -166,17 +158,17 @@ af_err af_lookup(af_array *out, const af_array in,
 
         af_array output = 0;
 
-        switch(idxType) {
-            case f32: output = lookup<float   >(in, indices, dim); break;
-            case f64: output = lookup<double  >(in, indices, dim); break;
-            case s32: output = lookup<int     >(in, indices, dim); break;
+        switch (idxType) {
+            case f32: output = lookup<float>(in, indices, dim); break;
+            case f64: output = lookup<double>(in, indices, dim); break;
+            case s32: output = lookup<int>(in, indices, dim); break;
             case u32: output = lookup<unsigned>(in, indices, dim); break;
-            case s16: output = lookup<short   >(in, indices, dim); break;
-            case u16: output = lookup<ushort  >(in, indices, dim); break;
-            case s64: output = lookup<intl    >(in, indices, dim); break;
-            case u64: output = lookup<uintl   >(in, indices, dim); break;
-            case  u8: output = lookup<uchar   >(in, indices, dim); break;
-            default : TYPE_ERROR(1, idxType);
+            case s16: output = lookup<short>(in, indices, dim); break;
+            case u16: output = lookup<ushort>(in, indices, dim); break;
+            case s64: output = lookup<intl>(in, indices, dim); break;
+            case u64: output = lookup<uintl>(in, indices, dim); break;
+            case u8: output = lookup<uchar>(in, indices, dim); break;
+            default: TYPE_ERROR(1, idxType);
         }
         std::swap(*out, output);
     }
@@ -188,22 +180,19 @@ af_err af_lookup(af_array *out, const af_array in,
 // expects 4 values which is handled appropriately
 // by the C-API af_index_gen
 template<typename T>
-static inline
-af_array genIndex(const af_array& in,  const af_index_t idxrs[])
-{
+static inline af_array genIndex(const af_array& in, const af_index_t idxrs[]) {
     return getHandle<T>(index<T>(getArray<T>(in), idxrs));
 }
 
-af_err af_index_gen(af_array *out, const af_array in,
-                    const dim_t ndims, const af_index_t* indexs)
-{
+af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
+                    const af_index_t* indexs) {
     try {
-        ARG_ASSERT(2, (ndims>0));
+        ARG_ASSERT(2, (ndims > 0));
         ARG_ASSERT(3, (indexs != NULL));
 
-        const ArrayInfo& iInfo  = getInfo(in);
-        const dim4& iDims  = iInfo.dims();
-        af_dtype inType = getInfo(in).getType();
+        const ArrayInfo& iInfo = getInfo(in);
+        const dim4& iDims      = iInfo.dims();
+        af_dtype inType        = getInfo(in).getType();
 
         if (iDims.ndims() <= 0) {
             *out = createHandle(dim4(0), inType);
@@ -228,29 +217,28 @@ af_err af_index_gen(af_array *out, const af_array in,
             }
         }
 
-        if (track==(int)ndims)
-            return af_index(out, in, ndims, seqs.data());
+        if (track == (int)ndims) return af_index(out, in, ndims, seqs.data());
 
         std::array<af_index_t, AF_MAX_DIMS> idxrs;
 
-        for (dim_t i=0; i<AF_MAX_DIMS; ++i) {
-            if (i<ndims) {
+        for (dim_t i = 0; i < AF_MAX_DIMS; ++i) {
+            if (i < ndims) {
                 bool isSeq = indexs[i].isSeq;
                 if (!isSeq) {
                     // check if all af_arrays have atleast one value
                     // to enable indexing along that dimension
                     const ArrayInfo& idxInfo = getInfo(indexs[i].idx.arr);
-                    af_dtype idxType = idxInfo.getType();
+                    af_dtype idxType         = idxInfo.getType();
 
                     ARG_ASSERT(3, (idxType != c32));
                     ARG_ASSERT(3, (idxType != c64));
-                    ARG_ASSERT(3, (idxType != b8 ));
+                    ARG_ASSERT(3, (idxType != b8));
 
-                    idxrs[i] = { { indexs[i].idx.arr },
-                                 isSeq, indexs[i].isBatch };
+                    idxrs[i] = {{indexs[i].idx.arr}, isSeq, indexs[i].isBatch};
                 } else {
                     // copy the af_seq to local variable
-                    af_seq inSeq = convert2Canonical(indexs[i].idx.seq, iDims[i]);
+                    af_seq inSeq =
+                        convert2Canonical(indexs[i].idx.seq, iDims[i]);
                     ARG_ASSERT(3, (inSeq.begin >= 0. || inSeq.end >= 0.));
                     if (signbit(inSeq.step)) {
                         ARG_ASSERT(3, inSeq.begin >= inSeq.end);
@@ -269,19 +257,19 @@ af_err af_index_gen(af_array *out, const af_array in,
         af_index_t* ptr = idxrs.data();
 
         af_array output = 0;
-        switch(inType) {
+        switch (inType) {
             case c64: output = genIndex<cdouble>(in, ptr); break;
-            case f64: output = genIndex<double >(in, ptr); break;
-            case c32: output = genIndex<cfloat >(in, ptr); break;
-            case f32: output = genIndex<float  >(in, ptr); break;
-            case u64: output = genIndex<uintl  >(in, ptr); break;
-            case s64: output = genIndex<intl   >(in, ptr); break;
-            case u32: output = genIndex<uint   >(in, ptr); break;
-            case s32: output = genIndex<int    >(in, ptr); break;
-            case u16: output = genIndex<ushort >(in, ptr); break;
-            case s16: output = genIndex<short  >(in, ptr); break;
-            case  u8: output = genIndex<uchar  >(in, ptr); break;
-            case  b8: output = genIndex<char   >(in, ptr); break;
+            case f64: output = genIndex<double>(in, ptr); break;
+            case c32: output = genIndex<cfloat>(in, ptr); break;
+            case f32: output = genIndex<float>(in, ptr); break;
+            case u64: output = genIndex<uintl>(in, ptr); break;
+            case s64: output = genIndex<intl>(in, ptr); break;
+            case u32: output = genIndex<uint>(in, ptr); break;
+            case s32: output = genIndex<int>(in, ptr); break;
+            case u16: output = genIndex<ushort>(in, ptr); break;
+            case s16: output = genIndex<short>(in, ptr); break;
+            case u8: output = genIndex<uchar>(in, ptr); break;
+            case b8: output = genIndex<char>(in, ptr); break;
             default: TYPE_ERROR(1, inType);
         }
         std::swap(*out, output);
@@ -290,13 +278,11 @@ af_err af_index_gen(af_array *out, const af_array in,
     return AF_SUCCESS;
 }
 
-af_seq af_make_seq(double begin, double end, double step)
-{
+af_seq af_make_seq(double begin, double end, double step) {
     return af_seq{begin, end, step};
 }
 
-af_err af_create_indexers(af_index_t** indexers)
-{
+af_err af_create_indexers(af_index_t** indexers) {
     try {
         af_index_t* out = new af_index_t[AF_MAX_DIMS];
         for (int i = 0; i < AF_MAX_DIMS; ++i) {
@@ -310,9 +296,8 @@ af_err af_create_indexers(af_index_t** indexers)
     return AF_SUCCESS;
 }
 
-af_err af_set_array_indexer(af_index_t* indexer,
-                            const af_array idx, const dim_t dim)
-{
+af_err af_set_array_indexer(af_index_t* indexer, const af_array idx,
+                            const dim_t dim) {
     try {
         ARG_ASSERT(0, (indexer != NULL));
         ARG_ASSERT(1, (idx != NULL));
@@ -324,8 +309,7 @@ af_err af_set_array_indexer(af_index_t* indexer,
 }
 
 af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx,
-                          const dim_t dim, const bool is_batch)
-{
+                          const dim_t dim, const bool is_batch) {
     try {
         ARG_ASSERT(0, (indexer != NULL));
         ARG_ASSERT(1, (idx != NULL));
@@ -340,22 +324,20 @@ af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx,
 
 af_err af_set_seq_param_indexer(af_index_t* indexer, const double begin,
                                 const double end, const double step,
-                                const dim_t dim, const bool is_batch)
-{
+                                const dim_t dim, const bool is_batch) {
     try {
         ARG_ASSERT(0, (indexer != NULL));
         ARG_ASSERT(4, (dim >= 0 && dim <= 3));
-        af_seq s = af_make_seq(begin, end, step);
+        af_seq s             = af_make_seq(begin, end, step);
         indexer[dim].idx.seq = s;
-        indexer[dim].isSeq = true;
+        indexer[dim].isSeq   = true;
         indexer[dim].isBatch = is_batch;
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_release_indexers(af_index_t* indexers)
-{
+af_err af_release_indexers(af_index_t* indexers) {
     try {
         delete[] indexers;
     }
diff --git a/src/api/c/indexing_common.hpp b/src/api/c/indexing_common.hpp
index e6e84ed84c..ae5ea3958a 100644
--- a/src/api/c/indexing_common.hpp
+++ b/src/api/c/indexing_common.hpp
@@ -38,4 +38,4 @@ af_index_t createSpanIndex();
 /// s{1, 2, 1};      will return the same sequence
 /// s{-1, 2, -1};    will return the sequence af_seq(9,2,-1)
 af_seq convert2Canonical(const af_seq s, const dim_t len);
-}
+}  // namespace common
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index e9f13cfa51..6a4c318a88 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/device.h>
-#include <af/version.h>
-#include <af/internal.h>
-#include <backend.hpp>
-#include <platform.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
+#include <backend.hpp>
 #include <common/err_common.hpp>
+#include <handle.hpp>
+#include <platform.hpp>
+#include <af/device.h>
+#include <af/dim4.hpp>
+#include <af/internal.h>
+#include <af/version.h>
 #include <cstring>
 
 using af::dim4;
@@ -28,26 +28,19 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
-af_err af_create_strided_array(af_array *arr,
-                               const void *data,
-                               const dim_t offset,
-                               const unsigned ndims,
+af_err af_create_strided_array(af_array *arr, const void *data,
+                               const dim_t offset, const unsigned ndims,
                                const dim_t *const dims_,
-                               const dim_t *const strides_,
-                               const af_dtype ty,
-                               const af_source location)
-{
+                               const dim_t *const strides_, const af_dtype ty,
+                               const af_source location) {
     try {
-
         ARG_ASSERT(2, offset >= 0);
-        ARG_ASSERT(3, ndims >=1 && ndims <= 4);
+        ARG_ASSERT(3, ndims >= 1 && ndims <= 4);
         ARG_ASSERT(4, dims_ != NULL);
         ARG_ASSERT(5, strides_ != NULL);
         ARG_ASSERT(5, strides_[0] == 1);
 
-        for (int i = 1; i < (int)ndims; i++) {
-            ARG_ASSERT(5, strides_[i] > 0);
-        }
+        for (int i = 1; i < (int)ndims; i++) { ARG_ASSERT(5, strides_[i] > 0); }
 
         dim4 dims(ndims, dims_);
         dim4 strides(ndims, strides_);
@@ -62,19 +55,55 @@ af_err af_create_strided_array(af_array *arr,
         AF_CHECK(af_init());
 
         switch (ty) {
-        case f32: res = getHandle(createStridedArray<float  >(dims, strides, offset, (float   *)data, isdev)); break;
-        case f64: res = getHandle(createStridedArray<double >(dims, strides, offset, (double  *)data, isdev)); break;
-        case c32: res = getHandle(createStridedArray<cfloat >(dims, strides, offset, (cfloat  *)data, isdev)); break;
-        case c64: res = getHandle(createStridedArray<cdouble>(dims, strides, offset, (cdouble *)data, isdev)); break;
-        case u32: res = getHandle(createStridedArray<uint   >(dims, strides, offset, (uint    *)data, isdev)); break;
-        case s32: res = getHandle(createStridedArray<int    >(dims, strides, offset, (int     *)data, isdev)); break;
-        case u64: res = getHandle(createStridedArray<uintl  >(dims, strides, offset, (uintl   *)data, isdev)); break;
-        case s64: res = getHandle(createStridedArray<intl   >(dims, strides, offset, (intl    *)data, isdev)); break;
-        case u16: res = getHandle(createStridedArray<ushort >(dims, strides, offset, (ushort  *)data, isdev)); break;
-        case s16: res = getHandle(createStridedArray<short  >(dims, strides, offset, (short   *)data, isdev)); break;
-        case b8 : res = getHandle(createStridedArray<char   >(dims, strides, offset, (char    *)data, isdev)); break;
-        case u8 : res = getHandle(createStridedArray<uchar  >(dims, strides, offset, (uchar   *)data, isdev)); break;
-        default: TYPE_ERROR(6, ty);
+            case f32:
+                res = getHandle(createStridedArray<float>(
+                    dims, strides, offset, (float *)data, isdev));
+                break;
+            case f64:
+                res = getHandle(createStridedArray<double>(
+                    dims, strides, offset, (double *)data, isdev));
+                break;
+            case c32:
+                res = getHandle(createStridedArray<cfloat>(
+                    dims, strides, offset, (cfloat *)data, isdev));
+                break;
+            case c64:
+                res = getHandle(createStridedArray<cdouble>(
+                    dims, strides, offset, (cdouble *)data, isdev));
+                break;
+            case u32:
+                res = getHandle(createStridedArray<uint>(dims, strides, offset,
+                                                         (uint *)data, isdev));
+                break;
+            case s32:
+                res = getHandle(createStridedArray<int>(dims, strides, offset,
+                                                        (int *)data, isdev));
+                break;
+            case u64:
+                res = getHandle(createStridedArray<uintl>(
+                    dims, strides, offset, (uintl *)data, isdev));
+                break;
+            case s64:
+                res = getHandle(createStridedArray<intl>(dims, strides, offset,
+                                                         (intl *)data, isdev));
+                break;
+            case u16:
+                res = getHandle(createStridedArray<ushort>(
+                    dims, strides, offset, (ushort *)data, isdev));
+                break;
+            case s16:
+                res = getHandle(createStridedArray<short>(
+                    dims, strides, offset, (short *)data, isdev));
+                break;
+            case b8:
+                res = getHandle(createStridedArray<char>(dims, strides, offset,
+                                                         (char *)data, isdev));
+                break;
+            case u8:
+                res = getHandle(createStridedArray<uchar>(
+                    dims, strides, offset, (uchar *)data, isdev));
+                break;
+            default: TYPE_ERROR(6, ty);
         }
 
         std::swap(*arr, res);
@@ -83,53 +112,48 @@ af_err af_create_strided_array(af_array *arr,
     return AF_SUCCESS;
 }
 
-af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array in)
-{
+af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3,
+                      const af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        *s0 = info.strides()[0];
-        *s1 = info.strides()[1];
-        *s2 = info.strides()[2];
-        *s3 = info.strides()[3];
+        const ArrayInfo &info = getInfo(in);
+        *s0                   = info.strides()[0];
+        *s1                   = info.strides()[1];
+        *s2                   = info.strides()[2];
+        *s3                   = info.strides()[3];
     }
     CATCHALL
     return AF_SUCCESS;
 }
 
-af_err af_get_offset(dim_t *offset, const af_array arr)
-{
+af_err af_get_offset(dim_t *offset, const af_array arr) {
     try {
-
         dim_t res = getInfo(arr).getOffset();
         std::swap(*offset, res);
     }
     CATCHALL;
     return AF_SUCCESS;
-
 }
 
-af_err af_get_raw_ptr(void **ptr, const af_array arr)
-{
+af_err af_get_raw_ptr(void **ptr, const af_array arr) {
     try {
-
         void *res = NULL;
 
         af_dtype ty = getInfo(arr).getType();
 
         switch (ty) {
-        case f32: res = (void *)getRawPtr(getArray<float  >(arr)); break;
-        case f64: res = (void *)getRawPtr(getArray<double >(arr)); break;
-        case c32: res = (void *)getRawPtr(getArray<cfloat >(arr)); break;
-        case c64: res = (void *)getRawPtr(getArray<cdouble>(arr)); break;
-        case u32: res = (void *)getRawPtr(getArray<uint   >(arr)); break;
-        case s32: res = (void *)getRawPtr(getArray<int    >(arr)); break;
-        case u64: res = (void *)getRawPtr(getArray<uintl  >(arr)); break;
-        case s64: res = (void *)getRawPtr(getArray<intl   >(arr)); break;
-        case u16: res = (void *)getRawPtr(getArray<ushort >(arr)); break;
-        case s16: res = (void *)getRawPtr(getArray<short  >(arr)); break;
-        case b8 : res = (void *)getRawPtr(getArray<char   >(arr)); break;
-        case u8 : res = (void *)getRawPtr(getArray<uchar  >(arr)); break;
-        default: TYPE_ERROR(6, ty);
+            case f32: res = (void *)getRawPtr(getArray<float>(arr)); break;
+            case f64: res = (void *)getRawPtr(getArray<double>(arr)); break;
+            case c32: res = (void *)getRawPtr(getArray<cfloat>(arr)); break;
+            case c64: res = (void *)getRawPtr(getArray<cdouble>(arr)); break;
+            case u32: res = (void *)getRawPtr(getArray<uint>(arr)); break;
+            case s32: res = (void *)getRawPtr(getArray<int>(arr)); break;
+            case u64: res = (void *)getRawPtr(getArray<uintl>(arr)); break;
+            case s64: res = (void *)getRawPtr(getArray<intl>(arr)); break;
+            case u16: res = (void *)getRawPtr(getArray<ushort>(arr)); break;
+            case s16: res = (void *)getRawPtr(getArray<short>(arr)); break;
+            case b8: res = (void *)getRawPtr(getArray<char>(arr)); break;
+            case u8: res = (void *)getRawPtr(getArray<uchar>(arr)); break;
+            default: TYPE_ERROR(6, ty);
         }
 
         std::swap(*ptr, res);
@@ -138,8 +162,7 @@ af_err af_get_raw_ptr(void **ptr, const af_array arr)
     return AF_SUCCESS;
 }
 
-af_err af_is_linear(bool *result, const af_array arr)
-{
+af_err af_is_linear(bool *result, const af_array arr) {
     try {
         *result = getInfo(arr).isLinear();
     }
@@ -147,28 +170,26 @@ af_err af_is_linear(bool *result, const af_array arr)
     return AF_SUCCESS;
 }
 
-af_err af_is_owner(bool *result, const af_array arr)
-{
+af_err af_is_owner(bool *result, const af_array arr) {
     try {
-
         bool res = false;
 
         af_dtype ty = getInfo(arr).getType();
 
         switch (ty) {
-        case f32: res = (void *)getArray<float  >(arr).isOwner(); break;
-        case f64: res = (void *)getArray<double >(arr).isOwner(); break;
-        case c32: res = (void *)getArray<cfloat >(arr).isOwner(); break;
-        case c64: res = (void *)getArray<cdouble>(arr).isOwner(); break;
-        case u32: res = (void *)getArray<uint   >(arr).isOwner(); break;
-        case s32: res = (void *)getArray<int    >(arr).isOwner(); break;
-        case u64: res = (void *)getArray<uintl  >(arr).isOwner(); break;
-        case s64: res = (void *)getArray<intl   >(arr).isOwner(); break;
-        case u16: res = (void *)getArray<ushort >(arr).isOwner(); break;
-        case s16: res = (void *)getArray<short  >(arr).isOwner(); break;
-        case b8 : res = (void *)getArray<char   >(arr).isOwner(); break;
-        case u8 : res = (void *)getArray<uchar  >(arr).isOwner(); break;
-        default: TYPE_ERROR(6, ty);
+            case f32: res = (void *)getArray<float>(arr).isOwner(); break;
+            case f64: res = (void *)getArray<double>(arr).isOwner(); break;
+            case c32: res = (void *)getArray<cfloat>(arr).isOwner(); break;
+            case c64: res = (void *)getArray<cdouble>(arr).isOwner(); break;
+            case u32: res = (void *)getArray<uint>(arr).isOwner(); break;
+            case s32: res = (void *)getArray<int>(arr).isOwner(); break;
+            case u64: res = (void *)getArray<uintl>(arr).isOwner(); break;
+            case s64: res = (void *)getArray<intl>(arr).isOwner(); break;
+            case u16: res = (void *)getArray<ushort>(arr).isOwner(); break;
+            case s16: res = (void *)getArray<short>(arr).isOwner(); break;
+            case b8: res = (void *)getArray<char>(arr).isOwner(); break;
+            case u8: res = (void *)getArray<uchar>(arr).isOwner(); break;
+            default: TYPE_ERROR(6, ty);
         }
 
         std::swap(*result, res);
@@ -177,27 +198,26 @@ af_err af_is_owner(bool *result, const af_array arr)
     return AF_SUCCESS;
 }
 
-af_err af_get_allocated_bytes(size_t *bytes, const af_array arr)
-{
+af_err af_get_allocated_bytes(size_t *bytes, const af_array arr) {
     try {
         af_dtype ty = getInfo(arr).getType();
 
         size_t res = 0;
 
         switch (ty) {
-        case f32: res = getArray<float  >(arr).getAllocatedBytes(); break;
-        case f64: res = getArray<double >(arr).getAllocatedBytes(); break;
-        case c32: res = getArray<cfloat >(arr).getAllocatedBytes(); break;
-        case c64: res = getArray<cdouble>(arr).getAllocatedBytes(); break;
-        case u32: res = getArray<uint   >(arr).getAllocatedBytes(); break;
-        case s32: res = getArray<int    >(arr).getAllocatedBytes(); break;
-        case u64: res = getArray<uintl  >(arr).getAllocatedBytes(); break;
-        case s64: res = getArray<intl   >(arr).getAllocatedBytes(); break;
-        case u16: res = getArray<ushort >(arr).getAllocatedBytes(); break;
-        case s16: res = getArray<short  >(arr).getAllocatedBytes(); break;
-        case b8 : res = getArray<char   >(arr).getAllocatedBytes(); break;
-        case u8 : res = getArray<uchar  >(arr).getAllocatedBytes(); break;
-        default: TYPE_ERROR(6, ty);
+            case f32: res = getArray<float>(arr).getAllocatedBytes(); break;
+            case f64: res = getArray<double>(arr).getAllocatedBytes(); break;
+            case c32: res = getArray<cfloat>(arr).getAllocatedBytes(); break;
+            case c64: res = getArray<cdouble>(arr).getAllocatedBytes(); break;
+            case u32: res = getArray<uint>(arr).getAllocatedBytes(); break;
+            case s32: res = getArray<int>(arr).getAllocatedBytes(); break;
+            case u64: res = getArray<uintl>(arr).getAllocatedBytes(); break;
+            case s64: res = getArray<intl>(arr).getAllocatedBytes(); break;
+            case u16: res = getArray<ushort>(arr).getAllocatedBytes(); break;
+            case s16: res = getArray<short>(arr).getAllocatedBytes(); break;
+            case b8: res = getArray<char>(arr).getAllocatedBytes(); break;
+            case u8: res = getArray<uchar>(arr).getAllocatedBytes(); break;
+            default: TYPE_ERROR(6, ty);
         }
 
         std::swap(*bytes, res);
diff --git a/src/api/c/inverse.cpp b/src/api/c/inverse.cpp
index 21e265e82f..1eee6eeb12 100644
--- a/src/api/c/inverse.cpp
+++ b/src/api/c/inverse.cpp
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <inverse.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array inverse(const af_array in)
-{
+static inline af_array inverse(const af_array in) {
     return getHandle(inverse<T>(getArray<T>(in)));
 }
 
-af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options)
-{
+af_err af_inverse(af_array* out, const af_array in, const af_mat_prop options) {
     try {
         const ArrayInfo& i_info = getInfo(in);
 
@@ -37,24 +35,24 @@ af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options)
         af_dtype type = i_info.getType();
 
         if (options != AF_MAT_NONE) {
-            AF_ERROR("Using this property is not yet supported in inverse", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in inverse",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
-        DIM_ASSERT(1, i_info.dims()[0] == i_info.dims()[1]);      // Only square matrices
-        ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
+        DIM_ASSERT(
+            1, i_info.dims()[0] == i_info.dims()[1]);  // Only square matrices
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
         af_array output;
 
-        if(i_info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (i_info.ndims() == 0) { return af_retain_array(out, in); }
 
-        switch(type) {
-            case f32: output = inverse<float  >(in);  break;
-            case f64: output = inverse<double >(in);  break;
-            case c32: output = inverse<cfloat >(in);  break;
-            case c64: output = inverse<cdouble>(in);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = inverse<float>(in); break;
+            case f64: output = inverse<double>(in); break;
+            case c32: output = inverse<cfloat>(in); break;
+            case c64: output = inverse<cdouble>(in); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 2ea2364450..4eaebb56a4 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -7,111 +7,110 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/data.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <join.hpp>
+#include <af/data.h>
 #include <vector>
 
 using af::dim4;
 using namespace detail;
 
 template<typename Tx, typename Ty>
-static inline af_array join(const int dim, const af_array first, const af_array second)
-{
-    return getHandle(join<Tx, Ty>(dim, getArray<Tx>(first), getArray<Ty>(second)));
+static inline af_array join(const int dim, const af_array first,
+                            const af_array second) {
+    return getHandle(
+        join<Tx, Ty>(dim, getArray<Tx>(first), getArray<Ty>(second)));
 }
 
 template<typename T>
-static inline af_array join_many(const int dim, const unsigned n_arrays, const af_array *inputs)
-{
+static inline af_array join_many(const int dim, const unsigned n_arrays,
+                                 const af_array *inputs) {
     std::vector<Array<T>> inputs_;
     inputs_.reserve(n_arrays);
 
-    for(int i = 0; i < (int)n_arrays; i++) {
+    for (int i = 0; i < (int)n_arrays; i++) {
         inputs_.push_back(getArray<T>(inputs[i]));
     }
     return getHandle(join<T>(dim, inputs_));
 }
 
-af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)
-{
+af_err af_join(af_array *out, const int dim, const af_array first,
+               const af_array second) {
     try {
-        const ArrayInfo& finfo = getInfo(first);
-        const ArrayInfo& sinfo = getInfo(second);
-        af::dim4  fdims = finfo.dims();
-        af::dim4  sdims = sinfo.dims();
+        const ArrayInfo &finfo = getInfo(first);
+        const ArrayInfo &sinfo = getInfo(second);
+        af::dim4 fdims         = finfo.dims();
+        af::dim4 sdims         = sinfo.dims();
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
         ARG_ASSERT(2, finfo.getType() == sinfo.getType());
-        if(sinfo.elements() == 0) {
-            return af_retain_array(out, first);
-        }
+        if (sinfo.elements() == 0) { return af_retain_array(out, first); }
 
-        if(finfo.elements() == 0) {
-            return af_retain_array(out, second);
-        }
+        if (finfo.elements() == 0) { return af_retain_array(out, second); }
 
         DIM_ASSERT(2, sinfo.elements() > 0);
         DIM_ASSERT(3, finfo.elements() > 0);
 
         // All dimensions except join dimension must be equal
         // Compute output dims
-        for(int i = 0; i < 4; i++) {
-            if(i != dim) DIM_ASSERT(2, fdims[i] == sdims[i]);
+        for (int i = 0; i < 4; i++) {
+            if (i != dim) DIM_ASSERT(2, fdims[i] == sdims[i]);
         }
 
         af_array output;
 
-        switch(finfo.getType()) {
-            case f32: output = join<float  , float  >(dim, first, second);  break;
-            case c32: output = join<cfloat , cfloat >(dim, first, second);  break;
-            case f64: output = join<double , double >(dim, first, second);  break;
-            case c64: output = join<cdouble, cdouble>(dim, first, second);  break;
-            case b8:  output = join<char   , char   >(dim, first, second);  break;
-            case s32: output = join<int    , int    >(dim, first, second);  break;
-            case u32: output = join<uint   , uint   >(dim, first, second);  break;
-            case s64: output = join<intl   , intl   >(dim, first, second);  break;
-            case u64: output = join<uintl  , uintl  >(dim, first, second);  break;
-            case s16: output = join<short  , short  >(dim, first, second);  break;
-            case u16: output = join<ushort , ushort >(dim, first, second);  break;
-            case u8:  output = join<uchar  , uchar  >(dim, first, second);  break;
-            default:  TYPE_ERROR(1, finfo.getType());
+        switch (finfo.getType()) {
+            case f32: output = join<float, float>(dim, first, second); break;
+            case c32: output = join<cfloat, cfloat>(dim, first, second); break;
+            case f64: output = join<double, double>(dim, first, second); break;
+            case c64:
+                output = join<cdouble, cdouble>(dim, first, second);
+                break;
+            case b8: output = join<char, char>(dim, first, second); break;
+            case s32: output = join<int, int>(dim, first, second); break;
+            case u32: output = join<uint, uint>(dim, first, second); break;
+            case s64: output = join<intl, intl>(dim, first, second); break;
+            case u64: output = join<uintl, uintl>(dim, first, second); break;
+            case s16: output = join<short, short>(dim, first, second); break;
+            case u16: output = join<ushort, ushort>(dim, first, second); break;
+            case u8: output = join<uchar, uchar>(dim, first, second); break;
+            default: TYPE_ERROR(1, finfo.getType());
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs)
-{
+af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
+                    const af_array *inputs) {
     try {
         ARG_ASSERT(3, n_arrays > 1 && n_arrays <= 10);
 
         std::vector<ArrayInfo> info;
         info.reserve(n_arrays);
         std::vector<af::dim4> dims(n_arrays);
-        for(int i = 0; i < (int)n_arrays; i++) {
+        for (int i = 0; i < (int)n_arrays; i++) {
             info.push_back(getInfo(inputs[i]));
             dims[i] = info[i].dims();
         }
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
 
-        for(int i = 1; i < (int)n_arrays; i++) {
+        for (int i = 1; i < (int)n_arrays; i++) {
             ARG_ASSERT(3, info[0].getType() == info[i].getType());
             DIM_ASSERT(3, info[i].elements() > 0);
         }
 
         // All dimensions except join dimension must be equal
         // Compute output dims
-        for(int i = 0; i < 4; i++) {
-            if(i != dim) {
-                for(int j = 1; j < (int)n_arrays; j++) {
+        for (int i = 0; i < 4; i++) {
+            if (i != dim) {
+                for (int j = 1; j < (int)n_arrays; j++) {
                     DIM_ASSERT(3, dims[0][i] == dims[j][i]);
                 }
             }
@@ -119,22 +118,22 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const
 
         af_array output;
 
-        switch(info[0].getType()) {
-            case f32: output = join_many<float  >(dim, n_arrays, inputs);  break;
-            case c32: output = join_many<cfloat >(dim, n_arrays, inputs);  break;
-            case f64: output = join_many<double >(dim, n_arrays, inputs);  break;
-            case c64: output = join_many<cdouble>(dim, n_arrays, inputs);  break;
-            case b8:  output = join_many<char   >(dim, n_arrays, inputs);  break;
-            case s32: output = join_many<int    >(dim, n_arrays, inputs);  break;
-            case u32: output = join_many<uint   >(dim, n_arrays, inputs);  break;
-            case s64: output = join_many<intl   >(dim, n_arrays, inputs);  break;
-            case u64: output = join_many<uintl  >(dim, n_arrays, inputs);  break;
-            case s16: output = join_many<short  >(dim, n_arrays, inputs);  break;
-            case u16: output = join_many<ushort >(dim, n_arrays, inputs);  break;
-            case u8:  output = join_many<uchar  >(dim, n_arrays, inputs);  break;
-            default:  TYPE_ERROR(1, info[0].getType());
+        switch (info[0].getType()) {
+            case f32: output = join_many<float>(dim, n_arrays, inputs); break;
+            case c32: output = join_many<cfloat>(dim, n_arrays, inputs); break;
+            case f64: output = join_many<double>(dim, n_arrays, inputs); break;
+            case c64: output = join_many<cdouble>(dim, n_arrays, inputs); break;
+            case b8: output = join_many<char>(dim, n_arrays, inputs); break;
+            case s32: output = join_many<int>(dim, n_arrays, inputs); break;
+            case u32: output = join_many<uint>(dim, n_arrays, inputs); break;
+            case s64: output = join_many<intl>(dim, n_arrays, inputs); break;
+            case u64: output = join_many<uintl>(dim, n_arrays, inputs); break;
+            case s16: output = join_many<short>(dim, n_arrays, inputs); break;
+            case u16: output = join_many<ushort>(dim, n_arrays, inputs); break;
+            case u8: output = join_many<uchar>(dim, n_arrays, inputs); break;
+            default: TYPE_ERROR(1, info[0].getType());
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index 82ef8c5fb1..cb5315588f 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <lu.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
 static inline void lu(af_array *lower, af_array *upper, af_array *pivot,
-                      const af_array in)
-{
-    Array<T> lowerArray = createEmptyArray<T>(af::dim4());
-    Array<T> upperArray = createEmptyArray<T>(af::dim4());
+                      const af_array in) {
+    Array<T> lowerArray   = createEmptyArray<T>(af::dim4());
+    Array<T> upperArray   = createEmptyArray<T>(af::dim4());
     Array<int> pivotArray = createEmptyArray<int>(af::dim4());
 
     lu<T>(lowerArray, upperArray, pivotArray, getArray<T>(in));
@@ -35,15 +34,14 @@ static inline void lu(af_array *lower, af_array *upper, af_array *pivot,
 }
 
 template<typename T>
-static inline af_array lu_inplace(af_array in, bool is_lapack_piv)
-{
+static inline af_array lu_inplace(af_array in, bool is_lapack_piv) {
     return getHandle(lu_inplace<T>(getArray<T>(in), !is_lapack_piv));
 }
 
-af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array in)
-{
+af_err af_lu(af_array *lower, af_array *upper, af_array *pivot,
+             const af_array in) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("lu can not be used in batch mode", AF_ERR_BATCH);
@@ -51,21 +49,21 @@ af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array i
 
         af_dtype type = i_info.getType();
 
-        ARG_ASSERT(3, i_info.isFloating());                       // Only floating and complex types
+        ARG_ASSERT(3, i_info.isFloating());  // Only floating and complex types
 
-        if(i_info.ndims() == 0) {
+        if (i_info.ndims() == 0) {
             AF_CHECK(af_create_handle(lower, 0, nullptr, type));
             AF_CHECK(af_create_handle(upper, 0, nullptr, type));
             AF_CHECK(af_create_handle(pivot, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
-        switch(type) {
-            case f32: lu<float  >(lower, upper, pivot, in);  break;
-            case f64: lu<double >(lower, upper, pivot, in);  break;
-            case c32: lu<cfloat >(lower, upper, pivot, in);  break;
-            case c64: lu<cdouble>(lower, upper, pivot, in);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: lu<float>(lower, upper, pivot, in); break;
+            case f64: lu<double>(lower, upper, pivot, in); break;
+            case c32: lu<cfloat>(lower, upper, pivot, in); break;
+            case c64: lu<cdouble>(lower, upper, pivot, in); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
@@ -73,42 +71,38 @@ af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array i
     return AF_SUCCESS;
 }
 
-af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv)
-{
+af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv) {
     try {
-
-        const ArrayInfo& i_info = getInfo(in);
-        af_dtype type = i_info.getType();
+        const ArrayInfo &i_info = getInfo(in);
+        af_dtype type           = i_info.getType();
 
         if (i_info.ndims() > 2) {
             AF_ERROR("lu can not be used in batch mode", AF_ERR_BATCH);
         }
 
-        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
-        if(i_info.ndims() == 0) {
+        if (i_info.ndims() == 0) {
             return af_create_handle(pivot, 0, nullptr, type);
         }
 
         af_array out;
 
-        switch(type) {
-            case f32: out = lu_inplace<float  >(in, is_lapack_piv);  break;
-            case f64: out = lu_inplace<double >(in, is_lapack_piv);  break;
-            case c32: out = lu_inplace<cfloat >(in, is_lapack_piv);  break;
-            case c64: out = lu_inplace<cdouble>(in, is_lapack_piv);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: out = lu_inplace<float>(in, is_lapack_piv); break;
+            case f64: out = lu_inplace<double>(in, is_lapack_piv); break;
+            case c32: out = lu_inplace<cfloat>(in, is_lapack_piv); break;
+            case c64: out = lu_inplace<cdouble>(in, is_lapack_piv); break;
+            default: TYPE_ERROR(1, type);
         }
-        if(pivot != NULL)
-            std::swap(*pivot, out);
+        if (pivot != NULL) std::swap(*pivot, out);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_is_lapack_available(bool *out)
-{
+af_err af_is_lapack_available(bool *out) {
     try {
         *out = isLAPACKAvailable();
     }
diff --git a/src/api/c/match_template.cpp b/src/api/c/match_template.cpp
index 2ce25b905a..e5fbef6f4a 100644
--- a/src/api/c/match_template.cpp
+++ b/src/api/c/match_template.cpp
@@ -7,38 +7,58 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <match_template.hpp>
+#include <af/defines.h>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename inType, typename outType>
-static
-af_array match_template(const af_array &sImg, const af_array tImg, af_match_type mType)
-{
-    switch(mType) {
-        case AF_SAD : return getHandle(match_template<inType, outType, AF_SAD >(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_ZSAD: return getHandle(match_template<inType, outType, AF_ZSAD>(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_LSAD: return getHandle(match_template<inType, outType, AF_LSAD>(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_SSD : return getHandle(match_template<inType, outType, AF_SSD >(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_ZSSD: return getHandle(match_template<inType, outType, AF_ZSSD>(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_LSSD: return getHandle(match_template<inType, outType, AF_LSSD>(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_NCC : return getHandle(match_template<inType, outType, AF_NCC >(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_ZNCC: return getHandle(match_template<inType, outType, AF_ZNCC>(getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_SHD : return getHandle(match_template<inType, outType, AF_SHD >(getArray<inType>(sImg), getArray<inType>(tImg)));
-        default:      return getHandle(match_template<inType, outType, AF_SAD >(getArray<inType>(sImg), getArray<inType>(tImg)));
+static af_array match_template(const af_array& sImg, const af_array tImg,
+                               af_match_type mType) {
+    switch (mType) {
+        case AF_SAD:
+            return getHandle(match_template<inType, outType, AF_SAD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_ZSAD:
+            return getHandle(match_template<inType, outType, AF_ZSAD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_LSAD:
+            return getHandle(match_template<inType, outType, AF_LSAD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_SSD:
+            return getHandle(match_template<inType, outType, AF_SSD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_ZSSD:
+            return getHandle(match_template<inType, outType, AF_ZSSD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_LSSD:
+            return getHandle(match_template<inType, outType, AF_LSSD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_NCC:
+            return getHandle(match_template<inType, outType, AF_NCC>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_ZNCC:
+            return getHandle(match_template<inType, outType, AF_ZNCC>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        case AF_SHD:
+            return getHandle(match_template<inType, outType, AF_SHD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
+        default:
+            return getHandle(match_template<inType, outType, AF_SAD>(
+                getArray<inType>(sImg), getArray<inType>(tImg)));
     }
 }
 
-af_err af_match_template(af_array *out, const af_array search_img, const af_array template_img, const af_match_type m_type)
-{
+af_err af_match_template(af_array* out, const af_array search_img,
+                         const af_array template_img,
+                         const af_match_type m_type) {
     try {
-        ARG_ASSERT(3, (m_type>=AF_SAD && m_type<=AF_LSSD));
+        ARG_ASSERT(3, (m_type >= AF_SAD && m_type <= AF_LSSD));
 
         const ArrayInfo& sInfo = getInfo(search_img);
         const ArrayInfo& tInfo = getInfo(template_img);
@@ -46,25 +66,49 @@ af_err af_match_template(af_array *out, const af_array search_img, const af_arra
         dim4 const sDims = sInfo.dims();
         dim4 const tDims = tInfo.dims();
 
-        dim_t sNumDims= sDims.ndims();
-        dim_t tNumDims= tDims.ndims();
-        ARG_ASSERT(1, (sNumDims>=2));
-        ARG_ASSERT(2, (tNumDims==2));
+        dim_t sNumDims = sDims.ndims();
+        dim_t tNumDims = tDims.ndims();
+        ARG_ASSERT(1, (sNumDims >= 2));
+        ARG_ASSERT(2, (tNumDims == 2));
 
         af_dtype sType = sInfo.getType();
-        ARG_ASSERT(1, (sType==tInfo.getType()));
+        ARG_ASSERT(1, (sType == tInfo.getType()));
 
         af_array output = 0;
-        switch(sType) {
-            case f64: output = match_template<double, double>(search_img, template_img, m_type); break;
-            case f32: output = match_template<float ,  float>(search_img, template_img, m_type); break;
-            case s32: output = match_template<int   ,  float>(search_img, template_img, m_type); break;
-            case u32: output = match_template<uint  ,  float>(search_img, template_img, m_type); break;
-            case s16: output = match_template<short ,  float>(search_img, template_img, m_type); break;
-            case u16: output = match_template<ushort,  float>(search_img, template_img, m_type); break;
-            case  b8: output = match_template<char  ,  float>(search_img, template_img, m_type); break;
-            case  u8: output = match_template<uchar ,  float>(search_img, template_img, m_type); break;
-            default : TYPE_ERROR(1, sType);
+        switch (sType) {
+            case f64:
+                output = match_template<double, double>(search_img,
+                                                        template_img, m_type);
+                break;
+            case f32:
+                output = match_template<float, float>(search_img, template_img,
+                                                      m_type);
+                break;
+            case s32:
+                output = match_template<int, float>(search_img, template_img,
+                                                    m_type);
+                break;
+            case u32:
+                output = match_template<uint, float>(search_img, template_img,
+                                                     m_type);
+                break;
+            case s16:
+                output = match_template<short, float>(search_img, template_img,
+                                                      m_type);
+                break;
+            case u16:
+                output = match_template<ushort, float>(search_img, template_img,
+                                                       m_type);
+                break;
+            case b8:
+                output = match_template<char, float>(search_img, template_img,
+                                                     m_type);
+                break;
+            case u8:
+                output = match_template<uchar, float>(search_img, template_img,
+                                                      m_type);
+                break;
+            default: TYPE_ERROR(1, sType);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 1f6540e97d..7e30ba3341 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -7,72 +7,69 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/data.h>
-#include <af/statistics.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
+#include <cast.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
-#include <mean.hpp>
-#include <arith.hpp>
 #include <math.hpp>
-#include <cast.hpp>
+#include <mean.hpp>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/statistics.h>
 
 #include "stats.h"
 
 using namespace detail;
 
 template<typename Ti, typename To>
-static To mean(const af_array &in)
-{
+static To mean(const af_array &in) {
     typedef typename baseOutType<To>::type Tw;
     return mean<Ti, Tw, To>(getArray<Ti>(in));
 }
 
 template<typename T>
-static T mean(const af_array &in, const af_array &weights)
-{
+static T mean(const af_array &in, const af_array &weights) {
     typedef typename baseOutType<T>::type Tw;
     return mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights));
 }
 
 template<typename Ti, typename To>
-static af_array mean(const af_array &in, const dim_t dim)
-{
+static af_array mean(const af_array &in, const dim_t dim) {
     typedef typename baseOutType<To>::type Tw;
     return getHandle<To>(mean<Ti, Tw, To>(getArray<Ti>(in), dim));
 }
 
 template<typename T>
-static af_array mean(const af_array &in, const af_array &weights, const dim_t dim)
-{
+static af_array mean(const af_array &in, const af_array &weights,
+                     const dim_t dim) {
     typedef typename baseOutType<T>::type Tw;
-    return getHandle<T>(mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights), dim));
+    return getHandle<T>(
+        mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights), dim));
 }
 
-af_err af_mean(af_array *out, const af_array in, const dim_t dim)
-{
+af_err af_mean(af_array *out, const af_array in, const dim_t dim) {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
-
-        af_array output = 0;
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        switch(type) {
-            case f64: output = mean<double  ,  double>(in, dim); break;
-            case f32: output = mean<float   ,  float >(in, dim); break;
-            case s32: output = mean<int     ,  float >(in, dim); break;
-            case u32: output = mean<unsigned,  float >(in, dim); break;
-            case s64: output = mean<intl    ,  double>(in, dim); break;
-            case u64: output = mean<uintl   ,  double>(in, dim); break;
-            case s16: output = mean<short   ,  float >(in, dim); break;
-            case u16: output = mean<ushort  ,  float >(in, dim); break;
-            case  u8: output = mean<uchar   ,  float >(in, dim); break;
-            case  b8: output = mean<char    ,  float >(in, dim); break;
-            case c32: output = mean<cfloat  ,  cfloat>(in, dim); break;
-            case c64: output = mean<cdouble , cdouble>(in, dim); break;
-            default : TYPE_ERROR(1, type);
+        ARG_ASSERT(2, (dim >= 0 && dim <= 3));
+
+        af_array output       = 0;
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        switch (type) {
+            case f64: output = mean<double, double>(in, dim); break;
+            case f32: output = mean<float, float>(in, dim); break;
+            case s32: output = mean<int, float>(in, dim); break;
+            case u32: output = mean<unsigned, float>(in, dim); break;
+            case s64: output = mean<intl, double>(in, dim); break;
+            case u64: output = mean<uintl, double>(in, dim); break;
+            case s16: output = mean<short, float>(in, dim); break;
+            case u16: output = mean<ushort, float>(in, dim); break;
+            case u8: output = mean<uchar, float>(in, dim); break;
+            case b8: output = mean<char, float>(in, dim); break;
+            case c32: output = mean<cfloat, cfloat>(in, dim); break;
+            case c64: output = mean<cdouble, cdouble>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
@@ -80,122 +77,128 @@ af_err af_mean(af_array *out, const af_array in, const dim_t dim)
     return AF_SUCCESS;
 }
 
-af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim)
-{
+af_err af_mean_weighted(af_array *out, const af_array in,
+                        const af_array weights, const dim_t dim) {
     try {
-        ARG_ASSERT(3, (dim>=0 && dim<=3));
+        ARG_ASSERT(3, (dim >= 0 && dim <= 3));
 
-        af_array output = 0;
-        const ArrayInfo& iInfo = getInfo(in);
-        const ArrayInfo& wInfo = getInfo(weights);
-        af_dtype iType  = iInfo.getType();
-        af_dtype wType  = wInfo.getType();
+        af_array output        = 0;
+        const ArrayInfo &iInfo = getInfo(in);
+        const ArrayInfo &wInfo = getInfo(weights);
+        af_dtype iType         = iInfo.getType();
+        af_dtype wType         = wInfo.getType();
 
-        ARG_ASSERT(2, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
+        ARG_ASSERT(
+            2,
+            (wType == f32 ||
+             wType ==
+                 f64)); /* verify that weights are non-complex real numbers */
 
-        //FIXME: We should avoid additional copies
+        // FIXME: We should avoid additional copies
         af_array w = weights;
         if (iInfo.dims() != wInfo.dims()) {
             dim4 iDims = iInfo.dims();
             dim4 wDims = wInfo.dims();
-            dim4 tDims(1,1,1,1);
+            dim4 tDims(1, 1, 1, 1);
             for (int i = 0; i < 4; i++) {
                 ARG_ASSERT(2, wDims[i] == 1 || wDims[i] == iDims[i]);
                 tDims[i] = iDims[i] / wDims[i];
             }
-            AF_CHECK(af_tile(&w, weights, tDims[0], tDims[1], tDims[2], tDims[3]));
+            AF_CHECK(
+                af_tile(&w, weights, tDims[0], tDims[1], tDims[2], tDims[3]));
         }
 
-        switch(iType) {
-            case f64: output = mean< double>(in, w, dim); break;
-            case f32: output = mean< float >(in, w, dim); break;
-            case s32: output = mean< float >(in, w, dim); break;
-            case u32: output = mean< float >(in, w, dim); break;
-            case s64: output = mean< double>(in, w, dim); break;
-            case u64: output = mean< double>(in, w, dim); break;
-            case s16: output = mean< float >(in, w, dim); break;
-            case u16: output = mean< float >(in, w, dim); break;
-            case  u8: output = mean< float >(in, w, dim); break;
-            case  b8: output = mean< float >(in, w, dim); break;
-            case c32: output = mean< cfloat>(in, w, dim); break;
+        switch (iType) {
+            case f64: output = mean<double>(in, w, dim); break;
+            case f32: output = mean<float>(in, w, dim); break;
+            case s32: output = mean<float>(in, w, dim); break;
+            case u32: output = mean<float>(in, w, dim); break;
+            case s64: output = mean<double>(in, w, dim); break;
+            case u64: output = mean<double>(in, w, dim); break;
+            case s16: output = mean<float>(in, w, dim); break;
+            case u16: output = mean<float>(in, w, dim); break;
+            case u8: output = mean<float>(in, w, dim); break;
+            case b8: output = mean<float>(in, w, dim); break;
+            case c32: output = mean<cfloat>(in, w, dim); break;
             case c64: output = mean<cdouble>(in, w, dim); break;
-            default : TYPE_ERROR(1, iType);
+            default: TYPE_ERROR(1, iType);
         }
 
-        if (w != weights) {
-            AF_CHECK(af_release_array(w));
-        }
+        if (w != weights) { AF_CHECK(af_release_array(w)); }
         std::swap(*out, output);
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_mean_all(double *realVal, double *imagVal, const af_array in)
-{
+af_err af_mean_all(double *realVal, double *imagVal, const af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        switch(type) {
-            case f64: *realVal = mean<double  , double>(in); break;
-            case f32: *realVal = mean<float   , float >(in); break;
-            case s32: *realVal = mean<int     , float >(in); break;
-            case u32: *realVal = mean<unsigned, float >(in); break;
-            case s64: *realVal = mean<intl    , double>(in); break;
-            case u64: *realVal = mean<uintl   , double>(in); break;
-            case s16: *realVal = mean<short   , float >(in); break;
-            case u16: *realVal = mean<ushort  , float >(in); break;
-            case  u8: *realVal = mean<uchar   , float >(in); break;
-            case  b8: *realVal = mean<char    , float >(in); break;
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        switch (type) {
+            case f64: *realVal = mean<double, double>(in); break;
+            case f32: *realVal = mean<float, float>(in); break;
+            case s32: *realVal = mean<int, float>(in); break;
+            case u32: *realVal = mean<unsigned, float>(in); break;
+            case s64: *realVal = mean<intl, double>(in); break;
+            case u64: *realVal = mean<uintl, double>(in); break;
+            case s16: *realVal = mean<short, float>(in); break;
+            case u16: *realVal = mean<ushort, float>(in); break;
+            case u8: *realVal = mean<uchar, float>(in); break;
+            case b8: *realVal = mean<char, float>(in); break;
             case c32: {
                 cfloat tmp = mean<cfloat, cfloat>(in);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
+                *realVal   = real(tmp);
+                *imagVal   = imag(tmp);
+            } break;
             case c64: {
                 cdouble tmp = mean<cdouble, cdouble>(in);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
-            default : TYPE_ERROR(1, type);
+                *realVal    = real(tmp);
+                *imagVal    = imag(tmp);
+            } break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_mean_all_weighted(double *realVal, double *imagVal, const af_array in, const af_array weights)
-{
+af_err af_mean_all_weighted(double *realVal, double *imagVal, const af_array in,
+                            const af_array weights) {
     try {
-        const ArrayInfo& iInfo = getInfo(in);
-        const ArrayInfo& wInfo = getInfo(weights);
-        af_dtype iType  = iInfo.getType();
-        af_dtype wType  = wInfo.getType();
-
-        ARG_ASSERT(3, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
-
-        switch(iType) {
+        const ArrayInfo &iInfo = getInfo(in);
+        const ArrayInfo &wInfo = getInfo(weights);
+        af_dtype iType         = iInfo.getType();
+        af_dtype wType         = wInfo.getType();
+
+        ARG_ASSERT(
+            3,
+            (wType == f32 ||
+             wType ==
+                 f64)); /* verify that weights are non-complex real numbers */
+
+        switch (iType) {
             case f64: *realVal = mean<double>(in, weights); break;
-            case f32: *realVal = mean< float>(in, weights); break;
-            case s32: *realVal = mean< float>(in, weights); break;
-            case u32: *realVal = mean< float>(in, weights); break;
+            case f32: *realVal = mean<float>(in, weights); break;
+            case s32: *realVal = mean<float>(in, weights); break;
+            case u32: *realVal = mean<float>(in, weights); break;
             case s64: *realVal = mean<double>(in, weights); break;
             case u64: *realVal = mean<double>(in, weights); break;
-            case s16: *realVal = mean< float>(in, weights); break;
-            case u16: *realVal = mean< float>(in, weights); break;
-            case  u8: *realVal = mean< float>(in, weights); break;
-            case  b8: *realVal = mean< float>(in, weights); break;
+            case s16: *realVal = mean<float>(in, weights); break;
+            case u16: *realVal = mean<float>(in, weights); break;
+            case u8: *realVal = mean<float>(in, weights); break;
+            case b8: *realVal = mean<float>(in, weights); break;
             case c32: {
                 cfloat tmp = mean<cfloat>(in, weights);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
+                *realVal   = real(tmp);
+                *imagVal   = imag(tmp);
+            } break;
             case c64: {
                 cdouble tmp = mean<cdouble>(in, weights);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
-            default : TYPE_ERROR(1, iType);
+                *realVal    = real(tmp);
+                *imagVal    = imag(tmp);
+            } break;
+            default: TYPE_ERROR(1, iType);
         }
     }
     CATCHALL;
diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index 15f3b7c2bc..a6725f96d6 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -7,55 +7,85 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <handle.hpp>
 #include <backend.hpp>
-#include <meanshift.hpp>
 #include <common/err_common.hpp>
+#include <handle.hpp>
+#include <meanshift.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array mean_shift(const af_array &in, const float &s_sigma, const float &c_sigma,
-                                  const unsigned niters, const bool is_color)
-{
-    return getHandle(meanshift<T>(getArray<T>(in), s_sigma, c_sigma, niters, is_color));
+static inline af_array mean_shift(const af_array &in, const float &s_sigma,
+                                  const float &c_sigma, const unsigned niters,
+                                  const bool is_color) {
+    return getHandle(
+        meanshift<T>(getArray<T>(in), s_sigma, c_sigma, niters, is_color));
 }
 
 af_err af_mean_shift(af_array *out, const af_array in,
                      const float spatial_sigma, const float chromatic_sigma,
-                     const unsigned num_iterations, const bool is_color)
-{
+                     const unsigned num_iterations, const bool is_color) {
     try {
-        ARG_ASSERT(2, (spatial_sigma>=0));
-        ARG_ASSERT(3, (chromatic_sigma>=0));
-        ARG_ASSERT(4, (num_iterations>0));
+        ARG_ASSERT(2, (spatial_sigma >= 0));
+        ARG_ASSERT(3, (chromatic_sigma >= 0));
+        ARG_ASSERT(4, (num_iterations > 0));
 
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type  = info.getType();
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
 
-        DIM_ASSERT(1, (dims.ndims()>=2));
-        if (is_color) DIM_ASSERT(1, (dims[2]==3));
+        DIM_ASSERT(1, (dims.ndims() >= 2));
+        if (is_color) DIM_ASSERT(1, (dims[2] == 3));
 
         af_array output;
-        switch(type) {
-            case f32: output = mean_shift<float >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case f64: output = mean_shift<double>(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case b8 : output = mean_shift<char  >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case s32: output = mean_shift<int   >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case u32: output = mean_shift<uint  >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case s16: output = mean_shift<short >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case u16: output = mean_shift<ushort>(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case s64: output = mean_shift<intl  >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case u64: output = mean_shift<uintl >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            case u8 : output = mean_shift<uchar >(in, spatial_sigma, chromatic_sigma, num_iterations, is_color); break;
-            default : TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                output = mean_shift<float>(in, spatial_sigma, chromatic_sigma,
+                                           num_iterations, is_color);
+                break;
+            case f64:
+                output = mean_shift<double>(in, spatial_sigma, chromatic_sigma,
+                                            num_iterations, is_color);
+                break;
+            case b8:
+                output = mean_shift<char>(in, spatial_sigma, chromatic_sigma,
+                                          num_iterations, is_color);
+                break;
+            case s32:
+                output = mean_shift<int>(in, spatial_sigma, chromatic_sigma,
+                                         num_iterations, is_color);
+                break;
+            case u32:
+                output = mean_shift<uint>(in, spatial_sigma, chromatic_sigma,
+                                          num_iterations, is_color);
+                break;
+            case s16:
+                output = mean_shift<short>(in, spatial_sigma, chromatic_sigma,
+                                           num_iterations, is_color);
+                break;
+            case u16:
+                output = mean_shift<ushort>(in, spatial_sigma, chromatic_sigma,
+                                            num_iterations, is_color);
+                break;
+            case s64:
+                output = mean_shift<intl>(in, spatial_sigma, chromatic_sigma,
+                                          num_iterations, is_color);
+                break;
+            case u64:
+                output = mean_shift<uintl>(in, spatial_sigma, chromatic_sigma,
+                                           num_iterations, is_color);
+                break;
+            case u8:
+                output = mean_shift<uchar>(in, spatial_sigma, chromatic_sigma,
+                                           num_iterations, is_color);
+                break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 70f2eefec7..57d3ff05c1 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -7,39 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/statistics.h>
-#include <af/index.h>
-#include <af/arith.h>
-#include <af/data.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
-#include <sort.hpp>
-#include <math.hpp>
 #include <cast.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
+#include <math.hpp>
+#include <sort.hpp>
+#include <af/arith.h>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/index.h>
+#include <af/statistics.h>
 
 using namespace detail;
 using af::dim4;
 
 template<typename T>
-static double median(const af_array& in)
-{
+static double median(const af_array& in) {
     dim_t nElems = getInfo(in).elements();
     dim4 dims(nElems, 1, 1, 1);
     ARG_ASSERT(0, nElems > 0);
 
     af_array temp = 0;
     AF_CHECK(af_moddims(&temp, in, 1, dims.get()));
-    const Array<T> input  = getArray<T>(temp);
+    const Array<T> input = getArray<T>(temp);
 
     // Shortcut cases for 1 or 2 elements
-    if(nElems == 1) {
+    if (nElems == 1) {
         T result;
         AF_CHECK(af_get_data_ptr((void*)&result, in));
         return result;
-    } else if(nElems == 2) {
+    } else if (nElems == 2) {
         T result[2];
         AF_CHECK(af_get_data_ptr((void*)&result, in));
         if (input.isFloating()) {
@@ -49,8 +48,8 @@ static double median(const af_array& in)
         }
     }
 
-    double mid      = (nElems + 1) / 2;
-    af_seq mdSpan[1]= {af_make_seq(mid-1, mid, 1)};
+    double mid       = (nElems + 1) / 2;
+    af_seq mdSpan[1] = {af_make_seq(mid - 1, mid, 1)};
 
     Array<T> sortedArr = sort<T>(input, 0, true);
 
@@ -80,24 +79,23 @@ static double median(const af_array& in)
 }
 
 template<typename T>
-static af_array median(const af_array& in, const dim_t dim)
-{
+static af_array median(const af_array& in, const dim_t dim) {
     const Array<T> input = getArray<T>(in);
 
     // Shortcut cases for 1 element along selected dimension
-    if(input.dims()[dim] == 1) {
+    if (input.dims()[dim] == 1) {
         Array<T> result = copyArray<T>(input);
         return getHandle<T>(result);
     }
 
-    Array<T> sortedIn   = sort<T>(input, dim, true);
+    Array<T> sortedIn = sort<T>(input, dim, true);
 
     int dimLength = input.dims()[dim];
     double mid    = (dimLength + 1) / 2;
     af_array left = 0;
 
     af_seq slices[4] = {af_span, af_span, af_span, af_span};
-    slices[dim] = af_make_seq(mid-1.0, mid-1.0, 1.0);
+    slices[dim]      = af_make_seq(mid - 1.0, mid - 1.0, 1.0);
 
     af_array sortedIn_handle = getHandle<T>(sortedIn);
     AF_CHECK(af_index(&left, sortedIn_handle, input.ndims(), slices));
@@ -117,9 +115,9 @@ static af_array median(const af_array& in, const dim_t dim)
         return out;
     } else {
         // ((mid-1)+mid)/2 is our guy
-        dim4 dims = input.dims();
+        dim4 dims      = input.dims();
         af_array right = 0;
-        slices[dim] = af_make_seq(mid, mid, 1.0);
+        slices[dim]    = af_make_seq(mid, mid, 1.0);
 
         AF_CHECK(af_index(&right, sortedIn_handle, dims.ndims(), slices));
 
@@ -129,7 +127,8 @@ static af_array median(const af_array& in, const dim_t dim)
 
         dim4 cdims = dims;
         cdims[dim] = 1;
-        AF_CHECK(af_constant(&carr, 0.5, cdims.ndims(), cdims.get(), input.isDouble() ? f64 : f32));
+        AF_CHECK(af_constant(&carr, 0.5, cdims.ndims(), cdims.get(),
+                             input.isDouble() ? f64 : f32));
 
         if (!input.isFloating()) {
             af_array lleft, rright;
@@ -137,7 +136,7 @@ static af_array median(const af_array& in, const dim_t dim)
             AF_CHECK(af_cast(&rright, right, f32));
             AF_CHECK(af_release_array(left));
             AF_CHECK(af_release_array(right));
-            left = lleft;
+            left  = lleft;
             right = rright;
         }
 
@@ -153,48 +152,46 @@ static af_array median(const af_array& in, const dim_t dim)
     }
 }
 
-af_err af_median_all(double *realVal, double *imagVal, const af_array in)
-{
+af_err af_median_all(double* realVal, double* imagVal, const af_array in) {
     UNUSED(imagVal);
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        af_dtype type         = info.getType();
 
         ARG_ASSERT(2, info.ndims() > 0);
-        switch(type) {
+        switch (type) {
             case f64: *realVal = median<double>(in); break;
-            case f32: *realVal = median<float >(in); break;
-            case s32: *realVal = median<int   >(in); break;
-            case u32: *realVal = median<uint  >(in); break;
-            case s16: *realVal = median<short >(in); break;
+            case f32: *realVal = median<float>(in); break;
+            case s32: *realVal = median<int>(in); break;
+            case u32: *realVal = median<uint>(in); break;
+            case s16: *realVal = median<short>(in); break;
             case u16: *realVal = median<ushort>(in); break;
-            case  u8: *realVal = median<uchar >(in); break;
-            default : TYPE_ERROR(1, type);
+            case u8: *realVal = median<uchar>(in); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_median(af_array* out, const af_array in, const dim_t dim)
-{
+af_err af_median(af_array* out, const af_array in, const dim_t dim) {
     try {
         ARG_ASSERT(2, (dim >= 0 && dim <= 4));
 
-        af_array output = 0;
+        af_array output       = 0;
         const ArrayInfo& info = getInfo(in);
 
         ARG_ASSERT(1, info.ndims() > 0);
         af_dtype type = info.getType();
-        switch(type) {
+        switch (type) {
             case f64: output = median<double>(in, dim); break;
-            case f32: output = median<float >(in, dim); break;
-            case s32: output = median<int   >(in, dim); break;
-            case u32: output = median<uint  >(in, dim); break;
-            case s16: output = median<short >(in, dim); break;
+            case f32: output = median<float>(in, dim); break;
+            case s32: output = median<int>(in, dim); break;
+            case u32: output = median<uint>(in, dim); break;
+            case s16: output = median<short>(in, dim); break;
             case u16: output = median<ushort>(in, dim); break;
-            case  u8: output = median<uchar >(in, dim); break;
-            default : TYPE_ERROR(1, type);
+            case u8: output = median<uchar>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index e983b77483..6740a7c893 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -7,25 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/device.h>
-#include <af/version.h>
-#include <af/backend.h>
-#include <backend.hpp>
-#include <platform.hpp>
 #include <Array.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <memory.hpp>
-#include <common/err_common.hpp>
+#include <platform.hpp>
+#include <af/backend.h>
+#include <af/device.h>
+#include <af/dim4.hpp>
+#include <af/version.h>
 #include <cstring>
 
 using namespace detail;
 
-af_err af_device_array(af_array *arr, const void *data,
-                       const unsigned ndims,
-                       const dim_t * const dims,
-                       const af_dtype type)
-{
+af_err af_device_array(af_array *arr, const void *data, const unsigned ndims,
+                       const dim_t *const dims, const af_dtype type) {
     try {
         AF_CHECK(af_init());
 
@@ -33,271 +30,276 @@ af_err af_device_array(af_array *arr, const void *data,
 
         DIM_ASSERT(1, ndims >= 1);
         dim4 d(1, 1, 1, 1);
-        for(unsigned i = 0; i < ndims; i++) {
+        for (unsigned i = 0; i < ndims; i++) {
             d[i] = dims[i];
             DIM_ASSERT(3, dims[i] >= 1);
         }
 
         switch (type) {
-        case f32: res = getHandle(createDeviceDataArray<float  >(d, data)); break;
-        case f64: res = getHandle(createDeviceDataArray<double >(d, data)); break;
-        case c32: res = getHandle(createDeviceDataArray<cfloat >(d, data)); break;
-        case c64: res = getHandle(createDeviceDataArray<cdouble>(d, data)); break;
-        case s32: res = getHandle(createDeviceDataArray<int    >(d, data)); break;
-        case u32: res = getHandle(createDeviceDataArray<uint   >(d, data)); break;
-        case s64: res = getHandle(createDeviceDataArray<intl   >(d, data)); break;
-        case u64: res = getHandle(createDeviceDataArray<uintl  >(d, data)); break;
-        case s16: res = getHandle(createDeviceDataArray<short  >(d, data)); break;
-        case u16: res = getHandle(createDeviceDataArray<ushort >(d, data)); break;
-        case u8 : res = getHandle(createDeviceDataArray<uchar  >(d, data)); break;
-        case b8 : res = getHandle(createDeviceDataArray<char   >(d, data)); break;
-        default: TYPE_ERROR(4, type);
+            case f32:
+                res = getHandle(createDeviceDataArray<float>(d, data));
+                break;
+            case f64:
+                res = getHandle(createDeviceDataArray<double>(d, data));
+                break;
+            case c32:
+                res = getHandle(createDeviceDataArray<cfloat>(d, data));
+                break;
+            case c64:
+                res = getHandle(createDeviceDataArray<cdouble>(d, data));
+                break;
+            case s32:
+                res = getHandle(createDeviceDataArray<int>(d, data));
+                break;
+            case u32:
+                res = getHandle(createDeviceDataArray<uint>(d, data));
+                break;
+            case s64:
+                res = getHandle(createDeviceDataArray<intl>(d, data));
+                break;
+            case u64:
+                res = getHandle(createDeviceDataArray<uintl>(d, data));
+                break;
+            case s16:
+                res = getHandle(createDeviceDataArray<short>(d, data));
+                break;
+            case u16:
+                res = getHandle(createDeviceDataArray<ushort>(d, data));
+                break;
+            case u8:
+                res = getHandle(createDeviceDataArray<uchar>(d, data));
+                break;
+            case b8:
+                res = getHandle(createDeviceDataArray<char>(d, data));
+                break;
+            default: TYPE_ERROR(4, type);
         }
 
         std::swap(*arr, res);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_get_device_ptr(void **data, const af_array arr)
-{
+af_err af_get_device_ptr(void **data, const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-            //FIXME: Perform copy if memory not continuous
-        case f32: *data = getDevicePtr(getArray<float  >(arr)); break;
-        case f64: *data = getDevicePtr(getArray<double >(arr)); break;
-        case c32: *data = getDevicePtr(getArray<cfloat >(arr)); break;
-        case c64: *data = getDevicePtr(getArray<cdouble>(arr)); break;
-        case s32: *data = getDevicePtr(getArray<int    >(arr)); break;
-        case u32: *data = getDevicePtr(getArray<uint   >(arr)); break;
-        case s64: *data = getDevicePtr(getArray<intl   >(arr)); break;
-        case u64: *data = getDevicePtr(getArray<uintl  >(arr)); break;
-        case s16: *data = getDevicePtr(getArray<short  >(arr)); break;
-        case u16: *data = getDevicePtr(getArray<ushort >(arr)); break;
-        case u8 : *data = getDevicePtr(getArray<uchar  >(arr)); break;
-        case b8 : *data = getDevicePtr(getArray<char   >(arr)); break;
-
-        default: TYPE_ERROR(4, type);
+                // FIXME: Perform copy if memory not continuous
+            case f32: *data = getDevicePtr(getArray<float>(arr)); break;
+            case f64: *data = getDevicePtr(getArray<double>(arr)); break;
+            case c32: *data = getDevicePtr(getArray<cfloat>(arr)); break;
+            case c64: *data = getDevicePtr(getArray<cdouble>(arr)); break;
+            case s32: *data = getDevicePtr(getArray<int>(arr)); break;
+            case u32: *data = getDevicePtr(getArray<uint>(arr)); break;
+            case s64: *data = getDevicePtr(getArray<intl>(arr)); break;
+            case u64: *data = getDevicePtr(getArray<uintl>(arr)); break;
+            case s16: *data = getDevicePtr(getArray<short>(arr)); break;
+            case u16: *data = getDevicePtr(getArray<ushort>(arr)); break;
+            case u8: *data = getDevicePtr(getArray<uchar>(arr)); break;
+            case b8: *data = getDevicePtr(getArray<char>(arr)); break;
+
+            default: TYPE_ERROR(4, type);
         }
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-template <typename T>
-inline void lockArray(const af_array arr)
-{
+template<typename T>
+inline void lockArray(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
     memLock((void *)getArray<T>(arr).getData().get());
 }
 
-af_err af_lock_device_ptr(const af_array arr)
-{
-    return af_lock_array(arr);
-}
+af_err af_lock_device_ptr(const af_array arr) { return af_lock_array(arr); }
 
-af_err af_lock_array(const af_array arr)
-{
+af_err af_lock_array(const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-        case f32: lockArray<float  >(arr); break;
-        case f64: lockArray<double >(arr); break;
-        case c32: lockArray<cfloat >(arr); break;
-        case c64: lockArray<cdouble>(arr); break;
-        case s32: lockArray<int    >(arr); break;
-        case u32: lockArray<uint   >(arr); break;
-        case s64: lockArray<intl   >(arr); break;
-        case u64: lockArray<uintl  >(arr); break;
-        case s16: lockArray<short  >(arr); break;
-        case u16: lockArray<ushort >(arr); break;
-        case u8 : lockArray<uchar  >(arr); break;
-        case b8 : lockArray<char   >(arr); break;
-        default: TYPE_ERROR(4, type);
+            case f32: lockArray<float>(arr); break;
+            case f64: lockArray<double>(arr); break;
+            case c32: lockArray<cfloat>(arr); break;
+            case c64: lockArray<cdouble>(arr); break;
+            case s32: lockArray<int>(arr); break;
+            case u32: lockArray<uint>(arr); break;
+            case s64: lockArray<intl>(arr); break;
+            case u64: lockArray<uintl>(arr); break;
+            case s16: lockArray<short>(arr); break;
+            case u16: lockArray<ushort>(arr); break;
+            case u8: lockArray<uchar>(arr); break;
+            case b8: lockArray<char>(arr); break;
+            default: TYPE_ERROR(4, type);
         }
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-
 template<typename T>
-inline bool checkUserLock(const af_array arr)
-{
+inline bool checkUserLock(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
     return isLocked((void *)getArray<T>(arr).getData().get());
 }
 
-af_err af_is_locked_array(bool *res, const af_array arr)
-{
+af_err af_is_locked_array(bool *res, const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-        case f32: *res = checkUserLock<float  >(arr); break;
-        case f64: *res = checkUserLock<double >(arr); break;
-        case c32: *res = checkUserLock<cfloat >(arr); break;
-        case c64: *res = checkUserLock<cdouble>(arr); break;
-        case s32: *res = checkUserLock<int    >(arr); break;
-        case u32: *res = checkUserLock<uint   >(arr); break;
-        case s64: *res = checkUserLock<intl   >(arr); break;
-        case u64: *res = checkUserLock<uintl  >(arr); break;
-        case s16: *res = checkUserLock<short  >(arr); break;
-        case u16: *res = checkUserLock<ushort >(arr); break;
-        case u8 : *res = checkUserLock<uchar  >(arr); break;
-        case b8 : *res = checkUserLock<char   >(arr); break;
-        default: TYPE_ERROR(4, type);
+            case f32: *res = checkUserLock<float>(arr); break;
+            case f64: *res = checkUserLock<double>(arr); break;
+            case c32: *res = checkUserLock<cfloat>(arr); break;
+            case c64: *res = checkUserLock<cdouble>(arr); break;
+            case s32: *res = checkUserLock<int>(arr); break;
+            case u32: *res = checkUserLock<uint>(arr); break;
+            case s64: *res = checkUserLock<intl>(arr); break;
+            case u64: *res = checkUserLock<uintl>(arr); break;
+            case s16: *res = checkUserLock<short>(arr); break;
+            case u16: *res = checkUserLock<ushort>(arr); break;
+            case u8: *res = checkUserLock<uchar>(arr); break;
+            case b8: *res = checkUserLock<char>(arr); break;
+            default: TYPE_ERROR(4, type);
         }
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-template <typename T>
-inline void unlockArray(const af_array arr)
-{
+template<typename T>
+inline void unlockArray(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
     memUnlock((void *)getArray<T>(arr).getData().get());
 }
 
-af_err af_unlock_device_ptr(const af_array arr)
-{
-    return af_unlock_array(arr);
-}
+af_err af_unlock_device_ptr(const af_array arr) { return af_unlock_array(arr); }
 
-af_err af_unlock_array(const af_array arr)
-{
+af_err af_unlock_array(const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-        case f32: unlockArray<float  >(arr); break;
-        case f64: unlockArray<double >(arr); break;
-        case c32: unlockArray<cfloat >(arr); break;
-        case c64: unlockArray<cdouble>(arr); break;
-        case s32: unlockArray<int    >(arr); break;
-        case u32: unlockArray<uint   >(arr); break;
-        case s64: unlockArray<intl   >(arr); break;
-        case u64: unlockArray<uintl  >(arr); break;
-        case s16: unlockArray<short  >(arr); break;
-        case u16: unlockArray<ushort >(arr); break;
-        case u8 : unlockArray<uchar  >(arr); break;
-        case b8 : unlockArray<char   >(arr); break;
-        default: TYPE_ERROR(4, type);
+            case f32: unlockArray<float>(arr); break;
+            case f64: unlockArray<double>(arr); break;
+            case c32: unlockArray<cfloat>(arr); break;
+            case c64: unlockArray<cdouble>(arr); break;
+            case s32: unlockArray<int>(arr); break;
+            case u32: unlockArray<uint>(arr); break;
+            case s64: unlockArray<intl>(arr); break;
+            case u64: unlockArray<uintl>(arr); break;
+            case s16: unlockArray<short>(arr); break;
+            case u16: unlockArray<ushort>(arr); break;
+            case u8: unlockArray<uchar>(arr); break;
+            case b8: unlockArray<char>(arr); break;
+            default: TYPE_ERROR(4, type);
         }
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-
-af_err af_alloc_device(void **ptr, const dim_t bytes)
-{
+af_err af_alloc_device(void **ptr, const dim_t bytes) {
     try {
         AF_CHECK(af_init());
         *ptr = memAllocUser(bytes);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_alloc_pinned(void **ptr, const dim_t bytes)
-{
+af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
     try {
         AF_CHECK(af_init());
         *ptr = (void *)pinnedAlloc<char>(bytes);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_free_device(void *ptr)
-{
+af_err af_free_device(void *ptr) {
     try {
         memFreeUser(ptr);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_free_pinned(void *ptr)
-{
+af_err af_free_pinned(void *ptr) {
     try {
         pinnedFree<char>((char *)ptr);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_alloc_host(void **ptr, const dim_t bytes)
-{
-    if((*ptr = malloc(bytes))) {
-      return AF_SUCCESS;
-    }
+af_err af_alloc_host(void **ptr, const dim_t bytes) {
+    if ((*ptr = malloc(bytes))) { return AF_SUCCESS; }
     return AF_ERR_NO_MEM;
 }
 
-af_err af_free_host(void *ptr)
-{
+af_err af_free_host(void *ptr) {
     free(ptr);
     return AF_SUCCESS;
 }
 
-af_err af_print_mem_info(const char *msg, const int device_id)
-{
+af_err af_print_mem_info(const char *msg, const int device_id) {
     try {
         int device = device_id;
-        if(device == -1) {
-            device = getActiveDeviceId();
-        }
+        if (device == -1) { device = getActiveDeviceId(); }
 
-        if(msg != NULL) ARG_ASSERT(0, strlen(msg) < 256); // 256 character limit on msg
+        if (msg != NULL)
+            ARG_ASSERT(0, strlen(msg) < 256);  // 256 character limit on msg
         ARG_ASSERT(1, device >= 0 && device < getDeviceCount());
 
         printMemInfo(msg ? msg : "", device);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_device_gc()
-{
+af_err af_device_gc() {
     try {
         garbageCollect();
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
-                          size_t *lock_bytes,  size_t *lock_buffers)
-{
+                          size_t *lock_bytes, size_t *lock_buffers) {
     try {
         deviceMemoryInfo(alloc_bytes, alloc_buffers, lock_bytes, lock_buffers);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_mem_step_size(const size_t step_bytes)
-{
-    try{
+af_err af_set_mem_step_size(const size_t step_bytes) {
+    try {
         detail::setMemStepSize(step_bytes);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_mem_step_size(size_t *step_bytes)
-{
+af_err af_get_mem_step_size(size_t *step_bytes) {
     try {
-        *step_bytes =  detail::getMemStepSize();
-    } CATCHALL;
+        *step_bytes = detail::getMemStepSize();
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 7378c4ae80..9975371e69 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -7,37 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/data.h>
 #include <backend.hpp>
 #include <common/err_common.hpp>
-#include <handle.hpp>
-#include <backend.hpp>
 #include <copy.hpp>
+#include <handle.hpp>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using namespace detail;
 
-namespace
-{
+namespace {
 template<typename T>
-af_array modDims(const af_array in, const dim4& newDims)
-{
+af_array modDims(const af_array in, const dim4& newDims) {
     return getHandle(::modDims(getArray<T>(in), newDims));
 }
 template<typename T>
-af_array flat(const af_array in)
-{
+af_array flat(const af_array in) {
     return getHandle(::flat(getArray<T>(in)));
 }
-}
+}  // namespace
 
-af_err af_moddims(af_array *out, const af_array in,
-                  const unsigned ndims, const dim_t * const dims)
-{
+af_err af_moddims(af_array* out, const af_array in, const unsigned ndims,
+                  const dim_t* const dims) {
     try {
-        if(ndims == 0) {
+        if (ndims == 0) {
             *out = retain(in);
             return AF_SUCCESS;
         }
@@ -47,37 +42,36 @@ af_err af_moddims(af_array *out, const af_array in,
         af_array output = 0;
         dim4 newDims(ndims, dims);
         const ArrayInfo& info = getInfo(in);
-        dim_t in_elements = info.elements();
-        dim_t new_elements = newDims.elements();
+        dim_t in_elements     = info.elements();
+        dim_t new_elements    = newDims.elements();
 
         DIM_ASSERT(1, in_elements == new_elements);
 
         af_dtype type = info.getType();
 
-        switch(type) {
-            case f32: output = modDims<float  >(in, newDims); break;
-            case c32: output = modDims<cfloat >(in, newDims); break;
-            case f64: output = modDims<double >(in, newDims); break;
+        switch (type) {
+            case f32: output = modDims<float>(in, newDims); break;
+            case c32: output = modDims<cfloat>(in, newDims); break;
+            case f64: output = modDims<double>(in, newDims); break;
             case c64: output = modDims<cdouble>(in, newDims); break;
-            case b8:  output = modDims<char   >(in, newDims); break;
-            case s32: output = modDims<int    >(in, newDims); break;
-            case u32: output = modDims<uint   >(in, newDims); break;
-            case u8:  output = modDims<uchar  >(in, newDims); break;
-            case s64: output = modDims<intl   >(in, newDims); break;
-            case u64: output = modDims<uintl  >(in, newDims); break;
-            case s16: output = modDims<short  >(in, newDims); break;
-            case u16: output = modDims<ushort >(in, newDims); break;
+            case b8: output = modDims<char>(in, newDims); break;
+            case s32: output = modDims<int>(in, newDims); break;
+            case u32: output = modDims<uint>(in, newDims); break;
+            case u8: output = modDims<uchar>(in, newDims); break;
+            case s64: output = modDims<intl>(in, newDims); break;
+            case u64: output = modDims<uintl>(in, newDims); break;
+            case s16: output = modDims<short>(in, newDims); break;
+            case u16: output = modDims<ushort>(in, newDims); break;
             default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL
 
     return AF_SUCCESS;
 }
 
-af_err af_flat(af_array *out, const af_array in)
-{
+af_err af_flat(af_array* out, const af_array in) {
     try {
         const ArrayInfo& info = getInfo(in);
 
@@ -85,24 +79,24 @@ af_err af_flat(af_array *out, const af_array in)
             *out = retain(in);
         } else {
             af_array output = 0;
-            af_dtype type = info.getType();
+            af_dtype type   = info.getType();
 
-            switch(type) {
-                case f32: output = flat<float  >(in); break;
-                case c32: output = flat<cfloat >(in); break;
-                case f64: output = flat<double >(in); break;
+            switch (type) {
+                case f32: output = flat<float>(in); break;
+                case c32: output = flat<cfloat>(in); break;
+                case f64: output = flat<double>(in); break;
                 case c64: output = flat<cdouble>(in); break;
-                case b8:  output = flat<char   >(in); break;
-                case s32: output = flat<int    >(in); break;
-                case u32: output = flat<uint   >(in); break;
-                case u8:  output = flat<uchar  >(in); break;
-                case s64: output = flat<intl   >(in); break;
-                case u64: output = flat<uintl  >(in); break;
-                case s16: output = flat<short  >(in); break;
-                case u16: output = flat<ushort >(in); break;
+                case b8: output = flat<char>(in); break;
+                case s32: output = flat<int>(in); break;
+                case u32: output = flat<uint>(in); break;
+                case u8: output = flat<uchar>(in); break;
+                case s64: output = flat<intl>(in); break;
+                case u64: output = flat<uintl>(in); break;
+                case s16: output = flat<short>(in); break;
+                case u16: output = flat<ushort>(in); break;
                 default: TYPE_ERROR(1, type);
             }
-            std::swap(*out,output);
+            std::swap(*out, output);
         }
     }
     CATCHALL;
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 5345afd233..379dd90edd 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -7,21 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/data.h>
 #include <af/image.h>
 #include <af/index.h>
-#include <af/data.h>
 
+#include <arith.hpp>
+#include <backend.hpp>
+#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
-#include <moments.hpp>
+#include <common/graphics_common.hpp>
 #include <handle.hpp>
+#include <join.hpp>
+#include <moments.hpp>
 #include <reorder.hpp>
 #include <tile.hpp>
-#include <join.hpp>
-#include <cast.hpp>
-#include <arith.hpp>
 
 #include <limits>
 #include <vector>
@@ -32,27 +32,27 @@ using std::vector;
 using namespace detail;
 
 template<typename T>
-static inline void moments(af_array *out, const af_array in, af_moment_type moment)
-{
+static inline void moments(af_array* out, const af_array in,
+                           af_moment_type moment) {
     Array<float> temp = moments<T>(getArray<T>(in), moment);
-    *out = getHandle<float>(temp);
+    *out              = getHandle<float>(temp);
 }
 
-af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
-{
+af_err af_moments(af_array* out, const af_array in,
+                  const af_moment_type moment) {
     try {
         const ArrayInfo& in_info = getInfo(in);
-        af_dtype type = in_info.getType();
+        af_dtype type            = in_info.getType();
 
-        switch(type) {
-            case f32: moments<float>          (out, in, moment); break;
-            case f64: moments<double>         (out, in, moment); break;
-            case u32: moments<unsigned>       (out, in, moment); break;
-            case s32: moments<int>            (out, in, moment); break;
-            case u16: moments<unsigned short> (out, in, moment); break;
-            case s16: moments<short>          (out, in, moment); break;
-            case b8:  moments<char>           (out, in, moment); break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: moments<float>(out, in, moment); break;
+            case f64: moments<double>(out, in, moment); break;
+            case u32: moments<unsigned>(out, in, moment); break;
+            case s32: moments<int>(out, in, moment); break;
+            case u16: moments<unsigned short>(out, in, moment); break;
+            case s16: moments<short>(out, in, moment); break;
+            case b8: moments<char>(out, in, moment); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
@@ -61,8 +61,7 @@ af_err af_moments(af_array *out, const af_array in, const af_moment_type moment)
 }
 
 template<typename T>
-static inline void moment_copy(double* out, const af_array moments)
-{
+static inline void moment_copy(double* out, const af_array moments) {
     auto info = getInfo(moments);
     vector<T> h_moments(info.elements());
     copyData(h_moments.data(), moments);
@@ -71,11 +70,11 @@ static inline void moment_copy(double* out, const af_array moments)
     copy(begin(h_moments), end(h_moments), out);
 }
 
-af_err af_moments_all(double* out, const af_array in, const af_moment_type moment)
-{
+af_err af_moments_all(double* out, const af_array in,
+                      const af_moment_type moment) {
     try {
         const ArrayInfo& in_info = getInfo(in);
-        dim4 idims = in_info.dims();
+        dim4 idims               = in_info.dims();
         DIM_ASSERT(1, idims[2] == 1 && idims[3] == 1);
 
         af_array moments_arr;
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 2d4a14b188..bec787d978 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -7,61 +7,58 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <morph.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, bool isDilation>
-static inline af_array morph(const af_array &in, const af_array &mask)
-{
-    const Array<T> &input = getArray<T>(in);
+static inline af_array morph(const af_array &in, const af_array &mask) {
+    const Array<T> &input  = getArray<T>(in);
     const Array<T> &filter = castArray<T>(mask);
-    Array<T> out = morph<T, isDilation>(input, filter);
+    Array<T> out           = morph<T, isDilation>(input, filter);
     return getHandle(out);
 }
 
 template<typename T, bool isDilation>
-static inline af_array morph3d(const af_array &in, const af_array &mask)
-{
-    const Array<T> &input = getArray<T>(in);
+static inline af_array morph3d(const af_array &in, const af_array &mask) {
+    const Array<T> &input  = getArray<T>(in);
     const Array<T> &filter = castArray<T>(mask);
-    Array<T> out = morph3d<T, isDilation>(input, filter);
+    Array<T> out           = morph3d<T, isDilation>(input, filter);
     return getHandle(out);
 }
 
 template<bool isDilation>
-static af_err morph(af_array *out, const af_array &in, const af_array &mask)
-{
+static af_err morph(af_array *out, const af_array &in, const af_array &mask) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        const ArrayInfo& mInfo= getInfo(mask);
-        af::dim4 dims  = info.dims();
-        af::dim4 mdims = mInfo.dims();
-        dim_t in_ndims = dims.ndims();
-        dim_t mask_ndims = mdims.ndims();
+        const ArrayInfo &info  = getInfo(in);
+        const ArrayInfo &mInfo = getInfo(mask);
+        af::dim4 dims          = info.dims();
+        af::dim4 mdims         = mInfo.dims();
+        dim_t in_ndims         = dims.ndims();
+        dim_t mask_ndims       = mdims.ndims();
 
         DIM_ASSERT(1, (in_ndims >= 2));
         DIM_ASSERT(2, (mask_ndims == 2));
 
         af_array output;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: output = morph<float , isDilation>(in, mask);      break;
-            case f64: output = morph<double, isDilation>(in, mask);      break;
-            case b8 : output = morph<char  , isDilation>(in, mask);      break;
-            case s32: output = morph<int   , isDilation>(in, mask);      break;
-            case u32: output = morph<uint  , isDilation>(in, mask);      break;
-            case s16: output = morph<short , isDilation>(in, mask);      break;
-            case u16: output = morph<ushort, isDilation>(in, mask);      break;
-            case u8 : output = morph<uchar , isDilation>(in, mask);      break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32: output = morph<float, isDilation>(in, mask); break;
+            case f64: output = morph<double, isDilation>(in, mask); break;
+            case b8: output = morph<char, isDilation>(in, mask); break;
+            case s32: output = morph<int, isDilation>(in, mask); break;
+            case u32: output = morph<uint, isDilation>(in, mask); break;
+            case s16: output = morph<short, isDilation>(in, mask); break;
+            case u16: output = morph<ushort, isDilation>(in, mask); break;
+            case u8: output = morph<uchar, isDilation>(in, mask); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
@@ -71,31 +68,30 @@ static af_err morph(af_array *out, const af_array &in, const af_array &mask)
 }
 
 template<bool isDilation>
-static af_err morph3d(af_array *out, const af_array &in, const af_array &mask)
-{
+static af_err morph3d(af_array *out, const af_array &in, const af_array &mask) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        const ArrayInfo& mInfo= getInfo(mask);
-        af::dim4 dims  = info.dims();
-        af::dim4 mdims = mInfo.dims();
-        dim_t in_ndims = dims.ndims();
-        dim_t mask_ndims = mdims.ndims();
+        const ArrayInfo &info  = getInfo(in);
+        const ArrayInfo &mInfo = getInfo(mask);
+        af::dim4 dims          = info.dims();
+        af::dim4 mdims         = mInfo.dims();
+        dim_t in_ndims         = dims.ndims();
+        dim_t mask_ndims       = mdims.ndims();
 
         DIM_ASSERT(1, (in_ndims >= 3));
         DIM_ASSERT(2, (mask_ndims == 3));
 
         af_array output;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: output = morph3d<float , isDilation>(in, mask);       break;
-            case f64: output = morph3d<double, isDilation>(in, mask);       break;
-            case b8 : output = morph3d<char  , isDilation>(in, mask);       break;
-            case s32: output = morph3d<int   , isDilation>(in, mask);       break;
-            case u32: output = morph3d<uint  , isDilation>(in, mask);       break;
-            case s16: output = morph3d<short , isDilation>(in, mask);       break;
-            case u16: output = morph3d<ushort, isDilation>(in, mask);       break;
-            case u8 : output = morph3d<uchar , isDilation>(in, mask);       break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32: output = morph3d<float, isDilation>(in, mask); break;
+            case f64: output = morph3d<double, isDilation>(in, mask); break;
+            case b8: output = morph3d<char, isDilation>(in, mask); break;
+            case s32: output = morph3d<int, isDilation>(in, mask); break;
+            case u32: output = morph3d<uint, isDilation>(in, mask); break;
+            case s16: output = morph3d<short, isDilation>(in, mask); break;
+            case u16: output = morph3d<ushort, isDilation>(in, mask); break;
+            case u8: output = morph3d<uchar, isDilation>(in, mask); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
@@ -103,22 +99,18 @@ static af_err morph3d(af_array *out, const af_array &in, const af_array &mask)
 
     return AF_SUCCESS;
 }
-af_err af_dilate(af_array *out, const af_array in, const af_array mask)
-{
-    return morph<true>(out,in,mask);
+af_err af_dilate(af_array *out, const af_array in, const af_array mask) {
+    return morph<true>(out, in, mask);
 }
 
-af_err af_erode(af_array *out, const af_array in, const af_array mask)
-{
-    return morph<false>(out,in,mask);
+af_err af_erode(af_array *out, const af_array in, const af_array mask) {
+    return morph<false>(out, in, mask);
 }
 
-af_err af_dilate3(af_array *out, const af_array in, const af_array mask)
-{
-    return morph3d<true>(out,in,mask);
+af_err af_dilate3(af_array *out, const af_array in, const af_array mask) {
+    return morph3d<true>(out, in, mask);
 }
 
-af_err af_erode3(af_array *out, const af_array in, const af_array mask)
-{
-    return morph3d<false>(out,in,mask);
+af_err af_erode3(af_array *out, const af_array in, const af_array mask) {
+    return morph3d<false>(out, in, mask);
 }
diff --git a/src/api/c/nearest_neighbour.cpp b/src/api/c/nearest_neighbour.cpp
index 6d53f5feed..6c88b1357e 100644
--- a/src/api/c/nearest_neighbour.cpp
+++ b/src/api/c/nearest_neighbour.cpp
@@ -7,45 +7,42 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <nearest_neighbour.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename Ti, typename To>
 static void nearest_neighbour(af_array* idx, af_array* dist,
-        const af_array query, const af_array train,
-        const dim_t dist_dim, const uint n_dist,
-        const af_match_type dist_type)
-{
+                              const af_array query, const af_array train,
+                              const dim_t dist_dim, const uint n_dist,
+                              const af_match_type dist_type) {
     Array<uint> oIdxArray = createEmptyArray<uint>(af::dim4());
-    Array<To>  oDistArray = createEmptyArray<To>(af::dim4());
+    Array<To> oDistArray  = createEmptyArray<To>(af::dim4());
 
-    nearest_neighbour<Ti, To>(oIdxArray, oDistArray, getArray<Ti>(query), getArray<Ti>(train),
-                              dist_dim, n_dist, dist_type);
+    nearest_neighbour<Ti, To>(oIdxArray, oDistArray, getArray<Ti>(query),
+                              getArray<Ti>(train), dist_dim, n_dist, dist_type);
 
     *idx  = getHandle<uint>(oIdxArray);
     *dist = getHandle<To>(oDistArray);
 }
 
-af_err af_nearest_neighbour(af_array* idx, af_array* dist,
-        const af_array query, const af_array train,
-        const dim_t dist_dim, const uint n_dist,
-        const af_match_type dist_type)
-{
+af_err af_nearest_neighbour(af_array* idx, af_array* dist, const af_array query,
+                            const af_array train, const dim_t dist_dim,
+                            const uint n_dist, const af_match_type dist_type) {
     try {
         const ArrayInfo& qInfo = getInfo(query);
         const ArrayInfo& tInfo = getInfo(train);
-        af_dtype qType  = qInfo.getType();
-        af_dtype tType  = tInfo.getType();
-        af::dim4 qDims  = qInfo.dims();
-        af::dim4 tDims  = tInfo.dims();
+        af_dtype qType         = qInfo.getType();
+        af_dtype tType         = tInfo.getType();
+        af::dim4 qDims         = qInfo.dims();
+        af::dim4 tDims         = tInfo.dims();
 
         uint train_samples = (dist_dim == 0) ? 1 : 0;
 
@@ -55,34 +52,79 @@ af_err af_nearest_neighbour(af_array* idx, af_array* dist,
         DIM_ASSERT(4, (dist_dim == 0 || dist_dim == 1));
         DIM_ASSERT(5, n_dist > 0 && n_dist <= (uint)tDims[train_samples]);
         ARG_ASSERT(5, n_dist > 0 && n_dist <= 256);
-        ARG_ASSERT(6, dist_type == AF_SAD || dist_type == AF_SSD || dist_type == AF_SHD);
+        ARG_ASSERT(6, dist_type == AF_SAD || dist_type == AF_SSD ||
+                          dist_type == AF_SHD);
         TYPE_ASSERT(qType == tType);
 
         // For Hamming, only u8, u16, u32 and u64 allowed.
         af_array oIdx;
         af_array oDist;
 
-        if(dist_type == AF_SHD) {
-            TYPE_ASSERT(qType == u8 || qType == u16 || qType == u32 || qType == u64);
-            switch(qType) {
-                case u8:  nearest_neighbour<uchar , uint>(&oIdx, &oDist, query, train, dist_dim, n_dist, AF_SHD); break;
-                case u16: nearest_neighbour<ushort, uint>(&oIdx, &oDist, query, train, dist_dim, n_dist, AF_SHD); break;
-                case u32: nearest_neighbour<uint  , uint>(&oIdx, &oDist, query, train, dist_dim, n_dist, AF_SHD); break;
-                case u64: nearest_neighbour<uintl , uint>(&oIdx, &oDist, query, train, dist_dim, n_dist, AF_SHD); break;
-                default : TYPE_ERROR(1, qType);
+        if (dist_type == AF_SHD) {
+            TYPE_ASSERT(qType == u8 || qType == u16 || qType == u32 ||
+                        qType == u64);
+            switch (qType) {
+                case u8:
+                    nearest_neighbour<uchar, uint>(&oIdx, &oDist, query, train,
+                                                   dist_dim, n_dist, AF_SHD);
+                    break;
+                case u16:
+                    nearest_neighbour<ushort, uint>(&oIdx, &oDist, query, train,
+                                                    dist_dim, n_dist, AF_SHD);
+                    break;
+                case u32:
+                    nearest_neighbour<uint, uint>(&oIdx, &oDist, query, train,
+                                                  dist_dim, n_dist, AF_SHD);
+                    break;
+                case u64:
+                    nearest_neighbour<uintl, uint>(&oIdx, &oDist, query, train,
+                                                   dist_dim, n_dist, AF_SHD);
+                    break;
+                default: TYPE_ERROR(1, qType);
             }
         } else {
-            switch(qType) {
-                case f32: nearest_neighbour<float , float >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case f64: nearest_neighbour<double, double>(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case s32: nearest_neighbour<int   , int   >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case u32: nearest_neighbour<uint  , uint  >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case s64: nearest_neighbour<intl  , intl  >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case u64: nearest_neighbour<uintl , uintl >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case s16: nearest_neighbour<short , int   >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case u16: nearest_neighbour<ushort, uint  >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                case u8:  nearest_neighbour<uchar , uint  >(&oIdx, &oDist, query, train, dist_dim, n_dist, dist_type); break;
-                default : TYPE_ERROR(1, qType);
+            switch (qType) {
+                case f32:
+                    nearest_neighbour<float, float>(&oIdx, &oDist, query, train,
+                                                    dist_dim, n_dist,
+                                                    dist_type);
+                    break;
+                case f64:
+                    nearest_neighbour<double, double>(&oIdx, &oDist, query,
+                                                      train, dist_dim, n_dist,
+                                                      dist_type);
+                    break;
+                case s32:
+                    nearest_neighbour<int, int>(&oIdx, &oDist, query, train,
+                                                dist_dim, n_dist, dist_type);
+                    break;
+                case u32:
+                    nearest_neighbour<uint, uint>(&oIdx, &oDist, query, train,
+                                                  dist_dim, n_dist, dist_type);
+                    break;
+                case s64:
+                    nearest_neighbour<intl, intl>(&oIdx, &oDist, query, train,
+                                                  dist_dim, n_dist, dist_type);
+                    break;
+                case u64:
+                    nearest_neighbour<uintl, uintl>(&oIdx, &oDist, query, train,
+                                                    dist_dim, n_dist,
+                                                    dist_type);
+                    break;
+                case s16:
+                    nearest_neighbour<short, int>(&oIdx, &oDist, query, train,
+                                                  dist_dim, n_dist, dist_type);
+                    break;
+                case u16:
+                    nearest_neighbour<ushort, uint>(&oIdx, &oDist, query, train,
+                                                    dist_dim, n_dist,
+                                                    dist_type);
+                    break;
+                case u8:
+                    nearest_neighbour<uchar, uint>(&oIdx, &oDist, query, train,
+                                                   dist_dim, n_dist, dist_type);
+                    break;
+                default: TYPE_ERROR(1, qType);
             }
         }
         std::swap(*idx, oIdx);
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index dc5b3b76a2..42eccd23b6 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -7,27 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <af/traits.hpp>
-#include <af/constants.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <math.hpp>
-#include <arith.hpp>
+#include <common/err_common.hpp>
+#include <complex.hpp>
+#include <handle.hpp>
 #include <lu.hpp>
+#include <math.hpp>
 #include <reduce.hpp>
-#include <complex.hpp>
+#include <af/array.h>
+#include <af/constants.h>
+#include <af/defines.h>
+#include <af/lapack.h>
+#include <af/traits.hpp>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-double matrixNorm(const Array<T> &A, double p)
-{
+double matrixNorm(const Array<T> &A, double p) {
     if (p == 1) {
         Array<T> colSum = reduce<af_add_t, T, T>(A, 0);
         return reduce_all<af_max_t, T, T>(colSum);
@@ -36,12 +35,12 @@ double matrixNorm(const Array<T> &A, double p)
         return reduce_all<af_max_t, T, T>(rowSum);
     }
 
-    AF_ERROR("This type of norm is not supported in ArrayFire\n", AF_ERR_NOT_SUPPORTED);
+    AF_ERROR("This type of norm is not supported in ArrayFire\n",
+             AF_ERR_NOT_SUPPORTED);
 }
 
 template<typename T>
-double vectorNorm(const Array<T> &A, double p)
-{
+double vectorNorm(const Array<T> &A, double p) {
     if (p == 1) {
         return reduce_all<af_add_t, T, T>(A);
     } else if (p == af::Inf) {
@@ -51,82 +50,68 @@ double vectorNorm(const Array<T> &A, double p)
         return std::sqrt(reduce_all<af_add_t, T, T>(A_sq));
     }
 
-    Array<T> P = createValueArray<T>(A.dims(), scalar<T>(p));
+    Array<T> P   = createValueArray<T>(A.dims(), scalar<T>(p));
     Array<T> A_p = arithOp<T, af_pow_t>(A, P, A.dims());
-    return std::pow(reduce_all<af_add_t, T, T>(A_p), T(1.0/p));
+    return std::pow(reduce_all<af_add_t, T, T>(A_p), T(1.0 / p));
 }
 
 template<typename T>
-double LPQNorm(const Array<T> &A, double p, double q)
-{
+double LPQNorm(const Array<T> &A, double p, double q) {
     Array<T> A_p_norm = createEmptyArray<T>(dim4());
 
     if (p == 1) {
         A_p_norm = reduce<af_add_t, T, T>(A, 0);
     } else {
-        Array<T> P = createValueArray<T>(A.dims(), scalar<T>(p));
-        Array<T> invP = createValueArray<T>(A.dims(), scalar<T>(1.0/p));
+        Array<T> P    = createValueArray<T>(A.dims(), scalar<T>(p));
+        Array<T> invP = createValueArray<T>(A.dims(), scalar<T>(1.0 / p));
 
-        Array<T> A_p = arithOp<T, af_pow_t>(A, P, A.dims());
+        Array<T> A_p     = arithOp<T, af_pow_t>(A, P, A.dims());
         Array<T> A_p_sum = reduce<af_add_t, T, T>(A_p, 0);
-        A_p_norm = arithOp<T, af_pow_t>(A_p_sum, invP, invP.dims());
+        A_p_norm         = arithOp<T, af_pow_t>(A_p_sum, invP, invP.dims());
     }
 
-    if (q == 1) {
-        return reduce_all<af_add_t, T, T>(A_p_norm);
-    }
+    if (q == 1) { return reduce_all<af_add_t, T, T>(A_p_norm); }
 
-    Array<T> Q = createValueArray<T>(A_p_norm.dims(), scalar<T>(q));
+    Array<T> Q          = createValueArray<T>(A_p_norm.dims(), scalar<T>(q));
     Array<T> A_p_norm_q = arithOp<T, af_pow_t>(A_p_norm, Q, Q.dims());
 
-    return std::pow(reduce_all<af_add_t, T, T>(A_p_norm_q), T(1.0/q));
+    return std::pow(reduce_all<af_add_t, T, T>(A_p_norm_q), T(1.0 / q));
 }
 
 template<typename T>
-double norm(const af_array a, const af_norm_type type, const double p, const double q)
-{
-
+double norm(const af_array a, const af_norm_type type, const double p,
+            const double q) {
     typedef typename af::dtype_traits<T>::base_type BT;
 
     const Array<BT> A = abs<BT, T>(getArray<T>(a));
 
     switch (type) {
+        case AF_NORM_EUCLID: return vectorNorm(A, 2);
 
-    case AF_NORM_EUCLID:
-        return vectorNorm(A, 2);
-
-    case AF_NORM_VECTOR_1:
-        return vectorNorm(A, 1);
+        case AF_NORM_VECTOR_1: return vectorNorm(A, 1);
 
-    case AF_NORM_VECTOR_INF:
-        return vectorNorm(A, af::Inf);
+        case AF_NORM_VECTOR_INF: return vectorNorm(A, af::Inf);
 
-    case AF_NORM_VECTOR_P:
-        return vectorNorm(A, p);
+        case AF_NORM_VECTOR_P: return vectorNorm(A, p);
 
-    case AF_NORM_MATRIX_1:
-        return matrixNorm(A, 1);
+        case AF_NORM_MATRIX_1: return matrixNorm(A, 1);
 
-    case AF_NORM_MATRIX_INF:
-        return matrixNorm(A, af::Inf);
+        case AF_NORM_MATRIX_INF: return matrixNorm(A, af::Inf);
 
-    case AF_NORM_MATRIX_2:
-        return matrixNorm(A, 2);
+        case AF_NORM_MATRIX_2: return matrixNorm(A, 2);
 
-    case AF_NORM_MATRIX_L_PQ:
-        return LPQNorm(A, p, q);
+        case AF_NORM_MATRIX_L_PQ: return LPQNorm(A, p, q);
 
-    default:
-        AF_ERROR("This type of norm is not supported in ArrayFire\n", AF_ERR_NOT_SUPPORTED);
+        default:
+            AF_ERROR("This type of norm is not supported in ArrayFire\n",
+                     AF_ERR_NOT_SUPPORTED);
     }
 }
 
-af_err af_norm(double *out, const af_array in,
-               const af_norm_type type, const double p, const double q)
-{
-
+af_err af_norm(double *out, const af_array in, const af_norm_type type,
+               const double p, const double q) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
@@ -134,18 +119,18 @@ af_err af_norm(double *out, const af_array in,
 
         af_dtype i_type = i_info.getType();
 
-        ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
         *out = 0;
 
-        if(i_info.ndims() == 0) { return AF_SUCCESS; }
+        if (i_info.ndims() == 0) { return AF_SUCCESS; }
 
-        switch(i_type) {
-        case f32: *out = norm<float  >(in, type, p, q);  break;
-        case f64: *out = norm<double >(in, type, p, q);  break;
-        case c32: *out = norm<cfloat >(in, type, p, q);  break;
-        case c64: *out = norm<cdouble>(in, type, p, q);  break;
-        default:  TYPE_ERROR(1, i_type);
+        switch (i_type) {
+            case f32: *out = norm<float>(in, type, p, q); break;
+            case f64: *out = norm<double>(in, type, p, q); break;
+            case c32: *out = norm<cfloat>(in, type, p, q); break;
+            case c64: *out = norm<cdouble>(in, type, p, q); break;
+            default: TYPE_ERROR(1, i_type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index ffa4153fe0..9987b21c77 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -23,132 +23,71 @@ using namespace detail;
 #define IS_NAN(val) !((val) == (val))
 
 template<typename T, af_op_t op>
-struct Binary
-{
-    static __DH__ T init()
-    {
-        return detail::scalar<T>(0);
-    }
+struct Binary {
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return lhs + rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
-struct Binary<T, af_add_t>
-{
-    static __DH__ T init()
-    {
-        return detail::scalar<T>(0);
-    }
+struct Binary<T, af_add_t> {
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return lhs + rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
-struct Binary<T, af_mul_t>
-{
-    static __DH__ T init()
-    {
-        return detail::scalar<T>(1);
-    }
+struct Binary<T, af_mul_t> {
+    static __DH__ T init() { return detail::scalar<T>(1); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return lhs * rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs * rhs; }
 };
 
 template<typename T>
-struct Binary<T, af_or_t>
-{
-    static __DH__ T init()
-    {
-        return detail::scalar<T>(0);
-    }
+struct Binary<T, af_or_t> {
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return lhs || rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs || rhs; }
 };
 
 template<typename T>
-struct Binary<T, af_and_t>
-{
-    static __DH__ T init()
-    {
-        return detail::scalar<T>(1);
-    }
+struct Binary<T, af_and_t> {
+    static __DH__ T init() { return detail::scalar<T>(1); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return lhs && rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs && rhs; }
 };
 
 template<typename T>
-struct Binary<T, af_notzero_t>
-{
-    static __DH__ T init()
-    {
-        return detail::scalar<T>(0);
-    }
+struct Binary<T, af_notzero_t> {
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return lhs + rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
-struct Binary<T, af_min_t>
-{
-    static __DH__ T init()
-    {
-        return detail::maxval<T>();
-    }
+struct Binary<T, af_min_t> {
+    static __DH__ T init() { return detail::maxval<T>(); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return detail::min(lhs, rhs);
-    }
+    __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); }
 };
 
 template<>
-struct Binary<char, af_min_t>
-{
-    static __DH__ char init()
-    {
-        return 1;
-    }
+struct Binary<char, af_min_t> {
+    static __DH__ char init() { return 1; }
 
-    __DH__ char operator() (char lhs, char rhs)
-    {
+    __DH__ char operator()(char lhs, char rhs) {
         return detail::min(lhs > 0, rhs > 0);
     }
 };
 
-#define SPECIALIZE_COMPLEX_MIN(T, Tr)           \
-    template<>                                  \
-    struct Binary<T, af_min_t>                  \
-    {                                           \
-        static __DH__ T init()                  \
-        {                                       \
-            return detail::scalar<T>(           \
-                detail::maxval<Tr>()            \
-                );                              \
-        }                                       \
-                                                \
-        __DH__ T operator() (T lhs, T rhs)      \
-        {                                       \
-            return detail::min(lhs, rhs);       \
-        }                                       \
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                       \
+    template<>                                                              \
+    struct Binary<T, af_min_t> {                                            \
+        static __DH__ T init() {                                            \
+            return detail::scalar<T>(detail::maxval<Tr>());                 \
+        }                                                                   \
+                                                                            \
+        __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); } \
     };
 
 SPECIALIZE_COMPLEX_MIN(cfloat, float)
@@ -157,48 +96,29 @@ SPECIALIZE_COMPLEX_MIN(cdouble, double)
 #undef SPECIALIZE_COMPLEX_MIN
 
 template<typename T>
-struct Binary<T, af_max_t>
-{
-    static __DH__ T init()
-    {
-        return detail::minval<T>();
-    }
+struct Binary<T, af_max_t> {
+    static __DH__ T init() { return detail::minval<T>(); }
 
-    __DH__ T operator() (T lhs, T rhs)
-    {
-        return detail::max(lhs, rhs);
-    }
+    __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); }
 };
 
 template<>
-struct Binary<char, af_max_t>
-{
-    static __DH__ char init()
-    {
-        return 0;
-    }
+struct Binary<char, af_max_t> {
+    static __DH__ char init() { return 0; }
 
-    __DH__ char operator() (char lhs, char rhs)
-    {
+    __DH__ char operator()(char lhs, char rhs) {
         return detail::max(lhs > 0, rhs > 0);
     }
 };
 
-#define SPECIALIZE_COMPLEX_MAX(T, Tr)           \
-    template<>                                  \
-    struct Binary<T, af_max_t>                  \
-    {                                           \
-        static __DH__ T init()                  \
-        {                                       \
-            return detail::scalar<T>(           \
-                detail::scalar<Tr>(0)           \
-                );                              \
-        }                                       \
-                                                \
-        __DH__ T operator() (T lhs, T rhs)      \
-        {                                       \
-            return detail::max(lhs, rhs);       \
-        }                                       \
+#define SPECIALIZE_COMPLEX_MAX(T, Tr)                                       \
+    template<>                                                              \
+    struct Binary<T, af_max_t> {                                            \
+        static __DH__ T init() {                                            \
+            return detail::scalar<T>(detail::scalar<Tr>(0));                \
+        }                                                                   \
+                                                                            \
+        __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); } \
     };
 
 SPECIALIZE_COMPLEX_MAX(cfloat, float)
@@ -207,55 +127,35 @@ SPECIALIZE_COMPLEX_MAX(cdouble, double)
 #undef SPECIALIZE_COMPLEX_MAX
 
 template<typename Ti, typename To, af_op_t op>
-struct Transform
-{
-    __DH__ To operator ()(Ti in)
-    {
-        return (To)(in);
-    }
+struct Transform {
+    __DH__ To operator()(Ti in) { return (To)(in); }
 };
 
 template<typename Ti, typename To>
-struct Transform<Ti, To, af_min_t>
-{
-    __DH__ To operator ()(Ti in)
-    {
-        return (To) (IS_NAN(in) ? Binary<To, af_min_t>::init() : in);
+struct Transform<Ti, To, af_min_t> {
+    __DH__ To operator()(Ti in) {
+        return (To)(IS_NAN(in) ? Binary<To, af_min_t>::init() : in);
     }
 };
 
 template<typename Ti, typename To>
-struct Transform<Ti, To, af_max_t>
-{
-    __DH__ To operator ()(Ti in)
-    {
-        return (To) (IS_NAN(in) ? Binary<To, af_max_t>::init() : in);
+struct Transform<Ti, To, af_max_t> {
+    __DH__ To operator()(Ti in) {
+        return (To)(IS_NAN(in) ? Binary<To, af_max_t>::init() : in);
     }
 };
 
 template<typename Ti, typename To>
-struct Transform<Ti, To, af_or_t>
-{
-    __DH__ To operator ()(Ti in)
-    {
-        return (in != detail::scalar<Ti>(0));
-    }
+struct Transform<Ti, To, af_or_t> {
+    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0)); }
 };
 
 template<typename Ti, typename To>
-struct Transform<Ti, To, af_and_t>
-{
-    __DH__ To operator ()(Ti in)
-    {
-        return (in != detail::scalar<Ti>(0));
-    }
+struct Transform<Ti, To, af_and_t> {
+    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0)); }
 };
 
 template<typename Ti, typename To>
-struct Transform<Ti, To, af_notzero_t>
-{
-    __DH__ To operator ()(Ti in)
-    {
-        return (in != detail::scalar<Ti>(0));
-    }
+struct Transform<Ti, To, af_notzero_t> {
+    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0)); }
 };
diff --git a/src/api/c/orb.cpp b/src/api/c/orb.cpp
index cbcdc3d73a..2f984a6299 100644
--- a/src/api/c/orb.cpp
+++ b/src/api/c/orb.cpp
@@ -7,37 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <features.hpp>
+#include <handle.hpp>
+#include <orb.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/features.h>
 #include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
-#include <orb.hpp>
-#include <features.hpp>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, typename convAccT>
-static void orb(af_features& feat_, af_array& descriptor,
-                const af_array& in, const float fast_thr,
-                const unsigned max_feat, const float scl_fctr,
-                const unsigned levels, const bool blur_img)
-{
+static void orb(af_features& feat_, af_array& descriptor, const af_array& in,
+                const float fast_thr, const unsigned max_feat,
+                const float scl_fctr, const unsigned levels,
+                const bool blur_img) {
     Array<float> x     = createEmptyArray<float>(dim4());
     Array<float> y     = createEmptyArray<float>(dim4());
     Array<float> score = createEmptyArray<float>(dim4());
     Array<float> ori   = createEmptyArray<float>(dim4());
     Array<float> size  = createEmptyArray<float>(dim4());
-    Array<uint > desc  = createEmptyArray<uint >(dim4());
+    Array<uint> desc   = createEmptyArray<uint>(dim4());
 
     af_features_t feat;
 
-    feat.n = orb<T, convAccT>(x, y, score, ori, size, desc,
-                              getArray<T>(in), fast_thr, max_feat,
-                              scl_fctr, levels, blur_img);
+    feat.n = orb<T, convAccT>(x, y, score, ori, size, desc, getArray<T>(in),
+                              fast_thr, max_feat, scl_fctr, levels, blur_img);
 
     feat.x           = getHandle(x);
     feat.y           = getHandle(y);
@@ -45,20 +43,20 @@ static void orb(af_features& feat_, af_array& descriptor,
     feat.orientation = getHandle(ori);
     feat.size        = getHandle(size);
 
-    feat_ = getFeaturesHandle(feat);
+    feat_      = getFeaturesHandle(feat);
     descriptor = getHandle<unsigned>(desc);
 }
 
-af_err af_orb(af_features* feat, af_array* desc,
-              const af_array in, const float fast_thr,
-              const unsigned max_feat, const float scl_fctr,
-              const unsigned levels, const bool blur_img)
-{
+af_err af_orb(af_features* feat, af_array* desc, const af_array in,
+              const float fast_thr, const unsigned max_feat,
+              const float scl_fctr, const unsigned levels,
+              const bool blur_img) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        af::dim4 dims         = info.dims();
 
-        ARG_ASSERT(2, (dims[0] >= 7 && dims[1] >= 7 && dims[2] == 1 && dims[3] == 1));
+        ARG_ASSERT(
+            2, (dims[0] >= 7 && dims[1] >= 7 && dims[2] == 1 && dims[3] == 1));
         ARG_ASSERT(3, fast_thr > 0.0f);
         ARG_ASSERT(4, max_feat > 0);
         ARG_ASSERT(5, scl_fctr > 1.0f);
@@ -68,13 +66,17 @@ af_err af_orb(af_features* feat, af_array* desc,
         DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
 
         af_array tmp_desc;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: orb<float , float >(*feat, tmp_desc, in, fast_thr, max_feat,
-                                          scl_fctr, levels, blur_img); break;
-            case f64: orb<double, double>(*feat, tmp_desc, in, fast_thr, max_feat,
-                                          scl_fctr, levels, blur_img); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                orb<float, float>(*feat, tmp_desc, in, fast_thr, max_feat,
+                                  scl_fctr, levels, blur_img);
+                break;
+            case f64:
+                orb<double, double>(*feat, tmp_desc, in, fast_thr, max_feat,
+                                    scl_fctr, levels, blur_img);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*desc, tmp_desc);
     }
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 42ecb167d8..86d5c677ad 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -7,13 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <backend.hpp>
 #include <Array.hpp>
+#include <backend.hpp>
 
-#include <af/array.h>
-#include <af/complex.h>
-#include <af/defines.h>
-#include <af/lapack.h>
 #include <arith.hpp>
 #include <blas.hpp>
 #include <cast.hpp>
@@ -28,33 +24,34 @@
 #include <svd.hpp>
 #include <tile.hpp>
 #include <transpose.hpp>
+#include <af/array.h>
+#include <af/complex.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using af::dtype_traits;
-using std::vector;
 using std::swap;
+using std::vector;
 
 using namespace detail;
 
 template<typename T>
-Array<T> getSubArray(const Array<T> &in, const bool copy,
-                       uint dim0begin = 0, uint dim0end = 0,
-                       uint dim1begin = 0, uint dim1end = 0,
-                       uint dim2begin = 0, uint dim2end = 0,
-                       uint dim3begin = 0, uint dim3end = 0) {
+Array<T> getSubArray(const Array<T> &in, const bool copy, uint dim0begin = 0,
+                     uint dim0end = 0, uint dim1begin = 0, uint dim1end = 0,
+                     uint dim2begin = 0, uint dim2end = 0, uint dim3begin = 0,
+                     uint dim3end = 0) {
     vector<af_seq> seqs = {
         {static_cast<double>(dim0begin), static_cast<double>(dim0end), 1.},
         {static_cast<double>(dim1begin), static_cast<double>(dim1end), 1.},
         {static_cast<double>(dim2begin), static_cast<double>(dim2end), 1.},
-        {static_cast<double>(dim3begin), static_cast<double>(dim3end), 1.}
-    };
+        {static_cast<double>(dim3begin), static_cast<double>(dim3end), 1.}};
     return createSubArray<T>(in, seqs, copy);
 }
 
 // Moore-Penrose Pseudoinverse
 template<typename T>
-Array<T> pinverseSvd(const Array<T> &in, const double tol)
-{
+Array<T> pinverseSvd(const Array<T> &in, const double tol) {
     in.eval();
     dim_t M = in.dims()[0];
     dim_t N = in.dims()[1];
@@ -63,33 +60,22 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol)
 
     // Compute SVD
     typedef typename dtype_traits<T>::base_type Tr;
-    // Ideally, these initializations should use createEmptyArray(), but for some
-    // reason, linux-opencl-k80 will produce wrong results for large arrays
-    Array<T> u = createValueArray<T>(dim4(M, M, P, Q), scalar<T>(0));
+    // Ideally, these initializations should use createEmptyArray(), but for
+    // some reason, linux-opencl-k80 will produce wrong results for large arrays
+    Array<T> u  = createValueArray<T>(dim4(M, M, P, Q), scalar<T>(0));
     Array<T> vT = createValueArray<T>(dim4(N, N, P, Q), scalar<T>(0));
-    Array<Tr> sVec = createValueArray<Tr>(dim4(min(M, N), 1, P, Q), scalar<Tr>(0));
+    Array<Tr> sVec =
+        createValueArray<Tr>(dim4(min(M, N), 1, P, Q), scalar<Tr>(0));
     for (dim_t j = 0; j < Q; ++j) {
         for (dim_t i = 0; i < P; ++i) {
-            Array<T> inSlice = getSubArray(in, false,
-                                           0, M - 1,
-                                           0, N - 1,
-                                           i, i,
-                                           j, j);
-            Array<Tr> sVecSlice = getSubArray(sVec, false,
-                                              0, sVec.dims()[0] - 1,
-                                              0, 0,
-                                              i, i,
-                                              j, j);
-            Array<T> uSlice = getSubArray(u, false,
-                                          0, u.dims()[0] - 1,
-                                          0, u.dims()[1] - 1,
-                                          i, i,
-                                          j, j);
-            Array<T> vTSlice = getSubArray(vT, false,
-                                           0, vT.dims()[0] - 1,
-                                           0, vT.dims()[1] - 1,
-                                           i, i,
-                                           j, j);
+            Array<T> inSlice =
+                getSubArray(in, false, 0, M - 1, 0, N - 1, i, i, j, j);
+            Array<Tr> sVecSlice = getSubArray(
+                sVec, false, 0, sVec.dims()[0] - 1, 0, 0, i, i, j, j);
+            Array<T> uSlice  = getSubArray(u, false, 0, u.dims()[0] - 1, 0,
+                                          u.dims()[1] - 1, i, i, j, j);
+            Array<T> vTSlice = getSubArray(vT, false, 0, vT.dims()[0] - 1, 0,
+                                           vT.dims()[1] - 1, i, i, j, j);
             svd<T, Tr>(sVecSlice, uSlice, vTSlice, inSlice);
         }
     }
@@ -101,30 +87,31 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol)
     Array<T> v = transpose(vT, true);
 
     // Build relative tolerance array
-    Array<Tr> sVecMax = reduce<af_max_t, Tr, Tr>(sVec, 0);
+    Array<Tr> sVecMax    = reduce<af_max_t, Tr, Tr>(sVec, 0);
     Array<T> sVecMaxCast = cast<T, Tr>(sVecMax);
-    double tolMulShape = tol * static_cast<double>(max(M, N));
-    Array<T> tolMulShapeArr = createValueArray<T>(sVecMaxCast.dims(),
-                                                  scalar<T>(tolMulShape));
-    Array<T> relTol = arithOp<T, af_mul_t>(tolMulShapeArr, sVecMaxCast,
-                                           sVecMaxCast.dims());
+    double tolMulShape   = tol * static_cast<double>(max(M, N));
+    Array<T> tolMulShapeArr =
+        createValueArray<T>(sVecMaxCast.dims(), scalar<T>(tolMulShape));
+    Array<T> relTol =
+        arithOp<T, af_mul_t>(tolMulShapeArr, sVecMaxCast, sVecMaxCast.dims());
     Array<T> relTolArr = tile<T>(relTol, dim4(sVecCast.dims()[0]));
 
     // Get reciprocal of sVec's non-zero values for s pinverse, except for
     // very small non-zero values though (< relTol), in order to avoid very
     // large reciprocals
-    Array<T> ones = createValueArray<T>(sVecCast.dims(), scalar<T>(1.));
+    Array<T> ones      = createValueArray<T>(sVecCast.dims(), scalar<T>(1.));
     Array<T> sVecRecip = arithOp<T, af_div_t>(ones, sVecCast, sVecCast.dims());
-    Array<char> cond = logicOp<T, af_ge_t>(sVecCast, relTolArr, sVecCast.dims());
+    Array<char> cond =
+        logicOp<T, af_ge_t>(sVecCast, relTolArr, sVecCast.dims());
     Array<T> zeros = createValueArray<T>(sVecCast.dims(), scalar<T>(0.));
     sVecRecip = createSelectNode<T>(cond, sVecRecip, zeros, sVecRecip.dims());
 
     // Make s vector into s pinverse array
-    Array<T> sVecRecipMod = modDims<T>(sVecRecip, dim4(sVecRecip.dims()[0],
-                                                       (sVecRecip.dims()[2]
-                                                        * sVecRecip.dims()[3])));
+    Array<T> sVecRecipMod = modDims<T>(
+        sVecRecip,
+        dim4(sVecRecip.dims()[0], (sVecRecip.dims()[2] * sVecRecip.dims()[3])));
     Array<T> sPinv = diagCreate<T>(sVecRecipMod, 0);
-    sPinv = modDims<T>(sPinv, dim4(sPinv.dims()[0], sPinv.dims()[1],
+    sPinv          = modDims<T>(sPinv, dim4(sPinv.dims()[0], sPinv.dims()[1],
                                    sVecRecip.dims()[2], sVecRecip.dims()[3]));
 
     Array<T> uT = transpose(u, true);
@@ -134,59 +121,50 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol)
     // Thus s+ produced by diagCreate() will have minimal dims as well,
     // and v could have an extra dim0 or u* could have an extra dim1
     if (v.dims()[1] > sPinv.dims()[0]) {
-        v = getSubArray(v, false,
-                        0, v.dims()[0] - 1,
-                        0, sPinv.dims()[0] - 1,
-                        0, v.dims()[2] - 1,
-                        0, v.dims()[3] - 1);
+        v = getSubArray(v, false, 0, v.dims()[0] - 1, 0, sPinv.dims()[0] - 1, 0,
+                        v.dims()[2] - 1, 0, v.dims()[3] - 1);
     }
     if (uT.dims()[0] > sPinv.dims()[1]) {
-        uT = getSubArray(uT, false,
-                         0, sPinv.dims()[1] - 1,
-                         0, uT.dims()[1] - 1,
-                         0, uT.dims()[2] - 1,
-                         0, uT.dims()[3] - 1);
+        uT = getSubArray(uT, false, 0, sPinv.dims()[1] - 1, 0, uT.dims()[1] - 1,
+                         0, uT.dims()[2] - 1, 0, uT.dims()[3] - 1);
     }
 
-    Array<T> out = matmul<T>(matmul<T>(v, sPinv, AF_MAT_NONE, AF_MAT_NONE),
-                             uT, AF_MAT_NONE, AF_MAT_NONE);
+    Array<T> out = matmul<T>(matmul<T>(v, sPinv, AF_MAT_NONE, AF_MAT_NONE), uT,
+                             AF_MAT_NONE, AF_MAT_NONE);
 
     return out;
 }
 
 template<typename T>
-static inline af_array pinverse(const af_array in, const double tol)
-{
+static inline af_array pinverse(const af_array in, const double tol) {
     return getHandle(pinverseSvd<T>(getArray<T>(in), tol));
 }
 
 af_err af_pinverse(af_array *out, const af_array in, const double tol,
-                   const af_mat_prop options)
-{
+                   const af_mat_prop options) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         af_dtype type = i_info.getType();
 
         if (options != AF_MAT_NONE) {
-            AF_ERROR("Using this property is not yet supported in inverse", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in inverse",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
-        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
-        ARG_ASSERT(2, tol >= 0.); // Ensure tolerance is not negative
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(2, tol >= 0.);            // Ensure tolerance is not negative
 
         af_array output;
 
-        if(i_info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (i_info.ndims() == 0) { return af_retain_array(out, in); }
 
-        switch(type) {
-            case f32: output = pinverse<float  >(in, tol);  break;
-            case f64: output = pinverse<double >(in, tol);  break;
-            case c32: output = pinverse<cfloat >(in, tol);  break;
-            case c64: output = pinverse<cdouble>(in, tol);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = pinverse<float>(in, tol); break;
+            case f64: output = pinverse<double>(in, tol); break;
+            case c32: output = pinverse<cfloat>(in, tol); break;
+            case c64: output = pinverse<cdouble>(in, tol); break;
+            default: TYPE_ERROR(1, type);
         }
         swap(*out, output);
     }
@@ -194,4 +172,3 @@ af_err af_pinverse(af_array *out, const af_array in, const double tol,
 
     return AF_SUCCESS;
 }
-
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 26b2cccec2..6d30820338 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -7,20 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/data.h>
 #include <af/graphics.h>
 #include <af/image.h>
-#include <af/data.h>
 
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
+#include <common/graphics_common.hpp>
+#include <handle.hpp>
+#include <join.hpp>
 #include <plot.hpp>
 #include <reduce.hpp>
-#include <join.hpp>
 #include <reorder.hpp>
 #include <transpose.hpp>
-#include <handle.hpp>
 
 using af::dim4;
 using namespace detail;
@@ -29,9 +29,8 @@ using namespace graphics;
 // Requires in_ to be in either [order, n] or [n, order] format
 template<typename T, int order>
 fg_chart setup_plot(fg_window window, const af_array in_,
-                    const af_cell* const props,
-                    fg_plot_type ptype, fg_marker_type mtype)
-{
+                    const af_cell* const props, fg_plot_type ptype,
+                    fg_marker_type mtype) {
     ForgeModule& _ = graphics::forgePlugin();
 
     Array<T> in = getArray<T>(in_);
@@ -42,16 +41,14 @@ fg_chart setup_plot(fg_window window, const af_array in_,
     DIM_ASSERT(1, (dims[0] == order || dims[1] == order));
 
     // The data expected by backend is 2D [order, n]
-    if(dims[1] == order) {
-        in = transpose(in, false);
-    }
+    if (dims[1] == order) { in = transpose(in, false); }
 
-    af::dim4 tdims = in.dims(); //transposed dimensions
+    af::dim4 tdims = in.dims();  // transposed dimensions
 
     ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
-    fg_chart chart = NULL;
+    fg_chart chart      = NULL;
     fg_chart_type ctype = order == 2 ? FG_CHART_2D : FG_CHART_3D;
 
     if (props->col > -1 && props->row > -1)
@@ -59,47 +56,43 @@ fg_chart setup_plot(fg_window window, const af_array in_,
     else
         chart = fgMngr.getChart(window, 0, 0, ctype);
 
-    fg_plot plot = fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
+    fg_plot plot =
+        fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
 
     // ArrayFire LOGO Orange shade
     FG_CHECK(_.fg_set_plot_color(plot, 0.929f, 0.529f, 0.212f, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
-    if(!fgMngr.getChartAxesOverride(chart)) {
+    if (!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
-        T     dmin[3], dmax[3];
-        FG_CHECK(_.fg_get_chart_axes_limits(&cmin[0], &cmax[0],
-                                          &cmin[1], &cmax[1],
-                                          &cmin[2], &cmax[2],
-                                          chart));
+        T dmin[3], dmax[3];
+        FG_CHECK(_.fg_get_chart_axes_limits(
+            &cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2], chart));
         copyData(dmin, reduce<af_min_t, T, T>(in, 1));
         copyData(dmax, reduce<af_max_t, T, T>(in, 1));
 
-        if(cmin[0] == 0 && cmax[0] == 0
-        && cmin[1] == 0 && cmax[1] == 0
-        && cmin[2] == 0 && cmax[2] == 0) {
+        if (cmin[0] == 0 && cmax[0] == 0 && cmin[1] == 0 && cmax[1] == 0 &&
+            cmin[2] == 0 && cmax[2] == 0) {
             // No previous limits. Set without checking
             cmin[0] = step_round(dmin[0], false);
             cmax[0] = step_round(dmax[0], true);
             cmin[1] = step_round(dmin[1], false);
             cmax[1] = step_round(dmax[1], true);
-            if(order == 3) cmin[2] = step_round(dmin[2], false);
-            if(order == 3) cmax[2] = step_round(dmax[2], true);
+            if (order == 3) cmin[2] = step_round(dmin[2], false);
+            if (order == 3) cmax[2] = step_round(dmax[2], true);
         } else {
-            if(cmin[0] > dmin[0])       cmin[0] = step_round(dmin[0], false);
-            if(cmax[0] < dmax[0])       cmax[0] = step_round(dmax[0], true);
-            if(cmin[1] > dmin[1])       cmin[1] = step_round(dmin[1], false);
-            if(cmax[1] < dmax[1])       cmax[1] = step_round(dmax[1], true);
-            if(order == 3) {
-                if(cmin[2] > dmin[2])   cmin[2] = step_round(dmin[2], false);
-                if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
+            if (cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
+            if (cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
+            if (cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
+            if (cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
+            if (order == 3) {
+                if (cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
+                if (cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
             }
         }
-        FG_CHECK(_.fg_set_chart_axes_limits(chart,
-                                          cmin[0], cmax[0],
-                                          cmin[1], cmax[1],
-                                          cmin[2], cmax[2]));
+        FG_CHECK(_.fg_set_chart_axes_limits(chart, cmin[0], cmax[0], cmin[1],
+                                            cmax[1], cmin[2], cmax[2]));
     }
     copy_plot<T>(in, plot);
 
@@ -107,32 +100,27 @@ fg_chart setup_plot(fg_window window, const af_array in_,
 }
 
 template<typename T>
-fg_chart setup_plot(fg_window window, const af_array in_,
-                    const int order, const af_cell* const props,
-                    fg_plot_type ptype, fg_marker_type mtype)
-{
-    if(order == 2)
+fg_chart setup_plot(fg_window window, const af_array in_, const int order,
+                    const af_cell* const props, fg_plot_type ptype,
+                    fg_marker_type mtype) {
+    if (order == 2)
         return setup_plot<T, 2>(window, in_, props, ptype, mtype);
-    else if(order == 3)
+    else if (order == 3)
         return setup_plot<T, 3>(window, in_, props, ptype, mtype);
     // Dummy to avoid warnings
     return NULL;
 }
 
-af_err plotWrapper(const af_window window,
-                   const af_array in, const int order_dim,
-                   const af_cell* const props,
-                   fg_plot_type ptype = FG_PLOT_LINE,
-                   fg_marker_type marker = FG_MARKER_NONE)
-{
+af_err plotWrapper(const af_window window, const af_array in,
+                   const int order_dim, const af_cell* const props,
+                   fg_plot_type ptype    = FG_PLOT_LINE,
+                   fg_marker_type marker = FG_MARKER_NONE) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& info = getInfo(in);
-        af::dim4  dims = info.dims();
-        af_dtype  type = info.getType();
+        af::dim4 dims         = info.dims();
+        af_dtype type         = info.getType();
 
         DIM_ASSERT(0, dims.ndims() == 2);
         DIM_ASSERT(0, dims[order_dim] == 2 || dims[order_dim] == 3);
@@ -141,24 +129,42 @@ af_err plotWrapper(const af_window window,
 
         fg_chart chart = NULL;
 
-        switch(type) {
-            case f32: chart = setup_plot<float  >(window, in, dims[order_dim], props, ptype, marker); break;
-            case s32: chart = setup_plot<int    >(window, in, dims[order_dim], props, ptype, marker); break;
-            case u32: chart = setup_plot<uint   >(window, in, dims[order_dim], props, ptype, marker); break;
-            case s16: chart = setup_plot<short  >(window, in, dims[order_dim], props, ptype, marker); break;
-            case u16: chart = setup_plot<ushort >(window, in, dims[order_dim], props, ptype, marker); break;
-            case u8 : chart = setup_plot<uchar  >(window, in, dims[order_dim], props, ptype, marker); break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                chart = setup_plot<float>(window, in, dims[order_dim], props,
+                                          ptype, marker);
+                break;
+            case s32:
+                chart = setup_plot<int>(window, in, dims[order_dim], props,
+                                        ptype, marker);
+                break;
+            case u32:
+                chart = setup_plot<uint>(window, in, dims[order_dim], props,
+                                         ptype, marker);
+                break;
+            case s16:
+                chart = setup_plot<short>(window, in, dims[order_dim], props,
+                                          ptype, marker);
+                break;
+            case u16:
+                chart = setup_plot<ushort>(window, in, dims[order_dim], props,
+                                           ptype, marker);
+                break;
+            case u8:
+                chart = setup_plot<uchar>(window, in, dims[order_dim], props,
+                                          ptype, marker);
+                break;
+            default: TYPE_ERROR(1, type);
         }
 
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                           gridDims.first, gridDims.second,
-                                           props->row * gridDims.second + props->col,
-                                           chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
@@ -167,28 +173,24 @@ af_err plotWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-af_err plotWrapper(const af_window window,
-                   const af_array X, const af_array Y, const af_array Z,
-                   const af_cell* const props,
-                   fg_plot_type ptype = FG_PLOT_LINE,
-                   fg_marker_type marker = FG_MARKER_NONE)
-{
+af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
+                   const af_array Z, const af_cell* const props,
+                   fg_plot_type ptype    = FG_PLOT_LINE,
+                   fg_marker_type marker = FG_MARKER_NONE) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& xInfo = getInfo(X);
-        af::dim4  xDims = xInfo.dims();
-        af_dtype  xType = xInfo.getType();
+        af::dim4 xDims         = xInfo.dims();
+        af_dtype xType         = xInfo.getType();
 
         const ArrayInfo& yInfo = getInfo(Y);
-        af::dim4  yDims = yInfo.dims();
-        af_dtype  yType = yInfo.getType();
+        af::dim4 yDims         = yInfo.dims();
+        af_dtype yType         = yInfo.getType();
 
         const ArrayInfo& zInfo = getInfo(Z);
-        af::dim4  zDims = zInfo.dims();
-        af_dtype  zType = zInfo.getType();
+        af::dim4 zDims         = zInfo.dims();
+        af_dtype zType         = zInfo.getType();
 
         DIM_ASSERT(0, xDims == yDims);
         DIM_ASSERT(0, xDims == zDims);
@@ -198,7 +200,7 @@ af_err plotWrapper(const af_window window,
         TYPE_ASSERT(xType == zType);
 
         // Join for set up vector
-        af_array in = 0;
+        af_array in    = 0;
         af_array pIn[] = {X, Y, Z};
         AF_CHECK(af_join_many(&in, 1, 3, pIn));
 
@@ -206,23 +208,35 @@ af_err plotWrapper(const af_window window,
 
         fg_chart chart = NULL;
 
-        switch(xType) {
-            case f32: chart = setup_plot<float  >(window, in, 3, props, ptype, marker); break;
-            case s32: chart = setup_plot<int    >(window, in, 3, props, ptype, marker); break;
-            case u32: chart = setup_plot<uint   >(window, in, 3, props, ptype, marker); break;
-            case s16: chart = setup_plot<short  >(window, in, 3, props, ptype, marker); break;
-            case u16: chart = setup_plot<ushort >(window, in, 3, props, ptype, marker); break;
-            case u8 : chart = setup_plot<uchar  >(window, in, 3, props, ptype, marker); break;
-            default:  TYPE_ERROR(1, xType);
+        switch (xType) {
+            case f32:
+                chart = setup_plot<float>(window, in, 3, props, ptype, marker);
+                break;
+            case s32:
+                chart = setup_plot<int>(window, in, 3, props, ptype, marker);
+                break;
+            case u32:
+                chart = setup_plot<uint>(window, in, 3, props, ptype, marker);
+                break;
+            case s16:
+                chart = setup_plot<short>(window, in, 3, props, ptype, marker);
+                break;
+            case u16:
+                chart = setup_plot<ushort>(window, in, 3, props, ptype, marker);
+                break;
+            case u8:
+                chart = setup_plot<uchar>(window, in, 3, props, ptype, marker);
+                break;
+            default: TYPE_ERROR(1, xType);
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
@@ -233,24 +247,20 @@ af_err plotWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-af_err plotWrapper(const af_window window,
-                   const af_array X, const af_array Y,
+af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
                    const af_cell* const props,
-                   fg_plot_type ptype = FG_PLOT_LINE,
-                   fg_marker_type marker = FG_MARKER_NONE)
-{
+                   fg_plot_type ptype    = FG_PLOT_LINE,
+                   fg_marker_type marker = FG_MARKER_NONE) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& xInfo = getInfo(X);
-        af::dim4  xDims = xInfo.dims();
-        af_dtype  xType = xInfo.getType();
+        af::dim4 xDims         = xInfo.dims();
+        af_dtype xType         = xInfo.getType();
 
         const ArrayInfo& yInfo = getInfo(Y);
-        af::dim4  yDims = yInfo.dims();
-        af_dtype  yType = yInfo.getType();
+        af::dim4 yDims         = yInfo.dims();
+        af_dtype yType         = yInfo.getType();
 
         DIM_ASSERT(0, xDims == yDims);
         DIM_ASSERT(0, xInfo.isVector());
@@ -265,23 +275,35 @@ af_err plotWrapper(const af_window window,
 
         fg_chart chart = NULL;
 
-        switch(xType) {
-            case f32: chart = setup_plot<float  >(window, in, 2, props, ptype, marker); break;
-            case s32: chart = setup_plot<int    >(window, in, 2, props, ptype, marker); break;
-            case u32: chart = setup_plot<uint   >(window, in, 2, props, ptype, marker); break;
-            case s16: chart = setup_plot<short  >(window, in, 2, props, ptype, marker); break;
-            case u16: chart = setup_plot<ushort >(window, in, 2, props, ptype, marker); break;
-            case u8 : chart = setup_plot<uchar  >(window, in, 2, props, ptype, marker); break;
-            default:  TYPE_ERROR(1, xType);
+        switch (xType) {
+            case f32:
+                chart = setup_plot<float>(window, in, 2, props, ptype, marker);
+                break;
+            case s32:
+                chart = setup_plot<int>(window, in, 2, props, ptype, marker);
+                break;
+            case u32:
+                chart = setup_plot<uint>(window, in, 2, props, ptype, marker);
+                break;
+            case s16:
+                chart = setup_plot<short>(window, in, 2, props, ptype, marker);
+                break;
+            case u16:
+                chart = setup_plot<ushort>(window, in, 2, props, ptype, marker);
+                break;
+            case u8:
+                chart = setup_plot<uchar>(window, in, 2, props, ptype, marker);
+                break;
+            default: TYPE_ERROR(1, xType);
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
@@ -294,45 +316,37 @@ af_err plotWrapper(const af_window window,
 
 // Plot API
 af_err af_draw_plot_nd(const af_window wind, const af_array in,
-                       const af_cell* const props)
-{
+                       const af_cell* const props) {
     return plotWrapper(wind, in, 1, props);
 }
 
-af_err af_draw_plot_2d(const af_window wind,
-                       const af_array X, const af_array Y,
-                       const af_cell* const props)
-{
+af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
+                       const af_cell* const props) {
     return plotWrapper(wind, X, Y, props);
 }
 
-af_err af_draw_plot_3d(const af_window wind,
-                       const af_array X, const af_array Y, const af_array Z,
-                       const af_cell* const props)
-{
+af_err af_draw_plot_3d(const af_window wind, const af_array X, const af_array Y,
+                       const af_array Z, const af_cell* const props) {
     return plotWrapper(wind, X, Y, Z, props);
 }
 
 // Deprecated Plot API
-af_err af_draw_plot(const af_window wind,
-                    const af_array X, const af_array Y,
-                    const af_cell* const props)
-{
+af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y,
+                    const af_cell* const props) {
     return plotWrapper(wind, X, Y, props);
 }
 
-af_err af_draw_plot3(const af_window wind,
-                     const af_array P, const af_cell* const props)
-{
+af_err af_draw_plot3(const af_window wind, const af_array P,
+                     const af_cell* const props) {
     try {
         const ArrayInfo& info = getInfo(P);
-        af::dim4  dims = info.dims();
+        af::dim4 dims         = info.dims();
 
-        if(dims.ndims() == 2 && dims[1] == 3) {
+        if (dims.ndims() == 2 && dims[1] == 3) {
             return plotWrapper(wind, P, 1, props);
-        } else if(dims.ndims() == 2 && dims[0] == 3) {
+        } else if (dims.ndims() == 2 && dims[0] == 3) {
             return plotWrapper(wind, P, 0, props);
-        } else if(dims.ndims() == 1 && dims[0] % 3 == 0) {
+        } else if (dims.ndims() == 1 && dims[0] % 3 == 0) {
             dim4 rdims(dims.elements() / 3, 3, 1, 1);
             af_array in = 0;
             AF_CHECK(af_moddims(&in, P, rdims.ndims(), rdims.get()));
@@ -340,8 +354,9 @@ af_err af_draw_plot3(const af_window wind,
             AF_CHECK(af_release_array(in));
             return err;
         } else {
-            AF_RETURN_ERROR("Input needs to be either [n, 3] or [3, n] or [3n, 1]",
-                            AF_ERR_SIZE);
+            AF_RETURN_ERROR(
+                "Input needs to be either [n, 3] or [3, n] or [3n, 1]",
+                AF_ERR_SIZE);
         }
     }
     CATCHALL;
@@ -352,63 +367,58 @@ af_err af_draw_plot3(const af_window wind,
 // Scatter API
 af_err af_draw_scatter_nd(const af_window wind, const af_array in,
                           const af_marker_type af_marker,
-                          const af_cell* const props)
-{
+                          const af_cell* const props) {
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
 }
 
-af_err af_draw_scatter_2d(const af_window wind,
-                          const af_array X, const af_array Y,
-                          const af_marker_type af_marker,
-                          const af_cell* const props)
-{
+af_err af_draw_scatter_2d(const af_window wind, const af_array X,
+                          const af_array Y, const af_marker_type af_marker,
+                          const af_cell* const props) {
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 }
 
-af_err af_draw_scatter_3d(const af_window wind,
-                          const af_array X, const af_array Y, const af_array Z,
+af_err af_draw_scatter_3d(const af_window wind, const af_array X,
+                          const af_array Y, const af_array Z,
                           const af_marker_type af_marker,
-                          const af_cell* const props)
-{
+                          const af_cell* const props) {
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
 }
 
 // Deprecated Scatter API
-af_err af_draw_scatter(const af_window wind,
-                       const af_array X, const af_array Y,
+af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
                        const af_marker_type af_marker,
-                       const af_cell* const props)
-{
+                       const af_cell* const props) {
     fg_marker_type fg_marker = getFGMarker(af_marker);
     return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
 }
 
-af_err af_draw_scatter3(const af_window wind,
-                        const af_array P, const af_marker_type af_marker,
-                        const af_cell* const props)
-{
+af_err af_draw_scatter3(const af_window wind, const af_array P,
+                        const af_marker_type af_marker,
+                        const af_cell* const props) {
     fg_marker_type fg_marker = getFGMarker(af_marker);
     try {
         const ArrayInfo& info = getInfo(P);
-        af::dim4  dims = info.dims();
+        af::dim4 dims         = info.dims();
 
-        if(dims.ndims() == 2 && dims[1] == 3) {
+        if (dims.ndims() == 2 && dims[1] == 3) {
             return plotWrapper(wind, P, 1, props, FG_PLOT_SCATTER, fg_marker);
-        } else if(dims.ndims() == 2 && dims[0] == 3) {
+        } else if (dims.ndims() == 2 && dims[0] == 3) {
             return plotWrapper(wind, P, 0, props, FG_PLOT_SCATTER, fg_marker);
-        } else if(dims.ndims() == 1 && dims[0] % 3 == 0) {
+        } else if (dims.ndims() == 1 && dims[0] % 3 == 0) {
             dim4 rdims(dims.elements() / 3, 3, 1, 1);
             af_array in = 0;
             AF_CHECK(af_moddims(&in, P, rdims.ndims(), rdims.get()));
-            af_err err = plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
+            af_err err =
+                plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
             AF_CHECK(af_release_array(in));
             return err;
         } else {
-            AF_RETURN_ERROR("Input needs to be either [n, 3] or [3, n] or [3n, 1]",
-                            AF_ERR_SIZE);
+            AF_RETURN_ERROR(
+                "Input needs to be either [n, 3] or [3, n] or [3n, 1]",
+                AF_ERR_SIZE);
         }
     }
     CATCHALL;
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index f3cae154ca..09f508f5dc 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -7,49 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <iostream>
-#include <iomanip>
-#include <vector>
-#include <string>
-#include <sstream>
-#include <af/array.h>
-#include <af/data.h>
-#include <af/internal.h>
-#include <copy.hpp>
-#include <print.hpp>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
-#include <type_util.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
+#include <print.hpp>
 #include <sparse_handle.hpp>
+#include <type_util.hpp>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/internal.h>
+#include <iomanip>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
 
 #include <af/index.h>
 
 using namespace detail;
-using std::ostream;
 using std::cout;
 using std::endl;
+using std::ostream;
 using std::vector;
 
 template<typename T>
-static void printer(ostream &out, const T* ptr, const ArrayInfo &info, unsigned dim, const int precision)
-{
-
-    dim_t stride =   info.strides()[dim];
-    dim_t d      =   info.dims()[dim];
+static void printer(ostream &out, const T *ptr, const ArrayInfo &info,
+                    unsigned dim, const int precision) {
+    dim_t stride = info.strides()[dim];
+    dim_t d      = info.dims()[dim];
     ToNum<T> toNum;
 
-    if(dim == 0) {
-        for(dim_t i = 0, j = 0; i < d; i++, j+=stride) {
-            out<<   std::fixed <<
-                    std::setw(precision + 6) <<
-                    std::setprecision(precision) << toNum(ptr[j]) << " ";
+    if (dim == 0) {
+        for (dim_t i = 0, j = 0; i < d; i++, j += stride) {
+            out << std::fixed << std::setw(precision + 6)
+                << std::setprecision(precision) << toNum(ptr[j]) << " ";
         }
         out << endl;
-    }
-    else {
-        for(dim_t i = 0; i < d; i++) {
+    } else {
+        for (dim_t i = 0; i < d; i++) {
             printer(out, ptr, info, dim - 1, precision);
             ptr += stride;
         }
@@ -58,26 +55,26 @@ static void printer(ostream &out, const T* ptr, const ArrayInfo &info, unsigned
 }
 
 template<typename T>
-static void print(const char *exp, af_array arr, const int precision, std::ostream &os = std::cout, bool transpose = true)
-{
-    if(exp == NULL) {
+static void print(const char *exp, af_array arr, const int precision,
+                  std::ostream &os = std::cout, bool transpose = true) {
+    if (exp == NULL) {
         os << "No Name Array" << std::endl;
     } else {
         os << exp << std::endl;
     }
 
-    const ArrayInfo& info = getInfo(arr);
+    const ArrayInfo &info = getInfo(arr);
 
     std::ios_base::fmtflags backup = os.flags();
 
     os << "[" << info.dims() << "]\n";
 #ifndef NDEBUG
-    os <<"   Offset: " << info.getOffset() << std::endl;
-    os <<"   Strides: [" << info.strides() << "]" << std::endl;
+    os << "   Offset: " << info.getOffset() << std::endl;
+    os << "   Strides: [" << info.strides() << "]" << std::endl;
 #endif
 
     // Handle empty array
-    if(info.elements() == 0) {
+    if (info.elements() == 0) {
         os << "<empty>" << std::endl;
         os.flags(backup);
         return;
@@ -86,83 +83,77 @@ static void print(const char *exp, af_array arr, const int precision, std::ostre
     vector<T> data(info.elements());
 
     af_array arrT;
-    if(transpose) {
+    if (transpose) {
         AF_CHECK(af_reorder(&arrT, arr, 1, 0, 2, 3));
     } else {
         arrT = arr;
     }
 
-    //FIXME: Use alternative function to avoid copies if possible
+    // FIXME: Use alternative function to avoid copies if possible
     AF_CHECK(af_get_data_ptr(&data.front(), arrT));
-    const ArrayInfo& infoT = getInfo(arrT);
+    const ArrayInfo &infoT = getInfo(arrT);
 
     printer(os, &data.front(), infoT, infoT.ndims() - 1, precision);
 
-    if(transpose) {
-        AF_CHECK(af_release_array(arrT));
-    }
+    if (transpose) { AF_CHECK(af_release_array(arrT)); }
 
     os.flags(backup);
 }
 
 template<typename T>
 static void printSparse(const char *exp, af_array arr, const int precision,
-                        std::ostream &os = std::cout, bool transpose = true)
-{
+                        std::ostream &os = std::cout, bool transpose = true) {
     common::SparseArray<T> sparse = getSparseArray<T>(arr);
     std::string name("No Name Sparse Array");
 
-    if(exp != NULL) {
-        name = std::string(exp);
-    }
+    if (exp != NULL) { name = std::string(exp); }
     os << name << std::endl;
     os << "Storage Format : ";
-    switch(sparse.getStorage()) {
-        case AF_STORAGE_DENSE: os << "AF_STORAGE_DENSE\n";    break;
-        case AF_STORAGE_CSR  : os << "AF_STORAGE_CSR\n";      break;
-        case AF_STORAGE_CSC  : os << "AF_STORAGE_CSC\n";      break;
-        case AF_STORAGE_COO  : os << "AF_STORAGE_COO\n";      break;
+    switch (sparse.getStorage()) {
+        case AF_STORAGE_DENSE: os << "AF_STORAGE_DENSE\n"; break;
+        case AF_STORAGE_CSR: os << "AF_STORAGE_CSR\n"; break;
+        case AF_STORAGE_CSC: os << "AF_STORAGE_CSC\n"; break;
+        case AF_STORAGE_COO: os << "AF_STORAGE_COO\n"; break;
     }
     os << "[" << sparse.dims() << "]\n";
 
-    print<T  >(std::string(name + ": Values").c_str(), getHandle(sparse.getValues()),
-               precision, os, transpose);
-    print<int>(std::string(name + ": RowIdx").c_str(), getHandle(sparse.getRowIdx()),
-               precision, os, transpose);
-    print<int>(std::string(name + ": ColIdx").c_str(), getHandle(sparse.getColIdx()),
-               precision, os, transpose);
+    print<T>(std::string(name + ": Values").c_str(),
+             getHandle(sparse.getValues()), precision, os, transpose);
+    print<int>(std::string(name + ": RowIdx").c_str(),
+               getHandle(sparse.getRowIdx()), precision, os, transpose);
+    print<int>(std::string(name + ": ColIdx").c_str(),
+               getHandle(sparse.getColIdx()), precision, os, transpose);
 }
 
-af_err af_print_array(af_array arr)
-{
+af_err af_print_array(af_array arr) {
     try {
-        const ArrayInfo& info = getInfo(arr, false);   // Don't assert sparse/dense
+        const ArrayInfo &info =
+            getInfo(arr, false);  // Don't assert sparse/dense
         af_dtype type = info.getType();
 
-        if(info.isSparse()) {
-            switch(type) {
-                case f32: printSparse<float  >(NULL, arr, 4); break;
-                case f64: printSparse<double >(NULL, arr, 4); break;
-                case c32: printSparse<cfloat >(NULL, arr, 4); break;
+        if (info.isSparse()) {
+            switch (type) {
+                case f32: printSparse<float>(NULL, arr, 4); break;
+                case f64: printSparse<double>(NULL, arr, 4); break;
+                case c32: printSparse<cfloat>(NULL, arr, 4); break;
                 case c64: printSparse<cdouble>(NULL, arr, 4); break;
-                default : TYPE_ERROR(0, type);
+                default: TYPE_ERROR(0, type);
             }
         } else {
-            switch(type)
-            {
-                case f32:   print<float>   (NULL, arr, 4);   break;
-                case c32:   print<cfloat>  (NULL, arr, 4);   break;
-                case f64:   print<double>  (NULL, arr, 4);   break;
-                case c64:   print<cdouble> (NULL, arr, 4);   break;
-                case b8:    print<char>    (NULL, arr, 4);   break;
-                case s32:   print<int>     (NULL, arr, 4);   break;
-                case u32:   print<unsigned>(NULL, arr, 4);   break;
-                case u8:    print<uchar>   (NULL, arr, 4);   break;
-                case s64:   print<intl>    (NULL, arr, 4);   break;
-                case u64:   print<uintl>   (NULL, arr, 4);   break;
-                case s16:   print<short>   (NULL, arr, 4);   break;
-                case u16:   print<ushort>  (NULL, arr, 4);   break;
-                default:    TYPE_ERROR(1, type);
+            switch (type) {
+                case f32: print<float>(NULL, arr, 4); break;
+                case c32: print<cfloat>(NULL, arr, 4); break;
+                case f64: print<double>(NULL, arr, 4); break;
+                case c64: print<cdouble>(NULL, arr, 4); break;
+                case b8: print<char>(NULL, arr, 4); break;
+                case s32: print<int>(NULL, arr, 4); break;
+                case u32: print<unsigned>(NULL, arr, 4); break;
+                case u8: print<uchar>(NULL, arr, 4); break;
+                case s64: print<intl>(NULL, arr, 4); break;
+                case u64: print<uintl>(NULL, arr, 4); break;
+                case s16: print<short>(NULL, arr, 4); break;
+                case u16: print<ushort>(NULL, arr, 4); break;
+                default: TYPE_ERROR(1, type);
             }
         }
     }
@@ -170,37 +161,37 @@ af_err af_print_array(af_array arr)
     return AF_SUCCESS;
 }
 
-af_err af_print_array_gen(const char *exp, const af_array arr, const int precision)
-{
+af_err af_print_array_gen(const char *exp, const af_array arr,
+                          const int precision) {
     try {
         ARG_ASSERT(0, exp != NULL);
-        const ArrayInfo& info = getInfo(arr, false);   // Don't assert sparse/dense
+        const ArrayInfo &info =
+            getInfo(arr, false);  // Don't assert sparse/dense
         af_dtype type = info.getType();
 
-        if(info.isSparse()) {
-            switch(type) {
-                case f32: printSparse<float  >(exp, arr, precision); break;
-                case f64: printSparse<double >(exp, arr, precision); break;
-                case c32: printSparse<cfloat >(exp, arr, precision); break;
+        if (info.isSparse()) {
+            switch (type) {
+                case f32: printSparse<float>(exp, arr, precision); break;
+                case f64: printSparse<double>(exp, arr, precision); break;
+                case c32: printSparse<cfloat>(exp, arr, precision); break;
                 case c64: printSparse<cdouble>(exp, arr, precision); break;
-                default : TYPE_ERROR(0, type);
+                default: TYPE_ERROR(0, type);
             }
         } else {
-            switch(type)
-            {
-                case f32:   print<float   >(exp, arr, precision);   break;
-                case c32:   print<cfloat  >(exp, arr, precision);   break;
-                case f64:   print<double  >(exp, arr, precision);   break;
-                case c64:   print<cdouble >(exp, arr, precision);   break;
-                case b8:    print<char    >(exp, arr, precision);   break;
-                case s32:   print<int     >(exp, arr, precision);   break;
-                case u32:   print<unsigned>(exp, arr, precision);   break;
-                case u8:    print<uchar   >(exp, arr, precision);   break;
-                case s64:   print<intl    >(exp, arr, precision);   break;
-                case u64:   print<uintl   >(exp, arr, precision);   break;
-                case s16:   print<short   >(exp, arr, precision);   break;
-                case u16:   print<ushort  >(exp, arr, precision);   break;
-                default:    TYPE_ERROR(1, type);
+            switch (type) {
+                case f32: print<float>(exp, arr, precision); break;
+                case c32: print<cfloat>(exp, arr, precision); break;
+                case f64: print<double>(exp, arr, precision); break;
+                case c64: print<cdouble>(exp, arr, precision); break;
+                case b8: print<char>(exp, arr, precision); break;
+                case s32: print<int>(exp, arr, precision); break;
+                case u32: print<unsigned>(exp, arr, precision); break;
+                case u8: print<uchar>(exp, arr, precision); break;
+                case s64: print<intl>(exp, arr, precision); break;
+                case u64: print<uintl>(exp, arr, precision); break;
+                case s16: print<short>(exp, arr, precision); break;
+                case u16: print<ushort>(exp, arr, precision); break;
+                default: TYPE_ERROR(1, type);
             }
         }
     }
@@ -209,44 +200,71 @@ af_err af_print_array_gen(const char *exp, const af_array arr, const int precisi
 }
 
 af_err af_array_to_string(char **output, const char *exp, const af_array arr,
-                          const int precision, bool transpose)
-{
+                          const int precision, bool transpose) {
     try {
         ARG_ASSERT(0, exp != NULL);
-        const ArrayInfo& info = getInfo(arr, false);   // Don't assert sparse/dense
+        const ArrayInfo &info =
+            getInfo(arr, false);  // Don't assert sparse/dense
         af_dtype type = info.getType();
         std::stringstream ss;
 
-        if(info.isSparse()) {
-            switch(type) {
-                case f32: printSparse<float  >(exp, arr, precision, ss, transpose); break;
-                case f64: printSparse<double >(exp, arr, precision, ss, transpose); break;
-                case c32: printSparse<cfloat >(exp, arr, precision, ss, transpose); break;
-                case c64: printSparse<cdouble>(exp, arr, precision, ss, transpose); break;
-                default : TYPE_ERROR(0, type);
+        if (info.isSparse()) {
+            switch (type) {
+                case f32:
+                    printSparse<float>(exp, arr, precision, ss, transpose);
+                    break;
+                case f64:
+                    printSparse<double>(exp, arr, precision, ss, transpose);
+                    break;
+                case c32:
+                    printSparse<cfloat>(exp, arr, precision, ss, transpose);
+                    break;
+                case c64:
+                    printSparse<cdouble>(exp, arr, precision, ss, transpose);
+                    break;
+                default: TYPE_ERROR(0, type);
             }
         } else {
-            switch(type)
-            {
-                case f32:   print<float   >(exp, arr, precision, ss, transpose);   break;
-                case c32:   print<cfloat  >(exp, arr, precision, ss, transpose);   break;
-                case f64:   print<double  >(exp, arr, precision, ss, transpose);   break;
-                case c64:   print<cdouble >(exp, arr, precision, ss, transpose);   break;
-                case b8:    print<char    >(exp, arr, precision, ss, transpose);   break;
-                case s32:   print<int     >(exp, arr, precision, ss, transpose);   break;
-                case u32:   print<unsigned>(exp, arr, precision, ss, transpose);   break;
-                case u8:    print<uchar   >(exp, arr, precision, ss, transpose);   break;
-                case s64:   print<intl    >(exp, arr, precision, ss, transpose);   break;
-                case u64:   print<uintl   >(exp, arr, precision, ss, transpose);   break;
-                case s16:   print<short   >(exp, arr, precision, ss, transpose);   break;
-                case u16:   print<ushort  >(exp, arr, precision, ss, transpose);   break;
-                default:    TYPE_ERROR(1, type);
+            switch (type) {
+                case f32:
+                    print<float>(exp, arr, precision, ss, transpose);
+                    break;
+                case c32:
+                    print<cfloat>(exp, arr, precision, ss, transpose);
+                    break;
+                case f64:
+                    print<double>(exp, arr, precision, ss, transpose);
+                    break;
+                case c64:
+                    print<cdouble>(exp, arr, precision, ss, transpose);
+                    break;
+                case b8: print<char>(exp, arr, precision, ss, transpose); break;
+                case s32: print<int>(exp, arr, precision, ss, transpose); break;
+                case u32:
+                    print<unsigned>(exp, arr, precision, ss, transpose);
+                    break;
+                case u8:
+                    print<uchar>(exp, arr, precision, ss, transpose);
+                    break;
+                case s64:
+                    print<intl>(exp, arr, precision, ss, transpose);
+                    break;
+                case u64:
+                    print<uintl>(exp, arr, precision, ss, transpose);
+                    break;
+                case s16:
+                    print<short>(exp, arr, precision, ss, transpose);
+                    break;
+                case u16:
+                    print<ushort>(exp, arr, precision, ss, transpose);
+                    break;
+                default: TYPE_ERROR(1, type);
             }
         }
         std::string str = ss.str();
-        af_alloc_host((void**)output, sizeof(char) * (str.size() + 1));
+        af_alloc_host((void **)output, sizeof(char) * (str.size() + 1));
         str.copy(*output, str.size());
-        (*output)[str.size()] = '\0'; // don't forget the terminating 0
+        (*output)[str.size()] = '\0';  // don't forget the terminating 0
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index e6477f420d..3791ffc381 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -7,21 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <qr.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline void qr(af_array *q, af_array *r, af_array *tau, const af_array in)
-{
+static inline void qr(af_array *q, af_array *r, af_array *tau,
+                      const af_array in) {
     Array<T> qArray = createEmptyArray<T>(af::dim4());
     Array<T> rArray = createEmptyArray<T>(af::dim4());
     Array<T> tArray = createEmptyArray<T>(af::dim4());
@@ -34,15 +34,13 @@ static inline void qr(af_array *q, af_array *r, af_array *tau, const af_array in
 }
 
 template<typename T>
-static inline af_array qr_inplace(af_array in)
-{
+static inline af_array qr_inplace(af_array in) {
     return getHandle(qr_inplace<T>(getArray<T>(in)));
 }
 
-af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
-{
+af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("qr can not be used in batch mode", AF_ERR_BATCH);
@@ -50,21 +48,21 @@ af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
 
         af_dtype type = i_info.getType();
 
-        if(i_info.ndims() == 0) {
-            AF_CHECK(af_create_handle(q,   0, nullptr, type));
-            AF_CHECK(af_create_handle(r,   0, nullptr, type));
+        if (i_info.ndims() == 0) {
+            AF_CHECK(af_create_handle(q, 0, nullptr, type));
+            AF_CHECK(af_create_handle(r, 0, nullptr, type));
             AF_CHECK(af_create_handle(tau, 0, nullptr, type));
             return AF_SUCCESS;
         }
 
-        ARG_ASSERT(3, i_info.isFloating());                       // Only floating and complex types
+        ARG_ASSERT(3, i_info.isFloating());  // Only floating and complex types
 
-        switch(type) {
-            case f32: qr<float  >(q, r, tau, in);  break;
-            case f64: qr<double >(q, r, tau, in);  break;
-            case c32: qr<cfloat >(q, r, tau, in);  break;
-            case c64: qr<cdouble>(q, r, tau, in);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: qr<float>(q, r, tau, in); break;
+            case f64: qr<double>(q, r, tau, in); break;
+            case c32: qr<cfloat>(q, r, tau, in); break;
+            case c64: qr<cdouble>(q, r, tau, in); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
@@ -72,10 +70,9 @@ af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_qr_inplace(af_array *tau, af_array in)
-{
+af_err af_qr_inplace(af_array *tau, af_array in) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &i_info = getInfo(in);
 
         if (i_info.ndims() > 2) {
             AF_ERROR("qr can not be used in batch mode", AF_ERR_BATCH);
@@ -83,23 +80,22 @@ af_err af_qr_inplace(af_array *tau, af_array in)
 
         af_dtype type = i_info.getType();
 
-        ARG_ASSERT(1, i_info.isFloating()); // Only floating and complex types
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
-        if(i_info.ndims() == 0) {
+        if (i_info.ndims() == 0) {
             return af_create_handle(tau, 0, nullptr, type);
         }
 
         af_array out;
 
-        switch(type) {
-            case f32: out = qr_inplace<float  >(in);  break;
-            case f64: out = qr_inplace<double >(in);  break;
-            case c32: out = qr_inplace<cfloat >(in);  break;
-            case c64: out = qr_inplace<cdouble>(in);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: out = qr_inplace<float>(in); break;
+            case f64: out = qr_inplace<double>(in); break;
+            case c32: out = qr_inplace<cfloat>(in); break;
+            case c64: out = qr_inplace<cdouble>(in); break;
+            default: TYPE_ERROR(1, type);
         }
-        if(tau != NULL)
-            std::swap(*tau, out);
+        if (tau != NULL) std::swap(*tau, out);
     }
     CATCHALL;
 
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 8dbec0a3d5..71a026dfab 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -9,16 +9,16 @@
 
 #include <af/random.h>
 
-#include <af/array.h>
-#include <af/data.h>
-#include <af/defines.h>
-#include <af/dim4.hpp>
 #include <backend.hpp>
+#include <common/MersenneTwister.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <random_engine.hpp>
-#include <common/MersenneTwister.hpp>
 #include <types.hpp>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 #include <memory>
 
 using namespace detail;
@@ -26,15 +26,13 @@ using namespace common;
 
 using af::dim4;
 
-Array<uint> emptyArray()
-{
+Array<uint> emptyArray() {
     static const Array<uint> EMPTY_ARRAY = createEmptyArray<uint>(af::dim4(0));
 
     return EMPTY_ARRAY;
 }
 
-struct RandomEngine
-{
+struct RandomEngine {
     af_random_engine_type type;
     std::shared_ptr<uintl> seed;
     std::shared_ptr<uintl> counter;
@@ -47,24 +45,28 @@ struct RandomEngine
     Array<uint> state;
 
     RandomEngine(void)
-        : type(AF_RANDOM_ENGINE_DEFAULT), seed(new uintl), counter(new uintl),
-        pos(emptyArray()), sh1(emptyArray()), sh2(emptyArray()), mask(0),
-        recursion_table(emptyArray()), temper_table(emptyArray()), state(emptyArray())
-    {
-        *seed = 0;
+        : type(AF_RANDOM_ENGINE_DEFAULT)
+        , seed(new uintl)
+        , counter(new uintl)
+        , pos(emptyArray())
+        , sh1(emptyArray())
+        , sh2(emptyArray())
+        , mask(0)
+        , recursion_table(emptyArray())
+        , temper_table(emptyArray())
+        , state(emptyArray()) {
+        *seed    = 0;
         *counter = 0;
     }
 };
 
-af_random_engine getRandomEngineHandle(const RandomEngine engine)
-{
+af_random_engine getRandomEngineHandle(const RandomEngine engine) {
     RandomEngine *engineHandle = new RandomEngine;
-    *engineHandle = engine;
+    *engineHandle              = engine;
     return static_cast<af_random_engine>(engineHandle);
 }
 
-RandomEngine* getRandomEngine(const af_random_engine engineHandle)
-{
+RandomEngine *getRandomEngine(const af_random_engine engineHandle) {
     if (engineHandle == 0) {
         AF_ERROR("Uninitialized random engine", AF_ERR_ARG);
     }
@@ -72,61 +74,63 @@ RandomEngine* getRandomEngine(const af_random_engine engineHandle)
 }
 
 template<typename T>
-static inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e)
-{
+static inline af_array uniformDistribution_(const af::dim4 &dims,
+                                            RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-        return getHandle(uniformDistribution<T>(dims, e->pos, e->sh1, e->sh2, e->mask,
-                    e->recursion_table, e->temper_table, e->state));
+        return getHandle(uniformDistribution<T>(dims, e->pos, e->sh1, e->sh2,
+                                                e->mask, e->recursion_table,
+                                                e->temper_table, e->state));
     } else {
-        return getHandle(uniformDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
+        return getHandle(
+            uniformDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
     }
 }
 
 template<typename T>
-static inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e)
-{
+static inline af_array normalDistribution_(const af::dim4 &dims,
+                                           RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-        return getHandle(normalDistribution<T>(dims, e->pos, e->sh1, e->sh2, e->mask,
-                    e->recursion_table, e->temper_table, e->state));
+        return getHandle(normalDistribution<T>(dims, e->pos, e->sh1, e->sh2,
+                                               e->mask, e->recursion_table,
+                                               e->temper_table, e->state));
     } else {
-        return getHandle(normalDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
+        return getHandle(
+            normalDistribution<T>(dims, e->type, *(e->seed), *(e->counter)));
     }
 }
 
-static void validateRandomType(const af_random_engine_type type)
-{
-    if ((type != AF_RANDOM_ENGINE_PHILOX_4X32_10)
-    &&  (type != AF_RANDOM_ENGINE_THREEFRY_2X32_16)
-    &&  (type != AF_RANDOM_ENGINE_MERSENNE_GP11213)
-    &&  (type != AF_RANDOM_ENGINE_PHILOX)
-    &&  (type != AF_RANDOM_ENGINE_THREEFRY)
-    &&  (type != AF_RANDOM_ENGINE_MERSENNE)
-    &&  (type != AF_RANDOM_ENGINE_DEFAULT)) {
+static void validateRandomType(const af_random_engine_type type) {
+    if ((type != AF_RANDOM_ENGINE_PHILOX_4X32_10) &&
+        (type != AF_RANDOM_ENGINE_THREEFRY_2X32_16) &&
+        (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) &&
+        (type != AF_RANDOM_ENGINE_PHILOX) &&
+        (type != AF_RANDOM_ENGINE_THREEFRY) &&
+        (type != AF_RANDOM_ENGINE_MERSENNE) &&
+        (type != AF_RANDOM_ENGINE_DEFAULT)) {
         AF_ERROR("Invalid random type", AF_ERR_ARG);
     }
 }
 
-af_err af_get_default_random_engine(af_random_engine *r)
-{
+af_err af_get_default_random_engine(af_random_engine *r) {
     try {
         AF_CHECK(af_init());
 
         thread_local RandomEngine re;
-        *r = static_cast<af_random_engine> (&re);
+        *r = static_cast<af_random_engine>(&re);
         return AF_SUCCESS;
     }
     CATCHALL;
 }
 
-af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_type rtype, uintl seed)
-{
+af_err af_create_random_engine(af_random_engine *engineHandle,
+                               af_random_engine_type rtype, uintl seed) {
     try {
         AF_CHECK(af_init());
         validateRandomType(rtype);
 
         RandomEngine e;
-        e.type = rtype;
-        *e.seed = seed;
+        e.type     = rtype;
+        *e.seed    = seed;
         *e.counter = 0;
 
         if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
@@ -135,30 +139,34 @@ af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_
             e.sh2  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh2);
             e.mask = mask;
 
-            e.recursion_table = createHostDataArray<uint>(af::dim4(TableLength), recursion_tbl);
-            e.temper_table    = createHostDataArray<uint>(af::dim4(TableLength), temper_tbl);
-            e.state           = createEmptyArray<uint>(af::dim4(MtStateLength));
+            e.recursion_table =
+                createHostDataArray<uint>(af::dim4(TableLength), recursion_tbl);
+            e.temper_table =
+                createHostDataArray<uint>(af::dim4(TableLength), temper_tbl);
+            e.state = createEmptyArray<uint>(af::dim4(MtStateLength));
 
             initMersenneState(e.state, seed, e.recursion_table);
         }
 
         *engineHandle = getRandomEngineHandle(e);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engine engineHandle)
-{
+af_err af_retain_random_engine(af_random_engine *outHandle,
+                               const af_random_engine engineHandle) {
     try {
         AF_CHECK(af_init());
         *outHandle = getRandomEngineHandle(*(getRandomEngine(engineHandle)));
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype)
-{
+af_err af_random_engine_set_type(af_random_engine *engine,
+                                 const af_random_engine_type rtype) {
     try {
         AF_CHECK(af_init());
         validateRandomType(rtype);
@@ -170,53 +178,56 @@ af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engin
                 e->sh2  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh2);
                 e->mask = mask;
 
-                e->recursion_table = createHostDataArray<uint>(af::dim4(TableLength), recursion_tbl);
-                e->temper_table    = createHostDataArray<uint>(af::dim4(TableLength), temper_tbl);
-                e->state           = createEmptyArray<uint>(af::dim4(MtStateLength));
+                e->recursion_table = createHostDataArray<uint>(
+                    af::dim4(TableLength), recursion_tbl);
+                e->temper_table = createHostDataArray<uint>(
+                    af::dim4(TableLength), temper_tbl);
+                e->state = createEmptyArray<uint>(af::dim4(MtStateLength));
 
                 initMersenneState(e->state, *(e->seed), e->recursion_table);
             } else if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-                e->pos  = emptyArray();
-                e->sh1  = emptyArray();
-                e->sh2  = emptyArray();
-                e->mask = 0;
+                e->pos             = emptyArray();
+                e->sh1             = emptyArray();
+                e->sh2             = emptyArray();
+                e->mask            = 0;
                 e->recursion_table = emptyArray();
                 e->temper_table    = emptyArray();
                 e->state           = emptyArray();
             }
             e->type = rtype;
         }
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine)
-{
+af_err af_random_engine_get_type(af_random_engine_type *rtype,
+                                 const af_random_engine engine) {
     try {
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(engine);
-        *rtype = e->type;
-    } CATCHALL;
+        *rtype          = e->type;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_default_random_engine_type(const af_random_engine_type rtype)
-{
+af_err af_set_default_random_engine_type(const af_random_engine_type rtype) {
     try {
         AF_CHECK(af_init());
         af_random_engine e;
         AF_CHECK(af_get_default_random_engine(&e));
         AF_CHECK(af_random_engine_set_type(&e, rtype));
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
-{
+af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed) {
     try {
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(*engine);
-        *(e->seed) = seed;
+        *(e->seed)      = seed;
         if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             initMersenneState(e->state, seed, e->recursion_table);
         } else {
@@ -227,40 +238,40 @@ af_err af_random_engine_set_seed(af_random_engine *engine, const uintl seed)
     return AF_SUCCESS;
 }
 
-af_err af_random_engine_get_seed(uintl * const seed, af_random_engine engine)
-{
+af_err af_random_engine_get_seed(uintl *const seed, af_random_engine engine) {
     try {
         AF_CHECK(af_init());
         RandomEngine *e = getRandomEngine(engine);
-        *seed = *(e->seed);
+        *seed           = *(e->seed);
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
-{
+af_err af_random_uniform(af_array *out, const unsigned ndims,
+                         const dim_t *const dims, const af_dtype type,
+                         af_random_engine engine) {
     try {
         AF_CHECK(af_init());
         af_array result;
 
-        af::dim4 d = verifyDims(ndims, dims);
+        af::dim4 d      = verifyDims(ndims, dims);
         RandomEngine *e = getRandomEngine(engine);
 
-        switch(type) {
-        case f32:   result = uniformDistribution_<float  >(d, e); break;
-        case c32:   result = uniformDistribution_<cfloat >(d, e); break;
-        case f64:   result = uniformDistribution_<double >(d, e); break;
-        case c64:   result = uniformDistribution_<cdouble>(d, e); break;
-        case s32:   result = uniformDistribution_<int    >(d, e); break;
-        case u32:   result = uniformDistribution_<uint   >(d, e); break;
-        case s64:   result = uniformDistribution_<intl   >(d, e); break;
-        case u64:   result = uniformDistribution_<uintl  >(d, e); break;
-        case s16:   result = uniformDistribution_<short  >(d, e); break;
-        case u16:   result = uniformDistribution_<ushort >(d, e); break;
-        case u8:    result = uniformDistribution_<uchar  >(d, e); break;
-        case b8:    result = uniformDistribution_<char   >(d, e); break;
-        default:    TYPE_ERROR(4, type);
+        switch (type) {
+            case f32: result = uniformDistribution_<float>(d, e); break;
+            case c32: result = uniformDistribution_<cfloat>(d, e); break;
+            case f64: result = uniformDistribution_<double>(d, e); break;
+            case c64: result = uniformDistribution_<cdouble>(d, e); break;
+            case s32: result = uniformDistribution_<int>(d, e); break;
+            case u32: result = uniformDistribution_<uint>(d, e); break;
+            case s64: result = uniformDistribution_<intl>(d, e); break;
+            case u64: result = uniformDistribution_<uintl>(d, e); break;
+            case s16: result = uniformDistribution_<short>(d, e); break;
+            case u16: result = uniformDistribution_<ushort>(d, e); break;
+            case u8: result = uniformDistribution_<uchar>(d, e); break;
+            case b8: result = uniformDistribution_<char>(d, e); break;
+            default: TYPE_ERROR(4, type);
         }
         std::swap(*out, result);
     }
@@ -268,21 +279,22 @@ af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * cons
     return AF_SUCCESS;
 }
 
-af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
-{
+af_err af_random_normal(af_array *out, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype type,
+                        af_random_engine engine) {
     try {
         AF_CHECK(af_init());
         af_array result;
 
-        af::dim4 d = verifyDims(ndims, dims);
+        af::dim4 d      = verifyDims(ndims, dims);
         RandomEngine *e = getRandomEngine(engine);
 
-        switch(type) {
-        case f32:   result = normalDistribution_<float  >(d, e); break;
-        case c32:   result = normalDistribution_<cfloat >(d, e); break;
-        case f64:   result = normalDistribution_<double >(d, e); break;
-        case c64:   result = normalDistribution_<cdouble>(d, e); break;
-        default:    TYPE_ERROR(4, type);
+        switch (type) {
+            case f32: result = normalDistribution_<float>(d, e); break;
+            case c32: result = normalDistribution_<cfloat>(d, e); break;
+            case f64: result = normalDistribution_<double>(d, e); break;
+            case c64: result = normalDistribution_<cdouble>(d, e); break;
+            default: TYPE_ERROR(4, type);
         }
         std::swap(*out, result);
     }
@@ -290,8 +302,7 @@ af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const
     return AF_SUCCESS;
 }
 
-af_err af_release_random_engine(af_random_engine engineHandle)
-{
+af_err af_release_random_engine(af_random_engine engineHandle) {
     try {
         AF_CHECK(af_init());
         delete getRandomEngine(engineHandle);
@@ -300,8 +311,8 @@ af_err af_release_random_engine(af_random_engine engineHandle)
     return AF_SUCCESS;
 }
 
-af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_randu(af_array *out, const unsigned ndims, const dim_t *const dims,
+                const af_dtype type) {
     try {
         AF_CHECK(af_init());
         af_array result;
@@ -309,22 +320,22 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
         RandomEngine *e = getRandomEngine(engine);
-        af::dim4 d = verifyDims(ndims, dims);
-
-        switch(type) {
-        case f32: result = uniformDistribution_<float  >(d, e); break;
-        case c32: result = uniformDistribution_<cfloat >(d, e); break;
-        case f64: result = uniformDistribution_<double >(d, e); break;
-        case c64: result = uniformDistribution_<cdouble>(d, e); break;
-        case s32: result = uniformDistribution_<int    >(d, e); break;
-        case u32: result = uniformDistribution_<uint   >(d, e); break;
-        case s64: result = uniformDistribution_<intl   >(d, e); break;
-        case u64: result = uniformDistribution_<uintl  >(d, e); break;
-        case s16: result = uniformDistribution_<short  >(d, e); break;
-        case u16: result = uniformDistribution_<ushort >(d, e); break;
-        case u8:  result = uniformDistribution_<uchar  >(d, e); break;
-        case b8:  result = uniformDistribution_<char   >(d, e); break;
-        default:  TYPE_ERROR(3, type);
+        af::dim4 d      = verifyDims(ndims, dims);
+
+        switch (type) {
+            case f32: result = uniformDistribution_<float>(d, e); break;
+            case c32: result = uniformDistribution_<cfloat>(d, e); break;
+            case f64: result = uniformDistribution_<double>(d, e); break;
+            case c64: result = uniformDistribution_<cdouble>(d, e); break;
+            case s32: result = uniformDistribution_<int>(d, e); break;
+            case u32: result = uniformDistribution_<uint>(d, e); break;
+            case s64: result = uniformDistribution_<intl>(d, e); break;
+            case u64: result = uniformDistribution_<uintl>(d, e); break;
+            case s16: result = uniformDistribution_<short>(d, e); break;
+            case u16: result = uniformDistribution_<ushort>(d, e); break;
+            case u8: result = uniformDistribution_<uchar>(d, e); break;
+            case b8: result = uniformDistribution_<char>(d, e); break;
+            default: TYPE_ERROR(3, type);
         }
         std::swap(*out, result);
     }
@@ -332,8 +343,8 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, c
     return AF_SUCCESS;
 }
 
-af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_randn(af_array *out, const unsigned ndims, const dim_t *const dims,
+                const af_dtype type) {
     try {
         AF_CHECK(af_init());
         af_array result;
@@ -341,14 +352,14 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
         RandomEngine *e = getRandomEngine(engine);
-        af::dim4 d = verifyDims(ndims, dims);
-
-        switch(type) {
-        case f32: result = normalDistribution_<float  >(d, e); break;
-        case c32: result = normalDistribution_<cfloat >(d, e); break;
-        case f64: result = normalDistribution_<double >(d, e); break;
-        case c64: result = normalDistribution_<cdouble>(d, e); break;
-        default:  TYPE_ERROR(3, type);
+        af::dim4 d      = verifyDims(ndims, dims);
+
+        switch (type) {
+            case f32: result = normalDistribution_<float>(d, e); break;
+            case c32: result = normalDistribution_<cfloat>(d, e); break;
+            case f64: result = normalDistribution_<double>(d, e); break;
+            case c64: result = normalDistribution_<cdouble>(d, e); break;
+            default: TYPE_ERROR(3, type);
         }
         std::swap(*out, result);
     }
@@ -356,24 +367,24 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, c
     return AF_SUCCESS;
 }
 
-af_err af_set_seed(const uintl seed)
-{
+af_err af_set_seed(const uintl seed) {
     try {
         AF_CHECK(af_init());
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
         AF_CHECK(af_random_engine_set_seed(&engine, seed));
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_get_seed(uintl *seed)
-{
+af_err af_get_seed(uintl *seed) {
     try {
         AF_CHECK(af_init());
         af_random_engine e;
         AF_CHECK(af_get_default_random_engine(&e));
         AF_CHECK(af_random_engine_get_seed(seed, e));
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 2a752c3f65..9816646e73 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <complex.hpp>
+#include <handle.hpp>
+#include <logic.hpp>
 #include <qr.hpp>
 #include <reduce.hpp>
-#include <logic.hpp>
-#include <complex.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline uint rank(const af_array in, double tol)
-{
+static inline uint rank(const af_array in, double tol) {
     typedef typename af::dtype_traits<T>::base_type BT;
     Array<T> In = getArray<T>(in);
 
@@ -40,14 +39,13 @@ static inline uint rank(const af_array in, double tol)
         R = abs<BT, T>(r);
     }
 
-    Array<BT> val = createValueArray<BT>(R.dims(), scalar<BT>(tol));
+    Array<BT> val  = createValueArray<BT>(R.dims(), scalar<BT>(tol));
     Array<char> gt = logicOp<BT, af_gt_t>(R, val, val.dims());
     Array<char> at = reduce<af_or_t, char, char>(gt, 1);
     return reduce_all<af_notzero_t, char, uint>(at);
 }
 
-af_err af_rank(uint *out, const af_array in, const double tol)
-{
+af_err af_rank(uint* out, const af_array in, const double tol) {
     try {
         const ArrayInfo& i_info = getInfo(in);
 
@@ -57,20 +55,20 @@ af_err af_rank(uint *out, const af_array in, const double tol)
 
         af_dtype type = i_info.getType();
 
-        ARG_ASSERT(1, i_info.isFloating());                       // Only floating and complex types
+        ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
         uint output;
-        if(i_info.ndims() == 0) {
+        if (i_info.ndims() == 0) {
             output = 0;
             return AF_SUCCESS;
         }
 
-        switch(type) {
-            case f32: output = rank<float  >(in, tol);  break;
-            case f64: output = rank<double >(in, tol);  break;
-            case c32: output = rank<cfloat >(in, tol);  break;
-            case c64: output = rank<cdouble>(in, tol);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = rank<float>(in, tol); break;
+            case f64: output = rank<double>(in, tol); break;
+            case c32: output = rank<cfloat>(in, tol); break;
+            case c64: output = rank<cdouble>(in, tol); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 9287ef09dc..9506ca87cd 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -7,37 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/defines.h>
-#include <af/dim4.hpp>
-#include <af/algorithm.h>
+#include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <ops.hpp>
-#include <backend.hpp>
-#include <reduce.hpp>
 #include <ireduce.hpp>
 #include <math.hpp>
+#include <ops.hpp>
+#include <reduce.hpp>
+#include <af/algorithm.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 using namespace detail;
 
 template<af_op_t op, typename Ti, typename To>
 static inline af_array reduce(const af_array in, const int dim,
-                              bool change_nan = false, double nanval = 0)
-{
-    return getHandle(reduce<op,Ti,To>(getArray<Ti>(in), dim, change_nan, nanval));
+                              bool change_nan = false, double nanval = 0) {
+    return getHandle(
+        reduce<op, Ti, To>(getArray<Ti>(in), dim, change_nan, nanval));
 }
 
 template<af_op_t op, typename To>
-static af_err reduce_type(af_array *out, const af_array in, const int dim)
-{
+static af_err reduce_type(af_array *out, const af_array in, const int dim) {
     try {
-
         ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(2, dim < 4);
 
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
         if (dim >= (int)in_info.ndims()) {
             *out = retain(in);
@@ -47,20 +45,20 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim)
         af_dtype type = in_info.getType();
         af_array res;
 
-        switch(type) {
-        case f32:  res = reduce<op, float  , To>(in, dim); break;
-        case f64:  res = reduce<op, double , To>(in, dim); break;
-        case c32:  res = reduce<op, cfloat , To>(in, dim); break;
-        case c64:  res = reduce<op, cdouble, To>(in, dim); break;
-        case u32:  res = reduce<op, uint   , To>(in, dim); break;
-        case s32:  res = reduce<op, int    , To>(in, dim); break;
-        case u64:  res = reduce<op, uintl  , To>(in, dim); break;
-        case s64:  res = reduce<op, intl   , To>(in, dim); break;
-        case u16:  res = reduce<op, ushort , To>(in, dim); break;
-        case s16:  res = reduce<op, short  , To>(in, dim); break;
-        case b8:   res = reduce<op, char   , To>(in, dim); break;
-        case u8:   res = reduce<op, uchar  , To>(in, dim); break;
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: res = reduce<op, float, To>(in, dim); break;
+            case f64: res = reduce<op, double, To>(in, dim); break;
+            case c32: res = reduce<op, cfloat, To>(in, dim); break;
+            case c64: res = reduce<op, cdouble, To>(in, dim); break;
+            case u32: res = reduce<op, uint, To>(in, dim); break;
+            case s32: res = reduce<op, int, To>(in, dim); break;
+            case u64: res = reduce<op, uintl, To>(in, dim); break;
+            case s64: res = reduce<op, intl, To>(in, dim); break;
+            case u16: res = reduce<op, ushort, To>(in, dim); break;
+            case s16: res = reduce<op, short, To>(in, dim); break;
+            case b8: res = reduce<op, char, To>(in, dim); break;
+            case u8: res = reduce<op, uchar, To>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
@@ -71,36 +69,32 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim)
 }
 
 template<af_op_t op>
-static af_err reduce_common(af_array *out, const af_array in, const int dim)
-{
+static af_err reduce_common(af_array *out, const af_array in, const int dim) {
     try {
-
         ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(2, dim < 4);
 
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
-        if (dim >= (int)in_info.ndims()) {
-            return af_retain_array(out, in);
-        }
+        if (dim >= (int)in_info.ndims()) { return af_retain_array(out, in); }
 
         af_dtype type = in_info.getType();
         af_array res;
 
-        switch(type) {
-        case f32:  res = reduce<op, float  , float  >(in, dim); break;
-        case f64:  res = reduce<op, double , double >(in, dim); break;
-        case c32:  res = reduce<op, cfloat , cfloat >(in, dim); break;
-        case c64:  res = reduce<op, cdouble, cdouble>(in, dim); break;
-        case u32:  res = reduce<op, uint   , uint   >(in, dim); break;
-        case s32:  res = reduce<op, int    , int    >(in, dim); break;
-        case u64:  res = reduce<op, uintl  , uintl  >(in, dim); break;
-        case s64:  res = reduce<op, intl   , intl   >(in, dim); break;
-        case u16:  res = reduce<op, ushort , ushort >(in, dim); break;
-        case s16:  res = reduce<op, short  , short  >(in, dim); break;
-        case b8:   res = reduce<op, char   , char   >(in, dim); break;
-        case u8:   res = reduce<op, uchar  , uchar  >(in, dim); break;
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: res = reduce<op, float, float>(in, dim); break;
+            case f64: res = reduce<op, double, double>(in, dim); break;
+            case c32: res = reduce<op, cfloat, cfloat>(in, dim); break;
+            case c64: res = reduce<op, cdouble, cdouble>(in, dim); break;
+            case u32: res = reduce<op, uint, uint>(in, dim); break;
+            case s32: res = reduce<op, int, int>(in, dim); break;
+            case u64: res = reduce<op, uintl, uintl>(in, dim); break;
+            case s64: res = reduce<op, intl, intl>(in, dim); break;
+            case u16: res = reduce<op, ushort, ushort>(in, dim); break;
+            case s16: res = reduce<op, short, short>(in, dim); break;
+            case b8: res = reduce<op, char, char>(in, dim); break;
+            case u8: res = reduce<op, uchar, uchar>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
@@ -112,14 +106,12 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim)
 
 template<af_op_t op>
 static af_err reduce_promote(af_array *out, const af_array in, const int dim,
-                             bool change_nan=false, double nanval=0)
-{
+                             bool change_nan = false, double nanval = 0) {
     try {
-
         ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(2, dim < 4);
 
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
         if (dim >= (int)in_info.ndims()) {
             *out = retain(in);
@@ -129,21 +121,47 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
         af_dtype type = in_info.getType();
         af_array res;
 
-        switch(type) {
-        case f32:  res = reduce<op, float  , float  >(in, dim, change_nan, nanval); break;
-        case f64:  res = reduce<op, double , double >(in, dim, change_nan, nanval); break;
-        case c32:  res = reduce<op, cfloat , cfloat >(in, dim, change_nan, nanval); break;
-        case c64:  res = reduce<op, cdouble, cdouble>(in, dim, change_nan, nanval); break;
-        case u32:  res = reduce<op, uint   , uint   >(in, dim, change_nan, nanval); break;
-        case s32:  res = reduce<op, int    , int    >(in, dim, change_nan, nanval); break;
-        case u64:  res = reduce<op, uintl  , uintl  >(in, dim, change_nan, nanval); break;
-        case s64:  res = reduce<op, intl   , intl   >(in, dim, change_nan, nanval); break;
-        case u16:  res = reduce<op, ushort , uint   >(in, dim, change_nan, nanval); break;
-        case s16:  res = reduce<op, short  , int    >(in, dim, change_nan, nanval); break;
-        case u8:   res = reduce<op, uchar  , uint   >(in, dim, change_nan, nanval); break;
-            // Make sure you are adding only "1" for every non zero value, even if op == af_add_t
-        case b8:   res = reduce<af_notzero_t, char  , uint   >(in, dim, change_nan, nanval); break;
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                res = reduce<op, float, float>(in, dim, change_nan, nanval);
+                break;
+            case f64:
+                res = reduce<op, double, double>(in, dim, change_nan, nanval);
+                break;
+            case c32:
+                res = reduce<op, cfloat, cfloat>(in, dim, change_nan, nanval);
+                break;
+            case c64:
+                res = reduce<op, cdouble, cdouble>(in, dim, change_nan, nanval);
+                break;
+            case u32:
+                res = reduce<op, uint, uint>(in, dim, change_nan, nanval);
+                break;
+            case s32:
+                res = reduce<op, int, int>(in, dim, change_nan, nanval);
+                break;
+            case u64:
+                res = reduce<op, uintl, uintl>(in, dim, change_nan, nanval);
+                break;
+            case s64:
+                res = reduce<op, intl, intl>(in, dim, change_nan, nanval);
+                break;
+            case u16:
+                res = reduce<op, ushort, uint>(in, dim, change_nan, nanval);
+                break;
+            case s16:
+                res = reduce<op, short, int>(in, dim, change_nan, nanval);
+                break;
+            case u8:
+                res = reduce<op, uchar, uint>(in, dim, change_nan, nanval);
+                break;
+                // Make sure you are adding only "1" for every non zero value,
+                // even if op == af_add_t
+            case b8:
+                res = reduce<af_notzero_t, char, uint>(in, dim, change_nan,
+                                                       nanval);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, res);
     }
@@ -152,85 +170,75 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
     return AF_SUCCESS;
 }
 
-af_err af_min(af_array *out, const af_array in, const int dim)
-{
+af_err af_min(af_array *out, const af_array in, const int dim) {
     return reduce_common<af_min_t>(out, in, dim);
 }
 
-af_err af_max(af_array *out, const af_array in, const int dim)
-{
+af_err af_max(af_array *out, const af_array in, const int dim) {
     return reduce_common<af_max_t>(out, in, dim);
 }
 
-af_err af_sum(af_array *out, const af_array in, const int dim)
-{
+af_err af_sum(af_array *out, const af_array in, const int dim) {
     return reduce_promote<af_add_t>(out, in, dim);
 }
 
-af_err af_product(af_array *out, const af_array in, const int dim)
-{
+af_err af_product(af_array *out, const af_array in, const int dim) {
     return reduce_promote<af_mul_t>(out, in, dim);
 }
 
-af_err af_sum_nan(af_array *out, const af_array in, const int dim, const double nanval)
-{
+af_err af_sum_nan(af_array *out, const af_array in, const int dim,
+                  const double nanval) {
     return reduce_promote<af_add_t>(out, in, dim, true, nanval);
 }
 
-af_err af_product_nan(af_array *out, const af_array in, const int dim, const double nanval)
-{
+af_err af_product_nan(af_array *out, const af_array in, const int dim,
+                      const double nanval) {
     return reduce_promote<af_mul_t>(out, in, dim, true, nanval);
 }
 
-af_err af_count(af_array *out, const af_array in, const int dim)
-{
+af_err af_count(af_array *out, const af_array in, const int dim) {
     return reduce_type<af_notzero_t, uint>(out, in, dim);
 }
 
-af_err af_all_true(af_array *out, const af_array in, const int dim)
-{
+af_err af_all_true(af_array *out, const af_array in, const int dim) {
     return reduce_type<af_and_t, char>(out, in, dim);
 }
 
-af_err af_any_true(af_array *out, const af_array in, const int dim)
-{
+af_err af_any_true(af_array *out, const af_array in, const int dim) {
     return reduce_type<af_or_t, char>(out, in, dim);
 }
 
 template<af_op_t op, typename Ti, typename To>
-static inline To reduce_all(const af_array in, bool change_nan = false, double nanval = 0)
-{
-    return reduce_all<op,Ti,To>(getArray<Ti>(in), change_nan, nanval);
+static inline To reduce_all(const af_array in, bool change_nan = false,
+                            double nanval = 0) {
+    return reduce_all<op, Ti, To>(getArray<Ti>(in), change_nan, nanval);
 }
 
 template<af_op_t op, typename To>
-static af_err reduce_all_type(double *real, double *imag, const af_array in)
-{
+static af_err reduce_all_type(double *real, double *imag, const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
-        af_dtype type = in_info.getType();
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
 
         ARG_ASSERT(0, real != NULL);
         *real = 0;
         if (imag) *imag = 0;
 
-        switch(type) {
-        case f32:  *real = (double)reduce_all<op, float  , To>(in); break;
-        case f64:  *real = (double)reduce_all<op, double , To>(in); break;
-        case c32:  *real = (double)reduce_all<op, cfloat , To>(in); break;
-        case c64:  *real = (double)reduce_all<op, cdouble, To>(in); break;
-        case u32:  *real = (double)reduce_all<op, uint   , To>(in); break;
-        case s32:  *real = (double)reduce_all<op, int    , To>(in); break;
-        case u64:  *real = (double)reduce_all<op, uintl  , To>(in); break;
-        case s64:  *real = (double)reduce_all<op, intl   , To>(in); break;
-        case u16:  *real = (double)reduce_all<op, ushort , To>(in); break;
-        case s16:  *real = (double)reduce_all<op, short  , To>(in); break;
-        case b8:   *real = (double)reduce_all<op, char   , To>(in); break;
-        case u8:   *real = (double)reduce_all<op, uchar  , To>(in); break;
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: *real = (double)reduce_all<op, float, To>(in); break;
+            case f64: *real = (double)reduce_all<op, double, To>(in); break;
+            case c32: *real = (double)reduce_all<op, cfloat, To>(in); break;
+            case c64: *real = (double)reduce_all<op, cdouble, To>(in); break;
+            case u32: *real = (double)reduce_all<op, uint, To>(in); break;
+            case s32: *real = (double)reduce_all<op, int, To>(in); break;
+            case u64: *real = (double)reduce_all<op, uintl, To>(in); break;
+            case s64: *real = (double)reduce_all<op, intl, To>(in); break;
+            case u16: *real = (double)reduce_all<op, ushort, To>(in); break;
+            case s16: *real = (double)reduce_all<op, short, To>(in); break;
+            case b8: *real = (double)reduce_all<op, char, To>(in); break;
+            case u8: *real = (double)reduce_all<op, uchar, To>(in); break;
+            default: TYPE_ERROR(1, type);
         }
-
     }
     CATCHALL;
 
@@ -238,50 +246,60 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in)
 }
 
 template<af_op_t op>
-static af_err reduce_all_common(double *real_val, double *imag_val, const af_array in)
-{
+static af_err reduce_all_common(double *real_val, double *imag_val,
+                                const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
-        af_dtype type = in_info.getType();
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
 
         ARG_ASSERT(2, in_info.ndims() > 0);
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
         if (imag_val != NULL) *imag_val = 0;
 
-        cfloat  cfval;
+        cfloat cfval;
         cdouble cdval;
 
-        switch(type) {
-        case f32:  *real_val = (double)reduce_all<op, float  , float  >(in); break;
-        case f64:  *real_val = (double)reduce_all<op, double , double >(in); break;
-        case u32:  *real_val = (double)reduce_all<op, uint   , uint   >(in); break;
-        case s32:  *real_val = (double)reduce_all<op, int    , int    >(in); break;
-        case u64:  *real_val = (double)reduce_all<op, uintl  , uintl  >(in); break;
-        case s64:  *real_val = (double)reduce_all<op, intl   , intl   >(in); break;
-        case u16:  *real_val = (double)reduce_all<op, ushort , ushort >(in); break;
-        case s16:  *real_val = (double)reduce_all<op, short  , short  >(in); break;
-        case b8:   *real_val = (double)reduce_all<op, char   , char   >(in); break;
-        case u8:   *real_val = (double)reduce_all<op, uchar  , uchar  >(in); break;
-
-        case c32:
-            cfval = reduce_all<op, cfloat, cfloat>(in);
-            ARG_ASSERT(1, imag_val != NULL);
-            *real_val = real(cfval);
-            *imag_val = imag(cfval);
-            break;
-
-        case c64:
-            cdval = reduce_all<op, cdouble, cdouble>(in);
-            ARG_ASSERT(1, imag_val != NULL);
-            *real_val = real(cdval);
-            *imag_val = imag(cdval);
-            break;
-
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                *real_val = (double)reduce_all<op, float, float>(in);
+                break;
+            case f64:
+                *real_val = (double)reduce_all<op, double, double>(in);
+                break;
+            case u32: *real_val = (double)reduce_all<op, uint, uint>(in); break;
+            case s32: *real_val = (double)reduce_all<op, int, int>(in); break;
+            case u64:
+                *real_val = (double)reduce_all<op, uintl, uintl>(in);
+                break;
+            case s64: *real_val = (double)reduce_all<op, intl, intl>(in); break;
+            case u16:
+                *real_val = (double)reduce_all<op, ushort, ushort>(in);
+                break;
+            case s16:
+                *real_val = (double)reduce_all<op, short, short>(in);
+                break;
+            case b8: *real_val = (double)reduce_all<op, char, char>(in); break;
+            case u8:
+                *real_val = (double)reduce_all<op, uchar, uchar>(in);
+                break;
+
+            case c32:
+                cfval = reduce_all<op, cfloat, cfloat>(in);
+                ARG_ASSERT(1, imag_val != NULL);
+                *real_val = real(cfval);
+                *imag_val = imag(cfval);
+                break;
+
+            case c64:
+                cdval = reduce_all<op, cdouble, cdouble>(in);
+                ARG_ASSERT(1, imag_val != NULL);
+                *real_val = real(cdval);
+                *imag_val = imag(cdval);
+                break;
+
+            default: TYPE_ERROR(1, type);
         }
-
     }
     CATCHALL;
 
@@ -289,49 +307,79 @@ static af_err reduce_all_common(double *real_val, double *imag_val, const af_arr
 }
 
 template<af_op_t op>
-static af_err reduce_all_promote(double *real_val, double *imag_val, const af_array in,
-                                 bool change_nan=false, double nanval=0)
-{
+static af_err reduce_all_promote(double *real_val, double *imag_val,
+                                 const af_array in, bool change_nan = false,
+                                 double nanval = 0) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
-        af_dtype type = in_info.getType();
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
 
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
         if (imag_val) *imag_val = 0;
 
-        cfloat  cfval;
+        cfloat cfval;
         cdouble cdval;
 
-        switch(type) {
-        case f32: *real_val = (double)reduce_all<op, float  , float  >(in, change_nan, nanval); break;
-        case f64: *real_val = (double)reduce_all<op, double , double >(in, change_nan, nanval); break;
-        case u32: *real_val = (double)reduce_all<op, uint   , uint   >(in, change_nan, nanval); break;
-        case s32: *real_val = (double)reduce_all<op, int    , int    >(in, change_nan, nanval); break;
-        case u64: *real_val = (double)reduce_all<op, uintl  , uintl  >(in, change_nan, nanval); break;
-        case s64: *real_val = (double)reduce_all<op, intl   , intl   >(in, change_nan, nanval); break;
-        case u16: *real_val = (double)reduce_all<op, ushort , uint   >(in, change_nan, nanval); break;
-        case s16: *real_val = (double)reduce_all<op, short  , int    >(in, change_nan, nanval); break;
-        case u8:  *real_val = (double)reduce_all<op, uchar  , uint   >(in, change_nan, nanval); break;
-            // Make sure you are adding only "1" for every non zero value, even if op == af_add_t
-        case b8:  *real_val = (double)reduce_all<af_notzero_t, char, uint>(in, change_nan, nanval); break;
-
-        case c32:
-            cfval = reduce_all<op, cfloat, cfloat>(in);
-            ARG_ASSERT(1, imag_val != NULL);
-            *real_val = real(cfval);
-            *imag_val = imag(cfval);
-            break;
-
-        case c64:
-            cdval = reduce_all<op, cdouble, cdouble>(in);
-            ARG_ASSERT(1, imag_val != NULL);
-            *real_val = real(cdval);
-            *imag_val = imag(cdval);
-            break;
-
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                *real_val = (double)reduce_all<op, float, float>(in, change_nan,
+                                                                 nanval);
+                break;
+            case f64:
+                *real_val = (double)reduce_all<op, double, double>(
+                    in, change_nan, nanval);
+                break;
+            case u32:
+                *real_val =
+                    (double)reduce_all<op, uint, uint>(in, change_nan, nanval);
+                break;
+            case s32:
+                *real_val =
+                    (double)reduce_all<op, int, int>(in, change_nan, nanval);
+                break;
+            case u64:
+                *real_val = (double)reduce_all<op, uintl, uintl>(in, change_nan,
+                                                                 nanval);
+                break;
+            case s64:
+                *real_val =
+                    (double)reduce_all<op, intl, intl>(in, change_nan, nanval);
+                break;
+            case u16:
+                *real_val = (double)reduce_all<op, ushort, uint>(in, change_nan,
+                                                                 nanval);
+                break;
+            case s16:
+                *real_val =
+                    (double)reduce_all<op, short, int>(in, change_nan, nanval);
+                break;
+            case u8:
+                *real_val =
+                    (double)reduce_all<op, uchar, uint>(in, change_nan, nanval);
+                break;
+                // Make sure you are adding only "1" for every non zero value,
+                // even if op == af_add_t
+            case b8:
+                *real_val = (double)reduce_all<af_notzero_t, char, uint>(
+                    in, change_nan, nanval);
+                break;
+
+            case c32:
+                cfval = reduce_all<op, cfloat, cfloat>(in);
+                ARG_ASSERT(1, imag_val != NULL);
+                *real_val = real(cfval);
+                *imag_val = imag(cfval);
+                break;
+
+            case c64:
+                cdval = reduce_all<op, cdouble, cdouble>(in);
+                ARG_ASSERT(1, imag_val != NULL);
+                *real_val = real(cdval);
+                *imag_val = imag(cdval);
+                break;
+
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
@@ -339,50 +387,42 @@ static af_err reduce_all_promote(double *real_val, double *imag_val, const af_ar
     return AF_SUCCESS;
 }
 
-af_err af_min_all(double *real, double *imag, const af_array in)
-{
+af_err af_min_all(double *real, double *imag, const af_array in) {
     return reduce_all_common<af_min_t>(real, imag, in);
 }
 
-af_err af_max_all(double *real, double *imag, const af_array in)
-{
+af_err af_max_all(double *real, double *imag, const af_array in) {
     return reduce_all_common<af_max_t>(real, imag, in);
 }
 
-af_err af_sum_all(double *real, double *imag, const af_array in)
-{
+af_err af_sum_all(double *real, double *imag, const af_array in) {
     return reduce_all_promote<af_add_t>(real, imag, in);
 }
 
-af_err af_product_all(double *real, double *imag, const af_array in)
-{
+af_err af_product_all(double *real, double *imag, const af_array in) {
     return reduce_all_promote<af_mul_t>(real, imag, in);
 }
 
-af_err af_count_all(double *real, double *imag, const af_array in)
-{
+af_err af_count_all(double *real, double *imag, const af_array in) {
     return reduce_all_type<af_notzero_t, uint>(real, imag, in);
 }
 
-af_err af_all_true_all(double *real, double *imag, const af_array in)
-{
+af_err af_all_true_all(double *real, double *imag, const af_array in) {
     return reduce_all_type<af_and_t, char>(real, imag, in);
 }
 
-af_err af_any_true_all(double *real, double *imag, const af_array in)
-{
-    return reduce_all_type<af_or_t , char>(real, imag, in);
+af_err af_any_true_all(double *real, double *imag, const af_array in) {
+    return reduce_all_type<af_or_t, char>(real, imag, in);
 }
 
 template<af_op_t op, typename T>
-static inline void ireduce(af_array *res, af_array *loc,
-                           const af_array in, const int dim)
-{
+static inline void ireduce(af_array *res, af_array *loc, const af_array in,
+                           const int dim) {
     const Array<T> In = getArray<T>(in);
-    dim4 odims = In.dims();
-    odims[dim] = 1;
+    dim4 odims        = In.dims();
+    odims[dim]        = 1;
 
-    Array<T> Res = createEmptyArray<T>(odims);
+    Array<T> Res    = createEmptyArray<T>(odims);
     Array<uint> Loc = createEmptyArray<uint>(odims);
     ireduce<op, T>(Res, Loc, In, dim);
 
@@ -391,14 +431,13 @@ static inline void ireduce(af_array *res, af_array *loc,
 }
 
 template<af_op_t op>
-static af_err ireduce_common(af_array *val, af_array *idx, const af_array in, const int dim)
-{
+static af_err ireduce_common(af_array *val, af_array *idx, const af_array in,
+                             const int dim) {
     try {
-
         ARG_ASSERT(3, dim >= 0);
-        ARG_ASSERT(3, dim <  4);
+        ARG_ASSERT(3, dim < 4);
 
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
         ARG_ASSERT(2, in_info.ndims() > 0);
 
         if (dim >= (int)in_info.ndims()) {
@@ -410,20 +449,20 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in, co
         af_dtype type = in_info.getType();
         af_array res, loc;
 
-        switch(type) {
-        case f32:  ireduce<op, float  >(&res, &loc, in, dim); break;
-        case f64:  ireduce<op, double >(&res, &loc, in, dim); break;
-        case c32:  ireduce<op, cfloat >(&res, &loc, in, dim); break;
-        case c64:  ireduce<op, cdouble>(&res, &loc, in, dim); break;
-        case u32:  ireduce<op, uint   >(&res, &loc, in, dim); break;
-        case s32:  ireduce<op, int    >(&res, &loc, in, dim); break;
-        case u64:  ireduce<op, uintl  >(&res, &loc, in, dim); break;
-        case s64:  ireduce<op, intl   >(&res, &loc, in, dim); break;
-        case u16:  ireduce<op, ushort >(&res, &loc, in, dim); break;
-        case s16:  ireduce<op, short  >(&res, &loc, in, dim); break;
-        case b8:   ireduce<op, char   >(&res, &loc, in, dim); break;
-        case u8:   ireduce<op, uchar  >(&res, &loc, in, dim); break;
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: ireduce<op, float>(&res, &loc, in, dim); break;
+            case f64: ireduce<op, double>(&res, &loc, in, dim); break;
+            case c32: ireduce<op, cfloat>(&res, &loc, in, dim); break;
+            case c64: ireduce<op, cdouble>(&res, &loc, in, dim); break;
+            case u32: ireduce<op, uint>(&res, &loc, in, dim); break;
+            case s32: ireduce<op, int>(&res, &loc, in, dim); break;
+            case u64: ireduce<op, uintl>(&res, &loc, in, dim); break;
+            case s64: ireduce<op, intl>(&res, &loc, in, dim); break;
+            case u16: ireduce<op, ushort>(&res, &loc, in, dim); break;
+            case s16: ireduce<op, short>(&res, &loc, in, dim); break;
+            case b8: ireduce<op, char>(&res, &loc, in, dim); break;
+            case u8: ireduce<op, uchar>(&res, &loc, in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*val, res);
@@ -434,90 +473,94 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in, co
     return AF_SUCCESS;
 }
 
-af_err af_imin(af_array *val, af_array *idx, const af_array in, const int dim)
-{
+af_err af_imin(af_array *val, af_array *idx, const af_array in, const int dim) {
     return ireduce_common<af_min_t>(val, idx, in, dim);
 }
 
-af_err af_imax(af_array *val, af_array *idx, const af_array in, const int dim)
-{
+af_err af_imax(af_array *val, af_array *idx, const af_array in, const int dim) {
     return ireduce_common<af_max_t>(val, idx, in, dim);
 }
 
 template<af_op_t op, typename T>
-static inline T ireduce_all(unsigned *loc, const af_array in)
-{
+static inline T ireduce_all(unsigned *loc, const af_array in) {
     return ireduce_all<op, T>(loc, getArray<T>(in));
 }
 
 template<af_op_t op>
 static af_err ireduce_all_common(double *real_val, double *imag_val,
-                                 unsigned *loc, const af_array in)
-{
+                                 unsigned *loc, const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
-        af_dtype type = in_info.getType();
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
 
         ARG_ASSERT(3, in_info.ndims() > 0);
         ARG_ASSERT(0, real_val != NULL);
         *real_val = 0;
         if (imag_val) *imag_val = 0;
 
-        cfloat  cfval;
+        cfloat cfval;
         cdouble cdval;
 
-        switch(type) {
-        case f32:  *real_val = (double)ireduce_all<op, float >(loc, in); break;
-        case f64:  *real_val = (double)ireduce_all<op, double>(loc, in); break;
-        case u32:  *real_val = (double)ireduce_all<op, uint  >(loc, in); break;
-        case s32:  *real_val = (double)ireduce_all<op, int   >(loc, in); break;
-        case u64:  *real_val = (double)ireduce_all<op, uintl >(loc, in); break;
-        case s64:  *real_val = (double)ireduce_all<op, intl  >(loc, in); break;
-        case u16:  *real_val = (double)ireduce_all<op, ushort>(loc, in); break;
-        case s16:  *real_val = (double)ireduce_all<op, short >(loc, in); break;
-        case b8:   *real_val = (double)ireduce_all<op, char  >(loc, in); break;
-        case u8:   *real_val = (double)ireduce_all<op, uchar >(loc, in); break;
-
-        case c32:
-            cfval = ireduce_all<op, cfloat>(loc, in);
-            ARG_ASSERT(1, imag_val != NULL);
-            *real_val = real(cfval);
-            *imag_val = imag(cfval);
-            break;
-
-        case c64:
-            cdval = ireduce_all<op, cdouble>(loc, in);
-            ARG_ASSERT(1, imag_val != NULL);
-            *real_val = real(cdval);
-            *imag_val = imag(cdval);
-            break;
-
-        default:   TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                *real_val = (double)ireduce_all<op, float>(loc, in);
+                break;
+            case f64:
+                *real_val = (double)ireduce_all<op, double>(loc, in);
+                break;
+            case u32: *real_val = (double)ireduce_all<op, uint>(loc, in); break;
+            case s32: *real_val = (double)ireduce_all<op, int>(loc, in); break;
+            case u64:
+                *real_val = (double)ireduce_all<op, uintl>(loc, in);
+                break;
+            case s64: *real_val = (double)ireduce_all<op, intl>(loc, in); break;
+            case u16:
+                *real_val = (double)ireduce_all<op, ushort>(loc, in);
+                break;
+            case s16:
+                *real_val = (double)ireduce_all<op, short>(loc, in);
+                break;
+            case b8: *real_val = (double)ireduce_all<op, char>(loc, in); break;
+            case u8: *real_val = (double)ireduce_all<op, uchar>(loc, in); break;
+
+            case c32:
+                cfval = ireduce_all<op, cfloat>(loc, in);
+                ARG_ASSERT(1, imag_val != NULL);
+                *real_val = real(cfval);
+                *imag_val = imag(cfval);
+                break;
+
+            case c64:
+                cdval = ireduce_all<op, cdouble>(loc, in);
+                ARG_ASSERT(1, imag_val != NULL);
+                *real_val = real(cdval);
+                *imag_val = imag(cdval);
+                break;
+
+            default: TYPE_ERROR(1, type);
         }
-
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_imin_all(double *real, double *imag, unsigned *idx, const af_array in)
-{
+af_err af_imin_all(double *real, double *imag, unsigned *idx,
+                   const af_array in) {
     return ireduce_all_common<af_min_t>(real, imag, idx, in);
 }
 
-af_err af_imax_all(double *real, double *imag, unsigned *idx, const af_array in)
-{
+af_err af_imax_all(double *real, double *imag, unsigned *idx,
+                   const af_array in) {
     return ireduce_all_common<af_max_t>(real, imag, idx, in);
 }
 
-af_err af_sum_nan_all(double *real, double *imag, const af_array in, const double nanval)
-{
+af_err af_sum_nan_all(double *real, double *imag, const af_array in,
+                      const double nanval) {
     return reduce_all_promote<af_add_t>(real, imag, in, true, nanval);
 }
 
-af_err af_product_nan_all(double *real, double *imag, const af_array in, const double nanval)
-{
+af_err af_product_nan_all(double *real, double *imag, const af_array in,
+                          const double nanval) {
     return reduce_all_promote<af_mul_t>(real, imag, in, true, nanval);
 }
diff --git a/src/api/c/regions.cpp b/src/api/c/regions.cpp
index ee1f0593e1..a106993569 100644
--- a/src/api/c/regions.cpp
+++ b/src/api/c/regions.cpp
@@ -7,48 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <regions.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static af_array regions(af_array const &in, af_connectivity connectivity)
-{
+static af_array regions(af_array const &in, af_connectivity connectivity) {
     return getHandle<T>(regions<T>(getArray<char>(in), connectivity));
 }
 
-af_err af_regions(af_array *out, const af_array in, const af_connectivity connectivity, const af_dtype type)
-{
+af_err af_regions(af_array *out, const af_array in,
+                  const af_connectivity connectivity, const af_dtype type) {
     try {
-        ARG_ASSERT(2, (connectivity==AF_CONNECTIVITY_4 || connectivity==AF_CONNECTIVITY_8));
+        ARG_ASSERT(2, (connectivity == AF_CONNECTIVITY_4 ||
+                       connectivity == AF_CONNECTIVITY_8));
 
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
         dim_t in_ndims = dims.ndims();
         DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
 
         af_dtype in_type = info.getType();
-        if (in_type != b8) {
-            TYPE_ERROR(1, in_type);
-        }
+        if (in_type != b8) { TYPE_ERROR(1, in_type); }
 
         af_array output;
-        switch(type) {
-            case f32: output = regions<float >(in, connectivity); break;
+        switch (type) {
+            case f32: output = regions<float>(in, connectivity); break;
             case f64: output = regions<double>(in, connectivity); break;
-            case s32: output = regions<int   >(in, connectivity); break;
-            case u32: output = regions<uint  >(in, connectivity); break;
-            case s16: output = regions<short >(in, connectivity); break;
+            case s32: output = regions<int>(in, connectivity); break;
+            case u32: output = regions<uint>(in, connectivity); break;
+            case s16: output = regions<short>(in, connectivity); break;
             case u16: output = regions<ushort>(in, connectivity); break;
-            default : TYPE_ERROR(0, type);
+            default: TYPE_ERROR(0, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 2fb0731e69..e6be05846c 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -7,46 +7,42 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/data.h>
-#include <af/blas.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <reorder.hpp>
 #include <transpose.hpp>
+#include <af/blas.h>
+#include <af/data.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array reorder(const af_array in, const af::dim4 &rdims0)
-{
+static inline af_array reorder(const af_array in, const af::dim4 &rdims0) {
     Array<T> In = getArray<T>(in);
-    dim4 rdims = rdims0;
+    dim4 rdims  = rdims0;
 
     if (rdims[0] == 1 && rdims[1] == 0) {
         In = transpose(In, false);
         std::swap(rdims[0], rdims[1]);
     }
-    const dim4 idims = In.dims();
+    const dim4 idims    = In.dims();
     const dim4 istrides = In.strides();
 
     // Ensure all JIT nodes are evaled
     In.eval();
 
     af_array out;
-    if (rdims[0] == 0 &&
-        rdims[1] == 1 &&
-        rdims[2] == 2 &&
-        rdims[3] == 3) {
+    if (rdims[0] == 0 && rdims[1] == 1 && rdims[2] == 2 && rdims[3] == 3) {
         Array<T> Out = In;
-        out = getHandle(Out);
+        out          = getHandle(Out);
     } else if (rdims[0] == 0) {
-        dim4 odims = dim4(1,1,1,1);
-        dim4 ostrides = dim4(1,1,1,1);
-        for(int i = 0; i < 4; i++) {
-            odims[i] = idims[rdims[i]];
+        dim4 odims    = dim4(1, 1, 1, 1);
+        dim4 ostrides = dim4(1, 1, 1, 1);
+        for (int i = 0; i < 4; i++) {
+            odims[i]    = idims[rdims[i]];
             ostrides[i] = istrides[rdims[i]];
         }
         Array<T> Out = In;
@@ -56,20 +52,17 @@ static inline af_array reorder(const af_array in, const af::dim4 &rdims0)
         out = getHandle(Out);
     } else {
         Array<T> Out = reorder<T>(In, rdims);
-        out = getHandle(Out);
+        out          = getHandle(Out);
     }
     return out;
 }
 
-af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims)
-{
+af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.elements() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.elements() == 0) { return af_retain_array(out, in); }
 
         DIM_ASSERT(1, info.elements() > 0);
 
@@ -84,41 +77,40 @@ af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims)
         // i = 2 => 3 found and cond is true so alldims[3] = -1
         // i = 3 => 1 found and cond is true so alldims[1] = -1
         // rdims = {2, 0, 3, 2} // Failure case
-        // i = 3 => 2 found so cond is false (since alldims[2] = -1 when i = 0) so failed.
+        // i = 3 => 2 found so cond is false (since alldims[2] = -1 when i = 0)
+        // so failed.
         dim_t allDims[] = {0, 1, 2, 3};
-        for(int i = 0; i < 4; i++) {
+        for (int i = 0; i < 4; i++) {
             DIM_ASSERT(i + 2, rdims[i] == allDims[rdims[i]]);
             allDims[rdims[i]] = -1;
         }
 
         af_array output;
 
-        switch(type) {
-            case f32: output = reorder<float  >(in, rdims);  break;
-            case c32: output = reorder<cfloat >(in, rdims);  break;
-            case f64: output = reorder<double >(in, rdims);  break;
-            case c64: output = reorder<cdouble>(in, rdims);  break;
-            case b8:  output = reorder<char   >(in, rdims);  break;
-            case s32: output = reorder<int    >(in, rdims);  break;
-            case u32: output = reorder<uint   >(in, rdims);  break;
-            case u8:  output = reorder<uchar  >(in, rdims);  break;
-            case s64: output = reorder<intl   >(in, rdims);  break;
-            case u64: output = reorder<uintl  >(in, rdims);  break;
-            case s16: output = reorder<short  >(in, rdims);  break;
-            case u16: output = reorder<ushort >(in, rdims);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = reorder<float>(in, rdims); break;
+            case c32: output = reorder<cfloat>(in, rdims); break;
+            case f64: output = reorder<double>(in, rdims); break;
+            case c64: output = reorder<cdouble>(in, rdims); break;
+            case b8: output = reorder<char>(in, rdims); break;
+            case s32: output = reorder<int>(in, rdims); break;
+            case u32: output = reorder<uint>(in, rdims); break;
+            case u8: output = reorder<uchar>(in, rdims); break;
+            case s64: output = reorder<intl>(in, rdims); break;
+            case u64: output = reorder<uintl>(in, rdims); break;
+            case s16: output = reorder<short>(in, rdims); break;
+            case u16: output = reorder<ushort>(in, rdims); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_reorder(af_array *out, const af_array in,
-               const unsigned x, const unsigned y,
-               const unsigned z, const unsigned w)
-{
+af_err af_reorder(af_array *out, const af_array in, const unsigned x,
+                  const unsigned y, const unsigned z, const unsigned w) {
     af::dim4 rdims(x, y, z, w);
     return af_reorder(out, in, rdims);
 }
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index fcb8f48b6f..585219b4be 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -6,16 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <af/data.h>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
-#include <implicit.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
+#include <implicit.hpp>
+#include <optypes.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
 
 #include <select.hpp>
 
@@ -23,21 +23,18 @@ using namespace detail;
 using af::dim4;
 
 template<typename T>
-void replace(af_array a, const af_array cond, const af_array b)
-{
-    select(getCopyOnWriteArray<T>(a), getArray<char>(cond), getArray<T>(a), getArray<T>(b));
+void replace(af_array a, const af_array cond, const af_array b) {
+    select(getCopyOnWriteArray<T>(a), getArray<char>(cond), getArray<T>(a),
+           getArray<T>(b));
 }
 
-af_err af_replace(af_array a, const af_array cond, const af_array b)
-{
+af_err af_replace(af_array a, const af_array cond, const af_array b) {
     try {
         const ArrayInfo& ainfo = getInfo(a);
         const ArrayInfo& binfo = getInfo(b);
         const ArrayInfo& cinfo = getInfo(cond);
 
-        if(cinfo.ndims() == 0) {
-            return AF_SUCCESS;
-        }
+        if (cinfo.ndims() == 0) { return AF_SUCCESS; }
 
         ARG_ASSERT(2, ainfo.getType() == binfo.getType());
         ARG_ASSERT(1, cinfo.getType() == b8);
@@ -55,33 +52,32 @@ af_err af_replace(af_array a, const af_array cond, const af_array b)
         }
 
         switch (ainfo.getType()) {
-        case f32: replace<float  >(a, cond, b); break;
-        case f64: replace<double >(a, cond, b); break;
-        case c32: replace<cfloat >(a, cond, b); break;
-        case c64: replace<cdouble>(a, cond, b); break;
-        case s32: replace<int    >(a, cond, b); break;
-        case u32: replace<uint   >(a, cond, b); break;
-        case s64: replace<intl   >(a, cond, b); break;
-        case u64: replace<uintl  >(a, cond, b); break;
-        case s16: replace<short  >(a, cond, b); break;
-        case u16: replace<ushort >(a, cond, b); break;
-        case u8:  replace<uchar  >(a, cond, b); break;
-        case b8:  replace<char   >(a, cond, b); break;
-        default:  TYPE_ERROR(2, ainfo.getType());
+            case f32: replace<float>(a, cond, b); break;
+            case f64: replace<double>(a, cond, b); break;
+            case c32: replace<cfloat>(a, cond, b); break;
+            case c64: replace<cdouble>(a, cond, b); break;
+            case s32: replace<int>(a, cond, b); break;
+            case u32: replace<uint>(a, cond, b); break;
+            case s64: replace<intl>(a, cond, b); break;
+            case u64: replace<uintl>(a, cond, b); break;
+            case s16: replace<short>(a, cond, b); break;
+            case u16: replace<ushort>(a, cond, b); break;
+            case u8: replace<uchar>(a, cond, b); break;
+            case b8: replace<char>(a, cond, b); break;
+            default: TYPE_ERROR(2, ainfo.getType());
         }
-
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 template<typename T>
-void replace_scalar(af_array a, const af_array cond, const double b)
-{
-    select_scalar<T, false>(getCopyOnWriteArray<T>(a), getArray<char>(cond), getArray<T>(a), b);
+void replace_scalar(af_array a, const af_array cond, const double b) {
+    select_scalar<T, false>(getCopyOnWriteArray<T>(a), getArray<char>(cond),
+                            getArray<T>(a), b);
 }
 
-af_err af_replace_scalar(af_array a, const af_array cond, const double b)
-{
+af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
     try {
         const ArrayInfo& ainfo = getInfo(a);
         const ArrayInfo& cinfo = getInfo(cond);
@@ -92,26 +88,24 @@ af_err af_replace_scalar(af_array a, const af_array cond, const double b)
         dim4 adims = ainfo.dims();
         dim4 cdims = cinfo.dims();
 
-        for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cdims[i] == adims[i]);
-        }
+        for (int i = 0; i < 4; i++) { DIM_ASSERT(1, cdims[i] == adims[i]); }
 
         switch (ainfo.getType()) {
-        case f32: replace_scalar<float  >(a, cond, b); break;
-        case f64: replace_scalar<double >(a, cond, b); break;
-        case c32: replace_scalar<cfloat >(a, cond, b); break;
-        case c64: replace_scalar<cdouble>(a, cond, b); break;
-        case s32: replace_scalar<int    >(a, cond, b); break;
-        case u32: replace_scalar<uint   >(a, cond, b); break;
-        case s64: replace_scalar<intl   >(a, cond, b); break;
-        case u64: replace_scalar<uintl  >(a, cond, b); break;
-        case s16: replace_scalar<short  >(a, cond, b); break;
-        case u16: replace_scalar<ushort >(a, cond, b); break;
-        case u8:  replace_scalar<uchar  >(a, cond, b); break;
-        case b8:  replace_scalar<char   >(a, cond, b); break;
-        default:  TYPE_ERROR(2, ainfo.getType());
+            case f32: replace_scalar<float>(a, cond, b); break;
+            case f64: replace_scalar<double>(a, cond, b); break;
+            case c32: replace_scalar<cfloat>(a, cond, b); break;
+            case c64: replace_scalar<cdouble>(a, cond, b); break;
+            case s32: replace_scalar<int>(a, cond, b); break;
+            case u32: replace_scalar<uint>(a, cond, b); break;
+            case s64: replace_scalar<intl>(a, cond, b); break;
+            case u64: replace_scalar<uintl>(a, cond, b); break;
+            case s16: replace_scalar<short>(a, cond, b); break;
+            case u16: replace_scalar<ushort>(a, cond, b); break;
+            case u8: replace_scalar<uchar>(a, cond, b); break;
+            case b8: replace_scalar<char>(a, cond, b); break;
+            default: TYPE_ERROR(2, ainfo.getType());
         }
-
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index bbd5a37784..9e912d6caf 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -7,45 +7,43 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/image.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <resize.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array resize(const af_array in, const dim_t odim0, const dim_t odim1,
-                              const af_interp_type method)
-{
+static inline af_array resize(const af_array in, const dim_t odim0,
+                              const dim_t odim1, const af_interp_type method) {
     return getHandle(resize<T>(getArray<T>(in), odim0, odim1, method));
 }
 
-af_err af_resize(af_array *out, const af_array in, const dim_t odim0, const dim_t odim1,
-                 const af_interp_type method)
-{
+af_err af_resize(af_array* out, const af_array in, const dim_t odim0,
+                 const dim_t odim1, const af_interp_type method) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        af_dtype type         = info.getType();
 
-        ARG_ASSERT(4, method == AF_INTERP_NEAREST  ||
-                      method == AF_INTERP_BILINEAR ||
-                      method == AF_INTERP_BILINEAR_COSINE ||
-                      method == AF_INTERP_BICUBIC ||
-                      method == AF_INTERP_BICUBIC_SPLINE ||
-                      method == AF_INTERP_LOWER);
+        ARG_ASSERT(4, method == AF_INTERP_NEAREST ||
+                          method == AF_INTERP_BILINEAR ||
+                          method == AF_INTERP_BILINEAR_COSINE ||
+                          method == AF_INTERP_BICUBIC ||
+                          method == AF_INTERP_BICUBIC_SPLINE ||
+                          method == AF_INTERP_LOWER);
 
         DIM_ASSERT(2, odim0 > 0);
         DIM_ASSERT(3, odim1 > 0);
 
-        bool is_resize_supported = (method == AF_INTERP_LOWER ||
-                                    method == AF_INTERP_NEAREST ||
-                                    method == AF_INTERP_BILINEAR);
+        bool is_resize_supported =
+            (method == AF_INTERP_LOWER || method == AF_INTERP_NEAREST ||
+             method == AF_INTERP_BILINEAR);
 
         if (!is_resize_supported) {
             // Fall back to scale for additional methods
@@ -54,22 +52,22 @@ af_err af_resize(af_array *out, const af_array in, const dim_t odim0, const dim_
 
         af_array output;
 
-        switch(type) {
-            case f32: output = resize<float  >(in, odim0, odim1, method);  break;
-            case f64: output = resize<double >(in, odim0, odim1, method);  break;
-            case c32: output = resize<cfloat >(in, odim0, odim1, method);  break;
-            case c64: output = resize<cdouble>(in, odim0, odim1, method);  break;
-            case s32: output = resize<int    >(in, odim0, odim1, method);  break;
-            case u32: output = resize<uint   >(in, odim0, odim1, method);  break;
-            case s64: output = resize<intl   >(in, odim0, odim1, method);  break;
-            case u64: output = resize<uintl  >(in, odim0, odim1, method);  break;
-            case s16: output = resize<short  >(in, odim0, odim1, method);  break;
-            case u16: output = resize<ushort >(in, odim0, odim1, method);  break;
-            case u8:  output = resize<uchar  >(in, odim0, odim1, method);  break;
-            case b8:  output = resize<char   >(in, odim0, odim1, method);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = resize<float>(in, odim0, odim1, method); break;
+            case f64: output = resize<double>(in, odim0, odim1, method); break;
+            case c32: output = resize<cfloat>(in, odim0, odim1, method); break;
+            case c64: output = resize<cdouble>(in, odim0, odim1, method); break;
+            case s32: output = resize<int>(in, odim0, odim1, method); break;
+            case u32: output = resize<uint>(in, odim0, odim1, method); break;
+            case s64: output = resize<intl>(in, odim0, odim1, method); break;
+            case u64: output = resize<uintl>(in, odim0, odim1, method); break;
+            case s16: output = resize<short>(in, odim0, odim1, method); break;
+            case u16: output = resize<ushort>(in, odim0, odim1, method); break;
+            case u8: output = resize<uchar>(in, odim0, odim1, method); break;
+            case b8: output = resize<char>(in, odim0, odim1, method); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index ba69f25965..0f308be153 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -7,36 +7,37 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/image.h>
 #include <af/index.h>
-#include <af/data.h>
 
+#include <arith.hpp>
+#include <backend.hpp>
+#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
-#include <arith.hpp>
+#include <join.hpp>
 #include <math.hpp>
-#include <cast.hpp>
 #include <tile.hpp>
-#include <join.hpp>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, typename cType>
-static af_array rgb2gray(const af_array& in, const float r, const float g, const float b)
-{
+static af_array rgb2gray(const af_array& in, const float r, const float g,
+                         const float b) {
     Array<cType> input = cast<cType>(getArray<T>(in));
-    dim4 inputDims = input.dims();
+    dim4 inputDims     = input.dims();
     dim4 matDims(inputDims[0], inputDims[1], 1, inputDims[3]);
 
     Array<cType> rCnst = createValueArray<cType>(matDims, scalar<cType>(r));
     Array<cType> gCnst = createValueArray<cType>(matDims, scalar<cType>(g));
     Array<cType> bCnst = createValueArray<cType>(matDims, scalar<cType>(b));
 
-    std::vector<af_seq> slice1(4, af_span), slice2(4, af_span), slice3(4, af_span);
+    std::vector<af_seq> slice1(4, af_span), slice2(4, af_span),
+        slice3(4, af_span);
     // extract three channels as three slices
     slice1[2] = {0, 0, 1};
     slice2[2] = {1, 1, 1};
@@ -48,30 +49,30 @@ static af_array rgb2gray(const af_array& in, const float r, const float g, const
 
     // r*Slice0
     Array<cType> expr1 = arithOp<cType, af_mul_t>(ch1Temp, rCnst, matDims);
-    //g*Slice1
+    // g*Slice1
     Array<cType> expr2 = arithOp<cType, af_mul_t>(ch2Temp, gCnst, matDims);
-    //b*Slice2
+    // b*Slice2
     Array<cType> expr3 = arithOp<cType, af_mul_t>(ch3Temp, bCnst, matDims);
-    //r*Slice0 + g*Slice1
+    // r*Slice0 + g*Slice1
     Array<cType> expr4 = arithOp<cType, af_add_t>(expr1, expr2, matDims);
-    //r*Slice0 + g*Slice1 + b*Slice2
-    Array<cType> result= arithOp<cType, af_add_t>(expr3, expr4, matDims);
+    // r*Slice0 + g*Slice1 + b*Slice2
+    Array<cType> result = arithOp<cType, af_add_t>(expr3, expr4, matDims);
 
     return getHandle<cType>(result);
 }
 
 template<typename T, typename cType>
-static af_array gray2rgb(const af_array& in, const float r, const float g, const float b)
-{
-    if (r==1.0 && g==1.0 && b==1.0) {
+static af_array gray2rgb(const af_array& in, const float r, const float g,
+                         const float b) {
+    if (r == 1.0 && g == 1.0 && b == 1.0) {
         dim4 tileDims(1, 1, 3, 1);
         return getHandle(tile(getArray<T>(in), tileDims));
     }
 
     af_array mod_input = 0;
-    dim4 inputDims = getInfo(in).dims();
+    dim4 inputDims     = getInfo(in).dims();
 
-    dim4 matDims(inputDims[0], inputDims[1], 1, inputDims[2]*inputDims[3]);
+    dim4 matDims(inputDims[0], inputDims[1], 1, inputDims[2] * inputDims[3]);
 
     AF_CHECK(af_moddims(&mod_input, in, matDims.ndims(), matDims.get()));
     Array<cType> mod_in = cast<cType>(getArray<cType>(mod_input));
@@ -92,8 +93,8 @@ static af_array gray2rgb(const af_array& in, const float r, const float g, const
 }
 
 template<typename T, typename cType, bool isRGB2GRAY>
-static af_array convert(const af_array& in, const float r, const float g, const float b)
-{
+static af_array convert(const af_array& in, const float r, const float g,
+                        const float b) {
     if (isRGB2GRAY) {
         return rgb2gray<T, cType>(in, r, g, b);
     } else {
@@ -102,32 +103,48 @@ static af_array convert(const af_array& in, const float r, const float g, const
 }
 
 template<bool isRGB2GRAY>
-af_err convert(af_array* out, const af_array in, const float r, const float g, const float b)
-{
+af_err convert(af_array* out, const af_array in, const float r, const float g,
+               const float b) {
     try {
-        const ArrayInfo& info     = getInfo(in);
-        af_dtype iType     = info.getType();
-        af::dim4 inputDims = info.dims();
+        const ArrayInfo& info = getInfo(in);
+        af_dtype iType        = info.getType();
+        af::dim4 inputDims    = info.dims();
 
         // 2D is not required.
-        if(info.elements() == 0) {
+        if (info.elements() == 0) {
             return af_create_handle(out, 0, nullptr, iType);
         }
 
         // If RGB is input, then assert 3 channels
         // else 1 channel
-        if (isRGB2GRAY) ARG_ASSERT(1, (inputDims[2]==3));
-        else            ARG_ASSERT(1, (inputDims[2]==1));
+        if (isRGB2GRAY)
+            ARG_ASSERT(1, (inputDims[2] == 3));
+        else
+            ARG_ASSERT(1, (inputDims[2] == 1));
 
         af_array output = 0;
-        switch(iType) {
-            case f64: output = convert<double, double, isRGB2GRAY>(in, r, g, b); break;
-            case f32: output = convert<float , float , isRGB2GRAY>(in, r, g, b); break;
-            case u32: output = convert<uint  , float , isRGB2GRAY>(in, r, g, b); break;
-            case s32: output = convert<int   , float , isRGB2GRAY>(in, r, g, b); break;
-            case u16: output = convert<ushort, float , isRGB2GRAY>(in, r, g, b); break;
-            case s16: output = convert<short , float , isRGB2GRAY>(in, r, g, b); break;
-            case u8:  output = convert<uchar , float , isRGB2GRAY>(in, r, g, b); break;
+        switch (iType) {
+            case f64:
+                output = convert<double, double, isRGB2GRAY>(in, r, g, b);
+                break;
+            case f32:
+                output = convert<float, float, isRGB2GRAY>(in, r, g, b);
+                break;
+            case u32:
+                output = convert<uint, float, isRGB2GRAY>(in, r, g, b);
+                break;
+            case s32:
+                output = convert<int, float, isRGB2GRAY>(in, r, g, b);
+                break;
+            case u16:
+                output = convert<ushort, float, isRGB2GRAY>(in, r, g, b);
+                break;
+            case s16:
+                output = convert<short, float, isRGB2GRAY>(in, r, g, b);
+                break;
+            case u8:
+                output = convert<uchar, float, isRGB2GRAY>(in, r, g, b);
+                break;
             default: TYPE_ERROR(1, iType); break;
         }
         std::swap(*out, output);
@@ -136,12 +153,12 @@ af_err convert(af_array* out, const af_array in, const float r, const float g, c
     return AF_SUCCESS;
 }
 
-af_err af_rgb2gray(af_array* out, const af_array in, const float rPercent, const float gPercent, const float bPercent)
-{
+af_err af_rgb2gray(af_array* out, const af_array in, const float rPercent,
+                   const float gPercent, const float bPercent) {
     return convert<true>(out, in, rPercent, gPercent, bPercent);
 }
 
-af_err af_gray2rgb(af_array* out, const af_array in, const float rFactor, const float gFactor, const float bFactor)
-{
+af_err af_gray2rgb(af_array* out, const af_array in, const float rFactor,
+                   const float gFactor, const float bFactor) {
     return convert<false>(out, in, rFactor, gFactor, bFactor);
 }
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index ac85eb3fa3..fd2a9252e3 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -7,37 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
+#include <backend.hpp>
+#include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <common/ArrayInfo.hpp>
-#include <backend.hpp>
 #include <rotate.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array rotate(const af_array in, const float theta, const af::dim4 &odims,
-                              const af_interp_type method)
-{
+static inline af_array rotate(const af_array in, const float theta,
+                              const af::dim4 &odims,
+                              const af_interp_type method) {
     return getHandle(rotate<T>(castArray<T>(in), theta, odims, method));
 }
 
-
 af_err af_rotate(af_array *out, const af_array in, const float theta,
-                 const bool crop,
-                 const af_interp_type method)
-{
+                 const bool crop, const af_interp_type method) {
     try {
         unsigned odims0 = 0, odims1 = 0;
 
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 idims = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 idims        = info.dims();
 
-        if(!crop) {
-            odims0 = idims[0] * fabs(std::cos(theta)) + idims[1] * fabs(std::sin(theta));
-            odims1 = idims[1] * fabs(std::cos(theta)) + idims[0] * fabs(std::sin(theta));
+        if (!crop) {
+            odims0 = idims[0] * fabs(std::cos(theta)) +
+                     idims[1] * fabs(std::sin(theta));
+            odims1 = idims[1] * fabs(std::cos(theta)) +
+                     idims[0] * fabs(std::sin(theta));
         } else {
             odims0 = idims[0];
             odims1 = idims[1];
@@ -45,38 +44,37 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
 
         af_dtype itype = info.getType();
 
-        ARG_ASSERT(4, method == AF_INTERP_NEAREST  ||
-                      method == AF_INTERP_BILINEAR ||
-                      method == AF_INTERP_BILINEAR_COSINE ||
-                      method == AF_INTERP_BICUBIC ||
-                      method == AF_INTERP_BICUBIC_SPLINE ||
-                      method == AF_INTERP_LOWER);
+        ARG_ASSERT(4, method == AF_INTERP_NEAREST ||
+                          method == AF_INTERP_BILINEAR ||
+                          method == AF_INTERP_BILINEAR_COSINE ||
+                          method == AF_INTERP_BICUBIC ||
+                          method == AF_INTERP_BICUBIC_SPLINE ||
+                          method == AF_INTERP_LOWER);
 
-        if(idims.elements() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (idims.elements() == 0) { return af_retain_array(out, in); }
         DIM_ASSERT(1, idims.elements() > 0);
 
         af::dim4 odims(odims0, odims1, idims[2], idims[3]);
 
         af_array output = 0;
-        switch(itype) {
-            case f32: output = rotate<float  >(in, theta, odims, method);  break;
-            case f64: output = rotate<double >(in, theta, odims, method);  break;
-            case c32: output = rotate<cfloat >(in, theta, odims, method);  break;
-            case c64: output = rotate<cdouble>(in, theta, odims, method);  break;
-            case s32: output = rotate<int    >(in, theta, odims, method);  break;
-            case u32: output = rotate<uint   >(in, theta, odims, method);  break;
-            case s64: output = rotate<intl   >(in, theta, odims, method);  break;
-            case u64: output = rotate<uintl  >(in, theta, odims, method);  break;
-            case s16: output = rotate<short  >(in, theta, odims, method);  break;
-            case u16: output = rotate<ushort >(in, theta, odims, method);  break;
-            case u8:  output = rotate<uchar  >(in, theta, odims, method);  break;
-            case b8:  output = rotate<uchar  >(in, theta, odims, method);  break;
-            default:  TYPE_ERROR(1, itype);
+        switch (itype) {
+            case f32: output = rotate<float>(in, theta, odims, method); break;
+            case f64: output = rotate<double>(in, theta, odims, method); break;
+            case c32: output = rotate<cfloat>(in, theta, odims, method); break;
+            case c64: output = rotate<cdouble>(in, theta, odims, method); break;
+            case s32: output = rotate<int>(in, theta, odims, method); break;
+            case u32: output = rotate<uint>(in, theta, odims, method); break;
+            case s64: output = rotate<intl>(in, theta, odims, method); break;
+            case u64: output = rotate<uintl>(in, theta, odims, method); break;
+            case s16: output = rotate<short>(in, theta, odims, method); break;
+            case u16: output = rotate<ushort>(in, theta, odims, method); break;
+            case u8: output = rotate<uchar>(in, theta, odims, method); break;
+            case b8: output = rotate<uchar>(in, theta, odims, method); break;
+            default: TYPE_ERROR(1, itype);
         }
-        std::swap(*out,output);
-    } CATCHALL
+        std::swap(*out, output);
+    }
+    CATCHALL
 
     return AF_SUCCESS;
 }
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index 5fde3eeb32..207b7b97f7 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -7,18 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <af/image.h>
-#include <handle.hpp>
 #include <common/err_common.hpp>
+#include <handle.hpp>
 #include <scan.hpp>
+#include <af/defines.h>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename To, typename Ti>
-static af_array sat(const af_array& in)
-{
+static af_array sat(const af_array& in) {
     const Array<To> input = castArray<To>(in);
 
     Array<To> hprefix_scan = scan<af_add_t, To, To>(input, 0);
@@ -27,28 +26,27 @@ static af_array sat(const af_array& in)
     return getHandle<To>(vprefix_scan);
 }
 
-af_err af_sat(af_array* out, const af_array in)
-{
-    try{
+af_err af_sat(af_array* out, const af_array in) {
+    try {
         const ArrayInfo& info = getInfo(in);
-        const dim4 dims = info.dims();
+        const dim4 dims       = info.dims();
 
         ARG_ASSERT(1, (dims.ndims() >= 2));
 
         af_dtype inputType = info.getType();
 
         af_array output = 0;
-        switch(inputType) {
+        switch (inputType) {
             case f64: output = sat<double, double>(in); break;
-            case f32: output = sat<float , float >(in); break;
-            case s32: output = sat<int   , int   >(in); break;
-            case u32: output = sat<uint  , uint  >(in); break;
-            case  b8: output = sat<int   , char  >(in); break;
-            case  u8: output = sat<uint  , uchar >(in); break;
-            case s64: output = sat<intl  , intl  >(in); break;
-            case u64: output = sat<uintl , uintl >(in); break;
-            case s16: output = sat<int   , short >(in); break;
-            case u16: output = sat<uint  , ushort>(in); break;
+            case f32: output = sat<float, float>(in); break;
+            case s32: output = sat<int, int>(in); break;
+            case u32: output = sat<uint, uint>(in); break;
+            case b8: output = sat<int, char>(in); break;
+            case u8: output = sat<uint, uchar>(in); break;
+            case s64: output = sat<intl, intl>(in); break;
+            case u64: output = sat<uintl, uintl>(in); break;
+            case s16: output = sat<int, short>(in); break;
+            case u16: output = sat<uint, ushort>(in); break;
             default: TYPE_ERROR(1, inputType);
         }
         std::swap(*out, output);
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index f0f35332d1..05811bae09 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -7,81 +7,112 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/defines.h>
-#include <af/dim4.hpp>
-#include <af/algorithm.h>
+#include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <ops.hpp>
 #include <scan.hpp>
 #include <scan_by_key.hpp>
-#include <backend.hpp>
+#include <af/algorithm.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 using namespace detail;
 
 template<af_op_t op, typename Ti, typename To>
-static inline af_array scan(const af_array in, const int dim, bool inclusive_scan = true)
-{
-    return getHandle(scan<op,Ti,To>(getArray<Ti>(in), dim, inclusive_scan));
+static inline af_array scan(const af_array in, const int dim,
+                            bool inclusive_scan = true) {
+    return getHandle(scan<op, Ti, To>(getArray<Ti>(in), dim, inclusive_scan));
 }
 
 template<af_op_t op, typename Ti, typename To>
-static inline af_array scan_key(const af_array key, const af_array in, const int dim, bool inclusive_scan = true)
-{
+static inline af_array scan_key(const af_array key, const af_array in,
+                                const int dim, bool inclusive_scan = true) {
     const ArrayInfo& key_info = getInfo(key);
-    af_dtype type = key_info.getType();
+    af_dtype type             = key_info.getType();
     af_array out;
 
-    switch(type) {
-        case    s32:   out = getHandle(scan<op, Ti,   int, To>(getArray<  int>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
-        case    u32:   out = getHandle(scan<op, Ti,  uint, To>(getArray< uint>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
-        case    s64:   out = getHandle(scan<op, Ti,  intl, To>(getArray< intl>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
-        case    u64:   out = getHandle(scan<op, Ti, uintl, To>(getArray<uintl>(key), castArray<Ti>(in), dim, inclusive_scan)); break;
-        default:
-            TYPE_ERROR(1, type);
+    switch (type) {
+        case s32:
+            out = getHandle(scan<op, Ti, int, To>(
+                getArray<int>(key), castArray<Ti>(in), dim, inclusive_scan));
+            break;
+        case u32:
+            out = getHandle(scan<op, Ti, uint, To>(
+                getArray<uint>(key), castArray<Ti>(in), dim, inclusive_scan));
+            break;
+        case s64:
+            out = getHandle(scan<op, Ti, intl, To>(
+                getArray<intl>(key), castArray<Ti>(in), dim, inclusive_scan));
+            break;
+        case u64:
+            out = getHandle(scan<op, Ti, uintl, To>(
+                getArray<uintl>(key), castArray<Ti>(in), dim, inclusive_scan));
+            break;
+        default: TYPE_ERROR(1, type);
     }
     return out;
 }
 
 template<typename Ti, typename To>
-static inline af_array scan_op(const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan = true)
-{
+static inline af_array scan_op(const af_array key, const af_array in,
+                               const int dim, af_binary_op op,
+                               bool inclusive_scan = true) {
     af_array out;
 
-    switch(op) {
-    case AF_BINARY_ADD:    out = scan_key<af_add_t, Ti, To>(key, in, dim, inclusive_scan); break;
-    case AF_BINARY_MUL:    out = scan_key<af_mul_t, Ti, To>(key, in, dim, inclusive_scan); break;
-    case AF_BINARY_MIN:    out = scan_key<af_min_t, Ti, To>(key, in, dim, inclusive_scan); break;
-    case AF_BINARY_MAX:    out = scan_key<af_max_t, Ti, To>(key, in, dim, inclusive_scan); break;
-    default:
-        AF_ERROR("Incorrect binary operation enum for argument number 3", AF_ERR_ARG); break;
+    switch (op) {
+        case AF_BINARY_ADD:
+            out = scan_key<af_add_t, Ti, To>(key, in, dim, inclusive_scan);
+            break;
+        case AF_BINARY_MUL:
+            out = scan_key<af_mul_t, Ti, To>(key, in, dim, inclusive_scan);
+            break;
+        case AF_BINARY_MIN:
+            out = scan_key<af_min_t, Ti, To>(key, in, dim, inclusive_scan);
+            break;
+        case AF_BINARY_MAX:
+            out = scan_key<af_max_t, Ti, To>(key, in, dim, inclusive_scan);
+            break;
+        default:
+            AF_ERROR("Incorrect binary operation enum for argument number 3",
+                     AF_ERR_ARG);
+            break;
     }
     return out;
 }
 
 template<typename Ti, typename To>
-static inline af_array scan_op(const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
-{
+static inline af_array scan_op(const af_array in, const int dim,
+                               af_binary_op op, bool inclusive_scan) {
     af_array out;
 
-    switch(op) {
-    case AF_BINARY_ADD:    out = scan<af_add_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_BINARY_MUL:    out = scan<af_mul_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_BINARY_MIN:    out = scan<af_min_t, Ti, To>(in, dim, inclusive_scan); break;
-    case AF_BINARY_MAX:    out = scan<af_max_t, Ti, To>(in, dim, inclusive_scan); break;
-    default:
-        AF_ERROR("Incorrect binary operation enum for argument number 2", AF_ERR_ARG); break;
+    switch (op) {
+        case AF_BINARY_ADD:
+            out = scan<af_add_t, Ti, To>(in, dim, inclusive_scan);
+            break;
+        case AF_BINARY_MUL:
+            out = scan<af_mul_t, Ti, To>(in, dim, inclusive_scan);
+            break;
+        case AF_BINARY_MIN:
+            out = scan<af_min_t, Ti, To>(in, dim, inclusive_scan);
+            break;
+        case AF_BINARY_MAX:
+            out = scan<af_max_t, Ti, To>(in, dim, inclusive_scan);
+            break;
+        default:
+            AF_ERROR("Incorrect binary operation enum for argument number 2",
+                     AF_ERR_ARG);
+            break;
     }
     return out;
 }
 
-af_err af_accum(af_array *out, const af_array in, const int dim)
-{
+af_err af_accum(af_array* out, const af_array in, const int dim) {
     try {
         ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(2, dim < 4);
 
         const ArrayInfo& in_info = getInfo(in);
 
@@ -93,22 +124,22 @@ af_err af_accum(af_array *out, const af_array in, const int dim)
         af_dtype type = in_info.getType();
         af_array res;
 
-        switch(type) {
-        case f32:  res = scan<af_add_t, float  , float  >(in, dim); break;
-        case f64:  res = scan<af_add_t, double , double >(in, dim); break;
-        case c32:  res = scan<af_add_t, cfloat , cfloat >(in, dim); break;
-        case c64:  res = scan<af_add_t, cdouble, cdouble>(in, dim); break;
-        case u32:  res = scan<af_add_t, uint   , uint   >(in, dim); break;
-        case s32:  res = scan<af_add_t, int    , int    >(in, dim); break;
-        case u64:  res = scan<af_add_t, uintl  , uintl  >(in, dim); break;
-        case s64:  res = scan<af_add_t, intl   , intl   >(in, dim); break;
-        case u16:  res = scan<af_add_t, ushort , uint   >(in, dim); break;
-        case s16:  res = scan<af_add_t, short  , int    >(in, dim); break;
-        case u8:   res = scan<af_add_t, uchar  , uint   >(in, dim); break;
-        // Make sure you are adding only "1" for every non zero value, even if op == af_add_t
-        case b8:   res = scan<af_notzero_t, char  , uint   >(in, dim); break;
-        default:
-            TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: res = scan<af_add_t, float, float>(in, dim); break;
+            case f64: res = scan<af_add_t, double, double>(in, dim); break;
+            case c32: res = scan<af_add_t, cfloat, cfloat>(in, dim); break;
+            case c64: res = scan<af_add_t, cdouble, cdouble>(in, dim); break;
+            case u32: res = scan<af_add_t, uint, uint>(in, dim); break;
+            case s32: res = scan<af_add_t, int, int>(in, dim); break;
+            case u64: res = scan<af_add_t, uintl, uintl>(in, dim); break;
+            case s64: res = scan<af_add_t, intl, intl>(in, dim); break;
+            case u16: res = scan<af_add_t, ushort, uint>(in, dim); break;
+            case s16: res = scan<af_add_t, short, int>(in, dim); break;
+            case u8: res = scan<af_add_t, uchar, uint>(in, dim); break;
+            // Make sure you are adding only "1" for every non zero value, even
+            // if op == af_add_t
+            case b8: res = scan<af_notzero_t, char, uint>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
@@ -118,11 +149,11 @@ af_err af_accum(af_array *out, const af_array in, const int dim)
     return AF_SUCCESS;
 }
 
-af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
-{
+af_err af_scan(af_array* out, const af_array in, const int dim, af_binary_op op,
+               bool inclusive_scan) {
     try {
         ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(2, dim < 4);
 
         const ArrayInfo& in_info = getInfo(in);
 
@@ -134,21 +165,44 @@ af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
         af_dtype type = in_info.getType();
         af_array res;
 
-        switch(type) {
-        case f32:  res = scan_op<float  , float  >(in, dim, op, inclusive_scan); break;
-        case f64:  res = scan_op<double , double >(in, dim, op, inclusive_scan); break;
-        case c32:  res = scan_op<cfloat , cfloat >(in, dim, op, inclusive_scan); break;
-        case c64:  res = scan_op<cdouble, cdouble>(in, dim, op, inclusive_scan); break;
-        case u32:  res = scan_op<uint   , uint   >(in, dim, op, inclusive_scan); break;
-        case s32:  res = scan_op<int    , int    >(in, dim, op, inclusive_scan); break;
-        case u64:  res = scan_op<uintl  , uintl  >(in, dim, op, inclusive_scan); break;
-        case s64:  res = scan_op<intl   , intl   >(in, dim, op, inclusive_scan); break;
-        case u16:  res = scan_op<ushort , uint   >(in, dim, op, inclusive_scan); break;
-        case s16:  res = scan_op<short  , int    >(in, dim, op, inclusive_scan); break;
-        case u8:   res = scan_op<uchar  , uint   >(in, dim, op, inclusive_scan); break;
-        case b8:   res = scan_op<char   , uint   >(in, dim, op, inclusive_scan); break;
-        default:
-            TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                res = scan_op<float, float>(in, dim, op, inclusive_scan);
+                break;
+            case f64:
+                res = scan_op<double, double>(in, dim, op, inclusive_scan);
+                break;
+            case c32:
+                res = scan_op<cfloat, cfloat>(in, dim, op, inclusive_scan);
+                break;
+            case c64:
+                res = scan_op<cdouble, cdouble>(in, dim, op, inclusive_scan);
+                break;
+            case u32:
+                res = scan_op<uint, uint>(in, dim, op, inclusive_scan);
+                break;
+            case s32:
+                res = scan_op<int, int>(in, dim, op, inclusive_scan);
+                break;
+            case u64:
+                res = scan_op<uintl, uintl>(in, dim, op, inclusive_scan);
+                break;
+            case s64:
+                res = scan_op<intl, intl>(in, dim, op, inclusive_scan);
+                break;
+            case u16:
+                res = scan_op<ushort, uint>(in, dim, op, inclusive_scan);
+                break;
+            case s16:
+                res = scan_op<short, int>(in, dim, op, inclusive_scan);
+                break;
+            case u8:
+                res = scan_op<uchar, uint>(in, dim, op, inclusive_scan);
+                break;
+            case b8:
+                res = scan_op<char, uint>(in, dim, op, inclusive_scan);
+                break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
@@ -158,13 +212,13 @@ af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
     return AF_SUCCESS;
 }
 
-af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
-{
+af_err af_scan_by_key(af_array* out, const af_array key, const af_array in,
+                      const int dim, af_binary_op op, bool inclusive_scan) {
     try {
         ARG_ASSERT(2, dim >= 0);
-        ARG_ASSERT(2, dim <  4);
+        ARG_ASSERT(2, dim < 4);
 
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo& in_info  = getInfo(in);
         const ArrayInfo& key_info = getInfo(key);
 
         if (dim >= (int)in_info.ndims()) {
@@ -177,21 +231,45 @@ af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, cons
         af_dtype type = in_info.getType();
         af_array res;
 
-        switch(type) {
-        case f32:  res = scan_op<float  , float  >(key, in, dim, op, inclusive_scan); break;
-        case f64:  res = scan_op<double , double >(key, in, dim, op, inclusive_scan); break;
-        case c32:  res = scan_op<cfloat , cfloat >(key, in, dim, op, inclusive_scan); break;
-        case c64:  res = scan_op<cdouble, cdouble>(key, in, dim, op, inclusive_scan); break;
-        case u32:  res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
-        case s32:  res = scan_op<int    , int    >(key, in, dim, op, inclusive_scan); break;
-        case u64:  res = scan_op<uintl  , uintl  >(key, in, dim, op, inclusive_scan); break;
-        case s64:  res = scan_op<intl   , intl   >(key, in, dim, op, inclusive_scan); break;
-        case u16:  res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
-        case s16:  res = scan_op<int    , int    >(key, in, dim, op, inclusive_scan); break;
-        case u8:   res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
-        case b8:   res = scan_op<uint   , uint   >(key, in, dim, op, inclusive_scan); break;
-        default:
-            TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                res = scan_op<float, float>(key, in, dim, op, inclusive_scan);
+                break;
+            case f64:
+                res = scan_op<double, double>(key, in, dim, op, inclusive_scan);
+                break;
+            case c32:
+                res = scan_op<cfloat, cfloat>(key, in, dim, op, inclusive_scan);
+                break;
+            case c64:
+                res =
+                    scan_op<cdouble, cdouble>(key, in, dim, op, inclusive_scan);
+                break;
+            case u32:
+                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
+                break;
+            case s32:
+                res = scan_op<int, int>(key, in, dim, op, inclusive_scan);
+                break;
+            case u64:
+                res = scan_op<uintl, uintl>(key, in, dim, op, inclusive_scan);
+                break;
+            case s64:
+                res = scan_op<intl, intl>(key, in, dim, op, inclusive_scan);
+                break;
+            case u16:
+                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
+                break;
+            case s16:
+                res = scan_op<int, int>(key, in, dim, op, inclusive_scan);
+                break;
+            case u8:
+                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
+                break;
+            case b8:
+                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
+                break;
+            default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index c0ac42d031..e330e9a958 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -6,159 +6,199 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <af/data.h>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
-#include <implicit.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
+#include <implicit.hpp>
+#include <optypes.hpp>
 #include <select.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
 
 using namespace detail;
 using af::dim4;
 
 template<typename T>
-af_array select(const af_array cond, const af_array a, const af_array b, const dim4 &odims)
-{
-    Array<T> out = createSelectNode(getArray<char>(cond), getArray<T>(a), getArray<T>(b), odims);
+af_array select(const af_array cond, const af_array a, const af_array b,
+                const dim4& odims) {
+    Array<T> out = createSelectNode(getArray<char>(cond), getArray<T>(a),
+                                    getArray<T>(b), odims);
     return getHandle<T>(out);
 }
 
-af_err af_select(af_array *out, const af_array cond, const af_array a, const af_array b)
-{
+af_err af_select(af_array* out, const af_array cond, const af_array a,
+                 const af_array b) {
     try {
-        const ArrayInfo& ainfo = getInfo(a);
-        const ArrayInfo& binfo = getInfo(b);
+        const ArrayInfo& ainfo     = getInfo(a);
+        const ArrayInfo& binfo     = getInfo(b);
         const ArrayInfo& cond_info = getInfo(cond);
 
-        if(cond_info.ndims() == 0) {
-            return af_retain_array(out, cond);
-        }
+        if (cond_info.ndims() == 0) { return af_retain_array(out, cond); }
 
         ARG_ASSERT(2, ainfo.getType() == binfo.getType());
         ARG_ASSERT(1, cond_info.getType() == b8);
 
-        dim4 adims = ainfo.dims();
-        dim4 bdims = binfo.dims();
+        dim4 adims     = ainfo.dims();
+        dim4 bdims     = binfo.dims();
         dim4 cond_dims = cond_info.dims();
         dim4 odims(1, 1, 1, 1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(2, (adims[i] == bdims[i] && adims[i] == cond_dims[i])
-                        || adims[i] == 1 || bdims[i] == 1 || cond_dims[i] == 1);
+            DIM_ASSERT(2, (adims[i] == bdims[i] && adims[i] == cond_dims[i]) ||
+                              adims[i] == 1 || bdims[i] == 1 ||
+                              cond_dims[i] == 1);
             odims[i] = std::max(std::max(adims[i], bdims[i]), cond_dims[i]);
         }
 
         af_array res;
 
         switch (ainfo.getType()) {
-        case f32: res = select<float  >(cond, a, b, odims); break;
-        case f64: res = select<double >(cond, a, b, odims); break;
-        case c32: res = select<cfloat >(cond, a, b, odims); break;
-        case c64: res = select<cdouble>(cond, a, b, odims); break;
-        case s32: res = select<int    >(cond, a, b, odims); break;
-        case u32: res = select<uint   >(cond, a, b, odims); break;
-        case s64: res = select<intl   >(cond, a, b, odims); break;
-        case u64: res = select<uintl  >(cond, a, b, odims); break;
-        case s16: res = select<short  >(cond, a, b, odims); break;
-        case u16: res = select<ushort >(cond, a, b, odims); break;
-        case u8:  res = select<uchar  >(cond, a, b, odims); break;
-        case b8:  res = select<char   >(cond, a, b, odims); break;
-        default:  TYPE_ERROR(2, ainfo.getType());
+            case f32: res = select<float>(cond, a, b, odims); break;
+            case f64: res = select<double>(cond, a, b, odims); break;
+            case c32: res = select<cfloat>(cond, a, b, odims); break;
+            case c64: res = select<cdouble>(cond, a, b, odims); break;
+            case s32: res = select<int>(cond, a, b, odims); break;
+            case u32: res = select<uint>(cond, a, b, odims); break;
+            case s64: res = select<intl>(cond, a, b, odims); break;
+            case u64: res = select<uintl>(cond, a, b, odims); break;
+            case s16: res = select<short>(cond, a, b, odims); break;
+            case u16: res = select<ushort>(cond, a, b, odims); break;
+            case u8: res = select<uchar>(cond, a, b, odims); break;
+            case b8: res = select<char>(cond, a, b, odims); break;
+            default: TYPE_ERROR(2, ainfo.getType());
         }
 
         std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
 template<typename T, bool flip>
-af_array select_scalar(const af_array cond, const af_array a, const double b, const dim4 &odims)
-{
-    Array<T> out = createSelectNode<T, flip>(getArray<char>(cond), getArray<T>(a), b, odims);
+af_array select_scalar(const af_array cond, const af_array a, const double b,
+                       const dim4& odims) {
+    Array<T> out = createSelectNode<T, flip>(getArray<char>(cond),
+                                             getArray<T>(a), b, odims);
     return getHandle<T>(out);
 }
 
-af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a, const double b)
-{
+af_err af_select_scalar_r(af_array* out, const af_array cond, const af_array a,
+                          const double b) {
     try {
         const ArrayInfo& ainfo = getInfo(a);
         const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
 
-        dim4 adims = ainfo.dims();
+        dim4 adims     = ainfo.dims();
         dim4 cond_dims = cinfo.dims();
         dim4 odims(1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cond_dims[i] == adims[i] || cond_dims[i] == 1 || adims[i] == 1);
+            DIM_ASSERT(1, cond_dims[i] == adims[i] || cond_dims[i] == 1 ||
+                              adims[i] == 1);
             odims[i] = std::max(cond_dims[i], adims[i]);
         }
 
         af_array res;
 
         switch (ainfo.getType()) {
-        case f32: res = select_scalar<float  , false>(cond, a, b, odims); break;
-        case f64: res = select_scalar<double , false>(cond, a, b, odims); break;
-        case c32: res = select_scalar<cfloat , false>(cond, a, b, odims); break;
-        case c64: res = select_scalar<cdouble, false>(cond, a, b, odims); break;
-        case s32: res = select_scalar<int    , false>(cond, a, b, odims); break;
-        case u32: res = select_scalar<uint   , false>(cond, a, b, odims); break;
-        case s16: res = select_scalar<short  , false>(cond, a, b, odims); break;
-        case u16: res = select_scalar<ushort , false>(cond, a, b, odims); break;
-        case s64: res = select_scalar<intl   , false>(cond, a, b, odims); break;
-        case u64: res = select_scalar<uintl  , false>(cond, a, b, odims); break;
-        case u8:  res = select_scalar<uchar  , false>(cond, a, b, odims); break;
-        case b8:  res = select_scalar<char   , false>(cond, a, b, odims); break;
-        default:  TYPE_ERROR(2, ainfo.getType());
+            case f32:
+                res = select_scalar<float, false>(cond, a, b, odims);
+                break;
+            case f64:
+                res = select_scalar<double, false>(cond, a, b, odims);
+                break;
+            case c32:
+                res = select_scalar<cfloat, false>(cond, a, b, odims);
+                break;
+            case c64:
+                res = select_scalar<cdouble, false>(cond, a, b, odims);
+                break;
+            case s32: res = select_scalar<int, false>(cond, a, b, odims); break;
+            case u32:
+                res = select_scalar<uint, false>(cond, a, b, odims);
+                break;
+            case s16:
+                res = select_scalar<short, false>(cond, a, b, odims);
+                break;
+            case u16:
+                res = select_scalar<ushort, false>(cond, a, b, odims);
+                break;
+            case s64:
+                res = select_scalar<intl, false>(cond, a, b, odims);
+                break;
+            case u64:
+                res = select_scalar<uintl, false>(cond, a, b, odims);
+                break;
+            case u8:
+                res = select_scalar<uchar, false>(cond, a, b, odims);
+                break;
+            case b8: res = select_scalar<char, false>(cond, a, b, odims); break;
+            default: TYPE_ERROR(2, ainfo.getType());
         }
 
         std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_select_scalar_l(af_array *out, const af_array cond, const double a, const af_array b)
-{
+af_err af_select_scalar_l(af_array* out, const af_array cond, const double a,
+                          const af_array b) {
     try {
         const ArrayInfo& binfo = getInfo(b);
         const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
 
-        dim4 bdims = binfo.dims();
+        dim4 bdims     = binfo.dims();
         dim4 cond_dims = cinfo.dims();
         dim4 odims(1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cond_dims[i] == bdims[i] || cond_dims[i] == 1 || bdims[i] == 1);
+            DIM_ASSERT(1, cond_dims[i] == bdims[i] || cond_dims[i] == 1 ||
+                              bdims[i] == 1);
             odims[i] = std::max(cond_dims[i], bdims[i]);
         }
 
         af_array res;
 
         switch (binfo.getType()) {
-        case f32: res = select_scalar<float  , true >(cond, b, a, odims); break;
-        case f64: res = select_scalar<double , true >(cond, b, a, odims); break;
-        case c32: res = select_scalar<cfloat , true >(cond, b, a, odims); break;
-        case c64: res = select_scalar<cdouble, true >(cond, b, a, odims); break;
-        case s32: res = select_scalar<int    , true >(cond, b, a, odims); break;
-        case u32: res = select_scalar<uint   , true >(cond, b, a, odims); break;
-        case s16: res = select_scalar<short  , true >(cond, b, a, odims); break;
-        case u16: res = select_scalar<ushort , true >(cond, b, a, odims); break;
-        case s64: res = select_scalar<intl   , true >(cond, b, a, odims); break;
-        case u64: res = select_scalar<uintl  , true >(cond, b, a, odims); break;
-        case u8:  res = select_scalar<uchar  , true >(cond, b, a, odims); break;
-        case b8:  res = select_scalar<char   , true >(cond, b, a, odims); break;
-        default:  TYPE_ERROR(2, binfo.getType());
+            case f32:
+                res = select_scalar<float, true>(cond, b, a, odims);
+                break;
+            case f64:
+                res = select_scalar<double, true>(cond, b, a, odims);
+                break;
+            case c32:
+                res = select_scalar<cfloat, true>(cond, b, a, odims);
+                break;
+            case c64:
+                res = select_scalar<cdouble, true>(cond, b, a, odims);
+                break;
+            case s32: res = select_scalar<int, true>(cond, b, a, odims); break;
+            case u32: res = select_scalar<uint, true>(cond, b, a, odims); break;
+            case s16:
+                res = select_scalar<short, true>(cond, b, a, odims);
+                break;
+            case u16:
+                res = select_scalar<ushort, true>(cond, b, a, odims);
+                break;
+            case s64: res = select_scalar<intl, true>(cond, b, a, odims); break;
+            case u64:
+                res = select_scalar<uintl, true>(cond, b, a, odims);
+                break;
+            case u8: res = select_scalar<uchar, true>(cond, b, a, odims); break;
+            case b8: res = select_scalar<char, true>(cond, b, a, odims); break;
+            default: TYPE_ERROR(2, binfo.getType());
         }
 
         std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index 63363b8fa8..df128f44ec 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -7,27 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/defines.h>
-#include <af/algorithm.h>
+#include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
 #include <set.hpp>
+#include <af/algorithm.h>
+#include <af/defines.h>
+#include <complex>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array setUnique(const af_array in, const bool is_sorted)
-{
+static inline af_array setUnique(const af_array in, const bool is_sorted) {
     return getHandle(setUnique(getArray<T>(in), is_sorted));
 }
 
-af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
-{
+af_err af_set_unique(af_array* out, const af_array in, const bool is_sorted) {
     try {
-
         const ArrayInfo& in_info = getInfo(in);
 
         if (in_info.isEmpty() || in_info.isScalar()) {
@@ -39,124 +36,128 @@ af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
         af_dtype type = in_info.getType();
 
         af_array res;
-        switch(type) {
-            case f32: res = setUnique<float  >(in, is_sorted); break;
-            case f64: res = setUnique<double >(in, is_sorted); break;
-            case s32: res = setUnique<int    >(in, is_sorted); break;
-            case u32: res = setUnique<uint   >(in, is_sorted); break;
-            case s16: res = setUnique<short  >(in, is_sorted); break;
-            case u16: res = setUnique<ushort >(in, is_sorted); break;
-            case s64: res = setUnique<intl   >(in, is_sorted); break;
-            case u64: res = setUnique<uintl  >(in, is_sorted); break;
-            case b8:  res = setUnique<char   >(in, is_sorted); break;
-            case u8:  res = setUnique<uchar  >(in, is_sorted); break;
+        switch (type) {
+            case f32: res = setUnique<float>(in, is_sorted); break;
+            case f64: res = setUnique<double>(in, is_sorted); break;
+            case s32: res = setUnique<int>(in, is_sorted); break;
+            case u32: res = setUnique<uint>(in, is_sorted); break;
+            case s16: res = setUnique<short>(in, is_sorted); break;
+            case u16: res = setUnique<ushort>(in, is_sorted); break;
+            case s64: res = setUnique<intl>(in, is_sorted); break;
+            case u64: res = setUnique<uintl>(in, is_sorted); break;
+            case b8: res = setUnique<char>(in, is_sorted); break;
+            case u8: res = setUnique<uchar>(in, is_sorted); break;
             default: TYPE_ERROR(1, type);
         }
 
         std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-
 template<typename T>
-static inline af_array setUnion(const af_array first, const af_array second, const bool is_unique)
-{
-    return getHandle(setUnion(getArray<T>(first), getArray<T>(second), is_unique));
+static inline af_array setUnion(const af_array first, const af_array second,
+                                const bool is_unique) {
+    return getHandle(
+        setUnion(getArray<T>(first), getArray<T>(second), is_unique));
 }
 
-af_err af_set_union(af_array *out, const af_array first, const af_array second, const bool is_unique)
-{
+af_err af_set_union(af_array* out, const af_array first, const af_array second,
+                    const bool is_unique) {
     try {
-
-        const ArrayInfo& first_info = getInfo(first);
+        const ArrayInfo& first_info  = getInfo(first);
         const ArrayInfo& second_info = getInfo(second);
 
         af_array res;
-        if(first_info.isEmpty()) {
-            return af_retain_array(out, second);
-        }
+        if (first_info.isEmpty()) { return af_retain_array(out, second); }
 
-        if(second_info.isEmpty()) {
-            return af_retain_array(out, first);
-        }
+        if (second_info.isEmpty()) { return af_retain_array(out, first); }
 
         ARG_ASSERT(1, (first_info.isVector() || first_info.isScalar()));
         ARG_ASSERT(1, (second_info.isVector() || second_info.isScalar()));
 
-        af_dtype first_type = first_info.getType();
+        af_dtype first_type  = first_info.getType();
         af_dtype second_type = second_info.getType();
 
         ARG_ASSERT(1, first_type == second_type);
 
-        switch(first_type) {
-            case f32: res = setUnion<float  >(first, second, is_unique); break;
-            case f64: res = setUnion<double >(first, second, is_unique); break;
-            case s32: res = setUnion<int    >(first, second, is_unique); break;
-            case u32: res = setUnion<uint   >(first, second, is_unique); break;
-            case s16: res = setUnion<short  >(first, second, is_unique); break;
-            case u16: res = setUnion<ushort >(first, second, is_unique); break;
-            case s64: res = setUnion<intl   >(first, second, is_unique); break;
-            case u64: res = setUnion<uintl  >(first, second, is_unique); break;
-            case b8:  res = setUnion<char   >(first, second, is_unique); break;
-            case u8:  res = setUnion<uchar  >(first, second, is_unique); break;
+        switch (first_type) {
+            case f32: res = setUnion<float>(first, second, is_unique); break;
+            case f64: res = setUnion<double>(first, second, is_unique); break;
+            case s32: res = setUnion<int>(first, second, is_unique); break;
+            case u32: res = setUnion<uint>(first, second, is_unique); break;
+            case s16: res = setUnion<short>(first, second, is_unique); break;
+            case u16: res = setUnion<ushort>(first, second, is_unique); break;
+            case s64: res = setUnion<intl>(first, second, is_unique); break;
+            case u64: res = setUnion<uintl>(first, second, is_unique); break;
+            case b8: res = setUnion<char>(first, second, is_unique); break;
+            case u8: res = setUnion<uchar>(first, second, is_unique); break;
             default: TYPE_ERROR(1, first_type);
         }
 
         std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-static inline af_array setIntersect(const af_array first, const af_array second, const bool is_unique)
-{
-    return getHandle(setIntersect(getArray<T>(first), getArray<T>(second), is_unique));
+static inline af_array setIntersect(const af_array first, const af_array second,
+                                    const bool is_unique) {
+    return getHandle(
+        setIntersect(getArray<T>(first), getArray<T>(second), is_unique));
 }
 
-af_err af_set_intersect(af_array *out, const af_array first, const af_array second, const bool is_unique)
-{
+af_err af_set_intersect(af_array* out, const af_array first,
+                        const af_array second, const bool is_unique) {
     try {
-
-        const ArrayInfo& first_info = getInfo(first);
+        const ArrayInfo& first_info  = getInfo(first);
         const ArrayInfo& second_info = getInfo(second);
 
-        //TODO: fix for set intersect from union
-        if(first_info.isEmpty()) {
-            return af_retain_array(out, first);
-        }
+        // TODO: fix for set intersect from union
+        if (first_info.isEmpty()) { return af_retain_array(out, first); }
 
-        if(second_info.isEmpty()) {
-            return af_retain_array(out, second);
-        }
+        if (second_info.isEmpty()) { return af_retain_array(out, second); }
 
         ARG_ASSERT(1, (first_info.isVector() || first_info.isScalar()));
         ARG_ASSERT(1, (second_info.isVector() || second_info.isScalar()));
 
-        af_dtype first_type = first_info.getType();
+        af_dtype first_type  = first_info.getType();
         af_dtype second_type = second_info.getType();
 
         ARG_ASSERT(1, first_type == second_type);
 
         af_array res;
-        switch(first_type) {
-            case f32: res = setIntersect<float  >(first, second, is_unique); break;
-            case f64: res = setIntersect<double >(first, second, is_unique); break;
-            case s32: res = setIntersect<int    >(first, second, is_unique); break;
-            case u32: res = setIntersect<uint   >(first, second, is_unique); break;
-            case s16: res = setIntersect<short  >(first, second, is_unique); break;
-            case u16: res = setIntersect<ushort >(first, second, is_unique); break;
-            case s64: res = setIntersect<intl   >(first, second, is_unique); break;
-            case u64: res = setIntersect<uintl  >(first, second, is_unique); break;
-            case b8:  res = setIntersect<char   >(first, second, is_unique); break;
-            case u8:  res = setIntersect<uchar  >(first, second, is_unique); break;
+        switch (first_type) {
+            case f32:
+                res = setIntersect<float>(first, second, is_unique);
+                break;
+            case f64:
+                res = setIntersect<double>(first, second, is_unique);
+                break;
+            case s32: res = setIntersect<int>(first, second, is_unique); break;
+            case u32: res = setIntersect<uint>(first, second, is_unique); break;
+            case s16:
+                res = setIntersect<short>(first, second, is_unique);
+                break;
+            case u16:
+                res = setIntersect<ushort>(first, second, is_unique);
+                break;
+            case s64: res = setIntersect<intl>(first, second, is_unique); break;
+            case u64:
+                res = setIntersect<uintl>(first, second, is_unique);
+                break;
+            case b8: res = setIntersect<char>(first, second, is_unique); break;
+            case u8: res = setIntersect<uchar>(first, second, is_unique); break;
             default: TYPE_ERROR(1, first_type);
         }
 
         std::swap(*out, res);
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index 98aa0eacac..44da4d8b57 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -7,60 +7,55 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/data.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <shift.hpp>
+#include <af/data.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array shift(const af_array in, const int sdims[4])
-{
+static inline af_array shift(const af_array in, const int sdims[4]) {
     return getHandle(shift<T>(getArray<T>(in), sdims));
 }
 
-af_err af_shift(af_array *out, const af_array in, const int sdims[4])
-{
+af_err af_shift(af_array *out, const af_array in, const int sdims[4]) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
         DIM_ASSERT(1, info.elements() > 0);
 
         af_array output;
 
-        switch(type) {
-            case f32: output = shift<float  >(in, sdims);  break;
-            case c32: output = shift<cfloat >(in, sdims);  break;
-            case f64: output = shift<double >(in, sdims);  break;
-            case c64: output = shift<cdouble>(in, sdims);  break;
-            case b8:  output = shift<char   >(in, sdims);  break;
-            case s32: output = shift<int    >(in, sdims);  break;
-            case u32: output = shift<uint   >(in, sdims);  break;
-            case s64: output = shift<intl   >(in, sdims);  break;
-            case u64: output = shift<uintl  >(in, sdims);  break;
-            case s16: output = shift<short  >(in, sdims);  break;
-            case u16: output = shift<ushort >(in, sdims);  break;
-            case u8:  output = shift<uchar  >(in, sdims);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = shift<float>(in, sdims); break;
+            case c32: output = shift<cfloat>(in, sdims); break;
+            case f64: output = shift<double>(in, sdims); break;
+            case c64: output = shift<cdouble>(in, sdims); break;
+            case b8: output = shift<char>(in, sdims); break;
+            case s32: output = shift<int>(in, sdims); break;
+            case u32: output = shift<uint>(in, sdims); break;
+            case s64: output = shift<intl>(in, sdims); break;
+            case u64: output = shift<uintl>(in, sdims); break;
+            case s16: output = shift<short>(in, sdims); break;
+            case u16: output = shift<ushort>(in, sdims); break;
+            case u8: output = shift<uchar>(in, sdims); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_shift(af_array *out, const af_array in,
-                const int x, const int y, const int z, const int w)
-{
+af_err af_shift(af_array *out, const af_array in, const int x, const int y,
+                const int z, const int w) {
     const int sdims[] = {x, y, z, w};
     return af_shift(out, in, sdims);
 }
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index a70ba534f5..4f6aaf05bb 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -7,25 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/features.h>
-#include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
 #include <features.hpp>
+#include <handle.hpp>
 #include <sift.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/features.h>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T, typename convAccT>
-static void sift(af_features& feat_, af_array& descriptors, const af_array& in, const unsigned n_layers,
-                 const float contrast_thr, const float edge_thr, const float init_sigma,
-                 const bool double_input, const float img_scale, const float feature_ratio,
-                 const bool compute_GLOH)
-{
+static void sift(af_features& feat_, af_array& descriptors, const af_array& in,
+                 const unsigned n_layers, const float contrast_thr,
+                 const float edge_thr, const float init_sigma,
+                 const bool double_input, const float img_scale,
+                 const float feature_ratio, const bool compute_GLOH) {
     Array<float> x     = createEmptyArray<float>(dim4());
     Array<float> y     = createEmptyArray<float>(dim4());
     Array<float> score = createEmptyArray<float>(dim4());
@@ -35,9 +35,10 @@ static void sift(af_features& feat_, af_array& descriptors, const af_array& in,
 
     af_features_t feat;
 
-    feat.n = sift<T, convAccT>(x, y, score, ori, size, desc, getArray<T>(in),
-                               n_layers, contrast_thr, edge_thr, init_sigma,
-                               double_input, img_scale, feature_ratio, compute_GLOH);
+    feat.n =
+        sift<T, convAccT>(x, y, score, ori, size, desc, getArray<T>(in),
+                          n_layers, contrast_thr, edge_thr, init_sigma,
+                          double_input, img_scale, feature_ratio, compute_GLOH);
 
     feat.x           = getHandle(x);
     feat.y           = getHandle(y);
@@ -45,20 +46,22 @@ static void sift(af_features& feat_, af_array& descriptors, const af_array& in,
     feat.orientation = getHandle(ori);
     feat.size        = getHandle(size);
 
-    feat_ = getFeaturesHandle(feat);
+    feat_       = getFeaturesHandle(feat);
     descriptors = getHandle<float>(desc);
 }
 
-af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsigned n_layers,
-               const float contrast_thr, const float edge_thr, const float init_sigma,
-               const bool double_input, const float img_scale, const float feature_ratio)
-{
+af_err af_sift(af_features* feat, af_array* desc, const af_array in,
+               const unsigned n_layers, const float contrast_thr,
+               const float edge_thr, const float init_sigma,
+               const bool double_input, const float img_scale,
+               const float feature_ratio) {
     try {
 #ifdef AF_WITH_NONFREE_SIFT
         const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        af::dim4 dims         = info.dims();
 
-        ARG_ASSERT(2, (dims[0] >= 15 && dims[1] >= 15 && dims[2] == 1 && dims[3] == 1));
+        ARG_ASSERT(2, (dims[0] >= 15 && dims[1] >= 15 && dims[2] == 1 &&
+                       dims[3] == 1));
         ARG_ASSERT(3, n_layers > 0);
         ARG_ASSERT(4, contrast_thr > 0.0f);
         ARG_ASSERT(5, edge_thr >= 1.0f);
@@ -70,15 +73,19 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
         DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
 
         af_array tmp_desc;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: sift<float , float >(*feat, tmp_desc, in, n_layers, contrast_thr,
-                                           edge_thr, init_sigma, double_input,
-                                           img_scale, feature_ratio, false); break;
-            case f64: sift<double, double>(*feat, tmp_desc, in, n_layers, contrast_thr,
-                                           edge_thr, init_sigma, double_input,
-                                           img_scale, feature_ratio, false); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                sift<float, float>(*feat, tmp_desc, in, n_layers, contrast_thr,
+                                   edge_thr, init_sigma, double_input,
+                                   img_scale, feature_ratio, false);
+                break;
+            case f64:
+                sift<double, double>(
+                    *feat, tmp_desc, in, n_layers, contrast_thr, edge_thr,
+                    init_sigma, double_input, img_scale, feature_ratio, false);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*desc, tmp_desc);
 #else
@@ -92,7 +99,9 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
         UNUSED(double_input);
         UNUSED(img_scale);
         UNUSED(feature_ratio);
-        AF_ERROR("ArrayFire was not built with nonfree support, SIFT disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, SIFT disabled\n",
+            AF_ERR_NONFREE);
 #endif
     }
     CATCHALL;
@@ -100,16 +109,18 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in, const unsig
     return AF_SUCCESS;
 }
 
-af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsigned n_layers,
-               const float contrast_thr, const float edge_thr, const float init_sigma,
-               const bool double_input, const float img_scale, const float feature_ratio)
-{
+af_err af_gloh(af_features* feat, af_array* desc, const af_array in,
+               const unsigned n_layers, const float contrast_thr,
+               const float edge_thr, const float init_sigma,
+               const bool double_input, const float img_scale,
+               const float feature_ratio) {
     try {
 #ifdef AF_WITH_NONFREE_SIFT
         const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        af::dim4 dims         = info.dims();
 
-        ARG_ASSERT(2, (dims[0] >= 15 && dims[1] >= 15 && dims[2] == 1 && dims[3] == 1));
+        ARG_ASSERT(2, (dims[0] >= 15 && dims[1] >= 15 && dims[2] == 1 &&
+                       dims[3] == 1));
         ARG_ASSERT(3, n_layers > 0);
         ARG_ASSERT(4, contrast_thr > 0.0f);
         ARG_ASSERT(5, edge_thr >= 1.0f);
@@ -121,15 +132,19 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsig
         DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
 
         af_array tmp_desc;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: sift<float , float >(*feat, tmp_desc, in, n_layers, contrast_thr,
-                                           edge_thr, init_sigma, double_input,
-                                           img_scale, feature_ratio, true); break;
-            case f64: sift<double, double>(*feat, tmp_desc, in, n_layers, contrast_thr,
-                                           edge_thr, init_sigma, double_input,
-                                           img_scale, feature_ratio, true); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                sift<float, float>(*feat, tmp_desc, in, n_layers, contrast_thr,
+                                   edge_thr, init_sigma, double_input,
+                                   img_scale, feature_ratio, true);
+                break;
+            case f64:
+                sift<double, double>(
+                    *feat, tmp_desc, in, n_layers, contrast_thr, edge_thr,
+                    init_sigma, double_input, img_scale, feature_ratio, true);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*desc, tmp_desc);
 #else
@@ -143,7 +158,9 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in, const unsig
         UNUSED(double_input);
         UNUSED(img_scale);
         UNUSED(feature_ratio);
-        AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, GLOH disabled\n",
+            AF_ERR_NONFREE);
 #endif
     }
     CATCHALL;
diff --git a/src/api/c/sobel.cpp b/src/api/c/sobel.cpp
index 2826756904..7e7c35b2ea 100644
--- a/src/api/c/sobel.cpp
+++ b/src/api/c/sobel.cpp
@@ -7,13 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <sobel.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 #include <utility>
 
 using af::dim4;
@@ -21,38 +21,48 @@ using namespace detail;
 
 typedef std::pair<af_array, af_array> ArrayPair;
 template<typename Ti, typename To>
-ArrayPair sobelDerivatives(const af_array &in, const unsigned &ker_size)
-{
-    typedef std::pair< Array<To>, Array<To> > BAPair;
-    BAPair  out = sobelDerivatives<Ti, To>(getArray<Ti>(in), ker_size);
-    return std::make_pair(getHandle<To>(out.first),
-                          getHandle<To>(out.second));
+ArrayPair sobelDerivatives(const af_array &in, const unsigned &ker_size) {
+    typedef std::pair<Array<To>, Array<To>> BAPair;
+    BAPair out = sobelDerivatives<Ti, To>(getArray<Ti>(in), ker_size);
+    return std::make_pair(getHandle<To>(out.first), getHandle<To>(out.second));
 }
 
-af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img, const unsigned ker_size)
-{
+af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img,
+                         const unsigned ker_size) {
     try {
-        //FIXME: ADD SUPPORT FOR OTHER KERNEL SIZES
-        //ARG_ASSERT(4, (ker_size==3 || ker_size==5 || ker_size==7));
-        ARG_ASSERT(4, (ker_size==3));
+        // FIXME: ADD SUPPORT FOR OTHER KERNEL SIZES
+        // ARG_ASSERT(4, (ker_size==3 || ker_size==5 || ker_size==7));
+        ARG_ASSERT(4, (ker_size == 3));
 
-        const ArrayInfo& info = getInfo(img);
-        af::dim4 dims  = info.dims();
+        const ArrayInfo &info = getInfo(img);
+        af::dim4 dims         = info.dims();
 
         DIM_ASSERT(3, (dims.ndims() >= 2));
 
         ArrayPair output;
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: output = sobelDerivatives<float , float> (img, ker_size); break;
-            case f64: output = sobelDerivatives<double, double>(img, ker_size); break;
-            case s32: output = sobelDerivatives<int   , int>   (img, ker_size); break;
-            case u32: output = sobelDerivatives<uint  , int>   (img, ker_size); break;
-            case s16: output = sobelDerivatives<short , int>   (img, ker_size); break;
-            case u16: output = sobelDerivatives<ushort, int>   (img, ker_size); break;
-            case b8 : output = sobelDerivatives<char  , int>   (img, ker_size); break;
-            case u8:  output = sobelDerivatives<uchar , int>   (img, ker_size); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                output = sobelDerivatives<float, float>(img, ker_size);
+                break;
+            case f64:
+                output = sobelDerivatives<double, double>(img, ker_size);
+                break;
+            case s32: output = sobelDerivatives<int, int>(img, ker_size); break;
+            case u32:
+                output = sobelDerivatives<uint, int>(img, ker_size);
+                break;
+            case s16:
+                output = sobelDerivatives<short, int>(img, ker_size);
+                break;
+            case u16:
+                output = sobelDerivatives<ushort, int>(img, ker_size);
+                break;
+            case b8: output = sobelDerivatives<char, int>(img, ker_size); break;
+            case u8:
+                output = sobelDerivatives<uchar, int>(img, ker_size);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*dx, output.first);
         std::swap(*dy, output.second);
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index 0e00183289..93c9459154 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -7,32 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/lapack.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <solve.hpp>
+#include <af/array.h>
+#include <af/defines.h>
+#include <af/lapack.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array solve(const af_array a, const af_array b, const af_mat_prop options)
-{
+static inline af_array solve(const af_array a, const af_array b,
+                             const af_mat_prop options) {
     return getHandle(solve<T>(getArray<T>(a), getArray<T>(b), options));
 }
 
-af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_prop options)
-{
+af_err af_solve(af_array* out, const af_array a, const af_array b,
+                const af_mat_prop options) {
     try {
         const ArrayInfo& a_info = getInfo(a);
         const ArrayInfo& b_info = getInfo(b);
 
-        if (a_info.ndims() > 2 ||
-            b_info.ndims() > 2) {
+        if (a_info.ndims() > 2 || b_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
         }
 
@@ -42,8 +41,8 @@ af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_
         dim4 adims = a_info.dims();
         dim4 bdims = b_info.dims();
 
-        ARG_ASSERT(1, a_info.isFloating());                       // Only floating and complex types
-        ARG_ASSERT(2, b_info.isFloating());                       // Only floating and complex types
+        ARG_ASSERT(1, a_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(2, b_info.isFloating());  // Only floating and complex types
 
         TYPE_ASSERT(a_type == b_type);
 
@@ -51,31 +50,34 @@ af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_
         DIM_ASSERT(1, bdims[2] == adims[2]);
         DIM_ASSERT(1, bdims[3] == adims[3]);
 
-        if(a_info.ndims() == 0 || b_info.ndims() == 0) {
+        if (a_info.ndims() == 0 || b_info.ndims() == 0) {
             return af_create_handle(out, 0, nullptr, a_type);
         }
 
-        bool is_triangle_solve = (options & AF_MAT_LOWER) || (options & AF_MAT_UPPER);
+        bool is_triangle_solve =
+            (options & AF_MAT_LOWER) || (options & AF_MAT_UPPER);
 
         if (options != AF_MAT_NONE && !is_triangle_solve) {
-            AF_ERROR("Using this property is not yet supported in solve", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in solve",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         if (is_triangle_solve) {
             DIM_ASSERT(1, adims[0] == adims[1]);
             if ((options & AF_MAT_TRANS || options & AF_MAT_CTRANS)) {
-                AF_ERROR("Using AF_MAT_TRANS is not yet supported in solve", AF_ERR_NOT_SUPPORTED);
+                AF_ERROR("Using AF_MAT_TRANS is not yet supported in solve",
+                         AF_ERR_NOT_SUPPORTED);
             }
         }
 
         af_array output;
 
-        switch(a_type) {
-            case f32: output = solve<float  >(a, b, options);  break;
-            case f64: output = solve<double >(a, b, options);  break;
-            case c32: output = solve<cfloat >(a, b, options);  break;
-            case c64: output = solve<cdouble>(a, b, options);  break;
-            default:  TYPE_ERROR(1, a_type);
+        switch (a_type) {
+            case f32: output = solve<float>(a, b, options); break;
+            case f64: output = solve<double>(a, b, options); break;
+            case c32: output = solve<cfloat>(a, b, options); break;
+            case c64: output = solve<cdouble>(a, b, options); break;
+            default: TYPE_ERROR(1, a_type);
         }
         std::swap(*out, output);
     }
@@ -86,22 +88,18 @@ af_err af_solve(af_array *out, const af_array a, const af_array b, const af_mat_
 
 template<typename T>
 static inline af_array solve_lu(const af_array a, const af_array pivot,
-                                const af_array b, const af_mat_prop options)
-{
+                                const af_array b, const af_mat_prop options) {
     return getHandle(solveLU<T>(getArray<T>(a), getArray<int>(pivot),
                                 getArray<T>(b), options));
 }
 
-af_err af_solve_lu(af_array *out, const af_array a,
-                   const af_array piv, const af_array b,
-                   const af_mat_prop options)
-{
+af_err af_solve_lu(af_array* out, const af_array a, const af_array piv,
+                   const af_array b, const af_mat_prop options) {
     try {
         const ArrayInfo& a_info = getInfo(a);
         const ArrayInfo& b_info = getInfo(b);
 
-        if (a_info.ndims() > 2 ||
-            b_info.ndims() > 2) {
+        if (a_info.ndims() > 2 || b_info.ndims() > 2) {
             AF_ERROR("solveLU can not be used in batch mode", AF_ERR_BATCH);
         }
 
@@ -110,12 +108,12 @@ af_err af_solve_lu(af_array *out, const af_array a,
 
         dim4 adims = a_info.dims();
         dim4 bdims = b_info.dims();
-        if(a_info.ndims() == 0 || b_info.ndims() == 0) {
-          return af_create_handle(out, 0, nullptr, a_type);
+        if (a_info.ndims() == 0 || b_info.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, a_type);
         }
 
-        ARG_ASSERT(1, a_info.isFloating());                       // Only floating and complex types
-        ARG_ASSERT(2, b_info.isFloating());                       // Only floating and complex types
+        ARG_ASSERT(1, a_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(2, b_info.isFloating());  // Only floating and complex types
 
         TYPE_ASSERT(a_type == b_type);
 
@@ -125,17 +123,18 @@ af_err af_solve_lu(af_array *out, const af_array a,
         DIM_ASSERT(1, bdims[3] == adims[3]);
 
         if (options != AF_MAT_NONE) {
-            AF_ERROR("Using this property is not yet supported in solveLU", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Using this property is not yet supported in solveLU",
+                     AF_ERR_NOT_SUPPORTED);
         }
 
         af_array output;
 
-        switch(a_type) {
-        case f32: output = solve_lu<float  >(a, piv, b, options);  break;
-        case f64: output = solve_lu<double >(a, piv, b, options);  break;
-        case c32: output = solve_lu<cfloat >(a, piv, b, options);  break;
-        case c64: output = solve_lu<cdouble>(a, piv, b, options);  break;
-        default:  TYPE_ERROR(1, a_type);
+        switch (a_type) {
+            case f32: output = solve_lu<float>(a, piv, b, options); break;
+            case f64: output = solve_lu<double>(a, piv, b, options); break;
+            case c32: output = solve_lu<cfloat>(a, piv, b, options); break;
+            case c64: output = solve_lu<cdouble>(a, piv, b, options); break;
+            default: TYPE_ERROR(1, a_type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index eb6bb67542..ffefbb580c 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -7,55 +7,53 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
-#include <af/algorithm.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <copy.hpp>
+#include <handle.hpp>
 #include <sort.hpp>
-#include <sort_index.hpp>
 #include <sort_by_key.hpp>
-#include <copy.hpp>
+#include <sort_index.hpp>
+#include <af/algorithm.h>
+#include <af/array.h>
+#include <af/defines.h>
 
-#include<cstdio>
+#include <cstdio>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array sort(const af_array in, const unsigned dim, const bool isAscending)
-{
+static inline af_array sort(const af_array in, const unsigned dim,
+                            const bool isAscending) {
     const Array<T> &inArray = getArray<T>(in);
     return getHandle(sort<T>(inArray, dim, isAscending));
 }
 
-af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending)
-{
+af_err af_sort(af_array *out, const af_array in, const unsigned dim,
+               const bool isAscending) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.elements() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.elements() == 0) { return af_retain_array(out, in); }
         DIM_ASSERT(1, info.elements() > 0);
 
         af_array val;
 
-        switch(type) {
-            case f32: val = sort<float  >(in, dim, isAscending);  break;
-            case f64: val = sort<double >(in, dim, isAscending);  break;
-            case s32: val = sort<int    >(in, dim, isAscending);  break;
-            case u32: val = sort<uint   >(in, dim, isAscending);  break;
-            case s16: val = sort<short  >(in, dim, isAscending);  break;
-            case u16: val = sort<ushort >(in, dim, isAscending);  break;
-            case s64: val = sort<intl   >(in, dim, isAscending);  break;
-            case u64: val = sort<uintl  >(in, dim, isAscending);  break;
-            case u8:  val = sort<uchar  >(in, dim, isAscending);  break;
-            case b8:  val = sort<char   >(in, dim, isAscending);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: val = sort<float>(in, dim, isAscending); break;
+            case f64: val = sort<double>(in, dim, isAscending); break;
+            case s32: val = sort<int>(in, dim, isAscending); break;
+            case u32: val = sort<uint>(in, dim, isAscending); break;
+            case s16: val = sort<short>(in, dim, isAscending); break;
+            case u16: val = sort<ushort>(in, dim, isAscending); break;
+            case s64: val = sort<intl>(in, dim, isAscending); break;
+            case u64: val = sort<uintl>(in, dim, isAscending); break;
+            case u8: val = sort<uchar>(in, dim, isAscending); break;
+            case b8: val = sort<char>(in, dim, isAscending); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, val);
     }
@@ -66,12 +64,11 @@ af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool
 
 template<typename T>
 static inline void sort_index(af_array *val, af_array *idx, const af_array in,
-                              const unsigned dim, const bool isAscending)
-{
+                              const unsigned dim, const bool isAscending) {
     const Array<T> &inArray = getArray<T>(in);
 
     // Initialize Dummy Arrays
-    Array<T> valArray = createEmptyArray<T>(af::dim4());
+    Array<T> valArray    = createEmptyArray<T>(af::dim4());
     Array<uint> idxArray = createEmptyArray<uint>(af::dim4());
 
     sort_index<T>(valArray, idxArray, inArray, dim, isAscending);
@@ -79,14 +76,14 @@ static inline void sort_index(af_array *val, af_array *idx, const af_array in,
     *idx = getHandle(idxArray);
 }
 
-af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const unsigned dim, const bool isAscending)
-{
+af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
+                     const unsigned dim, const bool isAscending) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.elements() <= 0) {
-            AF_CHECK(af_create_handle(out,     0, nullptr, type));
+        if (info.elements() <= 0) {
+            AF_CHECK(af_create_handle(out, 0, nullptr, type));
             AF_CHECK(af_create_handle(indices, 0, nullptr, type));
             return AF_SUCCESS;
         }
@@ -94,20 +91,30 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const
         af_array val;
         af_array idx;
 
-        switch(type) {
-            case f32: sort_index<float  >(&val, &idx, in, dim, isAscending);  break;
-            case f64: sort_index<double >(&val, &idx, in, dim, isAscending);  break;
-            case s32: sort_index<int    >(&val, &idx, in, dim, isAscending);  break;
-            case u32: sort_index<uint   >(&val, &idx, in, dim, isAscending);  break;
-            case s16: sort_index<short  >(&val, &idx, in, dim, isAscending);  break;
-            case u16: sort_index<ushort >(&val, &idx, in, dim, isAscending);  break;
-            case s64: sort_index<intl   >(&val, &idx, in, dim, isAscending);  break;
-            case u64: sort_index<uintl  >(&val, &idx, in, dim, isAscending);  break;
-            case u8:  sort_index<uchar  >(&val, &idx, in, dim, isAscending);  break;
-            case b8:  sort_index<char   >(&val, &idx, in, dim, isAscending);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                sort_index<float>(&val, &idx, in, dim, isAscending);
+                break;
+            case f64:
+                sort_index<double>(&val, &idx, in, dim, isAscending);
+                break;
+            case s32: sort_index<int>(&val, &idx, in, dim, isAscending); break;
+            case u32: sort_index<uint>(&val, &idx, in, dim, isAscending); break;
+            case s16:
+                sort_index<short>(&val, &idx, in, dim, isAscending);
+                break;
+            case u16:
+                sort_index<ushort>(&val, &idx, in, dim, isAscending);
+                break;
+            case s64: sort_index<intl>(&val, &idx, in, dim, isAscending); break;
+            case u64:
+                sort_index<uintl>(&val, &idx, in, dim, isAscending);
+                break;
+            case u8: sort_index<uchar>(&val, &idx, in, dim, isAscending); break;
+            case b8: sort_index<char>(&val, &idx, in, dim, isAscending); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out , val);
+        std::swap(*out, val);
         std::swap(*indices, idx);
     }
     CATCHALL;
@@ -116,9 +123,9 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in, const
 }
 
 template<typename Tk, typename Tv>
-static inline void sort_by_key(af_array *okey, af_array *oval, const af_array ikey, const af_array ival,
-                               const unsigned dim, const bool isAscending)
-{
+static inline void sort_by_key(af_array *okey, af_array *oval,
+                               const af_array ikey, const af_array ival,
+                               const unsigned dim, const bool isAscending) {
     const Array<Tk> &ikeyArray = getArray<Tk>(ikey);
     const Array<Tv> &ivalArray = getArray<Tv>(ival);
 
@@ -126,32 +133,57 @@ static inline void sort_by_key(af_array *okey, af_array *oval, const af_array ik
     Array<Tk> okeyArray = createEmptyArray<Tk>(af::dim4());
     Array<Tv> ovalArray = createEmptyArray<Tv>(af::dim4());
 
-    sort_by_key<Tk, Tv>(okeyArray, ovalArray, ikeyArray, ivalArray, dim, isAscending);
+    sort_by_key<Tk, Tv>(okeyArray, ovalArray, ikeyArray, ivalArray, dim,
+                        isAscending);
     *okey = getHandle(okeyArray);
     *oval = getHandle(ovalArray);
 }
 
 template<typename Tk>
-void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey, const af_array ival,
-                       const unsigned dim, const bool isAscending)
-{
-    const ArrayInfo& info = getInfo(ival);
-    af_dtype vtype = info.getType();
-
-    switch(vtype) {
-    case f32: sort_by_key<Tk, float  >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case f64: sort_by_key<Tk, double >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case c32: sort_by_key<Tk, cfloat >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case c64: sort_by_key<Tk, cdouble>(okey, oval, ikey, ival, dim, isAscending);  break;
-    case s32: sort_by_key<Tk, int    >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case u32: sort_by_key<Tk, uint   >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case s16: sort_by_key<Tk, short  >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case u16: sort_by_key<Tk, ushort >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case s64: sort_by_key<Tk, intl   >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case u64: sort_by_key<Tk, uintl  >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case u8:  sort_by_key<Tk, uchar  >(okey, oval, ikey, ival, dim, isAscending);  break;
-    case b8:  sort_by_key<Tk, char   >(okey, oval, ikey, ival, dim, isAscending);  break;
-    default:  TYPE_ERROR(1, vtype);
+void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey,
+                       const af_array ival, const unsigned dim,
+                       const bool isAscending) {
+    const ArrayInfo &info = getInfo(ival);
+    af_dtype vtype        = info.getType();
+
+    switch (vtype) {
+        case f32:
+            sort_by_key<Tk, float>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case f64:
+            sort_by_key<Tk, double>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case c32:
+            sort_by_key<Tk, cfloat>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case c64:
+            sort_by_key<Tk, cdouble>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case s32:
+            sort_by_key<Tk, int>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case u32:
+            sort_by_key<Tk, uint>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case s16:
+            sort_by_key<Tk, short>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case u16:
+            sort_by_key<Tk, ushort>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case s64:
+            sort_by_key<Tk, intl>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case u64:
+            sort_by_key<Tk, uintl>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case u8:
+            sort_by_key<Tk, uchar>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        case b8:
+            sort_by_key<Tk, char>(okey, oval, ikey, ival, dim, isAscending);
+            break;
+        default: TYPE_ERROR(1, vtype);
     }
 
     return;
@@ -159,17 +191,16 @@ void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey, cons
 
 af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                       const af_array keys, const af_array values,
-                      const unsigned dim, const bool isAscending)
-{
+                      const unsigned dim, const bool isAscending) {
     try {
-        const ArrayInfo& kinfo = getInfo(keys);
-        af_dtype ktype = kinfo.getType();
+        const ArrayInfo &kinfo = getInfo(keys);
+        af_dtype ktype         = kinfo.getType();
 
-        const ArrayInfo& vinfo = getInfo(values);
+        const ArrayInfo &vinfo = getInfo(values);
 
         DIM_ASSERT(4, kinfo.dims() == vinfo.dims());
-        if(kinfo.elements() == 0) {
-            AF_CHECK(af_create_handle(out_keys,   0, nullptr, ktype));
+        if (kinfo.elements() == 0) {
+            AF_CHECK(af_create_handle(out_keys, 0, nullptr, ktype));
             AF_CHECK(af_create_handle(out_values, 0, nullptr, ktype));
             return AF_SUCCESS;
         }
@@ -179,21 +210,51 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
         af_array oKey;
         af_array oVal;
 
-        switch(ktype) {
-            case f32: sort_by_key_tmplt<float  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case f64: sort_by_key_tmplt<double >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case s32: sort_by_key_tmplt<int    >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case u32: sort_by_key_tmplt<uint   >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case s16: sort_by_key_tmplt<short  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case u16: sort_by_key_tmplt<ushort >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case s64: sort_by_key_tmplt<intl   >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case u64: sort_by_key_tmplt<uintl  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case u8:  sort_by_key_tmplt<uchar  >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            case b8:  sort_by_key_tmplt<char   >(&oKey, &oVal, keys, values, dim, isAscending);  break;
-            default:  TYPE_ERROR(1, ktype);
+        switch (ktype) {
+            case f32:
+                sort_by_key_tmplt<float>(&oKey, &oVal, keys, values, dim,
+                                         isAscending);
+                break;
+            case f64:
+                sort_by_key_tmplt<double>(&oKey, &oVal, keys, values, dim,
+                                          isAscending);
+                break;
+            case s32:
+                sort_by_key_tmplt<int>(&oKey, &oVal, keys, values, dim,
+                                       isAscending);
+                break;
+            case u32:
+                sort_by_key_tmplt<uint>(&oKey, &oVal, keys, values, dim,
+                                        isAscending);
+                break;
+            case s16:
+                sort_by_key_tmplt<short>(&oKey, &oVal, keys, values, dim,
+                                         isAscending);
+                break;
+            case u16:
+                sort_by_key_tmplt<ushort>(&oKey, &oVal, keys, values, dim,
+                                          isAscending);
+                break;
+            case s64:
+                sort_by_key_tmplt<intl>(&oKey, &oVal, keys, values, dim,
+                                        isAscending);
+                break;
+            case u64:
+                sort_by_key_tmplt<uintl>(&oKey, &oVal, keys, values, dim,
+                                         isAscending);
+                break;
+            case u8:
+                sort_by_key_tmplt<uchar>(&oKey, &oVal, keys, values, dim,
+                                         isAscending);
+                break;
+            case b8:
+                sort_by_key_tmplt<char>(&oKey, &oVal, keys, values, dim,
+                                        isAscending);
+                break;
+            default: TYPE_ERROR(1, ktype);
         }
-        std::swap(*out_keys , oKey);
-        std::swap(*out_values , oVal);
+        std::swap(*out_keys, oKey);
+        std::swap(*out_values, oVal);
     }
     CATCHALL;
 
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index db09946f40..620781a6f7 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -7,29 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/sparse.h>
-#include <af/array.h>
-#include <af/algorithm.h>
-#include <sparse_handle.hpp>
-#include <sparse.hpp>
-#include <handle.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
-#include <arith.hpp>
+#include <handle.hpp>
 #include <lookup.hpp>
 #include <platform.hpp>
+#include <sparse.hpp>
+#include <sparse_handle.hpp>
+#include <af/algorithm.h>
+#include <af/array.h>
+#include <af/sparse.h>
 
 using namespace detail;
 using namespace common;
 using af::dim4;
 
-const SparseArrayBase& getSparseArrayBase(const af_array in, bool device_check)
-{
-    const SparseArrayBase *base = static_cast<SparseArrayBase*>(reinterpret_cast<void *>(in));
+const SparseArrayBase &getSparseArrayBase(const af_array in,
+                                          bool device_check) {
+    const SparseArrayBase *base =
+        static_cast<SparseArrayBase *>(reinterpret_cast<void *>(in));
 
-    if(!base->isSparse()) {
-        AF_ERROR("Input is not a SparseArray and cannot be used in Sparse functions",
-                 AF_ERR_ARG);
+    if (!base->isSparse()) {
+        AF_ERROR(
+            "Input is not a SparseArray and cannot be used in Sparse functions",
+            AF_ERR_ARG);
     }
 
     if (device_check && base->getDevId() != detail::getActiveDeviceId()) {
@@ -45,21 +47,17 @@ const SparseArrayBase& getSparseArrayBase(const af_array in, bool device_check)
 template<typename T>
 af_array createSparseArrayFromData(const af::dim4 &dims, const af_array values,
                                    const af_array rowIdx, const af_array colIdx,
-                                   const af::storage stype)
-{
+                                   const af::storage stype) {
     SparseArray<T> sparse = common::createArrayDataSparseArray(
-                            dims, getArray<T>(values),
-                            getArray<int>(rowIdx), getArray<int>(colIdx),
-                            stype);
+        dims, getArray<T>(values), getArray<int>(rowIdx), getArray<int>(colIdx),
+        stype);
     return getHandle(sparse);
 }
 
-af_err af_create_sparse_array(
-                 af_array *out,
-                 const dim_t nRows, const dim_t nCols,
-                 const af_array values, const af_array rowIdx, const af_array colIdx,
-                 const af_storage stype)
-{
+af_err af_create_sparse_array(af_array *out, const dim_t nRows,
+                              const dim_t nCols, const af_array values,
+                              const af_array rowIdx, const af_array colIdx,
+                              const af_storage stype) {
     try {
         // Checks:
         // rowIdx and colIdx arrays are of s32 type
@@ -70,15 +68,14 @@ af_err af_create_sparse_array(
         // stype is within acceptable range
         // type is floating type
 
-        if(!(stype == AF_STORAGE_CSR
-          || stype == AF_STORAGE_CSC
-          || stype == AF_STORAGE_COO)) {
+        if (!(stype == AF_STORAGE_CSR || stype == AF_STORAGE_CSC ||
+              stype == AF_STORAGE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
-        const ArrayInfo& vInfo = getInfo(values);
-        const ArrayInfo& rInfo = getInfo(rowIdx);
-        const ArrayInfo& cInfo = getInfo(colIdx);
+        const ArrayInfo &vInfo = getInfo(values);
+        const ArrayInfo &rInfo = getInfo(rowIdx);
+        const ArrayInfo &cInfo = getInfo(colIdx);
 
         TYPE_ASSERT(vInfo.isFloating());
         DIM_ASSERT(3, vInfo.isLinear());
@@ -88,62 +85,72 @@ af_err af_create_sparse_array(
         DIM_ASSERT(5, cInfo.isLinear());
 
         const size_t nNZ = vInfo.elements();
-        if(stype == AF_STORAGE_COO) {
-          DIM_ASSERT(4, rInfo.elements() == nNZ);
-          DIM_ASSERT(5, cInfo.elements() == nNZ);
-        } else if(stype == AF_STORAGE_CSR) {
-          DIM_ASSERT(4, (dim_t)rInfo.elements() == nRows + 1);
-          DIM_ASSERT(5, cInfo.elements() == nNZ);
-        } else if(stype == AF_STORAGE_CSC) {
-          DIM_ASSERT(4, rInfo.elements() == nNZ);
-          DIM_ASSERT(5, (dim_t)cInfo.elements() == nCols + 1);
+        if (stype == AF_STORAGE_COO) {
+            DIM_ASSERT(4, rInfo.elements() == nNZ);
+            DIM_ASSERT(5, cInfo.elements() == nNZ);
+        } else if (stype == AF_STORAGE_CSR) {
+            DIM_ASSERT(4, (dim_t)rInfo.elements() == nRows + 1);
+            DIM_ASSERT(5, cInfo.elements() == nNZ);
+        } else if (stype == AF_STORAGE_CSC) {
+            DIM_ASSERT(4, rInfo.elements() == nNZ);
+            DIM_ASSERT(5, (dim_t)cInfo.elements() == nCols + 1);
         }
 
         af_array output = 0;
 
         af::dim4 dims(nRows, nCols);
 
-        switch(vInfo.getType()) {
-            case f32: output = createSparseArrayFromData<float  >(dims, values, rowIdx, colIdx, stype); break;
-            case f64: output = createSparseArrayFromData<double >(dims, values, rowIdx, colIdx, stype); break;
-            case c32: output = createSparseArrayFromData<cfloat >(dims, values, rowIdx, colIdx, stype); break;
-            case c64: output = createSparseArrayFromData<cdouble>(dims, values, rowIdx, colIdx, stype); break;
-            default : TYPE_ERROR(1, vInfo.getType());
+        switch (vInfo.getType()) {
+            case f32:
+                output = createSparseArrayFromData<float>(dims, values, rowIdx,
+                                                          colIdx, stype);
+                break;
+            case f64:
+                output = createSparseArrayFromData<double>(dims, values, rowIdx,
+                                                           colIdx, stype);
+                break;
+            case c32:
+                output = createSparseArrayFromData<cfloat>(dims, values, rowIdx,
+                                                           colIdx, stype);
+                break;
+            case c64:
+                output = createSparseArrayFromData<cdouble>(
+                    dims, values, rowIdx, colIdx, stype);
+                break;
+            default: TYPE_ERROR(1, vInfo.getType());
         }
         std::swap(*out, output);
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-af_array createSparseArrayFromPtr(
-        const af::dim4 &dims, const dim_t nNZ,
-        const T * const values, const int * const rowIdx, const int * const colIdx,
-        const af::storage stype, const af::source source)
-{
+af_array createSparseArrayFromPtr(const af::dim4 &dims, const dim_t nNZ,
+                                  const T *const values,
+                                  const int *const rowIdx,
+                                  const int *const colIdx,
+                                  const af::storage stype,
+                                  const af::source source) {
     SparseArray<T> sparse = createEmptySparseArray<T>(dims, nNZ, stype);
 
-    if(nNZ) {
-        if(source == afHost)
-            sparse = common::createHostDataSparseArray(
-                            dims, nNZ, values, rowIdx, colIdx, stype);
+    if (nNZ) {
+        if (source == afHost)
+            sparse = common::createHostDataSparseArray(dims, nNZ, values,
+                                                       rowIdx, colIdx, stype);
         else if (source == afDevice)
-            sparse = common::createDeviceDataSparseArray(
-                            dims, nNZ, values, rowIdx, colIdx, stype);
+            sparse = common::createDeviceDataSparseArray(dims, nNZ, values,
+                                                         rowIdx, colIdx, stype);
     }
 
     return getHandle(sparse);
 }
 
 af_err af_create_sparse_array_from_ptr(
-                 af_array *out,
-                 const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-                 const void * const values, const int * const rowIdx, const int * const colIdx,
-                 const af_dtype type, const af_storage stype,
-                 const af_source source)
-{
+    af_array *out, const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+    const void *const values, const int *const rowIdx, const int *const colIdx,
+    const af_dtype type, const af_storage stype, const af_source source) {
     try {
         // Checks:
         // rowIdx and colIdx arrays are of s32 type
@@ -153,73 +160,78 @@ af_err af_create_sparse_array_from_ptr(
         // if CRC, rowIdx and values should have same dims, colIdx.dims = nCols
         // stype is within acceptable range
         // type is floating type
-        if(!(stype == AF_STORAGE_CSR
-          || stype == AF_STORAGE_CSC
-          || stype == AF_STORAGE_COO)) {
+        if (!(stype == AF_STORAGE_CSR || stype == AF_STORAGE_CSC ||
+              stype == AF_STORAGE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
-        TYPE_ASSERT(type == f32 || type == f64
-                 || type == c32 || type == c64);
-
+        TYPE_ASSERT(type == f32 || type == f64 || type == c32 || type == c64);
 
         af_array output = 0;
 
         af::dim4 dims(nRows, nCols);
 
-        switch(type) {
-            case f32: output = createSparseArrayFromPtr<float  >
-                               (dims, nNZ, static_cast<const float  *>(values), rowIdx, colIdx, stype, source);
-                      break;
-            case f64: output = createSparseArrayFromPtr<double >
-                               (dims, nNZ, static_cast<const double *>(values), rowIdx, colIdx, stype, source);
-                      break;
-            case c32: output = createSparseArrayFromPtr<cfloat >
-                               (dims, nNZ, static_cast<const cfloat *>(values), rowIdx, colIdx, stype, source);
-                      break;
-            case c64: output = createSparseArrayFromPtr<cdouble>
-                               (dims, nNZ, static_cast<const cdouble*>(values), rowIdx, colIdx, stype, source);
-                      break;
-            default : TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                output = createSparseArrayFromPtr<float>(
+                    dims, nNZ, static_cast<const float *>(values), rowIdx,
+                    colIdx, stype, source);
+                break;
+            case f64:
+                output = createSparseArrayFromPtr<double>(
+                    dims, nNZ, static_cast<const double *>(values), rowIdx,
+                    colIdx, stype, source);
+                break;
+            case c32:
+                output = createSparseArrayFromPtr<cfloat>(
+                    dims, nNZ, static_cast<const cfloat *>(values), rowIdx,
+                    colIdx, stype, source);
+                break;
+            case c64:
+                output = createSparseArrayFromPtr<cdouble>(
+                    dims, nNZ, static_cast<const cdouble *>(values), rowIdx,
+                    colIdx, stype, source);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-af_array createSparseArrayFromDense(
-        const af_array _in,
-        const af_storage stype)
-{
+af_array createSparseArrayFromDense(const af_array _in,
+                                    const af_storage stype) {
     const Array<T> in = getArray<T>(_in);
 
-    switch(stype) {
+    switch (stype) {
         case AF_STORAGE_CSR:
-            return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(in));
+            return getHandle(
+                sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(in));
         case AF_STORAGE_COO:
-            return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_COO>(in));
+            return getHandle(
+                sparseConvertDenseToStorage<T, AF_STORAGE_COO>(in));
         case AF_STORAGE_CSC:
-            //return getHandle(sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(in));
-        default: AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
+            // return getHandle(sparseConvertDenseToStorage<T,
+            // AF_STORAGE_CSC>(in));
+        default:
+            AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
     }
 }
 
 af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
-                                         const af_storage stype)
-{
+                                         const af_storage stype) {
     try {
         // Checks:
         // stype is within acceptable range
         // values is of floating point type
 
-        const ArrayInfo& info = getInfo(in);
+        const ArrayInfo &info = getInfo(in);
 
-        if(!(stype == AF_STORAGE_CSR
-          || stype == AF_STORAGE_CSC
-          || stype == AF_STORAGE_COO)) {
+        if (!(stype == AF_STORAGE_CSR || stype == AF_STORAGE_CSC ||
+              stype == AF_STORAGE_COO)) {
             AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
         }
 
@@ -230,52 +242,64 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
 
         af_array output = 0;
 
-        switch(info.getType()) {
-            case f32: output = createSparseArrayFromDense<float  >(in, stype); break;
-            case f64: output = createSparseArrayFromDense<double >(in, stype); break;
-            case c32: output = createSparseArrayFromDense<cfloat >(in, stype); break;
-            case c64: output = createSparseArrayFromDense<cdouble>(in, stype); break;
+        switch (info.getType()) {
+            case f32:
+                output = createSparseArrayFromDense<float>(in, stype);
+                break;
+            case f64:
+                output = createSparseArrayFromDense<double>(in, stype);
+                break;
+            case c32:
+                output = createSparseArrayFromDense<cfloat>(in, stype);
+                break;
+            case c64:
+                output = createSparseArrayFromDense<cdouble>(in, stype);
+                break;
             default: TYPE_ERROR(1, info.getType());
         }
         std::swap(*out, output);
-
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
-{
+af_array sparseConvertStorage(const af_array in_,
+                              const af_storage destStorage) {
     const SparseArray<T> in = getSparseArray<T>(in_);
 
-    if(destStorage == AF_STORAGE_DENSE) {
+    if (destStorage == AF_STORAGE_DENSE) {
         // Returns a regular af_array, not sparse
-        switch(in.getStorage()) {
+        switch (in.getStorage()) {
             case AF_STORAGE_CSR:
-                return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_CSR>(in));
+                return getHandle(
+                    detail::sparseConvertStorageToDense<T, AF_STORAGE_CSR>(in));
             case AF_STORAGE_COO:
-                return getHandle(detail::sparseConvertStorageToDense<T, AF_STORAGE_COO>(in));
+                return getHandle(
+                    detail::sparseConvertStorageToDense<T, AF_STORAGE_COO>(in));
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
-    } else if(destStorage == AF_STORAGE_CSR) {
+    } else if (destStorage == AF_STORAGE_CSR) {
         // Returns a sparse af_array
-        switch(in.getStorage()) {
-            case AF_STORAGE_CSR:
-                return retainSparseHandle<T>(in_);
+        switch (in.getStorage()) {
+            case AF_STORAGE_CSR: return retainSparseHandle<T>(in_);
             case AF_STORAGE_COO:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR, AF_STORAGE_COO>(in));
+                return getHandle(
+                    detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR,
+                                                          AF_STORAGE_COO>(in));
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
-    } else if(destStorage == AF_STORAGE_COO) {
+    } else if (destStorage == AF_STORAGE_COO) {
         // Returns a sparse af_array
-        switch(in.getStorage()) {
+        switch (in.getStorage()) {
             case AF_STORAGE_CSR:
-                return getHandle(detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO, AF_STORAGE_CSR>(in));
-            case AF_STORAGE_COO:
-                return retainSparseHandle<T>(in_);
+                return getHandle(
+                    detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO,
+                                                          AF_STORAGE_CSR>(in));
+            case AF_STORAGE_COO: return retainSparseHandle<T>(in_);
             default:
                 AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
         }
@@ -286,12 +310,11 @@ af_array sparseConvertStorage(const af_array in_, const af_storage destStorage)
 }
 
 af_err af_sparse_convert_to(af_array *out, const af_array in,
-                            const af_storage destStorage)
-{
+                            const af_storage destStorage) {
     try {
         // Handle dense case
-        const ArrayInfo& info = getInfo(in, false, true);
-        if(!info.isSparse()) {    // If input is dense
+        const ArrayInfo &info = getInfo(in, false, true);
+        if (!info.isSparse()) {  // If input is dense
             return af_create_sparse_array_from_dense(out, in, destStorage);
         }
 
@@ -299,26 +322,34 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
 
         const SparseArrayBase base = getSparseArrayBase(in);
 
-        // Dense not allowed as input -> Should never happen with SparseArrayBase
-        // CSC is currently not supported
-        ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE
-                   && base.getStorage() != AF_STORAGE_CSC);
+        // Dense not allowed as input -> Should never happen with
+        // SparseArrayBase CSC is currently not supported
+        ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE &&
+                          base.getStorage() != AF_STORAGE_CSC);
 
         // Conversion to and from CSC is not supported
         ARG_ASSERT(2, destStorage != AF_STORAGE_CSC);
 
-        if(base.getStorage() == destStorage) {
+        if (base.getStorage() == destStorage) {
             // Return a reference
             AF_CHECK(af_retain_array(out, in));
             return AF_SUCCESS;
         }
 
-        switch(base.getType()) {
-            case f32: output = sparseConvertStorage<float  >(in, destStorage); break;
-            case f64: output = sparseConvertStorage<double >(in, destStorage); break;
-            case c32: output = sparseConvertStorage<cfloat >(in, destStorage); break;
-            case c64: output = sparseConvertStorage<cdouble>(in, destStorage); break;
-            default : AF_ERROR("Output storage type is not valid", AF_ERR_ARG);
+        switch (base.getType()) {
+            case f32:
+                output = sparseConvertStorage<float>(in, destStorage);
+                break;
+            case f64:
+                output = sparseConvertStorage<double>(in, destStorage);
+                break;
+            case c32:
+                output = sparseConvertStorage<cfloat>(in, destStorage);
+                break;
+            case c64:
+                output = sparseConvertStorage<cdouble>(in, destStorage);
+                break;
+            default: AF_ERROR("Output storage type is not valid", AF_ERR_ARG);
         }
         std::swap(*out, output);
     }
@@ -326,8 +357,7 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
     return AF_SUCCESS;
 }
 
-af_err af_sparse_to_dense(af_array *out, const af_array in)
-{
+af_err af_sparse_to_dense(af_array *out, const af_array in) {
     try {
         af_array output = 0;
 
@@ -337,12 +367,20 @@ af_err af_sparse_to_dense(af_array *out, const af_array in)
         // To convert from dense to type, use the create* functions
         ARG_ASSERT(1, base.getStorage() != AF_STORAGE_DENSE);
 
-        switch(base.getType()) {
-            case f32: output = sparseConvertStorage<float  >(in, AF_STORAGE_DENSE); break;
-            case f64: output = sparseConvertStorage<double >(in, AF_STORAGE_DENSE); break;
-            case c32: output = sparseConvertStorage<cfloat >(in, AF_STORAGE_DENSE); break;
-            case c64: output = sparseConvertStorage<cdouble>(in, AF_STORAGE_DENSE); break;
-            default : AF_ERROR("Output storage type is not valid", AF_ERR_ARG);
+        switch (base.getType()) {
+            case f32:
+                output = sparseConvertStorage<float>(in, AF_STORAGE_DENSE);
+                break;
+            case f64:
+                output = sparseConvertStorage<double>(in, AF_STORAGE_DENSE);
+                break;
+            case c32:
+                output = sparseConvertStorage<cfloat>(in, AF_STORAGE_DENSE);
+                break;
+            case c64:
+                output = sparseConvertStorage<cdouble>(in, AF_STORAGE_DENSE);
+                break;
+            default: AF_ERROR("Output storage type is not valid", AF_ERR_ARG);
         }
         std::swap(*out, output);
     }
@@ -354,38 +392,35 @@ af_err af_sparse_to_dense(af_array *out, const af_array in)
 // Get Functions
 ////////////////////////////////////////////////////////////////////////////////
 template<typename T>
-af_array getSparseValues(const af_array in)
-{
+af_array getSparseValues(const af_array in) {
     return getHandle(getSparseArray<T>(in).getValues());
 }
 
-af_err af_sparse_get_info(af_array *values, af_array *rows, af_array *cols, af_storage *stype,
-                          const af_array in)
-{
+af_err af_sparse_get_info(af_array *values, af_array *rows, af_array *cols,
+                          af_storage *stype, const af_array in) {
     try {
-        if(values != NULL) AF_CHECK(af_sparse_get_values(values, in));
-        if(rows   != NULL) AF_CHECK(af_sparse_get_row_idx(rows , in));
-        if(cols   != NULL) AF_CHECK(af_sparse_get_col_idx(cols , in));
-        if(stype  != NULL) AF_CHECK(af_sparse_get_storage(stype, in));
+        if (values != NULL) AF_CHECK(af_sparse_get_values(values, in));
+        if (rows != NULL) AF_CHECK(af_sparse_get_row_idx(rows, in));
+        if (cols != NULL) AF_CHECK(af_sparse_get_col_idx(cols, in));
+        if (stype != NULL) AF_CHECK(af_sparse_get_storage(stype, in));
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_values(af_array *out, const af_array in)
-{
-    try{
+af_err af_sparse_get_values(af_array *out, const af_array in) {
+    try {
         const SparseArrayBase base = getSparseArrayBase(in);
 
         af_array output = 0;
 
-        switch(base.getType()) {
-            case f32: output = getSparseValues<float  >(in); break;
-            case f64: output = getSparseValues<double >(in); break;
-            case c32: output = getSparseValues<cfloat >(in); break;
+        switch (base.getType()) {
+            case f32: output = getSparseValues<float>(in); break;
+            case f64: output = getSparseValues<double>(in); break;
+            case c32: output = getSparseValues<cfloat>(in); break;
             case c64: output = getSparseValues<cdouble>(in); break;
-            default : TYPE_ERROR(1, base.getType());
+            default: TYPE_ERROR(1, base.getType());
         }
         std::swap(*out, output);
     }
@@ -393,38 +428,38 @@ af_err af_sparse_get_values(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_row_idx(af_array *out, const af_array in)
-{
+af_err af_sparse_get_row_idx(af_array *out, const af_array in) {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = getHandle(base.getRowIdx());
-    } CATCHALL;
+        *out                       = getHandle(base.getRowIdx());
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_col_idx(af_array *out, const af_array in)
-{
+af_err af_sparse_get_col_idx(af_array *out, const af_array in) {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = getHandle(base.getColIdx());
-    } CATCHALL;
+        *out                       = getHandle(base.getColIdx());
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_nnz(dim_t *out, const af_array in)
-{
+af_err af_sparse_get_nnz(dim_t *out, const af_array in) {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = base.getNNZ();
-    } CATCHALL;
+        *out                       = base.getNNZ();
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_sparse_get_storage(af_storage *out, const af_array in)
-{
+af_err af_sparse_get_storage(af_storage *out, const af_array in) {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
-        *out = base.getStorage();
-    } CATCHALL;
+        *out                       = base.getStorage();
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 453e4b085b..c7afce5306 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -8,86 +8,81 @@
  ********************************************************/
 
 #pragma once
-#include <af/array.h>
 #include <Array.hpp>
 #include <backend.hpp>
+#include <cast.hpp>
 #include <common/err_common.hpp>
-#include <math.hpp>
 #include <copy.hpp>
-#include <cast.hpp>
 #include <handle.hpp>
+#include <math.hpp>
+#include <af/array.h>
 #include <af/dim4.hpp>
 
 #include <common/SparseArray.hpp>
 
-const common::SparseArrayBase& getSparseArrayBase(const af_array arr, bool device_check = true);
+const common::SparseArrayBase &getSparseArrayBase(const af_array arr,
+                                                  bool device_check = true);
 
 template<typename T>
-const common::SparseArray<T>& getSparseArray(const af_array &arr)
-{
-    const common::SparseArray<T> *A = static_cast<const common::SparseArray<T>*>(arr);
+const common::SparseArray<T> &getSparseArray(const af_array &arr) {
+    const common::SparseArray<T> *A =
+        static_cast<const common::SparseArray<T> *>(arr);
     ARG_ASSERT(0, A->isSparse() == true);
     return *A;
 }
 
 template<typename T>
-common::SparseArray<T>& getSparseArray(af_array &arr)
-{
-    common::SparseArray<T> *A =  static_cast<common::SparseArray<T>*>(arr);
+common::SparseArray<T> &getSparseArray(af_array &arr) {
+    common::SparseArray<T> *A = static_cast<common::SparseArray<T> *>(arr);
     ARG_ASSERT(0, A->isSparse() == true);
     return *A;
 }
 
 template<typename T>
-static af_array
-getHandle(const common::SparseArray<T> &A)
-{
+static af_array getHandle(const common::SparseArray<T> &A) {
     common::SparseArray<T> *ret = new common::SparseArray<T>(A);
     return static_cast<af_array>(ret);
 }
 
 template<typename T>
-static void releaseSparseHandle(const af_array arr)
-{
-    common::destroySparseArray(static_cast<common::SparseArray<T>*>(arr));
+static void releaseSparseHandle(const af_array arr) {
+    common::destroySparseArray(static_cast<common::SparseArray<T> *>(arr));
 }
 
 template<typename T>
-af_array retainSparseHandle(const af_array in)
-{
-    const common::SparseArray<T> *sparse = static_cast<const common::SparseArray<T> *>(in);
+af_array retainSparseHandle(const af_array in) {
+    const common::SparseArray<T> *sparse =
+        static_cast<const common::SparseArray<T> *>(in);
     common::SparseArray<T> *out = new common::SparseArray<T>(*sparse);
     return static_cast<af_array>(out);
 }
 
 // based on castArray in handle.hpp
 template<typename To>
-common::SparseArray<To> castSparse(const af_array &in)
-{
-    const ArrayInfo& info = getInfo(in, false, true);
+common::SparseArray<To> castSparse(const af_array &in) {
+    const ArrayInfo &info = getInfo(in, false, true);
     using namespace common;
 
-#define CAST_SPARSE(Ti) do {                                            \
-        const SparseArray<Ti> sparse = getSparseArray<Ti>(in);          \
-        Array<To> values = detail::cast<To, Ti>(sparse.getValues());    \
-        return createArrayDataSparseArray(sparse.dims(), values,        \
-                                          sparse.getRowIdx(),           \
-                                          sparse.getColIdx(),           \
-                                          sparse.getStorage());         \
-    } while(0)
+#define CAST_SPARSE(Ti)                                                          \
+    do {                                                                         \
+        const SparseArray<Ti> sparse = getSparseArray<Ti>(in);                   \
+        Array<To> values             = detail::cast<To, Ti>(sparse.getValues()); \
+        return createArrayDataSparseArray(                                       \
+            sparse.dims(), values, sparse.getRowIdx(), sparse.getColIdx(),       \
+            sparse.getStorage());                                                \
+    } while (0)
 
-    switch(info.getType()) {
-    case f32: CAST_SPARSE(float);
-    case f64: CAST_SPARSE(double);
-    case c32: CAST_SPARSE(cfloat);
-    case c64: CAST_SPARSE(cdouble);
-    default: TYPE_ERROR(1, info.getType());
+    switch (info.getType()) {
+        case f32: CAST_SPARSE(float);
+        case f64: CAST_SPARSE(double);
+        case c32: CAST_SPARSE(cfloat);
+        case c64: CAST_SPARSE(cdouble);
+        default: TYPE_ERROR(1, info.getType());
     }
 }
 
 template<typename T>
-static af_array copySparseArray(const af_array in)
-{
-  const common::SparseArray<T> &inArray = getSparseArray<T>(in);
-  return getHandle<T>(common::copySparseArray<T>(inArray));
+static af_array copySparseArray(const af_array in) {
+    const common::SparseArray<T> &inArray = getSparseArray<T>(in);
+    return getHandle<T>(common::copySparseArray<T>(inArray));
 }
diff --git a/src/api/c/stats.h b/src/api/c/stats.h
index 1d6015a3d7..d7e5c6f390 100644
--- a/src/api/c/stats.h
+++ b/src/api/c/stats.h
@@ -10,7 +10,7 @@
 #pragma once
 
 template<typename T, typename Other>
-struct is_same{
+struct is_same {
     static const bool value = false;
 };
 
@@ -34,8 +34,7 @@ struct cond_type<false, T, Other> {
 
 template<typename T>
 struct baseOutType {
-    typedef typename cond_type< is_same<T, cdouble>::value ||
-                                is_same<T, double>::value,
-                                double,
-                                float>::type type;
+    typedef typename cond_type<is_same<T, cdouble>::value ||
+                                   is_same<T, double>::value,
+                               double, float>::type type;
 };
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 43afd1313c..b67c3c3dc4 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/statistics.h>
-#include <af/defines.h>
+#include <arith.hpp>
 #include <backend.hpp>
-#include <reduce.hpp>
+#include <cast.hpp>
 #include <handle.hpp>
-#include <mean.hpp>
-#include <arith.hpp>
-#include <unary.hpp>
 #include <math.hpp>
-#include <cast.hpp>
+#include <mean.hpp>
+#include <reduce.hpp>
 #include <tile.hpp>
+#include <unary.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/statistics.h>
 #include <cmath>
 #include <complex>
 
@@ -27,105 +27,109 @@
 using namespace detail;
 
 template<typename inType, typename outType>
-static outType stdev(const af_array& in)
-{
+static outType stdev(const af_array& in) {
     typedef typename baseOutType<outType>::type weightType;
     Array<inType> _in       = getArray<inType>(in);
     Array<outType> input    = cast<outType>(_in);
-    Array<outType> meanCnst = createValueArray<outType>(input.dims(), mean<inType, weightType, outType>(_in));
-    Array<outType> diff     = detail::arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
-    Array<outType> diffSq   = detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
-    outType result          = division(reduce_all<af_add_t, outType, outType>(diffSq), input.elements());
+    Array<outType> meanCnst = createValueArray<outType>(
+        input.dims(), mean<inType, weightType, outType>(_in));
+    Array<outType> diff =
+        detail::arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
+    Array<outType> diffSq =
+        detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
+    outType result = division(reduce_all<af_add_t, outType, outType>(diffSq),
+                              input.elements());
 
     return sqrt(result);
 }
 
 template<typename inType, typename outType>
-static af_array stdev(const af_array& in, int dim)
-{
+static af_array stdev(const af_array& in, int dim) {
     typedef typename baseOutType<outType>::type weightType;
     Array<inType> _in    = getArray<inType>(in);
     Array<outType> input = cast<outType>(_in);
-    dim4 iDims = input.dims();
+    dim4 iDims           = input.dims();
 
     Array<outType> meanArr = mean<inType, weightType, outType>(_in, dim);
 
     /* now tile meanArr along dim and use it for variance computation */
     dim4 tileDims(1);
-    tileDims[dim] = iDims[dim];
+    tileDims[dim]           = iDims[dim];
     Array<outType> tMeanArr = detail::tile<outType>(meanArr, tileDims);
     /* now mean array is ready */
 
-    Array<outType> diff    = detail::arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
-    Array<outType> diffSq  = detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
+    Array<outType> diff =
+        detail::arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
+    Array<outType> diffSq =
+        detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     Array<outType> redDiff = reduce<af_add_t, outType, outType>(diffSq, dim);
-    dim4 oDims = redDiff.dims();
+    dim4 oDims             = redDiff.dims();
 
-    Array<outType> divArr = createValueArray<outType>(oDims, scalar<outType>(iDims[dim]));
-    Array<outType> varArr = detail::arithOp<outType, af_div_t>(redDiff, divArr, redDiff.dims());
+    Array<outType> divArr =
+        createValueArray<outType>(oDims, scalar<outType>(iDims[dim]));
+    Array<outType> varArr =
+        detail::arithOp<outType, af_div_t>(redDiff, divArr, redDiff.dims());
     Array<outType> result = detail::unaryOp<outType, af_sqrt_t>(varArr);
 
     return getHandle<outType>(result);
 }
 
-af_err af_stdev_all(double *realVal, double *imagVal, const af_array in)
-{
-    UNUSED(imagVal); //TODO implement for complex values
+af_err af_stdev_all(double* realVal, double* imagVal, const af_array in) {
+    UNUSED(imagVal);  // TODO implement for complex values
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        switch(type) {
+        af_dtype type         = info.getType();
+        switch (type) {
             case f64: *realVal = stdev<double, double>(in); break;
-            case f32: *realVal = stdev<float , float >(in); break;
-            case s32: *realVal = stdev<int   , float >(in); break;
-            case u32: *realVal = stdev<uint  , float >(in); break;
-            case s16: *realVal = stdev<short , float >(in); break;
-            case u16: *realVal = stdev<ushort, float >(in); break;
-            case s64: *realVal = stdev<intl  , double>(in); break;
-            case u64: *realVal = stdev<uintl , double>(in); break;
-            case  u8: *realVal = stdev<uchar , float >(in); break;
-            case  b8: *realVal = stdev<char  , float >(in); break;
+            case f32: *realVal = stdev<float, float>(in); break;
+            case s32: *realVal = stdev<int, float>(in); break;
+            case u32: *realVal = stdev<uint, float>(in); break;
+            case s16: *realVal = stdev<short, float>(in); break;
+            case u16: *realVal = stdev<ushort, float>(in); break;
+            case s64: *realVal = stdev<intl, double>(in); break;
+            case u64: *realVal = stdev<uintl, double>(in); break;
+            case u8: *realVal = stdev<uchar, float>(in); break;
+            case b8: *realVal = stdev<char, float>(in); break;
             // TODO: FIXME: sqrt(complex) is not present in cuda/opencl backend
-            //case c32: {
+            // case c32: {
             //    cfloat tmp = stdev<cfloat,cfloat>(in);
             //    *realVal = real(tmp);
             //    *imagVal = imag(tmp);
             //    } break;
-            //case c64: {
+            // case c64: {
             //    cdouble tmp = stdev<cdouble,cdouble>(in);
             //    *realVal = real(tmp);
             //    *imagVal = imag(tmp);
             //    } break;
-            default : TYPE_ERROR(1, type);
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_stdev(af_array *out, const af_array in, const dim_t dim)
-{
+af_err af_stdev(af_array* out, const af_array in, const dim_t dim) {
     try {
-        ARG_ASSERT(2, (dim>=0 && dim<=3));
+        ARG_ASSERT(2, (dim >= 0 && dim <= 3));
 
-        af_array output = 0;
+        af_array output       = 0;
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        switch(type) {
-            case f64: output = stdev<double,  double>(in, dim); break;
-            case f32: output = stdev<float ,  float >(in, dim); break;
-            case s32: output = stdev<int   ,  float >(in, dim); break;
-            case u32: output = stdev<uint  ,  float >(in, dim); break;
-            case s16: output = stdev<short ,  float >(in, dim); break;
-            case u16: output = stdev<ushort,  float >(in, dim); break;
-            case s64: output = stdev<intl  ,  double>(in, dim); break;
-            case u64: output = stdev<uintl ,  double>(in, dim); break;
-            case  u8: output = stdev<uchar ,  float >(in, dim); break;
-            case  b8: output = stdev<char  ,  float >(in, dim); break;
+        af_dtype type         = info.getType();
+        switch (type) {
+            case f64: output = stdev<double, double>(in, dim); break;
+            case f32: output = stdev<float, float>(in, dim); break;
+            case s32: output = stdev<int, float>(in, dim); break;
+            case u32: output = stdev<uint, float>(in, dim); break;
+            case s16: output = stdev<short, float>(in, dim); break;
+            case u16: output = stdev<ushort, float>(in, dim); break;
+            case s64: output = stdev<intl, double>(in, dim); break;
+            case u64: output = stdev<uintl, double>(in, dim); break;
+            case u8: output = stdev<uchar, float>(in, dim); break;
+            case b8: output = stdev<char, float>(in, dim); break;
             // TODO: FIXME: sqrt(complex) is not present in cuda/opencl backend
-            //case c32: output = stdev<cfloat,  cfloat>(in, dim); break;
-            //case c64: output = stdev<cdouble,cdouble>(in, dim); break;
-            default : TYPE_ERROR(1, type);
+            // case c32: output = stdev<cfloat,  cfloat>(in, dim); break;
+            // case c64: output = stdev<cdouble,cdouble>(in, dim); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index ea3232cfa8..1392df6db9 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -37,8 +37,8 @@ using detail::ushort;
 static const char sfv_char = STREAM_FORMAT_VERSION;
 
 template<typename T>
-static int save(const char *key, const af_array arr, const char *filename, const bool append = false)
-{
+static int save(const char *key, const af_array arr, const char *filename,
+                const bool append = false) {
     // (char     )   Version (Once)
     // (int      )   No. of Arrays (Once)
     // (int    )   Length of the key
@@ -52,7 +52,7 @@ static int save(const char *key, const af_array arr, const char *filename, const
     std::string k(key);
     int klen = k.size();
 
-    const ArrayInfo& info = getInfo(arr);
+    const ArrayInfo &info = getInfo(arr);
     std::vector<T> data(info.elements());
 
     AF_CHECK(af_get_data_ptr(&data.front(), arr));
@@ -60,9 +60,7 @@ static int save(const char *key, const af_array arr, const char *filename, const
     char type = info.getType();
 
     intl odims[4];
-    for(int i = 0; i < 4; i++) {
-        odims[i] = info.dims()[i];
-    }
+    for (int i = 0; i < 4; i++) { odims[i] = info.dims()[i]; }
 
     intl offset = sizeof(char) + 4 * sizeof(intl) + info.elements() * sizeof(T);
     ///////////////////////////////////////////////////////////////////////////
@@ -70,36 +68,40 @@ static int save(const char *key, const af_array arr, const char *filename, const
     std::fstream fs;
     int n_arrays = 0;
 
-    if(append) {
+    if (append) {
         std::ifstream checkIfExists(filename);
         bool exists = checkIfExists.good();
         checkIfExists.close();
-        if(exists) {
-            fs.open(filename, std::fstream::in | std::fstream::out | std::fstream::binary);
+        if (exists) {
+            fs.open(filename, std::fstream::in | std::fstream::out |
+                                  std::fstream::binary);
         } else {
             fs.open(filename, std::fstream::out | std::fstream::binary);
         }
 
         // Throw exception if file is not open
-        if(!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+        if (!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
 
         // Assert Version
-        if(fs.peek() == std::fstream::traits_type::eof()) {
+        if (fs.peek() == std::fstream::traits_type::eof()) {
             // File is empty
             fs.clear();
         } else {
             char prev_version = 0;
             fs.read(&prev_version, sizeof(char));
 
-            AF_ASSERT(prev_version == sfv_char, "ArrayFire data format has changed. Can't append to file");
+            AF_ASSERT(
+                prev_version == sfv_char,
+                "ArrayFire data format has changed. Can't append to file");
 
-            fs.read((char*)&n_arrays, sizeof(int));
+            fs.read((char *)&n_arrays, sizeof(int));
         }
     } else {
-        fs.open(filename, std::fstream::out | std::fstream::binary | std::fstream::trunc);
+        fs.open(filename,
+                std::fstream::out | std::fstream::binary | std::fstream::trunc);
 
         // Throw exception if file is not open
-        if(!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+        if (!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
     }
 
     n_arrays++;
@@ -107,44 +109,44 @@ static int save(const char *key, const af_array arr, const char *filename, const
     // Write version and n_arrays to top of file
     fs.seekp(0);
     fs.write(&sfv_char, 1);
-    fs.write((char*)&n_arrays, sizeof(int));
+    fs.write((char *)&n_arrays, sizeof(int));
 
     // Write array to end of file. Irrespective of new or append
     fs.seekp(0, std::ios_base::end);
-    fs.write((char*)&klen, sizeof(int));
+    fs.write((char *)&klen, sizeof(int));
     fs.write(k.c_str(), klen);
-    fs.write((char*)&offset, sizeof(intl));
+    fs.write((char *)&offset, sizeof(intl));
     fs.write(&type, sizeof(char));
-    fs.write((char*)&odims, sizeof(intl) * 4);
-    fs.write((char*)&data.front(), sizeof(T) * data.size());
+    fs.write((char *)&odims, sizeof(intl) * 4);
+    fs.write((char *)&data.front(), sizeof(T) * data.size());
     fs.close();
 
     return n_arrays - 1;
 }
 
-af_err af_save_array(int *index, const char *key, const af_array arr, const char *filename, const bool append)
-{
+af_err af_save_array(int *index, const char *key, const af_array arr,
+                     const char *filename, const bool append) {
     try {
         ARG_ASSERT(0, key != NULL);
         ARG_ASSERT(2, filename != NULL);
 
-        const ArrayInfo& info = getInfo(arr);
-        af_dtype type = info.getType();
-        int id = -1;
-        switch(type) {
-            case f32:   id = save<float>   (key, arr, filename, append);   break;
-            case c32:   id = save<cfloat>  (key, arr, filename, append);   break;
-            case f64:   id = save<double>  (key, arr, filename, append);   break;
-            case c64:   id = save<cdouble> (key, arr, filename, append);   break;
-            case b8:    id = save<char>    (key, arr, filename, append);   break;
-            case s32:   id = save<int>     (key, arr, filename, append);   break;
-            case u32:   id = save<unsigned>(key, arr, filename, append);   break;
-            case u8:    id = save<uchar>   (key, arr, filename, append);   break;
-            case s64:   id = save<intl>    (key, arr, filename, append);   break;
-            case u64:   id = save<uintl>   (key, arr, filename, append);   break;
-            case s16:   id = save<short>   (key, arr, filename, append);   break;
-            case u16:   id = save<ushort>  (key, arr, filename, append);   break;
-            default:    TYPE_ERROR(1, type);
+        const ArrayInfo &info = getInfo(arr);
+        af_dtype type         = info.getType();
+        int id                = -1;
+        switch (type) {
+            case f32: id = save<float>(key, arr, filename, append); break;
+            case c32: id = save<cfloat>(key, arr, filename, append); break;
+            case f64: id = save<double>(key, arr, filename, append); break;
+            case c64: id = save<cdouble>(key, arr, filename, append); break;
+            case b8: id = save<char>(key, arr, filename, append); break;
+            case s32: id = save<int>(key, arr, filename, append); break;
+            case u32: id = save<unsigned>(key, arr, filename, append); break;
+            case u8: id = save<uchar>(key, arr, filename, append); break;
+            case s64: id = save<intl>(key, arr, filename, append); break;
+            case u64: id = save<uintl>(key, arr, filename, append); break;
+            case s16: id = save<short>(key, arr, filename, append); break;
+            case u16: id = save<ushort>(key, arr, filename, append); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*index, id);
     }
@@ -153,44 +155,40 @@ af_err af_save_array(int *index, const char *key, const af_array arr, const char
 }
 
 template<typename T>
-static af_array readDataToArray(std::fstream &fs)
-{
+static af_array readDataToArray(std::fstream &fs) {
     intl dims[4];
-    fs.read((char*)&dims, 4 * sizeof(intl));
+    fs.read((char *)&dims, 4 * sizeof(intl));
 
     dim4 d;
-    for(int i = 0; i < 4; i++) {
-        d[i] = dims[i];
-    }
+    for (int i = 0; i < 4; i++) { d[i] = dims[i]; }
 
     intl size = d.elements();
 
     std::vector<T> data(size);
-    fs.read((char*)&data.front(), size * sizeof(T));
+    fs.read((char *)&data.front(), size * sizeof(T));
 
     return getHandle(createHostDataArray<T>(d, &data.front()));
 }
 
-static af_array readArrayV1(const char *filename, const unsigned index)
-{
+static af_array readArrayV1(const char *filename, const unsigned index) {
     char version = 0;
     int n_arrays = 0;
 
     std::fstream fs(filename, std::fstream::in | std::fstream::binary);
 
     // Throw exception if file is not open
-    if(!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+    if (!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
 
-    if(fs.peek() == std::fstream::traits_type::eof()) {
+    if (fs.peek() == std::fstream::traits_type::eof()) {
         AF_ERROR("File is empty", AF_ERR_ARG);
     }
 
     fs.read(&version, sizeof(char));
-    fs.read((char*)&n_arrays, sizeof(int));
+    fs.read((char *)&n_arrays, sizeof(int));
 
     AF_ASSERT((int)index < n_arrays, "Index out of bounds");
 
-    for(int i = 0; i < (int)index; i++) {
+    for (int i = 0; i < (int)index; i++) {
         // (int    )   Length of the key
         // (cstring)   Key
         // (intl   )   Offset bytes to next array (type + dims + data)
@@ -198,34 +196,34 @@ static af_array readArrayV1(const char *filename, const unsigned index)
         // (intl   )   dim4 (x 4)
         // (T      )   data (x elements)
         int klen = -1;
-        fs.read((char*)&klen, sizeof(int));
+        fs.read((char *)&klen, sizeof(int));
 
-        //char* key = new char[klen];
-        //fs.read((char*)&key, klen * sizeof(char));
+        // char* key = new char[klen];
+        // fs.read((char*)&key, klen * sizeof(char));
 
         // Skip the array name tag
         fs.seekg(klen, std::ios_base::cur);
 
         // Read data offset
         intl offset = -1;
-        fs.read((char*)&offset, sizeof(intl));
+        fs.read((char *)&offset, sizeof(intl));
 
         // Skip data
         fs.seekg(offset, std::ios_base::cur);
     }
 
     int klen = -1;
-    fs.read((char*)&klen, sizeof(int));
+    fs.read((char *)&klen, sizeof(int));
 
-    //char* key = new char[klen];
-    //fs.read((char*)&key, klen * sizeof(char));
+    // char* key = new char[klen];
+    // fs.read((char*)&key, klen * sizeof(char));
 
     // Skip the array name tag
     fs.seekg(klen, std::ios_base::cur);
 
     // Read data offset
     intl offset = -1;
-    fs.read((char*)&offset, sizeof(intl));
+    fs.read((char *)&offset, sizeof(intl));
 
     // Read type and dims
     char type_ = -1;
@@ -234,39 +232,39 @@ static af_array readArrayV1(const char *filename, const unsigned index)
     af_dtype type = (af_dtype)type_;
 
     af_array out;
-    switch(type) {
-        case f32 : out = readDataToArray<float>  (fs);  break;
-        case c32 : out = readDataToArray<cfloat> (fs);  break;
-        case f64 : out = readDataToArray<double> (fs);  break;
-        case c64 : out = readDataToArray<cdouble>(fs);  break;
-        case b8  : out = readDataToArray<char>   (fs);  break;
-        case s32 : out = readDataToArray<int>    (fs);  break;
-        case u32 : out = readDataToArray<uint>   (fs);  break;
-        case u8  : out = readDataToArray<uchar>  (fs);  break;
-        case s64 : out = readDataToArray<intl>   (fs);  break;
-        case u64 : out = readDataToArray<uintl>  (fs);  break;
-        case s16 : out = readDataToArray<short>  (fs);  break;
-        case u16 : out = readDataToArray<ushort> (fs);  break;
-        default:    TYPE_ERROR(1, type);
+    switch (type) {
+        case f32: out = readDataToArray<float>(fs); break;
+        case c32: out = readDataToArray<cfloat>(fs); break;
+        case f64: out = readDataToArray<double>(fs); break;
+        case c64: out = readDataToArray<cdouble>(fs); break;
+        case b8: out = readDataToArray<char>(fs); break;
+        case s32: out = readDataToArray<int>(fs); break;
+        case u32: out = readDataToArray<uint>(fs); break;
+        case u8: out = readDataToArray<uchar>(fs); break;
+        case s64: out = readDataToArray<intl>(fs); break;
+        case u64: out = readDataToArray<uintl>(fs); break;
+        case s16: out = readDataToArray<short>(fs); break;
+        case u16: out = readDataToArray<ushort>(fs); break;
+        default: TYPE_ERROR(1, type);
     }
     fs.close();
 
     return out;
 }
 
-static af_array checkVersionAndRead(const char *filename, const unsigned index)
-{
+static af_array checkVersionAndRead(const char *filename,
+                                    const unsigned index) {
     char version = 0;
 
     std::string filenameStr = std::string(filename);
     std::fstream fs(filenameStr, std::fstream::in | std::fstream::binary);
     // Throw exception if file is not open
-    if(!fs.is_open()) {
+    if (!fs.is_open()) {
         std::string errStr = "Failed to open: " + filenameStr;
         AF_ERROR(errStr.c_str(), AF_ERR_ARG);
     }
 
-    if(fs.peek() == std::fstream::traits_type::eof()) {
+    if (fs.peek() == std::fstream::traits_type::eof()) {
         std::string errStr = filenameStr + " is empty";
         AF_ERROR(errStr.c_str(), AF_ERR_ARG);
     } else {
@@ -274,26 +272,25 @@ static af_array checkVersionAndRead(const char *filename, const unsigned index)
     }
     fs.close();
 
-    switch(version) {
+    switch (version) {
         case 1: return readArrayV1(filename, index);
         default: AF_ERROR("Invalid version", AF_ERR_ARG);
     }
 }
 
-int checkVersionAndFindIndex(const char *filename, const char *k)
-{
+int checkVersionAndFindIndex(const char *filename, const char *k) {
     char version = 0;
     std::string key(k);
     std::string filenameStr(filename);
     std::ifstream fs(filenameStr, std::ifstream::in | std::ifstream::binary);
 
     // Throw exception if file is not open
-    if(!fs.is_open()) {
+    if (!fs.is_open()) {
         std::string errStr = "Failed to open: " + filenameStr;
         AF_ERROR(errStr.c_str(), AF_ERR_ARG);
     }
 
-    if(fs.peek() == std::ifstream::traits_type::eof()) {
+    if (fs.peek() == std::ifstream::traits_type::eof()) {
         std::string errStr = filenameStr + " is empty";
         AF_ERROR(errStr.c_str(), AF_ERR_ARG);
     } else {
@@ -301,24 +298,24 @@ int checkVersionAndFindIndex(const char *filename, const char *k)
     }
 
     int index = -1;
-    if(version == 1) {
+    if (version == 1) {
         int n_arrays = -1;
-        fs.read((char*)&n_arrays, sizeof(int));
-        for(int i = 0; i < n_arrays; i++) {
+        fs.read((char *)&n_arrays, sizeof(int));
+        for (int i = 0; i < n_arrays; i++) {
             int klen = -1;
-            fs.read((char*)&klen, sizeof(int));
+            fs.read((char *)&klen, sizeof(int));
             string readKey;
             readKey.resize(klen);
             fs.read(&readKey.front(), klen);
 
-            if(key == readKey) {
+            if (key == readKey) {
                 // Ket matches, break
                 index = i;
                 break;
             } else {
                 // Key doesn't match. Skip the data
                 intl offset = -1;
-                fs.read((char*)&offset, sizeof(intl));
+                fs.read((char *)&offset, sizeof(intl));
                 fs.seekg(offset, std::ios_base::cur);
             }
         }
@@ -330,8 +327,8 @@ int checkVersionAndFindIndex(const char *filename, const char *k)
     return index;
 }
 
-af_err af_read_array_index(af_array *out, const char *filename, const unsigned index)
-{
+af_err af_read_array_index(af_array *out, const char *filename,
+                           const unsigned index) {
     try {
         AF_CHECK(af_init());
 
@@ -344,8 +341,7 @@ af_err af_read_array_index(af_array *out, const char *filename, const unsigned i
     return AF_SUCCESS;
 }
 
-af_err af_read_array_key(af_array *out, const char *filename, const char *key)
-{
+af_err af_read_array_key(af_array *out, const char *filename, const char *key) {
     try {
         AF_CHECK(af_init());
         ARG_ASSERT(1, filename != NULL);
@@ -354,8 +350,7 @@ af_err af_read_array_key(af_array *out, const char *filename, const char *key)
         // Find index of key. Then call read by index
         int index = checkVersionAndFindIndex(filename, key);
 
-        if(index == -1)
-            AF_ERROR("Key not found", AF_ERR_INVALID_ARRAY);
+        if (index == -1) AF_ERROR("Key not found", AF_ERR_INVALID_ARRAY);
 
         af_array output = checkVersionAndRead(filename, index);
         std::swap(*out, output);
@@ -364,8 +359,8 @@ af_err af_read_array_key(af_array *out, const char *filename, const char *key)
     return AF_SUCCESS;
 }
 
-af_err af_read_array_key_check(int *index, const char *filename, const char* key)
-{
+af_err af_read_array_key_check(int *index, const char *filename,
+                               const char *key) {
     try {
         ARG_ASSERT(1, filename != NULL);
         ARG_ASSERT(2, key != NULL);
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index ab3f77baf8..8f325acb8e 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -10,31 +10,29 @@
 #include <af/graphics.h>
 #include <af/image.h>
 
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
-#include <surface.hpp>
-#include <reduce.hpp>
+#include <common/graphics_common.hpp>
+#include <handle.hpp>
 #include <join.hpp>
-#include <tile.hpp>
+#include <reduce.hpp>
 #include <reorder.hpp>
-#include <handle.hpp>
+#include <surface.hpp>
+#include <tile.hpp>
 
 using af::dim4;
 using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg_chart setup_surface(fg_window window,
-                       const af_array xVals, const af_array yVals,
-                       const af_array zVals,
-                       const af_cell* const props)
-{
+fg_chart setup_surface(fg_window window, const af_array xVals,
+                       const af_array yVals, const af_array zVals,
+                       const af_cell* const props) {
     ForgeModule& _ = graphics::forgePlugin();
-    Array<T> xIn = getArray<T>(xVals);
-    Array<T> yIn = getArray<T>(yVals);
-    Array<T> zIn = getArray<T>(zVals);
+    Array<T> xIn   = getArray<T>(xVals);
+    Array<T> yIn   = getArray<T>(yVals);
+    Array<T> zIn   = getArray<T>(zVals);
 
     const ArrayInfo& Xinfo = getInfo(xVals);
     const ArrayInfo& Yinfo = getInfo(yVals);
@@ -44,7 +42,7 @@ fg_chart setup_surface(fg_window window,
     af::dim4 Y_dims = Yinfo.dims();
     af::dim4 Z_dims = Zinfo.dims();
 
-    if(Xinfo.isVector()){
+    if (Xinfo.isVector()) {
         // Convert xIn is a column vector
         xIn = modDims(xIn, xIn.elements());
         // Now tile along second dimension
@@ -52,7 +50,7 @@ fg_chart setup_surface(fg_window window,
         xIn = tile(xIn, x_tdims);
 
         // Convert yIn to a row vector
-        yIn= modDims(yIn, af::dim4(1, yIn.elements()));
+        yIn = modDims(yIn, af::dim4(1, yIn.elements()));
         // Now tile along first dimension
         dim4 y_tdims(X_dims[0], 1, 1, 1);
         yIn = tile(yIn, y_tdims);
@@ -60,36 +58,35 @@ fg_chart setup_surface(fg_window window,
 
     // Flatten xIn, yIn and zIn into row vectors
     dim4 rowDims = dim4(1, zIn.elements());
-    xIn = modDims(xIn, rowDims);
-    yIn = modDims(yIn, rowDims);
-    zIn = modDims(zIn, rowDims);
+    xIn          = modDims(xIn, rowDims);
+    yIn          = modDims(yIn, rowDims);
+    zIn          = modDims(zIn, rowDims);
 
     // Now join along first dimension, skip reorder
-    std::vector<Array<T> > inputs{xIn, yIn, zIn};
+    std::vector<Array<T>> inputs{xIn, yIn, zIn};
     Array<T> Z = join(0, inputs);
 
     ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
-    if (props->col>-1 && props->row>-1)
+    if (props->col > -1 && props->row > -1)
         chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
     else
         chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
 
-    fg_surface surface = fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
+    fg_surface surface =
+        fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
 
     FG_CHECK(_.fg_set_surface_color(surface, 0.0, 1.0, 0.0, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
-    if(!fgMngr.getChartAxesOverride(chart)) {
+    if (!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
-        T     dmin[3], dmax[3];
-        FG_CHECK(_.fg_get_chart_axes_limits(&cmin[0], &cmax[0],
-                                          &cmin[1], &cmax[1],
-                                          &cmin[2], &cmax[2],
-                                          chart));
+        T dmin[3], dmax[3];
+        FG_CHECK(_.fg_get_chart_axes_limits(
+            &cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2], chart));
         dmin[0] = reduce_all<af_min_t, T, T>(xIn);
         dmax[0] = reduce_all<af_max_t, T, T>(xIn);
         dmin[1] = reduce_all<af_min_t, T, T>(yIn);
@@ -97,9 +94,8 @@ fg_chart setup_surface(fg_window window,
         dmin[2] = reduce_all<af_min_t, T, T>(zIn);
         dmax[2] = reduce_all<af_max_t, T, T>(zIn);
 
-        if(cmin[0] == 0 && cmax[0] == 0
-        && cmin[1] == 0 && cmax[1] == 0
-        && cmin[2] == 0 && cmax[2] == 0) {
+        if (cmin[0] == 0 && cmax[0] == 0 && cmin[1] == 0 && cmax[1] == 0 &&
+            cmin[2] == 0 && cmax[2] == 0) {
             // No previous limits. Set without checking
             cmin[0] = step_round(dmin[0], false);
             cmax[0] = step_round(dmax[0], true);
@@ -108,76 +104,83 @@ fg_chart setup_surface(fg_window window,
             cmin[2] = step_round(dmin[2], false);
             cmax[2] = step_round(dmax[2], true);
         } else {
-            if(cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
-            if(cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
-            if(cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
-            if(cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
-            if(cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
-            if(cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
+            if (cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
+            if (cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
+            if (cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
+            if (cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
+            if (cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
+            if (cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
         }
 
-        FG_CHECK(_.fg_set_chart_axes_limits(chart,
-                                          cmin[0], cmax[0],
-                                          cmin[1], cmax[1],
-                                          cmin[2], cmax[2]));
+        FG_CHECK(_.fg_set_chart_axes_limits(chart, cmin[0], cmax[0], cmin[1],
+                                            cmax[1], cmin[2], cmax[2]));
     }
     copy_surface<T>(Z, surface);
 
     return chart;
 }
 
-af_err af_draw_surface(const af_window window,
-                       const af_array xVals, const af_array yVals,
-                       const af_array S, const af_cell* const props)
-{
+af_err af_draw_surface(const af_window window, const af_array xVals,
+                       const af_array yVals, const af_array S,
+                       const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& Xinfo = getInfo(xVals);
-        af::dim4 X_dims = Xinfo.dims();
-        af_dtype Xtype  = Xinfo.getType();
+        af::dim4 X_dims        = Xinfo.dims();
+        af_dtype Xtype         = Xinfo.getType();
 
         const ArrayInfo& Yinfo = getInfo(yVals);
-        af::dim4 Y_dims = Yinfo.dims();
-        af_dtype Ytype  = Yinfo.getType();
+        af::dim4 Y_dims        = Yinfo.dims();
+        af_dtype Ytype         = Yinfo.getType();
 
         const ArrayInfo& Sinfo = getInfo(S);
-        af::dim4 S_dims = Sinfo.dims();
-        af_dtype Stype  = Sinfo.getType();
+        af::dim4 S_dims        = Sinfo.dims();
+        af_dtype Stype         = Sinfo.getType();
 
         TYPE_ASSERT(Xtype == Ytype);
         TYPE_ASSERT(Ytype == Stype);
 
-        if(!Yinfo.isVector()){
+        if (!Yinfo.isVector()) {
             DIM_ASSERT(1, X_dims == Y_dims);
             DIM_ASSERT(3, Y_dims == S_dims);
-        }else{
-            DIM_ASSERT(3, ( X_dims[0] * Y_dims[0] == (dim_t)Sinfo.elements()));
+        } else {
+            DIM_ASSERT(3, (X_dims[0] * Y_dims[0] == (dim_t)Sinfo.elements()));
         }
 
         makeContextCurrent(window);
 
         fg_chart chart = NULL;
 
-        switch(Xtype) {
-            case f32: chart = setup_surface<float  >(window, xVals, yVals , S, props); break;
-            case s32: chart = setup_surface<int    >(window, xVals, yVals , S, props); break;
-            case u32: chart = setup_surface<uint   >(window, xVals, yVals , S, props); break;
-            case s16: chart = setup_surface<short  >(window, xVals, yVals , S, props); break;
-            case u16: chart = setup_surface<ushort >(window, xVals, yVals , S, props); break;
-            case u8 : chart = setup_surface<uchar  >(window, xVals, yVals , S, props); break;
-            default:  TYPE_ERROR(1, Xtype);
+        switch (Xtype) {
+            case f32:
+                chart = setup_surface<float>(window, xVals, yVals, S, props);
+                break;
+            case s32:
+                chart = setup_surface<int>(window, xVals, yVals, S, props);
+                break;
+            case u32:
+                chart = setup_surface<uint>(window, xVals, yVals, S, props);
+                break;
+            case s16:
+                chart = setup_surface<short>(window, xVals, yVals, S, props);
+                break;
+            case u16:
+                chart = setup_surface<ushort>(window, xVals, yVals, S, props);
+                break;
+            case u8:
+                chart = setup_surface<uchar>(window, xVals, yVals, S, props);
+                break;
+            default: TYPE_ERROR(1, Xtype);
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
diff --git a/src/api/c/susan.cpp b/src/api/c/susan.cpp
index 8ccee8ed16..6d630f5eff 100644
--- a/src/api/c/susan.cpp
+++ b/src/api/c/susan.cpp
@@ -7,69 +7,94 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/features.h>
-#include <af/vision.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
 #include <features.hpp>
+#include <handle.hpp>
 #include <susan.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/features.h>
+#include <af/vision.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static af_features susan(af_array const &in,
-                         const unsigned radius, const float diff_thr, const float geom_thr,
-                         const float feature_ratio, const unsigned edge)
-{
-    Array<float> x = createEmptyArray<float>(dim4());
-    Array<float> y = createEmptyArray<float>(dim4());
+static af_features susan(af_array const& in, const unsigned radius,
+                         const float diff_thr, const float geom_thr,
+                         const float feature_ratio, const unsigned edge) {
+    Array<float> x     = createEmptyArray<float>(dim4());
+    Array<float> y     = createEmptyArray<float>(dim4());
     Array<float> score = createEmptyArray<float>(dim4());
 
     af_features_t feat;
-    feat.n = susan<T>(x, y, score,
-                      getArray<T>(in), radius, diff_thr, geom_thr,
+    feat.n = susan<T>(x, y, score, getArray<T>(in), radius, diff_thr, geom_thr,
                       feature_ratio, edge);
 
-    feat.x           = getHandle(x);
-    feat.y           = getHandle(y);
-    feat.score       = getHandle(score);
-    feat.orientation = getHandle(feat.n > 0 ? createValueArray<float>(feat.n, 0.0) : createEmptyArray<float>(dim4()));
-    feat.size        = getHandle(feat.n > 0 ? createValueArray<float>(feat.n, 1.0) : createEmptyArray<float>(dim4()));
+    feat.x     = getHandle(x);
+    feat.y     = getHandle(y);
+    feat.score = getHandle(score);
+    feat.orientation =
+        getHandle(feat.n > 0 ? createValueArray<float>(feat.n, 0.0)
+                             : createEmptyArray<float>(dim4()));
+    feat.size = getHandle(feat.n > 0 ? createValueArray<float>(feat.n, 1.0)
+                                     : createEmptyArray<float>(dim4()));
 
     return getFeaturesHandle(feat);
 }
 
-af_err af_susan(af_features* out, const af_array in,
-                const unsigned radius, const float diff_thr, const float geom_thr,
-                const float feature_ratio, const unsigned edge)
-{
+af_err af_susan(af_features* out, const af_array in, const unsigned radius,
+                const float diff_thr, const float geom_thr,
+                const float feature_ratio, const unsigned edge) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af::dim4 dims  = info.dims();
+        af::dim4 dims         = info.dims();
 
-        ARG_ASSERT(1, dims.ndims()==2);
+        ARG_ASSERT(1, dims.ndims() == 2);
         ARG_ASSERT(2, radius < 10);
-        ARG_ASSERT(2, radius<=edge);
+        ARG_ASSERT(2, radius <= edge);
         ARG_ASSERT(3, diff_thr > 0.0f);
         ARG_ASSERT(4, geom_thr > 0.0f);
         ARG_ASSERT(5, (feature_ratio > 0.0f && feature_ratio <= 1.0f));
-        ARG_ASSERT(6, (dims[0] >= (dim_t)(2*edge+1) || dims[1] >= (dim_t)(2*edge+1)));
+        ARG_ASSERT(6, (dims[0] >= (dim_t)(2 * edge + 1) ||
+                       dims[1] >= (dim_t)(2 * edge + 1)));
 
-        af_dtype type  = info.getType();
-        switch(type) {
-            case f32: *out = susan<float >(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case f64: *out = susan<double>(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case b8 : *out = susan<char  >(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case s32: *out = susan<int   >(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case u32: *out = susan<uint  >(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case s16: *out = susan<short >(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case u16: *out = susan<ushort>(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            case u8 : *out = susan<uchar >(in, radius, diff_thr, geom_thr, feature_ratio, edge); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = info.getType();
+        switch (type) {
+            case f32:
+                *out = susan<float>(in, radius, diff_thr, geom_thr,
+                                    feature_ratio, edge);
+                break;
+            case f64:
+                *out = susan<double>(in, radius, diff_thr, geom_thr,
+                                     feature_ratio, edge);
+                break;
+            case b8:
+                *out = susan<char>(in, radius, diff_thr, geom_thr,
+                                   feature_ratio, edge);
+                break;
+            case s32:
+                *out = susan<int>(in, radius, diff_thr, geom_thr, feature_ratio,
+                                  edge);
+                break;
+            case u32:
+                *out = susan<uint>(in, radius, diff_thr, geom_thr,
+                                   feature_ratio, edge);
+                break;
+            case s16:
+                *out = susan<short>(in, radius, diff_thr, geom_thr,
+                                    feature_ratio, edge);
+                break;
+            case u16:
+                *out = susan<ushort>(in, radius, diff_thr, geom_thr,
+                                     feature_ratio, edge);
+                break;
+            case u8:
+                *out = susan<uchar>(in, radius, diff_thr, geom_thr,
+                                    feature_ratio, edge);
+                break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index d12b4a9144..cb208192fb 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -7,73 +7,72 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <af/array.h>
+#include <af/dim4.hpp>
 #include <af/lapack.h>
 
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <backend.hpp>
 #include <Array.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
 #include <svd.hpp>
+#include <af/defines.h>
 
 using namespace detail;
 
-template <typename T>
-static inline void svd(af_array *s, af_array *u, af_array *vt, const af_array in)
-{
-    const ArrayInfo& info = getInfo(in);  // ArrayInfo is the base class which
-    af::dim4 dims = info.dims();
-    int M = dims[0];
-    int N = dims[1];
+template<typename T>
+static inline void svd(af_array *s, af_array *u, af_array *vt,
+                       const af_array in) {
+    const ArrayInfo &info = getInfo(in);  // ArrayInfo is the base class which
+    af::dim4 dims         = info.dims();
+    int M                 = dims[0];
+    int N                 = dims[1];
 
     typedef typename af::dtype_traits<T>::base_type Tr;
 
-    //Allocate output arrays
-    Array<Tr> sA  = createEmptyArray<Tr>(af::dim4(min(M, N)));
-    Array<T > uA  = createEmptyArray<T >(af::dim4(M, M));
-    Array<T > vtA = createEmptyArray<T >(af::dim4(N, N));
+    // Allocate output arrays
+    Array<Tr> sA = createEmptyArray<Tr>(af::dim4(min(M, N)));
+    Array<T> uA  = createEmptyArray<T>(af::dim4(M, M));
+    Array<T> vtA = createEmptyArray<T>(af::dim4(N, N));
 
     svd<T, Tr>(sA, uA, vtA, getArray<T>(in));
 
-    *s = getHandle(sA);
-    *u = getHandle(uA);
+    *s  = getHandle(sA);
+    *u  = getHandle(uA);
     *vt = getHandle(vtA);
 }
 
-template <typename T>
-static inline void svdInPlace(af_array *s, af_array *u, af_array *vt, af_array in)
-{
-    const ArrayInfo& info = getInfo(in);  // ArrayInfo is the base class which
-    af::dim4 dims = info.dims();
-    int M = dims[0];
-    int N = dims[1];
+template<typename T>
+static inline void svdInPlace(af_array *s, af_array *u, af_array *vt,
+                              af_array in) {
+    const ArrayInfo &info = getInfo(in);  // ArrayInfo is the base class which
+    af::dim4 dims         = info.dims();
+    int M                 = dims[0];
+    int N                 = dims[1];
 
     typedef typename af::dtype_traits<T>::base_type Tr;
 
-    //Allocate output arrays
-    Array<Tr> sA  = createEmptyArray<Tr>(af::dim4(min(M, N)));
-    Array<T > uA  = createEmptyArray<T >(af::dim4(M, M));
-    Array<T > vtA = createEmptyArray<T >(af::dim4(N, N));
+    // Allocate output arrays
+    Array<Tr> sA = createEmptyArray<Tr>(af::dim4(min(M, N)));
+    Array<T> uA  = createEmptyArray<T>(af::dim4(M, M));
+    Array<T> vtA = createEmptyArray<T>(af::dim4(N, N));
 
     svdInPlace<T, Tr>(sA, uA, vtA, getArray<T>(in));
 
-    *s = getHandle(sA);
-    *u = getHandle(uA);
+    *s  = getHandle(sA);
+    *u  = getHandle(uA);
     *vt = getHandle(vtA);
 }
 
-af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
-{
+af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
         af_dtype type = info.getType();
 
-        if(dims.ndims() == 0) {
+        if (dims.ndims() == 0) {
             AF_CHECK(af_create_handle(u, 0, nullptr, type));
             AF_CHECK(af_create_handle(s, 0, nullptr, type));
             AF_CHECK(af_create_handle(vt, 0, nullptr, type));
@@ -81,36 +80,26 @@ af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
         }
 
         switch (type) {
-        case f64:
-            svd<double>(s, u, vt, in);
-            break;
-        case f32:
-            svd<float>(s, u, vt, in);
-            break;
-        case c64:
-            svd<cdouble>(s, u, vt, in);
-            break;
-        case c32:
-            svd<cfloat>(s, u, vt, in);
-            break;
-        default:
-            TYPE_ERROR(1, type);
+            case f64: svd<double>(s, u, vt, in); break;
+            case f32: svd<float>(s, u, vt, in); break;
+            case c64: svd<cdouble>(s, u, vt, in); break;
+            case c32: svd<cfloat>(s, u, vt, in); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
-{
+af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af::dim4 dims = info.dims();
+        const ArrayInfo &info = getInfo(in);
+        af::dim4 dims         = info.dims();
 
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
         af_dtype type = info.getType();
 
-        if(dims.ndims() == 0) {
+        if (dims.ndims() == 0) {
             AF_CHECK(af_create_handle(u, 0, nullptr, type));
             AF_CHECK(af_create_handle(s, 0, nullptr, type));
             AF_CHECK(af_create_handle(vt, 0, nullptr, type));
@@ -120,21 +109,11 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
         DIM_ASSERT(3, dims[0] >= dims[1]);
 
         switch (type) {
-        case f64:
-            svdInPlace<double>(s, u, vt, in);
-            break;
-        case f32:
-            svdInPlace<float>(s, u, vt, in);
-            break;
-        case c64:
-            svdInPlace<cdouble>(s, u, vt, in);
-            break;
-        case c32:
-            svdInPlace<cfloat>(s, u, vt, in);
-
-            break;
-        default:
-            TYPE_ERROR(1, type);
+            case f64: svdInPlace<double>(s, u, vt, in); break;
+            case f32: svdInPlace<float>(s, u, vt, in); break;
+            case c64: svdInPlace<cdouble>(s, u, vt, in); break;
+            case c32: svdInPlace<cfloat>(s, u, vt, in); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 55d9e37b44..411db64d79 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -7,40 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/data.h>
-#include <af/arith.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <tile.hpp>
-#include <arith.hpp>
+#include <af/arith.h>
+#include <af/data.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array tile(const af_array in, const af::dim4 &tileDims)
-{
+static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
     const Array<T> inArray = getArray<T>(in);
-    const dim4 inDims = inArray.dims();
-
+    const dim4 inDims      = inArray.dims();
 
     // FIXME: Always use JIT instead of checking for the condition.
-    // The current limitation exists for performance reasons. it should change in the future.
+    // The current limitation exists for performance reasons. it should change
+    // in the future.
 
     bool take_jit_path = true;
     dim4 outDims(1, 1, 1, 1);
 
-    // Check if JIT path can be taken. JIT path can only be taken if tiling a singleton dimension.
+    // Check if JIT path can be taken. JIT path can only be taken if tiling a
+    // singleton dimension.
     for (int i = 0; i < 4; i++) {
         take_jit_path &= (inDims[i] == 1 || tileDims[i] == 1);
         outDims[i] = inDims[i] * tileDims[i];
     }
 
     if (take_jit_path) {
-        // FIXME: This Should ideally call a NOP function, but adding 0 should be OK
-        // This does not allocate any memory, just a JIT node
+        // FIXME: This Should ideally call a NOP function, but adding 0 should
+        // be OK This does not allocate any memory, just a JIT node
         Array<T> tmpArray = createValueArray<T>(outDims, scalar<T>(0));
         return getHandle(arithOp<T, af_add_t>(inArray, tmpArray, outDims));
     } else {
@@ -48,46 +48,41 @@ static inline af_array tile(const af_array in, const af::dim4 &tileDims)
     }
 }
 
-af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims)
-{
+af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
 
-        if(info.ndims() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
         DIM_ASSERT(1, info.dims().elements() > 0);
         DIM_ASSERT(2, tileDims.elements() > 0);
 
         af_array output;
 
-        switch(type) {
-            case f32: output = tile<float  >(in, tileDims);  break;
-            case c32: output = tile<cfloat >(in, tileDims);  break;
-            case f64: output = tile<double >(in, tileDims);  break;
-            case c64: output = tile<cdouble>(in, tileDims);  break;
-            case b8:  output = tile<char   >(in, tileDims);  break;
-            case s32: output = tile<int    >(in, tileDims);  break;
-            case u32: output = tile<uint   >(in, tileDims);  break;
-            case s64: output = tile<intl   >(in, tileDims);  break;
-            case u64: output = tile<uintl  >(in, tileDims);  break;
-            case s16: output = tile<short  >(in, tileDims);  break;
-            case u16: output = tile<ushort >(in, tileDims);  break;
-            case u8:  output = tile<uchar  >(in, tileDims);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = tile<float>(in, tileDims); break;
+            case c32: output = tile<cfloat>(in, tileDims); break;
+            case f64: output = tile<double>(in, tileDims); break;
+            case c64: output = tile<cdouble>(in, tileDims); break;
+            case b8: output = tile<char>(in, tileDims); break;
+            case s32: output = tile<int>(in, tileDims); break;
+            case u32: output = tile<uint>(in, tileDims); break;
+            case s64: output = tile<intl>(in, tileDims); break;
+            case u64: output = tile<uintl>(in, tileDims); break;
+            case s16: output = tile<short>(in, tileDims); break;
+            case u16: output = tile<ushort>(in, tileDims); break;
+            case u8: output = tile<uchar>(in, tileDims); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_tile(af_array *out, const af_array in,
-               const unsigned x, const unsigned y,
-               const unsigned z, const unsigned w)
-{
+af_err af_tile(af_array *out, const af_array in, const unsigned x,
+               const unsigned y, const unsigned z, const unsigned w) {
     af::dim4 tileDims(x, y, z, w);
     return af_tile(out, in, tileDims);
 }
diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index 4aa85d9af1..f72652a471 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -7,23 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/statistics.h>
 #include <af/data.h>
+#include <af/statistics.h>
 
-#include <handle.hpp>
-#include <topk.hpp>
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
+#include <handle.hpp>
+#include <topk.hpp>
 
 using namespace detail;
 
 namespace {
 
 template<typename T>
-af_err topk(af_array *v, af_array* i, const af_array in,
-                    const int k, const int dim, const af_topk_function order)
-{
+af_err topk(af_array *v, af_array *i, const af_array in, const int k,
+            const int dim, const af_topk_function order) {
     auto vals = createEmptyArray<T>(af::dim4());
     auto idxs = createEmptyArray<unsigned>(af::dim4());
 
@@ -33,51 +32,52 @@ af_err topk(af_array *v, af_array* i, const af_array in,
     *i = getHandle<unsigned>(idxs);
     return AF_SUCCESS;
 }
-} //  namespace
+}  //  namespace
 
 af_err af_topk(af_array *values, af_array *indices, const af_array in,
-                     const int k, const int dim, const af_topk_function order)
-{
+               const int k, const int dim, const af_topk_function order) {
     try {
         af::topkFunction ord = (order == AF_TOPK_DEFAULT ? AF_TOPK_MAX : order);
 
         ArrayInfo inInfo = getInfo(in);
 
-        ARG_ASSERT(2, (inInfo.ndims()>0));
+        ARG_ASSERT(2, (inInfo.ndims() > 0));
 
         if (inInfo.elements() == 1) {
-            dim_t dims[1] = {1};
+            dim_t dims[1]   = {1};
             af_err errValue = af_constant(indices, 0, 1, dims, u32);
-            return errValue==AF_SUCCESS ? af_retain_array(values, in) : errValue;
+            return errValue == AF_SUCCESS ? af_retain_array(values, in)
+                                          : errValue;
         }
 
-        int rdim = dim;
+        int rdim     = dim;
         auto &inDims = inInfo.dims();
 
-        if (rdim==-1) {
+        if (rdim == -1) {
             for (dim_t d = 0; d < 4; d++) {
                 if (inDims[d] > 1) {
-                    rdim =  d;
+                    rdim = d;
                     break;
                 }
             }
         }
 
         ARG_ASSERT(2, (inInfo.dims()[rdim] >= k));
-        ARG_ASSERT(4, (k <= 256)); // TODO(umar): Remove this limitation
+        ARG_ASSERT(4, (k <= 256));  // TODO(umar): Remove this limitation
 
-        if (rdim!=0)
-            AF_ERROR("topk is supported along dimenion 0 only.", AF_ERR_NOT_SUPPORTED);
+        if (rdim != 0)
+            AF_ERROR("topk is supported along dimenion 0 only.",
+                     AF_ERR_NOT_SUPPORTED);
 
-        af_dtype type  = inInfo.getType();
+        af_dtype type = inInfo.getType();
 
-        switch(type) {
+        switch (type) {
             // TODO(umar): FIX RETURN VALUES HERE
-            case f32: topk<float >(values, indices, in, k, rdim, ord); break;
+            case f32: topk<float>(values, indices, in, k, rdim, ord); break;
             case f64: topk<double>(values, indices, in, k, rdim, ord); break;
-            case u32: topk<uint  >(values, indices, in, k, rdim, ord); break;
-            case s32: topk<int   >(values, indices, in, k, rdim, ord); break;
-            default : TYPE_ERROR(1, type);
+            case u32: topk<uint>(values, indices, in, k, rdim, ord); break;
+            case s32: topk<int>(values, indices, in, k, rdim, ord); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 35cbf9fe77..6c161d8877 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -7,26 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <af/defines.h>
+#include <backend.hpp>
+#include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <common/ArrayInfo.hpp>
-#include <backend.hpp>
 #include <transform.hpp>
+#include <af/defines.h>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array transform(const af_array in, const af_array tf, const af::dim4 &odims,
-                                 const af_interp_type method, const bool inverse, const bool perspective)
-{
-    return getHandle(transform<T>(getArray<T>(in), getArray<float>(tf), odims, method, inverse, perspective));
+static inline af_array transform(const af_array in, const af_array tf,
+                                 const af::dim4 &odims,
+                                 const af_interp_type method,
+                                 const bool inverse, const bool perspective) {
+    return getHandle(transform<T>(getArray<T>(in), getArray<float>(tf), odims,
+                                  method, inverse, perspective));
 }
 
-AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims)
-{
+AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims) {
     static const int baseDim = 2;
 
     dim_t iNd = iDims.ndims();
@@ -39,26 +40,25 @@ AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims)
     else if (iNd <= 4 && tNd == baseDim)
         return AF_BATCH_LHS;
     else if (iNd <= 4 && tNd <= 4) {
-        bool dimsMatch = true;
+        bool dimsMatch     = true;
         bool isInterleaved = true;
         for (dim_t i = baseDim; i < 4; i++) {
             dimsMatch &= (iDims[i] == tDims[i]);
-            isInterleaved &= (iDims[i] == 1 || tDims[i] == 1 || iDims[i] == tDims[i]);
+            isInterleaved &=
+                (iDims[i] == 1 || tDims[i] == 1 || iDims[i] == tDims[i]);
         }
         if (dimsMatch) return AF_BATCH_SAME;
         return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
-    }
-    else
+    } else
         return AF_BATCH_UNSUPPORTED;
 }
 
 af_err af_transform(af_array *out, const af_array in, const af_array tf,
                     const dim_t odim0, const dim_t odim1,
-                    const af_interp_type method, const bool inverse)
-{
+                    const af_interp_type method, const bool inverse) {
     try {
-        const ArrayInfo& t_info = getInfo(tf);
-        const ArrayInfo& i_info = getInfo(in);
+        const ArrayInfo &t_info = getInfo(tf);
+        const ArrayInfo &i_info = getInfo(in);
 
         af::dim4 idims = i_info.dims();
         af::dim4 tdims = t_info.dims();
@@ -66,37 +66,38 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
 
         // Assert type and interpolation
         ARG_ASSERT(2, t_info.getType() == f32);
-        ARG_ASSERT(5, method == AF_INTERP_NEAREST  ||
-                      method == AF_INTERP_BILINEAR ||
-                      method == AF_INTERP_BILINEAR_COSINE ||
-                      method == AF_INTERP_BICUBIC ||
-                      method == AF_INTERP_BICUBIC_SPLINE ||
-                      method == AF_INTERP_LOWER);
+        ARG_ASSERT(5, method == AF_INTERP_NEAREST ||
+                          method == AF_INTERP_BILINEAR ||
+                          method == AF_INTERP_BILINEAR_COSINE ||
+                          method == AF_INTERP_BICUBIC ||
+                          method == AF_INTERP_BICUBIC_SPLINE ||
+                          method == AF_INTERP_LOWER);
 
         // Assert dimesions
         // Image can be 2D or higher
         DIM_ASSERT(1, idims.elements() > 0);
         DIM_ASSERT(1, idims.ndims() >= 2);
 
-        // Transform can be 3x2 for affine transform or 3x3 for perspective transform
+        // Transform can be 3x2 for affine transform or 3x3 for perspective
+        // transform
         DIM_ASSERT(2, (tdims[0] == 3 && (tdims[1] == 2 || tdims[1] == 3)));
 
         // If transform is batched, the output dimensions must be specified
-        if(tdims[2] * tdims[3] > 1) {
+        if (tdims[2] * tdims[3] > 1) {
             ARG_ASSERT(3, odim0 > 0);
             ARG_ASSERT(4, odim1 > 0);
         }
 
         // If idims[2] > 1 and tdims[2] > 1, then both must be equal
         // else at least one of them must be 1
-        if(tdims[2] != 1 && idims[2] != 1)
+        if (tdims[2] != 1 && idims[2] != 1)
             DIM_ASSERT(2, idims[2] == tdims[2]);
         else
             DIM_ASSERT(2, idims[2] == 1 || tdims[2] == 1);
 
         // If idims[3] > 1 and tdims[3] > 1, then both must be equal
         // else at least one of them must be 1
-        if(tdims[3] != 1 && idims[3] != 1)
+        if (tdims[3] != 1 && idims[3] != 1)
             DIM_ASSERT(2, idims[3] == tdims[3]);
         else
             DIM_ASSERT(2, idims[3] == 1 || tdims[3] == 1);
@@ -108,67 +109,104 @@ af_err af_transform(af_array *out, const af_array in, const af_array tf,
             o1 = idims[1];
         }
 
-        switch(getTransformBatchKind(idims, tdims)) {
-            case AF_BATCH_NONE:     // Both are exactly 2D
+        switch (getTransformBatchKind(idims, tdims)) {
+            case AF_BATCH_NONE:  // Both are exactly 2D
             case AF_BATCH_LHS:   // Image is 3/4D, transform is 2D
-            case AF_BATCH_SAME:     // Both are 3/4D and have the same dims
+            case AF_BATCH_SAME:  // Both are 3/4D and have the same dims
                 o2 = idims[2];
                 o3 = idims[3];
                 break;
-            case AF_BATCH_RHS:   // Image is 2D, transform is 3/4D
+            case AF_BATCH_RHS:  // Image is 2D, transform is 3/4D
                 o2 = tdims[2];
                 o3 = tdims[3];
                 break;
-            case AF_BATCH_DIFF:     // Both are 3/4D, but have different dims
+            case AF_BATCH_DIFF:  // Both are 3/4D, but have different dims
                 o2 = idims[2] == 1 ? tdims[2] : idims[2];
                 o3 = idims[3] == 1 ? tdims[3] : idims[3];
                 break;
             case AF_BATCH_UNSUPPORTED:
             default:
-                AF_ERROR("Unsupported combination of batching parameters in transform",
-                          AF_ERR_NOT_SUPPORTED);
+                AF_ERROR(
+                    "Unsupported combination of batching parameters in "
+                    "transform",
+                    AF_ERR_NOT_SUPPORTED);
                 break;
         }
 
         af::dim4 odims(o0, o1, o2, o3);
 
         af_array output = 0;
-        switch(itype) {
-            case f32: output = transform<float  >(in, tf, odims, method, inverse, perspective);  break;
-            case f64: output = transform<double >(in, tf, odims, method, inverse, perspective);  break;
-            case c32: output = transform<cfloat >(in, tf, odims, method, inverse, perspective);  break;
-            case c64: output = transform<cdouble>(in, tf, odims, method, inverse, perspective);  break;
-            case s32: output = transform<int    >(in, tf, odims, method, inverse, perspective);  break;
-            case u32: output = transform<uint   >(in, tf, odims, method, inverse, perspective);  break;
-            case s64: output = transform<intl   >(in, tf, odims, method, inverse, perspective);  break;
-            case u64: output = transform<uintl  >(in, tf, odims, method, inverse, perspective);  break;
-            case s16: output = transform<short  >(in, tf, odims, method, inverse, perspective);  break;
-            case u16: output = transform<ushort >(in, tf, odims, method, inverse, perspective);  break;
-            case u8:  output = transform<uchar  >(in, tf, odims, method, inverse, perspective);  break;
-            case b8:  output = transform<char   >(in, tf, odims, method, inverse, perspective);  break;
-            default:  TYPE_ERROR(1, itype);
+        switch (itype) {
+            case f32:
+                output = transform<float>(in, tf, odims, method, inverse,
+                                          perspective);
+                break;
+            case f64:
+                output = transform<double>(in, tf, odims, method, inverse,
+                                           perspective);
+                break;
+            case c32:
+                output = transform<cfloat>(in, tf, odims, method, inverse,
+                                           perspective);
+                break;
+            case c64:
+                output = transform<cdouble>(in, tf, odims, method, inverse,
+                                            perspective);
+                break;
+            case s32:
+                output =
+                    transform<int>(in, tf, odims, method, inverse, perspective);
+                break;
+            case u32:
+                output = transform<uint>(in, tf, odims, method, inverse,
+                                         perspective);
+                break;
+            case s64:
+                output = transform<intl>(in, tf, odims, method, inverse,
+                                         perspective);
+                break;
+            case u64:
+                output = transform<uintl>(in, tf, odims, method, inverse,
+                                          perspective);
+                break;
+            case s16:
+                output = transform<short>(in, tf, odims, method, inverse,
+                                          perspective);
+                break;
+            case u16:
+                output = transform<ushort>(in, tf, odims, method, inverse,
+                                           perspective);
+                break;
+            case u8:
+                output = transform<uchar>(in, tf, odims, method, inverse,
+                                          perspective);
+                break;
+            case b8:
+                output = transform<char>(in, tf, odims, method, inverse,
+                                         perspective);
+                break;
+            default: TYPE_ERROR(1, itype);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_translate(af_array *out, const af_array in, const float trans0, const float trans1,
-                    const dim_t odim0, const dim_t odim1, const af_interp_type method)
-{
-
+af_err af_translate(af_array *out, const af_array in, const float trans0,
+                    const float trans1, const dim_t odim0, const dim_t odim1,
+                    const af_interp_type method) {
     try {
-        float trans_mat[6] = {1, 0, 0,
-                              0, 1, 0};
-        trans_mat[2] = trans0;
-        trans_mat[5] = trans1;
+        float trans_mat[6] = {1, 0, 0, 0, 1, 0};
+        trans_mat[2]       = trans0;
+        trans_mat[5]       = trans1;
 
         const af::dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
 
-        AF_CHECK(af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
+        AF_CHECK(
+            af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
         AF_CHECK(af_transform(out, in, t, odim0, odim1, method, true));
         AF_CHECK(af_release_array(t));
     }
@@ -177,18 +215,17 @@ af_err af_translate(af_array *out, const af_array in, const float trans0, const
     return AF_SUCCESS;
 }
 
-af_err af_scale(af_array *out, const af_array in, const float scale0, const float scale1,
-                const dim_t odim0, const dim_t odim1, const af_interp_type method)
-{
+af_err af_scale(af_array *out, const af_array in, const float scale0,
+                const float scale1, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method) {
     try {
-        const ArrayInfo& i_info = getInfo(in);
-        af::dim4 idims = i_info.dims();
+        const ArrayInfo &i_info = getInfo(in);
+        af::dim4 idims          = i_info.dims();
 
         dim_t _odim0 = odim0, _odim1 = odim1;
         float sx, sy;
 
-        if(_odim0 == 0 || _odim1 == 0) {
-
+        if (_odim0 == 0 || _odim1 == 0) {
             DIM_ASSERT(2, scale0 != 0);
             DIM_ASSERT(3, scale1 != 0);
 
@@ -197,7 +234,6 @@ af_err af_scale(af_array *out, const af_array in, const float scale0, const floa
             _odim1 = idims[1] / sy;
 
         } else if (scale0 == 0 || scale1 == 0) {
-
             DIM_ASSERT(4, odim0 != 0);
             DIM_ASSERT(5, odim1 != 0);
 
@@ -205,18 +241,17 @@ af_err af_scale(af_array *out, const af_array in, const float scale0, const floa
             sy = idims[1] / (float)_odim1;
 
         } else {
-
             sx = 1.f / scale0, sy = 1.f / scale1;
         }
 
-        float trans_mat[6] = {1, 0, 0,
-                              0, 1, 0};
-        trans_mat[0] = sx;
-        trans_mat[4] = sy;
+        float trans_mat[6] = {1, 0, 0, 0, 1, 0};
+        trans_mat[0]       = sx;
+        trans_mat[4]       = sy;
 
         const af::dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
-        AF_CHECK(af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
+        AF_CHECK(
+            af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
         AF_CHECK(af_transform(out, in, t, _odim0, _odim1, method, true));
         AF_CHECK(af_release_array(t));
     }
@@ -224,27 +259,25 @@ af_err af_scale(af_array *out, const af_array in, const float scale0, const floa
     return AF_SUCCESS;
 }
 
-af_err af_skew(af_array *out, const af_array in, const float skew0, const float skew1,
-               const dim_t odim0, const dim_t odim1, const af_interp_type method,
-               const bool inverse)
-{
+af_err af_skew(af_array *out, const af_array in, const float skew0,
+               const float skew1, const dim_t odim0, const dim_t odim1,
+               const af_interp_type method, const bool inverse) {
     try {
         float tx = std::tan(skew0);
         float ty = std::tan(skew1);
 
-        float trans_mat[6] = {1, 0, 0,
-                              0, 1, 0};
-        trans_mat[1] = ty;
-        trans_mat[3] = tx;
+        float trans_mat[6] = {1, 0, 0, 0, 1, 0};
+        trans_mat[1]       = ty;
+        trans_mat[3]       = tx;
 
-        if(inverse) {
-            if(tx == 0 || ty == 0) {
+        if (inverse) {
+            if (tx == 0 || ty == 0) {
                 trans_mat[1] = tx;
                 trans_mat[3] = ty;
             } else {
-                //calc_tranform_inverse(trans_mat);
-                //short cut of calc_transform_inverse
-                float d = 1.0f / (1.0f - tx * ty);
+                // calc_tranform_inverse(trans_mat);
+                // short cut of calc_transform_inverse
+                float d      = 1.0f / (1.0f - tx * ty);
                 trans_mat[0] = d;
                 trans_mat[1] = ty * d;
                 trans_mat[3] = tx * d;
@@ -253,7 +286,8 @@ af_err af_skew(af_array *out, const af_array in, const float skew0, const float
         }
         const af::dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
-        AF_CHECK(af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
+        AF_CHECK(
+            af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
         AF_CHECK(af_transform(out, in, t, odim0, odim1, method, true));
         AF_CHECK(af_release_array(t));
     }
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index 7b090a2f63..8ef7ded16d 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -7,74 +7,80 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/image.h>
-#include <common/err_common.hpp>
-#include <backend.hpp>
-#include <handle.hpp>
-#include <convolve.hpp>
 #include <arith.hpp>
+#include <backend.hpp>
 #include <blas.hpp>
+#include <common/err_common.hpp>
+#include <convolve.hpp>
+#include <handle.hpp>
 #include <join.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 #include <vector>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs, std::vector<af_seq> idx)
-{
+Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs,
+                         std::vector<af_seq> idx) {
     return matmul(lhs, createSubArray(rhs, idx), AF_MAT_NONE, AF_MAT_NONE);
 }
 
 template<typename T>
-static af_array transform_coordinates(const af_array& tf_, const float d0_, const float d1_)
-{
+static af_array transform_coordinates(const af_array &tf_, const float d0_,
+                                      const float d1_) {
     af::dim4 h_dims(4, 3);
-    T h_in[4*3] = { (T)0, (T)0,  (T)d1_, (T)d1_,
-                    (T)0, (T)d0_, (T)d0_, (T)0,
-                    (T)1, (T)1,  (T)1,  (T)1 };
+    T h_in[4 * 3] = {(T)0,   (T)0, (T)d1_, (T)d1_, (T)0, (T)d0_,
+                     (T)d0_, (T)0, (T)1,   (T)1,   (T)1, (T)1};
 
     const Array<T> tf = getArray<T>(tf_);
-    Array<T> in = createHostDataArray<T>(h_dims, h_in);
+    Array<T> in       = createHostDataArray<T>(h_dims, h_in);
 
     std::vector<af_seq> idx(2);
     idx[0] = af_make_seq(0, 2, 1);
 
     // w = 1.0 / matmul(tf, in(span, 2));
     // iw = matmul(tf, in(span, 2));
-    idx[1] = af_make_seq(2, 2, 1);
+    idx[1]      = af_make_seq(2, 2, 1);
     Array<T> iw = multiplyIndexed(in, tf, idx);
 
     // xt = w * matmul(tf, in(span, 0));
     // xt = matmul(tf, in(span, 0)) / iw;
     idx[1] = af_make_seq(0, 0, 1);
-    Array<T> xt = arithOp<T, af_div_t>(multiplyIndexed(in, tf, idx), iw, iw.dims());
+    Array<T> xt =
+        arithOp<T, af_div_t>(multiplyIndexed(in, tf, idx), iw, iw.dims());
 
     // yt = w * matmul(tf, in(span, 1));
     // yt = matmul(tf, in(span, 1)) / iw;
     idx[1] = af_make_seq(1, 1, 1);
-    Array<T> yw = arithOp<T, af_div_t>(multiplyIndexed(in, tf, idx), iw, iw.dims());
+    Array<T> yw =
+        arithOp<T, af_div_t>(multiplyIndexed(in, tf, idx), iw, iw.dims());
 
     // return join(1, xt, yt)
     Array<T> r = join(1, xt, yw);
     return getHandle(r);
 }
 
-af_err af_transform_coordinates(af_array *out, const af_array tf, const float d0_, const float d1_)
-{
+af_err af_transform_coordinates(af_array *out, const af_array tf,
+                                const float d0_, const float d1_) {
     try {
-        const ArrayInfo& tfInfo = getInfo(tf);
-        dim4 tfDims = tfInfo.dims();
-        ARG_ASSERT(1, (tfDims[0]==3 && tfDims[1]==3 && tfDims.ndims()==2));
+        const ArrayInfo &tfInfo = getInfo(tf);
+        dim4 tfDims             = tfInfo.dims();
+        ARG_ASSERT(1,
+                   (tfDims[0] == 3 && tfDims[1] == 3 && tfDims.ndims() == 2));
 
         af_array output;
-        af_dtype type  = tfInfo.getType();
-        switch(type) {
-            case f32: output = transform_coordinates<float >(tf, d0_, d1_); break;
-            case f64: output = transform_coordinates<double>(tf, d0_, d1_); break;
-            default : TYPE_ERROR(1, type);
+        af_dtype type = tfInfo.getType();
+        switch (type) {
+            case f32:
+                output = transform_coordinates<float>(tf, d0_, d1_);
+                break;
+            case f64:
+                output = transform_coordinates<double>(tf, d0_, d1_);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index d77f5257f1..52875f79e4 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -7,39 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
-#include <af/blas.h>
-#include <af/data.h>
-#include <af/arith.h>
+#include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
 #include <transpose.hpp>
+#include <af/arith.h>
+#include <af/blas.h>
+#include <af/data.h>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array trs(const af_array in, const bool conjugate)
-{
+static inline af_array trs(const af_array in, const bool conjugate) {
     return getHandle<T>(detail::transpose<T>(getArray<T>(in), conjugate));
 }
 
-af_err af_transpose(af_array *out, af_array in, const bool conjugate)
-{
+af_err af_transpose(af_array* out, af_array in, const bool conjugate) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        af::dim4 dims = info.dims();
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
 
-        if (dims.elements() == 0) {
-            return af_retain_array(out, in);
-        }
+        if (dims.elements() == 0) { return af_retain_array(out, in); }
 
-        if (dims[0]==1 || dims[1]==1) {
-            af::dim4 outDims(dims[1],dims[0],dims[2],dims[3]);
-            if(conjugate) {
+        if (dims[0] == 1 || dims[1] == 1) {
+            af::dim4 outDims(dims[1], dims[0], dims[2], dims[3]);
+            if (conjugate) {
                 af_array temp = 0;
                 AF_CHECK(af_conjg(&temp, in));
                 AF_CHECK(af_moddims(out, temp, outDims.ndims(), outDims.get()));
@@ -54,22 +50,22 @@ af_err af_transpose(af_array *out, af_array in, const bool conjugate)
         }
 
         af_array output;
-        switch(type) {
-            case f32: output = trs<float>  (in, conjugate);    break;
-            case c32: output = trs<cfloat> (in, conjugate);    break;
-            case f64: output = trs<double> (in, conjugate);    break;
-            case c64: output = trs<cdouble>(in, conjugate);    break;
-            case b8 : output = trs<char>   (in, conjugate);    break;
-            case s32: output = trs<int>    (in, conjugate);    break;
-            case u32: output = trs<uint>   (in, conjugate);    break;
-            case u8 : output = trs<uchar>  (in, conjugate);    break;
-            case s64: output = trs<intl>   (in, conjugate);    break;
-            case u64: output = trs<uintl>  (in, conjugate);    break;
-            case s16: output = trs<short>  (in, conjugate);    break;
-            case u16: output = trs<ushort> (in, conjugate);    break;
-            default : TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: output = trs<float>(in, conjugate); break;
+            case c32: output = trs<cfloat>(in, conjugate); break;
+            case f64: output = trs<double>(in, conjugate); break;
+            case c64: output = trs<cdouble>(in, conjugate); break;
+            case b8: output = trs<char>(in, conjugate); break;
+            case s32: output = trs<int>(in, conjugate); break;
+            case u32: output = trs<uint>(in, conjugate); break;
+            case u8: output = trs<uchar>(in, conjugate); break;
+            case s64: output = trs<intl>(in, conjugate); break;
+            case u64: output = trs<uintl>(in, conjugate); break;
+            case s16: output = trs<short>(in, conjugate); break;
+            case u16: output = trs<ushort>(in, conjugate); break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
@@ -77,39 +73,36 @@ af_err af_transpose(af_array *out, af_array in, const bool conjugate)
 }
 
 template<typename T>
-static inline void transpose_inplace(af_array in, const bool conjugate)
-{
+static inline void transpose_inplace(af_array in, const bool conjugate) {
     return detail::transpose_inplace<T>(getArray<T>(in), conjugate);
 }
 
-af_err af_transpose_inplace(af_array in, const bool conjugate)
-{
+af_err af_transpose_inplace(af_array in, const bool conjugate) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        af::dim4 dims = info.dims();
+        af_dtype type         = info.getType();
+        af::dim4 dims         = info.dims();
 
         // InPlace only works on square matrices
         DIM_ASSERT(0, dims[0] == dims[1]);
 
         // If singleton element
-        if(dims[0] == 1)
-            return AF_SUCCESS;
+        if (dims[0] == 1) return AF_SUCCESS;
 
-        switch(type) {
-            case f32: transpose_inplace<float>  (in, conjugate);    break;
-            case c32: transpose_inplace<cfloat> (in, conjugate);    break;
-            case f64: transpose_inplace<double> (in, conjugate);    break;
-            case c64: transpose_inplace<cdouble>(in, conjugate);    break;
-            case b8 : transpose_inplace<char>   (in, conjugate);    break;
-            case s32: transpose_inplace<int>    (in, conjugate);    break;
-            case u32: transpose_inplace<uint>   (in, conjugate);    break;
-            case u8 : transpose_inplace<uchar>  (in, conjugate);    break;
-            case s64: transpose_inplace<intl>   (in, conjugate);    break;
-            case u64: transpose_inplace<uintl>  (in, conjugate);    break;
-            case s16: transpose_inplace<short>  (in, conjugate);    break;
-            case u16: transpose_inplace<ushort> (in, conjugate);    break;
-            default : TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: transpose_inplace<float>(in, conjugate); break;
+            case c32: transpose_inplace<cfloat>(in, conjugate); break;
+            case f64: transpose_inplace<double>(in, conjugate); break;
+            case c64: transpose_inplace<cdouble>(in, conjugate); break;
+            case b8: transpose_inplace<char>(in, conjugate); break;
+            case s32: transpose_inplace<int>(in, conjugate); break;
+            case u32: transpose_inplace<uint>(in, conjugate); break;
+            case u8: transpose_inplace<uchar>(in, conjugate); break;
+            case s64: transpose_inplace<intl>(in, conjugate); break;
+            case u64: transpose_inplace<uintl>(in, conjugate); break;
+            case s16: transpose_inplace<short>(in, conjugate); break;
+            case u16: transpose_inplace<ushort>(in, conjugate); break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index f79cc72737..636a451cdb 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -9,34 +9,33 @@
 
 #include <type_util.hpp>
 
-#include <af/util.h>
 #include <common/err_common.hpp>
+#include <af/util.h>
 
-size_t size_of(af_dtype type)
-{
+size_t size_of(af_dtype type) {
     try {
-        switch(type) {
+        switch (type) {
             case f32: return sizeof(float);
             case f64: return sizeof(double);
             case s32: return sizeof(int);
             case u32: return sizeof(unsigned);
-            case u8 : return sizeof(unsigned char);
-            case b8 : return sizeof(unsigned char);
+            case u8: return sizeof(unsigned char);
+            case b8: return sizeof(unsigned char);
             case c32: return sizeof(float) * 2;
             case c64: return sizeof(double) * 2;
             case s16: return sizeof(short);
             case u16: return sizeof(unsigned short);
             case s64: return sizeof(long long);
             case u64: return sizeof(unsigned long long);
-            default : TYPE_ERROR(1, type);
+            default: TYPE_ERROR(1, type);
         }
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_get_size_of(size_t *size, af_dtype type)
-{
+af_err af_get_size_of(size_t *size, af_dtype type) {
     *size = size_of(type);
     return AF_SUCCESS;
 }
diff --git a/src/api/c/type_util.hpp b/src/api/c/type_util.hpp
index 881a9f8c44..1fa7dd7c87 100644
--- a/src/api/c/type_util.hpp
+++ b/src/api/c/type_util.hpp
@@ -12,22 +12,19 @@
 
 const char *getName(af_dtype type);
 
-//uchar to number converters
+// uchar to number converters
 template<typename T>
-struct ToNum
-{
+struct ToNum {
     inline T operator()(T val) { return val; }
 };
 
 template<>
-struct ToNum<unsigned char>
-{
+struct ToNum<unsigned char> {
     inline int operator()(unsigned char val) { return static_cast<int>(val); }
 };
 
 template<>
-struct ToNum<char>
-{
+struct ToNum<char> {
     inline int operator()(char val) { return static_cast<int>(val); }
 };
 
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 5028a9fd49..a2edd31f3f 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -13,28 +13,26 @@
 #endif
 #include <cmath>
 
-#include <af/array.h>
-#include <af/defines.h>
-#include <af/arith.h>
-#include <af/data.h>
+#include <arith.hpp>
+#include <backend.hpp>
+#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
-#include <optypes.hpp>
-#include <implicit.hpp>
 #include <common/err_common.hpp>
+#include <complex.hpp>
 #include <handle.hpp>
-#include <backend.hpp>
-#include <unary.hpp>
 #include <implicit.hpp>
-#include <complex.hpp>
 #include <logic.hpp>
-#include <cast.hpp>
-#include <arith.hpp>
+#include <optypes.hpp>
+#include <unary.hpp>
+#include <af/arith.h>
+#include <af/array.h>
+#include <af/data.h>
+#include <af/defines.h>
 
 using namespace detail;
 
 template<typename T, af_op_t op>
-static inline af_array unaryOp(const af_array in)
-{
+static inline af_array unaryOp(const af_array in) {
     af_array res = getHandle(unaryOp<T, op>(castArray<T>(in)));
     return res;
 }
@@ -43,23 +41,19 @@ template<typename Tc, typename Tr, af_op_t op>
 struct unaryOpCplxFun;
 
 template<typename Tc, typename Tr, af_op_t op>
-static inline Array<Tc> unaryOpCplx(const Array<Tc> &in)
-{
+static inline Array<Tc> unaryOpCplx(const Array<Tc> &in) {
     return unaryOpCplxFun<Tc, Tr, op>()(in);
 }
 
 template<typename Tc, typename Tr, af_op_t op>
-static inline af_array unaryOpCplx(const af_array in)
-{
+static inline af_array unaryOpCplx(const af_array in) {
     return getHandle(unaryOpCplx<Tc, Tr, op>(castArray<Tc>(in)));
 }
 
 template<af_op_t op>
-static af_err af_unary(af_array *out, const af_array in)
-{
+static af_err af_unary(af_array *out, const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
         ARG_ASSERT(1, in_info.isReal());
 
         af_dtype in_type = in_info.getType();
@@ -69,10 +63,9 @@ static af_err af_unary(af_array *out, const af_array in)
         af_dtype type = implicit(in_type, f32);
 
         switch (type) {
-        case f32 : res = unaryOp<float  , op>(in); break;
-        case f64 : res = unaryOp<double , op>(in); break;
-        default:
-            TYPE_ERROR(1, in_type); break;
+            case f32: res = unaryOp<float, op>(in); break;
+            case f64: res = unaryOp<double, op>(in); break;
+            default: TYPE_ERROR(1, in_type); break;
         }
 
         std::swap(*out, res);
@@ -82,10 +75,9 @@ static af_err af_unary(af_array *out, const af_array in)
 }
 
 template<af_op_t op>
-static af_err af_unary_complex(af_array *out, const af_array in)
-{
+static af_err af_unary_complex(af_array *out, const af_array in) {
     try {
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
         af_dtype in_type = in_info.getType();
         af_array res;
@@ -94,12 +86,11 @@ static af_err af_unary_complex(af_array *out, const af_array in)
         af_dtype type = implicit(in_type, f32);
 
         switch (type) {
-        case f32 : res = unaryOp<float  , op>(in); break;
-        case f64 : res = unaryOp<double , op>(in); break;
-        case c32 : res = unaryOpCplx<cfloat , float , op>(in); break;
-        case c64 : res = unaryOpCplx<cdouble, double, op>(in); break;
-        default:
-            TYPE_ERROR(1, in_type); break;
+            case f32: res = unaryOp<float, op>(in); break;
+            case f64: res = unaryOp<double, op>(in); break;
+            case c32: res = unaryOpCplx<cfloat, float, op>(in); break;
+            case c64: res = unaryOpCplx<cdouble, double, op>(in); break;
+            default: TYPE_ERROR(1, in_type); break;
         }
 
         std::swap(*out, res);
@@ -108,18 +99,16 @@ static af_err af_unary_complex(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-#define UNARY_FN(name, opcode)                          \
-    af_err af_##name(af_array *out, const af_array in)  \
-    {                                                   \
-        return af_unary<af_##opcode##_t>(out, in);          \
+#define UNARY_FN(name, opcode)                           \
+    af_err af_##name(af_array *out, const af_array in) { \
+        return af_unary<af_##opcode##_t>(out, in);       \
     }
 
 #define UNARY(fn) UNARY_FN(fn, fn)
 
-#define UNARY_COMPLEX(fn)                               \
-    af_err af_##fn(af_array *out, const af_array in)    \
-    {                                                   \
-        return af_unary_complex<af_##fn##_t>(out, in);  \
+#define UNARY_COMPLEX(fn)                              \
+    af_err af_##fn(af_array *out, const af_array in) { \
+        return af_unary_complex<af_##fn##_t>(out, in); \
     }
 
 UNARY(trunc)
@@ -159,10 +148,8 @@ UNARY_COMPLEX(tan)
 UNARY_COMPLEX(tanh)
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_exp_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_exp_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // exp(a + ib)
         // --> exp(a) * exp(ib)
         // --> exp(a) * (cos(a) + i * sin(b))
@@ -186,10 +173,8 @@ struct unaryOpCplxFun<Tc, Tr, af_exp_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_log_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_log_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // log(a + ib)
         // using r = abs(a + ib), phi == arg(a + ib)
         // --> log(r * exp(i * phi))
@@ -217,10 +202,8 @@ struct unaryOpCplxFun<Tc, Tr, af_log_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_sin_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_sin_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // sin(a + ib)
         // --> sin(a) * cos(ib) + cos(a) * sin(ib)
         // --> sin(a) * cosh(b) + i * cos(a) * sinh(b)
@@ -229,8 +212,8 @@ struct unaryOpCplxFun<Tc, Tr, af_sin_t>
         Array<Tr> b = imag<Tr, Tc>(z);
 
         // compute sin
-        Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
-        Array<Tr> cos_a = unaryOp<Tr, af_cos_t>(a);
+        Array<Tr> sin_a  = unaryOp<Tr, af_sin_t>(a);
+        Array<Tr> cos_a  = unaryOp<Tr, af_cos_t>(a);
         Array<Tr> sinh_b = unaryOp<Tr, af_sinh_t>(b);
         Array<Tr> cosh_b = unaryOp<Tr, af_cosh_t>(b);
 
@@ -245,10 +228,8 @@ struct unaryOpCplxFun<Tc, Tr, af_sin_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_cos_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_cos_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // cos(a + ib)
         // --> cos(a) * cos(ib) - sin(a) * sin(ib)
         // --> cos(a) * cosh(b) - i * sin(a) * sinh(b)
@@ -257,8 +238,8 @@ struct unaryOpCplxFun<Tc, Tr, af_cos_t>
         Array<Tr> b = imag<Tr, Tc>(z);
 
         // compute cos
-        Array<Tr> sin_a = unaryOp<Tr, af_sin_t>(a);
-        Array<Tr> cos_a = unaryOp<Tr, af_cos_t>(a);
+        Array<Tr> sin_a  = unaryOp<Tr, af_sin_t>(a);
+        Array<Tr> cos_a  = unaryOp<Tr, af_cos_t>(a);
         Array<Tr> sinh_b = unaryOp<Tr, af_sinh_t>(b);
         Array<Tr> cosh_b = unaryOp<Tr, af_cosh_t>(b);
 
@@ -267,19 +248,19 @@ struct unaryOpCplxFun<Tc, Tr, af_cos_t>
         // -1
         Array<Tr> neg_one = createValueArray<Tr>(a_out.dims(), -1);
         // sin(a) * sinh(b)
-        Array<Tr> b_out_neg = arithOp<Tr, af_mul_t>(sin_a, sinh_b, cos_a.dims());
+        Array<Tr> b_out_neg =
+            arithOp<Tr, af_mul_t>(sin_a, sinh_b, cos_a.dims());
         // -1 * sin(a) * sinh(b)
-        Array<Tr> b_out = arithOp<Tr, af_mul_t>(neg_one, b_out_neg, b_out_neg.dims());
+        Array<Tr> b_out =
+            arithOp<Tr, af_mul_t>(neg_one, b_out_neg, b_out_neg.dims());
         // cos(a) * cosh(b) - i * sin(a) * sinh(b)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_tan_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_tan_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // tan(a + ib) = sin(a + ib) / cos(a + ib)
         Array<Tc> sin_z = unaryOpCplx<Tc, Tr, af_sin_t>(z);
         Array<Tc> cos_z = unaryOpCplx<Tc, Tr, af_cos_t>(z);
@@ -288,10 +269,8 @@ struct unaryOpCplxFun<Tc, Tr, af_tan_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_sinh_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_sinh_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // sinh(a + ib)
         // --> sinh(a) * cosh(ib) + cosh(a) * sinh(ib)
         // --> sinh(a) * cos(b) + i * cosh(a) * sin(b)
@@ -302,8 +281,8 @@ struct unaryOpCplxFun<Tc, Tr, af_sinh_t>
         // compute sinh
         Array<Tr> sinh_a = unaryOp<Tr, af_sinh_t>(a);
         Array<Tr> cosh_a = unaryOp<Tr, af_cosh_t>(a);
-        Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
-        Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+        Array<Tr> sin_b  = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> cos_b  = unaryOp<Tr, af_cos_t>(b);
 
         // sinh(a) * cos(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(sinh_a, cos_b, sinh_a.dims());
@@ -316,10 +295,8 @@ struct unaryOpCplxFun<Tc, Tr, af_sinh_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_cosh_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_cosh_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // cosh(a + ib)
         // --> cosh(a) * cosh(ib) + sinh(a) * sinh(ib)
         // --> cosh(a) * cos(b) + i * sinh(a) * sin(b)
@@ -329,8 +306,8 @@ struct unaryOpCplxFun<Tc, Tr, af_cosh_t>
         // compute cosh
         Array<Tr> sinh_a = unaryOp<Tr, af_sinh_t>(a);
         Array<Tr> cosh_a = unaryOp<Tr, af_cosh_t>(a);
-        Array<Tr> sin_b = unaryOp<Tr, af_sin_t>(b);
-        Array<Tr> cos_b = unaryOp<Tr, af_cos_t>(b);
+        Array<Tr> sin_b  = unaryOp<Tr, af_sin_t>(b);
+        Array<Tr> cos_b  = unaryOp<Tr, af_cos_t>(b);
 
         // cosh(a) * cos(b)
         Array<Tr> a_out = arithOp<Tr, af_mul_t>(cosh_a, cos_b, cosh_a.dims());
@@ -343,10 +320,8 @@ struct unaryOpCplxFun<Tc, Tr, af_cosh_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_tanh_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_tanh_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // tanh(a + ib) = sinh(a + ib) / cosh(a + ib)
         Array<Tc> sinh_z = unaryOpCplx<Tc, Tr, af_sinh_t>(z);
         Array<Tc> cosh_z = unaryOpCplx<Tc, Tr, af_cosh_t>(z);
@@ -355,10 +330,8 @@ struct unaryOpCplxFun<Tc, Tr, af_tanh_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_acosh_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // dont simplify this expression, as it might lead to branch cuts
         // acosh(z) = log(z+sqrt(z+1)*sqrt(z-1))
 
@@ -371,9 +344,11 @@ struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
         // sqrt(z + 1)
         Array<Tc> sqrt_z_plus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z_plus_one);
         // sqrt(z - 1)
-        Array<Tc> sqrt_z_minus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z_minus_one);
+        Array<Tc> sqrt_z_minus_one =
+            unaryOpCplx<Tc, Tr, af_sqrt_t>(z_minus_one);
         // sqrt(z + 1) * sqrt(z - 1)
-        Array<Tc> sqrt_prod = arithOp<Tc, af_mul_t>(sqrt_z_plus_one, sqrt_z_minus_one, sqrt_z_plus_one.dims());
+        Array<Tc> sqrt_prod = arithOp<Tc, af_mul_t>(
+            sqrt_z_plus_one, sqrt_z_minus_one, sqrt_z_plus_one.dims());
         // z + sqrt(z + 1) * sqrt(z - 1)
         Array<Tc> w = arithOp<Tc, af_add_t>(z, sqrt_prod, z.dims());
         // log(z + sqrt(z + 1) * sqrt(z - 1))
@@ -382,10 +357,8 @@ struct unaryOpCplxFun<Tc, Tr, af_acosh_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_asinh_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // asinh(z) = log(z+sqrt(z^2+1))
         Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc>(1.0));
 
@@ -394,7 +367,8 @@ struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
         // ((a + 1) + i * b) --> z^2 + 1
         Array<Tc> z2_plus_one = arithOp<Tc, af_add_t>(z2, one, z.dims());
         // sqrt(z^2 + 1)
-        Array<Tc> sqrt_z2_plus_one = unaryOpCplx<Tc, Tr, af_sqrt_t>(z2_plus_one);
+        Array<Tc> sqrt_z2_plus_one =
+            unaryOpCplx<Tc, Tr, af_sqrt_t>(z2_plus_one);
         // z + sqrt(z^2 + 1)
         Array<Tc> w = arithOp<Tc, af_add_t>(z, sqrt_z2_plus_one, z.dims());
         // log(z + sqrt(z^2 + 1))
@@ -403,13 +377,13 @@ struct unaryOpCplxFun<Tc, Tr, af_asinh_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_atanh_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // atanh(z) = 0.5*(log(1+z)-log(1-z))
-        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
-        Array<Tc> half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.5, 0.0));
+        Array<Tc> one =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> half =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.5, 0.0));
 
         // (1 + z)
         Array<Tc> one_plus_z = arithOp<Tc, af_add_t>(one, z, one.dims());
@@ -420,31 +394,33 @@ struct unaryOpCplxFun<Tc, Tr, af_atanh_t>
         // log(1 - z)
         Array<Tc> log_one_minus_z = unaryOpCplx<Tc, Tr, af_log_t>(one_minus_z);
         // (log(1 + z) - log(1 - z))
-        Array<Tc> w = arithOp<Tc, af_sub_t>(log_one_plus_z, log_one_minus_z, log_one_plus_z.dims());
+        Array<Tc> w = arithOp<Tc, af_sub_t>(log_one_plus_z, log_one_minus_z,
+                                            log_one_plus_z.dims());
         // 0.5 * (log(1 + z) - log(1 - z))
         return arithOp<Tc, af_mul_t>(w, half, w.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_acos_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_acos_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // acos(z) = pi/2 + i*log(i*z+sqrt(1-z.^2))
         // --> pi/2 - asinz(z)
 
-        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> one =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
 
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
-        Array<Tc> pi_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(M_PI_2, 0.0));
+        Array<Tc> pi_half =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(M_PI_2, 0.0));
 
         // z^2
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
         // 1 - z^2
         Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
         // sqrt(1 - z^2)
-        Array<Tc> sqrt_one_minus_z2 = unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
+        Array<Tc> sqrt_one_minus_z2 =
+            unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
         // i*z
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
         // (i*z - sqrt(1 - z^2))
@@ -459,22 +435,23 @@ struct unaryOpCplxFun<Tc, Tr, af_acos_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_asin_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_asin_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // asin(z) = -i*log(i*z+sqrt(1-z^2))
 
-        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> one =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
-        Array<Tc> minus_i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, -1.0));
+        Array<Tc> minus_i =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, -1.0));
 
         // z^2
         Array<Tc> z2 = arithOp<Tc, af_mul_t>(z, z, z.dims());
         // 1 - z^2
         Array<Tc> one_minus_z2 = arithOp<Tc, af_sub_t>(one, z2, one.dims());
         // sqrt(1 - z^2)
-        Array<Tc> sqrt_one_minus_z2 = unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
+        Array<Tc> sqrt_one_minus_z2 =
+            unaryOpCplx<Tc, Tr, af_sqrt_t>(one_minus_z2);
         // i*z
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
         // (i*z + sqrt(1 - z^2))
@@ -487,16 +464,16 @@ struct unaryOpCplxFun<Tc, Tr, af_asin_t>
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_atan_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_atan_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // atan(z) = 0.5 * i * (log(1-i*z)-log(1+i*z))
-        Array<Tc> one = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
+        Array<Tc> one =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(1.0, 0.0));
         Array<Tc> i = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 1.0));
 
         // 0.5 * i
-        Array<Tc> i_half = createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 0.5));
+        Array<Tc> i_half =
+            createValueArray<Tc>(z.dims(), scalar<Tc, Tr>(0.0, 0.5));
         // i*z
         Array<Tc> iz = arithOp<Tc, af_mul_t>(i, z, z.dims());
         // 1 - i*z
@@ -508,17 +485,16 @@ struct unaryOpCplxFun<Tc, Tr, af_atan_t>
         // log(1 + i*z)
         Array<Tc> log_plus = unaryOpCplx<Tc, Tr, af_log_t>(one_plus_iz);
         // log(1 - i*z) - log(1 + i*z)
-        Array<Tc> log_diff = arithOp<Tc, af_sub_t>(log_minus, log_plus, z.dims());
+        Array<Tc> log_diff =
+            arithOp<Tc, af_sub_t>(log_minus, log_plus, z.dims());
         // 0.5 * i * (log(1 - i*z) - log(1 + i*z))
         return arithOp<Tc, af_mul_t>(i_half, log_diff, z.dims());
     }
 };
 
 template<typename Tc, typename Tr>
-struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
-{
-    Array<Tc> operator()(const Array<Tc> &z)
-    {
+struct unaryOpCplxFun<Tc, Tr, af_sqrt_t> {
+    Array<Tc> operator()(const Array<Tc> &z) {
         // sqrt(a + ib)
         // using r = abs(a + ib), phi == arg(a + ib)
         // --> sqrt(r * exp(i * phi))
@@ -529,11 +505,10 @@ struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
         Array<Tr> a = real<Tr, Tc>(z);
         Array<Tr> b = imag<Tr, Tc>(z);
 
-
         // phi = arg(a + ib)
         // --> phi = atan2(b, a)
         Array<Tr> phi = arithOp<Tr, af_atan2_t>(b, a, b.dims());
-        Array<Tr> r = abs<Tr>(z);
+        Array<Tr> r   = abs<Tr>(z);
 
         // compute sqrt
         Array<Tr> two = createValueArray<Tr>(phi.dims(), 2.0);
@@ -550,44 +525,41 @@ struct unaryOpCplxFun<Tc, Tr, af_sqrt_t>
         // sin(phi/2)
         Array<Tr> b_out_unit = unaryOp<Tr, af_sin_t>(phi_out);
         // sqrt(r) * cos(phi/2)
-        Array<Tr> a_out = arithOp<Tr, af_mul_t>(r_out, a_out_unit, r_out.dims());
+        Array<Tr> a_out =
+            arithOp<Tr, af_mul_t>(r_out, a_out_unit, r_out.dims());
         // sqrt(r) * sin(phi/2)
-        Array<Tr> b_out = arithOp<Tr, af_mul_t>(r_out, b_out_unit, r_out.dims());
+        Array<Tr> b_out =
+            arithOp<Tr, af_mul_t>(r_out, b_out_unit, r_out.dims());
 
         // sqrt(r) * cos(phi/2) + i * sqrt(r) * sin(phi/2)
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
     }
 };
 
-af_err af_not(af_array *out, const af_array in)
-{
+af_err af_not(af_array *out, const af_array in) {
     try {
-
         af_array tmp;
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
-        AF_CHECK(af_constant(&tmp, 0,
-                             in_info.ndims(),
-                             in_info.dims().get(), in_info.getType()));
+        AF_CHECK(af_constant(&tmp, 0, in_info.ndims(), in_info.dims().get(),
+                             in_info.getType()));
 
         AF_CHECK(af_eq(out, in, tmp, false));
 
         AF_CHECK(af_release_array(tmp));
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_arg(af_array *out, const af_array in)
-{
+af_err af_arg(af_array *out, const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
         if (!in_info.isComplex()) {
-            return af_constant(out, 0,
-                               in_info.ndims(),
-                               in_info.dims().get(), in_info.getType());
+            return af_constant(out, 0, in_info.ndims(), in_info.dims().get(),
+                               in_info.getType());
         }
 
         af_array real;
@@ -600,40 +572,36 @@ af_err af_arg(af_array *out, const af_array in)
 
         AF_CHECK(af_release_array(real));
         AF_CHECK(af_release_array(imag));
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_pow2(af_array *out, const af_array in)
-{
+af_err af_pow2(af_array *out, const af_array in) {
     try {
-
         af_array two;
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
-        AF_CHECK(af_constant(&two, 2,
-                             in_info.ndims(),
-                             in_info.dims().get(), in_info.getType()));
+        AF_CHECK(af_constant(&two, 2, in_info.ndims(), in_info.dims().get(),
+                             in_info.getType()));
 
         AF_CHECK(af_pow(out, two, in, false));
 
         AF_CHECK(af_release_array(two));
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_factorial(af_array *out, const af_array in)
-{
+af_err af_factorial(af_array *out, const af_array in) {
     try {
-
         af_array one;
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
-        AF_CHECK(af_constant(&one, 1,
-                             in_info.ndims(),
-                             in_info.dims().get(), in_info.getType()));
+        AF_CHECK(af_constant(&one, 1, in_info.ndims(), in_info.dims().get(),
+                             in_info.getType()));
 
         af_array inp1;
         AF_CHECK(af_add(&inp1, one, in, false));
@@ -642,47 +610,42 @@ af_err af_factorial(af_array *out, const af_array in)
 
         AF_CHECK(af_release_array(one));
         AF_CHECK(af_release_array(inp1));
-    } CATCHALL;
+    }
+    CATCHALL;
 
     return AF_SUCCESS;
 }
 
 template<typename T, af_op_t op>
-static inline af_array checkOp(const af_array in)
-{
+static inline af_array checkOp(const af_array in) {
     af_array res = getHandle(checkOp<T, op>(castArray<T>(in)));
     return res;
 }
 
 template<af_op_t op>
-struct cplxLogicOp
-{
-    af_array operator()(Array<char> resR, Array<char> resI, dim4 dims)
-    {
+struct cplxLogicOp {
+    af_array operator()(Array<char> resR, Array<char> resI, dim4 dims) {
         return getHandle(logicOp<char, af_or_t>(resR, resI, dims));
     }
 };
 
-template <>
-struct cplxLogicOp<af_iszero_t>
-{
-    af_array operator()(Array<char> resR, Array<char> resI, dim4 dims)
-    {
+template<>
+struct cplxLogicOp<af_iszero_t> {
+    af_array operator()(Array<char> resR, Array<char> resI, dim4 dims) {
         return getHandle(logicOp<char, af_and_t>(resR, resI, dims));
     }
 };
 
 template<typename T, typename BT, af_op_t op>
-static inline af_array checkOpCplx(const af_array in)
-{
+static inline af_array checkOpCplx(const af_array in) {
     Array<BT> R = real<BT, T>(getArray<T>(in));
     Array<BT> I = imag<BT, T>(getArray<T>(in));
 
     Array<char> resR = checkOp<BT, op>(R);
     Array<char> resI = checkOp<BT, op>(I);
 
-    const ArrayInfo& in_info = getInfo(in);
-    dim4 dims = in_info.dims();
+    const ArrayInfo &in_info = getInfo(in);
+    dim4 dims                = in_info.dims();
     cplxLogicOp<op> cplxLogic;
     af_array res = cplxLogic(resR, resI, dims);
 
@@ -690,11 +653,9 @@ static inline af_array checkOpCplx(const af_array in)
 }
 
 template<af_op_t op>
-static af_err af_check(af_array *out, const af_array in)
-{
+static af_err af_check(af_array *out, const af_array in) {
     try {
-
-        const ArrayInfo& in_info = getInfo(in);
+        const ArrayInfo &in_info = getInfo(in);
 
         af_dtype in_type = in_info.getType();
         af_array res;
@@ -703,12 +664,11 @@ static af_err af_check(af_array *out, const af_array in)
         af_dtype type = implicit(in_type, f32);
 
         switch (type) {
-        case f32 : res = checkOp<float  , op>(in); break;
-        case f64 : res = checkOp<double , op>(in); break;
-        case c32 : res = checkOpCplx<cfloat , float , op>(in); break;
-        case c64 : res = checkOpCplx<cdouble, double, op>(in); break;
-        default:
-            TYPE_ERROR(1, in_type); break;
+            case f32: res = checkOp<float, op>(in); break;
+            case f64: res = checkOp<double, op>(in); break;
+            case c32: res = checkOpCplx<cfloat, float, op>(in); break;
+            case c64: res = checkOpCplx<cdouble, double, op>(in); break;
+            default: TYPE_ERROR(1, in_type); break;
         }
 
         std::swap(*out, res);
@@ -717,13 +677,11 @@ static af_err af_check(af_array *out, const af_array in)
     return AF_SUCCESS;
 }
 
-#define CHECK(fn)                                       \
-    af_err af_##fn(af_array *out, const af_array in)    \
-    {                                                   \
-        return af_check<af_##fn##_t>(out, in);          \
+#define CHECK(fn)                                      \
+    af_err af_##fn(af_array *out, const af_array in) { \
+        return af_check<af_##fn##_t>(out, in);         \
     }
 
-
 CHECK(isinf)
 CHECK(isnan)
 CHECK(iszero)
diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index e9fd3dd7c5..8da2d81cd3 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -7,32 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <unwrap.hpp>
+#include <af/defines.h>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
 static inline af_array unwrap(const af_array in, const dim_t wx, const dim_t wy,
-                              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                              const bool is_column)
-{
-    return getHandle(unwrap<T>(getArray<T>(in), wx, wy, sx, sy, px, py, is_column));
+                              const dim_t sx, const dim_t sy, const dim_t px,
+                              const dim_t py, const bool is_column) {
+    return getHandle(
+        unwrap<T>(getArray<T>(in), wx, wy, sx, sy, px, py, is_column));
 }
 
-af_err af_unwrap(af_array *out, const af_array in, const dim_t wx, const dim_t wy,
-                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
-{
+af_err af_unwrap(af_array* out, const af_array in, const dim_t wx,
+                 const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
+                 const dim_t py, const bool is_column) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        af::dim4 idims = info.dims();
+        af_dtype type         = info.getType();
+        af::dim4 idims        = info.dims();
 
         ARG_ASSERT(2, wx > 0 && wx <= idims[0] + 2 * px);
         ARG_ASSERT(3, wy > 0 && wy <= idims[1] + 2 * py);
@@ -43,22 +43,46 @@ af_err af_unwrap(af_array *out, const af_array in, const dim_t wx, const dim_t w
 
         af_array output;
 
-        switch(type) {
-            case f32: output = unwrap<float  >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case f64: output = unwrap<double >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case c32: output = unwrap<cfloat >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case c64: output = unwrap<cdouble>(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case s32: output = unwrap<int    >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case u32: output = unwrap<uint   >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case s64: output = unwrap<intl   >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case u64: output = unwrap<uintl  >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case s16: output = unwrap<short  >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case u16: output = unwrap<ushort >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case u8:  output = unwrap<uchar  >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            case b8:  output = unwrap<char   >(in, wx, wy, sx, sy, px, py, is_column);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                output = unwrap<float>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case f64:
+                output = unwrap<double>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case c32:
+                output = unwrap<cfloat>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case c64:
+                output = unwrap<cdouble>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case s32:
+                output = unwrap<int>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u32:
+                output = unwrap<uint>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case s64:
+                output = unwrap<intl>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u64:
+                output = unwrap<uintl>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case s16:
+                output = unwrap<short>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u16:
+                output = unwrap<ushort>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u8:
+                output = unwrap<uchar>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case b8:
+                output = unwrap<char>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index ac18b8a639..2e02319333 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/statistics.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
+#include <arith.hpp>
 #include <backend.hpp>
+#include <cast.hpp>
+#include <common/err_common.hpp>
 #include <handle.hpp>
+#include <math.hpp>
 #include <mean.hpp>
 #include <reduce.hpp>
-#include <arith.hpp>
-#include <math.hpp>
-#include <cast.hpp>
 #include <tile.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/statistics.h>
 
 #include "stats.h"
 
@@ -32,158 +32,190 @@ using std::tie;
 using std::tuple;
 
 template<typename inType, typename outType>
-static outType varAll(const af_array& in, const bool isbiased)
-{
+static outType varAll(const af_array& in, const bool isbiased) {
     typedef typename baseOutType<outType>::type weightType;
-    Array<inType> inArr = getArray<inType>(in);
+    Array<inType> inArr  = getArray<inType>(in);
     Array<outType> input = cast<outType>(inArr);
 
-    Array<outType> meanCnst= createValueArray<outType>(input.dims(), mean<inType, weightType, outType>(inArr));
+    Array<outType> meanCnst = createValueArray<outType>(
+        input.dims(), mean<inType, weightType, outType>(inArr));
 
-    Array<outType> diff    = arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
+    Array<outType> diff =
+        arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
 
-    Array<outType> diffSq  = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
+    Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
 
-    outType result = division(reduce_all<af_add_t, outType, outType>(diffSq),
-        isbiased ? input.elements() : input.elements() - 1);
+    outType result =
+        division(reduce_all<af_add_t, outType, outType>(diffSq),
+                 isbiased ? input.elements() : input.elements() - 1);
 
     return result;
 }
 
 template<typename inType, typename outType>
-static outType varAll(const af_array& in, const af_array weights)
-{
+static outType varAll(const af_array& in, const af_array weights) {
     typedef typename baseOutType<outType>::type bType;
 
     Array<outType> input = cast<outType>(getArray<inType>(in));
     Array<outType> wts   = cast<outType>(getArray<bType>(weights));
 
-    bType wtsSum    = reduce_all<af_add_t, bType, bType>(getArray<bType>(weights));
+    bType wtsSum = reduce_all<af_add_t, bType, bType>(getArray<bType>(weights));
     outType wtdMean = mean<outType, bType>(input, getArray<bType>(weights));
 
     Array<outType> meanArr = createValueArray<outType>(input.dims(), wtdMean);
-    Array<outType> diff    = arithOp<outType, af_sub_t>(input, meanArr, input.dims());
-    Array<outType> diffSq  = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
+    Array<outType> diff =
+        arithOp<outType, af_sub_t>(input, meanArr, input.dims());
+    Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
 
-    Array<outType> accDiffSq = arithOp<outType, af_mul_t>(diffSq, wts, diffSq.dims());
+    Array<outType> accDiffSq =
+        arithOp<outType, af_mul_t>(diffSq, wts, diffSq.dims());
 
-    outType result = division(reduce_all<af_add_t, outType, outType>(accDiffSq), wtsSum);
+    outType result =
+        division(reduce_all<af_add_t, outType, outType>(accDiffSq), wtsSum);
 
     return result;
 }
 
 template<typename inType, typename outType>
-static
-tuple<Array<outType>, Array<outType>>
-meanvar(const Array<inType> &in, const Array<typename baseOutType<outType>::type>& weights,
-        const af_var_bias bias, const dim_t dim) {
-
+static tuple<Array<outType>, Array<outType>> meanvar(
+    const Array<inType>& in,
+    const Array<typename baseOutType<outType>::type>& weights,
+    const af_var_bias bias, const dim_t dim) {
     typedef typename baseOutType<outType>::type weightType;
     Array<outType> input = cast<outType>(in);
-    dim4 iDims = input.dims();
+    dim4 iDims           = input.dims();
 
     Array<outType> meanArr = createEmptyArray<outType>({0});
     Array<outType> normArr = createEmptyArray<outType>({0});
-    if(weights.isEmpty()) {
-        meanArr = mean<outType, weightType, outType>(input, dim);
-        auto val = 1.0 / (bias == AF_VARIANCE_POPULATION ? iDims[dim] : iDims[dim]-1);
-        normArr = createValueArray<outType>(meanArr.dims(), scalar<outType>(val));
+    if (weights.isEmpty()) {
+        meanArr  = mean<outType, weightType, outType>(input, dim);
+        auto val = 1.0 / (bias == AF_VARIANCE_POPULATION ? iDims[dim]
+                                                         : iDims[dim] - 1);
+        normArr =
+            createValueArray<outType>(meanArr.dims(), scalar<outType>(val));
     } else {
-        meanArr = mean<outType, weightType>(input, weights, dim);
-        Array<outType> wtsSum = cast<outType>(reduce<af_add_t, weightType, weightType>(weights, dim));
-        Array<outType> ones = createValueArray<outType>(wtsSum.dims(), scalar<outType>(1));
-        if(bias == AF_VARIANCE_SAMPLE) {
-          wtsSum = arithOp<outType, af_sub_t>(wtsSum, ones, ones.dims());
+        meanArr               = mean<outType, weightType>(input, weights, dim);
+        Array<outType> wtsSum = cast<outType>(
+            reduce<af_add_t, weightType, weightType>(weights, dim));
+        Array<outType> ones =
+            createValueArray<outType>(wtsSum.dims(), scalar<outType>(1));
+        if (bias == AF_VARIANCE_SAMPLE) {
+            wtsSum = arithOp<outType, af_sub_t>(wtsSum, ones, ones.dims());
         }
-        normArr  = arithOp<outType, af_div_t>(ones, wtsSum, meanArr.dims());
+        normArr = arithOp<outType, af_div_t>(ones, wtsSum, meanArr.dims());
     }
 
     /* now tile meanArr along dim and use it for variance computation */
     dim4 tileDims(1);
-    tileDims[dim] = iDims[dim];
+    tileDims[dim]           = iDims[dim];
     Array<outType> tMeanArr = tile<outType>(meanArr, tileDims);
     /* now mean array is ready */
 
-    Array<outType> diff    = arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
-    Array<outType> diffSq  = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
+    Array<outType> diff =
+        arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
+    Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     Array<outType> redDiff = reduce<af_add_t, outType, outType>(diffSq, dim);
 
-    Array<outType> variance = arithOp<outType, af_mul_t>(normArr, redDiff, redDiff.dims());
+    Array<outType> variance =
+        arithOp<outType, af_mul_t>(normArr, redDiff, redDiff.dims());
 
     return make_tuple(meanArr, variance);
 }
 
-
 template<typename inType, typename outType>
-static
-tuple<af_array, af_array>
-meanvar(const af_array &in, const af_array &weights,
-        const af_var_bias bias, const dim_t dim) {
-
-  typedef typename baseOutType<outType>::type weightType;
-  Array<outType> mean = createEmptyArray<outType>({0}), var = createEmptyArray<outType>({0});
-
-  Array<weightType> w = createEmptyArray<weightType>({0});
-  if(weights != 0) {
-    w = getArray<weightType>(weights);
-  }
-  tie(mean, var) = meanvar<inType, outType>(getArray<inType>(in), w,
-                           bias, dim);
-  return make_tuple(getHandle(mean), getHandle(var));
-
+static tuple<af_array, af_array> meanvar(const af_array& in,
+                                         const af_array& weights,
+                                         const af_var_bias bias,
+                                         const dim_t dim) {
+    typedef typename baseOutType<outType>::type weightType;
+    Array<outType> mean = createEmptyArray<outType>({0}),
+                   var  = createEmptyArray<outType>({0});
+
+    Array<weightType> w = createEmptyArray<weightType>({0});
+    if (weights != 0) { w = getArray<weightType>(weights); }
+    tie(mean, var) =
+        meanvar<inType, outType>(getArray<inType>(in), w, bias, dim);
+    return make_tuple(getHandle(mean), getHandle(var));
 }
 
 /// Calculates the variance
 ///
-/// \note Only calculates the weighted variance if the weights array is non-empty
+/// \note Only calculates the weighted variance if the weights array is
+/// non-empty
 template<typename inType, typename outType>
-static Array<outType>
-var(const Array<inType>& in,
+static Array<outType> var(
+    const Array<inType>& in,
     const Array<typename baseOutType<outType>::type>& weights,
-    const af_var_bias bias, int dim)
-{
+    const af_var_bias bias, int dim) {
     Array<outType> variance = createEmptyArray<outType>({0});
-    tie(ignore, variance) = meanvar<inType, outType>(in, weights, bias, dim);
+    tie(ignore, variance)   = meanvar<inType, outType>(in, weights, bias, dim);
     return variance;
 }
 
 template<typename inType, typename outType>
 static af_array var_(const af_array& in, const af_array& weights,
                      const af_var_bias bias, int dim) {
-  using bType = typename baseOutType<outType>::type;
-  if(weights == 0) {
-      Array<bType> empty = createEmptyArray<bType>({0});
-      return getHandle(var<inType, outType>(getArray<inType>(in), empty, bias, dim));
-  } else {
-      return getHandle(var<inType, outType>(getArray<inType>(in), getArray<bType>(weights), bias, dim));
-  }
+    using bType = typename baseOutType<outType>::type;
+    if (weights == 0) {
+        Array<bType> empty = createEmptyArray<bType>({0});
+        return getHandle(
+            var<inType, outType>(getArray<inType>(in), empty, bias, dim));
+    } else {
+        return getHandle(var<inType, outType>(
+            getArray<inType>(in), getArray<bType>(weights), bias, dim));
+    }
 }
 
-af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t dim)
-{
+af_err af_var(af_array* out, const af_array in, const bool isbiased,
+              const dim_t dim) {
     try {
-        ARG_ASSERT(3, (dim>=0 && dim<=3));
+        ARG_ASSERT(3, (dim >= 0 && dim <= 3));
 
-        af_array output = 0;
+        af_array output       = 0;
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
+        af_dtype type         = info.getType();
 
         af_array no_weights = 0;
-        af_var_bias bias = (isbiased) ? AF_VARIANCE_POPULATION : AF_VARIANCE_SAMPLE;
-        switch(type) {
-            case f32: output = var_<float ,  float >(in, no_weights, bias, dim); break;
-            case f64: output = var_<double,  double>(in, no_weights, bias, dim); break;
-            case s32: output = var_<int   ,  float >(in, no_weights, bias, dim); break;
-            case u32: output = var_<uint  ,  float >(in, no_weights, bias, dim); break;
-            case s16: output = var_<short ,  float >(in, no_weights, bias, dim); break;
-            case u16: output = var_<ushort,  float >(in, no_weights, bias, dim); break;
-            case s64: output = var_<intl  ,  double>(in, no_weights, bias, dim); break;
-            case u64: output = var_<uintl ,  double>(in, no_weights, bias, dim); break;
-            case  u8: output = var_<uchar ,  float >(in, no_weights, bias, dim); break;
-            case  b8: output = var_<char  ,  float >(in, no_weights, bias, dim); break;
-            case c32: output = var_<cfloat,  cfloat>(in, no_weights, bias, dim); break;
-            case c64: output = var_<cdouble,cdouble>(in, no_weights, bias, dim); break;
-            default : TYPE_ERROR(1, type);
+        af_var_bias bias =
+            (isbiased) ? AF_VARIANCE_POPULATION : AF_VARIANCE_SAMPLE;
+        switch (type) {
+            case f32:
+                output = var_<float, float>(in, no_weights, bias, dim);
+                break;
+            case f64:
+                output = var_<double, double>(in, no_weights, bias, dim);
+                break;
+            case s32:
+                output = var_<int, float>(in, no_weights, bias, dim);
+                break;
+            case u32:
+                output = var_<uint, float>(in, no_weights, bias, dim);
+                break;
+            case s16:
+                output = var_<short, float>(in, no_weights, bias, dim);
+                break;
+            case u16:
+                output = var_<ushort, float>(in, no_weights, bias, dim);
+                break;
+            case s64:
+                output = var_<intl, double>(in, no_weights, bias, dim);
+                break;
+            case u64:
+                output = var_<uintl, double>(in, no_weights, bias, dim);
+                break;
+            case u8:
+                output = var_<uchar, float>(in, no_weights, bias, dim);
+                break;
+            case b8:
+                output = var_<char, float>(in, no_weights, bias, dim);
+                break;
+            case c32:
+                output = var_<cfloat, cfloat>(in, no_weights, bias, dim);
+                break;
+            case c64:
+                output = var_<cdouble, cdouble>(in, no_weights, bias, dim);
+                break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
     }
@@ -191,33 +223,73 @@ af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t
     return AF_SUCCESS;
 }
 
-af_err af_var_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim)
-{
+af_err af_var_weighted(af_array* out, const af_array in, const af_array weights,
+                       const dim_t dim) {
     try {
-        ARG_ASSERT(3, (dim>=0 && dim<=3));
+        ARG_ASSERT(3, (dim >= 0 && dim <= 3));
 
-        af_array output = 0;
+        af_array output        = 0;
         const ArrayInfo& iInfo = getInfo(in);
         const ArrayInfo& wInfo = getInfo(weights);
-        af_dtype iType  = iInfo.getType();
-        af_dtype wType  = wInfo.getType();
-
-        ARG_ASSERT(2, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
-
-        switch(iType) {
-            case f64: output = var_<double,  double>(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case f32: output = var_<float ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case s32: output = var_<int   ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case u32: output = var_<uint  ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case s16: output = var_<short ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case u16: output = var_<ushort,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case s64: output = var_<intl  ,  double>(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case u64: output = var_<uintl ,  double>(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case  u8: output = var_<uchar ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case  b8: output = var_<char  ,  float >(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case c32: output = var_<cfloat,  cfloat>(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            case c64: output = var_<cdouble,cdouble>(in, weights, AF_VARIANCE_POPULATION, dim); break;
-            default : TYPE_ERROR(1, iType);
+        af_dtype iType         = iInfo.getType();
+        af_dtype wType         = wInfo.getType();
+
+        ARG_ASSERT(
+            2,
+            (wType == f32 ||
+             wType ==
+                 f64)); /* verify that weights are non-complex real numbers */
+
+        switch (iType) {
+            case f64:
+                output = var_<double, double>(in, weights,
+                                              AF_VARIANCE_POPULATION, dim);
+                break;
+            case f32:
+                output = var_<float, float>(in, weights, AF_VARIANCE_POPULATION,
+                                            dim);
+                break;
+            case s32:
+                output =
+                    var_<int, float>(in, weights, AF_VARIANCE_POPULATION, dim);
+                break;
+            case u32:
+                output =
+                    var_<uint, float>(in, weights, AF_VARIANCE_POPULATION, dim);
+                break;
+            case s16:
+                output = var_<short, float>(in, weights, AF_VARIANCE_POPULATION,
+                                            dim);
+                break;
+            case u16:
+                output = var_<ushort, float>(in, weights,
+                                             AF_VARIANCE_POPULATION, dim);
+                break;
+            case s64:
+                output = var_<intl, double>(in, weights, AF_VARIANCE_POPULATION,
+                                            dim);
+                break;
+            case u64:
+                output = var_<uintl, double>(in, weights,
+                                             AF_VARIANCE_POPULATION, dim);
+                break;
+            case u8:
+                output = var_<uchar, float>(in, weights, AF_VARIANCE_POPULATION,
+                                            dim);
+                break;
+            case b8:
+                output =
+                    var_<char, float>(in, weights, AF_VARIANCE_POPULATION, dim);
+                break;
+            case c32:
+                output = var_<cfloat, cfloat>(in, weights,
+                                              AF_VARIANCE_POPULATION, dim);
+                break;
+            case c64:
+                output = var_<cdouble, cdouble>(in, weights,
+                                                AF_VARIANCE_POPULATION, dim);
+                break;
+            default: TYPE_ERROR(1, iType);
         }
         std::swap(*out, output);
     }
@@ -225,105 +297,142 @@ af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
     return AF_SUCCESS;
 }
 
-af_err af_var_all(double *realVal, double *imagVal, const af_array in, const bool isbiased)
-{
+af_err af_var_all(double* realVal, double* imagVal, const af_array in,
+                  const bool isbiased) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        switch(type) {
+        af_dtype type         = info.getType();
+        switch (type) {
             case f64: *realVal = varAll<double, double>(in, isbiased); break;
-            case f32: *realVal = varAll<float , float >(in, isbiased); break;
-            case s32: *realVal = varAll<int   , float >(in, isbiased); break;
-            case u32: *realVal = varAll<uint  , float >(in, isbiased); break;
-            case s16: *realVal = varAll<short , float >(in, isbiased); break;
-            case u16: *realVal = varAll<ushort, float >(in, isbiased); break;
-            case s64: *realVal = varAll<intl  , double>(in, isbiased); break;
-            case u64: *realVal = varAll<uintl , double>(in, isbiased); break;
-            case  u8: *realVal = varAll<uchar , float >(in, isbiased); break;
-            case  b8: *realVal = varAll<char  , float >(in, isbiased); break;
+            case f32: *realVal = varAll<float, float>(in, isbiased); break;
+            case s32: *realVal = varAll<int, float>(in, isbiased); break;
+            case u32: *realVal = varAll<uint, float>(in, isbiased); break;
+            case s16: *realVal = varAll<short, float>(in, isbiased); break;
+            case u16: *realVal = varAll<ushort, float>(in, isbiased); break;
+            case s64: *realVal = varAll<intl, double>(in, isbiased); break;
+            case u64: *realVal = varAll<uintl, double>(in, isbiased); break;
+            case u8: *realVal = varAll<uchar, float>(in, isbiased); break;
+            case b8: *realVal = varAll<char, float>(in, isbiased); break;
             case c32: {
-                cfloat tmp = varAll<cfloat,cfloat>(in, isbiased);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
+                cfloat tmp = varAll<cfloat, cfloat>(in, isbiased);
+                *realVal   = real(tmp);
+                *imagVal   = imag(tmp);
+            } break;
             case c64: {
-                cdouble tmp = varAll<cdouble,cdouble>(in, isbiased);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
-            default : TYPE_ERROR(1, type);
+                cdouble tmp = varAll<cdouble, cdouble>(in, isbiased);
+                *realVal    = real(tmp);
+                *imagVal    = imag(tmp);
+            } break;
+            default: TYPE_ERROR(1, type);
         }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in, const af_array weights)
-{
+af_err af_var_all_weighted(double* realVal, double* imagVal, const af_array in,
+                           const af_array weights) {
     try {
         const ArrayInfo& iInfo = getInfo(in);
         const ArrayInfo& wInfo = getInfo(weights);
-        af_dtype iType  = iInfo.getType();
-        af_dtype wType  = wInfo.getType();
+        af_dtype iType         = iInfo.getType();
+        af_dtype wType         = wInfo.getType();
 
-        ARG_ASSERT(3, (wType==f32 || wType==f64)); /* verify that weights are non-complex real numbers */
+        ARG_ASSERT(
+            3,
+            (wType == f32 ||
+             wType ==
+                 f64)); /* verify that weights are non-complex real numbers */
 
-        switch(iType) {
+        switch (iType) {
             case f64: *realVal = varAll<double, double>(in, weights); break;
-            case f32: *realVal = varAll<float , float >(in, weights); break;
-            case s32: *realVal = varAll<int   , float >(in, weights); break;
-            case u32: *realVal = varAll<uint  , float >(in, weights); break;
-            case s16: *realVal = varAll<short , float >(in, weights); break;
-            case u16: *realVal = varAll<ushort, float >(in, weights); break;
-            case s64: *realVal = varAll<intl  , double >(in, weights); break;
-            case u64: *realVal = varAll<uintl , double >(in, weights); break;
-            case  u8: *realVal = varAll<uchar , float >(in, weights); break;
-            case  b8: *realVal = varAll<char  , float >(in, weights); break;
+            case f32: *realVal = varAll<float, float>(in, weights); break;
+            case s32: *realVal = varAll<int, float>(in, weights); break;
+            case u32: *realVal = varAll<uint, float>(in, weights); break;
+            case s16: *realVal = varAll<short, float>(in, weights); break;
+            case u16: *realVal = varAll<ushort, float>(in, weights); break;
+            case s64: *realVal = varAll<intl, double>(in, weights); break;
+            case u64: *realVal = varAll<uintl, double>(in, weights); break;
+            case u8: *realVal = varAll<uchar, float>(in, weights); break;
+            case b8: *realVal = varAll<char, float>(in, weights); break;
             case c32: {
-                cfloat tmp = varAll<cfloat,cfloat>(in, weights);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
+                cfloat tmp = varAll<cfloat, cfloat>(in, weights);
+                *realVal   = real(tmp);
+                *imagVal   = imag(tmp);
+            } break;
             case c64: {
-                cdouble tmp = varAll<cdouble,cdouble>(in, weights);
-                *realVal = real(tmp);
-                *imagVal = imag(tmp);
-                } break;
-            default : TYPE_ERROR(1, iType);
+                cdouble tmp = varAll<cdouble, cdouble>(in, weights);
+                *realVal    = real(tmp);
+                *imagVal    = imag(tmp);
+            } break;
+            default: TYPE_ERROR(1, iType);
         }
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
-                  const af_array weights, const af_var_bias bias, const dim_t dim) {
-
-  try {
-      const ArrayInfo& iInfo = getInfo(in);
-      if(weights != 0) {
-        const ArrayInfo& wInfo = getInfo(weights);
-        af_dtype wType  = wInfo.getType();
-        ARG_ASSERT(3, (wType==f32 || wType==f64));
-      }
-      af_dtype iType  = iInfo.getType();
-
-      switch(iType) {
-        case f32: tie(*mean, *var) = meanvar<float,   float>(in, weights, bias, dim); break;
-        case f64: tie(*mean, *var) = meanvar<double, double>(in, weights, bias, dim); break;
-        case s32: tie(*mean, *var) = meanvar<int,     float>(in, weights, bias, dim); break;
-        case u32: tie(*mean, *var) = meanvar<uint,    float>(in, weights, bias, dim); break;
-        case s16: tie(*mean, *var) = meanvar<short ,  float >(in, weights, bias, dim); break;
-        case u16: tie(*mean, *var) = meanvar<ushort,  float >(in, weights, bias, dim); break;
-        case s64: tie(*mean, *var) = meanvar<intl  ,  double>(in, weights, bias, dim); break;
-        case u64: tie(*mean, *var) = meanvar<uintl ,  double>(in, weights, bias, dim); break;
-        case  u8: tie(*mean, *var) = meanvar<uchar ,  float >(in, weights, bias, dim); break;
-        case  b8: tie(*mean, *var) = meanvar<char  ,  float >(in, weights, bias, dim); break;
-        case c32: tie(*mean, *var) = meanvar<cfloat,  cfloat>(in, weights, bias, dim); break;
-        case c64: tie(*mean, *var) = meanvar<cdouble,cdouble>(in, weights, bias, dim); break;
-        default : TYPE_ERROR(1, iType);
-      }
-  }
-  CATCHALL;
-  return AF_SUCCESS;
+af_err af_meanvar(af_array* mean, af_array* var, const af_array in,
+                  const af_array weights, const af_var_bias bias,
+                  const dim_t dim) {
+    try {
+        const ArrayInfo& iInfo = getInfo(in);
+        if (weights != 0) {
+            const ArrayInfo& wInfo = getInfo(weights);
+            af_dtype wType         = wInfo.getType();
+            ARG_ASSERT(3, (wType == f32 || wType == f64));
+        }
+        af_dtype iType = iInfo.getType();
+
+        switch (iType) {
+            case f32:
+                tie(*mean, *var) =
+                    meanvar<float, float>(in, weights, bias, dim);
+                break;
+            case f64:
+                tie(*mean, *var) =
+                    meanvar<double, double>(in, weights, bias, dim);
+                break;
+            case s32:
+                tie(*mean, *var) = meanvar<int, float>(in, weights, bias, dim);
+                break;
+            case u32:
+                tie(*mean, *var) = meanvar<uint, float>(in, weights, bias, dim);
+                break;
+            case s16:
+                tie(*mean, *var) =
+                    meanvar<short, float>(in, weights, bias, dim);
+                break;
+            case u16:
+                tie(*mean, *var) =
+                    meanvar<ushort, float>(in, weights, bias, dim);
+                break;
+            case s64:
+                tie(*mean, *var) =
+                    meanvar<intl, double>(in, weights, bias, dim);
+                break;
+            case u64:
+                tie(*mean, *var) =
+                    meanvar<uintl, double>(in, weights, bias, dim);
+                break;
+            case u8:
+                tie(*mean, *var) =
+                    meanvar<uchar, float>(in, weights, bias, dim);
+                break;
+            case b8:
+                tie(*mean, *var) = meanvar<char, float>(in, weights, bias, dim);
+                break;
+            case c32:
+                tie(*mean, *var) =
+                    meanvar<cfloat, cfloat>(in, weights, bias, dim);
+                break;
+            case c64:
+                tie(*mean, *var) =
+                    meanvar<cdouble, cdouble>(in, weights, bias, dim);
+                break;
+            default: TYPE_ERROR(1, iType);
+        }
+    }
+    CATCHALL;
+    return AF_SUCCESS;
 }
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 70a0f47c9d..bb6fdc1d3f 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -7,13 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/graphics.h>
 #include <af/data.h>
+#include <af/graphics.h>
 
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/graphics_common.hpp>
 #include <common/err_common.hpp>
-#include <backend.hpp>
+#include <common/graphics_common.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
@@ -22,23 +22,21 @@
 
 #include <vector>
 
-using std::vector;
 using af::dim4;
+using std::vector;
 using namespace detail;
 using namespace graphics;
 
 template<typename T>
-fg_chart setup_vector_field(fg_window window,
-                            const vector<af_array>& points,
+fg_chart setup_vector_field(fg_window window, const vector<af_array>& points,
                             const vector<af_array>& directions,
                             const af_cell* const props,
-                            const bool transpose_ = true)
-{
+                            const bool transpose_ = true) {
     ForgeModule& _ = graphics::forgePlugin();
-    vector< Array<T> > pnts;
-    vector< Array<T> > dirs;
+    vector<Array<T>> pnts;
+    vector<Array<T>> dirs;
 
-    for (unsigned i=0; i<points.size(); ++i) {
+    for (unsigned i = 0; i < points.size(); ++i) {
         pnts.push_back(getArray<T>(points[i]));
         dirs.push_back(getArray<T>(directions[i]));
     }
@@ -48,7 +46,7 @@ fg_chart setup_vector_field(fg_window window,
     Array<T> dIn = detail::join(1, dirs);
 
     // do transpose if required
-    if(transpose_) {
+    if (transpose_) {
         pIn = transpose<T>(pIn, false);
         dIn = transpose<T>(dIn, false);
     }
@@ -58,85 +56,82 @@ fg_chart setup_vector_field(fg_window window,
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
 
-    if(pIn.dims()[0] == 2) {
-        if (props->col>-1 && props->row>-1)
-            chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
+    if (pIn.dims()[0] == 2) {
+        if (props->col > -1 && props->row > -1)
+            chart =
+                fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
         else
             chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
     } else {
-        if (props->col>-1 && props->row>-1)
-            chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
+        if (props->col > -1 && props->row > -1)
+            chart =
+                fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
         else
             chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
     }
 
-    fg_vector_field vfield = fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
+    fg_vector_field vfield =
+        fgMngr.getVectorField(chart, pIn.dims()[1], getGLType<T>());
 
     // ArrayFire LOGO dark blue shade
     FG_CHECK(_.fg_set_vector_field_color(vfield, 0.130f, 0.173f, 0.263f, 1.0));
 
     // If chart axes limits do not have a manual override
     // then compute and set axes limits
-    if(!fgMngr.getChartAxesOverride(chart)) {
+    if (!fgMngr.getChartAxesOverride(chart)) {
         float cmin[3], cmax[3];
-        T     dmin[3], dmax[3];
-        FG_CHECK(_.fg_get_chart_axes_limits(&cmin[0], &cmax[0],
-                                          &cmin[1], &cmax[1],
-                                          &cmin[2], &cmax[2],
-                                          chart));
+        T dmin[3], dmax[3];
+        FG_CHECK(_.fg_get_chart_axes_limits(
+            &cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2], chart));
         copyData(dmin, reduce<af_min_t, T, T>(pIn, 1));
         copyData(dmax, reduce<af_max_t, T, T>(pIn, 1));
 
-        if(cmin[0] == 0 && cmax[0] == 0
-        && cmin[1] == 0 && cmax[1] == 0
-        && cmin[2] == 0 && cmax[2] == 0) {
+        if (cmin[0] == 0 && cmax[0] == 0 && cmin[1] == 0 && cmax[1] == 0 &&
+            cmin[2] == 0 && cmax[2] == 0) {
             // No previous limits. Set without checking
             cmin[0] = step_round(dmin[0], false);
             cmax[0] = step_round(dmax[0], true);
             cmin[1] = step_round(dmin[1], false);
             cmax[1] = step_round(dmax[1], true);
-            if(pIn.dims()[0] == 3) cmin[2] = step_round(dmin[2], false);
-            if(pIn.dims()[0] == 3) cmax[2] = step_round(dmax[2], true);
+            if (pIn.dims()[0] == 3) cmin[2] = step_round(dmin[2], false);
+            if (pIn.dims()[0] == 3) cmax[2] = step_round(dmax[2], true);
         } else {
-            if(cmin[0] > dmin[0])       cmin[0] = step_round(dmin[0], false);
-            if(cmax[0] < dmax[0])       cmax[0] = step_round(dmax[0], true);
-            if(cmin[1] > dmin[1])       cmin[1] = step_round(dmin[1], false);
-            if(cmax[1] < dmax[1])       cmax[1] = step_round(dmax[1], true);
-            if(pIn.dims()[0] == 3) {
-                if(cmin[2] > dmin[2])   cmin[2] = step_round(dmin[2], false);
-                if(cmax[2] < dmax[2])   cmax[2] = step_round(dmax[2], true);
+            if (cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
+            if (cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
+            if (cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
+            if (cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
+            if (pIn.dims()[0] == 3) {
+                if (cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
+                if (cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
             }
         }
-        FG_CHECK(_.fg_set_chart_axes_limits(chart,
-                                          cmin[0], cmax[0],
-                                          cmin[1], cmax[1],
-                                          cmin[2], cmax[2]));
+        FG_CHECK(_.fg_set_chart_axes_limits(chart, cmin[0], cmax[0], cmin[1],
+                                            cmax[1], cmin[2], cmax[2]));
     }
     copy_vector_field<T>(pIn, dIn, vfield);
 
     return chart;
 }
 
-af_err vectorFieldWrapper(const af_window window,
-                          const af_array points, const af_array directions,
-                          const af_cell* const props)
-{
+af_err vectorFieldWrapper(const af_window window, const af_array points,
+                          const af_array directions,
+                          const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& pInfo = getInfo(points);
-        af::dim4 pDims  = pInfo.dims();
-        af_dtype pType  = pInfo.getType();
+        af::dim4 pDims         = pInfo.dims();
+        af_dtype pType         = pInfo.getType();
 
         const ArrayInfo& dInfo = getInfo(directions);
-        af::dim4 dDims  = dInfo.dims();
-        af_dtype dType  = dInfo.getType();
+        af::dim4 dDims         = dInfo.dims();
+        af_dtype dType         = dInfo.getType();
 
         DIM_ASSERT(0, pDims == dDims);
         DIM_ASSERT(0, pDims.ndims() == 2);
-        DIM_ASSERT(0, pDims[1] == 2 || pDims[1] == 3); // Columns:P 2 means 2D and 3 means 3D
+        DIM_ASSERT(0,
+                   pDims[1] == 2 ||
+                       pDims[1] == 3);  // Columns:P 2 means 2D and 3 means 3D
 
         TYPE_ASSERT(pType == dType);
 
@@ -150,23 +145,35 @@ af_err vectorFieldWrapper(const af_window window,
         vector<af_array> dirs;
         dirs.push_back(directions);
 
-        switch(pType) {
-            case f32: chart = setup_vector_field<float  >(window, pnts, dirs, props); break;
-            case s32: chart = setup_vector_field<int    >(window, pnts, dirs, props); break;
-            case u32: chart = setup_vector_field<uint   >(window, pnts, dirs, props); break;
-            case s16: chart = setup_vector_field<short  >(window, pnts, dirs, props); break;
-            case u16: chart = setup_vector_field<ushort >(window, pnts, dirs, props); break;
-            case u8 : chart = setup_vector_field<uchar  >(window, pnts, dirs, props); break;
-            default:  TYPE_ERROR(1, pType);
+        switch (pType) {
+            case f32:
+                chart = setup_vector_field<float>(window, pnts, dirs, props);
+                break;
+            case s32:
+                chart = setup_vector_field<int>(window, pnts, dirs, props);
+                break;
+            case u32:
+                chart = setup_vector_field<uint>(window, pnts, dirs, props);
+                break;
+            case s16:
+                chart = setup_vector_field<short>(window, pnts, dirs, props);
+                break;
+            case u16:
+                chart = setup_vector_field<ushort>(window, pnts, dirs, props);
+                break;
+            case u8:
+                chart = setup_vector_field<uchar>(window, pnts, dirs, props);
+                break;
+            default: TYPE_ERROR(1, pType);
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
@@ -175,43 +182,36 @@ af_err vectorFieldWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-af_err vectorFieldWrapper(const af_window window,
-                          const af_array xPoints,
-                          const af_array yPoints,
-                          const af_array zPoints,
-                          const af_array xDirs,
-                          const af_array yDirs,
-                          const af_array zDirs,
-                          const af_cell* const props)
-{
+af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
+                          const af_array yPoints, const af_array zPoints,
+                          const af_array xDirs, const af_array yDirs,
+                          const af_array zDirs, const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_SUCCESS);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_SUCCESS); }
 
         const ArrayInfo& xpInfo = getInfo(xPoints);
         const ArrayInfo& ypInfo = getInfo(yPoints);
         const ArrayInfo& zpInfo = getInfo(zPoints);
 
-        af::dim4 xpDims  = xpInfo.dims();
-        af::dim4 ypDims  = ypInfo.dims();
-        af::dim4 zpDims  = zpInfo.dims();
+        af::dim4 xpDims = xpInfo.dims();
+        af::dim4 ypDims = ypInfo.dims();
+        af::dim4 zpDims = zpInfo.dims();
 
-        af_dtype xpType  = xpInfo.getType();
-        af_dtype ypType  = ypInfo.getType();
-        af_dtype zpType  = zpInfo.getType();
+        af_dtype xpType = xpInfo.getType();
+        af_dtype ypType = ypInfo.getType();
+        af_dtype zpType = zpInfo.getType();
 
         const ArrayInfo& xdInfo = getInfo(xDirs);
         const ArrayInfo& ydInfo = getInfo(yDirs);
         const ArrayInfo& zdInfo = getInfo(zDirs);
 
-        af::dim4 xdDims  = xdInfo.dims();
-        af::dim4 ydDims  = ydInfo.dims();
-        af::dim4 zdDims  = zdInfo.dims();
+        af::dim4 xdDims = xdInfo.dims();
+        af::dim4 ydDims = ydInfo.dims();
+        af::dim4 zdDims = zdInfo.dims();
 
-        af_dtype xdType  = xdInfo.getType();
-        af_dtype ydType  = ydInfo.getType();
-        af_dtype zdType  = zdInfo.getType();
+        af_dtype xdType = xdInfo.getType();
+        af_dtype ydType = ydInfo.getType();
+        af_dtype zdType = zdInfo.getType();
 
         // Assert all arrays are equal dimensions
         DIM_ASSERT(1, xpDims == xdDims);
@@ -246,23 +246,41 @@ af_err vectorFieldWrapper(const af_window window,
         directions.push_back(yDirs);
         directions.push_back(zDirs);
 
-        switch(xpType) {
-            case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
-            case s32: chart = setup_vector_field<int    >(window, points, directions, props); break;
-            case u32: chart = setup_vector_field<uint   >(window, points, directions, props); break;
-            case s16: chart = setup_vector_field<short  >(window, points, directions, props); break;
-            case u16: chart = setup_vector_field<ushort >(window, points, directions, props); break;
-            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
-            default:  TYPE_ERROR(1, xpType);
+        switch (xpType) {
+            case f32:
+                chart = setup_vector_field<float>(window, points, directions,
+                                                  props);
+                break;
+            case s32:
+                chart =
+                    setup_vector_field<int>(window, points, directions, props);
+                break;
+            case u32:
+                chart =
+                    setup_vector_field<uint>(window, points, directions, props);
+                break;
+            case s16:
+                chart = setup_vector_field<short>(window, points, directions,
+                                                  props);
+                break;
+            case u16:
+                chart = setup_vector_field<ushort>(window, points, directions,
+                                                   props);
+                break;
+            case u8:
+                chart = setup_vector_field<uchar>(window, points, directions,
+                                                  props);
+                break;
+            default: TYPE_ERROR(1, xpType);
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
@@ -271,33 +289,29 @@ af_err vectorFieldWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-af_err vectorFieldWrapper(const af_window window,
-                          const af_array xPoints, const af_array yPoints,
-                          const af_array xDirs, const af_array yDirs,
-                          const af_cell* const props)
-{
+af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
+                          const af_array yPoints, const af_array xDirs,
+                          const af_array yDirs, const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_SUCCESS);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_SUCCESS); }
 
         const ArrayInfo& xpInfo = getInfo(xPoints);
         const ArrayInfo& ypInfo = getInfo(yPoints);
 
-        af::dim4 xpDims  = xpInfo.dims();
-        af::dim4 ypDims  = ypInfo.dims();
+        af::dim4 xpDims = xpInfo.dims();
+        af::dim4 ypDims = ypInfo.dims();
 
-        af_dtype xpType  = xpInfo.getType();
-        af_dtype ypType  = ypInfo.getType();
+        af_dtype xpType = xpInfo.getType();
+        af_dtype ypType = ypInfo.getType();
 
         const ArrayInfo& xdInfo = getInfo(xDirs);
         const ArrayInfo& ydInfo = getInfo(yDirs);
 
-        af::dim4 xdDims  = xdInfo.dims();
-        af::dim4 ydDims  = ydInfo.dims();
+        af::dim4 xdDims = xdInfo.dims();
+        af::dim4 ydDims = ydInfo.dims();
 
-        af_dtype xdType  = xdInfo.getType();
-        af_dtype ydType  = ydInfo.getType();
+        af_dtype xdType = xdInfo.getType();
+        af_dtype ydType = ydInfo.getType();
 
         // Assert all arrays are equal dimensions
         DIM_ASSERT(1, xpDims == xdDims);
@@ -326,24 +340,42 @@ af_err vectorFieldWrapper(const af_window window,
         directions.push_back(xDirs);
         directions.push_back(yDirs);
 
-        switch(xpType) {
-            case f32: chart = setup_vector_field<float  >(window, points, directions, props); break;
-            case s32: chart = setup_vector_field<int    >(window, points, directions, props); break;
-            case u32: chart = setup_vector_field<uint   >(window, points, directions, props); break;
-            case s16: chart = setup_vector_field<short  >(window, points, directions, props); break;
-            case u16: chart = setup_vector_field<ushort >(window, points, directions, props); break;
-            case u8 : chart = setup_vector_field<uchar  >(window, points, directions, props); break;
-            default:  TYPE_ERROR(1, xpType);
+        switch (xpType) {
+            case f32:
+                chart = setup_vector_field<float>(window, points, directions,
+                                                  props);
+                break;
+            case s32:
+                chart =
+                    setup_vector_field<int>(window, points, directions, props);
+                break;
+            case u32:
+                chart =
+                    setup_vector_field<uint>(window, points, directions, props);
+                break;
+            case s16:
+                chart = setup_vector_field<short>(window, points, directions,
+                                                  props);
+                break;
+            case u16:
+                chart = setup_vector_field<ushort>(window, points, directions,
+                                                   props);
+                break;
+            case u8:
+                chart = setup_vector_field<uchar>(window, points, directions,
+                                                  props);
+                break;
+            default: TYPE_ERROR(1, xpType);
         }
 
         auto gridDims = forgeManager().getWindowGrid(window);
 
         ForgeModule& _ = graphics::forgePlugin();
-        if (props->col>-1 && props->row>-1) {
-            FG_CHECK(_.fg_draw_chart_to_cell(window,
-                                             gridDims.first, gridDims.second,
-                                             props->row * gridDims.second + props->col,
-                                             chart, props->title));
+        if (props->col > -1 && props->row > -1) {
+            FG_CHECK(_.fg_draw_chart_to_cell(
+                window, gridDims.first, gridDims.second,
+                props->row * gridDims.second + props->col, chart,
+                props->title));
         } else {
             FG_CHECK(_.fg_draw_chart(window, chart));
         }
@@ -352,30 +384,24 @@ af_err vectorFieldWrapper(const af_window window,
     return AF_SUCCESS;
 }
 
-af_err af_draw_vector_field_nd(const af_window wind,
-                               const af_array points,
+af_err af_draw_vector_field_nd(const af_window wind, const af_array points,
                                const af_array directions,
-                               const af_cell* const props)
-{
+                               const af_cell* const props) {
     return vectorFieldWrapper(wind, points, directions, props);
 }
 
-af_err af_draw_vector_field_3d(
-                const af_window wind,
-                const af_array xPoints, const af_array yPoints,
-                const af_array zPoints,
-                const af_array xDirs, const af_array yDirs,
-                const af_array zDirs,
-                const af_cell* const props)
-{
-    return vectorFieldWrapper(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
+af_err af_draw_vector_field_3d(const af_window wind, const af_array xPoints,
+                               const af_array yPoints, const af_array zPoints,
+                               const af_array xDirs, const af_array yDirs,
+                               const af_array zDirs,
+                               const af_cell* const props) {
+    return vectorFieldWrapper(wind, xPoints, yPoints, zPoints, xDirs, yDirs,
+                              zDirs, props);
 }
 
-af_err af_draw_vector_field_2d(
-                const af_window wind,
-                const af_array xPoints, const af_array yPoints,
-                const af_array xDirs, const af_array yDirs,
-                const af_cell* const props)
-{
+af_err af_draw_vector_field_2d(const af_window wind, const af_array xPoints,
+                               const af_array yPoints, const af_array xDirs,
+                               const af_array yDirs,
+                               const af_cell* const props) {
     return vectorFieldWrapper(wind, xPoints, yPoints, xDirs, yDirs, props);
 }
diff --git a/src/api/c/version.cpp b/src/api/c/version.cpp
index 91d24cb823..ce471bd9d1 100644
--- a/src/api/c/version.cpp
+++ b/src/api/c/version.cpp
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/util.h>
 #include <version.hpp>
+#include <af/util.h>
 
-af_err af_get_version(int *major, int *minor, int *patch)
-{
+af_err af_get_version(int *major, int *minor, int *patch) {
     *major = AF_VERSION_MAJOR;
     *minor = AF_VERSION_MINOR;
     *patch = AF_VERSION_PATCH;
@@ -19,7 +18,4 @@ af_err af_get_version(int *major, int *minor, int *patch)
     return AF_SUCCESS;
 }
 
-const char *af_get_revision()
-{
-    return AF_REVISION;
-}
+const char *af_get_revision() { return AF_REVISION; }
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 4e663a2bf7..8f2bf468fa 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -7,51 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
-#include <af/algorithm.h>
+#include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <ops.hpp>
 #include <where.hpp>
-#include <backend.hpp>
+#include <af/algorithm.h>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array where(const af_array in)
-{
+static inline af_array where(const af_array in) {
     // Making it more explicit that the output is uint
     return getHandle<uint>(where<T>(getArray<T>(in)));
 }
 
-af_err af_where(af_array *idx, const af_array in)
-{
+af_err af_where(af_array* idx, const af_array in) {
     try {
         const ArrayInfo& i_info = getInfo(in);
-        af_dtype type = i_info.getType();
+        af_dtype type           = i_info.getType();
 
-        if(i_info.ndims() == 0) {
+        if (i_info.ndims() == 0) {
             return af_create_handle(idx, 0, nullptr, u32);
         }
 
         af_array res;
-        switch(type) {
-        case f32: res = where<float  >(in); break;
-        case f64: res = where<double >(in); break;
-        case c32: res = where<cfloat >(in); break;
-        case c64: res = where<cdouble>(in); break;
-        case s32: res = where<int    >(in); break;
-        case u32: res = where<uint   >(in); break;
-        case s64: res = where<intl   >(in); break;
-        case u64: res = where<uintl  >(in); break;
-        case s16: res = where<short  >(in); break;
-        case u16: res = where<ushort >(in); break;
-        case u8 : res = where<uchar  >(in); break;
-        case b8 : res = where<char   >(in); break;
-        default:
-            TYPE_ERROR(1, type);
+        switch (type) {
+            case f32: res = where<float>(in); break;
+            case f64: res = where<double>(in); break;
+            case c32: res = where<cfloat>(in); break;
+            case c64: res = where<cdouble>(in); break;
+            case s32: res = where<int>(in); break;
+            case u32: res = where<uint>(in); break;
+            case s64: res = where<intl>(in); break;
+            case u64: res = where<uintl>(in); break;
+            case s16: res = where<short>(in); break;
+            case u16: res = where<ushort>(in); break;
+            case u8: res = where<uchar>(in); break;
+            case b8: res = where<char>(in); break;
+            default: TYPE_ERROR(1, type);
         }
         std::swap(*idx, res);
     }
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index e496ac7f08..a1d2e1afb9 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -7,21 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-
-#include <af/graphics.h>
 #include <af/algorithm.h>
+#include <af/graphics.h>
 
-#include <common/graphics_common.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
+#include <common/err_common.hpp>
+#include <common/graphics_common.hpp>
 #include <platform.hpp>
 
 using af::dim4;
 using namespace detail;
 using namespace graphics;
 
-af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
-{
+af_err af_create_window(af_window* out, const int width, const int height,
+                        const char* const title) {
     try {
         ForgeManager& fgMngr = forgeManager();
         fg_window mainWnd    = fgMngr.getMainWindow();
@@ -32,7 +31,8 @@ af_err af_create_window(af_window *out, const int width, const int height, const
 
         fg_window temp = nullptr;
 
-        FG_CHECK(forgePlugin().fg_create_window(&temp, width, height, title, mainWnd, false));
+        FG_CHECK(forgePlugin().fg_create_window(&temp, width, height, title,
+                                                mainWnd, false));
 
         fgMngr.setWindowChartGrid(temp, 1, 1);
 
@@ -42,62 +42,49 @@ af_err af_create_window(af_window *out, const int width, const int height, const
     return AF_SUCCESS;
 }
 
-af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
-{
+af_err af_set_position(const af_window wind, const unsigned x,
+                       const unsigned y) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         FG_CHECK(forgePlugin().fg_set_window_position(wind, x, y));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_title(const af_window wind, const char* const title)
-{
+af_err af_set_title(const af_window wind, const char* const title) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         FG_CHECK(forgePlugin().fg_set_window_title(wind, title));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
-{
+af_err af_set_size(const af_window wind, const unsigned w, const unsigned h) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         FG_CHECK(forgePlugin().fg_set_window_size(wind, w, h));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_grid(const af_window wind, const int rows, const int cols)
-{
+af_err af_grid(const af_window wind, const int rows, const int cols) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         forgeManager().setWindowChartGrid(wind, rows, cols);
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_axes_limits_compute(const af_window window,
-                                  const af_array x, const af_array y, const af_array z,
-                                  const bool exact, const af_cell* const props)
-{
+af_err af_set_axes_limits_compute(const af_window window, const af_array x,
+                                  const af_array y, const af_array z,
+                                  const bool exact,
+                                  const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         ForgeManager& fgMngr = forgeManager();
 
@@ -118,37 +105,34 @@ af_err af_set_axes_limits_compute(const af_window window,
         AF_CHECK(af_min_all(&ymin, NULL, y));
         AF_CHECK(af_max_all(&ymax, NULL, y));
 
-        if(ctype == FG_CHART_3D) {
+        if (ctype == FG_CHART_3D) {
             AF_CHECK(af_min_all(&zmin, NULL, z));
             AF_CHECK(af_max_all(&zmax, NULL, z));
         }
 
-        if(!exact) {
+        if (!exact) {
             xmin = step_round(xmin, false);
-            xmax = step_round(xmax, true );
+            xmax = step_round(xmax, true);
             ymin = step_round(ymin, false);
-            ymax = step_round(ymax, true );
+            ymax = step_round(ymax, true);
             zmin = step_round(zmin, false);
-            zmax = step_round(zmax, true );
+            zmax = step_round(zmax, true);
         }
 
         fgMngr.setChartAxesOverride(chart);
-        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, xmin, xmax,
-                                          ymin, ymax, zmin, zmax));
+        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, xmin, xmax, ymin,
+                                                        ymax, zmin, zmax));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_axes_limits_2d(const af_window window,
-                             const float xmin, const float xmax,
-                             const float ymin, const float ymax,
-                             const bool exact, const af_cell* const props)
-{
+af_err af_set_axes_limits_2d(const af_window window, const float xmin,
+                             const float xmax, const float ymin,
+                             const float ymax, const bool exact,
+                             const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         ForgeManager& fgMngr = forgeManager();
 
@@ -167,31 +151,28 @@ af_err af_set_axes_limits_2d(const af_window window,
         float _xmax = xmax;
         float _ymin = ymin;
         float _ymax = ymax;
-        if(!exact) {
+        if (!exact) {
             _xmin = step_round(_xmin, false);
-            _xmax = step_round(_xmax, true );
+            _xmax = step_round(_xmax, true);
             _ymin = step_round(_ymin, false);
-            _ymax = step_round(_ymax, true );
+            _ymax = step_round(_ymax, true);
         }
 
         fgMngr.setChartAxesOverride(chart);
-        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, _xmin, _xmax,
-                                          _ymin, _ymax, 0.0f, 0.0f));
+        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(
+            chart, _xmin, _xmax, _ymin, _ymax, 0.0f, 0.0f));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_axes_limits_3d(const af_window window,
-                             const float xmin, const float xmax,
-                             const float ymin, const float ymax,
-                             const float zmin, const float zmax,
-                             const bool exact, const af_cell* const props)
-{
+af_err af_set_axes_limits_3d(const af_window window, const float xmin,
+                             const float xmax, const float ymin,
+                             const float ymax, const float zmin,
+                             const float zmax, const bool exact,
+                             const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         ForgeManager& fgMngr = forgeManager();
 
@@ -212,33 +193,28 @@ af_err af_set_axes_limits_3d(const af_window window,
         float _ymax = ymax;
         float _zmin = zmin;
         float _zmax = zmax;
-        if(!exact) {
+        if (!exact) {
             _xmin = step_round(_xmin, false);
-            _xmax = step_round(_xmax, true );
+            _xmax = step_round(_xmax, true);
             _ymin = step_round(_ymin, false);
-            _ymax = step_round(_ymax, true );
+            _ymax = step_round(_ymax, true);
             _zmin = step_round(_zmin, false);
-            _zmax = step_round(_zmax, true );
+            _zmax = step_round(_zmax, true);
         }
 
         fgMngr.setChartAxesOverride(chart);
-        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(chart, _xmin, _xmax,
-                                          _ymin, _ymax, _zmin, _zmax));
+        FG_CHECK(forgePlugin().fg_set_chart_axes_limits(
+            chart, _xmin, _xmax, _ymin, _ymax, _zmin, _zmax));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_axes_titles(const af_window window,
-                          const char * const xtitle,
-                          const char * const ytitle,
-                          const char * const ztitle,
-                          const af_cell* const props)
-{
+af_err af_set_axes_titles(const af_window window, const char* const xtitle,
+                          const char* const ytitle, const char* const ztitle,
+                          const af_cell* const props) {
     try {
-        if(window == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         ForgeManager& fgMngr = forgeManager();
 
@@ -251,42 +227,34 @@ af_err af_set_axes_titles(const af_window window,
         else
             chart = fgMngr.getChart(window, 0, 0, ctype);
 
-        FG_CHECK(forgePlugin().fg_set_chart_axes_titles(chart, xtitle, ytitle, ztitle));
+        FG_CHECK(forgePlugin().fg_set_chart_axes_titles(chart, xtitle, ytitle,
+                                                        ztitle));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_show(const af_window wind)
-{
+af_err af_show(const af_window wind) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         FG_CHECK(forgePlugin().fg_swap_window_buffers(wind));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_is_window_closed(bool *out, const af_window wind)
-{
+af_err af_is_window_closed(bool* out, const af_window wind) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         FG_CHECK(forgePlugin().fg_close_window(out, wind));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err af_set_visibility(const af_window wind, const bool is_visible)
-{
+af_err af_set_visibility(const af_window wind, const bool is_visible) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         if (is_visible) {
             FG_CHECK(forgePlugin().fg_show_window(wind));
         } else {
@@ -297,12 +265,9 @@ af_err af_set_visibility(const af_window wind, const bool is_visible)
     return AF_SUCCESS;
 }
 
-af_err af_destroy_window(const af_window wind)
-{
+af_err af_destroy_window(const af_window wind) {
     try {
-        if(wind == 0) {
-            AF_ERROR("Not a valid window", AF_ERR_INTERNAL);
-        }
+        if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
         forgeManager().setWindowChartGrid(wind, 0, 0);
         FG_CHECK(forgePlugin().fg_release_window(wind));
     }
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index cd0baf2fb9..2ece64699d 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -7,39 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <af/defines.h>
-#include <common/err_common.hpp>
-#include <handle.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <wrap.hpp>
+#include <af/defines.h>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array wrap(const af_array in,
-                            const dim_t ox, const dim_t oy,
-                            const dim_t wx, const dim_t wy,
-                            const dim_t sx, const dim_t sy,
-                            const dim_t px, const dim_t py,
-                            const bool is_column)
-{
-    return getHandle(wrap<T>(getArray<T>(in), ox, oy, wx, wy, sx, sy, px, py, is_column));
+static inline af_array wrap(const af_array in, const dim_t ox, const dim_t oy,
+                            const dim_t wx, const dim_t wy, const dim_t sx,
+                            const dim_t sy, const dim_t px, const dim_t py,
+                            const bool is_column) {
+    return getHandle(
+        wrap<T>(getArray<T>(in), ox, oy, wx, wy, sx, sy, px, py, is_column));
 }
 
-af_err af_wrap(af_array *out, const af_array in,
-               const dim_t ox, const dim_t oy,
-               const dim_t wx, const dim_t wy,
-               const dim_t sx, const dim_t sy,
-               const dim_t px, const dim_t py,
-               const bool is_column)
-{
+af_err af_wrap(af_array* out, const af_array in, const dim_t ox, const dim_t oy,
+               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+               const dim_t px, const dim_t py, const bool is_column) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype type = info.getType();
-        af::dim4 idims = info.dims();
+        af_dtype type         = info.getType();
+        af::dim4 idims        = info.dims();
 
         ARG_ASSERT(2, wx > 0);
         ARG_ASSERT(3, wx > 0);
@@ -57,22 +51,58 @@ af_err af_wrap(af_array *out, const af_array in,
 
         af_array output;
 
-        switch(type) {
-            case f32: output = wrap<float  >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case f64: output = wrap<double >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case c32: output = wrap<cfloat >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case c64: output = wrap<cdouble>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case s32: output = wrap<int    >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case u32: output = wrap<uint   >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case s64: output = wrap<intl   >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case u64: output = wrap<uintl  >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case s16: output = wrap<short  >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case u16: output = wrap<ushort >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case u8:  output = wrap<uchar  >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            case b8:  output = wrap<char   >(in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-            default:  TYPE_ERROR(1, type);
+        switch (type) {
+            case f32:
+                output =
+                    wrap<float>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case f64:
+                output =
+                    wrap<double>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case c32:
+                output =
+                    wrap<cfloat>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case c64:
+                output = wrap<cdouble>(in, ox, oy, wx, wy, sx, sy, px, py,
+                                       is_column);
+                break;
+            case s32:
+                output =
+                    wrap<int>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u32:
+                output =
+                    wrap<uint>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case s64:
+                output =
+                    wrap<intl>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u64:
+                output =
+                    wrap<uintl>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case s16:
+                output =
+                    wrap<short>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u16:
+                output =
+                    wrap<ushort>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case u8:
+                output =
+                    wrap<uchar>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            case b8:
+                output =
+                    wrap<char>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+                break;
+            default: TYPE_ERROR(1, type);
         }
-        std::swap(*out,output);
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index 9d1b357e0a..1ee1065085 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <af/dim4.hpp>
-#include <af/image.h>
-#include <handle.hpp>
-#include <common/err_common.hpp>
-#include <backend.hpp>
 #include <arith.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <handle.hpp>
 #include <join.hpp>
 #include <math.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 
 using af::dim4;
 using namespace detail;
 
 template<typename T>
-static Array<T> mix(const Array<T>& X, const Array<T>& Y,
-                double xf, double yf)
-{
-    dim4 dims = X.dims();
+static Array<T> mix(const Array<T>& X, const Array<T>& Y, double xf,
+                    double yf) {
+    dim4 dims        = X.dims();
     Array<T> xf_cnst = createValueArray<T>(dims, xf);
     Array<T> yf_cnst = createValueArray<T>(dims, yf);
 
@@ -36,9 +35,8 @@ static Array<T> mix(const Array<T>& X, const Array<T>& Y,
 
 template<typename T>
 static Array<T> mix(const Array<T>& X, const Array<T>& Y, const Array<T>& Z,
-                double xf, double yf, double zf)
-{
-    dim4 dims = X.dims();
+                    double xf, double yf, double zf) {
+    dim4 dims        = X.dims();
     Array<T> xf_cnst = createValueArray<T>(dims, xf);
     Array<T> yf_cnst = createValueArray<T>(dims, yf);
     Array<T> zf_cnst = createValueArray<T>(dims, zf);
@@ -52,9 +50,9 @@ static Array<T> mix(const Array<T>& X, const Array<T>& Y, const Array<T>& Z,
 }
 
 template<typename T>
-static Array<T> digitize(const Array<T> ch, const double scale, const double offset)
-{
-    dim4 dims = ch.dims();
+static Array<T> digitize(const Array<T> ch, const double scale,
+                         const double offset) {
+    dim4 dims     = ch.dims();
     Array<T> base = createValueArray<T>(dims, scalar<T>(offset));
     Array<T> cnst = createValueArray<T>(dims, scalar<T>(scale));
     Array<T> scl  = arithOp<T, af_mul_t>(ch, cnst, dims);
@@ -62,25 +60,21 @@ static Array<T> digitize(const Array<T> ch, const double scale, const double off
 }
 
 template<typename T, bool isYCbCr2RGB>
-static af_array convert(const af_array& in, const af_ycc_std standard)
-{
+static af_array convert(const af_array& in, const af_ycc_std standard) {
     static const float INV_219 = 0.004566210;
     static const float INV_112 = 0.008928571;
-    const static float k[6] = {
-        0.1140f, 0.2990f,
-        0.0722f, 0.2126f,
-        0.0593f, 0.2627f
-    };
-    unsigned stdIdx = 0; // Default standard is AF_YCC_601
-    switch(standard) {
-        case AF_YCC_709 : stdIdx = 2; break;
+    const static float k[6]    = {0.1140f, 0.2990f, 0.0722f,
+                               0.2126f, 0.0593f, 0.2627f};
+    unsigned stdIdx            = 0;  // Default standard is AF_YCC_601
+    switch (standard) {
+        case AF_YCC_709: stdIdx = 2; break;
         case AF_YCC_2020: stdIdx = 4; break;
-        default         : stdIdx = 0; break;
+        default: stdIdx = 0; break;
     }
-    float kb = k[stdIdx];
-    float kr = k[stdIdx+1];
-    float kl = 1.0f - kb - kr;
-    float invKl = 1/kl;
+    float kb    = k[stdIdx];
+    float kr    = k[stdIdx + 1];
+    float kl    = 1.0f - kb - kr;
+    float invKl = 1 / kl;
 
     // extract three channels as three slices
     // prepare sequence objects
@@ -98,26 +92,27 @@ static af_array convert(const af_array& in, const af_ycc_std standard)
     Array<T> Z = createSubArray(input, indices, false);
 
     if (isYCbCr2RGB) {
-        dim4 dims = X.dims();
+        dim4 dims    = X.dims();
         Array<T> yc  = createValueArray<T>(dims, 16);
         Array<T> cc  = createValueArray<T>(dims, 128);
         Array<T> Y_  = arithOp<T, af_sub_t>(X, yc, dims);
         Array<T> Cb_ = arithOp<T, af_sub_t>(Y, cc, dims);
         Array<T> Cr_ = arithOp<T, af_sub_t>(Z, cc, dims);
-        Array<T> R   = mix<T>(Y_, Cr_, INV_219, INV_112*(1-kr));
-        Array<T> G   = mix<T>(Y_, Cr_, Cb_,
-                              INV_219,
-                              INV_112*(kr-1)*kr*invKl,
-                              INV_112*(kb-1)*kb*invKl);
-        Array<T> B   = mix<T>(Y_, Cb_, INV_219, INV_112*(1-kb));
+        Array<T> R   = mix<T>(Y_, Cr_, INV_219, INV_112 * (1 - kr));
+        Array<T> G =
+            mix<T>(Y_, Cr_, Cb_, INV_219, INV_112 * (kr - 1) * kr * invKl,
+                   INV_112 * (kb - 1) * kb * invKl);
+        Array<T> B = mix<T>(Y_, Cb_, INV_219, INV_112 * (1 - kb));
         // join channels
         Array<T> RG = join<T, T>(2, R, G);
         return getHandle(join<T, T>(2, RG, B));
     } else {
-        Array<T> Ey  = mix<T>(X, Y, Z, kr, kl, kb);
-        Array<T> Ecr = mix<T>(X, Y, Z, 0.5, 0.5*kl/(kr-1), 0.5*kb/(kr-1));
-        Array<T> Ecb = mix<T>(X, Y, Z, 0.5*kr/(kb-1), 0.5*kl/(kb-1), 0.5);
-        Array<T> Y = digitize<T>(Ey, 219.0, 16.0);
+        Array<T> Ey = mix<T>(X, Y, Z, kr, kl, kb);
+        Array<T> Ecr =
+            mix<T>(X, Y, Z, 0.5, 0.5 * kl / (kr - 1), 0.5 * kb / (kr - 1));
+        Array<T> Ecb =
+            mix<T>(X, Y, Z, 0.5 * kr / (kb - 1), 0.5 * kl / (kb - 1), 0.5);
+        Array<T> Y  = digitize<T>(Ey, 219.0, 16.0);
         Array<T> Cr = digitize<T>(Ecr, 224.0, 128.0);
         Array<T> Cb = digitize<T>(Ecb, 224.0, 128.0);
         // join channels
@@ -127,19 +122,20 @@ static af_array convert(const af_array& in, const af_ycc_std standard)
 }
 
 template<bool isYCbCr2RGB>
-af_err convert(af_array* out, const af_array& in, const af_ycc_std standard)
-{
+af_err convert(af_array* out, const af_array& in, const af_ycc_std standard) {
     try {
         const ArrayInfo& info = getInfo(in);
-        af_dtype iType = info.getType();
-        af::dim4 inputDims = info.dims();
+        af_dtype iType        = info.getType();
+        af::dim4 inputDims    = info.dims();
 
         ARG_ASSERT(1, (inputDims.ndims() >= 3));
 
         af_array output = 0;
         switch (iType) {
-            case f64: output = convert<double, isYCbCr2RGB>(in, standard); break;
-            case f32: output = convert<float , isYCbCr2RGB>(in, standard); break;
+            case f64:
+                output = convert<double, isYCbCr2RGB>(in, standard);
+                break;
+            case f32: output = convert<float, isYCbCr2RGB>(in, standard); break;
             default: TYPE_ERROR(1, iType); break;
         }
         std::swap(*out, output);
@@ -148,12 +144,12 @@ af_err convert(af_array* out, const af_array& in, const af_ycc_std standard)
     return AF_SUCCESS;
 }
 
-af_err af_ycbcr2rgb(af_array* out, const af_array in, const af_ycc_std standard)
-{
+af_err af_ycbcr2rgb(af_array* out, const af_array in,
+                    const af_ycc_std standard) {
     return convert<true>(out, in, standard);
 }
 
-af_err af_rgb2ycbcr(af_array* out, const af_array in, const af_ycc_std standard)
-{
+af_err af_rgb2ycbcr(af_array* out, const af_array in,
+                    const af_ycc_std standard) {
     return convert<false>(out, in, standard);
 }
diff --git a/src/api/cpp/anisotropic_diffusion.cpp b/src/api/cpp/anisotropic_diffusion.cpp
index be029b1c0c..cd0800c1fa 100644
--- a/src/api/cpp/anisotropic_diffusion.cpp
+++ b/src/api/cpp/anisotropic_diffusion.cpp
@@ -7,19 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 array anisotropicDiffusion(const array& in, const float timestep,
                            const float conductance, const unsigned iterations,
-                           const fluxFunction fftype,
-                           const diffusionEq eq)
-{
+                           const fluxFunction fftype, const diffusionEq eq) {
     af_array out = 0;
-    AF_THROW(af_anisotropic_diffusion(&out, in.get(), timestep, conductance, iterations, fftype, eq));
+    AF_THROW(af_anisotropic_diffusion(&out, in.get(), timestep, conductance,
+                                      iterations, fftype, eq));
     return array(out);
 }
-}
+}  // namespace af
diff --git a/src/api/cpp/approx.cpp b/src/api/cpp/approx.cpp
index fe840cd7ba..4e560572b4 100644
--- a/src/api/cpp/approx.cpp
+++ b/src/api/cpp/approx.cpp
@@ -11,43 +11,38 @@
 #include <af/signal.h>
 #include "error.hpp"
 
-namespace af
-{
-    array approx1(const array& yi, const array &xo, const interpType method, const float offGrid)
-    {
-        af_array yo = 0;
-        AF_THROW(af_approx1(&yo, yi.get(), xo.get(), method, offGrid));
-        return array(yo);
-    }
+namespace af {
+array approx1(const array &yi, const array &xo, const interpType method,
+              const float offGrid) {
+    af_array yo = 0;
+    AF_THROW(af_approx1(&yo, yi.get(), xo.get(), method, offGrid));
+    return array(yo);
+}
 
-    array approx2(const array& zi, const array &xo, const array &yo,
-                  const interpType method, const float offGrid)
-    {
-        af_array zo = 0;
-        AF_THROW(af_approx2(&zo, zi.get(), xo.get(), yo.get(), method, offGrid));
-        return array(zo);
-    }
+array approx2(const array &zi, const array &xo, const array &yo,
+              const interpType method, const float offGrid) {
+    af_array zo = 0;
+    AF_THROW(af_approx2(&zo, zi.get(), xo.get(), yo.get(), method, offGrid));
+    return array(zo);
+}
 
-     array approx1(const array &yi,
-                   const array &xo, const int xdim,
-                   const double xi_beg, const double xi_step,
-                   const interpType method, const float offGrid)
-     {
-        af_array yo = 0;
-        AF_THROW(af_approx1_uniform(&yo, yi.get(), xo.get(), xdim, xi_beg, xi_step, method, offGrid));
-        return array(yo);
-     }
+array approx1(const array &yi, const array &xo, const int xdim,
+              const double xi_beg, const double xi_step,
+              const interpType method, const float offGrid) {
+    af_array yo = 0;
+    AF_THROW(af_approx1_uniform(&yo, yi.get(), xo.get(), xdim, xi_beg, xi_step,
+                                method, offGrid));
+    return array(yo);
+}
 
-    array approx2(const array &zi,
-                  const array &xo, const int xdim, const double xi_beg, const double xi_step,
-                  const array &yo, const int ydim, const double yi_beg, const double yi_step,
-                  const interpType method, const float offGrid)
-    {
-        af_array zo = 0;
-        AF_THROW(af_approx2_uniform(&zo, zi.get(),
-                                    xo.get(), xdim, xi_beg, xi_step,
-                                    yo.get(), ydim, yi_beg, yi_step,
-                                    method, offGrid));
-        return array(zo);
-    }
+array approx2(const array &zi, const array &xo, const int xdim,
+              const double xi_beg, const double xi_step, const array &yo,
+              const int ydim, const double yi_beg, const double yi_step,
+              const interpType method, const float offGrid) {
+    af_array zo = 0;
+    AF_THROW(af_approx2_uniform(&zo, zi.get(), xo.get(), xdim, xi_beg, xi_step,
+                                yo.get(), ydim, yi_beg, yi_step, method,
+                                offGrid));
+    return array(zo);
 }
+}  // namespace af
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 450403cc9b..0469ba3e6a 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -6,852 +6,746 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/array.h>
+#include <af/algorithm.h>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/blas.h>
 #include <af/data.h>
-#include <af/traits.hpp>
-#include <af/util.h>
-#include <af/index.h>
 #include <af/device.h>
 #include <af/gfor.h>
-#include <af/algorithm.h>
+#include <af/index.h>
 #include <af/internal.h>
+#include <af/traits.hpp>
+#include <af/util.h>
 #include "error.hpp"
 
-#include <stdexcept>
 #include <memory>
+#include <stdexcept>
 
-namespace af
-{
-    static int gforDim(af_index_t *indices)
-    {
-        for (int i = 0; i < AF_MAX_DIMS; i++) {
-            if (indices[i].isBatch) return i;
-        }
-        return -1;
+namespace af {
+static int gforDim(af_index_t *indices) {
+    for (int i = 0; i < AF_MAX_DIMS; i++) {
+        if (indices[i].isBatch) return i;
     }
+    return -1;
+}
 
-    static af_array gforReorder(const af_array in, unsigned dim)
-    {
-        // This is here to stop gcc from complaining
-        if (dim > 3) AF_THROW_ERR("GFor: Dimension is invalid", AF_ERR_SIZE);
-        unsigned order[AF_MAX_DIMS] = {0, 1, 2, dim};
-        order[dim] = 3;
-        af_array out;
-        AF_THROW(af_reorder(&out, in, order[0], order[1], order[2], order[3]));
-        return out;
-    }
+static af_array gforReorder(const af_array in, unsigned dim) {
+    // This is here to stop gcc from complaining
+    if (dim > 3) AF_THROW_ERR("GFor: Dimension is invalid", AF_ERR_SIZE);
+    unsigned order[AF_MAX_DIMS] = {0, 1, 2, dim};
+    order[dim]                  = 3;
+    af_array out;
+    AF_THROW(af_reorder(&out, in, order[0], order[1], order[2], order[3]));
+    return out;
+}
 
-    static af::dim4 seqToDims(af_index_t *indices, af::dim4 parentDims, bool reorder = true)
-    {
-        try {
-            af::dim4 odims(1);
-            for (int i = 0; i < AF_MAX_DIMS; i++) {
-                if (indices[i].isSeq) {
-                    odims[i] = calcDim(indices[i].idx.seq, parentDims[i]);
-                } else {
-                    dim_t elems = 0;
-                    AF_THROW(af_get_elements(&elems, indices[i].idx.arr));
-                    odims[i] = elems;
-                }
+static af::dim4 seqToDims(af_index_t *indices, af::dim4 parentDims,
+                          bool reorder = true) {
+    try {
+        af::dim4 odims(1);
+        for (int i = 0; i < AF_MAX_DIMS; i++) {
+            if (indices[i].isSeq) {
+                odims[i] = calcDim(indices[i].idx.seq, parentDims[i]);
+            } else {
+                dim_t elems = 0;
+                AF_THROW(af_get_elements(&elems, indices[i].idx.arr));
+                odims[i] = elems;
             }
+        }
 
-            // Change the dimensions if inside GFOR
-            if (reorder) {
-                for (int i = 0; i < AF_MAX_DIMS; i++) {
-                    if (indices[i].isBatch) {
-                        int tmp = odims[i];
-                        odims[i] = odims[3];
-                        odims[3] = tmp;
-                        break;
-                    }
+        // Change the dimensions if inside GFOR
+        if (reorder) {
+            for (int i = 0; i < AF_MAX_DIMS; i++) {
+                if (indices[i].isBatch) {
+                    int tmp  = odims[i];
+                    odims[i] = odims[3];
+                    odims[3] = tmp;
+                    break;
                 }
             }
-            return odims;
-        } catch(std::logic_error &err) {
-            AF_THROW_ERR(err.what(), AF_ERR_SIZE);
         }
-    }
-
-    static unsigned numDims(const af_array arr)
-    {
-        unsigned nd;
-        AF_THROW(af_get_numdims(&nd, arr));
-        return nd;
-    }
+        return odims;
+    } catch (std::logic_error &err) { AF_THROW_ERR(err.what(), AF_ERR_SIZE); }
+}
 
-    static dim4 getDims(const af_array arr)
-    {
-        dim_t d0, d1, d2, d3;
-        AF_THROW(af_get_dims(&d0, &d1, &d2, &d3, arr));
-        return dim4(d0, d1, d2, d3);
-    }
+static unsigned numDims(const af_array arr) {
+    unsigned nd;
+    AF_THROW(af_get_numdims(&nd, arr));
+    return nd;
+}
 
-    struct array::array_proxy::array_proxy_impl
-    {
-        array *       parent_;        //< The original array
-        af_index_t    indices_[4];    //< Indexing array or seq objects
-        bool          is_linear_;
-
-        // if true the parent_ object will be deleted on distruction. This is
-        // necessary only when calling indexing functions in array_proxy objects.
-        bool          delete_on_destruction_;
-        array_proxy_impl(array &parent, af_index_t *idx, bool linear)
-            : parent_(&parent)
-            , indices_()
-            , is_linear_(linear)
-            , delete_on_destruction_(false)
-        {
-            std::copy(idx, idx + AF_MAX_DIMS, indices_);
-        }
+static dim4 getDims(const af_array arr) {
+    dim_t d0, d1, d2, d3;
+    AF_THROW(af_get_dims(&d0, &d1, &d2, &d3, arr));
+    return dim4(d0, d1, d2, d3);
+}
 
-        void delete_on_destruction(bool val) {
-            delete_on_destruction_ = val;
-        }
+struct array::array_proxy::array_proxy_impl {
+    array *parent_;          //< The original array
+    af_index_t indices_[4];  //< Indexing array or seq objects
+    bool is_linear_;
 
-        ~array_proxy_impl() {
-            if (delete_on_destruction_) delete parent_;
-        }
-      private:
-        array_proxy_impl(const array_proxy_impl&);
-        array_proxy_impl(const array_proxy_impl&&);
-        array_proxy_impl operator=(const array_proxy_impl&);
-        array_proxy_impl operator=(const array_proxy_impl&&);
-    };
-
-    array::array(const af_array handle): arr(handle)
-    {
+    // if true the parent_ object will be deleted on distruction. This is
+    // necessary only when calling indexing functions in array_proxy objects.
+    bool delete_on_destruction_;
+    array_proxy_impl(array &parent, af_index_t *idx, bool linear)
+        : parent_(&parent)
+        , indices_()
+        , is_linear_(linear)
+        , delete_on_destruction_(false) {
+        std::copy(idx, idx + AF_MAX_DIMS, indices_);
     }
 
-    static void initEmptyArray(af_array *arr, af::dtype ty,
-                               dim_t d0, dim_t d1=1, dim_t d2=1, dim_t d3=1)
-    {
-        dim_t my_dims[] = {d0, d1, d2, d3};
-        AF_THROW(af_create_handle(arr, AF_MAX_DIMS, my_dims, ty));
-    }
+    void delete_on_destruction(bool val) { delete_on_destruction_ = val; }
 
-    template<typename T>
-    static void initDataArray(af_array *arr, const T *ptr, af::source src,
-                              dim_t d0, dim_t d1=1, dim_t d2=1, dim_t d3=1)
-    {
-        af::dtype ty = (af::dtype)dtype_traits<T>::af_type;
-        dim_t my_dims[] = {d0, d1, d2, d3};
-        switch (src) {
-        case afHost:   AF_THROW(af_create_array(arr, (const void * const)ptr, AF_MAX_DIMS, my_dims, ty)); break;
-        case afDevice: AF_THROW(af_device_array(arr, (const void *      )ptr, AF_MAX_DIMS, my_dims, ty)); break;
-        default: AF_THROW_ERR("Can not create array from the requested source pointer",
-                              AF_ERR_ARG);
-        }
+    ~array_proxy_impl() {
+        if (delete_on_destruction_) delete parent_;
     }
 
-    array::array() : arr(0)
-    {
-        initEmptyArray(&arr, f32, 0, 1, 1, 1);
-    }
+   private:
+    array_proxy_impl(const array_proxy_impl &);
+    array_proxy_impl(const array_proxy_impl &&);
+    array_proxy_impl operator=(const array_proxy_impl &);
+    array_proxy_impl operator=(const array_proxy_impl &&);
+};
 
-    array::array(const dim4 &dims, af::dtype ty) : arr(0)
-    {
-        initEmptyArray(&arr, ty, dims[0], dims[1], dims[2], dims[3]);
-    }
+array::array(const af_array handle) : arr(handle) {}
 
-    array::array(dim_t d0, af::dtype ty) : arr(0)
-    {
-        initEmptyArray(&arr, ty, d0);
-    }
+static void initEmptyArray(af_array *arr, af::dtype ty, dim_t d0, dim_t d1 = 1,
+                           dim_t d2 = 1, dim_t d3 = 1) {
+    dim_t my_dims[] = {d0, d1, d2, d3};
+    AF_THROW(af_create_handle(arr, AF_MAX_DIMS, my_dims, ty));
+}
 
-    array::array(dim_t d0, dim_t d1, af::dtype ty) : arr(0)
-    {
-        initEmptyArray(&arr, ty, d0, d1);
+template<typename T>
+static void initDataArray(af_array *arr, const T *ptr, af::source src, dim_t d0,
+                          dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
+    af::dtype ty    = (af::dtype)dtype_traits<T>::af_type;
+    dim_t my_dims[] = {d0, d1, d2, d3};
+    switch (src) {
+        case afHost:
+            AF_THROW(af_create_array(arr, (const void *const)ptr, AF_MAX_DIMS,
+                                     my_dims, ty));
+            break;
+        case afDevice:
+            AF_THROW(af_device_array(arr, (const void *)ptr, AF_MAX_DIMS,
+                                     my_dims, ty));
+            break;
+        default:
+            AF_THROW_ERR(
+                "Can not create array from the requested source pointer",
+                AF_ERR_ARG);
     }
+}
 
-    array::array(dim_t d0, dim_t d1, dim_t d2, af::dtype ty) :
-        arr(0)
-    {
-        initEmptyArray(&arr, ty, d0, d1, d2);
-    }
+array::array() : arr(0) { initEmptyArray(&arr, f32, 0, 1, 1, 1); }
 
-    array::array(dim_t d0, dim_t d1, dim_t d2, dim_t d3, af::dtype ty) :
-        arr(0)
-    {
-        initEmptyArray(&arr, ty, d0, d1, d2, d3);
-    }
+array::array(const dim4 &dims, af::dtype ty) : arr(0) {
+    initEmptyArray(&arr, ty, dims[0], dims[1], dims[2], dims[3]);
+}
 
-#define INSTANTIATE(T)                                                  \
-    template<> AFAPI                                                    \
-    array::array(const dim4 &dims, const T *ptr, af::source src)        \
-        : arr(0)                                                        \
-    {                                                                   \
-        initDataArray<T>(&arr, ptr, src, dims[0], dims[1], dims[2],     \
-                dims[3]);                                               \
-    }                                                                   \
-    template<> AFAPI                                                    \
-    array::array(dim_t d0, const T *ptr, af::source src)                \
-        : arr(0)                                                        \
-    {                                                                   \
-        initDataArray<T>(&arr, ptr, src, d0);                           \
-    }                                                                   \
-    template<> AFAPI                                                    \
-    array::array(dim_t d0, dim_t d1, const T *ptr, af::source src)      \
-        : arr(0)                                                        \
-    {                                                                   \
-        initDataArray<T>(&arr, ptr, src, d0, d1);                       \
-    }                                                                   \
-    template<> AFAPI                                                    \
-    array::array(dim_t d0, dim_t d1, dim_t d2, const T *ptr,            \
-                 af::source src)                                        \
-        : arr(0)                                                        \
-    {                                                                   \
-        initDataArray<T>(&arr, ptr, src, d0, d1, d2);                   \
-    }                                                                   \
-    template<> AFAPI                                                    \
-    array::array(dim_t d0, dim_t d1, dim_t d2, dim_t d3, const T *ptr,  \
-                 af::source src) :                                      \
-        arr(0)                                                          \
-                                                                        \
-    {                                                                   \
-        initDataArray<T>(&arr, ptr, src, d0, d1, d2, d3);               \
-    }                                                                   \
-
-    INSTANTIATE(cdouble)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(double)
-    INSTANTIATE(float)
-    INSTANTIATE(unsigned)
-    INSTANTIATE(int)
-    INSTANTIATE(unsigned char)
-    INSTANTIATE(char)
-    INSTANTIATE(long long)
-    INSTANTIATE(unsigned long long)
-    INSTANTIATE(short)
-    INSTANTIATE(unsigned short)
+array::array(dim_t d0, af::dtype ty) : arr(0) { initEmptyArray(&arr, ty, d0); }
 
-#undef INSTANTIATE
+array::array(dim_t d0, dim_t d1, af::dtype ty) : arr(0) {
+    initEmptyArray(&arr, ty, d0, d1);
+}
 
-    array::~array()
-    {
-        af_array tmp = get();
-        // THOU SHALL NOT THROW IN DESTRUCTORS
-        af_release_array(tmp);
-    }
+array::array(dim_t d0, dim_t d1, dim_t d2, af::dtype ty) : arr(0) {
+    initEmptyArray(&arr, ty, d0, d1, d2);
+}
 
-    af::dtype array::type() const
-    {
-        af::dtype my_type;
-        AF_THROW(af_get_type(&my_type, arr));
-        return my_type;
-    }
+array::array(dim_t d0, dim_t d1, dim_t d2, dim_t d3, af::dtype ty) : arr(0) {
+    initEmptyArray(&arr, ty, d0, d1, d2, d3);
+}
 
-    dim_t array::elements() const
-    {
-        dim_t elems;
-        AF_THROW(af_get_elements(&elems, get()));
-        return elems;
-    }
+#define INSTANTIATE(T)                                                        \
+    template<>                                                                \
+    AFAPI array::array(const dim4 &dims, const T *ptr, af::source src)        \
+        : arr(0) {                                                            \
+        initDataArray<T>(&arr, ptr, src, dims[0], dims[1], dims[2], dims[3]); \
+    }                                                                         \
+    template<>                                                                \
+    AFAPI array::array(dim_t d0, const T *ptr, af::source src) : arr(0) {     \
+        initDataArray<T>(&arr, ptr, src, d0);                                 \
+    }                                                                         \
+    template<>                                                                \
+    AFAPI array::array(dim_t d0, dim_t d1, const T *ptr, af::source src)      \
+        : arr(0) {                                                            \
+        initDataArray<T>(&arr, ptr, src, d0, d1);                             \
+    }                                                                         \
+    template<>                                                                \
+    AFAPI array::array(dim_t d0, dim_t d1, dim_t d2, const T *ptr,            \
+                       af::source src)                                        \
+        : arr(0) {                                                            \
+        initDataArray<T>(&arr, ptr, src, d0, d1, d2);                         \
+    }                                                                         \
+    template<>                                                                \
+    AFAPI array::array(dim_t d0, dim_t d1, dim_t d2, dim_t d3, const T *ptr,  \
+                       af::source src)                                        \
+        : arr(0)                                                              \
+                                                                              \
+    {                                                                         \
+        initDataArray<T>(&arr, ptr, src, d0, d1, d2, d3);                     \
+    }
+
+INSTANTIATE(cdouble)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(unsigned)
+INSTANTIATE(int)
+INSTANTIATE(unsigned char)
+INSTANTIATE(char)
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
+INSTANTIATE(short)
+INSTANTIATE(unsigned short)
 
-    void array::host(void *data) const
-    {
-        AF_THROW(af_get_data_ptr(data, get()));
-    }
+#undef INSTANTIATE
 
-    af_array array::get()
-    {
-        return arr;
-    }
+array::~array() {
+    af_array tmp = get();
+    // THOU SHALL NOT THROW IN DESTRUCTORS
+    af_release_array(tmp);
+}
 
-    af_array array::get() const
-    {
-        return ((array *)(this))->get();
-    }
+af::dtype array::type() const {
+    af::dtype my_type;
+    AF_THROW(af_get_type(&my_type, arr));
+    return my_type;
+}
 
-    // Helper functions
-    dim4 array::dims() const
-    {
-        return getDims(get());
-    }
+dim_t array::elements() const {
+    dim_t elems;
+    AF_THROW(af_get_elements(&elems, get()));
+    return elems;
+}
 
-    dim_t array::dims(unsigned dim) const
-    {
-        return dims()[dim];
-    }
+void array::host(void *data) const { AF_THROW(af_get_data_ptr(data, get())); }
 
-    unsigned array::numdims() const
-    {
-        return numDims(get());
-    }
+af_array array::get() { return arr; }
 
-    size_t array::bytes() const
-    {
-        dim_t nElements;
-        AF_THROW(af_get_elements(&nElements, get()));
-        return nElements * getSizeOf(type());
-    }
+af_array array::get() const { return ((array *)(this))->get(); }
 
-    size_t array::allocated() const
-    {
-        size_t result = 0;
-        AF_THROW(af_get_allocated_bytes(&result, get()));
-        return result;
-    }
+// Helper functions
+dim4 array::dims() const { return getDims(get()); }
 
-    array array::copy() const
-    {
-        af_array other = 0;
-        AF_THROW(af_copy_array(&other, get()));
-        return array(other);
-    }
+dim_t array::dims(unsigned dim) const { return dims()[dim]; }
 
-#undef INSTANTIATE
-#define INSTANTIATE(fn)                         \
-    bool array::is##fn() const                  \
-    {                                           \
-        bool ret = false;                       \
-        AF_THROW(af_is_##fn(&ret, get()));      \
-        return ret;                             \
-    }
+unsigned array::numdims() const { return numDims(get()); }
+
+size_t array::bytes() const {
+    dim_t nElements;
+    AF_THROW(af_get_elements(&nElements, get()));
+    return nElements * getSizeOf(type());
+}
+
+size_t array::allocated() const {
+    size_t result = 0;
+    AF_THROW(af_get_allocated_bytes(&result, get()));
+    return result;
+}
 
-    INSTANTIATE(empty)
-    INSTANTIATE(scalar)
-    INSTANTIATE(vector)
-    INSTANTIATE(row)
-    INSTANTIATE(column)
-    INSTANTIATE(complex)
-    INSTANTIATE(double)
-    INSTANTIATE(single)
-    INSTANTIATE(realfloating)
-    INSTANTIATE(floating)
-    INSTANTIATE(integer)
-    INSTANTIATE(bool)
-    INSTANTIATE(sparse)
+array array::copy() const {
+    af_array other = 0;
+    AF_THROW(af_copy_array(&other, get()));
+    return array(other);
+}
 
 #undef INSTANTIATE
+#define INSTANTIATE(fn)                    \
+    bool array::is##fn() const {           \
+        bool ret = false;                  \
+        AF_THROW(af_is_##fn(&ret, get())); \
+        return ret;                        \
+    }
+
+INSTANTIATE(empty)
+INSTANTIATE(scalar)
+INSTANTIATE(vector)
+INSTANTIATE(row)
+INSTANTIATE(column)
+INSTANTIATE(complex)
+INSTANTIATE(double)
+INSTANTIATE(single)
+INSTANTIATE(realfloating)
+INSTANTIATE(floating)
+INSTANTIATE(integer)
+INSTANTIATE(bool)
+INSTANTIATE(sparse)
 
-    static array::array_proxy gen_indexing(const array &ref, const index &s0, const index &s1, const index &s2, const index &s3, bool linear = false)
-    {
-        ref.eval();
-        af_index_t inds[AF_MAX_DIMS];
-        inds[0] = s0.get();
-        inds[1] = s1.get();
-        inds[2] = s2.get();
-        inds[3] = s3.get();
+#undef INSTANTIATE
 
-        return array::array_proxy(const_cast<array&>(ref), inds, linear);
-    }
+static array::array_proxy gen_indexing(const array &ref, const index &s0,
+                                       const index &s1, const index &s2,
+                                       const index &s3, bool linear = false) {
+    ref.eval();
+    af_index_t inds[AF_MAX_DIMS];
+    inds[0] = s0.get();
+    inds[1] = s1.get();
+    inds[2] = s2.get();
+    inds[3] = s3.get();
+
+    return array::array_proxy(const_cast<array &>(ref), inds, linear);
+}
 
-    array::array_proxy array::operator()(const index &s0)
-    {
-        return const_cast<const array*>(this)->operator()(s0);
-    }
+array::array_proxy array::operator()(const index &s0) {
+    return const_cast<const array *>(this)->operator()(s0);
+}
 
-    array::array_proxy array::operator()(const index &s0, const index &s1, const index &s2, const index &s3)
-    {
-        return const_cast<const array*>(this)->operator()(s0, s1, s2, s3);
-    }
+array::array_proxy array::operator()(const index &s0, const index &s1,
+                                     const index &s2, const index &s3) {
+    return const_cast<const array *>(this)->operator()(s0, s1, s2, s3);
+}
 
-    const array::array_proxy array::operator()(const index &s0) const
-    {
-        index z = index(0);
-        if(isvector()){
-            switch(numDims(this->arr)) {
-                case 1: return gen_indexing(*this, s0, z, z, z);
-                case 2: return gen_indexing(*this, z, s0, z, z);
-                case 3: return gen_indexing(*this, z, z, s0, z);
-                case 4: return gen_indexing(*this, z, z, z, s0);
-                default: AF_THROW_ERR("ndims for Array is invalid", AF_ERR_SIZE);
-            }
-        }
-        else {
-            return gen_indexing(*this, s0, z, z, z, true);
+const array::array_proxy array::operator()(const index &s0) const {
+    index z = index(0);
+    if (isvector()) {
+        switch (numDims(this->arr)) {
+            case 1: return gen_indexing(*this, s0, z, z, z);
+            case 2: return gen_indexing(*this, z, s0, z, z);
+            case 3: return gen_indexing(*this, z, z, s0, z);
+            case 4: return gen_indexing(*this, z, z, z, s0);
+            default: AF_THROW_ERR("ndims for Array is invalid", AF_ERR_SIZE);
         }
+    } else {
+        return gen_indexing(*this, s0, z, z, z, true);
     }
+}
 
-    const array::array_proxy array::operator()(const index &s0, const index &s1, const index &s2, const index &s3) const
-    {
-        return gen_indexing(*this, s0, s1, s2, s3);
-    }
-
-    const array::array_proxy array::row(int index) const
-    {
-        return this->operator()(index, span, span, span);
-    }
+const array::array_proxy array::operator()(const index &s0, const index &s1,
+                                           const index &s2,
+                                           const index &s3) const {
+    return gen_indexing(*this, s0, s1, s2, s3);
+}
 
-    array::array_proxy array::row(int index)
-    {
-        return const_cast<const array*>(this)->row(index);
-    }
+const array::array_proxy array::row(int index) const {
+    return this->operator()(index, span, span, span);
+}
 
-    const array::array_proxy array::col(int index) const
-    {
-        return this->operator()(span, index, span, span);
-    }
+array::array_proxy array::row(int index) {
+    return const_cast<const array *>(this)->row(index);
+}
 
-    array::array_proxy array::col(int index)
-    {
-        return const_cast<const array*>(this)->col(index);
-    }
+const array::array_proxy array::col(int index) const {
+    return this->operator()(span, index, span, span);
+}
 
-    const array::array_proxy array::slice(int index) const
-    {
-        return this->operator()(span, span, index, span);
-    }
+array::array_proxy array::col(int index) {
+    return const_cast<const array *>(this)->col(index);
+}
 
-    array::array_proxy array::slice(int index)
-    {
-        return const_cast<const array*>(this)->slice(index);
-    }
+const array::array_proxy array::slice(int index) const {
+    return this->operator()(span, span, index, span);
+}
 
-    const array::array_proxy array::rows(int first, int last) const
-    {
-        seq idx(first, last, 1);
-        return this->operator()(idx, span, span, span);
-    }
+array::array_proxy array::slice(int index) {
+    return const_cast<const array *>(this)->slice(index);
+}
 
-    array::array_proxy array::rows(int first, int last)
-    {
-        return const_cast<const array*>(this)->rows(first, last);
-    }
+const array::array_proxy array::rows(int first, int last) const {
+    seq idx(first, last, 1);
+    return this->operator()(idx, span, span, span);
+}
 
-    const array::array_proxy array::cols(int first, int last) const
-    {
-        seq idx(first, last, 1);
-        return this->operator()(span, idx, span, span);
-    }
+array::array_proxy array::rows(int first, int last) {
+    return const_cast<const array *>(this)->rows(first, last);
+}
 
-    array::array_proxy array::cols(int first, int last)
-    {
-        return const_cast<const array*>(this)->cols(first, last);
-    }
+const array::array_proxy array::cols(int first, int last) const {
+    seq idx(first, last, 1);
+    return this->operator()(span, idx, span, span);
+}
 
-    const array::array_proxy array::slices(int first, int last) const
-    {
-        seq idx(first, last, 1);
-        return this->operator()(span, span, idx, span);
-    }
+array::array_proxy array::cols(int first, int last) {
+    return const_cast<const array *>(this)->cols(first, last);
+}
 
-    array::array_proxy array::slices(int first, int last)
-    {
-        return const_cast<const array*>(this)->slices(first, last);
-    }
+const array::array_proxy array::slices(int first, int last) const {
+    seq idx(first, last, 1);
+    return this->operator()(span, span, idx, span);
+}
 
-    const array array::as(af::dtype type) const
-    {
-        af_array out;
-        AF_THROW(af_cast(&out, this->get(), type));
-        return array(out);
-    }
+array::array_proxy array::slices(int first, int last) {
+    return const_cast<const array *>(this)->slices(first, last);
+}
 
-    array::array(const array& in) : arr(0)
-    {
-        AF_THROW(af_retain_array(&arr, in.get()));
-    }
+const array array::as(af::dtype type) const {
+    af_array out;
+    AF_THROW(af_cast(&out, this->get(), type));
+    return array(out);
+}
 
-    array::array(const array& input, const dim4& dims) : arr(0)
-    {
-        AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims.get()));
-    }
+array::array(const array &in) : arr(0) {
+    AF_THROW(af_retain_array(&arr, in.get()));
+}
 
-    array::array(const array& input, const dim_t dim0, const dim_t dim1, const dim_t dim2, const dim_t dim3)
-        : arr(0)
-    {
-        dim_t dims[] = {dim0, dim1, dim2, dim3};
-        AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims));
-    }
+array::array(const array &input, const dim4 &dims) : arr(0) {
+    AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims.get()));
+}
 
-    // Transpose and Conjugate Transpose
-    array array::T() const
-    {
-        return transpose(*this);
-    }
+array::array(const array &input, const dim_t dim0, const dim_t dim1,
+             const dim_t dim2, const dim_t dim3)
+    : arr(0) {
+    dim_t dims[] = {dim0, dim1, dim2, dim3};
+    AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims));
+}
 
-    array array::H() const
-    {
-        return transpose(*this, true);
-    }
+// Transpose and Conjugate Transpose
+array array::T() const { return transpose(*this); }
 
-    void array::set(af_array tmp)
-    {
-        if (arr) AF_THROW(af_release_array(arr));
-        arr = tmp;
-    }
+array array::H() const { return transpose(*this, true); }
 
-    // Assign values to an array
-    array::array_proxy&
-    af::array::array_proxy::operator=(const array &other)
-    {
-        unsigned nd = numDims(impl->parent_->get());
-        const dim4 this_dims = getDims(impl->parent_->get());
-        const dim4 other_dims = other.dims();
-        int dim = gforDim(impl->indices_);
-        af_array other_arr = other.get();
-
-        bool batch_assign = false;
-        bool is_reordered = false;
-        if (dim >= 0) {
-            //FIXME: Figure out a faster, cleaner way to do this
-            dim4 out_dims = seqToDims(impl->indices_, this_dims, false);
-
-            batch_assign = true;
-            for (int i = 0; i < AF_MAX_DIMS; i++) {
-                if (this->impl->indices_[i].isBatch) batch_assign &= (other_dims[i] == 1);
-                else                          batch_assign &= (other_dims[i] == out_dims[i]);
-            }
+void array::set(af_array tmp) {
+    if (arr) AF_THROW(af_release_array(arr));
+    arr = tmp;
+}
 
-            if (batch_assign) {
-                af_array out;
-                AF_THROW(af_tile(&out, other_arr,
-                                 out_dims[0] / other_dims[0],
-                                 out_dims[1] / other_dims[1],
-                                 out_dims[2] / other_dims[2],
-                                 out_dims[3] / other_dims[3]));
-                other_arr = out;
-
-            } else if (out_dims != other_dims) {
-                // HACK: This is a quick check to see if other has been reordered inside gfor
-                // TODO: Figure out if this breaks and implement a cleaner method
-                other_arr = gforReorder(other_arr, dim);
-                is_reordered = true;
-            }
+// Assign values to an array
+array::array_proxy &af::array::array_proxy::operator=(const array &other) {
+    unsigned nd           = numDims(impl->parent_->get());
+    const dim4 this_dims  = getDims(impl->parent_->get());
+    const dim4 other_dims = other.dims();
+    int dim               = gforDim(impl->indices_);
+    af_array other_arr    = other.get();
+
+    bool batch_assign = false;
+    bool is_reordered = false;
+    if (dim >= 0) {
+        // FIXME: Figure out a faster, cleaner way to do this
+        dim4 out_dims = seqToDims(impl->indices_, this_dims, false);
+
+        batch_assign = true;
+        for (int i = 0; i < AF_MAX_DIMS; i++) {
+            if (this->impl->indices_[i].isBatch)
+                batch_assign &= (other_dims[i] == 1);
+            else
+                batch_assign &= (other_dims[i] == out_dims[i]);
         }
 
-        af_array par_arr = 0;
-
-        if (impl->is_linear_) {
-            AF_THROW(af_flat(&par_arr, impl->parent_->get()));
-            nd = 1;
-        } else {
-            par_arr = impl->parent_->get();
+        if (batch_assign) {
+            af_array out;
+            AF_THROW(af_tile(&out, other_arr, out_dims[0] / other_dims[0],
+                             out_dims[1] / other_dims[1],
+                             out_dims[2] / other_dims[2],
+                             out_dims[3] / other_dims[3]));
+            other_arr = out;
+
+        } else if (out_dims != other_dims) {
+            // HACK: This is a quick check to see if other has been reordered
+            // inside gfor
+            // TODO: Figure out if this breaks and implement a cleaner method
+            other_arr    = gforReorder(other_arr, dim);
+            is_reordered = true;
         }
+    }
 
-        af_array tmp = 0;
-        AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices_, other_arr));
+    af_array par_arr = 0;
 
-        af_array res = 0;
-        if (impl->is_linear_) {
-            AF_THROW(af_moddims(&res, tmp, this_dims.ndims(), this_dims.get()));
-            AF_THROW(af_release_array(par_arr));
-            AF_THROW(af_release_array(tmp));
-        } else {
-            res = tmp;
-        }
+    if (impl->is_linear_) {
+        AF_THROW(af_flat(&par_arr, impl->parent_->get()));
+        nd = 1;
+    } else {
+        par_arr = impl->parent_->get();
+    }
 
-        impl->parent_->set(res);
+    af_array tmp = 0;
+    AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices_, other_arr));
 
-        if (dim >= 0 && (is_reordered || batch_assign)) {
-            if (other_arr) AF_THROW(af_release_array(other_arr));
-        }
-        return *this;
+    af_array res = 0;
+    if (impl->is_linear_) {
+        AF_THROW(af_moddims(&res, tmp, this_dims.ndims(), this_dims.get()));
+        AF_THROW(af_release_array(par_arr));
+        AF_THROW(af_release_array(tmp));
+    } else {
+        res = tmp;
     }
 
-    array::array_proxy&
-    af::array::array_proxy::operator=(const array::array_proxy &other)
-    {
-        array out = other;
-        return *this = out;
-    }
+    impl->parent_->set(res);
 
-    af::array::array_proxy::array_proxy(array& par, af_index_t *ssss, bool linear)
-        : impl(new array_proxy_impl(par, ssss, linear))
-    {
+    if (dim >= 0 && (is_reordered || batch_assign)) {
+        if (other_arr) AF_THROW(af_release_array(other_arr));
     }
+    return *this;
+}
 
-    af::array::array_proxy::array_proxy(const array_proxy &other)
-        : impl(new array_proxy_impl(*other.impl->parent_, other.impl->indices_, other.impl->is_linear_))
-    {
-    }
+array::array_proxy &af::array::array_proxy::operator=(
+    const array::array_proxy &other) {
+    array out    = other;
+    return *this = out;
+}
+
+af::array::array_proxy::array_proxy(array &par, af_index_t *ssss, bool linear)
+    : impl(new array_proxy_impl(par, ssss, linear)) {}
+
+af::array::array_proxy::array_proxy(const array_proxy &other)
+    : impl(new array_proxy_impl(*other.impl->parent_, other.impl->indices_,
+                                other.impl->is_linear_)) {}
 
 #if __cplusplus > 199711L
-    af::array::array_proxy::array_proxy(array_proxy &&other) {
-        impl = other.impl;
-    }
+af::array::array_proxy::array_proxy(array_proxy &&other) { impl = other.impl; }
 
-    array::array_proxy&
-    af::array::array_proxy::operator=(array_proxy &&other) {
-        array out = other;
-        return *this = out;
-    }
+array::array_proxy &af::array::array_proxy::operator=(array_proxy &&other) {
+    array out    = other;
+    return *this = out;
+}
 #endif
 
-    af::array::array_proxy::~array_proxy() {
-        if(impl) delete impl;
-    }
-
-    array array::array_proxy::as(dtype type) const
-    {
-        array out = *this;
-        return out.as(type);
-    }
+af::array::array_proxy::~array_proxy() {
+    if (impl) delete impl;
+}
 
-    dim_t array::array_proxy::dims(unsigned dim) const
-    {
-        array out = *this;
-        return out.dims(dim);
-    }
+array array::array_proxy::as(dtype type) const {
+    array out = *this;
+    return out.as(type);
+}
 
-    void array::array_proxy::host(void *ptr) const
-    {
-        array out = *this;
-        return out.host(ptr);
-    }
+dim_t array::array_proxy::dims(unsigned dim) const {
+    array out = *this;
+    return out.dims(dim);
+}
 
-#define MEM_FUNC(PREFIX, FUNC)                  \
-    PREFIX array::array_proxy::FUNC() const     \
-    {                                           \
-        array out = *this;                      \
-        return out.FUNC();                      \
-    }
+void array::array_proxy::host(void *ptr) const {
+    array out = *this;
+    return out.host(ptr);
+}
 
-    MEM_FUNC(dim_t                  , elements)
-    MEM_FUNC(array                  , T)
-    MEM_FUNC(array                  , H)
-    MEM_FUNC(dtype                  , type)
-    MEM_FUNC(dim4                   , dims)
-    MEM_FUNC(unsigned               , numdims)
-    MEM_FUNC(size_t                 , bytes)
-    MEM_FUNC(size_t                 , allocated)
-    MEM_FUNC(array                  , copy)
-    MEM_FUNC(bool                   , isempty)
-    MEM_FUNC(bool                   , isscalar)
-    MEM_FUNC(bool                   , isvector)
-    MEM_FUNC(bool                   , isrow)
-    MEM_FUNC(bool                   , iscolumn)
-    MEM_FUNC(bool                   , iscomplex)
-    MEM_FUNC(bool                   , isdouble)
-    MEM_FUNC(bool                   , issingle)
-    MEM_FUNC(bool                   , isrealfloating)
-    MEM_FUNC(bool                   , isfloating)
-    MEM_FUNC(bool                   , isinteger)
-    MEM_FUNC(bool                   , isbool)
-    MEM_FUNC(bool                   , issparse)
-    MEM_FUNC(void                   , eval)
-    MEM_FUNC(af_array               , get)
-    //MEM_FUNC(void                   , unlock)
+#define MEM_FUNC(PREFIX, FUNC)                \
+    PREFIX array::array_proxy::FUNC() const { \
+        array out = *this;                    \
+        return out.FUNC();                    \
+    }
+
+MEM_FUNC(dim_t, elements)
+MEM_FUNC(array, T)
+MEM_FUNC(array, H)
+MEM_FUNC(dtype, type)
+MEM_FUNC(dim4, dims)
+MEM_FUNC(unsigned, numdims)
+MEM_FUNC(size_t, bytes)
+MEM_FUNC(size_t, allocated)
+MEM_FUNC(array, copy)
+MEM_FUNC(bool, isempty)
+MEM_FUNC(bool, isscalar)
+MEM_FUNC(bool, isvector)
+MEM_FUNC(bool, isrow)
+MEM_FUNC(bool, iscolumn)
+MEM_FUNC(bool, iscomplex)
+MEM_FUNC(bool, isdouble)
+MEM_FUNC(bool, issingle)
+MEM_FUNC(bool, isrealfloating)
+MEM_FUNC(bool, isfloating)
+MEM_FUNC(bool, isinteger)
+MEM_FUNC(bool, isbool)
+MEM_FUNC(bool, issparse)
+MEM_FUNC(void, eval)
+MEM_FUNC(af_array, get)
+// MEM_FUNC(void                   , unlock)
 #undef MEM_FUNC
 
-
-#define ASSIGN_TYPE(TY, OP)                                   \
-    array::array_proxy&                                       \
-    array::array_proxy::operator OP(const TY &value)          \
-    {                                                         \
-        dim4 pdims = getDims(impl->parent_->get());           \
-        if (impl->is_linear_) pdims = dim4(pdims.elements()); \
-        dim4 dims = seqToDims(impl->indices_, pdims );        \
-        af::dtype ty = impl->parent_->type();                 \
-        array cst = constant(value, dims, ty);                \
-        return this->operator OP(cst);                        \
-    }                                                         \
-
-#define ASSIGN_OP(OP, op1)                      \
-    ASSIGN_TYPE(double             , OP)        \
-    ASSIGN_TYPE(float              , OP)        \
-    ASSIGN_TYPE(cdouble            , OP)        \
-    ASSIGN_TYPE(cfloat             , OP)        \
-    ASSIGN_TYPE(int                , OP)        \
-    ASSIGN_TYPE(unsigned           , OP)        \
-    ASSIGN_TYPE(long               , OP)        \
-    ASSIGN_TYPE(unsigned long      , OP)        \
-    ASSIGN_TYPE(long long          , OP)        \
-    ASSIGN_TYPE(unsigned long long , OP)        \
-    ASSIGN_TYPE(char               , OP)        \
-    ASSIGN_TYPE(unsigned char      , OP)        \
-    ASSIGN_TYPE(bool               , OP)        \
-    ASSIGN_TYPE(short              , OP)        \
-    ASSIGN_TYPE(unsigned short     , OP)        \
-
-    ASSIGN_OP(= , =)
-    ASSIGN_OP(+=, +)
-    ASSIGN_OP(-=, -)
-    ASSIGN_OP(*=, *)
-    ASSIGN_OP(/=, /)
+#define ASSIGN_TYPE(TY, OP)                                                \
+    array::array_proxy &array::array_proxy::operator OP(const TY &value) { \
+        dim4 pdims = getDims(impl->parent_->get());                        \
+        if (impl->is_linear_) pdims = dim4(pdims.elements());              \
+        dim4 dims    = seqToDims(impl->indices_, pdims);                   \
+        af::dtype ty = impl->parent_->type();                              \
+        array cst    = constant(value, dims, ty);                          \
+        return this->operator OP(cst);                                     \
+    }
+
+#define ASSIGN_OP(OP, op1)              \
+    ASSIGN_TYPE(double, OP)             \
+    ASSIGN_TYPE(float, OP)              \
+    ASSIGN_TYPE(cdouble, OP)            \
+    ASSIGN_TYPE(cfloat, OP)             \
+    ASSIGN_TYPE(int, OP)                \
+    ASSIGN_TYPE(unsigned, OP)           \
+    ASSIGN_TYPE(long, OP)               \
+    ASSIGN_TYPE(unsigned long, OP)      \
+    ASSIGN_TYPE(long long, OP)          \
+    ASSIGN_TYPE(unsigned long long, OP) \
+    ASSIGN_TYPE(char, OP)               \
+    ASSIGN_TYPE(unsigned char, OP)      \
+    ASSIGN_TYPE(bool, OP)               \
+    ASSIGN_TYPE(short, OP)              \
+    ASSIGN_TYPE(unsigned short, OP)
+
+ASSIGN_OP(=, =)
+ASSIGN_OP(+=, +)
+ASSIGN_OP(-=, -)
+ASSIGN_OP(*=, *)
+ASSIGN_OP(/=, /)
 #undef ASSIGN_OP
 
 #undef ASSIGN_TYPE
 
-#define SELF_OP(OP, op1)                                                          \
-    array::array_proxy& array::array_proxy::operator OP(const array_proxy &other) \
-    {                                                                             \
-        *this = *this op1 other;                                                  \
-        return *this;                                                             \
-    }                                                                             \
-    array::array_proxy& array::array_proxy::operator OP(const array &other)       \
-    {                                                                             \
-        *this = *this op1 other;                                                  \
-        return *this;                                                             \
-    }                                                                             \
-
-    SELF_OP(+=, +)
-    SELF_OP(-=, -)
-    SELF_OP(*=, *)
-    SELF_OP(/=, /)
+#define SELF_OP(OP, op1)                                                      \
+    array::array_proxy &array::array_proxy::operator OP(                      \
+        const array_proxy &other) {                                           \
+        *this = *this op1 other;                                              \
+        return *this;                                                         \
+    }                                                                         \
+    array::array_proxy &array::array_proxy::operator OP(const array &other) { \
+        *this = *this op1 other;                                              \
+        return *this;                                                         \
+    }
+
+SELF_OP(+=, +)
+SELF_OP(-=, -)
+SELF_OP(*=, *)
+SELF_OP(/=, /)
 #undef SELF_OP
 
-    array::array_proxy::operator array() const
-    {
-        af_array tmp = 0;
-        af_array arr = 0;
-
-        if(impl->is_linear_)  {
-            AF_THROW(af_flat(&arr, impl->parent_->get()));
-        } else {
-            arr = impl->parent_->get();
-        }
-
-        AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
-        if (impl->is_linear_)  {
-            AF_THROW(af_release_array(arr));
-        }
+array::array_proxy::operator array() const {
+    af_array tmp = 0;
+    af_array arr = 0;
 
-        return array(tmp);
+    if (impl->is_linear_) {
+        AF_THROW(af_flat(&arr, impl->parent_->get()));
+    } else {
+        arr = impl->parent_->get();
     }
 
-    array::array_proxy::operator array()
-    {
-        af_array tmp = 0;
-        af_array arr = 0;
+    AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
+    if (impl->is_linear_) { AF_THROW(af_release_array(arr)); }
 
-        if(impl->is_linear_)  {
-            AF_THROW(af_flat(&arr, impl->parent_->get()));
-        } else {
-            arr = impl->parent_->get();
-        }
-
-        AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
-        if(impl->is_linear_)  {
-            AF_THROW(af_release_array(arr));
-        }
+    return array(tmp);
+}
 
-        int dim = gforDim(impl->indices_);
-        if (tmp && dim >= 0) {
-            arr = gforReorder(tmp, dim);
-            if (tmp) AF_THROW(af_release_array(tmp));
-        } else {
-            arr = tmp;
-        }
+array::array_proxy::operator array() {
+    af_array tmp = 0;
+    af_array arr = 0;
 
-        return array(arr);
+    if (impl->is_linear_) {
+        AF_THROW(af_flat(&arr, impl->parent_->get()));
+    } else {
+        arr = impl->parent_->get();
     }
 
-#define MEM_INDEX(FUNC_SIG, USAGE)                    \
-    array::array_proxy                                \
-    array::array_proxy::FUNC_SIG                      \
-    {                                                 \
-        array* out = new array(*this);                \
-        array::array_proxy proxy = out->USAGE;        \
-        proxy.impl->delete_on_destruction(true);      \
-        return proxy;                                 \
-    }                                                 \
-                                                      \
-    const array::array_proxy                          \
-    array::array_proxy::FUNC_SIG const                \
-    {                                                 \
-        const array* out = new array(*this);          \
-        array::array_proxy proxy = out->USAGE;        \
-        proxy.impl->delete_on_destruction(true);      \
-        return proxy;                                 \
+    AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
+    if (impl->is_linear_) { AF_THROW(af_release_array(arr)); }
+
+    int dim = gforDim(impl->indices_);
+    if (tmp && dim >= 0) {
+        arr = gforReorder(tmp, dim);
+        if (tmp) AF_THROW(af_release_array(tmp));
+    } else {
+        arr = tmp;
     }
 
-    MEM_INDEX(row(int index)                , row(index));
-    MEM_INDEX(rows(int first, int last)     , rows(first, last));
-    MEM_INDEX(col(int index)                , col(index));
-    MEM_INDEX(cols(int first, int last)     , cols(first, last));
-    MEM_INDEX(slice(int index)              , slice(index));
-    MEM_INDEX(slices(int first, int last)   , slices(first, last));
+    return array(arr);
+}
 
-#undef MEM_INDEX
+#define MEM_INDEX(FUNC_SIG, USAGE)                                \
+    array::array_proxy array::array_proxy::FUNC_SIG {             \
+        array *out               = new array(*this);              \
+        array::array_proxy proxy = out->USAGE;                    \
+        proxy.impl->delete_on_destruction(true);                  \
+        return proxy;                                             \
+    }                                                             \
+                                                                  \
+    const array::array_proxy array::array_proxy::FUNC_SIG const { \
+        const array *out         = new array(*this);              \
+        array::array_proxy proxy = out->USAGE;                    \
+        proxy.impl->delete_on_destruction(true);                  \
+        return proxy;                                             \
+    }
+
+MEM_INDEX(row(int index), row(index));
+MEM_INDEX(rows(int first, int last), rows(first, last));
+MEM_INDEX(col(int index), col(index));
+MEM_INDEX(cols(int first, int last), cols(first, last));
+MEM_INDEX(slice(int index), slice(index));
+MEM_INDEX(slices(int first, int last), slices(first, last));
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Operator =
-    ///////////////////////////////////////////////////////////////////////////
-    array& array::operator=(const array &other)
-    {
-        if (this->get() == other.get()) {
-            return *this;
-        }
-        //TODO: Unsafe. loses data if af_weak_copy fails
-        if(this->arr != 0) {
-            AF_THROW(af_release_array(this->arr));
-        }
+#undef MEM_INDEX
 
-        af_array temp = 0;
-        AF_THROW(af_retain_array(&temp, other.get()));
-        this->arr = temp;
-        return *this;
-    }
-#define ASSIGN_TYPE(TY, OP)                                         \
-    array& array::operator OP(const TY &value)                      \
-    {                                                               \
-        af::dim4 dims = this->dims();                               \
-        af::dtype ty = this->type();                                \
-        array cst = constant(value, dims, ty);                      \
-        return operator OP(cst);                                    \
-    }                                                               \
-
-#define ASSIGN_OP(OP, op1)                                          \
-    array& array::operator OP(const array &other)                   \
-    {                                                               \
-        af_array out = 0;                                           \
-        AF_THROW(op1(&out, this->get(), other.get(), gforGet()));   \
-        this->set(out);                                             \
-        return *this;                                               \
-    }                                                               \
-    ASSIGN_TYPE(double             , OP)                            \
-    ASSIGN_TYPE(float              , OP)                            \
-    ASSIGN_TYPE(cdouble            , OP)                            \
-    ASSIGN_TYPE(cfloat             , OP)                            \
-    ASSIGN_TYPE(int                , OP)                            \
-    ASSIGN_TYPE(unsigned           , OP)                            \
-    ASSIGN_TYPE(long               , OP)                            \
-    ASSIGN_TYPE(unsigned long      , OP)                            \
-    ASSIGN_TYPE(long long          , OP)                            \
-    ASSIGN_TYPE(unsigned long long , OP)                            \
-    ASSIGN_TYPE(char               , OP)                            \
-    ASSIGN_TYPE(unsigned char      , OP)                            \
-    ASSIGN_TYPE(bool               , OP)                            \
-    ASSIGN_TYPE(short              , OP)                            \
-    ASSIGN_TYPE(unsigned short     , OP)                            \
-
-    ASSIGN_OP(+=, af_add)
-    ASSIGN_OP(-=, af_sub)
-    ASSIGN_OP(*=, af_mul)
-    ASSIGN_OP(/=, af_div)
+///////////////////////////////////////////////////////////////////////////
+// Operator =
+///////////////////////////////////////////////////////////////////////////
+array &array::operator=(const array &other) {
+    if (this->get() == other.get()) { return *this; }
+    // TODO: Unsafe. loses data if af_weak_copy fails
+    if (this->arr != 0) { AF_THROW(af_release_array(this->arr)); }
+
+    af_array temp = 0;
+    AF_THROW(af_retain_array(&temp, other.get()));
+    this->arr = temp;
+    return *this;
+}
+#define ASSIGN_TYPE(TY, OP)                        \
+    array &array::operator OP(const TY &value) {   \
+        af::dim4 dims = this->dims();              \
+        af::dtype ty  = this->type();              \
+        array cst     = constant(value, dims, ty); \
+        return operator OP(cst);                   \
+    }
+
+#define ASSIGN_OP(OP, op1)                                        \
+    array &array::operator OP(const array &other) {               \
+        af_array out = 0;                                         \
+        AF_THROW(op1(&out, this->get(), other.get(), gforGet())); \
+        this->set(out);                                           \
+        return *this;                                             \
+    }                                                             \
+    ASSIGN_TYPE(double, OP)                                       \
+    ASSIGN_TYPE(float, OP)                                        \
+    ASSIGN_TYPE(cdouble, OP)                                      \
+    ASSIGN_TYPE(cfloat, OP)                                       \
+    ASSIGN_TYPE(int, OP)                                          \
+    ASSIGN_TYPE(unsigned, OP)                                     \
+    ASSIGN_TYPE(long, OP)                                         \
+    ASSIGN_TYPE(unsigned long, OP)                                \
+    ASSIGN_TYPE(long long, OP)                                    \
+    ASSIGN_TYPE(unsigned long long, OP)                           \
+    ASSIGN_TYPE(char, OP)                                         \
+    ASSIGN_TYPE(unsigned char, OP)                                \
+    ASSIGN_TYPE(bool, OP)                                         \
+    ASSIGN_TYPE(short, OP)                                        \
+    ASSIGN_TYPE(unsigned short, OP)
+
+ASSIGN_OP(+=, af_add)
+ASSIGN_OP(-=, af_sub)
+ASSIGN_OP(*=, af_mul)
+ASSIGN_OP(/=, af_div)
 
 #undef ASSIGN_OP
 
 #undef ASSIGN_TYPE
 
-#define ASSIGN_TYPE(TY, OP)                                     \
-    array& array::operator OP(const TY &value)                  \
-    {                                                           \
-        af::dim4 dims = this->dims();                           \
-        af::dtype ty = this->type();                            \
-        array cst = constant(value, dims, ty);                  \
-        return operator OP(cst);                                \
-    }                                                           \
-
-#define ASSIGN_OP(OP)                           \
-    ASSIGN_TYPE(double             , OP)        \
-    ASSIGN_TYPE(float              , OP)        \
-    ASSIGN_TYPE(cdouble            , OP)        \
-    ASSIGN_TYPE(cfloat             , OP)        \
-    ASSIGN_TYPE(int                , OP)        \
-    ASSIGN_TYPE(unsigned           , OP)        \
-    ASSIGN_TYPE(long               , OP)        \
-    ASSIGN_TYPE(unsigned long      , OP)        \
-    ASSIGN_TYPE(long long          , OP)        \
-    ASSIGN_TYPE(unsigned long long , OP)        \
-    ASSIGN_TYPE(char               , OP)        \
-    ASSIGN_TYPE(unsigned char      , OP)        \
-    ASSIGN_TYPE(bool               , OP)        \
-    ASSIGN_TYPE(short              , OP)        \
-    ASSIGN_TYPE(unsigned short     , OP)        \
-
-    ASSIGN_OP(= )
+#define ASSIGN_TYPE(TY, OP)                        \
+    array &array::operator OP(const TY &value) {   \
+        af::dim4 dims = this->dims();              \
+        af::dtype ty  = this->type();              \
+        array cst     = constant(value, dims, ty); \
+        return operator OP(cst);                   \
+    }
+
+#define ASSIGN_OP(OP)                   \
+    ASSIGN_TYPE(double, OP)             \
+    ASSIGN_TYPE(float, OP)              \
+    ASSIGN_TYPE(cdouble, OP)            \
+    ASSIGN_TYPE(cfloat, OP)             \
+    ASSIGN_TYPE(int, OP)                \
+    ASSIGN_TYPE(unsigned, OP)           \
+    ASSIGN_TYPE(long, OP)               \
+    ASSIGN_TYPE(unsigned long, OP)      \
+    ASSIGN_TYPE(long long, OP)          \
+    ASSIGN_TYPE(unsigned long long, OP) \
+    ASSIGN_TYPE(char, OP)               \
+    ASSIGN_TYPE(unsigned char, OP)      \
+    ASSIGN_TYPE(bool, OP)               \
+    ASSIGN_TYPE(short, OP)              \
+    ASSIGN_TYPE(unsigned short, OP)
+
+ASSIGN_OP(=)
 
 #undef ASSIGN_OP
 
 #undef ASSIGN_TYPE
 
-af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
-{
+af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type) {
     // If same, do not do anything
     if (scalar_type == array_type) return scalar_type;
 
@@ -862,12 +756,13 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
     }
 
     // If 64 bit precision, do not lose precision
-    if (array_type == f64 || array_type == c64 ||
-        array_type == f32 || array_type == c32 ) return array_type;
+    if (array_type == f64 || array_type == c64 || array_type == f32 ||
+        array_type == c32)
+        return array_type;
 
     // Default to single precision by default when multiplying with scalar
     if ((scalar_type == f64 || scalar_type == c64) &&
-        (array_type  != f64 && array_type  != c64)) {
+        (array_type != f64 && array_type != c64)) {
         return f32;
     }
 
@@ -876,238 +771,209 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type)
 }
 
 #define BINARY_TYPE(TY, OP, func, dty)                          \
-    array operator OP(const array& plhs, const TY &value)       \
-    {                                                           \
+    array operator OP(const array &plhs, const TY &value) {     \
         af_array out;                                           \
         af::dtype cty = implicit_dtype(dty, plhs.type());       \
-        array cst = constant(value, plhs.dims(), cty);          \
+        array cst     = constant(value, plhs.dims(), cty);      \
         AF_THROW(func(&out, plhs.get(), cst.get(), gforGet())); \
         return array(out);                                      \
     }                                                           \
-    array operator OP(const TY &value, const array &other)      \
-    {                                                           \
+    array operator OP(const TY &value, const array &other) {    \
         const af_array rhs = other.get();                       \
         af_array out;                                           \
         af::dtype cty = implicit_dtype(dty, other.type());      \
-        array cst = constant(value, other.dims(), cty);         \
+        array cst     = constant(value, other.dims(), cty);     \
         AF_THROW(func(&out, cst.get(), rhs, gforGet()));        \
         return array(out);                                      \
-    }                                                           \
+    }
 
-#define BINARY_OP(OP, func)                                     \
-    array operator OP(const array &lhs, const array &rhs)       \
-    {                                                           \
-        af_array out;                                           \
-        AF_THROW(func(&out, lhs.get(), rhs.get(), gforGet()));  \
-        return array(out);                                      \
-    }                                                           \
-    BINARY_TYPE(double             , OP, func, f64)             \
-    BINARY_TYPE(float              , OP, func, f32)             \
-    BINARY_TYPE(cdouble            , OP, func, c64)             \
-    BINARY_TYPE(cfloat             , OP, func, c32)             \
-    BINARY_TYPE(int                , OP, func, s32)             \
-    BINARY_TYPE(unsigned           , OP, func, u32)             \
-    BINARY_TYPE(long               , OP, func, s64)             \
-    BINARY_TYPE(unsigned long      , OP, func, u64)             \
-    BINARY_TYPE(long long          , OP, func, s64)             \
-    BINARY_TYPE(unsigned long long , OP, func, u64)             \
-    BINARY_TYPE(char               , OP, func, b8)              \
-    BINARY_TYPE(unsigned char      , OP, func, u8)              \
-    BINARY_TYPE(bool               , OP, func, b8)              \
-    BINARY_TYPE(short              , OP, func, s16)             \
-    BINARY_TYPE(unsigned short     , OP, func, u16)             \
-
-    BINARY_OP(+, af_add)
-    BINARY_OP(-, af_sub)
-    BINARY_OP(*, af_mul)
-    BINARY_OP(/, af_div)
-    BINARY_OP(==, af_eq)
-    BINARY_OP(!=, af_neq)
-    BINARY_OP(< , af_lt)
-    BINARY_OP(<=, af_le)
-    BINARY_OP(> , af_gt)
-    BINARY_OP(>=, af_ge)
-    BINARY_OP(&&, af_and)
-    BINARY_OP(||, af_or)
-    BINARY_OP(%, af_mod)
-    BINARY_OP(&, af_bitand)
-    BINARY_OP(|, af_bitor)
-    BINARY_OP(^, af_bitxor)
-    BINARY_OP(<<, af_bitshiftl)
-    BINARY_OP(>>, af_bitshiftr)
+#define BINARY_OP(OP, func)                                    \
+    array operator OP(const array &lhs, const array &rhs) {    \
+        af_array out;                                          \
+        AF_THROW(func(&out, lhs.get(), rhs.get(), gforGet())); \
+        return array(out);                                     \
+    }                                                          \
+    BINARY_TYPE(double, OP, func, f64)                         \
+    BINARY_TYPE(float, OP, func, f32)                          \
+    BINARY_TYPE(cdouble, OP, func, c64)                        \
+    BINARY_TYPE(cfloat, OP, func, c32)                         \
+    BINARY_TYPE(int, OP, func, s32)                            \
+    BINARY_TYPE(unsigned, OP, func, u32)                       \
+    BINARY_TYPE(long, OP, func, s64)                           \
+    BINARY_TYPE(unsigned long, OP, func, u64)                  \
+    BINARY_TYPE(long long, OP, func, s64)                      \
+    BINARY_TYPE(unsigned long long, OP, func, u64)             \
+    BINARY_TYPE(char, OP, func, b8)                            \
+    BINARY_TYPE(unsigned char, OP, func, u8)                   \
+    BINARY_TYPE(bool, OP, func, b8)                            \
+    BINARY_TYPE(short, OP, func, s16)                          \
+    BINARY_TYPE(unsigned short, OP, func, u16)
+
+BINARY_OP(+, af_add)
+BINARY_OP(-, af_sub)
+BINARY_OP(*, af_mul)
+BINARY_OP(/, af_div)
+BINARY_OP(==, af_eq)
+BINARY_OP(!=, af_neq)
+BINARY_OP(<, af_lt)
+BINARY_OP(<=, af_le)
+BINARY_OP(>, af_gt)
+BINARY_OP(>=, af_ge)
+BINARY_OP(&&, af_and)
+BINARY_OP(||, af_or)
+BINARY_OP(%, af_mod)
+BINARY_OP(&, af_bitand)
+BINARY_OP(|, af_bitor)
+BINARY_OP(^, af_bitxor)
+BINARY_OP(<<, af_bitshiftl)
+BINARY_OP(>>, af_bitshiftr)
 
 #undef BINARY_OP
 
 #undef BINARY_TYPE
 
-    array array::operator-() const
-    {
-        af_array lhs = this->get();
-        af_array out;
-        array cst = constant(0, this->dims(), this->type());
-        AF_THROW(af_sub(&out, cst.get(), lhs, gforGet()));
-        return array(out);
-    }
+array array::operator-() const {
+    af_array lhs = this->get();
+    af_array out;
+    array cst = constant(0, this->dims(), this->type());
+    AF_THROW(af_sub(&out, cst.get(), lhs, gforGet()));
+    return array(out);
+}
 
-    array array::operator!() const
-    {
-        af_array lhs = this->get();
-        af_array out;
-        AF_THROW(af_not(&out, lhs));
-        return array(out);
-    }
+array array::operator!() const {
+    af_array lhs = this->get();
+    af_array out;
+    AF_THROW(af_not(&out, lhs));
+    return array(out);
+}
 
-    void array::eval() const
-    {
-        AF_THROW(af_eval(get()));
-    }
+void array::eval() const { AF_THROW(af_eval(get())); }
 
 // array instanciations
-#define INSTANTIATE(T)                                              \
-    template<> AFAPI T *array::host() const                         \
-    {                                                               \
-        if (type() != (af::dtype)dtype_traits<T>::af_type) {        \
-            AF_THROW_ERR("Requested type doesn't match with array", \
-                         AF_ERR_TYPE);                              \
-        }                                                           \
-        void *res;                                                  \
-        AF_THROW(af_alloc_host(&res, bytes()));                     \
-        AF_THROW(af_get_data_ptr(res, get()));                      \
-                                                                    \
-        return (T*)res;                                             \
-    }                                                               \
-    template<> AFAPI T array::scalar() const                        \
-    {                                                               \
-        af_dtype type = (af_dtype)af::dtype_traits<T>::af_type;     \
-        if (type != this->type())                                   \
-            AF_THROW_ERR("Requested type doesn't match array type", \
-                         AF_ERR_TYPE);                              \
-        T val;                                                      \
-        AF_THROW(af_get_scalar(&val, get()));                       \
-        return val;                                                 \
-    }                                                               \
-    template<> AFAPI T* array::device() const                       \
-    {                                                               \
-        void *ptr = NULL;                                           \
-        AF_THROW(af_get_device_ptr(&ptr, get()));                   \
-        return (T *)ptr;                                            \
-    }                                                               \
-    template<> AFAPI void array::write(const T *ptr,                \
-                                       const size_t bytes,          \
-                                       af::source src)              \
-    {                                                               \
-        if(src == afHost)   {                                       \
-            AF_THROW(af_write_array(get(), ptr, bytes,              \
-                                    (af::source)afHost));           \
-        }                                                           \
-        if(src == afDevice) {                                       \
-            AF_THROW(af_write_array(get(), ptr, bytes,              \
-                                    (af::source)afDevice));         \
-        }                                                           \
-    }                                                               \
-
-    INSTANTIATE(cdouble)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(double)
-    INSTANTIATE(float)
-    INSTANTIATE(unsigned)
-    INSTANTIATE(int)
-    INSTANTIATE(unsigned char)
-    INSTANTIATE(char)
-    INSTANTIATE(long long)
-    INSTANTIATE(unsigned long long)
-    INSTANTIATE(short)
-    INSTANTIATE(unsigned short)
-
-    template<> AFAPI void array::write(const void *ptr,
-                                       const size_t bytes,
-                                       af::source src)
-    {
-        AF_THROW(af_write_array(get(), ptr, bytes, src));
-    }
+#define INSTANTIATE(T)                                                         \
+    template<>                                                                 \
+    AFAPI T *array::host() const {                                             \
+        if (type() != (af::dtype)dtype_traits<T>::af_type) {                   \
+            AF_THROW_ERR("Requested type doesn't match with array",            \
+                         AF_ERR_TYPE);                                         \
+        }                                                                      \
+        void *res;                                                             \
+        AF_THROW(af_alloc_host(&res, bytes()));                                \
+        AF_THROW(af_get_data_ptr(res, get()));                                 \
+                                                                               \
+        return (T *)res;                                                       \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI T array::scalar() const {                                            \
+        af_dtype type = (af_dtype)af::dtype_traits<T>::af_type;                \
+        if (type != this->type())                                              \
+            AF_THROW_ERR("Requested type doesn't match array type",            \
+                         AF_ERR_TYPE);                                         \
+        T val;                                                                 \
+        AF_THROW(af_get_scalar(&val, get()));                                  \
+        return val;                                                            \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI T *array::device() const {                                           \
+        void *ptr = NULL;                                                      \
+        AF_THROW(af_get_device_ptr(&ptr, get()));                              \
+        return (T *)ptr;                                                       \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI void array::write(const T *ptr, const size_t bytes,                  \
+                            af::source src) {                                  \
+        if (src == afHost) {                                                   \
+            AF_THROW(af_write_array(get(), ptr, bytes, (af::source)afHost));   \
+        }                                                                      \
+        if (src == afDevice) {                                                 \
+            AF_THROW(af_write_array(get(), ptr, bytes, (af::source)afDevice)); \
+        }                                                                      \
+    }
+
+INSTANTIATE(cdouble)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(unsigned)
+INSTANTIATE(int)
+INSTANTIATE(unsigned char)
+INSTANTIATE(char)
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
+INSTANTIATE(short)
+INSTANTIATE(unsigned short)
+
+template<>
+AFAPI void array::write(const void *ptr, const size_t bytes, af::source src) {
+    AF_THROW(af_write_array(get(), ptr, bytes, src));
+}
 
 #undef INSTANTIATE
 
-    template<> AFAPI void* array::device() const
-    {
-        void *ptr = NULL;
-        AF_THROW(af_get_device_ptr(&ptr, get()));
-        return (void *)ptr;
-    }
-
+template<>
+AFAPI void *array::device() const {
+    void *ptr = NULL;
+    AF_THROW(af_get_device_ptr(&ptr, get()));
+    return (void *)ptr;
+}
 
 // array_proxy instanciations
-#define TEMPLATE_MEM_FUNC(TYPE, RETURN_TYPE, FUNC)      \
-    template <> AFAPI                                   \
-    RETURN_TYPE array::array_proxy::FUNC() const        \
-    {                                                   \
-        array out = *this;                              \
-        return out.FUNC<TYPE>();                        \
-    }
-
-#define INSTANTIATE(T)                                  \
-    TEMPLATE_MEM_FUNC(T, T*, host)                      \
-    TEMPLATE_MEM_FUNC(T, T , scalar)                    \
-    TEMPLATE_MEM_FUNC(T, T*, device)                    \
-
-    INSTANTIATE(cdouble)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(double)
-    INSTANTIATE(float)
-    INSTANTIATE(unsigned)
-    INSTANTIATE(int)
-    INSTANTIATE(unsigned char)
-    INSTANTIATE(char)
-    INSTANTIATE(long long)
-    INSTANTIATE(unsigned long long)
-    INSTANTIATE(short)
-    INSTANTIATE(unsigned short)
+#define TEMPLATE_MEM_FUNC(TYPE, RETURN_TYPE, FUNC)       \
+    template<>                                           \
+    AFAPI RETURN_TYPE array::array_proxy::FUNC() const { \
+        array out = *this;                               \
+        return out.FUNC<TYPE>();                         \
+    }
+
+#define INSTANTIATE(T)              \
+    TEMPLATE_MEM_FUNC(T, T *, host) \
+    TEMPLATE_MEM_FUNC(T, T, scalar) \
+    TEMPLATE_MEM_FUNC(T, T *, device)
+
+INSTANTIATE(cdouble)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(unsigned)
+INSTANTIATE(int)
+INSTANTIATE(unsigned char)
+INSTANTIATE(char)
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
+INSTANTIATE(short)
+INSTANTIATE(unsigned short)
 
 #undef INSTANTIATE
 #undef TEMPLATE_MEM_FUNC
 
-    //FIXME: These functions need to be implemented properly at a later point
-    void array::array_proxy::unlock() const {}
-    void array::array_proxy::lock() const {}
-    bool array::array_proxy::isLocked() const { return false; }
+// FIXME: These functions need to be implemented properly at a later point
+void array::array_proxy::unlock() const {}
+void array::array_proxy::lock() const {}
+bool array::array_proxy::isLocked() const { return false; }
 
-    int array::nonzeros() const { return count<int>(*this); }
+int array::nonzeros() const { return count<int>(*this); }
 
-    void array::lock() const
-    {
-        AF_THROW(af_lock_array(get()));
-    }
+void array::lock() const { AF_THROW(af_lock_array(get())); }
 
-    bool array::isLocked() const
-    {
-        bool res;
-        AF_THROW(af_is_locked_array(&res, get()));
-        return res;
-    }
+bool array::isLocked() const {
+    bool res;
+    AF_THROW(af_is_locked_array(&res, get()));
+    return res;
+}
 
-    void array::unlock() const
-    {
-        AF_THROW(af_unlock_array(get()));
-    }
+void array::unlock() const { AF_THROW(af_unlock_array(get())); }
 
-    void eval(int num, array **arrays)
-    {
-        std::vector<af_array> outputs(num);
-        for (int i = 0; i < num; i++) {
-            outputs[i] = arrays[i]->get();
-        }
-        AF_THROW(af_eval_multiple(num, &outputs[0]));
-    }
+void eval(int num, array **arrays) {
+    std::vector<af_array> outputs(num);
+    for (int i = 0; i < num; i++) { outputs[i] = arrays[i]->get(); }
+    AF_THROW(af_eval_multiple(num, &outputs[0]));
+}
 
-    void setManualEvalFlag(bool flag)
-    {
-        AF_THROW(af_set_manual_eval_flag(flag));
-    }
+void setManualEvalFlag(bool flag) { AF_THROW(af_set_manual_eval_flag(flag)); }
 
-    bool getManualEvalFlag()
-    {
-        bool flag;
-        AF_THROW(af_get_manual_eval_flag(&flag));
-        return flag;
-    }
+bool getManualEvalFlag() {
+    bool flag;
+    AF_THROW(af_get_manual_eval_flag(&flag));
+    return flag;
 }
+}  // namespace af
diff --git a/src/api/cpp/bilateral.cpp b/src/api/cpp/bilateral.cpp
index 047830c22b..d0626a9299 100644
--- a/src/api/cpp/bilateral.cpp
+++ b/src/api/cpp/bilateral.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array bilateral(const array &in, const float spatial_sigma, const float chromatic_sigma, const bool is_color)
-{
+array bilateral(const array &in, const float spatial_sigma,
+                const float chromatic_sigma, const bool is_color) {
     af_array out = 0;
-    AF_THROW(af_bilateral(&out, in.get(), spatial_sigma, chromatic_sigma, is_color));
+    AF_THROW(
+        af_bilateral(&out, in.get(), spatial_sigma, chromatic_sigma, is_color));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/binary.cpp b/src/api/cpp/binary.cpp
index 11ebbc45c6..96a04165d7 100644
--- a/src/api/cpp/binary.cpp
+++ b/src/api/cpp/binary.cpp
@@ -7,59 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
 #include <af/gfor.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
 #define INSTANTIATE(cppfunc, cfunc)                             \
-    array cppfunc(const array &lhs, const array &rhs)           \
-    {                                                           \
+    array cppfunc(const array &lhs, const array &rhs) {         \
         af_array out = 0;                                       \
         AF_THROW(cfunc(&out, lhs.get(), rhs.get(), gforGet())); \
         return array(out);                                      \
     }
 
-    INSTANTIATE(min , af_minof)
-    INSTANTIATE(max , af_maxof)
-    INSTANTIATE(pow , af_pow  )
-    INSTANTIATE(root, af_root )
-    INSTANTIATE(rem , af_rem  )
-    INSTANTIATE(mod , af_mod  )
+INSTANTIATE(min, af_minof)
+INSTANTIATE(max, af_maxof)
+INSTANTIATE(pow, af_pow)
+INSTANTIATE(root, af_root)
+INSTANTIATE(rem, af_rem)
+INSTANTIATE(mod, af_mod)
 
-    INSTANTIATE(complex, af_cplx2)
-    INSTANTIATE(atan2, af_atan2)
-    INSTANTIATE(hypot, af_hypot)
+INSTANTIATE(complex, af_cplx2)
+INSTANTIATE(atan2, af_atan2)
+INSTANTIATE(hypot, af_hypot)
 
-#define WRAPPER(func)                                       \
-    array func(const array &lhs, const double rhs)          \
-    {                                                       \
-        af::dtype ty = lhs.type();                          \
-        if (lhs.iscomplex()) {                              \
-            ty = lhs.issingle() ? f32 : f64;                \
-        }                                                   \
-        return func(lhs, constant(rhs, lhs.dims(), ty));    \
-    }                                                       \
-    array func(const double lhs, const array &rhs)          \
-    {                                                       \
-        af::dtype ty = rhs.type();                          \
-        if (rhs.iscomplex()) {                              \
-            ty = rhs.issingle() ? f32 : f64;                \
-        }                                                   \
-        return func(constant(lhs, rhs.dims(), ty), rhs);    \
+#define WRAPPER(func)                                             \
+    array func(const array &lhs, const double rhs) {              \
+        af::dtype ty = lhs.type();                                \
+        if (lhs.iscomplex()) { ty = lhs.issingle() ? f32 : f64; } \
+        return func(lhs, constant(rhs, lhs.dims(), ty));          \
+    }                                                             \
+    array func(const double lhs, const array &rhs) {              \
+        af::dtype ty = rhs.type();                                \
+        if (rhs.iscomplex()) { ty = rhs.issingle() ? f32 : f64; } \
+        return func(constant(lhs, rhs.dims(), ty), rhs);          \
     }
 
-    WRAPPER(min)
-    WRAPPER(max)
-    WRAPPER(pow)
-    WRAPPER(root)
-    WRAPPER(rem)
-    WRAPPER(mod)
-    WRAPPER(complex)
-    WRAPPER(atan2)
-    WRAPPER(hypot)
-}
+WRAPPER(min)
+WRAPPER(max)
+WRAPPER(pow)
+WRAPPER(root)
+WRAPPER(rem)
+WRAPPER(mod)
+WRAPPER(complex)
+WRAPPER(atan2)
+WRAPPER(hypot)
+}  // namespace af
diff --git a/src/api/cpp/blas.cpp b/src/api/cpp/blas.cpp
index 4a9db3ddb1..b985dd863b 100644
--- a/src/api/cpp/blas.cpp
+++ b/src/api/cpp/blas.cpp
@@ -11,96 +11,85 @@
 #include <af/blas.h>
 #include "error.hpp"
 
-namespace af
-{
-    array matmul(const array &lhs, const array &rhs,
-                 const matProp optLhs, const matProp optRhs)
-    {
-        af_array out = 0;
-        AF_THROW(af_matmul(&out, lhs.get(), rhs.get(), optLhs, optRhs));
-        return array(out);
-    }
+namespace af {
+array matmul(const array &lhs, const array &rhs, const matProp optLhs,
+             const matProp optRhs) {
+    af_array out = 0;
+    AF_THROW(af_matmul(&out, lhs.get(), rhs.get(), optLhs, optRhs));
+    return array(out);
+}
 
-    array matmulNT(const array &lhs, const array &rhs)
-    {
-        af_array out = 0;
-        AF_THROW(af_matmul(&out, lhs.get(), rhs.get(),
-                           AF_MAT_NONE, AF_MAT_TRANS));
-        return array(out);
-    }
+array matmulNT(const array &lhs, const array &rhs) {
+    af_array out = 0;
+    AF_THROW(af_matmul(&out, lhs.get(), rhs.get(), AF_MAT_NONE, AF_MAT_TRANS));
+    return array(out);
+}
 
-    array matmulTN(const array &lhs, const array &rhs)
-    {
-        af_array out = 0;
-        AF_THROW(af_matmul(&out, lhs.get(), rhs.get(),
-                           AF_MAT_TRANS, AF_MAT_NONE));
-        return array(out);
-    }
+array matmulTN(const array &lhs, const array &rhs) {
+    af_array out = 0;
+    AF_THROW(af_matmul(&out, lhs.get(), rhs.get(), AF_MAT_TRANS, AF_MAT_NONE));
+    return array(out);
+}
 
-    array matmulTT(const array &lhs, const array &rhs)
-    {
-        af_array out = 0;
-        AF_THROW(af_matmul(&out, lhs.get(), rhs.get(),
-                           AF_MAT_TRANS, AF_MAT_TRANS));
-        return array(out);
-    }
+array matmulTT(const array &lhs, const array &rhs) {
+    af_array out = 0;
+    AF_THROW(af_matmul(&out, lhs.get(), rhs.get(), AF_MAT_TRANS, AF_MAT_TRANS));
+    return array(out);
+}
 
-    array matmul(const array &a, const array &b, const array &c)
-    {
-        int tmp1 = a.dims(0) * b.dims(1);
-        int tmp2 = b.dims(0) * c.dims(1);
+array matmul(const array &a, const array &b, const array &c) {
+    int tmp1 = a.dims(0) * b.dims(1);
+    int tmp2 = b.dims(0) * c.dims(1);
 
-        if (tmp1 < tmp2) {
-            return matmul(matmul(a, b), c);
-        } else {
-            return matmul(a, matmul(b, c));
-        }
+    if (tmp1 < tmp2) {
+        return matmul(matmul(a, b), c);
+    } else {
+        return matmul(a, matmul(b, c));
     }
+}
 
-    array matmul(const array &a, const array &b, const array &c, const array &d)
-    {
-        int tmp1 = a.dims(0) * c.dims(1);
-        int tmp2 = b.dims(0) * d.dims(1);
+array matmul(const array &a, const array &b, const array &c, const array &d) {
+    int tmp1 = a.dims(0) * c.dims(1);
+    int tmp2 = b.dims(0) * d.dims(1);
 
-        if (tmp1 < tmp2) {
-            return matmul(matmul(a, b, c), d);
-        } else {
-            return matmul(a, matmul(b, c, d));
-        }
+    if (tmp1 < tmp2) {
+        return matmul(matmul(a, b, c), d);
+    } else {
+        return matmul(a, matmul(b, c, d));
     }
+}
 
-    array dot(const array &lhs, const array &rhs,
-              const matProp optLhs, const matProp optRhs)
-    {
-        af_array out = 0;
-        AF_THROW(af_dot(&out, lhs.get(), rhs.get(), optLhs, optRhs));
-        return array(out);
-    }
+array dot(const array &lhs, const array &rhs, const matProp optLhs,
+          const matProp optRhs) {
+    af_array out = 0;
+    AF_THROW(af_dot(&out, lhs.get(), rhs.get(), optLhs, optRhs));
+    return array(out);
+}
 
-#define INSTANTIATE_REAL(TYPE)                                                      \
-    template<> AFAPI                                                                \
-    TYPE dot(const array &lhs, const array &rhs,                                    \
-             const matProp optLhs, const matProp optRhs)                            \
-    {                                                                               \
-        double rval = 0, ival = 0;                                                  \
-        AF_THROW(af_dot_all(&rval, &ival, lhs.get(), rhs.get(), optLhs, optRhs));   \
-        return (TYPE)(rval);                                                        \
+#define INSTANTIATE_REAL(TYPE)                                               \
+    template<>                                                               \
+    AFAPI TYPE dot(const array &lhs, const array &rhs, const matProp optLhs, \
+                   const matProp optRhs) {                                   \
+        double rval = 0, ival = 0;                                           \
+        AF_THROW(                                                            \
+            af_dot_all(&rval, &ival, lhs.get(), rhs.get(), optLhs, optRhs)); \
+        return (TYPE)(rval);                                                 \
     }
 
-#define INSTANTIATE_CPLX(TYPE, REAL)                                                \
-    template<> AFAPI                                                                \
-    TYPE dot(const array &lhs, const array &rhs,                                    \
-             const matProp optLhs, const matProp optRhs)                            \
-    {                                                                               \
-        double rval = 0, ival = 0;                                                  \
-        AF_THROW(af_dot_all(&rval, &ival, lhs.get(), rhs.get(), optLhs, optRhs));   \
-        TYPE out((REAL)rval, (REAL)ival);                                           \
-        return out;                                                                 \
+#define INSTANTIATE_CPLX(TYPE, REAL)                                         \
+    template<>                                                               \
+    AFAPI TYPE dot(const array &lhs, const array &rhs, const matProp optLhs, \
+                   const matProp optRhs) {                                   \
+        double rval = 0, ival = 0;                                           \
+        AF_THROW(                                                            \
+            af_dot_all(&rval, &ival, lhs.get(), rhs.get(), optLhs, optRhs)); \
+        TYPE out((REAL)rval, (REAL)ival);                                    \
+        return out;                                                          \
     }
 
-    INSTANTIATE_REAL(float)
-    INSTANTIATE_REAL(double)
-    INSTANTIATE_CPLX(cfloat, float)
-    INSTANTIATE_CPLX(cdouble, double)
+INSTANTIATE_REAL(float)
+INSTANTIATE_REAL(double)
+INSTANTIATE_CPLX(cfloat, float)
+INSTANTIATE_CPLX(cdouble, double)
 
-}
+}  // namespace af
diff --git a/src/api/cpp/canny.cpp b/src/api/cpp/canny.cpp
index be8b14bd78..bdf7a382cc 100644
--- a/src/api/cpp/canny.cpp
+++ b/src/api/cpp/canny.cpp
@@ -7,17 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
-array canny(const array& in, const cannyThreshold ctType,
-            const float ltr, const float htr, const unsigned sW, const bool isFast)
-{
+namespace af {
+array canny(const array& in, const cannyThreshold ctType, const float ltr,
+            const float htr, const unsigned sW, const bool isFast) {
     af_array temp = 0;
     AF_THROW(af_canny(&temp, in.get(), ctType, ltr, htr, sW, isFast));
     return array(temp);
 }
-}
+}  // namespace af
diff --git a/src/api/cpp/clamp.cpp b/src/api/cpp/clamp.cpp
index 458f22ead0..cb3616d764 100644
--- a/src/api/cpp/clamp.cpp
+++ b/src/api/cpp/clamp.cpp
@@ -7,35 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
 #include <af/gfor.h>
 #include "error.hpp"
 
-namespace af
-{
-    array clamp(const array &in, const array &lo, const array &hi)
-    {
-        af_array out;
-        AF_THROW(af_clamp(&out, in.get(), lo.get(), hi.get(), gforGet()));
-        return array(out);
-    }
+namespace af {
+array clamp(const array &in, const array &lo, const array &hi) {
+    af_array out;
+    AF_THROW(af_clamp(&out, in.get(), lo.get(), hi.get(), gforGet()));
+    return array(out);
+}
 
-    array clamp(const array &in, const array &lo, const double hi)
-    {
-        return clamp(in, lo, constant(hi, lo.dims(), lo.type()));
-    }
+array clamp(const array &in, const array &lo, const double hi) {
+    return clamp(in, lo, constant(hi, lo.dims(), lo.type()));
+}
 
-    array clamp(const array &in, const double lo, const array &hi)
-    {
-        return clamp(in, constant(lo, hi.dims(), hi.type()), hi);
-    }
+array clamp(const array &in, const double lo, const array &hi) {
+    return clamp(in, constant(lo, hi.dims(), hi.type()), hi);
+}
 
-    array clamp(const array &in, const double lo, const double hi)
-    {
-        return clamp(in,
-                     constant(lo, in.dims(), in.type()),
-                     constant(hi, in.dims(), in.type()));
-    }
+array clamp(const array &in, const double lo, const double hi) {
+    return clamp(in, constant(lo, in.dims(), in.type()),
+                 constant(hi, in.dims(), in.type()));
 }
+}  // namespace af
diff --git a/src/api/cpp/colorspace.cpp b/src/api/cpp/colorspace.cpp
index 6241e54ddb..eda57cccb7 100644
--- a/src/api/cpp/colorspace.cpp
+++ b/src/api/cpp/colorspace.cpp
@@ -7,25 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
+#include <af/compatible.h>
 #include <af/defines.h>
 #include <af/image.h>
-#include <af/compatible.h>
-#include <af/array.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array colorspace(const array& image, const CSpace to, const CSpace from)
-{
+array colorspace(const array& image, const CSpace to, const CSpace from) {
     return colorSpace(image, to, from);
 }
 
-array colorSpace(const array& image, const CSpace to, const CSpace from)
-{
+array colorSpace(const array& image, const CSpace to, const CSpace from) {
     af_array temp = 0;
-    AF_THROW(af_color_space(&temp, image.get(), to ,from));
+    AF_THROW(af_color_space(&temp, image.get(), to, from));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/common.hpp b/src/api/cpp/common.hpp
index ed191670ec..355972fc8a 100644
--- a/src/api/cpp/common.hpp
+++ b/src/api/cpp/common.hpp
@@ -9,18 +9,15 @@
 
 #include <af/dim4.hpp>
 
-namespace af
-{
+namespace af {
 
 /// Get the first non-zero dimension
-static inline dim_t getFNSD(const int dim, af::dim4 dims)
-{
-    if(dim >= 0)
-        return dim;
+static inline dim_t getFNSD(const int dim, af::dim4 dims) {
+    if (dim >= 0) return dim;
 
     dim_t fNSD = 0;
-    for (dim_t i=0; i<4; ++i) {
-        if (dims[i]>1) {
+    for (dim_t i = 0; i < 4; ++i) {
+        if (dims[i] > 1) {
             fNSD = i;
             break;
         }
@@ -28,4 +25,4 @@ static inline dim_t getFNSD(const int dim, af::dim4 dims)
     return fNSD;
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/complex.cpp b/src/api/cpp/complex.cpp
index 4e9b5d4d7c..e1d4ada43b 100644
--- a/src/api/cpp/complex.cpp
+++ b/src/api/cpp/complex.cpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <af/complex.h>
-#include <istream>
 #include <cmath>
 #include <complex>
+#include <istream>
 
-namespace af
-{
+namespace af {
 using std::complex;
 
 float real(af_cfloat val) { return val.real; }
@@ -22,75 +21,73 @@ double real(af_cdouble val) { return val.real; }
 float imag(af_cfloat val) { return val.imag; }
 double imag(af_cdouble val) { return val.imag; }
 
-cfloat operator+(const cfloat &lhs, const cfloat &rhs)
-{
+cfloat operator+(const cfloat &lhs, const cfloat &rhs) {
     cfloat out(lhs.real + rhs.real, lhs.imag + rhs.imag);
     return out;
 }
 
-cdouble operator+(const cdouble &lhs, const cdouble &rhs)
-{
+cdouble operator+(const cdouble &lhs, const cdouble &rhs) {
     cdouble out(lhs.real + rhs.real, lhs.imag + rhs.imag);
     return out;
 }
 
-cfloat operator*(const cfloat &lhs, const cfloat &rhs)
-{
+cfloat operator*(const cfloat &lhs, const cfloat &rhs) {
     complex<float> clhs(lhs.real, lhs.imag);
     complex<float> crhs(rhs.real, rhs.imag);
     complex<float> out = clhs * crhs;
     return cfloat(out.real(), out.imag());
 }
 
-cdouble operator*(const cdouble &lhs, const cdouble &rhs)
-{
+cdouble operator*(const cdouble &lhs, const cdouble &rhs) {
     complex<double> clhs(lhs.real, lhs.imag);
     complex<double> crhs(rhs.real, rhs.imag);
     complex<double> out = clhs * crhs;
     return cdouble(out.real(), out.imag());
 }
 
-cfloat operator-(const cfloat &lhs, const cfloat &rhs)
-{
+cfloat operator-(const cfloat &lhs, const cfloat &rhs) {
     cfloat out(lhs.real - rhs.real, lhs.imag - rhs.imag);
     return out;
 }
 
-cdouble operator-(const cdouble &lhs, const cdouble &rhs)
-{
+cdouble operator-(const cdouble &lhs, const cdouble &rhs) {
     cdouble out(lhs.real - rhs.real, lhs.imag - rhs.imag);
     return out;
 }
 
-cfloat operator/(const cfloat &lhs, const cfloat &rhs)
-{
+cfloat operator/(const cfloat &lhs, const cfloat &rhs) {
     complex<float> clhs(lhs.real, lhs.imag);
     complex<float> crhs(rhs.real, rhs.imag);
     complex<float> out = clhs / crhs;
     return cfloat(out.real(), out.imag());
 }
 
-cdouble operator/(const cdouble &lhs, const cdouble &rhs)
-{
+cdouble operator/(const cdouble &lhs, const cdouble &rhs) {
     complex<double> clhs(lhs.real, lhs.imag);
     complex<double> crhs(rhs.real, rhs.imag);
     complex<double> out = clhs / crhs;
     return cdouble(out.real(), out.imag());
 }
 
-#define IMPL_OP(OP)                                             \
-    cfloat  operator OP(const cfloat  &lhs, const double  &rhs) \
-    { return lhs OP cfloat (rhs); }                             \
-    cdouble operator OP(const cdouble &lhs, const double  &rhs) \
-    { return lhs OP cdouble(rhs); }                             \
-    cfloat  operator OP(const double  &lhs, const cfloat  &rhs) \
-    { return cfloat (lhs) OP rhs; }                             \
-    cdouble operator OP(const double  &lhs, const cdouble &rhs) \
-    { return cdouble(lhs) OP rhs; }                             \
-    cdouble operator OP(const cfloat  &lhs, const cdouble &rhs) \
-    { return cdouble(real(lhs), imag(lhs)) OP rhs; }            \
-    cdouble operator OP(const cdouble &lhs, const cfloat  &rhs) \
-    { return lhs OP cdouble(real(rhs), imag(rhs)); }            \
+#define IMPL_OP(OP)                                              \
+    cfloat operator OP(const cfloat &lhs, const double &rhs) {   \
+        return lhs OP cfloat(rhs);                               \
+    }                                                            \
+    cdouble operator OP(const cdouble &lhs, const double &rhs) { \
+        return lhs OP cdouble(rhs);                              \
+    }                                                            \
+    cfloat operator OP(const double &lhs, const cfloat &rhs) {   \
+        return cfloat(lhs) OP rhs;                               \
+    }                                                            \
+    cdouble operator OP(const double &lhs, const cdouble &rhs) { \
+        return cdouble(lhs) OP rhs;                              \
+    }                                                            \
+    cdouble operator OP(const cfloat &lhs, const cdouble &rhs) { \
+        return cdouble(real(lhs), imag(lhs)) OP rhs;             \
+    }                                                            \
+    cdouble operator OP(const cdouble &lhs, const cfloat &rhs) { \
+        return lhs OP cdouble(real(rhs), imag(rhs));             \
+    }
 
 IMPL_OP(+)
 IMPL_OP(-)
@@ -99,62 +96,45 @@ IMPL_OP(/)
 
 #undef IMPL_OP
 
-bool operator!=(const cfloat &lhs, const cfloat &rhs)
-{
-    return !(lhs == rhs);
-}
+bool operator!=(const cfloat &lhs, const cfloat &rhs) { return !(lhs == rhs); }
 
-bool operator!=(const cdouble &lhs, const cdouble &rhs)
-{
+bool operator!=(const cdouble &lhs, const cdouble &rhs) {
     return !(lhs == rhs);
 }
 
-bool operator==(const cfloat &lhs, const cfloat &rhs)
-{
+bool operator==(const cfloat &lhs, const cfloat &rhs) {
     return lhs.real == rhs.real && lhs.imag == rhs.imag;
 }
 
-bool operator==(const cdouble &lhs, const cdouble &rhs)
-{
+bool operator==(const cdouble &lhs, const cdouble &rhs) {
     return lhs.real == rhs.real && lhs.imag == rhs.imag;
 }
 
-float abs(const cfloat &val)
-{
+float abs(const cfloat &val) {
     std::complex<float> out(val.real, val.imag);
     return abs(out);
 }
 
-double abs(const cdouble &val)
-{
+double abs(const cdouble &val) {
     std::complex<double> out(val.real, val.imag);
     return abs(out);
 }
 
-cfloat conj(const cfloat &val)
-{
-    return cfloat(val.real, -val.imag);
-}
+cfloat conj(const cfloat &val) { return cfloat(val.real, -val.imag); }
 
-cdouble conj(const cdouble &val)
-{
-    return cdouble(val.real, -val.imag);
-}
+cdouble conj(const cdouble &val) { return cdouble(val.real, -val.imag); }
 
-std::ostream& operator<< (std::ostream &os, const cfloat &in)
-{
+std::ostream &operator<<(std::ostream &os, const cfloat &in) {
     os << "(" << in.real << ", " << in.imag << ")";
     return os;
 }
 
-std::ostream& operator<< (std::ostream &os, const cdouble &in)
-{
+std::ostream &operator<<(std::ostream &os, const cdouble &in) {
     os << "(" << in.real << " " << in.imag << ")";
     return os;
 }
 
-std::istream& operator>> (std::istream &is, cfloat &in)
-{
+std::istream &operator>>(std::istream &is, cfloat &in) {
     char trash;
     is >> trash;
     is >> in.real;
@@ -164,8 +144,7 @@ std::istream& operator>> (std::istream &is, cfloat &in)
     return is;
 }
 
-std::istream& operator>> (std::istream &is, cdouble &in)
-{
+std::istream &operator>>(std::istream &is, cdouble &in) {
     char trash;
     is >> trash;
     is >> in.real;
@@ -175,4 +154,4 @@ std::istream& operator>> (std::istream &is, cdouble &in)
     return is;
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/convolve.cpp b/src/api/cpp/convolve.cpp
index aabfbe0cc2..9245b5b298 100644
--- a/src/api/cpp/convolve.cpp
+++ b/src/api/cpp/convolve.cpp
@@ -7,21 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/signal.h>
-#include <af/compatible.h>
 #include <af/array.h>
-#include "error.hpp"
+#include <af/compatible.h>
+#include <af/signal.h>
 #include <algorithm>
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array convolve(const array& signal, const array& filter, const convMode mode, convDomain domain)
-{
+array convolve(const array& signal, const array& filter, const convMode mode,
+               convDomain domain) {
     unsigned sN = signal.numdims();
     unsigned fN = filter.numdims();
 
-    switch(std::min(sN,fN)) {
+    switch (std::min(sN, fN)) {
         case 1: return convolve1(signal, filter, mode, domain);
         case 2: return convolve2(signal, filter, mode, domain);
         case 3: return convolve3(signal, filter, mode, domain);
@@ -29,37 +28,37 @@ array convolve(const array& signal, const array& filter, const convMode mode, co
     }
 }
 
-array convolve(const array& col_filter, const array& row_filter, const array& signal, const convMode mode)
-{
+array convolve(const array& col_filter, const array& row_filter,
+               const array& signal, const convMode mode) {
     af_array out = 0;
-    AF_THROW(af_convolve2_sep(&out, col_filter.get(), row_filter.get(), signal.get(), mode));
+    AF_THROW(af_convolve2_sep(&out, col_filter.get(), row_filter.get(),
+                              signal.get(), mode));
     return array(out);
 }
 
-array convolve1(const array& signal, const array& filter, const convMode mode, convDomain domain)
-{
+array convolve1(const array& signal, const array& filter, const convMode mode,
+                convDomain domain) {
     af_array out = 0;
     AF_THROW(af_convolve1(&out, signal.get(), filter.get(), mode, domain));
     return array(out);
 }
 
-array convolve2(const array& signal, const array& filter, const convMode mode, convDomain domain)
-{
+array convolve2(const array& signal, const array& filter, const convMode mode,
+                convDomain domain) {
     af_array out = 0;
     AF_THROW(af_convolve2(&out, signal.get(), filter.get(), mode, domain));
     return array(out);
 }
 
-array convolve3(const array& signal, const array& filter, const convMode mode, convDomain domain)
-{
+array convolve3(const array& signal, const array& filter, const convMode mode,
+                convDomain domain) {
     af_array out = 0;
     AF_THROW(af_convolve3(&out, signal.get(), filter.get(), mode, domain));
     return array(out);
 }
 
-array filter(const array& image, const array& kernel)
-{
+array filter(const array& image, const array& kernel) {
     return convolve(image, kernel, AF_CONV_DEFAULT, AF_CONV_AUTO);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/corrcoef.cpp b/src/api/cpp/corrcoef.cpp
index 01023b11ed..f90be68b5f 100644
--- a/src/api/cpp/corrcoef.cpp
+++ b/src/api/cpp/corrcoef.cpp
@@ -7,20 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/statistics.h>
 #include <af/array.h>
+#include <af/statistics.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-#define INSTANTIATE_CORRCOEF(T)                                    \
-    template<> AFAPI T corrcoef(const array& X, const array& Y)    \
-    {                                                              \
-        double real;                                               \
-        AF_THROW(af_corrcoef(&real, NULL, X.get(), Y.get()));      \
-        return (T)real;                                            \
-    }                                                              \
+#define INSTANTIATE_CORRCOEF(T)                               \
+    template<>                                                \
+    AFAPI T corrcoef(const array& X, const array& Y) {        \
+        double real;                                          \
+        AF_THROW(af_corrcoef(&real, NULL, X.get(), Y.get())); \
+        return (T)real;                                       \
+    }
 
 INSTANTIATE_CORRCOEF(float);
 INSTANTIATE_CORRCOEF(double);
@@ -35,4 +34,4 @@ INSTANTIATE_CORRCOEF(unsigned short);
 
 #undef INSTANTIATE_CORRCOEF
 
-}
+}  // namespace af
diff --git a/src/api/cpp/covariance.cpp b/src/api/cpp/covariance.cpp
index a38fe0410f..44608e4513 100644
--- a/src/api/cpp/covariance.cpp
+++ b/src/api/cpp/covariance.cpp
@@ -7,18 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/statistics.h>
 #include <af/array.h>
+#include <af/statistics.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array cov(const array& X, const array& Y, const bool isbiased)
-{
+array cov(const array& X, const array& Y, const bool isbiased) {
     af_array temp = 0;
     AF_THROW(af_cov(&temp, X.get(), Y.get(), isbiased));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index cbd9f5dcb0..163c0731fb 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -7,102 +7,99 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/arith.h>
 #include <af/array.h>
 #include <af/complex.h>
-#include <af/arith.h>
 #include <af/data.h>
-#include <af/traits.hpp>
+#include <af/defines.h>
 #include <af/gfor.h>
+#include <af/traits.hpp>
 #include "error.hpp"
-#include <af/defines.h>
 
 #include <type_traits>
 
-using std::enable_if;
 using af::array;
 using af::dim4;
 using af::dtype;
+using std::enable_if;
 
 namespace {
-    // NOTE: we are repeating this here so that we don't need to access the is_complex
-    // types in backend/common. This is done to isolate the C++ API from the internal
-    // API
-    template<typename T> struct is_complex              { static const bool value = false;  };
-    template<> struct           is_complex<af::cfloat>  { static const bool value = true;   };
-    template<> struct           is_complex<af::cdouble> { static const bool value = true;   };
-
-    template<typename T,
-             typename = typename enable_if<is_complex<T>::value == false, T>::type>
-    array
-    constant(T val, const dim4& dims, const dtype type)
-    {
-        af_array res;
-        if (type != s64 && type != u64) {
-            AF_THROW(af_constant(&res, (double)val,
-                                 dims.ndims(), dims.get(), type));
-        }
-        else if (type == s64) {
-                AF_THROW(af_constant_long (&res, (long long)val,
-                                           dims.ndims(),
-                                           dims.get()));
-        } else {
-            AF_THROW(af_constant_ulong(&res, (unsigned long long)val,
-                                       dims.ndims(),
-                                       dims.get()));
-        }
-        return array(res);
-    }
+// NOTE: we are repeating this here so that we don't need to access the
+// is_complex types in backend/common. This is done to isolate the C++ API from
+// the internal API
+template<typename T>
+struct is_complex {
+    static const bool value = false;
+};
+template<>
+struct is_complex<af::cfloat> {
+    static const bool value = true;
+};
+template<>
+struct is_complex<af::cdouble> {
+    static const bool value = true;
+};
+
+template<typename T,
+         typename = typename enable_if<is_complex<T>::value == false, T>::type>
+array constant(T val, const dim4 &dims, const dtype type) {
+    af_array res;
+    if (type != s64 && type != u64) {
+        AF_THROW(
+            af_constant(&res, (double)val, dims.ndims(), dims.get(), type));
+    } else if (type == s64) {
+        AF_THROW(
+            af_constant_long(&res, (long long)val, dims.ndims(), dims.get()));
+    } else {
+        AF_THROW(af_constant_ulong(&res, (unsigned long long)val, dims.ndims(),
+                                   dims.get()));
+    }
+    return array(res);
+}
 
-    template<typename T>
-    typename enable_if<is_complex<T>::value == true, array>::type
-    constant(T val, const dim4& dims, const dtype type)
-    {
-        if (type != c32 && type != c64) {
-            return ::constant(real(val), dims, type);
-        }
-        af_array res;
-        AF_THROW(af_constant_complex(&res,
-                                     real(val),
-                                     imag(val),
-                                     dims.ndims(),
-                                     dims.get(), type));
-        return array(res);
+template<typename T>
+typename enable_if<is_complex<T>::value == true, array>::type constant(
+    T val, const dim4 &dims, const dtype type) {
+    if (type != c32 && type != c64) {
+        return ::constant(real(val), dims, type);
     }
+    af_array res;
+    AF_THROW(af_constant_complex(&res, real(val), imag(val), dims.ndims(),
+                                 dims.get(), type));
+    return array(res);
 }
+}  // namespace
 
-namespace af
-{
-    template<typename T>
-    array constant(T val, const dim4& dims, const af::dtype type) {
-         return ::constant(val, dims, type);
-    }
+namespace af {
+template<typename T>
+array constant(T val, const dim4 &dims, const af::dtype type) {
+    return ::constant(val, dims, type);
+}
 
-     template<typename T>
-     array constant(T val, const dim_t d0, const af::dtype ty)
-     {
-         return ::constant(val, dim4(d0), ty);
-     }
+template<typename T>
+array constant(T val, const dim_t d0, const af::dtype ty) {
+    return ::constant(val, dim4(d0), ty);
+}
 
-    template<typename T>
-    array constant(T val, const dim_t d0, const dim_t d1, const af::dtype ty)
-    {
-        return ::constant(val, dim4(d0, d1), ty);
-    }
+template<typename T>
+array constant(T val, const dim_t d0, const dim_t d1, const af::dtype ty) {
+    return ::constant(val, dim4(d0, d1), ty);
+}
 
-    template<typename T>
-    array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const af::dtype ty)
-    {
-        return ::constant(val, dim4(d0, d1, d2), ty);
-    }
+template<typename T>
+array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2,
+               const af::dtype ty) {
+    return ::constant(val, dim4(d0, d1, d2), ty);
+}
 
-    template<typename T>
-    array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3, const af::dtype ty)
-    {
-        return ::constant(val, dim4(d0, d1, d2, d3), ty);
-    }
+template<typename T>
+array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2,
+               const dim_t d3, const af::dtype ty) {
+    return ::constant(val, dim4(d0, d1, d2, d3), ty);
+}
 
 #define CONSTANT(TYPE)                                                       \
-    template AFAPI array constant<TYPE>(TYPE val, const dim4&  dims,         \
+    template AFAPI array constant<TYPE>(TYPE val, const dim4 &dims,          \
                                         const af::dtype ty);                 \
     template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,            \
                                         const af::dtype ty);                 \
@@ -112,215 +109,191 @@ namespace af
                                         const dim_t d1, const dim_t d2,      \
                                         const af::dtype ty);                 \
     template AFAPI array constant<TYPE>(TYPE val, const dim_t d0,            \
-                                        const dim_t d1,                      \
-                                        const dim_t d2,                      \
+                                        const dim_t d1, const dim_t d2,      \
                                         const dim_t d3, const af::dtype ty);
-    CONSTANT(double);
-    CONSTANT(float);
-    CONSTANT(int);
-    CONSTANT(unsigned);
-    CONSTANT(char);
-    CONSTANT(unsigned char);
-    CONSTANT(cfloat);
-    CONSTANT(cdouble);
-    CONSTANT(long);
-    CONSTANT(unsigned long);
-    CONSTANT(long long);
-    CONSTANT(unsigned long long);
-    CONSTANT(bool);
-    CONSTANT(short);
-    CONSTANT(unsigned short);
+CONSTANT(double);
+CONSTANT(float);
+CONSTANT(int);
+CONSTANT(unsigned);
+CONSTANT(char);
+CONSTANT(unsigned char);
+CONSTANT(cfloat);
+CONSTANT(cdouble);
+CONSTANT(long);
+CONSTANT(unsigned long);
+CONSTANT(long long);
+CONSTANT(unsigned long long);
+CONSTANT(bool);
+CONSTANT(short);
+CONSTANT(unsigned short);
 
 #undef CONSTANT
 
-    array range(const dim4 &dims, const int seq_dim, const af::dtype ty)
-    {
-        af_array out;
-        AF_THROW(af_range(&out, dims.ndims(), dims.get(), seq_dim, ty));
-        return array(out);
-    }
-
-    array range(const dim_t d0, const dim_t d1, const dim_t d2,
-               const dim_t d3, const int seq_dim, const af::dtype ty)
-    {
-        return range(dim4(d0, d1, d2, d3), seq_dim, ty);
-    }
+array range(const dim4 &dims, const int seq_dim, const af::dtype ty) {
+    af_array out;
+    AF_THROW(af_range(&out, dims.ndims(), dims.get(), seq_dim, ty));
+    return array(out);
+}
 
-    array iota(const dim4 &dims, const dim4 &tile_dims, const af::dtype ty)
-    {
-        af_array out;
-        AF_THROW(af_iota(&out, dims.ndims(), dims.get(), tile_dims.ndims(), tile_dims.get(), ty));
-        return array(out);
-    }
+array range(const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3,
+            const int seq_dim, const af::dtype ty) {
+    return range(dim4(d0, d1, d2, d3), seq_dim, ty);
+}
 
-    array identity(const dim4 &dims, const af::dtype type)
-    {
-        af_array res;
-        AF_THROW(af_identity(&res, dims.ndims(), dims.get(), type));
-        return array(res);
-    }
+array iota(const dim4 &dims, const dim4 &tile_dims, const af::dtype ty) {
+    af_array out;
+    AF_THROW(af_iota(&out, dims.ndims(), dims.get(), tile_dims.ndims(),
+                     tile_dims.get(), ty));
+    return array(out);
+}
 
-    array identity(const dim_t d0, const af::dtype ty)
-    {
-        return identity(dim4(d0), ty);
-    }
+array identity(const dim4 &dims, const af::dtype type) {
+    af_array res;
+    AF_THROW(af_identity(&res, dims.ndims(), dims.get(), type));
+    return array(res);
+}
 
-    array identity(const dim_t d0,
-                const dim_t d1, const af::dtype ty)
-    {
-        return identity(dim4(d0, d1), ty);
-    }
+array identity(const dim_t d0, const af::dtype ty) {
+    return identity(dim4(d0), ty);
+}
 
-    array identity(const dim_t d0,
-                const dim_t d1, const dim_t d2, const af::dtype ty)
-    {
-        return identity(dim4(d0, d1, d2), ty);
-    }
+array identity(const dim_t d0, const dim_t d1, const af::dtype ty) {
+    return identity(dim4(d0, d1), ty);
+}
 
-    array identity(const dim_t d0,
-                const dim_t d1, const dim_t d2,
-                const dim_t d3, const af::dtype ty)
-    {
-        return identity(dim4(d0, d1, d2, d3), ty);
-    }
+array identity(const dim_t d0, const dim_t d1, const dim_t d2,
+               const af::dtype ty) {
+    return identity(dim4(d0, d1, d2), ty);
+}
 
-    array diag(const array &in, const int num, const bool extract)
-    {
-        af_array res;
-        if (extract) {
-            AF_THROW(af_diag_extract(&res, in.get(), num));
-        } else {
-            AF_THROW(af_diag_create(&res, in.get(), num));
-        }
+array identity(const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3,
+               const af::dtype ty) {
+    return identity(dim4(d0, d1, d2, d3), ty);
+}
 
-        return array(res);
+array diag(const array &in, const int num, const bool extract) {
+    af_array res;
+    if (extract) {
+        AF_THROW(af_diag_extract(&res, in.get(), num));
+    } else {
+        AF_THROW(af_diag_create(&res, in.get(), num));
     }
 
-    array moddims(const array& in, const unsigned ndims, const dim_t * const dims)
-    {
-        af_array out = 0;
-        AF_THROW(af_moddims(&out, in.get(), ndims, dims));
-        return array(out);
-    }
+    return array(res);
+}
 
-    array moddims(const array& in, const dim4& dims)
-    {
-        return af::moddims(in, dims.ndims(), dims.get());
-    }
+array moddims(const array &in, const unsigned ndims, const dim_t *const dims) {
+    af_array out = 0;
+    AF_THROW(af_moddims(&out, in.get(), ndims, dims));
+    return array(out);
+}
 
-    array moddims(const array& in, const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3)
-    {
-        dim_t dims[4] = {d0, d1, d2, d3};
-        return af::moddims(in, 4, dims);
-    }
+array moddims(const array &in, const dim4 &dims) {
+    return af::moddims(in, dims.ndims(), dims.get());
+}
 
-    array flat(const array& in)
-    {
-        af_array out = 0;
-        AF_THROW(af_flat(&out, in.get()));
-        return array(out);
-    }
+array moddims(const array &in, const dim_t d0, const dim_t d1, const dim_t d2,
+              const dim_t d3) {
+    dim_t dims[4] = {d0, d1, d2, d3};
+    return af::moddims(in, 4, dims);
+}
 
-    array join(const int dim, const array& first, const array& second)
-    {
-        af_array out = 0;
-        AF_THROW(af_join(&out, dim, first.get(), second.get()));
-        return array(out);
-    }
+array flat(const array &in) {
+    af_array out = 0;
+    AF_THROW(af_flat(&out, in.get()));
+    return array(out);
+}
 
-    array join(const int dim, const array& first, const array& second, const array &third)
-    {
-        af_array out = 0;
-        af_array inputs[3] = {first.get(), second.get(), third.get()};
-        AF_THROW(af_join_many(&out, dim, 3, inputs));
-        return array(out);
-    }
+array join(const int dim, const array &first, const array &second) {
+    af_array out = 0;
+    AF_THROW(af_join(&out, dim, first.get(), second.get()));
+    return array(out);
+}
 
-    array join(const int dim, const array& first, const array& second, const array &third, const array &fourth)
-    {
-        af_array out = 0;
-        af_array inputs[4] = {first.get(), second.get(), third.get(), fourth.get()};
-        AF_THROW(af_join_many(&out, dim, 4, inputs));
-        return array(out);
-    }
+array join(const int dim, const array &first, const array &second,
+           const array &third) {
+    af_array out       = 0;
+    af_array inputs[3] = {first.get(), second.get(), third.get()};
+    AF_THROW(af_join_many(&out, dim, 3, inputs));
+    return array(out);
+}
 
-    array tile(const array& in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)
-    {
-        af_array out = 0;
-        AF_THROW(af_tile(&out, in.get(), x, y, z, w));
-        return array(out);
-    }
+array join(const int dim, const array &first, const array &second,
+           const array &third, const array &fourth) {
+    af_array out       = 0;
+    af_array inputs[4] = {first.get(), second.get(), third.get(), fourth.get()};
+    AF_THROW(af_join_many(&out, dim, 4, inputs));
+    return array(out);
+}
 
-    array tile(const array& in, const af::dim4 &dims)
-    {
-        af_array out = 0;
-        AF_THROW(af_tile(&out, in.get(), dims[0], dims[1], dims[2], dims[3]));
-        return array(out);
-    }
+array tile(const array &in, const unsigned x, const unsigned y,
+           const unsigned z, const unsigned w) {
+    af_array out = 0;
+    AF_THROW(af_tile(&out, in.get(), x, y, z, w));
+    return array(out);
+}
 
-    array reorder(const array& in, const unsigned x, const unsigned y, const unsigned z, const unsigned w)
-    {
-        af_array out = 0;
-        AF_THROW(af_reorder(&out, in.get(), x, y, z, w));
-        return array(out);
-    }
+array tile(const array &in, const af::dim4 &dims) {
+    af_array out = 0;
+    AF_THROW(af_tile(&out, in.get(), dims[0], dims[1], dims[2], dims[3]));
+    return array(out);
+}
 
-    array shift(const array& in, const int x, const int y, const int z, const int w)
-    {
-        af_array out = 0;
-        AF_THROW(af_shift(&out, in.get(), x, y, z, w));
-        return array(out);
-    }
+array reorder(const array &in, const unsigned x, const unsigned y,
+              const unsigned z, const unsigned w) {
+    af_array out = 0;
+    AF_THROW(af_reorder(&out, in.get(), x, y, z, w));
+    return array(out);
+}
 
-    array flip(const array &in, const unsigned dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_flip(&out, in.get(), dim));
-        return array(out);
-    }
+array shift(const array &in, const int x, const int y, const int z,
+            const int w) {
+    af_array out = 0;
+    AF_THROW(af_shift(&out, in.get(), x, y, z, w));
+    return array(out);
+}
 
-    array lower(const array &in, bool is_unit_diag)
-    {
-        af_array res;
-        AF_THROW(af_lower(&res, in.get(), is_unit_diag));
-        return array(res);
-    }
+array flip(const array &in, const unsigned dim) {
+    af_array out = 0;
+    AF_THROW(af_flip(&out, in.get(), dim));
+    return array(out);
+}
 
-    array upper(const array &in, bool is_unit_diag)
-    {
-        af_array res;
-        AF_THROW(af_upper(&res, in.get(), is_unit_diag));
-        return array(res);
-    }
+array lower(const array &in, bool is_unit_diag) {
+    af_array res;
+    AF_THROW(af_lower(&res, in.get(), is_unit_diag));
+    return array(res);
+}
 
-    array select(const array &cond, const array &a, const array &b)
-    {
-        af_array res;
-        AF_THROW(af_select(&res, cond.get(), a.get(), b.get()));
-        return array(res);
-    }
+array upper(const array &in, bool is_unit_diag) {
+    af_array res;
+    AF_THROW(af_upper(&res, in.get(), is_unit_diag));
+    return array(res);
+}
 
-    array select(const array &cond, const array &a, const double &b)
-    {
-        af_array res;
-        AF_THROW(af_select_scalar_r(&res, cond.get(), a.get(), b));
-        return array(res);
-    }
+array select(const array &cond, const array &a, const array &b) {
+    af_array res;
+    AF_THROW(af_select(&res, cond.get(), a.get(), b.get()));
+    return array(res);
+}
 
-    array select(const array &cond, const double &a, const array &b)
-    {
-        af_array res;
-        AF_THROW(af_select_scalar_l(&res, cond.get(), a, b.get()));
-        return array(res);
-    }
+array select(const array &cond, const array &a, const double &b) {
+    af_array res;
+    AF_THROW(af_select_scalar_r(&res, cond.get(), a.get(), b));
+    return array(res);
+}
 
-    void replace(array &a, const array &cond, const array &b)
-    {
-        AF_THROW(af_replace(a.get(), cond.get(), b.get()));
-    }
+array select(const array &cond, const double &a, const array &b) {
+    af_array res;
+    AF_THROW(af_select_scalar_l(&res, cond.get(), a, b.get()));
+    return array(res);
+}
 
-    void replace(array &a, const array &cond, const double &b)
-    {
-        AF_THROW(af_replace_scalar(a.get(), cond.get(), b));
-    }
+void replace(array &a, const array &cond, const array &b) {
+    AF_THROW(af_replace(a.get(), cond.get(), b.get()));
+}
+
+void replace(array &a, const array &cond, const double &b) {
+    AF_THROW(af_replace_scalar(a.get(), cond.get(), b));
 }
+}  // namespace af
diff --git a/src/api/cpp/deconvolution.cpp b/src/api/cpp/deconvolution.cpp
index 923e0b271c..4b466a0b7e 100644
--- a/src/api/cpp/deconvolution.cpp
+++ b/src/api/cpp/deconvolution.cpp
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 array iterativeDeconv(const array& in, const array& ker,
                       const unsigned iterations, const float relaxFactor,
-                      const iterativeDeconvAlgo algo)
-{
+                      const iterativeDeconvAlgo algo) {
     af_array temp = 0;
-    AF_THROW(af_iterative_deconv(&temp, in.get(), ker.get(), iterations, relaxFactor, algo));
+    AF_THROW(af_iterative_deconv(&temp, in.get(), ker.get(), iterations,
+                                 relaxFactor, algo));
     return array(temp);
 }
 
-array inverseDeconv(const array& in, const array& psf,
-                    const float gamma, const inverseDeconvAlgo algo)
-{
+array inverseDeconv(const array& in, const array& psf, const float gamma,
+                    const inverseDeconvAlgo algo) {
     af_array temp = 0;
     AF_THROW(af_inverse_deconv(&temp, in.get(), psf.get(), gamma, algo));
     return array(temp);
 }
-}
+}  // namespace af
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 9499a3d644..f639451507 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -8,212 +8,173 @@
  ********************************************************/
 
 #include <af/array.h>
-#include <af/device.h>
+#include <af/backend.h>
 #include <af/compatible.h>
+#include <af/device.h>
 #include <af/traits.hpp>
-#include <af/backend.h>
-#include "type_util.hpp"
 #include "error.hpp"
+#include "type_util.hpp"
 
-namespace af
-{
-    void setBackend(const Backend bknd)
-    {
-        AF_THROW(af_set_backend(bknd));
-    }
-
-    unsigned getBackendCount()
-    {
-        unsigned temp = 1;
-        AF_THROW(af_get_backend_count(&temp));
-        return temp;
-    }
-
-    int getAvailableBackends()
-    {
-        int result = 0;
-        AF_THROW(af_get_available_backends(&result));
-        return result;
-    }
-
-    af::Backend getBackendId(const array &in)
-    {
-        af::Backend result = (af::Backend)0;
-        AF_THROW(af_get_backend_id(&result, in.get()));
-        return result;
-    }
+namespace af {
+void setBackend(const Backend bknd) { AF_THROW(af_set_backend(bknd)); }
 
-    int getDeviceId(const array &in)
-    {
-        int device = getDevice();;
-        AF_THROW(af_get_device_id(&device, in.get()));
-        return device;
-    }
+unsigned getBackendCount() {
+    unsigned temp = 1;
+    AF_THROW(af_get_backend_count(&temp));
+    return temp;
+}
 
-    af::Backend getActiveBackend()
-    {
-        af::Backend result = (af::Backend)0;
-        AF_THROW(af_get_active_backend(&result));
-        return result;
-    }
+int getAvailableBackends() {
+    int result = 0;
+    AF_THROW(af_get_available_backends(&result));
+    return result;
+}
 
-    void info()
-    {
-        AF_THROW(af_info());
-    }
+af::Backend getBackendId(const array &in) {
+    af::Backend result = (af::Backend)0;
+    AF_THROW(af_get_backend_id(&result, in.get()));
+    return result;
+}
 
-    const char* infoString(const bool verbose)
-    {
-        char *str = NULL;
-        AF_THROW(af_info_string(&str, verbose));
-        return (const char *)str;
-    }
+int getDeviceId(const array &in) {
+    int device = getDevice();
+    ;
+    AF_THROW(af_get_device_id(&device, in.get()));
+    return device;
+}
 
-    void deviceprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-    {
-        deviceInfo(d_name, d_platform, d_toolkit, d_compute);
-    }
-    void deviceInfo(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-    {
-        AF_THROW(af_device_info(d_name, d_platform, d_toolkit, d_compute));
-    }
+af::Backend getActiveBackend() {
+    af::Backend result = (af::Backend)0;
+    AF_THROW(af_get_active_backend(&result));
+    return result;
+}
 
-    int getDeviceCount()
-    {
-        int devices = -1;
-        AF_THROW(af_get_device_count(&devices));
-        return devices;
-    }
+void info() { AF_THROW(af_info()); }
 
-    int devicecount() { return getDeviceCount(); }
+const char *infoString(const bool verbose) {
+    char *str = NULL;
+    AF_THROW(af_info_string(&str, verbose));
+    return (const char *)str;
+}
 
-    void setDevice(const int device)
-    {
-        AF_THROW(af_set_device(device));
-    }
+void deviceprop(char *d_name, char *d_platform, char *d_toolkit,
+                char *d_compute) {
+    deviceInfo(d_name, d_platform, d_toolkit, d_compute);
+}
+void deviceInfo(char *d_name, char *d_platform, char *d_toolkit,
+                char *d_compute) {
+    AF_THROW(af_device_info(d_name, d_platform, d_toolkit, d_compute));
+}
 
-    void deviceset(const int device) { setDevice(device); }
+int getDeviceCount() {
+    int devices = -1;
+    AF_THROW(af_get_device_count(&devices));
+    return devices;
+}
 
-    int getDevice()
-    {
-        int device = 0;
-        AF_THROW(af_get_device(&device));
-        return device;
-    }
+int devicecount() { return getDeviceCount(); }
 
-    bool isDoubleAvailable(const int device)
-    {
-        bool temp;
-        AF_THROW(af_get_dbl_support(&temp, device));
-        return temp;
-    }
+void setDevice(const int device) { AF_THROW(af_set_device(device)); }
 
-    int deviceget() { return getDevice(); }
+void deviceset(const int device) { setDevice(device); }
 
-    void sync(int device)
-    {
-        AF_THROW(af_sync(device));
-    }
+int getDevice() {
+    int device = 0;
+    AF_THROW(af_get_device(&device));
+    return device;
+}
 
-    ///////////////////////////////////////////////////////////////////////////
-    // Alloc and free host, pinned, zero copy
-    void *alloc(const size_t elements, const af::dtype type)
-    {
-        void *ptr;
-        AF_THROW(af_alloc_device(&ptr, elements * size_of(type)));
-        // FIXME: Add to map
-        return ptr;
-    }
+bool isDoubleAvailable(const int device) {
+    bool temp;
+    AF_THROW(af_get_dbl_support(&temp, device));
+    return temp;
+}
 
-    void *pinned(const size_t elements, const af::dtype type)
-    {
-        void *ptr;
-        AF_THROW(af_alloc_pinned(&ptr, elements * size_of(type)));
-        // FIXME: Add to map
-        return ptr;
-    }
+int deviceget() { return getDevice(); }
 
-    void free(const void *ptr)
-    {
-        //FIXME: look up map and call the right free
-        AF_THROW(af_free_device((void *)ptr));
-    }
+void sync(int device) { AF_THROW(af_sync(device)); }
 
-    void freePinned(const void *ptr)
-    {
-        //FIXME: look up map and call the right free
-        AF_THROW(af_free_pinned((void *)ptr));
-    }
+///////////////////////////////////////////////////////////////////////////
+// Alloc and free host, pinned, zero copy
+void *alloc(const size_t elements, const af::dtype type) {
+    void *ptr;
+    AF_THROW(af_alloc_device(&ptr, elements * size_of(type)));
+    // FIXME: Add to map
+    return ptr;
+}
 
-    void *allocHost(const size_t elements, const af::dtype type)
-    {
-        void *ptr;
-        AF_THROW(af_alloc_host(&ptr, elements * size_of(type)));
-        return ptr;
-    }
+void *pinned(const size_t elements, const af::dtype type) {
+    void *ptr;
+    AF_THROW(af_alloc_pinned(&ptr, elements * size_of(type)));
+    // FIXME: Add to map
+    return ptr;
+}
 
-    void freeHost(const void *ptr)
-    {
-        AF_THROW(af_free_host((void *)ptr));
-    }
+void free(const void *ptr) {
+    // FIXME: look up map and call the right free
+    AF_THROW(af_free_device((void *)ptr));
+}
 
-    void printMemInfo(const char *msg, const int device_id)
-    {
-        AF_THROW(af_print_mem_info(msg, device_id));
-    }
+void freePinned(const void *ptr) {
+    // FIXME: look up map and call the right free
+    AF_THROW(af_free_pinned((void *)ptr));
+}
 
-    void deviceGC()
-    {
-        AF_THROW(af_device_gc());
-    }
+void *allocHost(const size_t elements, const af::dtype type) {
+    void *ptr;
+    AF_THROW(af_alloc_host(&ptr, elements * size_of(type)));
+    return ptr;
+}
 
-    void deviceMemInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                       size_t *lock_bytes,  size_t *lock_buffers)
-    {
-        AF_THROW(af_device_mem_info(alloc_bytes, alloc_buffers,
-                                    lock_bytes,  lock_buffers));
-    }
+void freeHost(const void *ptr) { AF_THROW(af_free_host((void *)ptr)); }
 
-    void setMemStepSize(const size_t step_bytes)
-    {
-        AF_THROW(af_set_mem_step_size(step_bytes));
-    }
+void printMemInfo(const char *msg, const int device_id) {
+    AF_THROW(af_print_mem_info(msg, device_id));
+}
 
-    size_t getMemStepSize()
-    {
-        size_t size_bytes = 0;
-        AF_THROW(af_get_mem_step_size(&size_bytes));
-        return size_bytes;
-    }
+void deviceGC() { AF_THROW(af_device_gc()); }
 
-#define INSTANTIATE(T)                                                      \
-    template<> AFAPI                                                        \
-    T* alloc(const size_t elements)                                         \
-    {                                                                       \
-        return (T*)alloc(elements, (af::dtype)dtype_traits<T>::af_type);    \
-    }                                                                       \
-    template<> AFAPI                                                        \
-    T* pinned(const size_t elements)                                        \
-    {                                                                       \
-        return (T*)pinned(elements, (af::dtype)dtype_traits<T>::af_type);   \
-    }                                                                       \
-    template<> AFAPI                                                        \
-    T* allocHost(const size_t elements)                                     \
-    {                                                                       \
-        return (T*)allocHost(elements, (af::dtype)dtype_traits<T>::af_type);\
-    }
+void deviceMemInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                   size_t *lock_bytes, size_t *lock_buffers) {
+    AF_THROW(af_device_mem_info(alloc_bytes, alloc_buffers, lock_bytes,
+                                lock_buffers));
+}
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(unsigned)
-    INSTANTIATE(unsigned char)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(unsigned short)
-    INSTANTIATE(long long)
-    INSTANTIATE(unsigned long long)
+void setMemStepSize(const size_t step_bytes) {
+    AF_THROW(af_set_mem_step_size(step_bytes));
+}
 
+size_t getMemStepSize() {
+    size_t size_bytes = 0;
+    AF_THROW(af_get_mem_step_size(&size_bytes));
+    return size_bytes;
 }
+
+#define INSTANTIATE(T)                                                        \
+    template<>                                                                \
+    AFAPI T *alloc(const size_t elements) {                                   \
+        return (T *)alloc(elements, (af::dtype)dtype_traits<T>::af_type);     \
+    }                                                                         \
+    template<>                                                                \
+    AFAPI T *pinned(const size_t elements) {                                  \
+        return (T *)pinned(elements, (af::dtype)dtype_traits<T>::af_type);    \
+    }                                                                         \
+    template<>                                                                \
+    AFAPI T *allocHost(const size_t elements) {                               \
+        return (T *)allocHost(elements, (af::dtype)dtype_traits<T>::af_type); \
+    }
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(unsigned)
+INSTANTIATE(unsigned char)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(unsigned short)
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
+
+}  // namespace af
diff --git a/src/api/cpp/diff.cpp b/src/api/cpp/diff.cpp
index 4e59f7692f..aca205bd9f 100644
--- a/src/api/cpp/diff.cpp
+++ b/src/api/cpp/diff.cpp
@@ -7,23 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include "error.hpp"
 
-namespace af
-{
-    array diff1(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_diff1(&out, in.get(), dim));
-        return array(out);
-    }
+namespace af {
+array diff1(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_diff1(&out, in.get(), dim));
+    return array(out);
+}
 
-    array diff2(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_diff2(&out, in.get(), dim));
-        return array(out);
-    }
+array diff2(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_diff2(&out, in.get(), dim));
+    return array(out);
 }
+}  // namespace af
diff --git a/src/api/cpp/dog.cpp b/src/api/cpp/dog.cpp
index a18fdb1895..67e2d50e4f 100644
--- a/src/api/cpp/dog.cpp
+++ b/src/api/cpp/dog.cpp
@@ -11,14 +11,12 @@
 #include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array dog(const array& in, const int radius1, const int radius2)
-{
+array dog(const array& in, const int radius1, const int radius2) {
     af_array temp = 0;
     AF_THROW(af_dog(&temp, in.get(), radius1, radius2));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index c3cb4fdf57..d2d8a8ee24 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -7,21 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/exception.h>
-#include <af/device.h>
 #include <common/defines.hpp>
+#include <af/device.h>
+#include <af/exception.h>
 
-#define AF_THROW(fn) do {                               \
-        af_err __err = fn;                              \
-        if (__err == AF_SUCCESS) break;                 \
-        char *msg = NULL; af_get_last_error(&msg, NULL);\
-        af::exception ex(msg, __PRETTY_FUNCTION__,      \
-                __AF_FILENAME__, __LINE__, __err);      \
-        af_free_host(msg);                              \
-        throw ex;                                       \
-    } while(0)
+#define AF_THROW(fn)                                                          \
+    do {                                                                      \
+        af_err __err = fn;                                                    \
+        if (__err == AF_SUCCESS) break;                                       \
+        char *msg = NULL;                                                     \
+        af_get_last_error(&msg, NULL);                                        \
+        af::exception ex(msg, __PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
+                         __err);                                              \
+        af_free_host(msg);                                                    \
+        throw ex;                                                             \
+    } while (0)
 
-#define AF_THROW_ERR(__msg, __err) do {                 \
-        throw af::exception(__msg, __PRETTY_FUNCTION__, \
-                __AF_FILENAME__, __LINE__, __err);      \
-    } while(0)
+#define AF_THROW_ERR(__msg, __err)                                       \
+    do {                                                                 \
+        throw af::exception(__msg, __PRETTY_FUNCTION__, __AF_FILENAME__, \
+                            __LINE__, __err);                            \
+    } while (0)
diff --git a/src/api/cpp/exampleFunction.cpp b/src/api/cpp/exampleFunction.cpp
index c3ec5b8442..017950d0a8 100644
--- a/src/api/cpp/exampleFunction.cpp
+++ b/src/api/cpp/exampleFunction.cpp
@@ -7,27 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>       // af::array class is declared here
+#include <af/array.h>  // af::array class is declared here
 
-#include <af/util.h>        // Include the header related to the function
+#include <af/util.h>  // Include the header related to the function
 
-#include "error.hpp"        // AF_THROW macro to use error code C-API
-                            // is going to return and throw corresponding
-                            // exceptions if call isn't a success
+#include "error.hpp"  // AF_THROW macro to use error code C-API
+                      // is going to return and throw corresponding
+                      // exceptions if call isn't a success
 
-namespace af
-{
+namespace af {
 
-array exampleFunction(const array& a, const af_someenum_t p)
-{
+array exampleFunction(const array& a, const af_someenum_t p) {
     // create a temporary af_array handle
     af_array temp = 0;
 
     // call C-API function
-    AF_THROW( af_example_function(&temp, a.get(), p) );
+    AF_THROW(af_example_function(&temp, a.get(), p));
 
     // array::get() returns af_array handle for the corresponding cpp af::array
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/exception.cpp b/src/api/cpp/exception.cpp
index f88f98b0f2..523da68a84 100644
--- a/src/api/cpp/exception.cpp
+++ b/src/api/cpp/exception.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <string.h> // strncpy
 #include <stdio.h>
+#include <string.h>  // strncpy
 #include <af/exception.h>
 #include <algorithm>
 
@@ -18,42 +18,40 @@
 
 namespace af {
 
-exception::exception(): m_err(AF_ERR_UNKNOWN)
-{
+exception::exception() : m_err(AF_ERR_UNKNOWN) {
     strncpy(m_msg, "unknown exception", sizeof(m_msg));
 }
 
-exception::exception(const char *msg): m_err(AF_ERR_UNKNOWN)
-{
+exception::exception(const char *msg) : m_err(AF_ERR_UNKNOWN) {
     strncpy(m_msg, msg, sizeof(m_msg));
-    m_msg[sizeof(m_msg)-1] = '\0';
+    m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
-exception::exception(const char *file, unsigned line, af_err err): m_err(err)
-{
-    snprintf(m_msg, sizeof(m_msg) - 1,
-             "ArrayFire Exception (%s:%d):\nIn %s:%u",
+exception::exception(const char *file, unsigned line, af_err err) : m_err(err) {
+    snprintf(m_msg, sizeof(m_msg) - 1, "ArrayFire Exception (%s:%d):\nIn %s:%u",
              af_err_to_string(err), (int)err, file, line);
 
-    m_msg[sizeof(m_msg)-1] = '\0';
+    m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
-exception::exception(const char *msg, const char *file, unsigned line, af_err err): m_err(err)
-{
+exception::exception(const char *msg, const char *file, unsigned line,
+                     af_err err)
+    : m_err(err) {
     snprintf(m_msg, sizeof(m_msg) - 1,
              "ArrayFire Exception (%s:%d):\n%s\nIn %s:%u",
              af_err_to_string(err), (int)(err), msg, file, line);
 
-    m_msg[sizeof(m_msg)-1] = '\0';
+    m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
-exception::exception(const char *msg, const char *func, const char *file, unsigned line, af_err err): m_err(err)
-{
+exception::exception(const char *msg, const char *func, const char *file,
+                     unsigned line, af_err err)
+    : m_err(err) {
     snprintf(m_msg, sizeof(m_msg) - 1,
              "ArrayFire Exception (%s:%d):\n%s\nIn function %s\nIn file %s:%u",
              af_err_to_string(err), (int)(err), msg, func, file, line);
 
-    m_msg[sizeof(m_msg)-1] = '\0';
+    m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/fast.cpp b/src/api/cpp/fast.cpp
index 3ba553bc62..308635b4f4 100644
--- a/src/api/cpp/fast.cpp
+++ b/src/api/cpp/fast.cpp
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
 features fast(const array& in, const float thr, const unsigned arc_length,
-                const bool non_max, const float feature_ratio,
-                const unsigned edge)
-{
+              const bool non_max, const float feature_ratio,
+              const unsigned edge) {
     af_features temp;
-    AF_THROW(af_fast(&temp, in.get(), thr, arc_length,
-                     non_max, feature_ratio, edge));
+    AF_THROW(af_fast(&temp, in.get(), thr, arc_length, non_max, feature_ratio,
+                     edge));
     return features(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/features.cpp b/src/api/cpp/features.cpp
index d9001ba412..d84e39ff53 100644
--- a/src/api/cpp/features.cpp
+++ b/src/api/cpp/features.cpp
@@ -7,99 +7,79 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <af/array.h>
+#include <af/features.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-    features::features()
-    {
-        AF_THROW(af_create_features(&feat, 0));
-    }
+features::features() { AF_THROW(af_create_features(&feat, 0)); }
 
-    features::features(const size_t n)
-    {
-        AF_THROW(af_create_features(&feat, (int)n));
-    }
+features::features(const size_t n) {
+    AF_THROW(af_create_features(&feat, (int)n));
+}
 
-    features::features(af_features f) : feat(f)
-    {
-    }
+features::features(af_features f) : feat(f) {}
 
-    features& features::operator= (const features& other)
-    {
-        if (this != &other) {
-            AF_THROW(af_release_features(feat));
-            AF_THROW(af_retain_features(&feat, other.get()));
-        }
-        return *this;
+features& features::operator=(const features& other) {
+    if (this != &other) {
+        AF_THROW(af_release_features(feat));
+        AF_THROW(af_retain_features(&feat, other.get()));
     }
+    return *this;
+}
 
-    features::~features()
-    {
-        // THOU SHALL NOT THROW IN DESTRUCTORS
-        if (feat) {
-            af_release_features(feat);
-        }
-    }
+features::~features() {
+    // THOU SHALL NOT THROW IN DESTRUCTORS
+    if (feat) { af_release_features(feat); }
+}
 
-    size_t features::getNumFeatures() const
-    {
-        dim_t n = 0;
-        AF_THROW(af_get_features_num(&n, feat));
-        return n;
-    }
+size_t features::getNumFeatures() const {
+    dim_t n = 0;
+    AF_THROW(af_get_features_num(&n, feat));
+    return n;
+}
 
-    array features::getX() const
-    {
-        af_array x = 0;
-        AF_THROW(af_get_features_xpos(&x, feat));
-        af_array tmp = 0;
-        AF_THROW(af_retain_array(&tmp, x));
-        return array(tmp);
-    }
+array features::getX() const {
+    af_array x = 0;
+    AF_THROW(af_get_features_xpos(&x, feat));
+    af_array tmp = 0;
+    AF_THROW(af_retain_array(&tmp, x));
+    return array(tmp);
+}
 
-    array features::getY() const
-    {
-        af_array y = 0;
-        AF_THROW(af_get_features_ypos(&y, feat));
-        af_array tmp = 0;
-        AF_THROW(af_retain_array(&tmp, y));
-        return array(tmp);
-    }
+array features::getY() const {
+    af_array y = 0;
+    AF_THROW(af_get_features_ypos(&y, feat));
+    af_array tmp = 0;
+    AF_THROW(af_retain_array(&tmp, y));
+    return array(tmp);
+}
 
-    array features::getScore() const
-    {
-        af_array s = 0;
-        AF_THROW(af_get_features_score(&s, feat));
-        af_array tmp = 0;
-        AF_THROW(af_retain_array(&tmp, s));
-        return array(tmp);
-    }
+array features::getScore() const {
+    af_array s = 0;
+    AF_THROW(af_get_features_score(&s, feat));
+    af_array tmp = 0;
+    AF_THROW(af_retain_array(&tmp, s));
+    return array(tmp);
+}
 
-    array features::getOrientation() const
-    {
-        af_array ori = 0;
-        AF_THROW(af_get_features_orientation(&ori, feat));
-        af_array tmp = 0;
-        AF_THROW(af_retain_array(&tmp, ori));
-        return array(tmp);
-    }
+array features::getOrientation() const {
+    af_array ori = 0;
+    AF_THROW(af_get_features_orientation(&ori, feat));
+    af_array tmp = 0;
+    AF_THROW(af_retain_array(&tmp, ori));
+    return array(tmp);
+}
 
-    array features::getSize() const
-    {
-        af_array s = 0;
-        AF_THROW(af_get_features_size(&s, feat));
-        af_array tmp = 0;
-        AF_THROW(af_retain_array(&tmp, s));
-        return array(tmp);
-    }
+array features::getSize() const {
+    af_array s = 0;
+    AF_THROW(af_get_features_size(&s, feat));
+    af_array tmp = 0;
+    AF_THROW(af_retain_array(&tmp, s));
+    return array(tmp);
+}
 
-    af_features features::get() const
-    {
-        return feat;
-    }
+af_features features::get() const { return feat; }
 
-};
+};  // namespace af
diff --git a/src/api/cpp/fft.cpp b/src/api/cpp/fft.cpp
index e7e96ca195..f72038a2f3 100644
--- a/src/api/cpp/fft.cpp
+++ b/src/api/cpp/fft.cpp
@@ -7,220 +7,200 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/signal.h>
 #include <af/array.h>
 #include <af/dim4.hpp>
+#include <af/signal.h>
 #include "error.hpp"
 
-namespace af
-{
-array fftNorm(const array& in, const double norm_factor, const dim_t odim0)
-{
+namespace af {
+array fftNorm(const array& in, const double norm_factor, const dim_t odim0) {
     af_array out = 0;
     AF_THROW(af_fft(&out, in.get(), norm_factor, odim0));
     return array(out);
 }
 
-array fft2Norm(const array& in, const double norm_factor, const dim_t odim0, const dim_t odim1)
-{
+array fft2Norm(const array& in, const double norm_factor, const dim_t odim0,
+               const dim_t odim1) {
     af_array out = 0;
     AF_THROW(af_fft2(&out, in.get(), norm_factor, odim0, odim1));
     return array(out);
 }
 
-array fft3Norm(const array& in, const double norm_factor, const dim_t odim0, const dim_t odim1, const dim_t odim2)
-{
+array fft3Norm(const array& in, const double norm_factor, const dim_t odim0,
+               const dim_t odim1, const dim_t odim2) {
     af_array out = 0;
     AF_THROW(af_fft3(&out, in.get(), norm_factor, odim0, odim1, odim2));
     return array(out);
 }
 
-array fft(const array& in, const dim_t odim0)
-{
+array fft(const array& in, const dim_t odim0) {
     return fftNorm(in, 1.0, odim0);
 }
 
-array fft2(const array& in, const dim_t odim0, const dim_t odim1)
-{
+array fft2(const array& in, const dim_t odim0, const dim_t odim1) {
     return fft2Norm(in, 1.0, odim0, odim1);
 }
 
-array fft3(const array& in, const dim_t odim0, const dim_t odim1, const dim_t odim2)
-{
+array fft3(const array& in, const dim_t odim0, const dim_t odim1,
+           const dim_t odim2) {
     return fft3Norm(in, 1.0, odim0, odim1, odim2);
 }
 
-array dft(const array& in, const double norm_factor, const dim4 outDims)
-{
+array dft(const array& in, const double norm_factor, const dim4 outDims) {
     array temp;
-    switch(in.dims().ndims()) {
+    switch (in.dims().ndims()) {
         case 1: temp = fftNorm(in, norm_factor, outDims[0]); break;
         case 2: temp = fft2Norm(in, norm_factor, outDims[0], outDims[1]); break;
-        case 3: temp = fft3Norm(in, norm_factor, outDims[0], outDims[1], outDims[2]); break;
+        case 3:
+            temp =
+                fft3Norm(in, norm_factor, outDims[0], outDims[1], outDims[2]);
+            break;
         default: AF_THROW(AF_ERR_NOT_SUPPORTED);
     }
     return temp;
 }
 
-array dft(const array& in, const dim4 outDims)
-{
-    return dft(in, 1.0, outDims);
-}
+array dft(const array& in, const dim4 outDims) { return dft(in, 1.0, outDims); }
 
-array dft(const array& in)
-{
-    return dft(in, 1.0, dim4(0,0,0,0));
-}
+array dft(const array& in) { return dft(in, 1.0, dim4(0, 0, 0, 0)); }
 
-array ifftNorm(const array& in, const double norm_factor, const dim_t odim0)
-{
+array ifftNorm(const array& in, const double norm_factor, const dim_t odim0) {
     af_array out = 0;
     AF_THROW(af_ifft(&out, in.get(), norm_factor, odim0));
     return array(out);
 }
 
-array ifft2Norm(const array& in, const double norm_factor, const dim_t odim0, const dim_t odim1)
-{
+array ifft2Norm(const array& in, const double norm_factor, const dim_t odim0,
+                const dim_t odim1) {
     af_array out = 0;
     AF_THROW(af_ifft2(&out, in.get(), norm_factor, odim0, odim1));
     return array(out);
 }
 
-array ifft3Norm(const array& in, const double norm_factor, const dim_t odim0, const dim_t odim1, const dim_t odim2)
-{
+array ifft3Norm(const array& in, const double norm_factor, const dim_t odim0,
+                const dim_t odim1, const dim_t odim2) {
     af_array out = 0;
     AF_THROW(af_ifft3(&out, in.get(), norm_factor, odim0, odim1, odim2));
     return array(out);
 }
 
-array ifft(const array& in, const dim_t odim0)
-{
-    const dim4 dims = in.dims();
-    dim_t dim0 = odim0==0 ? dims[0] : odim0;
-    double norm_factor = 1.0/dim0;
+array ifft(const array& in, const dim_t odim0) {
+    const dim4 dims    = in.dims();
+    dim_t dim0         = odim0 == 0 ? dims[0] : odim0;
+    double norm_factor = 1.0 / dim0;
     return ifftNorm(in, norm_factor, odim0);
 }
 
-array ifft2(const array& in, const dim_t odim0, const dim_t odim1)
-{
-    const dim4 dims = in.dims();
-    dim_t dim0 = odim0==0 ? dims[0] : odim0;
-    dim_t dim1 = odim1==0 ? dims[1] : odim1;
-    double norm_factor = 1.0/(dim0*dim1);
+array ifft2(const array& in, const dim_t odim0, const dim_t odim1) {
+    const dim4 dims    = in.dims();
+    dim_t dim0         = odim0 == 0 ? dims[0] : odim0;
+    dim_t dim1         = odim1 == 0 ? dims[1] : odim1;
+    double norm_factor = 1.0 / (dim0 * dim1);
     return ifft2Norm(in, norm_factor, odim0, odim1);
 }
 
-array ifft3(const array& in, const dim_t odim0, const dim_t odim1, const dim_t odim2)
-{
-    const dim4 dims = in.dims();
-    dim_t dim0 = odim0==0 ? dims[0] : odim0;
-    dim_t dim1 = odim1==0 ? dims[1] : odim1;
-    dim_t dim2 = odim2==0 ? dims[2] : odim2;
-    double norm_factor = 1.0/(dim0*dim1*dim2);
+array ifft3(const array& in, const dim_t odim0, const dim_t odim1,
+            const dim_t odim2) {
+    const dim4 dims    = in.dims();
+    dim_t dim0         = odim0 == 0 ? dims[0] : odim0;
+    dim_t dim1         = odim1 == 0 ? dims[1] : odim1;
+    dim_t dim2         = odim2 == 0 ? dims[2] : odim2;
+    double norm_factor = 1.0 / (dim0 * dim1 * dim2);
     return ifft3Norm(in, norm_factor, odim0, odim1, odim2);
 }
 
-array idft(const array& in, const double norm_factor, const dim4 outDims)
-{
+array idft(const array& in, const double norm_factor, const dim4 outDims) {
     array temp;
-    switch(in.dims().ndims()) {
-        case 1: temp =  ifftNorm(in, norm_factor, outDims[0]); break;
-        case 2: temp = ifft2Norm(in, norm_factor, outDims[0], outDims[1]); break;
-        case 3: temp = ifft3Norm(in, norm_factor, outDims[0], outDims[1], outDims[2]); break;
+    switch (in.dims().ndims()) {
+        case 1: temp = ifftNorm(in, norm_factor, outDims[0]); break;
+        case 2:
+            temp = ifft2Norm(in, norm_factor, outDims[0], outDims[1]);
+            break;
+        case 3:
+            temp =
+                ifft3Norm(in, norm_factor, outDims[0], outDims[1], outDims[2]);
+            break;
         default: AF_THROW(AF_ERR_NOT_SUPPORTED);
     }
     return temp;
 }
 
-array idft(const array& in, const dim4 outDims)
-{
+array idft(const array& in, const dim4 outDims) {
     return idft(in, 1.0, outDims);
 }
 
-array idft(const array& in)
-{
-    return idft(in, 1.0, dim4(0,0,0,0));
-}
+array idft(const array& in) { return idft(in, 1.0, dim4(0, 0, 0, 0)); }
 
-void fftInPlace(array& in, const double norm_factor)
-{
+void fftInPlace(array& in, const double norm_factor) {
     AF_THROW(af_fft_inplace(in.get(), norm_factor));
 }
 
-void fft2InPlace(array& in, const double norm_factor)
-{
+void fft2InPlace(array& in, const double norm_factor) {
     AF_THROW(af_fft2_inplace(in.get(), norm_factor));
 }
 
-void fft3InPlace(array& in, const double norm_factor)
-{
+void fft3InPlace(array& in, const double norm_factor) {
     AF_THROW(af_fft3_inplace(in.get(), norm_factor));
 }
 
-void ifftInPlace(array& in, const double norm_factor)
-{
+void ifftInPlace(array& in, const double norm_factor) {
     const dim4 dims = in.dims();
-    double norm = norm_factor *(1.0 / dims[0]);
+    double norm     = norm_factor * (1.0 / dims[0]);
     AF_THROW(af_ifft_inplace(in.get(), norm));
 }
 
-void ifft2InPlace(array& in, const double norm_factor)
-{
+void ifft2InPlace(array& in, const double norm_factor) {
     const dim4 dims = in.dims();
-    double norm = norm_factor *(1.0 / (dims[0] * dims[1]));
+    double norm     = norm_factor * (1.0 / (dims[0] * dims[1]));
     AF_THROW(af_ifft2_inplace(in.get(), norm));
 }
 
-void ifft3InPlace(array& in, const double norm_factor)
-{
+void ifft3InPlace(array& in, const double norm_factor) {
     const dim4 dims = in.dims();
-    double norm = norm_factor *(1.0 / (dims[0] * dims[1] * dims[2]));
+    double norm     = norm_factor * (1.0 / (dims[0] * dims[1] * dims[2]));
     AF_THROW(af_ifft3_inplace(in.get(), norm));
 }
 
 template<>
-AFAPI array fftR2C<1>(const array &in, const dim4 &dims,
-                      const double norm_factor)
-{
+AFAPI array fftR2C<1>(const array& in, const dim4& dims,
+                      const double norm_factor) {
     af_array res;
-    AF_THROW(af_fft_r2c(&res, in.get(), norm_factor == 0 ? 1.0 : norm_factor, dims[0]));
+    AF_THROW(af_fft_r2c(&res, in.get(), norm_factor == 0 ? 1.0 : norm_factor,
+                        dims[0]));
     return array(res);
 }
 
 template<>
-AFAPI array fftR2C<2>(const array &in, const dim4 &dims,
-                      const double norm_factor)
-{
+AFAPI array fftR2C<2>(const array& in, const dim4& dims,
+                      const double norm_factor) {
     af_array res;
-    AF_THROW(af_fft2_r2c(&res, in.get(), norm_factor == 0 ? 1.0 : norm_factor, dims[0], dims[1]));
+    AF_THROW(af_fft2_r2c(&res, in.get(), norm_factor == 0 ? 1.0 : norm_factor,
+                         dims[0], dims[1]));
     return array(res);
 }
 
 template<>
-AFAPI array fftR2C<3>(const array &in, const dim4 &dims,
-                      const double norm_factor)
-{
+AFAPI array fftR2C<3>(const array& in, const dim4& dims,
+                      const double norm_factor) {
     af_array res;
     AF_THROW(af_fft3_r2c(&res, in.get(), norm_factor == 0 ? 1.0 : norm_factor,
                          dims[0], dims[1], dims[2]));
     return array(res);
 }
 
-inline dim_t getOrigDim(dim_t d, bool is_odd)
-{
+inline dim_t getOrigDim(dim_t d, bool is_odd) {
     return 2 * (d - 1) + (is_odd ? 1 : 0);
 }
 
 template<>
-AFAPI array fftC2R<1>(const array &in, const bool is_odd,
-                      const double norm_factor)
-{
+AFAPI array fftC2R<1>(const array& in, const bool is_odd,
+                      const double norm_factor) {
     double norm = norm_factor;
 
     if (norm == 0) {
         dim4 idims = in.dims();
         dim_t dim0 = getOrigDim(idims[0], is_odd);
-        norm = 1.0/dim0;
+        norm       = 1.0 / dim0;
     }
 
     af_array res;
@@ -229,28 +209,25 @@ AFAPI array fftC2R<1>(const array &in, const bool is_odd,
 }
 
 template<>
-AFAPI array fftC2R<2>(const array &in, const bool is_odd,
-                      const double norm_factor)
-{
+AFAPI array fftC2R<2>(const array& in, const bool is_odd,
+                      const double norm_factor) {
     double norm = norm_factor;
 
     if (norm == 0) {
         dim4 idims = in.dims();
         dim_t dim0 = getOrigDim(idims[0], is_odd);
         dim_t dim1 = idims[1];
-        norm = 1.0/(dim0 * dim1);
+        norm       = 1.0 / (dim0 * dim1);
     }
 
-
     af_array res;
     AF_THROW(af_fft2_c2r(&res, in.get(), norm, is_odd));
     return array(res);
 }
 
 template<>
-AFAPI array fftC2R<3>(const array &in, const bool is_odd,
-                      const double norm_factor)
-{
+AFAPI array fftC2R<3>(const array& in, const bool is_odd,
+                      const double norm_factor) {
     double norm = norm_factor;
 
     if (norm == 0) {
@@ -258,7 +235,7 @@ AFAPI array fftC2R<3>(const array &in, const bool is_odd,
         dim_t dim0 = getOrigDim(idims[0], is_odd);
         dim_t dim1 = idims[1];
         dim_t dim2 = idims[2];
-        norm = 1.0/(dim0 * dim1 * dim2);
+        norm       = 1.0 / (dim0 * dim1 * dim2);
     }
 
     af_array res;
@@ -266,21 +243,17 @@ AFAPI array fftC2R<3>(const array &in, const bool is_odd,
     return array(res);
 }
 
-#define FFT_REAL(rank)                                      \
-    template<>                                              \
-    AFAPI array fftR2C<rank>(const array &in,               \
-                             const double norm_factor)      \
-    {                                                       \
-        return fftR2C<rank>(in, in.dims(), norm_factor);    \
-    }                                                       \
-                                                            \
+#define FFT_REAL(rank)                                                    \
+    template<>                                                            \
+    AFAPI array fftR2C<rank>(const array& in, const double norm_factor) { \
+        return fftR2C<rank>(in, in.dims(), norm_factor);                  \
+    }
 
 FFT_REAL(1)
 FFT_REAL(2)
 FFT_REAL(3)
 
-void setFFTPlanCacheSize(size_t cacheSize)
-{
+void setFFTPlanCacheSize(size_t cacheSize) {
     AF_THROW(af_set_fft_plan_cache_size(cacheSize));
 }
-}
+}  // namespace af
diff --git a/src/api/cpp/fftconvolve.cpp b/src/api/cpp/fftconvolve.cpp
index 3156922108..61fbf9937c 100644
--- a/src/api/cpp/fftconvolve.cpp
+++ b/src/api/cpp/fftconvolve.cpp
@@ -7,46 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/signal.h>
 #include <af/array.h>
-#include "error.hpp"
+#include <af/signal.h>
 #include <algorithm>
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array fftConvolve(const array& signal, const array& filter, const convMode mode)
-{
+array fftConvolve(const array& signal, const array& filter,
+                  const convMode mode) {
     unsigned sN = signal.numdims();
     unsigned fN = filter.numdims();
 
-    switch(std::min(sN,fN)) {
-        case 1:  return fftConvolve1(signal, filter, mode);
-        case 2:  return fftConvolve2(signal, filter, mode);
-        case 3:  return fftConvolve3(signal, filter, mode);
+    switch (std::min(sN, fN)) {
+        case 1: return fftConvolve1(signal, filter, mode);
+        case 2: return fftConvolve2(signal, filter, mode);
+        case 3: return fftConvolve3(signal, filter, mode);
         default: return fftConvolve3(signal, filter, mode);
     }
 }
 
-array fftConvolve1(const array& signal, const array& filter, const convMode mode)
-{
+array fftConvolve1(const array& signal, const array& filter,
+                   const convMode mode) {
     af_array out = 0;
     AF_THROW(af_fft_convolve1(&out, signal.get(), filter.get(), mode));
     return array(out);
 }
 
-array fftConvolve2(const array& signal, const array& filter, const convMode mode)
-{
+array fftConvolve2(const array& signal, const array& filter,
+                   const convMode mode) {
     af_array out = 0;
     AF_THROW(af_fft_convolve2(&out, signal.get(), filter.get(), mode));
     return array(out);
 }
 
-array fftConvolve3(const array& signal, const array& filter, const convMode mode)
-{
+array fftConvolve3(const array& signal, const array& filter,
+                   const convMode mode) {
     af_array out = 0;
     AF_THROW(af_fft_convolve3(&out, signal.get(), filter.get(), mode));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/filters.cpp b/src/api/cpp/filters.cpp
index 222aa99283..85246cad38 100644
--- a/src/api/cpp/filters.cpp
+++ b/src/api/cpp/filters.cpp
@@ -7,47 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/image.h>
 #include <af/signal.h>
-#include <af/array.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array medfilt(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
-{
-     af_array out = 0;
-     AF_THROW(af_medfilt(&out, in.get(), wind_length, wind_width, edge_pad));
-     return array(out);
+array medfilt(const array& in, const dim_t wind_length, const dim_t wind_width,
+              const borderType edge_pad) {
+    af_array out = 0;
+    AF_THROW(af_medfilt(&out, in.get(), wind_length, wind_width, edge_pad));
+    return array(out);
 }
 
-array medfilt1(const array& in, const dim_t wind_width, const borderType edge_pad)
-{
+array medfilt1(const array& in, const dim_t wind_width,
+               const borderType edge_pad) {
     af_array out = 0;
     AF_THROW(af_medfilt1(&out, in.get(), wind_width, edge_pad));
     return array(out);
 }
 
-array medfilt2(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
-{
+array medfilt2(const array& in, const dim_t wind_length, const dim_t wind_width,
+               const borderType edge_pad) {
     af_array out = 0;
     AF_THROW(af_medfilt2(&out, in.get(), wind_length, wind_width, edge_pad));
     return array(out);
 }
 
-array minfilt(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
-{
+array minfilt(const array& in, const dim_t wind_length, const dim_t wind_width,
+              const borderType edge_pad) {
     af_array out = 0;
     AF_THROW(af_minfilt(&out, in.get(), wind_length, wind_width, edge_pad));
     return array(out);
 }
 
-array maxfilt(const array& in, const dim_t wind_length, const dim_t wind_width, const borderType edge_pad)
-{
+array maxfilt(const array& in, const dim_t wind_length, const dim_t wind_width,
+              const borderType edge_pad) {
     af_array out = 0;
     AF_THROW(af_maxfilt(&out, in.get(), wind_length, wind_width, edge_pad));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/gaussian_kernel.cpp b/src/api/cpp/gaussian_kernel.cpp
index 68fd96819e..1b7e837192 100644
--- a/src/api/cpp/gaussian_kernel.cpp
+++ b/src/api/cpp/gaussian_kernel.cpp
@@ -6,27 +6,25 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/dim4.hpp>
-#include <af/image.h>
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include <af/compatible.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af{
-    array gaussianKernel(const int rows, const int cols,
-                         const double sig_r, const double sig_c)
-    {
-        af_array res;
-        AF_THROW(af_gaussian_kernel(&res, rows, cols, sig_r, sig_c));
-        return array(res);
-    }
-
-    // Compatible function
-    array gaussiankernel(const int rows, const int cols,
-                         const double sig_r, const double sig_c)
-    {
-        return gaussianKernel(rows, cols, sig_r, sig_c);
-    }
+namespace af {
+array gaussianKernel(const int rows, const int cols, const double sig_r,
+                     const double sig_c) {
+    af_array res;
+    AF_THROW(af_gaussian_kernel(&res, rows, cols, sig_r, sig_c));
+    return array(res);
+}
 
+// Compatible function
+array gaussiankernel(const int rows, const int cols, const double sig_r,
+                     const double sig_c) {
+    return gaussianKernel(rows, cols, sig_r, sig_c);
 }
+
+}  // namespace af
diff --git a/src/api/cpp/gfor.cpp b/src/api/cpp/gfor.cpp
index acc312ef56..fa37fd9ef1 100644
--- a/src/api/cpp/gfor.cpp
+++ b/src/api/cpp/gfor.cpp
@@ -7,37 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <af/seq.h>
-#include <af/array.h>
 #include <af/gfor.h>
+#include <af/seq.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-    thread_local bool gforStatus;
+thread_local bool gforStatus;
 
-    bool gforGet() { return gforStatus; }
-    void gforSet(bool val) { gforStatus = val; }
+bool gforGet() { return gforStatus; }
+void gforSet(bool val) { gforStatus = val; }
 
-    bool gforToggle()
-    {
-        bool status = gforGet();
-        status ^= 1;
-        gforSet(status);
-        return status;
-    }
-
-    array batchFunc(const array &lhs, const array &rhs, batchFunc_t func)
-    {
-        if (gforGet()) AF_THROW_ERR("batchFunc can not be used inside GFOR",
-                                    AF_ERR_ARG);
-        gforSet(true);
-        array res = func(lhs, rhs);
-        gforSet(false);
-        return res;
-    }
+bool gforToggle() {
+    bool status = gforGet();
+    status ^= 1;
+    gforSet(status);
+    return status;
+}
 
+array batchFunc(const array &lhs, const array &rhs, batchFunc_t func) {
+    if (gforGet())
+        AF_THROW_ERR("batchFunc can not be used inside GFOR", AF_ERR_ARG);
+    gforSet(true);
+    array res = func(lhs, rhs);
+    gforSet(false);
+    return res;
 }
+
+}  // namespace af
diff --git a/src/api/cpp/gradient.cpp b/src/api/cpp/gradient.cpp
index d4b21ec445..f38906dc8e 100644
--- a/src/api/cpp/gradient.cpp
+++ b/src/api/cpp/gradient.cpp
@@ -7,16 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include <utility>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void grad(array &rows, array &cols, const array& in)
-{
+void grad(array &rows, array &cols, const array &in) {
     af_array rows_handle = 0;
     af_array cols_handle = 0;
     AF_THROW(af_gradient(&rows_handle, &cols_handle, in.get()));
@@ -24,4 +22,4 @@ void grad(array &rows, array &cols, const array& in)
     cols = array(cols_handle);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 669d1fde5d..85faa917bc 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -12,227 +12,197 @@
 #include <af/graphics.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void Window::initWindow(const int width, const int height, const char* const title)
-{
+void Window::initWindow(const int width, const int height,
+                        const char* const title) {
     AF_THROW(af_create_window(&wnd, width, height, title));
 }
 
-Window::Window()
-    : wnd(0), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT)
-{
+Window::Window() : wnd(0), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT) {
     initWindow(1280, 720, "ArrayFire");
 }
 
 Window::Window(const char* const title)
-    : wnd(0), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT)
-{
+    : wnd(0), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT) {
     initWindow(1280, 720, title);
 }
 
 Window::Window(const int width, const int height, const char* const title)
-    : wnd(0), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT)
-{
+    : wnd(0), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT) {
     initWindow(width, height, title);
 }
 
 Window::Window(const af_window window)
-    : wnd(window), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT)
-{
-}
+    : wnd(window), _r(-1), _c(-1), _cmap(AF_COLORMAP_DEFAULT) {}
 
-Window::~Window()
-{
+Window::~Window() {
     // THOU SHALL NOT THROW IN DESTRUCTORS
-    if (wnd) {
-        af_destroy_window(wnd);
-    }
+    if (wnd) { af_destroy_window(wnd); }
 }
 
-void Window::setPos(const unsigned x, const unsigned y)
-{
+void Window::setPos(const unsigned x, const unsigned y) {
     AF_THROW(af_set_position(get(), x, y));
 }
 
-void Window::setTitle(const char* const title)
-{
+void Window::setTitle(const char* const title) {
     AF_THROW(af_set_title(get(), title));
 }
 
-void Window::setSize(const unsigned w, const unsigned h)
-{
+void Window::setSize(const unsigned w, const unsigned h) {
     AF_THROW(af_set_size(get(), w, h));
 }
 
-void Window::setColorMap(const ColorMap cmap)
-{
-    _cmap = cmap;
-}
+void Window::setColorMap(const ColorMap cmap) { _cmap = cmap; }
 
-void Window::image(const array& in, const char* const title)
-{
+void Window::image(const array& in, const char* const title) {
     af_cell temp{_r, _c, title, _cmap};
     AF_THROW(af_draw_image(get(), in.get(), &temp));
 }
 
-void Window::plot(const array& in, const char* const title)
-{
+void Window::plot(const array& in, const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_plot_nd(get(), in.get(), &temp));
 }
 
-void Window::plot(const array& X, const array& Y, const char* const title)
-{
+void Window::plot(const array& X, const array& Y, const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_plot_2d(get(), X.get(), Y.get(), &temp));
 }
 
-void Window::plot(const array& X, const array& Y, const array& Z, const char* const title)
-{
+void Window::plot(const array& X, const array& Y, const array& Z,
+                  const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_plot_3d(get(), X.get(), Y.get(), Z.get(), &temp));
 }
 
-void Window::plot3(const array& P, const char* const title)
-{
+void Window::plot3(const array& P, const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     P.eval();
     AF_THROW(af_draw_plot_nd(get(), P.get(), &temp));
 }
 
-void Window::scatter(const array& in, af::markerType marker, const char* const title)
-{
+void Window::scatter(const array& in, af::markerType marker,
+                     const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_scatter_nd(get(), in.get(), marker, &temp));
 }
 
-void Window::scatter(const array& X, const array& Y, af::markerType marker, const char* const title)
-{
+void Window::scatter(const array& X, const array& Y, af::markerType marker,
+                     const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_scatter_2d(get(), X.get(), Y.get(), marker, &temp));
 }
 
 void Window::scatter(const array& X, const array& Y, const array& Z,
-                     af::markerType marker, const char* const title)
-{
+                     af::markerType marker, const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_scatter_3d(get(), X.get(), Y.get(), Z.get(), marker, &temp));
+    AF_THROW(
+        af_draw_scatter_3d(get(), X.get(), Y.get(), Z.get(), marker, &temp));
 }
 
-void Window::scatter3(const array& P, af::markerType marker, const char* const title)
-{
+void Window::scatter3(const array& P, af::markerType marker,
+                      const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_scatter_nd(get(), P.get(), marker, &temp));
 }
 
-void Window::hist(const array& X, const double minval, const double maxval, const char* const title)
-{
+void Window::hist(const array& X, const double minval, const double maxval,
+                  const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_hist(get(), X.get(), minval, maxval, &temp));
 }
 
-void Window::surface(const array& S, const char* const title)
-{
+void Window::surface(const array& S, const char* const title) {
     af::array xVals = range(S.dims(0));
     af::array yVals = range(S.dims(1));
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_surface(get(), xVals.get(), yVals.get(), S.get(), &temp));
 }
 
-void Window::surface(const array& xVals, const array& yVals, const array& S, const char* const title)
-{
+void Window::surface(const array& xVals, const array& yVals, const array& S,
+                     const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
     AF_THROW(af_draw_surface(get(), xVals.get(), yVals.get(), S.get(), &temp));
 }
 
-void Window::vectorField(const array& points, const array& directions, const char* const title)
-{
+void Window::vectorField(const array& points, const array& directions,
+                         const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_vector_field_nd(get(), points.get(), directions.get(), &temp));
+    AF_THROW(
+        af_draw_vector_field_nd(get(), points.get(), directions.get(), &temp));
 }
 
-void Window::vectorField(const array& xPoints, const array& yPoints, const array& zPoints,
-                         const array& xDirs  , const array& yDirs  , const array& zDirs  ,
-                         const char* const title)
-{
+void Window::vectorField(const array& xPoints, const array& yPoints,
+                         const array& zPoints, const array& xDirs,
+                         const array& yDirs, const array& zDirs,
+                         const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_vector_field_3d(get(),
-                xPoints.get(), yPoints.get(), zPoints.get(),
-                xDirs.get()  , yDirs.get()  , zDirs.get()  ,
-                &temp));
+    AF_THROW(af_draw_vector_field_3d(get(), xPoints.get(), yPoints.get(),
+                                     zPoints.get(), xDirs.get(), yDirs.get(),
+                                     zDirs.get(), &temp));
 }
 
 void Window::vectorField(const array& xPoints, const array& yPoints,
-                         const array& xDirs  , const array& yDirs  ,
-                         const char* const title)
-{
+                         const array& xDirs, const array& yDirs,
+                         const char* const title) {
     af_cell temp{_r, _c, title, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_draw_vector_field_2d(get(),
-                xPoints.get(), yPoints.get(), xDirs.get(), yDirs.get(),
-                &temp));
+    AF_THROW(af_draw_vector_field_2d(get(), xPoints.get(), yPoints.get(),
+                                     xDirs.get(), yDirs.get(), &temp));
 }
 
-void Window::grid(const int rows, const int cols)
-{
+void Window::grid(const int rows, const int cols) {
     AF_THROW(af_grid(get(), rows, cols));
 }
 
-void Window::setAxesLimits(const array& x, const array& y, const bool exact)
-{
+void Window::setAxesLimits(const array& x, const array& y, const bool exact) {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), NULL, exact, &temp));
+    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), NULL, exact,
+                                        &temp));
 }
 
-void Window::setAxesLimits(const array& x, const array& y, const array &z, const bool exact)
-{
+void Window::setAxesLimits(const array& x, const array& y, const array& z,
+                           const bool exact) {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), z.get(), exact, &temp));
+    AF_THROW(af_set_axes_limits_compute(get(), x.get(), y.get(), z.get(), exact,
+                                        &temp));
 }
 
-void Window::setAxesLimits(const float xmin, const float xmax,
-                           const float ymin, const float ymax,
-                           const bool exact)
-{
+void Window::setAxesLimits(const float xmin, const float xmax, const float ymin,
+                           const float ymax, const bool exact) {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits_2d(get(), xmin, xmax, ymin, ymax, exact, &temp));
+    AF_THROW(
+        af_set_axes_limits_2d(get(), xmin, xmax, ymin, ymax, exact, &temp));
 }
 
-void Window::setAxesLimits(const float xmin, const float xmax,
-                           const float ymin, const float ymax,
-                           const float zmin, const float zmax,
-                           const bool exact)
-{
+void Window::setAxesLimits(const float xmin, const float xmax, const float ymin,
+                           const float ymax, const float zmin, const float zmax,
+                           const bool exact) {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
-    AF_THROW(af_set_axes_limits_3d(get(), xmin, xmax, ymin, ymax, zmin, zmax, exact, &temp));
+    AF_THROW(af_set_axes_limits_3d(get(), xmin, xmax, ymin, ymax, zmin, zmax,
+                                   exact, &temp));
 }
 
-void Window::setAxesTitles(const char * const xtitle,
-                           const char * const ytitle,
-                           const char * const ztitle)
-{
+void Window::setAxesTitles(const char* const xtitle, const char* const ytitle,
+                           const char* const ztitle) {
     af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
     AF_THROW(af_set_axes_titles(get(), xtitle, ytitle, ztitle, &temp));
 }
 
-void Window::show()
-{
+void Window::show() {
     AF_THROW(af_show(get()));
     _r = -1;
     _c = -1;
 }
 
-bool Window::close()
-{
+bool Window::close() {
     bool temp = true;
     AF_THROW(af_is_window_closed(&temp, get()));
     return temp;
 }
 
-void Window::setVisibility(const bool isVisible)
-{
+void Window::setVisibility(const bool isVisible) {
     AF_THROW(af_set_visibility(get(), isVisible));
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/hamming.cpp b/src/api/cpp/hamming.cpp
index 4ef8d1e5e1..3f1e84dcea 100644
--- a/src/api/cpp/hamming.cpp
+++ b/src/api/cpp/hamming.cpp
@@ -7,22 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void hammingMatcher(array& idx, array& dist,
-                     const array& query, const array& train,
-                     const dim_t dist_dim, const unsigned n_dist)
-{
+void hammingMatcher(array& idx, array& dist, const array& query,
+                    const array& train, const dim_t dist_dim,
+                    const unsigned n_dist) {
     af_array temp_idx  = 0;
     af_array temp_dist = 0;
-    AF_THROW(af_nearest_neighbour(&temp_idx, &temp_dist, query.get(), train.get(), dist_dim, n_dist, AF_SHD));
+    AF_THROW(af_nearest_neighbour(&temp_idx, &temp_dist, query.get(),
+                                  train.get(), dist_dim, n_dist, AF_SHD));
     idx  = array(temp_idx);
     dist = array(temp_dist);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/harris.cpp b/src/api/cpp/harris.cpp
index 849011d1da..a84cedfa44 100644
--- a/src/api/cpp/harris.cpp
+++ b/src/api/cpp/harris.cpp
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
 features harris(const array& in, const unsigned max_corners,
                 const float min_response, const float sigma,
-                const unsigned block_size, const float k_thr)
-{
+                const unsigned block_size, const float k_thr) {
     af_features temp;
-    AF_THROW(af_harris(&temp, in.get(), max_corners,
-                       min_response, sigma, block_size, k_thr));
+    AF_THROW(af_harris(&temp, in.get(), max_corners, min_response, sigma,
+                       block_size, k_thr));
     return features(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/histogram.cpp b/src/api/cpp/histogram.cpp
index 17bd7f0e08..6f7db329bc 100644
--- a/src/api/cpp/histogram.cpp
+++ b/src/api/cpp/histogram.cpp
@@ -7,38 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/algorithm.h>
-#include <af/compatible.h>
 #include <af/array.h>
+#include <af/compatible.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array histogram(const array &in, const unsigned nbins, const double minval, const double maxval)
-{
+array histogram(const array& in, const unsigned nbins, const double minval,
+                const double maxval) {
     af_array out = 0;
     AF_THROW(af_histogram(&out, in.get(), nbins, minval, maxval));
     return array(out);
 }
 
-array histogram(const array &in, const unsigned nbins)
-{
+array histogram(const array& in, const unsigned nbins) {
     af_array out = 0;
-    if(in.numdims() == 0) {
-        return in;
-    }
-    AF_THROW(af_histogram(&out, in.get(), nbins, min<double>(in), max<double>(in)));
+    if (in.numdims() == 0) { return in; }
+    AF_THROW(
+        af_histogram(&out, in.get(), nbins, min<double>(in), max<double>(in)));
     return array(out);
 }
 
-array histequal(const array& in, const array& hist) { return histEqual(in, hist); }
-array histEqual(const array& in, const array& hist)
-{
+array histequal(const array& in, const array& hist) {
+    return histEqual(in, hist);
+}
+array histEqual(const array& in, const array& hist) {
     af_array temp = 0;
     AF_THROW(af_hist_equal(&temp, in.get(), hist.get()));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/homography.cpp b/src/api/cpp/homography.cpp
index 77791047b4..4df2f8da87 100644
--- a/src/api/cpp/homography.cpp
+++ b/src/api/cpp/homography.cpp
@@ -7,26 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void homography(array &H, int &inliers,
-                const array &x_src, const array &y_src,
+void homography(array &H, int &inliers, const array &x_src, const array &y_src,
                 const array &x_dst, const array &y_dst,
                 const af_homography_type htype, const float inlier_thr,
-                const unsigned iterations, const af::dtype otype)
-{
+                const unsigned iterations, const af::dtype otype) {
     af_array outH;
-    AF_THROW(af_homography(&outH, &inliers,
-                           x_src.get(), y_src.get(),
-                           x_dst.get(), y_dst.get(),
-                           htype, inlier_thr, iterations, otype));
+    AF_THROW(af_homography(&outH, &inliers, x_src.get(), y_src.get(),
+                           x_dst.get(), y_dst.get(), htype, inlier_thr,
+                           iterations, otype));
 
     H = array(outH);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/hsv_rgb.cpp b/src/api/cpp/hsv_rgb.cpp
index e55442c832..b023e12013 100644
--- a/src/api/cpp/hsv_rgb.cpp
+++ b/src/api/cpp/hsv_rgb.cpp
@@ -1,31 +1,28 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
-
-    array hsv2rgb(const array& in)
-    {
-        af_array temp = 0;
-        AF_THROW(af_hsv2rgb(&temp, in.get()));
-        return array(temp);
-    }
+namespace af {
 
-    array rgb2hsv(const array& in)
-    {
-        af_array temp = 0;
-        AF_THROW(af_rgb2hsv(&temp, in.get()));
-        return array(temp);
-    }
+array hsv2rgb(const array& in) {
+    af_array temp = 0;
+    AF_THROW(af_hsv2rgb(&temp, in.get()));
+    return array(temp);
+}
 
+array rgb2hsv(const array& in) {
+    af_array temp = 0;
+    AF_THROW(af_rgb2hsv(&temp, in.get()));
+    return array(temp);
 }
+
+}  // namespace af
diff --git a/src/api/cpp/iir.cpp b/src/api/cpp/iir.cpp
index 8ef1ab7c70..7071978c3b 100644
--- a/src/api/cpp/iir.cpp
+++ b/src/api/cpp/iir.cpp
@@ -7,26 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/signal.h>
 #include <af/array.h>
-#include "error.hpp"
+#include <af/signal.h>
 #include <algorithm>
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array fir(const array& b, const array& x)
-{
+array fir(const array& b, const array& x) {
     af_array out = 0;
     AF_THROW(af_fir(&out, b.get(), x.get()));
     return array(out);
 }
 
-array iir(const array &b, const array& a, const array& x)
-{
+array iir(const array& b, const array& a, const array& x) {
     af_array out = 0;
     AF_THROW(af_iir(&out, b.get(), a.get(), x.get()));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/imageio.cpp b/src/api/cpp/imageio.cpp
index 75ef5fe9c4..21035ab3e6 100644
--- a/src/api/cpp/imageio.cpp
+++ b/src/api/cpp/imageio.cpp
@@ -7,72 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
-#include <af/compatible.h>
 #include <af/array.h>
+#include <af/compatible.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array loadImage(const char* filename, const bool is_color)
-{
+array loadImage(const char* filename, const bool is_color) {
     af_array out = 0;
     AF_THROW(af_load_image(&out, filename, is_color));
     return array(out);
 }
 
-array loadImageMem(const void* ptr)
-{
+array loadImageMem(const void* ptr) {
     af_array out = 0;
     AF_THROW(af_load_image_memory(&out, ptr));
     return array(out);
 }
 
-array loadimage(const char* filename, const bool is_color)
-{
+array loadimage(const char* filename, const bool is_color) {
     return loadImage(filename, is_color);
 }
 
-void saveImage(const char* filename, const array& in)
-{
+void saveImage(const char* filename, const array& in) {
     AF_THROW(af_save_image(filename, in.get()));
 }
 
-void* saveImageMem(const array& in, const imageFormat format)
-{
+void* saveImageMem(const array& in, const imageFormat format) {
     void* ptr = NULL;
     AF_THROW(af_save_image_memory(&ptr, in.get(), format));
     return ptr;
 }
 
-void saveimage(const char* filename, const array& in)
-{
+void saveimage(const char* filename, const array& in) {
     return saveImage(filename, in);
 }
 
-void deleteImageMem(void* ptr)
-{
-    AF_THROW(af_delete_image_memory(ptr));
-}
+void deleteImageMem(void* ptr) { AF_THROW(af_delete_image_memory(ptr)); }
 
-array loadImageNative(const char* filename)
-{
+array loadImageNative(const char* filename) {
     af_array out = 0;
     AF_THROW(af_load_image_native(&out, filename));
     return array(out);
 }
 
-void saveImageNative(const char* filename, const array& in)
-{
+void saveImageNative(const char* filename, const array& in) {
     AF_THROW(af_save_image_native(filename, in.get()));
 }
 
-bool isImageIOAvailable()
-{
+bool isImageIOAvailable() {
     bool out = false;
     AF_THROW(af_is_image_io_available(&out));
     return out;
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/index.cpp b/src/api/cpp/index.cpp
index ea23a41fc4..a585275c4a 100644
--- a/src/api/cpp/index.cpp
+++ b/src/api/cpp/index.cpp
@@ -7,88 +7,74 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/index.h>
-#include <af/array.h>
 #include <af/algorithm.h>
-#include "error.hpp"
+#include <af/array.h>
+#include <af/index.h>
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array lookup(const array &in, const array &idx, const int dim)
-{
+array lookup(const array &in, const array &idx, const int dim) {
     af_array out = 0;
     AF_THROW(af_lookup(&out, in.get(), idx.get(), getFNSD(dim, in.dims())));
     return array(out);
 }
 
-void copy(array &dst, const array &src,
-          const index &idx0,
-          const index &idx1,
-          const index &idx2,
-          const index &idx3)
-{
+void copy(array &dst, const array &src, const index &idx0, const index &idx1,
+          const index &idx2, const index &idx3) {
     unsigned nd = dst.numdims();
 
-    af_index_t indices[] = {idx0.get(),
-                            idx1.get(),
-                            idx2.get(),
-                            idx3.get()};
+    af_index_t indices[] = {idx0.get(), idx1.get(), idx2.get(), idx3.get()};
 
-    af_array lhs = dst.get();
+    af_array lhs       = dst.get();
     const af_array rhs = src.get();
     AF_THROW(af_assign_gen(&lhs, lhs, nd, indices, rhs));
 }
 
 index::index() {
     impl.idx.seq = af_span;
-    impl.isSeq = true;
+    impl.isSeq   = true;
     impl.isBatch = false;
 }
 
 index::index(const int idx) {
     impl.idx.seq = af_make_seq(idx, idx, 1);
-    impl.isSeq = true;
+    impl.isSeq   = true;
     impl.isBatch = false;
 }
 
-index::index(const af::seq& s0) {
+index::index(const af::seq &s0) {
     impl.idx.seq = s0.s;
-    impl.isSeq = true;
+    impl.isSeq   = true;
     impl.isBatch = s0.m_gfor;
 }
 
-index::index(const af_seq& s0) {
+index::index(const af_seq &s0) {
     impl.idx.seq = s0;
-    impl.isSeq = true;
+    impl.isSeq   = true;
     impl.isBatch = false;
 }
 
-index::index(const af::array& idx0) {
-    array idx = idx0.isbool() ? where(idx0) : idx0;
+index::index(const af::array &idx0) {
+    array idx    = idx0.isbool() ? where(idx0) : idx0;
     af_array arr = 0;
     AF_THROW(af_retain_array(&arr, idx.get()));
     impl.idx.arr = arr;
 
-    impl.isSeq = false;
+    impl.isSeq   = false;
     impl.isBatch = false;
 }
 
-index::index(const af::index& idx0) {
-    *this = idx0;
-}
+index::index(const af::index &idx0) { *this = idx0; }
 
 index::~index() {
-    if (!impl.isSeq && impl.idx.arr)
-        af_release_array(impl.idx.arr);
-
-
+    if (!impl.isSeq && impl.idx.arr) af_release_array(impl.idx.arr);
 }
 
-index & index::operator=(const index& idx0) {
+index &index::operator=(const index &idx0) {
     impl = idx0.get();
-    if(impl.isSeq == false){
+    if (impl.isSeq == false) {
         // increment reference count to avoid double free
         // when/if idx0 is destroyed
         AF_THROW(af_retain_array(&impl.idx.arr, impl.idx.arr));
@@ -98,30 +84,25 @@ index & index::operator=(const index& idx0) {
 
 #if __cplusplus > 199711L
 index::index(index &&idx0) {
-    impl = idx0.impl;
+    impl              = idx0.impl;
     idx0.impl.idx.arr = nullptr;
 }
 
-index& index::operator=(index &&idx0) {
-    impl = idx0.impl;
+index &index::operator=(index &&idx0) {
+    impl              = idx0.impl;
     idx0.impl.idx.arr = nullptr;
     return *this;
 }
 #endif
 
-
-static bool operator==(const af_seq& lhs, const af_seq& rhs) {
+static bool operator==(const af_seq &lhs, const af_seq &rhs) {
     return lhs.begin == rhs.begin && lhs.end == rhs.end && lhs.step == rhs.step;
 }
 
-bool index::isspan() const
-{
+bool index::isspan() const {
     return impl.isSeq == true && impl.idx.seq == af_span;
 }
 
-const af_index_t& index::get() const
-{
-    return impl;
-}
+const af_index_t &index::get() const { return impl; }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/internal.cpp b/src/api/cpp/internal.cpp
index bdce6e155c..b2d14360a2 100644
--- a/src/api/cpp/internal.cpp
+++ b/src/api/cpp/internal.cpp
@@ -7,57 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/internal.h>
 #include <af/array.h>
+#include <af/internal.h>
 #include "error.hpp"
 
-namespace af
-{
-    array createStridedArray(const void *data, const dim_t offset,
-                             const dim4 dims, const dim4 strides,
-                             const af::dtype ty,
-                             const af::source location)
-    {
-        af_array res;
-        AF_THROW(af_create_strided_array(&res, data, offset,
-                                         dims.ndims(), dims.get(), strides.get(),
-                                         ty, location));
-        return array(res);
-    }
-
-    dim4 getStrides(const array &in)
-    {
-        dim_t s0, s1, s2, s3;
-        AF_THROW(af_get_strides(&s0, &s1, &s2, &s3, in.get()));
-        return dim4(s0, s1, s2, s3);
-    }
-
-    dim_t getOffset(const array &in)
-    {
-        dim_t offset;
-        AF_THROW(af_get_offset(&offset, in.get()));
-        return offset;
-    }
-
-    void *getRawPtr(const array &in)
-    {
-        void *ptr = NULL;
-        AF_THROW(af_get_raw_ptr(&ptr, in.get()));
-        return ptr;
-    }
-
-    bool isLinear(const array &in)
-    {
-        bool is_linear = false;
-        AF_THROW(af_is_linear(&is_linear, in.get()));
-        return is_linear;
-    }
-
-    bool isOwner(const array &in)
-    {
-        bool is_owner = false;
-        AF_THROW(af_is_owner(&is_owner, in.get()));
-        return is_owner;
-    }
+namespace af {
+array createStridedArray(const void *data, const dim_t offset, const dim4 dims,
+                         const dim4 strides, const af::dtype ty,
+                         const af::source location) {
+    af_array res;
+    AF_THROW(af_create_strided_array(&res, data, offset, dims.ndims(),
+                                     dims.get(), strides.get(), ty, location));
+    return array(res);
+}
+
+dim4 getStrides(const array &in) {
+    dim_t s0, s1, s2, s3;
+    AF_THROW(af_get_strides(&s0, &s1, &s2, &s3, in.get()));
+    return dim4(s0, s1, s2, s3);
+}
+
+dim_t getOffset(const array &in) {
+    dim_t offset;
+    AF_THROW(af_get_offset(&offset, in.get()));
+    return offset;
+}
+
+void *getRawPtr(const array &in) {
+    void *ptr = NULL;
+    AF_THROW(af_get_raw_ptr(&ptr, in.get()));
+    return ptr;
+}
 
+bool isLinear(const array &in) {
+    bool is_linear = false;
+    AF_THROW(af_is_linear(&is_linear, in.get()));
+    return is_linear;
 }
+
+bool isOwner(const array &in) {
+    bool is_owner = false;
+    AF_THROW(af_is_owner(&is_owner, in.get()));
+    return is_owner;
+}
+
+}  // namespace af
diff --git a/src/api/cpp/lapack.cpp b/src/api/cpp/lapack.cpp
index 83a3163078..3556eaf46f 100644
--- a/src/api/cpp/lapack.cpp
+++ b/src/api/cpp/lapack.cpp
@@ -11,160 +11,140 @@
 #include <af/lapack.h>
 #include "error.hpp"
 
-namespace af
-{
-    void svd(array &u, array &s, array &vt, const array &in)
-    {
-        af_array sl = 0, ul = 0, vtl = 0;
-        AF_THROW(af_svd(&ul, &sl, &vtl, in.get()));
-        s = array(sl);
-        u = array(ul);
-        vt = array(vtl);
-    }
+namespace af {
+void svd(array &u, array &s, array &vt, const array &in) {
+    af_array sl = 0, ul = 0, vtl = 0;
+    AF_THROW(af_svd(&ul, &sl, &vtl, in.get()));
+    s  = array(sl);
+    u  = array(ul);
+    vt = array(vtl);
+}
 
-    void svdInPlace(array &u, array &s, array &vt, array &in)
-    {
-        af_array sl = 0, ul = 0, vtl = 0;
-        AF_THROW(af_svd_inplace(&ul, &sl, &vtl, in.get()));
-        s = array(sl);
-        u = array(ul);
-        vt = array(vtl);
-    }
+void svdInPlace(array &u, array &s, array &vt, array &in) {
+    af_array sl = 0, ul = 0, vtl = 0;
+    AF_THROW(af_svd_inplace(&ul, &sl, &vtl, in.get()));
+    s  = array(sl);
+    u  = array(ul);
+    vt = array(vtl);
+}
 
-    void lu(array &out, array &pivot, const array &in, const bool is_lapack_piv)
-    {
-        out = in.copy();
-        af_array p = 0;
-        AF_THROW(af_lu_inplace(&p, out.get(), is_lapack_piv));
-        pivot = array(p);
-    }
+void lu(array &out, array &pivot, const array &in, const bool is_lapack_piv) {
+    out        = in.copy();
+    af_array p = 0;
+    AF_THROW(af_lu_inplace(&p, out.get(), is_lapack_piv));
+    pivot = array(p);
+}
 
-    void lu(array &lower, array &upper, array &pivot, const array &in)
-    {
-        af_array l = 0, u = 0, p = 0;
-        AF_THROW(af_lu(&l, &u, &p, in.get()));
-        lower = array(l);
-        upper = array(u);
-        pivot = array(p);
-    }
+void lu(array &lower, array &upper, array &pivot, const array &in) {
+    af_array l = 0, u = 0, p = 0;
+    AF_THROW(af_lu(&l, &u, &p, in.get()));
+    lower = array(l);
+    upper = array(u);
+    pivot = array(p);
+}
 
-    void luInPlace(array &pivot, array &in, const bool is_lapack_piv)
-    {
-        af_array p = 0;
-        AF_THROW(af_lu_inplace(&p, in.get(), is_lapack_piv));
-        pivot = array(p);
-    }
+void luInPlace(array &pivot, array &in, const bool is_lapack_piv) {
+    af_array p = 0;
+    AF_THROW(af_lu_inplace(&p, in.get(), is_lapack_piv));
+    pivot = array(p);
+}
 
-    void qr(array &out, array &tau, const array &in)
-    {
-        out = in.copy();
-        af_array t = 0;
-        AF_THROW(af_qr_inplace(&t, out.get()));
-        tau = array(t);
-    }
+void qr(array &out, array &tau, const array &in) {
+    out        = in.copy();
+    af_array t = 0;
+    AF_THROW(af_qr_inplace(&t, out.get()));
+    tau = array(t);
+}
 
-    void qr(array &q, array &r, array &tau, const array &in)
-    {
-        af_array q_ = 0, r_ = 0, t_ = 0;
-        AF_THROW(af_qr(&q_, &r_, &t_, in.get()));
-        q = array(q_);
-        r = array(r_);
-        tau = array(t_);
-    }
+void qr(array &q, array &r, array &tau, const array &in) {
+    af_array q_ = 0, r_ = 0, t_ = 0;
+    AF_THROW(af_qr(&q_, &r_, &t_, in.get()));
+    q   = array(q_);
+    r   = array(r_);
+    tau = array(t_);
+}
 
-    void qrInPlace(array &tau, array &in)
-    {
-        af_array t = 0;
-        AF_THROW(af_qr_inplace(&t, in.get()));
-        tau = array(t);
-    }
+void qrInPlace(array &tau, array &in) {
+    af_array t = 0;
+    AF_THROW(af_qr_inplace(&t, in.get()));
+    tau = array(t);
+}
 
-    int cholesky(array &out, const array &in, const bool is_upper)
-    {
-        int info = 0;
-        af_array res;
-        AF_THROW(af_cholesky(&res, &info, in.get(), is_upper));
-        out = array(res);
-        return info;
-    }
+int cholesky(array &out, const array &in, const bool is_upper) {
+    int info = 0;
+    af_array res;
+    AF_THROW(af_cholesky(&res, &info, in.get(), is_upper));
+    out = array(res);
+    return info;
+}
 
-    int choleskyInPlace(array &in, const bool is_upper)
-    {
-        int info = 0;
-        AF_THROW(af_cholesky_inplace(&info, in.get(), is_upper));
-        return info;
-    }
+int choleskyInPlace(array &in, const bool is_upper) {
+    int info = 0;
+    AF_THROW(af_cholesky_inplace(&info, in.get(), is_upper));
+    return info;
+}
 
-    array solve(const array &a, const array &b, const matProp options)
-    {
-        af_array out;
-        AF_THROW(af_solve(&out, a.get(), b.get(), options));
-        return array(out);
-    }
+array solve(const array &a, const array &b, const matProp options) {
+    af_array out;
+    AF_THROW(af_solve(&out, a.get(), b.get(), options));
+    return array(out);
+}
 
-    array solveLU(const array &a, const array &piv,
-                  const array &b, const matProp options)
-    {
-        af_array out;
-        AF_THROW(af_solve_lu(&out, a.get(), piv.get(), b.get(), options));
-        return array(out);
-    }
+array solveLU(const array &a, const array &piv, const array &b,
+              const matProp options) {
+    af_array out;
+    AF_THROW(af_solve_lu(&out, a.get(), piv.get(), b.get(), options));
+    return array(out);
+}
 
-    array inverse(const array &in, const matProp options)
-    {
-        af_array out;
-        AF_THROW(af_inverse(&out, in.get(), options));
-        return array(out);
-    }
+array inverse(const array &in, const matProp options) {
+    af_array out;
+    AF_THROW(af_inverse(&out, in.get(), options));
+    return array(out);
+}
 
-    array pinverse(const array &in, const double tol, const matProp options)
-    {
-        af_array out;
-        AF_THROW(af_pinverse(&out, in.get(), tol, options));
-        return array(out);
-    }
+array pinverse(const array &in, const double tol, const matProp options) {
+    af_array out;
+    AF_THROW(af_pinverse(&out, in.get(), tol, options));
+    return array(out);
+}
 
-    unsigned rank(const array &in, const double tol)
-    {
-        unsigned r = 0;
-        AF_THROW(af_rank(&r, in.get(), tol));
-        return r;
-    }
+unsigned rank(const array &in, const double tol) {
+    unsigned r = 0;
+    AF_THROW(af_rank(&r, in.get(), tol));
+    return r;
+}
 
-#define INSTANTIATE_DET(TR, TC)                     \
-    template<> AFAPI                                \
-    TR det(const array &in)                         \
-    {                                               \
-        double real;                                \
-        double imag;                                \
-        AF_THROW(af_det(&real, &imag, in.get()));   \
-        return real;                                \
-    }                                               \
-    template<> AFAPI                                \
-    TC det(const array &in)                         \
-    {                                               \
-        double real;                                \
-        double imag;                                \
-        AF_THROW(af_det(&real, &imag, in.get()));   \
-        TC out((TR)real, (TR)imag);                 \
-        return out;                                 \
-    }                                               \
-
-    INSTANTIATE_DET(float, af_cfloat)
-    INSTANTIATE_DET(double, af_cdouble)
-
-    double norm(const array &in, const normType type,
-                const double p, const double q)
-    {
-        double out;
-        AF_THROW(af_norm(&out, in.get(), type, p, q));
-        return out;
-    }
+#define INSTANTIATE_DET(TR, TC)                   \
+    template<>                                    \
+    AFAPI TR det(const array &in) {               \
+        double real;                              \
+        double imag;                              \
+        AF_THROW(af_det(&real, &imag, in.get())); \
+        return real;                              \
+    }                                             \
+    template<>                                    \
+    AFAPI TC det(const array &in) {               \
+        double real;                              \
+        double imag;                              \
+        AF_THROW(af_det(&real, &imag, in.get())); \
+        TC out((TR)real, (TR)imag);               \
+        return out;                               \
+    }
+
+INSTANTIATE_DET(float, af_cfloat)
+INSTANTIATE_DET(double, af_cdouble)
+
+double norm(const array &in, const normType type, const double p,
+            const double q) {
+    double out;
+    AF_THROW(af_norm(&out, in.get(), type, p, q));
+    return out;
+}
 
-    bool isLAPACKAvailable()
-    {
-        bool out = false;
-        AF_THROW(af_is_lapack_available(&out));
-        return out;
-    }
+bool isLAPACKAvailable() {
+    bool out = false;
+    AF_THROW(af_is_lapack_available(&out));
+    return out;
 }
+}  // namespace af
diff --git a/src/api/cpp/matchTemplate.cpp b/src/api/cpp/matchTemplate.cpp
index 7b549e2101..e53cd89113 100644
--- a/src/api/cpp/matchTemplate.cpp
+++ b/src/api/cpp/matchTemplate.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array matchTemplate(const array &searchImg, const array &templateImg, const matchType mType)
-{
+array matchTemplate(const array &searchImg, const array &templateImg,
+                    const matchType mType) {
     af_array out = 0;
-    AF_THROW(af_match_template(&out, searchImg.get(), templateImg.get(), mType));
+    AF_THROW(
+        af_match_template(&out, searchImg.get(), templateImg.get(), mType));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/mean.cpp b/src/api/cpp/mean.cpp
index 980a0d1ba3..70f1772688 100644
--- a/src/api/cpp/mean.cpp
+++ b/src/api/cpp/mean.cpp
@@ -7,68 +7,65 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/statistics.h>
 #include <af/algorithm.h>
 #include <af/array.h>
-#include "error.hpp"
+#include <af/dim4.hpp>
+#include <af/statistics.h>
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array mean(const array &in, const dim_t dim)
-{
+array mean(const array& in, const dim_t dim) {
     af_array temp = 0;
     AF_THROW(af_mean(&temp, in.get(), getFNSD(dim, in.dims())));
     return array(temp);
 }
 
-array mean(const array &in, const array &weights, const dim_t dim)
-{
+array mean(const array& in, const array& weights, const dim_t dim) {
     af_array temp = 0;
-    AF_THROW(af_mean_weighted(&temp, in.get(), weights.get(), getFNSD(dim, in.dims())));
+    AF_THROW(af_mean_weighted(&temp, in.get(), weights.get(),
+                              getFNSD(dim, in.dims())));
     return array(temp);
 }
 
-#define INSTANTIATE_MEAN(T)                                     \
-    template<> AFAPI T mean(const array& in)                    \
-    {                                                           \
-        double ret_val;                                         \
-        AF_THROW(af_mean_all(&ret_val, NULL, in.get()));        \
-        return (T)ret_val;                                      \
-    }                                                           \
-    template<> AFAPI T mean(const array& in, const array& wts)  \
-    {                                                           \
-        double ret_val;                                         \
-        AF_THROW(af_mean_all_weighted(&ret_val, NULL,           \
-                    in.get(), wts.get()));                      \
-        return (T)ret_val;                                      \
-    }                                                           \
+#define INSTANTIATE_MEAN(T)                                                  \
+    template<>                                                               \
+    AFAPI T mean(const array& in) {                                          \
+        double ret_val;                                                      \
+        AF_THROW(af_mean_all(&ret_val, NULL, in.get()));                     \
+        return (T)ret_val;                                                   \
+    }                                                                        \
+    template<>                                                               \
+    AFAPI T mean(const array& in, const array& wts) {                        \
+        double ret_val;                                                      \
+        AF_THROW(af_mean_all_weighted(&ret_val, NULL, in.get(), wts.get())); \
+        return (T)ret_val;                                                   \
+    }
 
-template<> AFAPI af_cfloat mean(const array& in)
-{
+template<>
+AFAPI af_cfloat mean(const array& in) {
     double real, imag;
     AF_THROW(af_mean_all(&real, &imag, in.get()));
     return af_cfloat((float)real, (float)imag);
 }
 
-template<> AFAPI af_cdouble mean(const array& in)
-{
+template<>
+AFAPI af_cdouble mean(const array& in) {
     double real, imag;
     AF_THROW(af_mean_all(&real, &imag, in.get()));
     return af_cdouble(real, imag);
 }
 
-template<> AFAPI af_cfloat mean(const array& in, const array& weights)
-{
+template<>
+AFAPI af_cfloat mean(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_mean_all_weighted(&real, &imag, in.get(), weights.get()));
     return af_cfloat((float)real, (float)imag);
 }
 
-template<> AFAPI af_cdouble mean(const array& in, const array& weights)
-{
+template<>
+AFAPI af_cdouble mean(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_mean_all_weighted(&real, &imag, in.get(), weights.get()));
     return af_cdouble(real, imag);
@@ -87,4 +84,4 @@ INSTANTIATE_MEAN(unsigned short);
 
 #undef INSTANTIATE_MEAN
 
-}
+}  // namespace af
diff --git a/src/api/cpp/meanshift.cpp b/src/api/cpp/meanshift.cpp
index c83e011958..03a786bbcf 100644
--- a/src/api/cpp/meanshift.cpp
+++ b/src/api/cpp/meanshift.cpp
@@ -7,18 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array meanShift(const array& in, const float spatial_sigma, const float chromatic_sigma, const unsigned iter, const bool is_color)
-{
+array meanShift(const array& in, const float spatial_sigma,
+                const float chromatic_sigma, const unsigned iter,
+                const bool is_color) {
     af_array out = 0;
-    AF_THROW(af_mean_shift(&out, in.get(), spatial_sigma, chromatic_sigma, iter, is_color));
+    AF_THROW(af_mean_shift(&out, in.get(), spatial_sigma, chromatic_sigma, iter,
+                           is_color));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/median.cpp b/src/api/cpp/median.cpp
index d047d78a0f..5f4b88fb2a 100644
--- a/src/api/cpp/median.cpp
+++ b/src/api/cpp/median.cpp
@@ -7,21 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/statistics.h>
 #include <af/array.h>
-#include "error.hpp"
+#include <af/statistics.h>
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-#define INSTANTIATE_MEDIAN(T)                               \
-    template<> AFAPI T median(const array& in)              \
-    {                                                       \
-        double ret_val;                                     \
-        AF_THROW(af_median_all(&ret_val, NULL, in.get()));  \
-        return (T)ret_val;                                  \
-    }                                                       \
+#define INSTANTIATE_MEDIAN(T)                              \
+    template<>                                             \
+    AFAPI T median(const array& in) {                      \
+        double ret_val;                                    \
+        AF_THROW(af_median_all(&ret_val, NULL, in.get())); \
+        return (T)ret_val;                                 \
+    }
 
 INSTANTIATE_MEDIAN(float);
 INSTANTIATE_MEDIAN(double);
@@ -36,11 +35,10 @@ INSTANTIATE_MEDIAN(unsigned short);
 
 #undef INSTANTIATE_MEDIAN
 
-array median(const array& in, const dim_t dim)
-{
+array median(const array& in, const dim_t dim) {
     af_array temp = 0;
     AF_THROW(af_median(&temp, in.get(), getFNSD(dim, in.dims())));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/moments.cpp b/src/api/cpp/moments.cpp
index a572b59c55..3e28baf964 100644
--- a/src/api/cpp/moments.cpp
+++ b/src/api/cpp/moments.cpp
@@ -7,24 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/defines.h>
 #include <af/image.h>
-#include <af/array.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array moments(const array& in, const af_moment_type moment)
-{
+array moments(const array& in, const af_moment_type moment) {
     af_array out = 0;
     AF_THROW(af_moments(&out, in.get(), moment));
     return array(out);
 }
 
-void moments(double* out, const array& in, const af_moment_type moment)
-{
+void moments(double* out, const array& in, const af_moment_type moment) {
     AF_THROW(af_moments_all(out, in.get(), moment));
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/morph.cpp b/src/api/cpp/morph.cpp
index cda589e47d..8b6033ac80 100644
--- a/src/api/cpp/morph.cpp
+++ b/src/api/cpp/morph.cpp
@@ -7,39 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array dilate(const array& in, const array& mask)
-{
+array dilate(const array& in, const array& mask) {
     af_array out = 0;
     AF_THROW(af_dilate(&out, in.get(), mask.get()));
     return array(out);
 }
 
-array dilate3(const array& in, const array& mask)
-{
+array dilate3(const array& in, const array& mask) {
     af_array out = 0;
     AF_THROW(af_dilate3(&out, in.get(), mask.get()));
     return array(out);
 }
 
-array erode(const array& in, const array& mask)
-{
+array erode(const array& in, const array& mask) {
     af_array out = 0;
     AF_THROW(af_erode(&out, in.get(), mask.get()));
     return array(out);
 }
 
-array erode3(const array& in, const array& mask)
-{
+array erode3(const array& in, const array& mask) {
     af_array out = 0;
     AF_THROW(af_erode3(&out, in.get(), mask.get()));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/nearest_neighbour.cpp b/src/api/cpp/nearest_neighbour.cpp
index 5dae7abf9c..2c17f2c62c 100644
--- a/src/api/cpp/nearest_neighbour.cpp
+++ b/src/api/cpp/nearest_neighbour.cpp
@@ -7,23 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void nearestNeighbour(array& idx, array& dist,
-                      const array& query, const array& train,
-                      const dim_t dist_dim, const unsigned n_dist,
-                      const af_match_type dist_type)
-{
+void nearestNeighbour(array& idx, array& dist, const array& query,
+                      const array& train, const dim_t dist_dim,
+                      const unsigned n_dist, const af_match_type dist_type) {
     af_array temp_idx  = 0;
     af_array temp_dist = 0;
-    AF_THROW(af_nearest_neighbour(&temp_idx, &temp_dist, query.get(), train.get(), dist_dim, n_dist, dist_type));
+    AF_THROW(af_nearest_neighbour(&temp_idx, &temp_dist, query.get(),
+                                  train.get(), dist_dim, n_dist, dist_type));
     idx  = array(temp_idx);
     dist = array(temp_dist);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/orb.cpp b/src/api/cpp/orb.cpp
index 8a8e230ba4..3c9447d9b2 100644
--- a/src/api/cpp/orb.cpp
+++ b/src/api/cpp/orb.cpp
@@ -7,27 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void orb(features& feat, array& desc, const array& in,
-         const float fast_thr, const unsigned max_feat,
-         const float scl_fctr, const unsigned levels,
-         const bool blur_img)
-{
+void orb(features& feat, array& desc, const array& in, const float fast_thr,
+         const unsigned max_feat, const float scl_fctr, const unsigned levels,
+         const bool blur_img) {
     af_features temp_feat;
     af_array temp_desc = 0;
-    AF_THROW(af_orb(&temp_feat, &temp_desc, in.get(), fast_thr,
-                    max_feat, scl_fctr, levels, blur_img));
+    AF_THROW(af_orb(&temp_feat, &temp_desc, in.get(), fast_thr, max_feat,
+                    scl_fctr, levels, blur_img));
 
     dim_t num = 0;
-    AF_THROW(af_get_features_num(&num,  temp_feat));
+    AF_THROW(af_get_features_num(&num, temp_feat));
     feat = features(temp_feat);
     desc = array(temp_desc);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/random.cpp b/src/api/cpp/random.cpp
index b312b7fd45..57751a2bec 100644
--- a/src/api/cpp/random.cpp
+++ b/src/api/cpp/random.cpp
@@ -7,175 +7,134 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/random.h>
 #include <af/array.h>
-#include <af/dim4.hpp>
 #include <af/data.h>
+#include <af/dim4.hpp>
+#include <af/random.h>
 #include "error.hpp"
 
-namespace af
-{
-    randomEngine::randomEngine(randomEngineType type, unsigned long long seed) : engine(0)
-    {
-        AF_THROW(af_create_random_engine(&engine, type, seed));
-    }
-
-    randomEngine::randomEngine(const randomEngine& other) : engine(0)
-    {
-        if (this != &other) {
-            AF_THROW(af_retain_random_engine(&engine, other.get()));
-        }
-    }
+namespace af {
+randomEngine::randomEngine(randomEngineType type, unsigned long long seed)
+    : engine(0) {
+    AF_THROW(af_create_random_engine(&engine, type, seed));
+}
 
-    randomEngine::randomEngine(af_random_engine handle) : engine(handle)
-    {
+randomEngine::randomEngine(const randomEngine &other) : engine(0) {
+    if (this != &other) {
+        AF_THROW(af_retain_random_engine(&engine, other.get()));
     }
+}
 
-    randomEngine::~randomEngine()
-    {
-        if (engine) {
-            af_release_random_engine(engine);
-        }
-    }
+randomEngine::randomEngine(af_random_engine handle) : engine(handle) {}
 
-    randomEngine& randomEngine::operator= (const randomEngine& other)
-    {
-        if (this != &other) {
-            AF_THROW(af_release_random_engine(engine));
-            AF_THROW(af_retain_random_engine(&engine, other.get()));
-        }
-        return *this;
-    }
+randomEngine::~randomEngine() {
+    if (engine) { af_release_random_engine(engine); }
+}
 
-    randomEngineType randomEngine::getType(void)
-    {
-        af_random_engine_type type;
-        AF_THROW(af_random_engine_get_type(&type, engine));
-        return type;
+randomEngine &randomEngine::operator=(const randomEngine &other) {
+    if (this != &other) {
+        AF_THROW(af_release_random_engine(engine));
+        AF_THROW(af_retain_random_engine(&engine, other.get()));
     }
+    return *this;
+}
 
-    void randomEngine::setType(const randomEngineType type)
-    {
-        AF_THROW(af_random_engine_set_type(&engine, type));
-    }
+randomEngineType randomEngine::getType(void) {
+    af_random_engine_type type;
+    AF_THROW(af_random_engine_get_type(&type, engine));
+    return type;
+}
 
-    void randomEngine::setSeed(const unsigned long long seed)
-    {
-        AF_THROW(af_random_engine_set_seed(&engine, seed));
-    }
+void randomEngine::setType(const randomEngineType type) {
+    AF_THROW(af_random_engine_set_type(&engine, type));
+}
 
-    unsigned long long randomEngine::getSeed(void) const
-    {
-        unsigned long long seed;
-        AF_THROW(af_random_engine_get_seed(&seed, engine));
-        return seed;
-    }
+void randomEngine::setSeed(const unsigned long long seed) {
+    AF_THROW(af_random_engine_set_seed(&engine, seed));
+}
 
-    af_random_engine randomEngine::get(void) const
-    {
-        return engine;
-    }
+unsigned long long randomEngine::getSeed(void) const {
+    unsigned long long seed;
+    AF_THROW(af_random_engine_get_seed(&seed, engine));
+    return seed;
+}
 
-    array randu(const dim4 &dims, const dtype ty, randomEngine &r)
-    {
-        af_array out;
-        AF_THROW(af_random_uniform(&out, dims.ndims(), dims.get(), ty, r.get()));
-        return array(out);
-    }
+af_random_engine randomEngine::get(void) const { return engine; }
 
-    array randn(const dim4 &dims, const dtype ty, randomEngine &r)
-    {
-        af_array out;
-        AF_THROW(af_random_normal(&out, dims.ndims(), dims.get(), ty, r.get()));
-        return array(out);
-    }
+array randu(const dim4 &dims, const dtype ty, randomEngine &r) {
+    af_array out;
+    AF_THROW(af_random_uniform(&out, dims.ndims(), dims.get(), ty, r.get()));
+    return array(out);
+}
 
-    array randu(const dim4 &dims, const af::dtype type)
-    {
-        af_array res;
-        AF_THROW(af_randu(&res, dims.ndims(), dims.get(), type));
-        return array(res);
-    }
+array randn(const dim4 &dims, const dtype ty, randomEngine &r) {
+    af_array out;
+    AF_THROW(af_random_normal(&out, dims.ndims(), dims.get(), ty, r.get()));
+    return array(out);
+}
 
-    array randu(const dim_t d0, const af::dtype ty)
-    {
-        return randu(dim4(d0), ty);
-    }
+array randu(const dim4 &dims, const af::dtype type) {
+    af_array res;
+    AF_THROW(af_randu(&res, dims.ndims(), dims.get(), type));
+    return array(res);
+}
 
-    array randu(const dim_t d0,
-                const dim_t d1, const af::dtype ty)
-    {
-        return randu(dim4(d0, d1), ty);
-    }
+array randu(const dim_t d0, const af::dtype ty) { return randu(dim4(d0), ty); }
 
-    array randu(const dim_t d0,
-                const dim_t d1, const dim_t d2, const af::dtype ty)
-    {
-        return randu(dim4(d0, d1, d2), ty);
-    }
+array randu(const dim_t d0, const dim_t d1, const af::dtype ty) {
+    return randu(dim4(d0, d1), ty);
+}
 
-    array randu(const dim_t d0,
-                const dim_t d1, const dim_t d2,
-                const dim_t d3, const af::dtype ty)
-    {
-        return randu(dim4(d0, d1, d2, d3), ty);
-    }
+array randu(const dim_t d0, const dim_t d1, const dim_t d2,
+            const af::dtype ty) {
+    return randu(dim4(d0, d1, d2), ty);
+}
 
-    array randn(const dim4 &dims, const af::dtype type)
-    {
-        af_array res;
-        AF_THROW(af_randn(&res, dims.ndims(), dims.get(), type));
-        return array(res);
-    }
+array randu(const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3,
+            const af::dtype ty) {
+    return randu(dim4(d0, d1, d2, d3), ty);
+}
 
-    array randn(const dim_t d0, const af::dtype ty)
-    {
-        return randn(dim4(d0), ty);
-    }
+array randn(const dim4 &dims, const af::dtype type) {
+    af_array res;
+    AF_THROW(af_randn(&res, dims.ndims(), dims.get(), type));
+    return array(res);
+}
 
-    array randn(const dim_t d0,
-                const dim_t d1, const af::dtype ty)
-    {
-        return randn(dim4(d0, d1), ty);
-    }
+array randn(const dim_t d0, const af::dtype ty) { return randn(dim4(d0), ty); }
 
-    array randn(const dim_t d0,
-                const dim_t d1, const dim_t d2, const af::dtype ty)
-    {
-        return randn(dim4(d0, d1, d2), ty);
-    }
+array randn(const dim_t d0, const dim_t d1, const af::dtype ty) {
+    return randn(dim4(d0, d1), ty);
+}
 
-    array randn(const dim_t d0,
-                const dim_t d1, const dim_t d2,
-                const dim_t d3, const af::dtype ty)
-    {
-        return randn(dim4(d0, d1, d2, d3), ty);
-    }
+array randn(const dim_t d0, const dim_t d1, const dim_t d2,
+            const af::dtype ty) {
+    return randn(dim4(d0, d1, d2), ty);
+}
 
-    void setDefaultRandomEngineType(randomEngineType rtype)
-    {
-        AF_THROW(af_set_default_random_engine_type(rtype));
-    }
+array randn(const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3,
+            const af::dtype ty) {
+    return randn(dim4(d0, d1, d2, d3), ty);
+}
 
-    randomEngine getDefaultRandomEngine(void)
-    {
-        af_random_engine internal_handle = 0;
-        af_random_engine handle = 0;
-        AF_THROW(af_get_default_random_engine(&internal_handle));
-        AF_THROW(af_retain_random_engine(&handle, internal_handle));
-        return randomEngine(handle);
-    }
+void setDefaultRandomEngineType(randomEngineType rtype) {
+    AF_THROW(af_set_default_random_engine_type(rtype));
+}
 
-    void setSeed(const unsigned long long seed)
-    {
-        AF_THROW(af_set_seed(seed));
-    }
+randomEngine getDefaultRandomEngine(void) {
+    af_random_engine internal_handle = 0;
+    af_random_engine handle          = 0;
+    AF_THROW(af_get_default_random_engine(&internal_handle));
+    AF_THROW(af_retain_random_engine(&handle, internal_handle));
+    return randomEngine(handle);
+}
 
-    unsigned long long getSeed()
-    {
-        unsigned long long seed = 0;
-        AF_THROW(af_get_seed(&seed));
-        return seed;
-    }
+void setSeed(const unsigned long long seed) { AF_THROW(af_set_seed(seed)); }
 
+unsigned long long getSeed() {
+    unsigned long long seed = 0;
+    AF_THROW(af_get_seed(&seed));
+    return seed;
 }
+
+}  // namespace af
diff --git a/src/api/cpp/reduce.cpp b/src/api/cpp/reduce.cpp
index 18c12ee63d..a7c3a91a02 100644
--- a/src/api/cpp/reduce.cpp
+++ b/src/api/cpp/reduce.cpp
@@ -7,174 +7,152 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include <af/compatible.h>
-#include "error.hpp"
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
-    array sum(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_sum(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+namespace af {
+array sum(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_sum(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
 
-    array sum(const array &in, const int dim, const double nanval)
-    {
-        af_array out = 0;
-        AF_THROW(af_sum_nan(&out, in.get(), dim, nanval));
-        return array(out);
-    }
+array sum(const array &in, const int dim, const double nanval) {
+    af_array out = 0;
+    AF_THROW(af_sum_nan(&out, in.get(), dim, nanval));
+    return array(out);
+}
 
-    array product(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_product(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+array product(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_product(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
 
-    array product(const array &in, const int dim, const double nanval)
-    {
-        af_array out = 0;
-        AF_THROW(af_product_nan(&out, in.get(), dim, nanval));
-        return array(out);
-    }
+array product(const array &in, const int dim, const double nanval) {
+    af_array out = 0;
+    AF_THROW(af_product_nan(&out, in.get(), dim, nanval));
+    return array(out);
+}
 
-    array mul(const array &in, const int dim)
-    {
-        return product(in, dim);
-    }
+array mul(const array &in, const int dim) { return product(in, dim); }
 
-    array min(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_min(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+array min(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_min(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
 
-    array max(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_max(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+array max(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_max(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
 
-    // 2.1 compatibility
-    array alltrue(const array &in, const int dim) { return allTrue(in, dim); }
-    array allTrue(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_all_true(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+// 2.1 compatibility
+array alltrue(const array &in, const int dim) { return allTrue(in, dim); }
+array allTrue(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_all_true(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
 
-    // 2.1 compatibility
-    array anytrue(const array &in, const int dim) { return anyTrue(in, dim); }
-    array anyTrue(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_any_true(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+// 2.1 compatibility
+array anytrue(const array &in, const int dim) { return anyTrue(in, dim); }
+array anyTrue(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_any_true(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
 
-    array count(const array &in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_count(&out, in.get(), getFNSD(dim, in.dims())));
-        return array(out);
-    }
+array count(const array &in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_count(&out, in.get(), getFNSD(dim, in.dims())));
+    return array(out);
+}
+
+void min(array &val, array &idx, const array &in, const int dim) {
+    af_array out = 0;
+    af_array loc = 0;
+    AF_THROW(af_imin(&out, &loc, in.get(), getFNSD(dim, in.dims())));
+    val = array(out);
+    idx = array(loc);
+}
+
+void max(array &val, array &idx, const array &in, const int dim) {
+    af_array out = 0;
+    af_array loc = 0;
+    AF_THROW(af_imax(&out, &loc, in.get(), getFNSD(dim, in.dims())));
+    val = array(out);
+    idx = array(loc);
+}
 
-    void min(array &val, array &idx, const array &in, const int dim)
-    {
-        af_array out = 0;
-        af_array loc = 0;
-        AF_THROW(af_imin(&out, &loc, in.get(), getFNSD(dim, in.dims())));
-        val = array(out);
-        idx = array(loc);
+#define INSTANTIATE(fnC, fnCPP)                      \
+    INSTANTIATE_REAL(fnC, fnCPP, float)              \
+    INSTANTIATE_REAL(fnC, fnCPP, double)             \
+    INSTANTIATE_REAL(fnC, fnCPP, int)                \
+    INSTANTIATE_REAL(fnC, fnCPP, unsigned)           \
+    INSTANTIATE_REAL(fnC, fnCPP, long)               \
+    INSTANTIATE_REAL(fnC, fnCPP, unsigned long)      \
+    INSTANTIATE_REAL(fnC, fnCPP, long long)          \
+    INSTANTIATE_REAL(fnC, fnCPP, unsigned long long) \
+    INSTANTIATE_REAL(fnC, fnCPP, short)              \
+    INSTANTIATE_REAL(fnC, fnCPP, unsigned short)     \
+    INSTANTIATE_REAL(fnC, fnCPP, char)               \
+    INSTANTIATE_REAL(fnC, fnCPP, unsigned char)      \
+    INSTANTIATE_CPLX(fnC, fnCPP, af_cfloat, float)   \
+    INSTANTIATE_CPLX(fnC, fnCPP, af_cdouble, double)
+
+#define INSTANTIATE_REAL(fnC, fnCPP, T)                   \
+    template<>                                            \
+    AFAPI T fnCPP(const array &in) {                      \
+        double rval, ival;                                \
+        AF_THROW(af_##fnC##_all(&rval, &ival, in.get())); \
+        return (T)(rval);                                 \
     }
 
-    void max(array &val, array &idx, const array &in, const int dim)
-    {
-        af_array out = 0;
-        af_array loc = 0;
-        AF_THROW(af_imax(&out, &loc, in.get(), getFNSD(dim, in.dims())));
-        val = array(out);
-        idx = array(loc);
+#define INSTANTIATE_CPLX(fnC, fnCPP, T, Tr)               \
+    template<>                                            \
+    AFAPI T fnCPP(const array &in) {                      \
+        double rval, ival;                                \
+        AF_THROW(af_##fnC##_all(&rval, &ival, in.get())); \
+        T out((Tr)rval, (Tr)ival);                        \
+        return out;                                       \
     }
 
+INSTANTIATE(sum, sum)
+INSTANTIATE(product, product)
+INSTANTIATE(min, min)
+INSTANTIATE(max, max)
+INSTANTIATE(all_true, allTrue)
+INSTANTIATE(any_true, anyTrue)
+INSTANTIATE(count, count)
 
-#define INSTANTIATE(fnC, fnCPP)                         \
-    INSTANTIATE_REAL(fnC, fnCPP, float)                 \
-    INSTANTIATE_REAL(fnC, fnCPP, double)                \
-    INSTANTIATE_REAL(fnC, fnCPP, int)                   \
-    INSTANTIATE_REAL(fnC, fnCPP, unsigned)              \
-    INSTANTIATE_REAL(fnC, fnCPP, long)                  \
-    INSTANTIATE_REAL(fnC, fnCPP, unsigned long)         \
-    INSTANTIATE_REAL(fnC, fnCPP, long long)             \
-    INSTANTIATE_REAL(fnC, fnCPP, unsigned long long)    \
-    INSTANTIATE_REAL(fnC, fnCPP, short)                 \
-    INSTANTIATE_REAL(fnC, fnCPP, unsigned short)        \
-    INSTANTIATE_REAL(fnC, fnCPP, char)                  \
-    INSTANTIATE_REAL(fnC, fnCPP, unsigned char)         \
-    INSTANTIATE_CPLX(fnC, fnCPP, af_cfloat, float)      \
-    INSTANTIATE_CPLX(fnC, fnCPP, af_cdouble, double)    \
-
-#define INSTANTIATE_REAL(fnC, fnCPP, T)                     \
-    template<> AFAPI                                        \
-    T fnCPP(const array &in)                                \
-    {                                                       \
-        double rval, ival;                                  \
-        AF_THROW(af_##fnC##_all(&rval, &ival, in.get()));   \
-        return (T)(rval);                                   \
-    }                                                       \
-
-
-#define INSTANTIATE_CPLX(fnC, fnCPP, T, Tr)                 \
-    template<> AFAPI                                        \
-    T fnCPP(const array &in)                                \
-    {                                                       \
-        double rval, ival;                                  \
-        AF_THROW(af_##fnC##_all(&rval, &ival, in.get()));   \
-        T out((Tr)rval, (Tr)ival);                          \
-        return out;                                         \
-    }                                                       \
-
-    INSTANTIATE(sum, sum)
-    INSTANTIATE(product, product)
-    INSTANTIATE(min, min)
-    INSTANTIATE(max, max)
-    INSTANTIATE(all_true, allTrue)
-    INSTANTIATE(any_true, anyTrue)
-    INSTANTIATE(count, count)
-
-    INSTANTIATE_REAL(all_true, allTrue, bool);
-    INSTANTIATE_REAL(any_true, anyTrue, bool);
+INSTANTIATE_REAL(all_true, allTrue, bool);
+INSTANTIATE_REAL(any_true, anyTrue, bool);
 
 #undef INSTANTIATE_REAL
 #undef INSTANTIATE_CPLX
 
-#define INSTANTIATE_REAL(fnC, fnCPP, T)                             \
-    template<> AFAPI                                                \
-    T fnCPP(const array &in, const double nanval)                   \
-    {                                                               \
-        double rval, ival;                                          \
-        AF_THROW(af_##fnC##_all(&rval, &ival, in.get(), nanval));   \
-        return (T)(rval);                                           \
-    }                                                               \
-
-
-#define INSTANTIATE_CPLX(fnC, fnCPP, T, Tr)                         \
-    template<> AFAPI                                                \
-    T fnCPP(const array &in, const double nanval)                   \
-    {                                                               \
-        double rval, ival;                                          \
-        AF_THROW(af_##fnC##_all(&rval, &ival, in.get(), nanval));   \
-        T out((Tr)rval, (Tr)ival);                                  \
-        return out;                                                 \
-    }                                                               \
+#define INSTANTIATE_REAL(fnC, fnCPP, T)                           \
+    template<>                                                    \
+    AFAPI T fnCPP(const array &in, const double nanval) {         \
+        double rval, ival;                                        \
+        AF_THROW(af_##fnC##_all(&rval, &ival, in.get(), nanval)); \
+        return (T)(rval);                                         \
+    }
+
+#define INSTANTIATE_CPLX(fnC, fnCPP, T, Tr)                       \
+    template<>                                                    \
+    AFAPI T fnCPP(const array &in, const double nanval) {         \
+        double rval, ival;                                        \
+        AF_THROW(af_##fnC##_all(&rval, &ival, in.get(), nanval)); \
+        T out((Tr)rval, (Tr)ival);                                \
+        return out;                                               \
+    }
 
 INSTANTIATE(sum_nan, sum)
 INSTANTIATE(product_nan, product)
@@ -183,70 +161,66 @@ INSTANTIATE(product_nan, product)
 #undef INSTANTIATE_CPLX
 #undef INSTANTIATE
 
-#define INSTANTIATE_COMPAT(fnCPP, fnCompat, T)  \
-    template<> AFAPI                            \
-    T fnCompat(const array &in)                 \
-    {                                           \
-        return fnCPP<T>(in);                    \
+#define INSTANTIATE_COMPAT(fnCPP, fnCompat, T) \
+    template<>                                 \
+    AFAPI T fnCompat(const array &in) {        \
+        return fnCPP<T>(in);                   \
     }
 
-#define INSTANTIATE(fnCPP, fnCompat)                            \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, float)                  \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, double)                 \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, int)                    \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned)               \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, long)                   \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned long)          \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, long long)              \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned long long)     \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, char)                   \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned char)          \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, af_cfloat)              \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, af_cdouble)             \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, short)                  \
-    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned short)         \
-
-    INSTANTIATE(product, mul)
-    INSTANTIATE(allTrue, alltrue)
-    INSTANTIATE(anyTrue, anytrue)
-
-    INSTANTIATE_COMPAT(allTrue, alltrue, bool)
-    INSTANTIATE_COMPAT(anyTrue, anytrue, bool)
+#define INSTANTIATE(fnCPP, fnCompat)                        \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, float)              \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, double)             \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, int)                \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned)           \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, long)               \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned long)      \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, long long)          \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned long long) \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, char)               \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned char)      \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, af_cfloat)          \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, af_cdouble)         \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, short)              \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned short)
+
+INSTANTIATE(product, mul)
+INSTANTIATE(allTrue, alltrue)
+INSTANTIATE(anyTrue, anytrue)
+
+INSTANTIATE_COMPAT(allTrue, alltrue, bool)
+INSTANTIATE_COMPAT(anyTrue, anytrue, bool)
 
 #undef INSTANTIATE
 #undef INSTANTIATE_COMPAT
 
-#define INSTANTIATE_REAL(fn, T)                                 \
-    template<> AFAPI                                            \
-    void fn(T *val, unsigned *idx, const array &in)             \
-    {                                                           \
-        double rval, ival;                                      \
-        AF_THROW(af_i##fn##_all(&rval, &ival, idx, in.get()));  \
-        *val = (T)(rval);                                       \
-    }                                                           \
-
-
-#define INSTANTIATE_CPLX(fn, T, Tr)                             \
-    template<> AFAPI                                            \
-    void fn(T *val, unsigned *idx, const array &in)             \
-    {                                                           \
-        double rval, ival;                                      \
-        AF_THROW(af_i##fn##_all(&rval, &ival, idx, in.get()));  \
-        *val = T((Tr)rval, (Tr)ival);                           \
-    }                                                           \
-
-#define INSTANTIATE(fn)                         \
-    INSTANTIATE_REAL(fn, float)                 \
-    INSTANTIATE_REAL(fn, double)                \
-    INSTANTIATE_REAL(fn, int)                   \
-    INSTANTIATE_REAL(fn, unsigned)              \
-    INSTANTIATE_REAL(fn, char)                  \
-    INSTANTIATE_REAL(fn, unsigned char)         \
-    INSTANTIATE_REAL(fn, short)                 \
-    INSTANTIATE_REAL(fn, unsigned short)        \
-    INSTANTIATE_CPLX(fn, af_cfloat, float)      \
-    INSTANTIATE_CPLX(fn, af_cdouble, double)    \
-
-    INSTANTIATE(min)
-    INSTANTIATE(max)
-}
+#define INSTANTIATE_REAL(fn, T)                                \
+    template<>                                                 \
+    AFAPI void fn(T *val, unsigned *idx, const array &in) {    \
+        double rval, ival;                                     \
+        AF_THROW(af_i##fn##_all(&rval, &ival, idx, in.get())); \
+        *val = (T)(rval);                                      \
+    }
+
+#define INSTANTIATE_CPLX(fn, T, Tr)                            \
+    template<>                                                 \
+    AFAPI void fn(T *val, unsigned *idx, const array &in) {    \
+        double rval, ival;                                     \
+        AF_THROW(af_i##fn##_all(&rval, &ival, idx, in.get())); \
+        *val = T((Tr)rval, (Tr)ival);                          \
+    }
+
+#define INSTANTIATE(fn)                    \
+    INSTANTIATE_REAL(fn, float)            \
+    INSTANTIATE_REAL(fn, double)           \
+    INSTANTIATE_REAL(fn, int)              \
+    INSTANTIATE_REAL(fn, unsigned)         \
+    INSTANTIATE_REAL(fn, char)             \
+    INSTANTIATE_REAL(fn, unsigned char)    \
+    INSTANTIATE_REAL(fn, short)            \
+    INSTANTIATE_REAL(fn, unsigned short)   \
+    INSTANTIATE_CPLX(fn, af_cfloat, float) \
+    INSTANTIATE_CPLX(fn, af_cdouble, double)
+
+INSTANTIATE(min)
+INSTANTIATE(max)
+}  // namespace af
diff --git a/src/api/cpp/regions.cpp b/src/api/cpp/regions.cpp
index 73d40557e7..dce5319297 100644
--- a/src/api/cpp/regions.cpp
+++ b/src/api/cpp/regions.cpp
@@ -7,18 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array regions(const array& in, const af::connectivity connectivity, const af::dtype type)
-{
+array regions(const array& in, const af::connectivity connectivity,
+              const af::dtype type) {
     af_array temp = 0;
     AF_THROW(af_regions(&temp, in.get(), connectivity, type));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/resize.cpp b/src/api/cpp/resize.cpp
index e0e4f680e6..4bb1723437 100644
--- a/src/api/cpp/resize.cpp
+++ b/src/api/cpp/resize.cpp
@@ -7,32 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array resize(const array &in, const dim_t odim0, const dim_t odim1, const interpType method)
-{
+array resize(const array &in, const dim_t odim0, const dim_t odim1,
+             const interpType method) {
     af_array out = 0;
     AF_THROW(af_resize(&out, in.get(), odim0, odim1, method));
     return array(out);
 }
 
-array resize(const float scale0, const float scale1, const array &in, const interpType method)
-{
+array resize(const float scale0, const float scale1, const array &in,
+             const interpType method) {
     af_array out = 0;
-    AF_THROW(af_resize(&out, in.get(), in.dims(0) * scale0, in.dims(1) * scale1, method));
+    AF_THROW(af_resize(&out, in.get(), in.dims(0) * scale0, in.dims(1) * scale1,
+                       method));
     return array(out);
 }
 
-array resize(const float scale, const array &in, const interpType method)
-{
+array resize(const float scale, const array &in, const interpType method) {
     af_array out = 0;
-    AF_THROW(af_resize(&out, in.get(), in.dims(0) * scale, in.dims(1) * scale, method));
+    AF_THROW(af_resize(&out, in.get(), in.dims(0) * scale, in.dims(1) * scale,
+                       method));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/rgb_gray.cpp b/src/api/cpp/rgb_gray.cpp
index 0395228d20..995db9b225 100644
--- a/src/api/cpp/rgb_gray.cpp
+++ b/src/api/cpp/rgb_gray.cpp
@@ -7,25 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array rgb2gray(const array& in, const float rPercent, const float gPercent, const float bPercent)
-{
+array rgb2gray(const array& in, const float rPercent, const float gPercent,
+               const float bPercent) {
     af_array temp = 0;
     AF_THROW(af_rgb2gray(&temp, in.get(), rPercent, gPercent, bPercent));
     return array(temp);
 }
 
-array gray2rgb(const array& in, const float rFactor, const float gFactor, const float bFactor)
-{
+array gray2rgb(const array& in, const float rFactor, const float gFactor,
+               const float bFactor) {
     af_array temp = 0;
     AF_THROW(af_gray2rgb(&temp, in.get(), rFactor, gFactor, bFactor));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/rotate.cpp b/src/api/cpp/rotate.cpp
index ed42f69a2a..fb4b96f4b8 100644
--- a/src/api/cpp/rotate.cpp
+++ b/src/api/cpp/rotate.cpp
@@ -7,18 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array rotate(const array& in, const float theta, const bool crop, const interpType method)
-{
+array rotate(const array& in, const float theta, const bool crop,
+             const interpType method) {
     af_array out = 0;
     AF_THROW(af_rotate(&out, in.get(), theta, crop, method));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/sat.cpp b/src/api/cpp/sat.cpp
index b06c0a8dc6..f0dfe641f7 100644
--- a/src/api/cpp/sat.cpp
+++ b/src/api/cpp/sat.cpp
@@ -7,18 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array sat(const array& in)
-{
+array sat(const array& in) {
     af_array out = 0;
     AF_THROW(af_sat(&out, in.get()));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/scale.cpp b/src/api/cpp/scale.cpp
index a41aa0509f..fb59e1b95b 100644
--- a/src/api/cpp/scale.cpp
+++ b/src/api/cpp/scale.cpp
@@ -7,18 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array scale(const array& in, const float scale0, const float scale1, const dim_t odim0, const dim_t odim1, const interpType method)
-{
+array scale(const array& in, const float scale0, const float scale1,
+            const dim_t odim0, const dim_t odim1, const interpType method) {
     af_array out = 0;
     AF_THROW(af_scale(&out, in.get(), scale0, scale1, odim0, odim1, method));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/scan.cpp b/src/api/cpp/scan.cpp
index 0adf255041..840f23942b 100644
--- a/src/api/cpp/scan.cpp
+++ b/src/api/cpp/scan.cpp
@@ -7,30 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include "error.hpp"
 
-namespace af
-{
-    array accum(const array& in, const int dim)
-    {
-        af_array out = 0;
-        AF_THROW(af_accum(&out, in.get(), dim));
-        return array(out);
-    }
+namespace af {
+array accum(const array& in, const int dim) {
+    af_array out = 0;
+    AF_THROW(af_accum(&out, in.get(), dim));
+    return array(out);
+}
 
-    array scan(const array& in, const int dim, binaryOp op, bool inclusive_scan)
-    {
-        af_array out = 0;
-        AF_THROW(af_scan(&out, in.get(), dim, op, inclusive_scan));
-        return array(out);
-    }
+array scan(const array& in, const int dim, binaryOp op, bool inclusive_scan) {
+    af_array out = 0;
+    AF_THROW(af_scan(&out, in.get(), dim, op, inclusive_scan));
+    return array(out);
+}
 
-    array scanByKey(const array& key, const array& in, const int dim, binaryOp op, bool inclusive_scan)
-    {
-        af_array out = 0;
-        AF_THROW(af_scan_by_key(&out, key.get(), in.get(), dim, op, inclusive_scan));
-        return array(out);
-    }
+array scanByKey(const array& key, const array& in, const int dim, binaryOp op,
+                bool inclusive_scan) {
+    af_array out = 0;
+    AF_THROW(
+        af_scan_by_key(&out, key.get(), in.get(), dim, op, inclusive_scan));
+    return array(out);
 }
+}  // namespace af
diff --git a/src/api/cpp/seq.cpp b/src/api/cpp/seq.cpp
index c198ff9033..8a17759ef4 100644
--- a/src/api/cpp/seq.cpp
+++ b/src/api/cpp/seq.cpp
@@ -7,22 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/seq.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/seq.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 int end = -1;
 seq span(af_span);
 
-void seq::init(double begin, double end, double step)
-{
+void seq::init(double begin, double end, double step) {
     this->s.begin = begin;
     this->s.end   = end;
     this->s.step  = step;
-    if(step != 0) {       // Not Span
+    if (step != 0) {  // Not Span
         size = fabs((end - begin) / step) + 1;
     } else {
         size = 0;
@@ -31,19 +29,15 @@ void seq::init(double begin, double end, double step)
 
 #ifndef signbit
 // wtf windows?!
-inline int signbit(double x)
-{
+inline int signbit(double x) {
     if (x < 0) return -1;
-    return  0;
+    return 0;
 }
 #endif
 
-seq::~seq()
-{
-}
+seq::~seq() {}
 
-seq::seq(double n): m_gfor(false)
-{
+seq::seq(double n) : m_gfor(false) {
     if (n < 0) {
         init(0, n, 1);
     } else {
@@ -51,43 +45,35 @@ seq::seq(double n): m_gfor(false)
     }
 }
 
-seq::seq(const af_seq& s_): m_gfor(false)
-{
-    init(s_.begin, s_.end, s_.step);
-}
+seq::seq(const af_seq& s_) : m_gfor(false) { init(s_.begin, s_.end, s_.step); }
 
-seq& seq::operator=(const af_seq& s_)
-{
+seq& seq::operator=(const af_seq& s_) {
     init(s_.begin, s_.end, s_.step);
     return *this;
 }
 
-seq::seq(double begin, double end, double step): m_gfor(false)
-{
+seq::seq(double begin, double end, double step) : m_gfor(false) {
     if (step == 0) {
-        if (begin != end)   // Span
+        if (begin != end)  // Span
             AF_THROW_ERR("Invalid step size", AF_ERR_ARG);
     }
-    if ((signbit(end      ) == signbit(begin)) &&
-        (signbit(end-begin) != signbit(step )))
+    if ((signbit(end) == signbit(begin)) &&
+        (signbit(end - begin) != signbit(step)))
         AF_THROW_ERR("Sequence is invalid", AF_ERR_ARG);
     init(begin, end, step);
 }
 
 seq::seq(seq other, bool is_gfor)
-    : s(other.s),
-      size(other.size),
-      m_gfor(is_gfor)
-{ }
+    : s(other.s), size(other.size), m_gfor(is_gfor) {}
 
-seq::operator array() const
-{
+seq::operator array() const {
     double diff = s.end - s.begin;
-    dim_t len = (int)((diff + fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) / s.step);
+    dim_t len =
+        (int)((diff + fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) / s.step);
 
     array tmp = (m_gfor) ? range(1, 1, 1, len, 3) : range(len);
 
     array res = s.begin + s.step * tmp;
     return res;
 }
-}
+}  // namespace af
diff --git a/src/api/cpp/set.cpp b/src/api/cpp/set.cpp
index 31d8bb0738..b1a23fa1e4 100644
--- a/src/api/cpp/set.cpp
+++ b/src/api/cpp/set.cpp
@@ -12,43 +12,38 @@
 #include <af/compatible.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array setunique(const array &in, const bool is_sorted)
-{
+array setunique(const array &in, const bool is_sorted) {
     return setUnique(in, is_sorted);
 }
 
-array setUnique(const array &in, const bool is_sorted)
-{
+array setUnique(const array &in, const bool is_sorted) {
     af_array out = 0;
     AF_THROW(af_set_unique(&out, in.get(), is_sorted));
     return array(out);
 }
 
-array setunion(const array &first, const array &second, const bool is_unique)
-{
+array setunion(const array &first, const array &second, const bool is_unique) {
     return setUnion(first, second, is_unique);
 }
 
-array setUnion(const array &first, const array &second, const bool is_unique)
-{
+array setUnion(const array &first, const array &second, const bool is_unique) {
     af_array out = 0;
     AF_THROW(af_set_union(&out, first.get(), second.get(), is_unique));
     return array(out);
 }
 
-array setintersect(const array &first, const array &second, const bool is_unique)
-{
+array setintersect(const array &first, const array &second,
+                   const bool is_unique) {
     return setIntersect(first, second, is_unique);
 }
 
-array setIntersect(const array &first, const array &second, const bool is_unique)
-{
+array setIntersect(const array &first, const array &second,
+                   const bool is_unique) {
     af_array out = 0;
     AF_THROW(af_set_intersect(&out, first.get(), second.get(), is_unique));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/sift.cpp b/src/api/cpp/sift.cpp
index 8ae3ac6812..decc6851ff 100644
--- a/src/api/cpp/sift.cpp
+++ b/src/api/cpp/sift.cpp
@@ -7,23 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void sift(features& feat, array& desc, const array& in,
-          const unsigned n_layers, const float contrast_thr,
-          const float edge_thr, const float init_sigma,
-          const bool double_input, const float img_scale,
-          const float feature_ratio)
-{
+void sift(features& feat, array& desc, const array& in, const unsigned n_layers,
+          const float contrast_thr, const float edge_thr,
+          const float init_sigma, const bool double_input,
+          const float img_scale, const float feature_ratio) {
     af_features temp_feat;
     af_array temp_desc = 0;
     AF_THROW(af_sift(&temp_feat, &temp_desc, in.get(), n_layers, contrast_thr,
-                     edge_thr, init_sigma, double_input, img_scale, feature_ratio));
+                     edge_thr, init_sigma, double_input, img_scale,
+                     feature_ratio));
 
     dim_t num = 0;
     AF_THROW(af_get_features_num(&num, temp_feat));
@@ -31,16 +29,15 @@ void sift(features& feat, array& desc, const array& in,
     desc = array(temp_desc);
 }
 
-void gloh(features& feat, array& desc, const array& in,
-          const unsigned n_layers, const float contrast_thr,
-          const float edge_thr, const float init_sigma,
-          const bool double_input, const float img_scale,
-          const float feature_ratio)
-{
+void gloh(features& feat, array& desc, const array& in, const unsigned n_layers,
+          const float contrast_thr, const float edge_thr,
+          const float init_sigma, const bool double_input,
+          const float img_scale, const float feature_ratio) {
     af_features temp_feat;
     af_array temp_desc = 0;
     AF_THROW(af_gloh(&temp_feat, &temp_desc, in.get(), n_layers, contrast_thr,
-                     edge_thr, init_sigma, double_input, img_scale, feature_ratio));
+                     edge_thr, init_sigma, double_input, img_scale,
+                     feature_ratio));
 
     dim_t num = 0;
     AF_THROW(af_get_features_num(&num, temp_feat));
@@ -48,4 +45,4 @@ void gloh(features& feat, array& desc, const array& in,
     desc = array(temp_desc);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/skew.cpp b/src/api/cpp/skew.cpp
index 3913fbd6da..dd2e67edcc 100644
--- a/src/api/cpp/skew.cpp
+++ b/src/api/cpp/skew.cpp
@@ -7,18 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array skew(const array& in, const float skew0, const float skew1, const dim_t odim0, const dim_t odim1, const bool inverse, const interpType method)
-{
+array skew(const array& in, const float skew0, const float skew1,
+           const dim_t odim0, const dim_t odim1, const bool inverse,
+           const interpType method) {
     af_array out = 0;
-    AF_THROW(af_skew(&out, in.get(), skew0, skew1, odim0, odim1, method, inverse));
+    AF_THROW(
+        af_skew(&out, in.get(), skew0, skew1, odim0, odim1, method, inverse));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/sobel.cpp b/src/api/cpp/sobel.cpp
index ddeee47518..5ec491af7d 100644
--- a/src/api/cpp/sobel.cpp
+++ b/src/api/cpp/sobel.cpp
@@ -7,16 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/arith.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-void sobel(array &dx, array &dy, const array &img, const unsigned ker_size)
-{
+void sobel(array &dx, array &dy, const array &img, const unsigned ker_size) {
     af_array af_dx = 0;
     af_array af_dy = 0;
     AF_THROW(af_sobel_operator(&af_dx, &af_dy, img.get(), ker_size));
@@ -24,16 +22,15 @@ void sobel(array &dx, array &dy, const array &img, const unsigned ker_size)
     dy = array(af_dy);
 }
 
-array sobel(const array &img, const unsigned ker_size, const bool isFast)
-{
+array sobel(const array &img, const unsigned ker_size, const bool isFast) {
     array dx;
     array dy;
     sobel(dx, dy, img, ker_size);
     if (isFast) {
-        return abs(dx)+abs(dy);
+        return abs(dx) + abs(dy);
     } else {
-        return sqrt(dx*dx+dy*dy);
+        return sqrt(dx * dx + dy * dy);
     }
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/sort.cpp b/src/api/cpp/sort.cpp
index fcd4cfb612..64a851fe5c 100644
--- a/src/api/cpp/sort.cpp
+++ b/src/api/cpp/sort.cpp
@@ -7,33 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include "error.hpp"
 
-namespace af
-{
-    array sort(const array& in, const unsigned dim, const bool isAscending)
-    {
-        af_array out = 0;
-        AF_THROW(af_sort(&out, in.get(), dim, isAscending));
-        return array(out);
-    }
+namespace af {
+array sort(const array &in, const unsigned dim, const bool isAscending) {
+    af_array out = 0;
+    AF_THROW(af_sort(&out, in.get(), dim, isAscending));
+    return array(out);
+}
 
-    void sort(array &out, array &indices, const array& in, const unsigned dim, const bool isAscending)
-    {
-        af_array out_, indices_;
-        AF_THROW(af_sort_index(&out_, &indices_, in.get(), dim, isAscending));
-        out = array(out_);
-        indices = array(indices_);
-    }
+void sort(array &out, array &indices, const array &in, const unsigned dim,
+          const bool isAscending) {
+    af_array out_, indices_;
+    AF_THROW(af_sort_index(&out_, &indices_, in.get(), dim, isAscending));
+    out     = array(out_);
+    indices = array(indices_);
+}
 
-    void sort(array &out_keys, array &out_values, const array &keys, const array &values,
-              const unsigned dim, const bool isAscending)
-    {
-        af_array okeys, ovalues;
-        AF_THROW(af_sort_by_key(&okeys, &ovalues, keys.get(), values.get(), dim, isAscending));
-        out_keys = array(okeys);
-        out_values = array(ovalues);
-    }
+void sort(array &out_keys, array &out_values, const array &keys,
+          const array &values, const unsigned dim, const bool isAscending) {
+    af_array okeys, ovalues;
+    AF_THROW(af_sort_by_key(&okeys, &ovalues, keys.get(), values.get(), dim,
+                            isAscending));
+    out_keys   = array(okeys);
+    out_values = array(ovalues);
 }
+}  // namespace af
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index ab5a0b8158..1f9cabea4f 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -11,97 +11,83 @@
 #include <af/sparse.h>
 #include "error.hpp"
 
-namespace af
-{
-    array sparse(const dim_t nRows, const dim_t nCols,
-                 const array values, const array rowIdx, const array colIdx,
-                 const af::storage stype)
-    {
-        af_array out = 0;
-        AF_THROW(af_create_sparse_array(&out, nRows, nCols,
-                            values.get(), rowIdx.get(), colIdx.get(), stype));
-        return array(out);
-    }
-
-    array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-                 const void * const values,
-                 const int * const rowIdx, const int * const colIdx,
-                 const dtype type, const af::storage stype,
-                 const af::source src)
-    {
-        af_array out = 0;
-        AF_THROW(af_create_sparse_array_from_ptr(&out, nRows, nCols, nNZ,
-                            values, rowIdx, colIdx, type, stype, src));
-        return array(out);
-    }
+namespace af {
+array sparse(const dim_t nRows, const dim_t nCols, const array values,
+             const array rowIdx, const array colIdx, const af::storage stype) {
+    af_array out = 0;
+    AF_THROW(af_create_sparse_array(&out, nRows, nCols, values.get(),
+                                    rowIdx.get(), colIdx.get(), stype));
+    return array(out);
+}
 
+array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+             const void *const values, const int *const rowIdx,
+             const int *const colIdx, const dtype type, const af::storage stype,
+             const af::source src) {
+    af_array out = 0;
+    AF_THROW(af_create_sparse_array_from_ptr(&out, nRows, nCols, nNZ, values,
+                                             rowIdx, colIdx, type, stype, src));
+    return array(out);
+}
 
-    array sparse(const array dense, const af::storage stype)
-    {
-        af_array out = 0;
-        AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), stype));
-        return array(out);
-    }
+array sparse(const array dense, const af::storage stype) {
+    af_array out = 0;
+    AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), stype));
+    return array(out);
+}
 
-    array sparseConvertTo(const array in, const af::storage stype)
-    {
-        af_array out = 0;
-        AF_THROW(af_sparse_convert_to(&out, in.get(), stype));
-        return array(out);
-    }
+array sparseConvertTo(const array in, const af::storage stype) {
+    af_array out = 0;
+    AF_THROW(af_sparse_convert_to(&out, in.get(), stype));
+    return array(out);
+}
 
-    array dense(const array sparse)
-    {
-        af_array out = 0;
-        AF_THROW(af_sparse_to_dense(&out, sparse.get()));
-        return array(out);
-    }
+array dense(const array sparse) {
+    af_array out = 0;
+    AF_THROW(af_sparse_to_dense(&out, sparse.get()));
+    return array(out);
+}
 
-    void sparseGetInfo(array &values, array &rowIdx, array &colIdx, storage &stype,
-                       const array in)
-    {
-        af_array values_ = 0, rowIdx_ = 0, colIdx_ = 0;
-        af_storage stype_ = AF_STORAGE_DENSE;
-        AF_THROW(af_sparse_get_info(&values_, &rowIdx_, &colIdx_, &stype_, in.get()));
-        values = array(values_);
-        rowIdx = array(rowIdx_);
-        colIdx = array(colIdx_);
-        stype  = stype_;
-        return;
-    }
+void sparseGetInfo(array &values, array &rowIdx, array &colIdx, storage &stype,
+                   const array in) {
+    af_array values_ = 0, rowIdx_ = 0, colIdx_ = 0;
+    af_storage stype_ = AF_STORAGE_DENSE;
+    AF_THROW(
+        af_sparse_get_info(&values_, &rowIdx_, &colIdx_, &stype_, in.get()));
+    values = array(values_);
+    rowIdx = array(rowIdx_);
+    colIdx = array(colIdx_);
+    stype  = stype_;
+    return;
+}
 
-    array sparseGetValues(const array in)
-    {
-        af_array out = 0;
-        AF_THROW(af_sparse_get_values(&out, in.get()));
-        return array(out);
-    }
+array sparseGetValues(const array in) {
+    af_array out = 0;
+    AF_THROW(af_sparse_get_values(&out, in.get()));
+    return array(out);
+}
 
-    array sparseGetRowIdx(const array in)
-    {
-        af_array out = 0;
-        AF_THROW(af_sparse_get_row_idx(&out, in.get()));
-        return array(out);
-    }
+array sparseGetRowIdx(const array in) {
+    af_array out = 0;
+    AF_THROW(af_sparse_get_row_idx(&out, in.get()));
+    return array(out);
+}
 
-    array sparseGetColIdx(const array in)
-    {
-        af_array out = 0;
-        AF_THROW(af_sparse_get_col_idx(&out, in.get()));
-        return array(out);
-    }
+array sparseGetColIdx(const array in) {
+    af_array out = 0;
+    AF_THROW(af_sparse_get_col_idx(&out, in.get()));
+    return array(out);
+}
 
-    dim_t sparseGetNNZ(const array in)
-    {
-        dim_t out = 0;
-        AF_THROW(af_sparse_get_nnz(&out, in.get()));
-        return out;
-    }
+dim_t sparseGetNNZ(const array in) {
+    dim_t out = 0;
+    AF_THROW(af_sparse_get_nnz(&out, in.get()));
+    return out;
+}
 
-    af::storage sparseGetStorage(const array in)
-    {
-        af::storage out;
-        AF_THROW(af_sparse_get_storage(&out, in.get()));
-        return out;
-    }
+af::storage sparseGetStorage(const array in) {
+    af::storage out;
+    AF_THROW(af_sparse_get_storage(&out, in.get()));
+    return out;
 }
+}  // namespace af
diff --git a/src/api/cpp/stdev.cpp b/src/api/cpp/stdev.cpp
index 4812267bfc..7c8c116987 100644
--- a/src/api/cpp/stdev.cpp
+++ b/src/api/cpp/stdev.cpp
@@ -7,32 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/statistics.h>
-#include <af/array.h>
-#include "error.hpp"
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
 #define INSTANTIATE_STDEV(T)                              \
-    template<> AFAPI T stdev(const array& in)             \
-    {                                                     \
+    template<>                                            \
+    AFAPI T stdev(const array& in) {                      \
         double ret_val;                                   \
         AF_THROW(af_stdev_all(&ret_val, NULL, in.get())); \
-        return (T) ret_val;                               \
-    }                                                     \
+        return (T)ret_val;                                \
+    }
 
-template<> AFAPI af_cfloat stdev(const array& in)
-{
+template<>
+AFAPI af_cfloat stdev(const array& in) {
     double real, imag;
     AF_THROW(af_stdev_all(&real, &imag, in.get()));
     return af_cfloat((float)real, (float)imag);
 }
 
-template<> AFAPI af_cdouble stdev(const array& in)
-{
+template<>
+AFAPI af_cdouble stdev(const array& in) {
     double real, imag;
     AF_THROW(af_stdev_all(&real, &imag, in.get()));
     return af_cdouble(real, imag);
@@ -51,11 +50,10 @@ INSTANTIATE_STDEV(unsigned char);
 
 #undef INSTANTIATE_STDEV
 
-array stdev(const array& in, const dim_t dim)
-{
+array stdev(const array& in, const dim_t dim) {
     af_array temp = 0;
     AF_THROW(af_stdev(&temp, in.get(), getFNSD(dim, in.dims())));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/susan.cpp b/src/api/cpp/susan.cpp
index 1711b7b87f..2d2df19884 100644
--- a/src/api/cpp/susan.cpp
+++ b/src/api/cpp/susan.cpp
@@ -7,19 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/vision.h>
 #include <af/array.h>
+#include <af/vision.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-features susan(const array& in, const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge)
-{
+features susan(const array& in, const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge) {
     af_features temp;
-    AF_THROW(af_susan(&temp, in.get(), radius, diff_thr, geom_thr, feature_ratio, edge));
+    AF_THROW(af_susan(&temp, in.get(), radius, diff_thr, geom_thr,
+                      feature_ratio, edge));
     return features(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/timing.cpp b/src/api/cpp/timing.cpp
index 9b26f94236..c42ad90c87 100644
--- a/src/api/cpp/timing.cpp
+++ b/src/api/cpp/timing.cpp
@@ -7,17 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/timing.h>
-#include <af/device.h>
 #include <math.h>
-#include <vector>
+#include <af/device.h>
+#include <af/timing.h>
 #include <algorithm>
+#include <vector>
 
 using namespace af;
 
 // get current time
-static inline timer time_now(void)
-{
+static inline timer time_now(void) {
 #if defined(OS_WIN)
     timer time;
     QueryPerformanceCounter(&time.val);
@@ -31,22 +30,22 @@ static inline timer time_now(void)
 }
 
 // absolute difference between two times (in seconds)
-static inline double time_seconds(timer start, timer end)
-{
+static inline double time_seconds(timer start, timer end) {
 #if defined(OS_WIN)
     if (start.val.QuadPart > end.val.QuadPart) {
         timer temp = end;
-        end = start;
-        start = temp;
+        end        = start;
+        start      = temp;
     }
     timer system_freq;
     QueryPerformanceFrequency(&system_freq.val);
-    return (double)(end.val.QuadPart - start.val.QuadPart) / system_freq.val.QuadPart;
+    return (double)(end.val.QuadPart - start.val.QuadPart) /
+           system_freq.val.QuadPart;
 #elif defined(OS_MAC)
     if (start.val > end.val) {
         timer temp = start;
-        start = end;
-        end   = temp;
+        start      = end;
+        end        = temp;
     }
     // calculate platform timing epoch
     thread_local mach_timebase_info_data_t info;
@@ -56,36 +55,25 @@ static inline double time_seconds(timer start, timer end)
 #elif defined(OS_LNX)
     struct timeval elapsed;
     timersub(&start.val, &end.val, &elapsed);
-    long sec = elapsed.tv_sec;
+    long sec  = elapsed.tv_sec;
     long usec = elapsed.tv_usec;
-    double t = sec + usec * 1e-6;
+    double t  = sec + usec * 1e-6;
     return t >= 0 ? t : -t;
 #endif
 }
 
-
 namespace af {
 
 thread_local timer _timer_;
 
-timer timer::start()
-{
-    return _timer_ = time_now();
-}
-double timer::stop(timer start)
-{
-    return time_seconds(start, time_now());
-}
-double timer::stop()
-{
-    return time_seconds(_timer_, time_now());
-}
+timer timer::start() { return _timer_ = time_now(); }
+double timer::stop(timer start) { return time_seconds(start, time_now()); }
+double timer::stop() { return time_seconds(_timer_, time_now()); }
 
-double timeit(void(*fn)())
-{
+double timeit(void (*fn)()) {
     // parameters
-    static const int    trials   = 10;  // trial runs
-    static const int    s_trials = 5;   // trial runs
+    static const int trials      = 10;  // trial runs
+    static const int s_trials    = 5;   // trial runs
     static const double min_time = 1;   // seconds
 
     std::vector<double> sample_times(s_trials);
@@ -110,17 +98,16 @@ double timeit(void(*fn)())
     //   then run (min time / (trials * median_time)) batches
     // else
     //   run 1 batch
-    int batches = (int)ceilf(min_time / (trials * median_time));
+    int batches     = (int)ceilf(min_time / (trials * median_time));
     double run_time = 0;
 
-    for(int b = 0; b < batches; b++) {
+    for (int b = 0; b < batches; b++) {
         timer start = timer::start();
-        for (int i = 0; i < trials; ++i)
-            fn();
+        for (int i = 0; i < trials; ++i) fn();
         sync();
         run_time += timer::stop(start) / trials;
     }
     return run_time / batches;
 }
 
-} // namespace af
+}  // namespace af
diff --git a/src/api/cpp/topk.cpp b/src/api/cpp/topk.cpp
index 676067ca1b..55ebbe42cd 100644
--- a/src/api/cpp/topk.cpp
+++ b/src/api/cpp/topk.cpp
@@ -7,17 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/statistics.h>
-#include <af/array.h>
-#include "error.hpp"
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
-void topk(array &values, array &indices, const array& in, const int k,
-          const int dim, const topkFunction order)
-{
+namespace af {
+void topk(array &values, array &indices, const array &in, const int k,
+          const int dim, const topkFunction order) {
     af_array af_vals = 0;
     af_array af_idxs = 0;
 
@@ -26,4 +24,4 @@ void topk(array &values, array &indices, const array& in, const int k,
     values  = array(af_vals);
     indices = array(af_idxs);
 }
-}
+}  // namespace af
diff --git a/src/api/cpp/transform.cpp b/src/api/cpp/transform.cpp
index d2f369fffa..550841fa52 100644
--- a/src/api/cpp/transform.cpp
+++ b/src/api/cpp/transform.cpp
@@ -7,18 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array transform(const array& in, const array& transform, const dim_t odim0, const dim_t odim1, const interpType method, const bool inverse)
-{
+array transform(const array& in, const array& transform, const dim_t odim0,
+                const dim_t odim1, const interpType method,
+                const bool inverse) {
     af_array out = 0;
-    AF_THROW(af_transform(&out, in.get(), transform.get(), odim0, odim1, method, inverse));
+    AF_THROW(af_transform(&out, in.get(), transform.get(), odim0, odim1, method,
+                          inverse));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/transform_coordinates.cpp b/src/api/cpp/transform_coordinates.cpp
index 4d896e7194..3e4bb5500c 100644
--- a/src/api/cpp/transform_coordinates.cpp
+++ b/src/api/cpp/transform_coordinates.cpp
@@ -7,18 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array transformCoordinates(const array& tf, const float d0, const float d1)
-{
+array transformCoordinates(const array& tf, const float d0, const float d1) {
     af_array out = 0;
     AF_THROW(af_transform_coordinates(&out, tf.get(), d0, d1));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/translate.cpp b/src/api/cpp/translate.cpp
index cfb79ef43b..de6908b735 100644
--- a/src/api/cpp/translate.cpp
+++ b/src/api/cpp/translate.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array translate(const array& in, const float trans0, const float trans1, const dim_t odim0, const dim_t odim1, const interpType method)
-{
+array translate(const array& in, const float trans0, const float trans1,
+                const dim_t odim0, const dim_t odim1, const interpType method) {
     af_array out = 0;
-    AF_THROW(af_translate(&out, in.get(), trans0, trans1, odim0, odim1, method));
+    AF_THROW(
+        af_translate(&out, in.get(), trans0, trans1, odim0, odim1, method));
     return array(out);
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/transpose.cpp b/src/api/cpp/transpose.cpp
index 3c4c87af1d..dc5905fa75 100644
--- a/src/api/cpp/transpose.cpp
+++ b/src/api/cpp/transpose.cpp
@@ -7,23 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/blas.h>
 #include <af/array.h>
+#include <af/blas.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array transpose(const array& in, const bool conjugate)
-{
+array transpose(const array& in, const bool conjugate) {
     af_array out = 0;
     AF_THROW(af_transpose(&out, in.get(), conjugate));
     return array(out);
 }
 
-void transposeInPlace(array& in, const bool conjugate)
-{
+void transposeInPlace(array& in, const bool conjugate) {
     AF_THROW(af_transpose_inplace(in.get(), conjugate));
 }
 
-}
+}  // namespace af
diff --git a/src/api/cpp/unary.cpp b/src/api/cpp/unary.cpp
index 4b4ab667bb..eea4b4d83e 100644
--- a/src/api/cpp/unary.cpp
+++ b/src/api/cpp/unary.cpp
@@ -7,88 +7,84 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
 #define af_complex(...) af_cplx(__VA_ARGS__)
 
-#define INSTANTIATE(func)                                           \
-    array func(const array &in)                                     \
-    {                                                               \
-        af_array out = 0;                                           \
-        AF_THROW(af_##func(&out, in.get()));                        \
-        return array(out);                                          \
-    }
-
-    INSTANTIATE(complex)
-    INSTANTIATE(real  )
-    INSTANTIATE(imag  )
-    INSTANTIATE(arg   )
-    INSTANTIATE(abs   )
-    INSTANTIATE(conjg )
-
-    INSTANTIATE(sign  )
-    INSTANTIATE(round )
-    INSTANTIATE(trunc )
-    INSTANTIATE(floor )
-    INSTANTIATE(ceil  )
-
-    INSTANTIATE(sin   )
-    INSTANTIATE(cos   )
-    INSTANTIATE(tan   )
-
-    INSTANTIATE(asin  )
-    INSTANTIATE(acos  )
-    INSTANTIATE(atan  )
-
-    INSTANTIATE(sinh  )
-    INSTANTIATE(cosh  )
-    INSTANTIATE(tanh  )
-
-    INSTANTIATE(asinh )
-    INSTANTIATE(acosh )
-    INSTANTIATE(atanh )
-
-    INSTANTIATE(pow2  )
-    INSTANTIATE(exp   )
-    INSTANTIATE(expm1 )
-    INSTANTIATE(erf   )
-    INSTANTIATE(erfc  )
-    INSTANTIATE(sigmoid)
-
-    INSTANTIATE(log   )
-    INSTANTIATE(log1p )
-    INSTANTIATE(log10 )
-    INSTANTIATE(log2  )
-
-    INSTANTIATE(sqrt  )
-    INSTANTIATE(cbrt  )
-
-    INSTANTIATE(iszero)
-
-    INSTANTIATE(factorial)
-    INSTANTIATE(tgamma)
-    INSTANTIATE(lgamma)
-
-    // isinf and isnan are defined by C++.
-    // Thus we need a difference nomenclature.
-    array isInf(const array &in)
-    {
-        af_array out = 0;
-        AF_THROW(af_isinf(&out, in.get()));
-        return array(out);
+#define INSTANTIATE(func)                    \
+    array func(const array &in) {            \
+        af_array out = 0;                    \
+        AF_THROW(af_##func(&out, in.get())); \
+        return array(out);                   \
     }
 
-    array isNaN(const array &in)
-    {
-        af_array out = 0;
-        AF_THROW(af_isnan(&out, in.get()));
-        return array(out);
-    }
+INSTANTIATE(complex)
+INSTANTIATE(real)
+INSTANTIATE(imag)
+INSTANTIATE(arg)
+INSTANTIATE(abs)
+INSTANTIATE(conjg)
+
+INSTANTIATE(sign)
+INSTANTIATE(round)
+INSTANTIATE(trunc)
+INSTANTIATE(floor)
+INSTANTIATE(ceil)
+
+INSTANTIATE(sin)
+INSTANTIATE(cos)
+INSTANTIATE(tan)
+
+INSTANTIATE(asin)
+INSTANTIATE(acos)
+INSTANTIATE(atan)
+
+INSTANTIATE(sinh)
+INSTANTIATE(cosh)
+INSTANTIATE(tanh)
+
+INSTANTIATE(asinh)
+INSTANTIATE(acosh)
+INSTANTIATE(atanh)
+
+INSTANTIATE(pow2)
+INSTANTIATE(exp)
+INSTANTIATE(expm1)
+INSTANTIATE(erf)
+INSTANTIATE(erfc)
+INSTANTIATE(sigmoid)
+
+INSTANTIATE(log)
+INSTANTIATE(log1p)
+INSTANTIATE(log10)
+INSTANTIATE(log2)
+
+INSTANTIATE(sqrt)
+INSTANTIATE(cbrt)
+
+INSTANTIATE(iszero)
+
+INSTANTIATE(factorial)
+INSTANTIATE(tgamma)
+INSTANTIATE(lgamma)
+
+// isinf and isnan are defined by C++.
+// Thus we need a difference nomenclature.
+array isInf(const array &in) {
+    af_array out = 0;
+    AF_THROW(af_isinf(&out, in.get()));
+    return array(out);
+}
 
+array isNaN(const array &in) {
+    af_array out = 0;
+    AF_THROW(af_isnan(&out, in.get()));
+    return array(out);
 }
+
+}  // namespace af
diff --git a/src/api/cpp/unwrap.cpp b/src/api/cpp/unwrap.cpp
index d48d3124be..f0dc7d9803 100644
--- a/src/api/cpp/unwrap.cpp
+++ b/src/api/cpp/unwrap.cpp
@@ -11,13 +11,12 @@
 #include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
-    array unwrap(const array& in, const dim_t wx, const dim_t wy,
-                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
-    {
-        af_array out = 0;
-        AF_THROW(af_unwrap(&out, in.get(), wx, wy, sx, sy, px, py, is_column));
-        return array(out);
-    }
+namespace af {
+array unwrap(const array& in, const dim_t wx, const dim_t wy, const dim_t sx,
+             const dim_t sy, const dim_t px, const dim_t py,
+             const bool is_column) {
+    af_array out = 0;
+    AF_THROW(af_unwrap(&out, in.get(), wx, wy, sx, sy, px, py, is_column));
+    return array(out);
 }
+}  // namespace af
diff --git a/src/api/cpp/util.cpp b/src/api/cpp/util.cpp
index b3fafad987..b265fed161 100644
--- a/src/api/cpp/util.cpp
+++ b/src/api/cpp/util.cpp
@@ -9,70 +9,63 @@
 
 #include <af/array.h>
 #include <af/util.h>
-#include "error.hpp"
 #include <cstdio>
+#include "error.hpp"
 
 using namespace std;
 
-namespace af
-{
-    void print(const char *exp, const array &arr)
-    {
-        AF_THROW(af_print_array_gen(exp, arr.get(), 4));
-        return;
-    }
+namespace af {
+void print(const char *exp, const array &arr) {
+    AF_THROW(af_print_array_gen(exp, arr.get(), 4));
+    return;
+}
 
-    void print(const char *exp, const array &arr, const int precision)
-    {
-        AF_THROW(af_print_array_gen(exp, arr.get(), precision));
-        return;
-    }
+void print(const char *exp, const array &arr, const int precision) {
+    AF_THROW(af_print_array_gen(exp, arr.get(), precision));
+    return;
+}
 
-    int saveArray(const char *key, const array &arr, const char *filename, const bool append)
-    {
-        int index = -1;
-        AF_THROW(af_save_array(&index, key, arr.get(), filename, append));
-        return index;
-    }
+int saveArray(const char *key, const array &arr, const char *filename,
+              const bool append) {
+    int index = -1;
+    AF_THROW(af_save_array(&index, key, arr.get(), filename, append));
+    return index;
+}
 
-    array readArray(const char *filename, const unsigned index)
-    {
-        af_array out = 0;
-        AF_THROW(af_read_array_index(&out, filename, index));
-        return array(out);
-    }
+array readArray(const char *filename, const unsigned index) {
+    af_array out = 0;
+    AF_THROW(af_read_array_index(&out, filename, index));
+    return array(out);
+}
 
-    array readArray(const char *filename, const char *key)
-    {
-        af_array out = 0;
-        AF_THROW(af_read_array_key(&out, filename, key));
-        return array(out);
-    }
+array readArray(const char *filename, const char *key) {
+    af_array out = 0;
+    AF_THROW(af_read_array_key(&out, filename, key));
+    return array(out);
+}
 
-    int readArrayCheck(const char *filename, const char *key)
-    {
-        int out = -1;
-        AF_THROW(af_read_array_key_check(&out, filename, key));
-        return out;
-    }
+int readArrayCheck(const char *filename, const char *key) {
+    int out = -1;
+    AF_THROW(af_read_array_key_check(&out, filename, key));
+    return out;
+}
 
-    void toString(char **output, const char *exp, const array &arr, const int precision, const bool transpose)
-    {
-        AF_THROW(af_array_to_string(output, exp, arr.get(), precision, transpose));
-        return;
-    }
+void toString(char **output, const char *exp, const array &arr,
+              const int precision, const bool transpose) {
+    AF_THROW(af_array_to_string(output, exp, arr.get(), precision, transpose));
+    return;
+}
 
-    const char* toString(const char *exp, const array &arr, const int precision, const bool transpose)
-    {
-        char *output = NULL;
-        AF_THROW(af_array_to_string(&output, exp, arr.get(), precision, transpose));
-        return output;
-    }
+const char *toString(const char *exp, const array &arr, const int precision,
+                     const bool transpose) {
+    char *output = NULL;
+    AF_THROW(af_array_to_string(&output, exp, arr.get(), precision, transpose));
+    return output;
+}
 
-    size_t getSizeOf(af::dtype type)
-    {
-        size_t size = 0;
-        AF_THROW(af_get_size_of(&size, type));
-        return size;
-    }
+size_t getSizeOf(af::dtype type) {
+    size_t size = 0;
+    AF_THROW(af_get_size_of(&size, type));
+    return size;
 }
+}  // namespace af
diff --git a/src/api/cpp/var.cpp b/src/api/cpp/var.cpp
index e5c778d269..413c25a40a 100644
--- a/src/api/cpp/var.cpp
+++ b/src/api/cpp/var.cpp
@@ -7,68 +7,66 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/statistics.h>
-#include <af/array.h>
-#include "error.hpp"
 #include "common.hpp"
+#include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array var(const array& in, const bool isbiased, const dim_t dim)
-{
+array var(const array& in, const bool isbiased, const dim_t dim) {
     af_array temp = 0;
     AF_THROW(af_var(&temp, in.get(), isbiased, getFNSD(dim, in.dims())));
     return array(temp);
 }
 
-array var(const array& in, const array &weights, const dim_t dim)
-{
+array var(const array& in, const array& weights, const dim_t dim) {
     af_array temp = 0;
-    AF_THROW(af_var_weighted(&temp, in.get(), weights.get(), getFNSD(dim, in.dims())));
+    AF_THROW(af_var_weighted(&temp, in.get(), weights.get(),
+                             getFNSD(dim, in.dims())));
     return array(temp);
 }
 
-#define INSTANTIATE_VAR(T)                                          \
-    template<> AFAPI T var(const array& in, const bool isbiased)    \
-    {                                                               \
-        double ret_val;                                             \
-        AF_THROW(af_var_all(&ret_val, NULL, in.get(), isbiased));   \
-        return (T) ret_val;                                         \
-    }                                                               \
-                                                                    \
-    template<> AFAPI T var(const array& in, const array &weights)   \
-    {                                                               \
-        double ret_val;                                             \
-        AF_THROW(af_var_all_weighted(&ret_val, NULL,                \
-                                     in.get(), weights.get()));     \
-        return (T) ret_val;                                         \
-    }                                                               \
+#define INSTANTIATE_VAR(T)                                                 \
+    template<>                                                             \
+    AFAPI T var(const array& in, const bool isbiased) {                    \
+        double ret_val;                                                    \
+        AF_THROW(af_var_all(&ret_val, NULL, in.get(), isbiased));          \
+        return (T)ret_val;                                                 \
+    }                                                                      \
+                                                                           \
+    template<>                                                             \
+    AFAPI T var(const array& in, const array& weights) {                   \
+        double ret_val;                                                    \
+        AF_THROW(                                                          \
+            af_var_all_weighted(&ret_val, NULL, in.get(), weights.get())); \
+        return (T)ret_val;                                                 \
+    }
 
-template<> AFAPI af_cfloat var(const array& in, const bool isbiased)
-{
+template<>
+AFAPI af_cfloat var(const array& in, const bool isbiased) {
     double real, imag;
     AF_THROW(af_var_all(&real, &imag, in.get(), isbiased));
     return af_cfloat((float)real, (float)imag);
 }
 
-template<> AFAPI af_cdouble var(const array& in, const bool isbiased)
-{
+template<>
+AFAPI af_cdouble var(const array& in, const bool isbiased) {
     double real, imag;
     AF_THROW(af_var_all(&real, &imag, in.get(), isbiased));
     return af_cdouble(real, imag);
 }
 
-template<> AFAPI af_cfloat var(const array& in, const array &weights)
-{
+template<>
+AFAPI af_cfloat var(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_var_all_weighted(&real, &imag, in.get(), weights.get()));
     return af_cfloat((float)real, (float)imag);
 }
 
-template<> AFAPI af_cdouble var(const array& in, const array &weights)
-{
+template<>
+AFAPI af_cdouble var(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_var_all_weighted(&real, &imag, in.get(), weights.get()));
     return af_cdouble(real, imag);
@@ -87,4 +85,4 @@ INSTANTIATE_VAR(unsigned char);
 
 #undef INSTANTIATE_VAR
 
-}
+}  // namespace af
diff --git a/src/api/cpp/where.cpp b/src/api/cpp/where.cpp
index fd9705998a..b68f0616c4 100644
--- a/src/api/cpp/where.cpp
+++ b/src/api/cpp/where.cpp
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include <af/gfor.h>
 #include "error.hpp"
 
-namespace af
-{
-    array where(const array& in)
-    {
-        if (gforGet()) {
-            AF_THROW_ERR("WHERE can not be used inside GFOR", AF_ERR_RUNTIME);
-        }
-
-        af_array out = 0;
-        AF_THROW(af_where(&out, in.get()));
-        return array(out);
+namespace af {
+array where(const array& in) {
+    if (gforGet()) {
+        AF_THROW_ERR("WHERE can not be used inside GFOR", AF_ERR_RUNTIME);
     }
+
+    af_array out = 0;
+    AF_THROW(af_where(&out, in.get()));
+    return array(out);
 }
+}  // namespace af
diff --git a/src/api/cpp/wrap.cpp b/src/api/cpp/wrap.cpp
index ee95f84116..62194ff186 100644
--- a/src/api/cpp/wrap.cpp
+++ b/src/api/cpp/wrap.cpp
@@ -11,17 +11,13 @@
 #include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
-    array wrap(const array& in,
-               const dim_t ox, const dim_t oy,
-               const dim_t wx, const dim_t wy,
-               const dim_t sx, const dim_t sy,
-               const dim_t px, const dim_t py,
-               const bool is_column)
-    {
-        af_array out = 0;
-        AF_THROW(af_wrap(&out, in.get(), ox, oy, wx, wy, sx, sy, px, py, is_column));
-        return array(out);
-    }
+namespace af {
+array wrap(const array& in, const dim_t ox, const dim_t oy, const dim_t wx,
+           const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
+           const dim_t py, const bool is_column) {
+    af_array out = 0;
+    AF_THROW(
+        af_wrap(&out, in.get(), ox, oy, wx, wy, sx, sy, px, py, is_column));
+    return array(out);
 }
+}  // namespace af
diff --git a/src/api/cpp/ycbcr_rgb.cpp b/src/api/cpp/ycbcr_rgb.cpp
index 2716613d87..59c6ff7879 100644
--- a/src/api/cpp/ycbcr_rgb.cpp
+++ b/src/api/cpp/ycbcr_rgb.cpp
@@ -7,25 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/image.h>
 #include <af/array.h>
+#include <af/image.h>
 #include "error.hpp"
 
-namespace af
-{
+namespace af {
 
-array ycbcr2rgb(const array& in, const YCCStd standard)
-{
+array ycbcr2rgb(const array& in, const YCCStd standard) {
     af_array temp = 0;
     AF_THROW(af_ycbcr2rgb(&temp, in.get(), standard));
     return array(temp);
 }
 
-array rgb2ycbcr(const array& in, const YCCStd standard)
-{
+array rgb2ycbcr(const array& in, const YCCStd standard) {
     af_array temp = 0;
     AF_THROW(af_rgb2ycbcr(&temp, in.get(), standard));
     return array(temp);
 }
 
-}
+}  // namespace af
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index fe41f316d8..d0c77d402d 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -7,16 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/algorithm.h>
+#include <af/array.h>
 #include "symbol_manager.hpp"
 
-#define ALGO_HAPI_DEF(af_func) \
-af_err af_func(af_array* out, const af_array in, const int dim) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(out, in, dim); \
-}
+#define ALGO_HAPI_DEF(af_func)                                        \
+    af_err af_func(af_array *out, const af_array in, const int dim) { \
+        CHECK_ARRAYS(in);                                             \
+        return CALL(out, in, dim);                                    \
+    }
 
 ALGO_HAPI_DEF(af_sum)
 ALGO_HAPI_DEF(af_product)
@@ -31,24 +30,23 @@ ALGO_HAPI_DEF(af_diff2)
 
 #undef ALGO_HAPI_DEF
 
-#define ALGO_HAPI_DEF(af_func_nan) \
-af_err af_func_nan(af_array* out, const af_array in, const int dim, const double nanval) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(out, in, dim, nanval); \
-}
+#define ALGO_HAPI_DEF(af_func_nan)                                      \
+    af_err af_func_nan(af_array *out, const af_array in, const int dim, \
+                       const double nanval) {                           \
+        CHECK_ARRAYS(in);                                               \
+        return CALL(out, in, dim, nanval);                              \
+    }
 
 ALGO_HAPI_DEF(af_sum_nan)
 ALGO_HAPI_DEF(af_product_nan)
 
 #undef ALGO_HAPI_DEF
 
-#define ALGO_HAPI_DEF(af_func_all) \
-af_err af_func_all(double *real, double *imag, const af_array in) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(real, imag, in);\
-}
+#define ALGO_HAPI_DEF(af_func_all)                                      \
+    af_err af_func_all(double *real, double *imag, const af_array in) { \
+        CHECK_ARRAYS(in);                                               \
+        return CALL(real, imag, in);                                    \
+    }
 
 ALGO_HAPI_DEF(af_sum_all)
 ALGO_HAPI_DEF(af_product_all)
@@ -60,101 +58,91 @@ ALGO_HAPI_DEF(af_count_all)
 
 #undef ALGO_HAPI_DEF
 
-#define ALGO_HAPI_DEF(af_func_nan_all) \
-af_err af_func_nan_all(double *real, double *imag, const af_array in, const double nanval) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(real, imag, in, nanval);\
-}
+#define ALGO_HAPI_DEF(af_func_nan_all)                                    \
+    af_err af_func_nan_all(double *real, double *imag, const af_array in, \
+                           const double nanval) {                         \
+        CHECK_ARRAYS(in);                                                 \
+        return CALL(real, imag, in, nanval);                              \
+    }
 
 ALGO_HAPI_DEF(af_sum_nan_all)
 ALGO_HAPI_DEF(af_product_nan_all)
 
 #undef ALGO_HAPI_DEF
 
-
-#define ALGO_HAPI_DEF(af_ifunc) \
-af_err af_ifunc(af_array* out, af_array *idx, const af_array in, const int dim) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(out, idx, in, dim); \
-}
+#define ALGO_HAPI_DEF(af_ifunc)                                      \
+    af_err af_ifunc(af_array *out, af_array *idx, const af_array in, \
+                    const int dim) {                                 \
+        CHECK_ARRAYS(in);                                            \
+        return CALL(out, idx, in, dim);                              \
+    }
 
 ALGO_HAPI_DEF(af_imin)
 ALGO_HAPI_DEF(af_imax)
 
 #undef ALGO_HAPI_DEF
 
-#define ALGO_HAPI_DEF(af_ifunc_all) \
-af_err af_ifunc_all(double *real, double *imag, unsigned *idx, const af_array in) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(real, imag, idx, in);\
-}
+#define ALGO_HAPI_DEF(af_ifunc_all)                                \
+    af_err af_ifunc_all(double *real, double *imag, unsigned *idx, \
+                        const af_array in) {                       \
+        CHECK_ARRAYS(in);                                          \
+        return CALL(real, imag, idx, in);                          \
+    }
 
 ALGO_HAPI_DEF(af_imin_all)
 ALGO_HAPI_DEF(af_imax_all)
 
 #undef ALGO_HAPI_DEF
 
-
-af_err af_where(af_array *idx, const af_array in)
-{
+af_err af_where(af_array *idx, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(idx, in);
 }
 
-af_err af_scan(af_array* out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
-{
+af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
+               bool inclusive_scan) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dim, op, inclusive_scan);
 }
 
-af_err af_scan_by_key(af_array* out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan)
-{
+af_err af_scan_by_key(af_array *out, const af_array key, const af_array in,
+                      const int dim, af_binary_op op, bool inclusive_scan) {
     CHECK_ARRAYS(in, key);
     return CALL(out, key, in, dim, op, inclusive_scan);
 }
 
-af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending)
-{
+af_err af_sort(af_array *out, const af_array in, const unsigned dim,
+               const bool isAscending) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dim, isAscending);
 }
 
 af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
-                     const unsigned dim, const bool isAscending)
-{
+                     const unsigned dim, const bool isAscending) {
     CHECK_ARRAYS(in);
     return CALL(out, indices, in, dim, isAscending);
 }
 
 af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                       const af_array keys, const af_array values,
-                      const unsigned dim, const bool isAscending)
-{
+                      const unsigned dim, const bool isAscending) {
     CHECK_ARRAYS(keys, values);
     return CALL(out_keys, out_values, keys, values, dim, isAscending);
 }
 
-af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted)
-{
+af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted) {
     CHECK_ARRAYS(in);
     return CALL(out, in, is_sorted);
 }
 
-af_err af_set_union(af_array *out,
-                    const af_array first, const af_array second,
-                    const bool is_unique)
-{
+af_err af_set_union(af_array *out, const af_array first, const af_array second,
+                    const bool is_unique) {
     CHECK_ARRAYS(first, second);
     return CALL(out, first, second, is_unique);
 }
 
-af_err af_set_intersect(af_array *out,
-                        const af_array first, const af_array second,
-                        const bool is_unique)
-{
+af_err af_set_intersect(af_array *out, const af_array first,
+                        const af_array second, const bool is_unique) {
     CHECK_ARRAYS(first, second);
     return CALL(out, first, second, is_unique);
 }
diff --git a/src/api/unified/arith.cpp b/src/api/unified/arith.cpp
index 846cbf2a5e..7158af2e33 100644
--- a/src/api/unified/arith.cpp
+++ b/src/api/unified/arith.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/array.h>
 #include <af/arith.h>
+#include <af/array.h>
 #include "symbol_manager.hpp"
 
-#define BINARY_HAPI_DEF(af_func) \
-af_err af_func(af_array* out, const af_array lhs, const af_array rhs, const bool batchMode) \
-{ \
-    CHECK_ARRAYS(lhs, rhs); \
-    return CALL(out, lhs, rhs, batchMode); \
-}
+#define BINARY_HAPI_DEF(af_func)                                          \
+    af_err af_func(af_array* out, const af_array lhs, const af_array rhs, \
+                   const bool batchMode) {                                \
+        CHECK_ARRAYS(lhs, rhs);                                           \
+        return CALL(out, lhs, rhs, batchMode);                            \
+    }
 
 BINARY_HAPI_DEF(af_add)
 BINARY_HAPI_DEF(af_mul)
@@ -45,18 +45,16 @@ BINARY_HAPI_DEF(af_bitshiftl)
 BINARY_HAPI_DEF(af_bitshiftr)
 BINARY_HAPI_DEF(af_hypot)
 
-af_err af_cast(af_array *out, const af_array in, const af_dtype type)
-{
+af_err af_cast(af_array* out, const af_array in, const af_dtype type) {
     CHECK_ARRAYS(in);
     return CALL(out, in, type);
 }
 
-#define UNARY_HAPI_DEF(af_func) \
-af_err af_func(af_array* out, const af_array in) \
-{ \
-    CHECK_ARRAYS(in); \
-    return CALL(out, in); \
-}
+#define UNARY_HAPI_DEF(af_func)                        \
+    af_err af_func(af_array* out, const af_array in) { \
+        CHECK_ARRAYS(in);                              \
+        return CALL(out, in);                          \
+    }
 
 UNARY_HAPI_DEF(af_abs)
 UNARY_HAPI_DEF(af_arg)
@@ -101,9 +99,8 @@ UNARY_HAPI_DEF(af_isinf)
 UNARY_HAPI_DEF(af_isnan)
 UNARY_HAPI_DEF(af_not)
 
-af_err af_clamp(af_array *out, const af_array in,
-                const af_array lo, const af_array hi, const bool batch)
-{
+af_err af_clamp(af_array* out, const af_array in, const af_array lo,
+                const af_array hi, const bool batch) {
     CHECK_ARRAYS(in, lo, hi);
     return CALL(out, in, lo, hi, batch);
 }
diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index 81a0324228..388b9319a2 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -11,37 +11,36 @@
 #include <af/backend.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_array(af_array *arr, const void * const data, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_create_array(af_array *arr, const void *const data,
+                       const unsigned ndims, const dim_t *const dims,
+                       const af_dtype type) {
     return CALL(arr, data, ndims, dims, type);
 }
 
-af_err af_create_handle(af_array *arr, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_create_handle(af_array *arr, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype type) {
     return CALL(arr, ndims, dims, type);
 }
 
-af_err af_copy_array(af_array *arr, const af_array in)
-{
+af_err af_copy_array(af_array *arr, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(arr, in);
 }
 
-af_err af_write_array(af_array arr, const void *data, const size_t bytes, af_source src)
-{
+af_err af_write_array(af_array arr, const void *data, const size_t bytes,
+                      af_source src) {
     CHECK_ARRAYS(arr);
     return CALL(arr, data, bytes, src);
 }
 
-af_err af_get_data_ptr(void *data, const af_array arr)
-{
+af_err af_get_data_ptr(void *data, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(data, arr);
 }
 
-af_err af_release_array(af_array arr)
-{
-    af_backend curr = unified::AFSymbolManager::getInstance().getActiveBackend();
+af_err af_release_array(af_array arr) {
+    af_backend curr =
+        unified::AFSymbolManager::getInstance().getActiveBackend();
     af_backend other = curr;
 
     af_err err = af_get_backend_id(&other, arr);
@@ -53,54 +52,47 @@ af_err af_release_array(af_array arr)
     return err;
 }
 
-af_err af_retain_array(af_array *out, const af_array in)
-{
+af_err af_retain_array(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_get_data_ref_count(int *use_count, const af_array in)
-{
+af_err af_get_data_ref_count(int *use_count, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(use_count, in);
 }
 
-af_err af_eval(af_array in)
-{
+af_err af_eval(af_array in) {
     CHECK_ARRAYS(in);
     return CALL(in);
 }
 
-af_err af_get_elements(dim_t *elems, const af_array arr)
-{
+af_err af_get_elements(dim_t *elems, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(elems, arr);
 }
 
-af_err af_get_type(af_dtype *type, const af_array arr)
-{
+af_err af_get_type(af_dtype *type, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(type, arr);
 }
 
-af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3, const af_array arr)
-{
+af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
+                   const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(d0, d1, d2, d3, arr);
 }
 
-af_err af_get_numdims(unsigned *result, const af_array arr)
-{
+af_err af_get_numdims(unsigned *result, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(result, arr);
 }
 
-#define ARRAY_HAPI_DEF(af_func) \
-af_err af_func(bool *result, const af_array arr)\
-{\
-    CHECK_ARRAYS(arr); \
-    return CALL(result, arr);\
-}
+#define ARRAY_HAPI_DEF(af_func)                        \
+    af_err af_func(bool *result, const af_array arr) { \
+        CHECK_ARRAYS(arr);                             \
+        return CALL(result, arr);                      \
+    }
 
 ARRAY_HAPI_DEF(af_is_empty)
 ARRAY_HAPI_DEF(af_is_scalar)
@@ -117,8 +109,7 @@ ARRAY_HAPI_DEF(af_is_integer)
 ARRAY_HAPI_DEF(af_is_bool)
 ARRAY_HAPI_DEF(af_is_sparse)
 
-af_err af_get_scalar(void* output_value, const af_array arr)
-{
+af_err af_get_scalar(void *output_value, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(output_value, arr);
 }
diff --git a/src/api/unified/blas.cpp b/src/api/unified/blas.cpp
index 4bb5fced3d..4c8aa61e3f 100644
--- a/src/api/unified/blas.cpp
+++ b/src/api/unified/blas.cpp
@@ -10,39 +10,31 @@
 #include <af/blas.h>
 #include "symbol_manager.hpp"
 
-af_err af_matmul( af_array *out ,
-        const af_array lhs, const af_array rhs,
-        const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
+                 const af_mat_prop optLhs, const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
     return CALL(out, lhs, rhs, optLhs, optRhs);
 }
 
-
-af_err af_dot(af_array *out,
-        const af_array lhs, const af_array rhs,
-        const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
+              const af_mat_prop optLhs, const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
     return CALL(out, lhs, rhs, optLhs, optRhs);
 }
 
-af_err af_dot_all(double *rval, double *ival,
-        const af_array lhs, const af_array rhs,
-        const af_mat_prop optLhs, const af_mat_prop optRhs)
-{
+af_err af_dot_all(double *rval, double *ival, const af_array lhs,
+                  const af_array rhs, const af_mat_prop optLhs,
+                  const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
     return CALL(rval, ival, lhs, rhs, optLhs, optRhs);
 }
 
-af_err af_transpose(af_array *out, af_array in, const bool conjugate)
-{
+af_err af_transpose(af_array *out, af_array in, const bool conjugate) {
     CHECK_ARRAYS(in);
     return CALL(out, in, conjugate);
 }
 
-af_err af_transpose_inplace(af_array in, const bool conjugate)
-{
+af_err af_transpose_inplace(af_array in, const bool conjugate) {
     CHECK_ARRAYS(in);
     return CALL(in, conjugate);
 }
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index a87a691ded..df5b5accca 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -11,150 +11,133 @@
 #include <af/data.h>
 #include "symbol_manager.hpp"
 
-af_err af_constant(af_array *result, const double value,
-                   const unsigned ndims, const dim_t * const dims,
-                   const af_dtype type)
-{
+af_err af_constant(af_array *result, const double value, const unsigned ndims,
+                   const dim_t *const dims, const af_dtype type) {
     return CALL(result, value, ndims, dims, type);
 }
 
-
 af_err af_constant_complex(af_array *arr, const double real, const double imag,
-        const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+                           const unsigned ndims, const dim_t *const dims,
+                           const af_dtype type) {
     return CALL(arr, real, imag, ndims, dims, type);
 }
 
-
-af_err af_constant_long (af_array *arr, const long long val, const unsigned ndims, const dim_t * const dims)
-{
+af_err af_constant_long(af_array *arr, const long long val,
+                        const unsigned ndims, const dim_t *const dims) {
     return CALL(arr, val, ndims, dims);
 }
 
-
-af_err af_constant_ulong(af_array *arr, const unsigned long long val, const unsigned ndims, const dim_t * const dims)
-{
+af_err af_constant_ulong(af_array *arr, const unsigned long long val,
+                         const unsigned ndims, const dim_t *const dims) {
     return CALL(arr, val, ndims, dims);
 }
 
-af_err af_range(af_array *out, const unsigned ndims, const dim_t * const dims,
-        const int seq_dim, const af_dtype type)
-{
+af_err af_range(af_array *out, const unsigned ndims, const dim_t *const dims,
+                const int seq_dim, const af_dtype type) {
     return CALL(out, ndims, dims, seq_dim, type);
 }
 
-af_err af_iota(af_array *out, const unsigned ndims, const dim_t * const dims,
-        const unsigned t_ndims, const dim_t * const tdims, const af_dtype type)
-{
+af_err af_iota(af_array *out, const unsigned ndims, const dim_t *const dims,
+               const unsigned t_ndims, const dim_t *const tdims,
+               const af_dtype type) {
     return CALL(out, ndims, dims, t_ndims, tdims, type);
 }
 
-af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_identity(af_array *out, const unsigned ndims, const dim_t *const dims,
+                   const af_dtype type) {
     return CALL(out, ndims, dims, type);
 }
 
-af_err af_diag_create(af_array *out, const af_array in, const int num)
-{
+af_err af_diag_create(af_array *out, const af_array in, const int num) {
     CHECK_ARRAYS(in);
     return CALL(out, in, num);
 }
 
-af_err af_diag_extract(af_array *out, const af_array in, const int num)
-{
+af_err af_diag_extract(af_array *out, const af_array in, const int num) {
     CHECK_ARRAYS(in);
     return CALL(out, in, num);
 }
 
-af_err af_join(af_array *out, const int dim, const af_array first, const af_array second)
-{
+af_err af_join(af_array *out, const int dim, const af_array first,
+               const af_array second) {
     CHECK_ARRAYS(first, second);
     return CALL(out, dim, first, second);
 }
 
-af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs)
-{
-    for(unsigned i = 0; i < n_arrays; i++)
-        CHECK_ARRAYS(inputs[i]);
+af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
+                    const af_array *inputs) {
+    for (unsigned i = 0; i < n_arrays; i++) CHECK_ARRAYS(inputs[i]);
     return CALL(out, dim, n_arrays, inputs);
 }
 
-af_err af_tile(af_array *out, const af_array in,
-        const unsigned x, const unsigned y, const unsigned z, const unsigned w)
-{
+af_err af_tile(af_array *out, const af_array in, const unsigned x,
+               const unsigned y, const unsigned z, const unsigned w) {
     CHECK_ARRAYS(in);
     return CALL(out, in, x, y, z, w);
 }
 
-af_err af_reorder(af_array *out, const af_array in,
-        const unsigned x, const unsigned y, const unsigned z, const unsigned w)
-{
+af_err af_reorder(af_array *out, const af_array in, const unsigned x,
+                  const unsigned y, const unsigned z, const unsigned w) {
     CHECK_ARRAYS(in);
     return CALL(out, in, x, y, z, w);
 }
 
-af_err af_shift(af_array *out, const af_array in, const int x, const int y, const int z, const int w)
-{
+af_err af_shift(af_array *out, const af_array in, const int x, const int y,
+                const int z, const int w) {
     CHECK_ARRAYS(in);
     return CALL(out, in, x, y, z, w);
 }
 
-af_err af_moddims(af_array *out, const af_array in, const unsigned ndims, const dim_t * const dims)
-{
+af_err af_moddims(af_array *out, const af_array in, const unsigned ndims,
+                  const dim_t *const dims) {
     CHECK_ARRAYS(in);
     return CALL(out, in, ndims, dims);
 }
 
-af_err af_flat(af_array *out, const af_array in)
-{
+af_err af_flat(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_flip(af_array *out, const af_array in, const unsigned dim)
-{
+af_err af_flip(af_array *out, const af_array in, const unsigned dim) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dim);
 }
 
-af_err af_lower(af_array *out, const af_array in, bool is_unit_diag)
-{
+af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
     CHECK_ARRAYS(in);
     return CALL(out, in, is_unit_diag);
 }
 
-af_err af_upper(af_array *out, const af_array in, bool is_unit_diag)
-{
+af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
     CHECK_ARRAYS(in);
     return CALL(out, in, is_unit_diag);
 }
 
-af_err af_select(af_array *out, const af_array cond, const af_array a, const af_array b)
-{
+af_err af_select(af_array *out, const af_array cond, const af_array a,
+                 const af_array b) {
     CHECK_ARRAYS(cond, a, b);
     return CALL(out, cond, a, b);
 }
 
-af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a, const double b)
-{
+af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a,
+                          const double b) {
     CHECK_ARRAYS(cond, a);
     return CALL(out, cond, a, b);
 }
 
-af_err af_select_scalar_l(af_array *out, const af_array cond, const double a, const af_array b)
-{
+af_err af_select_scalar_l(af_array *out, const af_array cond, const double a,
+                          const af_array b) {
     CHECK_ARRAYS(cond, b);
     return CALL(out, cond, a, b);
 }
 
-af_err af_replace(af_array a, const af_array cond, const af_array b)
-{
+af_err af_replace(af_array a, const af_array cond, const af_array b) {
     CHECK_ARRAYS(a, cond, b);
     return CALL(a, cond, b);
 }
 
-af_err af_replace_scalar(af_array a, const af_array cond, const double b)
-{
+af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
     CHECK_ARRAYS(a, cond);
     return CALL(a, cond, b);
 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 096c430eed..b438770d51 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -7,206 +7,145 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/array.h>
 #include <af/backend.h>
 #include <af/device.h>
-#include <af/array.h>
 #include "symbol_manager.hpp"
 
-af_err af_set_backend(const af_backend bknd)
-{
+af_err af_set_backend(const af_backend bknd) {
     return unified::AFSymbolManager::getInstance().setBackend(bknd);
 }
 
-af_err af_get_backend_count(unsigned* num_backends)
-{
+af_err af_get_backend_count(unsigned *num_backends) {
     *num_backends = unified::AFSymbolManager::getInstance().getBackendCount();
     return AF_SUCCESS;
 }
 
-af_err af_get_available_backends(int* result)
-{
+af_err af_get_available_backends(int *result) {
     *result = unified::AFSymbolManager::getInstance().getAvailableBackends();
     return AF_SUCCESS;
 }
 
-af_err af_get_backend_id(af_backend *result, const af_array in)
-{
+af_err af_get_backend_id(af_backend *result, const af_array in) {
     // DO NOT CALL CHECK_ARRAYS HERE.
     // IT WILL RESULT IN AN INFINITE RECURSION
     return CALL(result, in);
 }
 
-af_err af_get_device_id(int *device, const af_array in)
-{
+af_err af_get_device_id(int *device, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(device, in);
 }
 
-af_err af_get_active_backend(af_backend *result)
-{
+af_err af_get_active_backend(af_backend *result) {
     *result = unified::AFSymbolManager::getInstance().getActiveBackend();
     return AF_SUCCESS;
 }
 
-af_err af_info()
-{
-    return CALL_NO_PARAMS();
-}
+af_err af_info() { return CALL_NO_PARAMS(); }
 
-af_err af_init()
-{
-    return CALL_NO_PARAMS();
-}
+af_err af_init() { return CALL_NO_PARAMS(); }
 
-af_err af_info_string(char **str, const bool verbose)
-{
+af_err af_info_string(char **str, const bool verbose) {
     return CALL(str, verbose);
 }
 
-af_err af_device_info(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-{
+af_err af_device_info(char *d_name, char *d_platform, char *d_toolkit,
+                      char *d_compute) {
     return CALL(d_name, d_platform, d_toolkit, d_compute);
 }
 
-af_err af_get_device_count(int *num_of_devices)
-{
-    return CALL(num_of_devices);
-}
+af_err af_get_device_count(int *num_of_devices) { return CALL(num_of_devices); }
 
-af_err af_get_dbl_support(bool* available, const int device)
-{
+af_err af_get_dbl_support(bool *available, const int device) {
     return CALL(available, device);
 }
 
-af_err af_set_device(const int device)
-{
-    return CALL(device);
-}
+af_err af_set_device(const int device) { return CALL(device); }
 
-af_err af_get_device(int *device)
-{
-    return CALL(device);
-}
+af_err af_get_device(int *device) { return CALL(device); }
 
-af_err af_sync(const int device)
-{
-    return CALL(device);
-}
+af_err af_sync(const int device) { return CALL(device); }
 
-af_err af_alloc_device(void **ptr, const dim_t bytes)
-{
+af_err af_alloc_device(void **ptr, const dim_t bytes) {
     return CALL(ptr, bytes);
 }
 
-af_err af_alloc_pinned(void **ptr, const dim_t bytes)
-{
+af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
     return CALL(ptr, bytes);
 }
 
-af_err af_free_device(void *ptr)
-{
-    return CALL(ptr);
-}
+af_err af_free_device(void *ptr) { return CALL(ptr); }
 
-af_err af_free_pinned(void *ptr)
-{
-    return CALL(ptr);
-}
+af_err af_free_pinned(void *ptr) { return CALL(ptr); }
 
-af_err af_alloc_host(void **ptr, const dim_t bytes)
-{
+af_err af_alloc_host(void **ptr, const dim_t bytes) {
     *ptr = malloc(bytes);
     return (*ptr == NULL) ? AF_ERR_NO_MEM : AF_SUCCESS;
 }
 
-af_err af_free_host(void *ptr)
-{
+af_err af_free_host(void *ptr) {
     free(ptr);
     return AF_SUCCESS;
 }
 
-af_err af_device_array(af_array *arr, const void *data, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_device_array(af_array *arr, const void *data, const unsigned ndims,
+                       const dim_t *const dims, const af_dtype type) {
     return CALL(arr, data, ndims, dims, type);
 }
 
 af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
-        size_t *lock_bytes, size_t *lock_buffers)
-{
+                          size_t *lock_bytes, size_t *lock_buffers) {
     return CALL(alloc_bytes, alloc_buffers, lock_bytes, lock_buffers);
 }
 
-af_err af_print_mem_info(const char *msg, const int device_id)
-{
+af_err af_print_mem_info(const char *msg, const int device_id) {
     return CALL(msg, device_id);
 }
 
-af_err af_device_gc()
-{
-    return CALL_NO_PARAMS();
-}
+af_err af_device_gc() { return CALL_NO_PARAMS(); }
 
-af_err af_set_mem_step_size(const size_t step_bytes)
-{
+af_err af_set_mem_step_size(const size_t step_bytes) {
     return CALL(step_bytes);
 }
 
-af_err af_get_mem_step_size(size_t *step_bytes)
-{
-    return CALL(step_bytes);
-}
+af_err af_get_mem_step_size(size_t *step_bytes) { return CALL(step_bytes); }
 
-af_err af_lock_device_ptr(const af_array arr)
-{
+af_err af_lock_device_ptr(const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(arr);
 }
 
-af_err af_unlock_device_ptr(const af_array arr)
-{
+af_err af_unlock_device_ptr(const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(arr);
 }
 
-af_err af_lock_array(const af_array arr)
-{
+af_err af_lock_array(const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(arr);
 }
 
-af_err af_unlock_array(const af_array arr)
-{
+af_err af_unlock_array(const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(arr);
 }
 
-af_err af_is_locked_array(bool *res, const af_array arr)
-{
+af_err af_is_locked_array(bool *res, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(res, arr);
 }
 
-af_err af_get_device_ptr(void **ptr, const af_array arr)
-{
+af_err af_get_device_ptr(void **ptr, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(ptr, arr);
 }
 
-af_err af_eval_multiple(const int num, af_array *arrays)
-{
-    for (int i = 0; i < num; i++) {
-        CHECK_ARRAYS(arrays[i]);
-    }
+af_err af_eval_multiple(const int num, af_array *arrays) {
+    for (int i = 0; i < num; i++) { CHECK_ARRAYS(arrays[i]); }
     return CALL(num, arrays);
 }
 
-af_err af_set_manual_eval_flag(bool flag)
-{
-    return CALL(flag);
-}
-
+af_err af_set_manual_eval_flag(bool flag) { return CALL(flag); }
 
-af_err af_get_manual_eval_flag(bool *flag)
-{
-    return CALL(flag);
-}
+af_err af_get_manual_eval_flag(bool *flag) { return CALL(flag); }
diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index 8fb04b21d1..23a90c4fb3 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -8,36 +8,34 @@
  ********************************************************/
 
 #include <af/array.h>
-#include <af/exception.h>
 #include <af/device.h>
+#include <af/exception.h>
 #include <algorithm>
 #include "symbol_manager.hpp"
 
-void af_get_last_error(char **str, dim_t *len)
-{
+void af_get_last_error(char **str, dim_t *len) {
     // Set error message from unified backend
     std::string &global_error_string = get_global_error_string();
     dim_t slen = std::min(MAX_ERR_SIZE, (int)global_error_string.size());
 
     // If this is true, the error is coming from the unified backend.
     if (slen != 0) {
-
         if (len && slen == 0) {
             *len = 0;
             *str = NULL;
             return;
         }
 
-        af_alloc_host((void**)str, sizeof(char) * (slen + 1));
+        af_alloc_host((void **)str, sizeof(char) * (slen + 1));
         global_error_string.copy(*str, slen);
 
-        (*str)[slen] = '\0';
+        (*str)[slen]        = '\0';
         global_error_string = std::string("");
 
         if (len) *len = slen;
     } else {
         // If false, the error is coming from active backend.
-        typedef void(*af_func)(char **, dim_t *);
+        typedef void (*af_func)(char **, dim_t *);
         af_func func = (af_func)LOAD_SYMBOL();
         func(str, len);
     }
diff --git a/src/api/unified/features.cpp b/src/api/unified/features.cpp
index 5eac8f72bb..98c8a3ca52 100644
--- a/src/api/unified/features.cpp
+++ b/src/api/unified/features.cpp
@@ -11,26 +11,22 @@
 #include <af/features.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_features(af_features *feat, dim_t num)
-{
+af_err af_create_features(af_features *feat, dim_t num) {
     return CALL(feat, num);
 }
 
-af_err af_retain_features(af_features *out, const af_features feat)
-{
+af_err af_retain_features(af_features *out, const af_features feat) {
     return CALL(out, feat);
 }
 
-af_err af_get_features_num(dim_t *num, const af_features feat)
-{
+af_err af_get_features_num(dim_t *num, const af_features feat) {
     return CALL(num, feat);
 }
 
-#define FEAT_HAPI_DEF(af_func)\
-af_err af_func(af_array *out, const af_features feat)\
-{\
-    return CALL(out, feat);\
-}
+#define FEAT_HAPI_DEF(af_func)                              \
+    af_err af_func(af_array *out, const af_features feat) { \
+        return CALL(out, feat);                             \
+    }
 
 FEAT_HAPI_DEF(af_get_features_xpos)
 FEAT_HAPI_DEF(af_get_features_ypos)
@@ -38,7 +34,4 @@ FEAT_HAPI_DEF(af_get_features_score)
 FEAT_HAPI_DEF(af_get_features_orientation)
 FEAT_HAPI_DEF(af_get_features_size)
 
-af_err af_release_features(af_features feat)
-{
-    return CALL(feat);
-}
+af_err af_release_features(af_features feat) { return CALL(feat); }
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 08f8d81d58..bb7afe7b6d 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -11,194 +11,175 @@
 #include <af/graphics.h>
 #include "symbol_manager.hpp"
 
-
-af_err af_create_window(af_window *out, const int width, const int height, const char* const title)
-{
+af_err af_create_window(af_window* out, const int width, const int height,
+                        const char* const title) {
     return CALL(out, width, height, title);
 }
 
-af_err af_set_position(const af_window wind, const unsigned x, const unsigned y)
-{
+af_err af_set_position(const af_window wind, const unsigned x,
+                       const unsigned y) {
     return CALL(wind, x, y);
 }
 
-af_err af_set_title(const af_window wind, const char* const title)
-{
+af_err af_set_title(const af_window wind, const char* const title) {
     return CALL(wind, title);
 }
 
-af_err af_set_size(const af_window wind, const unsigned w, const unsigned h)
-{
+af_err af_set_size(const af_window wind, const unsigned w, const unsigned h) {
     return CALL(wind, w, h);
 }
 
-af_err af_draw_image(const af_window wind, const af_array in, const af_cell* const props)
-{
+af_err af_draw_image(const af_window wind, const af_array in,
+                     const af_cell* const props) {
     CHECK_ARRAYS(in);
     return CALL(wind, in, props);
 }
 
-af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
-{
+af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y,
+                    const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
     return CALL(wind, X, Y, props);
 }
 
-af_err af_draw_plot3(const af_window wind, const af_array P, const af_cell* const props)
-{
+af_err af_draw_plot3(const af_window wind, const af_array P,
+                     const af_cell* const props) {
     CHECK_ARRAYS(P);
     return CALL(wind, P, props);
 }
 
-af_err af_draw_plot_nd(const af_window wind, const af_array in, const af_cell* const props)
-{
+af_err af_draw_plot_nd(const af_window wind, const af_array in,
+                       const af_cell* const props) {
     CHECK_ARRAYS(in);
     return CALL(wind, in, props);
 }
 
-af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y, const af_cell* const props)
-{
+af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
+                       const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
     return CALL(wind, X, Y, props);
 }
 
-af_err af_draw_plot_3d(const af_window wind, const af_array X, const af_array Y, const af_array Z,
-                       const af_cell* const props)
-{
+af_err af_draw_plot_3d(const af_window wind, const af_array X, const af_array Y,
+                       const af_array Z, const af_cell* const props) {
     CHECK_ARRAYS(X, Y, Z);
     return CALL(wind, X, Y, Z, props);
 }
 
-af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y, const af_marker_type marker, const af_cell* const props)
-{
+af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
+                       const af_marker_type marker,
+                       const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
     return CALL(wind, X, Y, marker, props);
 }
 
-af_err af_draw_scatter3(const af_window wind, const af_array P, const af_marker_type marker, const af_cell* const props)
-{
+af_err af_draw_scatter3(const af_window wind, const af_array P,
+                        const af_marker_type marker,
+                        const af_cell* const props) {
     CHECK_ARRAYS(P);
     return CALL(wind, P, marker, props);
 }
 
 af_err af_draw_scatter_nd(const af_window wind, const af_array in,
-                          const af_marker_type marker, const af_cell* const props)
-{
+                          const af_marker_type marker,
+                          const af_cell* const props) {
     CHECK_ARRAYS(in);
     return CALL(wind, in, marker, props);
 }
 
-af_err af_draw_scatter_2d(const af_window wind, const af_array X, const af_array Y,
-                          const af_marker_type marker, const af_cell* const props)
-{
+af_err af_draw_scatter_2d(const af_window wind, const af_array X,
+                          const af_array Y, const af_marker_type marker,
+                          const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
     return CALL(wind, X, Y, marker, props);
 }
 
-af_err af_draw_scatter_3d(const af_window wind,
-                          const af_array X, const af_array Y, const af_array Z,
-                          const af_marker_type marker, const af_cell* const props)
-{
+af_err af_draw_scatter_3d(const af_window wind, const af_array X,
+                          const af_array Y, const af_array Z,
+                          const af_marker_type marker,
+                          const af_cell* const props) {
     CHECK_ARRAYS(X, Y, Z);
     return CALL(wind, X, Y, Z, marker, props);
 }
 
-af_err af_draw_hist(const af_window wind, const af_array X, const double minval, const double maxval, const af_cell* const props)
-{
+af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
+                    const double maxval, const af_cell* const props) {
     CHECK_ARRAYS(X);
     return CALL(wind, X, minval, maxval, props);
 }
 
-af_err af_draw_surface(const af_window wind, const af_array xVals, const af_array yVals, const af_array S, const af_cell* const props)
-{
+af_err af_draw_surface(const af_window wind, const af_array xVals,
+                       const af_array yVals, const af_array S,
+                       const af_cell* const props) {
     CHECK_ARRAYS(xVals, yVals, S);
     return CALL(wind, xVals, yVals, S, props);
 }
 
-af_err af_draw_vector_field_nd(const af_window wind,
-                const af_array points, const af_array directions,
-                const af_cell* const props)
-{
+af_err af_draw_vector_field_nd(const af_window wind, const af_array points,
+                               const af_array directions,
+                               const af_cell* const props) {
     CHECK_ARRAYS(points, directions);
     return CALL(wind, points, directions, props);
 }
 
-af_err af_draw_vector_field_3d(
-                const af_window wind,
-                const af_array xPoints, const af_array yPoints, const af_array zPoints,
-                const af_array xDirs, const af_array yDirs, const af_array zDirs,
-                const af_cell* const props)
-{
+af_err af_draw_vector_field_3d(const af_window wind, const af_array xPoints,
+                               const af_array yPoints, const af_array zPoints,
+                               const af_array xDirs, const af_array yDirs,
+                               const af_array zDirs,
+                               const af_cell* const props) {
     CHECK_ARRAYS(xPoints, yPoints, zPoints, xDirs, yDirs, zDirs);
     return CALL(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
 }
 
-af_err af_draw_vector_field_2d(
-                const af_window wind,
-                const af_array xPoints, const af_array yPoints,
-                const af_array xDirs, const af_array yDirs,
-                const af_cell* const props)
-{
+af_err af_draw_vector_field_2d(const af_window wind, const af_array xPoints,
+                               const af_array yPoints, const af_array xDirs,
+                               const af_array yDirs,
+                               const af_cell* const props) {
     CHECK_ARRAYS(xPoints, yPoints, xDirs, yDirs);
     return CALL(wind, xPoints, yPoints, xDirs, yDirs, props);
 }
 
-af_err af_grid(const af_window wind, const int rows, const int cols)
-{
+af_err af_grid(const af_window wind, const int rows, const int cols) {
     return CALL(wind, rows, cols);
 }
 
-af_err af_set_axes_limits_compute(const af_window wind,
-                                  const af_array x, const af_array y, const af_array z,
-                                  const bool exact, const af_cell* const props)
-{
+af_err af_set_axes_limits_compute(const af_window wind, const af_array x,
+                                  const af_array y, const af_array z,
+                                  const bool exact,
+                                  const af_cell* const props) {
     CHECK_ARRAYS(x, y);
-    if(z) CHECK_ARRAYS(z);
+    if (z) CHECK_ARRAYS(z);
     return CALL(wind, x, y, z, exact, props);
 }
 
-af_err af_set_axes_limits_2d(const af_window wind,
-                             const float xmin, const float xmax,
-                             const float ymin, const float ymax,
-                             const bool exact, const af_cell* const props)
-{
+af_err af_set_axes_limits_2d(const af_window wind, const float xmin,
+                             const float xmax, const float ymin,
+                             const float ymax, const bool exact,
+                             const af_cell* const props) {
     return CALL(wind, xmin, xmax, ymin, ymax, exact, props);
 }
 
-af_err af_set_axes_limits_3d(const af_window wind,
-                             const float xmin, const float xmax,
-                             const float ymin, const float ymax,
-                             const float zmin, const float zmax,
-                             const bool exact, const af_cell* const props)
-{
+af_err af_set_axes_limits_3d(const af_window wind, const float xmin,
+                             const float xmax, const float ymin,
+                             const float ymax, const float zmin,
+                             const float zmax, const bool exact,
+                             const af_cell* const props) {
     return CALL(wind, xmin, xmax, ymin, ymax, zmin, zmax, exact, props);
 }
 
-af_err af_set_axes_titles(const af_window wind,
-                          const char * const xtitle,
-                          const char * const ytitle,
-                          const char * const ztitle,
-                          const af_cell* const props)
-{
+af_err af_set_axes_titles(const af_window wind, const char* const xtitle,
+                          const char* const ytitle, const char* const ztitle,
+                          const af_cell* const props) {
     return CALL(wind, xtitle, ytitle, ztitle, props);
 }
 
-af_err af_show(const af_window wind)
-{
-    return CALL(wind);
-}
+af_err af_show(const af_window wind) { return CALL(wind); }
 
-af_err af_is_window_closed(bool *out, const af_window wind)
-{
+af_err af_is_window_closed(bool* out, const af_window wind) {
     return CALL(out, wind);
 }
 
-af_err af_set_visibility(const af_window wind, const bool is_visible)
-{
+af_err af_set_visibility(const af_window wind, const bool is_visible) {
     return CALL(wind, is_visible);
 }
 
-af_err af_destroy_window(const af_window wind)
-{
-    return CALL(wind);
-}
+af_err af_destroy_window(const af_window wind) { return CALL(wind); }
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 0979b0610c..ade6308466 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -7,284 +7,251 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "symbol_manager.hpp"
 #include <af/array.h>
-#include <af/image.h>
 #include <af/defines.h>
+#include <af/image.h>
+#include "symbol_manager.hpp"
 
-af_err af_gradient(af_array *dx, af_array *dy, const af_array in)
-{
+af_err af_gradient(af_array *dx, af_array *dy, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(dx, dy, in);
 }
 
-af_err af_load_image(af_array *out, const char* filename, const bool isColor)
-{
+af_err af_load_image(af_array *out, const char *filename, const bool isColor) {
     return CALL(out, filename, isColor);
 }
 
-af_err af_save_image(const char* filename, const af_array in)
-{
+af_err af_save_image(const char *filename, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(filename, in);
 }
 
-af_err af_load_image_memory(af_array *out, const void* ptr)
-{
+af_err af_load_image_memory(af_array *out, const void *ptr) {
     return CALL(out, ptr);
 }
 
-af_err af_save_image_memory(void** ptr, const af_array in, const af_image_format format)
-{
+af_err af_save_image_memory(void **ptr, const af_array in,
+                            const af_image_format format) {
     CHECK_ARRAYS(in);
     return CALL(ptr, in, format);
 }
 
-af_err af_delete_image_memory(void* ptr)
-{
-    return CALL(ptr);
-}
+af_err af_delete_image_memory(void *ptr) { return CALL(ptr); }
 
-af_err af_load_image_native(af_array *out, const char* filename)
-{
+af_err af_load_image_native(af_array *out, const char *filename) {
     return CALL(out, filename);
 }
 
-af_err af_save_image_native(const char* filename, const af_array in)
-{
+af_err af_save_image_native(const char *filename, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(filename, in);
 }
 
-af_err af_is_image_io_available(bool *out)
-{
-    return CALL(out);
-}
+af_err af_is_image_io_available(bool *out) { return CALL(out); }
 
-af_err af_resize(af_array *out, const af_array in, const dim_t odim0, const dim_t odim1, const af_interp_type method)
-{
+af_err af_resize(af_array *out, const af_array in, const dim_t odim0,
+                 const dim_t odim1, const af_interp_type method) {
     CHECK_ARRAYS(in);
     return CALL(out, in, odim0, odim1, method);
 }
 
 af_err af_transform(af_array *out, const af_array in, const af_array transform,
-        const dim_t odim0, const dim_t odim1,
-        const af_interp_type method, const bool inverse)
-{
+                    const dim_t odim0, const dim_t odim1,
+                    const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(in, transform);
     return CALL(out, in, transform, odim0, odim1, method, inverse);
 }
 
 af_err af_transform_coordinates(af_array *out, const af_array tf,
-        const float d0, const float d1)
-{
+                                const float d0, const float d1) {
     CHECK_ARRAYS(tf);
     return CALL(out, tf, d0, d1);
 }
 
 af_err af_rotate(af_array *out, const af_array in, const float theta,
-        const bool crop, const af_interp_type method)
-{
+                 const bool crop, const af_interp_type method) {
     CHECK_ARRAYS(in);
     return CALL(out, in, theta, crop, method);
 }
 
-af_err af_translate(af_array *out, const af_array in, const float trans0, const float trans1,
-        const dim_t odim0, const dim_t odim1, const af_interp_type method)
-{
+af_err af_translate(af_array *out, const af_array in, const float trans0,
+                    const float trans1, const dim_t odim0, const dim_t odim1,
+                    const af_interp_type method) {
     CHECK_ARRAYS(in);
     return CALL(out, in, trans0, trans1, odim0, odim1, method);
 }
 
-af_err af_scale(af_array *out, const af_array in, const float scale0, const float scale1,
-        const dim_t odim0, const dim_t odim1, const af_interp_type method)
-{
+af_err af_scale(af_array *out, const af_array in, const float scale0,
+                const float scale1, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method) {
     CHECK_ARRAYS(in);
     return CALL(out, in, scale0, scale1, odim0, odim1, method);
 }
 
-af_err af_skew(af_array *out, const af_array in, const float skew0, const float skew1,
-        const dim_t odim0, const dim_t odim1, const af_interp_type method,
-        const bool inverse)
-{
+af_err af_skew(af_array *out, const af_array in, const float skew0,
+               const float skew1, const dim_t odim0, const dim_t odim1,
+               const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(in);
     return CALL(out, in, skew0, skew1, odim0, odim1, method, inverse);
 }
 
-af_err af_histogram(af_array *out, const af_array in, const unsigned nbins, const double minval, const double maxval)
-{
+af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
+                    const double minval, const double maxval) {
     CHECK_ARRAYS(in);
     return CALL(out, in, nbins, minval, maxval);
 }
 
-af_err af_dilate(af_array *out, const af_array in, const af_array mask)
-{
+af_err af_dilate(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
     return CALL(out, in, mask);
 }
 
-af_err af_dilate3(af_array *out, const af_array in, const af_array mask)
-{
+af_err af_dilate3(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
     return CALL(out, in, mask);
 }
 
-af_err af_erode(af_array *out, const af_array in, const af_array mask)
-{
+af_err af_erode(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
     return CALL(out, in, mask);
 }
 
-af_err af_erode3(af_array *out, const af_array in, const af_array mask)
-{
+af_err af_erode3(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
     return CALL(out, in, mask);
 }
 
-af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma, const float chromatic_sigma, const bool isColor)
-{
+af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma,
+                    const float chromatic_sigma, const bool isColor) {
     CHECK_ARRAYS(in);
     return CALL(out, in, spatial_sigma, chromatic_sigma, isColor);
 }
 
-af_err af_mean_shift(af_array *out, const af_array in, const float spatial_sigma, const float chromatic_sigma, const unsigned iter, const bool is_color)
-{
+af_err af_mean_shift(af_array *out, const af_array in,
+                     const float spatial_sigma, const float chromatic_sigma,
+                     const unsigned iter, const bool is_color) {
     CHECK_ARRAYS(in);
     return CALL(out, in, spatial_sigma, chromatic_sigma, iter, is_color);
 }
 
-af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
+                  const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
     return CALL(out, in, wind_length, wind_width, edge_pad);
 }
 
-af_err af_maxfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_maxfilt(af_array *out, const af_array in, const dim_t wind_length,
+                  const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
     return CALL(out, in, wind_length, wind_width, edge_pad);
 }
 
-af_err af_regions(af_array *out, const af_array in, const af_connectivity connectivity, const af_dtype ty)
-{
+af_err af_regions(af_array *out, const af_array in,
+                  const af_connectivity connectivity, const af_dtype ty) {
     CHECK_ARRAYS(in);
     return CALL(out, in, connectivity, ty);
 }
 
-af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img, const unsigned ker_size)
-{
+af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img,
+                         const unsigned ker_size) {
     CHECK_ARRAYS(img);
     return CALL(dx, dy, img, ker_size);
 }
 
-af_err af_rgb2gray(af_array* out, const af_array in, const float rPercent, const float gPercent, const float bPercent)
-{
+af_err af_rgb2gray(af_array *out, const af_array in, const float rPercent,
+                   const float gPercent, const float bPercent) {
     CHECK_ARRAYS(in);
     return CALL(out, in, rPercent, gPercent, bPercent);
 }
 
-af_err af_gray2rgb(af_array* out, const af_array in, const float rFactor, const float gFactor, const float bFactor)
-{
+af_err af_gray2rgb(af_array *out, const af_array in, const float rFactor,
+                   const float gFactor, const float bFactor) {
     CHECK_ARRAYS(in);
     return CALL(out, in, rFactor, gFactor, bFactor);
 }
 
-af_err af_hist_equal(af_array *out, const af_array in, const af_array hist)
-{
+af_err af_hist_equal(af_array *out, const af_array in, const af_array hist) {
     CHECK_ARRAYS(in, hist);
     return CALL(out, in, hist);
 }
 
-af_err af_gaussian_kernel(af_array *out,
-        const int rows, const int cols,
-        const double sigma_r, const double sigma_c)
-{
+af_err af_gaussian_kernel(af_array *out, const int rows, const int cols,
+                          const double sigma_r, const double sigma_c) {
     return CALL(out, rows, cols, sigma_r, sigma_c);
 }
 
-af_err af_hsv2rgb(af_array* out, const af_array in)
-{
+af_err af_hsv2rgb(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_rgb2hsv(af_array* out, const af_array in)
-{
+af_err af_rgb2hsv(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to, const af_cspace_t from)
-{
+af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to,
+                      const af_cspace_t from) {
     CHECK_ARRAYS(image);
     return CALL(out, image, to, from);
 }
 
-af_err af_unwrap(af_array *out, const af_array in, const dim_t wx, const dim_t wy,
-        const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-        const bool is_column)
-{
+af_err af_unwrap(af_array *out, const af_array in, const dim_t wx,
+                 const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
+                 const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
     return CALL(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
-af_err af_wrap(af_array *out,
-        const af_array in,
-        const dim_t ox, const dim_t oy,
-        const dim_t wx, const dim_t wy,
-        const dim_t sx, const dim_t sy,
-        const dim_t px, const dim_t py,
-        const bool is_column)
-{
+af_err af_wrap(af_array *out, const af_array in, const dim_t ox, const dim_t oy,
+               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+               const dim_t px, const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
     return CALL(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
 }
 
-af_err af_sat(af_array *out, const af_array in)
-{
+af_err af_sat(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_ycbcr2rgb(af_array* out, const af_array in, const af_ycc_std standard)
-{
+af_err af_ycbcr2rgb(af_array *out, const af_array in,
+                    const af_ycc_std standard) {
     CHECK_ARRAYS(in);
     return CALL(out, in, standard);
 }
 
-af_err af_rgb2ycbcr(af_array* out, const af_array in, const af_ycc_std standard)
-{
+af_err af_rgb2ycbcr(af_array *out, const af_array in,
+                    const af_ycc_std standard) {
     CHECK_ARRAYS(in);
     return CALL(out, in, standard);
 }
 
-af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
-                const float t1, const float t2, const unsigned sw, const bool isf)
-{
+af_err af_canny(af_array *out, const af_array in, const af_canny_threshold ct,
+                const float t1, const float t2, const unsigned sw,
+                const bool isf) {
     CHECK_ARRAYS(in);
     return CALL(out, in, ct, t1, t2, sw, isf);
 }
 
-af_err af_anisotropic_diffusion(af_array* out, const af_array in, const float dt,
-                                const float K, const unsigned iterations,
+af_err af_anisotropic_diffusion(af_array *out, const af_array in,
+                                const float dt, const float K,
+                                const unsigned iterations,
                                 const af_flux_function fftype,
-                                const af_diffusion_eq eq)
-{
+                                const af_diffusion_eq eq) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dt, K, iterations, fftype, eq);
 }
 
-af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
+af_err af_iterative_deconv(af_array *out, const af_array in, const af_array ker,
                            const unsigned iterations, const float relax_factor,
-                           const af_iterative_deconv_algo algo)
-{
+                           const af_iterative_deconv_algo algo) {
     CHECK_ARRAYS(in, ker);
     return CALL(out, in, ker, iterations, relax_factor, algo);
 }
 
-af_err af_inverse_deconv(af_array* out, const af_array in, const af_array psf,
-                         const float gamma,const af_inverse_deconv_algo algo)
-{
+af_err af_inverse_deconv(af_array *out, const af_array in, const af_array psf,
+                         const float gamma, const af_inverse_deconv_algo algo) {
     CHECK_ARRAYS(in, psf);
     return CALL(out, in, psf, gamma, algo);
 }
diff --git a/src/api/unified/index.cpp b/src/api/unified/index.cpp
index 4df5926d62..975fc746c5 100644
--- a/src/api/unified/index.cpp
+++ b/src/api/unified/index.cpp
@@ -11,78 +11,58 @@
 #include <af/index.h>
 #include "symbol_manager.hpp"
 
-af_err af_index(  af_array *out,
-        const af_array in,
-        const unsigned ndims, const af_seq* const index)
-{
+af_err af_index(af_array* out, const af_array in, const unsigned ndims,
+                const af_seq* const index) {
     CHECK_ARRAYS(in);
     return CALL(out, in, ndims, index);
 }
 
-af_err af_lookup( af_array *out,
-        const af_array in, const af_array indices,
-        const unsigned dim)
-{
+af_err af_lookup(af_array* out, const af_array in, const af_array indices,
+                 const unsigned dim) {
     CHECK_ARRAYS(in, indices);
     return CALL(out, in, indices, dim);
 }
 
-af_err af_assign_seq( af_array *out,
-        const af_array lhs,
-        const unsigned ndims, const af_seq* const indices,
-        const af_array rhs)
-{
+af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
+                     const af_seq* const indices, const af_array rhs) {
     CHECK_ARRAYS(lhs, rhs);
     return CALL(out, lhs, ndims, indices, rhs);
 }
 
-af_err af_index_gen(  af_array *out,
-        const af_array in,
-        const dim_t ndims, const af_index_t* indices)
-{
+af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
+                    const af_index_t* indices) {
     CHECK_ARRAYS(in);
     return CALL(out, in, ndims, indices);
 }
 
-af_err af_assign_gen( af_array *out,
-        const af_array lhs,
-        const dim_t ndims, const af_index_t* indices,
-        const af_array rhs)
-{
+af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
+                     const af_index_t* indices, const af_array rhs) {
     CHECK_ARRAYS(lhs, rhs);
     return CALL(out, lhs, ndims, indices, rhs);
 }
 
-af_seq af_make_seq(double begin, double end, double step)
-{
+af_seq af_make_seq(double begin, double end, double step) {
     af_seq seq = {begin, end, step};
     return seq;
 }
 
-af_err af_create_indexers(af_index_t** indexers)
-{
-    return CALL(indexers);
-}
+af_err af_create_indexers(af_index_t** indexers) { return CALL(indexers); }
 
-af_err af_set_array_indexer(af_index_t* indexer, const af_array idx, const dim_t dim)
-{
+af_err af_set_array_indexer(af_index_t* indexer, const af_array idx,
+                            const dim_t dim) {
     CHECK_ARRAYS(idx);
     return CALL(indexer, idx, dim);
 }
 
-af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx, const dim_t dim, const bool is_batch)
-{
+af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx,
+                          const dim_t dim, const bool is_batch) {
     return CALL(indexer, idx, dim, is_batch);
 }
 
-af_err af_set_seq_param_indexer(af_index_t* indexer,
-                              const double begin, const double end, const double step,
-                              const dim_t dim, const bool is_batch)
-{
+af_err af_set_seq_param_indexer(af_index_t* indexer, const double begin,
+                                const double end, const double step,
+                                const dim_t dim, const bool is_batch) {
     return CALL(indexer, begin, end, step, dim, is_batch);
 }
 
-af_err af_release_indexers(af_index_t* indexers)
-{
-    return CALL(indexers);
-}
+af_err af_release_indexers(af_index_t* indexers) { return CALL(indexers); }
diff --git a/src/api/unified/internal.cpp b/src/api/unified/internal.cpp
index 3a2d51cca5..c5dc3a9655 100644
--- a/src/api/unified/internal.cpp
+++ b/src/api/unified/internal.cpp
@@ -10,51 +10,41 @@
 #include <af/internal.h>
 #include "symbol_manager.hpp"
 
-
-af_err af_create_strided_array(af_array *arr,
-                               const void *data,
-                               const dim_t offset,
-                               const unsigned ndims,
+af_err af_create_strided_array(af_array *arr, const void *data,
+                               const dim_t offset, const unsigned ndims,
                                const dim_t *const dims_,
-                               const dim_t *const strides_,
-                               const af_dtype ty,
-                               const af_source location)
-{
+                               const dim_t *const strides_, const af_dtype ty,
+                               const af_source location) {
     return CALL(arr, data, offset, ndims, dims_, strides_, ty, location);
 }
 
-af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3, const af_array in)
-{
+af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3,
+                      const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(s0, s1, s2, s3, in);
 }
 
-af_err af_get_offset(dim_t *offset, const af_array arr)
-{
+af_err af_get_offset(dim_t *offset, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(offset, arr);
 }
 
-af_err af_get_raw_ptr(void **ptr, const af_array arr)
-{
+af_err af_get_raw_ptr(void **ptr, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(ptr, arr);
 }
 
-af_err af_is_linear(bool *result, const af_array arr)
-{
+af_err af_is_linear(bool *result, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(result, arr);
 }
 
-af_err af_is_owner(bool *result, const af_array arr)
-{
+af_err af_is_owner(bool *result, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(result, arr);
 }
 
-af_err af_get_allocated_bytes(size_t *bytes, const af_array arr)
-{
+af_err af_get_allocated_bytes(size_t *bytes, const af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(bytes, arr);
 }
diff --git a/src/api/unified/lapack.cpp b/src/api/unified/lapack.cpp
index 5f6a736204..7e22beaaf6 100644
--- a/src/api/unified/lapack.cpp
+++ b/src/api/unified/lapack.cpp
@@ -11,100 +11,85 @@
 #include <af/lapack.h>
 #include "symbol_manager.hpp"
 
-af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in)
-{
+af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(u, s, vt, in);
 }
 
-af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in)
-{
+af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in) {
     CHECK_ARRAYS(in);
     return CALL(u, s, vt, in);
 }
 
-af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array in)
-{
+af_err af_lu(af_array *lower, af_array *upper, af_array *pivot,
+             const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(lower, upper, pivot, in);
 }
 
-af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv)
-{
+af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv) {
     CHECK_ARRAYS(in);
     return CALL(pivot, in, is_lapack_piv);
 }
 
-af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in)
-{
+af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(q, r, tau, in);
 }
 
-af_err af_qr_inplace(af_array *tau, af_array in)
-{
+af_err af_qr_inplace(af_array *tau, af_array in) {
     CHECK_ARRAYS(in);
     return CALL(tau, in);
 }
 
-af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_upper)
-{
+af_err af_cholesky(af_array *out, int *info, const af_array in,
+                   const bool is_upper) {
     CHECK_ARRAYS(in);
     return CALL(out, info, in, is_upper);
 }
 
-af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper)
-{
+af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper) {
     CHECK_ARRAYS(in);
     return CALL(info, in, is_upper);
 }
 
 af_err af_solve(af_array *x, const af_array a, const af_array b,
-        const af_mat_prop options)
-{
+                const af_mat_prop options) {
     CHECK_ARRAYS(a, b);
     return CALL(x, a, b, options);
 }
 
 af_err af_solve_lu(af_array *x, const af_array a, const af_array piv,
-        const af_array b, const af_mat_prop options)
-{
+                   const af_array b, const af_mat_prop options) {
     CHECK_ARRAYS(a, piv, b);
     return CALL(x, a, piv, b, options);
 }
 
-af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options)
-{
+af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options) {
     CHECK_ARRAYS(in);
     return CALL(out, in, options);
 }
 
 af_err af_pinverse(af_array *out, const af_array in, const double tol,
-                   const af_mat_prop options)
-{
+                   const af_mat_prop options) {
     CHECK_ARRAYS(in);
     return CALL(out, in, tol, options);
 }
 
-af_err af_rank(unsigned *rank, const af_array in, const double tol)
-{
+af_err af_rank(unsigned *rank, const af_array in, const double tol) {
     CHECK_ARRAYS(in);
     return CALL(rank, in, tol);
 }
 
-af_err af_det(double *det_real, double *det_imag, const af_array in)
-{
+af_err af_det(double *det_real, double *det_imag, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(det_real, det_imag, in);
 }
 
-af_err af_norm(double *out, const af_array in, const af_norm_type type, const double p, const double q)
-{
+af_err af_norm(double *out, const af_array in, const af_norm_type type,
+               const double p, const double q) {
     CHECK_ARRAYS(in);
     return CALL(out, in, type, p, q);
 }
 
-af_err af_is_lapack_available(bool *out)
-{
-    return CALL(out);
-}
+af_err af_is_lapack_available(bool *out) { return CALL(out); }
diff --git a/src/api/unified/moments.cpp b/src/api/unified/moments.cpp
index d568bb5369..d79673fda9 100644
--- a/src/api/unified/moments.cpp
+++ b/src/api/unified/moments.cpp
@@ -11,14 +11,14 @@
 #include <af/image.h>
 #include "symbol_manager.hpp"
 
-af_err af_moments(af_array* out, const af_array in, const af_moment_type moment)
-{
+af_err af_moments(af_array* out, const af_array in,
+                  const af_moment_type moment) {
     CHECK_ARRAYS(in);
     return CALL(out, in, moment);
 }
 
-af_err af_moments_all(double* out, const af_array in, const af_moment_type moment)
-{
+af_err af_moments_all(double* out, const af_array in,
+                      const af_moment_type moment) {
     CHECK_ARRAYS(in);
     return CALL(out, in, moment);
 }
diff --git a/src/api/unified/random.cpp b/src/api/unified/random.cpp
index 0abea9a522..a40515a077 100644
--- a/src/api/unified/random.cpp
+++ b/src/api/unified/random.cpp
@@ -11,77 +11,69 @@
 #include <af/random.h>
 #include "symbol_manager.hpp"
 
-af_err af_get_default_random_engine(af_random_engine *r)
-{
-    return CALL(r);
-}
+af_err af_get_default_random_engine(af_random_engine *r) { return CALL(r); }
 
-af_err af_create_random_engine(af_random_engine *engineHandle, af_random_engine_type rtype, unsigned long long seed)
-{
+af_err af_create_random_engine(af_random_engine *engineHandle,
+                               af_random_engine_type rtype,
+                               unsigned long long seed) {
     return CALL(engineHandle, rtype, seed);
 }
 
-af_err af_retain_random_engine(af_random_engine *outHandle, const af_random_engine engineHandle)
-{
+af_err af_retain_random_engine(af_random_engine *outHandle,
+                               const af_random_engine engineHandle) {
     return CALL(outHandle, engineHandle);
 }
 
-af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine)
-{
+af_err af_random_engine_get_type(af_random_engine_type *rtype,
+                                 const af_random_engine engine) {
     return CALL(rtype, engine);
 }
 
-af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype)
-{
+af_err af_random_engine_set_type(af_random_engine *engine,
+                                 const af_random_engine_type rtype) {
     return CALL(engine, rtype);
 }
 
-af_err af_set_default_random_engine_type(const af_random_engine_type rtype)
-{
+af_err af_set_default_random_engine_type(const af_random_engine_type rtype) {
     return CALL(rtype);
 }
 
-af_err af_random_uniform(af_array *arr, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
-{
+af_err af_random_uniform(af_array *arr, const unsigned ndims,
+                         const dim_t *const dims, const af_dtype type,
+                         af_random_engine engine) {
     return CALL(arr, ndims, dims, type, engine);
 }
 
-af_err af_random_normal(af_array *arr, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine)
-{
+af_err af_random_normal(af_array *arr, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype type,
+                        af_random_engine engine) {
     return CALL(arr, ndims, dims, type, engine);
 }
 
-af_err af_release_random_engine(af_random_engine engineHandle)
-{
+af_err af_release_random_engine(af_random_engine engineHandle) {
     return CALL(engineHandle);
 }
 
-af_err af_random_engine_set_seed(af_random_engine *engine, const unsigned long long seed)
-{
+af_err af_random_engine_set_seed(af_random_engine *engine,
+                                 const unsigned long long seed) {
     return CALL(engine, seed);
 }
 
-af_err af_random_engine_get_seed(unsigned long long * const seed, af_random_engine engine)
-{
+af_err af_random_engine_get_seed(unsigned long long *const seed,
+                                 af_random_engine engine) {
     return CALL(seed, engine);
 }
 
-af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_randu(af_array *out, const unsigned ndims, const dim_t *const dims,
+                const af_dtype type) {
     return CALL(out, ndims, dims, type);
 }
 
-af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type)
-{
+af_err af_randn(af_array *out, const unsigned ndims, const dim_t *const dims,
+                const af_dtype type) {
     return CALL(out, ndims, dims, type);
 }
 
-af_err af_set_seed(const unsigned long long seed)
-{
-    return CALL(seed);
-}
+af_err af_set_seed(const unsigned long long seed) { return CALL(seed); }
 
-af_err af_get_seed(unsigned long long *seed)
-{
-    return CALL(seed);
-}
+af_err af_get_seed(unsigned long long *seed) { return CALL(seed); }
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 2fc73649e5..8d9c7ac4dd 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -12,49 +12,46 @@
 #include "symbol_manager.hpp"
 
 af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
-                  const af_interp_type method, const float offGrid)
-{
+                  const af_interp_type method, const float offGrid) {
     CHECK_ARRAYS(yi, xo);
     return CALL(yo, yi, xo, method, offGrid);
 }
 
-af_err af_approx2(af_array *zo, const af_array zi,
-                  const af_array xo, const af_array yo,
-                  const af_interp_type method, const float offGrid)
-{
+af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
+                  const af_array yo, const af_interp_type method,
+                  const float offGrid) {
     CHECK_ARRAYS(zi, xo, yo);
     return CALL(zo, zi, xo, yo, method, offGrid);
 }
 
-af_err af_approx1_uniform(af_array *yo, const af_array yi,
-                          const af_array xo, const int xdim,
-                          const double xi_beg, const double xi_step,
-                          const af_interp_type method, const float offGrid)
-{
+af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
+                          const int xdim, const double xi_beg,
+                          const double xi_step, const af_interp_type method,
+                          const float offGrid) {
     CHECK_ARRAYS(yi, xo);
     return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
 }
 
-af_err af_approx2_uniform(af_array *zo, const af_array zi,
-                          const af_array xo, const int xdim, const double xi_beg, const double xi_step,
-                          const af_array yo, const int ydim, const double yi_beg, const double yi_step,
-                          const af_interp_type method, const float offGrid)
-{
+af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
+                          const int xdim, const double xi_beg,
+                          const double xi_step, const af_array yo,
+                          const int ydim, const double yi_beg,
+                          const double yi_step, const af_interp_type method,
+                          const float offGrid) {
     CHECK_ARRAYS(zi, xo, yo);
-    return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step, method, offGrid);
+    return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+                method, offGrid);
 }
 
-af_err af_set_fft_plan_cache_size(size_t cache_size)
-{
+af_err af_set_fft_plan_cache_size(size_t cache_size) {
     return CALL(cache_size);
 }
 
-#define FFT_HAPI_DEF(af_func)\
-af_err af_func(af_array in, const double norm_factor)\
-{\
-    CHECK_ARRAYS(in); \
-    return CALL(in, norm_factor);\
-}
+#define FFT_HAPI_DEF(af_func)                               \
+    af_err af_func(af_array in, const double norm_factor) { \
+        CHECK_ARRAYS(in);                                   \
+        return CALL(in, norm_factor);                       \
+    }
 
 FFT_HAPI_DEF(af_fft_inplace)
 FFT_HAPI_DEF(af_fft2_inplace)
@@ -63,126 +60,126 @@ FFT_HAPI_DEF(af_ifft_inplace)
 FFT_HAPI_DEF(af_ifft2_inplace)
 FFT_HAPI_DEF(af_ifft3_inplace)
 
-af_err af_fft(af_array *out, const af_array in, const double norm_factor, const dim_t odim0)
-{
+af_err af_fft(af_array *out, const af_array in, const double norm_factor,
+              const dim_t odim0) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, odim0);
 }
 
-af_err af_fft2(af_array *out, const af_array in, const double norm_factor, const dim_t odim0, const dim_t odim1)
-{
+af_err af_fft2(af_array *out, const af_array in, const double norm_factor,
+               const dim_t odim0, const dim_t odim1) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, odim0, odim1);
 }
 
-af_err af_fft3(af_array *out, const af_array in, const double norm_factor, const dim_t odim0, const dim_t odim1, const dim_t odim2)
-{
+af_err af_fft3(af_array *out, const af_array in, const double norm_factor,
+               const dim_t odim0, const dim_t odim1, const dim_t odim2) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, odim0, odim1, odim2);
 }
 
-af_err af_ifft(af_array *out, const af_array in, const double norm_factor, const dim_t odim0)
-{
+af_err af_ifft(af_array *out, const af_array in, const double norm_factor,
+               const dim_t odim0) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, odim0);
 }
 
-af_err af_ifft2(af_array *out, const af_array in, const double norm_factor, const dim_t odim0, const dim_t odim1)
-{
+af_err af_ifft2(af_array *out, const af_array in, const double norm_factor,
+                const dim_t odim0, const dim_t odim1) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, odim0, odim1);
 }
 
-af_err af_ifft3(af_array *out, const af_array in, const double norm_factor, const dim_t odim0, const dim_t odim1, const dim_t odim2)
-{
+af_err af_ifft3(af_array *out, const af_array in, const double norm_factor,
+                const dim_t odim0, const dim_t odim1, const dim_t odim2) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, odim0, odim1, odim2);
 }
 
-af_err af_fft_r2c (af_array *out, const af_array in, const double norm_factor, const dim_t pad0)
-{
+af_err af_fft_r2c(af_array *out, const af_array in, const double norm_factor,
+                  const dim_t pad0) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, pad0);
 }
 
-af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1)
-{
+af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_factor,
+                   const dim_t pad0, const dim_t pad1) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, pad0, pad1);
 }
 
-af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_factor, const dim_t pad0, const dim_t pad1, const dim_t pad2)
-{
+af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_factor,
+                   const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     CHECK_ARRAYS(in);
     return CALL(out, in, norm_factor, pad0, pad1, pad2);
 }
 
-#define FFTC2R_HAPI_DEF(af_func)\
-af_err af_func(af_array *out, const af_array in, const double norm_factor, const bool is_odd)\
-{\
-    CHECK_ARRAYS(in); \
-    return CALL(out, in, norm_factor, is_odd);\
-}
+#define FFTC2R_HAPI_DEF(af_func)                                               \
+    af_err af_func(af_array *out, const af_array in, const double norm_factor, \
+                   const bool is_odd) {                                        \
+        CHECK_ARRAYS(in);                                                      \
+        return CALL(out, in, norm_factor, is_odd);                             \
+    }
 
 FFTC2R_HAPI_DEF(af_fft_c2r)
 FFTC2R_HAPI_DEF(af_fft2_c2r)
 FFTC2R_HAPI_DEF(af_fft3_c2r)
 
-#define CONV_HAPI_DEF(af_func)\
-af_err af_func(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain)\
-{\
-    CHECK_ARRAYS(signal, filter); \
-    return CALL(out, signal, filter, mode, domain);\
-}
+#define CONV_HAPI_DEF(af_func)                                     \
+    af_err af_func(af_array *out, const af_array signal,           \
+                   const af_array filter, const af_conv_mode mode, \
+                   af_conv_domain domain) {                        \
+        CHECK_ARRAYS(signal, filter);                              \
+        return CALL(out, signal, filter, mode, domain);            \
+    }
 
 CONV_HAPI_DEF(af_convolve1)
 CONV_HAPI_DEF(af_convolve2)
 CONV_HAPI_DEF(af_convolve3)
 
-#define FFT_CONV_HAPI_DEF(af_func)\
-af_err af_func(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode)\
-{\
-    CHECK_ARRAYS(signal, filter); \
-    return CALL(out, signal, filter, mode);\
-}
+#define FFT_CONV_HAPI_DEF(af_func)                                   \
+    af_err af_func(af_array *out, const af_array signal,             \
+                   const af_array filter, const af_conv_mode mode) { \
+        CHECK_ARRAYS(signal, filter);                                \
+        return CALL(out, signal, filter, mode);                      \
+    }
 
 FFT_CONV_HAPI_DEF(af_fft_convolve1)
 FFT_CONV_HAPI_DEF(af_fft_convolve2)
 FFT_CONV_HAPI_DEF(af_fft_convolve3)
 
-af_err af_convolve2_sep(af_array *out, const af_array col_filter, const af_array row_filter, const af_array signal, const af_conv_mode mode)
-{
+af_err af_convolve2_sep(af_array *out, const af_array col_filter,
+                        const af_array row_filter, const af_array signal,
+                        const af_conv_mode mode) {
     CHECK_ARRAYS(col_filter, row_filter, signal);
     return CALL(out, col_filter, row_filter, signal, mode);
 }
 
-af_err af_fir(af_array *y, const af_array b, const af_array x)
-{
+af_err af_fir(af_array *y, const af_array b, const af_array x) {
     CHECK_ARRAYS(b, x);
     return CALL(y, b, x);
 }
 
-af_err af_iir(af_array *y, const af_array b, const af_array a, const af_array x)
-{
+af_err af_iir(af_array *y, const af_array b, const af_array a,
+              const af_array x) {
     CHECK_ARRAYS(b, a, x);
     return CALL(y, b, a, x);
 }
 
-
-af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length,
+                  const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
     return CALL(out, in, wind_length, wind_width, edge_pad);
 }
 
-af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
+                   const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
     return CALL(out, in, wind_width, edge_pad);
 }
 
-af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length, const dim_t wind_width, const af_border_type edge_pad)
-{
+af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length,
+                   const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
     return CALL(out, in, wind_length, wind_width, edge_pad);
 }
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index a6a74dafc8..0f723edd04 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -10,81 +10,66 @@
 #include <af/sparse.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_sparse_array(
-       af_array *out,
-       const dim_t nRows, const dim_t nCols,
-       const af_array values, const af_array rowIdx, const af_array colIdx,
-       const af_storage stype)
-{
+af_err af_create_sparse_array(af_array *out, const dim_t nRows,
+                              const dim_t nCols, const af_array values,
+                              const af_array rowIdx, const af_array colIdx,
+                              const af_storage stype) {
     CHECK_ARRAYS(values, rowIdx, colIdx);
     return CALL(out, nRows, nCols, values, rowIdx, colIdx, stype);
 }
 
 af_err af_create_sparse_array_from_ptr(
-       af_array *out,
-       const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-       const void * const values,
-       const int * const rowIdx, const int * const colIdx,
-       const af_dtype type, const af_storage stype,
-       const af_source source)
-{
-    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, type, stype, source);
+    af_array *out, const dim_t nRows, const dim_t nCols, const dim_t nNZ,
+    const void *const values, const int *const rowIdx, const int *const colIdx,
+    const af_dtype type, const af_storage stype, const af_source source) {
+    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, type, stype,
+                source);
 }
 
-af_err af_create_sparse_array_from_dense(
-       af_array *out, const af_array in,
-       const af_storage stype)
-{
+af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
+                                         const af_storage stype) {
     CHECK_ARRAYS(in);
     return CALL(out, in, stype);
 }
 
 af_err af_sparse_convert_to(af_array *out, const af_array in,
-                            const af_storage destStorage)
-{
+                            const af_storage destStorage) {
     CHECK_ARRAYS(in);
     return CALL(out, in, destStorage);
 }
 
-af_err af_sparse_to_dense(af_array *out, const af_array in)
-{
+af_err af_sparse_to_dense(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx, af_storage *stype,
-                          const af_array in)
-{
+af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx,
+                          af_storage *stype, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(values, rowIdx, colIdx, stype, in);
 }
 
-af_err af_sparse_get_values(af_array *out, const af_array in)
-{
+af_err af_sparse_get_values(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_row_idx(af_array *out, const af_array in)
-{
+af_err af_sparse_get_row_idx(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_col_idx(af_array *out, const af_array in)
-{
+af_err af_sparse_get_col_idx(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_nnz(dim_t *out, const af_array in)
-{
+af_err af_sparse_get_nnz(dim_t *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
 
-af_err af_sparse_get_storage(af_storage *out, const af_array in)
-{
+af_err af_sparse_get_storage(af_storage *out, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(out, in);
 }
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index a6ba5e93c7..8654aeb725 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -11,100 +11,93 @@
 #include <af/statistics.h>
 #include "symbol_manager.hpp"
 
-af_err af_mean(af_array *out, const af_array in, const dim_t dim)
-{
+af_err af_mean(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dim);
 }
 
-af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim)
-{
+af_err af_mean_weighted(af_array *out, const af_array in,
+                        const af_array weights, const dim_t dim) {
     CHECK_ARRAYS(in, weights);
     return CALL(out, in, weights, dim);
 }
 
-af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t dim)
-{
+af_err af_var(af_array *out, const af_array in, const bool isbiased,
+              const dim_t dim) {
     CHECK_ARRAYS(in);
     return CALL(out, in, isbiased, dim);
 }
 
-af_err af_var_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim)
-{
+af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
+                       const dim_t dim) {
     CHECK_ARRAYS(in, weights);
     return CALL(out, in, weights, dim);
 }
 
 af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
-                  const af_array weights, const af_var_bias bias, const dim_t dim)
-{
+                  const af_array weights, const af_var_bias bias,
+                  const dim_t dim) {
     CHECK_ARRAYS(in, weights);
     return CALL(mean, var, in, weights, bias, dim);
 }
 
-af_err af_stdev(af_array *out, const af_array in, const dim_t dim)
-{
+af_err af_stdev(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dim);
 }
 
-af_err af_cov(af_array* out, const af_array X, const af_array Y, const bool isbiased)
-{
+af_err af_cov(af_array *out, const af_array X, const af_array Y,
+              const bool isbiased) {
     CHECK_ARRAYS(X, Y);
     return CALL(out, X, Y, isbiased);
 }
 
-af_err af_median(af_array* out, const af_array in, const dim_t dim)
-{
+af_err af_median(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
     return CALL(out, in, dim);
 }
 
-af_err af_mean_all(double *real, double *imag, const af_array in)
-{
+af_err af_mean_all(double *real, double *imag, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(real, imag, in);
 }
 
-af_err af_mean_all_weighted(double *real, double *imag, const af_array in, const af_array weights)
-{
+af_err af_mean_all_weighted(double *real, double *imag, const af_array in,
+                            const af_array weights) {
     CHECK_ARRAYS(in, weights);
     return CALL(real, imag, in, weights);
 }
 
-af_err af_var_all(double *realVal, double *imagVal, const af_array in, const bool isbiased)
-{
+af_err af_var_all(double *realVal, double *imagVal, const af_array in,
+                  const bool isbiased) {
     CHECK_ARRAYS(in);
     return CALL(realVal, imagVal, in, isbiased);
 }
 
-af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in, const af_array weights)
-{
+af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in,
+                           const af_array weights) {
     CHECK_ARRAYS(in, weights);
     return CALL(realVal, imagVal, in, weights);
 }
 
-af_err af_stdev_all(double *real, double *imag, const af_array in)
-{
+af_err af_stdev_all(double *real, double *imag, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(real, imag, in);
 }
 
-af_err af_median_all(double *realVal, double *imagVal, const af_array in)
-{
+af_err af_median_all(double *realVal, double *imagVal, const af_array in) {
     CHECK_ARRAYS(in);
     return CALL(realVal, imagVal, in);
 }
 
-af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y)
-{
+af_err af_corrcoef(double *realVal, double *imagVal, const af_array X,
+                   const af_array Y) {
     CHECK_ARRAYS(X, Y);
     return CALL(realVal, imagVal, X, Y);
 }
 
 af_err af_topk(af_array *values, af_array *indices, const af_array in,
-               const int k, const int dim, const af_topk_function order)
-{
+               const int k, const int dim, const af_topk_function order) {
     CHECK_ARRAYS(in);
     return CALL(values, indices, in, k, dim, order);
 }
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 273850d79d..125990793c 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -20,7 +20,6 @@
 #include <string>
 #include <type_traits>
 
-
 #ifdef OS_WIN
 #include <Windows.h>
 #else
@@ -36,8 +35,7 @@ using std::extent;
 using std::function;
 using std::string;
 
-namespace unified
-{
+namespace unified {
 
 #if defined(OS_WIN)
 static const char* LIB_AF_BKND_PREFIX = "";
@@ -47,24 +45,30 @@ static const char* LIB_AF_BKND_SUFFIX = ".dll";
 #else
 
 #if defined(__APPLE__)
-#  define SO_SUFFIX_HELPER(VER) "." #VER ".dylib"
+#define SO_SUFFIX_HELPER(VER) "." #VER ".dylib"
 #else
-#  define SO_SUFFIX_HELPER(VER) ".so." #VER
+#define SO_SUFFIX_HELPER(VER) ".so." #VER
 #endif
-   static const char* LIB_AF_BKND_PREFIX = "lib";
-#  define PATH_SEPARATOR "/"
+static const char* LIB_AF_BKND_PREFIX = "lib";
+#define PATH_SEPARATOR "/"
 
-#  define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
-   static const char* LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
+#define GET_SO_SUFFIX(VER) SO_SUFFIX_HELPER(VER)
+static const char* LIB_AF_BKND_SUFFIX = GET_SO_SUFFIX(AF_VERSION_MAJOR);
 #endif
 
 string getBkndLibName(const af_backend backend) {
     string ret;
     switch (backend) {
-        case AF_BACKEND_CUDA: ret = string(LIB_AF_BKND_PREFIX) + "afcuda" + LIB_AF_BKND_SUFFIX; break;
-        case AF_BACKEND_OPENCL: ret = string(LIB_AF_BKND_PREFIX) + "afopencl" + LIB_AF_BKND_SUFFIX; break;
-        case AF_BACKEND_CPU: ret = string(LIB_AF_BKND_PREFIX) + "afcpu" + LIB_AF_BKND_SUFFIX; break;
-        default: assert(1!=1 && "Invalid backend");
+        case AF_BACKEND_CUDA:
+            ret = string(LIB_AF_BKND_PREFIX) + "afcuda" + LIB_AF_BKND_SUFFIX;
+            break;
+        case AF_BACKEND_OPENCL:
+            ret = string(LIB_AF_BKND_PREFIX) + "afopencl" + LIB_AF_BKND_SUFFIX;
+            break;
+        case AF_BACKEND_CPU:
+            ret = string(LIB_AF_BKND_PREFIX) + "afcpu" + LIB_AF_BKND_SUFFIX;
+            break;
+        default: assert(1 != 1 && "Invalid backend");
     }
     return ret;
 }
@@ -74,47 +78,48 @@ string getBackendDirectoryName(const af_backend backend) {
         case AF_BACKEND_CUDA: ret = "cuda"; break;
         case AF_BACKEND_OPENCL: ret = "opencl"; break;
         case AF_BACKEND_CPU: ret = "cpu"; break;
-        default: assert(1!=1 && "Invalid backend");
-      }
+        default: assert(1 != 1 && "Invalid backend");
+    }
     return ret;
 }
 
-string join_path(string first) {
-    return first;
-}
+string join_path(string first) { return first; }
 
 template<typename... ARGS>
 string join_path(string first, ARGS... args) {
-    if(first.empty()) { return join_path(args...); }
-    else              { return first + PATH_SEPARATOR + join_path(args...); }
+    if (first.empty()) {
+        return join_path(args...);
+    } else {
+        return first + PATH_SEPARATOR + join_path(args...);
+    }
 }
 
 /*flag parameter is not used on windows platform */
-LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
-{
+LibHandle openDynLibrary(const af_backend bknd_idx, int flag = RTLD_LAZY) {
     // The default search path is the colon separated list of paths stored in
     // the environment variables:
-    string bkndLibName = getBkndLibName(bknd_idx);
-    string show_flag = getEnvVar("AF_SHOW_LOAD_PATH");
-    bool show_load_path = show_flag=="1";
+    string bkndLibName  = getBkndLibName(bknd_idx);
+    string show_flag    = getEnvVar("AF_SHOW_LOAD_PATH");
+    bool show_load_path = show_flag == "1";
 
     // FIXME(umar): avoid this if at all possible
-    auto getLogger = [&]{ return spdlog::get("unified"); };
+    auto getLogger = [&] { return spdlog::get("unified"); };
 
     string paths[] = {
-        "",  // Default paths
-        ".", // Shared libraries in current directory
+        "",   // Default paths
+        ".",  // Shared libraries in current directory
         // Running from the CMake Build directory
         join_path(".", "src", "backend", getBackendDirectoryName(bknd_idx)),
         // Running from the test directory
         join_path("..", "src", "backend", getBackendDirectoryName(bknd_idx)),
         // Environment variable PATHS
-        join_path(getEnvVar("AF_BUILD_PATH"), "src", "backend", getBackendDirectoryName(bknd_idx)),
+        join_path(getEnvVar("AF_BUILD_PATH"), "src", "backend",
+                  getBackendDirectoryName(bknd_idx)),
         join_path(getEnvVar("AF_PATH"), "lib"),
         join_path(getEnvVar("AF_PATH"), "lib64"),
         getEnvVar("AF_BUILD_LIB_CUSTOM_PATH"),
 
-        // Common install paths
+    // Common install paths
 #if !defined(OS_WIN)
         "/opt/arrayfire-3/lib/",
         "/opt/arrayfire/lib/",
@@ -125,7 +130,7 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
         join_path(getEnvVar("ProgramFiles"), "ArrayFire", "v3", "lib")
 #endif
     };
-    typedef af_err(*func)(int*);
+    typedef af_err (*func)(int*);
 
     LibHandle retVal = nullptr;
     for (size_t i = 0; i < extent<decltype(paths)>::value; i++) {
@@ -133,21 +138,19 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
         if ((retVal = loadLibrary(join_path(paths[i], bkndLibName).c_str()))) {
             AF_TRACE("Found: {}", join_path(paths[i], bkndLibName));
 
-            func count_func = (func)getFunctionPointer(retVal,
-                                                       "af_get_device_count");
-            if(count_func) {
+            func count_func =
+                (func)getFunctionPointer(retVal, "af_get_device_count");
+            if (count_func) {
                 int count = 0;
                 count_func(&count);
                 AF_TRACE("Device Count: {}.", count);
-                if(count == 0) {
+                if (count == 0) {
                     retVal = nullptr;
                     continue;
                 }
             }
 
-            if (show_load_path) {
-                printf("Using %s\n", bkndLibName.c_str());
-            }
+            if (show_load_path) { printf("Using %s\n", bkndLibName.c_str()); }
             break;
         }
     }
@@ -155,84 +158,71 @@ LibHandle openDynLibrary(const af_backend bknd_idx, int flag=RTLD_LAZY)
     return retVal;
 }
 
-void closeDynLibrary(LibHandle handle)
-{
-    unloadLibrary(handle);
-}
+void closeDynLibrary(LibHandle handle) { unloadLibrary(handle); }
 
-AFSymbolManager& AFSymbolManager::getInstance()
-{
+AFSymbolManager& AFSymbolManager::getInstance() {
     thread_local AFSymbolManager symbolManager;
     return symbolManager;
 }
 
-spdlog::logger* AFSymbolManager::getLogger() {
-    return logger.get();
-}
+spdlog::logger* AFSymbolManager::getLogger() { return logger.get(); }
 
 AFSymbolManager::AFSymbolManager()
-    : activeHandle(nullptr), defaultHandle(nullptr), numBackends(0),
-      backendsAvailable(0), logger(loggerFactory("unified"))
-{
+    : activeHandle(nullptr)
+    , defaultHandle(nullptr)
+    , numBackends(0)
+    , backendsAvailable(0)
+    , logger(loggerFactory("unified")) {
     // In order of priority.
-    static const af_backend order[] = { AF_BACKEND_CUDA,
-                                        AF_BACKEND_OPENCL,
-                                        AF_BACKEND_CPU};
+    static const af_backend order[] = {AF_BACKEND_CUDA, AF_BACKEND_OPENCL,
+                                       AF_BACKEND_CPU};
 
-    // Decremeting loop. The last successful backend loaded will be the most prefered one.
-    for(int i = NUM_BACKENDS - 1; i >= 0; i--) {
-        int backend = order[i] >> 1; // 2 4 1 -> 1 2 0
+    // Decremeting loop. The last successful backend loaded will be the most
+    // prefered one.
+    for (int i = NUM_BACKENDS - 1; i >= 0; i--) {
+        int backend          = order[i] >> 1;  // 2 4 1 -> 1 2 0
         bkndHandles[backend] = openDynLibrary(order[i]);
         if (bkndHandles[backend]) {
-            activeHandle = bkndHandles[backend];
+            activeHandle  = bkndHandles[backend];
             activeBackend = (af_backend)order[i];
             numBackends++;
             backendsAvailable += order[i];
         }
     }
-    if(activeBackend) {
-        AF_TRACE("AF_DEFAULT_BACKEND: {}", getBackendDirectoryName(activeBackend));
+    if (activeBackend) {
+        AF_TRACE("AF_DEFAULT_BACKEND: {}",
+                 getBackendDirectoryName(activeBackend));
     }
 
     // Keep a copy of default order handle inorder to use it in ::setBackend
     // when the user passes AF_BACKEND_DEFAULT
-    defaultHandle = activeHandle;
+    defaultHandle  = activeHandle;
     defaultBackend = activeBackend;
 }
 
-AFSymbolManager::~AFSymbolManager()
-{
-    for(int i=0; i<NUM_BACKENDS; ++i) {
-        if (bkndHandles[i]) {
-            closeDynLibrary(bkndHandles[i]);
-        }
+AFSymbolManager::~AFSymbolManager() {
+    for (int i = 0; i < NUM_BACKENDS; ++i) {
+        if (bkndHandles[i]) { closeDynLibrary(bkndHandles[i]); }
     }
 }
 
-unsigned AFSymbolManager::getBackendCount()
-{
-    return numBackends;
-}
+unsigned AFSymbolManager::getBackendCount() { return numBackends; }
 
-int AFSymbolManager::getAvailableBackends()
-{
-    return backendsAvailable;
-}
+int AFSymbolManager::getAvailableBackends() { return backendsAvailable; }
 
-af_err AFSymbolManager::setBackend(af::Backend bknd)
-{
-    if (bknd==AF_BACKEND_DEFAULT) {
+af_err AFSymbolManager::setBackend(af::Backend bknd) {
+    if (bknd == AF_BACKEND_DEFAULT) {
         if (defaultHandle) {
-            activeHandle = defaultHandle;
+            activeHandle  = defaultHandle;
             activeBackend = defaultBackend;
             return AF_SUCCESS;
         } else {
             UNIFIED_ERROR_LOAD_LIB();
         }
     }
-    int idx = bknd >> 1;    // Convert 1, 2, 4 -> 0, 1, 2
-    if(bkndHandles[idx]) {
-        activeHandle = bkndHandles[idx];
+    int idx = bknd >> 1;  // Convert 1, 2, 4 -> 0, 1, 2
+    if (bkndHandles[idx]) {
+        activeHandle  = bkndHandles[idx];
         activeBackend = bknd;
         return AF_SUCCESS;
     } else {
@@ -240,8 +230,7 @@ af_err AFSymbolManager::setBackend(af::Backend bknd)
     }
 }
 
-bool checkArray(af_backend activeBackend, af_array a)
-{
+bool checkArray(af_backend activeBackend, af_array a) {
     // Convert af_array into int to retrieve the backend info.
     // See ArrayInfo.hpp for more
     af_backend backend = (af_backend)0;
@@ -250,17 +239,17 @@ bool checkArray(af_backend activeBackend, af_array a)
     // backend return the expected error rather than AF_ERR_ARR_BKND_MISMATCH
     // Since a = 0, does not have a backend specified, it should be a
     // AF_ERR_ARG instead of AF_ERR_ARR_BKND_MISMATCH
-    if(a == 0) return true;
+    if (a == 0) return true;
 
-    unified::AFSymbolManager::getInstance().call("af_get_backend_id", &backend, a);
+    unified::AFSymbolManager::getInstance().call("af_get_backend_id", &backend,
+                                                 a);
     return backend == activeBackend;
 }
 
-bool checkArrays(af_backend activeBackend)
-{
+bool checkArrays(af_backend activeBackend) {
     UNUSED(activeBackend);
     // Dummy
     return true;
 }
 
-} // namespace unified
+}  // namespace unified
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index d17f9a5ee8..48459e90c2 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -8,28 +8,28 @@
  ********************************************************/
 #pragma once
 
-#include <af/defines.h>
-#include <common/err_common.hpp>
 #include <common/Logger.hpp>
+#include <common/err_common.hpp>
 #include <common/module_loading.hpp>
 #include <common/util.hpp>
+#include <af/defines.h>
 
+#include <spdlog/spdlog.h>
 #include <array>
 #include <cstdlib>
 #include <string>
 #include <unordered_map>
-#include <spdlog/spdlog.h>
 
-namespace unified
-{
+namespace unified {
 
 const int NUM_BACKENDS = 3;
 
-#define UNIFIED_ERROR_LOAD_LIB()                                        \
-    AF_RETURN_ERROR("Failed to load dynamic library. "                  \
-                    "See http://www.arrayfire.com/docs/unifiedbackend.htm " \
-                    "for instructions to set up environment for Unified backend.", \
-                    AF_ERR_LOAD_LIB)
+#define UNIFIED_ERROR_LOAD_LIB()                                       \
+    AF_RETURN_ERROR(                                                   \
+        "Failed to load dynamic library. "                             \
+        "See http://www.arrayfire.com/docs/unifiedbackend.htm "        \
+        "for instructions to set up environment for Unified backend.", \
+        AF_ERR_LOAD_LIB)
 
 static inline int backend_index(af::Backend be) {
     switch (be) {
@@ -41,69 +41,68 @@ static inline int backend_index(af::Backend be) {
 }
 
 class AFSymbolManager {
-    public:
-        static AFSymbolManager& getInstance();
-
-        ~AFSymbolManager();
+   public:
+    static AFSymbolManager& getInstance();
 
-        unsigned getBackendCount();
+    ~AFSymbolManager();
 
-        int getAvailableBackends();
+    unsigned getBackendCount();
 
-        af_err setBackend(af::Backend bnkd);
+    int getAvailableBackends();
 
-        af::Backend getActiveBackend() { return activeBackend; }
+    af_err setBackend(af::Backend bnkd);
 
-        template<typename... CalleeArgs>
-        af_err call(const char* symbolName, CalleeArgs... args) {
-            typedef af_err(*af_func)(CalleeArgs...);
-            if (!activeHandle) {
-                UNIFIED_ERROR_LOAD_LIB();
-            }
-            thread_local std::array<std::unordered_map<const char*, af_func>, NUM_BACKENDS> funcHandles;
+    af::Backend getActiveBackend() { return activeBackend; }
 
-            int index = backend_index(getActiveBackend());
-            af_func& funcHandle = funcHandles[index][symbolName];
+    template<typename... CalleeArgs>
+    af_err call(const char* symbolName, CalleeArgs... args) {
+        typedef af_err (*af_func)(CalleeArgs...);
+        if (!activeHandle) { UNIFIED_ERROR_LOAD_LIB(); }
+        thread_local std::array<std::unordered_map<const char*, af_func>,
+                                NUM_BACKENDS>
+            funcHandles;
 
-            if (!funcHandle) {
-                AF_TRACE("Loading: {}", symbolName);
-                funcHandle = (af_func)common::getFunctionPointer(activeHandle, symbolName);
-            }
-            if (!funcHandle) {
-                AF_TRACE("Failed to load symbol: {}", symbolName);
-                std::string str = "Failed to load symbol: ";
-                str += symbolName;
-                AF_RETURN_ERROR(str.c_str(),
-                                AF_ERR_LOAD_SYM);
-            }
+        int index           = backend_index(getActiveBackend());
+        af_func& funcHandle = funcHandles[index][symbolName];
 
-            return funcHandle(args...);
+        if (!funcHandle) {
+            AF_TRACE("Loading: {}", symbolName);
+            funcHandle =
+                (af_func)common::getFunctionPointer(activeHandle, symbolName);
+        }
+        if (!funcHandle) {
+            AF_TRACE("Failed to load symbol: {}", symbolName);
+            std::string str = "Failed to load symbol: ";
+            str += symbolName;
+            AF_RETURN_ERROR(str.c_str(), AF_ERR_LOAD_SYM);
         }
 
-        LibHandle getHandle() { return activeHandle; }
-        spdlog::logger* getLogger();
-
-    protected:
-        AFSymbolManager();
-
-        // Following two declarations are required to
-        // avoid copying accidental copy/assignment
-        // of instance returned by getInstance to other
-        // variables
-        AFSymbolManager(AFSymbolManager const&);
-        void operator=(AFSymbolManager const&);
-
-    private:
-
-        LibHandle bkndHandles[NUM_BACKENDS];
+        return funcHandle(args...);
+    }
 
-        LibHandle activeHandle;
-        LibHandle defaultHandle;
-        unsigned numBackends;
-        int backendsAvailable;
-        af_backend activeBackend;
-        af_backend defaultBackend;
-        std::shared_ptr<spdlog::logger> logger;
+    LibHandle getHandle() { return activeHandle; }
+    spdlog::logger* getLogger();
+
+   protected:
+    AFSymbolManager();
+
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    AFSymbolManager(AFSymbolManager const&);
+    void operator=(AFSymbolManager const&);
+
+   private:
+    LibHandle bkndHandles[NUM_BACKENDS];
+
+    LibHandle activeHandle;
+    LibHandle defaultHandle;
+    unsigned numBackends;
+    int backendsAvailable;
+    af_backend activeBackend;
+    af_backend defaultBackend;
+    std::shared_ptr<spdlog::logger> logger;
 };
 
 // Helper functions to ensure all the input arrays are on the active backend
@@ -111,28 +110,34 @@ bool checkArray(af_backend activeBackend, af_array a);
 bool checkArrays(af_backend activeBackend);
 
 template<typename T, typename... Args>
-bool checkArrays(af_backend activeBackend, T a, Args... arg)
-{
+bool checkArrays(af_backend activeBackend, T a, Args... arg) {
     return checkArray(activeBackend, a) && checkArrays(activeBackend, arg...);
 }
 
-} // namespace unified
+}  // namespace unified
 
 // Macro to check af_array as inputs. The arguments to this macro should be
 // only input af_arrays. Not outputs or other types.
-#define CHECK_ARRAYS(...) do {                                          \
-        af_backend backendId = unified::AFSymbolManager::getInstance().getActiveBackend(); \
-        if(!unified::checkArrays(backendId, __VA_ARGS__))               \
+#define CHECK_ARRAYS(...)                                                     \
+    do {                                                                      \
+        af_backend backendId =                                                \
+            unified::AFSymbolManager::getInstance().getActiveBackend();       \
+        if (!unified::checkArrays(backendId, __VA_ARGS__))                    \
             AF_RETURN_ERROR("Input array does not belong to current backend", \
-                            AF_ERR_ARR_BKND_MISMATCH);                  \
-    } while(0)
+                            AF_ERR_ARR_BKND_MISMATCH);                        \
+    } while (0)
 
 #if defined(OS_WIN)
-#define CALL(...) unified::AFSymbolManager::getInstance().call(__FUNCTION__, __VA_ARGS__)
-#define CALL_NO_PARAMS() unified::AFSymbolManager::getInstance().call(__FUNCTION__)
+#define CALL(...) \
+    unified::AFSymbolManager::getInstance().call(__FUNCTION__, __VA_ARGS__)
+#define CALL_NO_PARAMS() \
+    unified::AFSymbolManager::getInstance().call(__FUNCTION__)
 #else
-#define CALL(...) unified::AFSymbolManager::getInstance().call(__func__, __VA_ARGS__)
+#define CALL(...) \
+    unified::AFSymbolManager::getInstance().call(__func__, __VA_ARGS__)
 #define CALL_NO_PARAMS() unified::AFSymbolManager::getInstance().call(__func__)
 #endif
 
-#define LOAD_SYMBOL() common::getFunctionPointer(unified::AFSymbolManager::getInstance().getHandle(), __FUNCTION__)
+#define LOAD_SYMBOL()           \
+    common::getFunctionPointer( \
+        unified::AFSymbolManager::getInstance().getHandle(), __FUNCTION__)
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 1e83561306..8223f0b29d 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -11,48 +11,45 @@
 #include <af/util.h>
 #include "symbol_manager.hpp"
 
-af_err af_print_array(af_array arr)
-{
+af_err af_print_array(af_array arr) {
     CHECK_ARRAYS(arr);
     return CALL(arr);
 }
 
-af_err af_print_array_gen(const char *exp, const af_array arr, const int precision)
-{
+af_err af_print_array_gen(const char *exp, const af_array arr,
+                          const int precision) {
     CHECK_ARRAYS(arr);
     return CALL(exp, arr, precision);
 }
 
-af_err af_save_array(int *index, const char* key, const af_array arr, const char *filename, const bool append)
-{
+af_err af_save_array(int *index, const char *key, const af_array arr,
+                     const char *filename, const bool append) {
     CHECK_ARRAYS(arr);
     return CALL(index, key, arr, filename, append);
 }
 
-af_err af_read_array_index(af_array *out, const char *filename, const unsigned index)
-{
+af_err af_read_array_index(af_array *out, const char *filename,
+                           const unsigned index) {
     return CALL(out, filename, index);
 }
 
-af_err af_read_array_key(af_array *out, const char *filename, const char* key)
-{
+af_err af_read_array_key(af_array *out, const char *filename, const char *key) {
     return CALL(out, filename, key);
 }
 
-af_err af_read_array_key_check(int *index, const char *filename, const char* key)
-{
+af_err af_read_array_key_check(int *index, const char *filename,
+                               const char *key) {
     return CALL(index, filename, key);
 }
 
 af_err af_array_to_string(char **output, const char *exp, const af_array arr,
-        const int precision, const bool transpose)
-{
+                          const int precision, const bool transpose) {
     CHECK_ARRAYS(arr);
     return CALL(output, exp, arr, precision, transpose);
 }
 
-af_err af_example_function(af_array* out, const af_array a, const af_someenum_t param)
-{
+af_err af_example_function(af_array *out, const af_array a,
+                           const af_someenum_t param) {
     CHECK_ARRAYS(a);
     return CALL(out, a, param);
 }
diff --git a/src/api/unified/vision.cpp b/src/api/unified/vision.cpp
index cc43a61ed4..50c6a69dff 100644
--- a/src/api/unified/vision.cpp
+++ b/src/api/unified/vision.cpp
@@ -11,76 +11,90 @@
 #include <af/vision.h>
 #include "symbol_manager.hpp"
 
-af_err af_fast(af_features *out, const af_array in, const float thr, const unsigned arc_length, const bool non_max, const float feature_ratio, const unsigned edge)
-{
+af_err af_fast(af_features *out, const af_array in, const float thr,
+               const unsigned arc_length, const bool non_max,
+               const float feature_ratio, const unsigned edge) {
     CHECK_ARRAYS(in);
     return CALL(out, in, thr, arc_length, non_max, feature_ratio, edge);
 }
 
-af_err af_harris(af_features *out, const af_array in, const unsigned max_corners, const float min_response, const float sigma, const unsigned block_size, const float k_thr)
-{
+af_err af_harris(af_features *out, const af_array in,
+                 const unsigned max_corners, const float min_response,
+                 const float sigma, const unsigned block_size,
+                 const float k_thr) {
     CHECK_ARRAYS(in);
     return CALL(out, in, max_corners, min_response, sigma, block_size, k_thr);
 }
 
-af_err af_orb(af_features *feat, af_array *desc, const af_array in, const float fast_thr, const unsigned max_feat, const float scl_fctr, const unsigned levels, const bool blur_img)
-{
+af_err af_orb(af_features *feat, af_array *desc, const af_array in,
+              const float fast_thr, const unsigned max_feat,
+              const float scl_fctr, const unsigned levels,
+              const bool blur_img) {
     CHECK_ARRAYS(in);
     return CALL(feat, desc, in, fast_thr, max_feat, scl_fctr, levels, blur_img);
 }
 
-af_err af_sift(af_features *feat, af_array *desc, const af_array in, const unsigned n_layers, const float contrast_thr, const float edge_thr, const float init_sigma, const bool double_input, const float intensity_scale, const float feature_ratio)
-{
+af_err af_sift(af_features *feat, af_array *desc, const af_array in,
+               const unsigned n_layers, const float contrast_thr,
+               const float edge_thr, const float init_sigma,
+               const bool double_input, const float intensity_scale,
+               const float feature_ratio) {
     CHECK_ARRAYS(in);
-    return CALL(feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma, double_input, intensity_scale, feature_ratio);
+    return CALL(feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma,
+                double_input, intensity_scale, feature_ratio);
 }
 
-af_err af_gloh(af_features *feat, af_array *desc, const af_array in, const unsigned n_layers, const float contrast_thr, const float edge_thr, const float init_sigma, const bool double_input, const float intensity_scale, const float feature_ratio)
-{
+af_err af_gloh(af_features *feat, af_array *desc, const af_array in,
+               const unsigned n_layers, const float contrast_thr,
+               const float edge_thr, const float init_sigma,
+               const bool double_input, const float intensity_scale,
+               const float feature_ratio) {
     CHECK_ARRAYS(in);
-    return CALL(feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma, double_input, intensity_scale, feature_ratio);
+    return CALL(feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma,
+                double_input, intensity_scale, feature_ratio);
 }
 
-af_err af_hamming_matcher(af_array* idx, af_array* dist,
-        const af_array query, const af_array train,
-        const dim_t dist_dim, const unsigned n_dist)
-{
+af_err af_hamming_matcher(af_array *idx, af_array *dist, const af_array query,
+                          const af_array train, const dim_t dist_dim,
+                          const unsigned n_dist) {
     CHECK_ARRAYS(query, train);
     return CALL(idx, dist, query, train, dist_dim, n_dist);
 }
 
-af_err af_nearest_neighbour(af_array* idx, af_array* dist,
-        const af_array query, const af_array train,
-        const dim_t dist_dim, const unsigned n_dist,
-        const af_match_type dist_type)
-{
+af_err af_nearest_neighbour(af_array *idx, af_array *dist, const af_array query,
+                            const af_array train, const dim_t dist_dim,
+                            const unsigned n_dist,
+                            const af_match_type dist_type) {
     CHECK_ARRAYS(query, train);
     return CALL(idx, dist, query, train, dist_dim, n_dist, dist_type);
 }
 
-af_err af_match_template(af_array *out, const af_array search_img, const af_array template_img, const af_match_type m_type)
-{
+af_err af_match_template(af_array *out, const af_array search_img,
+                         const af_array template_img,
+                         const af_match_type m_type) {
     CHECK_ARRAYS(search_img, template_img);
     return CALL(out, search_img, template_img, m_type);
 }
 
-af_err af_susan(af_features* out, const af_array in, const unsigned radius, const float diff_thr, const float geom_thr,
-        const float feature_ratio, const unsigned edge)
-{
+af_err af_susan(af_features *out, const af_array in, const unsigned radius,
+                const float diff_thr, const float geom_thr,
+                const float feature_ratio, const unsigned edge) {
     CHECK_ARRAYS(in);
     return CALL(out, in, radius, diff_thr, geom_thr, feature_ratio, edge);
 }
 
-af_err af_dog(af_array *out, const af_array in, const int radius1, const int radius2)
-{
+af_err af_dog(af_array *out, const af_array in, const int radius1,
+              const int radius2) {
     CHECK_ARRAYS(in);
     return CALL(out, in, radius1, radius2);
 }
 
-af_err af_homography(af_array *H, int *inliers, const af_array x_src, const af_array y_src,
-                     const af_array x_dst, const af_array y_dst, const af_homography_type htype,
-                     const float inlier_thr, const unsigned iterations, const af_dtype type)
-{
+af_err af_homography(af_array *H, int *inliers, const af_array x_src,
+                     const af_array y_src, const af_array x_dst,
+                     const af_array y_dst, const af_homography_type htype,
+                     const float inlier_thr, const unsigned iterations,
+                     const af_dtype type) {
     CHECK_ARRAYS(x_src, y_src, x_dst, y_dst);
-    return CALL(H, inliers, x_src, y_src, x_dst, y_dst, htype, inlier_thr, iterations, type);
+    return CALL(H, inliers, x_src, y_src, x_dst, y_dst, htype, inlier_thr,
+                iterations, type);
 }
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index ae1f733e82..6e29345e44 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -8,56 +8,53 @@
  ********************************************************/
 
 #include <common/ArrayInfo.hpp>
-#include <numeric>
+#include <common/err_common.hpp>
 #include <algorithm>
 #include <functional>
-#include <common/err_common.hpp>
+#include <numeric>
 
 #include <backend.hpp>
 #include <platform.hpp>
 
 using af::dim4;
 
-dim4 calcStrides(const dim4 &parentDim)
-{
+dim4 calcStrides(const dim4 &parentDim) {
     dim4 out(1, 1, 1, 1);
-    dim_t *out_dims = out.get();
-    const dim_t *parent_dims =  parentDim.get();
+    dim_t *out_dims          = out.get();
+    const dim_t *parent_dims = parentDim.get();
 
-    for (dim_t i=1; i < 4; i++) {
-        out_dims[i] = out_dims[i - 1] * parent_dims[i-1];
+    for (dim_t i = 1; i < 4; i++) {
+        out_dims[i] = out_dims[i - 1] * parent_dims[i - 1];
     }
 
     return out;
 }
 
-int ArrayInfo::getDevId() const
-{
+int ArrayInfo::getDevId() const {
     // The actual device ID is only stored in the first 8 bits of devId
     // See ArrayInfo.hpp for more
     return devId & 0xff;
 }
 
-void ArrayInfo::setId(int id) const
-{
+void ArrayInfo::setId(int id) const {
     // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
-    int backendId = detail::getBackend() >> 1; // Convert enums 1, 2, 4 to ints 0, 1, 2
+    int backendId =
+        detail::getBackend() >> 1;  // Convert enums 1, 2, 4 to ints 0, 1, 2
     const_cast<ArrayInfo *>(this)->setId(id | 1 << (backendId + 8));
 }
 
-void ArrayInfo::setId(int id)
-{
+void ArrayInfo::setId(int id) {
     // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
-    int backendId = detail::getBackend() >> 1; // Convert enums 1, 2, 4 to ints 0, 1, 2
+    int backendId =
+        detail::getBackend() >> 1;  // Convert enums 1, 2, 4 to ints 0, 1, 2
     devId = id | 1 << (backendId + 8);
 }
 
-af_backend ArrayInfo::getBackendId() const
-{
+af_backend ArrayInfo::getBackendId() const {
     // devId >> 8 converts the backend info to 1, 2, 4 which are enums
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
@@ -65,117 +62,70 @@ af_backend ArrayInfo::getBackendId() const
     return (af_backend)backendId;
 }
 
-void ArrayInfo::modStrides(const dim4 &newStrides)
-{
-    dim_strides = newStrides;
-}
+void ArrayInfo::modStrides(const dim4 &newStrides) { dim_strides = newStrides; }
 
-void ArrayInfo::modDims(const dim4 &newDims)
-{
-    dim_size   = newDims;
+void ArrayInfo::modDims(const dim4 &newDims) {
+    dim_size = newDims;
     modStrides(calcStrides(newDims));
 }
 
-bool ArrayInfo::isEmpty() const
-{
-    return (elements() == 0);
-}
+bool ArrayInfo::isEmpty() const { return (elements() == 0); }
 
-bool ArrayInfo::isScalar() const
-{
-    return (elements() == 1);
-}
+bool ArrayInfo::isScalar() const { return (elements() == 1); }
 
-bool ArrayInfo::isRow() const
-{
-    return (dims()[0] == 1 && dims()[1] > 1 && dims()[2] == 1 && dims()[3] == 1);
+bool ArrayInfo::isRow() const {
+    return (dims()[0] == 1 && dims()[1] > 1 && dims()[2] == 1 &&
+            dims()[3] == 1);
 }
 
-bool ArrayInfo::isColumn() const
-{
-    return (dims()[0] > 1 && dims()[1] == 1 && dims()[2] == 1 && dims()[3] == 1);
+bool ArrayInfo::isColumn() const {
+    return (dims()[0] > 1 && dims()[1] == 1 && dims()[2] == 1 &&
+            dims()[3] == 1);
 }
 
-bool ArrayInfo::isVector() const
-{
-    int singular_dims = 0;
+bool ArrayInfo::isVector() const {
+    int singular_dims     = 0;
     int non_singular_dims = 0;
-    for(int i = 0; i < AF_MAX_DIMS; i++) {
+    for (int i = 0; i < AF_MAX_DIMS; i++) {
         non_singular_dims += (dims()[i] != 0 && dims()[i] != 1);
         singular_dims += (dims()[i] == 1);
     }
     return singular_dims == AF_MAX_DIMS - 1 && non_singular_dims == 1;
 }
 
-bool ArrayInfo::isComplex() const
-{
-    return ((type == c32) || (type == c64));
-}
+bool ArrayInfo::isComplex() const { return ((type == c32) || (type == c64)); }
 
-bool ArrayInfo::isReal() const
-{
-    return !isComplex();
-}
+bool ArrayInfo::isReal() const { return !isComplex(); }
 
-bool ArrayInfo::isDouble() const
-{
-    return (type == f64 || type == c64);
-}
+bool ArrayInfo::isDouble() const { return (type == f64 || type == c64); }
 
-bool ArrayInfo::isSingle() const
-{
-    return (type == f32 || type == c32);
-}
+bool ArrayInfo::isSingle() const { return (type == f32 || type == c32); }
 
-bool ArrayInfo::isRealFloating() const
-{
-    return (type == f64 || type == f32);
-}
+bool ArrayInfo::isRealFloating() const { return (type == f64 || type == f32); }
 
-bool ArrayInfo::isFloating() const
-{
-    return (!isInteger() && !isBool());
-}
+bool ArrayInfo::isFloating() const { return (!isInteger() && !isBool()); }
 
-bool ArrayInfo::isInteger() const
-{
-    return (type == s32
-         || type == u32
-         || type == s64
-         || type == u64
-         || type == s16
-         || type == u16
-         || type == u8);
+bool ArrayInfo::isInteger() const {
+    return (type == s32 || type == u32 || type == s64 || type == u64 ||
+            type == s16 || type == u16 || type == u8);
 }
 
-bool ArrayInfo::isBool() const
-{
-    return (type == b8);
-}
+bool ArrayInfo::isBool() const { return (type == b8); }
 
-bool ArrayInfo::isLinear() const
-{
-    if (ndims() == 1) {
-        return dim_strides[0] == 1;
-    }
+bool ArrayInfo::isLinear() const {
+    if (ndims() == 1) { return dim_strides[0] == 1; }
 
     dim_t count = 1;
     for (int i = 0; i < (int)ndims(); i++) {
-        if (count != dim_strides[i]) {
-            return false;
-        }
+        if (count != dim_strides[i]) { return false; }
         count *= dim_size[i];
     }
     return true;
 }
 
-bool ArrayInfo::isSparse() const
-{
-    return is_sparse;
-}
+bool ArrayInfo::isSparse() const { return is_sparse; }
 
-dim4 getOutDims(const dim4 &ldims, const dim4 &rdims, bool batchMode)
-{
+dim4 getOutDims(const dim4 &ldims, const dim4 &rdims, bool batchMode) {
     if (!batchMode) {
         DIM_ASSERT(1, ldims == rdims);
         return ldims;
@@ -192,11 +142,9 @@ dim4 getOutDims(const dim4 &ldims, const dim4 &rdims, bool batchMode)
 
 using std::vector;
 
-dim4
-toDims(const vector<af_seq>& seqs, const dim4 &parentDims)
-{
+dim4 toDims(const vector<af_seq> &seqs, const dim4 &parentDims) {
     dim4 outDims(1, 1, 1, 1);
-    for(unsigned i = 0; i < seqs.size(); i++ ) {
+    for (unsigned i = 0; i < seqs.size(); i++) {
         outDims[i] = af::calcDim(seqs[i], parentDims[i]);
         if (outDims[i] > parentDims[i])
             AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
@@ -204,12 +152,10 @@ toDims(const vector<af_seq>& seqs, const dim4 &parentDims)
     return outDims;
 }
 
-dim4
-toOffset(const vector<af_seq>& seqs, const dim4 &parentDims)
-{
+dim4 toOffset(const vector<af_seq> &seqs, const dim4 &parentDims) {
     dim4 outOffsets(0, 0, 0, 0);
-    for(unsigned i = 0; i < seqs.size(); i++ ) {
-        if (seqs[i].step !=0 && seqs[i].begin >= 0) {
+    for (unsigned i = 0; i < seqs.size(); i++) {
+        if (seqs[i].step != 0 && seqs[i].begin >= 0) {
             outOffsets[i] = seqs[i].begin;
         } else if (seqs[i].begin <= -1) {
             outOffsets[i] = parentDims[i] + seqs[i].begin;
@@ -223,30 +169,26 @@ toOffset(const vector<af_seq>& seqs, const dim4 &parentDims)
     return outOffsets;
 }
 
-dim4
-toStride(const vector<af_seq>& seqs, const af::dim4 &parentDims)
-{
+dim4 toStride(const vector<af_seq> &seqs, const af::dim4 &parentDims) {
     dim4 out(calcStrides(parentDims));
-    for(unsigned i = 0; i < seqs.size(); i++ ) {
-        if  (seqs[i].step != 0) {   out[i] *= seqs[i].step; }
+    for (unsigned i = 0; i < seqs.size(); i++) {
+        if (seqs[i].step != 0) { out[i] *= seqs[i].step; }
     }
     return out;
 }
 
-const ArrayInfo&
-getInfo(const af_array arr, bool sparse_check, bool device_check)
-{
-  const ArrayInfo *info = static_cast<ArrayInfo*>(reinterpret_cast<void *>(arr));
+const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
+                         bool device_check) {
+    const ArrayInfo *info =
+        static_cast<ArrayInfo *>(reinterpret_cast<void *>(arr));
 
-  // Check Sparse -> If false, then both standard Array<T> and SparseArray<T> are accepted
-  // Otherwise only regular Array<T> is accepted
-  if(sparse_check) {
-    ARG_ASSERT(0, info->isSparse() == false);
-  }
+    // Check Sparse -> If false, then both standard Array<T> and SparseArray<T>
+    // are accepted Otherwise only regular Array<T> is accepted
+    if (sparse_check) { ARG_ASSERT(0, info->isSparse() == false); }
 
-  if (device_check && info->getDevId() != detail::getActiveDeviceId()) {
-    AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
-  }
+    if (device_check && info->getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    }
 
-  return *info;
+    return *info;
 }
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index 3d9af5205a..99313ed4c4 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -9,96 +9,97 @@
 
 #pragma once
 #include <common/defines.hpp>
-#include <af/dim4.hpp>
 #include <af/device.h>
-#include <vector>
+#include <af/dim4.hpp>
 #include <cstddef>
+#include <vector>
 
-af::dim4
-calcStrides(const af::dim4 &parentDim);
+af::dim4 calcStrides(const af::dim4& parentDim);
 
-af::dim4 getOutDims(const af::dim4 &ldims, const af::dim4 &rdims, bool batchMode);
+af::dim4 getOutDims(const af::dim4& ldims, const af::dim4& rdims,
+                    bool batchMode);
 
 /// Array Arrayementation Info class
 // This class is the base class to all Array objects. The purpose of this class
 // was to have a way to retrieve basic information of an Array object without
 // specifying what type the object is at compile time.
-class ArrayInfo
-{
-private:
-    // The devId variable stores information about the deviceId as well as the backend.
-    // The 8 LSBs (0-7) are used to store the device ID.
-    // The 09th LSB is set to 1 if backend is CPU
-    // The 10th LSB is set to 1 if backend is CUDA
+class ArrayInfo {
+   private:
+    // The devId variable stores information about the deviceId as well as the
+    // backend. The 8 LSBs (0-7) are used to store the device ID. The 09th LSB
+    // is set to 1 if backend is CPU The 10th LSB is set to 1 if backend is CUDA
     // The 11th LSB is set to 1 if backend is OpenCL
     // This information can be retrieved directly from an af_array by doing
     //     int* devId = reinterpret_cast<int*>(a); // a is an af_array
-    //     af_backend backendID = *devId >> 8;   // Returns 1, 2, 4 for CPU, CUDA or OpenCL respectively
-    //     int        deviceID  = *devId & 0xff; // Returns devices ID between 0-255
+    //     af_backend backendID = *devId >> 8;   // Returns 1, 2, 4 for CPU,
+    //     CUDA or OpenCL respectively int        deviceID  = *devId & 0xff; //
+    //     Returns devices ID between 0-255
     // This is possible by doing a static_assert on devId
     //
-    // This can be changed in the future if the need arises for more devices as this
-    // implementation is internal. Make sure to change the bit shift ops when
-    // such a change is being made
-    int             devId;
-    af_dtype        type;
-    af::dim4        dim_size;
-    dim_t           offset;
-    af::dim4        dim_strides;
-    bool            is_sparse;
-
-public:
-    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride, af_dtype af_type):
-        devId(id),
-        type(af_type),
-        dim_size(size),
-        offset(offset_),
-        dim_strides(stride),
-        is_sparse(false)
-    {
+    // This can be changed in the future if the need arises for more devices as
+    // this implementation is internal. Make sure to change the bit shift ops
+    // when such a change is being made
+    int devId;
+    af_dtype type;
+    af::dim4 dim_size;
+    dim_t offset;
+    af::dim4 dim_strides;
+    bool is_sparse;
+
+   public:
+    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride,
+              af_dtype af_type)
+        : devId(id)
+        , type(af_type)
+        , dim_size(size)
+        , offset(offset_)
+        , dim_strides(stride)
+        , is_sparse(false) {
         setId(id);
 #if __cplusplus > 199711l
-    static_assert(offsetof(ArrayInfo, devId) == 0,
-                  "ArrayInfo::devId must be the first member variable of ArrayInfo. \
+        static_assert(
+            offsetof(ArrayInfo, devId) == 0,
+            "ArrayInfo::devId must be the first member variable of ArrayInfo. \
                    devId is used to encode the backend into the integer. \
                    This is then used in the unified backend to check mismatched arrays.");
 #endif
     }
 
-    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride, af_dtype af_type, bool sparse):
-        devId(id),
-        type(af_type),
-        dim_size(size),
-        offset(offset_),
-        dim_strides(stride),
-        is_sparse(sparse)
-    {
+    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride,
+              af_dtype af_type, bool sparse)
+        : devId(id)
+        , type(af_type)
+        , dim_size(size)
+        , offset(offset_)
+        , dim_strides(stride)
+        , is_sparse(sparse) {
         setId(id);
 #if __cplusplus > 199711l
-    static_assert(offsetof(ArrayInfo, devId) == 0,
-                  "ArrayInfo::devId must be the first member variable of ArrayInfo. \
+        static_assert(
+            offsetof(ArrayInfo, devId) == 0,
+            "ArrayInfo::devId must be the first member variable of ArrayInfo. \
                    devId is used to encode the backend into the integer. \
                    This is then used in the unified backend to check mismatched arrays.");
 #endif
     }
 
 #if __cplusplus > 199711L
-    //Copy constructors are deprecated if there is a
-    //user-defined destructor in c++11
-    ArrayInfo() = default;
+    // Copy constructors are deprecated if there is a
+    // user-defined destructor in c++11
+    ArrayInfo()                       = default;
     ArrayInfo(const ArrayInfo& other) = default;
 #endif
 
-    const af_dtype& getType() const     { return type;                  }
+    const af_dtype& getType() const { return type; }
 
-    dim_t getOffset() const             { return offset;                }
+    dim_t getOffset() const { return offset; }
 
-    const af::dim4& strides() const     { return dim_strides;           }
+    const af::dim4& strides() const { return dim_strides; }
 
-    size_t elements() const             { return dim_size.elements();   }
-    size_t ndims() const                { return dim_size.ndims();      }
-    const af::dim4& dims() const        { return dim_size;              }
-    size_t total() const                { return offset + dim_strides[3] * dim_size[3]; }
+    size_t elements() const { return dim_size.elements(); }
+    size_t ndims() const { return dim_size.ndims(); }
+    const af::dim4& dims() const { return dim_size; }
+    size_t total() const { return offset + dim_strides[3] * dim_size[3]; }
 
     int getDevId() const;
 
@@ -108,21 +109,17 @@ class ArrayInfo
 
     af_backend getBackendId() const;
 
-    void resetInfo(const af::dim4& dims)
-    {
-        dim_size = dims;
+    void resetInfo(const af::dim4& dims) {
+        dim_size    = dims;
         dim_strides = calcStrides(dims);
-        offset = 0;
+        offset      = 0;
     }
 
-    void resetDims(const af::dim4& dims)
-    {
-        dim_size = dims;
-    }
+    void resetDims(const af::dim4& dims) { dim_size = dims; }
 
-    void modDims(const af::dim4 &newDims);
+    void modDims(const af::dim4& newDims);
 
-    void modStrides(const af::dim4 &newStrides);
+    void modStrides(const af::dim4& newStrides);
 
     bool isEmpty() const;
 
@@ -155,11 +152,12 @@ class ArrayInfo
     bool isSparse() const;
 };
 #if __cplusplus > 199711l
-    static_assert(std::is_standard_layout<ArrayInfo>::value, "ArrayInfo must be a standard layout type");
+static_assert(std::is_standard_layout<ArrayInfo>::value,
+              "ArrayInfo must be a standard layout type");
 #endif
 
-af::dim4 toDims(const std::vector<af_seq>& seqs, const af::dim4 &parentDims);
+af::dim4 toDims(const std::vector<af_seq>& seqs, const af::dim4& parentDims);
 
-af::dim4 toOffset(const std::vector<af_seq>& seqs, const af::dim4 &parentDims);
+af::dim4 toOffset(const std::vector<af_seq>& seqs, const af::dim4& parentDims);
 
-af::dim4 toStride(const std::vector<af_seq>& seqs, const af::dim4 &parentDims);
+af::dim4 toStride(const std::vector<af_seq>& seqs, const af::dim4& parentDims);
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index daa0d1141f..7fee45cdf8 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -36,37 +36,33 @@ using std::string;
 
 namespace {
 
-    std::string libName(std::string name) {
-        return libraryPrefix + name + librarySuffix;
-    }
+std::string libName(std::string name) {
+    return libraryPrefix + name + librarySuffix;
 }
+}  // namespace
 
 namespace common {
 
-DependencyModule::DependencyModule(const char* plugin_file_name, const char** paths)
+DependencyModule::DependencyModule(const char* plugin_file_name,
+                                   const char** paths)
     : handle(nullptr) {
     // TODO(umar): Implement handling of non-standard paths
     UNUSED(paths);
-    if(plugin_file_name) {
+    if (plugin_file_name) {
         handle = loadLibrary(libName(plugin_file_name).c_str());
     }
 }
 
 DependencyModule::~DependencyModule() {
-    if(handle) {
-        unloadLibrary(handle);
-    }
+    if (handle) { unloadLibrary(handle); }
 }
 
-bool DependencyModule::isLoaded() {
-    return (bool)handle;
-}
+bool DependencyModule::isLoaded() { return (bool)handle; }
 
 bool DependencyModule::symbolsLoaded() {
-    return all_of(begin(functions), end(functions), [](void* ptr){ return ptr != nullptr; });
+    return all_of(begin(functions), end(functions),
+                  [](void* ptr) { return ptr != nullptr; });
 }
 
-string DependencyModule::getErrorMessage() {
-    return common::getErrorMessage();
-}
-}
+string DependencyModule::getErrorMessage() { return common::getErrorMessage(); }
+}  // namespace common
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 3e577f4116..2122612712 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -26,8 +26,9 @@ class DependencyModule {
     LibHandle handle;
     std::vector<void*> functions;
 
-  public:
-    DependencyModule(const char* plugin_file_name, const char** paths = nullptr);
+   public:
+    DependencyModule(const char* plugin_file_name,
+                     const char** paths = nullptr);
 
     ~DependencyModule();
 
@@ -49,12 +50,11 @@ class DependencyModule {
     std::string getErrorMessage();
 };
 
-}
+}  // namespace common
 
 /// Creates a function pointer
-#define MODULE_MEMBER(NAME)                     \
-    decltype(&::NAME) NAME
+#define MODULE_MEMBER(NAME) decltype(&::NAME) NAME
 
 /// Dynamically loads the function pointer at runtime
-#define MODULE_FUNCTION_INIT(NAME)                \
+#define MODULE_FUNCTION_INIT(NAME) \
     NAME = module.getSymbol<decltype(&::NAME)>(#NAME)
diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
index c2d9d7c9bd..bd341032a2 100644
--- a/src/backend/common/FFTPlanCache.hpp
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -13,66 +13,60 @@
 #include <string>
 #include <utility>
 
-namespace common
-{
+namespace common {
 // FFTPlanCache caches backend specific fft plans in FIFO order
 //
-// new plan |--> IF number of plans cached is at limit, pop the oldest entry and push new plan.
+// new plan |--> IF number of plans cached is at limit, pop the oldest entry and
+// push new plan.
 //          |
 //          |--> ELSE just push the plan
 // existing plan -> reuse a plan
 template<typename T, typename P>
-class FFTPlanCache
-{
-    using plan_t = typename std::shared_ptr<P>;
-    using plan_pair_t = typename std::pair<std::string, plan_t>;
+class FFTPlanCache {
+    using plan_t       = typename std::shared_ptr<P>;
+    using plan_pair_t  = typename std::pair<std::string, plan_t>;
     using plan_cache_t = typename std::deque<plan_pair_t>;
 
-    public:
-        FFTPlanCache() : mMaxCacheSize(5) {}
+   public:
+    FFTPlanCache() : mMaxCacheSize(5) {}
 
-        void setMaxCacheSize(size_t size)
-        {
-            mMaxCacheSize = size;
-            while (mCache.size()>mMaxCacheSize)
-                mCache.pop_back();
-        }
+    void setMaxCacheSize(size_t size) {
+        mMaxCacheSize = size;
+        while (mCache.size() > mMaxCacheSize) mCache.pop_back();
+    }
 
-        size_t getMaxCacheSize() const    { return mMaxCacheSize; }
+    size_t getMaxCacheSize() const { return mMaxCacheSize; }
 
-        // iterates through plan cache from front to back
-        // of the cache(queue)
-        // A valid shared_ptr of the plan in the cache is returned
-        // if found, and empty share_ptr otherwise.
-        plan_t find(const std::string& key) const
-        {
-            std::shared_ptr<P> res;
+    // iterates through plan cache from front to back
+    // of the cache(queue)
+    // A valid shared_ptr of the plan in the cache is returned
+    // if found, and empty share_ptr otherwise.
+    plan_t find(const std::string& key) const {
+        std::shared_ptr<P> res;
 
-            for(unsigned i=0; i<mCache.size(); ++i) {
-                if (key == mCache[i].first) {
-                    res = mCache[i].second;
-                    break;
-                }
+        for (unsigned i = 0; i < mCache.size(); ++i) {
+            if (key == mCache[i].first) {
+                res = mCache[i].second;
+                break;
             }
-
-            return res;
         }
 
-        // pushes plan to the front of cache(queue)
-        void push(const std::string key, plan_t plan)
-        {
-            if (mCache.size()>=mMaxCacheSize)
-                mCache.pop_back();
+        return res;
+    }
 
-            mCache.push_front(plan_pair_t(key, plan));
-        }
+    // pushes plan to the front of cache(queue)
+    void push(const std::string key, plan_t plan) {
+        if (mCache.size() >= mMaxCacheSize) mCache.pop_back();
+
+        mCache.push_front(plan_pair_t(key, plan));
+    }
 
-    protected:
-        FFTPlanCache(FFTPlanCache const&);
-        void operator=(FFTPlanCache const&);
+   protected:
+    FFTPlanCache(FFTPlanCache const&);
+    void operator=(FFTPlanCache const&);
 
-        size_t       mMaxCacheSize;
+    size_t mMaxCacheSize;
 
-        plan_cache_t mCache;
+    plan_cache_t mCache;
 };
-}
+}  // namespace common
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index d94ccb6fd9..b3f95d5d2c 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -18,88 +18,93 @@
 #include <memory>
 #include <vector>
 
-namespace common
-{
+namespace common {
 template<class T, typename R>
-class InteropManager
-{
+class InteropManager {
     using resource_t = typename std::shared_ptr<R>;
-    using  res_vec_t = typename std::vector<resource_t>;
-    using  res_map_t = typename std::map<void*, res_vec_t>;
+    using res_vec_t  = typename std::vector<resource_t>;
+    using res_map_t  = typename std::map<void *, res_vec_t>;
 
-    public:
-        InteropManager() {}
+   public:
+    InteropManager() {}
 
-        ~InteropManager() {
-            try {
-                destroyResources();
-            } catch (AfError &ex) {
-                std::string perr = getEnvVar("AF_PRINT_ERRORS");
-                if(!perr.empty()) {
-                    if(perr != "0") fprintf(stderr, "%s\n", ex.what());
-                }
+    ~InteropManager() {
+        try {
+            destroyResources();
+        } catch (AfError &ex) {
+            std::string perr = getEnvVar("AF_PRINT_ERRORS");
+            if (!perr.empty()) {
+                if (perr != "0") fprintf(stderr, "%s\n", ex.what());
             }
         }
+    }
 
-        res_vec_t getImageResources(const fg_window image) {
-            if (mInteropMap.find(image) == mInteropMap.end()) {
-                uint32_t buffer;
-                FG_CHECK(graphics::forgePlugin().fg_get_pixel_buffer(&buffer, image));
-                mInteropMap[image] =
-                    static_cast<T*>(this)->registerResources({buffer});
-            }
-            return mInteropMap[image];
+    res_vec_t getImageResources(const fg_window image) {
+        if (mInteropMap.find(image) == mInteropMap.end()) {
+            uint32_t buffer;
+            FG_CHECK(
+                graphics::forgePlugin().fg_get_pixel_buffer(&buffer, image));
+            mInteropMap[image] =
+                static_cast<T *>(this)->registerResources({buffer});
         }
+        return mInteropMap[image];
+    }
 
-        res_vec_t getPlotResources(const fg_plot plot) {
-            if (mInteropMap.find(plot) == mInteropMap.end()) {
-                uint32_t buffer;
-                FG_CHECK(graphics::forgePlugin().fg_get_plot_vertex_buffer(&buffer, plot));
-                mInteropMap[plot] =
-                    static_cast<T*>(this)->registerResources({buffer});
-            }
-            return mInteropMap[plot];
+    res_vec_t getPlotResources(const fg_plot plot) {
+        if (mInteropMap.find(plot) == mInteropMap.end()) {
+            uint32_t buffer;
+            FG_CHECK(graphics::forgePlugin().fg_get_plot_vertex_buffer(&buffer,
+                                                                       plot));
+            mInteropMap[plot] =
+                static_cast<T *>(this)->registerResources({buffer});
         }
+        return mInteropMap[plot];
+    }
 
-        res_vec_t getHistogramResources(const fg_histogram histogram) {
-            if (mInteropMap.find(histogram) == mInteropMap.end()) {
-                uint32_t buffer;
-                FG_CHECK(graphics::forgePlugin().fg_get_histogram_vertex_buffer(&buffer, histogram));
-                mInteropMap[histogram] =
-                    static_cast<T*>(this)->registerResources({buffer});
-            }
-            return mInteropMap[histogram];
+    res_vec_t getHistogramResources(const fg_histogram histogram) {
+        if (mInteropMap.find(histogram) == mInteropMap.end()) {
+            uint32_t buffer;
+            FG_CHECK(graphics::forgePlugin().fg_get_histogram_vertex_buffer(
+                &buffer, histogram));
+            mInteropMap[histogram] =
+                static_cast<T *>(this)->registerResources({buffer});
         }
+        return mInteropMap[histogram];
+    }
 
-        res_vec_t getSurfaceResources(const fg_surface surface) {
-            if (mInteropMap.find(surface) == mInteropMap.end()) {
-                uint32_t buffer;
-                FG_CHECK(graphics::forgePlugin().fg_get_surface_vertex_buffer(&buffer, surface));
-                mInteropMap[surface] =
-                    static_cast<T*>(this)->registerResources({buffer});
-            }
-            return mInteropMap[surface];
+    res_vec_t getSurfaceResources(const fg_surface surface) {
+        if (mInteropMap.find(surface) == mInteropMap.end()) {
+            uint32_t buffer;
+            FG_CHECK(graphics::forgePlugin().fg_get_surface_vertex_buffer(
+                &buffer, surface));
+            mInteropMap[surface] =
+                static_cast<T *>(this)->registerResources({buffer});
         }
+        return mInteropMap[surface];
+    }
 
-        res_vec_t getVectorFieldResources(const fg_vector_field field) {
-            if (mInteropMap.find(field) == mInteropMap.end()) {
-                uint32_t verts, dirs;
-                FG_CHECK(graphics::forgePlugin().fg_get_vector_field_vertex_buffer(&verts, field));
-                FG_CHECK(graphics::forgePlugin().fg_get_vector_field_direction_buffer(&dirs, field));
-                mInteropMap[field] =
-                    static_cast<T*>(this)->registerResources({verts, dirs});
-            }
-            return mInteropMap[field];
+    res_vec_t getVectorFieldResources(const fg_vector_field field) {
+        if (mInteropMap.find(field) == mInteropMap.end()) {
+            uint32_t verts, dirs;
+            FG_CHECK(graphics::forgePlugin().fg_get_vector_field_vertex_buffer(
+                &verts, field));
+            FG_CHECK(
+                graphics::forgePlugin().fg_get_vector_field_direction_buffer(
+                    &dirs, field));
+            mInteropMap[field] =
+                static_cast<T *>(this)->registerResources({verts, dirs});
         }
+        return mInteropMap[field];
+    }
 
-    protected:
-        InteropManager(InteropManager const&);
-        void operator=(InteropManager const&);
+   protected:
+    InteropManager(InteropManager const &);
+    void operator=(InteropManager const &);
 
-        void destroyResources() {
-            for(auto iter : mInteropMap) iter.second.clear();
-        }
+    void destroyResources() {
+        for (auto iter : mInteropMap) iter.second.clear();
+    }
 
-        res_map_t mInteropMap;
+    res_map_t mInteropMap;
 };
-}
+}  // namespace common
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index 905ec81bc2..d08732f950 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -8,17 +8,17 @@
  ********************************************************/
 
 #ifdef _WIN32
-#include <windows.h> // spdlog needs this
+#include <windows.h>  // spdlog needs this
 #endif
 
 #include <common/Logger.hpp>
 #include <common/util.hpp>
 
+#include <spdlog/sinks/stdout_sinks.h>
 #include <array>
 #include <cstdlib>
 #include <memory>
 #include <string>
-#include <spdlog/sinks/stdout_sinks.h>
 
 using std::array;
 using std::make_shared;
@@ -27,23 +27,22 @@ using std::string;
 using std::to_string;
 
 using spdlog::get;
-using spdlog::level::trace;
 using spdlog::logger;
 using spdlog::stdout_logger_mt;
+using spdlog::level::trace;
 
 namespace common {
-shared_ptr<logger>
-loggerFactory(string name) {
+shared_ptr<logger> loggerFactory(string name) {
     shared_ptr<logger> logger;
-    if(!(logger = get(name))) {
+    if (!(logger = get(name))) {
         logger = stdout_logger_mt(name);
         logger->set_pattern("[%n][%t] %v");
 
         // Log mode
         string env_var = getEnvVar("AF_TRACE");
-        if(env_var.find("all") != string::npos ||
-           env_var.find(name) != string::npos) {
-          logger->set_level(trace);
+        if (env_var.find("all") != string::npos ||
+            env_var.find(name) != string::npos) {
+            logger->set_level(trace);
         }
     }
     return logger;
@@ -51,12 +50,12 @@ loggerFactory(string name) {
 
 string bytesToString(size_t bytes) {
     static array<const char *, 5> units{{"B", "KB", "MB", "GB", "TB"}};
-    size_t count = 0;
-    double fbytes = static_cast<double>(bytes);
+    size_t count     = 0;
+    double fbytes    = static_cast<double>(bytes);
     size_t num_units = units.size();
-    for(count = 0; count < num_units && fbytes > 1000.0f; count++) {
+    for (count = 0; count < num_units && fbytes > 1000.0f; count++) {
         fbytes *= (1.0f / 1024.0f);
     }
     return fmt::format("{:.3g} {}", fbytes, units[count]);
 }
-}
+}  // namespace common
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index 5776fe9c3c..b00f9ac303 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -13,20 +13,24 @@
 #include <string>
 
 namespace spdlog {
-    class logger;
+class logger;
 }
 namespace common {
-    std::shared_ptr<spdlog::logger> loggerFactory(std::string name);
-    std::string bytesToString(size_t bytes);
-}
+std::shared_ptr<spdlog::logger> loggerFactory(std::string name);
+std::string bytesToString(size_t bytes);
+}  // namespace common
 
 #ifdef AF_WITH_LOGGING
 #define AF_STR_H(x) #x
 #define AF_STR_HELPER(x) AF_STR_H(x)
 #ifdef _MSC_VER
-#define AF_TRACE(...) getLogger()->trace("[ " __FILE__ "(" AF_STR_HELPER(__LINE__) ") ] " __VA_ARGS__)
+#define AF_TRACE(...)                \
+    getLogger()->trace("[ " __FILE__ \
+                       "(" AF_STR_HELPER(__LINE__) ") ] " __VA_ARGS__)
 #else
-#define AF_TRACE(...) getLogger()->trace("[ " __FILE__ ":" AF_STR_HELPER(__LINE__) " ] " __VA_ARGS__)
+#define AF_TRACE(...)                \
+    getLogger()->trace("[ " __FILE__ \
+                       ":" AF_STR_HELPER(__LINE__) " ] " __VA_ARGS__)
 #endif
 #else
 #define AF_TRACE(logger, ...) (void)0
diff --git a/src/backend/common/MatrixAlgebraHandle.hpp b/src/backend/common/MatrixAlgebraHandle.hpp
index bc3c55de59..c90ea613b0 100644
--- a/src/backend/common/MatrixAlgebraHandle.hpp
+++ b/src/backend/common/MatrixAlgebraHandle.hpp
@@ -12,28 +12,20 @@
 #include <common/err_common.hpp>
 #include <cstdio>
 
-namespace common
-{
+namespace common {
 template<typename T, typename H>
-class MatrixAlgebraHandle
-{
-    public:
-        MatrixAlgebraHandle() {
-            static_cast<T*>(this)->createHandle(&handle);
-        }
+class MatrixAlgebraHandle {
+   public:
+    MatrixAlgebraHandle() { static_cast<T*>(this)->createHandle(&handle); }
 
-        ~MatrixAlgebraHandle() {
-            static_cast<T*>(this)->destroyHandle(handle);
-        }
+    ~MatrixAlgebraHandle() { static_cast<T*>(this)->destroyHandle(handle); }
 
-        H get() const {
-            return handle;
-        }
+    H get() const { return handle; }
 
-    private:
-        MatrixAlgebraHandle(MatrixAlgebraHandle const&);
-        void operator=(MatrixAlgebraHandle const&);
+   private:
+    MatrixAlgebraHandle(MatrixAlgebraHandle const&);
+    void operator=(MatrixAlgebraHandle const&);
 
-        H handle;
+    H handle;
 };
-}
+}  // namespace common
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 94cdabe0b4..2159819ecd 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -23,21 +23,18 @@
 #include <vector>
 
 namespace spdlog {
-  class logger;
+class logger;
 }
-namespace common
-{
+namespace common {
 using mutex_t      = std::mutex;
 using lock_guard_t = std::lock_guard<mutex_t>;
 
-const unsigned MAX_BUFFERS   = 1000;
-const size_t ONE_GB = 1 << 30;
+const unsigned MAX_BUFFERS = 1000;
+const size_t ONE_GB        = 1 << 30;
 
 template<typename T>
-class MemoryManager
-{
-    typedef struct
-    {
+class MemoryManager {
+    typedef struct {
         bool manager_lock;
         bool user_lock;
         size_t bytes;
@@ -46,15 +43,14 @@ class MemoryManager
     using locked_t    = typename std::unordered_map<void *, locked_info>;
     using locked_iter = typename locked_t::iterator;
 
-    using free_t    = std::unordered_map<size_t, std::vector<void *> >;
+    using free_t    = std::unordered_map<size_t, std::vector<void *>>;
     using free_iter = free_t::iterator;
 
-    using uptr_t = std::unique_ptr<void, std::function<void(void*)>>;
+    using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
 
-    typedef struct memory_info
-    {
+    typedef struct memory_info {
         locked_t locked_map;
-        free_t   free_map;
+        free_t free_map;
 
         size_t lock_bytes;
         size_t lock_buffers;
@@ -62,10 +58,9 @@ class MemoryManager
         size_t total_buffers;
         size_t max_bytes;
 
-        memory_info()
-        {
-            // Calling getMaxMemorySize() here calls the virtual function that returns 0
-            // Call it from outside the constructor.
+        memory_info() {
+            // Calling getMaxMemorySize() here calls the virtual function that
+            // returns 0 Call it from outside the constructor.
             max_bytes     = ONE_GB;
             total_bytes   = 0;
             total_buffers = 0;
@@ -80,13 +75,13 @@ class MemoryManager
     std::shared_ptr<spdlog::logger> logger;
     bool debug_mode;
 
-    memory_info& getCurrentMemoryInfo();
+    memory_info &getCurrentMemoryInfo();
 
     inline int getActiveDeviceId();
     inline size_t getMaxMemorySize(int id);
     void cleanDeviceMemoryManager(int device);
 
-  public:
+   public:
     MemoryManager(int num_devices, unsigned max_buffers, bool debug);
 
     // Intended to be used with OpenCL backend, where
@@ -113,8 +108,8 @@ class MemoryManager
     /// manager.
     size_t allocated(void *ptr);
 
-    /// Frees or marks the pointer for deletion during the nex garbage collection
-    /// event
+    /// Frees or marks the pointer for deletion during the nex garbage
+    /// collection event
     void unlock(void *ptr, bool user_unlock);
 
     /// Frees all buffers which are not locked by the user or not being used.
@@ -122,7 +117,7 @@ class MemoryManager
 
     void printInfo(const char *msg, const int device);
     void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                    size_t *lock_bytes,  size_t *lock_buffers);
+                    size_t *lock_bytes, size_t *lock_buffers);
     void userLock(const void *ptr);
     void userUnlock(const void *ptr);
     bool isUserLocked(const void *ptr);
@@ -133,15 +128,16 @@ class MemoryManager
     inline void *nativeAlloc(const size_t bytes);
     inline void nativeFree(void *ptr);
     bool checkMemoryLimit();
-  protected:
-    spdlog::logger* getLogger();
-    MemoryManager() = delete;
-    ~MemoryManager() = default;
-    MemoryManager(const MemoryManager& other) = delete;
-    MemoryManager(const MemoryManager&& other) = delete;
-    MemoryManager& operator=(const MemoryManager& other) = delete;
-    MemoryManager& operator=(const MemoryManager&& other) = delete;
+
+   protected:
+    spdlog::logger *getLogger();
+    MemoryManager()                            = delete;
+    ~MemoryManager()                           = default;
+    MemoryManager(const MemoryManager &other)  = delete;
+    MemoryManager(const MemoryManager &&other) = delete;
+    MemoryManager &operator=(const MemoryManager &other) = delete;
+    MemoryManager &operator=(const MemoryManager &&other) = delete;
     mutex_t memory_mutex;
 };
 
-}
+}  // namespace common
diff --git a/src/backend/common/MemoryManagerImpl.hpp b/src/backend/common/MemoryManagerImpl.hpp
index dc9fa8ea1c..cf45ff348d 100644
--- a/src/backend/common/MemoryManagerImpl.hpp
+++ b/src/backend/common/MemoryManagerImpl.hpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/MemoryManager.hpp>
 #include <common/Logger.hpp>
+#include <common/MemoryManager.hpp>
 
 #include <string>
 #include <vector>
@@ -20,22 +20,21 @@ using std::vector;
 
 using spdlog::logger;
 
-namespace common
-{
+namespace common {
 template<typename T>
-typename MemoryManager<T>::memory_info&
+typename MemoryManager<T>::memory_info &
 MemoryManager<T>::getCurrentMemoryInfo() {
     return memory[this->getActiveDeviceId()];
 }
 
 template<typename T>
 inline int MemoryManager<T>::getActiveDeviceId() {
-    return static_cast<T*>(this)->getActiveDeviceId();
+    return static_cast<T *>(this)->getActiveDeviceId();
 }
 
 template<typename T>
 inline size_t MemoryManager<T>::getMaxMemorySize(int id) {
-    return static_cast<T*>(this)->getMaxMemorySize(id);
+    return static_cast<T *>(this)->getMaxMemorySize(id);
 }
 
 template<typename T>
@@ -45,9 +44,9 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
     // the lock is being held becasue the CPU backend calls sync.
-    vector<void*> free_ptrs;
-    size_t bytes_freed = 0;
-    memory_info& current = memory[device];
+    vector<void *> free_ptrs;
+    size_t bytes_freed   = 0;
+    memory_info &current = memory[device];
     {
         lock_guard_t lock(this->memory_mutex);
         // Return if all buffers are locked
@@ -58,9 +57,7 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
             size_t num_ptrs = kv.second.size();
             // Free memory by pushing the last element into the free_ptrs
             // vector which will be freed once outside of the lock
-            for(auto p : kv.second) {
-                free_ptrs.push_back(p);
-            }
+            for (auto p : kv.second) { free_ptrs.push_back(p); }
             current.total_bytes -= num_ptrs * kv.first;
             bytes_freed += num_ptrs * kv.first;
             current.total_buffers -= num_ptrs;
@@ -68,22 +65,20 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
         current.free_map.clear();
     }
 
-    AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(), bytesToString(bytes_freed));
+    AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(),
+             bytesToString(bytes_freed));
     // Free memory outside of the lock
-    for(auto ptr : free_ptrs) {
-        this->nativeFree(ptr);
-    }
+    for (auto ptr : free_ptrs) { this->nativeFree(ptr); }
 }
 
 template<typename T>
-MemoryManager<T>::MemoryManager(int num_devices,
-                                unsigned max_buffers,
+MemoryManager<T>::MemoryManager(int num_devices, unsigned max_buffers,
                                 bool debug)
-    : mem_step_size(1024),
-      max_buffers(max_buffers),
-      memory(num_devices),
-      logger (loggerFactory("mem")),
-      debug_mode(debug) {
+    : mem_step_size(1024)
+    , max_buffers(max_buffers)
+    , memory(num_devices)
+    , logger(loggerFactory("mem"))
+    , debug_mode(debug) {
     // Check for environment variables
 
     // Debug mode
@@ -93,26 +88,24 @@ MemoryManager<T>::MemoryManager(int num_devices,
 
     // Max Buffer count
     env_var = getEnvVar("AF_MAX_BUFFERS");
-    if (!env_var.empty())
-      this->max_buffers = max(1, stoi(env_var));
+    if (!env_var.empty()) this->max_buffers = max(1, stoi(env_var));
 }
 
 template<typename T>
 void MemoryManager<T>::addMemoryManagement(int device) {
     // If there is a memory manager allocated for this device id, we might
     // as well use it and the buffers allocated for it
-    if (static_cast<size_t>(device) < memory.size())
-        return;
+    if (static_cast<size_t>(device) < memory.size()) return;
 
     // Assuming, device need not be always the next device Lets resize to
     // current_size + device + 1 +1 is to account for device being 0-based
     // index of devices
-    memory.resize(memory.size()+device+1);
+    memory.resize(memory.size() + device + 1);
 }
 
 template<typename T>
 void MemoryManager<T>::removeMemoryManagement(int device) {
-    if ((size_t)device>=memory.size())
+    if ((size_t)device >= memory.size())
         AF_ERROR("No matching device found", AF_ERR_ARG);
 
     // Do garbage collection for the device and leave the memory_info struct
@@ -123,35 +116,32 @@ void MemoryManager<T>::removeMemoryManagement(int device) {
 template<typename T>
 void MemoryManager<T>::setMaxMemorySize() {
     for (unsigned n = 0; n < memory.size(); n++) {
-
         // Calls garbage collection when: total_bytes > memsize * 0.75 when
         // memsize < 4GB total_bytes > memsize - 1 GB when memsize >= 4GB If
         // memsize returned 0, then use 1GB
         size_t memsize = this->getMaxMemorySize(n);
-        memory[n].max_bytes = memsize == 0 ? ONE_GB :
-            max(memsize * 0.75, (double)(memsize - ONE_GB));
+        memory[n].max_bytes =
+            memsize == 0 ? ONE_GB
+                         : max(memsize * 0.75, (double)(memsize - ONE_GB));
     }
 }
 
 template<typename T>
 void *MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
-    void *ptr = nullptr;
-    size_t alloc_bytes =
-      this->debug_mode ? bytes :
-                          (divup(bytes, mem_step_size) * mem_step_size);
+    void *ptr          = nullptr;
+    size_t alloc_bytes = this->debug_mode
+                             ? bytes
+                             : (divup(bytes, mem_step_size) * mem_step_size);
 
     if (bytes > 0) {
-        memory_info& current = this->getCurrentMemoryInfo();
-        locked_info info = {!user_lock, user_lock, alloc_bytes};
+        memory_info &current = this->getCurrentMemoryInfo();
+        locked_info info     = {!user_lock, user_lock, alloc_bytes};
 
         // There is no memory cache in debug mode
         if (!this->debug_mode) {
-
             // FIXME: Add better checks for garbage collection
             // Perhaps look at total memory available as a metric
-            if (this->checkMemoryLimit()) {
-                this->garbageCollect();
-            }
+            if (this->checkMemoryLimit()) { this->garbageCollect(); }
 
             lock_guard_t lock(this->memory_mutex);
             free_iter iter = current.free_map.find(alloc_bytes);
@@ -192,8 +182,8 @@ void *MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
 template<typename T>
 size_t MemoryManager<T>::allocated(void *ptr) {
     if (!ptr) return 0;
-    memory_info& current = this->getCurrentMemoryInfo();
-    locked_iter iter = current.locked_map.find((void *)ptr);
+    memory_info &current = this->getCurrentMemoryInfo();
+    locked_iter iter     = current.locked_map.find((void *)ptr);
     if (iter == current.locked_map.end()) return 0;
     return (iter->second).bytes;
 }
@@ -204,10 +194,10 @@ void MemoryManager<T>::unlock(void *ptr, bool user_unlock) {
     if (!ptr) return;
 
     // Frees the pointer outside the lock.
-    uptr_t freed_ptr(nullptr, [this](void* p) { this->nativeFree(p); });
+    uptr_t freed_ptr(nullptr, [this](void *p) { this->nativeFree(p); });
     {
         lock_guard_t lock(this->memory_mutex);
-        memory_info& current = this->getCurrentMemoryInfo();
+        memory_info &current = this->getCurrentMemoryInfo();
 
         locked_iter iter = current.locked_map.find((void *)ptr);
 
@@ -252,46 +242,48 @@ void MemoryManager<T>::garbageCollect() {
 
 template<typename T>
 void MemoryManager<T>::printInfo(const char *msg, const int device) {
-    const memory_info& current = memory[device];
+    const memory_info &current = memory[device];
 
     printf("%s\n", msg);
-    printf("---------------------------------------------------------\n"
-            "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
-            "---------------------------------------------------------\n");
+    printf(
+        "---------------------------------------------------------\n"
+        "|     POINTER      |    SIZE    |  AF LOCK  | USER LOCK |\n"
+        "---------------------------------------------------------\n");
 
     lock_guard_t lock(this->memory_mutex);
-    for(auto& kv : current.locked_map) {
-        const char* status_mngr = "Yes";
-        const char* status_user = "Unknown";
-        if(kv.second.user_lock)     status_user = "Yes";
-        else                        status_user = " No";
-
-        const char* unit = "KB";
-        double size = (double)(kv.second.bytes) / 1024;
-        if(size >= 1024) {
+    for (auto &kv : current.locked_map) {
+        const char *status_mngr = "Yes";
+        const char *status_user = "Unknown";
+        if (kv.second.user_lock)
+            status_user = "Yes";
+        else
+            status_user = " No";
+
+        const char *unit = "KB";
+        double size      = (double)(kv.second.bytes) / 1024;
+        if (size >= 1024) {
             size = size / 1024;
             unit = "MB";
         }
 
-        printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
-                kv.first, size, unit, status_mngr, status_user);
+        printf("|  %14p  |  %6.f %s | %9s | %9s |\n", kv.first, size, unit,
+               status_mngr, status_user);
     }
 
-    for(auto &kv : current.free_map) {
+    for (auto &kv : current.free_map) {
+        const char *status_mngr = "No";
+        const char *status_user = "No";
 
-        const char* status_mngr = "No";
-        const char* status_user = "No";
-
-        const char* unit = "KB";
-        double size = (double)(kv.first) / 1024;
-        if(size >= 1024) {
+        const char *unit = "KB";
+        double size      = (double)(kv.first) / 1024;
+        if (size >= 1024) {
             size = size / 1024;
             unit = "MB";
         }
 
         for (auto &ptr : kv.second) {
-          printf("|  %14p  |  %6.f %s | %9s | %9s |\n",
-                  ptr, size, unit, status_mngr, status_user);
+            printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr, size, unit,
+                   status_mngr, status_user);
         }
     }
 
@@ -300,18 +292,18 @@ void MemoryManager<T>::printInfo(const char *msg, const int device) {
 
 template<typename T>
 void MemoryManager<T>::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                                  size_t *lock_bytes,  size_t *lock_buffers) {
-    const memory_info& current = this->getCurrentMemoryInfo();
+                                  size_t *lock_bytes, size_t *lock_buffers) {
+    const memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
-    if (alloc_bytes   ) *alloc_bytes   = current.total_bytes;
-    if (alloc_buffers ) *alloc_buffers = current.total_buffers;
-    if (lock_bytes    ) *lock_bytes    = current.lock_bytes;
-    if (lock_buffers  ) *lock_buffers  = current.lock_buffers;
+    if (alloc_bytes) *alloc_bytes = current.total_bytes;
+    if (alloc_buffers) *alloc_buffers = current.total_buffers;
+    if (lock_bytes) *lock_bytes = current.lock_bytes;
+    if (lock_buffers) *lock_buffers = current.lock_buffers;
 }
 
 template<typename T>
 void MemoryManager<T>::userLock(const void *ptr) {
-    memory_info& current = this->getCurrentMemoryInfo();
+    memory_info &current = this->getCurrentMemoryInfo();
 
     lock_guard_t lock(this->memory_mutex);
 
@@ -319,9 +311,7 @@ void MemoryManager<T>::userLock(const void *ptr) {
     if (iter != current.locked_map.end()) {
         iter->second.user_lock = true;
     } else {
-        locked_info info = {false,
-            true,
-            100}; //This number is not relevant
+        locked_info info = {false, true, 100};  // This number is not relevant
 
         current.locked_map[(void *)ptr] = info;
     }
@@ -334,7 +324,7 @@ void MemoryManager<T>::userUnlock(const void *ptr) {
 
 template<typename T>
 bool MemoryManager<T>::isUserLocked(const void *ptr) {
-    memory_info& current = this->getCurrentMemoryInfo();
+    memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
     locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
     if (iter != current.locked_map.end()) {
@@ -362,7 +352,7 @@ unsigned MemoryManager<T>::getMaxBuffers() {
 }
 
 template<typename T>
-logger* MemoryManager<T>::getLogger() {
+logger *MemoryManager<T>::getLogger() {
     return this->logger.get();
 }
 
@@ -374,18 +364,18 @@ void MemoryManager<T>::setMemStepSize(size_t new_step_size) {
 
 template<typename T>
 inline void *MemoryManager<T>::nativeAlloc(const size_t bytes) {
-    return static_cast<T*>(this)->nativeAlloc(bytes);
+    return static_cast<T *>(this)->nativeAlloc(bytes);
 }
 
 template<typename T>
 inline void MemoryManager<T>::nativeFree(void *ptr) {
-    static_cast<T*>(this)->nativeFree(ptr);
+    static_cast<T *>(this)->nativeFree(ptr);
 }
 
 template<typename T>
 bool MemoryManager<T>::checkMemoryLimit() {
-    const memory_info& current = this->getCurrentMemoryInfo();
+    const memory_info &current = this->getCurrentMemoryInfo();
     return current.lock_bytes >= current.max_bytes ||
-            current.total_buffers >= this->max_buffers;
-}
+           current.total_buffers >= this->max_buffers;
 }
+}  // namespace common
diff --git a/src/backend/common/MersenneTwister.hpp b/src/backend/common/MersenneTwister.hpp
index 5bf847f990..2810a1da0c 100644
--- a/src/backend/common/MersenneTwister.hpp
+++ b/src/backend/common/MersenneTwister.hpp
@@ -51,168 +51,213 @@
 
 #include <af/defines.h>
 
-namespace common
-{
-    const dim_t MaxBlocks = 32;
-    const dim_t TableLength = 16*MaxBlocks;
-    const dim_t MersenneN = 351;
-    const dim_t MtStateLength = MaxBlocks * MersenneN;
+namespace common {
+const dim_t MaxBlocks     = 32;
+const dim_t TableLength   = 16 * MaxBlocks;
+const dim_t MersenneN     = 351;
+const dim_t MtStateLength = MaxBlocks * MersenneN;
 
-    static unsigned pos[] = {
+static unsigned pos[] = {
     88, 84, 25, 42, 22, 11, 76, 11, 42, 60, 45, 80, 81, 16, 63, 38,
-    3, 55, 9, 75, 70, 63, 32, 70, 58, 33, 18, 9, 14, 91, 90, 86,
-    };
+    3,  55, 9,  75, 70, 63, 32, 70, 58, 33, 18, 9,  14, 91, 90, 86,
+};
 
-    static unsigned sh1[] = {
-    19, 15, 4, 20, 1, 16, 16, 15, 6, 6, 12, 6, 8, 1, 14, 28,
-    30, 1, 9, 17, 15, 15, 7, 12, 21, 7, 7, 12, 16, 4, 10, 6,
-    };
+static unsigned sh1[] = {
+    19, 15, 4, 20, 1,  16, 16, 15, 6,  6, 12, 6,  8,  1, 14, 28,
+    30, 1,  9, 17, 15, 15, 7,  12, 21, 7, 7,  12, 16, 4, 10, 6,
+};
 
-    static unsigned sh2[] = {
-    5, 12, 18, 9, 5, 1, 6, 16, 11, 11, 13, 9, 18, 19, 18, 1,
-    2, 16, 15, 6, 6, 17, 15, 10, 2, 10, 13, 13, 3, 2, 14, 7,
-    };
+static unsigned sh2[] = {
+    5, 12, 18, 9, 5, 1,  6,  16, 11, 11, 13, 9,  18, 19, 18, 1,
+    2, 16, 15, 6, 6, 17, 15, 10, 2,  10, 13, 13, 3,  2,  14, 7,
+};
 
-    static unsigned mask = 4294443008;
-    //static const unsigned mask[] = {
-    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
-    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
-    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
-    //4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
-    //};
+static unsigned mask = 4294443008;
+// static const unsigned mask[] = {
+// 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+// 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+// 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+// 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+// 4294443008, 4294443008, 4294443008, 4294443008, 4294443008, 4294443008,
+// 4294443008, 4294443008,
+//};
 
-    static unsigned recursion_tbl[] = {
-    0, 2879706668, 3137826695, 279165355, 570425344, 2309281324, 2567401351, 849590699,
-    38330, 2879669142, 3137862205, 279129105, 570463674, 2309243798, 2567436861, 849554449,
-    0, 2593609479, 1975655185, 4015344662, 357564441, 2412205854, 1620187912, 4194651151,
-    53865, 2593621358, 1975699832, 4015365759, 357618288, 2412217719, 1620232545, 4194672230,
-    0, 1350351572, 3879866427, 3074337519, 1908408359, 566016755, 2525106204, 3338578632,
-    56684, 1350330296, 3879914839, 3074324355, 1908464971, 565995423, 2525154672, 3338565540,
-    0, 1055977248, 2682116586, 2704094922, 271581238, 784396054, 2414729692, 2971481852,
-    23789, 1055962061, 2682094855, 2704108071, 271604955, 784380923, 2414708017, 2971494929,
-    0, 2953117369, 62376084, 3014866477, 122683469, 3075800820, 82299097, 3034789472,
-    18916, 2953099101, 62357872, 3014885321, 122702249, 3075782416, 82280765, 3034808196,
-    0, 1684682268, 3815655459, 2265218623, 723517535, 1330263619, 3360573564, 2888072800,
-    51884, 1684733104, 3815670415, 2265232531, 723569395, 1330314479, 3360588496, 2888086732,
-    0, 465136444, 2086871972, 1742358680, 574619745, 972647261, 1579361221, 1167739129,
-    19553, 465119069, 2086891461, 1742341369, 574639104, 972629820, 1579380644, 1167721624,
-    0, 2761653723, 3161842783, 418290052, 1969225846, 3522919853, 3373655081, 1838062066,
-    50425, 2761668898, 3161792678, 418274685, 1969276047, 3522935124, 3373605072, 1838046475,
-    0, 823868564, 2715863359, 2432431531, 1012924546, 226180118, 2642463165, 2895901993,
-    46626, 823888566, 2715844381, 2432385929, 1012971168, 226200116, 2642444191, 2895856395,
-    0, 3163477696, 1302313789, 4044451325, 2389704861, 855561821, 3287268256, 2137091424,
-    40970, 3163453130, 1302272823, 4044475895, 2389745815, 855537239, 3287227306, 2137116010,
-    0, 112997176, 3723016697, 3679750849, 4213178533, 4254872477, 650688860, 544508516,
-    42220, 113022932, 3722976533, 3679727149, 4213220425, 4254898033, 650649008, 544485000,
-    0, 612320333, 3070909104, 2473926397, 3292528821, 3762242808, 1934252549, 1463098952,
-    21839, 612307202, 3070889983, 2473937842, 3292550650, 3762229687, 1934233418, 1463110407,
-    0, 3452614784, 3739161126, 320187046, 3513778374, 481998918, 263131872, 3261442656,
-    46663, 3452571335, 3739198561, 320150753, 3513824897, 481955329, 263169191, 3261406247,
-    0, 588293362, 2445856652, 3000527742, 2311061713, 2865800227, 403230557, 991456175,
-    62105, 588273259, 2445819157, 3000539623, 2311123528, 2865780410, 403193284, 991467830,
-    0, 2177339548, 3971246860, 1836317584, 4080009455, 1928826995, 528772067, 2655255423,
-    59650, 2177333662, 3971252750, 1836257938, 4080069101, 1928821105, 528777953, 2655195773,
-    0, 3849091899, 1973261595, 2431774240, 442499322, 4279008193, 1878889953, 2324819674,
-    17524, 3849076559, 1973277039, 2431756884, 442516622, 4278992821, 1878905237, 2324802222,
-    0, 2208474059, 1390454348, 3510764935, 2555379979, 468886208, 3400574791, 1225917580,
-    43685, 2208434542, 1390415081, 3510808354, 2555423662, 468846693, 3400535522, 1225961001,
-    0, 753114312, 1662184071, 1341224527, 2666529043, 2987630043, 4259507092, 3506534236,
-    32220, 753131796, 1662162779, 1341197203, 2666560719, 2987646983, 4259485256, 3506506368,
-    0, 578983141, 3246792315, 3808725662, 1649410346, 1087542735, 2748718929, 2169801652,
-    22528, 578997477, 3246802555, 3808744094, 1649432874, 1087557071, 2748729169, 2169820084,
-    0, 2174674396, 1341825145, 3462677925, 3861905719, 1739515115, 2848629070, 676611218,
-    35444, 2174644136, 1341794829, 3462713297, 3861941059, 1739484831, 2848598842, 676646630,
-    0, 3312287941, 1270375145, 2396381740, 2464153923, 1468891526, 3646448554, 473293679,
-    38325, 3312260464, 1270413148, 2396354457, 2464191734, 1468863539, 3646486047, 473265882,
-    0, 1011438676, 4087471600, 3488123300, 455082329, 661214477, 3900824745, 3569912061,
-    52539, 1011456367, 4087419083, 3488105631, 455134306, 661231670, 3900772754, 3569894854,
-    0, 3529350863, 2972224887, 1668617144, 3278897504, 288202671, 1918405655, 2684687064,
-    37845, 3529313562, 2972196514, 1668644973, 3278934709, 288164986, 1918377922, 2684715277,
-    0, 1851876274, 2748239338, 3450842712, 3060793725, 3625018063, 364825751, 2078256933,
-    54612, 1851897574, 2748192958, 3450829580, 3060847657, 3625039771, 364779971, 2078243441,
-    0, 2798128189, 889378840, 2479543333, 3664773513, 2092436916, 4017281425, 1236981164,
-    52108, 2798176177, 889328532, 2479497129, 3664824837, 2092484152, 4017230365, 1236934176,
-    0, 1067241239, 3453461153, 4065029558, 4190110101, 3327970946, 873964340, 193686563,
-    27698, 1067229989, 3453472403, 4065001860, 4190137767, 3327959728, 873975558, 193658897,
-    0, 2213869621, 887682042, 3072068559, 4061135267, 1910831510, 3338203737, 1158417004,
-    40265, 2213832060, 887647923, 3072104070, 4061175018, 1910793439, 3338170128, 1158453029,
-    0, 3310321543, 1761913168, 2890651351, 2243953084, 1083145787, 3972311276, 697030507,
-    31607, 3310290160, 1761923623, 2890640800, 2243984075, 1083114828, 3972322203, 697019420,
-    0, 2168967706, 1652210191, 3812452373, 663749057, 2799162331, 1173011406, 3299699156,
-    62673, 2168923851, 1652182750, 3812465860, 663811344, 2799118090, 1172983583, 3299712261,
-    0, 2787116654, 1233955067, 4021071509, 3287286227, 1708132285, 2323425576, 744271686,
-    37500, 2787152914, 1233926791, 4021042409, 3287323567, 1708168641, 2323397460, 744242490,
-    0, 3709953686, 1938447361, 2930457239, 1075839468, 2634114938, 866803181, 4002102139,
-    50633, 3709969247, 1938463176, 2930507614, 1075889189, 2634130099, 866818084, 4002152114,
-    0, 1104625460, 154195956, 1223157952, 1706033657, 610746061, 1820382733, 760736057,
-    35538, 1104655846, 154164518, 1223123474, 1706068779, 610776095, 1820351711, 760701931,
-    };
+static unsigned recursion_tbl[] = {
+    0,          2879706668, 3137826695, 279165355,  570425344,  2309281324,
+    2567401351, 849590699,  38330,      2879669142, 3137862205, 279129105,
+    570463674,  2309243798, 2567436861, 849554449,  0,          2593609479,
+    1975655185, 4015344662, 357564441,  2412205854, 1620187912, 4194651151,
+    53865,      2593621358, 1975699832, 4015365759, 357618288,  2412217719,
+    1620232545, 4194672230, 0,          1350351572, 3879866427, 3074337519,
+    1908408359, 566016755,  2525106204, 3338578632, 56684,      1350330296,
+    3879914839, 3074324355, 1908464971, 565995423,  2525154672, 3338565540,
+    0,          1055977248, 2682116586, 2704094922, 271581238,  784396054,
+    2414729692, 2971481852, 23789,      1055962061, 2682094855, 2704108071,
+    271604955,  784380923,  2414708017, 2971494929, 0,          2953117369,
+    62376084,   3014866477, 122683469,  3075800820, 82299097,   3034789472,
+    18916,      2953099101, 62357872,   3014885321, 122702249,  3075782416,
+    82280765,   3034808196, 0,          1684682268, 3815655459, 2265218623,
+    723517535,  1330263619, 3360573564, 2888072800, 51884,      1684733104,
+    3815670415, 2265232531, 723569395,  1330314479, 3360588496, 2888086732,
+    0,          465136444,  2086871972, 1742358680, 574619745,  972647261,
+    1579361221, 1167739129, 19553,      465119069,  2086891461, 1742341369,
+    574639104,  972629820,  1579380644, 1167721624, 0,          2761653723,
+    3161842783, 418290052,  1969225846, 3522919853, 3373655081, 1838062066,
+    50425,      2761668898, 3161792678, 418274685,  1969276047, 3522935124,
+    3373605072, 1838046475, 0,          823868564,  2715863359, 2432431531,
+    1012924546, 226180118,  2642463165, 2895901993, 46626,      823888566,
+    2715844381, 2432385929, 1012971168, 226200116,  2642444191, 2895856395,
+    0,          3163477696, 1302313789, 4044451325, 2389704861, 855561821,
+    3287268256, 2137091424, 40970,      3163453130, 1302272823, 4044475895,
+    2389745815, 855537239,  3287227306, 2137116010, 0,          112997176,
+    3723016697, 3679750849, 4213178533, 4254872477, 650688860,  544508516,
+    42220,      113022932,  3722976533, 3679727149, 4213220425, 4254898033,
+    650649008,  544485000,  0,          612320333,  3070909104, 2473926397,
+    3292528821, 3762242808, 1934252549, 1463098952, 21839,      612307202,
+    3070889983, 2473937842, 3292550650, 3762229687, 1934233418, 1463110407,
+    0,          3452614784, 3739161126, 320187046,  3513778374, 481998918,
+    263131872,  3261442656, 46663,      3452571335, 3739198561, 320150753,
+    3513824897, 481955329,  263169191,  3261406247, 0,          588293362,
+    2445856652, 3000527742, 2311061713, 2865800227, 403230557,  991456175,
+    62105,      588273259,  2445819157, 3000539623, 2311123528, 2865780410,
+    403193284,  991467830,  0,          2177339548, 3971246860, 1836317584,
+    4080009455, 1928826995, 528772067,  2655255423, 59650,      2177333662,
+    3971252750, 1836257938, 4080069101, 1928821105, 528777953,  2655195773,
+    0,          3849091899, 1973261595, 2431774240, 442499322,  4279008193,
+    1878889953, 2324819674, 17524,      3849076559, 1973277039, 2431756884,
+    442516622,  4278992821, 1878905237, 2324802222, 0,          2208474059,
+    1390454348, 3510764935, 2555379979, 468886208,  3400574791, 1225917580,
+    43685,      2208434542, 1390415081, 3510808354, 2555423662, 468846693,
+    3400535522, 1225961001, 0,          753114312,  1662184071, 1341224527,
+    2666529043, 2987630043, 4259507092, 3506534236, 32220,      753131796,
+    1662162779, 1341197203, 2666560719, 2987646983, 4259485256, 3506506368,
+    0,          578983141,  3246792315, 3808725662, 1649410346, 1087542735,
+    2748718929, 2169801652, 22528,      578997477,  3246802555, 3808744094,
+    1649432874, 1087557071, 2748729169, 2169820084, 0,          2174674396,
+    1341825145, 3462677925, 3861905719, 1739515115, 2848629070, 676611218,
+    35444,      2174644136, 1341794829, 3462713297, 3861941059, 1739484831,
+    2848598842, 676646630,  0,          3312287941, 1270375145, 2396381740,
+    2464153923, 1468891526, 3646448554, 473293679,  38325,      3312260464,
+    1270413148, 2396354457, 2464191734, 1468863539, 3646486047, 473265882,
+    0,          1011438676, 4087471600, 3488123300, 455082329,  661214477,
+    3900824745, 3569912061, 52539,      1011456367, 4087419083, 3488105631,
+    455134306,  661231670,  3900772754, 3569894854, 0,          3529350863,
+    2972224887, 1668617144, 3278897504, 288202671,  1918405655, 2684687064,
+    37845,      3529313562, 2972196514, 1668644973, 3278934709, 288164986,
+    1918377922, 2684715277, 0,          1851876274, 2748239338, 3450842712,
+    3060793725, 3625018063, 364825751,  2078256933, 54612,      1851897574,
+    2748192958, 3450829580, 3060847657, 3625039771, 364779971,  2078243441,
+    0,          2798128189, 889378840,  2479543333, 3664773513, 2092436916,
+    4017281425, 1236981164, 52108,      2798176177, 889328532,  2479497129,
+    3664824837, 2092484152, 4017230365, 1236934176, 0,          1067241239,
+    3453461153, 4065029558, 4190110101, 3327970946, 873964340,  193686563,
+    27698,      1067229989, 3453472403, 4065001860, 4190137767, 3327959728,
+    873975558,  193658897,  0,          2213869621, 887682042,  3072068559,
+    4061135267, 1910831510, 3338203737, 1158417004, 40265,      2213832060,
+    887647923,  3072104070, 4061175018, 1910793439, 3338170128, 1158453029,
+    0,          3310321543, 1761913168, 2890651351, 2243953084, 1083145787,
+    3972311276, 697030507,  31607,      3310290160, 1761923623, 2890640800,
+    2243984075, 1083114828, 3972322203, 697019420,  0,          2168967706,
+    1652210191, 3812452373, 663749057,  2799162331, 1173011406, 3299699156,
+    62673,      2168923851, 1652182750, 3812465860, 663811344,  2799118090,
+    1172983583, 3299712261, 0,          2787116654, 1233955067, 4021071509,
+    3287286227, 1708132285, 2323425576, 744271686,  37500,      2787152914,
+    1233926791, 4021042409, 3287323567, 1708168641, 2323397460, 744242490,
+    0,          3709953686, 1938447361, 2930457239, 1075839468, 2634114938,
+    866803181,  4002102139, 50633,      3709969247, 1938463176, 2930507614,
+    1075889189, 2634130099, 866818084,  4002152114, 0,          1104625460,
+    154195956,  1223157952, 1706033657, 610746061,  1820382733, 760736057,
+    35538,      1104655846, 154164518,  1223123474, 1706068779, 610776095,
+    1820351711, 760701931,
+};
 
-    static unsigned temper_tbl[] = {
-    0, 101711872, 634912768, 600309760, 673972224, 775684096, 234094592, 199491584,
-    855825920, 890428928, 383442432, 281730560, 456056320, 490659328, 1056366080, 954654208,
-    0, 6581248, 135327744, 141859840, 922910720, 929491968, 1058172928, 1064705024,
-    5266944, 3420672, 138456576, 136626688, 928177664, 926331392, 1061301760, 1059471872,
-    0, 69272064, 134479872, 203751936, 289406976, 358679040, 423886848, 493158912,
-    544153088, 609098752, 678108672, 743054336, 825171456, 890117120, 959127040, 1024072704,
-    0, 608635904, 2523648, 610373120, 5439488, 605293568, 7700992, 607292928,
-    274488832, 874205696, 276487168, 876466176, 269442560, 877154816, 271178752, 879677440,
-    0, 1214875648, 67174400, 1281918976, 1614020608, 677218304, 1681195008, 744261632,
-    537288192, 1752159744, 604462592, 1819203072, 1077042688, 140236288, 1144217088, 207279616,
-    0, 3567010304, 194572288, 3741626880, 604008960, 4036767744, 798523904, 4211392512,
-    1563500032, 2309839872, 1453977088, 2184555520, 2033282048, 2913807872, 1923718144, 2788548096,
-    0, 136677376, 272809984, 409421824, 2109440, 134592512, 274919424, 407336960,
-    1891638784, 2028312064, 1619189248, 1755796992, 1893740032, 2026219008, 1621290496, 1753703936,
-    0, 4026617856, 172764160, 4199382016, 75673600, 4102283264, 248421376, 4275031040,
-    1158700544, 3037793792, 1331458560, 3210551808, 1100148224, 2979249664, 1272889856, 3151991296,
-    0, 2147483648, 0, 2147483648, 3221225472, 1073741824, 3221225472, 1073741824,
-    536878592, 2684362240, 536878592, 2684362240, 3758104064, 1610620416, 3758104064, 1610620416,
-    0, 3225420800, 2228224, 3227649024, 809369600, 4034790400, 807141376, 4032562176,
-    1073880576, 2151815680, 1075846656, 2153781760, 1882988032, 2960923136, 1881021952, 2958957056,
-    0, 206130176, 1107561472, 1313685504, 537462784, 742409216, 1645020160, 1849968640,
-    272670208, 470405632, 1380225536, 1577967104, 810128896, 1006688768, 1917688320, 2114246144,
-    0, 807406080, 275513344, 541854208, 1573888, 808979968, 276038656, 542379520,
-    269098496, 539628544, 6692352, 809899008, 269621760, 540151808, 8264192, 811470848,
-    0, 1612447744, 142082048, 1751384064, 7602176, 1617428480, 135004160, 1745879040,
-    10493440, 1622941184, 148381184, 1757683200, 13901312, 1623727616, 145497600, 1756372480,
-    0, 1275199488, 2793406464, 3934388224, 352321536, 1493303296, 3011510272, 4286709760,
-    689970688, 1696734720, 2409635328, 3282181632, 1008737792, 1881284096, 2594184704, 3600948736,
-    0, 150999040, 33619968, 184619008, 2103296, 153094144, 35723264, 186714112,
-    805543424, 956534272, 839032320, 990023168, 807634432, 958633472, 841123328, 992122368,
-    0, 3225487872, 3876324352, 659361280, 4198400000, 981436928, 489849856, 3715337728,
-    545267200, 3770749952, 3347847680, 130879488, 3669925376, 452957184, 1035115008, 4260597760,
-    0, 3910139904, 2496659456, 2109734912, 3687579648, 853278720, 1327235072, 2785804288,
-    164634112, 3770686976, 2634030592, 1947213312, 3525058048, 990649856, 1187782144, 2950438400,
-    0, 201461760, 3812622336, 4014084096, 33554432, 235016192, 3779067904, 3980529664,
-    1352670720, 1554124288, 3017809408, 3219262976, 1386225152, 1587678720, 2984254976, 3185708544,
-    0, 1469123584, 3497837568, 2280507392, 2818795520, 4287783936, 2021633024, 804167680,
-    5381632, 1472401920, 3492731392, 2277496320, 2823910912, 4290804224, 2016260608, 800898560,
-    0, 2550235136, 537106432, 3087144960, 1074806784, 3625041920, 1611913216, 4161951744,
-    212480, 2550316544, 536913408, 3087083008, 1075019264, 3625123328, 1611720192, 4161889792,
-    0, 543432704, 623958016, 89454592, 27283968, 566522368, 613452288, 83143168,
-    5381632, 540425728, 627230208, 84338176, 32656384, 563506176, 616731648, 78033920,
-    0, 134377472, 2521945088, 2656281600, 4236288, 138597376, 2517725184, 2652045312,
-    7249408, 141356544, 2520730112, 2654812672, 3029504, 137120256, 2524966400, 2659032576,
-    0, 269697536, 42663936, 311968256, 1346895872, 1079722496, 1388511232, 1120944640,
-    284057088, 16587776, 308633088, 41294848, 1084644864, 1354046464, 1110269440, 1379802112,
-    0, 54056960, 5527552, 57442304, 22155264, 40552448, 17188864, 37654528,
-    2153984, 51906048, 7636480, 55336448, 24301056, 38409728, 19305984, 35540480,
-    0, 3597599744, 21800448, 3609436672, 4467200, 3593154048, 17337344, 3613886464,
-    209935872, 3672922624, 231733248, 3684760576, 214397952, 3668471808, 227267072, 3689207296,
-    0, 70910976, 7421952, 72041472, 272663552, 343572480, 271696896, 336314368,
-    1879350784, 1950259712, 1886772736, 1951390208, 1615075840, 1685986816, 1614109184, 1678728704,
-    0, 1761935360, 185210880, 1645156352, 1101029376, 681926656, 1252685824, 598702080,
-    74128896, 1835933184, 258016768, 1717831168, 1170963968, 751730176, 1321297408, 667182592,
-    0, 3338677248, 1516357632, 2640438272, 302069760, 3573617664, 1214312448, 2405489664,
-    3368033792, 264253952, 2460079616, 1436678656, 3670091264, 499190272, 2158030336, 1201717760,
-    0, 554461696, 7452672, 561893888, 3422689792, 3977146368, 3430130176, 3984574464,
-    1102241280, 1623110656, 1103324672, 1624181760, 2377171968, 2898046464, 2378267648, 2899121664,
-    0, 697340416, 7652864, 702829568, 1074688000, 1772020224, 1081783808, 1776952320,
-    1141022208, 1838287872, 1148606464, 1843841536, 67956224, 765230080, 74983424, 770226688,
-    0, 423231488, 128225280, 513708032, 6653952, 425691136, 130095104, 519772160,
-    1146551808, 1567424000, 1139961344, 1523084800, 1144223232, 1560901120, 1134028288, 1521346048,
-    0, 33619968, 271785984, 305274880, 268632064, 302120960, 3153920, 36773888,
-    1077583360, 1111203328, 1342815744, 1376304640, 1345953280, 1379442176, 1074445824, 1108065792,
-    };
+static unsigned temper_tbl[] = {
+    0,          101711872,  634912768,  600309760,  673972224,  775684096,
+    234094592,  199491584,  855825920,  890428928,  383442432,  281730560,
+    456056320,  490659328,  1056366080, 954654208,  0,          6581248,
+    135327744,  141859840,  922910720,  929491968,  1058172928, 1064705024,
+    5266944,    3420672,    138456576,  136626688,  928177664,  926331392,
+    1061301760, 1059471872, 0,          69272064,   134479872,  203751936,
+    289406976,  358679040,  423886848,  493158912,  544153088,  609098752,
+    678108672,  743054336,  825171456,  890117120,  959127040,  1024072704,
+    0,          608635904,  2523648,    610373120,  5439488,    605293568,
+    7700992,    607292928,  274488832,  874205696,  276487168,  876466176,
+    269442560,  877154816,  271178752,  879677440,  0,          1214875648,
+    67174400,   1281918976, 1614020608, 677218304,  1681195008, 744261632,
+    537288192,  1752159744, 604462592,  1819203072, 1077042688, 140236288,
+    1144217088, 207279616,  0,          3567010304, 194572288,  3741626880,
+    604008960,  4036767744, 798523904,  4211392512, 1563500032, 2309839872,
+    1453977088, 2184555520, 2033282048, 2913807872, 1923718144, 2788548096,
+    0,          136677376,  272809984,  409421824,  2109440,    134592512,
+    274919424,  407336960,  1891638784, 2028312064, 1619189248, 1755796992,
+    1893740032, 2026219008, 1621290496, 1753703936, 0,          4026617856,
+    172764160,  4199382016, 75673600,   4102283264, 248421376,  4275031040,
+    1158700544, 3037793792, 1331458560, 3210551808, 1100148224, 2979249664,
+    1272889856, 3151991296, 0,          2147483648, 0,          2147483648,
+    3221225472, 1073741824, 3221225472, 1073741824, 536878592,  2684362240,
+    536878592,  2684362240, 3758104064, 1610620416, 3758104064, 1610620416,
+    0,          3225420800, 2228224,    3227649024, 809369600,  4034790400,
+    807141376,  4032562176, 1073880576, 2151815680, 1075846656, 2153781760,
+    1882988032, 2960923136, 1881021952, 2958957056, 0,          206130176,
+    1107561472, 1313685504, 537462784,  742409216,  1645020160, 1849968640,
+    272670208,  470405632,  1380225536, 1577967104, 810128896,  1006688768,
+    1917688320, 2114246144, 0,          807406080,  275513344,  541854208,
+    1573888,    808979968,  276038656,  542379520,  269098496,  539628544,
+    6692352,    809899008,  269621760,  540151808,  8264192,    811470848,
+    0,          1612447744, 142082048,  1751384064, 7602176,    1617428480,
+    135004160,  1745879040, 10493440,   1622941184, 148381184,  1757683200,
+    13901312,   1623727616, 145497600,  1756372480, 0,          1275199488,
+    2793406464, 3934388224, 352321536,  1493303296, 3011510272, 4286709760,
+    689970688,  1696734720, 2409635328, 3282181632, 1008737792, 1881284096,
+    2594184704, 3600948736, 0,          150999040,  33619968,   184619008,
+    2103296,    153094144,  35723264,   186714112,  805543424,  956534272,
+    839032320,  990023168,  807634432,  958633472,  841123328,  992122368,
+    0,          3225487872, 3876324352, 659361280,  4198400000, 981436928,
+    489849856,  3715337728, 545267200,  3770749952, 3347847680, 130879488,
+    3669925376, 452957184,  1035115008, 4260597760, 0,          3910139904,
+    2496659456, 2109734912, 3687579648, 853278720,  1327235072, 2785804288,
+    164634112,  3770686976, 2634030592, 1947213312, 3525058048, 990649856,
+    1187782144, 2950438400, 0,          201461760,  3812622336, 4014084096,
+    33554432,   235016192,  3779067904, 3980529664, 1352670720, 1554124288,
+    3017809408, 3219262976, 1386225152, 1587678720, 2984254976, 3185708544,
+    0,          1469123584, 3497837568, 2280507392, 2818795520, 4287783936,
+    2021633024, 804167680,  5381632,    1472401920, 3492731392, 2277496320,
+    2823910912, 4290804224, 2016260608, 800898560,  0,          2550235136,
+    537106432,  3087144960, 1074806784, 3625041920, 1611913216, 4161951744,
+    212480,     2550316544, 536913408,  3087083008, 1075019264, 3625123328,
+    1611720192, 4161889792, 0,          543432704,  623958016,  89454592,
+    27283968,   566522368,  613452288,  83143168,   5381632,    540425728,
+    627230208,  84338176,   32656384,   563506176,  616731648,  78033920,
+    0,          134377472,  2521945088, 2656281600, 4236288,    138597376,
+    2517725184, 2652045312, 7249408,    141356544,  2520730112, 2654812672,
+    3029504,    137120256,  2524966400, 2659032576, 0,          269697536,
+    42663936,   311968256,  1346895872, 1079722496, 1388511232, 1120944640,
+    284057088,  16587776,   308633088,  41294848,   1084644864, 1354046464,
+    1110269440, 1379802112, 0,          54056960,   5527552,    57442304,
+    22155264,   40552448,   17188864,   37654528,   2153984,    51906048,
+    7636480,    55336448,   24301056,   38409728,   19305984,   35540480,
+    0,          3597599744, 21800448,   3609436672, 4467200,    3593154048,
+    17337344,   3613886464, 209935872,  3672922624, 231733248,  3684760576,
+    214397952,  3668471808, 227267072,  3689207296, 0,          70910976,
+    7421952,    72041472,   272663552,  343572480,  271696896,  336314368,
+    1879350784, 1950259712, 1886772736, 1951390208, 1615075840, 1685986816,
+    1614109184, 1678728704, 0,          1761935360, 185210880,  1645156352,
+    1101029376, 681926656,  1252685824, 598702080,  74128896,   1835933184,
+    258016768,  1717831168, 1170963968, 751730176,  1321297408, 667182592,
+    0,          3338677248, 1516357632, 2640438272, 302069760,  3573617664,
+    1214312448, 2405489664, 3368033792, 264253952,  2460079616, 1436678656,
+    3670091264, 499190272,  2158030336, 1201717760, 0,          554461696,
+    7452672,    561893888,  3422689792, 3977146368, 3430130176, 3984574464,
+    1102241280, 1623110656, 1103324672, 1624181760, 2377171968, 2898046464,
+    2378267648, 2899121664, 0,          697340416,  7652864,    702829568,
+    1074688000, 1772020224, 1081783808, 1776952320, 1141022208, 1838287872,
+    1148606464, 1843841536, 67956224,   765230080,  74983424,   770226688,
+    0,          423231488,  128225280,  513708032,  6653952,    425691136,
+    130095104,  519772160,  1146551808, 1567424000, 1139961344, 1523084800,
+    1144223232, 1560901120, 1134028288, 1521346048, 0,          33619968,
+    271785984,  305274880,  268632064,  302120960,  3153920,    36773888,
+    1077583360, 1111203328, 1342815744, 1376304640, 1345953280, 1379442176,
+    1074445824, 1108065792,
+};
 
-}
+}  // namespace common
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index d0fc64918d..a06d0a13b6 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/SparseArray.hpp>
 #include <backend.hpp>
+#include <common/SparseArray.hpp>
 #include <copy.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -16,8 +16,7 @@
 
 using af::dtype_traits;
 
-namespace common
-{
+namespace common {
 
 using namespace detail;
 
@@ -29,76 +28,81 @@ using namespace detail;
 // SparseArrayBase::stype
 // _nNZ  -> Constructor Argument
 // _dims -> Constructor Argument
-#define ROW_LENGTH ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC) ? _nNZ : (_dims[0] + 1))
-#define COL_LENGTH ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSR) ? _nNZ : (_dims[1] + 1))
+#define ROW_LENGTH                                               \
+    ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC) ? _nNZ \
+                                                          : (_dims[0] + 1))
+#define COL_LENGTH                                               \
+    ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSR) ? _nNZ \
+                                                          : (_dims[1] + 1))
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage, af_dtype _type):
-    info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
-    stype(_storage),
-    rowIdx(createValueArray<int>(dim4(ROW_LENGTH), 0)),
-    colIdx(createValueArray<int>(dim4(COL_LENGTH), 0))
-{
+SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
+                                 af::storage _storage, af_dtype _type)
+    : info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true)
+    , stype(_storage)
+    , rowIdx(createValueArray<int>(dim4(ROW_LENGTH), 0))
+    , colIdx(createValueArray<int>(dim4(COL_LENGTH), 0)) {
 #if __cplusplus > 199711l
     static_assert(offsetof(SparseArrayBase, info) == 0,
-                  "SparseArrayBase::info must be the first member variable of SparseArrayBase.");
+                  "SparseArrayBase::info must be the first member variable of "
+                  "SparseArrayBase.");
 #endif
 }
 
 SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
-                    const int * const _rowIdx, const int * const _colIdx,
-                    const af::storage _storage, af_dtype _type,
-                    bool _is_device, bool _copy_device):
-    info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
-    stype(_storage),
-    rowIdx(_is_device ?
-          (!_copy_device ? createDeviceDataArray<int>(dim4(ROW_LENGTH), _rowIdx)
-                         : createValueArray<int>(dim4(ROW_LENGTH), 0))
-        : createHostDataArray<int>(dim4(ROW_LENGTH), _rowIdx)),
-    colIdx(_is_device ?
-          (!_copy_device ? createDeviceDataArray<int>(dim4(COL_LENGTH), _colIdx)
-                         : createValueArray<int>(dim4(COL_LENGTH), 0))
-        : createHostDataArray<int>(dim4(COL_LENGTH), _colIdx))
-{
+                                 const int *const _rowIdx,
+                                 const int *const _colIdx,
+                                 const af::storage _storage, af_dtype _type,
+                                 bool _is_device, bool _copy_device)
+    : info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true)
+    , stype(_storage)
+    , rowIdx(_is_device
+                 ? (!_copy_device
+                        ? createDeviceDataArray<int>(dim4(ROW_LENGTH), _rowIdx)
+                        : createValueArray<int>(dim4(ROW_LENGTH), 0))
+                 : createHostDataArray<int>(dim4(ROW_LENGTH), _rowIdx))
+    , colIdx(_is_device
+                 ? (!_copy_device
+                        ? createDeviceDataArray<int>(dim4(COL_LENGTH), _colIdx)
+                        : createValueArray<int>(dim4(COL_LENGTH), 0))
+                 : createHostDataArray<int>(dim4(COL_LENGTH), _colIdx)) {
 #if __cplusplus > 199711L
     static_assert(offsetof(SparseArrayBase, info) == 0,
-                  "SparseArrayBase::info must be the first member variable of SparseArrayBase.");
+                  "SparseArrayBase::info must be the first member variable of "
+                  "SparseArrayBase.");
 #endif
-    if(_is_device && _copy_device) {
+    if (_is_device && _copy_device) {
         writeDeviceDataArray<int>(rowIdx, _rowIdx, ROW_LENGTH * sizeof(int));
         writeDeviceDataArray<int>(colIdx, _colIdx, COL_LENGTH * sizeof(int));
     }
 }
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims,
-                    const Array<int> &_rowIdx, const Array<int> &_colIdx,
-                    const af::storage _storage, af_dtype _type,
-                    bool _copy):
-    info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true),
-    stype(_storage),
-    rowIdx(_copy ? copyArray<int>(_rowIdx): _rowIdx),
-    colIdx(_copy ? copyArray<int>(_colIdx): _colIdx)
-{
+SparseArrayBase::SparseArrayBase(af::dim4 _dims, const Array<int> &_rowIdx,
+                                 const Array<int> &_colIdx,
+                                 const af::storage _storage, af_dtype _type,
+                                 bool _copy)
+    : info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true)
+    , stype(_storage)
+    , rowIdx(_copy ? copyArray<int>(_rowIdx) : _rowIdx)
+    , colIdx(_copy ? copyArray<int>(_colIdx) : _colIdx) {
 #if __cplusplus > 199711L
     static_assert(offsetof(SparseArrayBase, info) == 0,
-                  "SparseArrayBase::info must be the first member variable of SparseArrayBase.");
+                  "SparseArrayBase::info must be the first member variable of "
+                  "SparseArrayBase.");
 #endif
 }
 
-SparseArrayBase::SparseArrayBase(const SparseArrayBase &base, bool copy):
-    info(base.info),
-    stype(base.stype),
-    rowIdx(copy ? copyArray<int>(base.rowIdx): base.rowIdx),
-    colIdx(copy ? copyArray<int>(base.colIdx): base.colIdx) {}
+SparseArrayBase::SparseArrayBase(const SparseArrayBase &base, bool copy)
+    : info(base.info)
+    , stype(base.stype)
+    , rowIdx(copy ? copyArray<int>(base.rowIdx) : base.rowIdx)
+    , colIdx(copy ? copyArray<int>(base.colIdx) : base.colIdx) {}
 
-SparseArrayBase::~SparseArrayBase()
-{
-}
+SparseArrayBase::~SparseArrayBase() {}
 
-dim_t SparseArrayBase::getNNZ() const
-{
-    if(stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC)
+dim_t SparseArrayBase::getNNZ() const {
+    if (stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC)
         return rowIdx.elements();
-    else if(stype == AF_STORAGE_CSR)
+    else if (stype == AF_STORAGE_CSR)
         return colIdx.elements();
 
     // This is to ensure future storages are properly configured
@@ -112,56 +116,49 @@ dim_t SparseArrayBase::getNNZ() const
 // Friend functions for Sparse Array Creation Implementations
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
-SparseArray<T> createEmptySparseArray(
-        const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage)
-{
+SparseArray<T> createEmptySparseArray(const af::dim4 &_dims, dim_t _nNZ,
+                                      const af::storage _storage) {
     return SparseArray<T>(_dims, _nNZ, _storage);
 }
 
 template<typename T>
-SparseArray<T> createHostDataSparseArray(
-        const af::dim4 &_dims, const dim_t nNZ,
-        const T * const _values,
-        const int * const _rowIdx, const int * const _colIdx,
-        const af::storage _storage)
-{
-    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, false);
+SparseArray<T> createHostDataSparseArray(const af::dim4 &_dims, const dim_t nNZ,
+                                         const T *const _values,
+                                         const int *const _rowIdx,
+                                         const int *const _colIdx,
+                                         const af::storage _storage) {
+    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage,
+                          false);
 }
 
 template<typename T>
 SparseArray<T> createDeviceDataSparseArray(
-        const af::dim4 &_dims, const dim_t nNZ,
-        const T * const _values,
-        const int * const _rowIdx, const int * const _colIdx,
-        const af::storage _storage, const bool _copy)
-{
-    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, true, _copy);
+    const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
+    const int *const _rowIdx, const int *const _colIdx,
+    const af::storage _storage, const bool _copy) {
+    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, true,
+                          _copy);
 }
 
 template<typename T>
 SparseArray<T> createArrayDataSparseArray(
-        const af::dim4 &_dims,
-        const Array<T> &_values,
-        const Array<int> &_rowIdx, const Array<int> &_colIdx,
-        const af::storage _storage, const bool _copy)
-{
+    const af::dim4 &_dims, const Array<T> &_values, const Array<int> &_rowIdx,
+    const Array<int> &_colIdx, const af::storage _storage, const bool _copy) {
     return SparseArray<T>(_dims, _values, _rowIdx, _colIdx, _storage, _copy);
 }
 
 template<typename T>
-SparseArray<T> copySparseArray(const SparseArray<T>& other) {
+SparseArray<T> copySparseArray(const SparseArray<T> &other) {
     return SparseArray<T>(other, true);
 }
 
 template<typename T>
-SparseArray<T> *initSparseArray()
-{
-    return new SparseArray<T>(dim4(), 0,  (af::storage)0);
+SparseArray<T> *initSparseArray() {
+    return new SparseArray<T>(dim4(), 0, (af::storage)0);
 }
 
 template<typename T>
-void destroySparseArray(SparseArray<T> *sparse)
-{
+void destroySparseArray(SparseArray<T> *sparse) {
     delete sparse;
 }
 
@@ -169,83 +166,79 @@ void destroySparseArray(SparseArray<T> *sparse)
 // Sparse Array Class Implementations
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
-SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage):
-    base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type),
-    values(createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
-{
+SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage)
+    : base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type)
+    , values(createValueArray<T>(dim4(_nNZ), scalar<T>(0))) {
 #if __cplusplus > 199711L
-        static_assert(std::is_standard_layout<SparseArray<T>>::value,
-                      "SparseArray<T> must be a standard layout type");
-        static_assert(offsetof(SparseArray<T>, base) == 0,
-                      "SparseArray<T>::base must be the first member variable of SparseArray<T>");
+    static_assert(std::is_standard_layout<SparseArray<T>>::value,
+                  "SparseArray<T> must be a standard layout type");
+    static_assert(offsetof(SparseArray<T>, base) == 0,
+                  "SparseArray<T>::base must be the first member variable of "
+                  "SparseArray<T>");
 #endif
 }
 
 template<typename T>
-SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,
-                     const T * const _values,
-                     const int * const _rowIdx, const int * const _colIdx,
-                     const af::storage _storage,
-                     bool _is_device, bool _copy_device):
-    base(_dims, _nNZ, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _is_device, _copy_device),
-    values(_is_device ?
-          (!_copy_device ? createDeviceDataArray<T>(dim4(_nNZ), _values)
-                         : createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
-        : createHostDataArray<T>(dim4(_nNZ), _values))
-{
-    if(_is_device && _copy_device) {
+SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, const T *const _values,
+                            const int *const _rowIdx, const int *const _colIdx,
+                            const af::storage _storage, bool _is_device,
+                            bool _copy_device)
+    : base(_dims, _nNZ, _rowIdx, _colIdx, _storage,
+           (af_dtype)dtype_traits<T>::af_type, _is_device, _copy_device)
+    , values(_is_device ? (!_copy_device
+                               ? createDeviceDataArray<T>(dim4(_nNZ), _values)
+                               : createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
+                        : createHostDataArray<T>(dim4(_nNZ), _values)) {
+    if (_is_device && _copy_device) {
         writeDeviceDataArray<T>(values, _values, _nNZ * sizeof(T));
     }
 }
 
 template<typename T>
-SparseArray<T>::SparseArray(af::dim4 _dims,
-            const Array<T> &_values,
-            const Array<int> &_rowIdx, const Array<int> &_colIdx,
-            const af::storage _storage, bool _copy):
-    base(_dims, _rowIdx, _colIdx, _storage, (af_dtype)dtype_traits<T>::af_type, _copy),
-    values(_copy ? copyArray<T>(_values): _values) {}
+SparseArray<T>::SparseArray(af::dim4 _dims, const Array<T> &_values,
+                            const Array<int> &_rowIdx,
+                            const Array<int> &_colIdx,
+                            const af::storage _storage, bool _copy)
+    : base(_dims, _rowIdx, _colIdx, _storage,
+           (af_dtype)dtype_traits<T>::af_type, _copy)
+    , values(_copy ? copyArray<T>(_values) : _values) {}
 
 template<typename T>
-SparseArray<T>::SparseArray(const SparseArray<T> &other, bool copy):
-    base(other.base, copy),
-    values(copy ? copyArray<T>(other.values): other.values) {}
+SparseArray<T>::SparseArray(const SparseArray<T> &other, bool copy)
+    : base(other.base, copy)
+    , values(copy ? copyArray<T>(other.values) : other.values) {}
 
 template<typename T>
 SparseArray<T>::~SparseArray() {}
 
-#define INSTANTIATE(T)                                                                              \
-    template SparseArray<T> createEmptySparseArray<T>(                                              \
-            const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);                         \
-    template SparseArray<T> createHostDataSparseArray<T>(                                           \
-            const af::dim4 &_dims, const dim_t _nNZ,                                                \
-            const T * const _values,                                                                \
-            const int * const _rowIdx, const int * const _colIdx,                                   \
-            const af::storage _storage);                                                            \
-    template SparseArray<T> createDeviceDataSparseArray<T>(                                         \
-            const af::dim4 &_dims, const dim_t _nNZ,                                                \
-            const T * const _values,                                                                \
-            const int * const _rowIdx, const int * const _colIdx,                                   \
-            const af::storage _storage, const bool _copy);                                          \
-    template SparseArray<T> createArrayDataSparseArray<T>(                                          \
-            const af::dim4 &_dims,                                                                  \
-            const Array<T> &_values,                                                                \
-            const Array<int> &_rowIdx, const Array<int> &_colIdx,                                   \
-            const af::storage _storage, const bool _copy);                                          \
-    template SparseArray<T> *initSparseArray<T>();                                                  \
-    template SparseArray<T> copySparseArray<T>(const SparseArray<T>& other);                        \
-    template void destroySparseArray<T>(SparseArray<T> *sparse);                                    \
-                                                                                                    \
-    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage _storage);         \
-    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,                                \
-                        const T * const _values,                                                    \
-                        const int * const _rowIdx, const int * const _colIdx,                       \
-                        const af::storage _storage,                                                 \
-                        bool _is_device, bool _copy_device);                                        \
-    template SparseArray<T>::SparseArray(af::dim4 _dims,                                            \
-                        const Array<T> &_values,                                                    \
-                        const Array<int> &_rowIdx, const Array<int> &_colIdx,                       \
-                        const af::storage _storage, bool _copy);                                    \
+#define INSTANTIATE(T)                                                       \
+    template SparseArray<T> createEmptySparseArray<T>(                       \
+        const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);      \
+    template SparseArray<T> createHostDataSparseArray<T>(                    \
+        const af::dim4 &_dims, const dim_t _nNZ, const T *const _values,     \
+        const int *const _rowIdx, const int *const _colIdx,                  \
+        const af::storage _storage);                                         \
+    template SparseArray<T> createDeviceDataSparseArray<T>(                  \
+        const af::dim4 &_dims, const dim_t _nNZ, const T *const _values,     \
+        const int *const _rowIdx, const int *const _colIdx,                  \
+        const af::storage _storage, const bool _copy);                       \
+    template SparseArray<T> createArrayDataSparseArray<T>(                   \
+        const af::dim4 &_dims, const Array<T> &_values,                      \
+        const Array<int> &_rowIdx, const Array<int> &_colIdx,                \
+        const af::storage _storage, const bool _copy);                       \
+    template SparseArray<T> *initSparseArray<T>();                           \
+    template SparseArray<T> copySparseArray<T>(const SparseArray<T> &other); \
+    template void destroySparseArray<T>(SparseArray<T> * sparse);            \
+                                                                             \
+    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,         \
+                                         af::storage _storage);              \
+    template SparseArray<T>::SparseArray(                                    \
+        af::dim4 _dims, dim_t _nNZ, const T *const _values,                  \
+        const int *const _rowIdx, const int *const _colIdx,                  \
+        const af::storage _storage, bool _is_device, bool _copy_device);     \
+    template SparseArray<T>::SparseArray(                                    \
+        af::dim4 _dims, const Array<T> &_values, const Array<int> &_rowIdx,  \
+        const Array<int> &_colIdx, const af::storage _storage, bool _copy);  \
     template SparseArray<T>::~SparseArray();
 
 // Instantiate only floating types
@@ -256,4 +249,4 @@ INSTANTIATE(cdouble);
 
 #undef INSTANTIATE
 
-} // namespace common
+}  // namespace common
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index b1c430e869..4059b34792 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -8,20 +8,20 @@
  ********************************************************/
 
 #pragma once
-#include <common/sparse_helpers.hpp>
-#include <common/ArrayInfo.hpp>
 #include <Array.hpp>
 #include <backend.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/sparse_helpers.hpp>
 
 #include <cstddef>
 #include <vector>
 
-namespace common
-{
+namespace common {
 
 using namespace detail;
 
-template<typename T> class SparseArray;
+template<typename T>
+class SparseArray;
 
 /// SparseArray Array Info class
 ///
@@ -31,26 +31,26 @@ template<typename T> class SparseArray;
 ///
 /// NOTE: This is not a template class to allow the frontend to determine the
 /// af_array type at runtime
-class SparseArrayBase
-{
-private:
-    ArrayInfo  info;        ///< NOTE: This must be the first element of SparseArray<T>.
-    af::storage stype;      ///< Storage format: CSR, CSC, COO
-    Array<int> rowIdx;      ///< Linear array containing row indices
-    Array<int> colIdx;      ///< Linear array containing col indices
-
-public:
-    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage, af_dtype _type);
-
-    SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
-                    const int * const _rowIdx, const int * const _colIdx,
-                    const af::storage _storage, af_dtype _type,
-                    bool _is_device = false, bool _copy_device = false);
-
-    SparseArrayBase(af::dim4 _dims,
-                    const Array<int> &_rowIdx, const Array<int> &_colIdx,
-                    const af::storage _storage, af_dtype _type,
-                    bool _copy = false);
+class SparseArrayBase {
+   private:
+    ArrayInfo
+        info;  ///< NOTE: This must be the first element of SparseArray<T>.
+    af::storage stype;  ///< Storage format: CSR, CSC, COO
+    Array<int> rowIdx;  ///< Linear array containing row indices
+    Array<int> colIdx;  ///< Linear array containing col indices
+
+   public:
+    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage,
+                    af_dtype _type);
+
+    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, const int *const _rowIdx,
+                    const int *const _colIdx, const af::storage _storage,
+                    af_dtype _type, bool _is_device = false,
+                    bool _copy_device = false);
+
+    SparseArrayBase(af::dim4 _dims, const Array<int> &_rowIdx,
+                    const Array<int> &_colIdx, const af::storage _storage,
+                    af_dtype _type, bool _copy = false);
 
     /// A copy constructor for SparseArray
     ///
@@ -66,83 +66,79 @@ class SparseArrayBase
     ////////////////////////////////////////////////////////////////////////////
     // Functions that call ArrayInfo object's functions
     ////////////////////////////////////////////////////////////////////////////
-#define INSTANTIATE_INFO(return_type, func)         \
-    return_type func() const { return info.func();  }
-
-    INSTANTIATE_INFO(const af_dtype&, getType       )
-    INSTANTIATE_INFO(size_t         , elements      )
-    INSTANTIATE_INFO(size_t         , ndims         )
-    INSTANTIATE_INFO(const af::dim4&, dims          )
-    INSTANTIATE_INFO(size_t         , total         )
-    INSTANTIATE_INFO(int            , getDevId      )
-    INSTANTIATE_INFO(af_backend     , getBackendId  )
-    INSTANTIATE_INFO(bool           , isEmpty       )
-    INSTANTIATE_INFO(bool           , isScalar      )
-    INSTANTIATE_INFO(bool           , isRow         )
-    INSTANTIATE_INFO(bool           , isColumn      )
-    INSTANTIATE_INFO(bool           , isVector      )
-    INSTANTIATE_INFO(bool           , isComplex     )
-    INSTANTIATE_INFO(bool           , isReal        )
-    INSTANTIATE_INFO(bool           , isDouble      )
-    INSTANTIATE_INFO(bool           , isSingle      )
-    INSTANTIATE_INFO(bool           , isRealFloating)
-    INSTANTIATE_INFO(bool           , isFloating    )
-    INSTANTIATE_INFO(bool           , isInteger     )
-    INSTANTIATE_INFO(bool           , isBool        )
-    INSTANTIATE_INFO(bool           , isLinear      )
-    INSTANTIATE_INFO(bool           , isSparse      )
+#define INSTANTIATE_INFO(return_type, func) \
+    return_type func() const { return info.func(); }
+
+    INSTANTIATE_INFO(const af_dtype &, getType)
+    INSTANTIATE_INFO(size_t, elements)
+    INSTANTIATE_INFO(size_t, ndims)
+    INSTANTIATE_INFO(const af::dim4 &, dims)
+    INSTANTIATE_INFO(size_t, total)
+    INSTANTIATE_INFO(int, getDevId)
+    INSTANTIATE_INFO(af_backend, getBackendId)
+    INSTANTIATE_INFO(bool, isEmpty)
+    INSTANTIATE_INFO(bool, isScalar)
+    INSTANTIATE_INFO(bool, isRow)
+    INSTANTIATE_INFO(bool, isColumn)
+    INSTANTIATE_INFO(bool, isVector)
+    INSTANTIATE_INFO(bool, isComplex)
+    INSTANTIATE_INFO(bool, isReal)
+    INSTANTIATE_INFO(bool, isDouble)
+    INSTANTIATE_INFO(bool, isSingle)
+    INSTANTIATE_INFO(bool, isRealFloating)
+    INSTANTIATE_INFO(bool, isFloating)
+    INSTANTIATE_INFO(bool, isInteger)
+    INSTANTIATE_INFO(bool, isBool)
+    INSTANTIATE_INFO(bool, isLinear)
+    INSTANTIATE_INFO(bool, isSparse)
 
 #undef INSTANTIATE_INFO
 
     // setId of info, values, rowIdx, colIdx
-    void setId(int id)
-    {
+    void setId(int id) {
         info.setId(id);
         rowIdx.setId(id);
         colIdx.setId(id);
     }
 
     /// Returns the row indices for the corresponding values in the SparseArray
-          Array<int>& getRowIdx()           { return rowIdx;            }
-    const Array<int>& getRowIdx()     const { return rowIdx;            }
+    Array<int> &getRowIdx() { return rowIdx; }
+    const Array<int> &getRowIdx() const { return rowIdx; }
 
     /// Returns the column indices for the corresponding values in the
     /// SparseArray
-          Array<int>& getColIdx()           { return colIdx;            }
-    const Array<int>& getColIdx()     const { return colIdx;            }
+    Array<int> &getColIdx() { return colIdx; }
+    const Array<int> &getColIdx() const { return colIdx; }
 
     /// Returns the number of non-zero elements in the array.
-    dim_t getNNZ()                    const;
+    dim_t getNNZ() const;
 
     /// Returns the storage format of the SparseArray
-    af::storage getStorage()          const { return stype;             }
+    af::storage getStorage() const { return stype; }
 };
 #if __cplusplus > 199711L
-        static_assert(std::is_standard_layout<SparseArrayBase>::value,
-                      "SparseArrayBase must be a standard layout type");
+static_assert(std::is_standard_layout<SparseArrayBase>::value,
+              "SparseArrayBase must be a standard layout type");
 #endif
 
 ////////////////////////////////////////////////////////////////////////////
 // Sparse Array Class
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
-class SparseArray
-{
-private:
-    SparseArrayBase  base;    ///< This must be the first element of SparseArray<T>.
-    Array<T>         values;  ///< Linear array containing actual values
+class SparseArray {
+   private:
+    SparseArrayBase
+        base;         ///< This must be the first element of SparseArray<T>.
+    Array<T> values;  ///< Linear array containing actual values
 
     SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage stype);
 
-    explicit
-    SparseArray(af::dim4 _dims, dim_t _nNZ,
-                const T * const _values,
-                const int * const _rowIdx, const int * const _colIdx,
-                const af::storage _storage,
-                bool _is_device = false, bool _copy_device = false);
+    explicit SparseArray(af::dim4 _dims, dim_t _nNZ, const T *const _values,
+                         const int *const _rowIdx, const int *const _colIdx,
+                         const af::storage _storage, bool _is_device = false,
+                         bool _copy_device = false);
 
-    SparseArray(af::dim4 _dims,
-                const Array<T> &_values,
+    SparseArray(af::dim4 _dims, const Array<T> &_values,
                 const Array<int> &_rowIdx, const Array<int> &_colIdx,
                 const af::storage _storage, bool _copy = false);
 
@@ -155,92 +151,87 @@ class SparseArray
     /// \param[in] deep_copy  If true a deep copy is performed
     SparseArray(const SparseArray<T> &in, bool deep_copy);
 
-public:
-
+   public:
     ~SparseArray();
 
 // Functions that call ArrayInfo object's functions
-#define INSTANTIATE_INFO(return_type, func)         \
-    return_type func() const { return base.func();  }
-
-    INSTANTIATE_INFO(const af_dtype&, getType       )
-    INSTANTIATE_INFO(size_t         , elements      )
-    INSTANTIATE_INFO(size_t         , ndims         )
-    INSTANTIATE_INFO(const af::dim4&, dims          )
-    INSTANTIATE_INFO(size_t         , total         )
-    INSTANTIATE_INFO(int            , getDevId      )
-    INSTANTIATE_INFO(af_backend     , getBackendId  )
-    INSTANTIATE_INFO(bool           , isEmpty       )
-    INSTANTIATE_INFO(bool           , isScalar      )
-    INSTANTIATE_INFO(bool           , isRow         )
-    INSTANTIATE_INFO(bool           , isColumn      )
-    INSTANTIATE_INFO(bool           , isVector      )
-    INSTANTIATE_INFO(bool           , isComplex     )
-    INSTANTIATE_INFO(bool           , isReal        )
-    INSTANTIATE_INFO(bool           , isDouble      )
-    INSTANTIATE_INFO(bool           , isSingle      )
-    INSTANTIATE_INFO(bool           , isRealFloating)
-    INSTANTIATE_INFO(bool           , isFloating    )
-    INSTANTIATE_INFO(bool           , isInteger     )
-    INSTANTIATE_INFO(bool           , isBool        )
-    INSTANTIATE_INFO(bool           , isLinear      )
-    INSTANTIATE_INFO(bool           , isSparse      )
+#define INSTANTIATE_INFO(return_type, func) \
+    return_type func() const { return base.func(); }
+
+    INSTANTIATE_INFO(const af_dtype &, getType)
+    INSTANTIATE_INFO(size_t, elements)
+    INSTANTIATE_INFO(size_t, ndims)
+    INSTANTIATE_INFO(const af::dim4 &, dims)
+    INSTANTIATE_INFO(size_t, total)
+    INSTANTIATE_INFO(int, getDevId)
+    INSTANTIATE_INFO(af_backend, getBackendId)
+    INSTANTIATE_INFO(bool, isEmpty)
+    INSTANTIATE_INFO(bool, isScalar)
+    INSTANTIATE_INFO(bool, isRow)
+    INSTANTIATE_INFO(bool, isColumn)
+    INSTANTIATE_INFO(bool, isVector)
+    INSTANTIATE_INFO(bool, isComplex)
+    INSTANTIATE_INFO(bool, isReal)
+    INSTANTIATE_INFO(bool, isDouble)
+    INSTANTIATE_INFO(bool, isSingle)
+    INSTANTIATE_INFO(bool, isRealFloating)
+    INSTANTIATE_INFO(bool, isFloating)
+    INSTANTIATE_INFO(bool, isInteger)
+    INSTANTIATE_INFO(bool, isBool)
+    INSTANTIATE_INFO(bool, isLinear)
+    INSTANTIATE_INFO(bool, isSparse)
 
     // Function from Base but not in ArrayInfo
-    INSTANTIATE_INFO(dim_t              , getNNZ    )
-    INSTANTIATE_INFO(af::storage        , getStorage)
+    INSTANTIATE_INFO(dim_t, getNNZ)
+    INSTANTIATE_INFO(af::storage, getStorage)
 
-    Array<int>& getRowIdx()                 { return base.getRowIdx(); }
-    Array<int>& getColIdx()                 { return base.getColIdx(); }
-    const Array<int>& getRowIdx()   const   { return base.getRowIdx(); }
-    const Array<int>& getColIdx()   const   { return base.getColIdx(); }
+    Array<int> &getRowIdx() { return base.getRowIdx(); }
+    Array<int> &getColIdx() { return base.getColIdx(); }
+    const Array<int> &getRowIdx() const { return base.getRowIdx(); }
+    const Array<int> &getColIdx() const { return base.getColIdx(); }
 
 #undef INSTANTIATE_INFO
 
-    void setId(int id)
-    {
+    void setId(int id) {
         base.setId(id);
         values.setId(id);
     }
 
     // Return the values array
-    Array<T>& getValues()                 { return values;  }
-    const Array<T>& getValues()     const { return values;  }
+    Array<T> &getValues() { return values; }
+    const Array<T> &getValues() const { return values; }
 
-    void eval() const
-    {
+    void eval() const {
         getValues().eval();
         getRowIdx().eval();
         getColIdx().eval();
     }
 
     // Friend functions for Sparse Array Creation
-    friend SparseArray<T> createEmptySparseArray<T>(
-            const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);
+    friend SparseArray<T> createEmptySparseArray<T>(const af::dim4 &_dims,
+                                                    dim_t _nNZ,
+                                                    const af::storage _storage);
 
     friend SparseArray<T> createHostDataSparseArray<T>(
-            const af::dim4 &_dims, const dim_t nNZ,
-            const T * const _values,
-            const int * const _rowIdx, const int * const _colIdx,
-            const af::storage _storage);
+        const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
+        const int *const _rowIdx, const int *const _colIdx,
+        const af::storage _storage);
 
     friend SparseArray<T> createDeviceDataSparseArray<T>(
-            const af::dim4 &_dims, const dim_t nNZ,
-            const T * const _values,
-            const int * const _rowIdx, const int * const _colIdx,
-            const af::storage _storage, const bool _copy);
+        const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
+        const int *const _rowIdx, const int *const _colIdx,
+        const af::storage _storage, const bool _copy);
 
     friend SparseArray<T> createArrayDataSparseArray<T>(
-            const af::dim4 &_dims,
-            const Array<T> &_values,
-            const Array<int> &_rowIdx, const Array<int> &_colIdx,
-            const af::storage _storage, const bool _copy);
+        const af::dim4 &_dims, const Array<T> &_values,
+        const Array<int> &_rowIdx, const Array<int> &_colIdx,
+        const af::storage _storage, const bool _copy);
 
     friend SparseArray<T> *initSparseArray<T>();
 
-    friend SparseArray<T> copySparseArray<T>(const SparseArray<T>& input);
+    friend SparseArray<T> copySparseArray<T>(const SparseArray<T> &input);
 
     friend void destroySparseArray<T>(SparseArray<T> *sparse);
 };
 
-} // namespace common
+}  // namespace common
diff --git a/src/backend/common/blas_headers.hpp b/src/backend/common/blas_headers.hpp
index 236bd21298..f76dfa0240 100644
--- a/src/backend/common/blas_headers.hpp
+++ b/src/backend/common/blas_headers.hpp
@@ -10,29 +10,29 @@
 #pragma once
 
 #ifdef USE_MKL
-    #include <mkl_cblas.h>
+#include <mkl_cblas.h>
 #else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-    #else
-        extern "C" {
-            #include <cblas.h>
-        }
-    #endif
+#ifdef __APPLE__
+#include <Accelerate/Accelerate.h>
+#else
+extern "C" {
+#include <cblas.h>
+}
+#endif
 #endif
 
 // TODO: Ask upstream for a more official way to detect it
 #ifdef OPENBLAS_CONST
-    #define IS_OPENBLAS
+#define IS_OPENBLAS
 #endif
 
 // Make sure we get the correct type signature for OpenBLAS
 // OpenBLAS defines blasint as it's index type. Emulate this
 // if we're not dealing with openblas and use it where applicable
 #ifdef IS_OPENBLAS
-    // blasint already defined
-    static const bool cplx_void_ptr = false;
+// blasint already defined
+static const bool cplx_void_ptr = false;
 #else
-    using blasint = int;
-    static const bool cplx_void_ptr = true;
+using blasint                   = int;
+static const bool cplx_void_ptr = true;
 #endif
diff --git a/src/backend/common/cblas.cpp b/src/backend/common/cblas.cpp
index 8f8e3434b2..09f255cb94 100644
--- a/src/backend/common/cblas.cpp
+++ b/src/backend/common/cblas.cpp
@@ -5,7 +5,7 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ ********************************************************/
 
 #ifdef USE_F77_BLAS
 #include <common/blas_headers.hpp>
@@ -14,62 +14,44 @@
 #include <cblas.h>
 #include <cblas_f77.h>
 
-static char transChar(CBLAS_TRANSPOSE Trans)
-{
-    switch(Trans) {
-        case CblasNoTrans:      return 'N';
-        case CblasTrans:        return 'T';
-        case CblasConjTrans:    return 'C';
-        default:                return '\0';
+static char transChar(CBLAS_TRANSPOSE Trans) {
+    switch (Trans) {
+        case CblasNoTrans: return 'N';
+        case CblasTrans: return 'T';
+        case CblasConjTrans: return 'C';
+        default: return '\0';
     }
 }
 
-#define GEMM_F77(X, TS, TV, TY)                                 \
-    void cblas_##X##gemm(                                       \
-        const CBLAS_ORDER Order, const CBLAS_TRANSPOSE TransA,  \
-        const CBLAS_TRANSPOSE TransB, const int M, const int N, \
-        const int K, const TS alpha, const TV *A,               \
-        const int lda, const TV *B, const int ldb,              \
-        const TS beta, TV *C, const int ldc)                    \
-    {                                                           \
-        char aT = transChar(TransA);                            \
-        char bT = transChar(TransB);                            \
-        X##gemm_(&aT, &bT, &M, &N, &K,                          \
-                 (const TY *)ADDR(alpha), (const TY *)A, &lda,  \
-                 (const TY *)B, &ldb,                           \
-                 (const TY *)ADDR(beta), (TY *)C, &ldc);        \
-    }                                                           \
-    void cblas_##X##gemv(                                       \
-        const CBLAS_ORDER order, const CBLAS_TRANSPOSE TransA,  \
-        const int M, const int N,                               \
-        const TS alpha, const TV *A, const int lda,             \
-        const TV *X, const int incX, const TS beta,             \
-        TV *Y, const int incY)                                  \
-    {                                                           \
-        char aT = transChar(TransA);                            \
-        X##gemv_(&aT, &M, &N,                                   \
-                 (const TY *)ADDR(alpha), (const TY *)A, &lda,  \
-                 (const TY *)X, &incX,                          \
-                 (const TY *)ADDR(beta), (TY *)Y, &incY);       \
-    }                                                           \
-    void cblas_##X##axpy(                                       \
-        const int N, const TS alpha,                            \
-        const TV *X, const int incX,                            \
-        TV *Y, const int incY)                                  \
-    {                                                           \
-        X##axpy_(&N,                                            \
-                 (const TY *)ADDR(alpha),                       \
-                 (const TY *)X, &incX,                          \
-                 (TY *)Y, &incY);                               \
-    }                                                           \
-    void cblas_##X##scal(                                       \
-        const int N, const TS alpha,                            \
-        TV *X, const int incX)                                  \
-    {                                                           \
-        X##scal_(&N,                                            \
-                 (const TY *)ADDR(alpha),                       \
-                 (TY *)X, &incX);                               \
-    }                                                           \
+#define GEMM_F77(X, TS, TV, TY)                                                \
+    void cblas_##X##gemm(                                                      \
+        const CBLAS_ORDER Order, const CBLAS_TRANSPOSE TransA,                 \
+        const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,   \
+        const TS alpha, const TV *A, const int lda, const TV *B,               \
+        const int ldb, const TS beta, TV *C, const int ldc) {                  \
+        char aT = transChar(TransA);                                           \
+        char bT = transChar(TransB);                                           \
+        X##gemm_(&aT, &bT, &M, &N, &K, (const TY *)ADDR(alpha), (const TY *)A, \
+                 &lda, (const TY *)B, &ldb, (const TY *)ADDR(beta), (TY *)C,   \
+                 &ldc);                                                        \
+    }                                                                          \
+    void cblas_##X##gemv(                                                      \
+        const CBLAS_ORDER order, const CBLAS_TRANSPOSE TransA, const int M,    \
+        const int N, const TS alpha, const TV *A, const int lda, const TV *X,  \
+        const int incX, const TS beta, TV *Y, const int incY) {                \
+        char aT = transChar(TransA);                                           \
+        X##gemv_(&aT, &M, &N, (const TY *)ADDR(alpha), (const TY *)A, &lda,    \
+                 (const TY *)X, &incX, (const TY *)ADDR(beta), (TY *)Y,        \
+                 &incY);                                                       \
+    }                                                                          \
+    void cblas_##X##axpy(const int N, const TS alpha, const TV *X,             \
+                         const int incX, TV *Y, const int incY) {              \
+        X##axpy_(&N, (const TY *)ADDR(alpha), (const TY *)X, &incX, (TY *)Y,   \
+                 &incY);                                                       \
+    }                                                                          \
+    void cblas_##X##scal(const int N, const TS alpha, TV *X, const int incX) { \
+        X##scal_(&N, (const TY *)ADDR(alpha), (TY *)X, &incX);                 \
+    }
 
 #define ADDR(val) &val
 GEMM_F77(s, float, float, float)
@@ -82,5 +64,5 @@ GEMM_F77(z, void *, void, double)
 #undef ADDR
 
 #else
-    #include <blas.hpp>
+#include <blas.hpp>
 #endif
diff --git a/src/backend/common/complex.hpp b/src/backend/common/complex.hpp
index 20692414fb..cb5a4cdabf 100644
--- a/src/backend/common/complex.hpp
+++ b/src/backend/common/complex.hpp
@@ -7,18 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <types.hpp>
-#include <backend.hpp>
 #include <Array.hpp>
+#include <backend.hpp>
+#include <types.hpp>
 
 #include <type_traits>
 
 namespace common {
 
 // The value returns true if the type is a complex type. False otherwise
-template<typename T> struct is_complex          { static const bool value = false;  };
-template<> struct           is_complex<detail::cfloat>  { static const bool value = true;   };
-template<> struct           is_complex<detail::cdouble> { static const bool value = true;   };
+template<typename T>
+struct is_complex {
+    static const bool value = false;
+};
+template<>
+struct is_complex<detail::cfloat> {
+    static const bool value = true;
+};
+template<>
+struct is_complex<detail::cdouble> {
+    static const bool value = true;
+};
 
 /// This is an enable_if for complex types.
 template<typename T, typename TYPE = void>
@@ -26,6 +35,7 @@ using if_complex = typename std::enable_if<is_complex<T>::value, TYPE>::type;
 
 /// This is an enable_if for real types.
 template<typename T, typename TYPE = void>
-using if_real = typename std::enable_if<is_complex<T>::value == false, TYPE>::type;
+using if_real =
+    typename std::enable_if<is_complex<T>::value == false, TYPE>::type;
 
-}
+}  // namespace common
diff --git a/src/backend/common/constants.cpp b/src/backend/common/constants.cpp
index 239385fef9..086cf14dd2 100644
--- a/src/backend/common/constants.cpp
+++ b/src/backend/common/constants.cpp
@@ -6,12 +6,11 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <limits>
 #include <af/constants.h>
+#include <limits>
 
-namespace af
-{
-    const double NaN = std::numeric_limits<double>::quiet_NaN();
-    const double Inf = std::numeric_limits<double>::infinity();
-    const double Pi  = 3.1415926535897932384626433832795028841971693993751;
-}
+namespace af {
+const double NaN = std::numeric_limits<double>::quiet_NaN();
+const double Inf = std::numeric_limits<double>::infinity();
+const double Pi  = 3.1415926535897932384626433832795028841971693993751;
+}  // namespace af
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 394520e23d..4317a632dd 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -11,36 +11,33 @@
 
 #include <string>
 
-inline std::string
-clipFilePath(std::string path, std::string str)
-{
+inline std::string clipFilePath(std::string path, std::string str) {
     try {
         std::string::size_type pos = path.rfind(str);
-        if(pos == std::string::npos) {
+        if (pos == std::string::npos) {
             return path;
         } else {
             return path.substr(pos);
         }
-    } catch(...) {
-        return path;
-    }
+    } catch (...) { return path; }
 }
 
-#define UNUSED(expr) do { (void)(expr); } while (0)
+#define UNUSED(expr) \
+    do { (void)(expr); } while (0)
 
 #if defined(_WIN32) || defined(_MSC_VER)
-    #define __PRETTY_FUNCTION__ __FUNCSIG__
-    #if _MSC_VER < 1900
-        #define snprintf sprintf_s
-    #endif
-    #define STATIC_ static
-    #define __AF_FILENAME__ (clipFilePath(__FILE__, "src\\").c_str())
+#define __PRETTY_FUNCTION__ __FUNCSIG__
+#if _MSC_VER < 1900
+#define snprintf sprintf_s
+#endif
+#define STATIC_ static
+#define __AF_FILENAME__ (clipFilePath(__FILE__, "src\\").c_str())
 #else
-    //#ifndef __PRETTY_FUNCTION__
-    //    #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
-    //#endif
-    #define STATIC_ inline
-    #define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
+//#ifndef __PRETTY_FUNCTION__
+//    #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
+//#endif
+#define STATIC_ inline
+#define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
 #endif
 
 typedef enum {
diff --git a/src/backend/common/dim4.cpp b/src/backend/common/dim4.cpp
index 46ddab69ae..74d0a83e63 100644
--- a/src/backend/common/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -7,63 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/err_common.hpp>
+#include <af/dim4.hpp>
+#include <cfloat>
+#include <cmath>
 #include <limits>
 #include <numeric>
-#include <cmath>
-#include <cfloat>
-#include <af/dim4.hpp>
-#include <common/err_common.hpp>
 
-namespace af
-{
+namespace af {
 
 #if __cplusplus > 199711l
-    static_assert(std::is_standard_layout<dim4>::value, "af::dim4 must be a standard layout type");
+static_assert(std::is_standard_layout<dim4>::value,
+              "af::dim4 must be a standard layout type");
 #endif
 
-using std::vector;
-using std::numeric_limits;
 using std::abs;
+using std::numeric_limits;
+using std::vector;
 
-dim4::dim4() : dims{0, 0, 0, 0}
-{
-}
+dim4::dim4() : dims{0, 0, 0, 0} {}
 
-dim4::dim4( dim_t first,
-            dim_t second,
-            dim_t third,
-            dim_t fourth) :
-  dims { first, second, third, fourth}
-{
-}
+dim4::dim4(dim_t first, dim_t second, dim_t third, dim_t fourth)
+    : dims{first, second, third, fourth} {}
 
 dim4::dim4(const dim4& other)
-  : dims{other.dims[0], other.dims[1], other.dims[2], other.dims[3]} {
-}
+    : dims{other.dims[0], other.dims[1], other.dims[2], other.dims[3]} {}
 
-dim4::dim4(const unsigned ndims_, const dim_t * const dims_)
-{
-    for (unsigned i = 0; i < 4; i++) {
-        dims[i] = ndims_ > i ? dims_[i] : 1;
-    }
+dim4::dim4(const unsigned ndims_, const dim_t* const dims_) {
+    for (unsigned i = 0; i < 4; i++) { dims[i] = ndims_ > i ? dims_[i] : 1; }
 }
 
+dim_t dim4::elements() const { return dims[0] * dims[1] * dims[2] * dims[3]; }
 
-dim_t
-dim4::elements() const
-{
-    return dims[0] * dims[1] * dims[2] * dims[3];
-}
-
-dim_t
-dim4::elements()
-{
-    return static_cast<const dim4&>(*this).elements();
-}
+dim_t dim4::elements() { return static_cast<const dim4&>(*this).elements(); }
 
-dim_t
-dim4::ndims() const
-{
+dim_t dim4::ndims() const {
     dim_t num = elements();
     if (num == 0) return 0;
     if (num == 1) return 1;
@@ -75,120 +53,82 @@ dim4::ndims() const
     return 1;
 }
 
-dim_t
-dim4::ndims()
-{
-    return static_cast<const dim4&>(*this).ndims();
-}
+dim_t dim4::ndims() { return static_cast<const dim4&>(*this).ndims(); }
 
-const dim_t&
-dim4::operator[](const unsigned dim) const
-{
-    return dims[dim];
-}
+const dim_t& dim4::operator[](const unsigned dim) const { return dims[dim]; }
 
-dim_t &
-dim4::operator[](const unsigned dim)
-{
+dim_t& dim4::operator[](const unsigned dim) {
     return const_cast<dim_t&>(static_cast<const dim4&>((*this))[dim]);
 }
 
-bool
-dim4::operator==(const dim4 &other) const
-{
+bool dim4::operator==(const dim4& other) const {
     bool ret = true;
-    for(unsigned i = 0; i < 4 && ret; i++) {
-        ret = (*this)[i] == other[i];
-    }
+    for (unsigned i = 0; i < 4 && ret; i++) { ret = (*this)[i] == other[i]; }
     return ret;
 }
 
-bool
-dim4::operator!=(const dim4 &other) const
-{
-    return !((*this) == other);
-}
+bool dim4::operator!=(const dim4& other) const { return !((*this) == other); }
 
-dim4&
-dim4::operator*=(const dim4 &other)
-{
-    for(unsigned i = 0; i < 4; i++) {
-        (*this)[i] *= other[i];
-    }
+dim4& dim4::operator*=(const dim4& other) {
+    for (unsigned i = 0; i < 4; i++) { (*this)[i] *= other[i]; }
     return *this;
 }
 
-dim4&
-dim4::operator+=(const dim4 &other)
-{
-    for(unsigned i = 0; i < 4; i++) {
-        (*this)[i] = (*this)[i] + other[i];
-    }
+dim4& dim4::operator+=(const dim4& other) {
+    for (unsigned i = 0; i < 4; i++) { (*this)[i] = (*this)[i] + other[i]; }
     return *this;
 }
 
-dim4&
-dim4::operator-=(const dim4 &other)
-{
-    for(unsigned i = 0; i < 4; i++) {
-        (*this)[i] = (*this)[i] - other[i];
-    }
+dim4& dim4::operator-=(const dim4& other) {
+    for (unsigned i = 0; i < 4; i++) { (*this)[i] = (*this)[i] - other[i]; }
     return *this;
 }
 
-dim4 operator+(const dim4& first, const dim4& second)
-{
+dim4 operator+(const dim4& first, const dim4& second) {
     dim4 dims;
-    for(unsigned i = 0; i < 4; i++) {
-        dims[i] = first[i] + second[i];
-    }
+    for (unsigned i = 0; i < 4; i++) { dims[i] = first[i] + second[i]; }
     return dims;
 }
 
-dim4 operator-(const dim4& first, const dim4& second)
-{
+dim4 operator-(const dim4& first, const dim4& second) {
     dim4 dims;
-    for(unsigned i = 0; i < 4; i++) {
-        dims[i] = first[i] - second[i];
-    }
+    for (unsigned i = 0; i < 4; i++) { dims[i] = first[i] - second[i]; }
     return dims;
 }
 
-dim4 operator*(const dim4& first, const dim4& second)
-{
+dim4 operator*(const dim4& first, const dim4& second) {
     dim4 dims;
-    for(unsigned i = 0; i < 4; i++) {
-        dims[i] = first[i] * second[i];
-    }
+    for (unsigned i = 0; i < 4; i++) { dims[i] = first[i] * second[i]; }
     return dims;
 }
 
+bool hasEnd(const af_seq& seq) { return (seq.begin <= -1 || seq.end <= -1); }
 
-bool
-hasEnd(const af_seq &seq)    { return (seq.begin <= -1 || seq.end <= -1); }
-
-bool
-isSpan(const af_seq &seq)   { return (seq.step == 0 && seq.begin == 1 && seq.end == 1); }
+bool isSpan(const af_seq& seq) {
+    return (seq.step == 0 && seq.begin == 1 && seq.end == 1);
+}
 
-size_t
-seqElements(const af_seq &seq) {
+size_t seqElements(const af_seq& seq) {
     size_t out = 0;
-    if      (seq.step > DBL_MIN)    { out = ((seq.end - seq.begin) / abs(seq.step)) + 1;    }
-    else if (seq.step < -DBL_MIN)   { out = ((seq.begin - seq.end) / abs(seq.step)) + 1;    }
-    else                            { out = numeric_limits<size_t>::max();                  }
+    if (seq.step > DBL_MIN) {
+        out = ((seq.end - seq.begin) / abs(seq.step)) + 1;
+    } else if (seq.step < -DBL_MIN) {
+        out = ((seq.begin - seq.end) / abs(seq.step)) + 1;
+    } else {
+        out = numeric_limits<size_t>::max();
+    }
 
     return out;
 }
 
-dim_t calcDim(const af_seq &seq, const dim_t &parentDim)
-{
+dim_t calcDim(const af_seq& seq, const dim_t& parentDim) {
     dim_t outDim = 1;
-    if  (isSpan(seq)) {
+    if (isSpan(seq)) {
         outDim = parentDim;
     } else if (hasEnd(seq)) {
         af_seq temp = {seq.begin, seq.end, seq.step};
         if (seq.begin < 0) temp.begin += parentDim;
-        if (seq.end   < 0) temp.end   += parentDim;
+        if (seq.end < 0) temp.end += parentDim;
         outDim = seqElements(temp);
     } else {
         DIM_ASSERT(1, seq.begin >= -DBL_MIN && seq.begin < parentDim);
@@ -199,4 +139,4 @@ dim_t calcDim(const af_seq &seq, const dim_t &parentDim)
     return outDim;
 }
 
-}
+}  // namespace af
diff --git a/src/backend/common/dispatch.cpp b/src/backend/common/dispatch.cpp
index c2d5d54053..50d35da9bc 100644
--- a/src/backend/common/dispatch.cpp
+++ b/src/backend/common/dispatch.cpp
@@ -9,13 +9,12 @@
 
 #include "dispatch.hpp"
 
-unsigned nextpow2(unsigned x)
-{
-       x = x - 1;
-       x = x | (x >> 1);
-       x = x | (x >> 2);
-       x = x | (x >> 4);
-       x = x | (x >> 8);
-       x = x | (x >>16);
-       return x + 1;
+unsigned nextpow2(unsigned x) {
+    x = x - 1;
+    x = x | (x >> 1);
+    x = x | (x >> 2);
+    x = x | (x >> 4);
+    x = x | (x >> 8);
+    x = x | (x >> 16);
+    return x + 1;
 }
diff --git a/src/backend/common/dispatch.hpp b/src/backend/common/dispatch.hpp
index 359fa61b59..099b0aa6a5 100644
--- a/src/backend/common/dispatch.hpp
+++ b/src/backend/common/dispatch.hpp
@@ -11,35 +11,29 @@
 
 #include <cmath>
 
-#define divup(a, b) (((a)+(b)-1)/(b))
+#define divup(a, b) (((a) + (b)-1) / (b))
 
 unsigned nextpow2(unsigned x);
 
 // isPrime & greatestPrimeFactor are tailored after
 // itk::Math::{IsPrimt, GreatestPrimeFactor}
-template <typename T>
-inline bool isPrime(T n)
-{
-    if( n <= 1 )
-        return false;
-
-    const T last = (T)std::sqrt( (double)n );
-    for (T x=2; x<=last; ++x)
-    {
-        if (n%x == 0)
-            return false;
+template<typename T>
+inline bool isPrime(T n) {
+    if (n <= 1) return false;
+
+    const T last = (T)std::sqrt((double)n);
+    for (T x = 2; x <= last; ++x) {
+        if (n % x == 0) return false;
     }
 
     return true;
 }
 
-template <typename T>
-inline T greatestPrimeFactor(T n)
-{
+template<typename T>
+inline T greatestPrimeFactor(T n) {
     T v = 2;
 
-    while (v <= n)
-    {
+    while (v <= n) {
         if (n % v == 0 && isPrime(v))
             n /= v;
         else
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 2aa9a39921..5be423e1d0 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/exception.h>
-#include <af/device.h>
 #include <common/err_common.hpp>
-#include <type_util.hpp>
-#include <common/util.hpp>
 #include <common/graphics_common.hpp>
+#include <common/util.hpp>
+#include <type_util.hpp>
+#include <af/device.h>
+#include <af/exception.h>
 
 #include <algorithm>
 #include <cstdio>
@@ -21,153 +21,89 @@
 #include <string>
 
 #ifdef AF_OPENCL
-#include <platform.hpp>
 #include <errorcodes.hpp>
+#include <platform.hpp>
 #endif
 
 using std::string;
 using std::stringstream;
 
-AfError::AfError(const char * const func,
-                 const char * const file,
-                 const int line,
-                 const char * const message, af_err err)
-    : logic_error   (message),
-      functionName  (func),
-      fileName      (file),
-      lineNumber(line),
-      error(err)
-{}
-
-AfError::AfError(string func,
-                 string file,
-                 const int line,
-                 string message, af_err err)
-    : logic_error   (message),
-      functionName  (func),
-      fileName      (file),
-      lineNumber(line),
-      error(err)
-{}
-
-const string&
-AfError::getFunctionName() const
-{
-    return functionName;
-}
+AfError::AfError(const char *const func, const char *const file, const int line,
+                 const char *const message, af_err err)
+    : logic_error(message)
+    , functionName(func)
+    , fileName(file)
+    , lineNumber(line)
+    , error(err) {}
 
-const string&
-AfError::getFileName() const
-{
-    return fileName;
-}
+AfError::AfError(string func, string file, const int line, string message,
+                 af_err err)
+    : logic_error(message)
+    , functionName(func)
+    , fileName(file)
+    , lineNumber(line)
+    , error(err) {}
 
-int
-AfError::getLine() const
-{
-    return lineNumber;
-}
+const string &AfError::getFunctionName() const { return functionName; }
 
-af_err
-AfError::getError() const
-{
-    return error;
-}
+const string &AfError::getFileName() const { return fileName; }
 
-AfError::~AfError() throw() {}
+int AfError::getLine() const { return lineNumber; }
 
-TypeError::TypeError(const char * const func,
-                     const char * const file,
-                     const int line,
-                     const int index, const af_dtype type)
-    : AfError (func, file, line, "Invalid data type", AF_ERR_TYPE),
-      argIndex(index),
-      errTypeName(getName(type))
-{}
-
-const string& TypeError::getTypeName() const
-{
-    return errTypeName;
-}
+af_err AfError::getError() const { return error; }
 
-int TypeError::getArgIndex() const
-{
-    return argIndex;
-}
+AfError::~AfError() throw() {}
 
-ArgumentError::ArgumentError(const char * const func,
-                             const char * const file,
-                             const int line,
-                             const int index,
-                             const char * const  expectString)
-    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG),
-      argIndex(index),
-      expected(expectString)
-{
+TypeError::TypeError(const char *const func, const char *const file,
+                     const int line, const int index, const af_dtype type)
+    : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE)
+    , argIndex(index)
+    , errTypeName(getName(type)) {}
 
-}
+const string &TypeError::getTypeName() const { return errTypeName; }
 
-const string& ArgumentError::getExpectedCondition() const
-{
-    return expected;
-}
+int TypeError::getArgIndex() const { return argIndex; }
 
-int ArgumentError::getArgIndex() const
-{
-    return argIndex;
-}
+ArgumentError::ArgumentError(const char *const func, const char *const file,
+                             const int line, const int index,
+                             const char *const expectString)
+    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG)
+    , argIndex(index)
+    , expected(expectString) {}
 
+const string &ArgumentError::getExpectedCondition() const { return expected; }
 
-SupportError::SupportError(const char * const func,
-                           const char * const file,
-                           const int line,
-                           const char * const back)
-    : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED),
-      backend(back)
-{}
+int ArgumentError::getArgIndex() const { return argIndex; }
 
-const string& SupportError::getBackendName() const
-{
-    return backend;
-}
+SupportError::SupportError(const char *const func, const char *const file,
+                           const int line, const char *const back)
+    : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED)
+    , backend(back) {}
 
-DimensionError::DimensionError(const char * const  func,
-                               const char * const file,
-                               const int line,
-                               const int index,
-                               const char * const  expectString)
-    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE),
-      argIndex(index),
-      expected(expectString)
-{
+const string &SupportError::getBackendName() const { return backend; }
 
-}
+DimensionError::DimensionError(const char *const func, const char *const file,
+                               const int line, const int index,
+                               const char *const expectString)
+    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE)
+    , argIndex(index)
+    , expected(expectString) {}
 
-const string& DimensionError::getExpectedCondition() const
-{
-    return expected;
-}
+const string &DimensionError::getExpectedCondition() const { return expected; }
 
-int DimensionError::getArgIndex() const
-{
-    return argIndex;
-}
+int DimensionError::getArgIndex() const { return argIndex; }
 
-void
-print_error(const string &msg)
-{
+void print_error(const string &msg) {
     std::string perr = getEnvVar("AF_PRINT_ERRORS");
-    if(!perr.empty()) {
-        if(perr != "0")
-            fprintf(stderr, "%s\n", msg.c_str());
+    if (!perr.empty()) {
+        if (perr != "0") fprintf(stderr, "%s\n", msg.c_str());
     }
     get_global_error_string() = msg;
 }
 
-af_err processException()
-{
-    stringstream    ss;
-    af_err          err= AF_ERR_INTERNAL;
+af_err processException() {
+    stringstream ss;
+    af_err err = AF_ERR_INTERNAL;
 
     try {
         throw;
@@ -188,9 +124,8 @@ af_err processException()
         print_error(ss.str());
         err = AF_ERR_ARG;
     } catch (const SupportError &ex) {
-        ss << ex.getFunctionName()
-           << " not supported for " << ex.getBackendName()
-           << " backend\n";
+        ss << ex.getFunctionName() << " not supported for "
+           << ex.getBackendName() << " backend\n";
 
         print_error(ss.str());
         err = AF_ERR_NOT_SUPPORTED;
@@ -209,17 +144,17 @@ af_err processException()
         print_error(ss.str());
         err = ex.getError();
 #ifdef AF_OPENCL
-    } catch(const cl::Error &ex) {
-      char opencl_err_msg[1024];
-      snprintf(opencl_err_msg, sizeof(opencl_err_msg),
-               "OpenCL Error (%d): %s when calling %s", ex.err(),
-               getErrorMessage(ex.err()).c_str(), ex.what());
-      print_error(opencl_err_msg);
-      if (ex.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {
-        err = AF_ERR_NO_MEM;
-      } else {
-        err = AF_ERR_INTERNAL;
-      }
+    } catch (const cl::Error &ex) {
+        char opencl_err_msg[1024];
+        snprintf(opencl_err_msg, sizeof(opencl_err_msg),
+                 "OpenCL Error (%d): %s when calling %s", ex.err(),
+                 getErrorMessage(ex.err()).c_str(), ex.what());
+        print_error(opencl_err_msg);
+        if (ex.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {
+            err = AF_ERR_NO_MEM;
+        } else {
+            err = AF_ERR_INTERNAL;
+        }
 #endif
     } catch (...) {
         print_error(ss.str());
@@ -229,37 +164,40 @@ af_err processException()
     return err;
 }
 
-std::string& get_global_error_string()
-{
+std::string &get_global_error_string() {
     thread_local std::string *global_error_string = new std::string("");
     return *global_error_string;
 }
 
-const char *af_err_to_string(const af_err err)
-{
+const char *af_err_to_string(const af_err err) {
     switch (err) {
-    case AF_SUCCESS:                return "Success";
-    case AF_ERR_NO_MEM:             return "Device out of memory";
-    case AF_ERR_DRIVER:             return "Driver not available or incompatible";
-    case AF_ERR_RUNTIME:            return "Runtime error ";
-    case AF_ERR_INVALID_ARRAY:      return "Invalid array";
-    case AF_ERR_ARG:                return "Invalid input argument";
-    case AF_ERR_SIZE:               return "Invalid input size";
-    case AF_ERR_TYPE:               return "Function does not support this data type";
-    case AF_ERR_DIFF_TYPE:          return "Input types are not the same";
-    case AF_ERR_BATCH:              return "Invalid batch configuration";
-    case AF_ERR_NOT_SUPPORTED:      return "Function not supported";
-    case AF_ERR_NOT_CONFIGURED:     return "Function not configured to build";
-    case AF_ERR_NONFREE:            return "Function unavailable. "
-                                           "ArrayFire compiled without Non-Free algorithms support";
-    case AF_ERR_NO_DBL:             return "Double precision not supported for this device";
-    case AF_ERR_NO_GFX:             return "Graphics functionality unavailable. "
-                                           "ArrayFire compiled without Graphics support";
-    case AF_ERR_LOAD_LIB:           return "Failed to load dynamic library. ";
-    case AF_ERR_LOAD_SYM:           return "Failed to load symbol";
-    case AF_ERR_ARR_BKND_MISMATCH:  return "There was a mismatch between an array and the current backend";
-    case AF_ERR_INTERNAL:           return "Internal error";
-    case AF_ERR_UNKNOWN:
-    default:                        return "Unknown error";
+        case AF_SUCCESS: return "Success";
+        case AF_ERR_NO_MEM: return "Device out of memory";
+        case AF_ERR_DRIVER: return "Driver not available or incompatible";
+        case AF_ERR_RUNTIME: return "Runtime error ";
+        case AF_ERR_INVALID_ARRAY: return "Invalid array";
+        case AF_ERR_ARG: return "Invalid input argument";
+        case AF_ERR_SIZE: return "Invalid input size";
+        case AF_ERR_TYPE: return "Function does not support this data type";
+        case AF_ERR_DIFF_TYPE: return "Input types are not the same";
+        case AF_ERR_BATCH: return "Invalid batch configuration";
+        case AF_ERR_NOT_SUPPORTED: return "Function not supported";
+        case AF_ERR_NOT_CONFIGURED: return "Function not configured to build";
+        case AF_ERR_NONFREE:
+            return "Function unavailable. "
+                   "ArrayFire compiled without Non-Free algorithms support";
+        case AF_ERR_NO_DBL:
+            return "Double precision not supported for this device";
+        case AF_ERR_NO_GFX:
+            return "Graphics functionality unavailable. "
+                   "ArrayFire compiled without Graphics support";
+        case AF_ERR_LOAD_LIB: return "Failed to load dynamic library. ";
+        case AF_ERR_LOAD_SYM: return "Failed to load symbol";
+        case AF_ERR_ARR_BKND_MISMATCH:
+            return "There was a mismatch between an array and the current "
+                   "backend";
+        case AF_ERR_INTERNAL: return "Internal error";
+        case AF_ERR_UNKNOWN:
+        default: return "Unknown error";
     }
 }
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index bc76c106df..e042ff40fa 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#include <af/defines.h>
 #include <common/defines.hpp>
+#include <af/defines.h>
 
 #include <cassert>
 #include <sstream>
@@ -18,31 +18,23 @@
 #include <string>
 #include <vector>
 
-class AfError   : public std::logic_error
-{
+class AfError : public std::logic_error {
     std::string functionName;
     std::string fileName;
     int lineNumber;
     af_err error;
     AfError();
 
-public:
+   public:
+    AfError(const char* const func, const char* const file, const int line,
+            const char* const message, af_err err);
 
-    AfError(const char * const func,
-            const char * const file,
-            const int line,
-            const char * const message, af_err err);
-
-    AfError(std::string func,
-            std::string file,
-            const int line,
+    AfError(std::string func, std::string file, const int line,
             std::string message, af_err err);
 
-    const std::string&
-    getFunctionName() const;
+    const std::string& getFunctionName() const;
 
-    const std::string&
-    getFileName() const;
+    const std::string& getFileName() const;
 
     int getLine() const;
 
@@ -52,158 +44,134 @@ class AfError   : public std::logic_error
 };
 
 // TODO: Perhaps add a way to return supported types
-class TypeError : public AfError
-{
+class TypeError : public AfError {
     int argIndex;
     std::string errTypeName;
     TypeError();
 
-public:
-
-    TypeError(const char * const func,
-              const char * const file,
-              const int line,
-              const int index,
-              const af_dtype type);
+   public:
+    TypeError(const char* const func, const char* const file, const int line,
+              const int index, const af_dtype type);
 
-    const std::string&
-    getTypeName() const;
+    const std::string& getTypeName() const;
 
     int getArgIndex() const;
 
     ~TypeError() throw() {}
 };
 
-class ArgumentError : public AfError
-{
+class ArgumentError : public AfError {
     int argIndex;
     std::string expected;
     ArgumentError();
 
-public:
+   public:
+    ArgumentError(const char* const func, const char* const file,
+                  const int line, const int index,
+                  const char* const expectString);
 
-    ArgumentError(const char * const func,
-                  const char * const file,
-                  const int line,
-                  const int index,
-                  const char * const expectString);
-
-    const std::string&
-    getExpectedCondition() const;
+    const std::string& getExpectedCondition() const;
 
     int getArgIndex() const;
 
-    ~ArgumentError() throw(){}
+    ~ArgumentError() throw() {}
 };
 
-class SupportError  :   public AfError
-{
+class SupportError : public AfError {
     std::string backend;
     SupportError();
 
-public:
-
-    SupportError(const char * const func,
-                 const char * const file,
-                 const int line,
-                 const char * const back);
+   public:
+    SupportError(const char* const func, const char* const file, const int line,
+                 const char* const back);
 
-    ~SupportError()throw() {}
+    ~SupportError() throw() {}
 
-    const std::string&
-    getBackendName() const;
+    const std::string& getBackendName() const;
 };
 
-class DimensionError : public AfError
-{
+class DimensionError : public AfError {
     int argIndex;
     std::string expected;
     DimensionError();
 
-public:
-
-    DimensionError(const char * const func,
-                   const char * const file,
-                   const int line,
-                   const int index,
-                   const char * const expectString);
+   public:
+    DimensionError(const char* const func, const char* const file,
+                   const int line, const int index,
+                   const char* const expectString);
 
-    const std::string&
-    getExpectedCondition() const;
+    const std::string& getExpectedCondition() const;
 
     int getArgIndex() const;
 
-    ~DimensionError() throw(){}
+    ~DimensionError() throw() {}
 };
 
 af_err processException();
 
-void print_error(const std::string &msg);
-
-#define DIM_ASSERT(INDEX, COND) do {                        \
-        if((COND) == false) {                               \
-            throw DimensionError(__PRETTY_FUNCTION__,       \
-                                 __AF_FILENAME__, __LINE__, \
-                                 INDEX, #COND);             \
-        }                                                   \
-    } while(0)
-
-#define ARG_ASSERT(INDEX, COND) do {                        \
-        if((COND) == false) {                               \
-            throw ArgumentError(__PRETTY_FUNCTION__,        \
-                                __AF_FILENAME__, __LINE__,  \
-                                INDEX, #COND);              \
-        }                                                   \
-    } while(0)
-
-#define TYPE_ERROR(INDEX, type) do {                        \
-        throw TypeError(__PRETTY_FUNCTION__,                \
-                        __AF_FILENAME__, __LINE__,          \
-                        INDEX, type);                       \
-    } while(0)                                              \
-
-
-#define AF_ERROR(MSG, ERR_TYPE) do {                        \
-        throw AfError(__PRETTY_FUNCTION__,                  \
-                      __AF_FILENAME__, __LINE__,            \
-                      MSG, ERR_TYPE);                       \
-    } while(0)
-
-#define AF_RETURN_ERROR(MSG, ERR_TYPE) do {                 \
-        AfError err(__PRETTY_FUNCTION__,                    \
-                    __AF_FILENAME__, __LINE__,              \
-                    MSG, ERR_TYPE);                         \
-        std::stringstream s;                                \
-        s << "Error in " << err.getFunctionName() << "\n"   \
-          << "In file " << err.getFileName()                \
-          << ":" << err.getLine()  << "\n"                  \
-          << err.what() << "\n";                            \
-        print_error(s.str());                               \
-        return ERR_TYPE;                                    \
-    } while(0)
-
-#define TYPE_ASSERT(COND) do {                              \
-        if ((COND) == false) {                              \
-            AF_ERROR("Type mismatch inputs",                \
-                     AF_ERR_DIFF_TYPE);                     \
-        }                                                   \
-    } while(0)
-
-#define AF_ASSERT(COND, MESSAGE)                            \
-    assert(MESSAGE && COND)
-
-#define CATCHALL                                            \
-    catch(...) {                                            \
-        return processException();                          \
+void print_error(const std::string& msg);
+
+#define DIM_ASSERT(INDEX, COND)                                        \
+    do {                                                               \
+        if ((COND) == false) {                                         \
+            throw DimensionError(__PRETTY_FUNCTION__, __AF_FILENAME__, \
+                                 __LINE__, INDEX, #COND);              \
+        }                                                              \
+    } while (0)
+
+#define ARG_ASSERT(INDEX, COND)                                       \
+    do {                                                              \
+        if ((COND) == false) {                                        \
+            throw ArgumentError(__PRETTY_FUNCTION__, __AF_FILENAME__, \
+                                __LINE__, INDEX, #COND);              \
+        }                                                             \
+    } while (0)
+
+#define TYPE_ERROR(INDEX, type)                                                \
+    do {                                                                       \
+        throw TypeError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, INDEX, \
+                        type);                                                 \
+    } while (0)
+
+#define AF_ERROR(MSG, ERR_TYPE)                                            \
+    do {                                                                   \
+        throw AfError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, MSG, \
+                      ERR_TYPE);                                           \
+    } while (0)
+
+#define AF_RETURN_ERROR(MSG, ERR_TYPE)                                       \
+    do {                                                                     \
+        AfError err(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, MSG,     \
+                    ERR_TYPE);                                               \
+        std::stringstream s;                                                 \
+        s << "Error in " << err.getFunctionName() << "\n"                    \
+          << "In file " << err.getFileName() << ":" << err.getLine() << "\n" \
+          << err.what() << "\n";                                             \
+        print_error(s.str());                                                \
+        return ERR_TYPE;                                                     \
+    } while (0)
+
+#define TYPE_ASSERT(COND)                                       \
+    do {                                                        \
+        if ((COND) == false) {                                  \
+            AF_ERROR("Type mismatch inputs", AF_ERR_DIFF_TYPE); \
+        }                                                       \
+    } while (0)
+
+#define AF_ASSERT(COND, MESSAGE) assert(MESSAGE&& COND)
+
+#define CATCHALL                   \
+    catch (...) {                  \
+        return processException(); \
     }
 
-#define AF_CHECK(fn) do {                                   \
-        af_err __err = fn;                                  \
-        if (__err == AF_SUCCESS) break;                     \
-        throw AfError(__PRETTY_FUNCTION__,                  \
-                      __AF_FILENAME__, __LINE__,            \
-                      "\n", __err);                         \
-    } while(0)
+#define AF_CHECK(fn)                                                        \
+    do {                                                                    \
+        af_err __err = fn;                                                  \
+        if (__err == AF_SUCCESS) break;                                     \
+        throw AfError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, "\n", \
+                      __err);                                               \
+    } while (0)
 
 static const int MAX_ERR_SIZE = 1024;
 std::string& get_global_error_string();
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index 02d2b83e98..39e3e2d23e 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -16,7 +16,7 @@
 #include <forge.h>
 
 class ForgeModule : public common::DependencyModule {
-    public:
+   public:
     ForgeModule();
 
     MODULE_MEMBER(fg_create_window);
@@ -90,11 +90,10 @@ namespace graphics {
 ForgeModule& forgePlugin();
 }
 
-#define FG_CHECK(fn)                           \
-    do {                                       \
-        fg_err e = (fn);                       \
-        if (e != FG_ERR_NONE) {                \
-            AF_ERROR("forge call failed",      \
-                    AF_ERR_INTERNAL);          \
-        }                                      \
-    } while(0);
+#define FG_CHECK(fn)                                        \
+    do {                                                    \
+        fg_err e = (fn);                                    \
+        if (e != FG_ERR_NONE) {                             \
+            AF_ERROR("forge call failed", AF_ERR_INTERNAL); \
+        }                                                   \
+    } while (0);
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 7281f53a88..c27c5b2f88 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -7,23 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/graphics_common.hpp>
-#include <common/err_common.hpp>
 #include <backend.hpp>
-#include <platform.hpp>
+#include <common/err_common.hpp>
+#include <common/graphics_common.hpp>
 #include <common/util.hpp>
+#include <platform.hpp>
 #include <mutex>
 #include <utility>
 
 using namespace std;
 
 /// Dynamically loads forge function pointer at runtime
-#define FG_MODULE_FUNCTION_INIT(NAME)                \
+#define FG_MODULE_FUNCTION_INIT(NAME) \
     NAME = DependencyModule::getSymbol<decltype(&::NAME)>(#NAME)
 
-ForgeModule::ForgeModule()
-    : DependencyModule("forge", nullptr)
-{
+ForgeModule::ForgeModule() : DependencyModule("forge", nullptr) {
     if (DependencyModule::isLoaded()) {
         FG_MODULE_FUNCTION_INIT(fg_create_window);
         FG_MODULE_FUNCTION_INIT(fg_get_window_context_handle);
@@ -92,9 +90,9 @@ ForgeModule::ForgeModule()
         FG_MODULE_FUNCTION_INIT(fg_release_chart);
 
         if (!DependencyModule::symbolsLoaded()) {
-            string error_message = "Error loading Forge: "
-                + DependencyModule::getErrorMessage()
-                + "\nForge or one of it's dependencies failed to "
+            string error_message =
+                "Error loading Forge: " + DependencyModule::getErrorMessage() +
+                "\nForge or one of it's dependencies failed to "
                 "load. Try installing Forge or check if Forge is in the "
                 "search path.";
             AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
@@ -103,37 +101,41 @@ ForgeModule::ForgeModule()
 }
 
 template<typename T>
-fg_dtype getGLType() { return FG_FLOAT32; }
+fg_dtype getGLType() {
+    return FG_FLOAT32;
+}
 
 fg_marker_type getFGMarker(const af_marker_type af_marker) {
     fg_marker_type fg_marker;
     switch (af_marker) {
-        case AF_MARKER_NONE     : fg_marker = FG_MARKER_NONE;        break;
-        case AF_MARKER_POINT    : fg_marker = FG_MARKER_POINT;       break;
-        case AF_MARKER_CIRCLE   : fg_marker = FG_MARKER_CIRCLE;      break;
-        case AF_MARKER_SQUARE   : fg_marker = FG_MARKER_SQUARE;      break;
-        case AF_MARKER_TRIANGLE : fg_marker = FG_MARKER_TRIANGLE;    break;
-        case AF_MARKER_CROSS    : fg_marker = FG_MARKER_CROSS;       break;
-        case AF_MARKER_PLUS     : fg_marker = FG_MARKER_PLUS;        break;
-        case AF_MARKER_STAR     : fg_marker = FG_MARKER_STAR;        break;
-        default                 : fg_marker = FG_MARKER_NONE;        break;
+        case AF_MARKER_NONE: fg_marker = FG_MARKER_NONE; break;
+        case AF_MARKER_POINT: fg_marker = FG_MARKER_POINT; break;
+        case AF_MARKER_CIRCLE: fg_marker = FG_MARKER_CIRCLE; break;
+        case AF_MARKER_SQUARE: fg_marker = FG_MARKER_SQUARE; break;
+        case AF_MARKER_TRIANGLE: fg_marker = FG_MARKER_TRIANGLE; break;
+        case AF_MARKER_CROSS: fg_marker = FG_MARKER_CROSS; break;
+        case AF_MARKER_PLUS: fg_marker = FG_MARKER_PLUS; break;
+        case AF_MARKER_STAR: fg_marker = FG_MARKER_STAR; break;
+        default: fg_marker = FG_MARKER_NONE; break;
     }
     return fg_marker;
 }
 
-#define INSTANTIATE_GET_FG_TYPE(T, ForgeEnum)\
-    template<> fg_dtype getGLType<T>() { return ForgeEnum; }
+#define INSTANTIATE_GET_FG_TYPE(T, ForgeEnum) \
+    template<>                                \
+    fg_dtype getGLType<T>() {                 \
+        return ForgeEnum;                     \
+    }
 
-INSTANTIATE_GET_FG_TYPE(float           , FG_FLOAT32);
-INSTANTIATE_GET_FG_TYPE(int             , FG_INT32  );
-INSTANTIATE_GET_FG_TYPE(unsigned        , FG_UINT32 );
-INSTANTIATE_GET_FG_TYPE(char            , FG_INT8   );
-INSTANTIATE_GET_FG_TYPE(unsigned char   , FG_UINT8  );
-INSTANTIATE_GET_FG_TYPE(unsigned short  , FG_UINT16 );
-INSTANTIATE_GET_FG_TYPE(short           , FG_INT16  );
+INSTANTIATE_GET_FG_TYPE(float, FG_FLOAT32);
+INSTANTIATE_GET_FG_TYPE(int, FG_INT32);
+INSTANTIATE_GET_FG_TYPE(unsigned, FG_UINT32);
+INSTANTIATE_GET_FG_TYPE(char, FG_INT8);
+INSTANTIATE_GET_FG_TYPE(unsigned char, FG_UINT8);
+INSTANTIATE_GET_FG_TYPE(unsigned short, FG_UINT16);
+INSTANTIATE_GET_FG_TYPE(short, FG_INT16);
 
-GLenum glErrorCheck(const char *msg, const char* file, int line)
-{
+GLenum glErrorCheck(const char* msg, const char* file, int line) {
 // Skipped in release mode
 #ifndef NDEBUG
     GLenum x = glGetError();
@@ -150,30 +152,27 @@ GLenum glErrorCheck(const char *msg, const char* file, int line)
 #endif
 }
 
-size_t getTypeSize(GLenum type)
-{
-    switch(type) {
-        case GL_FLOAT:          return sizeof(float);
-        case GL_INT:            return sizeof(int  );
-        case GL_UNSIGNED_INT:   return sizeof(unsigned);
-        case GL_SHORT:          return sizeof(short);
+size_t getTypeSize(GLenum type) {
+    switch (type) {
+        case GL_FLOAT: return sizeof(float);
+        case GL_INT: return sizeof(int);
+        case GL_UNSIGNED_INT: return sizeof(unsigned);
+        case GL_SHORT: return sizeof(short);
         case GL_UNSIGNED_SHORT: return sizeof(unsigned short);
-        case GL_BYTE:           return sizeof(char );
-        case GL_UNSIGNED_BYTE:  return sizeof(unsigned char);
+        case GL_BYTE: return sizeof(char);
+        case GL_UNSIGNED_BYTE: return sizeof(unsigned char);
         default: return sizeof(float);
     }
 }
 
-void makeContextCurrent(fg_window window)
-{
+void makeContextCurrent(fg_window window) {
     FG_CHECK(graphics::forgePlugin().fg_make_window_current(window));
     CheckGL("End makeContextCurrent");
 }
 
 // dir -> true = round up, false = round down
-double step_round(const double in, const bool dir)
-{
-    if(in == 0) return 0;
+double step_round(const double in, const bool dir) {
+    if (in == 0) return 0;
 
     static const double __log2 = log10(2);
     static const double __log4 = log10(4);
@@ -181,14 +180,16 @@ double step_round(const double in, const bool dir)
     static const double __log8 = log10(8);
 
     // log_in is of the form "s abc.xyz", where
-    // s is either + or -; + indicates abs(in) >= 1 and - indicates 0 < abs(in) < 1 (log10(1) is +0)
+    // s is either + or -; + indicates abs(in) >= 1 and - indicates 0 < abs(in)
+    // < 1 (log10(1) is +0)
     const double sign   = in < 0 ? -1 : 1;
     const double log_in = std::log10(std::fabs(in));
-    const double mag    = std::pow(10, std::floor(log_in)) * sign;  // Number of digits either left or right of 0
-    const double dec    = std::log10(in / mag); // log of the fraction
+    const double mag    = std::pow(10, std::floor(log_in)) *
+                       sign;  // Number of digits either left or right of 0
+    const double dec = std::log10(in / mag);  // log of the fraction
 
     // This means in is of the for 10^n
-    if(dec == 0) return in;
+    if (dec == 0) return in;
 
     // For negative numbers, -ve round down = +ve round up and vice versa
     bool op_dir = in > 0 ? dir : !dir;
@@ -196,26 +197,26 @@ double step_round(const double in, const bool dir)
     double mult = 1;
 
     // Round up
-    if(op_dir) {
-        if(dec <= __log2) {
+    if (op_dir) {
+        if (dec <= __log2) {
             mult = 2;
-        } else if(dec <= __log4) {
+        } else if (dec <= __log4) {
             mult = 4;
-        } else if(dec <= __log6) {
+        } else if (dec <= __log6) {
             mult = 6;
-        } else if(dec <= __log8) {
+        } else if (dec <= __log8) {
             mult = 8;
         } else {
             mult = 10;
         }
-    } else {    // Round down
-        if(dec < __log2) {
+    } else {  // Round down
+        if (dec < __log2) {
             mult = 1;
-        } else if(dec < __log4) {
+        } else if (dec < __log4) {
             mult = 2;
-        } else if(dec < __log6) {
+        } else if (dec < __log6) {
             mult = 4;
-        } else if(dec < __log8) {
+        } else if (dec < __log8) {
             mult = 6;
         } else {
             mult = 8;
@@ -227,35 +228,31 @@ double step_round(const double in, const bool dir)
 
 namespace graphics {
 
-ForgeModule& forgePlugin()
-{
-    return detail::forgeManager().plugin();
-}
+ForgeModule& forgePlugin() { return detail::forgeManager().plugin(); }
 
-ForgeManager::ForgeManager()
-    : mPlugin(new ForgeModule()) {}
+ForgeManager::ForgeManager() : mPlugin(new ForgeModule()) {}
 
-ForgeManager::~ForgeManager()
-{
+ForgeManager::~ForgeManager() {
     /* clear all OpenGL resource objects (images, plots, histograms etc) first
      * and then delete the windows */
-    for(ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
+    for (ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
         mPlugin->fg_release_image(iter->second);
 
-    for(PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
+    for (PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
         mPlugin->fg_release_plot(iter->second);
 
-    for(HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
+    for (HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
         mPlugin->fg_release_histogram(iter->second);
 
-    for(SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
+    for (SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
         mPlugin->fg_release_surface(iter->second);
 
-    for(VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
+    for (VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
         mPlugin->fg_release_vector_field(iter->second);
 
-    for(ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end(); iter++) {
-        for(int i = 0; i < (int)(iter->second).size(); i++) {
+    for (ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end();
+         iter++) {
+        for (int i = 0; i < (int)(iter->second).size(); i++) {
             fg_chart chrt = (iter->second)[i];
             if (chrt) {
                 mChartAxesOverrideMap.erase((chrt));
@@ -266,57 +263,50 @@ ForgeManager::~ForgeManager()
     mPlugin->fg_release_window(wnd->handle);
 }
 
-ForgeModule& ForgeManager::plugin() {
-    return *mPlugin;
-}
+ForgeModule& ForgeManager::plugin() { return *mPlugin; }
 
-fg_window ForgeManager::getMainWindow()
-{
+fg_window ForgeManager::getMainWindow() {
     static std::once_flag flag;
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
 
-    if (noGraphicsENV.empty()) { // If AF_DISABLE_GRAPHICS is not defined
-        std::call_once(flag,
-            [this] {
-                if (!this->mPlugin->isLoaded()) {
-                    string error_message = "Error loading Forge: "
-                        + this->mPlugin->getErrorMessage()
-                        + "\nForge or one of it's dependencies failed to "
-                        "load. Try installing Forge or check if Forge is in the "
-                        "search path.";
-                    AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
-                }
-                fg_window w = nullptr;
-                fg_err e = this->mPlugin->fg_create_window(&w, WIDTH, HEIGHT,
-                                                           "ArrayFire", NULL, true);
-                if (e != FG_ERR_NONE) {
-                    AF_ERROR("Graphics Window creation failed", AF_ERR_INTERNAL);
-                }
-                this->mPlugin->fg_make_window_current(w);
-                this->setWindowChartGrid(w, 1, 1);
-                this->wnd.reset(new Window({w}));
-                if (!gladLoadGL()) {
-                    AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB);
-                }
-            });
+    if (noGraphicsENV.empty()) {  // If AF_DISABLE_GRAPHICS is not defined
+        std::call_once(flag, [this] {
+            if (!this->mPlugin->isLoaded()) {
+                string error_message =
+                    "Error loading Forge: " + this->mPlugin->getErrorMessage() +
+                    "\nForge or one of it's dependencies failed to "
+                    "load. Try installing Forge or check if Forge is in the "
+                    "search path.";
+                AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+            }
+            fg_window w = nullptr;
+            fg_err e    = this->mPlugin->fg_create_window(&w, WIDTH, HEIGHT,
+                                                       "ArrayFire", NULL, true);
+            if (e != FG_ERR_NONE) {
+                AF_ERROR("Graphics Window creation failed", AF_ERR_INTERNAL);
+            }
+            this->mPlugin->fg_make_window_current(w);
+            this->setWindowChartGrid(w, 1, 1);
+            this->wnd.reset(new Window({w}));
+            if (!gladLoadGL()) { AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB); }
+        });
     }
 
     return wnd->handle;
 }
 
-void ForgeManager::setWindowChartGrid(const fg_window window,
-                                      const int r, const int c)
-{
+void ForgeManager::setWindowChartGrid(const fg_window window, const int r,
+                                      const int c) {
     ChartMapIter iter = mChartMap.find(window);
     GridMapIter gIter = mWndGridMap.find(window);
 
-    if(iter != mChartMap.end()) {
+    if (iter != mChartMap.end()) {
         // ChartVec found. Clear it.
         // This has to be cleared as there is no guarantee that existing
         // chart types(2D/3D) match the future grid requirements
-        for(int i = 0; i < (int)(iter->second).size(); i++) {
+        for (int i = 0; i < (int)(iter->second).size(); i++) {
             fg_chart chrt = (iter->second)[i];
             if (chrt) {
                 mChartAxesOverrideMap.erase(chrt);
@@ -327,17 +317,16 @@ void ForgeManager::setWindowChartGrid(const fg_window window,
         gIter->second = std::make_pair<int, int>(1, 1);
     }
 
-    if(r == 0 || c == 0) {
+    if (r == 0 || c == 0) {
         mChartMap.erase(window);
         mWndGridMap.erase(window);
     } else {
-        mChartMap[window] = std::vector<fg_chart>(r * c);
+        mChartMap[window]   = std::vector<fg_chart>(r * c);
         mWndGridMap[window] = std::make_pair(r, c);
     }
 }
 
-WindGridDims_t ForgeManager::getWindowGrid(const fg_window window)
-{
+WindGridDims_t ForgeManager::getWindowGrid(const fg_window window) {
     GridMapIter gIter = mWndGridMap.find(window);
 
     if (gIter == mWndGridMap.end()) {
@@ -347,20 +336,17 @@ WindGridDims_t ForgeManager::getWindowGrid(const fg_window window)
     return mWndGridMap[window];
 }
 
-fg_chart ForgeManager::getChart(const fg_window window,
-                                const int r, const int c,
-                                const fg_chart_type ctype)
-{
-    fg_chart chart = NULL;
+fg_chart ForgeManager::getChart(const fg_window window, const int r,
+                                const int c, const fg_chart_type ctype) {
+    fg_chart chart    = NULL;
     ChartMapIter iter = mChartMap.find(window);
     GridMapIter gIter = mWndGridMap.find(window);
 
     if (iter != mChartMap.end()) {
-
         int gRows = std::get<0>(gIter->second);
         int gCols = std::get<1>(gIter->second);
 
-        if(c >= gCols || r >= gRows)
+        if (c >= gCols || r >= gRows)
             AF_ERROR("Grid points are out of bounds", AF_ERR_TYPE);
 
         // upgrade to exclusive access to make changes
@@ -393,23 +379,23 @@ fg_chart ForgeManager::getChart(const fg_window window,
     return chart;
 }
 
-fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode, fg_dtype type)
-{
+fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode,
+                                fg_dtype type) {
     /* w, h needs to fall in the range of [0, 2^16]
      * for the ForgeManager to correctly retrieve
      * the necessary Forge Image object. So, this implementation
      * is a limitation on how big of an image can be rendered
      * using arrayfire graphics funtionality */
-    assert(w <= 2ll<<16);
-    assert(h <= 2ll<<16);
+    assert(w <= 2ll << 16);
+    assert(h <= 2ll << 16);
     long long key = ((w & _16BIT) << 16) | (h & _16BIT);
-    key = (((key << 16) | mode) << 16) | type;
+    key           = (((key << 16) | mode) << 16) | type;
 
     ChartKey_t keypair = std::make_pair(key, nullptr);
 
     ImgMapIter iter = mImgMap.find(keypair);
 
-    if (iter==mImgMap.end()) {
+    if (iter == mImgMap.end()) {
         fg_image img = nullptr;
         FG_CHECK(mPlugin->fg_create_image(&img, w, h, mode, type));
         mImgMap[keypair] = img;
@@ -419,26 +405,25 @@ fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode, fg_dtype t
 }
 
 fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
-                                fg_channel_format mode, fg_dtype type)
-{
+                                fg_channel_format mode, fg_dtype type) {
     /* w, h needs to fall in the range of [0, 2^16]
      * for the ForgeManager to correctly retrieve
      * the necessary Forge Image object. So, this implementation
      * is a limitation on how big of an image can be rendered
      * using arrayfire graphics funtionality */
-    assert(w <= 2ll<<16);
-    assert(h <= 2ll<<16);
+    assert(w <= 2ll << 16);
+    assert(h <= 2ll << 16);
     long long key = ((w & _16BIT) << 16) | (h & _16BIT);
-    key = (((key << 16) | mode) << 16) | type;
+    key           = (((key << 16) | mode) << 16) | type;
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
     ImgMapIter iter = mImgMap.find(keypair);
 
-    if (iter==mImgMap.end()) {
+    if (iter == mImgMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-        if(chart_type != FG_CHART_2D)
+        if (chart_type != FG_CHART_2D)
             AF_ERROR("Image can only be added to chart of type FG_CHART_2D",
                      AF_ERR_TYPE);
 
@@ -453,21 +438,22 @@ fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
 }
 
 fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
-                              fg_plot_type ptype, fg_marker_type mtype)
-{
+                              fg_plot_type ptype, fg_marker_type mtype) {
     long long key = ((nPoints & _48BIT) << 48);
-    key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) | (mtype & 0x000F));
+    key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) |
+            (mtype & 0x000F));
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
     PltMapIter iter = mPltMap.find(keypair);
 
-    if (iter==mPltMap.end()) {
+    if (iter == mPltMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
 
         fg_plot plt = nullptr;
-        FG_CHECK(mPlugin->fg_create_plot(&plt, nPoints, dtype, chart_type, ptype, mtype));
+        FG_CHECK(mPlugin->fg_create_plot(&plt, nPoints, dtype, chart_type,
+                                         ptype, mtype));
         mPltMap[keypair] = plt;
 
         FG_CHECK(mPlugin->fg_append_plot_to_chart(chart, plt));
@@ -476,18 +462,18 @@ fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
     return mPltMap[keypair];
 }
 
-fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins, fg_dtype type)
-{
+fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins,
+                                        fg_dtype type) {
     long long key = ((nBins & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
     HstMapIter iter = mHstMap.find(keypair);
 
-    if (iter==mHstMap.end()) {
+    if (iter == mHstMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-        if(chart_type != FG_CHART_2D)
+        if (chart_type != FG_CHART_2D)
             AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D",
                      AF_ERR_TYPE);
 
@@ -501,30 +487,30 @@ fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins, fg_dtype type
     return mHstMap[keypair];
 }
 
-fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY, fg_dtype type)
-{
+fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY,
+                                    fg_dtype type) {
     /* nX * nY needs to fall in the range of [0, 2^48]
      * for the ForgeManager to correctly retrieve
      * the necessary Forge Plot object. So, this implementation
      * is a limitation on how big of an plot graph can be rendered
      * using arrayfire graphics funtionality */
-    assert((long long)nX * nY <= 2ll<<48);
+    assert((long long)nX * nY <= 2ll << 48);
     long long key = (((nX * nY) & _48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
     SfcMapIter iter = mSfcMap.find(keypair);
 
-    if (iter==mSfcMap.end()) {
+    if (iter == mSfcMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-        if(chart_type != FG_CHART_3D)
+        if (chart_type != FG_CHART_3D)
             AF_ERROR("Surface can only be added to chart of type FG_CHART_3D",
                      AF_ERR_TYPE);
 
         fg_surface surf = nullptr;
         FG_CHECK(mPlugin->fg_create_surface(&surf, nX, nY, type,
-                                   FG_PLOT_SURFACE, FG_MARKER_NONE));
+                                            FG_PLOT_SURFACE, FG_MARKER_NONE));
         mSfcMap[keypair] = surf;
 
         FG_CHECK(mPlugin->fg_append_surface_to_chart(chart, surf));
@@ -533,20 +519,21 @@ fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY, fg_dtype typ
     return mSfcMap[keypair];
 }
 
-fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints, fg_dtype type)
-{
-    long long key = (((nPoints) & _48BIT) << 48) | (type & _16BIT);
+fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints,
+                                             fg_dtype type) {
+    long long key = (((nPoints)&_48BIT) << 48) | (type & _16BIT);
 
     ChartKey_t keypair = std::make_pair(key, chart);
 
     VcfMapIter iter = mVcfMap.find(keypair);
 
-    if (iter==mVcfMap.end()) {
+    if (iter == mVcfMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
 
         fg_vector_field vfield = nullptr;
-        FG_CHECK(mPlugin->fg_create_vector_field(&vfield, nPoints, type, chart_type));
+        FG_CHECK(mPlugin->fg_create_vector_field(&vfield, nPoints, type,
+                                                 chart_type));
         mVcfMap[keypair] = vfield;
 
         FG_CHECK(mPlugin->fg_append_vector_field_to_chart(chart, vfield));
@@ -555,8 +542,7 @@ fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints, fg_dty
     return mVcfMap[keypair];
 }
 
-bool ForgeManager::getChartAxesOverride(fg_chart chart)
-{
+bool ForgeManager::getChartAxesOverride(fg_chart chart) {
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
@@ -564,12 +550,11 @@ bool ForgeManager::getChartAxesOverride(fg_chart chart)
     return mChartAxesOverrideMap[chart];
 }
 
-void ForgeManager::setChartAxesOverride(fg_chart chart, bool flag)
-{
+void ForgeManager::setChartAxesOverride(fg_chart chart, bool flag) {
     ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
     mChartAxesOverrideMap[chart] = flag;
 }
-}
+}  // namespace graphics
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 5ffa3e82ef..61fb8019fc 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -9,13 +9,13 @@
 
 #pragma once
 
-#include <af/graphics.h>
 #include <common/forge_loader.hpp>
+#include <af/graphics.h>
 
-#include <vector>
 #include <map>
 #include <memory>
 #include <utility>
+#include <vector>
 
 // default to f32(float) type
 template<typename T>
@@ -23,9 +23,9 @@ fg_dtype getGLType();
 
 // Print for OpenGL errors
 // Returns 1 if an OpenGL error occurred, 0 otherwise.
-GLenum glErrorCheck(const char *msg, const char* file, int line);
+GLenum glErrorCheck(const char* msg, const char* file, int line);
 
-#define CheckGL(msg)      glErrorCheck     (msg, __AF_FILENAME__, __LINE__)
+#define CheckGL(msg) glErrorCheck(msg, __AF_FILENAME__, __LINE__)
 
 fg_marker_type getFGMarker(const af_marker_type af_marker);
 
@@ -34,10 +34,7 @@ void makeContextCurrent(fg_window window);
 double step_round(const double in, const bool dir);
 
 namespace graphics {
-enum Defaults {
-    WIDTH = 1280,
-    HEIGHT= 720
-};
+enum Defaults { WIDTH = 1280, HEIGHT = 720 };
 
 static const long long _16BIT = 0x000000000000FFFF;
 static const long long _32BIT = 0x00000000FFFFFFFF;
@@ -81,60 +78,57 @@ typedef ChartAxesOverride_t::iterator ChartAxesOverrideIter;
  *      fg_surface
  *      fg_vector_field
  * */
-class ForgeManager
-{
+class ForgeManager {
     struct Window {
         fg_window handle;
     };
 
-    private:
-        ForgeModule* mPlugin;
-        std::unique_ptr<Window> wnd;
+   private:
+    ForgeModule* mPlugin;
+    std::unique_ptr<Window> wnd;
 
-        ImageMap_t          mImgMap;
-        PlotMap_t           mPltMap;
-        HistogramMap_t      mHstMap;
-        SurfaceMap_t        mSfcMap;
-        VectorFieldMap_t    mVcfMap;
+    ImageMap_t mImgMap;
+    PlotMap_t mPltMap;
+    HistogramMap_t mHstMap;
+    SurfaceMap_t mSfcMap;
+    VectorFieldMap_t mVcfMap;
 
-        ChartMap_t          mChartMap;
-        WindGridMap_t       mWndGridMap;
-        ChartAxesOverride_t mChartAxesOverrideMap;
+    ChartMap_t mChartMap;
+    WindGridMap_t mWndGridMap;
+    ChartAxesOverride_t mChartAxesOverrideMap;
 
-    public:
-        ForgeManager();
-        ForgeManager(ForgeManager const&) = delete;
-        ForgeManager& operator=(ForgeManager const&) = delete;
-        ForgeManager(ForgeManager &&) = delete;
-        ForgeManager& operator=(ForgeManager &&) = delete;
-        ~ForgeManager();
-        ForgeModule& plugin();
-        fg_window getMainWindow();
+   public:
+    ForgeManager();
+    ForgeManager(ForgeManager const&) = delete;
+    ForgeManager& operator=(ForgeManager const&) = delete;
+    ForgeManager(ForgeManager&&)                 = delete;
+    ForgeManager& operator=(ForgeManager&&) = delete;
+    ~ForgeManager();
+    ForgeModule& plugin();
+    fg_window getMainWindow();
 
-        void            setWindowChartGrid(const fg_window window,
-                                           const int r, const int c);
+    void setWindowChartGrid(const fg_window window, const int r, const int c);
 
-        WindGridDims_t getWindowGrid(const fg_window window);
+    WindGridDims_t getWindowGrid(const fg_window window);
 
-        fg_chart getChart(const fg_window window, const int r, const int c,
-                          const fg_chart_type ctype);
+    fg_chart getChart(const fg_window window, const int r, const int c,
+                      const fg_chart_type ctype);
 
-        fg_image getImage(int w, int h, fg_channel_format mode,
-                          fg_dtype type);
+    fg_image getImage(int w, int h, fg_channel_format mode, fg_dtype type);
 
-        fg_image getImage(fg_chart chart, int w, int h,
-                          fg_channel_format mode, fg_dtype type);
+    fg_image getImage(fg_chart chart, int w, int h, fg_channel_format mode,
+                      fg_dtype type);
 
-        fg_plot getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
-                        fg_plot_type ptype, fg_marker_type mtype);
+    fg_plot getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
+                    fg_plot_type ptype, fg_marker_type mtype);
 
-        fg_histogram getHistogram(fg_chart chart, int nBins, fg_dtype type);
+    fg_histogram getHistogram(fg_chart chart, int nBins, fg_dtype type);
 
-        fg_surface getSurface(fg_chart chart, int nX, int nY, fg_dtype type);
+    fg_surface getSurface(fg_chart chart, int nX, int nY, fg_dtype type);
 
-        fg_vector_field getVectorField(fg_chart chart, int nPoints, fg_dtype type);
+    fg_vector_field getVectorField(fg_chart chart, int nPoints, fg_dtype type);
 
-        bool getChartAxesOverride(fg_chart chart);
-        void setChartAxesOverride(fg_chart chart, bool flag = true);
+    bool getChartAxesOverride(fg_chart chart);
+    void setChartAxesOverride(fg_chart chart, bool flag = true);
 };
-}
+}  // namespace graphics
diff --git a/src/backend/common/host_memory.cpp b/src/backend/common/host_memory.cpp
index b81d4fcf5c..a97aa12987 100644
--- a/src/backend/common/host_memory.cpp
+++ b/src/backend/common/host_memory.cpp
@@ -3,7 +3,8 @@
  * Site:    http://NadeauSoftware.com/
  * License: Creative Commons Attribution 3.0 Unported License
  *          http://creativecommons.org/licenses/by/3.0/deed.en_US
- * Source:  http://nadeausoftware.com/sites/NadeauSoftware.com/files/getMemorySize.c
+ * Source:
+ * http://nadeausoftware.com/sites/NadeauSoftware.com/files/getMemorySize.c
  */
 
 #include "host_memory.hpp"
@@ -11,10 +12,11 @@
 #if defined(_WIN32)
 #include <Windows.h>
 
-#elif defined(__unix__) || defined(__unix) || defined(unix) || (defined(__APPLE__) && defined(__MACH__))
-#include <unistd.h>
-#include <sys/types.h>
+#elif defined(__unix__) || defined(__unix) || defined(unix) || \
+    (defined(__APPLE__) && defined(__MACH__))
 #include <sys/param.h>
+#include <sys/types.h>
+#include <unistd.h>
 
 #if defined(BSD) && !defined(__gnu_hurd__)
 #include <sys/sysctl.h>
@@ -24,13 +26,11 @@
 #define NOMEMORYSIZE
 #endif
 
-namespace common
-{
+namespace common {
 
 #ifdef NOMEMORYSIZE
-size_t getHostMemorySize()
-{
-    return 0L; // Can't detect
+size_t getHostMemorySize() {
+    return 0L;  // Can't detect
 }
 
 #else
@@ -38,14 +38,13 @@ size_t getHostMemorySize()
 /**
  * Returns the size of physical memory (RAM) in bytes.
  */
-size_t getHostMemorySize()
-{
+size_t getHostMemorySize() {
 #if defined(_WIN32) && (defined(__CYGWIN__) || defined(__CYGWIN32__))
     /* Cygwin under Windows. ------------------------------------ */
     /* New 64-bit MEMORYSTATUSEX isn't available.  Use old 32.bit */
     MEMORYSTATUS status;
     status.dwLength = sizeof(status);
-    GlobalMemoryStatus( &status );
+    GlobalMemoryStatus(&status);
     return (size_t)status.dwTotalPhys;
 
 #elif defined(_WIN32)
@@ -53,61 +52,59 @@ size_t getHostMemorySize()
     /* Use new 64-bit MEMORYSTATUSEX, not old 32-bit MEMORYSTATUS */
     MEMORYSTATUSEX status;
     status.dwLength = sizeof(status);
-    GlobalMemoryStatusEx( &status );
+    GlobalMemoryStatusEx(&status);
     return (size_t)status.ullTotalPhys;
 
-#elif defined(__unix__) || defined(__unix) || defined(unix) || (defined(__APPLE__) && defined(__MACH__))
+#elif defined(__unix__) || defined(__unix) || defined(unix) || \
+    (defined(__APPLE__) && defined(__MACH__))
     /* UNIX variants. ------------------------------------------- */
-    /* Prefer sysctl() over sysconf() except sysctl() HW_REALMEM and HW_PHYSMEM */
+    /* Prefer sysctl() over sysconf() except sysctl() HW_REALMEM and HW_PHYSMEM
+     */
 
 #if defined(CTL_HW) && (defined(HW_MEMSIZE) || defined(HW_PHYSMEM64))
     int mib[2];
-    mib[0] = CTL_HW;
+    mib[0]       = CTL_HW;
 #if defined(HW_MEMSIZE)
-    mib[1] = HW_MEMSIZE;        /* OSX. --------------------- */
+    mib[1]       = HW_MEMSIZE; /* OSX. --------------------- */
 #elif defined(HW_PHYSMEM64)
-    mib[1] = HW_PHYSMEM64;      /* NetBSD, OpenBSD. --------- */
+    mib[1] = HW_PHYSMEM64; /* NetBSD, OpenBSD. --------- */
 #endif
-    int64_t size = 0;       /* 64-bit */
-    size_t len = sizeof( size );
-    if ( sysctl( mib, 2, &size, &len, NULL, 0 ) == 0 )
-        return (size_t)size;
-    return 0L;          /* Failed? */
+    int64_t size = 0;          /* 64-bit */
+    size_t len   = sizeof(size);
+    if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
+    return 0L; /* Failed? */
 
 #elif defined(_SC_AIX_REALMEM)
     /* AIX. ----------------------------------------------------- */
-    return (size_t)sysconf( _SC_AIX_REALMEM ) * (size_t)1024L;
+    return (size_t)sysconf(_SC_AIX_REALMEM) * (size_t)1024L;
 
 #elif defined(_SC_PHYS_PAGES) && defined(_SC_PAGESIZE)
     /* FreeBSD, Linux, OpenBSD, and Solaris. -------------------- */
-    return (size_t)sysconf( _SC_PHYS_PAGES ) *
-        (size_t)sysconf( _SC_PAGESIZE );
+    return (size_t)sysconf(_SC_PHYS_PAGES) * (size_t)sysconf(_SC_PAGESIZE);
 
 #elif defined(_SC_PHYS_PAGES) && defined(_SC_PAGE_SIZE)
     /* Legacy. -------------------------------------------------- */
-    return (size_t)sysconf( _SC_PHYS_PAGES ) *
-        (size_t)sysconf( _SC_PAGE_SIZE );
+    return (size_t)sysconf(_SC_PHYS_PAGES) * (size_t)sysconf(_SC_PAGE_SIZE);
 
 #elif defined(CTL_HW) && (defined(HW_PHYSMEM) || defined(HW_REALMEM))
     /* DragonFly BSD, FreeBSD, NetBSD, OpenBSD, and OSX. -------- */
     int mib[2];
-    mib[0] = CTL_HW;
+    mib[0]            = CTL_HW;
 #if defined(HW_REALMEM)
-    mib[1] = HW_REALMEM;        /* FreeBSD. ----------------- */
+    mib[1]            = HW_REALMEM; /* FreeBSD. ----------------- */
 #elif defined(HW_PYSMEM)
-    mib[1] = HW_PHYSMEM;        /* Others. ------------------ */
+    mib[1] = HW_PHYSMEM; /* Others. ------------------ */
 #endif
-    unsigned int size = 0;      /* 32-bit */
-    size_t len = sizeof( size );
-    if ( sysctl( mib, 2, &size, &len, NULL, 0 ) == 0 )
-        return (size_t)size;
-    return 0L;          /* Failed? */
+    unsigned int size = 0;          /* 32-bit */
+    size_t len        = sizeof(size);
+    if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
+    return 0L; /* Failed? */
 #endif /* sysctl and sysconf variants */
 
 #else
-    return 0L;          /* Unknown OS. */
+    return 0L; /* Unknown OS. */
 #endif
 }
 
-#endif // NOMEMORYSIZE
-} // namespace common
+#endif  // NOMEMORYSIZE
+}  // namespace common
diff --git a/src/backend/common/host_memory.hpp b/src/backend/common/host_memory.hpp
index 5955cbfbd9..69557fb576 100644
--- a/src/backend/common/host_memory.hpp
+++ b/src/backend/common/host_memory.hpp
@@ -10,8 +10,7 @@
 #pragma once
 #include <cstddef>
 
-namespace common
-{
+namespace common {
 
 size_t getHostMemorySize();
 
diff --git a/src/backend/common/jit/BinaryNode.hpp b/src/backend/common/jit/BinaryNode.hpp
index e3e5860db6..066dc9ac33 100644
--- a/src/backend/common/jit/BinaryNode.hpp
+++ b/src/backend/common/jit/BinaryNode.hpp
@@ -12,15 +12,12 @@
 #include <cmath>
 
 namespace common {
-class BinaryNode : public NaryNode
-{
-  public:
+class BinaryNode : public NaryNode {
+   public:
     BinaryNode(const char *out_type_str, const char *name_str,
-              const char *op_str,
-              common::Node_ptr lhs, common::Node_ptr rhs, int op)
-      : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}},
-                op, std::max(lhs->getHeight(), rhs->getHeight()) + 1)
-    {
-    }
+               const char *op_str, common::Node_ptr lhs, common::Node_ptr rhs,
+               int op)
+        : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}}, op,
+                   std::max(lhs->getHeight(), rhs->getHeight()) + 1) {}
 };
-}
+}  // namespace common
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 12bfad060c..525555341e 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -8,46 +8,40 @@
  ********************************************************/
 
 #pragma once
-#include <jit/kernel_generators.hpp>
 #include <backend.hpp>
+#include <jit/kernel_generators.hpp>
 
 #include <sstream>
 
 namespace common {
 
 template<typename DataType, typename ParamType>
-class BufferNodeBase : public common::Node
-{
-private:
+class BufferNodeBase : public common::Node {
+   private:
     DataType m_data;
     ParamType m_param;
     unsigned m_bytes;
     std::once_flag m_set_data_flag;
     bool m_linear_buffer;
 
-public:
-
-    BufferNodeBase(const char *type_str,
-                  const char *name_str)
-        : Node(type_str, name_str, 0, {})
-    {
-    }
+   public:
+    BufferNodeBase(const char *type_str, const char *name_str)
+        : Node(type_str, name_str, 0, {}) {}
 
     bool isBuffer() const final { return true; }
 
-
-    void setData(ParamType param, DataType data, const unsigned bytes, bool is_linear)
-    {
-        std::call_once(m_set_data_flag, [this, param, data, bytes, is_linear]() {
-                                          m_param = param;
-                                          m_data = data;
-                                          m_bytes = bytes;
-                                          m_linear_buffer = is_linear;
-                                        });
+    void setData(ParamType param, DataType data, const unsigned bytes,
+                 bool is_linear) {
+        std::call_once(m_set_data_flag,
+                       [this, param, data, bytes, is_linear]() {
+                           m_param         = param;
+                           m_data          = data;
+                           m_bytes         = bytes;
+                           m_linear_buffer = is_linear;
+                       });
     }
 
-    bool isLinear(dim_t dims[4]) const final
-    {
+    bool isLinear(dim_t dims[4]) const final {
         bool same_dims = true;
         for (int i = 0; same_dims && i < 4; i++) {
             same_dims &= (dims[i] == m_param.dims[i]);
@@ -55,34 +49,37 @@ class BufferNodeBase : public common::Node
         return m_linear_buffer && same_dims;
     }
 
-    void genKerName(std::stringstream &kerStream, const common::Node_ids& ids) const final
-    {
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
         kerStream << "_" << m_name_str;
-        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
+                  << std::dec;
     }
 
-
-    void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-    {
+    void genParams(std::stringstream &kerStream, int id,
+                   bool is_linear) const final {
         detail::generateParamDeclaration(kerStream, id, is_linear, m_type_str);
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const override {
-        return detail::setKernelArguments(start_id, is_linear, setArg, m_data, m_param);
+                std::function<void(int id, const void *ptr, size_t arg_size)>
+                    setArg) const override {
+        return detail::setKernelArguments(start_id, is_linear, setArg, m_data,
+                                          m_param);
     }
 
-    void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
-    {
+    void genOffsets(std::stringstream &kerStream, int id,
+                    bool is_linear) const final {
         detail::generateBufferOffsets(kerStream, id, is_linear, m_type_str);
     }
 
-    void genFuncs(std::stringstream &kerStream, const common::Node_ids& ids) const final
-    {
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
         detail::generateBufferRead(kerStream, ids.id, m_type_str);
     }
 
-    void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final {
+    void getInfo(unsigned &len, unsigned &buf_count,
+                 unsigned &bytes) const final {
         len++;
         buf_count++;
         bytes += m_bytes;
@@ -91,4 +88,4 @@ class BufferNodeBase : public common::Node
     size_t getBytes() const final { return m_bytes; }
 };
 
-}
+}  // namespace common
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index e3c04caedd..95b2c1cca4 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -12,54 +12,53 @@
 
 #include <array>
 #include <iomanip>
-#include <string>
 #include <sstream>
+#include <string>
 #include <utility>
 
 namespace common {
 
-    class NaryNode : public Node {
-    private:
-        const int m_num_children;
-        const int m_op;
-        const std::string m_op_str;
+class NaryNode : public Node {
+   private:
+    const int m_num_children;
+    const int m_op;
+    const std::string m_op_str;
 
-    public:
-        NaryNode(const char *out_type_str,
-                 const char *name_str,
-                 const char *op_str,
-                 const int num_children,
-                 const std::array<common::Node_ptr, Node::kMaxChildren> &&children,
-                 const int op, const int height)
-            : common::Node(out_type_str, name_str, height,
-                           std::forward<const std::array<common::Node_ptr, Node::kMaxChildren>>(children)),
-              m_num_children(num_children),
-              m_op(op),
-              m_op_str(op_str)
-        {
-        }
+   public:
+    NaryNode(const char *out_type_str, const char *name_str, const char *op_str,
+             const int num_children,
+             const std::array<common::Node_ptr, Node::kMaxChildren> &&children,
+             const int op, const int height)
+        : common::Node(
+              out_type_str, name_str, height,
+              std::forward<
+                  const std::array<common::Node_ptr, Node::kMaxChildren>>(
+                  children))
+        , m_num_children(num_children)
+        , m_op(op)
+        , m_op_str(op_str) {}
 
-        void genKerName(std::stringstream &kerStream, const common::Node_ids& ids) const final
-        {
-            // Make the dec representation of enum part of the Kernel name
-            kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec << m_op;
-            for (int i = 0; i < m_num_children; i++) {
-                kerStream << std::setw(3)
-                          << std::setfill('0')
-                          << std::dec
-                          << ids.child_ids[i];
-            }
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
+        // Make the dec representation of enum part of the Kernel name
+        kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec
+                  << m_op;
+        for (int i = 0; i < m_num_children; i++) {
+            kerStream << std::setw(3) << std::setfill('0') << std::dec
+                      << ids.child_ids[i];
         }
+        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
+                  << std::dec;
+    }
 
-        void genFuncs(std::stringstream &kerStream, const common::Node_ids& ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
-            for (int i = 0; i < m_num_children; i++) {
-                if (i > 0) kerStream << ", ";
-                kerStream << "val" << ids.child_ids[i];
-            }
-            kerStream << ");\n";
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
+        kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
+        for (int i = 0; i < m_num_children; i++) {
+            if (i > 0) kerStream << ", ";
+            kerStream << "val" << ids.child_ids[i];
         }
-    };
-}
+        kerStream << ");\n";
+    }
+};
+}  // namespace common
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 4ac215a86e..d6d8400af6 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -15,18 +15,17 @@ using namespace std;
 
 namespace common {
 
-  int Node::getNodesMap(Node_map_t &node_map,
-                vector<const Node *> &full_nodes,
-                vector<Node_ids> &full_ids) const {
+int Node::getNodesMap(Node_map_t &node_map, vector<const Node *> &full_nodes,
+                      vector<Node_ids> &full_ids) const {
     auto iter = node_map.find(this);
     if (iter == node_map.end()) {
         Node_ids ids;
 
         for (int i = 0; i < kMaxChildren && m_children[i] != nullptr; i++) {
-            ids.child_ids[i] = m_children[i]->getNodesMap(node_map, full_nodes,
-                                                          full_ids);
+            ids.child_ids[i] =
+                m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
         }
-        ids.id = node_map.size();
+        ids.id         = node_map.size();
         node_map[this] = ids.id;
         full_nodes.push_back(this);
         full_ids.push_back(ids);
@@ -35,4 +34,4 @@ namespace common {
     return iter->second;
 }
 
-}
+}  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index b7088b8c86..83c2b90ebd 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <platform.hpp>
 #include <optypes.hpp>
+#include <platform.hpp>
 
 #include <array>
 #include <functional>
@@ -19,98 +19,104 @@
 #include <vector>
 
 namespace common {
-    class Node;
-    struct Node_ids;
+class Node;
+struct Node_ids;
+
+using Node_ptr      = std::shared_ptr<Node>;
+using Node_map_t    = std::unordered_map<const Node *, int>;
+using Node_map_iter = Node_map_t::iterator;
 
-    using Node_ptr = std::shared_ptr<Node>;
-    using Node_map_t = std::unordered_map<const Node *, int> ;
-    using Node_map_iter = Node_map_t::iterator;
+class Node {
+   public:
+    static const int kMaxChildren = 3;
 
-    class Node
-    {
-    public:
-        static const int kMaxChildren = 3;
-    protected:
-        const std::array<Node_ptr, kMaxChildren> m_children;
-        const std::string m_type_str;
-        const std::string m_name_str;
-        const int m_height;
-        template<typename T> friend class NodeIterator;
+   protected:
+    const std::array<Node_ptr, kMaxChildren> m_children;
+    const std::string m_type_str;
+    const std::string m_name_str;
+    const int m_height;
+    template<typename T>
+    friend class NodeIterator;
 
-    public:
-        Node(const char *type_str, const char *name_str, const int height,
-             const std::array<Node_ptr, kMaxChildren> children)
-            : m_children(children),
-              m_type_str(type_str),
-              m_name_str(name_str),
-              m_height(height) {}
+   public:
+    Node(const char *type_str, const char *name_str, const int height,
+         const std::array<Node_ptr, kMaxChildren> children)
+        : m_children(children)
+        , m_type_str(type_str)
+        , m_name_str(name_str)
+        , m_height(height) {}
 
-        int getNodesMap(Node_map_t &node_map,
-                        std::vector<const Node *> &full_nodes,
-                        std::vector<Node_ids> &full_ids) const;
+    int getNodesMap(Node_map_t &node_map, std::vector<const Node *> &full_nodes,
+                    std::vector<Node_ids> &full_ids) const;
 
-        virtual void genKerName (std::stringstream &kerStream, const Node_ids& ids) const {
-            UNUSED(kerStream);
-            UNUSED(ids);
-        }
-        virtual void genParams  (std::stringstream &kerStream, int id, bool is_linear) const {
-            UNUSED(kerStream);
-            UNUSED(id);
-            UNUSED(is_linear);
-        }
-        virtual void genOffsets (std::stringstream &kerStream, int id, bool is_linear) const {
-            UNUSED(kerStream);
-            UNUSED(id);
-            UNUSED(is_linear);
-        }
-        virtual void genFuncs   (std::stringstream &kerStream, const Node_ids& ids) const {
-            UNUSED(kerStream);
-            UNUSED(ids);
-        }
+    virtual void genKerName(std::stringstream &kerStream,
+                            const Node_ids &ids) const {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
+    virtual void genParams(std::stringstream &kerStream, int id,
+                           bool is_linear) const {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+    virtual void genOffsets(std::stringstream &kerStream, int id,
+                            bool is_linear) const {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+    virtual void genFuncs(std::stringstream &kerStream,
+                          const Node_ids &ids) const {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
 
-        /// Calls the setArg function on each of the arguments passed into the kernel
-        ///
-        /// \param[in] start_id The index of the staring argument
-        /// \param[in] is_linear determines if the kernel should be linear or not
-        /// \param[in] setArg the function that will be called for each argument
-        ///
-        /// \returns the next index that will need to be set in the kernl. This
-        ///          is usually start_id + the number of times setArg is called
-        virtual int setArgs(int start_id, bool is_linear,
-                            std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const {
-            UNUSED(is_linear);
-            UNUSED(setArg);
-            return start_id;
-        }
+    /// Calls the setArg function on each of the arguments passed into the
+    /// kernel
+    ///
+    /// \param[in] start_id The index of the staring argument
+    /// \param[in] is_linear determines if the kernel should be linear or not
+    /// \param[in] setArg the function that will be called for each argument
+    ///
+    /// \returns the next index that will need to be set in the kernl. This
+    ///          is usually start_id + the number of times setArg is called
+    virtual int setArgs(
+        int start_id, bool is_linear,
+        std::function<void(int id, const void *ptr, size_t arg_size)> setArg)
+        const {
+        UNUSED(is_linear);
+        UNUSED(setArg);
+        return start_id;
+    }
 
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const {
-            UNUSED(buf_count);
-            UNUSED(bytes);
-            len++;
-        }
+    virtual void getInfo(unsigned &len, unsigned &buf_count,
+                         unsigned &bytes) const {
+        UNUSED(buf_count);
+        UNUSED(bytes);
+        len++;
+    }
 
-        // Return the size of the parameter in bytes that will be passed to the
-        // kernel
-        virtual short getParamBytes() const {
-            return 0;
-        }
+    // Return the size of the parameter in bytes that will be passed to the
+    // kernel
+    virtual short getParamBytes() const { return 0; }
 
-        // Return the size of the size of the buffer node in bytes. Zero otherwise
-        virtual size_t getBytes() const { return 0; }
-        virtual bool isBuffer() const { return false; }
-        virtual bool isLinear(dim_t dims[4]) const {
-            UNUSED(dims);
-            return true;
-        }
-        std::string getTypeStr() const { return m_type_str; }
-        int getHeight()  const { return m_height; }
-        std::string getNameStr() const { return m_name_str; }
+    // Return the size of the size of the buffer node in bytes. Zero otherwise
+    virtual size_t getBytes() const { return 0; }
+    virtual bool isBuffer() const { return false; }
+    virtual bool isLinear(dim_t dims[4]) const {
+        UNUSED(dims);
+        return true;
+    }
+    std::string getTypeStr() const { return m_type_str; }
+    int getHeight() const { return m_height; }
+    std::string getNameStr() const { return m_name_str; }
 
-        virtual ~Node() {}
-    };
+    virtual ~Node() {}
+};
 
-    struct Node_ids {
-        std::array<int, Node::kMaxChildren> child_ids;
-        int id;
-    };
-}
+struct Node_ids {
+    std::array<int, Node::kMaxChildren> child_ids;
+    int id;
+};
+}  // namespace common
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index c7bb8cb8a1..b5dd3a1998 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -15,47 +15,48 @@
 #include <vector>
 
 namespace common {
-class Node; // TODO(umar): Remove when CPU backend Node class is moved from JIT to common
+class Node;  // TODO(umar): Remove when CPU backend Node class is moved from JIT
+             // to common
 
 /// A node iterator that performs a breadth first traversal of the node tree
 template<typename Node = common::Node>
 class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
-  public:
+   public:
     using pointer   = Node*;
     using reference = Node&;
 
-  private:
+   private:
     std::vector<pointer> tree;
     size_t index;
 
     /// Copies the children of the \p n Node to the end of the tree vector
     void copy_children_to_end(Node* n) {
-        for(int i = 0; n->m_children[i] != nullptr && i < Node::kMaxChildren; i++) {
+        for (int i = 0; n->m_children[i] != nullptr && i < Node::kMaxChildren;
+             i++) {
             auto ptr = n->m_children[i].get();
-            if(find(begin(tree), end(tree), ptr) == end(tree)) {
+            if (find(begin(tree), end(tree), ptr) == end(tree)) {
                 tree.push_back(ptr);
             }
         }
     }
 
-  public:
-
+   public:
     /// NodeIterator Constructor
     ///
     /// \param[in] root The root node of the tree
     NodeIterator(pointer root) : tree{root}, index(0) {
-        tree.reserve(root->getHeight()*8);
+        tree.reserve(root->getHeight() * 8);
     }
 
     /// The equality operator
     ///
     /// \param[in] other the rhs of the node
     bool operator==(const NodeIterator& other) const noexcept {
-        // If the tree vector is empty in the other iterator then this means that the other
-        // iterator is a sentinel(end) node.
-        if(other.tree.empty()) {
-            // If the index is the same as the tree size then the index is past the
-            // end of the tree
+        // If the tree vector is empty in the other iterator then this means
+        // that the other iterator is a sentinel(end) node.
+        if (other.tree.empty()) {
+            // If the index is the same as the tree size then the index is past
+            // the end of the tree
             return index == tree.size();
         }
         return index == other.index && tree == other.tree;
@@ -67,9 +68,7 @@ class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
 
     /// Advances the iterator by one node in the tree
     NodeIterator& operator++() noexcept {
-        if(index < tree.size()) {
-            copy_children_to_end(tree[index]);
-        }
+        if (index < tree.size()) { copy_children_to_end(tree[index]); }
         index++;
         return *this;
     }
@@ -83,27 +82,21 @@ class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
 
     /// Advances the iterator by count nodes
     NodeIterator& operator+=(std::size_t count) noexcept {
-        while (count-- > 0) {
-            operator++();
-        }
+        while (count-- > 0) { operator++(); }
         return *this;
     }
 
-    reference operator*() const noexcept {
-        return *tree[index];
-    }
+    reference operator*() const noexcept { return *tree[index]; }
 
-    pointer operator->() const noexcept {
-        return tree[index];
-    }
+    pointer operator->() const noexcept { return tree[index]; }
 
     /// Creates a sentinel iterator. This is equivalent to the end iterator
-    NodeIterator() = default;
-    NodeIterator(const NodeIterator& other) = default;
+    NodeIterator()                              = default;
+    NodeIterator(const NodeIterator& other)     = default;
     NodeIterator(NodeIterator&& other) noexcept = default;
-    ~NodeIterator() noexcept = default;
+    ~NodeIterator() noexcept                    = default;
     NodeIterator& operator=(const NodeIterator& other) = default;
     NodeIterator& operator=(NodeIterator&& other) noexcept = default;
 };
 
-}
+}  // namespace common
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index fcd8bc8dff..b381728adc 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -14,53 +14,49 @@
 #include <types.hpp>
 #include <iomanip>
 
-namespace common
-{
-
-    template <typename T>
-    class ScalarNode : public common::Node
-    {
-    private:
-        const T m_val;
-
-    public:
-
-        ScalarNode(T val)
-            : Node(detail::getFullName<T>(), detail::shortname<T>(false), 0, {}),
-              m_val(val)
-        {
-        }
-
-        void genKerName(std::stringstream &kerStream,
-                        const common::Node_ids& ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
-
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            UNUSED(is_linear);
-            kerStream << m_type_str << " scalar" << id << ", \n";
-        }
-
-        int setArgs(int start_id, bool is_linear,
-                    std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const final
-        {
-            UNUSED(is_linear);
-            setArg(start_id, static_cast<const void*>(&m_val), sizeof(T));
-            return start_id + 1;
-        }
-
-        void genFuncs(std::stringstream &kerStream,
-                      const common::Node_ids& ids) const final
-        {
-            kerStream << m_type_str << " val" << ids.id
-                      << " = scalar" << ids.id << ";\n";
-        }
-
-        // Return the info for the params and the size of the buffers
-        virtual short getParamBytes() const final { return static_cast<short>(sizeof(T)); }
-    };
-
-}
+namespace common {
+
+template<typename T>
+class ScalarNode : public common::Node {
+   private:
+    const T m_val;
+
+   public:
+    ScalarNode(T val)
+        : Node(detail::getFullName<T>(), detail::shortname<T>(false), 0, {})
+        , m_val(val) {}
+
+    void genKerName(std::stringstream& kerStream,
+                    const common::Node_ids& ids) const final {
+        kerStream << "_" << m_name_str;
+        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
+                  << std::dec;
+    }
+
+    void genParams(std::stringstream& kerStream, int id,
+                   bool is_linear) const final {
+        UNUSED(is_linear);
+        kerStream << m_type_str << " scalar" << id << ", \n";
+    }
+
+    int setArgs(int start_id, bool is_linear,
+                std::function<void(int id, const void* ptr, size_t arg_size)>
+                    setArg) const final {
+        UNUSED(is_linear);
+        setArg(start_id, static_cast<const void*>(&m_val), sizeof(T));
+        return start_id + 1;
+    }
+
+    void genFuncs(std::stringstream& kerStream,
+                  const common::Node_ids& ids) const final {
+        kerStream << m_type_str << " val" << ids.id << " = scalar" << ids.id
+                  << ";\n";
+    }
+
+    // Return the info for the params and the size of the buffers
+    virtual short getParamBytes() const final {
+        return static_cast<short>(sizeof(T));
+    }
+};
+
+}  // namespace common
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 211c1831f5..d02ebab0e2 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -12,8 +12,8 @@
 #include <jit/BufferNode.hpp>
 #include <jit/kernel_generators.hpp>
 
-#include <iomanip>
 #include <backend.hpp>
+#include <iomanip>
 
 #include <array>
 #include <memory>
@@ -22,67 +22,64 @@
 
 namespace common {
 
-    template<typename BufferNode>
-    class ShiftNodeBase : public Node
-    {
-    private:
-        std::shared_ptr<BufferNode> m_buffer_node;
-        const std::array<int, 4> m_shifts;
+template<typename BufferNode>
+class ShiftNodeBase : public Node {
+   private:
+    std::shared_ptr<BufferNode> m_buffer_node;
+    const std::array<int, 4> m_shifts;
 
-    public:
-        ShiftNodeBase(const char *type_str,
-                  const char *name_str,
+   public:
+    ShiftNodeBase(const char *type_str, const char *name_str,
                   std::shared_ptr<BufferNode> buffer_node,
                   const std::array<int, 4> shifts)
-            : Node(type_str, name_str, 0, {}),
-              m_buffer_node(buffer_node),
-              m_shifts(shifts)
-        {
-        }
+        : Node(type_str, name_str, 0, {})
+        , m_buffer_node(buffer_node)
+        , m_shifts(shifts) {}
 
-        bool isLinear(dim_t dims[4]) const final
-        {
-            UNUSED(dims);
-            return false;
-        }
+    bool isLinear(dim_t dims[4]) const final {
+        UNUSED(dims);
+        return false;
+    }
 
-        void genKerName(std::stringstream &kerStream, const common::Node_ids& ids) const final
-        {
-            kerStream << "_" << m_name_str;
-            kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id << std::dec;
-        }
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
+        kerStream << "_" << m_name_str;
+        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
+                  << std::dec;
+    }
 
-        void genParams(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            m_buffer_node->genParams(kerStream, id, is_linear);
-            for (int i = 0; i < 4; i++) {
-                kerStream << "int shift" << id << "_" << i << ",\n";
-            }
+    void genParams(std::stringstream &kerStream, int id,
+                   bool is_linear) const final {
+        m_buffer_node->genParams(kerStream, id, is_linear);
+        for (int i = 0; i < 4; i++) {
+            kerStream << "int shift" << id << "_" << i << ",\n";
         }
+    }
 
-        int setArgs(int start_id, bool is_linear,
-                    std::function<void(int id, const void* ptr, size_t arg_size)> setArg) const {
-            int curr_id = m_buffer_node->setArgs(start_id, is_linear, setArg);
-            for (int i = 0; i < 4; i++) {
-                const int &d = m_shifts[i];
-                setArg(curr_id+i, static_cast<const void*>(&d), sizeof(int));
-            }
-            return curr_id + 4;
+    int setArgs(int start_id, bool is_linear,
+                std::function<void(int id, const void *ptr, size_t arg_size)>
+                    setArg) const {
+        int curr_id = m_buffer_node->setArgs(start_id, is_linear, setArg);
+        for (int i = 0; i < 4; i++) {
+            const int &d = m_shifts[i];
+            setArg(curr_id + i, static_cast<const void *>(&d), sizeof(int));
         }
+        return curr_id + 4;
+    }
 
-        void genOffsets(std::stringstream &kerStream, int id, bool is_linear) const final
-        {
-            detail::generateShiftNodeOffsets(kerStream, id, is_linear, m_type_str);
-        }
+    void genOffsets(std::stringstream &kerStream, int id,
+                    bool is_linear) const final {
+        detail::generateShiftNodeOffsets(kerStream, id, is_linear, m_type_str);
+    }
 
-        void genFuncs(std::stringstream &kerStream, const common::Node_ids& ids) const final
-        {
-            detail::generateShiftNodeRead(kerStream, ids.id, m_type_str);
-        }
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
+        detail::generateShiftNodeRead(kerStream, ids.id, m_type_str);
+    }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
-        {
-            m_buffer_node->getInfo(len, buf_count, bytes);
-        }
-    };
-}
+    void getInfo(unsigned &len, unsigned &buf_count,
+                 unsigned &bytes) const final {
+        m_buffer_node->getInfo(len, buf_count, bytes);
+    }
+};
+}  // namespace common
diff --git a/src/backend/common/jit/UnaryNode.hpp b/src/backend/common/jit/UnaryNode.hpp
index 9df843ec27..c169675148 100644
--- a/src/backend/common/jit/UnaryNode.hpp
+++ b/src/backend/common/jit/UnaryNode.hpp
@@ -10,18 +10,13 @@
 #pragma once
 #include <common/jit/NaryNode.hpp>
 
-
 namespace common {
 
-class UnaryNode : public NaryNode
-{
-public:
-  UnaryNode(const char *out_type_str, const char *name_str,
-            const char *op_str,
-            Node_ptr child, int op)
-    : NaryNode(out_type_str, name_str, op_str,
-               1, {{child}}, op, child->getHeight() + 1)
-  {
-  }
+class UnaryNode : public NaryNode {
+   public:
+    UnaryNode(const char *out_type_str, const char *name_str,
+              const char *op_str, Node_ptr child, int op)
+        : NaryNode(out_type_str, name_str, op_str, 1, {{child}}, op,
+                   child->getHeight() + 1) {}
 };
-}
+}  // namespace common
diff --git a/src/backend/common/lapacke.cpp b/src/backend/common/lapacke.cpp
index adfec9a0e1..3bba5b5a5a 100644
--- a/src/backend/common/lapacke.cpp
+++ b/src/backend/common/lapacke.cpp
@@ -5,12 +5,12 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ ********************************************************/
 
 #if defined(__APPLE__) && !defined(AF_CUDA)
-#include <common/lapacke.hpp>
-#include <common/defines.hpp>
 #include <Accelerate/Accelerate.h>
+#include <common/defines.hpp>
+#include <common/lapacke.hpp>
 #include <traits.hpp>
 
 #include <algorithm>
@@ -18,342 +18,292 @@
 #include <vector>
 
 #if INTPTR_MAX == INT16MAX
-    #define BS 16
+#define BS 16
 #elif INTPTR_MAX == INT32MAX
-    #define BS 32
+#define BS 32
 #elif INTPTR_MAX == INT64MAX
-    #define BS 64
+#define BS 64
 #else
-    #define BS 32
+#define BS 32
 #endif
 
-#define LAPACK_FUNC(X, T, TO)                                                       \
-int LAPACKE_##X##geqrf(int layout, int M, int N, T *A, int lda, T *tau)             \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int lwork = N * BS;                                                             \
-    T *work = new T[lwork];                                                         \
-    int info = 0;                                                                   \
-    X##geqrf_(&M, &N, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);               \
-    delete [] work;                                                                 \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##geqrf_work(int layout, int M, int N, T *A, int lda,                \
-                            T *tau, T *work, int lwork)                             \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##geqrf_(&M, &N, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);               \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##getrf(int layout, int M, int N, T *A, int lda, int *pivot)         \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##getrf_(&M, &N, (TO)A, &lda, pivot, &info);                                   \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##getrs(int layout, char trans, int M, int N, const T *A,            \
-                       int lda, const int *pivot, T *B, int ldb)                    \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##getrs_(&trans, &M, &N, (TO)A, &lda, (int *)pivot, (TO)B, &ldb, &info);       \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##potrf(int layout, char uplo, int N, T *A, int lda)                 \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##potrf_(&uplo, &N, (TO)A, &lda, &info);                                       \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##gesv(int layout, int N, int nrhs, T *A, int lda,                   \
-                      int *pivot, T *B, int ldb)                                    \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##gesv_(&N, &nrhs, (TO)A, &lda, pivot, (TO)B, &ldb, &info);                    \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##gels(int layout, char trans, int M, int N, int nrhs,               \
-                      T *A, int lda, T *B, int ldb)                                 \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int lwork = std::min(M, N) + std::max(M, std::max(N, nrhs)) * BS;               \
-    T *work = new T[lwork];                                                         \
-    int info = 0;                                                                   \
-    X##gels_(&trans, &M, &N, &nrhs, (TO)A, &lda,                                    \
-                       (TO)B, &ldb, (TO)work, &lwork, &info);                       \
-    delete [] work;                                                                 \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##getri(int layout, int N, T *A, int lda, const int *pivot)          \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int lwork = N * BS;                                                             \
-    T *work = new T[lwork];                                                         \
-    int info = 0;                                                                   \
-    X##getri_(&N, (TO)A, &lda, const_cast<int *>(pivot),                            \
-                        (TO)work, &lwork, &info);                                   \
-    delete [] work;                                                                 \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##trtri(int layout, char uplo, char diag, int N, T *A, int lda)      \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##trtri_(&uplo, &diag, &N, (TO)A, &lda, &info);                                \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##trtrs(int layout, char uplo, char trans, char diag,                \
-                       int N, int NRHS, const T *A, int lda, T *B, int ldb)         \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##trtrs_(&uplo, &trans, &diag, &N, &NRHS, (TO)A, &lda, (TO)B, &ldb, &info);    \
-    return info;                                                                    \
-}                                                                                   \
-int LAPACKE_##X##larft(int layout, char direct, char storev, int N, int K,          \
-                       const T *v, int ldv, const T *tau, T *t, int ldt)            \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    X##larft_(&direct, &storev, &N, &K, (TO)v, &ldv,                                \
-                        (TO)const_cast<T*>(tau), (TO)t, &ldt);                      \
-    return 0;                                                                       \
-}                                                                                   \
-int LAPACKE_##X##laswp(int layout, int N, T *A, int lda,                            \
-                       int k1, int k2, const int *pivot, int incx)                  \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    X##laswp_(&N, (TO)A, &lda, &k1, &k2, const_cast<int*>(pivot), &incx);           \
-    return 0;                                                                       \
-}                                                                                   \
-
-LAPACK_FUNC(s, float, float*)
-LAPACK_FUNC(d, double, double*)
-LAPACK_FUNC(c, cfloat, __CLPK_complex*)
-LAPACK_FUNC(z, cdouble, __CLPK_doublecomplex*)
-
-#define LAPACK_GQR(P, X, T, TO)                                                     \
-int LAPACKE_##X##P(int layout, int M, int N, int K, T *A, int lda, const T *tau)    \
-{                                                                                   \
-    UNUSED(layout);                                                       \
-    int lwork = N * 32;                                                             \
-    T *work = new T[lwork];                                                         \
-    int info = 0;                                                                   \
-    X##P##_(&M, &N, &K, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);             \
-    delete [] work;                                                                 \
-    return info;                                                                    \
-}                                                                                   \
-
-LAPACK_GQR(orgqr, s, float, float*)
-LAPACK_GQR(orgqr, d, double, double*)
-LAPACK_GQR(ungqr, c, cfloat, __CLPK_complex*)
-LAPACK_GQR(ungqr, z, cdouble, __CLPK_doublecomplex*)
-
-#define LAPACK_GQR_WORK(P, X, T, TO)                                                \
-int LAPACKE_##X##P##_work(int layout, int M, int N, int K, T *A, int lda,           \
-                          const T *tau, T *work, int lwork)                         \
-{                                                                                   \
-    UNUSED(layout);                                                                 \
-    int info = 0;                                                                   \
-    X##P##_(&M, &N, &K, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);             \
-    return info;                                                                    \
-}                                                                                   \
-
-LAPACK_GQR_WORK(orgqr, s, float, float*)
-LAPACK_GQR_WORK(orgqr, d, double, double*)
-LAPACK_GQR_WORK(ungqr, c, cfloat, __CLPK_complex*)
-LAPACK_GQR_WORK(ungqr, z, cdouble, __CLPK_doublecomplex*)
-
-#define LAPACK_MQR_WORK(P, X, T, TO)                                                \
-int LAPACKE_##X##P##_work(int layout, char side, char trans, int M, int N, int K,   \
-                          const T *A, int lda, const T *tau, T *c, int ldc,         \
-                          T *work, int lwork)                                       \
-{                                                                                   \
-    UNUSED(layout);                                                        \
-    int info = 0;                                                                   \
-    X##P##_(&side, &trans, &M, &N, &K, (TO)A, &lda, (TO)tau, (TO)c, &ldc,           \
-                      (TO)work, &lwork, &info);                                     \
-    return info;                                                                    \
-}                                                                                   \
+#define LAPACK_FUNC(X, T, TO)                                                  \
+    int LAPACKE_##X##geqrf(int layout, int M, int N, T *A, int lda, T *tau) {  \
+        UNUSED(layout);                                                        \
+        int lwork = N * BS;                                                    \
+        T *work   = new T[lwork];                                              \
+        int info  = 0;                                                         \
+        X##geqrf_(&M, &N, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);      \
+        delete[] work;                                                         \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##geqrf_work(int layout, int M, int N, T *A, int lda,       \
+                                T *tau, T *work, int lwork) {                  \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##geqrf_(&M, &N, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);      \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##getrf(int layout, int M, int N, T *A, int lda,            \
+                           int *pivot) {                                       \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##getrf_(&M, &N, (TO)A, &lda, pivot, &info);                          \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##getrs(int layout, char trans, int M, int N, const T *A,   \
+                           int lda, const int *pivot, T *B, int ldb) {         \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##getrs_(&trans, &M, &N, (TO)A, &lda, (int *)pivot, (TO)B, &ldb,      \
+                  &info);                                                      \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##potrf(int layout, char uplo, int N, T *A, int lda) {      \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##potrf_(&uplo, &N, (TO)A, &lda, &info);                              \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##gesv(int layout, int N, int nrhs, T *A, int lda,          \
+                          int *pivot, T *B, int ldb) {                         \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##gesv_(&N, &nrhs, (TO)A, &lda, pivot, (TO)B, &ldb, &info);           \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##gels(int layout, char trans, int M, int N, int nrhs,      \
+                          T *A, int lda, T *B, int ldb) {                      \
+        UNUSED(layout);                                                        \
+        int lwork = std::min(M, N) + std::max(M, std::max(N, nrhs)) * BS;      \
+        T *work   = new T[lwork];                                              \
+        int info  = 0;                                                         \
+        X##gels_(&trans, &M, &N, &nrhs, (TO)A, &lda, (TO)B, &ldb, (TO)work,    \
+                 &lwork, &info);                                               \
+        delete[] work;                                                         \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##getri(int layout, int N, T *A, int lda,                   \
+                           const int *pivot) {                                 \
+        UNUSED(layout);                                                        \
+        int lwork = N * BS;                                                    \
+        T *work   = new T[lwork];                                              \
+        int info  = 0;                                                         \
+        X##getri_(&N, (TO)A, &lda, const_cast<int *>(pivot), (TO)work, &lwork, \
+                  &info);                                                      \
+        delete[] work;                                                         \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##trtri(int layout, char uplo, char diag, int N, T *A,      \
+                           int lda) {                                          \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##trtri_(&uplo, &diag, &N, (TO)A, &lda, &info);                       \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##trtrs(int layout, char uplo, char trans, char diag,       \
+                           int N, int NRHS, const T *A, int lda, T *B,         \
+                           int ldb) {                                          \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##trtrs_(&uplo, &trans, &diag, &N, &NRHS, (TO)A, &lda, (TO)B, &ldb,   \
+                  &info);                                                      \
+        return info;                                                           \
+    }                                                                          \
+    int LAPACKE_##X##larft(int layout, char direct, char storev, int N, int K, \
+                           const T *v, int ldv, const T *tau, T *t, int ldt) { \
+        UNUSED(layout);                                                        \
+        X##larft_(&direct, &storev, &N, &K, (TO)v, &ldv,                       \
+                  (TO) const_cast<T *>(tau), (TO)t, &ldt);                     \
+        return 0;                                                              \
+    }                                                                          \
+    int LAPACKE_##X##laswp(int layout, int N, T *A, int lda, int k1, int k2,   \
+                           const int *pivot, int incx) {                       \
+        UNUSED(layout);                                                        \
+        X##laswp_(&N, (TO)A, &lda, &k1, &k2, const_cast<int *>(pivot), &incx); \
+        return 0;                                                              \
+    }
 
-LAPACK_MQR_WORK(ormqr, s, float, float*)
-LAPACK_MQR_WORK(ormqr, d, double, double*)
-LAPACK_MQR_WORK(unmqr, c, cfloat, __CLPK_complex*)
-LAPACK_MQR_WORK(unmqr, z, cdouble, __CLPK_doublecomplex*)
+LAPACK_FUNC(s, float, float *)
+LAPACK_FUNC(d, double, double *)
+LAPACK_FUNC(c, cfloat, __CLPK_complex *)
+LAPACK_FUNC(z, cdouble, __CLPK_doublecomplex *)
+
+#define LAPACK_GQR(P, X, T, TO)                                             \
+    int LAPACKE_##X##P(int layout, int M, int N, int K, T *A, int lda,      \
+                       const T *tau) {                                      \
+        UNUSED(layout);                                                     \
+        int lwork = N * 32;                                                 \
+        T *work   = new T[lwork];                                           \
+        int info  = 0;                                                      \
+        X##P##_(&M, &N, &K, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info); \
+        delete[] work;                                                      \
+        return info;                                                        \
+    }
 
+LAPACK_GQR(orgqr, s, float, float *)
+LAPACK_GQR(orgqr, d, double, double *)
+LAPACK_GQR(ungqr, c, cfloat, __CLPK_complex *)
+LAPACK_GQR(ungqr, z, cdouble, __CLPK_doublecomplex *)
+
+#define LAPACK_GQR_WORK(P, X, T, TO)                                          \
+    int LAPACKE_##X##P##_work(int layout, int M, int N, int K, T *A, int lda, \
+                              const T *tau, T *work, int lwork) {             \
+        UNUSED(layout);                                                       \
+        int info = 0;                                                         \
+        X##P##_(&M, &N, &K, (TO)A, &lda, (TO)tau, (TO)work, &lwork, &info);   \
+        return info;                                                          \
+    }
 
-#define LAPACK_GESDD_REAL(P, X, T, Tr, TO)          \
-    int LAPACKE_##X##P(int layout,                  \
-                       char jobz,                   \
-                       int m, int n,                \
-                       T* in, int ldin,             \
-                       Tr* s,                       \
-                       T* u, int ldu,               \
-                       T* vt, int ldvt)             \
-    {                                               \
-        UNUSED(layout);                             \
-        int info = 0;                               \
-        int lwork = -1;                             \
-        T work_param = 0;                           \
-        X##P##_(&jobz, &m, &n, (TO)in, &ldin,       \
-                s, (TO)u, &ldu, (TO)vt, &ldvt,      \
-                &work_param, &lwork,                \
-                NULL, &info);                       \
-        lwork = work_param;                         \
-        std::vector<T> work(lwork);                 \
-        std::vector<int> iwork(8 * std::min(m, n)); \
-        X##P##_(&jobz, &m, &n, (TO)in, &ldin,       \
-                s, (TO)u, &ldu, (TO)vt, &ldvt,      \
-                (TO)&work[0], &lwork,               \
-                &iwork[0], &info);                  \
-        return info;                                \
+LAPACK_GQR_WORK(orgqr, s, float, float *)
+LAPACK_GQR_WORK(orgqr, d, double, double *)
+LAPACK_GQR_WORK(ungqr, c, cfloat, __CLPK_complex *)
+LAPACK_GQR_WORK(ungqr, z, cdouble, __CLPK_doublecomplex *)
+
+#define LAPACK_MQR_WORK(P, X, T, TO)                                           \
+    int LAPACKE_##X##P##_work(int layout, char side, char trans, int M, int N, \
+                              int K, const T *A, int lda, const T *tau, T *c,  \
+                              int ldc, T *work, int lwork) {                   \
+        UNUSED(layout);                                                        \
+        int info = 0;                                                          \
+        X##P##_(&side, &trans, &M, &N, &K, (TO)A, &lda, (TO)tau, (TO)c, &ldc,  \
+                (TO)work, &lwork, &info);                                      \
+        return info;                                                           \
     }
 
-#define LAPACK_GESDD_CPLX(P, X, T, Tr, TO)          \
-    int LAPACKE_##X##P(int layout,                  \
-                       char jobz,                   \
-                       int m, int n,                \
-                       T* in, int ldin,             \
-                       Tr* s,                       \
-                       T* u, int ldu,               \
-                       T* vt, int ldvt)             \
-    {                                               \
-        UNUSED(layout);                             \
-        int info = 0;                               \
-        int max_mn = std::max(m, n);                \
-        int min_mn = std::max(m, n);                \
-        int lwork = 5 * max_mn;                     \
-        std::vector<T> work(lwork);                 \
-        std::vector<int> iwork(8 * std::min(m, n)); \
-        int irwork = std::max(1,                    \
-                              min_mn *              \
-                              std::max(5*min_mn+7,  \
-                                       2*max_mn+2*  \
-                                       min_mn+1));  \
-        std::vector<Tr> rwork(irwork);              \
-        X##P##_(&jobz, &m, &n, (TO)in, &ldin,       \
-                s, (TO)u, &ldu, (TO)vt, &ldvt,      \
-                (TO)&work[0], &lwork,               \
-                &rwork[0], &iwork[0], &info);       \
-        return info;                                \
+LAPACK_MQR_WORK(ormqr, s, float, float *)
+LAPACK_MQR_WORK(ormqr, d, double, double *)
+LAPACK_MQR_WORK(unmqr, c, cfloat, __CLPK_complex *)
+LAPACK_MQR_WORK(unmqr, z, cdouble, __CLPK_doublecomplex *)
+
+#define LAPACK_GESDD_REAL(P, X, T, Tr, TO)                                   \
+    int LAPACKE_##X##P(int layout, char jobz, int m, int n, T *in, int ldin, \
+                       Tr *s, T *u, int ldu, T *vt, int ldvt) {              \
+        UNUSED(layout);                                                      \
+        int info     = 0;                                                    \
+        int lwork    = -1;                                                   \
+        T work_param = 0;                                                    \
+        X##P##_(&jobz, &m, &n, (TO)in, &ldin, s, (TO)u, &ldu, (TO)vt, &ldvt, \
+                &work_param, &lwork, NULL, &info);                           \
+        lwork = work_param;                                                  \
+        std::vector<T> work(lwork);                                          \
+        std::vector<int> iwork(8 * std::min(m, n));                          \
+        X##P##_(&jobz, &m, &n, (TO)in, &ldin, s, (TO)u, &ldu, (TO)vt, &ldvt, \
+                (TO)&work[0], &lwork, &iwork[0], &info);                     \
+        return info;                                                         \
     }
 
+#define LAPACK_GESDD_CPLX(P, X, T, Tr, TO)                                   \
+    int LAPACKE_##X##P(int layout, char jobz, int m, int n, T *in, int ldin, \
+                       Tr *s, T *u, int ldu, T *vt, int ldvt) {              \
+        UNUSED(layout);                                                      \
+        int info   = 0;                                                      \
+        int max_mn = std::max(m, n);                                         \
+        int min_mn = std::max(m, n);                                         \
+        int lwork  = 5 * max_mn;                                             \
+        std::vector<T> work(lwork);                                          \
+        std::vector<int> iwork(8 * std::min(m, n));                          \
+        int irwork = std::max(                                               \
+            1,                                                               \
+            min_mn * std::max(5 * min_mn + 7, 2 * max_mn + 2 * min_mn + 1)); \
+        std::vector<Tr> rwork(irwork);                                       \
+        X##P##_(&jobz, &m, &n, (TO)in, &ldin, s, (TO)u, &ldu, (TO)vt, &ldvt, \
+                (TO)&work[0], &lwork, &rwork[0], &iwork[0], &info);          \
+        return info;                                                         \
+    }
 
-LAPACK_GESDD_REAL(gesdd, s, float  , float , float*)
-LAPACK_GESDD_REAL(gesdd, d, double , double, double*)
-LAPACK_GESDD_CPLX(gesdd, c, cfloat , float ,__CLPK_complex*)
-LAPACK_GESDD_CPLX(gesdd, z, cdouble, double,__CLPK_doublecomplex*)
+LAPACK_GESDD_REAL(gesdd, s, float, float, float *)
+LAPACK_GESDD_REAL(gesdd, d, double, double, double *)
+LAPACK_GESDD_CPLX(gesdd, c, cfloat, float, __CLPK_complex *)
+LAPACK_GESDD_CPLX(gesdd, z, cdouble, double, __CLPK_doublecomplex *)
 
-#define LAPACK_LAMCH(X, T) T LAPACKE_##X##lamch(char cmach) { return X##lamch_(&cmach); }
+#define LAPACK_LAMCH(X, T) \
+    T LAPACKE_##X##lamch(char cmach) { return X##lamch_(&cmach); }
 
-LAPACK_LAMCH(s, float )
+LAPACK_LAMCH(s, float)
 LAPACK_LAMCH(d, double)
 
-#define LAPACK_LACPY(X, T, TO)                                  \
-    int LAPACKE_##X##lacpy(int matrix_order, char uplo, int m,  \
-                           int n, const T* a,                   \
-                           int lda, T* b,                       \
-                           int ldb )                            \
-    {                                                           \
-        UNUSED(matrix_order);                                   \
-        int info = 0;                                           \
-        X##lacpy_(&uplo, &m, &n, (TO)a, &lda, (TO)b, &ldb);     \
-        return info;                                            \
+#define LAPACK_LACPY(X, T, TO)                                        \
+    int LAPACKE_##X##lacpy(int matrix_order, char uplo, int m, int n, \
+                           const T *a, int lda, T *b, int ldb) {      \
+        UNUSED(matrix_order);                                         \
+        int info = 0;                                                 \
+        X##lacpy_(&uplo, &m, &n, (TO)a, &lda, (TO)b, &ldb);           \
+        return info;                                                  \
     }
 
-LAPACK_LACPY(s, float, float*)
-LAPACK_LACPY(d, double, double*)
-LAPACK_LACPY(c, cfloat,__CLPK_complex*)
-LAPACK_LACPY(z, cdouble,__CLPK_doublecomplex*)
-
-#define LAPACK_GBR_WORK(P, X, T, TO)                                \
-    int LAPACKE_##X##P##_work(int matrix_order, char vect, int m,   \
-                              int n, int k, T* a,                   \
-                              int lda, const T* tau, T* work,       \
-                              int lwork )                           \
-    {                                                               \
-        UNUSED(matrix_order);                                       \
-        int info = 0;                                               \
-        X##P##_(&vect, &m, &n, &k, (TO)a, &lda,                     \
-                (TO)tau, (TO)work, &lwork, &info);                  \
-        return info;                                                \
+LAPACK_LACPY(s, float, float *)
+LAPACK_LACPY(d, double, double *)
+LAPACK_LACPY(c, cfloat, __CLPK_complex *)
+LAPACK_LACPY(z, cdouble, __CLPK_doublecomplex *)
+
+#define LAPACK_GBR_WORK(P, X, T, TO)                                       \
+    int LAPACKE_##X##P##_work(int matrix_order, char vect, int m, int n,   \
+                              int k, T *a, int lda, const T *tau, T *work, \
+                              int lwork) {                                 \
+        UNUSED(matrix_order);                                              \
+        int info = 0;                                                      \
+        X##P##_(&vect, &m, &n, &k, (TO)a, &lda, (TO)tau, (TO)work, &lwork, \
+                &info);                                                    \
+        return info;                                                       \
     }
 
-LAPACK_GBR_WORK(orgbr, s, float, float*)
-LAPACK_GBR_WORK(orgbr, d, double, double*)
-LAPACK_GBR_WORK(ungbr, c, cfloat,__CLPK_complex*)
-LAPACK_GBR_WORK(ungbr, z, cdouble,__CLPK_doublecomplex*)
-
-#define LAPACK_BDSQR_WORK(X, T, Tr, TO)                                 \
-    int LAPACKE_##X##bdsqr_work( int matrix_order, char uplo, int n,    \
-                                 int ncvt, int nru, int ncc,            \
-                                 Tr* d, Tr* e, T* vt,                   \
-                                 int ldvt, T* u,                        \
-                                 int ldu, T* c,                         \
-                                 int ldc, Tr* work)                     \
-    {                                                                   \
-        UNUSED(matrix_order);                                           \
-        int info = 0;                                                   \
-        X##bdsqr_(&uplo, &n, &ncvt, &nru, &ncc, d, e,                   \
-                  (TO)vt, &ldvt, (TO)u, &ldu,                           \
-                  (TO)c, &ldc, work, &info);                            \
-        return info;                                                    \
-    }                                                                   \
-
-
-LAPACK_BDSQR_WORK(s, float, float, float*)
-LAPACK_BDSQR_WORK(d, double, double, double*)
-LAPACK_BDSQR_WORK(c, cfloat, float,__CLPK_complex*)
-LAPACK_BDSQR_WORK(z, cdouble, double,__CLPK_doublecomplex*)
-
+LAPACK_GBR_WORK(orgbr, s, float, float *)
+LAPACK_GBR_WORK(orgbr, d, double, double *)
+LAPACK_GBR_WORK(ungbr, c, cfloat, __CLPK_complex *)
+LAPACK_GBR_WORK(ungbr, z, cdouble, __CLPK_doublecomplex *)
+
+#define LAPACK_BDSQR_WORK(X, T, Tr, TO)                                        \
+    int LAPACKE_##X##bdsqr_work(                                               \
+        int matrix_order, char uplo, int n, int ncvt, int nru, int ncc, Tr *d, \
+        Tr *e, T *vt, int ldvt, T *u, int ldu, T *c, int ldc, Tr *work) {      \
+        UNUSED(matrix_order);                                                  \
+        int info = 0;                                                          \
+        X##bdsqr_(&uplo, &n, &ncvt, &nru, &ncc, d, e, (TO)vt, &ldvt, (TO)u,    \
+                  &ldu, (TO)c, &ldc, work, &info);                             \
+        return info;                                                           \
+    }
 
-#define LAPACK_GEBRD_WORK(X, T, Tr, TO)                             \
-    int LAPACKE_##X##gebrd_work( int matrix_order, int m, int n,    \
-                                 T* a, int lda,                     \
-                                 Tr* d, Tr* e, T* tauq,             \
-                                 T* taup,                           \
-                                 T* work, int lwork )               \
-    {                                                               \
-        UNUSED(matrix_order);                                       \
-        int info = 0;                                               \
-        X##gebrd_(&m, &n, (TO)a, &lda, d, e, (TO)tauq, (TO)taup,    \
-                  (TO)work, &lwork, &info);                         \
-        return info;                                                \
+LAPACK_BDSQR_WORK(s, float, float, float *)
+LAPACK_BDSQR_WORK(d, double, double, double *)
+LAPACK_BDSQR_WORK(c, cfloat, float, __CLPK_complex *)
+LAPACK_BDSQR_WORK(z, cdouble, double, __CLPK_doublecomplex *)
+
+#define LAPACK_GEBRD_WORK(X, T, Tr, TO)                                        \
+    int LAPACKE_##X##gebrd_work(int matrix_order, int m, int n, T *a, int lda, \
+                                Tr *d, Tr *e, T *tauq, T *taup, T *work,       \
+                                int lwork) {                                   \
+        UNUSED(matrix_order);                                                  \
+        int info = 0;                                                          \
+        X##gebrd_(&m, &n, (TO)a, &lda, d, e, (TO)tauq, (TO)taup, (TO)work,     \
+                  &lwork, &info);                                              \
+        return info;                                                           \
     }
 
-LAPACK_GEBRD_WORK(s, float, float, float*)
-LAPACK_GEBRD_WORK(d, double, double, double*)
-LAPACK_GEBRD_WORK(c, cfloat, float, __CLPK_complex*)
-LAPACK_GEBRD_WORK(z, cdouble, double,__CLPK_doublecomplex*)
+LAPACK_GEBRD_WORK(s, float, float, float *)
+LAPACK_GEBRD_WORK(d, double, double, double *)
+LAPACK_GEBRD_WORK(c, cfloat, float, __CLPK_complex *)
+LAPACK_GEBRD_WORK(z, cdouble, double, __CLPK_doublecomplex *)
 
-#define LAPACK_LARFG_WORK(X, T, TO)                         \
-    int LAPACKE_##X##larfg_work( int n, T* alpha,           \
-                                 T* x, int incx,            \
-                                 T* tau )                   \
-    {                                                       \
-        int info = 0;                                       \
-        X##larfg_(&n, (TO)alpha, (TO)x, &incx, (TO)tau);    \
-        return info;                                        \
+#define LAPACK_LARFG_WORK(X, T, TO)                                        \
+    int LAPACKE_##X##larfg_work(int n, T *alpha, T *x, int incx, T *tau) { \
+        int info = 0;                                                      \
+        X##larfg_(&n, (TO)alpha, (TO)x, &incx, (TO)tau);                   \
+        return info;                                                       \
     }
 
-LAPACK_LARFG_WORK(s, float, float*)
-LAPACK_LARFG_WORK(d, double, double*)
-LAPACK_LARFG_WORK(c, cfloat, __CLPK_complex*)
-LAPACK_LARFG_WORK(z, cdouble, __CLPK_doublecomplex*)
+LAPACK_LARFG_WORK(s, float, float *)
+LAPACK_LARFG_WORK(d, double, double *)
+LAPACK_LARFG_WORK(c, cfloat, __CLPK_complex *)
+LAPACK_LARFG_WORK(z, cdouble, __CLPK_doublecomplex *)
 
-#define LAPACK_LACGV_WORK(X, T, TO)             \
-    int LAPACKE_##X##lacgv_work( int n, T* x,   \
-                                 int incx)      \
-    {                                           \
-        X##lacgv_(&n, (TO)x, &incx);            \
-        return 0;                               \
+#define LAPACK_LACGV_WORK(X, T, TO)                      \
+    int LAPACKE_##X##lacgv_work(int n, T *x, int incx) { \
+        X##lacgv_(&n, (TO)x, &incx);                     \
+        return 0;                                        \
     }
 
-LAPACK_LACGV_WORK(c, cfloat, __CLPK_complex*)
-LAPACK_LACGV_WORK(z, cdouble, __CLPK_doublecomplex*)
-
+LAPACK_LACGV_WORK(c, cfloat, __CLPK_complex *)
+LAPACK_LACGV_WORK(z, cdouble, __CLPK_doublecomplex *)
 
 #endif
diff --git a/src/backend/common/lapacke.hpp b/src/backend/common/lapacke.hpp
index 1fa3eabb89..88b485d7be 100644
--- a/src/backend/common/lapacke.hpp
+++ b/src/backend/common/lapacke.hpp
@@ -5,148 +5,136 @@
  * This file is distributed under 3-clause BSD license.
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ ********************************************************/
 
 #if defined(__APPLE__) && !defined(AF_CUDA)
-#include <types.hpp>
 #include <backend.hpp>
+#include <types.hpp>
 
-using detail::cfloat;
 using detail::cdouble;
+using detail::cfloat;
 
-#define LAPACK_FUNC(X, T)                                                           \
-int LAPACKE_##X##geqrf(int layout, int M, int N, T *A, int lda, T *tau);            \
-int LAPACKE_##X##geqrf_work(int layout, int M, int N, T *A, int lda,                \
-                            T *tau, T *work, int lwork);                            \
-int LAPACKE_##X##getrf(int layout, int M, int N, T *A, int lda, int *pivot);        \
-int LAPACKE_##X##potrf(int layout, char uplo, int N, T *A, int lda);                \
-int LAPACKE_##X##gesv(int layout, int N, int nrhs, T *A, int lda,                   \
-                      int *pivot, T *B, int ldb);                                   \
-int LAPACKE_##X##gels(int layout, char trans, int M, int N, int nrhs,               \
-                      T *A, int lda, T *B, int ldb);                                \
-int LAPACKE_##X##getri(int layout, int N, T *A, int lda, const int *pivot);         \
-int LAPACKE_##X##trtri(int layout, char uplo, char diag, int N, T *A, int lda);     \
-int LAPACKE_##X##larft(int layout, char direct, char storev, int N, int K,          \
-                       const T *v, int ldv, const T *tau, T *t, int ldt);           \
-int LAPACKE_##X##laswp(int layout, int N, T *A, int lda,                            \
-                       int k1, int k2, const int * pivot, int incx);                \
-int LAPACKE_##X##getrs(int layout, char trans, int M, int N, const T *A,            \
-                       int lda, const int *pivot, T *B, int ldb);                   \
-int LAPACKE_##X##trtrs(int layout, char uplo, char trans, char diag,                \
-                       int N, int NRHS, const T *A, int lda, T *B, int ldb);        \
+#define LAPACK_FUNC(X, T)                                                      \
+    int LAPACKE_##X##geqrf(int layout, int M, int N, T *A, int lda, T *tau);   \
+    int LAPACKE_##X##geqrf_work(int layout, int M, int N, T *A, int lda,       \
+                                T *tau, T *work, int lwork);                   \
+    int LAPACKE_##X##getrf(int layout, int M, int N, T *A, int lda,            \
+                           int *pivot);                                        \
+    int LAPACKE_##X##potrf(int layout, char uplo, int N, T *A, int lda);       \
+    int LAPACKE_##X##gesv(int layout, int N, int nrhs, T *A, int lda,          \
+                          int *pivot, T *B, int ldb);                          \
+    int LAPACKE_##X##gels(int layout, char trans, int M, int N, int nrhs,      \
+                          T *A, int lda, T *B, int ldb);                       \
+    int LAPACKE_##X##getri(int layout, int N, T *A, int lda,                   \
+                           const int *pivot);                                  \
+    int LAPACKE_##X##trtri(int layout, char uplo, char diag, int N, T *A,      \
+                           int lda);                                           \
+    int LAPACKE_##X##larft(int layout, char direct, char storev, int N, int K, \
+                           const T *v, int ldv, const T *tau, T *t, int ldt);  \
+    int LAPACKE_##X##laswp(int layout, int N, T *A, int lda, int k1, int k2,   \
+                           const int *pivot, int incx);                        \
+    int LAPACKE_##X##getrs(int layout, char trans, int M, int N, const T *A,   \
+                           int lda, const int *pivot, T *B, int ldb);          \
+    int LAPACKE_##X##trtrs(int layout, char uplo, char trans, char diag,       \
+                           int N, int NRHS, const T *A, int lda, T *B,         \
+                           int ldb);
 
 LAPACK_FUNC(s, float)
 LAPACK_FUNC(d, double)
 LAPACK_FUNC(c, cfloat)
 LAPACK_FUNC(z, cdouble)
 
-#define LAPACK_GQR(P, X, T)                                                         \
-int LAPACKE_##X##P(int layout, int M, int N, int K, T *A, int lda, const T *tau);   \
+#define LAPACK_GQR(P, X, T)                                            \
+    int LAPACKE_##X##P(int layout, int M, int N, int K, T *A, int lda, \
+                       const T *tau);
 
 LAPACK_GQR(orgqr, s, float)
 LAPACK_GQR(orgqr, d, double)
 LAPACK_GQR(ungqr, c, cfloat)
 LAPACK_GQR(ungqr, z, cdouble)
 
-#define LAPACK_GQR_WORK(P, X, T)                                                    \
-int LAPACKE_##X##P##_work(int layout, int M, int N, int K, T *A, int lda,           \
-                          const T *tau, T *work, int lwork);                        \
+#define LAPACK_GQR_WORK(P, X, T)                                              \
+    int LAPACKE_##X##P##_work(int layout, int M, int N, int K, T *A, int lda, \
+                              const T *tau, T *work, int lwork);
 
 LAPACK_GQR_WORK(orgqr, s, float)
 LAPACK_GQR_WORK(orgqr, d, double)
 LAPACK_GQR_WORK(ungqr, c, cfloat)
 LAPACK_GQR_WORK(ungqr, z, cdouble)
 
-#define LAPACK_MQR_WORK(P, X, T)                                                    \
-int LAPACKE_##X##P##_work(int layout, char side, char trans, int M, int N, int K,   \
-                          const T *A, int lda, const T *tau, T *c, int ldc,         \
-                          T *work, int lwork);                                      \
+#define LAPACK_MQR_WORK(P, X, T)                                               \
+    int LAPACKE_##X##P##_work(int layout, char side, char trans, int M, int N, \
+                              int K, const T *A, int lda, const T *tau, T *c,  \
+                              int ldc, T *work, int lwork);
 
 LAPACK_MQR_WORK(ormqr, s, float)
 LAPACK_MQR_WORK(ormqr, d, double)
 LAPACK_MQR_WORK(unmqr, c, cfloat)
 LAPACK_MQR_WORK(unmqr, z, cdouble)
 
-#define LAPACK_GESDD(P, X, T, Tr)               \
-    int LAPACKE_##X##P(int layout,              \
-                       char jobz,               \
-                       int m, int n,            \
-                       T* in, int ldin,         \
-                       Tr* s,                   \
-                       T* u, int ldu,           \
-                       T* vt, int ldvt);        \
-
-LAPACK_GESDD(gesdd, s, float  , float )
-LAPACK_GESDD(gesdd, d, double , double)
-LAPACK_GESDD(gesdd, c, cfloat , float )
+#define LAPACK_GESDD(P, X, T, Tr)                                            \
+    int LAPACKE_##X##P(int layout, char jobz, int m, int n, T *in, int ldin, \
+                       Tr *s, T *u, int ldu, T *vt, int ldvt);
+
+LAPACK_GESDD(gesdd, s, float, float)
+LAPACK_GESDD(gesdd, d, double, double)
+LAPACK_GESDD(gesdd, c, cfloat, float)
 LAPACK_GESDD(gesdd, z, cdouble, double)
 
 #define LAPACK_LAMCH(X, T) T LAPACKE_##X##lamch(char cmach);
 
-LAPACK_LAMCH(s, float )
+LAPACK_LAMCH(s, float)
 LAPACK_LAMCH(d, double)
 
-#define LAPACK_LACPY(X, T)                                      \
-    int LAPACKE_##X##lacpy(int matrix_order, char uplo, int m,  \
-                           int n, const T* a,                   \
-                           int lda, T* b,                       \
-                           int ldb );                           \
-
+#define LAPACK_LACPY(X, T)                                            \
+    int LAPACKE_##X##lacpy(int matrix_order, char uplo, int m, int n, \
+                           const T *a, int lda, T *b, int ldb);
 
 LAPACK_LACPY(s, float)
 LAPACK_LACPY(d, double)
 LAPACK_LACPY(c, cfloat)
 LAPACK_LACPY(z, cdouble)
 
-#define LAPACK_GBR_WORK(P, X, T)                                    \
-    int LAPACKE_##X##P##_work(int matrix_order, char vect, int m,   \
-                              int n, int k, T* a,                   \
-                              int lda, const T* tau, T* work,       \
-                              int lwork );                          \
+#define LAPACK_GBR_WORK(P, X, T)                                           \
+    int LAPACKE_##X##P##_work(int matrix_order, char vect, int m, int n,   \
+                              int k, T *a, int lda, const T *tau, T *work, \
+                              int lwork);
 
 LAPACK_GBR_WORK(orgbr, s, float)
 LAPACK_GBR_WORK(orgbr, d, double)
 LAPACK_GBR_WORK(ungbr, c, cfloat)
 LAPACK_GBR_WORK(ungbr, z, cdouble)
 
-#define LAPACK_BDSQR_WORK(X, T, Tr)                                     \
-    int LAPACKE_##X##bdsqr_work( int matrix_order, char uplo, int n,    \
-                                 int ncvt, int nru, int ncc,            \
-                                 Tr* d, Tr* e, T* vt,                   \
-                                 int ldvt, T* u,                        \
-                                 int ldu, T* c,                         \
-                                 int ldc, Tr* work );                   \
+#define LAPACK_BDSQR_WORK(X, T, Tr)                                            \
+    int LAPACKE_##X##bdsqr_work(                                               \
+        int matrix_order, char uplo, int n, int ncvt, int nru, int ncc, Tr *d, \
+        Tr *e, T *vt, int ldvt, T *u, int ldu, T *c, int ldc, Tr *work);
 
 LAPACK_BDSQR_WORK(s, float, float)
 LAPACK_BDSQR_WORK(d, double, double)
 LAPACK_BDSQR_WORK(c, cfloat, float)
 LAPACK_BDSQR_WORK(z, cdouble, double)
 
-#define LAPACK_GEBRD_WORK(X, T, Tr)                                 \
-    int LAPACKE_##X##gebrd_work( int matrix_order, int m, int n,    \
-                                 T* a, int lda,                     \
-                                 Tr* d, Tr* e, T* tauq,             \
-                                 T* taup,                           \
-                                 T* work, int lwork );              \
+#define LAPACK_GEBRD_WORK(X, T, Tr)                                            \
+    int LAPACKE_##X##gebrd_work(int matrix_order, int m, int n, T *a, int lda, \
+                                Tr *d, Tr *e, T *tauq, T *taup, T *work,       \
+                                int lwork);
 
 LAPACK_GEBRD_WORK(s, float, float)
 LAPACK_GEBRD_WORK(d, double, double)
 LAPACK_GEBRD_WORK(c, cfloat, float)
 LAPACK_GEBRD_WORK(z, cdouble, double)
 
-#define LAPACK_LARFG_WORK(X, T)                     \
-    int LAPACKE_##X##larfg_work( int n, T* alpha,   \
-                                 T* x, int incx,    \
-                                 T* tau );          \
+#define LAPACK_LARFG_WORK(X, T) \
+    int LAPACKE_##X##larfg_work(int n, T *alpha, T *x, int incx, T *tau);
 
 LAPACK_LARFG_WORK(s, float)
 LAPACK_LARFG_WORK(d, double)
 LAPACK_LARFG_WORK(c, cfloat)
 LAPACK_LARFG_WORK(z, cdouble)
 
-#define LAPACK_LACGV_WORK(X, T)                 \
-    int LAPACKE_##X##lacgv_work( int n, T* x,   \
-                                 int incx );    \
+#define LAPACK_LACGV_WORK(X, T) \
+    int LAPACKE_##X##lacgv_work(int n, T *x, int incx);
 
 LAPACK_LACGV_WORK(c, cfloat)
 LAPACK_LACGV_WORK(z, cdouble)
diff --git a/src/backend/common/module_loading.hpp b/src/backend/common/module_loading.hpp
index 83ced96b84..5a28c5bb9e 100644
--- a/src/backend/common/module_loading.hpp
+++ b/src/backend/common/module_loading.hpp
@@ -19,4 +19,4 @@ void unloadLibrary(LibHandle handle);
 
 std::string getErrorMessage();
 
-}
+}  // namespace common
diff --git a/src/backend/common/module_loading_unix.cpp b/src/backend/common/module_loading_unix.cpp
index b2ddb3fe61..cd9efab751 100644
--- a/src/backend/common/module_loading_unix.cpp
+++ b/src/backend/common/module_loading_unix.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/module_loading.hpp>
 #include <common/defines.hpp>
+#include <common/module_loading.hpp>
 
 #include <dlfcn.h>
 
@@ -18,19 +18,17 @@ using std::string;
 namespace common {
 
 void* getFunctionPointer(LibHandle handle, const char* symbolName) {
-  return dlsym(handle, symbolName);
+    return dlsym(handle, symbolName);
 }
 
 LibHandle loadLibrary(const char* library_name) {
     return dlopen(library_name, RTLD_LAZY);
 }
-void unloadLibrary(LibHandle handle) {
-    dlclose(handle);
-}
+void unloadLibrary(LibHandle handle) { dlclose(handle); }
 
 string getErrorMessage() {
     string error_message(dlerror());
     return error_message;
 }
 
-}
+}  // namespace common
diff --git a/src/backend/common/module_loading_windows.cpp b/src/backend/common/module_loading_windows.cpp
index d331118716..7415792951 100644
--- a/src/backend/common/module_loading_windows.cpp
+++ b/src/backend/common/module_loading_windows.cpp
@@ -7,42 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/module_loading.hpp>
 #include <common/defines.hpp>
+#include <common/module_loading.hpp>
 
-#include <string>
 #include <Windows.h>
+#include <string>
 
 using std::string;
 
 namespace common {
 
 void* getFunctionPointer(LibHandle handle, const char* symbolName) {
-  return GetProcAddress(handle, symbolName);
+    return GetProcAddress(handle, symbolName);
 }
 
 LibHandle loadLibrary(const char* library_name) {
     return LoadLibrary(library_name);
 }
 
-void unloadLibrary(LibHandle handle) {
-    FreeLibrary(handle);
-}
+void unloadLibrary(LibHandle handle) { FreeLibrary(handle); }
 
 string getErrorMessage() {
     const char* lpMsgBuf;
     DWORD dw = GetLastError();
 
-    FormatMessage(FORMAT_MESSAGE_ALLOCATE_BUFFER |
-                  FORMAT_MESSAGE_FROM_SYSTEM |
-                  FORMAT_MESSAGE_IGNORE_INSERTS,
-                  NULL,
-                  dw,
-                  MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
-                  (LPTSTR) &lpMsgBuf,
-                  0, NULL );
+    FormatMessage(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM |
+                      FORMAT_MESSAGE_IGNORE_INSERTS,
+                  NULL, dw, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
+                  (LPTSTR)&lpMsgBuf, 0, NULL);
     string error_message(lpMsgBuf);
     return error_message;
 }
 
-}
+}  // namespace common
diff --git a/src/backend/common/sparse_helpers.hpp b/src/backend/common/sparse_helpers.hpp
index 5e50efacb7..60929efde4 100644
--- a/src/backend/common/sparse_helpers.hpp
+++ b/src/backend/common/sparse_helpers.hpp
@@ -10,41 +10,41 @@
 #pragma once
 #include <Array.hpp>
 
-namespace common
-{
+namespace common {
 
 using namespace detail;
 
 class SparseArrayBase;
-template<typename T> class SparseArray;
+template<typename T>
+class SparseArray;
 
 ////////////////////////////////////////////////////////////////////////////
 // Friend functions for Sparse Array Creation
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
-SparseArray<T> createEmptySparseArray(
-        const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);
+SparseArray<T> createEmptySparseArray(const af::dim4 &_dims, dim_t _nNZ,
+                                      const af::storage _storage);
 
 template<typename T>
-SparseArray<T> createHostDataSparseArray(
-        const af::dim4 &_dims, const dim_t nNZ,
-        const T * const _values,
-        const int * const _rowIdx, const int * const _colIdx,
-        const af::storage _storage);
+SparseArray<T> createHostDataSparseArray(const af::dim4 &_dims, const dim_t nNZ,
+                                         const T *const _values,
+                                         const int *const _rowIdx,
+                                         const int *const _colIdx,
+                                         const af::storage _storage);
 
 template<typename T>
 SparseArray<T> createDeviceDataSparseArray(
-        const af::dim4 &_dims, const dim_t nNZ,
-        const T * const _values,
-        const int * const _rowIdx, const int * const _colIdx,
-        const af::storage _storage, const bool _copy = false);
+    const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
+    const int *const _rowIdx, const int *const _colIdx,
+    const af::storage _storage, const bool _copy = false);
 
 template<typename T>
-SparseArray<T> createArrayDataSparseArray(
-        const af::dim4 &_dims,
-        const Array<T> &_values,
-        const Array<int> &_rowIdx, const Array<int> &_colIdx,
-        const af::storage _storage, const bool _copy = false);
+SparseArray<T> createArrayDataSparseArray(const af::dim4 &_dims,
+                                          const Array<T> &_values,
+                                          const Array<int> &_rowIdx,
+                                          const Array<int> &_colIdx,
+                                          const af::storage _storage,
+                                          const bool _copy = false);
 
 template<typename T>
 SparseArray<T> *initSparseArray();
@@ -57,6 +57,6 @@ void destroySparseArray(SparseArray<T> *sparse);
 /// \param[in] input    The sparse array that is to be copied
 /// \returns A deep copy of the input sparse array
 template<typename T>
-SparseArray<T> copySparseArray(const SparseArray<T>& input);
+SparseArray<T> copySparseArray(const SparseArray<T> &input);
 
-} // namespace common
+}  // namespace common
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index 2579197766..f3df973473 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 /// This file contains platform independent utility functions
-#include <string>
 #include <cstdlib>
+#include <string>
 
 #if defined(OS_WIN)
 #include <Windows.h>
@@ -19,10 +19,10 @@
 
 using std::string;
 
-string getEnvVar(const std::string &key)
-{
+string getEnvVar(const std::string &key) {
 #if defined(OS_WIN)
-    DWORD bufSize = 32767; // limit according to GetEnvironment Variable documentation
+    DWORD bufSize =
+        32767;  // limit according to GetEnvironment Variable documentation
     string retVal;
     retVal.resize(bufSize);
     bufSize = GetEnvironmentVariable(key.c_str(), &retVal[0], bufSize);
@@ -33,26 +33,25 @@ string getEnvVar(const std::string &key)
         return retVal;
     }
 #else
-    char * str = getenv(key.c_str());
-    return str==NULL ? string("") : string(str);
+    char *str = getenv(key.c_str());
+    return str == NULL ? string("") : string(str);
 #endif
 }
 
-const char *getName(af_dtype type)
-{
-  switch(type) {
-  case f32: return "float";
-  case f64: return "double";
-  case c32: return "complex float";
-  case c64: return "complex double";
-  case u32: return "unsigned int";
-  case s32: return "int";
-  case u16: return "unsigned short";
-  case s16: return "short";
-  case u64: return "unsigned long long";
-  case s64: return "long long";
-  case u8 : return "unsigned char";
-  case b8 : return "bool";
-  default : return "unknown type";
-  }
+const char *getName(af_dtype type) {
+    switch (type) {
+        case f32: return "float";
+        case f64: return "double";
+        case c32: return "complex float";
+        case c64: return "complex double";
+        case u32: return "unsigned int";
+        case s32: return "int";
+        case u16: return "unsigned short";
+        case s16: return "short";
+        case u64: return "unsigned long long";
+        case s64: return "long long";
+        case u8: return "unsigned char";
+        case b8: return "bool";
+        default: return "unknown type";
+    }
 }
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index f0847e2b3d..877aaa199d 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -7,18 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-
 #include <Array.hpp>
 #include <kernel/Array.hpp>
 
-#include <jit/BufferNode.hpp>
-#include <jit/Node.hpp>
-#include <jit/ScalarNode.hpp>
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/jit/NodeIterator.hpp>
 #include <common/err_common.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
+#include <jit/BufferNode.hpp>
+#include <jit/Node.hpp>
+#include <jit/ScalarNode.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
@@ -29,46 +28,56 @@
 #include <af/seq.h>
 #include <af/traits.hpp>
 
-#include <algorithm> // IWYU pragma: keep
-#include <cstring>
+#include <algorithm>  // IWYU pragma: keep
 #include <cstddef>
+#include <cstring>
 #include <type_traits>
 
-namespace cpu
-{
+namespace cpu {
 
+using common::NodeIterator;
 using jit::BufferNode;
 using jit::Node;
-using jit::Node_ptr;
 using jit::Node_map_t;
-using common::NodeIterator;
+using jit::Node_ptr;
 
 using af::dim4;
-using std::vector;
-using std::is_standard_layout;
 using std::copy;
+using std::is_standard_layout;
+using std::vector;
 
 template<typename T>
-Node_ptr bufferNodePtr()
-{
+Node_ptr bufferNodePtr() {
     return Node_ptr(reinterpret_cast<Node *>(new BufferNode<T>()));
 }
 
 template<typename T>
-Array<T>::Array(dim4 dims):
-    info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-    data(memAlloc<T>(dims.elements()).release(), memFree<T>), data_dims(dims),
-    node(bufferNodePtr<T>()), ready(true), owner(true)
-{ }
+Array<T>::Array(dim4 dims)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(memAlloc<T>(dims.elements()).release(), memFree<T>)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {}
 
 template<typename T>
-Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
-    info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-    data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()).release(), memFree<T>), data_dims(dims),
-    node(bufferNodePtr<T>()), ready(true), owner(true)
-{
-    static_assert(is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
-    static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
+Array<T>::Array(dim4 dims, const T *const in_data, bool is_device,
+                bool copy_device)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data((is_device & !copy_device) ? (T *)in_data
+                                      : memAlloc<T>(dims.elements()).release(),
+           memFree<T>)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
+    static_assert(is_standard_layout<Array<T>>::value,
+                  "Array<T> must be a standard layout type");
+    static_assert(
+        offsetof(Array<T>, info) == 0,
+        "Array<T>::info must be the first member variable of Array<T>");
     if (!is_device || copy_device) {
         // Ensure the memory being written to isnt used anywhere else.
         getQueue().sync();
@@ -77,31 +86,37 @@ Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_de
 }
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, Node_ptr n) :
-    info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-    data(), data_dims(dims),
-    node(n), ready(false), owner(true)
-{
-}
+Array<T>::Array(af::dim4 dims, Node_ptr n)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data()
+    , data_dims(dims)
+    , node(n)
+    , ready(false)
+    , owner(true) {}
 
 template<typename T>
-Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &strides) :
-    info(parent.getDevId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-    data(parent.getData()), data_dims(parent.getDataDims()),
-    node(bufferNodePtr<T>()),
-    ready(true), owner(false)
-{ }
+Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
+                const dim4 &strides)
+    : info(parent.getDevId(), dims, offset_, strides,
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(parent.getData())
+    , data_dims(parent.getDataDims())
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(false) {}
 
 template<typename T>
 Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
-                const T * const in_data, bool is_device) :
-    info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-    data(is_device ? (T*)in_data : memAlloc<T>(info.total()).release(), memFree<T>),
-    data_dims(dims),
-    node(bufferNodePtr<T>()),
-    ready(true),
-    owner(true)
-{
+                const T *const in_data, bool is_device)
+    : info(getActiveDeviceId(), dims, offset_, strides,
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(is_device ? (T *)in_data : memAlloc<T>(info.total()).release(),
+           memFree<T>)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
     if (!is_device) {
         // Ensure the memory being written to isnt used anywhere else.
         getQueue().sync();
@@ -110,10 +125,10 @@ Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
 }
 
 template<typename T>
-void Array<T>::eval()
-{
+void Array<T>::eval() {
     if (isReady()) return;
-    if (getQueue().is_worker()) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+    if (getQueue().is_worker())
+        AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
 
     this->setId(getActiveDeviceId());
 
@@ -122,19 +137,17 @@ void Array<T>::eval()
     getQueue().enqueue(kernel::evalArray<T>, *this, this->node);
     // Reset shared_ptr
     this->node = bufferNodePtr<T>();
-    ready = true;
+    ready      = true;
 }
 
 template<typename T>
-void Array<T>::eval() const
-{
+void Array<T>::eval() const {
     if (isReady()) return;
     const_cast<Array<T> *>(this)->eval();
 }
 
 template<typename T>
-T* Array<T>::device()
-{
+T *Array<T>::device() {
     getQueue().sync();
     if (!isOwner() || getOffset() || data.use_count() > 1) {
         *this = copyArray<T>(*this);
@@ -143,17 +156,18 @@ T* Array<T>::device()
 }
 
 template<typename T>
-void evalMultiple(vector<Array<T>*> array_ptrs)
-{
-    vector<Array<T>*> output_arrays;
+void evalMultiple(vector<Array<T> *> array_ptrs) {
+    vector<Array<T> *> output_arrays;
     vector<Node_ptr> nodes;
     vector<Param<T>> params;
-    if (getQueue().is_worker()) AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
-    for (Array<T>* array : array_ptrs) {
+    if (getQueue().is_worker())
+        AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+    for (Array<T> *array : array_ptrs) {
         if (array->ready) continue;
 
         array->setId(getActiveDeviceId());
-        array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
+        array->data =
+            shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
         output_arrays.push_back(array);
         params.push_back(*array);
@@ -162,64 +176,49 @@ void evalMultiple(vector<Array<T>*> array_ptrs)
 
     if (output_arrays.size() > 0) {
         getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
-        for (Array<T>* array : output_arrays) {
+        for (Array<T> *array : output_arrays) {
             array->ready = true;
-            array->node = bufferNodePtr<T>();
+            array->node  = bufferNodePtr<T>();
         }
     }
     return;
 }
 
 template<typename T>
-Node_ptr Array<T>::getNode() const
-{
+Node_ptr Array<T>::getNode() const {
     if (node->isBuffer()) {
         BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
-        unsigned bytes = this->getDataDims().elements() * sizeof(T);
-        bufNode->setData(data,
-                         bytes,
-                         getOffset(),
-                         dims().get(),
-                         strides().get(),
-                         isLinear());
+        unsigned bytes         = this->getDataDims().elements() * sizeof(T);
+        bufNode->setData(data, bytes, getOffset(), dims().get(),
+                         strides().get(), isLinear());
     }
     return node;
 }
 
 template<typename T>
-Array<T>
-createHostDataArray(const dim4 &size, const T * const data)
-{
+Array<T> createHostDataArray(const dim4 &size, const T *const data) {
     return Array<T>(size, data, false);
 }
 
 template<typename T>
-Array<T>
-createDeviceDataArray(const dim4 &size, const void *data)
-{
-    return Array<T>(size, (const T * const) data, true);
+Array<T> createDeviceDataArray(const dim4 &size, const void *data) {
+    return Array<T>(size, (const T *const)data, true);
 }
 
 template<typename T>
-Array<T>
-createValueArray(const dim4 &size, const T& value)
-{
+Array<T> createValueArray(const dim4 &size, const T &value) {
     jit::ScalarNode<T> *node = new jit::ScalarNode<T>(value);
     return createNodeArray<T>(size, Node_ptr(node));
 }
 
 template<typename T>
-Array<T>
-createEmptyArray(const dim4 &size)
-{
+Array<T> createEmptyArray(const dim4 &size) {
     return Array<T>(size);
 }
 
 template<typename T>
-Array<T>
-createNodeArray(const dim4 &dims, Node_ptr node)
-{
-    Array<T> out =  Array<T>(dims, node);
+Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    Array<T> out = Array<T>(dims, node);
 
     if (evalFlag()) {
         if (node->getHeight() >= (int)getMaxJitSize()) {
@@ -228,28 +227,25 @@ createNodeArray(const dim4 &dims, Node_ptr node)
             size_t alloc_bytes, alloc_buffers;
             size_t lock_bytes, lock_buffers;
 
-            deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
-                             &lock_bytes, &lock_buffers);
+            deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
+                             &lock_buffers);
 
             // Check if approaching the memory limit
-            if (lock_bytes > getMaxBytes() ||
-                lock_buffers > getMaxBuffers()) {
-
+            if (lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers()) {
                 Node *n = node.get();
 
                 NodeIterator<jit::Node> it(n);
                 NodeIterator<jit::Node> end_node;
-                size_t bytes = accumulate(it, end_node,
-                                          size_t(0),
-                                          [=](const size_t prev, const Node& n) {
-                                              // getBytes returns the size of the data Array. Sub arrays will
-                                              // be represented by their parent size.
-                                              return prev + n.getBytes();
-                                          });
-
-                if (2 * bytes > lock_bytes) {
-                    out.eval();
-                }
+                size_t bytes =
+                    accumulate(it, end_node, size_t(0),
+                               [=](const size_t prev, const Node &n) {
+                                   // getBytes returns the size of the data
+                                   // Array. Sub arrays will be represented by
+                                   // their parent size.
+                                   return prev + n.getBytes();
+                               });
+
+                if (2 * bytes > lock_bytes) { out.eval(); }
             }
         }
     }
@@ -258,14 +254,12 @@ createNodeArray(const dim4 &dims, Node_ptr node)
 }
 
 template<typename T>
-Array<T> createSubArray(const Array<T>& parent,
-                        const vector<af_seq> &index,
-                        bool copy)
-{
+Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
+                        bool copy) {
     parent.eval();
 
-    dim4 dDims = parent.getDataDims();
-    dim4 dStrides = calcStrides(dDims);
+    dim4 dDims          = parent.getDataDims();
+    dim4 dStrides       = calcStrides(dDims);
     dim4 parent_strides = parent.strides();
 
     if (dStrides != parent_strides) {
@@ -273,9 +267,9 @@ Array<T> createSubArray(const Array<T>& parent,
         return createSubArray(parentCopy, index, copy);
     }
 
-    dim4 pDims = parent.dims();
-    dim4 dims    = toDims  (index, pDims);
-    dim4 strides = toStride (index, dDims);
+    dim4 pDims   = parent.dims();
+    dim4 dims    = toDims(index, pDims);
+    dim4 strides = toStride(index, dDims);
 
     // Find total offsets after indexing
     dim4 offsets = toOffset(index, pDims);
@@ -286,11 +280,7 @@ Array<T> createSubArray(const Array<T>& parent,
 
     if (!copy) return out;
 
-    if (strides[0] != 1 ||
-        strides[1] <  0 ||
-        strides[2] <  0 ||
-        strides[3] <  0) {
-
+    if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
         out = copyArray(out);
     }
 
@@ -298,19 +288,14 @@ Array<T> createSubArray(const Array<T>& parent,
 }
 
 template<typename T>
-void
-destroyArray(Array<T> *A)
-{
+void destroyArray(Array<T> *A) {
     delete A;
 }
 
 template<typename T>
-void
-writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
-{
-    if(!arr.isOwner()) {
-        arr = copyArray<T>(arr);
-    }
+void writeHostDataArray(Array<T> &arr, const T *const data,
+                        const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
     arr.eval();
     // Ensure the memory being written to isnt used anywhere else.
     getQueue().sync();
@@ -318,50 +303,44 @@ writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
 }
 
 template<typename T>
-void
-writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
-{
-    if(!arr.isOwner()) {
-        arr = copyArray<T>(arr);
-    }
-    memcpy(arr.get(), (const T * const)data, bytes);
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
+    memcpy(arr.get(), (const T *const)data, bytes);
 }
 
-
 template<typename T>
-void
-Array<T>::setDataDims(const dim4 &new_dims)
-{
+void Array<T>::setDataDims(const dim4 &new_dims) {
     modDims(new_dims);
     data_dims = new_dims;
-    if (node->isBuffer()) {
-        node = bufferNodePtr<T>();
-    }
+    if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
-#define INSTANTIATE(T)                                                  \
-    template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
-    template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
-    template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
-    template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
-    template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
-                                                       const vector<af_seq> &index, \
-                                                       bool copy);      \
-    template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, Node_ptr node); \
-    template       void Array<T>::eval();                               \
-    template       void Array<T>::eval() const;                         \
-    template       T*   Array<T>::device();                             \
-    template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
-                                   bool is_device, bool copy_device);   \
-    template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
-                                   const T * const in_data,             \
-                                   bool is_device);                     \
-    template       Node_ptr Array<T>::getNode() const;                  \
-    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, const size_t bytes); \
-    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, const size_t bytes); \
-    template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
-    template       void Array<T>::setDataDims(const dim4 &new_dims);    \
+#define INSTANTIATE(T)                                                       \
+    template Array<T> createHostDataArray<T>(const dim4 &size,               \
+                                             const T *const data);           \
+    template Array<T> createDeviceDataArray<T>(const dim4 &size,             \
+                                               const void *data);            \
+    template Array<T> createValueArray<T>(const dim4 &size, const T &value); \
+    template Array<T> createEmptyArray<T>(const dim4 &size);                 \
+    template Array<T> createSubArray<T>(                                     \
+        const Array<T> &parent, const vector<af_seq> &index, bool copy);     \
+    template void destroyArray<T>(Array<T> * A);                             \
+    template Array<T> createNodeArray<T>(const dim4 &size, Node_ptr node);   \
+    template void Array<T>::eval();                                          \
+    template void Array<T>::eval() const;                                    \
+    template T *Array<T>::device();                                          \
+    template Array<T>::Array(af::dim4 dims, const T *const in_data,          \
+                             bool is_device, bool copy_device);              \
+    template Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset,  \
+                             const T *const in_data, bool is_device);        \
+    template Node_ptr Array<T>::getNode() const;                             \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data, \
+                                        const size_t bytes);                 \
+    template void writeDeviceDataArray<T>(                                   \
+        Array<T> & arr, const void *const data, const size_t bytes);         \
+    template void evalMultiple<T>(vector<Array<T> *> arrays);                \
+    template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -376,4 +355,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 251f2ee5e3..ba5b08c2e8 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-//This is the array implementation class.
+// This is the array implementation class.
 #pragma once
 #include <Param.hpp>
-#include <jit/Node.hpp>
 #include <common/ArrayInfo.hpp>
+#include <jit/Node.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
@@ -24,238 +24,229 @@
 #include <memory>
 #include <vector>
 
-namespace cpu
-{
-    namespace kernel
-    {
-        template<typename T> void evalArray(Param<T> in, jit::Node_ptr node);
-
-        template<typename T>
-        void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> nodes);
-
-    }
-
-    template<typename T> class Array;
-
-    using std::shared_ptr;
-    using af::dim4;
-
-    template<typename T>
-    void evalMultiple(std::vector<Array<T> *> arrays);
+namespace cpu {
+namespace kernel {
+template<typename T>
+void evalArray(Param<T> in, jit::Node_ptr node);
 
-    // Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, jit::Node_ptr node);
+template<typename T>
+void evalMultiple(std::vector<Param<T>> arrays,
+                  std::vector<jit::Node_ptr> nodes);
 
-    // Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    Array<T> createValueArray(const af::dim4 &size, const T& value);
+}  // namespace kernel
 
-    // Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    Array<T> createHostDataArray(const af::dim4 &size, const T * const data);
+template<typename T>
+class Array;
 
-    template<typename T>
-    Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
+using af::dim4;
+using std::shared_ptr;
 
-    template<typename T>
-    Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
-                                const T * const in_data, bool is_device) {
-        return Array<T>(dims, strides, offset, in_data, is_device);
-    }
-
-    /// Copies data to an existing Array object from a host pointer
-    template<typename T>
-    void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
+template<typename T>
+void evalMultiple(std::vector<Array<T> *> arrays);
 
-    /// Copies data to an existing Array object from a device pointer
-    template<typename T>
-    void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
+// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createNodeArray(const af::dim4 &size, jit::Node_ptr node);
 
-    /// Creates an empty array of a given size. No data is initialized
-    ///
-    /// \param[in] size The dimension of the output array
-    template<typename T>
-    Array<T> createEmptyArray(const af::dim4 &size);
+// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createValueArray(const af::dim4 &size, const T &value);
 
-    template<typename T>
-    Array<T> createSubArray(const Array<T>& parent,
-                            const std::vector<af_seq> &index,
-                            bool copy=true);
+// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createHostDataArray(const af::dim4 &size, const T *const data);
 
-    // Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    void destroyArray(Array<T> *A);
+template<typename T>
+Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
 
-    template<typename T>
-    void *getDevicePtr(const Array<T>& arr)
-    {
-        T *ptr = arr.device();
-        memLock(ptr);
-
-        return (void *)ptr;
-    }
+template<typename T>
+Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
+                            const T *const in_data, bool is_device) {
+    return Array<T>(dims, strides, offset, in_data, is_device);
+}
 
-    template<typename T>
-    void *getRawPtr(const Array<T>& arr)
-    {
-        getQueue().sync();
-        return (void *)(arr.get(false));
-    }
+/// Copies data to an existing Array object from a host pointer
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data, const size_t bytes);
 
-    // Array Array Implementation
-    template<typename T>
-    class Array
-    {
-        ArrayInfo info; // Must be the first element of Array<T>
+/// Copies data to an existing Array object from a device pointer
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes);
 
-        //data if parent. empty if child
-        std::shared_ptr<T> data;
-        af::dim4 data_dims;
-        jit::Node_ptr node;
+/// Creates an empty array of a given size. No data is initialized
+///
+/// \param[in] size The dimension of the output array
+template<typename T>
+Array<T> createEmptyArray(const af::dim4 &size);
 
-        bool ready;
-        bool owner;
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent,
+                        const std::vector<af_seq> &index, bool copy = true);
 
-        Array() = default;
-        Array(dim4 dims);
+// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+void destroyArray(Array<T> *A);
 
-        explicit Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device=false);
-        Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        explicit Array(af::dim4 dims, jit::Node_ptr n);
-        Array(af::dim4 dims, af::dim4 strides, dim_t offset,
-              const T * const in_data, bool is_device = false);
+template<typename T>
+void *getDevicePtr(const Array<T> &arr) {
+    T *ptr = arr.device();
+    memLock(ptr);
 
-    public:
+    return (void *)ptr;
+}
 
-        void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
-        void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
-        void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }
-        void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides);  }
-        void setId(int id)                          { info.setId(id);               }
+template<typename T>
+void *getRawPtr(const Array<T> &arr) {
+    getQueue().sync();
+    return (void *)(arr.get(false));
+}
 
-#define INFO_FUNC(RET_TYPE, NAME)   \
+// Array Array Implementation
+template<typename T>
+class Array {
+    ArrayInfo info;  // Must be the first element of Array<T>
+
+    // data if parent. empty if child
+    std::shared_ptr<T> data;
+    af::dim4 data_dims;
+    jit::Node_ptr node;
+
+    bool ready;
+    bool owner;
+
+    Array() = default;
+    Array(dim4 dims);
+
+    explicit Array(dim4 dims, const T *const in_data, bool is_device,
+                   bool copy_device = false);
+    Array(const Array<T> &parnt, const dim4 &dims, const dim_t &offset,
+          const dim4 &stride);
+    explicit Array(af::dim4 dims, jit::Node_ptr n);
+    Array(af::dim4 dims, af::dim4 strides, dim_t offset, const T *const in_data,
+          bool is_device = false);
+
+   public:
+    void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
+    void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
+    void modDims(const af::dim4 &newDims) { info.modDims(newDims); }
+    void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides); }
+    void setId(int id) { info.setId(id); }
+
+#define INFO_FUNC(RET_TYPE, NAME) \
     RET_TYPE NAME() const { return info.NAME(); }
 
-        INFO_FUNC(const af_dtype& ,getType)
-        INFO_FUNC(const af::dim4& ,strides)
-        INFO_FUNC(size_t          ,elements)
-        INFO_FUNC(size_t          ,ndims)
-        INFO_FUNC(const af::dim4& ,dims )
-        INFO_FUNC(int             ,getDevId)
+    INFO_FUNC(const af_dtype &, getType)
+    INFO_FUNC(const af::dim4 &, strides)
+    INFO_FUNC(size_t, elements)
+    INFO_FUNC(size_t, ndims)
+    INFO_FUNC(const af::dim4 &, dims)
+    INFO_FUNC(int, getDevId)
 
 #undef INFO_FUNC
 
-#define INFO_IS_FUNC(NAME)\
-    bool NAME () const { return info.NAME(); }
-
-        INFO_IS_FUNC(isEmpty)
-        INFO_IS_FUNC(isScalar)
-        INFO_IS_FUNC(isRow)
-        INFO_IS_FUNC(isColumn)
-        INFO_IS_FUNC(isVector)
-        INFO_IS_FUNC(isComplex)
-        INFO_IS_FUNC(isReal)
-        INFO_IS_FUNC(isDouble)
-        INFO_IS_FUNC(isSingle)
-        INFO_IS_FUNC(isRealFloating)
-        INFO_IS_FUNC(isFloating)
-        INFO_IS_FUNC(isInteger)
-        INFO_IS_FUNC(isBool)
-        INFO_IS_FUNC(isLinear)
-        INFO_IS_FUNC(isSparse)
+#define INFO_IS_FUNC(NAME) \
+    bool NAME() const { return info.NAME(); }
+
+    INFO_IS_FUNC(isEmpty)
+    INFO_IS_FUNC(isScalar)
+    INFO_IS_FUNC(isRow)
+    INFO_IS_FUNC(isColumn)
+    INFO_IS_FUNC(isVector)
+    INFO_IS_FUNC(isComplex)
+    INFO_IS_FUNC(isReal)
+    INFO_IS_FUNC(isDouble)
+    INFO_IS_FUNC(isSingle)
+    INFO_IS_FUNC(isRealFloating)
+    INFO_IS_FUNC(isFloating)
+    INFO_IS_FUNC(isInteger)
+    INFO_IS_FUNC(isBool)
+    INFO_IS_FUNC(isLinear)
+    INFO_IS_FUNC(isSparse)
 
 #undef INFO_IS_FUNC
 
-        ~Array() = default;
+    ~Array() = default;
 
-        bool isReady() const { return ready; }
+    bool isReady() const { return ready; }
 
-        bool isOwner() const { return owner; }
+    bool isOwner() const { return owner; }
 
-        void eval();
-        void eval() const;
+    void eval();
+    void eval() const;
 
-        dim_t getOffset() const { return info.getOffset(); }
-        shared_ptr<T> getData() const {return data; }
+    dim_t getOffset() const { return info.getOffset(); }
+    shared_ptr<T> getData() const { return data; }
 
-        dim4 getDataDims() const
-        {
-            return data_dims;
-        }
-
-        void setDataDims(const dim4 &new_dims);
-
-        size_t getAllocatedBytes() const
-        {
-            if (!isReady()) return 0;
-            size_t bytes = memoryManager().allocated(data.get());
-            // External device poitner
-            if (bytes == 0 && data.get()) {
-                return data_dims.elements() * sizeof(T);
-            }
-            return  bytes;
-        }
+    dim4 getDataDims() const { return data_dims; }
 
-        T* device();
+    void setDataDims(const dim4 &new_dims);
 
-        T* device() const
-        {
-            return const_cast<Array<T>*>(this)->device();
+    size_t getAllocatedBytes() const {
+        if (!isReady()) return 0;
+        size_t bytes = memoryManager().allocated(data.get());
+        // External device poitner
+        if (bytes == 0 && data.get()) {
+            return data_dims.elements() * sizeof(T);
         }
+        return bytes;
+    }
 
-        T* get(bool withOffset = true)
-        {
-            return const_cast<T*>(static_cast<const Array<T>*>(this)->get(withOffset));
-        }
+    T *device();
 
-        const T* get(bool withOffset = true) const
-        {
-            if (!data.get()) eval();
-            return data.get() + (withOffset ? getOffset() : 0);
-        }
+    T *device() const { return const_cast<Array<T> *>(this)->device(); }
 
-        int useCount() const
-        {
-            if (!data.get()) eval();
-            return static_cast<int>(data.use_count());
-        }
+    T *get(bool withOffset = true) {
+        return const_cast<T *>(
+            static_cast<const Array<T> *>(this)->get(withOffset));
+    }
 
-        operator Param<T>()
-        {
-            return Param<T>(this->get(), this->dims(), this->strides());
-        }
+    const T *get(bool withOffset = true) const {
+        if (!data.get()) eval();
+        return data.get() + (withOffset ? getOffset() : 0);
+    }
 
-        operator CParam<T>() const
-        {
-            return CParam<T>(this->get(), this->dims(), this->strides());
-        }
+    int useCount() const {
+        if (!data.get()) eval();
+        return static_cast<int>(data.use_count());
+    }
 
-        jit::Node_ptr getNode() const;
+    operator Param<T>() {
+        return Param<T>(this->get(), this->dims(), this->strides());
+    }
+
+    operator CParam<T>() const {
+        return CParam<T>(this->get(), this->dims(), this->strides());
+    }
 
-        friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
+    jit::Node_ptr getNode() const;
 
-        friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
-        friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
-        friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
-        friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
-                                              const T * const in_data, bool is_device);
+    friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
+    friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
+    friend Array<T> createHostDataArray<T>(const af::dim4 &size,
+                                           const T *const data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size,
+                                             const void *data);
+    friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
+                                          dim_t offset, const T *const in_data,
+                                          bool is_device);
 
-        friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, jit::Node_ptr node);
+    friend Array<T> createEmptyArray<T>(const af::dim4 &size);
+    friend Array<T> createNodeArray<T>(const af::dim4 &dims,
+                                       jit::Node_ptr node);
 
-        friend Array<T> createSubArray<T>(const Array<T>& parent,
-                                          const std::vector<af_seq> &index,
-                                          bool copy);
+    friend Array<T> createSubArray<T>(const Array<T> &parent,
+                                      const std::vector<af_seq> &index,
+                                      bool copy);
 
-        friend void kernel::evalArray<T>(Param<T> in, jit::Node_ptr node);
-        friend void kernel::evalMultiple<T>(std::vector<Param<T>> arrays,
-                                            std::vector<jit::Node_ptr> nodes);
+    friend void kernel::evalArray<T>(Param<T> in, jit::Node_ptr node);
+    friend void kernel::evalMultiple<T>(std::vector<Param<T>> arrays,
+                                        std::vector<jit::Node_ptr> nodes);
 
-        friend void destroyArray<T>(Array<T> *arr);
-        friend void *getDevicePtr<T>(const Array<T>& arr);
-        friend void *getRawPtr<T>(const Array<T>& arr);
-    };
+    friend void destroyArray<T>(Array<T> *arr);
+    friend void *getDevicePtr<T>(const Array<T> &arr);
+    friend void *getRawPtr<T>(const Array<T> &arr);
+};
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
index 2b748ffc9a..55006f1c62 100644
--- a/src/backend/cpu/Param.hpp
+++ b/src/backend/cpu/Param.hpp
@@ -8,131 +8,92 @@
  ********************************************************/
 
 #pragma once
+#include <backend.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <backend.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-class CParam
-{
-private:
+class CParam {
+   private:
     const T *m_ptr;
     af::dim4 m_dims;
     af::dim4 m_strides;
 
-public:
-    CParam(const T *iptr, const af::dim4 &idims, const af::dim4 &istrides) :
-        m_ptr(iptr)
-    {
+   public:
+    CParam(const T *iptr, const af::dim4 &idims, const af::dim4 &istrides)
+        : m_ptr(iptr) {
         for (int i = 0; i < 4; i++) {
-            m_dims[i] = idims[i];
+            m_dims[i]    = idims[i];
             m_strides[i] = istrides[i];
         }
     }
 
-    const T *get() const
-    {
-        return m_ptr;
-    }
+    const T *get() const { return m_ptr; }
 
-    af::dim4 dims() const
-    {
-        return m_dims;
-    }
+    af::dim4 dims() const { return m_dims; }
 
-    af::dim4 strides() const
-    {
-        return m_strides;
-    }
+    af::dim4 strides() const { return m_strides; }
 
-    dim_t dims(int i) const
-    {
-        return m_dims[i];
-    }
+    dim_t dims(int i) const { return m_dims[i]; }
 
-    dim_t strides(int i) const
-    {
-        return m_strides[i];
-    }
+    dim_t strides(int i) const { return m_strides[i]; }
 };
 
 template<typename T>
-class Param
-{
-private:
+class Param {
+   private:
     T *m_ptr;
     af::dim4 m_dims;
     af::dim4 m_strides;
 
-public:
-    Param() : m_ptr(nullptr)
-    {
-    }
+   public:
+    Param() : m_ptr(nullptr) {}
 
-    Param(T *iptr, const af::dim4 &idims, const af::dim4 &istrides) :
-        m_ptr(iptr)
-    {
+    Param(T *iptr, const af::dim4 &idims, const af::dim4 &istrides)
+        : m_ptr(iptr) {
         for (int i = 0; i < 4; i++) {
-            m_dims[i] = idims[i];
+            m_dims[i]    = idims[i];
             m_strides[i] = istrides[i];
         }
     }
 
-    T *get()
-    {
-        return m_ptr;
-    }
+    T *get() { return m_ptr; }
 
-    operator CParam<T>() const
-    {
+    operator CParam<T>() const {
         return CParam<T>(const_cast<T *>(m_ptr), m_dims, m_strides);
     }
 
-    af::dim4 dims() const
-    {
-        return m_dims;
-    }
+    af::dim4 dims() const { return m_dims; }
 
-    af::dim4 strides() const
-    {
-        return m_strides;
-    }
+    af::dim4 strides() const { return m_strides; }
 
-    dim_t dims(int i) const
-    {
-        return m_dims[i];
-    }
+    dim_t dims(int i) const { return m_dims[i]; }
 
-    dim_t strides(int i) const
-    {
-        return m_strides[i];
-    }
+    dim_t strides(int i) const { return m_strides[i]; }
 };
 
-template<typename T> class Array;
+template<typename T>
+class Array;
 
-// These functions are needed to convert Array<T> to Param<T> when queueing up functions.
-// This is necessary because the memory used by Array<T> can be put back into the queue faster.
-// This is fine becacuse we only have 1 compute queue. This ensures there's no race conditions.
+// These functions are needed to convert Array<T> to Param<T> when queueing up
+// functions. This is necessary because the memory used by Array<T> can be put
+// back into the queue faster. This is fine becacuse we only have 1 compute
+// queue. This ensures there's no race conditions.
 template<typename T>
-T toParam(const T &val)
-{
+T toParam(const T &val) {
     return val;
 }
 
 template<typename T>
-Param<T> toParam(Array<T> &val)
-{
+Param<T> toParam(Array<T> &val) {
     return (Param<T>)(val);
 }
 
 template<typename T>
-CParam<T> toParam(const Array<T> &val)
-{
+CParam<T> toParam(const Array<T> &val) {
     return (CParam<T>)(val);
 }
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
index de430aff56..2e750127ec 100644
--- a/src/backend/cpu/ParamIterator.hpp
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <af/dim4.hpp>
 #include <Param.hpp>
+#include <af/dim4.hpp>
 
 #include <array>
 #include <cstddef>
@@ -32,23 +32,23 @@ class ParamIterator {
     // The iterator's stride
     const af::dim4 stride;
 
-    /// Calculates the iterator offsets. These are different from the original offsets
-    /// because they define the stride from the end of the last element in the previous
-    /// dimension to the first element on the next dimension.
-    static dim4 calculate_iterator_stride(const dim4 &dims, const dim4 &stride) noexcept {
-        dim4 out(stride[0],
-                 stride[1] - (stride[0] * dims[0]),
+    /// Calculates the iterator offsets. These are different from the original
+    /// offsets because they define the stride from the end of the last element
+    /// in the previous dimension to the first element on the next dimension.
+    static dim4 calculate_iterator_stride(const dim4& dims,
+                                          const dim4& stride) noexcept {
+        dim4 out(stride[0], stride[1] - (stride[0] * dims[0]),
                  stride[2] - (stride[1] * dims[1]),
                  stride[3] - (stride[2] * dims[2]));
 
         return out;
     }
 
-  public:
-    using difference_type = ptrdiff_t;
-    using value_type = T;
-    using pointer = T*;
-    using reference = T&;
+   public:
+    using difference_type   = ptrdiff_t;
+    using value_type        = T;
+    using pointer           = T*;
+    using reference         = T&;
     using iterator_category = std::forward_iterator_tag;
 
     /// Creates a sentinel iterator. This is equivalent to the end iterator
@@ -69,8 +69,7 @@ class ParamIterator {
         : ptr(in.get())
         , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]}
         , dims(in.dims())
-        , stride(calculate_iterator_stride(dims, in.strides())) {
-    }
+        , stride(calculate_iterator_stride(dims, in.strides())) {}
 
     /// The equality operator
     bool operator==(const ParamIterator& other) const noexcept {
@@ -84,12 +83,10 @@ class ParamIterator {
 
     /// Advances the iterator
     ParamIterator& operator++() noexcept {
-        for(int i = 0; i < AF_MAX_DIMS; i++) {
+        for (int i = 0; i < AF_MAX_DIMS; i++) {
             dim_index[i]--;
             ptr += stride[i];
-            if(dim_index[i]) {
-                return *this;
-            }
+            if (dim_index[i]) { return *this; }
             dim_index[i] = dims[i];
         }
         ptr = nullptr;
@@ -105,45 +102,40 @@ class ParamIterator {
 
     /// Advances the iterator by count elements
     ParamIterator& operator+=(std::size_t count) noexcept {
-        while (count-- > 0) {
-            operator++();
-        }
+        while (count-- > 0) { operator++(); }
         return *this;
     }
 
-    const reference operator*() const noexcept {
-        return *ptr;
-    }
+    const reference operator*() const noexcept { return *ptr; }
 
-    const pointer operator->() const noexcept {
-        return ptr;
-    }
+    const pointer operator->() const noexcept { return ptr; }
 
     ParamIterator(const ParamIterator<T>& other) = default;
-    ParamIterator(ParamIterator<T>&& other) = default;
-    ~ParamIterator() noexcept = default;
-    ParamIterator<T>& operator=(const ParamIterator<T>& other) noexcept = default;
+    ParamIterator(ParamIterator<T>&& other)      = default;
+    ~ParamIterator() noexcept                    = default;
+    ParamIterator<T>& operator=(const ParamIterator<T>& other) noexcept =
+        default;
     ParamIterator<T>& operator=(ParamIterator<T>&& other) noexcept = default;
 };
 
-  template<typename T>
-  ParamIterator<T> begin(Param<T>& param) {
-      return ParamIterator<T>(param);
-  }
-
-  template<typename T>
-  ParamIterator<T> end(Param<T>& param) {
-      return ParamIterator<T>();
-  }
+template<typename T>
+ParamIterator<T> begin(Param<T>& param) {
+    return ParamIterator<T>(param);
+}
 
-  template<typename T>
-  ParamIterator<const T> begin(CParam<T>& param) {
-      return ParamIterator<const T>(param);
-  }
+template<typename T>
+ParamIterator<T> end(Param<T>& param) {
+    return ParamIterator<T>();
+}
 
-  template<typename T>
-  ParamIterator<const T> end(CParam<T>& param) {
-      return ParamIterator<const T>();
-  }
+template<typename T>
+ParamIterator<const T> begin(CParam<T>& param) {
+    return ParamIterator<const T>(param);
+}
 
+template<typename T>
+ParamIterator<const T> end(CParam<T>& param) {
+    return ParamIterator<const T>();
 }
+
+}  // namespace cpu
diff --git a/src/backend/cpu/anisotropic_diffusion.cpp b/src/backend/cpu/anisotropic_diffusion.cpp
index 906fcc0df6..3a7f518979 100644
--- a/src/backend/cpu/anisotropic_diffusion.cpp
+++ b/src/backend/cpu/anisotropic_diffusion.cpp
@@ -11,23 +11,24 @@
 #include <kernel/anisotropic_diffusion.hpp>
 #include <platform.hpp>
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
-void anisotropicDiffusion(Array<T>& inout, const float dt,
-                          const float mct, const af::fluxFunction fftype,
-                          const af::diffusionEq eq)
-{
-    if (eq==AF_DIFFUSION_MCDE)
-        getQueue().enqueue(kernel::anisotropicDiffusion<T, true>, inout, dt, mct, fftype);
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
+                          const af::diffusionEq eq) {
+    if (eq == AF_DIFFUSION_MCDE)
+        getQueue().enqueue(kernel::anisotropicDiffusion<T, true>, inout, dt,
+                           mct, fftype);
     else
-        getQueue().enqueue(kernel::anisotropicDiffusion<T, false>, inout, dt, mct, fftype);
+        getQueue().enqueue(kernel::anisotropicDiffusion<T, false>, inout, dt,
+                           mct, fftype);
 }
 
-#define INSTANTIATE(T)\
-template void anisotropicDiffusion<T>(Array<T> &inout, const float dt, const float mct,\
-                                      const af::fluxFunction fftype, const af::diffusionEq eq);
+#define INSTANTIATE(T)                                     \
+    template void anisotropicDiffusion<T>(                 \
+        Array<T> & inout, const float dt, const float mct, \
+        const af::fluxFunction fftype, const af::diffusionEq eq);
 
 INSTANTIATE(double)
-INSTANTIATE( float)
-}
+INSTANTIATE(float)
+}  // namespace cpu
diff --git a/src/backend/cpu/anisotropic_diffusion.hpp b/src/backend/cpu/anisotropic_diffusion.hpp
index 5c7a9078df..bf82cbde46 100644
--- a/src/backend/cpu/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/anisotropic_diffusion.hpp
@@ -7,15 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-
 #include "af/defines.h"
 
-namespace cpu
-{
-template <typename T> class Array;
+namespace cpu {
+template<typename T>
+class Array;
 
 template<typename T>
-void anisotropicDiffusion(Array<T>& inout, const float dt,
-                          const float mct, const af::fluxFunction fftype,
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
                           const af::diffusionEq eq);
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 5ff4038f1a..8ca9f0c656 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -12,113 +12,92 @@
 #include <platform.hpp>
 #include <af/dim4.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename Ty, typename Tp>
-void approx1(Array<Ty> &yo, const Array<Ty> &yi,
-             const Array<Tp> &xo, const int xdim,
-             const Tp &xi_beg, const Tp &xi_step,
-             const af_interp_type method, const float offGrid)
-{
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid) {
     yi.eval();
     xo.eval();
 
-    switch(method) {
-    case AF_INTERP_NEAREST:
-    case AF_INTERP_LOWER:
-        getQueue().enqueue(kernel::approx1<Ty, Tp, 1>,
-                           yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
-        break;
-    case AF_INTERP_LINEAR:
-    case AF_INTERP_LINEAR_COSINE:
-        getQueue().enqueue(kernel::approx1<Ty, Tp, 2>,
-                           yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
-        break;
-    case AF_INTERP_CUBIC:
-    case AF_INTERP_CUBIC_SPLINE:
-        getQueue().enqueue(kernel::approx1<Ty, Tp, 3>,
-                           yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
-        break;
-    default:
-        break;
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            getQueue().enqueue(kernel::approx1<Ty, Tp, 1>, yo, yi, xo, xdim,
+                               xi_beg, xi_step, offGrid, method);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+            getQueue().enqueue(kernel::approx1<Ty, Tp, 2>, yo, yi, xo, xdim,
+                               xi_beg, xi_step, offGrid, method);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+            getQueue().enqueue(kernel::approx1<Ty, Tp, 3>, yo, yi, xo, xdim,
+                               xi_beg, xi_step, offGrid, method);
+            break;
+        default: break;
     }
 }
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi,
-                  const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                  const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid)
-{
+Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
+                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                  const af_interp_type method, const float offGrid) {
     zi.eval();
     xo.eval();
     yo.eval();
 
-    dim4 odims = zi.dims();
+    dim4 odims  = zi.dims();
     odims[xdim] = xo.dims()[xdim];
     odims[ydim] = xo.dims()[ydim];
 
     Array<Ty> zo = createEmptyArray<Ty>(odims);
 
-    switch(method) {
-    case AF_INTERP_NEAREST:
-    case AF_INTERP_LOWER:
-        getQueue().enqueue(kernel::approx2<Ty, Tp, 1>,
-                           zo, zi,
-                           xo, xdim, xi_beg, xi_step,
-                           yo, ydim, yi_beg, yi_step, offGrid, method);
-        break;
-    case AF_INTERP_LINEAR:
-    case AF_INTERP_BILINEAR:
-    case AF_INTERP_LINEAR_COSINE:
-    case AF_INTERP_BILINEAR_COSINE:
-        getQueue().enqueue(kernel::approx2<Ty, Tp, 2>,
-                           zo, zi,
-                           xo, xdim, xi_beg, xi_step,
-                           yo, ydim, yi_beg, yi_step,
-                           offGrid, method);
-        break;
-    case AF_INTERP_CUBIC:
-    case AF_INTERP_BICUBIC:
-    case AF_INTERP_CUBIC_SPLINE:
-    case AF_INTERP_BICUBIC_SPLINE:
-        getQueue().enqueue(kernel::approx2<Ty, Tp, 3>,
-                           zo, zi,
-                           xo, xdim, xi_beg, xi_step,
-                           yo, ydim, yi_beg, yi_step,
-                           offGrid, method);
-        break;
-    default:
-        break;
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            getQueue().enqueue(kernel::approx2<Ty, Tp, 1>, zo, zi, xo, xdim,
+                               xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+                               offGrid, method);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+        case AF_INTERP_BILINEAR_COSINE:
+            getQueue().enqueue(kernel::approx2<Ty, Tp, 2>, zo, zi, xo, xdim,
+                               xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+                               offGrid, method);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+        case AF_INTERP_BICUBIC_SPLINE:
+            getQueue().enqueue(kernel::approx2<Ty, Tp, 3>, zo, zi, xo, xdim,
+                               xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+                               offGrid, method);
+            break;
+        default: break;
     }
     return zo;
 }
 
-#define INSTANTIATE(Ty, Tp)                                         \
-    template void approx1<Ty, Tp>(Array<Ty> &yo,                    \
-                                  const Array<Ty> &yi,              \
-                                  const Array<Tp> &xo,              \
-                                  const int xdim,                   \
-                                  const Tp &xi_beg,                 \
-                                  const Tp &xi_step,                \
-                                  const af_interp_type method,      \
-                                  const float offGrid);             \
-    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
-                                       const Array<Tp> &xo,         \
-                                       const int xdim,              \
-                                       const Tp &xi_beg,            \
-                                       const Tp &xi_step,           \
-                                       const Array<Tp> &yo,         \
-                                       const int ydim,              \
-                                       const Tp &yi_beg,            \
-                                       const Tp &yi_step,           \
-                                       const af_interp_type method, \
-                                       const float offGrid);        \
+#define INSTANTIATE(Ty, Tp)                                       \
+    template void approx1<Ty, Tp>(                                \
+        Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const af_interp_type method, const float offGrid);        \
+    template Array<Ty> approx2<Ty, Tp>(                           \
+        const Array<Ty> &zi, const Array<Tp> &xo, const int xdim, \
+        const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo, \
+        const int ydim, const Tp &yi_beg, const Tp &yi_step,      \
+        const af_interp_type method, const float offGrid);
 
-INSTANTIATE(float  , float )
-INSTANTIATE(double , double)
-INSTANTIATE(cfloat , float )
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index 1bc134463b..49a67e39d0 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -10,17 +10,15 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
-namespace cpu
-{
-    template<typename Ty, typename Tp>
-    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
-                 const Array<Tp> &xo, const int xdim,
-                 const Tp &xi_beg, const Tp &xi_step,
-                 const af_interp_type method, const float offGrid);
+namespace cpu {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid);
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &zi,
-                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                      const af_interp_type method, const float offGrid);
-}
+template<typename Ty, typename Tp>
+Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
+                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                  const af_interp_type method, const float offGrid);
+}  // namespace cpu
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 780a776955..dc40eeb228 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -7,31 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <err_cpu.hpp>
-#include <cmath>
 #include <jit/BinaryNode.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
+#include <cmath>
 
-namespace cpu
-{
-
-#define ARITH_FN(OP, op)                        \
-    template<typename T>                        \
-    struct BinOp<T, T, OP>                      \
-    {                                           \
-        void eval(jit::array<T> &out,           \
-                  const jit::array<T> &lhs,     \
-                  const jit::array<T> &rhs,     \
-                  int lim) const                \
-        {                                       \
-            for (int i = 0; i < lim; i++) {     \
-                out[i] = lhs[i] op rhs[i];      \
-            }                                   \
-        }                                       \
-    };                                          \
+namespace cpu {
 
+#define ARITH_FN(OP, op)                                                 \
+    template<typename T>                                                 \
+    struct BinOp<T, T, OP> {                                             \
+        void eval(jit::array<T> &out, const jit::array<T> &lhs,          \
+                  const jit::array<T> &rhs, int lim) const {             \
+            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
+        }                                                                \
+    };
 
 ARITH_FN(af_add_t, +)
 ARITH_FN(af_sub_t, -)
@@ -40,34 +32,42 @@ ARITH_FN(af_div_t, /)
 
 #undef ARITH_FN
 
-template<typename T> static T __mod(T lhs, T rhs)
-{
+template<typename T>
+static T __mod(T lhs, T rhs) {
     T res = lhs % rhs;
     return (res < 0) ? abs(rhs - res) : res;
 }
 
-template<typename T> static T __rem(T lhs, T rhs) { return lhs % rhs; }
-
-template<> STATIC_ float __mod<float>(float lhs, float rhs) { return fmod(lhs, rhs); }
-template<> STATIC_ double __mod<double>(double lhs, double rhs) { return fmod(lhs, rhs); }
-template<> STATIC_ float __rem<float>(float lhs, float rhs) { return remainder(lhs, rhs); }
-template<> STATIC_ double __rem<double>(double lhs, double rhs) { return remainder(lhs, rhs); }
+template<typename T>
+static T __rem(T lhs, T rhs) {
+    return lhs % rhs;
+}
 
+template<>
+STATIC_ float __mod<float>(float lhs, float rhs) {
+    return fmod(lhs, rhs);
+}
+template<>
+STATIC_ double __mod<double>(double lhs, double rhs) {
+    return fmod(lhs, rhs);
+}
+template<>
+STATIC_ float __rem<float>(float lhs, float rhs) {
+    return remainder(lhs, rhs);
+}
+template<>
+STATIC_ double __rem<double>(double lhs, double rhs) {
+    return remainder(lhs, rhs);
+}
 
-#define NUMERIC_FN(OP, FN)                      \
-    template<typename T>                        \
-    struct BinOp<T, T, OP>                      \
-    {                                           \
-        void eval(jit::array<T> &out,           \
-                  const jit::array<T> &lhs,     \
-                  const jit::array<T> &rhs,     \
-                  int lim)                      \
-        {                                       \
-            for (int i = 0; i < lim; i++) {     \
-                out[i] = FN(lhs[i] , rhs[i]);   \
-            }                                   \
-        }                                       \
-    };                                          \
+#define NUMERIC_FN(OP, FN)                                                 \
+    template<typename T>                                                   \
+    struct BinOp<T, T, OP> {                                               \
+        void eval(jit::array<T> &out, const jit::array<T> &lhs,            \
+                  const jit::array<T> &rhs, int lim) {                     \
+            for (int i = 0; i < lim; i++) { out[i] = FN(lhs[i], rhs[i]); } \
+        }                                                                  \
+    };
 
 NUMERIC_FN(af_max_t, max)
 NUMERIC_FN(af_min_t, min)
@@ -78,14 +78,15 @@ NUMERIC_FN(af_atan2_t, atan2)
 NUMERIC_FN(af_hypot_t, hypot)
 
 template<typename T, af_op_t op>
-Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-{
+Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
+                 const af::dim4 &odims) {
     jit::Node_ptr lhs_node = lhs.getNode();
     jit::Node_ptr rhs_node = rhs.getNode();
 
-    jit::BinaryNode<T, T, op> *node = new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
+    jit::BinaryNode<T, T, op> *node =
+        new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
     return createNodeArray<T>(odims, jit::Node_ptr(node));
 }
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index 294b4397d5..ccee91957c 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/assign.hpp>
 #include <assign.hpp>
+#include <kernel/assign.hpp>
 
 #include <Array.hpp>
 #include <Param.hpp>
@@ -27,27 +27,23 @@
 using af::dim4;
 using std::vector;
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
-void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
-{
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
     out.eval();
     rhs.eval();
 
     vector<bool> isSeq(4);
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output dimensions, offsets & offsets
-    for (dim_t x=0; x<4; ++x) {
-        if (idxrs[x].isSeq) {
-            seqs[x] = idxrs[x].idx.seq;
-        }
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
         isSeq[x] = idxrs[x].isSeq;
     }
 
-    vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
             idxArrs[x].eval();
@@ -59,20 +55,21 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
                        move(isSeq), move(seqs), move(idxParams));
 }
 
-#define INSTANTIATE(T) \
-    template void assign<T>(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
+#define INSTANTIATE(T)                                                \
+    template void assign<T>(Array<T> & out, const af_index_t idxrs[], \
+                            const Array<T>& rhs);
 
 INSTANTIATE(cdouble)
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
-INSTANTIATE(float  )
-INSTANTIATE(uintl  )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(int    )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(ushort )
-INSTANTIATE(short  )
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(uintl)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(int)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/assign.hpp b/src/backend/cpu/assign.hpp
index 77dea299f7..8a9536c14d 100644
--- a/src/backend/cpu/assign.hpp
+++ b/src/backend/cpu/assign.hpp
@@ -9,11 +9,11 @@
 
 #include <af/index.h>
 
-namespace cpu
-{
-template <typename T> class Array;
+namespace cpu {
+template<typename T>
+class Array;
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index ff12ad1dbc..53629e9b9e 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -17,30 +17,32 @@
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
-{
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
+                         const float &c_sigma) {
     in.eval();
-    const dim4 dims     = in.dims();
+    const dim4 dims    = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
-    getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in, s_sigma, c_sigma);
+    getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in,
+                       s_sigma, c_sigma);
     return out;
 }
 
-#define INSTANTIATE(inT, outT)\
-template Array<outT> bilateral<inT, outT,true >(const Array<inT> &in, const float &s_sigma, const float &c_sigma);\
-template Array<outT> bilateral<inT, outT,false>(const Array<inT> &in, const float &s_sigma, const float &c_sigma);
+#define INSTANTIATE(inT, outT)                                             \
+    template Array<outT> bilateral<inT, outT, true>(                       \
+        const Array<inT> &in, const float &s_sigma, const float &c_sigma); \
+    template Array<outT> bilateral<inT, outT, false>(                      \
+        const Array<inT> &in, const float &s_sigma, const float &c_sigma);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float ,  float)
-INSTANTIATE(char  ,  float)
-INSTANTIATE(int   ,  float)
-INSTANTIATE(uint  ,  float)
-INSTANTIATE(uchar ,  float)
-INSTANTIATE(short ,  float)
-INSTANTIATE(ushort,  float)
-
-}
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/bilateral.hpp b/src/backend/cpu/bilateral.hpp
index 51542a7c59..57e9d15f13 100644
--- a/src/backend/cpu/bilateral.hpp
+++ b/src/backend/cpu/bilateral.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma);
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
+                         const float &c_sigma);
 
 }
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 31ba8ffa7e..cfcbcdcb44 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -16,8 +16,8 @@
 #include <Array.hpp>
 #include <Param.hpp>
 #include <common/blas_headers.hpp>
-#include <common/err_common.hpp>
 #include <common/complex.hpp>
+#include <common/err_common.hpp>
 #include <kernel/dot.hpp>
 #include <platform.hpp>
 #include <types.hpp>
@@ -26,28 +26,28 @@
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 
-
 #include <algorithm>
 #include <type_traits>
 #include <vector>
 
 using std::vector;
 
-namespace cpu
-{
+namespace cpu {
 
 using af::dtype_traits;
 
 using std::add_const;
 using std::add_pointer;
+using std::conditional;
 using std::enable_if;
 using std::is_floating_point;
 using std::remove_const;
-using std::conditional;
 
 using common::is_complex;
 
-// Some implementations of BLAS require void* for complex pointers while others use float*/double*
+// clang-format off
+// Some implementations of BLAS require void* for complex pointers while others
+// use float*/double*
 //
 // Sample cgemm API
 // OpenBLAS
@@ -96,115 +96,114 @@ using common::is_complex;
 //                  const void *alpha, const void *A, const int lda,
 //                  const void *B, const int ldb, const void *beta,
 //                  void *C, const int ldc);
+// clang-format on
 
 template<typename T>
 struct blas_base {
-    using type = typename conditional<is_complex<T>::value && cplx_void_ptr,
-                                      void,
-                                      typename dtype_traits<T>::base_type>::type;
+    using type =
+        typename conditional<is_complex<T>::value && cplx_void_ptr, void,
+                             typename dtype_traits<T>::base_type>::type;
 };
 
 template<typename T>
-using cptr_type   =   typename conditional<is_complex<T>::value,
-                                           const typename blas_base<T>::type *,
-                                           const T*>::type;
+using cptr_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type *, const T *>::type;
 template<typename T>
-using ptr_type   =    typename conditional<is_complex<T>::value,
-                                           typename blas_base<T>::type *,
-                                           T*>::type;
+using ptr_type = typename conditional<is_complex<T>::value,
+                                      typename blas_base<T>::type *, T *>::type;
 template<typename T>
-using scale_type =    typename conditional<is_complex<T>::value,
-                                           const typename blas_base<T>::type *,
-                                           const T>::type;
+using scale_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type *, const T>::type;
 
 template<typename T>
-using batch_scale_type = typename conditional<is_complex<T>::value,
-                                              const typename blas_base<T>::type*,
-                                              const T*>::type;
+using batch_scale_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type *, const T *>::type;
 
 template<typename T>
-using gemm_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE,
-                                const CBLAS_TRANSPOSE,
-                                const blasint, const blasint, const blasint,
-                                scale_type<T>, cptr_type<T>, const blasint,
-                                cptr_type<T>, const blasint,
-                                scale_type<T>, ptr_type<T>, const blasint);
+using gemm_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
+                               const CBLAS_TRANSPOSE, const blasint,
+                               const blasint, const blasint, scale_type<T>,
+                               cptr_type<T>, const blasint, cptr_type<T>,
+                               const blasint, scale_type<T>, ptr_type<T>,
+                               const blasint);
 
 template<typename T>
-using gemv_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE,
-                                const blasint, const blasint,
-                                scale_type<T>, cptr_type<T>, const blasint,
-                                cptr_type<T>, const blasint,
-                                scale_type<T>, ptr_type<T>, const blasint);
+using gemv_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
+                               const blasint, const blasint, scale_type<T>,
+                               cptr_type<T>, const blasint, cptr_type<T>,
+                               const blasint, scale_type<T>, ptr_type<T>,
+                               const blasint);
 
 #ifdef USE_MKL
 template<typename T>
-using gemm_batch_func_def = void (*)( const CBLAS_LAYOUT,
-                                      const CBLAS_TRANSPOSE*,
-                                      const CBLAS_TRANSPOSE*,
-                                      const MKL_INT*, const MKL_INT*, const MKL_INT*,
-                                      batch_scale_type<T>, cptr_type<T>*, const MKL_INT*,
-                                      cptr_type<T>*, const MKL_INT*, batch_scale_type<T>,
-                                      ptr_type<T>*, const MKL_INT*,
-                                      const MKL_INT, const MKL_INT*);
+using gemm_batch_func_def = void (*)(
+    const CBLAS_LAYOUT, const CBLAS_TRANSPOSE *, const CBLAS_TRANSPOSE *,
+    const MKL_INT *, const MKL_INT *, const MKL_INT *, batch_scale_type<T>,
+    cptr_type<T> *, const MKL_INT *, cptr_type<T> *, const MKL_INT *,
+    batch_scale_type<T>, ptr_type<T> *, const MKL_INT *, const MKL_INT,
+    const MKL_INT *);
 #endif
 
-#define BLAS_FUNC_DEF( FUNC )                           \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+#define BLAS_FUNC_DEF(FUNC) \
+    template<typename T>    \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define BLAS_FUNC( FUNC, TYPE, PREFIX )                 \
-  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &cblas_##PREFIX##FUNC; }
+#define BLAS_FUNC(FUNC, TYPE, PREFIX)           \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &cblas_##PREFIX##FUNC;           \
+    }
 
-BLAS_FUNC_DEF( gemm )
-BLAS_FUNC(gemm , float   , s)
-BLAS_FUNC(gemm , double  , d)
-BLAS_FUNC(gemm , cfloat  , c)
-BLAS_FUNC(gemm , cdouble , z)
+BLAS_FUNC_DEF(gemm)
+BLAS_FUNC(gemm, float, s)
+BLAS_FUNC(gemm, double, d)
+BLAS_FUNC(gemm, cfloat, c)
+BLAS_FUNC(gemm, cdouble, z)
 
 BLAS_FUNC_DEF(gemv)
-BLAS_FUNC(gemv , float   , s)
-BLAS_FUNC(gemv , double  , d)
-BLAS_FUNC(gemv , cfloat  , c)
-BLAS_FUNC(gemv , cdouble , z)
+BLAS_FUNC(gemv, float, s)
+BLAS_FUNC(gemv, double, d)
+BLAS_FUNC(gemv, cfloat, c)
+BLAS_FUNC(gemv, cdouble, z)
 
 #ifdef USE_MKL
-BLAS_FUNC_DEF( gemm_batch )
-BLAS_FUNC(gemm_batch , float   , s)
-BLAS_FUNC(gemm_batch , double  , d)
-BLAS_FUNC(gemm_batch , cfloat  , c)
-BLAS_FUNC(gemm_batch , cdouble , z)
+BLAS_FUNC_DEF(gemm_batch)
+BLAS_FUNC(gemm_batch, float, s)
+BLAS_FUNC(gemm_batch, double, d)
+BLAS_FUNC(gemm_batch, cfloat, c)
+BLAS_FUNC(gemm_batch, cdouble, z)
 #endif
 
 template<typename T, int value>
 typename enable_if<is_floating_point<T>::value, scale_type<T>>::type
-getScale() { return T(value); }
+getScale() {
+    return T(value);
+}
 
 template<typename T, int value>
-typename enable_if<is_complex<T>::value, scale_type<T>>::type
-getScale()
-{
+typename enable_if<is_complex<T>::value, scale_type<T>>::type getScale() {
     static T val(value);
     return (const typename blas_base<T>::type *)&val;
 }
 
 CBLAS_TRANSPOSE
-toCblasTranspose(af_mat_prop opt)
-{
+toCblasTranspose(af_mat_prop opt) {
     CBLAS_TRANSPOSE out = CblasNoTrans;
-    switch(opt) {
-        case AF_MAT_NONE        : out = CblasNoTrans;   break;
-        case AF_MAT_TRANS       : out = CblasTrans;     break;
-        case AF_MAT_CTRANS      : out = CblasConjTrans; break;
-        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    switch (opt) {
+        case AF_MAT_NONE: out = CblasNoTrans; break;
+        case AF_MAT_TRANS: out = CblasTrans; break;
+        case AF_MAT_CTRANS: out = CblasConjTrans; break;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
 }
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
     lhs.eval();
     rhs.eval();
 
@@ -217,11 +216,11 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
     auto lDims = lhs.dims();
     auto rDims = rhs.dims();
-    int M = lDims[aRowDim];
-    int N = rDims[bColDim];
-    int K = lDims[aColDim];
+    int M      = lDims[aRowDim];
+    int N      = rDims[bColDim];
+    int K      = lDims[aColDim];
 
-    using BT  =       typename blas_base<T>::type;
+    using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
     dim_t d2 = std::max(lDims[2], rDims[2]);
@@ -229,7 +228,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     const dim4 oDims(M, N, d2, d3);
     Array<T> out = createEmptyArray<T>(oDims);
 
-    auto func = [=] (Param<T> output, CParam<T> left, CParam<T> right) {
+    auto func = [=](Param<T> output, CParam<T> left, CParam<T> right) {
         auto alpha = getScale<T, 1>();
         auto beta  = getScale<T, 0>();
 
@@ -239,14 +238,15 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
         if (oDims.ndims() <= 2) {
             if (rDims[bColDim] == 1) {
-                dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                dim_t incr =
+                    (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
                 gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
-                        left.get(), lStrides[1], right.get(), incr, beta,
-                        output.get(), 1);
+                               left.get(), lStrides[1], right.get(), incr, beta,
+                               output.get(), 1);
             } else {
                 gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
-                        left.get(), lStrides[1], right.get(), rStrides[1], beta,
-                        output.get(), output.dims(0));
+                               left.get(), lStrides[1], right.get(),
+                               rStrides[1], beta, output.get(), output.dims(0));
             }
         } else {
             int batchSize = oDims[2] * oDims[3];
@@ -256,20 +256,23 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
             const bool is_r_d2_batched = oDims[2] == rDims[2];
             const bool is_r_d3_batched = oDims[3] == rDims[3];
 
-            vector< CBT* > lptrs(batchSize);
-            vector< CBT* > rptrs(batchSize);
-            vector<  BT* > optrs(batchSize);
+            vector<CBT *> lptrs(batchSize);
+            vector<CBT *> rptrs(batchSize);
+            vector<BT *> optrs(batchSize);
 
             for (int n = 0; n < batchSize; n++) {
                 int w = n / oDims[2];
                 int z = n - w * oDims[2];
 
-                int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
-                int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+                int loff = z * (is_l_d2_batched * lStrides[2]) +
+                           w * (is_l_d3_batched * lStrides[3]);
+                int roff = z * (is_r_d2_batched * rStrides[2]) +
+                           w * (is_r_d3_batched * rStrides[3]);
 
-                lptrs[n] = reinterpret_cast<CBT*>(left.get() + loff);
-                rptrs[n] = reinterpret_cast<CBT*>(right.get() + roff);
-                optrs[n] = reinterpret_cast<BT*>(output.get() + z * oStrides[2] + w * oStrides[3]);
+                lptrs[n] = reinterpret_cast<CBT *>(left.get() + loff);
+                rptrs[n] = reinterpret_cast<CBT *>(right.get() + roff);
+                optrs[n] = reinterpret_cast<BT *>(
+                    output.get() + z * oStrides[2] + w * oStrides[3]);
             }
 
 #ifdef USE_MKL
@@ -280,18 +283,20 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
             const MKL_INT ldc = oStrides[1];
 
             gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts, &M, &N, &K,
-                    &alpha, lptrs.data(), &lda, rptrs.data(), &ldb, &beta,
-                    optrs.data(), &ldc, 1, &batchSize);
+                                 &alpha, lptrs.data(), &lda, rptrs.data(), &ldb,
+                                 &beta, optrs.data(), &ldc, 1, &batchSize);
 #else
             for (int n = 0; n < batchSize; n++) {
-                if(rDims[bColDim] == 1) {
-                    dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                    gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
-                            lptrs[n], lStrides[1], rptrs[n], incr, beta, optrs[n], 1);
+                if (rDims[bColDim] == 1) {
+                    dim_t incr =
+                        (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                    gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1],
+                                   alpha, lptrs[n], lStrides[1], rptrs[n], incr,
+                                   beta, optrs[n], 1);
                 } else {
                     gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
-                            lptrs[n], lStrides[1], rptrs[n], rStrides[1], beta,
-                            optrs[n], output.dims(0));
+                                   lptrs[n], lStrides[1], rptrs[n], rStrides[1],
+                                   beta, optrs[n], output.dims(0));
                 }
             }
 #endif
@@ -303,21 +308,24 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 }
 
 template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs) {
     lhs.eval();
     rhs.eval();
 
     Array<T> out = createEmptyArray<T>(af::dim4(1));
-    if(optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
-        getQueue().enqueue(kernel::dot<T, false, true>, out, lhs, rhs, optLhs, optRhs);
+    if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
+        getQueue().enqueue(kernel::dot<T, false, true>, out, lhs, rhs, optLhs,
+                           optRhs);
     } else if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_NONE) {
-        getQueue().enqueue(kernel::dot<T, true, false>,out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, true, false>, out, lhs, rhs, optLhs,
+                           optRhs);
     } else if (optLhs == AF_MAT_NONE && optRhs == AF_MAT_CONJ) {
-        getQueue().enqueue(kernel::dot<T, true, false>,out, rhs, lhs, optRhs, optLhs);
+        getQueue().enqueue(kernel::dot<T, true, false>, out, rhs, lhs, optRhs,
+                           optLhs);
     } else {
-        getQueue().enqueue(kernel::dot<T, false, false>,out, lhs, rhs, optLhs, optRhs);
+        getQueue().enqueue(kernel::dot<T, false, false>, out, lhs, rhs, optLhs,
+                           optRhs);
     }
     return out;
 }
@@ -325,8 +333,9 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
 #undef BT
 #undef REINTEPRET_CAST
 
-#define INSTANTIATE_BLAS(TYPE)                                                          \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
+#define INSTANTIATE_BLAS(TYPE)                                \
+    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
+                                      const Array<TYPE> &rhs, \
                                       af_mat_prop optLhs, af_mat_prop optRhs);
 
 INSTANTIATE_BLAS(float)
@@ -334,13 +343,14 @@ INSTANTIATE_BLAS(cfloat)
 INSTANTIATE_BLAS(double)
 INSTANTIATE_BLAS(cdouble)
 
-#define INSTANTIATE_DOT(TYPE)                                                               \
-    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,          \
-                                   af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_DOT(TYPE)                                                  \
+    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
+                                   const Array<TYPE> &rhs, af_mat_prop optLhs, \
+                                   af_mat_prop optRhs);
 
 INSTANTIATE_DOT(float)
 INSTANTIATE_DOT(double)
 INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 3a6f4a7e4f..5a85e3cbc2 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -11,14 +11,13 @@
 
 #include <af/defines.h>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs);
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs);
 template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs);
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/canny.cpp b/src/backend/cpu/canny.cpp
index 0e14fc67ca..830cda2601 100644
--- a/src/backend/cpu/canny.cpp
+++ b/src/backend/cpu/canny.cpp
@@ -15,11 +15,10 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-namespace cpu
-{
+namespace cpu {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
-                                   const Array<float>& gx, const Array<float>& gy)
-{
+                                   const Array<float>& gx,
+                                   const Array<float>& gy) {
     mag.eval();
     gx.eval();
     gy.eval();
@@ -32,8 +31,8 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
     return out;
 }
 
-Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak)
-{
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak) {
     strong.eval();
     weak.eval();
 
@@ -44,4 +43,4 @@ Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char
 
     return out;
 }
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/canny.hpp b/src/backend/cpu/canny.hpp
index 0d2ce29e0d..e2910fd2a1 100644
--- a/src/backend/cpu/canny.hpp
+++ b/src/backend/cpu/canny.hpp
@@ -9,10 +9,11 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
-                                   const Array<float>& gx, const Array<float>& gy);
+                                   const Array<float>& gx,
+                                   const Array<float>& gy);
 
-Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak);
-}
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak);
+}  // namespace cpu
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 7b8aea2715..6b5fa0fd0a 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -8,101 +8,71 @@
  ********************************************************/
 
 #pragma once
-#include <af/dim4.hpp>
-#include <complex>
+#include <Array.hpp>
 #include <err_cpu.hpp>
+#include <jit/UnaryNode.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
 #include <types.hpp>
-#include <jit/UnaryNode.hpp>
-#include <Array.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename To, typename Ti>
-struct UnOp<To, Ti, af_cast_t>
-{
-    void eval(jit::array<To> &out,
-              const jit::array<Ti> &in, int lim)
-    {
-        for (int i = 0; i < lim; i++) {
-            out[i] = To(in[i]);
-        }
+struct UnOp<To, Ti, af_cast_t> {
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(in[i]); }
     }
 };
 
 template<typename To>
-struct UnOp<To, std::complex<float>, af_cast_t>
-{
+struct UnOp<To, std::complex<float>, af_cast_t> {
     typedef std::complex<float> Ti;
-    void eval(jit::array<To> &out,
-              const jit::array<Ti> &in, int lim)
-    {
-        for (int i = 0; i < lim; i++) {
-            out[i] = To(std::abs(in[i]));
-        }
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(std::abs(in[i])); }
     }
 };
 
 template<typename To>
-struct UnOp<To, std::complex<double>, af_cast_t>
-{
+struct UnOp<To, std::complex<double>, af_cast_t> {
     typedef std::complex<double> Ti;
-    void eval(jit::array<To> &out,
-              const jit::array<Ti> &in, int lim)
-    {
-        for (int i = 0; i < lim; i++) {
-            out[i] = To(std::abs(in[i]));
-        }
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(std::abs(in[i])); }
     }
 };
 
 // DO NOT REMOVE THE TWO SPECIALIZATIONS BELOW
-// These specializations are required because we partially specialize when Ti = std::complex<T>
-// The partial specializations above expect output to be real.
-// so they To(std::abs(v)) instead of To(v) which results in incorrect values when To is complex.
+// These specializations are required because we partially specialize when Ti =
+// std::complex<T> The partial specializations above expect output to be real.
+// so they To(std::abs(v)) instead of To(v) which results in incorrect values
+// when To is complex.
 
 template<>
-struct UnOp<std::complex<float>, std::complex<double>, af_cast_t>
-{
+struct UnOp<std::complex<float>, std::complex<double>, af_cast_t> {
     typedef std::complex<double> Ti;
     typedef std::complex<float> To;
-    void eval(jit::array<To> &out,
-              const jit::array<Ti> &in, int lim)
-    {
-        for (int i = 0; i < lim; i++) {
-            out[i] = To(in[i]);
-        }
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(in[i]); }
     }
 };
 
 template<>
-struct UnOp<std::complex<double>, std::complex<float>, af_cast_t>
-{
+struct UnOp<std::complex<double>, std::complex<float>, af_cast_t> {
     typedef std::complex<float> Ti;
     typedef std::complex<double> To;
-    void eval(jit::array<To> &out,
-              const jit::array<Ti> &in, int lim)
-    {
-        for (int i = 0; i < lim; i++) {
-            out[i] = To(in[i]);
-        }
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(in[i]); }
     }
 };
 
-#define CAST_B8(T)                                      \
-    template<>                                          \
-    struct UnOp<char, T, af_cast_t>                     \
-    {                                                   \
-        void eval(jit::array<char> &out,                \
-                  const jit::array<T> &in, int lim)     \
-        {                                               \
-            for (int i = 0; i < lim; i++) {             \
-                out[i] = char(in[i] != 0);              \
-            }                                           \
-        }                                               \
-    };                                                  \
+#define CAST_B8(T)                                                           \
+    template<>                                                               \
+    struct UnOp<char, T, af_cast_t> {                                        \
+        void eval(jit::array<char> &out, const jit::array<T> &in, int lim) { \
+            for (int i = 0; i < lim; i++) { out[i] = char(in[i] != 0); }     \
+        }                                                                    \
+    };
 
 CAST_B8(float)
 CAST_B8(double)
@@ -111,31 +81,25 @@ CAST_B8(uchar)
 CAST_B8(char)
 
 template<typename To, typename Ti>
-struct CastWrapper
-{
-    Array<To> operator()(const Array<Ti> &in)
-    {
+struct CastWrapper {
+    Array<To> operator()(const Array<Ti> &in) {
         jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<To, Ti, af_cast_t> *node = new jit::UnaryNode<To, Ti, af_cast_t>(in_node);
-        return createNodeArray<To>(in.dims(), jit::Node_ptr(
-                                       reinterpret_cast<jit::Node *>(node)));
+        jit::UnaryNode<To, Ti, af_cast_t> *node =
+            new jit::UnaryNode<To, Ti, af_cast_t>(in_node);
+        return createNodeArray<To>(
+            in.dims(), jit::Node_ptr(reinterpret_cast<jit::Node *>(node)));
     }
 };
 
 template<typename T>
-struct CastWrapper<T, T>
-{
-    Array<T> operator()(const Array<T> &in)
-    {
-        return in;
-    }
+struct CastWrapper<T, T> {
+    Array<T> operator()(const Array<T> &in) { return in; }
 };
 
 template<typename To, typename Ti>
-Array<To> cast(const Array<Ti> &in)
-{
+Array<To> cast(const Array<Ti> &in) {
     CastWrapper<To, Ti> cast_op;
     return cast_op(in);
 }
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 7ba6eea0e5..a19a4afc37 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -16,63 +16,62 @@
 #include <copy.hpp>
 #include <types.hpp>
 
-#include <af/dim4.hpp>
-#include <triangle.hpp>
 #include <lapack_helper.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <triangle.hpp>
+#include <af/dim4.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-using potrf_func_def = int (*)(ORDER_TYPE, char,
-                               int,
-                               T*, int);
-
-#define CH_FUNC_DEF( FUNC )                                     \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+using potrf_func_def = int (*)(ORDER_TYPE, char, int, T *, int);
 
+#define CH_FUNC_DEF(FUNC) \
+    template<typename T>  \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define CH_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
+#define CH_FUNC(FUNC, TYPE, PREFIX)             \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-CH_FUNC_DEF( potrf )
-CH_FUNC(potrf , float  , s)
-CH_FUNC(potrf , double , d)
-CH_FUNC(potrf , cfloat , c)
-CH_FUNC(potrf , cdouble, z)
+CH_FUNC_DEF(potrf)
+CH_FUNC(potrf, float, s)
+CH_FUNC(potrf, double, d)
+CH_FUNC(potrf, cfloat, c)
+CH_FUNC(potrf, cdouble, z)
 
 template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     in.eval();
 
     Array<T> out = copyArray<T>(in);
-    *info = cholesky_inplace(out, is_upper);
+    *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper) triangle<T, true , false>(out, out);
-    else          triangle<T, false, false>(out, out);
+    if (is_upper)
+        triangle<T, true, false>(out, out);
+    else
+        triangle<T, false, false>(out, out);
 
     return out;
 }
 
 template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
     in.eval();
 
     dim4 iDims = in.dims();
-    int N = iDims[0];
+    int N      = iDims[0];
 
     char uplo = 'L';
-    if(is_upper)
-        uplo = 'U';
+    if (is_upper) uplo = 'U';
 
-    int info = 0;
-    auto func = [&] (int *info, Param<T> in) {
-        *info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(), in.strides(1));
+    int info  = 0;
+    auto func = [&](int *info, Param<T> in) {
+        *info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, in.get(),
+                                in.strides(1));
     };
 
     getQueue().enqueue(func, &info, in);
@@ -82,45 +81,42 @@ int cholesky_inplace(Array<T> &in, const bool is_upper)
     return info;
 }
 
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
 
 INSTANTIATE_CH(float)
 INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
-}
+}  // namespace cpu
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
 
 INSTANTIATE_CH(float)
 INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
-}
+}  // namespace cpu
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/cpu/cholesky.hpp b/src/backend/cpu/cholesky.hpp
index 002189a55f..9317718d72 100644
--- a/src/backend/cpu/cholesky.hpp
+++ b/src/backend/cpu/cholesky.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
+namespace cpu {
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 
-    template<typename T>
-    int cholesky_inplace(Array<T> &in, const bool is_upper);
-}
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper);
+}  // namespace cpu
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index a68e8ffbea..65e7a2e343 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -7,98 +7,86 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <err_cpu.hpp>
 #include <jit/BinaryNode.hpp>
 #include <jit/UnaryNode.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
-namespace cpu
-{
-
-    template<typename To, typename Ti>
-    struct BinOp<To, Ti, af_cplx2_t>
-    {
-        void eval(jit::array<To> &out,
-                  const jit::array<Ti> &lhs,
-                  const jit::array<Ti> &rhs,
-                  int lim)
-        {
-            for (int i = 0; i < lim; i++) {
-                out[i] = To(lhs[i], rhs[i]);
-            }
-        }
-    };
+namespace cpu {
 
-    template<typename To, typename Ti>
-    Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
-    {
-        jit::Node_ptr lhs_node = lhs.getNode();
-        jit::Node_ptr rhs_node = rhs.getNode();
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_cplx2_t> {
+    void eval(jit::array<To> &out, const jit::array<Ti> &lhs,
+              const jit::array<Ti> &rhs, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(lhs[i], rhs[i]); }
+    }
+};
 
-        jit::BinaryNode<To, Ti, af_cplx2_t> *node =
-            new jit::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
+template<typename To, typename Ti>
+Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
+               const af::dim4 &odims) {
+    jit::Node_ptr lhs_node = lhs.getNode();
+    jit::Node_ptr rhs_node = rhs.getNode();
 
-        return createNodeArray<To>(odims, jit::Node_ptr(node));
-    }
+    jit::BinaryNode<To, Ti, af_cplx2_t> *node =
+        new jit::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
 
-#define CPLX_UNARY_FN(op)                               \
-    template<typename To, typename Ti>                  \
-    struct UnOp<To, Ti, af_##op##_t>                    \
-    {                                                   \
-        void eval(jit::array<To> &out,                  \
-                  const jit::array<Ti> &in, int lim)    \
-        {                                               \
-            for (int i = 0; i < lim; i++) {             \
-                out[i] = std::op(in[i]);                \
-            }                                           \
-        }                                               \
-    };                                                  \
-
-    CPLX_UNARY_FN(real)
-    CPLX_UNARY_FN(imag)
-    CPLX_UNARY_FN(conj)
-    CPLX_UNARY_FN(abs)
-
-    template<typename To, typename Ti>
-    Array<To> real(const Array<Ti> &in)
-    {
-        jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<To, Ti, af_real_t> *node = new jit::UnaryNode<To, Ti, af_real_t>(in_node);
-
-        return createNodeArray<To>(in.dims(),
-                                   jit::Node_ptr(static_cast<jit::Node *>(node)));
-    }
+    return createNodeArray<To>(odims, jit::Node_ptr(node));
+}
 
-    template<typename To, typename Ti>
-    Array<To> imag(const Array<Ti> &in)
-    {
-        jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<To, Ti, af_imag_t> *node = new jit::UnaryNode<To, Ti, af_imag_t>(in_node);
+#define CPLX_UNARY_FN(op)                                                   \
+    template<typename To, typename Ti>                                      \
+    struct UnOp<To, Ti, af_##op##_t> {                                      \
+        void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) { \
+            for (int i = 0; i < lim; i++) { out[i] = std::op(in[i]); }      \
+        }                                                                   \
+    };
 
-        return createNodeArray<To>(in.dims(),
-                                   jit::Node_ptr(static_cast<jit::Node *>(node)));
-    }
+CPLX_UNARY_FN(real)
+CPLX_UNARY_FN(imag)
+CPLX_UNARY_FN(conj)
+CPLX_UNARY_FN(abs)
 
-    template<typename To, typename Ti>
-    Array<To> abs(const Array<Ti> &in)
-    {
-        jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<To, Ti, af_abs_t> *node = new jit::UnaryNode<To, Ti, af_abs_t>(in_node);
+template<typename To, typename Ti>
+Array<To> real(const Array<Ti> &in) {
+    jit::Node_ptr in_node = in.getNode();
+    jit::UnaryNode<To, Ti, af_real_t> *node =
+        new jit::UnaryNode<To, Ti, af_real_t>(in_node);
 
-        return createNodeArray<To>(in.dims(),
-                                   jit::Node_ptr(static_cast<jit::Node *>(node)));
-    }
+    return createNodeArray<To>(in.dims(),
+                               jit::Node_ptr(static_cast<jit::Node *>(node)));
+}
 
-    template<typename T>
-    Array<T> conj(const Array<T> &in)
-    {
-        jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<T, T, af_conj_t> *node = new jit::UnaryNode<T, T, af_conj_t>(in_node);
+template<typename To, typename Ti>
+Array<To> imag(const Array<Ti> &in) {
+    jit::Node_ptr in_node = in.getNode();
+    jit::UnaryNode<To, Ti, af_imag_t> *node =
+        new jit::UnaryNode<To, Ti, af_imag_t>(in_node);
 
-        return createNodeArray<T>(in.dims(),
-                                  jit::Node_ptr(static_cast<jit::Node *>(node)));
-    }
+    return createNodeArray<To>(in.dims(),
+                               jit::Node_ptr(static_cast<jit::Node *>(node)));
+}
+
+template<typename To, typename Ti>
+Array<To> abs(const Array<Ti> &in) {
+    jit::Node_ptr in_node = in.getNode();
+    jit::UnaryNode<To, Ti, af_abs_t> *node =
+        new jit::UnaryNode<To, Ti, af_abs_t>(in_node);
+
+    return createNodeArray<To>(in.dims(),
+                               jit::Node_ptr(static_cast<jit::Node *>(node)));
+}
+
+template<typename T>
+Array<T> conj(const Array<T> &in) {
+    jit::Node_ptr in_node = in.getNode();
+    jit::UnaryNode<T, T, af_conj_t> *node =
+        new jit::UnaryNode<T, T, af_conj_t>(in_node);
+
+    return createNodeArray<T>(in.dims(),
+                              jit::Node_ptr(static_cast<jit::Node *>(node)));
 }
+}  // namespace cpu
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 6dafadca9a..fdc6830931 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -18,45 +18,44 @@
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename accT, int baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
-{
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+                  AF_BATCH_KIND kind) {
     signal.eval();
     filter.eval();
 
-    auto sDims    = signal.dims();
-    auto fDims    = filter.dims();
+    auto sDims = signal.dims();
+    auto fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
-                oDims[d] = sDims[d]+fDims[d]-1;
+        for (dim_t d = 0; d < 4; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
+                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_RHS) {
-            for (dim_t i=baseDim; i<4; ++i)
-                oDims[i] = fDims[i];
+        if (kind == AF_BATCH_RHS) {
+            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::convolve_nd<T, accT, baseDim, expand>,out, signal, filter, kind);
+    getQueue().enqueue(kernel::convolve_nd<T, accT, baseDim, expand>, out,
+                       signal, filter, kind);
 
     return out;
 }
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter)
-{
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter) {
     signal.eval();
     c_filter.eval();
     r_filter.eval();
@@ -66,12 +65,13 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
     dim4 oDims = sDims;
 
     if (expand) {
-        auto cfDims   = c_filter.dims();
-        auto rfDims   = r_filter.dims();
+        auto cfDims = c_filter.dims();
+        auto rfDims = r_filter.dims();
 
         dim_t cflen = (dim_t)cfDims.elements();
         dim_t rflen = (dim_t)rfDims.elements();
-        // separable convolve only does AF_BATCH_NONE and standard batch(AF_BATCH_LHS)
+        // separable convolve only does AF_BATCH_NONE and standard
+        // batch(AF_BATCH_LHS)
         tDims[0] += cflen - 1;
         oDims[0] += cflen - 1;
         oDims[1] += rflen - 1;
@@ -80,32 +80,49 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
     Array<T> out  = createEmptyArray<T>(oDims);
     Array<T> temp = createEmptyArray<T>(tDims);
 
-    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal, c_filter, r_filter, temp);
+    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal,
+                       c_filter, r_filter, temp);
 
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                            \
-    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve2<T, accT, true >(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
+#define INSTANTIATE(T, accT)                                                 \
+    template Array<T> convolve<T, accT, 1, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 1, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve<T, accT, 2, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 2, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve<T, accT, 3, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 3, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve2<T, accT, true>(Array<T> const& signal,       \
+                                               Array<accT> const& c_filter,  \
+                                               Array<accT> const& r_filter); \
+    template Array<T> convolve2<T, accT, false>(Array<T> const& signal,      \
+                                                Array<accT> const& c_filter, \
+                                                Array<accT> const& r_filter);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
-
-}
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index cfbd8a0499..ba366c51e6 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -10,13 +10,14 @@
 #include <Array.hpp>
 #include <common/defines.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename accT, int baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+                  AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index d2651fb291..eae7901047 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -9,12 +9,12 @@
 
 #include <Array.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/complex.hpp>
 #include <copy.hpp>
 #include <err_cpu.hpp>
 #include <kernel/copy.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <common/complex.hpp>
 
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -24,27 +24,25 @@
 
 using common::is_complex;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void copyData(T *to, const Array<T> &from)
-{
+void copyData(T *to, const Array<T> &from) {
     from.eval();
     // Ensure all operations on 'from' are complete before copying data to host.
     getQueue().sync();
-    if(from.isLinear()) {
+    if (from.isLinear()) {
         // FIXME: Check for errors / exceptions
-        memcpy(to, from.get(), from.elements()*sizeof(T));
+        memcpy(to, from.get(), from.elements() * sizeof(T));
     } else {
         dim4 ostrides = calcStrides(from.dims());
-        kernel::stridedCopy<T>(to, ostrides, from.get(), from.dims(), from.strides(), from.ndims() - 1);
+        kernel::stridedCopy<T>(to, ostrides, from.get(), from.dims(),
+                               from.strides(), from.ndims() - 1);
     }
 }
 
 template<typename T>
-Array<T> copyArray(const Array<T> &A)
-{
+Array<T> copyArray(const Array<T> &A) {
     A.eval();
     Array<T> out = createEmptyArray<T>(A.dims());
     getQueue().enqueue(kernel::copy<T, T>, out, A);
@@ -52,85 +50,97 @@ Array<T> copyArray(const Array<T> &A)
 }
 
 template<typename inType, typename outType>
-void copyArray(Array<outType> &out, Array<inType> const &in)
-{
-    static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
-                  "Cannot copy from complex Array<T> to a non complex Array<T>");
+void copyArray(Array<outType> &out, Array<inType> const &in) {
+    static_assert(
+        !(is_complex<inType>::value && !is_complex<outType>::value),
+        "Cannot copy from complex Array<T> to a non complex Array<T>");
     out.eval();
     in.eval();
     getQueue().enqueue(kernel::copy<outType, inType>, out, in);
 }
 
-#define INSTANTIATE(T)                                                  \
-    template void      copyData<T> (T *data, const Array<T> &from);     \
-    template Array<T>  copyArray<T>(const Array<T> &A);                 \
+#define INSTANTIATE(T)                                         \
+    template void copyData<T>(T * data, const Array<T> &from); \
+    template Array<T> copyArray<T>(const Array<T> &A);
 
-INSTANTIATE(float  )
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
-
-#define INSTANTIATE_COPY_ARRAY(SRC_T)                                    \
-    template void copyArray<SRC_T, float  >(Array<float  > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, double >(Array<double > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, cfloat >(Array<cfloat > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, int    >(Array<int    > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, uint   >(Array<uint   > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, intl   >(Array<intl   > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, uintl  >(Array<uintl  > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, short  >(Array<short  > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, ushort >(Array<ushort > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, uchar  >(Array<uchar  > &dst, Array<SRC_T> const &src);  \
-    template void copyArray<SRC_T, char   >(Array<char   > &dst, Array<SRC_T> const &src);
-
-INSTANTIATE_COPY_ARRAY(float )
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+#define INSTANTIATE_COPY_ARRAY(SRC_T)                                 \
+    template void copyArray<SRC_T, float>(Array<float> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, double>(Array<double> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,     \
+                                            Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, int>(Array<int> & dst,             \
+                                        Array<SRC_T> const &src);     \
+    template void copyArray<SRC_T, uint>(Array<uint> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, intl>(Array<intl> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, short>(Array<short> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, char>(Array<char> & dst,           \
+                                         Array<SRC_T> const &src);
+
+INSTANTIATE_COPY_ARRAY(float)
 INSTANTIATE_COPY_ARRAY(double)
-INSTANTIATE_COPY_ARRAY(int   )
-INSTANTIATE_COPY_ARRAY(uint  )
-INSTANTIATE_COPY_ARRAY(intl  )
-INSTANTIATE_COPY_ARRAY(uintl )
-INSTANTIATE_COPY_ARRAY(uchar )
-INSTANTIATE_COPY_ARRAY(char  )
+INSTANTIATE_COPY_ARRAY(int)
+INSTANTIATE_COPY_ARRAY(uint)
+INSTANTIATE_COPY_ARRAY(intl)
+INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(uchar)
+INSTANTIATE_COPY_ARRAY(char)
 INSTANTIATE_COPY_ARRAY(ushort)
-INSTANTIATE_COPY_ARRAY(short )
+INSTANTIATE_COPY_ARRAY(short)
 
-#define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                            \
-    template void copyArray<SRC_T, cfloat  >(Array<cfloat  > &dst, Array<SRC_T> const &src);    \
-    template void copyArray<SRC_T, cdouble   >(Array<cdouble > &dst, Array<SRC_T> const &src);
+#define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                        \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,      \
+                                           Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,    \
+                                            Array<SRC_T> const &src);
 
-INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat )
+INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat)
 INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
-T getScalar(const Array<T> &in)
-{
+T getScalar(const Array<T> &in) {
     in.eval();
     getQueue().sync();
     return in.get()[0];
 }
 
-#define INSTANTIATE_GETSCALAR(T) \
-    template T getScalar(const Array<T> &in);
+#define INSTANTIATE_GETSCALAR(T) template T getScalar(const Array<T> &in);
 
-INSTANTIATE_GETSCALAR(float  )
-INSTANTIATE_GETSCALAR(double )
-INSTANTIATE_GETSCALAR(cfloat )
+INSTANTIATE_GETSCALAR(float)
+INSTANTIATE_GETSCALAR(double)
+INSTANTIATE_GETSCALAR(cfloat)
 INSTANTIATE_GETSCALAR(cdouble)
-INSTANTIATE_GETSCALAR(int    )
-INSTANTIATE_GETSCALAR(uint   )
-INSTANTIATE_GETSCALAR(uchar  )
-INSTANTIATE_GETSCALAR(char   )
-INSTANTIATE_GETSCALAR(intl   )
-INSTANTIATE_GETSCALAR(uintl  )
-INSTANTIATE_GETSCALAR(short  )
-INSTANTIATE_GETSCALAR(ushort )
-}
+INSTANTIATE_GETSCALAR(int)
+INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(uchar)
+INSTANTIATE_GETSCALAR(char)
+INSTANTIATE_GETSCALAR(intl)
+INSTANTIATE_GETSCALAR(uintl)
+INSTANTIATE_GETSCALAR(short)
+INSTANTIATE_GETSCALAR(ushort)
+}  // namespace cpu
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index d7781640b9..8dd45d281f 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -9,55 +9,53 @@
 #pragma once
 
 #include <Array.hpp>
-#include <queue.hpp>
 #include <kernel/pad_array_borders.hpp>
 #include <math.hpp>
+#include <queue.hpp>
 
-namespace af { class dim4; }
+namespace af {
+class dim4;
+}
 
-namespace cpu
-{
+namespace cpu {
 
-    template<typename T>
-    void copyData(T *data, const Array<T> &A);
+template<typename T>
+void copyData(T *data, const Array<T> &A);
 
-    template<typename T>
-    Array<T> copyArray(const Array<T> &A);
+template<typename T>
+Array<T> copyArray(const Array<T> &A);
 
-    template<typename inType, typename outType>
-    void copyArray(Array<outType> &out, const Array<inType> &in);
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, const Array<inType> &in);
 
-    template<typename inType, typename outType>
-    Array<outType> padArray(const Array<inType>& in, const dim4& dims,
-                            outType default_value=outType(0),
-                            double factor=1.0);
+template<typename inType, typename outType>
+Array<outType> padArray(const Array<inType> &in, const dim4 &dims,
+                        outType default_value = outType(0),
+                        double factor         = 1.0);
 
-    template<typename T>
-    Array<T> padArrayBorders(const Array<T>& in,
-                             const dim4& lowerBoundPadding,
-                             const dim4& upperBoundPadding,
-                             const af::borderType btype)
-    {
-        const dim4& iDims = in.dims();
+template<typename T>
+Array<T> padArrayBorders(const Array<T> &in, const dim4 &lowerBoundPadding,
+                         const dim4 &upperBoundPadding,
+                         const af::borderType btype) {
+    const dim4 &iDims = in.dims();
 
-        dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
-                   lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
-                   lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
-                   lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
+    dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
+               lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
+               lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
+               lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
 
-        auto ret = (btype == AF_PAD_ZERO ?
-                    createValueArray<T>(oDims, scalar<T>(0)) :
-                    createEmptyArray<T>(oDims));
-        ret.eval();
+    auto ret = (btype == AF_PAD_ZERO ? createValueArray<T>(oDims, scalar<T>(0))
+                                     : createEmptyArray<T>(oDims));
+    ret.eval();
 
-        getQueue().enqueue(kernel::padBorders<T>, ret, in,
-                           lowerBoundPadding, upperBoundPadding, btype);
-        return ret;
-    }
+    getQueue().enqueue(kernel::padBorders<T>, ret, in, lowerBoundPadding,
+                       upperBoundPadding, btype);
+    return ret;
+}
 
-    template<typename T>
-    void multiply_inplace(Array<T> &in, double val);
+template<typename T>
+void multiply_inplace(Array<T> &in, double val);
 
-    template<typename T>
-    T getScalar(const Array<T> &in);
-}
+template<typename T>
+T getScalar(const Array<T> &in);
+}  // namespace cpu
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 659f0f85e6..2b2c0c6a16 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -7,27 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/diagonal.hpp>
 #include <diagonal.hpp>
+#include <kernel/diagonal.hpp>
 
 #include <Array.hpp>
+#include <platform.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <platform.hpp>
 
 #include <algorithm>
 #include <cstdlib>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> diagCreate(const Array<T> &in, const int num)
-{
+Array<T> diagCreate(const Array<T> &in, const int num) {
     in.eval();
 
-    int size = in.dims()[0] + std::abs(num);
-    int batch = in.dims()[1];
+    int size     = in.dims()[0] + std::abs(num);
+    int batch    = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
     getQueue().enqueue(kernel::diagCreate<T>, out, in, num);
@@ -36,13 +34,12 @@ Array<T> diagCreate(const Array<T> &in, const int num)
 }
 
 template<typename T>
-Array<T> diagExtract(const Array<T> &in, const int num)
-{
+Array<T> diagExtract(const Array<T> &in, const int num) {
     in.eval();
 
     const dim4 idims = in.dims();
-    dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
-    Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
+    dim_t size       = std::min(idims[0], idims[1]) - std::abs(num);
+    Array<T> out     = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
     getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
 
@@ -50,8 +47,8 @@ Array<T> diagExtract(const Array<T> &in, const int num)
 }
 
 #define INSTANTIATE_DIAGONAL(T)                                          \
-    template Array<T>  diagExtract<T>    (const Array<T> &in, const int num); \
-    template Array<T>  diagCreate <T>    (const Array<T> &in, const int num);
+    template Array<T> diagExtract<T>(const Array<T> &in, const int num); \
+    template Array<T> diagCreate<T>(const Array<T> &in, const int num);
 
 INSTANTIATE_DIAGONAL(float)
 INSTANTIATE_DIAGONAL(double)
@@ -66,4 +63,4 @@ INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/diagonal.hpp b/src/backend/cpu/diagonal.hpp
index e71ec435ee..f58ce6fcdb 100644
--- a/src/backend/cpu/diagonal.hpp
+++ b/src/backend/cpu/diagonal.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> diagCreate(const Array<T> &in, const int num);
+namespace cpu {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num);
 
-    template<typename T>
-    Array<T> diagExtract(const Array<T> &in, const int num);
-}
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num);
+}  // namespace cpu
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index f39dd8e47e..411d207f89 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -10,17 +10,15 @@
 #include <diff.hpp>
 
 #include <Array.hpp>
-#include <platform.hpp>
 #include <kernel/diff.hpp>
+#include <platform.hpp>
 
 #include <af/dim4.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T>  diff1(const Array<T> &in, const int dim)
-{
+Array<T> diff1(const Array<T> &in, const int dim) {
     in.eval();
 
     // Decrement dimension of select dimension
@@ -35,8 +33,7 @@ Array<T>  diff1(const Array<T> &in, const int dim)
 }
 
 template<typename T>
-Array<T>  diff2(const Array<T> &in, const int dim)
-{
+Array<T> diff2(const Array<T> &in, const int dim) {
     in.eval();
 
     // Decrement dimension of select dimension
@@ -50,9 +47,9 @@ Array<T>  diff2(const Array<T> &in, const int dim)
     return outArray;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T>  diff1<T>  (const Array<T> &in, const int dim);   \
-    template Array<T>  diff2<T>  (const Array<T> &in, const int dim);   \
+#define INSTANTIATE(T)                                             \
+    template Array<T> diff1<T>(const Array<T> &in, const int dim); \
+    template Array<T> diff2<T>(const Array<T> &in, const int dim);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -67,4 +64,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/diff.hpp b/src/backend/cpu/diff.hpp
index b8d7bf495a..32913b9391 100644
--- a/src/backend/cpu/diff.hpp
+++ b/src/backend/cpu/diff.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> diff1(const Array<T> &in, const int dim);
+namespace cpu {
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim);
 
-    template<typename T>
-    Array<T> diff2(const Array<T> &in, const int dim);
-}
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim);
+}  // namespace cpu
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index 4e9464db1d..966b403a35 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -9,7 +9,8 @@
 
 #include <common/err_common.hpp>
 
-#define CPU_NOT_SUPPORTED(message) do {                 \
-        throw SupportError(__PRETTY_FUNCTION__,         \
-                __AF_FILENAME__, __LINE__, message);    \
-    } while(0)
+#define CPU_NOT_SUPPORTED(message)                                         \
+    do {                                                                   \
+        throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
+                           message);                                       \
+    } while (0)
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index a7109a6058..1dc9b4a935 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -7,55 +7,54 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>                    // header with cpu backend specific
-                                        // Array class implementation that inherits
-                                        // ArrayInfo base class
+#include <Array.hpp>  // header with cpu backend specific
+                      // Array class implementation that inherits
+                      // ArrayInfo base class
 
-#include <exampleFunction.hpp>          // cpu backend function header
-#include <kernel/exampleFunction.hpp>   // Function implementation header
+#include <exampleFunction.hpp>         // cpu backend function header
+#include <kernel/exampleFunction.hpp>  // Function implementation header
 
-#include <err_cpu.hpp>                  // error check functions and Macros
-                                        // specific to cpu backend
-#include <af/dim4.hpp>
+#include <err_cpu.hpp>  // error check functions and Macros
+                        // specific to cpu backend
 #include <platform.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method)
-{
-    a.eval();                           // All input Arrays should call eval mandatorily
-                                        // in CPU backend function implementations. Since
-                                        // the cpu fns are asynchronous launches, any Arrays
-                                        // that are either views/JIT nodes needs to evaluated
-                                        // before they are passed onto functions that are
-                                        // enqueued onto the queues.
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method) {
+    a.eval();  // All input Arrays should call eval mandatorily
+               // in CPU backend function implementations. Since
+               // the cpu fns are asynchronous launches, any Arrays
+               // that are either views/JIT nodes needs to evaluated
+               // before they are passed onto functions that are
+               // enqueued onto the queues.
     b.eval();
 
-    dim4 outputDims;                    // this should be '= in.dims();' in most cases
-                                        // but would definitely depend on the type of
-                                        // algorithm you are implementing.
+    dim4 outputDims;  // this should be '= in.dims();' in most cases
+                      // but would definitely depend on the type of
+                      // algorithm you are implementing.
 
     Array<T> out = createEmptyArray<T>(outputDims);
-                                        // Please use the create***Array<T> helper
-                                        // functions defined in Array.hpp to create
-                                        // different types of Arrays. Please check the
-                                        // file to know what are the different types you
-                                        // can create.
+    // Please use the create***Array<T> helper
+    // functions defined in Array.hpp to create
+    // different types of Arrays. Please check the
+    // file to know what are the different types you
+    // can create.
 
     // Enqueue the function call on the worker thread
     // This code will be present in src/backend/cpu/kernel/exampleFunction.hpp
     getQueue().enqueue(kernel::exampleFunction<T>, out, a, b, method);
 
-    return out;                         // return the result
+    return out;  // return the result
 }
 
-
-#define INSTANTIATE(T)  \
-    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
+#define INSTANTIATE(T)                                                         \
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, \
+                                         const af_someenum_t method);
 
 // INSTANTIATIONS for all the types which
 // are present in the switch case statement
@@ -69,4 +68,4 @@ INSTANTIATE(char)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/exampleFunction.hpp b/src/backend/cpu/exampleFunction.hpp
index c5203c738d..822ad57186 100644
--- a/src/backend/cpu/exampleFunction.hpp
+++ b/src/backend/cpu/exampleFunction.hpp
@@ -10,9 +10,8 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
+namespace cpu {
+template<typename T>
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method);
 }
-
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 28569656b1..91dc6bb19f 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -7,32 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/fast.hpp>
 #include <fast.hpp>
+#include <kernel/fast.hpp>
 
 #include <Array.hpp>
-#include <af/dim4.hpp>
 #include <math.h>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <af/dim4.hpp>
 
-#include <cstddef>
 #include <algorithm>
+#include <cstddef>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
               const bool nonmax, const float feature_ratio,
-              const unsigned edge)
-{
+              const unsigned edge) {
     in.eval();
 
-    dim4 in_dims = in.dims();
+    dim4 in_dims            = in.dims();
     const unsigned max_feat = ceil(in.elements() * feature_ratio);
 
     // Matrix containing scores for detected features, scores are stored in the
@@ -47,15 +45,15 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
 
     // Arrays containing all features detected before non-maximal suppression.
     dim4 max_feat_dims(max_feat);
-    Array<float> x = createEmptyArray<float>(max_feat_dims);
-    Array<float> y = createEmptyArray<float>(max_feat_dims);
+    Array<float> x     = createEmptyArray<float>(max_feat_dims);
+    Array<float> y     = createEmptyArray<float>(max_feat_dims);
     Array<float> score = createEmptyArray<float>(max_feat_dims);
 
     // Feature counter
     unsigned count = 0;
 
     kernel::locate_features<T>(in, V, x, y, score, &count, thr, arc_length,
-                       nonmax, max_feat, edge);
+                               nonmax, max_feat, edge);
 
     // If more features than max_feat were detected, feat wasn't populated
     // with them anyway, so the real number of features will be that of
@@ -63,8 +61,8 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     unsigned feat_found = std::min(max_feat, count);
     dim4 feat_found_dims(feat_found);
 
-    Array<float> x_total = createEmptyArray<float>(af::dim4());
-    Array<float> y_total = createEmptyArray<float>(af::dim4());
+    Array<float> x_total     = createEmptyArray<float>(af::dim4());
+    Array<float> y_total     = createEmptyArray<float>(af::dim4());
     Array<float> score_total = createEmptyArray<float>(af::dim4());
 
     if (nonmax == 1) {
@@ -73,36 +71,34 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
         score_total = createEmptyArray<float>(feat_found_dims);
 
         count = 0;
-        kernel::non_maximal(V, x, y,
-                            x_total, y_total, score_total,
-                            &count, feat_found, edge);
+        kernel::non_maximal(V, x, y, x_total, y_total, score_total, &count,
+                            feat_found, edge);
 
         feat_found = std::min(max_feat, count);
     } else {
-        x_total = x;
-        y_total = y;
+        x_total     = x;
+        y_total     = y;
         score_total = score;
     }
 
     if (feat_found > 0) {
         feat_found_dims = dim4(feat_found);
 
-        x_out = createEmptyArray<float>(feat_found_dims);
-        y_out = createEmptyArray<float>(feat_found_dims);
+        x_out     = createEmptyArray<float>(feat_found_dims);
+        y_out     = createEmptyArray<float>(feat_found_dims);
         score_out = createEmptyArray<float>(feat_found_dims);
 
-        float *x_total_ptr = x_total.get();
-        float *y_total_ptr = y_total.get();
+        float *x_total_ptr     = x_total.get();
+        float *y_total_ptr     = y_total.get();
         float *score_total_ptr = score_total.get();
 
-
-        float *x_out_ptr = x_out.get();
-        float *y_out_ptr = y_out.get();
+        float *x_out_ptr     = x_out.get();
+        float *y_out_ptr     = y_out.get();
         float *score_out_ptr = score_out.get();
 
         for (size_t i = 0; i < feat_found; i++) {
-            x_out_ptr[i] = x_total_ptr[i];
-            y_out_ptr[i] = y_total_ptr[i];
+            x_out_ptr[i]     = x_total_ptr[i];
+            y_out_ptr[i]     = y_total_ptr[i];
             score_out_ptr[i] = score_total_ptr[i];
         }
     }
@@ -110,18 +106,19 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     return feat_found;
 }
 
-#define INSTANTIATE(T)                                                                              \
-    template unsigned fast<T>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,    \
-                              const Array<T> &in, const float thr, const unsigned arc_length,       \
-                              const bool nonmax, const float feature_ratio, const unsigned edge);
+#define INSTANTIATE(T)                                                        \
+    template unsigned fast<T>(                                                \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const float thr, const unsigned arc_length,       \
+        const bool nonmax, const float feature_ratio, const unsigned edge);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/fast.hpp b/src/backend/cpu/fast.hpp
index f49c62b2bd..21c0904c66 100644
--- a/src/backend/cpu/fast.hpp
+++ b/src/backend/cpu/fast.hpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-namespace cpu
-{
-template <typename T> class Array;
+namespace cpu {
+template<typename T>
+class Array;
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
@@ -17,4 +17,4 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const bool non_max, const float feature_ratio,
               const unsigned edge);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 2b93af14f1..b0f0fc97ae 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/fft.hpp>
 #include <fft.hpp>
+#include <kernel/fft.hpp>
 
 #include <Array.hpp>
 #include <platform.hpp>
@@ -17,69 +17,65 @@
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
-void setFFTPlanCacheSize(size_t numPlans)
-{
-    UNUSED(numPlans);
-}
+void setFFTPlanCacheSize(size_t numPlans) { UNUSED(numPlans); }
 
 template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in)
-{
+void fft_inplace(Array<T> &in) {
     in.eval();
-    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in, in.getDataDims());
+    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in,
+                       in.getDataDims());
 }
 
 template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in)
-{
+Array<Tc> fft_r2c(const Array<Tr> &in) {
     in.eval();
 
-    dim4 odims = in.dims();
-    odims[0] = odims[0] / 2 + 1;
+    dim4 odims    = in.dims();
+    odims[0]      = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, out.getDataDims(), in, in.getDataDims());
+    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, out.getDataDims(),
+                       in, in.getDataDims());
 
     return out;
 }
 
 template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
-{
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     in.eval();
 
     Array<Tr> out = createEmptyArray<Tr>(odims);
-    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>,
-                       out, out.getDataDims(),
-                       in, in.getDataDims(),
-                       odims);
+    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
+                       in, in.getDataDims(), odims);
 
     return out;
 }
 
-#define INSTANTIATE(T)                                      \
-    template void fft_inplace<T, 1, true >(Array<T> &in);   \
-    template void fft_inplace<T, 2, true >(Array<T> &in);   \
-    template void fft_inplace<T, 3, true >(Array<T> &in);   \
-    template void fft_inplace<T, 1, false>(Array<T> &in);   \
-    template void fft_inplace<T, 2, false>(Array<T> &in);   \
-    template void fft_inplace<T, 3, false>(Array<T> &in);
+#define INSTANTIATE(T)                                     \
+    template void fft_inplace<T, 1, true>(Array<T> & in);  \
+    template void fft_inplace<T, 2, true>(Array<T> & in);  \
+    template void fft_inplace<T, 3, true>(Array<T> & in);  \
+    template void fft_inplace<T, 1, false>(Array<T> & in); \
+    template void fft_inplace<T, 2, false>(Array<T> & in); \
+    template void fft_inplace<T, 3, false>(Array<T> & in);
 
-INSTANTIATE(cfloat )
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-#define INSTANTIATE_REAL(Tr, Tc)                                        \
-    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in);         \
-    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in);         \
-    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in);         \
-    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in, const dim4 &odims); \
-    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in, const dim4 &odims); \
-    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in, const dim4 &odims); \
-
-INSTANTIATE_REAL(float , cfloat )
+#define INSTANTIATE_REAL(Tr, Tc)                                \
+    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in); \
+    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in); \
+    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in); \
+    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in,  \
+                                          const dim4 &odims);   \
+    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in,  \
+                                          const dim4 &odims);   \
+    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in,  \
+                                          const dim4 &odims);
+
+INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/fft.hpp b/src/backend/cpu/fft.hpp
index f669517a00..84dde77218 100644
--- a/src/backend/cpu/fft.hpp
+++ b/src/backend/cpu/fft.hpp
@@ -11,10 +11,11 @@
 
 #include <cstddef>
 
-namespace af { class dim4; }
+namespace af {
+class dim4;
+}
 
-namespace cpu
-{
+namespace cpu {
 
 void setFFTPlanCacheSize(size_t numPlans);
 
@@ -26,4 +27,4 @@ Array<Tc> fft_r2c(const Array<Tr> &in);
 
 template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims);
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index f80abd6d32..95e950c3cf 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <common/dispatch.hpp>
-#include <fft.hpp>
+#include <copy.hpp>
 #include <err_cpu.hpp>
+#include <fft.hpp>
 #include <fftw3.h>
-#include <copy.hpp>
+#include <kernel/fftconvolve.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/fftconvolve.hpp>
+#include <af/dim4.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind)
-{
+                     const bool expand, AF_BATCH_KIND kind) {
     signal.eval();
     filter.eval();
 
@@ -40,12 +39,13 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched FFT capabilities
-    fft_dims[baseDim - 1] = nextpow2((unsigned)((int)ceil(sd[0] / 2.f) + fd[0] - 1));
+    fft_dims[baseDim - 1] =
+        nextpow2((unsigned)((int)ceil(sd[0] / 2.f) + fd[0] - 1));
     packed_dims[0] = 2 * fft_dims[baseDim - 1];
     fftScale *= fft_dims[baseDim - 1];
 
     for (dim_t k = 1; k < baseDim; k++) {
-        packed_dims[k] = nextpow2((unsigned)(sd[k] + fd[k] - 1));
+        packed_dims[k]            = nextpow2((unsigned)(sd[k] + fd[k] - 1));
         fft_dims[baseDim - k - 1] = packed_dims[k];
         fftScale *= fft_dims[baseDim - k - 1];
     }
@@ -59,21 +59,21 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<convT> packed = createEmptyArray<convT>(packed_dims);
 
-    sig_tmp_dims[0]    = filter_tmp_dims[0] = packed_dims[0];
+    sig_tmp_dims[0] = filter_tmp_dims[0] = packed_dims[0];
     sig_tmp_strides[0] = filter_tmp_strides[0] = 1;
 
     for (dim_t k = 1; k < 4; k++) {
         if (k < baseDim) {
             sig_tmp_dims[k]    = packed_dims[k];
             filter_tmp_dims[k] = packed_dims[k];
-        }
-        else {
+        } else {
             sig_tmp_dims[k]    = sd[k];
             filter_tmp_dims[k] = fd[k];
         }
 
-        sig_tmp_strides[k]    = sig_tmp_strides[k - 1] * sig_tmp_dims[k - 1];
-        filter_tmp_strides[k] = filter_tmp_strides[k - 1] * filter_tmp_dims[k - 1];
+        sig_tmp_strides[k] = sig_tmp_strides[k - 1] * sig_tmp_dims[k - 1];
+        filter_tmp_strides[k] =
+            filter_tmp_strides[k - 1] * filter_tmp_dims[k - 1];
     }
 
     // Number of packed complex elements in dimension 0
@@ -81,55 +81,40 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    getQueue().enqueue(kernel::packData<convT, T>, packed, sig_tmp_dims, sig_tmp_strides, signal);
+    getQueue().enqueue(kernel::packData<convT, T>, packed, sig_tmp_dims,
+                       sig_tmp_strides, signal);
 
     // Pad filter array with 0s
-    const dim_t offset = sig_tmp_strides[3]*sig_tmp_dims[3];
-    getQueue().enqueue(kernel::padArray<convT, T>, packed, filter_tmp_dims, filter_tmp_strides,
-                       filter, offset);
+    const dim_t offset = sig_tmp_strides[3] * sig_tmp_dims[3];
+    getQueue().enqueue(kernel::padArray<convT, T>, packed, filter_tmp_dims,
+                       filter_tmp_strides, filter, offset);
 
     dim4 fftDims(1, 1, 1, 1);
-    for (int i=0; i<baseDim; ++i)
-        fftDims[i] = fft_dims[i];
+    for (int i = 0; i < baseDim; ++i) fftDims[i] = fft_dims[i];
 
-    auto upstream_dft = [=] (Param<convT> packed, const dim4 fftDims) {
+    auto upstream_dft = [=](Param<convT> packed, const dim4 fftDims) {
         int fft_dims[baseDim];
-        for (int i=0; i<baseDim; ++i)
-            fft_dims[i] = fftDims[i];
-        const dim4 packed_dims = packed.dims();
+        for (int i = 0; i < baseDim; ++i) fft_dims[i] = fftDims[i];
+        const dim4 packed_dims        = packed.dims();
         const af::dim4 packed_strides = packed.strides();
         // Compute forward FFT
         if (isDouble) {
-            fftw_plan plan = fftw_plan_many_dft(baseDim,
-                                                fft_dims,
-                                                packed_dims[baseDim],
-                                                (fftw_complex*)packed.get(),
-                                                NULL,
-                                                packed_strides[0],
-                                                packed_strides[baseDim] / 2,
-                                                (fftw_complex*)packed.get(),
-                                                NULL,
-                                                packed_strides[0],
-                                                packed_strides[baseDim] / 2,
-                                                FFTW_FORWARD,
-                                                FFTW_ESTIMATE);
+            fftw_plan plan = fftw_plan_many_dft(
+                baseDim, fft_dims, packed_dims[baseDim],
+                (fftw_complex*)packed.get(), NULL, packed_strides[0],
+                packed_strides[baseDim] / 2, (fftw_complex*)packed.get(), NULL,
+                packed_strides[0], packed_strides[baseDim] / 2, FFTW_FORWARD,
+                FFTW_ESTIMATE);
 
             fftw_execute(plan);
             fftw_destroy_plan(plan);
         } else {
-            fftwf_plan plan = fftwf_plan_many_dft(baseDim,
-                                                  fft_dims,
-                                                  packed_dims[baseDim],
-                                                  (fftwf_complex*)packed.get(),
-                                                  NULL,
-                                                  packed_strides[0],
-                                                  packed_strides[baseDim] / 2,
-                                                  (fftwf_complex*)packed.get(),
-                                                  NULL,
-                                                  packed_strides[0],
-                                                  packed_strides[baseDim] / 2,
-                                                  FFTW_FORWARD,
-                                                  FFTW_ESTIMATE);
+            fftwf_plan plan = fftwf_plan_many_dft(
+                baseDim, fft_dims, packed_dims[baseDim],
+                (fftwf_complex*)packed.get(), NULL, packed_strides[0],
+                packed_strides[baseDim] / 2, (fftwf_complex*)packed.get(), NULL,
+                packed_strides[0], packed_strides[baseDim] / 2, FFTW_FORWARD,
+                FFTW_ESTIMATE);
 
             fftwf_execute(plan);
             fftwf_destroy_plan(plan);
@@ -138,49 +123,33 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     getQueue().enqueue(upstream_dft, packed, fftDims);
 
     // Multiply filter and signal FFT arrays
-    getQueue().enqueue(kernel::complexMultiply<convT>, packed,
-                       sig_tmp_dims, sig_tmp_strides,
-                       filter_tmp_dims, filter_tmp_strides,
+    getQueue().enqueue(kernel::complexMultiply<convT>, packed, sig_tmp_dims,
+                       sig_tmp_strides, filter_tmp_dims, filter_tmp_strides,
                        kind, offset);
 
-    auto upstream_idft = [=] (Param<convT> packed, const dim4 fftDims) {
+    auto upstream_idft = [=](Param<convT> packed, const dim4 fftDims) {
         int fft_dims[baseDim];
-        for (int i=0; i<baseDim; ++i)
-            fft_dims[i] = fftDims[i];
-        const dim4 packed_dims = packed.dims();
+        for (int i = 0; i < baseDim; ++i) fft_dims[i] = fftDims[i];
+        const dim4 packed_dims        = packed.dims();
         const af::dim4 packed_strides = packed.strides();
         // Compute inverse FFT
         if (isDouble) {
-            fftw_plan plan = fftw_plan_many_dft(baseDim,
-                                                fft_dims,
-                                                packed_dims[baseDim],
-                                                (fftw_complex*)packed.get(),
-                                                NULL,
-                                                packed_strides[0],
-                                                packed_strides[baseDim] / 2,
-                                                (fftw_complex*)packed.get(),
-                                                NULL,
-                                                packed_strides[0],
-                                                packed_strides[baseDim] / 2,
-                                                FFTW_BACKWARD,
-                                                FFTW_ESTIMATE);
+            fftw_plan plan = fftw_plan_many_dft(
+                baseDim, fft_dims, packed_dims[baseDim],
+                (fftw_complex*)packed.get(), NULL, packed_strides[0],
+                packed_strides[baseDim] / 2, (fftw_complex*)packed.get(), NULL,
+                packed_strides[0], packed_strides[baseDim] / 2, FFTW_BACKWARD,
+                FFTW_ESTIMATE);
 
             fftw_execute(plan);
             fftw_destroy_plan(plan);
         } else {
-            fftwf_plan plan = fftwf_plan_many_dft(baseDim,
-                                                  fft_dims,
-                                                  packed_dims[baseDim],
-                                                  (fftwf_complex*)packed.get(),
-                                                  NULL,
-                                                  packed_strides[0],
-                                                  packed_strides[baseDim] / 2,
-                                                  (fftwf_complex*)packed.get(),
-                                                  NULL,
-                                                  packed_strides[0],
-                                                  packed_strides[baseDim] / 2,
-                                                  FFTW_BACKWARD,
-                                                  FFTW_ESTIMATE);
+            fftwf_plan plan = fftwf_plan_many_dft(
+                baseDim, fft_dims, packed_dims[baseDim],
+                (fftwf_complex*)packed.get(), NULL, packed_strides[0],
+                packed_strides[baseDim] / 2, (fftwf_complex*)packed.get(), NULL,
+                packed_strides[0], packed_strides[baseDim] / 2, FFTW_BACKWARD,
+                FFTW_ESTIMATE);
 
             fftwf_execute(plan);
             fftwf_destroy_plan(plan);
@@ -191,47 +160,50 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     // Compute output dimensions
     dim4 oDims(1);
     if (expand) {
-        for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
-                oDims[d] = sd[d]+fd[d]-1;
+        for (dim_t d = 0; d < 4; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sd[d] + fd[d] - 1;
             } else {
-                oDims[d] = (d<baseDim ? sd[d]+fd[d]-1 : sd[d]);
+                oDims[d] = (d < baseDim ? sd[d] + fd[d] - 1 : sd[d]);
             }
         }
     } else {
         oDims = sd;
-        if (kind==AF_BATCH_RHS) {
-            for (dim_t i=baseDim; i<4; ++i)
-                oDims[i] = fd[i];
+        if (kind == AF_BATCH_RHS) {
+            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fd[i];
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out, packed, filter,
-                       sig_half_d0, fftScale, sig_tmp_dims, sig_tmp_strides, filter_tmp_dims,
-                       filter_tmp_strides, expand, kind);
+    getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out,
+                       packed, filter, sig_half_d0, fftScale, sig_tmp_dims,
+                       sig_tmp_strides, filter_tmp_dims, filter_tmp_strides,
+                       expand, kind);
 
     return out;
 }
 
-#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                                                   \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 1>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 2>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 3>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
-
-INSTANTIATE(double, double, cdouble, true , false)
-INSTANTIATE(float , float,  cfloat,  false, false)
-INSTANTIATE(uint  , float,  cfloat,  false, true)
-INSTANTIATE(int   , float,  cfloat,  false, true)
-INSTANTIATE(uchar , float,  cfloat,  false, true)
-INSTANTIATE(char  , float,  cfloat,  false, true)
-INSTANTIATE(uintl , float,  cfloat,  false, true)
-INSTANTIATE(intl  , float,  cfloat,  false, true)
-INSTANTIATE(ushort, float,  cfloat,  false, true)
-INSTANTIATE(short , float,  cfloat,  false, true)
-
-} // namespace cpu
+#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                      \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 1>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);                                               \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 2>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);                                               \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 3>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);
+
+INSTANTIATE(double, double, cdouble, true, false)
+INSTANTIATE(float, float, cfloat, false, false)
+INSTANTIATE(uint, float, cfloat, false, true)
+INSTANTIATE(int, float, cfloat, false, true)
+INSTANTIATE(uchar, float, cfloat, false, true)
+INSTANTIATE(char, float, cfloat, false, true)
+INSTANTIATE(uintl, float, cfloat, false, true)
+INSTANTIATE(intl, float, cfloat, false, true)
+INSTANTIATE(ushort, float, cfloat, false, true)
+INSTANTIATE(short, float, cfloat, false, true)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/fftconvolve.hpp b/src/backend/cpu/fftconvolve.hpp
index f00d5d3468..671e27ac6b 100644
--- a/src/backend/cpu/fftconvolve.hpp
+++ b/src/backend/cpu/fftconvolve.hpp
@@ -9,10 +9,11 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind);
 
 }
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index aa417f49e1..341ef94fde 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -8,20 +8,18 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_cpu.hpp>
 #include <gradient.hpp>
+#include <kernel/gradient.hpp>
 #include <math.hpp>
-#include <stdexcept>
-#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/gradient.hpp>
+#include <stdexcept>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
-{
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
     grad0.eval();
     grad1.eval();
     in.eval();
@@ -29,12 +27,13 @@ void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
     getQueue().enqueue(kernel::gradient<T>, grad0, grad1, in);
 }
 
-#define INSTANTIATE(T)                                                                  \
-    template void gradient<T>(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);    \
+#define INSTANTIATE(T)                                            \
+    template void gradient<T>(Array<T> & grad0, Array<T> & grad1, \
+                              const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/gradient.hpp b/src/backend/cpu/gradient.hpp
index b2070585b1..cc18462ba1 100644
--- a/src/backend/cpu/gradient.hpp
+++ b/src/backend/cpu/gradient.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
+namespace cpu {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
 }
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index dd7a94a98b..100045e8eb 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -7,28 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <harris.hpp>
 #include <convolve.hpp>
 #include <gradient.hpp>
-#include <sort_index.hpp>
-#include <cstring>
+#include <harris.hpp>
+#include <kernel/harris.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <math.hpp>
-#include <kernel/harris.hpp>
+#include <sort_index.hpp>
+#include <af/dim4.hpp>
+#include <cstring>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename convAccT>
-unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
-                const Array<T> &in, const unsigned max_corners, const float min_response,
-                const float sigma, const unsigned filter_len, const float k_thr)
-{
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &resp_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr) {
     in.eval();
 
     dim4 idims = in.dims();
@@ -40,10 +40,10 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
     } else {
-      gaussian1D<convAccT>(h_filter.get(), (int)filter_len, sigma);
+        gaussian1D<convAccT>(h_filter.get(), (int)filter_len, sigma);
     }
-    Array<convAccT> filter = createDeviceDataArray<convAccT>(dim4(filter_len),
-                                                             (const void*)h_filter.release());
+    Array<convAccT> filter = createDeviceDataArray<convAccT>(
+        dim4(filter_len), (const void *)h_filter.release());
     unsigned border_len = filter_len / 2 + 1;
 
     Array<T> ix = createEmptyArray<T>(idims);
@@ -57,7 +57,8 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     Array<T> iyy = createEmptyArray<T>(idims);
 
     // Compute second-order derivatives
-    getQueue().enqueue(kernel::second_order_deriv<T>, ixx, ixy, iyy, in.elements(), ix, iy);
+    getQueue().enqueue(kernel::second_order_deriv<T>, ixx, ixy, iyy,
+                       in.elements(), ix, iy);
 
     // Convolve second-order derivatives with proper window filter
     ixx = convolve2<T, convAccT, false>(ixx, filter, filter);
@@ -68,8 +69,8 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
 
     Array<T> responses = createEmptyArray<T>(dim4(in.elements()));
 
-    getQueue().enqueue(kernel::harris_responses<T>, responses, idims[0], idims[1],
-                       ixx, ixy, iyy, k_thr, border_len);
+    getQueue().enqueue(kernel::harris_responses<T>, responses, idims[0],
+                       idims[1], ixx, ixy, iyy, k_thr, border_len);
 
     Array<float> xCorners    = createEmptyArray<float>(dim4(corner_lim));
     Array<float> yCorners    = createEmptyArray<float>(dim4(corner_lim));
@@ -81,31 +82,34 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     getQueue().sync();
     unsigned corners_found = 0;
     kernel::non_maximal<T>(xCorners, yCorners, respCorners, &corners_found,
-                   idims[0], idims[1], responses, min_r, border_len, corner_lim);
+                           idims[0], idims[1], responses, min_r, border_len,
+                           corner_lim);
 
-    const unsigned corners_out = min(corners_found,
-                                    (max_corners > 0) ? max_corners : corner_lim);
-    if (corners_out == 0)
-        return 0;
+    const unsigned corners_out =
+        min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
+    if (corners_out == 0) return 0;
 
     if (max_corners > 0 && corners_found > corners_out) {
         respCorners.resetDims(dim4(corners_found));
-        Array<float> harris_sorted = createEmptyArray<float>(dim4(corners_found));
-        Array<unsigned> harris_idx = createEmptyArray<unsigned>(dim4(corners_found));
+        Array<float> harris_sorted =
+            createEmptyArray<float>(dim4(corners_found));
+        Array<unsigned> harris_idx =
+            createEmptyArray<unsigned>(dim4(corners_found));
 
         // Sort Harris responses
         sort_index<float>(harris_sorted, harris_idx, respCorners, 0, false);
 
-        x_out = createEmptyArray<float>(dim4(corners_out));
-        y_out = createEmptyArray<float>(dim4(corners_out));
+        x_out    = createEmptyArray<float>(dim4(corners_out));
+        y_out    = createEmptyArray<float>(dim4(corners_out));
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
         // Keep only the corners with higher Harris responses
-        getQueue().enqueue(kernel::keep_corners, x_out, y_out, resp_out, xCorners, yCorners,
-                           harris_sorted, harris_idx, corners_out);
+        getQueue().enqueue(kernel::keep_corners, x_out, y_out, resp_out,
+                           xCorners, yCorners, harris_sorted, harris_idx,
+                           corners_out);
     } else if (max_corners == 0 && corners_found < corner_lim) {
-        x_out = createEmptyArray<float>(dim4(corners_out));
-        y_out = createEmptyArray<float>(dim4(corners_out));
+        x_out    = createEmptyArray<float>(dim4(corners_out));
+        y_out    = createEmptyArray<float>(dim4(corners_out));
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
         auto copyFunc = [=](Param<float> x_out, Param<float> y_out,
@@ -114,13 +118,14 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
                             const unsigned corners_out) {
             memcpy(x_out.get(), x_crnrs.get(), corners_out * sizeof(float));
             memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
-            memcpy(outResponses.get(), inResponses.get(), corners_out * sizeof(float));
+            memcpy(outResponses.get(), inResponses.get(),
+                   corners_out * sizeof(float));
         };
-        getQueue().enqueue(copyFunc, x_out, y_out, resp_out,
-                           xCorners, yCorners, respCorners, corners_out);
+        getQueue().enqueue(copyFunc, x_out, y_out, resp_out, xCorners, yCorners,
+                           respCorners, corners_out);
     } else {
-        x_out = xCorners;
-        y_out = yCorners;
+        x_out    = xCorners;
+        y_out    = yCorners;
         resp_out = respCorners;
         x_out.resetDims(dim4(corners_out));
         y_out.resetDims(dim4(corners_out));
@@ -130,12 +135,14 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out
     return corners_out;
 }
 
-#define INSTANTIATE(T, convAccT)                                                                                    \
-    template unsigned harris<T, convAccT>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,        \
-                                          const Array<T> &in, const unsigned max_corners, const float min_response, \
-                                          const float sigma, const unsigned block_size, const float k_thr);
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned harris<T, convAccT>(                                    \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned max_corners,                       \
+        const float min_response, const float sigma,                          \
+        const unsigned block_size, const float k_thr);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/harris.hpp b/src/backend/cpu/harris.hpp
index 67caf9b0d5..c2f587b18d 100644
--- a/src/backend/cpu/harris.hpp
+++ b/src/backend/cpu/harris.hpp
@@ -7,17 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename convAccT>
-unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
-                const Array<T> &in, const unsigned max_corners, const float min_response,
-                const float sigma, const unsigned filter_len, const float k_thr);
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &resp_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr);
 
 }
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index c269e87874..4c68d6858e 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -7,17 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <hist_graphics.hpp>
 #include <err_cpu.hpp>
+#include <hist_graphics.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
 namespace cpu {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, fg_histogram hist)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_histogram(const Array<T> &data, fg_histogram hist) {
+    ForgeModule &_ = graphics::forgePlugin();
     data.eval();
     getQueue().sync();
 
@@ -33,8 +32,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
     CheckGL("End copy_histogram");
 }
 
-#define INSTANTIATE(T)  \
-template void copy_histogram<T>(const Array<T> &, fg_histogram);
+#define INSTANTIATE(T) \
+    template void copy_histogram<T>(const Array<T> &, fg_histogram);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
@@ -43,4 +42,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/hist_graphics.hpp b/src/backend/cpu/hist_graphics.hpp
index be397ffcfb..1fd68a1adb 100644
--- a/src/backend/cpu/hist_graphics.hpp
+++ b/src/backend/cpu/hist_graphics.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#include <common/graphics_common.hpp>
 #include <Array.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cpu {
 
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 8c255163b1..0ed3c3f198 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -7,49 +7,50 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <histogram.hpp>
+#include <kernel/histogram.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/histogram.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in,
-                         const unsigned &nbins,
-                         const double &minval, const double &maxval)
-{
+Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
+                         const double &minval, const double &maxval) {
     in.eval();
 
     const dim4 inDims  = in.dims();
-    dim4 outDims       = dim4(nbins,1,inDims[2],inDims[3]);
+    dim4 outDims       = dim4(nbins, 1, inDims[2], inDims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
     out.eval();
 
-    getQueue().enqueue(kernel::histogram<outType, inType, isLinear>,
-            out, in, nbins, minval, maxval);
+    getQueue().enqueue(kernel::histogram<outType, inType, isLinear>, out, in,
+                       nbins, minval, maxval);
 
     return out;
 }
 
-#define INSTANTIATE(in_t,out_t)\
-template Array<out_t> histogram<in_t, out_t, true>(const Array<in_t> &in, const unsigned &nbins, const double &minval, const double &maxval); \
-template Array<out_t> histogram<in_t, out_t, false>(const Array<in_t> &in, const unsigned &nbins, const double &minval, const double &maxval);
+#define INSTANTIATE(in_t, out_t)                                            \
+    template Array<out_t> histogram<in_t, out_t, true>(                     \
+        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
+        const double &maxval);                                              \
+    template Array<out_t> histogram<in_t, out_t, false>(                    \
+        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
+        const double &maxval);
 
-INSTANTIATE(float , uint)
+INSTANTIATE(float, uint)
 INSTANTIATE(double, uint)
-INSTANTIATE(char  , uint)
-INSTANTIATE(int   , uint)
-INSTANTIATE(uint  , uint)
-INSTANTIATE(uchar , uint)
-INSTANTIATE(short , uint)
+INSTANTIATE(char, uint)
+INSTANTIATE(int, uint)
+INSTANTIATE(uint, uint)
+INSTANTIATE(uchar, uint)
+INSTANTIATE(short, uint)
 INSTANTIATE(ushort, uint)
-INSTANTIATE(intl  , uint)
-INSTANTIATE(uintl , uint)
+INSTANTIATE(intl, uint)
+INSTANTIATE(uintl, uint)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/histogram.hpp b/src/backend/cpu/histogram.hpp
index 9a735232ca..854c1452e1 100644
--- a/src/backend/cpu/histogram.hpp
+++ b/src/backend/cpu/histogram.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval);
+Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
+                         const double &minval, const double &maxval);
 
 }
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index fa970f3953..aba40175ac 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -7,110 +7,102 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <arith.hpp>
 #include <err_cpu.hpp>
 #include <homography.hpp>
-#include <arith.hpp>
-#include <cstring>
-#include <cfloat>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <af/dim4.hpp>
+#include <cfloat>
+#include <cstring>
 
 #include <array>
 
 using af::dim4;
 using std::array;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-T sq(T a)
-{
+T sq(T a) {
     return a * a;
 }
 
-#define APTR(Y, X) (A_ptr[(Y) * Adims[0] + (X)])
+#define APTR(Y, X) (A_ptr[(Y)*Adims[0] + (X)])
 
-static const float RANSACConfidence = 0.99f;
-static const float LMEDSConfidence = 0.99f;
+static const float RANSACConfidence  = 0.99f;
+static const float LMEDSConfidence   = 0.99f;
 static const float LMEDSOutlierRatio = 0.4f;
 
 template<typename T>
-struct EPS
-{
+struct EPS {
     T eps() { return FLT_EPSILON; }
 };
 
 template<>
-struct EPS<float>
-{
+struct EPS<float> {
     static float eps() { return FLT_EPSILON; }
 };
 
 template<>
-struct EPS<double>
-{
+struct EPS<double> {
     static double eps() { return DBL_EPSILON; }
 };
 
 template<typename T, int M, int N>
-void JacobiSVD(T* S, T* V)
-{
+void JacobiSVD(T* S, T* V) {
     const int iterations = 30;
     array<T, N> d;
 
     for (int i = 0; i < N; i++) {
         T sd = 0;
         for (int j = 0; j < M; j++) {
-            T t = S[i*M + j];
-            sd += t*t;
+            T t = S[i * M + j];
+            sd += t * t;
         }
         d[i] = sd;
 
-        V[i*N + i] = 1;
+        V[i * N + i] = 1;
     }
 
     for (int it = 0; it < iterations; it++) {
         bool converged = false;
 
-        for (int i = 0; i < N-1; i++) {
-            for (int j = i+1; j < N; j++) {
-                T* Si = S + i*M;
-                T* Sj = S + j*M;
-                T* Vi = V + i*N;
-                T* Vj = V + j*N;
+        for (int i = 0; i < N - 1; i++) {
+            for (int j = i + 1; j < N; j++) {
+                T* Si = S + i * M;
+                T* Sj = S + j * M;
+                T* Vi = V + i * N;
+                T* Vj = V + j * N;
 
                 T p = (T)0;
-                for (int k = 0; k < M; k++)
-                    p += Si[k]*Sj[k];
+                for (int k = 0; k < M; k++) p += Si[k] * Sj[k];
 
-                if (std::abs(p) <= M*EPS<T>::eps()*std::sqrt(d[i]*d[j]))
+                if (std::abs(p) <= M * EPS<T>::eps() * std::sqrt(d[i] * d[j]))
                     continue;
 
-                T y = d[i] - d[j];
-                T r = hypot(p*2, y);
-                T r2 = r*2;
+                T y  = d[i] - d[j];
+                T r  = hypot(p * 2, y);
+                T r2 = r * 2;
                 T c, s;
                 if (y >= 0) {
                     c = std::sqrt((r + y) / r2);
-                    s = p / (r2*c);
-                }
-                else {
+                    s = p / (r2 * c);
+                } else {
                     s = std::sqrt((r - y) / r2);
-                    c = p / (r2*s);
+                    c = p / (r2 * s);
                 }
 
                 T a = 0, b = 0;
                 for (int k = 0; k < M; k++) {
-                    T t0 = c*Si[k] + s*Sj[k];
-                    T t1 = c*Sj[k] - s*Si[k];
+                    T t0  = c * Si[k] + s * Sj[k];
+                    T t1  = c * Sj[k] - s * Si[k];
                     Si[k] = t0;
                     Sj[k] = t1;
 
-                    a += t0*t0;
-                    b += t1*t1;
+                    a += t0 * t0;
+                    b += t1 * t1;
                 }
                 d[i] = a;
                 d[j] = b;
@@ -125,37 +117,36 @@ void JacobiSVD(T* S, T* V)
 
                 converged = true;
             }
-            if (!converged)
-                break;
+            if (!converged) break;
         }
     }
 }
 
-unsigned updateIterations(float inlier_ratio, unsigned iter)
-{
-    float w = std::min(std::max(inlier_ratio, 0.0f), 1.0f);
+unsigned updateIterations(float inlier_ratio, unsigned iter) {
+    float w  = std::min(std::max(inlier_ratio, 0.0f), 1.0f);
     float wn = pow(1 - w, 4.f);
 
     float d = 1.f - wn;
-    if (d < FLT_MIN)
-        return 0;
+    if (d < FLT_MIN) return 0;
 
     d = log(d);
 
     float p = std::min(std::max(RANSACConfidence, 0.0f), 1.0f);
     float n = log(1.f - p);
 
-    return n <= d*iter ? iter : (unsigned)round(n/d);
+    return n <= d * iter ? iter : (unsigned)round(n / d);
 }
 
 template<typename T>
-int computeHomography(T* H_ptr, const float* rnd_ptr,
-                      const float* x_src_ptr, const float* y_src_ptr,
-                      const float* x_dst_ptr, const float* y_dst_ptr)
-{
-    if ((unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[1] || (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[2] ||
-        (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[3] || (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[2] ||
-        (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[3] || (unsigned)rnd_ptr[2] == (unsigned)rnd_ptr[3])
+int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
+                      const float* y_src_ptr, const float* x_dst_ptr,
+                      const float* y_dst_ptr) {
+    if ((unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[1] ||
+        (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[2] ||
+        (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[3] ||
+        (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[2] ||
+        (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[3] ||
+        (unsigned)rnd_ptr[2] == (unsigned)rnd_ptr[3])
         return 1;
 
     float src_pt_x[4], src_pt_y[4], dst_pt_x[4], dst_pt_y[4];
@@ -166,10 +157,14 @@ int computeHomography(T* H_ptr, const float* rnd_ptr,
         dst_pt_y[j] = y_dst_ptr[(unsigned)rnd_ptr[j]];
     }
 
-    float x_src_mean = (src_pt_x[0] + src_pt_x[1] + src_pt_x[2] + src_pt_x[3]) / 4.f;
-    float y_src_mean = (src_pt_y[0] + src_pt_y[1] + src_pt_y[2] + src_pt_y[3]) / 4.f;
-    float x_dst_mean = (dst_pt_x[0] + dst_pt_x[1] + dst_pt_x[2] + dst_pt_x[3]) / 4.f;
-    float y_dst_mean = (dst_pt_y[0] + dst_pt_y[1] + dst_pt_y[2] + dst_pt_y[3]) / 4.f;
+    float x_src_mean =
+        (src_pt_x[0] + src_pt_x[1] + src_pt_x[2] + src_pt_x[3]) / 4.f;
+    float y_src_mean =
+        (src_pt_y[0] + src_pt_y[1] + src_pt_y[2] + src_pt_y[3]) / 4.f;
+    float x_dst_mean =
+        (dst_pt_x[0] + dst_pt_x[1] + dst_pt_x[2] + dst_pt_x[3]) / 4.f;
+    float y_dst_mean =
+        (dst_pt_y[0] + dst_pt_y[1] + dst_pt_y[2] + dst_pt_y[3]) / 4.f;
 
     float src_var = 0.0f, dst_var = 0.0f;
     for (unsigned j = 0; j < 4; j++) {
@@ -187,7 +182,7 @@ int computeHomography(T* H_ptr, const float* rnd_ptr,
     A.eval();
     getQueue().sync();
     af::dim4 Adims = A.dims();
-    T* A_ptr = A.get();
+    T* A_ptr       = A.get();
 
     for (unsigned j = 0; j < 4; j++) {
         float srcx = (src_pt_x[j] - x_src_mean) * src_scale;
@@ -195,19 +190,19 @@ int computeHomography(T* H_ptr, const float* rnd_ptr,
         float dstx = (dst_pt_x[j] - x_dst_mean) * dst_scale;
         float dsty = (dst_pt_y[j] - y_dst_mean) * dst_scale;
 
-        APTR(3, j*2) = -srcx;
-        APTR(4, j*2) = -srcy;
-        APTR(5, j*2) = -1.0f;
-        APTR(6, j*2) = dsty*srcx;
-        APTR(7, j*2) = dsty*srcy;
-        APTR(8, j*2) = dsty;
-
-        APTR(0, j*2+1) = srcx;
-        APTR(1, j*2+1) = srcy;
-        APTR(2, j*2+1) = 1.0f;
-        APTR(6, j*2+1) = -dstx*srcx;
-        APTR(7, j*2+1) = -dstx*srcy;
-        APTR(8, j*2+1) = -dstx;
+        APTR(3, j * 2) = -srcx;
+        APTR(4, j * 2) = -srcy;
+        APTR(5, j * 2) = -1.0f;
+        APTR(6, j * 2) = dsty * srcx;
+        APTR(7, j * 2) = dsty * srcy;
+        APTR(8, j * 2) = dsty;
+
+        APTR(0, j * 2 + 1) = srcx;
+        APTR(1, j * 2 + 1) = srcy;
+        APTR(2, j * 2 + 1) = 1.0f;
+        APTR(6, j * 2 + 1) = -dstx * srcx;
+        APTR(7, j * 2 + 1) = -dstx * srcy;
+        APTR(8, j * 2 + 1) = -dstx;
     }
 
     Array<T> V = createValueArray<T>(af::dim4(Adims[1], Adims[1]), (T)0);
@@ -216,42 +211,43 @@ int computeHomography(T* H_ptr, const float* rnd_ptr,
     JacobiSVD<T, 9, 9>(A.get(), V.get());
 
     dim4 Vdims = V.dims();
-    T* V_ptr = V.get();
+    T* V_ptr   = V.get();
 
     array<T, 9> vH;
-    for (unsigned j = 0; j < 9; j++)
-        vH[j] = V_ptr[8 * Vdims[0] + j];
-
-    H_ptr[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
-    H_ptr[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
-    H_ptr[2] = x_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-                          (vH[2] - src_scale*y_src_mean*vH[1] - src_scale*x_src_mean*vH[0])/dst_scale;
-
-    H_ptr[3] = src_scale*y_dst_mean*vH[6] + src_scale*vH[3]/dst_scale;
-    H_ptr[4] = src_scale*y_dst_mean*vH[7] + src_scale*vH[4]/dst_scale;
-    H_ptr[5] = y_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-                          (vH[5] - src_scale*y_src_mean*vH[4] - src_scale*x_src_mean*vH[3])/dst_scale;
-
-    H_ptr[6] = src_scale*vH[6];
-    H_ptr[7] = src_scale*vH[7];
-    H_ptr[8] = vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6];
+    for (unsigned j = 0; j < 9; j++) vH[j] = V_ptr[8 * Vdims[0] + j];
+
+    H_ptr[0] = src_scale * x_dst_mean * vH[6] + src_scale * vH[0] / dst_scale;
+    H_ptr[1] = src_scale * x_dst_mean * vH[7] + src_scale * vH[1] / dst_scale;
+    H_ptr[2] = x_dst_mean * (vH[8] - src_scale * y_src_mean * vH[7] -
+                             src_scale * x_src_mean * vH[6]) +
+               (vH[2] - src_scale * y_src_mean * vH[1] -
+                src_scale * x_src_mean * vH[0]) /
+                   dst_scale;
+
+    H_ptr[3] = src_scale * y_dst_mean * vH[6] + src_scale * vH[3] / dst_scale;
+    H_ptr[4] = src_scale * y_dst_mean * vH[7] + src_scale * vH[4] / dst_scale;
+    H_ptr[5] = y_dst_mean * (vH[8] - src_scale * y_src_mean * vH[7] -
+                             src_scale * x_src_mean * vH[6]) +
+               (vH[5] - src_scale * y_src_mean * vH[4] -
+                src_scale * x_src_mean * vH[3]) /
+                   dst_scale;
+
+    H_ptr[6] = src_scale * vH[6];
+    H_ptr[7] = src_scale * vH[7];
+    H_ptr[8] =
+        vH[8] - src_scale * y_src_mean * vH[7] - src_scale * x_src_mean * vH[6];
 
     return 0;
 }
 
-// LMedS: http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
+// LMedS:
+// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
 template<typename T>
-int findBestHomography(Array<T> &bestH,
-                       const Array<float> &x_src,
-                       const Array<float> &y_src,
-                       const Array<float> &x_dst,
-                       const Array<float> &y_dst,
-                       const Array<float> &rnd,
-                       const unsigned iterations,
-                       const unsigned nsamples,
-                       const float inlier_thr,
-                       const af_homography_type htype)
-{
+int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
+                       const Array<float>& y_src, const Array<float>& x_dst,
+                       const Array<float>& y_dst, const Array<float>& rnd,
+                       const unsigned iterations, const unsigned nsamples,
+                       const float inlier_thr, const af_homography_type htype) {
     const float* x_src_ptr = x_src.get();
     const float* y_src_ptr = y_src.get();
     const float* x_dst_ptr = x_dst.get();
@@ -264,47 +260,57 @@ int findBestHomography(Array<T> &bestH,
     const af::dim4 rdims = rnd.dims();
     const af::dim4 Hdims = H.dims();
 
-    unsigned iter = iterations;
-    unsigned bestIdx = 0;
+    unsigned iter        = iterations;
+    unsigned bestIdx     = 0;
     unsigned bestInliers = 0;
-    float minMedian = FLT_MAX;
+    float minMedian      = FLT_MAX;
 
     for (unsigned i = 0; i < iter; i++) {
         const unsigned Hidx = Hdims[0] * i;
-        T* H_ptr = H.get() + Hidx;
+        T* H_ptr            = H.get() + Hidx;
 
-        const unsigned ridx = rdims[0] * i;
+        const unsigned ridx  = rdims[0] * i;
         const float* rnd_ptr = rnd.get() + ridx;
 
-        if (computeHomography<T>(H_ptr, rnd_ptr, x_src_ptr, y_src_ptr, x_dst_ptr, y_dst_ptr))
+        if (computeHomography<T>(H_ptr, rnd_ptr, x_src_ptr, y_src_ptr,
+                                 x_dst_ptr, y_dst_ptr))
             continue;
 
         if (htype == AF_HOMOGRAPHY_RANSAC) {
             unsigned inliers_count = 0;
             for (unsigned j = 0; j < nsamples; j++) {
-                float z =  H_ptr[6]*x_src_ptr[j] + H_ptr[7]*y_src_ptr[j] + H_ptr[8];
-                float x = (H_ptr[0]*x_src_ptr[j] + H_ptr[1]*y_src_ptr[j] + H_ptr[2]) / z;
-                float y = (H_ptr[3]*x_src_ptr[j] + H_ptr[4]*y_src_ptr[j] + H_ptr[5]) / z;
+                float z = H_ptr[6] * x_src_ptr[j] + H_ptr[7] * y_src_ptr[j] +
+                          H_ptr[8];
+                float x = (H_ptr[0] * x_src_ptr[j] + H_ptr[1] * y_src_ptr[j] +
+                           H_ptr[2]) /
+                          z;
+                float y = (H_ptr[3] * x_src_ptr[j] + H_ptr[4] * y_src_ptr[j] +
+                           H_ptr[5]) /
+                          z;
 
                 float dist = sq(x_dst_ptr[j] - x) + sq(y_dst_ptr[j] - y);
-                if (dist < (inlier_thr*inlier_thr))
-                    inliers_count++;
+                if (dist < (inlier_thr * inlier_thr)) inliers_count++;
             }
-            iter = updateIterations((nsamples - inliers_count) / (float)nsamples, iter);
+            iter = updateIterations(
+                (nsamples - inliers_count) / (float)nsamples, iter);
             if (inliers_count > bestInliers) {
-                bestIdx = i;
+                bestIdx     = i;
                 bestInliers = inliers_count;
             }
-        }
-        else if (htype == AF_HOMOGRAPHY_LMEDS) {
+        } else if (htype == AF_HOMOGRAPHY_LMEDS) {
             std::vector<float> err(nsamples);
             for (unsigned j = 0; j < nsamples; j++) {
-                float z =  H_ptr[6]*x_src_ptr[j] + H_ptr[7]*y_src_ptr[j] + H_ptr[8];
-                float x = (H_ptr[0]*x_src_ptr[j] + H_ptr[1]*y_src_ptr[j] + H_ptr[2]) / z;
-                float y = (H_ptr[3]*x_src_ptr[j] + H_ptr[4]*y_src_ptr[j] + H_ptr[5]) / z;
+                float z = H_ptr[6] * x_src_ptr[j] + H_ptr[7] * y_src_ptr[j] +
+                          H_ptr[8];
+                float x = (H_ptr[0] * x_src_ptr[j] + H_ptr[1] * y_src_ptr[j] +
+                           H_ptr[2]) /
+                          z;
+                float y = (H_ptr[3] * x_src_ptr[j] + H_ptr[4] * y_src_ptr[j] +
+                           H_ptr[5]) /
+                          z;
 
                 float dist = sq(x_dst_ptr[j] - x) + sq(y_dst_ptr[j] - y);
-                err[j] = sqrt(dist);
+                err[j]     = sqrt(dist);
             }
 
             std::stable_sort(err.begin(), err.end());
@@ -315,26 +321,32 @@ int findBestHomography(Array<T> &bestH,
 
             if (median < minMedian && median > FLT_EPSILON) {
                 minMedian = median;
-                bestIdx = i;
+                bestIdx   = i;
             }
         }
     }
 
-    memcpy(bestH.get(), H.get() + bestIdx*9, 9 * sizeof(T));
+    memcpy(bestH.get(), H.get() + bestIdx * 9, 9 * sizeof(T));
 
     if (htype == AF_HOMOGRAPHY_LMEDS) {
-        float sigma = std::max(1.4826f * (1 + 5.f/(nsamples - 4)) * (float)sqrt(minMedian), 1e-6f);
+        float sigma = std::max(
+            1.4826f * (1 + 5.f / (nsamples - 4)) * (float)sqrt(minMedian),
+            1e-6f);
         float dist_thr = sq(2.5f * sigma);
-        T* bestH_ptr = bestH.get();
+        T* bestH_ptr   = bestH.get();
 
         for (unsigned j = 0; j < nsamples; j++) {
-            float z =  bestH_ptr[6]*x_src_ptr[j] + bestH_ptr[7]*y_src_ptr[j] + bestH_ptr[8];
-            float x = (bestH_ptr[0]*x_src_ptr[j] + bestH_ptr[1]*y_src_ptr[j] + bestH_ptr[2]) / z;
-            float y = (bestH_ptr[3]*x_src_ptr[j] + bestH_ptr[4]*y_src_ptr[j] + bestH_ptr[5]) / z;
+            float z = bestH_ptr[6] * x_src_ptr[j] +
+                      bestH_ptr[7] * y_src_ptr[j] + bestH_ptr[8];
+            float x = (bestH_ptr[0] * x_src_ptr[j] +
+                       bestH_ptr[1] * y_src_ptr[j] + bestH_ptr[2]) /
+                      z;
+            float y = (bestH_ptr[3] * x_src_ptr[j] +
+                       bestH_ptr[4] * y_src_ptr[j] + bestH_ptr[5]) /
+                      z;
 
             float dist = sq(x_dst_ptr[j] - x) + sq(y_dst_ptr[j] - y);
-            if (dist <= dist_thr)
-                bestInliers++;
+            if (dist <= dist_thr) bestInliers++;
         }
     }
 
@@ -342,46 +354,44 @@ int findBestHomography(Array<T> &bestH,
 }
 
 template<typename T>
-int homography(Array<T> &bestH,
-               const Array<float> &x_src,
-               const Array<float> &y_src,
-               const Array<float> &x_dst,
-               const Array<float> &y_dst,
-               const Array<float> &initial,
-               const af_homography_type htype,
-               const float inlier_thr,
-               const unsigned iterations)
-{
+int homography(Array<T>& bestH, const Array<float>& x_src,
+               const Array<float>& y_src, const Array<float>& x_dst,
+               const Array<float>& y_dst, const Array<float>& initial,
+               const af_homography_type htype, const float inlier_thr,
+               const unsigned iterations) {
     x_src.eval();
     y_src.eval();
     x_dst.eval();
     y_dst.eval();
 
-    const af::dim4 idims = x_src.dims();
+    const af::dim4 idims    = x_src.dims();
     const unsigned nsamples = idims[0];
 
     unsigned iter = iterations;
     if (htype == AF_HOMOGRAPHY_LMEDS)
-        iter = std::min(iter, (unsigned)(log(1.f - LMEDSConfidence) / log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
+        iter = std::min(
+            iter, (unsigned)(log(1.f - LMEDSConfidence) /
+                             log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
 
     af::dim4 rdims(4, iter);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
+    Array<float> rnd  = arithOp<float, af_mul_t>(initial, fctr, rdims);
     rnd.eval();
     getQueue().sync();
 
-    return findBestHomography<T>(bestH, x_src, y_src, x_dst, y_dst, rnd, iter, nsamples, inlier_thr, htype);
+    return findBestHomography<T>(bestH, x_src, y_src, x_dst, y_dst, rnd, iter,
+                                 nsamples, inlier_thr, htype);
 }
 
-#define INSTANTIATE(T)                                                                  \
-    template int homography<T>(Array<T> &bestH,                                         \
-                               const Array<float> &x_src, const Array<float> &y_src,    \
-                               const Array<float> &x_dst, const Array<float> &y_dst,    \
-                               const Array<float> &initial,                             \
-                               const af_homography_type htype, const float inlier_thr,  \
-                               const unsigned iterations);
+#define INSTANTIATE(T)                                          \
+    template int homography<T>(                                 \
+        Array<T> & bestH, const Array<float>& x_src,            \
+        const Array<float>& y_src, const Array<float>& x_dst,   \
+        const Array<float>& y_dst, const Array<float>& initial, \
+        const af_homography_type htype, const float inlier_thr, \
+        const unsigned iterations);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/homography.hpp b/src/backend/cpu/homography.hpp
index 1d4db42b1b..25acd7cb23 100644
--- a/src/backend/cpu/homography.hpp
+++ b/src/backend/cpu/homography.hpp
@@ -9,14 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-int homography(Array<T> &H,
-               const Array<float> &x_src, const Array<float> &y_src,
-               const Array<float> &x_dst, const Array<float> &y_dst,
-               const Array<float> &initial,
+int homography(Array<T> &H, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index 5c572cd4a9..1d2758f1a9 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -1,27 +1,25 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <hsv_rgb.hpp>
+#include <kernel/hsv_rgb.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/hsv_rgb.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> hsv2rgb(const Array<T>& in)
-{
+Array<T> hsv2rgb(const Array<T>& in) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
@@ -32,8 +30,7 @@ Array<T> hsv2rgb(const Array<T>& in)
 }
 
 template<typename T>
-Array<T> rgb2hsv(const Array<T>& in)
-{
+Array<T> rgb2hsv(const Array<T>& in) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
@@ -43,11 +40,11 @@ Array<T> rgb2hsv(const Array<T>& in)
     return out;
 }
 
-#define INSTANTIATE(T)  \
+#define INSTANTIATE(T)                                \
     template Array<T> hsv2rgb<T>(const Array<T>& in); \
-    template Array<T> rgb2hsv<T>(const Array<T>& in); \
+    template Array<T> rgb2hsv<T>(const Array<T>& in);
 
 INSTANTIATE(double)
-INSTANTIATE(float )
+INSTANTIATE(float)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/hsv_rgb.hpp b/src/backend/cpu/hsv_rgb.hpp
index 5c870a7a39..eac988b035 100644
--- a/src/backend/cpu/hsv_rgb.hpp
+++ b/src/backend/cpu/hsv_rgb.hpp
@@ -1,16 +1,15 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in);
@@ -18,4 +17,4 @@ Array<T> hsv2rgb(const Array<T>& in);
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index c5e11029fc..7ae8f4a96c 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -7,19 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <identity.hpp>
+#include <kernel/identity.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/identity.hpp>
+#include <af/dim4.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> identity(const dim4& dims)
-{
+Array<T> identity(const dim4& dims) {
     Array<T> out = createEmptyArray<T>(dims);
 
     getQueue().enqueue(kernel::identity<T>, out);
@@ -27,8 +25,8 @@ Array<T> identity(const dim4& dims)
     return out;
 }
 
-#define INSTANTIATE_IDENTITY(T)                              \
-    template Array<T>  identity<T>    (const af::dim4 &dims);
+#define INSTANTIATE_IDENTITY(T) \
+    template Array<T> identity<T>(const af::dim4& dims);
 
 INSTANTIATE_IDENTITY(float)
 INSTANTIATE_IDENTITY(double)
@@ -43,4 +41,4 @@ INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/identity.hpp b/src/backend/cpu/identity.hpp
index 4fd81b6d43..805214585c 100644
--- a/src/backend/cpu/identity.hpp
+++ b/src/backend/cpu/identity.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> identity(const dim4& dim);
+namespace cpu {
+template<typename T>
+Array<T> identity(const dim4& dim);
 }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index f37ae4795b..1f0ce4ed7c 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -7,22 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <iir.hpp>
 #include <convolve.hpp>
+#include <iir.hpp>
+#include <kernel/iir.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/iir.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
-{
+Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
     b.eval();
     a.eval();
     x.eval();
@@ -35,7 +33,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
     // Extract the first N elements
     Array<T> c = convolve<T, T, 1, true>(x, b, type);
     dim4 cdims = c.dims();
-    cdims[0] = x.dims()[0];
+    cdims[0]   = x.dims()[0];
     c.resetDims(cdims);
 
     Array<T> y = createEmptyArray<T>(c.dims());
@@ -45,14 +43,13 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
     return y;
 }
 
-#define INSTANTIATE(T)                          \
-    template Array<T> iir(const Array<T> &b,    \
-                          const Array<T> &a,    \
-                          const Array<T> &x);   \
+#define INSTANTIATE(T)                                          \
+    template Array<T> iir(const Array<T> &b, const Array<T> &a, \
+                          const Array<T> &x);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/iir.hpp b/src/backend/cpu/iir.hpp
index 4969dd0b95..2286fd91e6 100644
--- a/src/backend/cpu/iir.hpp
+++ b/src/backend/cpu/iir.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index be4be570b8..95836264ed 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -11,9 +11,9 @@
 // https://gist.github.com/SnopyDogy/a9a22497a893ec86aa3e
 
 #include <Array.hpp>
-#include <image.hpp>
-#include <err_cpu.hpp>
 #include <common/graphics_common.hpp>
+#include <err_cpu.hpp>
+#include <image.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
@@ -22,14 +22,13 @@ using af::dim4;
 namespace cpu {
 
 template<typename T>
-void copy_image(const Array<T> &in, fg_image image)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_image(const Array<T> &in, fg_image image) {
+    ForgeModule &_ = graphics::forgePlugin();
     in.eval();
     getQueue().sync();
 
     CheckGL("Before CopyArrayToImage");
-    const T *d_X = in.get();
+    const T *d_X       = in.get();
     unsigned data_size = 0, buffer = 0;
     FG_CHECK(_.fg_get_pixel_buffer(&buffer, image));
     FG_CHECK(_.fg_get_image_size(&data_size, image));
@@ -41,8 +40,7 @@ void copy_image(const Array<T> &in, fg_image image)
     CheckGL("In CopyArrayToImage");
 }
 
-#define INSTANTIATE(T)  \
-template void copy_image<T>(const Array<T> &, fg_image);
+#define INSTANTIATE(T) template void copy_image<T>(const Array<T> &, fg_image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -53,4 +51,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 4e5915be72..aacbc784f2 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -7,44 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <index.hpp>
 #include <handle.hpp>
-#include <vector>
+#include <index.hpp>
+#include <kernel/index.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <af/dim4.hpp>
 #include <utility>
-#include <kernel/index.hpp>
+#include <vector>
 
-using std::vector;
 using af::dim4;
+using std::vector;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> index(const Array<T>& in, const af_index_t idxrs[])
-{
+Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     in.eval();
 
     vector<bool> isSeq(4);
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (unsigned x=0; x<isSeq.size(); ++x) {
-        if (idxrs[x].isSeq) {
-            seqs[x] = idxrs[x].idx.seq;
-        }
+    for (unsigned x = 0; x < isSeq.size(); ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
         isSeq[x] = idxrs[x].isSeq;
     }
 
     // retrieve
     dim4 oDims = toDims(seqs, in.dims());
 
-    vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (unsigned x=0; x<isSeq.size(); ++x) {
+    for (unsigned x = 0; x < isSeq.size(); ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
             idxArrs[x].eval();
@@ -66,16 +62,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     template Array<T> index<T>(const Array<T>& in, const af_index_t idxrs[]);
 
 INSTANTIATE(cdouble)
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
-INSTANTIATE(float  )
-INSTANTIATE(uintl  )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(int    )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(ushort )
-INSTANTIATE(short  )
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(uintl)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(int)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/index.hpp b/src/backend/cpu/index.hpp
index d14b31b967..d397db3ed7 100644
--- a/src/backend/cpu/index.hpp
+++ b/src/backend/cpu/index.hpp
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/index.h>
 #include <Array.hpp>
+#include <af/index.h>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index fc2c3ad406..abfd63031d 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -7,48 +7,47 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <inverse.hpp>
 #include <common/err_common.hpp>
+#include <inverse.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
 
-#include <af/dim4.hpp>
+#include <err_cpu.hpp>
 #include <handle.hpp>
 #include <range.hpp>
+#include <af/dim4.hpp>
 #include <cassert>
-#include <err_cpu.hpp>
 
+#include <identity.hpp>
 #include <lapack_helper.hpp>
 #include <lu.hpp>
-#include <identity.hpp>
-#include <solve.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <solve.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-using getri_func_def = int (*)(ORDER_TYPE, int,
-                               T *, int,
-                               const int *);
+using getri_func_def = int (*)(ORDER_TYPE, int, T *, int, const int *);
 
-#define INV_FUNC_DEF( FUNC )                                        \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+#define INV_FUNC_DEF(FUNC) \
+    template<typename T>   \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define INV_FUNC( FUNC, TYPE, PREFIX )                              \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
+#define INV_FUNC(FUNC, TYPE, PREFIX)            \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-INV_FUNC_DEF( getri )
-INV_FUNC(getri , float  , s)
-INV_FUNC(getri , double , d)
-INV_FUNC(getri , cfloat , c)
-INV_FUNC(getri , cdouble, z)
+INV_FUNC_DEF(getri)
+INV_FUNC(getri, float, s)
+INV_FUNC(getri, double, d)
+INV_FUNC(getri, cfloat, c)
+INV_FUNC(getri, cdouble, z)
 
 template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
+Array<T> inverse(const Array<T> &in) {
     in.eval();
 
     int M = in.dims()[0];
@@ -59,12 +58,11 @@ Array<T> inverse(const Array<T> &in)
         return solve(in, I);
     }
 
-    Array<T> A = copyArray<T>(in);
+    Array<T> A       = copyArray<T>(in);
     Array<int> pivot = lu_inplace<T>(A, false);
 
-    auto func = [=] (Param<T> A, Param<int> pivot, int M) {
-        getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
-                        A.get(), A.strides(1),
+    auto func = [=](Param<T> A, Param<int> pivot, int M) {
+        getri_func<T>()(AF_LAPACK_COL_MAJOR, M, A.get(), A.strides(1),
                         pivot.get());
     };
     getQueue().enqueue(func, A, pivot, M);
@@ -72,36 +70,31 @@ Array<T> inverse(const Array<T> &in)
     return A;
 }
 
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cpu
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
-    AF_ERROR("Linear Algebra is disabled on CPU",
-              AF_ERR_NOT_CONFIGURED);
+Array<T> inverse(const Array<T> &in) {
+    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cpu
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/cpu/inverse.hpp b/src/backend/cpu/inverse.hpp
index 5823a6a318..460b2fd954 100644
--- a/src/backend/cpu/inverse.hpp
+++ b/src/backend/cpu/inverse.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> inverse(const Array<T> &in);
+namespace cpu {
+template<typename T>
+Array<T> inverse(const Array<T> &in);
 }
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 2a2abce0a6..8ae2a8c00f 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -9,19 +9,17 @@
 
 #include <Array.hpp>
 #include <iota.hpp>
+#include <kernel/iota.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/iota.hpp>
 
 using namespace std;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
-{
+Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
     dim4 outdims = dims * tile_dims;
 
     Array<T> out = createEmptyArray<T>(outdims);
@@ -31,8 +29,8 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
     return out;
 }
 
-#define INSTANTIATE(T)                                                          \
-    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims); \
+#define INSTANTIATE(T) \
+    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -44,4 +42,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/iota.hpp b/src/backend/cpu/iota.hpp
index 1ab9a9f3e0..c8551a14c4 100644
--- a/src/backend/cpu/iota.hpp
+++ b/src/backend/cpu/iota.hpp
@@ -10,9 +10,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
+namespace cpu {
+template<typename T>
+Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
 }
-
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 9cab39c502..06137be7d3 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -7,62 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
+#include <kernel/ireduce.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/ireduce.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<af_op_t op, typename T>
 using ireduce_dim_func = std::function<void(Param<T>, Param<uint>, const dim_t,
                                             CParam<T>, const dim_t, const int)>;
 
 template<af_op_t op, typename T>
-void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim)
-{
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim) {
     out.eval();
     loc.eval();
     in.eval();
 
-    dim4 odims = in.dims();
-    odims[dim] = 1;
-    static const ireduce_dim_func<op, T> ireduce_funcs[] = { kernel::ireduce_dim<op, T, 1>()
-                                                           , kernel::ireduce_dim<op, T, 2>()
-                                                           , kernel::ireduce_dim<op, T, 3>()
-                                                           , kernel::ireduce_dim<op, T, 4>()};
+    dim4 odims                                           = in.dims();
+    odims[dim]                                           = 1;
+    static const ireduce_dim_func<op, T> ireduce_funcs[] = {
+        kernel::ireduce_dim<op, T, 1>(), kernel::ireduce_dim<op, T, 2>(),
+        kernel::ireduce_dim<op, T, 3>(), kernel::ireduce_dim<op, T, 4>()};
 
     getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
 }
 
 template<af_op_t op, typename T>
-T ireduce_all(unsigned *loc, const Array<T> &in)
-{
+T ireduce_all(unsigned *loc, const Array<T> &in) {
     in.eval();
     getQueue().sync();
 
-    af::dim4 dims = in.dims();
+    af::dim4 dims    = in.dims();
     af::dim4 strides = in.strides();
-    const T *inPtr = in.get();
+    const T *inPtr   = in.get();
 
     kernel::MinMaxOp<op, T> Op(inPtr[0], 0);
 
-    for(dim_t l = 0; l < dims[3]; l++) {
+    for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
 
-        for(dim_t k = 0; k < dims[2]; k++) {
+        for (dim_t k = 0; k < dims[2]; k++) {
             dim_t off2 = k * strides[2];
 
-            for(dim_t j = 0; j < dims[1]; j++) {
+            for (dim_t j = 0; j < dims[1]; j++) {
                 dim_t off1 = j * strides[1];
 
-                for(dim_t i = 0; i < dims[0]; i++) {
+                for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
                     Op(inPtr[idx], idx);
                 }
@@ -74,37 +71,37 @@ T ireduce_all(unsigned *loc, const Array<T> &in)
     return Op.m_val;
 }
 
-#define INSTANTIATE(ROp, T)                                             \
-    template void ireduce<ROp, T>(Array<T> &out, Array<uint> &loc,      \
-                                  const Array<T> &in, const int dim);   \
-    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);  \
+#define INSTANTIATE(ROp, T)                                           \
+    template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim); \
+    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
 
-//min
-INSTANTIATE(af_min_t, float  )
-INSTANTIATE(af_min_t, double )
-INSTANTIATE(af_min_t, cfloat )
+// min
+INSTANTIATE(af_min_t, float)
+INSTANTIATE(af_min_t, double)
+INSTANTIATE(af_min_t, cfloat)
 INSTANTIATE(af_min_t, cdouble)
-INSTANTIATE(af_min_t, int    )
-INSTANTIATE(af_min_t, uint   )
-INSTANTIATE(af_min_t, intl   )
-INSTANTIATE(af_min_t, uintl  )
-INSTANTIATE(af_min_t, char   )
-INSTANTIATE(af_min_t, uchar  )
-INSTANTIATE(af_min_t, short  )
-INSTANTIATE(af_min_t, ushort )
-
-//max
-INSTANTIATE(af_max_t, float  )
-INSTANTIATE(af_max_t, double )
-INSTANTIATE(af_max_t, cfloat )
+INSTANTIATE(af_min_t, int)
+INSTANTIATE(af_min_t, uint)
+INSTANTIATE(af_min_t, intl)
+INSTANTIATE(af_min_t, uintl)
+INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, uchar)
+INSTANTIATE(af_min_t, short)
+INSTANTIATE(af_min_t, ushort)
+
+// max
+INSTANTIATE(af_max_t, float)
+INSTANTIATE(af_max_t, double)
+INSTANTIATE(af_max_t, cfloat)
 INSTANTIATE(af_max_t, cdouble)
-INSTANTIATE(af_max_t, int    )
-INSTANTIATE(af_max_t, uint   )
-INSTANTIATE(af_max_t, intl   )
-INSTANTIATE(af_max_t, uintl  )
-INSTANTIATE(af_max_t, char   )
-INSTANTIATE(af_max_t, uchar  )
-INSTANTIATE(af_max_t, short  )
-INSTANTIATE(af_max_t, ushort )
-
-}
+INSTANTIATE(af_max_t, int)
+INSTANTIATE(af_max_t, uint)
+INSTANTIATE(af_max_t, intl)
+INSTANTIATE(af_max_t, uintl)
+INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, uchar)
+INSTANTIATE(af_max_t, short)
+INSTANTIATE(af_max_t, ushort)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/ireduce.hpp b/src/backend/cpu/ireduce.hpp
index 00206293fe..9efe8312f6 100644
--- a/src/backend/cpu/ireduce.hpp
+++ b/src/backend/cpu/ireduce.hpp
@@ -10,12 +10,11 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cpu
-{
-    template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc,
-                 const Array<T> &in, const int dim);
+namespace cpu {
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim);
 
-    template<af_op_t op, typename T>
-    T ireduce_all(unsigned *loc, const Array<T> &in);
-}
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in);
+}  // namespace cpu
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 41a61cbe37..4e69165717 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -8,66 +8,53 @@
  ********************************************************/
 
 #pragma once
+#include <math.hpp>
 #include <optypes.hpp>
+#include <array>
 #include <vector>
-#include <math.hpp>
 #include "Node.hpp"
-#include <array>
-
-namespace cpu
-{
-
-    template<typename To, typename Ti, af_op_t op>
-    struct BinOp
-    {
-        void eval(jit::array<To> &out,
-                  const jit::array<Ti> &lhs,
-                  const jit::array<Ti> &rhs,
-                  int lim) const
-        {
-            UNUSED(lhs);
-            UNUSED(rhs);
-            for (int i = 0; i < lim; i++) {
-                out[i] = scalar<To>(0);
-            }
-        }
-    };
-
-namespace jit
-{
-
-    template<typename To, typename Ti, af_op_t op>
-    class BinaryNode  : public TNode<To>
-    {
-
-    protected:
-        BinOp<To, Ti, op> m_op;
-        TNode<Ti> *m_lhs, *m_rhs;
-
-    public:
-        BinaryNode(Node_ptr lhs, Node_ptr rhs) :
-            TNode<To>(0, std::max(lhs->getHeight(), rhs->getHeight()) + 1, {{lhs, rhs}}),
-            m_lhs(reinterpret_cast<TNode<Ti> *>(lhs.get())),
-            m_rhs(reinterpret_cast<TNode<Ti> *>(rhs.get()))
-        {
-        }
-
-        void calc(int x, int y, int z, int w, int lim) final
-        {
-            UNUSED(x);
-            UNUSED(y);
-            UNUSED(z);
-            UNUSED(w);
-            m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
-        }
-
-        void calc(int idx, int lim) final
-        {
-            UNUSED(idx);
-            m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
-        }
-    };
-
-}
 
-}
+namespace cpu {
+
+template<typename To, typename Ti, af_op_t op>
+struct BinOp {
+    void eval(jit::array<To> &out, const jit::array<Ti> &lhs,
+              const jit::array<Ti> &rhs, int lim) const {
+        UNUSED(lhs);
+        UNUSED(rhs);
+        for (int i = 0; i < lim; i++) { out[i] = scalar<To>(0); }
+    }
+};
+
+namespace jit {
+
+template<typename To, typename Ti, af_op_t op>
+class BinaryNode : public TNode<To> {
+   protected:
+    BinOp<To, Ti, op> m_op;
+    TNode<Ti> *m_lhs, *m_rhs;
+
+   public:
+    BinaryNode(Node_ptr lhs, Node_ptr rhs)
+        : TNode<To>(0, std::max(lhs->getHeight(), rhs->getHeight()) + 1,
+                    {{lhs, rhs}})
+        , m_lhs(reinterpret_cast<TNode<Ti> *>(lhs.get()))
+        , m_rhs(reinterpret_cast<TNode<Ti> *>(rhs.get())) {}
+
+    void calc(int x, int y, int z, int w, int lim) final {
+        UNUSED(x);
+        UNUSED(y);
+        UNUSED(z);
+        UNUSED(w);
+        m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
+    }
+
+    void calc(int idx, int lim) final {
+        UNUSED(idx);
+        m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
+    }
+};
+
+}  // namespace jit
+
+}  // namespace cpu
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index f81cc49902..f3729c6198 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -9,102 +9,81 @@
 
 #pragma once
 #include <optypes.hpp>
+#include <mutex>
 #include <vector>
 #include "Node.hpp"
-#include <mutex>
-namespace cpu
-{
-
-namespace jit
-{
+namespace cpu {
 
-    using std::shared_ptr;
-    template<typename T>
-    class BufferNode : public TNode<T>
-    {
+namespace jit {
 
-    protected:
-        shared_ptr<T> m_sptr;
-        T *m_ptr;
-        unsigned m_bytes;
-        dim_t m_strides[4];
-        dim_t m_dims[4];
-        std::once_flag m_set_data_flag;
-        bool m_linear_buffer;
-    public:
+using std::shared_ptr;
+template<typename T>
+class BufferNode : public TNode<T> {
+   protected:
+    shared_ptr<T> m_sptr;
+    T *m_ptr;
+    unsigned m_bytes;
+    dim_t m_strides[4];
+    dim_t m_dims[4];
+    std::once_flag m_set_data_flag;
+    bool m_linear_buffer;
 
-        BufferNode() : TNode<T>(0, 0, {})
-        {}
-
-        void setData(shared_ptr<T> data,
-                     unsigned bytes,
-                     dim_t data_off,
-                     const dim_t *dims,
-                     const dim_t *strides,
-                     const bool is_linear)
-        {
-            std::call_once(m_set_data_flag,
-                           [this, data, bytes,
-                            data_off, dims, strides, is_linear]()
-                           {
-                               m_sptr = data;
-                               m_ptr = data.get() + data_off;
-                               m_bytes = bytes;
-                               m_linear_buffer = is_linear;
-                               for (int i = 0; i < 4; i++) {
-                                   m_strides[i] = strides[i];
-                                   m_dims[i] = dims[i];
-                               }
-                           });
-        }
+   public:
+    BufferNode() : TNode<T>(0, 0, {}) {}
 
-        void calc(int x, int y, int z, int w, int lim) final
-        {
-            dim_t l_off = 0;
-            l_off += (w < (int)m_dims[3]) * w * m_strides[3];
-            l_off += (z < (int)m_dims[2]) * z * m_strides[2];
-            l_off += (y < (int)m_dims[1]) * y * m_strides[1];
-            T *in_ptr = m_ptr + l_off;
-            T *out_ptr = this->m_val.data();
-            for(int i = 0; i < lim; i++) {
-                out_ptr[i] = in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0];
+    void setData(shared_ptr<T> data, unsigned bytes, dim_t data_off,
+                 const dim_t *dims, const dim_t *strides,
+                 const bool is_linear) {
+        std::call_once(m_set_data_flag, [this, data, bytes, data_off, dims,
+                                         strides, is_linear]() {
+            m_sptr          = data;
+            m_ptr           = data.get() + data_off;
+            m_bytes         = bytes;
+            m_linear_buffer = is_linear;
+            for (int i = 0; i < 4; i++) {
+                m_strides[i] = strides[i];
+                m_dims[i]    = dims[i];
             }
-        }
+        });
+    }
 
-        void calc(int idx, int lim) final
-        {
-            T *in_ptr = m_ptr + idx;
-            T *out_ptr = this->m_val.data();
-            for(int i = 0; i < lim; i++) {
-                out_ptr[i] = in_ptr[i];
-            }
+    void calc(int x, int y, int z, int w, int lim) final {
+        dim_t l_off = 0;
+        l_off += (w < (int)m_dims[3]) * w * m_strides[3];
+        l_off += (z < (int)m_dims[2]) * z * m_strides[2];
+        l_off += (y < (int)m_dims[1]) * y * m_strides[1];
+        T *in_ptr  = m_ptr + l_off;
+        T *out_ptr = this->m_val.data();
+        for (int i = 0; i < lim; i++) {
+            out_ptr[i] = in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0];
         }
+    }
 
-        void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const final
-        {
-            len++;
-            buf_count++;
-            bytes += m_bytes;
-            return;
-        }
+    void calc(int idx, int lim) final {
+        T *in_ptr  = m_ptr + idx;
+        T *out_ptr = this->m_val.data();
+        for (int i = 0; i < lim; i++) { out_ptr[i] = in_ptr[i]; }
+    }
 
-        size_t getBytes() const final {
-            return m_bytes;
-        }
+    void getInfo(unsigned &len, unsigned &buf_count,
+                 unsigned &bytes) const final {
+        len++;
+        buf_count++;
+        bytes += m_bytes;
+        return;
+    }
 
-        bool isLinear(const dim_t *dims) const final
-        {
-            return m_linear_buffer &&
-                dims[0] == m_dims[0] &&
-                dims[1] == m_dims[1] &&
-                dims[2] == m_dims[2] &&
-                dims[3] == m_dims[3];
-        }
+    size_t getBytes() const final { return m_bytes; }
 
-        bool isBuffer() const final { return true; }
+    bool isLinear(const dim_t *dims) const final {
+        return m_linear_buffer && dims[0] == m_dims[0] &&
+               dims[1] == m_dims[1] && dims[2] == m_dims[2] &&
+               dims[3] == m_dims[3];
+    }
 
-    };
+    bool isBuffer() const final { return true; }
+};
 
-}
+}  // namespace jit
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index ddb913c682..952f015072 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -12,109 +12,104 @@
 #include <optypes.hpp>
 
 #include <array>
-#include <vector>
 #include <memory>
 #include <unordered_map>
+#include <vector>
 
 namespace common {
-    template<typename T>
-    class NodeIterator;
+template<typename T>
+class NodeIterator;
 }
 
-namespace cpu
-{
-
-namespace jit
-{
-    class Node;
-    constexpr int VECTOR_LENGTH = 256;
-
-    using Node_ptr = std::shared_ptr<Node>;
-    using Node_map_t = std::unordered_map<Node *, int>;
-    using Node_map_iter = Node_map_t::iterator;
-
-    template<typename T>
-    using array = std::array<T, VECTOR_LENGTH>;
-
-    class Node
-    {
-    public:
-        static const int kMaxChildren = 2;
-    protected:
-        const int m_height;
-        const std::array<Node_ptr, kMaxChildren> m_children;
-        template<typename T> friend class common::NodeIterator;
-
-    public:
-        Node(const int height, const std::array<Node_ptr, kMaxChildren> children) :
-            m_height(height),
-            m_children(children)
-        {}
-
-        int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes)
-        {
-            auto iter = node_map.find(this);
-            if (iter == node_map.end()) {
-                for (auto &child : m_children) {
-                    if (child == nullptr) break;
-                    child->getNodesMap(node_map, full_nodes);
-                }
-                int id = static_cast<int>(node_map.size());
-                node_map[this] = id;
-                full_nodes.push_back(this);
-                return id;
-            }
-            return iter->second;
-        }
-
-        int getHeight() { return m_height; }
-
-        virtual void calc(int x, int y, int z, int w, int lim) {
-            UNUSED(x);
-            UNUSED(y);
-            UNUSED(z);
-            UNUSED(w);
-            UNUSED(lim);
-        }
+namespace cpu {
 
-        virtual void calc(int idx, int lim) {
-            UNUSED(idx);
-            UNUSED(lim);
-        }
+namespace jit {
+class Node;
+constexpr int VECTOR_LENGTH = 256;
 
-        virtual void getInfo(unsigned &len, unsigned &buf_count, unsigned &bytes) const {
-            UNUSED(buf_count);
-            UNUSED(bytes);
-            len++;
-        }
+using Node_ptr      = std::shared_ptr<Node>;
+using Node_map_t    = std::unordered_map<Node *, int>;
+using Node_map_iter = Node_map_t::iterator;
 
-        virtual bool isLinear(const dim_t *dims) const {
-            UNUSED(dims);
-            return true;
-        }
-        virtual bool isBuffer() const { return false; }
-        virtual ~Node() {}
+template<typename T>
+using array = std::array<T, VECTOR_LENGTH>;
 
-        virtual size_t getBytes() const {
-          return 0;
-        }
-    };
+class Node {
+   public:
+    static const int kMaxChildren = 2;
 
+   protected:
+    const int m_height;
+    const std::array<Node_ptr, kMaxChildren> m_children;
     template<typename T>
-    class TNode : public Node
-    {
-    public:
-        alignas(16) jit::array<T> m_val;
-    public:
-        TNode(T val, const int height, const std::array<Node_ptr, kMaxChildren> children) :
-            Node(height, children)
-            {
-                m_val.fill(val);
+    friend class common::NodeIterator;
+
+   public:
+    Node(const int height, const std::array<Node_ptr, kMaxChildren> children)
+        : m_height(height), m_children(children) {}
+
+    int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes) {
+        auto iter = node_map.find(this);
+        if (iter == node_map.end()) {
+            for (auto &child : m_children) {
+                if (child == nullptr) break;
+                child->getNodesMap(node_map, full_nodes);
             }
-    };
-
-    template<typename T>
-    using TNode_ptr = std::shared_ptr<TNode<T>>;
-}
-
-}
+            int id         = static_cast<int>(node_map.size());
+            node_map[this] = id;
+            full_nodes.push_back(this);
+            return id;
+        }
+        return iter->second;
+    }
+
+    int getHeight() { return m_height; }
+
+    virtual void calc(int x, int y, int z, int w, int lim) {
+        UNUSED(x);
+        UNUSED(y);
+        UNUSED(z);
+        UNUSED(w);
+        UNUSED(lim);
+    }
+
+    virtual void calc(int idx, int lim) {
+        UNUSED(idx);
+        UNUSED(lim);
+    }
+
+    virtual void getInfo(unsigned &len, unsigned &buf_count,
+                         unsigned &bytes) const {
+        UNUSED(buf_count);
+        UNUSED(bytes);
+        len++;
+    }
+
+    virtual bool isLinear(const dim_t *dims) const {
+        UNUSED(dims);
+        return true;
+    }
+    virtual bool isBuffer() const { return false; }
+    virtual ~Node() {}
+
+    virtual size_t getBytes() const { return 0; }
+};
+
+template<typename T>
+class TNode : public Node {
+   public:
+    alignas(16) jit::array<T> m_val;
+
+   public:
+    TNode(T val, const int height,
+          const std::array<Node_ptr, kMaxChildren> children)
+        : Node(height, children) {
+        m_val.fill(val);
+    }
+};
+
+template<typename T>
+using TNode_ptr = std::shared_ptr<TNode<T>>;
+}  // namespace jit
+
+}  // namespace cpu
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index 5adae29f22..afb4ca8768 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -12,21 +12,15 @@
 #include <vector>
 #include "Node.hpp"
 
-namespace cpu
-{
+namespace cpu {
 
-namespace jit
-{
+namespace jit {
 
-    template<typename T>
-    class ScalarNode : public TNode<T>
-    {
+template<typename T>
+class ScalarNode : public TNode<T> {
+   public:
+    ScalarNode(T val) : TNode<T>(val, 0, {}) {}
+};
+}  // namespace jit
 
-    public:
-        ScalarNode(T val) : TNode<T>(val, 0, {})
-        {
-        }
-    };
-}
-
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 0948f4ea4a..31d87ebce0 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -8,60 +8,46 @@
  ********************************************************/
 
 #pragma once
+#include <math.hpp>
 #include <optypes.hpp>
 #include <vector>
-#include <math.hpp>
 #include "Node.hpp"
 
-namespace cpu
-{
-    template<typename To, typename Ti, af_op_t op>
-    struct UnOp
-    {
-        void eval(jit::array<To> &out,
-                  const jit::array<Ti> &in, int lim) const
-        {
-            for (int i = 0; i < lim; i++) {
-                out[i] = To(in[i]);
-            }
-        }
-    };
-
-namespace jit
-{
-
-    template<typename To, typename Ti, af_op_t op>
-    class UnaryNode  : public TNode<To>
-    {
-
-    protected:
-        UnOp<To, Ti, op> m_op;
-        TNode<Ti> *m_child;
-
-    public:
-        UnaryNode(Node_ptr child) :
-            TNode<To>(0, child->getHeight() + 1, {{child}}),
-            m_child(reinterpret_cast<TNode<Ti> *>(child.get()))
-        {
-        }
-
-        void calc(int x, int y, int z, int w, int lim) final
-        {
-            UNUSED(x);
-            UNUSED(y);
-            UNUSED(z);
-            UNUSED(w);
-            m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
-        }
-
-        void calc(int idx, int lim) final
-        {
-            UNUSED(idx);
-            m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
-        }
-
-    };
-
-}
-
-}
+namespace cpu {
+template<typename To, typename Ti, af_op_t op>
+struct UnOp {
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) const {
+        for (int i = 0; i < lim; i++) { out[i] = To(in[i]); }
+    }
+};
+
+namespace jit {
+
+template<typename To, typename Ti, af_op_t op>
+class UnaryNode : public TNode<To> {
+   protected:
+    UnOp<To, Ti, op> m_op;
+    TNode<Ti> *m_child;
+
+   public:
+    UnaryNode(Node_ptr child)
+        : TNode<To>(0, child->getHeight() + 1, {{child}})
+        , m_child(reinterpret_cast<TNode<Ti> *>(child.get())) {}
+
+    void calc(int x, int y, int z, int w, int lim) final {
+        UNUSED(x);
+        UNUSED(y);
+        UNUSED(z);
+        UNUSED(w);
+        m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
+    }
+
+    void calc(int idx, int lim) final {
+        UNUSED(idx);
+        m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
+    }
+};
+
+}  // namespace jit
+
+}  // namespace cpu
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index a7af895b06..0cb2c93315 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -9,16 +9,14 @@
 
 #include <Array.hpp>
 #include <join.hpp>
+#include <kernel/join.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/join.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
-{
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
     first.eval();
     second.eval();
 
@@ -28,8 +26,8 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
     af::dim4 fdims = first.dims();
     af::dim4 sdims = second.dims();
 
-    for(int i = 0; i < 4; i++) {
-        if(i == dim) {
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
             odims[i] = fdims[i] + sdims[i];
         } else {
             odims[i] = fdims[i];
@@ -44,10 +42,8 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
 }
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
-{
-    for (unsigned i=0; i<inputs.size(); ++i)
-        inputs[i].eval();
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
+    for (unsigned i = 0; i < inputs.size(); ++i) inputs[i].eval();
     // All dimensions except join dimension must be equal
     // Compute output dims
     af::dim4 odims;
@@ -55,13 +51,13 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
     std::vector<af::dim4> idims(n_arrays);
 
     dim_t dim_size = 0;
-    for(unsigned i = 0; i < idims.size(); i++) {
+    for (unsigned i = 0; i < idims.size(); i++) {
         idims[i] = inputs[i].dims();
         dim_size += idims[i][dim];
     }
 
-    for(int i = 0; i < 4; i++) {
-        if(i == dim) {
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
             odims[i] = dim_size;
         } else {
             odims[i] = idims[0][i];
@@ -71,7 +67,7 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
     std::vector<CParam<T>> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
-    switch(n_arrays) {
+    switch (n_arrays) {
         case 1:
             getQueue().enqueue(kernel::join<T, 1>, dim, out, inputParams);
             break;
@@ -100,33 +96,35 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs)
             getQueue().enqueue(kernel::join<T, 9>, dim, out, inputParams);
             break;
         case 10:
-            getQueue().enqueue(kernel::join<T,10>, dim, out, inputParams);
+            getQueue().enqueue(kernel::join<T, 10>, dim, out, inputParams);
             break;
     }
 
     return out;
 }
 
-#define INSTANTIATE(Tx, Ty) \
-    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+#define INSTANTIATE(Tx, Ty)                                                \
+    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, \
+                                    const Array<Ty> &second);
 
-INSTANTIATE(float,   float)
-INSTANTIATE(double,  double)
-INSTANTIATE(cfloat,  cfloat)
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, cfloat)
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(int,     int)
-INSTANTIATE(uint,    uint)
-INSTANTIATE(intl,    intl)
-INSTANTIATE(uintl,   uintl)
-INSTANTIATE(uchar,   uchar)
-INSTANTIATE(char,    char)
-INSTANTIATE(ushort,  ushort)
-INSTANTIATE(short,   short)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(uchar, uchar)
+INSTANTIATE(char, char)
+INSTANTIATE(ushort, ushort)
+INSTANTIATE(short, short)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)      \
-    template Array<T> join<T>(const int dim, const std::vector<Array<T>> &inputs);
+#define INSTANTIATE(T)                       \
+    template Array<T> join<T>(const int dim, \
+                              const std::vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -142,4 +140,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 #undef INSTANTIATE
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/join.hpp b/src/backend/cpu/join.hpp
index aa2ae8d76c..847d6dc7eb 100644
--- a/src/backend/cpu/join.hpp
+++ b/src/backend/cpu/join.hpp
@@ -10,11 +10,10 @@
 #include <Array.hpp>
 #include <vector>
 
-namespace cpu
-{
-    template<typename Tx, typename Ty>
-    Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+namespace cpu {
+template<typename Tx, typename Ty>
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
 
-    template<typename T>
-    Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
-}
+template<typename T>
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 85a50a8879..a8b3fbb512 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -9,18 +9,16 @@
 
 #pragma once
 #include <Param.hpp>
-#include <platform.hpp>
 #include <jit/Node.hpp>
+#include <platform.hpp>
 #include <vector>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> output_nodes_)
-{
+void evalMultiple(std::vector<Param<T>> arrays,
+                  std::vector<jit::Node_ptr> output_nodes_) {
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
@@ -32,18 +30,18 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> outpu
     int narrays = static_cast<int>(arrays.size());
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
-        output_nodes.push_back(reinterpret_cast<jit::TNode<T> *>(output_nodes_[i].get()));
+        output_nodes.push_back(
+            reinterpret_cast<jit::TNode<T> *>(output_nodes_[i].get()));
         output_nodes_[i]->getNodesMap(nodes, full_nodes);
     }
 
     bool is_linear = true;
-    for(auto node : full_nodes) {
-        is_linear &= node->isLinear(odims.get());
-    }
+    for (auto node : full_nodes) { is_linear &= node->isLinear(odims.get()); }
 
     if (is_linear) {
         int num = arrays[0].dims().elements();
-        int cnum = jit::VECTOR_LENGTH * std::ceil(double(num) / jit::VECTOR_LENGTH);
+        int cnum =
+            jit::VECTOR_LENGTH * std::ceil(double(num) / jit::VECTOR_LENGTH);
         for (int i = 0; i < cnum; i += jit::VECTOR_LENGTH) {
             int lim = std::min(jit::VECTOR_LENGTH, num - i);
             for (int n = 0; n < (int)full_nodes.size(); n++) {
@@ -51,12 +49,10 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> outpu
             }
             for (int n = 0; n < (int)output_nodes.size(); n++) {
                 std::copy(output_nodes[n]->m_val.begin(),
-                          output_nodes[n]->m_val.begin() + lim,
-                          ptrs[n] + i);
+                          output_nodes[n]->m_val.begin() + lim, ptrs[n] + i);
             }
         }
     } else {
-
         for (int w = 0; w < (int)odims[3]; w++) {
             dim_t offw = w * ostrs[3];
 
@@ -66,10 +62,11 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> outpu
                 for (int y = 0; y < (int)odims[1]; y++) {
                     dim_t offy = y * ostrs[1] + offz;
 
-                    int dim0 = odims[0];
-                    int cdim0 = jit::VECTOR_LENGTH * std::ceil(double(dim0) / jit::VECTOR_LENGTH);
+                    int dim0  = odims[0];
+                    int cdim0 = jit::VECTOR_LENGTH *
+                                std::ceil(double(dim0) / jit::VECTOR_LENGTH);
                     for (int x = 0; x < (int)cdim0; x += jit::VECTOR_LENGTH) {
-                        int lim = std::min(jit::VECTOR_LENGTH, dim0 - x);
+                        int lim  = std::min(jit::VECTOR_LENGTH, dim0 - x);
                         dim_t id = x + offy;
 
                         for (int n = 0; n < (int)full_nodes.size(); n++) {
@@ -88,10 +85,9 @@ void evalMultiple(std::vector<Param<T>> arrays, std::vector<jit::Node_ptr> outpu
 }
 
 template<typename T>
-void evalArray(Param<T> arr, jit::Node_ptr node)
-{
+void evalArray(Param<T> arr, jit::Node_ptr node) {
     evalMultiple<T>({arr}, {node});
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/anisotropic_diffusion.hpp b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
index e7798c3b9f..0a8e773f00 100644
--- a/src/backend/cpu/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
@@ -12,89 +12,80 @@
 #include <Array.hpp>
 #include <math.hpp>
 
+#include <algorithm>
 #include <cassert>
 #include <cmath>
-#include <algorithm>
 
 using std::exp;
 using std::pow;
 using std::sqrt;
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-int index(int x, int y, int stride1)
-{
-    return y*stride1+x;
-}
+int index(int x, int y, int stride1) { return y * stride1 + x; }
 
-float quad(float value)
-{
-  return 1.0f/(1.0f+value);
-}
+float quad(float value) { return 1.0f / (1.0f + value); }
 
-float computeGradientBasedUpdate(const float mct,
-                                 const float NW, const float N, const float NE,
-                                 const float  W, const float C, const float  E,
-                                 const float SW, const float S, const float SE, const af_flux_function fftype)
-{
+float computeGradientBasedUpdate(const float mct, const float NW, const float N,
+                                 const float NE, const float W, const float C,
+                                 const float E, const float SW, const float S,
+                                 const float SE,
+                                 const af_flux_function fftype) {
     float delta = 0.f;
 
     float dx, dy, df, db, cx, cxd;
 
     // centralized derivatives
-    dx = (E-W)*0.5f;
-    dy = (S-N)*0.5f;
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
 
     // half-d's and conductance along first dimension
-    df  = E - C;
-    db  = C - W;
+    df = E - C;
+    db = C - W;
 
-    float gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f)) * mct ;
-    float gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f)) * mct;
-    if (fftype==AF_FLUX_EXPONENTIAL) {
+    float gmsqf = (df * df + 0.25f * pow(dy + 0.5f * (SE - NE), 2.f)) * mct;
+    float gmsqb = (db * db + 0.25f * pow(dy + 0.5f * (SW - NW), 2.f)) * mct;
+    if (fftype == AF_FLUX_EXPONENTIAL) {
         cx  = exp(gmsqf);
         cxd = exp(gmsqb);
     } else {
         cx  = quad(gmsqf);
         cxd = quad(gmsqb);
     }
-    delta = (cx*df - cxd*db);
+    delta = (cx * df - cxd * db);
 
     // half-d's and conductance along second dimension
-    df  = S - C;
-    db  = C - N;
+    df = S - C;
+    db = C - N;
 
-    gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f)) * mct;
-    gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f)) * mct;
-    if (fftype==AF_FLUX_EXPONENTIAL) {
+    gmsqf = (df * df + 0.25f * pow(dx + 0.5f * (SE - SW), 2.f)) * mct;
+    gmsqb = (db * db + 0.25f * pow(dx + 0.5f * (NE - NW), 2.f)) * mct;
+    if (fftype == AF_FLUX_EXPONENTIAL) {
         cx  = exp(gmsqf);
         cxd = exp(gmsqb);
     } else {
         cx  = quad(gmsqf);
         cxd = quad(gmsqb);
     }
-    delta += (cx*df - cxd*db);
+    delta += (cx * df - cxd * db);
 
     return delta;
 }
 
-float computeCurvatureBasedUpdate(const float mct,
-                                  const float NW, const float N, const float NE,
-                                  const float  W, const float C, const float  E,
-                                  const float SW, const float S, const float SE)
-{
-    float delta = 0.f;
+float computeCurvatureBasedUpdate(const float mct, const float NW,
+                                  const float N, const float NE, const float W,
+                                  const float C, const float E, const float SW,
+                                  const float S, const float SE) {
+    float delta     = 0.f;
     float prop_grad = 0.f;
 
     float df0, db0;
     float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
 
     // centralized derivatives
-    dx = (E-W)*0.5f;
-    dy = (S-N)*0.5f;
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
 
     // half-d's and conductance along first dimension
     df  = E - C;
@@ -102,59 +93,61 @@ float computeCurvatureBasedUpdate(const float mct,
     df0 = df;
     db0 = db;
 
-    gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2.f));
-    gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2.f));
+    gmsqf = (df * df + 0.25f * pow(dy + 0.5f * (SE - NE), 2.f));
+    gmsqb = (db * db + 0.25f * pow(dy + 0.5f * (SW - NW), 2.f));
 
     gmf = sqrt(1.0e-10f + gmsqf);
     gmb = sqrt(1.0e-10f + gmsqb);
 
-    cx  = exp( gmsqf * mct );
-    cxd = exp( gmsqb * mct );
+    cx  = exp(gmsqf * mct);
+    cxd = exp(gmsqb * mct);
 
-    delta = ((df/gmf)*cx - (db/gmb)*cxd);
+    delta = ((df / gmf) * cx - (db / gmb) * cxd);
 
     // half-d's and conductance along second dimension
-    df  = S - C;
-    db  = C - N;
+    df = S - C;
+    db = C - N;
 
-    gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2.f));
-    gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2.f));
-    gmf = sqrt(1.0e-10f + gmsqf);
-    gmb = sqrt(1.0e-10f + gmsqb);
+    gmsqf = (df * df + 0.25f * pow(dx + 0.5f * (SE - SW), 2.f));
+    gmsqb = (db * db + 0.25f * pow(dx + 0.5f * (NE - NW), 2.f));
+    gmf   = sqrt(1.0e-10f + gmsqf);
+    gmb   = sqrt(1.0e-10f + gmsqb);
 
-    cx  = exp( gmsqf * mct );
-    cxd = exp( gmsqb * mct );
+    cx  = exp(gmsqf * mct);
+    cxd = exp(gmsqb * mct);
 
-    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
 
-    if (delta>0.f) {
-        prop_grad += (pow(fminf(db0, 0.0f),2.0f) + pow(fmaxf(df0, 0.0f), 2.0f));
-        prop_grad += (pow(fminf( db, 0.0f),2.0f) + pow(fmaxf( df, 0.0f), 2.0f));
+    if (delta > 0.f) {
+        prop_grad +=
+            (pow(fminf(db0, 0.0f), 2.0f) + pow(fmaxf(df0, 0.0f), 2.0f));
+        prop_grad += (pow(fminf(db, 0.0f), 2.0f) + pow(fmaxf(df, 0.0f), 2.0f));
     } else {
-        prop_grad += (pow(fmaxf(db0, 0.0f),2.0f) + pow(fminf(df0, 0.0f), 2.0f));
-        prop_grad += (pow(fmaxf( db, 0.0f),2.0f) + pow(fminf( df, 0.0f), 2.0f));
+        prop_grad +=
+            (pow(fmaxf(db0, 0.0f), 2.0f) + pow(fminf(df0, 0.0f), 2.0f));
+        prop_grad += (pow(fmaxf(db, 0.0f), 2.0f) + pow(fminf(df, 0.0f), 2.0f));
     }
 
-    return sqrt(prop_grad)*delta;
+    return sqrt(prop_grad) * delta;
 }
 
 template<typename T, bool isMCDE>
-void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const af_flux_function fftype)
-{
-    const auto dims = inout.dims();
-    const auto strides = inout.strides();
+void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
+                          const af_flux_function fftype) {
+    const auto dims     = inout.dims();
+    const auto strides  = inout.strides();
     const auto d1stride = strides[1];
-    const int d0 = dims[0] - 1;
-    const int d1 = dims[1] - 1;
-    const int d2 = dims[2];
-    const int d3 = dims[3];
-
-    for(int b3=0; b3<d3; ++b3) {
-        for(int b2=0; b2<d2; ++b2) {
-            T* img = inout.get() + b2*strides[2] + b3*strides[3];
-            for(int j=1; j<d1; ++j) {
-                for(int i=1; i<d0; ++i) {
-                    float C = 0.f;
+    const int d0        = dims[0] - 1;
+    const int d1        = dims[1] - 1;
+    const int d2        = dims[2];
+    const int d3        = dims[3];
+
+    for (int b3 = 0; b3 < d3; ++b3) {
+        for (int b2 = 0; b2 < d2; ++b2) {
+            T* img = inout.get() + b2 * strides[2] + b3 * strides[3];
+            for (int j = 1; j < d1; ++j) {
+                for (int i = 1; i < d0; ++i) {
+                    float C     = 0.f;
                     float delta = 0.f;
 
                     const int ip1 = i + 1;
@@ -164,36 +157,34 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const
 
                     if (isMCDE) {
                         delta = computeCurvatureBasedUpdate(
-                                mct,
-                                img[ index(im1, jm1, d1stride) ],
-                                img[ index(i  , jm1, d1stride) ],
-                                img[ index(ip1, jm1, d1stride) ],
-                                img[ index(im1, j  , d1stride) ],
-                            C = img[ index(i  , j,   d1stride) ],
-                                img[ index(ip1, j  , d1stride) ],
-                                img[ index(im1, jp1, d1stride) ],
-                                img[ index(i  , jp1, d1stride) ],
-                                img[ index(ip1, jp1, d1stride) ]);
+                            mct, img[index(im1, jm1, d1stride)],
+                            img[index(i, jm1, d1stride)],
+                            img[index(ip1, jm1, d1stride)],
+                            img[index(im1, j, d1stride)],
+                            C = img[index(i, j, d1stride)],
+                            img[index(ip1, j, d1stride)],
+                            img[index(im1, jp1, d1stride)],
+                            img[index(i, jp1, d1stride)],
+                            img[index(ip1, jp1, d1stride)]);
 
                     } else {
                         delta = computeGradientBasedUpdate(
-                                mct,
-                                img[ index(im1, jm1,  d1stride) ],
-                                img[ index(i  , jm1,  d1stride) ],
-                                img[ index(ip1, jm1,  d1stride) ],
-                                img[ index(im1, j  ,  d1stride) ],
-                            C = img[ index(i  , j,    d1stride) ],
-                                img[ index(ip1, j  ,  d1stride) ],
-                                img[ index(im1, jp1,  d1stride) ],
-                                img[ index(i  , jp1,  d1stride) ],
-                                img[ index(ip1, jp1,  d1stride) ], fftype);
+                            mct, img[index(im1, jm1, d1stride)],
+                            img[index(i, jm1, d1stride)],
+                            img[index(ip1, jm1, d1stride)],
+                            img[index(im1, j, d1stride)],
+                            C = img[index(i, j, d1stride)],
+                            img[index(ip1, j, d1stride)],
+                            img[index(im1, jp1, d1stride)],
+                            img[index(i, jp1, d1stride)],
+                            img[index(ip1, jp1, d1stride)], fftype);
                     }
 
-                    img[i + j*d1stride] = (T)(C + delta*dt);
+                    img[i + j * d1stride] = (T)(C + delta * dt);
                 }
             }
         }
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index f0b001836b..35f3a2bd78 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -12,49 +12,48 @@
 #include <math.hpp>
 #include "interp.hpp"
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename InT, typename LocT, int order>
-void approx1(Param<InT> yo, CParam<InT> yi,
-             CParam<LocT> xo, const int xdim,
-             const LocT &xi_beg, const LocT &xi_step,
-             const float offGrid, af_interp_type method)
-{
-    InT *yo_ptr = yo.get();
+void approx1(Param<InT> yo, CParam<InT> yi, CParam<LocT> xo, const int xdim,
+             const LocT &xi_beg, const LocT &xi_step, const float offGrid,
+             af_interp_type method) {
+    InT *yo_ptr        = yo.get();
     const LocT *xo_ptr = xo.get();
 
-    const af::dim4 yo_dims     = yo.dims();
-    const af::dim4 yi_dims     = yi.dims();
-    const af::dim4 xo_dims     = xo.dims();
+    const af::dim4 yo_dims = yo.dims();
+    const af::dim4 yi_dims = yi.dims();
+    const af::dim4 xo_dims = xo.dims();
 
-    const af::dim4 yo_strides  = yo.strides();
-    const af::dim4 yi_strides  = yi.strides();
-    const af::dim4 xo_strides  = xo.strides();
+    const af::dim4 yo_strides = yo.strides();
+    const af::dim4 yi_strides = yi.strides();
+    const af::dim4 xo_strides = xo.strides();
 
     Interp1<InT, LocT, order> interp;
-    bool is_xo_off[] = {xo_dims[0] > 1, xo_dims[1] > 1, xo_dims[2] > 1, xo_dims[3] > 1};
+    bool is_xo_off[] = {xo_dims[0] > 1, xo_dims[1] > 1, xo_dims[2] > 1,
+                        xo_dims[3] > 1};
     bool is_yi_off[] = {true, true, true, true};
-    is_yi_off[xdim] = false;
+    is_yi_off[xdim]  = false;
 
-    for(dim_t idw = 0; idw < yo_dims[3]; idw++) {
-        for(dim_t idz = 0; idz < yo_dims[2]; idz++) {
+    for (dim_t idw = 0; idw < yo_dims[3]; idw++) {
+        for (dim_t idz = 0; idz < yo_dims[2]; idz++) {
             dim_t yo_off_zw = idw * yo_strides[3] + idz * yo_strides[2];
-            dim_t yi_off_zw = idw * yi_strides[3] * is_yi_off[3] + idz * yi_strides[2] * is_yi_off[2];
-            dim_t xo_off_zw = idw * xo_strides[3] * is_xo_off[3] + idz * xo_strides[2] * is_xo_off[2];
-
-            for(dim_t idy = 0; idy < yo_dims[1]; idy++) {
+            dim_t yi_off_zw = idw * yi_strides[3] * is_yi_off[3] +
+                              idz * yi_strides[2] * is_yi_off[2];
+            dim_t xo_off_zw = idw * xo_strides[3] * is_xo_off[3] +
+                              idz * xo_strides[2] * is_xo_off[2];
 
+            for (dim_t idy = 0; idy < yo_dims[1]; idy++) {
                 dim_t yo_off = yo_off_zw + idy * yo_strides[1];
                 dim_t yi_off = yi_off_zw + idy * yi_strides[1] * is_yi_off[1];
                 dim_t xo_off = xo_off_zw + idy * xo_strides[1] * is_xo_off[1];
 
-                for(dim_t idx = 0; idx < yo_dims[0]; idx++) {
-
+                for (dim_t idx = 0; idx < yo_dims[0]; idx++) {
                     dim_t yi_idx = idx * is_yi_off[0];
-                    const LocT x = (xo_ptr[xo_off + idx * is_xo_off[0]] - xi_beg) / xi_step;
+                    const LocT x =
+                        (xo_ptr[xo_off + idx * is_xo_off[0]] - xi_beg) /
+                        xi_step;
 
                     // FIXME: Only cubic interpolation is doing clamping
                     // We need to make it consistent across all methods
@@ -64,7 +63,8 @@ void approx1(Param<InT> yo, CParam<InT> yi,
                     if (x < 0 || yi_dims[xdim] < x + 1) {
                         yo_ptr[yo_off + idx] = scalar<InT>(offGrid);
                     } else {
-                        interp(yo, yo_off + idx, yi, yi_off + yi_idx, x, method, 1, clamp, xdim);
+                        interp(yo, yo_off + idx, yi, yi_off + yi_idx, x, method,
+                               1, clamp, xdim);
                     }
                 }
             }
@@ -73,45 +73,46 @@ void approx1(Param<InT> yo, CParam<InT> yi,
 }
 
 template<typename InT, typename LocT, int order>
-void approx2(Param<InT> zo, CParam<InT> zi,
-             CParam<LocT> xo, const int xdim, const LocT &xi_beg, const LocT &xi_step,
-             CParam<LocT> yo, const int ydim, const LocT &yi_beg, const LocT &yi_step,
-             float const offGrid, af_interp_type method)
-{
-    InT *zo_ptr = zo.get();
+void approx2(Param<InT> zo, CParam<InT> zi, CParam<LocT> xo, const int xdim,
+             const LocT &xi_beg, const LocT &xi_step, CParam<LocT> yo,
+             const int ydim, const LocT &yi_beg, const LocT &yi_step,
+             float const offGrid, af_interp_type method) {
+    InT *zo_ptr        = zo.get();
     const LocT *xo_ptr = xo.get();
     const LocT *yo_ptr = yo.get();
 
-    af::dim4 const zo_dims     = zo.dims();
-    af::dim4 const zi_dims     = zi.dims();
-    af::dim4 const xo_dims     = xo.dims();
-    af::dim4 const zo_strides  = zo.strides();
-    af::dim4 const zi_strides  = zi.strides();
-    af::dim4 const xo_strides  = xo.strides();
-    af::dim4 const yo_strides  = yo.strides();
+    af::dim4 const zo_dims    = zo.dims();
+    af::dim4 const zi_dims    = zi.dims();
+    af::dim4 const xo_dims    = xo.dims();
+    af::dim4 const zo_strides = zo.strides();
+    af::dim4 const zi_strides = zi.strides();
+    af::dim4 const xo_strides = xo.strides();
+    af::dim4 const yo_strides = yo.strides();
 
     Interp2<InT, LocT, order> interp;
-    bool is_xo_off[] = {xo_dims[0] > 1, xo_dims[1] > 1, xo_dims[2] > 1, xo_dims[3] > 1};
+    bool is_xo_off[] = {xo_dims[0] > 1, xo_dims[1] > 1, xo_dims[2] > 1,
+                        xo_dims[3] > 1};
     bool is_zi_off[] = {true, true, true, true};
-    is_zi_off[xdim] = false;
-    is_zi_off[ydim] = false;
-
-    for(dim_t idw = 0; idw < zo_dims[3]; idw++) {
-        for(dim_t idz = 0; idz < zo_dims[2]; idz++) {
+    is_zi_off[xdim]  = false;
+    is_zi_off[ydim]  = false;
 
+    for (dim_t idw = 0; idw < zo_dims[3]; idw++) {
+        for (dim_t idz = 0; idz < zo_dims[2]; idz++) {
             dim_t zo_off_zw = idw * zo_strides[3] + idz * zo_strides[2];
-            dim_t zi_off_zw = idw * zi_strides[3] * is_zi_off[3] + idz * zi_strides[2] * is_zi_off[2];
-            dim_t xo_off_zw = idw * xo_strides[3] * is_xo_off[3] + idz * xo_strides[2] * is_xo_off[2];
-            dim_t yo_off_zw = idw * yo_strides[3] * is_xo_off[3] + idz * yo_strides[2] * is_xo_off[2];
-
-            for(dim_t idy = 0; idy < zo_dims[1]; idy++) {
+            dim_t zi_off_zw = idw * zi_strides[3] * is_zi_off[3] +
+                              idz * zi_strides[2] * is_zi_off[2];
+            dim_t xo_off_zw = idw * xo_strides[3] * is_xo_off[3] +
+                              idz * xo_strides[2] * is_xo_off[2];
+            dim_t yo_off_zw = idw * yo_strides[3] * is_xo_off[3] +
+                              idz * yo_strides[2] * is_xo_off[2];
+
+            for (dim_t idy = 0; idy < zo_dims[1]; idy++) {
                 dim_t xo_off = xo_off_zw + idy * xo_strides[1] * is_xo_off[1];
                 dim_t yo_off = yo_off_zw + idy * yo_strides[1] * is_xo_off[1];
                 dim_t zi_off = zi_off_zw + idy * zi_strides[1] * is_zi_off[1];
                 dim_t zo_off = zo_off_zw + idy * zo_strides[1];
 
-                for(dim_t idx = 0; idx < zo_dims[0]; idx++) {
-
+                for (dim_t idx = 0; idx < zo_dims[0]; idx++) {
                     const LocT x = (xo_ptr[xo_off + idx] - xi_beg) / xi_step;
                     const LocT y = (yo_ptr[yo_off + idx] - yi_beg) / yi_step;
 
@@ -122,16 +123,17 @@ void approx2(Param<InT> zo, CParam<InT> zi,
                     // Not changing the behavior because tests will fail
                     bool clamp = order == 3;
 
-                    if (x < 0 || zi_dims[xdim] < x + 1 ||
-                        y < 0 || zi_dims[ydim] < y + 1 ) {
+                    if (x < 0 || zi_dims[xdim] < x + 1 || y < 0 ||
+                        zi_dims[ydim] < y + 1) {
                         zo_ptr[zo_off + idx] = scalar<InT>(offGrid);
                     } else {
-                        interp(zo, zo_off + idx, zi, zi_off + zi_idx, x, y, method, 1, clamp, xdim, ydim);
+                        interp(zo, zo_off + idx, zi, zi_off + zi_idx, x, y,
+                               method, 1, clamp, xdim, ydim);
                     }
                 }
             }
         }
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 184405f5f8..8a055db0c5 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -19,16 +19,13 @@
 
 #include <vector>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void assign(Param<T> out, af::dim4 dDims,
-            CParam<T> rhs, std::vector<bool> const isSeq,
-            std::vector<af_seq> const seqs, std::vector< CParam<uint> > idxArrs)
-{
+void assign(Param<T> out, af::dim4 dDims, CParam<T> rhs,
+            std::vector<bool> const isSeq, std::vector<af_seq> const seqs,
+            std::vector<CParam<uint>> idxArrs) {
     af::dim4 pDims = out.dims();
     // retrieve dimensions & strides for array to which rhs is being copied to
     af::dim4 dst_offsets = toOffset(seqs, dDims);
@@ -37,41 +34,41 @@ void assign(Param<T> out, af::dim4 dDims,
     af::dim4 src_dims    = rhs.dims();
     af::dim4 src_strides = rhs.strides();
     // declare pointers to af_array index data
-    uint const * const ptr0 = idxArrs[0].get();
-    uint const * const ptr1 = idxArrs[1].get();
-    uint const * const ptr2 = idxArrs[2].get();
-    uint const * const ptr3 = idxArrs[3].get();
+    uint const* const ptr0 = idxArrs[0].get();
+    uint const* const ptr1 = idxArrs[1].get();
+    uint const* const ptr2 = idxArrs[2].get();
+    uint const* const ptr3 = idxArrs[3].get();
 
-    const T * src= rhs.get();
-    T * dst      = out.get();
+    const T* src = rhs.get();
+    T* dst       = out.get();
 
-    for(dim_t l=0; l<src_dims[3]; ++l) {
+    for (dim_t l = 0; l < src_dims[3]; ++l) {
+        dim_t src_loff = l * src_strides[3];
 
-        dim_t src_loff = l*src_strides[3];
-
-        dim_t dst_lIdx = trimIndex(isSeq[3] ? l+dst_offsets[3] : ptr3[l], pDims[3]);
+        dim_t dst_lIdx =
+            trimIndex(isSeq[3] ? l + dst_offsets[3] : ptr3[l], pDims[3]);
         dim_t dst_loff = dst_lIdx * dst_strides[3];
 
-        for(dim_t k=0; k<src_dims[2]; ++k) {
-
-            dim_t src_koff = k*src_strides[2];
+        for (dim_t k = 0; k < src_dims[2]; ++k) {
+            dim_t src_koff = k * src_strides[2];
 
-            dim_t dst_kIdx = trimIndex(isSeq[2] ? k+dst_offsets[2] : ptr2[k], pDims[2]);
+            dim_t dst_kIdx =
+                trimIndex(isSeq[2] ? k + dst_offsets[2] : ptr2[k], pDims[2]);
             dim_t dst_koff = dst_kIdx * dst_strides[2];
 
-            for(dim_t j=0; j<src_dims[1]; ++j) {
-
-                dim_t src_joff = j*src_strides[1];
+            for (dim_t j = 0; j < src_dims[1]; ++j) {
+                dim_t src_joff = j * src_strides[1];
 
-                dim_t dst_jIdx = trimIndex(isSeq[1] ? j+dst_offsets[1] : ptr1[j], pDims[1]);
+                dim_t dst_jIdx = trimIndex(
+                    isSeq[1] ? j + dst_offsets[1] : ptr1[j], pDims[1]);
                 dim_t dst_joff = dst_jIdx * dst_strides[1];
 
-                for(dim_t i=0; i<src_dims[0]; ++i) {
-
-                    dim_t src_ioff = i*src_strides[0];
+                for (dim_t i = 0; i < src_dims[0]; ++i) {
+                    dim_t src_ioff = i * src_strides[0];
                     dim_t src_idx  = src_ioff + src_joff + src_koff + src_loff;
 
-                    dim_t dst_iIdx = trimIndex(isSeq[0] ? i+dst_offsets[0] : ptr0[i], pDims[0]);
+                    dim_t dst_iIdx = trimIndex(
+                        isSeq[0] ? i + dst_offsets[0] : ptr0[i], pDims[0]);
                     dim_t dst_ioff = dst_iIdx * dst_strides[0];
                     dim_t dst_idx  = dst_ioff + dst_joff + dst_koff + dst_loff;
 
@@ -82,5 +79,5 @@ void assign(Param<T> out, af::dim4 dDims,
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index 37f66b2b41..d5c0e34473 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -13,14 +13,12 @@
 #include <utility.hpp>
 #include <cmath>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename OutT, typename InT, bool IsColor>
-void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma, float const c_sigma)
-{
+void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma,
+               float const c_sigma) {
     af::dim4 const dims     = in.dims();
     af::dim4 const istrides = in.strides();
     af::dim4 const ostrides = out.strides();
@@ -29,54 +27,58 @@ void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma, float const
     float space_       = std::min(11.5f, std::max(s_sigma, 0.f));
     float color_       = std::max(c_sigma, 0.f);
     dim_t const radius = std::max((dim_t)(space_ * 1.5f), (dim_t)1);
-    float const svar   = space_*space_;
-    float const cvar   = color_*color_;
+    float const svar   = space_ * space_;
+    float const cvar   = color_ * color_;
 
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-
-        OutT *outData = out.get() + b3 * ostrides[3];
-        InT const * inData  = in.get() + b3 * istrides[3];
+    for (dim_t b3 = 0; b3 < dims[3]; ++b3) {
+        OutT *outData     = out.get() + b3 * ostrides[3];
+        InT const *inData = in.get() + b3 * istrides[3];
 
         // b3 for loop handles following batch configurations
         //  - gfor
         //  - input based batch
         //      - when input is 4d array for color images
-        for(dim_t b2=0; b2<dims[2]; ++b2) {
+        for (dim_t b2 = 0; b2 < dims[2]; ++b2) {
             // b2 for loop handles following batch configurations
             //  - channels
             //  - input based batch
             //      - when input is 3d array for grayscale images
-            for(dim_t j=0; j<dims[1]; ++j) {
+            for (dim_t j = 0; j < dims[1]; ++j) {
                 // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
+                for (dim_t i = 0; i < dims[0]; ++i) {
                     // i steps along 1st dimension
-                    OutT norm = 0.0;
-                    OutT res  = 0.0;
+                    OutT norm         = 0.0;
+                    OutT res          = 0.0;
                     OutT const center = (OutT)inData[getIdx(istrides, i, j)];
-                    for(dim_t wj=-radius; wj<=radius; ++wj) {
+                    for (dim_t wj = -radius; wj <= radius; ++wj) {
                         // clamps offsets
-                        dim_t tj = clamp(j+wj, dim_t(0), dims[1]-1);
-                        for(dim_t wi=-radius; wi<=radius; ++wi) {
+                        dim_t tj = clamp(j + wj, dim_t(0), dims[1] - 1);
+                        for (dim_t wi = -radius; wi <= radius; ++wi) {
                             // clamps offsets
-                            dim_t ti = clamp(i+wi, dim_t(0), dims[0]-1);
+                            dim_t ti = clamp(i + wi, dim_t(0), dims[0] - 1);
                             // proceed
-                            OutT const val= (OutT)inData[getIdx(istrides, ti, tj)];
-                            OutT const gauss_space = (wi*wi+wj*wj)/(-2.0*svar);
-                            OutT const gauss_range = ((center-val)*(center-val))/(-2.0*cvar);
-                            OutT const weight = std::exp(gauss_space+gauss_range);
+                            OutT const val =
+                                (OutT)inData[getIdx(istrides, ti, tj)];
+                            OutT const gauss_space =
+                                (wi * wi + wj * wj) / (-2.0 * svar);
+                            OutT const gauss_range =
+                                ((center - val) * (center - val)) /
+                                (-2.0 * cvar);
+                            OutT const weight =
+                                std::exp(gauss_space + gauss_range);
                             norm += weight;
-                            res += val*weight;
+                            res += val * weight;
                         }
-                    } // filter loop ends here
+                    }  // filter loop ends here
 
-                    outData[getIdx(ostrides, i, j)] = res/norm;
-                } //1st dimension loop ends here
-            } //2nd dimension loop ends here
+                    outData[getIdx(ostrides, i, j)] = res / norm;
+                }  // 1st dimension loop ends here
+            }      // 2nd dimension loop ends here
             outData += ostrides[2];
-            inData  += istrides[2];
+            inData += istrides[2];
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
index 4fa156c502..4c7c1a7246 100644
--- a/src/backend/cpu/kernel/canny.hpp
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -9,94 +9,89 @@
 
 #pragma once
 #include <Param.hpp>
+#include <array>
 #include <cassert>
 #include <list>
-#include <array>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 template<typename T>
-void nonMaxSuppression(Param<T> output, CParam<T> magnitude,
-                       CParam<T> dxParam, CParam<T> dyParam)
-{
+void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dxParam,
+                       CParam<T> dyParam) {
     const af::dim4 dims    = magnitude.dims();
     const af::dim4 strides = magnitude.strides();
 
-          T* out = output.get();
+    T* out       = output.get();
     const T* mag = magnitude.get();
     const T* dX  = dxParam.get();
     const T* dY  = dyParam.get();
 
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        for(dim_t b2=0; b2<dims[2]; ++b2) {
-
+    for (dim_t b3 = 0; b3 < dims[3]; ++b3) {
+        for (dim_t b2 = 0; b2 < dims[2]; ++b2) {
             dim_t offset;
 
-            offset = dims[0]+1;
-
-            for(dim_t j=1; j<dims[1]-1; ++j, offset+=2) {
+            offset = dims[0] + 1;
 
-                for(dim_t i=1; i<dims[0]-1; ++i, ++offset) {
-                    if (mag[offset]==0)
+            for (dim_t j = 1; j < dims[1] - 1; ++j, offset += 2) {
+                for (dim_t i = 1; i < dims[0] - 1; ++i, ++offset) {
+                    if (mag[offset] == 0)
                         out[offset] = (T)0;
                     else {
-                        const float se = mag[offset+dims[0]+1];
-                        const float nw = mag[offset-dims[0]-1];
-                        const float ea = mag[offset+1];
-                        const float we = mag[offset-1];
-                        const float ne = mag[offset-dims[0]+1];
-                        const float sw = mag[offset+dims[0]-1];
-                        const float no = mag[offset-dims[0]];
-                        const float so = mag[offset+dims[0]];
+                        const float se = mag[offset + dims[0] + 1];
+                        const float nw = mag[offset - dims[0] - 1];
+                        const float ea = mag[offset + 1];
+                        const float we = mag[offset - 1];
+                        const float ne = mag[offset - dims[0] + 1];
+                        const float sw = mag[offset + dims[0] - 1];
+                        const float no = mag[offset - dims[0]];
+                        const float so = mag[offset + dims[0]];
                         const float dx = dX[offset];
                         const float dy = dY[offset];
 
                         float a1, a2, b1, b2, alpha;
 
-                        if (dx>=0) {
-                            if (dy>=0) {
-                                const bool isTrue = (dx-dy)>=0;
+                        if (dx >= 0) {
+                            if (dy >= 0) {
+                                const bool isTrue = (dx - dy) >= 0;
 
                                 a1    = isTrue ? ea : so;
                                 a2    = isTrue ? we : no;
                                 b1    = se;
                                 b2    = nw;
-                                alpha = isTrue ? dy/dx : dx/dy;
+                                alpha = isTrue ? dy / dx : dx / dy;
                             } else {
-                                const bool isTrue = (dx+dy)>=0;
+                                const bool isTrue = (dx + dy) >= 0;
 
                                 a1    = isTrue ? ea : no;
                                 a2    = isTrue ? we : so;
                                 b1    = ne;
                                 b2    = sw;
-                                alpha = isTrue ? -dy/dx : dx/-dy;
+                                alpha = isTrue ? -dy / dx : dx / -dy;
                             }
                         } else {
-                            if (dy>=0) {
-                                const bool isTrue = (dx+dy)>=0;
+                            if (dy >= 0) {
+                                const bool isTrue = (dx + dy) >= 0;
 
                                 a1    = isTrue ? so : we;
                                 a2    = isTrue ? no : ea;
                                 b1    = sw;
                                 b2    = ne;
-                                alpha = isTrue ? -dx/dy : dy/-dx;
+                                alpha = isTrue ? -dx / dy : dy / -dx;
                             } else {
-                                const bool isTrue = (-dx+dy)>=0;
+                                const bool isTrue = (-dx + dy) >= 0;
 
                                 a1    = isTrue ? we : no;
                                 a2    = isTrue ? ea : so;
                                 b1    = nw;
                                 b2    = se;
-                                alpha = isTrue ? -dy/dx : dx/-dy;
+                                alpha = isTrue ? -dy / dx : dx / -dy;
                             }
                         }
 
-                        float mag1 = (1-alpha)*a1 + alpha*b1;
-                        float mag2 = (1-alpha)*a2 + alpha*b2;
+                        float mag1 = (1 - alpha) * a1 + alpha * b1;
+                        float mag2 = (1 - alpha) * a2 + alpha * b2;
 
-                        if (mag[offset]>mag1 && mag[offset]>mag2) {
+                        if (mag[offset] > mag1 && mag[offset] > mag2) {
                             out[offset] = mag[offset];
                         } else {
                             out[offset] = (T)0;
@@ -107,84 +102,76 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude,
 
             out += strides[2];
             mag += strides[2];
-            dX  += strides[2];
-            dY  += strides[2];
+            dX += strides[2];
+            dY += strides[2];
         }
         out += strides[3];
         mag += strides[3];
-        dX  += strides[3];
-        dY  += strides[3];
+        dX += strides[3];
+        dY += strides[3];
     }
 }
 
 template<typename T>
-void traceEdge(T* out, const T* strong, const T* weak, int t, int width)
-{
-    if (!out || !strong || !weak)
-        return;
+void traceEdge(T* out, const T* strong, const T* weak, int t, int width) {
+    if (!out || !strong || !weak) return;
 
     const T EDGE = 1;
 
-    std::list<dim_t> edges; // list of edges to be checked
+    std::list<dim_t> edges;  // list of edges to be checked
     edges.push_back(t);
 
     do {
         t = edges.front();
-        edges.pop_front(); // remove the last after read
+        edges.pop_front();  // remove the last after read
 
         // get indices of 8 neighbours
         std::array<dim_t, 8> potentials;
 
-        potentials[0] = t - width - 1;              // north-west
-        potentials[1] = potentials[0] + 1;          // north
-        potentials[2] = potentials[1] + 1;          // north-east
-        potentials[3] = t - 1;                      // west
-        potentials[4] = t + 1;                      // east
-        potentials[5] = t + width - 1;              // south-west
-        potentials[6] = potentials[5] + 1;          // south
-        potentials[7] = potentials[6] + 1;          // south-east
+        potentials[0] = t - width - 1;      // north-west
+        potentials[1] = potentials[0] + 1;  // north
+        potentials[2] = potentials[1] + 1;  // north-east
+        potentials[3] = t - 1;              // west
+        potentials[4] = t + 1;              // east
+        potentials[5] = t + width - 1;      // south-west
+        potentials[6] = potentials[5] + 1;  // south
+        potentials[7] = potentials[6] + 1;  // south-east
 
         // test 8 neighbours and add them into edge
         // list only if they are also edges
-        for (auto it: potentials)
-        {
-            if (weak[it] > 0 && out[it] != EDGE)
-            {
+        for (auto it : potentials) {
+            if (weak[it] > 0 && out[it] != EDGE) {
                 out[it] = EDGE;
                 edges.emplace_back(it);
             }
         }
-    } while(!edges.empty());
+    } while (!edges.empty());
 }
 
-
 template<typename T>
-void edgeTrackingHysteresis(Param<T> out, CParam<T> strong, CParam<T> weak)
-{
+void edgeTrackingHysteresis(Param<T> out, CParam<T> strong, CParam<T> weak) {
     const af::dim4 dims = strong.dims();
 
-    dim_t t    = dims[0] + 1;   // skip the first coloumn and first element of second coloumn
-    dim_t jMax = dims[1] - 1;   // max Y value to traverse, ignore right coloumn
-    dim_t iMax = dims[0] - 1;   // max X value to traverse, ignore bottom border
+    dim_t t = dims[0] +
+              1;  // skip the first coloumn and first element of second coloumn
+    dim_t jMax = dims[1] - 1;  // max Y value to traverse, ignore right coloumn
+    dim_t iMax = dims[0] - 1;  // max X value to traverse, ignore bottom border
 
-          T* optr = out.get();
+    T* optr       = out.get();
     const T* sptr = strong.get();
     const T* wptr = weak.get();
 
-    for (dim_t j = 1; j <= jMax; ++j)
-    {
-        for (dim_t i = 1; i <= iMax; ++i, ++t)
-        {
+    for (dim_t j = 1; j <= jMax; ++j) {
+        for (dim_t i = 1; i <= iMax; ++i, ++t) {
             // if current pixel(sptr) is part of a edge
             // and output doesn't have it marked already,
             // mark it and trace the pixels from here.
-            if (sptr[t] > 0 && optr[t]!=1)
-            {
+            if (sptr[t] > 0 && optr[t] != 1) {
                 optr[t] = 1;
                 traceEdge(optr, sptr, wptr, t, dims[0]);
             }
         }
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 0a8a927adf..a1a5fbdfcd 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -9,126 +9,128 @@
 
 #pragma once
 #include <Param.hpp>
-#include <af/defines.h>
 #include <math.hpp>
+#include <af/defines.h>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename InT, typename AccT, bool Expand>
-void one2one_1d(InT *optr, InT const * const iptr, AccT const * const fptr, af::dim4 const & oDims,
-                af::dim4 const & sDims, af::dim4 const & fDims, af::dim4 const & sStrides)
-{
-    dim_t start = (Expand ? 0 : fDims[0]/2);
+void one2one_1d(InT *optr, InT const *const iptr, AccT const *const fptr,
+                af::dim4 const &oDims, af::dim4 const &sDims,
+                af::dim4 const &fDims, af::dim4 const &sStrides) {
+    dim_t start = (Expand ? 0 : fDims[0] / 2);
     dim_t end   = (Expand ? oDims[0] : start + sDims[0]);
-    for(dim_t i=start; i<end; ++i) {
+    for (dim_t i = start; i < end; ++i) {
         AccT accum = 0.0;
-        for(dim_t f=0; f<fDims[0]; ++f) {
-            dim_t iIdx = i-f;
-            InT s_val = ((iIdx>=0 &&iIdx<sDims[0])? iptr[iIdx*sStrides[0]] : InT(0));
+        for (dim_t f = 0; f < fDims[0]; ++f) {
+            dim_t iIdx = i - f;
+            InT s_val =
+                ((iIdx >= 0 && iIdx < sDims[0]) ? iptr[iIdx * sStrides[0]]
+                                                : InT(0));
             accum += AccT(s_val * fptr[f]);
         }
-        optr[i-start] = InT(accum);
+        optr[i - start] = InT(accum);
     }
 }
 
 template<typename InT, typename AccT, bool Expand>
-void one2one_2d(InT *optr, InT const * const iptr, AccT const * const fptr, af::dim4 const & oDims,
-                af::dim4 const & sDims, af::dim4 const & fDims, af::dim4 const & oStrides,
-                af::dim4 const & sStrides, af::dim4 const & fStrides)
-{
-    dim_t jStart = (Expand ? 0 : fDims[1]/2);
+void one2one_2d(InT *optr, InT const *const iptr, AccT const *const fptr,
+                af::dim4 const &oDims, af::dim4 const &sDims,
+                af::dim4 const &fDims, af::dim4 const &oStrides,
+                af::dim4 const &sStrides, af::dim4 const &fStrides) {
+    dim_t jStart = (Expand ? 0 : fDims[1] / 2);
     dim_t jEnd   = (Expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (Expand ? 0 : fDims[0]/2);
+    dim_t iStart = (Expand ? 0 : fDims[0] / 2);
     dim_t iEnd   = (Expand ? oDims[0] : iStart + sDims[0]);
 
-    for(dim_t j=jStart; j<jEnd; ++j) {
-        dim_t joff = (j-jStart)*oStrides[1];
-
-        for(dim_t i=iStart; i<iEnd; ++i) {
+    for (dim_t j = jStart; j < jEnd; ++j) {
+        dim_t joff = (j - jStart) * oStrides[1];
 
+        for (dim_t i = iStart; i < iEnd; ++i) {
             AccT accum = AccT(0);
-            for(dim_t wj=0; wj<fDims[1]; ++wj) {
-                dim_t jIdx  = j-wj;
-                dim_t w_joff = wj*fStrides[1];
-                dim_t s_joff = jIdx * sStrides[1];
-                bool isJValid = (jIdx>=0 && jIdx<sDims[1]);
+            for (dim_t wj = 0; wj < fDims[1]; ++wj) {
+                dim_t jIdx    = j - wj;
+                dim_t w_joff  = wj * fStrides[1];
+                dim_t s_joff  = jIdx * sStrides[1];
+                bool isJValid = (jIdx >= 0 && jIdx < sDims[1]);
 
-                for(dim_t wi=0; wi<fDims[0]; ++wi) {
-                    dim_t iIdx = i-wi;
+                for (dim_t wi = 0; wi < fDims[0]; ++wi) {
+                    dim_t iIdx = i - wi;
 
                     InT s_val = InT(0);
-                    if ( isJValid && (iIdx>=0 && iIdx<sDims[0])) {
-                        s_val = iptr[s_joff+iIdx*sStrides[0]];
+                    if (isJValid && (iIdx >= 0 && iIdx < sDims[0])) {
+                        s_val = iptr[s_joff + iIdx * sStrides[0]];
                     }
 
-                    accum += AccT(s_val * fptr[w_joff+wi*fStrides[0]]);
+                    accum += AccT(s_val * fptr[w_joff + wi * fStrides[0]]);
                 }
             }
-            optr[joff+i-iStart] = InT(accum);
+            optr[joff + i - iStart] = InT(accum);
         }
     }
 }
 
 template<typename InT, typename AccT, bool Expand>
-void one2one_3d(InT *optr, InT const * const iptr, AccT const * const fptr, af::dim4 const & oDims,
-                af::dim4 const & sDims, af::dim4 const & fDims, af::dim4 const & oStrides,
-                af::dim4 const & sStrides, af::dim4 const & fStrides)
-{
-    dim_t kStart = (Expand ? 0 : fDims[2]/2);
+void one2one_3d(InT *optr, InT const *const iptr, AccT const *const fptr,
+                af::dim4 const &oDims, af::dim4 const &sDims,
+                af::dim4 const &fDims, af::dim4 const &oStrides,
+                af::dim4 const &sStrides, af::dim4 const &fStrides) {
+    dim_t kStart = (Expand ? 0 : fDims[2] / 2);
     dim_t kEnd   = (Expand ? oDims[2] : kStart + sDims[2]);
-    dim_t jStart = (Expand ? 0 : fDims[1]/2);
+    dim_t jStart = (Expand ? 0 : fDims[1] / 2);
     dim_t jEnd   = (Expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (Expand ? 0 : fDims[0]/2);
+    dim_t iStart = (Expand ? 0 : fDims[0] / 2);
     dim_t iEnd   = (Expand ? oDims[0] : iStart + sDims[0]);
 
-    for(dim_t k=kStart; k<kEnd; ++k) {
-        dim_t koff = (k-kStart)*oStrides[2];
+    for (dim_t k = kStart; k < kEnd; ++k) {
+        dim_t koff = (k - kStart) * oStrides[2];
 
-        for(dim_t j=jStart; j<jEnd; ++j) {
-            dim_t joff = (j-jStart)*oStrides[1];
-
-            for(dim_t i=iStart; i<iEnd; ++i) {
+        for (dim_t j = jStart; j < jEnd; ++j) {
+            dim_t joff = (j - jStart) * oStrides[1];
 
+            for (dim_t i = iStart; i < iEnd; ++i) {
                 AccT accum = AccT(0);
-                for(dim_t wk=0; wk<fDims[2]; ++wk) {
-                    dim_t kIdx  = k-wk;
-                    dim_t w_koff = wk*fStrides[2];
-                    dim_t s_koff = kIdx * sStrides[2];
-                    bool isKValid = (kIdx>=0 && kIdx<sDims[2]);
+                for (dim_t wk = 0; wk < fDims[2]; ++wk) {
+                    dim_t kIdx    = k - wk;
+                    dim_t w_koff  = wk * fStrides[2];
+                    dim_t s_koff  = kIdx * sStrides[2];
+                    bool isKValid = (kIdx >= 0 && kIdx < sDims[2]);
 
-                    for(dim_t wj=0; wj<fDims[1]; ++wj) {
-                        dim_t jIdx  = j-wj;
-                        dim_t w_joff = wj*fStrides[1];
-                        dim_t s_joff = jIdx * sStrides[1];
-                        bool isJValid = (jIdx>=0 && jIdx<sDims[1]);
+                    for (dim_t wj = 0; wj < fDims[1]; ++wj) {
+                        dim_t jIdx    = j - wj;
+                        dim_t w_joff  = wj * fStrides[1];
+                        dim_t s_joff  = jIdx * sStrides[1];
+                        bool isJValid = (jIdx >= 0 && jIdx < sDims[1]);
 
-                        for(dim_t wi=0; wi<fDims[0]; ++wi) {
-                            dim_t iIdx = i-wi;
+                        for (dim_t wi = 0; wi < fDims[0]; ++wi) {
+                            dim_t iIdx = i - wi;
 
                             InT s_val = InT(0);
-                            if ( isKValid && isJValid && (iIdx>=0 && iIdx<sDims[0])) {
-                                s_val = iptr[s_koff+s_joff+iIdx*sStrides[0]];
+                            if (isKValid && isJValid &&
+                                (iIdx >= 0 && iIdx < sDims[0])) {
+                                s_val =
+                                    iptr[s_koff + s_joff + iIdx * sStrides[0]];
                             }
 
-                            accum += AccT(s_val * fptr[w_koff+w_joff+wi*fStrides[0]]);
+                            accum +=
+                                AccT(s_val *
+                                     fptr[w_koff + w_joff + wi * fStrides[0]]);
                         }
                     }
                 }
-                optr[koff+joff+i-iStart] = InT(accum);
-            } //i loop ends here
-        } // j loop ends here
-    } // k loop ends here
+                optr[koff + joff + i - iStart] = InT(accum);
+            }  // i loop ends here
+        }      // j loop ends here
+    }          // k loop ends here
 }
 
 template<typename InT, typename AccT, dim_t baseDim, bool Expand>
-void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter, AF_BATCH_KIND kind)
-{
-    InT * optr = out.get();
-    InT const * const iptr = signal.get();
-    AccT const * const fptr = filter.get();
+void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
+                 AF_BATCH_KIND kind) {
+    InT *optr              = out.get();
+    InT const *const iptr  = signal.get();
+    AccT const *const fptr = filter.get();
 
     af::dim4 const oDims = out.dims();
     af::dim4 const sDims = signal.dims();
@@ -138,46 +140,66 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter, AF_BAT
     af::dim4 const sStrides = signal.strides();
     af::dim4 const fStrides = filter.strides();
 
-    dim_t out_step[4]  = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
-    dim_t in_step[4]   = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
-    dim_t filt_step[4] = {0, 0, 0, 0}; /* first value is never used, and declared for code simplicity */
-    dim_t batch[4]     = {0, 1, 1, 1}; /* first value is never used, and declared for code simplicity */
-
-    for (dim_t i=1; i<4; ++i) {
-        switch(kind) {
+    dim_t out_step[4] = {
+        0, 0, 0,
+        0}; /* first value is never used, and declared for code simplicity */
+    dim_t in_step[4] = {
+        0, 0, 0,
+        0}; /* first value is never used, and declared for code simplicity */
+    dim_t filt_step[4] = {
+        0, 0, 0,
+        0}; /* first value is never used, and declared for code simplicity */
+    dim_t batch[4] = {
+        0, 1, 1,
+        1}; /* first value is never used, and declared for code simplicity */
+
+    for (dim_t i = 1; i < 4; ++i) {
+        switch (kind) {
             case AF_BATCH_LHS:
                 out_step[i] = oStrides[i];
                 in_step[i]  = sStrides[i];
-                if (i>=baseDim) batch[i] = sDims[i];
+                if (i >= baseDim) batch[i] = sDims[i];
                 break;
             case AF_BATCH_SAME:
                 out_step[i]  = oStrides[i];
                 in_step[i]   = sStrides[i];
                 filt_step[i] = fStrides[i];
-                if (i>=baseDim) batch[i] = sDims[i];
+                if (i >= baseDim) batch[i] = sDims[i];
                 break;
             case AF_BATCH_RHS:
                 out_step[i]  = oStrides[i];
                 filt_step[i] = fStrides[i];
-                if (i>=baseDim) batch[i] = fDims[i];
-                break;
-            default:
+                if (i >= baseDim) batch[i] = fDims[i];
                 break;
+            default: break;
         }
     }
 
-    for (dim_t b3=0; b3<batch[3]; ++b3) {
-        for (dim_t b2=0; b2<batch[2]; ++b2) {
-            for (dim_t b1=0; b1<batch[1]; ++b1) {
-
-                InT * out          = optr + b1 * out_step[1] + b2 * out_step[2] + b3 * out_step[3];
-                InT const *in      = iptr + b1 *  in_step[1] + b2 *  in_step[2] + b3 *  in_step[3];
-                AccT const *filt = fptr + b1 *filt_step[1] + b2 *filt_step[2] + b3 *filt_step[3];
-
-                switch(baseDim) {
-                    case 1: one2one_1d<InT, AccT, Expand>(out, in, filt, oDims, sDims, fDims, sStrides);                     break;
-                    case 2: one2one_2d<InT, AccT, Expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
-                    case 3: one2one_3d<InT, AccT, Expand>(out, in, filt, oDims, sDims, fDims, oStrides, sStrides, fStrides); break;
+    for (dim_t b3 = 0; b3 < batch[3]; ++b3) {
+        for (dim_t b2 = 0; b2 < batch[2]; ++b2) {
+            for (dim_t b1 = 0; b1 < batch[1]; ++b1) {
+                InT *out = optr + b1 * out_step[1] + b2 * out_step[2] +
+                           b3 * out_step[3];
+                InT const *in =
+                    iptr + b1 * in_step[1] + b2 * in_step[2] + b3 * in_step[3];
+                AccT const *filt = fptr + b1 * filt_step[1] +
+                                   b2 * filt_step[2] + b3 * filt_step[3];
+
+                switch (baseDim) {
+                    case 1:
+                        one2one_1d<InT, AccT, Expand>(out, in, filt, oDims,
+                                                      sDims, fDims, sStrides);
+                        break;
+                    case 2:
+                        one2one_2d<InT, AccT, Expand>(out, in, filt, oDims,
+                                                      sDims, fDims, oStrides,
+                                                      sStrides, fStrides);
+                        break;
+                    case 3:
+                        one2one_3d<InT, AccT, Expand>(out, in, filt, oDims,
+                                                      sDims, fDims, oStrides,
+                                                      sStrides, fStrides);
+                        break;
                 }
             }
         }
@@ -185,51 +207,52 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter, AF_BAT
 }
 
 template<typename InT, typename AccT, dim_t conv_dim, bool Expand>
-void convolve2_separable(InT *optr, InT const * const iptr, AccT const * const fptr,
-                        af::dim4 const & oDims, af::dim4 const & sDims, af::dim4 const & orgDims, dim_t fDim,
-                        af::dim4 const & oStrides, af::dim4 const & sStrides, dim_t fStride)
-{
+void convolve2_separable(InT *optr, InT const *const iptr,
+                         AccT const *const fptr, af::dim4 const &oDims,
+                         af::dim4 const &sDims, af::dim4 const &orgDims,
+                         dim_t fDim, af::dim4 const &oStrides,
+                         af::dim4 const &sStrides, dim_t fStride) {
     UNUSED(orgDims);
     UNUSED(sStrides);
     UNUSED(fStride);
-    for(dim_t j=0; j<oDims[1]; ++j) {
-        dim_t jOff = j*oStrides[1];
-        dim_t cj = j + (conv_dim==1)*(Expand ? 0: fDim>>1);
+    for (dim_t j = 0; j < oDims[1]; ++j) {
+        dim_t jOff = j * oStrides[1];
+        dim_t cj   = j + (conv_dim == 1) * (Expand ? 0 : fDim >> 1);
 
-        for(dim_t i=0; i<oDims[0]; ++i) {
-            dim_t iOff = i*oStrides[0];
-            dim_t ci = i + (conv_dim==0)*(Expand ? 0 : fDim>>1);
+        for (dim_t i = 0; i < oDims[0]; ++i) {
+            dim_t iOff = i * oStrides[0];
+            dim_t ci   = i + (conv_dim == 0) * (Expand ? 0 : fDim >> 1);
 
             AccT accum = scalar<AccT>(0);
 
-            for(dim_t f=0; f<fDim; ++f) {
+            for (dim_t f = 0; f < fDim; ++f) {
                 InT f_val = fptr[f];
                 InT s_val;
 
-                if (conv_dim==0) {
-                    dim_t offi = ci - f;
-                    bool isCIValid = offi>=0 && offi<sDims[0];
-                    bool isCJValid = cj>=0 && cj<sDims[1];
-                    s_val = (isCJValid && isCIValid ? iptr[cj*sDims[0]+offi] : scalar<InT>(0));
+                if (conv_dim == 0) {
+                    dim_t offi     = ci - f;
+                    bool isCIValid = offi >= 0 && offi < sDims[0];
+                    bool isCJValid = cj >= 0 && cj < sDims[1];
+                    s_val = (isCJValid && isCIValid ? iptr[cj * sDims[0] + offi]
+                                                    : scalar<InT>(0));
                 } else {
-                    dim_t offj = cj - f;
-                    bool isCIValid = ci>=0 && ci<sDims[0];
-                    bool isCJValid = offj>=0 && offj<sDims[1];
-                    s_val = (isCJValid && isCIValid ? iptr[offj*sDims[0]+ci] : scalar<InT>(0));
+                    dim_t offj     = cj - f;
+                    bool isCIValid = ci >= 0 && ci < sDims[0];
+                    bool isCJValid = offj >= 0 && offj < sDims[1];
+                    s_val = (isCJValid && isCIValid ? iptr[offj * sDims[0] + ci]
+                                                    : scalar<InT>(0));
                 }
 
                 accum += AccT(s_val * f_val);
             }
-            optr[iOff+jOff] = InT(accum);
+            optr[iOff + jOff] = InT(accum);
         }
     }
 }
 
 template<typename InT, typename AccT, bool Expand>
-void convolve2(Param<InT> out, CParam<InT> signal,
-               CParam<AccT> c_filter, CParam<AccT> r_filter,
-               Param<InT> temp)
-{
+void convolve2(Param<InT> out, CParam<InT> signal, CParam<AccT> c_filter,
+               CParam<AccT> r_filter, Param<InT> temp) {
     dim_t cflen = (dim_t)c_filter.dims().elements();
     dim_t rflen = (dim_t)r_filter.dims().elements();
 
@@ -240,28 +263,26 @@ void convolve2(Param<InT> out, CParam<InT> signal,
     auto sStrides = signal.strides();
     auto tStrides = temp.strides();
 
-    for (dim_t b3=0; b3<oDims[3]; ++b3) {
-
-        dim_t i_b3Off = b3*sStrides[3];
-        dim_t t_b3Off = b3*tStrides[3];
-        dim_t o_b3Off = b3*oStrides[3];
+    for (dim_t b3 = 0; b3 < oDims[3]; ++b3) {
+        dim_t i_b3Off = b3 * sStrides[3];
+        dim_t t_b3Off = b3 * tStrides[3];
+        dim_t o_b3Off = b3 * oStrides[3];
 
-        for (dim_t b2=0; b2<oDims[2]; ++b2) {
+        for (dim_t b2 = 0; b2 < oDims[2]; ++b2) {
+            InT const *const iptr = signal.get() + b2 * sStrides[2] + i_b3Off;
+            InT *tptr             = temp.get() + b2 * tStrides[2] + t_b3Off;
+            InT *optr             = out.get() + b2 * oStrides[2] + o_b3Off;
 
-            InT const * const iptr = signal.get()+ b2*sStrides[2] + i_b3Off;
-            InT *tptr = temp.get() + b2*tStrides[2] + t_b3Off;
-            InT *optr = out.get()  + b2*oStrides[2] + o_b3Off;
+            convolve2_separable<InT, AccT, 0, Expand>(
+                tptr, iptr, c_filter.get(), temp.dims(), sDims, sDims, cflen,
+                tStrides, sStrides, c_filter.strides(0));
 
-            convolve2_separable<InT, AccT, 0, Expand>(tptr, iptr, c_filter.get(),
-                                                      temp.dims(), sDims, sDims, cflen,
-                                                      tStrides, sStrides, c_filter.strides(0));
-
-            convolve2_separable<InT, AccT, 1, Expand>(optr, tptr, r_filter.get(),
-                                                      oDims, temp.dims(), sDims, rflen,
-                                                      oStrides, tStrides, r_filter.strides(0));
+            convolve2_separable<InT, AccT, 1, Expand>(
+                optr, tptr, r_filter.get(), oDims, temp.dims(), sDims, rflen,
+                oStrides, tStrides, r_filter.strides(0));
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index 1c6c8c0018..b81b6328f8 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -10,31 +10,28 @@
 #pragma once
 #include <Param.hpp>
 #include <math.hpp>
-#include <af/dim4.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 
-#include <cstring> //memcpy
+#include <cstring>  //memcpy
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void stridedCopy(T* dst, af::dim4 const & ostrides, T const * src,
-                 af::dim4 const & dims, af::dim4 const & strides, unsigned dim)
-{
-    if(dim == 0) {
-        if(strides[dim] == 1) {
-            //FIXME: Check for errors / exceptions
+void stridedCopy(T* dst, af::dim4 const& ostrides, T const* src,
+                 af::dim4 const& dims, af::dim4 const& strides, unsigned dim) {
+    if (dim == 0) {
+        if (strides[dim] == 1) {
+            // FIXME: Check for errors / exceptions
             std::memcpy(dst, src, dims[dim] * sizeof(T));
         } else {
-            for(dim_t i = 0; i < dims[dim]; i++) {
-                dst[i] = src[strides[dim]*i];
+            for (dim_t i = 0; i < dims[dim]; i++) {
+                dst[i] = src[strides[dim] * i];
             }
         }
     } else {
-        for(dim_t i = dims[dim]; i > 0; i--) {
+        for (dim_t i = dims[dim]; i > 0; i--) {
             stridedCopy<T>(dst, ostrides, src, dims, strides, dim - 1);
             src += strides[dim];
             dst += ostrides[dim];
@@ -43,46 +40,45 @@ void stridedCopy(T* dst, af::dim4 const & ostrides, T const * src,
 }
 
 template<typename OutT, typename InT>
-void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value, double factor)
-{
-    af::dim4 src_dims       = src.dims();
-    af::dim4 dst_dims       = dst.dims();
-    af::dim4 src_strides    = src.strides();
-    af::dim4 dst_strides    = dst.strides();
+void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value,
+                  double factor) {
+    af::dim4 src_dims    = src.dims();
+    af::dim4 dst_dims    = dst.dims();
+    af::dim4 src_strides = src.strides();
+    af::dim4 dst_strides = dst.strides();
 
-    InT const * const src_ptr = src.get();
-    OutT * dst_ptr      = dst.get();
+    InT const* const src_ptr = src.get();
+    OutT* dst_ptr            = dst.get();
 
     dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
     dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
     dim_t trgt_j = std::min(dst_dims[1], src_dims[1]);
     dim_t trgt_i = std::min(dst_dims[0], src_dims[0]);
 
-    for(dim_t l=0; l<dst_dims[3]; ++l) {
-
-        dim_t src_loff = l*src_strides[3];
-        dim_t dst_loff = l*dst_strides[3];
-        bool isLvalid = l<trgt_l;
-
-        for(dim_t k=0; k<dst_dims[2]; ++k) {
+    for (dim_t l = 0; l < dst_dims[3]; ++l) {
+        dim_t src_loff = l * src_strides[3];
+        dim_t dst_loff = l * dst_strides[3];
+        bool isLvalid  = l < trgt_l;
 
-            dim_t src_koff = k*src_strides[2];
-            dim_t dst_koff = k*dst_strides[2];
-            bool isKvalid = k<trgt_k;
+        for (dim_t k = 0; k < dst_dims[2]; ++k) {
+            dim_t src_koff = k * src_strides[2];
+            dim_t dst_koff = k * dst_strides[2];
+            bool isKvalid  = k < trgt_k;
 
-            for(dim_t j=0; j<dst_dims[1]; ++j) {
+            for (dim_t j = 0; j < dst_dims[1]; ++j) {
+                dim_t src_joff = j * src_strides[1];
+                dim_t dst_joff = j * dst_strides[1];
+                bool isJvalid  = j < trgt_j;
 
-                dim_t src_joff = j*src_strides[1];
-                dim_t dst_joff = j*dst_strides[1];
-                bool isJvalid = j<trgt_j;
-
-                for(dim_t i=0; i<dst_dims[0]; ++i) {
+                for (dim_t i = 0; i < dst_dims[0]; ++i) {
                     OutT temp = default_value;
-                    if (isLvalid && isKvalid && isJvalid && i<trgt_i) {
-                        dim_t src_idx = i*src_strides[0] + src_joff + src_koff + src_loff;
-                        temp = OutT(src_ptr[src_idx])*OutT(factor);
+                    if (isLvalid && isKvalid && isJvalid && i < trgt_i) {
+                        dim_t src_idx =
+                            i * src_strides[0] + src_joff + src_koff + src_loff;
+                        temp = OutT(src_ptr[src_idx]) * OutT(factor);
                     }
-                    dim_t dst_idx = i*dst_strides[0] + dst_joff + dst_koff + dst_loff;
+                    dim_t dst_idx =
+                        i * dst_strides[0] + dst_joff + dst_koff + dst_loff;
                     dst_ptr[dst_idx] = temp;
                 }
             }
@@ -91,55 +87,51 @@ void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value, double f
 }
 
 template<typename OutT, typename InT>
-struct CopyImpl
-{
-    static void copy(Param<OutT> dst, CParam<InT> src)
-    {
+struct CopyImpl {
+    static void copy(Param<OutT> dst, CParam<InT> src) {
         copyElemwise(dst, src, scalar<OutT>(0), 1.0);
     }
 };
 
 template<typename T>
-struct CopyImpl<T, T>
-{
-    static void copy(Param<T> dst, CParam<T> src)
-    {
-        af::dim4 src_dims       = src.dims();
-        af::dim4 dst_dims       = dst.dims();
-        af::dim4 src_strides    = src.strides();
-        af::dim4 dst_strides    = dst.strides();
-
-        T const * src_ptr = src.get();
-        T * dst_ptr       = dst.get();
+struct CopyImpl<T, T> {
+    static void copy(Param<T> dst, CParam<T> src) {
+        af::dim4 src_dims    = src.dims();
+        af::dim4 dst_dims    = dst.dims();
+        af::dim4 src_strides = src.strides();
+        af::dim4 dst_strides = dst.strides();
+
+        T const* src_ptr = src.get();
+        T* dst_ptr       = dst.get();
 
         // find the major-most dimension, which is linear in both arrays
         int linear_end = 0;
-        dim_t count = 1;
-        while (linear_end < 4
-                && count == src_strides[linear_end]
-                && count == dst_strides[linear_end]) {
+        dim_t count    = 1;
+        while (linear_end < 4 && count == src_strides[linear_end] &&
+               count == dst_strides[linear_end]) {
             count *= src_dims[linear_end];
             ++linear_end;
         }
 
         // traverse through the array using strides only until neccessary
-        copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, 3, linear_end);
+        copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims,
+                3, linear_end);
     }
 
-    static void copy_go(
-            T * dst_ptr,       const af::dim4 & dst_strides, const af::dim4 & dst_dims,
-            T const * src_ptr, const af::dim4 & src_strides, const af::dim4 & src_dims,
-            int dim, int linear_end)
-    {
+    static void copy_go(T* dst_ptr, const af::dim4& dst_strides,
+                        const af::dim4& dst_dims, T const* src_ptr,
+                        const af::dim4& src_strides, const af::dim4& src_dims,
+                        int dim, int linear_end) {
         // if we are in a higher dimension, copy the entire stride if possible
         if (linear_end == dim + 1) {
-            std::memcpy(dst_ptr, src_ptr, sizeof(T) * src_strides[dim] * src_dims[dim]);
+            std::memcpy(dst_ptr, src_ptr,
+                        sizeof(T) * src_strides[dim] * src_dims[dim]);
             return;
         }
 
         // 0th dimension is recursion bottom - copy element by element
         if (dim == 0) {
-            for(dim_t i=0; i<dst_dims[0]; ++i) {
+            for (dim_t i = 0; i < dst_dims[0]; ++i) {
                 *dst_ptr = *src_ptr;
                 dst_ptr += dst_strides[0];
                 src_ptr += src_strides[0];
@@ -148,8 +140,9 @@ struct CopyImpl<T, T>
         }
 
         // otherwise recurse to a lower dimenstion
-        for(dim_t i=0; i<dst_dims[dim]; ++i) {
-            copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides, src_dims, dim - 1, linear_end);
+        for (dim_t i = 0; i < dst_dims[dim]; ++i) {
+            copy_go(dst_ptr, dst_strides, dst_dims, src_ptr, src_strides,
+                    src_dims, dim - 1, linear_end);
             dst_ptr += dst_strides[dim];
             src_ptr += src_strides[dim];
         }
@@ -157,10 +150,9 @@ struct CopyImpl<T, T>
 };
 
 template<typename OutT, typename InT>
-void copy(Param<OutT> dst, CParam<InT> src)
-{
+void copy(Param<OutT> dst, CParam<InT> src) {
     CopyImpl<OutT, InT>::copy(dst, src);
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
index bc7d19a37c..e5de90f41d 100644
--- a/src/backend/cpu/kernel/diagonal.hpp
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -13,27 +13,22 @@
 
 #include <af/dim4.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void diagCreate(Param<T> out, CParam<T> in, int const num)
-{
+void diagCreate(Param<T> out, CParam<T> in, int const num) {
     int batch = in.dims(1);
     int size  = out.dims(0);
 
-    T const * iptr = in.get();
-    T * optr = out.get();
+    T const *iptr = in.get();
+    T *optr       = out.get();
 
     for (int k = 0; k < batch; k++) {
         for (int j = 0; j < size; j++) {
             for (int i = 0; i < size; i++) {
                 T val = scalar<T>(0);
-                if (i == j - num) {
-                    val = (num > 0) ? iptr[i] : iptr[j];
-                }
+                if (i == j - num) { val = (num > 0) ? iptr[i] : iptr[j]; }
                 optr[i + j * out.strides(1)] = val;
             }
         }
@@ -43,27 +38,27 @@ void diagCreate(Param<T> out, CParam<T> in, int const num)
 }
 
 template<typename T>
-void diagExtract(Param<T> out, CParam<T> in, int const num)
-{
+void diagExtract(Param<T> out, CParam<T> in, int const num) {
     af::dim4 const odims = out.dims();
     af::dim4 const idims = in.dims();
 
     int const i_off = (num > 0) ? (num * in.strides(1)) : (-num);
 
     for (int l = 0; l < (int)odims[3]; l++) {
-
         for (int k = 0; k < (int)odims[2]; k++) {
-            const T *iptr = in.get() + l * in.strides(3) + k * in.strides(2) + i_off;
+            const T *iptr =
+                in.get() + l * in.strides(3) + k * in.strides(2) + i_off;
             T *optr = out.get() + l * out.strides(3) + k * out.strides(2);
 
             for (int i = 0; i < (int)odims[0]; i++) {
                 T val = scalar<T>(0);
-                if (i < idims[0] && i < idims[1]) val =  iptr[i * in.strides(1) + i];
+                if (i < idims[0] && i < idims[1])
+                    val = iptr[i * in.strides(1) + i];
                 optr[i] = val;
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
index db86532230..72283e7a7e 100644
--- a/src/backend/cpu/kernel/diff.hpp
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -11,14 +11,11 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void diff1(Param<T> out, CParam<T> in, int const dim)
-{
+void diff1(Param<T> out, CParam<T> in, int const dim) {
     af::dim4 dims = out.dims();
     // Bool for dimension
     bool is_dim0 = dim == 0;
@@ -26,20 +23,20 @@ void diff1(Param<T> out, CParam<T> in, int const dim)
     bool is_dim2 = dim == 2;
     bool is_dim3 = dim == 3;
 
-    T const * const inPtr = in.get();
-    T * outPtr = out.get();
+    T const* const inPtr = in.get();
+    T* outPtr            = out.get();
 
     // TODO: Improve this
-    for(dim_t l = 0; l < dims[3]; l++) {
-        for(dim_t k = 0; k < dims[2]; k++) {
-            for(dim_t j = 0; j < dims[1]; j++) {
-                for(dim_t i = 0; i < dims[0]; i++) {
-                    // Operation: out[index] = in[index + 1 * dim_size] - in[index]
-                    int idx = getIdx(in.strides(), i, j, k, l);
-                    int jdx = getIdx(in.strides(),
-                            i + is_dim0, j + is_dim1,
-                            k + is_dim2, l + is_dim3);
-                    int odx = getIdx(out.strides(), i, j, k, l);
+    for (dim_t l = 0; l < dims[3]; l++) {
+        for (dim_t k = 0; k < dims[2]; k++) {
+            for (dim_t j = 0; j < dims[1]; j++) {
+                for (dim_t i = 0; i < dims[0]; i++) {
+                    // Operation: out[index] = in[index + 1 * dim_size] -
+                    // in[index]
+                    int idx     = getIdx(in.strides(), i, j, k, l);
+                    int jdx     = getIdx(in.strides(), i + is_dim0, j + is_dim1,
+                                     k + is_dim2, l + is_dim3);
+                    int odx     = getIdx(out.strides(), i, j, k, l);
                     outPtr[odx] = inPtr[jdx] - inPtr[idx];
                 }
             }
@@ -48,8 +45,7 @@ void diff1(Param<T> out, CParam<T> in, int const dim)
 }
 
 template<typename T>
-void diff2(Param<T> out, CParam<T> in, int const dim)
-{
+void diff2(Param<T> out, CParam<T> in, int const dim) {
     af::dim4 dims = out.dims();
     // Bool for dimension
     bool is_dim0 = dim == 0;
@@ -57,29 +53,30 @@ void diff2(Param<T> out, CParam<T> in, int const dim)
     bool is_dim2 = dim == 2;
     bool is_dim3 = dim == 3;
 
-    T const * const inPtr = in.get();
-    T * outPtr = out.get();
+    T const* const inPtr = in.get();
+    T* outPtr            = out.get();
 
     // TODO: Improve this
-    for(dim_t l = 0; l < dims[3]; l++) {
-        for(dim_t k = 0; k < dims[2]; k++) {
-            for(dim_t j = 0; j < dims[1]; j++) {
-                for(dim_t i = 0; i < dims[0]; i++) {
-                    // Operation: out[index] = in[index + 1 * dim_size] - in[index]
+    for (dim_t l = 0; l < dims[3]; l++) {
+        for (dim_t k = 0; k < dims[2]; k++) {
+            for (dim_t j = 0; j < dims[1]; j++) {
+                for (dim_t i = 0; i < dims[0]; i++) {
+                    // Operation: out[index] = in[index + 1 * dim_size] -
+                    // in[index]
                     int idx = getIdx(in.strides(), i, j, k, l);
-                    int jdx = getIdx(in.strides(),
-                            i + is_dim0, j + is_dim1,
-                            k + is_dim2, l + is_dim3);
-                    int kdx = getIdx(in.strides(),
-                            i + 2 * is_dim0, j + 2 * is_dim1,
-                            k + 2 * is_dim2, l + 2 * is_dim3);
+                    int jdx = getIdx(in.strides(), i + is_dim0, j + is_dim1,
+                                     k + is_dim2, l + is_dim3);
+                    int kdx =
+                        getIdx(in.strides(), i + 2 * is_dim0, j + 2 * is_dim1,
+                               k + 2 * is_dim2, l + 2 * is_dim3);
                     int odx = getIdx(out.strides(), i, j, k, l);
-                    outPtr[odx] = inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
+                    outPtr[odx] =
+                        inPtr[kdx] + inPtr[idx] - inPtr[jdx] - inPtr[jdx];
                 }
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
index 6d80a488e8..8946534bb8 100644
--- a/src/backend/cpu/kernel/dot.hpp
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -11,37 +11,41 @@
 #include <Param.hpp>
 #include <complex>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-template<typename T> T
-conj(T  x) { return x; }
+template<typename T>
+T conj(T x) {
+    return x;
+}
 
-template<> cfloat  conj<cfloat> (cfloat  c) { return std::conj(c); }
-template<> cdouble conj<cdouble>(cdouble c) { return std::conj(c); }
+template<>
+cfloat conj<cfloat>(cfloat c) {
+    return std::conj(c);
+}
+template<>
+cdouble conj<cdouble>(cdouble c) {
+    return std::conj(c);
+}
 
 template<typename T, bool conjugate, bool both_conjugate>
-void dot(Param<T> output, CParam<T> lhs, CParam<T> rhs,
-         af_mat_prop optLhs, af_mat_prop optRhs)
-{
+void dot(Param<T> output, CParam<T> lhs, CParam<T> rhs, af_mat_prop optLhs,
+         af_mat_prop optRhs) {
     UNUSED(optLhs);
     UNUSED(optRhs);
     int N = lhs.dims(0);
 
-    T out = 0;
+    T out       = 0;
     const T *pL = lhs.get();
     const T *pR = rhs.get();
 
-    for(int i = 0; i < N; i++)
+    for (int i = 0; i < N; i++)
         out += (conjugate ? kernel::conj(pL[i]) : pL[i]) * pR[i];
 
-    if(both_conjugate) out = kernel::conj(out);
+    if (both_conjugate) out = kernel::conj(out);
 
     *output.get() = out;
-
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/exampleFunction.hpp b/src/backend/cpu/kernel/exampleFunction.hpp
index a9e01916ee..853f96e60c 100644
--- a/src/backend/cpu/kernel/exampleFunction.hpp
+++ b/src/backend/cpu/kernel/exampleFunction.hpp
@@ -11,34 +11,33 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void exampleFunction(Param<T> out, CParam<T> a, CParam<T> b, const af_someenum_t method)
-{
+void exampleFunction(Param<T> out, CParam<T> a, CParam<T> b,
+                     const af_someenum_t method) {
     UNUSED(method);
-    dim4 oDims    = out.dims();
+    dim4 oDims = out.dims();
 
-    dim4 aStrides = a.strides();        // you can retrieve strides
+    dim4 aStrides = a.strides();  // you can retrieve strides
     dim4 bStrides = b.strides();
     dim4 oStrides = out.strides();
 
-    const T* src1 = a.get();            // cpu::Param<T>::get returns the pointer to the
-                                        // memory allocated for that Param (with proper offsets)
-    const T* src2 = b.get();            // cpu::Param<T>::get returns the pointer to the
-                                        // memory allocated for that Param (with proper offsets)
+    const T* src1 =
+        a.get();  // cpu::Param<T>::get returns the pointer to the
+                  // memory allocated for that Param (with proper offsets)
+    const T* src2 =
+        b.get();  // cpu::Param<T>::get returns the pointer to the
+                  // memory allocated for that Param (with proper offsets)
     T* dst = out.get();
 
     // Implement your algorithm and write results to dst
-    for(int j=0; j<oDims[1]; ++j) {
-        for (int i=0; i<oDims[0]; ++i) {
-
-            int src1Idx = i + j*aStrides[1];
-            int src2Idx = i + j*bStrides[1];
-            int dstIdx  = i + j*oStrides[1];
+    for (int j = 0; j < oDims[1]; ++j) {
+        for (int i = 0; i < oDims[0]; ++i) {
+            int src1Idx = i + j * aStrides[1];
+            int src2Idx = i + j * bStrides[1];
+            int dstIdx  = i + j * oStrides[1];
 
             // kernel algorithm goes here
             dst[dstIdx] = src1[src1Idx] + src2[src2Idx];
@@ -46,5 +45,5 @@ void exampleFunction(Param<T> out, CParam<T> a, CParam<T> b, const af_someenum_t
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index 2806b16584..f2a3d148ee 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -11,162 +11,154 @@
 #include <Param.hpp>
 #include <math.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-inline int idx_y(int i)
-{
-    if (i >= 8)
-        return clamp(-(i-8-4), -3, 3);
+inline int idx_y(int i) {
+    if (i >= 8) return clamp(-(i - 8 - 4), -3, 3);
 
-    return clamp(i-4, -3, 3);
+    return clamp(i - 4, -3, 3);
 }
 
-inline int idx_x(int i)
-{
-    if (i < 12)
-        return idx_y(i+4);
+inline int idx_x(int i) {
+    if (i < 12) return idx_y(i + 4);
 
-    return idx_y(i-12);
+    return idx_y(i - 12);
 }
 
-inline int idx(int y, int x, unsigned idim0)
-{
-    return x * idim0 + y;
-}
+inline int idx(int y, int x, unsigned idim0) { return x * idim0 + y; }
 
 // test_greater()
 // Tests if a pixel x > p + thr
-inline int test_greater(float x, float p, float thr)
-{
-    return (x > p + thr);
-}
+inline int test_greater(float x, float p, float thr) { return (x > p + thr); }
 
 // test_smaller()
 // Tests if a pixel x < p - thr
-inline int test_smaller(float x, float p, float thr)
-{
-    return (x < p - thr);
-}
+inline int test_smaller(float x, float p, float thr) { return (x < p - thr); }
 
 // test_pixel()
 // Returns -1 when x < p - thr
 // Returns  0 when x >= p - thr && x <= p + thr
 // Returns  1 when x > p + thr
 template<typename T>
-inline int test_pixel(const T* image, const float p, float thr, int y, int x, unsigned idim0)
-{
-    return -test_smaller((float)image[idx(y,x,idim0)], p, thr) + test_greater((float)image[idx(y,x,idim0)], p, thr);
+inline int test_pixel(const T *image, const float p, float thr, int y, int x,
+                      unsigned idim0) {
+    return -test_smaller((float)image[idx(y, x, idim0)], p, thr) +
+           test_greater((float)image[idx(y, x, idim0)], p, thr);
 }
 
 // abs_diff()
 // Returns absolute difference of x and y
-inline int abs_diff(int x, int y)
-{
-    return abs(x - y);
-}
-inline unsigned abs_diff(unsigned x, unsigned y)
-{
+inline int abs_diff(int x, int y) { return abs(x - y); }
+inline unsigned abs_diff(unsigned x, unsigned y) {
     return (unsigned)abs((int)x - (int)y);
 }
-inline float abs_diff(float x, float y)
-{
-    return fabs(x - y);
-}
-inline double abs_diff(double x, double y)
-{
-    return fabs(x - y);
-}
+inline float abs_diff(float x, float y) { return fabs(x - y); }
+inline double abs_diff(double x, double y) { return fabs(x - y); }
 
 template<typename T>
-void locate_features(CParam<T> in, Param<float> score,
-                     Param<float> x_out, Param<float> y_out,
-                     Param<float> score_out, unsigned* count, float const thr,
+void locate_features(CParam<T> in, Param<float> score, Param<float> x_out,
+                     Param<float> y_out, Param<float> score_out,
+                     unsigned *count, float const thr,
                      unsigned const arc_length, unsigned const nonmax,
-                     unsigned const max_feat, unsigned const edge)
-{
+                     unsigned const max_feat, unsigned const edge) {
     af::dim4 in_dims = in.dims();
-    T const * in_ptr = in.get();
+    T const *in_ptr  = in.get();
 
     for (int y = edge; y < (int)(in_dims[0] - edge); y++) {
         for (int x = edge; x < (int)(in_dims[1] - edge); x++) {
             float p = in_ptr[idx(y, x, in_dims[0])];
 
-            // Start by testing opposite pixels of the circle that will result in
-            // a non-kepoint
+            // Start by testing opposite pixels of the circle that will result
+            // in a non-kepoint
             int d;
-            d  = test_pixel<T>(in_ptr, p, thr, y-3,   x, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+3,   x, in_dims[0]);
-            if (d == 0)
-                continue;
-
-            d &= test_pixel<T>(in_ptr, p, thr, y-2, x+2, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+2, x-2, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y  , x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y  , x-3, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y+2, x+2, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-2, x-2, in_dims[0]);
-            if (d == 0)
-                continue;
-
-            d &= test_pixel<T>(in_ptr, p, thr, y-3, x+1, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+3, x-1, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y-1, x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y+1, x-3, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y+1, x+3, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-1, x-3, in_dims[0]);
-            d &= test_pixel<T>(in_ptr, p, thr, y+3, x+1, in_dims[0]) | test_pixel<T>(in_ptr, p, thr, y-3, x-1, in_dims[0]);
-            if (d == 0)
-                continue;
+            d = test_pixel<T>(in_ptr, p, thr, y - 3, x, in_dims[0]) |
+                test_pixel<T>(in_ptr, p, thr, y + 3, x, in_dims[0]);
+            if (d == 0) continue;
+
+            d &= test_pixel<T>(in_ptr, p, thr, y - 2, x + 2, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y + 2, x - 2, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y, x + 3, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y, x - 3, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y + 2, x + 2, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y - 2, x - 2, in_dims[0]);
+            if (d == 0) continue;
+
+            d &= test_pixel<T>(in_ptr, p, thr, y - 3, x + 1, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y + 3, x - 1, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y - 1, x + 3, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y + 1, x - 3, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y + 1, x + 3, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y - 1, x - 3, in_dims[0]);
+            d &= test_pixel<T>(in_ptr, p, thr, y + 3, x + 1, in_dims[0]) |
+                 test_pixel<T>(in_ptr, p, thr, y - 3, x - 1, in_dims[0]);
+            if (d == 0) continue;
 
             int sum = 0;
 
             // Sum responses [-1, 0 or 1] of first arc_length pixels
             for (int i = 0; i < static_cast<int>(arc_length); i++)
-                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
+                sum += test_pixel<T>(in_ptr, p, thr, y + idx_y(i), x + idx_x(i),
+                                     in_dims[0]);
 
-            // Test maximum and mininmum responses of first segment of arc_length
-            // pixels
+            // Test maximum and mininmum responses of first segment of
+            // arc_length pixels
             int max_sum = 0, min_sum = 0;
             max_sum = std::max(max_sum, sum);
             min_sum = std::min(min_sum, sum);
 
-            // Sum responses and test the remaining 16-arc_length pixels of the circle
+            // Sum responses and test the remaining 16-arc_length pixels of the
+            // circle
             for (int i = arc_length; i < 16; i++) {
-                sum -= test_pixel<T>(in_ptr, p, thr, y+idx_y(i-arc_length), x+idx_x(i-arc_length), in_dims[0]);
-                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
+                sum -= test_pixel<T>(in_ptr, p, thr, y + idx_y(i - arc_length),
+                                     x + idx_x(i - arc_length), in_dims[0]);
+                sum += test_pixel<T>(in_ptr, p, thr, y + idx_y(i), x + idx_x(i),
+                                     in_dims[0]);
                 max_sum = std::max(max_sum, sum);
                 min_sum = std::min(min_sum, sum);
             }
 
             // To completely test all possible segments, it's necessary to test
             // segments that include the top junction of the circle
-            for (int i = 0; i < static_cast<int>(arc_length-1); i++) {
-                sum -= test_pixel<T>(in_ptr, p, thr, y+idx_y(16-arc_length+i), x+idx_x(16-arc_length+i), in_dims[0]);
-                sum += test_pixel<T>(in_ptr, p, thr, y+idx_y(i), x+idx_x(i), in_dims[0]);
+            for (int i = 0; i < static_cast<int>(arc_length - 1); i++) {
+                sum -= test_pixel<T>(
+                    in_ptr, p, thr, y + idx_y(16 - arc_length + i),
+                    x + idx_x(16 - arc_length + i), in_dims[0]);
+                sum += test_pixel<T>(in_ptr, p, thr, y + idx_y(i), x + idx_x(i),
+                                     in_dims[0]);
                 max_sum = std::max(max_sum, sum);
                 min_sum = std::min(min_sum, sum);
             }
 
             float s_bright = 0, s_dark = 0;
             for (int i = 0; i < 16; i++) {
-                float p_x = (float)in_ptr[idx(y+idx_y(i), x+idx_x(i), in_dims[0])];
+                float p_x =
+                    (float)in_ptr[idx(y + idx_y(i), x + idx_x(i), in_dims[0])];
 
-                s_bright += test_greater(p_x, p, thr) * (abs_diff(p_x, p) - thr);
-                s_dark   += test_smaller(p_x, p, thr) * (abs_diff(p, p_x) - thr);
+                s_bright +=
+                    test_greater(p_x, p, thr) * (abs_diff(p_x, p) - thr);
+                s_dark += test_smaller(p_x, p, thr) * (abs_diff(p, p_x) - thr);
             }
 
-            // If sum at some point was equal to (+-)arc_length, there is a segment
-            // that for which all pixels are much brighter or much brighter than
-            // central pixel p.
-            if (max_sum == static_cast<int>(arc_length) || min_sum == -static_cast<int>(arc_length)) {
+            // If sum at some point was equal to (+-)arc_length, there is a
+            // segment that for which all pixels are much brighter or much
+            // brighter than central pixel p.
+            if (max_sum == static_cast<int>(arc_length) ||
+                min_sum == -static_cast<int>(arc_length)) {
                 unsigned j = *count;
                 ++*count;
                 if (j < max_feat) {
-                    float *x_out_ptr = x_out.get();
-                    float *y_out_ptr = y_out.get();
+                    float *x_out_ptr     = x_out.get();
+                    float *y_out_ptr     = y_out.get();
                     float *score_out_ptr = score_out.get();
-                    x_out_ptr[j]     = static_cast<float>(x);
-                    y_out_ptr[j]     = static_cast<float>(y);
-                    score_out_ptr[j] = static_cast<float>(std::max(s_bright, s_dark));
+                    x_out_ptr[j]         = static_cast<float>(x);
+                    y_out_ptr[j]         = static_cast<float>(y);
+                    score_out_ptr[j] =
+                        static_cast<float>(std::max(s_bright, s_dark));
                     if (nonmax == 1) {
-                        float* score_ptr = score.get();
-                        score_ptr[idx(y, x, in_dims[0])] = std::max(s_bright, s_dark);
+                        float *score_ptr = score.get();
+                        score_ptr[idx(y, x, in_dims[0])] =
+                            std::max(s_bright, s_dark);
                     }
                 }
             }
@@ -176,11 +168,11 @@ void locate_features(CParam<T> in, Param<float> score,
 
 void non_maximal(CParam<float> score, CParam<float> x_in, CParam<float> y_in,
                  Param<float> x_out, Param<float> y_out, Param<float> score_out,
-                 unsigned* count, unsigned const total_feat, unsigned const edge)
-{
-    float const * score_ptr = score.get();
-    float const * x_in_ptr = x_in.get();
-    float const * y_in_ptr = y_in.get();
+                 unsigned *count, unsigned const total_feat,
+                 unsigned const edge) {
+    float const *score_ptr = score.get();
+    float const *x_in_ptr  = x_in.get();
+    float const *y_in_ptr  = y_in.get();
 
     af::dim4 score_dims = score.dims();
 
@@ -190,13 +182,14 @@ void non_maximal(CParam<float> score, CParam<float> x_in, CParam<float> y_in,
 
         float v = score_ptr[y + score_dims[0] * x];
         float max_v;
-        max_v = std::max(score_ptr[y-1 + score_dims[0] * (x-1)], score_ptr[y-1 + score_dims[0] * x]);
-        max_v = std::max(max_v, score_ptr[y-1 + score_dims[0] * (x+1)]);
-        max_v = std::max(max_v, score_ptr[y   + score_dims[0] * (x-1)]);
-        max_v = std::max(max_v, score_ptr[y   + score_dims[0] * (x+1)]);
-        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x-1)]);
-        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x)  ]);
-        max_v = std::max(max_v, score_ptr[y+1 + score_dims[0] * (x+1)]);
+        max_v = std::max(score_ptr[y - 1 + score_dims[0] * (x - 1)],
+                         score_ptr[y - 1 + score_dims[0] * x]);
+        max_v = std::max(max_v, score_ptr[y - 1 + score_dims[0] * (x + 1)]);
+        max_v = std::max(max_v, score_ptr[y + score_dims[0] * (x - 1)]);
+        max_v = std::max(max_v, score_ptr[y + score_dims[0] * (x + 1)]);
+        max_v = std::max(max_v, score_ptr[y + 1 + score_dims[0] * (x - 1)]);
+        max_v = std::max(max_v, score_ptr[y + 1 + score_dims[0] * (x)]);
+        max_v = std::max(max_v, score_ptr[y + 1 + score_dims[0] * (x + 1)]);
 
         if (y >= score_dims[1] - edge - 1 || y <= edge + 1 ||
             x >= score_dims[0] - edge - 1 || x <= edge + 1)
@@ -208,8 +201,8 @@ void non_maximal(CParam<float> score, CParam<float> x_in, CParam<float> y_in,
             unsigned j = *count;
             ++*count;
 
-            float *x_out_ptr = x_out.get();
-            float *y_out_ptr = y_out.get();
+            float *x_out_ptr     = x_out.get();
+            float *y_out_ptr     = y_out.get();
             float *score_out_ptr = score_out.get();
 
             x_out_ptr[j]     = static_cast<float>(x);
@@ -219,5 +212,5 @@ void non_maximal(CParam<float> score, CParam<float> x_in, CParam<float> y_in,
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
index ef717a049d..94df2374ef 100644
--- a/src/backend/cpu/kernel/fft.hpp
+++ b/src/backend/cpu/kernel/fft.hpp
@@ -8,42 +8,36 @@
  ********************************************************/
 
 #pragma once
-#include <fftw3.h>
 #include <Param.hpp>
+#include <fftw3.h>
 #include <types.hpp>
 
 #include <af/dim4.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<int rank>
-void computeDims(int rdims[rank], const af::dim4 &idims)
-{
-    for (int i = 0; i < rank; i++) {
-        rdims[i] = idims[(rank -1) - i];
-    }
+void computeDims(int rdims[rank], const af::dim4 &idims) {
+    for (int i = 0; i < rank; i++) { rdims[i] = idims[(rank - 1) - i]; }
 }
 
 template<typename T>
 struct fftw_transform;
 
-#define TRANSFORM(PRE, TY)                                              \
-    template<>                                                          \
-    struct fftw_transform<TY>                                           \
-    {                                                                   \
-        typedef PRE##_plan plan_t;                                      \
-        typedef PRE##_complex ctype_t;                                  \
-                                                                        \
-        template<typename... Args>                                      \
-            plan_t create(Args... args)                                 \
-        { return PRE##_plan_many_dft(args...); }                        \
-        void execute(plan_t plan) { return PRE##_execute(plan); }       \
-        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); }  \
-    };                                                                  \
-
+#define TRANSFORM(PRE, TY)                                             \
+    template<>                                                         \
+    struct fftw_transform<TY> {                                        \
+        typedef PRE##_plan plan_t;                                     \
+        typedef PRE##_complex ctype_t;                                 \
+                                                                       \
+        template<typename... Args>                                     \
+        plan_t create(Args... args) {                                  \
+            return PRE##_plan_many_dft(args...);                       \
+        }                                                              \
+        void execute(plan_t plan) { return PRE##_execute(plan); }      \
+        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); } \
+    };
 
 TRANSFORM(fftwf, cfloat)
 TRANSFORM(fftw, cdouble)
@@ -51,37 +45,34 @@ TRANSFORM(fftw, cdouble)
 template<typename To, typename Ti>
 struct fftw_real_transform;
 
-#define TRANSFORM_REAL(PRE, To, Ti, POST)                               \
-    template<>                                                          \
-    struct fftw_real_transform<To, Ti>                                  \
-    {                                                                   \
-        typedef PRE##_plan plan_t;                                      \
-        typedef PRE##_complex ctype_t;                                  \
-                                                                        \
-        template<typename... Args>                                      \
-            plan_t create(Args... args)                                 \
-        { return PRE##_plan_many_dft_##POST(args...); }                 \
-        void execute(plan_t plan) { return PRE##_execute(plan); }       \
-        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); }  \
-    };                                                                  \
-
-
-TRANSFORM_REAL(fftwf, cfloat , float , r2c)
-TRANSFORM_REAL(fftw , cdouble, double, r2c)
-TRANSFORM_REAL(fftwf, float , cfloat , c2r)
-TRANSFORM_REAL(fftw , double, cdouble, c2r)
-
+#define TRANSFORM_REAL(PRE, To, Ti, POST)                              \
+    template<>                                                         \
+    struct fftw_real_transform<To, Ti> {                               \
+        typedef PRE##_plan plan_t;                                     \
+        typedef PRE##_complex ctype_t;                                 \
+                                                                       \
+        template<typename... Args>                                     \
+        plan_t create(Args... args) {                                  \
+            return PRE##_plan_many_dft_##POST(args...);                \
+        }                                                              \
+        void execute(plan_t plan) { return PRE##_execute(plan); }      \
+        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); } \
+    };
+
+TRANSFORM_REAL(fftwf, cfloat, float, r2c)
+TRANSFORM_REAL(fftw, cdouble, double, r2c)
+TRANSFORM_REAL(fftwf, float, cfloat, c2r)
+TRANSFORM_REAL(fftw, double, cdouble, c2r)
 
 template<typename T, int rank, bool direction>
-void fft_inplace(Param<T> in, const af::dim4 iDataDims)
-{
+void fft_inplace(Param<T> in, const af::dim4 iDataDims) {
     int t_dims[rank];
     int in_embed[rank];
 
     const af::dim4 idims = in.dims();
 
-    computeDims<rank>(t_dims  , idims);
-    computeDims<rank>(in_embed , iDataDims);
+    computeDims<rank>(t_dims, idims);
+    computeDims<rank>(in_embed, iDataDims);
 
     const af::dim4 istrides = in.strides();
 
@@ -91,38 +82,30 @@ void fft_inplace(Param<T> in, const af::dim4 iDataDims)
     fftw_transform<T> transform;
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= idims[i];
-    }
-
-    plan = transform.create(rank,
-                            t_dims,
-                            (int)batch,
-                            (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            direction ? FFTW_FORWARD : FFTW_BACKWARD,
-                            FFTW_ESTIMATE);
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+
+    plan = transform.create(
+        rank, t_dims, (int)batch, (ctype_t *)in.get(), in_embed,
+        (int)istrides[0], (int)istrides[rank], (ctype_t *)in.get(), in_embed,
+        (int)istrides[0], (int)istrides[rank],
+        direction ? FFTW_FORWARD : FFTW_BACKWARD, FFTW_ESTIMATE);
 
     transform.execute(plan);
     transform.destroy(plan);
 }
 
 template<typename Tc, typename Tr, int rank>
-void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in, const af::dim4 iDataDims)
-{
+void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in,
+             const af::dim4 iDataDims) {
     af::dim4 idims = in.dims();
 
     int t_dims[rank];
     int in_embed[rank];
     int out_embed[rank];
 
-    computeDims<rank>(t_dims  , idims);
-    computeDims<rank>(in_embed , iDataDims);
-    computeDims<rank>(out_embed , oDataDims);
+    computeDims<rank>(t_dims, idims);
+    computeDims<rank>(in_embed, iDataDims);
+    computeDims<rank>(out_embed, oDataDims);
 
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -133,37 +116,27 @@ void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in, const af::d
     fftw_real_transform<Tc, Tr> transform;
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= idims[i];
-    }
-
-    plan = transform.create(rank,
-                            t_dims,
-                            (int)batch,
-                            (Tr *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            (ctype_t *)out.get(),
-                            out_embed, (int)ostrides[0],
-                            (int)ostrides[rank],
-                            FFTW_ESTIMATE);
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+
+    plan = transform.create(rank, t_dims, (int)batch, (Tr *)in.get(), in_embed,
+                            (int)istrides[0], (int)istrides[rank],
+                            (ctype_t *)out.get(), out_embed, (int)ostrides[0],
+                            (int)ostrides[rank], FFTW_ESTIMATE);
 
     transform.execute(plan);
     transform.destroy(plan);
 }
 
 template<typename Tr, typename Tc, int rank>
-void fft_c2r(Param<Tr> out, const af::dim4 oDataDims,
-             CParam<Tc> in, const af::dim4 iDataDims,
-             const af::dim4 odims)
-{
+void fft_c2r(Param<Tr> out, const af::dim4 oDataDims, CParam<Tc> in,
+             const af::dim4 iDataDims, const af::dim4 odims) {
     int t_dims[rank];
     int in_embed[rank];
     int out_embed[rank];
 
-    computeDims<rank>(t_dims  , odims);
-    computeDims<rank>(in_embed , iDataDims);
-    computeDims<rank>(out_embed , oDataDims);
+    computeDims<rank>(t_dims, odims);
+    computeDims<rank>(in_embed, iDataDims);
+    computeDims<rank>(out_embed, oDataDims);
 
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -174,24 +147,16 @@ void fft_c2r(Param<Tr> out, const af::dim4 oDataDims,
     fftw_real_transform<Tr, Tc> transform;
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= odims[i];
-    }
-
-    plan = transform.create(rank,
-                            t_dims,
-                            (int)batch,
-                            (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0],
-                            (int)istrides[rank],
-                            (Tr *)out.get(),
-                            out_embed, (int)ostrides[0],
-                            (int)ostrides[rank],
-                            FFTW_ESTIMATE);
+    for (int i = rank; i < 4; i++) { batch *= odims[i]; }
+
+    plan = transform.create(rank, t_dims, (int)batch, (ctype_t *)in.get(),
+                            in_embed, (int)istrides[0], (int)istrides[rank],
+                            (Tr *)out.get(), out_embed, (int)ostrides[0],
+                            (int)ostrides[rank], FFTW_ESTIMATE);
 
     transform.execute(plan);
     transform.destroy(plan);
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 419c060a5b..78205869c7 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -10,19 +10,17 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename To, typename Ti>
-void packData(Param<To> out, const af::dim4 od, const af::dim4 os, CParam<Ti> in)
-{
+void packData(Param<To> out, const af::dim4 od, const af::dim4 os,
+              CParam<Ti> in) {
     To* out_ptr = out.get();
 
     const af::dim4 id = in.dims();
     const af::dim4 is = in.strides();
-    const Ti* in_ptr = in.get();
+    const Ti* in_ptr  = in.get();
 
     int id0_half = divup(id[0], 2);
     bool odd_id0 = (id[0] % 2 == 1);
@@ -31,20 +29,22 @@ void packData(Param<To> out, const af::dim4 od, const af::dim4 os, CParam<Ti> in
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
             for (int d1 = 0; d1 < (int)od[1]; d1++) {
                 for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
-                    const dim_t oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-
-                    if (d0 < (int)id0_half && d1 < (int)id[1] && d2 < (int)id[2] && d3 < (int)id[3]) {
-                        const dim_t iidx = d3*is[3] + d2*is[2] + d1*is[1] + d0;
-                        out_ptr[oidx]   = (To)in_ptr[iidx];
-                        if (d0 == id0_half-1 && odd_id0)
-                            out_ptr[oidx+1] = (To)0;
+                    const dim_t oidx =
+                        d3 * os[3] + d2 * os[2] + d1 * os[1] + d0 * 2;
+
+                    if (d0 < (int)id0_half && d1 < (int)id[1] &&
+                        d2 < (int)id[2] && d3 < (int)id[3]) {
+                        const dim_t iidx =
+                            d3 * is[3] + d2 * is[2] + d1 * is[1] + d0;
+                        out_ptr[oidx] = (To)in_ptr[iidx];
+                        if (d0 == id0_half - 1 && odd_id0)
+                            out_ptr[oidx + 1] = (To)0;
                         else
-                            out_ptr[oidx+1] = (To)in_ptr[iidx+id0_half];
-                    }
-                    else {
+                            out_ptr[oidx + 1] = (To)in_ptr[iidx + id0_half];
+                    } else {
                         // Pad remaining elements with 0s
-                        out_ptr[oidx]   = (To)0;
-                        out_ptr[oidx+1] = (To)0;
+                        out_ptr[oidx]     = (To)0;
+                        out_ptr[oidx + 1] = (To)0;
                     }
                 }
             }
@@ -54,29 +54,31 @@ void packData(Param<To> out, const af::dim4 od, const af::dim4 os, CParam<Ti> in
 
 template<typename To, typename Ti>
 void padArray(Param<To> out, const af::dim4 od, const af::dim4 os,
-              CParam<Ti> in, const dim_t offset)
-{
-    To* out_ptr = out.get() + offset;
+              CParam<Ti> in, const dim_t offset) {
+    To* out_ptr       = out.get() + offset;
     const af::dim4 id = in.dims();
     const af::dim4 is = in.strides();
-    const Ti* in_ptr = in.get();
+    const Ti* in_ptr  = in.get();
 
     for (int d3 = 0; d3 < (int)od[3]; d3++) {
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
             for (int d1 = 0; d1 < (int)od[1]; d1++) {
                 for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
-                    const dim_t oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
-
-                    if (d0 < (int)id[0] && d1 < (int)id[1] && d2 < (int)id[2] && d3 < (int)id[3]) {
-                        // Copy input elements to real elements, set imaginary elements to 0
-                        const dim_t iidx = d3*is[3] + d2*is[2] + d1*is[1] + d0;
-                        out_ptr[oidx]   = (To)in_ptr[iidx];
-                        out_ptr[oidx+1] = (To)0;
-                    }
-                    else {
+                    const dim_t oidx =
+                        d3 * os[3] + d2 * os[2] + d1 * os[1] + d0 * 2;
+
+                    if (d0 < (int)id[0] && d1 < (int)id[1] && d2 < (int)id[2] &&
+                        d3 < (int)id[3]) {
+                        // Copy input elements to real elements, set imaginary
+                        // elements to 0
+                        const dim_t iidx =
+                            d3 * is[3] + d2 * is[2] + d1 * is[1] + d0;
+                        out_ptr[oidx]     = (To)in_ptr[iidx];
+                        out_ptr[oidx + 1] = (To)0;
+                    } else {
                         // Pad remaining of the matrix to 0s
-                        out_ptr[oidx]   = (To)0;
-                        out_ptr[oidx+1] = (To)0;
+                        out_ptr[oidx]     = (To)0;
+                        out_ptr[oidx + 1] = (To)0;
                     }
                 }
             }
@@ -85,16 +87,16 @@ void padArray(Param<To> out, const af::dim4 od, const af::dim4 os,
 }
 
 template<typename T>
-void complexMultiply(Param<T> packed, const af::dim4 sig_dims, const af::dim4 sig_strides,
-                     const af::dim4 fit_dims, const af::dim4 fit_strides,
-                     AF_BATCH_KIND kind, const dim_t offset)
-{
-    T* out_ptr = packed.get() + (kind==AF_BATCH_RHS? offset : 0);
+void complexMultiply(Param<T> packed, const af::dim4 sig_dims,
+                     const af::dim4 sig_strides, const af::dim4 fit_dims,
+                     const af::dim4 fit_strides, AF_BATCH_KIND kind,
+                     const dim_t offset) {
+    T* out_ptr = packed.get() + (kind == AF_BATCH_RHS ? offset : 0);
     T* in1_ptr = packed.get();
     T* in2_ptr = packed.get() + offset;
 
-    const af::dim4& od  = (kind==AF_BATCH_RHS ? fit_dims : sig_dims);
-    const af::dim4& os  = (kind==AF_BATCH_RHS ? fit_strides : sig_strides);
+    const af::dim4& od  = (kind == AF_BATCH_RHS ? fit_dims : sig_dims);
+    const af::dim4& os  = (kind == AF_BATCH_RHS ? fit_strides : sig_strides);
     const af::dim4& i1d = sig_dims;
     const af::dim4& i2d = fit_dims;
     const af::dim4& i1s = sig_strides;
@@ -106,7 +108,8 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims, const af::dim4 si
                 for (int d0 = 0; d0 < (int)od[0] / 2; d0++) {
                     if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
                         // Complex multiply each signal to equivalent filter
-                        const int ridx = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+                        const int ridx =
+                            d3 * os[3] + d2 * os[2] + d1 * os[1] + d0 * 2;
                         const int iidx = ridx + 1;
 
                         T a = in1_ptr[ridx];
@@ -114,12 +117,12 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         T c = in2_ptr[ridx];
                         T d = in2_ptr[iidx];
 
-                        out_ptr[ridx] = a*c - b*d;
-                        out_ptr[iidx] = a*d + b*c;
-                    }
-                    else if (kind == AF_BATCH_LHS) {
+                        out_ptr[ridx] = a * c - b * d;
+                        out_ptr[iidx] = a * d + b * c;
+                    } else if (kind == AF_BATCH_LHS) {
                         // Complex multiply all signals to filter
-                        const int ridx1 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+                        const int ridx1 =
+                            d3 * os[3] + d2 * os[2] + d1 * os[1] + d0 * 2;
                         const int iidx1 = ridx1 + 1;
                         const int ridx2 = ridx1 % (i2s[3] * i2d[3]);
                         const int iidx2 = iidx1 % (i2s[3] * i2d[3]);
@@ -129,12 +132,12 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         T c = in2_ptr[ridx2];
                         T d = in2_ptr[iidx2];
 
-                        out_ptr[ridx1] = a*c - b*d;
-                        out_ptr[iidx1] = a*d + b*c;
-                    }
-                    else if (kind == AF_BATCH_RHS) {
+                        out_ptr[ridx1] = a * c - b * d;
+                        out_ptr[iidx1] = a * d + b * c;
+                    } else if (kind == AF_BATCH_RHS) {
                         // Complex multiply signal to all filters
-                        const int ridx2 = d3*os[3] + d2*os[2] + d1*os[1] + d0*2;
+                        const int ridx2 =
+                            d3 * os[3] + d2 * os[2] + d1 * os[1] + d0 * 2;
                         const int iidx2 = ridx2 + 1;
                         const int ridx1 = ridx2 % (i1s[3] * i1d[3]);
                         const int iidx1 = iidx2 % (i1s[3] * i1d[3]);
@@ -144,8 +147,8 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims, const af::dim4 si
                         T c = in2_ptr[ridx2];
                         T d = in2_ptr[iidx2];
 
-                        out_ptr[ridx2] = a*c - b*d;
-                        out_ptr[iidx2] = a*d + b*c;
+                        out_ptr[ridx2] = a * c - b * d;
+                        out_ptr[iidx2] = a * d + b * c;
                     }
                 }
             }
@@ -157,8 +160,7 @@ template<typename To, typename Ti, bool roundOut>
 void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                    const Ti* in_ptr, const af::dim4& id, const af::dim4& is,
                    const af::dim4& fd, const int half_di0, const int baseDim,
-                   const int fftScale, const bool expand)
-{
+                   const int fftScale, const bool expand) {
     UNUSED(id);
     for (int d3 = 0; d3 < (int)od[3]; d3++) {
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
@@ -170,40 +172,44 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                         id1 = d1 * is[1];
                         id2 = d2 * is[2];
                         id3 = d3 * is[3];
-                    }
-                    else {
-                        id0 = d0 + fd[0]/2;
-                        id1 = (d1 + (baseDim > 1)*(fd[1]/2)) * is[1];
-                        id2 = (d2 + (baseDim > 2)*(fd[2]/2)) * is[2];
+                    } else {
+                        id0 = d0 + fd[0] / 2;
+                        id1 = (d1 + (baseDim > 1) * (fd[1] / 2)) * is[1];
+                        id2 = (d2 + (baseDim > 2) * (fd[2] / 2)) * is[2];
                         id3 = d3 * is[3];
                     }
 
-                    int oidx = d3*os[3] + d2*os[2] + d1*os[1] + d0;
+                    int oidx = d3 * os[3] + d2 * os[2] + d1 * os[1] + d0;
 
-                    // Divide output elements to cuFFT resulting scale, round result if output
-                    // type is single or double precision floating-point
+                    // Divide output elements to cuFFT resulting scale, round
+                    // result if output type is single or double precision
+                    // floating-point
                     if (id0 < half_di0) {
                         // Copy top elements
                         int iidx = id3 + id2 + id1 + id0 * 2;
                         if (roundOut)
-                            out_ptr[oidx] = (To)roundf((float)(in_ptr[iidx] / fftScale));
+                            out_ptr[oidx] =
+                                (To)roundf((float)(in_ptr[iidx] / fftScale));
                         else
                             out_ptr[oidx] = (To)(in_ptr[iidx] / fftScale);
-                    }
-                    else if (id0 < half_di0 + (int)fd[0] - 1) {
+                    } else if (id0 < half_di0 + (int)fd[0] - 1) {
                         // Add signal and filter elements to central part
                         int iidx1 = id3 + id2 + id1 + id0 * 2;
                         int iidx2 = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
                         if (roundOut)
-                            out_ptr[oidx] = (To)roundf((float)((in_ptr[iidx1] + in_ptr[iidx2]) / fftScale));
+                            out_ptr[oidx] = (To)roundf(
+                                (float)((in_ptr[iidx1] + in_ptr[iidx2]) /
+                                        fftScale));
                         else
-                            out_ptr[oidx] = (To)((in_ptr[iidx1] + in_ptr[iidx2]) / fftScale);
-                    }
-                    else {
+                            out_ptr[oidx] = (To)(
+                                (in_ptr[iidx1] + in_ptr[iidx2]) / fftScale);
+                    } else {
                         // Copy bottom elements
-                        const int iidx = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
+                        const int iidx =
+                            id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
                         if (roundOut)
-                            out_ptr[oidx] = (To)roundf((float)(in_ptr[iidx] / fftScale));
+                            out_ptr[oidx] =
+                                (To)roundf((float)(in_ptr[iidx] / fftScale));
                         else
                             out_ptr[oidx] = (To)(in_ptr[iidx] / fftScale);
                     }
@@ -214,33 +220,34 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
 }
 
 template<typename T, typename convT, bool roundOut, int baseDim>
-void reorder(Param<T> out, Param<convT> packed,
-             CParam<T> filter, const dim_t sig_half_d0, const dim_t fftScale,
+void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
+             const dim_t sig_half_d0, const dim_t fftScale,
              const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
              const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
-             bool expand, AF_BATCH_KIND kind)
-{
-    T* out_ptr = out.get();
-    const af::dim4 out_dims = out.dims();
+             bool expand, AF_BATCH_KIND kind) {
+    T* out_ptr                 = out.get();
+    const af::dim4 out_dims    = out.dims();
     const af::dim4 out_strides = out.strides();
 
     const af::dim4 filter_dims = filter.dims();
 
-    convT* packed_ptr = packed.get();
+    convT* packed_ptr     = packed.get();
     convT* sig_tmp_ptr    = packed_ptr;
     convT* filter_tmp_ptr = packed_ptr + sig_tmp_strides[3] * sig_tmp_dims[3];
 
     // Reorder the output
     if (kind == AF_BATCH_RHS) {
-        reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
-                filter_tmp_ptr, filter_tmp_dims, filter_tmp_strides,
-                filter_dims, sig_half_d0, baseDim, fftScale, expand);
+        reorderHelper<T, convT, roundOut>(
+            out_ptr, out_dims, out_strides, filter_tmp_ptr, filter_tmp_dims,
+            filter_tmp_strides, filter_dims, sig_half_d0, baseDim, fftScale,
+            expand);
     } else {
-        reorderHelper<T, convT, roundOut>(out_ptr, out_dims, out_strides,
-                sig_tmp_ptr, sig_tmp_dims, sig_tmp_strides,
-                filter_dims, sig_half_d0, baseDim, fftScale, expand);
+        reorderHelper<T, convT, roundOut>(
+            out_ptr, out_dims, out_strides, sig_tmp_ptr, sig_tmp_dims,
+            sig_tmp_strides, filter_dims, sig_half_d0, baseDim, fftScale,
+            expand);
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/gradient.hpp b/src/backend/cpu/kernel/gradient.hpp
index 33deb9d125..35f1fa8248 100644
--- a/src/backend/cpu/kernel/gradient.hpp
+++ b/src/backend/cpu/kernel/gradient.hpp
@@ -9,15 +9,13 @@
 
 #pragma once
 #include <Param.hpp>
+#include <math.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in)
-{
+void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     const af::dim4 dims = in.dims();
 
     T *d_grad0    = grad0.get();
@@ -31,25 +29,25 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in)
     T v5 = scalar<T>(0.5);
     T v1 = scalar<T>(1.0);
 
-    for(dim_t idw = 0; idw < dims[3]; idw++) {
+    for (dim_t idw = 0; idw < dims[3]; idw++) {
         const dim_t inW = idw * inst[3];
         const dim_t g0W = idw * g0st[3];
         const dim_t g1W = idw * g1st[3];
-        for(dim_t idz = 0; idz < dims[2]; idz++) {
+        for (dim_t idz = 0; idz < dims[2]; idz++) {
             const dim_t inZW = inW + idz * inst[2];
             const dim_t g0ZW = g0W + idz * g0st[2];
             const dim_t g1ZW = g1W + idz * g1st[2];
-            dim_t xl, xr, yl,yr;
+            dim_t xl, xr, yl, yr;
             T f0, f1;
-            for(dim_t idy = 0; idy < dims[1]; idy++) {
+            for (dim_t idy = 0; idy < dims[1]; idy++) {
                 const dim_t inYZW = inZW + idy * inst[1];
                 const dim_t g0YZW = g0ZW + idy * g0st[1];
                 const dim_t g1YZW = g1ZW + idy * g1st[1];
-                if(idy == 0) {
+                if (idy == 0) {
                     yl = inYZW + inst[1];
                     yr = inYZW;
                     f1 = v1;
-                } else if(idy == dims[1] - 1) {
+                } else if (idy == dims[1] - 1) {
                     yl = inYZW;
                     yr = inYZW - inst[1];
                     f1 = v1;
@@ -58,15 +56,15 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in)
                     yr = inYZW - inst[1];
                     f1 = v5;
                 }
-                for(dim_t idx = 0; idx < dims[0]; idx++) {
+                for (dim_t idx = 0; idx < dims[0]; idx++) {
                     const dim_t inMem = inYZW + idx;
                     const dim_t g0Mem = g0YZW + idx;
                     const dim_t g1Mem = g1YZW + idx;
-                    if(idx == 0) {
+                    if (idx == 0) {
                         xl = inMem + 1;
                         xr = inMem;
                         f0 = v1;
-                    } else if(idx == dims[0] - 1) {
+                    } else if (idx == dims[0] - 1) {
                         xl = inMem;
                         xr = inMem - 1;
                         f0 = v1;
@@ -84,5 +82,5 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in)
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/harris.hpp b/src/backend/cpu/kernel/harris.hpp
index 8e871d0713..7ea9350642 100644
--- a/src/backend/cpu/kernel/harris.hpp
+++ b/src/backend/cpu/kernel/harris.hpp
@@ -11,15 +11,12 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
 void second_order_deriv(Param<T> ixx, Param<T> ixy, Param<T> iyy,
-                        const unsigned in_len, CParam<T> ix, CParam<T> iy)
-{
+                        const unsigned in_len, CParam<T> ix, CParam<T> iy) {
     T* ixx_out     = ixx.get();
     T* ixy_out     = ixy.get();
     T* iyy_out     = iyy.get();
@@ -35,8 +32,7 @@ void second_order_deriv(Param<T> ixx, Param<T> ixy, Param<T> iyy,
 template<typename T>
 void harris_responses(Param<T> resp, const unsigned idim0, const unsigned idim1,
                       CParam<T> ixx, CParam<T> ixy, CParam<T> iyy,
-                      const float k_thr, const unsigned border_len)
-{
+                      const float k_thr, const unsigned border_len) {
     T* resp_out      = resp.get();
     const T* ixx_in  = ixx.get();
     const T* ixy_in  = ixy.get();
@@ -48,23 +44,23 @@ void harris_responses(Param<T> resp, const unsigned idim0, const unsigned idim1,
             const unsigned idx = x * idim0 + y;
 
             // Calculates matrix trace and determinant
-            T tr = ixx_in[idx] + iyy_in[idx];
+            T tr  = ixx_in[idx] + iyy_in[idx];
             T det = ixx_in[idx] * iyy_in[idx] - ixy_in[idx] * ixy_in[idx];
 
             // Calculates local Harris response
-            resp_out[idx] = det - k_thr * (tr*tr);
+            resp_out[idx] = det - k_thr * (tr * tr);
         }
     }
 }
 
 template<typename T>
-void non_maximal(Param<float> xOut, Param<float> yOut, Param<float> respOut, unsigned* count,
-                 const unsigned idim0, const unsigned idim1, CParam<T> respIn,
-                 const float min_resp, const unsigned border_len, const unsigned max_corners)
-{
-    float* x_out = xOut.get();
-    float* y_out = yOut.get();
-    float* resp_out = respOut.get();
+void non_maximal(Param<float> xOut, Param<float> yOut, Param<float> respOut,
+                 unsigned* count, const unsigned idim0, const unsigned idim1,
+                 CParam<T> respIn, const float min_resp,
+                 const unsigned border_len, const unsigned max_corners) {
+    float* x_out     = xOut.get();
+    float* y_out     = yOut.get();
+    float* resp_out  = respOut.get();
     const T* resp_in = respIn.get();
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
@@ -75,16 +71,18 @@ void non_maximal(Param<float> xOut, Param<float> yOut, Param<float> respOut, uns
 
             // Find maximum neighborhood response
             T max_v;
-            max_v = max(resp_in[(x-1) * idim0 + y-1], resp_in[x * idim0 + y-1]);
-            max_v = max(max_v, resp_in[(x+1) * idim0 + y-1]);
-            max_v = max(max_v, resp_in[(x-1) * idim0 + y  ]);
-            max_v = max(max_v, resp_in[(x+1) * idim0 + y  ]);
-            max_v = max(max_v, resp_in[(x-1) * idim0 + y+1]);
-            max_v = max(max_v, resp_in[(x)   * idim0 + y+1]);
-            max_v = max(max_v, resp_in[(x+1) * idim0 + y+1]);
+            max_v = std::max(resp_in[(x - 1) * idim0 + y - 1],
+                             resp_in[x * idim0 + y - 1]);
+            max_v = std::max(max_v, resp_in[(x + 1) * idim0 + y - 1]);
+            max_v = std::max(max_v, resp_in[(x - 1) * idim0 + y]);
+            max_v = std::max(max_v, resp_in[(x + 1) * idim0 + y]);
+            max_v = std::max(max_v, resp_in[(x - 1) * idim0 + y + 1]);
+            max_v = std::max(max_v, resp_in[(x)*idim0 + y + 1]);
+            max_v = std::max(max_v, resp_in[(x + 1) * idim0 + y + 1]);
 
-            // Stores corner to {x,y,resp}_out if it's response is maximum compared
-            // to its 8-neighborhood and greater or equal minimum response
+            // Stores corner to {x,y,resp}_out if it's response is maximum
+            // compared to its 8-neighborhood and greater or equal minimum
+            // response
             if (v > max_v && v >= (T)min_resp) {
                 const unsigned idx = *count;
                 *count += 1;
@@ -98,26 +96,25 @@ void non_maximal(Param<float> xOut, Param<float> yOut, Param<float> respOut, uns
     }
 }
 
-static void keep_corners(Param<float> xOut, Param<float> yOut, Param<float> respOut,
-                         CParam<float> xIn, CParam<float> yIn,
-                         CParam<float> respIn, CParam<unsigned> respIdx,
-                         const unsigned n_corners)
-{
-    float* x_out = xOut.get();
-    float* y_out = yOut.get();
-    float* resp_out = respOut.get();
-    const float* x_in = xIn.get();
-    const float* y_in = yIn.get();
+static void keep_corners(Param<float> xOut, Param<float> yOut,
+                         Param<float> respOut, CParam<float> xIn,
+                         CParam<float> yIn, CParam<float> respIn,
+                         CParam<unsigned> respIdx, const unsigned n_corners) {
+    float* x_out         = xOut.get();
+    float* y_out         = yOut.get();
+    float* resp_out      = respOut.get();
+    const float* x_in    = xIn.get();
+    const float* y_in    = yIn.get();
     const float* resp_in = respIn.get();
     const uint* resp_idx = respIdx.get();
 
     // Keep only the first n_feat features
     for (unsigned f = 0; f < n_corners; f++) {
-        x_out[f] = x_in[resp_idx[f]];
-        y_out[f] = y_in[resp_idx[f]];
+        x_out[f]    = x_in[resp_idx[f]];
+        y_out[f]    = y_in[resp_idx[f]];
         resp_out[f] = resp_in[f];
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 639130dc45..3ec8e12d04 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -10,39 +10,38 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename OutT, typename InT, bool IsLinear>
-void histogram(Param<OutT> out, CParam<InT> in,
-               unsigned const nbins, double const minval, double const maxval)
-{
-    dim4 const outDims   = out.dims();
-    float const step     = (maxval - minval)/(float)nbins;
-    dim4 const inDims    = in.dims();
-    dim4 const iStrides  = in.strides();
-    dim4 const oStrides  = out.strides();
-    dim_t const nElems   = inDims[0]*inDims[1];
+void histogram(Param<OutT> out, CParam<InT> in, unsigned const nbins,
+               double const minval, double const maxval) {
+    dim4 const outDims  = out.dims();
+    float const step    = (maxval - minval) / (float)nbins;
+    dim4 const inDims   = in.dims();
+    dim4 const iStrides = in.strides();
+    dim4 const oStrides = out.strides();
+    dim_t const nElems  = inDims[0] * inDims[1];
 
-
-    for(dim_t b3 = 0; b3 < outDims[3]; b3++) {
-        OutT *outData    = out.get() + b3 * oStrides[3];
-        const InT* inData= in.get() + b3 * iStrides[3];
-        for(dim_t b2 = 0; b2 < outDims[2]; b2++) {
-            for(dim_t i=0; i<nElems; i++) {
-                int idx = IsLinear ? i : ((i % inDims[0]) + (i / inDims[0])*iStrides[1]);
+    for (dim_t b3 = 0; b3 < outDims[3]; b3++) {
+        OutT* outData     = out.get() + b3 * oStrides[3];
+        const InT* inData = in.get() + b3 * iStrides[3];
+        for (dim_t b2 = 0; b2 < outDims[2]; b2++) {
+            for (dim_t i = 0; i < nElems; i++) {
+                int idx =
+                    IsLinear
+                        ? i
+                        : ((i % inDims[0]) + (i / inDims[0]) * iStrides[1]);
                 int bin = (int)((inData[idx] - minval) / step);
-                bin = std::max(bin, 0);
-                bin = std::min(bin, (int)(nbins - 1));
+                bin     = std::max(bin, 0);
+                bin     = std::min(bin, (int)(nbins - 1));
                 outData[bin]++;
             }
-            inData  += iStrides[2];
+            inData += iStrides[2];
             outData += oStrides[2];
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/hsv_rgb.hpp b/src/backend/cpu/kernel/hsv_rgb.hpp
index 828b4deda4..dd75815be2 100644
--- a/src/backend/cpu/kernel/hsv_rgb.hpp
+++ b/src/backend/cpu/kernel/hsv_rgb.hpp
@@ -1,40 +1,37 @@
 /*******************************************************
-* Copyright (c) 2015, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
 #pragma once
 #include <Param.hpp>
 #include <cmath>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void hsv2rgb(Param<T> out, CParam<T> in)
-{
+void hsv2rgb(Param<T> out, CParam<T> in) {
     const af::dim4 dims    = in.dims();
     const af::dim4 strides = in.strides();
-    dim_t obStride  = out.strides(3);
-    dim_t coff      = strides[2];
-    dim_t bCount    = dims[3];
+    dim_t obStride         = out.strides(3);
+    dim_t coff             = strides[2];
+    dim_t bCount           = dims[3];
 
-    for(dim_t b=0; b<bCount; ++b) {
+    for (dim_t b = 0; b < bCount; ++b) {
         const T* src = in.get() + b * strides[3];
         T* dst       = out.get() + b * obStride;
 
-        for(dim_t j=0; j<dims[1]; ++j) {
-            dim_t jOff = j*strides[1];
+        for (dim_t j = 0; j < dims[1]; ++j) {
+            dim_t jOff = j * strides[1];
             // j steps along 2nd dimension
-            for(dim_t i=0; i<dims[0]; ++i) {
+            for (dim_t i = 0; i < dims[0]; ++i) {
                 // i steps along 1st dimension
-                dim_t hIdx = i*strides[0] + jOff;
+                dim_t hIdx = i * strides[0] + jOff;
                 dim_t sIdx = hIdx + coff;
                 dim_t vIdx = sIdx + coff;
 
@@ -45,11 +42,11 @@ void hsv2rgb(Param<T> out, CParam<T> in)
                 T R, G, B;
                 R = G = B = 0;
 
-                int   m = (int)(H * 6);
-                T f = H * 6 - m;
-                T p = V * (1 - S);
-                T q = V * (1 - f * S);
-                T t = V * (1 - (1 - f) * S);
+                int m = (int)(H * 6);
+                T f   = H * 6 - m;
+                T p   = V * (1 - S);
+                T q   = V * (1 - f * S);
+                T t   = V * (1 - (1 - f) * S);
 
                 switch (m % 6) {
                     case 0: R = V, G = t, B = p; break;
@@ -69,55 +66,54 @@ void hsv2rgb(Param<T> out, CParam<T> in)
 }
 
 template<typename T>
-void rgb2hsv(Param<T> out, CParam<T> in)
-{
+void rgb2hsv(Param<T> out, CParam<T> in) {
     const af::dim4 dims    = in.dims();
     const af::dim4 strides = in.strides();
     af::dim4 oStrides      = out.strides();
-    dim_t bCount    = dims[3];
+    dim_t bCount           = dims[3];
 
-    for(dim_t b=0; b<bCount; ++b) {
+    for (dim_t b = 0; b < bCount; ++b) {
         const T* src = in.get() + b * strides[3];
         T* dst       = out.get() + b * oStrides[3];
 
-        for(dim_t j=0; j<dims[1]; ++j) {
+        for (dim_t j = 0; j < dims[1]; ++j) {
             // j steps along 2nd dimension
             dim_t oj = j * oStrides[1];
             dim_t ij = j * strides[1];
 
-            for(dim_t i=0; i<dims[0]; ++i) {
+            for (dim_t i = 0; i < dims[0]; ++i) {
                 // i steps along 1st dimension
                 dim_t oIdx0 = i * oStrides[0] + oj;
                 dim_t oIdx1 = oIdx0 + oStrides[2];
                 dim_t oIdx2 = oIdx1 + oStrides[2];
 
-                dim_t iIdx0 = i * strides[0]  + ij;
+                dim_t iIdx0 = i * strides[0] + ij;
                 dim_t iIdx1 = iIdx0 + strides[2];
                 dim_t iIdx2 = iIdx1 + strides[2];
 
-                T R = src[iIdx0];
-                T G = src[iIdx1];
-                T B = src[iIdx2];
-                T Cmax = std::max(std::max(R, G), B);
-                T Cmin = std::min(std::min(R, G), B);
-                T delta= Cmax-Cmin;
+                T R     = src[iIdx0];
+                T G     = src[iIdx1];
+                T B     = src[iIdx2];
+                T Cmax  = std::max(std::max(R, G), B);
+                T Cmin  = std::min(std::min(R, G), B);
+                T delta = Cmax - Cmin;
 
                 T H = 0;
 
-                if (Cmax!=Cmin) {
-                    if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
-                    if (Cmax==G) H = (B-R)/delta + 2;
-                    if (Cmax==B) H = (R-G)/delta + 4;
+                if (Cmax != Cmin) {
+                    if (Cmax == R) H = (G - B) / delta + (G < B ? 6 : 0);
+                    if (Cmax == G) H = (B - R) / delta + 2;
+                    if (Cmax == B) H = (R - G) / delta + 4;
                     H = H / 6.0f;
                 }
 
                 dst[oIdx0] = H;
-                dst[oIdx1] = (Cmax==0.0f ? 0 : delta/Cmax);
+                dst[oIdx1] = (Cmax == 0.0f ? 0 : delta / Cmax);
                 dst[oIdx2] = Cmax;
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/identity.hpp b/src/backend/cpu/kernel/identity.hpp
index 6ba4d0bd90..1c3b1cf12e 100644
--- a/src/backend/cpu/kernel/identity.hpp
+++ b/src/backend/cpu/kernel/identity.hpp
@@ -11,26 +11,24 @@
 #include <Param.hpp>
 #include <math.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void identity(Param<T> out)
-{
-    T *ptr = out.get();
-    const af::dim4 out_dims  = out.dims();
+void identity(Param<T> out) {
+    T *ptr                  = out.get();
+    const af::dim4 out_dims = out.dims();
 
     for (dim_t k = 0; k < out_dims[2] * out_dims[3]; k++) {
         for (dim_t j = 0; j < out_dims[1]; j++) {
             for (dim_t i = 0; i < out_dims[0]; i++) {
-                ptr[j * out_dims[0] + i]  = (i == j) ? scalar<T>(1) : scalar<T>(0);
+                ptr[j * out_dims[0] + i] =
+                    (i == j) ? scalar<T>(1) : scalar<T>(0);
             }
         }
         ptr += out_dims[0] * out_dims[1];
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/iir.hpp b/src/backend/cpu/kernel/iir.hpp
index 1b31e1523c..b355c7dcbb 100644
--- a/src/backend/cpu/kernel/iir.hpp
+++ b/src/backend/cpu/kernel/iir.hpp
@@ -10,16 +10,13 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void iir(Param<T> y, Param<T> c, CParam<T> a)
-{
+void iir(Param<T> y, Param<T> c, CParam<T> a) {
     dim4 ydims = c.dims();
-    int num_a = a.dims(0);
+    int num_a  = a.dims(0);
 
     for (int l = 0; l < (int)ydims[3]; l++) {
         dim_t yidx3 = l * y.strides(3);
@@ -27,13 +24,11 @@ void iir(Param<T> y, Param<T> c, CParam<T> a)
         dim_t aidx3 = l * a.strides(3);
 
         for (int k = 0; k < (int)ydims[2]; k++) {
-
             dim_t yidx2 = k * y.strides(2) + yidx3;
             dim_t cidx2 = k * c.strides(2) + cidx3;
             dim_t aidx2 = k * a.strides(2) + aidx3;
 
             for (int j = 0; j < (int)ydims[1]; j++) {
-
                 dim_t yidx1 = j * y.strides(1) + yidx2;
                 dim_t cidx1 = j * c.strides(1) + cidx2;
                 dim_t aidx1 = j * a.strides(1) + aidx2;
@@ -41,12 +36,11 @@ void iir(Param<T> y, Param<T> c, CParam<T> a)
                 std::vector<T> h_z(num_a);
 
                 const T *h_a = a.get() + (a.dims().ndims() > 1 ? aidx1 : 0);
-                T *h_c = c.get() + cidx1;
-                T *h_y = y.get() + yidx1;
+                T *h_c       = c.get() + cidx1;
+                T *h_y       = y.get() + yidx1;
 
                 for (int i = 0; i < (int)ydims[0]; i++) {
-
-                    T y = h_y[i] = (h_c[i] + h_z[0]) /  h_a[0];
+                    T y = h_y[i] = (h_c[i] + h_z[0]) / h_a[0];
                     for (int ii = 1; ii < num_a; ii++) {
                         h_z[ii - 1] = h_z[ii] - h_a[ii] * y;
                     }
@@ -56,5 +50,5 @@ void iir(Param<T> y, Param<T> c, CParam<T> a)
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index fa7b18d4d2..605d1009d9 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -8,62 +8,59 @@
  ********************************************************/
 
 #pragma once
-#include <vector>
 #include <Param.hpp>
 #include <utility.hpp>
+#include <vector>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
 void index(Param<T> out, CParam<T> in, const af::dim4 dDims,
            std::vector<bool> const isSeq, std::vector<af_seq> const seqs,
-           std::vector<CParam<uint>> idxArrs)
-{
+           std::vector<CParam<uint>> idxArrs) {
     const af::dim4 iDims    = in.dims();
     const af::dim4 iOffs    = toOffset(seqs, dDims);
     const af::dim4 iStrds   = in.strides();
     const af::dim4 oDims    = out.dims();
     const af::dim4 oStrides = out.strides();
-    const T *src        = in.get();
-    T *dst        = out.get();
-    const uint* ptr0    = idxArrs[0].get();
-    const uint* ptr1    = idxArrs[1].get();
-    const uint* ptr2    = idxArrs[2].get();
-    const uint* ptr3    = idxArrs[3].get();
-
-    for (dim_t l=0; l<oDims[3]; ++l) {
-
-        dim_t lOff   = l*oStrides[3];
-        dim_t inIdx3 = trimIndex(isSeq[3] ? l+iOffs[3] : ptr3[l], iDims[3]);
-        dim_t inOff3 = inIdx3*iStrds[3];
+    const T* src            = in.get();
+    T* dst                  = out.get();
+    const uint* ptr0        = idxArrs[0].get();
+    const uint* ptr1        = idxArrs[1].get();
+    const uint* ptr2        = idxArrs[2].get();
+    const uint* ptr3        = idxArrs[3].get();
 
-        for (dim_t k=0; k<oDims[2]; ++k) {
+    for (dim_t l = 0; l < oDims[3]; ++l) {
+        dim_t lOff   = l * oStrides[3];
+        dim_t inIdx3 = trimIndex(isSeq[3] ? l + iOffs[3] : ptr3[l], iDims[3]);
+        dim_t inOff3 = inIdx3 * iStrds[3];
 
-            dim_t kOff   = k*oStrides[2];
-            dim_t inIdx2 = trimIndex(isSeq[2] ? k+iOffs[2] : ptr2[k], iDims[2]);
-            dim_t inOff2 = inIdx2*iStrds[2];
+        for (dim_t k = 0; k < oDims[2]; ++k) {
+            dim_t kOff = k * oStrides[2];
+            dim_t inIdx2 =
+                trimIndex(isSeq[2] ? k + iOffs[2] : ptr2[k], iDims[2]);
+            dim_t inOff2 = inIdx2 * iStrds[2];
 
-            for (dim_t j=0; j<oDims[1]; ++j) {
+            for (dim_t j = 0; j < oDims[1]; ++j) {
+                dim_t jOff = j * oStrides[1];
+                dim_t inIdx1 =
+                    trimIndex(isSeq[1] ? j + iOffs[1] : ptr1[j], iDims[1]);
+                dim_t inOff1 = inIdx1 * iStrds[1];
 
-                dim_t jOff   = j*oStrides[1];
-                dim_t inIdx1 = trimIndex(isSeq[1] ? j+iOffs[1] : ptr1[j], iDims[1]);
-                dim_t inOff1 = inIdx1*iStrds[1];
+                for (dim_t i = 0; i < oDims[0]; ++i) {
+                    dim_t iOff = i * oStrides[0];
+                    dim_t inIdx0 =
+                        trimIndex(isSeq[0] ? i + iOffs[0] : ptr0[i], iDims[0]);
+                    dim_t inOff0 = inIdx0 * iStrds[0];
 
-                for (dim_t i=0; i<oDims[0]; ++i) {
-
-                    dim_t iOff   = i*oStrides[0];
-                    dim_t inIdx0 = trimIndex(isSeq[0] ? i+iOffs[0] : ptr0[i], iDims[0]);
-                    dim_t inOff0 = inIdx0*iStrds[0];
-
-                    dst[lOff+kOff+jOff+iOff] = src[inOff3+inOff2+inOff1+inOff0];
+                    dst[lOff + kOff + jOff + iOff] =
+                        src[inOff3 + inOff2 + inOff1 + inOff0];
                 }
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index 1553bf21fe..b0a9c18f5e 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -8,36 +8,32 @@
  ********************************************************/
 #pragma once
 #include <Param.hpp>
+#include <common/complex.hpp>
 #include <math.hpp>
 #include <af/constants.h>
 #include <type_traits>
-#include <common/complex.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 using std::conditional;
 using std::is_same;
 
 template<typename T>
-using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+using wtype_t =
+    typename conditional<is_same<T, double>::value, double, float>::type;
 
 template<typename T>
-using vtype_t = typename conditional<common::is_complex<T>::value,
-                                     T, wtype_t<T>
-                                     >::type;
+using vtype_t =
+    typename conditional<common::is_complex<T>::value, T, wtype_t<T>>::type;
 
 template<typename InT, typename LocT>
-InT linearInterpFunc(InT val[2], LocT ratio)
-{
+InT linearInterpFunc(InT val[2], LocT ratio) {
     return (1 - ratio) * val[0] + ratio * val[1];
 }
 
 template<typename InT, typename LocT>
-InT bilinearInterpFunc(InT val[2][2], LocT xratio, LocT yratio)
-{
+InT bilinearInterpFunc(InT val[2][2], LocT xratio, LocT yratio) {
     InT res[2];
     res[0] = linearInterpFunc(val[0], xratio);
     res[1] = linearInterpFunc(val[1], xratio);
@@ -45,17 +41,14 @@ InT bilinearInterpFunc(InT val[2][2], LocT xratio, LocT yratio)
 }
 
 template<typename InT, typename LocT>
-InT cubicInterpFunc(InT val[4], LocT xratio, bool spline)
-{
+InT cubicInterpFunc(InT val[4], LocT xratio, bool spline) {
     InT a0, a1, a2, a3;
     if (spline) {
-        a0 =
-            scalar<InT>(-0.5) * val[0] + scalar<InT>( 1.5) * val[1] +
-            scalar<InT>(-1.5) * val[2] + scalar<InT>( 0.5) * val[3];
+        a0 = scalar<InT>(-0.5) * val[0] + scalar<InT>(1.5) * val[1] +
+             scalar<InT>(-1.5) * val[2] + scalar<InT>(0.5) * val[3];
 
-        a1 =
-            scalar<InT>( 1.0) * val[0] + scalar<InT>(-2.5) * val[1] +
-            scalar<InT>( 2.0) * val[2] + scalar<InT>(-0.5) * val[3];
+        a1 = scalar<InT>(1.0) * val[0] + scalar<InT>(-2.5) * val[1] +
+             scalar<InT>(2.0) * val[2] + scalar<InT>(-0.5) * val[3];
 
         a2 = scalar<InT>(-0.5) * val[0] + scalar<InT>(0.5) * val[2];
 
@@ -74,8 +67,7 @@ InT cubicInterpFunc(InT val[4], LocT xratio, bool spline)
 }
 
 template<typename InT, typename LocT>
-InT bicubicInterpFunc(InT val[4][4], LocT xratio, LocT yratio, bool spline)
-{
+InT bicubicInterpFunc(InT val[4][4], LocT xratio, LocT yratio, bool spline) {
     InT res[4];
     res[0] = cubicInterpFunc(val[0], xratio, spline);
     res[1] = cubicInterpFunc(val[1], xratio, spline);
@@ -85,29 +77,24 @@ InT bicubicInterpFunc(InT val[4][4], LocT xratio, LocT yratio, bool spline)
 }
 
 template<typename InT, typename LocT, int order>
-struct Interp1
-{
-};
+struct Interp1 {};
 
 template<typename InT, typename LocT>
-struct Interp1<InT, LocT, 1>
-{
-    void operator()(Param<InT> &out, int ooff,
-                    CParam<InT> &in, int ioff, LocT x,
-                    af_interp_type  method, int batch, bool clamp,
-                    int xdim = 0, int batch_dim = 1)
-    {
-        const InT *inptr = in.get();
-        const dim4 idims = in.dims();
+struct Interp1<InT, LocT, 1> {
+    void operator()(Param<InT> &out, int ooff, CParam<InT> &in, int ioff,
+                    LocT x, af_interp_type method, int batch, bool clamp,
+                    int xdim = 0, int batch_dim = 1) {
+        const InT *inptr    = in.get();
+        const dim4 idims    = in.dims();
         const dim4 istrides = in.strides();
 
-        InT *outptr = out.get();
+        InT *outptr         = out.get();
         const dim4 ostrides = out.strides();
 
-        const int x_lim = idims[xdim];
+        const int x_lim    = idims[xdim];
         const int x_stride = istrides[xdim];
 
-        int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
+        int xid   = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
         bool cond = xid >= 0 && xid < x_lim;
         if (clamp) xid = std::max(0, std::min(xid, x_lim));
 
@@ -115,41 +102,39 @@ struct Interp1<InT, LocT, 1>
 
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * istrides[batch_dim];
-            outptr[ooff + n * ostrides[batch_dim]] = (cond || clamp) ? inptr[idx_n] : scalar<InT>(0);
+            outptr[ooff + n * ostrides[batch_dim]] =
+                (cond || clamp) ? inptr[idx_n] : scalar<InT>(0);
         }
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp1<InT, LocT, 2>
-{
-    void operator()(Param<InT> &out, int ooff,
-                    CParam<InT> &in, int ioff, LocT x,
-                    af_interp_type  method, int batch, bool clamp,
-                    int xdim = 0, int batch_dim = 1)
-    {
+struct Interp1<InT, LocT, 2> {
+    void operator()(Param<InT> &out, int ooff, CParam<InT> &in, int ioff,
+                    LocT x, af_interp_type method, int batch, bool clamp,
+                    int xdim = 0, int batch_dim = 1) {
         typedef vtype_t<InT> VT;
 
         const int grid_x = floor(x);    // nearest grid
         const LocT off_x = x - grid_x;  // fractional offset
 
-        const InT *inptr = in.get();
-        const dim4 idims = in.dims();
+        const InT *inptr    = in.get();
+        const dim4 idims    = in.dims();
         const dim4 istrides = in.strides();
-        InT *outptr = out.get();
+        InT *outptr         = out.get();
         const dim4 ostrides = out.strides();
 
-        const int x_lim = idims[xdim];
+        const int x_lim    = idims[xdim];
         const int x_stride = istrides[xdim];
-        const int idx = ioff + grid_x * x_stride;
+        const int idx      = ioff + grid_x * x_stride;
 
         bool cond[2] = {true, grid_x + 1 < x_lim};
-        int  offx[2] = {0 , cond[1] ? 1 : 0};
+        int offx[2]  = {0, cond[1] ? 1 : 0};
 
         LocT ratio = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
-            ratio = (1 - std::cos(ratio * af::Pi))/2;
+            ratio = (1 - std::cos(ratio * af::Pi)) / 2;
         }
 
         const VT zero = scalar<VT>(0);
@@ -157,39 +142,39 @@ struct Interp1<InT, LocT, 2>
             int idx_n = idx + n * istrides[batch_dim];
             VT val[2] = {zero, zero};
             for (int i = 0; i < 2; i++) {
-                if (clamp || cond[i]) val[i] = inptr[idx_n + offx[i] * x_stride];
+                if (clamp || cond[i])
+                    val[i] = inptr[idx_n + offx[i] * x_stride];
             }
-            outptr[ooff + n * ostrides[batch_dim]] = linearInterpFunc(val, ratio);
+            outptr[ooff + n * ostrides[batch_dim]] =
+                linearInterpFunc(val, ratio);
         }
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp1<InT, LocT, 3>
-{
-    void operator()(Param<InT> &out, int ooff,
-                    CParam<InT> &in, int ioff, LocT x,
-                    af_interp_type  method, int batch, bool clamp,
-                    int xdim = 0, int batch_dim = 1)
-    {
+struct Interp1<InT, LocT, 3> {
+    void operator()(Param<InT> &out, int ooff, CParam<InT> &in, int ioff,
+                    LocT x, af_interp_type method, int batch, bool clamp,
+                    int xdim = 0, int batch_dim = 1) {
         typedef vtype_t<InT> VT;
 
         const int grid_x = floor(x);    // nearest grid
         const LocT off_x = x - grid_x;  // fractional offset
 
-        const InT *inptr = in.get();
-        const dim4 idims = in.dims();
+        const InT *inptr    = in.get();
+        const dim4 idims    = in.dims();
         const dim4 istrides = in.strides();
-        InT *outptr = out.get();
+        InT *outptr         = out.get();
         const dim4 ostrides = out.strides();
 
-
-        const int x_lim = idims[xdim];
+        const int x_lim    = idims[xdim];
         const int x_stride = istrides[xdim];
-        const int idx = ioff + grid_x * x_stride;
+        const int idx      = ioff + grid_x * x_stride;
 
-        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
-        int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                        grid_x + 2 < x_lim};
+        int off[4]   = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
+                      cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
         const VT zero = scalar<VT>(0);
         for (int n = 0; n < batch; n++) {
@@ -199,39 +184,35 @@ struct Interp1<InT, LocT, 3>
                 if (clamp || cond[i]) val[i] = inptr[idx_n + off[i] * x_stride];
             }
             bool spline = method == AF_INTERP_CUBIC_SPLINE;
-            outptr[ooff + n * ostrides[batch_dim]] =  cubicInterpFunc(val, off_x, spline);
+            outptr[ooff + n * ostrides[batch_dim]] =
+                cubicInterpFunc(val, off_x, spline);
         }
     }
 };
 
 template<typename InT, typename LocT, int order>
-struct Interp2
-{
-};
+struct Interp2 {};
 
 template<typename InT, typename LocT>
-struct Interp2<InT, LocT, 1>
-{
-    void operator()(Param<InT> &out, int ooff,
-                    CParam<InT> &in, int ioff, LocT x, LocT y,
-                    af_interp_type  method, int nimages, bool clamp,
-                    int xdim = 0, int ydim = 1, int batch_dim = 2)
-    {
-        const InT *inptr = in.get();
+struct Interp2<InT, LocT, 1> {
+    void operator()(Param<InT> &out, int ooff, CParam<InT> &in, int ioff,
+                    LocT x, LocT y, af_interp_type method, int nimages,
+                    bool clamp, int xdim = 0, int ydim = 1, int batch_dim = 2) {
+        const InT *inptr    = in.get();
         const dim4 istrides = in.strides();
-        const dim4 idims = in.dims();
+        const dim4 idims    = in.dims();
 
-        InT *outptr = out.get();
+        InT *outptr         = out.get();
         const dim4 ostrides = out.strides();
 
         int xid = (method == AF_INTERP_LOWER ? std::floor(x) : std::round(x));
         int yid = (method == AF_INTERP_LOWER ? std::floor(y) : std::round(y));
 
-        const int x_lim = idims[xdim];
-        const int y_lim = idims[ydim];
+        const int x_lim    = idims[xdim];
+        const int y_lim    = idims[ydim];
         const int x_stride = istrides[xdim];
         const int y_stride = istrides[ydim];
-        const int idx = ioff + yid * y_stride + xid * x_stride;
+        const int idx      = ioff + yid * y_stride + xid * x_stride;
 
         bool condX = xid >= 0 && xid < x_lim;
         bool condY = yid >= 0 && yid < y_lim;
@@ -244,26 +225,24 @@ struct Interp2<InT, LocT, 1>
         bool cond = condX && condY;
         for (int n = 0; n < nimages; n++) {
             int idx_n = idx + n * istrides[batch_dim];
-            outptr[ooff + n * ostrides[batch_dim]] = (clamp || cond) ? inptr[idx_n] : scalar<InT>(0);
+            outptr[ooff + n * ostrides[batch_dim]] =
+                (clamp || cond) ? inptr[idx_n] : scalar<InT>(0);
         }
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp2<InT, LocT, 2>
-{
-    void operator()(Param<InT> &out, int ooff,
-                    CParam<InT> &in, int ioff, LocT x, LocT y,
-                    af_interp_type  method, int nimages, bool clamp,
-                    int xdim = 0, int ydim = 1, int batch_dim = 2)
-    {
+struct Interp2<InT, LocT, 2> {
+    void operator()(Param<InT> &out, int ooff, CParam<InT> &in, int ioff,
+                    LocT x, LocT y, af_interp_type method, int nimages,
+                    bool clamp, int xdim = 0, int ydim = 1, int batch_dim = 2) {
         typedef vtype_t<InT> VT;
 
-        const InT *inptr = in.get();
-        const dim4 idims = in.dims();
+        const InT *inptr    = in.get();
+        const dim4 idims    = in.dims();
         const dim4 istrides = in.strides();
 
-        InT *outptr = out.get();
+        InT *outptr         = out.get();
         const dim4 ostrides = out.strides();
 
         const int grid_x = floor(x);
@@ -272,11 +251,11 @@ struct Interp2<InT, LocT, 2>
         const int grid_y = floor(y);
         const LocT off_y = y - grid_y;
 
-        const int x_lim = idims[xdim];
-        const int y_lim = idims[ydim];
+        const int x_lim    = idims[xdim];
+        const int y_lim    = idims[ydim];
         const int x_stride = istrides[xdim];
         const int y_stride = istrides[ydim];
-        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
         bool condX[2] = {true, x + 1 < x_lim};
         bool condY[2] = {true, y + 1 < y_lim};
@@ -290,8 +269,8 @@ struct Interp2<InT, LocT, 2>
         if (method == AF_INTERP_LINEAR_COSINE ||
             method == AF_INTERP_BILINEAR_COSINE) {
             // Smooth the factional part with cosine
-            xratio = (1 - std::cos(xratio * af::Pi))/2;
-            yratio = (1 - std::cos(yratio * af::Pi))/2;
+            xratio = (1 - std::cos(xratio * af::Pi)) / 2;
+            yratio = (1 - std::cos(yratio * af::Pi)) / 2;
         }
 
         for (int n = 0; n < nimages; n++) {
@@ -304,26 +283,24 @@ struct Interp2<InT, LocT, 2>
                     val[j][i] = cond ? inptr[off_y + offX[i] * x_stride] : zero;
                 }
             }
-            outptr[ooff + n * ostrides[batch_dim]] = bilinearInterpFunc(val, off_x, off_y);
+            outptr[ooff + n * ostrides[batch_dim]] =
+                bilinearInterpFunc(val, off_x, off_y);
         }
     }
 };
 
 template<typename InT, typename LocT>
-struct Interp2<InT, LocT, 3>
-{
-    void operator()(Param<InT> &out, int ooff,
-                    CParam<InT> &in, int ioff, LocT x, LocT y,
-                    af_interp_type  method, int nimages, bool clamp,
-                    int xdim = 0, int ydim = 1, int batch_dim = 2)
-    {
+struct Interp2<InT, LocT, 3> {
+    void operator()(Param<InT> &out, int ooff, CParam<InT> &in, int ioff,
+                    LocT x, LocT y, af_interp_type method, int nimages,
+                    bool clamp, int xdim = 0, int ydim = 1, int batch_dim = 2) {
         typedef vtype_t<InT> VT;
 
-        const InT *inptr = in.get();
-        const dim4 idims = in.dims();
+        const InT *inptr    = in.get();
+        const dim4 idims    = in.dims();
         const dim4 istrides = in.strides();
 
-        InT *outptr = out.get();
+        InT *outptr         = out.get();
         const dim4 ostrides = out.strides();
 
         const int grid_x = floor(x);
@@ -332,36 +309,43 @@ struct Interp2<InT, LocT, 3>
         const int grid_y = floor(y);
         const LocT off_y = y - grid_y;
 
-        const int x_lim = idims[xdim];
-        const int y_lim = idims[ydim];
+        const int x_lim    = idims[xdim];
+        const int y_lim    = idims[ydim];
         const int x_stride = istrides[xdim];
         const int y_stride = istrides[ydim];
-        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
         // used for setting values at boundaries
-        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
-        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim, grid_y + 2 < y_lim};
-        int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-        int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
-
-        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
-        VT zero = scalar<VT>(0);
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                         grid_x + 2 < x_lim};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim,
+                         grid_y + 2 < y_lim};
+        int offX[4]   = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0,
+                       condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int offY[4]   = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0,
+                       condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE ||
+                       method == AF_INTERP_BICUBIC_SPLINE);
+        VT zero     = scalar<VT>(0);
         for (int n = 0; n < nimages; n++) {
             int idx_n = idx + n * istrides[batch_dim];
 
-            //for bicubic interpolation, work with 4x4 val at a time
+            // for bicubic interpolation, work with 4x4 val at a time
             VT val[4][4];
             for (int j = 0; j < 4; j++) {
                 int ioff_j = idx_n + offY[j] * y_stride;
                 for (int i = 0; i < 4; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = cond ? inptr[ioff_j + offX[i] * x_stride] : zero;
+                    val[j][i] =
+                        cond ? inptr[ioff_j + offX[i] * x_stride] : zero;
                 }
             }
-            outptr[ooff + n * ostrides[batch_dim]] = bicubicInterpFunc(val, off_x, off_y, spline);
+            outptr[ooff + n * ostrides[batch_dim]] =
+                bicubicInterpFunc(val, off_x, off_y, spline);
         }
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index 4769cf6318..74be5ee6bc 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -10,35 +10,32 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void iota(Param<T> output, const af::dim4 &sdims)
-{
+void iota(Param<T> output, const af::dim4& sdims) {
     const af::dim4 dims    = output.dims();
-    T* out             = output.get();
+    T* out                 = output.get();
     const af::dim4 strides = output.strides();
 
-    for(dim_t w = 0; w < dims[3]; w++) {
+    for (dim_t w = 0; w < dims[3]; w++) {
         dim_t offW = w * strides[3];
-        T valW = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
-        for(dim_t z = 0; z < dims[2]; z++) {
+        T valW     = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
+        for (dim_t z = 0; z < dims[2]; z++) {
             dim_t offWZ = offW + z * strides[2];
-            T valZ = valW + (z % sdims[2]) * sdims[0] * sdims[1];
-            for(dim_t y = 0; y < dims[1]; y++) {
+            T valZ      = valW + (z % sdims[2]) * sdims[0] * sdims[1];
+            for (dim_t y = 0; y < dims[1]; y++) {
                 dim_t offWZY = offWZ + y * strides[1];
-                T valY = valZ + (y % sdims[1]) * sdims[0];
-                for(dim_t x = 0; x < dims[0]; x++) {
+                T valY       = valZ + (y % sdims[1]) * sdims[0];
+                for (dim_t x = 0; x < dims[0]; x++) {
                     dim_t id = offWZY + x;
-                    out[id] = valY + (x % sdims[0]);
+                    out[id]  = valY + (x % sdims[0]);
                 }
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index 56bd121f28..74ef7ba60e 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -11,32 +11,30 @@
 #include <Param.hpp>
 #include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-template<typename T> double cabs(const T in) { return (double)in; }
+template<typename T>
+double cabs(const T in) {
+    return (double)in;
+}
 static double cabs(const char in) { return (double)(in > 0); }
 static double cabs(const cfloat &in) { return (double)abs(in); }
 static double cabs(const cdouble &in) { return (double)abs(in); }
-template<typename T> static bool is_nan(T in) { return in != in; }
+template<typename T>
+static bool is_nan(T in) {
+    return in != in;
+}
 
 template<af_op_t op, typename T>
-struct MinMaxOp
-{
+struct MinMaxOp {
     T m_val;
     uint m_idx;
-    MinMaxOp(T val, uint idx) :
-        m_val(val), m_idx(idx)
-    {
-        if (is_nan(val)) {
-            m_val = Binary<T, op>::init();
-        }
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = Binary<T, op>::init(); }
     }
 
-    void operator()(T val, uint idx)
-    {
+    void operator()(T val, uint idx) {
         if ((cabs(val) < cabs(m_val) ||
              (cabs(val) == cabs(m_val) && idx > m_idx))) {
             m_val = val;
@@ -46,20 +44,14 @@ struct MinMaxOp
 };
 
 template<typename T>
-struct MinMaxOp<af_max_t, T>
-{
+struct MinMaxOp<af_max_t, T> {
     T m_val;
     uint m_idx;
-    MinMaxOp(T val, uint idx) :
-        m_val(val), m_idx(idx)
-    {
-        if (is_nan(val)) {
-            m_val = Binary<T, af_max_t>::init();
-        }
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = Binary<T, af_max_t>::init(); }
     }
 
-    void operator()(T val, uint idx)
-    {
+    void operator()(T val, uint idx) {
         if ((cabs(val) > cabs(m_val) ||
              (cabs(val) == cabs(m_val) && idx <= m_idx))) {
             m_val = val;
@@ -69,34 +61,33 @@ struct MinMaxOp<af_max_t, T>
 };
 
 template<af_op_t op, typename T, int D>
-struct ireduce_dim
-{
-    void operator()(Param<T> output, Param<uint> locParam, const dim_t outOffset,
-                    CParam<T> input, const dim_t inOffset, const int dim)
-    {
+struct ireduce_dim {
+    void operator()(Param<T> output, Param<uint> locParam,
+                    const dim_t outOffset, CParam<T> input,
+                    const dim_t inOffset, const int dim) {
         const af::dim4 odims    = output.dims();
         const af::dim4 ostrides = output.strides();
         const af::dim4 istrides = input.strides();
-        const int D1 = D - 1;
+        const int D1            = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
-            ireduce_dim<op, T, D1>()(output, locParam, outOffset + i * ostrides[D1],
-                                     input, inOffset + i * istrides[D1], dim);
+            ireduce_dim<op, T, D1>()(output, locParam,
+                                     outOffset + i * ostrides[D1], input,
+                                     inOffset + i * istrides[D1], dim);
         }
     }
 };
 
 template<af_op_t op, typename T>
-struct ireduce_dim<op, T, 0>
-{
-    void operator()(Param<T> output, Param<uint> locParam, const dim_t outOffset,
-                    CParam<T> input, const dim_t inOffset, const int dim)
-    {
-        const af::dim4 idims = input.dims();
+struct ireduce_dim<op, T, 0> {
+    void operator()(Param<T> output, Param<uint> locParam,
+                    const dim_t outOffset, CParam<T> input,
+                    const dim_t inOffset, const int dim) {
+        const af::dim4 idims    = input.dims();
         const af::dim4 istrides = input.strides();
 
-        T const * const in = input.get();
-        T * out = output.get();
-        uint * loc = locParam.get();
+        T const *const in = input.get();
+        T *out            = output.get();
+        uint *loc         = locParam.get();
 
         dim_t stride = istrides[dim];
         MinMaxOp<op, T> Op(in[inOffset], 0);
@@ -109,5 +100,5 @@ struct ireduce_dim<op, T, 0>
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index 0ffdc851fe..d23b9b757f 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -10,14 +10,11 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<int dim>
-af::dim4 calcOffset(const af::dim4 dims)
-{
+af::dim4 calcOffset(const af::dim4 dims) {
     af::dim4 offset;
     offset[0] = (dim == 0) ? dims[0] : 0;
     offset[1] = (dim == 1) ? dims[1] : 0;
@@ -28,24 +25,24 @@ af::dim4 calcOffset(const af::dim4 dims)
 
 template<typename To, typename Tx, int dim>
 void join_append(To *out, const Tx *X, const af::dim4 &offset,
-           const af::dim4 &xdims, const af::dim4 &ost, const af::dim4 &xst)
-{
-    for(dim_t ow = 0; ow < xdims[3]; ow++) {
+                 const af::dim4 &xdims, const af::dim4 &ost,
+                 const af::dim4 &xst) {
+    for (dim_t ow = 0; ow < xdims[3]; ow++) {
         const dim_t xW = ow * xst[3];
         const dim_t oW = (ow + offset[3]) * ost[3];
 
-        for(dim_t oz = 0; oz < xdims[2]; oz++) {
+        for (dim_t oz = 0; oz < xdims[2]; oz++) {
             const dim_t xZW = xW + oz * xst[2];
             const dim_t oZW = oW + (oz + offset[2]) * ost[2];
 
-            for(dim_t oy = 0; oy < xdims[1]; oy++) {
+            for (dim_t oy = 0; oy < xdims[1]; oy++) {
                 const dim_t xYZW = xZW + oy * xst[1];
                 const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
 
-                for(dim_t ox = 0; ox < xdims[0]; ox++) {
+                for (dim_t ox = 0; ox < xdims[0]; ox++) {
                     const dim_t iMem = xYZW + ox;
                     const dim_t oMem = oYZW + (ox + offset[0]);
-                    out[oMem] = X[iMem];
+                    out[oMem]        = X[iMem];
                 }
             }
         }
@@ -53,88 +50,94 @@ void join_append(To *out, const Tx *X, const af::dim4 &offset,
 }
 
 template<typename Tx, typename Ty>
-void join(Param<Tx> out, const int dim, CParam<Tx> first, CParam<Ty> second)
-{
-    Tx* outPtr = out.get();
-    const Tx* fptr = first.get();
-    const Ty* sptr = second.get();
+void join(Param<Tx> out, const int dim, CParam<Tx> first, CParam<Ty> second) {
+    Tx *outPtr     = out.get();
+    const Tx *fptr = first.get();
+    const Ty *sptr = second.get();
 
-    af::dim4 zero(0,0,0,0);
+    af::dim4 zero(0, 0, 0, 0);
     const af::dim4 fdims = first.dims();
     const af::dim4 sdims = second.dims();
 
-    switch(dim) {
+    switch (dim) {
         case 0:
-            join_append<Tx, Tx, 0>(outPtr, fptr, zero,
-                                   fdims, out.strides(), first.strides());
-            join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims),
-                                   sdims, out.strides(), second.strides());
+            join_append<Tx, Tx, 0>(outPtr, fptr, zero, fdims, out.strides(),
+                                   first.strides());
+            join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims), sdims,
+                                   out.strides(), second.strides());
             break;
         case 1:
-            join_append<Tx, Tx, 1>(outPtr, fptr, zero,
-                                   fdims, out.strides(), first.strides());
-            join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims),
-                                   sdims, out.strides(), second.strides());
+            join_append<Tx, Tx, 1>(outPtr, fptr, zero, fdims, out.strides(),
+                                   first.strides());
+            join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims), sdims,
+                                   out.strides(), second.strides());
             break;
         case 2:
-            join_append<Tx, Tx, 2>(outPtr, fptr, zero,
-                                   fdims, out.strides(), first.strides());
-            join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims),
-                                   sdims, out.strides(), second.strides());
+            join_append<Tx, Tx, 2>(outPtr, fptr, zero, fdims, out.strides(),
+                                   first.strides());
+            join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims), sdims,
+                                   out.strides(), second.strides());
             break;
         case 3:
-            join_append<Tx, Tx, 3>(outPtr, fptr, zero,
-                                   fdims, out.strides(), first.strides());
-            join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims),
-                                   sdims, out.strides(), second.strides());
+            join_append<Tx, Tx, 3>(outPtr, fptr, zero, fdims, out.strides(),
+                                   first.strides());
+            join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims), sdims,
+                                   out.strides(), second.strides());
             break;
     }
 }
 
 template<typename T, int n_arrays>
-void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs)
-{
-    af::dim4 zero(0,0,0,0);
+void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs) {
+    af::dim4 zero(0, 0, 0, 0);
     af::dim4 d = zero;
-    switch(dim) {
+    switch (dim) {
         case 0:
             join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
+                                 inputs[0].dims(), out.strides(),
+                                 inputs[0].strides());
+            for (int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
-                join_append<T, T, 0>(out.get(), inputs[i].get(), calcOffset<0>(d),
-                                     inputs[i].dims(), out.strides(), inputs[i].strides());
+                join_append<T, T, 0>(out.get(), inputs[i].get(),
+                                     calcOffset<0>(d), inputs[i].dims(),
+                                     out.strides(), inputs[i].strides());
             }
             break;
         case 1:
             join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
+                                 inputs[0].dims(), out.strides(),
+                                 inputs[0].strides());
+            for (int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
-                join_append<T, T, 1>(out.get(), inputs[i].get(), calcOffset<1>(d),
-                                     inputs[i].dims(), out.strides(), inputs[i].strides());
+                join_append<T, T, 1>(out.get(), inputs[i].get(),
+                                     calcOffset<1>(d), inputs[i].dims(),
+                                     out.strides(), inputs[i].strides());
             }
             break;
         case 2:
             join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
+                                 inputs[0].dims(), out.strides(),
+                                 inputs[0].strides());
+            for (int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
-                join_append<T, T, 2>(out.get(), inputs[i].get(), calcOffset<2>(d),
-                        inputs[i].dims(), out.strides(), inputs[i].strides());
+                join_append<T, T, 2>(out.get(), inputs[i].get(),
+                                     calcOffset<2>(d), inputs[i].dims(),
+                                     out.strides(), inputs[i].strides());
             }
             break;
         case 3:
             join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(), inputs[0].strides());
-            for(int i = 1; i < n_arrays; i++) {
+                                 inputs[0].dims(), out.strides(),
+                                 inputs[0].strides());
+            for (int i = 1; i < n_arrays; i++) {
                 d += inputs[i - 1].dims();
-                join_append<T, T, 3>(out.get(), inputs[i].get(), calcOffset<3>(d),
-                                     inputs[i].dims(), out.strides(), inputs[i].strides());
+                join_append<T, T, 3>(out.get(), inputs[i].get(),
+                                     calcOffset<3>(d), inputs[i].dims(),
+                                     out.strides(), inputs[i].strides());
             }
             break;
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/lookup.hpp b/src/backend/cpu/kernel/lookup.hpp
index a9ec855c3d..fe333eb8cd 100644
--- a/src/backend/cpu/kernel/lookup.hpp
+++ b/src/backend/cpu/kernel/lookup.hpp
@@ -8,54 +8,55 @@
  ********************************************************/
 
 #pragma once
-#include <vector>
 #include <Param.hpp>
 #include <utility.hpp>
+#include <vector>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename InT, typename IndexT>
-void lookup(Param<InT> out, CParam<InT> input,
-            CParam<IndexT> indices, unsigned const dim)
-{
+void lookup(Param<InT> out, CParam<InT> input, CParam<IndexT> indices,
+            unsigned const dim) {
     const af::dim4 iDims    = input.dims();
     const af::dim4 oDims    = out.dims();
     const af::dim4 iStrides = input.strides();
     const af::dim4 oStrides = out.strides();
-    const InT *inPtr   = input.get();
-    const IndexT *idxPtr = indices.get();
+    const InT *inPtr        = input.get();
+    const IndexT *idxPtr    = indices.get();
 
     InT *outPtr = out.get();
 
-    for (dim_t l=0; l<oDims[3]; ++l) {
-
-        dim_t iLOff = iStrides[3]*(dim==3 ? trimIndex((dim_t)idxPtr[l], iDims[3]): l);
-        dim_t oLOff = l*oStrides[3];
-
-        for (dim_t k=0; k<oDims[2]; ++k) {
-
-            dim_t iKOff = iStrides[2]*(dim==2 ? trimIndex((dim_t)idxPtr[k], iDims[2]): k);
-            dim_t oKOff = k*oStrides[2];
-
-            for (dim_t j=0; j<oDims[1]; ++j) {
-
-                dim_t iJOff = iStrides[1]*(dim==1 ? trimIndex((dim_t)idxPtr[j], iDims[1]): j);
-                dim_t oJOff = j*oStrides[1];
-
-                for (dim_t i=0; i<oDims[0]; ++i) {
-
-                    dim_t iIOff = iStrides[0]*(dim==0 ? trimIndex((dim_t)idxPtr[i], iDims[0]): i);
-                    dim_t oIOff = i*oStrides[0];
-
-                    outPtr[oLOff+oKOff+oJOff+oIOff] = inPtr[iLOff+iKOff+iJOff+iIOff];
+    for (dim_t l = 0; l < oDims[3]; ++l) {
+        dim_t iLOff = iStrides[3] *
+                      (dim == 3 ? trimIndex((dim_t)idxPtr[l], iDims[3]) : l);
+        dim_t oLOff = l * oStrides[3];
+
+        for (dim_t k = 0; k < oDims[2]; ++k) {
+            dim_t iKOff =
+                iStrides[2] *
+                (dim == 2 ? trimIndex((dim_t)idxPtr[k], iDims[2]) : k);
+            dim_t oKOff = k * oStrides[2];
+
+            for (dim_t j = 0; j < oDims[1]; ++j) {
+                dim_t iJOff =
+                    iStrides[1] *
+                    (dim == 1 ? trimIndex((dim_t)idxPtr[j], iDims[1]) : j);
+                dim_t oJOff = j * oStrides[1];
+
+                for (dim_t i = 0; i < oDims[0]; ++i) {
+                    dim_t iIOff =
+                        iStrides[0] *
+                        (dim == 0 ? trimIndex((dim_t)idxPtr[i], iDims[0]) : i);
+                    dim_t oIOff = i * oStrides[0];
+
+                    outPtr[oLOff + oKOff + oJOff + oIOff] =
+                        inPtr[iLOff + iKOff + iJOff + iIOff];
                 }
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/lu.hpp b/src/backend/cpu/kernel/lu.hpp
index 0717cd223f..c1473a7918 100644
--- a/src/backend/cpu/kernel/lu.hpp
+++ b/src/backend/cpu/kernel/lu.hpp
@@ -10,16 +10,13 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void lu_split(Param<T> lower, Param<T> upper, CParam<T> in)
-{
-    T *l = lower.get();
-    T *u = upper.get();
+void lu_split(Param<T> lower, Param<T> upper, CParam<T> in) {
+    T *l       = lower.get();
+    T *u       = upper.get();
     const T *i = in.get();
 
     af::dim4 ldm = lower.dims();
@@ -29,34 +26,34 @@ void lu_split(Param<T> lower, Param<T> upper, CParam<T> in)
     af::dim4 ust = upper.strides();
     af::dim4 ist = in.strides();
 
-    for(dim_t ow = 0; ow < idm[3]; ow++) {
+    for (dim_t ow = 0; ow < idm[3]; ow++) {
         const dim_t lW = ow * lst[3];
         const dim_t uW = ow * ust[3];
         const dim_t iW = ow * ist[3];
 
-        for(dim_t oz = 0; oz < idm[2]; oz++) {
+        for (dim_t oz = 0; oz < idm[2]; oz++) {
             const dim_t lZW = lW + oz * lst[2];
             const dim_t uZW = uW + oz * ust[2];
             const dim_t iZW = iW + oz * ist[2];
 
-            for(dim_t oy = 0; oy < idm[1]; oy++) {
+            for (dim_t oy = 0; oy < idm[1]; oy++) {
                 const dim_t lYZW = lZW + oy * lst[1];
                 const dim_t uYZW = uZW + oy * ust[1];
                 const dim_t iYZW = iZW + oy * ist[1];
 
-                for(dim_t ox = 0; ox < idm[0]; ox++) {
+                for (dim_t ox = 0; ox < idm[0]; ox++) {
                     const dim_t lMem = lYZW + ox;
                     const dim_t uMem = uYZW + ox;
                     const dim_t iMem = iYZW + ox;
-                    if(ox > oy) {
-                        if(oy < ldm[1]) l[lMem] = i[iMem];
-                        if(ox < udm[0]) u[uMem] = scalar<T>(0);
+                    if (ox > oy) {
+                        if (oy < ldm[1]) l[lMem] = i[iMem];
+                        if (ox < udm[0]) u[uMem] = scalar<T>(0);
                     } else if (oy > ox) {
-                        if(oy < ldm[1]) l[lMem] = scalar<T>(0);
-                        if(ox < udm[0]) u[uMem] = i[iMem];
-                    } else if(ox == oy) {
-                        if(oy < ldm[1]) l[lMem] = scalar<T>(1.0);
-                        if(ox < udm[0]) u[uMem] = i[iMem];
+                        if (oy < ldm[1]) l[lMem] = scalar<T>(0);
+                        if (ox < udm[0]) u[uMem] = i[iMem];
+                    } else if (ox == oy) {
+                        if (oy < ldm[1]) l[lMem] = scalar<T>(1.0);
+                        if (ox < udm[0]) u[uMem] = i[iMem];
                     }
                 }
             }
@@ -64,16 +61,15 @@ void lu_split(Param<T> lower, Param<T> upper, CParam<T> in)
     }
 }
 
-void convertPivot(Param<int> p, Param<int> pivot)
-{
+void convertPivot(Param<int> p, Param<int> pivot) {
     int *d_pi = pivot.get();
     int *d_po = p.get();
     dim_t d0  = pivot.dims(0);
-    for(int j = 0; j < (int)d0; j++) {
+    for (int j = 0; j < (int)d0; j++) {
         // 1 indexed in pivot
         std::swap(d_po[j], d_po[d_pi[j] - 1]);
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
index 9a0402ca80..48df0cbffe 100644
--- a/src/backend/cpu/kernel/match_template.hpp
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -10,68 +10,63 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename OutT, typename InT, af_match_type MatchT>
-void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg)
-{
-    const af::dim4 sDims = sImg.dims();
-    const af::dim4 tDims = tImg.dims();
+void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
+    const af::dim4 sDims    = sImg.dims();
+    const af::dim4 tDims    = tImg.dims();
     const af::dim4 sStrides = sImg.strides();
     const af::dim4 tStrides = tImg.strides();
 
-    const dim_t tDim0  = tDims[0];
-    const dim_t tDim1  = tDims[1];
-    const dim_t sDim0  = sDims[0];
-    const dim_t sDim1  = sDims[1];
+    const dim_t tDim0 = tDims[0];
+    const dim_t tDim1 = tDims[1];
+    const dim_t sDim0 = sDims[0];
+    const dim_t sDim1 = sDims[1];
 
     const af::dim4 oStrides = out.strides();
 
-    OutT tImgMean = OutT(0);
+    OutT tImgMean        = OutT(0);
     dim_t winNumElements = tImg.dims().elements();
-    bool needMean = MatchT==AF_ZSAD || MatchT==AF_LSAD ||
-        MatchT==AF_ZSSD || MatchT==AF_LSSD ||
-        MatchT==AF_ZNCC;
-    const InT * tpl = tImg.get();
+    bool needMean        = MatchT == AF_ZSAD || MatchT == AF_LSAD ||
+                    MatchT == AF_ZSSD || MatchT == AF_LSSD || MatchT == AF_ZNCC;
+    const InT* tpl = tImg.get();
 
     if (needMean) {
-        for(dim_t tj=0; tj<tDim1; tj++) {
-            dim_t tjStride = tj*tStrides[1];
+        for (dim_t tj = 0; tj < tDim1; tj++) {
+            dim_t tjStride = tj * tStrides[1];
 
-            for(dim_t ti=0; ti<tDim0; ti++) {
-                tImgMean += (OutT)tpl[tjStride+ti*tStrides[0]];
+            for (dim_t ti = 0; ti < tDim0; ti++) {
+                tImgMean += (OutT)tpl[tjStride + ti * tStrides[0]];
             }
         }
         tImgMean /= winNumElements;
     }
 
-    OutT * dst      = out.get();
-    const InT * src = sImg.get();
-
-    for(dim_t b3=0; b3<sDims[3]; ++b3) {
-        for(dim_t b2=0; b2<sDims[2]; ++b2) {
+    OutT* dst      = out.get();
+    const InT* src = sImg.get();
 
+    for (dim_t b3 = 0; b3 < sDims[3]; ++b3) {
+        for (dim_t b2 = 0; b2 < sDims[2]; ++b2) {
             // slide through image window after window
-            for(dim_t sj=0; sj<sDim1; sj++) {
-
-                dim_t ojStride = sj*oStrides[1];
+            for (dim_t sj = 0; sj < sDim1; sj++) {
+                dim_t ojStride = sj * oStrides[1];
 
-                for(dim_t si=0; si<sDim0; si++) {
+                for (dim_t si = 0; si < sDim0; si++) {
                     OutT disparity = OutT(0);
 
                     // mean for window
                     // this variable will be used based on MatchT value
                     OutT wImgMean = OutT(0);
                     if (needMean) {
-                        for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
-                            dim_t jStride = j*sStrides[1];
+                        for (dim_t tj = 0, j = sj; tj < tDim1; tj++, j++) {
+                            dim_t jStride = j * sStrides[1];
 
-                            for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
-                                InT sVal = ((j<sDim1 && i<sDim0) ?
-                                        src[jStride + i*sStrides[0]] : InT(0));
+                            for (dim_t ti = 0, i = si; ti < tDim0; ti++, i++) {
+                                InT sVal = ((j < sDim1 && i < sDim0)
+                                                ? src[jStride + i * sStrides[0]]
+                                                : InT(0));
                                 wImgMean += (OutT)sVal;
                             }
                         }
@@ -79,45 +74,51 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg)
                     }
 
                     // run the window match metric
-                    for(dim_t tj=0,j=sj; tj<tDim1; tj++, j++) {
-                        dim_t jStride = j*sStrides[1];
-                        dim_t tjStride = tj*tStrides[1];
-
-                        for(dim_t ti=0, i=si; ti<tDim0; ti++, i++) {
-                            InT sVal = ((j<sDim1 && i<sDim0) ?
-                                    src[jStride + i*sStrides[0]] : InT(0));
-                            InT tVal = tpl[tjStride+ti*tStrides[0]];
+                    for (dim_t tj = 0, j = sj; tj < tDim1; tj++, j++) {
+                        dim_t jStride  = j * sStrides[1];
+                        dim_t tjStride = tj * tStrides[1];
+
+                        for (dim_t ti = 0, i = si; ti < tDim0; ti++, i++) {
+                            InT sVal = ((j < sDim1 && i < sDim0)
+                                            ? src[jStride + i * sStrides[0]]
+                                            : InT(0));
+                            InT tVal = tpl[tjStride + ti * tStrides[0]];
                             OutT temp;
-                            switch(MatchT) {
+                            switch (MatchT) {
                                 case AF_SAD:
-                                    disparity += fabs((OutT)sVal-(OutT)tVal);
+                                    disparity += fabs((OutT)sVal - (OutT)tVal);
                                     break;
                                 case AF_ZSAD:
                                     disparity += fabs((OutT)sVal - wImgMean -
-                                            (OutT)tVal + tImgMean);
+                                                      (OutT)tVal + tImgMean);
                                     break;
                                 case AF_LSAD:
-                                    disparity += fabs((OutT)sVal-(wImgMean/tImgMean)*tVal);
+                                    disparity +=
+                                        fabs((OutT)sVal -
+                                             (wImgMean / tImgMean) * tVal);
                                     break;
                                 case AF_SSD:
-                                    disparity += ((OutT)sVal-(OutT)tVal)*((OutT)sVal-(OutT)tVal);
+                                    disparity += ((OutT)sVal - (OutT)tVal) *
+                                                 ((OutT)sVal - (OutT)tVal);
                                     break;
                                 case AF_ZSSD:
-                                    temp = ((OutT)sVal - wImgMean - (OutT)tVal + tImgMean);
-                                    disparity += temp*temp;
+                                    temp = ((OutT)sVal - wImgMean - (OutT)tVal +
+                                            tImgMean);
+                                    disparity += temp * temp;
                                     break;
                                 case AF_LSSD:
-                                    temp = ((OutT)sVal-(wImgMean/tImgMean)*tVal);
-                                    disparity += temp*temp;
+                                    temp = ((OutT)sVal -
+                                            (wImgMean / tImgMean) * tVal);
+                                    disparity += temp * temp;
                                     break;
                                 case AF_NCC:
-                                    //TODO: furture implementation
+                                    // TODO: furture implementation
                                     break;
                                 case AF_ZNCC:
-                                    //TODO: furture implementation
+                                    // TODO: furture implementation
                                     break;
                                 case AF_SHD:
-                                    //TODO: furture implementation
+                                    // TODO: furture implementation
                                     break;
                             }
                         }
@@ -135,6 +136,5 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg)
     }
 };
 
-
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index 5863a3f1ed..db4b12473b 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -9,73 +9,65 @@
 
 #pragma once
 #include <Array.hpp>
+#include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename Ti, typename To, typename Tw>
-struct MeanOp
-{
+struct MeanOp {
     Transform<Ti, To, af_add_t> transform;
     To runningMean;
     Tw runningCount;
-    MeanOp(Ti mean, Tw count) :
-        transform(),
-        runningMean(transform(mean)),
-        runningCount(count)
-    {
-    }
+    MeanOp(Ti mean, Tw count)
+        : transform(), runningMean(transform(mean)), runningCount(count) {}
 
-    void operator()(Ti _newMean, Tw newCount)
-    {
+    void operator()(Ti _newMean, Tw newCount) {
         To newMean = transform(_newMean);
         if ((newCount != 0) || (runningCount != 0)) {
             Tw runningScale = runningCount;
-            Tw newScale = newCount;
+            Tw newScale     = newCount;
             runningCount += newCount;
-            runningScale = runningScale/runningCount;
-            newScale = newScale/runningCount;
-            runningMean = (runningScale*runningMean) + (newScale*newMean);
+            runningScale = runningScale / runningCount;
+            newScale     = newScale / runningCount;
+            runningMean  = (runningScale * runningMean) + (newScale * newMean);
         }
     }
 };
 
 template<typename T, typename Tw, int D>
-struct mean_weighted_dim
-{
+struct mean_weighted_dim {
     void operator()(Param<T> output, const dim_t outOffset,
-                    const CParam< T>  input, const dim_t inOffset,
-                    const CParam<Tw> weight, const dim_t wtOffset, const int dim)
-    {
+                    const CParam<T> input, const dim_t inOffset,
+                    const CParam<Tw> weight, const dim_t wtOffset,
+                    const int dim) {
         const af::dim4 odims    = output.dims();
         const af::dim4 ostrides = output.strides();
-        const af::dim4 istrides =  input.strides();
+        const af::dim4 istrides = input.strides();
         const af::dim4 wstrides = weight.strides();
-        const int D1 = D - 1;
+        const int D1            = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
             mean_weighted_dim<T, Tw, D1>()(output, outOffset + i * ostrides[D1],
-                    input, inOffset + i * istrides[D1],
-                    weight, wtOffset + i * wstrides[D1], dim);
+                                           input, inOffset + i * istrides[D1],
+                                           weight, wtOffset + i * wstrides[D1],
+                                           dim);
         }
     }
 };
 
 template<typename T, typename Tw>
-struct mean_weighted_dim<T, Tw, 0>
-{
+struct mean_weighted_dim<T, Tw, 0> {
     void operator()(Param<T> output, const dim_t outOffset,
-                    const CParam< T>  input, const dim_t inOffset,
-                    const CParam<Tw> weight, const dim_t wtOffset, const int dim)
-    {
-        const af::dim4 idims = input.dims();
-        const af::dim4 istrides =  input.strides();
+                    const CParam<T> input, const dim_t inOffset,
+                    const CParam<Tw> weight, const dim_t wtOffset,
+                    const int dim) {
+        const af::dim4 idims    = input.dims();
+        const af::dim4 istrides = input.strides();
         const af::dim4 wstrides = weight.strides();
 
-        T const * const  in =  input.get();
-        Tw const * const wt = weight.get();
-        T * out = output.get();
+        T const* const in  = input.get();
+        Tw const* const wt = weight.get();
+        T* out             = output.get();
 
         dim_t istride = istrides[dim];
         dim_t wstride = wstrides[dim];
@@ -89,33 +81,31 @@ struct mean_weighted_dim<T, Tw, 0>
 };
 
 template<typename Ti, typename Tw, typename To, int D>
-struct mean_dim
-{
+struct mean_dim {
     void operator()(Param<To> output, const dim_t outOffset,
-                    const CParam<Ti> input, const dim_t inOffset, const int dim)
-    {
+                    const CParam<Ti> input, const dim_t inOffset,
+                    const int dim) {
         const af::dim4 odims    = output.dims();
         const af::dim4 ostrides = output.strides();
-        const af::dim4 istrides =  input.strides();
-        const int D1 = D - 1;
+        const af::dim4 istrides = input.strides();
+        const int D1            = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
             mean_dim<Ti, Tw, To, D1>()(output, outOffset + i * ostrides[D1],
-                                      input, inOffset + i * istrides[D1], dim);
+                                       input, inOffset + i * istrides[D1], dim);
         }
     }
 };
 
 template<typename Ti, typename Tw, typename To>
-struct mean_dim<Ti, Tw, To, 0>
-{
+struct mean_dim<Ti, Tw, To, 0> {
     void operator()(Param<To> output, const dim_t outOffset,
-                    const CParam<Ti> input, const dim_t inOffset, const int dim)
-    {
-        const af::dim4 idims = input.dims();
-        const af::dim4 istrides =  input.strides();
+                    const CParam<Ti> input, const dim_t inOffset,
+                    const int dim) {
+        const af::dim4 idims    = input.dims();
+        const af::dim4 istrides = input.strides();
 
-        Ti const * const  in =  input.get();
-        To * out = output.get();
+        Ti const* const in = input.get();
+        To* out            = output.get();
 
         dim_t istride = istrides[dim];
         MeanOp<Ti, To, Tw> Op(0, 0);
@@ -127,5 +117,5 @@ struct mean_dim<Ti, Tw, To, 0>
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index f7e696de0e..141153bb75 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -9,19 +9,17 @@
 
 #pragma once
 #include <Param.hpp>
-#include <vector>
 #include <utility.hpp>
 #include <type_traits>
+#include <vector>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 template<typename T, bool IsColor>
 void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
-               const float chromaticSigma, const unsigned numIterations)
-{
-    typedef typename std::conditional< std::is_same<T, double>::value, double, float >::type AccType;
+               const float chromaticSigma, const unsigned numIterations) {
+    typedef typename std::conditional<std::is_same<T, double>::value, double,
+                                      float>::type AccType;
 
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
@@ -31,108 +29,113 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
     const dim_t radius      = std::max((int)(spatialSigma * 1.5f), 1);
     const AccType cvar      = chromaticSigma * chromaticSigma;
 
+    std::array<AccType, 4> currentCenterColors{{0}};
+    std::array<AccType, 4> currentMeanColors{{0}};
+    std::array<AccType, 4> tempColors{{0}};
+    for (dim_t b3 = 0; b3 < dims[3]; ++b3) {
+        for (unsigned b2 = 0; b2 < bCount; ++b2) {
+            T* outData      = out.get() + b2 * ostrides[2] + b3 * ostrides[3];
+            const T* inData = in.get() + b2 * istrides[2] + b3 * istrides[3];
 
-    std::array<AccType, 4> currentCenterColors{{ 0 }};
-    std::array<AccType, 4> currentMeanColors{{ 0 }};
-    std::array<AccType, 4> tempColors{{ 0 }};
-    for (dim_t b3=0; b3<dims[3]; ++b3) {
-        for (unsigned b2=0; b2<bCount; ++b2) {
-
-            T *      outData = out.get() + b2 * ostrides[2] + b3 * ostrides[3];
-            const T * inData = in.get()  + b2 * istrides[2] + b3 * istrides[3];
-
-            for (dim_t j=0; j<dims[1]; ++j) {
-
-                dim_t j_in_off  = j*istrides[1];
-                dim_t j_out_off = j*ostrides[1];
-
-                for (dim_t i=0; i<dims[0]; ++i) {
+            for (dim_t j = 0; j < dims[1]; ++j) {
+                dim_t j_in_off  = j * istrides[1];
+                dim_t j_out_off = j * ostrides[1];
 
-                    dim_t i_in_off  = i*istrides[0];
-                    dim_t i_out_off = i*ostrides[0];
+                for (dim_t i = 0; i < dims[0]; ++i) {
+                    dim_t i_in_off  = i * istrides[0];
+                    dim_t i_out_off = i * ostrides[0];
 
-                    for (unsigned ch=0; ch<channels; ++ch)
-                        currentCenterColors[ch] = static_cast<AccType>(inData[j_in_off + i_in_off + ch*istrides[2]]);
+                    for (unsigned ch = 0; ch < channels; ++ch)
+                        currentCenterColors[ch] = static_cast<AccType>(
+                            inData[j_in_off + i_in_off + ch * istrides[2]]);
 
                     int meanPosJ = j;
                     int meanPosI = i;
 
                     // scope of meanshift iterations begin
-                    for (unsigned it=0; it<numIterations; ++it) {
-
-                        int oldMeanPosJ   = meanPosJ;
-                        int oldMeanPosI   = meanPosI;
-                        unsigned count = 0;
-                        int shift_y = 0;
-                        int shift_x = 0;
+                    for (unsigned it = 0; it < numIterations; ++it) {
+                        int oldMeanPosJ = meanPosJ;
+                        int oldMeanPosI = meanPosI;
+                        unsigned count  = 0;
+                        int shift_y     = 0;
+                        int shift_x     = 0;
 
                         currentMeanColors.fill(0);
                         // Windowing operation
-                        for (dim_t wj=-radius; wj<=radius; ++wj) {
-
+                        for (dim_t wj = -radius; wj <= radius; ++wj) {
                             int hit_count = 0;
-                            dim_t tj = meanPosJ + wj;
-                            if (tj<0 || tj>dims[1]-1) continue;
-
-                            dim_t tjstride = tj*istrides[1];
+                            dim_t tj      = meanPosJ + wj;
+                            if (tj < 0 || tj > dims[1] - 1) continue;
 
-                            for (dim_t wi=-radius; wi<=radius; ++wi) {
+                            dim_t tjstride = tj * istrides[1];
 
+                            for (dim_t wi = -radius; wi <= radius; ++wi) {
                                 dim_t ti = meanPosI + wi;
-                                if (ti<0 || ti>dims[0]-1) continue;
+                                if (ti < 0 || ti > dims[0] - 1) continue;
 
-                                dim_t tistride = ti*istrides[0];
+                                dim_t tistride = ti * istrides[0];
 
                                 AccType norm = 0;
-                                for (unsigned ch=0; ch<channels; ++ch) {
-                                    tempColors[ch] = static_cast<AccType>(inData[ tistride + tjstride + ch*istrides[2] ]);
-                                    AccType diff = currentCenterColors[ch] - tempColors[ch];
+                                for (unsigned ch = 0; ch < channels; ++ch) {
+                                    tempColors[ch] = static_cast<AccType>(
+                                        inData[tistride + tjstride +
+                                               ch * istrides[2]]);
+                                    AccType diff = currentCenterColors[ch] -
+                                                   tempColors[ch];
                                     norm += (diff * diff);
                                 }
                                 if (norm <= cvar) {
-                                    for(unsigned ch=0; ch<channels; ++ch)
+                                    for (unsigned ch = 0; ch < channels; ++ch)
                                         currentMeanColors[ch] += tempColors[ch];
 
                                     shift_x += ti;
                                     ++hit_count;
                                 }
                             }
-                            count   += hit_count;
-                            shift_y += tj*hit_count;
+                            count += hit_count;
+                            shift_y += tj * hit_count;
                         }
 
-                        if (count==0) break;
+                        if (count == 0) break;
 
-                        const AccType fcount = 1/static_cast<AccType>(count);
+                        const AccType fcount = 1 / static_cast<AccType>(count);
 
-                        meanPosJ = static_cast<int>(std::trunc(shift_y*fcount));
-                        meanPosI = static_cast<int>(std::trunc(shift_x*fcount));
+                        meanPosJ =
+                            static_cast<int>(std::trunc(shift_y * fcount));
+                        meanPosI =
+                            static_cast<int>(std::trunc(shift_x * fcount));
 
-                        for (unsigned ch=0; ch<channels; ++ch)
-                            currentMeanColors[ch] = std::trunc(currentMeanColors[ch]*fcount);
+                        for (unsigned ch = 0; ch < channels; ++ch)
+                            currentMeanColors[ch] =
+                                std::trunc(currentMeanColors[ch] * fcount);
 
                         AccType norm = 0;
-                        for (unsigned ch=0; ch<channels; ++ch) {
-                            AccType diff = currentMeanColors[ch] - currentCenterColors[ch];
-                            norm += (diff*diff);
+                        for (unsigned ch = 0; ch < channels; ++ch) {
+                            AccType diff =
+                                currentMeanColors[ch] - currentCenterColors[ch];
+                            norm += (diff * diff);
                         }
 
-                        //stop the process if mean converged or within given tolerance range
-                        bool stop = (meanPosJ==oldMeanPosJ && oldMeanPosI==meanPosI) ||
-                                    ((abs(oldMeanPosJ-meanPosJ) + abs(oldMeanPosI-meanPosI) + norm) <= 1);
+                        // stop the process if mean converged or within given
+                        // tolerance range
+                        bool stop = (meanPosJ == oldMeanPosJ &&
+                                     oldMeanPosI == meanPosI) ||
+                                    ((abs(oldMeanPosJ - meanPosJ) +
+                                      abs(oldMeanPosI - meanPosI) + norm) <= 1);
 
-                        for (unsigned ch=0; ch<channels; ++ch)
+                        for (unsigned ch = 0; ch < channels; ++ch)
                             currentCenterColors[ch] = currentMeanColors[ch];
 
                         if (stop) break;
-                    } // scope of meanshift iterations end
+                    }  // scope of meanshift iterations end
 
-                    for (dim_t ch=0; ch<channels; ++ch)
-                        outData[j_out_off + i_out_off + ch*ostrides[2]] = static_cast<T>(currentCenterColors[ch]);
+                    for (dim_t ch = 0; ch < channels; ++ch)
+                        outData[j_out_off + i_out_off + ch * ostrides[2]] =
+                            static_cast<T>(currentCenterColors[ch]);
                 }
             }
         }
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index af17a7a081..6f804a0aae 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -9,17 +9,14 @@
 
 #pragma once
 #include <Param.hpp>
-#include <vector>
 #include <algorithm>
+#include <vector>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, af_border_type Pad>
-void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid)
-{
+void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid) {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -27,173 +24,158 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid)
     std::vector<T> wind_vals;
     wind_vals.reserve(w_wid);
 
-    for(int b3=0; b3<(int)dims[3]; b3++) {
-        T const * in_ptr = in.get() + b3 * istrides[3];
-        T * out_ptr = out.get() + b3 * ostrides[3];
-
-        for(int b2=0; b2<(int)dims[2]; b2++) {
+    for (int b3 = 0; b3 < (int)dims[3]; b3++) {
+        T const* in_ptr = in.get() + b3 * istrides[3];
+        T* out_ptr      = out.get() + b3 * ostrides[3];
 
-            for(int col=0; col<(int)dims[1]; col++) {
-
-                int ocol_off = col*ostrides[1];
-
-                for(int row=0; row<(int)dims[0]; row++) {
+        for (int b2 = 0; b2 < (int)dims[2]; b2++) {
+            for (int col = 0; col < (int)dims[1]; col++) {
+                int ocol_off = col * ostrides[1];
 
+                for (int row = 0; row < (int)dims[0]; row++) {
                     wind_vals.clear();
-                    for(int wi=0; wi<(int)w_wid; ++wi) {
-
-                        int im_row = row + wi-w_wid/2;
+                    for (int wi = 0; wi < (int)w_wid; ++wi) {
+                        int im_row = row + wi - w_wid / 2;
                         int im_roff;
-                        switch(Pad) {
+                        switch (Pad) {
                             case AF_PAD_ZERO:
                                 im_roff = im_row * istrides[0];
-                                if (im_row < 0 || im_row>=(int)dims[0])
+                                if (im_row < 0 || im_row >= (int)dims[0])
                                     wind_vals.push_back(0);
                                 else
                                     wind_vals.push_back(in_ptr[im_roff]);
                                 break;
-                            case AF_PAD_SYM:
-                                {
-                                    if (im_row < 0) {
-                                        im_row *= -1;
-                                    }
-
-                                    if (im_row>=(int)dims[0]) {
-                                        im_row = 2*((int)dims[0]-1) - im_row;
-                                    }
+                            case AF_PAD_SYM: {
+                                if (im_row < 0) { im_row *= -1; }
 
-                                    im_roff = im_row * istrides[0];
-                                    wind_vals.push_back(in_ptr[im_roff]);
+                                if (im_row >= (int)dims[0]) {
+                                    im_row = 2 * ((int)dims[0] - 1) - im_row;
                                 }
-                                break;
+
+                                im_roff = im_row * istrides[0];
+                                wind_vals.push_back(in_ptr[im_roff]);
+                            } break;
                         }
                     }
 
-                    int off = wind_vals.size()/2;
-                    std::stable_sort(wind_vals.begin(),wind_vals.end());
-                    if (wind_vals.size()%2==0)
-                        out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
+                    int off = wind_vals.size() / 2;
+                    std::stable_sort(wind_vals.begin(), wind_vals.end());
+                    if (wind_vals.size() % 2 == 0)
+                        out_ptr[ocol_off + row * ostrides[0]] =
+                            (wind_vals[off] + wind_vals[off - 1]) / 2;
                     else {
-                        out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
+                        out_ptr[ocol_off + row * ostrides[0]] = wind_vals[off];
                     }
                 }
             }
-            in_ptr  += istrides[2];
+            in_ptr += istrides[2];
             out_ptr += ostrides[2];
         }
     }
 }
 
-
 template<typename T, af_border_type Pad>
-void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid)
-{
+void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
 
     std::vector<T> wind_vals;
-    wind_vals.reserve(w_len*w_wid);
-
-    for(int b3=0; b3<(int)dims[3]; b3++) {
-        T const * in_ptr = in.get() + b3 * istrides[3];
-        T * out_ptr = out.get() + b3 * ostrides[3];
-
-        for(int b2=0; b2<(int)dims[2]; b2++) {
+    wind_vals.reserve(w_len * w_wid);
 
-            for(int col=0; col<(int)dims[1]; col++) {
+    for (int b3 = 0; b3 < (int)dims[3]; b3++) {
+        T const* in_ptr = in.get() + b3 * istrides[3];
+        T* out_ptr      = out.get() + b3 * ostrides[3];
 
-                int ocol_off = col*ostrides[1];
-
-                for(int row=0; row<(int)dims[0]; row++) {
+        for (int b2 = 0; b2 < (int)dims[2]; b2++) {
+            for (int col = 0; col < (int)dims[1]; col++) {
+                int ocol_off = col * ostrides[1];
 
+                for (int row = 0; row < (int)dims[0]; row++) {
                     wind_vals.clear();
 
-                    for(int wj=0; wj<(int)w_wid; ++wj) {
-
+                    for (int wj = 0; wj < (int)w_wid; ++wj) {
                         bool isColOff = false;
 
-                        int im_col = col + wj-w_wid/2;
+                        int im_col = col + wj - w_wid / 2;
                         int im_coff;
-                        switch(Pad) {
+                        switch (Pad) {
                             case AF_PAD_ZERO:
                                 im_coff = im_col * istrides[1];
-                                if (im_col < 0 || im_col>=(int)dims[1])
+                                if (im_col < 0 || im_col >= (int)dims[1])
                                     isColOff = true;
                                 break;
-                            case AF_PAD_SYM:
-                                {
-                                    if (im_col < 0) {
-                                        im_col *= -1;
-                                        isColOff = true;
-                                    }
-
-                                    if (im_col>=(int)dims[1]) {
-                                        im_col = 2*((int)dims[1]-1) - im_col;
-                                        isColOff = true;
-                                    }
+                            case AF_PAD_SYM: {
+                                if (im_col < 0) {
+                                    im_col *= -1;
+                                    isColOff = true;
+                                }
 
-                                    im_coff = im_col * istrides[1];
+                                if (im_col >= (int)dims[1]) {
+                                    im_col   = 2 * ((int)dims[1] - 1) - im_col;
+                                    isColOff = true;
                                 }
-                                break;
-                        }
 
-                        for(int wi=0; wi<(int)w_len; ++wi) {
+                                im_coff = im_col * istrides[1];
+                            } break;
+                        }
 
+                        for (int wi = 0; wi < (int)w_len; ++wi) {
                             bool isRowOff = false;
 
-                            int im_row = row + wi-w_len/2;
+                            int im_row = row + wi - w_len / 2;
                             int im_roff;
-                            switch(Pad) {
+                            switch (Pad) {
                                 case AF_PAD_ZERO:
                                     im_roff = im_row * istrides[0];
-                                    if (im_row < 0 || im_row>=(int)dims[0])
+                                    if (im_row < 0 || im_row >= (int)dims[0])
                                         isRowOff = true;
                                     break;
-                                case AF_PAD_SYM:
-                                    {
-                                        if (im_row < 0) {
-                                            im_row *= -1;
-                                            isRowOff = true;
-                                        }
-
-                                        if (im_row>=(int)dims[0]) {
-                                            im_row = 2*((int)dims[0]-1) - im_row;
-                                            isRowOff = true;
-                                        }
-
-                                        im_roff = im_row * istrides[0];
+                                case AF_PAD_SYM: {
+                                    if (im_row < 0) {
+                                        im_row *= -1;
+                                        isRowOff = true;
                                     }
-                                    break;
+
+                                    if (im_row >= (int)dims[0]) {
+                                        im_row =
+                                            2 * ((int)dims[0] - 1) - im_row;
+                                        isRowOff = true;
+                                    }
+
+                                    im_roff = im_row * istrides[0];
+                                } break;
                             }
 
-                            if(isRowOff || isColOff) {
-                                switch(Pad) {
+                            if (isRowOff || isColOff) {
+                                switch (Pad) {
                                     case AF_PAD_ZERO:
                                         wind_vals.push_back(0);
                                         break;
                                     case AF_PAD_SYM:
-                                        wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                                        wind_vals.push_back(
+                                            in_ptr[im_coff + im_roff]);
                                         break;
                                 }
                             } else
-                                wind_vals.push_back(in_ptr[im_coff+im_roff]);
+                                wind_vals.push_back(in_ptr[im_coff + im_roff]);
                         }
                     }
 
-                    std::stable_sort(wind_vals.begin(),wind_vals.end());
-                    int off = wind_vals.size()/2;
-                    if (wind_vals.size()%2==0)
-                        out_ptr[ocol_off+row*ostrides[0]] = (wind_vals[off]+wind_vals[off-1])/2;
+                    std::stable_sort(wind_vals.begin(), wind_vals.end());
+                    int off = wind_vals.size() / 2;
+                    if (wind_vals.size() % 2 == 0)
+                        out_ptr[ocol_off + row * ostrides[0]] =
+                            (wind_vals[off] + wind_vals[off - 1]) / 2;
                     else
-                        out_ptr[ocol_off+row*ostrides[0]] = wind_vals[off];
+                        out_ptr[ocol_off + row * ostrides[0]] = wind_vals[off];
                 }
             }
-            in_ptr  += istrides[2];
+            in_ptr += istrides[2];
             out_ptr += ostrides[2];
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index fd99884cd7..f67b2deb48 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -8,48 +8,45 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <Param.hpp>
-#include <utility.hpp>
 #include <math.hpp>
+#include <utility.hpp>
+#include <af/defines.h>
 
-namespace cpu
-{
-namespace kernel
-{
-
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void moments(Param<float> output, CParam<T> input, af_moment_type moment)
-{
-    T const * const in       = input.get();
-    af::dim4  const idims    = input.dims();
-    af::dim4  const istrides = input.strides();
-    af::dim4  const ostrides = output.strides();
+void moments(Param<float> output, CParam<T> input, af_moment_type moment) {
+    T const *const in       = input.get();
+    af::dim4 const idims    = input.dims();
+    af::dim4 const istrides = input.strides();
+    af::dim4 const ostrides = output.strides();
 
     float *out = output.get();
 
-    for(dim_t w = 0; w < idims[3]; w++) {
-        for(dim_t z = 0; z < idims[2]; z++) {
+    for (dim_t w = 0; w < idims[3]; w++) {
+        for (dim_t z = 0; z < idims[2]; z++) {
             dim_t out_off = w * ostrides[3] + z * ostrides[2];
-            for(dim_t y = 0; y < idims[1]; y++) {
-                dim_t in_off = y * istrides[1] + z * istrides[2] + w * istrides[3];
-                for(dim_t x = 0; x < idims[0]; x++) {
-                    dim_t m_off=0;
-                    float val = in[in_off + x];
-                    if((moment & AF_MOMENT_M00) > 0) {
+            for (dim_t y = 0; y < idims[1]; y++) {
+                dim_t in_off =
+                    y * istrides[1] + z * istrides[2] + w * istrides[3];
+                for (dim_t x = 0; x < idims[0]; x++) {
+                    dim_t m_off = 0;
+                    float val   = in[in_off + x];
+                    if ((moment & AF_MOMENT_M00) > 0) {
                         out[out_off + m_off] += val;
                         m_off++;
                     }
-                    if((moment & AF_MOMENT_M01) > 0) {
+                    if ((moment & AF_MOMENT_M01) > 0) {
                         out[out_off + m_off] += x * val;
                         m_off++;
                     }
-                    if((moment & AF_MOMENT_M10) > 0) {
+                    if ((moment & AF_MOMENT_M10) > 0) {
                         out[out_off + m_off] += y * val;
                         m_off++;
                     }
-                    if((moment & AF_MOMENT_M11) > 0) {
+                    if ((moment & AF_MOMENT_M11) > 0) {
                         out[out_off + m_off] += x * y * val;
                         m_off++;
                     }
@@ -59,6 +56,5 @@ void moments(Param<float> output, CParam<T> input, af_moment_type moment)
     }
 }
 
-
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index a54200fc14..56104e089a 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -8,29 +8,26 @@
  ********************************************************/
 
 #pragma once
-#include <limits>
 #include <Param.hpp>
-#include <utility.hpp>
 #include <ops.hpp>
+#include <utility.hpp>
+#include <limits>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 template<typename T>
-void getOffsets(std::vector<dim_t>& offsets,
-                const af::dim4& strides, const CParam<T>& mask)
-{
+void getOffsets(std::vector<dim_t>& offsets, const af::dim4& strides,
+                const CParam<T>& mask) {
     const af::dim4 fstrides = mask.strides();
-    const T * filter = mask.get();
+    const T* filter         = mask.get();
     const dim_t dim0 = mask.dims()[0], dim1 = mask.dims()[1];
-    const dim_t R0 = dim0/2;
-    const dim_t R1 = dim1/2;
+    const dim_t R0 = dim0 / 2;
+    const dim_t R1 = dim1 / 2;
 
     offsets.reserve(mask.dims().elements());
     for (dim_t j = 0; j < dim1; ++j) {
         for (dim_t i = 0; i < dim0; ++i) {
-            if (filter[ getIdx(fstrides, i, j) ] > (T)0) {
+            if (filter[getIdx(fstrides, i, j)] > (T)0) {
                 dim_t offset = (j - R1) * strides[1] + (i - R0) * strides[0];
                 offsets.push_back(offset);
             }
@@ -46,22 +43,22 @@ struct MorphFilterOp {
 };
 
 template<typename T, bool IsDilation>
-void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask)
-{
+void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask) {
     MorphFilterOp<T, IsDilation> filterOp;
-    T init = IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T init =
+        IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 
     const af::dim4 ostrides = paddedOut.strides();
-    T * outData             = paddedOut.get();
+    T* outData              = paddedOut.get();
     const af::dim4 istrides = paddedIn.strides();
     const af::dim4 dims     = paddedIn.dims();
-    const T * inData        = paddedIn.get();
+    const T* inData         = paddedIn.get();
 
     std::vector<dim_t> offsets;
     getOffsets(offsets, istrides, mask);
 
     const dim_t batchSize = dims[0] * dims[1];
-    const int  batchCount = dims[2] * dims[3];
+    const int batchCount  = dims[2] * dims[3];
     for (int b = 0; b < batchCount; ++b) {
         for (dim_t n = 0; n < batchSize; ++n) {
             T filterResult = init;
@@ -73,72 +70,76 @@ void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask)
             outData[n] = filterResult;
         }
         outData += ostrides[2];
-        inData  += istrides[2];
+        inData += istrides[2];
     }
 }
 
 template<typename T, bool IsDilation>
-void morph3d(Param<T> out, CParam<T> in, CParam<T> mask)
-{
+void morph3d(Param<T> out, CParam<T> in, CParam<T> mask) {
     const af::dim4 dims     = in.dims();
     const af::dim4 window   = mask.dims();
-    const dim_t R0      = window[0]/2;
-    const dim_t R1      = window[1]/2;
-    const dim_t R2      = window[2]/2;
+    const dim_t R0          = window[0] / 2;
+    const dim_t R1          = window[1] / 2;
+    const dim_t R2          = window[2] / 2;
     const af::dim4 istrides = in.strides();
     const af::dim4 fstrides = mask.strides();
-    const dim_t bCount  = dims[3];
+    const dim_t bCount      = dims[3];
     const af::dim4 ostrides = out.strides();
-    T* outData          = out.get();
-    const T*   inData   = in.get();
-    const T*   filter   = mask.get();
+    T* outData              = out.get();
+    const T* inData         = in.get();
+    const T* filter         = mask.get();
 
-    T init = IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T init =
+        IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 
-    for(dim_t batchId=0; batchId<bCount; ++batchId) {
+    for (dim_t batchId = 0; batchId < bCount; ++batchId) {
         // either channels or batch is handled by outer most loop
-        for(dim_t k=0; k<dims[2]; ++k) {
+        for (dim_t k = 0; k < dims[2]; ++k) {
             // k steps along 3rd dimension
-            for(dim_t j=0; j<dims[1]; ++j) {
+            for (dim_t j = 0; j < dims[1]; ++j) {
                 // j steps along 2nd dimension
-                for(dim_t i=0; i<dims[0]; ++i) {
+                for (dim_t i = 0; i < dims[0]; ++i) {
                     // i steps along 1st dimension
                     T filterResult = init;
 
-                    // wk, wj,wi steps along 2nd & 1st dimensions of filter window respectively
-                    for(dim_t wk=0; wk<window[2]; wk++) {
-                        for(dim_t wj=0; wj<window[1]; wj++) {
-                            for(dim_t wi=0; wi<window[0]; wi++) {
+                    // wk, wj,wi steps along 2nd & 1st dimensions of filter
+                    // window respectively
+                    for (dim_t wk = 0; wk < window[2]; wk++) {
+                        for (dim_t wj = 0; wj < window[1]; wj++) {
+                            for (dim_t wi = 0; wi < window[0]; wi++) {
+                                dim_t offk = k + wk - R2;
+                                dim_t offj = j + wj - R1;
+                                dim_t offi = i + wi - R0;
 
-                                dim_t offk = k+wk-R2;
-                                dim_t offj = j+wj-R1;
-                                dim_t offi = i+wi-R0;
+                                T maskValue =
+                                    filter[getIdx(fstrides, wi, wj, wk)];
 
-                                T maskValue = filter[ getIdx(fstrides, wi, wj, wk) ];
-
-                                if ((maskValue > (T)0) && offi>=0 && offj>=0 && offk>=0 &&
-                                        offi<dims[0] && offj<dims[1] && offk<dims[2]) {
-
-                                    T inValue   = inData[ getIdx(istrides, offi, offj, offk) ];
+                                if ((maskValue > (T)0) && offi >= 0 &&
+                                    offj >= 0 && offk >= 0 && offi < dims[0] &&
+                                    offj < dims[1] && offk < dims[2]) {
+                                    T inValue = inData[getIdx(istrides, offi,
+                                                              offj, offk)];
 
                                     if (IsDilation)
-                                        filterResult = std::max(filterResult, inValue);
+                                        filterResult =
+                                            std::max(filterResult, inValue);
                                     else
-                                        filterResult = std::min(filterResult, inValue);
+                                        filterResult =
+                                            std::min(filterResult, inValue);
                                 }
 
-                            } // window 1st dimension loop ends here
-                        }  // window 1st dimension loop ends here
-                    }// filter window loop ends here
+                            }  // window 1st dimension loop ends here
+                        }      // window 1st dimension loop ends here
+                    }          // filter window loop ends here
 
-                    outData[ getIdx(ostrides, i, j, k) ] = filterResult;
-                } //1st dimension loop ends here
-            } // 2nd dimension loop ends here
-        } // 3rd dimension loop ends here
+                    outData[getIdx(ostrides, i, j, k)] = filterResult;
+                }  // 1st dimension loop ends here
+            }      // 2nd dimension loop ends here
+        }          // 3rd dimension loop ends here
         // next iteration will be next batch if any
         outData += ostrides[3];
-        inData  += istrides[3];
+        inData += istrides[3];
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 0a23bee43a..599c04356b 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -10,10 +10,8 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 #if defined(_WIN32) || defined(_MSC_VER)
 
@@ -23,84 +21,56 @@ namespace kernel
 #endif
 
 template<typename T, typename To, af_match_type dist_type>
-struct dist_op
-{
-    To operator()(T v1, T v2)
-    {
-        return v1 - v2;     // Garbage distance
+struct dist_op {
+    To operator()(T v1, T v2) {
+        return v1 - v2;  // Garbage distance
     }
 };
 
 template<typename T, typename To>
-struct dist_op<T, To, AF_SAD>
-{
-    To operator()(T v1, T v2)
-    {
-        return std::abs((double)v1 - (double)v2);
-    }
+struct dist_op<T, To, AF_SAD> {
+    To operator()(T v1, T v2) { return std::abs((double)v1 - (double)v2); }
 };
 
 template<typename T, typename To>
-struct dist_op<T, To, AF_SSD>
-{
-    To operator()(T v1, T v2)
-    {
-        return (v1 - v2) * (v1 - v2);
-    }
+struct dist_op<T, To, AF_SSD> {
+    To operator()(T v1, T v2) { return (v1 - v2) * (v1 - v2); }
 };
 
 template<typename To>
-struct dist_op<uint, To, AF_SHD>
-{
-    To operator()(uint v1, uint v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
+struct dist_op<uint, To, AF_SHD> {
+    To operator()(uint v1, uint v2) { return __builtin_popcount(v1 ^ v2); }
 };
 
 template<typename To>
-struct dist_op<uintl, To, AF_SHD>
-{
-    To operator()(uintl v1, uintl v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
+struct dist_op<uintl, To, AF_SHD> {
+    To operator()(uintl v1, uintl v2) { return __builtin_popcount(v1 ^ v2); }
 };
 
 template<typename To>
-struct dist_op<uchar, To, AF_SHD>
-{
-    To operator()(uchar v1, uchar v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
+struct dist_op<uchar, To, AF_SHD> {
+    To operator()(uchar v1, uchar v2) { return __builtin_popcount(v1 ^ v2); }
 };
 
 template<typename To>
-struct dist_op<ushort, To, AF_SHD>
-{
-    To operator()(ushort v1, ushort v2)
-    {
-        return __builtin_popcount(v1 ^ v2);
-    }
+struct dist_op<ushort, To, AF_SHD> {
+    To operator()(ushort v1, ushort v2) { return __builtin_popcount(v1 ^ v2); }
 };
 
 template<typename T, typename To, af_match_type dist_type>
-void nearest_neighbour(Param<To> dists,
-                       CParam<T> query, CParam<T> train,
-                       const uint dist_dim)
-{
-    uint sample_dim = (dist_dim == 0) ? 1 : 0;
+void nearest_neighbour(Param<To> dists, CParam<T> query, CParam<T> train,
+                       const uint dist_dim) {
+    uint sample_dim  = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
 
     const unsigned distLength = qDims[dist_dim];
-    const unsigned nQuery = qDims[sample_dim];
-    const unsigned nTrain = tDims[sample_dim];
+    const unsigned nQuery     = qDims[sample_dim];
+    const unsigned nTrain     = tDims[sample_dim];
 
     const T* qPtr = query.get();
     const T* tPtr = train.get();
-    To* dPtr = dists.get();
+    To* dPtr      = dists.get();
 
     dist_op<T, To, dist_type> op;
 
@@ -112,8 +82,7 @@ void nearest_neighbour(Param<To> dists,
                 if (sample_dim == 0) {
                     qIdx = k * qDims[0] + i;
                     tIdx = k * tDims[0] + j;
-                }
-                else {
+                } else {
                     qIdx = i * qDims[0] + k;
                     tIdx = j * tDims[0] + k;
                 }
@@ -121,10 +90,10 @@ void nearest_neighbour(Param<To> dists,
                 local_dist += op(qPtr[qIdx], tPtr[tIdx]);
             }
 
-            dPtr[i*nTrain + j] = local_dist;
+            dPtr[i * nTrain + j] = local_dist;
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/orb.hpp b/src/backend/cpu/kernel/orb.hpp
index a1c7362d90..33c642cd8d 100644
--- a/src/backend/cpu/kernel/orb.hpp
+++ b/src/backend/cpu/kernel/orb.hpp
@@ -11,297 +11,100 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 // Reference pattern, generated for a patch size of 31x31, as suggested by
 // original ORB paper
 #define REF_PAT_SIZE 31
 #define REF_PAT_SAMPLES 256
 #define REF_PAT_COORDS 4
-#define REF_PAT_LENGTH (REF_PAT_SAMPLES*REF_PAT_COORDS)
+#define REF_PAT_LENGTH (REF_PAT_SAMPLES * REF_PAT_COORDS)
 
 // Current reference pattern was borrowed from OpenCV, to build a pattern with
 // similar quality, a training process must be applied, as described in
 // sections 4.2 and 4.3 of the original ORB paper.
 const int ref_pat[REF_PAT_LENGTH] = {
-    8,-3, 9,5,
-    4,2, 7,-12,
-    -11,9, -8,2,
-    7,-12, 12,-13,
-    2,-13, 2,12,
-    1,-7, 1,6,
-    -2,-10, -2,-4,
-    -13,-13, -11,-8,
-    -13,-3, -12,-9,
-    10,4, 11,9,
-    -13,-8, -8,-9,
-    -11,7, -9,12,
-    7,7, 12,6,
-    -4,-5, -3,0,
-    -13,2, -12,-3,
-    -9,0, -7,5,
-    12,-6, 12,-1,
-    -3,6, -2,12,
-    -6,-13, -4,-8,
-    11,-13, 12,-8,
-    4,7, 5,1,
-    5,-3, 10,-3,
-    3,-7, 6,12,
-    -8,-7, -6,-2,
-    -2,11, -1,-10,
-    -13,12, -8,10,
-    -7,3, -5,-3,
-    -4,2, -3,7,
-    -10,-12, -6,11,
-    5,-12, 6,-7,
-    5,-6, 7,-1,
-    1,0, 4,-5,
-    9,11, 11,-13,
-    4,7, 4,12,
-    2,-1, 4,4,
-    -4,-12, -2,7,
-    -8,-5, -7,-10,
-    4,11, 9,12,
-    0,-8, 1,-13,
-    -13,-2, -8,2,
-    -3,-2, -2,3,
-    -6,9, -4,-9,
-    8,12, 10,7,
-    0,9, 1,3,
-    7,-5, 11,-10,
-    -13,-6, -11,0,
-    10,7, 12,1,
-    -6,-3, -6,12,
-    10,-9, 12,-4,
-    -13,8, -8,-12,
-    -13,0, -8,-4,
-    3,3, 7,8,
-    5,7, 10,-7,
-    -1,7, 1,-12,
-    3,-10, 5,6,
-    2,-4, 3,-10,
-    -13,0, -13,5,
-    -13,-7, -12,12,
-    -13,3, -11,8,
-    -7,12, -4,7,
-    6,-10, 12,8,
-    -9,-1, -7,-6,
-    -2,-5, 0,12,
-    -12,5, -7,5,
-    3,-10, 8,-13,
-    -7,-7, -4,5,
-    -3,-2, -1,-7,
-    2,9, 5,-11,
-    -11,-13, -5,-13,
-    -1,6, 0,-1,
-    5,-3, 5,2,
-    -4,-13, -4,12,
-    -9,-6, -9,6,
-    -12,-10, -8,-4,
-    10,2, 12,-3,
-    7,12, 12,12,
-    -7,-13, -6,5,
-    -4,9, -3,4,
-    7,-1, 12,2,
-    -7,6, -5,1,
-    -13,11, -12,5,
-    -3,7, -2,-6,
-    7,-8, 12,-7,
-    -13,-7, -11,-12,
-    1,-3, 12,12,
-    2,-6, 3,0,
-    -4,3, -2,-13,
-    -1,-13, 1,9,
-    7,1, 8,-6,
-    1,-1, 3,12,
-    9,1, 12,6,
-    -1,-9, -1,3,
-    -13,-13, -10,5,
-    7,7, 10,12,
-    12,-5, 12,9,
-    6,3, 7,11,
-    5,-13, 6,10,
-    2,-12, 2,3,
-    3,8, 4,-6,
-    2,6, 12,-13,
-    9,-12, 10,3,
-    -8,4, -7,9,
-    -11,12, -4,-6,
-    1,12, 2,-8,
-    6,-9, 7,-4,
-    2,3, 3,-2,
-    6,3, 11,0,
-    3,-3, 8,-8,
-    7,8, 9,3,
-    -11,-5, -6,-4,
-    -10,11, -5,10,
-    -5,-8, -3,12,
-    -10,5, -9,0,
-    8,-1, 12,-6,
-    4,-6, 6,-11,
-    -10,12, -8,7,
-    4,-2, 6,7,
-    -2,0, -2,12,
-    -5,-8, -5,2,
-    7,-6, 10,12,
-    -9,-13, -8,-8,
-    -5,-13, -5,-2,
-    8,-8, 9,-13,
-    -9,-11, -9,0,
-    1,-8, 1,-2,
-    7,-4, 9,1,
-    -2,1, -1,-4,
-    11,-6, 12,-11,
-    -12,-9, -6,4,
-    3,7, 7,12,
-    5,5, 10,8,
-    0,-4, 2,8,
-    -9,12, -5,-13,
-    0,7, 2,12,
-    -1,2, 1,7,
-    5,11, 7,-9,
-    3,5, 6,-8,
-    -13,-4, -8,9,
-    -5,9, -3,-3,
-    -4,-7, -3,-12,
-    6,5, 8,0,
-    -7,6, -6,12,
-    -13,6, -5,-2,
-    1,-10, 3,10,
-    4,1, 8,-4,
-    -2,-2, 2,-13,
-    2,-12, 12,12,
-    -2,-13, 0,-6,
-    4,1, 9,3,
-    -6,-10, -3,-5,
-    -3,-13, -1,1,
-    7,5, 12,-11,
-    4,-2, 5,-7,
-    -13,9, -9,-5,
-    7,1, 8,6,
-    7,-8, 7,6,
-    -7,-4, -7,1,
-    -8,11, -7,-8,
-    -13,6, -12,-8,
-    2,4, 3,9,
-    10,-5, 12,3,
-    -6,-5, -6,7,
-    8,-3, 9,-8,
-    2,-12, 2,8,
-    -11,-2, -10,3,
-    -12,-13, -7,-9,
-    -11,0, -10,-5,
-    5,-3, 11,8,
-    -2,-13, -1,12,
-    -1,-8, 0,9,
-    -13,-11, -12,-5,
-    -10,-2, -10,11,
-    -3,9, -2,-13,
-    2,-3, 3,2,
-    -9,-13, -4,0,
-    -4,6, -3,-10,
-    -4,12, -2,-7,
-    -6,-11, -4,9,
-    6,-3, 6,11,
-    -13,11, -5,5,
-    11,11, 12,6,
-    7,-5, 12,-2,
-    -1,12, 0,7,
-    -4,-8, -3,-2,
-    -7,1, -6,7,
-    -13,-12, -8,-13,
-    -7,-2, -6,-8,
-    -8,5, -6,-9,
-    -5,-1, -4,5,
-    -13,7, -8,10,
-    1,5, 5,-13,
-    1,0, 10,-13,
-    9,12, 10,-1,
-    5,-8, 10,-9,
-    -1,11, 1,-13,
-    -9,-3, -6,2,
-    -1,-10, 1,12,
-    -13,1, -8,-10,
-    8,-11, 10,-6,
-    2,-13, 3,-6,
-    7,-13, 12,-9,
-    -10,-10, -5,-7,
-    -10,-8, -8,-13,
-    4,-6, 8,5,
-    3,12, 8,-13,
-    -4,2, -3,-3,
-    5,-13, 10,-12,
-    4,-13, 5,-1,
-    -9,9, -4,3,
-    0,3, 3,-9,
-    -12,1, -6,1,
-    3,2, 4,-8,
-    -10,-10, -10,9,
-    8,-13, 12,12,
-    -8,-12, -6,-5,
-    2,2, 3,7,
-    10,6, 11,-8,
-    6,8, 8,-12,
-    -7,10, -6,5,
-    -3,-9, -3,9,
-    -1,-13, -1,5,
-    -3,-7, -3,4,
-    -8,-2, -8,3,
-    4,2, 12,12,
-    2,-5, 3,11,
-    6,-9, 11,-13,
-    3,-1, 7,12,
-    11,-1, 12,4,
-    -3,0, -3,6,
-    4,-11, 4,12,
-    2,-4, 2,1,
-    -10,-6, -8,1,
-    -13,7, -11,1,
-    -13,12, -11,-13,
-    6,0, 11,-13,
-    0,-1, 1,4,
-    -13,3, -9,-2,
-    -9,8, -6,-3,
-    -13,-6, -8,-2,
-    5,-9, 8,10,
-    2,7, 3,-9,
-    -1,-6, -1,-1,
-    9,5, 11,-2,
-    11,-3, 12,-8,
-    3,0, 3,5,
-    -1,4, 0,10,
-    3,-6, 4,5,
-    -13,0, -10,5,
-    5,8, 12,11,
-    8,9, 9,-6,
-    7,-4, 8,-12,
-    -10,4, -10,9,
-    7,3, 12,4,
-    9,-7, 10,-2,
-    7,0, 12,-2,
-    -1,-6, 0,-11,
+    8,   -3,  9,   5,   4,   2,   7,   -12, -11, 9,   -8,  2,   7,   -12, 12,
+    -13, 2,   -13, 2,   12,  1,   -7,  1,   6,   -2,  -10, -2,  -4,  -13, -13,
+    -11, -8,  -13, -3,  -12, -9,  10,  4,   11,  9,   -13, -8,  -8,  -9,  -11,
+    7,   -9,  12,  7,   7,   12,  6,   -4,  -5,  -3,  0,   -13, 2,   -12, -3,
+    -9,  0,   -7,  5,   12,  -6,  12,  -1,  -3,  6,   -2,  12,  -6,  -13, -4,
+    -8,  11,  -13, 12,  -8,  4,   7,   5,   1,   5,   -3,  10,  -3,  3,   -7,
+    6,   12,  -8,  -7,  -6,  -2,  -2,  11,  -1,  -10, -13, 12,  -8,  10,  -7,
+    3,   -5,  -3,  -4,  2,   -3,  7,   -10, -12, -6,  11,  5,   -12, 6,   -7,
+    5,   -6,  7,   -1,  1,   0,   4,   -5,  9,   11,  11,  -13, 4,   7,   4,
+    12,  2,   -1,  4,   4,   -4,  -12, -2,  7,   -8,  -5,  -7,  -10, 4,   11,
+    9,   12,  0,   -8,  1,   -13, -13, -2,  -8,  2,   -3,  -2,  -2,  3,   -6,
+    9,   -4,  -9,  8,   12,  10,  7,   0,   9,   1,   3,   7,   -5,  11,  -10,
+    -13, -6,  -11, 0,   10,  7,   12,  1,   -6,  -3,  -6,  12,  10,  -9,  12,
+    -4,  -13, 8,   -8,  -12, -13, 0,   -8,  -4,  3,   3,   7,   8,   5,   7,
+    10,  -7,  -1,  7,   1,   -12, 3,   -10, 5,   6,   2,   -4,  3,   -10, -13,
+    0,   -13, 5,   -13, -7,  -12, 12,  -13, 3,   -11, 8,   -7,  12,  -4,  7,
+    6,   -10, 12,  8,   -9,  -1,  -7,  -6,  -2,  -5,  0,   12,  -12, 5,   -7,
+    5,   3,   -10, 8,   -13, -7,  -7,  -4,  5,   -3,  -2,  -1,  -7,  2,   9,
+    5,   -11, -11, -13, -5,  -13, -1,  6,   0,   -1,  5,   -3,  5,   2,   -4,
+    -13, -4,  12,  -9,  -6,  -9,  6,   -12, -10, -8,  -4,  10,  2,   12,  -3,
+    7,   12,  12,  12,  -7,  -13, -6,  5,   -4,  9,   -3,  4,   7,   -1,  12,
+    2,   -7,  6,   -5,  1,   -13, 11,  -12, 5,   -3,  7,   -2,  -6,  7,   -8,
+    12,  -7,  -13, -7,  -11, -12, 1,   -3,  12,  12,  2,   -6,  3,   0,   -4,
+    3,   -2,  -13, -1,  -13, 1,   9,   7,   1,   8,   -6,  1,   -1,  3,   12,
+    9,   1,   12,  6,   -1,  -9,  -1,  3,   -13, -13, -10, 5,   7,   7,   10,
+    12,  12,  -5,  12,  9,   6,   3,   7,   11,  5,   -13, 6,   10,  2,   -12,
+    2,   3,   3,   8,   4,   -6,  2,   6,   12,  -13, 9,   -12, 10,  3,   -8,
+    4,   -7,  9,   -11, 12,  -4,  -6,  1,   12,  2,   -8,  6,   -9,  7,   -4,
+    2,   3,   3,   -2,  6,   3,   11,  0,   3,   -3,  8,   -8,  7,   8,   9,
+    3,   -11, -5,  -6,  -4,  -10, 11,  -5,  10,  -5,  -8,  -3,  12,  -10, 5,
+    -9,  0,   8,   -1,  12,  -6,  4,   -6,  6,   -11, -10, 12,  -8,  7,   4,
+    -2,  6,   7,   -2,  0,   -2,  12,  -5,  -8,  -5,  2,   7,   -6,  10,  12,
+    -9,  -13, -8,  -8,  -5,  -13, -5,  -2,  8,   -8,  9,   -13, -9,  -11, -9,
+    0,   1,   -8,  1,   -2,  7,   -4,  9,   1,   -2,  1,   -1,  -4,  11,  -6,
+    12,  -11, -12, -9,  -6,  4,   3,   7,   7,   12,  5,   5,   10,  8,   0,
+    -4,  2,   8,   -9,  12,  -5,  -13, 0,   7,   2,   12,  -1,  2,   1,   7,
+    5,   11,  7,   -9,  3,   5,   6,   -8,  -13, -4,  -8,  9,   -5,  9,   -3,
+    -3,  -4,  -7,  -3,  -12, 6,   5,   8,   0,   -7,  6,   -6,  12,  -13, 6,
+    -5,  -2,  1,   -10, 3,   10,  4,   1,   8,   -4,  -2,  -2,  2,   -13, 2,
+    -12, 12,  12,  -2,  -13, 0,   -6,  4,   1,   9,   3,   -6,  -10, -3,  -5,
+    -3,  -13, -1,  1,   7,   5,   12,  -11, 4,   -2,  5,   -7,  -13, 9,   -9,
+    -5,  7,   1,   8,   6,   7,   -8,  7,   6,   -7,  -4,  -7,  1,   -8,  11,
+    -7,  -8,  -13, 6,   -12, -8,  2,   4,   3,   9,   10,  -5,  12,  3,   -6,
+    -5,  -6,  7,   8,   -3,  9,   -8,  2,   -12, 2,   8,   -11, -2,  -10, 3,
+    -12, -13, -7,  -9,  -11, 0,   -10, -5,  5,   -3,  11,  8,   -2,  -13, -1,
+    12,  -1,  -8,  0,   9,   -13, -11, -12, -5,  -10, -2,  -10, 11,  -3,  9,
+    -2,  -13, 2,   -3,  3,   2,   -9,  -13, -4,  0,   -4,  6,   -3,  -10, -4,
+    12,  -2,  -7,  -6,  -11, -4,  9,   6,   -3,  6,   11,  -13, 11,  -5,  5,
+    11,  11,  12,  6,   7,   -5,  12,  -2,  -1,  12,  0,   7,   -4,  -8,  -3,
+    -2,  -7,  1,   -6,  7,   -13, -12, -8,  -13, -7,  -2,  -6,  -8,  -8,  5,
+    -6,  -9,  -5,  -1,  -4,  5,   -13, 7,   -8,  10,  1,   5,   5,   -13, 1,
+    0,   10,  -13, 9,   12,  10,  -1,  5,   -8,  10,  -9,  -1,  11,  1,   -13,
+    -9,  -3,  -6,  2,   -1,  -10, 1,   12,  -13, 1,   -8,  -10, 8,   -11, 10,
+    -6,  2,   -13, 3,   -6,  7,   -13, 12,  -9,  -10, -10, -5,  -7,  -10, -8,
+    -8,  -13, 4,   -6,  8,   5,   3,   12,  8,   -13, -4,  2,   -3,  -3,  5,
+    -13, 10,  -12, 4,   -13, 5,   -1,  -9,  9,   -4,  3,   0,   3,   3,   -9,
+    -12, 1,   -6,  1,   3,   2,   4,   -8,  -10, -10, -10, 9,   8,   -13, 12,
+    12,  -8,  -12, -6,  -5,  2,   2,   3,   7,   10,  6,   11,  -8,  6,   8,
+    8,   -12, -7,  10,  -6,  5,   -3,  -9,  -3,  9,   -1,  -13, -1,  5,   -3,
+    -7,  -3,  4,   -8,  -2,  -8,  3,   4,   2,   12,  12,  2,   -5,  3,   11,
+    6,   -9,  11,  -13, 3,   -1,  7,   12,  11,  -1,  12,  4,   -3,  0,   -3,
+    6,   4,   -11, 4,   12,  2,   -4,  2,   1,   -10, -6,  -8,  1,   -13, 7,
+    -11, 1,   -13, 12,  -11, -13, 6,   0,   11,  -13, 0,   -1,  1,   4,   -13,
+    3,   -9,  -2,  -9,  8,   -6,  -3,  -13, -6,  -8,  -2,  5,   -9,  8,   10,
+    2,   7,   3,   -9,  -1,  -6,  -1,  -1,  9,   5,   11,  -2,  11,  -3,  12,
+    -8,  3,   0,   3,   5,   -1,  4,   0,   10,  3,   -6,  4,   5,   -13, 0,
+    -10, 5,   5,   8,   12,  11,  8,   9,   9,   -6,  7,   -4,  8,   -12, -10,
+    4,   -10, 9,   7,   3,   12,  4,   9,   -7,  10,  -2,  7,   0,   12,  -2,
+    -1,  -6,  0,   -11,
 };
 
 template<typename T>
-void keep_features(
-    float* x_out,
-    float* y_out,
-    float* score_out,
-    float* size_out,
-    const float* x_in,
-    const float* y_in,
-    const float* score_in,
-    const unsigned* score_idx,
-    const float* size_in,
-    const unsigned n_feat)
-{
+void keep_features(float* x_out, float* y_out, float* score_out,
+                   float* size_out, const float* x_in, const float* y_in,
+                   const float* score_in, const unsigned* score_idx,
+                   const float* size_in, const unsigned n_feat) {
     // Keep only the first n_feat features
     for (unsigned f = 0; f < n_feat; f++) {
-        x_out[f] = x_in[score_idx[f]];
-        y_out[f] = y_in[score_idx[f]];
+        x_out[f]     = x_in[score_idx[f]];
+        y_out[f]     = y_in[score_idx[f]];
         score_out[f] = score_in[f];
         if (size_in != nullptr && size_out != nullptr)
             size_out[f] = size_in[score_idx[f]];
@@ -309,33 +112,23 @@ void keep_features(
 }
 
 template<typename T, bool use_scl>
-void harris_response(
-    float* x_out,
-    float* y_out,
-    float* score_out,
-    float* size_out,
-    const float* x_in,
-    const float* y_in,
-    const float* scl_in,
-    const unsigned total_feat,
-    unsigned* usable_feat,
-    CParam<T> image,
-    const unsigned block_size,
-    const float k_thr,
-    const unsigned patch_size)
-{
+void harris_response(float* x_out, float* y_out, float* score_out,
+                     float* size_out, const float* x_in, const float* y_in,
+                     const float* scl_in, const unsigned total_feat,
+                     unsigned* usable_feat, CParam<T> image,
+                     const unsigned block_size, const float k_thr,
+                     const unsigned patch_size) {
     const af::dim4 idims = image.dims();
-    const T* image_ptr = image.get();
+    const T* image_ptr   = image.get();
     for (unsigned f = 0; f < total_feat; f++) {
         unsigned x, y;
         float scl = 1.f;
         if (use_scl) {
             // Update x and y coordinates according to scale
             scl = scl_in[f];
-            x = (unsigned)round(x_in[f] * scl);
-            y = (unsigned)round(y_in[f] * scl);
-        }
-        else {
+            x   = (unsigned)round(x_in[f] * scl);
+            y   = (unsigned)round(y_in[f] * scl);
+        } else {
             x = (unsigned)round(x_in[f]);
             y = (unsigned)round(y_in[f]);
         }
@@ -347,7 +140,8 @@ void harris_response(
         // the image, sqrt(2.f) is the radius when angle is 45 degrees and
         // represents widest case possible
         unsigned patch_r = ceil(size * sqrt(2.f) / 2.f);
-        if (x < patch_r || y < patch_r || x >= idims[1] - patch_r || y >= idims[0] - patch_r)
+        if (x < patch_r || y < patch_r || x >= idims[1] - patch_r ||
+            y >= idims[0] - patch_r)
             continue;
 
         unsigned r = block_size / 2;
@@ -359,54 +153,49 @@ void harris_response(
             int j = k % block_size - r;
 
             // Calculate local x and y derivatives
-            float ix = image_ptr[(x+i+1) * idims[0] + y+j] - image_ptr[(x+i-1) * idims[0] + y+j];
-            float iy = image_ptr[(x+i) * idims[0] + y+j+1] - image_ptr[(x+i) * idims[0] + y+j-1];
+            float ix = image_ptr[(x + i + 1) * idims[0] + y + j] -
+                       image_ptr[(x + i - 1) * idims[0] + y + j];
+            float iy = image_ptr[(x + i) * idims[0] + y + j + 1] -
+                       image_ptr[(x + i) * idims[0] + y + j - 1];
 
             // Accumulate second order derivatives
-            ixx += ix*ix;
-            iyy += iy*iy;
-            ixy += ix*iy;
+            ixx += ix * ix;
+            iyy += iy * iy;
+            ixy += ix * iy;
         }
 
         unsigned idx = *usable_feat;
         *usable_feat += 1;
-        float tr = ixx + iyy;
-        float det = ixx*iyy - ixy*ixy;
+        float tr  = ixx + iyy;
+        float det = ixx * iyy - ixy * ixy;
 
         // Calculate Harris responses
-        float resp = det - k_thr * (tr*tr);
+        float resp = det - k_thr * (tr * tr);
 
         // Scale factor
         // TODO: improve response scaling
         float rscale = 0.001f;
-        rscale = rscale * rscale * rscale * rscale;
+        rscale       = rscale * rscale * rscale * rscale;
 
-        x_out[idx] = x;
-        y_out[idx] = y;
+        x_out[idx]     = x;
+        y_out[idx]     = y;
         score_out[idx] = resp * rscale;
-        if (use_scl)
-            size_out[idx] = size;
+        if (use_scl) size_out[idx] = size;
     }
 }
 
 template<typename T>
-void centroid_angle(
-    const float* x_in,
-    const float* y_in,
-    float* orientation_out,
-    const unsigned total_feat,
-    CParam<T> image,
-    const unsigned patch_size)
-{
+void centroid_angle(const float* x_in, const float* y_in,
+                    float* orientation_out, const unsigned total_feat,
+                    CParam<T> image, const unsigned patch_size) {
     const af::dim4 idims = image.dims();
-    const T* image_ptr = image.get();
+    const T* image_ptr   = image.get();
     for (unsigned f = 0; f < total_feat; f++) {
         unsigned x = (unsigned)round(x_in[f]);
         unsigned y = (unsigned)round(y_in[f]);
 
         unsigned r = patch_size / 2;
-        if (x < r || y < r || x > idims[1] - r || y > idims[0] - r)
-            continue;
+        if (x < r || y < r || x > idims[1] - r || y > idims[0] - r) continue;
 
         T m01 = (T)0, m10 = (T)0;
         unsigned patch_size_sq = patch_size * patch_size;
@@ -415,32 +204,25 @@ void centroid_angle(
             int j = k % patch_size - r;
 
             // Calculate first order moments
-            T p = image_ptr[(x+i) * idims[0] + y+j];
+            T p = image_ptr[(x + i) * idims[0] + y + j];
             m01 += j * p;
             m10 += i * p;
         }
 
-        float angle = atan2(m01, m10);
+        float angle        = atan2(m01, m10);
         orientation_out[f] = angle;
     }
 }
 
 template<typename T>
-inline T get_pixel(
-    unsigned x,
-    unsigned y,
-    const float ori,
-    const unsigned size,
-    const int dist_x,
-    const int dist_y,
-    CParam<T> image,
-    const unsigned patch_size)
-{
+inline T get_pixel(unsigned x, unsigned y, const float ori, const unsigned size,
+                   const int dist_x, const int dist_y, CParam<T> image,
+                   const unsigned patch_size) {
     const af::dim4 idims = image.dims();
-    const T* image_ptr = image.get();
-    float ori_sin = sin(ori);
-    float ori_cos = cos(ori);
-    float patch_scl = (float)size / (float)patch_size;
+    const T* image_ptr   = image.get();
+    float ori_sin        = sin(ori);
+    float ori_cos        = cos(ori);
+    float patch_scl      = (float)size / (float)patch_size;
 
     // Calculate point coordinates based on orientation and size
     x += round(dist_x * patch_scl * ori_cos - dist_y * patch_scl * ori_sin);
@@ -450,27 +232,18 @@ inline T get_pixel(
 }
 
 template<typename T>
-void extract_orb(
-    unsigned* desc_out,
-    const unsigned n_feat,
-    float* x_in_out,
-    float* y_in_out,
-    const float* ori_in,
-    float* size_out,
-    CParam<T> image,
-    const float scl,
-    const unsigned patch_size)
-{
+void extract_orb(unsigned* desc_out, const unsigned n_feat, float* x_in_out,
+                 float* y_in_out, const float* ori_in, float* size_out,
+                 CParam<T> image, const float scl, const unsigned patch_size) {
     const af::dim4 idims = image.dims();
     for (unsigned f = 0; f < n_feat; f++) {
-        unsigned x = (unsigned)round(x_in_out[f]);
-        unsigned y = (unsigned)round(y_in_out[f]);
-        float ori = ori_in[f];
+        unsigned x    = (unsigned)round(x_in_out[f]);
+        unsigned y    = (unsigned)round(y_in_out[f]);
+        float ori     = ori_in[f];
         unsigned size = patch_size;
 
         unsigned r = ceil(patch_size * sqrt(2.f) / 2.f);
-        if (x < r || y < r || x >= idims[1] - r || y >= idims[0] - r)
-            continue;
+        if (x < r || y < r || x >= idims[1] - r || y >= idims[0] - r) continue;
 
         // Descriptor fixed at 256 bits for now
         // Storing descriptor as a vector of 8 x 32-bit unsigned numbers
@@ -479,16 +252,20 @@ void extract_orb(
 
             // j < 32 for 256 bits descriptor
             for (unsigned j = 0; j < 32; j++) {
-                // Get position from distribution pattern and values of points p1 and p2
-                int dist_x = ref_pat[i*32*4 + j*4];
-                int dist_y = ref_pat[i*32*4 + j*4+1];
-                T p1 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
-
-                dist_x = ref_pat[i*32*4 + j*4+2];
-                dist_y = ref_pat[i*32*4 + j*4+3];
-                T p2 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
-
-                // Calculate bit based on p1 and p2 and shifts it to correct position
+                // Get position from distribution pattern and values of points
+                // p1 and p2
+                int dist_x = ref_pat[i * 32 * 4 + j * 4];
+                int dist_y = ref_pat[i * 32 * 4 + j * 4 + 1];
+                T p1       = get_pixel(x, y, ori, size, dist_x, dist_y, image,
+                                 patch_size);
+
+                dist_x = ref_pat[i * 32 * 4 + j * 4 + 2];
+                dist_y = ref_pat[i * 32 * 4 + j * 4 + 3];
+                T p2   = get_pixel(x, y, ori, size, dist_x, dist_y, image,
+                                 patch_size);
+
+                // Calculate bit based on p1 and p2 and shifts it to correct
+                // position
                 v |= (p1 < p2) << j;
             }
 
@@ -502,7 +279,5 @@ void extract_orb(
     }
 }
 
-
-
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
index 61e99c95a2..2daa4c588d 100644
--- a/src/backend/cpu/kernel/pad_array_borders.hpp
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -12,91 +12,81 @@
 
 #include <algorithm>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 namespace {
-static inline dim_t
-idxByndEdge(const dim_t i, const dim_t lb,
-            const dim_t len, const af::borderType btype)
-{
+static inline dim_t idxByndEdge(const dim_t i, const dim_t lb, const dim_t len,
+                                const af::borderType btype) {
     dim_t retVal;
-    switch(btype) {
+    switch (btype) {
         case AF_PAD_SYM:
-            retVal = ((i < lb || i>= (lb+len)) ? ((len-1)-((i-lb)%len)) : i-lb);
+            retVal =
+                ((i < lb || i >= (lb + len)) ? ((len - 1) - ((i - lb) % len))
+                                             : i - lb);
             break;
         case AF_PAD_CLAMP_TO_EDGE:
-            retVal = std::max(dim_t(0), std::min(i-lb, len-1));
-            break;
-        default:
-            retVal = 0;
+            retVal = std::max(dim_t(0), std::min(i - lb, len - 1));
             break;
+        default: retVal = 0; break;
     }
     return retVal;
 }
-}
+}  // namespace
 
 template<typename T>
-void padBorders(Param<T> out, CParam<T> in,
-                const dim4 lBoundPadSize,
-                const dim4 uBoundPadSize,
-                const af::borderType btype)
-{
+void padBorders(Param<T> out, CParam<T> in, const dim4 lBoundPadSize,
+                const dim4 uBoundPadSize, const af::borderType btype) {
     const dim4& oDims = out.dims();
     const dim4& oStrs = out.strides();
     const dim4& iDims = in.dims();
     const dim4& iStrs = in.strides();
 
-    T const * const src = in.get();
-    T * dst             = out.get();
+    T const* const src = in.get();
+    T* dst             = out.get();
 
     const dim4 validRegEnds(
-            oDims[0] - uBoundPadSize[0],
-            oDims[1] - uBoundPadSize[1],
-            oDims[2] - uBoundPadSize[2],
-            oDims[3] - uBoundPadSize[3]);
-    const bool isInputLinear = iStrs[0]==1;
+        oDims[0] - uBoundPadSize[0], oDims[1] - uBoundPadSize[1],
+        oDims[2] - uBoundPadSize[2], oDims[3] - uBoundPadSize[3]);
+    const bool isInputLinear = iStrs[0] == 1;
 
     /*
      * VALID REGION COPYING DOES
      * NOT NEED ANY BOUND CHECKS
      * */
-    for (dim_t l=lBoundPadSize[3]; l<validRegEnds[3]; ++l) {
-        dim_t oLOff   = oStrs[3] * l;
-        dim_t iLOff   = iStrs[3] * (l - lBoundPadSize[3]);
+    for (dim_t l = lBoundPadSize[3]; l < validRegEnds[3]; ++l) {
+        dim_t oLOff = oStrs[3] * l;
+        dim_t iLOff = iStrs[3] * (l - lBoundPadSize[3]);
 
-        for (dim_t k=lBoundPadSize[2]; k<validRegEnds[2]; ++k) {
-            dim_t oKOff   = oStrs[2] * k;
-            dim_t iKOff   = iStrs[2] * (k - lBoundPadSize[2]);
+        for (dim_t k = lBoundPadSize[2]; k < validRegEnds[2]; ++k) {
+            dim_t oKOff = oStrs[2] * k;
+            dim_t iKOff = iStrs[2] * (k - lBoundPadSize[2]);
 
-            for (dim_t j=lBoundPadSize[1]; j<validRegEnds[1]; ++j) {
-                dim_t oJOff   = oStrs[1] * j;
-                dim_t iJOff   = iStrs[1] * (j - lBoundPadSize[1]);
+            for (dim_t j = lBoundPadSize[1]; j < validRegEnds[1]; ++j) {
+                dim_t oJOff = oStrs[1] * j;
+                dim_t iJOff = iStrs[1] * (j - lBoundPadSize[1]);
 
                 if (isInputLinear) {
-                    T const * const sptr = src + iLOff + iKOff + iJOff;
-                    T * dptr = dst + oLOff + oKOff + oJOff + lBoundPadSize[0];
+                    T const* const sptr = src + iLOff + iKOff + iJOff;
+                    T* dptr = dst + oLOff + oKOff + oJOff + lBoundPadSize[0];
 
-                    std::copy(sptr, sptr+iDims[0], dptr);
+                    std::copy(sptr, sptr + iDims[0], dptr);
                 } else {
-                    for (dim_t i=lBoundPadSize[0]; i<validRegEnds[0]; ++i) {
-                        dim_t oIOff   = oStrs[0] * i;
-                        dim_t iIOff   = iStrs[0] * (i - lBoundPadSize[0]);
+                    for (dim_t i = lBoundPadSize[0]; i < validRegEnds[0]; ++i) {
+                        dim_t oIOff = oStrs[0] * i;
+                        dim_t iIOff = iStrs[0] * (i - lBoundPadSize[0]);
 
-                        dst[oLOff+oKOff+oJOff+oIOff] =
+                        dst[oLOff + oKOff + oJOff + oIOff] =
                             src[iLOff + iKOff + iJOff + iIOff];
                     }
                 }
-            } // second dimension loop
-        }// third dimension loop
-    } // fourth dimension loop
+            }  // second dimension loop
+        }      // third dimension loop
+    }          // fourth dimension loop
 
     // If we have to do zero padding,
     // just return as the output is filled with
     // zeros during allocation
-    if (btype == AF_PAD_ZERO)
-        return;
+    if (btype == AF_PAD_ZERO) return;
 
     /*
      * PADDED REGIONS NEED BOUND
@@ -104,36 +94,36 @@ void padBorders(Param<T> out, CParam<T> in,
      * LOOPS SHALL ONLY PROCESS
      * PADDED REGIONS AND SKIP REST
      * */
-    for (dim_t l=0; l<oDims[3]; ++l) {
-        bool skipL  = (l>=lBoundPadSize[3] && l<validRegEnds[3]);
+    for (dim_t l = 0; l < oDims[3]; ++l) {
+        bool skipL  = (l >= lBoundPadSize[3] && l < validRegEnds[3]);
         dim_t oLOff = oStrs[3] * l;
-        dim_t iLOff = iStrs[3] *
-            idxByndEdge(l, lBoundPadSize[3], iDims[3], btype);
-        for (dim_t k=0; k<oDims[2]; ++k) {
-            bool skipK  = (k>=lBoundPadSize[2] && k<validRegEnds[2]);
+        dim_t iLOff =
+            iStrs[3] * idxByndEdge(l, lBoundPadSize[3], iDims[3], btype);
+        for (dim_t k = 0; k < oDims[2]; ++k) {
+            bool skipK  = (k >= lBoundPadSize[2] && k < validRegEnds[2]);
             dim_t oKOff = oStrs[2] * k;
-            dim_t iKOff = iStrs[2] *
-                idxByndEdge(k, lBoundPadSize[2], iDims[2], btype);
-            for (dim_t j=0; j<oDims[1]; ++j) {
-                bool skipJ  = (j>=lBoundPadSize[1] && j<validRegEnds[1]);
+            dim_t iKOff =
+                iStrs[2] * idxByndEdge(k, lBoundPadSize[2], iDims[2], btype);
+            for (dim_t j = 0; j < oDims[1]; ++j) {
+                bool skipJ  = (j >= lBoundPadSize[1] && j < validRegEnds[1]);
                 dim_t oJOff = oStrs[1] * j;
                 dim_t iJOff = iStrs[1] *
-                    idxByndEdge(j, lBoundPadSize[1], iDims[1], btype);
-                for (dim_t i=0; i<oDims[0]; ++i) {
-                    bool skipI = (i>=lBoundPadSize[0] && i<validRegEnds[0]);
-                    if (skipI && skipJ && skipK && skipL)
-                        continue;
+                              idxByndEdge(j, lBoundPadSize[1], iDims[1], btype);
+                for (dim_t i = 0; i < oDims[0]; ++i) {
+                    bool skipI = (i >= lBoundPadSize[0] && i < validRegEnds[0]);
+                    if (skipI && skipJ && skipK && skipL) continue;
 
                     dim_t oIOff = oStrs[0] * i;
-                    dim_t iIOff = iStrs[0] *
-                        idxByndEdge(i, lBoundPadSize[0], iDims[0], btype);
+                    dim_t iIOff = iStrs[0] * idxByndEdge(i, lBoundPadSize[0],
+                                                         iDims[0], btype);
 
-                    dst[oLOff+oKOff+oJOff+oIOff] = src[iLOff+iKOff+iJOff+iIOff];
+                    dst[oLOff + oKOff + oJOff + oIOff] =
+                        src[iLOff + iKOff + iJOff + iIOff];
 
-                } // first dimension loop
-            } // second dimension loop
-        } // third dimension loop
-    } // fourth dimension loop
-}
-}
+                }  // first dimension loop
+            }      // second dimension loop
+        }          // third dimension loop
+    }              // fourth dimension loop
 }
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 0e08e83e3a..256f0ed548 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -11,288 +11,276 @@
 
 #include <Param.hpp>
 #include <err_cpu.hpp>
+#include <kernel/random_engine_mersenne.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
-#include <kernel/random_engine_mersenne.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
-    //Utils
-    static const double PI_VAL = 3.1415926535897932384626433832795028841971693993751058209749445923078164;
-
-    //Conversion to floats adapted from Random123
-    #define UINTMAX 0xffffffff
-    #define FLT_FACTOR ((1.0f)/(UINTMAX + (1.0f)))
-    #define HALF_FLT_FACTOR ((0.5f)*FLT_FACTOR)
-
-    #define UINTLMAX 0xffffffffffffffff
-    #define DBL_FACTOR ((1.0)/(UINTLMAX + (1.0)))
-    #define HALF_DBL_FACTOR ((0.5)*DBL_FACTOR)
-
-    template <typename T>
-    T transform(uint *val, int index)
-    {
-        T *oval = (T*)val;
-        return oval[index];
-    }
+namespace cpu {
+namespace kernel {
+// Utils
+static const double PI_VAL =
+    3.1415926535897932384626433832795028841971693993751058209749445923078164;
+
+// Conversion to floats adapted from Random123
+#define UINTMAX 0xffffffff
+#define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
+#define HALF_FLT_FACTOR ((0.5f) * FLT_FACTOR)
+
+#define UINTLMAX 0xffffffffffffffff
+#define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
+#define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
+
+template<typename T>
+T transform(uint *val, int index) {
+    T *oval = (T *)val;
+    return oval[index];
+}
 
-    template <> char transform<char>(uint *val, int index)
-    {
-        char v = val[index>>2]>>(8<<(index & 3));
-        v = (v&0x1) ? 1 : 0;
-        return v;
-    }
+template<>
+char transform<char>(uint *val, int index) {
+    char v = val[index >> 2] >> (8 << (index & 3));
+    v      = (v & 0x1) ? 1 : 0;
+    return v;
+}
 
-    template <> uchar transform<uchar>(uint *val, int index)
-    {
-        uchar v = val[index>>2]>>(8<<(index & 3));
-        return v;
-    }
+template<>
+uchar transform<uchar>(uint *val, int index) {
+    uchar v = val[index >> 2] >> (8 << (index & 3));
+    return v;
+}
 
-    template <> ushort transform<ushort>(uint *val, int index)
-    {
-        ushort v = val[index>>1]>>(16<<(index & 1));
-        return v;
-    }
+template<>
+ushort transform<ushort>(uint *val, int index) {
+    ushort v = val[index >> 1] >> (16 << (index & 1));
+    return v;
+}
 
-    template <> short transform<short>(uint *val, int index)
-    {
-        return transform<ushort>(val, index);
-    }
+template<>
+short transform<short>(uint *val, int index) {
+    return transform<ushort>(val, index);
+}
 
-    template <> uint transform<uint>(uint *val, int index)
-    {
-        return val[index];
-    }
+template<>
+uint transform<uint>(uint *val, int index) {
+    return val[index];
+}
 
-    template <> int transform<int>(uint *val, int index)
-    {
-        return transform<uint>(val, index);
-    }
+template<>
+int transform<int>(uint *val, int index) {
+    return transform<uint>(val, index);
+}
 
-    template <> uintl transform<uintl>(uint *val, int index)
-    {
-        uintl v = (((uintl)val[index<<1])<<32) | ((uintl)val[(index<<1)+1]);
-        return v;
-    }
+template<>
+uintl transform<uintl>(uint *val, int index) {
+    uintl v = (((uintl)val[index << 1]) << 32) | ((uintl)val[(index << 1) + 1]);
+    return v;
+}
 
-    template <> intl transform<intl>(uint *val, int index)
-    {
-        return transform<uintl>(val, index);
-    }
+template<>
+intl transform<intl>(uint *val, int index) {
+    return transform<uintl>(val, index);
+}
 
-    //Generates rationals in [0, 1)
-    template <> float transform<float>(uint *val, int index)
-    {
-        return 1.f - (val[index]*FLT_FACTOR + HALF_FLT_FACTOR);
-    }
+// Generates rationals in [0, 1)
+template<>
+float transform<float>(uint *val, int index) {
+    return 1.f - (val[index] * FLT_FACTOR + HALF_FLT_FACTOR);
+}
 
-    //Generates rationals in [0, 1)
-    template <> double transform<double>(uint *val, int index)
-    {
-        uintl v = transform<uintl>(val, index);
-        return 1.0 - (v*DBL_FACTOR + HALF_DBL_FACTOR);
-    }
+// Generates rationals in [0, 1)
+template<>
+double transform<double>(uint *val, int index) {
+    uintl v = transform<uintl>(val, index);
+    return 1.0 - (v * DBL_FACTOR + HALF_DBL_FACTOR);
+}
 
-    template <typename T>
-    void philoxUniform(T* out, size_t elements, const uintl seed, uintl counter)
-    {
-        uint hi = seed>>32;
-        uint lo = seed;
-        uint hic = counter>>32;
-        uint loc = counter;
-        uint key[2] = {lo, hi};
-        uint ctr[4] = {loc, hic, 0, 0};
-
-        int reset = (4*sizeof(uint))/sizeof(T);
-        for (int i = 0; i < (int)elements; i += reset) {
-            philox(key, ctr);
-            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
-            for (int j = 0; j < lim; ++j) {
-                out[i + j] = transform<T>(ctr, j);
-            }
-        }
+template<typename T>
+void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
+    uint hi     = seed >> 32;
+    uint lo     = seed;
+    uint hic    = counter >> 32;
+    uint loc    = counter;
+    uint key[2] = {lo, hi};
+    uint ctr[4] = {loc, hic, 0, 0};
+
+    int reset = (4 * sizeof(uint)) / sizeof(T);
+    for (int i = 0; i < (int)elements; i += reset) {
+        philox(key, ctr);
+        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
+        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(ctr, j); }
     }
+}
 
-    template <typename T>
-    void threefryUniform(T* out, size_t elements, const uintl seed, uintl counter)
-    {
-        uint hi = seed>>32;
-        uint lo = seed;
-        uint hic = counter>>32;
-        uint loc = counter;
-        uint key[2] = {lo, hi};
-        uint ctr[2] = {loc, hic};
-        uint val[2];
-
-        int reset = (2*sizeof(uint))/sizeof(T);
-        for (int i = 0; i < (int)elements; i += reset) {
-            threefry(key, ctr, val);
-            ++ctr[0];
-            ctr[1] += (ctr[0] == 0);
-            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
-            for (int j = 0; j < lim; ++j) {
-                out[i + j] = transform<T>(val, j);
-            }
-        }
+template<typename T>
+void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
+    uint hi     = seed >> 32;
+    uint lo     = seed;
+    uint hic    = counter >> 32;
+    uint loc    = counter;
+    uint key[2] = {lo, hi};
+    uint ctr[2] = {loc, hic};
+    uint val[2];
+
+    int reset = (2 * sizeof(uint)) / sizeof(T);
+    for (int i = 0; i < (int)elements; i += reset) {
+        threefry(key, ctr, val);
+        ++ctr[0];
+        ctr[1] += (ctr[0] == 0);
+        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
+        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(val, j); }
     }
+}
 
-    template <typename T>
-    void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
-    {
-        /*
-         * The log of a real value x where 0 < x < 1 is negative.
-         */
-        T r = sqrt((T)(-2.0) * log((T)(1.0) - r1));
-        T theta = 2 * (T)PI_VAL * ((T)(1.0) - r2);
-        *out1 = r*sin(theta);
-        *out2 = r*cos(theta);
-    }
+template<typename T>
+void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
+    T r     = sqrt((T)(-2.0) * log((T)(1.0) - r1));
+    T theta = 2 * (T)PI_VAL * ((T)(1.0) - r2);
+    *out1   = r * sin(theta);
+    *out2   = r * cos(theta);
+}
 
-    void boxMullerTransform(uint val[4], double *temp)
-    {
-        boxMullerTransform(&temp[0], &temp[1], transform<double>(val, 0), transform<double>(val,1));
-    }
+void boxMullerTransform(uint val[4], double *temp) {
+    boxMullerTransform(&temp[0], &temp[1], transform<double>(val, 0),
+                       transform<double>(val, 1));
+}
 
-    void boxMullerTransform(uint val[4], float *temp)
-    {
-        boxMullerTransform(&temp[0], &temp[1], transform<float>(val, 0), transform<float>(val, 1));
-        boxMullerTransform(&temp[2], &temp[3], transform<float>(val, 2), transform<float>(val, 3));
-    }
+void boxMullerTransform(uint val[4], float *temp) {
+    boxMullerTransform(&temp[0], &temp[1], transform<float>(val, 0),
+                       transform<float>(val, 1));
+    boxMullerTransform(&temp[2], &temp[3], transform<float>(val, 2),
+                       transform<float>(val, 3));
+}
 
-    template <typename T>
-    void philoxNormal(T* out, size_t elements, const uintl seed, uintl counter)
-    {
-        uint hi = seed>>32;
-        uint lo = seed;
-        uint hic = counter>>32;
-        uint loc = counter;
-        uint key[2] = {lo, hi};
-        uint ctr[4] = {loc, hic, 0, 0};
-        T temp[(4*sizeof(uint))/sizeof(T)];
-
-        int reset = (4*sizeof(uint))/sizeof(T);
-        for (int i = 0; i < (int)elements; i += reset) {
-            philox(key, ctr);
-            boxMullerTransform(ctr, temp);
-            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
-            for (int j = 0; j < lim; ++j) {
-                out[i + j] = temp[j];
-            }
-        }
+template<typename T>
+void philoxNormal(T *out, size_t elements, const uintl seed, uintl counter) {
+    uint hi     = seed >> 32;
+    uint lo     = seed;
+    uint hic    = counter >> 32;
+    uint loc    = counter;
+    uint key[2] = {lo, hi};
+    uint ctr[4] = {loc, hic, 0, 0};
+    T temp[(4 * sizeof(uint)) / sizeof(T)];
+
+    int reset = (4 * sizeof(uint)) / sizeof(T);
+    for (int i = 0; i < (int)elements; i += reset) {
+        philox(key, ctr);
+        boxMullerTransform(ctr, temp);
+        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
+        for (int j = 0; j < lim; ++j) { out[i + j] = temp[j]; }
     }
+}
 
-    template <typename T>
-    void threefryNormal(T* out, size_t elements, const uintl seed, uintl counter)
-    {
-        uint hi = seed>>32;
-        uint lo = seed;
-        uint hic = counter>>32;
-        uint loc = counter;
-        uint key[2] = {lo, hi};
-        uint ctr[2] = {loc, hic};
-        uint val[4];
-        T temp[(4*sizeof(uint))/sizeof(T)];
-
-        int reset = (4*sizeof(uint))/sizeof(T);
-        for (int i = 0; i < (int)elements; i += reset) {
-            threefry(key, ctr, val);
-            ++ctr[0];
-            ctr[1] += (ctr[0] == 0);
-            threefry(key, ctr, val+2);
-            ++ctr[0];
-            ctr[1] += (ctr[0] == 0);
-            boxMullerTransform(val, temp);
-            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
-            for (int j = 0; j < lim; ++j) {
-                out[i + j] = temp[j];
-            }
-        }
+template<typename T>
+void threefryNormal(T *out, size_t elements, const uintl seed, uintl counter) {
+    uint hi     = seed >> 32;
+    uint lo     = seed;
+    uint hic    = counter >> 32;
+    uint loc    = counter;
+    uint key[2] = {lo, hi};
+    uint ctr[2] = {loc, hic};
+    uint val[4];
+    T temp[(4 * sizeof(uint)) / sizeof(T)];
+
+    int reset = (4 * sizeof(uint)) / sizeof(T);
+    for (int i = 0; i < (int)elements; i += reset) {
+        threefry(key, ctr, val);
+        ++ctr[0];
+        ctr[1] += (ctr[0] == 0);
+        threefry(key, ctr, val + 2);
+        ++ctr[0];
+        ctr[1] += (ctr[0] == 0);
+        boxMullerTransform(val, temp);
+        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
+        for (int j = 0; j < lim; ++j) { out[i + j] = temp[j]; }
     }
+}
 
-    template <typename T>
-    void uniformDistributionMT(T* out, size_t elements,
-            uint * const state,
-            const uint * const pos,
-            const uint * const sh1,
-            const uint * const sh2,
-            uint mask,
-            const uint * const recursion_table,
-            const uint * const temper_table)
-    {
-        uint l_state[STATE_SIZE];
-        uint o[4];
-        uint lpos = pos[0];
-        uint lsh1 = sh1[0];
-        uint lsh2 = sh2[0];
-
-        state_read(l_state, state);
-
-        int reset = (4*sizeof(uint))/sizeof(T);
-        for (int i = 0; i < (int)elements; i += reset) {
-            mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table, temper_table);
-            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
-            for (int j = 0; j < lim; ++j) {
-                out[i + j] = transform<T>(o, j);
-            }
-        }
-
-        state_write(state, l_state);
+template<typename T>
+void uniformDistributionMT(T *out, size_t elements, uint *const state,
+                           const uint *const pos, const uint *const sh1,
+                           const uint *const sh2, uint mask,
+                           const uint *const recursion_table,
+                           const uint *const temper_table) {
+    uint l_state[STATE_SIZE];
+    uint o[4];
+    uint lpos = pos[0];
+    uint lsh1 = sh1[0];
+    uint lsh2 = sh2[0];
+
+    state_read(l_state, state);
+
+    int reset = (4 * sizeof(uint)) / sizeof(T);
+    for (int i = 0; i < (int)elements; i += reset) {
+        mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table,
+                 temper_table);
+        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
+        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(o, j); }
     }
 
-    template <typename T>
-    void normalDistributionMT(T* out, size_t elements,
-            uint * const state,
-            const uint * const pos,
-            const uint * const sh1,
-            const uint * const sh2,
-            uint mask,
-            const uint * const recursion_table,
-            const uint * const temper_table)
-    {
-        T temp[(4*sizeof(uint))/sizeof(T)];
-        uint l_state[STATE_SIZE];
-        uint o[4];
-        uint lpos = pos[0];
-        uint lsh1 = sh1[0];
-        uint lsh2 = sh2[0];
-
-        state_read(l_state, state);
-
-        int reset = (4*sizeof(uint))/sizeof(T);
-        for (int i = 0; i < (int)elements; i += reset) {
-            mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table, temper_table);
-            boxMullerTransform(o, temp);
-            int lim = (reset < (int)(elements - i))? reset : (int)(elements - i);
-            for (int j = 0; j < lim; ++j) {
-                out[i + j] = temp[j];
-            }
-        }
-
-        state_write(state, l_state);
-    }
+    state_write(state, l_state);
+}
 
-    template <typename T>
-    void uniformDistributionCBRNG(T* out, size_t elements, af_random_engine_type type, const uintl seed, uintl counter)
-    {
-        switch(type) {
-            case AF_RANDOM_ENGINE_PHILOX_4X32_10   :   philoxUniform(out, elements, seed, counter); break;
-            case AF_RANDOM_ENGINE_THREEFRY_2X32_16 : threefryUniform(out, elements, seed, counter); break;
-            default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
+template<typename T>
+void normalDistributionMT(T *out, size_t elements, uint *const state,
+                          const uint *const pos, const uint *const sh1,
+                          const uint *const sh2, uint mask,
+                          const uint *const recursion_table,
+                          const uint *const temper_table) {
+    T temp[(4 * sizeof(uint)) / sizeof(T)];
+    uint l_state[STATE_SIZE];
+    uint o[4];
+    uint lpos = pos[0];
+    uint lsh1 = sh1[0];
+    uint lsh2 = sh2[0];
+
+    state_read(l_state, state);
+
+    int reset = (4 * sizeof(uint)) / sizeof(T);
+    for (int i = 0; i < (int)elements; i += reset) {
+        mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table,
+                 temper_table);
+        boxMullerTransform(o, temp);
+        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
+        for (int j = 0; j < lim; ++j) { out[i + j] = temp[j]; }
     }
 
-    template <typename T>
-    void normalDistributionCBRNG(T* out, size_t elements, af_random_engine_type type, const uintl seed, uintl counter)
-    {
-        switch(type) {
-            case AF_RANDOM_ENGINE_PHILOX_4X32_10   :   philoxNormal(out, elements, seed, counter); break;
-            case AF_RANDOM_ENGINE_THREEFRY_2X32_16 : threefryNormal(out, elements, seed, counter); break;
-            default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
-    }
+    state_write(state, l_state);
+}
 
+template<typename T>
+void uniformDistributionCBRNG(T *out, size_t elements,
+                              af_random_engine_type type, const uintl seed,
+                              uintl counter) {
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            philoxUniform(out, elements, seed, counter);
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            threefryUniform(out, elements, seed, counter);
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 }
+
+template<typename T>
+void normalDistributionCBRNG(T *out, size_t elements,
+                             af_random_engine_type type, const uintl seed,
+                             uintl counter) {
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            philoxNormal(out, elements, seed, counter);
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            threefryNormal(out, elements, seed, counter);
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 }
+
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/random_engine_mersenne.hpp b/src/backend/cpu/kernel/random_engine_mersenne.hpp
index d4074a78e2..ada96f231e 100644
--- a/src/backend/cpu/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cpu/kernel/random_engine_mersenne.hpp
@@ -44,89 +44,76 @@
 
 #pragma once
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-    static const int N = 351;
-    static const int STATE_SIZE = 256*3;
+static const int N          = 351;
+static const int STATE_SIZE = 256 * 3;
 
-    uint recursion(const uint * const recursion_table, const uint mask,
-            const uint sh1, const uint sh2, const uint x1, const uint x2, uint y)
-    {
-        uint x = (x1 & mask) ^ x2;
-        x ^= x << sh1;
-        y = x ^ (y >> sh2);
-        uint mat = recursion_table[y & 0x0f];
-        return y ^ mat;
-    }
+uint recursion(const uint* const recursion_table, const uint mask,
+               const uint sh1, const uint sh2, const uint x1, const uint x2,
+               uint y) {
+    uint x = (x1 & mask) ^ x2;
+    x ^= x << sh1;
+    y        = x ^ (y >> sh2);
+    uint mat = recursion_table[y & 0x0f];
+    return y ^ mat;
+}
 
-    uint temper(const uint * const temper_table, const uint v, uint t)
-    {
-         t ^= t >> 16;
-         t ^= t >> 8;
-         uint mat = temper_table[t & 0x0f];
-         return v ^ mat;
-    }
+uint temper(const uint* const temper_table, const uint v, uint t) {
+    t ^= t >> 16;
+    t ^= t >> 8;
+    uint mat = temper_table[t & 0x0f];
+    return v ^ mat;
+}
 
-    void mersenne(uint * const out,
-            uint * const state,
-            int i,
-            uint pos,
-            uint sh1,
-            uint sh2,
-            uint mask,
-            const uint * const recursion_table,
-            const uint * const temper_table)
-    {
-        int index    = i % STATE_SIZE;
-        int offsetX1 = (STATE_SIZE - N + index          ) % STATE_SIZE;
-        int offsetX2 = (STATE_SIZE - N + index + 1      ) % STATE_SIZE;
-        int offsetY  = (STATE_SIZE - N + index + pos    ) % STATE_SIZE;
-        int offsetT  = (STATE_SIZE - N + index + pos - 1) % STATE_SIZE;
-        for (int i = 0; i < 4; ++i) {
-            state[index] = recursion(recursion_table, mask, sh1, sh2,
-                    state[offsetX1], state[offsetX2], state[offsetY]);
-            out[i] = temper(temper_table, state[index], state[offsetT]);
-            offsetX1 = (offsetX1 + 1) % STATE_SIZE;
-            offsetX2 = (offsetX2 + 1) % STATE_SIZE;
-            offsetY  = (offsetY  + 1) % STATE_SIZE;
-            offsetT  = (offsetT  + 1) % STATE_SIZE;
-            index    = (index    + 1) % STATE_SIZE;
-        }
+void mersenne(uint* const out, uint* const state, int i, uint pos, uint sh1,
+              uint sh2, uint mask, const uint* const recursion_table,
+              const uint* const temper_table) {
+    int index    = i % STATE_SIZE;
+    int offsetX1 = (STATE_SIZE - N + index) % STATE_SIZE;
+    int offsetX2 = (STATE_SIZE - N + index + 1) % STATE_SIZE;
+    int offsetY  = (STATE_SIZE - N + index + pos) % STATE_SIZE;
+    int offsetT  = (STATE_SIZE - N + index + pos - 1) % STATE_SIZE;
+    for (int i = 0; i < 4; ++i) {
+        state[index] =
+            recursion(recursion_table, mask, sh1, sh2, state[offsetX1],
+                      state[offsetX2], state[offsetY]);
+        out[i]   = temper(temper_table, state[index], state[offsetT]);
+        offsetX1 = (offsetX1 + 1) % STATE_SIZE;
+        offsetX2 = (offsetX2 + 1) % STATE_SIZE;
+        offsetY  = (offsetY + 1) % STATE_SIZE;
+        offsetT  = (offsetT + 1) % STATE_SIZE;
+        index    = (index + 1) % STATE_SIZE;
     }
+}
 
-    void state_read(uint * const l_state, const uint * const state)
-    {
-        for (int i = 0; i < N; ++i) {
-            l_state[STATE_SIZE - N + i] = state[i];
-        }
-    }
+void state_read(uint* const l_state, const uint* const state) {
+    for (int i = 0; i < N; ++i) { l_state[STATE_SIZE - N + i] = state[i]; }
+}
 
-    void state_write(uint * const state, const uint * const l_state)
-    {
-        for (int i = 0; i < N; ++i) {
-            state[i] = l_state[STATE_SIZE - N + i];
-        }
-    }
+void state_write(uint* const state, const uint* const l_state) {
+    for (int i = 0; i < N; ++i) { state[i] = l_state[STATE_SIZE - N + i]; }
+}
 
-    void initMersenneState(uint * const state, const uint * const tbl, const uintl seed)
-    {
-        uint hidden_seed = tbl[4] ^ (tbl[8] << 16);
-        uint tmp = hidden_seed;
-        tmp += tmp >> 16;
-        tmp += tmp >> 8;
-        tmp &= 0xff;
-        tmp |= tmp << 8;
-        tmp |= tmp << 16;
-        state[0] = seed;
-        state[1] = hidden_seed ^ ((uint)(1812433253) * (state[0] ^ (state[0] >> 30)) + 1);
-        for (int i = 2; i < N; ++i) {
-            state[i] = tmp;
-            state[i] ^= (uint)(1812433253) * (state[i-1] ^ (state[i-1] >> 30)) + i;
-        }
+void initMersenneState(uint* const state, const uint* const tbl,
+                       const uintl seed) {
+    uint hidden_seed = tbl[4] ^ (tbl[8] << 16);
+    uint tmp         = hidden_seed;
+    tmp += tmp >> 16;
+    tmp += tmp >> 8;
+    tmp &= 0xff;
+    tmp |= tmp << 8;
+    tmp |= tmp << 16;
+    state[0] = seed;
+    state[1] =
+        hidden_seed ^ ((uint)(1812433253) * (state[0] ^ (state[0] >> 30)) + 1);
+    for (int i = 2; i < N; ++i) {
+        state[i] = tmp;
+        state[i] ^=
+            (uint)(1812433253) * (state[i - 1] ^ (state[i - 1] >> 30)) + i;
     }
-
-}
 }
+
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index 30e945d720..7b2efd45f9 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -47,56 +47,59 @@
 
 #pragma once
 
-namespace cpu
-{
-namespace kernel
-{
-    //Utils
-    //Source of these constants :
-    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
+namespace cpu {
+namespace kernel {
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
 
-    static const uint m4x32_0 = 0xD2511F53;
-    static const uint m4x32_1 = 0xCD9E8D57;
-    static const uint w32_0   = 0x9E3779B9;
-    static const uint w32_1   = 0xBB67AE85;
+static const uint m4x32_0 = 0xD2511F53;
+static const uint m4x32_1 = 0xCD9E8D57;
+static const uint w32_0   = 0x9E3779B9;
+static const uint w32_1   = 0xBB67AE85;
 
-    void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
-    {
-        *hi = (((uintl)a) * ((uintl)b))>>32;
-        *lo = a*b;
-    }
-
-    void philoxBump(uint * const k)
-    {
-        k[0] += w32_0;
-        k[1] += w32_1;
-    }
-
-    void philoxRound(const uint * const k, uint * const c)
-    {
-        uint hi0, lo0, hi1, lo1;
-        mulhilo(m4x32_0, c[0], &hi0, &lo0);
-        mulhilo(m4x32_1, c[2], &hi1, &lo1);
-        c[0] = hi1^c[1]^k[0];
-        c[1] = lo1;
-        c[2] = hi0^c[3]^k[1];
-        c[3] = lo0;
-    }
+void mulhilo(const uint a, const uint b, uint* const hi, uint* const lo) {
+    *hi = (((uintl)a) * ((uintl)b)) >> 32;
+    *lo = a * b;
+}
 
-    void philox(uint * const key, uint * const ctr)
-    {
-        //10 Rounds
-                           philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-        philoxBump(key);   philoxRound(key, ctr);
-    }
+void philoxBump(uint* const k) {
+    k[0] += w32_0;
+    k[1] += w32_1;
+}
 
+void philoxRound(const uint* const k, uint* const c) {
+    uint hi0, lo0, hi1, lo1;
+    mulhilo(m4x32_0, c[0], &hi0, &lo0);
+    mulhilo(m4x32_1, c[2], &hi1, &lo1);
+    c[0] = hi1 ^ c[1] ^ k[0];
+    c[1] = lo1;
+    c[2] = hi0 ^ c[3] ^ k[1];
+    c[3] = lo0;
 }
+
+void philox(uint* const key, uint* const ctr) {
+    // 10 Rounds
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
 }
+
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
index 7c95d7e6a7..8affc5bcaa 100644
--- a/src/backend/cpu/kernel/random_engine_threefry.hpp
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -46,80 +46,113 @@
 
 #pragma once
 
-namespace cpu
-{
-namespace kernel
-{
-    //Utils
-    //Source of these constants :
-    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
-
-    static const uint SKEIN_KS_PARITY = 0x1BD11BDA;
-
-    static const uint R0 = 13;
-    static const uint R1 = 15;
-    static const uint R2 = 26;
-    static const uint R3 =  6;
-    static const uint R4 = 17;
-    static const uint R5 = 29;
-    static const uint R6 = 16;
-    static const uint R7 = 24;
-
-    static inline uint rotL(uint x, uint N)
-    {
-        return (x << (N & 31)) | (x >> ((32-N) & 31));
-    }
-
-    static inline void threefry(uint k[2], uint c[2], uint X[2])
-    {
-        uint ks[3];
-
-        ks[2] = SKEIN_KS_PARITY;
-        ks[0] = k[0];
-        X[0] = c[0];
-        ks[2] ^= k[0];
-        ks[1] = k[1];
-        X[1] = c[1];
-        ks[2] ^= k[1];
-
-        X[0] += ks[0]; X[1] += ks[1];
-
-        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
-
-        /* InjectKey(r=1) */
-        X[0] += ks[1]; X[1] += ks[2];
-        X[1] += 1;     /* X[2-1] += r  */
-
-        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
-
-        /* InjectKey(r=2) */
-        X[0] += ks[2]; X[1] += ks[0];
-        X[1] += 2;
-
-        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
-
-        /* InjectKey(r=3) */
-        X[0] += ks[0]; X[1] += ks[1];
-        X[1] += 3;
-
-        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
-
-        /* InjectKey(r=4) */
-        X[0] += ks[1]; X[1] += ks[2];
-        X[1] += 4;
-    }
-
+namespace cpu {
+namespace kernel {
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
+
+static const uint SKEIN_KS_PARITY = 0x1BD11BDA;
+
+static const uint R0 = 13;
+static const uint R1 = 15;
+static const uint R2 = 26;
+static const uint R3 = 6;
+static const uint R4 = 17;
+static const uint R5 = 29;
+static const uint R6 = 16;
+static const uint R7 = 24;
+
+static inline uint rotL(uint x, uint N) {
+    return (x << (N & 31)) | (x >> ((32 - N) & 31));
 }
+
+static inline void threefry(uint k[2], uint c[2], uint X[2]) {
+    uint ks[3];
+
+    ks[2] = SKEIN_KS_PARITY;
+    ks[0] = k[0];
+    X[0]  = c[0];
+    ks[2] ^= k[0];
+    ks[1] = k[1];
+    X[1]  = c[1];
+    ks[2] ^= k[1];
+
+    X[0] += ks[0];
+    X[1] += ks[1];
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=1) */
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 1; /* X[2-1] += r  */
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=2) */
+    X[0] += ks[2];
+    X[1] += ks[0];
+    X[1] += 2;
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=3) */
+    X[0] += ks[0];
+    X[1] += ks[1];
+    X[1] += 3;
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=4) */
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 4;
 }
+
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/range.hpp b/src/backend/cpu/kernel/range.hpp
index 982cba91b3..12ae94d5b7 100644
--- a/src/backend/cpu/kernel/range.hpp
+++ b/src/backend/cpu/kernel/range.hpp
@@ -10,34 +10,31 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, int dim>
-void range(Param<T> output)
-{
+void range(Param<T> output) {
     T* out = output.get();
 
-    const dim4 dims = output.dims();
+    const dim4 dims    = output.dims();
     const dim4 strides = output.strides();
 
-    for(dim_t w = 0; w < dims[3]; w++) {
+    for (dim_t w = 0; w < dims[3]; w++) {
         dim_t offW = w * strides[3];
-        for(dim_t z = 0; z < dims[2]; z++) {
+        for (dim_t z = 0; z < dims[2]; z++) {
             dim_t offWZ = offW + z * strides[2];
-            for(dim_t y = 0; y < dims[1]; y++) {
+            for (dim_t y = 0; y < dims[1]; y++) {
                 dim_t offWZY = offWZ + y * strides[1];
-                for(dim_t x = 0; x < dims[0]; x++) {
+                for (dim_t x = 0; x < dims[0]; x++) {
                     dim_t id = offWZY + x;
-                    if(dim == 0) {
+                    if (dim == 0) {
                         out[id] = x;
-                    } else if(dim == 1) {
+                    } else if (dim == 1) {
                         out[id] = y;
-                    } else if(dim == 2) {
+                    } else if (dim == 2) {
                         out[id] = z;
-                    } else if(dim == 3) {
+                    } else if (dim == 3) {
                         out[id] = w;
                     }
                 }
@@ -46,6 +43,5 @@ void range(Param<T> output)
     }
 }
 
-}
-}
-
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index fff50c747b..c036152216 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -9,19 +9,16 @@
 
 #pragma once
 #include <Param.hpp>
+#include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<af_op_t op, typename Ti, typename To, int D>
-struct reduce_dim
-{
-    void operator()(Param<To> out, const dim_t outOffset,
-                    CParam<Ti> in, const dim_t inOffset,
-                    const int dim, bool change_nan, double nanval)
-    {
+struct reduce_dim {
+    void operator()(Param<To> out, const dim_t outOffset, CParam<Ti> in,
+                    const dim_t inOffset, const int dim, bool change_nan,
+                    double nanval) {
         static const int D1 = D - 1;
         reduce_dim<op, Ti, To, D1> reduce_dim_next;
 
@@ -30,29 +27,26 @@ struct reduce_dim
         const af::dim4 odims    = out.dims();
 
         for (dim_t i = 0; i < odims[D1]; i++) {
-            reduce_dim_next(out, outOffset + i * ostrides[D1],
-                            in, inOffset + i * istrides[D1],
-                            dim, change_nan, nanval);
+            reduce_dim_next(out, outOffset + i * ostrides[D1], in,
+                            inOffset + i * istrides[D1], dim, change_nan,
+                            nanval);
         }
     }
 };
 
 template<af_op_t op, typename Ti, typename To>
-struct reduce_dim<op, Ti, To, 0>
-{
-
+struct reduce_dim<op, Ti, To, 0> {
     Transform<Ti, To, op> transform;
     Binary<To, op> reduce;
-    void operator()(Param<To> out, const dim_t outOffset,
-                    CParam<Ti> in, const dim_t inOffset,
-                    const int dim, bool change_nan, double nanval)
-    {
+    void operator()(Param<To> out, const dim_t outOffset, CParam<Ti> in,
+                    const dim_t inOffset, const int dim, bool change_nan,
+                    double nanval) {
         const af::dim4 istrides = in.strides();
         const af::dim4 idims    = in.dims();
 
-        To * const outPtr = out.get() + outOffset;
-        Ti const * const inPtr = in.get() + inOffset;
-        dim_t stride = istrides[dim];
+        To* const outPtr      = out.get() + outOffset;
+        Ti const* const inPtr = in.get() + inOffset;
+        dim_t stride          = istrides[dim];
 
         To out_val = Binary<To, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
@@ -65,5 +59,5 @@ struct reduce_dim<op, Ti, To, 0>
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/regions.hpp b/src/backend/cpu/kernel/regions.hpp
index 9d10e333ec..40aa507b74 100644
--- a/src/backend/cpu/kernel/regions.hpp
+++ b/src/backend/cpu/kernel/regions.hpp
@@ -10,81 +10,55 @@
 #pragma once
 #include <Param.hpp>
 #include <memory.hpp>
+#include <map>
+#include <set>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-class LabelNode
-{
-private:
+class LabelNode {
+   private:
     T label;
     T minLabel;
     unsigned rank;
     LabelNode* parent;
 
-public:
-    LabelNode() : label(0), minLabel(0), rank(0), parent(this) { }
-    LabelNode(T label) : label(label), minLabel(label), rank(0), parent(this) { }
+   public:
+    LabelNode() : label(0), minLabel(0), rank(0), parent(this) {}
+    LabelNode(T label) : label(label), minLabel(label), rank(0), parent(this) {}
 
-    T getLabel()
-    {
-        return label;
-    }
+    T getLabel() { return label; }
 
-    T getMinLabel()
-    {
-        return minLabel;
-    }
+    T getMinLabel() { return minLabel; }
 
-    LabelNode* getParent()
-    {
-        return parent;
-    }
+    LabelNode* getParent() { return parent; }
 
-    unsigned getRank()
-    {
-        return rank;
-    }
+    unsigned getRank() { return rank; }
 
-    void setMinLabel(T l)
-    {
-        minLabel = l;
-    }
+    void setMinLabel(T l) { minLabel = l; }
 
-    void setParent(LabelNode* p)
-    {
-        parent = p;
-    }
+    void setParent(LabelNode* p) { parent = p; }
 
-    void setRank(unsigned r)
-    {
-        rank = r;
-    }
+    void setRank(unsigned r) { rank = r; }
 };
 
 template<typename T>
-static LabelNode<T>* find(LabelNode<T>* x)
-{
-    if (x->getParent() != x)
-        x->setParent(find(x->getParent()));
+static LabelNode<T>* find(LabelNode<T>* x) {
+    if (x->getParent() != x) x->setParent(find(x->getParent()));
     return x->getParent();
 }
 
 template<typename T>
-static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
-{
+static void setUnion(LabelNode<T>* x, LabelNode<T>* y) {
     LabelNode<T>* xRoot = find(x);
     LabelNode<T>* yRoot = find(y);
-    if (xRoot == yRoot)
-        return;
+    if (xRoot == yRoot) return;
 
     T xMinLabel = xRoot->getMinLabel();
     T yMinLabel = yRoot->getMinLabel();
-    xRoot->setMinLabel(min(xMinLabel, yMinLabel));
-    yRoot->setMinLabel(min(xMinLabel, yMinLabel));
+    xRoot->setMinLabel(std::min(xMinLabel, yMinLabel));
+    yRoot->setMinLabel(std::min(xMinLabel, yMinLabel));
 
     if (xRoot->getRank() < yRoot->getRank())
         xRoot->setParent(yRoot);
@@ -97,15 +71,14 @@ static void setUnion(LabelNode<T>* x, LabelNode<T>* y)
 }
 
 template<typename T>
-void regions(Param<T> out, CParam<char> in, af_connectivity connectivity)
-{
+void regions(Param<T> out, CParam<char> in, af_connectivity connectivity) {
     const af::dim4 inDims = in.dims();
-    const char *inPtr  = in.get();
-    T *outPtr = out.get();
+    const char* inPtr     = in.get();
+    T* outPtr             = out.get();
 
     // Map labels
-    typedef typename std::unique_ptr< LabelNode<T> > UnqLabelPtr;
-    typedef typename std::map<T, UnqLabelPtr > LabelMap;
+    typedef typename std::unique_ptr<LabelNode<T>> UnqLabelPtr;
+    typedef typename std::map<T, UnqLabelPtr> LabelMap;
     typedef typename LabelMap::iterator LabelMapIterator;
 
     LabelMap lmap;
@@ -120,32 +93,34 @@ void regions(Param<T> out, CParam<char> in, af_connectivity connectivity)
                 std::vector<T> l;
 
                 // Test neighbors
-                if (i > 0 && outPtr[j * (int)inDims[0] + i-1] > 0)
-                    l.push_back(outPtr[j * inDims[0] + i-1]);
-                if (j > 0 && outPtr[(j-1) * (int)inDims[0] + i] > 0)
-                    l.push_back(outPtr[(j-1) * inDims[0] + i]);
-                if (connectivity == AF_CONNECTIVITY_8 && i > 0 &&
-                        j > 0 && outPtr[(j-1) * inDims[0] + i-1] > 0)
-                    l.push_back(outPtr[(j-1) * inDims[0] + i-1]);
+                if (i > 0 && outPtr[j * (int)inDims[0] + i - 1] > 0)
+                    l.push_back(outPtr[j * inDims[0] + i - 1]);
+                if (j > 0 && outPtr[(j - 1) * (int)inDims[0] + i] > 0)
+                    l.push_back(outPtr[(j - 1) * inDims[0] + i]);
+                if (connectivity == AF_CONNECTIVITY_8 && i > 0 && j > 0 &&
+                    outPtr[(j - 1) * inDims[0] + i - 1] > 0)
+                    l.push_back(outPtr[(j - 1) * inDims[0] + i - 1]);
                 if (connectivity == AF_CONNECTIVITY_8 &&
-                        i < (int)inDims[0] - 1 && j > 0 && outPtr[(j-1) * inDims[0] + i+1] != 0)
-                    l.push_back(outPtr[(j-1) * inDims[0] + i+1]);
+                    i < (int)inDims[0] - 1 && j > 0 &&
+                    outPtr[(j - 1) * inDims[0] + i + 1] != 0)
+                    l.push_back(outPtr[(j - 1) * inDims[0] + i + 1]);
 
                 if (!l.empty()) {
                     T minl = l[0];
                     for (size_t k = 0; k < l.size(); k++) {
-                        minl = min(l[k], minl);
+                        minl                        = std::min(l[k], minl);
                         LabelMapIterator currentMap = lmap.find(l[k]);
-                        LabelNode<T> *node = currentMap->second.get();
+                        LabelNode<T>* node          = currentMap->second.get();
                         // Group labels of the same region under a disjoint set
-                        for (size_t m = k+1; m < l.size(); m++)
+                        for (size_t m = k + 1; m < l.size(); m++)
                             setUnion(node, lmap.find(l[m])->second.get());
                     }
                     // Set label to smallest neighbor label
                     outPtr[idx] = minl;
                 } else {
                     // Insert new label in map
-                    lmap.insert(std::make_pair(label, UnqLabelPtr(new LabelNode<T>(label))));
+                    lmap.insert(std::make_pair(
+                        label, UnqLabelPtr(new LabelNode<T>(label))));
                     outPtr[idx] = label++;
                 }
             }
@@ -158,14 +133,14 @@ void regions(Param<T> out, CParam<char> in, af_connectivity connectivity)
         for (int i = 0; i < (int)inDims[0]; i++) {
             int idx = j * (int)inDims[0] + i;
             if (inPtr[idx] != 0) {
-                T l = outPtr[idx];
+                T l                         = outPtr[idx];
                 LabelMapIterator currentMap = lmap.find(l);
 
                 if (currentMap != lmap.end()) {
                     LabelNode<T>* node = currentMap->second.get();
 
                     LabelNode<T>* nodeRoot = find(node);
-                    outPtr[idx] = nodeRoot->getMinLabel();
+                    outPtr[idx]            = nodeRoot->getMinLabel();
 
                     // Mark removed labels (those that are part of a region
                     // that contains a smaller label)
@@ -183,11 +158,12 @@ void regions(Param<T> out, CParam<char> in, af_connectivity connectivity)
         for (int i = 0; i < (int)inDims[0]; i++) {
             int idx = j * (int)inDims[0] + i;
             if (outPtr[idx] > 0) {
-                outPtr[idx] -= distance(removed.begin(), removed.lower_bound(outPtr[idx]));
+                outPtr[idx] -=
+                    distance(removed.begin(), removed.lower_bound(outPtr[idx]));
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/reorder.hpp b/src/backend/cpu/kernel/reorder.hpp
index 60cf0748a4..b038d4920b 100644
--- a/src/backend/cpu/kernel/reorder.hpp
+++ b/src/backend/cpu/kernel/reorder.hpp
@@ -10,37 +10,34 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void reorder(Param<T> out, CParam<T> in, const af::dim4 oDims, const af::dim4 rdims)
-{
-    T* outPtr = out.get();
+void reorder(Param<T> out, CParam<T> in, const af::dim4 oDims,
+             const af::dim4 rdims) {
+    T* outPtr      = out.get();
     const T* inPtr = in.get();
 
     const af::dim4 ist = in.strides();
     const af::dim4 ost = out.strides();
 
-
-    dim_t ids[4]  = {0};
-    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+    dim_t ids[4] = {0};
+    for (dim_t ow = 0; ow < oDims[3]; ow++) {
         const dim_t oW = ow * ost[3];
-        ids[rdims[3]] = ow;
-        for(dim_t oz = 0; oz < oDims[2]; oz++) {
+        ids[rdims[3]]  = ow;
+        for (dim_t oz = 0; oz < oDims[2]; oz++) {
             const dim_t oZW = oW + oz * ost[2];
-            ids[rdims[2]] = oz;
-            for(dim_t oy = 0; oy < oDims[1]; oy++) {
+            ids[rdims[2]]   = oz;
+            for (dim_t oy = 0; oy < oDims[1]; oy++) {
                 const dim_t oYZW = oZW + oy * ost[1];
-                ids[rdims[1]] = oy;
-                for(dim_t ox = 0; ox < oDims[0]; ox++) {
+                ids[rdims[1]]    = oy;
+                for (dim_t ox = 0; ox < oDims[0]; ox++) {
                     const dim_t oIdx = oYZW + ox;
 
-                    ids[rdims[0]] = ox;
+                    ids[rdims[0]]    = ox;
                     const dim_t iIdx = ids[3] * ist[3] + ids[2] * ist[2] +
-                                          ids[1] * ist[1] + ids[0];
+                                       ids[1] * ist[1] + ids[0];
 
                     outPtr[oIdx] = inPtr[iIdx];
                 }
@@ -49,6 +46,5 @@ void reorder(Param<T> out, CParam<T> in, const af::dim4 oDims, const af::dim4 rd
     }
 }
 
-}
-}
-
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index 0dc171688f..0a3d3a0e33 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -9,13 +9,12 @@
 
 #pragma once
 #include <Param.hpp>
-#include <af/traits.hpp>
 #include <common/complex.hpp>
+#include <math.hpp>
+#include <af/traits.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 /**
  * noop function for round to avoid compilation
@@ -26,29 +25,24 @@ namespace kernel
  * is to be used only for positive numbers, i m using it here
  * for calculating dimensions of arrays
  */
-dim_t round2int(float value)
-{
-    return (dim_t)(value+0.5f);
-}
+dim_t round2int(float value) { return (dim_t)(value + 0.5f); }
 
 using std::conditional;
 using std::is_same;
 
 template<typename T>
-using wtype_t = typename conditional<is_same<T, double>::value, double, float>::type;
+using wtype_t =
+    typename conditional<is_same<T, double>::value, double, float>::type;
 
 template<typename T>
-using vtype_t = typename conditional<common::is_complex<T>::value,
-                                     T, wtype_t<T>
-                                    >::type;
+using vtype_t =
+    typename conditional<common::is_complex<T>::value, T, wtype_t<T>>::type;
 
 template<typename T, af_interp_type method>
-struct resize_op
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-              const af::dim4 &ostrides, const af::dim4 &istrides,
-              const dim_t x, const dim_t y)
-    {
+struct resize_op {
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims,
+                    const af::dim4 &idims, const af::dim4 &ostrides,
+                    const af::dim4 &istrides, const dim_t x, const dim_t y) {
         UNUSED(outPtr);
         UNUSED(inPtr);
         UNUSED(odims);
@@ -62,12 +56,10 @@ struct resize_op
 };
 
 template<typename T>
-struct resize_op<T, AF_INTERP_NEAREST>
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const dim_t x, const dim_t y)
-    {
+struct resize_op<T, AF_INTERP_NEAREST> {
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims,
+                    const af::dim4 &idims, const af::dim4 &ostrides,
+                    const af::dim4 &istrides, const dim_t x, const dim_t y) {
         // Compute Indices
         dim_t i_x = round2int((float)x / (odims[0] / (float)idims[0]));
         dim_t i_y = round2int((float)y / (odims[1] / (float)idims[1]));
@@ -76,40 +68,39 @@ struct resize_op<T, AF_INTERP_NEAREST>
         if (i_y >= idims[1]) i_y = idims[1] - 1;
 
         dim_t i_off = i_y * istrides[1] + i_x;
-        dim_t o_off =   y * ostrides[1] + x;
+        dim_t o_off = y * ostrides[1] + x;
         // Copy values from all channels
-        for(dim_t w = 0; w < odims[3]; w++) {
+        for (dim_t w = 0; w < odims[3]; w++) {
             dim_t wost = w * ostrides[3];
             dim_t wist = w * istrides[3];
-            for(dim_t z = 0; z < odims[2]; z++) {
-                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
+            for (dim_t z = 0; z < odims[2]; z++) {
+                outPtr[o_off + z * ostrides[2] + wost] =
+                    inPtr[i_off + z * istrides[2] + wist];
             }
         }
     }
 };
 
 template<typename T>
-struct resize_op<T, AF_INTERP_BILINEAR>
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const dim_t x, const dim_t y)
-    {
+struct resize_op<T, AF_INTERP_BILINEAR> {
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims,
+                    const af::dim4 &idims, const af::dim4 &ostrides,
+                    const af::dim4 &istrides, const dim_t x, const dim_t y) {
         // Compute Indices
         float f_x = (float)x / (odims[0] / (float)idims[0]);
         float f_y = (float)y / (odims[1] / (float)idims[1]);
 
-        dim_t i1_x  = floor(f_x);
-        dim_t i1_y  = floor(f_y);
+        dim_t i1_x = floor(f_x);
+        dim_t i1_y = floor(f_y);
 
         if (i1_x >= idims[0]) i1_x = idims[0] - 1;
         if (i1_y >= idims[1]) i1_y = idims[1] - 1;
 
-        float b   = f_x - i1_x;
-        float a   = f_y - i1_y;
+        float b = f_x - i1_x;
+        float a = f_y - i1_y;
 
-        dim_t i2_x  = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
-        dim_t i2_y  = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
+        dim_t i2_x = (i1_x + 1 >= idims[0] ? idims[0] - 1 : i1_x + 1);
+        dim_t i2_y = (i1_y + 1 >= idims[1] ? idims[1] - 1 : i1_y + 1);
 
         typedef typename af::dtype_traits<T>::base_type BT;
         typedef wtype_t<BT> WT;
@@ -117,10 +108,10 @@ struct resize_op<T, AF_INTERP_BILINEAR>
 
         dim_t o_off = y * ostrides[1] + x;
         // Copy values from all channels
-        for(dim_t w = 0; w < odims[3]; w++) {
+        for (dim_t w = 0; w < odims[3]; w++) {
             dim_t wst = w * istrides[3];
-            for(dim_t z = 0; z < odims[2]; z++) {
-                dim_t zst = z * istrides[2];
+            for (dim_t z = 0; z < odims[2]; z++) {
+                dim_t zst         = z * istrides[2];
                 dim_t channel_off = zst + wst;
                 VT p1 = inPtr[i1_y * istrides[1] + i1_x + channel_off];
                 VT p2 = inPtr[i2_y * istrides[1] + i1_x + channel_off];
@@ -128,22 +119,20 @@ struct resize_op<T, AF_INTERP_BILINEAR>
                 VT p4 = inPtr[i2_y * istrides[1] + i2_x + channel_off];
 
                 outPtr[o_off + z * ostrides[2] + w * ostrides[3]] =
-                                scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
-                                scalar<WT>((    a   ) * (1.0f - b)) * p2 +
-                                scalar<WT>((1.0f - a) * (    b   )) * p3 +
-                                scalar<WT>((    a   ) * (    b   )) * p4;
+                    scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
+                    scalar<WT>((a) * (1.0f - b)) * p2 +
+                    scalar<WT>((1.0f - a) * (b)) * p3 +
+                    scalar<WT>((a) * (b)) * p4;
             }
         }
     }
 };
 
 template<typename T>
-struct resize_op<T, AF_INTERP_LOWER>
-{
-    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims, const af::dim4 &idims,
-            const af::dim4 &ostrides, const af::dim4 &istrides,
-            const dim_t x, const dim_t y)
-    {
+struct resize_op<T, AF_INTERP_LOWER> {
+    void operator()(T *outPtr, const T *inPtr, const af::dim4 &odims,
+                    const af::dim4 &idims, const af::dim4 &ostrides,
+                    const af::dim4 &istrides, const dim_t x, const dim_t y) {
         // Compute Indices
         dim_t i_x = floor((float)x / (odims[0] / (float)idims[0]));
         dim_t i_y = floor((float)y / (odims[1] / (float)idims[1]));
@@ -152,35 +141,35 @@ struct resize_op<T, AF_INTERP_LOWER>
         if (i_y >= idims[1]) i_y = idims[1] - 1;
 
         dim_t i_off = i_y * istrides[1] + i_x;
-        dim_t o_off =   y * ostrides[1] + x;
+        dim_t o_off = y * ostrides[1] + x;
         // Copy values from all channels
-        for(dim_t w = 0; w < odims[3]; w++) {
+        for (dim_t w = 0; w < odims[3]; w++) {
             dim_t wost = w * ostrides[3];
             dim_t wist = w * istrides[3];
-            for(dim_t z = 0; z < odims[2]; z++) {
-                outPtr[o_off + z * ostrides[2] + wost] = inPtr[i_off + z * istrides[2] + wist];
+            for (dim_t z = 0; z < odims[2]; z++) {
+                outPtr[o_off + z * ostrides[2] + wost] =
+                    inPtr[i_off + z * istrides[2] + wist];
             }
         }
     }
 };
 
 template<typename T, af_interp_type method>
-void resize(Param<T> out, CParam<T> in)
-{
+void resize(Param<T> out, CParam<T> in) {
     af::dim4 idims    = in.dims();
     af::dim4 odims    = out.dims();
     const T *inPtr    = in.get();
-          T *outPtr   = out.get();
+    T *outPtr         = out.get();
     af::dim4 ostrides = out.strides();
     af::dim4 istrides = in.strides();
 
     resize_op<T, method> op;
-    for(dim_t y = 0; y < odims[1]; y++) {
-        for(dim_t x = 0; x < odims[0]; x++) {
+    for (dim_t y = 0; y < odims[1]; y++) {
+        for (dim_t x = 0; x < odims[0]; x++) {
             op(outPtr, inPtr, odims, idims, ostrides, istrides, x, y);
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index cc5a1be81e..af2e21f31d 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -9,22 +9,19 @@
 
 #pragma once
 #include <Param.hpp>
-#include <math.hpp>
 #include <err_cpu.hpp>
-#include "interp.hpp"
+#include <math.hpp>
 #include <af/traits.hpp>
+#include "interp.hpp"
 
 using af::dtype_traits;
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, int order>
-void rotate(Param<T> output, CParam<T> input,
-            const float theta, af_interp_type method)
-{
+void rotate(Param<T> output, CParam<T> input, const float theta,
+            af_interp_type method) {
     typedef typename dtype_traits<T>::base_type BT;
     typedef wtype_t<BT> WT;
     Interp2<T, WT, order> interp;
@@ -41,45 +38,45 @@ void rotate(Param<T> output, CParam<T> input,
         const float ny = 0.5 * (idims[1] - 1);
         const float mx = 0.5 * (odims[0] - 1);
         const float my = 0.5 * (odims[1] - 1);
-        const float sx = (mx * c + my *-s);
+        const float sx = (mx * c + my * -s);
         const float sy = (mx * s + my * c);
-        tx = -(sx - nx);
-        ty = -(sy - ny);
+        tx             = -(sx - nx);
+        ty             = -(sy - ny);
     }
 
-    const float tmat[6] = {std::round( c * 1000) / 1000.0f,
-                           std::round(-s * 1000) / 1000.0f,
-                           std::round(tx * 1000) / 1000.0f,
-                           std::round( s * 1000) / 1000.0f,
-                           std::round( c * 1000) / 1000.0f,
-                           std::round(ty * 1000) / 1000.0f,
-                          };
+    const float tmat[6] = {
+        std::round(c * 1000) / 1000.0f,  std::round(-s * 1000) / 1000.0f,
+        std::round(tx * 1000) / 1000.0f, std::round(s * 1000) / 1000.0f,
+        std::round(c * 1000) / 1000.0f,  std::round(ty * 1000) / 1000.0f,
+    };
 
     int nimages = odims[2];
-    T *out = output.get();
+    T *out      = output.get();
 
     for (int idw = 0; idw < (int)odims[3]; idw++) {
-
         int out_offw = idw * ostrides[3];
         int in_offw  = idw * istrides[3];
 
         // Do transform for image
-        for(int idy = 0; idy < (int)odims[1]; idy++) {
-            for(int idx = 0; idx < (int)odims[0]; idx++) {
+        for (int idy = 0; idy < (int)odims[1]; idy++) {
+            for (int idx = 0; idx < (int)odims[0]; idx++) {
                 WT xidi = idx * tmat[0] + idy * tmat[1] + tmat[2];
                 WT yidi = idx * tmat[3] + idy * tmat[4] + tmat[5];
 
-                // Special conditions to deal with boundaries for bilinear and bicubic
-                // FIXME: Ideally this condition should be removed or be present for all methods
-                // But tests are expecting a different behavior for bilinear and nearest
+                // Special conditions to deal with boundaries for bilinear and
+                // bicubic
+                // FIXME: Ideally this condition should be removed or be present
+                // for all methods But tests are expecting a different behavior
+                // for bilinear and nearest
                 bool condX = xidi >= -0.0001 && xidi < idims[0];
                 bool condY = yidi >= -0.0001 && yidi < idims[1];
-                int ooff = out_offw + idy * ostrides[1] + idx;
+                int ooff   = out_offw + idy * ostrides[1] + idx;
                 if (order == 1 || (condX && condY)) {
-                    // FIXME: Nearest and lower do not do clamping, but other methods do
-                    // Make it consistent
+                    // FIXME: Nearest and lower do not do clamping, but other
+                    // methods do Make it consistent
                     bool clamp = order != 1;
-                    interp(output, ooff, input, in_offw, xidi, yidi, method, nimages, clamp);
+                    interp(output, ooff, input, in_offw, xidi, yidi, method,
+                           nimages, clamp);
                 } else {
                     for (int n = 0; n < nimages; n++) {
                         out[ooff + n * ostrides[2]] = scalar<T>(0);
@@ -90,5 +87,5 @@ void rotate(Param<T> output, CParam<T> input,
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index af4b702938..f721e5a8d9 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -9,19 +9,15 @@
 
 #pragma once
 #include <Param.hpp>
+#include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<af_op_t op, typename Ti, typename To, int D, bool inclusive_scan>
-struct scan_dim
-{
-    void operator()(Param<To> out, dim_t outOffset,
-                    CParam<Ti> in, dim_t inOffset,
-                    const int dim) const
-    {
+struct scan_dim {
+    void operator()(Param<To> out, dim_t outOffset, CParam<Ti> in,
+                    dim_t inOffset, const int dim) const {
         const dim4 odims    = out.dims();
         const dim4 ostrides = out.strides();
         const dim4 istrides = in.strides();
@@ -37,14 +33,11 @@ struct scan_dim
 };
 
 template<af_op_t op, typename Ti, typename To, bool inclusive_scan>
-struct scan_dim<op, Ti, To, 0, inclusive_scan>
-{
-    void operator()(Param<To> output, dim_t outOffset,
-                    CParam<Ti> input,  dim_t inOffset,
-                    const int dim) const
-    {
+struct scan_dim<op, Ti, To, 0, inclusive_scan> {
+    void operator()(Param<To> output, dim_t outOffset, CParam<Ti> input,
+                    dim_t inOffset, const int dim) const {
         const Ti* in = input.get() + inOffset;
-              To* out= output.get()+ outOffset;
+        To* out      = output.get() + outOffset;
 
         const dim4 ostrides = output.strides();
         const dim4 istrides = input.strides();
@@ -60,10 +53,10 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan>
         To out_val = Binary<To, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
             To in_val = transform(in[i * istride]);
-            out_val = scan(in_val, out_val);
+            out_val   = scan(in_val, out_val);
             if (!inclusive_scan) {
-                //The loop shifts the output index by 1.
-                //The last index wraps around and writes the first element.
+                // The loop shifts the output index by 1.
+                // The last index wraps around and writes the first element.
                 if (i == (idims[dim] - 1)) {
                     out[0] = Binary<To, op>::init();
                 } else {
@@ -76,5 +69,5 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan>
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index 767dd4e27f..bd9c3e627a 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -9,23 +9,19 @@
 
 #pragma once
 #include <Param.hpp>
+#include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<af_op_t op, typename Ti, typename Tk, typename To, int D>
-struct scan_dim_by_key
-{
+struct scan_dim_by_key {
     bool inclusive_scan;
     scan_dim_by_key(bool inclusiveSanKey) : inclusive_scan(inclusiveSanKey) {}
 
-    void operator()(Param<To> out, dim_t outOffset,
-                    CParam<Tk> key, dim_t keyOffset,
-                    CParam<Ti> in, dim_t inOffset,
-                    const int dim) const
-    {
+    void operator()(Param<To> out, dim_t outOffset, CParam<Tk> key,
+                    dim_t keyOffset, CParam<Ti> in, dim_t inOffset,
+                    const int dim) const {
         const dim4 odims    = out.dims();
         const dim4 ostrides = out.strides();
         const dim4 kstrides = key.strides();
@@ -43,19 +39,16 @@ struct scan_dim_by_key
 };
 
 template<af_op_t op, typename Ti, typename Tk, typename To>
-struct scan_dim_by_key<op, Ti, Tk, To, 0>
-{
+struct scan_dim_by_key<op, Ti, Tk, To, 0> {
     bool inclusive_scan;
     scan_dim_by_key(bool inclusiveSanKey) : inclusive_scan(inclusiveSanKey) {}
 
-    void operator()(Param<To> output, dim_t outOffset,
-                    CParam<Tk> keyinput, dim_t keyOffset,
-                    CParam<Ti> input, dim_t inOffset,
-                    const int dim) const
-    {
-        const Ti* in  = input.get()    + inOffset;
+    void operator()(Param<To> output, dim_t outOffset, CParam<Tk> keyinput,
+                    dim_t keyOffset, CParam<Ti> input, dim_t inOffset,
+                    const int dim) const {
+        const Ti* in  = input.get() + inOffset;
         const Tk* key = keyinput.get() + keyOffset;
-              To* out = output.get()   + outOffset;
+        To* out       = output.get() + outOffset;
 
         const dim4 ostrides = output.strides();
         const dim4 kstrides = keyinput.strides();
@@ -74,14 +67,12 @@ struct scan_dim_by_key<op, Ti, Tk, To, 0>
         Tk key_val = key[0];
 
         dim_t k = !inclusive_scan;
-        if (!inclusive_scan) {
-            out[0] = Binary<To, op>::init();
-        }
+        if (!inclusive_scan) { out[0] = Binary<To, op>::init(); }
 
         for (dim_t i = 0; i < idims[dim] - (!inclusive_scan); i++, k++) {
             To in_val = transform(in[i * istride]);
             if (key[k * kstride] != key_val) {
-                out_val = !inclusive_scan? Binary<To, op>::init() : in_val;
+                out_val = !inclusive_scan ? Binary<To, op>::init() : in_val;
                 key_val = key[k * kstride];
             } else {
                 out_val = scan(in_val, out_val);
@@ -91,5 +82,5 @@ struct scan_dim_by_key<op, Ti, Tk, To, 0>
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index 468fba0aac..6b7534995e 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -10,66 +10,59 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b)
-{
-    af::dim4 adims = a.dims();
+void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b) {
+    af::dim4 adims    = a.dims();
     af::dim4 astrides = a.strides();
-    af::dim4 bdims = b.dims();
+    af::dim4 bdims    = b.dims();
     af::dim4 bstrides = b.strides();
 
-    af::dim4 cdims = cond.dims();
+    af::dim4 cdims    = cond.dims();
     af::dim4 cstrides = cond.strides();
 
-    af::dim4 odims = out.dims();
+    af::dim4 odims    = out.dims();
     af::dim4 ostrides = out.strides();
 
     bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
-        adims[2] == odims[2], adims[3] == odims[3]};
+                        adims[2] == odims[2], adims[3] == odims[3]};
 
     bool is_b_same[] = {bdims[0] == odims[0], bdims[1] == odims[1],
-        bdims[2] == odims[2], bdims[3] == odims[3]};
+                        bdims[2] == odims[2], bdims[3] == odims[3]};
 
     bool is_c_same[] = {cdims[0] == odims[0], cdims[1] == odims[1],
-        cdims[2] == odims[2], cdims[3] == odims[3]};
+                        cdims[2] == odims[2], cdims[3] == odims[3]};
 
-    const T *aptr = a.get();
-    const T *bptr = b.get();
-    T *optr = out.get();
+    const T *aptr    = a.get();
+    const T *bptr    = b.get();
+    T *optr          = out.get();
     const char *cptr = cond.get();
 
     for (int l = 0; l < odims[3]; l++) {
-
-        int o_off3   = ostrides[3] * l;
-        int a_off3   = astrides[3] * is_a_same[3] * l;
-        int b_off3   = bstrides[3] * is_b_same[3] * l;
-        int c_off3   = cstrides[3] * is_c_same[3] * l;
+        int o_off3 = ostrides[3] * l;
+        int a_off3 = astrides[3] * is_a_same[3] * l;
+        int b_off3 = bstrides[3] * is_b_same[3] * l;
+        int c_off3 = cstrides[3] * is_c_same[3] * l;
 
         for (int k = 0; k < odims[2]; k++) {
-
-            int o_off2   = ostrides[2] * k + o_off3;
-            int a_off2   = astrides[2] * is_a_same[2] * k + a_off3;
-            int b_off2   = bstrides[2] * is_b_same[2] * k + b_off3;
-            int c_off2   = cstrides[2] * is_c_same[2] * k + c_off3;
+            int o_off2 = ostrides[2] * k + o_off3;
+            int a_off2 = astrides[2] * is_a_same[2] * k + a_off3;
+            int b_off2 = bstrides[2] * is_b_same[2] * k + b_off3;
+            int c_off2 = cstrides[2] * is_c_same[2] * k + c_off3;
 
             for (int j = 0; j < odims[1]; j++) {
-
-                int o_off1   = ostrides[1] * j + o_off2;
-                int a_off1   = astrides[1] * is_a_same[1] * j + a_off2;
-                int b_off1   = bstrides[1] * is_b_same[1] * j + b_off2;
-                int c_off1   = cstrides[1] * is_c_same[1] * j + c_off2;
+                int o_off1 = ostrides[1] * j + o_off2;
+                int a_off1 = astrides[1] * is_a_same[1] * j + a_off2;
+                int b_off1 = bstrides[1] * is_b_same[1] * j + b_off2;
+                int c_off1 = cstrides[1] * is_c_same[1] * j + c_off2;
 
                 for (int i = 0; i < odims[0]; i++) {
-
                     bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
-                    T    aval = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
-                    T    bval = is_b_same[0] ? bptr[b_off1 + i] : bptr[b_off1];
-                    T    oval = cval ? aval : bval;
+                    T aval    = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
+                    T bval    = is_b_same[0] ? bptr[b_off1 + i] : bptr[b_off1];
+                    T oval    = cval ? aval : bval;
                     optr[o_off1 + i] = oval;
                 }
             }
@@ -78,21 +71,20 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b)
 }
 
 template<typename T, bool flip>
-void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b)
-{
+void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a,
+                   const double b) {
     af::dim4 astrides = a.strides();
-    af::dim4 adims = a.dims();
+    af::dim4 adims    = a.dims();
     af::dim4 cstrides = cond.strides();
-    af::dim4 cdims = cond.dims();
+    af::dim4 cdims    = cond.dims();
 
-    af::dim4 odims = out.dims();
+    af::dim4 odims    = out.dims();
     af::dim4 ostrides = out.strides();
 
-    const T *aptr = a.get();
-    T *optr = out.get();
+    const T *aptr    = a.get();
+    T *optr          = out.get();
     const char *cptr = cond.get();
 
-
     bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
                         adims[2] == odims[2], adims[3] == odims[3]};
 
@@ -100,27 +92,23 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b)
                         cdims[2] == odims[2], cdims[3] == odims[3]};
 
     for (int l = 0; l < odims[3]; l++) {
-
         int o_off3 = ostrides[3] * l;
         int a_off3 = astrides[3] * is_a_same[3] * l;
         int c_off3 = cstrides[3] * is_c_same[3] * l;
 
         for (int k = 0; k < odims[2]; k++) {
-
             int o_off2 = ostrides[2] * k + o_off3;
             int a_off2 = astrides[2] * is_a_same[2] * k + a_off3;
             int c_off2 = cstrides[2] * is_c_same[2] * k + c_off3;
 
             for (int j = 0; j < odims[1]; j++) {
-
                 int o_off1 = ostrides[1] * j + o_off2;
                 int a_off1 = astrides[1] * is_a_same[1] * j + a_off2;
                 int c_off1 = cstrides[1] * is_c_same[1] * j + c_off2;
 
                 for (int i = 0; i < odims[0]; i++) {
-
                     bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
-                    T    aval = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
+                    T aval    = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
                     optr[o_off1 + i] = (flip ^ cval) ? aval : b;
                 }
             }
@@ -128,7 +116,5 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b)
     }
 }
 
-
-
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/shift.hpp b/src/backend/cpu/kernel/shift.hpp
index 02a58fdac3..ea844439e9 100644
--- a/src/backend/cpu/kernel/shift.hpp
+++ b/src/backend/cpu/kernel/shift.hpp
@@ -11,20 +11,16 @@
 #include <Param.hpp>
 #include <cassert>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-static inline dim_t simple_mod(const dim_t i, const dim_t dim)
-{
+static inline dim_t simple_mod(const dim_t i, const dim_t dim) {
     return (i < dim) ? i : (i - dim);
 }
 
 template<typename T>
-void shift(Param<T> out, CParam<T> in, const af::dim4 sdims)
-{
-    T* outPtr = out.get();
+void shift(Param<T> out, CParam<T> in, const af::dim4 sdims) {
+    T* outPtr      = out.get();
     const T* inPtr = in.get();
 
     const af::dim4 oDims = out.dims();
@@ -33,28 +29,29 @@ void shift(Param<T> out, CParam<T> in, const af::dim4 sdims)
 
     int sdims_[4];
     // Need to do this because we are mapping output to input in the kernel
-    for(int i = 0; i < 4; i++) {
+    for (int i = 0; i < 4; i++) {
         // sdims_[i] will always be positive and always [0, oDims[i]].
-        // Negative shifts are converted to position by going the other way round
+        // Negative shifts are converted to position by going the other way
+        // round
         sdims_[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
         assert(sdims_[i] >= 0 && sdims_[i] <= oDims[i]);
     }
 
-    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+    for (dim_t ow = 0; ow < oDims[3]; ow++) {
         const int oW = ow * ost[3];
         const int iw = simple_mod((ow + sdims_[3]), oDims[3]);
         const int iW = iw * ist[3];
-        for(dim_t oz = 0; oz < oDims[2]; oz++) {
+        for (dim_t oz = 0; oz < oDims[2]; oz++) {
             const int oZW = oW + oz * ost[2];
-            const int iz = simple_mod((oz + sdims_[2]), oDims[2]);
+            const int iz  = simple_mod((oz + sdims_[2]), oDims[2]);
             const int iZW = iW + iz * ist[2];
-            for(dim_t oy = 0; oy < oDims[1]; oy++) {
+            for (dim_t oy = 0; oy < oDims[1]; oy++) {
                 const int oYZW = oZW + oy * ost[1];
-                const int iy = simple_mod((oy + sdims_[1]), oDims[1]);
+                const int iy   = simple_mod((oy + sdims_[1]), oDims[1]);
                 const int iYZW = iZW + iy * ist[1];
-                for(dim_t ox = 0; ox < oDims[0]; ox++) {
+                for (dim_t ox = 0; ox < oDims[0]; ox++) {
                     const int oIdx = oYZW + ox;
-                    const int ix = simple_mod((ox + sdims_[0]), oDims[0]);
+                    const int ix   = simple_mod((ox + sdims_[0]), oDims[0]);
                     const int iIdx = iYZW + ix;
 
                     outPtr[oIdx] = inPtr[iIdx];
@@ -64,5 +61,5 @@ void shift(Param<T> out, CParam<T> in, const af::dim4 sdims)
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index c436ac6b3e..a5cc4741dc 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -70,11 +70,9 @@
 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 static const float PI_VAL = 3.14159265358979323846f;
 
@@ -129,25 +127,21 @@ static const unsigned GLOHAngularBins = 8;
 // Number of GLOH bins per histogram in descriptor
 static const unsigned GLOHHistBins = 16;
 
-typedef struct
-{
-    float    f[4];
+typedef struct {
+    float f[4];
     unsigned l;
 } feat_t;
 
-bool feat_cmp(feat_t i, feat_t j)
-{
+bool feat_cmp(feat_t i, feat_t j) {
     for (int k = 0; k < 4; k++)
-        if (i.f[k] != j.f[k])
-            return (i.f[k] < j.f[k]);
-    if (i.l != j.l)
-        return (i.l < j.l);
+        if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
+    if (i.l != j.l) return (i.l < j.l);
 
     return true;
 }
 
-void array_to_feat(std::vector<feat_t>& feat, float *x, float *y, unsigned *layer, float *resp, float *size, unsigned nfeat)
-{
+void array_to_feat(std::vector<feat_t>& feat, float* x, float* y,
+                   unsigned* layer, float* resp, float* size, unsigned nfeat) {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -159,26 +153,23 @@ void array_to_feat(std::vector<feat_t>& feat, float *x, float *y, unsigned *laye
 }
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * PI_VAL * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
 template<typename T>
-Array<T> gauss_filter(float sigma)
-{
+Array<T> gauss_filter(float sigma) {
     // Using 6-sigma rule
     unsigned gauss_len = std::min((unsigned)round(sigma * 6 + 1) | 1, 31u);
 
@@ -189,102 +180,91 @@ Array<T> gauss_filter(float sigma)
 }
 
 template<int N>
-void gaussianElimination(float* A, float* b, float* x)
-{
+void gaussianElimination(float* A, float* b, float* x) {
     // forward elimination
-    for (int i = 0; i < N-1; i++) {
-        for (int j = i+1; j < N; j++) {
-            float s = A[j*N+i] / A[i*N+i];
+    for (int i = 0; i < N - 1; i++) {
+        for (int j = i + 1; j < N; j++) {
+            float s = A[j * N + i] / A[i * N + i];
 
-            for (int k = i; k < N; k++)
-                A[j*N+k] -= s * A[i*N+k];
+            for (int k = i; k < N; k++) A[j * N + k] -= s * A[i * N + k];
 
             b[j] -= s * b[i];
         }
     }
 
-    for (int i = 0; i < N; i++)
-        x[i] = 0;
+    for (int i = 0; i < N; i++) x[i] = 0;
 
     // backward substitution
     float sum = 0;
-    for (int i = 0; i <= N-2; i++) {
+    for (int i = 0; i <= N - 2; i++) {
         sum = b[i];
-        for (int j = i+1; j < N; j++)
-            sum -= A[i*N+j] * x[j];
-        x[i] = sum / A[i*N+i];
+        for (int j = i + 1; j < N; j++) sum -= A[i * N + j] * x[j];
+        x[i] = sum / A[i * N + i];
     }
 }
 
 template<typename T>
-void sub(
-    Array<T>& out,
-    const Array<T>& in1,
-    const Array<T>& in2)
-{
-    size_t nel = in1.elements();
-    T* out_ptr = out.get();
+void sub(Array<T>& out, const Array<T>& in1, const Array<T>& in2) {
+    size_t nel       = in1.elements();
+    T* out_ptr       = out.get();
     const T* in1_ptr = in1.get();
     const T* in2_ptr = in2.get();
 
-    for (size_t i = 0; i < nel; i++) {
-        out_ptr[i] = in1_ptr[i] - in2_ptr[i];
-    }
+    for (size_t i = 0; i < nel; i++) { out_ptr[i] = in1_ptr[i] - in2_ptr[i]; }
 }
 
-#define CPTR(Y, X) (center_ptr[(Y) * idims[0] + (X)])
-#define PPTR(Y, X) (prev_ptr[(Y) * idims[0] + (X)])
-#define NPTR(Y, X) (next_ptr[(Y) * idims[0] + (X)])
+#define CPTR(Y, X) (center_ptr[(Y)*idims[0] + (X)])
+#define PPTR(Y, X) (prev_ptr[(Y)*idims[0] + (X)])
+#define NPTR(Y, X) (next_ptr[(Y)*idims[0] + (X)])
 
 // Determines whether a pixel is a scale-space extremum by comparing it to its
 // 3x3x3 pixel neighborhood.
 template<typename T>
-void detectExtrema(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    unsigned* counter,
-    const Array<T>& prev,
-    const Array<T>& center,
-    const Array<T>& next,
-    const unsigned layer,
-    const unsigned max_feat,
-    const float threshold)
-{
+void detectExtrema(float* x_out, float* y_out, unsigned* layer_out,
+                   unsigned* counter, const Array<T>& prev,
+                   const Array<T>& center, const Array<T>& next,
+                   const unsigned layer, const unsigned max_feat,
+                   const float threshold) {
     const af::dim4 idims = center.dims();
     const T* prev_ptr    = prev.get();
     const T* center_ptr  = center.get();
     const T* next_ptr    = next.get();
 
-    for (int y = ImgBorder; y < idims[1]-ImgBorder; y++) {
-        for (int x = ImgBorder; x < idims[0]-ImgBorder; x++) {
-            float p = center_ptr[y*idims[0] + x];
+    for (int y = ImgBorder; y < idims[1] - ImgBorder; y++) {
+        for (int x = ImgBorder; x < idims[0] - ImgBorder; x++) {
+            float p = center_ptr[y * idims[0] + x];
 
             // Find extrema
             if (abs((float)p) > threshold &&
-                ((p > 0 && p > CPTR(y-1, x-1) && p > CPTR(y-1, x) &&
-                  p > CPTR(y-1, x+1) && p > CPTR(y, x-1) && p > CPTR(y,   x+1)  &&
-                  p > CPTR(y+1, x-1) && p > CPTR(y+1, x) && p > CPTR(y+1, x+1)  &&
-                  p > PPTR(y-1, x-1) && p > PPTR(y-1, x) && p > PPTR(y-1, x+1)  &&
-                  p > PPTR(y,   x-1) && p > PPTR(y  , x) && p > PPTR(y,   x+1)  &&
-                  p > PPTR(y+1, x-1) && p > PPTR(y+1, x) && p > PPTR(y+1, x+1)  &&
-                  p > NPTR(y-1, x-1) && p > NPTR(y-1, x) && p > NPTR(y-1, x+1)  &&
-                  p > NPTR(y,   x-1) && p > NPTR(y  , x) && p > NPTR(y,   x+1)  &&
-                  p > NPTR(y+1, x-1) && p > NPTR(y+1, x) && p > NPTR(y+1, x+1)) ||
-                 (p < 0 && p < CPTR(y-1, x-1) && p < CPTR(y-1, x) &&
-                  p < CPTR(y-1, x+1) && p < CPTR(y, x-1) && p < CPTR(y,   x+1)  &&
-                  p < CPTR(y+1, x-1) && p < CPTR(y+1, x) && p < CPTR(y+1, x+1)  &&
-                  p < PPTR(y-1, x-1) && p < PPTR(y-1, x) && p < PPTR(y-1, x+1)  &&
-                  p < PPTR(y,   x-1) && p < PPTR(y  , x) && p < PPTR(y,   x+1)  &&
-                  p < PPTR(y+1, x-1) && p < PPTR(y+1, x) && p < PPTR(y+1, x+1)  &&
-                  p < NPTR(y-1, x-1) && p < NPTR(y-1, x) && p < NPTR(y-1, x+1)  &&
-                  p < NPTR(y,   x-1) && p < NPTR(y  , x) && p < NPTR(y,   x+1)  &&
-                  p < NPTR(y+1, x-1) && p < NPTR(y+1, x) && p < NPTR(y+1, x+1)))) {
-
-                if (*counter < max_feat)
-                {
-                    x_out[*counter] = (float)y;
-                    y_out[*counter] = (float)x;
+                ((p > 0 && p > CPTR(y - 1, x - 1) && p > CPTR(y - 1, x) &&
+                  p > CPTR(y - 1, x + 1) && p > CPTR(y, x - 1) &&
+                  p > CPTR(y, x + 1) && p > CPTR(y + 1, x - 1) &&
+                  p > CPTR(y + 1, x) && p > CPTR(y + 1, x + 1) &&
+                  p > PPTR(y - 1, x - 1) && p > PPTR(y - 1, x) &&
+                  p > PPTR(y - 1, x + 1) && p > PPTR(y, x - 1) &&
+                  p > PPTR(y, x) && p > PPTR(y, x + 1) &&
+                  p > PPTR(y + 1, x - 1) && p > PPTR(y + 1, x) &&
+                  p > PPTR(y + 1, x + 1) && p > NPTR(y - 1, x - 1) &&
+                  p > NPTR(y - 1, x) && p > NPTR(y - 1, x + 1) &&
+                  p > NPTR(y, x - 1) && p > NPTR(y, x) && p > NPTR(y, x + 1) &&
+                  p > NPTR(y + 1, x - 1) && p > NPTR(y + 1, x) &&
+                  p > NPTR(y + 1, x + 1)) ||
+                 (p < 0 && p < CPTR(y - 1, x - 1) && p < CPTR(y - 1, x) &&
+                  p < CPTR(y - 1, x + 1) && p < CPTR(y, x - 1) &&
+                  p < CPTR(y, x + 1) && p < CPTR(y + 1, x - 1) &&
+                  p < CPTR(y + 1, x) && p < CPTR(y + 1, x + 1) &&
+                  p < PPTR(y - 1, x - 1) && p < PPTR(y - 1, x) &&
+                  p < PPTR(y - 1, x + 1) && p < PPTR(y, x - 1) &&
+                  p < PPTR(y, x) && p < PPTR(y, x + 1) &&
+                  p < PPTR(y + 1, x - 1) && p < PPTR(y + 1, x) &&
+                  p < PPTR(y + 1, x + 1) && p < NPTR(y - 1, x - 1) &&
+                  p < NPTR(y - 1, x) && p < NPTR(y - 1, x + 1) &&
+                  p < NPTR(y, x - 1) && p < NPTR(y, x) && p < NPTR(y, x + 1) &&
+                  p < NPTR(y + 1, x - 1) && p < NPTR(y + 1, x) &&
+                  p < NPTR(y + 1, x + 1)))) {
+                if (*counter < max_feat) {
+                    x_out[*counter]     = (float)y;
+                    y_out[*counter]     = (float)x;
                     layer_out[*counter] = layer;
                     (*counter)++;
                 }
@@ -297,65 +277,57 @@ void detectExtrema(
 // accuracy to form an image feature. Rejects features with low contrast.
 // Based on Section 4 of Lowe's paper.
 template<typename T>
-void interpolateExtrema(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    float* response_out,
-    float* size_out,
-    unsigned* counter,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const unsigned extrema_feat,
-    std::vector< Array<T> >& dog_pyr,
-    const unsigned max_feat,
-    const unsigned octave,
-    const unsigned n_layers,
-    const float contrast_thr,
-    const float edge_thr,
-    const float sigma,
-    const float img_scale)
-{
+void interpolateExtrema(float* x_out, float* y_out, unsigned* layer_out,
+                        float* response_out, float* size_out, unsigned* counter,
+                        const float* x_in, const float* y_in,
+                        const unsigned* layer_in, const unsigned extrema_feat,
+                        std::vector<Array<T>>& dog_pyr, const unsigned max_feat,
+                        const unsigned octave, const unsigned n_layers,
+                        const float contrast_thr, const float edge_thr,
+                        const float sigma, const float img_scale) {
     for (int f = 0; f < (int)extrema_feat; f++) {
-        const float first_deriv_scale = img_scale*0.5f;
+        const float first_deriv_scale  = img_scale * 0.5f;
         const float second_deriv_scale = img_scale;
-        const float cross_deriv_scale = img_scale*0.25f;
+        const float cross_deriv_scale  = img_scale * 0.25f;
 
         float xl = 0, xy = 0, xx = 0, contr = 0;
         int i = 0;
 
-        unsigned x = x_in[f];
-        unsigned y = y_in[f];
+        unsigned x     = x_in[f];
+        unsigned y     = y_in[f];
         unsigned layer = layer_in[f];
 
-        const T* prev_ptr   = dog_pyr[octave*(n_layers+2) + layer-1].get();
-        const T* center_ptr = dog_pyr[octave*(n_layers+2) + layer].get();
-        const T* next_ptr   = dog_pyr[octave*(n_layers+2) + layer+1].get();
+        const T* prev_ptr = dog_pyr[octave * (n_layers + 2) + layer - 1].get();
+        const T* center_ptr = dog_pyr[octave * (n_layers + 2) + layer].get();
+        const T* next_ptr = dog_pyr[octave * (n_layers + 2) + layer + 1].get();
 
-        af::dim4 idims = dog_pyr[octave*(n_layers+2)].dims();
+        af::dim4 idims = dog_pyr[octave * (n_layers + 2)].dims();
 
         bool converges = true;
 
         for (i = 0; i < MaxInterpSteps; i++) {
-            float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                           (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                           (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
-
-            float d2  = CPTR(x, y) * 2.f;
-            float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-            float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-            float dss = (NPTR(x, y  ) + PPTR(x, y  ) - d2) * second_deriv_scale;
-            float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                         CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
-            float dxs = (NPTR(x+1, y) - NPTR(x-1, y) -
-                         PPTR(x+1, y) + PPTR(x-1, y)) * cross_deriv_scale;
-            float dys = (NPTR(x, y+1) - NPTR(x-1, y-1) -
-                         PPTR(x, y-1) + PPTR(x-1, y-1)) * cross_deriv_scale;
-
-            float H[9] = {dxx, dxy, dxs,
-                          dxy, dyy, dys,
-                          dxs, dys, dss};
+            float dD[3] = {
+                (float)(CPTR(x + 1, y) - CPTR(x - 1, y)) * first_deriv_scale,
+                (float)(CPTR(x, y + 1) - CPTR(x, y - 1)) * first_deriv_scale,
+                (float)(NPTR(x, y) - PPTR(x, y)) * first_deriv_scale};
+
+            float d2 = CPTR(x, y) * 2.f;
+            float dxx =
+                (CPTR(x + 1, y) + CPTR(x - 1, y) - d2) * second_deriv_scale;
+            float dyy =
+                (CPTR(x, y + 1) + CPTR(x, y - 1) - d2) * second_deriv_scale;
+            float dss = (NPTR(x, y) + PPTR(x, y) - d2) * second_deriv_scale;
+            float dxy = (CPTR(x + 1, y + 1) - CPTR(x - 1, y + 1) -
+                         CPTR(x + 1, y - 1) + CPTR(x - 1, y - 1)) *
+                        cross_deriv_scale;
+            float dxs = (NPTR(x + 1, y) - NPTR(x - 1, y) - PPTR(x + 1, y) +
+                         PPTR(x - 1, y)) *
+                        cross_deriv_scale;
+            float dys = (NPTR(x, y + 1) - NPTR(x - 1, y - 1) - PPTR(x, y - 1) +
+                         PPTR(x - 1, y - 1)) *
+                        cross_deriv_scale;
+
+            float H[9] = {dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss};
 
             float X[3];
             gaussianElimination<3>(H, dD, X);
@@ -364,57 +336,57 @@ void interpolateExtrema(
             xy = -X[1];
             xx = -X[0];
 
-            if (fabs(xl) < 0.5f && fabs(xy) < 0.5f && fabs(xx) < 0.5f)
-                break;
+            if (fabs(xl) < 0.5f && fabs(xy) < 0.5f && fabs(xx) < 0.5f) break;
 
             x += round(xx);
             y += round(xy);
             layer += round(xl);
 
-            if (layer < 1 || layer > n_layers ||
-                x < ImgBorder || x >= idims[1] - ImgBorder ||
-                y < ImgBorder || y >= idims[0] - ImgBorder) {
+            if (layer < 1 || layer > n_layers || x < ImgBorder ||
+                x >= idims[1] - ImgBorder || y < ImgBorder ||
+                y >= idims[0] - ImgBorder) {
                 converges = false;
                 break;
             }
         }
 
         // ensure convergence of interpolation
-        if (i >= MaxInterpSteps || !converges)
-            continue;
+        if (i >= MaxInterpSteps || !converges) continue;
 
-        float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                       (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                       (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
+        float dD[3] = {
+            (float)(CPTR(x + 1, y) - CPTR(x - 1, y)) * first_deriv_scale,
+            (float)(CPTR(x, y + 1) - CPTR(x, y - 1)) * first_deriv_scale,
+            (float)(NPTR(x, y) - PPTR(x, y)) * first_deriv_scale};
         float X[3] = {xx, xy, xl};
 
-        float P = dD[0]*X[0] + dD[1]*X[1] + dD[2]*X[2];
+        float P = dD[0] * X[0] + dD[1] * X[1] + dD[2] * X[2];
 
-        contr = center_ptr[x*idims[0]+y]*img_scale + P * 0.5f;
-        if(abs(contr) < (contrast_thr / n_layers))
-            continue;
+        contr = center_ptr[x * idims[0] + y] * img_scale + P * 0.5f;
+        if (abs(contr) < (contrast_thr / n_layers)) continue;
 
         // principal curvatures are computed using the trace and det of Hessian
         float d2  = CPTR(x, y) * 2.f;
-        float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-        float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-        float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                     CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
+        float dxx = (CPTR(x + 1, y) + CPTR(x - 1, y) - d2) * second_deriv_scale;
+        float dyy = (CPTR(x, y + 1) + CPTR(x, y - 1) - d2) * second_deriv_scale;
+        float dxy = (CPTR(x + 1, y + 1) - CPTR(x - 1, y + 1) -
+                     CPTR(x + 1, y - 1) + CPTR(x - 1, y - 1)) *
+                    cross_deriv_scale;
 
-        float tr = dxx + dyy;
+        float tr  = dxx + dyy;
         float det = dxx * dyy - dxy * dxy;
 
         // add FLT_EPSILON for double-precision compatibility
-        if (det <= 0 || tr*tr*edge_thr >= (edge_thr + 1)*(edge_thr + 1)*det+FLT_EPSILON)
+        if (det <= 0 || tr * tr * edge_thr >=
+                            (edge_thr + 1) * (edge_thr + 1) * det + FLT_EPSILON)
             continue;
 
-        if (*counter < max_feat)
-        {
-            x_out[*counter] = (x + xx) * (1 << octave);
-            y_out[*counter] = (y + xy) * (1 << octave);
-            layer_out[*counter] = layer;
+        if (*counter < max_feat) {
+            x_out[*counter]        = (x + xx) * (1 << octave);
+            y_out[*counter]        = (y + xy) * (1 << octave);
+            layer_out[*counter]    = layer;
             response_out[*counter] = abs(contr);
-            size_out[*counter] = sigma*pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
+            size_out[*counter] =
+                sigma * pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
             (*counter)++;
         }
     }
@@ -425,64 +397,50 @@ void interpolateExtrema(
 #undef NPTR
 
 // Remove duplicate keypoints
-void removeDuplicates(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    float* response_out,
-    float* size_out,
-    unsigned* counter,
-    const std::vector<feat_t>& sorted_feat)
-{
+void removeDuplicates(float* x_out, float* y_out, unsigned* layer_out,
+                      float* response_out, float* size_out, unsigned* counter,
+                      const std::vector<feat_t>& sorted_feat) {
     size_t nfeat = sorted_feat.size();
 
     for (size_t f = 0; f < nfeat; f++) {
         float prec_fctr = 1e4f;
 
-        if (f < nfeat-1) {
-            if (round(sorted_feat[f].f[0]*prec_fctr) == round(sorted_feat[f+1].f[0]*prec_fctr) &&
-                round(sorted_feat[f].f[1]*prec_fctr) == round(sorted_feat[f+1].f[1]*prec_fctr) &&
-                round(sorted_feat[f].f[2]*prec_fctr) == round(sorted_feat[f+1].f[2]*prec_fctr) &&
-                round(sorted_feat[f].f[3]*prec_fctr) == round(sorted_feat[f+1].f[3]*prec_fctr) &&
-                sorted_feat[f].l == sorted_feat[f+1].l)
+        if (f < nfeat - 1) {
+            if (round(sorted_feat[f].f[0] * prec_fctr) ==
+                    round(sorted_feat[f + 1].f[0] * prec_fctr) &&
+                round(sorted_feat[f].f[1] * prec_fctr) ==
+                    round(sorted_feat[f + 1].f[1] * prec_fctr) &&
+                round(sorted_feat[f].f[2] * prec_fctr) ==
+                    round(sorted_feat[f + 1].f[2] * prec_fctr) &&
+                round(sorted_feat[f].f[3] * prec_fctr) ==
+                    round(sorted_feat[f + 1].f[3] * prec_fctr) &&
+                sorted_feat[f].l == sorted_feat[f + 1].l)
                 continue;
         }
 
-        x_out[*counter] = sorted_feat[f].f[0];
-        y_out[*counter] = sorted_feat[f].f[1];
+        x_out[*counter]        = sorted_feat[f].f[0];
+        y_out[*counter]        = sorted_feat[f].f[1];
         response_out[*counter] = sorted_feat[f].f[2];
-        size_out[*counter] = sorted_feat[f].f[3];
-        layer_out[*counter] = sorted_feat[f].l;
+        size_out[*counter]     = sorted_feat[f].f[3];
+        layer_out[*counter]    = sorted_feat[f].l;
         (*counter)++;
     }
 }
 
-#define IPTR(Y, X) (img_ptr[(Y) * idims[0] + (X)])
+#define IPTR(Y, X) (img_ptr[(Y)*idims[0] + (X)])
 
 // Computes a canonical orientation for each image feature in an array.  Based
 // on Section 5 of Lowe's paper.  This function adds features to the array when
 // there is more than one dominant orientation at a given feature location.
 template<typename T>
-void calcOrientation(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    float* response_out,
-    float* size_out,
-    float* ori_out,
-    unsigned* counter,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const unsigned total_feat,
-    const std::vector< Array<T> >& gauss_pyr,
-    const unsigned max_feat,
-    const unsigned octave,
-    const unsigned n_layers,
-    const bool double_input)
-{
+void calcOrientation(float* x_out, float* y_out, unsigned* layer_out,
+                     float* response_out, float* size_out, float* ori_out,
+                     unsigned* counter, const float* x_in, const float* y_in,
+                     const unsigned* layer_in, const float* response_in,
+                     const float* size_in, const unsigned total_feat,
+                     const std::vector<Array<T>>& gauss_pyr,
+                     const unsigned max_feat, const unsigned octave,
+                     const unsigned n_layers, const bool double_input) {
     const int n = OriHistBins;
 
     float hist[OriHistBins];
@@ -490,87 +448,80 @@ void calcOrientation(
 
     for (unsigned f = 0; f < total_feat; f++) {
         // Load keypoint information
-        const float real_x = x_in[f];
-        const float real_y = y_in[f];
+        const float real_x   = x_in[f];
+        const float real_y   = y_in[f];
         const unsigned layer = layer_in[f];
         const float response = response_in[f];
-        const float size = size_in[f];
+        const float size     = size_in[f];
 
         const int pt_x = (int)round(real_x / (1 << octave));
         const int pt_y = (int)round(real_y / (1 << octave));
 
         // Calculate auxiliary parameters
-        const float scl_octv = size*0.5f / (1 << octave);
-        const int radius = (int)round(OriRadius * scl_octv);
-        const float sigma = OriSigFctr * scl_octv;
-        const int len = (radius*2+1);
+        const float scl_octv  = size * 0.5f / (1 << octave);
+        const int radius      = (int)round(OriRadius * scl_octv);
+        const float sigma     = OriSigFctr * scl_octv;
+        const int len         = (radius * 2 + 1);
         const float exp_denom = 2.f * sigma * sigma;
 
         // Points img to correct Gaussian pyramid layer
-        const Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
-        const T* img_ptr = img.get();
+        const Array<T> img = gauss_pyr[octave * (n_layers + 3) + layer];
+        const T* img_ptr   = img.get();
 
-        for (int i = 0; i < OriHistBins; i++)
-            hist[i] = 0.f;
+        for (int i = 0; i < OriHistBins; i++) hist[i] = 0.f;
 
         af::dim4 idims = img.dims();
 
         // Calculate orientation histogram
-        for (int l = 0; l < len*len; l++) {
+        for (int l = 0; l < len * len; l++) {
             int i = l / len - radius;
             int j = l % len - radius;
 
             int y = pt_y + i;
             int x = pt_x + j;
-            if (y < 1 || y >= idims[0] - 1 ||
-                x < 1 || x >= idims[1] - 1)
+            if (y < 1 || y >= idims[0] - 1 || x < 1 || x >= idims[1] - 1)
                 continue;
 
-            float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-            float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+            float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
 
-            float mag = sqrt(dx*dx+dy*dy);
-            float ori = atan2(dy,dx);
-            float w = exp(-(i*i + j*j)/exp_denom);
+            float mag = sqrt(dx * dx + dy * dy);
+            float ori = atan2(dy, dx);
+            float w   = exp(-(i * i + j * j) / exp_denom);
 
-            int bin = round(n*(ori+PI_VAL)/(2.f*PI_VAL));
-            bin = bin < n ? bin : 0;
+            int bin = round(n * (ori + PI_VAL) / (2.f * PI_VAL));
+            bin     = bin < n ? bin : 0;
 
-            hist[bin] += w*mag;
+            hist[bin] += w * mag;
         }
 
         for (int i = 0; i < SmoothOriPasses; i++) {
+            for (int j = 0; j < n; j++) { temphist[j] = hist[j]; }
             for (int j = 0; j < n; j++) {
-                temphist[j] = hist[j];
-            }
-            for (int j = 0; j < n; j++) {
-                float prev = (j == 0) ? temphist[n-1] : temphist[j-1];
-                float next = (j+1 == n) ? temphist[0] : temphist[j+1];
-                hist[j] = 0.25f * prev + 0.5f * temphist[j] + 0.25f * next;
+                float prev = (j == 0) ? temphist[n - 1] : temphist[j - 1];
+                float next = (j + 1 == n) ? temphist[0] : temphist[j + 1];
+                hist[j]    = 0.25f * prev + 0.5f * temphist[j] + 0.25f * next;
             }
         }
 
         float omax = hist[0];
-        for (int i = 1; i < n; i++)
-            omax = max(omax, hist[i]);
+        for (int i = 1; i < n; i++) omax = max(omax, hist[i]);
 
         float mag_thr = (float)(omax * OriPeakRatio);
         int l, r;
         for (int j = 0; j < n; j++) {
             l = (j == 0) ? n - 1 : j - 1;
             r = (j + 1) % n;
-            if (hist[j] > hist[l] &&
-                hist[j] > hist[r] &&
-                hist[j] >= mag_thr) {
+            if (hist[j] > hist[l] && hist[j] > hist[r] && hist[j] >= mag_thr) {
                 if (*counter < max_feat) {
                     float bin = j + 0.5f * (hist[l] - hist[r]) /
-                        (hist[l] - 2.0f*hist[j] + hist[r]);
+                                        (hist[l] - 2.0f * hist[j] + hist[r]);
                     bin = (bin < 0.0f) ? bin + n : (bin >= n) ? bin - n : bin;
-                    float ori = 360.f - ((360.f/n) * bin);
+                    float ori = 360.f - ((360.f / n) * bin);
 
                     float new_real_x = real_x;
                     float new_real_y = real_y;
-                    float new_size = size;
+                    float new_size   = size;
 
                     if (double_input) {
                         float scale = 0.5f;
@@ -579,12 +530,12 @@ void calcOrientation(
                         new_size *= scale;
                     }
 
-                    x_out[*counter] = new_real_x;
-                    y_out[*counter] = new_real_y;
-                    layer_out[*counter] = layer;
+                    x_out[*counter]        = new_real_x;
+                    y_out[*counter]        = new_real_y;
+                    layer_out[*counter]    = layer;
                     response_out[*counter] = response;
-                    size_out[*counter] = new_size;
-                    ori_out[*counter] = ori;
+                    size_out[*counter]     = new_size;
+                    ori_out[*counter]      = ori;
                     (*counter)++;
                 }
             }
@@ -592,72 +543,56 @@ void calcOrientation(
     }
 }
 
-void normalizeDesc(
-    float* desc,
-    const int histlen)
-{
+void normalizeDesc(float* desc, const int histlen) {
     float len_sq = 0.0f;
 
-    for (int i = 0; i < histlen; i++)
-        len_sq += desc[i] * desc[i];
+    for (int i = 0; i < histlen; i++) len_sq += desc[i] * desc[i];
 
     float len_inv = 1.0f / sqrt(len_sq);
 
-    for (int i = 0; i < histlen; i++) {
-        desc[i] *= len_inv;
-    }
+    for (int i = 0; i < histlen; i++) { desc[i] *= len_inv; }
 }
 
 // Computes feature descriptors for features in an array.  Based on Section 6
 // of Lowe's paper.
 template<typename T>
-void computeDescriptor(
-    float* desc_out,
-    const unsigned desc_len,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const float* ori_in,
-    const unsigned total_feat,
-    const std::vector< Array<T> >& gauss_pyr,
-    const int d,
-    const int n,
-    const float scale,
-    const unsigned octave,
-    const unsigned n_layers)
-{
+void computeDescriptor(float* desc_out, const unsigned desc_len,
+                       const float* x_in, const float* y_in,
+                       const unsigned* layer_in, const float* response_in,
+                       const float* size_in, const float* ori_in,
+                       const unsigned total_feat,
+                       const std::vector<Array<T>>& gauss_pyr, const int d,
+                       const int n, const float scale, const unsigned octave,
+                       const unsigned n_layers) {
     UNUSED(response_in);
     float desc[128];
 
     for (unsigned f = 0; f < total_feat; f++) {
         const unsigned layer = layer_in[f];
-        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-        const float size = size_in[f];
-        const int fx = round(x_in[f] * scale);
-        const int fy = round(y_in[f] * scale);
+        float ori            = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori                  = (ori > PI_VAL) ? ori - PI_VAL * 2 : ori;
+        const float size     = size_in[f];
+        const int fx         = round(x_in[f] * scale);
+        const int fy         = round(y_in[f] * scale);
 
         // Points img to correct Gaussian pyramid layer
-        Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
+        Array<T> img     = gauss_pyr[octave * (n_layers + 3) + layer];
         const T* img_ptr = img.get();
-        af::dim4 idims = img.dims();
+        af::dim4 idims   = img.dims();
 
-        float cos_t = cos(ori);
-        float sin_t = sin(ori);
+        float cos_t        = cos(ori);
+        float sin_t        = sin(ori);
         float bins_per_rad = n / (PI_VAL * 2.f);
-        float exp_denom = d * d * 0.5f;
-        float hist_width = DescrSclFctr * size * scale * 0.5f;
-        int radius = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
+        float exp_denom    = d * d * 0.5f;
+        float hist_width   = DescrSclFctr * size * scale * 0.5f;
+        int radius         = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
 
-        int len = radius*2+1;
+        int len = radius * 2 + 1;
 
-        for (int i = 0; i < (int)desc_len; i++)
-            desc[i] = 0.f;
+        for (int i = 0; i < (int)desc_len; i++) desc[i] = 0.f;
 
         // Calculate orientation histogram
-        for (int l = 0; l < len*len; l++) {
+        for (int l = 0; l < len * len; l++) {
             int i = l / len - radius;
             int j = l % len - radius;
 
@@ -666,24 +601,22 @@ void computeDescriptor(
 
             float x_rot = (j * cos_t - i * sin_t) / hist_width;
             float y_rot = (j * sin_t + i * cos_t) / hist_width;
-            float xbin = x_rot + d/2 - 0.5f;
-            float ybin = y_rot + d/2 - 0.5f;
+            float xbin  = x_rot + d / 2 - 0.5f;
+            float ybin  = y_rot + d / 2 - 0.5f;
 
-            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d &&
-                y > 0 && y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d && y > 0 &&
+                y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
+                float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+                float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
 
-                float grad_mag = sqrt(dx*dx + dy*dy);
+                float grad_mag = sqrt(dx * dx + dy * dy);
                 float grad_ori = atan2(dy, dx) - ori;
-                while (grad_ori < 0.0f)
-                    grad_ori += PI_VAL*2;
-                while (grad_ori >= PI_VAL*2)
-                    grad_ori -= PI_VAL*2;
+                while (grad_ori < 0.0f) grad_ori += PI_VAL * 2;
+                while (grad_ori >= PI_VAL * 2) grad_ori -= PI_VAL * 2;
 
-                float w = exp(-(x_rot*x_rot + y_rot*y_rot) / exp_denom);
+                float w    = exp(-(x_rot * x_rot + y_rot * y_rot) / exp_denom);
                 float obin = grad_ori * bins_per_rad;
-                float mag = grad_mag*w;
+                float mag  = grad_mag * w;
 
                 int x0 = floor(xbin);
                 int y0 = floor(ybin);
@@ -699,11 +632,13 @@ void computeDescriptor(
                         for (int xl = 0; xl <= 1; xl++) {
                             int xb = x0 + xl;
                             if (xb >= 0 && xb < d) {
-                                float v_x = v_y * ((xl == 0) ? 1.0f - xbin : xbin);
+                                float v_x =
+                                    v_y * ((xl == 0) ? 1.0f - xbin : xbin);
                                 for (int ol = 0; ol <= 1; ol++) {
                                     int ob = (o0 + ol) % n;
-                                    float v_o = v_x * ((ol == 0) ? 1.0f - obin : obin);
-                                    desc[(yb*d + xb)*n + ob] += v_o;
+                                    float v_o =
+                                        v_x * ((ol == 0) ? 1.0f - obin : obin);
+                                    desc[(yb * d + xb) * n + ob] += v_o;
                                 }
                             }
                         }
@@ -721,54 +656,45 @@ void computeDescriptor(
 
         // Calculate final descriptor values
         for (int k = 0; k < (int)desc_len; k++) {
-            desc_out[f*desc_len+k] = round(min(255.f, desc[k] * IntDescrFctr));
+            desc_out[f * desc_len + k] =
+                round(min(255.f, desc[k] * IntDescrFctr));
         }
     }
 }
 
-// Computes GLOH feature descriptors for features in an array. Based on Section III-B
-// of Mikolajczyk and Schmid paper.
+// Computes GLOH feature descriptors for features in an array. Based on Section
+// III-B of Mikolajczyk and Schmid paper.
 template<typename T>
-void computeGLOHDescriptor(
-    float* desc_out,
-    const unsigned desc_len,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const float* ori_in,
-    const unsigned total_feat,
-    const std::vector< Array<T> >& gauss_pyr,
-    const int d,
-    const unsigned rb,
-    const unsigned ab,
-    const unsigned hb,
-    const float scale,
-    const unsigned octave,
-    const unsigned n_layers)
-{
+void computeGLOHDescriptor(float* desc_out, const unsigned desc_len,
+                           const float* x_in, const float* y_in,
+                           const unsigned* layer_in, const float* response_in,
+                           const float* size_in, const float* ori_in,
+                           const unsigned total_feat,
+                           const std::vector<Array<T>>& gauss_pyr, const int d,
+                           const unsigned rb, const unsigned ab,
+                           const unsigned hb, const float scale,
+                           const unsigned octave, const unsigned n_layers) {
     UNUSED(response_in);
     float desc[272];
 
     for (unsigned f = 0; f < total_feat; f++) {
         const unsigned layer = layer_in[f];
-        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-        const float size = size_in[f];
-        const int fx = round(x_in[f] * scale);
-        const int fy = round(y_in[f] * scale);
+        float ori            = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori                  = (ori > PI_VAL) ? ori - PI_VAL * 2 : ori;
+        const float size     = size_in[f];
+        const int fx         = round(x_in[f] * scale);
+        const int fy         = round(y_in[f] * scale);
 
         // Points img to correct Gaussian pyramid layer
-        Array<T> img = gauss_pyr[octave*(n_layers+3) + layer];
+        Array<T> img     = gauss_pyr[octave * (n_layers + 3) + layer];
         const T* img_ptr = img.get();
-        af::dim4 idims = img.dims();
+        af::dim4 idims   = img.dims();
 
-        float cos_t = cos(ori);
-        float sin_t = sin(ori);
-        float hist_bins_per_rad = hb / (PI_VAL * 2.f);
+        float cos_t              = cos(ori);
+        float sin_t              = sin(ori);
+        float hist_bins_per_rad  = hb / (PI_VAL * 2.f);
         float polar_bins_per_rad = ab / (PI_VAL * 2.f);
-        float exp_denom = GLOHRadii[rb-1] * 0.5f;
+        float exp_denom          = GLOHRadii[rb - 1] * 0.5f;
 
         float hist_width = DescrSclFctr * size * scale * 0.5f;
 
@@ -779,16 +705,15 @@ void computeGLOHDescriptor(
         // (rw) in the range of 0.25f-0.75f gives different results,
         // increasing it tends to show a better recall rate but with a
         // smaller amount of correct matches
-        //float rw = 0.5f;
-        //int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
+        // float rw = 0.5f;
+        // int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
 
-        int len = radius*2+1;
+        int len = radius * 2 + 1;
 
-        for (int i = 0; i < (int)desc_len; i++)
-            desc[i] = 0.f;
+        for (int i = 0; i < (int)desc_len; i++) desc[i] = 0.f;
 
         // Calculate orientation histogram
-        for (int l = 0; l < len*len; l++) {
+        for (int l = 0; l < len * len; l++) {
             int i = l / len - radius;
             int j = l % len - radius;
 
@@ -798,33 +723,36 @@ void computeGLOHDescriptor(
             float x_rot = (j * cos_t - i * sin_t);
             float y_rot = (j * sin_t + i * cos_t);
 
-            float r = sqrt(x_rot*x_rot + y_rot*y_rot) / radius * GLOHRadii[rb-1];
+            float r = sqrt(x_rot * x_rot + y_rot * y_rot) / radius *
+                      GLOHRadii[rb - 1];
             float theta = atan2(y_rot, x_rot);
-            while (theta < 0.0f)
-                theta += PI_VAL*2;
-            while (theta >= PI_VAL*2)
-                theta -= PI_VAL*2;
+            while (theta < 0.0f) theta += PI_VAL * 2;
+            while (theta >= PI_VAL * 2) theta -= PI_VAL * 2;
 
             float tbin = theta * polar_bins_per_rad;
-            float rbin = (r < GLOHRadii[0]) ? r / GLOHRadii[0] :
-                         ((r < GLOHRadii[1]) ? 1 + (r - GLOHRadii[0]) / (float)(GLOHRadii[1] - GLOHRadii[0]) :
-                         min(2 + (r - GLOHRadii[1]) / (float)(GLOHRadii[2] - GLOHRadii[1]), 3.f-FLT_EPSILON));
-
-            if (r <= GLOHRadii[rb-1] &&
-                y > 0 && y < idims[0] - 1 && x > 0 && x < idims[1] - 1) {
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
-
-                float grad_mag = sqrt(dx*dx + dy*dy);
+            float rbin =
+                (r < GLOHRadii[0])
+                    ? r / GLOHRadii[0]
+                    : ((r < GLOHRadii[1])
+                           ? 1 + (r - GLOHRadii[0]) /
+                                     (float)(GLOHRadii[1] - GLOHRadii[0])
+                           : min(2 + (r - GLOHRadii[1]) /
+                                         (float)(GLOHRadii[2] - GLOHRadii[1]),
+                                 3.f - FLT_EPSILON));
+
+            if (r <= GLOHRadii[rb - 1] && y > 0 && y < idims[0] - 1 && x > 0 &&
+                x < idims[1] - 1) {
+                float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+                float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
+
+                float grad_mag = sqrt(dx * dx + dy * dy);
                 float grad_ori = atan2(dy, dx) - ori;
-                while (grad_ori < 0.0f)
-                    grad_ori += PI_VAL*2;
-                while (grad_ori >= PI_VAL*2)
-                    grad_ori -= PI_VAL*2;
+                while (grad_ori < 0.0f) grad_ori += PI_VAL * 2;
+                while (grad_ori >= PI_VAL * 2) grad_ori -= PI_VAL * 2;
 
-                float w = exp(-r / exp_denom);
+                float w    = exp(-r / exp_denom);
                 float obin = grad_ori * hist_bins_per_rad;
-                float mag = grad_mag*w;
+                float mag  = grad_mag * w;
 
                 int t0 = floor(tbin);
                 int r0 = floor(rbin);
@@ -834,16 +762,20 @@ void computeGLOHDescriptor(
                 obin -= o0;
 
                 for (int rl = 0; rl <= 1; rl++) {
-                    int rb = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
+                    int rb    = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
                     float v_r = mag * ((rl == 0) ? 1.0f - rbin : rbin);
                     if (rb >= 0 && rb <= 2) {
                         for (int tl = 0; tl <= 1; tl++) {
-                            int tb = (t0 + tl) % ab;
+                            int tb    = (t0 + tl) % ab;
                             float v_t = v_r * ((tl == 0) ? 1.0f - tbin : tbin);
                             for (int ol = 0; ol <= 1; ol++) {
                                 int ob = (o0 + ol) % hb;
-                                float v_o = v_t * ((ol == 0) ? 1.0f - obin : obin);
-                                unsigned idx = (rb > 0) * (hb + ((rb-1) * ab + tb)*hb) + ob;
+                                float v_o =
+                                    v_t * ((ol == 0) ? 1.0f - obin : obin);
+                                unsigned idx =
+                                    (rb > 0) *
+                                        (hb + ((rb - 1) * ab + tb) * hb) +
+                                    ob;
                                 desc[idx] += v_o;
                             }
                         }
@@ -861,7 +793,8 @@ void computeGLOHDescriptor(
 
         // Calculate final descriptor values
         for (int k = 0; k < (int)desc_len; k++) {
-            desc_out[f*desc_len+k] = round(min(255.f, desc[k] * IntDescrFctr));
+            desc_out[f * desc_len + k] =
+                round(min(255.f, desc[k] * IntDescrFctr));
         }
     }
 }
@@ -869,25 +802,26 @@ void computeGLOHDescriptor(
 #undef IPTR
 
 template<typename T, typename convAccT>
-Array<T> createInitialImage(
-    const Array<T>& img,
-    const float init_sigma,
-    const bool double_input)
-{
+Array<T> createInitialImage(const Array<T>& img, const float init_sigma,
+                            const bool double_input) {
     af::dim4 idims = img.dims();
 
     Array<T> init_img = createEmptyArray<T>(af::dim4());
 
-    float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma * 4), 0.1f)
-                             : std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma), 0.1f);
+    float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma -
+                                                    InitSigma * InitSigma * 4),
+                                        0.1f)
+                             : std::max((float)sqrt(init_sigma * init_sigma -
+                                                    InitSigma * InitSigma),
+                                        0.1f);
 
     Array<T> filter = gauss_filter<T>(s);
 
     if (double_input) {
-        Array<T> double_img = resize<T>(img, idims[0] * 2, idims[1] * 2, AF_INTERP_BILINEAR);
+        Array<T> double_img =
+            resize<T>(img, idims[0] * 2, idims[1] * 2, AF_INTERP_BILINEAR);
         init_img = convolve2<T, convAccT, false>(double_img, filter, filter);
-    }
-    else {
+    } else {
         init_img = convolve2<T, convAccT, false>(img, filter, filter);
     }
 
@@ -895,41 +829,41 @@ Array<T> createInitialImage(
 }
 
 template<typename T, typename convAccT>
-std::vector< Array<T> > buildGaussPyr(
-    const Array<T>& init_img,
-    const unsigned n_octaves,
-    const unsigned n_layers,
-    const float init_sigma)
-{
+std::vector<Array<T>> buildGaussPyr(const Array<T>& init_img,
+                                    const unsigned n_octaves,
+                                    const unsigned n_layers,
+                                    const float init_sigma) {
     // Precompute Gaussian sigmas using the following formula:
     // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
     std::vector<float> sig_layers(n_layers + 3);
     sig_layers[0] = init_sigma;
-    float k = std::pow(2.0f, 1.0f / n_layers);
+    float k       = std::pow(2.0f, 1.0f / n_layers);
     for (unsigned i = 1; i < n_layers + 3; i++) {
-        float sig_prev = std::pow(k, i-1) * init_sigma;
+        float sig_prev  = std::pow(k, i - 1) * init_sigma;
         float sig_total = sig_prev * k;
-        sig_layers[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);
+        sig_layers[i] = std::sqrt(sig_total * sig_total - sig_prev * sig_prev);
     }
 
     // Gaussian Pyramid
-    std::vector< Array<T> > gauss_pyr(n_octaves * (n_layers+3), createEmptyArray<T>(af::dim4()));
+    std::vector<Array<T>> gauss_pyr(n_octaves * (n_layers + 3),
+                                    createEmptyArray<T>(af::dim4()));
     for (unsigned o = 0; o < n_octaves; o++) {
-        for (unsigned l = 0; l < n_layers+3; l++) {
-            unsigned src_idx = (l == 0) ? (o-1)*(n_layers+3) + n_layers : o*(n_layers+3) + l-1;
-            unsigned idx = o*(n_layers+3) + l;
+        for (unsigned l = 0; l < n_layers + 3; l++) {
+            unsigned src_idx = (l == 0) ? (o - 1) * (n_layers + 3) + n_layers
+                                        : o * (n_layers + 3) + l - 1;
+            unsigned idx = o * (n_layers + 3) + l;
 
             if (o == 0 && l == 0) {
                 gauss_pyr[idx] = init_img;
-            }
-            else if (l == 0) {
+            } else if (l == 0) {
                 af::dim4 sdims = gauss_pyr[src_idx].dims();
-                gauss_pyr[idx] = resize<T>(gauss_pyr[src_idx], sdims[0] / 2, sdims[1] / 2, AF_INTERP_BILINEAR);
-            }
-            else {
+                gauss_pyr[idx] = resize<T>(gauss_pyr[src_idx], sdims[0] / 2,
+                                           sdims[1] / 2, AF_INTERP_BILINEAR);
+            } else {
                 Array<T> filter = gauss_filter<T>(sig_layers[l]);
 
-                gauss_pyr[idx] = convolve2<T, convAccT, false>(gauss_pyr[src_idx], filter, filter);
+                gauss_pyr[idx] = convolve2<T, convAccT, false>(
+                    gauss_pyr[src_idx], filter, filter);
             }
         }
     }
@@ -938,18 +872,17 @@ std::vector< Array<T> > buildGaussPyr(
 }
 
 template<typename T>
-std::vector< Array<T> > buildDoGPyr(
-    std::vector< Array<T> >& gauss_pyr,
-    const unsigned n_octaves,
-    const unsigned n_layers)
-{
+std::vector<Array<T>> buildDoGPyr(std::vector<Array<T>>& gauss_pyr,
+                                  const unsigned n_octaves,
+                                  const unsigned n_layers) {
     // DoG Pyramid
-    std::vector< Array<T> > dog_pyr(n_octaves * (n_layers+2), createEmptyArray<T>(af::dim4()));
+    std::vector<Array<T>> dog_pyr(n_octaves * (n_layers + 2),
+                                  createEmptyArray<T>(af::dim4()));
     for (unsigned o = 0; o < n_octaves; o++) {
-        for (unsigned l = 0; l < n_layers+2; l++) {
-            unsigned idx    = o*(n_layers+2) + l;
-            unsigned bottom = o*(n_layers+3) + l;
-            unsigned top    = o*(n_layers+3) + l+1;
+        for (unsigned l = 0; l < n_layers + 2; l++) {
+            unsigned idx    = o * (n_layers + 2) + l;
+            unsigned bottom = o * (n_layers + 3) + l;
+            unsigned top    = o * (n_layers + 3) + l + 1;
 
             dog_pyr[idx] = createEmptyArray<T>(gauss_pyr[bottom].dims());
 
@@ -960,7 +893,6 @@ std::vector< Array<T> > buildDoGPyr(
     return dog_pyr;
 }
 
-
 template<typename T, typename convAccT>
 unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
                    Array<float>& ori, Array<float>& size, Array<float>& desc,
@@ -968,11 +900,10 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
                    const float contrast_thr, const float edge_thr,
                    const float init_sigma, const bool double_input,
                    const float img_scale, const float feature_ratio,
-                   const bool compute_GLOH)
-{
-    using std::vector;
-    using std::unique_ptr;
+                   const bool compute_GLOH) {
     using std::function;
+    using std::unique_ptr;
+    using std::vector;
     in.eval();
     getQueue().sync();
     af::dim4 idims = in.dims();
@@ -982,11 +913,14 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
 
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
-    Array<T> init_img = createInitialImage<T, convAccT>(in, init_sigma, double_input);
+    Array<T> init_img =
+        createInitialImage<T, convAccT>(in, init_sigma, double_input);
 
-    std::vector< Array<T> > gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+    std::vector<Array<T>> gauss_pyr =
+        buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-    std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
+    std::vector<Array<T>> dog_pyr =
+        buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
 
     vector<uptr<float>> x_pyr(n_octaves);
     vector<uptr<float>> y_pyr(n_octaves);
@@ -997,78 +931,77 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
     vector<unsigned> feat_pyr(n_octaves, 0);
     unsigned total_feat = 0;
 
-    const unsigned d = DescrWidth;
-    const unsigned n = DescrHistBins;
+    const unsigned d  = DescrWidth;
+    const unsigned n  = DescrHistBins;
     const unsigned rb = GLOHRadialBins;
     const unsigned ab = GLOHAngularBins;
     const unsigned hb = GLOHHistBins;
-    const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
+    const unsigned desc_len =
+        (compute_GLOH) ? (1 + (rb - 1) * ab) * hb : d * d * n;
 
     for (unsigned i = 0; i < n_octaves; i++) {
-        af::dim4 ddims = dog_pyr[i*(n_layers+2)].dims();
-        if (ddims[0]-2*ImgBorder < 1 ||
-            ddims[1]-2*ImgBorder < 1)
+        af::dim4 ddims = dog_pyr[i * (n_layers + 2)].dims();
+        if (ddims[0] - 2 * ImgBorder < 1 || ddims[1] - 2 * ImgBorder < 1)
             continue;
 
-        const unsigned imel = ddims[0] * ddims[1];
+        const unsigned imel     = ddims[0] * ddims[1];
         const unsigned max_feat = ceil(imel * feature_ratio);
 
-        auto extrema_x = memAlloc<float>(max_feat);
-        auto extrema_y = memAlloc<float>(max_feat);
-        auto extrema_layer = memAlloc<unsigned>(max_feat);
+        auto extrema_x        = memAlloc<float>(max_feat);
+        auto extrema_y        = memAlloc<float>(max_feat);
+        auto extrema_layer    = memAlloc<unsigned>(max_feat);
         unsigned extrema_feat = 0;
 
         for (unsigned j = 1; j <= n_layers; j++) {
-            unsigned prev   = i*(n_layers+2) + j-1;
-            unsigned center = i*(n_layers+2) + j;
-            unsigned next   = i*(n_layers+2) + j+1;
+            unsigned prev   = i * (n_layers + 2) + j - 1;
+            unsigned center = i * (n_layers + 2) + j;
+            unsigned next   = i * (n_layers + 2) + j + 1;
 
             unsigned layer = j;
 
             float extrema_thr = 0.5f * contrast_thr / n_layers;
-            detectExtrema<T>(extrema_x.get(), extrema_y.get(), extrema_layer.get(), &extrema_feat,
-                             dog_pyr[prev], dog_pyr[center], dog_pyr[next],
-                             layer, max_feat, extrema_thr);
+            detectExtrema<T>(extrema_x.get(), extrema_y.get(),
+                             extrema_layer.get(), &extrema_feat, dog_pyr[prev],
+                             dog_pyr[center], dog_pyr[next], layer, max_feat,
+                             extrema_thr);
         }
 
         extrema_feat = min(extrema_feat, max_feat);
 
-        if (extrema_feat == 0) {
-            continue;
-        }
+        if (extrema_feat == 0) { continue; }
 
         unsigned interp_feat = 0;
 
-        auto interp_x = memAlloc<float>(extrema_feat);
-        auto interp_y = memAlloc<float>(extrema_feat);
-        auto interp_layer = memAlloc<unsigned>(extrema_feat);
+        auto interp_x        = memAlloc<float>(extrema_feat);
+        auto interp_y        = memAlloc<float>(extrema_feat);
+        auto interp_layer    = memAlloc<unsigned>(extrema_feat);
         auto interp_response = memAlloc<float>(extrema_feat);
-        auto interp_size = memAlloc<float>(extrema_feat);
+        auto interp_size     = memAlloc<float>(extrema_feat);
 
-        interpolateExtrema<T>(interp_x.get(), interp_y.get(), interp_layer.get(),
-                              interp_response.get(), interp_size.get(), &interp_feat,
-                              extrema_x.get(), extrema_y.get(), extrema_layer.get(), extrema_feat,
-                              dog_pyr, max_feat, i, n_layers,
+        interpolateExtrema<T>(interp_x.get(), interp_y.get(),
+                              interp_layer.get(), interp_response.get(),
+                              interp_size.get(), &interp_feat, extrema_x.get(),
+                              extrema_y.get(), extrema_layer.get(),
+                              extrema_feat, dog_pyr, max_feat, i, n_layers,
                               contrast_thr, edge_thr, init_sigma, img_scale);
 
         interp_feat = min(interp_feat, max_feat);
 
-        if (interp_feat == 0) {
-            continue;
-        }
+        if (interp_feat == 0) { continue; }
 
         std::vector<feat_t> sorted_feat;
-        array_to_feat(sorted_feat, interp_x.get(), interp_y.get(), interp_layer.get(),
-                      interp_response.get(), interp_size.get(), interp_feat);
+        array_to_feat(sorted_feat, interp_x.get(), interp_y.get(),
+                      interp_layer.get(), interp_response.get(),
+                      interp_size.get(), interp_feat);
         std::stable_sort(sorted_feat.begin(), sorted_feat.end(), feat_cmp);
 
         unsigned nodup_feat = 0;
 
-        auto nodup_x = memAlloc<float>(interp_feat);
-        auto nodup_y = memAlloc<float>(interp_feat);
-        auto nodup_layer = memAlloc<unsigned>(interp_feat);
+        auto nodup_x        = memAlloc<float>(interp_feat);
+        auto nodup_y        = memAlloc<float>(interp_feat);
+        auto nodup_layer    = memAlloc<unsigned>(interp_feat);
         auto nodup_response = memAlloc<float>(interp_feat);
-        auto nodup_size = memAlloc<float>(interp_feat);
+        auto nodup_size     = memAlloc<float>(interp_feat);
 
         removeDuplicates(nodup_x.get(), nodup_y.get(), nodup_layer.get(),
                          nodup_response.get(), nodup_size.get(), &nodup_feat,
@@ -1076,53 +1009,52 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
 
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        auto oriented_x = memAlloc<float>(max_oriented_feat);
-        auto oriented_y = memAlloc<float>(max_oriented_feat);
-        auto oriented_layer = memAlloc<unsigned>(max_oriented_feat);
+        auto oriented_x        = memAlloc<float>(max_oriented_feat);
+        auto oriented_y        = memAlloc<float>(max_oriented_feat);
+        auto oriented_layer    = memAlloc<unsigned>(max_oriented_feat);
         auto oriented_response = memAlloc<float>(max_oriented_feat);
-        auto oriented_size = memAlloc<float>(max_oriented_feat);
-        auto oriented_ori = memAlloc<float>(max_oriented_feat);
+        auto oriented_size     = memAlloc<float>(max_oriented_feat);
+        auto oriented_ori      = memAlloc<float>(max_oriented_feat);
 
         unsigned oriented_feat = 0;
 
-        calcOrientation<T>(oriented_x.get(), oriented_y.get(), oriented_layer.get(),
-                           oriented_response.get(), oriented_size.get(), oriented_ori.get(), &oriented_feat,
-                           nodup_x.get(), nodup_y.get(), nodup_layer.get(),
-                           nodup_response.get(), nodup_size.get(), nodup_feat,
-                           gauss_pyr, max_oriented_feat, i, n_layers, double_input);
-
+        calcOrientation<T>(
+            oriented_x.get(), oriented_y.get(), oriented_layer.get(),
+            oriented_response.get(), oriented_size.get(), oriented_ori.get(),
+            &oriented_feat, nodup_x.get(), nodup_y.get(), nodup_layer.get(),
+            nodup_response.get(), nodup_size.get(), nodup_feat, gauss_pyr,
+            max_oriented_feat, i, n_layers, double_input);
 
-        if (oriented_feat == 0) {
-            continue;
-        }
+        if (oriented_feat == 0) { continue; }
 
         auto desc = memAlloc<float>(oriented_feat * desc_len);
 
-        float scale = 1.f/(1 << i);
+        float scale = 1.f / (1 << i);
         if (double_input) scale *= 2.f;
 
         if (compute_GLOH)
-            computeGLOHDescriptor<T>(desc.get(), desc_len,
-                                     oriented_x.get(), oriented_y.get(), oriented_layer.get(),
-                                     oriented_response.get(), oriented_size.get(), oriented_ori.get(),
-                                     oriented_feat, gauss_pyr, d, rb, ab, hb,
-                                     scale, i, n_layers);
+            computeGLOHDescriptor<T>(
+                desc.get(), desc_len, oriented_x.get(), oriented_y.get(),
+                oriented_layer.get(), oriented_response.get(),
+                oriented_size.get(), oriented_ori.get(), oriented_feat,
+                gauss_pyr, d, rb, ab, hb, scale, i, n_layers);
         else
-            computeDescriptor<T>(desc.get(), desc_len,
-                                 oriented_x.get(), oriented_y.get(), oriented_layer.get(),
-                                 oriented_response.get(), oriented_size.get(), oriented_ori.get(),
-                                 oriented_feat, gauss_pyr, d, n, scale, i, n_layers);
+            computeDescriptor<T>(desc.get(), desc_len, oriented_x.get(),
+                                 oriented_y.get(), oriented_layer.get(),
+                                 oriented_response.get(), oriented_size.get(),
+                                 oriented_ori.get(), oriented_feat, gauss_pyr,
+                                 d, n, scale, i, n_layers);
 
         total_feat += oriented_feat;
         feat_pyr[i] = oriented_feat;
 
         if (oriented_feat > 0) {
-          x_pyr[i] = std::move(oriented_x);
-          y_pyr[i] = std::move(oriented_y);
-          response_pyr[i] = std::move(oriented_response);
-          ori_pyr[i] = std::move(oriented_ori);
-          size_pyr[i] = std::move(oriented_size);
-          desc_pyr[i] = std::move(desc);
+            x_pyr[i]        = std::move(oriented_x);
+            y_pyr[i]        = std::move(oriented_y);
+            response_pyr[i] = std::move(oriented_response);
+            ori_pyr[i]      = std::move(oriented_ori);
+            size_pyr[i]     = std::move(oriented_size);
+            desc_pyr[i]     = std::move(desc);
         }
     }
 
@@ -1138,25 +1070,28 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
         size  = createEmptyArray<float>(total_feat_dims);
         desc  = createEmptyArray<float>(desc_dims);
 
-        float* x_ptr = x.get();
-        float* y_ptr = y.get();
+        float* x_ptr     = x.get();
+        float* y_ptr     = y.get();
         float* score_ptr = score.get();
-        float* ori_ptr = ori.get();
-        float* size_ptr = size.get();
-        float* desc_ptr = desc.get();
+        float* ori_ptr   = ori.get();
+        float* size_ptr  = size.get();
+        float* desc_ptr  = desc.get();
 
         unsigned offset = 0;
         for (unsigned i = 0; i < n_octaves; i++) {
-            if (feat_pyr[i] == 0)
-                continue;
-
-            memcpy(x_ptr+offset,     x_pyr[i].get(),        feat_pyr[i] * sizeof(float));
-            memcpy(y_ptr+offset,     y_pyr[i].get(),        feat_pyr[i] * sizeof(float));
-            memcpy(score_ptr+offset, response_pyr[i].get(), feat_pyr[i] * sizeof(float));
-            memcpy(ori_ptr+offset,   ori_pyr[i].get(),      feat_pyr[i] * sizeof(float));
-            memcpy(size_ptr+offset,  size_pyr[i].get(),     feat_pyr[i] * sizeof(float));
-
-            memcpy(desc_ptr+(offset*desc_len), desc_pyr[i].get(), feat_pyr[i] * desc_len * sizeof(float));
+            if (feat_pyr[i] == 0) continue;
+
+            memcpy(x_ptr + offset, x_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(y_ptr + offset, y_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(score_ptr + offset, response_pyr[i].get(),
+                   feat_pyr[i] * sizeof(float));
+            memcpy(ori_ptr + offset, ori_pyr[i].get(),
+                   feat_pyr[i] * sizeof(float));
+            memcpy(size_ptr + offset, size_pyr[i].get(),
+                   feat_pyr[i] * sizeof(float));
+
+            memcpy(desc_ptr + (offset * desc_len), desc_pyr[i].get(),
+                   feat_pyr[i] * desc_len * sizeof(float));
             offset += feat_pyr[i];
         }
     }
@@ -1164,4 +1099,4 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
     return total_feat;
 }
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index 0f629a3d13..255a6bb741 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -11,66 +11,76 @@
 #include <Param.hpp>
 #include <cassert>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename Ti, typename To, bool isDX>
-void derivative(Param<To> output, CParam<Ti> input)
-{
-    const af::dim4 dims    = input.dims();
+void derivative(Param<To> output, CParam<Ti> input) {
+    const af::dim4 dims     = input.dims();
     const af::dim4 istrides = input.strides();
     const af::dim4 ostrides = output.strides();
 
-    for(dim_t b3=0; b3<dims[3]; ++b3) {
-        To* optr     = output.get() + b3 * ostrides[3];
-        const Ti* iptr     = input.get() + b3 * istrides[3];
-        for(dim_t b2=0; b2<dims[2]; ++b2) {
-
-            for(dim_t j=0; j<dims[1]; ++j) {
-
-                int joff  = j;
-                int _joff = j-1;
-                int joff_ = j+1;
-                int joffset = j*ostrides[1];
-
-                for(dim_t i=0; i<dims[0]; ++i) {
-
+    for (dim_t b3 = 0; b3 < dims[3]; ++b3) {
+        To* optr       = output.get() + b3 * ostrides[3];
+        const Ti* iptr = input.get() + b3 * istrides[3];
+        for (dim_t b2 = 0; b2 < dims[2]; ++b2) {
+            for (dim_t j = 0; j < dims[1]; ++j) {
+                int joff    = j;
+                int _joff   = j - 1;
+                int joff_   = j + 1;
+                int joffset = j * ostrides[1];
+
+                for (dim_t i = 0; i < dims[0]; ++i) {
                     To accum = To(0);
 
-                    int  ioff = i;
-                    int _ioff = i-1;
-                    int ioff_ = i+1;
-
-                    To NW = (_ioff>=0 && _joff>=0) ?
-                        iptr[_joff*istrides[1]+_ioff*istrides[0]] : 0;
-                    To SW = (ioff_<(int)dims[0] && _joff>=0) ?
-                        iptr[_joff*istrides[1]+ioff_*istrides[0]] : 0;
-                    To NE = (_ioff>=0 && joff_<(int)dims[1]) ?
-                        iptr[joff_*istrides[1]+_ioff*istrides[0]] : 0;
-                    To SE = (ioff_<(int)dims[0] && joff_<(int)dims[1]) ?
-                                   iptr[joff_*istrides[1]+ioff_*istrides[0]] : 0;
+                    int ioff  = i;
+                    int _ioff = i - 1;
+                    int ioff_ = i + 1;
+
+                    To NW =
+                        (_ioff >= 0 && _joff >= 0)
+                            ? iptr[_joff * istrides[1] + _ioff * istrides[0]]
+                            : 0;
+                    To SW =
+                        (ioff_ < (int)dims[0] && _joff >= 0)
+                            ? iptr[_joff * istrides[1] + ioff_ * istrides[0]]
+                            : 0;
+                    To NE =
+                        (_ioff >= 0 && joff_ < (int)dims[1])
+                            ? iptr[joff_ * istrides[1] + _ioff * istrides[0]]
+                            : 0;
+                    To SE =
+                        (ioff_ < (int)dims[0] && joff_ < (int)dims[1])
+                            ? iptr[joff_ * istrides[1] + ioff_ * istrides[0]]
+                            : 0;
 
                     if (isDX) {
-                        To W  = _joff>=0 ?
-                                   iptr[_joff*istrides[1]+ioff*istrides[0]] : 0;
+                        To W =
+                            _joff >= 0
+                                ? iptr[_joff * istrides[1] + ioff * istrides[0]]
+                                : 0;
 
-                        To E  = joff_<(int)dims[1] ?
-                                      iptr[joff_*istrides[1]+ioff*istrides[0]] : 0;
+                        To E =
+                            joff_ < (int)dims[1]
+                                ? iptr[joff_ * istrides[1] + ioff * istrides[0]]
+                                : 0;
 
-                        accum = NW+SW - (NE+SE) + 2*(W-E);
+                        accum = NW + SW - (NE + SE) + 2 * (W - E);
                     } else {
-                        To N  = _ioff>=0 ?
-                                   iptr[joff*istrides[1]+_ioff*istrides[0]] : 0;
+                        To N =
+                            _ioff >= 0
+                                ? iptr[joff * istrides[1] + _ioff * istrides[0]]
+                                : 0;
 
-                        To S  = ioff_<(int)dims[0] ?
-                                      iptr[joff*istrides[1]+ioff_*istrides[0]] : 0;
+                        To S =
+                            ioff_ < (int)dims[0]
+                                ? iptr[joff * istrides[1] + ioff_ * istrides[0]]
+                                : 0;
 
-                        accum = NW+NE - (SW+SE) + 2*(N-S);
+                        accum = NW + NE - (SW + SE) + 2 * (N - S);
                     }
 
-                    optr[joffset+i*ostrides[0]] = accum;
+                    optr[joffset + i * ostrides[0]] = accum;
                 }
             }
 
@@ -80,5 +90,5 @@ void derivative(Param<To> output, CParam<Ti> input)
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index dd842dceb1..f0bbf07a71 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -9,34 +9,30 @@
 
 #pragma once
 #include <Param.hpp>
+#include <err_cpu.hpp>
 #include <math.hpp>
 #include <algorithm>
-#include <numeric>
-#include <err_cpu.hpp>
 #include <functional>
+#include <numeric>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 // Based off of http://stackoverflow.com/a/12399290
 template<typename T>
-void sort0Iterative(Param<T> val, bool isAscending)
-{
+void sort0Iterative(Param<T> val, bool isAscending) {
     // initialize original index locations
     T *val_ptr = val.get();
 
     function<bool(T, T)> op = std::greater<T>();
-    if(isAscending) { op = std::less<T>(); }
+    if (isAscending) { op = std::less<T>(); }
 
     T *comp_ptr = nullptr;
-    for(dim_t w = 0; w < val.dims(3); w++) {
+    for (dim_t w = 0; w < val.dims(3); w++) {
         dim_t valW = w * val.strides(3);
-        for(dim_t z = 0; z < val.dims(2); z++) {
+        for (dim_t z = 0; z < val.dims(2); z++) {
             dim_t valWZ = valW + z * val.strides(2);
-            for(dim_t y = 0; y < val.dims(1); y++) {
-
+            for (dim_t y = 0; y < val.dims(1); y++) {
                 dim_t valOffset = valWZ + y * val.strides(1);
 
                 comp_ptr = val_ptr + valOffset;
@@ -47,5 +43,5 @@ void sort0Iterative(Param<T> val, bool isAscending)
     return;
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 450fbfb092..9f67a570c0 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -10,19 +10,18 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename Tk, typename Tv>
 void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
 template<typename Tk, typename Tv>
-void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim, bool isAscending);
+void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim,
+                      bool isAscending);
 
 template<typename Tk, typename Tv>
 void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index e3cca6f663..05d6709bda 100644
--- a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -11,10 +11,8 @@
 
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar
 
-namespace cpu
-{
-namespace kernel
-{
-    INSTANTIATE1(TYPE)
-}
+namespace cpu {
+namespace kernel {
+INSTANTIATE1(TYPE)
 }
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index 3f7fe4904e..c10ac89747 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -8,26 +8,23 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <err_cpu.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <kernel/sort_helper.hpp>
-#include <Param.hpp>
 #include <math.hpp>
 #include <algorithm>
+#include <functional>
 #include <numeric>
 #include <queue>
-#include <err_cpu.hpp>
-#include <functional>
 #include <tuple>
 #include <utility>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename Tk, typename Tv>
-void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
-{
+void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
     // Get pointers and initialize original index locations
     Tk *okey_ptr = okey.get();
     Tv *oval_ptr = oval.get();
@@ -37,33 +34,35 @@ void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
     dim_t size = okey.dims(0);
     std::vector<CurrentPair> pairKeyVal(size);
 
-    for(dim_t w = 0; w < okey.dims(3); w++) {
+    for (dim_t w = 0; w < okey.dims(3); w++) {
         dim_t okeyW = w * okey.strides(3);
         dim_t ovalW = w * oval.strides(3);
 
-        for(dim_t z = 0; z < okey.dims(2); z++) {
+        for (dim_t z = 0; z < okey.dims(2); z++) {
             dim_t okeyWZ = okeyW + z * okey.strides(2);
             dim_t ovalWZ = ovalW + z * oval.strides(2);
 
-            for(dim_t y = 0; y < okey.dims(1); y++) {
-
+            for (dim_t y = 0; y < okey.dims(1); y++) {
                 dim_t okeyOffset = okeyWZ + y * okey.strides(1);
                 dim_t ovalOffset = ovalWZ + y * oval.strides(1);
 
                 Tk *okey_col_ptr = okey_ptr + okeyOffset;
                 Tv *oval_col_ptr = oval_ptr + ovalOffset;
 
-                for(dim_t x = 0; x < size; x++) {
-                   pairKeyVal[x] = std::make_tuple(okey_col_ptr[x], oval_col_ptr[x]);
+                for (dim_t x = 0; x < size; x++) {
+                    pairKeyVal[x] =
+                        std::make_tuple(okey_col_ptr[x], oval_col_ptr[x]);
                 }
 
-                if(isAscending) {
-                    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), IPCompare<Tk, Tv, true>());
+                if (isAscending) {
+                    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(),
+                                     IPCompare<Tk, Tv, true>());
                 } else {
-                    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), IPCompare<Tk, Tv, false>());
+                    std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(),
+                                     IPCompare<Tk, Tv, false>());
                 }
 
-                for(unsigned x = 0; x < size; x++) {
+                for (unsigned x = 0; x < size; x++) {
                     okey_ptr[okeyOffset + x] = std::get<0>(pairKeyVal[x]);
                     oval_ptr[ovalOffset + x] = std::get<1>(pairKeyVal[x]);
                 }
@@ -75,36 +74,37 @@ void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
 }
 
 template<typename Tk, typename Tv>
-void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim, bool isAscending)
-{
+void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim,
+                      bool isAscending) {
     af::dim4 inDims = okey.dims();
 
     af::dim4 tileDims(1);
     af::dim4 seqDims = inDims;
-    tileDims[dim] = inDims[dim];
-    seqDims[dim] = 1;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
 
     std::vector<uint> key(inDims.elements());
     // IOTA
     {
-        af::dim4 dims    = inDims;
-        uint* out        = key.data();
+        af::dim4 dims = inDims;
+        uint *out     = key.data();
         af::dim4 strides(1);
-        for(int i = 1; i < 4; i++)
-            strides[i] = strides[i-1] * dims[i-1];
+        for (int i = 1; i < 4; i++) strides[i] = strides[i - 1] * dims[i - 1];
 
-        for(dim_t w = 0; w < dims[3]; w++) {
+        for (dim_t w = 0; w < dims[3]; w++) {
             dim_t offW = w * strides[3];
-            dim_t okeyW = (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
-            for(dim_t z = 0; z < dims[2]; z++) {
+            dim_t okeyW =
+                (w % seqDims[3]) * seqDims[0] * seqDims[1] * seqDims[2];
+            for (dim_t z = 0; z < dims[2]; z++) {
                 dim_t offWZ = offW + z * strides[2];
-                dim_t okeyZ = okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
-                for(dim_t y = 0; y < dims[1]; y++) {
+                dim_t okeyZ =
+                    okeyW + (z % seqDims[2]) * seqDims[0] * seqDims[1];
+                for (dim_t y = 0; y < dims[1]; y++) {
                     dim_t offWZY = offWZ + y * strides[1];
-                    dim_t okeyY = okeyZ + (y % seqDims[1]) * seqDims[0];
-                    for(dim_t x = 0; x < dims[0]; x++) {
+                    dim_t okeyY  = okeyZ + (y % seqDims[1]) * seqDims[0];
+                    for (dim_t x = 0; x < dims[0]; x++) {
                         dim_t id = offWZY + x;
-                        out[id] = okeyY + (x % seqDims[0]);
+                        out[id]  = okeyY + (x % seqDims[0]);
                     }
                 }
             }
@@ -119,57 +119,57 @@ void sortByKeyBatched(Param<Tk> okey, Param<Tv> oval, const int dim, bool isAsce
     size_t size = okey.dims().elements();
     std::vector<CurrentTuple> tupleKeyValIdx(size);
 
-    for(unsigned i = 0; i < size; i++) {
+    for (unsigned i = 0; i < size; i++) {
         tupleKeyValIdx[i] = std::make_tuple(okey_ptr[i], oval_ptr[i], key[i]);
     }
 
-
-    if(isAscending) {
-        std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(), KIPCompareV<Tk, Tv, true>());
-    }
-    else {
-        std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(), KIPCompareV<Tk, Tv, false>());
+    if (isAscending) {
+        std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(),
+                         KIPCompareV<Tk, Tv, true>());
+    } else {
+        std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(),
+                         KIPCompareV<Tk, Tv, false>());
     }
 
-    std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(), KIPCompareK<Tk, Tv, true>());
+    std::stable_sort(tupleKeyValIdx.begin(), tupleKeyValIdx.end(),
+                     KIPCompareK<Tk, Tv, true>());
 
-    for(unsigned x = 0; x < okey.dims().elements(); x++) {
+    for (unsigned x = 0; x < okey.dims().elements(); x++) {
         okey_ptr[x] = std::get<0>(tupleKeyValIdx[x]);
         oval_ptr[x] = std::get<1>(tupleKeyValIdx[x]);
     }
-
 }
 
 template<typename Tk, typename Tv>
-void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
-{
-    int higherDims =  okey.dims(1) * okey.dims(2) * okey.dims(3);
+void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
+    int higherDims = okey.dims(1) * okey.dims(2) * okey.dims(3);
     // TODO Make a better heurisitic
-    if(higherDims > 4)
+    if (higherDims > 4)
         kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
     else
         kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
 }
 
-#define INSTANTIATE(Tk, Tv)                                                             \
-    template void sort0ByKey<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, bool isAscending); \
-    template void sort0ByKeyIterative<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,           \
-                                              bool isAscending);                        \
-    template void sortByKeyBatched<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,              \
+#define INSTANTIATE(Tk, Tv)                                                   \
+    template void sort0ByKey<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,          \
+                                     bool isAscending);                       \
+    template void sort0ByKeyIterative<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, \
+                                              bool isAscending);              \
+    template void sortByKeyBatched<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,    \
                                            const int dim, bool isAscending);
 
-#define INSTANTIATE1(Tk) \
-    INSTANTIATE(Tk, float  ) \
-    INSTANTIATE(Tk, double ) \
-    INSTANTIATE(Tk, cfloat ) \
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
     INSTANTIATE(Tk, cdouble) \
-    INSTANTIATE(Tk, int    ) \
-    INSTANTIATE(Tk, uint   ) \
-    INSTANTIATE(Tk, short  ) \
-    INSTANTIATE(Tk, ushort ) \
-    INSTANTIATE(Tk, char   ) \
-    INSTANTIATE(Tk, uchar  ) \
-    INSTANTIATE(Tk, intl   ) \
-    INSTANTIATE(Tk, uintl  )
-}
-}
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
index 47dfd8b8ca..955460bf86 100644
--- a/src/backend/cpu/kernel/sort_helper.hpp
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -10,52 +10,53 @@
 #include <err_cpu.hpp>
 #include <tuple>
 
-namespace cpu
-{
-    namespace kernel
-    {
-        template <typename Tk, typename Tv>
-        using IndexPair = std::tuple<Tk, Tv>;
+namespace cpu {
+namespace kernel {
+template<typename Tk, typename Tv>
+using IndexPair = std::tuple<Tk, Tv>;
 
-        template <typename Tk, typename Tv, bool isAscending>
-        struct IPCompare
-        {
-            bool operator()(const IndexPair<Tk, Tv> &lhs, const IndexPair<Tk, Tv> &rhs)
-            {
-                // Check stable sort condition
-                Tk lhsVal = std::get<0>(lhs);
-                Tk rhsVal = std::get<0>(rhs);
-                if(isAscending) return (lhsVal < rhsVal);
-                else return (lhsVal > rhsVal);
-            }
-        };
+template<typename Tk, typename Tv, bool isAscending>
+struct IPCompare {
+    bool operator()(const IndexPair<Tk, Tv> &lhs,
+                    const IndexPair<Tk, Tv> &rhs) {
+        // Check stable sort condition
+        Tk lhsVal = std::get<0>(lhs);
+        Tk rhsVal = std::get<0>(rhs);
+        if (isAscending)
+            return (lhsVal < rhsVal);
+        else
+            return (lhsVal > rhsVal);
+    }
+};
 
-        template <typename Tk, typename Tv>
-        using KeyIndexPair = std::tuple<Tk, Tv, uint>;
+template<typename Tk, typename Tv>
+using KeyIndexPair = std::tuple<Tk, Tv, uint>;
 
-        template <typename Tk, typename Tv, bool isAscending>
-        struct KIPCompareV
-        {
-            bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
-            {
-                // Check stable sort condition
-                Tk lhsVal = std::get<0>(lhs);
-                Tk rhsVal = std::get<0>(rhs);
-                if(isAscending) return (lhsVal < rhsVal);
-                else return (lhsVal > rhsVal);
-            }
-        };
+template<typename Tk, typename Tv, bool isAscending>
+struct KIPCompareV {
+    bool operator()(const KeyIndexPair<Tk, Tv> &lhs,
+                    const KeyIndexPair<Tk, Tv> &rhs) {
+        // Check stable sort condition
+        Tk lhsVal = std::get<0>(lhs);
+        Tk rhsVal = std::get<0>(rhs);
+        if (isAscending)
+            return (lhsVal < rhsVal);
+        else
+            return (lhsVal > rhsVal);
+    }
+};
 
-        template <typename Tk, typename Tv, bool isAscending>
-        struct KIPCompareK
-        {
-            bool operator()(const KeyIndexPair<Tk, Tv> &lhs, const KeyIndexPair<Tk, Tv> &rhs)
-            {
-                uint lhsVal = std::get<2>(lhs);
-                uint rhsVal = std::get<2>(rhs);
-                if(isAscending) return (lhsVal < rhsVal);
-                else return (lhsVal > rhsVal);
-            }
-        };
+template<typename Tk, typename Tv, bool isAscending>
+struct KIPCompareK {
+    bool operator()(const KeyIndexPair<Tk, Tv> &lhs,
+                    const KeyIndexPair<Tk, Tv> &rhs) {
+        uint lhsVal = std::get<2>(lhs);
+        uint rhsVal = std::get<2>(rhs);
+        if (isAscending)
+            return (lhsVal < rhsVal);
+        else
+            return (lhsVal > rhsVal);
     }
-}
+};
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index 87521ce564..a8b796a702 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -9,32 +9,29 @@
 
 #pragma once
 #include <Param.hpp>
-#include <utility.hpp>
-#include <math.hpp>
 #include <kernel/sort_helper.hpp>
+#include <math.hpp>
+#include <utility.hpp>
 #include <algorithm>
 #include <tuple>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void coo2dense(Param<T> output,
-               CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
-{
-    const T *vPtr = values.get();
-    const int * rPtr = rowIdx.get();
-    const int * cPtr = colIdx.get();
+void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+               CParam<int> colIdx) {
+    const T *vPtr   = values.get();
+    const int *rPtr = rowIdx.get();
+    const int *cPtr = colIdx.get();
 
-    T * outPtr = output.get();
+    T *outPtr = output.get();
 
     af::dim4 ostrides = output.strides();
 
     int nNZ = values.dims(0);
-    for(int i = 0; i < nNZ; i++) {
-        T   v = vPtr[i];
+    for (int i = 0; i < nNZ; i++) {
+        T v   = vPtr[i];
         int r = rPtr[i];
         int c = cPtr[i];
 
@@ -46,22 +43,21 @@ void coo2dense(Param<T> output,
 
 template<typename T>
 void dense2csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
-               CParam<T> in)
-{
-    const T * iPtr = in.get();
-    T       * vPtr = values.get();
-    int     * rPtr = rowIdx.get();
-    int     * cPtr = colIdx.get();
-
-    int stride = in.strides(1);
+               CParam<T> in) {
+    const T *iPtr = in.get();
+    T *vPtr       = values.get();
+    int *rPtr     = rowIdx.get();
+    int *cPtr     = colIdx.get();
+
+    int stride    = in.strides(1);
     af::dim4 dims = in.dims();
 
     int offset = 0;
     for (int i = 0; i < dims[0]; ++i) {
         rPtr[i] = offset;
         for (int j = 0; j < dims[1]; ++j) {
-            if (iPtr[j*stride + i] != scalar<T>(0)) {
-                vPtr[offset] = iPtr[j*stride + i];
+            if (iPtr[j * stride + i] != scalar<T>(0)) {
+                vPtr[offset]   = iPtr[j * stride + i];
                 cPtr[offset++] = j;
             }
         }
@@ -70,11 +66,10 @@ void dense2csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 }
 
 template<typename T>
-void csr2dense(Param<T> out,
-               CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
-{
-    T   *oPtr = out.get();
-    const T   *vPtr = values.get();
+void csr2dense(Param<T> out, CParam<T> values, CParam<int> rowIdx,
+               CParam<int> colIdx) {
+    T *oPtr         = out.get();
+    const T *vPtr   = values.get();
     const int *rPtr = rowIdx.get();
     const int *cPtr = colIdx.get();
 
@@ -82,23 +77,23 @@ void csr2dense(Param<T> out,
 
     int r = rowIdx.dims(0);
     for (int i = 0; i < r - 1; i++) {
-        for (int ii = rPtr[i]; ii < rPtr[i+1]; ++ii) {
-            int j = cPtr[ii];
-            T v = vPtr[ii];
-            oPtr[j*stride + i] = v;
+        for (int ii = rPtr[i]; ii < rPtr[i + 1]; ++ii) {
+            int j                = cPtr[ii];
+            T v                  = vPtr[ii];
+            oPtr[j * stride + i] = v;
         }
     }
 }
 
 // Modified code from sort helper
-template <typename T>
-using SpKeyIndexPair = std::tuple<int, T, int>; // sorting index, value, other index
-
-template <typename T>
-struct SpKIPCompareK
-{
-    bool operator()(const SpKeyIndexPair<T> &lhs, const SpKeyIndexPair<T> &rhs)
-    {
+template<typename T>
+using SpKeyIndexPair =
+    std::tuple<int, T, int>;  // sorting index, value, other index
+
+template<typename T>
+struct SpKIPCompareK {
+    bool operator()(const SpKeyIndexPair<T> &lhs,
+                    const SpKeyIndexPair<T> &rhs) {
         int lhsVal = std::get<0>(lhs);
         int rhsVal = std::get<0>(rhs);
         // Always returns ascending
@@ -108,19 +103,18 @@ struct SpKIPCompareK
 
 template<typename T>
 void csr2coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
-             CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx)
-{
+             CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx) {
     // First calculate the linear index
-    T         * ovPtr = ovalues.get();
-    int       * orPtr = orowIdx.get();
-    int       * ocPtr = ocolIdx.get();
+    T *ovPtr   = ovalues.get();
+    int *orPtr = orowIdx.get();
+    int *ocPtr = ocolIdx.get();
 
-    const T   *ivPtr = ivalues.get();
+    const T *ivPtr   = ivalues.get();
     const int *irPtr = irowIdx.get();
     const int *icPtr = icolIdx.get();
 
     // Create cordinate form of the row array
-    for(int i = 0; i < (int)irowIdx.dims().elements() - 1; i++) {
+    for (int i = 0; i < (int)irowIdx.dims().elements() - 1; i++) {
         std::fill_n(orPtr + irPtr[i], irPtr[i + 1] - irPtr[i], i);
     }
 
@@ -130,27 +124,25 @@ void csr2coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
     int size = ovalues.dims(0);
     std::vector<CurrentPair> pairKeyVal(size);
 
-    for(int x = 0; x < size; x++) {
-       pairKeyVal[x] = std::make_tuple(icPtr[x], ivPtr[x], orPtr[x]);
+    for (int x = 0; x < size; x++) {
+        pairKeyVal[x] = std::make_tuple(icPtr[x], ivPtr[x], orPtr[x]);
     }
 
     std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), SpKIPCompareK<T>());
 
-    for(int x = 0; x < (int)ovalues.dims().elements(); x++) {
+    for (int x = 0; x < (int)ovalues.dims().elements(); x++) {
         std::tie(ocPtr[x], ovPtr[x], orPtr[x]) = pairKeyVal[x];
     }
-
 }
 
 template<typename T>
 void coo2csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
-             CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx)
-{
-    T   * ovPtr = ovalues.get();
+             CParam<T> ivalues, CParam<int> irowIdx, CParam<int> icolIdx) {
+    T *ovPtr   = ovalues.get();
     int *orPtr = orowIdx.get();
     int *ocPtr = ocolIdx.get();
 
-    const T   *ivPtr = ivalues.get();
+    const T *ivPtr   = ivalues.get();
     const int *irPtr = irowIdx.get();
     const int *icPtr = icolIdx.get();
 
@@ -158,26 +150,26 @@ void coo2csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
     // Uses code from sort_by_key kernels
     typedef SpKeyIndexPair<T> CurrentPair;
     int size = ovalues.dims(0);
-    std::vector<CurrentPair>pairKeyVal(size);
+    std::vector<CurrentPair> pairKeyVal(size);
 
-    for(int x = 0; x < size; x++) {
-       pairKeyVal[x] = std::make_tuple(irPtr[x], ivPtr[x], icPtr[x]);
+    for (int x = 0; x < size; x++) {
+        pairKeyVal[x] = std::make_tuple(irPtr[x], ivPtr[x], icPtr[x]);
     }
 
     std::stable_sort(pairKeyVal.begin(), pairKeyVal.end(), SpKIPCompareK<T>());
 
     ovPtr[0] = 0;
-    for(int x = 0; x < (int)ovalues.dims().elements(); x++) {
-        int row = -2; // Some value that will make orPtr[row + 1] error out
+    for (int x = 0; x < (int)ovalues.dims().elements(); x++) {
+        int row = -2;  // Some value that will make orPtr[row + 1] error out
         std::tie(row, ovPtr[x], ocPtr[x]) = pairKeyVal[x];
         orPtr[row + 1]++;
     }
 
     // Compress row storage
-    for(int x = 1; x < (int)orowIdx.dims().elements(); x++) {
+    for (int x = 1; x < (int)orowIdx.dims().elements(); x++) {
         orPtr[x] += orPtr[x - 1];
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index 9eef3e98f0..f9492d5aaa 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -13,16 +13,12 @@
 
 #include <cmath>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, af_op_t op>
-struct arith_op
-{
-    T operator()(T v1, T v2)
-    {
+struct arith_op {
+    T operator()(T v1, T v2) {
         UNUSED(v1);
         UNUSED(v2);
         return scalar<T>(0);
@@ -30,143 +26,124 @@ struct arith_op
 };
 
 template<typename T>
-struct arith_op<T, af_add_t>
-{
-    T operator()(T v1, T v2)
-    {
-        return v1 + v2;
-    }
+struct arith_op<T, af_add_t> {
+    T operator()(T v1, T v2) { return v1 + v2; }
 };
 
 template<typename T>
-struct arith_op<T, af_sub_t>
-{
-    T operator()(T v1, T v2)
-    {
-        return v1 - v2;
-    }
+struct arith_op<T, af_sub_t> {
+    T operator()(T v1, T v2) { return v1 - v2; }
 };
 
 template<typename T>
-struct arith_op<T, af_mul_t>
-{
-    T operator()(T v1, T v2)
-    {
-        return v1 * v2;
-    }
+struct arith_op<T, af_mul_t> {
+    T operator()(T v1, T v2) { return v1 * v2; }
 };
 
 template<typename T>
-struct arith_op<T, af_div_t>
-{
-    T operator()(T v1, T v2)
-    {
-        return v1 / v2;
-    }
+struct arith_op<T, af_div_t> {
+    T operator()(T v1, T v2) { return v1 / v2; }
 };
 
 template<typename T, af_op_t op, af_storage type>
-void sparseArithOpD(Param<T> output,
-                    CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
-                    CParam<T> rhs, const bool reverse = false)
-{
-    T * oPtr = output.get();
-    const T   * hPtr = rhs.get();
+void sparseArithOpD(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+                    CParam<int> colIdx, CParam<T> rhs,
+                    const bool reverse = false) {
+    T *oPtr       = output.get();
+    const T *hPtr = rhs.get();
 
-    const T   * vPtr = values.get();
-    const int * rPtr = rowIdx.get();
-    const int * cPtr = colIdx.get();
+    const T *vPtr   = values.get();
+    const int *rPtr = rowIdx.get();
+    const int *cPtr = colIdx.get();
 
     dim4 odims    = output.dims();
-    dim4 ostrides = output.strides();;
-    dim4 hstrides = rhs.strides();;
+    dim4 ostrides = output.strides();
+    ;
+    dim4 hstrides = rhs.strides();
+    ;
 
     std::vector<int> temp;
-    if(type == AF_STORAGE_CSR) {
+    if (type == AF_STORAGE_CSR) {
         temp.resize(values.dims().elements());
-        for(int i = 0; i < rowIdx.dims(0) - 1; i++) {
-            for(int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) {
-                temp[ii] = i;
-            }
+        for (int i = 0; i < rowIdx.dims(0) - 1; i++) {
+            for (int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) { temp[ii] = i; }
         }
-    //} else if(type == AF_STORAGE_CSC) {   // For future
+        //} else if(type == AF_STORAGE_CSC) {   // For future
     }
 
     const int *xx = (type == AF_STORAGE_CSR) ? temp.data() : rPtr;
     const int *yy = (type == AF_STORAGE_CSC) ? temp.data() : cPtr;
 
-    for(int i = 0; i < (int)values.dims().elements(); i++) {
+    for (int i = 0; i < (int)values.dims().elements(); i++) {
         // Bad index data
-        if(xx[i] >= odims[0] || yy [i]>= odims[1]) continue;
+        if (xx[i] >= odims[0] || yy[i] >= odims[1]) continue;
 
         int offset = xx[i] + yy[i] * ostrides[1];
         int hoff   = xx[i] + yy[i] * hstrides[1];
 
-        if(reverse) oPtr[offset] = arith_op<T, op>()(hPtr[hoff], vPtr[i]);
-        else        oPtr[offset] = arith_op<T, op>()(vPtr[i], hPtr[hoff]);
+        if (reverse)
+            oPtr[offset] = arith_op<T, op>()(hPtr[hoff], vPtr[i]);
+        else
+            oPtr[offset] = arith_op<T, op>()(vPtr[i], hPtr[hoff]);
     }
 }
 
 template<typename T, af_op_t op, af_storage type>
 void sparseArithOpS(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
-                    CParam<T> rhs, const bool reverse = false)
-{
-          T   * vPtr = values.get();
-    const int * rPtr = rowIdx.get();
-    const int * cPtr = colIdx.get();
+                    CParam<T> rhs, const bool reverse = false) {
+    T *vPtr         = values.get();
+    const int *rPtr = rowIdx.get();
+    const int *cPtr = colIdx.get();
 
-    const T   * hPtr = rhs.get();
+    const T *hPtr = rhs.get();
 
     dim4 dims     = rhs.dims();
     dim4 hstrides = rhs.strides();
 
     std::vector<int> temp;
-    if(type == AF_STORAGE_CSR) {
+    if (type == AF_STORAGE_CSR) {
         temp.resize(values.dims().elements());
-        for(int i = 0; i < rowIdx.dims(0) - 1; i++) {
-            for(int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) {
-                temp[ii] = i;
-            }
+        for (int i = 0; i < rowIdx.dims(0) - 1; i++) {
+            for (int ii = rPtr[i]; ii < rPtr[i + 1]; ii++) { temp[ii] = i; }
         }
-    //} else if(type == AF_STORAGE_CSC) {   // For future
+        //} else if(type == AF_STORAGE_CSC) {   // For future
     }
 
     const int *xx = (type == AF_STORAGE_CSR) ? temp.data() : rPtr;
     const int *yy = (type == AF_STORAGE_CSC) ? temp.data() : cPtr;
 
-    for(int i = 0; i < (int)values.dims().elements(); i++) {
+    for (int i = 0; i < (int)values.dims().elements(); i++) {
         // Bad index data
-        if(xx[i] >= dims[0] || yy [i]>= dims[1]) continue;
+        if (xx[i] >= dims[0] || yy[i] >= dims[1]) continue;
 
         int hoff = xx[i] + yy[i] * hstrides[1];
 
-        if(reverse) vPtr[i] = arith_op<T, op>()(hPtr[hoff], vPtr[i]);
-        else        vPtr[i] = arith_op<T, op>()(vPtr[i], hPtr[hoff]);
+        if (reverse)
+            vPtr[i] = arith_op<T, op>()(hPtr[hoff], vPtr[i]);
+        else
+            vPtr[i] = arith_op<T, op>()(vPtr[i], hPtr[hoff]);
     }
 }
 
 // The following functions can handle CSR
 // storage format only as of now.
-static
-void calcOutNNZ(Param<int> outRowIdx,
-                const uint M, const uint N,
-                CParam<int> lRowIdx, CParam<int> lColIdx,
-                CParam<int> rRowIdx, CParam<int> rColIdx)
-{
-          int *orPtr = outRowIdx.get();
+static void calcOutNNZ(Param<int> outRowIdx, const uint M, const uint N,
+                       CParam<int> lRowIdx, CParam<int> lColIdx,
+                       CParam<int> rRowIdx, CParam<int> rColIdx) {
+    int *orPtr       = outRowIdx.get();
     const int *lrPtr = lRowIdx.get();
     const int *lcPtr = lColIdx.get();
     const int *rrPtr = rRowIdx.get();
     const int *rcPtr = rColIdx.get();
 
     unsigned csrOutCount = 0;
-    for (uint row=0; row<M; ++row) {
-        const int lEnd = lrPtr[row+1];
-        const int rEnd = rrPtr[row+1];
+    for (uint row = 0; row < M; ++row) {
+        const int lEnd = lrPtr[row + 1];
+        const int rEnd = rrPtr[row + 1];
 
         uint rowNNZ = 0;
-        int l = lrPtr[row];
-        int r = rrPtr[row];
+        int l       = lrPtr[row];
+        int r       = rrPtr[row];
         while (l < lEnd && r < rEnd) {
             int lci = lcPtr[l];
             int rci = rcPtr[r];
@@ -177,27 +154,26 @@ void calcOutNNZ(Param<int> outRowIdx,
         }
         // Elements from lhs or rhs are exhausted.
         // Just count left over elements
-        rowNNZ += (lEnd-l);
-        rowNNZ += (rEnd-r);
+        rowNNZ += (lEnd - l);
+        rowNNZ += (rEnd - r);
 
         orPtr[row] = csrOutCount;
         csrOutCount += rowNNZ;
     }
-    //Write out the Rows+1 entry
+    // Write out the Rows+1 entry
     orPtr[M] = csrOutCount;
 }
 
 template<typename T, af_op_t op>
-void sparseArithOp(Param<T> oVals, Param<int> oColIdx,
-                   CParam<int> oRowIdx, const uint Rows,
-                   CParam<T> lvals, CParam<int> lRowIdx, CParam<int> lColIdx,
-                   CParam<T> rvals, CParam<int> rRowIdx, CParam<int> rColIdx)
-{
+void sparseArithOp(Param<T> oVals, Param<int> oColIdx, CParam<int> oRowIdx,
+                   const uint Rows, CParam<T> lvals, CParam<int> lRowIdx,
+                   CParam<int> lColIdx, CParam<T> rvals, CParam<int> rRowIdx,
+                   CParam<int> rColIdx) {
     const int *orPtr = oRowIdx.get();
-    const   T *lvPtr = lvals.get();
+    const T *lvPtr   = lvals.get();
     const int *lrPtr = lRowIdx.get();
     const int *lcPtr = lColIdx.get();
-    const   T *rvPtr = rvals.get();
+    const T *rvPtr   = rvals.get();
     const int *rrPtr = rRowIdx.get();
     const int *rcPtr = rColIdx.get();
 
@@ -205,17 +181,17 @@ void sparseArithOp(Param<T> oVals, Param<int> oColIdx,
 
     auto ZERO = scalar<T>(0);
 
-    for (uint row=0; row<Rows; ++row) {
-        const int lEnd = lrPtr[row+1];
-        const int rEnd = rrPtr[row+1];
+    for (uint row = 0; row < Rows; ++row) {
+        const int lEnd = lrPtr[row + 1];
+        const int rEnd = rrPtr[row + 1];
         const int offs = orPtr[row];
 
-          T *ovPtr = oVals.get() + offs;
+        T *ovPtr   = oVals.get() + offs;
         int *ocPtr = oColIdx.get() + offs;
 
         uint rowNNZ = 0;
-        int l = lrPtr[row];
-        int r = rrPtr[row];
+        int l       = lrPtr[row];
+        int r       = rrPtr[row];
         while (l < lEnd && r < rEnd) {
             int lci = lcPtr[l];
             int rci = rcPtr[r];
@@ -223,26 +199,26 @@ void sparseArithOp(Param<T> oVals, Param<int> oColIdx,
             T lhs = (lci <= rci ? lvPtr[l] : ZERO);
             T rhs = (lci >= rci ? rvPtr[r] : ZERO);
 
-            ovPtr[ rowNNZ ] = binOp(lhs, rhs);
-            ocPtr[ rowNNZ ] = (lci <= rci) ? lci : rci;
+            ovPtr[rowNNZ] = binOp(lhs, rhs);
+            ocPtr[rowNNZ] = (lci <= rci) ? lci : rci;
 
             l += (lci <= rci);
             r += (lci >= rci);
             rowNNZ++;
         }
         while (l < lEnd) {
-            ovPtr[ rowNNZ ] = binOp(lvPtr[l], ZERO);
-            ocPtr[ rowNNZ ] = lcPtr[l];
+            ovPtr[rowNNZ] = binOp(lvPtr[l], ZERO);
+            ocPtr[rowNNZ] = lcPtr[l];
             l++;
             rowNNZ++;
         }
         while (r < rEnd) {
-            ovPtr[ rowNNZ ] = binOp(ZERO, rvPtr[r]);
-            ocPtr[ rowNNZ ] = rcPtr[r];
+            ovPtr[rowNNZ] = binOp(ZERO, rvPtr[r]);
+            ocPtr[rowNNZ] = rcPtr[r];
             r++;
             rowNNZ++;
         }
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/susan.hpp b/src/backend/cpu/kernel/susan.hpp
index 9d5d2e0009..13dee51519 100644
--- a/src/backend/cpu/kernel/susan.hpp
+++ b/src/backend/cpu/kernel/susan.hpp
@@ -10,37 +10,33 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void susan_responses(Param<T> output, CParam<T> input,
-                     const dim_t idim0, const dim_t idim1,
-                     const int radius, const float t, const float g,
-                     const unsigned border_len)
-{
+void susan_responses(Param<T> output, CParam<T> input, const dim_t idim0,
+                     const dim_t idim1, const int radius, const float t,
+                     const float g, const unsigned border_len) {
     T* resp_out = output.get();
     const T* in = input.get();
 
     const unsigned r = border_len;
-    const int rSqrd = radius*radius;
+    const int rSqrd  = radius * radius;
 
     for (dim_t y = r; y < idim1 - r; ++y) {
         for (dim_t x = r; x < idim0 - r; ++x) {
             const dim_t idx = y * idim0 + x;
-            T m_0 = in[idx];
-            float nM = 0.0f;
+            T m_0           = in[idx];
+            float nM        = 0.0f;
 
-            for (int i=-radius; i<=radius; ++i) {
-                for (int j=-radius; j<=radius; ++j) {
-                    if (i*i + j*j < rSqrd) {
-                        int p = x + i;
-                        int q = y + j;
-                        T m = in[p + idim0 * q];
-                        float exp_pow = std::pow((m - m_0)/t, 6.0);
-                        float cM = std::exp(-exp_pow);
+            for (int i = -radius; i <= radius; ++i) {
+                for (int j = -radius; j <= radius; ++j) {
+                    if (i * i + j * j < rSqrd) {
+                        int p         = x + i;
+                        int q         = y + j;
+                        T m           = in[p + idim0 * q];
+                        float exp_pow = std::pow((m - m_0) / t, 6.0);
+                        float cM      = std::exp(-exp_pow);
                         nM += cM;
                     }
                 }
@@ -52,15 +48,15 @@ void susan_responses(Param<T> output, CParam<T> input,
 }
 
 template<typename T>
-void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> response,
-                 shared_ptr<unsigned> counter, const dim_t idim0, const dim_t idim1,
-                 CParam<T> input, const unsigned border_len, const unsigned max_corners)
-{
-    float* x_out    = xcoords.get();
-    float* y_out    = ycoords.get();
-    float* resp_out = response.get();
-    unsigned* count = counter.get();
-    const T* resp_in= input.get();
+void non_maximal(Param<float> xcoords, Param<float> ycoords,
+                 Param<float> response, shared_ptr<unsigned> counter,
+                 const dim_t idim0, const dim_t idim1, CParam<T> input,
+                 const unsigned border_len, const unsigned max_corners) {
+    float* x_out     = xcoords.get();
+    float* y_out     = ycoords.get();
+    float* resp_out  = response.get();
+    unsigned* count  = counter.get();
+    const T* resp_in = input.get();
 
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
@@ -71,16 +67,18 @@ void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> respon
 
             // Find maximum neighborhood response
             T max_v;
-            max_v = max(resp_in[(y-1) * idim0 + x-1], resp_in[y * idim0 + x-1]);
-            max_v = max(max_v, resp_in[(y+1) * idim0 + x-1]);
-            max_v = max(max_v, resp_in[(y-1) * idim0 + x  ]);
-            max_v = max(max_v, resp_in[(y+1) * idim0 + x  ]);
-            max_v = max(max_v, resp_in[(y-1) * idim0 + x+1]);
-            max_v = max(max_v, resp_in[(y)   * idim0 + x+1]);
-            max_v = max(max_v, resp_in[(y+1) * idim0 + x+1]);
+            max_v = std::max(resp_in[(y - 1) * idim0 + x - 1],
+                             resp_in[y * idim0 + x - 1]);
+            max_v = std::max(max_v, resp_in[(y + 1) * idim0 + x - 1]);
+            max_v = std::max(max_v, resp_in[(y - 1) * idim0 + x]);
+            max_v = std::max(max_v, resp_in[(y + 1) * idim0 + x]);
+            max_v = std::max(max_v, resp_in[(y - 1) * idim0 + x + 1]);
+            max_v = std::max(max_v, resp_in[(y)*idim0 + x + 1]);
+            max_v = std::max(max_v, resp_in[(y + 1) * idim0 + x + 1]);
 
-            // Stores corner to {x,y,resp}_out if it's response is maximum compared
-            // to its 8-neighborhood and greater or equal minimum response
+            // Stores corner to {x,y,resp}_out if it's response is maximum
+            // compared to its 8-neighborhood and greater or equal minimum
+            // response
             if (v > max_v) {
                 const dim_t idx = *count;
                 *count += 1;
@@ -94,5 +92,5 @@ void non_maximal(Param<float> xcoords, Param<float> ycoords, Param<float> respon
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/tile.hpp b/src/backend/cpu/kernel/tile.hpp
index 65a9eb20bd..5fdaba9db7 100644
--- a/src/backend/cpu/kernel/tile.hpp
+++ b/src/backend/cpu/kernel/tile.hpp
@@ -10,45 +10,41 @@
 #pragma once
 #include <Param.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-void tile(Param<T> out, CParam<T> in)
-{
-
-    T* outPtr = out.get();
+void tile(Param<T> out, CParam<T> in) {
+    T* outPtr      = out.get();
     const T* inPtr = in.get();
 
     const af::dim4 iDims = in.dims();
     const af::dim4 oDims = out.dims();
-    const af::dim4 ist = in.strides();
-    const af::dim4 ost = out.strides();
+    const af::dim4 ist   = in.strides();
+    const af::dim4 ost   = out.strides();
 
-    for(dim_t ow = 0; ow < oDims[3]; ow++) {
+    for (dim_t ow = 0; ow < oDims[3]; ow++) {
         const dim_t iw = ow % iDims[3];
         const dim_t iW = iw * ist[3];
         const dim_t oW = ow * ost[3];
-        for(dim_t oz = 0; oz < oDims[2]; oz++) {
-            const dim_t iz = oz % iDims[2];
+        for (dim_t oz = 0; oz < oDims[2]; oz++) {
+            const dim_t iz  = oz % iDims[2];
             const dim_t iZW = iW + iz * ist[2];
             const dim_t oZW = oW + oz * ost[2];
-            for(dim_t oy = 0; oy < oDims[1]; oy++) {
-                const dim_t iy = oy % iDims[1];
+            for (dim_t oy = 0; oy < oDims[1]; oy++) {
+                const dim_t iy   = oy % iDims[1];
                 const dim_t iYZW = iZW + iy * ist[1];
                 const dim_t oYZW = oZW + oy * ost[1];
-                for(dim_t ox = 0; ox < oDims[0]; ox++) {
-                    const dim_t ix = ox % iDims[0];
+                for (dim_t ox = 0; ox < oDims[0]; ox++) {
+                    const dim_t ix   = ox % iDims[0];
                     const dim_t iMem = iYZW + ix;
                     const dim_t oMem = oYZW + ox;
-                    outPtr[oMem] = inPtr[iMem];
+                    outPtr[oMem]     = inPtr[iMem];
                 }
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index c78a2d7a93..f0e388cbe7 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -10,39 +10,41 @@
 #pragma once
 #include <Param.hpp>
 #include <err_cpu.hpp>
+#include <af/traits.hpp>
 #include <type_traits>
 #include "interp.hpp"
-#include <af/traits.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
-template <typename T>
-void calc_transform_inverse(T *txo, const T *txi, const bool perspective)
-{
+template<typename T>
+void calc_transform_inverse(T *txo, const T *txi, const bool perspective) {
     if (perspective) {
-        txo[0] =   txi[4]*txi[8] - txi[5]*txi[7];
-        txo[1] = -(txi[1]*txi[8] - txi[2]*txi[7]);
-        txo[2] =   txi[1]*txi[5] - txi[2]*txi[4];
-
-        txo[3] = -(txi[3]*txi[8] - txi[5]*txi[6]);
-        txo[4] =   txi[0]*txi[8] - txi[2]*txi[6];
-        txo[5] = -(txi[0]*txi[5] - txi[2]*txi[3]);
-
-        txo[6] =   txi[3]*txi[7] - txi[4]*txi[6];
-        txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
-        txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
-
-        T det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
-
-        txo[0] /= det; txo[1] /= det; txo[2] /= det;
-        txo[3] /= det; txo[4] /= det; txo[5] /= det;
-        txo[6] /= det; txo[7] /= det; txo[8] /= det;
-    }
-    else {
-        T det = txi[0]*txi[4] - txi[1]*txi[3];
+        txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
+        txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
+        txo[2] = txi[1] * txi[5] - txi[2] * txi[4];
+
+        txo[3] = -(txi[3] * txi[8] - txi[5] * txi[6]);
+        txo[4] = txi[0] * txi[8] - txi[2] * txi[6];
+        txo[5] = -(txi[0] * txi[5] - txi[2] * txi[3]);
+
+        txo[6] = txi[3] * txi[7] - txi[4] * txi[6];
+        txo[7] = -(txi[0] * txi[7] - txi[1] * txi[6]);
+        txo[8] = txi[0] * txi[4] - txi[1] * txi[3];
+
+        T det = txi[0] * txo[0] + txi[1] * txo[3] + txi[2] * txo[6];
+
+        txo[0] /= det;
+        txo[1] /= det;
+        txo[2] /= det;
+        txo[3] /= det;
+        txo[4] /= det;
+        txo[5] /= det;
+        txo[6] /= det;
+        txo[7] /= det;
+        txo[8] /= det;
+    } else {
+        T det = txi[0] * txi[4] - txi[1] * txi[3];
 
         txo[0] = txi[4] / det;
         txo[1] = txi[3] / det;
@@ -54,27 +56,23 @@ void calc_transform_inverse(T *txo, const T *txi, const bool perspective)
     }
 }
 
-template <typename T>
+template<typename T>
 void calc_transform_inverse(T *tmat, const T *tmat_ptr, const bool inverse,
-                            const bool perspective, const unsigned transf_len)
-{
+                            const bool perspective, const unsigned transf_len) {
     // The way kernel is structured, it expects an inverse
     // transform matrix by default.
     // If it is an forward transform, then we need its inverse
-    if(inverse) {
-        for(int i = 0; i < (int)transf_len; i++)
-            tmat[i] = tmat_ptr[i];
+    if (inverse) {
+        for (int i = 0; i < (int)transf_len; i++) tmat[i] = tmat_ptr[i];
     } else {
         calc_transform_inverse(tmat, tmat_ptr, perspective);
     }
 }
 
 template<typename T, int order>
-void transform(Param<T> output, CParam<T> input,
-               CParam<float> transform, const bool inverse,
-               const bool perspective,
-               af_interp_type method)
-{
+void transform(Param<T> output, CParam<T> input, CParam<float> transform,
+               const bool inverse, const bool perspective,
+               af_interp_type method) {
     typedef typename af::dtype_traits<T>::base_type BT;
     typedef wtype_t<BT> WT;
 
@@ -85,8 +83,8 @@ void transform(Param<T> output, CParam<T> input,
     const af::dim4 istrides = input.strides();
     const af::dim4 ostrides = output.strides();
 
-    T * out = output.get();
-    const float* tf = transform.get();
+    T *out          = output.get();
+    const float *tf = transform.get();
 
     int batch_size = 1;
     if (idims[2] != tdims[2]) batch_size = idims[2];
@@ -94,18 +92,19 @@ void transform(Param<T> output, CParam<T> input,
     Interp2<T, WT, order> interp;
     for (int idw = 0; idw < (int)odims[3]; idw++) {
         dim_t out_offw = idw * ostrides[3];
-        dim_t in_offw = (idims[3] > 1) * idw * istrides[3];
-        dim_t tf_offw = (tdims[3] > 1) * idw * tstrides[3];
+        dim_t in_offw  = (idims[3] > 1) * idw * istrides[3];
+        dim_t tf_offw  = (tdims[3] > 1) * idw * tstrides[3];
 
         for (int idz = 0; idz < (int)odims[2]; idz += batch_size) {
             dim_t out_offzw = out_offw + idz * ostrides[2];
-            dim_t in_offzw = in_offw + (idims[2] > 1) * idz * istrides[2];
-            dim_t tf_offzw = tf_offw + (tdims[2] > 1) * idz * tstrides[2];
+            dim_t in_offzw  = in_offw + (idims[2] > 1) * idz * istrides[2];
+            dim_t tf_offzw  = tf_offw + (tdims[2] > 1) * idz * tstrides[2];
 
             const float *tptr = tf + tf_offzw;
 
             float tmat[9];
-            calc_transform_inverse(tmat, tptr, inverse, perspective, perspective ? 9 : 6);
+            calc_transform_inverse(tmat, tptr, inverse, perspective,
+                                   perspective ? 9 : 6);
 
             for (int idy = 0; idy < (int)odims[1]; idy++) {
                 for (int idx = 0; idx < (int)odims[0]; idx++) {
@@ -113,23 +112,24 @@ void transform(Param<T> output, CParam<T> input,
                     WT yidi = idx * tmat[3] + idy * tmat[4] + tmat[5];
 
                     if (perspective) {
-                        WT W    = idx * tmat[6] + idy * tmat[7] + tmat[8];
+                        WT W = idx * tmat[6] + idy * tmat[7] + tmat[8];
                         xidi /= W;
                         yidi /= W;
                     }
 
-                    // FIXME: Nearest and lower do not do clamping, but other methods do
-                    // Make it consistent
+                    // FIXME: Nearest and lower do not do clamping, but other
+                    // methods do Make it consistent
                     bool clamp = order != 1;
                     bool condX = xidi >= -0.0001 && xidi < idims[0];
                     bool condY = yidi >= -0.0001 && yidi < idims[1];
 
                     int ooff = out_offzw + idy * ostrides[1] + idx;
                     if (condX && condY) {
-                        interp(output, ooff, input, in_offzw, xidi, yidi, method, batch_size, clamp);
+                        interp(output, ooff, input, in_offzw, xidi, yidi,
+                               method, batch_size, clamp);
                     } else {
                         for (int n = 0; n < batch_size; n++) {
-                            out[ooff + n * ostrides[2]] =  scalar<T>(0);
+                            out[ooff + n * ostrides[2]] = scalar<T>(0);
                         }
                     }
                 }
@@ -138,5 +138,5 @@ void transform(Param<T> output, CParam<T> input,
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
index 28f548b942..cfe9c4001f 100644
--- a/src/backend/cpu/kernel/transpose.hpp
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -9,42 +9,36 @@
 
 #pragma once
 #include <Param.hpp>
-#include <utility.hpp>
 #include <err_cpu.hpp>
+#include <utility.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T>
-T getConjugate(const T &in)
-{
+T getConjugate(const T &in) {
     // For non-complex types return same
     return in;
 }
 
 template<>
-cfloat getConjugate(const cfloat &in)
-{
+cfloat getConjugate(const cfloat &in) {
     return std::conj(in);
 }
 
 template<>
-cdouble getConjugate(const cdouble &in)
-{
+cdouble getConjugate(const cdouble &in) {
     return std::conj(in);
 }
 
 template<typename T, bool conjugate>
-void transpose(Param<T> output, CParam<T> input)
-{
+void transpose(Param<T> output, CParam<T> input) {
     const dim4 odims    = output.dims();
     const dim4 ostrides = output.strides();
     const dim4 istrides = input.strides();
 
-    T * out = output.get();
-    T const * const in = input.get();
+    T *out            = output.get();
+    T const *const in = input.get();
 
     for (dim_t l = 0; l < odims[3]; ++l) {
         for (dim_t k = 0; k < odims[2]; ++k) {
@@ -55,9 +49,9 @@ void transpose(Param<T> output, CParam<T> input)
                 for (dim_t i = 0; i < odims[0]; ++i) {
                     // calculate array indices based on offsets and strides
                     // the helper getIdx takes care of indices
-                    const dim_t inIdx  = getIdx(istrides,j,i,k,l);
-                    const dim_t outIdx = getIdx(ostrides,i,j,k,l);
-                    if(conjugate)
+                    const dim_t inIdx  = getIdx(istrides, j, i, k, l);
+                    const dim_t outIdx = getIdx(ostrides, i, j, k, l);
+                    if (conjugate)
                         out[outIdx] = getConjugate(in[inIdx]);
                     else
                         out[outIdx] = in[inIdx];
@@ -71,18 +65,17 @@ void transpose(Param<T> output, CParam<T> input)
 }
 
 template<typename T>
-void transpose(Param<T> out, CParam<T> in, const bool conjugate)
-{
-    return (conjugate ? transpose<T, true>(out, in) : transpose<T, false>(out, in));
+void transpose(Param<T> out, CParam<T> in, const bool conjugate) {
+    return (conjugate ? transpose<T, true>(out, in)
+                      : transpose<T, false>(out, in));
 }
 
 template<typename T, bool conjugate>
-void transpose_inplace(Param<T> input)
-{
+void transpose_inplace(Param<T> input) {
     const dim4 idims    = input.dims();
     const dim4 istrides = input.strides();
 
-    T * in = input.get();
+    T *in = input.get();
 
     for (dim_t l = 0; l < idims[3]; ++l) {
         for (dim_t k = 0; k < idims[2]; ++k) {
@@ -95,14 +88,13 @@ void transpose_inplace(Param<T> input)
                 for (dim_t i = j + 1; i < idims[0]; ++i) {
                     // calculate array indices based on offsets and strides
                     // the helper getIdx takes care of indices
-                    const dim_t iIdx  = getIdx(istrides,j,i,k,l);
-                    const dim_t oIdx = getIdx(istrides,i,j,k,l);
-                    if(conjugate) {
+                    const dim_t iIdx = getIdx(istrides, j, i, k, l);
+                    const dim_t oIdx = getIdx(istrides, i, j, k, l);
+                    if (conjugate) {
                         in[iIdx] = getConjugate(in[iIdx]);
                         in[oIdx] = getConjugate(in[oIdx]);
                         std::swap(in[iIdx], in[oIdx]);
-                    }
-                    else {
+                    } else {
                         std::swap(in[iIdx], in[oIdx]);
                     }
                 }
@@ -112,10 +104,10 @@ void transpose_inplace(Param<T> input)
 }
 
 template<typename T>
-void transpose_inplace(Param<T> in, const bool conjugate)
-{
-    return (conjugate ? transpose_inplace<T, true >(in) : transpose_inplace<T, false>(in));
+void transpose_inplace(Param<T> in, const bool conjugate) {
+    return (conjugate ? transpose_inplace<T, true>(in)
+                      : transpose_inplace<T, false>(in));
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index 5d83daa0ed..6bab5e7693 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -9,16 +9,14 @@
 
 #pragma once
 #include <Param.hpp>
+#include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Param<T> out, CParam<T> in)
-{
-    T *o = out.get();
+void triangle(Param<T> out, CParam<T> in) {
+    T *o       = out.get();
     const T *i = in.get();
 
     af::dim4 odm = out.dims();
@@ -26,35 +24,34 @@ void triangle(Param<T> out, CParam<T> in)
     af::dim4 ost = out.strides();
     af::dim4 ist = in.strides();
 
-    for(dim_t ow = 0; ow < odm[3]; ow++) {
+    for (dim_t ow = 0; ow < odm[3]; ow++) {
         const dim_t oW = ow * ost[3];
         const dim_t iW = ow * ist[3];
 
-        for(dim_t oz = 0; oz < odm[2]; oz++) {
+        for (dim_t oz = 0; oz < odm[2]; oz++) {
             const dim_t oZW = oW + oz * ost[2];
             const dim_t iZW = iW + oz * ist[2];
 
-            for(dim_t oy = 0; oy < odm[1]; oy++) {
+            for (dim_t oy = 0; oy < odm[1]; oy++) {
                 const dim_t oYZW = oZW + oy * ost[1];
                 const dim_t iYZW = iZW + oy * ist[1];
 
-                for(dim_t ox = 0; ox < odm[0]; ox++) {
+                for (dim_t ox = 0; ox < odm[0]; ox++) {
                     const dim_t oMem = oYZW + ox;
                     const dim_t iMem = iYZW + ox;
 
-                    bool cond = is_upper ? (oy >= ox) : (oy <= ox);
+                    bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
                     bool do_unit_diag = (is_unit_diag && ox == oy);
-                    if(cond) {
+                    if (cond) {
                         o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
                     } else {
                         o[oMem] = scalar<T>(0);
                     }
-
                 }
             }
         }
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
index 52742b5dd0..e928136abb 100644
--- a/src/backend/cpu/kernel/unwrap.hpp
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -10,18 +10,17 @@
 #pragma once
 #include <Param.hpp>
 #include <err_cpu.hpp>
+#include <ops.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, int d>
 void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
-{
-    const T *inPtr = in.get();
-    T *outPtr      = out.get();
+                const dim_t sx, const dim_t sy, const dim_t px,
+                const dim_t py) {
+    const T* inPtr = in.get();
+    T* outPtr      = out.get();
 
     af::dim4 idims    = in.dims();
     af::dim4 odims    = out.dims();
@@ -30,15 +29,14 @@ void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
 
     dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
 
-    for(dim_t w = 0; w < odims[3]; w++) {
-        for(dim_t z = 0; z < odims[2]; z++) {
+    for (dim_t w = 0; w < odims[3]; w++) {
+        for (dim_t z = 0; z < odims[2]; z++) {
+            dim_t cOut    = w * ostrides[3] + z * ostrides[2];
+            dim_t cIn     = w * istrides[3] + z * istrides[2];
+            const T* iptr = inPtr + cIn;
+            T* optr_      = outPtr + cOut;
 
-            dim_t cOut = w * ostrides[3] + z * ostrides[2];
-            dim_t cIn  = w * istrides[3] + z * istrides[2];
-            const T* iptr = inPtr  + cIn;
-            T* optr_= outPtr + cOut;
-
-            for(dim_t col = 0; col < odims[d]; col++) {
+            for (dim_t col = 0; col < odims[d]; col++) {
                 // Offset output ptr
                 T* optr = optr_ + col * ostrides[d];
 
@@ -52,19 +50,23 @@ void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                 dim_t spx = startx - px;
                 dim_t spy = starty - py;
 
-                // Short cut condition ensuring all values within input dimensions
-                bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 && spy + wy < idims[1]);
+                // Short cut condition ensuring all values within input
+                // dimensions
+                bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 &&
+                             spy + wy < idims[1]);
 
-                for(dim_t y = 0; y < wy; y++) {
-                    for(dim_t x = 0; x < wx; x++) {
+                for (dim_t y = 0; y < wy; y++) {
+                    for (dim_t x = 0; x < wx; x++) {
                         dim_t xpad = spx + x;
                         dim_t ypad = spy + y;
 
                         dim_t oloc = (y * wx + x);
                         if (d == 0) oloc *= ostrides[1];
 
-                        if(cond || (xpad >= 0 && xpad < idims[0] && ypad >= 0 && ypad < idims[1])) {
-                            dim_t iloc = (ypad * istrides[1] + xpad * istrides[0]);
+                        if (cond || (xpad >= 0 && xpad < idims[0] &&
+                                     ypad >= 0 && ypad < idims[1])) {
+                            dim_t iloc =
+                                (ypad * istrides[1] + xpad * istrides[0]);
                             optr[oloc] = iptr[iloc];
                         } else {
                             optr[oloc] = scalar<T>(0.0);
@@ -76,5 +78,5 @@ void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index fbbfc07e59..43b2b995e1 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -11,17 +11,14 @@
 #include <Param.hpp>
 #include <err_cpu.hpp>
 
-namespace cpu
-{
-namespace kernel
-{
+namespace cpu {
+namespace kernel {
 
 template<typename T, int d>
 void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
-{
-    const T *inPtr = in.get();
-    T *outPtr      = out.get();
+              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py) {
+    const T* inPtr = in.get();
+    T* outPtr      = out.get();
 
     af::dim4 idims    = in.dims();
     af::dim4 odims    = out.dims();
@@ -30,15 +27,14 @@ void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
 
     dim_t nx = (odims[0] + 2 * px - wx) / sx + 1;
 
-    for(dim_t w = 0; w < idims[3]; w++) {
-        for(dim_t z = 0; z < idims[2]; z++) {
+    for (dim_t w = 0; w < idims[3]; w++) {
+        for (dim_t z = 0; z < idims[2]; z++) {
+            dim_t cIn      = w * istrides[3] + z * istrides[2];
+            dim_t cOut     = w * ostrides[3] + z * ostrides[2];
+            const T* iptr_ = inPtr + cIn;
+            T* optr        = outPtr + cOut;
 
-            dim_t cIn  = w * istrides[3] + z * istrides[2];
-            dim_t cOut = w * ostrides[3] + z * ostrides[2];
-            const T* iptr_ = inPtr  + cIn;
-            T* optr= outPtr + cOut;
-
-            for(dim_t col = 0; col < idims[d]; col++) {
+            for (dim_t col = 0; col < idims[d]; col++) {
                 // Offset output ptr
                 const T* iptr = iptr_ + col * istrides[d];
 
@@ -52,19 +48,23 @@ void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                 dim_t spx = startx - px;
                 dim_t spy = starty - py;
 
-                // Short cut condition ensuring all values within input dimensions
-                bool cond = (spx >= 0 && spx + wx < odims[0] && spy >= 0 && spy + wy < odims[1]);
+                // Short cut condition ensuring all values within input
+                // dimensions
+                bool cond = (spx >= 0 && spx + wx < odims[0] && spy >= 0 &&
+                             spy + wy < odims[1]);
 
-                for(dim_t y = 0; y < wy; y++) {
-                    for(dim_t x = 0; x < wx; x++) {
+                for (dim_t y = 0; y < wy; y++) {
+                    for (dim_t x = 0; x < wx; x++) {
                         dim_t xpad = spx + x;
                         dim_t ypad = spy + y;
 
                         dim_t iloc = (y * wx + x);
                         if (d == 0) iloc *= istrides[1];
 
-                        if(cond || (xpad >= 0 && xpad < odims[0] && ypad >= 0 && ypad < odims[1])) {
-                            dim_t oloc = (ypad * ostrides[1] + xpad * ostrides[0]);
+                        if (cond || (xpad >= 0 && xpad < odims[0] &&
+                                     ypad >= 0 && ypad < odims[1])) {
+                            dim_t oloc =
+                                (ypad * ostrides[1] + xpad * ostrides[0]);
                             // FIXME: When using threads, atomize this
                             optr[oloc] += iptr[iloc];
                         }
@@ -75,5 +75,5 @@ void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
     }
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/lapack_helper.hpp b/src/backend/cpu/lapack_helper.hpp
index 0ecea31bea..a7bc77aaf3 100644
--- a/src/backend/cpu/lapack_helper.hpp
+++ b/src/backend/cpu/lapack_helper.hpp
@@ -18,16 +18,16 @@
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
 #ifdef USE_MKL
-    #include<mkl_lapacke.h>
+#include <mkl_lapacke.h>
 #else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-        #include <common/lapacke.hpp>
-        #undef AF_LAPACK_COL_MAJOR
-        #define AF_LAPACK_COL_MAJOR 0
-    #else // NETLIB LAPACKE
-        #include<lapacke.h>
-    #endif
+#ifdef __APPLE__
+#include <Accelerate/Accelerate.h>
+#include <common/lapacke.hpp>
+#undef AF_LAPACK_COL_MAJOR
+#define AF_LAPACK_COL_MAJOR 0
+#else  // NETLIB LAPACKE
+#include <lapacke.h>
+#endif
 #endif
 
 #endif
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index 726643fd6a..f356eaf6fa 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -7,60 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <err_cpu.hpp>
-#include <types.hpp>
 #include <jit/BinaryNode.hpp>
+#include <optypes.hpp>
+#include <types.hpp>
+#include <af/dim4.hpp>
 
-namespace cpu
-{
-
-#define LOGIC_FN(OP, op)                        \
-    template<typename T>                        \
-    struct BinOp<char, T, OP>                   \
-    {                                           \
-        void eval(jit::array<char> &out,        \
-                  const jit::array<T> &lhs,     \
-                  const jit::array<T> &rhs,     \
-                  int lim)                      \
-        {                                       \
-            for (int i = 0; i < lim; i++) {     \
-                out[i] = lhs[i] op rhs[i];      \
-            }                                   \
-        }                                       \
-    };                                          \
-
-
-    LOGIC_FN(af_eq_t, ==)
-    LOGIC_FN(af_neq_t, !=)
-    LOGIC_FN(af_lt_t, <)
-    LOGIC_FN(af_gt_t, >)
-    LOGIC_FN(af_le_t, <=)
-    LOGIC_FN(af_ge_t, >=)
-    LOGIC_FN(af_and_t, &&)
-    LOGIC_FN(af_or_t, ||)
+namespace cpu {
+
+#define LOGIC_FN(OP, op)                                                 \
+    template<typename T>                                                 \
+    struct BinOp<char, T, OP> {                                          \
+        void eval(jit::array<char> &out, const jit::array<T> &lhs,       \
+                  const jit::array<T> &rhs, int lim) {                   \
+            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
+        }                                                                \
+    };
+
+LOGIC_FN(af_eq_t, ==)
+LOGIC_FN(af_neq_t, !=)
+LOGIC_FN(af_lt_t, <)
+LOGIC_FN(af_gt_t, >)
+LOGIC_FN(af_le_t, <=)
+LOGIC_FN(af_ge_t, >=)
+LOGIC_FN(af_and_t, &&)
+LOGIC_FN(af_or_t, ||)
 
 #undef LOGIC_FN
 
-#define LOGIC_CPLX_FN(T, OP, op)                \
-    template<>                                  \
-    struct BinOp<char, std::complex<T>, OP>     \
-    {                                           \
-        typedef std::complex<T> Ti;             \
-        void eval(jit::array<char> &out,        \
-                  const jit::array<Ti> &lhs,    \
-                  const jit::array<Ti> &rhs,    \
-                  int lim)                      \
-        {                                       \
-            for (int i = 0; i < lim; i++) {     \
-                T lhs_mag = std::abs(lhs[i]);   \
-                T rhs_mag = std::abs(rhs[i]);   \
-                out[i] = lhs_mag op rhs_mag;    \
-            }                                   \
-        }                                       \
-    };                                          \
+#define LOGIC_CPLX_FN(T, OP, op)                                    \
+    template<>                                                      \
+    struct BinOp<char, std::complex<T>, OP> {                       \
+        typedef std::complex<T> Ti;                                 \
+        void eval(jit::array<char> &out, const jit::array<Ti> &lhs, \
+                  const jit::array<Ti> &rhs, int lim) {             \
+            for (int i = 0; i < lim; i++) {                         \
+                T lhs_mag = std::abs(lhs[i]);                       \
+                T rhs_mag = std::abs(rhs[i]);                       \
+                out[i]    = lhs_mag op rhs_mag;                     \
+            }                                                       \
+        }                                                           \
+    };
 
 LOGIC_CPLX_FN(float, af_lt_t, <)
 LOGIC_CPLX_FN(float, af_le_t, <=)
@@ -69,7 +57,6 @@ LOGIC_CPLX_FN(float, af_ge_t, >=)
 LOGIC_CPLX_FN(float, af_and_t, &&)
 LOGIC_CPLX_FN(float, af_or_t, ||)
 
-
 LOGIC_CPLX_FN(double, af_lt_t, <)
 LOGIC_CPLX_FN(double, af_le_t, <=)
 LOGIC_CPLX_FN(double, af_gt_t, >)
@@ -79,50 +66,44 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
 
 #undef LOGIC_CPLX_FN
 
-    template<typename T, af_op_t op>
-    Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        jit::Node_ptr lhs_node = lhs.getNode();
-        jit::Node_ptr rhs_node = rhs.getNode();
-
-        jit::BinaryNode<char, T, op> *node = new jit::BinaryNode<char, T, op>(lhs_node, rhs_node);
-
-        return createNodeArray<char>(odims, jit::Node_ptr(node));
-    }
+template<typename T, af_op_t op>
+Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
+                    const af::dim4 &odims) {
+    jit::Node_ptr lhs_node = lhs.getNode();
+    jit::Node_ptr rhs_node = rhs.getNode();
 
+    jit::BinaryNode<char, T, op> *node =
+        new jit::BinaryNode<char, T, op>(lhs_node, rhs_node);
 
+    return createNodeArray<char>(odims, jit::Node_ptr(node));
+}
 
-#define BITWISE_FN(OP, op)                      \
-    template<typename T>                        \
-    struct BinOp<T, T, OP>                      \
-    {                                           \
-        void eval(jit::array<T> &out,           \
-                  const jit::array<T> &lhs,     \
-                  const jit::array<T> &rhs,     \
-                  int lim)                      \
-        {                                       \
-            for (int i = 0; i < lim; i++) {     \
-                out[i] = lhs[i] op rhs[i];      \
-            }                                   \
-        }                                       \
-    };                                          \
-
-    BITWISE_FN(af_bitor_t, |)
-    BITWISE_FN(af_bitand_t, &)
-    BITWISE_FN(af_bitxor_t, ^)
-    BITWISE_FN(af_bitshiftl_t, <<)
-    BITWISE_FN(af_bitshiftr_t, >>)
+#define BITWISE_FN(OP, op)                                               \
+    template<typename T>                                                 \
+    struct BinOp<T, T, OP> {                                             \
+        void eval(jit::array<T> &out, const jit::array<T> &lhs,          \
+                  const jit::array<T> &rhs, int lim) {                   \
+            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
+        }                                                                \
+    };
+
+BITWISE_FN(af_bitor_t, |)
+BITWISE_FN(af_bitand_t, &)
+BITWISE_FN(af_bitxor_t, ^)
+BITWISE_FN(af_bitshiftl_t, <<)
+BITWISE_FN(af_bitshiftr_t, >>)
 
 #undef BITWISE_FN
 
-    template<typename T, af_op_t op>
-    Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        jit::Node_ptr lhs_node = lhs.getNode();
-        jit::Node_ptr rhs_node = rhs.getNode();
+template<typename T, af_op_t op>
+Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
+               const af::dim4 &odims) {
+    jit::Node_ptr lhs_node = lhs.getNode();
+    jit::Node_ptr rhs_node = rhs.getNode();
 
-        jit::BinaryNode<T, T, op> *node = new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
+    jit::BinaryNode<T, T, op> *node =
+        new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-        return createNodeArray<T>(odims, jit::Node_ptr(node));
-    }
+    return createNodeArray<T>(odims, jit::Node_ptr(node));
 }
+}  // namespace cpu
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index c7c6e214a4..a0bf4bbac2 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <kernel/lookup.hpp>
 #include <lookup.hpp>
-#include <cstdlib>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/lookup.hpp>
+#include <cstdlib>
 
-namespace cpu
-{
+namespace cpu {
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input,
-                   const Array<idx_t> &indices, const unsigned dim)
-{
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim) {
     input.eval();
     indices.eval();
 
     const dim4 iDims = input.dims();
 
     dim4 oDims(1);
-    for (int d=0; d<4; ++d)
-        oDims[d] = (d==int(dim) ? indices.elements() : iDims[d]);
+    for (int d = 0; d < 4; ++d)
+        oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
@@ -35,27 +33,36 @@ Array<in_t> lookup(const Array<in_t> &input,
     return out;
 }
 
-#define INSTANTIATE(T)  \
-template Array<T>  lookup<T, float   >(const Array<T>&, const Array<float   >&, const unsigned); \
-template Array<T>  lookup<T, double  >(const Array<T>&, const Array<double  >&, const unsigned); \
-template Array<T>  lookup<T, int     >(const Array<T>&, const Array<int     >&, const unsigned); \
-template Array<T>  lookup<T, unsigned>(const Array<T>&, const Array<unsigned>&, const unsigned); \
-template Array<T>  lookup<T, short   >(const Array<T>&, const Array<short   >&, const unsigned); \
-template Array<T>  lookup<T, ushort  >(const Array<T>&, const Array<ushort  >&, const unsigned); \
-template Array<T>  lookup<T, intl    >(const Array<T>&, const Array<intl    >&, const unsigned); \
-template Array<T>  lookup<T, uintl   >(const Array<T>&, const Array<uintl   >&, const unsigned); \
-template Array<T>  lookup<T, uchar   >(const Array<T>&, const Array<uchar   >&, const unsigned);
-
-INSTANTIATE(float   );
-INSTANTIATE(cfloat  );
-INSTANTIATE(double  );
-INSTANTIATE(cdouble );
-INSTANTIATE(int     );
+#define INSTANTIATE(T)                                                         \
+    template Array<T> lookup<T, float>(const Array<T> &, const Array<float> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, double>(                                       \
+        const Array<T> &, const Array<double> &, const unsigned);              \
+    template Array<T> lookup<T, int>(const Array<T> &, const Array<int> &,     \
+                                     const unsigned);                          \
+    template Array<T> lookup<T, unsigned>(                                     \
+        const Array<T> &, const Array<unsigned> &, const unsigned);            \
+    template Array<T> lookup<T, short>(const Array<T> &, const Array<short> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, ushort>(                                       \
+        const Array<T> &, const Array<ushort> &, const unsigned);              \
+    template Array<T> lookup<T, intl>(const Array<T> &, const Array<intl> &,   \
+                                      const unsigned);                         \
+    template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
+                                       const unsigned);
+
+INSTANTIATE(float);
+INSTANTIATE(cfloat);
+INSTANTIATE(double);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
 INSTANTIATE(unsigned);
-INSTANTIATE(intl    );
-INSTANTIATE(uintl   );
-INSTANTIATE(uchar   );
-INSTANTIATE(char    );
-INSTANTIATE(ushort  );
-INSTANTIATE(short   );
-}
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(uchar);
+INSTANTIATE(char);
+INSTANTIATE(ushort);
+INSTANTIATE(short);
+}  // namespace cpu
diff --git a/src/backend/cpu/lookup.hpp b/src/backend/cpu/lookup.hpp
index 95c729f154..cd5f72a78d 100644
--- a/src/backend/cpu/lookup.hpp
+++ b/src/backend/cpu/lookup.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input,
-                   const Array<idx_t> &indices, const unsigned dim);
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim);
 }
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index f078b02274..efedf867a8 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <lu.hpp>
 #include <common/err_common.hpp>
+#include <lu.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <af/dim4.hpp>
 #include <handle.hpp>
 #include <kernel/lu.hpp>
 #include <lapack_helper.hpp>
@@ -19,44 +18,46 @@
 #include <platform.hpp>
 #include <queue.hpp>
 #include <range.hpp>
+#include <af/dim4.hpp>
 
 #include <cassert>
 #include <iostream>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-using getrf_func_def = int (*)(ORDER_TYPE, int, int, T*, int, int*);
-
-#define LU_FUNC_DEF( FUNC )                                     \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+using getrf_func_def = int (*)(ORDER_TYPE, int, int, T *, int, int *);
 
+#define LU_FUNC_DEF(FUNC) \
+    template<typename T>  \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define LU_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
+#define LU_FUNC(FUNC, TYPE, PREFIX)             \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-LU_FUNC_DEF( getrf )
-LU_FUNC(getrf , float  , s)
-LU_FUNC(getrf , double , d)
-LU_FUNC(getrf , cfloat , c)
-LU_FUNC(getrf , cdouble, z)
+LU_FUNC_DEF(getrf)
+LU_FUNC(getrf, float, s)
+LU_FUNC(getrf, double, d)
+LU_FUNC(getrf, cfloat, c)
+LU_FUNC(getrf, cdouble, z)
 
 template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
     lower.eval();
     upper.eval();
     pivot.eval();
     in.eval();
 
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<T> in_copy = copyArray<T>(in);
-    pivot = lu_inplace(in_copy);
+    pivot            = lu_inplace(in_copy);
 
     // SPLIT into lower and upper
     dim4 ldims(M, min(M, N));
@@ -68,20 +69,21 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 }
 
 template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     in.eval();
 
     dim4 iDims = in.dims();
-    Array<int> pivot = createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
+    Array<int> pivot =
+        createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
 
-    auto func = [=] (Param<T> in, Param<int> pivot) {
+    auto func = [=](Param<T> in, Param<int> pivot) {
         dim4 iDims = in.dims();
-        getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(), in.strides(1), pivot.get());
+        getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(),
+                        in.strides(1), pivot.get());
     };
     getQueue().enqueue(func, in, pivot);
 
-    if(convert_pivot) {
+    if (convert_pivot) {
         Array<int> p = range<int>(dim4(iDims[0]), 0);
         getQueue().enqueue(kernel::convertPivot, p, pivot);
         return p;
@@ -90,49 +92,42 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     }
 }
 
-bool isLAPACKAvailable()
-{
-    return true;
-}
+bool isLAPACKAvailable() { return true; }
 
-}
+}  // namespace cpu
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-bool isLAPACKAvailable()
-{
-    return false;
-}
+bool isLAPACKAvailable() { return false; }
 
-}
+}  // namespace cpu
 
 #endif  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
 
 INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/lu.hpp b/src/backend/cpu/lu.hpp
index 1164664534..4092d4445c 100644
--- a/src/backend/cpu/lu.hpp
+++ b/src/backend/cpu/lu.hpp
@@ -9,13 +9,13 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+namespace cpu {
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in);
 
-    template<typename T>
-    Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
-    bool isLAPACKAvailable();
-}
+bool isLAPACKAvailable();
+}  // namespace cpu
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 7e0457fee9..c429dae52e 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -7,49 +7,57 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <kernel/match_template.hpp>
 #include <match_template.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/match_template.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename InT, typename OutT, af_match_type MatchT>
-Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg)
-{
+Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg) {
     sImg.eval();
     tImg.eval();
 
     Array<OutT> out = createEmptyArray<OutT>(sImg.dims());
 
-    getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg, tImg);
+    getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg,
+                       tImg);
 
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)\
-    template Array<out_t> match_template<in_t, out_t, AF_SAD >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_LSAD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_SSD >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_LSSD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_NCC >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_SHD >(const Array<in_t> &sImg, const Array<in_t> &tImg);
+#define INSTANTIATE(in_t, out_t)                                \
+    template Array<out_t> match_template<in_t, out_t, AF_SAD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_LSAD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_SSD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_LSSD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_NCC>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_SHD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float ,  float)
-INSTANTIATE(char  ,  float)
-INSTANTIATE(int   ,  float)
-INSTANTIATE(uint  ,  float)
-INSTANTIATE(uchar ,  float)
-INSTANTIATE(short ,  float)
-INSTANTIATE(ushort,  float)
-
-}
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/match_template.hpp b/src/backend/cpu/match_template.hpp
index 777f645ff9..ae32d6c839 100644
--- a/src/backend/cpu/match_template.hpp
+++ b/src/backend/cpu/match_template.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename inType, typename outType, af_match_type mType>
-Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg);
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg);
 
 }
diff --git a/src/backend/cpu/math.cpp b/src/backend/cpu/math.cpp
index 556a817b1f..b061c44b93 100644
--- a/src/backend/cpu/math.cpp
+++ b/src/backend/cpu/math.cpp
@@ -9,43 +9,32 @@
 #include <common/defines.hpp>
 #include <math.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 uint abs(uint val) { return val; }
 uchar abs(uchar val) { return val; }
 uintl abs(uintl val) { return val; }
 
-cfloat  scalar(float val)
-{
-    cfloat  cval = {(float)val, 0};
+cfloat scalar(float val) {
+    cfloat cval = {(float)val, 0};
     return cval;
 }
 
-cdouble scalar(double val)
-{
-    cdouble  cval = {val, 0};
+cdouble scalar(double val) {
+    cdouble cval = {val, 0};
     return cval;
 }
 
-cfloat min(cfloat lhs, cfloat rhs)
-{
-    return abs(lhs) < abs(rhs) ? lhs : rhs;
-}
+cfloat min(cfloat lhs, cfloat rhs) { return abs(lhs) < abs(rhs) ? lhs : rhs; }
 
-cdouble min(cdouble lhs, cdouble rhs)
-{
+cdouble min(cdouble lhs, cdouble rhs) {
     return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
-cfloat max(cfloat lhs, cfloat rhs)
-{
-    return abs(lhs) > abs(rhs) ? lhs : rhs;
-}
+cfloat max(cfloat lhs, cfloat rhs) { return abs(lhs) > abs(rhs) ? lhs : rhs; }
 
-cdouble max(cdouble lhs, cdouble rhs)
-{
+cdouble max(cdouble lhs, cdouble rhs) {
     return abs(lhs) > abs(rhs) ? lhs : rhs;
 }
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 4488935329..1d83c72ccf 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -9,73 +9,99 @@
 
 #pragma once
 
+#include <common/defines.hpp>
 #include <types.hpp>
 #include <af/defines.h>
-#include <common/defines.hpp>
 
-#include <limits>
 #include <algorithm>
+#include <limits>
 #include <numeric>
 
-namespace cpu
-{
-    template<typename T> static inline T abs(T val) { return std::abs(val); }
-    uint abs(uint val);
-    uchar abs(uchar val);
-    uintl abs(uintl val);
+namespace cpu {
+template<typename T>
+static inline T abs(T val) {
+    return std::abs(val);
+}
+uint abs(uint val);
+uchar abs(uchar val);
+uintl abs(uintl val);
 
-    template<typename T> static inline T min(T lhs, T rhs) { return std::min(lhs, rhs); }
-    cfloat min(cfloat lhs, cfloat rhs);
-    cdouble min(cdouble lhs, cdouble rhs);
+template<typename T>
+static inline T min(T lhs, T rhs) {
+    return std::min(lhs, rhs);
+}
+cfloat min(cfloat lhs, cfloat rhs);
+cdouble min(cdouble lhs, cdouble rhs);
 
-    template<typename T> static inline T max(T lhs, T rhs) { return std::max(lhs, rhs); }
-    cfloat max(cfloat lhs, cfloat rhs);
-    cdouble max(cdouble lhs, cdouble rhs);
+template<typename T>
+static inline T max(T lhs, T rhs) {
+    return std::max(lhs, rhs);
+}
+cfloat max(cfloat lhs, cfloat rhs);
+cdouble max(cdouble lhs, cdouble rhs);
 
-    template<typename T> static inline T division(T lhs, double rhs) { return lhs / rhs; }
+template<typename T>
+static inline T division(T lhs, double rhs) {
+    return lhs / rhs;
+}
 
-    template<> STATIC_ cfloat division<cfloat>(cfloat lhs, double rhs)
-    {
-        cfloat retVal(real(lhs) / static_cast<float>(rhs), imag(lhs) / static_cast<float>(rhs));
-        return retVal;
-    }
+template<>
+STATIC_ cfloat division<cfloat>(cfloat lhs, double rhs) {
+    cfloat retVal(real(lhs) / static_cast<float>(rhs),
+                  imag(lhs) / static_cast<float>(rhs));
+    return retVal;
+}
 
-    template<> STATIC_ cdouble division<cdouble>(cdouble lhs, double rhs)
-    {
-        cdouble retVal(real(lhs) / rhs, imag(lhs) / rhs);
-        return retVal;
-    }
+template<>
+STATIC_ cdouble division<cdouble>(cdouble lhs, double rhs) {
+    cdouble retVal(real(lhs) / rhs, imag(lhs) / rhs);
+    return retVal;
+}
 
-    template <typename T> STATIC_ T      maxval() { return  std::numeric_limits<T     >::max();      }
-    template <typename T> STATIC_ T      minval() { return  std::numeric_limits<T     >::min();      }
-    template <>           STATIC_ float  maxval() { return  std::numeric_limits<float >::infinity(); }
-    template <>           STATIC_ double maxval() { return  std::numeric_limits<double>::infinity(); }
-    template <>           STATIC_ float  minval() { return -std::numeric_limits<float >::infinity(); }
-    template <>           STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
+template<typename T>
+STATIC_ T maxval() {
+    return std::numeric_limits<T>::max();
+}
+template<typename T>
+STATIC_ T minval() {
+    return std::numeric_limits<T>::min();
+}
+template<>
+STATIC_ float maxval() {
+    return std::numeric_limits<float>::infinity();
+}
+template<>
+STATIC_ double maxval() {
+    return std::numeric_limits<double>::infinity();
+}
+template<>
+STATIC_ float minval() {
+    return -std::numeric_limits<float>::infinity();
+}
+template<>
+STATIC_ double minval() {
+    return -std::numeric_limits<double>::infinity();
+}
 
-    template<typename T>
-    static T scalar(double val)
-    {
-        return (T)(val);
-    }
+template<typename T>
+static T scalar(double val) {
+    return (T)(val);
+}
 
-    template<typename To, typename Ti>
-    static To scalar(Ti real, Ti imag)
-    {
-        To  cval = {real, imag};
-        return cval;
-    }
+template<typename To, typename Ti>
+static To scalar(Ti real, Ti imag) {
+    To cval = {real, imag};
+    return cval;
+}
 
-    cfloat  scalar(float val);
+cfloat scalar(float val);
 
-    cdouble scalar(double val);
+cdouble scalar(double val);
 
 #if __cplusplus < 201703L
-    template<typename T>
-    static inline
-    T clamp(const T value, const T lo, const T hi)
-    {
-        return (value<lo ? lo : (value>hi ? hi : value));
-    }
-#endif
+template<typename T>
+static inline T clamp(const T value, const T lo, const T hi) {
+    return (value < lo ? lo : (value > hi ? hi : value));
 }
+#endif
+}  // namespace cpu
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index ff7fa4de28..9710819a3e 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -7,86 +7,83 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <kernel/mean.hpp>
 #include <mean.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/mean.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename Ti, typename Tw, typename To>
-using mean_dim_func = std::function<void(Param<To>, const dim_t,
-                                            const CParam<Ti>, const dim_t, const int)>;
+using mean_dim_func = std::function<void(
+    Param<To>, const dim_t, const CParam<Ti>, const dim_t, const int)>;
 
 template<typename Ti, typename Tw, typename To>
-Array<To> mean(const Array<Ti>& in, const int dim)
-{
+Array<To> mean(const Array<Ti> &in, const int dim) {
     in.eval();
 
-    dim4 odims = in.dims();
-    odims[dim] = 1;
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
     Array<To> out = createEmptyArray<To>(odims);
-    static const mean_dim_func<Ti, Tw, To> mean_funcs[] = { kernel::mean_dim<Ti, Tw, To, 1>(),
-                                                            kernel::mean_dim<Ti, Tw, To, 2>(),
-                                                            kernel::mean_dim<Ti, Tw, To, 3>(),
-                                                            kernel::mean_dim<Ti, Tw, To, 4>()};
+    static const mean_dim_func<Ti, Tw, To> mean_funcs[] = {
+        kernel::mean_dim<Ti, Tw, To, 1>(), kernel::mean_dim<Ti, Tw, To, 2>(),
+        kernel::mean_dim<Ti, Tw, To, 3>(), kernel::mean_dim<Ti, Tw, To, 4>()};
 
     getQueue().enqueue(mean_funcs[in.ndims() - 1], out, 0, in, 0, dim);
     return out;
 }
 
 template<typename T, typename Tw>
-using mean_weighted_dim_func = std::function<void(Param<T>, const dim_t,
-                                            const CParam<T>, const dim_t, const CParam<Tw>, const dim_t, const int)>;
+using mean_weighted_dim_func =
+    std::function<void(Param<T>, const dim_t, const CParam<T>, const dim_t,
+                       const CParam<Tw>, const dim_t, const int)>;
 
 template<typename T, typename Tw>
-Array<T> mean(const Array<T>& in, const Array<Tw>& wt, const int dim)
-{
+Array<T> mean(const Array<T> &in, const Array<Tw> &wt, const int dim) {
     in.eval();
     wt.eval();
 
-    dim4 odims = in.dims();
-    odims[dim] = 1;
+    dim4 odims   = in.dims();
+    odims[dim]   = 1;
     Array<T> out = createEmptyArray<T>(odims);
-    static const mean_weighted_dim_func<T, Tw> mean_funcs[] = { kernel::mean_weighted_dim<T, Tw, 1>(),
-                                                                kernel::mean_weighted_dim<T, Tw, 2>(),
-                                                                kernel::mean_weighted_dim<T, Tw, 3>(),
-                                                                kernel::mean_weighted_dim<T, Tw, 4>()};
+    static const mean_weighted_dim_func<T, Tw> mean_funcs[] = {
+        kernel::mean_weighted_dim<T, Tw, 1>(),
+        kernel::mean_weighted_dim<T, Tw, 2>(),
+        kernel::mean_weighted_dim<T, Tw, 3>(),
+        kernel::mean_weighted_dim<T, Tw, 4>()};
 
     getQueue().enqueue(mean_funcs[in.ndims() - 1], out, 0, in, 0, wt, 0, dim);
     return out;
 }
 
 template<typename T, typename Tw>
-T mean(const Array<T>& in, const Array<Tw>& wt)
-{
+T mean(const Array<T> &in, const Array<Tw> &wt) {
     in.eval();
     wt.eval();
     getQueue().sync();
 
-    af::dim4 dims = in.dims();
+    af::dim4 dims    = in.dims();
     af::dim4 strides = in.strides();
-    const T  *inPtr = in.get();
-    const Tw *wtPtr = wt.get();
+    const T *inPtr   = in.get();
+    const Tw *wtPtr  = wt.get();
 
     kernel::MeanOp<T, T, Tw> Op(inPtr[0], wtPtr[0]);
 
-    for(dim_t l = 0; l < dims[3]; l++) {
+    for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
 
-        for(dim_t k = 0; k < dims[2]; k++) {
+        for (dim_t k = 0; k < dims[2]; k++) {
             dim_t off2 = k * strides[2];
 
-            for(dim_t j = 0; j < dims[1]; j++) {
+            for (dim_t j = 0; j < dims[1]; j++) {
                 dim_t off1 = j * strides[1];
 
-                for(dim_t i = 0; i < dims[0]; i++) {
+                for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
                     Op(inPtr[idx], wtPtr[idx]);
                 }
@@ -98,27 +95,26 @@ T mean(const Array<T>& in, const Array<Tw>& wt)
 }
 
 template<typename Ti, typename Tw, typename To>
-To mean(const Array<Ti>& in)
-{
+To mean(const Array<Ti> &in) {
     in.eval();
     getQueue().sync();
 
-    af::dim4 dims = in.dims();
+    af::dim4 dims    = in.dims();
     af::dim4 strides = in.strides();
-    const Ti *inPtr = in.get();
+    const Ti *inPtr  = in.get();
 
     kernel::MeanOp<Ti, To, Tw> Op(0, 0);
 
-    for(dim_t l = 0; l < dims[3]; l++) {
+    for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
 
-        for(dim_t k = 0; k < dims[2]; k++) {
+        for (dim_t k = 0; k < dims[2]; k++) {
             dim_t off2 = k * strides[2];
 
-            for(dim_t j = 0; j < dims[1]; j++) {
+            for (dim_t j = 0; j < dims[1]; j++) {
                 dim_t off1 = j * strides[1];
 
-                for(dim_t i = 0; i < dims[0]; i++) {
+                for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
                     Op(inPtr[idx], 1);
                 }
@@ -129,30 +125,31 @@ To mean(const Array<Ti>& in)
     return Op.runningMean;
 }
 
-#define INSTANTIATE(Ti, Tw, To)                                         \
+#define INSTANTIATE(Ti, Tw, To)                        \
     template To mean<Ti, Tw, To>(const Array<Ti> &in); \
-    template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim); \
-
-INSTANTIATE(double  , double,  double);
-INSTANTIATE(float   , float ,  float );
-INSTANTIATE(int     , float ,  float );
-INSTANTIATE(unsigned, float ,  float );
-INSTANTIATE(intl    , double,  double);
-INSTANTIATE(uintl   , double,  double);
-INSTANTIATE(short   , float ,  float );
-INSTANTIATE(ushort  , float ,  float );
-INSTANTIATE(uchar   , float ,  float );
-INSTANTIATE(char    , float ,  float );
-INSTANTIATE(cfloat  , float ,  cfloat);
-INSTANTIATE(cdouble , double, cdouble);
-
-#define INSTANTIATE_WGT(T, Tw)                                         \
-    template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts); \
-    template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, const int dim); \
-
-INSTANTIATE_WGT(double , double);
-INSTANTIATE_WGT(float  , float );
-INSTANTIATE_WGT(cfloat , float );
+    template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim);
+
+INSTANTIATE(double, double, double);
+INSTANTIATE(float, float, float);
+INSTANTIATE(int, float, float);
+INSTANTIATE(unsigned, float, float);
+INSTANTIATE(intl, double, double);
+INSTANTIATE(uintl, double, double);
+INSTANTIATE(short, float, float);
+INSTANTIATE(ushort, float, float);
+INSTANTIATE(uchar, float, float);
+INSTANTIATE(char, float, float);
+INSTANTIATE(cfloat, float, cfloat);
+INSTANTIATE(cdouble, double, cdouble);
+
+#define INSTANTIATE_WGT(T, Tw)                                              \
+    template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts);       \
+    template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, \
+                                  const int dim);
+
+INSTANTIATE_WGT(double, double);
+INSTANTIATE_WGT(float, float);
+INSTANTIATE_WGT(cfloat, float);
 INSTANTIATE_WGT(cdouble, double);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/mean.hpp b/src/backend/cpu/mean.hpp
index a3d40e5e90..d51a71bd2d 100644
--- a/src/backend/cpu/mean.hpp
+++ b/src/backend/cpu/mean.hpp
@@ -10,17 +10,16 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cpu
-{
-    template<typename Ti, typename Tw, typename To>
-    Array<To> mean(const Array<Ti>& in, const int dim);
+namespace cpu {
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim);
 
-    template<typename T, typename Tw>
-    Array<T> mean(const Array<T>& in, const Array<Tw>& wt, const int dim);
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wt, const int dim);
 
-    template<typename T, typename Tw>
-    T mean(const Array<T>& in, const Array<Tw>& wts);
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts);
 
-    template<typename Ti, typename Tw, typename To>
-    To mean(const Array<Ti>& in);
-}
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in);
+}  // namespace cpu
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index a571a6b315..81b40236dd 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -7,50 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <meanshift.hpp>
-#include <cmath>
-#include <algorithm>
 #include <err_cpu.hpp>
+#include <kernel/meanshift.hpp>
 #include <math.hpp>
+#include <meanshift.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/meanshift.hpp>
+#include <af/dim4.hpp>
+#include <algorithm>
+#include <cmath>
 
 using af::dim4;
 using std::vector;
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
-Array<T>  meanshift(const Array<T> &in,
-                    const float &spatialSigma, const float &chromaticSigma,
-                    const unsigned& numInterations, const bool& isColor)
-{
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numInterations,
+                   const bool &isColor) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
     if (isColor)
-        getQueue().enqueue(kernel::meanShift<T, true>, out, in, spatialSigma, chromaticSigma, numInterations);
+        getQueue().enqueue(kernel::meanShift<T, true>, out, in, spatialSigma,
+                           chromaticSigma, numInterations);
     else
-        getQueue().enqueue(kernel::meanShift<T, false>, out, in, spatialSigma, chromaticSigma, numInterations);
+        getQueue().enqueue(kernel::meanShift<T, false>, out, in, spatialSigma,
+                           chromaticSigma, numInterations);
 
     return out;
 }
 
-#define INSTANTIATE(T) \
-    template Array<T>  meanshift<T>(const Array<T>&, const float&, const float&, const unsigned&, const bool&);
+#define INSTANTIATE(T)                                              \
+    template Array<T> meanshift<T>(const Array<T> &, const float &, \
+                                   const float &, const unsigned &, \
+                                   const bool &);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
-INSTANTIATE(intl  )
-INSTANTIATE(uintl )
-}
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace cpu
diff --git a/src/backend/cpu/meanshift.hpp b/src/backend/cpu/meanshift.hpp
index 43299b52f7..b8ba8d2c24 100644
--- a/src/backend/cpu/meanshift.hpp
+++ b/src/backend/cpu/meanshift.hpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
-Array<T>  meanshift(const Array<T> &in,
-                    const float &spatialSigma, const float &chromaticSigma,
-                    const unsigned& numIterations, const bool& isColor);
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor);
 }
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index a1c44a4323..deff345b6f 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <kernel/medfilt.hpp>
 #include <medfilt.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/medfilt.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, af_border_type pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
-{
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
@@ -32,8 +30,7 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 }
 
 template<typename T, af_border_type pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
-{
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
@@ -43,19 +40,23 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
     return out;
 }
 
-#define INSTANTIATE(T)\
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);              \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);    \
-    template Array<T> medfilt2<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
-
-INSTANTIATE(float )
+#define INSTANTIATE(T)                                                         \
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in,             \
+                                               dim_t w_wid);                   \
+    template Array<T> medfilt1<T, AF_PAD_SYM>(const Array<T> &in,              \
+                                              dim_t w_wid);                    \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in,             \
+                                               dim_t w_len, dim_t w_wid);      \
+    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, \
+                                              dim_t w_wid);
+
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
 INSTANTIATE(ushort)
-INSTANTIATE(short )
+INSTANTIATE(short)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/medfilt.hpp b/src/backend/cpu/medfilt.hpp
index 2bb5836841..db177afdbc 100644
--- a/src/backend/cpu/medfilt.hpp
+++ b/src/backend/cpu/medfilt.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
@@ -18,4 +17,4 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 2a658e9eb4..60858b1551 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -29,114 +29,80 @@ template class common::MemoryManager<cpu::MemoryManager>;
 
 using common::bytesToString;
 
-using std::unique_ptr;
 using std::function;
+using std::unique_ptr;
 
-namespace cpu
-{
-void setMemStepSize(size_t step_bytes)
-{
+namespace cpu {
+void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
-size_t getMemStepSize(void)
-{
-    return memoryManager().getMemStepSize();
-}
+size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
 
-size_t getMaxBytes()
-{
-    return memoryManager().getMaxBytes();
-}
+size_t getMaxBytes() { return memoryManager().getMaxBytes(); }
 
-unsigned getMaxBuffers()
-{
-    return memoryManager().getMaxBuffers();
-}
+unsigned getMaxBuffers() { return memoryManager().getMaxBuffers(); }
 
-void garbageCollect()
-{
-    memoryManager().garbageCollect();
-}
+void garbageCollect() { memoryManager().garbageCollect(); }
 
-void printMemInfo(const char *msg, const int device)
-{
+void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
 }
 
 template<typename T>
-unique_ptr<T[], function<void(T *)>>
-memAlloc(const size_t &elements)
-{
+unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements) {
     T *ptr = nullptr;
 
     ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
     return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
 }
 
-void* memAllocUser(const size_t &bytes)
-{
+void *memAllocUser(const size_t &bytes) {
     void *ptr = nullptr;
-    ptr = memoryManager().alloc(bytes, true);
+    ptr       = memoryManager().alloc(bytes, true);
     return ptr;
 }
 
 template<typename T>
-void memFree(T *ptr)
-{
+void memFree(T *ptr) {
     return memoryManager().unlock((void *)ptr, false);
 }
 
-void memFreeUser(void *ptr)
-{
-    memoryManager().unlock((void *)ptr, true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
 
-void memLock(const void *ptr)
-{
-    memoryManager().userLock((void *)ptr);
-}
+void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
-bool isLocked(const void *ptr)
-{
+bool isLocked(const void *ptr) {
     return memoryManager().isUserLocked((void *)ptr);
 }
 
-void memUnlock(const void *ptr)
-{
-    memoryManager().userUnlock((void *)ptr);
-}
+void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                      size_t *lock_bytes,  size_t *lock_buffers)
-{
-    memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
-                                  lock_bytes,  lock_buffers);
+                      size_t *lock_bytes, size_t *lock_buffers) {
+    memoryManager().bufferInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                               lock_buffers);
 }
 
 template<typename T>
-T* pinnedAlloc(const size_t &elements)
-{
+T *pinnedAlloc(const size_t &elements) {
     return (T *)memoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
-void pinnedFree(T* ptr)
-{
+void pinnedFree(T *ptr) {
     return memoryManager().unlock((void *)ptr, false);
 }
 
-bool checkMemoryLimit()
-{
-    return memoryManager().checkMemoryLimit();
-}
+bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
+
+#define INSTANTIATE(T)                                                \
+    template std::unique_ptr<T[], std::function<void(T *)>> memAlloc( \
+        const size_t &elements);                                      \
+    template void memFree(T *ptr);                                    \
+    template T *pinnedAlloc(const size_t &elements);                  \
+    template void pinnedFree(T *ptr);
 
-#define INSTANTIATE(T)                                                                        \
-    template std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements); \
-    template void memFree(T* ptr);                                                            \
-    template T* pinnedAlloc(const size_t &elements);                                          \
-    template void pinnedFree(T* ptr);                                                         \
- 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
@@ -148,50 +114,44 @@ INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(ushort)
-INSTANTIATE(short )
+INSTANTIATE(short)
 
 MemoryManager::MemoryManager()
-    : common::MemoryManager<cpu::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
-                                                AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)
-{
+    : common::MemoryManager<cpu::MemoryManager>(
+          getDeviceCount(), common::MAX_BUFFERS,
+          AF_MEM_DEBUG || AF_CPU_MEM_DEBUG) {
     this->setMaxMemorySize();
 }
 
-MemoryManager::~MemoryManager()
-{
+MemoryManager::~MemoryManager() {
     for (int n = 0; n < cpu::getDeviceCount(); n++) {
         try {
             cpu::setDevice(n);
             garbageCollect();
-        } catch(AfError err) {
-            continue; // Do not throw any errors while shutting down
+        } catch (AfError err) {
+            continue;  // Do not throw any errors while shutting down
         }
     }
 }
 
-int MemoryManager::getActiveDeviceId()
-{
-    return cpu::getActiveDeviceId();
-}
+int MemoryManager::getActiveDeviceId() { return cpu::getActiveDeviceId(); }
 
-size_t MemoryManager::getMaxMemorySize(int id)
-{
+size_t MemoryManager::getMaxMemorySize(int id) {
     return cpu::getDeviceMemorySize(id);
 }
 
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
+void *MemoryManager::nativeAlloc(const size_t bytes) {
     void *ptr = malloc(bytes);
     AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
     return ptr;
 }
 
-void MemoryManager::nativeFree(void *ptr)
-{
+void MemoryManager::nativeFree(void *ptr) {
     AF_TRACE("nativeFree: {: >8} {}", " ", ptr);
-    // Make sure this pointer is not being used on the queue before freeing the memory.
+    // Make sure this pointer is not being used on the queue before freeing the
+    // memory.
     getQueue().sync();
     return free((void *)ptr);
 }
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 99e3d57e02..af80156b42 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -8,39 +8,42 @@
  ********************************************************/
 #pragma once
 
-#include <af/defines.h>
 #include <common/MemoryManager.hpp>
+#include <af/defines.h>
 
 #include <functional>
 #include <memory>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
 
-template<typename T> std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
+template<typename T>
+std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-template<typename T> void memFree(T* ptr);
-void memFreeUser(void* ptr);
+template<typename T>
+void memFree(T *ptr);
+void memFreeUser(void *ptr);
 
 void memLock(const void *ptr);
 void memUnlock(const void *ptr);
 bool isLocked(const void *ptr);
 
-template<typename T> T* pinnedAlloc(const size_t &elements);
-template<typename T> void pinnedFree(T* ptr);
+template<typename T>
+T *pinnedAlloc(const size_t &elements);
+template<typename T>
+void pinnedFree(T *ptr);
 
 size_t getMaxBytes();
 unsigned getMaxBuffers();
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                      size_t *lock_bytes,  size_t *lock_buffers);
+                      size_t *lock_bytes, size_t *lock_buffers);
 void garbageCollect();
 void pinnedGarbageCollect();
 
@@ -50,14 +53,13 @@ void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 bool checkMemoryLimit();
 
-class MemoryManager : public common::MemoryManager<cpu::MemoryManager>
-{
-    public:
-        MemoryManager();
-        ~MemoryManager();
-        int getActiveDeviceId();
-        size_t getMaxMemorySize(int id);
-        void *nativeAlloc(const size_t bytes);
-        void nativeFree(void *ptr);
+class MemoryManager : public common::MemoryManager<cpu::MemoryManager> {
+   public:
+    MemoryManager();
+    ~MemoryManager();
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
 };
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index 63549bc084..04eeac5dc6 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -7,15 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <Array.hpp>
 #include <err_cpu.hpp>
-#include <platform.hpp>
 #include <kernel/moments.hpp>
+#include <platform.hpp>
 #include <queue.hpp>
+#include <af/defines.h>
 
-namespace cpu
-{
+namespace cpu {
 
 static inline int bitCount(int v) {
     v = v - ((v >> 1) & 0x55555555);
@@ -26,8 +25,7 @@ static inline int bitCount(int v) {
 using af::dim4;
 
 template<typename T>
-Array<float> moments(const Array<T> &in, const af_moment_type moment)
-{
+Array<float> moments(const Array<T> &in, const af_moment_type moment) {
     in.eval();
     dim4 odims, idims = in.dims();
     dim_t moments_dim = bitCount(moment);
@@ -45,9 +43,9 @@ Array<float> moments(const Array<T> &in, const af_moment_type moment)
     return out;
 }
 
-
-#define INSTANTIATE(T)  \
-    template Array<float> moments<T>(const Array<T> &in, const af_moment_type moment);
+#define INSTANTIATE(T)                                   \
+    template Array<float> moments<T>(const Array<T> &in, \
+                                     const af_moment_type moment);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -58,5 +56,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
-
+}  // namespace cpu
diff --git a/src/backend/cpu/moments.hpp b/src/backend/cpu/moments.hpp
index f3627fad54..20a4ff4ed0 100644
--- a/src/backend/cpu/moments.hpp
+++ b/src/backend/cpu/moments.hpp
@@ -10,9 +10,7 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
-namespace cpu
-{
-    template<typename T>
-    Array<float> moments(const Array<T> &in, const af_moment_type moment);
+namespace cpu {
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment);
 }
-
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index ecb6681882..7b4a5e2786 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -7,22 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
+#include <kernel/morph.hpp>
 #include <morph.hpp>
-#include <algorithm>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/morph.hpp>
+#include <af/dim4.hpp>
+#include <algorithm>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask)
-{
+Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     af::borderType padType = isDilation ? AF_PAD_ZERO : AF_PAD_CLAMP_TO_EDGE;
 
     in.eval();
@@ -31,11 +29,10 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
     const af::dim4 idims = in.dims();
     const af::dim4 mdims = mask.dims();
 
-    const af::dim4 lpad(mdims[0]/2, mdims[1]/2, 0, 0);
+    const af::dim4 lpad(mdims[0] / 2, mdims[1] / 2, 0, 0);
     const af::dim4 upad(lpad);
     const af::dim4 odims(lpad[0] + idims[0] + upad[0],
-                         lpad[1] + idims[1] + upad[1],
-                         idims[2], idims[3]);
+                         lpad[1] + idims[1] + upad[1], idims[2], idims[3]);
 
     auto out = createEmptyArray<T>(odims);
     auto inp = padArrayBorders(in, lpad, upad, padType);
@@ -43,15 +40,14 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
     getQueue().enqueue(kernel::morph<T, isDilation>, out, inp, mask);
 
     std::vector<af_seq> idxs(4, af_span);
-    idxs[0] = af_seq{double(lpad[0]), double(lpad[0]+idims[0]-1), 1.0};
-    idxs[1] = af_seq{double(lpad[1]), double(lpad[1]+idims[1]-1), 1.0};
+    idxs[0] = af_seq{double(lpad[0]), double(lpad[0] + idims[0] - 1), 1.0};
+    idxs[1] = af_seq{double(lpad[1]), double(lpad[1] + idims[1] - 1), 1.0};
 
     return createSubArray(out, idxs);
 }
 
 template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
-{
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
     in.eval();
     mask.eval();
 
@@ -62,18 +58,22 @@ Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
     return out;
 }
 
-#define INSTANTIATE(T)\
-    template Array<T> morph  <T, true >(const Array<T> &in, const Array<T> &mask);\
-    template Array<T> morph  <T, false>(const Array<T> &in, const Array<T> &mask);\
-    template Array<T> morph3d<T, true >(const Array<T> &in, const Array<T> &mask);\
-    template Array<T> morph3d<T, false>(const Array<T> &in, const Array<T> &mask);
-
-INSTANTIATE(float )
+#define INSTANTIATE(T)                                        \
+    template Array<T> morph<T, true>(const Array<T> &in,      \
+                                     const Array<T> &mask);   \
+    template Array<T> morph<T, false>(const Array<T> &in,     \
+                                      const Array<T> &mask);  \
+    template Array<T> morph3d<T, true>(const Array<T> &in,    \
+                                       const Array<T> &mask); \
+    template Array<T> morph3d<T, false>(const Array<T> &in,   \
+                                        const Array<T> &mask);
+
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
 INSTANTIATE(ushort)
-INSTANTIATE(short )
-}
+INSTANTIATE(short)
+}  // namespace cpu
diff --git a/src/backend/cpu/morph.hpp b/src/backend/cpu/morph.hpp
index 006553db38..a4ded63686 100644
--- a/src/backend/cpu/morph.hpp
+++ b/src/backend/cpu/morph.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask);
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index e917c84287..e033e1ef1b 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -7,26 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_cpu.hpp>
+#include <kernel/nearest_neighbour.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <topk.hpp>
-#include <err_cpu.hpp>
-#include <kernel/nearest_neighbour.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename To>
-void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
-                       const Array<T>& query, const Array<T>& train,
-                       const uint dist_dim, const uint n_dist,
-                       const af_match_type dist_type)
-{
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist, const af_match_type dist_type) {
     idx.eval();
     dist.eval();
     query.eval();
@@ -35,48 +32,49 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
     uint sample_dim  = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
-    const dim4 outDims (n_dist, qDims[sample_dim]);
+    const dim4 outDims(n_dist, qDims[sample_dim]);
     const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
 
     Array<To> tmp_dists = createEmptyArray<To>(distDims);
 
     idx  = createEmptyArray<uint>(outDims);
-    dist = createEmptyArray<To  >(outDims);
+    dist = createEmptyArray<To>(outDims);
 
-    switch(dist_type) {
+    switch (dist_type) {
         case AF_SAD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>, tmp_dists, query, train, dist_dim);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SAD>,
+                               tmp_dists, query, train, dist_dim);
             break;
         case AF_SSD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>, tmp_dists, query, train, dist_dim);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SSD>,
+                               tmp_dists, query, train, dist_dim);
             break;
         case AF_SHD:
-            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>, tmp_dists, query, train, dist_dim);
+            getQueue().enqueue(kernel::nearest_neighbour<T, To, AF_SHD>,
+                               tmp_dists, query, train, dist_dim);
             break;
-        default:
-            AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
+        default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
 
     cpu::topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
-
 }
 
-#define INSTANTIATE(T, To)                                                              \
-    template void nearest_neighbour<T, To>(Array<uint>& idx, Array<To>& dist,           \
-                                         const Array<T>& query, const Array<T>& train,  \
-                                         const uint dist_dim, const uint n_dist,        \
-                                         const af_match_type dist_type);
+#define INSTANTIATE(T, To)                                             \
+    template void nearest_neighbour<T, To>(                            \
+        Array<uint> & idx, Array<To> & dist, const Array<T>& query,    \
+        const Array<T>& train, const uint dist_dim, const uint n_dist, \
+        const af_match_type dist_type);
 
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
-INSTANTIATE(int   , int)
-INSTANTIATE(uint  , uint)
-INSTANTIATE(intl  , intl)
-INSTANTIATE(uintl , uintl)
-INSTANTIATE(uchar , uint)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(uchar, uint)
 INSTANTIATE(ushort, uint)
-INSTANTIATE(short , int)
+INSTANTIATE(short, int)
 
-INSTANTIATE(uintl , uint)    // For Hamming
+INSTANTIATE(uintl, uint)  // For Hamming
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/nearest_neighbour.hpp b/src/backend/cpu/nearest_neighbour.hpp
index 4cf1dc2a0a..22e190cb16 100644
--- a/src/backend/cpu/nearest_neighbour.hpp
+++ b/src/backend/cpu/nearest_neighbour.hpp
@@ -9,13 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename To>
-void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
-                       const Array<T>& query, const Array<T>& train,
-                       const uint dist_dim, const uint n_dist,
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
 
 }
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 326ae03086..330fc42d7d 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -7,45 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <resize.hpp>
-#include <fast.hpp>
-#include <sort_index.hpp>
 #include <convolve.hpp>
+#include <fast.hpp>
+#include <kernel/orb.hpp>
 #include <memory.hpp>
-#include <cstring>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/orb.hpp>
+#include <resize.hpp>
+#include <sort_index.hpp>
+#include <af/dim4.hpp>
+#include <cstring>
 
 using af::dim4;
 
-using std::vector;
 using std::function;
 using std::unique_ptr;
+using std::vector;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename convAccT>
-unsigned orb(Array<float> &x, Array<float> &y,
-             Array<float> &score, Array<float> &ori,
-             Array<float> &size, Array<uint> &desc,
-             const Array<T>& image,
-             const float fast_thr, const unsigned max_feat,
-             const float scl_fctr, const unsigned levels,
-             const bool blur_img)
-{
+unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
+             Array<float>& ori, Array<float>& size, Array<uint>& desc,
+             const Array<T>& image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img) {
     image.eval();
     getQueue().sync();
 
     unsigned patch_size = REF_PAT_SIZE;
 
     const af::dim4 idims = image.dims();
-    unsigned min_side = std::min(idims[0], idims[1]);
-    unsigned max_levels = 0;
-    float scl_sum = 0.f;
+    unsigned min_side    = std::min(idims[0], idims[1]);
+    unsigned max_levels  = 0;
+    float scl_sum        = 0.f;
 
     for (unsigned i = 0; i < levels; i++) {
         min_side /= scl_fctr;
@@ -54,7 +50,7 @@ unsigned orb(Array<float> &x, Array<float> &y,
         if (min_side < patch_size || max_levels == levels) break;
 
         max_levels++;
-        scl_sum += 1.f / (float)std::pow(scl_fctr,(float)i);
+        scl_sum += 1.f / (float)std::pow(scl_fctr, (float)i);
     }
 
     vector<unique_ptr<float[], function<void(float*)>>> h_x_pyr(max_levels);
@@ -62,7 +58,8 @@ unsigned orb(Array<float> &x, Array<float> &y,
     vector<unique_ptr<float[], function<void(float*)>>> h_score_pyr(max_levels);
     vector<unique_ptr<float[], function<void(float*)>>> h_ori_pyr(max_levels);
     vector<unique_ptr<float[], function<void(float*)>>> h_size_pyr(max_levels);
-    vector<unique_ptr<unsigned[], function<void(unsigned*)>>> h_desc_pyr(max_levels);
+    vector<unique_ptr<unsigned[], function<void(unsigned*)>>> h_desc_pyr(
+        max_levels);
 
     std::vector<unsigned> feat_pyr(max_levels);
     unsigned total_feat = 0;
@@ -70,51 +67,50 @@ unsigned orb(Array<float> &x, Array<float> &y,
     // Compute number of features to keep for each level
     std::vector<unsigned> lvl_best(max_levels);
     unsigned feat_sum = 0;
-    for (unsigned i = 0; i < max_levels-1; i++) {
-        float lvl_scl = (float)std::pow(scl_fctr,(float)i);
-        lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl);
+    for (unsigned i = 0; i < max_levels - 1; i++) {
+        float lvl_scl = (float)std::pow(scl_fctr, (float)i);
+        lvl_best[i]   = ceil((max_feat / scl_sum) / lvl_scl);
         feat_sum += lvl_best[i];
     }
-    lvl_best[max_levels-1] = max_feat - feat_sum;
+    lvl_best[max_levels - 1] = max_feat - feat_sum;
 
     // Maintain a reference to previous level image
     Array<T> prev_img = createEmptyArray<T>(af::dim4());
     af::dim4 prev_ldims;
 
     af::dim4 gauss_dims(9);
-    std::unique_ptr<T[], std::function<void(T *)>> h_gauss;
+    std::unique_ptr<T[], std::function<void(T*)>> h_gauss;
     Array<T> gauss_filter = createEmptyArray<T>(af::dim4());
 
     for (unsigned i = 0; i < max_levels; i++) {
         af::dim4 ldims;
-        const float lvl_scl = (float)std::pow(scl_fctr,(float)i);
-        Array<T> lvl_img = createEmptyArray<T>(af::dim4());
+        const float lvl_scl = (float)std::pow(scl_fctr, (float)i);
+        Array<T> lvl_img    = createEmptyArray<T>(af::dim4());
 
         if (i == 0) {
             // First level is used in its original size
             lvl_img = image;
-            ldims = image.dims();
+            ldims   = image.dims();
 
-            prev_img = image;
+            prev_img   = image;
             prev_ldims = image.dims();
-        }
-        else {
+        } else {
             // Resize previous level image to current level dimensions
             ldims[0] = round(idims[0] / lvl_scl);
             ldims[1] = round(idims[1] / lvl_scl);
 
-            lvl_img = resize<T>(prev_img, ldims[0], ldims[1], AF_INTERP_BILINEAR);
+            lvl_img =
+                resize<T>(prev_img, ldims[0], ldims[1], AF_INTERP_BILINEAR);
 
-            prev_img = lvl_img;
+            prev_img   = lvl_img;
             prev_ldims = lvl_img.dims();
         }
         prev_img.eval();
         lvl_img.eval();
         getQueue().sync();
 
-
-        Array<float> x_feat = createEmptyArray<float>(dim4());
-        Array<float> y_feat = createEmptyArray<float>(dim4());
+        Array<float> x_feat     = createEmptyArray<float>(dim4());
+        Array<float> y_feat     = createEmptyArray<float>(dim4());
         Array<float> score_feat = createEmptyArray<float>(dim4());
 
         // Round feature size to nearest odd integer
@@ -125,36 +121,32 @@ unsigned orb(Array<float> &x, Array<float> &y,
         // represents widest case possible
         unsigned edge = ceil(size * sqrt(2.f) / 2.f);
 
-        unsigned lvl_feat = fast(x_feat, y_feat, score_feat,
-                                 lvl_img, fast_thr, 9, 1, 0.15f, edge);
+        unsigned lvl_feat = fast(x_feat, y_feat, score_feat, lvl_img, fast_thr,
+                                 9, 1, 0.15f, edge);
 
-        if (lvl_feat == 0) {
-            continue;
-        }
+        if (lvl_feat == 0) { continue; }
 
         float* h_x_feat = x_feat.get();
         float* h_y_feat = y_feat.get();
 
-        auto h_x_harris = memAlloc<float>(lvl_feat);
-        auto h_y_harris = memAlloc<float>(lvl_feat);
+        auto h_x_harris     = memAlloc<float>(lvl_feat);
+        auto h_y_harris     = memAlloc<float>(lvl_feat);
         auto h_score_harris = memAlloc<float>(lvl_feat);
 
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
         unsigned usable_feat = 0;
-        kernel::harris_response<T, false>(h_x_harris.get(), h_y_harris.get(), h_score_harris.get(), nullptr,
-                                  h_x_feat, h_y_feat, nullptr,
-                                  lvl_feat, &usable_feat,
-                                  lvl_img,
-                                  7, 0.04f, patch_size);
+        kernel::harris_response<T, false>(
+            h_x_harris.get(), h_y_harris.get(), h_score_harris.get(), nullptr,
+            h_x_feat, h_y_feat, nullptr, lvl_feat, &usable_feat, lvl_img, 7,
+            0.04f, patch_size);
 
-        if (usable_feat == 0) {
-            continue;
-        }
+        if (usable_feat == 0) { continue; }
 
         // Sort features according to Harris responses
         af::dim4 usable_feat_dims(usable_feat);
-        Array<float> score_harris = createDeviceDataArray<float>(usable_feat_dims, h_score_harris.get());
+        Array<float> score_harris = createDeviceDataArray<float>(
+            usable_feat_dims, h_score_harris.get());
         Array<float> harris_sorted = createEmptyArray<float>(af::dim4());
         Array<unsigned> harris_idx = createEmptyArray<unsigned>(af::dim4());
 
@@ -163,40 +155,45 @@ unsigned orb(Array<float> &x, Array<float> &y,
 
         usable_feat = std::min(usable_feat, lvl_best[i]);
 
-        if(usable_feat == 0) {
-          h_score_harris.release();
-          continue;
+        if (usable_feat == 0) {
+            h_score_harris.release();
+            continue;
         }
 
-        auto h_x_lvl = memAlloc<float>(usable_feat);
-        auto h_y_lvl = memAlloc<float>(usable_feat);
+        auto h_x_lvl     = memAlloc<float>(usable_feat);
+        auto h_y_lvl     = memAlloc<float>(usable_feat);
         auto h_score_lvl = memAlloc<float>(usable_feat);
 
         // Keep only features with higher Harris responses
-        kernel::keep_features<T>(h_x_lvl.get(), h_y_lvl.get(), h_score_lvl.get(), nullptr,
-                                 h_x_harris.get(), h_y_harris.get(), harris_sorted.get(), harris_idx.get(),
-                         nullptr, usable_feat);
+        kernel::keep_features<T>(h_x_lvl.get(), h_y_lvl.get(),
+                                 h_score_lvl.get(), nullptr, h_x_harris.get(),
+                                 h_y_harris.get(), harris_sorted.get(),
+                                 harris_idx.get(), nullptr, usable_feat);
 
-        auto h_ori_lvl = memAlloc<float>(usable_feat);
+        auto h_ori_lvl  = memAlloc<float>(usable_feat);
         auto h_size_lvl = memAlloc<float>(usable_feat);
 
         // Compute orientation of features
-        kernel::centroid_angle<T>(h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(), usable_feat,
-                          lvl_img, patch_size);
+        kernel::centroid_angle<T>(h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(),
+                                  usable_feat, lvl_img, patch_size);
 
         Array<T> lvl_filt = createEmptyArray<T>(dim4());
 
         if (blur_img) {
-            // Calculate a separable Gaussian kernel, if one is not already stored
+            // Calculate a separable Gaussian kernel, if one is not already
+            // stored
             if (!h_gauss) {
                 h_gauss = memAlloc<T>(gauss_dims[0]);
                 gaussian1D(h_gauss.get(), gauss_dims[0], 2.f);
-                gauss_filter = createDeviceDataArray<T>(gauss_dims, h_gauss.get());
+                gauss_filter =
+                    createDeviceDataArray<T>(gauss_dims, h_gauss.get());
                 gauss_filter.eval();
             }
 
-            // Filter level image with Gaussian kernel to reduce noise sensitivity
-            lvl_filt = convolve2<T, convAccT, false>(lvl_img, gauss_filter, gauss_filter);
+            // Filter level image with Gaussian kernel to reduce noise
+            // sensitivity
+            lvl_filt = convolve2<T, convAccT, false>(lvl_img, gauss_filter,
+                                                     gauss_filter);
         }
         lvl_filt.eval();
         getQueue().sync();
@@ -205,29 +202,30 @@ unsigned orb(Array<float> &x, Array<float> &y,
         auto h_desc_lvl = memAlloc<unsigned>(usable_feat * 8);
         memset(h_desc_lvl.get(), 0, usable_feat * 8 * sizeof(unsigned));
         if (blur_img)
-          kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat,
-                                   h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(), h_size_lvl.get(),
-                           lvl_filt, lvl_scl, patch_size);
+            kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat, h_x_lvl.get(),
+                                   h_y_lvl.get(), h_ori_lvl.get(),
+                                   h_size_lvl.get(), lvl_filt, lvl_scl,
+                                   patch_size);
         else
-          kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat,
-                                   h_x_lvl.get(), h_y_lvl.get(), h_ori_lvl.get(), h_size_lvl.get(),
-                           lvl_img, lvl_scl, patch_size);
+            kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat, h_x_lvl.get(),
+                                   h_y_lvl.get(), h_ori_lvl.get(),
+                                   h_size_lvl.get(), lvl_img, lvl_scl,
+                                   patch_size);
 
         // Store results to pyramids
         total_feat += usable_feat;
-        feat_pyr[i] = usable_feat;
-        h_x_pyr[i] = std::move(h_x_lvl);
-        h_y_pyr[i] = std::move(h_y_lvl);
+        feat_pyr[i]    = usable_feat;
+        h_x_pyr[i]     = std::move(h_x_lvl);
+        h_y_pyr[i]     = std::move(h_y_lvl);
         h_score_pyr[i] = std::move(h_score_lvl);
-        h_ori_pyr[i] = std::move(h_ori_lvl);
-        h_size_pyr[i] = std::move(h_size_lvl);
-        h_desc_pyr[i] = std::move(h_desc_lvl);
+        h_ori_pyr[i]   = std::move(h_ori_lvl);
+        h_size_pyr[i]  = std::move(h_size_lvl);
+        h_desc_pyr[i]  = std::move(h_desc_lvl);
         h_score_harris.release();
         h_gauss.release();
     }
 
-    if (total_feat > 0 ) {
-
+    if (total_feat > 0) {
         // Allocate feature Arrays
         const af::dim4 total_feat_dims(total_feat);
         const af::dim4 desc_dims(8, total_feat);
@@ -237,48 +235,47 @@ unsigned orb(Array<float> &x, Array<float> &y,
         score = createEmptyArray<float>(total_feat_dims);
         ori   = createEmptyArray<float>(total_feat_dims);
         size  = createEmptyArray<float>(total_feat_dims);
-        desc  = createEmptyArray<uint >(desc_dims);
+        desc  = createEmptyArray<uint>(desc_dims);
 
-        float* h_x = x.get();
-        float* h_y = y.get();
+        float* h_x     = x.get();
+        float* h_y     = y.get();
         float* h_score = score.get();
-        float* h_ori = ori.get();
-        float* h_size = size.get();
+        float* h_ori   = ori.get();
+        float* h_size  = size.get();
 
         unsigned* h_desc = desc.get();
 
         unsigned offset = 0;
         for (unsigned i = 0; i < max_levels; i++) {
-            if (feat_pyr[i] == 0)
-                continue;
-
-            if (i > 0)
-                offset += feat_pyr[i-1];
+            if (feat_pyr[i] == 0) continue;
 
-            memcpy(h_x+offset, h_x_pyr[i].get(), feat_pyr[i] * sizeof(float));
-            memcpy(h_y+offset, h_y_pyr[i].get(), feat_pyr[i] * sizeof(float));
-            memcpy(h_score+offset, h_score_pyr[i].get(), feat_pyr[i] * sizeof(float));
-            memcpy(h_ori+offset, h_ori_pyr[i].get(), feat_pyr[i] * sizeof(float));
-            memcpy(h_size+offset, h_size_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            if (i > 0) offset += feat_pyr[i - 1];
 
-            memcpy(h_desc+(offset*8), h_desc_pyr[i].get(), feat_pyr[i] * 8 * sizeof(unsigned));
+            memcpy(h_x + offset, h_x_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(h_y + offset, h_y_pyr[i].get(), feat_pyr[i] * sizeof(float));
+            memcpy(h_score + offset, h_score_pyr[i].get(),
+                   feat_pyr[i] * sizeof(float));
+            memcpy(h_ori + offset, h_ori_pyr[i].get(),
+                   feat_pyr[i] * sizeof(float));
+            memcpy(h_size + offset, h_size_pyr[i].get(),
+                   feat_pyr[i] * sizeof(float));
 
+            memcpy(h_desc + (offset * 8), h_desc_pyr[i].get(),
+                   feat_pyr[i] * 8 * sizeof(unsigned));
         }
     }
 
     return total_feat;
 }
 
-#define INSTANTIATE(T, convAccT)                                                        \
-    template unsigned orb<T, convAccT>(Array<float> &x, Array<float> &y,                \
-                                       Array<float> &score, Array<float> &ori,          \
-                                       Array<float> &size, Array<uint> &desc,           \
-                                       const Array<T>& image,                           \
-                                       const float fast_thr, const unsigned max_feat,   \
-                                       const float scl_fctr, const unsigned levels,     \
-                                       const bool blur_img);
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned orb<T, convAccT>(                                       \
+        Array<float> & x, Array<float> & y, Array<float> & score,             \
+        Array<float> & ori, Array<float> & size, Array<uint> & desc,          \
+        const Array<T>& image, const float fast_thr, const unsigned max_feat, \
+        const float scl_fctr, const unsigned levels, const bool blur_img);
 
-INSTANTIATE(float , float )
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/orb.hpp b/src/backend/cpu/orb.hpp
index 0b4ebd8931..cfb5904935 100644
--- a/src/backend/cpu/orb.hpp
+++ b/src/backend/cpu/orb.hpp
@@ -7,21 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename convAccT>
 unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              Array<float> &orientation, Array<float> &size,
-             Array<unsigned> &desc,
-             const Array<T>& image,
-             const float fast_thr, const unsigned max_feat,
-             const float scl_fctr, const unsigned levels,
-             const bool blur_img);
+             Array<unsigned> &desc, const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img);
 
 }
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
index 4f7b611b34..2e23f2ff97 100644
--- a/src/backend/cpu/padarray.cpp
+++ b/src/backend/cpu/padarray.cpp
@@ -7,34 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <type_traits>
 #include <Array.hpp>
 #include <copy.hpp>
-#include <cstring>
-#include <algorithm>
-#include <complex>
-#include <vector>
-#include <cassert>
 #include <err_cpu.hpp>
+#include <kernel/copy.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/copy.hpp>
+#include <algorithm>
+#include <cassert>
+#include <complex>
+#include <cstring>
+#include <type_traits>
+#include <vector>
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
-void multiply_inplace(Array<T> &in, double val)
-{
+void multiply_inplace(Array<T>& in, double val) {
     in.eval();
-    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in,
-                       static_cast<T>(0), val);
+    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, static_cast<T>(0),
+                       val);
 }
 
 template<typename inType, typename outType>
 Array<outType> padArray(const Array<inType>& in, const dim4& dims,
-                        outType default_value, double factor)
-{
+                        outType default_value, double factor) {
     Array<outType> ret = createValueArray<outType>(dims, default_value);
     ret.eval();
     in.eval();
@@ -44,52 +41,79 @@ Array<outType> padArray(const Array<inType>& in, const dim4& dims,
     return ret;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template void      multiply_inplace<T> (Array<T> &in, double norm); \
+#define INSTANTIATE(T) \
+    template void multiply_inplace<T>(Array<T> & in, double norm);
 
-INSTANTIATE(float  )
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
+#define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
+    template Array<float> padArray<SRC_T, float>(                         \
+        const Array<SRC_T>& src, const dim4& dims, float default_value,   \
+        double factor);                                                   \
+    template Array<double> padArray<SRC_T, double>(                       \
+        const Array<SRC_T>& src, const dim4& dims, double default_value,  \
+        double factor);                                                   \
+    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
+        const Array<SRC_T>& src, const dim4& dims, cfloat default_value,  \
+        double factor);                                                   \
+    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
+        const Array<SRC_T>& src, const dim4& dims, cdouble default_value, \
+        double factor);                                                   \
+    template Array<int> padArray<SRC_T, int>(                             \
+        const Array<SRC_T>& src, const dim4& dims, int default_value,     \
+        double factor);                                                   \
+    template Array<uint> padArray<SRC_T, uint>(                           \
+        const Array<SRC_T>& src, const dim4& dims, uint default_value,    \
+        double factor);                                                   \
+    template Array<intl> padArray<SRC_T, intl>(                           \
+        const Array<SRC_T>& src, const dim4& dims, intl default_value,    \
+        double factor);                                                   \
+    template Array<uintl> padArray<SRC_T, uintl>(                         \
+        const Array<SRC_T>& src, const dim4& dims, uintl default_value,   \
+        double factor);                                                   \
+    template Array<short> padArray<SRC_T, short>(                         \
+        const Array<SRC_T>& src, const dim4& dims, short default_value,   \
+        double factor);                                                   \
+    template Array<ushort> padArray<SRC_T, ushort>(                       \
+        const Array<SRC_T>& src, const dim4& dims, ushort default_value,  \
+        double factor);                                                   \
+    template Array<uchar> padArray<SRC_T, uchar>(                         \
+        const Array<SRC_T>& src, const dim4& dims, uchar default_value,   \
+        double factor);                                                   \
+    template Array<char> padArray<SRC_T, char>(                           \
+        const Array<SRC_T>& src, const dim4& dims, char default_value,    \
+        double factor);
 
-#define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
-    template Array<float  > padArray<SRC_T, float  >(const Array<SRC_T>& src, const dim4& dims, float   default_value, double factor); \
-    template Array<double > padArray<SRC_T, double >(const Array<SRC_T>& src, const dim4& dims, double  default_value, double factor); \
-    template Array<cfloat > padArray<SRC_T, cfloat >(const Array<SRC_T>& src, const dim4& dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(const Array<SRC_T>& src, const dim4& dims, cdouble default_value, double factor); \
-    template Array<int    > padArray<SRC_T, int    >(const Array<SRC_T>& src, const dim4& dims, int     default_value, double factor); \
-    template Array<uint   > padArray<SRC_T, uint   >(const Array<SRC_T>& src, const dim4& dims, uint    default_value, double factor); \
-    template Array<intl   > padArray<SRC_T, intl   >(const Array<SRC_T>& src, const dim4& dims, intl    default_value, double factor); \
-    template Array<uintl  > padArray<SRC_T, uintl  >(const Array<SRC_T>& src, const dim4& dims, uintl   default_value, double factor); \
-    template Array<short  > padArray<SRC_T, short  >(const Array<SRC_T>& src, const dim4& dims, short   default_value, double factor); \
-    template Array<ushort > padArray<SRC_T, ushort >(const Array<SRC_T>& src, const dim4& dims, ushort  default_value, double factor); \
-    template Array<uchar  > padArray<SRC_T, uchar  >(const Array<SRC_T>& src, const dim4& dims, uchar   default_value, double factor); \
-    template Array<char   > padArray<SRC_T, char   >(const Array<SRC_T>& src, const dim4& dims, char    default_value, double factor); \
-
-INSTANTIATE_PAD_ARRAY(float )
+INSTANTIATE_PAD_ARRAY(float)
 INSTANTIATE_PAD_ARRAY(double)
-INSTANTIATE_PAD_ARRAY(int   )
-INSTANTIATE_PAD_ARRAY(uint  )
-INSTANTIATE_PAD_ARRAY(intl  )
-INSTANTIATE_PAD_ARRAY(uintl )
-INSTANTIATE_PAD_ARRAY(uchar )
-INSTANTIATE_PAD_ARRAY(char  )
+INSTANTIATE_PAD_ARRAY(int)
+INSTANTIATE_PAD_ARRAY(uint)
+INSTANTIATE_PAD_ARRAY(intl)
+INSTANTIATE_PAD_ARRAY(uintl)
+INSTANTIATE_PAD_ARRAY(uchar)
+INSTANTIATE_PAD_ARRAY(char)
 INSTANTIATE_PAD_ARRAY(ushort)
-INSTANTIATE_PAD_ARRAY(short )
+INSTANTIATE_PAD_ARRAY(short)
 
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
-    template Array<cfloat > padArray<SRC_T, cfloat >(const Array<SRC_T>& src, const dim4& dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(const Array<SRC_T>& src, const dim4& dims, cdouble default_value, double factor); \
+#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
+    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
+        const Array<SRC_T>& src, const dim4& dims, cfloat default_value,  \
+        double factor);                                                   \
+    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
+        const Array<SRC_T>& src, const dim4& dims, cdouble default_value, \
+        double factor);
 
-INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
+INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
 INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 7a5421f75b..0da0f20e83 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/version.h>
-#include <platform.hpp>
-#include <version.hpp>
 #include <common/defines.hpp>
 #include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
+#include <platform.hpp>
+#include <version.hpp>
+#include <af/version.h>
 
 #include <cctype>
 #include <sstream>
@@ -22,17 +22,21 @@ using namespace std;
 #ifdef CPUID_CAPABLE
 
 CPUInfo::CPUInfo()
-    : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)
-{
+    : mVendorId("")
+    , mModelName("")
+    , mNumSMT(0)
+    , mNumCores(0)
+    , mNumLogCpus(0)
+    , mIsHTT(false) {
     // Get vendor name EAX=0
     CPUID cpuID1(1, 0);
-    mIsHTT   = cpuID1.EDX() & HTT_POS;
+    mIsHTT = cpuID1.EDX() & HTT_POS;
 
     CPUID cpuID0(0, 0);
     uint32_t HFS = cpuID0.EAX();
-    mVendorId += string((const char *)&cpuID0.EBX(), 4);
-    mVendorId += string((const char *)&cpuID0.EDX(), 4);
-    mVendorId += string((const char *)&cpuID0.ECX(), 4);
+    mVendorId += string((const char*)&cpuID0.EBX(), 4);
+    mVendorId += string((const char*)&cpuID0.EDX(), 4);
+    mVendorId += string((const char*)&cpuID0.ECX(), 4);
 
     string upVId = mVendorId;
 
@@ -41,29 +45,29 @@ CPUInfo::CPUInfo()
     // Get num of cores
     if (upVId.find("INTEL") != std::string::npos) {
         mVendorId = "Intel";
-        if(HFS >= 11) {
-            for (int lvl=0; lvl<MAX_INTEL_TOP_LVL; ++lvl) {
-                    CPUID cpuID4(0x0B, lvl);
-                    uint32_t currLevel = (LVL_TYPE & cpuID4.ECX())>>8;
-                    switch(currLevel) {
-                        case 0x01: mNumSMT     = LVL_CORES & cpuID4.EBX(); break;
-                        case 0x02: mNumLogCpus = LVL_CORES & cpuID4.EBX(); break;
-                        default: break;
-                    }
+        if (HFS >= 11) {
+            for (int lvl = 0; lvl < MAX_INTEL_TOP_LVL; ++lvl) {
+                CPUID cpuID4(0x0B, lvl);
+                uint32_t currLevel = (LVL_TYPE & cpuID4.ECX()) >> 8;
+                switch (currLevel) {
+                    case 0x01: mNumSMT = LVL_CORES & cpuID4.EBX(); break;
+                    case 0x02: mNumLogCpus = LVL_CORES & cpuID4.EBX(); break;
+                    default: break;
+                }
             }
             // Fixes Possible divide by zero error
             // TODO: Fix properly
-            mNumCores = mNumLogCpus/(mNumSMT == 0 ? 1 : mNumSMT);
+            mNumCores = mNumLogCpus / (mNumSMT == 0 ? 1 : mNumSMT);
         } else {
-            if (HFS>=1) {
+            if (HFS >= 1) {
                 mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
-                if (HFS>=4) {
+                if (HFS >= 4) {
                     mNumCores = 1 + ((CPUID(4, 0).EAX() >> 26) & 0x3F);
                 }
             }
             if (mIsHTT) {
-                if (!(mNumCores>1)) {
-                    mNumCores = 1;
+                if (!(mNumCores > 1)) {
+                    mNumCores   = 1;
                     mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
                 }
             } else {
@@ -72,15 +76,15 @@ CPUInfo::CPUInfo()
         }
     } else if (upVId.find("AMD") != std::string::npos) {
         mVendorId = "AMD";
-        if (HFS>=1) {
+        if (HFS >= 1) {
             mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
-            if (CPUID(0x80000000, 0).EAX() >=8) {
+            if (CPUID(0x80000000, 0).EAX() >= 8) {
                 mNumCores = 1 + ((CPUID(0x80000008, 0).ECX() & 0xFF));
             }
         }
         if (mIsHTT) {
-            if (!(mNumCores>1)) {
-                mNumCores = 1;
+            if (!(mNumCores > 1)) {
+                mNumCores   = 1;
                 mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
             }
         } else {
@@ -91,7 +95,7 @@ CPUInfo::CPUInfo()
     }
     // Get processor brand string
     // This seems to be working for both Intel & AMD vendors
-    for(unsigned i=0x80000002; i<0x80000005; ++i) {
+    for (unsigned i = 0x80000002; i < 0x80000005; ++i) {
         CPUID cpuID(i, 0);
         mModelName += string((const char*)&cpuID.EAX(), 4);
         mModelName += string((const char*)&cpuID.EBX(), 4);
@@ -104,67 +108,67 @@ CPUInfo::CPUInfo()
 #else
 
 CPUInfo::CPUInfo()
-  : mVendorId(""), mModelName(""), mNumSMT(0), mNumCores(0), mNumLogCpus(0), mIsHTT(false)
-{
-  mVendorId = "Unknown";
-  mModelName= "Unknown";
-  mNumSMT   = 1;
-  mNumCores = 1;
-  mNumLogCpus = 1;
+    : mVendorId("")
+    , mModelName("")
+    , mNumSMT(0)
+    , mNumCores(0)
+    , mNumLogCpus(0)
+    , mIsHTT(false) {
+    mVendorId   = "Unknown";
+    mModelName  = "Unknown";
+    mNumSMT     = 1;
+    mNumCores   = 1;
+    mNumLogCpus = 1;
 }
 
 #endif
 
-namespace cpu
-{
+namespace cpu {
 
-static const std::string get_system(void)
-{
-    std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
+static const std::string get_system(void) {
+    std::string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
 #if defined(OS_LNX)
-    "Linux";
+           "Linux";
 #elif defined(OS_WIN)
-    "Windows";
+           "Windows";
 #elif defined(OS_MAC)
-    "Mac OSX";
+           "Mac OSX";
 #endif
 }
 
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
-static inline std::string &ltrim(std::string &s)
-{
-    s.erase(s.begin(), std::find_if(s.begin(), s.end(),
-                                    std::not1(std::ptr_fun<int, int>(std::isspace))));
+static inline std::string& ltrim(std::string& s) {
+    s.erase(s.begin(),
+            std::find_if(s.begin(), s.end(),
+                         std::not1(std::ptr_fun<int, int>(std::isspace))));
     return s;
 }
 
-int getBackend()
-{
-    return AF_BACKEND_CPU;
-}
+int getBackend() { return AF_BACKEND_CPU; }
 
-std::string getDeviceInfo()
-{
+std::string getDeviceInfo() {
     const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
 
     std::ostringstream info;
 
-    info << "ArrayFire v" << AF_VERSION
-         << " (CPU, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
+    info << "ArrayFire v" << AF_VERSION << " (CPU, " << get_system()
+         << ", build " << AF_REVISION << ")" << std::endl;
 
     std::string model = cinfo.model();
 
     size_t memMB = getDeviceMemorySize(getActiveDeviceId()) / 1048576;
 
-    info << string("[0] ") << cinfo.vendor() <<": " << ltrim(model);
+    info << string("[0] ") << cinfo.vendor() << ": " << ltrim(model);
 
-    if(memMB) info << ", " << memMB << " MB, ";
-    else      info << ", Unknown MB, ";
+    if (memMB)
+        info << ", " << memMB << " MB, ";
+    else
+        info << ", Unknown MB, ";
 
-    info << "Max threads("<< cinfo.threads()<<") ";
+    info << "Max threads(" << cinfo.threads() << ") ";
 #ifndef NDEBUG
     info << AF_COMPILER_STR;
 #endif
@@ -173,14 +177,12 @@ std::string getDeviceInfo()
     return info.str();
 }
 
-bool isDoubleSupported(int device)
-{
+bool isDoubleSupported(int device) {
     UNUSED(device);
     return DeviceManager::IS_DOUBLE_SUPPORTED;
 }
 
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-{
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
     const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
 
     snprintf(d_name, 64, "%s", cinfo.vendor().c_str());
@@ -190,8 +192,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     snprintf(d_compute, 10, "%s", "0.0");
 }
 
-unsigned getMaxJitSize()
-{
+unsigned getMaxJitSize() {
     const int MAX_JIT_LEN = 100;
 
     thread_local int length = 0;
@@ -206,82 +207,61 @@ unsigned getMaxJitSize()
     return length;
 }
 
-int getDeviceCount()
-{
-    return DeviceManager::NUM_DEVICES;
-}
+int getDeviceCount() { return DeviceManager::NUM_DEVICES; }
 
 // Get the currently active device id
-int getActiveDeviceId()
-{
-    return DeviceManager::ACTIVE_DEVICE_ID;
-}
+int getActiveDeviceId() { return DeviceManager::ACTIVE_DEVICE_ID; }
 
-size_t getDeviceMemorySize(int device)
-{
+size_t getDeviceMemorySize(int device) {
     UNUSED(device);
     return common::getHostMemorySize();
 }
 
-size_t getHostMemorySize()
-{
-    return common::getHostMemorySize();
-}
+size_t getHostMemorySize() { return common::getHostMemorySize(); }
 
-int setDevice(int device)
-{
+int setDevice(int device) {
     thread_local bool flag = false;
     if (!flag && device != 0) {
 #ifndef NDEBUG
-        fprintf(stderr, "WARNING af_set_device(device): device can only be 0 for CPU\n");
+        fprintf(
+            stderr,
+            "WARNING af_set_device(device): device can only be 0 for CPU\n");
 #endif
         flag = true;
     }
     return 0;
 }
 
-queue& getQueue(int device)
-{
+queue& getQueue(int device) {
     return DeviceManager::getInstance().queues[device];
 }
 
-CPUInfo DeviceManager::getCPUInfo() const
-{
-    return cinfo;
-}
+CPUInfo DeviceManager::getCPUInfo() const { return cinfo; }
 
-void sync(int device)
-{
-    getQueue(device).sync();
-}
+void sync(int device) { getQueue(device).sync(); }
 
-bool& evalFlag()
-{
+bool& evalFlag() {
     thread_local bool flag = true;
     return flag;
 }
 
 DeviceManager::DeviceManager()
     : queues(MAX_QUEUES)
-    , memManager(new MemoryManager()),
-      fgMngr(new graphics::ForgeManager()){}
+    , memManager(new MemoryManager())
+    , fgMngr(new graphics::ForgeManager()) {}
 
-
-MemoryManager& memoryManager()
-{
+MemoryManager& memoryManager() {
     DeviceManager& inst = DeviceManager::getInstance();
     return *(inst.memManager);
 }
 
-graphics::ForgeManager& forgeManager()
-{
+graphics::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
-DeviceManager& DeviceManager::getInstance()
-{
+DeviceManager& DeviceManager::getInstance() {
     static DeviceManager* my_instance = new DeviceManager();
     return *my_instance;
 }
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 6530d0d914..dc33f6032f 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -9,21 +9,23 @@
 
 #pragma once
 
-#include <array>
+#include <memory.hpp>
+#include <queue.hpp>
 #include <algorithm>
+#include <array>
 #include <memory>
 #include <string>
-#include <memory.hpp>
-#include <queue.hpp>
 
-#if defined(AF_WITH_CPUID) && (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) || defined(_WIN64))
+#if defined(AF_WITH_CPUID) &&                                       \
+    (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || \
+     defined(_M_IX86) || defined(_WIN64))
 #define CPUID_CAPABLE
 #endif
 
 #ifdef _WIN32
-#include <limits.h>
 #include <intrin.h>
-typedef unsigned __int32  uint32_t;
+#include <limits.h>
+typedef unsigned __int32 uint32_t;
 #else
 #include <stdint.h>
 #endif
@@ -35,62 +37,62 @@ typedef unsigned __int32  uint32_t;
 class CPUID {
     uint32_t regs[4];
 
-    public:
+   public:
     explicit CPUID(unsigned funcId, unsigned subFuncId) {
 #ifdef _WIN32
-        __cpuidex((int *)regs, (int)funcId, (int)subFuncId);
+        __cpuidex((int*)regs, (int)funcId, (int)subFuncId);
 
 #else
-        asm volatile
-            ("cpuid" : "=a" (regs[0]), "=b" (regs[1]), "=c" (regs[2]), "=d" (regs[3])
-             : "a" (funcId), "c" (subFuncId));
+        asm volatile("cpuid"
+                     : "=a"(regs[0]), "=b"(regs[1]), "=c"(regs[2]),
+                       "=d"(regs[3])
+                     : "a"(funcId), "c"(subFuncId));
 #endif
     }
 
-    inline const uint32_t &EAX() const { return regs[0]; }
-    inline const uint32_t &EBX() const { return regs[1]; }
-    inline const uint32_t &ECX() const { return regs[2]; }
-    inline const uint32_t &EDX() const { return regs[3]; }
+    inline const uint32_t& EAX() const { return regs[0]; }
+    inline const uint32_t& EBX() const { return regs[1]; }
+    inline const uint32_t& ECX() const { return regs[2]; }
+    inline const uint32_t& EDX() const { return regs[3]; }
 };
 
 #endif
 
 class CPUInfo {
-    public:
-        CPUInfo();
-        std::string  vendor() const { return mVendorId;   }
-        std::string  model()  const { return mModelName;  }
-        int threads() const { return mNumLogCpus; }
-
-    private:
-        // Bit positions for data extractions
-        static const uint32_t LVL_NUM   = 0x000000FF;
-        static const uint32_t LVL_TYPE  = 0x0000FF00;
-        static const uint32_t LVL_CORES = 0x0000FFFF;
-        static const uint32_t HTT_POS   = 0x10000000;
-
-        // Attributes
-        std::string mVendorId;
-        std::string mModelName;
-        int    mNumSMT;
-        int    mNumCores;
-        int    mNumLogCpus;
-        bool   mIsHTT;
+   public:
+    CPUInfo();
+    std::string vendor() const { return mVendorId; }
+    std::string model() const { return mModelName; }
+    int threads() const { return mNumLogCpus; }
+
+   private:
+    // Bit positions for data extractions
+    static const uint32_t LVL_NUM   = 0x000000FF;
+    static const uint32_t LVL_TYPE  = 0x0000FF00;
+    static const uint32_t LVL_CORES = 0x0000FFFF;
+    static const uint32_t HTT_POS   = 0x10000000;
+
+    // Attributes
+    std::string mVendorId;
+    std::string mModelName;
+    int mNumSMT;
+    int mNumCores;
+    int mNumLogCpus;
+    bool mIsHTT;
 };
 
 namespace graphics {
-  class ForgeManager;
+class ForgeManager;
 }
 
-namespace cpu
-{
+namespace cpu {
 int getBackend();
 
 std::string getDeviceInfo();
 
 bool isDoubleSupported(int device);
 
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
 
@@ -104,7 +106,7 @@ size_t getHostMemorySize();
 
 int setDevice(int device);
 
-queue& getQueue(int device=0);
+queue& getQueue(int device = 0);
 
 void sync(int device);
 
@@ -114,38 +116,36 @@ MemoryManager& memoryManager();
 
 graphics::ForgeManager& forgeManager();
 
-class DeviceManager
-{
-    public:
-        static const int MAX_QUEUES = 1;
-        static const int NUM_DEVICES = 1;
-        static const int ACTIVE_DEVICE_ID = 0;
-        static const bool IS_DOUBLE_SUPPORTED = true;
+class DeviceManager {
+   public:
+    static const int MAX_QUEUES           = 1;
+    static const int NUM_DEVICES          = 1;
+    static const int ACTIVE_DEVICE_ID     = 0;
+    static const bool IS_DOUBLE_SUPPORTED = true;
 
-        static DeviceManager& getInstance();
+    static DeviceManager& getInstance();
 
-        friend queue& getQueue(int device);
+    friend queue& getQueue(int device);
 
-        friend MemoryManager& memoryManager();
+    friend MemoryManager& memoryManager();
 
-        friend graphics::ForgeManager& forgeManager();
+    friend graphics::ForgeManager& forgeManager();
 
-        CPUInfo getCPUInfo() const;
+    CPUInfo getCPUInfo() const;
 
-    private:
-        DeviceManager();
-        // Following two declarations are required to
-        // avoid copying accidental copy/assignment
-        // of instance returned by getInstance to other
-        // variables
-        DeviceManager(DeviceManager const&) = delete;
-        void operator=(DeviceManager const&) = delete;
-
-        // Attributes
-        std::unique_ptr<graphics::ForgeManager> fgMngr;
-        std::unique_ptr<MemoryManager> memManager;
-        std::vector<queue> queues;
-        const CPUInfo cinfo;
+   private:
+    DeviceManager();
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&) = delete;
+    void operator=(DeviceManager const&) = delete;
 
+    // Attributes
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<MemoryManager> memManager;
+    std::vector<queue> queues;
+    const CPUInfo cinfo;
 };
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index 4e196167a9..bc4afa5059 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <plot.hpp>
-#include <err_cpu.hpp>
 #include <common/graphics_common.hpp>
+#include <err_cpu.hpp>
 #include <platform.hpp>
+#include <plot.hpp>
 #include <queue.hpp>
 
 using af::dim4;
@@ -19,9 +19,8 @@ using af::dim4;
 namespace cpu {
 
 template<typename T>
-void copy_plot(const Array<T> &P, fg_plot plot)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_plot(const Array<T> &P, fg_plot plot) {
+    ForgeModule &_ = graphics::forgePlugin();
     P.eval();
     getQueue().sync();
 
@@ -37,8 +36,7 @@ void copy_plot(const Array<T> &P, fg_plot plot)
     CheckGL("In CopyArrayToVBO");
 }
 
-#define INSTANTIATE(T)  \
-template void copy_plot<T>(const Array<T> &, fg_plot);
+#define INSTANTIATE(T) template void copy_plot<T>(const Array<T> &, fg_plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -48,4 +46,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/print.hpp b/src/backend/cpu/print.hpp
index a21b904c45..9d9d8da4f1 100644
--- a/src/backend/cpu/print.hpp
+++ b/src/backend/cpu/print.hpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-namespace cpu
-{
-    // Nothing here
+namespace cpu {
+// Nothing here
 }
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index 3a36e62cbb..e0cfb94334 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -7,59 +7,62 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <qr.hpp>
 #include <common/err_common.hpp>
+#include <qr.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <cassert>
 #include <err_cpu.hpp>
-#include <triangle.hpp>
+#include <handle.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <triangle.hpp>
+#include <af/dim4.hpp>
+#include <cassert>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-using geqrf_func_def = int (*)(ORDER_TYPE, int, int, T*, int, T*);
+using geqrf_func_def = int (*)(ORDER_TYPE, int, int, T *, int, T *);
 
 template<typename T>
-using gqr_func_def = int (*)(ORDER_TYPE, int, int, int, T*, int, const T*);
-
-#define QR_FUNC_DEF( FUNC )                                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define QR_FUNC( FUNC, TYPE, PREFIX )                               \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-QR_FUNC_DEF( geqrf )
-QR_FUNC(geqrf , float  , s)
-QR_FUNC(geqrf , double , d)
-QR_FUNC(geqrf , cfloat , c)
-QR_FUNC(geqrf , cdouble, z)
-
-#define GQR_FUNC_DEF( FUNC )                                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define GQR_FUNC( FUNC, TYPE, PREFIX )                               \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()             \
-{ return & LAPACK_NAME(PREFIX); }
-
-GQR_FUNC_DEF( gqr )
-GQR_FUNC(gqr , float  , sorgqr)
-GQR_FUNC(gqr , double , dorgqr)
-GQR_FUNC(gqr , cfloat , cungqr)
-GQR_FUNC(gqr , cdouble, zungqr)
+using gqr_func_def = int (*)(ORDER_TYPE, int, int, int, T *, int, const T *);
+
+#define QR_FUNC_DEF(FUNC) \
+    template<typename T>  \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define QR_FUNC(FUNC, TYPE, PREFIX)             \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
+
+QR_FUNC_DEF(geqrf)
+QR_FUNC(geqrf, float, s)
+QR_FUNC(geqrf, double, d)
+QR_FUNC(geqrf, cfloat, c)
+QR_FUNC(geqrf, cdouble, z)
+
+#define GQR_FUNC_DEF(FUNC) \
+    template<typename T>   \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define GQR_FUNC(FUNC, TYPE, PREFIX)            \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX);            \
+    }
+
+GQR_FUNC_DEF(gqr)
+GQR_FUNC(gqr, float, sorgqr)
+GQR_FUNC(gqr, double, dorgqr)
+GQR_FUNC(gqr, cfloat, cungqr)
+GQR_FUNC(gqr, cdouble, zungqr)
 
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     q.eval();
     r.eval();
     t.eval();
@@ -79,16 +82,16 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
 
     triangle<T, true, false>(r, q);
 
-    auto func = [=] (Param<T> q, Param<T> t, int M, int N) {
-        gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(), q.strides(1), t.get());
+    auto func = [=](Param<T> q, Param<T> t, int M, int N) {
+        gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(),
+                      q.strides(1), t.get());
     };
     q.resetDims(dim4(M, M));
     getQueue().enqueue(func, q, t, M, N);
 }
 
 template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
+Array<T> qr_inplace(Array<T> &in) {
     in.eval();
 
     dim4 iDims = in.dims();
@@ -96,47 +99,45 @@ Array<T> qr_inplace(Array<T> &in)
     int N      = iDims[1];
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
-    auto func = [=] (Param<T> in, Param<T> t, int M, int N) {
-        geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides(1), t.get());
+    auto func = [=](Param<T> in, Param<T> t, int M, int N) {
+        geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, in.get(), in.strides(1),
+                        t.get());
     };
     getQueue().enqueue(func, in, t, M, N);
 
     return t;
 }
 
-}
+}  // namespace cpu
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
+Array<T> qr_inplace(Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-}
+}  // namespace cpu
 
 #endif  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
 
 INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/qr.hpp b/src/backend/cpu/qr.hpp
index cb4adc003d..b8a43d4d02 100644
--- a/src/backend/cpu/qr.hpp
+++ b/src/backend/cpu/qr.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+namespace cpu {
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
 
-    template<typename T>
-    Array<T> qr_inplace(Array<T> &in);
-}
+template<typename T>
+Array<T> qr_inplace(Array<T> &in);
+}  // namespace cpu
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 3c50240023..26f96159e8 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -7,34 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <common/util.hpp>
 #include <memory.hpp>
-#include <Param.hpp>
 
-//FIXME: Is there a better way to check for std::future not being supported ?
-#if defined(AF_DISABLE_CPU_ASYNC) || (defined(__GNUC__) && (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2))
+// FIXME: Is there a better way to check for std::future not being supported ?
+#if defined(AF_DISABLE_CPU_ASYNC) || \
+    (defined(__GNUC__) &&            \
+     (__GCC_ATOMIC_INT_LOCK_FREE < 2 || __GCC_ATOMIC_POINTER_LOCK_FREE < 2))
 
 #include <functional>
 using std::function;
 #include <err_cpu.hpp>
 #define __SYNCHRONOUS_ARCH 1
-class queue_impl
-{
-public:
-    template <typename F, typename... Args>
+class queue_impl {
+   public:
+    template<typename F, typename... Args>
     void enqueue(const F func, Args... args) const {
         AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
     }
 
-    void sync() const {
-        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
-    }
+    void sync() const { AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL); }
 
     bool is_worker() const {
         AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
         return false;
     }
-
 };
 
 #else
@@ -50,45 +48,41 @@ typedef async_queue queue_impl;
 namespace cpu {
 
 /// Wraps the async_queue class
-class queue
-{
-public:
+class queue {
+   public:
     queue()
-        :
-        count(0),
-        sync_calls( __SYNCHRONOUS_ARCH == 1 || getEnvVar("AF_SYNCHRONOUS_CALLS") == "1")
-    {}
-
-    template <typename F, typename... Args>
-    void enqueue(const F func, Args... args)
-    {
+        : count(0)
+        , sync_calls(__SYNCHRONOUS_ARCH == 1 ||
+                     getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
+
+    template<typename F, typename... Args>
+    void enqueue(const F func, Args... args) {
         count++;
-        if(sync_calls) { func(toParam(args)... ); }
-        else           { aQueue.enqueue(func, toParam(args)... ); }
+        if (sync_calls) {
+            func(toParam(args)...);
+        } else {
+            aQueue.enqueue(func, toParam(args)...);
+        }
 #ifndef NDEBUG
         sync();
 #else
-        if (checkMemoryLimit() || count >= 25) {
-            sync();
-        }
+        if (checkMemoryLimit() || count >= 25) { sync(); }
 #endif
     }
 
-    void sync()
-    {
+    void sync() {
         count = 0;
-        if(!sync_calls) aQueue.sync();
+        if (!sync_calls) aQueue.sync();
     }
 
-    bool is_worker() const
-    {
+    bool is_worker() const {
         return (!sync_calls) ? aQueue.is_worker() : false;
     }
 
-    private:
-        int count;
-        const bool sync_calls;
-        queue_impl aQueue;
+   private:
+    int count;
+    const bool sync_calls;
+    queue_impl aQueue;
 };
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index a0e92162ad..477d4a7d51 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -7,156 +7,156 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
+#include <af/dim4.hpp>
 #include <cassert>
 
-namespace cpu
-{
-    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
-    {
-        getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
-    }
+namespace cpu {
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> tbl) {
+    getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
+}
 
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        getQueue().enqueue(kernel::uniformDistributionCBRNG<T>, out.get(), out.elements(), type, seed, counter);
-        counter += out.elements();
-        return out;
-    }
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type, const uintl seed,
+                             uintl &counter) {
+    Array<T> out = createEmptyArray<T>(dims);
+    getQueue().enqueue(kernel::uniformDistributionCBRNG<T>, out.get(),
+                       out.elements(), type, seed, counter);
+    counter += out.elements();
+    return out;
+}
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        getQueue().enqueue(kernel::normalDistributionCBRNG<T>, out.get(), out.elements(), type, seed, counter);
-        counter += out.elements();
-        return out;
-    }
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl seed,
+                            uintl &counter) {
+    Array<T> out = createEmptyArray<T>(dims);
+    getQueue().enqueue(kernel::normalDistributionCBRNG<T>, out.get(),
+                       out.elements(), type, seed, counter);
+    counter += out.elements();
+    return out;
+}
 
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        getQueue().enqueue(kernel::uniformDistributionMT<T>,
-              out.get(), out.elements(),
-              state.get(), pos.get(),
-              sh1.get(), sh2.get(),
-              mask, recursion_table.get(),
-              temper_table.get());
-        return out;
-    }
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state) {
+    Array<T> out = createEmptyArray<T>(dims);
+    getQueue().enqueue(kernel::uniformDistributionMT<T>, out.get(),
+                       out.elements(), state.get(), pos.get(), sh1.get(),
+                       sh2.get(), mask, recursion_table.get(),
+                       temper_table.get());
+    return out;
+}
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        getQueue().enqueue(kernel::normalDistributionMT<T>,
-              out.get(), out.elements(),
-              state.get(), pos.get(),
-              sh1.get(), sh2.get(),
-              mask, recursion_table.get(),
-              temper_table.get());
-        return out;
-    }
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state) {
+    Array<T> out = createEmptyArray<T>(dims);
+    getQueue().enqueue(kernel::normalDistributionMT<T>, out.get(),
+                       out.elements(), state.get(), pos.get(), sh1.get(),
+                       sh2.get(), mask, recursion_table.get(),
+                       temper_table.get());
+    return out;
+}
 
-#define INSTANTIATE_UNIFORM(T)                                                                                                  \
-    template                                                                                                                    \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter);  \
-    template                                                                                                                    \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+#define INSTANTIATE_UNIFORM(T)                                   \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl seed, uintl &counter);                       \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
 
-#define INSTANTIATE_NORMAL(T)                                                                                                   \
-    template                                                                                                                    \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter);   \
-    template                                                                                                                    \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+#define INSTANTIATE_NORMAL(T)                                                  \
+    template Array<T> normalDistribution<T>(const af::dim4 &dims,              \
+                                            const af_random_engine_type type,  \
+                                            const uintl seed, uintl &counter); \
+    template Array<T> normalDistribution<T>(                                   \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,                \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,               \
+        Array<uint> temper_table, Array<uint> state);
 
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                     \
-    template<>                                                                                                                  \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)   \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        TR *outPtr = (TR*)out.get();                                                                                            \
-        size_t elements = out.elements()*2;                                                                                     \
-        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                        \
-        counter += elements;                                                                                                    \
-        return out;                                                                                                             \
-    }                                                                                                                           \
-    template<>                                                                                                                  \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        TR *outPtr = (TR*)out.get();                                                                                            \
-        size_t elements = out.elements()*2;                                                                                     \
-        getQueue().enqueue(kernel::uniformDistributionMT<TR>,                                                                   \
-              outPtr, elements,                                                                                                 \
-              state.get(), pos.get(),                                                                                           \
-              sh1.get(), sh2.get(),                                                                                             \
-              mask, recursion_table.get(),                                                                                      \
-              temper_table.get());                                                                                              \
-        return out;                                                                                                             \
-    }                                                                                                                           \
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                              \
+    template<>                                                           \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                \
+                                    const af_random_engine_type type,    \
+                                    const uintl seed, uintl &counter) {  \
+        Array<T> out    = createEmptyArray<T>(dims);                     \
+        TR *outPtr      = (TR *)out.get();                               \
+        size_t elements = out.elements() * 2;                            \
+        getQueue().enqueue(kernel::uniformDistributionCBRNG<TR>, outPtr, \
+                           elements, type, seed, counter);               \
+        counter += elements;                                             \
+        return out;                                                      \
+    }                                                                    \
+    template<>                                                           \
+    Array<T> uniformDistribution<T>(                                     \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,          \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,         \
+        Array<uint> temper_table, Array<uint> state) {                   \
+        Array<T> out    = createEmptyArray<T>(dims);                     \
+        TR *outPtr      = (TR *)out.get();                               \
+        size_t elements = out.elements() * 2;                            \
+        getQueue().enqueue(kernel::uniformDistributionMT<TR>, outPtr,    \
+                           elements, state.get(), pos.get(), sh1.get(),  \
+                           sh2.get(), mask, recursion_table.get(),       \
+                           temper_table.get());                          \
+        return out;                                                      \
+    }
 
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                  \
-    template<>                                                                                                              \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl seed, uintl &counter)\
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        TR *outPtr = (TR*)out.get();                                                                                        \
-        size_t elements = out.elements()*2;                                                                                 \
-        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr, elements, type, seed, counter);                     \
-        counter += elements;                                                                                                \
-        return out;                                                                                                         \
-    }                                                                                                                       \
-    template<>                                                                                                              \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                    \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                   \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                       \
-    {                                                                                                                       \
-        Array<T> out = createEmptyArray<T>(dims);                                                                           \
-        TR *outPtr = (TR*)out.get();                                                                                        \
-        size_t elements = out.elements()*2;                                                                                 \
-        getQueue().enqueue(kernel::normalDistributionMT<TR>,                                                                \
-              outPtr, elements,                                                                                             \
-              state.get(), pos.get(),                                                                                       \
-              sh1.get(), sh2.get(),                                                                                         \
-              mask, recursion_table.get(),                                                                                  \
-              temper_table.get());                                                                                          \
-        return out;                                                                                                         \
-    }                                                                                                                       \
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                     \
+    template<>                                                                 \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                       \
+                                   const af_random_engine_type type,           \
+                                   const uintl seed, uintl &counter) {         \
+        Array<T> out    = createEmptyArray<T>(dims);                           \
+        TR *outPtr      = (TR *)out.get();                                     \
+        size_t elements = out.elements() * 2;                                  \
+        getQueue().enqueue(kernel::normalDistributionCBRNG<TR>, outPtr,        \
+                           elements, type, seed, counter);                     \
+        counter += elements;                                                   \
+        return out;                                                            \
+    }                                                                          \
+    template<>                                                                 \
+    Array<T> normalDistribution<T>(                                            \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,                \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,               \
+        Array<uint> temper_table, Array<uint> state) {                         \
+        Array<T> out    = createEmptyArray<T>(dims);                           \
+        TR *outPtr      = (TR *)out.get();                                     \
+        size_t elements = out.elements() * 2;                                  \
+        getQueue().enqueue(kernel::normalDistributionMT<TR>, outPtr, elements, \
+                           state.get(), pos.get(), sh1.get(), sh2.get(), mask, \
+                           recursion_table.get(), temper_table.get());         \
+        return out;                                                            \
+    }
 
-    INSTANTIATE_UNIFORM(float )
-    INSTANTIATE_UNIFORM(double)
-    INSTANTIATE_UNIFORM(int   )
-    INSTANTIATE_UNIFORM(uint  )
-    INSTANTIATE_UNIFORM(intl  )
-    INSTANTIATE_UNIFORM(uintl )
-    INSTANTIATE_UNIFORM(char  )
-    INSTANTIATE_UNIFORM(uchar )
-    INSTANTIATE_UNIFORM(short )
-    INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(float)
+INSTANTIATE_UNIFORM(double)
+INSTANTIATE_UNIFORM(int)
+INSTANTIATE_UNIFORM(uint)
+INSTANTIATE_UNIFORM(intl)
+INSTANTIATE_UNIFORM(uintl)
+INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(uchar)
+INSTANTIATE_UNIFORM(short)
+INSTANTIATE_UNIFORM(ushort)
 
-    INSTANTIATE_NORMAL(float )
-    INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(float)
+INSTANTIATE_NORMAL(double)
 
-    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
-    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index ac24ad277f..bb50388e86 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -10,28 +10,36 @@
 #pragma once
 
 #include <Array.hpp>
-#include <af/defines.h>
 #include <backend.hpp>
+#include <af/defines.h>
 
-namespace cpu
-{
-    Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
-
-    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
-
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const unsigned long long seed, unsigned long long &counter);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const unsigned long long seed, unsigned long long &counter);
-
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
-}
+namespace cpu {
+Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
+
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> tbl);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const unsigned long long seed,
+                             unsigned long long &counter);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type,
+                            const unsigned long long seed,
+                            unsigned long long &counter);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state);
+}  // namespace cpu
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index e91ba1e241..98455398b4 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -8,43 +8,41 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <range.hpp>
-#include <math.hpp>
-#include <stdexcept>
 #include <err_cpu.hpp>
-#include <algorithm>
-#include <numeric>
+#include <kernel/range.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/range.hpp>
+#include <range.hpp>
+#include <algorithm>
+#include <numeric>
+#include <stdexcept>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> range(const dim4& dims, const int seq_dim)
-{
+Array<T> range(const dim4& dims, const int seq_dim) {
     // Set dimension along which the sequence should be
     // Other dimensions are simply tiled
     int _seq_dim = seq_dim;
-    if(seq_dim < 0) {
-        _seq_dim = 0;   // column wise sequence
+    if (seq_dim < 0) {
+        _seq_dim = 0;  // column wise sequence
     }
 
     Array<T> out = createEmptyArray<T>(dims);
-    switch(_seq_dim) {
+    switch (_seq_dim) {
         case 0: getQueue().enqueue(kernel::range<T, 0>, out); break;
         case 1: getQueue().enqueue(kernel::range<T, 1>, out); break;
         case 2: getQueue().enqueue(kernel::range<T, 2>, out); break;
         case 3: getQueue().enqueue(kernel::range<T, 3>, out); break;
-        default : AF_ERROR("Invalid rep selection", AF_ERR_ARG);
+        default: AF_ERROR("Invalid rep selection", AF_ERR_ARG);
     }
 
     return out;
 }
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> range<T>(const af::dim4 &dims, const int seq_dims);   \
+#define INSTANTIATE(T) \
+    template Array<T> range<T>(const af::dim4& dims, const int seq_dims);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -56,4 +54,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/range.hpp b/src/backend/cpu/range.hpp
index cb373c1216..9b30f261f7 100644
--- a/src/backend/cpu/range.hpp
+++ b/src/backend/cpu/range.hpp
@@ -10,8 +10,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> range(const dim4& dim, const int seq_dim = -1);
+namespace cpu {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim = -1);
 }
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 9604814724..6e735e289b 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -7,61 +7,55 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <reduce.hpp>
+#include <kernel/reduce.hpp>
 #include <ops.hpp>
-#include <functional>
-#include <complex>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/reduce.hpp>
+#include <reduce.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+#include <functional>
 
 using af::dim4;
 
 template<>
-struct Binary<cdouble, af_add_t>
-{
-    static cdouble init()
-    {
-        return cdouble(0,0);
-    }
+struct Binary<cdouble, af_add_t> {
+    static cdouble init() { return cdouble(0, 0); }
 
-    cdouble operator()(cdouble lhs, cdouble rhs)
-    {
-        return cdouble(real(lhs)+real(rhs), imag(lhs)+imag(rhs));
+    cdouble operator()(cdouble lhs, cdouble rhs) {
+        return cdouble(real(lhs) + real(rhs), imag(lhs) + imag(rhs));
     }
 };
 
-namespace cpu
-{
+namespace cpu {
 
 template<af_op_t op, typename Ti, typename To>
-using reduce_dim_func = std::function<void(Param<To>, const dim_t,
-                                           CParam<Ti>, const dim_t,
-                                           const int, bool, double)>;
+using reduce_dim_func = std::function<void(
+    Param<To>, const dim_t, CParam<Ti>, const dim_t, const int, bool, double)>;
 
 template<af_op_t op, typename Ti, typename To>
-Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nanval)
-{
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
+                 double nanval) {
     dim4 odims = in.dims();
     odims[dim] = 1;
     in.eval();
 
     Array<To> out = createEmptyArray<To>(odims);
-    static const reduce_dim_func<op, Ti, To>  reduce_funcs[4] = { kernel::reduce_dim<op, Ti, To, 1>()
-                                                                , kernel::reduce_dim<op, Ti, To, 2>()
-                                                                , kernel::reduce_dim<op, Ti, To, 3>()
-                                                                , kernel::reduce_dim<op, Ti, To, 4>()};
+    static const reduce_dim_func<op, Ti, To> reduce_funcs[4] = {
+        kernel::reduce_dim<op, Ti, To, 1>(),
+        kernel::reduce_dim<op, Ti, To, 2>(),
+        kernel::reduce_dim<op, Ti, To, 3>(),
+        kernel::reduce_dim<op, Ti, To, 4>()};
 
-    getQueue().enqueue(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim, change_nan, nanval);
+    getQueue().enqueue(reduce_funcs[in.ndims() - 1], out, 0, in, 0, dim,
+                       change_nan, nanval);
 
     return out;
 }
 
 template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
-{
+To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     in.eval();
     getQueue().sync();
 
@@ -71,20 +65,20 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
     To out = Binary<To, op>::init();
 
     // Decrement dimension of select dimension
-    af::dim4 dims = in.dims();
+    af::dim4 dims    = in.dims();
     af::dim4 strides = in.strides();
-    const Ti *inPtr = in.get();
+    const Ti *inPtr  = in.get();
 
-    for(dim_t l = 0; l < dims[3]; l++) {
+    for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
 
-        for(dim_t k = 0; k < dims[2]; k++) {
+        for (dim_t k = 0; k < dims[2]; k++) {
             dim_t off2 = k * strides[2];
 
-            for(dim_t j = 0; j < dims[1]; j++) {
+            for (dim_t j = 0; j < dims[1]; j++) {
                 dim_t off1 = j * strides[1];
 
-                for(dim_t i = 0; i < dims[0]; i++) {
+                for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
 
                     To in_val = transform(inPtr[idx]);
@@ -98,116 +92,116 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
     return out;
 }
 
-#define INSTANTIATE(ROp, Ti, To)                                        \
+#define INSTANTIATE(ROp, Ti, To)                                               \
     template Array<To> reduce<ROp, Ti, To>(const Array<Ti> &in, const int dim, \
-                                           bool change_nan, double nanval); \
-    template To reduce_all<ROp, Ti, To>(const Array<Ti> &in,            \
-                                        bool change_nan, double nanval);
-
-//min
-INSTANTIATE(af_min_t, float  , float  )
-INSTANTIATE(af_min_t, double , double )
-INSTANTIATE(af_min_t, cfloat , cfloat )
+                                           bool change_nan, double nanval);    \
+    template To reduce_all<ROp, Ti, To>(const Array<Ti> &in, bool change_nan,  \
+                                        double nanval);
+
+// min
+INSTANTIATE(af_min_t, float, float)
+INSTANTIATE(af_min_t, double, double)
+INSTANTIATE(af_min_t, cfloat, cfloat)
 INSTANTIATE(af_min_t, cdouble, cdouble)
-INSTANTIATE(af_min_t, int    , int    )
-INSTANTIATE(af_min_t, uint   , uint   )
-INSTANTIATE(af_min_t, intl   , intl   )
-INSTANTIATE(af_min_t, uintl  , uintl  )
-INSTANTIATE(af_min_t, char   , char   )
-INSTANTIATE(af_min_t, uchar  , uchar  )
-INSTANTIATE(af_min_t, short  , short  )
-INSTANTIATE(af_min_t, ushort , ushort )
-
-//max
-INSTANTIATE(af_max_t, float  , float  )
-INSTANTIATE(af_max_t, double , double )
-INSTANTIATE(af_max_t, cfloat , cfloat )
+INSTANTIATE(af_min_t, int, int)
+INSTANTIATE(af_min_t, uint, uint)
+INSTANTIATE(af_min_t, intl, intl)
+INSTANTIATE(af_min_t, uintl, uintl)
+INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, uchar, uchar)
+INSTANTIATE(af_min_t, short, short)
+INSTANTIATE(af_min_t, ushort, ushort)
+
+// max
+INSTANTIATE(af_max_t, float, float)
+INSTANTIATE(af_max_t, double, double)
+INSTANTIATE(af_max_t, cfloat, cfloat)
 INSTANTIATE(af_max_t, cdouble, cdouble)
-INSTANTIATE(af_max_t, int    , int    )
-INSTANTIATE(af_max_t, uint   , uint   )
-INSTANTIATE(af_max_t, intl   , intl   )
-INSTANTIATE(af_max_t, uintl  , uintl  )
-INSTANTIATE(af_max_t, char   , char   )
-INSTANTIATE(af_max_t, uchar  , uchar  )
-INSTANTIATE(af_max_t, short  , short  )
-INSTANTIATE(af_max_t, ushort , ushort )
-
-//sum
-INSTANTIATE(af_add_t, float  , float  )
-INSTANTIATE(af_add_t, double , double )
-INSTANTIATE(af_add_t, cfloat , cfloat )
+INSTANTIATE(af_max_t, int, int)
+INSTANTIATE(af_max_t, uint, uint)
+INSTANTIATE(af_max_t, intl, intl)
+INSTANTIATE(af_max_t, uintl, uintl)
+INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, uchar, uchar)
+INSTANTIATE(af_max_t, short, short)
+INSTANTIATE(af_max_t, ushort, ushort)
+
+// sum
+INSTANTIATE(af_add_t, float, float)
+INSTANTIATE(af_add_t, double, double)
+INSTANTIATE(af_add_t, cfloat, cfloat)
 INSTANTIATE(af_add_t, cdouble, cdouble)
-INSTANTIATE(af_add_t, int    , int    )
-INSTANTIATE(af_add_t, int    , float  )
-INSTANTIATE(af_add_t, uint   , uint   )
-INSTANTIATE(af_add_t, uint   , float  )
-INSTANTIATE(af_add_t, intl   , intl   )
-INSTANTIATE(af_add_t, intl   , double )
-INSTANTIATE(af_add_t, uintl  , uintl  )
-INSTANTIATE(af_add_t, uintl  , double )
-INSTANTIATE(af_add_t, char   , int    )
-INSTANTIATE(af_add_t, char   , float  )
-INSTANTIATE(af_add_t, uchar  , uint   )
-INSTANTIATE(af_add_t, uchar  , float  )
-INSTANTIATE(af_add_t, short  , int    )
-INSTANTIATE(af_add_t, short  , float  )
-INSTANTIATE(af_add_t, ushort , uint   )
-INSTANTIATE(af_add_t, ushort , float  )
-
-//mul
-INSTANTIATE(af_mul_t, float  , float  )
-INSTANTIATE(af_mul_t, double , double )
-INSTANTIATE(af_mul_t, cfloat , cfloat )
+INSTANTIATE(af_add_t, int, int)
+INSTANTIATE(af_add_t, int, float)
+INSTANTIATE(af_add_t, uint, uint)
+INSTANTIATE(af_add_t, uint, float)
+INSTANTIATE(af_add_t, intl, intl)
+INSTANTIATE(af_add_t, intl, double)
+INSTANTIATE(af_add_t, uintl, uintl)
+INSTANTIATE(af_add_t, uintl, double)
+INSTANTIATE(af_add_t, char, int)
+INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, uchar, uint)
+INSTANTIATE(af_add_t, uchar, float)
+INSTANTIATE(af_add_t, short, int)
+INSTANTIATE(af_add_t, short, float)
+INSTANTIATE(af_add_t, ushort, uint)
+INSTANTIATE(af_add_t, ushort, float)
+
+// mul
+INSTANTIATE(af_mul_t, float, float)
+INSTANTIATE(af_mul_t, double, double)
+INSTANTIATE(af_mul_t, cfloat, cfloat)
 INSTANTIATE(af_mul_t, cdouble, cdouble)
-INSTANTIATE(af_mul_t, int    , int    )
-INSTANTIATE(af_mul_t, uint   , uint   )
-INSTANTIATE(af_mul_t, intl   , intl   )
-INSTANTIATE(af_mul_t, uintl  , uintl  )
-INSTANTIATE(af_mul_t, char   , int    )
-INSTANTIATE(af_mul_t, uchar  , uint   )
-INSTANTIATE(af_mul_t, short  , int    )
-INSTANTIATE(af_mul_t, ushort , uint   )
+INSTANTIATE(af_mul_t, int, int)
+INSTANTIATE(af_mul_t, uint, uint)
+INSTANTIATE(af_mul_t, intl, intl)
+INSTANTIATE(af_mul_t, uintl, uintl)
+INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, uchar, uint)
+INSTANTIATE(af_mul_t, short, int)
+INSTANTIATE(af_mul_t, ushort, uint)
 
 // count
-INSTANTIATE(af_notzero_t, float  , uint)
-INSTANTIATE(af_notzero_t, double , uint)
-INSTANTIATE(af_notzero_t, cfloat , uint)
+INSTANTIATE(af_notzero_t, float, uint)
+INSTANTIATE(af_notzero_t, double, uint)
+INSTANTIATE(af_notzero_t, cfloat, uint)
 INSTANTIATE(af_notzero_t, cdouble, uint)
-INSTANTIATE(af_notzero_t, int    , uint)
-INSTANTIATE(af_notzero_t, uint   , uint)
-INSTANTIATE(af_notzero_t, intl   , uint)
-INSTANTIATE(af_notzero_t, uintl  , uint)
-INSTANTIATE(af_notzero_t, char   , uint)
-INSTANTIATE(af_notzero_t, uchar  , uint)
-INSTANTIATE(af_notzero_t, short  , uint)
-INSTANTIATE(af_notzero_t, ushort , uint)
-
-//anytrue
-INSTANTIATE(af_or_t, float  , char)
-INSTANTIATE(af_or_t, double , char)
-INSTANTIATE(af_or_t, cfloat , char)
+INSTANTIATE(af_notzero_t, int, uint)
+INSTANTIATE(af_notzero_t, uint, uint)
+INSTANTIATE(af_notzero_t, intl, uint)
+INSTANTIATE(af_notzero_t, uintl, uint)
+INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, uchar, uint)
+INSTANTIATE(af_notzero_t, short, uint)
+INSTANTIATE(af_notzero_t, ushort, uint)
+
+// anytrue
+INSTANTIATE(af_or_t, float, char)
+INSTANTIATE(af_or_t, double, char)
+INSTANTIATE(af_or_t, cfloat, char)
 INSTANTIATE(af_or_t, cdouble, char)
-INSTANTIATE(af_or_t, int    , char)
-INSTANTIATE(af_or_t, uint   , char)
-INSTANTIATE(af_or_t, intl   , char)
-INSTANTIATE(af_or_t, uintl  , char)
-INSTANTIATE(af_or_t, char   , char)
-INSTANTIATE(af_or_t, uchar  , char)
-INSTANTIATE(af_or_t, short  , char)
-INSTANTIATE(af_or_t, ushort , char)
-
-//alltrue
-INSTANTIATE(af_and_t, float  , char)
-INSTANTIATE(af_and_t, double , char)
-INSTANTIATE(af_and_t, cfloat , char)
+INSTANTIATE(af_or_t, int, char)
+INSTANTIATE(af_or_t, uint, char)
+INSTANTIATE(af_or_t, intl, char)
+INSTANTIATE(af_or_t, uintl, char)
+INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, uchar, char)
+INSTANTIATE(af_or_t, short, char)
+INSTANTIATE(af_or_t, ushort, char)
+
+// alltrue
+INSTANTIATE(af_and_t, float, char)
+INSTANTIATE(af_and_t, double, char)
+INSTANTIATE(af_and_t, cfloat, char)
 INSTANTIATE(af_and_t, cdouble, char)
-INSTANTIATE(af_and_t, int    , char)
-INSTANTIATE(af_and_t, uint   , char)
-INSTANTIATE(af_and_t, intl   , char)
-INSTANTIATE(af_and_t, uintl  , char)
-INSTANTIATE(af_and_t, char   , char)
-INSTANTIATE(af_and_t, uchar  , char)
-INSTANTIATE(af_and_t, short  , char)
-INSTANTIATE(af_and_t, ushort , char)
-
-}
+INSTANTIATE(af_and_t, int, char)
+INSTANTIATE(af_and_t, uint, char)
+INSTANTIATE(af_and_t, intl, char)
+INSTANTIATE(af_and_t, uintl, char)
+INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, uchar, char)
+INSTANTIATE(af_and_t, short, char)
+INSTANTIATE(af_and_t, ushort, char)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/reduce.hpp b/src/backend/cpu/reduce.hpp
index 2af78566c2..e8acbd9543 100644
--- a/src/backend/cpu/reduce.hpp
+++ b/src/backend/cpu/reduce.hpp
@@ -10,11 +10,11 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cpu
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan=false, double nanval=0);
+namespace cpu {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
+                 double nanval = 0);
 
-    template<af_op_t op, typename Ti, typename To>
-    To reduce_all(const Array<Ti> &in, bool change_nan=false, double nanval=0);
-}
+template<af_op_t op, typename Ti, typename To>
+To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
+}  // namespace cpu
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index 4886544623..e6895b7983 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <regions.hpp>
 #include <err_cpu.hpp>
+#include <kernel/regions.hpp>
 #include <math.hpp>
-#include <map>
-#include <set>
-#include <algorithm>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/regions.hpp>
+#include <regions.hpp>
+#include <af/dim4.hpp>
+#include <algorithm>
+#include <map>
+#include <set>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> regions(const Array<char> &in, af_connectivity connectivity)
-{
+Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
     in.eval();
 
     Array<T> out = createValueArray(in.dims(), (T)0);
@@ -37,14 +35,15 @@ Array<T> regions(const Array<char> &in, af_connectivity connectivity)
     return out;
 }
 
-#define INSTANTIATE(T)\
-    template Array<T> regions<T>(const Array<char> &in, af_connectivity connectivity);
+#define INSTANTIATE(T)                                  \
+    template Array<T> regions<T>(const Array<char> &in, \
+                                 af_connectivity connectivity);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(short )
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/regions.hpp b/src/backend/cpu/regions.hpp
index 2e94711d28..0e2ce0f319 100644
--- a/src/backend/cpu/regions.hpp
+++ b/src/backend/cpu/regions.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index bd156585ee..57d63584d4 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -8,31 +8,28 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <reorder.hpp>
+#include <kernel/reorder.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/reorder.hpp>
+#include <reorder.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
-{
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
     in.eval();
 
     const af::dim4 iDims = in.dims();
     af::dim4 oDims(0);
-    for(int i = 0; i < 4; i++)
-        oDims[i] = iDims[rdims[i]];
+    for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
 
     Array<T> out = createEmptyArray<T>(oDims);
     getQueue().enqueue(kernel::reorder<T>, out, in, oDims, rdims);
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);  \
+#define INSTANTIATE(T) \
+    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -47,4 +44,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/reorder.hpp b/src/backend/cpu/reorder.hpp
index d4f81e78ca..bc689f74c2 100644
--- a/src/backend/cpu/reorder.hpp
+++ b/src/backend/cpu/reorder.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
+namespace cpu {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
 }
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 342b269d69..17bd317818 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -8,19 +8,17 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <resize.hpp>
+#include <kernel/resize.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/resize.hpp>
+#include <resize.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                const af_interp_type method)
-{
+                const af_interp_type method) {
     af::dim4 idims = in.dims();
     af::dim4 odims(odim0, odim1, idims[2], idims[3]);
     // Create output placeholder
@@ -28,21 +26,25 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
     out.eval();
     in.eval();
 
-    switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
-            getQueue().enqueue(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_NEAREST>, out, in);
+            break;
         case AF_INTERP_BILINEAR:
-            getQueue().enqueue(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_BILINEAR>, out, in);
+            break;
         case AF_INTERP_LOWER:
-            getQueue().enqueue(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
+            getQueue().enqueue(kernel::resize<T, AF_INTERP_LOWER>, out, in);
+            break;
         default: break;
     }
     return out;
 }
 
-#define INSTANTIATE(T)                                                                     \
-    template Array<T> resize<T> (const Array<T> &in, const dim_t odim0, const dim_t odim1, \
-                                 const af_interp_type method);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> resize<T>(const Array<T> &in, const dim_t odim0, \
+                                const dim_t odim1,                     \
+                                const af_interp_type method);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -57,4 +59,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/resize.hpp b/src/backend/cpu/resize.hpp
index a96a04f249..83852f1e29 100644
--- a/src/backend/cpu/resize.hpp
+++ b/src/backend/cpu/resize.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                    const af_interp_type method);
+namespace cpu {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method);
 }
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 2f853a4a47..074e9d6bf5 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -8,47 +8,43 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <rotate.hpp>
+#include <kernel/rotate.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/rotate.hpp>
+#include <rotate.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                 const af_interp_type method)
-{
+                const af_interp_type method) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(odims);
 
-    switch(method) {
-    case AF_INTERP_NEAREST:
-    case AF_INTERP_LOWER:
-        getQueue().enqueue(kernel::rotate<T, 1>, out, in, theta, method);
-        break;
-    case AF_INTERP_BILINEAR:
-    case AF_INTERP_BILINEAR_COSINE:
-        getQueue().enqueue(kernel::rotate<T, 2>, out, in, theta, method);
-        break;
-    case AF_INTERP_BICUBIC:
-    case AF_INTERP_BICUBIC_SPLINE:
-        getQueue().enqueue(kernel::rotate<T, 3>, out, in, theta, method);
-        break;
-    default:
-        AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-        break;
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            getQueue().enqueue(kernel::rotate<T, 1>, out, in, theta, method);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            getQueue().enqueue(kernel::rotate<T, 2>, out, in, theta, method);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            getQueue().enqueue(kernel::rotate<T, 3>, out, in, theta, method);
+            break;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
 
     return out;
 }
 
-
-#define INSTANTIATE(T)                                                              \
-    template Array<T> rotate(const Array<T> &in, const float theta,                 \
-                             const af::dim4 &odims, const af_interp_type method);
+#define INSTANTIATE(T)                                              \
+    template Array<T> rotate(const Array<T> &in, const float theta, \
+                             const af::dim4 &odims,                 \
+                             const af_interp_type method);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -63,4 +59,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/rotate.hpp b/src/backend/cpu/rotate.hpp
index 93d838737c..094bc24f92 100644
--- a/src/backend/cpu/rotate.hpp
+++ b/src/backend/cpu/rotate.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                    const af_interp_type method);
+namespace cpu {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method);
 }
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 4f71220273..9893cbb282 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -7,91 +7,90 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <scan.hpp>
+#include <kernel/scan.hpp>
 #include <ops.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/scan.hpp>
+#include <scan.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        dim4 dims     = in.dims();
-        Array<To> out = createEmptyArray<To>(dims);
-        in.eval();
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
+    dim4 dims     = in.dims();
+    Array<To> out = createEmptyArray<To>(dims);
+    in.eval();
 
-        if (inclusive_scan) {
-            switch (in.ndims()) {
-                case 1:
-                    kernel::scan_dim<op, Ti, To, 1, true> func1;
-                    getQueue().enqueue(func1, out, 0, in, 0, dim);
-                    break;
-                case 2:
-                    kernel::scan_dim<op, Ti, To, 2, true> func2;
-                    getQueue().enqueue(func2, out, 0, in, 0, dim);
-                    break;
-                case 3:
-                    kernel::scan_dim<op, Ti, To, 3, true> func3;
-                    getQueue().enqueue(func3, out, 0, in, 0, dim);
-                    break;
-                case 4:
-                    kernel::scan_dim<op, Ti, To, 4, true> func4;
-                    getQueue().enqueue(func4, out, 0, in, 0, dim);
-                    break;
-            }
-        } else {
-            switch (in.ndims()) {
-                case 1:
-                    kernel::scan_dim<op, Ti, To, 1, false> func1;
-                    getQueue().enqueue(func1, out, 0, in, 0, dim);
-                    break;
-                case 2:
-                    kernel::scan_dim<op, Ti, To, 2, false> func2;
-                    getQueue().enqueue(func2, out, 0, in, 0, dim);
-                    break;
-                case 3:
-                    kernel::scan_dim<op, Ti, To, 3, false> func3;
-                    getQueue().enqueue(func3, out, 0, in, 0, dim);
-                    break;
-                case 4:
-                    kernel::scan_dim<op, Ti, To, 4, false> func4;
-                    getQueue().enqueue(func4, out, 0, in, 0, dim);
-                    break;
-            }
+    if (inclusive_scan) {
+        switch (in.ndims()) {
+            case 1:
+                kernel::scan_dim<op, Ti, To, 1, true> func1;
+                getQueue().enqueue(func1, out, 0, in, 0, dim);
+                break;
+            case 2:
+                kernel::scan_dim<op, Ti, To, 2, true> func2;
+                getQueue().enqueue(func2, out, 0, in, 0, dim);
+                break;
+            case 3:
+                kernel::scan_dim<op, Ti, To, 3, true> func3;
+                getQueue().enqueue(func3, out, 0, in, 0, dim);
+                break;
+            case 4:
+                kernel::scan_dim<op, Ti, To, 4, true> func4;
+                getQueue().enqueue(func4, out, 0, in, 0, dim);
+                break;
+        }
+    } else {
+        switch (in.ndims()) {
+            case 1:
+                kernel::scan_dim<op, Ti, To, 1, false> func1;
+                getQueue().enqueue(func1, out, 0, in, 0, dim);
+                break;
+            case 2:
+                kernel::scan_dim<op, Ti, To, 2, false> func2;
+                getQueue().enqueue(func2, out, 0, in, 0, dim);
+                break;
+            case 3:
+                kernel::scan_dim<op, Ti, To, 3, false> func3;
+                getQueue().enqueue(func3, out, 0, in, 0, dim);
+                break;
+            case 4:
+                kernel::scan_dim<op, Ti, To, 4, false> func4;
+                getQueue().enqueue(func4, out, 0, in, 0, dim);
+                break;
         }
-
-        return out;
     }
 
-#define INSTANTIATE_SCAN(ROp, Ti, To)\
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
+    return out;
+}
 
-#define INSTANTIATE_SCAN_ALL(ROp)                       \
-    INSTANTIATE_SCAN(ROp, float  , float  )             \
-    INSTANTIATE_SCAN(ROp, double , double )             \
-    INSTANTIATE_SCAN(ROp, cfloat , cfloat )             \
-    INSTANTIATE_SCAN(ROp, cdouble, cdouble)             \
-    INSTANTIATE_SCAN(ROp, int    , int    )             \
-    INSTANTIATE_SCAN(ROp, uint   , uint   )             \
-    INSTANTIATE_SCAN(ROp, intl   , intl   )             \
-    INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
-    INSTANTIATE_SCAN(ROp, char   , int    )             \
-    INSTANTIATE_SCAN(ROp, char   , uint   )             \
-    INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
-    INSTANTIATE_SCAN(ROp, short  , int    )             \
-    INSTANTIATE_SCAN(ROp, ushort , uint   )
+#define INSTANTIATE_SCAN(ROp, Ti, To)                                        \
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti>& in, const int dim, \
+                                         bool inclusive_scan);
 
-    INSTANTIATE_SCAN(af_notzero_t, char, uint)
-    INSTANTIATE_SCAN_ALL(af_add_t)
-    INSTANTIATE_SCAN_ALL(af_mul_t)
-    INSTANTIATE_SCAN_ALL(af_min_t)
-    INSTANTIATE_SCAN_ALL(af_max_t)
-}
+#define INSTANTIATE_SCAN_ALL(ROp)           \
+    INSTANTIATE_SCAN(ROp, float, float)     \
+    INSTANTIATE_SCAN(ROp, double, double)   \
+    INSTANTIATE_SCAN(ROp, cfloat, cfloat)   \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble) \
+    INSTANTIATE_SCAN(ROp, int, int)         \
+    INSTANTIATE_SCAN(ROp, uint, uint)       \
+    INSTANTIATE_SCAN(ROp, intl, intl)       \
+    INSTANTIATE_SCAN(ROp, uintl, uintl)     \
+    INSTANTIATE_SCAN(ROp, char, int)        \
+    INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, uchar, uint)      \
+    INSTANTIATE_SCAN(ROp, short, int)       \
+    INSTANTIATE_SCAN(ROp, ushort, uint)
+
+INSTANTIATE_SCAN(af_notzero_t, char, uint)
+INSTANTIATE_SCAN_ALL(af_add_t)
+INSTANTIATE_SCAN_ALL(af_mul_t)
+INSTANTIATE_SCAN_ALL(af_min_t)
+INSTANTIATE_SCAN_ALL(af_max_t)
+}  // namespace cpu
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index 5620e44cd8..f00f75e82d 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cpu
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+namespace cpu {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index 3f0aad80a9..63b592703e 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -7,71 +7,64 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <scan_by_key.hpp>
+#include <kernel/scan_by_key.hpp>
 #include <ops.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/scan_by_key.hpp>
+#include <scan_by_key.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 
-namespace cpu
-{
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        dim4 dims     = in.dims();
-        Array<To> out = createEmptyArray<To>(dims);
-        kernel::scan_dim_by_key<op, Ti, Tk, To, 1> func1(inclusive_scan);
-        kernel::scan_dim_by_key<op, Ti, Tk, To, 2> func2(inclusive_scan);
-        kernel::scan_dim_by_key<op, Ti, Tk, To, 3> func3(inclusive_scan);
-        kernel::scan_dim_by_key<op, Ti, Tk, To, 4> func4(inclusive_scan);
-
-        in.eval();
-        key.eval();
+namespace cpu {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan) {
+    dim4 dims     = in.dims();
+    Array<To> out = createEmptyArray<To>(dims);
+    kernel::scan_dim_by_key<op, Ti, Tk, To, 1> func1(inclusive_scan);
+    kernel::scan_dim_by_key<op, Ti, Tk, To, 2> func2(inclusive_scan);
+    kernel::scan_dim_by_key<op, Ti, Tk, To, 3> func3(inclusive_scan);
+    kernel::scan_dim_by_key<op, Ti, Tk, To, 4> func4(inclusive_scan);
 
-        switch (in.ndims()) {
-        case 1:
-            getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim);
-            break;
-        case 2:
-            getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim);
-            break;
-        case 3:
-            getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim);
-            break;
-        case 4:
-            getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim);
-            break;
-        }
+    in.eval();
+    key.eval();
 
-        return out;
+    switch (in.ndims()) {
+        case 1: getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim); break;
+        case 2: getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim); break;
+        case 3: getQueue().enqueue(func3, out, 0, key, 0, in, 0, dim); break;
+        case 4: getQueue().enqueue(func4, out, 0, key, 0, in, 0, dim); break;
     }
 
-#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
-    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+    return out;
+}
+
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)                  \
+    template Array<To> scan<ROp, Ti, Tk, To>(                     \
+        const Array<Tk>& key, const Array<Ti>& in, const int dim, \
+        bool inclusive_scan);
 
-#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
-    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)           \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl, Tk, uintl)
 
 #define INSTANTIATE_SCAN_BY_KEY_ALL_OP(ROp) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint ) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl ) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)   \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl)  \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
 
-    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_add_t)
-    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_mul_t)
-    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_min_t)
-    INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_max_t)
-}
+INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_add_t)
+INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_mul_t)
+INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_min_t)
+INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_max_t)
+}  // namespace cpu
diff --git a/src/backend/cpu/scan_by_key.hpp b/src/backend/cpu/scan_by_key.hpp
index 6b0cb1b5bd..f239189136 100644
--- a/src/backend/cpu/scan_by_key.hpp
+++ b/src/backend/cpu/scan_by_key.hpp
@@ -10,8 +10,8 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cpu
-{
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+namespace cpu {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan = true);
 }
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 982c1100a6..62bebb1dd4 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -8,19 +8,18 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <select.hpp>
+#include <kernel/select.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/select.hpp>
+#include <select.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b)
-{
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b) {
     out.eval();
     cond.eval();
     a.eval();
@@ -29,37 +28,35 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Arr
 }
 
 template<typename T, bool flip>
-void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b)
-{
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const double &b) {
     out.eval();
     cond.eval();
     a.eval();
     getQueue().enqueue(kernel::select_scalar<T, flip>, out, cond, a, b);
 }
 
-#define INSTANTIATE(T)                                              \
-    template void select<T>(Array<T> &out, const Array<char> &cond, \
-                            const Array<T> &a, const Array<T> &b);  \
-    template void select_scalar<T, true >(Array<T> &out,            \
-                                          const Array<char> &cond,  \
-                                          const Array<T> &a,        \
-                                          const double &b);         \
-    template void select_scalar<T, false>(Array<T> &out, const      \
-                                          Array<char> &cond,        \
-                                          const Array<T> &a,        \
-                                          const double &b);         \
-
-INSTANTIATE(float  )
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
+#define INSTANTIATE(T)                                                        \
+    template void select<T>(Array<T> & out, const Array<char> &cond,          \
+                            const Array<T> &a, const Array<T> &b);            \
+    template void select_scalar<T, true>(Array<T> & out,                      \
+                                         const Array<char> &cond,             \
+                                         const Array<T> &a, const double &b); \
+    template void select_scalar<T, false>(Array<T> & out,                     \
+                                          const Array<char> &cond,            \
+                                          const Array<T> &a, const double &b);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(char   )
-INSTANTIATE(uchar  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
-
-}
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/select.hpp b/src/backend/cpu/select.hpp
index 51c3b0d6ac..dfe13ae9ea 100644
--- a/src/backend/cpu/select.hpp
+++ b/src/backend/cpu/select.hpp
@@ -9,27 +9,28 @@
 #pragma once
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b);
+namespace cpu {
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b);
 
-    template<typename T, bool flip>
-    void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const double &b);
 
-    template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const Array<T> &b, const af::dim4 &odims)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
-        select(out, cond, a, b);
-        return out;
-    }
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const af::dim4 &odims) {
+    Array<T> out = createEmptyArray<T>(odims);
+    select(out, cond, a, b);
+    return out;
+}
 
-    template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a, const double &b, const af::dim4 &odims)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
-        select_scalar<T, flip>(out, cond, a, b);
-        return out;
-    }
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const double &b, const af::dim4 &odims) {
+    Array<T> out = createEmptyArray<T>(odims);
+    select_scalar<T, flip>(out, cond, a, b);
+    return out;
 }
+}  // namespace cpu
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 659ef1e490..b588de332f 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -7,40 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <algorithm>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <set.hpp>
 #include <copy.hpp>
-#include <sort.hpp>
 #include <err_cpu.hpp>
-#include <vector>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <set.hpp>
+#include <sort.hpp>
+#include <af/dim4.hpp>
+#include <algorithm>
+#include <complex>
+#include <vector>
 
-namespace cpu
-{
+namespace cpu {
 
 using namespace std;
 using af::dim4;
 
 template<typename T>
-Array<T> setUnique(const Array<T> &in,
-                    const bool is_sorted)
-{
+Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(af::dim4());
-    if (is_sorted) out = copyArray<T>(in);
-    else           out = sort<T>(in, 0, true);
+    if (is_sorted)
+        out = copyArray<T>(in);
+    else
+        out = sort<T>(in, 0, true);
 
     // Need to sync old jobs since we need to
     // operator on pointers directly in std::unique
     getQueue().sync();
 
-    T *ptr = out.get();
-    T *last = std::unique(ptr, ptr + in.elements());
+    T *ptr     = out.get();
+    T *last    = std::unique(ptr, ptr + in.elements());
     dim_t dist = (dim_t)std::distance(ptr, last);
 
     dim4 dims(dist, 1, 1, 1);
@@ -49,15 +48,13 @@ Array<T> setUnique(const Array<T> &in,
 }
 
 template<typename T>
-Array<T> setUnion(const Array<T> &first,
-                   const Array<T> &second,
-                   const bool is_unique)
-{
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique) {
     first.eval();
     second.eval();
     getQueue().sync();
 
-    Array<T> uFirst = first;
+    Array<T> uFirst  = first;
     Array<T> uSecond = second;
 
     if (!is_unique) {
@@ -68,14 +65,14 @@ Array<T> setUnion(const Array<T> &first,
 
     dim_t first_elements  = uFirst.elements();
     dim_t second_elements = uSecond.elements();
-    dim_t elements = first_elements + second_elements;
+    dim_t elements        = first_elements + second_elements;
 
     Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
     T *ptr = out.get();
-    T *last = std::set_union(uFirst.get() , uFirst.get()  + first_elements,
-                             uSecond.get(), uSecond.get() + second_elements,
-                             ptr);
+    T *last =
+        std::set_union(uFirst.get(), uFirst.get() + first_elements,
+                       uSecond.get(), uSecond.get() + second_elements, ptr);
 
     dim_t dist = (dim_t)std::distance(ptr, last);
     dim4 dims(dist, 1, 1, 1);
@@ -85,15 +82,13 @@ Array<T> setUnion(const Array<T> &first,
 }
 
 template<typename T>
-Array<T> setIntersect(const Array<T> &first,
-                      const Array<T> &second,
-                      const bool is_unique)
-{
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique) {
     first.eval();
     second.eval();
     getQueue().sync();
 
-    Array<T> uFirst = first;
+    Array<T> uFirst  = first;
     Array<T> uSecond = second;
 
     if (!is_unique) {
@@ -103,14 +98,14 @@ Array<T> setIntersect(const Array<T> &first,
 
     dim_t first_elements  = uFirst.elements();
     dim_t second_elements = uSecond.elements();
-    dim_t elements = std::max(first_elements, second_elements);
+    dim_t elements        = std::max(first_elements, second_elements);
 
     Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
-    T *ptr = out.get();
-    T *last = std::set_intersection(uFirst.get() , uFirst.get()  + first_elements,
-                                    uSecond.get(), uSecond.get() + second_elements,
-                                    ptr);
+    T *ptr  = out.get();
+    T *last = std::set_intersection(uFirst.get(), uFirst.get() + first_elements,
+                                    uSecond.get(),
+                                    uSecond.get() + second_elements, ptr);
 
     dim_t dist = (dim_t)std::distance(ptr, last);
     dim4 dims(dist, 1, 1, 1);
@@ -119,10 +114,12 @@ Array<T> setIntersect(const Array<T> &first,
     return out;
 }
 
-#define INSTANTIATE(T)                                                  \
+#define INSTANTIATE(T)                                                        \
     template Array<T> setUnique<T>(const Array<T> &in, const bool is_sorted); \
-    template Array<T> setUnion<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
-    template Array<T> setIntersect<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
+    template Array<T> setUnion<T>(                                            \
+        const Array<T> &first, const Array<T> &second, const bool is_unique); \
+    template Array<T> setIntersect<T>(                                        \
+        const Array<T> &first, const Array<T> &second, const bool is_unique);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -135,4 +132,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/set.hpp b/src/backend/cpu/set.hpp
index a0e48c7076..eac24a6ba3 100644
--- a/src/backend/cpu/set.hpp
+++ b/src/backend/cpu/set.hpp
@@ -9,16 +9,15 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T> Array<T> setUnique(const Array<T> &in,
-                                            const bool is_sorted);
+namespace cpu {
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted);
 
-    template<typename T> Array<T> setUnion(const Array<T> &first,
-                                           const Array<T> &second,
-                                           const bool is_unique);
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique);
 
-    template<typename T> Array<T> setIntersect(const Array<T> &first,
-                                               const Array<T> &second,
-                                               const bool is_unique);
-}
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique);
+}  // namespace cpu
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index 041f1ab8ba..e2a3d3060b 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -8,17 +8,15 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <shift.hpp>
+#include <kernel/shift.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/shift.hpp>
+#include <shift.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> shift(const Array<T> &in, const int sdims[4])
-{
+Array<T> shift(const Array<T> &in, const int sdims[4]) {
     in.eval();
 
     Array<T> out = createEmptyArray<T>(in.dims());
@@ -29,8 +27,8 @@ Array<T> shift(const Array<T> &in, const int sdims[4])
     return out;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]); \
+#define INSTANTIATE(T) \
+    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -45,4 +43,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/shift.hpp b/src/backend/cpu/shift.hpp
index e55cc564aa..4f992e7fb0 100644
--- a/src/backend/cpu/shift.hpp
+++ b/src/backend/cpu/shift.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> shift(const Array<T> &in, const int sdims[4]);
+namespace cpu {
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]);
 }
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index db2d630dbe..15281c1a53 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <err_cpu.hpp>
-#include <resize.hpp>
-#include <sort_index.hpp>
 #include <convolve.hpp>
+#include <err_cpu.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <cstring>
+#include <resize.hpp>
+#include <sort_index.hpp>
+#include <af/dim4.hpp>
 #include <cfloat>
+#include <cstring>
 #include <vector>
 
 #ifdef AF_WITH_NONFREE_SIFT
@@ -25,8 +25,7 @@
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename convAccT>
 unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
@@ -35,12 +34,11 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float contrast_thr, const float edge_thr,
               const float init_sigma, const bool double_input,
               const float img_scale, const float feature_ratio,
-              const bool compute_GLOH)
-{
+              const bool compute_GLOH) {
 #ifdef AF_WITH_NONFREE_SIFT
-    return sift_impl<T, convAccT>(x, y, score, ori, size, desc, in, n_layers,
-                                  contrast_thr, edge_thr, init_sigma, double_input,
-                                  img_scale, feature_ratio, compute_GLOH);
+    return sift_impl<T, convAccT>(
+        x, y, score, ori, size, desc, in, n_layers, contrast_thr, edge_thr,
+        init_sigma, double_input, img_scale, feature_ratio, compute_GLOH);
 #else
     UNUSED(x);
     UNUSED(y);
@@ -57,23 +55,26 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
     UNUSED(img_scale);
     UNUSED(feature_ratio);
     if (compute_GLOH)
-        AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, GLOH disabled\n",
+            AF_ERR_NONFREE);
     else
-        AF_ERROR("ArrayFire was not built with nonfree support, SIFT disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, SIFT disabled\n",
+            AF_ERR_NONFREE);
 #endif
 }
 
-#define INSTANTIATE(T, convAccT)\
-    template unsigned sift<T, convAccT>(Array<float>& x, Array<float>& y,                   \
-                                        Array<float>& score, Array<float>& ori,             \
-                                        Array<float>& size, Array<float>& desc,             \
-                                        const Array<T>& in, const unsigned n_layers,        \
-                                        const float contrast_thr, const float edge_thr,     \
-                                        const float init_sigma, const bool double_input,    \
-                                        const float img_scale, const float feature_ratio,   \
-                                        const bool compute_GLOH);
+#define INSTANTIATE(T, convAccT)                                               \
+    template unsigned sift<T, convAccT>(                                       \
+        Array<float> & x, Array<float> & y, Array<float> & score,              \
+        Array<float> & ori, Array<float> & size, Array<float> & desc,          \
+        const Array<T>& in, const unsigned n_layers, const float contrast_thr, \
+        const float edge_thr, const float init_sigma, const bool double_input, \
+        const float img_scale, const float feature_ratio,                      \
+        const bool compute_GLOH);
 
-INSTANTIATE(float , float )
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sift.hpp b/src/backend/cpu/sift.hpp
index 1ceea4b8c7..66f0d191bb 100644
--- a/src/backend/cpu/sift.hpp
+++ b/src/backend/cpu/sift.hpp
@@ -7,13 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, typename convAccT>
 unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 8d0baa446e..f1b00d46e7 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -7,23 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <sobel.hpp>
 #include <convolve.hpp>
+#include <kernel/sobel.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/sobel.hpp>
+#include <sobel.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename Ti, typename To>
-std::pair< Array<To>, Array<To> >
-sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
-{
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size) {
     UNUSED(ker_size);
     img.eval();
     // ket_size is for future proofing, this argument is not used
@@ -31,23 +29,23 @@ sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
-    getQueue().enqueue(kernel::derivative<Ti, To, true >, dx, img);
+    getQueue().enqueue(kernel::derivative<Ti, To, true>, dx, img);
     getQueue().enqueue(kernel::derivative<Ti, To, false>, dy, img);
 
     return std::make_pair(dx, dy);
 }
 
-#define INSTANTIATE(Ti, To)                                               \
-    template std::pair< Array<To>, Array<To> >                            \
-    sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
+#define INSTANTIATE(Ti, To)                                    \
+    template std::pair<Array<To>, Array<To>> sobelDerivatives( \
+        const Array<Ti> &img, const unsigned &ker_size);
 
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
-INSTANTIATE(int   , int)
-INSTANTIATE(uint  , int)
-INSTANTIATE(char  , int)
-INSTANTIATE(uchar , int)
-INSTANTIATE(short , int)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, int)
+INSTANTIATE(char, int)
+INSTANTIATE(uchar, int)
+INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sobel.hpp b/src/backend/cpu/sobel.hpp
index 23678a9748..dcd41b9366 100644
--- a/src/backend/cpu/sobel.hpp
+++ b/src/backend/cpu/sobel.hpp
@@ -10,11 +10,10 @@
 #include <Array.hpp>
 #include <utility>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename Ti, typename To>
-std::pair< Array<To>, Array<To> >
-sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size);
 
 }
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 75276601d5..431eeacf83 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -7,88 +7,85 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <solve.hpp>
 #include <common/err_common.hpp>
+#include <solve.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <af/dim4.hpp>
-#include <handle.hpp>
-#include <cassert>
 #include <err_cpu.hpp>
+#include <handle.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <af/dim4.hpp>
+#include <cassert>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-using gesv_func_def = int (*)(ORDER_TYPE, int, int,
-                              T *, int, int *, T *, int);
+using gesv_func_def = int (*)(ORDER_TYPE, int, int, T *, int, int *, T *, int);
 
 template<typename T>
-using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int,
-                              T *, int, T *, int);
+using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int, T *, int, T *,
+                              int);
 
 template<typename T>
-using getrs_func_def = int (*)(ORDER_TYPE, char, int, int,
-                               const T *, int, const int *, T *, int);
+using getrs_func_def = int (*)(ORDER_TYPE, char, int, int, const T *, int,
+                               const int *, T *, int);
 
 template<typename T>
 using trtrs_func_def = int (*)(ORDER_TYPE, char, char, char, int, int,
                                const T *, int, T *, int);
 
+#define SOLVE_FUNC_DEF(FUNC) \
+    template<typename T>     \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define SOLVE_FUNC_DEF( FUNC )                                 \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                       \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()       \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-SOLVE_FUNC_DEF( gesv )
-SOLVE_FUNC(gesv , float  , s)
-SOLVE_FUNC(gesv , double , d)
-SOLVE_FUNC(gesv , cfloat , c)
-SOLVE_FUNC(gesv , cdouble, z)
-
-SOLVE_FUNC_DEF( gels )
-SOLVE_FUNC(gels , float  , s)
-SOLVE_FUNC(gels , double , d)
-SOLVE_FUNC(gels , cfloat , c)
-SOLVE_FUNC(gels , cdouble, z)
-
-SOLVE_FUNC_DEF( getrs )
-SOLVE_FUNC(getrs , float  , s)
-SOLVE_FUNC(getrs , double , d)
-SOLVE_FUNC(getrs , cfloat , c)
-SOLVE_FUNC(getrs , cdouble, z)
+#define SOLVE_FUNC(FUNC, TYPE, PREFIX)          \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-SOLVE_FUNC_DEF( trtrs )
-SOLVE_FUNC(trtrs , float  , s)
-SOLVE_FUNC(trtrs , double , d)
-SOLVE_FUNC(trtrs , cfloat , c)
-SOLVE_FUNC(trtrs , cdouble, z)
+SOLVE_FUNC_DEF(gesv)
+SOLVE_FUNC(gesv, float, s)
+SOLVE_FUNC(gesv, double, d)
+SOLVE_FUNC(gesv, cfloat, c)
+SOLVE_FUNC(gesv, cdouble, z)
+
+SOLVE_FUNC_DEF(gels)
+SOLVE_FUNC(gels, float, s)
+SOLVE_FUNC(gels, double, d)
+SOLVE_FUNC(gels, cfloat, c)
+SOLVE_FUNC(gels, cdouble, z)
+
+SOLVE_FUNC_DEF(getrs)
+SOLVE_FUNC(getrs, float, s)
+SOLVE_FUNC(getrs, double, d)
+SOLVE_FUNC(getrs, cfloat, c)
+SOLVE_FUNC(getrs, cdouble, z)
+
+SOLVE_FUNC_DEF(trtrs)
+SOLVE_FUNC(trtrs, float, s)
+SOLVE_FUNC(trtrs, double, d)
+SOLVE_FUNC(trtrs, cfloat, c)
+SOLVE_FUNC(trtrs, cdouble, z)
 
 template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
     UNUSED(options);
     A.eval();
     pivot.eval();
     b.eval();
 
-    int N        = A.dims()[0];
-    int NRHS     = b.dims()[1];
-    Array< T > B = copyArray<T>(b);
+    int N      = A.dims()[0];
+    int NRHS   = b.dims()[1];
+    Array<T> B = copyArray<T>(b);
 
-    auto func = [=] (Param<T> A, Param<T> B, Param<int> pivot, int N, int NRHS) {
-        getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                        N, NRHS, A.get(), A.strides(1),
-                        pivot.get(), B.get(), B.strides(1));
+    auto func = [=](Param<T> A, Param<T> B, Param<int> pivot, int N, int NRHS) {
+        getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N', N, NRHS, A.get(),
+                        A.strides(1), pivot.get(), B.get(), B.strides(1));
     };
     getQueue().enqueue(func, A, B, pivot, N, NRHS);
 
@@ -96,8 +93,8 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
 }
 
 template<typename T>
-Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
-{
+Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
+                       const af_mat_prop options) {
     A.eval();
     b.eval();
 
@@ -105,24 +102,21 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop o
     int N      = B.dims()[0];
     int NRHS   = B.dims()[1];
 
-    auto func = [=] (Param<T> A, Param<T> B, int N, int NRHS, const af_mat_prop options) {
-        trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
-                        options & AF_MAT_UPPER ? 'U' : 'L',
-                        'N', // transpose flag
-                        options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
-                        N, NRHS,
-                        A.get(), A.strides(1),
-                        B.get(), B.strides(1));
+    auto func = [=](Param<T> A, Param<T> B, int N, int NRHS,
+                    const af_mat_prop options) {
+        trtrs_func<T>()(AF_LAPACK_COL_MAJOR, options & AF_MAT_UPPER ? 'U' : 'L',
+                        'N',  // transpose flag
+                        options & AF_MAT_DIAG_UNIT ? 'U' : 'N', N, NRHS,
+                        A.get(), A.strides(1), B.get(), B.strides(1));
     };
     getQueue().enqueue(func, A, B, N, NRHS, options);
 
     return B;
 }
 
-
 template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
     a.eval();
     b.eval();
 
@@ -137,23 +131,22 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
     Array<T> A = copyArray<T>(a);
     Array<T> B = padArray<T, T>(b, dim4(max(M, N), K));
 
-    if(M == N) {
+    if (M == N) {
         Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
 
-        auto func = [=] (Param<T> A, Param<T> B, Param<int> pivot, int N, int K) {
+        auto func = [=](Param<T> A, Param<T> B, Param<int> pivot, int N,
+                        int K) {
             gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides(1),
                            pivot.get(), B.get(), B.strides(1));
         };
         getQueue().enqueue(func, A, B, pivot, N, K);
     } else {
-        auto func = [=] (Param<T> A, Param<T> B, int M, int N, int K) {
+        auto func = [=](Param<T> A, Param<T> B, int M, int N, int K) {
             int sM = A.strides(1);
             int sN = A.strides(2) / sM;
 
-            gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                    M, N, K,
-                    A.get(), A.strides(1),
-                    B.get(), max(sM, sN));
+            gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N', M, N, K, A.get(),
+                           A.strides(1), B.get(), max(sM, sN));
         };
         B.resetDims(dim4(N, K));
         getQueue().enqueue(func, A, B, M, N, K);
@@ -162,42 +155,40 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
     return B;
 }
 
-}
+}  // namespace cpu
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-}
+}  // namespace cpu
 
 #endif  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
     template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
 
 INSTANTIATE_SOLVE(float)
 INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/solve.hpp b/src/backend/cpu/solve.hpp
index 8580707b4a..2469a39451 100644
--- a/src/backend/cpu/solve.hpp
+++ b/src/backend/cpu/solve.hpp
@@ -9,12 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
+namespace cpu {
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options = AF_MAT_NONE);
 
-    template<typename T>
-    Array<T> solveLU(const Array<T> &a, const Array<int> &pivot,
-                     const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
-}
+template<typename T>
+Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options = AF_MAT_NONE);
+}  // namespace cpu
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 8a7e91fe3c..413d684434 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -8,37 +8,35 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <sort.hpp>
-#include <math.hpp>
 #include <copy.hpp>
-#include <algorithm>
-#include <functional>
+#include <iota.hpp>
+#include <kernel/sort.hpp>
+#include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <range.hpp>
-#include <iota.hpp>
-#include <sort_by_key.hpp>
 #include <reorder.hpp>
-#include <kernel/sort.hpp>
+#include <sort.hpp>
+#include <sort_by_key.hpp>
+#include <algorithm>
+#include <functional>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, int dim>
-void sortBatched(Array<T>& val, bool isAscending)
-{
+void sortBatched(Array<T>& val, bool isAscending) {
     af::dim4 inDims = val.dims();
 
     // Sort dimension
     af::dim4 tileDims(1);
     af::dim4 seqDims = inDims;
-    tileDims[dim] = inDims[dim];
-    seqDims[dim] = 1;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
 
     Array<uint> key = iota<uint>(seqDims, tileDims);
 
     Array<uint> resKey = createEmptyArray<uint>(dim4());
-    Array<T   > resVal = createEmptyArray<T>(dim4());
+    Array<T> resVal    = createEmptyArray<T>(dim4());
 
     val.setDataDims(inDims.elements());
     key.setDataDims(inDims.elements());
@@ -49,27 +47,25 @@ void sortBatched(Array<T>& val, bool isAscending)
     sort_by_key<uint, T>(key, val, resKey, resVal, 0, true);
     val.eval();
 
-    val.setDataDims(inDims); // This is correct only for dim0
+    val.setDataDims(inDims);  // This is correct only for dim0
 }
 
 template<typename T>
-void sort0(Array<T>& val, bool isAscending)
-{
+void sort0(Array<T>& val, bool isAscending) {
     int higherDims = val.elements() / val.dims()[0];
     // TODO Make a better heurisitic
-    if(higherDims > 10)
+    if (higherDims > 10)
         sortBatched<T, 0>(val, isAscending);
     else
         getQueue().enqueue(kernel::sort0Iterative<T>, val, isAscending);
 }
 
 template<typename T>
-Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
-{
+Array<T> sort(const Array<T>& in, const unsigned dim, bool isAscending) {
     in.eval();
 
     Array<T> out = copyArray<T>(in);
-    switch(dim) {
+    switch (dim) {
         case 0: sort0<T>(out, isAscending); break;
         case 1: sortBatched<T, 1>(out, isAscending); break;
         case 2: sortBatched<T, 2>(out, isAscending); break;
@@ -77,14 +73,14 @@ Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
-    if(dim != 0) {
+    if (dim != 0) {
         af::dim4 preorderDims = out.dims();
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
-        preorderDims[0] = out.dims()[dim];
-        for(int i = 1; i <= (int)dim; i++) {
+        preorderDims[0]  = out.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
             reorderDims[i - 1] = i;
-            preorderDims[i] = out.dims()[i - 1];
+            preorderDims[i]    = out.dims()[i - 1];
         }
 
         out.setDataDims(preorderDims);
@@ -93,13 +89,14 @@ Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
     return out;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
+#define INSTANTIATE(T)                                                \
+    template Array<T> sort<T>(const Array<T>& in, const unsigned dim, \
+                              bool isAscending);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
-//INSTANTIATE(cfloat)
-//INSTANTIATE(cdouble)
+// INSTANTIATE(cfloat)
+// INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
@@ -109,4 +106,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sort.hpp b/src/backend/cpu/sort.hpp
index cb924873a1..4ec954685c 100644
--- a/src/backend/cpu/sort.hpp
+++ b/src/backend/cpu/sort.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
+namespace cpu {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 0a139a319f..9f7dd825d1 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -8,43 +8,47 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <sort_by_key.hpp>
+#include <copy.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <copy.hpp>
 #include <range.hpp>
 #include <reorder.hpp>
-#include <kernel/sort_by_key.hpp>
+#include <sort_by_key.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename Tk, typename Tv>
-void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-                 const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim, bool isAscending)
-{
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const uint dim, bool isAscending) {
     ikey.eval();
     ival.eval();
 
     okey = copyArray<Tk>(ikey);
     oval = copyArray<Tv>(ival);
 
-    switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0ByKey<Tk, Tv>, okey, oval, isAscending); break;
+    switch (dim) {
+        case 0:
+            getQueue().enqueue(kernel::sort0ByKey<Tk, Tv>, okey, oval,
+                               isAscending);
+            break;
         case 1:
         case 2:
-        case 3: getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv>, okey, oval, dim, isAscending); break;
+        case 3:
+            getQueue().enqueue(kernel::sortByKeyBatched<Tk, Tv>, okey, oval,
+                               dim, isAscending);
+            break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
-    if(dim != 0) {
+    if (dim != 0) {
         af::dim4 preorderDims = okey.dims();
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
-        preorderDims[0] = okey.dims()[dim];
-        for(int i = 1; i <= (int)dim; i++) {
+        preorderDims[0]  = okey.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
             reorderDims[i - 1] = i;
-            preorderDims[i] = okey.dims()[i - 1];
+            preorderDims[i]    = okey.dims()[i - 1];
         }
 
         okey.setDataDims(preorderDims);
@@ -55,26 +59,24 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
     }
 }
 
-#define INSTANTIATE(Tk, Tv)                                             \
-    template void                                                       \
-    sort_by_key<Tk, Tv>(Array<Tk> &okey, Array<Tv> &oval,               \
-                        const Array<Tk> &ikey, const Array<Tv> &ival,   \
-                        const uint dim, bool isAscending);
-
-#define INSTANTIATE1(Tk)       \
-    INSTANTIATE(Tk, float)     \
-    INSTANTIATE(Tk, double)    \
-    INSTANTIATE(Tk, cfloat)    \
-    INSTANTIATE(Tk, cdouble)   \
-    INSTANTIATE(Tk, int)       \
-    INSTANTIATE(Tk, uint)      \
-    INSTANTIATE(Tk, char)      \
-    INSTANTIATE(Tk, uchar)     \
-    INSTANTIATE(Tk, short)     \
-    INSTANTIATE(Tk, ushort)    \
-    INSTANTIATE(Tk, intl)      \
-    INSTANTIATE(Tk, uintl)     \
+#define INSTANTIATE(Tk, Tv)                                        \
+    template void sort_by_key<Tk, Tv>(                             \
+        Array<Tk> & okey, Array<Tv> & oval, const Array<Tk> &ikey, \
+        const Array<Tv> &ival, const uint dim, bool isAscending);
 
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
 
 INSTANTIATE1(float)
 INSTANTIATE1(double)
@@ -87,4 +89,4 @@ INSTANTIATE1(ushort)
 INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sort_by_key.hpp b/src/backend/cpu/sort_by_key.hpp
index c18d14be6f..a8c6fc2078 100644
--- a/src/backend/cpu/sort_by_key.hpp
+++ b/src/backend/cpu/sort_by_key.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename Tk, typename Tv>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending);
+namespace cpu {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index fa43ce589f..c123d65ff1 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -8,23 +8,22 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <sort_index.hpp>
+#include <copy.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <math.hpp>
-#include <algorithm>
-#include <numeric>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <range.hpp>
-#include <copy.hpp>
 #include <reorder.hpp>
-#include <kernel/sort_by_key.hpp>
+#include <sort_index.hpp>
+#include <algorithm>
+#include <numeric>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim, bool isAscending)
-{
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
+                const uint dim, bool isAscending) {
     in.eval();
 
     // okey is values, oval is indices
@@ -32,22 +31,28 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uin
     oval = range<uint>(in.dims(), dim);
     oval.eval();
 
-    switch(dim) {
-        case 0: getQueue().enqueue(kernel::sort0ByKey<T, uint>, okey, oval, isAscending); break;
+    switch (dim) {
+        case 0:
+            getQueue().enqueue(kernel::sort0ByKey<T, uint>, okey, oval,
+                               isAscending);
+            break;
         case 1:
         case 2:
-        case 3: getQueue().enqueue(kernel::sortByKeyBatched<T, uint>, okey, oval, dim, isAscending); break;
+        case 3:
+            getQueue().enqueue(kernel::sortByKeyBatched<T, uint>, okey, oval,
+                               dim, isAscending);
+            break;
         default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
     }
 
-    if(dim != 0) {
+    if (dim != 0) {
         af::dim4 preorderDims = okey.dims();
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
-        preorderDims[0] = okey.dims()[dim];
-        for(int i = 1; i <= (int)dim; i++) {
+        preorderDims[0]  = okey.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
             reorderDims[i - 1] = i;
-            preorderDims[i] = okey.dims()[i - 1];
+            preorderDims[i]    = okey.dims()[i - 1];
         }
 
         okey.setDataDims(preorderDims);
@@ -58,14 +63,15 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uin
     }
 }
 
-#define INSTANTIATE(T)                                                  \
-    template void sort_index<T>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim, bool isAscending);
+#define INSTANTIATE(T)                                              \
+    template void sort_index<T>(Array<T> & val, Array<uint> & idx,  \
+                                const Array<T> &in, const uint dim, \
+                                bool isAscending);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
-//INSTANTIATE(cfloat)
-//INSTANTIATE(cdouble)
+// INSTANTIATE(cfloat)
+// INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
@@ -75,4 +81,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sort_index.hpp b/src/backend/cpu/sort_index.hpp
index 2052752eb0..001f152b95 100644
--- a/src/backend/cpu/sort_index.hpp
+++ b/src/backend/cpu/sort_index.hpp
@@ -9,8 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim, bool isAscending);
+namespace cpu {
+template<typename T>
+void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in,
+                const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index dbea4d8d08..f34e99d318 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse.hpp>
 #include <kernel/sparse.hpp>
+#include <sparse.hpp>
 
 #include <stdexcept>
 #include <string>
 
 #include <arith.hpp>
 #include <cast.hpp>
+#include <common/complex.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <common/err_common.hpp>
-#include <common/complex.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -28,13 +28,12 @@
 
 namespace cpu {
 
-using common::SparseArray;
 using common::createArrayDataSparseArray;
 using common::createEmptySparseArray;
+using common::SparseArray;
 
 template<typename T, af_storage stype>
-SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
-{
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     in.eval();
 
     if (stype == AF_STORAGE_CSR) {
@@ -43,7 +42,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
         auto sparse = createEmptySparseArray<T>(in.dims(), nNZ, stype);
         sparse.eval();
 
-        Array<T  > values = sparse.getValues();
+        Array<T> values   = sparse.getValues();
         Array<int> rowIdx = sparse.getRowIdx();
         Array<int> colIdx = sparse.getColIdx();
 
@@ -58,82 +57,100 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
         auto cnst = createValueArray<int>(dim4(nNZ), in.dims()[0]);
         cnst.eval();
 
-        auto rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
-        auto colIdx = arithOp<int, af_div_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
+        auto rowIdx =
+            arithOp<int, af_mod_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
+        auto colIdx =
+            arithOp<int, af_div_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
 
         Array<T> values = copyArray<T>(in);
         values.modDims(dim4(values.elements()));
         values = lookup<T, int>(values, nonZeroIdx, 0);
 
-        return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, stype);
+        return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+                                             stype);
     } else {
-        AF_ERROR("CPU Backend only supports Dense to CSR or COO", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("CPU Backend only supports Dense to CSR or COO",
+                 AF_ERR_NOT_SUPPORTED);
     }
 }
 
 template<typename T, af_storage stype>
-Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
-{
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
     in.eval();
 
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
     dense.eval();
 
-    Array<T  > values = in.getValues();
+    Array<T> values   = in.getValues();
     Array<int> rowIdx = in.getRowIdx();
     Array<int> colIdx = in.getColIdx();
 
-    if(stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR)
         getQueue().enqueue(kernel::csr2dense<T>, dense, values, rowIdx, colIdx);
     else if (stype == AF_STORAGE_COO)
         getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
     else
-        AF_ERROR("CPU Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("CPU Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
 
     return dense;
 }
 
 template<typename T, af_storage dest, af_storage src>
-SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
-{
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     in.eval();
 
-    auto converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    auto converted =
+        createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
     converted.eval();
 
-    function<void (Param<T>, Param<int>, Param<int>,
-                   CParam<T>, CParam<int>, CParam<int>)> converter;
+    function<void(Param<T>, Param<int>, Param<int>, CParam<T>, CParam<int>,
+                  CParam<int>)>
+        converter;
 
-    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+    if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
         converter = kernel::csr2coo<T>;
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
         converter = kernel::coo2csr<T>;
     } else {
         // Should never come here
-        AF_ERROR("CPU Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("CPU Backend invalid conversion combination",
+                 AF_ERR_NOT_SUPPORTED);
     }
-    getQueue().enqueue(converter, converted.getValues(),
-                       converted.getRowIdx(), converted.getColIdx(),
-                       in.getValues(), in.getRowIdx(), in.getColIdx());
+    getQueue().enqueue(converter, converted.getValues(), converted.getRowIdx(),
+                       converted.getColIdx(), in.getValues(), in.getRowIdx(),
+                       in.getColIdx());
     return converted;
 }
 
-#define INSTANTIATE_TO_STORAGE(T, S)                                                                \
-template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T>&); \
-template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T>&); \
-template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T>&); \
-
-#define INSTANTIATE_SPARSE(T)                                                               \
-template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in); \
-template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in); \
-template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in); \
-template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in); \
-template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in); \
-template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in); \
-                                                                                            \
-INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                   \
-INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                   \
-INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                   \
+#define INSTANTIATE_TO_STORAGE(T, S)                     \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>( \
+        const SparseArray<T> &);                         \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>( \
+        const SparseArray<T> &);                         \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>( \
+        const SparseArray<T> &);
+
+#define INSTANTIATE_SPARSE(T)                                               \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>( \
+        const Array<T> &in);                                                \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>( \
+        const Array<T> &in);                                                \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>( \
+        const Array<T> &in);                                                \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(       \
+        const SparseArray<T> &in);                                          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(       \
+        const SparseArray<T> &in);                                          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(       \
+        const SparseArray<T> &in);                                          \
+                                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
@@ -143,4 +160,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_TO_STORAGE
 #undef INSTANTIATE_SPARSE
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 1b132e63de..9246a529a1 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -12,8 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
-namespace cpu
-{
+namespace cpu {
 template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
 
@@ -21,5 +20,6 @@ template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
 template<typename T, af_storage dest, af_storage src>
-common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
-}
+common::SparseArray<T> sparseConvertStorageToStorage(
+    const common::SparseArray<T> &in);
+}  // namespace cpu
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index 5fd96cef5a..8772680985 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse_arith.hpp>
-#include <common/SparseArray.hpp>
-#include <sparse.hpp>
-#include <optypes.hpp>
-#include <af/dim4.hpp>
 #include <arith.hpp>
+#include <common/SparseArray.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <common/err_common.hpp>
 #include <math.hpp>
+#include <optypes.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <sparse.hpp>
+#include <sparse_arith.hpp>
+#include <af/dim4.hpp>
 
 #include <kernel/sparse_arith.hpp>
 
@@ -27,112 +27,107 @@
 #include <string>
 #include <vector>
 
-namespace cpu
-{
+namespace cpu {
 
 using namespace common;
 
 template<typename T>
-T getInf()
-{
+T getInf() {
     return scalar<T>(std::numeric_limits<T>::infinity());
 }
 
 template<>
-cfloat getInf()
-{
-    return scalar<cfloat, float>(
-            std::numeric_limits<float>::infinity(),
-            std::numeric_limits<float>::infinity()
-            );
+cfloat getInf() {
+    return scalar<cfloat, float>(std::numeric_limits<float>::infinity(),
+                                 std::numeric_limits<float>::infinity());
 }
 
 template<>
-cdouble getInf()
-{
-    return scalar<cdouble, double>(
-            std::numeric_limits<double>::infinity(),
-            std::numeric_limits<double>::infinity()
-            );
+cdouble getInf() {
+    return scalar<cdouble, double>(std::numeric_limits<double>::infinity(),
+                                   std::numeric_limits<double>::infinity());
 }
 
 template<typename T, af_op_t op>
-Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
-{
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
+                  const bool reverse) {
     lhs.eval();
     rhs.eval();
 
-    Array<T> out = createEmptyArray<T>(dim4(0));
+    Array<T> out  = createEmptyArray<T>(dim4(0));
     Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
-    switch(op) {
+    switch (op) {
         case af_add_t: out = copyArray<T>(rhs); break;
-        case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
-        default      : out = copyArray<T>(rhs);
+        case af_sub_t:
+            out = reverse ? copyArray<T>(rhs)
+                          : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
+            break;
+        default: out = copyArray<T>(rhs);
     }
     out.eval();
-    switch(lhs.getStorage()) {
+    switch (lhs.getStorage()) {
         case AF_STORAGE_CSR:
             getQueue().enqueue(kernel::sparseArithOpD<T, op, AF_STORAGE_CSR>,
-                               out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-                               rhs, reverse);
+                               out, lhs.getValues(), lhs.getRowIdx(),
+                               lhs.getColIdx(), rhs, reverse);
             break;
         case AF_STORAGE_COO:
             getQueue().enqueue(kernel::sparseArithOpD<T, op, AF_STORAGE_COO>,
-                               out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-                               rhs, reverse);
+                               out, lhs.getValues(), lhs.getRowIdx(),
+                               lhs.getColIdx(), rhs, reverse);
             break;
         default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
     }
 
     return out;
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
-{
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
+                       const bool reverse) {
     lhs.eval();
     rhs.eval();
 
-    SparseArray<T> out = createArrayDataSparseArray<T>(lhs.dims(), lhs.getValues(),
-                                                       lhs.getRowIdx(), lhs.getColIdx(),
-                                                       lhs.getStorage(), true);
+    SparseArray<T> out = createArrayDataSparseArray<T>(
+        lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+        lhs.getStorage(), true);
     out.eval();
-    switch(out.getStorage()) {
+    switch (out.getStorage()) {
         case AF_STORAGE_CSR:
             getQueue().enqueue(kernel::sparseArithOpS<T, op, AF_STORAGE_CSR>,
-                               out.getValues(), out.getRowIdx(), out.getColIdx(),
-                               rhs, reverse);
+                               out.getValues(), out.getRowIdx(),
+                               out.getColIdx(), rhs, reverse);
             break;
         case AF_STORAGE_COO:
             getQueue().enqueue(kernel::sparseArithOpS<T, op, AF_STORAGE_COO>,
-                               out.getValues(), out.getRowIdx(), out.getColIdx(),
-                               rhs, reverse);
+                               out.getValues(), out.getRowIdx(),
+                               out.getColIdx(), rhs, reverse);
             break;
         default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
     }
 
     return out;
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
-{
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     af::storage sfmt = lhs.getStorage();
 
     lhs.eval();
     rhs.eval();
 
     const dim4 dims = lhs.dims();
-    const uint M = dims[0];
-    const uint N = dims[1];
+    const uint M    = dims[0];
+    const uint N    = dims[1];
 
-    auto rowArr = createEmptyArray<int>(dim4(M+1));
+    auto rowArr = createEmptyArray<int>(dim4(M + 1));
 
-    getQueue().enqueue(kernel::calcOutNNZ, rowArr, M, N,
-                       lhs.getRowIdx(), lhs.getColIdx(),
-                       rhs.getRowIdx(), rhs.getColIdx());
+    getQueue().enqueue(kernel::calcOutNNZ, rowArr, M, N, lhs.getRowIdx(),
+                       lhs.getColIdx(), rhs.getRowIdx(), rhs.getColIdx());
     getQueue().sync();
 
     uint nnz = rowArr.get()[M];
@@ -141,43 +136,42 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
 
     copyArray(out.getRowIdx(), rowArr);
 
-    getQueue().enqueue(kernel::sparseArithOp<T, op>,
-                       out.getValues(), out.getColIdx(),
-                       out.getRowIdx(), M,
-                       lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-                       rhs.getValues(), rhs.getRowIdx(), rhs.getColIdx());
+    getQueue().enqueue(kernel::sparseArithOp<T, op>, out.getValues(),
+                       out.getColIdx(), out.getRowIdx(), M, lhs.getValues(),
+                       lhs.getRowIdx(), lhs.getColIdx(), rhs.getValues(),
+                       rhs.getRowIdx(), rhs.getColIdx());
     return out;
 }
 
-#define INSTANTIATE(T)                                                                              \
-    template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                           const bool reverse);                                     \
-    template Array<T> arithOpD<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template SparseArray<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                 const bool reverse);                               \
-    template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_sub_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_mul_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_div_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);
-
-INSTANTIATE(float  )
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
+#define INSTANTIATE(T)                                                         \
+    template Array<T> arithOpD<T, af_add_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_sub_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_mul_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_div_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_mul_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_div_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_mul_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_div_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
index 364dbb18ea..f37f55a42d 100644
--- a/src/backend/cpu/sparse_arith.hpp
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -11,22 +11,21 @@
 
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
-#include <sparse.hpp>
 #include <optypes.hpp>
+#include <sparse.hpp>
 
-namespace cpu
-{
+namespace cpu {
 // These two functions cannot be overloaded by return type.
 // So have to give them separate names.
 template<typename T, af_op_t op>
 Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                 const bool reverse = false);
+                  const bool reverse = false);
 
 template<typename T, af_op_t op>
-common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                               const bool reverse = false);
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const Array<T> &rhs, const bool reverse = false);
 
 template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 18d8f59590..d7b14c6d7d 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -13,34 +13,33 @@
 #include <mkl_spblas.h>
 #endif
 
-#include <af/dim4.hpp>
-#include <complex.hpp>
 #include <common/complex.hpp>
 #include <common/err_common.hpp>
+#include <complex.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <types.hpp>
+#include <af/dim4.hpp>
 
+#include <cassert>
 #include <stdexcept>
 #include <string>
-#include <cassert>
 
 namespace cpu {
 
 #ifdef USE_MKL
-using sp_cfloat = MKL_Complex8;
+using sp_cfloat  = MKL_Complex8;
 using sp_cdouble = MKL_Complex16;
 #else
-using sp_cfloat = cfloat;
+using sp_cfloat  = cfloat;
 using sp_cdouble = cdouble;
 
 // From mkl_spblas.h
-typedef enum
-{
-    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
-    SPARSE_OPERATION_TRANSPOSE              = 11,
-    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
+typedef enum {
+    SPARSE_OPERATION_NON_TRANSPOSE       = 10,
+    SPARSE_OPERATION_TRANSPOSE           = 11,
+    SPARSE_OPERATION_CONJUGATE_TRANSPOSE = 12,
 } sparse_operation_t;
 #endif
 
@@ -50,47 +49,43 @@ struct blas_base {
 };
 
 template<typename T>
-struct blas_base <T, typename std::enable_if< common::is_complex<T>::value>::type> {
+struct blas_base<T,
+                 typename std::enable_if<common::is_complex<T>::value>::type> {
     using type = typename std::conditional<std::is_same<T, cdouble>::value,
-                                      sp_cdouble, sp_cfloat>
-                                     ::type;
+                                           sp_cdouble, sp_cfloat>::type;
 };
 
 template<typename T>
-using cptr_type    = typename std::conditional< common::is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T*>::type;
+using cptr_type = typename std::conditional<common::is_complex<T>::value,
+                                            const typename blas_base<T>::type *,
+                                            const T *>::type;
 template<typename T>
-using ptr_type     = typename std::conditional< common::is_complex<T>::value,
-                                                typename blas_base<T>::type *,
-                                                T*>::type;
+using ptr_type =
+    typename std::conditional<common::is_complex<T>::value,
+                              typename blas_base<T>::type *, T *>::type;
 template<typename T>
-using scale_type   = typename std::conditional< common::is_complex<T>::value,
-                                                const typename blas_base<T>::type,
-                                                const T>::type;
+using scale_type =
+    typename std::conditional<common::is_complex<T>::value,
+                              const typename blas_base<T>::type, const T>::type;
 
 template<typename To, typename Ti>
-To getScaleValue(Ti val)
-{
+To getScaleValue(Ti val) {
     return (To)(val);
 }
 
 template<typename T, int value>
-scale_type<T> getScale()
-{
+scale_type<T> getScale() {
     static T val(value);
     return getScaleValue<scale_type<T>, T>(val);
 }
 
-sparse_operation_t
-toSparseTranspose(af_mat_prop opt)
-{
+sparse_operation_t toSparseTranspose(af_mat_prop opt) {
     sparse_operation_t out = SPARSE_OPERATION_NON_TRANSPOSE;
-    switch(opt) {
-        case AF_MAT_NONE        : out = SPARSE_OPERATION_NON_TRANSPOSE;         break;
-        case AF_MAT_TRANS       : out = SPARSE_OPERATION_TRANSPOSE;             break;
-        case AF_MAT_CTRANS      : out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE;   break;
-        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    switch (opt) {
+        case AF_MAT_NONE: out = SPARSE_OPERATION_NON_TRANSPOSE; break;
+        case AF_MAT_TRANS: out = SPARSE_OPERATION_TRANSPOSE; break;
+        case AF_MAT_CTRANS: out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE; break;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
 }
@@ -98,8 +93,7 @@ toSparseTranspose(af_mat_prop opt)
 #ifdef USE_MKL
 
 template<>
-const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
-{
+const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
     sp_cfloat ret;
     ret.real = val.real();
     ret.imag = val.imag();
@@ -107,8 +101,7 @@ const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
 }
 
 template<>
-const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
-{
+const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val) {
     sp_cdouble ret;
     ret.real = val.real();
     ret.imag = val.imag();
@@ -124,28 +117,29 @@ const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
 //                 MKL_Complex16 *values);
 
 template<typename T>
-using create_csr_func_def = sparse_status_t (*)
-                           (sparse_matrix_t *,
-                            sparse_index_base_t,
-                            int, int,
-                            int *, int *, int*,
-                            ptr_type<T>);
+using create_csr_func_def = sparse_status_t (*)(sparse_matrix_t *,
+                                                sparse_index_base_t, int, int,
+                                                int *, int *, int *,
+                                                ptr_type<T>);
 
-#define SPARSE_FUNC_DEF( FUNC )                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+#define SPARSE_FUNC_DEF(FUNC) \
+    template<typename T>      \
+    FUNC##_func_def<T> FUNC##_func();
 
-SPARSE_FUNC_DEF( create_csr )
+SPARSE_FUNC_DEF(create_csr)
 
 #undef SPARSE_FUNC_DEF
 
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
-  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &mkl_sparse_##PREFIX##_##FUNC; }
+#define SPARSE_FUNC(FUNC, TYPE, PREFIX)         \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &mkl_sparse_##PREFIX##_##FUNC;   \
+    }
 
-SPARSE_FUNC(create_csr , float   , s)
-SPARSE_FUNC(create_csr , double  , d)
-SPARSE_FUNC(create_csr , cfloat  , c)
-SPARSE_FUNC(create_csr , cdouble , z)
+SPARSE_FUNC(create_csr, float, s)
+SPARSE_FUNC(create_csr, double, d)
+SPARSE_FUNC(create_csr, cfloat, c)
+SPARSE_FUNC(create_csr, cdouble, z)
 
 #undef SPARSE_FUNC
 
@@ -171,50 +165,43 @@ SPARSE_FUNC(create_csr , cdouble , z)
 //                 MKL_INT ldy);
 
 template<typename T>
-using mv_func_def         = sparse_status_t (*)
-                           (sparse_operation_t,
-                            scale_type<T>,
-                            const sparse_matrix_t,
-                            struct matrix_descr,
-                            cptr_type<T>,
-                            scale_type<T>,
-                            ptr_type<T>);
+using mv_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
+                                        const sparse_matrix_t,
+                                        struct matrix_descr, cptr_type<T>,
+                                        scale_type<T>, ptr_type<T>);
 
 template<typename T>
-using mm_func_def         = sparse_status_t (*)
-                           (sparse_operation_t,
-                            scale_type<T>,
-                            const sparse_matrix_t,
-                            struct matrix_descr,
-                            sparse_layout_t,
-                            cptr_type<T>,
-                            int, int,
-                            scale_type<T>,
-                            ptr_type<T>, int);
-
-#define SPARSE_FUNC_DEF( FUNC )                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
-  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &mkl_sparse_##PREFIX##_##FUNC; }
-
-SPARSE_FUNC_DEF( mv )
-SPARSE_FUNC(mv , float   , s)
-SPARSE_FUNC(mv , double  , d)
-SPARSE_FUNC(mv , cfloat  , c)
-SPARSE_FUNC(mv , cdouble , z)
-
-SPARSE_FUNC_DEF( mm )
-SPARSE_FUNC(mm , float   , s)
-SPARSE_FUNC(mm , double  , d)
-SPARSE_FUNC(mm , cfloat  , c)
-SPARSE_FUNC(mm , cdouble , z)
+using mm_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
+                                        const sparse_matrix_t,
+                                        struct matrix_descr, sparse_layout_t,
+                                        cptr_type<T>, int, int, scale_type<T>,
+                                        ptr_type<T>, int);
+
+#define SPARSE_FUNC_DEF(FUNC) \
+    template<typename T>      \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define SPARSE_FUNC(FUNC, TYPE, PREFIX)         \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &mkl_sparse_##PREFIX##_##FUNC;   \
+    }
+
+SPARSE_FUNC_DEF(mv)
+SPARSE_FUNC(mv, float, s)
+SPARSE_FUNC(mv, double, d)
+SPARSE_FUNC(mv, cfloat, c)
+SPARSE_FUNC(mv, cdouble, z)
+
+SPARSE_FUNC_DEF(mm)
+SPARSE_FUNC(mm, float, s)
+SPARSE_FUNC(mm, double, d)
+SPARSE_FUNC(mm, cfloat, c)
+SPARSE_FUNC(mm, cdouble, z)
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                af_mat_prop optLhs, af_mat_prop optRhs) {
     // MKL: CSRMM Does not support optRhs
     UNUSED(optRhs);
 
@@ -225,28 +212,24 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     sparse_operation_t lOpts = toSparseTranspose(optLhs);
 
     int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
-    //int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    // int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
-    //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
+    // Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
     static const int rColDim = 1;
 
-    const dim4& lDims = lhs.dims();
-    const dim4& rDims = rhs.dims();
+    const dim4 &lDims = lhs.dims();
+    const dim4 &rDims = rhs.dims();
 
     int M = lDims[lRowDim];
     int N = rDims[rColDim];
-    //int K = lDims[lColDim];
+    // int K = lDims[lColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
 
-    auto func = [=] (Param<T> output,
-                     CParam<T> values,
-                     CParam<int> rowIdx,
-                     CParam<int> colIdx,
-                     const dim_t sdim0,
-                     const dim_t sdim1,
-                     CParam<T> right) {
+    auto func = [=](Param<T> output, CParam<T> values, CParam<int> rowIdx,
+                    CParam<int> colIdx, const dim_t sdim0, const dim_t sdim1,
+                    CParam<T> right) {
         auto alpha = getScale<T, 1>();
         auto beta  = getScale<T, 0>();
 
@@ -258,9 +241,8 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
         T *vptr = const_cast<T *>(values.get());
 
         sparse_matrix_t csrLhs;
-        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, sdim0, sdim1,
-                             pB, pE,
-                             const_cast<int *>(colIdx.get()),
+        create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, sdim0, sdim1, pB,
+                             pE, const_cast<int *>(colIdx.get()),
                              reinterpret_cast<ptr_type<T>>(vptr));
 
         struct matrix_descr descrLhs;
@@ -268,78 +250,66 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
         mkl_sparse_optimize(csrLhs);
 
-        if(rDims[rColDim] == 1) {
+        if (rDims[rColDim] == 1) {
             mkl_sparse_set_mv_hint(csrLhs, lOpts, descrLhs, 1);
-            mv_func<T>()(
-                lOpts, alpha,
-                csrLhs, descrLhs,
-                reinterpret_cast<cptr_type<T>>(right.get()),
-                beta,
-                reinterpret_cast<ptr_type<T>>(output.get()));
+            mv_func<T>()(lOpts, alpha, csrLhs, descrLhs,
+                         reinterpret_cast<cptr_type<T>>(right.get()), beta,
+                         reinterpret_cast<ptr_type<T>>(output.get()));
         } else {
-            mkl_sparse_set_mm_hint(csrLhs, lOpts, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR, N, 1);
+            mkl_sparse_set_mm_hint(csrLhs, lOpts, descrLhs,
+                                   SPARSE_LAYOUT_COLUMN_MAJOR, N, 1);
             mm_func<T>()(
-                lOpts, alpha,
-                csrLhs, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR,
-                reinterpret_cast<cptr_type<T>>(right.get()),
-                N, ldb, beta,
+                lOpts, alpha, csrLhs, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR,
+                reinterpret_cast<cptr_type<T>>(right.get()), N, ldb, beta,
                 reinterpret_cast<ptr_type<T>>(output.get()), ldc);
         }
         mkl_sparse_destroy(csrLhs);
     };
 
-
-    const Array<T  > values = lhs.getValues();
+    const Array<T> values   = lhs.getValues();
     const Array<int> rowIdx = lhs.getRowIdx();
     const Array<int> colIdx = lhs.getColIdx();
-    af::dim4 ldims = lhs.dims();
+    af::dim4 ldims          = lhs.dims();
 
-    getQueue().enqueue(func, out, values, rowIdx, colIdx, ldims[0], ldims[1], rhs);
+    getQueue().enqueue(func, out, values, rowIdx, colIdx, ldims[0], ldims[1],
+                       rhs);
 
     return out;
 }
 
-#else // #if USE_MKL
+#else  // #if USE_MKL
 
 template<typename T>
-T getConjugate(const T &in)
-{
+T getConjugate(const T &in) {
     // For non-complex types return same
     return in;
 }
 
 template<>
-cfloat getConjugate(const cfloat &in)
-{
+cfloat getConjugate(const cfloat &in) {
     return std::conj(in);
 }
 
 template<>
-cdouble getConjugate(const cdouble &in)
-{
+cdouble getConjugate(const cdouble &in) {
     return std::conj(in);
 }
 
 template<typename T, bool conjugate>
-void mv(Param<T> output,
-        CParam<T> values,
-        CParam<int> rowIdx,
-        CParam<int> colIdx,
-        CParam<T> right,
-        int M)
-{
+void mv(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+        CParam<int> colIdx, CParam<T> right, int M) {
     UNUSED(M);
-    const T   *valPtr = values.get();
+    const T *valPtr   = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
-    const T   *rightPtr = right.get();
+    const T *rightPtr = right.get();
 
-    T* outPtr = output.get();
+    T *outPtr = output.get();
 
-    for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
+    for (int i = 0; i < rowIdx.dims(0) - 1; ++i) {
         outPtr[i] = scalar<T>(0);
-        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-            //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
+        for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+            // If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
             if (conjugate) {
                 outPtr[i] += getConjugate(valPtr[j]) * rightPtr[colPtr[j]];
             } else {
@@ -350,26 +320,19 @@ void mv(Param<T> output,
 }
 
 template<typename T, bool conjugate>
-void mtv(Param<T> output,
-         CParam<T> values,
-         CParam<int> rowIdx,
-         CParam<int> colIdx,
-         CParam<T> right,
-         int M)
-{
-    const T *valPtr = values.get();
+void mtv(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+         CParam<int> colIdx, CParam<T> right, int M) {
+    const T *valPtr   = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
     const T *rightPtr = right.get();
-    T* outPtr = output.get();
+    T *outPtr         = output.get();
 
-    for (int i = 0; i < M; ++i) {
-        outPtr[i] = scalar<T>(0);
-    }
+    for (int i = 0; i < M; ++i) { outPtr[i] = scalar<T>(0); }
 
-    for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
-        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-            //If stride[0] of right is not 1 then rightPtr[i*stride]
+    for (int i = 0; i < rowIdx.dims(0) - 1; ++i) {
+        for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+            // If stride[0] of right is not 1 then rightPtr[i*stride]
             if (conjugate) {
                 outPtr[colPtr[j]] += getConjugate(valPtr[j]) * rightPtr[i];
             } else {
@@ -380,26 +343,21 @@ void mtv(Param<T> output,
 }
 
 template<typename T, bool conjugate>
-void mm(Param<T> output,
-        CParam<T> values,
-        CParam<int> rowIdx,
-        CParam<int> colIdx,
-        CParam<T> right,
-        int M, int N,
-        int ldb, int ldc)
-{
+void mm(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+        CParam<int> colIdx, CParam<T> right, int M, int N, int ldb, int ldc) {
     UNUSED(M);
-    const T *valPtr = values.get();
+    const T *valPtr   = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
     const T *rightPtr = right.get();
-    T *outPtr = output.get();
+    T *outPtr         = output.get();
 
     for (int o = 0; o < N; ++o) {
-        for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
+        for (int i = 0; i < rowIdx.dims(0) - 1; ++i) {
             outPtr[i] = scalar<T>(0);
-            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-                //If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
+            for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+                // If stride[0] of right is not 1 then
+                // rightPtr[colPtr[j]*stride]
                 if (conjugate) {
                     outPtr[i] += getConjugate(valPtr[j]) * rightPtr[colPtr[j]];
                 } else {
@@ -413,28 +371,20 @@ void mm(Param<T> output,
 }
 
 template<typename T, bool conjugate>
-void mtm(Param<T> output,
-         CParam<T> values,
-         CParam<int> rowIdx,
-         CParam<int> colIdx,
-         CParam<T> right,
-         int M, int N,
-         int ldb, int ldc)
-{
-    const T *valPtr = values.get();
+void mtm(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+         CParam<int> colIdx, CParam<T> right, int M, int N, int ldb, int ldc) {
+    const T *valPtr   = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
     const T *rightPtr = right.get();
-    T *outPtr = output.get();
+    T *outPtr         = output.get();
 
     for (int o = 0; o < N; ++o) {
-        for (int i = 0; i < M; ++i) {
-            outPtr[i] = scalar<T>(0);
-        }
+        for (int i = 0; i < M; ++i) { outPtr[i] = scalar<T>(0); }
 
-        for (int i = 0; i < rowIdx.dims(0)-1; ++i) {
-            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-                //If stride[0] of right is not 1 then rightPtr[i*stride]
+        for (int i = 0; i < rowIdx.dims(0) - 1; ++i) {
+            for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+                // If stride[0] of right is not 1 then rightPtr[i*stride]
                 if (conjugate) {
                     outPtr[colPtr[j]] += getConjugate(valPtr[j]) * rightPtr[i];
                 } else {
@@ -449,8 +399,7 @@ void mtm(Param<T> output,
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                af_mat_prop optLhs, af_mat_prop optRhs) {
     UNUSED(optRhs);
     lhs.eval();
     rhs.eval();
@@ -462,24 +411,20 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     static const int rColDim = 1;
 
-    const dim4& lDims = lhs.dims();
-    const dim4& rDims = rhs.dims();
-
-    int M = lDims[lRowDim];
-    int N = rDims[rColDim];
+    const dim4 &lDims = lhs.dims();
+    const dim4 &rDims = rhs.dims();
+    int M             = lDims[lRowDim];
+    int N             = rDims[rColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
 
-    auto func = [=] (Param<T> output,
-                     CParam<T> values,
-                     CParam<int> rowIdx,
-                     CParam<int> colIdx,
-                     CParam<T> right) {
+    auto func = [=](Param<T> output, CParam<T> values, CParam<int> rowIdx,
+                    CParam<int> colIdx, CParam<T> right) {
         int ldb = right.strides(1);
         int ldc = output.strides(1);
 
-        if(rDims[rColDim] == 1) {
+        if (rDims[rColDim] == 1) {
             if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
                 mv<T, false>(output, values, rowIdx, colIdx, right, M);
             } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
@@ -489,16 +434,19 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
             }
         } else {
             if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
-                mm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+                mm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb,
+                             ldc);
             } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
-                mtm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+                mtm<T, false>(output, values, rowIdx, colIdx, right, M, N, ldb,
+                              ldc);
             } else if (lOpts == SPARSE_OPERATION_CONJUGATE_TRANSPOSE) {
-                mtm<T, true>(output, values, rowIdx, colIdx, right, M, N, ldb, ldc);
+                mtm<T, true>(output, values, rowIdx, colIdx, right, M, N, ldb,
+                             ldc);
             }
         }
     };
 
-    const Array<T  > values = lhs.getValues();
+    const Array<T> values   = lhs.getValues();
     const Array<int> rowIdx = lhs.getRowIdx();
     const Array<int> colIdx = lhs.getColIdx();
 
@@ -507,16 +455,16 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     return out;
 }
 
-#endif // #if USE_MKL
-
-#define INSTANTIATE_SPARSE(T)                                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
-                                af_mat_prop optLhs, af_mat_prop optRhs);                \
+#endif  // #if USE_MKL
 
+#define INSTANTIATE_SPARSE(T)                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
+                                const Array<T> rhs, af_mat_prop optLhs, \
+                                af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
 INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/sparse_blas.hpp b/src/backend/cpu/sparse_blas.hpp
index d73aacbf12..8d8d3d531c 100644
--- a/src/backend/cpu/sparse_blas.hpp
+++ b/src/backend/cpu/sparse_blas.hpp
@@ -11,12 +11,10 @@
 #include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
-
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 46e01c7dbf..7eb1034d49 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -8,20 +8,19 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <surface.hpp>
-#include <err_cpu.hpp>
 #include <common/graphics_common.hpp>
+#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <surface.hpp>
 
 using af::dim4;
 
 namespace cpu {
 
 template<typename T>
-void copy_surface(const Array<T> &P, fg_surface surface)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_surface(const Array<T> &P, fg_surface surface) {
+    ForgeModule &_ = graphics::forgePlugin();
     P.eval();
     getQueue().sync();
 
@@ -37,8 +36,8 @@ void copy_surface(const Array<T> &P, fg_surface surface)
     CheckGL("In CopyArrayToVBO");
 }
 
-#define INSTANTIATE(T)  \
-template void copy_surface<T>(const Array<T> &, fg_surface);
+#define INSTANTIATE(T) \
+    template void copy_surface<T>(const Array<T> &, fg_surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -48,4 +47,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index ad8d780301..ccdfbcd040 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -7,43 +7,43 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
-#include <cmath>
+#include <kernel/susan.hpp>
 #include <math.hpp>
-#include <memory>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <kernel/susan.hpp>
+#include <af/features.h>
+#include <cmath>
+#include <memory>
 
 using af::features;
 using std::shared_ptr;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
-               const Array<T> &in,
-               const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge)
-{
+               const Array<T> &in, const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge) {
     in.eval();
 
-    dim4 idims = in.dims();
+    dim4 idims                = in.dims();
     const unsigned corner_lim = in.elements() * feature_ratio;
 
-    auto x_corners    = createEmptyArray<float>(dim4(corner_lim));
-    auto y_corners    = createEmptyArray<float>(dim4(corner_lim));
-    auto resp_corners = createEmptyArray<float>(dim4(corner_lim));
-    auto response     = createEmptyArray<T>(dim4(in.elements()));
-    auto corners_found= std::shared_ptr<unsigned>(memAlloc<unsigned>(1).release(), memFree<unsigned>);
+    auto x_corners     = createEmptyArray<float>(dim4(corner_lim));
+    auto y_corners     = createEmptyArray<float>(dim4(corner_lim));
+    auto resp_corners  = createEmptyArray<float>(dim4(corner_lim));
+    auto response      = createEmptyArray<T>(dim4(in.elements()));
+    auto corners_found = std::shared_ptr<unsigned>(
+        memAlloc<unsigned>(1).release(), memFree<unsigned>);
     corners_found.get()[0] = 0;
 
-    getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0], idims[1],
-                       radius, diff_thr, geom_thr, edge);
-    getQueue().enqueue(kernel::non_maximal<T>, x_corners, y_corners, resp_corners, corners_found,
-                       idims[0], idims[1], response, edge, corner_lim);
+    getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0],
+                       idims[1], radius, diff_thr, geom_thr, edge);
+    getQueue().enqueue(kernel::non_maximal<T>, x_corners, y_corners,
+                       resp_corners, corners_found, idims[0], idims[1],
+                       response, edge, corner_lim);
     getQueue().sync();
 
     const unsigned corners_out = min((corners_found.get())[0], corner_lim);
@@ -53,8 +53,8 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
         resp_out = createEmptyArray<float>(dim4());
         return 0;
     } else {
-        x_out = x_corners;
-        y_out = y_corners;
+        x_out    = x_corners;
+        y_out    = y_corners;
         resp_out = resp_corners;
         x_out.resetDims(dim4(corners_out));
         y_out.resetDims(dim4(corners_out));
@@ -63,18 +63,19 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     }
 }
 
-#define INSTANTIATE(T) \
-template unsigned susan<T>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,   \
-                           const Array<T> &in, const unsigned radius, const float diff_thr,     \
-                           const float geom_thr, const float feature_ratio, const unsigned edge);
+#define INSTANTIATE(T)                                                        \
+    template unsigned susan<T>(                                               \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned radius, const float diff_thr,      \
+        const float geom_thr, const float feature_ratio, const unsigned edge);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/susan.hpp b/src/backend/cpu/susan.hpp
index 2e57711be7..29504b8f2b 100644
--- a/src/backend/cpu/susan.hpp
+++ b/src/backend/cpu/susan.hpp
@@ -7,18 +7,18 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-               const Array<T> &in,
-               const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge);
+unsigned susan(Array<float> &x_out, Array<float> &y_out,
+               Array<float> &score_out, const Array<T> &in,
+               const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge);
 
 }
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index f75e16b3c8..d484ac42a5 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -8,128 +8,118 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <svd.hpp>
 #include <common/err_common.hpp>
 #include <err_cpu.hpp>
+#include <svd.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <lapack_helper.hpp>
 #include <copy.hpp>
+#include <lapack_helper.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
-#define SVD_FUNC_DEF( FUNC )                                            \
-    template<typename T,typename Tr> svd_func_def<T, Tr> svd_func();
+#define SVD_FUNC_DEF(FUNC)            \
+    template<typename T, typename Tr> \
+    svd_func_def<T, Tr> svd_func();
 
-#define SVD_FUNC( FUNC, T, Tr, PREFIX )                     \
-    template<> svd_func_def<T, Tr>     svd_func<T, Tr>()    \
-    { return & LAPACK_NAME(PREFIX##FUNC); }
+#define SVD_FUNC(FUNC, T, Tr, PREFIX)       \
+    template<>                              \
+    svd_func_def<T, Tr> svd_func<T, Tr>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);  \
+    }
 
 #if defined(USE_MKL) || defined(__APPLE__)
 
 template<typename T, typename Tr>
-using svd_func_def = int (*)(ORDER_TYPE,
-                             char jobz,
-                             int m, int n,
-                             T* in, int ldin,
-                             Tr* s,
-                             T* u, int ldu,
-                             T* vt, int ldvt);
-
-SVD_FUNC_DEF( gesdd )
-SVD_FUNC(gesdd, float  , float , s)
-SVD_FUNC(gesdd, double , double, d)
-SVD_FUNC(gesdd, cfloat , float , c)
+using svd_func_def = int (*)(ORDER_TYPE, char jobz, int m, int n, T *in,
+                             int ldin, Tr *s, T *u, int ldu, T *vt, int ldvt);
+
+SVD_FUNC_DEF(gesdd)
+SVD_FUNC(gesdd, float, float, s)
+SVD_FUNC(gesdd, double, double, d)
+SVD_FUNC(gesdd, cfloat, float, c)
 SVD_FUNC(gesdd, cdouble, double, z)
 
-#else   // Atlas causes memory freeing issues with using gesdd
+#else  // Atlas causes memory freeing issues with using gesdd
 
 template<typename T, typename Tr>
-using svd_func_def = int (*)(ORDER_TYPE,
-                             char jobu, char jobvt,
-                             int m, int n,
-                             T* in, int ldin,
-                             Tr* s,
-                             T* u, int ldu,
-                             T* vt, int ldvt,
-                             Tr *superb);
-
-SVD_FUNC_DEF( gesvd )
-SVD_FUNC(gesvd, float  , float , s)
-SVD_FUNC(gesvd, double , double, d)
-SVD_FUNC(gesvd, cfloat , float , c)
+using svd_func_def = int (*)(ORDER_TYPE, char jobu, char jobvt, int m, int n,
+                             T *in, int ldin, Tr *s, T *u, int ldu, T *vt,
+                             int ldvt, Tr *superb);
+
+SVD_FUNC_DEF(gesvd)
+SVD_FUNC(gesvd, float, float, s)
+SVD_FUNC(gesvd, double, double, d)
+SVD_FUNC(gesvd, cfloat, float, c)
 SVD_FUNC(gesvd, cdouble, double, z)
 
 #endif
 
-template <typename T, typename Tr>
-void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-{
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     s.eval();
     u.eval();
     vt.eval();
     in.eval();
 
-    auto func = [=] (Param<Tr> s, Param<T> u, Param<T> vt, Param<T> in) {
+    auto func = [=](Param<Tr> s, Param<T> u, Param<T> vt, Param<T> in) {
         dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
+        int M      = iDims[0];
+        int N      = iDims[1];
 
 #if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(), in.strides(1),
-                s.get(), u.get(), u.strides(1), vt.get(), vt.strides(1));
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, in.get(),
+                          in.strides(1), s.get(), u.get(), u.strides(1),
+                          vt.get(), vt.strides(1));
 #else
         std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(), in.strides(1),
-                s.get(), u.get(), u.strides(1), vt.get(), vt.strides(1), &superb[0]);
+        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, in.get(),
+                          in.strides(1), s.get(), u.get(), u.strides(1),
+                          vt.get(), vt.strides(1), &superb[0]);
 #endif
     };
     getQueue().enqueue(func, s, u, vt, in);
 }
 
-template <typename T, typename Tr>
-void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-{
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
     Array<T> in_copy = copyArray<T>(in);
     svdInPlace(s, u, vt, in_copy);
 }
 
-}
+}  // namespace cpu
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
-template <typename T, typename Tr>
-void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-{
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-template <typename T, typename Tr>
-void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-{
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
 }
 
-}
+}  // namespace cpu
 
 #endif  // WITH_LINEAR_ALGEBRA
 
-namespace cpu
-{
+namespace cpu {
 
-#define INSTANTIATE_SVD(T, Tr)                                          \
-    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, const Array<T> &in); \
-    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, Array<T> &in);
+#define INSTANTIATE_SVD(T, Tr)                                           \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
 
-INSTANTIATE_SVD(float  , float )
-INSTANTIATE_SVD(double , double)
-INSTANTIATE_SVD(cfloat , float )
+INSTANTIATE_SVD(float, float)
+INSTANTIATE_SVD(double, double)
+INSTANTIATE_SVD(cfloat, float)
 INSTANTIATE_SVD(cdouble, double)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/svd.hpp b/src/backend/cpu/svd.hpp
index 2d409aec31..2019ea57c5 100644
--- a/src/backend/cpu/svd.hpp
+++ b/src/backend/cpu/svd.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
+namespace cpu {
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 
-    template<typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
-}
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+}  // namespace cpu
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 0fe52c6398..2c21396fd5 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -8,23 +8,21 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <tile.hpp>
-#include <platform.hpp>
 #include <kernel/tile.hpp>
+#include <platform.hpp>
+#include <tile.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
-{
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
     in.eval();
 
     const af::dim4 iDims = in.dims();
-    af::dim4 oDims = iDims;
+    af::dim4 oDims       = iDims;
     oDims *= tileDims;
 
-    if(iDims.elements() == 0 || oDims.elements() == 0) {
+    if (iDims.elements() == 0 || oDims.elements() == 0) {
         throw std::runtime_error("Elements are 0");
     }
 
@@ -35,8 +33,8 @@ Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);  \
+#define INSTANTIATE(T) \
+    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -51,4 +49,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/tile.hpp b/src/backend/cpu/tile.hpp
index 0b4fbd8e9e..4e71919789 100644
--- a/src/backend/cpu/tile.hpp
+++ b/src/backend/cpu/tile.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
+namespace cpu {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
 }
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index 24446bb00e..4a5a5b56a4 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -22,58 +22,56 @@ using std::min;
 using std::partial_sort_copy;
 using std::vector;
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
 void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
-          const int k, const int dim, const af::topkFunction order)
-{
+          const int k, const int dim, const af::topkFunction order) {
     // The out_dims is of size k along the dimension of the topk operation
     // and the same as the input dimension otherwise.
     dim4 out_dims(1);
     int ndims = in.dims().ndims();
-    for(int i = 0; i < ndims; i++) {
-      if (i == dim) {
-        out_dims[i] = min(k, (int)in.dims()[i]);
-      } else {
-        out_dims[i] = in.dims()[i];
-      }
+    for (int i = 0; i < ndims; i++) {
+        if (i == dim) {
+            out_dims[i] = min(k, (int)in.dims()[i]);
+        } else {
+            out_dims[i] = in.dims()[i];
+        }
     }
 
     auto values  = createEmptyArray<T>(out_dims);
     auto indices = createEmptyArray<unsigned>(out_dims);
 
     auto func = [=](Param<T> values, Param<unsigned> indices, CParam<T> in) {
-        const T* ptr = in.get();
+        const T* ptr   = in.get();
         unsigned* iptr = indices.get();
-        T* vptr = values.get();
+        T* vptr        = values.get();
 
         // Create a linear index
         vector<uint> idx(in.dims().elements());
         iota(begin(idx), end(idx), 0);
 
         int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
-        for(int i = 0; i < iter; i++) {
+        for (int i = 0; i < iter; i++) {
             auto idx_itr = begin(idx) + i * in.strides()[1];
-            auto kiptr = iptr + k * i;
+            auto kiptr   = iptr + k * i;
 
-            if(order == AF_TOPK_MIN) {
+            if (order == AF_TOPK_MIN) {
                 // Sort the top k values in each column
-                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
-                                  kiptr , kiptr + k,
-                                  [ptr](const uint lhs, const uint rhs) -> bool {
-                                      return ptr[lhs] < ptr[rhs];
-                                  });
+                partial_sort_copy(
+                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                    [ptr](const uint lhs, const uint rhs) -> bool {
+                        return ptr[lhs] < ptr[rhs];
+                    });
             } else {
-                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
-                                  kiptr , kiptr + k,
-                                  [ptr](const uint lhs, const uint rhs) -> bool {
-                                    return ptr[lhs] >= ptr[rhs];
-                                  });
+                partial_sort_copy(
+                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                    [ptr](const uint lhs, const uint rhs) -> bool {
+                        return ptr[lhs] >= ptr[rhs];
+                    });
             }
 
             auto kvptr = vptr + k * i;
-            for(int j = 0; j < k; j++) {
+            for (int j = 0; j < k; j++) {
                 // Update the value arrays with the original values
                 kvptr[j] = ptr[kiptr[j]];
                 // Convert linear indices back to column indices
@@ -88,13 +86,14 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
     idxs = indices;
 }
 
-#define INSTANTIATE(T)\
-template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, const int, const int, const af::topkFunction);
+#define INSTANTIATE(T)                                                  \
+    template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, \
+                          const int, const int, const af::topkFunction);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
+INSTANTIATE(int)
+INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/topk.hpp b/src/backend/cpu/topk.hpp
index b4b5764972..75cb5e7cfe 100644
--- a/src/backend/cpu/topk.hpp
+++ b/src/backend/cpu/topk.hpp
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-namespace cpu
-{
+namespace cpu {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 1e43b325b2..9dc5a5cae3 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -8,51 +8,49 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <transform.hpp>
+#include <kernel/transform.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <kernel/transform.hpp>
+#include <transform.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                    const af_interp_type method, const bool inverse, const bool perspective)
-{
+Array<T> transform(const Array<T> &in, const Array<float> &tf,
+                   const af::dim4 &odims, const af_interp_type method,
+                   const bool inverse, const bool perspective) {
     in.eval();
     tf.eval();
 
     Array<T> out = createEmptyArray<T>(odims);
 
-    switch(method) {
-    case AF_INTERP_NEAREST:
-    case AF_INTERP_LOWER:
-        getQueue().enqueue(kernel::transform<T, 1>, out, in, tf,
-                           inverse, perspective, method);
-        break;
-    case AF_INTERP_BILINEAR:
-    case AF_INTERP_BILINEAR_COSINE:
-        getQueue().enqueue(kernel::transform<T, 2>, out, in, tf,
-                           inverse, perspective, method);
-        break;
-    case AF_INTERP_BICUBIC:
-    case AF_INTERP_BICUBIC_SPLINE:
-        getQueue().enqueue(kernel::transform<T, 3>, out, in, tf,
-                           inverse, perspective, method);
-        break;
-    default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            getQueue().enqueue(kernel::transform<T, 1>, out, in, tf, inverse,
+                               perspective, method);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            getQueue().enqueue(kernel::transform<T, 2>, out, in, tf, inverse,
+                               perspective, method);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            getQueue().enqueue(kernel::transform<T, 3>, out, in, tf, inverse,
+                               perspective, method);
+            break;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
 
     return out;
 }
 
-
-#define INSTANTIATE(T)                                                              \
-template Array<T> transform(const Array<T> &in, const Array<float> &tf,             \
-                            const af::dim4 &odims, const af_interp_type method,     \
-                            const bool inverse, const bool perspective);
-
+#define INSTANTIATE(T)                                                      \
+    template Array<T> transform(const Array<T> &in, const Array<float> &tf, \
+                                const af::dim4 &odims,                      \
+                                const af_interp_type method,                \
+                                const bool inverse, const bool perspective);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -67,4 +65,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/transform.hpp b/src/backend/cpu/transform.hpp
index bfe4ef71a0..0d33e97ea2 100644
--- a/src/backend/cpu/transform.hpp
+++ b/src/backend/cpu/transform.hpp
@@ -9,9 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse, const bool perspective);
+namespace cpu {
+template<typename T>
+Array<T> transform(const Array<T> &in, const Array<float> &tf,
+                   const af::dim4 &odims, const af_interp_type method,
+                   const bool inverse, const bool perspective);
 }
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 2cfe936624..f55ed82a15 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -7,28 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <transpose.hpp>
-#include <platform.hpp>
 #include <kernel/transpose.hpp>
-#include <utility>
+#include <platform.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
 #include <cassert>
+#include <utility>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> transpose(const Array<T> &in, const bool conjugate)
-{
+Array<T> transpose(const Array<T> &in, const bool conjugate) {
     in.eval();
 
     const dim4 inDims  = in.dims();
-    const dim4 outDims = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
+    const dim4 outDims = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
     // create an array with first two dimensions swapped
-    Array<T> out  = createEmptyArray<T>(outDims);
+    Array<T> out = createEmptyArray<T>(outDims);
 
     getQueue().enqueue(kernel::transpose<T>, out, in, conjugate);
 
@@ -36,27 +34,26 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
 }
 
 template<typename T>
-void transpose_inplace(Array<T> &in, const bool conjugate)
-{
+void transpose_inplace(Array<T> &in, const bool conjugate) {
     in.eval();
     getQueue().enqueue(kernel::transpose_inplace<T>, in, conjugate);
 }
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> transpose(const Array<T> &in, const bool conjugate);  \
+#define INSTANTIATE(T)                                                     \
+    template Array<T> transpose(const Array<T> &in, const bool conjugate); \
     template void transpose_inplace(Array<T> &in, const bool conjugate);
 
-INSTANTIATE(float  )
-INSTANTIATE(cfloat )
-INSTANTIATE(double )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
 INSTANTIATE(cdouble)
-INSTANTIATE(char   )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/transpose.hpp b/src/backend/cpu/transpose.hpp
index 2a8f72d96e..27337bd0fb 100644
--- a/src/backend/cpu/transpose.hpp
+++ b/src/backend/cpu/transpose.hpp
@@ -9,13 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T>  transpose(const Array<T> &in, const bool conjugate);
+Array<T> transpose(const Array<T> &in, const bool conjugate);
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 8a392ea5c0..db2baaf559 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -7,41 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <triangle.hpp>
+#include <kernel/triangle.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <kernel/triangle.hpp>
+#include <triangle.hpp>
+#include <af/dim4.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in)
-{
+void triangle(Array<T> &out, const Array<T> &in) {
     in.eval();
     getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in)
-{
+Array<T> triangle(const Array<T> &in) {
     in.eval();
     Array<T> out = createEmptyArray<T>(in.dims());
     triangle<T, is_upper, is_unit_diag>(out, in);
     return out;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template void triangle<T, true ,  true>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, false,  true>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, true , false>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, false, false>(Array<T> &out, const Array<T> &in); \
-    template Array<T> triangle<T, true ,  true>(const Array<T> &in);    \
-    template Array<T> triangle<T, false,  true>(const Array<T> &in);    \
-    template Array<T> triangle<T, true , false>(const Array<T> &in);    \
-    template Array<T> triangle<T, false, false>(const Array<T> &in);    \
+#define INSTANTIATE(T)                                                         \
+    template void triangle<T, true, true>(Array<T> & out, const Array<T> &in); \
+    template void triangle<T, false, true>(Array<T> & out,                     \
+                                           const Array<T> &in);                \
+    template void triangle<T, true, false>(Array<T> & out,                     \
+                                           const Array<T> &in);                \
+    template void triangle<T, false, false>(Array<T> & out,                    \
+                                            const Array<T> &in);               \
+    template Array<T> triangle<T, true, true>(const Array<T> &in);             \
+    template Array<T> triangle<T, false, true>(const Array<T> &in);            \
+    template Array<T> triangle<T, true, false>(const Array<T> &in);            \
+    template Array<T> triangle<T, false, false>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -56,4 +56,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/triangle.hpp b/src/backend/cpu/triangle.hpp
index 531de0ff79..d7bf864d12 100644
--- a/src/backend/cpu/triangle.hpp
+++ b/src/backend/cpu/triangle.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T, bool is_upper, bool is_unit_diag>
-    void triangle(Array<T> &out, const Array<T> &in);
+namespace cpu {
+template<typename T, bool is_upper, bool is_unit_diag>
+void triangle(Array<T> &out, const Array<T> &in);
 
-    template<typename T, bool is_upper, bool is_unit_diag>
-    Array<T> triangle(const Array<T> &in);
-}
+template<typename T, bool is_upper, bool is_unit_diag>
+Array<T> triangle(const Array<T> &in);
+}  // namespace cpu
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 073f2f258d..5025cbb543 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -10,8 +10,7 @@
 #pragma once
 #include <complex>
 
-namespace cpu
-{
+namespace cpu {
 using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
 using intl    = long long;
@@ -19,4 +18,4 @@ using uint    = unsigned int;
 using uchar   = unsigned char;
 using uintl   = unsigned long long;
 using ushort  = unsigned short;
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index f81ad78442..2584cd30e0 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -8,32 +8,25 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <err_cpu.hpp>
 #include <jit/UnaryNode.hpp>
+#include <optypes.hpp>
 #include <cmath>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-T sigmoid(T in)
-{
+T sigmoid(T in) {
     return (1.0) / (1 + std::exp(-in));
 }
 
-#define UNARY_OP_FN(op, fn)                         \
-    template<typename T>                            \
-    struct UnOp<T, T, af_##op##_t>                  \
-    {                                               \
-        void eval(jit::array<T> &out,               \
-                  const jit::array<T> &in, int lim) \
-        {                                           \
-            for (int i = 0; i < lim; i++) {         \
-                out[i] = fn(in[i]);                 \
-            }                                       \
-        }                                           \
-    };                                              \
+#define UNARY_OP_FN(op, fn)                                               \
+    template<typename T>                                                  \
+    struct UnOp<T, T, af_##op##_t> {                                      \
+        void eval(jit::array<T> &out, const jit::array<T> &in, int lim) { \
+            for (int i = 0; i < lim; i++) { out[i] = fn(in[i]); }         \
+        }                                                                 \
+    };
 
 #define UNARY_OP(op) UNARY_OP_FN(op, std::op)
 
@@ -79,41 +72,36 @@ UNARY_OP(lgamma)
 #undef UNARY_OP
 #undef UNARY_OP_FN
 
-    template<typename T, af_op_t op>
-    Array<T> unaryOp(const Array<T> &in)
-    {
-        jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<T, T, op> *node = new jit::UnaryNode<T, T, op>(in_node);
+template<typename T, af_op_t op>
+Array<T> unaryOp(const Array<T> &in) {
+    jit::Node_ptr in_node          = in.getNode();
+    jit::UnaryNode<T, T, op> *node = new jit::UnaryNode<T, T, op>(in_node);
 
-        return createNodeArray<T>(in.dims(), jit::Node_ptr(node));
-    }
+    return createNodeArray<T>(in.dims(), jit::Node_ptr(node));
+}
 
 #define iszero(a) ((a) == 0)
 
-#define CHECK_FN(name ,op)                          \
-    template<typename T>                            \
-    struct UnOp<char, T, af_##name##_t>             \
-    {                                               \
-        void eval(jit::array<char> &out,            \
-                  const jit::array<T> &in, int lim) \
-        {                                           \
-            for (int i = 0; i < lim; i++) {         \
-                out[i] = op(in[i]);                 \
-            }                                       \
-        }                                           \
-    };                                              \
-
-    CHECK_FN(isinf, std::isinf)
-    CHECK_FN(isnan, std::isnan)
-    CHECK_FN(iszero, iszero)
-
-    template<typename T, af_op_t op>
-    Array<char> checkOp(const Array<T> &in)
-    {
-        jit::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<char, T, op> *node = new jit::UnaryNode<char, T, op>(in_node);
-
-        return createNodeArray<char>(in.dims(), jit::Node_ptr(node));
-    }
-
+#define CHECK_FN(name, op)                                                   \
+    template<typename T>                                                     \
+    struct UnOp<char, T, af_##name##_t> {                                    \
+        void eval(jit::array<char> &out, const jit::array<T> &in, int lim) { \
+            for (int i = 0; i < lim; i++) { out[i] = op(in[i]); }            \
+        }                                                                    \
+    };
+
+CHECK_FN(isinf, std::isinf)
+CHECK_FN(isnan, std::isnan)
+CHECK_FN(iszero, iszero)
+#undef iszero
+
+template<typename T, af_op_t op>
+Array<char> checkOp(const Array<T> &in) {
+    jit::Node_ptr in_node = in.getNode();
+    jit::UnaryNode<char, T, op> *node =
+        new jit::UnaryNode<char, T, op>(in_node);
+
+    return createNodeArray<char>(in.dims(), jit::Node_ptr(node));
 }
+
+}  // namespace cpu
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index 277f66240f..a003205bec 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -8,47 +8,45 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <unwrap.hpp>
 #include <common/dispatch.hpp>
+#include <kernel/unwrap.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <kernel/unwrap.hpp>
+#include <unwrap.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
-{
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const bool is_column) {
     in.eval();
 
     af::dim4 idims = in.dims();
-    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-    dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
+    dim_t nx       = (idims[0] + 2 * px - wx) / sx + 1;
+    dim_t ny       = (idims[1] + 2 * py - wy) / sy + 1;
 
     af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
 
-    if (!is_column) {
-        std::swap(odims[0], odims[1]);
-    }
+    if (!is_column) { std::swap(odims[0], odims[1]); }
 
     Array<T> outArray = createEmptyArray<T>(odims);
 
     if (is_column) {
-        getQueue().enqueue(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx,
+                           sy, px, py);
     } else {
-        getQueue().enqueue(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx,
+                           sy, px, py);
     }
 
     return outArray;
 }
 
-
-#define INSTANTIATE(T)                                                                  \
-    template Array<T> unwrap<T> (const Array<T> &in, const dim_t wx, const dim_t wy,    \
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
-
+#define INSTANTIATE(T)                                                      \
+    template Array<T> unwrap<T>(                                            \
+        const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
+        const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -63,4 +61,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/unwrap.hpp b/src/backend/cpu/unwrap.hpp
index 447fcfe0b1..b1d15490cf 100644
--- a/src/backend/cpu/unwrap.hpp
+++ b/src/backend/cpu/unwrap.hpp
@@ -9,9 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+namespace cpu {
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const bool is_column);
 }
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index 25bfacb7b5..f7d74f9162 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -9,47 +9,41 @@
 
 #pragma once
 #include <af/constants.h>
-#include <cmath>
 #include <algorithm>
+#include <cmath>
 #include "backend.hpp"
 
-namespace cpu
-{
-static inline
-dim_t trimIndex(int const & idx, dim_t const & len)
-{
+namespace cpu {
+static inline dim_t trimIndex(int const& idx, dim_t const& len) {
     int ret_val = idx;
-    if (ret_val<0) {
-        int offset  = (abs(ret_val)-1)%len;
-        ret_val = offset;
-    } else if (ret_val>=(int)len) {
-        int offset  = abs(ret_val)%len;
-        ret_val = len-offset-1;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= (int)len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
     }
     return ret_val;
 }
 
-static inline
-unsigned getIdx(af::dim4 const & strides, int i, int j = 0, int k = 0, int l = 0)
-{
+static inline unsigned getIdx(af::dim4 const& strides, int i, int j = 0,
+                              int k = 0, int l = 0) {
     return (l * strides[3] + k * strides[2] + j * strides[1] + i * strides[0]);
 }
 
 template<typename T>
-void gaussian1D(T* out, int const dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, int const dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / std::sqrt(2 * af::Pi * sigma*sigma) * std::exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / std::sqrt(2 * af::Pi * sigma * sigma) *
+               std::exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
-}
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
+}  // namespace cpu
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index f5064179a7..2f9f2d34e4 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -8,11 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <vector_field.hpp>
-#include <err_cpu.hpp>
 #include <common/graphics_common.hpp>
+#include <err_cpu.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <vector_field.hpp>
 
 using af::dim4;
 
@@ -20,9 +20,8 @@ namespace cpu {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vfield)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+                       fg_vector_field vfield) {
+    ForgeModule &_ = graphics::forgePlugin();
     points.eval();
     directions.eval();
     getQueue().sync();
@@ -47,9 +46,9 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
     CheckGL("In CopyArrayToVBO");
 }
 
-#define INSTANTIATE(T)                                                  \
-template void copy_vector_field<T>(const Array<T> &, const Array<T> &,  \
-                                   fg_vector_field);
+#define INSTANTIATE(T)                                                     \
+    template void copy_vector_field<T>(const Array<T> &, const Array<T> &, \
+                                       fg_vector_field);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -59,4 +58,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index b41d3963de..acd735ef6f 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -7,35 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <memory.hpp>
-#include <where.hpp>
 #include <ops.hpp>
-#include <vector>
 #include <platform.hpp>
+#include <where.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+#include <vector>
 
 using af::dim4;
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<uint> where(const Array<T> &in)
-{
+Array<uint> where(const Array<T> &in) {
     in.eval();
     getQueue().sync();
 
     const dim_t *dims    = in.dims().get();
     const dim_t *strides = in.strides().get();
-    static const T zero = scalar<T>(0);
+    static const T zero  = scalar<T>(0);
 
     const T *iptr = in.get();
     auto out_vec  = memAlloc<uint>(in.elements());
 
     dim_t count = 0;
-    dim_t idx = 0;
+    dim_t idx   = 0;
     for (dim_t w = 0; w < dims[3]; w++) {
         uint offw = w * strides[3];
 
@@ -46,7 +44,6 @@ Array<uint> where(const Array<T> &in)
                 uint offy = y * strides[1] + offz;
 
                 for (dim_t x = 0; x < dims[0]; x++) {
-
                     T val = iptr[offy + x];
                     if (val != zero) {
                         out_vec[count] = idx;
@@ -63,20 +60,19 @@ Array<uint> where(const Array<T> &in)
     return out;
 }
 
-#define INSTANTIATE(T)                                  \
-    template Array<uint> where<T>(const Array<T> &in);    \
+#define INSTANTIATE(T) template Array<uint> where<T>(const Array<T> &in);
 
-INSTANTIATE(float  )
-INSTANTIATE(cfloat )
-INSTANTIATE(double )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
 INSTANTIATE(cdouble)
-INSTANTIATE(char   )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(uchar  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/where.hpp b/src/backend/cpu/where.hpp
index 368d8457d8..8ec35b1526 100644
--- a/src/backend/cpu/where.hpp
+++ b/src/backend/cpu/where.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<uint> where(const Array<T>& in);
+namespace cpu {
+template<typename T>
+Array<uint> where(const Array<T>& in);
 }
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index f2ee1bca4c..d55baeb19c 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -8,23 +8,18 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <wrap.hpp>
 #include <common/dispatch.hpp>
+#include <kernel/wrap.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <kernel/wrap.hpp>
+#include <wrap.hpp>
 
-namespace cpu
-{
+namespace cpu {
 
 template<typename T>
-Array<T> wrap(const Array<T> &in,
-              const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy,
-              const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py,
-              const bool is_column)
-{
+Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py, const bool is_column) {
     af::dim4 idims = in.dims();
     af::dim4 odims(ox, oy, idims[2], idims[3]);
 
@@ -33,22 +28,21 @@ Array<T> wrap(const Array<T> &in,
     in.eval();
 
     if (is_column) {
-        getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px,
+                           py);
     } else {
-        getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px, py);
+        getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px,
+                           py);
     }
 
     return out;
 }
 
-
-#define INSTANTIATE(T)                                          \
-    template Array<T> wrap<T> (const Array<T> &in,              \
-                               const dim_t ox, const dim_t oy,  \
-                               const dim_t wx, const dim_t wy,  \
-                               const dim_t sx, const dim_t sy,  \
-                               const dim_t px, const dim_t py,  \
-                               const bool is_column);
+#define INSTANTIATE(T)                                                        \
+    template Array<T> wrap<T>(const Array<T> &in, const dim_t ox,             \
+                              const dim_t oy, const dim_t wx, const dim_t wy, \
+                              const dim_t sx, const dim_t sy, const dim_t px, \
+                              const dim_t py, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -63,4 +57,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/wrap.hpp b/src/backend/cpu/wrap.hpp
index 00263e7074..2463e49f76 100644
--- a/src/backend/cpu/wrap.hpp
+++ b/src/backend/cpu/wrap.hpp
@@ -9,13 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cpu
-{
-    template<typename T>
-    Array<T> wrap(const Array<T> &in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column);
+namespace cpu {
+template<typename T>
+Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py, const bool is_column);
 }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index ed6b27085a..0348e8a3d7 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -8,445 +8,438 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <jit/BufferNode.hpp>
 #include <common/jit/NodeIterator.hpp>
-#include <af/dim4.hpp>
 #include <copy.hpp>
 #include <err_cuda.hpp>
+#include <jit/BufferNode.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <scalar.hpp>
+#include <af/dim4.hpp>
 
 #include <cstddef>
 #include <memory>
 #include <numeric>
 
 using af::dim4;
-using cuda::jit::BufferNode;
 using common::Node;
-using common::NodeIterator;
 using common::Node_ptr;
+using common::NodeIterator;
+using cuda::jit::BufferNode;
 
 using std::accumulate;
 using std::shared_ptr;
 using std::vector;
 
-namespace cuda
-{
-    template<typename T>
-    Node_ptr bufferNodePtr()
-    {
-        return Node_ptr(new BufferNode<T>(getFullName<T>(),
-                                          shortname<T>(true)));
-    }
+namespace cuda {
+template<typename T>
+Node_ptr bufferNodePtr() {
+    return Node_ptr(new BufferNode<T>(getFullName<T>(), shortname<T>(true)));
+}
 
-    template<typename T>
-    Array<T>::Array(af::dim4 dims) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data((dims.elements() ? memAlloc<T>(dims.elements()).release() : nullptr), memFree<T>), data_dims(dims),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
-    {}
-
-    template<typename T>
-    Array<T>::Array(af::dim4 dims, const T * const in_data, bool is_device, bool copy_device) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(((is_device & !copy_device) ? const_cast<T*>(in_data) : memAlloc<T>(dims.elements()).release()), memFree<T>),
-        data_dims(dims),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
-    {
+template<typename T>
+Array<T>::Array(af::dim4 dims)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data((dims.elements() ? memAlloc<T>(dims.elements()).release() : nullptr),
+           memFree<T>)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {}
+
+template<typename T>
+Array<T>::Array(af::dim4 dims, const T *const in_data, bool is_device,
+                bool copy_device)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(
+          ((is_device & !copy_device) ? const_cast<T *>(in_data)
+                                      : memAlloc<T>(dims.elements()).release()),
+          memFree<T>)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
 #if __cplusplus > 199711L
-        static_assert(std::is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
-        static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
+    static_assert(std::is_standard_layout<Array<T>>::value,
+                  "Array<T> must be a standard layout type");
+    static_assert(
+        offsetof(Array<T>, info) == 0,
+        "Array<T>::info must be the first member variable of Array<T>");
 #endif
-        if (!is_device) {
-            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                                       cudaMemcpyHostToDevice, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-        } else if (copy_device) {
-            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                                       cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-        }
-    }
-
-    template<typename T>
-    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &strides) :
-        info(parent.getDevId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-        data(parent.getData()), data_dims(parent.getDataDims()),
-        node(bufferNodePtr<T>()),
-        ready(true), owner(false)
-    { }
-
-    template<typename T>
-    Array<T>::Array(Param<T> &tmp, bool owner_) :
-        info(getActiveDeviceId(),
-             af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]),
-             0,
-             af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2], tmp.strides[3]),
-             (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.ptr, owner_ ? std::function<void(T*)>(memFree<T>) : std::function<void(T*)>([](T*){})),
-        data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3])),
-        node(bufferNodePtr<T>()), ready(true), owner(owner_)
-    {
+    if (!is_device) {
+        CUDA_CHECK(
+            cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
+                            cudaMemcpyHostToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+    } else if (copy_device) {
+        CUDA_CHECK(
+            cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
+}
 
-    template<typename T>
-    Array<T>::Array(af::dim4 dims, common::Node_ptr n) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(), data_dims(dims),
-        node(n), ready(false), owner(true)
-    {
+template<typename T>
+Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
+                const dim4 &strides)
+    : info(parent.getDevId(), dims, offset_, strides,
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(parent.getData())
+    , data_dims(parent.getDataDims())
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(false) {}
+
+template<typename T>
+Array<T>::Array(Param<T> &tmp, bool owner_)
+    : info(getActiveDeviceId(),
+           af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]), 0,
+           af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2],
+                    tmp.strides[3]),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(tmp.ptr, owner_ ? std::function<void(T *)>(memFree<T>)
+                           : std::function<void(T *)>([](T *) {}))
+    , data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]))
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(owner_) {}
+
+template<typename T>
+Array<T>::Array(af::dim4 dims, common::Node_ptr n)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data()
+    , data_dims(dims)
+    , node(n)
+    , ready(false)
+    , owner(true) {}
+
+template<typename T>
+Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
+                const T *const in_data, bool is_device)
+    : info(getActiveDeviceId(), dims, offset_, strides,
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(is_device ? (T *)in_data : memAlloc<T>(info.total()).release(),
+           memFree<T>)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
+    if (!is_device) {
+        cudaStream_t stream = getActiveStream();
+        CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data,
+                                   info.total() * sizeof(T),
+                                   cudaMemcpyHostToDevice, stream));
+        CUDA_CHECK(cudaStreamSynchronize(stream));
     }
+}
 
-    template<typename T>
-    Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
-                    const T * const in_data, bool is_device) :
-        info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-        data(is_device ? (T*)in_data : memAlloc<T>(info.total()).release(), memFree<T>),
-        data_dims(dims),
-        node(bufferNodePtr<T>()),
-        ready(true),
-        owner(true)
-    {
-        if (!is_device) {
-            cudaStream_t stream = getActiveStream();
-            CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data, info.total() * sizeof(T),
-                                       cudaMemcpyHostToDevice, stream));
-            CUDA_CHECK(cudaStreamSynchronize(stream));
-        }
-    }
+template<typename T>
+void Array<T>::eval() {
+    if (isReady()) return;
 
-    template<typename T>
-    void Array<T>::eval()
-    {
-        if (isReady()) return;
+    this->setId(getActiveDeviceId());
+    this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
 
-        this->setId(getActiveDeviceId());
-        this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
+    ready = true;
+    evalNodes<T>(*this, this->getNode().get());
+    // FIXME: Replace the current node in any JIT possible trees with the new
+    // BufferNode
+    node = bufferNodePtr<T>();
+}
 
-        ready = true;
-        evalNodes<T>(*this, this->getNode().get());
-        // FIXME: Replace the current node in any JIT possible trees with the new BufferNode
-        node = bufferNodePtr<T>();
+template<typename T>
+T *Array<T>::device() {
+    if (!isOwner() || getOffset() || data.use_count() > 1) {
+        *this = copyArray<T>(*this);
     }
+    return this->get();
+}
 
-    template<typename T>
-    T* Array<T>::device()
-    {
-        if (!isOwner() || getOffset() || data.use_count() > 1) {
-            *this = copyArray<T>(*this);
-        }
-        return this->get();
-    }
+template<typename T>
+void Array<T>::eval() const {
+    if (isReady()) return;
+    const_cast<Array<T> *>(this)->eval();
+}
 
-    template<typename T>
-    void Array<T>::eval() const
-    {
-        if (isReady()) return;
-        const_cast<Array<T> *>(this)->eval();
-    }
+template<typename T>
+void evalMultiple(std::vector<Array<T> *> arrays) {
+    vector<Param<T>> outputs;
+    vector<Array<T> *> output_arrays;
+    vector<Node *> nodes;
 
-    template<typename T>
-    void evalMultiple(std::vector<Array<T>*> arrays)
-    {
-        vector<Param<T> > outputs;
-        vector<Array<T> *> output_arrays;
-        vector<Node*> nodes;
+    for (Array<T> *array : arrays) {
+        if (array->isReady()) { continue; }
 
-        for (Array<T>* array : arrays) {
-            if (array->isReady()) {
-                continue;
-            }
+        array->ready = true;
+        array->setId(getActiveDeviceId());
+        array->data =
+            shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
-            array->ready = true;
-            array->setId(getActiveDeviceId());
-            array->data = shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
+        outputs.push_back(*array);
+        output_arrays.push_back(array);
+        nodes.push_back(array->node.get());
+    }
 
-            outputs.push_back(*array);
-            output_arrays.push_back(array);
-            nodes.push_back(array->node.get());
-        }
+    evalNodes(outputs, nodes);
 
-        evalNodes(outputs, nodes);
+    for (Array<T> *array : output_arrays) array->node = bufferNodePtr<T>();
 
-        for(Array<T>* array : output_arrays)
-            array->node = bufferNodePtr<T>();
+    return;
+}
 
-        return;
-    }
+template<typename T>
+Array<T>::~Array() {}
 
-    template<typename T>
-    Array<T>::~Array() {}
-
-    template<typename T>
-    Node_ptr Array<T>::getNode()
-    {
-        if (node->isBuffer()) {
-            unsigned bytes = this->getDataDims().elements() * sizeof(T);
-            BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
-            Param<T> param = *this;
-            bufNode->setData(param, data, bytes, isLinear());
-        }
-        return node;
+template<typename T>
+Node_ptr Array<T>::getNode() {
+    if (node->isBuffer()) {
+        unsigned bytes         = this->getDataDims().elements() * sizeof(T);
+        BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
+        Param<T> param         = *this;
+        bufNode->setData(param, data, bytes, isLinear());
     }
+    return node;
+}
 
-    template<typename T>
-    Node_ptr Array<T>::getNode() const
-    {
-        if (node->isBuffer()) {
-            return const_cast<Array<T> *>(this)->getNode();
-        }
-        return node;
-    }
+template<typename T>
+Node_ptr Array<T>::getNode() const {
+    if (node->isBuffer()) { return const_cast<Array<T> *>(this)->getNode(); }
+    return node;
+}
+
+template<typename T>
+Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    Array<T> out = Array<T>(dims, node);
+
+    if (evalFlag()) {
+        if (node->getHeight() >= (int)getMaxJitSize()) {
+            out.eval();
+        } else {
+            size_t alloc_bytes, alloc_buffers;
+            size_t lock_bytes, lock_buffers;
+
+            deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
+                             &lock_buffers);
+
+            bool isBufferLimit =
+                lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
+
+            // We eval in the following cases.
+            //
+            // 1. Too many bytes are locked up by JIT causing memory
+            //    pressure. Too many bytes is assumed to be half of all bytes
+            //    allocated so far.
+            //
+            // 2. Too many buffers in a nonlinear kernel cause param space
+            //    overflow. This happens when the number of nodes reaches 50
+            //    (51 including output). Too many buffers can occur in a tree
+            //    of size 25 in the worst case.
+            //
+            // TODO: Find better solution than the following emperical solution.
+            if (node->getHeight() > 25 || isBufferLimit) {
+                // This is the size of the params that are passed by default
+                constexpr int param_base_size =
+                    sizeof(Param<T>) + (4 * sizeof(uint));
+
+                // This is the maximum size of the params that can be allowed by
+                // CUDA NOTE: This number should have been (4096 -
+                // some_buffer_size) BUT kernels who's kernel sizes come close
+                // to this value are not passing and cuModuleLoadDataEx is
+                // failing with CUDA_ERROR_INVALID_IMAGE(200). 35*sizeof(int)
+                // seems to be the magic number that passes all tests. I have no
+                // idea why this is the case.
+                constexpr int max_param_size =
+                    (4096 - (sizeof(Param<T>) + 35 * sizeof(uint)));
+                Node *n = node.get();
+
+                struct tree_info {
+                    size_t buffer_size;
+                    int num_buffers;
+                    int param_scalar_size;
+                    bool is_linear;
+                };
+                NodeIterator<> end_node;
+                dim4 outdim    = out.dims();
+                tree_info info = accumulate(
+                    NodeIterator<>(n), end_node, tree_info{0, 0, 0, true},
+                    [=](tree_info &prev, const Node &node) {
+                        if (node.isBuffer()) {
+                            const auto &buf_node =
+                                static_cast<const BufferNode<T> &>(node);
+                            prev.buffer_size += buf_node.getBytes();
+                            prev.num_buffers++;
+                            prev.is_linear &=
+                                buf_node.isLinear((dim_t *)outdim.get());
+                        } else {
+                            prev.param_scalar_size += node.getParamBytes();
+                        }
+                        // getBytes returns the size of the data Array. Sub
+                        // arrays will be represented by their parent size.
+                        return prev;
+                    });
+                int param_size = param_base_size + info.param_scalar_size;
+                if (info.is_linear) {
+                    param_size += info.num_buffers * sizeof(T *);
+                } else {
+                    param_size += info.num_buffers * sizeof(Param<T>);
+                }
 
-    template<typename T>
-    Array<T> createNodeArray(const dim4 &dims, Node_ptr node)
-    {
-        Array<T> out =  Array<T>(dims, node);
-
-        if (evalFlag()) {
-
-            if (node->getHeight() >= (int)getMaxJitSize()) {
-                out.eval();
-            } else {
-
-                size_t alloc_bytes, alloc_buffers;
-                size_t lock_bytes, lock_buffers;
-
-                deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
-                                 &lock_bytes, &lock_buffers);
-
-                bool isBufferLimit =
-                    lock_bytes > getMaxBytes() ||
-                    lock_buffers > getMaxBuffers();
-
-                // We eval in the following cases.
-                //
-                // 1. Too many bytes are locked up by JIT causing memory
-                //    pressure. Too many bytes is assumed to be half of all bytes
-                //    allocated so far.
-                //
-                // 2. Too many buffers in a nonlinear kernel cause param space
-                //    overflow. This happens when the number of nodes reaches 50
-                //    (51 including output). Too many buffers can occur in a tree
-                //    of size 25 in the worst case.
-                //
-                // TODO: Find better solution than the following emperical solution.
-                if (node->getHeight() > 25 || isBufferLimit) {
-                    // This is the size of the params that are passed by default
-                    constexpr int param_base_size = sizeof(Param<T>) + (4 * sizeof(uint));
-
-                    // This is the maximum size of the params that can be allowed by CUDA
-                    // NOTE: This number should have been (4096 - some_buffer_size) BUT
-                    // kernels who's kernel sizes come close to this value are not passing
-                    // and cuModuleLoadDataEx is failing with CUDA_ERROR_INVALID_IMAGE(200).
-                    // 35*sizeof(int) seems to be the magic number that passes all tests.
-                    // I have no idea why this is the case.
-                    constexpr int max_param_size = (4096 - (sizeof(Param<T>) + 35*sizeof(uint)));
-                    Node *n = node.get();
-
-                    struct  tree_info {
-                        size_t buffer_size;
-                        int num_buffers;
-                        int param_scalar_size;
-                        bool is_linear;
-                    };
-                    NodeIterator<> end_node;
-                    dim4 outdim = out.dims();
-                    tree_info info = accumulate(NodeIterator<>(n), end_node,
-                                                tree_info{0, 0, 0, true},
-                                                [=](tree_info& prev, const Node& node) {
-                                                    if(node.isBuffer()) {
-                                                        const auto& buf_node = static_cast<const BufferNode<T>&>(node);
-                                                        prev.buffer_size += buf_node.getBytes();
-                                                        prev.num_buffers++;
-                                                        prev.is_linear &= buf_node.isLinear((dim_t*)outdim.get());
-                                                    } else {
-                                                        prev.param_scalar_size += node.getParamBytes();
-                                                    }
-                                                    // getBytes returns the size of the data Array. Sub arrays will
-                                                    // be represented by their parent size.
-                                                    return prev;
-                                                });
-                    int param_size = param_base_size + info.param_scalar_size;
-                    if(info.is_linear) {
-                        param_size += info.num_buffers * sizeof(T*);
-                    } else {
-                        param_size += info.num_buffers * sizeof(Param<T>);
-                    }
-
-
-                    // TODO: the buffer_size check here is very conservative. It will trigger
-                    // an evaluation of the node in most cases. We should be checking the
-                    // amount of memory available to guard this eval
-                    if (param_size >= max_param_size  || info.buffer_size * 2 > lock_bytes) {
-                        out.eval();
-                    }
+                // TODO: the buffer_size check here is very conservative. It
+                // will trigger an evaluation of the node in most cases. We
+                // should be checking the amount of memory available to guard
+                // this eval
+                if (param_size >= max_param_size ||
+                    info.buffer_size * 2 > lock_bytes) {
+                    out.eval();
                 }
             }
         }
-
-        return out;
     }
 
-    template<typename T>
-    Array<T> createHostDataArray(const dim4 &size, const T * const data)
-    {
-        return Array<T>(size, data, false);
-    }
-
-    template<typename T>
-    Array<T> createDeviceDataArray(const dim4 &size, const void *data)
-    {
-        return Array<T>(size, (const T * const)data, true);
-    }
-
-    template<typename T>
-    Array<T> createValueArray(const dim4 &size, const T& value)
-    {
-        return createScalarNode<T>(size, value);
-    }
-
-    template<typename T>
-    Array<T> createEmptyArray(const dim4 &size)
-    {
-        return Array<T>(size);
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T> createSubArray(const Array<T>& parent,
-                            const std::vector<af_seq> &index,
-                            bool copy)
-    {
-        parent.eval();
+template<typename T>
+Array<T> createHostDataArray(const dim4 &size, const T *const data) {
+    return Array<T>(size, data, false);
+}
 
-        dim4 dDims = parent.getDataDims();
-        dim4 dStrides = calcStrides(dDims);
-        dim4 parent_strides = parent.strides();
+template<typename T>
+Array<T> createDeviceDataArray(const dim4 &size, const void *data) {
+    return Array<T>(size, (const T *const)data, true);
+}
 
-        if (dStrides != parent_strides) {
-            const Array<T> parentCopy = copyArray(parent);
-            return createSubArray(parentCopy, index, copy);
-        }
+template<typename T>
+Array<T> createValueArray(const dim4 &size, const T &value) {
+    return createScalarNode<T>(size, value);
+}
 
-        dim4 pDims = parent.dims();
-        dim4 dims    = toDims  (index, pDims);
-        dim4 strides = toStride (index, dDims);
+template<typename T>
+Array<T> createEmptyArray(const dim4 &size) {
+    return Array<T>(size);
+}
 
-        // Find total offsets after indexing
-        dim4 offsets = toOffset(index, pDims);
-        dim_t offset = parent.getOffset();
-        for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent,
+                        const std::vector<af_seq> &index, bool copy) {
+    parent.eval();
 
-        Array<T> out = Array<T>(parent, dims, offset, strides);
+    dim4 dDims          = parent.getDataDims();
+    dim4 dStrides       = calcStrides(dDims);
+    dim4 parent_strides = parent.strides();
 
-        if (!copy) return out;
+    if (dStrides != parent_strides) {
+        const Array<T> parentCopy = copyArray(parent);
+        return createSubArray(parentCopy, index, copy);
+    }
 
-        if (strides[0] != 1 ||
-            strides[1] <  0 ||
-            strides[2] <  0 ||
-            strides[3] <  0) {
+    dim4 pDims   = parent.dims();
+    dim4 dims    = toDims(index, pDims);
+    dim4 strides = toStride(index, dDims);
 
-            out = copyArray(out);
-        }
+    // Find total offsets after indexing
+    dim4 offsets = toOffset(index, pDims);
+    dim_t offset = parent.getOffset();
+    for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
 
-        return out;
-    }
+    Array<T> out = Array<T>(parent, dims, offset, strides);
 
-    template<typename T>
-    Array<T> createParamArray(Param<T> &tmp, bool owner)
-    {
-        return Array<T>(tmp, owner);
-    }
+    if (!copy) return out;
 
-    template<typename T>
-    void destroyArray(Array<T> *A)
-    {
-        delete A;
+    if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
+        out = copyArray(out);
     }
 
-    template<typename T>
-    void
-    writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
-    {
-        if (!arr.isOwner()) {
-            arr = copyArray<T>(arr);
-        }
+    return out;
+}
 
-        T *ptr = arr.get();
+template<typename T>
+Array<T> createParamArray(Param<T> &tmp, bool owner) {
+    return Array<T>(tmp, owner);
+}
 
-        CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+template<typename T>
+void destroyArray(Array<T> *A) {
+    delete A;
+}
 
-        return;
-    }
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data,
+                        const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
 
-    template<typename T>
-    void
-    writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
-    {
-        if (!arr.isOwner()) {
-            arr = copyArray<T>(arr);
-        }
+    T *ptr = arr.get();
 
-        T *ptr = arr.get();
+    CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice,
+                               cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-        CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+    return;
+}
 
-        return;
-    }
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
 
-    template<typename T>
-    void
-    Array<T>::setDataDims(const dim4 &new_dims)
-    {
-        modDims(new_dims);
-        data_dims = new_dims;
-        if (node->isBuffer()) {
-            node = bufferNodePtr<T>();
-        }
-    }
+    T *ptr = arr.get();
 
+    CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyDeviceToDevice,
+                               cuda::getActiveStream()));
 
-#define INSTANTIATE(T)                                                  \
-    template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
-    template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data); \
-    template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
-    template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
-    template       Array<T>  createParamArray<T>      (Param<T> &tmp, bool owner); \
-    template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
-                                                       const std::vector<af_seq> &index, \
-                                                       bool copy);      \
-    template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, common::Node_ptr node); \
-    template       Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset, \
-                                   const T * const in_data,             \
-                                   bool is_device);                     \
-    template       Array<T>::Array(af::dim4 dims, const T * const in_data, \
-                                   bool is_device, bool copy_device);   \
-    template       Array<T>::~Array        ();                          \
-    template       Node_ptr Array<T>::getNode() const;                  \
-    template       void Array<T>::eval();                               \
-    template       void Array<T>::eval() const;                         \
-    template       T*   Array<T>::device();                             \
-    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, \
-                                                       const size_t bytes); \
-    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, \
-                                                       const size_t bytes); \
-    template       void      evalMultiple<T>     (std::vector<Array<T>*> arrays); \
-    template       void Array<T>::setDataDims(const dim4 &new_dims);    \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return;
+}
 
+template<typename T>
+void Array<T>::setDataDims(const dim4 &new_dims) {
+    modDims(new_dims);
+    data_dims = new_dims;
+    if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> createHostDataArray<T>(const dim4 &size,                \
+                                             const T *const data);            \
+    template Array<T> createDeviceDataArray<T>(const dim4 &size,              \
+                                               const void *data);             \
+    template Array<T> createValueArray<T>(const dim4 &size, const T &value);  \
+    template Array<T> createEmptyArray<T>(const dim4 &size);                  \
+    template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);        \
+    template Array<T> createSubArray<T>(                                      \
+        const Array<T> &parent, const std::vector<af_seq> &index, bool copy); \
+    template void destroyArray<T>(Array<T> * A);                              \
+    template Array<T> createNodeArray<T>(const dim4 &size,                    \
+                                         common::Node_ptr node);              \
+    template Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset,   \
+                             const T *const in_data, bool is_device);         \
+    template Array<T>::Array(af::dim4 dims, const T *const in_data,           \
+                             bool is_device, bool copy_device);               \
+    template Array<T>::~Array();                                              \
+    template Node_ptr Array<T>::getNode() const;                              \
+    template void Array<T>::eval();                                           \
+    template void Array<T>::eval() const;                                     \
+    template T *Array<T>::device();                                           \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
+                                        const size_t bytes);                  \
+    template void writeDeviceDataArray<T>(                                    \
+        Array<T> & arr, const void *const data, const size_t bytes);          \
+    template void evalMultiple<T>(std::vector<Array<T> *> arrays);            \
+    template void Array<T>::setDataDims(const dim4 &new_dims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index f74fd27715..5e1997f9b6 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -8,244 +8,238 @@
  ********************************************************/
 #pragma once
 
-#include <af/dim4.hpp>
-#include <common/ArrayInfo.hpp>
-#include "traits.hpp"
+#include <Param.hpp>
 #include <backend.hpp>
-#include <types.hpp>
-#include <traits.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/jit/Node.hpp>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
-#include <Param.hpp>
-#include <common/jit/Node.hpp>
-#include <vector>
 #include <memory.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <af/dim4.hpp>
+#include <vector>
+#include "traits.hpp"
 
-namespace cuda
-{
-    using af::dim4;
+namespace cuda {
+using af::dim4;
 
-    template<typename T> class Array;
+template<typename T>
+class Array;
 
-    template<typename T>
-    void evalNodes(Param<T> out, common::Node *node);
+template<typename T>
+void evalNodes(Param<T> out, common::Node *node);
 
-    template<typename T>
-    void evalNodes(std::vector<Param<T> > &out, std::vector<common::Node *> nodes);
+template<typename T>
+void evalNodes(std::vector<Param<T>> &out, std::vector<common::Node *> nodes);
 
-    template<typename T>
-    void evalMultiple(std::vector<Array<T> *> arrays);
+template<typename T>
+void evalMultiple(std::vector<Array<T> *> arrays);
 
-    template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
+template<typename T>
+Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
 
-    template<typename T>
-    Array<T> createValueArray(const af::dim4 &size, const T& value);
+template<typename T>
+Array<T> createValueArray(const af::dim4 &size, const T &value);
 
-    template<typename T>
-    Array<T> createHostDataArray(const af::dim4 &size, const T * const data);
+template<typename T>
+Array<T> createHostDataArray(const af::dim4 &size, const T *const data);
 
-    template<typename T>
-    Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
+template<typename T>
+Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
 
-    template<typename T>
-    Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
-                                const T * const in_data, bool is_device) {
-        return Array<T>(dims, strides, offset, in_data, is_device);
-    }
+template<typename T>
+Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
+                            const T *const in_data, bool is_device) {
+    return Array<T>(dims, strides, offset, in_data, is_device);
+}
 
-    /// Copies data to an existing Array object from a host pointer
-    template<typename T>
-    void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
-
-    /// Copies data to an existing Array object from a device pointer
-    template<typename T>
-    void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
-
-    /// Creates an empty array of a given size. No data is initialized
-    ///
-    /// \param[in] size The dimension of the output array
-    template<typename T>
-    Array<T> createEmptyArray(const af::dim4 &size);
-
-    /// Create an Array object from Param<T> object.
-    ///
-    /// \param[in] in    The Param<T> array that is created.
-    /// \param[in] owner If true, the new Array<T> object is the owner of the data. If false
-    ///                  the Array<T> will not delete the object on destruction
-    template<typename T>
-    Array<T> createParamArray(Param<T> &in, bool owner);
-
-    template<typename T>
-    Array<T> createSubArray(const Array<T>& parent,
-                            const std::vector<af_seq> &index,
-                            bool copy=true);
-
-    // Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    void destroyArray(Array<T> *A);
-
-    template<typename T>
-    void *getDevicePtr(const Array<T>& arr)
-    {
-        T *ptr = arr.device();
-        memLock(ptr);
-        return (void *)ptr;
-    }
+/// Copies data to an existing Array object from a host pointer
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data, const size_t bytes);
+
+/// Copies data to an existing Array object from a device pointer
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes);
+
+/// Creates an empty array of a given size. No data is initialized
+///
+/// \param[in] size The dimension of the output array
+template<typename T>
+Array<T> createEmptyArray(const af::dim4 &size);
+
+/// Create an Array object from Param<T> object.
+///
+/// \param[in] in    The Param<T> array that is created.
+/// \param[in] owner If true, the new Array<T> object is the owner of the data.
+/// If false
+///                  the Array<T> will not delete the object on destruction
+template<typename T>
+Array<T> createParamArray(Param<T> &in, bool owner);
+
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent,
+                        const std::vector<af_seq> &index, bool copy = true);
+
+// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+void destroyArray(Array<T> *A);
+
+template<typename T>
+void *getDevicePtr(const Array<T> &arr) {
+    T *ptr = arr.device();
+    memLock(ptr);
+    return (void *)ptr;
+}
 
-    template<typename T>
-    void *getRawPtr(const Array<T>& arr)
-    {
-        return (void *)(arr.get(false));
-    }
+template<typename T>
+void *getRawPtr(const Array<T> &arr) {
+    return (void *)(arr.get(false));
+}
 
-    template<typename T>
-    class Array
-    {
-        ArrayInfo       info; // This must be the first element of Array<T>
-        std::shared_ptr<T> data;
-        af::dim4 data_dims;
+template<typename T>
+class Array {
+    ArrayInfo info;  // This must be the first element of Array<T>
+    std::shared_ptr<T> data;
+    af::dim4 data_dims;
 
-        common::Node_ptr node;
-        bool ready;
-        bool owner;
+    common::Node_ptr node;
+    bool ready;
+    bool owner;
 
-        Array(af::dim4 dims);
+    Array(af::dim4 dims);
 
-        explicit Array(af::dim4 dims, const T * const in_data, bool is_device = false, bool copy_device = false);
-        Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        Array(Param<T> &tmp, bool owner);
-        Array(af::dim4 dims, common::Node_ptr n);
-    public:
+    explicit Array(af::dim4 dims, const T *const in_data,
+                   bool is_device = false, bool copy_device = false);
+    Array(const Array<T> &parnt, const dim4 &dims, const dim_t &offset,
+          const dim4 &stride);
+    Array(Param<T> &tmp, bool owner);
+    Array(af::dim4 dims, common::Node_ptr n);
 
-        Array(af::dim4 dims, af::dim4 strides, dim_t offset,
-              const T * const in_data, bool is_device = false);
+   public:
+    Array(af::dim4 dims, af::dim4 strides, dim_t offset, const T *const in_data,
+          bool is_device = false);
 
-        void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
-        void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
-        void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }
-        void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides);  }
-        void setId(int id)                          { info.setId(id);               }
+    void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
+    void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
+    void modDims(const af::dim4 &newDims) { info.modDims(newDims); }
+    void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides); }
+    void setId(int id) { info.setId(id); }
 
-#define INFO_FUNC(RET_TYPE, NAME)   \
+#define INFO_FUNC(RET_TYPE, NAME) \
     RET_TYPE NAME() const { return info.NAME(); }
 
-        INFO_FUNC(const af_dtype& ,getType)
-        INFO_FUNC(const af::dim4& ,strides)
-        INFO_FUNC(size_t          ,elements)
-        INFO_FUNC(size_t          ,ndims)
-        INFO_FUNC(const af::dim4& ,dims )
-        INFO_FUNC(int             ,getDevId)
+    INFO_FUNC(const af_dtype &, getType)
+    INFO_FUNC(const af::dim4 &, strides)
+    INFO_FUNC(size_t, elements)
+    INFO_FUNC(size_t, ndims)
+    INFO_FUNC(const af::dim4 &, dims)
+    INFO_FUNC(int, getDevId)
 
 #undef INFO_FUNC
 
-#define INFO_IS_FUNC(NAME)\
-    bool NAME () const { return info.NAME(); }
-
-        INFO_IS_FUNC(isEmpty);
-        INFO_IS_FUNC(isScalar);
-        INFO_IS_FUNC(isRow);
-        INFO_IS_FUNC(isColumn);
-        INFO_IS_FUNC(isVector);
-        INFO_IS_FUNC(isComplex);
-        INFO_IS_FUNC(isReal);
-        INFO_IS_FUNC(isDouble);
-        INFO_IS_FUNC(isSingle);
-        INFO_IS_FUNC(isRealFloating);
-        INFO_IS_FUNC(isFloating);
-        INFO_IS_FUNC(isInteger);
-        INFO_IS_FUNC(isBool);
-        INFO_IS_FUNC(isLinear);
-        INFO_IS_FUNC(isSparse);
+#define INFO_IS_FUNC(NAME) \
+    bool NAME() const { return info.NAME(); }
+
+    INFO_IS_FUNC(isEmpty);
+    INFO_IS_FUNC(isScalar);
+    INFO_IS_FUNC(isRow);
+    INFO_IS_FUNC(isColumn);
+    INFO_IS_FUNC(isVector);
+    INFO_IS_FUNC(isComplex);
+    INFO_IS_FUNC(isReal);
+    INFO_IS_FUNC(isDouble);
+    INFO_IS_FUNC(isSingle);
+    INFO_IS_FUNC(isRealFloating);
+    INFO_IS_FUNC(isFloating);
+    INFO_IS_FUNC(isInteger);
+    INFO_IS_FUNC(isBool);
+    INFO_IS_FUNC(isLinear);
+    INFO_IS_FUNC(isSparse);
 
 #undef INFO_IS_FUNC
 
-        ~Array();
-
-        bool isReady() const { return ready; }
-        bool isOwner() const { return owner; }
-
-        void eval();
-        void eval() const;
-
-        dim_t getOffset() const { return info.getOffset(); }
-        std::shared_ptr<T> getData() const { return data; }
+    ~Array();
 
-        dim4 getDataDims() const
-        {
-            return data_dims;
-        }
+    bool isReady() const { return ready; }
+    bool isOwner() const { return owner; }
 
-        void setDataDims(const dim4 &new_dims);
-
-        size_t getAllocatedBytes() const
-        {
-            if (!isReady()) return 0;
-            size_t bytes = memoryManager().allocated(data.get());
-            // External device poitner
-            if (bytes == 0 && data.get()) {
-                return data_dims.elements() * sizeof(T);
-            }
-            return  bytes;
-        }
+    void eval();
+    void eval() const;
 
-        T* device();
+    dim_t getOffset() const { return info.getOffset(); }
+    std::shared_ptr<T> getData() const { return data; }
 
-        T* device() const
-        {
-            return const_cast<Array<T>*>(this)->device();
-        }
-
-        T* get(bool withOffset = true)
-        {
-            if (!isReady()) eval();
-            return const_cast<T*>(static_cast<const Array<T>*>(this)->get(withOffset));
-        }
+    dim4 getDataDims() const { return data_dims; }
 
-        //FIXME: implement withOffset parameter
-        const   T* get(bool withOffset = true) const
-        {
-            if (!isReady()) eval();
-            return data.get() + (withOffset ? getOffset() : 0);
-        }
+    void setDataDims(const dim4 &new_dims);
 
-        int useCount() const
-        {
-            if (!isReady()) eval();
-            return data.use_count();
+    size_t getAllocatedBytes() const {
+        if (!isReady()) return 0;
+        size_t bytes = memoryManager().allocated(data.get());
+        // External device poitner
+        if (bytes == 0 && data.get()) {
+            return data_dims.elements() * sizeof(T);
         }
+        return bytes;
+    }
 
-        operator Param<T>()
-        {
-            return Param<T>(this->get(), this->dims().get(), this->strides().get());
-        }
+    T *device();
 
-        operator CParam<T>() const
-        {
-            return CParam<T>(this->get(), this->dims().get(), this->strides().get());
-        }
+    T *device() const { return const_cast<Array<T> *>(this)->device(); }
 
-        common::Node_ptr getNode();
-        common::Node_ptr getNode() const;
+    T *get(bool withOffset = true) {
+        if (!isReady()) eval();
+        return const_cast<T *>(
+            static_cast<const Array<T> *>(this)->get(withOffset));
+    }
 
-        friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
-        friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
-        friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
-        friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data);
-        friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
-                                              const T * const in_data, bool is_device);
+    // FIXME: implement withOffset parameter
+    const T *get(bool withOffset = true) const {
+        if (!isReady()) eval();
+        return data.get() + (withOffset ? getOffset() : 0);
+    }
 
-        friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, common::Node_ptr node);
+    int useCount() const {
+        if (!isReady()) eval();
+        return data.use_count();
+    }
 
-        friend Array<T> createSubArray<T>(const Array<T>& parent,
-                                          const std::vector<af_seq> &index,
-                                          bool copy);
+    operator Param<T>() {
+        return Param<T>(this->get(), this->dims().get(), this->strides().get());
+    }
 
-        friend void destroyArray<T>(Array<T> *arr);
-        friend void *getDevicePtr<T>(const Array<T>& arr);
-        friend void *getRawPtr<T>(const Array<T>& arr);
-    };
+    operator CParam<T>() const {
+        return CParam<T>(this->get(), this->dims().get(),
+                         this->strides().get());
+    }
 
-}
+    common::Node_ptr getNode();
+    common::Node_ptr getNode() const;
+
+    friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
+    friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
+    friend Array<T> createHostDataArray<T>(const af::dim4 &size,
+                                           const T *const data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size,
+                                             const void *data);
+    friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
+                                          dim_t offset, const T *const in_data,
+                                          bool is_device);
+
+    friend Array<T> createEmptyArray<T>(const af::dim4 &size);
+    friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
+    friend Array<T> createNodeArray<T>(const af::dim4 &dims,
+                                       common::Node_ptr node);
+
+    friend Array<T> createSubArray<T>(const Array<T> &parent,
+                                      const std::vector<af_seq> &index,
+                                      bool copy);
+
+    friend void destroyArray<T>(Array<T> *arr);
+    friend void *getDevicePtr<T>(const Array<T> &arr);
+    friend void *getRawPtr<T>(const Array<T> &arr);
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index a66ec039b4..de4e4dc71f 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -13,36 +13,35 @@
 
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <common/graphics_common.hpp>
-#define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#define __gl_h_  // FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#include <GraphicsResourceManager.hpp>
 #include <cuda_gl_interop.h>
-#include <platform.hpp>
 #include <err_cuda.hpp>
-#include <GraphicsResourceManager.hpp>
+#include <platform.hpp>
 
 namespace cuda {
 GraphicsResourceManager::ShrdResVector
-GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
-{
+GraphicsResourceManager::registerResources(std::vector<uint32_t> resources) {
     ShrdResVector output;
 
     auto deleter = [](cudaGraphicsResource_t* handle) {
-        //FIXME Having a CUDA_CHECK around unregister
-        //call is causing invalid GL context.
-        //Moving ForgeManager class singleton as data
-        //member of DeviceManager with proper ordering
-        //of member destruction doesn't help either.
-        //Calling makeContextCurrent also doesn't help.
+        // FIXME Having a CUDA_CHECK around unregister
+        // call is causing invalid GL context.
+        // Moving ForgeManager class singleton as data
+        // member of DeviceManager with proper ordering
+        // of member destruction doesn't help either.
+        // Calling makeContextCurrent also doesn't help.
         cudaGraphicsUnregisterResource(*handle);
         delete handle;
     };
 
-    for (auto id: resources) {
+    for (auto id : resources) {
         cudaGraphicsResource_t r;
-        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(&r, id,
-                    cudaGraphicsMapFlagsWriteDiscard));
+        CUDA_CHECK(cudaGraphicsGLRegisterBuffer(
+            &r, id, cudaGraphicsMapFlagsWriteDiscard));
         output.emplace_back(new cudaGraphicsResource_t(r), deleter);
     }
 
     return output;
 }
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index 109770a08e..ff6a261ba1 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -10,22 +10,23 @@
 #pragma once
 
 #include <common/InteropManager.hpp>
+#include <driver_types.h>
 
 #include <map>
 #include <vector>
 
 namespace cuda {
-class GraphicsResourceManager :
-    public common::InteropManager<GraphicsResourceManager, cudaGraphicsResource_t>
-{
-    public:
-        using ShrdResVector = std::vector< std::shared_ptr<cudaGraphicsResource_t> >;
+class GraphicsResourceManager
+    : public common::InteropManager<GraphicsResourceManager,
+                                    cudaGraphicsResource_t> {
+   public:
+    using ShrdResVector = std::vector<std::shared_ptr<cudaGraphicsResource_t>>;
 
-        GraphicsResourceManager() {}
-        ShrdResVector registerResources(std::vector<uint32_t> resources);
+    GraphicsResourceManager() {}
+    ShrdResVector registerResources(std::vector<uint32_t> resources);
 
-    protected:
-        GraphicsResourceManager(GraphicsResourceManager const&);
-        void operator=(GraphicsResourceManager const&);
+   protected:
+    GraphicsResourceManager(GraphicsResourceManager const&);
+    void operator=(GraphicsResourceManager const&);
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index b2c17832f9..7f15f86026 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -8,33 +8,30 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <backend.hpp>
+#include <af/defines.h>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-class Param
-{
-public:
+class Param {
+   public:
     T *ptr;
     dim_t dims[4];
     dim_t strides[4];
 
-    __DH__ Param() : ptr(nullptr)
-    {
-    }
+    __DH__ Param() : ptr(nullptr) {}
 
-    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides) :
-        ptr(iptr)
-    {
+    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides)
+        : ptr(iptr) {
         for (int i = 0; i < 4; i++) {
-            dims[i] = idims[i];
+            dims[i]    = idims[i];
             strides[i] = istrides[i];
         }
     }
-    size_t elements() const noexcept { return dims[0] * dims[1] * dims[2] * dims[3]; }
+    size_t elements() const noexcept {
+        return dims[0] * dims[1] * dims[2] * dims[3];
+    }
 };
 
 template<typename T>
@@ -47,26 +44,23 @@ Param<T> flat(Param<T> in) {
 }
 
 template<typename T>
-class CParam
-{
-public:
+class CParam {
+   public:
     const T *ptr;
     dim_t dims[4];
     dim_t strides[4];
 
-    __DH__ CParam(const T *iptr, const dim_t *idims, const dim_t *istrides) :
-        ptr(iptr)
-    {
+    __DH__ CParam(const T *iptr, const dim_t *idims, const dim_t *istrides)
+        : ptr(iptr) {
         for (int i = 0; i < 4; i++) {
-            dims[i] = idims[i];
+            dims[i]    = idims[i];
             strides[i] = istrides[i];
         }
     }
 
-    __DH__ CParam(Param<T> &in) : ptr(in.ptr)
-    {
+    __DH__ CParam(Param<T> &in) : ptr(in.ptr) {
         for (int i = 0; i < 4; i++) {
-            dims[i] = in.dims[i];
+            dims[i]    = in.dims[i];
             strides[i] = in.strides[i];
         }
     }
@@ -74,4 +68,4 @@ class CParam
     __DH__ ~CParam() {}
 };
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/ThrustAllocator.cuh b/src/backend/cuda/ThrustAllocator.cuh
index 04af03565a..917cc5e9ba 100644
--- a/src/backend/cuda/ThrustAllocator.cuh
+++ b/src/backend/cuda/ThrustAllocator.cuh
@@ -9,37 +9,35 @@
 
 #pragma once
 
+#include <memory.hpp>
 #include <thrust/device_malloc_allocator.h>
 #include <thrust/device_vector.h>
-#include <memory.hpp>
 
-//Below Class definition is found at the following URL
-//http://stackoverflow.com/questions/9007343/mix-custom-memory-managment-and-thrust-in-cuda
+// Below Class definition is found at the following URL
+// http://stackoverflow.com/questions/9007343/mix-custom-memory-managment-and-thrust-in-cuda
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-struct ThrustAllocator : thrust::device_malloc_allocator<T>
-{
+struct ThrustAllocator : thrust::device_malloc_allocator<T> {
     // shorthand for the name of the base class
     typedef thrust::device_malloc_allocator<T> super_t;
 
     // get access to some of the base class's typedefs
     // note that because we inherited from device_malloc_allocator,
     // pointer is actually thrust::device_ptr<T>
-    typedef typename super_t::pointer   pointer;
+    typedef typename super_t::pointer pointer;
 
     typedef typename super_t::size_type size_type;
 
-    pointer allocate(size_type elements)
-    {
-        return thrust::device_ptr<T>(memAlloc<T>(elements).release());// delegate to ArrayFire allocator
+    pointer allocate(size_type elements) {
+        return thrust::device_ptr<T>(
+            memAlloc<T>(elements)
+                .release());  // delegate to ArrayFire allocator
     }
 
-    void deallocate(pointer p, size_type n)
-    {
+    void deallocate(pointer p, size_type n) {
         UNUSED(n);
-        memFree<T>(p.get());// delegate to ArrayFire allocator
+        memFree<T>(p.get());  // delegate to ArrayFire allocator
     }
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/all.cu b/src/backend/cuda/all.cu
index b70f98ab28..07cc308329 100644
--- a/src/backend/cuda/all.cu
+++ b/src/backend/cuda/all.cu
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    //alltrue
-    INSTANTIATE(af_and_t, float  , char)
-    INSTANTIATE(af_and_t, double , char)
-    INSTANTIATE(af_and_t, cfloat , char)
-    INSTANTIATE(af_and_t, cdouble, char)
-    INSTANTIATE(af_and_t, int    , char)
-    INSTANTIATE(af_and_t, uint   , char)
-    INSTANTIATE(af_and_t, intl   , char)
-    INSTANTIATE(af_and_t, uintl  , char)
-    INSTANTIATE(af_and_t, char   , char)
-    INSTANTIATE(af_and_t, uchar  , char)
-    INSTANTIATE(af_and_t, short  , char)
-    INSTANTIATE(af_and_t, ushort , char)
-}
+namespace cuda {
+// alltrue
+INSTANTIATE(af_and_t, float, char)
+INSTANTIATE(af_and_t, double, char)
+INSTANTIATE(af_and_t, cfloat, char)
+INSTANTIATE(af_and_t, cdouble, char)
+INSTANTIATE(af_and_t, int, char)
+INSTANTIATE(af_and_t, uint, char)
+INSTANTIATE(af_and_t, intl, char)
+INSTANTIATE(af_and_t, uintl, char)
+INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, uchar, char)
+INSTANTIATE(af_and_t, short, char)
+INSTANTIATE(af_and_t, ushort, char)
+}  // namespace cuda
diff --git a/src/backend/cuda/anisotropic_diffusion.cu b/src/backend/cuda/anisotropic_diffusion.cu
index 56fcc0e224..100485fcbf 100644
--- a/src/backend/cuda/anisotropic_diffusion.cu
+++ b/src/backend/cuda/anisotropic_diffusion.cu
@@ -7,28 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <anisotropic_diffusion.hpp>
 #include <kernel/anisotropic_diffusion.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-void anisotropicDiffusion(Array<T>& inout, const float dt,
-                          const float mct, const af::fluxFunction fftype,
-                          const af::diffusionEq eq)
-{
-    if (eq==AF_DIFFUSION_MCDE)
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
+                          const af::diffusionEq eq) {
+    if (eq == AF_DIFFUSION_MCDE)
         kernel::anisotropicDiffusion<T, true>(inout, dt, mct, fftype);
     else
         kernel::anisotropicDiffusion<T, false>(inout, dt, mct, fftype);
 }
 
-#define INSTANTIATE(T)\
-template void anisotropicDiffusion<T>(Array<T> &inout, const float dt, const float mct,\
-                                      const af::fluxFunction fftype, const af::diffusionEq eq);
+#define INSTANTIATE(T)                                     \
+    template void anisotropicDiffusion<T>(                 \
+        Array<T> & inout, const float dt, const float mct, \
+        const af::fluxFunction fftype, const af::diffusionEq eq);
 
 INSTANTIATE(double)
-INSTANTIATE( float)
-}
+INSTANTIATE(float)
+}  // namespace cuda
diff --git a/src/backend/cuda/anisotropic_diffusion.hpp b/src/backend/cuda/anisotropic_diffusion.hpp
index 568d4f25f7..4dca3740f2 100644
--- a/src/backend/cuda/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/anisotropic_diffusion.hpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-void anisotropicDiffusion(Array<T>& inout, const float dt,
-                          const float mct, const af::fluxFunction fftype,
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
                           const af::diffusionEq eq);
 }
diff --git a/src/backend/cuda/any.cu b/src/backend/cuda/any.cu
index aa13fbb67b..eb8004dd92 100644
--- a/src/backend/cuda/any.cu
+++ b/src/backend/cuda/any.cu
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    //anytrue
-    INSTANTIATE(af_or_t, float  , char)
-    INSTANTIATE(af_or_t, double , char)
-    INSTANTIATE(af_or_t, cfloat , char)
-    INSTANTIATE(af_or_t, cdouble, char)
-    INSTANTIATE(af_or_t, int    , char)
-    INSTANTIATE(af_or_t, uint   , char)
-    INSTANTIATE(af_or_t, intl   , char)
-    INSTANTIATE(af_or_t, uintl  , char)
-    INSTANTIATE(af_or_t, char   , char)
-    INSTANTIATE(af_or_t, uchar  , char)
-    INSTANTIATE(af_or_t, short  , char)
-    INSTANTIATE(af_or_t, ushort , char)
-}
+namespace cuda {
+// anytrue
+INSTANTIATE(af_or_t, float, char)
+INSTANTIATE(af_or_t, double, char)
+INSTANTIATE(af_or_t, cfloat, char)
+INSTANTIATE(af_or_t, cdouble, char)
+INSTANTIATE(af_or_t, int, char)
+INSTANTIATE(af_or_t, uint, char)
+INSTANTIATE(af_or_t, intl, char)
+INSTANTIATE(af_or_t, uintl, char)
+INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, uchar, char)
+INSTANTIATE(af_or_t, short, char)
+INSTANTIATE(af_or_t, ushort, char)
+}  // namespace cuda
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cu
index 1ddffdf73b..faf8cde44d 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cu
@@ -9,105 +9,86 @@
 
 #include <Array.hpp>
 #include <approx.hpp>
+#include <err_cuda.hpp>
 #include <kernel/approx.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
 
-namespace cuda
-{
-    template<typename Ty, typename Tp>
-    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
-                 const Array<Tp> &xo, const int xdim,
-                 const Tp &xi_beg, const Tp &xi_step,
-                 const af_interp_type method, const float offGrid)
-    {
-        switch(method) {
+namespace cuda {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp, 1> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
+            kernel::approx1<Ty, Tp, 1>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp, 2> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
+            kernel::approx1<Ty, Tp, 2>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp, 3> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
+            kernel::approx1<Ty, Tp, 3>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
-        default:
-            break;
-        }
+        default: break;
     }
+}
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &zi,
-                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                      const af_interp_type method, const float offGrid)
-    {
-        af::dim4 odims = zi.dims();
-        odims[xdim] = xo.dims()[xdim];
-        odims[ydim] = xo.dims()[ydim];
+template<typename Ty, typename Tp>
+Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
+                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                  const af_interp_type method, const float offGrid) {
+    af::dim4 odims = zi.dims();
+    odims[xdim]    = xo.dims()[xdim];
+    odims[ydim]    = xo.dims()[ydim];
 
-        // Create output placeholder
-        Array<Ty> zo = createEmptyArray<Ty>(odims);
+    // Create output placeholder
+    Array<Ty> zo = createEmptyArray<Ty>(odims);
 
-        switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp, 1> (zo, zi,
-                                        xo, xdim, xi_beg, xi_step,
-                                        yo, ydim, yi_beg, yi_step,
-                                        offGrid, method);
+            kernel::approx2<Ty, Tp, 1>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp, 2> (zo, zi,
-                                        xo, xdim, xi_beg, xi_step,
-                                        yo, ydim, yi_beg, yi_step,
-                                        offGrid, method);
+            kernel::approx2<Ty, Tp, 2>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp, 3> (zo, zi,
-                                        xo, xdim, xi_beg, xi_step,
-                                        yo, ydim, yi_beg, yi_step,
-                                        offGrid, method);
+            kernel::approx2<Ty, Tp, 3>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
-        default:
-            break;
-        }
-        return zo;
+        default: break;
     }
+    return zo;
+}
 
-#define INSTANTIATE(Ty, Tp)                                         \
-    template void approx1<Ty, Tp>(Array<Ty> &yo,                    \
-                                  const Array<Ty> &yi,              \
-                                  const Array<Tp> &xo,              \
-                                  const int xdim,                   \
-                                  const Tp &xi_beg,                 \
-                                  const Tp &xi_step,                \
-                                  const af_interp_type method,      \
-                                  const float offGrid);             \
-    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
-                                       const Array<Tp> &xo,         \
-                                       const int xdim,              \
-                                       const Tp &xi_beg,            \
-                                       const Tp &xi_step,           \
-                                       const Array<Tp> &yo,         \
-                                       const int ydim,              \
-                                       const Tp &yi_beg,            \
-                                       const Tp &yi_step,           \
-                                       const af_interp_type method, \
-                                       const float offGrid);        \
+#define INSTANTIATE(Ty, Tp)                                       \
+    template void approx1<Ty, Tp>(                                \
+        Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const af_interp_type method, const float offGrid);        \
+    template Array<Ty> approx2<Ty, Tp>(                           \
+        const Array<Ty> &zi, const Array<Tp> &xo, const int xdim, \
+        const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo, \
+        const int ydim, const Tp &yi_beg, const Tp &yi_step,      \
+        const af_interp_type method, const float offGrid);
 
-    INSTANTIATE(float  , float )
-    INSTANTIATE(double , double)
-    INSTANTIATE(cfloat , float )
-    INSTANTIATE(cdouble, double)
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/approx.hpp b/src/backend/cuda/approx.hpp
index 02289136fb..c3f21afd38 100644
--- a/src/backend/cuda/approx.hpp
+++ b/src/backend/cuda/approx.hpp
@@ -9,17 +9,15 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename Ty, typename Tp>
-    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
-                 const Array<Tp> &xo, const int xdim,
-                 const Tp &xi_beg, const Tp &xi_step,
-                 const af_interp_type method, const float offGrid);
+namespace cuda {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid);
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &zi,
-                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                      const af_interp_type method, const float offGrid);
-}
+template<typename Ty, typename Tp>
+Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
+                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                  const af_interp_type method, const float offGrid);
+}  // namespace cuda
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index 87117e90bb..8aa453ceb5 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -7,16 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <binary.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    template<typename T, af_op_t op>
-    Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<T, T, op>(lhs, rhs, odims);
-    }
+namespace cuda {
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
+                 const af::dim4 &odims) {
+    return createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/assign.cu b/src/backend/cuda/assign.cu
index 2806ea69ef..092b6cc1f2 100644
--- a/src/backend/cuda/assign.cu
+++ b/src/backend/cuda/assign.cu
@@ -7,73 +7,70 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
 #include <assign.hpp>
-#include <kernel/assign.hpp>
 #include <err_cuda.hpp>
+#include <handle.hpp>
+#include <kernel/assign.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
-{
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
     kernel::AssignKernelParam_t p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (dim_t x=0; x<4; ++x) {
-        if (idxrs[x].isSeq) {
-            seqs[x] = idxrs[x].idx.seq;
-        }
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
     }
 
     // retrieve dimensions, strides and offsets
     dim4 dDims = out.dims();
     // retrieve dimensions & strides for array
     // to which rhs is being copied to
-    dim4 dstOffs = toOffset(seqs, dDims);
-    dim4 dstStrds= toStride(seqs, dDims);
+    dim4 dstOffs  = toOffset(seqs, dDims);
+    dim4 dstStrds = toStride(seqs, dDims);
 
-    for (dim_t i=0; i<4; ++i) {
+    for (dim_t i = 0; i < 4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
         p.offs[i]  = dstOffs[i];
         p.strds[i] = dstStrds[i];
     }
 
-    std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         // set idxPtrs to null
         p.ptr[x] = 0;
         // set index pointers were applicable
         if (!p.isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
-            p.ptr[x] = idxArrs[x].get();
+            p.ptr[x]   = idxArrs[x].get();
         }
     }
 
     kernel::assign<T>(out, rhs, p);
 }
 
-#define INSTANTIATE(T) \
-    template void assign<T>(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
+#define INSTANTIATE(T)                                                \
+    template void assign<T>(Array<T> & out, const af_index_t idxrs[], \
+                            const Array<T>& rhs);
 
 INSTANTIATE(cdouble)
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
-INSTANTIATE(float  )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(char   )
-INSTANTIATE(uchar  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/assign.hpp b/src/backend/cuda/assign.hpp
index 730df7c473..1e2eff86bf 100644
--- a/src/backend/cuda/assign.hpp
+++ b/src/backend/cuda/assign.hpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/index.h>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
diff --git a/src/backend/cuda/bilateral.cu b/src/backend/cuda/bilateral.cu
index bef64db1a3..ade1977757 100644
--- a/src/backend/cuda/bilateral.cu
+++ b/src/backend/cuda/bilateral.cu
@@ -7,35 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <bilateral.hpp>
 #include <kernel/bilateral.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
-{
-    Array<outType>out = createEmptyArray<outType>(in.dims());
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
+                         const float &c_sigma) {
+    Array<outType> out = createEmptyArray<outType>(in.dims());
     kernel::bilateral<inType, outType, isColor>(out, in, s_sigma, c_sigma);
     return out;
 }
 
-#define INSTANTIATE(inT, outT)\
-template Array<outT> bilateral<inT, outT,true >(const Array<inT> &in, const float &s_sigma, const float &c_sigma);\
-template Array<outT> bilateral<inT, outT,false>(const Array<inT> &in, const float &s_sigma, const float &c_sigma);
+#define INSTANTIATE(inT, outT)                                             \
+    template Array<outT> bilateral<inT, outT, true>(                       \
+        const Array<inT> &in, const float &s_sigma, const float &c_sigma); \
+    template Array<outT> bilateral<inT, outT, false>(                      \
+        const Array<inT> &in, const float &s_sigma, const float &c_sigma);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float ,  float)
-INSTANTIATE(char  ,  float)
-INSTANTIATE(int   ,  float)
-INSTANTIATE(uint  ,  float)
-INSTANTIATE(uchar ,  float)
-INSTANTIATE(short ,  float)
-INSTANTIATE(ushort,  float)
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/bilateral.hpp b/src/backend/cuda/bilateral.hpp
index 23000086bd..bbed9202b9 100644
--- a/src/backend/cuda/bilateral.hpp
+++ b/src/backend/cuda/bilateral.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma);
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
+                         const float &c_sigma);
 
 }
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index f07199b478..11803d2752 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -8,52 +8,34 @@
  ********************************************************/
 
 #pragma once
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
-#include <math.hpp>
 #include <common/jit/BinaryNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-
-    template<typename To, typename Ti, af_op_t op>
-    struct BinOp
-    {
-        const char *name()
-        {
-            return "__invalid";
-        }
-    };
+namespace cuda {
 
-#define BINARY_TYPE_1(fn)                      \
-    template<typename To, typename Ti>          \
-    struct BinOp<To, Ti, af_##fn##_t>           \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__"#fn;                     \
-        }                                       \
-    };                                          \
-                                                \
-    template<typename To>                       \
-    struct BinOp<To, cfloat, af_##fn##_t>       \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn"f";                 \
-        }                                       \
-    };                                          \
-                                                \
-    template<typename To>                       \
-    struct BinOp<To, cdouble, af_##fn##_t>      \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn;                    \
-        }                                       \
-    };                                          \
+template<typename To, typename Ti, af_op_t op>
+struct BinOp {
+    const char *name() { return "__invalid"; }
+};
 
+#define BINARY_TYPE_1(fn)                            \
+    template<typename To, typename Ti>               \
+    struct BinOp<To, Ti, af_##fn##_t> {              \
+        const char *name() { return "__" #fn; }      \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cfloat, af_##fn##_t> {          \
+        const char *name() { return "__c" #fn "f"; } \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cdouble, af_##fn##_t> {         \
+        const char *name() { return "__c" #fn; }     \
+    };
 
 BINARY_TYPE_1(eq)
 BINARY_TYPE_1(neq)
@@ -75,47 +57,27 @@ BINARY_TYPE_1(bitshiftr)
 
 #undef BINARY_TYPE_1
 
-#define BINARY_TYPE_2(fn)                       \
-    template<typename To, typename Ti>          \
-    struct BinOp<To, Ti, af_##fn##_t>           \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__"#fn;                     \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, float, af_##fn##_t>        \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "f"#fn;                      \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, double, af_##fn##_t>       \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "f"#fn;                      \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, cfloat, af_##fn##_t>       \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn"f";                 \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, cdouble, af_##fn##_t>      \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn;                    \
-        }                                       \
-    };                                          \
+#define BINARY_TYPE_2(fn)                            \
+    template<typename To, typename Ti>               \
+    struct BinOp<To, Ti, af_##fn##_t> {              \
+        const char *name() { return "__" #fn; }      \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, float, af_##fn##_t> {           \
+        const char *name() { return "f" #fn; }       \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, double, af_##fn##_t> {          \
+        const char *name() { return "f" #fn; }       \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, cfloat, af_##fn##_t> {          \
+        const char *name() { return "__c" #fn "f"; } \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, cdouble, af_##fn##_t> {         \
+        const char *name() { return "__c" #fn; }     \
+    };
 
 BINARY_TYPE_2(min)
 BINARY_TYPE_2(max)
@@ -127,80 +89,58 @@ struct BinOp<To, Ti, af_pow_t> {
     const char *name() { return "__pow"; }
 };
 
-#define POW_BINARY_OP(INTYPE, OPNAME)       \
-template<typename To>                       \
-struct BinOp<To, INTYPE, af_pow_t> {        \
-    const char *name() { return OPNAME; }   \
-};
+#define POW_BINARY_OP(INTYPE, OPNAME)         \
+    template<typename To>                     \
+    struct BinOp<To, INTYPE, af_pow_t> {      \
+        const char *name() { return OPNAME; } \
+    };
 
-POW_BINARY_OP(double, "pow"    )
-POW_BINARY_OP( float, "powf"   )
-POW_BINARY_OP(  intl, "__powll")
-POW_BINARY_OP( uintl, "__powul")
-POW_BINARY_OP(  uint, "__powui")
-POW_BINARY_OP(   int, "__powsi")
+POW_BINARY_OP(double, "pow")
+POW_BINARY_OP(float, "powf")
+POW_BINARY_OP(intl, "__powll")
+POW_BINARY_OP(uintl, "__powul")
+POW_BINARY_OP(uint, "__powui")
+POW_BINARY_OP(int, "__powsi")
 
 #undef POW_BINARY_OP
 
 template<typename Ti>
-struct BinOp<cfloat, Ti, af_cplx2_t>
-{
-    const char *name()
-    {
-        return "__cplx2f";
-    }
+struct BinOp<cfloat, Ti, af_cplx2_t> {
+    const char *name() { return "__cplx2f"; }
 };
 
 template<typename Ti>
-struct BinOp<cdouble, Ti, af_cplx2_t>
-{
-    const char *name()
-    {
-        return "__cplx2";
-    }
+struct BinOp<cdouble, Ti, af_cplx2_t> {
+    const char *name() { return "__cplx2"; }
 };
 
 template<typename To, typename Ti>
-struct BinOp<To, Ti, af_cplx2_t>
-{
-    const char *name()
-    {
-        return "noop";
-    }
+struct BinOp<To, Ti, af_cplx2_t> {
+    const char *name() { return "noop"; }
 };
 
 template<typename To, typename Ti>
-struct BinOp<To, Ti, af_atan2_t>
-{
-    const char *name()
-    {
-        return "atan2";
-    }
+struct BinOp<To, Ti, af_atan2_t> {
+    const char *name() { return "atan2"; }
 };
 
 template<typename To, typename Ti>
-struct BinOp<To, Ti, af_hypot_t>
-{
-    const char *name()
-    {
-        return "hypot";
-    }
+struct BinOp<To, Ti, af_hypot_t> {
+    const char *name() { return "hypot"; }
 };
 
 template<typename To, typename Ti, af_op_t op>
-Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
-{
+Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
+                           const af::dim4 &odims) {
     BinOp<To, Ti, op> bop;
 
     common::Node_ptr lhs_node = lhs.getNode();
     common::Node_ptr rhs_node = rhs.getNode();
-    common::BinaryNode *node = new common::BinaryNode(getFullName<To>(),
-                                                      shortname<To>(true),
-                                                      bop.name(),
-                                                      lhs_node,
-                                                      rhs_node, (int)(op));
+    common::BinaryNode *node =
+        new common::BinaryNode(getFullName<To>(), shortname<To>(true),
+                               bop.name(), lhs_node, rhs_node, (int)(op));
 
     return createNodeArray<To>(odims, common::Node_ptr(node));
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 4f1c071d23..d7443a2a82 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -8,155 +8,143 @@
  ********************************************************/
 
 #include <blas.hpp>
-#include <cuda_runtime.h>
 #include <cublas_v2.h>
+#include <cuda_runtime.h>
 #include <platform.hpp>
 
-#include <stdexcept>
-#include <string>
-#include <cassert>
-#include <math.hpp>
+#include <arith.hpp>
 #include <common/err_common.hpp>
-#include <err_cuda.hpp>
+#include <complex.hpp>
 #include <cublas.hpp>
-#include <arith.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <reduce.hpp>
-#include <complex.hpp>
+#include <cassert>
+#include <stdexcept>
+#include <string>
 
-namespace cuda
-{
+namespace cuda {
 
-cublasOperation_t
-toCblasTranspose(af_mat_prop opt)
-{
+cublasOperation_t toCblasTranspose(af_mat_prop opt) {
     cublasOperation_t out = CUBLAS_OP_N;
-    switch(opt) {
-        case AF_MAT_NONE        : out = CUBLAS_OP_N;    break;
-        case AF_MAT_TRANS           : out = CUBLAS_OP_T;    break;
-        case AF_MAT_CTRANS : out = CUBLAS_OP_C;    break;
-        default                     : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    switch (opt) {
+        case AF_MAT_NONE: out = CUBLAS_OP_N; break;
+        case AF_MAT_TRANS: out = CUBLAS_OP_T; break;
+        case AF_MAT_CTRANS: out = CUBLAS_OP_C; break;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
 }
 
 template<typename T>
-struct gemm_func_def_t
-{
-    typedef cublasStatus_t (*gemm_func_def)(    cublasHandle_t,
-                                                cublasOperation_t, cublasOperation_t,
-                                                int, int, int,
-                                                const T *,  const T *, int,
-                                                            const T *, int,
-                                                const T *,        T *, int);
+struct gemm_func_def_t {
+    typedef cublasStatus_t (*gemm_func_def)(cublasHandle_t, cublasOperation_t,
+                                            cublasOperation_t, int, int, int,
+                                            const T *, const T *, int,
+                                            const T *, int, const T *, T *,
+                                            int);
 };
 
 template<typename T>
-struct gemmBatched_func_def_t
-{
-    typedef cublasStatus_t (*gemmBatched_func_def)(    cublasHandle_t,
-                                                       cublasOperation_t, cublasOperation_t,
-                                                       int, int, int,
-                                                       const T *,  const T **, int,
-                                                       const T **, int,
-                                                       const T *,        T **, int,
-                                                       int);
+struct gemmBatched_func_def_t {
+    typedef cublasStatus_t (*gemmBatched_func_def)(
+        cublasHandle_t, cublasOperation_t, cublasOperation_t, int, int, int,
+        const T *, const T **, int, const T **, int, const T *, T **, int, int);
 };
 
 template<typename T>
-struct gemv_func_def_t
-{
-    typedef cublasStatus_t (*gemv_func_def)(    cublasHandle_t,
-                                                cublasOperation_t,
-                                                int, int,
-                                                const T *,  const T *, int,
-                                                            const T *, int,
-                                                const T *,        T *, int);
+struct gemv_func_def_t {
+    typedef cublasStatus_t (*gemv_func_def)(cublasHandle_t, cublasOperation_t,
+                                            int, int, const T *, const T *, int,
+                                            const T *, int, const T *, T *,
+                                            int);
 };
 
 template<typename T>
-struct trsm_func_def_t
-{
-    typedef cublasStatus_t (*trsm_func_def)(    cublasHandle_t,
-                                                cublasSideMode_t,
-                                                cublasFillMode_t,
-                                                cublasOperation_t,
-                                                cublasDiagType_t,
-                                                int, int,
-                                                const T *,
-                                                const T *, int,
-                                                T *, int);
+struct trsm_func_def_t {
+    typedef cublasStatus_t (*trsm_func_def)(cublasHandle_t, cublasSideMode_t,
+                                            cublasFillMode_t, cublasOperation_t,
+                                            cublasDiagType_t, int, int,
+                                            const T *, const T *, int, T *,
+                                            int);
 };
 
-#define BLAS_FUNC_DEF( FUNC )                       \
-template<typename T>                                \
-typename FUNC##_func_def_t<T>::FUNC##_func_def      \
-FUNC##_func();
+#define BLAS_FUNC_DEF(FUNC) \
+    template<typename T>    \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
 
-#define BLAS_FUNC( FUNC, TYPE, PREFIX )         \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def       FUNC##_func<TYPE>()  { return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cublas##PREFIX##FUNC; }
+#define BLAS_FUNC(FUNC, TYPE, PREFIX)                                       \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cublas##PREFIX##FUNC;                                        \
+    }
 
 BLAS_FUNC_DEF(gemm)
-BLAS_FUNC(gemm, float,  S)
+BLAS_FUNC(gemm, float, S)
 BLAS_FUNC(gemm, cfloat, C)
 BLAS_FUNC(gemm, double, D)
-BLAS_FUNC(gemm, cdouble,Z)
+BLAS_FUNC(gemm, cdouble, Z)
 
 BLAS_FUNC_DEF(gemmBatched)
-BLAS_FUNC(gemmBatched, float,  S)
+BLAS_FUNC(gemmBatched, float, S)
 BLAS_FUNC(gemmBatched, cfloat, C)
 BLAS_FUNC(gemmBatched, double, D)
-BLAS_FUNC(gemmBatched, cdouble,Z)
+BLAS_FUNC(gemmBatched, cdouble, Z)
 
 BLAS_FUNC_DEF(gemv)
-BLAS_FUNC(gemv, float,  S)
+BLAS_FUNC(gemv, float, S)
 BLAS_FUNC(gemv, cfloat, C)
 BLAS_FUNC(gemv, double, D)
-BLAS_FUNC(gemv, cdouble,Z)
+BLAS_FUNC(gemv, cdouble, Z)
 
 BLAS_FUNC_DEF(trsm)
-BLAS_FUNC(trsm, float,  S)
+BLAS_FUNC(trsm, float, S)
 BLAS_FUNC(trsm, cfloat, C)
 BLAS_FUNC(trsm, double, D)
-BLAS_FUNC(trsm, cdouble,Z)
+BLAS_FUNC(trsm, cdouble, Z)
 
 #undef BLAS_FUNC
 #undef BLAS_FUNC_DEF
 
 template<typename T, bool conjugate>
-struct dot_func_def_t
-{
-    typedef cublasStatus_t (*dot_func_def)(    cublasHandle_t,
-                                                int,
-                                                const T *,  int,
-                                                const T *,  int,
-                                                T *);
+struct dot_func_def_t {
+    typedef cublasStatus_t (*dot_func_def)(cublasHandle_t, int, const T *, int,
+                                           const T *, int, T *);
 };
 
-#define BLAS_FUNC_DEF( FUNC )                                   \
-template<typename T, bool conjugate>                            \
-typename FUNC##_func_def_t<T, conjugate>::FUNC##_func_def       \
-FUNC##_func();
+#define BLAS_FUNC_DEF(FUNC)              \
+    template<typename T, bool conjugate> \
+    typename FUNC##_func_def_t<T, conjugate>::FUNC##_func_def FUNC##_func();
 
-#define BLAS_FUNC( FUNC, TYPE, CONJUGATE, PREFIX )                           \
-template<> typename FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def      \
-FUNC##_func<TYPE, CONJUGATE>()  { return (FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def)&cublas##PREFIX##FUNC; }
+#define BLAS_FUNC(FUNC, TYPE, CONJUGATE, PREFIX)                       \
+    template<>                                                         \
+    typename FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def       \
+        FUNC##_func<TYPE, CONJUGATE>() {                               \
+        return (FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def) & \
+               cublas##PREFIX##FUNC;                                   \
+    }
 
 BLAS_FUNC_DEF(dot)
-BLAS_FUNC(dot, float,  true,  S)
-BLAS_FUNC(dot, double, true,  D)
-BLAS_FUNC(dot, float,  false, S)
+BLAS_FUNC(dot, float, true, S)
+BLAS_FUNC(dot, double, true, D)
+BLAS_FUNC(dot, float, false, S)
 BLAS_FUNC(dot, double, false, D)
 
 #undef BLAS_FUNC
 
-#define BLAS_FUNC( FUNC, TYPE, CONJUGATE, PREFIX, SUFFIX)                \
-template<> typename FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def  \
-FUNC##_func<TYPE, CONJUGATE>()  { return (FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def)&cublas##PREFIX##FUNC##SUFFIX; }
+#define BLAS_FUNC(FUNC, TYPE, CONJUGATE, PREFIX, SUFFIX)               \
+    template<>                                                         \
+    typename FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def       \
+        FUNC##_func<TYPE, CONJUGATE>() {                               \
+        return (FUNC##_func_def_t<TYPE, CONJUGATE>::FUNC##_func_def) & \
+               cublas##PREFIX##FUNC##SUFFIX;                           \
+    }
 
 BLAS_FUNC_DEF(dot)
-BLAS_FUNC(dot, cfloat,  true , C, c)
-BLAS_FUNC(dot, cdouble, true , Z, c)
-BLAS_FUNC(dot, cfloat,  false, C, u)
+BLAS_FUNC(dot, cfloat, true, C, c)
+BLAS_FUNC(dot, cdouble, true, Z, c)
+BLAS_FUNC(dot, cfloat, false, C, u)
 BLAS_FUNC(dot, cdouble, false, Z, u)
 
 #undef BLAS_FUNC
@@ -165,9 +153,8 @@ BLAS_FUNC(dot, cdouble, false, Z, u)
 using namespace std;
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
     cublasOperation_t lOpts = toCblasTranspose(optLhs);
     cublasOperation_t rOpts = toCblasTranspose(optRhs);
 
@@ -177,13 +164,13 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
-    int M = lDims[aRowDim];
-    int N = rDims[bColDim];
-    int K = lDims[aColDim];
+    int M      = lDims[aRowDim];
+    int N      = rDims[bColDim];
+    int K      = lDims[aColDim];
 
-    dim_t d2 = std::max(lDims[2], rDims[2]);
-    dim_t d3 = std::max(lDims[3], rDims[3]);
-    dim4 oDims = dim4(M, N, d2, d3);
+    dim_t d2     = std::max(lDims[2], rDims[2]);
+    dim_t d3     = std::max(lDims[3], rDims[3]);
+    dim4 oDims   = dim4(M, N, d2, d3);
     Array<T> out = createEmptyArray<T>(oDims);
 
     T alpha = scalar<T>(1);
@@ -194,31 +181,17 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     dim4 oStrides = out.strides();
 
     if (oDims.ndims() <= 2) {
-        if(rDims[bColDim] == 1) {
+        if (rDims[bColDim] == 1) {
             dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-            N = lDims[aColDim];
-            CUBLAS_CHECK(gemv_func<T>()(
-                             blasHandle(),
-                             lOpts,
-                             lDims[0],
-                             lDims[1],
-                             &alpha,
-                             lhs.get(), lStrides[1],
-                             rhs.get(), incr,
-                             &beta,
-                             out.get(), 1));
+            N          = lDims[aColDim];
+            CUBLAS_CHECK(gemv_func<T>()(blasHandle(), lOpts, lDims[0], lDims[1],
+                                        &alpha, lhs.get(), lStrides[1],
+                                        rhs.get(), incr, &beta, out.get(), 1));
         } else {
-            CUBLAS_CHECK(gemm_func<T>()(
-                             blasHandle(),
-                             lOpts,
-                             rOpts,
-                             M, N, K,
-                             &alpha,
-                             lhs.get(), lStrides[1],
-                             rhs.get(), rStrides[1],
-                             &beta,
-                             out.get(),
-                             oDims[0]));
+            CUBLAS_CHECK(gemm_func<T>()(blasHandle(), lOpts, rOpts, M, N, K,
+                                        &alpha, lhs.get(), lStrides[1],
+                                        rhs.get(), rStrides[1], &beta,
+                                        out.get(), oDims[0]));
         }
     } else {
         int batchSize = oDims[2] * oDims[3];
@@ -234,13 +207,15 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
         const T *lptr = lhs.get();
         const T *rptr = rhs.get();
-        T *optr = out.get();
+        T *optr       = out.get();
 
         for (int n = 0; n < batchSize; n++) {
-            int w = n / oDims[2];
-            int z = n - w * oDims[2];
-            int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
-            int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+            int w    = n / oDims[2];
+            int z    = n - w * oDims[2];
+            int loff = z * (is_l_d2_batched * lStrides[2]) +
+                       w * (is_l_d3_batched * lStrides[3]);
+            int roff = z * (is_r_d2_batched * rStrides[2]) +
+                       w * (is_r_d3_batched * rStrides[3]);
             lptrs[n] = lptr + loff;
             rptrs[n] = rptr + roff;
             optrs[n] = optr + z * oStrides[2] + w * oStrides[3];
@@ -252,40 +227,26 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
         size_t bytes = batchSize * sizeof(T **);
         CUDA_CHECK(cudaMemcpyAsync(d_lptrs.get(), lptrs.data(), bytes,
-                                   cudaMemcpyHostToDevice,
-                                   getActiveStream()));
+                                   cudaMemcpyHostToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(d_rptrs.get(), rptrs.data(), bytes,
-                                   cudaMemcpyHostToDevice,
-                                   getActiveStream()));
+                                   cudaMemcpyHostToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(d_optrs.get(), optrs.data(), bytes,
-                                   cudaMemcpyHostToDevice,
-                                   getActiveStream()));
+                                   cudaMemcpyHostToDevice, getActiveStream()));
 
         CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         CUBLAS_CHECK(gemmBatched_func<T>()(
-                         blasHandle(),
-                         lOpts,
-                         rOpts,
-                         M, N, K,
-                         &alpha,
-                         (const T **)d_lptrs.get(), lStrides[1],
-                         (const T **)d_rptrs.get(), rStrides[1],
-                         &beta,
-                         (T **)d_optrs.get(),
-                         oStrides[1],
-                         batchSize));
-
+            blasHandle(), lOpts, rOpts, M, N, K, &alpha,
+            (const T **)d_lptrs.get(), lStrides[1], (const T **)d_rptrs.get(),
+            rStrides[1], &beta, (T **)d_optrs.get(), oStrides[1], batchSize));
     }
 
     return out;
-
 }
 
 template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs) {
     const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
     const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
 
@@ -294,13 +255,12 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
 }
 
 template<typename T>
-void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans,
-          bool is_upper, bool is_left, bool is_unit)
-{
-    //dim4 lDims = lhs.dims();
+void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans, bool is_upper,
+          bool is_left, bool is_unit) {
+    // dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
-    int M = rDims[0];
-    int N = rDims[1];
+    int M      = rDims[0];
+    int N      = rDims[1];
 
     T alpha = scalar<T>(1);
 
@@ -308,20 +268,16 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans,
     dim4 rStrides = rhs.strides();
 
     CUBLAS_CHECK(trsm_func<T>()(
-                     blasHandle(),
-                     is_left  ? CUBLAS_SIDE_LEFT : CUBLAS_SIDE_RIGHT,
-                     is_upper ? CUBLAS_FILL_MODE_UPPER : CUBLAS_FILL_MODE_LOWER,
-                     toCblasTranspose(trans),
-                     is_unit  ? CUBLAS_DIAG_UNIT : CUBLAS_DIAG_NON_UNIT,
-                     M, N,
-                     &alpha,
-                     lhs.get(), lStrides[1],
-                     rhs.get(), rStrides[1]));
+        blasHandle(), is_left ? CUBLAS_SIDE_LEFT : CUBLAS_SIDE_RIGHT,
+        is_upper ? CUBLAS_FILL_MODE_UPPER : CUBLAS_FILL_MODE_LOWER,
+        toCblasTranspose(trans),
+        is_unit ? CUBLAS_DIAG_UNIT : CUBLAS_DIAG_NON_UNIT, M, N, &alpha,
+        lhs.get(), lStrides[1], rhs.get(), rStrides[1]));
 }
 
-
-#define INSTANTIATE_BLAS(TYPE)                                                          \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
+#define INSTANTIATE_BLAS(TYPE)                                \
+    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
+                                      const Array<TYPE> &rhs, \
                                       af_mat_prop optLhs, af_mat_prop optRhs);
 
 INSTANTIATE_BLAS(float)
@@ -329,22 +285,24 @@ INSTANTIATE_BLAS(cfloat)
 INSTANTIATE_BLAS(double)
 INSTANTIATE_BLAS(cdouble)
 
-#define INSTANTIATE_DOT(TYPE)                                                           \
-    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,      \
-                                   af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_DOT(TYPE)                                                  \
+    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
+                                   const Array<TYPE> &rhs, af_mat_prop optLhs, \
+                                   af_mat_prop optRhs);
 
 INSTANTIATE_DOT(float)
 INSTANTIATE_DOT(double)
 INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
 
-#define INSTANTIATE_TRSM(TYPE)                                                          \
-    template void trsm<TYPE>(const Array<TYPE> &lhs, Array<TYPE> &rhs,                  \
-                             af_mat_prop trans, bool is_upper, bool is_left, bool is_unit);
+#define INSTANTIATE_TRSM(TYPE)                                               \
+    template void trsm<TYPE>(const Array<TYPE> &lhs, Array<TYPE> &rhs,       \
+                             af_mat_prop trans, bool is_upper, bool is_left, \
+                             bool is_unit);
 
 INSTANTIATE_TRSM(float)
 INSTANTIATE_TRSM(cfloat)
 INSTANTIATE_TRSM(double)
 INSTANTIATE_TRSM(cdouble)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index ff43715495..c7199e257f 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -9,19 +9,18 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs);
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs);
 
 template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs);
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs);
 
 template<typename T>
 void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans = AF_MAT_NONE,
           bool is_upper = false, bool is_left = true, bool is_unit = false);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/canny.cu b/src/backend/cuda/canny.cu
index c8c2ab9a76..a3aa187cc6 100644
--- a/src/backend/cuda/canny.cu
+++ b/src/backend/cuda/canny.cu
@@ -14,11 +14,10 @@
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
-                                   const Array<float>& gx, const Array<float>& gy)
-{
+                                   const Array<float>& gx,
+                                   const Array<float>& gy) {
     Array<float> out = createValueArray<float>(mag.dims(), 0);
 
     kernel::nonMaxSuppression<float>(out, mag, gx, gy);
@@ -26,12 +25,12 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
     return out;
 }
 
-Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak)
-{
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak) {
     Array<char> out = createValueArray<char>(strong.dims(), 0);
 
     kernel::edgeTrackingHysteresis<char>(out, strong, weak);
 
     return out;
 }
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/canny.hpp b/src/backend/cuda/canny.hpp
index 8c9a286b15..bbd90a9ca2 100644
--- a/src/backend/cuda/canny.hpp
+++ b/src/backend/cuda/canny.hpp
@@ -9,10 +9,11 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
-                                   const Array<float>& gx, const Array<float>& gy);
+                                   const Array<float>& gx,
+                                   const Array<float>& gy);
 
-Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak);
-}
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak);
+}  // namespace cuda
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 2c219dbcce..0297efee61 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -8,35 +8,26 @@
  ********************************************************/
 
 #pragma once
-#include <af/dim4.hpp>
-#include <complex>
+#include <Array.hpp>
+#include <common/jit/UnaryNode.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
-#include <common/jit/UnaryNode.hpp>
 #include <types.hpp>
-#include <Array.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename To, typename Ti>
-struct CastOp
-{
-    const char *name()
-    {
-        return "";
-    }
+struct CastOp {
+    const char *name() { return ""; }
 };
 
-#define CAST_FN(TYPE)                           \
-    template<typename Ti>                       \
-        struct CastOp<TYPE, Ti>                 \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "("#TYPE")";                 \
-        }                                       \
+#define CAST_FN(TYPE)                                \
+    template<typename Ti>                            \
+    struct CastOp<TYPE, Ti> {                        \
+        const char *name() { return "(" #TYPE ")"; } \
     };
 
 CAST_FN(int)
@@ -47,14 +38,10 @@ CAST_FN(short)
 CAST_FN(float)
 CAST_FN(double)
 
-#define CAST_CFN(TYPE)                          \
-    template<typename Ti>                       \
-    struct CastOp<TYPE, Ti>                     \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__convert_"#TYPE;           \
-        }                                       \
+#define CAST_CFN(TYPE)                                    \
+    template<typename Ti>                                 \
+    struct CastOp<TYPE, Ti> {                             \
+        const char *name() { return "__convert_" #TYPE; } \
     };
 
 CAST_CFN(cfloat)
@@ -62,73 +49,49 @@ CAST_CFN(cdouble)
 CAST_CFN(char)
 
 template<>
-struct CastOp<cfloat, cdouble>
-{
-    const char *name()
-    {
-        return "__convert_z2c";
-    }
+struct CastOp<cfloat, cdouble> {
+    const char *name() { return "__convert_z2c"; }
 };
 
 template<>
-struct CastOp<cdouble, cfloat>
-{
-    const char *name()
-    {
-        return "__convert_c2z";
-    }
+struct CastOp<cdouble, cfloat> {
+    const char *name() { return "__convert_c2z"; }
 };
 
 template<>
-struct CastOp<cfloat, cfloat>
-{
-    const char *name()
-    {
-        return "__convert_c2c";
-    }
+struct CastOp<cfloat, cfloat> {
+    const char *name() { return "__convert_c2c"; }
 };
 
 template<>
-struct CastOp<cdouble, cdouble>
-{
-    const char *name()
-    {
-        return "__convert_z2z";
-    }
+struct CastOp<cdouble, cdouble> {
+    const char *name() { return "__convert_z2z"; }
 };
 
 #undef CAST_FN
 #undef CAST_CFN
 
 template<typename To, typename Ti>
-struct CastWrapper
-{
-    Array<To> operator()(const Array<Ti> &in)
-    {
+struct CastWrapper {
+    Array<To> operator()(const Array<Ti> &in) {
         CastOp<To, Ti> cop;
         common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
-                                                        shortname<To>(true),
-                                                        cop.name(),
-                                                        in_node, af_cast_t);
+        common::UnaryNode *node =
+            new common::UnaryNode(getFullName<To>(), shortname<To>(true),
+                                  cop.name(), in_node, af_cast_t);
         return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 };
 
 template<typename T>
-struct CastWrapper<T, T>
-{
-    Array<T> operator()(const Array<T> &in)
-    {
-        return in;
-    }
+struct CastWrapper<T, T> {
+    Array<T> operator()(const Array<T> &in) { return in; }
 };
 
 template<typename To, typename Ti>
-Array<To> cast(const Array<Ti> &in)
-{
+Array<To> cast(const Array<Ti> &in) {
     CastWrapper<To, Ti> cast_op;
     return cast_op(in);
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index 30d7185704..4128ecc9e9 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -7,23 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <cholesky.hpp>
+#include <common/err_common.hpp>
 
-#include <platform.hpp>
+#include <copy.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
 #include <memory.hpp>
-#include <copy.hpp>
+#include <platform.hpp>
 #include <triangle.hpp>
 
-#include <math.hpp>
 #include <common/err_common.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-//cusolverStatus_t cusolverDn<>potrf_bufferSize(
+// cusolverStatus_t cusolverDn<>potrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        cublasFillMode_t uplo,
 //        int n,
@@ -31,7 +30,7 @@ namespace cuda
 //        int lda,
 //        int *Lwork );
 //
-//cusolverStatus_t cusolverDn<>potrf(
+// cusolverStatus_t cusolverDn<>potrf(
 //        cusolverDnHandle_t handle,
 //        cublasFillMode_t uplo,
 //        int n,
@@ -40,103 +39,90 @@ namespace cuda
 //        int *devInfo );
 
 template<typename T>
-struct potrf_func_def_t
-{
-    typedef cusolverStatus_t (*potrf_func_def) (
-                              cusolverDnHandle_t,
-                              cublasFillMode_t,
-                              int,
-                              T *, int,
-                              T *,
-                              int, int *);
+struct potrf_func_def_t {
+    typedef cusolverStatus_t (*potrf_func_def)(cusolverDnHandle_t,
+                                               cublasFillMode_t, int, T *, int,
+                                               T *, int, int *);
 };
 
 template<typename T>
-struct potrf_buf_func_def_t
-{
-    typedef cusolverStatus_t (*potrf_buf_func_def) (
-                              cusolverDnHandle_t,
-                              cublasFillMode_t,
-                              int,
-                              T *, int,
-                              int *);
+struct potrf_buf_func_def_t {
+    typedef cusolverStatus_t (*potrf_buf_func_def)(cusolverDnHandle_t,
+                                                   cublasFillMode_t, int, T *,
+                                                   int, int *);
 };
 
-#define CH_FUNC_DEF( FUNC )                                                     \
-template<typename T>                                                            \
-typename FUNC##_func_def_t<T>::FUNC##_func_def                                  \
-FUNC##_func();                                                                  \
-                                                                                \
-template<typename T>                                                            \
-typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def                          \
-FUNC##_buf_func();                                                              \
-
-#define CH_FUNC( FUNC, TYPE, PREFIX )                                                           \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>()                \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusolverDn##PREFIX##FUNC; }                 \
-                                                                                                \
-template<> typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def FUNC##_buf_func<TYPE>()    \
-{ return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def)&cusolverDn##PREFIX##FUNC##_bufferSize; }
-
-CH_FUNC_DEF( potrf )
-CH_FUNC(potrf , float  , S)
-CH_FUNC(potrf , double , D)
-CH_FUNC(potrf , cfloat , C)
-CH_FUNC(potrf , cdouble, Z)
+#define CH_FUNC_DEF(FUNC)                                         \
+    template<typename T>                                          \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func(); \
+                                                                  \
+    template<typename T>                                          \
+    typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def FUNC##_buf_func();
+
+#define CH_FUNC(FUNC, TYPE, PREFIX)                                         \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusolverDn##PREFIX##FUNC;                                    \
+    }                                                                       \
+                                                                            \
+    template<>                                                              \
+    typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def               \
+        FUNC##_buf_func<TYPE>() {                                           \
+        return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def) &         \
+               cusolverDn##PREFIX##FUNC##_bufferSize;                       \
+    }
+
+CH_FUNC_DEF(potrf)
+CH_FUNC(potrf, float, S)
+CH_FUNC(potrf, double, D)
+CH_FUNC(potrf, cfloat, C)
+CH_FUNC(potrf, cdouble, Z)
 
 template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
-
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
-    *info = cholesky_inplace(out, is_upper);
+    *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper) triangle<T, true , false>(out, out);
-    else          triangle<T, false, false>(out, out);
+    if (is_upper)
+        triangle<T, true, false>(out, out);
+    else
+        triangle<T, false, false>(out, out);
 
     return out;
 }
 
 template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
     dim4 iDims = in.dims();
-    int N = iDims[0];
+    int N      = iDims[0];
 
     int lwork = 0;
 
     cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER;
-    if(is_upper)
-        uplo = CUBLAS_FILL_MODE_UPPER;
+    if (is_upper) uplo = CUBLAS_FILL_MODE_UPPER;
 
-    CUSOLVER_CHECK(potrf_buf_func<T>()(solverDnHandle(),
-                                       uplo,
-                                       N,
-                                       in.get(), in.strides()[1],
-                                       &lwork));
+    CUSOLVER_CHECK(potrf_buf_func<T>()(solverDnHandle(), uplo, N, in.get(),
+                                       in.strides()[1], &lwork));
 
     auto workspace = memAlloc<T>(lwork);
-    auto d_info = memAlloc<int>(1);
+    auto d_info    = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(potrf_func<T>()(solverDnHandle(),
-                                   uplo,
-                                   N,
-                                   in.get(), in.strides()[1],
-                                   workspace.get(), lwork,
+    CUSOLVER_CHECK(potrf_func<T>()(solverDnHandle(), uplo, N, in.get(),
+                                   in.strides()[1], workspace.get(), lwork,
                                    d_info.get()));
 
-
-    //FIXME: should return h_info
+    // FIXME: should return h_info
     return 0;
 }
 
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
 
 INSTANTIATE_CH(float)
 INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cholesky.hpp b/src/backend/cuda/cholesky.hpp
index f39f8f01e4..82bfcc3580 100644
--- a/src/backend/cuda/cholesky.hpp
+++ b/src/backend/cuda/cholesky.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
+namespace cuda {
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 
-    template<typename T>
-    int cholesky_inplace(Array<T> &in, const bool is_upper);
-}
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper);
+}  // namespace cuda
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 945c545df5..e0eba61c8a 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -7,73 +7,80 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <binary.hpp>
 #include <common/jit/UnaryNode.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    template<typename To, typename Ti>
-    Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
-    }
+namespace cuda {
+template<typename To, typename Ti>
+Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
+               const af::dim4 &odims) {
+    return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
+}
 
-    template<typename To, typename Ti>
-    Array<To> real(const Array<Ti> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
-                                                        shortname<To>(true),
-                                                        "__creal",
-                                                        in_node, af_real_t);
+template<typename To, typename Ti>
+Array<To> real(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node  = new common::UnaryNode(
+        getFullName<To>(), shortname<To>(true), "__creal", in_node, af_real_t);
 
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
 
-    template<typename To, typename Ti>
-    Array<To> imag(const Array<Ti> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
-                                                        shortname<To>(true),
-                                                        "__cimag",
-                                                        in_node, af_imag_t);
+template<typename To, typename Ti>
+Array<To> imag(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node  = new common::UnaryNode(
+        getFullName<To>(), shortname<To>(true), "__cimag", in_node, af_imag_t);
 
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
 
-    template<typename T> static const char *abs_name() { return "fabs"; }
-    template<> STATIC_ const char *abs_name<cfloat>() { return "__cabsf"; }
-    template<> STATIC_ const char *abs_name<cdouble>() { return "__cabs"; }
+template<typename T>
+static const char *abs_name() {
+    return "fabs";
+}
+template<>
+STATIC_ const char *abs_name<cfloat>() {
+    return "__cabsf";
+}
+template<>
+STATIC_ const char *abs_name<cdouble>() {
+    return "__cabs";
+}
 
-    template<typename To, typename Ti>
-    Array<To> abs(const Array<Ti> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(getFullName<To>(),
-                                                        shortname<To>(true),
-                                                        abs_name<Ti>(),
-                                                        in_node, af_abs_t);
+template<typename To, typename Ti>
+Array<To> abs(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(getFullName<To>(), shortname<To>(true),
+                              abs_name<Ti>(), in_node, af_abs_t);
 
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
 
-    template<typename T> static const char *conj_name() { return "__noop"; }
-    template<> STATIC_ const char *conj_name<cfloat>() { return "__cconjf"; }
-    template<> STATIC_ const char *conj_name<cdouble>() { return "__cconj"; }
+template<typename T>
+static const char *conj_name() {
+    return "__noop";
+}
+template<>
+STATIC_ const char *conj_name<cfloat>() {
+    return "__cconjf";
+}
+template<>
+STATIC_ const char *conj_name<cdouble>() {
+    return "__cconj";
+}
 
-    template<typename T>
-    Array<T> conj(const Array<T> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(getFullName<T>(),
-                                                        shortname<T>(true),
-                                                        conj_name<T>(),
-                                                        in_node, af_conj_t);
+template<typename T>
+Array<T> conj(const Array<T> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(getFullName<T>(), shortname<T>(true),
+                              conj_name<T>(), in_node, af_conj_t);
 
-        return createNodeArray<T>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 8a512f52e6..e4e4015ef0 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -7,41 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
-#include <kernel/convolve.hpp>
 #include <err_cuda.hpp>
+#include <kernel/convolve.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
-{
-    const dim4 sDims    = signal.dims();
-    const dim4 fDims    = filter.dims();
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+                  AF_BATCH_KIND kind) {
+    const dim4 sDims = signal.dims();
+    const dim4 fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
-                oDims[d] = sDims[d]+fDims[d]-1;
+        for (dim_t d = 0; d < 4; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
+                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_RHS) {
-            for (dim_t i=baseDim; i<4; ++i)
-                oDims[i] = fDims[i];
+        if (kind == AF_BATCH_RHS) {
+            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
         }
     }
 
-    Array<T> out   = createEmptyArray<T>(oDims);
+    Array<T> out = createEmptyArray<T>(oDims);
 
     kernel::convolve_nd<T, accT, baseDim, expand>(out, signal, filter, kind);
 
@@ -49,17 +47,17 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
 }
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter)
-{
-    const dim4 cfDims   = c_filter.dims();
-    const dim4 rfDims   = r_filter.dims();
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter) {
+    const dim4 cfDims = c_filter.dims();
+    const dim4 rfDims = r_filter.dims();
 
-    const dim_t cfLen= cfDims.elements();
-    const dim_t rfLen= rfDims.elements();
+    const dim_t cfLen = cfDims.elements();
+    const dim_t rfLen = rfDims.elements();
 
     const dim4 sDims = signal.dims();
-    dim4 tDims = sDims;
-    dim4 oDims = sDims;
+    dim4 tDims       = sDims;
+    dim4 oDims       = sDims;
 
     if (expand) {
         tDims[0] += cfLen - 1;
@@ -67,8 +65,8 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
         oDims[1] += rfLen - 1;
     }
 
-    Array<T> temp= createEmptyArray<T>(tDims);
-    Array<T> out = createEmptyArray<T>(oDims);
+    Array<T> temp = createEmptyArray<T>(tDims);
+    Array<T> out  = createEmptyArray<T>(oDims);
 
     kernel::convolve2<T, accT, 0, expand>(temp, signal, c_filter);
     kernel::convolve2<T, accT, 1, expand>(out, temp, r_filter);
@@ -76,27 +74,43 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                            \
-    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve2<T, accT, true >(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
+#define INSTANTIATE(T, accT)                                                 \
+    template Array<T> convolve<T, accT, 1, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 1, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve<T, accT, 2, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 2, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve<T, accT, 3, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 3, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve2<T, accT, true>(Array<T> const& signal,       \
+                                               Array<accT> const& c_filter,  \
+                                               Array<accT> const& r_filter); \
+    template Array<T> convolve2<T, accT, false>(Array<T> const& signal,      \
+                                                Array<accT> const& c_filter, \
+                                                Array<accT> const& r_filter);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
-
-}
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/convolve.hpp b/src/backend/cuda/convolve.hpp
index 6ee841bfb5..01b211dbc5 100644
--- a/src/backend/cuda/convolve.hpp
+++ b/src/backend/cuda/convolve.hpp
@@ -9,13 +9,14 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+                  AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 2cc50b4aea..9b4a624d9a 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -7,206 +7,235 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cuda_runtime_api.h>
 #include <Array.hpp>
 #include <copy.hpp>
-#include <kernel/memcopy.hpp>
+#include <cuda_runtime_api.h>
 #include <err_cuda.hpp>
+#include <kernel/memcopy.hpp>
 #include <math.hpp>
 
 #include <common/complex.hpp>
 
 using common::is_complex;
 
-namespace cuda
-{
-
-    template<typename T>
-    void copyData(T *dst, const Array<T> &src)
-    {
-        // FIXME: Merge this with copyArray
-        src.eval();
-
-        Array<T> out = src;
-        const T *ptr = NULL;
-
-        if (src.isLinear() || // No offsets, No strides
-            src.ndims() == 1 // Simple offset, no strides.
-            ) {
-
-            //A.get() gets data with offsets
-            ptr = src.get();
-        } else {
-            //FIXME: Think about implementing eval
-            out = copyArray(src);
-            ptr = out.get();
-        }
-
-        auto stream = cuda::getActiveStream();
-        CUDA_CHECK(cudaMemcpyAsync(dst, ptr,
-                                   src.elements() * sizeof(T),
-                                   cudaMemcpyDeviceToHost,
-                                   stream));
-        CUDA_CHECK(cudaStreamSynchronize(stream));
-        return;
+namespace cuda {
+
+template<typename T>
+void copyData(T *dst, const Array<T> &src) {
+    // FIXME: Merge this with copyArray
+    src.eval();
+
+    Array<T> out = src;
+    const T *ptr = NULL;
+
+    if (src.isLinear() ||  // No offsets, No strides
+        src.ndims() == 1   // Simple offset, no strides.
+    ) {
+        // A.get() gets data with offsets
+        ptr = src.get();
+    } else {
+        // FIXME: Think about implementing eval
+        out = copyArray(src);
+        ptr = out.get();
     }
 
-    template<typename T>
-    Array<T> copyArray(const Array<T> &src)
-    {
-        Array<T> out = createEmptyArray<T>(src.dims());
+    auto stream = cuda::getActiveStream();
+    CUDA_CHECK(cudaMemcpyAsync(dst, ptr, src.elements() * sizeof(T),
+                               cudaMemcpyDeviceToHost, stream));
+    CUDA_CHECK(cudaStreamSynchronize(stream));
+    return;
+}
 
-        if (src.isLinear()) {
-            CUDA_CHECK(cudaMemcpyAsync(out.get(), src.get(),
-                                       src.elements() * sizeof(T),
-                                       cudaMemcpyDeviceToDevice,
-                                       cuda::getActiveStream()));
-        } else {
-            // FIXME: Seems to fail when using Param<T>
-            kernel::memcopy(out.get(), out.strides().get(), src.get(), src.dims().get(),
-                            src.strides().get(), (uint)src.ndims());
-        }
-        return out;
+template<typename T>
+Array<T> copyArray(const Array<T> &src) {
+    Array<T> out = createEmptyArray<T>(src.dims());
+
+    if (src.isLinear()) {
+        CUDA_CHECK(
+            cudaMemcpyAsync(out.get(), src.get(), src.elements() * sizeof(T),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+    } else {
+        // FIXME: Seems to fail when using Param<T>
+        kernel::memcopy(out.get(), out.strides().get(), src.get(),
+                        src.dims().get(), src.strides().get(),
+                        (uint)src.ndims());
     }
+    return out;
+}
 
-    template<typename inType, typename outType>
-    Array<outType> padArray(Array<inType> const &in, dim4 const &dims, outType default_value, double factor)
-    {
-        ARG_ASSERT(1, (in.ndims() == (size_t)dims.ndims()));
-        Array<outType> ret = createEmptyArray<outType>(dims);
-        kernel::copy<inType, outType>(ret, in, in.ndims(), default_value, factor);
-        return ret;
-    }
+template<typename inType, typename outType>
+Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                        outType default_value, double factor) {
+    ARG_ASSERT(1, (in.ndims() == (size_t)dims.ndims()));
+    Array<outType> ret = createEmptyArray<outType>(dims);
+    kernel::copy<inType, outType>(ret, in, in.ndims(), default_value, factor);
+    return ret;
+}
 
-    template<typename T>
-    void multiply_inplace(Array<T> &in, double val)
-    {
-        kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val);
-    }
+template<typename T>
+void multiply_inplace(Array<T> &in, double val) {
+    kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val);
+}
 
-    template<typename inType, typename outType>
-    struct copyWrapper {
-        void operator()(Array<outType> &out, Array<inType> const &in)
-        {
-            kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0), 1);
-        }
-    };
-
-    template<typename T>
-    struct copyWrapper<T, T> {
-        void operator()(Array<T> &out, Array<T> const &in)
-        {
-            if (out.isLinear() &&
-                in.isLinear() &&
-                out.elements() == in.elements())
-            {
-                CUDA_CHECK(cudaMemcpyAsync(out.get(), in.get(),
-                                           in.elements() * sizeof(T),
-                                           cudaMemcpyDeviceToDevice,
-                                           cuda::getActiveStream()));
-            } else {
-                kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1);
-            }
-        }
-    };
-
-    template<typename inType, typename outType>
-    void copyArray(Array<outType> &out, Array<inType> const &in)
-    {
-        static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
-                      "Cannot copy from complex value to a non complex value");
-        ARG_ASSERT(1, (in.ndims() == (size_t)out.dims().ndims()));
-        copyWrapper<inType, outType> copyFn;
-        copyFn(out, in);
+template<typename inType, typename outType>
+struct copyWrapper {
+    void operator()(Array<outType> &out, Array<inType> const &in) {
+        kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
+                                      1);
     }
-
-#define INSTANTIATE(T)                                                  \
-    template void      copyData<T> (T *dst, const Array<T> &src);       \
-    template Array<T>  copyArray<T>(const Array<T> &src);               \
-    template void      multiply_inplace<T> (Array<T> &in, double norm); \
-
-    INSTANTIATE(float  )
-    INSTANTIATE(double )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(char   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
-
-#define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
-    template Array<float  > padArray<SRC_T, float  >(Array<SRC_T> const &src, dim4 const &dims, float   default_value, double factor); \
-    template Array<double > padArray<SRC_T, double >(Array<SRC_T> const &src, dim4 const &dims, double  default_value, double factor); \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
-    template Array<int    > padArray<SRC_T, int    >(Array<SRC_T> const &src, dim4 const &dims, int     default_value, double factor); \
-    template Array<uint   > padArray<SRC_T, uint   >(Array<SRC_T> const &src, dim4 const &dims, uint    default_value, double factor); \
-    template Array<intl   > padArray<SRC_T, intl   >(Array<SRC_T> const &src, dim4 const &dims, intl    default_value, double factor); \
-    template Array<uintl  > padArray<SRC_T, uintl  >(Array<SRC_T> const &src, dim4 const &dims, uintl   default_value, double factor); \
-    template Array<short  > padArray<SRC_T, short  >(Array<SRC_T> const &src, dim4 const &dims, short   default_value, double factor); \
-    template Array<ushort > padArray<SRC_T, ushort >(Array<SRC_T> const &src, dim4 const &dims, ushort  default_value, double factor); \
-    template Array<uchar  > padArray<SRC_T, uchar  >(Array<SRC_T> const &src, dim4 const &dims, uchar   default_value, double factor); \
-    template Array<char   > padArray<SRC_T, char   >(Array<SRC_T> const &src, dim4 const &dims, char    default_value, double factor); \
-    template void copyArray<SRC_T, float  >(Array<float  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, double >(Array<double > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, cfloat >(Array<cfloat > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, int    >(Array<int    > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, uint   >(Array<uint   > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, intl   >(Array<intl   > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, uintl  >(Array<uintl  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, short  >(Array<short  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, ushort >(Array<ushort > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, uchar  >(Array<uchar  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, char   >(Array<char   > &dst, Array<SRC_T> const &src);
-
-    INSTANTIATE_PAD_ARRAY(float )
-    INSTANTIATE_PAD_ARRAY(double)
-    INSTANTIATE_PAD_ARRAY(int   )
-    INSTANTIATE_PAD_ARRAY(uint  )
-    INSTANTIATE_PAD_ARRAY(intl  )
-    INSTANTIATE_PAD_ARRAY(uintl )
-    INSTANTIATE_PAD_ARRAY(short )
-    INSTANTIATE_PAD_ARRAY(ushort)
-    INSTANTIATE_PAD_ARRAY(uchar )
-    INSTANTIATE_PAD_ARRAY(char  )
-
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
-    template void copyArray<SRC_T, cfloat  >(Array<cfloat  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, cdouble   >(Array<cdouble > &dst, Array<SRC_T> const &src);
-
-    INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
-    INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
-
-    template<typename T>
-    T getScalar(const Array<T> &in)
-    {
-        T retVal;
-        CUDA_CHECK(cudaMemcpyAsync(&retVal, in.get(), sizeof(T),
-                                   cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-        return retVal;
+};
+
+template<typename T>
+struct copyWrapper<T, T> {
+    void operator()(Array<T> &out, Array<T> const &in) {
+        if (out.isLinear() && in.isLinear() &&
+            out.elements() == in.elements()) {
+            CUDA_CHECK(cudaMemcpyAsync(
+                out.get(), in.get(), in.elements() * sizeof(T),
+                cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        } else {
+            kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1);
+        }
     }
+};
+
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, Array<inType> const &in) {
+    static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
+                  "Cannot copy from complex value to a non complex value");
+    ARG_ASSERT(1, (in.ndims() == (size_t)out.dims().ndims()));
+    copyWrapper<inType, outType> copyFn;
+    copyFn(out, in);
+}
 
-#define INSTANTIATE_GETSCALAR(T) \
-    template T getScalar(const Array<T> &in);
-
-    INSTANTIATE_GETSCALAR(float  )
-    INSTANTIATE_GETSCALAR(double )
-    INSTANTIATE_GETSCALAR(cfloat )
-    INSTANTIATE_GETSCALAR(cdouble)
-    INSTANTIATE_GETSCALAR(int    )
-    INSTANTIATE_GETSCALAR(uint   )
-    INSTANTIATE_GETSCALAR(uchar  )
-    INSTANTIATE_GETSCALAR(char   )
-    INSTANTIATE_GETSCALAR(intl   )
-    INSTANTIATE_GETSCALAR(uintl  )
-    INSTANTIATE_GETSCALAR(short  )
-    INSTANTIATE_GETSCALAR(ushort )
+#define INSTANTIATE(T)                                       \
+    template void copyData<T>(T * dst, const Array<T> &src); \
+    template Array<T> copyArray<T>(const Array<T> &src);     \
+    template void multiply_inplace<T>(Array<T> & in, double norm);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+#define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
+    template Array<float> padArray<SRC_T, float>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, float default_value,   \
+        double factor);                                                   \
+    template Array<double> padArray<SRC_T, double>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, double default_value,  \
+        double factor);                                                   \
+    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
+        double factor);                                                   \
+    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
+        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
+        double factor);                                                   \
+    template Array<int> padArray<SRC_T, int>(                             \
+        Array<SRC_T> const &src, dim4 const &dims, int default_value,     \
+        double factor);                                                   \
+    template Array<uint> padArray<SRC_T, uint>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, uint default_value,    \
+        double factor);                                                   \
+    template Array<intl> padArray<SRC_T, intl>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, intl default_value,    \
+        double factor);                                                   \
+    template Array<uintl> padArray<SRC_T, uintl>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, uintl default_value,   \
+        double factor);                                                   \
+    template Array<short> padArray<SRC_T, short>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, short default_value,   \
+        double factor);                                                   \
+    template Array<ushort> padArray<SRC_T, ushort>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, ushort default_value,  \
+        double factor);                                                   \
+    template Array<uchar> padArray<SRC_T, uchar>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, uchar default_value,   \
+        double factor);                                                   \
+    template Array<char> padArray<SRC_T, char>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, char default_value,    \
+        double factor);                                                   \
+    template void copyArray<SRC_T, float>(Array<float> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, double>(Array<double> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
+                                            Array<SRC_T> const &src);     \
+    template void copyArray<SRC_T, int>(Array<int> & dst,                 \
+                                        Array<SRC_T> const &src);         \
+    template void copyArray<SRC_T, uint>(Array<uint> & dst,               \
+                                         Array<SRC_T> const &src);        \
+    template void copyArray<SRC_T, intl>(Array<intl> & dst,               \
+                                         Array<SRC_T> const &src);        \
+    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, short>(Array<short> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, char>(Array<char> & dst,               \
+                                         Array<SRC_T> const &src);
+
+INSTANTIATE_PAD_ARRAY(float)
+INSTANTIATE_PAD_ARRAY(double)
+INSTANTIATE_PAD_ARRAY(int)
+INSTANTIATE_PAD_ARRAY(uint)
+INSTANTIATE_PAD_ARRAY(intl)
+INSTANTIATE_PAD_ARRAY(uintl)
+INSTANTIATE_PAD_ARRAY(short)
+INSTANTIATE_PAD_ARRAY(ushort)
+INSTANTIATE_PAD_ARRAY(uchar)
+INSTANTIATE_PAD_ARRAY(char)
+
+#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
+    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
+        double factor);                                                   \
+    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
+        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
+        double factor);                                                   \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
+                                            Array<SRC_T> const &src);
+
+INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
+INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+
+template<typename T>
+T getScalar(const Array<T> &in) {
+    T retVal;
+    CUDA_CHECK(cudaMemcpyAsync(&retVal, in.get(), sizeof(T),
+                               cudaMemcpyDeviceToHost,
+                               cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+    return retVal;
 }
+
+#define INSTANTIATE_GETSCALAR(T) template T getScalar(const Array<T> &in);
+
+INSTANTIATE_GETSCALAR(float)
+INSTANTIATE_GETSCALAR(double)
+INSTANTIATE_GETSCALAR(cfloat)
+INSTANTIATE_GETSCALAR(cdouble)
+INSTANTIATE_GETSCALAR(int)
+INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(uchar)
+INSTANTIATE_GETSCALAR(char)
+INSTANTIATE_GETSCALAR(intl)
+INSTANTIATE_GETSCALAR(uintl)
+INSTANTIATE_GETSCALAR(short)
+INSTANTIATE_GETSCALAR(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index 7d2b316fb0..be778832c4 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -10,41 +10,39 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    // Copies(blocking) data from an Array<T> object to a contiguous host side
-    // pointer.
-    //
-    // \param dst The destination pointer on the host system.
-    // \param src    The source array
-    template<typename T>
-    void copyData(T *dst, const Array<T> &src);
-
-    // Create a deep copy of the \p src Array with the same size and shape. The new
-    // Array will not maintain the subarray metadata of the \p src array.
-    //
-    // \param   src  The source Array<T> object.
-    // \returns      A new Array<T> object with the same shape and data as the
-    //               \p src Array<T>
-    template<typename T>
-    Array<T> copyArray(const Array<T> &src);
-
-    template<typename inType, typename outType>
-    void copyArray(Array<outType> &out, const Array<inType> &in);
-
-    template<typename inType, typename outType>
-    Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                            outType default_value, double factor=1.0);
-
-    template<typename T>
-    Array<T> padArrayBorders(Array<T> const& in,
-                             dim4 const& lowerBoundPadding,
-                             dim4 const& upperBoundPadding,
-                             const af::borderType btype);
-
-    template<typename T>
-    void multiply_inplace(Array<T> &in, double val);
-
-    template<typename T>
-    T getScalar(const Array<T> &in);
-}
+namespace cuda {
+// Copies(blocking) data from an Array<T> object to a contiguous host side
+// pointer.
+//
+// \param dst The destination pointer on the host system.
+// \param src    The source array
+template<typename T>
+void copyData(T *dst, const Array<T> &src);
+
+// Create a deep copy of the \p src Array with the same size and shape. The new
+// Array will not maintain the subarray metadata of the \p src array.
+//
+// \param   src  The source Array<T> object.
+// \returns      A new Array<T> object with the same shape and data as the
+//               \p src Array<T>
+template<typename T>
+Array<T> copyArray(const Array<T> &src);
+
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, const Array<inType> &in);
+
+template<typename inType, typename outType>
+Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                        outType default_value, double factor = 1.0);
+
+template<typename T>
+Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
+                         dim4 const &upperBoundPadding,
+                         const af::borderType btype);
+
+template<typename T>
+void multiply_inplace(Array<T> &in, double val);
+
+template<typename T>
+T getScalar(const Array<T> &in);
+}  // namespace cuda
diff --git a/src/backend/cuda/count.cu b/src/backend/cuda/count.cu
index 365897f75d..25590f4704 100644
--- a/src/backend/cuda/count.cu
+++ b/src/backend/cuda/count.cu
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    // count
-    INSTANTIATE(af_notzero_t, float  , uint)
-    INSTANTIATE(af_notzero_t, double , uint)
-    INSTANTIATE(af_notzero_t, cfloat , uint)
-    INSTANTIATE(af_notzero_t, cdouble, uint)
-    INSTANTIATE(af_notzero_t, int    , uint)
-    INSTANTIATE(af_notzero_t, uint   , uint)
-    INSTANTIATE(af_notzero_t, intl   , uint)
-    INSTANTIATE(af_notzero_t, uintl  , uint)
-    INSTANTIATE(af_notzero_t, short  , uint)
-    INSTANTIATE(af_notzero_t, ushort , uint)
-    INSTANTIATE(af_notzero_t, char   , uint)
-    INSTANTIATE(af_notzero_t, uchar  , uint)
-}
+namespace cuda {
+// count
+INSTANTIATE(af_notzero_t, float, uint)
+INSTANTIATE(af_notzero_t, double, uint)
+INSTANTIATE(af_notzero_t, cfloat, uint)
+INSTANTIATE(af_notzero_t, cdouble, uint)
+INSTANTIATE(af_notzero_t, int, uint)
+INSTANTIATE(af_notzero_t, uint, uint)
+INSTANTIATE(af_notzero_t, intl, uint)
+INSTANTIATE(af_notzero_t, uintl, uint)
+INSTANTIATE(af_notzero_t, short, uint)
+INSTANTIATE(af_notzero_t, ushort, uint)
+INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, uchar, uint)
+}  // namespace cuda
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 902c9aed94..aeabf961c2 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -7,31 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <platform.hpp>
 #include <common/err_common.hpp>
 #include <cublas.hpp>
+#include <platform.hpp>
 
-namespace cuda
-{
-const char *errorString(cublasStatus_t err)
-{
-    switch(err)
-    {
-    case CUBLAS_STATUS_SUCCESS         : return "CUBLAS_STATUS_SUCCESS"         ;
-    case CUBLAS_STATUS_NOT_INITIALIZED : return "CUBLAS_STATUS_NOT_INITIALIZED" ;
-    case CUBLAS_STATUS_ALLOC_FAILED    : return "CUBLAS_STATUS_ALLOC_FAILED"    ;
-    case CUBLAS_STATUS_INVALID_VALUE   : return "CUBLAS_STATUS_INVALID_VALUE"   ;
-    case CUBLAS_STATUS_ARCH_MISMATCH   : return "CUBLAS_STATUS_ARCH_MISMATCH"   ;
-    case CUBLAS_STATUS_MAPPING_ERROR   : return "CUBLAS_STATUS_MAPPING_ERROR"   ;
-    case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
-    case CUBLAS_STATUS_INTERNAL_ERROR  : return "CUBLAS_STATUS_INTERNAL_ERROR"  ;
-    case CUBLAS_STATUS_NOT_SUPPORTED   : return "CUBLAS_STATUS_NOT_SUPPORTED"   ;
-    default: return "UNKNOWN";
+namespace cuda {
+const char* errorString(cublasStatus_t err) {
+    switch (err) {
+        case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
+        case CUBLAS_STATUS_NOT_INITIALIZED:
+            return "CUBLAS_STATUS_NOT_INITIALIZED";
+        case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
+        case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
+        case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
+        case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
+        case CUBLAS_STATUS_EXECUTION_FAILED:
+            return "CUBLAS_STATUS_EXECUTION_FAILED";
+        case CUBLAS_STATUS_INTERNAL_ERROR:
+            return "CUBLAS_STATUS_INTERNAL_ERROR";
+        case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
+        default: return "UNKNOWN";
     }
 }
 
-void cublasHandle::createHandle(BlasHandle* handle)
-{
+void cublasHandle::createHandle(BlasHandle* handle) {
     CUBLAS_CHECK(cublasCreate(handle));
 }
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index 6914d957ca..cf767dc30e 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -8,37 +8,31 @@
  ********************************************************/
 
 #pragma once
-#include <cublas_v2.h>
-#include <common/defines.hpp>
 #include <common/MatrixAlgebraHandle.hpp>
+#include <common/defines.hpp>
+#include <cublas_v2.h>
 
-namespace cuda
-{
+namespace cuda {
 typedef cublasHandle_t BlasHandle;
 
-const char * errorString(cublasStatus_t err);
+const char* errorString(cublasStatus_t err);
 
-#define CUBLAS_CHECK(fn) do {                   \
-        cublasStatus_t _error = fn;             \
-        if (_error != CUBLAS_STATUS_SUCCESS) {  \
-            char _err_msg[1024];                \
-            snprintf(_err_msg,                  \
-                     sizeof(_err_msg),          \
-                     "CUBLAS Error (%d): %s\n", \
-                     (int)(_error),             \
-                     errorString(_error));      \
-                                                \
-            AF_ERROR(_err_msg,                  \
-                     AF_ERR_INTERNAL);          \
-        }                                       \
-    } while(0)
+#define CUBLAS_CHECK(fn)                                                    \
+    do {                                                                    \
+        cublasStatus_t _error = fn;                                         \
+        if (_error != CUBLAS_STATUS_SUCCESS) {                              \
+            char _err_msg[1024];                                            \
+            snprintf(_err_msg, sizeof(_err_msg), "CUBLAS Error (%d): %s\n", \
+                     (int)(_error), errorString(_error));                   \
+                                                                            \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
+        }                                                                   \
+    } while (0)
 
-class cublasHandle : public common::MatrixAlgebraHandle<cublasHandle, BlasHandle>
-{
-    public:
-        void createHandle(BlasHandle* handle);
-        void destroyHandle(BlasHandle handle) {
-            cublasDestroy(handle);
-        }
+class cublasHandle
+    : public common::MatrixAlgebraHandle<cublasHandle, BlasHandle> {
+   public:
+    void createHandle(BlasHandle* handle);
+    void destroyHandle(BlasHandle handle) { cublasDestroy(handle); }
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index 2dcf2eaa97..ec85eae175 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -7,27 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <platform.hpp>
 #include <cufft.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 
-namespace cuda
-{
-const char * _cufftGetResultString(cufftResult res)
-{
-    switch (res)
-    {
-        case CUFFT_SUCCESS:
-            return "cuFFT: success";
+namespace cuda {
+const char *_cufftGetResultString(cufftResult res) {
+    switch (res) {
+        case CUFFT_SUCCESS: return "cuFFT: success";
 
-        case CUFFT_INVALID_PLAN:
-            return "cuFFT: invalid plan handle passed";
+        case CUFFT_INVALID_PLAN: return "cuFFT: invalid plan handle passed";
 
-        case CUFFT_ALLOC_FAILED:
-            return "cuFFT: resources allocation failed";
+        case CUFFT_ALLOC_FAILED: return "cuFFT: resources allocation failed";
 
-        case CUFFT_INVALID_TYPE:
-            return "cuFFT: invalid type (deprecated)";
+        case CUFFT_INVALID_TYPE: return "cuFFT: invalid type (deprecated)";
 
         case CUFFT_INVALID_VALUE:
             return "cuFFT: invalid parameters passed to cuFFT API";
@@ -35,17 +28,13 @@ const char * _cufftGetResultString(cufftResult res)
         case CUFFT_INTERNAL_ERROR:
             return "cuFFT: internal error detected using cuFFT";
 
-        case CUFFT_EXEC_FAILED:
-            return "cuFFT: FFT execution failed";
+        case CUFFT_EXEC_FAILED: return "cuFFT: FFT execution failed";
 
-        case CUFFT_SETUP_FAILED:
-            return "cuFFT: library initialization failed";
+        case CUFFT_SETUP_FAILED: return "cuFFT: library initialization failed";
 
-        case CUFFT_INVALID_SIZE:
-            return "cuFFT: invalid size parameters passed";
+        case CUFFT_INVALID_SIZE: return "cuFFT: invalid size parameters passed";
 
-        case CUFFT_UNALIGNED_DATA:
-            return "cuFFT: unaligned data (deprecated)";
+        case CUFFT_UNALIGNED_DATA: return "cuFFT: unaligned data (deprecated)";
 
         case CUFFT_INCOMPLETE_PARAMETER_LIST:
             return "cuFFT: call is missing parameters";
@@ -53,45 +42,38 @@ const char * _cufftGetResultString(cufftResult res)
         case CUFFT_INVALID_DEVICE:
             return "cuFFT: plan execution different than plan creation";
 
-        case CUFFT_PARSE_ERROR:
-            return "cuFFT: plan parse error";
+        case CUFFT_PARSE_ERROR: return "cuFFT: plan parse error";
 
-        case CUFFT_NO_WORKSPACE:
-            return "cuFFT: no workspace provided";
+        case CUFFT_NO_WORKSPACE: return "cuFFT: no workspace provided";
 
-        case CUFFT_NOT_IMPLEMENTED:
-            return "cuFFT: not implemented";
+        case CUFFT_NOT_IMPLEMENTED: return "cuFFT: not implemented";
 
-        case CUFFT_LICENSE_ERROR:
-            return "cuFFT: license error";
+        case CUFFT_LICENSE_ERROR: return "cuFFT: license error";
 
 #if CUDA_VERSION >= 8000
-        case CUFFT_NOT_SUPPORTED:
-            return "cuFFT: not supported";
+        case CUFFT_NOT_SUPPORTED: return "cuFFT: not supported";
 #endif
     }
 
     return "cuFFT: unknown error";
 }
 
-SharedPlan findPlan(int rank, int *n,
-                    int *inembed, int istride, int idist,
-                    int *onembed, int ostride, int odist,
-                    cufftType type, int batch)
-{
+SharedPlan findPlan(int rank, int *n, int *inembed, int istride, int idist,
+                    int *onembed, int ostride, int odist, cufftType type,
+                    int batch) {
     // create the key string
     char key_str_temp[64];
     sprintf(key_str_temp, "%d:", rank);
 
     std::string key_string(key_str_temp);
 
-    for(int r=0; r<rank; ++r) {
+    for (int r = 0; r < rank; ++r) {
         sprintf(key_str_temp, "%d:", n[r]);
         key_string.append(std::string(key_str_temp));
     }
 
-    if (inembed!=NULL) {
-        for(int r=0; r<rank; ++r) {
+    if (inembed != NULL) {
+        for (int r = 0; r < rank; ++r) {
             sprintf(key_str_temp, "%d:", inembed[r]);
             key_string.append(std::string(key_str_temp));
         }
@@ -99,8 +81,8 @@ SharedPlan findPlan(int rank, int *n,
         key_string.append(std::string(key_str_temp));
     }
 
-    if (onembed!=NULL) {
-        for(int r=0; r<rank; ++r) {
+    if (onembed != NULL) {
+        for (int r = 0; r < rank; ++r) {
             sprintf(key_str_temp, "%d:", onembed[r]);
             key_string.append(std::string(key_str_temp));
         }
@@ -114,23 +96,20 @@ SharedPlan findPlan(int rank, int *n,
     PlanCache &planner = cuda::fftManager();
     SharedPlan retVal  = planner.find(key_string);
 
-    if (retVal)
-        return retVal;
+    if (retVal) return retVal;
 
-    PlanType* temp = (PlanType*)malloc(sizeof(PlanType));
-    cufftResult res = cufftPlanMany(temp, rank, n,
-                                    inembed, istride, idist, onembed, ostride, odist,
-                                    type, batch);
+    PlanType *temp  = (PlanType *)malloc(sizeof(PlanType));
+    cufftResult res = cufftPlanMany(temp, rank, n, inembed, istride, idist,
+                                    onembed, ostride, odist, type, batch);
 
     // If plan creation fails, clean up the memory we hold on to and try again
     if (res != CUFFT_SUCCESS) {
         cuda::garbageCollect();
-        CUFFT_CHECK(cufftPlanMany(temp, rank, n,
-                                  inembed, istride, idist, onembed, ostride, odist,
-                                  type, batch));
+        CUFFT_CHECK(cufftPlanMany(temp, rank, n, inembed, istride, idist,
+                                  onembed, ostride, odist, type, batch));
     }
 
-    retVal.reset(temp, [](PlanType* p) {
+    retVal.reset(temp, [](PlanType *p) {
         cufftDestroy(*p);
         free(p);
     });
@@ -139,4 +118,4 @@ SharedPlan findPlan(int rank, int *n,
 
     return retVal;
 }
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cufft.hpp b/src/backend/cuda/cufft.hpp
index 04a22d4514..bba83ca546 100644
--- a/src/backend/cuda/cufft.hpp
+++ b/src/backend/cuda/cufft.hpp
@@ -8,44 +8,37 @@
  ********************************************************/
 
 #pragma once
+#include <common/FFTPlanCache.hpp>
 #include <common/err_common.hpp>
 #include <cufft.h>
-#include <common/FFTPlanCache.hpp>
 #include <cstdio>
 
-namespace cuda
-{
+namespace cuda {
 typedef cufftHandle PlanType;
 typedef std::shared_ptr<PlanType> SharedPlan;
 
-const char * _cufftGetResultString(cufftResult res);
+const char *_cufftGetResultString(cufftResult res);
 
-SharedPlan findPlan(int rank, int *n,
-                    int *inembed, int istride, int idist,
-                    int *onembed, int ostride, int odist,
-                    cufftType type, int batch);
+SharedPlan findPlan(int rank, int *n, int *inembed, int istride, int idist,
+                    int *onembed, int ostride, int odist, cufftType type,
+                    int batch);
 
-class PlanCache : public common::FFTPlanCache<PlanCache, PlanType>
-{
-    friend SharedPlan findPlan(int rank, int *n,
-                               int *inembed, int istride, int idist,
-                               int *onembed, int ostride, int odist,
+class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
+    friend SharedPlan findPlan(int rank, int *n, int *inembed, int istride,
+                               int idist, int *onembed, int ostride, int odist,
                                cufftType type, int batch);
 };
-}
+}  // namespace cuda
 
-#define CUFFT_CHECK(fn) do {                        \
-        cufftResult _cufft_res = fn;                \
-        if (_cufft_res != CUFFT_SUCCESS) {          \
-            char cufft_res_msg[1024];               \
-            snprintf(cufft_res_msg,                 \
-                     sizeof(cufft_res_msg),         \
-                     "cuFFT Error (%d): %s\n",      \
-                     (int)(_cufft_res),             \
-                     cuda::_cufftGetResultString(   \
-                         _cufft_res));              \
-                                                    \
-            AF_ERROR(cufft_res_msg,                 \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
+#define CUFFT_CHECK(fn)                                           \
+    do {                                                          \
+        cufftResult _cufft_res = fn;                              \
+        if (_cufft_res != CUFFT_SUCCESS) {                        \
+            char cufft_res_msg[1024];                             \
+            snprintf(cufft_res_msg, sizeof(cufft_res_msg),        \
+                     "cuFFT Error (%d): %s\n", (int)(_cufft_res), \
+                     cuda::_cufftGetResultString(_cufft_res));    \
+                                                                  \
+            AF_ERROR(cufft_res_msg, AF_ERR_INTERNAL);             \
+        }                                                         \
+    } while (0)
diff --git a/src/backend/cuda/cusolverDn.cpp b/src/backend/cuda/cusolverDn.cpp
index e02f50cae2..afe88d3374 100644
--- a/src/backend/cuda/cusolverDn.cpp
+++ b/src/backend/cuda/cusolverDn.cpp
@@ -8,29 +8,37 @@
  ********************************************************/
 
 #include <cusolverDn.hpp>
-#include <platform.hpp>
 #include <debug_cuda.hpp>
+#include <platform.hpp>
 #include <stdexcept>
 #include <string>
 
-namespace cuda
-{
-const char *errorString(cusolverStatus_t err)
-{
-    switch(err) {
-    case CUSOLVER_STATUS_SUCCESS                  : return "CUSOLVER_STATUS_SUCCESS"                  ;
-    case CUSOLVER_STATUS_NOT_INITIALIZED          : return "CUSOLVER_STATUS_NOT_INITIALIZED"          ;
-    case CUSOLVER_STATUS_ALLOC_FAILED             : return "CUSOLVER_STATUS_ALLOC_FAILED"             ;
-    case CUSOLVER_STATUS_INVALID_VALUE            : return "CUSOLVER_STATUS_INVALID_VALUE"            ;
-    case CUSOLVER_STATUS_ARCH_MISMATCH            : return "CUSOLVER_STATUS_ARCH_MISMATCH"            ;
-    case CUSOLVER_STATUS_MAPPING_ERROR            : return "CUSOLVER_STATUS_MAPPING_ERROR"            ;
-    case CUSOLVER_STATUS_EXECUTION_FAILED         : return "CUSOLVER_STATUS_EXECUTION_FAILED"         ;
-    case CUSOLVER_STATUS_INTERNAL_ERROR           : return "CUSOLVER_STATUS_INTERNAL_ERROR"           ;
-    case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED: return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
-    case CUSOLVER_STATUS_NOT_SUPPORTED            : return "CUSOLVER_STATUS_NOT_SUPPORTED"            ;
-    case CUSOLVER_STATUS_ZERO_PIVOT               : return "CUSOLVER_STATUS_ZERO_PIVOT"               ;
-    case CUSOLVER_STATUS_INVALID_LICENSE          : return "CUSOLVER_STATUS_INVALID_LICENSE"          ;
-    default: return "UNKNOWN";
+namespace cuda {
+const char *errorString(cusolverStatus_t err) {
+    switch (err) {
+        case CUSOLVER_STATUS_SUCCESS: return "CUSOLVER_STATUS_SUCCESS";
+        case CUSOLVER_STATUS_NOT_INITIALIZED:
+            return "CUSOLVER_STATUS_NOT_INITIALIZED";
+        case CUSOLVER_STATUS_ALLOC_FAILED:
+            return "CUSOLVER_STATUS_ALLOC_FAILED";
+        case CUSOLVER_STATUS_INVALID_VALUE:
+            return "CUSOLVER_STATUS_INVALID_VALUE";
+        case CUSOLVER_STATUS_ARCH_MISMATCH:
+            return "CUSOLVER_STATUS_ARCH_MISMATCH";
+        case CUSOLVER_STATUS_MAPPING_ERROR:
+            return "CUSOLVER_STATUS_MAPPING_ERROR";
+        case CUSOLVER_STATUS_EXECUTION_FAILED:
+            return "CUSOLVER_STATUS_EXECUTION_FAILED";
+        case CUSOLVER_STATUS_INTERNAL_ERROR:
+            return "CUSOLVER_STATUS_INTERNAL_ERROR";
+        case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
+            return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
+        case CUSOLVER_STATUS_NOT_SUPPORTED:
+            return "CUSOLVER_STATUS_NOT_SUPPORTED";
+        case CUSOLVER_STATUS_ZERO_PIVOT: return "CUSOLVER_STATUS_ZERO_PIVOT";
+        case CUSOLVER_STATUS_INVALID_LICENSE:
+            return "CUSOLVER_STATUS_INVALID_LICENSE";
+        default: return "UNKNOWN";
     }
 }
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index c9a3f38240..4d46ec9439 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -9,41 +9,35 @@
 
 #pragma once
 
+#include <common/MatrixAlgebraHandle.hpp>
+#include <common/defines.hpp>
 #include <cuda_runtime.h>
 #include <cusolverDn.h>
-#include <common/defines.hpp>
-#include <common/MatrixAlgebraHandle.hpp>
 
-namespace cuda
-{
+namespace cuda {
 typedef cusolverDnHandle_t SolveHandle;
 
-const char * errorString(cusolverStatus_t err);
+const char* errorString(cusolverStatus_t err);
 
-#define CUSOLVER_CHECK(fn) do {                     \
-        cusolverStatus_t _error = fn;               \
-        if (_error != CUSOLVER_STATUS_SUCCESS) {    \
-            char _err_msg[1024];                    \
-            snprintf(_err_msg,                      \
-                     sizeof(_err_msg),              \
-                     "CUBLAS Error (%d): %s\n",     \
-                     (int)(_error),                 \
-                     errorString(_error));          \
-                                                    \
-            AF_ERROR(_err_msg,                      \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
+#define CUSOLVER_CHECK(fn)                                                  \
+    do {                                                                    \
+        cusolverStatus_t _error = fn;                                       \
+        if (_error != CUSOLVER_STATUS_SUCCESS) {                            \
+            char _err_msg[1024];                                            \
+            snprintf(_err_msg, sizeof(_err_msg), "CUBLAS Error (%d): %s\n", \
+                     (int)(_error), errorString(_error));                   \
+                                                                            \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
+        }                                                                   \
+    } while (0)
 
-class cusolverDnHandle : public common::MatrixAlgebraHandle<cusolverDnHandle, SolveHandle>
-{
-    public:
-        void createHandle(SolveHandle* handle) {
-            CUSOLVER_CHECK(cusolverDnCreate(handle));
-        }
+class cusolverDnHandle
+    : public common::MatrixAlgebraHandle<cusolverDnHandle, SolveHandle> {
+   public:
+    void createHandle(SolveHandle* handle) {
+        CUSOLVER_CHECK(cusolverDnCreate(handle));
+    }
 
-        void destroyHandle(SolveHandle handle) {
-            cusolverDnDestroy(handle);
-        }
+    void destroyHandle(SolveHandle handle) { cusolverDnDestroy(handle); }
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cusparse.cpp b/src/backend/cuda/cusparse.cpp
index 1e6776fff1..79323f27b4 100644
--- a/src/backend/cuda/cusparse.cpp
+++ b/src/backend/cuda/cusparse.cpp
@@ -7,32 +7,37 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <platform.hpp>
 #include <cusparse.hpp>
+#include <platform.hpp>
 #include <stdexcept>
 #include <string>
 
-namespace cuda
-{
-const char *errorString(cusparseStatus_t err)
-{
-    switch(err) {
-    case CUSPARSE_STATUS_SUCCESS                  : return "CUSPARSE_STATUS_SUCCESS"                  ;
-    case CUSPARSE_STATUS_NOT_INITIALIZED          : return "CUSPARSE_STATUS_NOT_INITIALIZED"          ;
-    case CUSPARSE_STATUS_ALLOC_FAILED             : return "CUSPARSE_STATUS_ALLOC_FAILED"             ;
-    case CUSPARSE_STATUS_INVALID_VALUE            : return "CUSPARSE_STATUS_INVALID_VALUE"            ;
-    case CUSPARSE_STATUS_ARCH_MISMATCH            : return "CUSPARSE_STATUS_ARCH_MISMATCH"            ;
-    case CUSPARSE_STATUS_MAPPING_ERROR            : return "CUSPARSE_STATUS_MAPPING_ERROR"            ;
-    case CUSPARSE_STATUS_EXECUTION_FAILED         : return "CUSPARSE_STATUS_EXECUTION_FAILED"         ;
-    case CUSPARSE_STATUS_INTERNAL_ERROR           : return "CUSPARSE_STATUS_INTERNAL_ERROR"           ;
-    case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED: return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
-    case CUSPARSE_STATUS_ZERO_PIVOT               : return "CUSPARSE_STATUS_ZERO_PIVOT"               ;
-    default: return "UNKNOWN";
+namespace cuda {
+const char* errorString(cusparseStatus_t err) {
+    switch (err) {
+        case CUSPARSE_STATUS_SUCCESS: return "CUSPARSE_STATUS_SUCCESS";
+        case CUSPARSE_STATUS_NOT_INITIALIZED:
+            return "CUSPARSE_STATUS_NOT_INITIALIZED";
+        case CUSPARSE_STATUS_ALLOC_FAILED:
+            return "CUSPARSE_STATUS_ALLOC_FAILED";
+        case CUSPARSE_STATUS_INVALID_VALUE:
+            return "CUSPARSE_STATUS_INVALID_VALUE";
+        case CUSPARSE_STATUS_ARCH_MISMATCH:
+            return "CUSPARSE_STATUS_ARCH_MISMATCH";
+        case CUSPARSE_STATUS_MAPPING_ERROR:
+            return "CUSPARSE_STATUS_MAPPING_ERROR";
+        case CUSPARSE_STATUS_EXECUTION_FAILED:
+            return "CUSPARSE_STATUS_EXECUTION_FAILED";
+        case CUSPARSE_STATUS_INTERNAL_ERROR:
+            return "CUSPARSE_STATUS_INTERNAL_ERROR";
+        case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
+            return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
+        case CUSPARSE_STATUS_ZERO_PIVOT: return "CUSPARSE_STATUS_ZERO_PIVOT";
+        default: return "UNKNOWN";
     }
 }
 
-void cusparseHandle::createHandle(SparseHandle* handle)
-{
+void cusparseHandle::createHandle(SparseHandle* handle) {
     CUSPARSE_CHECK(cusparseCreate(handle));
 }
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 0598f7a8c3..bbac77d5df 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -8,35 +8,31 @@
  ********************************************************/
 
 #pragma once
-#include <cusparse_v2.h>
-#include <common/defines.hpp>
 #include <common/MatrixAlgebraHandle.hpp>
+#include <common/defines.hpp>
+#include <cusparse_v2.h>
 
-namespace cuda
-{
+namespace cuda {
 typedef cusparseHandle_t SparseHandle;
 
-const char * errorString(cusparseStatus_t err);
+const char* errorString(cusparseStatus_t err);
 
-#define CUSPARSE_CHECK(fn) do {                         \
-        cusparseStatus_t _error = fn;                   \
-        if (_error != CUSPARSE_STATUS_SUCCESS) {        \
-            char _err_msg[1024];                        \
-            snprintf(_err_msg, sizeof(_err_msg),        \
-                     "CUSPARSE Error (%d): %s\n",       \
-                     (int)(_error),                     \
-                     errorString( _error));             \
-                                                        \
-            AF_ERROR(_err_msg, AF_ERR_INTERNAL);        \
-        }                                               \
-    } while(0)
+#define CUSPARSE_CHECK(fn)                                                    \
+    do {                                                                      \
+        cusparseStatus_t _error = fn;                                         \
+        if (_error != CUSPARSE_STATUS_SUCCESS) {                              \
+            char _err_msg[1024];                                              \
+            snprintf(_err_msg, sizeof(_err_msg), "CUSPARSE Error (%d): %s\n", \
+                     (int)(_error), errorString(_error));                     \
+                                                                              \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
+        }                                                                     \
+    } while (0)
 
-class cusparseHandle : public common::MatrixAlgebraHandle<cusparseHandle, SparseHandle>
-{
-    public:
-        void createHandle(SparseHandle* handle);
-        void destroyHandle(SparseHandle handle) {
-            cusparseDestroy(handle);
-        }
+class cusparseHandle
+    : public common::MatrixAlgebraHandle<cusparseHandle, SparseHandle> {
+   public:
+    void createHandle(SparseHandle* handle);
+    void destroyHandle(SparseHandle handle) { cusparseDestroy(handle); }
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index 5cf036b503..56170c7088 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -8,62 +8,61 @@
  ********************************************************/
 
 #pragma once
-#include <platform.hpp>
 #include <err_cuda.hpp>
-#include <thrust/version.h>
+#include <platform.hpp>
 #include <thrust/system/cuda/detail/par.h>
+#include <thrust/version.h>
 #include <ThrustAllocator.cuh>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T> >;
+using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T>>;
 }
 
 #define THRUST_STREAM thrust::cuda::par.on(cuda::getActiveStream())
 
-#if THRUST_MAJOR_VERSION>=1 && THRUST_MINOR_VERSION>=8
+#if THRUST_MAJOR_VERSION >= 1 && THRUST_MINOR_VERSION >= 8
 
 #define THRUST_SELECT(fn, ...) fn(THRUST_STREAM, __VA_ARGS__)
 #define THRUST_SELECT_OUT(res, fn, ...) res = fn(THRUST_STREAM, __VA_ARGS__)
 
 #else
 
-#define THRUST_SELECT(fn, ...) \
-    do {                          \
+#define THRUST_SELECT(fn, ...)                                      \
+    do {                                                            \
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        fn(__VA_ARGS__);       \
-    } while(0)
+        fn(__VA_ARGS__);                                            \
+    } while (0)
 
-#define THRUST_SELECT_OUT(res, fn, ...) \
-    do {                          \
+#define THRUST_SELECT_OUT(res, fn, ...)                             \
+    do {                                                            \
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        res = fn(__VA_ARGS__);       \
-    } while(0)
+        res = fn(__VA_ARGS__);                                      \
+    } while (0)
 
 #endif
 
 #define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...) \
-	fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__)
+    fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__)
 
 #define CUDA_LAUNCH(fn, blks, thrds, ...) \
-	CUDA_LAUNCH_SMEM(fn, blks, thrds, 0, __VA_ARGS__)
+    CUDA_LAUNCH_SMEM(fn, blks, thrds, 0, __VA_ARGS__)
 
 // FIXME: Add a special flag for debug
 #ifndef NDEBUG
 
-#define POST_LAUNCH_CHECK() do {                        \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-    } while(0)                                          \
+#define POST_LAUNCH_CHECK() \
+    do { CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); } while (0)
 
 #else
 
-#define POST_LAUNCH_CHECK() do {                                        \
-    if(cuda::synchronize_calls()) {                                     \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-    } else {                                                            \
-        CUDA_CHECK(cudaPeekAtLastError());                              \
-    }                                                                   \
-  } while(0)                                                            \
+#define POST_LAUNCH_CHECK()                                             \
+    do {                                                                \
+        if (cuda::synchronize_calls()) {                                \
+            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
+        } else {                                                        \
+            CUDA_CHECK(cudaPeekAtLastError());                          \
+        }                                                               \
+    } while (0)
 
 #endif
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cu
index 6b6736f3bc..aa111bcc53 100644
--- a/src/backend/cuda/diagonal.cu
+++ b/src/backend/cuda/diagonal.cu
@@ -7,54 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <diagonal.hpp>
-#include <math.hpp>
 #include <err_cuda.hpp>
 #include <kernel/diagonal.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> diagCreate(const Array<T> &in, const int num)
-    {
-        int size = in.dims()[0] + std::abs(num);
-        int batch = in.dims()[1];
-        Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
+namespace cuda {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num) {
+    int size     = in.dims()[0] + std::abs(num);
+    int batch    = in.dims()[1];
+    Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
-        kernel::diagCreate<T>(out, in, num);
+    kernel::diagCreate<T>(out, in, num);
 
-        return out;
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T> diagExtract(const Array<T> &in, const int num)
-    {
-        const dim_t *idims = in.dims().get();
-        dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
-        Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num) {
+    const dim_t *idims = in.dims().get();
+    dim_t size         = std::min(idims[0], idims[1]) - std::abs(num);
+    Array<T> out       = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-        kernel::diagExtract<T>(out, in, num);
+    kernel::diagExtract<T>(out, in, num);
 
-        return out;
-    }
+    return out;
+}
 
 #define INSTANTIATE_DIAGONAL(T)                                          \
-    template Array<T>  diagExtract<T>    (const Array<T> &in, const int num); \
-    template Array<T>  diagCreate <T>    (const Array<T> &in, const int num);
-
-    INSTANTIATE_DIAGONAL(float)
-    INSTANTIATE_DIAGONAL(double)
-    INSTANTIATE_DIAGONAL(cfloat)
-    INSTANTIATE_DIAGONAL(cdouble)
-    INSTANTIATE_DIAGONAL(int)
-    INSTANTIATE_DIAGONAL(uint)
-    INSTANTIATE_DIAGONAL(intl)
-    INSTANTIATE_DIAGONAL(uintl)
-    INSTANTIATE_DIAGONAL(char)
-    INSTANTIATE_DIAGONAL(uchar)
-    INSTANTIATE_DIAGONAL(short)
-    INSTANTIATE_DIAGONAL(ushort)
-
-}
+    template Array<T> diagExtract<T>(const Array<T> &in, const int num); \
+    template Array<T> diagCreate<T>(const Array<T> &in, const int num);
+
+INSTANTIATE_DIAGONAL(float)
+INSTANTIATE_DIAGONAL(double)
+INSTANTIATE_DIAGONAL(cfloat)
+INSTANTIATE_DIAGONAL(cdouble)
+INSTANTIATE_DIAGONAL(int)
+INSTANTIATE_DIAGONAL(uint)
+INSTANTIATE_DIAGONAL(intl)
+INSTANTIATE_DIAGONAL(uintl)
+INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(uchar)
+INSTANTIATE_DIAGONAL(short)
+INSTANTIATE_DIAGONAL(ushort)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/diagonal.hpp b/src/backend/cuda/diagonal.hpp
index c385efe08d..b36c1d181f 100644
--- a/src/backend/cuda/diagonal.hpp
+++ b/src/backend/cuda/diagonal.hpp
@@ -10,11 +10,10 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> diagCreate(const Array<T> &in, const int num);
+namespace cuda {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num);
 
-    template<typename T>
-    Array<T> diagExtract(const Array<T> &in, const int num);
-}
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num);
+}  // namespace cuda
diff --git a/src/backend/cuda/diff.cu b/src/backend/cuda/diff.cu
index 96135f93f8..d0516286d5 100644
--- a/src/backend/cuda/diff.cu
+++ b/src/backend/cuda/diff.cu
@@ -9,68 +9,59 @@
 
 #include <Array.hpp>
 #include <diff.hpp>
+#include <err_cuda.hpp>
 #include <kernel/diff.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
 
-    template<typename T, bool isDiff2>
-    static Array<T> diff(const Array<T> &in, const int dim)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
-        oDims[dim] -= (isDiff2 + 1);
+namespace cuda {
 
-        if(iDims.elements() == 0 || oDims.elements() == 0) {
-            AF_ERROR("Elements are 0", AF_ERR_SIZE);
-        }
+template<typename T, bool isDiff2>
+static Array<T> diff(const Array<T> &in, const int dim) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims       = iDims;
+    oDims[dim] -= (isDiff2 + 1);
 
-        Array<T> out = createEmptyArray<T>(oDims);
+    if (iDims.elements() == 0 || oDims.elements() == 0) {
+        AF_ERROR("Elements are 0", AF_ERR_SIZE);
+    }
 
-        switch (dim) {
-            case (0):   kernel::diff<T, 0, isDiff2>(out, in, in.ndims());
-                break;
-            case (1):   kernel::diff<T, 1, isDiff2>(out, in, in.ndims());
-                break;
-            case (2):   kernel::diff<T, 2, isDiff2>(out, in, in.ndims());
-                break;
-            case (3):   kernel::diff<T, 3, isDiff2>(out, in, in.ndims());
-                break;
-        }
+    Array<T> out = createEmptyArray<T>(oDims);
 
-        return out;
+    switch (dim) {
+        case (0): kernel::diff<T, 0, isDiff2>(out, in, in.ndims()); break;
+        case (1): kernel::diff<T, 1, isDiff2>(out, in, in.ndims()); break;
+        case (2): kernel::diff<T, 2, isDiff2>(out, in, in.ndims()); break;
+        case (3): kernel::diff<T, 3, isDiff2>(out, in, in.ndims()); break;
     }
 
-    template<typename T>
-    Array<T> diff1(const Array<T> &in, const int dim)
-    {
-        return diff<T, false>(in, dim);
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T> diff2(const Array<T> &in, const int dim)
-    {
-        return diff<T, true>(in, dim);
-    }
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim) {
+    return diff<T, false>(in, dim);
+}
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> diff1<T>  (const Array<T> &in, const int dim);   \
-    template Array<T> diff2<T>  (const Array<T> &in, const int dim);   \
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim) {
+    return diff<T, true>(in, dim);
+}
 
+#define INSTANTIATE(T)                                             \
+    template Array<T> diff1<T>(const Array<T> &in, const int dim); \
+    template Array<T> diff2<T>(const Array<T> &in, const int dim);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/diff.hpp b/src/backend/cuda/diff.hpp
index eac2ef60b3..30ac6661e9 100644
--- a/src/backend/cuda/diff.hpp
+++ b/src/backend/cuda/diff.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> diff1(const Array<T> &in, const int dim);
+namespace cuda {
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim);
 
-    template<typename T>
-    Array<T> diff2(const Array<T> &in, const int dim);
-}
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim);
+}  // namespace cuda
diff --git a/src/backend/cuda/dilate.cu b/src/backend/cuda/dilate.cu
index 9115ba8f63..ef7dc60b21 100644
--- a/src/backend/cuda/dilate.cu
+++ b/src/backend/cuda/dilate.cu
@@ -9,16 +9,15 @@
 
 #include "morph_impl.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-INSTANTIATE(float , true)
+INSTANTIATE(float, true)
 INSTANTIATE(double, true)
-INSTANTIATE(char  , true)
-INSTANTIATE(int   , true)
-INSTANTIATE(uint  , true)
-INSTANTIATE(uchar , true)
-INSTANTIATE(short , true)
+INSTANTIATE(char, true)
+INSTANTIATE(int, true)
+INSTANTIATE(uint, true)
+INSTANTIATE(uchar, true)
+INSTANTIATE(short, true)
 INSTANTIATE(ushort, true)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/dilate3d.cu b/src/backend/cuda/dilate3d.cu
index 4846e40ad9..ba49e49f6e 100644
--- a/src/backend/cuda/dilate3d.cu
+++ b/src/backend/cuda/dilate3d.cu
@@ -9,16 +9,15 @@
 
 #include "morph3d_impl.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-INSTANTIATE(float , true)
+INSTANTIATE(float, true)
 INSTANTIATE(double, true)
-INSTANTIATE(char  , true)
-INSTANTIATE(int   , true)
-INSTANTIATE(uint  , true)
-INSTANTIATE(uchar , true)
-INSTANTIATE(short , true)
+INSTANTIATE(char, true)
+INSTANTIATE(int, true)
+INSTANTIATE(uint, true)
+INSTANTIATE(uchar, true)
+INSTANTIATE(short, true)
 INSTANTIATE(ushort, true)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/driver.cpp b/src/backend/cuda/driver.cpp
index 809b792c5c..088f2f04de 100644
--- a/src/backend/cuda/driver.cpp
+++ b/src/backend/cuda/driver.cpp
@@ -7,17 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <driver.h>
 #include <stdio.h>
 #include <string.h>
-#include <driver.h>
 
 #ifdef OS_WIN
-#include <windows.h>
 #include <stdlib.h>
+#include <windows.h>
 #define snprintf _snprintf
 
-int nvDriverVersion(char *result, int len)
-{
+int nvDriverVersion(char *result, int len) {
 #ifndef OS_WIN
     LPCTSTR lptstrFilename = "nvcuda.dll";
     DWORD dwLen, dwHandle;
@@ -40,8 +39,8 @@ int nvDriverVersion(char *result, int len)
     rv = VerQueryValue(lpData, "\\", (LPVOID *)&lpBuffer, &buflen);
     if (!rv) return 0;
 
-    version = (HIWORD(lpBuffer->dwFileVersionLS) - 10)*10000 +
-               LOWORD(lpBuffer->dwFileVersionLS);
+    version = (HIWORD(lpBuffer->dwFileVersionLS) - 10) * 10000 +
+              LOWORD(lpBuffer->dwFileVersionLS);
     fversion = version / 100.f;
 
     snprintf(result, len, "%.2f", fversion);
@@ -55,25 +54,21 @@ int nvDriverVersion(char *result, int len)
 
 #else
 
-int nvDriverVersion(char *result, int len)
-{
+int nvDriverVersion(char *result, int len) {
     int pos = 0, epos = 0, i = 0;
     char buffer[1024];
     FILE *f = NULL;
 
-    if (NULL == (f = fopen("/proc/driver/nvidia/version", "r"))) {
-        return 0;
-    }
+    if (NULL == (f = fopen("/proc/driver/nvidia/version", "r"))) { return 0; }
     if (fgets(buffer, 1024, f) == NULL) {
-        if(f) fclose(f);
+        if (f) fclose(f);
         return 0;
     }
 
-    //just close it now since we've already read what we need
-    if(f) fclose(f);
+    // just close it now since we've already read what we need
+    if (f) fclose(f);
 
     for (i = 1; i < 8; i++) {
-
         while (buffer[pos] != ' ' && buffer[pos] != '\t')
             if (pos >= 1024 || buffer[pos] == '\0' || buffer[pos] == '\n')
                 return 0;
@@ -96,7 +91,7 @@ int nvDriverVersion(char *result, int len)
 
     buffer[epos] = '\0';
 
-    strncpy(result, buffer+pos, len);
+    strncpy(result, buffer + pos, len);
 
     return 1;
 }
diff --git a/src/backend/cuda/erode.cu b/src/backend/cuda/erode.cu
index 25ca46c129..9e0f41c42c 100644
--- a/src/backend/cuda/erode.cu
+++ b/src/backend/cuda/erode.cu
@@ -9,16 +9,15 @@
 
 #include "morph_impl.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-INSTANTIATE(float , false)
+INSTANTIATE(float, false)
 INSTANTIATE(double, false)
-INSTANTIATE(char  , false)
-INSTANTIATE(int   , false)
-INSTANTIATE(uint  , false)
-INSTANTIATE(uchar , false)
-INSTANTIATE(short , false)
+INSTANTIATE(char, false)
+INSTANTIATE(int, false)
+INSTANTIATE(uint, false)
+INSTANTIATE(uchar, false)
+INSTANTIATE(short, false)
 INSTANTIATE(ushort, false)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/erode3d.cu b/src/backend/cuda/erode3d.cu
index c54b301ba5..7c3128bc19 100644
--- a/src/backend/cuda/erode3d.cu
+++ b/src/backend/cuda/erode3d.cu
@@ -9,16 +9,15 @@
 
 #include "morph3d_impl.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-INSTANTIATE(float , false)
+INSTANTIATE(float, false)
 INSTANTIATE(double, false)
-INSTANTIATE(char  , false)
-INSTANTIATE(int   , false)
-INSTANTIATE(uint  , false)
-INSTANTIATE(uchar , false)
-INSTANTIATE(short , false)
+INSTANTIATE(char, false)
+INSTANTIATE(int, false)
+INSTANTIATE(uint, false)
+INSTANTIATE(uchar, false)
+INSTANTIATE(short, false)
 INSTANTIATE(ushort, false)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 822ca4b689..c53df653f3 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -8,32 +8,31 @@
  ********************************************************/
 
 #pragma once
-#include <stdio.h>
 #include <common/defines.hpp>
 #include <common/err_common.hpp>
+#include <stdio.h>
 
-#define CUDA_NOT_SUPPORTED(message) do {                \
-        throw SupportError(__PRETTY_FUNCTION__,         \
-                __AF_FILENAME__, __LINE__, message);    \
-    } while(0)
+#define CUDA_NOT_SUPPORTED(message)                                        \
+    do {                                                                   \
+        throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
+                           message);                                       \
+    } while (0)
 
-#define CUDA_CHECK(fn) do {                                         \
-        cudaError_t _cuda_error = fn;                               \
-        if (_cuda_error != cudaSuccess) {                           \
-            char cuda_err_msg[1024];                                \
-            snprintf(cuda_err_msg,                                  \
-                     sizeof(cuda_err_msg),                          \
-                     "CUDA Error (%d): %s\n",                       \
-                     (int)(_cuda_error),                            \
-                     cudaGetErrorString(                            \
-                         cudaGetLastError()));                      \
-                                                                    \
-            if (_cuda_error == cudaErrorMemoryAllocation) {         \
-                AF_ERROR(cuda_err_msg, AF_ERR_NO_MEM);              \
-            } else if (_cuda_error == cudaErrorDevicesUnavailable) {\
-                AF_ERROR(cuda_err_msg, AF_ERR_DRIVER);              \
-            } else {                                                \
-                AF_ERROR(cuda_err_msg, AF_ERR_INTERNAL);            \
-            }                                                       \
-        }                                                           \
-    } while(0)
+#define CUDA_CHECK(fn)                                               \
+    do {                                                             \
+        cudaError_t _cuda_error = fn;                                \
+        if (_cuda_error != cudaSuccess) {                            \
+            char cuda_err_msg[1024];                                 \
+            snprintf(cuda_err_msg, sizeof(cuda_err_msg),             \
+                     "CUDA Error (%d): %s\n", (int)(_cuda_error),    \
+                     cudaGetErrorString(cudaGetLastError()));        \
+                                                                     \
+            if (_cuda_error == cudaErrorMemoryAllocation) {          \
+                AF_ERROR(cuda_err_msg, AF_ERR_NO_MEM);               \
+            } else if (_cuda_error == cudaErrorDevicesUnavailable) { \
+                AF_ERROR(cuda_err_msg, AF_ERR_DRIVER);               \
+            } else {                                                 \
+                AF_ERROR(cuda_err_msg, AF_ERR_INTERNAL);             \
+            }                                                        \
+        }                                                            \
+    } while (0)
diff --git a/src/backend/cuda/exampleFunction.cu b/src/backend/cuda/exampleFunction.cu
index c97a46b5cf..15bf8cdc6f 100644
--- a/src/backend/cuda/exampleFunction.cu
+++ b/src/backend/cuda/exampleFunction.cu
@@ -7,48 +7,47 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>                    // header with cuda backend specific
-                                        // Array class implementation that inherits
-                                        // ArrayInfo base class
+#include <Array.hpp>  // header with cuda backend specific
+                      // Array class implementation that inherits
+                      // ArrayInfo base class
 
-#include <exampleFunction.hpp>          // cuda backend function header
+#include <exampleFunction.hpp>  // cuda backend function header
 
-#include <err_cuda.hpp>                 // error check functions and Macros
-                                        // specific to cuda backend
+#include <err_cuda.hpp>  // error check functions and Macros
+                         // specific to cuda backend
 
-#include <kernel/exampleFunction.hpp>   // this header under the folder src/cuda/kernel
-                                        // defines the CUDA kernel and its wrapper
-                                        // function to which the main computation of your
-                                        // algorithm should be relayed to
+#include <kernel/exampleFunction.hpp>  // this header under the folder src/cuda/kernel
+          // defines the CUDA kernel and its wrapper
+          // function to which the main computation of your
+          // algorithm should be relayed to
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method)
-{
-    dim4 outputDims;                    // this should be '= in.dims();' in most cases
-                                        // but would definitely depend on the type of
-                                        // algorithm you are implementing.
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method) {
+    dim4 outputDims;  // this should be '= in.dims();' in most cases
+                      // but would definitely depend on the type of
+                      // algorithm you are implementing.
 
     Array<T> out = createEmptyArray<T>(outputDims);
-                                        // Please use the create***Array<T> helper
-                                        // functions defined in Array.hpp to create
-                                        // different types of Arrays. Please check the
-                                        // file to know what are the different types you
-                                        // can create.
+    // Please use the create***Array<T> helper
+    // functions defined in Array.hpp to create
+    // different types of Arrays. Please check the
+    // file to know what are the different types you
+    // can create.
 
     // Relay the actual computation to CUDA kernel wrapper
     kernel::exampleFunc<T>(out, a, b, method);
 
-    return out;                         // return the result
+    return out;  // return the result
 }
 
-
-#define INSTANTIATE(T)  \
-    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
+#define INSTANTIATE(T)                                                         \
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, \
+                                         const af_someenum_t method);
 
 // INSTANTIATIONS for all the types which
 // are present in the switch case statement
@@ -62,4 +61,4 @@ INSTANTIATE(char)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/exampleFunction.hpp b/src/backend/cuda/exampleFunction.hpp
index 1f97d7073d..b0c20927ab 100644
--- a/src/backend/cuda/exampleFunction.hpp
+++ b/src/backend/cuda/exampleFunction.hpp
@@ -9,8 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
+namespace cuda {
+template<typename T>
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method);
 }
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index 41f3705610..538a59b1e1 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -7,54 +7,54 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/fast.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 using af::dim4;
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
-              const bool non_max, const float feature_ratio, const unsigned edge)
-{
+              const bool non_max, const float feature_ratio,
+              const unsigned edge) {
     unsigned nfeat;
     float *d_x_out;
     float *d_y_out;
     float *d_score_out;
 
-    kernel::fast<T>(&nfeat, &d_x_out, &d_y_out, &d_score_out, in,
-                    thr, arc_length, non_max, feature_ratio, edge);
+    kernel::fast<T>(&nfeat, &d_x_out, &d_y_out, &d_score_out, in, thr,
+                    arc_length, non_max, feature_ratio, edge);
 
     if (nfeat > 0) {
         const dim4 out_dims(nfeat);
 
-        x_out = createDeviceDataArray<float>(out_dims, d_x_out);
-        y_out = createDeviceDataArray<float>(out_dims, d_y_out);
+        x_out     = createDeviceDataArray<float>(out_dims, d_x_out);
+        y_out     = createDeviceDataArray<float>(out_dims, d_y_out);
         score_out = createDeviceDataArray<float>(out_dims, d_score_out);
     }
 
     return nfeat;
 }
 
-#define INSTANTIATE(T)                                                                              \
-    template unsigned fast<T>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,    \
-                              const Array<T> &in, const float thr, const unsigned arc_length,       \
-                              const bool nonmax, const float feature_ratio, const unsigned edge);
+#define INSTANTIATE(T)                                                        \
+    template unsigned fast<T>(                                                \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const float thr, const unsigned arc_length,       \
+        const bool nonmax, const float feature_ratio, const unsigned edge);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/fast.hpp b/src/backend/cuda/fast.hpp
index e3f3606d7d..84f509c5aa 100644
--- a/src/backend/cuda/fast.hpp
+++ b/src/backend/cuda/fast.hpp
@@ -7,17 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
-              const bool non_max, const float feature_ratio, const unsigned edge);
+              const bool non_max, const float feature_ratio,
+              const unsigned edge);
 
 }
diff --git a/src/backend/cuda/fast_pyramid.cu b/src/backend/cuda/fast_pyramid.cu
index b00f728b9d..9dab0988e2 100644
--- a/src/backend/cuda/fast_pyramid.cu
+++ b/src/backend/cuda/fast_pyramid.cu
@@ -7,47 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/fast_pyramid.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 using af::dim4;
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
                   std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
                   std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
-                  const Array<T>& image,
-                  const float fast_thr, const unsigned max_feat,
-                  const float scl_fctr, const unsigned levels,
-                  const unsigned patch_size)
-{
-    kernel::fast_pyramid<T>(feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
-                            image, fast_thr, max_feat, scl_fctr, levels, patch_size);
+                  const Array<T>& image, const float fast_thr,
+                  const unsigned max_feat, const float scl_fctr,
+                  const unsigned levels, const unsigned patch_size) {
+    kernel::fast_pyramid<T>(feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl,
+                            img_pyr, image, fast_thr, max_feat, scl_fctr,
+                            levels, patch_size);
 }
 
-#define INSTANTIATE(T)\
-    template void fast_pyramid<T>(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,    \
-                                  std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,    \
-                                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,      \
-                                  const Array<T>& image,                                            \
-                                  const float fast_thr, const unsigned max_feat,                    \
-                                  const float scl_fctr, const unsigned levels,                      \
-                                  const unsigned patch_size);
+#define INSTANTIATE(T)                                                        \
+    template void fast_pyramid<T>(                                            \
+        std::vector<unsigned> & feat_pyr, std::vector<float*> & d_x_pyr,      \
+        std::vector<float*> & d_y_pyr, std::vector<unsigned> & lvl_best,      \
+        std::vector<float> & lvl_scl, std::vector<Array<T>> & img_pyr,        \
+        const Array<T>& image, const float fast_thr, const unsigned max_feat, \
+        const float scl_fctr, const unsigned levels,                          \
+        const unsigned patch_size);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/fast_pyramid.hpp b/src/backend/cuda/fast_pyramid.hpp
index d380f61fb0..a7c9d79f86 100644
--- a/src/backend/cuda/fast_pyramid.hpp
+++ b/src/backend/cuda/fast_pyramid.hpp
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
                   std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
                   std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
-                  const Array<T>& image,
-                  const float fast_thr, const unsigned max_feat,
-                  const float scl_fctr, const unsigned levels,
-                  const unsigned patch_size);
+                  const Array<T>& image, const float fast_thr,
+                  const unsigned max_feat, const float scl_fctr,
+                  const unsigned levels, const unsigned patch_size);
 
 }
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cpp
index 9af0f25ccc..bb1219171e 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cpp
@@ -7,73 +7,63 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
-#include <fft.hpp>
-#include <debug_cuda.hpp>
 #include <cufft.hpp>
+#include <debug_cuda.hpp>
+#include <fft.hpp>
 #include <math.hpp>
 #include <memory.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using std::string;
 
-namespace cuda
-{
-void setFFTPlanCacheSize(size_t numPlans)
-{
+namespace cuda {
+void setFFTPlanCacheSize(size_t numPlans) {
     fftManager().setMaxCacheSize(numPlans);
 }
 
 template<typename T>
 struct cufft_transform;
 
-#define CUFFT_FUNC(T, TRANSFORM_TYPE)                               \
-    template<>                                                      \
-    struct cufft_transform<T>                                       \
-    {                                                               \
-        enum { type = CUFFT_##TRANSFORM_TYPE };                     \
-        cufftResult                                                 \
-            operator() (cufftHandle plan, T *in, T *out, int dir) { \
-            return cufftExec##TRANSFORM_TYPE(plan, in, out, dir);   \
-        }                                                           \
+#define CUFFT_FUNC(T, TRANSFORM_TYPE)                                      \
+    template<>                                                             \
+    struct cufft_transform<T> {                                            \
+        enum { type = CUFFT_##TRANSFORM_TYPE };                            \
+        cufftResult operator()(cufftHandle plan, T *in, T *out, int dir) { \
+            return cufftExec##TRANSFORM_TYPE(plan, in, out, dir);          \
+        }                                                                  \
     };
 
-CUFFT_FUNC(cfloat , C2C)
+CUFFT_FUNC(cfloat, C2C)
 CUFFT_FUNC(cdouble, Z2Z)
 
 template<typename To, typename Ti>
 struct cufft_real_transform;
 
-#define CUFFT_REAL_FUNC(To, Ti, TRANSFORM_TYPE)                 \
-    template<>                                                  \
-    struct cufft_real_transform<To, Ti>                         \
-    {                                                           \
-        enum { type = CUFFT_##TRANSFORM_TYPE };                 \
-        cufftResult                                             \
-            operator() (cufftHandle plan, Ti *in, To *out) {    \
-            return cufftExec##TRANSFORM_TYPE(plan, in, out);    \
-        }                                                       \
+#define CUFFT_REAL_FUNC(To, Ti, TRANSFORM_TYPE)                     \
+    template<>                                                      \
+    struct cufft_real_transform<To, Ti> {                           \
+        enum { type = CUFFT_##TRANSFORM_TYPE };                     \
+        cufftResult operator()(cufftHandle plan, Ti *in, To *out) { \
+            return cufftExec##TRANSFORM_TYPE(plan, in, out);        \
+        }                                                           \
     };
 
-CUFFT_REAL_FUNC(cfloat , float , R2C)
+CUFFT_REAL_FUNC(cfloat, float, R2C)
 CUFFT_REAL_FUNC(cdouble, double, D2Z)
 
-CUFFT_REAL_FUNC(float , cfloat , C2R)
+CUFFT_REAL_FUNC(float, cfloat, C2R)
 CUFFT_REAL_FUNC(double, cdouble, Z2D)
 
 template<int rank>
-void computeDims(int rdims[rank], const dim4 &idims)
-{
-    for (int i = 0; i < rank; i++) {
-        rdims[i] = idims[(rank -1) - i];
-    }
+void computeDims(int rdims[rank], const dim4 &idims) {
+    for (int i = 0; i < rank; i++) { rdims[i] = idims[(rank - 1) - i]; }
 }
 
 template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in)
-{
+void fft_inplace(Array<T> &in) {
     const dim4 idims    = in.dims();
     const dim4 istrides = in.strides();
 
@@ -84,23 +74,21 @@ void fft_inplace(Array<T> &in)
     computeDims<rank>(in_embed, in.getDataDims());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= idims[i];
-    }
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
-    SharedPlan plan = findPlan(rank, t_dims,
-                               in_embed , istrides[0], istrides[rank],
-                               in_embed , istrides[0], istrides[rank],
-                               (cufftType)cufft_transform<T>::type, batch);
+    SharedPlan plan =
+        findPlan(rank, t_dims, in_embed, istrides[0], istrides[rank], in_embed,
+                 istrides[0], istrides[rank],
+                 (cufftType)cufft_transform<T>::type, batch);
 
     cufft_transform<T> transform;
     CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
-    CUFFT_CHECK(transform(*plan.get(), (T *)in.get(), in.get(), direction ? CUFFT_FORWARD : CUFFT_INVERSE));
+    CUFFT_CHECK(transform(*plan.get(), (T *)in.get(), in.get(),
+                          direction ? CUFFT_FORWARD : CUFFT_INVERSE));
 }
 
 template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in)
-{
+Array<Tc> fft_r2c(const Array<Tr> &in) {
     dim4 idims = in.dims();
     dim4 odims = in.dims();
 
@@ -116,17 +104,15 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     computeDims<rank>(out_embed, out.getDataDims());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= idims[i];
-    }
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
     dim4 istrides = in.strides();
     dim4 ostrides = out.strides();
 
-    SharedPlan plan = findPlan(rank, t_dims,
-                               in_embed  , istrides[0], istrides[rank],
-                               out_embed , ostrides[0], ostrides[rank],
-                               (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
+    SharedPlan plan =
+        findPlan(rank, t_dims, in_embed, istrides[0], istrides[rank], out_embed,
+                 ostrides[0], ostrides[rank],
+                 (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
 
     cufft_real_transform<Tc, Tr> transform;
     CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
@@ -135,8 +121,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
 }
 
 template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
-{
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     Array<Tr> out = createEmptyArray<Tr>(odims);
 
     int t_dims[rank];
@@ -147,44 +132,45 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
     computeDims<rank>(out_embed, out.getDataDims());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= odims[i];
-    }
+    for (int i = rank; i < 4; i++) { batch *= odims[i]; }
 
     dim4 istrides = in.strides();
     dim4 ostrides = out.strides();
 
     cufft_real_transform<Tr, Tc> transform;
 
-    SharedPlan plan = findPlan(rank, t_dims,
-                               in_embed  , istrides[0], istrides[rank],
-                               out_embed , ostrides[0], ostrides[rank],
-                               (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
+    SharedPlan plan =
+        findPlan(rank, t_dims, in_embed, istrides[0], istrides[rank], out_embed,
+                 ostrides[0], ostrides[rank],
+                 (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
 
     CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (Tc *)in.get(), out.get()));
     return out;
 }
 
-#define INSTANTIATE(T)                                      \
-    template void fft_inplace<T, 1, true >(Array<T> &in);   \
-    template void fft_inplace<T, 2, true >(Array<T> &in);   \
-    template void fft_inplace<T, 3, true >(Array<T> &in);   \
-    template void fft_inplace<T, 1, false>(Array<T> &in);   \
-    template void fft_inplace<T, 2, false>(Array<T> &in);   \
-    template void fft_inplace<T, 3, false>(Array<T> &in);
-
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-
-#define INSTANTIATE_REAL(Tr, Tc)                                        \
-    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in);         \
-    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in);         \
-    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in);         \
-    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in, const dim4 &odims); \
-    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in, const dim4 &odims); \
-    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in, const dim4 &odims); \
-
-    INSTANTIATE_REAL(float , cfloat )
-    INSTANTIATE_REAL(double, cdouble)
-}
+#define INSTANTIATE(T)                                     \
+    template void fft_inplace<T, 1, true>(Array<T> & in);  \
+    template void fft_inplace<T, 2, true>(Array<T> & in);  \
+    template void fft_inplace<T, 3, true>(Array<T> & in);  \
+    template void fft_inplace<T, 1, false>(Array<T> & in); \
+    template void fft_inplace<T, 2, false>(Array<T> & in); \
+    template void fft_inplace<T, 3, false>(Array<T> & in);
+
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+
+#define INSTANTIATE_REAL(Tr, Tc)                                \
+    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in); \
+    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in); \
+    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in); \
+    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in,  \
+                                          const dim4 &odims);   \
+    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in,  \
+                                          const dim4 &odims);   \
+    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in,  \
+                                          const dim4 &odims);
+
+INSTANTIATE_REAL(float, cfloat)
+INSTANTIATE_REAL(double, cdouble)
+}  // namespace cuda
diff --git a/src/backend/cuda/fft.hpp b/src/backend/cuda/fft.hpp
index 1d85b8ad84..b66be18e82 100644
--- a/src/backend/cuda/fft.hpp
+++ b/src/backend/cuda/fft.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 void setFFTPlanCacheSize(size_t numPlans);
 
@@ -23,4 +22,4 @@ Array<Tc> fft_r2c(const Array<Tr> &in);
 template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cu
index cda209c72e..68d28f6f1e 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cu
@@ -7,33 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <fftconvolve.hpp>
 #include <kernel/fftconvolve.hpp>
-#include <err_cuda.hpp>
+#include <af/dim4.hpp>
 
 #include <fft.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-static const dim4 calcPackedSize(Array<T> const& i1,
-                                 Array<T> const& i2,
-                                 const dim_t baseDim)
-{
+static const dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
+                                 const dim_t baseDim) {
     const dim4 i1d = i1.dims();
     const dim4 i2d = i2.dims();
 
     dim_t pd[4] = {1, 1, 1, 1};
 
-
     dim_t max_d0 = (i1d[0] > i2d[0]) ? i1d[0] : i2d[0];
     dim_t min_d0 = (i1d[0] < i2d[0]) ? i1d[0] : i2d[0];
-    pd[0]  = nextpow2((unsigned)((int)ceil(max_d0 / 2.f) + min_d0 - 1));
+    pd[0]        = nextpow2((unsigned)((int)ceil(max_d0 / 2.f) + min_d0 - 1));
 
     for (dim_t k = 1; k < 4; k++) {
         if (k < baseDim) {
@@ -46,31 +42,31 @@ static const dim4 calcPackedSize(Array<T> const& i1,
     return dim4(pd[0], pd[1], pd[2], pd[3]);
 }
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind)
-{
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind) {
     const dim4 sDims = signal.dims();
     const dim4 fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
-                oDims[d] = sDims[d]+fDims[d]-1;
+        for (dim_t d = 0; d < 4; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
+                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_RHS) {
-            for (dim_t i=baseDim; i<4; ++i)
-                oDims[i] = fDims[i];
+        if (kind == AF_BATCH_RHS) {
+            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
         }
     }
 
-    const dim4 spDims = calcPackedSize<T>(signal, filter, baseDim);
-    const dim4 fpDims = calcPackedSize<T>(filter, signal, baseDim);
+    const dim4 spDims       = calcPackedSize<T>(signal, filter, baseDim);
+    const dim4 fpDims       = calcPackedSize<T>(filter, signal, baseDim);
     Array<cT> signal_packed = createEmptyArray<cT>(spDims);
     Array<cT> filter_packed = createEmptyArray<cT>(fpDims);
 
@@ -86,37 +82,44 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     if (kind == AF_BATCH_RHS) {
         fft_inplace<cT, baseDim, false>(filter_packed);
         if (expand)
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, filter_packed, signal, filter);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true>(
+                out, filter_packed, signal, filter);
         else
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(out, filter_packed, signal, filter);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(
+                out, filter_packed, signal, filter);
     } else {
         fft_inplace<cT, baseDim, false>(signal_packed);
         if (expand)
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true >(out, signal_packed, signal, filter);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true>(
+                out, signal_packed, signal, filter);
         else
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(out, signal_packed, signal, filter);
+            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(
+                out, signal_packed, signal, filter);
     }
 
     return out;
 }
 
-#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                                                   \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 1>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 2>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 3>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
-
-INSTANTIATE(double, double, cdouble, true , false)
-INSTANTIATE(float , float,  cfloat,  false, false)
-INSTANTIATE(uint  , float,  cfloat,  false, true)
-INSTANTIATE(int   , float,  cfloat,  false, true)
-INSTANTIATE(uchar , float,  cfloat,  false, true)
-INSTANTIATE(char  , float,  cfloat,  false, true)
-INSTANTIATE(ushort, float,  cfloat,  false, true)
-INSTANTIATE(short , float,  cfloat,  false, true)
-INSTANTIATE(uintl , float,  cfloat,  false, true)
-INSTANTIATE(intl  , float,  cfloat,  false, true)
-
-}
+#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                      \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 1>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);                                               \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 2>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);                                               \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 3>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);
+
+INSTANTIATE(double, double, cdouble, true, false)
+INSTANTIATE(float, float, cfloat, false, false)
+INSTANTIATE(uint, float, cfloat, false, true)
+INSTANTIATE(int, float, cfloat, false, true)
+INSTANTIATE(uchar, float, cfloat, false, true)
+INSTANTIATE(char, float, cfloat, false, true)
+INSTANTIATE(ushort, float, cfloat, false, true)
+INSTANTIATE(short, float, cfloat, false, true)
+INSTANTIATE(uintl, float, cfloat, false, true)
+INSTANTIATE(intl, float, cfloat, false, true)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/fftconvolve.hpp b/src/backend/cuda/fftconvolve.hpp
index 66597c40df..86748ea16a 100644
--- a/src/backend/cuda/fftconvolve.hpp
+++ b/src/backend/cuda/fftconvolve.hpp
@@ -9,10 +9,11 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind);
 
 }
diff --git a/src/backend/cuda/gradient.cu b/src/backend/cuda/gradient.cu
index 30b36b0bfa..425fc91e3e 100644
--- a/src/backend/cuda/gradient.cu
+++ b/src/backend/cuda/gradient.cu
@@ -8,25 +8,24 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <gradient.hpp>
 #include <kernel/gradient.hpp>
 #include <math.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
-    {
-        kernel::gradient<T>(grad0, grad1, in);
-    }
+namespace cuda {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
+    kernel::gradient<T>(grad0, grad1, in);
+}
 
-#define INSTANTIATE(T)                                                                  \
-    template void gradient<T>(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);    \
+#define INSTANTIATE(T)                                            \
+    template void gradient<T>(Array<T> & grad0, Array<T> & grad1, \
+                              const Array<T> &in);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+}  // namespace cuda
diff --git a/src/backend/cuda/gradient.hpp b/src/backend/cuda/gradient.hpp
index 3cc27d92c9..1378fba097 100644
--- a/src/backend/cuda/gradient.hpp
+++ b/src/backend/cuda/gradient.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
+namespace cuda {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
 }
diff --git a/src/backend/cuda/harris.cu b/src/backend/cuda/harris.cu
index 6116182f3c..375b9e1570 100644
--- a/src/backend/cuda/harris.cu
+++ b/src/backend/cuda/harris.cu
@@ -7,48 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/harris.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 using af::dim4;
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename convAccT>
-unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-                const Array<T> &in, const unsigned max_corners, const float min_response,
-                const float sigma, const unsigned filter_len, const float k_thr)
-{
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &score_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr) {
     unsigned nfeat;
     float *d_x_out;
     float *d_y_out;
     float *d_score_out;
 
     kernel::harris<T, convAccT>(&nfeat, &d_x_out, &d_y_out, &d_score_out, in,
-                                max_corners, min_response, sigma, filter_len, k_thr);
+                                max_corners, min_response, sigma, filter_len,
+                                k_thr);
 
     if (nfeat > 0) {
         const dim4 out_dims(nfeat);
 
-        x_out = createDeviceDataArray<float>(out_dims, d_x_out);
-        y_out = createDeviceDataArray<float>(out_dims, d_y_out);
+        x_out     = createDeviceDataArray<float>(out_dims, d_x_out);
+        y_out     = createDeviceDataArray<float>(out_dims, d_y_out);
         score_out = createDeviceDataArray<float>(out_dims, d_score_out);
     }
 
     return nfeat;
 }
 
-#define INSTANTIATE(T, convAccT)                                                                                \
-    template unsigned harris<T, convAccT>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,    \
-                                const Array<T> &in, const unsigned max_corners, const float min_response,       \
-                                const float sigma, const unsigned filter_len, const float k_thr);
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned harris<T, convAccT>(                                    \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned max_corners,                       \
+        const float min_response, const float sigma,                          \
+        const unsigned filter_len, const float k_thr);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/harris.hpp b/src/backend/cuda/harris.hpp
index 6bc3ace78e..ce51eaf3de 100644
--- a/src/backend/cuda/harris.hpp
+++ b/src/backend/cuda/harris.hpp
@@ -7,17 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename convAccT>
-unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-                const Array<T> &in, const unsigned max_corners, const float min_response,
-                const float sigma, const unsigned filter_len, const float k_thr);
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &score_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr);
 
 }
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index b6a1e8ec1b..2dcda99e89 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -8,27 +8,26 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <hist_graphics.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
+#include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <hist_graphics.hpp>
 
 namespace cuda {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, fg_histogram hist)
-{
+void copy_histogram(const Array<T> &data, fg_histogram hist) {
     auto stream = cuda::getActiveStream();
-    if(DeviceManager::checkGraphicsInteropCapability()) {
+    if (DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
         auto res = interopManager().getHistogramResources(hist);
 
         size_t bytes = 0;
-        T* d_vbo = NULL;
+        T *d_vbo     = NULL;
         cudaGraphicsMapResources(1, res[0].get(), stream);
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo,
-                                             &bytes, *(res[0].get()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
+                                             *(res[0].get()));
         cudaMemcpyAsync(d_vbo, d_P, bytes, cudaMemcpyDeviceToDevice, stream);
         cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
@@ -36,14 +35,14 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule &_ = graphics::forgePlugin();
         unsigned bytes = 0, buffer = 0;
         FG_CHECK(_.fg_get_histogram_vertex_buffer(&buffer, hist));
         FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, data.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
@@ -55,8 +54,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
     }
 }
 
-#define INSTANTIATE(T)  \
-template void copy_histogram<T>(const Array<T> &, fg_histogram);
+#define INSTANTIATE(T) \
+    template void copy_histogram<T>(const Array<T> &, fg_histogram);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
@@ -65,4 +64,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/hist_graphics.hpp b/src/backend/cuda/hist_graphics.hpp
index eca5c2f57d..10cae9ae94 100644
--- a/src/backend/cuda/hist_graphics.hpp
+++ b/src/backend/cuda/hist_graphics.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#include <common/graphics_common.hpp>
 #include <Array.hpp>
+#include <common/graphics_common.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/histogram.cu b/src/backend/cuda/histogram.cu
index 3482fb8c3e..ecf5211289 100644
--- a/src/backend/cuda/histogram.cu
+++ b/src/backend/cuda/histogram.cu
@@ -7,45 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
-#include <err_cuda.hpp>
+#include <af/dim4.hpp>
 #include <vector>
 
 using af::dim4;
 using std::vector;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval)
-{
-
-    const dim4 dims     = in.dims();
-    dim4 outDims        = dim4(nbins, 1, dims[2], dims[3]);
-    Array<outType> out  = createValueArray<outType>(outDims, outType(0));
+Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
+                         const double &minval, const double &maxval) {
+    const dim4 dims    = in.dims();
+    dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
+    Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
-    kernel::histogram<inType, outType, isLinear>(out, in, nbins, minval, maxval);
+    kernel::histogram<inType, outType, isLinear>(out, in, nbins, minval,
+                                                 maxval);
 
     return out;
 }
 
-#define INSTANTIATE(in_t,out_t)\
-template Array<out_t> histogram<in_t, out_t, true>(const Array<in_t> &in, const unsigned &nbins, const double &minval, const double &maxval); \
-template Array<out_t> histogram<in_t, out_t, false>(const Array<in_t> &in, const unsigned &nbins, const double &minval, const double &maxval);
+#define INSTANTIATE(in_t, out_t)                                            \
+    template Array<out_t> histogram<in_t, out_t, true>(                     \
+        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
+        const double &maxval);                                              \
+    template Array<out_t> histogram<in_t, out_t, false>(                    \
+        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
+        const double &maxval);
 
-INSTANTIATE(float , uint)
+INSTANTIATE(float, uint)
 INSTANTIATE(double, uint)
-INSTANTIATE(char  , uint)
-INSTANTIATE(int   , uint)
-INSTANTIATE(uint  , uint)
-INSTANTIATE(uchar , uint)
-INSTANTIATE(short , uint)
+INSTANTIATE(char, uint)
+INSTANTIATE(int, uint)
+INSTANTIATE(uint, uint)
+INSTANTIATE(uchar, uint)
+INSTANTIATE(short, uint)
 INSTANTIATE(ushort, uint)
-INSTANTIATE(intl  , uint)
-INSTANTIATE(uintl , uint)
+INSTANTIATE(intl, uint)
+INSTANTIATE(uintl, uint)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/histogram.hpp b/src/backend/cuda/histogram.hpp
index 0ef5f2e72a..c02556df2e 100644
--- a/src/backend/cuda/histogram.hpp
+++ b/src/backend/cuda/histogram.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval);
+Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
+                         const double &minval, const double &maxval);
 
 }
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index 108a35dd10..102bf35f18 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -7,65 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <err_cuda.hpp>
 #include <arith.hpp>
+#include <err_cuda.hpp>
 #include <kernel/homography.hpp>
+#include <af/dim4.hpp>
 #include <algorithm>
 
 #include <cfloat>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 #define RANSACConfidence 0.99f
 #define LMEDSConfidence 0.99f
 #define LMEDSOutlierRatio 0.4f
 
 template<typename T>
-int homography(Array<T> &bestH,
-               const Array<float> &x_src,
-               const Array<float> &y_src,
-               const Array<float> &x_dst,
-               const Array<float> &y_dst,
-               const Array<float> &initial,
-               const af_homography_type htype,
-               const float inlier_thr,
-               const unsigned iterations)
-{
-    const af::dim4 idims = x_src.dims();
+int homography(Array<T> &bestH, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
+               const af_homography_type htype, const float inlier_thr,
+               const unsigned iterations) {
+    const af::dim4 idims    = x_src.dims();
     const unsigned nsamples = idims[0];
 
-    unsigned iter = iterations;
+    unsigned iter    = iterations;
     Array<float> err = createEmptyArray<float>(dim4());
     if (htype == AF_HOMOGRAPHY_LMEDS) {
-        iter = ::std::min(iter, (unsigned)(log(1.f - LMEDSConfidence) / log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
+        iter = ::std::min(
+            iter, (unsigned)(log(1.f - LMEDSConfidence) /
+                             log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
         err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
     }
 
     af::dim4 rdims(4, iter);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
+    Array<float> rnd  = arithOp<float, af_mul_t>(initial, fctr, rdims);
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter), (T)0);
 
-    return kernel::computeH<T>(bestH, tmpH, err,
-                               x_src, y_src, x_dst, y_dst,
+    return kernel::computeH<T>(bestH, tmpH, err, x_src, y_src, x_dst, y_dst,
                                rnd, iter, nsamples, inlier_thr, htype);
 }
 
-#define INSTANTIATE(T)                                                                  \
-    template int homography<T>(Array<T> &H,                                             \
-                               const Array<float> &x_src, const Array<float> &y_src,    \
-                               const Array<float> &x_dst, const Array<float> &y_dst,    \
-                               const Array<float> &initial,                             \
-                               const af_homography_type htype, const float inlier_thr,  \
-                               const unsigned iterations);
+#define INSTANTIATE(T)                                                      \
+    template int homography<T>(                                             \
+        Array<T> & H, const Array<float> &x_src, const Array<float> &y_src, \
+        const Array<float> &x_dst, const Array<float> &y_dst,               \
+        const Array<float> &initial, const af_homography_type htype,        \
+        const float inlier_thr, const unsigned iterations);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/homography.hpp b/src/backend/cuda/homography.hpp
index 564c0766b7..38ad486e93 100644
--- a/src/backend/cuda/homography.hpp
+++ b/src/backend/cuda/homography.hpp
@@ -9,14 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-int homography(Array<T> &H,
-               const Array<float> &x_src, const Array<float> &y_src,
-               const Array<float> &x_dst, const Array<float> &y_dst,
-               const Array<float> &initial,
+int homography(Array<T> &H, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
diff --git a/src/backend/cuda/hsv_rgb.cu b/src/backend/cuda/hsv_rgb.cu
index 9f693c9fa1..c985853e73 100644
--- a/src/backend/cuda/hsv_rgb.cu
+++ b/src/backend/cuda/hsv_rgb.cu
@@ -1,27 +1,25 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <hsv_rgb.hpp>
 #include <kernel/hsv_rgb.hpp>
-#include <err_cuda.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> hsv2rgb(const Array<T>& in)
-{
-    Array<T> out   = createEmptyArray<T>(in.dims());
+Array<T> hsv2rgb(const Array<T>& in) {
+    Array<T> out = createEmptyArray<T>(in.dims());
 
     kernel::hsv2rgb_convert<T, true>(out, in);
 
@@ -29,20 +27,19 @@ Array<T> hsv2rgb(const Array<T>& in)
 }
 
 template<typename T>
-Array<T> rgb2hsv(const Array<T>& in)
-{
-    Array<T> out   = createEmptyArray<T>(in.dims());
+Array<T> rgb2hsv(const Array<T>& in) {
+    Array<T> out = createEmptyArray<T>(in.dims());
 
     kernel::hsv2rgb_convert<T, false>(out, in);
 
     return out;
 }
 
-#define INSTANTIATE(T)  \
+#define INSTANTIATE(T)                                \
     template Array<T> hsv2rgb<T>(const Array<T>& in); \
-    template Array<T> rgb2hsv<T>(const Array<T>& in); \
+    template Array<T> rgb2hsv<T>(const Array<T>& in);
 
 INSTANTIATE(double)
-INSTANTIATE(float )
+INSTANTIATE(float)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/hsv_rgb.hpp b/src/backend/cuda/hsv_rgb.hpp
index aef0837d4f..7758ce5181 100644
--- a/src/backend/cuda/hsv_rgb.hpp
+++ b/src/backend/cuda/hsv_rgb.hpp
@@ -1,16 +1,15 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in);
@@ -18,4 +17,4 @@ Array<T> hsv2rgb(const Array<T>& in);
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/identity.cu b/src/backend/cuda/identity.cu
index a47bdc2c9c..3f781b9151 100644
--- a/src/backend/cuda/identity.cu
+++ b/src/backend/cuda/identity.cu
@@ -7,36 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <identity.hpp>
 #include <debug_cuda.hpp>
+#include <identity.hpp>
 #include <kernel/identity.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> identity(const dim4& dims)
-    {
-        Array<T> out  = createEmptyArray<T>(dims);
-        kernel::identity<T>(out);
-        return out;
-    }
+namespace cuda {
+template<typename T>
+Array<T> identity(const dim4& dims) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::identity<T>(out);
+    return out;
+}
 
-#define INSTANTIATE_IDENTITY(T)                              \
-    template Array<T>  identity<T>    (const af::dim4 &dims);
+#define INSTANTIATE_IDENTITY(T) \
+    template Array<T> identity<T>(const af::dim4& dims);
 
-    INSTANTIATE_IDENTITY(float)
-    INSTANTIATE_IDENTITY(double)
-    INSTANTIATE_IDENTITY(cfloat)
-    INSTANTIATE_IDENTITY(cdouble)
-    INSTANTIATE_IDENTITY(int)
-    INSTANTIATE_IDENTITY(uint)
-    INSTANTIATE_IDENTITY(intl)
-    INSTANTIATE_IDENTITY(uintl)
-    INSTANTIATE_IDENTITY(char)
-    INSTANTIATE_IDENTITY(uchar)
-    INSTANTIATE_IDENTITY(short)
-    INSTANTIATE_IDENTITY(ushort)
+INSTANTIATE_IDENTITY(float)
+INSTANTIATE_IDENTITY(double)
+INSTANTIATE_IDENTITY(cfloat)
+INSTANTIATE_IDENTITY(cdouble)
+INSTANTIATE_IDENTITY(int)
+INSTANTIATE_IDENTITY(uint)
+INSTANTIATE_IDENTITY(intl)
+INSTANTIATE_IDENTITY(uintl)
+INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(uchar)
+INSTANTIATE_IDENTITY(short)
+INSTANTIATE_IDENTITY(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/identity.hpp b/src/backend/cuda/identity.hpp
index 2dbf9a5776..77b58f6ab7 100644
--- a/src/backend/cuda/identity.hpp
+++ b/src/backend/cuda/identity.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> identity(const dim4& dim);
+namespace cuda {
+template<typename T>
+Array<T> identity(const dim4& dim);
 }
diff --git a/src/backend/cuda/iir.cu b/src/backend/cuda/iir.cu
index eced5a3aee..d03653cb71 100644
--- a/src/backend/cuda/iir.cu
+++ b/src/backend/cuda/iir.cu
@@ -7,56 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <iir.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
 #include <arith.hpp>
 #include <convolve.hpp>
+#include <err_cuda.hpp>
+#include <iir.hpp>
 #include <kernel/iir.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
-    {
-
-        AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
-        if (x.ndims() != b.ndims()) {
-            type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
-        }
+namespace cuda {
+template<typename T>
+Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
+    AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
+    if (x.ndims() != b.ndims()) {
+        type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
+    }
 
-        // Extract the first N elements
-        Array<T> c = convolve<T, T, 1, true>(x, b, type);
-        dim4 cdims = c.dims();
-        cdims[0] = x.dims()[0];
-        c.resetDims(cdims);
+    // Extract the first N elements
+    Array<T> c = convolve<T, T, 1, true>(x, b, type);
+    dim4 cdims = c.dims();
+    cdims[0]   = x.dims()[0];
+    c.resetDims(cdims);
 
-        int num_a = a.dims()[0];
+    int num_a = a.dims()[0];
 
-        if (num_a == 1) return c;
+    if (num_a == 1) return c;
 
-        dim4 ydims = c.dims();
-        Array<T> y = createEmptyArray<T>(ydims);
+    dim4 ydims = c.dims();
+    Array<T> y = createEmptyArray<T>(ydims);
 
-        if (a.ndims() > 1) {
-            kernel::iir<T,  true>(y, c, a);
-        } else {
-            kernel::iir<T, false>(y, c, a);
-        }
-        return y;
+    if (a.ndims() > 1) {
+        kernel::iir<T, true>(y, c, a);
+    } else {
+        kernel::iir<T, false>(y, c, a);
     }
+    return y;
+}
 
-#define INSTANTIATE(T)                          \
-    template Array<T> iir(const Array<T> &b,    \
-                          const Array<T> &a,    \
-                          const Array<T> &x);   \
+#define INSTANTIATE(T)                                          \
+    template Array<T> iir(const Array<T> &b, const Array<T> &a, \
+                          const Array<T> &x);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+}  // namespace cuda
diff --git a/src/backend/cuda/iir.hpp b/src/backend/cuda/iir.hpp
index a3f88581dc..f2ff082d2a 100644
--- a/src/backend/cuda/iir.hpp
+++ b/src/backend/cuda/iir.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index dba78928f2..de253d1dd6 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -11,35 +11,34 @@
 // https://gist.github.com/SnopyDogy/a9a22497a893ec86aa3e
 
 #include <Array.hpp>
-#include <image.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
+#include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <image.hpp>
 
 using af::dim4;
 
 namespace cuda {
 
 template<typename T>
-void copy_image(const Array<T> &in, fg_image image)
-{
+void copy_image(const Array<T> &in, fg_image image) {
     auto stream = cuda::getActiveStream();
-    if(DeviceManager::checkGraphicsInteropCapability()) {
+    if (DeviceManager::checkGraphicsInteropCapability()) {
         auto res = interopManager().getImageResources(image);
 
         const T *d_X = in.get();
         size_t bytes = 0;
-        T* d_pixels = NULL;
+        T *d_pixels  = NULL;
         cudaGraphicsMapResources(1, res[0].get(), stream);
-        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels,
-                                             &bytes, *(res[0].get()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_pixels, &bytes,
+                                             *(res[0].get()));
         cudaMemcpyAsync(d_pixels, d_X, bytes, cudaMemcpyDeviceToDevice, stream);
         cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
     } else {
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule &_ = graphics::forgePlugin();
         CheckGL("Begin CUDA fallback-resource copy");
         unsigned data_size = 0, buffer = 0;
         FG_CHECK(_.fg_get_image_size(&data_size, image));
@@ -47,7 +46,8 @@ void copy_image(const Array<T> &in, fg_image image)
 
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
         glBufferData(GL_PIXEL_UNPACK_BUFFER, data_size, 0, GL_STREAM_DRAW);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr =
+            (GLubyte *)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, in.get(), data_size,
                                        cudaMemcpyDeviceToHost, stream));
@@ -59,8 +59,7 @@ void copy_image(const Array<T> &in, fg_image image)
     }
 }
 
-#define INSTANTIATE(T)      \
-template void copy_image<T>(const Array<T> &, fg_image);
+#define INSTANTIATE(T) template void copy_image<T>(const Array<T> &, fg_image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -71,4 +70,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cu
index 962d1fc486..583e4ff3af 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cu
@@ -7,60 +7,56 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <handle.hpp>
 #include <index.hpp>
 #include <kernel/index.hpp>
-#include <err_cuda.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> index(const Array<T>& in, const af_index_t idxrs[])
-{
+Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     kernel::IndexKernelParam_t p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (dim_t x=0; x<4; ++x) {
-        if (idxrs[x].isSeq) {
-            seqs[x] = idxrs[x].idx.seq;
-        }
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
     }
 
     // retrieve dimensions, strides and offsets
-    dim4 iDims = in.dims();
-    dim4 dDims = in.getDataDims();
-    dim4 oDims = toDims  (seqs, iDims);
-    dim4 iOffs = toOffset(seqs, dDims);
-    dim4 iStrds= in.strides();
+    dim4 iDims  = in.dims();
+    dim4 dDims  = in.getDataDims();
+    dim4 oDims  = toDims(seqs, iDims);
+    dim4 iOffs  = toOffset(seqs, dDims);
+    dim4 iStrds = in.strides();
 
-    for (dim_t i=0; i<4; ++i) {
+    for (dim_t i = 0; i < 4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
         p.offs[i]  = iOffs[i];
         p.strds[i] = iStrds[i];
     }
 
-    std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         // set idxPtrs to null
         p.ptr[x] = 0;
         // set index pointers were applicable
         if (!p.isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
-            p.ptr[x] = idxArrs[x].get();
+            p.ptr[x]   = idxArrs[x].get();
             // set output array ith dimension value
             oDims[x] = idxArrs[x].elements();
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
-    if(oDims.elements() == 0) { return out; }
+    if (oDims.elements() == 0) { return out; }
 
     kernel::index<T>(out, in, p);
 
@@ -71,16 +67,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     template Array<T> index<T>(const Array<T>& in, const af_index_t idxrs[]);
 
 INSTANTIATE(cdouble)
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
-INSTANTIATE(float  )
-INSTANTIATE(uint   )
-INSTANTIATE(int    )
-INSTANTIATE(uintl  )
-INSTANTIATE(intl   )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(ushort )
-INSTANTIATE(short  )
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(int)
+INSTANTIATE(uintl)
+INSTANTIATE(intl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/index.hpp b/src/backend/cuda/index.hpp
index 67d106d59b..3a439c9941 100644
--- a/src/backend/cuda/index.hpp
+++ b/src/backend/cuda/index.hpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <af/index.h>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
diff --git a/src/backend/cuda/inverse.cu b/src/backend/cuda/inverse.cu
index e2d0e971d3..22c1ae88b3 100644
--- a/src/backend/cuda/inverse.cu
+++ b/src/backend/cuda/inverse.cu
@@ -7,28 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <inverse.hpp>
 #include <common/err_common.hpp>
+#include <inverse.hpp>
 
-#include <solve.hpp>
 #include <identity.hpp>
+#include <solve.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
+Array<T> inverse(const Array<T> &in) {
     Array<T> I = identity<T>(in.dims());
     return solve<T>(in, I);
 }
 
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/inverse.hpp b/src/backend/cuda/inverse.hpp
index d9d35746fb..27ba153175 100644
--- a/src/backend/cuda/inverse.hpp
+++ b/src/backend/cuda/inverse.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> inverse(const Array<T> &in);
+namespace cuda {
+template<typename T>
+Array<T> inverse(const Array<T> &in);
 }
diff --git a/src/backend/cuda/iota.cu b/src/backend/cuda/iota.cu
index cd06e63770..81e8fbca6b 100644
--- a/src/backend/cuda/iota.cu
+++ b/src/backend/cuda/iota.cu
@@ -8,36 +8,33 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <iota.hpp>
 #include <kernel/iota.hpp>
 #include <math.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
-    {
-        dim4 outdims = dims * tile_dims;
 
-        Array<T> out = createEmptyArray<T>(outdims);
-        kernel::iota<T>(out, dims);
+namespace cuda {
+template<typename T>
+Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
+    dim4 outdims = dims * tile_dims;
 
-        return out;
-    }
+    Array<T> out = createEmptyArray<T>(outdims);
+    kernel::iota<T>(out, dims);
 
-#define INSTANTIATE(T)                                                          \
-    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims); \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return out;
 }
 
+#define INSTANTIATE(T) \
+    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/iota.hpp b/src/backend/cuda/iota.hpp
index 19922def39..bbc01a94e8 100644
--- a/src/backend/cuda/iota.hpp
+++ b/src/backend/cuda/iota.hpp
@@ -10,10 +10,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
+namespace cuda {
+template<typename T>
+Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
 }
-
-
diff --git a/src/backend/cuda/ireduce.cu b/src/backend/cuda/ireduce.cu
index 945908c2ea..6dc0f72efd 100644
--- a/src/backend/cuda/ireduce.cu
+++ b/src/backend/cuda/ireduce.cu
@@ -7,64 +7,61 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <ireduce.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
 #undef _GLIBCXX_USE_INT128
-#include <complex>
-#include <kernel/ireduce.hpp>
 #include <err_cuda.hpp>
+#include <kernel/ireduce.hpp>
+#include <complex>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
-    template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc,
-                 const Array<T> &in, const int dim)
-    {
-        kernel::ireduce<T, op>(out, loc.get(), in, dim);
-    }
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim) {
+    kernel::ireduce<T, op>(out, loc.get(), in, dim);
+}
 
-    template<af_op_t op, typename T>
-    T ireduce_all(unsigned *loc, const Array<T> &in)
-    {
-        return kernel::ireduce_all<T, op>(loc, in);
-    }
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in) {
+    return kernel::ireduce_all<T, op>(loc, in);
+}
 
-#define INSTANTIATE(ROp, T)                                             \
-    template void ireduce<ROp, T>(Array<T> &out, Array<uint> &loc,      \
-                                  const Array<T> &in, const int dim);   \
-    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);  \
+#define INSTANTIATE(ROp, T)                                           \
+    template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim); \
+    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
 
-    //min
-    INSTANTIATE(af_min_t, float  )
-    INSTANTIATE(af_min_t, double )
-    INSTANTIATE(af_min_t, cfloat )
-    INSTANTIATE(af_min_t, cdouble)
-    INSTANTIATE(af_min_t, int    )
-    INSTANTIATE(af_min_t, uint   )
-    INSTANTIATE(af_min_t, intl   )
-    INSTANTIATE(af_min_t, uintl  )
-    INSTANTIATE(af_min_t, short  )
-    INSTANTIATE(af_min_t, ushort )
-    INSTANTIATE(af_min_t, char   )
-    INSTANTIATE(af_min_t, uchar  )
+// min
+INSTANTIATE(af_min_t, float)
+INSTANTIATE(af_min_t, double)
+INSTANTIATE(af_min_t, cfloat)
+INSTANTIATE(af_min_t, cdouble)
+INSTANTIATE(af_min_t, int)
+INSTANTIATE(af_min_t, uint)
+INSTANTIATE(af_min_t, intl)
+INSTANTIATE(af_min_t, uintl)
+INSTANTIATE(af_min_t, short)
+INSTANTIATE(af_min_t, ushort)
+INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, uchar)
 
-    //max
-    INSTANTIATE(af_max_t, float  )
-    INSTANTIATE(af_max_t, double )
-    INSTANTIATE(af_max_t, cfloat )
-    INSTANTIATE(af_max_t, cdouble)
-    INSTANTIATE(af_max_t, int    )
-    INSTANTIATE(af_max_t, uint   )
-    INSTANTIATE(af_max_t, intl   )
-    INSTANTIATE(af_max_t, uintl  )
-    INSTANTIATE(af_max_t, short  )
-    INSTANTIATE(af_max_t, ushort )
-    INSTANTIATE(af_max_t, char   )
-    INSTANTIATE(af_max_t, uchar  )
-}
+// max
+INSTANTIATE(af_max_t, float)
+INSTANTIATE(af_max_t, double)
+INSTANTIATE(af_max_t, cfloat)
+INSTANTIATE(af_max_t, cdouble)
+INSTANTIATE(af_max_t, int)
+INSTANTIATE(af_max_t, uint)
+INSTANTIATE(af_max_t, intl)
+INSTANTIATE(af_max_t, uintl)
+INSTANTIATE(af_max_t, short)
+INSTANTIATE(af_max_t, ushort)
+INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, uchar)
+}  // namespace cuda
diff --git a/src/backend/cuda/ireduce.hpp b/src/backend/cuda/ireduce.hpp
index a446553d5a..a41927cced 100644
--- a/src/backend/cuda/ireduce.hpp
+++ b/src/backend/cuda/ireduce.hpp
@@ -10,12 +10,11 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-    template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc,
-                 const Array<T> &in, const int dim);
+namespace cuda {
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim);
 
-    template<af_op_t op, typename T>
-    T ireduce_all(unsigned *loc, const Array<T> &in);
-}
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in);
+}  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 9b1cb17249..fc5270ea99 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel_headers/jit.hpp>
 #include <common/dispatch.hpp>
 #include <err_cuda.hpp>
+#include <kernel_headers/jit.hpp>
 
 #include <Array.hpp>
-#include <copy.hpp>
 #include <common/jit/Node.hpp>
-#include <platform.hpp>
+#include <copy.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 
-#include <af/dim4.hpp>
 #include <nvrtc.h>
+#include <af/dim4.hpp>
 
 #include <array>
 #include <cstdio>
@@ -29,8 +29,7 @@
 #include <thread>
 #include <vector>
 
-namespace cuda
-{
+namespace cuda {
 
 using common::Node;
 using common::Node_ids;
@@ -49,14 +48,14 @@ using std::vector;
 
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
-                          const vector<Node_ids> &full_ids,
-                          bool is_linear)
-{
+                          const vector<Node_ids> &full_ids, bool is_linear) {
     stringstream funcName;
     stringstream hashName;
 
-    if (is_linear) funcName << "L_"; //Kernel Linear
-    else           funcName << "G_"; //Kernel General
+    if (is_linear)
+        funcName << "L_";  // Kernel Linear
+    else
+        funcName << "G_";  // Kernel General
 
     for (const auto &node : output_nodes) {
         funcName << node->getNameStr() << "_";
@@ -76,10 +75,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
 static string getKernelString(const string funcName,
                               const vector<const Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
-                              const vector<int> &output_ids,
-                              bool is_linear)
-{
-
+                              const vector<int> &output_ids, bool is_linear) {
     const std::string includeFileStr(jit_cuh, jit_cuh_len);
 
     const std::string paramTStr = R"JIT(
@@ -101,15 +97,16 @@ struct Param
     // This part of the code does not change with the kernel.
 
     static const char *kernelVoid = "extern \"C\" __global__ void\n";
-    static const char *dimParams = "uint blocks_x, uint blocks_y, uint blocks_x_total, uint num_odims";
+    static const char *dimParams =
+        "uint blocks_x, uint blocks_y, uint blocks_x_total, uint num_odims";
 
-    static const char * loopStart = R"JIT(
+    static const char *loopStart = R"JIT(
     for (int blockIdx_x = blockIdx.x; blockIdx_x < blocks_x_total; blockIdx_x += gridDim.x) {
     )JIT";
-    static const char *loopEnd = "}\n\n";
+    static const char *loopEnd   = "}\n\n";
 
     static const char *blockStart = "{\n\n";
-    static const char *blockEnd = "\n\n}";
+    static const char *blockEnd   = "\n\n}";
 
     static const char *linearIndex = R"JIT(
         uint threadId = threadIdx.x;
@@ -158,7 +155,7 @@ struct Param
     stringstream outrefstream;
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
-        const auto &node = full_nodes[i];
+        const auto &node     = full_nodes[i];
         const auto &ids_curr = full_ids[i];
         // Generate input parameters, only needs current id
         node->genParams(inParamStream, ids_curr.id, is_linear);
@@ -174,7 +171,8 @@ struct Param
     for (int i = 0; i < (int)output_ids.size(); i++) {
         int id = output_ids[i];
         // Generate output parameters
-        outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out" << id << ", \n";
+        outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out"
+                       << id << ", \n";
         // Generate code to write the output
         outWriteStream << "out" << id << ".ptr[idx] = val" << id << ";\n";
     }
@@ -213,85 +211,78 @@ typedef struct {
     CUfunction ker;
 } kc_entry_t;
 
-#define CU_CHECK(fn) do {                                 \
-        CUresult res = fn;                                \
-        if (res == CUDA_SUCCESS) break;                   \
-        char cu_err_msg[1024];                            \
-        const char *cu_err_name;                          \
-        const char *cu_err_string;                        \
-        cuGetErrorName(res, &cu_err_name);                \
-        cuGetErrorString(res, &cu_err_string);            \
-        snprintf(cu_err_msg,                              \
-                 sizeof(cu_err_msg),                      \
-                 "CU Error %s(%d): %s\n",                 \
-                 cu_err_name, (int)(res), cu_err_string); \
-        AF_ERROR(cu_err_msg,                              \
-                 AF_ERR_INTERNAL);                        \
-    } while(0)
+#define CU_CHECK(fn)                                                      \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[1024];                                            \
+        const char *cu_err_name;                                          \
+        const char *cu_err_string;                                        \
+        cuGetErrorName(res, &cu_err_name);                                \
+        cuGetErrorString(res, &cu_err_string);                            \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), cu_err_string);                 \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+    } while (0)
 
 #ifndef NDEBUG
-#define CU_LINK_CHECK(fn) do {                            \
-        CUresult res = fn;                                \
-        if (res == CUDA_SUCCESS) break;                   \
-        char cu_err_msg[1024];                            \
-        const char *cu_err_name;                          \
-        cuGetErrorName(res, &cu_err_name);                \
-        snprintf(cu_err_msg,                              \
-                 sizeof(cu_err_msg),                      \
-                 "CU Error %s(%d): %s\n",                 \
-                 cu_err_name, (int)(res), linkError);     \
-        AF_ERROR(cu_err_msg,                              \
-                 AF_ERR_INTERNAL);                        \
-    } while(0)
+#define CU_LINK_CHECK(fn)                                                 \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[1024];                                            \
+        const char *cu_err_name;                                          \
+        cuGetErrorName(res, &cu_err_name);                                \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), linkError);                     \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+    } while (0)
 #else
 #define CU_LINK_CHECK(fn) CU_CHECK(fn)
 #endif
 
 #ifndef NDEBUG
-#define NVRTC_CHECK(fn) do {                            \
-        nvrtcResult res = fn;                           \
-        if (res == NVRTC_SUCCESS) break;                \
-        size_t logSize;                                 \
-        nvrtcGetProgramLogSize(prog, &logSize);         \
-        unique_ptr<char []> log(new char[logSize +1]);  \
-        char *logptr = log.get();                       \
-        nvrtcGetProgramLog(prog, logptr);               \
-        logptr[logSize] = '\x0';                        \
-        printf("%s\n", logptr);                         \
-        AF_ERROR("NVRTC ERROR",                         \
-                 AF_ERR_INTERNAL);                      \
-    } while(0)
+#define NVRTC_CHECK(fn)                                \
+    do {                                               \
+        nvrtcResult res = fn;                          \
+        if (res == NVRTC_SUCCESS) break;               \
+        size_t logSize;                                \
+        nvrtcGetProgramLogSize(prog, &logSize);        \
+        unique_ptr<char[]> log(new char[logSize + 1]); \
+        char *logptr = log.get();                      \
+        nvrtcGetProgramLog(prog, logptr);              \
+        logptr[logSize] = '\x0';                       \
+        printf("%s\n", logptr);                        \
+        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
+    } while (0)
 #else
-#define NVRTC_CHECK(fn) do {                        \
-        nvrtcResult res = fn;                       \
-        if (res == NVRTC_SUCCESS) break;            \
-        char nvrtc_err_msg[1024];                   \
-        snprintf(nvrtc_err_msg,                     \
-                 sizeof(nvrtc_err_msg),             \
-                 "NVRTC Error(%d): %s\n",           \
-                 res, nvrtcGetErrorString(res));    \
-        AF_ERROR(nvrtc_err_msg,                     \
-                 AF_ERR_INTERNAL);                  \
-    } while(0)
+#define NVRTC_CHECK(fn)                                                   \
+    do {                                                                  \
+        nvrtcResult res = fn;                                             \
+        if (res == NVRTC_SUCCESS) break;                                  \
+        char nvrtc_err_msg[1024];                                         \
+        snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
+                 "NVRTC Error(%d): %s\n", res, nvrtcGetErrorString(res)); \
+        AF_ERROR(nvrtc_err_msg, AF_ERR_INTERNAL);                         \
+    } while (0)
 #endif
 
-std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
-{
+std::vector<char> compileToPTX(const char *ker_name, string jit_ker) {
     nvrtcProgram prog;
     size_t ptx_size;
     std::vector<char> ptx;
-    NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(),
-                                   ker_name, 0, NULL, NULL));
+    NVRTC_CHECK(
+        nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 0, NULL, NULL));
 
     auto dev = getDeviceProp(getActiveDeviceId());
     array<char, 32> arch;
     snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
              dev.major, dev.minor);
-    const char* compiler_options[] = {
-      arch.data(),
+    const char *compiler_options[] = {
+        arch.data(),
 #if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
-      "--device-debug",
-      "--generate-line-info"
+        "--device-debug",
+        "--generate-line-info"
 #endif
     };
     int num_options = std::extent<decltype(compiler_options)>::value;
@@ -304,33 +295,25 @@ std::vector<char> compileToPTX(const char *ker_name, string jit_ker)
     return ptx;
 }
 
-static kc_entry_t compileKernel(const char *ker_name, string jit_ker)
-{
-    const size_t linkLogSize = 1024;
-    char linkInfo[linkLogSize] = {0};
+static kc_entry_t compileKernel(const char *ker_name, string jit_ker) {
+    const size_t linkLogSize    = 1024;
+    char linkInfo[linkLogSize]  = {0};
     char linkError[linkLogSize] = {0};
 
     auto ptx = compileToPTX(ker_name, jit_ker);
 
     CUlinkState linkState;
     CUjit_option linkOptions[] = {
-        CU_JIT_INFO_LOG_BUFFER,
-        CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
-        CU_JIT_ERROR_LOG_BUFFER,
-        CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
-        CU_JIT_LOG_VERBOSE
-    };
+        CU_JIT_INFO_LOG_BUFFER, CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
+        CU_JIT_ERROR_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
+        CU_JIT_LOG_VERBOSE};
 
     void *linkOptionValues[] = {
-        linkInfo,
-        reinterpret_cast<void*>(linkLogSize),
-        linkError,
-        reinterpret_cast<void*>(linkLogSize),
-        reinterpret_cast<void*>(1)
-    };
+        linkInfo, reinterpret_cast<void *>(linkLogSize), linkError,
+        reinterpret_cast<void *>(linkLogSize), reinterpret_cast<void *>(1)};
 
     CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
-    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.data(),
+    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void *)ptx.data(),
                                 ptx.size(), ker_name, 0, NULL, NULL));
 
     void *cubin = nullptr;
@@ -350,21 +333,22 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<int> &output_ids,
                             const vector<const Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
-                            const bool is_linear)
-{
+                            const bool is_linear) {
     typedef map<string, kc_entry_t> kc_t;
 
     thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
-    string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    int device      = getActiveDeviceId();
+    string funcName =
+        getFuncName(output_nodes, full_nodes, full_ids, is_linear);
+    int device = getActiveDeviceId();
 
     kc_t::iterator idx = kernelCaches[device].find(funcName);
     kc_entry_t entry{nullptr, nullptr};
 
     if (idx == kernelCaches[device].end()) {
-        string jit_ker = getKernelString(funcName, full_nodes, full_ids, output_ids, is_linear);
-        entry = compileKernel(funcName.c_str(), jit_ker);
+        string jit_ker = getKernelString(funcName, full_nodes, full_ids,
+                                         output_ids, is_linear);
+        entry          = compileKernel(funcName.c_str(), jit_ker);
         kernelCaches[device][funcName] = entry;
     } else {
         entry = idx->second;
@@ -374,8 +358,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
 }
 
 template<typename T>
-void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
-{
+void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
     int num_outputs = (int)outputs.size();
     int device      = getActiveDeviceId();
 
@@ -405,35 +388,35 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
         is_linear &= node->isLinear(outputs[0].dims);
     }
 
-    CUfunction ker = getKernel(output_nodes, output_ids,
-                               full_nodes, full_ids,
-                               is_linear);
+    CUfunction ker =
+        getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
 
     int threads_x = 1, threads_y = 1;
     int blocks_x_ = 1, blocks_y_ = 1;
-    int blocks_x  = 1, blocks_y = 1, blocks_z = 1, blocks_x_total;
+    int blocks_x = 1, blocks_y = 1, blocks_z = 1, blocks_x_total;
 
-    cudaDeviceProp properties = getDeviceProp(device);
+    cudaDeviceProp properties    = getDeviceProp(device);
     const long long max_blocks_x = properties.maxGridSize[0];
     const long long max_blocks_y = properties.maxGridSize[1];
 
     int num_odims = 4;
     while (num_odims >= 1) {
-        if (outputs[0].dims[num_odims - 1] == 1) num_odims--;
-        else break;
+        if (outputs[0].dims[num_odims - 1] == 1)
+            num_odims--;
+        else
+            break;
     }
 
     if (is_linear) {
         threads_x = 256;
-        threads_y =  1;
+        threads_y = 1;
 
-        blocks_x_total = divup((outputs[0].dims[0] *
-                                outputs[0].dims[1] *
-                                outputs[0].dims[2] *
-                                outputs[0].dims[3]), threads_x);
+        blocks_x_total = divup((outputs[0].dims[0] * outputs[0].dims[1] *
+                                outputs[0].dims[2] * outputs[0].dims[3]),
+                               threads_x);
 
         int repeat_x = divup(blocks_x_total, max_blocks_x);
-        blocks_x = divup(blocks_x_total, repeat_x);
+        blocks_x     = divup(blocks_x_total, repeat_x);
     } else {
         threads_x = 32;
         threads_y = 8;
@@ -448,16 +431,17 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
         blocks_y = divup(blocks_y, blocks_z);
 
         blocks_x_total = blocks_x;
-        int repeat_x = divup(blocks_x_total, max_blocks_x);
-        blocks_x = divup(blocks_x_total, repeat_x);
+        int repeat_x   = divup(blocks_x_total, max_blocks_x);
+        blocks_x       = divup(blocks_x_total, repeat_x);
     }
 
     vector<void *> args;
 
     for (const auto &node : full_nodes) {
-        node->setArgs(0, is_linear, [&] (int /*id*/, const void* ptr, size_t /*size*/){
-                                        args.push_back(const_cast<void*>(ptr));
-                                    });
+        node->setArgs(0, is_linear,
+                      [&](int /*id*/, const void *ptr, size_t /*size*/) {
+                          args.push_back(const_cast<void *>(ptr));
+                      });
     }
 
     for (int i = 0; i < num_outputs; i++) {
@@ -469,16 +453,8 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
     args.push_back((void *)&blocks_x_total);
     args.push_back((void *)&num_odims);
 
-    CU_CHECK(cuLaunchKernel(ker,
-                            blocks_x,
-                            blocks_y,
-                            blocks_z,
-                            threads_x,
-                            threads_y,
-                            1,
-                            0,
-                            getActiveStream(),
-                            args.data(),
+    CU_CHECK(cuLaunchKernel(ker, blocks_x, blocks_y, blocks_z, threads_x,
+                            threads_y, 1, 0, getActiveStream(), args.data(),
                             NULL));
 
     // Reset the thread local vectors
@@ -489,8 +465,7 @@ void evalNodes(vector<Param<T>>& outputs, vector<Node *> output_nodes)
 }
 
 template<typename T>
-void evalNodes(Param<T> out, Node *node)
-{
+void evalNodes(Param<T> out, Node *node) {
     vector<Param<T>> outputs;
     vector<Node *> output_nodes;
 
@@ -500,29 +475,33 @@ void evalNodes(Param<T> out, Node *node)
     return;
 }
 
-template void evalNodes<float  >(Param<float  > out, Node *node);
-template void evalNodes<double >(Param<double > out, Node *node);
-template void evalNodes<cfloat >(Param<cfloat > out, Node *node);
+template void evalNodes<float>(Param<float> out, Node *node);
+template void evalNodes<double>(Param<double> out, Node *node);
+template void evalNodes<cfloat>(Param<cfloat> out, Node *node);
 template void evalNodes<cdouble>(Param<cdouble> out, Node *node);
-template void evalNodes<int    >(Param<int    > out, Node *node);
-template void evalNodes<uint   >(Param<uint   > out, Node *node);
-template void evalNodes<char   >(Param<char   > out, Node *node);
-template void evalNodes<uchar  >(Param<uchar  > out, Node *node);
-template void evalNodes<intl   >(Param<intl   > out, Node *node);
-template void evalNodes<uintl  >(Param<uintl  > out, Node *node);
-template void evalNodes<short  >(Param<short  > out, Node *node);
-template void evalNodes<ushort >(Param<ushort > out, Node *node);
-
-template void evalNodes<float  >(vector<Param<float  > > &out, vector<Node *> node);
-template void evalNodes<double >(vector<Param<double > > &out, vector<Node *> node);
-template void evalNodes<cfloat >(vector<Param<cfloat > > &out, vector<Node *> node);
-template void evalNodes<cdouble>(vector<Param<cdouble> > &out, vector<Node *> node);
-template void evalNodes<int    >(vector<Param<int    > > &out, vector<Node *> node);
-template void evalNodes<uint   >(vector<Param<uint   > > &out, vector<Node *> node);
-template void evalNodes<char   >(vector<Param<char   > > &out, vector<Node *> node);
-template void evalNodes<uchar  >(vector<Param<uchar  > > &out, vector<Node *> node);
-template void evalNodes<intl   >(vector<Param<intl   > > &out, vector<Node *> node);
-template void evalNodes<uintl  >(vector<Param<uintl  > > &out, vector<Node *> node);
-template void evalNodes<short  >(vector<Param<short  > > &out, vector<Node *> node);
-template void evalNodes<ushort >(vector<Param<ushort > > &out, vector<Node *> node);
-}
+template void evalNodes<int>(Param<int> out, Node *node);
+template void evalNodes<uint>(Param<uint> out, Node *node);
+template void evalNodes<char>(Param<char> out, Node *node);
+template void evalNodes<uchar>(Param<uchar> out, Node *node);
+template void evalNodes<intl>(Param<intl> out, Node *node);
+template void evalNodes<uintl>(Param<uintl> out, Node *node);
+template void evalNodes<short>(Param<short> out, Node *node);
+template void evalNodes<ushort>(Param<ushort> out, Node *node);
+
+template void evalNodes<float>(vector<Param<float>> &out, vector<Node *> node);
+template void evalNodes<double>(vector<Param<double>> &out,
+                                vector<Node *> node);
+template void evalNodes<cfloat>(vector<Param<cfloat>> &out,
+                                vector<Node *> node);
+template void evalNodes<cdouble>(vector<Param<cdouble>> &out,
+                                 vector<Node *> node);
+template void evalNodes<int>(vector<Param<int>> &out, vector<Node *> node);
+template void evalNodes<uint>(vector<Param<uint>> &out, vector<Node *> node);
+template void evalNodes<char>(vector<Param<char>> &out, vector<Node *> node);
+template void evalNodes<uchar>(vector<Param<uchar>> &out, vector<Node *> node);
+template void evalNodes<intl>(vector<Param<intl>> &out, vector<Node *> node);
+template void evalNodes<uintl>(vector<Param<uintl>> &out, vector<Node *> node);
+template void evalNodes<short>(vector<Param<short>> &out, vector<Node *> node);
+template void evalNodes<ushort>(vector<Param<ushort>> &out,
+                                vector<Node *> node);
+}  // namespace cuda
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
index 3d27022881..371a263245 100644
--- a/src/backend/cuda/jit/BufferNode.hpp
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -8,14 +8,12 @@
  ********************************************************/
 
 #pragma once
-#include "../Param.hpp"
 #include <common/jit/BufferNodeBase.hpp>
+#include "../Param.hpp"
 
-namespace cuda
-{
-namespace jit
-{
-  template<typename T>
-  using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
-}
+namespace cuda {
+namespace jit {
+template<typename T>
+using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index e165cecd7d..3414e439b9 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -20,85 +20,90 @@ namespace cuda {
 
 namespace {
 
-  /// Creates a string that will be used to declare the parameter of kernel
-  void generateParamDeclaration(std::stringstream& kerStream, int id, bool is_linear,
-                                const std::string& m_type_str) {
-      if (is_linear) {
-          kerStream << m_type_str << " *in" << id << "_ptr,\n";
-      } else {
-          kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
-      }
-  }
-
-
-  /// Calls the setArg function to set the arguments for a kernel call
-  template<typename T>
-  int setKernelArguments(int start_id, bool is_linear,
-                         std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-                         const std::shared_ptr<T>& ptr, const Param<T>& info) {
-      UNUSED(ptr);
-      if (is_linear) {
-          setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
-      } else {
-          setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
-      }
-      return start_id + 1;
-  }
-
-  /// Generates the code to calculate the offsets for a buffer
-  void generateBufferOffsets(std::stringstream &kerStream, int id,
-                             bool is_linear, const std::string& type_str) {
-      std::string idx_str = std::string("int idx") + std::to_string(id);
+/// Creates a string that will be used to declare the parameter of kernel
+void generateParamDeclaration(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& m_type_str) {
+    if (is_linear) {
+        kerStream << m_type_str << " *in" << id << "_ptr,\n";
+    } else {
+        kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
+    }
+}
 
-      if (is_linear) {
-          kerStream << idx_str << " = idx;\n";
-      } else {
-          std::string info_str = std::string("in") + std::to_string(id);
-          kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                    << info_str << ".strides[3] * id3 + (id2 < " << info_str << ".dims[2]) * "
-                    << info_str << ".strides[2] * id2 + (id1 < " << info_str << ".dims[1]) * "
-                    << info_str << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * id0;\n";
-          kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
-      }
-  }
+/// Calls the setArg function to set the arguments for a kernel call
+template<typename T>
+int setKernelArguments(
+    int start_id, bool is_linear,
+    std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+    const std::shared_ptr<T>& ptr, const Param<T>& info) {
+    UNUSED(ptr);
+    if (is_linear) {
+        setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
+    } else {
+        setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
+    }
+    return start_id + 1;
+}
 
-  /// Generates the code to read a buffer and store it in a local variable
-  void generateBufferRead(std::stringstream &kerStream, int id,
-                          const std::string& type_str) {
-      kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id << "];\n";
-  }
+/// Generates the code to calculate the offsets for a buffer
+void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
+                           const std::string& type_str) {
+    std::string idx_str = std::string("int idx") + std::to_string(id);
 
-  void generateShiftNodeOffsets(std::stringstream &kerStream, int id,
-                                bool is_linear, const std::string& type_str) {
-      UNUSED(is_linear);
-      std::string idx_str = std::string("idx") + std::to_string(id);
-      std::string info_str = std::string("in") + std::to_string(id);
-      std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
-      std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+    if (is_linear) {
+        kerStream << idx_str << " = idx;\n";
+    } else {
+        std::string info_str = std::string("in") + std::to_string(id);
+        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
+                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
+                  << info_str << ".dims[1]) * " << info_str
+                  << ".strides[1] * id1 + (id0 < " << info_str
+                  << ".dims[0]) * id0;\n";
+        kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
+    }
+}
 
-      for (int i = 0; i < 4; i++) {
-          kerStream << "int " << id_str << i
-                    << " = __circular_mod(id" << i
-                    << " + " << shift_str << i
-                    << ", " << info_str << ".dims[" << i << "]);\n";
-      }
+/// Generates the code to read a buffer and store it in a local variable
+void generateBufferRead(std::stringstream& kerStream, int id,
+                        const std::string& type_str) {
+    kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
+              << "];\n";
+}
 
-      kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str << ".dims[3]) * "
-                << info_str << ".strides[3] * " << id_str << "3;\n";
-      kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
-                << info_str << ".strides[2] * " << id_str << "2;\n";
-      kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
-                << info_str << ".strides[1] * " << id_str << "1;\n";
-      kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
-                << id_str << "0;\n";
-      kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
-  }
+void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& type_str) {
+    UNUSED(is_linear);
+    std::string idx_str   = std::string("idx") + std::to_string(id);
+    std::string info_str  = std::string("in") + std::to_string(id);
+    std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
+    std::string shift_str = std::string("shift") + std::to_string(id) + "_";
 
-  void generateShiftNodeRead(std::stringstream &kerStream, int id,
-                             const std::string& type_str) {
-      kerStream << type_str << " val" << id
-                << " = in" << id << "_ptr[idx" << id << "];\n";
-  }
+    for (int i = 0; i < 4; i++) {
+        kerStream << "int " << id_str << i << " = __circular_mod(id" << i
+                  << " + " << shift_str << i << ", " << info_str << ".dims["
+                  << i << "]);\n";
+    }
 
+    kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str
+              << ".dims[3]) * " << info_str << ".strides[3] * " << id_str
+              << "3;\n";
+    kerStream << idx_str << " += (" << id_str << "2 < " << info_str
+              << ".dims[2]) * " << info_str << ".strides[2] * " << id_str
+              << "2;\n";
+    kerStream << idx_str << " += (" << id_str << "1 < " << info_str
+              << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
+              << "1;\n";
+    kerStream << idx_str << " += (" << id_str << "0 < " << info_str
+              << ".dims[0]) * " << id_str << "0;\n";
+    kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
 }
+
+void generateShiftNodeRead(std::stringstream& kerStream, int id,
+                           const std::string& type_str) {
+    kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
+              << "];\n";
 }
+
+}  // namespace
+}  // namespace cuda
diff --git a/src/backend/cuda/join.cu b/src/backend/cuda/join.cu
index 729cec4c3f..3ab17e55d4 100644
--- a/src/backend/cuda/join.cu
+++ b/src/backend/cuda/join.cu
@@ -8,198 +8,176 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_cuda.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
-    template<int dim>
-    af::dim4 calcOffset(const af::dim4 dims)
-    {
-        af::dim4 offset;
-        offset[0] = (dim == 0) ? dims[0] : 0;
-        offset[1] = (dim == 1) ? dims[1] : 0;
-        offset[2] = (dim == 2) ? dims[2] : 0;
-        offset[3] = (dim == 3) ? dims[3] : 0;
-        return offset;
-    }
 
-    template<typename Tx, typename Ty>
-    Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
-    {
-        // All dimensions except join dimension must be equal
-        // Compute output dims
-        af::dim4 odims;
-        af::dim4 fdims = first.dims();
-        af::dim4 sdims = second.dims();
-
-        for(int i = 0; i < 4; i++) {
-            if(i == dim) {
-                odims[i] = fdims[i] + sdims[i];
-            } else {
-                odims[i] = fdims[i];
-            }
-        }
+namespace cuda {
+template<int dim>
+af::dim4 calcOffset(const af::dim4 dims) {
+    af::dim4 offset;
+    offset[0] = (dim == 0) ? dims[0] : 0;
+    offset[1] = (dim == 1) ? dims[1] : 0;
+    offset[2] = (dim == 2) ? dims[2] : 0;
+    offset[3] = (dim == 3) ? dims[3] : 0;
+    return offset;
+}
 
-        Array<Tx> out = createEmptyArray<Tx>(odims);
-
-        af::dim4 zero(0,0,0,0);
-
-        switch(dim) {
-            case 0:
-                kernel::join<Tx, Tx, 0>(out, first,  zero);
-                kernel::join<Tx, Ty, 0>(out, second, calcOffset<0>(fdims));
-                break;
-            case 1:
-                kernel::join<Tx, Tx, 1>(out, first,  zero);
-                kernel::join<Tx, Ty, 1>(out, second, calcOffset<1>(fdims));
-                break;
-            case 2:
-                kernel::join<Tx, Tx, 2>(out, first,  zero);
-                kernel::join<Tx, Ty, 2>(out, second, calcOffset<2>(fdims));
-                break;
-            case 3:
-                kernel::join<Tx, Tx, 3>(out, first,  zero);
-                kernel::join<Tx, Ty, 3>(out, second, calcOffset<3>(fdims));
-                break;
+template<typename Tx, typename Ty>
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
+    // All dimensions except join dimension must be equal
+    // Compute output dims
+    af::dim4 odims;
+    af::dim4 fdims = first.dims();
+    af::dim4 sdims = second.dims();
+
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
+            odims[i] = fdims[i] + sdims[i];
+        } else {
+            odims[i] = fdims[i];
         }
-
-        return out;
     }
 
-    template<typename T, int n_arrays>
-    void join_wrapper(const int dim, Array<T> &out, const std::vector<Array<T> > &inputs)
-    {
-        af::dim4 zero(0,0,0,0);
-        af::dim4 d = zero;
-
-        switch(dim) {
-            case 0:
-                kernel::join<T, T, 0>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 0>(out, inputs[i], calcOffset<0>(d));
-                }
-                break;
-            case 1:
-                kernel::join<T, T, 1>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 1>(out, inputs[i], calcOffset<1>(d));
-                }
-                break;
-            case 2:
-                kernel::join<T, T, 1>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 2>(out, inputs[i], calcOffset<2>(d));
-                }
-                break;
-            case 3:
-                kernel::join<T, T, 3>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 3>(out, inputs[i], calcOffset<3>(d));
-                }
-                break;
-        }
+    Array<Tx> out = createEmptyArray<Tx>(odims);
+
+    af::dim4 zero(0, 0, 0, 0);
+
+    switch (dim) {
+        case 0:
+            kernel::join<Tx, Tx, 0>(out, first, zero);
+            kernel::join<Tx, Ty, 0>(out, second, calcOffset<0>(fdims));
+            break;
+        case 1:
+            kernel::join<Tx, Tx, 1>(out, first, zero);
+            kernel::join<Tx, Ty, 1>(out, second, calcOffset<1>(fdims));
+            break;
+        case 2:
+            kernel::join<Tx, Tx, 2>(out, first, zero);
+            kernel::join<Tx, Ty, 2>(out, second, calcOffset<2>(fdims));
+            break;
+        case 3:
+            kernel::join<Tx, Tx, 3>(out, first, zero);
+            kernel::join<Tx, Ty, 3>(out, second, calcOffset<3>(fdims));
+            break;
     }
 
-    template<typename T>
-    Array<T> join(const int dim, const std::vector<Array<T> > &inputs)
-    {
-        // All dimensions except join dimension must be equal
-        // Compute output dims
-        af::dim4 odims;
-        const dim_t n_arrays = inputs.size();
-        std::vector<af::dim4> idims(n_arrays);
-
-        dim_t dim_size = 0;
-        for(int i = 0; i < (int)idims.size(); i++) {
-            idims[i] = inputs[i].dims();
-            dim_size += idims[i][dim];
-        }
+    return out;
+}
 
-        for(int i = 0; i < 4; i++) {
-            if(i == dim) {
-                odims[i] = dim_size;
-            } else {
-                odims[i] = idims[0][i];
+template<typename T, int n_arrays>
+void join_wrapper(const int dim, Array<T> &out,
+                  const std::vector<Array<T>> &inputs) {
+    af::dim4 zero(0, 0, 0, 0);
+    af::dim4 d = zero;
+
+    switch (dim) {
+        case 0:
+            kernel::join<T, T, 0>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 0>(out, inputs[i], calcOffset<0>(d));
             }
-        }
+            break;
+        case 1:
+            kernel::join<T, T, 1>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 1>(out, inputs[i], calcOffset<1>(d));
+            }
+            break;
+        case 2:
+            kernel::join<T, T, 1>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 2>(out, inputs[i], calcOffset<2>(d));
+            }
+            break;
+        case 3:
+            kernel::join<T, T, 3>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 3>(out, inputs[i], calcOffset<3>(d));
+            }
+            break;
+    }
+}
 
-        Array<T> out = createEmptyArray<T>(odims);
-
-        switch(n_arrays) {
-            case 1:
-                join_wrapper<T, 1>(dim, out, inputs);
-                break;
-            case 2:
-                join_wrapper<T, 2>(dim, out, inputs);
-                break;
-            case 3:
-                join_wrapper<T, 3>(dim, out, inputs);
-                break;
-            case 4:
-                join_wrapper<T, 4>(dim, out, inputs);
-                break;
-            case 5:
-                join_wrapper<T, 5>(dim, out, inputs);
-                break;
-            case 6:
-                join_wrapper<T, 6>(dim, out, inputs);
-                break;
-            case 7:
-                join_wrapper<T, 7>(dim, out, inputs);
-                break;
-            case 8:
-                join_wrapper<T, 8>(dim, out, inputs);
-                break;
-            case 9:
-                join_wrapper<T, 9>(dim, out, inputs);
-                break;
-            case 10:
-                join_wrapper<T,10>(dim, out, inputs);
-                break;
+template<typename T>
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
+    // All dimensions except join dimension must be equal
+    // Compute output dims
+    af::dim4 odims;
+    const dim_t n_arrays = inputs.size();
+    std::vector<af::dim4> idims(n_arrays);
+
+    dim_t dim_size = 0;
+    for (int i = 0; i < (int)idims.size(); i++) {
+        idims[i] = inputs[i].dims();
+        dim_size += idims[i][dim];
+    }
+
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
+            odims[i] = dim_size;
+        } else {
+            odims[i] = idims[0][i];
         }
-        return out;
     }
 
-#define INSTANTIATE(Tx, Ty)                                                                             \
-    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, const Array<Ty> &second);   \
-
-    INSTANTIATE(float  , float  )
-    INSTANTIATE(double , double )
-    INSTANTIATE(cfloat , cfloat )
-    INSTANTIATE(cdouble, cdouble)
-    INSTANTIATE(int    , int    )
-    INSTANTIATE(uint   , uint   )
-    INSTANTIATE(intl   , intl   )
-    INSTANTIATE(uintl  , uintl  )
-    INSTANTIATE(short  , short  )
-    INSTANTIATE(ushort , ushort )
-    INSTANTIATE(uchar  , uchar  )
-    INSTANTIATE(char   , char   )
+    Array<T> out = createEmptyArray<T>(odims);
+
+    switch (n_arrays) {
+        case 1: join_wrapper<T, 1>(dim, out, inputs); break;
+        case 2: join_wrapper<T, 2>(dim, out, inputs); break;
+        case 3: join_wrapper<T, 3>(dim, out, inputs); break;
+        case 4: join_wrapper<T, 4>(dim, out, inputs); break;
+        case 5: join_wrapper<T, 5>(dim, out, inputs); break;
+        case 6: join_wrapper<T, 6>(dim, out, inputs); break;
+        case 7: join_wrapper<T, 7>(dim, out, inputs); break;
+        case 8: join_wrapper<T, 8>(dim, out, inputs); break;
+        case 9: join_wrapper<T, 9>(dim, out, inputs); break;
+        case 10: join_wrapper<T, 10>(dim, out, inputs); break;
+    }
+    return out;
+}
+
+#define INSTANTIATE(Tx, Ty)                                                \
+    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, \
+                                    const Array<Ty> &second);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(short, short)
+INSTANTIATE(ushort, ushort)
+INSTANTIATE(uchar, uchar)
+INSTANTIATE(char, char)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                                                                              \
-    template Array<T> join<T>(const int dim, const std::vector<Array<T> > &inputs);
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
+#define INSTANTIATE(T)                       \
+    template Array<T> join<T>(const int dim, \
+                              const std::vector<Array<T>> &inputs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
 
 #undef INSTANTIATE
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/join.hpp b/src/backend/cuda/join.hpp
index 722d46cc05..3d0ecd760d 100644
--- a/src/backend/cuda/join.hpp
+++ b/src/backend/cuda/join.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename Tx, typename Ty>
-    Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+namespace cuda {
+template<typename Tx, typename Ty>
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
 
-    template<typename T>
-    Array<T> join(const int dim, const std::vector<Array<T> > &inputs);
-}
+template<typename T>
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index d1ed3d2b9a..31cadece1e 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -7,98 +7,92 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
 
-inline __device__
-int clamp(const int value, const int low, const int high)
-{
+inline __device__ int clamp(const int value, const int low, const int high) {
     return max(low, min(value, high));
 }
 
-__forceinline__ __device__
-int index(const int x, const int y,
-          const int dim0, const int dim1,
-          const int stride0, const int stride1)
-{
-    return clamp(x, 0, dim0-1)*stride0 + clamp(y, 0, dim1-1)*stride1;
+__forceinline__ __device__ int index(const int x, const int y, const int dim0,
+                                     const int dim1, const int stride0,
+                                     const int stride1) {
+    return clamp(x, 0, dim0 - 1) * stride0 + clamp(y, 0, dim1 - 1) * stride1;
 }
 
-__device__
-float quadratic(const float value)
-{
-    return 1.0/(1.0+value);
-}
+__device__ float quadratic(const float value) { return 1.0 / (1.0 + value); }
 
-__device__
-float computeGradientBasedUpdate(const float mct, const float C,
-                                 const float  S,  const float  N, const float  W, const float E,
-                                 const float SE,  const float SW, const float NE, const float NW,
-                                 const af_flux_function fftype)
-{
+__device__ float computeGradientBasedUpdate(const float mct, const float C,
+                                            const float S, const float N,
+                                            const float W, const float E,
+                                            const float SE, const float SW,
+                                            const float NE, const float NW,
+                                            const af_flux_function fftype) {
     float delta = 0;
 
     float dx, dy, df, db, cx, cxd;
 
     // centralized derivatives
-    dx = (E-W)*0.5f;
-    dy = (S-N)*0.5f;
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
 
     // half-d's and conductance along first dimension
-    df  = E - C;
-    db  = C - W;
+    df = E - C;
+    db = C - W;
 
-    if (fftype==AF_FLUX_EXPONENTIAL) {
-        cx  = expf( (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2)) * mct );
-        cxd = expf( (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2)) * mct );
+    if (fftype == AF_FLUX_EXPONENTIAL) {
+        cx  = expf((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd = expf((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
     } else {
-        cx  = quadratic( (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2)) * mct );
-        cxd = quadratic( (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2)) * mct );
+        cx =
+            quadratic((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd =
+            quadratic((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
     }
-    delta += (cx*df - cxd*db);
+    delta += (cx * df - cxd * db);
 
     // half-d's and conductance along second dimension
-    df  = S - C;
-    db  = C - N;
+    df = S - C;
+    db = C - N;
 
-    if (fftype==AF_FLUX_EXPONENTIAL) {
-        cx  = expf( (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2)) * mct );
-        cxd = expf( (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2)) * mct );
+    if (fftype == AF_FLUX_EXPONENTIAL) {
+        cx  = expf((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd = expf((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
     } else {
-        cx  = quadratic( (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2)) * mct );
-        cxd = quadratic( (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2)) * mct );
+        cx =
+            quadratic((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd =
+            quadratic((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
     }
-    delta += (cx*df - cxd*db);
+    delta += (cx * df - cxd * db);
 
     return delta;
 }
 
-__device__
-float computeCurvatureBasedUpdate(const float mct, const float C,
-                                  const float  S, const float  N, const float  W, const float  E,
-                                  const float SE, const float SW, const float NE, const float NW,
-                                  const af_flux_function fftype)
-{
-    float delta = 0;
+__device__ float computeCurvatureBasedUpdate(const float mct, const float C,
+                                             const float S, const float N,
+                                             const float W, const float E,
+                                             const float SE, const float SW,
+                                             const float NE, const float NW,
+                                             const af_flux_function fftype) {
+    float delta     = 0;
     float prop_grad = 0;
 
     float df0, db0;
     float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
 
     // centralized derivatives
-    dx = (E-W)*0.5f;
-    dy = (S-N)*0.5f;
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
 
     // half-d's and conductance along first dimension
     df  = E - C;
@@ -106,50 +100,54 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     df0 = df;
     db0 = db;
 
-    gmsqf = (df*df + 0.25f*powf(dy+0.5f*(SE - NE), 2));
-    gmsqb = (db*db + 0.25f*powf(dy+0.5f*(SW - NW), 2));
+    gmsqf = (df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2));
+    gmsqb = (db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2));
 
     gmf = sqrtf(1.0e-10 + gmsqf);
     gmb = sqrtf(1.0e-10 + gmsqb);
 
-    cx  = expf( gmsqf * mct );
-    cxd = expf( gmsqb * mct );
+    cx  = expf(gmsqf * mct);
+    cxd = expf(gmsqb * mct);
 
-    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
 
     // half-d's and conductance along second dimension
-    df  = S - C;
-    db  = C - N;
+    df = S - C;
+    db = C - N;
 
-    gmsqf = (df*df + 0.25f*powf(dx+0.5f*(SE - SW), 2));
-    gmsqb = (db*db + 0.25f*powf(dx+0.5f*(NE - NW), 2));
-    gmf = sqrtf(1.0e-10 + gmsqf);
-    gmb = sqrtf(1.0e-10 + gmsqb);
+    gmsqf = (df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2));
+    gmsqb = (db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2));
+    gmf   = sqrtf(1.0e-10 + gmsqf);
+    gmb   = sqrtf(1.0e-10 + gmsqb);
 
-    cx  = expf( gmsqf * mct );
-    cxd = expf( gmsqb * mct );
+    cx  = expf(gmsqf * mct);
+    cxd = expf(gmsqb * mct);
 
-    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
 
-    if (delta>0){
-        prop_grad += (powf(fminf(db0, 0.0f),2.0f) + powf(fmaxf(df0, 0.0f), 2.0f));
-        prop_grad += (powf(fminf( db, 0.0f),2.0f) + powf(fmaxf( df, 0.0f), 2.0f));
+    if (delta > 0) {
+        prop_grad +=
+            (powf(fminf(db0, 0.0f), 2.0f) + powf(fmaxf(df0, 0.0f), 2.0f));
+        prop_grad +=
+            (powf(fminf(db, 0.0f), 2.0f) + powf(fmaxf(df, 0.0f), 2.0f));
     } else {
-        prop_grad += (powf(fmaxf(db0, 0.0f),2.0f) + powf(fminf(df0, 0.0f), 2.0f));
-        prop_grad += (powf(fmaxf( db, 0.0f),2.0f) + powf(fminf( df, 0.0f), 2.0f));
+        prop_grad +=
+            (powf(fmaxf(db0, 0.0f), 2.0f) + powf(fminf(df0, 0.0f), 2.0f));
+        prop_grad +=
+            (powf(fmaxf(db, 0.0f), 2.0f) + powf(fminf(df, 0.0f), 2.0f));
     }
 
-    return sqrtf(prop_grad)*delta;
+    return sqrtf(prop_grad) * delta;
 }
 
 template<typename T, bool isMCDE>
-static __global__
-void diffUpdate(Param<T> inout, const float dt, const float mct, const af_flux_function fftype,
-                const unsigned blkX, const unsigned blkY)
-{
-    const unsigned RADIUS = 1;
-    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2*RADIUS; //Coloumns
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2*RADIUS; //Rows
+static __global__ void diffUpdate(Param<T> inout, const float dt,
+                                  const float mct,
+                                  const af_flux_function fftype,
+                                  const unsigned blkX, const unsigned blkY) {
+    const unsigned RADIUS         = 1;
+    const unsigned SHRD_MEM_WIDTH = THREADS_X + 2 * RADIUS;   // Coloumns
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2 * RADIUS;  // Rows
 
     __shared__ float shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
@@ -159,27 +157,26 @@ void diffUpdate(Param<T> inout, const float dt, const float mct, const af_flux_f
     const int b2 = blockIdx.x / blkX;
     const int b3 = blockIdx.y / blkY;
 
-    const int gx = blockDim.x * (blockIdx.x - b2*blkX) + lx;
-    const int gy = blockDim.y * (blockIdx.y - b3*blkY) + ly;
+    const int gx = blockDim.x * (blockIdx.x - b2 * blkX) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
 
-    T* img = (T *)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
+    T* img = (T*)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
 
 #pragma unroll
-    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=blockDim.y, gy2+=blockDim.y)
-    {
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y) {
 #pragma unroll
-        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=blockDim.x, gx2+=blockDim.x)
-        {
-            int idx = index(gx2-RADIUS, gy2-RADIUS, inout.dims[0], inout.dims[1],
-                            inout.strides[0], inout.strides[1]);
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x) {
+            int idx       = index(gx2 - RADIUS, gy2 - RADIUS, inout.dims[0],
+                            inout.dims[1], inout.strides[0], inout.strides[1]);
             shrdMem[b][a] = img[idx];
         }
     }
 
     __syncthreads();
 
-    if (gx<inout.dims[0] && gy<inout.dims[1])
-    {
+    if (gx < inout.dims[0] && gy < inout.dims[1]) {
         int i       = lx + RADIUS;
         int j       = ly + RADIUS;
         float C     = shrdMem[j][i];
@@ -187,23 +184,24 @@ void diffUpdate(Param<T> inout, const float dt, const float mct, const af_flux_f
 
         if (isMCDE) {
             delta = computeCurvatureBasedUpdate(
-                    mct, C, shrdMem[j][i+1], shrdMem[j][i-1], shrdMem[j-1][i], shrdMem[j+1][i],
-                    shrdMem[j+1][i+1], shrdMem[j-1][i+1], shrdMem[j+1][i-1], shrdMem[j-1][i-1],
-                    fftype);
+                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
         } else {
             delta = computeGradientBasedUpdate(
-                    mct, C, shrdMem[j][i+1], shrdMem[j][i-1], shrdMem[j-1][i], shrdMem[j+1][i],
-                    shrdMem[j+1][i+1], shrdMem[j-1][i+1], shrdMem[j+1][i-1], shrdMem[j-1][i-1],
-                    fftype);
+                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
         }
 
-        img[gx*inout.strides[0] + gy*inout.strides[1]] = (T)(C + delta*dt);
+        img[gx * inout.strides[0] + gy * inout.strides[1]] =
+            (T)(C + delta * dt);
     }
 }
 
 template<typename T, bool isMCDE>
-void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const af_flux_function fftype)
-{
+void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
+                          const af_flux_function fftype) {
     dim3 threads(THREADS_X, THREADS_Y, 1);
 
     int blkX = divup(inout.dims[0], threads.x);
@@ -211,17 +209,19 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct, const
 
     dim3 blocks(blkX * inout.dims[2], blkY * inout.dims[3], 1);
 
-    const int maxBlkY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    const int blkZ    = divup(blocks.y, maxBlkY);
+    const int maxBlkY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int blkZ = divup(blocks.y, maxBlkY);
 
-    if(blkZ > 1) {
+    if (blkZ > 1) {
         blocks.y = maxBlkY;
         blocks.z = blkZ;
     }
 
-    CUDA_LAUNCH((diffUpdate<T, isMCDE>), blocks, threads, inout, dt, mct, fftype, blkX, blkY);
+    CUDA_LAUNCH((diffUpdate<T, isMCDE>), blocks, threads, inout, dt, mct,
+                fftype, blkX, blkY);
 
     POST_LAUNCH_CHECK();
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index c9db048f40..c3878a0a54 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -7,169 +7,169 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include "interp.hpp"
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const int TX = 16;
-        static const int TY = 16;
-        static const int THREADS = 256;
-
-        template<typename Ty, typename Tp, int order>
-        __global__
-        void approx1_kernel(Param<Ty> yo, CParam<Ty> yi,
-                            CParam<Tp> xo, const int xdim,
-                            const Tp xi_beg, const Tp xi_step,
-                            const float offGrid, const int blocksMatX, const bool batch,
-                            af_interp_type method)
-        {
-            const int idy = blockIdx.x / blocksMatX;
-            const int blockIdx_x = blockIdx.x - idy * blocksMatX;
-            const int idx = blockIdx_x * blockDim.x + threadIdx.x;
-
-            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / yo.dims[2];
-            const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * yo.dims[2];
-
-            if (idx >= yo.dims[0] || idy >= yo.dims[1] ||
-                idz >= yo.dims[2] || idw >= yo.dims[3])
-                return;
-
-            bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
-            bool is_yi_off[] = {true, true, true, true};
-            is_yi_off[xdim] = false;
-
-            const int yo_idx = idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx;
-            int xo_idx = idx * is_xo_off[0];
-            xo_idx += idw * xo.strides[3] * is_xo_off[3];
-            xo_idx += idz * xo.strides[2] * is_xo_off[2];
-            xo_idx += idy * xo.strides[1] * is_xo_off[1];
-
-            const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
-            if (x < 0 || yi.dims[xdim] < x+1) {
-                yo.ptr[yo_idx] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            int yi_idx = idx * is_yi_off[0];
-            yi_idx += idw * yi.strides[3] * is_yi_off[3];
-            yi_idx += idz * yi.strides[2] * is_yi_off[2];
-            yi_idx += idy * yi.strides[1] * is_yi_off[1];
-
-            // FIXME: Only cubic interpolation is doing clamping
-            // We need to make it consistent across all methods
-            // Not changing the behavior because tests will fail
-            bool clamp = order == 3;
-
-            Interp1<Ty, Tp, order> interp;
-            interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp, xdim);
-        }
-
-        template<typename Ty, typename Tp, int order>
-        __global__
-        void approx2_kernel(Param<Ty> zo, CParam<Ty> zi,
-                            CParam<Tp> xo, const int xdim, const Tp xi_beg, const Tp xi_step,
-                            CParam<Tp> yo, const int ydim, const Tp yi_beg, const Tp yi_step,
-                            const float offGrid,
-                            const int blocksMatX, const int blocksMatY, const bool batch,
-                            af_interp_type method)
-        {
-            const int idz = blockIdx.x / blocksMatX;
-            const int blockIdx_x = blockIdx.x - idz * blocksMatX;
-            const int idx = threadIdx.x + blockIdx_x * blockDim.x;
-
-            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksMatY;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksMatY;
-            const int idy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if (idx >= zo.dims[0] || idy >= zo.dims[1] ||
-                idz >= zo.dims[2] || idw >= zo.dims[3])
-                return;
-
-            bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
-            bool is_zi_off[] = {true, true, true, true};
-            is_zi_off[xdim] = false;
-            is_zi_off[ydim] = false;
-
-            const int zo_idx = idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx;
-            int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
-            int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
-            xo_idx += idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2]  * is_xo_off[2];
-            yo_idx += idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2]  * is_xo_off[2];
-
-            const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
-            const Tp y = (yo.ptr[yo_idx] - yi_beg) / yi_step;
-            if (x < 0 || y < 0 || zi.dims[xdim] < x+1 || zi.dims[ydim] < y+1) {
-                zo.ptr[zo_idx] = scalar<Ty>(offGrid);
-                return;
-            }
-
-            int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0];
-            zi_idx += idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
-
-            // FIXME: Only cubic interpolation is doing clamping
-            // We need to make it consistent across all methods
-            // Not changing the behavior because tests will fail
-            bool clamp = order == 3;
-
-            Interp2<Ty, Tp, order> interp;
-            interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp, xdim, ydim);
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename Ty, typename Tp, int order>
-        void approx1(Param<Ty> yo, CParam<Ty> yi,
-                     CParam<Tp> xo, const int xdim,
-                     const Tp &xi_beg, const Tp &xi_step,
-                     const float offGrid,
-                     af_interp_type method)
-        {
-            dim3 threads(THREADS, 1, 1);
-            int blocksPerMat = divup(yo.dims[0], threads.x);
-            dim3 blocks(blocksPerMat * yo.dims[1], yo.dims[2] * yo.dims[3]);
-
-            bool batch = !(xo.dims[1] == 1 && xo.dims[2] == 1 && xo.dims[3] == 1);
-
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
-
-            CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads,
-                        yo, yi, xo, xdim, xi_beg, xi_step, offGrid, blocksPerMat, batch, method);
-            POST_LAUNCH_CHECK();
-        }
-
-        template <typename Ty, typename Tp, int order>
-        void approx2(Param<Ty> zo, CParam<Ty> zi,
-                     CParam<Tp> xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                     CParam<Tp> yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                     const float offGrid,
-                     af_interp_type method)
-        {
-            dim3 threads(TX, TY, 1);
-            int blocksPerMatX = divup(zo.dims[0], threads.x);
-            int blocksPerMatY = divup(zo.dims[1], threads.y);
-            dim3 blocks(blocksPerMatX * zo.dims[2], blocksPerMatY * zo.dims[3]);
-
-            bool batch = !(xo.dims[2] == 1 && xo.dims[3] == 1);
-
-            const int maxBlocksY    = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
-
-            CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads,
-                        zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
-                        offGrid, blocksPerMatX, blocksPerMatY, batch, method);
-            POST_LAUNCH_CHECK();
-        }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const int TX      = 16;
+static const int TY      = 16;
+static const int THREADS = 256;
+
+template<typename Ty, typename Tp, int order>
+__global__ void approx1_kernel(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
+                               const int xdim, const Tp xi_beg,
+                               const Tp xi_step, const float offGrid,
+                               const int blocksMatX, const bool batch,
+                               af_interp_type method) {
+    const int idy        = blockIdx.x / blocksMatX;
+    const int blockIdx_x = blockIdx.x - idy * blocksMatX;
+    const int idx        = blockIdx_x * blockDim.x + threadIdx.x;
+
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / yo.dims[2];
+    const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * yo.dims[2];
+
+    if (idx >= yo.dims[0] || idy >= yo.dims[1] || idz >= yo.dims[2] ||
+        idw >= yo.dims[3])
+        return;
+
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                        xo.dims[3] > 1};
+    bool is_yi_off[] = {true, true, true, true};
+    is_yi_off[xdim]  = false;
+
+    const int yo_idx =
+        idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx;
+    int xo_idx = idx * is_xo_off[0];
+    xo_idx += idw * xo.strides[3] * is_xo_off[3];
+    xo_idx += idz * xo.strides[2] * is_xo_off[2];
+    xo_idx += idy * xo.strides[1] * is_xo_off[1];
+
+    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+    if (x < 0 || yi.dims[xdim] < x + 1) {
+        yo.ptr[yo_idx] = scalar<Ty>(offGrid);
+        return;
     }
+
+    int yi_idx = idx * is_yi_off[0];
+    yi_idx += idw * yi.strides[3] * is_yi_off[3];
+    yi_idx += idz * yi.strides[2] * is_yi_off[2];
+    yi_idx += idy * yi.strides[1] * is_yi_off[1];
+
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    bool clamp = order == 3;
+
+    Interp1<Ty, Tp, order> interp;
+    interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp, xdim);
+}
+
+template<typename Ty, typename Tp, int order>
+__global__ void approx2_kernel(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
+                               const int xdim, const Tp xi_beg,
+                               const Tp xi_step, CParam<Tp> yo, const int ydim,
+                               const Tp yi_beg, const Tp yi_step,
+                               const float offGrid, const int blocksMatX,
+                               const int blocksMatY, const bool batch,
+                               af_interp_type method) {
+    const int idz        = blockIdx.x / blocksMatX;
+    const int blockIdx_x = blockIdx.x - idz * blocksMatX;
+    const int idx        = threadIdx.x + blockIdx_x * blockDim.x;
+
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksMatY;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksMatY;
+    const int idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (idx >= zo.dims[0] || idy >= zo.dims[1] || idz >= zo.dims[2] ||
+        idw >= zo.dims[3])
+        return;
+
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                        xo.dims[3] > 1};
+    bool is_zi_off[] = {true, true, true, true};
+    is_zi_off[xdim]  = false;
+    is_zi_off[ydim]  = false;
+
+    const int zo_idx =
+        idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx;
+    int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
+    int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
+    xo_idx +=
+        idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2] * is_xo_off[2];
+    yo_idx +=
+        idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2] * is_xo_off[2];
+
+    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+    const Tp y = (yo.ptr[yo_idx] - yi_beg) / yi_step;
+    if (x < 0 || y < 0 || zi.dims[xdim] < x + 1 || zi.dims[ydim] < y + 1) {
+        zo.ptr[zo_idx] = scalar<Ty>(offGrid);
+        return;
+    }
+
+    int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0];
+    zi_idx +=
+        idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
+
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    bool clamp = order == 3;
+
+    Interp2<Ty, Tp, order> interp;
+    interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp, xdim, ydim);
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename Ty, typename Tp, int order>
+void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
+             const Tp &xi_beg, const Tp &xi_step, const float offGrid,
+             af_interp_type method) {
+    dim3 threads(THREADS, 1, 1);
+    int blocksPerMat = divup(yo.dims[0], threads.x);
+    dim3 blocks(blocksPerMat * yo.dims[1], yo.dims[2] * yo.dims[3]);
+
+    bool batch = !(xo.dims[1] == 1 && xo.dims[2] == 1 && xo.dims[3] == 1);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads, yo, yi, xo,
+                xdim, xi_beg, xi_step, offGrid, blocksPerMat, batch, method);
+    POST_LAUNCH_CHECK();
+}
+
+template<typename Ty, typename Tp, int order>
+void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
+             const Tp &xi_beg, const Tp &xi_step, CParam<Tp> yo, const int ydim,
+             const Tp &yi_beg, const Tp &yi_step, const float offGrid,
+             af_interp_type method) {
+    dim3 threads(TX, TY, 1);
+    int blocksPerMatX = divup(zo.dims[0], threads.x);
+    int blocksPerMatY = divup(zo.dims[1], threads.y);
+    dim3 blocks(blocksPerMatX * zo.dims[2], blocksPerMatY * zo.dims[3]);
+
+    bool batch = !(xo.dims[2] == 1 && xo.dims[3] == 1);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads, zo, zi, xo,
+                xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step, offGrid,
+                blocksPerMatX, blocksPerMatY, batch, method);
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index b3ad4ee47b..a7e56b18ae 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -7,34 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
+#include <debug_cuda.hpp>
 #include <math.hpp>
 #include <utility.hpp>
-#include <debug_cuda.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 32;
-static const int THREADS_Y =  8;
+static const int THREADS_Y = 8;
 
 typedef struct {
-    int  offs[4];
+    int offs[4];
     int strds[4];
-    bool     isSeq[4];
-    uint*      ptr[4];
+    bool isSeq[4];
+    uint* ptr[4];
 } AssignKernelParam_t;
 
 template<typename T>
-__global__
-void AssignKernel(Param<T> out, CParam<T> in, const AssignKernelParam_t p,
-                 const int nBBS0, const int nBBS1)
-{
+__global__ void AssignKernel(Param<T> out, CParam<T> in,
+                             const AssignKernelParam_t p, const int nBBS0,
+                             const int nBBS1) {
     // retrieve index pointers
     // these can be 0 where af_array index is not used
     const uint* ptr0 = p.ptr[0];
@@ -47,36 +44,45 @@ void AssignKernel(Param<T> out, CParam<T> in, const AssignKernelParam_t p,
     const bool s2 = p.isSeq[2];
     const bool s3 = p.isSeq[3];
 
-    const int gz =  blockIdx.x / nBBS0;
+    const int gz = blockIdx.x / nBBS0;
     const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
-    const int gx = blockDim.x * (blockIdx.x - gz*nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y)  - gw*nBBS1) + threadIdx.y;
+    const int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + threadIdx.x;
+    const int gy =
+        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) +
+        threadIdx.y;
 
-    if (gx<in.dims[0] && gy<in.dims[1] && gz<in.dims[2] && gw<in.dims[3]) {
+    if (gx < in.dims[0] && gy < in.dims[1] && gz < in.dims[2] &&
+        gw < in.dims[3]) {
         // calculate pointer offsets for input
-        int i = p.strds[0] * trimIndex(s0 ? gx+p.offs[0] : ptr0[gx], out.dims[0]);
-        int j = p.strds[1] * trimIndex(s1 ? gy+p.offs[1] : ptr1[gy], out.dims[1]);
-        int k = p.strds[2] * trimIndex(s2 ? gz+p.offs[2] : ptr2[gz], out.dims[2]);
-        int l = p.strds[3] * trimIndex(s3 ? gw+p.offs[3] : ptr3[gw], out.dims[3]);
+        int i =
+            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], out.dims[0]);
+        int j =
+            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], out.dims[1]);
+        int k =
+            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], out.dims[2]);
+        int l =
+            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], out.dims[3]);
         // offset input and output pointers
-        const T *src = (const T*)in.ptr + (gx*in.strides[0]+gy*in.strides[1]+ gz*in.strides[2]+gw*in.strides[3]);
-        T *dst = (T*)out.ptr +(i+j+k+l);
+        const T* src =
+            (const T*)in.ptr + (gx * in.strides[0] + gy * in.strides[1] +
+                                gz * in.strides[2] + gw * in.strides[3]);
+        T* dst = (T*)out.ptr + (i + j + k + l);
         // set the output
         dst[0] = src[0];
     }
 }
 
 template<typename T>
-void assign(Param<T> out, CParam<T> in, const AssignKernelParam_t& p)
-{
+void assign(Param<T> out, CParam<T> in, const AssignKernelParam_t& p) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blks_x = divup(in.dims[0], threads.x);
     int blks_y = divup(in.dims[1], threads.y);
 
-    dim3 blocks(blks_x*in.dims[2], blks_y*in.dims[3]);
+    dim3 blocks(blks_x * in.dims[2], blks_y * in.dims[3]);
 
-    const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
@@ -85,6 +91,6 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam_t& p)
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/atomics.hpp b/src/backend/cuda/kernel/atomics.hpp
index 1b9ff2701b..47ed2f4747 100644
--- a/src/backend/cuda/kernel/atomics.hpp
+++ b/src/backend/cuda/kernel/atomics.hpp
@@ -7,53 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-namespace cuda
-{
-    namespace kernel
-    {
-        template<typename T>
-        __device__ T atomicAdd(T *ptr, T val)
-        {
-            return ::atomicAdd(ptr, val);
-        }
+namespace cuda {
+namespace kernel {
+template<typename T>
+__device__ T atomicAdd(T *ptr, T val) {
+    return ::atomicAdd(ptr, val);
+}
 
-#define SPECIALIZE(T, fn1, fn2)                                         \
-        template<>                                                      \
-        __device__ T atomicAdd<T>(T* ptr, T val)                        \
-        {                                                               \
-            unsigned long long int* ptr_as_ull =                        \
-                (unsigned long long int*)ptr;                           \
-            unsigned long long int old = *ptr_as_ull, assumed;          \
-            do {                                                        \
-                assumed = old;                                          \
-                old = atomicCAS(ptr_as_ull, assumed,                    \
-                                fn2(val + fn1(assumed)));               \
-            } while (assumed != old);                                   \
-            return fn1(old);                                            \
-        }                                                               \
+#define SPECIALIZE(T, fn1, fn2)                                                \
+    template<>                                                                 \
+    __device__ T atomicAdd<T>(T * ptr, T val) {                                \
+        unsigned long long int *ptr_as_ull = (unsigned long long int *)ptr;    \
+        unsigned long long int old         = *ptr_as_ull, assumed;             \
+        do {                                                                   \
+            assumed = old;                                                     \
+            old     = atomicCAS(ptr_as_ull, assumed, fn2(val + fn1(assumed))); \
+        } while (assumed != old);                                              \
+        return fn1(old);                                                       \
+    }
 
-        SPECIALIZE(double, __longlong_as_double, __double_as_longlong)
-        SPECIALIZE(intl, intl, uintl)
-        SPECIALIZE(uintl, uintl, uintl)
+SPECIALIZE(double, __longlong_as_double, __double_as_longlong)
+SPECIALIZE(intl, intl, uintl)
+SPECIALIZE(uintl, uintl, uintl)
 
-        template<>
-        __device__ cfloat atomicAdd<cfloat>(cfloat *ptr, cfloat val)
-        {
-            float *fptr = (float *)(ptr);
-            cfloat res;
-            res.x = ::atomicAdd(fptr + 0, val.x);
-            res.y = ::atomicAdd(fptr + 1, val.y);
-            return res;
-        }
+template<>
+__device__ cfloat atomicAdd<cfloat>(cfloat *ptr, cfloat val) {
+    float *fptr = (float *)(ptr);
+    cfloat res;
+    res.x = ::atomicAdd(fptr + 0, val.x);
+    res.y = ::atomicAdd(fptr + 1, val.y);
+    return res;
+}
 
-        template<>
-        __device__ cdouble atomicAdd<cdouble>(cdouble *ptr, cdouble val)
-        {
-            double *fptr = (double *)(ptr);
-            cdouble res;
-            res.x = atomicAdd(fptr + 0, val.x);
-            res.y = atomicAdd(fptr + 1, val.y);
-            return res;
-        }
-    }
+template<>
+__device__ cdouble atomicAdd<cdouble>(cdouble *ptr, cdouble val) {
+    double *fptr = (double *)(ptr);
+    cdouble res;
+    res.x = atomicAdd(fptr + 0, val.x);
+    res.y = atomicAdd(fptr + 1, val.y);
+    return res;
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index d5932432c6..045897b89a 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -7,118 +7,117 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include "shared.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-inline __device__
-int lIdx(int x, int y, int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
+inline __device__ int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
 }
 
 template<typename inType, typename outType>
-inline __device__
-void load2ShrdMem(outType * shrd, const inType * const in,
-                  int lx, int ly, int shrdStride,
-                  int dim0, int dim1,
-                  int gx, int gy,
-                  int inStride1, int inStride0)
-{
-    shrd[ly*shrdStride+lx] = in[lIdx(clamp(gx, 0, dim0-1), clamp(gy, 0, dim1-1), inStride1, inStride0)];
+inline __device__ void load2ShrdMem(outType *shrd, const inType *const in,
+                                    int lx, int ly, int shrdStride, int dim0,
+                                    int dim1, int gx, int gy, int inStride1,
+                                    int inStride0) {
+    shrd[ly * shrdStride + lx] = in[lIdx(
+        clamp(gx, 0, dim0 - 1), clamp(gy, 0, dim1 - 1), inStride1, inStride0)];
 }
 
 template<typename inType, typename outType>
-static __global__
-void bilateralKernel(Param<outType> out, CParam<inType> in,
-                     float sigma_space, float sigma_color,
-                     int gaussOff, int nBBS0, int nBBS1)
-{
+static __global__ void bilateralKernel(Param<outType> out, CParam<inType> in,
+                                       float sigma_space, float sigma_color,
+                                       int gaussOff, int nBBS0, int nBBS1) {
     SharedMemory<outType> shared;
     outType *localMem = shared.getPointer();
     outType *gauss2d  = localMem + gaussOff;
 
-    const int radius      = max((int)(sigma_space * 1.5f), 1);
-    const int padding     = 2 * radius;
-    const int window_size = padding + 1;
-    const int shrdLen     = THREADS_X + padding;
-    const float variance_range = sigma_color * sigma_color;
-    const float variance_space = sigma_space * sigma_space;
-    const float variance_space_neg2 = -2.0 * variance_space;
+    const int radius                    = max((int)(sigma_space * 1.5f), 1);
+    const int padding                   = 2 * radius;
+    const int window_size               = padding + 1;
+    const int shrdLen                   = THREADS_X + padding;
+    const float variance_range          = sigma_color * sigma_color;
+    const float variance_space          = sigma_space * sigma_space;
+    const float variance_space_neg2     = -2.0 * variance_space;
     const float inv_variance_range_neg2 = -0.5 / variance_range;
 
     // gfor batch offsets
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
-    const inType* iptr  = (const inType *) in.ptr + (b2 * in.strides[2]  + b3 * in.strides[3] );
-    outType*       optr = (outType *     )out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const inType *iptr =
+        (const inType *)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    outType *optr =
+        (outType *)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
 
     int lx = threadIdx.x;
     int ly = threadIdx.y;
 
-    const int gx = THREADS_X * (blockIdx.x-b2*nBBS0) + lx;
-    const int gy = THREADS_Y * (blockIdx.y-b3*nBBS1) + ly;
+    const int gx = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
 
     // generate gauss2d spatial variance values for block
-    if (lx<window_size && ly<window_size) {
+    if (lx < window_size && ly < window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = __expf(((x*x) + (y*y)) / variance_space_neg2);
+        gauss2d[ly * window_size + lx] =
+            __expf(((x * x) + (y * y)) / variance_space_neg2);
     }
 
     // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen; b+=blockDim.y, gy2+=blockDim.y) {
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += blockDim.y, gy2 += blockDim.y) {
         // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-            load2ShrdMem<inType, outType>(localMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
-                                          gx2-radius, gy2-radius, in.strides[1], in.strides[0]);
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            load2ShrdMem<inType, outType>(
+                localMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
+                gx2 - radius, gy2 - radius, in.strides[1], in.strides[0]);
         }
     }
 
     __syncthreads();
 
-    if (gx<in.dims[0] && gy<in.dims[1]) {
+    if (gx < in.dims[0] && gy < in.dims[1]) {
         lx += radius;
         ly += radius;
-        const outType center_color = localMem[ly*shrdLen+lx];
-        outType res  = 0;
-        outType norm = 0;
-        int joff = (ly - radius)*shrdLen + (lx - radius);
-        int goff = 0;
+        const outType center_color = localMem[ly * shrdLen + lx];
+        outType res                = 0;
+        outType norm               = 0;
+        int joff                   = (ly - radius) * shrdLen + (lx - radius);
+        int goff                   = 0;
 
 #pragma unroll
-        for(int wj=0; wj<window_size; ++wj) {
+        for (int wj = 0; wj < window_size; ++wj) {
 #pragma unroll
-            for(int wi=0; wi<window_size; ++wi) {
-                const outType tmp_color   = localMem[joff+wi];
-                const outType c = center_color - tmp_color;
-                const outType gauss_range = __expf(c * c * inv_variance_range_neg2);
-                const outType weight      = gauss2d[goff+wi] * gauss_range;
+            for (int wi = 0; wi < window_size; ++wi) {
+                const outType tmp_color = localMem[joff + wi];
+                const outType c         = center_color - tmp_color;
+                const outType gauss_range =
+                    __expf(c * c * inv_variance_range_neg2);
+                const outType weight = gauss2d[goff + wi] * gauss_range;
                 norm += weight;
-                res  += tmp_color * weight;
+                res += tmp_color * weight;
             }
             joff += shrdLen;
             goff += window_size;
         }
-        optr[gy*out.strides[1]+gx] = res / norm;
+        optr[gy * out.strides[1] + gx] = res / norm;
     }
 }
 
 template<typename inType, typename outType, bool isColor>
-void bilateral(Param<outType> out, CParam<inType> in, float s_sigma, float c_sigma)
-{
+void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
+               float c_sigma) {
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], THREADS_X);
@@ -127,25 +126,29 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma, float c_sig
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
     // calculate shared memory size
-    int radius = (int)std::max(s_sigma * 1.5f, 1.f);
-    int num_shrd_elems    = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
-    int num_gauss_elems   = (2 * radius + 1)*(2 * radius + 1);
-    size_t total_shrd_size   = sizeof(outType) * (num_shrd_elems + num_gauss_elems);
-
-    size_t MAX_SHRD_SIZE = cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock;
+    int radius          = (int)std::max(s_sigma * 1.5f, 1.f);
+    int num_shrd_elems  = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
+    int num_gauss_elems = (2 * radius + 1) * (2 * radius + 1);
+    size_t total_shrd_size =
+        sizeof(outType) * (num_shrd_elems + num_gauss_elems);
+
+    size_t MAX_SHRD_SIZE =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock;
     if (total_shrd_size > MAX_SHRD_SIZE) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nCUDA Bilateral filter doesn't support %f spatial sigma\n", s_sigma);
+                 "\nCUDA Bilateral filter doesn't support %f spatial sigma\n",
+                 s_sigma);
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    CUDA_LAUNCH_SMEM((bilateralKernel<inType, outType>), blocks, threads, total_shrd_size,
-        out, in, s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y);
+    CUDA_LAUNCH_SMEM((bilateralKernel<inType, outType>), blocks, threads,
+                     total_shrd_size, out, in, s_sigma, c_sigma, num_shrd_elems,
+                     blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 8a7377fd46..ed683bca24 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -7,17 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int STRONG = 1;
 static const int WEAK   = 2;
 static const int NOEDGE = 0;
@@ -25,19 +23,17 @@ static const int NOEDGE = 0;
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-__forceinline__ __device__
-int lIdx(int x, int y, int stride0, int stride1)
-{
-    return (x*stride0 + y*stride1);
+__forceinline__ __device__ int lIdx(int x, int y, int stride0, int stride1) {
+    return (x * stride0 + y * stride1);
 }
 
 template<typename T>
-static __global__
-void nonMaxSuppressionKernel(Param<float> output, CParam<T> in, CParam<T> dx, CParam<T> dy,
-                             unsigned nBBS0, unsigned nBBS1)
-{
-    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2; //Coloumns
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2; //Rows
+static __global__ void nonMaxSuppressionKernel(Param<float> output,
+                                               CParam<T> in, CParam<T> dx,
+                                               CParam<T> dy, unsigned nBBS0,
+                                               unsigned nBBS1) {
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Coloumns
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
 
     // Declared shared memory with 1 pixel border
     __shared__ T shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
@@ -51,34 +47,38 @@ void nonMaxSuppressionKernel(Param<float> output, CParam<T> in, CParam<T> dx, CP
     const unsigned b3 = blockIdx.y / nBBS1;
 
     // global indices
-    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    const T* mag = (const T *)in.ptr +
-                      (b2 * in.strides[2] + b3 * in.strides[3]) + in.strides[1] + 1;
-    const T* dX  = (const T *)dx.ptr  +
-                      (b2 * dx.strides[2]  + b3 * dx.strides[3] ) + dx.strides[1] + 1;
-    const T* dY  = (const T *)dy.ptr  +
-                      (b2 * dy.strides[2]  + b3 * dy.strides[3] ) + dy.strides[1] + 1;
-          T* out = (float *  )output.ptr +
-                      (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+    const T* mag = (const T*)in.ptr +
+                   (b2 * in.strides[2] + b3 * in.strides[3]) + in.strides[1] +
+                   1;
+    const T* dX = (const T*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]) +
+                  dx.strides[1] + 1;
+    const T* dY = (const T*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]) +
+                  dy.strides[1] + 1;
+    T* out = (float*)output.ptr +
+             (b2 * output.strides[2] + b3 * output.strides[3]) +
+             output.strides[1] + 1;
 
     // pull image to shared memory
 #pragma unroll
-    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=blockDim.y, gy2+=blockDim.y)
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y)
 #pragma unroll
-        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=blockDim.x, gx2+=blockDim.x)
-            shrdMem[b][a] = mag[ lIdx(gx2-1, gy2-1, in.strides[0], in.strides[1]) ];
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x)
+            shrdMem[b][a] =
+                mag[lIdx(gx2 - 1, gy2 - 1, in.strides[0], in.strides[1])];
 
     int i = lx + 1;
     int j = ly + 1;
 
     __syncthreads();
 
-    if (gx<in.dims[0]-2 && gy<in.dims[1]-2)
-    {
+    if (gx < in.dims[0] - 2 && gy < in.dims[1] - 2) {
         int idx = lIdx(gx, gy, in.strides[0], in.strides[1]);
 
         const float cmag = shrdMem[j][i];
@@ -88,59 +88,59 @@ void nonMaxSuppressionKernel(Param<float> output, CParam<T> in, CParam<T> dx, CP
         else {
             const float dx = dX[idx];
             const float dy = dY[idx];
-            const float se = shrdMem[j+1][i+1];
-            const float nw = shrdMem[j-1][i-1];
-            const float ea = shrdMem[j  ][i+1];
-            const float we = shrdMem[j  ][i-1];
-            const float ne = shrdMem[j-1][i+1];
-            const float sw = shrdMem[j+1][i-1];
-            const float no = shrdMem[j-1][i  ];
-            const float so = shrdMem[j+1][i  ];
+            const float se = shrdMem[j + 1][i + 1];
+            const float nw = shrdMem[j - 1][i - 1];
+            const float ea = shrdMem[j][i + 1];
+            const float we = shrdMem[j][i - 1];
+            const float ne = shrdMem[j - 1][i + 1];
+            const float sw = shrdMem[j + 1][i - 1];
+            const float no = shrdMem[j - 1][i];
+            const float so = shrdMem[j + 1][i];
 
             float a1, a2, b1, b2, alpha;
 
-            if (dx>=0) {
-                if (dy>=0) {
-                    const bool isTrue = (dx-dy)>=0;
+            if (dx >= 0) {
+                if (dy >= 0) {
+                    const bool isTrue = (dx - dy) >= 0;
 
                     a1    = isTrue ? ea : so;
                     a2    = isTrue ? we : no;
                     b1    = se;
                     b2    = nw;
-                    alpha = isTrue ? dy/dx : dx/dy;
+                    alpha = isTrue ? dy / dx : dx / dy;
                 } else {
-                    const bool isTrue = (dx+dy)>=0;
+                    const bool isTrue = (dx + dy) >= 0;
 
                     a1    = isTrue ? ea : no;
                     a2    = isTrue ? we : so;
                     b1    = ne;
                     b2    = sw;
-                    alpha = isTrue ? -dy/dx : dx/-dy;
+                    alpha = isTrue ? -dy / dx : dx / -dy;
                 }
             } else {
-                if (dy>=0) {
-                    const bool isTrue = (dx+dy)>=0;
+                if (dy >= 0) {
+                    const bool isTrue = (dx + dy) >= 0;
 
                     a1    = isTrue ? so : we;
                     a2    = isTrue ? no : ea;
                     b1    = sw;
                     b2    = ne;
-                    alpha = isTrue ? -dx/dy : dy/-dx;
+                    alpha = isTrue ? -dx / dy : dy / -dx;
                 } else {
-                    const bool isTrue = (-dx+dy)>=0;
+                    const bool isTrue = (-dx + dy) >= 0;
 
                     a1    = isTrue ? we : no;
                     a2    = isTrue ? ea : so;
                     b1    = nw;
                     b2    = se;
-                    alpha = isTrue ? -dy/dx : dx/-dy;
+                    alpha = isTrue ? -dy / dx : dx / -dy;
                 }
             }
 
-            float mag1 = (1-alpha)*a1 + alpha*b1;
-            float mag2 = (1-alpha)*a2 + alpha*b2;
+            float mag1 = (1 - alpha) * a1 + alpha * b1;
+            float mag2 = (1 - alpha) * a2 + alpha * b2;
 
-            if (cmag>mag1 && cmag>mag2) {
+            if (cmag > mag1 && cmag > mag2) {
                 out[idx] = cmag;
             } else {
                 out[idx] = (T)0;
@@ -150,46 +150,46 @@ void nonMaxSuppressionKernel(Param<float> output, CParam<T> in, CParam<T> dx, CP
 }
 
 template<typename T>
-void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx, CParam<T> dy)
-{
+void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
+                       CParam<T> dy) {
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     // Launch only threads to process non-border pixels
-    int blk_x = divup(magnitude.dims[0]-2, threads.x);
-    int blk_y = divup(magnitude.dims[1]-2, threads.y);
+    int blk_x = divup(magnitude.dims[0] - 2, threads.x);
+    int blk_y = divup(magnitude.dims[1] - 2, threads.y);
 
     // launch batch * blk_x blocks along x dimension
     dim3 blocks(blk_x * magnitude.dims[2], blk_y * magnitude.dims[3]);
 
-    CUDA_LAUNCH(nonMaxSuppressionKernel<T>, blocks, threads, output, magnitude, dx, dy, blk_x, blk_y);
+    CUDA_LAUNCH(nonMaxSuppressionKernel<T>, blocks, threads, output, magnitude,
+                dx, dy, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T>
-static __global__
-void initEdgeOutKernel(Param<T> output, CParam<T> strong, CParam<T> weak,
-                       unsigned nBBS0, unsigned nBBS1)
-{
+static __global__ void initEdgeOutKernel(Param<T> output, CParam<T> strong,
+                                         CParam<T> weak, unsigned nBBS0,
+                                         unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = blockIdx.x / nBBS0;
     const unsigned b3 = blockIdx.y / nBBS1;
 
     // global indices
-    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + threadIdx.y;
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    const T* wPtr = weak.ptr +
-                         (b2 * weak.strides[2] + b3 * weak.strides[3]) + weak.strides[1] + 1;
+    const T* wPtr = weak.ptr + (b2 * weak.strides[2] + b3 * weak.strides[3]) +
+                    weak.strides[1] + 1;
     const T* sPtr = strong.ptr +
-                         (b2 * strong.strides[2] + b3 * strong.strides[3]) + strong.strides[1] + 1;
-          T* oPtr = output.ptr +
-                         (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+                    (b2 * strong.strides[2] + b3 * strong.strides[3]) +
+                    strong.strides[1] + 1;
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
+              output.strides[1] + 1;
 
-    if (gx<(output.dims[0]-2) && gy<(output.dims[1]-2))
-    {
+    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2)) {
         int idx   = lIdx(gx, gy, output.strides[0], output.strides[1]);
         oPtr[idx] = (sPtr[idx] > 0 ? STRONG : (wPtr[idx] > 0 ? WEAK : NOEDGE));
     }
@@ -200,19 +200,20 @@ void initEdgeOutKernel(Param<T> output, CParam<T> strong, CParam<T> weak,
 // the breath first search algorithm
 __device__ int hasChanged = 0;
 
-#define VALID_BLOCK_IDX(j, i) ( (j)>0 && (j)<(SHRD_MEM_HEIGHT-1) && (i)>0 && (i)<(SHRD_MEM_WIDTH-1) )
+#define VALID_BLOCK_IDX(j, i)                             \
+    ((j) > 0 && (j) < (SHRD_MEM_HEIGHT - 1) && (i) > 0 && \
+     (i) < (SHRD_MEM_WIDTH - 1))
 
 template<typename T>
-static __global__
-void edgeTrackKernel(Param<T> output, unsigned nBBS0, unsigned nBBS1)
-{
-    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2; // Cols
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2; // Rows
+static __global__ void edgeTrackKernel(Param<T> output, unsigned nBBS0,
+                                       unsigned nBBS1) {
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Cols
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
 
     // shared memory with 1 pixel border
     // strong and weak images are binary(char) images thus,
     // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
-    __shared__ int outMem  [ SHRD_MEM_HEIGHT ] [ SHRD_MEM_WIDTH ];
+    __shared__ int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
     // local thread indices
     const int lx = threadIdx.x;
@@ -223,26 +224,28 @@ void edgeTrackKernel(Param<T> output, unsigned nBBS0, unsigned nBBS1)
     const unsigned b3 = blockIdx.y / nBBS1;
 
     // global indices
-    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
+              output.strides[1] + 1;
 
     // pull image to shared memory
 #pragma unroll
-    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=blockDim.y, gy2+=blockDim.y)
-    {
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y) {
 #pragma unroll
-        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=blockDim.x, gx2+=blockDim.x)
-        {
-            int x = gx2-1;
-            int y = gy2-1;
-            if (x>=0 && x<output.dims[0] && y>=0 && y<output.dims[1])
-                outMem[b][a]  = oPtr[ lIdx(x, y, output.strides[0], output.strides[1]) ];
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x) {
+            int x = gx2 - 1;
+            int y = gy2 - 1;
+            if (x >= 0 && x < output.dims[0] && y >= 0 && y < output.dims[1])
+                outMem[b][a] =
+                    oPtr[lIdx(x, y, output.strides[0], output.strides[1])];
             else
-                outMem[b][a]  = NOEDGE;
+                outMem[b][a] = NOEDGE;
         }
     }
 
@@ -253,131 +256,139 @@ void edgeTrackKernel(Param<T> output, unsigned nBBS0, unsigned nBBS1)
 
     int continueIter = 1;
 
-    while (continueIter)
-    {
+    while (continueIter) {
         int cu = outMem[j][i];
-        int nw = outMem[j-1][i-1];
-        int no = outMem[j-1][i  ];
-        int ne = outMem[j-1][i+1];
-        int ea = outMem[j  ][i+1];
-        int se = outMem[j+1][i+1];
-        int so = outMem[j+1][i  ];
-        int sw = outMem[j+1][i-1];
-        int we = outMem[j  ][i-1];
+        int nw = outMem[j - 1][i - 1];
+        int no = outMem[j - 1][i];
+        int ne = outMem[j - 1][i + 1];
+        int ea = outMem[j][i + 1];
+        int se = outMem[j + 1][i + 1];
+        int so = outMem[j + 1][i];
+        int sw = outMem[j + 1][i - 1];
+        int we = outMem[j][i - 1];
 
-        bool hasStrongNeighbour = nw==STRONG || no==STRONG || ne==STRONG || ea==STRONG ||
-                                  se==STRONG || so==STRONG || sw==STRONG || we==STRONG;
+        bool hasStrongNeighbour =
+            nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
+            se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
 
-        if (cu==WEAK && hasStrongNeighbour)
-            outMem[j][i] = STRONG;
+        if (cu == WEAK && hasStrongNeighbour) outMem[j][i] = STRONG;
 
         __syncthreads();
 
-        //Check if there are any STRONG pixels with weak neighbours.
-        //This search however ignores 1-pixel border encompassing the
-        //shared memory tile region.
+        // Check if there are any STRONG pixels with weak neighbours.
+        // This search however ignores 1-pixel border encompassing the
+        // shared memory tile region.
 
         cu = outMem[j][i];
 
-        bool _nw = outMem[j-1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i-1);
-        bool _no = outMem[j-1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j-1, i  );
-        bool _ne = outMem[j-1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i+1);
-        bool _ea = outMem[j  ][i+1] == WEAK  &&  VALID_BLOCK_IDX(j  , i+1);
-        bool _se = outMem[j+1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i+1);
-        bool _so = outMem[j+1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j+1, i  );
-        bool _sw = outMem[j+1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i-1);
-        bool _we = outMem[j  ][i-1] == WEAK  &&  VALID_BLOCK_IDX(j  , i-1);
-
-        bool hasWeakNeighbour = _nw || _no || _ne || _ea || _se || _so || _sw || _we;
-
-        continueIter = __syncthreads_or(cu==STRONG && hasWeakNeighbour);
+        bool _nw =
+            outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
+        bool _no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+        bool _ne =
+            outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
+        bool _ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+        bool _se =
+            outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
+        bool _so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+        bool _sw =
+            outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
+        bool _we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+
+        bool hasWeakNeighbour =
+            _nw || _no || _ne || _ea || _se || _so || _sw || _we;
+
+        continueIter = __syncthreads_or(cu == STRONG && hasWeakNeighbour);
     };
 
     // Check if any 1-pixel border ring
     // has weak pixels with strong candidates
     // within the main region, then increment hasChanged.
     int cu = outMem[j][i];
-    int nw = outMem[j-1][i-1];
-    int no = outMem[j-1][i  ];
-    int ne = outMem[j-1][i+1];
-    int ea = outMem[j  ][i+1];
-    int se = outMem[j+1][i+1];
-    int so = outMem[j+1][i  ];
-    int sw = outMem[j+1][i-1];
-    int we = outMem[j  ][i-1];
-
-    bool hasWeakNeighbour = nw==WEAK || no==WEAK || ne==WEAK || ea==WEAK ||
-                            se==WEAK || so==WEAK || sw==WEAK || we==WEAK;
-
-    if (__syncthreads_or(cu==STRONG && hasWeakNeighbour) && lx==0 && ly==0)
+    int nw = outMem[j - 1][i - 1];
+    int no = outMem[j - 1][i];
+    int ne = outMem[j - 1][i + 1];
+    int ea = outMem[j][i + 1];
+    int se = outMem[j + 1][i + 1];
+    int so = outMem[j + 1][i];
+    int sw = outMem[j + 1][i - 1];
+    int we = outMem[j][i - 1];
+
+    bool hasWeakNeighbour = nw == WEAK || no == WEAK || ne == WEAK ||
+                            ea == WEAK || se == WEAK || so == WEAK ||
+                            sw == WEAK || we == WEAK;
+
+    if (__syncthreads_or(cu == STRONG && hasWeakNeighbour) && lx == 0 &&
+        ly == 0)
         atomicAdd(&hasChanged, 1);
 
     // Update output with shared memory result
-    if (gx<(output.dims[0]-2) && gy<(output.dims[1]-2))
-        oPtr[ lIdx(gx, gy, output.strides[0], output.strides[1]) ] = outMem[j][i];
+    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2))
+        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1])] = outMem[j][i];
 }
 
 template<typename T>
-static __global__
-void suppressLeftOverKernel(Param<T> output, unsigned nBBS0, unsigned nBBS1)
-{
+static __global__ void suppressLeftOverKernel(Param<T> output, unsigned nBBS0,
+                                              unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = blockIdx.x / nBBS0;
     const unsigned b3 = blockIdx.y / nBBS1;
 
     // global indices
-    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + threadIdx.y;
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) + output.strides[1] + 1;
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
+              output.strides[1] + 1;
 
-    if (gx<(output.dims[0]-2) && gy<(output.dims[1]-2))
-    {
+    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2)) {
         int idx = lIdx(gx, gy, output.strides[0], output.strides[1]);
         T val   = oPtr[idx];
-        if (val==WEAK)
-            oPtr[idx] = NOEDGE;
+        if (val == WEAK) oPtr[idx] = NOEDGE;
     }
 }
 
 template<typename T>
-void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak)
-{
+void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     // Launch only threads to process non-border pixels
-    int blk_x = divup(weak.dims[0]-2, threads.x);
-    int blk_y = divup(weak.dims[1]-2, threads.y);
+    int blk_x = divup(weak.dims[0] - 2, threads.x);
+    int blk_y = divup(weak.dims[1] - 2, threads.y);
 
     // launch batch * blk_x blocks along x dimension
     dim3 blocks(blk_x * weak.dims[2], blk_y * weak.dims[3]);
 
-    CUDA_LAUNCH(initEdgeOutKernel<T>, blocks, threads, output, strong, weak, blk_x, blk_y);
+    CUDA_LAUNCH(initEdgeOutKernel<T>, blocks, threads, output, strong, weak,
+                blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 
     int notFinished = 1;
 
-    while(notFinished) {
+    while (notFinished) {
         notFinished = 0;
-        CUDA_CHECK(cudaMemcpyToSymbolAsync(hasChanged, &notFinished, sizeof(int),
-                    0, cudaMemcpyHostToDevice, cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyToSymbolAsync(
+            hasChanged, &notFinished, sizeof(int), 0, cudaMemcpyHostToDevice,
+            cuda::getStream(cuda::getActiveDeviceId())));
 
         CUDA_LAUNCH(edgeTrackKernel<T>, blocks, threads, output, blk_x, blk_y);
 
         POST_LAUNCH_CHECK();
 
-        CUDA_CHECK(cudaMemcpyFromSymbolAsync(&notFinished, hasChanged, sizeof(int),
-                    0, cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyFromSymbolAsync(
+            &notFinished, hasChanged, sizeof(int), 0, cudaMemcpyDeviceToHost,
+            cuda::getStream(cuda::getActiveDeviceId())));
 
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(
+            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
     }
 
-    CUDA_LAUNCH(suppressLeftOverKernel<T>, blocks, threads, output, blk_x, blk_y);
+    CUDA_LAUNCH(suppressLeftOverKernel<T>, blocks, threads, output, blk_x,
+                blk_y);
 
     POST_LAUNCH_CHECK();
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/config.hpp b/src/backend/cuda/kernel/config.hpp
index c879e4fec6..975d6ff987 100644
--- a/src/backend/cuda/kernel/config.hpp
+++ b/src/backend/cuda/kernel/config.hpp
@@ -9,14 +9,12 @@
 
 #pragma once
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
-    static const uint THREADS_PER_BLOCK = 256;
-    static const uint THREADS_X = 32;
-    static const uint THREADS_Y = THREADS_PER_BLOCK / THREADS_X;
-    static const uint REPEAT    = 32;
-}
-}
+static const uint THREADS_PER_BLOCK = 256;
+static const uint THREADS_X         = 32;
+static const uint THREADS_Y         = THREADS_PER_BLOCK / THREADS_X;
+static const uint REPEAT            = 32;
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
index f4f294aa6a..c0b605875f 100644
--- a/src/backend/cuda/kernel/convolve.cu
+++ b/src/backend/cuda/kernel/convolve.cu
@@ -7,28 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
+#include <convolve.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include "shared.hpp"
-#include <convolve.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
-static const int THREADS   = 256;
+static const int THREADS = 256;
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-static const int CUBE_X    =  8;
-static const int CUBE_Y    =  8;
-static const int CUBE_Z    =  4;
+static const int CUBE_X = 8;
+static const int CUBE_Y = 8;
+static const int CUBE_Z = 4;
 
 // below shared MAX_*_LEN's are calculated based on
 // a maximum shared memory configuration of 48KB per block
@@ -39,99 +37,103 @@ static const int MAX_CONV3_FILTER_LEN = 5;
 
 // we shall declare the maximum size required of above all three cases
 // and re-use the same constant memory locations for every case
-__constant__ char cFilter[2*(2*(MAX_CONV1_FILTER_LEN-1)+THREADS)*sizeof(double)];
+__constant__ char
+    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + THREADS) * sizeof(double)];
 
 template<typename T, typename aT, bool expand>
-__global__
-void convolve1(Param<T> out, CParam<T> signal, int fLen,
-               int nBBS0, int nBBS1,
-               int o1, int o2, int o3,
-               int s1, int s2, int s3)
-{
+__global__ void convolve1(Param<T> out, CParam<T> signal, int fLen, int nBBS0,
+                          int nBBS1, int o1, int o2, int o3, int s1, int s2,
+                          int s3) {
     SharedMemory<T> shared;
-    T * shrdMem = shared.getPointer();
-
-    const int padding = fLen-1;
-    const int shrdLen = blockDim.x + 2*padding;
-    const unsigned b1 = blockIdx.x/nBBS0;   /* [0 {1} 2 3] */
-    const unsigned b3 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;   /* [0 1 2 {3}] */
-    const unsigned b2 = (blockIdx.y + blockIdx.z * gridDim.y) - nBBS1*b3;/* [0 1 {2} 3] */
-    if(b2 >= out.dims[2] || b3 >= out.dims[3])
-        return;
-
-    T *dst = (T *)out.ptr + (b1 * out.strides[1] +  /* activated with batched input signal */
-                             o1 * out.strides[1] +  /* activated with batched input filter */
-                             b2 * out.strides[2] +  /* activated with batched input signal */
-                             o2 * out.strides[2] +  /* activated with batched input filter */
-                             b3 * out.strides[3] +  /* activated with batched input signal */
-                             o3 * out.strides[3]);  /* activated with batched input filter */
-
-    const T *src = (const T *)signal.ptr + (b1 * signal.strides[1] + /* activated with batched input signal */
-                                            s1 * signal.strides[1] + /* activated with batched input filter */
-                                            b2 * signal.strides[2] + /* activated with batched input signal */
-                                            s2 * signal.strides[2] + /* activated with batched input filter */
-                                            b3 * signal.strides[3] + /* activated with batched input signal */
-                                            s3 * signal.strides[3]); /* activated with batched input filter */
+    T *shrdMem = shared.getPointer();
+
+    const int padding = fLen - 1;
+    const int shrdLen = blockDim.x + 2 * padding;
+    const unsigned b1 = blockIdx.x / nBBS0; /* [0 {1} 2 3] */
+    const unsigned b3 =
+        (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1; /* [0 1 2 {3}] */
+    const unsigned b2 =
+        (blockIdx.y + blockIdx.z * gridDim.y) - nBBS1 * b3; /* [0 1 {2} 3] */
+    if (b2 >= out.dims[2] || b3 >= out.dims[3]) return;
+
+    T *dst = (T *)out.ptr +
+             (b1 * out.strides[1] + /* activated with batched input signal */
+              o1 * out.strides[1] + /* activated with batched input filter */
+              b2 * out.strides[2] + /* activated with batched input signal */
+              o2 * out.strides[2] + /* activated with batched input filter */
+              b3 * out.strides[3] + /* activated with batched input signal */
+              o3 * out.strides[3]); /* activated with batched input filter */
+
+    const T *src =
+        (const T *)signal.ptr +
+        (b1 * signal.strides[1] + /* activated with batched input signal */
+         s1 * signal.strides[1] + /* activated with batched input filter */
+         b2 * signal.strides[2] + /* activated with batched input signal */
+         s2 * signal.strides[2] + /* activated with batched input filter */
+         b3 * signal.strides[3] + /* activated with batched input signal */
+         s3 * signal.strides[3]); /* activated with batched input filter */
 
     const aT *impulse = (const aT *)cFilter;
 
-    int gx  = blockDim.x*(blockIdx.x-b1*nBBS0);
+    int gx = blockDim.x * (blockIdx.x - b1 * nBBS0);
 
     int s0 = signal.strides[0];
     int d0 = signal.dims[0];
-    for (int i=threadIdx.x; i<shrdLen; i+=blockDim.x) {
-        int idx= gx-padding + i;
-        shrdMem[i]  = (idx>=0 && idx<d0) ? src[idx*s0] : scalar<T>(0);
+    for (int i = threadIdx.x; i < shrdLen; i += blockDim.x) {
+        int idx    = gx - padding + i;
+        shrdMem[i] = (idx >= 0 && idx < d0) ? src[idx * s0] : scalar<T>(0);
     }
     __syncthreads();
     gx += threadIdx.x;
 
-    if (gx<out.dims[0]) {
-        int lx   = threadIdx.x + padding + (expand ? 0 : fLen>>1);
+    if (gx < out.dims[0]) {
+        int lx   = threadIdx.x + padding + (expand ? 0 : fLen >> 1);
         aT accum = scalar<aT>(0);
-        for(int f=0; f<fLen; ++f) {
-            accum = accum + (shrdMem[lx-f]*impulse[f]);
+        for (int f = 0; f < fLen; ++f) {
+            accum = accum + (shrdMem[lx - f] * impulse[f]);
         }
         dst[gx] = (T)accum;
     }
 }
 
 template<typename T, typename aT, bool expand, int fLen0, int fLen1>
-__global__
-void convolve2(Param<T> out, CParam<T> signal, int nBBS0,
-               int nBBS1, int o2, int o3, int s2, int s3)
-{
-    const size_t C_SIZE  = (THREADS_X+2*(fLen0-1))* (THREADS_Y+2*(fLen1-1));
+__global__ void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
+                          int o2, int o3, int s2, int s3) {
+    const size_t C_SIZE =
+        (THREADS_X + 2 * (fLen0 - 1)) * (THREADS_Y + 2 * (fLen1 - 1));
     __shared__ T shrdMem[C_SIZE];
 
-    const int radius0  = fLen0-1;
-    const int radius1  = fLen1-1;
-    const int padding0 = 2*radius0;
-    const int padding1 = 2*radius1;
+    const int radius0  = fLen0 - 1;
+    const int radius1  = fLen1 - 1;
+    const int padding0 = 2 * radius0;
+    const int padding1 = 2 * radius1;
     const int shrdLen0 = THREADS_X + padding0;
     const int shrdLen1 = THREADS_Y + padding1;
 
-    unsigned b0  = blockIdx.x / nBBS0;
-    unsigned b1  = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
-    T *dst = (T *)out.ptr + (b0 * out.strides[2] + /* activated with batched input signal */
-                             o2 * out.strides[2] + /* activated with batched input filter */
-                             b1 * out.strides[3] + /* activated with batched input signal */
-                             o3 * out.strides[3]); /* activated with batched input filter */
+    unsigned b0 = blockIdx.x / nBBS0;
+    unsigned b1 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
+    T *dst      = (T *)out.ptr +
+             (b0 * out.strides[2] + /* activated with batched input signal */
+              o2 * out.strides[2] + /* activated with batched input filter */
+              b1 * out.strides[3] + /* activated with batched input signal */
+              o3 * out.strides[3]); /* activated with batched input filter */
+
+    const T *src =
+        (const T *)signal.ptr +
+        (b0 * signal.strides[2] + /* activated with batched input signal */
+         s2 * signal.strides[2] + /* activated with batched input filter */
+         b1 * signal.strides[3] + /* activated with batched input signal */
+         s3 * signal.strides[3]); /* activated with batched input filter */
 
-    const T *src = (const T *)signal.ptr + (b0 * signal.strides[2] + /* activated with batched input signal */
-                                            s2 * signal.strides[2] + /* activated with batched input filter */
-                                            b1 * signal.strides[3] + /* activated with batched input signal */
-                                            s3 * signal.strides[3]); /* activated with batched input filter */
-
-    const aT *impulse  = (const aT *)cFilter;
+    const aT *impulse = (const aT *)cFilter;
 
-    int lx  = threadIdx.x;
-    int ly  = threadIdx.y;
-    int gx  = THREADS_X * (blockIdx.x-b0*nBBS0) + lx;
-    int gy  = THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) -b1*nBBS1) + ly;
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+    int gx = THREADS_X * (blockIdx.x - b0 * nBBS0) + lx;
+    int gy =
+        THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) - b1 * nBBS1) + ly;
 
-    if(b1 >= out.dims[3])
-        return;
+    if (b1 >= out.dims[3]) return;
 
     int s0 = signal.strides[0];
     int s1 = signal.strides[1];
@@ -140,75 +142,76 @@ void convolve2(Param<T> out, CParam<T> signal, int nBBS0,
     // below loops are traditional loops, they only run multiple
     // times filter length is more than launch size
 #pragma unroll
-    for (int b=ly, gy2=gy; b<shrdLen1; b+=THREADS_Y, gy2+=THREADS_Y) {
-        int j = gy2-radius1;
-        bool is_j  = j>=0 && j<d1;
+    for (int b = ly, gy2 = gy; b < shrdLen1; b += THREADS_Y, gy2 += THREADS_Y) {
+        int j     = gy2 - radius1;
+        bool is_j = j >= 0 && j < d1;
         // move row_set THREADS_Y along coloumns
 #pragma unroll
-        for (int a=lx, gx2=gx; a<shrdLen0; a+=THREADS_X, gx2+=THREADS_X) {
-            int i = gx2-radius0;
-            bool is_i  = i>=0 && i<d0;
-            shrdMem[b*shrdLen0+a] = (is_i && is_j ? src[i*s0+j*s1] : scalar<T>(0));
+        for (int a = lx, gx2 = gx; a < shrdLen0;
+             a += THREADS_X, gx2 += THREADS_X) {
+            int i     = gx2 - radius0;
+            bool is_i = i >= 0 && i < d0;
+            shrdMem[b * shrdLen0 + a] =
+                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
         }
     }
     __syncthreads();
 
-    if (gx<out.dims[0] && gy<out.dims[1]) {
-        int ci = lx + radius0 + (expand ? 0 : fLen0>>1);
-        int cj = ly + radius1 + (expand ? 0 : fLen1>>1);
+    if (gx < out.dims[0] && gy < out.dims[1]) {
+        int ci = lx + radius0 + (expand ? 0 : fLen0 >> 1);
+        int cj = ly + radius1 + (expand ? 0 : fLen1 >> 1);
 
         aT accum = scalar<aT>(0);
 #pragma unroll
-        for(int fj=0; fj<fLen1; ++fj) {
+        for (int fj = 0; fj < fLen1; ++fj) {
 #pragma unroll
-            for(int fi=0; fi<fLen0; ++fi) {
-                aT f_val = impulse[fj*fLen0+fi];
-                T s_val = shrdMem[(cj-fj)*shrdLen0 + (ci-fi)];
-                accum   = accum + s_val*f_val;
+            for (int fi = 0; fi < fLen0; ++fi) {
+                aT f_val = impulse[fj * fLen0 + fi];
+                T s_val  = shrdMem[(cj - fj) * shrdLen0 + (ci - fi)];
+                accum    = accum + s_val * f_val;
             }
         }
-        dst[gy*out.strides[1]+gx] = (T)accum;
+        dst[gy * out.strides[1] + gx] = (T)accum;
     }
 }
 
-__inline__ __device__
-int index(int i, int j, int k, int jstride, int kstride)
-{
-    return i+j*jstride+k*kstride;
+__inline__ __device__ int index(int i, int j, int k, int jstride, int kstride) {
+    return i + j * jstride + k * kstride;
 }
 
 template<typename T, typename aT, bool expand>
-__global__
-void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
-               int fLen2, int nBBS, int o3, int s3)
-{
+__global__ void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
+                          int fLen2, int nBBS, int o3, int s3) {
     SharedMemory<T> shared;
 
-    T * shrdMem       = shared.getPointer();
-    int radius0  = fLen0-1;
-    int radius1  = fLen1-1;
-    int radius2  = fLen2-1;
-    int shrdLen0 = blockDim.x + 2*radius0;
-    int shrdLen1 = blockDim.y + 2*radius1;
-    int shrdLen2 = blockDim.z + 2*radius2;
+    T *shrdMem   = shared.getPointer();
+    int radius0  = fLen0 - 1;
+    int radius1  = fLen1 - 1;
+    int radius2  = fLen2 - 1;
+    int shrdLen0 = blockDim.x + 2 * radius0;
+    int shrdLen1 = blockDim.y + 2 * radius1;
+    int shrdLen2 = blockDim.z + 2 * radius2;
     int skStride = shrdLen0 * shrdLen1;
     int fStride  = fLen0 * fLen1;
-    unsigned b2  = blockIdx.x/nBBS;
+    unsigned b2  = blockIdx.x / nBBS;
 
-    T *dst = (T *)out.ptr + (b2 * out.strides[3] + /* activated with batched input signal */
-                             o3 * out.strides[3]); /* activated with batched input filter */
+    T *dst = (T *)out.ptr +
+             (b2 * out.strides[3] + /* activated with batched input signal */
+              o3 * out.strides[3]); /* activated with batched input filter */
 
-    const T *src = (const T *)signal.ptr + (b2 * signal.strides[3] + /* activated with batched input signal */
-                                            s3 * signal.strides[3]); /* activated with batched input filter */
+    const T *src =
+        (const T *)signal.ptr +
+        (b2 * signal.strides[3] + /* activated with batched input signal */
+         s3 * signal.strides[3]); /* activated with batched input filter */
 
-    const aT *impulse  = (const aT *)cFilter;
+    const aT *impulse = (const aT *)cFilter;
 
-    int lx  = threadIdx.x;
-    int ly  = threadIdx.y;
-    int lz  = threadIdx.z;
-    int gx  = blockDim.x * (blockIdx.x-b2*nBBS) + lx;
-    int gy  = blockDim.y * blockIdx.y + ly;
-    int gz  = blockDim.z * blockIdx.z + lz;
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+    int lz = threadIdx.z;
+    int gx = blockDim.x * (blockIdx.x - b2 * nBBS) + lx;
+    int gy = blockDim.y * blockIdx.y + ly;
+    int gz = blockDim.z * blockIdx.z + lz;
 
     int s0 = signal.strides[0];
     int s1 = signal.strides[1];
@@ -217,39 +220,42 @@ void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
     int d1 = signal.dims[1];
     int d2 = signal.dims[2];
 #pragma unroll
-    for (int c=lz, gz2=gz; c<shrdLen2; c+=CUBE_Z, gz2+=CUBE_Z) {
-        int k = gz2-radius2;
-        bool is_k  = k>=0 && k<d2;
+    for (int c = lz, gz2 = gz; c < shrdLen2; c += CUBE_Z, gz2 += CUBE_Z) {
+        int k     = gz2 - radius2;
+        bool is_k = k >= 0 && k < d2;
 #pragma unroll
-        for (int b=ly, gy2=gy; b<shrdLen1; b+=CUBE_Y, gy2+=CUBE_Y) {
-            int j = gy2-radius1;
-            bool is_j  = j>=0 && j<d1;
+        for (int b = ly, gy2 = gy; b < shrdLen1; b += CUBE_Y, gy2 += CUBE_Y) {
+            int j     = gy2 - radius1;
+            bool is_j = j >= 0 && j < d1;
 #pragma unroll
-            for (int a=lx, gx2=gx; a<shrdLen0; a+=CUBE_X, gx2+=CUBE_X) {
-                int i = gx2-radius0;
-                bool is_i  = i>=0 && i<d0;
-                shrdMem[c*skStride+b*shrdLen0+a] =
-                    (is_i && is_j && is_k ? src[i*s0+j*s1+k*s2] : scalar<T>(0));
+            for (int a = lx, gx2 = gx; a < shrdLen0;
+                 a += CUBE_X, gx2 += CUBE_X) {
+                int i     = gx2 - radius0;
+                bool is_i = i >= 0 && i < d0;
+                shrdMem[c * skStride + b * shrdLen0 + a] =
+                    (is_i && is_j && is_k ? src[i * s0 + j * s1 + k * s2]
+                                          : scalar<T>(0));
             }
         }
     }
     __syncthreads();
 
-    if (gx<out.dims[0] && gy<out.dims[1] && gz<out.dims[2]) {
-        int ci = lx + radius0 + (expand ? 0 : fLen0>>1);
-        int cj = ly + radius1 + (expand ? 0 : fLen1>>1);
-        int ck = lz + radius2 + (expand ? 0 : fLen2>>1);
+    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2]) {
+        int ci = lx + radius0 + (expand ? 0 : fLen0 >> 1);
+        int cj = ly + radius1 + (expand ? 0 : fLen1 >> 1);
+        int ck = lz + radius2 + (expand ? 0 : fLen2 >> 1);
 
         aT accum = scalar<aT>(0);
 #pragma unroll
-        for(int fk=0; fk<fLen2; ++fk) {
+        for (int fk = 0; fk < fLen2; ++fk) {
 #pragma unroll
-            for(int fj=0; fj<fLen1; ++fj) {
+            for (int fj = 0; fj < fLen1; ++fj) {
 #pragma unroll
-                for(int fi=0; fi<fLen0; ++fi) {
+                for (int fi = 0; fi < fLen0; ++fi) {
                     aT f_val = impulse[index(fi, fj, fk, fLen0, fStride)];
-                    T s_val = shrdMem[index(ci-fi, cj-fj, ck-fk, shrdLen0, skStride)];
-                    accum   = accum + s_val*f_val;
+                    T s_val = shrdMem[index(ci - fi, cj - fj, ck - fk, shrdLen0,
+                                            skStride)];
+                    accum   = accum + s_val * f_val;
                 }
             }
         }
@@ -258,152 +264,181 @@ void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
 }
 
 struct conv_kparam_t {
-    dim3              mBlocks;
-    dim3             mThreads;
-    size_t        mSharedSize;
-    int           mBlk_x;
-    int           mBlk_y;
-    bool       outHasNoOffset;
-    bool        inHasNoOffset;
-    bool     launchMoreBlocks;
-    int             o[3];
-    int             s[3];
+    dim3 mBlocks;
+    dim3 mThreads;
+    size_t mSharedSize;
+    int mBlk_x;
+    int mBlk_y;
+    bool outHasNoOffset;
+    bool inHasNoOffset;
+    bool launchMoreBlocks;
+    int o[3];
+    int s[3];
 };
 
 template<typename T>
-void prepareKernelArgs(conv_kparam_t &params, dim_t oDims[], dim_t fDims[], int baseDim)
-{
+void prepareKernelArgs(conv_kparam_t &params, dim_t oDims[], dim_t fDims[],
+                       int baseDim) {
     int batchDims[4] = {1, 1, 1, 1};
-    for(int i=baseDim; i<4; ++i) {
+    for (int i = baseDim; i < 4; ++i) {
         batchDims[i] = (params.launchMoreBlocks ? 1 : oDims[i]);
     }
 
-    const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    if (baseDim==1) {
-        params.mThreads    = dim3(THREADS, 1);
-        params.mBlk_x      = divup(oDims[0], params.mThreads.x);
-        params.mBlk_y      = batchDims[2];
-        params.mBlocks     = dim3(params.mBlk_x * batchDims[1], params.mBlk_y * batchDims[3]);
-        params.mSharedSize = (params.mThreads.x+2*(fDims[0]-1)) * sizeof(T);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    if (baseDim == 1) {
+        params.mThreads = dim3(THREADS, 1);
+        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
+        params.mBlk_y   = batchDims[2];
+        params.mBlocks =
+            dim3(params.mBlk_x * batchDims[1], params.mBlk_y * batchDims[3]);
+        params.mSharedSize =
+            (params.mThreads.x + 2 * (fDims[0] - 1)) * sizeof(T);
         params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
         params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
-    } else if (baseDim==2) {
-        params.mThreads    = dim3(THREADS_X, THREADS_Y);
-        params.mBlk_x      = divup(oDims[0], params.mThreads.x);
-        params.mBlk_y      = divup(oDims[1], params.mThreads.y);
-        params.mBlocks     = dim3(params.mBlk_x * batchDims[2], params.mBlk_y * batchDims[3]);
+    } else if (baseDim == 2) {
+        params.mThreads = dim3(THREADS_X, THREADS_Y);
+        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
+        params.mBlk_y   = divup(oDims[1], params.mThreads.y);
+        params.mBlocks =
+            dim3(params.mBlk_x * batchDims[2], params.mBlk_y * batchDims[3]);
         params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
         params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
-    } else if (baseDim==3) {
-        params.mThreads    = dim3(CUBE_X, CUBE_Y, CUBE_Z);
-        params.mBlk_x      = divup(oDims[0], params.mThreads.x);
-        params.mBlk_y      = divup(oDims[1], params.mThreads.y);
-        int blk_z     = divup(oDims[2], params.mThreads.z);
-        params.mBlocks     = dim3(params.mBlk_x * batchDims[3], params.mBlk_y, blk_z);
-        params.mSharedSize = (params.mThreads.x+2*(fDims[0]-1)) *
-                             (params.mThreads.y+2*(fDims[1]-1)) *
-                             (params.mThreads.z+2*(fDims[2]-1)) * sizeof(T);
-        //todo: fold into x dimension according to old style
+    } else if (baseDim == 3) {
+        params.mThreads = dim3(CUBE_X, CUBE_Y, CUBE_Z);
+        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
+        params.mBlk_y   = divup(oDims[1], params.mThreads.y);
+        int blk_z       = divup(oDims[2], params.mThreads.z);
+        params.mBlocks =
+            dim3(params.mBlk_x * batchDims[3], params.mBlk_y, blk_z);
+        params.mSharedSize = (params.mThreads.x + 2 * (fDims[0] - 1)) *
+                             (params.mThreads.y + 2 * (fDims[1] - 1)) *
+                             (params.mThreads.z + 2 * (fDims[2] - 1)) *
+                             sizeof(T);
+        // todo: fold into x dimension according to old style
         params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
         params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
     }
 }
 
 template<typename T, typename aT, bool expand, int f0, int f1>
-void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig)
-{
-    CUDA_LAUNCH((convolve2<T, aT, expand, f0, f1>), p.mBlocks, p.mThreads,
-            out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
+void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig) {
+    CUDA_LAUNCH((convolve2<T, aT, expand, f0, f1>), p.mBlocks, p.mThreads, out,
+                sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, typename aT, bool expand, int f0>
-void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f1)
-{
-    switch(f1) {
-        case  1: conv2Helper<T, aT, expand, f0,  1>(p, out, sig); break;
-        case  2: conv2Helper<T, aT, expand, f0,  2>(p, out, sig); break;
-        case  3: conv2Helper<T, aT, expand, f0,  3>(p, out, sig); break;
-        case  4: conv2Helper<T, aT, expand, f0,  4>(p, out, sig); break;
-        case  5: conv2Helper<T, aT, expand, f0,  5>(p, out, sig); break;
-        default:
-            {
-                char errMessage[256];
-                snprintf(errMessage, sizeof(errMessage),
-                        "\nCUDA Convolution doesn't support %dx%d kernel\n", f0, f1);
-                CUDA_NOT_SUPPORTED(errMessage);
-            };
+void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f1) {
+    switch (f1) {
+        case 1: conv2Helper<T, aT, expand, f0, 1>(p, out, sig); break;
+        case 2: conv2Helper<T, aT, expand, f0, 2>(p, out, sig); break;
+        case 3: conv2Helper<T, aT, expand, f0, 3>(p, out, sig); break;
+        case 4: conv2Helper<T, aT, expand, f0, 4>(p, out, sig); break;
+        case 5: conv2Helper<T, aT, expand, f0, 5>(p, out, sig); break;
+        default: {
+            char errMessage[256];
+            snprintf(errMessage, sizeof(errMessage),
+                     "\nCUDA Convolution doesn't support %dx%d kernel\n", f0,
+                     f1);
+            CUDA_NOT_SUPPORTED(errMessage);
+        };
     }
 }
 
 template<typename T, typename aT, bool expand>
-void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f0, int f1)
-{
-    switch(f0) {
-        case  1: conv2Helper<T, aT, expand,  1>(p, out, sig, f1); break;
-        case  2: conv2Helper<T, aT, expand,  2>(p, out, sig, f1); break;
-        case  3: conv2Helper<T, aT, expand,  3>(p, out, sig, f1); break;
-        case  4: conv2Helper<T, aT, expand,  4>(p, out, sig, f1); break;
-        case  5: conv2Helper<T, aT, expand,  5>(p, out, sig, f1); break;
+void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f0,
+                 int f1) {
+    switch (f0) {
+        case 1: conv2Helper<T, aT, expand, 1>(p, out, sig, f1); break;
+        case 2: conv2Helper<T, aT, expand, 2>(p, out, sig, f1); break;
+        case 3: conv2Helper<T, aT, expand, 3>(p, out, sig, f1); break;
+        case 4: conv2Helper<T, aT, expand, 4>(p, out, sig, f1); break;
+        case 5: conv2Helper<T, aT, expand, 5>(p, out, sig, f1); break;
         default: {
-            if (f0==f1) {
-                switch(f1) {
-                    case  6: conv2Helper<T, aT, expand,  6,  6>(p, out, sig); break;
-                    case  7: conv2Helper<T, aT, expand,  7,  7>(p, out, sig); break;
-                    case  8: conv2Helper<T, aT, expand,  8,  8>(p, out, sig); break;
-                    case  9: conv2Helper<T, aT, expand,  9,  9>(p, out, sig); break;
-                    case 10: conv2Helper<T, aT, expand, 10, 10>(p, out, sig); break;
-                    case 11: conv2Helper<T, aT, expand, 11, 11>(p, out, sig); break;
-                    case 12: conv2Helper<T, aT, expand, 12, 12>(p, out, sig); break;
-                    case 13: conv2Helper<T, aT, expand, 13, 13>(p, out, sig); break;
-                    case 14: conv2Helper<T, aT, expand, 14, 14>(p, out, sig); break;
-                    case 15: conv2Helper<T, aT, expand, 15, 15>(p, out, sig); break;
-                    case 16: conv2Helper<T, aT, expand, 16, 16>(p, out, sig); break;
-                    case 17: conv2Helper<T, aT, expand, 17, 17>(p, out, sig); break;
-                    default:
-                      {
-                          char errMessage[256];
-                          snprintf(errMessage, sizeof(errMessage),
-                                  "\nCUDA 2D convolution doesn't support %dx%d kernel\n", f0, f1);
-                          CUDA_NOT_SUPPORTED(errMessage);
-                      };
+            if (f0 == f1) {
+                switch (f1) {
+                    case 6:
+                        conv2Helper<T, aT, expand, 6, 6>(p, out, sig);
+                        break;
+                    case 7:
+                        conv2Helper<T, aT, expand, 7, 7>(p, out, sig);
+                        break;
+                    case 8:
+                        conv2Helper<T, aT, expand, 8, 8>(p, out, sig);
+                        break;
+                    case 9:
+                        conv2Helper<T, aT, expand, 9, 9>(p, out, sig);
+                        break;
+                    case 10:
+                        conv2Helper<T, aT, expand, 10, 10>(p, out, sig);
+                        break;
+                    case 11:
+                        conv2Helper<T, aT, expand, 11, 11>(p, out, sig);
+                        break;
+                    case 12:
+                        conv2Helper<T, aT, expand, 12, 12>(p, out, sig);
+                        break;
+                    case 13:
+                        conv2Helper<T, aT, expand, 13, 13>(p, out, sig);
+                        break;
+                    case 14:
+                        conv2Helper<T, aT, expand, 14, 14>(p, out, sig);
+                        break;
+                    case 15:
+                        conv2Helper<T, aT, expand, 15, 15>(p, out, sig);
+                        break;
+                    case 16:
+                        conv2Helper<T, aT, expand, 16, 16>(p, out, sig);
+                        break;
+                    case 17:
+                        conv2Helper<T, aT, expand, 17, 17>(p, out, sig);
+                        break;
+                    default: {
+                        char errMessage[256];
+                        snprintf(errMessage, sizeof(errMessage),
+                                 "\nCUDA 2D convolution doesn't support %dx%d "
+                                 "kernel\n",
+                                 f0, f1);
+                        CUDA_NOT_SUPPORTED(errMessage);
+                    };
                 }
             } else {
-              {
-                  char errMessage[256];
-                  snprintf(errMessage, sizeof(errMessage),
-                          "\nCUDA 2D convolution doesn't support rectangular kernels\n");
-                  CUDA_NOT_SUPPORTED(errMessage);
-              };
+                {
+                    char errMessage[256];
+                    snprintf(errMessage, sizeof(errMessage),
+                             "\nCUDA 2D convolution doesn't support "
+                             "rectangular kernels\n");
+                    CUDA_NOT_SUPPORTED(errMessage);
+                };
             }
-          } break;
+        } break;
     }
 }
 
 template<typename T, typename aT, bool expand>
-void convolve_1d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
-{
+void convolve_1d(conv_kparam_t &p, Param<T> out, CParam<T> sig,
+                 CParam<aT> filt) {
     prepareKernelArgs<T>(p, out.dims, filt.dims, 1);
 
     int filterLen = filt.dims[0];
 
-    for (int b3=0; b3<filt.dims[3]; ++b3) {
+    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
         int f3Off = b3 * filt.strides[3];
 
-        for (int b2=0; b2<filt.dims[2]; ++b2) {
+        for (int b2 = 0; b2 < filt.dims[2]; ++b2) {
             int f2Off = b2 * filt.strides[2];
 
-            for (int b1=0; b1<filt.dims[1]; ++b1) {
+            for (int b1 = 0; b1 < filt.dims[1]; ++b1) {
                 int f1Off = b1 * filt.strides[1];
 
                 // FIXME: if the filter array is strided, direct copy of symbols
                 // might cause issues
-                CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter,
-                                              filt.ptr+(f1Off+f2Off+f3Off),
-                                              filterLen*sizeof(aT),
-                                              0, cudaMemcpyDeviceToDevice,
-                                              cuda::getActiveStream()));
+                CUDA_CHECK(cudaMemcpyToSymbolAsync(
+                    kernel::cFilter, filt.ptr + (f1Off + f2Off + f3Off),
+                    filterLen * sizeof(aT), 0, cudaMemcpyDeviceToDevice,
+                    cuda::getActiveStream()));
 
                 p.o[0] = (p.outHasNoOffset ? 0 : b1);
                 p.o[1] = (p.outHasNoOffset ? 0 : b2);
@@ -412,9 +447,10 @@ void convolve_1d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
                 p.s[1] = (p.inHasNoOffset ? 0 : b2);
                 p.s[2] = (p.inHasNoOffset ? 0 : b3);
 
-                CUDA_LAUNCH_SMEM((convolve1<T, aT, expand>), p.mBlocks, p.mThreads, p.mSharedSize,
-                    out, sig, filt.dims[0], p.mBlk_x, p.mBlk_y,
-                    p.o[0], p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
+                CUDA_LAUNCH_SMEM((convolve1<T, aT, expand>), p.mBlocks,
+                                 p.mThreads, p.mSharedSize, out, sig,
+                                 filt.dims[0], p.mBlk_x, p.mBlk_y, p.o[0],
+                                 p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
 
                 POST_LAUNCH_CHECK();
             }
@@ -423,25 +459,24 @@ void convolve_1d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
 }
 
 template<typename T, typename aT, bool expand>
-void convolve_2d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
-{
+void convolve_2d(conv_kparam_t &p, Param<T> out, CParam<T> sig,
+                 CParam<aT> filt) {
     prepareKernelArgs<T>(p, out.dims, filt.dims, 2);
 
     int filterLen = filt.dims[0] * filt.dims[1];
 
-    for (int b3=0; b3<filt.dims[3]; ++b3) {
+    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
         int f3Off = b3 * filt.strides[3];
 
-        for (int b2=0; b2<filt.dims[2]; ++b2) {
+        for (int b2 = 0; b2 < filt.dims[2]; ++b2) {
             int f2Off = b2 * filt.strides[2];
 
             // FIXME: if the filter array is strided, direct copy of symbols
             // might cause issues
-            CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter,
-                                          filt.ptr+(f2Off+f3Off),
-                                          filterLen*sizeof(aT),
-                                          0, cudaMemcpyDeviceToDevice,
-                                          cuda::getActiveStream()));
+            CUDA_CHECK(cudaMemcpyToSymbolAsync(
+                kernel::cFilter, filt.ptr + (f2Off + f3Off),
+                filterLen * sizeof(aT), 0, cudaMemcpyDeviceToDevice,
+                cuda::getActiveStream()));
 
             p.o[1] = (p.outHasNoOffset ? 0 : b2);
             p.o[2] = (p.outHasNoOffset ? 0 : b3);
@@ -454,64 +489,73 @@ void convolve_2d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
 }
 
 template<typename T, typename aT, bool expand>
-void convolve_3d(conv_kparam_t &p, Param<T> out, CParam<T> sig, CParam<aT> filt)
-{
+void convolve_3d(conv_kparam_t &p, Param<T> out, CParam<T> sig,
+                 CParam<aT> filt) {
     prepareKernelArgs<T>(p, out.dims, filt.dims, 3);
 
     int filterLen = filt.dims[0] * filt.dims[1] * filt.dims[2];
 
-    for (int b3=0; b3<filt.dims[3]; ++b3) {
+    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
         int f3Off = b3 * filt.strides[3];
 
         // FIXME: if the filter array is strided, direct copy of symbols
         // might cause issues
-        CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter,
-                    filt.ptr+f3Off,
-                    filterLen*sizeof(aT),
-                    0, cudaMemcpyDeviceToDevice,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyToSymbolAsync(
+            kernel::cFilter, filt.ptr + f3Off, filterLen * sizeof(aT), 0,
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
         p.s[2] = (p.inHasNoOffset ? 0 : b3);
 
-        CUDA_LAUNCH_SMEM((convolve3<T, aT, expand>), p.mBlocks, p.mThreads, p.mSharedSize,
-            out, sig, filt.dims[0], filt.dims[1], filt.dims[2], p.mBlk_x, p.o[2], p.s[2]);
+        CUDA_LAUNCH_SMEM((convolve3<T, aT, expand>), p.mBlocks, p.mThreads,
+                         p.mSharedSize, out, sig, filt.dims[0], filt.dims[1],
+                         filt.dims[2], p.mBlk_x, p.o[2], p.s[2]);
 
         POST_LAUNCH_CHECK();
     }
 }
 
 template<typename T, typename aT, int baseDim, bool expand>
-void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, AF_BATCH_KIND kind)
-{
+void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt,
+                 AF_BATCH_KIND kind) {
     bool callKernel = true;
 
     int MCFL2 = kernel::MAX_CONV2_FILTER_LEN;
     int MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
-    switch(baseDim) {
-        case 1: if (filt.dims[0]>kernel::MAX_CONV1_FILTER_LEN) callKernel = false; break;
-        case 2: if ((filt.dims[0]*filt.dims[1]) > (MCFL2 * MCFL2)) callKernel = false; break;
-        case 3: if ((filt.dims[0]*filt.dims[1]*filt.dims[2]) > (MCFL3 * MCFL3 * MCFL3)) callKernel = false; break;
+    switch (baseDim) {
+        case 1:
+            if (filt.dims[0] > kernel::MAX_CONV1_FILTER_LEN) callKernel = false;
+            break;
+        case 2:
+            if ((filt.dims[0] * filt.dims[1]) > (MCFL2 * MCFL2))
+                callKernel = false;
+            break;
+        case 3:
+            if ((filt.dims[0] * filt.dims[1] * filt.dims[2]) >
+                (MCFL3 * MCFL3 * MCFL3))
+                callKernel = false;
+            break;
     }
 
     if (!callKernel) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nCUDA N Dimensional Convolution doesn't support %lldx%lldx%lld kernel\n",
+                 "\nCUDA N Dimensional Convolution doesn't support "
+                 "%lldx%lldx%lld kernel\n",
                  filt.dims[0], filt.dims[1], filt.dims[2]);
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
     conv_kparam_t param;
-    for (int i=0; i<3; ++i) {
+    for (int i = 0; i < 3; ++i) {
         param.o[i] = 0;
         param.s[i] = 0;
     }
-    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_RHS;
-    param.outHasNoOffset   = kind==AF_BATCH_LHS  || kind==AF_BATCH_NONE;
-    param.inHasNoOffset    = kind!=AF_BATCH_SAME;
+    param.launchMoreBlocks = kind == AF_BATCH_SAME || kind == AF_BATCH_RHS;
+    param.outHasNoOffset   = kind == AF_BATCH_LHS || kind == AF_BATCH_NONE;
+    param.inHasNoOffset    = kind != AF_BATCH_SAME;
 
-    switch(baseDim) {
+    switch (baseDim) {
         case 1: convolve_1d<T, aT, expand>(param, out, signal, filt); break;
         case 2: convolve_2d<T, aT, expand>(param, out, signal, filt); break;
         case 3: convolve_3d<T, aT, expand>(param, out, signal, filt); break;
@@ -520,28 +564,39 @@ void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt, AF_BATCH_KIND
     POST_LAUNCH_CHECK();
 }
 
-#define INSTANTIATE(T, aT)  \
-    template void convolve_nd<T, aT, 1, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
-    template void convolve_nd<T, aT, 1, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
-    template void convolve_nd<T, aT, 2, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
-    template void convolve_nd<T, aT, 2, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
-    template void convolve_nd<T, aT, 3, true >(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
-    template void convolve_nd<T, aT, 3, false>(Param<T> out, CParam<T> signal, CParam<aT> filter, AF_BATCH_KIND kind);\
-
+#define INSTANTIATE(T, aT)                                                     \
+    template void convolve_nd<T, aT, 1, true>(Param<T> out, CParam<T> signal,  \
+                                              CParam<aT> filter,               \
+                                              AF_BATCH_KIND kind);             \
+    template void convolve_nd<T, aT, 1, false>(Param<T> out, CParam<T> signal, \
+                                               CParam<aT> filter,              \
+                                               AF_BATCH_KIND kind);            \
+    template void convolve_nd<T, aT, 2, true>(Param<T> out, CParam<T> signal,  \
+                                              CParam<aT> filter,               \
+                                              AF_BATCH_KIND kind);             \
+    template void convolve_nd<T, aT, 2, false>(Param<T> out, CParam<T> signal, \
+                                               CParam<aT> filter,              \
+                                               AF_BATCH_KIND kind);            \
+    template void convolve_nd<T, aT, 3, true>(Param<T> out, CParam<T> signal,  \
+                                              CParam<aT> filter,               \
+                                              AF_BATCH_KIND kind);             \
+    template void convolve_nd<T, aT, 3, false>(Param<T> out, CParam<T> signal, \
+                                               CParam<aT> filter,              \
+                                               AF_BATCH_KIND kind);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
-
-}
-
-}
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace kernel
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 16e34b97e2..9daf1af3ce 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -7,25 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include "shared.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T, typename accType, int baseDim, bool expand>
-void convolve_nd(Param<T> out, CParam<T> signal, CParam<accType> filter, AF_BATCH_KIND kind);
+void convolve_nd(Param<T> out, CParam<T> signal, CParam<accType> filter,
+                 AF_BATCH_KIND kind);
 
 template<typename T, typename accType, int conv_dim, bool expand>
 void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter);
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve_separable.cu b/src/backend/cuda/kernel/convolve_separable.cu
index 27df028fce..a929d8cf15 100644
--- a/src/backend/cuda/kernel/convolve_separable.cu
+++ b/src/backend/cuda/kernel/convolve_separable.cu
@@ -7,17 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
+#include <convolve.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
-#include <convolve.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
@@ -28,94 +26,102 @@ static const int MAX_SCONV_FILTER_LEN = 31;
 
 // we shall declare the maximum size required of above all three cases
 // and re-use the same constant memory locations for every case
-__constant__ char sFilter[2*THREADS_Y*(2*(MAX_SCONV_FILTER_LEN-1)+THREADS_X)*sizeof(double)];
+__constant__ char sFilter[2 * THREADS_Y *
+                          (2 * (MAX_SCONV_FILTER_LEN - 1) + THREADS_X) *
+                          sizeof(double)];
 
 template<typename T, typename accType, int conv_dim, bool expand, int fLen>
-__global__
-void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1)
-{
-    const int smem_len =   (conv_dim==0 ?
-                                (THREADS_X+2*(fLen-1))* THREADS_Y:
-                                (THREADS_Y+2*(fLen-1))* THREADS_X);
+__global__ void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0,
+                                    int nBBS1) {
+    const int smem_len =
+        (conv_dim == 0 ? (THREADS_X + 2 * (fLen - 1)) * THREADS_Y
+                       : (THREADS_Y + 2 * (fLen - 1)) * THREADS_X);
     __shared__ T shrdMem[smem_len];
 
-    const int radius  = fLen-1;
-    const int padding = 2*radius;
+    const int radius  = fLen - 1;
+    const int padding = 2 * radius;
     const int s0      = signal.strides[0];
     const int s1      = signal.strides[1];
     const int d0      = signal.dims[0];
     const int d1      = signal.dims[1];
-    const int shrdLen = THREADS_X + (conv_dim==0 ? padding : 0);
+    const int shrdLen = THREADS_X + (conv_dim == 0 ? padding : 0);
 
-    unsigned b2  = blockIdx.x/nBBS0;
-    unsigned b3  = blockIdx.y/nBBS1;
-    T *dst       = (T *)out.ptr          + (b2*out.strides[2] + b3*out.strides[3]);
-    const T *src = (const T *)signal.ptr + (b2*signal.strides[2] + b3*signal.strides[3]);
-    const accType *impulse  = (const accType *)sFilter;
+    unsigned b2  = blockIdx.x / nBBS0;
+    unsigned b3  = blockIdx.y / nBBS1;
+    T *dst       = (T *)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const T *src = (const T *)signal.ptr +
+                   (b2 * signal.strides[2] + b3 * signal.strides[3]);
+    const accType *impulse = (const accType *)sFilter;
 
     int lx = threadIdx.x;
     int ly = threadIdx.y;
-    int ox = THREADS_X * (blockIdx.x-b2*nBBS0) + lx;
-    int oy = THREADS_Y * (blockIdx.y-b3*nBBS1) + ly;
+    int ox = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
+    int oy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
     int gx = ox;
     int gy = oy;
 
     // below if-else statement is based on template parameter
-    if (conv_dim==0) {
-        gx += (expand ? 0 : fLen>>1);
-        int endX = ((fLen-1)<<1) + THREADS_X;
+    if (conv_dim == 0) {
+        gx += (expand ? 0 : fLen >> 1);
+        int endX = ((fLen - 1) << 1) + THREADS_X;
 
 #pragma unroll
-        for(int lx = threadIdx.x, glb_x = gx; lx<endX; lx += THREADS_X, glb_x += THREADS_X) {
-            int i = glb_x - radius;
-            int j = gy;
-            bool is_i  = i>=0 && i<d0;
-            bool is_j  = j>=0 && j<d1;
-            shrdMem[ly*shrdLen+lx] = (is_i && is_j ? src[i*s0 + j*s1] : scalar<T>(0));
+        for (int lx = threadIdx.x, glb_x = gx; lx < endX;
+             lx += THREADS_X, glb_x += THREADS_X) {
+            int i     = glb_x - radius;
+            int j     = gy;
+            bool is_i = i >= 0 && i < d0;
+            bool is_j = j >= 0 && j < d1;
+            shrdMem[ly * shrdLen + lx] =
+                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
         }
 
-    } else if (conv_dim==1) {
-        gy += (expand ? 0 : fLen>>1);
-        int endY = ((fLen-1)<<1) + THREADS_Y;
+    } else if (conv_dim == 1) {
+        gy += (expand ? 0 : fLen >> 1);
+        int endY = ((fLen - 1) << 1) + THREADS_Y;
 
 #pragma unroll
-        for(int ly = threadIdx.y, glb_y = gy; ly<endY; ly += THREADS_Y, glb_y += THREADS_Y) {
-            int i = gx;
-            int j = glb_y - radius;
-            bool is_i  = i>=0 && i<d0;
-            bool is_j  = j>=0 && j<d1;
-            shrdMem[ly*shrdLen+lx] = (is_i && is_j ? src[i*s0 + j*s1] : scalar<T>(0));
+        for (int ly = threadIdx.y, glb_y = gy; ly < endY;
+             ly += THREADS_Y, glb_y += THREADS_Y) {
+            int i     = gx;
+            int j     = glb_y - radius;
+            bool is_i = i >= 0 && i < d0;
+            bool is_j = j >= 0 && j < d1;
+            shrdMem[ly * shrdLen + lx] =
+                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
         }
     }
     __syncthreads();
 
-    if (ox<out.dims[0] && oy<out.dims[1]) {
+    if (ox < out.dims[0] && oy < out.dims[1]) {
         // below conditional statement is based on template parameter
-        int i  = (conv_dim==0 ? lx : ly) + radius;
+        int i         = (conv_dim == 0 ? lx : ly) + radius;
         accType accum = scalar<accType>(0);
 #pragma unroll
-        for(int f=0; f<fLen; ++f) {
+        for (int f = 0; f < fLen; ++f) {
             accType f_val = impulse[f];
             // below conditional statement is based on template parameter
-            int s_idx = (conv_dim==0 ? (ly*shrdLen+(i-f)) : ((i-f)*shrdLen+lx));
-            T s_val = shrdMem[s_idx];
-            accum   = accum + s_val*f_val;
+            int s_idx = (conv_dim == 0 ? (ly * shrdLen + (i - f))
+                                       : ((i - f) * shrdLen + lx));
+            T s_val   = shrdMem[s_idx];
+            accum     = accum + s_val * f_val;
         }
-        dst[oy*out.strides[1]+ox] = (T)accum;
+        dst[oy * out.strides[1] + ox] = (T)accum;
     }
 }
 
 template<typename T, typename aT, int cDim, bool expand, int f>
-void conv2Helper(dim3 blks, dim3 thrds, Param<T> out, CParam<T> sig, int nBBS0, int nBBS1)
-{
-   CUDA_LAUNCH((convolve2_separable<T, aT, cDim, expand, f>), blks, thrds, out, sig, nBBS0, nBBS1);
+void conv2Helper(dim3 blks, dim3 thrds, Param<T> out, CParam<T> sig, int nBBS0,
+                 int nBBS1) {
+    CUDA_LAUNCH((convolve2_separable<T, aT, cDim, expand, f>), blks, thrds, out,
+                sig, nBBS0, nBBS1);
 }
 
 template<typename T, typename accType, int conv_dim, bool expand>
-void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter)
-{
-    int fLen = filter.dims[0] * filter.dims[1] * filter.dims[2] * filter.dims[3];
-    if(fLen > kernel::MAX_SCONV_FILTER_LEN) {
+void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter) {
+    int fLen =
+        filter.dims[0] * filter.dims[1] * filter.dims[2] * filter.dims[3];
+    if (fLen > kernel::MAX_SCONV_FILTER_LEN) {
         // TODO call upon fft
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
@@ -129,75 +135,168 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter)
     int blk_x = divup(out.dims[0], threads.x);
     int blk_y = divup(out.dims[1], threads.y);
 
-    dim3 blocks(blk_x*signal.dims[2], blk_y*signal.dims[3]);
-
-
-   // FIX ME: if the filter array is strided, direct copy of symbols
-   // might cause issues
-   CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::sFilter, filter.ptr, fLen*sizeof(accType), 0,
-               cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
-    switch(fLen) {
-        case  2: conv2Helper<T, accType, conv_dim, expand,  2>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  3: conv2Helper<T, accType, conv_dim, expand,  3>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  4: conv2Helper<T, accType, conv_dim, expand,  4>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  5: conv2Helper<T, accType, conv_dim, expand,  5>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  6: conv2Helper<T, accType, conv_dim, expand,  6>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  7: conv2Helper<T, accType, conv_dim, expand,  7>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  8: conv2Helper<T, accType, conv_dim, expand,  8>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case  9: conv2Helper<T, accType, conv_dim, expand,  9>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 10: conv2Helper<T, accType, conv_dim, expand, 10>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 11: conv2Helper<T, accType, conv_dim, expand, 11>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 12: conv2Helper<T, accType, conv_dim, expand, 12>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 13: conv2Helper<T, accType, conv_dim, expand, 13>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 14: conv2Helper<T, accType, conv_dim, expand, 14>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 15: conv2Helper<T, accType, conv_dim, expand, 15>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 16: conv2Helper<T, accType, conv_dim, expand, 16>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 17: conv2Helper<T, accType, conv_dim, expand, 17>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 18: conv2Helper<T, accType, conv_dim, expand, 18>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 19: conv2Helper<T, accType, conv_dim, expand, 19>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 20: conv2Helper<T, accType, conv_dim, expand, 20>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 21: conv2Helper<T, accType, conv_dim, expand, 21>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 22: conv2Helper<T, accType, conv_dim, expand, 22>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 23: conv2Helper<T, accType, conv_dim, expand, 23>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 24: conv2Helper<T, accType, conv_dim, expand, 24>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 25: conv2Helper<T, accType, conv_dim, expand, 25>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 26: conv2Helper<T, accType, conv_dim, expand, 26>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 27: conv2Helper<T, accType, conv_dim, expand, 27>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 28: conv2Helper<T, accType, conv_dim, expand, 28>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 29: conv2Helper<T, accType, conv_dim, expand, 29>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 30: conv2Helper<T, accType, conv_dim, expand, 30>(blocks, threads, out, signal, blk_x, blk_y); break;
-        case 31: conv2Helper<T, accType, conv_dim, expand, 31>(blocks, threads, out, signal, blk_x, blk_y); break;
-        default:
-            {
-                char errMessage[256];
-                snprintf(errMessage, sizeof(errMessage),
-                        "\nCUDA Separable convolution doesn't support %d kernel\n", fLen);
-                CUDA_NOT_SUPPORTED(errMessage);
-            };
+    dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
+
+    // FIX ME: if the filter array is strided, direct copy of symbols
+    // might cause issues
+    CUDA_CHECK(cudaMemcpyToSymbolAsync(
+        kernel::sFilter, filter.ptr, fLen * sizeof(accType), 0,
+        cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+
+    switch (fLen) {
+        case 2:
+            conv2Helper<T, accType, conv_dim, expand, 2>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 3:
+            conv2Helper<T, accType, conv_dim, expand, 3>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 4:
+            conv2Helper<T, accType, conv_dim, expand, 4>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 5:
+            conv2Helper<T, accType, conv_dim, expand, 5>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 6:
+            conv2Helper<T, accType, conv_dim, expand, 6>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 7:
+            conv2Helper<T, accType, conv_dim, expand, 7>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 8:
+            conv2Helper<T, accType, conv_dim, expand, 8>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 9:
+            conv2Helper<T, accType, conv_dim, expand, 9>(blocks, threads, out,
+                                                         signal, blk_x, blk_y);
+            break;
+        case 10:
+            conv2Helper<T, accType, conv_dim, expand, 10>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 11:
+            conv2Helper<T, accType, conv_dim, expand, 11>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 12:
+            conv2Helper<T, accType, conv_dim, expand, 12>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 13:
+            conv2Helper<T, accType, conv_dim, expand, 13>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 14:
+            conv2Helper<T, accType, conv_dim, expand, 14>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 15:
+            conv2Helper<T, accType, conv_dim, expand, 15>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 16:
+            conv2Helper<T, accType, conv_dim, expand, 16>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 17:
+            conv2Helper<T, accType, conv_dim, expand, 17>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 18:
+            conv2Helper<T, accType, conv_dim, expand, 18>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 19:
+            conv2Helper<T, accType, conv_dim, expand, 19>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 20:
+            conv2Helper<T, accType, conv_dim, expand, 20>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 21:
+            conv2Helper<T, accType, conv_dim, expand, 21>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 22:
+            conv2Helper<T, accType, conv_dim, expand, 22>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 23:
+            conv2Helper<T, accType, conv_dim, expand, 23>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 24:
+            conv2Helper<T, accType, conv_dim, expand, 24>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 25:
+            conv2Helper<T, accType, conv_dim, expand, 25>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 26:
+            conv2Helper<T, accType, conv_dim, expand, 26>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 27:
+            conv2Helper<T, accType, conv_dim, expand, 27>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 28:
+            conv2Helper<T, accType, conv_dim, expand, 28>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 29:
+            conv2Helper<T, accType, conv_dim, expand, 29>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 30:
+            conv2Helper<T, accType, conv_dim, expand, 30>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        case 31:
+            conv2Helper<T, accType, conv_dim, expand, 31>(blocks, threads, out,
+                                                          signal, blk_x, blk_y);
+            break;
+        default: {
+            char errMessage[256];
+            snprintf(errMessage, sizeof(errMessage),
+                     "\nCUDA Separable convolution doesn't support %d kernel\n",
+                     fLen);
+            CUDA_NOT_SUPPORTED(errMessage);
+        };
     }
 
-   POST_LAUNCH_CHECK();
+    POST_LAUNCH_CHECK();
 }
 
-#define INSTANTIATE(T, accType)                                         \
-    template void convolve2<T, accType, 0, true >(Param<T> out, CParam<T> signal, CParam<accType> filter); \
-    template void convolve2<T, accType, 0, false>(Param<T> out, CParam<T> signal, CParam<accType> filter); \
-    template void convolve2<T, accType, 1, true >(Param<T> out, CParam<T> signal, CParam<accType> filter); \
-    template void convolve2<T, accType, 1, false>(Param<T> out, CParam<T> signal, CParam<accType> filter); \
-
+#define INSTANTIATE(T, accType)                                  \
+    template void convolve2<T, accType, 0, true>(                \
+        Param<T> out, CParam<T> signal, CParam<accType> filter); \
+    template void convolve2<T, accType, 0, false>(               \
+        Param<T> out, CParam<T> signal, CParam<accType> filter); \
+    template void convolve2<T, accType, 1, true>(                \
+        Param<T> out, CParam<T> signal, CParam<accType> filter); \
+    template void convolve2<T, accType, 1, false>(               \
+        Param<T> out, CParam<T> signal, CParam<accType> filter);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
-}
-}
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index 34d34d7cf8..a5343a4052 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -7,95 +7,88 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
-#include <platform.hpp>
 #include <debug_cuda.hpp>
-#include <Param.hpp>
+#include <err_cuda.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
-    template<typename T>
-    __global__ static void
-    diagCreateKernel(Param<T> out, CParam<T> in, int num, int blocks_x)
-    {
-        unsigned idz = blockIdx.x / blocks_x;
-        unsigned blockIdx_x = blockIdx.x - idz * blocks_x;
-
-        unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
-        unsigned idy = threadIdx.y + (blockIdx.y + blockIdx.z * gridDim.y) * blockDim.y;
+namespace cuda {
+namespace kernel {
+template<typename T>
+__global__ static void diagCreateKernel(Param<T> out, CParam<T> in, int num,
+                                        int blocks_x) {
+    unsigned idz        = blockIdx.x / blocks_x;
+    unsigned blockIdx_x = blockIdx.x - idz * blocks_x;
 
-        if (idx >= out.dims[0] ||
-            idy >= out.dims[1] ||
-            idz >= out.dims[2]) return;
+    unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
+    unsigned idy =
+        threadIdx.y + (blockIdx.y + blockIdx.z * gridDim.y) * blockDim.y;
 
+    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2]) return;
 
-        T *optr = out.ptr + idz * out.strides[2] + idy * out.strides[1] + idx;
-        const T *iptr = in.ptr  + idz *  in.strides[1] + ((num > 0) ? idx : idy);
+    T *optr       = out.ptr + idz * out.strides[2] + idy * out.strides[1] + idx;
+    const T *iptr = in.ptr + idz * in.strides[1] + ((num > 0) ? idx : idy);
 
-        T val = (idx == (idy - num)) ? *iptr : scalar<T>(0);
-        *optr = val;
-    }
+    T val = (idx == (idy - num)) ? *iptr : scalar<T>(0);
+    *optr = val;
+}
 
-    template<typename T>
-    static void diagCreate(Param<T> out, CParam<T> in, int num)
-    {
-        dim3 threads(32, 8);
-        int blocks_x = divup(out.dims[0], threads.x);
-        int blocks_y = divup(out.dims[1], threads.y);
-        dim3 blocks(blocks_x * out.dims[2], blocks_y);
-
-        const int maxBlocksY   = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
-        if(blocksPerMatZ > 1) {
-            blocks.y = maxBlocksY;
-            blocks.z = blocksPerMatZ;
-        }
-
-        CUDA_LAUNCH((diagCreateKernel<T>), blocks, threads, out, in, num, blocks_x);
-        POST_LAUNCH_CHECK();
+template<typename T>
+static void diagCreate(Param<T> out, CParam<T> in, int num) {
+    dim3 threads(32, 8);
+    int blocks_x = divup(out.dims[0], threads.x);
+    int blocks_y = divup(out.dims[1], threads.y);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
+    if (blocksPerMatZ > 1) {
+        blocks.y = maxBlocksY;
+        blocks.z = blocksPerMatZ;
     }
 
-    template<typename T>
-    __global__ static void
-    diagExtractKernel(Param<T> out, CParam<T> in, int num, int blocks_z)
-    {
-        unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_z;
-        unsigned idz = (blockIdx.y + blockIdx.z * gridDim.y)  - idw * blocks_z;
-
-        unsigned idx = threadIdx.x + blockIdx.x * blockDim.x;
-
-        if (idx >= out.dims[0] ||
-            idz >= out.dims[2] ||
-            idw >= out.dims[3]) return;
-
-        T *optr = out.ptr + idz * out.strides[2] + idw * out.strides[3] + idx;
+    CUDA_LAUNCH((diagCreateKernel<T>), blocks, threads, out, in, num, blocks_x);
+    POST_LAUNCH_CHECK();
+}
 
-        if (idx >= in.dims[0] || idx >= in.dims[1]) *optr = scalar<T>(0);
+template<typename T>
+__global__ static void diagExtractKernel(Param<T> out, CParam<T> in, int num,
+                                         int blocks_z) {
+    unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_z;
+    unsigned idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_z;
 
-        int i_off = (num > 0) ? (num * in.strides[1] + idx) : (idx - num);
-        const T *iptr = in.ptr  + idz *  in.strides[2] + idw *  in.strides[3] + i_off;
-        *optr = iptr[idx * in.strides[1]];
-    }
+    unsigned idx = threadIdx.x + blockIdx.x * blockDim.x;
 
-    template<typename T>
-    static void diagExtract(Param<T> out, CParam<T> in, int num)
-    {
-        dim3 threads(256, 1);
-        int blocks_x = divup(out.dims[0], threads.x);
-        int blocks_z = out.dims[2];
-        dim3 blocks(blocks_x, out.dims[3] * blocks_z);
+    if (idx >= out.dims[0] || idz >= out.dims[2] || idw >= out.dims[3]) return;
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    T *optr = out.ptr + idz * out.strides[2] + idw * out.strides[3] + idx;
 
-        CUDA_LAUNCH((diagExtractKernel<T>), blocks, threads, out, in, num, blocks_z);
-        POST_LAUNCH_CHECK();
-    }
+    if (idx >= in.dims[0] || idx >= in.dims[1]) *optr = scalar<T>(0);
 
+    int i_off     = (num > 0) ? (num * in.strides[1] + idx) : (idx - num);
+    const T *iptr = in.ptr + idz * in.strides[2] + idw * in.strides[3] + i_off;
+    *optr         = iptr[idx * in.strides[1]];
 }
+
+template<typename T>
+static void diagExtract(Param<T> out, CParam<T> in, int num) {
+    dim3 threads(256, 1);
+    int blocks_x = divup(out.dims[0], threads.x);
+    int blocks_z = out.dims[2];
+    dim3 blocks(blocks_x, out.dims[3] * blocks_z);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((diagExtractKernel<T>), blocks, threads, out, in, num,
+                blocks_z);
+    POST_LAUNCH_CHECK();
 }
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index e3aa47cee0..a3a23c546b 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -7,95 +7,90 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 16;
-        static const unsigned TY = 16;
-
-        template<typename T, bool D>
-        inline __host__ __device__
-        void diff_this(T* out, const T* in, const unsigned oMem, const unsigned iMem0,
-                       const unsigned iMem1, const unsigned iMem2)
-        {
-            //iMem2 can never be 0
-            if(D == 0) {                        // Diff1
-                out[oMem] = in[iMem1] - in[iMem0];
-            } else {                                // Diff2
-                out[oMem] = in[iMem2] - in[iMem1] - in[iMem1] + in[iMem0];
-            }
-        }
-
-        /////////////////////////////////////////////////////////////////////////////
-        // 1st and 2nd Order Differential for 4D along all dimensions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, unsigned dim, bool isDiff2>
-        __global__
-        void diff_kernel(Param<T> out, CParam<T> in, const unsigned oElem,
-                         const unsigned blocksPerMatX, const unsigned blocksPerMatY)
-        {
-            unsigned idz = blockIdx.x / blocksPerMatX;
-            unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-            unsigned blockIdx_x = blockIdx.x - idz * blocksPerMatX;
-            unsigned blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
-
-            unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
-            unsigned idy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if(idx >= out.dims[0] ||
-               idy >= out.dims[1] ||
-               idz >= out.dims[2] ||
-               idw >= out.dims[3])
-                return;
-
-            unsigned iMem0 = idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
-            unsigned iMem1 = iMem0 + in.strides[dim];
-            unsigned iMem2 = iMem1 + in.strides[dim];
-
-            unsigned oMem = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1] + idx;
-
-            iMem2 *= isDiff2;
-
-            diff_this<T, isDiff2>(out.ptr, in.ptr, oMem, iMem0, iMem1, iMem2);
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, unsigned dim, bool isDiff2>
-        void diff(Param<T> out, CParam<T> in, const int indims)
-        {
-            dim3 threads(TX, TY, 1);
-
-            if (dim == 0 && indims == 1) {
-                threads = dim3(TX * TY, 1, 1);
-            }
-
-            int blocksPerMatX = divup(out.dims[0], TX);
-            int blocksPerMatY = divup(out.dims[1], TY);
-            dim3 blocks(blocksPerMatX * out.dims[2],
-                        blocksPerMatY * out.dims[3],
-                        1);
-
-            const int oElem = out.dims[0] * out.dims[1] * out.dims[2] * out.dims[3];
-
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
-
-            CUDA_LAUNCH((diff_kernel<T, dim, isDiff2>), blocks, threads,
-                out, in, oElem, blocksPerMatX, blocksPerMatY);
-
-            POST_LAUNCH_CHECK();
-        }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX = 16;
+static const unsigned TY = 16;
+
+template<typename T, bool D>
+inline __host__ __device__ void diff_this(T* out, const T* in,
+                                          const unsigned oMem,
+                                          const unsigned iMem0,
+                                          const unsigned iMem1,
+                                          const unsigned iMem2) {
+    // iMem2 can never be 0
+    if (D == 0) {  // Diff1
+        out[oMem] = in[iMem1] - in[iMem0];
+    } else {  // Diff2
+        out[oMem] = in[iMem2] - in[iMem1] - in[iMem1] + in[iMem0];
     }
 }
+
+/////////////////////////////////////////////////////////////////////////////
+// 1st and 2nd Order Differential for 4D along all dimensions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, unsigned dim, bool isDiff2>
+__global__ void diff_kernel(Param<T> out, CParam<T> in, const unsigned oElem,
+                            const unsigned blocksPerMatX,
+                            const unsigned blocksPerMatY) {
+    unsigned idz = blockIdx.x / blocksPerMatX;
+    unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    unsigned blockIdx_x = blockIdx.x - idz * blocksPerMatX;
+    unsigned blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
+
+    unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
+    unsigned idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2] ||
+        idw >= out.dims[3])
+        return;
+
+    unsigned iMem0 =
+        idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
+    unsigned iMem1 = iMem0 + in.strides[dim];
+    unsigned iMem2 = iMem1 + in.strides[dim];
+
+    unsigned oMem = idw * out.strides[3] + idz * out.strides[2] +
+                    idy * out.strides[1] + idx;
+
+    iMem2 *= isDiff2;
+
+    diff_this<T, isDiff2>(out.ptr, in.ptr, oMem, iMem0, iMem1, iMem2);
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, unsigned dim, bool isDiff2>
+void diff(Param<T> out, CParam<T> in, const int indims) {
+    dim3 threads(TX, TY, 1);
+
+    if (dim == 0 && indims == 1) { threads = dim3(TX * TY, 1, 1); }
+
+    int blocksPerMatX = divup(out.dims[0], TX);
+    int blocksPerMatY = divup(out.dims[1], TY);
+    dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
+
+    const int oElem = out.dims[0] * out.dims[1] * out.dims[2] * out.dims[3];
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((diff_kernel<T, dim, isDiff2>), blocks, threads, out, in, oElem,
+                blocksPerMatX, blocksPerMatY);
+
+    POST_LAUNCH_CHECK();
+}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 6454c1dc0b..5386dd8fb4 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -7,47 +7,44 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>                     // CUDA specific math functions
+#include <math.hpp>  // CUDA specific math functions
 
-#include <Param.hpp>                    // This header has the declaration of structures
-                                        // that are passed onto kernel. Operator overloads
-                                        // for creating Param objects from cuda::Array<T>
-                                        // objects is automatic, no special work is needed.
-                                        // Hence, the CUDA kernel wrapper function takes in
-                                        // Param and CParam(constant version of Param) instead
-                                        // of cuda::Array<T>
+#include <Param.hpp>  // This header has the declaration of structures
+                      // that are passed onto kernel. Operator overloads
+                      // for creating Param objects from cuda::Array<T>
+                      // objects is automatic, no special work is needed.
+                      // Hence, the CUDA kernel wrapper function takes in
+                      // Param and CParam(constant version of Param) instead
+                      // of cuda::Array<T>
 
-#include <common/dispatch.hpp>                 // common utility header for CUDA & OpenCL backends
-                                        // has the divup macro
+#include <common/dispatch.hpp>  // common utility header for CUDA & OpenCL backends
+                                // has the divup macro
 
-#include <err_cuda.hpp>                 // CUDA specific error check functions and macros
+#include <err_cuda.hpp>  // CUDA specific error check functions and macros
 
-#include <debug_cuda.hpp>               // For Debug only related CUDA validations
+#include <debug_cuda.hpp>  // For Debug only related CUDA validations
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
-static const unsigned TX = 16;          // Kernel Launch Config Values
-static const unsigned TY = 16;          // Kernel Launch Config Values
+static const unsigned TX = 16;  // Kernel Launch Config Values
+static const unsigned TY = 16;  // Kernel Launch Config Values
 
 template<typename T>
-__global__
-void exampleFuncKernel(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p)
-{
+__global__ void exampleFuncKernel(Param<T> c, CParam<T> a, CParam<T> b,
+                                  const af_someenum_t p) {
     // get current thread global identifiers along required dimensions
     int i = blockDim.x * blockIdx.x + threadIdx.x;
     int j = blockDim.y * blockIdx.y + threadIdx.y;
 
-    if ( i<a.dims[0] && j<a.dims[1] ) {
+    if (i < a.dims[0] && j < a.dims[1]) {
         // if needed use strides array to compute linear index of arrays
-        int src1Idx = i + j*a.strides[1];
-        int src2Idx = i + j*b.strides[1];
-        int dstIdx  = i + j*c.strides[1];
+        int src1Idx = i + j * a.strides[1];
+        int src2Idx = i + j * b.strides[1];
+        int dstIdx  = i + j * c.strides[1];
 
-        T* dst = c.ptr;
+        T* dst        = c.ptr;
         const T* src1 = a.ptr;
         const T* src2 = b.ptr;
 
@@ -56,33 +53,32 @@ void exampleFuncKernel(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t
     }
 }
 
-
-template <typename T>                   // CUDA kernel wrapper function
-void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p)
-{
-
-    dim3 threads(TX, TY, 1);            // set your cuda launch config for blocks
+template<typename T>  // CUDA kernel wrapper function
+void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
+    dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
     int blk_x = divup(c.dims[0], threads.x);
     int blk_y = divup(c.dims[1], threads.y);
-    dim3 blocks(blk_x, blk_y);          // set your opencl launch config for grid
+    dim3 blocks(blk_x, blk_y);  // set your opencl launch config for grid
 
     // launch your kernel
     // One must use CUDA_LAUNCH macro to launch their kernels to ensure
     // that the kernel is launched on an appropriate stream
     //
-    // Use CUDA_LAUNCH macro for launching kernels that don't use dynamic shared memory
+    // Use CUDA_LAUNCH macro for launching kernels that don't use dynamic shared
+    // memory
     //
-    // Use CUDA_LAUNCH_SMEM macro for launching kernsl that use dynamic shared memory
+    // Use CUDA_LAUNCH_SMEM macro for launching kernsl that use dynamic shared
+    // memory
     //
-    // CUDA_LAUNCH_SMEM takes in an additional parameter, size of shared memory, after
-    // threads paramters, which are then followed by kernel parameters
+    // CUDA_LAUNCH_SMEM takes in an additional parameter, size of shared memory,
+    // after threads paramters, which are then followed by kernel parameters
     CUDA_LAUNCH((exampleFuncKernel<T>), blocks, threads, c, a, b, p);
 
-    POST_LAUNCH_CHECK();                // Macro for post kernel launch checks
-                                        // these checks are carried  ONLY IN DEBUG mode
+    POST_LAUNCH_CHECK();  // Macro for post kernel launch checks
+                          // these checks are carried  ONLY IN DEBUG mode
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 37587c0bca..9cc96a464d 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -7,53 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "shared.hpp"
 #include <common/dispatch.hpp>
-#include <cub/block/block_reduce.cuh>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <kernel/fast_lut.hpp>
-#include <memory.hpp>
 #include <math.hpp>
+#include <memory.hpp>
+#include <cub/block/block_reduce.cuh>
+#include "shared.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
-inline __device__
-int idx_y(const int i)
-{
+inline __device__ int idx_y(const int i) {
     int j = i - 4;
     int k = min(j, 8 - j);
     return clamp(k, -3, 3);
 }
 
-inline __device__
-int idx_x(const int i)
-{
-    return idx_y((i + 4) & 15);
-}
+inline __device__ int idx_x(const int i) { return idx_y((i + 4) & 15); }
 
-inline __device__
-int idx(const int x, const int y)
-{
+inline __device__ int idx(const int x, const int y) {
     return ((threadIdx.x + 3 + x) + (blockDim.x + 6) * (threadIdx.y + 3 + y));
 }
 
 // test_greater()
 // Tests if a pixel x > p + thr
-inline __device__
-int test_greater(const float x, const float p, const float thr)
-{
+inline __device__ int test_greater(const float x, const float p,
+                                   const float thr) {
     return (x > p + thr);
 }
 
 // test_smaller()
 // Tests if a pixel x < p - thr
-inline __device__
-int test_smaller(const float x, const float p, const float thr)
-{
+inline __device__ int test_smaller(const float x, const float p,
+                                   const float thr) {
     return (x < p - thr);
 }
 
@@ -62,197 +50,164 @@ int test_smaller(const float x, const float p, const float thr)
 // Returns  0 when x >= p - thr && x <= p + thr
 // Returns  1 when x > p + thr
 template<typename T>
-inline __device__
-int test_pixel(const T* local_image, const float p, const float thr, const int x, const int y)
-{
-    return -test_smaller((float)local_image[idx(x,y)], p, thr) + test_greater((float)local_image[idx(x,y)], p, thr);
+inline __device__ int test_pixel(const T *local_image, const float p,
+                                 const float thr, const int x, const int y) {
+    return -test_smaller((float)local_image[idx(x, y)], p, thr) +
+           test_greater((float)local_image[idx(x, y)], p, thr);
 }
 
 // max_val()
 // Returns max of x and y
-inline __device__
-int max_val(const int x, const int y)
-{
-    return max(x, y);
-}
-inline __device__
-unsigned max_val(const unsigned x, const unsigned y)
-{
+inline __device__ int max_val(const int x, const int y) { return max(x, y); }
+inline __device__ unsigned max_val(const unsigned x, const unsigned y) {
     return max(x, y);
 }
-inline __device__
-short max_val(const short x, const short y)
-{
+inline __device__ short max_val(const short x, const short y) {
     return max(x, y);
 }
-inline __device__
-ushort max_val(const ushort x, const ushort y)
-{
+inline __device__ ushort max_val(const ushort x, const ushort y) {
     return max(x, y);
 }
-inline __device__
-float max_val(const float x, const float y)
-{
+inline __device__ float max_val(const float x, const float y) {
     return fmax(x, y);
 }
-inline __device__
-double max_val(const double x, const double y)
-{
+inline __device__ double max_val(const double x, const double y) {
     return fmax(x, y);
 }
 
 // abs_diff()
 // Returns absolute difference of x and y
-inline __device__ int abs_diff(const int x, const int y)
-{
+inline __device__ int abs_diff(const int x, const int y) {
     int i = x - y;
     return max(-i, i);
 }
-inline __device__ unsigned abs_diff(const unsigned x, const unsigned y)
-{
+inline __device__ unsigned abs_diff(const unsigned x, const unsigned y) {
     int i = (int)x - (int)y;
     return max(-i, i);
 }
-inline __device__ short abs_diff(const short x, const short y)
-{
+inline __device__ short abs_diff(const short x, const short y) {
     short i = x - y;
     return max(-i, i);
 }
-inline __device__ ushort abs_diff(const ushort x, const ushort y)
-{
+inline __device__ ushort abs_diff(const ushort x, const ushort y) {
     int i = (int)x - (int)y;
     return (ushort)max(-i, i);
 }
-inline __device__ float abs_diff(const float x, const float y)
-{
+inline __device__ float abs_diff(const float x, const float y) {
     return fabs(x - y);
 }
-inline __device__ double abs_diff(const double x, const double y)
-{
+inline __device__ double abs_diff(const double x, const double y) {
     return fabs(x - y);
 }
 
 template<typename T, int arc_length>
-__device__
-void locate_features_core(
-    T* local_image,
-    float* score,
-    const unsigned idim0,
-    const unsigned idim1,
-    const float thr,
-    int x, int y,
-    const unsigned edge)
-{
+__device__ void locate_features_core(T *local_image, float *score,
+                                     const unsigned idim0, const unsigned idim1,
+                                     const float thr, int x, int y,
+                                     const unsigned edge) {
     if (x >= idim0 - edge || y >= idim1 - edge) return;
 
     score[y * idim0 + x] = 0.f;
 
-    float p = local_image[idx( 0, 0)];
+    float p = local_image[idx(0, 0)];
 
     // Start by testing opposite pixels of the circle that will result in
     // a non-kepoint
-    int d = test_pixel<T>(local_image, p, thr, -3,  0) | test_pixel<T>(local_image, p, thr, 3,  0);
-    if (d == 0)
-        return;
-
-    d &= test_pixel<T>(local_image, p, thr, -2,  2) | test_pixel<T>(local_image, p, thr,  2, -2);
-    d &= test_pixel<T>(local_image, p, thr,  0,  3) | test_pixel<T>(local_image, p, thr,  0, -3);
-    d &= test_pixel<T>(local_image, p, thr,  2,  2) | test_pixel<T>(local_image, p, thr, -2, -2);
-    if (d == 0)
-        return;
-
-    d &= test_pixel<T>(local_image, p, thr, -3,  1) | test_pixel<T>(local_image, p, thr,  3, -1);
-    d &= test_pixel<T>(local_image, p, thr, -1,  3) | test_pixel<T>(local_image, p, thr,  1, -3);
-    d &= test_pixel<T>(local_image, p, thr,  1,  3) | test_pixel<T>(local_image, p, thr, -1, -3);
-    d &= test_pixel<T>(local_image, p, thr,  3,  1) | test_pixel<T>(local_image, p, thr, -3, -1);
-    if (d == 0)
-        return;
+    int d = test_pixel<T>(local_image, p, thr, -3, 0) |
+            test_pixel<T>(local_image, p, thr, 3, 0);
+    if (d == 0) return;
+
+    d &= test_pixel<T>(local_image, p, thr, -2, 2) |
+         test_pixel<T>(local_image, p, thr, 2, -2);
+    d &= test_pixel<T>(local_image, p, thr, 0, 3) |
+         test_pixel<T>(local_image, p, thr, 0, -3);
+    d &= test_pixel<T>(local_image, p, thr, 2, 2) |
+         test_pixel<T>(local_image, p, thr, -2, -2);
+    if (d == 0) return;
+
+    d &= test_pixel<T>(local_image, p, thr, -3, 1) |
+         test_pixel<T>(local_image, p, thr, 3, -1);
+    d &= test_pixel<T>(local_image, p, thr, -1, 3) |
+         test_pixel<T>(local_image, p, thr, 1, -3);
+    d &= test_pixel<T>(local_image, p, thr, 1, 3) |
+         test_pixel<T>(local_image, p, thr, -1, -3);
+    d &= test_pixel<T>(local_image, p, thr, 3, 1) |
+         test_pixel<T>(local_image, p, thr, -3, -1);
+    if (d == 0) return;
 
     int bright = 0, dark = 0;
     float s_bright = 0, s_dark = 0;
 
-    // Force less loop unrolls to control maximum number of registers and
-    // launch more blocks
-    #pragma unroll 4
+// Force less loop unrolls to control maximum number of registers and
+// launch more blocks
+#pragma unroll 4
     for (int i = 0; i < 16; i++) {
         // Get pixel from the circle
-        float p_x = local_image[idx(idx_x(i),idx_y(i))];
+        float p_x = local_image[idx(idx_x(i), idx_y(i))];
 
         // Compute binary vectors with responses for each pixel on circle
         bright |= test_greater(p_x, p, thr) << i;
-        dark   |= test_smaller(p_x, p, thr) << i;
+        dark |= test_smaller(p_x, p, thr) << i;
 
         // Compute scores for brighter and darker pixels
         float weight = abs_diff(p_x, p) - thr;
         s_bright += test_greater(p_x, p, thr) * weight;
-        s_dark   += test_smaller(p_x, p, thr) * weight;
+        s_dark += test_smaller(p_x, p, thr) * weight;
     }
 
     // Checks LUT to verify if there is a segment for which all pixels are much
     // brighter or much darker than central pixel p.
-    if ((int)FAST_LUT[bright] >= arc_length || (int)FAST_LUT[dark] >= arc_length)
+    if ((int)FAST_LUT[bright] >= arc_length ||
+        (int)FAST_LUT[dark] >= arc_length)
         score[x + idim0 * y] = max_val(s_bright, s_dark);
 }
 
 template<typename T>
-__device__
-void load_shared_image(CParam<T> in,
-                       T *local_image,
-                       unsigned ix, unsigned iy,
-                       unsigned bx, unsigned by,
-                       unsigned x, unsigned y,
-                       unsigned lx, unsigned ly,
-                       const unsigned edge)
-{
+__device__ void load_shared_image(CParam<T> in, T *local_image, unsigned ix,
+                                  unsigned iy, unsigned bx, unsigned by,
+                                  unsigned x, unsigned y, unsigned lx,
+                                  unsigned ly, const unsigned edge) {
     // Copy an image patch to shared memory, with a 3-pixel edge
     if (ix < lx && iy < ly && x - 3 < in.dims[0] && y - 3 < in.dims[1]) {
-        local_image[(ix)      + (bx+6) * (iy)]    = in.ptr[(x-3)    + in.dims[0] * (y-3)];
+        local_image[(ix) + (bx + 6) * (iy)] =
+            in.ptr[(x - 3) + in.dims[0] * (y - 3)];
         if (x + lx - 3 < in.dims[0])
-            local_image[(ix + lx) + (bx+6) * (iy)]    = in.ptr[(x+lx-3) + in.dims[0] * (y-3)];
+            local_image[(ix + lx) + (bx + 6) * (iy)] =
+                in.ptr[(x + lx - 3) + in.dims[0] * (y - 3)];
         if (y + ly - 3 < in.dims[1])
-            local_image[(ix)      + (bx+6) * (iy+ly)] = in.ptr[(x-3)    + in.dims[0] * (y+ly-3)];
+            local_image[(ix) + (bx + 6) * (iy + ly)] =
+                in.ptr[(x - 3) + in.dims[0] * (y + ly - 3)];
         if (x + lx - 3 < in.dims[0] && y + ly - 3 < in.dims[1])
-            local_image[(ix + lx) + (bx+6) * (iy+ly)] = in.ptr[(x+lx-3) + in.dims[0] * (y+ly-3)];
+            local_image[(ix + lx) + (bx + 6) * (iy + ly)] =
+                in.ptr[(x + lx - 3) + in.dims[0] * (y + ly - 3)];
     }
 }
 
 template<typename T, int arc_length>
-__global__
-void locate_features(
-    CParam<T> in,
-    float* score,
-    const float thr,
-    const unsigned edge)
-{
+__global__ void locate_features(CParam<T> in, float *score, const float thr,
+                                const unsigned edge) {
     unsigned ix = threadIdx.x;
     unsigned iy = threadIdx.y;
     unsigned bx = blockDim.x;
     unsigned by = blockDim.y;
-    unsigned x = bx * blockIdx.x + ix + edge;
-    unsigned y = by * blockIdx.y + iy + edge;
+    unsigned x  = bx * blockIdx.x + ix + edge;
+    unsigned y  = by * blockIdx.y + iy + edge;
     unsigned lx = bx / 2 + 3;
     unsigned ly = by / 2 + 3;
 
     SharedMemory<T> shared;
-    T* local_image_curr = shared.getPointer();
+    T *local_image_curr = shared.getPointer();
     load_shared_image(in, local_image_curr, ix, iy, bx, by, x, y, lx, ly, edge);
     __syncthreads();
-    locate_features_core<T, arc_length>(local_image_curr, score,
-                                        in.dims[0], in.dims[1], thr, x, y, edge);
+    locate_features_core<T, arc_length>(local_image_curr, score, in.dims[0],
+                                        in.dims[1], thr, x, y, edge);
 }
 
 template<bool nonmax>
-__global__
-void non_max_counts(
-    unsigned *d_counts,
-    unsigned *d_offsets,
-    unsigned *d_total,
-    float *flags,
-    const float* score,
-    const unsigned idim0,
-    const unsigned idim1,
-    const unsigned edge)
-{
+__global__ void non_max_counts(unsigned *d_counts, unsigned *d_offsets,
+                               unsigned *d_total, float *flags,
+                               const float *score, const unsigned idim0,
+                               const unsigned idim1, const unsigned edge) {
     const int xid = blockIdx.x * blockDim.x * 2 + threadIdx.x;
     const int yid = blockIdx.y * blockDim.y * 8 + threadIdx.y;
     const int tid = blockDim.x * threadIdx.y + threadIdx.x;
@@ -264,15 +219,16 @@ void non_max_counts(
     const int yend = (blockIdx.y + 1) * blockDim.y * 8;
 
     const int bid = blockIdx.y * gridDim.x + blockIdx.x;
-    using BlockReduce = cub::BlockReduce<unsigned, 32, cub::BLOCK_REDUCE_WARP_REDUCTIONS, 8>;
+    using BlockReduce =
+        cub::BlockReduce<unsigned, 32, cub::BLOCK_REDUCE_WARP_REDUCTIONS, 8>;
 
     __shared__ typename BlockReduce::TempStorage temp_storage;
 
     unsigned count = 0;
     for (int y = yid; y < yend; y += yoff) {
-        if (y >= idim1 - edge-1 || y <= edge+1) continue;
+        if (y >= idim1 - edge - 1 || y <= edge + 1) continue;
         for (int x = xid; x < xend; x += xoff) {
-            if (x >= idim0 - edge-1 || x <= edge+1) continue;
+            if (x >= idim0 - edge - 1 || x <= edge + 1) continue;
 
             float v = score[y * idim0 + x];
             if (v == 0) {
@@ -282,15 +238,16 @@ void non_max_counts(
 
             if (nonmax) {
                 float max_v = v;
-                max_v = max_val(score[x-1 + idim0 * (y-1)], score[x-1 + idim0 * y]);
-                max_v = max_val(max_v, score[x-1 + idim0 * (y+1)]);
-                max_v = max_val(max_v, score[x   + idim0 * (y-1)]);
-                max_v = max_val(max_v, score[x   + idim0 * (y+1)]);
-                max_v = max_val(max_v, score[x+1 + idim0 * (y-1)]);
-                max_v = max_val(max_v, score[x+1 + idim0 * (y)  ]);
-                max_v = max_val(max_v, score[x+1 + idim0 * (y+1)]);
-
-                v = (v > max_v) ? v : 0;
+                max_v       = max_val(score[x - 1 + idim0 * (y - 1)],
+                                score[x - 1 + idim0 * y]);
+                max_v       = max_val(max_v, score[x - 1 + idim0 * (y + 1)]);
+                max_v       = max_val(max_v, score[x + idim0 * (y - 1)]);
+                max_v       = max_val(max_v, score[x + idim0 * (y + 1)]);
+                max_v       = max_val(max_v, score[x + 1 + idim0 * (y - 1)]);
+                max_v       = max_val(max_v, score[x + 1 + idim0 * (y)]);
+                max_v       = max_val(max_v, score[x + 1 + idim0 * (y + 1)]);
+
+                v                    = (v > max_v) ? v : 0;
                 flags[y * idim0 + x] = v;
                 if (v == 0) continue;
             }
@@ -303,25 +260,17 @@ void non_max_counts(
 
     if (tid == 0) {
         unsigned total = sum ? atomicAdd(d_total, sum) : 0;
-        d_counts [bid] = sum;
+        d_counts[bid]  = sum;
         d_offsets[bid] = total;
     }
 }
 
 template<typename T>
-__global__
-void get_features(
-    float *x_out,
-    float *y_out,
-    float *score_out,
-    const T* flags,
-    const unsigned *d_counts,
-    const unsigned *d_offsets,
-    const unsigned total,
-    const unsigned idim0,
-    const unsigned idim1,
-    const unsigned edge)
-{
+__global__ void get_features(float *x_out, float *y_out, float *score_out,
+                             const T *flags, const unsigned *d_counts,
+                             const unsigned *d_offsets, const unsigned total,
+                             const unsigned idim0, const unsigned idim1,
+                             const unsigned edge) {
     const int xid = blockIdx.x * blockDim.x * 2 + threadIdx.x;
     const int yid = blockIdx.y * blockDim.y * 8 + threadIdx.y;
     const int tid = blockDim.x * threadIdx.y + threadIdx.x;
@@ -338,47 +287,41 @@ void get_features(
     __shared__ unsigned s_idx;
 
     if (tid == 0) {
-        s_count  = d_counts [bid];
-        s_idx    = d_offsets[bid];
+        s_count = d_counts[bid];
+        s_idx   = d_offsets[bid];
     }
     __syncthreads();
 
     // Blocks that are empty, please bail
     if (s_count == 0) return;
     for (int y = yid; y < yend; y += yoff) {
-        if (y >= idim1 - edge-1 || y <= edge+1) continue;
+        if (y >= idim1 - edge - 1 || y <= edge + 1) continue;
         for (int x = xid; x < xend; x += xoff) {
-            if (x >= idim0 - edge-1 || x <= edge+1) continue;
+            if (x >= idim0 - edge - 1 || x <= edge + 1) continue;
 
             float v = flags[y * idim0 + x];
             if (v == 0) continue;
 
             unsigned id = atomicAdd(&s_idx, 1u);
             if (id >= total) return;
-            y_out[id] = x;
-            x_out[id] = y;
+            y_out[id]     = x;
+            x_out[id]     = y;
             score_out[id] = v;
         }
     }
 }
 
 template<typename T>
-void fast(unsigned* out_feat,
-          float** x_out,
-          float** y_out,
-          float** score_out,
-          const Array<T>& in,
-          const float thr,
-          const unsigned arc_length,
-          const unsigned nonmax,
-          const float feature_ratio,
-          const unsigned edge)
-{
-    dim4 indims = in.dims();
+void fast(unsigned *out_feat, float **x_out, float **y_out, float **score_out,
+          const Array<T> &in, const float thr, const unsigned arc_length,
+          const unsigned nonmax, const float feature_ratio,
+          const unsigned edge) {
+    dim4 indims             = in.dims();
     const unsigned max_feat = ceil(indims[0] * indims[1] * feature_ratio);
 
     dim3 threads(16, 16);
-    dim3 blocks(divup(indims[0]-edge*2, threads.x), divup(indims[1]-edge*2, threads.y));
+    dim3 blocks(divup(indims[0] - edge * 2, threads.x),
+                divup(indims[1] - edge * 2, threads.y));
 
     // Matrix containing scores for detected features, scores are stored in the
     // same coordinates as features, dimensions should be equal to in.
@@ -388,81 +331,91 @@ void fast(unsigned* out_feat,
     uptr<float> d_flags_alloc;
     if (nonmax) {
         d_flags_alloc = memAlloc<float>(indims[0] * indims[1]);
-        d_flags = d_flags_alloc.get();
+        d_flags       = d_flags_alloc.get();
     }
 
     // Shared memory size
     size_t shared_size = (threads.x + 6) * (threads.y + 6) * sizeof(T);
 
-    switch(arc_length) {
-    case 9:
-        CUDA_LAUNCH_SMEM((locate_features<T, 9>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 10:
-        CUDA_LAUNCH_SMEM((locate_features<T,10>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 11:
-        CUDA_LAUNCH_SMEM((locate_features<T,11>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 12:
-        CUDA_LAUNCH_SMEM((locate_features<T,12>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 13:
-        CUDA_LAUNCH_SMEM((locate_features<T,13>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 14:
-        CUDA_LAUNCH_SMEM((locate_features<T,14>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 15:
-        CUDA_LAUNCH_SMEM((locate_features<T,15>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
-    case 16:
-        CUDA_LAUNCH_SMEM((locate_features<T,16>), blocks, threads, shared_size, in, d_score.get(), thr, edge);
-        break;
+    switch (arc_length) {
+        case 9:
+            CUDA_LAUNCH_SMEM((locate_features<T, 9>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 10:
+            CUDA_LAUNCH_SMEM((locate_features<T, 10>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 11:
+            CUDA_LAUNCH_SMEM((locate_features<T, 11>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 12:
+            CUDA_LAUNCH_SMEM((locate_features<T, 12>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 13:
+            CUDA_LAUNCH_SMEM((locate_features<T, 13>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 14:
+            CUDA_LAUNCH_SMEM((locate_features<T, 14>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 15:
+            CUDA_LAUNCH_SMEM((locate_features<T, 15>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
+        case 16:
+            CUDA_LAUNCH_SMEM((locate_features<T, 16>), blocks, threads,
+                             shared_size, in, d_score.get(), thr, edge);
+            break;
     }
 
     POST_LAUNCH_CHECK();
 
     threads.x = 32;
-    threads.y =  8;
+    threads.y = 8;
 
     blocks.x = divup(indims[0], 64);
     blocks.y = divup(indims[1], 64);
 
     unsigned *d_total = (unsigned *)(d_score.get() + (indims[0] * indims[1]));
-    CUDA_CHECK(cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getActiveStream()));
+    CUDA_CHECK(
+        cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getActiveStream()));
     auto d_counts  = memAlloc<unsigned>(blocks.x * blocks.y);
     auto d_offsets = memAlloc<unsigned>(blocks.x * blocks.y);
 
     if (nonmax)
-        CUDA_LAUNCH((non_max_counts<true >), blocks, threads,
-                    d_counts.get(), d_offsets.get(), d_total, d_flags,
-                    d_score.get(), indims[0], indims[1], edge);
+        CUDA_LAUNCH((non_max_counts<true>), blocks, threads, d_counts.get(),
+                    d_offsets.get(), d_total, d_flags, d_score.get(), indims[0],
+                    indims[1], edge);
     else
-        CUDA_LAUNCH((non_max_counts<false>), blocks, threads,
-                    d_counts.get(), d_offsets.get(), d_total, d_flags,
-                    d_score.get(), indims[0], indims[1], edge);
+        CUDA_LAUNCH((non_max_counts<false>), blocks, threads, d_counts.get(),
+                    d_offsets.get(), d_total, d_flags, d_score.get(), indims[0],
+                    indims[1], edge);
 
     POST_LAUNCH_CHECK();
 
     // Dimensions of output array
     unsigned total;
-    CUDA_CHECK(cudaMemcpyAsync(&total, d_total, sizeof(unsigned), cudaMemcpyDeviceToHost,
-                cuda::getActiveStream()));
+    CUDA_CHECK(cudaMemcpyAsync(&total, d_total, sizeof(unsigned),
+                               cudaMemcpyDeviceToHost,
+                               cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     total = total < max_feat ? total : max_feat;
 
     if (total > 0) {
-        auto x_out_alloc = memAlloc<float>(total);
-        auto y_out_alloc = memAlloc<float>(total);
+        auto x_out_alloc     = memAlloc<float>(total);
+        auto y_out_alloc     = memAlloc<float>(total);
         auto score_out_alloc = memAlloc<float>(total);
-        *x_out     = x_out_alloc.get();
-        *y_out     = y_out_alloc.get();
-        *score_out = score_out_alloc.get();
+        *x_out               = x_out_alloc.get();
+        *y_out               = y_out_alloc.get();
+        *score_out           = score_out_alloc.get();
 
-        CUDA_LAUNCH((get_features<float>), blocks, threads,
-                *x_out, *y_out, *score_out, d_flags, d_counts.get(),
-                d_offsets.get(), total, indims[0], indims[1], edge);
+        CUDA_LAUNCH((get_features<float>), blocks, threads, *x_out, *y_out,
+                    *score_out, d_flags, d_counts.get(), d_offsets.get(), total,
+                    indims[0], indims[1], edge);
 
         POST_LAUNCH_CHECK();
 
diff --git a/src/backend/cuda/kernel/fast_lut.hpp b/src/backend/cuda/kernel/fast_lut.hpp
index aba857120e..55ebcc5de2 100644
--- a/src/backend/cuda/kernel/fast_lut.hpp
+++ b/src/backend/cuda/kernel/fast_lut.hpp
@@ -8,2051 +8,3453 @@
  ********************************************************/
 
 __constant__ unsigned char FAST_LUT[] = {
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
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-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,
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-     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
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+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  12, 0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    11, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  14, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  10, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  11, 0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  12, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  11, 0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  13, 0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  11, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  10, 0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  12, 0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  11,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+    0,  0,  0,  0,  0,  9,  15, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,
+    0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,
+    11, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,
+    0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  12, 0,  0,  0,  0,  0,
+    0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,
+    0,  0,  0,  0,  0,  0,  0,  11, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,
+    0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,
+    0,  13, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,
+    0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  11, 0,  0,  0,  0,
+    0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,
+    9,  0,  0,  0,  0,  0,  0,  0,  12, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,
+    0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,
+    0,  0,  11, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10,
+    0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  14, 0,  0,  0,
+    0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,
+    0,  9,  0,  0,  0,  0,  0,  0,  0,  11, 0,  0,  0,  0,  0,  0,  0,  9,  0,
+    0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,
+    0,  0,  0,  12, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,
+    10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  11, 0,  0,
+    0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,
+    0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  13, 0,  0,  0,  0,  0,  0,  0,  9,
+    0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,
+    0,  0,  0,  0,  11, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,
+    0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  12, 0,
+    0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,
+    0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,  11, 0,  0,  0,  0,  0,  0,  0,
+    9,  0,  0,  0,  0,  0,  0,  0,  10, 0,  0,  0,  0,  0,  0,  0,  9,  0,  0,
+    0,  0,  0,  0,  0,  15, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,
+    0,  0,  11, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  12,
+    0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  11, 0,  0,  0,
+    9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  13, 0,  0,  0,  9,  0,  0,
+    0,  10, 0,  0,  0,  9,  0,  0,  0,  11, 0,  0,  0,  9,  0,  0,  0,  10, 0,
+    0,  0,  9,  0,  0,  0,  12, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,
+    0,  0,  0,  11, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,
+    14, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  11, 0,  0,
+    0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  12, 0,  0,  0,  9,  0,
+    0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  11, 0,  0,  0,  9,  0,  0,  0,  10,
+    0,  0,  0,  9,  0,  0,  0,  13, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,
+    9,  0,  0,  0,  11, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,
+    0,  12, 0,  0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  11, 0,
+    0,  0,  9,  0,  0,  0,  10, 0,  0,  0,  9,  0,  0,  0,  15, 0,  9,  0,  10,
+    0,  9,  0,  11, 0,  9,  0,  10, 0,  9,  0,  12, 0,  9,  0,  10, 0,  9,  0,
+    11, 0,  9,  0,  10, 0,  9,  0,  13, 0,  9,  0,  10, 0,  9,  0,  11, 0,  9,
+    0,  10, 0,  9,  0,  12, 0,  9,  0,  10, 0,  9,  0,  11, 0,  9,  0,  10, 0,
+    9,  0,  14, 0,  9,  0,  10, 0,  9,  0,  11, 0,  9,  0,  10, 0,  9,  0,  12,
+    0,  9,  0,  10, 0,  9,  0,  11, 0,  9,  0,  10, 0,  9,  0,  13, 0,  9,  0,
+    10, 0,  9,  0,  11, 0,  9,  0,  10, 0,  9,  0,  12, 0,  9,  0,  10, 0,  9,
+    0,  11, 0,  9,  0,  10, 0,  9,  0,  15, 9,  10, 9,  11, 9,  10, 9,  12, 9,
+    10, 9,  11, 9,  10, 9,  13, 9,  10, 9,  11, 9,  10, 9,  12, 9,  10, 9,  11,
+    9,  10, 9,  14, 9,  10, 9,  11, 9,  10, 9,  12, 9,  10, 9,  11, 9,  10, 9,
+    13, 9,  10, 9,  11, 9,  10, 9,  12, 9,  10, 9,  11, 9,  10, 9,  15, 10, 11,
+    10, 12, 10, 11, 10, 13, 10, 11, 10, 12, 10, 11, 10, 14, 10, 11, 10, 12, 10,
+    11, 10, 13, 10, 11, 10, 12, 10, 11, 10, 15, 11, 12, 11, 13, 11, 12, 11, 14,
+    11, 12, 11, 13, 11, 12, 11, 15, 12, 13, 12, 14, 12, 13, 12, 15, 13, 14, 13,
+    15, 14, 15, 15, 16};
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index a7b2888375..9ee4008e73 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -8,37 +8,28 @@
  ********************************************************/
 
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
 
 #include "fast.hpp"
 #include "resize.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T>
-void fast_pyramid(std::vector<unsigned>& feat_pyr,
-                  std::vector<float*>& d_x_pyr,
-                  std::vector<float*>& d_y_pyr,
-                  std::vector<unsigned>& lvl_best,
-                  std::vector<float>& lvl_scl,
-                  std::vector<Array<T>>& img_pyr,
-                  const Array<T>& in,
-                  const float fast_thr,
-                  const unsigned max_feat,
-                  const float scl_fctr,
-                  const unsigned levels,
-                  const unsigned patch_size)
-{
-    dim4 indims = in.dims();
-    unsigned min_side = std::min(indims[0], indims[1]);
+void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
+                  std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
+                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
+                  const Array<T>& in, const float fast_thr,
+                  const unsigned max_feat, const float scl_fctr,
+                  const unsigned levels, const unsigned patch_size) {
+    dim4 indims         = in.dims();
+    unsigned min_side   = std::min(indims[0], indims[1]);
     unsigned max_levels = 0;
-    float scl_sum = 0.f;
+    float scl_sum       = 0.f;
 
     for (unsigned i = 0; i < levels; i++) {
         min_side /= scl_fctr;
@@ -47,22 +38,22 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
         if (min_side < patch_size || max_levels == levels) break;
 
         max_levels++;
-        scl_sum += 1.f / (float)std::pow(scl_fctr,(float)i);
+        scl_sum += 1.f / (float)std::pow(scl_fctr, (float)i);
     }
 
     // Compute number of features to keep for each level
     lvl_best.resize(max_levels);
     lvl_scl.resize(max_levels);
     unsigned feat_sum = 0;
-    for (unsigned i = 0; i < max_levels-1; i++) {
-        float scl = (float)std::pow(scl_fctr,(float)i);
+    for (unsigned i = 0; i < max_levels - 1; i++) {
+        float scl  = (float)std::pow(scl_fctr, (float)i);
         lvl_scl[i] = scl;
 
         lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl[i]);
         feat_sum += lvl_best[i];
     }
-    lvl_scl[max_levels-1] = (float)std::pow(scl_fctr,(float)max_levels-1);
-    lvl_best[max_levels-1] = max_feat - feat_sum;
+    lvl_scl[max_levels - 1]  = (float)std::pow(scl_fctr, (float)max_levels - 1);
+    lvl_best[max_levels - 1] = max_feat - feat_sum;
 
     // Hold multi-scale image pyramids
     static const dim4 dims0;
@@ -75,14 +66,13 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
         if (i == 0) {
             // First level is used in its original size
             img_pyr.push_back(in);
-        }
-        else {
+        } else {
             // Resize previous level image to current level dimensions
             dim4 dims(round(indims[0] / lvl_scl[i]),
                       round(indims[1] / lvl_scl[i]));
 
             img_pyr.push_back(createEmptyArray<T>(dims));
-            resize<T, AF_INTERP_BILINEAR>(img_pyr[i], img_pyr[i-1]);
+            resize<T, AF_INTERP_BILINEAR>(img_pyr[i], img_pyr[i - 1]);
         }
     }
 
@@ -91,9 +81,9 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
     d_y_pyr.resize(max_levels);
 
     for (unsigned i = 0; i < max_levels; i++) {
-        unsigned lvl_feat = 0;
-        float* d_x_feat = NULL;
-        float* d_y_feat = NULL;
+        unsigned lvl_feat   = 0;
+        float* d_x_feat     = NULL;
+        float* d_y_feat     = NULL;
         float* d_score_feat = NULL;
 
         // Round feature size to nearest odd integer
@@ -105,8 +95,8 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
         unsigned edge = ceil(size * sqrt(2.f) / 2.f);
 
         // Detects FAST features
-        fast(&lvl_feat, &d_x_feat, &d_y_feat, &d_score_feat,
-             img_pyr[i], fast_thr, 9, 1, 0.15f, edge);
+        fast(&lvl_feat, &d_x_feat, &d_y_feat, &d_score_feat, img_pyr[i],
+             fast_thr, 9, 1, 0.15f, edge);
 
         // FAST score is not used
         // TODO: should be handled by fast()
@@ -114,17 +104,16 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr,
 
         if (lvl_feat == 0) {
             feat_pyr[i] = 0;
-            d_x_pyr[i] = NULL;
-            d_x_pyr[i] = NULL;
-        }
-        else {
+            d_x_pyr[i]  = NULL;
+            d_x_pyr[i]  = NULL;
+        } else {
             feat_pyr[i] = lvl_feat;
-            d_x_pyr[i] = d_x_feat;
-            d_y_pyr[i] = d_y_feat;
+            d_x_pyr[i]  = d_x_feat;
+            d_y_pyr[i]  = d_y_feat;
         }
     }
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index f030f7ee3e..cfa25ed76a 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -7,34 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <Param.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS = 256;
 
 template<typename To, typename Ti>
-__global__ void packData(
-    Param<To> out,
-    CParam<Ti> in,
-    const int di0_half,
-    const bool odd_di0)
-{
+__global__ void packData(Param<To> out, CParam<Ti> in, const int di0_half,
+                         const bool odd_di0) {
     const int t = blockDim.x * blockIdx.x + threadIdx.x;
 
     const int tMax = out.strides[3] * out.dims[3];
 
-    if (t >= tMax)
-        return;
+    if (t >= tMax) return;
 
     const int do1 = out.dims[1];
     const int do2 = out.dims[2];
@@ -60,16 +53,15 @@ __global__ void packData(
 
     const int iidx1 = ti3 + ti2 + ti1 + ti0;
     const int iidx2 = iidx1 + di0_half;
-    const int oidx = to3*so3 + to2*so2 + to1*so1 + to0;
+    const int oidx  = to3 * so3 + to2 * so2 + to1 * so1 + to0;
 
     if (to0 < di0_half && to1 < di1 && to2 < di2) {
         out.ptr[oidx].x = in.ptr[iidx1];
-        if (ti0 == di0_half-1 && odd_di0)
+        if (ti0 == di0_half - 1 && odd_di0)
             out.ptr[oidx].y = 0;
         else
             out.ptr[oidx].y = in.ptr[iidx2];
-    }
-    else {
+    } else {
         // Pad remaining elements with 0s
         out.ptr[oidx].x = 0;
         out.ptr[oidx].y = 0;
@@ -77,16 +69,12 @@ __global__ void packData(
 }
 
 template<typename To, typename Ti>
-__global__ void padArray(
-    Param<To> out,
-    CParam<Ti> in)
-{
+__global__ void padArray(Param<To> out, CParam<Ti> in) {
     const int t = blockDim.x * blockIdx.x + threadIdx.x;
 
     const int tMax = out.strides[3] * out.dims[3];
 
-    if (t >= tMax)
-        return;
+    if (t >= tMax) return;
 
     const int do1 = out.dims[1];
     const int do2 = out.dims[2];
@@ -114,14 +102,13 @@ __global__ void padArray(
 
     const int iidx = ti3 + ti2 + ti1 + ti0;
 
-    const int t2 = to3*so3 + to2*so2 + to1*so1 + to0;
+    const int t2 = to3 * so3 + to2 * so2 + to1 * so1 + to0;
 
     if (to0 < di0 && to1 < di1 && to2 < di2 && to3 < di3) {
         // Copy input elements to real elements, set imaginary elements to 0
         out.ptr[t2].x = in.ptr[iidx];
         out.ptr[t2].y = 0;
-    }
-    else {
+    } else {
         // Pad remaining of the matrix to 0s
         out.ptr[t2].x = 0;
         out.ptr[t2].y = 0;
@@ -129,16 +116,11 @@ __global__ void padArray(
 }
 
 template<typename convT, AF_BATCH_KIND kind>
-__global__ void complexMultiply(
-    Param<convT> out,
-    Param<convT> in1,
-    Param<convT> in2,
-    const int nelem)
-{
+__global__ void complexMultiply(Param<convT> out, Param<convT> in1,
+                                Param<convT> in2, const int nelem) {
     const int t = blockDim.x * blockIdx.x + threadIdx.x;
 
-    if (t >= nelem)
-        return;
+    if (t >= nelem) return;
 
     if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
         // Complex multiply each signal to equivalent filter
@@ -147,10 +129,9 @@ __global__ void complexMultiply(
         convT c1 = in1.ptr[ridx];
         convT c2 = in2.ptr[ridx];
 
-        out.ptr[ridx].x = c1.x*c2.x - c1.y*c2.y;
-        out.ptr[ridx].y = c1.x*c2.y + c1.y*c2.x;
-    }
-    else if (kind == AF_BATCH_LHS) {
+        out.ptr[ridx].x = c1.x * c2.x - c1.y * c2.y;
+        out.ptr[ridx].y = c1.x * c2.y + c1.y * c2.x;
+    } else if (kind == AF_BATCH_LHS) {
         // Complex multiply all signals to filter
         const int ridx1 = t;
         const int ridx2 = t % (in2.strides[3] * in2.dims[3]);
@@ -158,10 +139,9 @@ __global__ void complexMultiply(
         convT c1 = in1.ptr[ridx1];
         convT c2 = in2.ptr[ridx2];
 
-        out.ptr[ridx1].x = c1.x*c2.x - c1.y*c2.y;
-        out.ptr[ridx1].y = c1.x*c2.y + c1.y*c2.x;
-    }
-    else if (kind == AF_BATCH_RHS) {
+        out.ptr[ridx1].x = c1.x * c2.x - c1.y * c2.y;
+        out.ptr[ridx1].y = c1.x * c2.y + c1.y * c2.x;
+    } else if (kind == AF_BATCH_RHS) {
         // Complex multiply signal to all filters
         const int ridx1 = t % (in1.strides[3] * in1.dims[3]);
         const int ridx2 = t;
@@ -169,26 +149,20 @@ __global__ void complexMultiply(
         convT c1 = in1.ptr[ridx1];
         convT c2 = in2.ptr[ridx2];
 
-        out.ptr[ridx2].x = c1.x*c2.x - c1.y*c2.y;
-        out.ptr[ridx2].y = c1.x*c2.y + c1.y*c2.x;
+        out.ptr[ridx2].x = c1.x * c2.x - c1.y * c2.y;
+        out.ptr[ridx2].y = c1.x * c2.y + c1.y * c2.x;
     }
 }
 
 template<typename To, typename Ti, bool expand, bool roundOut>
-__global__ void reorderOutput(
-    Param<To> out,
-    Param<Ti> in,
-    CParam<To> filter,
-    const int half_di0,
-    const int baseDim,
-    const int fftScale)
-{
+__global__ void reorderOutput(Param<To> out, Param<Ti> in, CParam<To> filter,
+                              const int half_di0, const int baseDim,
+                              const int fftScale) {
     const int t = blockIdx.x * blockDim.x + threadIdx.x;
 
     const int tMax = out.strides[3] * out.dims[3];
 
-    if (t >= tMax)
-        return;
+    if (t >= tMax) return;
 
     const int do1 = out.dims[1];
     const int do2 = out.dims[2];
@@ -205,7 +179,7 @@ __global__ void reorderOutput(
     const int to2 = (t / so2) % do2;
     const int to3 = (t / so3);
 
-    int oidx = to3*so3 + to2*so2 + to1*so1 + to0;
+    int oidx = to3 * so3 + to2 * so2 + to1 * so1 + to0;
 
     int ti0, ti1, ti2, ti3;
     if (expand) {
@@ -213,11 +187,10 @@ __global__ void reorderOutput(
         ti1 = to1 * si1;
         ti2 = to2 * si2;
         ti3 = to3 * si3;
-    }
-    else {
-        ti0 = to0 + filter.dims[0]/2;
-        ti1 = (to1 + (baseDim > 1)*(filter.dims[1]/2)) * si1;
-        ti2 = (to2 + (baseDim > 2)*(filter.dims[2]/2)) * si2;
+    } else {
+        ti0 = to0 + filter.dims[0] / 2;
+        ti1 = (to1 + (baseDim > 1) * (filter.dims[1] / 2)) * si1;
+        ti2 = (to2 + (baseDim > 2) * (filter.dims[2] / 2)) * si2;
         ti3 = to3 * si3;
     }
 
@@ -230,17 +203,17 @@ __global__ void reorderOutput(
             out.ptr[oidx] = (To)roundf(in.ptr[iidx].x / fftScale);
         else
             out.ptr[oidx] = (To)(in.ptr[iidx].x / fftScale);
-    }
-    else if (ti0 < half_di0 + filter.dims[0] - 1) {
+    } else if (ti0 < half_di0 + filter.dims[0] - 1) {
         // Add signal and filter elements to central part
         int iidx1 = ti3 + ti2 + ti1 + ti0;
         int iidx2 = ti3 + ti2 + ti1 + (ti0 - half_di0);
         if (roundOut)
-            out.ptr[oidx] = (To)roundf((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
+            out.ptr[oidx] =
+                (To)roundf((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
         else
-            out.ptr[oidx] = (To)((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
-    }
-    else {
+            out.ptr[oidx] =
+                (To)((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
+    } else {
         // Copy bottom elements
         const int iidx = ti3 + ti2 + ti1 + (ti0 - half_di0);
         if (roundOut)
@@ -251,14 +224,11 @@ __global__ void reorderOutput(
 }
 
 template<typename convT, typename T>
-void packDataHelper(Param<convT> sig_packed,
-                    Param<convT> filter_packed,
-                    CParam<T> sig,
-                    CParam<T> filter)
-{
+void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
+                    CParam<T> sig, CParam<T> filter) {
     dim_t *sd = sig.dims;
 
-    int sig_packed_elem = 1;
+    int sig_packed_elem    = 1;
     int filter_packed_elem = 1;
 
     for (int i = 0; i < 4; i++) {
@@ -267,7 +237,7 @@ void packDataHelper(Param<convT> sig_packed,
     }
 
     // Number of packed complex elements in dimension 0
-    int sig_half_d0 = divup(sd[0], 2);
+    int sig_half_d0      = divup(sd[0], 2);
     bool sig_half_d0_odd = (sd[0] % 2 == 1);
 
     dim3 threads(THREADS);
@@ -275,7 +245,8 @@ void packDataHelper(Param<convT> sig_packed,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    CUDA_LAUNCH((packData<convT, T>), blocks, threads, sig_packed, sig, sig_half_d0, sig_half_d0_odd);
+    CUDA_LAUNCH((packData<convT, T>), blocks, threads, sig_packed, sig,
+                sig_half_d0, sig_half_d0_odd);
     POST_LAUNCH_CHECK();
 
     blocks = dim3(divup(filter_packed_elem, threads.x));
@@ -287,11 +258,9 @@ void packDataHelper(Param<convT> sig_packed,
 
 // TODO(umar): This needs a better name
 template<typename T, typename convT>
-void complexMultiplyHelper(Param<convT> sig_packed,
-                           Param<convT> filter_packed,
-                           AF_BATCH_KIND kind)
-{
-    int sig_packed_elem = 1;
+void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
+                           AF_BATCH_KIND kind) {
+    int sig_packed_elem    = 1;
     int filter_packed_elem = 1;
 
     for (int i = 0; i < 4; i++) {
@@ -302,15 +271,16 @@ void complexMultiplyHelper(Param<convT> sig_packed,
     dim3 threads(THREADS);
     dim3 blocks(divup(sig_packed_elem / 2, threads.x));
 
-    int mul_elem = (sig_packed_elem < filter_packed_elem) ?
-                        filter_packed_elem : sig_packed_elem;
+    int mul_elem = (sig_packed_elem < filter_packed_elem) ? filter_packed_elem
+                                                          : sig_packed_elem;
     blocks = dim3(divup(mul_elem, threads.x));
 
     // Multiply filter and signal FFT arrays
-    switch(kind) {
+    switch (kind) {
         case AF_BATCH_NONE:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_NONE>), blocks, threads,
-                    sig_packed, sig_packed, filter_packed, mul_elem);
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_NONE>), blocks,
+                        threads, sig_packed, sig_packed, filter_packed,
+                        mul_elem);
             break;
         case AF_BATCH_LHS:
             CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_LHS>), blocks, threads,
@@ -318,31 +288,27 @@ void complexMultiplyHelper(Param<convT> sig_packed,
             break;
         case AF_BATCH_RHS:
             CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_RHS>), blocks, threads,
-                    filter_packed, sig_packed, filter_packed, mul_elem);
+                        filter_packed, sig_packed, filter_packed, mul_elem);
             break;
         case AF_BATCH_SAME:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_SAME>), blocks, threads,
-                    sig_packed, sig_packed, filter_packed, mul_elem);
+            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_SAME>), blocks,
+                        threads, sig_packed, sig_packed, filter_packed,
+                        mul_elem);
             break;
         case AF_BATCH_UNSUPPORTED:
-        default:
-            break;
+        default: break;
     }
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, typename convT, bool roundOut, int baseDim, bool expand>
-void reorderOutputHelper(Param<T> out,
-                         Param<convT> packed,
-                         CParam<T> sig,
-                         CParam<T> filter)
-{
-    dim_t *sd = sig.dims;
+void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
+                         CParam<T> filter) {
+    dim_t *sd    = sig.dims;
     int fftScale = 1;
 
     // Calculate the scale by which to divide cuFFT results
-    for (int k = 0; k < baseDim; k++)
-        fftScale *= packed.dims[k];
+    for (int k = 0; k < baseDim; k++) fftScale *= packed.dims[k];
 
     // Number of packed complex elements in dimension 0
     int sig_half_d0 = divup(sd[0], 2);
@@ -351,10 +317,10 @@ void reorderOutputHelper(Param<T> out,
     dim3 blocks(divup(out.strides[3] * out.dims[3], threads.x));
 
     CUDA_LAUNCH((reorderOutput<T, convT, expand, roundOut>), blocks, threads,
-            out, packed, filter, sig_half_d0, baseDim, fftScale);
+                out, packed, filter, sig_half_d0, baseDim, fftScale);
     POST_LAUNCH_CHECK();
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index c303a40799..a0a6a7299d 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -7,113 +7,112 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX = 32;
+static const unsigned TY = 8;
 
 #define sidx(y, x) scratch[y + 1][x + 1]
 
-        template<typename T>
-        __global__
-        void gradient_kernel(Param<T> grad0, Param<T> grad1, CParam<T> in,
-                             const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int idz =  blockIdx.x / blocksPerMatX;
-            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-            const int blockIdx_x =  blockIdx.x - idz * blocksPerMatX;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
-
-            const int xB = blockIdx_x * blockDim.x;
-            const int yB = blockIdx_y * blockDim.y;
-
-            const int idx = threadIdx.x + xB;
-            const int idy = threadIdx.y + yB;
-
-            bool cond = (idx >= in.dims[0] || idy >= in.dims[1] ||
-                         idz >= in.dims[2] || idw >= in.dims[3]);
-
-            int xmax = (TX > (in.dims[0] - xB)) ? (in.dims[0] - xB) : TX;
-            int ymax = (TY > (in.dims[1] - yB)) ? (in.dims[1] - yB) : TY;
-
-            int iIdx = idw * in.strides[3] + idz * in.strides[2]
-                          + idy * in.strides[1] + idx;
-
-            int g0dx = idw * grad0.strides[3] + idz * grad0.strides[2]
-                          + idy * grad0.strides[1] + idx;
-
-            int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2]
-                          + idy * grad1.strides[1] + idx;
-
-            __shared__ T scratch[TY + 2][TX + 2];
-
-            // Multipliers - 0.5 for interior, 1 for edge cases
-            float xf = 0.5 * (1 + (idx == 0 || idx >= (in.dims[0] - 1)));
-            float yf = 0.5 * (1 + (idy == 0 || idy >= (in.dims[1] - 1)));
-
-            // Copy data to scratch space
-            sidx(threadIdx.y, threadIdx.x) = cond ? scalar<T>(0) : in.ptr[iIdx];
-
-            __syncthreads();
-
-            // Copy buffer zone data. Corner (0,0) etc, are not used.
-            // Cols
-            if(threadIdx.y == 0) {
-                // Y-1
-                sidx(-1, threadIdx.x) = (cond || idy == 0) ?
-                                        sidx(0, threadIdx.x) : in.ptr[iIdx - in.strides[1]];
-                sidx(ymax, threadIdx.x) = (cond || (idy + ymax) >= in.dims[1]) ?
-                                        sidx(ymax - 1, threadIdx.x) : in.ptr[iIdx + ymax * in.strides[1]];
-            }
-            // Rows
-            if(threadIdx.x == 0) {
-                sidx(threadIdx.y, -1) = (cond || idx == 0) ?
-                                        sidx(threadIdx.y, 0) : in.ptr[iIdx - 1];
-                sidx(threadIdx.y, xmax) = (cond || (idx + xmax) >= in.dims[0]) ?
-                                        sidx(threadIdx.y, xmax - 1) : in.ptr[iIdx + xmax];
-            }
-
-            __syncthreads();
-
-            if (cond) return;
-
-            grad0.ptr[g0dx] = xf * (sidx(threadIdx.y, threadIdx.x + 1)
-                                 -  sidx(threadIdx.y, threadIdx.x - 1));
-            grad1.ptr[g1dx] = yf * (sidx(threadIdx.y + 1, threadIdx.x)
-                                 -  sidx(threadIdx.y - 1, threadIdx.x));
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in)
-        {
-            dim3 threads(TX, TY, 1);
-
-            int blocksPerMatX = divup(in.dims[0], TX);
-            int blocksPerMatY = divup(in.dims[1], TY);
-            dim3 blocks(blocksPerMatX * in.dims[2],
-                        blocksPerMatY * in.dims[3],
-                        1);
-
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
-
-            CUDA_LAUNCH((gradient_kernel<T>), blocks, threads,
-                    grad0, grad1, in, blocksPerMatX, blocksPerMatY);
-            POST_LAUNCH_CHECK();
-        }
+template<typename T>
+__global__ void gradient_kernel(Param<T> grad0, Param<T> grad1, CParam<T> in,
+                                const int blocksPerMatX,
+                                const int blocksPerMatY) {
+    const int idz = blockIdx.x / blocksPerMatX;
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - idz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
+
+    const int xB = blockIdx_x * blockDim.x;
+    const int yB = blockIdx_y * blockDim.y;
+
+    const int idx = threadIdx.x + xB;
+    const int idy = threadIdx.y + yB;
+
+    bool cond = (idx >= in.dims[0] || idy >= in.dims[1] || idz >= in.dims[2] ||
+                 idw >= in.dims[3]);
+
+    int xmax = (TX > (in.dims[0] - xB)) ? (in.dims[0] - xB) : TX;
+    int ymax = (TY > (in.dims[1] - yB)) ? (in.dims[1] - yB) : TY;
+
+    int iIdx =
+        idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
+
+    int g0dx = idw * grad0.strides[3] + idz * grad0.strides[2] +
+               idy * grad0.strides[1] + idx;
+
+    int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2] +
+               idy * grad1.strides[1] + idx;
+
+    __shared__ T scratch[TY + 2][TX + 2];
+
+    // Multipliers - 0.5 for interior, 1 for edge cases
+    float xf = 0.5 * (1 + (idx == 0 || idx >= (in.dims[0] - 1)));
+    float yf = 0.5 * (1 + (idy == 0 || idy >= (in.dims[1] - 1)));
+
+    // Copy data to scratch space
+    sidx(threadIdx.y, threadIdx.x) = cond ? scalar<T>(0) : in.ptr[iIdx];
+
+    __syncthreads();
+
+    // Copy buffer zone data. Corner (0,0) etc, are not used.
+    // Cols
+    if (threadIdx.y == 0) {
+        // Y-1
+        sidx(-1, threadIdx.x) = (cond || idy == 0)
+                                    ? sidx(0, threadIdx.x)
+                                    : in.ptr[iIdx - in.strides[1]];
+        sidx(ymax, threadIdx.x) = (cond || (idy + ymax) >= in.dims[1])
+                                      ? sidx(ymax - 1, threadIdx.x)
+                                      : in.ptr[iIdx + ymax * in.strides[1]];
     }
+    // Rows
+    if (threadIdx.x == 0) {
+        sidx(threadIdx.y, -1) =
+            (cond || idx == 0) ? sidx(threadIdx.y, 0) : in.ptr[iIdx - 1];
+        sidx(threadIdx.y, xmax) = (cond || (idx + xmax) >= in.dims[0])
+                                      ? sidx(threadIdx.y, xmax - 1)
+                                      : in.ptr[iIdx + xmax];
+    }
+
+    __syncthreads();
+
+    if (cond) return;
+
+    grad0.ptr[g0dx] = xf * (sidx(threadIdx.y, threadIdx.x + 1) -
+                            sidx(threadIdx.y, threadIdx.x - 1));
+    grad1.ptr[g1dx] = yf * (sidx(threadIdx.y + 1, threadIdx.x) -
+                            sidx(threadIdx.y - 1, threadIdx.x));
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
+    dim3 threads(TX, TY, 1);
+
+    int blocksPerMatX = divup(in.dims[0], TX);
+    int blocksPerMatY = divup(in.dims[1], TY);
+    dim3 blocks(blocksPerMatX * in.dims[2], blocksPerMatY * in.dims[3], 1);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((gradient_kernel<T>), blocks, threads, grad0, grad1, in,
+                blocksPerMatX, blocksPerMatY);
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 58fbec280b..21badbb305 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -7,74 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/constants.h>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
+#include <af/constants.h>
 
 #include "config.hpp"
 #include "convolve.hpp"
 #include "gradient.hpp"
-#include "sort_by_key.hpp"
 #include "range.hpp"
+#include "sort_by_key.hpp"
 
 #include <vector>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const unsigned BLOCK_SIZE = 16;
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * af::Pi * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * af::Pi * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
 // max_val()
 // Returns max of x and y
-inline __device__ int max_val(const int x, const int y)
-{
-    return max(x, y);
-}
-inline __device__ unsigned max_val(const unsigned x, const unsigned y)
-{
+inline __device__ int max_val(const int x, const int y) { return max(x, y); }
+inline __device__ unsigned max_val(const unsigned x, const unsigned y) {
     return max(x, y);
 }
-inline __device__ float max_val(const float x, const float y)
-{
+inline __device__ float max_val(const float x, const float y) {
     return fmax(x, y);
 }
-inline __device__ double max_val(const double x, const double y)
-{
+inline __device__ double max_val(const double x, const double y) {
     return fmax(x, y);
 }
 
 template<typename T>
-__global__ void second_order_deriv(
-    T* ixx_out,
-    T* ixy_out,
-    T* iyy_out,
-    const unsigned in_len,
-    const T* ix_in,
-    const T* iy_in)
-{
+__global__ void second_order_deriv(T* ixx_out, T* ixy_out, T* iyy_out,
+                                   const unsigned in_len, const T* ix_in,
+                                   const T* iy_in) {
     const unsigned x = blockDim.x * blockIdx.x + threadIdx.x;
 
     if (x < in_len) {
@@ -85,16 +70,10 @@ __global__ void second_order_deriv(
 }
 
 template<typename T>
-__global__ void harris_responses(
-    T* resp_out,
-    const unsigned idim0,
-    const unsigned idim1,
-    const T* ixx_in,
-    const T* ixy_in,
-    const T* iyy_in,
-    const float k_thr,
-    const unsigned border_len)
-{
+__global__ void harris_responses(T* resp_out, const unsigned idim0,
+                                 const unsigned idim1, const T* ixx_in,
+                                 const T* ixy_in, const T* iyy_in,
+                                 const float k_thr, const unsigned border_len) {
     const unsigned r = border_len;
 
     const unsigned x = blockDim.x * blockIdx.x + threadIdx.x + r;
@@ -104,27 +83,20 @@ __global__ void harris_responses(
         const unsigned idx = x * idim0 + y;
 
         // Calculates matrix trace and determinant
-        T tr = ixx_in[idx] + iyy_in[idx];
+        T tr  = ixx_in[idx] + iyy_in[idx];
         T det = ixx_in[idx] * iyy_in[idx] - ixy_in[idx] * ixy_in[idx];
 
         // Calculates local Harris response
-        resp_out[idx] = det - k_thr * (tr*tr);
+        resp_out[idx] = det - k_thr * (tr * tr);
     }
 }
 
 template<typename T>
-__global__ void non_maximal(
-    float* x_out,
-    float* y_out,
-    float* resp_out,
-    unsigned* count,
-    const unsigned idim0,
-    const unsigned idim1,
-    const T* resp_in,
-    const float min_resp,
-    const unsigned border_len,
-    const unsigned max_corners)
-{
+__global__ void non_maximal(float* x_out, float* y_out, float* resp_out,
+                            unsigned* count, const unsigned idim0,
+                            const unsigned idim1, const T* resp_in,
+                            const float min_resp, const unsigned border_len,
+                            const unsigned max_corners) {
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
 
@@ -136,13 +108,14 @@ __global__ void non_maximal(
 
         // Find maximum neighborhood response
         T max_v;
-        max_v = max_val(resp_in[(x-1) * idim0 + y-1], resp_in[x * idim0 + y-1]);
-        max_v = max_val(max_v, resp_in[(x+1) * idim0 + y-1]);
-        max_v = max_val(max_v, resp_in[(x-1) * idim0 + y  ]);
-        max_v = max_val(max_v, resp_in[(x+1) * idim0 + y  ]);
-        max_v = max_val(max_v, resp_in[(x-1) * idim0 + y+1]);
-        max_v = max_val(max_v, resp_in[(x)   * idim0 + y+1]);
-        max_v = max_val(max_v, resp_in[(x+1) * idim0 + y+1]);
+        max_v = max_val(resp_in[(x - 1) * idim0 + y - 1],
+                        resp_in[x * idim0 + y - 1]);
+        max_v = max_val(max_v, resp_in[(x + 1) * idim0 + y - 1]);
+        max_v = max_val(max_v, resp_in[(x - 1) * idim0 + y]);
+        max_v = max_val(max_v, resp_in[(x + 1) * idim0 + y]);
+        max_v = max_val(max_v, resp_in[(x - 1) * idim0 + y + 1]);
+        max_v = max_val(max_v, resp_in[(x)*idim0 + y + 1]);
+        max_v = max_val(max_v, resp_in[(x + 1) * idim0 + y + 1]);
 
         // Stores corner to {x,y,resp}_out if it's response is maximum compared
         // to its 8-neighborhood and greater or equal minimum response
@@ -157,69 +130,53 @@ __global__ void non_maximal(
     }
 }
 
-__global__ void keep_corners(
-    float* x_out,
-    float* y_out,
-    float* resp_out,
-    const float* x_in,
-    const float* y_in,
-    const float* resp_in,
-    const unsigned* resp_idx,
-    const unsigned n_corners)
-{
+__global__ void keep_corners(float* x_out, float* y_out, float* resp_out,
+                             const float* x_in, const float* y_in,
+                             const float* resp_in, const unsigned* resp_idx,
+                             const unsigned n_corners) {
     const unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
 
     // Keep only the first n_feat features
     if (f < n_corners) {
-        x_out[f] = x_in[(unsigned)resp_idx[f]];
-        y_out[f] = y_in[(unsigned)resp_idx[f]];
+        x_out[f]    = x_in[(unsigned)resp_idx[f]];
+        y_out[f]    = y_in[(unsigned)resp_idx[f]];
         resp_out[f] = resp_in[f];
     }
 }
 
-int compare(const void* a, const void* b)
-{
-    return *(float*)a > *(float*)b;
-}
+int compare(const void* a, const void* b) { return *(float*)a > *(float*)b; }
 
 template<typename T, typename convAccT>
-void harris(unsigned* corners_out,
-            float** x_out,
-            float** y_out,
-            float** resp_out,
-            CParam<T> in,
-            const unsigned max_corners,
-            const float min_response,
-            const float sigma,
-            const unsigned filter_len,
-            const float k_thr)
-{
+void harris(unsigned* corners_out, float** x_out, float** y_out,
+            float** resp_out, CParam<T> in, const unsigned max_corners,
+            const float min_response, const float sigma,
+            const unsigned filter_len, const float k_thr) {
     // Window filter
     std::vector<convAccT> h_filter(filter_len);
     // Decide between rectangular or circular filter
     if (sigma < 0.5f) {
         for (unsigned i = 0; i < filter_len; i++)
             h_filter[i] = (T)1.f / (filter_len);
-    }
-    else {
+    } else {
         gaussian1D<convAccT>(h_filter.data(), (int)filter_len, sigma);
     }
 
     // Copy filter to device object
     Param<convAccT> filter;
-    filter.dims[0] = filter_len;
+    filter.dims[0]    = filter_len;
     filter.strides[0] = 1;
 
     for (int k = 1; k < 4; k++) {
-        filter.dims[k] = 1;
+        filter.dims[k]    = 1;
         filter.strides[k] = filter.dims[k - 1] * filter.strides[k - 1];
     }
 
-    int filter_elem = filter.strides[3] * filter.dims[3];
+    int filter_elem   = filter.strides[3] * filter.dims[3];
     auto filter_alloc = memAlloc<convAccT>(filter_elem);
-    filter.ptr = filter_alloc.get();
-    CUDA_CHECK(cudaMemcpyAsync(filter.ptr, h_filter.data(), filter_elem * sizeof(convAccT),
-                cudaMemcpyHostToDevice, cuda::getActiveStream()));
+    filter.ptr        = filter_alloc.get();
+    CUDA_CHECK(cudaMemcpyAsync(
+        filter.ptr, h_filter.data(), filter_elem * sizeof(convAccT),
+        cudaMemcpyHostToDevice, cuda::getActiveStream()));
 
     const unsigned border_len = filter_len / 2 + 1;
 
@@ -230,8 +187,8 @@ void harris(unsigned* corners_out,
     }
     auto ix_alloc = memAlloc<T>(ix.dims[3] * ix.strides[3]);
     auto iy_alloc = memAlloc<T>(iy.dims[3] * iy.strides[3]);
-    ix.ptr = ix_alloc.get();
-    iy.ptr = iy_alloc.get();
+    ix.ptr        = ix_alloc.get();
+    iy.ptr        = iy_alloc.get();
 
     // Compute first-order derivatives as gradients
     gradient<T>(iy, ix, in);
@@ -242,28 +199,28 @@ void harris(unsigned* corners_out,
         ixx.dims[i] = ixy.dims[i] = iyy.dims[i] = in.dims[i];
         ixx_tmp.dims[i] = ixy_tmp.dims[i] = iyy_tmp.dims[i] = in.dims[i];
         ixx.strides[i] = ixy.strides[i] = iyy.strides[i] = in.strides[i];
-        ixx_tmp.strides[i] = ixy_tmp.strides[i] = iyy_tmp.strides[i] = in.strides[i];
+        ixx_tmp.strides[i] = ixy_tmp.strides[i] = iyy_tmp.strides[i] =
+            in.strides[i];
     }
     auto ixx_alloc = memAlloc<T>(ixx.dims[3] * ixx.strides[3]);
     auto ixy_alloc = memAlloc<T>(ixy.dims[3] * ixy.strides[3]);
     auto iyy_alloc = memAlloc<T>(iyy.dims[3] * iyy.strides[3]);
-    ixx.ptr = ixx_alloc.get();
-    ixy.ptr = ixy_alloc.get();
-    iyy.ptr = iyy_alloc.get();
+    ixx.ptr        = ixx_alloc.get();
+    ixy.ptr        = ixy_alloc.get();
+    iyy.ptr        = iyy_alloc.get();
 
     // Compute second-order derivatives
     dim3 threads(THREADS_PER_BLOCK, 1);
     dim3 blocks(divup(in.dims[3] * in.strides[3], threads.x), 1);
-    CUDA_LAUNCH((second_order_deriv<T>), blocks, threads,
-            ixx.ptr, ixy.ptr, iyy.ptr,
-            in.dims[3] * in.strides[3], ix.ptr, iy.ptr);
+    CUDA_LAUNCH((second_order_deriv<T>), blocks, threads, ixx.ptr, ixy.ptr,
+                iyy.ptr, in.dims[3] * in.strides[3], ix.ptr, iy.ptr);
 
     auto ixx_tmp_alloc = memAlloc<T>(ixx_tmp.dims[3] * ixx_tmp.strides[3]);
     auto ixy_tmp_alloc = memAlloc<T>(ixy_tmp.dims[3] * ixy_tmp.strides[3]);
     auto iyy_tmp_alloc = memAlloc<T>(iyy_tmp.dims[3] * iyy_tmp.strides[3]);
-    ixx_tmp.ptr = ixx_tmp_alloc.get();
-    ixy_tmp.ptr = ixy_tmp_alloc.get();
-    iyy_tmp.ptr = iyy_tmp_alloc.get();
+    ixx_tmp.ptr        = ixx_tmp_alloc.get();
+    ixy_tmp.ptr        = ixy_tmp_alloc.get();
+    iyy_tmp.ptr        = iyy_tmp_alloc.get();
 
     // Convolve second-order derivatives with proper window filter
     convolve2<T, convAccT, 0, false>(ixx_tmp, CParam<T>(ixx), filter);
@@ -279,38 +236,40 @@ void harris(unsigned* corners_out,
 
     auto d_corners_found = memAlloc<unsigned>(1);
     CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
-                cuda::getActiveStream()));
+                               cuda::getActiveStream()));
 
-    auto d_x_corners = memAlloc<float>(corner_lim);
-    auto d_y_corners = memAlloc<float>(corner_lim);
+    auto d_x_corners    = memAlloc<float>(corner_lim);
+    auto d_y_corners    = memAlloc<float>(corner_lim);
     auto d_resp_corners = memAlloc<float>(corner_lim);
 
     auto d_responses = memAlloc<T>(in.dims[3] * in.strides[3]);
 
     // Calculate Harris responses for all pixels
     threads = dim3(BLOCK_SIZE, BLOCK_SIZE);
-    blocks = dim3(divup(in.dims[1] - border_len*2, threads.x),
-                  divup(in.dims[0] - border_len*2, threads.y));
-    CUDA_LAUNCH((harris_responses<T>), blocks, threads,
-            d_responses.get(), in.dims[0], in.dims[1],
-            ixx.ptr, ixy.ptr, iyy.ptr, k_thr, border_len);
+    blocks  = dim3(divup(in.dims[1] - border_len * 2, threads.x),
+                  divup(in.dims[0] - border_len * 2, threads.y));
+    CUDA_LAUNCH((harris_responses<T>), blocks, threads, d_responses.get(),
+                in.dims[0], in.dims[1], ixx.ptr, ixy.ptr, iyy.ptr, k_thr,
+                border_len);
 
     const float min_r = (max_corners > 0) ? 0.f : min_response;
 
     // Perform non-maximal suppression
-    CUDA_LAUNCH((non_maximal<T>), blocks, threads,
-                d_x_corners.get(), d_y_corners.get(), d_resp_corners.get(), d_corners_found.get(),
-                in.dims[0], in.dims[1], d_responses.get(), min_r, border_len, corner_lim);
+    CUDA_LAUNCH((non_maximal<T>), blocks, threads, d_x_corners.get(),
+                d_y_corners.get(), d_resp_corners.get(), d_corners_found.get(),
+                in.dims[0], in.dims[1], d_responses.get(), min_r, border_len,
+                corner_lim);
 
     unsigned corners_found = 0;
-    CUDA_CHECK(cudaMemcpyAsync(&corners_found, d_corners_found.get(), sizeof(unsigned),
-                cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+    CUDA_CHECK(cudaMemcpyAsync(&corners_found, d_corners_found.get(),
+                               sizeof(unsigned), cudaMemcpyDeviceToHost,
+                               cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-    *corners_out = min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
+    *corners_out =
+        min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
 
-    if (*corners_out == 0)
-        return;
+    if (*corners_out == 0) return;
 
     if (max_corners > 0 && corners_found > *corners_out) {
         Param<float> harris_responses;
@@ -321,67 +280,70 @@ void harris(unsigned* corners_out,
 
         for (int k = 1; k < 4; k++) {
             harris_responses.dims[k] = 1;
-            harris_responses.strides[k] = harris_responses.dims[k - 1] * harris_responses.strides[k - 1];
+            harris_responses.strides[k] =
+                harris_responses.dims[k - 1] * harris_responses.strides[k - 1];
             harris_idx.dims[k] = 1;
-            harris_idx.strides[k] = harris_idx.dims[k - 1] * harris_idx.strides[k - 1];
+            harris_idx.strides[k] =
+                harris_idx.dims[k - 1] * harris_idx.strides[k - 1];
         }
 
         int sort_elem = harris_responses.strides[3] * harris_responses.dims[3];
         harris_responses.ptr = d_resp_corners.get();
         // Create indices using range
         auto harris_idx_alloc = memAlloc<unsigned>(sort_elem);
-        harris_idx.ptr = harris_idx_alloc.get();
+        harris_idx.ptr        = harris_idx_alloc.get();
         kernel::range<uint>(harris_idx, 0);
 
         // Sort Harris responses
         sort0ByKey<float, uint>(harris_responses, harris_idx, false);
 
-        auto x_out_alloc = memAlloc<float>(*corners_out);
-        auto y_out_alloc = memAlloc<float>(*corners_out);
+        auto x_out_alloc    = memAlloc<float>(*corners_out);
+        auto y_out_alloc    = memAlloc<float>(*corners_out);
         auto resp_out_alloc = memAlloc<float>(*corners_out);
-        *x_out = x_out_alloc.get();
-        *y_out = y_out_alloc.get();
-        *resp_out = resp_out_alloc.get();
+        *x_out              = x_out_alloc.get();
+        *y_out              = y_out_alloc.get();
+        *resp_out           = resp_out_alloc.get();
 
         // Keep only the first corners_to_keep corners with higher Harris
         // responses
         threads = dim3(THREADS_PER_BLOCK, 1);
-        blocks = dim3(divup(*corners_out, threads.x), 1);
-        CUDA_LAUNCH(keep_corners, blocks, threads,
-                *x_out, *y_out, *resp_out, d_x_corners.get(), d_y_corners.get(),
-                harris_responses.ptr, harris_idx.ptr, *corners_out);
+        blocks  = dim3(divup(*corners_out, threads.x), 1);
+        CUDA_LAUNCH(keep_corners, blocks, threads, *x_out, *y_out, *resp_out,
+                    d_x_corners.get(), d_y_corners.get(), harris_responses.ptr,
+                    harris_idx.ptr, *corners_out);
 
         x_out_alloc.release();
         y_out_alloc.release();
         resp_out_alloc.release();
-    }
-    else if (max_corners == 0 && corners_found < corner_lim) {
-        auto x_out_alloc = memAlloc<float>(*corners_out);
-        auto y_out_alloc = memAlloc<float>(*corners_out);
+    } else if (max_corners == 0 && corners_found < corner_lim) {
+        auto x_out_alloc    = memAlloc<float>(*corners_out);
+        auto y_out_alloc    = memAlloc<float>(*corners_out);
         auto resp_out_alloc = memAlloc<float>(*corners_out);
-        *x_out = x_out_alloc.get();
-        *y_out = y_out_alloc.get();
-        *resp_out = resp_out_alloc.get();
-
-        CUDA_CHECK(cudaMemcpyAsync(*x_out, d_x_corners.get(), *corners_out * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*y_out, d_y_corners.get(), *corners_out * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*resp_out, d_resp_corners.get(), *corners_out * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        *x_out              = x_out_alloc.get();
+        *y_out              = y_out_alloc.get();
+        *resp_out           = resp_out_alloc.get();
+
+        CUDA_CHECK(cudaMemcpyAsync(
+            *x_out, d_x_corners.get(), *corners_out * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *y_out, d_y_corners.get(), *corners_out * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *resp_out, d_resp_corners.get(), *corners_out * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         x_out_alloc.release();
         y_out_alloc.release();
-        resp_out_alloc.release(); 
-    }
-    else {
-        *x_out = d_x_corners.release();
-        *y_out = d_y_corners.release();
+        resp_out_alloc.release();
+    } else {
+        *x_out    = d_x_corners.release();
+        *y_out    = d_y_corners.release();
         *resp_out = d_resp_corners.release();
     }
     filter_alloc.release();
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 8d2a919558..40d91cfc21 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -12,53 +12,51 @@
 #include <debug_cuda.hpp>
 #include "shared.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 constexpr int MAX_BINS  = 4000;
-constexpr int THREADS_X =  256;
-constexpr int THRD_LOAD =   16;
+constexpr int THREADS_X = 256;
+constexpr int THRD_LOAD = 16;
 
-__forceinline__ __device__ int minimum(int a, int b)
-{
-  return (a < b ? a : b);
-}
+__forceinline__ __device__ int minimum(int a, int b) { return (a < b ? a : b); }
 
 template<typename inType, typename outType, bool isLinear>
-static __global__
-void histogramKernel(Param<outType> out, CParam<inType> in,
-                     int len, int nbins, float minval, float maxval, int nBBS)
-{
+static __global__ void histogramKernel(Param<outType> out, CParam<inType> in,
+                                       int len, int nbins, float minval,
+                                       float maxval, int nBBS) {
     SharedMemory<outType> shared;
-    outType * shrdMem = shared.getPointer();
+    outType *shrdMem = shared.getPointer();
 
     // offset input and output to account for batch ops
     unsigned b2 = blockIdx.x / nBBS;
-    const inType *iptr  =  in.ptr + b2 *  in.strides[2] + blockIdx.y *  in.strides[3];
-    outType      *optr  = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
+    const inType *iptr =
+        in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
+    outType *optr = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
 
-    int start  = (blockIdx.x-b2*nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
-    int end    = minimum((start + THRD_LOAD * blockDim.x), len);
-    float step = (maxval-minval) / (float)nbins;
+    int start = (blockIdx.x - b2 * nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
+    int end   = minimum((start + THRD_LOAD * blockDim.x), len);
+    float step = (maxval - minval) / (float)nbins;
 
-    // If nbins > max shared memory allocated, then just use atomicAdd on global memory
+    // If nbins > max shared memory allocated, then just use atomicAdd on global
+    // memory
     bool use_global = nbins > MAX_BINS;
 
     // Skip initializing shared memory
     if (!use_global) {
-        for (int i = threadIdx.x; i < nbins; i += blockDim.x)
-            shrdMem[i] = 0;
+        for (int i = threadIdx.x; i < nbins; i += blockDim.x) shrdMem[i] = 0;
         __syncthreads();
     }
 
     for (int row = start; row < end; row += blockDim.x) {
-        int idx = isLinear ? row : ((row % in.dims[0]) + (row / in.dims[0])*in.strides[1]);
+        int idx =
+            isLinear
+                ? row
+                : ((row % in.dims[0]) + (row / in.dims[0]) * in.strides[1]);
         int bin = (int)((iptr[idx] - minval) / step);
-        bin     = (bin < 0)      ? 0         : bin;
-        bin     = (bin >= nbins) ? (nbins-1) : bin;
+        bin     = (bin < 0) ? 0 : bin;
+        bin     = (bin >= nbins) ? (nbins - 1) : bin;
 
         if (use_global) {
             atomicAdd((optr + bin), 1);
@@ -77,24 +75,25 @@ void histogramKernel(Param<outType> out, CParam<inType> in,
 }
 
 template<typename inType, typename outType, bool isLinear>
-void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval, float maxval)
-{
+void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
+               float maxval) {
     dim3 threads(kernel::THREADS_X, 1);
 
     int nElems = in.dims[0] * in.dims[1];
-    int blk_x  = divup(nElems, THRD_LOAD*THREADS_X);
+    int blk_x  = divup(nElems, THRD_LOAD * THREADS_X);
 
     dim3 blocks(blk_x * in.dims[2], in.dims[3]);
 
     // If nbins > MAX_BINS, we are using global memory so smem_size can be 0;
     int smem_size = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 0;
 
-    CUDA_LAUNCH_SMEM((histogramKernel<inType, outType, isLinear>), blocks, threads, smem_size,
-            out, in, nElems, nbins, minval, maxval, blk_x);
+    CUDA_LAUNCH_SMEM((histogramKernel<inType, outType, isLinear>), blocks,
+                     threads, smem_size, out, in, nElems, nbins, minval, maxval,
+                     blk_x);
 
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 2e34fa5c40..7d3033f647 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
 #include "ireduce.hpp"
 #include "reduce.hpp"
@@ -17,33 +17,27 @@
 
 #include <cfloat>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T>
-__device__ T sq(T a)
-{
+__device__ T sq(T a) {
     return a * a;
 }
 
 template<typename T>
-struct EPS
-{
+struct EPS {
     __device__ T eps() { return FLT_EPSILON; }
 };
 
 template<>
-struct EPS<float>
-{
+struct EPS<float> {
     __device__ static float eps() { return FLT_EPSILON; }
 };
 
 template<>
-struct EPS<double>
-{
+struct EPS<double> {
     __device__ static double eps() { return DBL_EPSILON; }
 };
 
@@ -54,32 +48,30 @@ struct EPS<double>
 extern __shared__ char sh[];
 
 template<typename T>
-__device__ void JacobiSVD(int m, int n)
-{
+__device__ void JacobiSVD(int m, int n) {
     const int iterations = 30;
 
     int tid_x = threadIdx.x;
     int bsz_x = blockDim.x;
     int tid_y = threadIdx.y;
-    //int gid_y = blockIdx.y * blockDim.y + tid_y;
+    // int gid_y = blockIdx.y * blockDim.y + tid_y;
 
     __shared__ T s_acc1[256];
     __shared__ T s_acc2[256];
 
-    __shared__ T s_d[16*9];
+    __shared__ T s_d[16 * 9];
 
     T* s_V = (T*)sh;
-    T* s_S = (T*)sh + 16*81;
+    T* s_S = (T*)sh + 16 * 81;
 
-
-    int doff = tid_y *  n;
+    int doff = tid_y * n;
     int soff = tid_y * 81;
 
     if (tid_x < n) {
         T acc1 = 0;
         for (int i = 0; i < m; i++) {
             int stid = soff + tid_x * m + i;
-            T t = s_S[stid];
+            T t      = s_S[stid];
             acc1 += t * t;
             s_V[stid] = (tid_x == i) ? 1 : 0;
         }
@@ -88,17 +80,16 @@ __device__ void JacobiSVD(int m, int n)
     __syncthreads();
 
     for (int it = 0; it < iterations; it++) {
-        for (int i = 0; i < n-1; i++) {
-            for (int j = i+1; j < n; j++) {
-                T* Si = s_S + soff + i*m;
-                T* Sj = s_S + soff + j*m;
+        for (int i = 0; i < n - 1; i++) {
+            for (int j = i + 1; j < n; j++) {
+                T* Si = s_S + soff + i * m;
+                T* Sj = s_S + soff + j * m;
 
-                T* Vi = s_V + soff + i*n;
-                T* Vj = s_V + soff + j*n;
+                T* Vi = s_V + soff + i * n;
+                T* Vj = s_V + soff + j * n;
 
                 T p = (T)0;
-                for (int k = 0; k < m; k++)
-                    p += Si[k]*Sj[k];
+                for (int k = 0; k < m; k++) p += Si[k] * Sj[k];
 
                 T di = s_d[doff + i];
                 T dj = s_d[doff + j];
@@ -106,27 +97,26 @@ __device__ void JacobiSVD(int m, int n)
 
                 T c = 0, s = 0;
                 T t0 = 0, t1 = 0;
-                int cond = (fabs(p) > m*EPS<T>::eps()*sqrt(di * dj));
+                int cond = (fabs(p) > m * EPS<T>::eps() * sqrt(di * dj));
                 T a = 0, b = 0;
 
                 if (cond) {
-                    T y = di - dj;
-                    T r = hypot(p*2, y);
-                    T r2 = r*2;
+                    T y  = di - dj;
+                    T r  = hypot(p * 2, y);
+                    T r2 = r * 2;
                     if (y >= 0) {
                         c = sqrt((r + y) / r2);
-                        s = p / (r2*c);
-                    }
-                    else {
+                        s = p / (r2 * c);
+                    } else {
                         s = sqrt((r - y) / r2);
-                        c = p / (r2*s);
+                        c = p / (r2 * s);
                     }
 
-                    for (int k = tid_x; k < m; k+=bsz_x) {
-                        t0 = c*Si[k] + s*Sj[k];
-                        t1 = c*Sj[k] - s*Si[k];
-                        Si[k] = t0;
-                        Sj[k] = t1;
+                    for (int k = tid_x; k < m; k += bsz_x) {
+                        t0                        = c * Si[k] + s * Sj[k];
+                        t1                        = c * Sj[k] - s * Si[k];
+                        Si[k]                     = t0;
+                        Sj[k]                     = t1;
                         s_acc1[tid_y * bsz_x + k] = t0 * t0;
                         s_acc2[tid_y * bsz_x + k] = t1 * t1;
                     }
@@ -161,33 +151,18 @@ __device__ void JacobiSVD(int m, int n)
 }
 
 __device__ bool computeMeanScale(
-    float* x_src_mean,
-    float* y_src_mean,
-    float* x_dst_mean,
-    float* y_dst_mean,
-    float* src_scale,
-    float* dst_scale,
-    float* src_pt_x,
-    float* src_pt_y,
-    float* dst_pt_x,
-    float* dst_pt_y,
-    CParam<float> x_src,
-    CParam<float> y_src,
-    CParam<float> x_dst,
-    CParam<float> y_dst,
-    CParam<float> rnd,
-    int i)
-{
+    float* x_src_mean, float* y_src_mean, float* x_dst_mean, float* y_dst_mean,
+    float* src_scale, float* dst_scale, float* src_pt_x, float* src_pt_y,
+    float* dst_pt_x, float* dst_pt_y, CParam<float> x_src, CParam<float> y_src,
+    CParam<float> x_dst, CParam<float> y_dst, CParam<float> rnd, int i) {
     const unsigned ridx = rnd.dims[0] * i;
-    unsigned r[4] = { (unsigned)rnd.ptr[ridx],
-                      (unsigned)rnd.ptr[ridx+1],
-                      (unsigned)rnd.ptr[ridx+2],
-                      (unsigned)rnd.ptr[ridx+3] };
+    unsigned r[4]       = {(unsigned)rnd.ptr[ridx], (unsigned)rnd.ptr[ridx + 1],
+                     (unsigned)rnd.ptr[ridx + 2], (unsigned)rnd.ptr[ridx + 3]};
 
     // If one of the points is repeated, it's a bad samples, will still
     // compute homography to ensure all threads pass __syncthreads()
-    bool bad = (r[0] == r[1] || r[0] == r[2] || r[0] == r[3] ||
-                r[1] == r[2] || r[1] == r[3] || r[2] == r[3]);
+    bool bad = (r[0] == r[1] || r[0] == r[2] || r[0] == r[3] || r[1] == r[2] ||
+                r[1] == r[3] || r[2] == r[3]);
 
     for (unsigned j = 0; j < 4; j++) {
         src_pt_x[j] = x_src.ptr[r[j]];
@@ -203,8 +178,10 @@ __device__ bool computeMeanScale(
 
     float src_var = 0.0f, dst_var = 0.0f;
     for (unsigned j = 0; j < 4; j++) {
-        src_var += sq(src_pt_x[j] - *x_src_mean) + sq(src_pt_y[j] - *y_src_mean);
-        dst_var += sq(dst_pt_x[j] - *x_dst_mean) + sq(dst_pt_y[j] - *y_dst_mean);
+        src_var +=
+            sq(src_pt_x[j] - *x_src_mean) + sq(src_pt_y[j] - *y_src_mean);
+        dst_var +=
+            sq(dst_pt_x[j] - *x_dst_mean) + sq(dst_pt_y[j] - *y_dst_mean);
     }
 
     src_var /= 4.f;
@@ -216,20 +193,15 @@ __device__ bool computeMeanScale(
     return !bad;
 }
 
-#define SSPTR(Z, Y, X) (s_S[(Z) * 81 + (Y) * 9 + (X)])
+#define SSPTR(Z, Y, X) (s_S[(Z)*81 + (Y)*9 + (X)])
 
 template<typename T>
-__global__ void buildLinearSystem(
-    Param<T> H,
-    CParam<float> x_src,
-    CParam<float> y_src,
-    CParam<float> x_dst,
-    CParam<float> y_dst,
-    CParam<float> rnd,
-    const unsigned iterations)
-{
+__global__ void buildLinearSystem(Param<T> H, CParam<float> x_src,
+                                  CParam<float> y_src, CParam<float> x_dst,
+                                  CParam<float> y_dst, CParam<float> rnd,
+                                  const unsigned iterations) {
     unsigned tid_y = threadIdx.y;
-    unsigned i = blockIdx.y * blockDim.y + tid_y;
+    unsigned i     = blockIdx.y * blockDim.y + tid_y;
 
     if (i < iterations) {
         float x_src_mean, y_src_mean;
@@ -237,43 +209,39 @@ __global__ void buildLinearSystem(
         float src_scale, dst_scale;
         float src_pt_x[4], src_pt_y[4], dst_pt_x[4], dst_pt_y[4];
 
-        computeMeanScale(&x_src_mean, &y_src_mean,
-                         &x_dst_mean, &y_dst_mean,
-                         &src_scale, &dst_scale,
-                         src_pt_x, src_pt_y,
-                         dst_pt_x, dst_pt_y,
-                         x_src, y_src, x_dst, y_dst,
-                         rnd, i);
+        computeMeanScale(&x_src_mean, &y_src_mean, &x_dst_mean, &y_dst_mean,
+                         &src_scale, &dst_scale, src_pt_x, src_pt_y, dst_pt_x,
+                         dst_pt_y, x_src, y_src, x_dst, y_dst, rnd, i);
 
         T* s_V = (T*)sh;
-        T* s_S = (T*)sh + 16*81;
+        T* s_S = (T*)sh + 16 * 81;
 
         // Compute input matrix
-        for (unsigned j = threadIdx.x; j < 4; j+=blockDim.x) {
+        for (unsigned j = threadIdx.x; j < 4; j += blockDim.x) {
             float srcx = (src_pt_x[j] - x_src_mean) * src_scale;
             float srcy = (src_pt_y[j] - y_src_mean) * src_scale;
             float dstx = (dst_pt_x[j] - x_dst_mean) * dst_scale;
             float dsty = (dst_pt_y[j] - y_dst_mean) * dst_scale;
 
-            SSPTR(tid_y, 0, j*2) = 0.0f;
-            SSPTR(tid_y, 1, j*2) = 0.0f;
-            SSPTR(tid_y, 2, j*2) = 0.0f;
-            SSPTR(tid_y, 3, j*2) = -srcx;
-            SSPTR(tid_y, 4, j*2) = -srcy;
-            SSPTR(tid_y, 5, j*2) = -1.0f;
-            SSPTR(tid_y, 6, j*2) = dsty*srcx;
-            SSPTR(tid_y, 7, j*2) = dsty*srcy;
-            SSPTR(tid_y, 8, j*2) = dsty;
-
-            SSPTR(tid_y, 0, j*2+1) = srcx;
-            SSPTR(tid_y, 1, j*2+1) = srcy;
-            SSPTR(tid_y, 2, j*2+1) = 1.0f;
-            SSPTR(tid_y, 3, j*2+1) = 0.0f;
-            SSPTR(tid_y, 4, j*2+1) = 0.0f;
-            SSPTR(tid_y, 5, j*2+1) = 0.0f;
-            SSPTR(tid_y, 6, j*2+1) = -dstx*srcx;
-            SSPTR(tid_y, 7, j*2+1) = -dstx*srcy;
-            SSPTR(tid_y, 8, j*2+1) = -dstx;
+            SSPTR(tid_y, 0, j * 2) = 0.0f;
+            SSPTR(tid_y, 1, j * 2) = 0.0f;
+            SSPTR(tid_y, 2, j * 2) = 0.0f;
+            SSPTR(tid_y, 3, j * 2) = -srcx;
+            SSPTR(tid_y, 4, j * 2) = -srcy;
+            SSPTR(tid_y, 5, j * 2) = -1.0f;
+            SSPTR(tid_y, 6, j * 2) = dsty * srcx;
+            SSPTR(tid_y, 7, j * 2) = dsty * srcy;
+            SSPTR(tid_y, 8, j * 2) = dsty;
+
+            SSPTR(tid_y, 0, j * 2 + 1) = srcx;
+            SSPTR(tid_y, 1, j * 2 + 1) = srcy;
+            SSPTR(tid_y, 2, j * 2 + 1) = 1.0f;
+            SSPTR(tid_y, 3, j * 2 + 1) = 0.0f;
+            SSPTR(tid_y, 4, j * 2 + 1) = 0.0f;
+            SSPTR(tid_y, 5, j * 2 + 1) = 0.0f;
+            SSPTR(tid_y, 6, j * 2 + 1) = -dstx * srcx;
+            SSPTR(tid_y, 7, j * 2 + 1) = -dstx * srcy;
+            SSPTR(tid_y, 8, j * 2 + 1) = -dstx;
 
             if (j == 4) {
                 SSPTR(tid_y, 0, 8) = 0.0f;
@@ -292,52 +260,53 @@ __global__ void buildLinearSystem(
         JacobiSVD<T>(9, 9);
 
         T vH[9], H_tmp[9];
-        for (unsigned j = 0; j < 9; j++)
-            vH[j] = s_V[tid_y * 81 + 8 * 9 + j];
-
-        H_tmp[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
-        H_tmp[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
-        H_tmp[2] = x_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-                              (vH[2] - src_scale*y_src_mean*vH[1] - src_scale*x_src_mean*vH[0])/dst_scale;
-
-        H_tmp[3] = src_scale*y_dst_mean*vH[6] + src_scale*vH[3]/dst_scale;
-        H_tmp[4] = src_scale*y_dst_mean*vH[7] + src_scale*vH[4]/dst_scale;
-        H_tmp[5] = y_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-                              (vH[5] - src_scale*y_src_mean*vH[4] - src_scale*x_src_mean*vH[3])/dst_scale;
-
-        H_tmp[6] = src_scale*vH[6];
-        H_tmp[7] = src_scale*vH[7];
-        H_tmp[8] = vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6];
+        for (unsigned j = 0; j < 9; j++) vH[j] = s_V[tid_y * 81 + 8 * 9 + j];
+
+        H_tmp[0] =
+            src_scale * x_dst_mean * vH[6] + src_scale * vH[0] / dst_scale;
+        H_tmp[1] =
+            src_scale * x_dst_mean * vH[7] + src_scale * vH[1] / dst_scale;
+        H_tmp[2] = x_dst_mean * (vH[8] - src_scale * y_src_mean * vH[7] -
+                                 src_scale * x_src_mean * vH[6]) +
+                   (vH[2] - src_scale * y_src_mean * vH[1] -
+                    src_scale * x_src_mean * vH[0]) /
+                       dst_scale;
+
+        H_tmp[3] =
+            src_scale * y_dst_mean * vH[6] + src_scale * vH[3] / dst_scale;
+        H_tmp[4] =
+            src_scale * y_dst_mean * vH[7] + src_scale * vH[4] / dst_scale;
+        H_tmp[5] = y_dst_mean * (vH[8] - src_scale * y_src_mean * vH[7] -
+                                 src_scale * x_src_mean * vH[6]) +
+                   (vH[5] - src_scale * y_src_mean * vH[4] -
+                    src_scale * x_src_mean * vH[3]) /
+                       dst_scale;
+
+        H_tmp[6] = src_scale * vH[6];
+        H_tmp[7] = src_scale * vH[7];
+        H_tmp[8] = vH[8] - src_scale * y_src_mean * vH[7] -
+                   src_scale * x_src_mean * vH[6];
 
         const unsigned Hidx = H.dims[0] * i;
-        T* H_ptr = H.ptr + Hidx;
-        for (int h = 0; h < 9; h++)
-            H_ptr[h] = H_tmp[h];
+        T* H_ptr            = H.ptr + Hidx;
+        for (int h = 0; h < 9; h++) H_ptr[h] = H_tmp[h];
     }
 }
 
 #undef SSPTR
 
-// LMedS: http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
+// LMedS:
+// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
 template<typename T>
 __global__ void computeEvalHomography(
-    Param<unsigned> inliers,
-    Param<unsigned> idx,
-    Param<T> H,
-    Param<float> err,
-    CParam<float> x_src,
-    CParam<float> y_src,
-    CParam<float> x_dst,
-    CParam<float> y_dst,
-    CParam<float> rnd,
-    const unsigned iterations,
-    const unsigned nsamples,
-    const float inlier_thr,
-    const af_homography_type htype)
-{
+    Param<unsigned> inliers, Param<unsigned> idx, Param<T> H, Param<float> err,
+    CParam<float> x_src, CParam<float> y_src, CParam<float> x_dst,
+    CParam<float> y_dst, CParam<float> rnd, const unsigned iterations,
+    const unsigned nsamples, const float inlier_thr,
+    const af_homography_type htype) {
     unsigned bid_x = blockIdx.x;
     unsigned tid_x = threadIdx.x;
-    unsigned i = bid_x * blockDim.x + tid_x;
+    unsigned i     = bid_x * blockDim.x + tid_x;
 
     __shared__ unsigned s_inliers[256];
     __shared__ unsigned s_idx[256];
@@ -348,36 +317,43 @@ __global__ void computeEvalHomography(
 
     if (i < iterations) {
         const unsigned Hidx = H.dims[0] * i;
-        T* H_ptr = H.ptr + Hidx;
+        T* H_ptr            = H.ptr + Hidx;
         T H_tmp[9];
-        for (int h = 0; h < 9; h++)
-            H_tmp[h] = H_ptr[h];
+        for (int h = 0; h < 9; h++) H_tmp[h] = H_ptr[h];
 
         if (htype == AF_HOMOGRAPHY_RANSAC) {
             // Compute inliers
             unsigned inliers_count = 0;
             for (unsigned j = 0; j < nsamples; j++) {
-                float z =  H_tmp[6]*x_src.ptr[j] + H_tmp[7]*y_src.ptr[j] + H_tmp[8];
-                float x = (H_tmp[0]*x_src.ptr[j] + H_tmp[1]*y_src.ptr[j] + H_tmp[2]) / z;
-                float y = (H_tmp[3]*x_src.ptr[j] + H_tmp[4]*y_src.ptr[j] + H_tmp[5]) / z;
+                float z = H_tmp[6] * x_src.ptr[j] + H_tmp[7] * y_src.ptr[j] +
+                          H_tmp[8];
+                float x = (H_tmp[0] * x_src.ptr[j] + H_tmp[1] * y_src.ptr[j] +
+                           H_tmp[2]) /
+                          z;
+                float y = (H_tmp[3] * x_src.ptr[j] + H_tmp[4] * y_src.ptr[j] +
+                           H_tmp[5]) /
+                          z;
 
                 float dist = sq(x_dst.ptr[j] - x) + sq(y_dst.ptr[j] - y);
-                if (dist < inlier_thr*inlier_thr)
-                    inliers_count++;
+                if (dist < inlier_thr * inlier_thr) inliers_count++;
             }
 
             s_inliers[tid_x] = inliers_count;
             s_idx[tid_x]     = i;
-        }
-        else if (htype == AF_HOMOGRAPHY_LMEDS) {
+        } else if (htype == AF_HOMOGRAPHY_LMEDS) {
             // Compute error
             for (unsigned j = 0; j < nsamples; j++) {
-                float z =  H_tmp[6]*x_src.ptr[j] + H_tmp[7]*y_src.ptr[j] + H_tmp[8];
-                float x = (H_tmp[0]*x_src.ptr[j] + H_tmp[1]*y_src.ptr[j] + H_tmp[2]) / z;
-                float y = (H_tmp[3]*x_src.ptr[j] + H_tmp[4]*y_src.ptr[j] + H_tmp[5]) / z;
+                float z = H_tmp[6] * x_src.ptr[j] + H_tmp[7] * y_src.ptr[j] +
+                          H_tmp[8];
+                float x = (H_tmp[0] * x_src.ptr[j] + H_tmp[1] * y_src.ptr[j] +
+                           H_tmp[2]) /
+                          z;
+                float y = (H_tmp[3] * x_src.ptr[j] + H_tmp[4] * y_src.ptr[j] +
+                           H_tmp[5]) /
+                          z;
 
                 float dist = sq(x_dst.ptr[j] - x) + sq(y_dst.ptr[j] - y);
-                err.ptr[i*err.dims[0] + j] = sqrt(dist);
+                err.ptr[i * err.dims[0] + j] = sqrt(dist);
             }
         }
     }
@@ -400,29 +376,25 @@ __global__ void computeEvalHomography(
     }
 }
 
-__global__ void computeMedian(
-    Param<float> median,
-    Param<unsigned> idx,
-    CParam<float> err,
-    const unsigned iterations)
-{
+__global__ void computeMedian(Param<float> median, Param<unsigned> idx,
+                              CParam<float> err, const unsigned iterations) {
     const unsigned tid = threadIdx.x;
     const unsigned bid = blockIdx.x;
-    const unsigned i = bid * blockDim.x + threadIdx.x;
+    const unsigned i   = bid * blockDim.x + threadIdx.x;
 
     __shared__ float s_median[256];
     __shared__ unsigned s_idx[256];
 
     s_median[tid] = FLT_MAX;
-    s_idx[tid] = 0;
+    s_idx[tid]    = 0;
 
     if (i < iterations) {
         const int nsamples = err.dims[0];
-        float m = err.ptr[i*nsamples + nsamples / 2];
+        float m            = err.ptr[i * nsamples + nsamples / 2];
         if (nsamples % 2 == 0)
-            m = (m + err.ptr[i*nsamples + nsamples / 2 - 1]) * 0.5f;
+            m = (m + err.ptr[i * nsamples + nsamples / 2 - 1]) * 0.5f;
 
-        s_idx[tid] = i;
+        s_idx[tid]    = i;
         s_median[tid] = m;
     }
     __syncthreads();
@@ -438,24 +410,20 @@ __global__ void computeMedian(
     }
 
     median.ptr[bid] = s_median[0];
-    idx.ptr[bid] = s_idx[0];
+    idx.ptr[bid]    = s_idx[0];
 }
 
-#define DIVUP(A, B) (((A) + (B) - 1) / (B))
+#define DIVUP(A, B) (((A) + (B)-1) / (B))
 
-__global__ void findMinMedian(
-    float* minMedian,
-    unsigned* minIdx,
-    CParam<float> median,
-    CParam<unsigned> idx)
-{
+__global__ void findMinMedian(float* minMedian, unsigned* minIdx,
+                              CParam<float> median, CParam<unsigned> idx) {
     const int tid = threadIdx.x;
 
     __shared__ float s_minMedian[256];
     __shared__ unsigned s_minIdx[256];
 
     s_minMedian[tid] = FLT_MAX;
-    s_minIdx[tid] = 0;
+    s_minIdx[tid]    = 0;
     __syncthreads();
 
     const int loop = DIVUP(median.dims[0], blockDim.x);
@@ -464,7 +432,7 @@ __global__ void findMinMedian(
         int j = i * blockDim.x + tid;
         if (j < median.dims[0] && median.ptr[j] < s_minMedian[tid]) {
             s_minMedian[tid] = median.ptr[j];
-            s_minIdx[tid] = idx.ptr[j];
+            s_minIdx[tid]    = idx.ptr[j];
         }
         __syncthreads();
     }
@@ -480,25 +448,20 @@ __global__ void findMinMedian(
     }
 
     *minMedian = s_minMedian[0];
-    *minIdx = s_minIdx[0];
+    *minIdx    = s_minIdx[0];
 }
 
 #undef DIVUP
 
 template<typename T>
-__global__ void computeLMedSInliers(
-    Param<unsigned> inliers,
-    CParam<T> H,
-    CParam<float> x_src,
-    CParam<float> y_src,
-    CParam<float> x_dst,
-    CParam<float> y_dst,
-    const float minMedian,
-    const unsigned nsamples)
-{
+__global__ void computeLMedSInliers(Param<unsigned> inliers, CParam<T> H,
+                                    CParam<float> x_src, CParam<float> y_src,
+                                    CParam<float> x_dst, CParam<float> y_dst,
+                                    const float minMedian,
+                                    const unsigned nsamples) {
     unsigned tid = threadIdx.x;
     unsigned bid = blockIdx.x;
-    unsigned i = bid * blockDim.x + tid;
+    unsigned i   = bid * blockDim.x + tid;
 
     __shared__ T s_H[9];
     __shared__ unsigned s_inliers[256];
@@ -506,27 +469,25 @@ __global__ void computeLMedSInliers(
     s_inliers[tid] = 0;
     __syncthreads();
 
-    if (tid < 9)
-        s_H[tid] = H.ptr[tid];
+    if (tid < 9) s_H[tid] = H.ptr[tid];
     __syncthreads();
 
-    float sigma = max(1.4826f * (1 + 5.f/(nsamples - 4)) * (float)sqrt(minMedian), 1e-6f);
+    float sigma = max(
+        1.4826f * (1 + 5.f / (nsamples - 4)) * (float)sqrt(minMedian), 1e-6f);
     float dist_thr = sq(2.5f * sigma);
 
     if (i < nsamples) {
-        float z =  s_H[6]*x_src.ptr[i] + s_H[7]*y_src.ptr[i] + s_H[8];
-        float x = (s_H[0]*x_src.ptr[i] + s_H[1]*y_src.ptr[i] + s_H[2]) / z;
-        float y = (s_H[3]*x_src.ptr[i] + s_H[4]*y_src.ptr[i] + s_H[5]) / z;
+        float z = s_H[6] * x_src.ptr[i] + s_H[7] * y_src.ptr[i] + s_H[8];
+        float x = (s_H[0] * x_src.ptr[i] + s_H[1] * y_src.ptr[i] + s_H[2]) / z;
+        float y = (s_H[3] * x_src.ptr[i] + s_H[4] * y_src.ptr[i] + s_H[5]) / z;
 
         float dist = sq(x_dst.ptr[i] - x) + sq(y_dst.ptr[i] - y);
-        if (dist <= dist_thr)
-            s_inliers[tid] = 1;
+        if (dist <= dist_thr) s_inliers[tid] = 1;
     }
     __syncthreads();
 
     for (unsigned t = 128; t > 0; t >>= 1) {
-        if (tid < t)
-            s_inliers[tid] += s_inliers[tid + t];
+        if (tid < t) s_inliers[tid] += s_inliers[tid + t];
         __syncthreads();
     }
 
@@ -534,41 +495,34 @@ __global__ void computeLMedSInliers(
 }
 
 template<typename T>
-int computeH(
-    Param<T> bestH,
-    Param<T> H,
-    Param<float> err,
-    CParam<float> x_src,
-    CParam<float> y_src,
-    CParam<float> x_dst,
-    CParam<float> y_dst,
-    CParam<float> rnd,
-    const unsigned iterations,
-    const unsigned nsamples,
-    const float inlier_thr,
-    const af_homography_type htype)
-{
+int computeH(Param<T> bestH, Param<T> H, Param<float> err, CParam<float> x_src,
+             CParam<float> y_src, CParam<float> x_dst, CParam<float> y_dst,
+             CParam<float> rnd, const unsigned iterations,
+             const unsigned nsamples, const float inlier_thr,
+             const af_homography_type htype) {
     dim3 threads(16, 16);
     dim3 blocks(1, divup(iterations, threads.y));
 
     // Build linear system and solve SVD
     size_t ls_shared_sz = threads.x * 81 * 2 * sizeof(T);
-    CUDA_LAUNCH_SMEM((buildLinearSystem<T>), blocks, threads, ls_shared_sz,
-                H, x_src, y_src, x_dst, y_dst, rnd, iterations);
+    CUDA_LAUNCH_SMEM((buildLinearSystem<T>), blocks, threads, ls_shared_sz, H,
+                     x_src, y_src, x_dst, y_dst, rnd, iterations);
     POST_LAUNCH_CHECK();
 
     threads = dim3(256);
-    blocks = dim3(divup(iterations, threads.x));
+    blocks  = dim3(divup(iterations, threads.x));
 
     // Allocate some temporary buffers
     dim4 idx_dims(blocks.x);
-    Array<unsigned> idx = createEmptyArray<unsigned>(idx_dims);
-    Array<unsigned> inliers = createEmptyArray<unsigned>((htype == AF_HOMOGRAPHY_RANSAC) ? blocks.x : divup(nsamples, threads.x));
+    Array<unsigned> idx     = createEmptyArray<unsigned>(idx_dims);
+    Array<unsigned> inliers = createEmptyArray<unsigned>(
+        (htype == AF_HOMOGRAPHY_RANSAC) ? blocks.x
+                                        : divup(nsamples, threads.x));
 
     // Compute (and for RANSAC, evaluate) homographies
-    CUDA_LAUNCH((computeEvalHomography<T>), blocks, threads,
-                 inliers, idx, H, err, x_src, y_src, x_dst, y_dst,
-                 rnd, iterations, nsamples, inlier_thr, htype);
+    CUDA_LAUNCH((computeEvalHomography<T>), blocks, threads, inliers, idx, H,
+                err, x_src, y_src, x_dst, y_dst, rnd, iterations, nsamples,
+                inlier_thr, htype);
     POST_LAUNCH_CHECK();
 
     unsigned inliersH, idxH;
@@ -582,8 +536,8 @@ int computeH(
         float minMedian;
 
         // Compute median of every iteration
-        CUDA_LAUNCH((computeMedian), blocks, threads,
-                    median, idx, err, iterations);
+        CUDA_LAUNCH((computeMedian), blocks, threads, median, idx, err,
+                    iterations);
         POST_LAUNCH_CHECK();
 
         // Reduce medians, only in case iterations > 256
@@ -591,55 +545,63 @@ int computeH(
             blocks = dim3(1);
 
             auto finalMedian = memAlloc<float>(1);
-            auto finalIdx = memAlloc<unsigned>(1);
+            auto finalIdx    = memAlloc<unsigned>(1);
 
-            CUDA_LAUNCH((findMinMedian), blocks, threads,
-                        finalMedian.get(), finalIdx.get(), median, idx);
+            CUDA_LAUNCH((findMinMedian), blocks, threads, finalMedian.get(),
+                        finalIdx.get(), median, idx);
             POST_LAUNCH_CHECK();
 
-            CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian.get(), sizeof(float),
-                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx.get(), sizeof(unsigned),
-                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+            CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian.get(),
+                                       sizeof(float), cudaMemcpyDeviceToHost,
+                                       cuda::getActiveStream()));
+            CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx.get(),
+                                       sizeof(unsigned), cudaMemcpyDeviceToHost,
+                                       cuda::getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         } else {
             CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.get(), sizeof(float),
-                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+                                       cudaMemcpyDeviceToHost,
+                                       cuda::getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(&minIdx, idx.get(), sizeof(unsigned),
-                        cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+                                       cudaMemcpyDeviceToHost,
+                                       cuda::getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         }
 
         // Copy best homography to output
-        CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + minIdx * 9, 9*sizeof(T),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + minIdx * 9, 9 * sizeof(T),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getActiveStream()));
 
         blocks = dim3(divup(nsamples, threads.x));
         // sync stream for the device to host copies to be visible for
         // the subsequent kernel launch
 
-        CUDA_LAUNCH((computeLMedSInliers<T>), blocks, threads,
-                    inliers, bestH, x_src, y_src, x_dst, y_dst,
-                    minMedian, nsamples);
+        CUDA_LAUNCH((computeLMedSInliers<T>), blocks, threads, inliers, bestH,
+                    x_src, y_src, x_dst, y_dst, minMedian, nsamples);
         POST_LAUNCH_CHECK();
 
         // Adds up the total number of inliers
         Array<unsigned> totalInliers = createEmptyArray<unsigned>(1);
-        kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
+        kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0,
+                                                     false, 0.0);
 
-        CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.get(), sizeof(unsigned),
-                    cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.get(),
+                                   sizeof(unsigned), cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
         unsigned blockIdx;
         inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
         // Copies back index and number of inliers of best homography estimation
-        CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.get()+blockIdx, sizeof(unsigned),
-                    cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.get() + blockIdx,
+                                   sizeof(unsigned), cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9*sizeof(T),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9 * sizeof(T),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getActiveStream()));
     }
 
     // sync stream for the device to host copies to be visible for
@@ -649,6 +611,6 @@ int computeH(
     return (int)inliersH;
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 4dec5609a9..5712f848e5 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -7,34 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool isHSV2RGB>
-__global__
-void convert(Param<T> out, CParam<T> in, int nBBS)
-{
+__global__ void convert(Param<T> out, CParam<T> in, int nBBS) {
     // batch offsets
-    unsigned batchId= blockIdx.x / nBBS;
-    const T* src    = (const T *) in.ptr + (batchId *  in.strides[3]);
-    T*       dst    = (T *      )out.ptr + (batchId * out.strides[3]);
+    unsigned batchId = blockIdx.x / nBBS;
+    const T* src     = (const T*)in.ptr + (batchId * in.strides[3]);
+    T* dst           = (T*)out.ptr + (batchId * out.strides[3]);
     // global indices
-    int gx = blockDim.x * (blockIdx.x-batchId*nBBS) + threadIdx.x;
+    int gx = blockDim.x * (blockIdx.x - batchId * nBBS) + threadIdx.x;
     int gy = blockDim.y * (blockIdx.y + blockIdx.z * gridDim.y) + threadIdx.y;
 
     if (gx < out.dims[0] && gy < out.dims[1] && batchId < out.dims[3]) {
-
         int oIdx0 = gx + gy * out.strides[1];
         int oIdx1 = oIdx0 + out.strides[2];
         int oIdx2 = oIdx1 + out.strides[2];
@@ -43,7 +38,7 @@ void convert(Param<T> out, CParam<T> in, int nBBS)
         int iIdx1 = iIdx0 + in.strides[2];
         int iIdx2 = iIdx1 + in.strides[2];
 
-        if(isHSV2RGB) {
+        if (isHSV2RGB) {
             T H = src[iIdx0];
             T S = src[iIdx1];
             T V = src[iIdx2];
@@ -51,11 +46,11 @@ void convert(Param<T> out, CParam<T> in, int nBBS)
             T R, G, B;
             R = G = B = 0;
 
-            int   i = (int)(H * 6);
-            T f = H * 6 - i;
-            T p = V * (1 - S);
-            T q = V * (1 - f * S);
-            T t = V * (1 - (1 - f) * S);
+            int i = (int)(H * 6);
+            T f   = H * 6 - i;
+            T p   = V * (1 - S);
+            T q   = V * (1 - f * S);
+            T t   = V * (1 - (1 - f) * S);
 
             switch (i % 6) {
                 case 0: R = V, G = t, B = p; break;
@@ -70,32 +65,31 @@ void convert(Param<T> out, CParam<T> in, int nBBS)
             dst[oIdx1] = G;
             dst[oIdx2] = B;
         } else {
-            T R = src[iIdx0];
-            T G = src[iIdx1];
-            T B = src[iIdx2];
-            T Cmax = fmax(fmax(R, G), B);
-            T Cmin = fmin(fmin(R, G), B);
-            T delta= Cmax-Cmin;
+            T R     = src[iIdx0];
+            T G     = src[iIdx1];
+            T B     = src[iIdx2];
+            T Cmax  = fmax(fmax(R, G), B);
+            T Cmin  = fmin(fmin(R, G), B);
+            T delta = Cmax - Cmin;
 
             T H = 0;
 
-            if (Cmax!=Cmin) {
-                if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
-                if (Cmax==G) H = (B-R)/delta + 2;
-                if (Cmax==B) H = (R-G)/delta + 4;
+            if (Cmax != Cmin) {
+                if (Cmax == R) H = (G - B) / delta + (G < B ? 6 : 0);
+                if (Cmax == G) H = (B - R) / delta + 2;
+                if (Cmax == B) H = (R - G) / delta + 4;
                 H = H / 6.0f;
             }
 
             dst[oIdx0] = H;
-            dst[oIdx1] = Cmax==0.0f ? 0 : delta/Cmax;
+            dst[oIdx1] = Cmax == 0.0f ? 0 : delta / Cmax;
             dst[oIdx2] = Cmax;
         }
     }
 }
 
 template<typename T, bool isHSV2RGB>
-void hsv2rgb_convert(Param<T> out, CParam<T> in)
-{
+void hsv2rgb_convert(Param<T> out, CParam<T> in) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
@@ -103,9 +97,10 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in)
 
     // all images are three channels, so batch
     // parameter would be along 4th dimension
-    dim3 blocks(blk_x*in.dims[3], blk_y);
+    dim3 blocks(blk_x * in.dims[3], blk_y);
 
-    const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
@@ -114,6 +109,6 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in)
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 885cf26712..d6b42b3657 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -7,59 +7,55 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
-#include <platform.hpp>
 #include <debug_cuda.hpp>
-#include <Param.hpp>
+#include <err_cuda.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
-    template<typename T>
-    __global__
-    static void identity_kernel(Param<T> out, int blocks_x, int blocks_y)
-    {
-        const dim_t idz = blockIdx.x / blocks_x;
-        const dim_t idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+template<typename T>
+__global__ static void identity_kernel(Param<T> out, int blocks_x,
+                                       int blocks_y) {
+    const dim_t idz = blockIdx.x / blocks_x;
+    const dim_t idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
 
-        const dim_t blockIdx_x = blockIdx.x - idz * blocks_x;
-        const dim_t blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_y;
+    const dim_t blockIdx_x = blockIdx.x - idz * blocks_x;
+    const dim_t blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_y;
 
-        const dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
-        const dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
+    const dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
+    const dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
 
-        if(idx >= out.dims[0] ||
-           idy >= out.dims[1] ||
-           idz >= out.dims[2] ||
-           idw >= out.dims[3])
-            return;
+    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2] ||
+        idw >= out.dims[3])
+        return;
 
-        const T one  = scalar<T>(1);
-        const T zero = scalar<T>(0);
+    const T one  = scalar<T>(1);
+    const T zero = scalar<T>(0);
 
-        T *ptr = out.ptr + idz * out.strides[2] + idw * out.strides[3];
-        T val = (idx == idy) ? one : zero;
-        ptr[idx + idy * out.strides[1]] = val;
-    }
+    T *ptr = out.ptr + idz * out.strides[2] + idw * out.strides[3];
+    T val  = (idx == idy) ? one : zero;
+    ptr[idx + idy * out.strides[1]] = val;
+}
 
-    template<typename T>
-    static void identity(Param<T> out)
-    {
-        dim3 threads(32, 8);
-        int blocks_x = divup(out.dims[0], threads.x);
-        int blocks_y = divup(out.dims[1], threads.y);
-        dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
+template<typename T>
+static void identity(Param<T> out) {
+    dim3 threads(32, 8);
+    int blocks_x = divup(out.dims[0], threads.x);
+    int blocks_y = divup(out.dims[1], threads.y);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        CUDA_LAUNCH((identity_kernel<T>), blocks, threads, out, blocks_x, blocks_y);
-        POST_LAUNCH_CHECK();
-    }
-}
+    CUDA_LAUNCH((identity_kernel<T>), blocks, threads, out, blocks_x, blocks_y);
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index 6cf3b00449..f54459a089 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -7,89 +7,83 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-    namespace kernel
-    {
+namespace kernel {
 
-        static const int MAX_A_SIZE = 1024;
+static const int MAX_A_SIZE = 1024;
 
-        template<typename T, bool batch_a>
-        __global__
-        void iir_kernel(Param<T> y, CParam<T> c, CParam<T> a,
-                        const int blocks_y)
-        {
-            __shared__ T s_z[MAX_A_SIZE];
-            __shared__ T s_a[MAX_A_SIZE];
-            __shared__ T s_y;
+template<typename T, bool batch_a>
+__global__ void iir_kernel(Param<T> y, CParam<T> c, CParam<T> a,
+                           const int blocks_y) {
+    __shared__ T s_z[MAX_A_SIZE];
+    __shared__ T s_a[MAX_A_SIZE];
+    __shared__ T s_y;
 
-            const int idz = blockIdx.x;
-            const int idw = blockIdx.y / blocks_y;
-            const int idy = blockIdx.y - idw * blocks_y;
+    const int idz = blockIdx.x;
+    const int idw = blockIdx.y / blocks_y;
+    const int idy = blockIdx.y - idw * blocks_y;
 
-            const int tx = threadIdx.x;
-            const int num_a = a.dims[0];
+    const int tx    = threadIdx.x;
+    const int num_a = a.dims[0];
 
-            int y_off = idw * y.strides[3] + idz * y.strides[2] + idy * y.strides[1];
-            int c_off = idw * c.strides[3] + idz * c.strides[2] + idy * c.strides[1];
-            int a_off = 0;
+    int y_off = idw * y.strides[3] + idz * y.strides[2] + idy * y.strides[1];
+    int c_off = idw * c.strides[3] + idz * c.strides[2] + idy * c.strides[1];
+    int a_off = 0;
 
-            if (batch_a) a_off = idw * a.strides[3] + idz * a.strides[2] + idy * a.strides[1];
+    if (batch_a)
+        a_off = idw * a.strides[3] + idz * a.strides[2] + idy * a.strides[1];
 
-            T *d_y = y.ptr + y_off;
-            const T *d_c = c.ptr + c_off;
-            const T *d_a = a.ptr + a_off;
-            const int repeat = (num_a + blockDim.x - 1) / blockDim.x;
-
-            for (int ii = 0; ii < MAX_A_SIZE / blockDim.x; ii++) {
-                int id = ii * blockDim.x + tx;
-                s_z[id] = scalar<T>(0);
-                s_a[id] = (id < num_a) ? d_a[id] : scalar<T>(0);
-            }
-            __syncthreads();
+    T *d_y           = y.ptr + y_off;
+    const T *d_c     = c.ptr + c_off;
+    const T *d_a     = a.ptr + a_off;
+    const int repeat = (num_a + blockDim.x - 1) / blockDim.x;
 
+    for (int ii = 0; ii < MAX_A_SIZE / blockDim.x; ii++) {
+        int id  = ii * blockDim.x + tx;
+        s_z[id] = scalar<T>(0);
+        s_a[id] = (id < num_a) ? d_a[id] : scalar<T>(0);
+    }
+    __syncthreads();
 
-            for (int i = 0; i < y.dims[0]; i++) {
-                if (tx == 0) {
-                    s_y = (d_c[i] + s_z[0]) / s_a[0];
-                    d_y[i] = s_y;
-                }
-                __syncthreads();
+    for (int i = 0; i < y.dims[0]; i++) {
+        if (tx == 0) {
+            s_y    = (d_c[i] + s_z[0]) / s_a[0];
+            d_y[i] = s_y;
+        }
+        __syncthreads();
 
 #pragma unroll
-                for (int ii = 0; ii < repeat; ii++) {
-                    int id = ii * blockDim.x + tx + 1;
+        for (int ii = 0; ii < repeat; ii++) {
+            int id = ii * blockDim.x + tx + 1;
 
-                    T z = s_z[id] - s_a[id] * s_y;
-                    __syncthreads();
+            T z = s_z[id] - s_a[id] * s_y;
+            __syncthreads();
 
-                    s_z[id - 1] = z;
-                    __syncthreads();
-                }
-            }
+            s_z[id - 1] = z;
+            __syncthreads();
         }
+    }
+}
 
-        template<typename T, bool batch_a>
-        void iir(Param<T> y, CParam<T> c, CParam<T> a)
-        {
-            const int blocks_y = y.dims[1];
-            const int blocks_x = y.dims[2];
+template<typename T, bool batch_a>
+void iir(Param<T> y, CParam<T> c, CParam<T> a) {
+    const int blocks_y = y.dims[1];
+    const int blocks_x = y.dims[2];
 
-            dim3 blocks(blocks_x,
-                        blocks_y * y.dims[3]);
+    dim3 blocks(blocks_x, blocks_y * y.dims[3]);
 
-            int threads = 256;
-            while (threads > y.dims[0] && threads > 32) threads /= 2;
+    int threads = 256;
+    while (threads > y.dims[0] && threads > 32) threads /= 2;
 
-            CUDA_LAUNCH((iir_kernel<T, batch_a>), blocks, threads, y, c, a, blocks_y);
-        }
-
-    }
+    CUDA_LAUNCH((iir_kernel<T, batch_a>), blocks, threads, y, c, a, blocks_y);
 }
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index d600aefacc..55de91119c 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -7,34 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
+#include <debug_cuda.hpp>
 #include <math.hpp>
 #include <utility.hpp>
-#include <debug_cuda.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 32;
-static const int THREADS_Y =  8;
+static const int THREADS_Y = 8;
 
 typedef struct {
-    int  offs[4];
+    int offs[4];
     int strds[4];
-    bool     isSeq[4];
-    uint*      ptr[4];
+    bool isSeq[4];
+    uint* ptr[4];
 } IndexKernelParam_t;
 
 template<typename T>
-__global__
-void indexKernel(Param<T> out, CParam<T> in, const IndexKernelParam_t p,
-                 const int nBBS0, const int nBBS1)
-{
+__global__ void indexKernel(Param<T> out, CParam<T> in,
+                            const IndexKernelParam_t p, const int nBBS0,
+                            const int nBBS1) {
     // retrieve index pointers
     // these can be 0 where af_array index is not used
     const uint* ptr0 = p.ptr[0];
@@ -47,37 +44,45 @@ void indexKernel(Param<T> out, CParam<T> in, const IndexKernelParam_t p,
     const bool s2 = p.isSeq[2];
     const bool s3 = p.isSeq[3];
 
-    const int gz = blockIdx.x/nBBS0;
-    const int gx = blockDim.x * (blockIdx.x - gz*nBBS0) + threadIdx.x;
+    const int gz = blockIdx.x / nBBS0;
+    const int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + threadIdx.x;
 
-    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) /nBBS1;
-    const int gy = blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw*nBBS1) + threadIdx.y;
+    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
+    const int gy =
+        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) +
+        threadIdx.y;
 
-    if (gx<out.dims[0] && gy<out.dims[1] && gz<out.dims[2] && gw<out.dims[3]) {
+    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2] &&
+        gw < out.dims[3]) {
         // calculate pointer offsets for input
-        int i = p.strds[0] * trimIndex(s0 ? gx+p.offs[0] : ptr0[gx], in.dims[0]);
-        int j = p.strds[1] * trimIndex(s1 ? gy+p.offs[1] : ptr1[gy], in.dims[1]);
-        int k = p.strds[2] * trimIndex(s2 ? gz+p.offs[2] : ptr2[gz], in.dims[2]);
-        int l = p.strds[3] * trimIndex(s3 ? gw+p.offs[3] : ptr3[gw], in.dims[3]);
+        int i =
+            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], in.dims[0]);
+        int j =
+            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], in.dims[1]);
+        int k =
+            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], in.dims[2]);
+        int l =
+            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], in.dims[3]);
         // offset input and output pointers
-        const T *src = (const T*)in.ptr + (i+j+k+l);
-        T *dst = (T*)out.ptr +(gx*out.strides[0]+gy*out.strides[1]+ gz*out.strides[2]+gw*out.strides[3]);
+        const T* src = (const T*)in.ptr + (i + j + k + l);
+        T* dst = (T*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
+                                gz * out.strides[2] + gw * out.strides[3]);
         // set the output
         dst[0] = src[0];
     }
 }
 
 template<typename T>
-void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p)
-{
+void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blks_x = divup(out.dims[0], threads.x);
     int blks_y = divup(out.dims[1], threads.y);
 
-    dim3 blocks(blks_x*out.dims[2], blks_y*out.dims[3]);
+    dim3 blocks(blks_x * out.dims[2], blks_y * out.dims[3]);
 
-    const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
@@ -86,6 +91,6 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p)
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index cc1b05eef9..a899f69156 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -9,50 +9,40 @@
 
 #include <math_constants.h>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
 template<typename T>
-struct itype_t
-{
+struct itype_t {
     typedef float wtype;
     typedef float vtype;
 };
 
 template<>
-struct itype_t<double>
-{
+struct itype_t<double> {
     typedef double wtype;
     typedef double vtype;
 };
 
 template<>
-struct itype_t<cfloat>
-{
-    typedef float  wtype;
+struct itype_t<cfloat> {
+    typedef float wtype;
     typedef cfloat vtype;
 };
 
 template<>
-struct itype_t<cdouble>
-{
-    typedef double  wtype;
+struct itype_t<cdouble> {
+    typedef double wtype;
     typedef cdouble vtype;
 };
 
 template<typename Ty, typename Tp>
-__device__
-Ty linearInterpFunc(Ty val[2], Tp ratio)
-{
+__device__ Ty linearInterpFunc(Ty val[2], Tp ratio) {
     return (1 - ratio) * val[0] + ratio * val[1];
 }
 
 template<typename Ty, typename Tp>
-__device__
-Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio)
-{
+__device__ Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio) {
     Ty res[2];
     res[0] = linearInterpFunc(val[0], xratio);
     res[1] = linearInterpFunc(val[1], xratio);
@@ -60,18 +50,14 @@ Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio)
 }
 
 template<typename Ty, typename Tp>
-__device__ inline static
-Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline)
-{
+__device__ inline static Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) {
     Ty a0, a1, a2, a3;
     if (spline) {
-        a0 =
-            scalar<Ty>(-0.5) * val[0] + scalar<Ty>( 1.5) * val[1] +
-            scalar<Ty>(-1.5) * val[2] + scalar<Ty>( 0.5) * val[3];
+        a0 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(1.5) * val[1] +
+             scalar<Ty>(-1.5) * val[2] + scalar<Ty>(0.5) * val[3];
 
-        a1 =
-            scalar<Ty>( 1.0) * val[0] + scalar<Ty>(-2.5) * val[1] +
-            scalar<Ty>( 2.0) * val[2] + scalar<Ty>(-0.5) * val[3];
+        a1 = scalar<Ty>(1.0) * val[0] + scalar<Ty>(-2.5) * val[1] +
+             scalar<Ty>(2.0) * val[2] + scalar<Ty>(-0.5) * val[3];
 
         a2 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(0.5) * val[2];
 
@@ -90,9 +76,8 @@ Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline)
 }
 
 template<typename Ty, typename Tp>
-__device__ inline static
-Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio, bool spline)
-{
+__device__ inline static Ty bicubicInterpFunc(Ty val[4][4], Tp xratio,
+                                              Tp yratio, bool spline) {
     Ty res[4];
     res[0] = cubicInterpFunc(val[0], xratio, spline);
     res[1] = cubicInterpFunc(val[1], xratio, spline);
@@ -102,127 +87,118 @@ Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio, bool spline)
 }
 
 template<typename Ty, typename Tp, int order>
-struct Interp1
-{
-};
+struct Interp1 {};
 
 template<typename Ty, typename Tp>
-struct Interp1<Ty, Tp, 1>
-{
-    __device__ void operator()(Param<Ty> out, int ooff,
-                               CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch, bool clamp,
-                               int xdim = 0, int batch_dim = 1)
-    {
+struct Interp1<Ty, Tp, 1> {
+    __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
+                               Tp x, af_interp_type method, int batch,
+                               bool clamp, int xdim = 0, int batch_dim = 1) {
         Ty zero = scalar<Ty>(0);
 
-        const int x_lim = in.dims[xdim];
+        const int x_lim    = in.dims[xdim];
         const int x_stride = in.strides[xdim];
 
-        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         bool cond = xid >= 0 && xid < x_lim;
         if (clamp) xid = max(0, min(xid, x_lim));
 
         const int idx = ioff + xid * x_stride;
 
         for (int n = 0; n < batch; n++) {
-            Ty outval = (cond || clamp) ? in.ptr[idx + n * in.strides[batch_dim]] : zero;
+            Ty outval = (cond || clamp)
+                            ? in.ptr[idx + n * in.strides[batch_dim]]
+                            : zero;
             out.ptr[ooff + n * out.strides[batch_dim]] = outval;
         }
     }
 };
 
 template<typename Ty, typename Tp>
-struct Interp1<Ty, Tp, 2>
-{
-    __device__ void operator()(Param<Ty> out, int ooff,
-                               CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch, bool clamp,
-                               int xdim = 0, int batch_dim = 1)
-    {
+struct Interp1<Ty, Tp, 2> {
+    __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
+                               Tp x, af_interp_type method, int batch,
+                               bool clamp, int xdim = 0, int batch_dim = 1) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
         const int grid_x = floor(x);    // nearest grid
-        const WT off_x = x - grid_x;    // fractional offset
+        const WT off_x   = x - grid_x;  // fractional offset
 
-        const int x_lim = in.dims[xdim];
+        const int x_lim    = in.dims[xdim];
         const int x_stride = in.strides[xdim];
-        const int idx = ioff + grid_x * x_stride;
+        const int idx      = ioff + grid_x * x_stride;
 
         bool cond[2] = {true, grid_x + 1 < x_lim};
-        int  offx[2]  = {0, cond[1] ? 1 : 0};
-        WT ratio = off_x;
+        int offx[2]  = {0, cond[1] ? 1 : 0};
+        WT ratio     = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
-            ratio = (1 - cos(ratio * CUDART_PI))/2;
+            ratio = (1 - cos(ratio * CUDART_PI)) / 2;
         }
 
         Ty zero = scalar<Ty>(0);
 
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * in.strides[batch_dim];
-            VT val[2] = {(clamp || cond[0]) ? in.ptr[idx_n + offx[0] * x_stride] : zero,
-                         (clamp || cond[1]) ? in.ptr[idx_n + offx[1] * x_stride] : zero};
-            out.ptr[ooff + n * out.strides[batch_dim]] = linearInterpFunc(val, ratio);
+            VT val[2] = {
+                (clamp || cond[0]) ? in.ptr[idx_n + offx[0] * x_stride] : zero,
+                (clamp || cond[1]) ? in.ptr[idx_n + offx[1] * x_stride] : zero};
+            out.ptr[ooff + n * out.strides[batch_dim]] =
+                linearInterpFunc(val, ratio);
         }
     }
 };
 
 template<typename Ty, typename Tp>
-struct Interp1<Ty, Tp, 3>
-{
-    __device__ void operator()(Param<Ty> out, int ooff,
-                               CParam<Ty> in, int ioff, Tp x,
-                               af_interp_type method, int batch, bool clamp,
-                               int xdim = 0, int batch_dim = 1)
-    {
+struct Interp1<Ty, Tp, 3> {
+    __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
+                               Tp x, af_interp_type method, int batch,
+                               bool clamp, int xdim = 0, int batch_dim = 1) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
         const int grid_x = floor(x);    // nearest grid
-        const WT off_x = x - grid_x;    // fractional offset
+        const WT off_x   = x - grid_x;  // fractional offset
 
-        const int x_lim = in.dims[xdim];
+        const int x_lim    = in.dims[xdim];
         const int x_stride = in.strides[xdim];
-        const int idx = ioff + grid_x * x_stride;
+        const int idx      = ioff + grid_x * x_stride;
 
-        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
-        int  offx[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                        grid_x + 2 < x_lim};
+        int offx[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
+                       cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
         bool spline = method == AF_INTERP_CUBIC_SPLINE;
-        Ty zero = scalar<Ty>(0);
+        Ty zero     = scalar<Ty>(0);
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * in.strides[batch_dim];
             VT val[4];
             for (int i = 0; i < 4; i++) {
-                val[i] = (clamp || cond[i]) ? in.ptr[idx_n + offx[i] * x_stride] : zero;
+                val[i] = (clamp || cond[i]) ? in.ptr[idx_n + offx[i] * x_stride]
+                                            : zero;
             }
-            out.ptr[ooff + n * out.strides[batch_dim]] = cubicInterpFunc(val, off_x, spline);
+            out.ptr[ooff + n * out.strides[batch_dim]] =
+                cubicInterpFunc(val, off_x, spline);
         }
     }
 };
 
 template<typename Ty, typename Tp, int order>
-struct Interp2
-{
-};
+struct Interp2 {};
 
 template<typename Ty, typename Tp>
-struct Interp2<Ty, Tp, 1>
-{
-    __device__ void operator()(Param<Ty> out, int ooff,
-                               CParam<Ty> in, int ioff, Tp x, Tp y,
-                               af_interp_type method,
-                               int batch, bool clamp,
-                               int xdim = 0, int ydim = 1,
-                               int batch_dim = 2)
-    {
+struct Interp2<Ty, Tp, 1> {
+    __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
+                               Tp x, Tp y, af_interp_type method, int batch,
+                               bool clamp, int xdim = 0, int ydim = 1,
+                               int batch_dim = 2) {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
 
-        const int x_lim = in.dims[xdim];
-        const int y_lim = in.dims[ydim];
+        const int x_lim    = in.dims[xdim];
+        const int y_lim    = in.dims[ydim];
         const int x_stride = in.strides[xdim];
         const int y_stride = in.strides[ydim];
 
@@ -236,53 +212,49 @@ struct Interp2<Ty, Tp, 1>
         bool condX = xid >= 0 && xid < x_lim;
         bool condY = yid >= 0 && yid < y_lim;
 
-        Ty zero = scalar<Ty>(0);
+        Ty zero   = scalar<Ty>(0);
         bool cond = condX && condY;
 
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * in.strides[batch_dim];
-            Ty val = (clamp || cond) ? in.ptr[idx_n] : zero;
+            Ty val    = (clamp || cond) ? in.ptr[idx_n] : zero;
             out.ptr[ooff + n * out.strides[batch_dim]] = val;
         }
     }
 };
 
 template<typename Ty, typename Tp>
-struct Interp2<Ty, Tp, 2>
-{
-    __device__ void operator()(Param<Ty> out, int ooff,
-                               CParam<Ty> in, int ioff, Tp x, Tp y,
-                               af_interp_type method,
-                               int batch, bool clamp,
-                               int xdim = 0, int ydim = 1,
-                               int batch_dim = 2)
-    {
+struct Interp2<Ty, Tp, 2> {
+    __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
+                               Tp x, Tp y, af_interp_type method, int batch,
+                               bool clamp, int xdim = 0, int ydim = 1,
+                               int batch_dim = 2) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
         const int grid_x = floor(x);
-        const WT off_x = x - grid_x;
+        const WT off_x   = x - grid_x;
 
         const int grid_y = floor(y);
-        const WT off_y = y - grid_y;
+        const WT off_y   = y - grid_y;
 
-        const int x_lim = in.dims[xdim];
-        const int y_lim = in.dims[ydim];
+        const int x_lim    = in.dims[xdim];
+        const int y_lim    = in.dims[ydim];
         const int x_stride = in.strides[xdim];
         const int y_stride = in.strides[ydim];
-        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
         bool condX[2] = {true, x + 1 < x_lim};
         bool condY[2] = {true, y + 1 < y_lim};
-        int  offx[2]  = {0, condX[1] ? 1 : 0};
-        int  offy[2]  = {0, condY[1] ? 1 : 0};
+        int offx[2]   = {0, condX[1] ? 1 : 0};
+        int offy[2]   = {0, condY[1] ? 1 : 0};
 
         WT xratio = off_x, yratio = off_y;
         if (method == AF_INTERP_LINEAR_COSINE ||
             method == AF_INTERP_BILINEAR_COSINE) {
             // Smooth the factional part with cosine
-            xratio = (1 - cos(xratio * CUDART_PI))/2;
-            yratio = (1 - cos(yratio * CUDART_PI))/2;
+            xratio = (1 - cos(xratio * CUDART_PI)) / 2;
+            yratio = (1 - cos(yratio * CUDART_PI)) / 2;
         }
 
         Ty zero = scalar<Ty>(0);
@@ -294,48 +266,51 @@ struct Interp2<Ty, Tp, 2>
                 int ioff_j = idx_n + offy[j] * y_stride;
                 for (int i = 0; i < 2; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = (cond) ? in.ptr[ioff_j + offx[i] * x_stride] : zero;
+                    val[j][i] =
+                        (cond) ? in.ptr[ioff_j + offx[i] * x_stride] : zero;
                 }
             }
-            out.ptr[ooff + n * out.strides[batch_dim]] = bilinearInterpFunc(val, xratio, yratio);
+            out.ptr[ooff + n * out.strides[batch_dim]] =
+                bilinearInterpFunc(val, xratio, yratio);
         }
     }
 };
 
 template<typename Ty, typename Tp>
-struct Interp2<Ty, Tp, 3>
-{
-    __device__ void operator()(Param<Ty> out, int ooff,
-                               CParam<Ty> in, int ioff, Tp x, Tp y,
-                               af_interp_type method,
-                               int batch, bool clamp,
-                               int xdim = 0, int ydim = 1,
-                               int batch_dim = 2)
-    {
+struct Interp2<Ty, Tp, 3> {
+    __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
+                               Tp x, Tp y, af_interp_type method, int batch,
+                               bool clamp, int xdim = 0, int ydim = 1,
+                               int batch_dim = 2) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
         const int grid_x = floor(x);
-        const WT off_x = x - grid_x;
+        const WT off_x   = x - grid_x;
 
         const int grid_y = floor(y);
-        const WT off_y = y - grid_y;
+        const WT off_y   = y - grid_y;
 
-        const int x_lim = in.dims[xdim];
-        const int y_lim = in.dims[ydim];
+        const int x_lim    = in.dims[xdim];
+        const int y_lim    = in.dims[ydim];
         const int x_stride = in.strides[xdim];
         const int y_stride = in.strides[ydim];
-        const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
         // used for setting values at boundaries
-        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
-        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim, grid_y + 2 < y_lim};
-        int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-        int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
-
-        //for bicubic interpolation, work with 4x4 val at a time
-        Ty zero = scalar<Ty>(0);
-        bool spline = (method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE);
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                         grid_x + 2 < x_lim};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim,
+                         grid_y + 2 < y_lim};
+        int offX[4]   = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0,
+                       condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int offY[4]   = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0,
+                       condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+        // for bicubic interpolation, work with 4x4 val at a time
+        Ty zero     = scalar<Ty>(0);
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE ||
+                       method == AF_INTERP_BICUBIC_SPLINE);
         for (int n = 0; n < batch; n++) {
             int idx_n = idx + n * in.strides[batch_dim];
             VT val[4][4];
@@ -345,14 +320,16 @@ struct Interp2<Ty, Tp, 3>
 #pragma unroll
                 for (int i = 0; i < 4; i++) {
                     bool cond = clamp || (condX[i] && condY[j]);
-                    val[j][i] = (cond) ? in.ptr[ioff_j + offX[i] * x_stride] : zero;
+                    val[j][i] =
+                        (cond) ? in.ptr[ioff_j + offX[i] * x_stride] : zero;
                 }
             }
 
-            out.ptr[ooff + n * out.strides[batch_dim]] = bicubicInterpFunc(val, off_x, off_y, spline);
+            out.ptr[ooff + n * out.strides[batch_dim]] =
+                bicubicInterpFunc(val, off_x, off_y, spline);
         }
     }
 };
 
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index bded6043ef..9744515a33 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -7,87 +7,78 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned IOTA_TX = 32;
-        static const unsigned IOTA_TY = 8;
-        static const unsigned TILEX = 512;
-        static const unsigned TILEY = 32;
-
-        template<typename T>
-        __global__
-        void iota_kernel(Param<T> out,
-                         const int s0, const int s1, const int s2, const int s3,
-                         const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-
-            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if(xx >= out.dims[0] ||
-               yy >= out.dims[1] ||
-               oz >= out.dims[2] ||
-               ow >= out.dims[3])
-                return;
-
-            const int ozw = ow * out.strides[3] + oz * out.strides[2];
-
-            T val = (ow % s3) * s2 * s1 * s0;
-            val  += (oz % s2) * s1 * s0;
-
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
-
-            for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                int oyzw = ozw + oy * out.strides[1];
-                T valY = val + (oy % s1) * s0;
-                for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                    int oidx = oyzw + ox;
-
-                    out.ptr[oidx] = valY + (ox % s0);
-                }
-            }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned IOTA_TX = 32;
+static const unsigned IOTA_TY = 8;
+static const unsigned TILEX   = 512;
+static const unsigned TILEY   = 32;
+
+template<typename T>
+__global__ void iota_kernel(Param<T> out, const int s0, const int s1,
+                            const int s2, const int s3, const int blocksPerMatX,
+                            const int blocksPerMatY) {
+    const int oz         = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
+
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int ozw = ow * out.strides[3] + oz * out.strides[2];
+
+    T val = (ow % s3) * s2 * s1 * s0;
+    val += (oz % s2) * s1 * s0;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        int oyzw = ozw + oy * out.strides[1];
+        T valY   = val + (oy % s1) * s0;
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            int oidx = oyzw + ox;
+
+            out.ptr[oidx] = valY + (ox % s0);
         }
+    }
+}
 
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void iota(Param<T> out, const af::dim4 &sdims) {
+    dim3 threads(IOTA_TX, IOTA_TY, 1);
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void iota(Param<T> out, const af::dim4 &sdims)
-        {
-            dim3 threads(IOTA_TX, IOTA_TY, 1);
-
-            int blocksPerMatX = divup(out.dims[0], TILEX);
-            int blocksPerMatY = divup(out.dims[1], TILEY);
+    int blocksPerMatX = divup(out.dims[0], TILEX);
+    int blocksPerMatY = divup(out.dims[1], TILEY);
 
-            dim3 blocks(blocksPerMatX * out.dims[2],
-                        blocksPerMatY * out.dims[3],
-                        1);
+    dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            CUDA_LAUNCH((iota_kernel<T>), blocks, threads,
-                    out, sdims[0], sdims[1], sdims[2], sdims[3],
-                    blocksPerMatX, blocksPerMatY);
+    CUDA_LAUNCH((iota_kernel<T>), blocks, threads, out, sdims[0], sdims[1],
+                sdims[2], sdims[3], blocksPerMatX, blocksPerMatY);
 
-            POST_LAUNCH_CHECK();
-        }
-    }
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 6d162ec5ac..864b083a8a 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -7,555 +7,560 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include "config.hpp"
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
 #include <memory>
+#include "config.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
-    template<typename T> __host__ __device__
-    static double cabs(const T& in) { return (double)in; }
+namespace cuda {
+namespace kernel {
+template<typename T>
+__host__ __device__ static double cabs(const T &in) {
+    return (double)in;
+}
 
-    template<> __host__ __device__
-    double cabs<char>(const char& in) { return (double)(in > 0); }
+template<>
+__host__ __device__ double cabs<char>(const char &in) {
+    return (double)(in > 0);
+}
 
-    template<> __host__ __device__
-    double cabs<cfloat>(const cfloat &in) { return (double)abs(in); }
+template<>
+__host__ __device__ double cabs<cfloat>(const cfloat &in) {
+    return (double)abs(in);
+}
 
-    template<> __host__ __device__
-    double cabs<cdouble>(const cdouble &in) { return (double)abs(in); }
+template<>
+__host__ __device__ double cabs<cdouble>(const cdouble &in) {
+    return (double)abs(in);
+}
 
-    template<typename T> __host__ __device__
-    static bool is_nan(const T& in) { return in != in; }
+template<typename T>
+__host__ __device__ static bool is_nan(const T &in) {
+    return in != in;
+}
 
-    template<> __host__ __device__
-    bool is_nan<cfloat>(const cfloat &in) {
-        return in.x != in.x || in.y != in.y;
-    }
+template<>
+__host__ __device__ bool is_nan<cfloat>(const cfloat &in) {
+    return in.x != in.x || in.y != in.y;
+}
 
-    template<> __host__ __device__
-    bool is_nan<cdouble>(const cdouble &in) {
-        return in.x != in.x || in.y != in.y;
-    }
+template<>
+__host__ __device__ bool is_nan<cdouble>(const cdouble &in) {
+    return in.x != in.x || in.y != in.y;
+}
 
-    template<af_op_t op, typename T>
-    struct MinMaxOp
-    {
-        T m_val;
-        uint m_idx;
-        __host__ __device__ MinMaxOp(T val, uint idx) :
-            m_val(val), m_idx(idx)
-        {
-            if (is_nan(val)) {
-                m_val = Binary<T, op>::init();
-            }
-        }
+template<af_op_t op, typename T>
+struct MinMaxOp {
+    T m_val;
+    uint m_idx;
+    __host__ __device__ MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = Binary<T, op>::init(); }
+    }
 
-        __host__ __device__ void operator()(T val, uint idx)
-        {
-            if ((cabs(val) < cabs(m_val) ||
-                 (cabs(val) == cabs(m_val) && idx > m_idx))) {
-                m_val = val;
-                m_idx = idx;
-            }
-        }
-    };
-
-    template<typename T>
-    struct MinMaxOp<af_max_t, T>
-    {
-        T m_val;
-        uint m_idx;
-        __host__ __device__ MinMaxOp(T val, uint idx) :
-            m_val(val), m_idx(idx)
-        {
-            if (is_nan(val)) {
-                m_val = Binary<T, af_max_t>::init();
-            }
+    __host__ __device__ void operator()(T val, uint idx) {
+        if ((cabs(val) < cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx > m_idx))) {
+            m_val = val;
+            m_idx = idx;
         }
+    }
+};
+
+template<typename T>
+struct MinMaxOp<af_max_t, T> {
+    T m_val;
+    uint m_idx;
+    __host__ __device__ MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = Binary<T, af_max_t>::init(); }
+    }
 
-        __host__ __device__ void operator()(T val, uint idx)
-        {
-            if ((cabs(val) > cabs(m_val) ||
-                 (cabs(val) == cabs(m_val) && idx <= m_idx))) {
-                m_val = val;
-                m_idx = idx;
-            }
-        }
-    };
-
-    template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
-    __global__
-    static void ireduce_dim_kernel(Param<T> out, uint *olptr,
-                                  CParam <T> in, const uint *ilptr,
-                                  uint blocks_x, uint blocks_y, uint offset_dim)
-    {
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-        const uint tid  = tidy * THREADS_X + tidx;
-
-        const uint zid = blockIdx.x / blocks_x;
-        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const uint xid = blockIdx_x * blockDim.x + tidx;
-        const uint yid = blockIdx_y; // yid  of output. updated for input later.
-
-        uint ids[4] = {xid, yid, zid, wid};
-
-        const T *iptr = in.ptr;
-        T *optr = out.ptr;
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        olptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        const uint blockIdx_dim = ids[dim];
-
-        ids[dim] = ids[dim] * blockDim.y + tidy;
-        iptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
-        if (!is_first) ilptr  += ids[3] * in.strides[3] + ids[2] * in.strides[2] + ids[1] * in.strides[1] + ids[0];
-        const uint id_dim_in = ids[dim];
-
-        const uint istride_dim = in.strides[dim];
-
-        bool is_valid =
-            (ids[0] < in.dims[0]) &&
-            (ids[1] < in.dims[1]) &&
-            (ids[2] < in.dims[2]) &&
-            (ids[3] < in.dims[3]);
-
-        T val = Binary<T, op>::init();
-        uint idx = id_dim_in;
-
-        if (is_valid && id_dim_in < in.dims[dim]) {
-            val = *iptr;
-            if (!is_first) idx = *ilptr;
+    __host__ __device__ void operator()(T val, uint idx) {
+        if ((cabs(val) > cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx <= m_idx))) {
+            m_val = val;
+            m_idx = idx;
         }
+    }
+};
+
+template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
+__global__ static void ireduce_dim_kernel(Param<T> out, uint *olptr,
+                                          CParam<T> in, const uint *ilptr,
+                                          uint blocks_x, uint blocks_y,
+                                          uint offset_dim) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * THREADS_X + tidx;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint xid = blockIdx_x * blockDim.x + tidx;
+    const uint yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    uint ids[4] = {xid, yid, zid, wid};
+
+    const T *iptr = in.ptr;
+    T *optr       = out.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    olptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+             ids[1] * out.strides[1] + ids[0];
+    const uint blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y + tidy;
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    if (!is_first)
+        ilptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+                 ids[1] * in.strides[1] + ids[0];
+    const uint id_dim_in = ids[dim];
+
+    const uint istride_dim = in.strides[dim];
+
+    bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
+                    (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
+
+    T val    = Binary<T, op>::init();
+    uint idx = id_dim_in;
+
+    if (is_valid && id_dim_in < in.dims[dim]) {
+        val = *iptr;
+        if (!is_first) idx = *ilptr;
+    }
 
-        MinMaxOp<op, T> Op(val, idx);
-
-        const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
+    MinMaxOp<op, T> Op(val, idx);
 
-        __shared__ T s_val[THREADS_X * DIMY];
-        __shared__ uint s_idx[THREADS_X * DIMY];
+    const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
 
-        for (int id = id_dim_in_start;
-             is_valid && (id < in.dims[dim]);
-             id += offset_dim * blockDim.y) {
+    __shared__ T s_val[THREADS_X * DIMY];
+    __shared__ uint s_idx[THREADS_X * DIMY];
 
-            iptr = iptr + offset_dim * blockDim.y * istride_dim;
-            if (!is_first) {
-                ilptr = ilptr + offset_dim * blockDim.y * istride_dim;
-                Op(*iptr, *ilptr);
-            } else {
-                Op(*iptr, id);
-            }
+    for (int id = id_dim_in_start; is_valid && (id < in.dims[dim]);
+         id += offset_dim * blockDim.y) {
+        iptr = iptr + offset_dim * blockDim.y * istride_dim;
+        if (!is_first) {
+            ilptr = ilptr + offset_dim * blockDim.y * istride_dim;
+            Op(*iptr, *ilptr);
+        } else {
+            Op(*iptr, id);
         }
+    }
 
-        s_val[tid] = Op.m_val;
-        s_idx[tid] = Op.m_idx;
+    s_val[tid] = Op.m_val;
+    s_idx[tid] = Op.m_idx;
 
-        T *s_vptr = s_val + tid;
-        uint *s_iptr = s_idx + tid;
-        __syncthreads();
+    T *s_vptr    = s_val + tid;
+    uint *s_iptr = s_idx + tid;
+    __syncthreads();
 
-        if (DIMY == 8) {
-            if (tidy < 4) {
-                Op(s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
-                *s_vptr = Op.m_val;
-                *s_iptr = Op.m_idx;
-            }
-            __syncthreads();
-        }
-
-        if (DIMY >= 4) {
-            if (tidy < 2) {
-                Op(s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
-                *s_vptr = Op.m_val;
-                *s_iptr = Op.m_idx;
-            }
-            __syncthreads();
+    if (DIMY == 8) {
+        if (tidy < 4) {
+            Op(s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
+            *s_vptr = Op.m_val;
+            *s_iptr = Op.m_idx;
         }
+        __syncthreads();
+    }
 
-        if (DIMY >= 2) {
-            if (tidy < 1) {
-                Op(s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
-                *s_vptr = Op.m_val;
-                *s_iptr = Op.m_idx;
-            }
-            __syncthreads();
+    if (DIMY >= 4) {
+        if (tidy < 2) {
+            Op(s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
+            *s_vptr = Op.m_val;
+            *s_iptr = Op.m_idx;
         }
+        __syncthreads();
+    }
 
-        if (tidy == 0 && is_valid &&
-            (blockIdx_dim < out.dims[dim])) {
-            *optr = *s_vptr;
-            *olptr = *s_iptr;
+    if (DIMY >= 2) {
+        if (tidy < 1) {
+            Op(s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
+            *s_vptr = Op.m_val;
+            *s_iptr = Op.m_idx;
         }
+        __syncthreads();
+    }
 
+    if (tidy == 0 && is_valid && (blockIdx_dim < out.dims[dim])) {
+        *optr  = *s_vptr;
+        *olptr = *s_iptr;
     }
+}
 
-    template<typename T, af_op_t op, int dim, bool is_first>
-    void ireduce_dim_launcher(Param<T> out, uint *olptr,
-                             CParam<T> in, const uint *ilptr,
-                             const uint threads_y, const dim_t blocks_dim[4])
-    {
-        dim3 threads(THREADS_X, threads_y);
+template<typename T, af_op_t op, int dim, bool is_first>
+void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
+                          const uint *ilptr, const uint threads_y,
+                          const dim_t blocks_dim[4]) {
+    dim3 threads(THREADS_X, threads_y);
 
-        dim3 blocks(blocks_dim[0] * blocks_dim[2],
-                    blocks_dim[1] * blocks_dim[3]);
+    dim3 blocks(blocks_dim[0] * blocks_dim[2], blocks_dim[1] * blocks_dim[3]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        switch (threads_y) {
+    switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 8>), blocks, threads,
-                out, olptr, in, ilptr, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 8>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim]);
+            break;
         case 4:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 4>), blocks, threads,
-                out, olptr, in, ilptr, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 4>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim]);
+            break;
         case 2:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 2>), blocks, threads,
-                out, olptr, in, ilptr, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 2>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim]);
+            break;
         case 1:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 1>), blocks, threads,
-                out, olptr, in, ilptr, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 1>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim]);
+            break;
     }
 
-    template<typename T, af_op_t op, int dim>
-    void ireduce_dim(Param<T> out,  uint *olptr, CParam<T> in)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
-        uint threads_x = THREADS_X;
+    POST_LAUNCH_CHECK();
+}
 
-        dim_t blocks_dim[] = {divup(in.dims[0], threads_x),
-                             in.dims[1], in.dims[2], in.dims[3]};
+template<typename T, af_op_t op, int dim>
+void ireduce_dim(Param<T> out, uint *olptr, CParam<T> in) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
+    uint threads_x = THREADS_X;
 
-        blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
+    dim_t blocks_dim[] = {divup(in.dims[0], threads_x), in.dims[1], in.dims[2],
+                          in.dims[3]};
 
-        Param<T> tmp = out;
-        uint *tlptr = olptr;
-        uptr<T> tmp_alloc;
-        uptr<uint> tlptr_alloc;
+    blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
-        if (blocks_dim[dim] > 1) {
-            int tmp_elements = 1;
-            tmp.dims[dim] = blocks_dim[dim];
+    Param<T> tmp = out;
+    uint *tlptr  = olptr;
+    uptr<T> tmp_alloc;
+    uptr<uint> tlptr_alloc;
 
-            for (int k = 0; k < 4; k++) tmp_elements *= tmp.dims[k];
-            tmp_alloc = memAlloc<T>(tmp_elements);
-            tlptr_alloc = memAlloc<uint>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
-            tlptr = tlptr_alloc.get();
+    if (blocks_dim[dim] > 1) {
+        int tmp_elements = 1;
+        tmp.dims[dim]    = blocks_dim[dim];
 
-            for (int k = dim + 1; k < 4; k++) tmp.strides[k] *= blocks_dim[dim];
-        }
+        for (int k = 0; k < 4; k++) tmp_elements *= tmp.dims[k];
+        tmp_alloc   = memAlloc<T>(tmp_elements);
+        tlptr_alloc = memAlloc<uint>(tmp_elements);
+        tmp.ptr     = tmp_alloc.get();
+        tlptr       = tlptr_alloc.get();
 
-        ireduce_dim_launcher<T, op, dim, true>(tmp, tlptr, in, NULL, threads_y, blocks_dim);
+        for (int k = dim + 1; k < 4; k++) tmp.strides[k] *= blocks_dim[dim];
+    }
 
-        if (blocks_dim[dim] > 1) {
-            blocks_dim[dim] = 1;
+    ireduce_dim_launcher<T, op, dim, true>(tmp, tlptr, in, NULL, threads_y,
+                                           blocks_dim);
 
-            ireduce_dim_launcher<T, op, dim, false>(out, olptr, tmp, tlptr,
-                                                    threads_y, blocks_dim);
-        }
+    if (blocks_dim[dim] > 1) {
+        blocks_dim[dim] = 1;
 
+        ireduce_dim_launcher<T, op, dim, false>(out, olptr, tmp, tlptr,
+                                                threads_y, blocks_dim);
     }
+}
 
-    template<typename T, af_op_t op>
-    __device__ void warp_reduce(T *s_ptr, uint *s_idx, uint tidx)
-    {
-        MinMaxOp<op, T> Op(s_ptr[tidx], s_idx[tidx]);
+template<typename T, af_op_t op>
+__device__ void warp_reduce(T *s_ptr, uint *s_idx, uint tidx) {
+    MinMaxOp<op, T> Op(s_ptr[tidx], s_idx[tidx]);
 #pragma unroll
-        for (int n = 16; n >= 1; n >>= 1) {
-            if (tidx < n) {
-                Op(s_ptr[tidx + n], s_idx[tidx + n]);
-                s_ptr[tidx] = Op.m_val;
-                s_idx[tidx] = Op.m_idx;
-            }
-            __syncthreads();
+    for (int n = 16; n >= 1; n >>= 1) {
+        if (tidx < n) {
+            Op(s_ptr[tidx + n], s_idx[tidx + n]);
+            s_ptr[tidx] = Op.m_val;
+            s_idx[tidx] = Op.m_idx;
         }
+        __syncthreads();
     }
+}
 
+template<typename T, af_op_t op, bool is_first, uint DIMX>
+__global__ static void ireduce_first_kernel(Param<T> out, uint *olptr,
+                                            CParam<T> in, const uint *ilptr,
+                                            uint blocks_x, uint blocks_y,
+                                            uint repeat) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * blockDim.x + tidx;
 
-    template<typename T, af_op_t op, bool is_first, uint DIMX>
-    __global__
-    static void ireduce_first_kernel(Param<T> out, uint *olptr,
-                                    CParam<T>  in, const uint *ilptr,
-                                    uint blocks_x, uint blocks_y, uint repeat)
-    {
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-        const uint tid  = tidy * blockDim.x + tidx;
-
-        const uint zid = blockIdx.x / blocks_x;
-        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
-        const uint yid = blockIdx_y * blockDim.y + tidy;
-
-        const T *iptr = in.ptr;
-        T *optr = out.ptr;
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
+    const uint yid = blockIdx_y * blockDim.y + tidy;
 
-        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
-        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    const T *iptr = in.ptr;
+    T *optr       = out.ptr;
 
-        if (!is_first) ilptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
-        olptr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
 
-        if (yid >= in.dims[1] ||
-            zid >= in.dims[2] ||
-            wid >= in.dims[3]) return;
+    if (!is_first)
+        ilptr +=
+            wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    olptr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
 
-        int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+    if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
 
-        T val = Binary<T, op>::init();
-        uint idx = xid;
+    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-        if (xid < lim) {
-            val = iptr[xid];
-            if (!is_first) idx = ilptr[xid];
-        }
+    T val    = Binary<T, op>::init();
+    uint idx = xid;
 
-        MinMaxOp<op, T> Op(val, idx);
+    if (xid < lim) {
+        val = iptr[xid];
+        if (!is_first) idx = ilptr[xid];
+    }
 
-        __shared__ T s_val[THREADS_PER_BLOCK];
-        __shared__ uint s_idx[THREADS_PER_BLOCK];
+    MinMaxOp<op, T> Op(val, idx);
 
+    __shared__ T s_val[THREADS_PER_BLOCK];
+    __shared__ uint s_idx[THREADS_PER_BLOCK];
 
-        for (int id = xid + DIMX; id < lim; id += DIMX) {
-            Op(iptr[id], (!is_first) ? ilptr[id] : id);
-        }
+    for (int id = xid + DIMX; id < lim; id += DIMX) {
+        Op(iptr[id], (!is_first) ? ilptr[id] : id);
+    }
 
-        s_val[tid] = Op.m_val;
-        s_idx[tid] = Op.m_idx;
-        __syncthreads();
+    s_val[tid] = Op.m_val;
+    s_idx[tid] = Op.m_idx;
+    __syncthreads();
 
-        T *s_vptr = s_val + tidy * DIMX;
-        uint *s_iptr = s_idx + tidy * DIMX;
+    T *s_vptr    = s_val + tidy * DIMX;
+    uint *s_iptr = s_idx + tidy * DIMX;
 
-        if (DIMX == 256) {
-            if (tidx < 128) {
-                Op(s_vptr[tidx + 128], s_iptr[tidx + 128]);
-                s_vptr[tidx] = Op.m_val;
-                s_iptr[tidx] = Op.m_idx;
-            }
-            __syncthreads();
+    if (DIMX == 256) {
+        if (tidx < 128) {
+            Op(s_vptr[tidx + 128], s_iptr[tidx + 128]);
+            s_vptr[tidx] = Op.m_val;
+            s_iptr[tidx] = Op.m_idx;
         }
+        __syncthreads();
+    }
 
-        if (DIMX >= 128) {
-            if (tidx <  64) {
-                Op(s_vptr[tidx +  64], s_iptr[tidx +  64]);
-                s_vptr[tidx] = Op.m_val;
-                s_iptr[tidx] = Op.m_idx;
-            }
-            __syncthreads();
+    if (DIMX >= 128) {
+        if (tidx < 64) {
+            Op(s_vptr[tidx + 64], s_iptr[tidx + 64]);
+            s_vptr[tidx] = Op.m_val;
+            s_iptr[tidx] = Op.m_idx;
         }
+        __syncthreads();
+    }
 
-        if (DIMX >=  64) {
-            if (tidx <  32) {
-                Op(s_vptr[tidx +  32], s_iptr[tidx +  32]);
-                s_vptr[tidx] = Op.m_val;
-                s_iptr[tidx] = Op.m_idx;
-            }
-            __syncthreads();
+    if (DIMX >= 64) {
+        if (tidx < 32) {
+            Op(s_vptr[tidx + 32], s_iptr[tidx + 32]);
+            s_vptr[tidx] = Op.m_val;
+            s_iptr[tidx] = Op.m_idx;
         }
+        __syncthreads();
+    }
 
-        warp_reduce<T, op>(s_vptr, s_iptr, tidx);
+    warp_reduce<T, op>(s_vptr, s_iptr, tidx);
 
-        if (tidx == 0) {
-            optr[blockIdx_x] = s_vptr[0];
-            olptr[blockIdx_x] = s_iptr[0];
-        }
+    if (tidx == 0) {
+        optr[blockIdx_x]  = s_vptr[0];
+        olptr[blockIdx_x] = s_iptr[0];
     }
+}
 
-    template<typename T, af_op_t op, bool is_first>
-    void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in, const uint *ilptr,
-                               const uint blocks_x, const uint blocks_y, const uint threads_x)
-    {
-
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * in.dims[2],
-                    blocks_y * in.dims[3]);
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+template<typename T, af_op_t op, bool is_first>
+void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
+                            const uint *ilptr, const uint blocks_x,
+                            const uint blocks_y, const uint threads_x) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        uint repeat = divup(in.dims[0], (blocks_x * threads_x));
+    uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-        switch (threads_x) {
+    switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first,  32>), blocks, threads,
-                out, olptr, in, ilptr, blocks_x, blocks_y, repeat); break;
+            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 32>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
+                        repeat);
+            break;
         case 64:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first,  64>), blocks, threads,
-                out, olptr, in, ilptr, blocks_x, blocks_y, repeat); break;
+            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 64>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
+                        repeat);
+            break;
         case 128:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first,  128>), blocks, threads,
-                out, olptr, in, ilptr, blocks_x, blocks_y, repeat); break;
+            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 128>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
+                        repeat);
+            break;
         case 256:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first,  256>), blocks, threads,
-                out, olptr, in, ilptr, blocks_x, blocks_y, repeat); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 256>), blocks,
+                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
+                        repeat);
+            break;
     }
 
-    template<typename T, af_op_t op>
-    void ireduce_first(Param<T> out, uint *olptr, CParam<T> in)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(in.dims[1], threads_y);
+    POST_LAUNCH_CHECK();
+}
 
-        Param<T> tmp = out;
-        uint *tlptr = olptr;
-        uptr<T> tmp_alloc;
-        uptr<uint> tlptr_alloc;
-        if (blocks_x > 1) {
-            auto elements = blocks_x * in.dims[1] * in.dims[2] * in.dims[3];
-            tmp_alloc = memAlloc<T>(elements);
-            tlptr_alloc = memAlloc<uint>(elements);
-            tmp.ptr = tmp_alloc.get();
-            tlptr = tlptr_alloc.get();
-
-            tmp.dims[0] = blocks_x;
-            for (int k = 1; k < 4; k++) tmp.strides[k] *= blocks_x;
-        }
+template<typename T, af_op_t op>
+void ireduce_first(Param<T> out, uint *olptr, CParam<T> in) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.dims[1], threads_y);
+
+    Param<T> tmp = out;
+    uint *tlptr  = olptr;
+    uptr<T> tmp_alloc;
+    uptr<uint> tlptr_alloc;
+    if (blocks_x > 1) {
+        auto elements = blocks_x * in.dims[1] * in.dims[2] * in.dims[3];
+        tmp_alloc     = memAlloc<T>(elements);
+        tlptr_alloc   = memAlloc<uint>(elements);
+        tmp.ptr       = tmp_alloc.get();
+        tlptr         = tlptr_alloc.get();
+
+        tmp.dims[0] = blocks_x;
+        for (int k = 1; k < 4; k++) tmp.strides[k] *= blocks_x;
+    }
 
-        ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x, blocks_y, threads_x);
+    ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x,
+                                        blocks_y, threads_x);
 
-        if (blocks_x > 1) {
-            ireduce_first_launcher<T, op, false>(out, olptr, tmp, tlptr, 1, blocks_y, threads_x);
-        }
+    if (blocks_x > 1) {
+        ireduce_first_launcher<T, op, false>(out, olptr, tmp, tlptr, 1,
+                                             blocks_y, threads_x);
     }
+}
 
-    template<typename T, af_op_t op>
-    void ireduce(Param<T> out, uint *olptr, CParam<T> in, int dim)
-    {
-        switch (dim) {
-        case 0: return ireduce_first<T, op   >(out, olptr, in);
-        case 1: return ireduce_dim  <T, op, 1>(out, olptr, in);
-        case 2: return ireduce_dim  <T, op, 2>(out, olptr, in);
-        case 3: return ireduce_dim  <T, op, 3>(out, olptr, in);
-        }
+template<typename T, af_op_t op>
+void ireduce(Param<T> out, uint *olptr, CParam<T> in, int dim) {
+    switch (dim) {
+        case 0: return ireduce_first<T, op>(out, olptr, in);
+        case 1: return ireduce_dim<T, op, 1>(out, olptr, in);
+        case 2: return ireduce_dim<T, op, 2>(out, olptr, in);
+        case 3: return ireduce_dim<T, op, 3>(out, olptr, in);
     }
+}
 
-    template<typename T, af_op_t op>
-    T ireduce_all(uint *idx, CParam<T> in)
-    {
-        using std::unique_ptr;
-        int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
-
-        // FIXME: Use better heuristics to get to the optimum number
-        if (in_elements > 4096) {
+template<typename T, af_op_t op>
+T ireduce_all(uint *idx, CParam<T> in) {
+    using std::unique_ptr;
+    int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+        bool is_linear = (in.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            is_linear &=
+                (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+        }
 
-            bool is_linear = (in.strides[0] == 1);
+        if (is_linear) {
+            in.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
-                is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
-            }
-
-            if (is_linear) {
-                in.dims[0] = in_elements;
-                for (int k = 1; k < 4; k++) {
-                    in.dims[k] = 1;
-                    in.strides[k] = in_elements;
-                }
+                in.dims[k]    = 1;
+                in.strides[k] = in_elements;
             }
+        }
 
-            uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-            uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-            Param<T> tmp;
-            uint *tlptr;
-
-            uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-            uint blocks_y = divup(in.dims[1], threads_y);
+        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-            tmp.dims[0] = blocks_x;
-            tmp.strides[0] = 1;
+        Param<T> tmp;
+        uint *tlptr;
 
-            for (int k = 1; k < 4; k++) {
-                tmp.dims[k] = in.dims[k];
-                tmp.strides[k] = tmp.dims[k - 1] * tmp.strides[k - 1];
-            }
+        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.dims[1], threads_y);
 
-            int tmp_elements = tmp.strides[3] * tmp.dims[3];
-
-            //TODO: Use scoped_ptr
-            auto tmp_alloc = memAlloc<T>(tmp_elements);
-            auto tlptr_alloc = memAlloc<uint>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
-            tlptr = tlptr_alloc.get();
-            ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x, blocks_y, threads_x);
-
-            unique_ptr<T[]>       h_ptr(new T[tmp_elements]);
-            unique_ptr<uint[]>    h_lptr(new uint[tmp_elements]);
-            T*      h_ptr_raw = h_ptr.get();
-            uint*   h_lptr_raw = h_lptr.get();
-
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(T),
-                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaMemcpyAsync(h_lptr_raw, tlptr, tmp_elements * sizeof(uint),
-                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-            if (!is_linear) {
-                // Converting n-d index into a linear index
-                // in is of size   [   dims0, dims1, dims2, dims3]
-                // tidx is of size [blocks_x, dims1, dims2, dims3]
-                // i / blocks_x gives you the batch number "N"
-                // "N * dims0 + i" gives the linear index
-                for (int i = 0; i < tmp_elements; i++) {
-                    h_lptr_raw[i] += (i / blocks_x) * in.dims[0];
-                }
-            }
+        tmp.dims[0]    = blocks_x;
+        tmp.strides[0] = 1;
 
-            MinMaxOp<op, T> Op(h_ptr_raw[0], h_lptr_raw[0]);
+        for (int k = 1; k < 4; k++) {
+            tmp.dims[k]    = in.dims[k];
+            tmp.strides[k] = tmp.dims[k - 1] * tmp.strides[k - 1];
+        }
 
-            for (int i = 1; i < tmp_elements; i++) {
-                Op(h_ptr_raw[i], h_lptr_raw[i]);
+        int tmp_elements = tmp.strides[3] * tmp.dims[3];
+
+        // TODO: Use scoped_ptr
+        auto tmp_alloc   = memAlloc<T>(tmp_elements);
+        auto tlptr_alloc = memAlloc<uint>(tmp_elements);
+        tmp.ptr          = tmp_alloc.get();
+        tlptr            = tlptr_alloc.get();
+        ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x,
+                                            blocks_y, threads_x);
+
+        unique_ptr<T[]> h_ptr(new T[tmp_elements]);
+        unique_ptr<uint[]> h_lptr(new uint[tmp_elements]);
+        T *h_ptr_raw     = h_ptr.get();
+        uint *h_lptr_raw = h_lptr.get();
+
+        CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(T),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
+        CUDA_CHECK(
+            cudaMemcpyAsync(h_lptr_raw, tlptr, tmp_elements * sizeof(uint),
+                            cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+
+        if (!is_linear) {
+            // Converting n-d index into a linear index
+            // in is of size   [   dims0, dims1, dims2, dims3]
+            // tidx is of size [blocks_x, dims1, dims2, dims3]
+            // i / blocks_x gives you the batch number "N"
+            // "N * dims0 + i" gives the linear index
+            for (int i = 0; i < tmp_elements; i++) {
+                h_lptr_raw[i] += (i / blocks_x) * in.dims[0];
             }
+        }
 
-            *idx = Op.m_idx;
-            return Op.m_val;
-        } else {
-
-            unique_ptr<T[]> h_ptr(new T[in_elements]);
-            T* h_ptr_raw = h_ptr.get();
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
-                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-            MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
-            for (int i = 1; i < in_elements; i++) {
-                Op(h_ptr_raw[i], i);
-            }
+        MinMaxOp<op, T> Op(h_ptr_raw[0], h_lptr_raw[0]);
 
-            *idx = Op.m_idx;
-            return Op.m_val;
+        for (int i = 1; i < tmp_elements; i++) {
+            Op(h_ptr_raw[i], h_lptr_raw[i]);
         }
-    }
 
+        *idx = Op.m_idx;
+        return Op.m_val;
+    } else {
+        unique_ptr<T[]> h_ptr(new T[in_elements]);
+        T *h_ptr_raw = h_ptr.get();
+        CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+
+        MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
+        for (int i = 1; i < in_elements; i++) { Op(h_ptr_raw[i], i); }
+
+        *idx = Op.m_idx;
+        return Op.m_val;
+    }
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index d8e6b741dc..635041af8e 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -40,10 +40,10 @@ typedef cuDoubleComplex cdouble;
 #define __neq(lhs, rhs) (lhs) != (rhs)
 
 #define __conj(in) (in)
-#define __real(in) (in)
-#define __imag(in) (0)
+#define __real(in)(in)
+#define __imag(in)(0)
 #define __abs(in) abs(in)
-#define __sigmoid(in) (1.0/(1 + exp(-(in))))
+#define __sigmoid(in) (1.0 / (1 + exp(-(in))))
 
 #define __bitor(lhs, rhs) ((lhs) | (rhs))
 #define __bitand(lhs, rhs) ((lhs) & (rhs))
@@ -56,11 +56,16 @@ typedef cuDoubleComplex cdouble;
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
 
-#define __pow(lhs, rhs) __float2int_rn(pow(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
-#define __powll(lhs, rhs) __double2ll_rn(pow(__ll2double_rn(lhs), __ll2double_rn(rhs)))
-#define __powul(lhs, rhs) __double2ull_rn(pow(__ull2double_rn(lhs), __ull2double_rn(rhs)))
-#define __powui(lhs, rhs) __double2uint_rn(pow(__uint2double_rn(lhs), __uint2double_rn(rhs)))
-#define __powsi(lhs, rhs) __double2int_rn(pow(__int2double_rn(lhs), __int2double_rn(rhs)))
+#define __pow(lhs, rhs) \
+    __float2int_rn(pow(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
+#define __powll(lhs, rhs) \
+    __double2ll_rn(pow(__ll2double_rn(lhs), __ll2double_rn(rhs)))
+#define __powul(lhs, rhs) \
+    __double2ull_rn(pow(__ull2double_rn(lhs), __ull2double_rn(rhs)))
+#define __powui(lhs, rhs) \
+    __double2uint_rn(pow(__uint2double_rn(lhs), __uint2double_rn(rhs)))
+#define __powsi(lhs, rhs) \
+    __double2int_rn(pow(__int2double_rn(lhs), __int2double_rn(rhs)))
 
 #define __convert_char(val) (char)((val) != 0)
 #define frem(lhs, rhs) remainder((lhs), (rhs))
@@ -74,59 +79,50 @@ typedef cuDoubleComplex cdouble;
 #define __cimagf(in) ((in).y)
 #define __cabsf(in) hypotf(in.x, in.y)
 
-__device__ cfloat __cplx2f(float x, float y)
-{
+__device__ cfloat __cplx2f(float x, float y) {
     cfloat res = {x, y};
     return res;
 }
 
-__device__ cfloat __cconjf(cfloat in)
-{
+__device__ cfloat __cconjf(cfloat in) {
     cfloat res = {in.x, -in.y};
     return res;
 }
 
-__device__ cfloat __caddf(cfloat lhs, cfloat rhs)
-{
+__device__ cfloat __caddf(cfloat lhs, cfloat rhs) {
     cfloat res = {lhs.x + rhs.x, lhs.y + rhs.y};
     return res;
 }
 
-__device__ cfloat __csubf(cfloat lhs, cfloat rhs)
-{
+__device__ cfloat __csubf(cfloat lhs, cfloat rhs) {
     cfloat res = {lhs.x - rhs.x, lhs.y - rhs.y};
     return res;
 }
 
-__device__ cfloat __cmulf(cfloat lhs, cfloat rhs)
-{
+__device__ cfloat __cmulf(cfloat lhs, cfloat rhs) {
     cfloat out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-__device__ cfloat __cdivf(cfloat lhs, cfloat rhs)
-{
+__device__ cfloat __cdivf(cfloat lhs, cfloat rhs) {
     // Normalize by absolute value and multiply
-    float rhs_abs = __cabsf(rhs);
+    float rhs_abs     = __cabsf(rhs);
     float inv_rhs_abs = 1.0f / rhs_abs;
-    float rhs_x = inv_rhs_abs * rhs.x;
-    float rhs_y = inv_rhs_abs * rhs.y;
-    cfloat out = {lhs.x * rhs_x + lhs.y * rhs_y,
-                  lhs.y * rhs_x - lhs.x * rhs_y};
+    float rhs_x       = inv_rhs_abs * rhs.x;
+    float rhs_y       = inv_rhs_abs * rhs.y;
+    cfloat out = {lhs.x * rhs_x + lhs.y * rhs_y, lhs.y * rhs_x - lhs.x * rhs_y};
     out.x *= inv_rhs_abs;
     out.y *= inv_rhs_abs;
     return out;
 }
 
-__device__ cfloat __cminf(cfloat lhs, cfloat rhs)
-{
+__device__ cfloat __cminf(cfloat lhs, cfloat rhs) {
     return __cabsf(lhs) < __cabsf(rhs) ? lhs : rhs;
 }
 
-__device__ cfloat __cmaxf(cfloat lhs, cfloat rhs)
-{
+__device__ cfloat __cmaxf(cfloat lhs, cfloat rhs) {
     return __cabsf(lhs) > __cabsf(rhs) ? lhs : rhs;
 }
 #define __candf(lhs, rhs) __cabsf(lhs) && __cabsf(rhs)
@@ -148,59 +144,51 @@ __device__ cfloat __cmaxf(cfloat lhs, cfloat rhs)
 #define __cimag(in) ((in).y)
 #define __cabs(in) hypot(in.x, in.y)
 
-__device__ cdouble __cplx2(double x, double y)
-{
+__device__ cdouble __cplx2(double x, double y) {
     cdouble res = {x, y};
     return res;
 }
 
-__device__ cdouble __cconj(cdouble in)
-{
+__device__ cdouble __cconj(cdouble in) {
     cdouble res = {in.x, -in.y};
     return res;
 }
 
-__device__ cdouble __cadd(cdouble lhs, cdouble rhs)
-{
+__device__ cdouble __cadd(cdouble lhs, cdouble rhs) {
     cdouble res = {lhs.x + rhs.x, lhs.y + rhs.y};
     return res;
 }
 
-__device__ cdouble __csub(cdouble lhs, cdouble rhs)
-{
+__device__ cdouble __csub(cdouble lhs, cdouble rhs) {
     cdouble res = {lhs.x - rhs.x, lhs.y - rhs.y};
     return res;
 }
 
-__device__ cdouble __cmul(cdouble lhs, cdouble rhs)
-{
+__device__ cdouble __cmul(cdouble lhs, cdouble rhs) {
     cdouble out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-__device__ cdouble __cdiv(cdouble lhs, cdouble rhs)
-{
+__device__ cdouble __cdiv(cdouble lhs, cdouble rhs) {
     // Normalize by absolute value and multiply
-    double rhs_abs = __cabs(rhs);
+    double rhs_abs     = __cabs(rhs);
     double inv_rhs_abs = 1.0 / rhs_abs;
-    double rhs_x = inv_rhs_abs * rhs.x;
-    double rhs_y = inv_rhs_abs * rhs.y;
-    cdouble out = {lhs.x * rhs_x + lhs.y * rhs_y,
+    double rhs_x       = inv_rhs_abs * rhs.x;
+    double rhs_y       = inv_rhs_abs * rhs.y;
+    cdouble out        = {lhs.x * rhs_x + lhs.y * rhs_y,
                    lhs.y * rhs_x - lhs.x * rhs_y};
     out.x *= inv_rhs_abs;
     out.y *= inv_rhs_abs;
     return out;
 }
 
-__device__ cdouble __cmin(cdouble lhs, cdouble rhs)
-{
+__device__ cdouble __cmin(cdouble lhs, cdouble rhs) {
     return __cabs(lhs) < __cabs(rhs) ? lhs : rhs;
 }
 
-__device__ cdouble __cmax(cdouble lhs, cdouble rhs)
-{
+__device__ cdouble __cmax(cdouble lhs, cdouble rhs) {
     return __cabs(lhs) > __cabs(rhs) ? lhs : rhs;
 }
 #define __cand(lhs, rhs) __cabs(lhs) && __cabs(rhs)
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index 75d9dcd160..e873c120e4 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -7,80 +7,74 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 256;
-        static const unsigned TILEY = 32;
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
+static const unsigned TILEX = 256;
+static const unsigned TILEY = 32;
 
-        template<typename To, typename Ti, int dim>
-        __global__
-        void join_kernel(Param<To> out, CParam<Ti> in,
-                         const int o0, const int o1, const int o2, const int o3,
-                         const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
+template<typename To, typename Ti, int dim>
+__global__ void join_kernel(Param<To> out, CParam<Ti> in, const int o0,
+                            const int o1, const int o2, const int o3,
+                            const int blocksPerMatX, const int blocksPerMatY) {
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
 
-            const int iz = blockIdx.x / blocksPerMatX;
-            const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int iz         = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
+    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
 
-            To *d_out      = out.ptr;
-            Ti const *d_in = in.ptr;
+    To *d_out      = out.ptr;
+    Ti const *d_in = in.ptr;
 
-            const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
-            const int blockIdx_y = (blockIdx.y + (blockIdx.z * gridDim.y)) - iw * blocksPerMatY;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+    const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
+    const int blockIdx_y =
+        (blockIdx.y + (blockIdx.z * gridDim.y)) - iw * blocksPerMatY;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            if(iz < in.dims[2] && iw < in.dims[3]) {
-                d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
-                d_in  = d_in  + iz * in.strides[2] + iw * in.strides[3];
+    if (iz < in.dims[2] && iw < in.dims[3]) {
+        d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
+        d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
 
-                for (int iy = yy; iy < in.dims[1]; iy += incy) {
-                    Ti const *d_in_ = d_in + iy * in.strides[1];
-                    To *d_out_ = d_out + (iy + o1) * out.strides[1];
+        for (int iy = yy; iy < in.dims[1]; iy += incy) {
+            Ti const *d_in_ = d_in + iy * in.strides[1];
+            To *d_out_      = d_out + (iy + o1) * out.strides[1];
 
-                    for (int ix = xx; ix < in.dims[0]; ix += incx) {
-                        d_out_[ix + o0] = d_in_[ix];
-                    }
-                }
+            for (int ix = xx; ix < in.dims[0]; ix += incx) {
+                d_out_[ix + o0] = d_in_[ix];
             }
         }
+    }
+}
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename To, typename Tx, int dim>
-        void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset)
-        {
-            dim3 threads(TX, TY, 1);
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename To, typename Tx, int dim>
+void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset) {
+    dim3 threads(TX, TY, 1);
 
-            int blocksPerMatX = divup(X.dims[0], TILEX);
-            int blocksPerMatY = divup(X.dims[1], TILEY);
+    int blocksPerMatX = divup(X.dims[0], TILEX);
+    int blocksPerMatY = divup(X.dims[1], TILEY);
 
-            dim3 blocks(blocksPerMatX * X.dims[2],
-                        blocksPerMatY * X.dims[3],
-                        1);
+    dim3 blocks(blocksPerMatX * X.dims[2], blocksPerMatY * X.dims[3], 1);
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            CUDA_LAUNCH((join_kernel<To, Tx, dim>), blocks, threads,
-                       out, X, offset[0], offset[1], offset[2], offset[3],
-                       blocksPerMatX, blocksPerMatY);
-            POST_LAUNCH_CHECK();
-        }
-    }
+    CUDA_LAUNCH((join_kernel<To, Tx, dim>), blocks, threads, out, X, offset[0],
+                offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 0ad5362eff..67eeec8891 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -7,107 +7,111 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
+#include <debug_cuda.hpp>
 #include <math.hpp>
 #include <utility.hpp>
-#include <debug_cuda.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int THREADS   = 256;
 static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
-static const int THRD_LOAD = THREADS_X/THREADS_Y;
+static const int THRD_LOAD = THREADS_X / THREADS_Y;
 
 template<typename in_t, typename idx_t>
-__global__
-void lookup1D(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int vDim)
-{
+__global__ void lookup1D(Param<in_t> out, CParam<in_t> in,
+                         CParam<idx_t> indices, int vDim) {
     int idx = threadIdx.x + blockIdx.x * THREADS * THRD_LOAD;
 
     const in_t* inPtr   = (const in_t*)in.ptr;
     const idx_t* idxPtr = (const idx_t*)indices.ptr;
 
-    in_t* outPtr  = (in_t*)out.ptr;
+    in_t* outPtr = (in_t*)out.ptr;
 
     int en = min(out.dims[vDim], idx + THRD_LOAD * THREADS);
 
     for (int oIdx = idx; oIdx < en; oIdx += THREADS) {
-        int iIdx = trimIndex(idxPtr[oIdx], in.dims[vDim]);
+        int iIdx     = trimIndex(idxPtr[oIdx], in.dims[vDim]);
         outPtr[oIdx] = inPtr[iIdx];
     }
 }
 
 template<typename in_t, typename idx_t, unsigned dim>
-__global__
-void lookupND(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
-                    int nBBS0, int nBBS1)
-{
+__global__ void lookupND(Param<in_t> out, CParam<in_t> in,
+                         CParam<idx_t> indices, int nBBS0, int nBBS1) {
     int lx = threadIdx.x;
     int ly = threadIdx.y;
 
-    int gz = blockIdx.x/nBBS0;
-    int gw = (blockIdx.y + blockIdx.z * gridDim.y)/nBBS1;
-
-    int gx = blockDim.x * (blockIdx.x - gz*nBBS0) + lx;
-    int gy = blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw*nBBS1) + ly;
+    int gz = blockIdx.x / nBBS0;
+    int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
 
-    const idx_t *idxPtr = (const idx_t*)indices.ptr;
+    int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + lx;
+    int gy =
+        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) + ly;
 
-    int i = in.strides[0]*(dim==0 ? trimIndex((int)idxPtr[gx], in.dims[0]): gx);
-    int j = in.strides[1]*(dim==1 ? trimIndex((int)idxPtr[gy], in.dims[1]): gy);
-    int k = in.strides[2]*(dim==2 ? trimIndex((int)idxPtr[gz], in.dims[2]): gz);
-    int l = in.strides[3]*(dim==3 ? trimIndex((int)idxPtr[gw], in.dims[3]): gw);
-
-    const in_t *inPtr = (const in_t*)in.ptr + (i+j+k+l);
-    in_t *outPtr = (in_t*)out.ptr +(gx*out.strides[0]+gy*out.strides[1]+
-                                    gz*out.strides[2]+gw*out.strides[3]);
+    const idx_t* idxPtr = (const idx_t*)indices.ptr;
 
-    if (gx<out.dims[0] && gy<out.dims[1] && gz<out.dims[2] && gw<out.dims[3]) {
+    int i = in.strides[0] *
+            (dim == 0 ? trimIndex((int)idxPtr[gx], in.dims[0]) : gx);
+    int j = in.strides[1] *
+            (dim == 1 ? trimIndex((int)idxPtr[gy], in.dims[1]) : gy);
+    int k = in.strides[2] *
+            (dim == 2 ? trimIndex((int)idxPtr[gz], in.dims[2]) : gz);
+    int l = in.strides[3] *
+            (dim == 3 ? trimIndex((int)idxPtr[gw], in.dims[3]) : gw);
+
+    const in_t* inPtr = (const in_t*)in.ptr + (i + j + k + l);
+    in_t* outPtr = (in_t*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
+                                     gz * out.strides[2] + gw * out.strides[3]);
+
+    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2] &&
+        gw < out.dims[3]) {
         outPtr[0] = inPtr[0];
     }
 }
 
 template<typename in_t, typename idx_t, unsigned dim>
-void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims)
-{
+void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
+            int nDims) {
     /* find which dimension has non-zero # of elements */
     int vDim = 0;
-    for (int i=0; i<4; i++) {
-        if (in.dims[i]==1)
+    for (int i = 0; i < 4; i++) {
+        if (in.dims[i] == 1)
             vDim++;
         else
             break;
     }
 
-    if (dim==0 && nDims==1 && dim==vDim) {
+    if (dim == 0 && nDims == 1 && dim == vDim) {
         const dim3 threads(THREADS, 1);
 
-        int blks = divup(out.dims[vDim], THREADS*THRD_LOAD);
+        int blks = divup(out.dims[vDim], THREADS * THRD_LOAD);
 
         dim3 blocks(blks, 1);
 
-        CUDA_LAUNCH((lookup1D<in_t, idx_t>), blocks, threads, out, in, indices, vDim);
+        CUDA_LAUNCH((lookup1D<in_t, idx_t>), blocks, threads, out, in, indices,
+                    vDim);
     } else {
         const dim3 threads(THREADS_X, THREADS_Y);
 
         int blks_x = divup(out.dims[0], threads.x);
         int blks_y = divup(out.dims[1], threads.y);
 
-        dim3 blocks(blks_x*out.dims[2], blks_y*out.dims[3]);
+        dim3 blocks(blks_x * out.dims[2], blks_y * out.dims[3]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        const int maxBlocksY =
+            cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
         blocks.z = divup(blocks.y, maxBlocksY);
         blocks.y = divup(blocks.y, blocks.z);
 
-        CUDA_LAUNCH((lookupND<in_t, idx_t, dim>), blocks, threads, out, in, indices, blks_x, blks_y);
+        CUDA_LAUNCH((lookupND<in_t, idx_t, dim>), blocks, threads, out, in,
+                    indices, blks_x, blks_y);
     }
 
     POST_LAUNCH_CHECK();
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index d2294a4985..f9b95437bb 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -7,95 +7,87 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 128;
-        static const unsigned TILEY = 32;
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
+static const unsigned TILEX = 128;
+static const unsigned TILEY = 32;
 
-        template<typename T, bool same_dims>
-        __global__
-        void lu_split_kernel(Param<T> lower, Param<T> upper, Param<T> in,
-                             const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = blockIdx.y / blocksPerMatY;
+template<typename T, bool same_dims>
+__global__ void lu_split_kernel(Param<T> lower, Param<T> upper, Param<T> in,
+                                const int blocksPerMatX,
+                                const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = blockIdx.y / blocksPerMatY;
 
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
 
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
 
-            T *d_l = lower.ptr;
-            T *d_u = upper.ptr;
-            T *d_i = in.ptr;
+    T *d_l = lower.ptr;
+    T *d_u = upper.ptr;
+    T *d_i = in.ptr;
 
-            if(oz < in.dims[2] && ow < in.dims[3]) {
-                d_i = d_i + oz * in.strides[2]    + ow * in.strides[3];
-                d_l = d_l + oz * lower.strides[2] + ow * lower.strides[3];
-                d_u = d_u + oz * upper.strides[2] + ow * upper.strides[3];
+    if (oz < in.dims[2] && ow < in.dims[3]) {
+        d_i = d_i + oz * in.strides[2] + ow * in.strides[3];
+        d_l = d_l + oz * lower.strides[2] + ow * lower.strides[3];
+        d_u = d_u + oz * upper.strides[2] + ow * upper.strides[3];
 
-                for (int oy = yy; oy < in.dims[1]; oy += incy) {
-                    T *Yd_i = d_i + oy * in.strides[1];
-                    T *Yd_l = d_l +  oy * lower.strides[1];
-                    T *Yd_u = d_u +  oy * upper.strides[1];
-                    for (int ox = xx; ox < in.dims[0]; ox += incx) {
-                        if(ox > oy) {
-                            if(same_dims || oy < lower.dims[1])
-                                Yd_l[ox] = Yd_i[ox];
-                            if(!same_dims || ox < upper.dims[0])
-                                Yd_u[ox] = scalar<T>(0);
-                        } else if (oy > ox) {
-                            if(same_dims || oy < lower.dims[1])
-                                Yd_l[ox] = scalar<T>(0);
-                            if(!same_dims || ox < upper.dims[0])
-                                Yd_u[ox] = Yd_i[ox];
-                        } else if(ox == oy) {
-                            if(same_dims || oy < lower.dims[1])
-                                Yd_l[ox] = scalar<T>(1.0);
-                            if(!same_dims || ox < upper.dims[0])
-                                Yd_u[ox] = Yd_i[ox];
-                        }
-                    }
+        for (int oy = yy; oy < in.dims[1]; oy += incy) {
+            T *Yd_i = d_i + oy * in.strides[1];
+            T *Yd_l = d_l + oy * lower.strides[1];
+            T *Yd_u = d_u + oy * upper.strides[1];
+            for (int ox = xx; ox < in.dims[0]; ox += incx) {
+                if (ox > oy) {
+                    if (same_dims || oy < lower.dims[1]) Yd_l[ox] = Yd_i[ox];
+                    if (!same_dims || ox < upper.dims[0])
+                        Yd_u[ox] = scalar<T>(0);
+                } else if (oy > ox) {
+                    if (same_dims || oy < lower.dims[1])
+                        Yd_l[ox] = scalar<T>(0);
+                    if (!same_dims || ox < upper.dims[0]) Yd_u[ox] = Yd_i[ox];
+                } else if (ox == oy) {
+                    if (same_dims || oy < lower.dims[1])
+                        Yd_l[ox] = scalar<T>(1.0);
+                    if (!same_dims || ox < upper.dims[0]) Yd_u[ox] = Yd_i[ox];
                 }
             }
         }
+    }
+}
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void lu_split(Param<T> lower, Param<T> upper, Param<T> in)
-        {
-            dim3 threads(TX, TY, 1);
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
+    dim3 threads(TX, TY, 1);
 
-            int blocksPerMatX = divup(in.dims[0], TILEX);
-            int blocksPerMatY = divup(in.dims[1], TILEY);
-            dim3 blocks(blocksPerMatX * in.dims[2],
-                        blocksPerMatY * in.dims[3],
-                        1);
+    int blocksPerMatX = divup(in.dims[0], TILEX);
+    int blocksPerMatY = divup(in.dims[1], TILEY);
+    dim3 blocks(blocksPerMatX * in.dims[2], blocksPerMatY * in.dims[3], 1);
 
-            if(lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1]) {
-                CUDA_LAUNCH((lu_split_kernel<T, true>), blocks, threads, lower, upper, in, blocksPerMatX, blocksPerMatY);
-            } else {
-                CUDA_LAUNCH((lu_split_kernel<T, false>), blocks, threads, lower, upper, in, blocksPerMatX, blocksPerMatY);
-            }
-            POST_LAUNCH_CHECK();
-        }
+    if (lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1]) {
+        CUDA_LAUNCH((lu_split_kernel<T, true>), blocks, threads, lower, upper,
+                    in, blocksPerMatX, blocksPerMatY);
+    } else {
+        CUDA_LAUNCH((lu_split_kernel<T, false>), blocks, threads, lower, upper,
+                    in, blocksPerMatX, blocksPerMatY);
     }
+    POST_LAUNCH_CHECK();
 }
-
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index dbc687cb79..454054c276 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -7,64 +7,63 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename inType, typename outType, af_match_type mType, bool needMean>
-__global__
-void matchTemplate(Param<outType> out, CParam<inType> srch, CParam<inType> tmplt,
-                   int nBBS0, int nBBS1)
-{
+__global__ void matchTemplate(Param<outType> out, CParam<inType> srch,
+                              CParam<inType> tmplt, int nBBS0, int nBBS1) {
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
 
-    int gx = threadIdx.x + (blockIdx.x - b2*nBBS0) * blockDim.x;
-    int gy = threadIdx.y + (blockIdx.y - b3*nBBS1)* blockDim.y;
+    int gx = threadIdx.x + (blockIdx.x - b2 * nBBS0) * blockDim.x;
+    int gy = threadIdx.y + (blockIdx.y - b3 * nBBS1) * blockDim.y;
 
     if (gx < srch.dims[0] && gy < srch.dims[1]) {
-
-        const int tDim0 = tmplt.dims[0];
-        const int tDim1 = tmplt.dims[1];
-        const int sDim0 = srch.dims[0];
-        const int sDim1 = srch.dims[1];
-        const inType* tptr   = (const inType*) tmplt.ptr;
-        int winNumElems = tDim0*tDim1;
+        const int tDim0    = tmplt.dims[0];
+        const int tDim1    = tmplt.dims[1];
+        const int sDim0    = srch.dims[0];
+        const int sDim1    = srch.dims[1];
+        const inType* tptr = (const inType*)tmplt.ptr;
+        int winNumElems    = tDim0 * tDim1;
 
         outType tImgMean = outType(0);
         if (needMean) {
-            for(int tj=0; tj<tDim1; tj++) {
-                int tjStride = tj*tmplt.strides[1];
+            for (int tj = 0; tj < tDim1; tj++) {
+                int tjStride = tj * tmplt.strides[1];
 
-                for(int ti=0; ti<tDim0; ti++) {
-                    tImgMean += (outType)tptr[ tjStride + ti*tmplt.strides[0] ];
+                for (int ti = 0; ti < tDim0; ti++) {
+                    tImgMean += (outType)tptr[tjStride + ti * tmplt.strides[0]];
                 }
             }
             tImgMean /= winNumElems;
         }
 
-        const inType* sptr  = (const inType*) srch.ptr + (b2 * srch.strides[2] + b3 * srch.strides[3]);
-        outType* optr       = (outType*) out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+        const inType* sptr = (const inType*)srch.ptr +
+                             (b2 * srch.strides[2] + b3 * srch.strides[3]);
+        outType* optr =
+            (outType*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
 
         // mean for window
         // this variable will be used based on mType value
         outType wImgMean = outType(0);
         if (needMean) {
-            for(int tj=0,j=gy; tj<tDim1; tj++, j++) {
-                int jStride = j*srch.strides[1];
+            for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
+                int jStride = j * srch.strides[1];
 
-                for(int ti=0, i=gx; ti<tDim0; ti++, i++) {
-                    inType sVal = ((j<sDim1 && i<sDim0) ? sptr[jStride + i*srch.strides[0]] : inType(0));
+                for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
+                    inType sVal = ((j < sDim1 && i < sDim0)
+                                       ? sptr[jStride + i * srch.strides[0]]
+                                       : inType(0));
                     wImgMean += (outType)sVal;
                 }
             }
@@ -74,72 +73,75 @@ void matchTemplate(Param<outType> out, CParam<inType> srch, CParam<inType> tmplt
         // run the window match metric
         outType disparity = outType(0);
 
-        for(int tj=0,j=gy; tj<tDim1; tj++, j++) {
-
-            int jStride  = j*srch.strides[1];
-            int tjStride = tj*tmplt.strides[1];
+        for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
+            int jStride  = j * srch.strides[1];
+            int tjStride = tj * tmplt.strides[1];
 
-            for(int ti=0, i=gx; ti<tDim0; ti++, i++) {
-
-                inType sVal = ((j<sDim1 && i<sDim0) ? sptr[jStride + i*srch.strides[0]] : inType(0));
-                inType tVal = tptr[ tjStride + ti*tmplt.strides[0] ];
+            for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
+                inType sVal = ((j < sDim1 && i < sDim0)
+                                   ? sptr[jStride + i * srch.strides[0]]
+                                   : inType(0));
+                inType tVal = tptr[tjStride + ti * tmplt.strides[0]];
 
                 outType temp;
-                switch(mType) {
+                switch (mType) {
                     case AF_SAD:
-                        disparity += fabs((outType)sVal-(outType)tVal);
+                        disparity += fabs((outType)sVal - (outType)tVal);
                         break;
                     case AF_ZSAD:
                         disparity += fabs((outType)sVal - wImgMean -
-                                (outType)tVal + tImgMean);
+                                          (outType)tVal + tImgMean);
                         break;
                     case AF_LSAD:
-                        disparity += fabs((outType)sVal-(wImgMean/tImgMean)*tVal);
+                        disparity +=
+                            fabs((outType)sVal - (wImgMean / tImgMean) * tVal);
                         break;
                     case AF_SSD:
-                        disparity += ((outType)sVal-(outType)tVal)*((outType)sVal-(outType)tVal);
+                        disparity += ((outType)sVal - (outType)tVal) *
+                                     ((outType)sVal - (outType)tVal);
                         break;
                     case AF_ZSSD:
-                        temp = ((outType)sVal - wImgMean - (outType)tVal + tImgMean);
-                        disparity += temp*temp;
+                        temp = ((outType)sVal - wImgMean - (outType)tVal +
+                                tImgMean);
+                        disparity += temp * temp;
                         break;
                     case AF_LSSD:
-                        temp = ((outType)sVal-(wImgMean/tImgMean)*tVal);
-                        disparity += temp*temp;
+                        temp = ((outType)sVal - (wImgMean / tImgMean) * tVal);
+                        disparity += temp * temp;
                         break;
                     case AF_NCC:
-                        //TODO: furture implementation
+                        // TODO: furture implementation
                         break;
                     case AF_ZNCC:
-                        //TODO: furture implementation
+                        // TODO: furture implementation
                         break;
                     case AF_SHD:
-                        //TODO: furture implementation
+                        // TODO: furture implementation
                         break;
                 }
             }
         }
 
-        optr[gy*out.strides[1]+gx] = disparity;
+        optr[gy * out.strides[1] + gx] = disparity;
     }
 }
 
 template<typename inType, typename outType, af_match_type mType, bool needMean>
-void matchTemplate(Param<outType> out, CParam<inType> srch, CParam<inType> tmplt)
-{
+void matchTemplate(Param<outType> out, CParam<inType> srch,
+                   CParam<inType> tmplt) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(srch.dims[0], threads.x);
     int blk_y = divup(srch.dims[1], threads.y);
 
-    dim3 blocks(blk_x*srch.dims[2], blk_y*srch.dims[3]);
+    dim3 blocks(blk_x * srch.dims[2], blk_y * srch.dims[3]);
 
-    CUDA_LAUNCH((matchTemplate<inType, outType, mType, needMean>), blocks, threads,
-            out, srch, tmplt, blk_x, blk_y);
+    CUDA_LAUNCH((matchTemplate<inType, outType, mType, needMean>), blocks,
+                threads, out, srch, tmplt, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index f232e14152..2944b81cf8 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -7,567 +7,578 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
-#include <backend.hpp>
-#include <copy.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
+#include <copy.hpp>
 #include <debug_cuda.hpp>
-#include "config.hpp"
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
+#include "config.hpp"
 
 #include <memory>
 #include <vector>
 
 using std::vector;
 
-namespace cuda
-{
-namespace kernel
-{
-
-    template<typename To, typename Tw>
-    __device__ __host__
-    void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt)
-    {
-        if (((*l_wt) != 0) || (r_wt != 0)) {
-            Tw l_scale = (*l_wt);
-            (*l_wt) += r_wt;
-            l_scale = l_scale/(*l_wt);
-
-            Tw r_scale = r_wt/(*l_wt);
-            (*lhs) = (l_scale * (*lhs)) + (r_scale * rhs);
-        }
+namespace cuda {
+namespace kernel {
+
+template<typename To, typename Tw>
+__device__ __host__ void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
+    if (((*l_wt) != 0) || (r_wt != 0)) {
+        Tw l_scale = (*l_wt);
+        (*l_wt) += r_wt;
+        l_scale = l_scale / (*l_wt);
+
+        Tw r_scale = r_wt / (*l_wt);
+        (*lhs)     = (l_scale * (*lhs)) + (r_scale * rhs);
     }
+}
 
-    template<typename Ti, typename Tw, typename To, uint dim, uint DIMY>
-    __global__
-    static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
-                                CParam <Ti> in, CParam<Tw> iwt,
-                                uint blocks_x, uint blocks_y, uint offset_dim)
-    {
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-        const uint tid  = tidy * THREADS_X + tidx;
-
-        const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const uint xid = blockIdx_x * blockDim.x + tidx;
-        const uint yid = blockIdx_y; // yid  of output. updated for input later.
-
-        uint ids[4] = {xid, yid, zid, wid};
-
-        const Ti *iptr = in.ptr;
-        const Tw *iwptr = iwt.ptr;
-        To *optr = out.ptr;
-        Tw *owptr = owt.ptr;
-
-        int ooffset = ids[3] * out.strides[3] +
-                      ids[2] * out.strides[2] +
-                      ids[1] * out.strides[1] + ids[0];
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        optr += ooffset;
-        if (owptr != NULL) owptr += ooffset;
-
-        const uint blockIdx_dim = ids[dim];
-
-        ids[dim] = ids[dim] * blockDim.y + tidy;
-
-        int ioffset = ids[3] * in.strides[3] +
-                      ids[2] * in.strides[2] +
-                      ids[1] * in.strides[1] + ids[0];
-        iptr  += ioffset;
-        if (iwptr != NULL) iwptr  += ioffset;
-
-        const uint id_dim_in = ids[dim];
-        const uint istride_dim = in.strides[dim];
-
-        bool is_valid =
-            (ids[0] < in.dims[0]) &&
-            (ids[1] < in.dims[1]) &&
-            (ids[2] < in.dims[2]) &&
-            (ids[3] < in.dims[3]);
+template<typename Ti, typename Tw, typename To, uint dim, uint DIMY>
+__global__ static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
+                                       CParam<Ti> in, CParam<Tw> iwt,
+                                       uint blocks_x, uint blocks_y,
+                                       uint offset_dim) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * THREADS_X + tidx;
 
-        Transform<Ti, To, af_add_t> transform;
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = blockIdx.y / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const uint xid        = blockIdx_x * blockDim.x + tidx;
+    const uint yid = blockIdx_y;  // yid  of output. updated for input later.
 
-        To val = Binary<To, af_add_t>::init();
-        Tw weight = Binary<Tw, af_add_t>::init();
+    uint ids[4] = {xid, yid, zid, wid};
 
-        if (is_valid && id_dim_in < in.dims[dim]) {
-            val = transform(*iptr);
-            if (iwptr != NULL) {
-                weight = *iwptr;
-            } else {
-                weight = (Tw)1;
-            }
-        }
+    const Ti *iptr  = in.ptr;
+    const Tw *iwptr = iwt.ptr;
+    To *optr        = out.ptr;
+    Tw *owptr       = owt.ptr;
 
-        const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
+    int ooffset = ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+                  ids[1] * out.strides[1] + ids[0];
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    optr += ooffset;
+    if (owptr != NULL) owptr += ooffset;
 
-        __shared__ To s_val[THREADS_X * DIMY];
-        __shared__ Tw s_idx[THREADS_X * DIMY];
+    const uint blockIdx_dim = ids[dim];
 
-        for (int id = id_dim_in_start;
-             is_valid && (id < in.dims[dim]);
-             id += offset_dim * blockDim.y) {
+    ids[dim] = ids[dim] * blockDim.y + tidy;
 
-            iptr = iptr + offset_dim * blockDim.y * istride_dim;
-            if (iwptr != NULL) {
-                iwptr = iwptr + offset_dim * blockDim.y * istride_dim;
-                stable_mean(&val, &weight, transform(*iptr), *iwptr);
-            } else {
-                // Faster version of stable_mean when iwptr is NULL
-                val = val + (transform(*iptr) - val) / (weight + 1);
-                weight = weight + 1;
-            }
+    int ioffset = ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+                  ids[1] * in.strides[1] + ids[0];
+    iptr += ioffset;
+    if (iwptr != NULL) iwptr += ioffset;
+
+    const uint id_dim_in   = ids[dim];
+    const uint istride_dim = in.strides[dim];
+
+    bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
+                    (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
+
+    Transform<Ti, To, af_add_t> transform;
+
+    To val    = Binary<To, af_add_t>::init();
+    Tw weight = Binary<Tw, af_add_t>::init();
+
+    if (is_valid && id_dim_in < in.dims[dim]) {
+        val = transform(*iptr);
+        if (iwptr != NULL) {
+            weight = *iwptr;
+        } else {
+            weight = (Tw)1;
         }
+    }
 
-        s_val[tid] = val;
-        s_idx[tid] = weight;
+    const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
 
-        To *s_vptr = s_val + tid;
-        Tw *s_iptr = s_idx + tid;
-        __syncthreads();
+    __shared__ To s_val[THREADS_X * DIMY];
+    __shared__ Tw s_idx[THREADS_X * DIMY];
 
-        if (DIMY == 8) {
-            if (tidy < 4) {
-                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
-            }
-            __syncthreads();
+    for (int id = id_dim_in_start; is_valid && (id < in.dims[dim]);
+         id += offset_dim * blockDim.y) {
+        iptr = iptr + offset_dim * blockDim.y * istride_dim;
+        if (iwptr != NULL) {
+            iwptr = iwptr + offset_dim * blockDim.y * istride_dim;
+            stable_mean(&val, &weight, transform(*iptr), *iwptr);
+        } else {
+            // Faster version of stable_mean when iwptr is NULL
+            val    = val + (transform(*iptr) - val) / (weight + 1);
+            weight = weight + 1;
         }
+    }
 
-        if (DIMY >= 4) {
-            if (tidy < 2) {
-                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
-            }
-            __syncthreads();
+    s_val[tid] = val;
+    s_idx[tid] = weight;
+
+    To *s_vptr = s_val + tid;
+    Tw *s_iptr = s_idx + tid;
+    __syncthreads();
+
+    if (DIMY == 8) {
+        if (tidy < 4) {
+            stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 4],
+                        s_iptr[THREADS_X * 4]);
         }
+        __syncthreads();
+    }
 
-        if (DIMY >= 2) {
-            if (tidy < 1) {
-                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
-            }
-            __syncthreads();
+    if (DIMY >= 4) {
+        if (tidy < 2) {
+            stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 2],
+                        s_iptr[THREADS_X * 2]);
         }
+        __syncthreads();
+    }
 
-        if (tidy == 0 && is_valid &&
-            (blockIdx_dim < out.dims[dim])) {
-            *optr = *s_vptr;
-            if (owptr != NULL) *owptr = *s_iptr;
+    if (DIMY >= 2) {
+        if (tidy < 1) {
+            stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 1],
+                        s_iptr[THREADS_X * 1]);
         }
+        __syncthreads();
+    }
 
+    if (tidy == 0 && is_valid && (blockIdx_dim < out.dims[dim])) {
+        *optr = *s_vptr;
+        if (owptr != NULL) *owptr = *s_iptr;
     }
+}
 
-    template<typename Ti, typename Tw, typename To, int dim>
-    void mean_dim_launcher(Param<To> out, Param<Tw> owt,
-                           CParam<Ti> in, CParam<Tw> iwt,
-                           const uint threads_y, const dim_t blocks_dim[4])
-    {
-        dim3 threads(THREADS_X, threads_y);
+template<typename Ti, typename Tw, typename To, int dim>
+void mean_dim_launcher(Param<To> out, Param<Tw> owt, CParam<Ti> in,
+                       CParam<Tw> iwt, const uint threads_y,
+                       const dim_t blocks_dim[4]) {
+    dim3 threads(THREADS_X, threads_y);
 
-        dim3 blocks(blocks_dim[0] * blocks_dim[2],
-                    blocks_dim[1] * blocks_dim[3]);
+    dim3 blocks(blocks_dim[0] * blocks_dim[2], blocks_dim[1] * blocks_dim[3]);
 
-        switch (threads_y) {
+    switch (threads_y) {
         case 8:
             CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 8>), blocks, threads,
-                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+                        out, owt, in, iwt, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim]);
+            break;
         case 4:
             CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 4>), blocks, threads,
-                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+                        out, owt, in, iwt, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim]);
+            break;
         case 2:
             CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 2>), blocks, threads,
-                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
+                        out, owt, in, iwt, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim]);
+            break;
         case 1:
             CUDA_LAUNCH((mean_dim_kernel<Ti, Tw, To, dim, 1>), blocks, threads,
-                out, owt, in, iwt, blocks_dim[0], blocks_dim[1], blocks_dim[dim]); break;
-        }
-
-        POST_LAUNCH_CHECK();
+                        out, owt, in, iwt, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim]);
+            break;
     }
 
-    template<typename Ti, typename Tw, typename To, int dim>
-    void mean_dim(Param<To> out, CParam<Ti> in, CParam<Tw> iwt)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
-        uint threads_x = THREADS_X;
+    POST_LAUNCH_CHECK();
+}
 
-        dim_t blocks_dim[] = {divup(in.dims[0], threads_x),
-                             in.dims[1], in.dims[2], in.dims[3]};
+template<typename Ti, typename Tw, typename To, int dim>
+void mean_dim(Param<To> out, CParam<Ti> in, CParam<Tw> iwt) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
+    uint threads_x = THREADS_X;
 
-        blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
+    dim_t blocks_dim[] = {divup(in.dims[0], threads_x), in.dims[1], in.dims[2],
+                          in.dims[3]};
 
-        Array<To> tmpOut = createEmptyArray<To>(dim4());
-        Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
+    blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
-        if (blocks_dim[dim] > 1) {
-            dim4 dims(4, out.dims);
-            dims[dim] = blocks_dim[dim];
-            tmpOut = createEmptyArray<To>(dims);
-            tmpWt  = createEmptyArray<Tw>(dims);
-        }
-        else {
-            tmpOut = createParamArray(out, false);
-        }
+    Array<To> tmpOut = createEmptyArray<To>(dim4());
+    Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
 
-        mean_dim_launcher<Ti, Tw, To, dim>(tmpOut, tmpWt, in, iwt, threads_y, blocks_dim);
-
-        if (blocks_dim[dim] > 1) {
-            blocks_dim[dim] = 1;
+    if (blocks_dim[dim] > 1) {
+        dim4 dims(4, out.dims);
+        dims[dim] = blocks_dim[dim];
+        tmpOut    = createEmptyArray<To>(dims);
+        tmpWt     = createEmptyArray<Tw>(dims);
+    } else {
+        tmpOut = createParamArray(out, false);
+    }
 
-            Array<Tw> owt = createEmptyArray<Tw>(dim4());
-            mean_dim_launcher<To, Tw, To, dim>(out, owt, tmpOut, tmpWt,
-                                                    threads_y, blocks_dim);
+    mean_dim_launcher<Ti, Tw, To, dim>(tmpOut, tmpWt, in, iwt, threads_y,
+                                       blocks_dim);
 
-        }
+    if (blocks_dim[dim] > 1) {
+        blocks_dim[dim] = 1;
 
+        Array<Tw> owt = createEmptyArray<Tw>(dim4());
+        mean_dim_launcher<To, Tw, To, dim>(out, owt, tmpOut, tmpWt, threads_y,
+                                           blocks_dim);
     }
+}
 
-    template<typename T, typename Tw>
-    __device__ void warp_reduce(T *s_ptr, Tw *s_idx, uint tidx)
-    {
+template<typename T, typename Tw>
+__device__ void warp_reduce(T *s_ptr, Tw *s_idx, uint tidx) {
 #pragma unroll
-        for (int n = 16; n >= 1; n >>= 1) {
-            if (tidx < n) {
-                stable_mean(s_ptr + tidx, s_idx + tidx, s_ptr[tidx + n], s_idx[tidx + n]);
-            }
-            __syncthreads();
+    for (int n = 16; n >= 1; n >>= 1) {
+        if (tidx < n) {
+            stable_mean(s_ptr + tidx, s_idx + tidx, s_ptr[tidx + n],
+                        s_idx[tidx + n]);
         }
+        __syncthreads();
     }
+}
 
-    //Calculate mean along the first dimension. If wt is an empty CParam, use
-    //weight as 1 and treat it as count. If owt is empty Param, do not write
-    //temporary reduced counts/weights to it.
-    template<typename Ti, typename Tw, typename To, uint DIMX>
-    __global__
-    static void mean_first_kernel(Param<To> out, Param<Tw> owt,
-                                  CParam<Ti> in, CParam<Tw> iwt,
-                                  uint blocks_x, uint blocks_y, uint repeat)
-    {
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-        const uint tid  = tidy * blockDim.x + tidx;
-
-        const uint zid = blockIdx.x / blocks_x;
-        const uint wid = blockIdx.y / blocks_y;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
-        const uint yid = blockIdx_y * blockDim.y + tidy;
-
-        const Ti *iptr = in.ptr;
-        const Tw *iwptr = iwt.ptr;
-        To *optr = out.ptr;
-        Tw *owptr = owt.ptr;
-
-        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
-        if (iwptr != NULL) iwptr += wid *  iwt.strides[3] + zid *  iwt.strides[2] + yid *  iwt.strides[1];
-        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-        if (owptr != NULL) owptr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-
-        if (yid >= in.dims[1] ||
-            zid >= in.dims[2] ||
-            wid >= in.dims[3]) return;
-
-        int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
-
-        Transform<Ti, To, af_add_t> transform;
-
-        To val = Binary<To, af_add_t>::init();
-        Tw weight = Binary<Tw, af_add_t>::init();
-
-        if (xid < lim) {
-            val = transform(iptr[xid]);
-            if (iwptr != NULL) {
-                weight = iwptr[xid];
-            } else {
-                weight = (Tw)1;
-            }
-        }
-
-        __shared__ To s_val[THREADS_PER_BLOCK];
-        __shared__ Tw s_idx[THREADS_PER_BLOCK];
-
+// Calculate mean along the first dimension. If wt is an empty CParam, use
+// weight as 1 and treat it as count. If owt is empty Param, do not write
+// temporary reduced counts/weights to it.
+template<typename Ti, typename Tw, typename To, uint DIMX>
+__global__ static void mean_first_kernel(Param<To> out, Param<Tw> owt,
+                                         CParam<Ti> in, CParam<Tw> iwt,
+                                         uint blocks_x, uint blocks_y,
+                                         uint repeat) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * blockDim.x + tidx;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = blockIdx.y / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const uint xid        = blockIdx_x * blockDim.x * repeat + tidx;
+    const uint yid        = blockIdx_y * blockDim.y + tidy;
+
+    const Ti *iptr  = in.ptr;
+    const Tw *iwptr = iwt.ptr;
+    To *optr        = out.ptr;
+    Tw *owptr       = owt.ptr;
+
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    if (iwptr != NULL)
+        iwptr +=
+            wid * iwt.strides[3] + zid * iwt.strides[2] + yid * iwt.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    if (owptr != NULL)
+        owptr +=
+            wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+    if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
+
+    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+
+    Transform<Ti, To, af_add_t> transform;
+
+    To val    = Binary<To, af_add_t>::init();
+    Tw weight = Binary<Tw, af_add_t>::init();
+
+    if (xid < lim) {
+        val = transform(iptr[xid]);
         if (iwptr != NULL) {
-            for (int id = xid + DIMX; id < lim; id += DIMX) {
-                stable_mean(&val, &weight, transform(iptr[id]), iwptr[id]);
-            }
+            weight = iwptr[xid];
         } else {
-            for (int id = xid + DIMX; id < lim; id += DIMX) {
-                // Faster version of stable_mean when iwptr is NULL
-                val = val + (transform(iptr[id]) - val) / (weight + 1);
-                weight = weight + 1;
-            }
+            weight = (Tw)1;
         }
+    }
 
-        s_val[tid] = val;
-        s_idx[tid] = weight;
-        __syncthreads();
-
-        To *s_vptr = s_val + tidy * DIMX;
-        Tw *s_iptr = s_idx + tidy * DIMX;
+    __shared__ To s_val[THREADS_PER_BLOCK];
+    __shared__ Tw s_idx[THREADS_PER_BLOCK];
 
-        if (DIMX == 256) {
-            if (tidx < 128) {
-                stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx + 128], s_iptr[tidx + 128]);
-            }
-            __syncthreads();
+    if (iwptr != NULL) {
+        for (int id = xid + DIMX; id < lim; id += DIMX) {
+            stable_mean(&val, &weight, transform(iptr[id]), iwptr[id]);
         }
-
-        if (DIMX >= 128) {
-            if (tidx <  64) {
-                stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx +  64], s_iptr[tidx +  64]);
-            }
-            __syncthreads();
+    } else {
+        for (int id = xid + DIMX; id < lim; id += DIMX) {
+            // Faster version of stable_mean when iwptr is NULL
+            val    = val + (transform(iptr[id]) - val) / (weight + 1);
+            weight = weight + 1;
         }
+    }
 
-        if (DIMX >=  64) {
-            if (tidx <  32) {
-                stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx +  32], s_iptr[tidx +  32]);
-            }
-            __syncthreads();
+    s_val[tid] = val;
+    s_idx[tid] = weight;
+    __syncthreads();
+
+    To *s_vptr = s_val + tidy * DIMX;
+    Tw *s_iptr = s_idx + tidy * DIMX;
+
+    if (DIMX == 256) {
+        if (tidx < 128) {
+            stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx + 128],
+                        s_iptr[tidx + 128]);
         }
+        __syncthreads();
+    }
 
-        warp_reduce<To, Tw>(s_vptr, s_iptr, tidx);
+    if (DIMX >= 128) {
+        if (tidx < 64) {
+            stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx + 64],
+                        s_iptr[tidx + 64]);
+        }
+        __syncthreads();
+    }
 
-        if (tidx == 0) {
-            optr[blockIdx_x] = s_vptr[0];
-            if (owptr != NULL) owptr[blockIdx_x] = s_iptr[0];
+    if (DIMX >= 64) {
+        if (tidx < 32) {
+            stable_mean(s_vptr + tidx, s_iptr + tidx, s_vptr[tidx + 32],
+                        s_iptr[tidx + 32]);
         }
+        __syncthreads();
     }
 
+    warp_reduce<To, Tw>(s_vptr, s_iptr, tidx);
 
-    template<typename Ti, typename Tw, typename To>
-    void mean_first_launcher(Param<To> out, Param<Tw> owt, CParam<Ti> in, CParam<Tw> iwt,
-                             const uint blocks_x, const uint blocks_y, const uint threads_x)
-    {
+    if (tidx == 0) {
+        optr[blockIdx_x] = s_vptr[0];
+        if (owptr != NULL) owptr[blockIdx_x] = s_iptr[0];
+    }
+}
 
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * in.dims[2],
-                    blocks_y * in.dims[3]);
+template<typename Ti, typename Tw, typename To>
+void mean_first_launcher(Param<To> out, Param<Tw> owt, CParam<Ti> in,
+                         CParam<Tw> iwt, const uint blocks_x,
+                         const uint blocks_y, const uint threads_x) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
 
-        uint repeat = divup(in.dims[0], (blocks_x * threads_x));
+    uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-        switch (threads_x) {
+    switch (threads_x) {
         case 32:
             CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 32>), blocks, threads,
-                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+                        out, owt, in, iwt, blocks_x, blocks_y, repeat);
+            break;
         case 64:
             CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 64>), blocks, threads,
-                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+                        out, owt, in, iwt, blocks_x, blocks_y, repeat);
+            break;
         case 128:
             CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 128>), blocks, threads,
-                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
+                        out, owt, in, iwt, blocks_x, blocks_y, repeat);
+            break;
         case 256:
             CUDA_LAUNCH((mean_first_kernel<Ti, Tw, To, 256>), blocks, threads,
-                out, owt, in, iwt, blocks_x, blocks_y, repeat); break;
-        }
-
-        POST_LAUNCH_CHECK();
+                        out, owt, in, iwt, blocks_x, blocks_y, repeat);
+            break;
     }
 
-    template<typename Ti, typename Tw, typename To>
-    void mean_first(Param<To> out, CParam<Ti> in, CParam<Tw> iwt)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(in.dims[1], threads_y);
-
-        Array<To> tmpOut = createEmptyArray<To>(dim4());
-        Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
-        if (blocks_x > 1) {
-          tmpOut = createEmptyArray<To>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
-          tmpWt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
-        } else {
-          tmpOut = createParamArray(out, false);
-        }
-
-        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
+    POST_LAUNCH_CHECK();
+}
 
-        if (blocks_x > 1) {
-            Param<Tw> owt;
-            owt.ptr = NULL;
-            mean_first_launcher<To, Tw, To>(out, owt, tmpOut, tmpWt, 1, blocks_y, threads_x);
-        }
+template<typename Ti, typename Tw, typename To>
+void mean_first(Param<To> out, CParam<Ti> in, CParam<Tw> iwt) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.dims[1], threads_y);
+
+    Array<To> tmpOut = createEmptyArray<To>(dim4());
+    Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
+    if (blocks_x > 1) {
+        tmpOut = createEmptyArray<To>(
+            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+        tmpWt = createEmptyArray<Tw>(
+            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+    } else {
+        tmpOut = createParamArray(out, false);
     }
 
-    template<typename Ti, typename Tw, typename To>
-    void mean_weighted(Param<To> out, CParam<Ti> in, CParam<Tw> iwt, int dim)
-    {
-        switch (dim) {
-        case 0: return mean_first<Ti, Tw, To   >(out, in, iwt);
-        case 1: return mean_dim  <Ti, Tw, To, 1>(out, in, iwt);
-        case 2: return mean_dim  <Ti, Tw, To, 2>(out, in, iwt);
-        case 3: return mean_dim  <Ti, Tw, To, 3>(out, in, iwt);
-        }
+    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y,
+                                    threads_x);
+
+    if (blocks_x > 1) {
+        Param<Tw> owt;
+        owt.ptr = NULL;
+        mean_first_launcher<To, Tw, To>(out, owt, tmpOut, tmpWt, 1, blocks_y,
+                                        threads_x);
     }
+}
 
-    template<typename Ti, typename Tw, typename To>
-    void mean(Param<To> out, CParam<Ti> in, int dim)
-    {
-        Param<Tw> dummy_weight;
-        mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
+template<typename Ti, typename Tw, typename To>
+void mean_weighted(Param<To> out, CParam<Ti> in, CParam<Tw> iwt, int dim) {
+    switch (dim) {
+        case 0: return mean_first<Ti, Tw, To>(out, in, iwt);
+        case 1: return mean_dim<Ti, Tw, To, 1>(out, in, iwt);
+        case 2: return mean_dim<Ti, Tw, To, 2>(out, in, iwt);
+        case 3: return mean_dim<Ti, Tw, To, 3>(out, in, iwt);
     }
+}
 
-    template<typename T, typename Tw>
-    T mean_all_weighted(CParam<T> in, CParam<Tw> iwt)
-    {
-        int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+template<typename Ti, typename Tw, typename To>
+void mean(Param<To> out, CParam<Ti> in, int dim) {
+    Param<Tw> dummy_weight;
+    mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
+}
 
-        // FIXME: Use better heuristics to get to the optimum number
-        if (in_elements > 4096) {
+template<typename T, typename Tw>
+T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
+    int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+        bool in_is_linear = (in.strides[0] == 1);
+        bool wt_is_linear = (iwt.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            in_is_linear &=
+                (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+            wt_is_linear &=
+                (iwt.strides[k] == (iwt.strides[k - 1] * iwt.dims[k - 1]));
+        }
 
-            bool in_is_linear = (in.strides[0] == 1);
-            bool wt_is_linear = (iwt.strides[0] == 1);
+        if (in_is_linear && wt_is_linear) {
+            in.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
-                in_is_linear &= ( in.strides[k] == ( in.strides[k - 1] *  in.dims[k - 1]));
-                wt_is_linear &= (iwt.strides[k] == (iwt.strides[k - 1] * iwt.dims[k - 1]));
+                in.dims[k]    = 1;
+                in.strides[k] = in_elements;
             }
 
-            if (in_is_linear && wt_is_linear) {
-                in.dims[0] = in_elements;
-                for (int k = 1; k < 4; k++) {
-                    in.dims[k] = 1;
-                    in.strides[k] = in_elements;
-                }
-
-                for (int k = 0; k < 4; k++) {
-                    iwt.dims[k] = in.dims[k];
-                    iwt.strides[k] = in.strides[k];
-                }
+            for (int k = 0; k < 4; k++) {
+                iwt.dims[k]    = in.dims[k];
+                iwt.strides[k] = in.strides[k];
             }
+        }
 
-            uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-            uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-            uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-            uint blocks_y = divup(in.dims[1], threads_y);
-
-            Array<T> tmpOut = createEmptyArray<T>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
-            Array<Tw> tmpWt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
-
-            int tmp_elements = tmpOut.elements();
-
-            mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y, threads_x);
+        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-            vector<T>  h_ptr(tmp_elements);
-            vector<Tw> h_wptr(tmp_elements);
+        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.dims[1], threads_y);
 
-            copyData<T>(h_ptr.data(),  tmpOut);
-            copyData<Tw>(h_wptr.data(), tmpWt);
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        Array<T> tmpOut =
+            createEmptyArray<T>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+        Array<Tw> tmpWt = createEmptyArray<Tw>(
+            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
 
-            T val = h_ptr[0];
-            Tw weight = h_wptr[0];
+        int tmp_elements = tmpOut.elements();
 
-            for (int i = 1; i < tmp_elements; i++) {
-                stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
-            }
+        mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x,
+                                      blocks_y, threads_x);
 
-            return val;
-        } else {
+        vector<T> h_ptr(tmp_elements);
+        vector<Tw> h_wptr(tmp_elements);
 
-            vector<T>  h_ptr(in_elements);
-            vector<Tw> h_wptr(in_elements);
+        copyData<T>(h_ptr.data(), tmpOut);
+        copyData<Tw>(h_wptr.data(), tmpWt);
+        CUDA_CHECK(
+            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr, in_elements * sizeof(T),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), iwt.ptr, in_elements * sizeof(Tw),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        T val     = h_ptr[0];
+        Tw weight = h_wptr[0];
 
-            T val = h_ptr[0];
-            Tw weight = h_wptr[0];
-            for (int i = 1; i < in_elements; i++) {
-                stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
-            }
+        for (int i = 1; i < tmp_elements; i++) {
+            stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
+        }
 
-            return val;
+        return val;
+    } else {
+        vector<T> h_ptr(in_elements);
+        vector<Tw> h_wptr(in_elements);
+
+        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr,
+                                   in_elements * sizeof(T),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), iwt.ptr,
+                                   in_elements * sizeof(Tw),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(
+            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+
+        T val     = h_ptr[0];
+        Tw weight = h_wptr[0];
+        for (int i = 1; i < in_elements; i++) {
+            stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
         }
-    }
 
-    template<typename Ti, typename Tw, typename To>
-    To mean_all(CParam<Ti> in)
-    {
-        using std::unique_ptr;
-        int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+        return val;
+    }
+}
 
-        // FIXME: Use better heuristics to get to the optimum number
-        if (in_elements > 4096) {
+template<typename Ti, typename Tw, typename To>
+To mean_all(CParam<Ti> in) {
+    using std::unique_ptr;
+    int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+        bool is_linear = (in.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            is_linear &=
+                (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+        }
 
-            bool is_linear = (in.strides[0] == 1);
+        if (is_linear) {
+            in.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
-                is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
-            }
-
-            if (is_linear) {
-                in.dims[0] = in_elements;
-                for (int k = 1; k < 4; k++) {
-                    in.dims[k] = 1;
-                    in.strides[k] = in_elements;
-                }
+                in.dims[k]    = 1;
+                in.strides[k] = in_elements;
             }
+        }
 
-            uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-            uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-
-            uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-            uint blocks_y = divup(in.dims[1], threads_y);
+        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-            Param<Tw> iwt;
-            Array<To> tmpOut = createEmptyArray<To>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
-            Array<Tw> tmpCt = createEmptyArray<Tw>({blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.dims[1], threads_y);
 
-            mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x, blocks_y, threads_x);
+        Param<Tw> iwt;
+        Array<To> tmpOut = createEmptyArray<To>(
+            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+        Array<Tw> tmpCt = createEmptyArray<Tw>(
+            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
 
-            int tmp_elements = tmpOut.elements();
-            vector<To> h_ptr(tmp_elements);
-            vector<Tw> h_cptr(tmp_elements);
+        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x,
+                                        blocks_y, threads_x);
 
-            copyData<To>(h_ptr.data(), tmpOut);
-            copyData<Tw>(h_cptr.data(), tmpCt);
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        int tmp_elements = tmpOut.elements();
+        vector<To> h_ptr(tmp_elements);
+        vector<Tw> h_cptr(tmp_elements);
 
-            To val = h_ptr[0];
-            Tw weight = h_cptr[0];
+        copyData<To>(h_ptr.data(), tmpOut);
+        copyData<Tw>(h_cptr.data(), tmpCt);
+        CUDA_CHECK(
+            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-            for (int i = 1; i < tmp_elements; i++) {
-                stable_mean(&val, &weight, h_ptr[i], h_cptr[i]);
-            }
+        To val    = h_ptr[0];
+        Tw weight = h_cptr[0];
 
-            return val;
-        } else {
-
-            vector<Ti> h_ptr(in_elements);
+        for (int i = 1; i < tmp_elements; i++) {
+            stable_mean(&val, &weight, h_ptr[i], h_cptr[i]);
+        }
 
-            CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr, in_elements * sizeof(Ti),
-                       cudaMemcpyDeviceToHost, cuda::getStream(cuda::getActiveDeviceId())));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        return val;
+    } else {
+        vector<Ti> h_ptr(in_elements);
 
-            Transform<Ti, To, af_add_t> transform;
-            Tw count = (Tw)1;
+        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr,
+                                   in_elements * sizeof(Ti),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(
+            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-            To val = transform(h_ptr[0]);
-            Tw weight = count;
-            for (int i = 1; i < in_elements; i++) {
-                stable_mean(&val, &weight, transform(h_ptr[i]), count);
-            }
+        Transform<Ti, To, af_add_t> transform;
+        Tw count = (Tw)1;
 
-            return val;
+        To val    = transform(h_ptr[0]);
+        Tw weight = count;
+        for (int i = 1; i < in_elements; i++) {
+            stable_mean(&val, &weight, transform(h_ptr[i]), count);
         }
-    }
 
+        return val;
+    }
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index 18f1592869..4d8304e964 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -7,35 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 
 #include <type_traits>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename AccType, typename T, int channels>
-static __global__
-void meanshiftKernel(Param<T> out, CParam<T> in, int radius, float cvar, uint numIters,
-                     int nBBS0, int nBBS1)
-{
-    unsigned b2   = blockIdx.x / nBBS0;
-    unsigned b3   = blockIdx.y / nBBS1;
-    const T* iptr = (const T *) in.ptr + (b2 *  in.strides[2] + b3 *  in.strides[3]);
-    T*       optr = (T *      )out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-    const int gx  = blockDim.x * (blockIdx.x-b2*nBBS0) + threadIdx.x;
-    const int gy  = blockDim.y * (blockIdx.y-b3*nBBS1) + threadIdx.y;
-
-    if (gx>=in.dims[0] || gy>=in.dims[1])
-        return;
+static __global__ void meanshiftKernel(Param<T> out, CParam<T> in, int radius,
+                                       float cvar, uint numIters, int nBBS0,
+                                       int nBBS1) {
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+    const T* iptr =
+        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr      = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
+
+    if (gx >= in.dims[0] || gy >= in.dims[1]) return;
 
     int meanPosI = gx;
     int meanPosJ = gy;
@@ -46,15 +43,15 @@ void meanshiftKernel(Param<T> out, CParam<T> in, int radius, float cvar, uint nu
     AccType currentMeanColors[channels];
 
 #pragma unroll
-    for (int ch=0; ch<channels; ++ch)
-        currentCenterColors[ch] = iptr[ (gx*in.strides[0] + gy*in.strides[1] + ch*in.strides[2]) ];
+    for (int ch = 0; ch < channels; ++ch)
+        currentCenterColors[ch] = iptr[(
+            gx * in.strides[0] + gy * in.strides[1] + ch * in.strides[2])];
 
-    const int dim0LenLmt = in.dims[0]-1;
-    const int dim1LenLmt = in.dims[1]-1;
+    const int dim0LenLmt = in.dims[0] - 1;
+    const int dim1LenLmt = in.dims[1] - 1;
 
     // scope of meanshift iterations begin
-    for (uint it=0; it<numIters; ++it) {
-
+    for (uint it = 0; it < numIters; ++it) {
         int oldMeanPosJ = meanPosJ;
         int oldMeanPosI = meanPosI;
         unsigned count  = 0;
@@ -63,32 +60,33 @@ void meanshiftKernel(Param<T> out, CParam<T> in, int radius, float cvar, uint nu
         int shift_y = 0;
 
 #pragma unroll
-        for (int ch=0; ch<channels; ++ch)
-            currentMeanColors[ch] = 0;
+        for (int ch = 0; ch < channels; ++ch) currentMeanColors[ch] = 0;
 
-        for (int wj=-radius; wj<=radius; ++wj) {
+        for (int wj = -radius; wj <= radius; ++wj) {
             int hit_count = 0;
-            int tj = meanPosJ + wj;
+            int tj        = meanPosJ + wj;
 
-            if (tj<0 || tj>dim1LenLmt) continue;
-
-            for(int wi=-radius; wi<=radius; ++wi) {
+            if (tj < 0 || tj > dim1LenLmt) continue;
 
+            for (int wi = -radius; wi <= radius; ++wi) {
                 int ti = meanPosI + wi;
 
-                if (ti<0 || ti>dim0LenLmt) continue;
+                if (ti < 0 || ti > dim0LenLmt) continue;
 
                 AccType norm = 0;
 #pragma unroll
-                for (int ch=0; ch<channels; ++ch) {
-                    tempColors[ch] = iptr[ (ti*in.strides[0] + tj*in.strides[1] + ch*in.strides[2]) ];
-                    AccType diff = (AccType)currentCenterColors[ch] - (AccType)tempColors[ch];
+                for (int ch = 0; ch < channels; ++ch) {
+                    tempColors[ch] =
+                        iptr[(ti * in.strides[0] + tj * in.strides[1] +
+                              ch * in.strides[2])];
+                    AccType diff = (AccType)currentCenterColors[ch] -
+                                   (AccType)tempColors[ch];
                     norm += (diff * diff);
                 }
 
                 if (norm <= cvar) {
 #pragma unroll
-                    for (int ch=0; ch<channels; ++ch)
+                    for (int ch = 0; ch < channels; ++ch)
                         currentMeanColors[ch] += (AccType)tempColors[ch];
 
                     shift_x += ti;
@@ -96,70 +94,74 @@ void meanshiftKernel(Param<T> out, CParam<T> in, int radius, float cvar, uint nu
                 }
             }
             count += hit_count;
-            shift_y += tj*hit_count;
+            shift_y += tj * hit_count;
         }
 
-        if (count==0) break;
+        if (count == 0) break;
 
-        const AccType fcount = 1/(AccType)count;
+        const AccType fcount = 1 / (AccType)count;
 
-        meanPosI = __float2int_rz(shift_x*fcount);
-        meanPosJ = __float2int_rz(shift_y*fcount);
+        meanPosI = __float2int_rz(shift_x * fcount);
+        meanPosJ = __float2int_rz(shift_y * fcount);
 
 #pragma unroll
-        for (int ch=0; ch<channels; ++ch)
-            currentMeanColors[ch] = __float2int_rz(currentMeanColors[ch]*fcount);
+        for (int ch = 0; ch < channels; ++ch)
+            currentMeanColors[ch] =
+                __float2int_rz(currentMeanColors[ch] * fcount);
 
         AccType norm = 0;
 #pragma unroll
-        for (int ch=0; ch<channels; ++ch) {
-            AccType diff = (AccType)currentCenterColors[ch] - currentMeanColors[ch];
+        for (int ch = 0; ch < channels; ++ch) {
+            AccType diff =
+                (AccType)currentCenterColors[ch] - currentMeanColors[ch];
             norm += (diff * diff);
         }
 
-        bool stop = (meanPosJ==oldMeanPosJ && meanPosI==oldMeanPosI) ||
-                    ((abs(oldMeanPosJ-meanPosJ) + abs(oldMeanPosI-meanPosI) + norm) <= 1);
+        bool stop = (meanPosJ == oldMeanPosJ && meanPosI == oldMeanPosI) ||
+                    ((abs(oldMeanPosJ - meanPosJ) +
+                      abs(oldMeanPosI - meanPosI) + norm) <= 1);
 
 #pragma unroll
-        for (int ch=0; ch<channels; ++ch)
+        for (int ch = 0; ch < channels; ++ch)
             currentCenterColors[ch] = (T)(currentMeanColors[ch]);
 
         if (stop) break;
-    } // scope of meanshift iterations end
+    }  // scope of meanshift iterations end
 
 #pragma unroll
-    for (int ch=0; ch<channels; ++ch)
-        optr[ (gx*out.strides[0] + gy*out.strides[1] + ch*out.strides[2]) ] = currentCenterColors[ch];
+    for (int ch = 0; ch < channels; ++ch)
+        optr[(gx * out.strides[0] + gy * out.strides[1] +
+              ch * out.strides[2])] = currentCenterColors[ch];
 }
 
 template<typename T, bool IsColor>
-void meanshift(Param<T> out, CParam<T> in,
-       const float spatialSigma, const float chromaticSigma, const uint numIters)
-{
-    typedef typename std::conditional< std::is_same<T, double>::value, double, float >::type AccType;
+void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
+               const float chromaticSigma, const uint numIters) {
+    typedef typename std::conditional<std::is_same<T, double>::value, double,
+                                      float>::type AccType;
 
     static dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], THREADS_X);
     int blk_y = divup(in.dims[1], THREADS_Y);
 
-    const int bCount   = (IsColor ? 1 : in.dims[2]);
+    const int bCount = (IsColor ? 1 : in.dims[2]);
 
     dim3 blocks(blk_x * bCount, blk_y * in.dims[3]);
 
     // clamp spatical and chromatic sigma's
-    int radius   = std::max( (int)(spatialSigma * 1.5f), 1 );
+    int radius = std::max((int)(spatialSigma * 1.5f), 1);
 
-    const float cvar = chromaticSigma*chromaticSigma;
+    const float cvar = chromaticSigma * chromaticSigma;
 
     if (IsColor)
-        CUDA_LAUNCH((meanshiftKernel<AccType, T, 3>), blocks, threads,
-                out, in, radius, cvar, numIters, blk_x, blk_y);
+        CUDA_LAUNCH((meanshiftKernel<AccType, T, 3>), blocks, threads, out, in,
+                    radius, cvar, numIters, blk_x, blk_y);
     else
-        CUDA_LAUNCH((meanshiftKernel<AccType, T, 1>), blocks, threads,
-                out, in, radius, cvar, numIters, blk_x, blk_y);
+        CUDA_LAUNCH((meanshiftKernel<AccType, T, 1>), blocks, threads, out, in,
+                    radius, cvar, numIters, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 84743981ff..8816098b85 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -7,18 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <platform.hpp>
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
+#include <platform.hpp>
 #include "shared.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int MAX_MEDFILTER1_LEN = 121;
 static const int MAX_MEDFILTER2_LEN = 15;
@@ -26,102 +24,94 @@ static const int MAX_MEDFILTER2_LEN = 15;
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-
 // Exchange trick: Morgan McGuire, ShaderX 2008
-#define swap(a,b)    { T tmp = a; a = min(a,b); b = max(tmp,b); }
+#define swap(a, b)           \
+    {                        \
+        T tmp = a;           \
+        a     = min(a, b);   \
+        b     = max(tmp, b); \
+    }
 
-__forceinline__ __device__
-int lIdx(int x, int y, int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
+__forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
 }
 
 template<typename T, af_border_type pad>
-__device__
-void load2ShrdMem(T * shrd, const T * in,
-                  int lx, int ly, int shrdStride,
-                  int dim0, int dim1,
-                  int gx, int gy,
-                  int inStride1, int inStride0)
-{
-    switch(pad) {
-        case AF_PAD_ZERO:
-            {
-                if (gx<0 || gx>=dim0 || gy<0 || gy>=dim1)
-                    shrd[lIdx(lx, ly, shrdStride, 1)] = T(0);
-                else
-                    shrd[lIdx(lx, ly, shrdStride, 1)] = in[lIdx(gx, gy, inStride1, inStride0)];
-            }
-            break;
-        case AF_PAD_SYM:
-            {
-                if (gx<0) gx *= -1;
-                if (gy<0) gy *= -1;
-                if (gx>=dim0) gx = 2*(dim0-1) - gx;
-                if (gy>=dim1) gy = 2*(dim1-1) - gy;
-
-                shrd[lIdx(lx, ly, shrdStride, 1)] = in[lIdx(gx, gy, inStride1, inStride0)];
-            }
-            break;
+__device__ void load2ShrdMem(T* shrd, const T* in, int lx, int ly,
+                             int shrdStride, int dim0, int dim1, int gx, int gy,
+                             int inStride1, int inStride0) {
+    switch (pad) {
+        case AF_PAD_ZERO: {
+            if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
+                shrd[lIdx(lx, ly, shrdStride, 1)] = T(0);
+            else
+                shrd[lIdx(lx, ly, shrdStride, 1)] =
+                    in[lIdx(gx, gy, inStride1, inStride0)];
+        } break;
+        case AF_PAD_SYM: {
+            if (gx < 0) gx *= -1;
+            if (gy < 0) gy *= -1;
+            if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
+            if (gy >= dim1) gy = 2 * (dim1 - 1) - gy;
+
+            shrd[lIdx(lx, ly, shrdStride, 1)] =
+                in[lIdx(gx, gy, inStride1, inStride0)];
+        } break;
     }
 }
 
 template<typename T, af_border_type pad>
-__device__
-void load2ShrdMem_1d(T * shrd, const T * in,
-                    int lx, int dim0, int gx, int inStride0)
-{
-    switch(pad) {
-        case AF_PAD_ZERO:
-        {
-            if (gx<0 || gx>=dim0)
+__device__ void load2ShrdMem_1d(T* shrd, const T* in, int lx, int dim0, int gx,
+                                int inStride0) {
+    switch (pad) {
+        case AF_PAD_ZERO: {
+            if (gx < 0 || gx >= dim0)
                 shrd[lx] = T(0);
             else
                 shrd[lx] = in[gx];
-        }
-        break;
-        case AF_PAD_SYM:
-        {
-            if (gx<0) gx *= -1;
-            if (gx>=dim0) gx = 2*(dim0-1) - gx;
+        } break;
+        case AF_PAD_SYM: {
+            if (gx < 0) gx *= -1;
+            if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
 
             shrd[lx] = in[gx];
-        }
-        break;
+        } break;
     }
 }
 
 template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
-__global__
-void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
-{
-    __shared__ T shrdMem[(THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)];
+__global__ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1) {
+    __shared__ T shrdMem[(THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1)];
 
     // calculate necessary offset and window parameters
-    const int padding = w_len-1;
-    const int halo    = padding/2;
+    const int padding = w_len - 1;
+    const int halo    = padding / 2;
     const int shrdLen = blockDim.x + padding;
 
     // batch offsets
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
-    const T* iptr    = (const T *) in.ptr + (b2 *  in.strides[2] + b3 *  in.strides[3]);
-    T*       optr    = (T *      )out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const T* iptr =
+        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
 
     // local neighborhood indices
     int lx = threadIdx.x;
     int ly = threadIdx.y;
 
     // global indices
-    int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
-    int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+    int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
 
     // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen; b+=blockDim.y, gy2+=blockDim.y) {
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += blockDim.y, gy2 += blockDim.y) {
         // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-            load2ShrdMem<T, pad>(shrdMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
-                    gx2-halo, gy2-halo, in.strides[1], in.strides[0]);
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            load2ShrdMem<T, pad>(shrdMem, iptr, a, b, shrdLen, in.dims[0],
+                                 in.dims[1], gx2 - halo, gy2 - halo,
+                                 in.strides[1], in.strides[0]);
         }
     }
 
@@ -129,15 +119,14 @@ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
 
     // Only continue if we're at a valid location
     if (gx < in.dims[0] && gy < in.dims[1]) {
-
-        const int ARR_SIZE = w_len * (w_wid-w_wid/2);
+        const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
         // pull top half from shared memory into local memory
         T v[ARR_SIZE];
 #pragma unroll
-        for(int k = 0; k <= w_wid/2; k++) {
+        for (int k = 0; k <= w_wid / 2; k++) {
 #pragma unroll
-            for(int i = 0; i < w_len; i++) {
-                v[w_len*k + i] = shrdMem[lIdx(lx+i,ly+k,shrdLen,1)];
+            for (int i = 0; i < w_len; i++) {
+                v[w_len * k + i] = shrdMem[lIdx(lx + i, ly + k, shrdLen, 1)];
             }
         }
 
@@ -145,41 +134,35 @@ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
         // initial sort
         // ensure min in first half, max in second half
 #pragma unroll
-        for(int i = 0; i < ARR_SIZE/2; i++) {
-            swap(v[i], v[ARR_SIZE-1-i]);
+        for (int i = 0; i < ARR_SIZE / 2; i++) {
+            swap(v[i], v[ARR_SIZE - 1 - i]);
         }
         // move min in first half to first pos
 #pragma unroll
-        for(int i = 1; i < (ARR_SIZE+1)/2; i++) {
-            swap(v[0], v[i]);
-        }
+        for (int i = 1; i < (ARR_SIZE + 1) / 2; i++) { swap(v[0], v[i]); }
         // move max in second half to last pos
 #pragma unroll
-        for(int i = ARR_SIZE-2; i >= ARR_SIZE/2; i--) {
-            swap(v[i], v[ARR_SIZE-1]);
+        for (int i = ARR_SIZE - 2; i >= ARR_SIZE / 2; i--) {
+            swap(v[i], v[ARR_SIZE - 1]);
         }
 
-        int last = ARR_SIZE-1;
-
-        for(int k = 1+w_wid/2; k < w_wid; k++) {
-
-            for(int j = 0; j < w_len; j++) {
+        int last = ARR_SIZE - 1;
 
+        for (int k = 1 + w_wid / 2; k < w_wid; k++) {
+            for (int j = 0; j < w_len; j++) {
                 // add new contestant to first position in array
-                v[0] = shrdMem[lIdx(lx+j, ly+k, shrdLen, 1)];
+                v[0] = shrdMem[lIdx(lx + j, ly + k, shrdLen, 1)];
 
                 last--;
 
                 // place max in last half, min in first half
-                for(int i = 0; i < (last+1)/2; i++) {
-                    swap(v[i], v[last-i]);
+                for (int i = 0; i < (last + 1) / 2; i++) {
+                    swap(v[i], v[last - i]);
                 }
                 // now perform swaps on each half such that
                 // max is in last pos, min is in first pos
-                for(int i = 1; i <= last/2; i++) {
-                    swap(v[0], v[i]);
-                }
-                for(int i = last-1; i >= (last+1)/2; i--) {
+                for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
+                for (int i = last - 1; i >= (last + 1) / 2; i--) {
                     swap(v[i], v[last]);
                 }
             }
@@ -188,36 +171,33 @@ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1)
         // no more new contestants
         // may still have to sort the last row
         // each outer loop drops the min and max
-        for(int k = 1; k < w_len/2; k++) {
+        for (int k = 1; k < w_len / 2; k++) {
             // move max/min into respective halves
-            for(int i = k; i < w_len/2; i++) {
-                swap(v[i], v[w_len-1-i]);
+            for (int i = k; i < w_len / 2; i++) {
+                swap(v[i], v[w_len - 1 - i]);
             }
             // move min into first pos
-            for(int i = k+1; i <= w_len/2; i++) {
-                swap(v[k], v[i]);
-            }
+            for (int i = k + 1; i <= w_len / 2; i++) { swap(v[k], v[i]); }
             // move max into last pos
-            for(int i = w_len-k-2; i >= w_len/2; i--) {
-                swap(v[i], v[w_len-1-k]);
+            for (int i = w_len - k - 2; i >= w_len / 2; i--) {
+                swap(v[i], v[w_len - 1 - k]);
             }
         }
 
         // pick the middle element of the first row
-        optr[gy*out.strides[1]+gx*out.strides[0]] = v[w_len/2];
+        optr[gy * out.strides[1] + gx * out.strides[0]] = v[w_len / 2];
     }
 }
 
 template<typename T, af_border_type pad, unsigned ARR_SIZE>
-__global__
-void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0)
-{
+__global__ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid,
+                         int nBBS0) {
     SharedMemory<T> shared;
-    T * shrdMem = shared.getPointer();
+    T* shrdMem = shared.getPointer();
 
     // calculate necessary offset and window parameters
-    const int padding = w_wid-1;
-    const int halo    = padding/2;
+    const int padding = w_wid - 1;
+    const int halo    = padding / 2;
     const int shrdLen = blockDim.x + padding;
 
     // batch offsets
@@ -225,8 +205,11 @@ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0)
     unsigned b2 = blockIdx.y;
     unsigned b3 = blockIdx.z;
 
-    const T* iptr    = (const T *) in.ptr + (b1 * in.strides[1] + b2 *  in.strides[2] + b3 *  in.strides[3]);
-    T*       optr    = (T *      )out.ptr + (b1 * in.strides[1] + b2 * out.strides[2] + b3 * out.strides[3]);
+    const T* iptr =
+        (const T*)in.ptr +
+        (b1 * in.strides[1] + b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr = (T*)out.ptr +
+              (b1 * in.strides[1] + b2 * out.strides[2] + b3 * out.strides[3]);
 
     // local neighborhood indices
     int lx = threadIdx.x;
@@ -235,58 +218,54 @@ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0)
     int gx = blockDim.x * (blockIdx.x - b1 * nBBS0) + lx;
 
     // pull signal to local memory
-    for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-        load2ShrdMem_1d<T, pad>(shrdMem, iptr, a, in.dims[0], gx2-halo, in.strides[0]);
+    for (int a = lx, gx2 = gx; a < shrdLen;
+         a += blockDim.x, gx2 += blockDim.x) {
+        load2ShrdMem_1d<T, pad>(shrdMem, iptr, a, in.dims[0], gx2 - halo,
+                                in.strides[0]);
     }
 
     __syncthreads();
 
     // Only continue if we're at a valid location
     if (gx < in.dims[0]) {
-        const int ARR_BOUNDARY = (w_wid-w_wid/2) + 1;
+        const int ARR_BOUNDARY = (w_wid - w_wid / 2) + 1;
         // pull top half from shared memory into local memory
         T v[ARR_SIZE];
 
 #pragma unroll
-        for(int k = 0; k <= w_wid/2 + 1; k++) {
-            v[k] = shrdMem[lx+k];
-        }
+        for (int k = 0; k <= w_wid / 2 + 1; k++) { v[k] = shrdMem[lx + k]; }
         // with each pass, remove min and max values and add new value
         // initial sort
         // ensure min in first half, max in second half
 #pragma unroll
-        for(int i = 0; i < ARR_BOUNDARY/2; i++) {
-            swap(v[i], v[ARR_BOUNDARY-1-i]);
+        for (int i = 0; i < ARR_BOUNDARY / 2; i++) {
+            swap(v[i], v[ARR_BOUNDARY - 1 - i]);
         }
         // move min in first half to first pos
 #pragma unroll
-        for(int i = 1; i < (ARR_BOUNDARY+1)/2; i++) {
-            swap(v[0], v[i]);
-        }
+        for (int i = 1; i < (ARR_BOUNDARY + 1) / 2; i++) { swap(v[0], v[i]); }
         // move max in second half to last pos
 #pragma unroll
-        for(int i = ARR_BOUNDARY-2; i >= ARR_BOUNDARY/2; i--) {
-            swap(v[i], v[ARR_BOUNDARY-1]);
+        for (int i = ARR_BOUNDARY - 2; i >= ARR_BOUNDARY / 2; i--) {
+            swap(v[i], v[ARR_BOUNDARY - 1]);
         }
 
-        int last = ARR_BOUNDARY-1;
+        int last = ARR_BOUNDARY - 1;
 
-        for(int k = w_wid/2 + 2; k < w_wid; k++) {
+        for (int k = w_wid / 2 + 2; k < w_wid; k++) {
             // add new contestant to first position in array
             v[0] = shrdMem[lx + k];
 
             last--;
 
             // place max in last half, min in first half
-            for(int i = 0; i < (last+1)/2; i++) {
-                swap(v[i], v[last-i]);
+            for (int i = 0; i < (last + 1) / 2; i++) {
+                swap(v[i], v[last - i]);
             }
             // now perform swaps on each half such that
             // max is in last pos, min is in first pos
-            for(int i = 1; i <= last/2; i++) {
-                swap(v[0], v[i]);
-            }
-            for(int i = last-1; i >= (last+1)/2; i--) {
+            for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
+            for (int i = last - 1; i >= (last + 1) / 2; i--) {
                 swap(v[i], v[last]);
             }
         }
@@ -294,77 +273,126 @@ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0)
         // no more new contestants
         // may still have to sort the last row
         // each outer loop drops the min and max
-        for(int k = 0; k < last; k++) {
+        for (int k = 0; k < last; k++) {
             // move max/min into respective halves
-            for(int i = k; i < ARR_BOUNDARY/2; i++) {
-                swap(v[i], v[ARR_BOUNDARY-1-i]);
+            for (int i = k; i < ARR_BOUNDARY / 2; i++) {
+                swap(v[i], v[ARR_BOUNDARY - 1 - i]);
             }
             // move min into first pos
-            for(int i = k+1; i <= ARR_BOUNDARY/2; i++) {
+            for (int i = k + 1; i <= ARR_BOUNDARY / 2; i++) {
                 swap(v[k], v[i]);
             }
             // move max into last pos
-            for(int i = ARR_BOUNDARY-k-2; i >= ARR_BOUNDARY/2; i--) {
-                swap(v[i], v[ARR_BOUNDARY-1-k]);
+            for (int i = ARR_BOUNDARY - k - 2; i >= ARR_BOUNDARY / 2; i--) {
+                swap(v[i], v[ARR_BOUNDARY - 1 - k]);
             }
         }
 
         // pick the middle element of the first row
-        optr[gx*out.strides[0]] = v[last/2];
+        optr[gx * out.strides[0]] = v[last / 2];
     }
 }
 
 template<typename T, af_border_type pad>
-void medfilt2(Param<T> out, CParam<T> in, int w_len, int w_wid)
-{
+void medfilt2(Param<T> out, CParam<T> in, int w_len, int w_wid) {
     UNUSED(w_wid);
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
     int blk_y = divup(in.dims[1], threads.y);
 
-    dim3 blocks(blk_x*in.dims[2], blk_y*in.dims[3]);
+    dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
-    switch(w_len) {
-        case  3: CUDA_LAUNCH((medfilt2<T,pad, 3, 3>), blocks, threads, out, in, blk_x, blk_y); break;
-        case  5: CUDA_LAUNCH((medfilt2<T,pad, 5, 5>), blocks, threads, out, in, blk_x, blk_y); break;
-        case  7: CUDA_LAUNCH((medfilt2<T,pad, 7, 7>), blocks, threads, out, in, blk_x, blk_y); break;
-        case  9: CUDA_LAUNCH((medfilt2<T,pad, 9, 9>), blocks, threads, out, in, blk_x, blk_y); break;
-        case 11: CUDA_LAUNCH((medfilt2<T,pad,11,11>), blocks, threads, out, in, blk_x, blk_y); break;
-        case 13: CUDA_LAUNCH((medfilt2<T,pad,13,13>), blocks, threads, out, in, blk_x, blk_y); break;
-        case 15: CUDA_LAUNCH((medfilt2<T,pad,15,15>), blocks, threads, out, in, blk_x, blk_y); break;
+    switch (w_len) {
+        case 3:
+            CUDA_LAUNCH((medfilt2<T, pad, 3, 3>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
+        case 5:
+            CUDA_LAUNCH((medfilt2<T, pad, 5, 5>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
+        case 7:
+            CUDA_LAUNCH((medfilt2<T, pad, 7, 7>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
+        case 9:
+            CUDA_LAUNCH((medfilt2<T, pad, 9, 9>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
+        case 11:
+            CUDA_LAUNCH((medfilt2<T, pad, 11, 11>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
+        case 13:
+            CUDA_LAUNCH((medfilt2<T, pad, 13, 13>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
+        case 15:
+            CUDA_LAUNCH((medfilt2<T, pad, 15, 15>), blocks, threads, out, in,
+                        blk_x, blk_y);
+            break;
     }
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_border_type pad>
-void medfilt1(Param<T> out, CParam<T> in, int w_wid)
-{
+void medfilt1(Param<T> out, CParam<T> in, int w_wid) {
     const dim3 threads(THREADS_X);
 
     int blk_x = divup(in.dims[0], threads.x);
 
-    dim3 blocks(blk_x*in.dims[1], in.dims[2], in.dims[3] );
+    dim3 blocks(blk_x * in.dims[1], in.dims[2], in.dims[3]);
 
     const size_t shrdMemBytes = sizeof(T) * (THREADS_X + w_wid - 1);
 
-    switch(w_wid) {
-        case  3: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 3>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case  5: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 4>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case  7: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 5>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case  9: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 6>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case 11: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 7>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case 13: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 8>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case 15: CUDA_LAUNCH_SMEM((medfilt1<T,pad, 9>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case 17: CUDA_LAUNCH_SMEM((medfilt1<T,pad,10>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        case 19: CUDA_LAUNCH_SMEM((medfilt1<T,pad,11>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
-        default: CUDA_LAUNCH_SMEM((medfilt1<T,pad,62>), blocks, threads, shrdMemBytes, out, in, w_wid, blk_x); break;
+    switch (w_wid) {
+        case 3:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 3>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 5:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 4>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 7:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 5>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 9:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 6>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 11:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 7>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 13:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 8>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 15:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 9>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 17:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 10>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        case 19:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 11>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
+        default:
+            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 62>), blocks, threads,
+                             shrdMemBytes, out, in, w_wid, blk_x);
+            break;
     }
 
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 1e451a0f4d..a2b6dd39c9 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -7,228 +7,217 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <backend.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 
 #include <algorithm>
 
-namespace cuda
-{
-namespace kernel
-{
-
-    typedef struct
-    {
-        int dim[4];
-    } dims_t;
-
-    static const uint DIMX = 32;
-    static const uint DIMY =  8;
-
-    template<typename T>
-    __global__ static void
-    memcopy_kernel(T *out, const dims_t ostrides,
-                   const T *in, const dims_t idims,
-                   const dims_t istrides, uint blocks_x, uint blocks_y)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int xid = blockIdx_x * blockDim.x + tidx;
-
-        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const int yid = blockIdx_y * blockDim.y + tidy;
-        // FIXME: Do more work per block
-        T * const optr = out + wid * ostrides.dim[3] + zid * ostrides.dim[2] + yid * ostrides.dim[1];
-        const T * iptr = in + wid * istrides.dim[3] + zid * istrides.dim[2] + yid * istrides.dim[1];
-
-        int istride0 = istrides.dim[0];
-        if (xid < idims.dim[0] &&
-            yid < idims.dim[1] &&
-            zid < idims.dim[2] &&
-            wid < idims.dim[3]) {
-            optr[xid] = iptr[xid * istride0];
-        }
+namespace cuda {
+namespace kernel {
+
+typedef struct {
+    int dim[4];
+} dims_t;
+
+static const uint DIMX = 32;
+static const uint DIMY = 8;
+
+template<typename T>
+__global__ static void memcopy_kernel(T *out, const dims_t ostrides,
+                                      const T *in, const dims_t idims,
+                                      const dims_t istrides, uint blocks_x,
+                                      uint blocks_y) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+
+    const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int yid = blockIdx_y * blockDim.y + tidy;
+    // FIXME: Do more work per block
+    T *const optr = out + wid * ostrides.dim[3] + zid * ostrides.dim[2] +
+                    yid * ostrides.dim[1];
+    const T *iptr = in + wid * istrides.dim[3] + zid * istrides.dim[2] +
+                    yid * istrides.dim[1];
+
+    int istride0 = istrides.dim[0];
+    if (xid < idims.dim[0] && yid < idims.dim[1] && zid < idims.dim[2] &&
+        wid < idims.dim[3]) {
+        optr[xid] = iptr[xid * istride0];
     }
+}
 
-    template<typename T>
-    void memcopy(T *out, const dim_t *ostrides,
-                 const T *in, const dim_t *idims,
-                 const dim_t *istrides, uint ndims)
-    {
-        dim3 threads(DIMX, DIMY);
-
-        if (ndims == 1) {
-            threads.x *= threads.y;
-            threads.y  = 1;
-       }
+template<typename T>
+void memcopy(T *out, const dim_t *ostrides, const T *in, const dim_t *idims,
+             const dim_t *istrides, uint ndims) {
+    dim3 threads(DIMX, DIMY);
 
-        // FIXME: DO more work per block
-        uint blocks_x = divup(idims[0], threads.x);
-        uint blocks_y = divup(idims[1], threads.y);
+    if (ndims == 1) {
+        threads.x *= threads.y;
+        threads.y = 1;
+    }
 
-        dim3 blocks(blocks_x * idims[2],
-                    blocks_y * idims[3]);
+    // FIXME: DO more work per block
+    uint blocks_x = divup(idims[0], threads.x);
+    uint blocks_y = divup(idims[1], threads.y);
 
-        dims_t _ostrides = {{(int)ostrides[0], (int)ostrides[1], (int)ostrides[2], (int)ostrides[3]}};
-        dims_t _istrides = {{(int)istrides[0], (int)istrides[1], (int)istrides[2], (int)istrides[3]}};
-        dims_t _idims = {{(int)idims[0], (int)idims[1], (int)idims[2], (int)idims[3]}};
+    dim3 blocks(blocks_x * idims[2], blocks_y * idims[3]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    dims_t _ostrides = {{(int)ostrides[0], (int)ostrides[1], (int)ostrides[2],
+                         (int)ostrides[3]}};
+    dims_t _istrides = {{(int)istrides[0], (int)istrides[1], (int)istrides[2],
+                         (int)istrides[3]}};
+    dims_t _idims    = {
+        {(int)idims[0], (int)idims[1], (int)idims[2], (int)idims[3]}};
 
-        CUDA_LAUNCH((memcopy_kernel<T>), blocks, threads,
-            out, _ostrides, in, _idims, _istrides, blocks_x, blocks_y);
-        POST_LAUNCH_CHECK();
-    }
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
+    CUDA_LAUNCH((memcopy_kernel<T>), blocks, threads, out, _ostrides, in,
+                _idims, _istrides, blocks_x, blocks_y);
+    POST_LAUNCH_CHECK();
+}
 
-    ////////////////////////////// BEGIN - templated help functions for copy_kernel ////////////////////////////////
-    template<typename T>
-    __inline__ __device__ static
-    T scale(T value, double factor) {
-        return (T)(value*factor);
-    }
+////////////////////////////// BEGIN - templated help functions for copy_kernel
+///////////////////////////////////
+template<typename T>
+__inline__ __device__ static T scale(T value, double factor) {
+    return (T)(value * factor);
+}
 
-    template<>
-    __inline__ __device__
-    cfloat scale<cfloat>(cfloat value, double factor) {
-        return make_cuFloatComplex(value.x*factor, value.y*factor);
-    }
+template<>
+__inline__ __device__ cfloat scale<cfloat>(cfloat value, double factor) {
+    return make_cuFloatComplex(value.x * factor, value.y * factor);
+}
 
-    template<>
-    __inline__ __device__
-    cdouble scale<cdouble>(cdouble value, double factor) {
-        return make_cuDoubleComplex(value.x*factor, value.y*factor);
-    }
+template<>
+__inline__ __device__ cdouble scale<cdouble>(cdouble value, double factor) {
+    return make_cuDoubleComplex(value.x * factor, value.y * factor);
+}
 
-    template<typename inType, typename outType>
-    __inline__ __device__
-    outType convertType(inType value) {
-        return (outType)value;
-    }
+template<typename inType, typename outType>
+__inline__ __device__ outType convertType(inType value) {
+    return (outType)value;
+}
 
-    template<>
-    __inline__ __device__
-    cdouble convertType<cfloat, cdouble>(cfloat value) {
-        return cuComplexFloatToDouble(value);
-    }
+template<>
+__inline__ __device__ cdouble convertType<cfloat, cdouble>(cfloat value) {
+    return cuComplexFloatToDouble(value);
+}
 
-    template<>
-    __inline__ __device__
-    cfloat convertType<cdouble, cfloat>(cdouble value) {
-        return cuComplexDoubleToFloat(value);
-    }
+template<>
+__inline__ __device__ cfloat convertType<cdouble, cfloat>(cdouble value) {
+    return cuComplexDoubleToFloat(value);
+}
 
-#define OTHER_SPECIALIZATIONS(IN_T)                     \
-    template<>                                          \
-    __inline__ __device__                               \
-    cfloat convertType<IN_T, cfloat>(IN_T value) {      \
-        return make_cuFloatComplex(value, 0.0f);        \
-    }                                                   \
-                                                        \
-    template<>                                          \
-    __inline__ __device__                               \
-    cdouble convertType<IN_T, cdouble>(IN_T value) {    \
-        return make_cuDoubleComplex(value, 0.0);        \
+#define OTHER_SPECIALIZATIONS(IN_T)                                        \
+    template<>                                                             \
+    __inline__ __device__ cfloat convertType<IN_T, cfloat>(IN_T value) {   \
+        return make_cuFloatComplex(value, 0.0f);                           \
+    }                                                                      \
+                                                                           \
+    template<>                                                             \
+    __inline__ __device__ cdouble convertType<IN_T, cdouble>(IN_T value) { \
+        return make_cuDoubleComplex(value, 0.0);                           \
     }
 
-    OTHER_SPECIALIZATIONS(float )
-    OTHER_SPECIALIZATIONS(double)
-    OTHER_SPECIALIZATIONS(int   )
-    OTHER_SPECIALIZATIONS(uint  )
-    OTHER_SPECIALIZATIONS(intl   )
-    OTHER_SPECIALIZATIONS(uintl  )
-    OTHER_SPECIALIZATIONS(short  )
-    OTHER_SPECIALIZATIONS(ushort )
-    OTHER_SPECIALIZATIONS(uchar )
-    OTHER_SPECIALIZATIONS(char  )
-    ////////////////////////////// END - templated help functions for copy_kernel //////////////////////////////////
-
-
-    template<typename inType, typename outType, bool same_dims>
-    __global__ static void
-    copy_kernel(Param<outType> dst, CParam<inType> src, outType default_value,
-                double factor, const dims_t trgt, uint blk_x, uint blk_y)
-    {
-        const uint lx = threadIdx.x;
-        const uint ly = threadIdx.y;
-
-        const uint gz = blockIdx.x / blk_x;
-        const uint gw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blk_y;
-        const uint blockIdx_x = blockIdx.x - (blk_x) * gz;
-        const uint blockIdx_y = (blockIdx.y + (blockIdx.z * gridDim.y)) - (blk_y) * gw;
-        const uint gx = blockIdx_x * blockDim.x + lx;
-        const uint gy = blockIdx_y * blockDim.y + ly;
-
-        const inType * in = src.ptr + (gw * src.strides[3] + gz * src.strides[2] + gy * src.strides[1]);
-        outType * out     = dst.ptr + (gw * dst.strides[3] + gz * dst.strides[2] + gy * dst.strides[1]);
-
-        int istride0 = src.strides[0];
-        int ostride0 = dst.strides[0];
-
-        if (gy < dst.dims[1] && gz < dst.dims[2] && gw < dst.dims[3]) {
-            int loop_offset = blockDim.x * blk_x;
-            bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
-            for(int rep=gx; rep<dst.dims[0]; rep+=loop_offset) {
-                outType temp = default_value;
-                if (same_dims || (rep < trgt.dim[0] && cond)) {
-                    temp = convertType<inType, outType>(scale<inType>(in[rep * istride0], factor));
-                }
-                out[rep*ostride0] = temp;
+OTHER_SPECIALIZATIONS(float)
+OTHER_SPECIALIZATIONS(double)
+OTHER_SPECIALIZATIONS(int)
+OTHER_SPECIALIZATIONS(uint)
+OTHER_SPECIALIZATIONS(intl)
+OTHER_SPECIALIZATIONS(uintl)
+OTHER_SPECIALIZATIONS(short)
+OTHER_SPECIALIZATIONS(ushort)
+OTHER_SPECIALIZATIONS(uchar)
+OTHER_SPECIALIZATIONS(char)
+////////////////////////////// END - templated help functions for copy_kernel
+/////////////////////////////////////
+
+template<typename inType, typename outType, bool same_dims>
+__global__ static void copy_kernel(Param<outType> dst, CParam<inType> src,
+                                   outType default_value, double factor,
+                                   const dims_t trgt, uint blk_x, uint blk_y) {
+    const uint lx = threadIdx.x;
+    const uint ly = threadIdx.y;
+
+    const uint gz         = blockIdx.x / blk_x;
+    const uint gw         = (blockIdx.y + (blockIdx.z * gridDim.y)) / blk_y;
+    const uint blockIdx_x = blockIdx.x - (blk_x)*gz;
+    const uint blockIdx_y =
+        (blockIdx.y + (blockIdx.z * gridDim.y)) - (blk_y)*gw;
+    const uint gx = blockIdx_x * blockDim.x + lx;
+    const uint gy = blockIdx_y * blockDim.y + ly;
+
+    const inType *in = src.ptr + (gw * src.strides[3] + gz * src.strides[2] +
+                                  gy * src.strides[1]);
+    outType *out     = dst.ptr + (gw * dst.strides[3] + gz * dst.strides[2] +
+                              gy * dst.strides[1]);
+
+    int istride0 = src.strides[0];
+    int ostride0 = dst.strides[0];
+
+    if (gy < dst.dims[1] && gz < dst.dims[2] && gw < dst.dims[3]) {
+        int loop_offset = blockDim.x * blk_x;
+        bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
+        for (int rep = gx; rep < dst.dims[0]; rep += loop_offset) {
+            outType temp = default_value;
+            if (same_dims || (rep < trgt.dim[0] && cond)) {
+                temp = convertType<inType, outType>(
+                    scale<inType>(in[rep * istride0], factor));
             }
+            out[rep * ostride0] = temp;
         }
     }
+}
 
-    template<typename inType, typename outType>
-    void copy(Param<outType> dst, CParam<inType> src, int ndims, outType default_value, double factor)
-    {
-        dim3 threads(DIMX, DIMY);
-        size_t local_size[] = {DIMX, DIMY};
-
-        //FIXME: Why isn't threads being updated??
-        local_size[0] *= local_size[1];
-        if (ndims == 1) {
-            local_size[1] = 1;
-        }
+template<typename inType, typename outType>
+void copy(Param<outType> dst, CParam<inType> src, int ndims,
+          outType default_value, double factor) {
+    dim3 threads(DIMX, DIMY);
+    size_t local_size[] = {DIMX, DIMY};
 
-        uint blk_x = divup(dst.dims[0], local_size[0]);
-        uint blk_y = divup(dst.dims[1], local_size[1]);
+    // FIXME: Why isn't threads being updated??
+    local_size[0] *= local_size[1];
+    if (ndims == 1) { local_size[1] = 1; }
 
-        dim3 blocks(blk_x * dst.dims[2],
-                    blk_y * dst.dims[3]);
+    uint blk_x = divup(dst.dims[0], local_size[0]);
+    uint blk_y = divup(dst.dims[1], local_size[1]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    dim3 blocks(blk_x * dst.dims[2], blk_y * dst.dims[3]);
 
-        int trgt_l  = std::min(dst.dims[3], src.dims[3]);
-        int trgt_k  = std::min(dst.dims[2], src.dims[2]);
-        int trgt_j  = std::min(dst.dims[1], src.dims[1]);
-        int trgt_i  = std::min(dst.dims[0], src.dims[0]);
-        dims_t trgt_dims = {{trgt_i, trgt_j, trgt_k, trgt_l}};
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        bool same_dims = ( (src.dims[0]==dst.dims[0]) &&
-                           (src.dims[1]==dst.dims[1]) &&
-                           (src.dims[2]==dst.dims[2]) &&
-                           (src.dims[3]==dst.dims[3]) );
+    int trgt_l       = std::min(dst.dims[3], src.dims[3]);
+    int trgt_k       = std::min(dst.dims[2], src.dims[2]);
+    int trgt_j       = std::min(dst.dims[1], src.dims[1]);
+    int trgt_i       = std::min(dst.dims[0], src.dims[0]);
+    dims_t trgt_dims = {{trgt_i, trgt_j, trgt_k, trgt_l}};
 
-        if (same_dims)
-            CUDA_LAUNCH((copy_kernel<inType, outType, true >), blocks, threads,
-                    dst, src, default_value, factor, trgt_dims, blk_x, blk_y);
-        else
-            CUDA_LAUNCH((copy_kernel<inType, outType, false>), blocks, threads,
-                    dst, src, default_value, factor, trgt_dims, blk_x, blk_y);
+    bool same_dims =
+        ((src.dims[0] == dst.dims[0]) && (src.dims[1] == dst.dims[1]) &&
+         (src.dims[2] == dst.dims[2]) && (src.dims[3] == dst.dims[3]));
 
-        POST_LAUNCH_CHECK();
-    }
+    if (same_dims)
+        CUDA_LAUNCH((copy_kernel<inType, outType, true>), blocks, threads, dst,
+                    src, default_value, factor, trgt_dims, blk_x, blk_y);
+    else
+        CUDA_LAUNCH((copy_kernel<inType, outType, false>), blocks, threads, dst,
+                    src, default_value, factor, trgt_dims, blk_x, blk_y);
 
+    POST_LAUNCH_CHECK();
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index d9fad5236f..a263f77839 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -7,84 +7,77 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
+#include <af/defines.h>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
-    // Kernel Launch Config Values
-    static const int THREADS = 128;
+// Kernel Launch Config Values
+static const int THREADS = 128;
 
-    template<typename T>
-    __global__
-    void moments_kernel(Param<float> out, CParam<T> in, af_moment_type moment, const bool pBatch)
-    {
-        const dim_t idw = blockIdx.y / in.dims[2];
-        const dim_t idz = blockIdx.y - idw * in.dims[2];
+template<typename T>
+__global__ void moments_kernel(Param<float> out, CParam<T> in,
+                               af_moment_type moment, const bool pBatch) {
+    const dim_t idw = blockIdx.y / in.dims[2];
+    const dim_t idz = blockIdx.y - idw * in.dims[2];
 
-        const dim_t idy = blockIdx.x;
-        dim_t idx = threadIdx.x;
+    const dim_t idy = blockIdx.x;
+    dim_t idx       = threadIdx.x;
 
-        if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3] )
-            return;
+    if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3]) return;
 
-        extern __shared__ float blk_moment_sum[];
-        if(threadIdx.x < out.dims[0]) {
-            blk_moment_sum[threadIdx.x] = 0.f;
-        }
-        __syncthreads();
+    extern __shared__ float blk_moment_sum[];
+    if (threadIdx.x < out.dims[0]) { blk_moment_sum[threadIdx.x] = 0.f; }
+    __syncthreads();
 
-        dim_t mId = idy * in.strides[1] + idx;
-        if(pBatch) {
-            mId += idw * in.strides[3] + idz * in.strides[2];
-        }
+    dim_t mId = idy * in.strides[1] + idx;
+    if (pBatch) { mId += idw * in.strides[3] + idz * in.strides[2]; }
 
-        for(; idx<in.dims[0]; idx+=blockDim.x) {
-            dim_t m_off = 0;
-            float val = (float)in.ptr[mId];
-            mId += blockDim.x;
-
-            if((moment & AF_MOMENT_M00) > 0) {
-                atomicAdd(blk_moment_sum + m_off++, val);
-            }
-            if((moment & AF_MOMENT_M01) > 0) {
-                atomicAdd(blk_moment_sum + m_off++, idx * val);
-            }
-            if((moment & AF_MOMENT_M10) > 0) {
-                atomicAdd(blk_moment_sum + m_off++, idy * val);
-            }
-            if((moment & AF_MOMENT_M11) > 0) {
-                atomicAdd(blk_moment_sum + m_off, idx * idy * val);
-            }
-        }
+    for (; idx < in.dims[0]; idx += blockDim.x) {
+        dim_t m_off = 0;
+        float val   = (float)in.ptr[mId];
+        mId += blockDim.x;
 
-        __syncthreads();
-
-        float *offset = const_cast<float *>(out.ptr + (idw * out.strides[3] + idz * out.strides[2]) + threadIdx.x);
-        if(threadIdx.x < out.dims[0])
-            atomicAdd(offset, blk_moment_sum[threadIdx.x]);
+        if ((moment & AF_MOMENT_M00) > 0) {
+            atomicAdd(blk_moment_sum + m_off++, val);
+        }
+        if ((moment & AF_MOMENT_M01) > 0) {
+            atomicAdd(blk_moment_sum + m_off++, idx * val);
+        }
+        if ((moment & AF_MOMENT_M10) > 0) {
+            atomicAdd(blk_moment_sum + m_off++, idy * val);
+        }
+        if ((moment & AF_MOMENT_M11) > 0) {
+            atomicAdd(blk_moment_sum + m_off, idx * idy * val);
+        }
     }
 
-    // Wrapper functions
-    template <typename T>
-    void moments(Param<float> out, CParam<T> in, const af_moment_type moment) {
-        dim3 threads(THREADS, 1, 1);
-        dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
+    __syncthreads();
 
-        bool pBatch = !(in.dims[2] == 1 && in.dims[3] == 1);
+    float *offset = const_cast<float *>(
+        out.ptr + (idw * out.strides[3] + idz * out.strides[2]) + threadIdx.x);
+    if (threadIdx.x < out.dims[0])
+        atomicAdd(offset, blk_moment_sum[threadIdx.x]);
+}
 
-        CUDA_LAUNCH_SMEM((moments_kernel<T>), blocks, threads, sizeof(float) * out.dims[0],
-                     out, in, moment, pBatch);
-        POST_LAUNCH_CHECK();
-    }
+// Wrapper functions
+template<typename T>
+void moments(Param<float> out, CParam<T> in, const af_moment_type moment) {
+    dim3 threads(THREADS, 1, 1);
+    dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
 
+    bool pBatch = !(in.dims[2] == 1 && in.dims[3] == 1);
+
+    CUDA_LAUNCH_SMEM((moments_kernel<T>), blocks, threads,
+                     sizeof(float) * out.dims[0], out, in, moment, pBatch);
+    POST_LAUNCH_CHECK();
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 3b6e5f5d67..b533b91af0 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -7,106 +7,103 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <limits>
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include <ops.hpp>
+#include <limits>
 #include "shared.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int MAX_MORPH_FILTER_LEN = 17;
 // cFilter is used by both 2d morph and 3d morph
 // Maximum kernel size supported for 2d morph is 19x19*8 = 2888
 // Maximum kernel size supported for 3d morph is 7x7x7*8 = 2744
 // We will declare a char array as __constant__ array and allocate
 // size necessary to hold doubles of FILTER_LEN*FILTER_LEN
-__constant__ char cFilter[MAX_MORPH_FILTER_LEN*MAX_MORPH_FILTER_LEN*sizeof(double)];
+__constant__ char
+    cFilter[MAX_MORPH_FILTER_LEN * MAX_MORPH_FILTER_LEN * sizeof(double)];
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-static const int CUBE_X    =  8;
-static const int CUBE_Y    =  8;
-static const int CUBE_Z    =  8;
+static const int CUBE_X = 8;
+static const int CUBE_Y = 8;
+static const int CUBE_Z = 8;
 
-__forceinline__ __device__ int lIdx(int x, int y,
-        int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
+__forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
 }
 
 template<typename T, bool isDilation>
-inline __device__ void load2ShrdMem(T * shrd, const T * const in,
-        int lx, int ly, int shrdStride,
-        int dim0, int dim1,
-        int gx, int gy,
-        int inStride1, int inStride0)
-{
-    T val = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-    if (gx>=0 && gx<dim0 && gy>=0 && gy<dim1) {
-        val = in[ lIdx(gx, gy, inStride1, inStride0) ];
+inline __device__ void load2ShrdMem(T* shrd, const T* const in, int lx, int ly,
+                                    int shrdStride, int dim0, int dim1, int gx,
+                                    int gy, int inStride1, int inStride0) {
+    T val =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1) {
+        val = in[lIdx(gx, gy, inStride1, inStride0)];
     }
-    shrd[ lIdx(lx, ly, shrdStride, 1) ] = val;
+    shrd[lIdx(lx, ly, shrdStride, 1)] = val;
 }
 
-
 // kernel assumes mask/filter is square and hence does the
 // necessary operations accordingly.
 //
 // Notes on template arguments for morphKernel:
 //   * T is the data type of the image & kernel
-//   * isDilation indicates if the current kernel invocation is an erosion operation or dilation
-//   operation
-//   * SeLength is the structuring element length a.k.a the kernel window length. This template
-//   parameter takes precedence over the kernel argument `windLen`.
+//   * isDilation indicates if the current kernel invocation is an erosion
+//   operation or dilation operation
+//   * SeLength is the structuring element length a.k.a the kernel window
+//   length. This template parameter takes precedence over the kernel argument
+//   `windLen`.
 //
 // Please make sure at least one of the following variables is not 0.
 //  * SeLength (structuring element a.k.a window/kernel)
 //  * windLen
 // If SeLength is > 0, then that will override the kernel argument.
-template<typename T, bool isDilation, int SeLength=0>
-static __global__ void morphKernel(Param<T> out, CParam<T> in,
-                                   int nBBS0, int nBBS1,
-                                   int windLen=0)
-{
-    windLen = (SeLength>0 ? SeLength : windLen);
+template<typename T, bool isDilation, int SeLength = 0>
+static __global__ void morphKernel(Param<T> out, CParam<T> in, int nBBS0,
+                                   int nBBS1, int windLen = 0) {
+    windLen = (SeLength > 0 ? SeLength : windLen);
 
     // get shared memory pointer
     SharedMemory<T> shared;
-    T * shrdMem = shared.getPointer();
+    T* shrdMem = shared.getPointer();
 
     // calculate necessary offset and window parameters
-    const int halo     = windLen/2;
-    const int padding  = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
+    const int halo = windLen / 2;
+    const int padding =
+        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
     const int shrdLen  = blockDim.x + padding + 1;
     const int shrdLen1 = blockDim.y + padding;
 
     // gfor batch offsets
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
-    const T* iptr    = (const T *) in.ptr + (b2 *  in.strides[2] + b3 *  in.strides[3]);
-    T*       optr    = (T *      )out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const T* iptr =
+        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
 
     const int lx = threadIdx.x;
     const int ly = threadIdx.y;
 
     // global indices
-    const int gx = blockDim.x * (blockIdx.x-b2*nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y-b3*nBBS1) + ly;
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
 
     // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen1; b+=blockDim.y, gy2+=blockDim.y) {
+    for (int b = ly, gy2 = gy; b < shrdLen1;
+         b += blockDim.y, gy2 += blockDim.y) {
         // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-            load2ShrdMem<T, isDilation>(shrdMem, iptr, a, b, shrdLen,
-                                        in.dims[0], in.dims[1],
-                                        gx2-halo, gy2-halo, in.strides[1], in.strides[0]);
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            load2ShrdMem<T, isDilation>(
+                shrdMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
+                gx2 - halo, gy2 - halo, in.strides[1], in.strides[0]);
         }
     }
 
@@ -115,16 +112,17 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
 
     __syncthreads();
 
-    const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    const T* d_filt = (const T*)cFilter;
+    T acc =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 #pragma unroll
-    for(int wj=0; wj<windLen; ++wj) {
-        int joff   = wj*windLen;
-        int w_joff = (j+wj-halo)*shrdLen;
+    for (int wj = 0; wj < windLen; ++wj) {
+        int joff   = wj * windLen;
+        int w_joff = (j + wj - halo) * shrdLen;
 #pragma unroll
-        for(int wi=0; wi<windLen; ++wi) {
-            if (d_filt[joff+wi] > (T)0) {
-                T cur  = shrdMem[w_joff + (i+wi-halo)];
+        for (int wi = 0; wi < windLen; ++wi) {
+            if (d_filt[joff + wi] > (T)0) {
+                T cur = shrdMem[w_joff + (i + wi - halo)];
                 if (isDilation)
                     acc = max(acc, cur);
                 else
@@ -133,98 +131,99 @@ static __global__ void morphKernel(Param<T> out, CParam<T> in,
         }
     }
 
-    if (gx<in.dims[0] && gy<in.dims[1]) {
-        int outIdx = lIdx(gx, gy, out.strides[1], out.strides[0]);
+    if (gx < in.dims[0] && gy < in.dims[1]) {
+        int outIdx   = lIdx(gx, gy, out.strides[1], out.strides[0]);
         optr[outIdx] = acc;
     }
 }
 
-__forceinline__ __device__ int lIdx3D(int x, int y, int z,
-        int stride2, int stride1, int stride0)
-{
-    return (z*stride2 + y*stride1 + x*stride0);
+__forceinline__ __device__ int lIdx3D(int x, int y, int z, int stride2,
+                                      int stride1, int stride0) {
+    return (z * stride2 + y * stride1 + x * stride0);
 }
 
 template<typename T, bool isDilation>
-inline __device__ void load2ShrdVolume(T * shrd, const T * const in,
-        int lx, int ly, int lz,
-        int shrdStride1, int shrdStride2,
-        int dim0, int dim1, int dim2,
-        int gx, int gy, int gz,
-        int inStride2, int inStride1, int inStride0)
-{
-    T val = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-    if (gx>=0 && gx<dim0 &&
-        gy>=0 && gy<dim1 &&
-        gz>=0 && gz<dim2) {
-        val = in[gx*inStride0 + gy*inStride1 + gz*inStride2];
+inline __device__ void load2ShrdVolume(T* shrd, const T* const in, int lx,
+                                       int ly, int lz, int shrdStride1,
+                                       int shrdStride2, int dim0, int dim1,
+                                       int dim2, int gx, int gy, int gz,
+                                       int inStride2, int inStride1,
+                                       int inStride0) {
+    T val =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1 && gz >= 0 && gz < dim2) {
+        val = in[gx * inStride0 + gy * inStride1 + gz * inStride2];
     }
-    shrd[lx + ly*shrdStride1 + lz*shrdStride2] = val;
+    shrd[lx + ly * shrdStride1 + lz * shrdStride2] = val;
 }
 
 // kernel assumes mask/filter is square and hence does the
 // necessary operations accordingly.
 template<typename T, bool isDilation, int windLen>
-static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
-{
+static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS) {
     // get shared memory pointer
     SharedMemory<T> shared;
-    T * shrdMem = shared.getPointer();
+    T* shrdMem = shared.getPointer();
 
-    const int halo      = windLen/2;
-    const int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
+    const int halo = windLen / 2;
+    const int padding =
+        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
 
-    const int se_area   = windLen*windLen;
-    const int shrdLen   = blockDim.x + padding + 1;
-    const int shrdLen1  = blockDim.y + padding;
-    const int shrdLen2  = blockDim.z + padding;
-    const int shrdArea  = shrdLen * shrdLen1;
+    const int se_area  = windLen * windLen;
+    const int shrdLen  = blockDim.x + padding + 1;
+    const int shrdLen1 = blockDim.y + padding;
+    const int shrdLen2 = blockDim.z + padding;
+    const int shrdArea = shrdLen * shrdLen1;
 
     // gfor batch offsets
     unsigned batchId = blockIdx.x / nBBS;
 
-    const T* iptr    = (const T *) in.ptr + (batchId *  in.strides[3]);
-    T*       optr    = (T *      )out.ptr + (batchId * out.strides[3]);
+    const T* iptr = (const T*)in.ptr + (batchId * in.strides[3]);
+    T* optr       = (T*)out.ptr + (batchId * out.strides[3]);
 
     const int lx = threadIdx.x;
     const int ly = threadIdx.y;
     const int lz = threadIdx.z;
 
-    const int gx = blockDim.x * (blockIdx.x-batchId*nBBS) + lx;
+    const int gx = blockDim.x * (blockIdx.x - batchId * nBBS) + lx;
     const int gy = blockDim.y * blockIdx.y + ly;
     const int gz = blockDim.z * blockIdx.z + lz;
 
-    for (int c=lz, gz2=gz; c<shrdLen2; c+=blockDim.z, gz2+=blockDim.z) {
-        for (int b=ly, gy2=gy; b<shrdLen1; b+=blockDim.y, gy2+=blockDim.y) {
-            for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-                load2ShrdVolume<T, isDilation>(shrdMem, iptr, a, b, c, shrdLen, shrdArea,
-                                               in.dims[0], in.dims[1], in.dims[2],
-                                               gx2-halo, gy2-halo, gz2-halo,
-                                               in.strides[2], in.strides[1], in.strides[0]);
+    for (int c = lz, gz2 = gz; c < shrdLen2;
+         c += blockDim.z, gz2 += blockDim.z) {
+        for (int b = ly, gy2 = gy; b < shrdLen1;
+             b += blockDim.y, gy2 += blockDim.y) {
+            for (int a = lx, gx2 = gx; a < shrdLen;
+                 a += blockDim.x, gx2 += blockDim.x) {
+                load2ShrdVolume<T, isDilation>(
+                    shrdMem, iptr, a, b, c, shrdLen, shrdArea, in.dims[0],
+                    in.dims[1], in.dims[2], gx2 - halo, gy2 - halo, gz2 - halo,
+                    in.strides[2], in.strides[1], in.strides[0]);
             }
         }
     }
 
     __syncthreads();
     // indices of voxel owned by current thread
-    int i  = lx + halo;
-    int j  = ly + halo;
-    int k  = lz + halo;
+    int i = lx + halo;
+    int j = ly + halo;
+    int k = lz + halo;
 
-    const T * d_filt = (const T *)cFilter;
-    T acc = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    const T* d_filt = (const T*)cFilter;
+    T acc =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
 #pragma unroll
-    for(int wk=0; wk<windLen; ++wk) {
-        int koff   = wk*se_area;
-        int w_koff = (k+wk-halo)*shrdArea;
+    for (int wk = 0; wk < windLen; ++wk) {
+        int koff   = wk * se_area;
+        int w_koff = (k + wk - halo) * shrdArea;
 #pragma unroll
-        for(int wj=0; wj<windLen; ++wj) {
-        int joff   = wj*windLen;
-        int w_joff = (j+wj-halo)*shrdLen;
+        for (int wj = 0; wj < windLen; ++wj) {
+            int joff   = wj * windLen;
+            int w_joff = (j + wj - halo) * shrdLen;
 #pragma unroll
-            for(int wi=0; wi<windLen; ++wi) {
-                if (d_filt[koff+joff+wi]) {
-                    T cur  = shrdMem[w_koff+w_joff + i+wi-halo];
+            for (int wi = 0; wi < windLen; ++wi) {
+                if (d_filt[koff + joff + wi]) {
+                    T cur = shrdMem[w_koff + w_joff + i + wi - halo];
                     if (isDilation)
                         acc = max(acc, cur);
                     else
@@ -234,17 +233,15 @@ static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS)
         }
     }
 
-    if (gx<in.dims[0] && gy<in.dims[1] && gz<in.dims[2]) {
-        int outIdx = gz * out.strides[2] +
-                          gy * out.strides[1] +
-                          gx * out.strides[0];
+    if (gx < in.dims[0] && gy < in.dims[1] && gz < in.dims[2]) {
+        int outIdx =
+            gz * out.strides[2] + gy * out.strides[1] + gx * out.strides[0];
         optr[outIdx] = acc;
     }
 }
 
 template<typename T, bool isDilation>
-void morph(Param<T> out, CParam<T> in, int windLen)
-{
+void morph(Param<T> out, CParam<T> in, int windLen) {
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], THREADS_X);
@@ -253,30 +250,59 @@ void morph(Param<T> out, CParam<T> in, int windLen)
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
     // calculate shared memory size
-    int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
-    int shrdLen   = kernel::THREADS_X + padding + 1; // +1 for to avoid bank conflicts
-    int shrdSize  = shrdLen * (kernel::THREADS_Y + padding) * sizeof(T);
-
-    switch(windLen) {
-        case  2: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  2>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  3: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  3>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  4: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  4>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  5: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  5>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  6: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  6>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  7: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  7>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  8: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  8>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case  9: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation,  9>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        case 10: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 10>), blocks, threads, shrdSize, out, in, blk_x, blk_y); break;
-        default: CUDA_LAUNCH_SMEM((morphKernel<T, isDilation>), blocks, threads, shrdSize, out, in, blk_x, blk_y, windLen);
-                 break;
+    int padding = (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
+    int shrdLen =
+        kernel::THREADS_X + padding + 1;  // +1 for to avoid bank conflicts
+    int shrdSize = shrdLen * (kernel::THREADS_Y + padding) * sizeof(T);
+
+    switch (windLen) {
+        case 2:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 2>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 3:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 3>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 4:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 4>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 5:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 5>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 6:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 6>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 7:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 7>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 8:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 8>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 9:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 9>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        case 10:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 10>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y);
+            break;
+        default:
+            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation>), blocks, threads,
+                             shrdSize, out, in, blk_x, blk_y, windLen);
+            break;
     }
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, bool isDilation>
-void morph3d(Param<T> out, CParam<T> in, int windLen)
-{
+void morph3d(Param<T> out, CParam<T> in, int windLen) {
     dim3 threads(kernel::CUBE_X, kernel::CUBE_Y, kernel::CUBE_Z);
 
     int blk_x = divup(in.dims[0], CUBE_X);
@@ -285,21 +311,43 @@ void morph3d(Param<T> out, CParam<T> in, int windLen)
     dim3 blocks(blk_x * in.dims[3], blk_y, blk_z);
 
     // calculate shared memory size
-    int padding   = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
-    int shrdLen   = kernel::CUBE_X + padding + 1; // +1 for to avoid bank conflicts
-    int shrdSize  = shrdLen * (kernel::CUBE_Y + padding) * (kernel::CUBE_Z + padding) * sizeof(T);
-
-    switch(windLen) {
-        case  2: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 2>), blocks, threads, shrdSize, out, in, blk_x); break;
-        case  3: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 3>), blocks, threads, shrdSize, out, in, blk_x); break;
-        case  4: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 4>), blocks, threads, shrdSize, out, in, blk_x); break;
-        case  5: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 5>), blocks, threads, shrdSize, out, in, blk_x); break;
-        case  6: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 6>), blocks, threads, shrdSize, out, in, blk_x); break;
-        case  7: CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 7>), blocks, threads, shrdSize, out, in, blk_x); break;
-        default: CUDA_NOT_SUPPORTED("Morph 3D does not support kernels larger than 7.");
+    int padding = (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
+    int shrdLen =
+        kernel::CUBE_X + padding + 1;  // +1 for to avoid bank conflicts
+    int shrdSize = shrdLen * (kernel::CUBE_Y + padding) *
+                   (kernel::CUBE_Z + padding) * sizeof(T);
+
+    switch (windLen) {
+        case 2:
+            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 2>), blocks, threads,
+                             shrdSize, out, in, blk_x);
+            break;
+        case 3:
+            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 3>), blocks, threads,
+                             shrdSize, out, in, blk_x);
+            break;
+        case 4:
+            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 4>), blocks, threads,
+                             shrdSize, out, in, blk_x);
+            break;
+        case 5:
+            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 5>), blocks, threads,
+                             shrdSize, out, in, blk_x);
+            break;
+        case 6:
+            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 6>), blocks, threads,
+                             shrdSize, out, in, blk_x);
+            break;
+        case 7:
+            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 7>), blocks, threads,
+                             shrdSize, out, in, blk_x);
+            break;
+        default:
+            CUDA_NOT_SUPPORTED(
+                "Morph 3D does not support kernels larger than 7.");
     }
 
     POST_LAUNCH_CHECK();
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 15d0d004d7..f615a733db 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -7,107 +7,73 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
-#include <backend.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const unsigned THREADS = 256;
 
 template<typename T, typename To, af_match_type dist_type>
-struct dist_op
-{
-    __DH__ To operator()(T v1, T v2)
-    {
-        return v1 - v2;
-    }
+struct dist_op {
+    __DH__ To operator()(T v1, T v2) { return v1 - v2; }
 };
 
 template<typename T, typename To>
-struct dist_op<T, To, AF_SAD>
-{
-    __device__ To operator()(T v1, T v2)
-    {
+struct dist_op<T, To, AF_SAD> {
+    __device__ To operator()(T v1, T v2) {
         return fabsf((float)v1 - (float)v2);
     }
 };
 
 template<typename To>
-struct dist_op<double, To, AF_SAD>
-{
-    __device__ To operator()(double v1, double v2)
-    {
+struct dist_op<double, To, AF_SAD> {
+    __device__ To operator()(double v1, double v2) {
         return fabs((double)v1 - (double)v2);
     }
 };
 
 template<typename T, typename To>
-struct dist_op<T, To, AF_SSD>
-{
-    __device__ To operator()(T v1, T v2)
-    {
-        return (v1 - v2) * (v1 - v2);
-    }
+struct dist_op<T, To, AF_SSD> {
+    __device__ To operator()(T v1, T v2) { return (v1 - v2) * (v1 - v2); }
 };
 
 template<typename To>
-struct dist_op<uint, To, AF_SHD>
-{
-    __device__ To operator()(uint v1, uint v2)
-    {
-        return __popc(v1 ^ v2);
-    }
+struct dist_op<uint, To, AF_SHD> {
+    __device__ To operator()(uint v1, uint v2) { return __popc(v1 ^ v2); }
 };
 
 template<typename To>
-struct dist_op<uintl, To, AF_SHD>
-{
-    __device__ To operator()(uintl v1, uintl v2)
-    {
-        return __popc(v1 ^ v2);
-    }
+struct dist_op<uintl, To, AF_SHD> {
+    __device__ To operator()(uintl v1, uintl v2) { return __popc(v1 ^ v2); }
 };
 
 template<typename To>
-struct dist_op<ushort, To, AF_SHD>
-{
-    __device__ To operator()(ushort v1, ushort v2)
-    {
-        return __popc(v1 ^ v2);
-    }
+struct dist_op<ushort, To, AF_SHD> {
+    __device__ To operator()(ushort v1, ushort v2) { return __popc(v1 ^ v2); }
 };
 
 template<typename To>
-struct dist_op<uchar, To, AF_SHD>
-{
-    __device__ To operator()(uchar v1, uchar v2)
-    {
-        return __popc(v1 ^ v2);
-    }
+struct dist_op<uchar, To, AF_SHD> {
+    __device__ To operator()(uchar v1, uchar v2) { return __popc(v1 ^ v2); }
 };
 
 template<typename T, typename To, af_match_type dist_type, bool use_shmem>
-__global__ void all_distances(
-    To* out_dist,
-    CParam<T> query,
-    CParam<T> train,
-    const To max_dist,
-    const unsigned feat_len,
-    const unsigned max_feat_len,
-    const unsigned feat_offset)
-{
+__global__ void all_distances(To* out_dist, CParam<T> query, CParam<T> train,
+                              const To max_dist, const unsigned feat_len,
+                              const unsigned max_feat_len,
+                              const unsigned feat_offset) {
     unsigned nquery = query.dims[0];
     unsigned ntrain = train.dims[0];
 
-    unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
+    unsigned f   = blockDim.x * blockIdx.x + threadIdx.x;
     unsigned tid = threadIdx.x;
 
     __shared__ To s_dist[THREADS];
@@ -125,7 +91,8 @@ __global__ void all_distances(
         if (use_shmem) {
             unsigned end_feat = min(feat_offset + max_feat_len, feat_len);
             for (unsigned i = feat_offset; i < end_feat; i++) {
-                s_train[(i - feat_offset) * blockDim.x + tid] = train.ptr[i * ntrain + f];
+                s_train[(i - feat_offset) * blockDim.x + tid] =
+                    train.ptr[i * ntrain + f];
             }
         }
     }
@@ -150,10 +117,11 @@ __global__ void all_distances(
                 // Calculate Hamming distance for 32-bits of descriptor and
                 // accumulates to dist
                 if (use_shmem) {
-                    dist += op(s_train[(k - feat_offset) * blockDim.x + tid], s_query[k - feat_offset]);
-                }
-                else {
-                    dist += op(train.ptr[k * ntrain + f], s_query[k - feat_offset]);
+                    dist += op(s_train[(k - feat_offset) * blockDim.x + tid],
+                               s_query[k - feat_offset]);
+                } else {
+                    dist +=
+                        op(train.ptr[k * ntrain + f], s_query[k - feat_offset]);
                 }
             }
 
@@ -166,7 +134,7 @@ __global__ void all_distances(
         // Store best match in training features from block to the current
         // query feature
         if (valid_feat) {
-            if(feat_offset == 0)
+            if (feat_offset == 0)
                 out_dist[j * ntrain + f] = s_dist[tid];
             else
                 out_dist[j * ntrain + f] += s_dist[tid];
@@ -176,13 +144,11 @@ __global__ void all_distances(
 }
 
 template<typename T, typename To, af_match_type dist_type>
-void all_distances(Param<To> dist,
-                       CParam<T> query,
-                       CParam<T> train,
-                       const dim_t dist_dim)
-{
+void all_distances(Param<To> dist, CParam<T> query, CParam<T> train,
+                   const dim_t dist_dim) {
     const dim_t feat_len = query.dims[dist_dim];
-    const unsigned max_kern_feat_len = min(THREADS, feat_len);
+    const unsigned max_kern_feat_len =
+        std::min(THREADS, static_cast<unsigned>(feat_len));
     const To max_dist = maxval<To>();
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
@@ -196,25 +162,29 @@ void all_distances(Param<To> dist,
     int device          = getActiveDeviceId();
     cudaDeviceProp prop = getDeviceProp(device);
     size_t avail_smem   = prop.sharedMemPerBlock;
-    size_t smem_predef  = 2 * THREADS * sizeof(unsigned) + max_kern_feat_len * sizeof(T);
-    size_t strain_sz    = threads.x * max_kern_feat_len * sizeof(T);
-    bool use_shmem      = (avail_smem >= (smem_predef + strain_sz)) ? true : false;
-    unsigned smem_sz    = (use_shmem) ? smem_predef + strain_sz : smem_predef;
+    size_t smem_predef =
+        2 * THREADS * sizeof(unsigned) + max_kern_feat_len * sizeof(T);
+    size_t strain_sz = threads.x * max_kern_feat_len * sizeof(T);
+    bool use_shmem   = (avail_smem >= (smem_predef + strain_sz)) ? true : false;
+    unsigned smem_sz = (use_shmem) ? smem_predef + strain_sz : smem_predef;
 
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
-    for(dim_t feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
+    for (dim_t feat_offset = 0; feat_offset < feat_len;
+         feat_offset += THREADS) {
         if (use_shmem) {
-            CUDA_LAUNCH_SMEM((all_distances<T,To,dist_type,true>), blocks, threads, smem_sz,
-                             dist.ptr, query, train, max_dist, feat_len, max_kern_feat_len, feat_offset);
+            CUDA_LAUNCH_SMEM((all_distances<T, To, dist_type, true>), blocks,
+                             threads, smem_sz, dist.ptr, query, train, max_dist,
+                             feat_len, max_kern_feat_len, feat_offset);
         } else {
-            CUDA_LAUNCH_SMEM((all_distances<T,To,dist_type,false>), blocks, threads, smem_sz,
-                             dist.ptr, query, train, max_dist, feat_len, max_kern_feat_len, feat_offset);
+            CUDA_LAUNCH_SMEM((all_distances<T, To, dist_type, false>), blocks,
+                             threads, smem_sz, dist.ptr, query, train, max_dist,
+                             feat_len, max_kern_feat_len, feat_offset);
         }
     }
     POST_LAUNCH_CHECK();
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 526cc077f1..d1246a928e 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -8,23 +8,21 @@
  ********************************************************/
 
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
 
 #include "convolve.hpp"
 #include "orb_patch.hpp"
-#include "sort_by_key.hpp"
 #include "range.hpp"
+#include "sort_by_key.hpp"
 
-using std::vector;
 using std::unique_ptr;
+using std::vector;
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS   = 256;
 static const int THREADS_X = 16;
@@ -33,38 +31,31 @@ static const int THREADS_Y = 16;
 static const float PI_VAL = 3.14159265358979323846f;
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * PI_VAL * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
-inline __device__ float block_reduce_sum(float val)
-{
-    __shared__ float data[THREADS_X*THREADS_Y];
+inline __device__ float block_reduce_sum(float val) {
+    __shared__ float data[THREADS_X * THREADS_Y];
 
     unsigned idx = threadIdx.x * blockDim.x + threadIdx.y;
 
     data[idx] = val;
     __syncthreads();
 
-    for (unsigned i = blockDim.y / 2; i > 0; i >>= 1)
-    {
-        if (threadIdx.y < i)
-        {
-            data[idx] += data[idx + i];
-        }
+    for (unsigned i = blockDim.y / 2; i > 0; i >>= 1) {
+        if (threadIdx.y < i) { data[idx] += data[idx + i]; }
 
         __syncthreads();
     }
@@ -73,24 +64,17 @@ inline __device__ float block_reduce_sum(float val)
 }
 
 template<typename T>
-__global__ void keep_features(
-    float* x_out,
-    float* y_out,
-    float* score_out,
-    float* size_out,
-    const float* x_in,
-    const float* y_in,
-    const float* score_in,
-    const unsigned* score_idx,
-    const float* size_in,
-    const unsigned n_feat)
-{
+__global__ void keep_features(float* x_out, float* y_out, float* score_out,
+                              float* size_out, const float* x_in,
+                              const float* y_in, const float* score_in,
+                              const unsigned* score_idx, const float* size_in,
+                              const unsigned n_feat) {
     unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
 
     // Keep only the first n_feat features
     if (f < n_feat) {
-        x_out[f] = x_in[score_idx[f]];
-        y_out[f] = y_in[score_idx[f]];
+        x_out[f]     = x_in[score_idx[f]];
+        y_out[f]     = y_in[score_idx[f]];
         score_out[f] = score_in[f];
         if (size_in != NULL && size_out != NULL)
             size_out[f] = size_in[score_idx[f]];
@@ -98,18 +82,11 @@ __global__ void keep_features(
 }
 
 template<typename T, bool use_scl>
-__global__ void harris_response(
-        float* score_out,
-        float* size_out,
-        const float* x_in,
-        const float* y_in,
-        const float* scl_in,
-        const unsigned total_feat,
-        CParam<T> image,
-        const unsigned block_size,
-        const float k_thr,
-        const unsigned patch_size)
-{
+__global__ void harris_response(float* score_out, float* size_out,
+                                const float* x_in, const float* y_in,
+                                const float* scl_in, const unsigned total_feat,
+                                CParam<T> image, const unsigned block_size,
+                                const float k_thr, const unsigned patch_size) {
     unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
 
     float ixx = 0.f, iyy = 0.f, ixy = 0.f;
@@ -121,10 +98,9 @@ __global__ void harris_response(
         if (use_scl) {
             // Update x and y coordinates according to scale
             scl = scl_in[f];
-            x = (unsigned)round(x_in[f] * scl);
-            y = (unsigned)round(y_in[f] * scl);
-        }
-        else {
+            x   = (unsigned)round(x_in[f] * scl);
+            y   = (unsigned)round(y_in[f] * scl);
+        } else {
             x = (unsigned)round(x_in[f]);
             y = (unsigned)round(y_in[f]);
         }
@@ -136,7 +112,8 @@ __global__ void harris_response(
         // the image, sqrt(2.f) is the radius when angle is 45 degrees and
         // represents widest case possible
         unsigned patch_r = ceil(size * sqrt(2.f) / 2.f);
-        if (x < patch_r || y < patch_r || x >= image.dims[1] - patch_r || y >= image.dims[0] - patch_r)
+        if (x < patch_r || y < patch_r || x >= image.dims[1] - patch_r ||
+            y >= image.dims[0] - patch_r)
             return;
 
         unsigned r = block_size / 2;
@@ -147,13 +124,15 @@ __global__ void harris_response(
             int j = k % block_size - r;
 
             // Calculate local x and y derivatives
-            float ix = image.ptr[(x+i+1) * image.dims[0] + y+j] - image.ptr[(x+i-1) * image.dims[0] + y+j];
-            float iy = image.ptr[(x+i) * image.dims[0] + y+j+1] - image.ptr[(x+i) * image.dims[0] + y+j-1];
+            float ix = image.ptr[(x + i + 1) * image.dims[0] + y + j] -
+                       image.ptr[(x + i - 1) * image.dims[0] + y + j];
+            float iy = image.ptr[(x + i) * image.dims[0] + y + j + 1] -
+                       image.ptr[(x + i) * image.dims[0] + y + j - 1];
 
             // Accumulate second order derivatives
-            ixx += ix*ix;
-            iyy += iy*iy;
-            ixy += ix*iy;
+            ixx += ix * ix;
+            iyy += iy * iy;
+            ixy += ix * iy;
         }
     }
     __syncthreads();
@@ -163,32 +142,27 @@ __global__ void harris_response(
     ixy = block_reduce_sum(ixy);
 
     if (f < total_feat && threadIdx.y == 0) {
-        float tr = ixx + iyy;
-        float det = ixx*iyy - ixy*ixy;
+        float tr  = ixx + iyy;
+        float det = ixx * iyy - ixy * ixy;
 
         // Calculate Harris responses
-        float resp = det - k_thr * (tr*tr);
+        float resp = det - k_thr * (tr * tr);
 
         // Scale factor
         // TODO: improve response scaling
         float rscale = 0.001f;
-        rscale = rscale * rscale * rscale * rscale;
+        rscale       = rscale * rscale * rscale * rscale;
 
         score_out[f] = resp * rscale;
-        if (use_scl)
-            size_out[f] = size;
+        if (use_scl) size_out[f] = size;
     }
 }
 
 template<typename T>
-__global__ void centroid_angle(
-    const float* x_in,
-    const float* y_in,
-    float* orientation_out,
-    const unsigned total_feat,
-    CParam<T> image,
-    const unsigned patch_size)
-{
+__global__ void centroid_angle(const float* x_in, const float* y_in,
+                               float* orientation_out,
+                               const unsigned total_feat, CParam<T> image,
+                               const unsigned patch_size) {
     unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
 
     if (f < total_feat) {
@@ -206,7 +180,7 @@ __global__ void centroid_angle(
             int j = k % patch_size - r;
 
             // Calculate first order moments
-            T p = image.ptr[(x+i) * image.dims[0] + y+j];
+            T p = image.ptr[(x + i) * image.dims[0] + y + j];
             m01 += j * p;
             m10 += i * p;
         }
@@ -215,25 +189,19 @@ __global__ void centroid_angle(
         m10 = block_reduce_sum(m10);
 
         if (threadIdx.y == 0) {
-            float angle = atan2((float)m01, (float)m10);
+            float angle        = atan2((float)m01, (float)m10);
             orientation_out[f] = angle;
         }
     }
 }
 
 template<typename T>
-inline __device__ T get_pixel(
-    unsigned x,
-    unsigned y,
-    const float ori,
-    const unsigned size,
-    const int dist_x,
-    const int dist_y,
-    CParam<T> image,
-    const unsigned patch_size)
-{
-    float ori_sin = sin(ori);
-    float ori_cos = cos(ori);
+inline __device__ T get_pixel(unsigned x, unsigned y, const float ori,
+                              const unsigned size, const int dist_x,
+                              const int dist_y, CParam<T> image,
+                              const unsigned patch_size) {
+    float ori_sin   = sin(ori);
+    float ori_cos   = cos(ori);
     float patch_scl = (float)size / (float)patch_size;
 
     // Calculate point coordinates based on orientation and size
@@ -244,23 +212,17 @@ inline __device__ T get_pixel(
 }
 
 template<typename T>
-__global__ void extract_orb(
-    unsigned* desc_out,
-    const unsigned n_feat,
-    float* x_in_out,
-    float* y_in_out,
-    const float* ori_in,
-    float* size_out,
-    CParam<T> image,
-    const float scl,
-    const unsigned patch_size)
-{
+__global__ void extract_orb(unsigned* desc_out, const unsigned n_feat,
+                            float* x_in_out, float* y_in_out,
+                            const float* ori_in, float* size_out,
+                            CParam<T> image, const float scl,
+                            const unsigned patch_size) {
     unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
 
     if (f < n_feat) {
-        unsigned x = (unsigned)round(x_in_out[f]);
-        unsigned y = (unsigned)round(y_in_out[f]);
-        float ori = ori_in[f];
+        unsigned x    = (unsigned)round(x_in_out[f]);
+        unsigned y    = (unsigned)round(y_in_out[f]);
+        float ori     = ori_in[f];
         unsigned size = patch_size;
 
         unsigned r = ceil(patch_size * sqrt(2.f) / 2.f);
@@ -274,21 +236,25 @@ __global__ void extract_orb(
 
             // j < 16 for 256 bits descriptor
             for (unsigned j = 0; j < 16; j++) {
-                // Get position from distribution pattern and values of points p1 and p2
-                int dist_x = d_ref_pat[i*16*4 + j*4];
-                int dist_y = d_ref_pat[i*16*4 + j*4+1];
-                T p1 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
-
-                dist_x = d_ref_pat[i*16*4 + j*4+2];
-                dist_y = d_ref_pat[i*16*4 + j*4+3];
-                T p2 = get_pixel(x, y, ori, size, dist_x, dist_y, image, patch_size);
-
-                // Calculate bit based on p1 and p2 and shifts it to correct position
-                v |= (p1 < p2) << (j + 16*(i % 2));
+                // Get position from distribution pattern and values of points
+                // p1 and p2
+                int dist_x = d_ref_pat[i * 16 * 4 + j * 4];
+                int dist_y = d_ref_pat[i * 16 * 4 + j * 4 + 1];
+                T p1       = get_pixel(x, y, ori, size, dist_x, dist_y, image,
+                                 patch_size);
+
+                dist_x = d_ref_pat[i * 16 * 4 + j * 4 + 2];
+                dist_y = d_ref_pat[i * 16 * 4 + j * 4 + 3];
+                T p2   = get_pixel(x, y, ori, size, dist_x, dist_y, image,
+                                 patch_size);
+
+                // Calculate bit based on p1 and p2 and shifts it to correct
+                // position
+                v |= (p1 < p2) << (j + 16 * (i % 2));
             }
 
             // Store 16 bits of descriptor
-            atomicAdd(&desc_out[f * 8 + i/2], v);
+            atomicAdd(&desc_out[f * 8 + i / 2], v);
         }
 
         if (threadIdx.y == 0) {
@@ -299,28 +265,14 @@ __global__ void extract_orb(
     }
 }
 
-
-
 template<typename T, typename convAccT>
-void orb(unsigned* out_feat,
-         float** d_x,
-         float** d_y,
-         float** d_score,
-         float** d_ori,
-         float** d_size,
-         unsigned** d_desc,
-         vector<unsigned>& feat_pyr,
-         vector<float*>& d_x_pyr,
-         vector<float*>& d_y_pyr,
-         vector<unsigned>& lvl_best,
-         vector<float>& lvl_scl,
-         vector<Array<T>>& img_pyr,
-         const float fast_thr,
-         const unsigned max_feat,
-         const float scl_fctr,
-         const unsigned levels,
-         const bool blur_img)
-{
+void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
+         float** d_ori, float** d_size, unsigned** d_desc,
+         vector<unsigned>& feat_pyr, vector<float*>& d_x_pyr,
+         vector<float*>& d_y_pyr, vector<unsigned>& lvl_best,
+         vector<float>& lvl_scl, vector<Array<T>>& img_pyr,
+         const float fast_thr, const unsigned max_feat, const float scl_fctr,
+         const unsigned levels, const bool blur_img) {
     UNUSED(fast_thr);
     UNUSED(max_feat);
     UNUSED(scl_fctr);
@@ -331,7 +283,8 @@ void orb(unsigned* out_feat,
 
     // In future implementations, the user will be capable of passing his
     // distribution instead of using the reference one
-    //CUDA_CHECK(cudaMemcpyToSymbolAsync(d_ref_pat, h_ref_pat, 256 * 4 * sizeof(int), 0,
+    // CUDA_CHECK(cudaMemcpyToSymbolAsync(d_ref_pat, h_ref_pat, 256 * 4 *
+    // sizeof(int), 0,
     // cudaMemcpyHostToDevice, cuda::getActiveStream()));
 
     vector<float*> d_score_pyr(max_levels);
@@ -352,26 +305,26 @@ void orb(unsigned* out_feat,
         gauss_filter = createHostDataArray<convAccT>(gauss_dim, h_gauss.data());
         CUDA_CHECK(cudaMemcpyAsync(gauss_filter.get(), h_gauss.data(),
                                    h_gauss.size() * sizeof(convAccT),
-                                   cudaMemcpyHostToDevice, cuda::getActiveStream()));
+                                   cudaMemcpyHostToDevice,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 
     for (int i = 0; i < (int)max_levels; i++) {
-        if (feat_pyr[i] == 0 || lvl_best[i] == 0) {
-            continue;
-        }
+        if (feat_pyr[i] == 0 || lvl_best[i] == 0) { continue; }
 
-        //auto d_score_harris = memAlloc<float>(feat_pyr[i]);
+        // auto d_score_harris = memAlloc<float>(feat_pyr[i]);
         dim4 score_dim(feat_pyr[i]);
-        Array<float> d_score_harris = createEmptyArray<float>(score_dim); //harris_sorted
+        Array<float> d_score_harris =
+            createEmptyArray<float>(score_dim);  // harris_sorted
 
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
         dim3 threads(THREADS_X, THREADS_Y);
         dim3 blocks(divup(feat_pyr[i], threads.x), 1);
-        CUDA_LAUNCH((harris_response<T,false>), blocks, threads,
-                    d_score_harris.get(), NULL, d_x_pyr[i], d_y_pyr[i],
-                    NULL, feat_pyr[i], img_pyr[i], 7, 0.04f, patch_size);
+        CUDA_LAUNCH((harris_response<T, false>), blocks, threads,
+                    d_score_harris.get(), NULL, d_x_pyr[i], d_y_pyr[i], NULL,
+                    feat_pyr[i], img_pyr[i], 7, 0.04f, patch_size);
         POST_LAUNCH_CHECK();
 
         dim4 feat_dim(feat_pyr[i]);
@@ -385,16 +338,16 @@ void orb(unsigned* out_feat,
 
         feat_pyr[i] = std::min(feat_pyr[i], lvl_best[i]);
 
-        float* d_x_lvl = memAlloc<float>(feat_pyr[i]).release();
-        float* d_y_lvl = memAlloc<float>(feat_pyr[i]).release();
+        float* d_x_lvl     = memAlloc<float>(feat_pyr[i]).release();
+        float* d_y_lvl     = memAlloc<float>(feat_pyr[i]).release();
         float* d_score_lvl = memAlloc<float>(feat_pyr[i]).release();
 
         // Keep only features with higher Harris responses
         threads = dim3(THREADS, 1);
-        blocks = dim3(divup(feat_pyr[i], threads.x), 1);
-        CUDA_LAUNCH((keep_features<T>), blocks, threads,
-                    d_x_lvl, d_y_lvl, d_score_lvl, NULL,
-                    d_x_pyr[i], d_y_pyr[i], d_score_harris.get(), harris_idx.get(), NULL, feat_pyr[i]);
+        blocks  = dim3(divup(feat_pyr[i], threads.x), 1);
+        CUDA_LAUNCH((keep_features<T>), blocks, threads, d_x_lvl, d_y_lvl,
+                    d_score_lvl, NULL, d_x_pyr[i], d_y_pyr[i],
+                    d_score_harris.get(), harris_idx.get(), NULL, feat_pyr[i]);
         POST_LAUNCH_CHECK();
 
         memFree(d_x_pyr[i]);
@@ -405,8 +358,8 @@ void orb(unsigned* out_feat,
         // Compute orientation of features
         threads = dim3(THREADS_X, THREADS_Y);
         blocks  = dim3(divup(feat_pyr[i], threads.x), 1);
-        CUDA_LAUNCH((centroid_angle<T>), blocks, threads,
-                d_x_lvl, d_y_lvl, d_ori_lvl, feat_pyr[i], img_pyr[i], patch_size);
+        CUDA_LAUNCH((centroid_angle<T>), blocks, threads, d_x_lvl, d_y_lvl,
+                    d_ori_lvl, feat_pyr[i], img_pyr[i], patch_size);
         POST_LAUNCH_CHECK();
 
         if (blur_img) {
@@ -420,25 +373,26 @@ void orb(unsigned* out_feat,
         float* d_size_lvl = memAlloc<float>(feat_pyr[i]).release();
 
         unsigned* d_desc_lvl = memAlloc<unsigned>(feat_pyr[i] * 8).release();
-        CUDA_CHECK(cudaMemsetAsync(d_desc_lvl, 0, feat_pyr[i] * 8 * sizeof(unsigned),
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemsetAsync(d_desc_lvl, 0,
+                                   feat_pyr[i] * 8 * sizeof(unsigned),
+                                   cuda::getActiveStream()));
 
         // Compute ORB descriptors
         threads = dim3(THREADS_X, THREADS_Y);
         blocks  = dim3(divup(feat_pyr[i], threads.x), 1);
-        CUDA_LAUNCH((extract_orb<T>), blocks, threads,
-                d_desc_lvl, feat_pyr[i], d_x_lvl, d_y_lvl, d_ori_lvl, d_size_lvl,
-                img_pyr[i], lvl_scl[i], patch_size);
+        CUDA_LAUNCH((extract_orb<T>), blocks, threads, d_desc_lvl, feat_pyr[i],
+                    d_x_lvl, d_y_lvl, d_ori_lvl, d_size_lvl, img_pyr[i],
+                    lvl_scl[i], patch_size);
         POST_LAUNCH_CHECK();
 
         // Store results to pyramids
         total_feat += feat_pyr[i];
-        d_x_pyr[i] = d_x_lvl;
-        d_y_pyr[i] = d_y_lvl;
+        d_x_pyr[i]     = d_x_lvl;
+        d_y_pyr[i]     = d_y_lvl;
         d_score_pyr[i] = d_score_lvl;
-        d_ori_pyr[i] = d_ori_lvl;
-        d_size_pyr[i] = d_size_lvl;
-        d_desc_pyr[i] = d_desc_lvl;
+        d_ori_pyr[i]   = d_ori_lvl;
+        d_size_pyr[i]  = d_size_lvl;
+        d_desc_pyr[i]  = d_desc_lvl;
     }
 
     // If no features are found, set found features to 0 and return
@@ -448,32 +402,37 @@ void orb(unsigned* out_feat,
     }
 
     // Allocate output memory
-    *d_x     = memAlloc<float>(total_feat).release();
-    *d_y     = memAlloc<float>(total_feat).release();
-    *d_score = memAlloc<float>(total_feat).release();
-    *d_ori   = memAlloc<float>(total_feat).release();
-    *d_size  = memAlloc<float>(total_feat).release();
-    *d_desc  = memAlloc<unsigned>(total_feat * 8).release();
+    *d_x            = memAlloc<float>(total_feat).release();
+    *d_y            = memAlloc<float>(total_feat).release();
+    *d_score        = memAlloc<float>(total_feat).release();
+    *d_ori          = memAlloc<float>(total_feat).release();
+    *d_size         = memAlloc<float>(total_feat).release();
+    *d_desc         = memAlloc<unsigned>(total_feat * 8).release();
     unsigned offset = 0;
     for (unsigned i = 0; i < max_levels; i++) {
-        if (feat_pyr[i] == 0)
-            continue;
-
-        if (i > 0)
-            offset += feat_pyr[i-1];
-
-        CUDA_CHECK(cudaMemcpyAsync(*d_x+offset, d_x_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_y+offset, d_y_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_score+offset, d_score_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_ori+offset, d_ori_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_size+offset, d_size_pyr[i], feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_desc+(offset*8), d_desc_pyr[i], feat_pyr[i] * 8 * sizeof(unsigned),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        if (feat_pyr[i] == 0) continue;
+
+        if (i > 0) offset += feat_pyr[i - 1];
+
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_x + offset, d_x_pyr[i], feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_y + offset, d_y_pyr[i], feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_score + offset, d_score_pyr[i], feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_ori + offset, d_ori_pyr[i], feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_size + offset, d_size_pyr[i], feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(*d_desc + (offset * 8), d_desc_pyr[i],
+                                   feat_pyr[i] * 8 * sizeof(unsigned),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getActiveStream()));
 
         memFree(d_x_pyr[i]);
         memFree(d_y_pyr[i]);
@@ -487,6 +446,6 @@ void orb(unsigned* out_feat,
     *out_feat = total_feat;
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/orb_patch.hpp b/src/backend/cuda/kernel/orb_patch.hpp
index 7330feed6e..8a6ec2633b 100644
--- a/src/backend/cuda/kernel/orb_patch.hpp
+++ b/src/backend/cuda/kernel/orb_patch.hpp
@@ -9,281 +9,92 @@
 
 #pragma once
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 // Reference pattern, generated for a patch size of 31x31, as suggested by
 // original ORB paper
 #define REF_PAT_SIZE 31
 #define REF_PAT_SAMPLES 256
 #define REF_PAT_COORDS 4
-#define REF_PAT_LENGTH (REF_PAT_SAMPLES*REF_PAT_COORDS)
+#define REF_PAT_LENGTH (REF_PAT_SAMPLES * REF_PAT_COORDS)
 
 // Current reference pattern was borrowed from OpenCV, a randomly generated
 // pattern will not achieve same quality as it must be trained like described
 // in sections 4.2 and 4.3 of the original ORB paper.
 __constant__ int d_ref_pat[REF_PAT_LENGTH] = {
-    8,-3, 9,5,
-    4,2, 7,-12,
-    -11,9, -8,2,
-    7,-12, 12,-13,
-    2,-13, 2,12,
-    1,-7, 1,6,
-    -2,-10, -2,-4,
-    -13,-13, -11,-8,
-    -13,-3, -12,-9,
-    10,4, 11,9,
-    -13,-8, -8,-9,
-    -11,7, -9,12,
-    7,7, 12,6,
-    -4,-5, -3,0,
-    -13,2, -12,-3,
-    -9,0, -7,5,
-    12,-6, 12,-1,
-    -3,6, -2,12,
-    -6,-13, -4,-8,
-    11,-13, 12,-8,
-    4,7, 5,1,
-    5,-3, 10,-3,
-    3,-7, 6,12,
-    -8,-7, -6,-2,
-    -2,11, -1,-10,
-    -13,12, -8,10,
-    -7,3, -5,-3,
-    -4,2, -3,7,
-    -10,-12, -6,11,
-    5,-12, 6,-7,
-    5,-6, 7,-1,
-    1,0, 4,-5,
-    9,11, 11,-13,
-    4,7, 4,12,
-    2,-1, 4,4,
-    -4,-12, -2,7,
-    -8,-5, -7,-10,
-    4,11, 9,12,
-    0,-8, 1,-13,
-    -13,-2, -8,2,
-    -3,-2, -2,3,
-    -6,9, -4,-9,
-    8,12, 10,7,
-    0,9, 1,3,
-    7,-5, 11,-10,
-    -13,-6, -11,0,
-    10,7, 12,1,
-    -6,-3, -6,12,
-    10,-9, 12,-4,
-    -13,8, -8,-12,
-    -13,0, -8,-4,
-    3,3, 7,8,
-    5,7, 10,-7,
-    -1,7, 1,-12,
-    3,-10, 5,6,
-    2,-4, 3,-10,
-    -13,0, -13,5,
-    -13,-7, -12,12,
-    -13,3, -11,8,
-    -7,12, -4,7,
-    6,-10, 12,8,
-    -9,-1, -7,-6,
-    -2,-5, 0,12,
-    -12,5, -7,5,
-    3,-10, 8,-13,
-    -7,-7, -4,5,
-    -3,-2, -1,-7,
-    2,9, 5,-11,
-    -11,-13, -5,-13,
-    -1,6, 0,-1,
-    5,-3, 5,2,
-    -4,-13, -4,12,
-    -9,-6, -9,6,
-    -12,-10, -8,-4,
-    10,2, 12,-3,
-    7,12, 12,12,
-    -7,-13, -6,5,
-    -4,9, -3,4,
-    7,-1, 12,2,
-    -7,6, -5,1,
-    -13,11, -12,5,
-    -3,7, -2,-6,
-    7,-8, 12,-7,
-    -13,-7, -11,-12,
-    1,-3, 12,12,
-    2,-6, 3,0,
-    -4,3, -2,-13,
-    -1,-13, 1,9,
-    7,1, 8,-6,
-    1,-1, 3,12,
-    9,1, 12,6,
-    -1,-9, -1,3,
-    -13,-13, -10,5,
-    7,7, 10,12,
-    12,-5, 12,9,
-    6,3, 7,11,
-    5,-13, 6,10,
-    2,-12, 2,3,
-    3,8, 4,-6,
-    2,6, 12,-13,
-    9,-12, 10,3,
-    -8,4, -7,9,
-    -11,12, -4,-6,
-    1,12, 2,-8,
-    6,-9, 7,-4,
-    2,3, 3,-2,
-    6,3, 11,0,
-    3,-3, 8,-8,
-    7,8, 9,3,
-    -11,-5, -6,-4,
-    -10,11, -5,10,
-    -5,-8, -3,12,
-    -10,5, -9,0,
-    8,-1, 12,-6,
-    4,-6, 6,-11,
-    -10,12, -8,7,
-    4,-2, 6,7,
-    -2,0, -2,12,
-    -5,-8, -5,2,
-    7,-6, 10,12,
-    -9,-13, -8,-8,
-    -5,-13, -5,-2,
-    8,-8, 9,-13,
-    -9,-11, -9,0,
-    1,-8, 1,-2,
-    7,-4, 9,1,
-    -2,1, -1,-4,
-    11,-6, 12,-11,
-    -12,-9, -6,4,
-    3,7, 7,12,
-    5,5, 10,8,
-    0,-4, 2,8,
-    -9,12, -5,-13,
-    0,7, 2,12,
-    -1,2, 1,7,
-    5,11, 7,-9,
-    3,5, 6,-8,
-    -13,-4, -8,9,
-    -5,9, -3,-3,
-    -4,-7, -3,-12,
-    6,5, 8,0,
-    -7,6, -6,12,
-    -13,6, -5,-2,
-    1,-10, 3,10,
-    4,1, 8,-4,
-    -2,-2, 2,-13,
-    2,-12, 12,12,
-    -2,-13, 0,-6,
-    4,1, 9,3,
-    -6,-10, -3,-5,
-    -3,-13, -1,1,
-    7,5, 12,-11,
-    4,-2, 5,-7,
-    -13,9, -9,-5,
-    7,1, 8,6,
-    7,-8, 7,6,
-    -7,-4, -7,1,
-    -8,11, -7,-8,
-    -13,6, -12,-8,
-    2,4, 3,9,
-    10,-5, 12,3,
-    -6,-5, -6,7,
-    8,-3, 9,-8,
-    2,-12, 2,8,
-    -11,-2, -10,3,
-    -12,-13, -7,-9,
-    -11,0, -10,-5,
-    5,-3, 11,8,
-    -2,-13, -1,12,
-    -1,-8, 0,9,
-    -13,-11, -12,-5,
-    -10,-2, -10,11,
-    -3,9, -2,-13,
-    2,-3, 3,2,
-    -9,-13, -4,0,
-    -4,6, -3,-10,
-    -4,12, -2,-7,
-    -6,-11, -4,9,
-    6,-3, 6,11,
-    -13,11, -5,5,
-    11,11, 12,6,
-    7,-5, 12,-2,
-    -1,12, 0,7,
-    -4,-8, -3,-2,
-    -7,1, -6,7,
-    -13,-12, -8,-13,
-    -7,-2, -6,-8,
-    -8,5, -6,-9,
-    -5,-1, -4,5,
-    -13,7, -8,10,
-    1,5, 5,-13,
-    1,0, 10,-13,
-    9,12, 10,-1,
-    5,-8, 10,-9,
-    -1,11, 1,-13,
-    -9,-3, -6,2,
-    -1,-10, 1,12,
-    -13,1, -8,-10,
-    8,-11, 10,-6,
-    2,-13, 3,-6,
-    7,-13, 12,-9,
-    -10,-10, -5,-7,
-    -10,-8, -8,-13,
-    4,-6, 8,5,
-    3,12, 8,-13,
-    -4,2, -3,-3,
-    5,-13, 10,-12,
-    4,-13, 5,-1,
-    -9,9, -4,3,
-    0,3, 3,-9,
-    -12,1, -6,1,
-    3,2, 4,-8,
-    -10,-10, -10,9,
-    8,-13, 12,12,
-    -8,-12, -6,-5,
-    2,2, 3,7,
-    10,6, 11,-8,
-    6,8, 8,-12,
-    -7,10, -6,5,
-    -3,-9, -3,9,
-    -1,-13, -1,5,
-    -3,-7, -3,4,
-    -8,-2, -8,3,
-    4,2, 12,12,
-    2,-5, 3,11,
-    6,-9, 11,-13,
-    3,-1, 7,12,
-    11,-1, 12,4,
-    -3,0, -3,6,
-    4,-11, 4,12,
-    2,-4, 2,1,
-    -10,-6, -8,1,
-    -13,7, -11,1,
-    -13,12, -11,-13,
-    6,0, 11,-13,
-    0,-1, 1,4,
-    -13,3, -9,-2,
-    -9,8, -6,-3,
-    -13,-6, -8,-2,
-    5,-9, 8,10,
-    2,7, 3,-9,
-    -1,-6, -1,-1,
-    9,5, 11,-2,
-    11,-3, 12,-8,
-    3,0, 3,5,
-    -1,4, 0,10,
-    3,-6, 4,5,
-    -13,0, -10,5,
-    5,8, 12,11,
-    8,9, 9,-6,
-    7,-4, 8,-12,
-    -10,4, -10,9,
-    7,3, 12,4,
-    9,-7, 10,-2,
-    7,0, 12,-2,
-    -1,-6, 0,-11,
+    8,   -3,  9,   5,   4,   2,   7,   -12, -11, 9,   -8,  2,   7,   -12, 12,
+    -13, 2,   -13, 2,   12,  1,   -7,  1,   6,   -2,  -10, -2,  -4,  -13, -13,
+    -11, -8,  -13, -3,  -12, -9,  10,  4,   11,  9,   -13, -8,  -8,  -9,  -11,
+    7,   -9,  12,  7,   7,   12,  6,   -4,  -5,  -3,  0,   -13, 2,   -12, -3,
+    -9,  0,   -7,  5,   12,  -6,  12,  -1,  -3,  6,   -2,  12,  -6,  -13, -4,
+    -8,  11,  -13, 12,  -8,  4,   7,   5,   1,   5,   -3,  10,  -3,  3,   -7,
+    6,   12,  -8,  -7,  -6,  -2,  -2,  11,  -1,  -10, -13, 12,  -8,  10,  -7,
+    3,   -5,  -3,  -4,  2,   -3,  7,   -10, -12, -6,  11,  5,   -12, 6,   -7,
+    5,   -6,  7,   -1,  1,   0,   4,   -5,  9,   11,  11,  -13, 4,   7,   4,
+    12,  2,   -1,  4,   4,   -4,  -12, -2,  7,   -8,  -5,  -7,  -10, 4,   11,
+    9,   12,  0,   -8,  1,   -13, -13, -2,  -8,  2,   -3,  -2,  -2,  3,   -6,
+    9,   -4,  -9,  8,   12,  10,  7,   0,   9,   1,   3,   7,   -5,  11,  -10,
+    -13, -6,  -11, 0,   10,  7,   12,  1,   -6,  -3,  -6,  12,  10,  -9,  12,
+    -4,  -13, 8,   -8,  -12, -13, 0,   -8,  -4,  3,   3,   7,   8,   5,   7,
+    10,  -7,  -1,  7,   1,   -12, 3,   -10, 5,   6,   2,   -4,  3,   -10, -13,
+    0,   -13, 5,   -13, -7,  -12, 12,  -13, 3,   -11, 8,   -7,  12,  -4,  7,
+    6,   -10, 12,  8,   -9,  -1,  -7,  -6,  -2,  -5,  0,   12,  -12, 5,   -7,
+    5,   3,   -10, 8,   -13, -7,  -7,  -4,  5,   -3,  -2,  -1,  -7,  2,   9,
+    5,   -11, -11, -13, -5,  -13, -1,  6,   0,   -1,  5,   -3,  5,   2,   -4,
+    -13, -4,  12,  -9,  -6,  -9,  6,   -12, -10, -8,  -4,  10,  2,   12,  -3,
+    7,   12,  12,  12,  -7,  -13, -6,  5,   -4,  9,   -3,  4,   7,   -1,  12,
+    2,   -7,  6,   -5,  1,   -13, 11,  -12, 5,   -3,  7,   -2,  -6,  7,   -8,
+    12,  -7,  -13, -7,  -11, -12, 1,   -3,  12,  12,  2,   -6,  3,   0,   -4,
+    3,   -2,  -13, -1,  -13, 1,   9,   7,   1,   8,   -6,  1,   -1,  3,   12,
+    9,   1,   12,  6,   -1,  -9,  -1,  3,   -13, -13, -10, 5,   7,   7,   10,
+    12,  12,  -5,  12,  9,   6,   3,   7,   11,  5,   -13, 6,   10,  2,   -12,
+    2,   3,   3,   8,   4,   -6,  2,   6,   12,  -13, 9,   -12, 10,  3,   -8,
+    4,   -7,  9,   -11, 12,  -4,  -6,  1,   12,  2,   -8,  6,   -9,  7,   -4,
+    2,   3,   3,   -2,  6,   3,   11,  0,   3,   -3,  8,   -8,  7,   8,   9,
+    3,   -11, -5,  -6,  -4,  -10, 11,  -5,  10,  -5,  -8,  -3,  12,  -10, 5,
+    -9,  0,   8,   -1,  12,  -6,  4,   -6,  6,   -11, -10, 12,  -8,  7,   4,
+    -2,  6,   7,   -2,  0,   -2,  12,  -5,  -8,  -5,  2,   7,   -6,  10,  12,
+    -9,  -13, -8,  -8,  -5,  -13, -5,  -2,  8,   -8,  9,   -13, -9,  -11, -9,
+    0,   1,   -8,  1,   -2,  7,   -4,  9,   1,   -2,  1,   -1,  -4,  11,  -6,
+    12,  -11, -12, -9,  -6,  4,   3,   7,   7,   12,  5,   5,   10,  8,   0,
+    -4,  2,   8,   -9,  12,  -5,  -13, 0,   7,   2,   12,  -1,  2,   1,   7,
+    5,   11,  7,   -9,  3,   5,   6,   -8,  -13, -4,  -8,  9,   -5,  9,   -3,
+    -3,  -4,  -7,  -3,  -12, 6,   5,   8,   0,   -7,  6,   -6,  12,  -13, 6,
+    -5,  -2,  1,   -10, 3,   10,  4,   1,   8,   -4,  -2,  -2,  2,   -13, 2,
+    -12, 12,  12,  -2,  -13, 0,   -6,  4,   1,   9,   3,   -6,  -10, -3,  -5,
+    -3,  -13, -1,  1,   7,   5,   12,  -11, 4,   -2,  5,   -7,  -13, 9,   -9,
+    -5,  7,   1,   8,   6,   7,   -8,  7,   6,   -7,  -4,  -7,  1,   -8,  11,
+    -7,  -8,  -13, 6,   -12, -8,  2,   4,   3,   9,   10,  -5,  12,  3,   -6,
+    -5,  -6,  7,   8,   -3,  9,   -8,  2,   -12, 2,   8,   -11, -2,  -10, 3,
+    -12, -13, -7,  -9,  -11, 0,   -10, -5,  5,   -3,  11,  8,   -2,  -13, -1,
+    12,  -1,  -8,  0,   9,   -13, -11, -12, -5,  -10, -2,  -10, 11,  -3,  9,
+    -2,  -13, 2,   -3,  3,   2,   -9,  -13, -4,  0,   -4,  6,   -3,  -10, -4,
+    12,  -2,  -7,  -6,  -11, -4,  9,   6,   -3,  6,   11,  -13, 11,  -5,  5,
+    11,  11,  12,  6,   7,   -5,  12,  -2,  -1,  12,  0,   7,   -4,  -8,  -3,
+    -2,  -7,  1,   -6,  7,   -13, -12, -8,  -13, -7,  -2,  -6,  -8,  -8,  5,
+    -6,  -9,  -5,  -1,  -4,  5,   -13, 7,   -8,  10,  1,   5,   5,   -13, 1,
+    0,   10,  -13, 9,   12,  10,  -1,  5,   -8,  10,  -9,  -1,  11,  1,   -13,
+    -9,  -3,  -6,  2,   -1,  -10, 1,   12,  -13, 1,   -8,  -10, 8,   -11, 10,
+    -6,  2,   -13, 3,   -6,  7,   -13, 12,  -9,  -10, -10, -5,  -7,  -10, -8,
+    -8,  -13, 4,   -6,  8,   5,   3,   12,  8,   -13, -4,  2,   -3,  -3,  5,
+    -13, 10,  -12, 4,   -13, 5,   -1,  -9,  9,   -4,  3,   0,   3,   3,   -9,
+    -12, 1,   -6,  1,   3,   2,   4,   -8,  -10, -10, -10, 9,   8,   -13, 12,
+    12,  -8,  -12, -6,  -5,  2,   2,   3,   7,   10,  6,   11,  -8,  6,   8,
+    8,   -12, -7,  10,  -6,  5,   -3,  -9,  -3,  9,   -1,  -13, -1,  5,   -3,
+    -7,  -3,  4,   -8,  -2,  -8,  3,   4,   2,   12,  12,  2,   -5,  3,   11,
+    6,   -9,  11,  -13, 3,   -1,  7,   12,  11,  -1,  12,  4,   -3,  0,   -3,
+    6,   4,   -11, 4,   12,  2,   -4,  2,   1,   -10, -6,  -8,  1,   -13, 7,
+    -11, 1,   -13, 12,  -11, -13, 6,   0,   11,  -13, 0,   -1,  1,   4,   -13,
+    3,   -9,  -2,  -9,  8,   -6,  -3,  -13, -6,  -8,  -2,  5,   -9,  8,   10,
+    2,   7,   3,   -9,  -1,  -6,  -1,  -1,  9,   5,   11,  -2,  11,  -3,  12,
+    -8,  3,   0,   3,   5,   -1,  4,   0,   10,  3,   -6,  4,   5,   -13, 0,
+    -10, 5,   5,   8,   12,  11,  8,   9,   9,   -6,  7,   -4,  8,   -12, -10,
+    4,   -10, 9,   7,   3,   12,  4,   9,   -7,  10,  -2,  7,   0,   12,  -2,
+    -1,  -6,  0,   -11,
 };
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index d11ae987dc..ecc4135d65 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -8,33 +8,29 @@
  ********************************************************/
 
 #pragma once
-#include <platform.hpp>
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int PADB_THREADS_X = 32;
-static const int PADB_THREADS_Y =  8;
+static const int PADB_THREADS_Y = 8;
 
 template<af_border_type BType>
-__device__
-int idxByndEdge(const int i, const int lb, const int len)
-{
+__device__ int idxByndEdge(const int i, const int lb, const int len) {
     uint retVal;
-    switch(BType) {
+    switch (BType) {
         case AF_PAD_SYM:
-            retVal = ((i<lb || i>=(lb+len)) ? ((len-1) - ((i-lb)%len)) : i-lb);
-            break;
-        case AF_PAD_CLAMP_TO_EDGE:
-            retVal = clamp(i-lb, 0, len-1);
+            retVal =
+                ((i < lb || i >= (lb + len)) ? ((len - 1) - ((i - lb) % len))
+                                             : i - lb);
             break;
-        default: //AF_PAD_ZERO
+        case AF_PAD_CLAMP_TO_EDGE: retVal = clamp(i - lb, 0, len - 1); break;
+        default:  // AF_PAD_ZERO
             retVal = 0;
             break;
     }
@@ -42,21 +38,18 @@ int idxByndEdge(const int i, const int lb, const int len)
 }
 
 template<typename T, af_border_type BType>
-__global__
-void padBordersKernel(Param<T> out, CParam<T> in,
-                      const int l0, const int l1,
-                      const int l2, const int l3,
-                      unsigned blk_x, unsigned blk_y)
-{
+__global__ void padBordersKernel(Param<T> out, CParam<T> in, const int l0,
+                                 const int l1, const int l2, const int l3,
+                                 unsigned blk_x, unsigned blk_y) {
     const int lx = threadIdx.x;
     const int ly = threadIdx.y;
     const int k  = blockIdx.x / blk_x;
     const int l  = blockIdx.y / blk_y;
 
-    const int blockIdx_x = blockIdx.x - (blk_x) * k;
-    const int blockIdx_y = blockIdx.y - (blk_y) * l;
-    const int i = blockIdx_x * blockDim.x + lx;
-    const int j = blockIdx_y * blockDim.y + ly;
+    const int blockIdx_x = blockIdx.x - (blk_x)*k;
+    const int blockIdx_y = blockIdx.y - (blk_y)*l;
+    const int i          = blockIdx_x * blockDim.x + lx;
+    const int j          = blockIdx_y * blockDim.y + ly;
 
     const int d0 = in.dims[0];
     const int d1 = in.dims[1];
@@ -67,43 +60,41 @@ void padBordersKernel(Param<T> out, CParam<T> in,
     const int s2 = in.strides[2];
     const int s3 = in.strides[3];
 
-    const T * src = in.ptr ;
-          T * dst = out.ptr;
+    const T* src = in.ptr;
+    T* dst       = out.ptr;
 
-    bool isNotPadding = ( l>=l3 && l<(d3+l3) ) &&
-                        ( k>=l2 && k<(d2+l2) ) &&
-                        ( j>=l1 && j<(d1+l1) ) &&
-                        ( i>=l0 && i<(d0+l0) );
+    bool isNotPadding =
+        (l >= l3 && l < (d3 + l3)) && (k >= l2 && k < (d2 + l2)) &&
+        (j >= l1 && j < (d1 + l1)) && (i >= l0 && i < (d0 + l0));
     T value = scalar<T>(0);
 
     if (isNotPadding) {
-        unsigned iLOff = (l-l3) * s3;
-        unsigned iKOff = (k-l2) * s2;
-        unsigned iJOff = (j-l1) * s1;
-        unsigned iIOff = (i-l0) * s0;
+        unsigned iLOff = (l - l3) * s3;
+        unsigned iKOff = (k - l2) * s2;
+        unsigned iJOff = (j - l1) * s1;
+        unsigned iIOff = (i - l0) * s0;
 
-        value = src[ iLOff + iKOff + iJOff + iIOff ];
-    } else if (BType!=AF_PAD_ZERO) {
+        value = src[iLOff + iKOff + iJOff + iIOff];
+    } else if (BType != AF_PAD_ZERO) {
         unsigned iLOff = idxByndEdge<BType>(l, l3, d3) * s3;
         unsigned iKOff = idxByndEdge<BType>(k, l2, d2) * s2;
         unsigned iJOff = idxByndEdge<BType>(j, l1, d1) * s1;
         unsigned iIOff = idxByndEdge<BType>(i, l0, d0) * s0;
 
-        value = src[ iLOff + iKOff + iJOff + iIOff ];
+        value = src[iLOff + iKOff + iJOff + iIOff];
     }
 
-    if (i<out.dims[0] && j<out.dims[1] &&
-        k<out.dims[2] && l<out.dims[3]) {
-        unsigned off = (l*out.strides[3] + k*out.strides[2] +
-                        j*out.strides[1] + i*out.strides[0]);
-        dst[ off ] = value;
+    if (i < out.dims[0] && j < out.dims[1] && k < out.dims[2] &&
+        l < out.dims[3]) {
+        unsigned off = (l * out.strides[3] + k * out.strides[2] +
+                        j * out.strides[1] + i * out.strides[0]);
+        dst[off]     = value;
     }
 }
 
 template<typename T>
-void padBorders(Param<T> out, CParam<T> in,
-                dim4 const lBoundPadding, const af::borderType btype)
-{
+void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
+                const af::borderType btype) {
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
 
     int blk_x = divup(out.dims[0], PADB_THREADS_X);
@@ -111,27 +102,24 @@ void padBorders(Param<T> out, CParam<T> in,
 
     dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
 
-    switch(btype) {
+    switch (btype) {
         case AF_PAD_SYM:
-            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_SYM>),
-                    blocks, threads, out, in,
-                    lBoundPadding[0], lBoundPadding[1],
-                    lBoundPadding[2], lBoundPadding[3],
-                    blk_x, blk_y); break;
+            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_SYM>), blocks, threads, out,
+                        in, lBoundPadding[0], lBoundPadding[1],
+                        lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
+            break;
         case AF_PAD_CLAMP_TO_EDGE:
-            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_CLAMP_TO_EDGE>),
-                    blocks, threads, out, in,
-                    lBoundPadding[0], lBoundPadding[1],
-                    lBoundPadding[2], lBoundPadding[3],
-                    blk_x, blk_y); break;
+            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_CLAMP_TO_EDGE>), blocks,
+                        threads, out, in, lBoundPadding[0], lBoundPadding[1],
+                        lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
+            break;
         default:
-            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_ZERO>),
-                    blocks, threads, out, in,
-                    lBoundPadding[0], lBoundPadding[1],
-                    lBoundPadding[2], lBoundPadding[3],
-                    blk_x, blk_y); break;
+            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_ZERO>), blocks, threads,
+                        out, in, lBoundPadding[0], lBoundPadding[1],
+                        lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
+            break;
     }
     POST_LAUNCH_CHECK();
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 9e01e948f3..6140b9efec 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -10,674 +10,766 @@
 #pragma once
 
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include <af/defines.h>
+#include <err_cuda.hpp>
+#include <kernel/random_engine_mersenne.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
-#include <kernel/random_engine_mersenne.hpp>
 #include <random_engine.hpp>
+#include <af/defines.h>
+
+namespace cuda {
+namespace kernel {
+// Utils
+
+static const int THREADS = 256;
+#define PI_VAL \
+    3.1415926535897932384626433832795028841971693993751058209749445923078164
+
+// Conversion to floats adapted from Random123
+#define UINTMAX 0xffffffff
+#define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
+#define HALF_FLT_FACTOR ((0.5f) * FLT_FACTOR)
+
+#define UINTLMAX 0xffffffffffffffff
+#define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
+#define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
+
+// Generates rationals in (0, 1]
+__device__ static float getFloat(const uint &num) {
+    return (num * FLT_FACTOR + HALF_FLT_FACTOR);
+}
+
+// Generates rationals in (0, 1]
+__device__ static double getDouble(const uint &num1, const uint &num2) {
+    uintl num = (((uintl)num1) << 32) | ((uintl)num2);
+    return (num * DBL_FACTOR + HALF_DBL_FACTOR);
+}
+
+template<typename T>
+__device__ static void boxMullerTransform(T *const out1, T *const out2,
+                                          const T &r1, const T &r2) {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
+    T r     = sqrt((T)(-2.0) * log(r1));
+    T theta = 2 * (T)PI_VAL * r2;
+    *out1   = r * sin(theta);
+    *out2   = r * cos(theta);
+}
+
+// Writes without boundary checking
+
+__device__ static void writeOut128Bytes(uchar *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]                   = r1;
+    out[index + blockDim.x]      = r1 >> 8;
+    out[index + 2 * blockDim.x]  = r1 >> 16;
+    out[index + 3 * blockDim.x]  = r1 >> 24;
+    out[index + 4 * blockDim.x]  = r2;
+    out[index + 5 * blockDim.x]  = r2 >> 8;
+    out[index + 6 * blockDim.x]  = r2 >> 16;
+    out[index + 7 * blockDim.x]  = r2 >> 24;
+    out[index + 8 * blockDim.x]  = r3;
+    out[index + 9 * blockDim.x]  = r3 >> 8;
+    out[index + 10 * blockDim.x] = r3 >> 16;
+    out[index + 11 * blockDim.x] = r3 >> 24;
+    out[index + 12 * blockDim.x] = r4;
+    out[index + 13 * blockDim.x] = r4 >> 8;
+    out[index + 14 * blockDim.x] = r4 >> 16;
+    out[index + 15 * blockDim.x] = r4 >> 24;
+}
+
+__device__ static void writeOut128Bytes(char *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]                   = (r1)&0x1;
+    out[index + blockDim.x]      = (r1 >> 1) & 0x1;
+    out[index + 2 * blockDim.x]  = (r1 >> 2) & 0x1;
+    out[index + 3 * blockDim.x]  = (r1 >> 3) & 0x1;
+    out[index + 4 * blockDim.x]  = (r2)&0x1;
+    out[index + 5 * blockDim.x]  = (r2 >> 1) & 0x1;
+    out[index + 6 * blockDim.x]  = (r2 >> 2) & 0x1;
+    out[index + 7 * blockDim.x]  = (r2 >> 3) & 0x1;
+    out[index + 8 * blockDim.x]  = (r3)&0x1;
+    out[index + 9 * blockDim.x]  = (r3 >> 1) & 0x1;
+    out[index + 10 * blockDim.x] = (r3 >> 2) & 0x1;
+    out[index + 11 * blockDim.x] = (r3 >> 3) & 0x1;
+    out[index + 12 * blockDim.x] = (r4)&0x1;
+    out[index + 13 * blockDim.x] = (r4 >> 1) & 0x1;
+    out[index + 14 * blockDim.x] = (r4 >> 2) & 0x1;
+    out[index + 15 * blockDim.x] = (r4 >> 3) & 0x1;
+}
+
+__device__ static void writeOut128Bytes(short *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]                  = r1;
+    out[index + blockDim.x]     = r1 >> 16;
+    out[index + 2 * blockDim.x] = r2;
+    out[index + 3 * blockDim.x] = r2 >> 16;
+    out[index + 4 * blockDim.x] = r3;
+    out[index + 5 * blockDim.x] = r3 >> 16;
+    out[index + 6 * blockDim.x] = r4;
+    out[index + 7 * blockDim.x] = r4 >> 16;
+}
+
+__device__ static void writeOut128Bytes(ushort *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((short *)(out), index, r1, r2, r3, r4);
+}
+
+__device__ static void writeOut128Bytes(int *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]                  = r1;
+    out[index + blockDim.x]     = r2;
+    out[index + 2 * blockDim.x] = r3;
+    out[index + 3 * blockDim.x] = r4;
+}
+
+__device__ static void writeOut128Bytes(uint *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((int *)(out), index, r1, r2, r3, r4);
+}
+
+__device__ static void writeOut128Bytes(intl *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    intl c1                 = r2;
+    c1                      = (c1 << 32) | r1;
+    intl c2                 = r4;
+    c2                      = (c2 << 32) | r3;
+    out[index]              = c1;
+    out[index + blockDim.x] = c2;
+}
+
+__device__ static void writeOut128Bytes(uintl *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((intl *)(out), index, r1, r2, r3, r4);
+}
+
+__device__ static void writeOut128Bytes(float *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]                  = 1.f - getFloat(r1);
+    out[index + blockDim.x]     = 1.f - getFloat(r2);
+    out[index + 2 * blockDim.x] = 1.f - getFloat(r3);
+    out[index + 3 * blockDim.x] = 1.f - getFloat(r4);
+}
+
+__device__ static void writeOut128Bytes(cfloat *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index].x              = 1.f - getFloat(r1);
+    out[index].y              = 1.f - getFloat(r2);
+    out[index + blockDim.x].x = 1.f - getFloat(r3);
+    out[index + blockDim.x].y = 1.f - getFloat(r4);
+}
+
+__device__ static void writeOut128Bytes(double *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]              = 1.0 - getDouble(r1, r2);
+    out[index + blockDim.x] = 1.0 - getDouble(r3, r4);
+}
+
+__device__ static void writeOut128Bytes(cdouble *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index].x = 1.0 - getDouble(r1, r2);
+    out[index].y = 1.0 - getDouble(r3, r4);
+}
+
+// Normalized writes without boundary checking
+
+__device__ static void boxMullerWriteOut128Bytes(float *out, const uint &index,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], &out[index + blockDim.x], getFloat(r1),
+                       getFloat(r2));
+    boxMullerTransform(&out[index + 2 * blockDim.x],
+                       &out[index + 3 * blockDim.x], getFloat(r1),
+                       getFloat(r2));
+}
+
+__device__ static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index].x, &out[index].y, getFloat(r1),
+                       getFloat(r2));
+    boxMullerTransform(&out[index + blockDim.x].x, &out[index + blockDim.x].y,
+                       getFloat(r3), getFloat(r4));
+}
 
-namespace cuda
-{
-namespace kernel
-{
-    //Utils
-
-    static const int THREADS = 256;
-    #define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
-
-    //Conversion to floats adapted from Random123
-    #define UINTMAX 0xffffffff
-    #define FLT_FACTOR ((1.0f)/(UINTMAX + (1.0f)))
-    #define HALF_FLT_FACTOR ((0.5f)*FLT_FACTOR)
-
-    #define UINTLMAX 0xffffffffffffffff
-    #define DBL_FACTOR ((1.0)/(UINTLMAX + (1.0)))
-    #define HALF_DBL_FACTOR ((0.5)*DBL_FACTOR)
-
-    //Generates rationals in (0, 1]
-    __device__ static float getFloat(const uint &num)
-    {
-        return (num*FLT_FACTOR + HALF_FLT_FACTOR);
-    }
-
-    //Generates rationals in (0, 1]
-    __device__ static double getDouble(const uint &num1, const uint &num2)
-    {
-        uintl num = (((uintl)num1)<<32) | ((uintl)num2);
-        return (num*DBL_FACTOR + HALF_DBL_FACTOR);
-    }
-
-    template <typename T>
-    __device__ static void boxMullerTransform(T * const out1, T * const out2, const T &r1, const T &r2)
-    {
-        /*
-         * The log of a real value x where 0 < x < 1 is negative.
-         */
-        T r = sqrt((T)(-2.0) * log(r1));
-        T theta = 2 * (T)PI_VAL * r2;
-        *out1 = r*sin(theta);
-        *out2 = r*cos(theta);
-    }
-
-    //Writes without boundary checking
-
-    __device__ static void writeOut128Bytes(uchar *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index]                 =     r1;
-        out[index +    blockDim.x] =  r1>>8;
-        out[index +  2*blockDim.x] = r1>>16;
-        out[index +  3*blockDim.x] = r1>>24;
-        out[index +  4*blockDim.x] =     r2;
-        out[index +  5*blockDim.x] =  r2>>8;
-        out[index +  6*blockDim.x] = r2>>16;
-        out[index +  7*blockDim.x] = r2>>24;
-        out[index +  8*blockDim.x] =     r3;
-        out[index +  9*blockDim.x] =  r3>>8;
-        out[index + 10*blockDim.x] = r3>>16;
-        out[index + 11*blockDim.x] = r3>>24;
-        out[index + 12*blockDim.x] =     r4;
-        out[index + 13*blockDim.x] =  r4>>8;
-        out[index + 14*blockDim.x] = r4>>16;
-        out[index + 15*blockDim.x] = r4>>24;
-    }
-
-    __device__ static void writeOut128Bytes(char *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index]                 = (r1   )&0x1;
-        out[index +    blockDim.x] = (r1>>1)&0x1;
-        out[index +  2*blockDim.x] = (r1>>2)&0x1;
-        out[index +  3*blockDim.x] = (r1>>3)&0x1;
-        out[index +  4*blockDim.x] = (r2   )&0x1;
-        out[index +  5*blockDim.x] = (r2>>1)&0x1;
-        out[index +  6*blockDim.x] = (r2>>2)&0x1;
-        out[index +  7*blockDim.x] = (r2>>3)&0x1;
-        out[index +  8*blockDim.x] = (r3   )&0x1;
-        out[index +  9*blockDim.x] = (r3>>1)&0x1;
-        out[index + 10*blockDim.x] = (r3>>2)&0x1;
-        out[index + 11*blockDim.x] = (r3>>3)&0x1;
-        out[index + 12*blockDim.x] = (r4   )&0x1;
-        out[index + 13*blockDim.x] = (r4>>1)&0x1;
-        out[index + 14*blockDim.x] = (r4>>2)&0x1;
-        out[index + 15*blockDim.x] = (r4>>3)&0x1;
-    }
-
-    __device__ static void writeOut128Bytes(short *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index]                =     r1;
-        out[index +   blockDim.x] = r1>>16;
-        out[index + 2*blockDim.x] =     r2;
-        out[index + 3*blockDim.x] = r2>>16;
-        out[index + 4*blockDim.x] =     r3;
-        out[index + 5*blockDim.x] = r3>>16;
-        out[index + 6*blockDim.x] =     r4;
-        out[index + 7*blockDim.x] = r4>>16;
-    }
-
-    __device__ static void writeOut128Bytes(ushort *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        writeOut128Bytes((short*)(out), index, r1, r2, r3, r4);
-    }
-
-    __device__ static void writeOut128Bytes(int *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index]                = r1;
-        out[index +   blockDim.x] = r2;
-        out[index + 2*blockDim.x] = r3;
-        out[index + 3*blockDim.x] = r4;
-    }
-
-    __device__ static void writeOut128Bytes(uint *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        writeOut128Bytes((int*)(out), index, r1, r2, r3, r4);
-    }
-
-    __device__ static void writeOut128Bytes(intl *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        intl c1 = r2;
-        c1 = (c1<<32) | r1;
-        intl c2 = r4;
-        c2 = (c2<<32) | r3;
-        out[index]              = c1;
-        out[index + blockDim.x] = c2;
-    }
-
-    __device__ static void writeOut128Bytes(uintl *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        writeOut128Bytes((intl*)(out), index, r1, r2, r3, r4);
-    }
-
-    __device__ static void writeOut128Bytes(float *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index]                = 1.f - getFloat(r1);
-        out[index +   blockDim.x] = 1.f - getFloat(r2);
-        out[index + 2*blockDim.x] = 1.f - getFloat(r3);
-        out[index + 3*blockDim.x] = 1.f - getFloat(r4);
-    }
-
-    __device__ static void writeOut128Bytes(cfloat *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index].x              = 1.f - getFloat(r1);
-        out[index].y              = 1.f - getFloat(r2);
+__device__ static void boxMullerWriteOut128Bytes(double *out, const uint &index,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], &out[index + blockDim.x], getDouble(r1, r2),
+                       getDouble(r3, r4));
+}
+
+__device__ static void boxMullerWriteOut128Bytes(cdouble *out,
+                                                 const uint &index,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index].x, &out[index].y, getDouble(r1, r2),
+                       getDouble(r3, r4));
+}
+
+// Writes with boundary checking
+
+__device__ static void partialWriteOut128Bytes(uchar *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = r1 >> 8; }
+    if (index + 2 * blockDim.x < elements) {
+        out[index + 2 * blockDim.x] = r1 >> 16;
+    }
+    if (index + 3 * blockDim.x < elements) {
+        out[index + 3 * blockDim.x] = r1 >> 24;
+    }
+    if (index + 4 * blockDim.x < elements) { out[index + 4 * blockDim.x] = r2; }
+    if (index + 5 * blockDim.x < elements) {
+        out[index + 5 * blockDim.x] = r2 >> 8;
+    }
+    if (index + 6 * blockDim.x < elements) {
+        out[index + 6 * blockDim.x] = r2 >> 16;
+    }
+    if (index + 7 * blockDim.x < elements) {
+        out[index + 7 * blockDim.x] = r2 >> 24;
+    }
+    if (index + 8 * blockDim.x < elements) { out[index + 8 * blockDim.x] = r3; }
+    if (index + 9 * blockDim.x < elements) {
+        out[index + 9 * blockDim.x] = r3 >> 8;
+    }
+    if (index + 10 * blockDim.x < elements) {
+        out[index + 10 * blockDim.x] = r3 >> 16;
+    }
+    if (index + 11 * blockDim.x < elements) {
+        out[index + 11 * blockDim.x] = r3 >> 24;
+    }
+    if (index + 12 * blockDim.x < elements) {
+        out[index + 12 * blockDim.x] = r4;
+    }
+    if (index + 13 * blockDim.x < elements) {
+        out[index + 13 * blockDim.x] = r4 >> 8;
+    }
+    if (index + 14 * blockDim.x < elements) {
+        out[index + 14 * blockDim.x] = r4 >> 16;
+    }
+    if (index + 15 * blockDim.x < elements) {
+        out[index + 15 * blockDim.x] = r4 >> 24;
+    }
+}
+
+__device__ static void partialWriteOut128Bytes(char *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = (r1)&0x1; }
+    if (index + blockDim.x < elements) {
+        out[index + blockDim.x] = (r1 >> 1) & 0x1;
+    }
+    if (index + 2 * blockDim.x < elements) {
+        out[index + 2 * blockDim.x] = (r1 >> 2) & 0x1;
+    }
+    if (index + 3 * blockDim.x < elements) {
+        out[index + 3 * blockDim.x] = (r1 >> 3) & 0x1;
+    }
+    if (index + 4 * blockDim.x < elements) {
+        out[index + 4 * blockDim.x] = (r2)&0x1;
+    }
+    if (index + 5 * blockDim.x < elements) {
+        out[index + 5 * blockDim.x] = (r2 >> 1) & 0x1;
+    }
+    if (index + 6 * blockDim.x < elements) {
+        out[index + 6 * blockDim.x] = (r2 >> 2) & 0x1;
+    }
+    if (index + 7 * blockDim.x < elements) {
+        out[index + 7 * blockDim.x] = (r2 >> 3) & 0x1;
+    }
+    if (index + 8 * blockDim.x < elements) {
+        out[index + 8 * blockDim.x] = (r3)&0x1;
+    }
+    if (index + 9 * blockDim.x < elements) {
+        out[index + 9 * blockDim.x] = (r3 >> 1) & 0x1;
+    }
+    if (index + 10 * blockDim.x < elements) {
+        out[index + 10 * blockDim.x] = (r3 >> 2) & 0x1;
+    }
+    if (index + 11 * blockDim.x < elements) {
+        out[index + 11 * blockDim.x] = (r3 >> 3) & 0x1;
+    }
+    if (index + 12 * blockDim.x < elements) {
+        out[index + 12 * blockDim.x] = (r4)&0x1;
+    }
+    if (index + 13 * blockDim.x < elements) {
+        out[index + 13 * blockDim.x] = (r4 >> 1) & 0x1;
+    }
+    if (index + 14 * blockDim.x < elements) {
+        out[index + 14 * blockDim.x] = (r4 >> 2) & 0x1;
+    }
+    if (index + 15 * blockDim.x < elements) {
+        out[index + 15 * blockDim.x] = (r4 >> 3) & 0x1;
+    }
+}
+
+__device__ static void partialWriteOut128Bytes(short *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = r1 >> 16; }
+    if (index + 2 * blockDim.x < elements) { out[index + 2 * blockDim.x] = r2; }
+    if (index + 3 * blockDim.x < elements) {
+        out[index + 3 * blockDim.x] = r2 >> 16;
+    }
+    if (index + 4 * blockDim.x < elements) { out[index + 4 * blockDim.x] = r3; }
+    if (index + 5 * blockDim.x < elements) {
+        out[index + 5 * blockDim.x] = r3 >> 16;
+    }
+    if (index + 6 * blockDim.x < elements) { out[index + 6 * blockDim.x] = r4; }
+    if (index + 7 * blockDim.x < elements) {
+        out[index + 7 * blockDim.x] = r4 >> 16;
+    }
+}
+
+__device__ static void partialWriteOut128Bytes(ushort *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((short *)(out), index, r1, r2, r3, r4, elements);
+}
+
+__device__ static void partialWriteOut128Bytes(int *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = r2; }
+    if (index + 2 * blockDim.x < elements) { out[index + 2 * blockDim.x] = r3; }
+    if (index + 3 * blockDim.x < elements) { out[index + 3 * blockDim.x] = r4; }
+}
+
+__device__ static void partialWriteOut128Bytes(uint *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((int *)(out), index, r1, r2, r3, r4, elements);
+}
+
+__device__ static void partialWriteOut128Bytes(intl *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    intl c1 = r2;
+    c1      = (c1 << 32) | r1;
+    intl c2 = r4;
+    c2      = (c2 << 32) | r3;
+    if (index < elements) { out[index] = c1; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = c2; }
+}
+
+__device__ static void partialWriteOut128Bytes(uintl *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((intl *)(out), index, r1, r2, r3, r4, elements);
+}
+
+__device__ static void partialWriteOut128Bytes(float *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = 1.f - getFloat(r1); }
+    if (index + blockDim.x < elements) {
+        out[index + blockDim.x] = 1.f - getFloat(r2);
+    }
+    if (index + 2 * blockDim.x < elements) {
+        out[index + 2 * blockDim.x] = 1.f - getFloat(r3);
+    }
+    if (index + 3 * blockDim.x < elements) {
+        out[index + 3 * blockDim.x] = 1.f - getFloat(r4);
+    }
+}
+
+__device__ static void partialWriteOut128Bytes(cfloat *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) {
+        out[index].x = 1.f - getFloat(r1);
+        out[index].y = 1.f - getFloat(r2);
+    }
+    if (index + blockDim.x < elements) {
         out[index + blockDim.x].x = 1.f - getFloat(r3);
         out[index + blockDim.x].y = 1.f - getFloat(r4);
     }
+}
 
-    __device__ static void writeOut128Bytes(double *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        out[index]              = 1.0 - getDouble(r1, r2);
+__device__ static void partialWriteOut128Bytes(double *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = 1.0 - getDouble(r1, r2); }
+    if (index + blockDim.x < elements) {
         out[index + blockDim.x] = 1.0 - getDouble(r3, r4);
     }
+}
 
-    __device__ static void writeOut128Bytes(cdouble *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
+__device__ static void partialWriteOut128Bytes(cdouble *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) {
         out[index].x = 1.0 - getDouble(r1, r2);
         out[index].y = 1.0 - getDouble(r3, r4);
     }
+}
 
-    //Normalized writes without boundary checking
-
-    __device__ static void boxMullerWriteOut128Bytes(float *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        boxMullerTransform(&out[index]               , &out[index +   blockDim.x], getFloat(r1), getFloat(r2));
-        boxMullerTransform(&out[index + 2*blockDim.x], &out[index + 3*blockDim.x], getFloat(r1), getFloat(r2));
-    }
-
-    __device__ static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        boxMullerTransform(&out[index].x             , &out[index].y             , getFloat(r1), getFloat(r2));
-        boxMullerTransform(&out[index + blockDim.x].x, &out[index + blockDim.x].y, getFloat(r3), getFloat(r4));
-    }
-
-    __device__ static void boxMullerWriteOut128Bytes(double *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        boxMullerTransform(&out[index], &out[index + blockDim.x], getDouble(r1, r2), getDouble(r3, r4));
-    }
-
-    __device__ static void boxMullerWriteOut128Bytes(cdouble *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4)
-    {
-        boxMullerTransform(&out[index].x, &out[index].y, getDouble(r1, r2), getDouble(r3, r4));
-    }
-
-    //Writes with boundary checking
-
-    __device__ static void partialWriteOut128Bytes(uchar *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index                 < elements) {out[index]                 =     r1;}
-        if (index +    blockDim.x < elements) {out[index +    blockDim.x] =  r1>>8;}
-        if (index +  2*blockDim.x < elements) {out[index +  2*blockDim.x] = r1>>16;}
-        if (index +  3*blockDim.x < elements) {out[index +  3*blockDim.x] = r1>>24;}
-        if (index +  4*blockDim.x < elements) {out[index +  4*blockDim.x] =     r2;}
-        if (index +  5*blockDim.x < elements) {out[index +  5*blockDim.x] =  r2>>8;}
-        if (index +  6*blockDim.x < elements) {out[index +  6*blockDim.x] = r2>>16;}
-        if (index +  7*blockDim.x < elements) {out[index +  7*blockDim.x] = r2>>24;}
-        if (index +  8*blockDim.x < elements) {out[index +  8*blockDim.x] =     r3;}
-        if (index +  9*blockDim.x < elements) {out[index +  9*blockDim.x] =  r3>>8;}
-        if (index + 10*blockDim.x < elements) {out[index + 10*blockDim.x] = r3>>16;}
-        if (index + 11*blockDim.x < elements) {out[index + 11*blockDim.x] = r3>>24;}
-        if (index + 12*blockDim.x < elements) {out[index + 12*blockDim.x] =     r4;}
-        if (index + 13*blockDim.x < elements) {out[index + 13*blockDim.x] =  r4>>8;}
-        if (index + 14*blockDim.x < elements) {out[index + 14*blockDim.x] = r4>>16;}
-        if (index + 15*blockDim.x < elements) {out[index + 15*blockDim.x] = r4>>24;}
-    }
-
-    __device__ static void partialWriteOut128Bytes(char *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index                 < elements) {out[index]                 = (r1   )&0x1;}
-        if (index +    blockDim.x < elements) {out[index +    blockDim.x] = (r1>>1)&0x1;}
-        if (index +  2*blockDim.x < elements) {out[index +  2*blockDim.x] = (r1>>2)&0x1;}
-        if (index +  3*blockDim.x < elements) {out[index +  3*blockDim.x] = (r1>>3)&0x1;}
-        if (index +  4*blockDim.x < elements) {out[index +  4*blockDim.x] = (r2   )&0x1;}
-        if (index +  5*blockDim.x < elements) {out[index +  5*blockDim.x] = (r2>>1)&0x1;}
-        if (index +  6*blockDim.x < elements) {out[index +  6*blockDim.x] = (r2>>2)&0x1;}
-        if (index +  7*blockDim.x < elements) {out[index +  7*blockDim.x] = (r2>>3)&0x1;}
-        if (index +  8*blockDim.x < elements) {out[index +  8*blockDim.x] = (r3   )&0x1;}
-        if (index +  9*blockDim.x < elements) {out[index +  9*blockDim.x] = (r3>>1)&0x1;}
-        if (index + 10*blockDim.x < elements) {out[index + 10*blockDim.x] = (r3>>2)&0x1;}
-        if (index + 11*blockDim.x < elements) {out[index + 11*blockDim.x] = (r3>>3)&0x1;}
-        if (index + 12*blockDim.x < elements) {out[index + 12*blockDim.x] = (r4   )&0x1;}
-        if (index + 13*blockDim.x < elements) {out[index + 13*blockDim.x] = (r4>>1)&0x1;}
-        if (index + 14*blockDim.x < elements) {out[index + 14*blockDim.x] = (r4>>2)&0x1;}
-        if (index + 15*blockDim.x < elements) {out[index + 15*blockDim.x] = (r4>>3)&0x1;}
-    }
-
-    __device__ static void partialWriteOut128Bytes(short *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index                < elements) {out[index]                =     r1;}
-        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = r1>>16;}
-        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] =     r2;}
-        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = r2>>16;}
-        if (index + 4*blockDim.x < elements) {out[index + 4*blockDim.x] =     r3;}
-        if (index + 5*blockDim.x < elements) {out[index + 5*blockDim.x] = r3>>16;}
-        if (index + 6*blockDim.x < elements) {out[index + 6*blockDim.x] =     r4;}
-        if (index + 7*blockDim.x < elements) {out[index + 7*blockDim.x] = r4>>16;}
-    }
-
-    __device__ static void partialWriteOut128Bytes(ushort *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        partialWriteOut128Bytes((short*)(out), index, r1, r2, r3, r4, elements);
-    }
-
-    __device__ static void partialWriteOut128Bytes(int *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index                < elements) {out[index]                = r1;}
-        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = r2;}
-        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = r3;}
-        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = r4;}
-    }
-
-    __device__ static void partialWriteOut128Bytes(uint *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        partialWriteOut128Bytes((int*)(out), index, r1, r2, r3, r4, elements);
-    }
-
-    __device__ static void partialWriteOut128Bytes(intl *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        intl c1 = r2;
-        c1 = (c1<<32) | r1;
-        intl c2 = r4;
-        c2 = (c2<<32) | r3;
-        if (index              < elements) {out[index]              = c1;}
-        if (index + blockDim.x < elements) {out[index + blockDim.x] = c2;}
-    }
-
-    __device__ static void partialWriteOut128Bytes(uintl *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        partialWriteOut128Bytes((intl*)(out), index, r1, r2, r3, r4, elements);
-    }
-
-    __device__ static void partialWriteOut128Bytes(float *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index                < elements) {out[index]                = 1.f - getFloat(r1);}
-        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = 1.f - getFloat(r2);}
-        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = 1.f - getFloat(r3);}
-        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = 1.f - getFloat(r4);}
-    }
-
-    __device__ static void partialWriteOut128Bytes(cfloat *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index              < elements) {
-            out[index].x              = 1.f - getFloat(r1);
-            out[index].y              = 1.f - getFloat(r2);
-        }
-        if (index + blockDim.x < elements) {
-            out[index + blockDim.x].x = 1.f - getFloat(r3);
-            out[index + blockDim.x].y = 1.f - getFloat(r4);
-        }
-    }
+// Normalized writes with boundary checking
+
+__device__ static void partialBoxMullerWriteOut128Bytes(
+    float *out, const uint &index, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    float n1, n2, n3, n4;
+    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
+    if (index < elements) { out[index] = n1; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = n2; }
+    if (index + 2 * blockDim.x < elements) { out[index + 2 * blockDim.x] = n3; }
+    if (index + 3 * blockDim.x < elements) { out[index + 3 * blockDim.x] = n4; }
+}
 
-    __device__ static void partialWriteOut128Bytes(double *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index              < elements) {out[index]              = 1.0 - getDouble(r1, r2);}
-        if (index + blockDim.x < elements) {out[index + blockDim.x] = 1.0 - getDouble(r3, r4);}
+__device__ static void partialBoxMullerWriteOut128Bytes(
+    cfloat *out, const uint &index, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    float n1, n2, n3, n4;
+    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
+    if (index < elements) {
+        out[index].x = n1;
+        out[index].y = n2;
     }
-
-    __device__ static void partialWriteOut128Bytes(cdouble *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        if (index < elements) {
-            out[index].x = 1.0 - getDouble(r1, r2);
-            out[index].y = 1.0 - getDouble(r3, r4);
-        }
+    if (index + blockDim.x < elements) {
+        out[index + blockDim.x].x = n3;
+        out[index + blockDim.x].y = n4;
     }
+}
 
-    //Normalized writes with boundary checking
-
-    __device__ static void partialBoxMullerWriteOut128Bytes(float *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        float n1, n2, n3, n4;
-        boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
-        boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
-        if (index                < elements) {out[index]                = n1;}
-        if (index +   blockDim.x < elements) {out[index +   blockDim.x] = n2;}
-        if (index + 2*blockDim.x < elements) {out[index + 2*blockDim.x] = n3;}
-        if (index + 3*blockDim.x < elements) {out[index + 3*blockDim.x] = n4;}
-    }
-
-    __device__ static void partialBoxMullerWriteOut128Bytes(cfloat *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        float n1, n2, n3, n4;
-        boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
-        boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
-        if (index              < elements) {
-            out[index].x              = n1;
-            out[index].y              = n2;
-        }
-        if (index + blockDim.x < elements) {
-            out[index + blockDim.x].x = n3;
-            out[index + blockDim.x].y = n4;
-        }
-    }
+__device__ static void partialBoxMullerWriteOut128Bytes(
+    double *out, const uint &index, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    double n1, n2;
+    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    if (index < elements) { out[index] = n1; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = n2; }
+}
 
-    __device__ static void partialBoxMullerWriteOut128Bytes(double *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        double n1, n2;
-        boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
-        if (index              < elements) {out[index]              = n1;}
-        if (index + blockDim.x < elements) {out[index + blockDim.x] = n2;}
+__device__ static void partialBoxMullerWriteOut128Bytes(
+    cdouble *out, const uint &index, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    double n1, n2;
+    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    if (index < elements) {
+        out[index].x = n1;
+        out[index].y = n2;
     }
+}
 
-    __device__ static void partialBoxMullerWriteOut128Bytes(cdouble *out, const uint &index,
-            const uint &r1, const uint &r2, const uint &r3, const uint &r4, const uint &elements)
-    {
-        double n1, n2;
-        boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
-        if (index < elements) {
-            out[index].x = n1;
-            out[index].y = n2;
-        }
+template<typename T>
+__global__ void uniformPhilox(T *out, uint hi, uint lo, uint hic, uint loc,
+                              uint elementsPerBlock, uint elements) {
+    uint index  = blockIdx.x * elementsPerBlock + threadIdx.x;
+    uint key[2] = {lo, hi};
+    uint ctr[4] = {loc, hic, 0, 0};
+    ctr[0] += index;
+    ctr[1] += (ctr[0] < loc);
+    ctr[2] += (ctr[1] < hic);
+    if (blockIdx.x != (gridDim.x - 1)) {
+        philox(key, ctr);
+        writeOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
+    } else {
+        philox(key, ctr);
+        partialWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3],
+                                elements);
     }
+}
 
-    template <typename T>
-    __global__ void uniformPhilox(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
-    {
-        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {lo, hi};
-        uint ctr[4] = {loc, hic, 0, 0};
-        ctr[0] += index;
-        ctr[1] += (ctr[0] < loc);
-        ctr[2] += (ctr[1] < hic);
-        if (blockIdx.x != (gridDim.x - 1)) {
-            philox(key, ctr);
-            writeOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
-        } else {
-            philox(key, ctr);
-            partialWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
-        }
+template<typename T>
+__global__ void uniformThreefry(T *out, uint hi, uint lo, uint hic, uint loc,
+                                uint elementsPerBlock, uint elements) {
+    uint index  = blockIdx.x * elementsPerBlock + threadIdx.x;
+    uint key[2] = {lo, hi};
+    uint ctr[2] = {loc, hic};
+    ctr[0] += index;
+    ctr[1] += (ctr[0] < loc);
+    uint o[4];
+
+    threefry(key, ctr, o);
+    uint step = elementsPerBlock / 2;
+    ctr[0] += step;
+    ctr[1] += (ctr[0] < step);
+    threefry(key, ctr, o + 2);
+
+    if (blockIdx.x != (gridDim.x - 1)) {
+        writeOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
+    } else {
+        partialWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
     }
+}
 
-    template <typename T>
-    __global__ void uniformThreefry(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
-    {
-        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {lo, hi};
-        uint ctr[2] = {loc, hic};
-        ctr[0] += index;
-        ctr[1] += (ctr[0] < loc);
-        uint o[4];
-
-	threefry(key, ctr, o);
-	uint step = elementsPerBlock / 2;
-	ctr[0] += step;
-	ctr[1] += (ctr[0] < step);
-	threefry(key, ctr, o + 2);
-
-        if (blockIdx.x != (gridDim.x - 1)) {
-            writeOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
+template<typename T>
+__global__ void uniformMersenne(T *const out, uint *const gState,
+                                const uint *const pos_tbl,
+                                const uint *const sh1_tbl,
+                                const uint *const sh2_tbl, uint mask,
+                                const uint *const g_recursion_table,
+                                const uint *const g_temper_table,
+                                uint elementsPerBlock, size_t elements) {
+    __shared__ uint state[STATE_SIZE];
+    __shared__ uint recursion_table[TABLE_SIZE];
+    __shared__ uint temper_table[TABLE_SIZE];
+    uint start                    = blockIdx.x * elementsPerBlock;
+    uint end                      = start + elementsPerBlock;
+    end                           = (end > elements) ? elements : end;
+    int elementsPerBlockIteration = (blockDim.x * 4 * sizeof(uint)) / sizeof(T);
+    int iter = divup((end - start), elementsPerBlockIteration);
+
+    uint pos = pos_tbl[blockIdx.x];
+    uint sh1 = sh1_tbl[blockIdx.x];
+    uint sh2 = sh2_tbl[blockIdx.x];
+    state_read(state, gState);
+    read_table(recursion_table, g_recursion_table);
+    read_table(temper_table, g_temper_table);
+    __syncthreads();
+
+    uint index = start;
+    uint o[4];
+    int offsetX1 = (STATE_SIZE - N + threadIdx.x) % STATE_SIZE;
+    int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1) % STATE_SIZE;
+    int offsetY  = (STATE_SIZE - N + threadIdx.x + pos) % STATE_SIZE;
+    int offsetT  = (STATE_SIZE - N + threadIdx.x + pos - 1) % STATE_SIZE;
+    int offsetO  = threadIdx.x;
+
+    for (int i = 0; i < iter; ++i) {
+        for (int ii = 0; ii < 4; ++ii) {
+            uint r = recursion(recursion_table, mask, sh1, sh2, state[offsetX1],
+                               state[offsetX2], state[offsetY]);
+            state[offsetO] = r;
+            o[ii]          = temper(temper_table, r, state[offsetT]);
+            offsetX1       = (offsetX1 + blockDim.x) % STATE_SIZE;
+            offsetX2       = (offsetX2 + blockDim.x) % STATE_SIZE;
+            offsetY        = (offsetY + blockDim.x) % STATE_SIZE;
+            offsetT        = (offsetT + blockDim.x) % STATE_SIZE;
+            offsetO        = (offsetO + blockDim.x) % STATE_SIZE;
+            __syncthreads();
+        }
+        if (i == iter - 1) {
+            partialWriteOut128Bytes(out, index + threadIdx.x, o[0], o[1], o[2],
+                                    o[3], elements);
         } else {
-            partialWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
+            writeOut128Bytes(out, index + threadIdx.x, o[0], o[1], o[2], o[3]);
         }
+        index += elementsPerBlockIteration;
     }
+    state_write(gState, state);
+}
 
-    template <typename T>
-    __global__ void uniformMersenne(T * const out,
-            uint * const gState,
-            const uint * const pos_tbl,
-            const uint * const sh1_tbl,
-            const uint * const sh2_tbl,
-            uint mask,
-            const uint * const g_recursion_table,
-            const uint * const g_temper_table,
-            uint elementsPerBlock, size_t elements)
-    {
-        __shared__ uint state[STATE_SIZE];
-        __shared__ uint recursion_table[TABLE_SIZE];
-        __shared__ uint temper_table[TABLE_SIZE];
-        uint start = blockIdx.x*elementsPerBlock;
-        uint end = start + elementsPerBlock;
-        end = (end > elements)? elements : end;
-        int elementsPerBlockIteration = (blockDim.x*4*sizeof(uint))/sizeof(T);
-        int iter = divup((end - start), elementsPerBlockIteration);
-
-        uint pos = pos_tbl[blockIdx.x];
-        uint sh1 = sh1_tbl[blockIdx.x];
-        uint sh2 = sh2_tbl[blockIdx.x];
-        state_read(state, gState);
-        read_table(recursion_table, g_recursion_table);
-        read_table(temper_table, g_temper_table);
-        __syncthreads();
-
-        uint index = start;
-        uint o[4];
-        int offsetX1 = (STATE_SIZE - N + threadIdx.x          ) % STATE_SIZE;
-        int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1      ) % STATE_SIZE;
-        int offsetY  = (STATE_SIZE - N + threadIdx.x + pos    ) % STATE_SIZE;
-        int offsetT  = (STATE_SIZE - N + threadIdx.x + pos - 1) % STATE_SIZE;
-        int offsetO  = threadIdx.x;
-
-        for (int i = 0; i < iter; ++i) {
-            for (int ii = 0; ii < 4; ++ii) {
-                uint r = recursion(recursion_table, mask, sh1, sh2,
-                        state[offsetX1],
-                        state[offsetX2],
-                        state[offsetY ]);
-                state[offsetO] = r;
-                o[ii] = temper(temper_table, r, state[offsetT]);
-                offsetX1 = (offsetX1 + blockDim.x) % STATE_SIZE;
-                offsetX2 = (offsetX2 + blockDim.x) % STATE_SIZE;
-                offsetY  = (offsetY  + blockDim.x) % STATE_SIZE;
-                offsetT  = (offsetT  + blockDim.x) % STATE_SIZE;
-                offsetO  = (offsetO  + blockDim.x) % STATE_SIZE;
-                __syncthreads();
-            }
-            if (i == iter - 1) {
-                partialWriteOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
-            } else {
-                writeOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
-            }
-            index += elementsPerBlockIteration;
-        }
-        state_write(gState, state);
-    }
-
-    template <typename T>
-    __global__ void normalPhilox(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
-    {
-        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {lo, hi};
-        uint ctr[4] = {loc, hic, 0, 0};
-        ctr[0] += index;
-        ctr[1] += (ctr[0] < loc);
-        ctr[2] += (ctr[1] < hic);
-        if (blockIdx.x != (gridDim.x - 1)) {
-            philox(key, ctr);
-            boxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
-        } else {
-            philox(key, ctr);
-            partialBoxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
-        }
+template<typename T>
+__global__ void normalPhilox(T *out, uint hi, uint lo, uint hic, uint loc,
+                             uint elementsPerBlock, uint elements) {
+    uint index  = blockIdx.x * elementsPerBlock + threadIdx.x;
+    uint key[2] = {lo, hi};
+    uint ctr[4] = {loc, hic, 0, 0};
+    ctr[0] += index;
+    ctr[1] += (ctr[0] < loc);
+    ctr[2] += (ctr[1] < hic);
+    if (blockIdx.x != (gridDim.x - 1)) {
+        philox(key, ctr);
+        boxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
+    } else {
+        philox(key, ctr);
+        partialBoxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2],
+                                         ctr[3], elements);
     }
+}
 
-    template <typename T>
-    __global__ void normalThreefry(T *out, uint hi, uint lo, uint hic, uint loc, uint elementsPerBlock, uint elements)
-    {
-        uint index = blockIdx.x*elementsPerBlock + threadIdx.x;
-        uint key[2] = {lo, hi};
-        uint ctr[2] = {loc, hic};
-        ctr[0] += index;
-        ctr[1] += (ctr[0] < loc);
-        uint o[4];
-
-	threefry(key, ctr, o);
-	uint step = elementsPerBlock / 2;
-	ctr[0] += step;
-	ctr[1] += (ctr[0] < step);
-	threefry(key, ctr, o + 2);
-
-	if (blockIdx.x != (gridDim.x - 1)) {
-            boxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
-        } else {
-            partialBoxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3], elements);
-        }
+template<typename T>
+__global__ void normalThreefry(T *out, uint hi, uint lo, uint hic, uint loc,
+                               uint elementsPerBlock, uint elements) {
+    uint index  = blockIdx.x * elementsPerBlock + threadIdx.x;
+    uint key[2] = {lo, hi};
+    uint ctr[2] = {loc, hic};
+    ctr[0] += index;
+    ctr[1] += (ctr[0] < loc);
+    uint o[4];
+
+    threefry(key, ctr, o);
+    uint step = elementsPerBlock / 2;
+    ctr[0] += step;
+    ctr[1] += (ctr[0] < step);
+    threefry(key, ctr, o + 2);
+
+    if (blockIdx.x != (gridDim.x - 1)) {
+        boxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3]);
+    } else {
+        partialBoxMullerWriteOut128Bytes(out, index, o[0], o[1], o[2], o[3],
+                                         elements);
     }
+}
 
-    template <typename T>
-    __global__ void normalMersenne(T * const out,
-            uint * const gState,
-            const uint * const pos_tbl,
-            const uint * const sh1_tbl,
-            const uint * const sh2_tbl,
-            uint mask,
-            const uint * const g_recursion_table,
-            const uint * const g_temper_table,
-            uint elementsPerBlock, uint elements)
-    {
-
-        __shared__ uint state[STATE_SIZE];
-        __shared__ uint recursion_table[TABLE_SIZE];
-        __shared__ uint temper_table[TABLE_SIZE];
-        uint start = blockIdx.x*elementsPerBlock;
-        uint end = start + elementsPerBlock;
-        end = (end > elements)? elements : end;
-        int iter = divup((end - start)*sizeof(T), blockDim.x*4*sizeof(uint));
-
-        uint pos = pos_tbl[blockIdx.x];
-        uint sh1 = sh1_tbl[blockIdx.x];
-        uint sh2 = sh2_tbl[blockIdx.x];
-        state_read(state, gState);
-        read_table(recursion_table, g_recursion_table);
-        read_table(temper_table, g_temper_table);
-        __syncthreads();
-
-        uint index = start;
-        int elementsPerBlockIteration = blockDim.x*4*sizeof(uint)/sizeof(T);
-        uint o[4];
-        int offsetX1 = (STATE_SIZE - N + threadIdx.x          ) % STATE_SIZE;
-        int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1      ) % STATE_SIZE;
-        int offsetY  = (STATE_SIZE - N + threadIdx.x + pos    ) % STATE_SIZE;
-        int offsetT  = (STATE_SIZE - N + threadIdx.x + pos - 1) % STATE_SIZE;
-        int offsetO  = threadIdx.x;
-
-        for (int i = 0; i < iter; ++i) {
-            for (int ii = 0; ii < 4; ++ii) {
-                uint r = recursion(recursion_table, mask, sh1, sh2,
-                        state[offsetX1],
-                        state[offsetX2],
-                        state[offsetY ]);
-                state[offsetO] = r;
-                o[ii] = temper(temper_table, r, state[offsetT]);
-                offsetX1 = (offsetX1 + blockDim.x) % STATE_SIZE;
-                offsetX2 = (offsetX2 + blockDim.x) % STATE_SIZE;
-                offsetY  = (offsetY  + blockDim.x) % STATE_SIZE;
-                offsetT  = (offsetT  + blockDim.x) % STATE_SIZE;
-                offsetO  = (offsetO  + blockDim.x) % STATE_SIZE;
-                __syncthreads();
-            }
-            if (i == iter - 1) {
-                partialBoxMullerWriteOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3], elements);
-            } else {
-                boxMullerWriteOut128Bytes(out, index+threadIdx.x, o[0], o[1], o[2], o[3]);
-            }
-            index += elementsPerBlockIteration;
-        }
-        state_write(gState, state);
-    }
-
-    template <typename T>
-    void uniformDistributionMT(T* out, size_t elements,
-            uint * const state,
-            const uint * const pos,
-            const uint * const sh1,
-            const uint * const sh2,
-            uint mask,
-            const uint * const recursion_table,
-            const uint * const temper_table)
-    {
-        int threads = THREADS;
-        int min_elements_per_block = 32*threads*4*sizeof(uint)/sizeof(T);
-        int blocks = divup(elements, min_elements_per_block);
-        blocks = (blocks > BLOCKS)? BLOCKS : blocks;
-        uint elementsPerBlock = divup(elements, blocks);
-        CUDA_LAUNCH(uniformMersenne, blocks, threads, out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
-    }
-
-    template <typename T>
-    void normalDistributionMT(T* out, size_t elements,
-            uint * const state,
-            const uint * const pos,
-            const uint * const sh1,
-            const uint * const sh2,
-            uint mask,
-            const uint * const recursion_table,
-            const uint * const temper_table)
-    {
-        int threads = THREADS;
-        int min_elements_per_block = 32*threads*4*sizeof(uint)/sizeof(T);
-        int blocks = divup(elements, min_elements_per_block);
-        blocks = (blocks > BLOCKS)? BLOCKS : blocks;
-        uint elementsPerBlock = divup(elements, blocks);
-        CUDA_LAUNCH(normalMersenne, blocks, threads, out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
-    }
-
-    template <typename T>
-    void uniformDistributionCBRNG(T* out, size_t elements, const af_random_engine_type type, const uintl &seed, uintl &counter)
-    {
-        int threads = THREADS;
-        int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
-        int blocks = divup(elements, elementsPerBlock);
-        uint hi = seed>>32;
-        uint lo = seed;
-        uint hic = counter>>32;
-        uint loc = counter;
-        switch (type) {
-        case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
-            CUDA_LAUNCH(uniformPhilox, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
-        case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
-            CUDA_LAUNCH(uniformThreefry, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
-        default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+template<typename T>
+__global__ void normalMersenne(T *const out, uint *const gState,
+                               const uint *const pos_tbl,
+                               const uint *const sh1_tbl,
+                               const uint *const sh2_tbl, uint mask,
+                               const uint *const g_recursion_table,
+                               const uint *const g_temper_table,
+                               uint elementsPerBlock, uint elements) {
+    __shared__ uint state[STATE_SIZE];
+    __shared__ uint recursion_table[TABLE_SIZE];
+    __shared__ uint temper_table[TABLE_SIZE];
+    uint start = blockIdx.x * elementsPerBlock;
+    uint end   = start + elementsPerBlock;
+    end        = (end > elements) ? elements : end;
+    int iter = divup((end - start) * sizeof(T), blockDim.x * 4 * sizeof(uint));
+
+    uint pos = pos_tbl[blockIdx.x];
+    uint sh1 = sh1_tbl[blockIdx.x];
+    uint sh2 = sh2_tbl[blockIdx.x];
+    state_read(state, gState);
+    read_table(recursion_table, g_recursion_table);
+    read_table(temper_table, g_temper_table);
+    __syncthreads();
+
+    uint index                    = start;
+    int elementsPerBlockIteration = blockDim.x * 4 * sizeof(uint) / sizeof(T);
+    uint o[4];
+    int offsetX1 = (STATE_SIZE - N + threadIdx.x) % STATE_SIZE;
+    int offsetX2 = (STATE_SIZE - N + threadIdx.x + 1) % STATE_SIZE;
+    int offsetY  = (STATE_SIZE - N + threadIdx.x + pos) % STATE_SIZE;
+    int offsetT  = (STATE_SIZE - N + threadIdx.x + pos - 1) % STATE_SIZE;
+    int offsetO  = threadIdx.x;
+
+    for (int i = 0; i < iter; ++i) {
+        for (int ii = 0; ii < 4; ++ii) {
+            uint r = recursion(recursion_table, mask, sh1, sh2, state[offsetX1],
+                               state[offsetX2], state[offsetY]);
+            state[offsetO] = r;
+            o[ii]          = temper(temper_table, r, state[offsetT]);
+            offsetX1       = (offsetX1 + blockDim.x) % STATE_SIZE;
+            offsetX2       = (offsetX2 + blockDim.x) % STATE_SIZE;
+            offsetY        = (offsetY + blockDim.x) % STATE_SIZE;
+            offsetT        = (offsetT + blockDim.x) % STATE_SIZE;
+            offsetO        = (offsetO + blockDim.x) % STATE_SIZE;
+            __syncthreads();
         }
-        counter += elements;
-    }
-
-    template <typename T>
-    void normalDistributionCBRNG(T *out, size_t elements, const af_random_engine_type type, const uintl &seed, uintl &counter)
-    {
-        int threads = THREADS;
-        int elementsPerBlock = threads*4*sizeof(uint)/sizeof(T);
-        int blocks = divup(elements, elementsPerBlock);
-        uint hi = seed>>32;
-        uint lo = seed;
-        uint hic = counter>>32;
-        uint loc = counter;
-        switch (type) {
-        case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
-            CUDA_LAUNCH(normalPhilox, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
-        case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
-            CUDA_LAUNCH(normalThreefry, blocks, threads, out, hi, lo, hic, loc, elementsPerBlock, elements); break;
-        default : AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+        if (i == iter - 1) {
+            partialBoxMullerWriteOut128Bytes(out, index + threadIdx.x, o[0],
+                                             o[1], o[2], o[3], elements);
+        } else {
+            boxMullerWriteOut128Bytes(out, index + threadIdx.x, o[0], o[1],
+                                      o[2], o[3]);
         }
-        counter += elements;
+        index += elementsPerBlockIteration;
     }
+    state_write(gState, state);
+}
+
+template<typename T>
+void uniformDistributionMT(T *out, size_t elements, uint *const state,
+                           const uint *const pos, const uint *const sh1,
+                           const uint *const sh2, uint mask,
+                           const uint *const recursion_table,
+                           const uint *const temper_table) {
+    int threads                = THREADS;
+    int min_elements_per_block = 32 * threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks                 = divup(elements, min_elements_per_block);
+    blocks                     = (blocks > BLOCKS) ? BLOCKS : blocks;
+    uint elementsPerBlock      = divup(elements, blocks);
+    CUDA_LAUNCH(uniformMersenne, blocks, threads, out, state, pos, sh1, sh2,
+                mask, recursion_table, temper_table, elementsPerBlock,
+                elements);
+}
+
+template<typename T>
+void normalDistributionMT(T *out, size_t elements, uint *const state,
+                          const uint *const pos, const uint *const sh1,
+                          const uint *const sh2, uint mask,
+                          const uint *const recursion_table,
+                          const uint *const temper_table) {
+    int threads                = THREADS;
+    int min_elements_per_block = 32 * threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks                 = divup(elements, min_elements_per_block);
+    blocks                     = (blocks > BLOCKS) ? BLOCKS : blocks;
+    uint elementsPerBlock      = divup(elements, blocks);
+    CUDA_LAUNCH(normalMersenne, blocks, threads, out, state, pos, sh1, sh2,
+                mask, recursion_table, temper_table, elementsPerBlock,
+                elements);
+}
+
+template<typename T>
+void uniformDistributionCBRNG(T *out, size_t elements,
+                              const af_random_engine_type type,
+                              const uintl &seed, uintl &counter) {
+    int threads          = THREADS;
+    int elementsPerBlock = threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks           = divup(elements, elementsPerBlock);
+    uint hi              = seed >> 32;
+    uint lo              = seed;
+    uint hic             = counter >> 32;
+    uint loc             = counter;
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            CUDA_LAUNCH(uniformPhilox, blocks, threads, out, hi, lo, hic, loc,
+                        elementsPerBlock, elements);
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            CUDA_LAUNCH(uniformThreefry, blocks, threads, out, hi, lo, hic, loc,
+                        elementsPerBlock, elements);
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
+    counter += elements;
 }
+
+template<typename T>
+void normalDistributionCBRNG(T *out, size_t elements,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter) {
+    int threads          = THREADS;
+    int elementsPerBlock = threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks           = divup(elements, elementsPerBlock);
+    uint hi              = seed >> 32;
+    uint lo              = seed;
+    uint hic             = counter >> 32;
+    uint loc             = counter;
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            CUDA_LAUNCH(normalPhilox, blocks, threads, out, hi, lo, hic, loc,
+                        elementsPerBlock, elements);
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            CUDA_LAUNCH(normalThreefry, blocks, threads, out, hi, lo, hic, loc,
+                        elementsPerBlock, elements);
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
+    counter += elements;
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index 41cf57ef41..6e8862574e 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -42,91 +42,89 @@
  * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *******************************************************/
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
-    constexpr int N = 351;
-    constexpr int BLOCKS = 32;
-    constexpr int STATE_SIZE = (256*3);
-    constexpr int TABLE_SIZE = 16;
+constexpr int N          = 351;
+constexpr int BLOCKS     = 32;
+constexpr int STATE_SIZE = (256 * 3);
+constexpr int TABLE_SIZE = 16;
 
-    //Utils
-    static inline __device__ void read_table(uint * const sharedTable, const uint * const table)
-    {
-        const uint * const t = table + (blockIdx.x * TABLE_SIZE);
-        if (threadIdx.x < TABLE_SIZE) {
-            sharedTable[threadIdx.x] = t[threadIdx.x];
-        }
-    }
+// Utils
+static inline __device__ void read_table(uint *const sharedTable,
+                                         const uint *const table) {
+    const uint *const t = table + (blockIdx.x * TABLE_SIZE);
+    if (threadIdx.x < TABLE_SIZE) { sharedTable[threadIdx.x] = t[threadIdx.x]; }
+}
 
-    static inline __device__ void state_read(uint * const state, const uint * const gState)
-    {
-        const uint * const g = gState + (blockIdx.x * N);
-        state[STATE_SIZE - N + threadIdx.x] = g[threadIdx.x];
-        if (threadIdx.x < N - blockDim.x) {
-            state[STATE_SIZE - N + blockDim.x + threadIdx.x] = g[blockDim.x + threadIdx.x];
-        }
+static inline __device__ void state_read(uint *const state,
+                                         const uint *const gState) {
+    const uint *const g                 = gState + (blockIdx.x * N);
+    state[STATE_SIZE - N + threadIdx.x] = g[threadIdx.x];
+    if (threadIdx.x < N - blockDim.x) {
+        state[STATE_SIZE - N + blockDim.x + threadIdx.x] =
+            g[blockDim.x + threadIdx.x];
     }
+}
 
-    static inline __device__ void state_write(uint * const gState, const uint * const state)
-    {
-        uint * const g = gState + (blockIdx.x * N);
-        g[threadIdx.x] = state[STATE_SIZE - N + threadIdx.x];
-        if (threadIdx.x < N - blockDim.x) {
-            g[blockDim.x + threadIdx.x] = state[STATE_SIZE - N + blockDim.x + threadIdx.x];
-        }
+static inline __device__ void state_write(uint *const gState,
+                                          const uint *const state) {
+    uint *const g  = gState + (blockIdx.x * N);
+    g[threadIdx.x] = state[STATE_SIZE - N + threadIdx.x];
+    if (threadIdx.x < N - blockDim.x) {
+        g[blockDim.x + threadIdx.x] =
+            state[STATE_SIZE - N + blockDim.x + threadIdx.x];
     }
+}
 
-    static inline __device__ uint recursion(const uint * const recursion_table,
-            const uint mask, const uint sh1, const uint sh2,
-            const uint x1, const uint x2, uint y)
-    {
-        uint x = (x1 & mask) ^ x2;
-        x ^= x << sh1;
-        y = x ^ (y >> sh2);
-        uint mat = recursion_table[y & 0x0f];
-        return y ^ mat;
-    }
+static inline __device__ uint recursion(const uint *const recursion_table,
+                                        const uint mask, const uint sh1,
+                                        const uint sh2, const uint x1,
+                                        const uint x2, uint y) {
+    uint x = (x1 & mask) ^ x2;
+    x ^= x << sh1;
+    y        = x ^ (y >> sh2);
+    uint mat = recursion_table[y & 0x0f];
+    return y ^ mat;
+}
 
-    static inline __device__ uint temper(const uint * const temper_table, const uint v, uint t)
-    {
-        t ^= t >> 16;
-        t ^= t >> 8;
-        uint mat = temper_table[t & 0x0f];
-        return v ^ mat;
-    }
+static inline __device__ uint temper(const uint *const temper_table,
+                                     const uint v, uint t) {
+    t ^= t >> 16;
+    t ^= t >> 8;
+    uint mat = temper_table[t & 0x0f];
+    return v ^ mat;
+}
 
-    //Initialization
+// Initialization
 
-    __global__ void initState(uint *state, const uint *tbl, uintl seed)
-    {
-        __shared__ uint lstate[N];
-        const uint *ltbl = tbl + (TABLE_SIZE*blockIdx.x);
-        uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
-        uint tmp = hidden_seed;
-        tmp += tmp >> 16;
-        tmp += tmp >> 8;
-        tmp &= 0xff;
-        tmp |= tmp << 8;
-        tmp |= tmp << 16;
-        lstate[threadIdx.x] = tmp;
-        __syncthreads();
-        if (threadIdx.x == 0) {
-            lstate[0] = seed;
-            lstate[1] = hidden_seed;
-            for (int i = 1; i < N; ++i) {
-                lstate[i] ^= ((uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i);
-            }
+__global__ void initState(uint *state, const uint *tbl, uintl seed) {
+    __shared__ uint lstate[N];
+    const uint *ltbl = tbl + (TABLE_SIZE * blockIdx.x);
+    uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
+    uint tmp         = hidden_seed;
+    tmp += tmp >> 16;
+    tmp += tmp >> 8;
+    tmp &= 0xff;
+    tmp |= tmp << 8;
+    tmp |= tmp << 16;
+    lstate[threadIdx.x] = tmp;
+    __syncthreads();
+    if (threadIdx.x == 0) {
+        lstate[0] = seed;
+        lstate[1] = hidden_seed;
+        for (int i = 1; i < N; ++i) {
+            lstate[i] ^=
+                ((uint)(1812433253) * (lstate[i - 1] ^ (lstate[i - 1] >> 30)) +
+                 i);
         }
-        __syncthreads();
-        state[N*blockIdx.x + threadIdx.x] = lstate[threadIdx.x];
-    }
-
-    void initMersenneState(uint *state, const uint *tbl, uintl seed)
-    {
-        CUDA_LAUNCH(initState, BLOCKS, N, state, tbl, seed);
     }
+    __syncthreads();
+    state[N * blockIdx.x + threadIdx.x] = lstate[threadIdx.x];
 }
+
+void initMersenneState(uint *state, const uint *tbl, uintl seed) {
+    CUDA_LAUNCH(initState, BLOCKS, N, state, tbl, seed);
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index bad322698c..6f1764225d 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -46,55 +46,60 @@
 
 #pragma once
 
-namespace cuda
-{
-namespace kernel
-{
-    //Utils
-    //Source of these constants :
-    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
+namespace cuda {
+namespace kernel {
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
 
-    static const uint m4x32_0 = 0xD2511F53;
-    static const uint m4x32_1 = 0xCD9E8D57;
-    static const uint w32_0   = 0x9E3779B9;
-    static const uint w32_1   = 0xBB67AE85;
+static const uint m4x32_0 = 0xD2511F53;
+static const uint m4x32_1 = 0xCD9E8D57;
+static const uint w32_0   = 0x9E3779B9;
+static const uint w32_1   = 0xBB67AE85;
 
-    static inline __device__ void mulhilo(const uint &a, const uint &b, uint &hi, uint &lo)
-    {
-        hi = __umulhi(a,b);
-        lo = a*b;
-    }
-
-    static inline __device__ void philoxBump(uint k[2])
-    {
-        k[0] += w32_0;
-        k[1] += w32_1;
-    }
+static inline __device__ void mulhilo(const uint &a, const uint &b, uint &hi,
+                                      uint &lo) {
+    hi = __umulhi(a, b);
+    lo = a * b;
+}
 
-    static inline __device__ void philoxRound(const uint m0, const uint m1, const uint k[2], uint c[4])
-    {
-        uint hi0, lo0, hi1, lo1;
-        mulhilo(m0, c[0], hi0, lo0);
-        mulhilo(m1, c[2], hi1, lo1);
-        c[0] = hi1^c[1]^k[0];
-        c[1] = lo1;
-        c[2] = hi0^c[3]^k[1];
-        c[3] = lo0;
-    }
+static inline __device__ void philoxBump(uint k[2]) {
+    k[0] += w32_0;
+    k[1] += w32_1;
+}
 
-    static inline __device__ void philox(uint key[2], uint ctr[4])
-    {
-        //10 Rounds
-                           philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-        philoxBump(key);   philoxRound(m4x32_0, m4x32_1, key, ctr);
-    }
+static inline __device__ void philoxRound(const uint m0, const uint m1,
+                                          const uint k[2], uint c[4]) {
+    uint hi0, lo0, hi1, lo1;
+    mulhilo(m0, c[0], hi0, lo0);
+    mulhilo(m1, c[2], hi1, lo1);
+    c[0] = hi1 ^ c[1] ^ k[0];
+    c[1] = lo1;
+    c[2] = hi0 ^ c[3] ^ k[1];
+    c[3] = lo0;
 }
+
+static inline __device__ void philox(uint key[2], uint ctr[4]) {
+    // 10 Rounds
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
index 6c0894060b..dbafbfae44 100644
--- a/src/backend/cuda/kernel/random_engine_threefry.hpp
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -46,85 +46,117 @@
 
 #pragma once
 
-namespace cuda
-{
-namespace kernel
-{
-    //Utils
-    //Source of these constants :
-    //github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
-
-    static const uint  SKEIN_KS_PARITY32 = 0x1BD11BDA;
-
-    static const uint R0=13;
-    static const uint R1=15;
-    static const uint R2=26;
-    static const uint R3= 6;
-    static const uint R4=17;
-    static const uint R5=29;
-    static const uint R6=16;
-    static const uint R7=24;
-
-    static inline __device__ void setSkeinParity(uint *ptr)
-    {
-        *ptr = SKEIN_KS_PARITY32;
-    }
-
-    static inline __device__ uint rotL(uint x, uint N)
-    {
-        return (x << (N & 31)) | (x >> ((32-N) & 31));
-    }
-
-    __device__ void threefry(uint k[2], uint c[2], uint X[2])
-    {
-        uint ks[3];
-
-        setSkeinParity(&ks[2]);
-        ks[0] = k[0];
-        X[0] = c[0];
-        ks[2] ^= k[0];
-        ks[1] = k[1];
-        X[1] = c[1];
-        ks[2] ^= k[1];
-
-        X[0] += ks[0]; X[1] += ks[1];
-
-        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
-
-        /* InjectKey(r=1) */
-        X[0] += ks[1]; X[1] += ks[2];
-        X[1] += 1;     /* X[2-1] += r  */
-
-        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
-
-        /* InjectKey(r=2) */
-        X[0] += ks[2]; X[1] += ks[0];
-        X[1] += 2;
-
-        X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
-
-        /* InjectKey(r=3) */
-        X[0] += ks[0]; X[1] += ks[1];
-        X[1] += 3;
-
-        X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-        X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
-
-        /* InjectKey(r=4) */
-        X[0] += ks[1]; X[1] += ks[2];
-        X[1] += 4;
-    }
+namespace cuda {
+namespace kernel {
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
+
+static const uint SKEIN_KS_PARITY32 = 0x1BD11BDA;
+
+static const uint R0 = 13;
+static const uint R1 = 15;
+static const uint R2 = 26;
+static const uint R3 = 6;
+static const uint R4 = 17;
+static const uint R5 = 29;
+static const uint R6 = 16;
+static const uint R7 = 24;
+
+static inline __device__ void setSkeinParity(uint *ptr) {
+    *ptr = SKEIN_KS_PARITY32;
+}
 
+static inline __device__ uint rotL(uint x, uint N) {
+    return (x << (N & 31)) | (x >> ((32 - N) & 31));
 }
+
+__device__ void threefry(uint k[2], uint c[2], uint X[2]) {
+    uint ks[3];
+
+    setSkeinParity(&ks[2]);
+    ks[0] = k[0];
+    X[0]  = c[0];
+    ks[2] ^= k[0];
+    ks[1] = k[1];
+    X[1]  = c[1];
+    ks[2] ^= k[1];
+
+    X[0] += ks[0];
+    X[1] += ks[1];
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=1) */
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 1; /* X[2-1] += r  */
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=2) */
+    X[0] += ks[2];
+    X[1] += ks[0];
+    X[1] += 2;
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=3) */
+    X[0] += ks[0];
+    X[1] += ks[1];
+    X[1] += 3;
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=4) */
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 4;
 }
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index dea3aea343..590079aae2 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -7,87 +7,80 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned RANGE_TX = 32;
-        static const unsigned RANGE_TY = 8;
-        static const unsigned RANGE_TILEX = 512;
-        static const unsigned RANGE_TILEY = 32;
-
-        template<typename T>
-        __global__
-        void range_kernel(Param<T> out, const int dim,
-                          const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int mul0 = (dim == 0);
-            const int mul1 = (dim == 1);
-            const int mul2 = (dim == 2);
-            const int mul3 = (dim == 3);
-
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if(xx >= out.dims[0] ||
-               yy >= out.dims[1] ||
-               oz >= out.dims[2] ||
-               ow >= out.dims[3])
-                return;
-
-            const int ozw = ow * out.strides[3] + oz * out.strides[2];
-
-            T valZW = (mul3 * ow) + (mul2 * oz);
-
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
-
-            for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                T valYZW = valZW + (mul1 * oy);
-                int oyzw = ozw + oy * out.strides[1];
-                for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                    int oidx = oyzw + ox;
-                    T val = valYZW + (ox * mul0);
-
-                    out.ptr[oidx] = val;
-                }
-            }
-        }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned RANGE_TX    = 32;
+static const unsigned RANGE_TY    = 8;
+static const unsigned RANGE_TILEX = 512;
+static const unsigned RANGE_TILEY = 32;
 
+template<typename T>
+__global__ void range_kernel(Param<T> out, const int dim,
+                             const int blocksPerMatX, const int blocksPerMatY) {
+    const int mul0 = (dim == 0);
+    const int mul1 = (dim == 1);
+    const int mul2 = (dim == 2);
+    const int mul3 = (dim == 3);
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void range(Param<T> out, const int dim)
-        {
-            dim3 threads(RANGE_TX, RANGE_TY, 1);
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
-            int blocksPerMatX = divup(out.dims[0], RANGE_TILEX);
-            int blocksPerMatY = divup(out.dims[1], RANGE_TILEY);
-            dim3 blocks(blocksPerMatX * out.dims[2],
-                        blocksPerMatY * out.dims[3],
-                        1);
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            CUDA_LAUNCH((range_kernel<T>), blocks, threads, out, dim, blocksPerMatX, blocksPerMatY);
-            POST_LAUNCH_CHECK();
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int ozw = ow * out.strides[3] + oz * out.strides[2];
+
+    T valZW = (mul3 * ow) + (mul2 * oz);
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        T valYZW = valZW + (mul1 * oy);
+        int oyzw = ozw + oy * out.strides[1];
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            int oidx = oyzw + ox;
+            T val    = valYZW + (ox * mul0);
+
+            out.ptr[oidx] = val;
         }
     }
 }
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void range(Param<T> out, const int dim) {
+    dim3 threads(RANGE_TX, RANGE_TY, 1);
+
+    int blocksPerMatX = divup(out.dims[0], RANGE_TILEX);
+    int blocksPerMatY = divup(out.dims[1], RANGE_TILEY);
+    dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((range_kernel<T>), blocks, threads, out, dim, blocksPerMatX,
+                blocksPerMatY);
+    POST_LAUNCH_CHECK();
+}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index e5c6c28ea8..712925b501 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
-#include "config.hpp"
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
+#include "config.hpp"
 
 #include <cub/warp/warp_reduce.cuh>
 
@@ -23,396 +23,392 @@
 
 using std::unique_ptr;
 
-namespace cuda
-{
-namespace kernel
-{
-    template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
-    __global__
-    static void reduce_dim_kernel(Param<To> out,
-                                  CParam <Ti> in,
-                                  uint blocks_x, uint blocks_y, uint offset_dim,
-                                  bool change_nan, To nanval)
-    {
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-        const uint tid  = tidy * THREADS_X + tidx;
-
-        const uint zid = blockIdx.x / blocks_x;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint xid = blockIdx_x * blockDim.x + tidx;
-
-        __shared__ To s_val[THREADS_X * DIMY];
-
-        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const uint yid = blockIdx_y; // yid  of output. updated for input later.
-
-        uint ids[4] = {xid, yid, zid, wid};
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        To * const optr = out.ptr + ids[3] * out.strides[3] +
-                                    ids[2] * out.strides[2] +
-                                    ids[1] * out.strides[1] + ids[0];
-
-        const uint blockIdx_dim = ids[dim];
-        ids[dim] = ids[dim] * blockDim.y + tidy;
-
-        const Ti * iptr = in.ptr + ids[3] * in.strides[3] +
-                                   ids[2] * in.strides[2] +
-                                   ids[1] * in.strides[1] + ids[0];
-
-        const uint id_dim_in = ids[dim];
-        const uint istride_dim = in.strides[dim];
-
-        bool is_valid =
-            (ids[0] < in.dims[0]) &&
-            (ids[1] < in.dims[1]) &&
-            (ids[2] < in.dims[2]) &&
-            (ids[3] < in.dims[3]);
+namespace cuda {
+namespace kernel {
+template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
+__global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
+                                         uint blocks_x, uint blocks_y,
+                                         uint offset_dim, bool change_nan,
+                                         To nanval) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * THREADS_X + tidx;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint xid        = blockIdx_x * blockDim.x + tidx;
+
+    __shared__ To s_val[THREADS_X * DIMY];
+
+    const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    uint ids[4] = {xid, yid, zid, wid};
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    To *const optr = out.ptr + ids[3] * out.strides[3] +
+                     ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+
+    const uint blockIdx_dim = ids[dim];
+    ids[dim]                = ids[dim] * blockDim.y + tidy;
+
+    const Ti *iptr = in.ptr + ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+                     ids[1] * in.strides[1] + ids[0];
+
+    const uint id_dim_in   = ids[dim];
+    const uint istride_dim = in.strides[dim];
+
+    bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
+                    (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> reduce;
+    To out_val = Binary<To, op>::init();
+    for (int id = id_dim_in; is_valid && (id < in.dims[dim]);
+         id += offset_dim * blockDim.y) {
+        To in_val = transform(*iptr);
+        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+        out_val = reduce(in_val, out_val);
+        iptr    = iptr + offset_dim * blockDim.y * istride_dim;
+    }
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
-        To out_val = Binary<To, op>::init();
-        for (int id = id_dim_in; is_valid && (id < in.dims[dim]); id += offset_dim * blockDim.y) {
-            To in_val = transform(*iptr);
-            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
-            out_val = reduce(in_val, out_val);
-            iptr = iptr + offset_dim * blockDim.y * istride_dim;
-        }
+    s_val[tid] = out_val;
 
-        s_val[tid] = out_val;
+    To *s_ptr = s_val + tid;
+    __syncthreads();
 
-        To *s_ptr = s_val + tid;
+    if (DIMY == 8) {
+        if (tidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
         __syncthreads();
+    }
 
-        if (DIMY == 8) {
-            if (tidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
-            __syncthreads();
-        }
-
-        if (DIMY >= 4) {
-            if (tidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
-            __syncthreads();
-        }
+    if (DIMY >= 4) {
+        if (tidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
+        __syncthreads();
+    }
 
-        if (DIMY >= 2) {
-            if (tidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
-            __syncthreads();
-        }
+    if (DIMY >= 2) {
+        if (tidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
+        __syncthreads();
+    }
 
-        if (tidy == 0 && is_valid &&
-            (blockIdx_dim < out.dims[dim])) {
-            *optr = *s_ptr;
-        }
+    if (tidy == 0 && is_valid && (blockIdx_dim < out.dims[dim])) {
+        *optr = *s_ptr;
     }
+}
 
-    template<typename Ti, typename To, af_op_t op, int dim>
-    void reduce_dim_launcher(Param<To> out, CParam<Ti> in,
-                             const uint threads_y, const dim_t blocks_dim[4],
-                             bool change_nan, double nanval)
-    {
-        dim3 threads(THREADS_X, threads_y);
+template<typename Ti, typename To, af_op_t op, int dim>
+void reduce_dim_launcher(Param<To> out, CParam<Ti> in, const uint threads_y,
+                         const dim_t blocks_dim[4], bool change_nan,
+                         double nanval) {
+    dim3 threads(THREADS_X, threads_y);
 
-        dim3 blocks(blocks_dim[0] * blocks_dim[2],
-                    blocks_dim[1] * blocks_dim[3]);
+    dim3 blocks(blocks_dim[0] * blocks_dim[2], blocks_dim[1] * blocks_dim[3]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        switch (threads_y) {
+    switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 8>), blocks,
+                        threads, out, in, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim], change_nan, scalar<To>(nanval));
+            break;
         case 4:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 4>), blocks,
+                        threads, out, in, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim], change_nan, scalar<To>(nanval));
+            break;
         case 2:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 2>), blocks,
+                        threads, out, in, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim], change_nan, scalar<To>(nanval));
+            break;
         case 1:
-            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1>), blocks, threads,
-                out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
-                change_nan, scalar<To>(nanval)); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((reduce_dim_kernel<Ti, To, op, dim, 1>), blocks,
+                        threads, out, in, blocks_dim[0], blocks_dim[1],
+                        blocks_dim[dim], change_nan, scalar<To>(nanval));
+            break;
     }
 
-    template<typename Ti, typename To, af_op_t op, int dim>
-    void reduce_dim(Param<To> out,  CParam<Ti> in, bool change_nan, double nanval)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
-        uint threads_x = THREADS_X;
+    POST_LAUNCH_CHECK();
+}
 
-        dim_t blocks_dim[] = { divup(in.dims[0], threads_x),
-                               in.dims[1], in.dims[2], in.dims[3] };
+template<typename Ti, typename To, af_op_t op, int dim>
+void reduce_dim(Param<To> out, CParam<Ti> in, bool change_nan, double nanval) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
+    uint threads_x = THREADS_X;
 
-        blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
+    dim_t blocks_dim[] = {divup(in.dims[0], threads_x), in.dims[1], in.dims[2],
+                          in.dims[3]};
 
-        Param<To> tmp = out;
-        uptr<To> tmp_alloc;
-        if (blocks_dim[dim] > 1) {
-            int tmp_elements = 1;
-            tmp.dims[dim] = blocks_dim[dim];
+    blocks_dim[dim] = divup(in.dims[dim], threads_y * REPEAT);
 
-            for (int k = 0; k < 4; k++) tmp_elements *= tmp.dims[k];
-            tmp_alloc = memAlloc<To>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
+    Param<To> tmp = out;
+    uptr<To> tmp_alloc;
+    if (blocks_dim[dim] > 1) {
+        int tmp_elements = 1;
+        tmp.dims[dim]    = blocks_dim[dim];
 
-            for (int k = dim + 1; k < 4; k++) tmp.strides[k] *= blocks_dim[dim];
-        }
+        for (int k = 0; k < 4; k++) tmp_elements *= tmp.dims[k];
+        tmp_alloc = memAlloc<To>(tmp_elements);
+        tmp.ptr   = tmp_alloc.get();
 
-        reduce_dim_launcher<Ti, To, op, dim>(tmp, in, threads_y, blocks_dim, change_nan, nanval);
+        for (int k = dim + 1; k < 4; k++) tmp.strides[k] *= blocks_dim[dim];
+    }
 
-        if (blocks_dim[dim] > 1) {
-            blocks_dim[dim] = 1;
+    reduce_dim_launcher<Ti, To, op, dim>(tmp, in, threads_y, blocks_dim,
+                                         change_nan, nanval);
 
-            if (op == af_notzero_t) {
-                reduce_dim_launcher<To, To, af_add_t, dim>(out, tmp, threads_y, blocks_dim,
-                                                           change_nan, nanval);
-            } else {
-                reduce_dim_launcher<To, To,       op, dim>(out, tmp, threads_y, blocks_dim,
-                                                           change_nan, nanval);
-            }
+    if (blocks_dim[dim] > 1) {
+        blocks_dim[dim] = 1;
 
+        if (op == af_notzero_t) {
+            reduce_dim_launcher<To, To, af_add_t, dim>(
+                out, tmp, threads_y, blocks_dim, change_nan, nanval);
+        } else {
+            reduce_dim_launcher<To, To, op, dim>(
+                out, tmp, threads_y, blocks_dim, change_nan, nanval);
         }
     }
+}
 
+template<typename Ti, typename To, af_op_t op, uint DIMX>
+__global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
+                                           uint blocks_x, uint blocks_y,
+                                           uint repeat, bool change_nan,
+                                           To nanval) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * blockDim.x + tidx;
 
-    template<typename Ti, typename To, af_op_t op, uint DIMX>
-    __global__
-    static void reduce_first_kernel(Param<To> out,
-                                    CParam<Ti>  in,
-                                    uint blocks_x, uint blocks_y, uint repeat,
-                                    bool change_nan, To nanval) {
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint xid        = blockIdx_x * blockDim.x * repeat + tidx;
 
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-        const uint tid  = tidy * blockDim.x + tidx;
+    Binary<To, op> reduce;
+    Transform<Ti, To, op> transform;
 
-        const uint zid = blockIdx.x / blocks_x;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
+    __shared__ To s_val[THREADS_PER_BLOCK];
 
-        Binary<To, op> reduce;
-        Transform<Ti, To, op> transform;
+    const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint yid = blockIdx_y * blockDim.y + tidy;
 
-        __shared__ To s_val[THREADS_PER_BLOCK];
+    const Ti *const iptr = in.ptr + (wid * in.strides[3] + zid * in.strides[2] +
+                                     yid * in.strides[1]);
 
-        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const uint yid = blockIdx_y * blockDim.y + tidy;
+    if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
 
-        const Ti * const iptr = in.ptr + (wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1]);
+    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-        if (yid >= in.dims[1] ||
-            zid >= in.dims[2] ||
-            wid >= in.dims[3]) return;
-
-
-        int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+    To out_val = Binary<To, op>::init();
+    for (int id = xid; id < lim; id += DIMX) {
+        To in_val = transform(iptr[id]);
+        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+        out_val = reduce(in_val, out_val);
+    }
 
-        To out_val = Binary<To, op>::init();
-        for (int id = xid; id < lim; id += DIMX) {
-            To in_val = transform(iptr[id]);
-            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
-            out_val = reduce(in_val, out_val);
-        }
+    s_val[tid] = out_val;
 
-        s_val[tid] = out_val;
+    __syncthreads();
+    To *s_ptr = s_val + tidy * DIMX;
 
+    if (DIMX == 256) {
+        if (tidx < 128) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
         __syncthreads();
-        To *s_ptr = s_val + tidy * DIMX;
-
-        if (DIMX == 256) {
-            if (tidx < 128)
-                s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
-            __syncthreads();
-        }
+    }
 
-        if (DIMX >= 128) {
-            if (tidx <  64) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  64]);
-            __syncthreads();
-        }
+    if (DIMX >= 128) {
+        if (tidx < 64) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 64]);
+        __syncthreads();
+    }
 
-        if (DIMX >=  64) {
-            if (tidx <  32) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx +  32]);
-            __syncthreads();
-        }
+    if (DIMX >= 64) {
+        if (tidx < 32) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 32]);
+        __syncthreads();
+    }
 
-        typedef cub::WarpReduce<To> WarpReduce;
-        __shared__ typename WarpReduce::TempStorage temp_storage;
+    typedef cub::WarpReduce<To> WarpReduce;
+    __shared__ typename WarpReduce::TempStorage temp_storage;
 
-        To warp_val = s_ptr[tidx];
-        out_val = WarpReduce(temp_storage).Reduce(warp_val, reduce);
+    To warp_val = s_ptr[tidx];
+    out_val     = WarpReduce(temp_storage).Reduce(warp_val, reduce);
 
-        To * const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1]);
-        if (tidx == 0)
-            optr[blockIdx_x] = out_val;
-    }
+    To *const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] +
+                                yid * out.strides[1]);
+    if (tidx == 0) optr[blockIdx_x] = out_val;
+}
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce_first_launcher(Param<To> out, CParam<Ti> in,
-                               const uint blocks_x, const uint blocks_y, const uint threads_x,
-                               bool change_nan, double nanval)
-    {
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * in.dims[2],
-                    blocks_y * in.dims[3]);
+template<typename Ti, typename To, af_op_t op>
+void reduce_first_launcher(Param<To> out, CParam<Ti> in, const uint blocks_x,
+                           const uint blocks_y, const uint threads_x,
+                           bool change_nan, double nanval) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
 
-        uint repeat = divup(in.dims[0], (blocks_x * threads_x));
+    uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        switch (threads_x) {
+    switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  32>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op, 32>), blocks, threads,
+                        out, in, blocks_x, blocks_y, repeat, change_nan,
+                        scalar<To>(nanval));
+            break;
         case 64:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  64>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op, 64>), blocks, threads,
+                        out, in, blocks_x, blocks_y, repeat, change_nan,
+                        scalar<To>(nanval));
+            break;
         case 128:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  128>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op, 128>), blocks, threads,
+                        out, in, blocks_x, blocks_y, repeat, change_nan,
+                        scalar<To>(nanval));
+            break;
         case 256:
-            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op,  256>), blocks, threads,
-                out, in, blocks_x, blocks_y, repeat, change_nan, scalar<To>(nanval)); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((reduce_first_kernel<Ti, To, op, 256>), blocks, threads,
+                        out, in, blocks_x, blocks_y, repeat, change_nan,
+                        scalar<To>(nanval));
+            break;
     }
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce_first(Param<To> out, CParam<Ti> in, bool change_nan, double nanval)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
+    POST_LAUNCH_CHECK();
+}
 
-        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(in.dims[1], threads_y);
+template<typename Ti, typename To, af_op_t op>
+void reduce_first(Param<To> out, CParam<Ti> in, bool change_nan,
+                  double nanval) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.dims[1], threads_y);
+
+    Param<To> tmp = out;
+    uptr<To> tmp_alloc;
+    if (blocks_x > 1) {
+        tmp_alloc =
+            memAlloc<To>(blocks_x * in.dims[1] * in.dims[2] * in.dims[3]);
+        tmp.ptr = tmp_alloc.get();
+
+        tmp.dims[0] = blocks_x;
+        for (int k = 1; k < 4; k++) tmp.strides[k] *= blocks_x;
+    }
 
-        Param<To> tmp = out;
-        uptr<To> tmp_alloc;
-        if (blocks_x > 1) {
-            tmp_alloc = memAlloc<To>(blocks_x * in.dims[1] * in.dims[2] * in.dims[3]);
-            tmp.ptr = tmp_alloc.get();
+    reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
+                                      change_nan, nanval);
 
-            tmp.dims[0] = blocks_x;
-            for (int k = 1; k < 4; k++) tmp.strides[k] *= blocks_x;
+    if (blocks_x > 1) {
+        // FIXME: Is there an alternative to the if condition?
+        if (op == af_notzero_t) {
+            reduce_first_launcher<To, To, af_add_t>(
+                out, tmp, 1, blocks_y, threads_x, change_nan, nanval);
+        } else {
+            reduce_first_launcher<To, To, op>(out, tmp, 1, blocks_y, threads_x,
+                                              change_nan, nanval);
         }
+    }
+}
 
-        reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x, change_nan, nanval);
-
-        if (blocks_x > 1) {
-            //FIXME: Is there an alternative to the if condition?
-            if (op == af_notzero_t) {
-                reduce_first_launcher<To, To, af_add_t>(out, tmp, 1, blocks_y, threads_x,
-                                                        change_nan, nanval);
-            } else {
-                reduce_first_launcher<To, To,       op>(out, tmp, 1, blocks_y, threads_x,
-                                                        change_nan, nanval);
-            }
-
-        }
+template<typename Ti, typename To, af_op_t op>
+void reduce(Param<To> out, CParam<Ti> in, int dim, bool change_nan,
+            double nanval) {
+    switch (dim) {
+        case 0: return reduce_first<Ti, To, op>(out, in, change_nan, nanval);
+        case 1: return reduce_dim<Ti, To, op, 1>(out, in, change_nan, nanval);
+        case 2: return reduce_dim<Ti, To, op, 2>(out, in, change_nan, nanval);
+        case 3: return reduce_dim<Ti, To, op, 3>(out, in, change_nan, nanval);
     }
+}
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce(Param<To> out, CParam<Ti> in, int dim, bool change_nan, double nanval)
-    {
-        switch (dim) {
-        case 0: return reduce_first<Ti, To, op   >(out, in, change_nan, nanval);
-        case 1: return reduce_dim  <Ti, To, op, 1>(out, in, change_nan, nanval);
-        case 2: return reduce_dim  <Ti, To, op, 2>(out, in, change_nan, nanval);
-        case 3: return reduce_dim  <Ti, To, op, 3>(out, in, change_nan, nanval);
-        }
+template<typename Ti, typename To, af_op_t op>
+To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
+    int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+    bool is_linear  = (in.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
     }
 
-    template<typename Ti, typename To, af_op_t op>
-    To reduce_all(CParam<Ti> in, bool change_nan, double nanval)
-    {
-        int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
-        bool is_linear = (in.strides[0] == 1);
-        for (int k = 1; k < 4; k++) {
-            is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096 || !is_linear) {
+        if (is_linear) {
+            in.dims[0] = in_elements;
+            for (int k = 1; k < 4; k++) {
+                in.dims[k]    = 1;
+                in.strides[k] = in_elements;
+            }
         }
 
-        // FIXME: Use better heuristics to get to the optimum number
-        if (in_elements > 4096 || !is_linear) {
+        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-            if (is_linear) {
-                in.dims[0] = in_elements;
-                for (int k = 1; k < 4; k++) {
-                    in.dims[k] = 1;
-                    in.strides[k] = in_elements;
-                }
-            }
+        Param<To> tmp;
 
-            uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-            uint threads_y = THREADS_PER_BLOCK / threads_x;
+        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.dims[1], threads_y);
 
-            Param<To> tmp;
+        tmp.dims[0]    = blocks_x;
+        tmp.strides[0] = 1;
 
-            uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-            uint blocks_y = divup(in.dims[1], threads_y);
+        for (int k = 1; k < 4; k++) {
+            tmp.dims[k]    = in.dims[k];
+            tmp.strides[k] = tmp.dims[k - 1] * tmp.strides[k - 1];
+        }
 
-            tmp.dims[0] = blocks_x;
-            tmp.strides[0] = 1;
+        int tmp_elements = tmp.strides[3] * tmp.dims[3];
 
-            for (int k = 1; k < 4; k++) {
-                tmp.dims[k] = in.dims[k];
-                tmp.strides[k] = tmp.dims[k - 1] * tmp.strides[k - 1];
-            }
+        auto tmp_alloc = memAlloc<To>(tmp_elements);
+        tmp.ptr        = tmp_alloc.get();
+        reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y,
+                                          threads_x, change_nan, nanval);
 
-            int tmp_elements = tmp.strides[3] * tmp.dims[3];
+        std::vector<To> h_data(tmp_elements);
+        CUDA_CHECK(
+            cudaMemcpyAsync(h_data.data(), tmp.ptr, tmp_elements * sizeof(To),
+                            cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-            auto tmp_alloc = memAlloc<To>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
-            reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y, threads_x,
-                                              change_nan, nanval);
-
-            std::vector<To> h_data(tmp_elements);
-            CUDA_CHECK(cudaMemcpyAsync(h_data.data(), tmp.ptr, tmp_elements * sizeof(To),
-                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+        Binary<To, op> reduce;
+        To out = Binary<To, op>::init();
+        for (int i = 0; i < tmp_elements; i++) { out = reduce(out, h_data[i]); }
 
-            Binary<To, op> reduce;
-            To out = Binary<To, op>::init();
-            for (int i = 0; i < tmp_elements; i++) {
-                out = reduce(out, h_data[i]);
-            }
+        return out;
+    } else {
+        std::vector<Ti> h_data(in_elements);
+        CUDA_CHECK(
+            cudaMemcpyAsync(h_data.data(), in.ptr, in_elements * sizeof(Ti),
+                            cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-            return out;
-        } else {
-            std::vector<Ti> h_data(in_elements);
-            CUDA_CHECK(cudaMemcpyAsync(h_data.data(), in.ptr, in_elements * sizeof(Ti),
-                       cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-            Transform<Ti, To, op> transform;
-            Binary<To, op> reduce;
-            To out = Binary<To, op>::init();
-            To nanval_to = scalar<To>(nanval);
-
-            for (int i = 0; i < in_elements; i++) {
-                To in_val = transform(h_data[i]);
-                if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval_to;
-                out = reduce(out, in_val);
-            }
+        Transform<Ti, To, op> transform;
+        Binary<To, op> reduce;
+        To out       = Binary<To, op>::init();
+        To nanval_to = scalar<To>(nanval);
 
-            return out;
+        for (int i = 0; i < in_elements; i++) {
+            To in_val = transform(h_data[i]);
+            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval_to;
+            out = reduce(out, in_val);
         }
-    }
 
+        return out;
+    }
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 4f44734e64..cb3ffa4d67 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -8,19 +8,19 @@
  ********************************************************/
 
 #include <common/dispatch.hpp>
+#include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
-#include <debug_cuda.hpp>
-#include <stdio.h>
 #include <memory.hpp>
-#include <thrust/system/cuda/detail/par.h>
+#include <stdio.h>
 #include <thrust/adjacent_difference.h>
 #include <thrust/binary_search.h>
 #include <thrust/device_vector.h>
-#include <thrust/iterator/counting_iterator.h>
 #include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
 #include <thrust/scan.h>
 #include <thrust/sort.h>
+#include <thrust/system/cuda/detail/par.h>
 #include <thrust/transform_scan.h>
 
 static const int THREADS_X = 16;
@@ -33,33 +33,30 @@ __device__ static int continue_flag = 1;
 
 // Wrapper function for texture fetch
 template<typename T>
-static inline __device__
-T fetch(const int n, cuda::Param<T> equiv_map, cudaTextureObject_t tex)
-{
+static inline __device__ T fetch(const int n, cuda::Param<T> equiv_map,
+                                 cudaTextureObject_t tex) {
     return tex1Dfetch<T>(tex, n);
 }
 
-template<> __device__
-STATIC_ double fetch<double>(const int n,
-                             cuda::Param<double> equiv_map,
-                             cudaTextureObject_t tex)
-{
+template<>
+__device__ STATIC_ double fetch<double>(const int n,
+                                        cuda::Param<double> equiv_map,
+                                        cudaTextureObject_t tex) {
     return equiv_map.ptr[n];
 }
 
 // The initial label kernel distinguishes between valid (nonzero)
 // pixels and "background" (zero) pixels.
 template<typename T, int n_per_thread>
-__global__
-static void initial_label(cuda::Param<T> equiv_map, cuda::CParam<char> bin)
-{
+__global__ static void initial_label(cuda::Param<T> equiv_map,
+                                     cuda::CParam<char> bin) {
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
 
-    // If in bounds and a valid pixel, set the initial label.
-    #pragma unroll
+// If in bounds and a valid pixel, set the initial label.
+#pragma unroll
     for (int xb = 0; xb < n_per_thread; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < n_per_thread; ++yb) {
             const int x = base_x + (xb * blockDim.x);
             const int y = base_y + (yb * blockDim.y);
@@ -72,22 +69,23 @@ static void initial_label(cuda::Param<T> equiv_map, cuda::CParam<char> bin)
 }
 
 template<typename T, int n_per_thread>
-__global__
-static void final_relabel(cuda::Param<T> equiv_map, cuda::CParam<char> bin, const T* d_tmp)
-{
+__global__ static void final_relabel(cuda::Param<T> equiv_map,
+                                     cuda::CParam<char> bin, const T* d_tmp) {
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
 
-    // If in bounds and a valid pixel, set the initial label.
-    #pragma unroll
+// If in bounds and a valid pixel, set the initial label.
+#pragma unroll
     for (int xb = 0; xb < n_per_thread; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < n_per_thread; ++yb) {
             const int x = base_x + (xb * blockDim.x);
             const int y = base_y + (yb * blockDim.y);
             const int n = y * bin.dims[0] + x;
             if (x < bin.dims[0] && y < bin.dims[1]) {
-                equiv_map.ptr[n] = (bin.ptr[n] > (char)0) ? d_tmp[(int)equiv_map.ptr[n]] : (T)0;
+                equiv_map.ptr[n] = (bin.ptr[n] > (char)0)
+                                       ? d_tmp[(int)equiv_map.ptr[n]]
+                                       : (T)0;
             }
         }
     }
@@ -96,18 +94,19 @@ static void final_relabel(cuda::Param<T> equiv_map, cuda::CParam<char> bin, cons
 // When two labels are equivalent, choose the lower label, but
 // do not choose zero, which indicates invalid.
 template<typename T>
-__device__ __inline__
-static T relabel(const T a, const T b)
-{
+__device__ __inline__ static T relabel(const T a, const T b) {
     T aa = (a == 0) ? cuda::maxval<T>() : a;
     T bb = (b == 0) ? cuda::maxval<T>() : b;
     return min(aa, bb);
 }
 
-//Calculates the number of warps at compile time
+// Calculates the number of warps at compile time
 template<unsigned thread_count>
 struct warp_count {
-    enum { value = ((thread_count % 32) == 0 ? thread_count/32 : thread_count/32 + 1)};
+    enum {
+        value = ((thread_count % 32) == 0 ? thread_count / 32
+                                          : thread_count / 32 + 1)
+    };
 };
 
 // The following kernel updates the equivalency map.  This kernel
@@ -119,10 +118,9 @@ struct warp_count {
 // num_warps = 8; // (Could compute this from block dim)
 // Number of elements to handle per thread in each dimension
 // int n_per_thread = 2; // 2x2 per thread = 4 total elems per thread
-template <typename T, int block_dim, int n_per_thread, bool full_conn>
-__global__
-static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex)
-{
+template<typename T, int block_dim, int n_per_thread, bool full_conn>
+__global__ static void update_equiv(cuda::Param<T> equiv_map,
+                                    const cudaTextureObject_t tex) {
     // Basic coordinates
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
@@ -134,33 +132,32 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
 
     // Per element write flags and label, initially 0
     char write[n_per_thread * n_per_thread];
-    T    best_label[n_per_thread * n_per_thread];
+    T best_label[n_per_thread * n_per_thread];
 
-    #pragma unroll
+#pragma unroll
     for (int i = 0; i < n_per_thread * n_per_thread; ++i) {
         write[i]      = (char)0;
         best_label[i] = (T)0;
     }
 
     // Cached tile of the equivalency map
-    __shared__ T s_tile[n_per_thread*block_dim][(n_per_thread*block_dim)];
+    __shared__ T s_tile[n_per_thread * block_dim][(n_per_thread * block_dim)];
 
-    #pragma unroll
+#pragma unroll
     for (int xb = 0; xb < n_per_thread; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < n_per_thread; ++yb) {
-
             // Indexing variables
-            const int x = base_x + (xb * blockDim.x);
-            const int y = base_y + (yb * blockDim.y);
-            const int tx = threadIdx.x + (xb * blockDim.x);
-            const int ty = threadIdx.y + (yb * blockDim.y);
+            const int x     = base_x + (xb * blockDim.x);
+            const int y     = base_y + (yb * blockDim.y);
+            const int tx    = threadIdx.x + (xb * blockDim.x);
+            const int ty    = threadIdx.y + (yb * blockDim.y);
             const int tid_i = xb * n_per_thread + yb;
-            const int n = y * width + x;
+            const int n     = y * width + x;
 
             // Get the label for this pixel if we're  in bounds
-            const T orig_label = (x < width && y < height) ?
-                fetch<T>(n, equiv_map, tex) : (T)0;
+            const T orig_label =
+                (x < width && y < height) ? fetch<T>(n, equiv_map, tex) : (T)0;
             s_tile[ty][tx] = orig_label;
 
             // Find the lowest label of the nearest valid pixel
@@ -168,45 +165,53 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
             best_label[tid_i] = orig_label;
 
             if (orig_label != (T)0) {
-                const int south_y = min(y, height-2) + 1;
+                const int south_y = min(y, height - 2) + 1;
                 const int north_y = max(y, 1) - 1;
-                const int east_x = min(x, width-2) + 1;
-                const int west_x = max(x, 1) - 1;
+                const int east_x  = min(x, width - 2) + 1;
+                const int west_x  = max(x, 1) - 1;
 
                 // Check bottom
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        fetch((south_y) * width + x, equiv_map, tex));
+                best_label[tid_i] =
+                    relabel(best_label[tid_i],
+                            fetch((south_y)*width + x, equiv_map, tex));
 
                 // Check right neighbor
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        fetch(y * width + east_x, equiv_map, tex));
+                best_label[tid_i] =
+                    relabel(best_label[tid_i],
+                            fetch(y * width + east_x, equiv_map, tex));
 
                 // Check left neighbor
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        fetch(y * width + west_x, equiv_map, tex));
+                best_label[tid_i] =
+                    relabel(best_label[tid_i],
+                            fetch(y * width + west_x, equiv_map, tex));
 
                 // Check top neighbor
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        fetch((north_y) * width + x, equiv_map, tex));
+                best_label[tid_i] =
+                    relabel(best_label[tid_i],
+                            fetch((north_y)*width + x, equiv_map, tex));
 
                 if (full_conn) {
                     // Check NW corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                            fetch((north_y) * width + west_x, equiv_map, tex));
+                    best_label[tid_i] = relabel(
+                        best_label[tid_i],
+                        fetch((north_y)*width + west_x, equiv_map, tex));
 
                     // Check NE corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                            fetch((north_y) * width + east_x, equiv_map, tex));
+                    best_label[tid_i] = relabel(
+                        best_label[tid_i],
+                        fetch((north_y)*width + east_x, equiv_map, tex));
 
                     // Check SW corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                        fetch((south_y) * width + west_x, equiv_map, tex));
+                    best_label[tid_i] = relabel(
+                        best_label[tid_i],
+                        fetch((south_y)*width + west_x, equiv_map, tex));
 
                     // Check SE corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                            fetch((south_y) * width + east_x, equiv_map, tex));
-                } // if connectivity == 8
-            } // if orig_label != 0
+                    best_label[tid_i] = relabel(
+                        best_label[tid_i],
+                        fetch((south_y)*width + east_x, equiv_map, tex));
+                }  // if connectivity == 8
+            }      // if orig_label != 0
 
             // Process the equivalency list.
             T last_label = orig_label;
@@ -214,13 +219,13 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
 
             while (best_label[tid_i] != (T)0 && new_label < last_label) {
                 last_label = new_label;
-                new_label = fetch(new_label - (T)1, equiv_map, tex);
+                new_label  = fetch(new_label - (T)1, equiv_map, tex);
             }
 
             if (orig_label != new_label) {
-                tid_changed = true;
+                tid_changed    = true;
                 s_tile[ty][tx] = new_label;
-                write[tid_i] = (char)1;
+                write[tid_i]   = (char)1;
             }
             best_label[tid_i] = new_label;
         }
@@ -230,68 +235,67 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
 
     // Iterate until no pixel in the tile changes
     while (continue_iter) {
-
         // Reset whether or not this thread's pixels have changed.
         tid_changed = false;
 
-        #pragma unroll
+#pragma unroll
         for (int xb = 0; xb < n_per_thread; ++xb) {
-            #pragma unroll
+#pragma unroll
             for (int yb = 0; yb < n_per_thread; ++yb) {
-
                 // Indexing
-                const int tx = threadIdx.x + (xb * blockDim.x);
-                const int ty = threadIdx.y + (yb * blockDim.y);
+                const int tx    = threadIdx.x + (xb * blockDim.x);
+                const int ty    = threadIdx.y + (yb * blockDim.y);
                 const int tid_i = xb * n_per_thread + yb;
 
                 T last_label = best_label[tid_i];
 
                 if (best_label[tid_i] != 0) {
-
-                    const int north_y   = max(ty, 1) -1;
-                    const int south_y   = min(ty, n_per_thread*block_dim - 2) +1;
-                    const int east_x    = min(tx, n_per_thread*block_dim - 2) +1;
-                    const int west_x    = max(tx, 1) -1;
+                    const int north_y = max(ty, 1) - 1;
+                    const int south_y =
+                        min(ty, n_per_thread * block_dim - 2) + 1;
+                    const int east_x =
+                        min(tx, n_per_thread * block_dim - 2) + 1;
+                    const int west_x = max(tx, 1) - 1;
 
                     // Check bottom
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[south_y][tx]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[south_y][tx]);
 
                     // Check right neighbor
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[ty][east_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[ty][east_x]);
 
                     // Check left neighbor
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[ty][west_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[ty][west_x]);
 
                     // Check top neighbor
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[north_y][tx]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[north_y][tx]);
 
                     if (full_conn) {
                         // Check NW corner
-                        best_label[tid_i] = relabel(best_label[tid_i],
-                                                    s_tile[north_y][west_x]);
+                        best_label[tid_i] =
+                            relabel(best_label[tid_i], s_tile[north_y][west_x]);
 
                         // Check NE corner
-                        best_label[tid_i] = relabel(best_label[tid_i],
-                                                    s_tile[north_y][east_x]);
+                        best_label[tid_i] =
+                            relabel(best_label[tid_i], s_tile[north_y][east_x]);
 
                         // Check SW corner
-                        best_label[tid_i] = relabel(best_label[tid_i],
-                                                    s_tile[south_y][west_x]);
+                        best_label[tid_i] =
+                            relabel(best_label[tid_i], s_tile[south_y][west_x]);
 
                         // Check SE corner
-                        best_label[tid_i] = relabel(best_label[tid_i],
-                                                    s_tile[south_y][east_x]);
-                    } // if connectivity == 8
+                        best_label[tid_i] =
+                            relabel(best_label[tid_i], s_tile[south_y][east_x]);
+                    }  // if connectivity == 8
 
                     // This thread's value changed during this iteration if the
                     // best label is not the same as the last label.
                     const bool changed = best_label[tid_i] != last_label;
-                    write[tid_i] = write[tid_i] || changed;
-                    tid_changed  =  tid_changed || changed;
+                    write[tid_i]       = write[tid_i] || changed;
+                    tid_changed        = tid_changed || changed;
                 }
             }
         }
@@ -301,12 +305,12 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
         // If we have to continue iterating, update the tile of the
         // equiv map in shared memory
         if (continue_iter) {
-            #pragma unroll
+#pragma unroll
             for (int xb = 0; xb < n_per_thread; ++xb) {
-                #pragma unroll
+#pragma unroll
                 for (int yb = 0; yb < n_per_thread; ++yb) {
-                    const int tx = threadIdx.x + (xb * blockDim.x);
-                    const int ty = threadIdx.y + (yb * blockDim.y);
+                    const int tx    = threadIdx.x + (xb * blockDim.x);
+                    const int ty    = threadIdx.y + (yb * blockDim.y);
                     const int tid_i = xb * n_per_thread + yb;
                     // Update tile in shared memory
                     s_tile[ty][tx] = best_label[tid_i];
@@ -314,45 +318,43 @@ static void update_equiv(cuda::Param<T> equiv_map, const cudaTextureObject_t tex
             }
             __syncthreads();
         }
-    } // while (continue_iter)
+    }  // while (continue_iter)
 
-    // Write out equiv_map
-    #pragma unroll
+// Write out equiv_map
+#pragma unroll
     for (int xb = 0; xb < n_per_thread; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < n_per_thread; ++yb) {
-            const int x = base_x + (xb * blockDim.x);
-            const int y = base_y + (yb * blockDim.y);
-            const int n = y * width + x;
+            const int x     = base_x + (xb * blockDim.x);
+            const int y     = base_y + (yb * blockDim.y);
+            const int n     = y * width + x;
             const int tid_i = xb * n_per_thread + yb;
             if (x < width && y < height && write[tid_i]) {
-                equiv_map.ptr[n]  = best_label[tid_i];
-                continue_flag = 1;
+                equiv_map.ptr[n] = best_label[tid_i];
+                continue_flag    = 1;
             }
         }
     }
 }
 
 template<typename T>
-struct clamp_to_one : public thrust::unary_function<T,T>
-{
-    __host__ __device__ T operator()(const T& in) const
-    {
+struct clamp_to_one : public thrust::unary_function<T, T> {
+    __host__ __device__ T operator()(const T& in) const {
         return (in >= (T)1) ? (T)1 : in;
     }
 };
 
 template<typename T, bool full_conn, int n_per_thread>
-void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
-{
+void regions(cuda::Param<T> out, cuda::CParam<char> in,
+             cudaTextureObject_t tex) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
-    const int blk_x = divup(in.dims[0], threads.x*2);
-    const int blk_y = divup(in.dims[1], threads.y*2);
+    const int blk_x = divup(in.dims[0], threads.x * 2);
+    const int blk_y = divup(in.dims[1], threads.y * 2);
 
     const dim3 blocks(blk_x, blk_y);
 
-    CUDA_LAUNCH((initial_label<T,n_per_thread>), blocks, threads, out, in);
+    CUDA_LAUNCH((initial_label<T, n_per_thread>), blocks, threads, out, in);
 
     POST_LAUNCH_CHECK();
 
@@ -360,17 +362,18 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
 
     while (h_continue) {
         h_continue = 0;
-        CUDA_CHECK(cudaMemcpyToSymbolAsync(continue_flag, &h_continue, sizeof(int),
-                    0, cudaMemcpyHostToDevice,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyToSymbolAsync(
+            continue_flag, &h_continue, sizeof(int), 0, cudaMemcpyHostToDevice,
+            cuda::getActiveStream()));
 
-        CUDA_LAUNCH((update_equiv<T, 16, n_per_thread, full_conn>), blocks, threads, out, tex);
+        CUDA_LAUNCH((update_equiv<T, 16, n_per_thread, full_conn>), blocks,
+                    threads, out, tex);
 
         POST_LAUNCH_CHECK();
 
-        CUDA_CHECK(cudaMemcpyFromSymbolAsync(&h_continue, continue_flag, sizeof(int),
-                    0, cudaMemcpyDeviceToHost,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyFromSymbolAsync(
+            &h_continue, continue_flag, sizeof(int), 0, cudaMemcpyDeviceToHost,
+            cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 
@@ -381,8 +384,8 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     int size = in.dims[0] * in.dims[1];
     auto tmp = cuda::memAlloc<T>(size);
     CUDA_CHECK(cudaMemcpyAsync(tmp.get(), out.ptr, size * sizeof(T),
-                          cudaMemcpyDeviceToDevice,
-                          cuda::getActiveStream()));
+                               cudaMemcpyDeviceToDevice,
+                               cuda::getActiveStream()));
 
     // Wrap raw device ptr
     thrust::device_ptr<T> wrapped_tmp = thrust::device_pointer_cast(tmp.get());
@@ -399,34 +402,28 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in, cudaTextureObject_t tex)
     // component(1's) or it has only one component other than
     // background(0's). Either way, no further
     // post-processing of labels is required.
-    if (num_bins<=2)
-        return;
+    if (num_bins <= 2) return;
 
     cuda::ThrustVector<T> labels(num_bins);
 
     // Find the end of each section of values
     thrust::counting_iterator<T> search_begin(0);
-    THRUST_SELECT(thrust::upper_bound, wrapped_tmp,  wrapped_tmp  + size,
-                        search_begin, search_begin + num_bins,
-                        labels.begin());
+    THRUST_SELECT(thrust::upper_bound, wrapped_tmp, wrapped_tmp + size,
+                  search_begin, search_begin + num_bins, labels.begin());
 
-    THRUST_SELECT(thrust::adjacent_difference, labels.begin(), labels.end(), labels.begin());
+    THRUST_SELECT(thrust::adjacent_difference, labels.begin(), labels.end(),
+                  labels.begin());
 
     // Operators for the scan
     clamp_to_one<T> clamp;
     thrust::plus<T> add;
 
     // Perform scan -- this computes the correct labels for each component
-    THRUST_SELECT(thrust::transform_exclusive_scan,
-                                     labels.begin(),
-                                     labels.end(),
-                                     labels.begin(),
-                                     clamp,
-                                     0,
-                                     add);
+    THRUST_SELECT(thrust::transform_exclusive_scan, labels.begin(),
+                  labels.end(), labels.begin(), clamp, 0, add);
     // Apply the correct labels to the equivalency map
-    CUDA_LAUNCH((final_relabel<T,n_per_thread>), blocks,threads,
-            out, in, thrust::raw_pointer_cast(&labels[0]));
+    CUDA_LAUNCH((final_relabel<T, n_per_thread>), blocks, threads, out, in,
+                thrust::raw_pointer_cast(&labels[0]));
 
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 5515f29b1b..918cab33d0 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -7,89 +7,82 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 512;
-        static const unsigned TILEY = 32;
-
-        template<typename T>
-        __global__
-        void reorder_kernel(Param<T> out, CParam<T> in, const int d0, const int d1,
-                            const int d2, const int d3,
-                            const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            if(xx >= out.dims[0] ||
-               yy >= out.dims[1] ||
-               oz >= out.dims[2] ||
-               ow >= out.dims[3])
-                return;
-
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
-
-            const int rdims[] = {d0, d1, d2, d3};
-            const int o_off   = ow * out.strides[3] + oz * out.strides[2];
-                  int ids[4]  = {0};
-            ids[rdims[3]] = ow;
-            ids[rdims[2]] = oz;
-
-            for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                ids[rdims[1]] = oy;
-                for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                    ids[rdims[0]] = ox;
-
-                    const int oIdx = o_off + oy * out.strides[1] + ox;
-
-                    const int iIdx = ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-                                          ids[1] * in.strides[1] + ids[0];
-
-                    out.ptr[oIdx] = in.ptr[iIdx];
-                }
-            }
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void reorder(Param<T> out, CParam<T> in, const dim_t *rdims)
-        {
-            dim3 threads(TX, TY, 1);
-
-            int blocksPerMatX = divup(out.dims[0], TILEX);
-            int blocksPerMatY = divup(out.dims[1], TILEY);
-            dim3 blocks(blocksPerMatX * out.dims[2],
-                        blocksPerMatY * out.dims[3],
-                        1);
-
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
-
-            CUDA_LAUNCH((reorder_kernel<T>), blocks, threads,
-                out, in, rdims[0], rdims[1], rdims[2], rdims[3],
-                blocksPerMatX, blocksPerMatY);
-            POST_LAUNCH_CHECK();
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
+static const unsigned TILEX = 512;
+static const unsigned TILEY = 32;
+
+template<typename T>
+__global__ void reorder_kernel(Param<T> out, CParam<T> in, const int d0,
+                               const int d1, const int d2, const int d3,
+                               const int blocksPerMatX,
+                               const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    const int rdims[] = {d0, d1, d2, d3};
+    const int o_off   = ow * out.strides[3] + oz * out.strides[2];
+    int ids[4]        = {0};
+    ids[rdims[3]]     = ow;
+    ids[rdims[2]]     = oz;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        ids[rdims[1]] = oy;
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            ids[rdims[0]] = ox;
+
+            const int oIdx = o_off + oy * out.strides[1] + ox;
+
+            const int iIdx = ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+                             ids[1] * in.strides[1] + ids[0];
+
+            out.ptr[oIdx] = in.ptr[iIdx];
         }
     }
 }
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
+    dim3 threads(TX, TY, 1);
+
+    int blocksPerMatX = divup(out.dims[0], TILEX);
+    int blocksPerMatY = divup(out.dims[1], TILEY);
+    dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((reorder_kernel<T>), blocks, threads, out, in, rdims[0],
+                rdims[1], rdims[2], rdims[3], blocksPerMatX, blocksPerMatY);
+    POST_LAUNCH_CHECK();
+}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index 7cb5f53ea2..b45c6c85e3 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -7,186 +7,172 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 16;
-        static const unsigned TY = 16;
-
-        template<typename T>
-        struct itype_t
-        {
-            typedef float wtype;
-            typedef float vtype;
-        };
-
-        template<>
-        struct itype_t<double>
-        {
-            typedef double wtype;
-            typedef double vtype;
-        };
-
-        template<>
-        struct itype_t<cfloat>
-        {
-            typedef float  wtype;
-            typedef cfloat vtype;
-        };
-
-        template<>
-        struct itype_t<cdouble>
-        {
-            typedef double  wtype;
-            typedef cdouble vtype;
-        };
-
-        ///////////////////////////////////////////////////////////////////////////
-        // nearest-neighbor resampling
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        __host__ __device__
-        void resize_n(Param<T> out, CParam<T> in,
-                      const int o_off, const int i_off,
-                      const int blockIdx_x, const int blockIdx_y,
-                      const float xf, const float yf)
-        {
-            const int ox = threadIdx.x + blockIdx_x * blockDim.x;
-            const int oy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            int ix = round(ox * xf);
-            int iy = round(oy * yf);
-
-            if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-            if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
-            if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
-
-            out.ptr[o_off + ox + oy * out.strides[1]] = in.ptr[i_off + ix + iy * in.strides[1]];
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // bilinear resampling
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        __host__ __device__
-        void resize_b(Param<T> out, CParam<T> in,
-                      const int o_off, const int i_off,
-                      const int blockIdx_x, const int blockIdx_y,
-                      const float xf_, const float yf_)
-        {
-            const int ox = threadIdx.x + blockIdx_x * blockDim.x;
-            const int oy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            float xf = ox * xf_;
-            float yf = oy * yf_;
-
-            int ix = floorf(xf);
-            int iy = floorf(yf);
-
-            if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-            if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
-            if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
-
-            float b = xf - ix;
-            float a = yf - iy;
-
-            const int ix2 = ix + 1 <  in.dims[0] ? ix + 1 : ix;
-            const int iy2 = iy + 1 <  in.dims[1] ? iy + 1 : iy;
-
-            typedef typename itype_t<T>::wtype WT;
-            typedef typename itype_t<T>::vtype VT;
-
-            const T *iptr = in.ptr + i_off;
-
-            const VT p1 = iptr[ix  + in.strides[1] * iy ];
-            const VT p2 = iptr[ix  + in.strides[1] * iy2];
-            const VT p3 = iptr[ix2 + in.strides[1] * iy ] ;
-            const VT p4 = iptr[ix2 + in.strides[1] * iy2];
-
-            VT val = scalar<WT>((1.0f-a) * (1.0f-b)) * p1 +
-                     scalar<WT>((a)      * (1.0f-b)) * p2 +
-                     scalar<WT>((1.0f-a) * (b)     ) * p3 +
-                     scalar<WT>((a)      * (b)     ) * p4;
-
-            out.ptr[o_off + ox + oy * out.strides[1]] = val;
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // lower resampling
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        __host__ __device__
-        void resize_l(Param<T> out, CParam<T> in,
-                      const int o_off, const int i_off,
-                      const int blockIdx_x, const int blockIdx_y,
-                      const float xf, const float yf)
-        {
-            const int ox = threadIdx.x + blockIdx_x * blockDim.x;
-            const int oy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            int ix = (ox * xf);
-            int iy = (oy * yf);
-
-            if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-            if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
-            if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
-
-            out.ptr[o_off + ox + oy * out.strides[1]] = in.ptr[i_off + ix + iy * in.strides[1]];
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Resize Kernel
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, af_interp_type method>
-        __global__
-        void resize_kernel(Param<T> out, CParam<T> in,
-                           const int b0, const int b1, const float xf, const float yf)
-        {
-            const int bIdx = blockIdx.x / b0;
-            const int bIdy = blockIdx.y / b1;
-            // channel adjustment
-            const int i_off = bIdx * in.strides[2]  + bIdy * in.strides[3];
-            const int o_off = bIdx * out.strides[2] + bIdy * out.strides[3];
-            const int blockIdx_x =  blockIdx.x - bIdx * b0;
-            const int blockIdx_y =  blockIdx.y - bIdy * b1;
-
-            // core
-            if(method == AF_INTERP_NEAREST) {
-                resize_n(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
-            } else if(method == AF_INTERP_BILINEAR) {
-                resize_b(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
-            } else if(method == AF_INTERP_LOWER) {
-                resize_l(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
-            }
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename T, af_interp_type method>
-        void resize(Param<T> out, CParam<T> in)
-        {
-            dim3 threads(TX, TY, 1);
-            dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
-            int blocksPerMatX = blocks.x;
-            int blocksPerMatY = blocks.y;
-
-            if (in.dims[2] > 1) { blocks.x *= in.dims[2]; }
-            if (in.dims[3] > 1) { blocks.y *= in.dims[3]; }
-            float xf = (float)in.dims[0] / (float)out.dims[0];
-            float yf = (float)in.dims[1] / (float)out.dims[1];
-
-            CUDA_LAUNCH((resize_kernel<T, method>), blocks, threads,
-                    out, in, blocksPerMatX, blocksPerMatY, xf, yf);
-            POST_LAUNCH_CHECK();
-        }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX = 16;
+static const unsigned TY = 16;
+
+template<typename T>
+struct itype_t {
+    typedef float wtype;
+    typedef float vtype;
+};
+
+template<>
+struct itype_t<double> {
+    typedef double wtype;
+    typedef double vtype;
+};
+
+template<>
+struct itype_t<cfloat> {
+    typedef float wtype;
+    typedef cfloat vtype;
+};
+
+template<>
+struct itype_t<cdouble> {
+    typedef double wtype;
+    typedef cdouble vtype;
+};
+
+///////////////////////////////////////////////////////////////////////////
+// nearest-neighbor resampling
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+__host__ __device__ void resize_n(Param<T> out, CParam<T> in, const int o_off,
+                                  const int i_off, const int blockIdx_x,
+                                  const int blockIdx_y, const float xf,
+                                  const float yf) {
+    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
+    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    int ix = round(ox * xf);
+    int iy = round(oy * yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    out.ptr[o_off + ox + oy * out.strides[1]] =
+        in.ptr[i_off + ix + iy * in.strides[1]];
+}
+
+///////////////////////////////////////////////////////////////////////////
+// bilinear resampling
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+__host__ __device__ void resize_b(Param<T> out, CParam<T> in, const int o_off,
+                                  const int i_off, const int blockIdx_x,
+                                  const int blockIdx_y, const float xf_,
+                                  const float yf_) {
+    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
+    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    float xf = ox * xf_;
+    float yf = oy * yf_;
+
+    int ix = floorf(xf);
+    int iy = floorf(yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    float b = xf - ix;
+    float a = yf - iy;
+
+    const int ix2 = ix + 1 < in.dims[0] ? ix + 1 : ix;
+    const int iy2 = iy + 1 < in.dims[1] ? iy + 1 : iy;
+
+    typedef typename itype_t<T>::wtype WT;
+    typedef typename itype_t<T>::vtype VT;
+
+    const T *iptr = in.ptr + i_off;
+
+    const VT p1 = iptr[ix + in.strides[1] * iy];
+    const VT p2 = iptr[ix + in.strides[1] * iy2];
+    const VT p3 = iptr[ix2 + in.strides[1] * iy];
+    const VT p4 = iptr[ix2 + in.strides[1] * iy2];
+
+    VT val = scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
+             scalar<WT>((a) * (1.0f - b)) * p2 +
+             scalar<WT>((1.0f - a) * (b)) * p3 + scalar<WT>((a) * (b)) * p4;
+
+    out.ptr[o_off + ox + oy * out.strides[1]] = val;
+}
+
+///////////////////////////////////////////////////////////////////////////
+// lower resampling
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+__host__ __device__ void resize_l(Param<T> out, CParam<T> in, const int o_off,
+                                  const int i_off, const int blockIdx_x,
+                                  const int blockIdx_y, const float xf,
+                                  const float yf) {
+    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
+    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    int ix = (ox * xf);
+    int iy = (oy * yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    out.ptr[o_off + ox + oy * out.strides[1]] =
+        in.ptr[i_off + ix + iy * in.strides[1]];
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Resize Kernel
+///////////////////////////////////////////////////////////////////////////
+template<typename T, af_interp_type method>
+__global__ void resize_kernel(Param<T> out, CParam<T> in, const int b0,
+                              const int b1, const float xf, const float yf) {
+    const int bIdx = blockIdx.x / b0;
+    const int bIdy = blockIdx.y / b1;
+    // channel adjustment
+    const int i_off      = bIdx * in.strides[2] + bIdy * in.strides[3];
+    const int o_off      = bIdx * out.strides[2] + bIdy * out.strides[3];
+    const int blockIdx_x = blockIdx.x - bIdx * b0;
+    const int blockIdx_y = blockIdx.y - bIdy * b1;
+
+    // core
+    if (method == AF_INTERP_NEAREST) {
+        resize_n(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
+    } else if (method == AF_INTERP_BILINEAR) {
+        resize_b(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
+    } else if (method == AF_INTERP_LOWER) {
+        resize_l(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
     }
 }
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, af_interp_type method>
+void resize(Param<T> out, CParam<T> in) {
+    dim3 threads(TX, TY, 1);
+    dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
+    int blocksPerMatX = blocks.x;
+    int blocksPerMatY = blocks.y;
+
+    if (in.dims[2] > 1) { blocks.x *= in.dims[2]; }
+    if (in.dims[3] > 1) { blocks.y *= in.dims[3]; }
+    float xf = (float)in.dims[0] / (float)out.dims[0];
+    float yf = (float)in.dims[1] / (float)out.dims[1];
+
+    CUDA_LAUNCH((resize_kernel<T, method>), blocks, threads, out, in,
+                blocksPerMatX, blocksPerMatY, xf, yf);
+    POST_LAUNCH_CHECK();
+}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index c7a2df3219..708a221f86 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -7,133 +7,130 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include "interp.hpp"
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        constexpr unsigned TX = 16;
-        constexpr unsigned TY = 16;
-        // Used for batching images
-        constexpr int TI = 4;
-
-        typedef struct {
-            float tmat[6];
-        } tmat_t;
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Rotate Kernel
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, int order>
-        __global__ static void
-        rotate_kernel(Param<T> out, CParam<T> in, const tmat_t t,
-                      const int nimages, const int nbatches,
-                      const int blocksXPerImage, const int blocksYPerImage,
-                      af_interp_type method)
-        {
-            // Compute which image set
-            const int setId = blockIdx.x / blocksXPerImage;
-            const int blockIdx_x = blockIdx.x - setId * blocksXPerImage;
-
-            const int batch = blockIdx.y / blocksYPerImage;
-            const int blockIdx_y = blockIdx.y - batch * blocksYPerImage;
-
-            // Get thread indices
-            const int xido = blockIdx_x * blockDim.x + threadIdx.x;
-            const int yido = blockIdx_y * blockDim.y + threadIdx.y;
-
-            const int limages = min(out.dims[2] - setId * nimages, nimages);
-
-            if(xido >= out.dims[0] || yido >= out.dims[1])
-                return;
-
-            // Compute input index
-            typedef typename itype_t<T>::wtype WT;
-            WT xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
-            WT yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
-
-            // Global offset
-            //          Offset for transform channel + Offset for image channel.
-            int outoff =  setId * nimages * out.strides[2] + batch * out.strides[3];
-            int inoff  =  setId * nimages * in.strides[2]  + batch * in.strides[3];
-            const int loco = outoff + (yido * out.strides[1] + xido);
-
-            if (order > 1) {
-                // Special conditions to deal with boundaries for bilinear and bicubic
-                // FIXME: Ideally this condition should be removed or be present for all methods
-                // But tests are expecting a different behavior for bilinear and nearest
-                if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi || in.dims[1] < yidi) {
-                    for(int i = 0; i < nimages; i++) {
-                        out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
-                    }
-                    return;
-                }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+constexpr unsigned TX = 16;
+constexpr unsigned TY = 16;
+// Used for batching images
+constexpr int TI = 4;
+
+typedef struct {
+    float tmat[6];
+} tmat_t;
+
+///////////////////////////////////////////////////////////////////////////
+// Rotate Kernel
+///////////////////////////////////////////////////////////////////////////
+template<typename T, int order>
+__global__ static void rotate_kernel(Param<T> out, CParam<T> in, const tmat_t t,
+                                     const int nimages, const int nbatches,
+                                     const int blocksXPerImage,
+                                     const int blocksYPerImage,
+                                     af_interp_type method) {
+    // Compute which image set
+    const int setId      = blockIdx.x / blocksXPerImage;
+    const int blockIdx_x = blockIdx.x - setId * blocksXPerImage;
+
+    const int batch      = blockIdx.y / blocksYPerImage;
+    const int blockIdx_y = blockIdx.y - batch * blocksYPerImage;
+
+    // Get thread indices
+    const int xido = blockIdx_x * blockDim.x + threadIdx.x;
+    const int yido = blockIdx_y * blockDim.y + threadIdx.y;
+
+    const int limages = min(out.dims[2] - setId * nimages, nimages);
+
+    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
+
+    // Compute input index
+    typedef typename itype_t<T>::wtype WT;
+    WT xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
+    WT yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
+
+    // Global offset
+    //          Offset for transform channel + Offset for image channel.
+    int outoff     = setId * nimages * out.strides[2] + batch * out.strides[3];
+    int inoff      = setId * nimages * in.strides[2] + batch * in.strides[3];
+    const int loco = outoff + (yido * out.strides[1] + xido);
+
+    if (order > 1) {
+        // Special conditions to deal with boundaries for bilinear and bicubic
+        // FIXME: Ideally this condition should be removed or be present for all
+        // methods But tests are expecting a different behavior for bilinear and
+        // nearest
+        if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi ||
+            in.dims[1] < yidi) {
+            for (int i = 0; i < nimages; i++) {
+                out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
             }
-
-            Interp2<T, WT, order> interp;
-            // FIXME: Nearest and lower do not do clamping, but other methods do
-            // Make it consistent
-            bool clamp = order != 1;
-            interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
+            return;
         }
+    }
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename T, int order>
-        void rotate(Param<T> out, CParam<T> in, const float theta, af_interp_type method)
-        {
-            const float c = cos(-theta), s = sin(-theta);
-            float tx, ty;
-            {
-                const float nx = 0.5 * (in.dims[0] - 1);
-                const float ny = 0.5 * (in.dims[1] - 1);
-                const float mx = 0.5 * (out.dims[0] - 1);
-                const float my = 0.5 * (out.dims[1] - 1);
-                const float sx = (mx * c + my *-s);
-                const float sy = (mx * s + my * c);
-                tx = -(sx - nx);
-                ty = -(sy - ny);
-            }
+    Interp2<T, WT, order> interp;
+    // FIXME: Nearest and lower do not do clamping, but other methods do
+    // Make it consistent
+    bool clamp = order != 1;
+    interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
+}
 
-            // Rounding error. Anything more than 3 decimal points wont make a diff
-            tmat_t t;
-            t.tmat[0] = round( c * 1000) / 1000.0f;
-            t.tmat[1] = round(-s * 1000) / 1000.0f;
-            t.tmat[2] = round(tx * 1000) / 1000.0f;
-            t.tmat[3] = round( s * 1000) / 1000.0f;
-            t.tmat[4] = round( c * 1000) / 1000.0f;
-            t.tmat[5] = round(ty * 1000) / 1000.0f;
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, int order>
+void rotate(Param<T> out, CParam<T> in, const float theta,
+            af_interp_type method) {
+    const float c = cos(-theta), s = sin(-theta);
+    float tx, ty;
+    {
+        const float nx = 0.5 * (in.dims[0] - 1);
+        const float ny = 0.5 * (in.dims[1] - 1);
+        const float mx = 0.5 * (out.dims[0] - 1);
+        const float my = 0.5 * (out.dims[1] - 1);
+        const float sx = (mx * c + my * -s);
+        const float sy = (mx * s + my * c);
+        tx             = -(sx - nx);
+        ty             = -(sy - ny);
+    }
 
-            int nimages = in.dims[2];
-            int nbatches = in.dims[3];
+    // Rounding error. Anything more than 3 decimal points wont make a diff
+    tmat_t t;
+    t.tmat[0] = round(c * 1000) / 1000.0f;
+    t.tmat[1] = round(-s * 1000) / 1000.0f;
+    t.tmat[2] = round(tx * 1000) / 1000.0f;
+    t.tmat[3] = round(s * 1000) / 1000.0f;
+    t.tmat[4] = round(c * 1000) / 1000.0f;
+    t.tmat[5] = round(ty * 1000) / 1000.0f;
 
-            dim3 threads(TX, TY, 1);
-            dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
+    int nimages  = in.dims[2];
+    int nbatches = in.dims[3];
 
-            const int blocksXPerImage = blocks.x;
-            const int blocksYPerImage = blocks.y;
+    dim3 threads(TX, TY, 1);
+    dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
 
-            if(nimages > TI) {
-                int tile_images = divup(nimages, TI);
-                nimages = TI;
-                blocks.x = blocks.x * tile_images;
-            }
+    const int blocksXPerImage = blocks.x;
+    const int blocksYPerImage = blocks.y;
 
-            blocks.y = blocks.y * nbatches;
+    if (nimages > TI) {
+        int tile_images = divup(nimages, TI);
+        nimages         = TI;
+        blocks.x        = blocks.x * tile_images;
+    }
 
-            CUDA_LAUNCH((rotate_kernel<T, order>), blocks, threads,
-                        out, in, t, nimages, nbatches,
-                        blocksXPerImage, blocksYPerImage, method);
+    blocks.y = blocks.y * nbatches;
 
-            POST_LAUNCH_CHECK();
-        }
-    }
+    CUDA_LAUNCH((rotate_kernel<T, order>), blocks, threads, out, in, t, nimages,
+                nbatches, blocksXPerImage, blocksYPerImage, method);
+
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
index 56cd4fe70b..19654bfe33 100644
--- a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
+++ b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
@@ -7,20 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
 #include <backend.hpp>
-#include <kernel/scan_first_by_key_impl.hpp>
 #include <kernel/scan_dim_by_key_impl.hpp>
+#include <kernel/scan_first_by_key_impl.hpp>
+#include <ops.hpp>
 
 // This file instantiates scan_dim_by_key as separate object files from CMake
 // The line below is read by CMake to determenine the instantiations
 // SBK_BINARY_OPS:af_add_t af_mul_t af_max_t af_min_t
 
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATE_SCAN_FIRST_BY_KEY_OP(SBK_BINARY_OP)
-    INSTANTIATE_SCAN_DIM_BY_KEY_OP(SBK_BINARY_OP)
-}
-}
+namespace cuda {
+namespace kernel {
+INSTANTIATE_SCAN_FIRST_BY_KEY_OP(SBK_BINARY_OP)
+INSTANTIATE_SCAN_DIM_BY_KEY_OP(SBK_BINARY_OP)
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 8412b6df9c..9901054a4c 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -7,304 +7,286 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
 #include "config.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
-
-    template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass, uint DIMY, bool inclusive_scan>
-    __global__
-    static void scan_dim_kernel(Param<To> out,
-                                Param<To> tmp,
-                                CParam<Ti>  in,
-                                uint blocks_x,
-                                uint blocks_y,
-                                uint blocks_dim,
-                                uint lim)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-        const int tid  = tidy * THREADS_X + tidx;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x + tidx;
-        const int yid = blockIdx_y; // yid  of output. updated for input later.
-
-        int ids[4] = {xid, yid, zid, wid};
-
-        const Ti *iptr = in.ptr;
-        To *optr = out.ptr;
-        To *tptr = tmp.ptr;
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] + ids[1] * tmp.strides[1] + ids[0];
-        const int blockIdx_dim = ids[dim];
-
-        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        iptr  += ids[3] *  in.strides[3] + ids[2] *  in.strides[2] + ids[1] *  in.strides[1] + ids[0];
-        int id_dim = ids[dim];
-        const int out_dim = out.dims[dim];
-
-        bool is_valid =
-            (ids[0] < out.dims[0]) &&
-            (ids[1] < out.dims[1]) &&
-            (ids[2] < out.dims[2]) &&
-            (ids[3] < out.dims[3]);
-
-        const int ostride_dim = out.strides[dim];
-        const int istride_dim =  in.strides[dim];
-
-        __shared__ To s_val[THREADS_X * DIMY * 2];
-        __shared__ To s_tmp[THREADS_X];
-        To *sptr =  s_val + tid;
-
-        Transform<Ti, To, op> transform;
-        Binary<To, op> binop;
-
-        const To init = Binary<To, op>::init();
-        To val = init;
-
-        const bool isLast = (tidy == (DIMY - 1));
-
-        for (int k = 0; k < lim; k++) {
-
-            if (isLast) s_tmp[tidx] = val;
-
-            bool cond = (is_valid) && (id_dim < out_dim);
-            val = cond ? transform(*iptr) : init;
-            *sptr = val;
-            __syncthreads();
-
-            int start = 0;
+namespace cuda {
+namespace kernel {
+
+template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass,
+         uint DIMY, bool inclusive_scan>
+__global__ static void scan_dim_kernel(Param<To> out, Param<To> tmp,
+                                       CParam<Ti> in, uint blocks_x,
+                                       uint blocks_y, uint blocks_dim,
+                                       uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+    const int tid  = tidy * THREADS_X + tidx;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const Ti *iptr = in.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    int id_dim        = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    const int ostride_dim = out.strides[dim];
+    const int istride_dim = in.strides[dim];
+
+    __shared__ To s_val[THREADS_X * DIMY * 2];
+    __shared__ To s_tmp[THREADS_X];
+    To *sptr = s_val + tid;
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const bool isLast = (tidy == (DIMY - 1));
+
+    for (int k = 0; k < lim; k++) {
+        if (isLast) s_tmp[tidx] = val;
+
+        bool cond = (is_valid) && (id_dim < out_dim);
+        val       = cond ? transform(*iptr) : init;
+        *sptr     = val;
+        __syncthreads();
+
+        int start = 0;
 #pragma unroll
-            for (int off = 1; off < DIMY; off *= 2) {
+        for (int off = 1; off < DIMY; off *= 2) {
+            if (tidy >= off) val = binop(val, sptr[(start - off) * THREADS_X]);
+            start                   = DIMY - start;
+            sptr[start * THREADS_X] = val;
 
-                if (tidy >= off) val = binop(val, sptr[(start - off) * THREADS_X]);
-                start = DIMY - start;
-                sptr[start * THREADS_X] = val;
-
-                __syncthreads();
-            }
-
-            val = binop(val, s_tmp[tidx]);
-            if (inclusive_scan) {
-                if (cond) {
-                    *optr = val;
-                }
-            } else if (is_valid) {
-                if (id_dim == (out_dim - 1)) {
-                    *(optr - (id_dim*ostride_dim)) = init;
-                } else if (id_dim < (out_dim - 1)) {
-                    *(optr + ostride_dim) = val;
-                }
-            }
-            id_dim += blockDim.y;
-            iptr += blockDim.y * istride_dim;
-            optr += blockDim.y * ostride_dim;
             __syncthreads();
         }
 
-        if (!isFinalPass &&
-            is_valid &&
-            (blockIdx_dim < tmp.dims[dim]) &&
-            isLast) {
-            *tptr = val;
+        val = binop(val, s_tmp[tidx]);
+        if (inclusive_scan) {
+            if (cond) { *optr = val; }
+        } else if (is_valid) {
+            if (id_dim == (out_dim - 1)) {
+                *(optr - (id_dim * ostride_dim)) = init;
+            } else if (id_dim < (out_dim - 1)) {
+                *(optr + ostride_dim) = val;
             }
+        }
+        id_dim += blockDim.y;
+        iptr += blockDim.y * istride_dim;
+        optr += blockDim.y * ostride_dim;
+        __syncthreads();
     }
 
-    template<typename To, af_op_t op, int dim>
-    __global__
-    static void bcast_dim_kernel(Param<To> out,
-                                 CParam<To> tmp,
-                                 uint blocks_x,
-                                 uint blocks_y,
-                                 uint blocks_dim,
-                                 uint lim,
-                                 bool inclusive_scan)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x + tidx;
-        const int yid = blockIdx_y; // yid  of output. updated for input later.
-
-        int ids[4] = {xid, yid, zid, wid};
-
-        const To *tptr = tmp.ptr;
-        To *optr = out.ptr;
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] + ids[1] * tmp.strides[1] + ids[0];
-        const int blockIdx_dim = ids[dim];
-
-        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-        optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        const int id_dim = ids[dim];
-        const int out_dim = out.dims[dim];
-
-        // Shift broadcast one step to the right for exclusive scan (#2366)
-        int offset = inclusive_scan ? 0 : out.strides[dim];
-        optr += offset;
-
-        bool is_valid =
-            (ids[0] < out.dims[0]) &&
-            (ids[1] < out.dims[1]) &&
-            (ids[2] < out.dims[2]) &&
-            (ids[3] < out.dims[3]);
-
-        if (!is_valid) return;
-        if (blockIdx_dim == 0) return;
-
-        To accum = *(tptr - tmp.strides[dim]);
-
-        Binary<To, op> binop;
-        const int ostride_dim = out.strides[dim];
-
-        for (int k = 0, id = id_dim;
-             is_valid && k < lim && (id < out_dim);
-             k++, id += blockDim.y) {
-
-            *optr = binop(*optr,accum);
-            optr += blockDim.y * ostride_dim;
-        }
+    if (!isFinalPass && is_valid && (blockIdx_dim < tmp.dims[dim]) && isLast) {
+        *tptr = val;
     }
+}
 
-    template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass, bool inclusive_scan>
-    static void scan_dim_launcher(Param<To> out,
-                           Param<To> tmp,
-                           CParam<Ti> in,
-                           const uint threads_y,
-                           const dim_t blocks_all[4])
-    {
-        dim3 threads(THREADS_X, threads_y);
+template<typename To, af_op_t op, int dim>
+__global__ static void bcast_dim_kernel(Param<To> out, CParam<To> tmp,
+                                        uint blocks_x, uint blocks_y,
+                                        uint blocks_dim, uint lim,
+                                        bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const To *tptr = tmp.ptr;
+    To *optr       = out.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    const int id_dim  = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    // Shift broadcast one step to the right for exclusive scan (#2366)
+    int offset = inclusive_scan ? 0 : out.strides[dim];
+    optr += offset;
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    if (!is_valid) return;
+    if (blockIdx_dim == 0) return;
+
+    To accum = *(tptr - tmp.strides[dim]);
+
+    Binary<To, op> binop;
+    const int ostride_dim = out.strides[dim];
+
+    for (int k = 0, id = id_dim; is_valid && k < lim && (id < out_dim);
+         k++, id += blockDim.y) {
+        *optr = binop(*optr, accum);
+        optr += blockDim.y * ostride_dim;
+    }
+}
 
-        dim3 blocks(blocks_all[0] * blocks_all[2],
-                    blocks_all[1] * blocks_all[3]);
+template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass,
+         bool inclusive_scan>
+static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                              const uint threads_y, const dim_t blocks_all[4]) {
+    dim3 threads(THREADS_X, threads_y);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        switch (threads_y) {
+    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+
+    switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 8, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 8,
+                                         inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_all[0],
+                        blocks_all[1], blocks_all[dim], lim);
+            break;
         case 4:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 4, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 4,
+                                         inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_all[0],
+                        blocks_all[1], blocks_all[dim], lim);
+            break;
         case 2:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 2, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 2,
+                                         inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_all[0],
+                        blocks_all[1], blocks_all[dim], lim);
+            break;
         case 1:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 1, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim], lim); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 1,
+                                         inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_all[0],
+                        blocks_all[1], blocks_all[dim], lim);
+            break;
     }
 
+    POST_LAUNCH_CHECK();
+}
 
+template<typename To, af_op_t op, int dim>
+static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
+                               const uint threads_y, const dim_t blocks_all[4],
+                               bool inclusive_scan) {
+    dim3 threads(THREADS_X, threads_y);
 
-    template<typename To, af_op_t op, int dim>
-    static void bcast_dim_launcher(Param<To> out,
-                                   CParam<To> tmp,
-                                   const uint threads_y,
-                                   const dim_t blocks_all[4],
-                                   bool inclusive_scan)
-    {
-
-        dim3 threads(THREADS_X, threads_y);
-
-        dim3 blocks(blocks_all[0] * blocks_all[2],
-                    blocks_all[1] * blocks_all[3]);
-
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
-
-        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
-
-        CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads,
-                    out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim], lim, inclusive_scan);
+    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-        POST_LAUNCH_CHECK();
-    }
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-    template<typename Ti, typename To, af_op_t op, int dim, bool inclusive_scan>
-    static void scan_dim(Param<To> out, CParam<Ti> in)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
-        uint threads_x = THREADS_X;
+    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-        dim_t blocks_all[] = {divup(out.dims[0], threads_x),
-                             out.dims[1], out.dims[2], out.dims[3]};
+    CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads, out, tmp,
+                blocks_all[0], blocks_all[1], blocks_all[dim], lim,
+                inclusive_scan);
 
-        blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
+    POST_LAUNCH_CHECK();
+}
 
-        if (blocks_all[dim] == 1) {
+template<typename Ti, typename To, af_op_t op, int dim, bool inclusive_scan>
+static void scan_dim(Param<To> out, CParam<Ti> in) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
+    uint threads_x = THREADS_X;
 
-            scan_dim_launcher<Ti, To, op, dim, true, inclusive_scan>(out, out, in,
-                                                     threads_y,
-                                                     blocks_all);
+    dim_t blocks_all[] = {divup(out.dims[0], threads_x), out.dims[1],
+                          out.dims[2], out.dims[3]};
 
-        } else {
+    blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
 
-            Param<To> tmp = out;
+    if (blocks_all[dim] == 1) {
+        scan_dim_launcher<Ti, To, op, dim, true, inclusive_scan>(
+            out, out, in, threads_y, blocks_all);
 
-            tmp.dims[dim] = blocks_all[dim];
-            tmp.strides[0] = 1;
-            for (int k = 1; k < 4; k++) tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
+    } else {
+        Param<To> tmp = out;
 
-            int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            auto tmp_alloc = memAlloc<To>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
+        tmp.dims[dim]  = blocks_all[dim];
+        tmp.strides[0] = 1;
+        for (int k = 1; k < 4; k++)
+            tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
 
-            scan_dim_launcher<Ti, To, op, dim, false, inclusive_scan>(out, tmp, in,
-                                                      threads_y,
-                                                      blocks_all);
+        int tmp_elements = tmp.strides[3] * tmp.dims[3];
+        auto tmp_alloc   = memAlloc<To>(tmp_elements);
+        tmp.ptr          = tmp_alloc.get();
 
-            int bdim = blocks_all[dim];
-            blocks_all[dim] = 1;
+        scan_dim_launcher<Ti, To, op, dim, false, inclusive_scan>(
+            out, tmp, in, threads_y, blocks_all);
 
-            //FIXME: Is there an alternative to the if condition ?
-            if (op == af_notzero_t) {
-                scan_dim_launcher<To, To, af_add_t, dim, true, true>(tmp, tmp, tmp,
-                                                               threads_y,
-                                                               blocks_all);
-            } else {
-                scan_dim_launcher<To, To,       op, dim, true, true>(tmp, tmp, tmp,
-                                                               threads_y,
-                                                               blocks_all);
-            }
+        int bdim        = blocks_all[dim];
+        blocks_all[dim] = 1;
 
-            blocks_all[dim] = bdim;
-            bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all, inclusive_scan);
+        // FIXME: Is there an alternative to the if condition ?
+        if (op == af_notzero_t) {
+            scan_dim_launcher<To, To, af_add_t, dim, true, true>(
+                tmp, tmp, tmp, threads_y, blocks_all);
+        } else {
+            scan_dim_launcher<To, To, op, dim, true, true>(
+                tmp, tmp, tmp, threads_y, blocks_all);
         }
-    }
 
+        blocks_all[dim] = bdim;
+        bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all,
+                                        inclusive_scan);
+    }
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.hpp b/src/backend/cuda/kernel/scan_dim_by_key.hpp
index a609510b92..2b6ba16149 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key.hpp
@@ -8,14 +8,13 @@
  ********************************************************/
 #pragma once
 
-#include <ops.hpp>
 #include <Param.hpp>
+#include <ops.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        template<typename Ti, typename Tk, typename To, af_op_t op>
-        void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, bool inclusive_scan);
-    }
+namespace cuda {
+namespace kernel {
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim,
+                     bool inclusive_scan);
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 05fbf12b0f..72deb5c880 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -8,567 +8,541 @@
  ********************************************************/
 #pragma once
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
 #include "config.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 
-    template<typename Tk>
-    __device__
-    inline static char calculate_head_flags_dim(const Tk *kptr, int id, int stride)
-    {
-        return (id == 0)? 1 : ((*kptr) != (*(kptr - stride)));
+template<typename Tk>
+__device__ inline static char calculate_head_flags_dim(const Tk *kptr, int id,
+                                                       int stride) {
+    return (id == 0) ? 1 : ((*kptr) != (*(kptr - stride)));
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
+__global__ static void scan_dim_nonfinal_kernel(Param<To> out, Param<To> tmp,
+                                                Param<char> tflg,
+                                                Param<int> tlid, CParam<Ti> in,
+                                                CParam<Tk> key, int dim,
+                                                uint blocks_x, uint blocks_y,
+                                                uint lim, bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+    const int tid  = tidy * THREADS_X + tidx;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+    char *tfptr    = tflg.ptr;
+    int *tiptr     = tlid.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    tfptr += ids[3] * tflg.strides[3] + ids[2] * tflg.strides[2] +
+             ids[1] * tflg.strides[1] + ids[0];
+    tiptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] +
+             ids[1] * tlid.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    kptr += ids[3] * key.strides[3] + ids[2] * key.strides[2] +
+            ids[1] * key.strides[1] + ids[0];
+    int id_dim        = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    const int ostride_dim = out.strides[dim];
+    const int istride_dim = in.strides[dim];
+
+    __shared__ char s_flg[THREADS_X * DIMY * 2];
+    __shared__ To s_val[THREADS_X * DIMY * 2];
+    __shared__ char s_ftmp[THREADS_X];
+    __shared__ To s_tmp[THREADS_X];
+    __shared__ int boundaryid[THREADS_X];
+    To *sptr    = s_val + tid;
+    char *sfptr = s_flg + tid;
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const bool isLast = (tidy == (DIMY - 1));
+    if (isLast) {
+        s_tmp[tidx]      = val;
+        s_ftmp[tidx]     = 0;
+        boundaryid[tidx] = -1;
     }
+    __syncthreads();
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
-    __global__
-    static void scan_dim_nonfinal_kernel(Param<To> out,
-                                         Param<To> tmp,
-                                         Param<char> tflg,
-                                         Param<int> tlid,
-                                         CParam<Ti>  in,
-                                         CParam<Tk> key,
-                                         int dim,
-                                         uint blocks_x,
-                                         uint blocks_y,
-                                         uint lim,
-                                         bool inclusive_scan)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-        const int tid  = tidy * THREADS_X + tidx;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x + tidx;
-        const int yid = blockIdx_y; // yid  of output. updated for input later.
-
-        int ids[4] = {xid, yid, zid, wid};
-
-        const Ti *iptr = in.ptr;
-        const Tk *kptr = key.ptr;
-        To *optr = out.ptr;
-        To *tptr = tmp.ptr;
-        char *tfptr = tflg.ptr;
-        int *tiptr = tlid.ptr;
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        tptr    += ids[3] *  tmp.strides[3] + ids[2] *  tmp.strides[2] + ids[1] *  tmp.strides[1] + ids[0];
-        tfptr   += ids[3] * tflg.strides[3] + ids[2] * tflg.strides[2] + ids[1] * tflg.strides[1] + ids[0];
-        tiptr   += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] + ids[1] * tlid.strides[1] + ids[0];
-        const int blockIdx_dim = ids[dim];
-
-        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        iptr  += ids[3] *  in.strides[3] + ids[2] *  in.strides[2] + ids[1] *  in.strides[1] + ids[0];
-        kptr  += ids[3] * key.strides[3] + ids[2] * key.strides[2] + ids[1] * key.strides[1] + ids[0];
-        int id_dim = ids[dim];
-        const int out_dim = out.dims[dim];
-
-        bool is_valid =
-            (ids[0] < out.dims[0]) &&
-            (ids[1] < out.dims[1]) &&
-            (ids[2] < out.dims[2]) &&
-            (ids[3] < out.dims[3]);
-
-        const int ostride_dim = out.strides[dim];
-        const int istride_dim =  in.strides[dim];
-
-        __shared__ char s_flg[THREADS_X * DIMY * 2];
-        __shared__ To s_val[THREADS_X * DIMY * 2];
-        __shared__ char s_ftmp[THREADS_X];
-        __shared__ To s_tmp[THREADS_X];
-        __shared__ int boundaryid[THREADS_X];
-        To *sptr =  s_val + tid;
-        char *sfptr = s_flg + tid;
-
-        Transform<Ti, To, op> transform;
-        Binary<To, op> binop;
-
-        const To init = Binary<To, op>::init();
-        To val = init;
-
-        const bool isLast = (tidy == (DIMY - 1));
-        if (isLast) {
-            s_tmp[tidx] = val;
-            s_ftmp[tidx] = 0;
-            boundaryid[tidx] = -1;
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+        if (id_dim < out_dim) {
+            flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
+        } else {
+            flag = 0;
         }
-        __syncthreads();
-
-        char flag = 0;
-        for (int k = 0; k < lim; k++) {
 
-            if (id_dim < out_dim) {
-                flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id_dim >= out_dim) {
+                val = init;
             } else {
-                flag = 0;
+                val = transform(*iptr);
             }
-
-            //Load val from global in
-            if (inclusive_scan) {
-                if (id_dim >= out_dim) {
-                    val = init;
-                } else {
-                    val = transform(*iptr);
-                }
+        } else {
+            if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
+                val = init;
             } else {
-                if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
-                    val = init;
-                } else {
-                    val = transform(*(iptr - istride_dim));
-                }
+                val = transform(*(iptr - istride_dim));
             }
+        }
 
-            //Add partial result from last iteration before scan operation
-            if ((tidy == 0) && (flag == 0)) {
-                val = binop(val, s_tmp[tidx]);
-                flag = s_ftmp[tidx];
-            }
+        // Add partial result from last iteration before scan operation
+        if ((tidy == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidx]);
+            flag = s_ftmp[tidx];
+        }
 
-            //Write to shared memory
-            *sptr = val;
-            *sfptr = flag;
-            __syncthreads();
+        // Write to shared memory
+        *sptr  = val;
+        *sfptr = flag;
+        __syncthreads();
 
-            //Segmented Scan
-            int start = 0;
+        // Segmented Scan
+        int start = 0;
 #pragma unroll
-            for (int off = 1; off < DIMY; off *= 2) {
-
-                if (tidy >= off) {
-                    val = sfptr[start * THREADS_X] ? val : binop(val, sptr[(start - off) * THREADS_X]);
-                    flag = sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
-                }
-                start = DIMY - start;
-                sptr[start * THREADS_X] = val;
-                sfptr[start * THREADS_X] = flag;
-
-                __syncthreads();
+        for (int off = 1; off < DIMY; off *= 2) {
+            if (tidy >= off) {
+                val = sfptr[start * THREADS_X]
+                          ? val
+                          : binop(val, sptr[(start - off) * THREADS_X]);
+                flag =
+                    sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
             }
+            start                    = DIMY - start;
+            sptr[start * THREADS_X]  = val;
+            sfptr[start * THREADS_X] = flag;
 
-            //Identify segment boundary
-            if (tidy == 0) {
-                if ((s_ftmp[tidx] == 0) && (sfptr[start * THREADS_X] == 1)) {
-                    boundaryid[tidx] = id_dim;
-                }
-            } else {
-                if ((sfptr[(start - 1) * THREADS_X] == 0) && (sfptr[start * THREADS_X] == 1)) {
-                    boundaryid[tidx] = id_dim;
-                }
-            }
-            __syncthreads();
-
-            if (is_valid && (id_dim < out_dim)) *optr = val;
-            if (isLast) {
-                s_tmp[tidx] = val;
-                s_ftmp[tidx] = flag;
-            }
-            id_dim += blockDim.y;
-            kptr += blockDim.y * key.strides[dim];
-            iptr += blockDim.y * istride_dim;
-            optr += blockDim.y * ostride_dim;
             __syncthreads();
         }
 
-        if (is_valid &&
-            (blockIdx_dim < tmp.dims[dim]) &&
-            isLast) {
-            *tptr = val;
-            *tfptr = flag;
-            int boundary = boundaryid[tidx];
-            *tiptr = (boundary == -1) ? id_dim : boundary;
+        // Identify segment boundary
+        if (tidy == 0) {
+            if ((s_ftmp[tidx] == 0) && (sfptr[start * THREADS_X] == 1)) {
+                boundaryid[tidx] = id_dim;
             }
-    }
+        } else {
+            if ((sfptr[(start - 1) * THREADS_X] == 0) &&
+                (sfptr[start * THREADS_X] == 1)) {
+                boundaryid[tidx] = id_dim;
+            }
+        }
+        __syncthreads();
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
-    __global__
-    static void scan_dim_final_kernel(Param<To> out,
-                                      CParam<Ti>  in,
-                                      CParam<Tk> key,
-                                      int dim,
-                                      uint blocks_x,
-                                      uint blocks_y,
-                                      uint lim,
-                                      bool calculateFlags,
-                                      bool inclusive_scan)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-        const int tid  = tidy * THREADS_X + tidx;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x + tidx;
-        const int yid = blockIdx_y; // yid  of output. updated for input later.
-
-        int ids[4] = {xid, yid, zid, wid};
-
-        const Ti *iptr = in.ptr;
-        const Tk *kptr = key.ptr;
-        To *optr = out.ptr;
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-
-        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        iptr  += ids[3] *  in.strides[3] + ids[2] *  in.strides[2] + ids[1] *  in.strides[1] + ids[0];
-        kptr  += ids[3] * key.strides[3] + ids[2] * key.strides[2] + ids[1] * key.strides[1] + ids[0];
-        int id_dim = ids[dim];
-        const int out_dim = out.dims[dim];
-
-        bool is_valid =
-            (ids[0] < out.dims[0]) &&
-            (ids[1] < out.dims[1]) &&
-            (ids[2] < out.dims[2]) &&
-            (ids[3] < out.dims[3]);
-
-        const int ostride_dim = out.strides[dim];
-        const int istride_dim =  in.strides[dim];
-
-        __shared__ char s_flg[THREADS_X * DIMY * 2];
-        __shared__ To s_val[THREADS_X * DIMY * 2];
-        __shared__ char s_ftmp[THREADS_X];
-        __shared__ To s_tmp[THREADS_X];
-        To *sptr =  s_val + tid;
-        char *sfptr = s_flg + tid;
-
-        Transform<Ti, To, op> transform;
-        Binary<To, op> binop;
-
-        const To init = Binary<To, op>::init();
-        To val = init;
-
-        const bool isLast = (tidy == (DIMY - 1));
+        if (is_valid && (id_dim < out_dim)) *optr = val;
         if (isLast) {
-            s_tmp[tidx] = val;
-            s_ftmp[tidx] = 0;
+            s_tmp[tidx]  = val;
+            s_ftmp[tidx] = flag;
         }
+        id_dim += blockDim.y;
+        kptr += blockDim.y * key.strides[dim];
+        iptr += blockDim.y * istride_dim;
+        optr += blockDim.y * ostride_dim;
         __syncthreads();
+    }
 
-        char flag = 0;
-        for (int k = 0; k < lim; k++) {
+    if (is_valid && (blockIdx_dim < tmp.dims[dim]) && isLast) {
+        *tptr        = val;
+        *tfptr       = flag;
+        int boundary = boundaryid[tidx];
+        *tiptr       = (boundary == -1) ? id_dim : boundary;
+    }
+}
 
-            if (calculateFlags) {
-                if (id_dim < out_dim) {
-                    flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
-                } else {
-                    flag = 0;
-                }
+template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
+__global__ static void scan_dim_final_kernel(Param<To> out, CParam<Ti> in,
+                                             CParam<Tk> key, int dim,
+                                             uint blocks_x, uint blocks_y,
+                                             uint lim, bool calculateFlags,
+                                             bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+    const int tid  = tidy * THREADS_X + tidx;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    kptr += ids[3] * key.strides[3] + ids[2] * key.strides[2] +
+            ids[1] * key.strides[1] + ids[0];
+    int id_dim        = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    const int ostride_dim = out.strides[dim];
+    const int istride_dim = in.strides[dim];
+
+    __shared__ char s_flg[THREADS_X * DIMY * 2];
+    __shared__ To s_val[THREADS_X * DIMY * 2];
+    __shared__ char s_ftmp[THREADS_X];
+    __shared__ To s_tmp[THREADS_X];
+    To *sptr    = s_val + tid;
+    char *sfptr = s_flg + tid;
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const bool isLast = (tidy == (DIMY - 1));
+    if (isLast) {
+        s_tmp[tidx]  = val;
+        s_ftmp[tidx] = 0;
+    }
+    __syncthreads();
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+        if (calculateFlags) {
+            if (id_dim < out_dim) {
+                flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
             } else {
-                flag = *kptr;
+                flag = 0;
             }
+        } else {
+            flag = *kptr;
+        }
 
-            //Load val from global in
-            if (inclusive_scan) {
-                if (id_dim >= out_dim) {
-                    val = init;
-                } else {
-                    val = transform(*iptr);
-                }
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id_dim >= out_dim) {
+                val = init;
             } else {
-                if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
-                    val = init;
-                } else {
-                    val = transform(*(iptr - istride_dim));
-                }
+                val = transform(*iptr);
             }
-
-            //Add partial result from last iteration before scan operation
-            if ((tidy == 0) && (flag == 0)) {
-                val = binop(val, s_tmp[tidx]);
-                flag = s_ftmp[tidx];
+        } else {
+            if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
+                val = init;
+            } else {
+                val = transform(*(iptr - istride_dim));
             }
+        }
 
-            //Write to shared memory
-            *sptr = val;
-            *sfptr = flag;
-            __syncthreads();
-
-            //Segmented Scan
-            int start = 0;
-#pragma unroll
-            for (int off = 1; off < DIMY; off *= 2) {
+        // Add partial result from last iteration before scan operation
+        if ((tidy == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidx]);
+            flag = s_ftmp[tidx];
+        }
 
-                if (tidy >= off) {
-                    val = sfptr[start * THREADS_X] ? val : binop(val, sptr[(start - off) * THREADS_X]);
-                    flag = sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
-                }
-                start = DIMY - start;
-                sptr[start * THREADS_X] = val;
-                sfptr[start * THREADS_X] = flag;
+        // Write to shared memory
+        *sptr  = val;
+        *sfptr = flag;
+        __syncthreads();
 
-                __syncthreads();
+        // Segmented Scan
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMY; off *= 2) {
+            if (tidy >= off) {
+                val = sfptr[start * THREADS_X]
+                          ? val
+                          : binop(val, sptr[(start - off) * THREADS_X]);
+                flag =
+                    sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
             }
+            start                    = DIMY - start;
+            sptr[start * THREADS_X]  = val;
+            sfptr[start * THREADS_X] = flag;
 
-            if (is_valid && (id_dim < out_dim)) *optr = val;
-            if (isLast) {
-                s_tmp[tidx] = val;
-                s_ftmp[tidx] = flag;
-            }
-            id_dim += blockDim.y;
-            kptr += blockDim.y * key.strides[dim];
-            iptr += blockDim.y * istride_dim;
-            optr += blockDim.y * ostride_dim;
             __syncthreads();
         }
 
+        if (is_valid && (id_dim < out_dim)) *optr = val;
+        if (isLast) {
+            s_tmp[tidx]  = val;
+            s_ftmp[tidx] = flag;
+        }
+        id_dim += blockDim.y;
+        kptr += blockDim.y * key.strides[dim];
+        iptr += blockDim.y * istride_dim;
+        optr += blockDim.y * ostride_dim;
+        __syncthreads();
     }
+}
 
-    template<typename To, af_op_t op>
-    __global__
-    static void bcast_dim_kernel(Param<To> out,
-                                 CParam<To> tmp,
-                                 Param<int> tlid,
-                                 int dim,
-                                 uint blocks_x,
-                                 uint blocks_y,
-                                 uint blocks_dim,
-                                 uint lim)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x + tidx;
-        const int yid = blockIdx_y; // yid  of output. updated for input later.
-
-        int ids[4] = {xid, yid, zid, wid};
-
-        const To *tptr = tmp.ptr;
-        To *optr = out.ptr;
-        const int *iptr = tlid.ptr;
-
-        // There is only one element per block for out
-        // There are blockDim.y elements per block for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        tptr += ids[3] *  tmp.strides[3] + ids[2] *  tmp.strides[2] + ids[1] *  tmp.strides[1] + ids[0];
-        iptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] + ids[1] * tlid.strides[1] + ids[0];
-        const int blockIdx_dim = ids[dim];
-
-        ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-        optr  += ids[3] * out.strides[3] + ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
-        const int id_dim = ids[dim];
-
-        bool is_valid =
-            (ids[0] < out.dims[0]) &&
-            (ids[1] < out.dims[1]) &&
-            (ids[2] < out.dims[2]) &&
-            (ids[3] < out.dims[3]);
-
-        if (!is_valid) return;
-        if (blockIdx_dim == 0) return;
-
-        int boundary = *iptr;
-        To accum = *(tptr - tmp.strides[dim]);
-
-        Binary<To, op> binop;
-        const int ostride_dim = out.strides[dim];
-
-        for (int k = 0, id = id_dim;
-             is_valid && k < lim && (id < boundary);
-             k++, id += blockDim.y) {
-
-            *optr = binop(*optr,accum);
-            optr += blockDim.y * ostride_dim;
-        }
+template<typename To, af_op_t op>
+__global__ static void bcast_dim_kernel(Param<To> out, CParam<To> tmp,
+                                        Param<int> tlid, int dim, uint blocks_x,
+                                        uint blocks_y, uint blocks_dim,
+                                        uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const To *tptr  = tmp.ptr;
+    To *optr        = out.ptr;
+    const int *iptr = tlid.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    iptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] +
+            ids[1] * tlid.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    const int id_dim = ids[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    if (!is_valid) return;
+    if (blockIdx_dim == 0) return;
+
+    int boundary = *iptr;
+    To accum     = *(tptr - tmp.strides[dim]);
+
+    Binary<To, op> binop;
+    const int ostride_dim = out.strides[dim];
+
+    for (int k = 0, id = id_dim; is_valid && k < lim && (id < boundary);
+         k++, id += blockDim.y) {
+        *optr = binop(*optr, accum);
+        optr += blockDim.y * ostride_dim;
     }
+}
 
-    template<typename Ti, typename Tk, typename To, af_op_t op>
-    static void scan_dim_final_launcher(Param<To> out,
-                                        CParam<Ti> in,
-                                        CParam<Tk> key,
-                                        const int dim,
-                                        const uint threads_y,
-                                        const dim_t blocks_all[4],
-                                        bool calculateFlags,
-                                        bool inclusive_scan)
-    {
-        dim3 threads(THREADS_X, threads_y);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
+                                    CParam<Tk> key, const int dim,
+                                    const uint threads_y,
+                                    const dim_t blocks_all[4],
+                                    bool calculateFlags, bool inclusive_scan) {
+    dim3 threads(THREADS_X, threads_y);
 
-        dim3 blocks(blocks_all[0] * blocks_all[2],
-                    blocks_all[1] * blocks_all[3]);
+    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-        switch (threads_y) {
+    switch (threads_y) {
         case 8:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 8>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 8>), blocks,
+                        threads, out, in, key, dim, blocks_all[0],
+                        blocks_all[1], lim, calculateFlags, inclusive_scan);
+            break;
         case 4:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 4>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 4>), blocks,
+                        threads, out, in, key, dim, blocks_all[0],
+                        blocks_all[1], lim, calculateFlags, inclusive_scan);
+            break;
         case 2:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 2>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 2>), blocks,
+                        threads, out, in, key, dim, blocks_all[0],
+                        blocks_all[1], lim, calculateFlags, inclusive_scan);
+            break;
         case 1:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 1>), blocks, threads,
-                        out, in, key, dim, blocks_all[0], blocks_all[1], lim, calculateFlags, inclusive_scan); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 1>), blocks,
+                        threads, out, in, key, dim, blocks_all[0],
+                        blocks_all[1], lim, calculateFlags, inclusive_scan);
+            break;
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op>
-    static void scan_dim_nonfinal_launcher(Param<To> out,
-                                           Param<To> tmp,
-                                           Param<char> tflg,
-                                           Param<int> tlid,
-                                           CParam<Ti> in,
-                                           CParam<Tk> key,
-                                           const int dim,
-                                           const uint threads_y,
-                                           const dim_t blocks_all[4],
-                                           bool inclusive_scan)
-    {
-        dim3 threads(THREADS_X, threads_y);
-
-        dim3 blocks(blocks_all[0] * blocks_all[2],
-                    blocks_all[1] * blocks_all[3]);
-
-        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
-
-        switch (threads_y) {
-        case 8:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 8>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
-        case 4:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 4>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
-        case 2:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 2>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
-        case 1:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 1>), blocks, threads,
-                        out, tmp, tflg, tlid, in, key, dim, blocks_all[0], blocks_all[1], lim, inclusive_scan); break;
-        }
-
-        POST_LAUNCH_CHECK();
-    }
-
-    template<typename To, af_op_t op>
-    static void bcast_dim_launcher(Param<To> out,
-                                   CParam<To> tmp,
-                                   Param<int> tlid,
-                                   const int dim,
-                                   const uint threads_y,
-                                   const dim_t blocks_all[4])
-    {
-
-        dim3 threads(THREADS_X, threads_y);
+    POST_LAUNCH_CHECK();
+}
 
-        dim3 blocks(blocks_all[0] * blocks_all[2],
-                    blocks_all[1] * blocks_all[3]);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
+                                       Param<char> tflg, Param<int> tlid,
+                                       CParam<Ti> in, CParam<Tk> key,
+                                       const int dim, const uint threads_y,
+                                       const dim_t blocks_all[4],
+                                       bool inclusive_scan) {
+    dim3 threads(THREADS_X, threads_y);
 
-        uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
+    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-        CUDA_LAUNCH((bcast_dim_kernel<To, op>), blocks, threads,
-                    out, tmp, tlid, dim, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
+    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-        POST_LAUNCH_CHECK();
+    switch (threads_y) {
+        case 8:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 8>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, dim,
+                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
+            break;
+        case 4:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 4>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, dim,
+                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
+            break;
+        case 2:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 2>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, dim,
+                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
+            break;
+        case 1:
+            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 1>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, dim,
+                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
+            break;
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op>
-    void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, bool inclusive_scan)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
-        uint threads_x = THREADS_X;
-
-        dim_t blocks_all[] = {divup(out.dims[0], threads_x),
-                             out.dims[1], out.dims[2], out.dims[3]};
+    POST_LAUNCH_CHECK();
+}
 
-        blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
+template<typename To, af_op_t op>
+static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
+                               const int dim, const uint threads_y,
+                               const dim_t blocks_all[4]) {
+    dim3 threads(THREADS_X, threads_y);
 
-        if (blocks_all[dim] == 1) {
+    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-            scan_dim_final_launcher<Ti, Tk, To, op>(out, in, key,
-                                                    dim,
-                                                    threads_y,
-                                                    blocks_all,
-                                                    true, inclusive_scan);
+    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-        } else {
-            Param<To> tmp = out;
-            Param<char> tmpflg;
-            Param<int> tmpid;
-
-            tmp.dims[dim] = blocks_all[dim];
-            tmp.strides[0] = 1;
-            for (int k = 1; k < 4; k++) tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
-            for (int k = 0; k < 4; k++) {
-                tmpflg.strides[k] = tmp.strides[k];
-                tmpid.strides[k] = tmp.strides[k];
-                tmpflg.dims[k] = tmp.dims[k];
-                tmpid.dims[k] = tmp.dims[k];
-            }
+    CUDA_LAUNCH((bcast_dim_kernel<To, op>), blocks, threads, out, tmp, tlid,
+                dim, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
 
-            int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            auto tmp_alloc = memAlloc<To>(tmp_elements);
-            auto tmpflg_alloc = memAlloc<char>(tmp_elements);
-            auto tmpid_alloc = memAlloc<int>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
-            tmpflg.ptr = tmpflg_alloc.get();
-            tmpid.ptr = tmpid_alloc.get();
-
-            scan_dim_nonfinal_launcher<Ti, Tk, To, op>(out, tmp, tmpflg,
-                                                       tmpid, in, key,
-                                                       dim,
-                                                       threads_y,
-                                                       blocks_all,
-                                                       inclusive_scan);
-
-            int bdim = blocks_all[dim];
-            blocks_all[dim] = 1;
-            scan_dim_final_launcher<To, char, To,       op>(tmp, tmp, tmpflg,
-                                                            dim,
-                                                            threads_y,
-                                                            blocks_all, false, true);
-
-            blocks_all[dim] = bdim;
-            bcast_dim_launcher<To, op>(out, tmp, tmpid, dim, threads_y, blocks_all);
+    POST_LAUNCH_CHECK();
+}
 
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim,
+                     bool inclusive_scan) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
+    uint threads_x = THREADS_X;
+
+    dim_t blocks_all[] = {divup(out.dims[0], threads_x), out.dims[1],
+                          out.dims[2], out.dims[3]};
+
+    blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
+
+    if (blocks_all[dim] == 1) {
+        scan_dim_final_launcher<Ti, Tk, To, op>(
+            out, in, key, dim, threads_y, blocks_all, true, inclusive_scan);
+
+    } else {
+        Param<To> tmp = out;
+        Param<char> tmpflg;
+        Param<int> tmpid;
+
+        tmp.dims[dim]  = blocks_all[dim];
+        tmp.strides[0] = 1;
+        for (int k = 1; k < 4; k++)
+            tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
+        for (int k = 0; k < 4; k++) {
+            tmpflg.strides[k] = tmp.strides[k];
+            tmpid.strides[k]  = tmp.strides[k];
+            tmpflg.dims[k]    = tmp.dims[k];
+            tmpid.dims[k]     = tmp.dims[k];
         }
-    }
 
+        int tmp_elements  = tmp.strides[3] * tmp.dims[3];
+        auto tmp_alloc    = memAlloc<To>(tmp_elements);
+        auto tmpflg_alloc = memAlloc<char>(tmp_elements);
+        auto tmpid_alloc  = memAlloc<int>(tmp_elements);
+        tmp.ptr           = tmp_alloc.get();
+        tmpflg.ptr        = tmpflg_alloc.get();
+        tmpid.ptr         = tmpid_alloc.get();
+
+        scan_dim_nonfinal_launcher<Ti, Tk, To, op>(out, tmp, tmpflg, tmpid, in,
+                                                   key, dim, threads_y,
+                                                   blocks_all, inclusive_scan);
+
+        int bdim        = blocks_all[dim];
+        blocks_all[dim] = 1;
+        scan_dim_final_launcher<To, char, To, op>(
+            tmp, tmp, tmpflg, dim, threads_y, blocks_all, false, true);
+
+        blocks_all[dim] = bdim;
+        bcast_dim_launcher<To, op>(out, tmp, tmpid, dim, threads_y, blocks_all);
+    }
 }
 
-#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)                    \
-    template void scan_dim_by_key<Ti, Tk, To, ROp>(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, bool inclusive_scan); \
-
-#define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-
-#define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)         \
-    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int  )   \
-    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uint )   \
-    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl )   \
+}  // namespace kernel
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)           \
+    template void scan_dim_by_key<Ti, Tk, To, ROp>(            \
+        Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim, \
+        bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)         \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl, Tk, uintl)
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)      \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int)  \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uint) \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uintl)
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 5b4fee09e1..fac586d222 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -7,263 +7,240 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
 #include "config.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
-    template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX, bool inclusive_scan>
-    __global__
-    static void scan_first_kernel(Param<To> out,
-                                  Param<To> tmp,
-                                  CParam<Ti>  in,
-                                  uint blocks_x,
-                                  uint blocks_y,
-                                  uint lim)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
+namespace cuda {
+namespace kernel {
+template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX,
+         bool inclusive_scan>
+__global__ static void scan_first_kernel(Param<To> out, Param<To> tmp,
+                                         CParam<Ti> in, uint blocks_x,
+                                         uint blocks_y, uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
 
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x * lim + tidx;
-        const int yid = blockIdx_y * blockDim.y + tidy;
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid = blockIdx_y * blockDim.y + tidy;
 
-        bool cond_yzw = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    bool cond_yzw =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
 
-        if (!cond_yzw) return; // retire warps early
+    if (!cond_yzw) return;  // retire warps early
 
-        const Ti *iptr = in.ptr;
-        To *optr = out.ptr;
-        To *tptr = tmp.ptr;
+    const Ti *iptr = in.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
 
-        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
-        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-        tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
 
+    const int DIMY            = THREADS_PER_BLOCK / DIMX;
+    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
 
-        const int DIMY = THREADS_PER_BLOCK / DIMX;
-        const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+    __shared__ To s_val[SHARED_MEM_SIZE];
+    __shared__ To s_tmp[DIMY];
 
-        __shared__ To s_val[SHARED_MEM_SIZE];
-        __shared__ To s_tmp[DIMY];
+    To *sptr = s_val + tidy * (2 * DIMX + 1);
 
-        To *sptr = s_val + tidy * (2 * DIMX + 1);
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> binop;
+    const To init = Binary<To, op>::init();
+    int id        = xid;
+    To val        = init;
 
-        const To init = Binary<To, op>::init();
-        int id = xid;
-        To val = init;
+    const bool isLast = (tidx == (DIMX - 1));
 
-        const bool isLast = (tidx == (DIMX - 1));
+    for (int k = 0; k < lim; k++) {
+        if (isLast) s_tmp[tidy] = val;
 
-        for (int k = 0; k < lim; k++) {
+        bool cond  = (id < out.dims[0]);
+        val        = cond ? transform(iptr[id]) : init;
+        sptr[tidx] = val;
+        __syncthreads();
 
-            if (isLast) s_tmp[tidy] = val;
-
-            bool cond = (id < out.dims[0]);
-            val = cond ? transform(iptr[id]) : init;
-            sptr[tidx] = val;
-            __syncthreads();
-
-
-            int start = 0;
+        int start = 0;
 #pragma unroll
-            for (int off = 1; off < DIMX; off *= 2) {
-
-                if (tidx >= off) val = binop(val, sptr[(start - off) + tidx]);
-                start = DIMX - start;
-                sptr[start + tidx] = val;
-
-                __syncthreads();
-            }
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (tidx >= off) val = binop(val, sptr[(start - off) + tidx]);
+            start              = DIMX - start;
+            sptr[start + tidx] = val;
 
-            val = binop(val, s_tmp[tidy]);
-
-            if (inclusive_scan) {
-                if (cond) {
-                    optr[id] = val;
-                }
-            } else {
-                if (id == (out.dims[0] - 1)) {
-                    optr[0] = init;
-                } else if (id < (out.dims[0] - 1)) {
-                    optr[id + 1] = val;
-                }
-            }
-            id += blockDim.x;
             __syncthreads();
         }
 
-        if (!isFinalPass && isLast) {
-            tptr[blockIdx_x] = val;
-        }
-    }
+        val = binop(val, s_tmp[tidy]);
 
-    template <typename To, af_op_t op>
-    __global__ static void bcast_first_kernel(Param<To> out,
-                                              CParam<To> tmp,
-                                              uint blocks_x,
-                                              uint blocks_y,
-                                              uint lim,
-                                              bool inclusive_scan) {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x * lim + tidx;
-        const int yid = blockIdx_y * blockDim.y + tidy;
-
-        if (blockIdx_x == 0) return;
-
-        bool cond = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-        if (!cond) return;
-
-        To *optr = out.ptr;
-        const To *tptr = tmp.ptr;
-
-        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-        tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-
-        Binary<To, op> binop;
-        To accum = tptr[blockIdx_x - 1];
-
-        // Shift broadcast one step to the right for exclusive scan (#2366)
-        int offset = !inclusive_scan;
-        for (int k = 0, id = xid + offset;
-             k < lim && id < out.dims[0];
-             k++, id += blockDim.x) {
-            optr[id] = binop(accum, optr[id]);
+        if (inclusive_scan) {
+            if (cond) { optr[id] = val; }
+        } else {
+            if (id == (out.dims[0] - 1)) {
+                optr[0] = init;
+            } else if (id < (out.dims[0] - 1)) {
+                optr[id + 1] = val;
+            }
         }
+        id += blockDim.x;
+        __syncthreads();
     }
 
-    template<typename Ti, typename To, af_op_t op, bool isFinalPass, bool inclusive_scan>
-    static void scan_first_launcher(Param<To> out,
-                             Param<To> tmp,
-                             CParam<Ti> in,
-                             const uint blocks_x,
-                             const uint blocks_y,
-                             const uint threads_x)
-    {
+    if (!isFinalPass && isLast) { tptr[blockIdx_x] = val; }
+}
+
+template<typename To, af_op_t op>
+__global__ static void bcast_first_kernel(Param<To> out, CParam<To> tmp,
+                                          uint blocks_x, uint blocks_y,
+                                          uint lim, bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid = blockIdx_y * blockDim.y + tidy;
+
+    if (blockIdx_x == 0) return;
+
+    bool cond =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond) return;
+
+    To *optr       = out.ptr;
+    const To *tptr = tmp.ptr;
+
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+
+    Binary<To, op> binop;
+    To accum = tptr[blockIdx_x - 1];
+
+    // Shift broadcast one step to the right for exclusive scan (#2366)
+    int offset = !inclusive_scan;
+    for (int k = 0, id = xid + offset; k < lim && id < out.dims[0];
+         k++, id += blockDim.x) {
+        optr[id] = binop(accum, optr[id]);
+    }
+}
 
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * out.dims[2],
-                    blocks_y * out.dims[3]);
+template<typename Ti, typename To, af_op_t op, bool isFinalPass,
+         bool inclusive_scan>
+static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                                const uint blocks_x, const uint blocks_y,
+                                const uint threads_x) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        uint lim = divup(out.dims[0], (threads_x * blocks_x));
+    uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-        switch (threads_x) {
+    switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  32, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 32,
+                                           inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
+            break;
         case 64:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  64, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 64,
+                                           inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
+            break;
         case 128:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  128, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_x, blocks_y, lim); break;
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 128,
+                                           inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
+            break;
         case 256:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass,  256, inclusive_scan>), blocks, threads,
-                out, tmp, in, blocks_x, blocks_y, lim); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 256,
+                                           inclusive_scan>),
+                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
+            break;
     }
 
+    POST_LAUNCH_CHECK();
+}
 
+template<typename To, af_op_t op>
+static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
+                                 const uint blocks_x, const uint blocks_y,
+                                 const uint threads_x, bool inclusive_scan) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    template<typename To, af_op_t op>
-    static void bcast_first_launcher(Param<To> out,
-                                     CParam<To> tmp,
-                                     const uint blocks_x,
-                                     const uint blocks_y,
-                                     const uint threads_x,
-                                     bool inclusive_scan)
-    {
-
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * out.dims[2],
-                    blocks_y * out.dims[3]);
-
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-        uint lim = divup(out.dims[0], (threads_x * blocks_x));
+    uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-        CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads,
-                    out, tmp, blocks_x, blocks_y, lim, inclusive_scan);
+    CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp,
+                blocks_x, blocks_y, lim, inclusive_scan);
 
-        POST_LAUNCH_CHECK();
-    }
+    POST_LAUNCH_CHECK();
+}
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_first(Param<To> out, CParam<Ti> in)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
+static void scan_first(Param<To> out, CParam<Ti> in) {
+    uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-        uint blocks_x = divup(out.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(out.dims[1], threads_y);
+    uint blocks_x = divup(out.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(out.dims[1], threads_y);
 
-        if (blocks_x == 1) {
+    if (blocks_x == 1) {
+        scan_first_launcher<Ti, To, op, true, inclusive_scan>(
+            out, out, in, blocks_x, blocks_y, threads_x);
 
-            scan_first_launcher<Ti, To, op, true, inclusive_scan>(out, out, in,
-                                                  blocks_x, blocks_y,
-                                                  threads_x);
+    } else {
+        Param<To> tmp = out;
 
-        } else {
+        tmp.dims[0]    = blocks_x;
+        tmp.strides[0] = 1;
+        for (int k = 1; k < 4; k++)
+            tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
 
-            Param<To> tmp = out;
-
-            tmp.dims[0] = blocks_x;
-            tmp.strides[0] = 1;
-            for (int k = 1; k < 4; k++) tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
-
-            int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            auto tmp_alloc = memAlloc<To>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
-
-            scan_first_launcher<Ti, To, op, false, inclusive_scan>(out, tmp, in,
-                                                   blocks_x, blocks_y,
-                                                   threads_x);
-
-            //FIXME: Is there an alternative to the if condition ?
-            if (op == af_notzero_t) {
-                scan_first_launcher<To, To, af_add_t, true, true>(tmp, tmp, tmp,
-                                                             1, blocks_y,
-                                                             threads_x);
-            } else {
-                scan_first_launcher<To, To,       op, true, true>(tmp, tmp, tmp,
-                                                            1, blocks_y,
-                                                            threads_x);
-            }
+        int tmp_elements = tmp.strides[3] * tmp.dims[3];
+        auto tmp_alloc   = memAlloc<To>(tmp_elements);
+        tmp.ptr          = tmp_alloc.get();
 
-            bcast_first_launcher<To, op>(out, tmp, blocks_x, blocks_y, threads_x, inclusive_scan);
+        scan_first_launcher<Ti, To, op, false, inclusive_scan>(
+            out, tmp, in, blocks_x, blocks_y, threads_x);
 
+        // FIXME: Is there an alternative to the if condition ?
+        if (op == af_notzero_t) {
+            scan_first_launcher<To, To, af_add_t, true, true>(
+                tmp, tmp, tmp, 1, blocks_y, threads_x);
+        } else {
+            scan_first_launcher<To, To, op, true, true>(tmp, tmp, tmp, 1,
+                                                        blocks_y, threads_x);
         }
-    }
 
+        bcast_first_launcher<To, op>(out, tmp, blocks_x, blocks_y, threads_x,
+                                     inclusive_scan);
+    }
 }
-}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_first_by_key.hpp b/src/backend/cuda/kernel/scan_first_by_key.hpp
index 2acdd9f782..8b758810c1 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key.hpp
@@ -8,14 +8,13 @@
  ********************************************************/
 #pragma once
 
-#include <ops.hpp>
 #include <Param.hpp>
+#include <ops.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        template<typename Ti, typename Tk, typename To, af_op_t op>
-        void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan);
-    }
+namespace cuda {
+namespace kernel {
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key,
+                       bool inclusive_scan);
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 995ffd6f1c..c3c23fb2c5 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -8,499 +8,469 @@
  ********************************************************/
 #pragma once
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
 #include "config.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
-    template<typename Tk>
-    __device__
-    inline static char calculate_head_flags(const Tk *kptr, int id, int previd)
-    {
-        return (id == 0)? 1 : (kptr[id] != kptr[previd]);
-    }
+namespace cuda {
+namespace kernel {
+template<typename Tk>
+__device__ inline static char calculate_head_flags(const Tk *kptr, int id,
+                                                   int previd) {
+    return (id == 0) ? 1 : (kptr[id] != kptr[previd]);
+}
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
-    __global__
-    static void scan_nonfinal_kernel(Param<To> out,
-                                     Param<To> tmp,
-                                     Param<char> tflg,
-                                     Param<int> tlid,
-                                     CParam<Ti> in,
-                                     CParam<Tk> key,
-                                     uint blocks_x,
-                                     uint blocks_y,
-                                     uint lim,
-                                     bool inclusive_scan)
-    {
-        Transform<Ti, To, op> transform;
-        Binary<To, op> binop;
-        const To init = Binary<To, op>::init();
-        To val = init;
-
-        const int istride = in.strides[0];
-        const int DIMY = THREADS_PER_BLOCK / DIMX;
-        const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
-        __shared__ char s_flg[SHARED_MEM_SIZE];
-        __shared__ To s_val[SHARED_MEM_SIZE];
-        __shared__ char s_ftmp[DIMY];
-        __shared__ To s_tmp[DIMY];
-        __shared__ int boundaryid[DIMY];
-
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x * lim + tidx;
-        const int yid = blockIdx_y * blockDim.y + tidy;
-        bool cond_yzw = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-        if (!cond_yzw) return; // retire warps early
-
-        To *sptr = s_val + tidy * (2 * DIMX + 1);
-        char *sfptr = s_flg + tidy * (2 * DIMX + 1);
-        int id = xid;
-
-        const bool isLast = (tidx == (DIMX - 1));
-        if (isLast) {
-            s_tmp[tidy] = init;
-            s_ftmp[tidy] = 0;
-            boundaryid[tidy] = -1;
+template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
+__global__ static void scan_nonfinal_kernel(Param<To> out, Param<To> tmp,
+                                            Param<char> tflg, Param<int> tlid,
+                                            CParam<Ti> in, CParam<Tk> key,
+                                            uint blocks_x, uint blocks_y,
+                                            uint lim, bool inclusive_scan) {
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const int istride         = in.strides[0];
+    const int DIMY            = THREADS_PER_BLOCK / DIMX;
+    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+    __shared__ char s_flg[SHARED_MEM_SIZE];
+    __shared__ To s_val[SHARED_MEM_SIZE];
+    __shared__ char s_ftmp[DIMY];
+    __shared__ To s_tmp[DIMY];
+    __shared__ int boundaryid[DIMY];
+
+    const int tidx       = threadIdx.x;
+    const int tidy       = threadIdx.y;
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid        = blockIdx_y * blockDim.y + tidy;
+    bool cond_yzw =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond_yzw) return;  // retire warps early
+
+    To *sptr    = s_val + tidy * (2 * DIMX + 1);
+    char *sfptr = s_flg + tidy * (2 * DIMX + 1);
+    int id      = xid;
+
+    const bool isLast = (tidx == (DIMX - 1));
+    if (isLast) {
+        s_tmp[tidy]      = init;
+        s_ftmp[tidy]     = 0;
+        boundaryid[tidy] = -1;
+    }
+    __syncthreads();
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+    char *tfptr    = tflg.ptr;
+    int *tiptr     = tlid.ptr;
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+    tfptr +=
+        wid * tflg.strides[3] + zid * tflg.strides[2] + yid * tflg.strides[1];
+    tiptr +=
+        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+        if (id < out.dims[0]) {
+            flag = calculate_head_flags(kptr, id, id - 1);
+        } else {
+            flag = 0;
         }
-        __syncthreads();
-
-        const Ti *iptr = in.ptr;
-        const Tk *kptr = key.ptr;
-        To *optr = out.ptr;
-        To *tptr = tmp.ptr;
-        char *tfptr = tflg.ptr;
-        int *tiptr = tlid.ptr;
-        iptr  += wid *   in.strides[3] + zid *   in.strides[2] + yid *   in.strides[1];
-        kptr  += wid *  key.strides[3] + zid *  key.strides[2] + yid *  key.strides[1];
-        optr  += wid *  out.strides[3] + zid *  out.strides[2] + yid *  out.strides[1];
-        tptr  += wid *  tmp.strides[3] + zid *  tmp.strides[2] + yid *  tmp.strides[1];
-        tfptr += wid * tflg.strides[3] + zid * tflg.strides[2] + yid * tflg.strides[1];
-        tiptr += wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
 
-        char flag = 0;
-        for (int k = 0; k < lim; k++) {
-            if (id < out.dims[0]) {
-                flag = calculate_head_flags(kptr, id, id - 1);
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id >= out.dims[0]) {
+                val = init;
             } else {
-                flag = 0;
+                val = transform(iptr[id]);
             }
-
-            //Load val from global in
-            if (inclusive_scan) {
-                if (id >= out.dims[0]) {
-                    val = init;
-                } else {
-                    val = transform(iptr[id]);
-                }
+        } else {
+            if ((id == 0) || (id >= out.dims[0]) || flag) {
+                val = init;
             } else {
-                if ((id == 0) || (id >= out.dims[0]) || flag) {
-                    val = init;
-                } else {
-                    val = transform(iptr[id-istride]);
-                }
+                val = transform(iptr[id - istride]);
             }
+        }
 
-            //Add partial result from last iteration before scan operation
-            if ((tidx == 0) && (flag == 0)) {
-                val = binop(val, s_tmp[tidy]);
-                flag = s_ftmp[tidy];
-            }
+        // Add partial result from last iteration before scan operation
+        if ((tidx == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidy]);
+            flag = s_ftmp[tidy];
+        }
 
-            //Write to shared memory
-            sptr[tidx] = val;
-            sfptr[tidx] = flag;
-            __syncthreads();
+        // Write to shared memory
+        sptr[tidx]  = val;
+        sfptr[tidx] = flag;
+        __syncthreads();
 
-            //Segmented Scan
-            int start = 0;
+        // Segmented Scan
+        int start = 0;
 #pragma unroll
-            for (int off = 1; off < DIMX; off *= 2) {
-                if (tidx >= off) {
-                    val = sfptr[start + tidx]? val : binop(val, sptr[(start - off) + tidx]);
-                    flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
-                }
-                start = DIMX - start;
-                sptr[start + tidx] = val;
-                sfptr[start + tidx] = flag;
-
-                __syncthreads();
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (tidx >= off) {
+                val = sfptr[start + tidx]
+                          ? val
+                          : binop(val, sptr[(start - off) + tidx]);
+                flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
             }
+            start               = DIMX - start;
+            sptr[start + tidx]  = val;
+            sfptr[start + tidx] = flag;
 
-            //Identify segment boundary
-            if (tidx == 0) {
-                if ((s_ftmp[tidy] == 0) && (sfptr[tidx] == 1)) {
-                    boundaryid[tidy] = id;
-                }
-            } else {
-                if ((sfptr[tidx-1] == 0) && (sfptr[tidx] == 1)) {
-                    boundaryid[tidy] = id;
-                }
-            }
             __syncthreads();
+        }
 
-            if (id < out.dims[0]) optr[id] = val;
-            if (isLast) {
-                s_tmp[tidy] = val;
-                s_ftmp[tidy] = flag;
+        // Identify segment boundary
+        if (tidx == 0) {
+            if ((s_ftmp[tidy] == 0) && (sfptr[tidx] == 1)) {
+                boundaryid[tidy] = id;
+            }
+        } else {
+            if ((sfptr[tidx - 1] == 0) && (sfptr[tidx] == 1)) {
+                boundaryid[tidy] = id;
             }
-            id += blockDim.x;
-            __syncthreads();
         }
+        __syncthreads();
+
+        if (id < out.dims[0]) optr[id] = val;
         if (isLast) {
-            tptr[blockIdx_x] = val;
-            tfptr[blockIdx_x] = flag;
-            int boundary = boundaryid[tidy];
-            tiptr[blockIdx_x] = (boundary == -1)? id : boundary;
+            s_tmp[tidy]  = val;
+            s_ftmp[tidy] = flag;
         }
+        id += blockDim.x;
+        __syncthreads();
+    }
+    if (isLast) {
+        tptr[blockIdx_x]  = val;
+        tfptr[blockIdx_x] = flag;
+        int boundary      = boundaryid[tidy];
+        tiptr[blockIdx_x] = (boundary == -1) ? id : boundary;
+    }
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
+__global__ static void scan_final_kernel(Param<To> out, CParam<Ti> in,
+                                         CParam<Tk> key, uint blocks_x,
+                                         uint blocks_y, uint lim,
+                                         bool calculateFlags,
+                                         bool inclusive_scan) {
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const int istride         = in.strides[0];
+    const int DIMY            = THREADS_PER_BLOCK / DIMX;
+    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+    __shared__ char s_flg[SHARED_MEM_SIZE];
+    __shared__ To s_val[SHARED_MEM_SIZE];
+    __shared__ char s_ftmp[DIMY];
+    __shared__ To s_tmp[DIMY];
+
+    const int tidx       = threadIdx.x;
+    const int tidy       = threadIdx.y;
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid        = blockIdx_y * blockDim.y + tidy;
+    bool cond_yzw =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond_yzw) return;  // retire warps early
+
+    To *sptr    = s_val + tidy * (2 * DIMX + 1);
+    char *sfptr = s_flg + tidy * (2 * DIMX + 1);
+    int id      = xid;
+
+    const bool isLast = (tidx == (DIMX - 1));
+    if (isLast) {
+        s_tmp[tidy]  = init;
+        s_ftmp[tidy] = 0;
     }
+    __syncthreads();
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
-    __global__
-    static void scan_final_kernel(Param<To> out,
-                                  CParam<Ti> in,
-                                  CParam<Tk> key,
-                                  uint blocks_x,
-                                  uint blocks_y,
-                                  uint lim,
-                                  bool calculateFlags,
-                                  bool inclusive_scan)
-    {
-        Transform<Ti, To, op> transform;
-        Binary<To, op> binop;
-        const To init = Binary<To, op>::init();
-        To val = init;
-
-        const int istride = in.strides[0];
-        const int DIMY = THREADS_PER_BLOCK / DIMX;
-        const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
-        __shared__ char s_flg[SHARED_MEM_SIZE];
-        __shared__ To s_val[SHARED_MEM_SIZE];
-        __shared__ char s_ftmp[DIMY];
-        __shared__ To s_tmp[DIMY];
-
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x * lim + tidx;
-        const int yid = blockIdx_y * blockDim.y + tidy;
-        bool cond_yzw = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-        if (!cond_yzw) return; // retire warps early
-
-        To *sptr = s_val + tidy * (2 * DIMX + 1);
-        char *sfptr = s_flg + tidy * (2 * DIMX + 1);
-        int id = xid;
-
-        const bool isLast = (tidx == (DIMX - 1));
-        if (isLast) {
-            s_tmp[tidy] = init;
-            s_ftmp[tidy] = 0;
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+    for (int k = 0; k < lim; k++) {
+        char flag = 0;
+        if (calculateFlags) {
+            if (id < out.dims[0]) {
+                flag = calculate_head_flags(kptr, id, id - key.strides[0]);
+            }
+        } else {
+            flag = kptr[id];
         }
-        __syncthreads();
 
-        const Ti *iptr = in.ptr;
-        const Tk *kptr = key.ptr;
-        To *optr = out.ptr;
-        iptr += wid *  in.strides[3] + zid *  in.strides[2] + yid *  in.strides[1];
-        kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
-        optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-
-        for (int k = 0; k < lim; k++) {
-            char flag = 0;
-            if (calculateFlags) {
-                if (id < out.dims[0]) {
-                    flag = calculate_head_flags(kptr, id, id - key.strides[0]);
-                }
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id >= out.dims[0]) {
+                val = init;
             } else {
-                flag = kptr[id];
+                val = transform(iptr[id]);
             }
-
-            //Load val from global in
-            if (inclusive_scan) {
-                if (id >= out.dims[0]) {
-                    val = init;
-                } else {
-                    val = transform(iptr[id]);
-                }
+        } else {
+            if ((id == 0) || (id >= out.dims[0]) || flag) {
+                val = init;
             } else {
-                if ((id == 0) || (id >= out.dims[0]) || flag) {
-                    val = init;
-                } else {
-                    val = transform(iptr[id-istride]);
-                }
+                val = transform(iptr[id - istride]);
             }
+        }
 
-            //Add partial result from last iteration before scan operation
-            if ((tidx == 0) && (flag == 0)) {
-                val = binop(val, s_tmp[tidy]);
-                flag = flag | s_ftmp[tidy];
-            }
+        // Add partial result from last iteration before scan operation
+        if ((tidx == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidy]);
+            flag = flag | s_ftmp[tidy];
+        }
 
-            //Write to shared memory
-            sptr[tidx] = val;
-            sfptr[tidx] = flag;
-            __syncthreads();
+        // Write to shared memory
+        sptr[tidx]  = val;
+        sfptr[tidx] = flag;
+        __syncthreads();
 
-            //Segmented Scan
-            int start = 0;
+        // Segmented Scan
+        int start = 0;
 #pragma unroll
-            for (int off = 1; off < DIMX; off *= 2) {
-                if (tidx >= off) {
-                    val = sfptr[start + tidx]? val : binop(val, sptr[(start - off) + tidx]);
-                    flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
-                }
-                start = DIMX - start;
-                sptr[start + tidx] = val;
-                sfptr[start + tidx] = flag;
-
-                __syncthreads();
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (tidx >= off) {
+                val = sfptr[start + tidx]
+                          ? val
+                          : binop(val, sptr[(start - off) + tidx]);
+                flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
             }
+            start               = DIMX - start;
+            sptr[start + tidx]  = val;
+            sfptr[start + tidx] = flag;
 
-            if (id < out.dims[0]) optr[id] = val;
-            if (isLast) {
-                s_tmp[tidy] = val;
-                s_ftmp[tidy] = flag;
-            }
-            id += blockDim.x;
             __syncthreads();
         }
+
+        if (id < out.dims[0]) optr[id] = val;
+        if (isLast) {
+            s_tmp[tidy]  = val;
+            s_ftmp[tidy] = flag;
+        }
+        id += blockDim.x;
+        __syncthreads();
     }
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
+                                   Param<char> tflg, Param<int> tlid,
+                                   CParam<Ti> in, CParam<Tk> key,
+                                   const uint blocks_x, const uint blocks_y,
+                                   const uint threads_x, bool inclusive_scan) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    template<typename Ti, typename Tk, typename To, af_op_t op>
-    static void scan_nonfinal_launcher(Param<To> out,
-                                       Param<To> tmp,
-                                       Param<char> tflg,
-                                       Param<int> tlid,
-                                       CParam<Ti> in,
-                                       CParam<Tk> key,
-                                       const uint blocks_x,
-                                       const uint blocks_y,
-                                       const uint threads_x,
-                                       bool inclusive_scan)
-    {
-
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * out.dims[2],
-                    blocks_y * out.dims[3]);
-
-        uint lim = divup(out.dims[0], (threads_x * blocks_x));
-
-        switch (threads_x) {
+    uint lim = divup(out.dims[0], (threads_x * blocks_x));
+
+    switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 32>),
-                        blocks, threads, out, tmp, tflg, tlid, in, key,
-                        blocks_x, blocks_y, lim, inclusive_scan); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 32>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
+                        blocks_y, lim, inclusive_scan);
+            break;
         case 64:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 64>),
-                        blocks, threads, out, tmp, tflg, tlid, in, key,
-                        blocks_x, blocks_y, lim, inclusive_scan); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 64>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
+                        blocks_y, lim, inclusive_scan);
+            break;
         case 128:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 128>),
-                        blocks, threads, out, tmp, tflg, tlid, in, key,
-                        blocks_x, blocks_y, lim, inclusive_scan); break;
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 128>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
+                        blocks_y, lim, inclusive_scan);
+            break;
         case 256:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 256>),
-                        blocks, threads, out, tmp, tflg, tlid, in, key,
-                        blocks_x, blocks_y, lim, inclusive_scan); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 256>), blocks,
+                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
+                        blocks_y, lim, inclusive_scan);
+            break;
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op>
-    static void scan_final_launcher(Param<To> out,
-                                    CParam<Ti> in,
-                                    CParam<Tk> key,
-                                    const uint blocks_x,
-                                    const uint blocks_y,
-                                    const uint threads_x,
-                                    bool calculateFlags,
-                                    bool inclusive_scan)
-    {
+    POST_LAUNCH_CHECK();
+}
 
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * out.dims[2],
-                    blocks_y * out.dims[3]);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
+                                const uint blocks_x, const uint blocks_y,
+                                const uint threads_x, bool calculateFlags,
+                                bool inclusive_scan) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-        uint lim = divup(out.dims[0], (threads_x * blocks_x));
+    uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-        switch (threads_x) {
+    switch (threads_x) {
         case 32:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 32>),
-                        blocks, threads, out, in, key, blocks_x,
-                        blocks_y, lim, calculateFlags, inclusive_scan); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 32>), blocks,
+                        threads, out, in, key, blocks_x, blocks_y, lim,
+                        calculateFlags, inclusive_scan);
+            break;
         case 64:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 64>),
-                        blocks, threads, out, in, key, blocks_x,
-                        blocks_y, lim, calculateFlags, inclusive_scan); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 64>), blocks,
+                        threads, out, in, key, blocks_x, blocks_y, lim,
+                        calculateFlags, inclusive_scan);
+            break;
         case 128:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 128>),
-                        blocks, threads, out, in, key, blocks_x,
-                        blocks_y, lim, calculateFlags, inclusive_scan); break;
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 128>), blocks,
+                        threads, out, in, key, blocks_x, blocks_y, lim,
+                        calculateFlags, inclusive_scan);
+            break;
         case 256:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 256>),
-                        blocks, threads, out, in, key, blocks_x,
-                        blocks_y, lim, calculateFlags, inclusive_scan); break;
-        }
-
-        POST_LAUNCH_CHECK();
+            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 256>), blocks,
+                        threads, out, in, key, blocks_x, blocks_y, lim,
+                        calculateFlags, inclusive_scan);
+            break;
     }
 
-    template<typename To, af_op_t op>
-    __global__
-    static void bcast_first_kernel(Param<To> out,
-                                   Param<To> tmp,
-                                   Param<int> tlid,
-                                   uint blocks_x,
-                                   uint blocks_y,
-                                   uint lim)
-    {
-        const int tidx = threadIdx.x;
-        const int tidy = threadIdx.y;
-
-        const int zid = blockIdx.x / blocks_x;
-        const int wid = blockIdx.y / blocks_y;
-        const int blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const int blockIdx_y = blockIdx.y - (blocks_y) * wid;
-        const int xid = blockIdx_x * blockDim.x * lim + tidx;
-        const int yid = blockIdx_y * blockDim.y + tidy;
-
-        if (blockIdx_x == 0) return;
-
-        bool cond = (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-        if (!cond) return;
-
-        To *optr = out.ptr;
-        const To *tptr = tmp.ptr;
-        const int *iptr = tlid.ptr;
-
-        optr += wid *  out.strides[3] + zid *  out.strides[2] + yid *  out.strides[1];
-        tptr += wid *  tmp.strides[3] + zid *  tmp.strides[2] + yid *  tmp.strides[1];
-        iptr += wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
-
-        Binary<To, op> binop;
-        int boundary = iptr[blockIdx_x];
-        To accum = tptr[blockIdx_x - 1];
-
-        for (int k = 0, id = xid;
-             k < lim && id < boundary;
-             k++, id += blockDim.x) {
-
-            optr[id] = binop(accum, optr[id]);
-        }
-    }
+    POST_LAUNCH_CHECK();
+}
 
-    template<typename To, af_op_t op>
-    static void bcast_first_launcher(Param<To> out,
-                                     Param<To> tmp,
-                                     Param<int> tlid,
-                                     const dim_t blocks_x,
-                                     const dim_t blocks_y,
-                                     const uint threads_x)
-    {
-
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * out.dims[2],
-                    blocks_y * out.dims[3]);
-        uint lim = divup(out.dims[0], (threads_x * blocks_x));
-        CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp, tlid, blocks_x, blocks_y, lim);
-
-        POST_LAUNCH_CHECK();
+template<typename To, af_op_t op>
+__global__ static void bcast_first_kernel(Param<To> out, Param<To> tmp,
+                                          Param<int> tlid, uint blocks_x,
+                                          uint blocks_y, uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid        = blockIdx_y * blockDim.y + tidy;
+
+    if (blockIdx_x == 0) return;
+
+    bool cond =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond) return;
+
+    To *optr        = out.ptr;
+    const To *tptr  = tmp.ptr;
+    const int *iptr = tlid.ptr;
+
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+    iptr +=
+        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+    Binary<To, op> binop;
+    int boundary = iptr[blockIdx_x];
+    To accum     = tptr[blockIdx_x - 1];
+
+    for (int k = 0, id = xid; k < lim && id < boundary; k++, id += blockDim.x) {
+        optr[id] = binop(accum, optr[id]);
     }
+}
 
-    template<typename Ti, typename Tk, typename To, af_op_t op>
-    void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-        uint blocks_x = static_cast<uint>(divup(out.dims[0], threads_x * REPEAT));
-        uint blocks_y = static_cast<uint>(divup(out.dims[1], threads_y));
-
-        if (blocks_x == 1) {
-            scan_final_launcher<Ti, Tk, To, op>(
-                out, in, key,
-                blocks_x, blocks_y, threads_x,
-                true, inclusive_scan);
-
-        } else {
-
-            Param<To> tmp = out;
-            Param<char> tmpflg;
-            Param<int> tmpid;
-
-            tmp.dims[0] = blocks_x;
-            tmpflg.dims[0] = blocks_x;
-            tmpid.dims[0] = blocks_x;
-            tmp.strides[0] = 1;
-            tmpflg.strides[0] = 1;
-            tmpid.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmpflg.dims[k] = out.dims[k];
-                tmpid.dims[k] = out.dims[k];
-                tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
-                tmpflg.strides[k] = tmpflg.strides[k - 1] * tmpflg.dims[k - 1];
-                tmpid.strides[k] = tmpid.strides[k - 1] * tmpid.dims[k - 1];
-            }
+template<typename To, af_op_t op>
+static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
+                                 const dim_t blocks_x, const dim_t blocks_y,
+                                 const uint threads_x) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
+    uint lim = divup(out.dims[0], (threads_x * blocks_x));
+    CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp, tlid,
+                blocks_x, blocks_y, lim);
+
+    POST_LAUNCH_CHECK();
+}
 
-            int tmp_elements = tmp.strides[3] * tmp.dims[3];
-            auto tmp_alloc = memAlloc<To>(tmp_elements);
-            auto tmpflg_alloc = memAlloc<char>(tmp_elements);
-            auto tmpid_alloc = memAlloc<int>(tmp_elements);
-            tmp.ptr = tmp_alloc.get();
-            tmpflg.ptr = tmpflg_alloc.get();
-            tmpid.ptr = tmpid_alloc.get();
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key,
+                       bool inclusive_scan) {
+    uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = static_cast<uint>(divup(out.dims[0], threads_x * REPEAT));
+    uint blocks_y = static_cast<uint>(divup(out.dims[1], threads_y));
+
+    if (blocks_x == 1) {
+        scan_final_launcher<Ti, Tk, To, op>(out, in, key, blocks_x, blocks_y,
+                                            threads_x, true, inclusive_scan);
+
+    } else {
+        Param<To> tmp = out;
+        Param<char> tmpflg;
+        Param<int> tmpid;
+
+        tmp.dims[0]       = blocks_x;
+        tmpflg.dims[0]    = blocks_x;
+        tmpid.dims[0]     = blocks_x;
+        tmp.strides[0]    = 1;
+        tmpflg.strides[0] = 1;
+        tmpid.strides[0]  = 1;
+        for (int k = 1; k < 4; k++) {
+            tmpflg.dims[k]    = out.dims[k];
+            tmpid.dims[k]     = out.dims[k];
+            tmp.strides[k]    = tmp.strides[k - 1] * tmp.dims[k - 1];
+            tmpflg.strides[k] = tmpflg.strides[k - 1] * tmpflg.dims[k - 1];
+            tmpid.strides[k]  = tmpid.strides[k - 1] * tmpid.dims[k - 1];
+        }
 
-            scan_nonfinal_launcher<Ti, Tk, To, op>(
-                out, tmp, tmpflg, tmpid, in, key,
-                blocks_x, blocks_y, threads_x,
-                inclusive_scan);
+        int tmp_elements  = tmp.strides[3] * tmp.dims[3];
+        auto tmp_alloc    = memAlloc<To>(tmp_elements);
+        auto tmpflg_alloc = memAlloc<char>(tmp_elements);
+        auto tmpid_alloc  = memAlloc<int>(tmp_elements);
+        tmp.ptr           = tmp_alloc.get();
+        tmpflg.ptr        = tmpflg_alloc.get();
+        tmpid.ptr         = tmpid_alloc.get();
 
-            scan_final_launcher<To, char, To, op>(
-                tmp, tmp, tmpflg,
-                1, blocks_y, threads_x,
-                false, true);
+        scan_nonfinal_launcher<Ti, Tk, To, op>(out, tmp, tmpflg, tmpid, in, key,
+                                               blocks_x, blocks_y, threads_x,
+                                               inclusive_scan);
 
-            bcast_first_launcher<To, op>(out, tmp, tmpid, blocks_x, blocks_y, threads_x);
+        scan_final_launcher<To, char, To, op>(tmp, tmp, tmpflg, 1, blocks_y,
+                                              threads_x, false, true);
 
-        }
+        bcast_first_launcher<To, op>(out, tmp, tmpid, blocks_x, blocks_y,
+                                     threads_x);
     }
 }
-
-#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)\
-    template void scan_first_by_key<Ti, Tk, To, ROp>(Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan); \
-
-#define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)        \
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float  , Tk, float  )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, double , Tk, double )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cfloat , Tk, cfloat )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cdouble, Tk, cdouble)\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, int    , Tk, int    )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
-
-#define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)       \
-    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int  ) \
-    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uint ) \
-    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl ) \
+}  // namespace kernel
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To) \
+    template void scan_first_by_key<Ti, Tk, To, ROp>(  \
+        Param<To> out, CParam<Ti> in, CParam<Tk> key, bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)         \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl, Tk, uintl)
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)      \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int)  \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uint) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uintl)
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 4e310f4f7b..51442e80b3 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -7,170 +7,154 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-
-        static const uint DIMX = 32;
-        static const uint DIMY =  8;
-        static const int REPEAT = 64;
-
-        __device__ __host__
-        int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4])
-        {
-            int off = 0;
-            off += ids[3] * (dims[3] == refdims[3]) * strides[3];
-            off += ids[2] * (dims[2] == refdims[2]) * strides[2];
-            off += ids[1] * (dims[1] == refdims[1]) * strides[1];
-            return off;
-        }
+namespace cuda {
+namespace kernel {
 
-        template<typename T, bool is_same>
-        __global__
-        void select_kernel(Param<T> out, CParam<char> cond,
-                           CParam<T> a, CParam<T> b, int blk_x, int blk_y)
-        {
-            const int idz = blockIdx.x / blk_x;
-            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
-
-
-            const int blockIdx_x = blockIdx.x - idz * blk_x;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
-
-            const int idy = blockIdx_y * blockDim.y + threadIdx.y;
-            const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
-
-            if (idw >= out.dims[3] ||
-                idz >= out.dims[2] ||
-                idy >= out.dims[1])  {
-                return;
-            }
-
-            const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
-            T *optr = out.ptr + off;
-
-            const T *aptr = a.ptr;
-            const T *bptr = b.ptr;
-            const char *cptr = cond.ptr;
-
-            int ids[] = {idx0, idy, idz, idw};
-            aptr += getOffset(a.dims, a.strides, out.dims, ids);
-            bptr += getOffset(b.dims, b.strides, out.dims, ids);
-            cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
-
-            if (is_same) {
-                for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
-                    optr[idx] = cptr[idx] ? aptr[idx] : bptr[idx];
-                }
-            } else {
-                bool csame = cond.dims[0] == out.dims[0];
-                bool asame = a.dims[0] == out.dims[0];
-                bool bsame = b.dims[0] == out.dims[0];
-                for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
-                    optr[idx] = cptr[csame * idx] ? aptr[asame * idx] : bptr[bsame * idx];
-                }
-            }
-        }
+static const uint DIMX  = 32;
+static const uint DIMY  = 8;
+static const int REPEAT = 64;
 
-        template<typename T>
-        void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b, int ndims)
-        {
-            bool is_same = true;
-            for (int i = 0; i < 4; i++) {
-                is_same &= (a.dims[i] == b.dims[i]);
-            }
+__device__ __host__ int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims,
+                                  int ids[4]) {
+    int off = 0;
+    off += ids[3] * (dims[3] == refdims[3]) * strides[3];
+    off += ids[2] * (dims[2] == refdims[2]) * strides[2];
+    off += ids[1] * (dims[1] == refdims[1]) * strides[1];
+    return off;
+}
 
-            dim3 threads(DIMX, DIMY);
+template<typename T, bool is_same>
+__global__ void select_kernel(Param<T> out, CParam<char> cond, CParam<T> a,
+                              CParam<T> b, int blk_x, int blk_y) {
+    const int idz = blockIdx.x / blk_x;
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
 
-            if (ndims == 1) {
-                threads.x *= threads.y;
-                threads.y = 1;
-            }
+    const int blockIdx_x = blockIdx.x - idz * blk_x;
+    const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
 
-            int blk_x = divup(out.dims[0], REPEAT * threads.x);
-            int blk_y = divup(out.dims[1], threads.y);
+    const int idy  = blockIdx_y * blockDim.y + threadIdx.y;
+    const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
 
+    if (idw >= out.dims[3] || idz >= out.dims[2] || idy >= out.dims[1]) {
+        return;
+    }
 
-            dim3 blocks(blk_x * out.dims[2],
-                        blk_y * out.dims[3]);
+    const int off =
+        idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
+    T *optr = out.ptr + off;
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    const T *aptr    = a.ptr;
+    const T *bptr    = b.ptr;
+    const char *cptr = cond.ptr;
 
-            if (is_same) {
-                CUDA_LAUNCH((select_kernel<T, true>), blocks, threads,
-                            out, cond, a, b, blk_x, blk_y);
-            } else {
-                CUDA_LAUNCH((select_kernel<T, false>), blocks, threads,
-                            out, cond, a, b, blk_x, blk_y);
-            }
+    int ids[] = {idx0, idy, idz, idw};
+    aptr += getOffset(a.dims, a.strides, out.dims, ids);
+    bptr += getOffset(b.dims, b.strides, out.dims, ids);
+    cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
 
+    if (is_same) {
+        for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+            optr[idx] = cptr[idx] ? aptr[idx] : bptr[idx];
+        }
+    } else {
+        bool csame = cond.dims[0] == out.dims[0];
+        bool asame = a.dims[0] == out.dims[0];
+        bool bsame = b.dims[0] == out.dims[0];
+        for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+            optr[idx] =
+                cptr[csame * idx] ? aptr[asame * idx] : bptr[bsame * idx];
         }
+    }
+}
+
+template<typename T>
+void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
+            int ndims) {
+    bool is_same = true;
+    for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
-        template<typename T, bool flip>
-        __global__
-        void select_scalar_kernel(Param<T> out, CParam<char> cond,
-                                  CParam<T> a, T b, int blk_x, int blk_y)
-        {
-            const int idz = blockIdx.x / blk_x;
-            const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
+    dim3 threads(DIMX, DIMY);
 
-            const int blockIdx_x = blockIdx.x - idz * blk_x;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
+    if (ndims == 1) {
+        threads.x *= threads.y;
+        threads.y = 1;
+    }
+
+    int blk_x = divup(out.dims[0], REPEAT * threads.x);
+    int blk_y = divup(out.dims[1], threads.y);
 
-            const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
-            const int idy = blockIdx_y * blockDim.y + threadIdx.y;
+    dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
 
-            const int off = idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            T *optr = out.ptr + off;
+    if (is_same) {
+        CUDA_LAUNCH((select_kernel<T, true>), blocks, threads, out, cond, a, b,
+                    blk_x, blk_y);
+    } else {
+        CUDA_LAUNCH((select_kernel<T, false>), blocks, threads, out, cond, a, b,
+                    blk_x, blk_y);
+    }
+}
 
-            const T *aptr = a.ptr;
-            const char *cptr = cond.ptr;
+template<typename T, bool flip>
+__global__ void select_scalar_kernel(Param<T> out, CParam<char> cond,
+                                     CParam<T> a, T b, int blk_x, int blk_y) {
+    const int idz = blockIdx.x / blk_x;
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
 
-            int ids[] = {idx0, idy, idz, idw};
-            aptr += getOffset(a.dims, a.strides, out.dims, ids);
-            cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
+    const int blockIdx_x = blockIdx.x - idz * blk_x;
+    const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
 
-            if (idw >= out.dims[3] ||
-                idz >= out.dims[2] ||
-                idy >= out.dims[1]) {
-                return;
-            }
+    const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
+    const int idy  = blockIdx_y * blockDim.y + threadIdx.y;
 
-            for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
-                optr[idx] = ((cptr[idx]) ^ flip) ? aptr[idx] : b;
-            }
-        }
+    const int off =
+        idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
 
-        template<typename T, bool flip>
-        void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b, int ndims)
-        {
-            dim3 threads(DIMX, DIMY);
+    T *optr = out.ptr + off;
 
-            if (ndims == 1) {
-                threads.x *= threads.y;
-                threads.y = 1;
-            }
+    const T *aptr    = a.ptr;
+    const char *cptr = cond.ptr;
 
-            int blk_x = divup(out.dims[0], REPEAT * threads.x);
-            int blk_y = divup(out.dims[1], threads.y);
+    int ids[] = {idx0, idy, idz, idw};
+    aptr += getOffset(a.dims, a.strides, out.dims, ids);
+    cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
 
+    if (idw >= out.dims[3] || idz >= out.dims[2] || idy >= out.dims[1]) {
+        return;
+    }
 
-            dim3 blocks(blk_x * out.dims[2],
-                        blk_y * out.dims[3]);
+    for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+        optr[idx] = ((cptr[idx]) ^ flip) ? aptr[idx] : b;
+    }
+}
 
-            CUDA_LAUNCH((select_scalar_kernel<T, flip>), blocks, threads,
-                        out, cond, a, scalar<T>(b), blk_x, blk_y);
+template<typename T, bool flip>
+void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
+                   int ndims) {
+    dim3 threads(DIMX, DIMY);
 
-        }
+    if (ndims == 1) {
+        threads.x *= threads.y;
+        threads.y = 1;
     }
+
+    int blk_x = divup(out.dims[0], REPEAT * threads.x);
+    int blk_y = divup(out.dims[1], threads.y);
+
+    dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
+
+    CUDA_LAUNCH((select_scalar_kernel<T, flip>), blocks, threads, out, cond, a,
+                scalar<T>(b), blk_x, blk_y);
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/shared.hpp b/src/backend/cuda/kernel/shared.hpp
index ab7f6d9764..bb23ea14e5 100644
--- a/src/backend/cuda/kernel/shared.hpp
+++ b/src/backend/cuda/kernel/shared.hpp
@@ -9,32 +9,29 @@
 
 #pragma once
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
-template <typename T>
-struct SharedMemory
-{
+template<typename T>
+struct SharedMemory {
     // return a pointer to the runtime-sized shared memory array.
-    __device__ T* getPointer()
-    {
-        extern __device__ void Error_UnsupportedType(); // Ensure that we won't compile any un-specialized types
+    __device__ T* getPointer() {
+        extern __device__ void
+        Error_UnsupportedType();  // Ensure that we won't compile any
+                                  // un-specialized types
         Error_UnsupportedType();
         return (T*)0;
     }
 };
 
-#define SPECIALIZE(T)                           \
-    template <>                                 \
-    struct SharedMemory <T>                     \
-    {                                           \
-        __device__ T* getPointer() {            \
-            extern __shared__ T ptr_##T##_[];   \
-                return ptr_##T##_;              \
-        }                                       \
+#define SPECIALIZE(T)                         \
+    template<>                                \
+    struct SharedMemory<T> {                  \
+        __device__ T* getPointer() {          \
+            extern __shared__ T ptr_##T##_[]; \
+            return ptr_##T##_;                \
+        }                                     \
     };
 
 SPECIALIZE(float)
@@ -52,5 +49,5 @@ SPECIALIZE(uintl)
 
 #undef SPECIALIZE
 
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index ed7243b8c3..8763c0ac13 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -71,8 +71,8 @@
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
 #include "shared.hpp"
 
@@ -81,19 +81,17 @@
 
 #include <thrust/device_ptr.h>
 #include <thrust/device_vector.h>
+#include <thrust/gather.h>
 #include <thrust/generate.h>
+#include <thrust/random.h>
 #include <thrust/sequence.h>
 #include <thrust/sort.h>
-#include <thrust/gather.h>
-#include <thrust/random.h>
 
 #include <cfloat>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const dim_t SIFT_THREADS   = 256;
 static const dim_t SIFT_THREADS_X = 32;
@@ -153,194 +151,159 @@ static const unsigned GLOHAngularBins = 8;
 static const unsigned GLOHHistBins = 16;
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * PI_VAL * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
 template<typename T>
-Array<T> gauss_filter(float sigma)
-{
+Array<T> gauss_filter(float sigma) {
     // Using 6-sigma rule
     unsigned gauss_len = std::min((unsigned)round(sigma * 6 + 1) | 1, 31u);
 
     std::vector<T> h_gauss(gauss_len);
     gaussian1D(h_gauss.data(), gauss_len, sigma);
 
-    Array<T> gauss_filter = createHostDataArray(dim4(gauss_len), h_gauss.data());
+    Array<T> gauss_filter =
+        createHostDataArray(dim4(gauss_len), h_gauss.data());
     return gauss_filter;
 }
 
 template<int N>
-__inline__ __device__ void gaussianElimination(float* A, float* b, float* x)
-{
-    // forward elimination
-    #pragma unroll
-    for (int i = 0; i < N-1; i++) {
-        #pragma unroll
-        for (int j = i+1; j < N; j++) {
-            float s = A[j*N+i] / A[i*N+i];
-
-            #pragma unroll
-            for (int k = i; k < N; k++)
-                A[j*N+k] -= s * A[i*N+k];
+__inline__ __device__ void gaussianElimination(float* A, float* b, float* x) {
+// forward elimination
+#pragma unroll
+    for (int i = 0; i < N - 1; i++) {
+#pragma unroll
+        for (int j = i + 1; j < N; j++) {
+            float s = A[j * N + i] / A[i * N + i];
+
+#pragma unroll
+            for (int k = i; k < N; k++) A[j * N + k] -= s * A[i * N + k];
 
             b[j] -= s * b[i];
         }
     }
 
-    #pragma unroll
-    for (int i = 0; i < N; i++)
-        x[i] = 0;
+#pragma unroll
+    for (int i = 0; i < N; i++) x[i] = 0;
 
     // backward substitution
     float sum = 0;
-    #pragma unroll
-    for (int i = 0; i <= N-2; i++) {
+#pragma unroll
+    for (int i = 0; i <= N - 2; i++) {
         sum = b[i];
-        #pragma unroll
-        for (int j = i+1; j < N; j++)
-            sum -= A[i*N+j] * x[j];
-        x[i] = sum / A[i*N+i];
+#pragma unroll
+        for (int j = i + 1; j < N; j++) sum -= A[i * N + j] * x[j];
+        x[i] = sum / A[i * N + i];
     }
 }
 
-__inline__ __device__ void normalizeDesc(
-    float* desc,
-    float* accum,
-    const int histlen)
-{
+__inline__ __device__ void normalizeDesc(float* desc, float* accum,
+                                         const int histlen) {
     int tid_x = threadIdx.x;
     int tid_y = threadIdx.y;
     int bsz_x = blockDim.x;
 
     for (int i = tid_x; i < histlen; i += bsz_x)
-        accum[i] = desc[tid_y*histlen+i]*desc[tid_y*histlen+i];
+        accum[i] = desc[tid_y * histlen + i] * desc[tid_y * histlen + i];
     __syncthreads();
 
-    if (tid_x < 64)
-        accum[tid_x] += accum[tid_x+64];
+    if (tid_x < 64) accum[tid_x] += accum[tid_x + 64];
     __syncthreads();
-    if (tid_x < 32)
-        accum[tid_x] += accum[tid_x+32];
+    if (tid_x < 32) accum[tid_x] += accum[tid_x + 32];
     __syncthreads();
-    if (tid_x < 16)
-        accum[tid_x] += accum[tid_x+16];
+    if (tid_x < 16) accum[tid_x] += accum[tid_x + 16];
     __syncthreads();
-    if (tid_x < 8)
-        accum[tid_x] += accum[tid_x+8];
+    if (tid_x < 8) accum[tid_x] += accum[tid_x + 8];
     __syncthreads();
-    if (tid_x < 4)
-        accum[tid_x] += accum[tid_x+4];
+    if (tid_x < 4) accum[tid_x] += accum[tid_x + 4];
     __syncthreads();
-    if (tid_x < 2)
-        accum[tid_x] += accum[tid_x+2];
+    if (tid_x < 2) accum[tid_x] += accum[tid_x + 2];
     __syncthreads();
-    if (tid_x < 1)
-        accum[tid_x] += accum[tid_x+1];
+    if (tid_x < 1) accum[tid_x] += accum[tid_x + 1];
     __syncthreads();
 
-    float len_sq = accum[0];
+    float len_sq  = accum[0];
     float len_inv = 1.0f / sqrtf(len_sq);
 
     for (int i = tid_x; i < histlen; i += bsz_x) {
-        desc[tid_y*histlen+i] *= len_inv;
+        desc[tid_y * histlen + i] *= len_inv;
     }
     __syncthreads();
 }
 
-__inline__ __device__ void normalizeGLOHDesc(
-    float* desc,
-    float* accum,
-    const int histlen)
-{
+__inline__ __device__ void normalizeGLOHDesc(float* desc, float* accum,
+                                             const int histlen) {
     int tid_x = threadIdx.x;
     int tid_y = threadIdx.y;
     int bsz_x = blockDim.x;
 
     for (int i = tid_x; i < histlen; i += bsz_x)
-        accum[i] = desc[tid_y*histlen+i]*desc[tid_y*histlen+i];
+        accum[i] = desc[tid_y * histlen + i] * desc[tid_y * histlen + i];
     __syncthreads();
 
-    if (tid_x < 128)
-        accum[tid_x] += accum[tid_x+128];
+    if (tid_x < 128) accum[tid_x] += accum[tid_x + 128];
     __syncthreads();
-    if (tid_x < 64)
-        accum[tid_x] += accum[tid_x+64];
+    if (tid_x < 64) accum[tid_x] += accum[tid_x + 64];
     __syncthreads();
-    if (tid_x < 32)
-        accum[tid_x] += accum[tid_x+32];
+    if (tid_x < 32) accum[tid_x] += accum[tid_x + 32];
     __syncthreads();
     if (tid_x < 16)
         // GLOH is 272-dimensional, accumulating last 16 descriptors
-        accum[tid_x] += accum[tid_x+16] + accum[tid_x+256];
+        accum[tid_x] += accum[tid_x + 16] + accum[tid_x + 256];
     __syncthreads();
-    if (tid_x < 8)
-        accum[tid_x] += accum[tid_x+8];
+    if (tid_x < 8) accum[tid_x] += accum[tid_x + 8];
     __syncthreads();
-    if (tid_x < 4)
-        accum[tid_x] += accum[tid_x+4];
+    if (tid_x < 4) accum[tid_x] += accum[tid_x + 4];
     __syncthreads();
-    if (tid_x < 2)
-        accum[tid_x] += accum[tid_x+2];
+    if (tid_x < 2) accum[tid_x] += accum[tid_x + 2];
     __syncthreads();
-    if (tid_x < 1)
-        accum[tid_x] += accum[tid_x+1];
+    if (tid_x < 1) accum[tid_x] += accum[tid_x + 1];
     __syncthreads();
 
-    float len_sq = accum[0];
+    float len_sq  = accum[0];
     float len_inv = 1.0f / sqrtf(len_sq);
 
     for (int i = tid_x; i < histlen; i += bsz_x) {
-        desc[tid_y*histlen+i] *= len_inv;
+        desc[tid_y * histlen + i] *= len_inv;
     }
     __syncthreads();
 }
 
 template<typename T>
-__global__ void sub(
-    Param<T> out,
-    CParam<T> in,
-    const unsigned nel,
-    const unsigned n_layers)
-{
+__global__ void sub(Param<T> out, CParam<T> in, const unsigned nel,
+                    const unsigned n_layers) {
     unsigned i = blockIdx.x * blockDim.x + threadIdx.x;
 
     if (i < nel) {
         for (unsigned l = 0; l < n_layers; l++)
-            out.ptr[l*nel + i] = in.ptr[(l+1)*nel + i] - in.ptr[l*nel + i];
+            out.ptr[l * nel + i] =
+                in.ptr[(l + 1) * nel + i] - in.ptr[l * nel + i];
     }
 }
 
-#define SCPTR(Y, X) (s_center[(Y) * s_i + (X)])
-#define SPPTR(Y, X) (s_prev[(Y) * s_i + (X)])
-#define SNPTR(Y, X) (s_next[(Y) * s_i + (X)])
-#define DPTR(Z, Y, X) (dog.ptr[(Z) * imel + (Y) * dim0 + (X)])
+#define SCPTR(Y, X) (s_center[(Y)*s_i + (X)])
+#define SPPTR(Y, X) (s_prev[(Y)*s_i + (X)])
+#define SNPTR(Y, X) (s_next[(Y)*s_i + (X)])
+#define DPTR(Z, Y, X) (dog.ptr[(Z)*imel + (Y)*dim0 + (X)])
 
 // Determines whether a pixel is a scale-space extremum by comparing it to its
 // 3x3x3 pixel neighborhood.
 template<typename T>
-__global__ void detectExtrema(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    unsigned* counter,
-    CParam<T> dog,
-    const unsigned max_feat,
-    const float threshold)
-{
+__global__ void detectExtrema(float* x_out, float* y_out, unsigned* layer_out,
+                              unsigned* counter, CParam<T> dog,
+                              const unsigned max_feat, const float threshold) {
     const int dim0 = dog.dims[0];
     const int dim1 = dog.dims[1];
     const int imel = dim0 * dim1;
@@ -349,71 +312,86 @@ __global__ void detectExtrema(
     const int tid_j = threadIdx.y;
     const int bsz_i = blockDim.x;
     const int bsz_j = blockDim.y;
-    const int i = blockIdx.x * bsz_i + tid_i+IMG_BORDER;
-    const int j = blockIdx.y * bsz_j + tid_j+IMG_BORDER;
+    const int i     = blockIdx.x * bsz_i + tid_i + IMG_BORDER;
+    const int j     = blockIdx.y * bsz_j + tid_j + IMG_BORDER;
 
-    const int x = tid_i+1;
-    const int y = tid_j+1;
+    const int x = tid_i + 1;
+    const int y = tid_j + 1;
 
     // One pixel border for each side
-    const int s_i = bsz_i+2;
-    const int s_j = bsz_j+2;
+    const int s_i = bsz_i + 2;
+    const int s_j = bsz_j + 2;
 
     SharedMemory<float> shared;
-    float* shrdMem = shared.getPointer();
+    float* shrdMem  = shared.getPointer();
     float* s_next   = shrdMem;
     float* s_center = shrdMem + s_i * s_j;
     float* s_prev   = shrdMem + s_i * s_j * 2;
 
-    for (int l = 1; l < dog.dims[2]-1; l++) {
-        const int s_i_half = s_i/2;
-        const int s_j_half = s_j/2;
-        if (tid_i < s_i_half && tid_j < s_j_half && i < dim0-IMG_BORDER+1 && j < dim1-IMG_BORDER+1) {
-            SNPTR(tid_j, tid_i) = DPTR(l+1, j-1, i-1);
-            SCPTR(tid_j, tid_i) = DPTR(l,   j-1, i-1);
-            SPPTR(tid_j, tid_i) = DPTR(l-1, j-1, i-1);
-
-            SNPTR(tid_j, tid_i+s_i_half) = DPTR((l+1), j-1, i-1+s_i_half);
-            SCPTR(tid_j, tid_i+s_i_half) = DPTR((l  ), j-1, i-1+s_i_half);
-            SPPTR(tid_j, tid_i+s_i_half) = DPTR((l-1), j-1, i-1+s_i_half);
-
-            SNPTR(tid_j+s_j_half, tid_i) = DPTR(l+1, j-1+s_j_half, i-1);
-            SCPTR(tid_j+s_j_half, tid_i) = DPTR(l,   j-1+s_j_half, i-1);
-            SPPTR(tid_j+s_j_half, tid_i) = DPTR(l-1, j-1+s_j_half, i-1);
-
-            SNPTR(tid_j+s_j_half, tid_i+s_i_half) = DPTR(l+1, j-1+s_j_half, i-1+s_i_half);
-            SCPTR(tid_j+s_j_half, tid_i+s_i_half) = DPTR(l,   j-1+s_j_half, i-1+s_i_half);
-            SPPTR(tid_j+s_j_half, tid_i+s_i_half) = DPTR(l-1, j-1+s_j_half, i-1+s_i_half);
+    for (int l = 1; l < dog.dims[2] - 1; l++) {
+        const int s_i_half = s_i / 2;
+        const int s_j_half = s_j / 2;
+        if (tid_i < s_i_half && tid_j < s_j_half && i < dim0 - IMG_BORDER + 1 &&
+            j < dim1 - IMG_BORDER + 1) {
+            SNPTR(tid_j, tid_i) = DPTR(l + 1, j - 1, i - 1);
+            SCPTR(tid_j, tid_i) = DPTR(l, j - 1, i - 1);
+            SPPTR(tid_j, tid_i) = DPTR(l - 1, j - 1, i - 1);
+
+            SNPTR(tid_j, tid_i + s_i_half) =
+                DPTR((l + 1), j - 1, i - 1 + s_i_half);
+            SCPTR(tid_j, tid_i + s_i_half) = DPTR((l), j - 1, i - 1 + s_i_half);
+            SPPTR(tid_j, tid_i + s_i_half) =
+                DPTR((l - 1), j - 1, i - 1 + s_i_half);
+
+            SNPTR(tid_j + s_j_half, tid_i) =
+                DPTR(l + 1, j - 1 + s_j_half, i - 1);
+            SCPTR(tid_j + s_j_half, tid_i) = DPTR(l, j - 1 + s_j_half, i - 1);
+            SPPTR(tid_j + s_j_half, tid_i) =
+                DPTR(l - 1, j - 1 + s_j_half, i - 1);
+
+            SNPTR(tid_j + s_j_half, tid_i + s_i_half) =
+                DPTR(l + 1, j - 1 + s_j_half, i - 1 + s_i_half);
+            SCPTR(tid_j + s_j_half, tid_i + s_i_half) =
+                DPTR(l, j - 1 + s_j_half, i - 1 + s_i_half);
+            SPPTR(tid_j + s_j_half, tid_i + s_i_half) =
+                DPTR(l - 1, j - 1 + s_j_half, i - 1 + s_i_half);
         }
         __syncthreads();
 
         float p = SCPTR(y, x);
 
-        if (abs(p) > threshold && i < dim0-IMG_BORDER && j < dim1-IMG_BORDER &&
-            ((p > 0 && p > SCPTR(y-1, x-1) && p > SCPTR(y-1, x) &&
-              p > SCPTR(y-1, x+1) && p > SCPTR(y, x-1) && p > SCPTR(y,   x+1)  &&
-              p > SCPTR(y+1, x-1) && p > SCPTR(y+1, x) && p > SCPTR(y+1, x+1)  &&
-              p > SPPTR(y-1, x-1) && p > SPPTR(y-1, x) && p > SPPTR(y-1, x+1)  &&
-              p > SPPTR(y,   x-1) && p > SPPTR(y  , x) && p > SPPTR(y,   x+1)  &&
-              p > SPPTR(y+1, x-1) && p > SPPTR(y+1, x) && p > SPPTR(y+1, x+1)  &&
-              p > SNPTR(y-1, x-1) && p > SNPTR(y-1, x) && p > SNPTR(y-1, x+1)  &&
-              p > SNPTR(y,   x-1) && p > SNPTR(y  , x) && p > SNPTR(y,   x+1)  &&
-              p > SNPTR(y+1, x-1) && p > SNPTR(y+1, x) && p > SNPTR(y+1, x+1)) ||
-             (p < 0 && p < SCPTR(y-1, x-1) && p < SCPTR(y-1, x) &&
-              p < SCPTR(y-1, x+1) && p < SCPTR(y, x-1) && p < SCPTR(y,   x+1)  &&
-              p < SCPTR(y+1, x-1) && p < SCPTR(y+1, x) && p < SCPTR(y+1, x+1)  &&
-              p < SPPTR(y-1, x-1) && p < SPPTR(y-1, x) && p < SPPTR(y-1, x+1)  &&
-              p < SPPTR(y,   x-1) && p < SPPTR(y  , x) && p < SPPTR(y,   x+1)  &&
-              p < SPPTR(y+1, x-1) && p < SPPTR(y+1, x) && p < SPPTR(y+1, x+1)  &&
-              p < SNPTR(y-1, x-1) && p < SNPTR(y-1, x) && p < SNPTR(y-1, x+1)  &&
-              p < SNPTR(y,   x-1) && p < SNPTR(y  , x) && p < SNPTR(y,   x+1)  &&
-              p < SNPTR(y+1, x-1) && p < SNPTR(y+1, x) && p < SNPTR(y+1, x+1)))) {
-
+        if (abs(p) > threshold && i < dim0 - IMG_BORDER &&
+            j < dim1 - IMG_BORDER &&
+            ((p > 0 && p > SCPTR(y - 1, x - 1) && p > SCPTR(y - 1, x) &&
+              p > SCPTR(y - 1, x + 1) && p > SCPTR(y, x - 1) &&
+              p > SCPTR(y, x + 1) && p > SCPTR(y + 1, x - 1) &&
+              p > SCPTR(y + 1, x) && p > SCPTR(y + 1, x + 1) &&
+              p > SPPTR(y - 1, x - 1) && p > SPPTR(y - 1, x) &&
+              p > SPPTR(y - 1, x + 1) && p > SPPTR(y, x - 1) &&
+              p > SPPTR(y, x) && p > SPPTR(y, x + 1) &&
+              p > SPPTR(y + 1, x - 1) && p > SPPTR(y + 1, x) &&
+              p > SPPTR(y + 1, x + 1) && p > SNPTR(y - 1, x - 1) &&
+              p > SNPTR(y - 1, x) && p > SNPTR(y - 1, x + 1) &&
+              p > SNPTR(y, x - 1) && p > SNPTR(y, x) && p > SNPTR(y, x + 1) &&
+              p > SNPTR(y + 1, x - 1) && p > SNPTR(y + 1, x) &&
+              p > SNPTR(y + 1, x + 1)) ||
+             (p < 0 && p < SCPTR(y - 1, x - 1) && p < SCPTR(y - 1, x) &&
+              p < SCPTR(y - 1, x + 1) && p < SCPTR(y, x - 1) &&
+              p < SCPTR(y, x + 1) && p < SCPTR(y + 1, x - 1) &&
+              p < SCPTR(y + 1, x) && p < SCPTR(y + 1, x + 1) &&
+              p < SPPTR(y - 1, x - 1) && p < SPPTR(y - 1, x) &&
+              p < SPPTR(y - 1, x + 1) && p < SPPTR(y, x - 1) &&
+              p < SPPTR(y, x) && p < SPPTR(y, x + 1) &&
+              p < SPPTR(y + 1, x - 1) && p < SPPTR(y + 1, x) &&
+              p < SPPTR(y + 1, x + 1) && p < SNPTR(y - 1, x - 1) &&
+              p < SNPTR(y - 1, x) && p < SNPTR(y - 1, x + 1) &&
+              p < SNPTR(y, x - 1) && p < SNPTR(y, x) && p < SNPTR(y, x + 1) &&
+              p < SNPTR(y + 1, x - 1) && p < SNPTR(y + 1, x) &&
+              p < SNPTR(y + 1, x + 1)))) {
             unsigned idx = atomicAdd(counter, 1u);
-            if (idx < max_feat)
-            {
-                x_out[idx] = (float)j;
-                y_out[idx] = (float)i;
+            if (idx < max_feat) {
+                x_out[idx]     = (float)j;
+                y_out[idx]     = (float)i;
                 layer_out[idx] = l;
             }
         }
@@ -424,75 +402,66 @@ __global__ void detectExtrema(
 #undef SCPTR
 #undef SPPTR
 #undef SNPTR
-#define CPTR(Y, X) (center_ptr[(Y) * dim0 + (X)])
-#define PPTR(Y, X) (prev_ptr[(Y) * dim0 + (X)])
-#define NPTR(Y, X) (next_ptr[(Y) * dim0 + (X)])
+#define CPTR(Y, X) (center_ptr[(Y)*dim0 + (X)])
+#define PPTR(Y, X) (prev_ptr[(Y)*dim0 + (X)])
+#define NPTR(Y, X) (next_ptr[(Y)*dim0 + (X)])
 
 // Interpolates a scale-space extremum's location and scale to subpixel
 // accuracy to form an image feature. Rejects features with low contrast.
 // Based on Section 4 of Lowe's paper.
 template<typename T>
 __global__ void interpolateExtrema(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    float* response_out,
-    float* size_out,
-    unsigned* counter,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const unsigned extrema_feat,
-    const CParam<T> dog_octave,
-    const unsigned max_feat,
-    const unsigned octave,
-    const unsigned n_layers,
-    const float contrast_thr,
-    const float edge_thr,
-    const float sigma,
-    const float img_scale)
-{
+    float* x_out, float* y_out, unsigned* layer_out, float* response_out,
+    float* size_out, unsigned* counter, const float* x_in, const float* y_in,
+    const unsigned* layer_in, const unsigned extrema_feat,
+    const CParam<T> dog_octave, const unsigned max_feat, const unsigned octave,
+    const unsigned n_layers, const float contrast_thr, const float edge_thr,
+    const float sigma, const float img_scale) {
     const unsigned f = blockIdx.x * blockDim.x + threadIdx.x;
 
     if (f < extrema_feat) {
-        const float first_deriv_scale = img_scale*0.5f;
+        const float first_deriv_scale  = img_scale * 0.5f;
         const float second_deriv_scale = img_scale;
-        const float cross_deriv_scale = img_scale*0.25f;
+        const float cross_deriv_scale  = img_scale * 0.25f;
 
         float xl = 0, xy = 0, xx = 0, contr = 0;
         int i = 0;
 
-        unsigned x = x_in[f];
-        unsigned y = y_in[f];
+        unsigned x     = x_in[f];
+        unsigned y     = y_in[f];
         unsigned layer = layer_in[f];
 
         const int dim0 = dog_octave.dims[0];
         const int dim1 = dog_octave.dims[1];
         const int imel = dim0 * dim1;
 
-        const T* prev_ptr   = dog_octave.ptr + (layer-1) * imel;
-        const T* center_ptr = dog_octave.ptr + (layer)   * imel;
-        const T* next_ptr   = dog_octave.ptr + (layer+1) * imel;
-
-        for(i = 0; i < MAX_INTERP_STEPS; i++) {
-            float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                           (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                           (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
-
-            float d2  = CPTR(x, y) * 2.f;
-            float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-            float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-            float dss = (NPTR(x, y  ) + PPTR(x, y  ) - d2) * second_deriv_scale;
-            float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                         CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
-            float dxs = (NPTR(x+1, y) - NPTR(x-1, y) -
-                         PPTR(x+1, y) + PPTR(x-1, y)) * cross_deriv_scale;
-            float dys = (NPTR(x, y+1) - NPTR(x-1, y-1) -
-                         PPTR(x, y-1) + PPTR(x-1, y-1)) * cross_deriv_scale;
-
-            float H[9] = {dxx, dxy, dxs,
-                          dxy, dyy, dys,
-                          dxs, dys, dss};
+        const T* prev_ptr   = dog_octave.ptr + (layer - 1) * imel;
+        const T* center_ptr = dog_octave.ptr + (layer)*imel;
+        const T* next_ptr   = dog_octave.ptr + (layer + 1) * imel;
+
+        for (i = 0; i < MAX_INTERP_STEPS; i++) {
+            float dD[3] = {
+                (float)(CPTR(x + 1, y) - CPTR(x - 1, y)) * first_deriv_scale,
+                (float)(CPTR(x, y + 1) - CPTR(x, y - 1)) * first_deriv_scale,
+                (float)(NPTR(x, y) - PPTR(x, y)) * first_deriv_scale};
+
+            float d2 = CPTR(x, y) * 2.f;
+            float dxx =
+                (CPTR(x + 1, y) + CPTR(x - 1, y) - d2) * second_deriv_scale;
+            float dyy =
+                (CPTR(x, y + 1) + CPTR(x, y - 1) - d2) * second_deriv_scale;
+            float dss = (NPTR(x, y) + PPTR(x, y) - d2) * second_deriv_scale;
+            float dxy = (CPTR(x + 1, y + 1) - CPTR(x - 1, y + 1) -
+                         CPTR(x + 1, y - 1) + CPTR(x - 1, y - 1)) *
+                        cross_deriv_scale;
+            float dxs = (NPTR(x + 1, y) - NPTR(x - 1, y) - PPTR(x + 1, y) +
+                         PPTR(x - 1, y)) *
+                        cross_deriv_scale;
+            float dys = (NPTR(x, y + 1) - NPTR(x - 1, y - 1) - PPTR(x, y - 1) +
+                         PPTR(x - 1, y - 1)) *
+                        cross_deriv_scale;
+
+            float H[9] = {dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss};
 
             float X[3];
             gaussianElimination<3>(H, dD, X);
@@ -501,57 +470,57 @@ __global__ void interpolateExtrema(
             xy = -X[1];
             xx = -X[0];
 
-            if (abs(xl) < 0.5f && abs(xy) < 0.5f && abs(xx) < 0.5f)
-                break;
+            if (abs(xl) < 0.5f && abs(xy) < 0.5f && abs(xx) < 0.5f) break;
 
             x += round(xx);
             y += round(xy);
             layer += round(xl);
 
-            if (layer < 1 || layer > n_layers ||
-                x < IMG_BORDER || x >= dim1 - IMG_BORDER ||
-                y < IMG_BORDER || y >= dim0 - IMG_BORDER)
+            if (layer < 1 || layer > n_layers || x < IMG_BORDER ||
+                x >= dim1 - IMG_BORDER || y < IMG_BORDER ||
+                y >= dim0 - IMG_BORDER)
                 return;
         }
 
         // ensure convergence of interpolation
-        if (i >= MAX_INTERP_STEPS)
-            return;
+        if (i >= MAX_INTERP_STEPS) return;
 
-        float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                       (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                       (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
+        float dD[3] = {
+            (float)(CPTR(x + 1, y) - CPTR(x - 1, y)) * first_deriv_scale,
+            (float)(CPTR(x, y + 1) - CPTR(x, y - 1)) * first_deriv_scale,
+            (float)(NPTR(x, y) - PPTR(x, y)) * first_deriv_scale};
         float X[3] = {xx, xy, xl};
 
-        float P = dD[0]*X[0] + dD[1]*X[1] + dD[2]*X[2];
+        float P = dD[0] * X[0] + dD[1] * X[1] + dD[2] * X[2];
 
-        contr = CPTR(x, y)*img_scale + P * 0.5f;
-        if (abs(contr) < (contrast_thr / n_layers))
-            return;
+        contr = CPTR(x, y) * img_scale + P * 0.5f;
+        if (abs(contr) < (contrast_thr / n_layers)) return;
 
         // principal curvatures are computed using the trace and det of Hessian
         float d2  = CPTR(x, y) * 2.f;
-        float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-        float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-        float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                     CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
+        float dxx = (CPTR(x + 1, y) + CPTR(x - 1, y) - d2) * second_deriv_scale;
+        float dyy = (CPTR(x, y + 1) + CPTR(x, y - 1) - d2) * second_deriv_scale;
+        float dxy = (CPTR(x + 1, y + 1) - CPTR(x - 1, y + 1) -
+                     CPTR(x + 1, y - 1) + CPTR(x - 1, y - 1)) *
+                    cross_deriv_scale;
 
-        float tr = dxx + dyy;
+        float tr  = dxx + dyy;
         float det = dxx * dyy - dxy * dxy;
 
         // add FLT_EPSILON for double-precision compatibility
-        if (det <= 0 || tr*tr*edge_thr >= (edge_thr + 1)*(edge_thr + 1)*det+FLT_EPSILON)
+        if (det <= 0 || tr * tr * edge_thr >=
+                            (edge_thr + 1) * (edge_thr + 1) * det + FLT_EPSILON)
             return;
 
         unsigned ridx = atomicAdd(counter, 1u);
 
-        if (ridx < max_feat)
-        {
-            x_out[ridx] = (x + xx) * (1 << octave);
-            y_out[ridx] = (y + xy) * (1 << octave);
-            layer_out[ridx] = layer;
+        if (ridx < max_feat) {
+            x_out[ridx]        = (x + xx) * (1 << octave);
+            y_out[ridx]        = (y + xy) * (1 << octave);
+            layer_out[ridx]    = layer;
             response_out[ridx] = abs(contr);
-            size_out[ridx] = sigma*pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
+            size_out[ridx] =
+                sigma * pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
         }
     }
 }
@@ -561,70 +530,51 @@ __global__ void interpolateExtrema(
 #undef NPTR
 
 // Remove duplicate keypoints
-__global__ void removeDuplicates(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    float* response_out,
-    float* size_out,
-    unsigned* counter,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const unsigned total_feat)
-{
+__global__ void removeDuplicates(float* x_out, float* y_out,
+                                 unsigned* layer_out, float* response_out,
+                                 float* size_out, unsigned* counter,
+                                 const float* x_in, const float* y_in,
+                                 const unsigned* layer_in,
+                                 const float* response_in, const float* size_in,
+                                 const unsigned total_feat) {
     const unsigned f = blockIdx.x * blockDim.x + threadIdx.x;
 
-    if (f >= total_feat)
-        return;
+    if (f >= total_feat) return;
 
     float prec_fctr = 1e4f;
 
-    if (f < total_feat-1) {
-        if (round(x_in[f]*prec_fctr) == round(x_in[f+1]*prec_fctr) &&
-            round(y_in[f]*prec_fctr) == round(y_in[f+1]*prec_fctr) &&
-            layer_in[f] == layer_in[f+1] &&
-            round(response_in[f]*prec_fctr) == round(response_in[f+1]*prec_fctr) &&
-            round(size_in[f]*prec_fctr) == round(size_in[f+1]*prec_fctr))
+    if (f < total_feat - 1) {
+        if (round(x_in[f] * prec_fctr) == round(x_in[f + 1] * prec_fctr) &&
+            round(y_in[f] * prec_fctr) == round(y_in[f + 1] * prec_fctr) &&
+            layer_in[f] == layer_in[f + 1] &&
+            round(response_in[f] * prec_fctr) ==
+                round(response_in[f + 1] * prec_fctr) &&
+            round(size_in[f] * prec_fctr) == round(size_in[f + 1] * prec_fctr))
             return;
     }
 
     unsigned idx = atomicAdd(counter, 1);
 
-    x_out[idx] = x_in[f];
-    y_out[idx] = y_in[f];
-    layer_out[idx] = layer_in[f];
+    x_out[idx]        = x_in[f];
+    y_out[idx]        = y_in[f];
+    layer_out[idx]    = layer_in[f];
     response_out[idx] = response_in[f];
-    size_out[idx] = size_in[f];
+    size_out[idx]     = size_in[f];
 }
 
-#define IPTR(Y, X) (img_ptr[(Y) * dim0 + (X)])
+#define IPTR(Y, X) (img_ptr[(Y)*dim0 + (X)])
 
 // Computes a canonical orientation for each image feature in an array.  Based
 // on Section 5 of Lowe's paper.  This function adds features to the array when
 // there is more than one dominant orientation at a given feature location.
 template<typename T>
 __global__ void calcOrientation(
-    float* x_out,
-    float* y_out,
-    unsigned* layer_out,
-    float* response_out,
-    float* size_out,
-    float* ori_out,
-    unsigned* counter,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const unsigned total_feat,
-    const CParam<T> gauss_octave,
-    const unsigned max_feat,
-    const unsigned octave,
-    const bool double_input)
-{
+    float* x_out, float* y_out, unsigned* layer_out, float* response_out,
+    float* size_out, float* ori_out, unsigned* counter, const float* x_in,
+    const float* y_in, const unsigned* layer_in, const float* response_in,
+    const float* size_in, const unsigned total_feat,
+    const CParam<T> gauss_octave, const unsigned max_feat,
+    const unsigned octave, const bool double_input) {
     const int tid_x = threadIdx.x;
     const int tid_y = threadIdx.y;
     const int bsz_x = blockDim.x;
@@ -635,13 +585,13 @@ __global__ void calcOrientation(
     const int n = ORI_HIST_BINS;
 
     SharedMemory<float> shared;
-    float* shrdMem = shared.getPointer();
-    float* hist = shrdMem;
-    float* temphist = shrdMem + n*8;
+    float* shrdMem  = shared.getPointer();
+    float* hist     = shrdMem;
+    float* temphist = shrdMem + n * 8;
 
     // Initialize temporary histogram
     for (int i = tid_x; i < ORI_HIST_BINS; i += bsz_x)
-        hist[tid_y*n + i] = 0.f;
+        hist[tid_y * n + i] = 0.f;
     __syncthreads();
 
     float real_x, real_y, response, size;
@@ -649,20 +599,20 @@ __global__ void calcOrientation(
 
     if (f < total_feat) {
         // Load keypoint information
-        real_x = x_in[f];
-        real_y = y_in[f];
-        layer = layer_in[f];
+        real_x   = x_in[f];
+        real_y   = y_in[f];
+        layer    = layer_in[f];
         response = response_in[f];
-        size = size_in[f];
+        size     = size_in[f];
 
         const int pt_x = (int)round(real_x / (1 << octave));
         const int pt_y = (int)round(real_y / (1 << octave));
 
         // Calculate auxiliary parameters
-        const float scl_octv = size*0.5f / (1 << octave);
-        const int radius = (int)round(ORI_RADIUS * scl_octv);
-        const float sigma = ORI_SIG_FCTR * scl_octv;
-        const int len = (radius*2+1);
+        const float scl_octv  = size * 0.5f / (1 << octave);
+        const int radius      = (int)round(ORI_RADIUS * scl_octv);
+        const float sigma     = ORI_SIG_FCTR * scl_octv;
+        const int len         = (radius * 2 + 1);
         const float exp_denom = 2.f * sigma * sigma;
 
         const int dim0 = gauss_octave.dims[0];
@@ -673,87 +623,97 @@ __global__ void calcOrientation(
         const T* img_ptr = gauss_octave.ptr + layer * imel;
 
         // Calculate orientation histogram
-        for (int l = tid_x; l < len*len; l += bsz_x) {
+        for (int l = tid_x; l < len * len; l += bsz_x) {
             int i = l / len - radius;
             int j = l % len - radius;
 
             int y = pt_y + i;
             int x = pt_x + j;
-            if (y < 1 || y >= dim0 - 1 ||
-                x < 1 || x >= dim1 - 1)
-                continue;
+            if (y < 1 || y >= dim0 - 1 || x < 1 || x >= dim1 - 1) continue;
 
-            float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-            float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+            float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
 
-            float mag = sqrt(dx*dx+dy*dy);
-            float ori = atan2(dy,dx);
-            float w = exp(-(i*i + j*j)/exp_denom);
+            float mag = sqrt(dx * dx + dy * dy);
+            float ori = atan2(dy, dx);
+            float w   = exp(-(i * i + j * j) / exp_denom);
 
-            int bin = round(n*(ori+PI_VAL)/(2.f*PI_VAL));
-            bin = bin < n ? bin : 0;
+            int bin = round(n * (ori + PI_VAL) / (2.f * PI_VAL));
+            bin     = bin < n ? bin : 0;
 
-            atomicAdd(&hist[tid_y*n+bin], w*mag);
+            atomicAdd(&hist[tid_y * n + bin], w * mag);
         }
     }
     __syncthreads();
 
     for (int i = 0; i < SMOOTH_ORI_PASSES; i++) {
         for (int j = tid_x; j < n; j += bsz_x) {
-            temphist[tid_y*n+j] = hist[tid_y*n+j];
+            temphist[tid_y * n + j] = hist[tid_y * n + j];
         }
         __syncthreads();
         for (int j = tid_x; j < n; j += bsz_x) {
-            float prev = (j == 0) ? temphist[tid_y*n+n-1] : temphist[tid_y*n+j-1];
-            float next = (j+1 == n) ? temphist[tid_y*n] : temphist[tid_y*n+j+1];
-            hist[tid_y*n+j] = 0.25f * prev + 0.5f * temphist[tid_y*n+j] + 0.25f * next;
+            float prev = (j == 0) ? temphist[tid_y * n + n - 1]
+                                  : temphist[tid_y * n + j - 1];
+            float next = (j + 1 == n) ? temphist[tid_y * n]
+                                      : temphist[tid_y * n + j + 1];
+            hist[tid_y * n + j] =
+                0.25f * prev + 0.5f * temphist[tid_y * n + j] + 0.25f * next;
         }
         __syncthreads();
     }
 
     for (int i = tid_x; i < n; i += bsz_x)
-        temphist[tid_y*n+i] = hist[tid_y*n+i];
+        temphist[tid_y * n + i] = hist[tid_y * n + i];
     __syncthreads();
 
     if (tid_x < 16)
-        temphist[tid_y*n+tid_x] = fmax(hist[tid_y*n+tid_x], hist[tid_y*n+tid_x+16]);
+        temphist[tid_y * n + tid_x] =
+            fmax(hist[tid_y * n + tid_x], hist[tid_y * n + tid_x + 16]);
     __syncthreads();
     if (tid_x < 8)
-        temphist[tid_y*n+tid_x] = fmax(temphist[tid_y*n+tid_x], temphist[tid_y*n+tid_x+8]);
+        temphist[tid_y * n + tid_x] =
+            fmax(temphist[tid_y * n + tid_x], temphist[tid_y * n + tid_x + 8]);
     __syncthreads();
     if (tid_x < 4) {
-        temphist[tid_y*n+tid_x] = fmax(temphist[tid_y*n+tid_x], hist[tid_y*n+tid_x+32]);
-        temphist[tid_y*n+tid_x] = fmax(temphist[tid_y*n+tid_x], temphist[tid_y*n+tid_x+4]);
+        temphist[tid_y * n + tid_x] =
+            fmax(temphist[tid_y * n + tid_x], hist[tid_y * n + tid_x + 32]);
+        temphist[tid_y * n + tid_x] =
+            fmax(temphist[tid_y * n + tid_x], temphist[tid_y * n + tid_x + 4]);
     }
     __syncthreads();
     if (tid_x < 2)
-        temphist[tid_y*n+tid_x] = fmax(temphist[tid_y*n+tid_x], temphist[tid_y*n+tid_x+2]);
+        temphist[tid_y * n + tid_x] =
+            fmax(temphist[tid_y * n + tid_x], temphist[tid_y * n + tid_x + 2]);
     __syncthreads();
     if (tid_x < 1)
-        temphist[tid_y*n+tid_x] = fmax(temphist[tid_y*n+tid_x], temphist[tid_y*n+tid_x+1]);
+        temphist[tid_y * n + tid_x] =
+            fmax(temphist[tid_y * n + tid_x], temphist[tid_y * n + tid_x + 1]);
     __syncthreads();
-    float omax = temphist[tid_y*n];
+    float omax = temphist[tid_y * n];
 
     if (f < total_feat) {
         float mag_thr = (float)(omax * ORI_PEAK_RATIO);
         int l, r;
-        for (int j = tid_x; j < n; j+=bsz_x) {
+        for (int j = tid_x; j < n; j += bsz_x) {
             l = (j == 0) ? n - 1 : j - 1;
             r = (j + 1) % n;
-            if (hist[tid_y*n+j] > hist[tid_y*n+l] &&
-                hist[tid_y*n+j] > hist[tid_y*n+r] &&
-                hist[tid_y*n+j] >= mag_thr) {
+            if (hist[tid_y * n + j] > hist[tid_y * n + l] &&
+                hist[tid_y * n + j] > hist[tid_y * n + r] &&
+                hist[tid_y * n + j] >= mag_thr) {
                 int idx = atomicAdd(counter, 1);
 
                 if (idx < max_feat) {
-                    float bin = j + 0.5f * (hist[tid_y*n+l] - hist[tid_y*n+r]) /
-                                (hist[tid_y*n+l] - 2.0f*hist[tid_y*n+j] + hist[tid_y*n+r]);
+                    float bin =
+                        j +
+                        0.5f * (hist[tid_y * n + l] - hist[tid_y * n + r]) /
+                            (hist[tid_y * n + l] - 2.0f * hist[tid_y * n + j] +
+                             hist[tid_y * n + r]);
                     bin = (bin < 0.0f) ? bin + n : (bin >= n) ? bin - n : bin;
-                    float ori = 360.f - ((360.f/n) * bin);
+                    float ori = 360.f - ((360.f / n) * bin);
 
                     float new_real_x = real_x;
                     float new_real_y = real_y;
-                    float new_size = size;
+                    float new_size   = size;
 
                     if (double_input) {
                         float scale = 0.5f;
@@ -762,12 +722,12 @@ __global__ void calcOrientation(
                         new_size *= scale;
                     }
 
-                    x_out[idx] = new_real_x;
-                    y_out[idx] = new_real_y;
-                    layer_out[idx] = layer;
+                    x_out[idx]        = new_real_x;
+                    y_out[idx]        = new_real_y;
+                    layer_out[idx]    = layer;
                     response_out[idx] = response;
-                    size_out[idx] = new_size;
-                    ori_out[idx] = ori;
+                    size_out[idx]     = new_size;
+                    ori_out[idx]      = ori;
                 }
             }
         }
@@ -778,22 +738,11 @@ __global__ void calcOrientation(
 // of Lowe's paper.
 template<typename T>
 __global__ void computeDescriptor(
-    float* desc_out,
-    const unsigned desc_len,
-    const unsigned histsz,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const float* ori_in,
-    const unsigned total_feat,
-    const CParam<T> gauss_octave,
-    const int d,
-    const int n,
-    const float scale,
-    const int n_layers)
-{
+    float* desc_out, const unsigned desc_len, const unsigned histsz,
+    const float* x_in, const float* y_in, const unsigned* layer_in,
+    const float* response_in, const float* size_in, const float* ori_in,
+    const unsigned total_feat, const CParam<T> gauss_octave, const int d,
+    const int n, const float scale, const int n_layers) {
     const int tid_x = threadIdx.x;
     const int tid_y = threadIdx.y;
     const int bsz_x = blockDim.x;
@@ -803,20 +752,20 @@ __global__ void computeDescriptor(
 
     SharedMemory<float> shared;
     float* shrdMem = shared.getPointer();
-    float* desc = shrdMem;
-    float* accum = shrdMem + desc_len * histsz;
+    float* desc    = shrdMem;
+    float* accum   = shrdMem + desc_len * histsz;
 
-    for (int i = tid_x; i < desc_len*histsz; i += bsz_x)
-        desc[tid_y*desc_len+i] = 0.f;
+    for (int i = tid_x; i < desc_len * histsz; i += bsz_x)
+        desc[tid_y * desc_len + i] = 0.f;
     __syncthreads();
 
     if (f < total_feat) {
         const unsigned layer = layer_in[f];
-        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-        const float size = size_in[f];
-        const int fx = round(x_in[f] * scale);
-        const int fy = round(y_in[f] * scale);
+        float ori            = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori                  = (ori > PI_VAL) ? ori - PI_VAL * 2 : ori;
+        const float size     = size_in[f];
+        const int fx         = round(x_in[f] * scale);
+        const int fy         = round(y_in[f] * scale);
 
         const int dim0 = gauss_octave.dims[0];
         const int dim1 = gauss_octave.dims[1];
@@ -825,18 +774,18 @@ __global__ void computeDescriptor(
         // Points img to correct Gaussian pyramid layer
         const T* img_ptr = gauss_octave.ptr + layer * imel;
 
-        float cos_t = cosf(ori);
-        float sin_t = sinf(ori);
+        float cos_t        = cosf(ori);
+        float sin_t        = sinf(ori);
         float bins_per_rad = n / (PI_VAL * 2.f);
-        float exp_denom = d * d * 0.5f;
-        float hist_width = DESCR_SCL_FCTR * size * scale * 0.5f;
-        int radius = hist_width * sqrtf(2.f) * (d + 1.f) * 0.5f + 0.5f;
+        float exp_denom    = d * d * 0.5f;
+        float hist_width   = DESCR_SCL_FCTR * size * scale * 0.5f;
+        int radius         = hist_width * sqrtf(2.f) * (d + 1.f) * 0.5f + 0.5f;
 
-        int len = radius*2+1;
+        int len            = radius * 2 + 1;
         const int hist_off = (tid_x % histsz) * desc_len;
 
         // Calculate orientation histogram
-        for (int l = tid_x; l < len*len; l += bsz_x) {
+        for (int l = tid_x; l < len * len; l += bsz_x) {
             int i = l / len - radius;
             int j = l % len - radius;
 
@@ -845,24 +794,22 @@ __global__ void computeDescriptor(
 
             float x_rot = (j * cos_t - i * sin_t) / hist_width;
             float y_rot = (j * sin_t + i * cos_t) / hist_width;
-            float xbin = x_rot + d/2 - 0.5f;
-            float ybin = y_rot + d/2 - 0.5f;
+            float xbin  = x_rot + d / 2 - 0.5f;
+            float ybin  = y_rot + d / 2 - 0.5f;
 
-            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d &&
-                y > 0 && y < dim0 - 1 && x > 0 && x < dim1 - 1) {
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d && y > 0 &&
+                y < dim0 - 1 && x > 0 && x < dim1 - 1) {
+                float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+                float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
 
-                float grad_mag = sqrtf(dx*dx + dy*dy);
+                float grad_mag = sqrtf(dx * dx + dy * dy);
                 float grad_ori = atan2f(dy, dx) - ori;
-                while (grad_ori < 0.0f)
-                    grad_ori += PI_VAL*2;
-                while (grad_ori >= PI_VAL*2)
-                    grad_ori -= PI_VAL*2;
+                while (grad_ori < 0.0f) grad_ori += PI_VAL * 2;
+                while (grad_ori >= PI_VAL * 2) grad_ori -= PI_VAL * 2;
 
-                float w = exp(-(x_rot*x_rot + y_rot*y_rot) / exp_denom);
+                float w    = exp(-(x_rot * x_rot + y_rot * y_rot) / exp_denom);
                 float obin = grad_ori * bins_per_rad;
-                float mag = grad_mag*w;
+                float mag  = grad_mag * w;
 
                 int x0 = floor(xbin);
                 int y0 = floor(ybin);
@@ -874,19 +821,24 @@ __global__ void computeDescriptor(
                 for (int yl = 0; yl <= 1; yl++) {
                     int yb = y0 + yl;
                     if (yb >= 0 && yb < d) {
-	                    float v_y = mag * ((yl == 0) ? 1.0f - ybin : ybin);
-	                    for (int xl = 0; xl <= 1; xl++) {
-	                        int xb = x0 + xl;
-	                        if (xb >= 0 && xb < d) {
-		                        float v_x = v_y * ((xl == 0) ? 1.0f - xbin : xbin);
-		                        for (int ol = 0; ol <= 1; ol++) {
-		                            int ob = (o0 + ol) % n;
-		                            float v_o = v_x * ((ol == 0) ? 1.0f - obin : obin);
-		                            atomicAdd(&desc[hist_off + tid_y*desc_len + (yb*d + xb)*n + ob], v_o);
-		                        }
-		                    }
-	                    }
-	                }
+                        float v_y = mag * ((yl == 0) ? 1.0f - ybin : ybin);
+                        for (int xl = 0; xl <= 1; xl++) {
+                            int xb = x0 + xl;
+                            if (xb >= 0 && xb < d) {
+                                float v_x =
+                                    v_y * ((xl == 0) ? 1.0f - xbin : xbin);
+                                for (int ol = 0; ol <= 1; ol++) {
+                                    int ob = (o0 + ol) % n;
+                                    float v_o =
+                                        v_x * ((ol == 0) ? 1.0f - obin : obin);
+                                    atomicAdd(
+                                        &desc[hist_off + tid_y * desc_len +
+                                              (yb * d + xb) * n + ob],
+                                        v_o);
+                                }
+                            }
+                        }
+                    }
                 }
             }
         }
@@ -894,20 +846,20 @@ __global__ void computeDescriptor(
     __syncthreads();
 
     // Combine histograms (reduces previous atomicAdd overhead)
-    for (int l = tid_x; l < desc_len*4; l += bsz_x)
-        desc[l] += desc[l+4*desc_len];
+    for (int l = tid_x; l < desc_len * 4; l += bsz_x)
+        desc[l] += desc[l + 4 * desc_len];
     __syncthreads();
-    for (int l = tid_x; l < desc_len*2; l += bsz_x)
-        desc[l    ] += desc[l+2*desc_len];
+    for (int l = tid_x; l < desc_len * 2; l += bsz_x)
+        desc[l] += desc[l + 2 * desc_len];
     __syncthreads();
-    for (int l = tid_x; l < desc_len; l += bsz_x)
-        desc[l] += desc[l+desc_len];
+    for (int l = tid_x; l < desc_len; l += bsz_x) desc[l] += desc[l + desc_len];
     __syncthreads();
 
     normalizeDesc(desc, accum, desc_len);
 
     for (int i = tid_x; i < desc_len; i += bsz_x)
-        desc[tid_y*desc_len+i] = min(desc[tid_y*desc_len+i], DESC_MAG_THR);
+        desc[tid_y * desc_len + i] =
+            min(desc[tid_y * desc_len + i], DESC_MAG_THR);
     __syncthreads();
 
     normalizeDesc(desc, accum, desc_len);
@@ -915,32 +867,21 @@ __global__ void computeDescriptor(
     if (f < total_feat) {
         // Calculate final descriptor values
         for (int k = tid_x; k < desc_len; k += bsz_x)
-            desc_out[f*desc_len+k] = round(min(255.f, desc[tid_y*desc_len+k] * INT_DESCR_FCTR));
+            desc_out[f * desc_len + k] =
+                round(min(255.f, desc[tid_y * desc_len + k] * INT_DESCR_FCTR));
     }
 }
 
-// Computes GLOH feature descriptors for features in an array. Based on Section III-B
-// of Mikolajczyk and Schmid paper.
+// Computes GLOH feature descriptors for features in an array. Based on Section
+// III-B of Mikolajczyk and Schmid paper.
 template<typename T>
 __global__ void computeGLOHDescriptor(
-    float* desc_out,
-    const unsigned desc_len,
-    const unsigned histsz,
-    const float* x_in,
-    const float* y_in,
-    const unsigned* layer_in,
-    const float* response_in,
-    const float* size_in,
-    const float* ori_in,
-    const unsigned total_feat,
-    const CParam<T> gauss_octave,
-    const int d,
-    const unsigned rb,
-    const unsigned ab,
-    const unsigned hb,
-    const float scale,
-    const int n_layers)
-{
+    float* desc_out, const unsigned desc_len, const unsigned histsz,
+    const float* x_in, const float* y_in, const unsigned* layer_in,
+    const float* response_in, const float* size_in, const float* ori_in,
+    const unsigned total_feat, const CParam<T> gauss_octave, const int d,
+    const unsigned rb, const unsigned ab, const unsigned hb, const float scale,
+    const int n_layers) {
     const int tid_x = threadIdx.x;
     const int tid_y = threadIdx.y;
     const int bsz_x = blockDim.x;
@@ -950,20 +891,20 @@ __global__ void computeGLOHDescriptor(
 
     SharedMemory<float> shared;
     float* shrdMem = shared.getPointer();
-    float* desc = shrdMem;
-    float* accum = shrdMem + desc_len * histsz;
+    float* desc    = shrdMem;
+    float* accum   = shrdMem + desc_len * histsz;
 
-    for (int i = tid_x; i < desc_len*histsz; i += bsz_x)
-        desc[tid_y*desc_len+i] = 0.f;
+    for (int i = tid_x; i < desc_len * histsz; i += bsz_x)
+        desc[tid_y * desc_len + i] = 0.f;
     __syncthreads();
 
     if (f < total_feat) {
         const unsigned layer = layer_in[f];
-        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-        const float size = size_in[f];
-        const int fx = round(x_in[f] * scale);
-        const int fy = round(y_in[f] * scale);
+        float ori            = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori                  = (ori > PI_VAL) ? ori - PI_VAL * 2 : ori;
+        const float size     = size_in[f];
+        const int fx         = round(x_in[f] * scale);
+        const int fy         = round(y_in[f] * scale);
 
         const int dim0 = gauss_octave.dims[0];
         const int dim1 = gauss_octave.dims[1];
@@ -972,11 +913,11 @@ __global__ void computeGLOHDescriptor(
         // Points img to correct Gaussian pyramid layer
         const T* img_ptr = gauss_octave.ptr + layer * imel;
 
-        float cos_t = cosf(ori);
-        float sin_t = sinf(ori);
-        float hist_bins_per_rad = hb / (PI_VAL * 2.f);
+        float cos_t              = cosf(ori);
+        float sin_t              = sinf(ori);
+        float hist_bins_per_rad  = hb / (PI_VAL * 2.f);
         float polar_bins_per_rad = ab / (PI_VAL * 2.f);
-        float exp_denom = GLOHRadii[rb-1] * 0.5f;
+        float exp_denom          = GLOHRadii[rb - 1] * 0.5f;
 
         float hist_width = DESCR_SCL_FCTR * size * scale * 0.5f;
 
@@ -987,14 +928,14 @@ __global__ void computeGLOHDescriptor(
         // (rw) in the range of 0.25f-0.75f gives different results,
         // increasing it tends to show a better recall rate but with a
         // smaller amount of correct matches
-        //float rw = 0.5f;
-        //int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
+        // float rw = 0.5f;
+        // int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
 
-        int len = radius*2+1;
+        int len            = radius * 2 + 1;
         const int hist_off = (tid_x % histsz) * desc_len;
 
         // Calculate orientation histogram
-        for (int l = tid_x; l < len*len; l += bsz_x) {
+        for (int l = tid_x; l < len * len; l += bsz_x) {
             int i = l / len - radius;
             int j = l % len - radius;
 
@@ -1004,33 +945,36 @@ __global__ void computeGLOHDescriptor(
             float x_rot = (j * cos_t - i * sin_t);
             float y_rot = (j * sin_t + i * cos_t);
 
-            float r = sqrt(x_rot*x_rot + y_rot*y_rot) / radius * GLOHRadii[rb-1];
+            float r = sqrt(x_rot * x_rot + y_rot * y_rot) / radius *
+                      GLOHRadii[rb - 1];
             float theta = atan2(y_rot, x_rot);
-            while (theta < 0.0f)
-                theta += PI_VAL*2;
-            while (theta >= PI_VAL*2)
-                theta -= PI_VAL*2;
+            while (theta < 0.0f) theta += PI_VAL * 2;
+            while (theta >= PI_VAL * 2) theta -= PI_VAL * 2;
 
             float tbin = theta * polar_bins_per_rad;
-            float rbin = (r < GLOHRadii[0]) ? r / GLOHRadii[0] :
-                         ((r < GLOHRadii[1]) ? 1 + (r - GLOHRadii[0]) / (float)(GLOHRadii[1] - GLOHRadii[0]) :
-                         min(2 + (r - GLOHRadii[1]) / (float)(GLOHRadii[2] - GLOHRadii[1]), 3.f-FLT_EPSILON));
-
-            if (r <= GLOHRadii[rb-1] &&
-                y > 0 && y < dim0 - 1 && x > 0 && x < dim1 - 1) {
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
-
-                float grad_mag = sqrtf(dx*dx + dy*dy);
+            float rbin =
+                (r < GLOHRadii[0])
+                    ? r / GLOHRadii[0]
+                    : ((r < GLOHRadii[1])
+                           ? 1 + (r - GLOHRadii[0]) /
+                                     (float)(GLOHRadii[1] - GLOHRadii[0])
+                           : min(2 + (r - GLOHRadii[1]) /
+                                         (float)(GLOHRadii[2] - GLOHRadii[1]),
+                                 3.f - FLT_EPSILON));
+
+            if (r <= GLOHRadii[rb - 1] && y > 0 && y < dim0 - 1 && x > 0 &&
+                x < dim1 - 1) {
+                float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+                float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
+
+                float grad_mag = sqrtf(dx * dx + dy * dy);
                 float grad_ori = atan2f(dy, dx) - ori;
-                while (grad_ori < 0.0f)
-                    grad_ori += PI_VAL*2;
-                while (grad_ori >= PI_VAL*2)
-                    grad_ori -= PI_VAL*2;
+                while (grad_ori < 0.0f) grad_ori += PI_VAL * 2;
+                while (grad_ori >= PI_VAL * 2) grad_ori -= PI_VAL * 2;
 
-                float w = exp(-r / exp_denom);
+                float w    = exp(-r / exp_denom);
                 float obin = grad_ori * hist_bins_per_rad;
-                float mag = grad_mag*w;
+                float mag  = grad_mag * w;
 
                 int t0 = floor(tbin);
                 int r0 = floor(rbin);
@@ -1040,17 +984,23 @@ __global__ void computeGLOHDescriptor(
                 obin -= o0;
 
                 for (int rl = 0; rl <= 1; rl++) {
-                    int rb = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
+                    int rb    = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
                     float v_r = mag * ((rl == 0) ? 1.0f - rbin : rbin);
                     if (rb >= 0 && rb <= 2) {
                         for (int tl = 0; tl <= 1; tl++) {
-                            int tb = (t0 + tl) % ab;
+                            int tb    = (t0 + tl) % ab;
                             float v_t = v_r * ((tl == 0) ? 1.0f - tbin : tbin);
                             for (int ol = 0; ol <= 1; ol++) {
                                 int ob = (o0 + ol) % hb;
-                                float v_o = v_t * ((ol == 0) ? 1.0f - obin : obin);
-                                unsigned idx = (rb > 0) * (hb + ((rb-1) * ab + tb)*hb) + ob;
-                                atomicAdd(&desc[hist_off + tid_y*desc_len + idx], v_o);
+                                float v_o =
+                                    v_t * ((ol == 0) ? 1.0f - obin : obin);
+                                unsigned idx =
+                                    (rb > 0) *
+                                        (hb + ((rb - 1) * ab + tb) * hb) +
+                                    ob;
+                                atomicAdd(
+                                    &desc[hist_off + tid_y * desc_len + idx],
+                                    v_o);
                             }
                         }
                     }
@@ -1061,20 +1011,20 @@ __global__ void computeGLOHDescriptor(
     __syncthreads();
 
     // Combine histograms (reduces previous atomicAdd overhead)
-    for (int l = tid_x; l < desc_len*4; l += bsz_x)
-        desc[l] += desc[l+4*desc_len];
+    for (int l = tid_x; l < desc_len * 4; l += bsz_x)
+        desc[l] += desc[l + 4 * desc_len];
     __syncthreads();
-    for (int l = tid_x; l < desc_len*2; l += bsz_x)
-        desc[l    ] += desc[l+2*desc_len];
+    for (int l = tid_x; l < desc_len * 2; l += bsz_x)
+        desc[l] += desc[l + 2 * desc_len];
     __syncthreads();
-    for (int l = tid_x; l < desc_len; l += bsz_x)
-        desc[l] += desc[l+desc_len];
+    for (int l = tid_x; l < desc_len; l += bsz_x) desc[l] += desc[l + desc_len];
     __syncthreads();
 
     normalizeGLOHDesc(desc, accum, desc_len);
 
     for (int i = tid_x; i < desc_len; i += bsz_x)
-        desc[tid_y*desc_len+i] = min(desc[tid_y*desc_len+i], DESC_MAG_THR);
+        desc[tid_y * desc_len + i] =
+            min(desc[tid_y * desc_len + i], DESC_MAG_THR);
     __syncthreads();
 
     normalizeGLOHDesc(desc, accum, desc_len);
@@ -1082,33 +1032,35 @@ __global__ void computeGLOHDescriptor(
     if (f < total_feat) {
         // Calculate final descriptor values
         for (int k = tid_x; k < desc_len; k += bsz_x)
-            desc_out[f*desc_len+k] = round(min(255.f, desc[tid_y*desc_len+k] * INT_DESCR_FCTR));
+            desc_out[f * desc_len + k] =
+                round(min(255.f, desc[tid_y * desc_len + k] * INT_DESCR_FCTR));
     }
 }
 
 #undef IPTR
 
 template<typename T, typename convAccT>
-Array<T> createInitialImage(
-    CParam<T> img,
-    const float init_sigma,
-    const bool double_input)
-{
+Array<T> createInitialImage(CParam<T> img, const float init_sigma,
+                            const bool double_input) {
     dim4 dims((double_input) ? img.dims[0] * 2 : img.dims[0],
               (double_input) ? img.dims[1] * 2 : img.dims[1]);
     Array<T> init_img = createEmptyArray<T>(dims);
     Array<T> init_tmp = createEmptyArray<T>(dims);
 
-    float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma - INIT_SIGMA * INIT_SIGMA * 4), 0.1f)
-                             : std::max((float)sqrt(init_sigma * init_sigma - INIT_SIGMA * INIT_SIGMA), 0.1f);
+    float s = (double_input)
+                  ? std::max((float)sqrt(init_sigma * init_sigma -
+                                         INIT_SIGMA * INIT_SIGMA * 4),
+                             0.1f)
+                  : std::max((float)sqrt(init_sigma * init_sigma -
+                                         INIT_SIGMA * INIT_SIGMA),
+                             0.1f);
 
     Array<convAccT> filter = gauss_filter<convAccT>(s);
 
     if (double_input) {
         resize<T, AF_INTERP_BILINEAR>(init_img, img);
         convolve2<T, convAccT, 0, false>(init_tmp, init_img, filter);
-    }
-    else
+    } else
         convolve2<T, convAccT, 0, false>(init_tmp, img, filter);
 
     convolve2<T, convAccT, 1, false>(init_img, CParam<T>(init_tmp), filter);
@@ -1117,148 +1069,139 @@ Array<T> createInitialImage(
 }
 
 template<typename T, typename convAccT>
-std::vector< Array<T> > buildGaussPyr(
-    Param<T> init_img,
-    const unsigned n_octaves,
-    const unsigned n_layers,
-    const float init_sigma)
-{
+std::vector<Array<T>> buildGaussPyr(Param<T> init_img, const unsigned n_octaves,
+                                    const unsigned n_layers,
+                                    const float init_sigma) {
     // Precompute Gaussian sigmas using the following formula:
     // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
     std::vector<float> sig_layers(n_layers + 3);
     sig_layers[0] = init_sigma;
-    float k = std::pow(2.0f, 1.0f / n_layers);
+    float k       = std::pow(2.0f, 1.0f / n_layers);
     for (unsigned i = 1; i < n_layers + 3; i++) {
-        float sig_prev = std::pow(k, i-1) * init_sigma;
+        float sig_prev  = std::pow(k, i - 1) * init_sigma;
         float sig_total = sig_prev * k;
-        sig_layers[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);
+        sig_layers[i] = std::sqrt(sig_total * sig_total - sig_prev * sig_prev);
     }
 
     // Gaussian Pyramid
     std::vector<Array<T>> gauss_pyr;
     std::vector<Array<T>> tmp_pyr;
     gauss_pyr.reserve(n_octaves);
-    tmp_pyr.reserve(n_octaves * (n_layers+3));
+    tmp_pyr.reserve(n_octaves * (n_layers + 3));
     for (unsigned o = 0; o < n_octaves; o++) {
-        gauss_pyr.push_back(createEmptyArray<T>({(o == 0) ? init_img.dims[0] : gauss_pyr[o-1].dims()[0] / 2,
-                                                (o == 0) ? init_img.dims[1] : gauss_pyr[o-1].dims()[1] / 2,
-                                                n_layers+3}));
+        gauss_pyr.push_back(createEmptyArray<T>(
+            {(o == 0) ? init_img.dims[0] : gauss_pyr[o - 1].dims()[0] / 2,
+             (o == 0) ? init_img.dims[1] : gauss_pyr[o - 1].dims()[1] / 2,
+             n_layers + 3}));
 
-        for (unsigned l = 0; l < n_layers+3; l++) {
-            unsigned src_idx = (l == 0) ? (o-1)*(n_layers+3) + n_layers : o*(n_layers+3) + l-1;
-            unsigned idx = o*(n_layers+3) + l;
+        for (unsigned l = 0; l < n_layers + 3; l++) {
+            unsigned src_idx = (l == 0) ? (o - 1) * (n_layers + 3) + n_layers
+                                        : o * (n_layers + 3) + l - 1;
+            unsigned idx = o * (n_layers + 3) + l;
 
             if (o == 0 && l == 0) {
                 tmp_pyr.push_back(createParamArray(init_img, false));
-            }
-            else if (l == 0) {
-                tmp_pyr.push_back(createEmptyArray<T>({ tmp_pyr[src_idx].dims()[0] / 2,
-                                                        tmp_pyr[src_idx].dims()[1] / 2}));
+            } else if (l == 0) {
+                tmp_pyr.push_back(
+                    createEmptyArray<T>({tmp_pyr[src_idx].dims()[0] / 2,
+                                         tmp_pyr[src_idx].dims()[1] / 2}));
                 resize<T, AF_INTERP_BILINEAR>(tmp_pyr[idx], tmp_pyr[src_idx]);
-            }
-            else {
+            } else {
                 tmp_pyr.push_back(createEmptyArray<T>(tmp_pyr[src_idx].dims()));
                 Array<T> tmp = createEmptyArray<T>(tmp_pyr[src_idx].dims());
                 Array<convAccT> filter = gauss_filter<convAccT>(sig_layers[l]);
 
                 convolve2<T, convAccT, 0, false>(tmp, tmp_pyr[src_idx], filter);
-                convolve2<T, convAccT, 1, false>(tmp_pyr[idx], CParam<T>(tmp), filter);
+                convolve2<T, convAccT, 1, false>(tmp_pyr[idx], CParam<T>(tmp),
+                                                 filter);
 
-                //memFree(tmp.ptr);
+                // memFree(tmp.ptr);
             }
 
-            const unsigned imel = tmp_pyr[idx].elements();
+            const unsigned imel   = tmp_pyr[idx].elements();
             const unsigned offset = imel * l;
 
-            CUDA_CHECK(cudaMemcpyAsync(gauss_pyr[o].get() + offset, tmp_pyr[idx].get(),
-                        imel * sizeof(T), cudaMemcpyDeviceToDevice,
-                        cuda::getActiveStream()));
+            CUDA_CHECK(cudaMemcpyAsync(gauss_pyr[o].get() + offset,
+                                       tmp_pyr[idx].get(), imel * sizeof(T),
+                                       cudaMemcpyDeviceToDevice,
+                                       cuda::getActiveStream()));
         }
     }
     return gauss_pyr;
 }
 
 template<typename T>
-std::vector< Array<T> > buildDoGPyr(
-    std::vector< Array<T> >& gauss_pyr,
-    const unsigned n_octaves,
-    const unsigned n_layers)
-{
+std::vector<Array<T>> buildDoGPyr(std::vector<Array<T>>& gauss_pyr,
+                                  const unsigned n_octaves,
+                                  const unsigned n_layers) {
     // DoG Pyramid
-    std::vector< Array<T> > dog_pyr;
+    std::vector<Array<T>> dog_pyr;
     dog_pyr.reserve(n_octaves);
 
     for (unsigned o = 0; o < n_octaves; o++) {
-        dog_pyr.push_back(createEmptyArray<T>({  gauss_pyr[o].dims()[0],
-                gauss_pyr[o].dims()[1],
-                gauss_pyr[o].dims()[2]-1,
-                gauss_pyr[o].dims()[3]}));
+        dog_pyr.push_back(createEmptyArray<T>(
+            {gauss_pyr[o].dims()[0], gauss_pyr[o].dims()[1],
+             gauss_pyr[o].dims()[2] - 1, gauss_pyr[o].dims()[3]}));
 
         const unsigned nel = dog_pyr[o].dims()[1] * dog_pyr[o].strides()[1];
-        const unsigned dog_layers = n_layers+2;
+        const unsigned dog_layers = n_layers + 2;
 
         dim3 threads(SIFT_THREADS);
         dim3 blocks(divup(nel, threads.x));
-        CUDA_LAUNCH((sub<T>), blocks, threads,
-                    dog_pyr[o], gauss_pyr[o], nel, dog_layers);
+        CUDA_LAUNCH((sub<T>), blocks, threads, dog_pyr[o], gauss_pyr[o], nel,
+                    dog_layers);
         POST_LAUNCH_CHECK();
     }
 
     return dog_pyr;
 }
 
-template <typename T>
-void update_permutation(thrust::device_ptr<T>& keys, cuda::ThrustVector<int>& permutation)
-{
+template<typename T>
+void update_permutation(thrust::device_ptr<T>& keys,
+                        cuda::ThrustVector<int>& permutation) {
     // temporary storage for keys
     cuda::ThrustVector<T> temp(permutation.size());
 
     // permute the keys with the current reordering
-    THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(), keys, temp.begin());
+    THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(),
+                  keys, temp.begin());
 
     // stable_sort the permuted keys and update the permutation
-    THRUST_SELECT((thrust::stable_sort_by_key), temp.begin(), temp.end(), permutation.begin());
+    THRUST_SELECT((thrust::stable_sort_by_key), temp.begin(), temp.end(),
+                  permutation.begin());
 }
 
-template <typename T>
-void apply_permutation(thrust::device_ptr<T>& keys, cuda::ThrustVector<int>& permutation)
-{
+template<typename T>
+void apply_permutation(thrust::device_ptr<T>& keys,
+                       cuda::ThrustVector<int>& permutation) {
     // copy keys to temporary vector
-    cuda::ThrustVector<T> temp(keys, keys+permutation.size());
+    cuda::ThrustVector<T> temp(keys, keys + permutation.size());
 
     // permute the keys
-    THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(), temp.begin(), keys);
+    THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(),
+                  temp.begin(), keys);
 }
 
 template<typename T, typename convAccT>
-void sift(unsigned* out_feat,
-          unsigned* out_dlen,
-          float** d_x,
-          float** d_y,
-          float** d_score,
-          float** d_ori,
-          float** d_size,
-          float** d_desc,
-          CParam<T> img,
-          const unsigned n_layers,
-          const float contrast_thr,
-          const float edge_thr,
-          const float init_sigma,
-          const bool double_input,
-          const float img_scale,
-          const float feature_ratio,
-          const bool compute_GLOH)
-{
+void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
+          float** d_score, float** d_ori, float** d_size, float** d_desc,
+          CParam<T> img, const unsigned n_layers, const float contrast_thr,
+          const float edge_thr, const float init_sigma, const bool double_input,
+          const float img_scale, const float feature_ratio,
+          const bool compute_GLOH) {
     unsigned min_dim = min(img.dims[0], img.dims[1]);
     if (double_input) min_dim *= 2;
 
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
-    Array<T> init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
+    Array<T> init_img =
+        createInitialImage<T, convAccT>(img, init_sigma, double_input);
 
-    std::vector< Array<T> > gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+    std::vector<Array<T>> gauss_pyr =
+        buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-    std::vector< Array<T> > dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
+    std::vector<Array<T>> dog_pyr =
+        buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers);
 
     std::vector<uptr<float>> d_x_pyr(n_octaves);
     std::vector<uptr<float>> d_y_pyr(n_octaves);
@@ -1269,86 +1212,97 @@ void sift(unsigned* out_feat,
     std::vector<unsigned> feat_pyr(n_octaves);
     unsigned total_feat = 0;
 
-    const unsigned d = DESCR_WIDTH;
-    const unsigned n = DESCR_HIST_BINS;
+    const unsigned d  = DESCR_WIDTH;
+    const unsigned n  = DESCR_HIST_BINS;
     const unsigned rb = GLOHRadialBins;
     const unsigned ab = GLOHAngularBins;
     const unsigned hb = GLOHHistBins;
-    const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
+    const unsigned desc_len =
+        (compute_GLOH) ? (1 + (rb - 1) * ab) * hb : d * d * n;
 
     uptr<unsigned> d_count = memAlloc<unsigned>(1);
     for (unsigned i = 0; i < n_octaves; i++) {
-        if (dog_pyr[i].dims()[0]-2*IMG_BORDER < 1 ||
-            dog_pyr[i].dims()[1]-2*IMG_BORDER < 1)
+        if (dog_pyr[i].dims()[0] - 2 * IMG_BORDER < 1 ||
+            dog_pyr[i].dims()[1] - 2 * IMG_BORDER < 1)
             continue;
 
-        const unsigned imel = dog_pyr[i].dims()[0] * dog_pyr[i].dims()[1];
+        const unsigned imel     = dog_pyr[i].dims()[0] * dog_pyr[i].dims()[1];
         const unsigned max_feat = ceil(imel * feature_ratio);
 
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
                                    cuda::getActiveStream()));
 
-        uptr<float>    d_extrema_x = memAlloc<float>(max_feat);
-        uptr<float>    d_extrema_y = memAlloc<float>(max_feat);
+        uptr<float> d_extrema_x        = memAlloc<float>(max_feat);
+        uptr<float> d_extrema_y        = memAlloc<float>(max_feat);
         uptr<unsigned> d_extrema_layer = memAlloc<unsigned>(max_feat);
 
         int dim0 = dog_pyr[i].dims()[0];
         int dim1 = dog_pyr[i].dims()[1];
 
         dim3 threads(SIFT_THREADS_X, SIFT_THREADS_Y);
-        dim3 blocks(divup(dim0-2*IMG_BORDER, threads.x), divup(dim1-2*IMG_BORDER, threads.y));
+        dim3 blocks(divup(dim0 - 2 * IMG_BORDER, threads.x),
+                    divup(dim1 - 2 * IMG_BORDER, threads.y));
 
         float extrema_thr = 0.5f * contrast_thr / n_layers;
-        const size_t extrema_shared_size = (threads.x+2) * (threads.y+2) * 3 * sizeof(float);
-        CUDA_LAUNCH_SMEM((detectExtrema<T>), blocks, threads, extrema_shared_size,
-                          d_extrema_x.get(), d_extrema_y.get(), d_extrema_layer.get(), d_count.get(),
-                          dog_pyr[i], max_feat, extrema_thr);
+        const size_t extrema_shared_size =
+            (threads.x + 2) * (threads.y + 2) * 3 * sizeof(float);
+        CUDA_LAUNCH_SMEM((detectExtrema<T>), blocks, threads,
+                         extrema_shared_size, d_extrema_x.get(),
+                         d_extrema_y.get(), d_extrema_layer.get(),
+                         d_count.get(), dog_pyr[i], max_feat, extrema_thr);
         POST_LAUNCH_CHECK();
 
         unsigned extrema_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&extrema_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&extrema_feat, d_count.get(),
+                                   sizeof(unsigned), cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         extrema_feat = min(extrema_feat, max_feat);
 
         if (extrema_feat == 0) { continue; }
 
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
-                                  cuda::getActiveStream()));
+                                   cuda::getActiveStream()));
 
-        auto d_interp_x = memAlloc<float>(extrema_feat);
-        auto d_interp_y = memAlloc<float>(extrema_feat);
-        auto d_interp_layer = memAlloc<unsigned>(extrema_feat);
+        auto d_interp_x        = memAlloc<float>(extrema_feat);
+        auto d_interp_y        = memAlloc<float>(extrema_feat);
+        auto d_interp_layer    = memAlloc<unsigned>(extrema_feat);
         auto d_interp_response = memAlloc<float>(extrema_feat);
-        auto d_interp_size = memAlloc<float>(extrema_feat);
+        auto d_interp_size     = memAlloc<float>(extrema_feat);
 
         threads = dim3(SIFT_THREADS, 1);
-        blocks = dim3(divup(extrema_feat, threads.x), 1);
+        blocks  = dim3(divup(extrema_feat, threads.x), 1);
 
-        CUDA_LAUNCH((interpolateExtrema<T>), blocks, threads,
-                    d_interp_x.get(), d_interp_y.get(), d_interp_layer.get(),
+        CUDA_LAUNCH((interpolateExtrema<T>), blocks, threads, d_interp_x.get(),
+                    d_interp_y.get(), d_interp_layer.get(),
                     d_interp_response.get(), d_interp_size.get(), d_count.get(),
-                    d_extrema_x.get(), d_extrema_y.get(), d_extrema_layer.get(), extrema_feat,
-                    dog_pyr[i], max_feat, i, n_layers,
+                    d_extrema_x.get(), d_extrema_y.get(), d_extrema_layer.get(),
+                    extrema_feat, dog_pyr[i], max_feat, i, n_layers,
                     contrast_thr, edge_thr, init_sigma, img_scale);
         POST_LAUNCH_CHECK();
 
         unsigned interp_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&interp_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&interp_feat, d_count.get(),
+                                   sizeof(unsigned), cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         interp_feat = min(interp_feat, max_feat);
 
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
                                    cuda::getActiveStream()));
 
-        if (interp_feat == 0) {continue;}
+        if (interp_feat == 0) { continue; }
 
-        thrust::device_ptr<float> interp_x_ptr = thrust::device_pointer_cast(d_interp_x.get());
-        thrust::device_ptr<float> interp_y_ptr = thrust::device_pointer_cast(d_interp_y.get());
-        thrust::device_ptr<unsigned> interp_layer_ptr = thrust::device_pointer_cast(d_interp_layer.get());
-        thrust::device_ptr<float> interp_response_ptr = thrust::device_pointer_cast(d_interp_response.get());
-        thrust::device_ptr<float> interp_size_ptr = thrust::device_pointer_cast(d_interp_size.get());
+        thrust::device_ptr<float> interp_x_ptr =
+            thrust::device_pointer_cast(d_interp_x.get());
+        thrust::device_ptr<float> interp_y_ptr =
+            thrust::device_pointer_cast(d_interp_y.get());
+        thrust::device_ptr<unsigned> interp_layer_ptr =
+            thrust::device_pointer_cast(d_interp_layer.get());
+        thrust::device_ptr<float> interp_response_ptr =
+            thrust::device_pointer_cast(d_interp_response.get());
+        thrust::device_ptr<float> interp_size_ptr =
+            thrust::device_pointer_cast(d_interp_size.get());
 
         cuda::ThrustVector<int> permutation(interp_feat);
         thrust::sequence(permutation.begin(), permutation.end());
@@ -1365,53 +1319,58 @@ void sift(unsigned* out_feat,
         apply_permutation<float>(interp_y_ptr, permutation);
         apply_permutation<float>(interp_x_ptr, permutation);
 
-        auto d_nodup_x = memAlloc<float>(interp_feat);
-        auto d_nodup_y = memAlloc<float>(interp_feat);
-        auto d_nodup_layer = memAlloc<unsigned>(interp_feat);
+        auto d_nodup_x        = memAlloc<float>(interp_feat);
+        auto d_nodup_y        = memAlloc<float>(interp_feat);
+        auto d_nodup_layer    = memAlloc<unsigned>(interp_feat);
         auto d_nodup_response = memAlloc<float>(interp_feat);
-        auto d_nodup_size = memAlloc<float>(interp_feat);
+        auto d_nodup_size     = memAlloc<float>(interp_feat);
 
         threads = dim3(SIFT_THREADS, 1);
-        blocks = dim3(divup(interp_feat, threads.x), 1);
+        blocks  = dim3(divup(interp_feat, threads.x), 1);
 
-        CUDA_LAUNCH((removeDuplicates), blocks, threads,
-                    d_nodup_x.get(), d_nodup_y.get(), d_nodup_layer.get(),
+        CUDA_LAUNCH((removeDuplicates), blocks, threads, d_nodup_x.get(),
+                    d_nodup_y.get(), d_nodup_layer.get(),
                     d_nodup_response.get(), d_nodup_size.get(), d_count.get(),
                     d_interp_x.get(), d_interp_y.get(), d_interp_layer.get(),
                     d_interp_response.get(), d_interp_size.get(), interp_feat);
         POST_LAUNCH_CHECK();
 
         unsigned nodup_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&nodup_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&nodup_feat, d_count.get(), sizeof(unsigned),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
                                    cuda::getActiveStream()));
 
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        auto d_oriented_x = memAlloc<float>(max_oriented_feat);
-        auto d_oriented_y = memAlloc<float>(max_oriented_feat);
-        auto d_oriented_layer = memAlloc<unsigned>(max_oriented_feat);
+        auto d_oriented_x        = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_y        = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_layer    = memAlloc<unsigned>(max_oriented_feat);
         auto d_oriented_response = memAlloc<float>(max_oriented_feat);
-        auto d_oriented_size = memAlloc<float>(max_oriented_feat);
-        auto d_oriented_ori = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_size     = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_ori      = memAlloc<float>(max_oriented_feat);
 
         threads = dim3(SIFT_THREADS_X, SIFT_THREADS_Y);
-        blocks = dim3(1, divup(nodup_feat, threads.y));
-
-        const size_t ori_shared_size = ORI_HIST_BINS * threads.y * 2 * sizeof(float);
-        CUDA_LAUNCH_SMEM((calcOrientation<T>), blocks, threads, ori_shared_size,
-                         d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
-                         d_oriented_response.get(), d_oriented_size.get(), d_oriented_ori.get(), d_count.get(),
-                         d_nodup_x.get(), d_nodup_y.get(), d_nodup_layer.get(),
-                         d_nodup_response.get(), d_nodup_size.get(), nodup_feat,
-                         CParam<T>(gauss_pyr[i]), max_oriented_feat, i, double_input);
+        blocks  = dim3(1, divup(nodup_feat, threads.y));
+
+        const size_t ori_shared_size =
+            ORI_HIST_BINS * threads.y * 2 * sizeof(float);
+        CUDA_LAUNCH_SMEM(
+            (calcOrientation<T>), blocks, threads, ori_shared_size,
+            d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
+            d_oriented_response.get(), d_oriented_size.get(),
+            d_oriented_ori.get(), d_count.get(), d_nodup_x.get(),
+            d_nodup_y.get(), d_nodup_layer.get(), d_nodup_response.get(),
+            d_nodup_size.get(), nodup_feat, CParam<T>(gauss_pyr[i]),
+            max_oriented_feat, i, double_input);
         POST_LAUNCH_CHECK();
 
         unsigned oriented_feat = 0;
-        CUDA_CHECK(cudaMemcpyAsync(&oriented_feat, d_count.get(), sizeof(unsigned), cudaMemcpyDeviceToHost,
-                    cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&oriented_feat, d_count.get(),
+                                   sizeof(unsigned), cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         oriented_feat = min(oriented_feat, max_oriented_feat);
 
@@ -1419,40 +1378,43 @@ void sift(unsigned* out_feat,
 
         auto d_desc = memAlloc<float>(oriented_feat * desc_len);
 
-        float scale = 1.f/(1 << i);
+        float scale = 1.f / (1 << i);
         if (double_input) scale *= 2.f;
 
         threads = dim3(SIFT_THREADS, 1);
         blocks  = dim3(1, divup(oriented_feat, threads.y));
 
-        const unsigned histsz = 8;
-        const size_t shared_size = desc_len * (histsz+1) * sizeof(float);
+        const unsigned histsz    = 8;
+        const size_t shared_size = desc_len * (histsz + 1) * sizeof(float);
 
         if (compute_GLOH)
-            CUDA_LAUNCH_SMEM((computeGLOHDescriptor<T>), blocks, threads, shared_size,
-                             d_desc.get(), desc_len, histsz,
-                             d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
-                             d_oriented_response.get(), d_oriented_size.get(), d_oriented_ori.get(),
-                             oriented_feat, gauss_pyr[i], d, rb, ab, hb,
-                             scale, n_layers);
+            CUDA_LAUNCH_SMEM((computeGLOHDescriptor<T>), blocks, threads,
+                             shared_size, d_desc.get(), desc_len, histsz,
+                             d_oriented_x.get(), d_oriented_y.get(),
+                             d_oriented_layer.get(), d_oriented_response.get(),
+                             d_oriented_size.get(), d_oriented_ori.get(),
+                             oriented_feat, gauss_pyr[i], d, rb, ab, hb, scale,
+                             n_layers);
         else
-            CUDA_LAUNCH_SMEM((computeDescriptor<T>), blocks, threads, shared_size,
-                             d_desc.get(), desc_len, histsz,
-                             d_oriented_x.get(), d_oriented_y.get(), d_oriented_layer.get(),
-                             d_oriented_response.get(), d_oriented_size.get(), d_oriented_ori.get(),
-                             oriented_feat, CParam<T>(gauss_pyr[i]), d, n, scale, n_layers);
+            CUDA_LAUNCH_SMEM((computeDescriptor<T>), blocks, threads,
+                             shared_size, d_desc.get(), desc_len, histsz,
+                             d_oriented_x.get(), d_oriented_y.get(),
+                             d_oriented_layer.get(), d_oriented_response.get(),
+                             d_oriented_size.get(), d_oriented_ori.get(),
+                             oriented_feat, CParam<T>(gauss_pyr[i]), d, n,
+                             scale, n_layers);
         POST_LAUNCH_CHECK();
 
         total_feat += oriented_feat;
         feat_pyr[i] = oriented_feat;
 
         if (oriented_feat > 0) {
-            d_x_pyr[i] = std::move(d_oriented_x);
-            d_y_pyr[i] = std::move(d_oriented_y);
+            d_x_pyr[i]        = std::move(d_oriented_x);
+            d_y_pyr[i]        = std::move(d_oriented_y);
             d_response_pyr[i] = std::move(d_oriented_response);
-            d_ori_pyr[i] = std::move(d_oriented_ori);
-            d_size_pyr[i] = std::move(d_oriented_size);
-            d_desc_pyr[i] = std::move(d_desc);
+            d_ori_pyr[i]      = std::move(d_oriented_ori);
+            d_size_pyr[i]     = std::move(d_oriented_size);
+            d_desc_pyr[i]     = std::move(d_desc);
         }
     }
 
@@ -1466,23 +1428,29 @@ void sift(unsigned* out_feat,
 
     unsigned offset = 0;
     for (unsigned i = 0; i < n_octaves; i++) {
-        if (feat_pyr[i] == 0)
-            continue;
-
-        CUDA_CHECK(cudaMemcpyAsync(*d_x+offset, d_x_pyr[i].get(), feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_y+offset, d_y_pyr[i].get(), feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_score+offset, d_response_pyr[i].get(), feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_ori+offset, d_ori_pyr[i].get(), feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(*d_size+offset, d_size_pyr[i].get(), feat_pyr[i] * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
-        CUDA_CHECK(cudaMemcpyAsync(*d_desc+(offset*desc_len), d_desc_pyr[i].get(),
-                    feat_pyr[i] * desc_len * sizeof(float),
-                    cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        if (feat_pyr[i] == 0) continue;
+
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_x + offset, d_x_pyr[i].get(), feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_y + offset, d_y_pyr[i].get(), feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(*d_score + offset, d_response_pyr[i].get(),
+                                   feat_pyr[i] * sizeof(float),
+                                   cudaMemcpyDeviceToDevice,
+                                   cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_ori + offset, d_ori_pyr[i].get(), feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(
+            *d_size + offset, d_size_pyr[i].get(), feat_pyr[i] * sizeof(float),
+            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+
+        CUDA_CHECK(
+            cudaMemcpyAsync(*d_desc + (offset * desc_len), d_desc_pyr[i].get(),
+                            feat_pyr[i] * desc_len * sizeof(float),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         offset += feat_pyr[i];
     }
@@ -1492,6 +1460,6 @@ void sift(unsigned* out_feat,
     *out_dlen = desc_len;
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index 92a6eb6761..2b1649f382 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -7,64 +7,60 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename Ti>
-__device__
-Ti load2ShrdMem(const Ti * in,
-               int dim0, int dim1,
-               int gx, int gy,
-               int inStride1, int inStride0)
-{
-    if (gx<0 || gx>=dim0 || gy<0 || gy>=dim1)
+__device__ Ti load2ShrdMem(const Ti* in, int dim0, int dim1, int gx, int gy,
+                           int inStride1, int inStride0) {
+    if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
         return Ti(0);
     else
-        return in[gx*inStride0+gy*inStride1];
+        return in[gx * inStride0 + gy * inStride1];
 }
 
 template<typename Ti, typename To>
-__global__
-void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0, int nBBS1)
-{
-    __shared__ Ti shrdMem[THREADS_X+2][THREADS_Y+2];
+__global__ void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0,
+                         int nBBS1) {
+    __shared__ Ti shrdMem[THREADS_X + 2][THREADS_Y + 2];
 
     // calculate necessary offset and window parameters
     const int radius  = 1;
-    const int padding = 2*radius;
+    const int padding = 2 * radius;
     const int shrdLen = blockDim.x + padding;
 
     // batch offsets
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
-    const Ti* iptr     = (const Ti *)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
-    To*       dxptr    = (To *      )dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]);
-    To*       dyptr    = (To *      )dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]);
+    const Ti* iptr =
+        (const Ti*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    To* dxptr = (To*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]);
+    To* dyptr = (To*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]);
 
     // local neighborhood indices
     int lx = threadIdx.x;
     int ly = threadIdx.y;
 
     // global indices
-    int gx = THREADS_X * (blockIdx.x-b2*nBBS0) + lx;
-    int gy = THREADS_Y * (blockIdx.y-b3*nBBS1) + ly;
-
-    for (int b=ly, gy2=gy; b<shrdLen; b+=blockDim.y, gy2+=blockDim.y) {
-        for (int a=lx, gx2=gx; a<shrdLen; a+=blockDim.x, gx2+=blockDim.x) {
-            shrdMem[a][b] = load2ShrdMem<Ti>(iptr, in.dims[0], in.dims[1],
-                                             gx2-radius, gy2-radius,
-                                             in.strides[1], in.strides[0]);
+    int gx = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
+    int gy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += blockDim.y, gy2 += blockDim.y) {
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            shrdMem[a][b] =
+                load2ShrdMem<Ti>(iptr, in.dims[0], in.dims[1], gx2 - radius,
+                                 gy2 - radius, in.strides[1], in.strides[0]);
         }
     }
 
@@ -72,47 +68,49 @@ void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0, int nBBS1)
 
     // Only continue if we're at a valid location
     if (gx < in.dims[0] && gy < in.dims[1]) {
-        int i = lx + radius;
-        int j = ly + radius;
-        int _i = i-1;
-        int i_ = i+1;
-        int _j = j-1;
-        int j_ = j+1;
+        int i  = lx + radius;
+        int j  = ly + radius;
+        int _i = i - 1;
+        int i_ = i + 1;
+        int _j = j - 1;
+        int j_ = j + 1;
 
         float NW = shrdMem[_i][_j];
         float SW = shrdMem[i_][_j];
         float NE = shrdMem[_i][j_];
         float SE = shrdMem[i_][j_];
 
-        float t1 = shrdMem[i][_j];
-        float t2 = shrdMem[i][j_];
-        dxptr[gy*dx.strides[1]+gx] = (NW+SW - (NE+SE) + 2*(t1-t2));
-
-        t1 = shrdMem[_i][j];
-        t2 = shrdMem[i_][j];
-        dyptr[gy*dy.strides[1]+gx] = (NW+NE - (SW+SE) + 2*(t1-t2));
+        float t1                       = shrdMem[i][_j];
+        float t2                       = shrdMem[i][j_];
+        dxptr[gy * dx.strides[1] + gx] = (NW + SW - (NE + SE) + 2 * (t1 - t2));
 
+        t1                             = shrdMem[_i][j];
+        t2                             = shrdMem[i_][j];
+        dyptr[gy * dy.strides[1] + gx] = (NW + NE - (SW + SE) + 2 * (t1 - t2));
     }
 }
 
 template<typename Ti, typename To>
-void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in, const unsigned &ker_size)
-{
+void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
+           const unsigned& ker_size) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
     int blk_y = divup(in.dims[1], threads.y);
 
-    dim3 blocks(blk_x*in.dims[2], blk_y*in.dims[3]);
+    dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
-    //TODO: add more cases when 5x5 and 7x7 kernels are done
-    switch(ker_size) {
-        case  3: CUDA_LAUNCH((sobel3x3<Ti, To>), blocks, threads, dx, dy, in, blk_x, blk_y); break;
+    // TODO: add more cases when 5x5 and 7x7 kernels are done
+    switch (ker_size) {
+        case 3:
+            CUDA_LAUNCH((sobel3x3<Ti, To>), blocks, threads, dx, dy, in, blk_x,
+                        blk_y);
+            break;
     }
 
     POST_LAUNCH_CHECK();
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index aa0dcbc924..b03af555f9 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -7,84 +7,75 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <iota.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <thrust/sort.h>
-#include <kernel/thrust_sort_by_key.hpp>
+#include <err_cuda.hpp>
 #include <handle.hpp>
+#include <iota.hpp>
+#include <kernel/thrust_sort_by_key.hpp>
+#include <math.hpp>
+#include <thrust/sort.h>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Wrapper functions
-        template<typename T>
-        void sort0Iterative(Param<T> val, bool isAscending)
-        {
-            for(int w = 0; w < val.dims[3]; w++) {
-                int valW = w * val.strides[3];
-                for(int z = 0; z < val.dims[2]; z++) {
-                    int valWZ = valW + z * val.strides[2];
-                    for(int y = 0; y < val.dims[1]; y++) {
-
-                        int valOffset = valWZ + y * val.strides[1];
+namespace cuda {
+namespace kernel {
+// Wrapper functions
+template<typename T>
+void sort0Iterative(Param<T> val, bool isAscending) {
+    for (int w = 0; w < val.dims[3]; w++) {
+        int valW = w * val.strides[3];
+        for (int z = 0; z < val.dims[2]; z++) {
+            int valWZ = valW + z * val.strides[2];
+            for (int y = 0; y < val.dims[1]; y++) {
+                int valOffset = valWZ + y * val.strides[1];
 
-                        if(isAscending) {
-                            THRUST_SELECT(thrust::sort,
-                                          val.ptr + valOffset,
-                                          val.ptr + valOffset + val.dims[0]);
-                        } else {
-                            THRUST_SELECT(thrust::sort,
-                                          val.ptr + valOffset,
-                                          val.ptr + valOffset + val.dims[0],
-                                          thrust::greater<T>());
-                        }
-                    }
+                if (isAscending) {
+                    THRUST_SELECT(thrust::sort, val.ptr + valOffset,
+                                  val.ptr + valOffset + val.dims[0]);
+                } else {
+                    THRUST_SELECT(thrust::sort, val.ptr + valOffset,
+                                  val.ptr + valOffset + val.dims[0],
+                                  thrust::greater<T>());
                 }
             }
-            POST_LAUNCH_CHECK();
         }
+    }
+    POST_LAUNCH_CHECK();
+}
 
-        template<typename T>
-        void sortBatched(Param<T> pVal, int dim, bool isAscending)
-        {
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pVal.dims[i];
+template<typename T>
+void sortBatched(Param<T> pVal, int dim, bool isAscending) {
+    af::dim4 inDims;
+    for (int i = 0; i < 4; i++) inDims[i] = pVal.dims[i];
 
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
+    // Sort dimension
+    // tileDims * seqDims = inDims
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
 
-            // Create/call iota
-            Array<uint> pKey = iota<uint>(seqDims, tileDims);
+    // Create/call iota
+    Array<uint> pKey = iota<uint>(seqDims, tileDims);
 
-            pVal = flat(pVal);
+    pVal = flat(pVal);
 
-            // Sort indices
-            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-            thrustSortByKey(pVal.ptr, pKey.get(), pVal.dims[0], isAscending);
+    // Sort indices
+    // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+    thrustSortByKey(pVal.ptr, pKey.get(), pVal.dims[0], isAscending);
 
-            // Needs to be ascending (true) in order to maintain the indices properly
-            thrustSortByKey(pKey.get(), pVal.ptr, pVal.dims[0], true);
-        }
+    // Needs to be ascending (true) in order to maintain the indices properly
+    thrustSortByKey(pKey.get(), pVal.ptr, pVal.dims[0], true);
+}
 
-        template<typename T>
-        void sort0(Param<T> val, bool isAscending)
-        {
-            int higherDims =  val.dims[1] * val.dims[2] * val.dims[3];
+template<typename T>
+void sort0(Param<T> val, bool isAscending) {
+    int higherDims = val.dims[1] * val.dims[2] * val.dims[3];
 
-            if(higherDims > 10)
-                sortBatched<T>(val, 0, isAscending);
-            else
-                kernel::sort0Iterative<T>(val, isAscending);
-        }
-    }
+    if (higherDims > 10)
+        sortBatched<T>(val, 0, isAscending);
+    else
+        kernel::sort0Iterative<T>(val, isAscending);
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index 3f45326aea..e2edb286e3 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -7,100 +7,91 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <memory.hpp>
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <iota.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Wrapper functions
-        template<typename Tk, typename Tv>
-        void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending)
-        {
-            for(int w = 0; w < okey.dims[3]; w++) {
-                int okeyW = w * okey.strides[3];
-                int ovalW = w * oval.strides[3];
-                for(int z = 0; z < okey.dims[2]; z++) {
-                    int okeyWZ = okeyW + z * okey.strides[2];
-                    int ovalWZ = ovalW + z * oval.strides[2];
-                    for(int y = 0; y < okey.dims[1]; y++) {
-
-                        int okeyOffset = okeyWZ + y * okey.strides[1];
-                        int ovalOffset = ovalWZ + y * oval.strides[1];
+namespace cuda {
+namespace kernel {
+// Wrapper functions
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
+    for (int w = 0; w < okey.dims[3]; w++) {
+        int okeyW = w * okey.strides[3];
+        int ovalW = w * oval.strides[3];
+        for (int z = 0; z < okey.dims[2]; z++) {
+            int okeyWZ = okeyW + z * okey.strides[2];
+            int ovalWZ = ovalW + z * oval.strides[2];
+            for (int y = 0; y < okey.dims[1]; y++) {
+                int okeyOffset = okeyWZ + y * okey.strides[1];
+                int ovalOffset = ovalWZ + y * oval.strides[1];
 
-                        thrustSortByKey<Tk, Tv>(okey.ptr + okeyOffset,
-                                                oval.ptr + ovalOffset,
-                                                okey.dims[0],
-                                                isAscending);
-                    }
-                }
+                thrustSortByKey<Tk, Tv>(okey.ptr + okeyOffset,
+                                        oval.ptr + ovalOffset, okey.dims[0],
+                                        isAscending);
             }
-            POST_LAUNCH_CHECK();
         }
+    }
+    POST_LAUNCH_CHECK();
+}
 
-        template<typename Tk, typename Tv>
-        void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim, bool isAscending)
-        {
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pKey.dims[i];
+template<typename Tk, typename Tv>
+void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
+                      bool isAscending) {
+    af::dim4 inDims;
+    for (int i = 0; i < 4; i++) inDims[i] = pKey.dims[i];
 
-            const dim_t elements = inDims.elements();
+    const dim_t elements = inDims.elements();
 
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
+    // Sort dimension
+    // tileDims * seqDims = inDims
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
 
-            // Create/call iota
-            Array<uint> Seq = iota<uint>(seqDims, tileDims);
+    // Create/call iota
+    Array<uint> Seq = iota<uint>(seqDims, tileDims);
 
-            Tk *Key = pKey.ptr;
-            auto cKey = memAlloc<Tk>(elements);
-            CUDA_CHECK(cudaMemcpyAsync(cKey.get(), Key, elements * sizeof(Tk),
-                                       cudaMemcpyDeviceToDevice,
-                                       getActiveStream()));
+    Tk *Key   = pKey.ptr;
+    auto cKey = memAlloc<Tk>(elements);
+    CUDA_CHECK(cudaMemcpyAsync(cKey.get(), Key, elements * sizeof(Tk),
+                               cudaMemcpyDeviceToDevice, getActiveStream()));
 
-            Tv *Val = pVal.ptr;
-            thrustSortByKey(Key, Val, elements, isAscending);
-            thrustSortByKey(cKey.get(), Seq.get(), elements, isAscending);
+    Tv *Val = pVal.ptr;
+    thrustSortByKey(Key, Val, elements, isAscending);
+    thrustSortByKey(cKey.get(), Seq.get(), elements, isAscending);
 
-            auto cSeq = memAlloc<uint>(elements);
-            CUDA_CHECK(cudaMemcpyAsync(cSeq.get(), Seq.get(), elements * sizeof(uint),
-                                       cudaMemcpyDeviceToDevice,
-                                       getActiveStream()));
+    auto cSeq = memAlloc<uint>(elements);
+    CUDA_CHECK(cudaMemcpyAsync(cSeq.get(), Seq.get(), elements * sizeof(uint),
+                               cudaMemcpyDeviceToDevice, getActiveStream()));
 
-            // This always needs to be ascending
-            thrustSortByKey(Seq.get(), Val, elements, true);
-            thrustSortByKey(cSeq.get(), Key, elements, true);
+    // This always needs to be ascending
+    thrustSortByKey(Seq.get(), Val, elements, true);
+    thrustSortByKey(cSeq.get(), Key, elements, true);
 
-            // No need of doing moddims here because the original Array<T>
-            // dimensions have not been changed
-            //val.modDims(inDims);
-        }
+    // No need of doing moddims here because the original Array<T>
+    // dimensions have not been changed
+    // val.modDims(inDims);
+}
 
-        template<typename Tk, typename Tv>
-        void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending)
-        {
-            int higherDims =  okey.dims[1] * okey.dims[2] * okey.dims[3];
+template<typename Tk, typename Tv>
+void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
+    int higherDims = okey.dims[1] * okey.dims[2] * okey.dims[3];
 
-            // Batced sort performs 4x sort by keys But this is only useful
-            // before GPU is saturated The GPU is saturated at around 100,000
-            // integers Call batched sort only if both conditions are met
-            if(higherDims > 4 && okey.dims[0] < 100000)
-                kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
-            else
-                kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
-        }
-    }
+    // Batced sort performs 4x sort by keys But this is only useful
+    // before GPU is saturated The GPU is saturated at around 100,000
+    // integers Call batched sort only if both conditions are met
+    if (higherDims > 4 && okey.dims[0] < 100000)
+        kernel::sortByKeyBatched<Tk, Tv>(okey, oval, 0, isAscending);
+    else
+        kernel::sort0ByKeyIterative<Tk, Tv>(okey, oval, isAscending);
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index c7b3283dad..299d82eaf3 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -7,57 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
+
+namespace cuda {
+namespace kernel {
+static const int reps = 4;
+
+/////////////////////////////////////////////////////////////////////////////
+// Kernel to convert COO into Dense
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+__global__ void coo2dense_kernel(Param<T> output, CParam<T> values,
+                                 CParam<int> rowIdx, CParam<int> colIdx) {
+    int id = blockIdx.x * blockDim.x * reps + threadIdx.x;
+    if (id >= values.dims[0]) return;
 
-namespace cuda
-{
-    namespace kernel
-    {
-        static const int reps = 4;
-
-        /////////////////////////////////////////////////////////////////////////////
-        // Kernel to convert COO into Dense
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        __global__
-        void coo2dense_kernel(Param<T> output, CParam<T> values,
-                              CParam<int> rowIdx, CParam<int> colIdx)
-        {
-            int id = blockIdx.x * blockDim.x * reps + threadIdx.x;
-            if(id >= values.dims[0])
-                return;
-
-            for(int i = threadIdx.x; i <= reps * blockDim.x; i += blockDim.x) {
-                if(i >= values.dims[0])
-                    return;
-
-                T   v = values.ptr[i];
-                int r = rowIdx.ptr[i];
-                int c = colIdx.ptr[i];
-
-                int offset = r + c * output.strides[1];
-
-                output.ptr[offset] = v;
-            }
-        }
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx)
-        {
-            dim3 threads(256, 1, 1);
-
-            dim3 blocks(divup(output.dims[0], threads.x * reps), 1, 1);
-
-            CUDA_LAUNCH((coo2dense_kernel<T>), blocks, threads, output, values, rowIdx, colIdx);
-
-            POST_LAUNCH_CHECK();
-        }
+    for (int i = threadIdx.x; i <= reps * blockDim.x; i += blockDim.x) {
+        if (i >= values.dims[0]) return;
+
+        T v   = values.ptr[i];
+        int r = rowIdx.ptr[i];
+        int c = colIdx.ptr[i];
+
+        int offset = r + c * output.strides[1];
+
+        output.ptr[offset] = v;
     }
 }
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+               CParam<int> colIdx) {
+    dim3 threads(256, 1, 1);
+
+    dim3 blocks(divup(output.dims[0], threads.x * reps), 1, 1);
+
+    CUDA_LAUNCH((coo2dense_kernel<T>), blocks, threads, output, values, rowIdx,
+                colIdx);
+
+    POST_LAUNCH_CHECK();
+}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index adfa3ae7ba..ebc9b4ec37 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -7,241 +7,212 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
+#include <optypes.hpp>
 #include <platform.hpp>
-#include <backend.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
-static const unsigned TX = 32;
-static const unsigned TY = 8;
+static const unsigned TX      = 32;
+static const unsigned TY      = 8;
 static const unsigned THREADS = TX * TY;
 
 template<typename T, af_op_t op>
-struct arith_op
-{
-    __DH__ T operator()(T v1, T v2)
-    {
-        return T(0);
-    }
+struct arith_op {
+    __DH__ T operator()(T v1, T v2) { return T(0); }
 };
 
 template<typename T>
-struct arith_op<T, af_add_t>
-{
-    __device__ T operator()(T v1, T v2)
-    {
-        return v1 + v2;
-    }
+struct arith_op<T, af_add_t> {
+    __device__ T operator()(T v1, T v2) { return v1 + v2; }
 };
 
 template<typename T>
-struct arith_op<T, af_sub_t>
-{
-    __device__ T operator()(T v1, T v2)
-    {
-        return v1 - v2;
-    }
+struct arith_op<T, af_sub_t> {
+    __device__ T operator()(T v1, T v2) { return v1 - v2; }
 };
 
 template<typename T>
-struct arith_op<T, af_mul_t>
-{
-    __device__ T operator()(T v1, T v2)
-    {
-        return v1 * v2;
-    }
+struct arith_op<T, af_mul_t> {
+    __device__ T operator()(T v1, T v2) { return v1 * v2; }
 };
 
 template<typename T>
-struct arith_op<T, af_div_t>
-{
-    __device__ T operator()(T v1, T v2)
-    {
-        return v1 / v2;
-    }
+struct arith_op<T, af_div_t> {
+    __device__ T operator()(T v1, T v2) { return v1 / v2; }
 };
 
 template<typename T, af_op_t op>
-__global__
-void sparseArithCSRKernel(Param<T> out,
-                          CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
-                          CParam<T> rhs,
-                          const bool reverse)
-{
-    const int row         = blockIdx.x * TY + threadIdx.y;
+__global__ void sparseArithCSRKernel(Param<T> out, CParam<T> values,
+                                     CParam<int> rowIdx, CParam<int> colIdx,
+                                     CParam<T> rhs, const bool reverse) {
+    const int row = blockIdx.x * TY + threadIdx.y;
 
-    if(row >= out.dims[0]) return;
+    if (row >= out.dims[0]) return;
 
-    const int rowStartIdx = rowIdx.ptr[row  ];
-    const int rowEndIdx   = rowIdx.ptr[row+1];
+    const int rowStartIdx = rowIdx.ptr[row];
+    const int rowEndIdx   = rowIdx.ptr[row + 1];
 
     // Repeat loop until all values in the row are computed
-    for(int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
+    for (int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
         const int col = colIdx.ptr[idx];
 
-        if(row >= out.dims[0] || col >= out.dims[1]) continue;    // Bad indices
+        if (row >= out.dims[0] || col >= out.dims[1]) continue;  // Bad indices
 
         // Get Values
         const T val  = values.ptr[idx];
         const T rval = rhs.ptr[col * rhs.strides[1] + row];
 
         const int offset = col * out.strides[1] + row;
-        if(reverse) out.ptr[offset] = arith_op<T, op>()(rval, val);
-        else        out.ptr[offset] = arith_op<T, op>()(val, rval);
+        if (reverse)
+            out.ptr[offset] = arith_op<T, op>()(rval, val);
+        else
+            out.ptr[offset] = arith_op<T, op>()(val, rval);
     }
 }
 
 template<typename T, af_op_t op>
-__global__
-void sparseArithCOOKernel(Param<T> out,
-                          CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
-                          CParam<T> rhs,
-                          const bool reverse)
-{
+__global__ void sparseArithCOOKernel(Param<T> out, CParam<T> values,
+                                     CParam<int> rowIdx, CParam<int> colIdx,
+                                     CParam<T> rhs, const bool reverse) {
     const int idx = blockIdx.x * THREADS + threadIdx.x;
 
-    if(idx >= values.dims[0]) return;
+    if (idx >= values.dims[0]) return;
 
     const int row = rowIdx.ptr[idx];
     const int col = colIdx.ptr[idx];
 
-    if(row >= out.dims[0] || col >= out.dims[1]) return;    // Bad indices
+    if (row >= out.dims[0] || col >= out.dims[1]) return;  // Bad indices
 
     // Get Values
     const T val  = values.ptr[idx];
     const T rval = rhs.ptr[col * rhs.strides[1] + row];
 
     const int offset = col * out.strides[1] + row;
-    if(reverse) out.ptr[offset] = arith_op<T, op>()(rval, val);
-    else        out.ptr[offset] = arith_op<T, op>()(val, rval);
+    if (reverse)
+        out.ptr[offset] = arith_op<T, op>()(rval, val);
+    else
+        out.ptr[offset] = arith_op<T, op>()(val, rval);
 }
 
 template<typename T, af_op_t op>
-void sparseArithOpCSR(Param<T> out,
-                      CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
-                      CParam<T> rhs,
-                      const bool reverse)
-{
+void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
+                      CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(out.dims[0], TY), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads,
-                 out, values, rowIdx, colIdx, rhs, reverse);
+    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads, out, values,
+                rowIdx, colIdx, rhs, reverse);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op>
-void sparseArithOpCOO(Param<T> out,
-                      CParam<T> values, CParam<int> rowIdx, CParam<int> colIdx,
-                      CParam<T> rhs,
-                      const bool reverse)
-{
+void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
+                      CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(values.dims[0], THREADS), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads,
-                 out, values, rowIdx, colIdx, rhs, reverse);
+    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads, out, values,
+                rowIdx, colIdx, rhs, reverse);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op>
-__global__
-void sparseArithCSRKernel(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
-                          CParam<T> rhs, const bool reverse)
-{
-    const int row         = blockIdx.x * TY + threadIdx.y;
+__global__ void sparseArithCSRKernel(Param<T> values, Param<int> rowIdx,
+                                     Param<int> colIdx, CParam<T> rhs,
+                                     const bool reverse) {
+    const int row = blockIdx.x * TY + threadIdx.y;
 
-    if(row >= rhs.dims[0]) return;
+    if (row >= rhs.dims[0]) return;
 
-    const int rowStartIdx = rowIdx.ptr[row  ];
-    const int rowEndIdx   = rowIdx.ptr[row+1];
+    const int rowStartIdx = rowIdx.ptr[row];
+    const int rowEndIdx   = rowIdx.ptr[row + 1];
 
     // Repeat loop until all values in the row are computed
-    for(int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
+    for (int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
         const int col = colIdx.ptr[idx];
 
-        if(row >= rhs.dims[0] || col >= rhs.dims[1]) continue;    // Bad indices
+        if (row >= rhs.dims[0] || col >= rhs.dims[1]) continue;  // Bad indices
 
         // Get Values
         const T val  = values.ptr[idx];
         const T rval = rhs.ptr[col * rhs.strides[1] + row];
 
-        if(reverse) values.ptr[idx] = arith_op<T, op>()(rval, val);
-        else        values.ptr[idx] = arith_op<T, op>()(val, rval);
+        if (reverse)
+            values.ptr[idx] = arith_op<T, op>()(rval, val);
+        else
+            values.ptr[idx] = arith_op<T, op>()(val, rval);
     }
 }
 
 template<typename T, af_op_t op>
-__global__
-void sparseArithCOOKernel(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
-                          CParam<T> rhs, const bool reverse)
-{
+__global__ void sparseArithCOOKernel(Param<T> values, Param<int> rowIdx,
+                                     Param<int> colIdx, CParam<T> rhs,
+                                     const bool reverse) {
     const int idx = blockIdx.x * THREADS + threadIdx.x;
 
-    if(idx >= values.dims[0]) return;
+    if (idx >= values.dims[0]) return;
 
     const int row = rowIdx.ptr[idx];
     const int col = colIdx.ptr[idx];
 
-    if(row >= rhs.dims[0] || col >= rhs.dims[1]) return;    // Bad indices
+    if (row >= rhs.dims[0] || col >= rhs.dims[1]) return;  // Bad indices
 
     // Get Values
     const T val  = values.ptr[idx];
     const T rval = rhs.ptr[col * rhs.strides[1] + row];
 
-    if(reverse) values.ptr[idx] = arith_op<T, op>()(rval, val);
-    else        values.ptr[idx] = arith_op<T, op>()(val, rval);
+    if (reverse)
+        values.ptr[idx] = arith_op<T, op>()(rval, val);
+    else
+        values.ptr[idx] = arith_op<T, op>()(val, rval);
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
-                      CParam<T> rhs, const bool reverse)
-{
+                      CParam<T> rhs, const bool reverse) {
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(rhs.dims[0], TY), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads,
-                 values, rowIdx, colIdx, rhs, reverse);
+    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads, values, rowIdx,
+                colIdx, rhs, reverse);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
-                      CParam<T> rhs,
-                      const bool reverse)
-{
+                      CParam<T> rhs, const bool reverse) {
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(values.dims[0], THREADS), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads,
-                 values, rowIdx, colIdx, rhs, reverse);
+    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads, values, rowIdx,
+                colIdx, rhs, reverse);
 
     POST_LAUNCH_CHECK();
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 765b468a43..f9e57793e4 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -7,90 +7,80 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 #include "config.hpp"
 #include "shared.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
 static const unsigned BLOCK_X = 16;
 static const unsigned BLOCK_Y = 16;
 
-inline __device__ int max_val(const int x, const int y)
-{
-    return max(x, y);
-}
-inline __device__ unsigned max_val(const unsigned x, const unsigned y)
-{
+inline __device__ int max_val(const int x, const int y) { return max(x, y); }
+inline __device__ unsigned max_val(const unsigned x, const unsigned y) {
     return max(x, y);
 }
-inline __device__ float max_val(const float x, const float y)
-{
+inline __device__ float max_val(const float x, const float y) {
     return fmax(x, y);
 }
-inline __device__ double max_val(const double x, const double y)
-{
+inline __device__ double max_val(const double x, const double y) {
     return fmax(x, y);
 }
 
 template<typename T>
-__global__
-void susanKernel(T* out, const T* in,
-                 const unsigned idim0, const unsigned idim1,
-                 const unsigned radius, const float t, const float g,
-                 const unsigned edge)
-{
-    const int rSqrd   = radius*radius;
-    const int windLen = 2*radius+1;
-    const int shrdLen = BLOCK_X + windLen-1;
+__global__ void susanKernel(T* out, const T* in, const unsigned idim0,
+                            const unsigned idim1, const unsigned radius,
+                            const float t, const float g, const unsigned edge) {
+    const int rSqrd   = radius * radius;
+    const int windLen = 2 * radius + 1;
+    const int shrdLen = BLOCK_X + windLen - 1;
 
     SharedMemory<T> shared;
     T* shrdMem = shared.getPointer();
 
     const unsigned lx = threadIdx.x;
     const unsigned ly = threadIdx.y;
-    const unsigned gx  = blockDim.x * blockIdx.x + lx + edge;
-    const unsigned gy  = blockDim.y * blockIdx.y + ly + edge;
+    const unsigned gx = blockDim.x * blockIdx.x + lx + edge;
+    const unsigned gy = blockDim.y * blockIdx.y + ly + edge;
 
-    const unsigned nucleusIdx = (ly+radius)*shrdLen + lx+radius;
-    shrdMem[nucleusIdx] = gx<idim0 && gy<idim1 ? in[gy*idim0+gx] : 0;
-    T m_0 = shrdMem[nucleusIdx];
+    const unsigned nucleusIdx = (ly + radius) * shrdLen + lx + radius;
+    shrdMem[nucleusIdx] = gx < idim0 && gy < idim1 ? in[gy * idim0 + gx] : 0;
+    T m_0               = shrdMem[nucleusIdx];
 
 #pragma unroll
-    for (int b=ly, gy2=gy; b<shrdLen; b+=BLOCK_Y, gy2+=BLOCK_Y) {
-        int j = gy2-radius;
+    for (int b = ly, gy2 = gy; b < shrdLen; b += BLOCK_Y, gy2 += BLOCK_Y) {
+        int j = gy2 - radius;
 #pragma unroll
-        for (int a=lx, gx2=gx; a<shrdLen; a+=BLOCK_X, gx2+=BLOCK_X) {
-            int i = gx2-radius;
-            shrdMem[b*shrdLen+a] = (i<idim0 && j<idim1 ? in[j*idim0+i]: m_0);
+        for (int a = lx, gx2 = gx; a < shrdLen; a += BLOCK_X, gx2 += BLOCK_X) {
+            int i = gx2 - radius;
+            shrdMem[b * shrdLen + a] =
+                (i < idim0 && j < idim1 ? in[j * idim0 + i] : m_0);
         }
     }
     __syncthreads();
 
     if (gx < idim0 - edge && gy < idim1 - edge) {
-        unsigned idx = gy*idim0 + gx;
-        float nM  = 0.0f;
+        unsigned idx = gy * idim0 + gx;
+        float nM     = 0.0f;
 #pragma unroll
-        for (int p=0; p<windLen; ++p) {
+        for (int p = 0; p < windLen; ++p) {
 #pragma unroll
-            for (int q=0; q<windLen; ++q) {
+            for (int q = 0; q < windLen; ++q) {
                 int i = p - radius;
                 int j = q - radius;
                 int a = lx + radius + i;
                 int b = ly + radius + j;
-                if (i*i + j*j < rSqrd) {
-                    float c = m_0;
-                    float m = shrdMem[b * shrdLen + a];
-                    float exp_pow = powf((m - c)/t, 6.0f);
-                    float cM = expf(-exp_pow);
+                if (i * i + j * j < rSqrd) {
+                    float c       = m_0;
+                    float m       = shrdMem[b * shrdLen + a];
+                    float exp_pow = powf((m - c) / t, 6.0f);
+                    float cM      = expf(-exp_pow);
                     nM += cM;
                 }
             }
@@ -100,27 +90,26 @@ void susanKernel(T* out, const T* in,
 }
 
 template<typename T>
-void susan_responses(T* out, const T* in,
-                     const unsigned idim0, const unsigned idim1,
-                     const int radius, const float t, const float g,
-                     const unsigned edge)
-{
+void susan_responses(T* out, const T* in, const unsigned idim0,
+                     const unsigned idim1, const int radius, const float t,
+                     const float g, const unsigned edge) {
     dim3 threads(BLOCK_X, BLOCK_Y);
-    dim3 blocks(divup(idim0-edge*2, BLOCK_X), divup(idim1-edge*2, BLOCK_Y));
-    const size_t SMEM_SIZE = (BLOCK_X+2*radius)*(BLOCK_Y+2*radius)*sizeof(T);
+    dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
+                divup(idim1 - edge * 2, BLOCK_Y));
+    const size_t SMEM_SIZE =
+        (BLOCK_X + 2 * radius) * (BLOCK_Y + 2 * radius) * sizeof(T);
 
-    CUDA_LAUNCH_SMEM((susanKernel<T>), blocks, threads, SMEM_SIZE,
-            out, in, idim0, idim1, radius, t, g, edge);
+    CUDA_LAUNCH_SMEM((susanKernel<T>), blocks, threads, SMEM_SIZE, out, in,
+                     idim0, idim1, radius, t, g, edge);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T>
-__global__
-void nonMaxKernel(float* x_out, float* y_out, float* resp_out, unsigned* count,
-                  const unsigned idim0, const unsigned idim1, const T* resp_in,
-                  const unsigned edge, const unsigned max_corners)
-{
+__global__ void nonMaxKernel(float* x_out, float* y_out, float* resp_out,
+                             unsigned* count, const unsigned idim0,
+                             const unsigned idim1, const T* resp_in,
+                             const unsigned edge, const unsigned max_corners) {
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = edge + 1;
 
@@ -132,13 +121,14 @@ void nonMaxKernel(float* x_out, float* y_out, float* resp_out, unsigned* count,
 
         // Find maximum neighborhood response
         T max_v;
-        max_v = max_val(resp_in[(gy-1) * idim0 + gx-1], resp_in[gy * idim0 + gx-1]);
-        max_v = max_val(max_v, resp_in[(gy+1) * idim0 + gx-1]);
-        max_v = max_val(max_v, resp_in[(gy-1) * idim0 + gx  ]);
-        max_v = max_val(max_v, resp_in[(gy+1) * idim0 + gx  ]);
-        max_v = max_val(max_v, resp_in[(gy-1) * idim0 + gx+1]);
-        max_v = max_val(max_v, resp_in[(gy)   * idim0 + gx+1]);
-        max_v = max_val(max_v, resp_in[(gy+1) * idim0 + gx+1]);
+        max_v = max_val(resp_in[(gy - 1) * idim0 + gx - 1],
+                        resp_in[gy * idim0 + gx - 1]);
+        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx - 1]);
+        max_v = max_val(max_v, resp_in[(gy - 1) * idim0 + gx]);
+        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx]);
+        max_v = max_val(max_v, resp_in[(gy - 1) * idim0 + gx + 1]);
+        max_v = max_val(max_v, resp_in[(gy)*idim0 + gx + 1]);
+        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx + 1]);
 
         // Stores corner to {x,y,resp}_out if it's response is maximum compared
         // to its 8-neighborhood and greater or equal minimum response
@@ -154,27 +144,29 @@ void nonMaxKernel(float* x_out, float* y_out, float* resp_out, unsigned* count,
 }
 
 template<typename T>
-void nonMaximal(float* x_out, float* y_out, float* resp_out,
-                 unsigned* count, const unsigned idim0, const unsigned idim1,
-                 const T * resp_in, const unsigned edge, const unsigned max_corners)
-{
+void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
+                const unsigned idim0, const unsigned idim1, const T* resp_in,
+                const unsigned edge, const unsigned max_corners) {
     dim3 threads(BLOCK_X, BLOCK_Y);
-    dim3 blocks(divup(idim0-edge*2, BLOCK_X), divup(idim1-edge*2, BLOCK_Y));
+    dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
+                divup(idim1 - edge * 2, BLOCK_Y));
 
     auto d_corners_found = memAlloc<unsigned>(1);
     CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
-                cuda::getActiveStream()));
+                               cuda::getActiveStream()));
 
-    CUDA_LAUNCH((nonMaxKernel<T>), blocks, threads,
-                x_out, y_out, resp_out, d_corners_found.get(), idim0, idim1, resp_in, edge, max_corners);
+    CUDA_LAUNCH((nonMaxKernel<T>), blocks, threads, x_out, y_out, resp_out,
+                d_corners_found.get(), idim0, idim1, resp_in, edge,
+                max_corners);
 
     POST_LAUNCH_CHECK();
 
     CUDA_CHECK(cudaMemcpyAsync(count, d_corners_found.get(), sizeof(unsigned),
-                cudaMemcpyDeviceToHost, cuda::getActiveStream()));
+                               cudaMemcpyDeviceToHost,
+                               cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key.hpp b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
index 17476ef0a6..cb5cb376b1 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
@@ -9,12 +9,10 @@
 
 #pragma once
 #include <Param.hpp>
-namespace cuda
-{
-    namespace kernel
-    {
-        // Wrapper functions
-        template<typename Tk, typename Tv>
-        void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending);
-    }
-}
+namespace cuda {
+namespace kernel {
+// Wrapper functions
+template<typename Tk, typename Tv>
+void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending);
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
index 62a7f21ac1..cf19942149 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
@@ -14,10 +14,8 @@
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar
 // SBK_INSTS:0 1
 
-namespace cuda
-{
-namespace kernel
-{
-    INSTANTIATESBK_INST(SBK_TYPE)
-}
+namespace cuda {
+namespace kernel {
+INSTANTIATESBK_INST(SBK_TYPE)
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 905b3e9bee..3a5c22b926 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -12,48 +12,40 @@
 #include <thrust/sort.h>
 #include <types.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Wrapper functions
-        template<typename Tk, typename Tv>
-        void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending)
-        {
-            if (isAscending) {
-                THRUST_SELECT(thrust::stable_sort_by_key,
-                              keyPtr,
-                              keyPtr + elements,
-                              valPtr);
-            } else {
-                THRUST_SELECT(thrust::stable_sort_by_key,
-                              keyPtr,
-                              keyPtr + elements,
-                              valPtr, thrust::greater<Tk>());
-            }
-            POST_LAUNCH_CHECK();
-        }
+namespace cuda {
+namespace kernel {
+// Wrapper functions
+template<typename Tk, typename Tv>
+void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending) {
+    if (isAscending) {
+        THRUST_SELECT(thrust::stable_sort_by_key, keyPtr, keyPtr + elements,
+                      valPtr);
+    } else {
+        THRUST_SELECT(thrust::stable_sort_by_key, keyPtr, keyPtr + elements,
+                      valPtr, thrust::greater<Tk>());
+    }
+    POST_LAUNCH_CHECK();
+}
 
-#define INSTANTIATE(Tk, Tv)                                             \
-        template void thrustSortByKey<Tk, Tv>(Tk *keyPtr, Tv *valPtr,   \
-                                              int elements,             \
-                                              bool isAscending);        \
+#define INSTANTIATE(Tk, Tv)                                         \
+    template void thrustSortByKey<Tk, Tv>(Tk * keyPtr, Tv * valPtr, \
+                                          int elements, bool isAscending);
 
-#define INSTANTIATE0(Tk    ) \
-    INSTANTIATE(Tk, float  ) \
-    INSTANTIATE(Tk, double ) \
-    INSTANTIATE(Tk, cfloat ) \
+#define INSTANTIATE0(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
     INSTANTIATE(Tk, cdouble) \
-    INSTANTIATE(Tk, char   ) \
-    INSTANTIATE(Tk, uchar  )
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)
 
-#define INSTANTIATE1(Tk    ) \
-    INSTANTIATE(Tk, int    ) \
-    INSTANTIATE(Tk, uint   ) \
-    INSTANTIATE(Tk, short  ) \
-    INSTANTIATE(Tk, ushort ) \
-    INSTANTIATE(Tk, intl   ) \
-    INSTANTIATE(Tk, uintl  )
+#define INSTANTIATE1(Tk)    \
+    INSTANTIATE(Tk, int)    \
+    INSTANTIATE(Tk, uint)   \
+    INSTANTIATE(Tk, short)  \
+    INSTANTIATE(Tk, ushort) \
+    INSTANTIATE(Tk, intl)   \
+    INSTANTIATE(Tk, uintl)
 
-    }
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 6908a53a32..d9d9740cc7 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -7,83 +7,77 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 512;
-        static const unsigned TILEY = 32;
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
+static const unsigned TILEX = 512;
+static const unsigned TILEY = 32;
 
-        template<typename T>
-        __global__
-        void tile_kernel(Param<T> out, CParam<T> in,
-                         const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int oz =  blockIdx.x / blocksPerMatX;
-            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+template<typename T>
+__global__ void tile_kernel(Param<T> out, CParam<T> in, const int blocksPerMatX,
+                            const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
-            const int blockIdx_x =  blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
 
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-            if(xx >= out.dims[0] ||
-               yy >= out.dims[1] ||
-               oz >= out.dims[2] ||
-               ow >= out.dims[3])
-                return;
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
 
-            const int iz = oz % in.dims[2];
-            const int iw = ow % in.dims[3];
-            const int izw = iw * in.strides[3] + iz * in.strides[2];
-            const int ozw = ow * out.strides[3] + oz * out.strides[2];
+    const int iz  = oz % in.dims[2];
+    const int iw  = ow % in.dims[3];
+    const int izw = iw * in.strides[3] + iz * in.strides[2];
+    const int ozw = ow * out.strides[3] + oz * out.strides[2];
 
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
 
-            for(int oy = yy; oy < out.dims[1]; oy += incy) {
-                const int iy = oy % in.dims[1];
-                for(int ox = xx; ox < out.dims[0]; ox += incx) {
-                    const int ix = ox % in.dims[0];
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        const int iy = oy % in.dims[1];
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            const int ix = ox % in.dims[0];
 
-                    int iMem = izw + iy * in.strides[1] + ix;
-                    int oMem = ozw + oy * out.strides[1] + ox;
+            int iMem = izw + iy * in.strides[1] + ix;
+            int oMem = ozw + oy * out.strides[1] + ox;
 
-                    out.ptr[oMem] = in.ptr[iMem];
-                }
-            }
+            out.ptr[oMem] = in.ptr[iMem];
         }
+    }
+}
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T>
-        void tile(Param<T> out, CParam<T> in)
-        {
-            dim3 threads(TX, TY, 1);
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void tile(Param<T> out, CParam<T> in) {
+    dim3 threads(TX, TY, 1);
 
-            int blocksPerMatX = divup(out.dims[0], TILEX);
-            int blocksPerMatY = divup(out.dims[1], TILEY);
-            dim3 blocks(blocksPerMatX * out.dims[2],
-                        blocksPerMatY * out.dims[3],
-                        1);
+    int blocksPerMatX = divup(out.dims[0], TILEX);
+    int blocksPerMatY = divup(out.dims[1], TILEY);
+    dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            CUDA_LAUNCH((tile_kernel<T>), blocks, threads, out, in, blocksPerMatX, blocksPerMatY);
-            POST_LAUNCH_CHECK();
-        }
-    }
+    CUDA_LAUNCH((tile_kernel<T>), blocks, threads, out, in, blocksPerMatX,
+                blocksPerMatY);
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 7ad01dad94..803380d89a 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -9,36 +9,31 @@
 
 #include <cub/block/block_radix_sort.cuh>
 
+#include <Array.hpp>
+#include <Param.hpp>
 #include <backend.hpp>
-#include <common/dispatch.hpp>
 #include <common/ArrayInfo.hpp>
-#include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
-#include <Array.hpp>
 
 #include <limits>
 
 using cub::BlockRadixSort;
 
-namespace cuda
-{
-namespace kernel
-{
+namespace cuda {
+namespace kernel {
 static const int TOPK_THRDS_PER_BLK = 256;
-static const int TOPK_IDX_THRD_LOAD  = 4;
+static const int TOPK_IDX_THRD_LOAD = 4;
 
 template<typename T, bool READ_INDEX>
-static __global__
-void
-kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
-            CParam<T> ivals, CParam<uint> iidxs,
-            const int k, const af::topkFunction order,
-            uint numLaunchBlocksY)
-{
+static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
+                                   CParam<T> ivals, CParam<uint> iidxs,
+                                   const int k, const af::topkFunction order,
+                                   uint numLaunchBlocksY) {
     using ValueType = uint;
-    using  BlockRadixSortT = BlockRadixSort<T, TOPK_THRDS_PER_BLK,
-                                            TOPK_IDX_THRD_LOAD, ValueType>;
+    using BlockRadixSortT =
+        BlockRadixSort<T, TOPK_THRDS_PER_BLK, TOPK_IDX_THRD_LOAD, ValueType>;
 
     __shared__ typename BlockRadixSortT::TempStorage smem;
 
@@ -50,26 +45,22 @@ kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
     const uint gxStride = blockDim.x * gridDim.x;
     const uint elements = ivals.dims[0];
 
-    const T*    kdata = ivals.ptr + by * ivals.strides[1]
-                                  + bz * ivals.strides[2]
-                                  + bw * ivals.strides[3];
+    const T* kdata = ivals.ptr + by * ivals.strides[1] + bz * ivals.strides[2] +
+                     bw * ivals.strides[3];
 
-    const ValueType* idata = iidxs.ptr + by * iidxs.strides[1]
-                                       + bz * iidxs.strides[2]
-                                       + bw * iidxs.strides[3];
+    const ValueType* idata = iidxs.ptr + by * iidxs.strides[1] +
+                             bz * iidxs.strides[2] + bw * iidxs.strides[3];
 
-    T*    ores = ovals.ptr + by * ovals.strides[1]
-                           + bz * ovals.strides[2]
-                           + bw * ovals.strides[3];
-    uint* ires = oidxs.ptr + by * oidxs.strides[1]
-                           + bz * oidxs.strides[2]
-                           + bw * oidxs.strides[3];
+    T* ores = ovals.ptr + by * ovals.strides[1] + bz * ovals.strides[2] +
+              bw * ovals.strides[3];
+    uint* ires = oidxs.ptr + by * oidxs.strides[1] + bz * oidxs.strides[2] +
+                 bw * oidxs.strides[3];
 
-    T    keys[TOPK_IDX_THRD_LOAD];
+    T keys[TOPK_IDX_THRD_LOAD];
     ValueType vals[TOPK_IDX_THRD_LOAD];
 
-    for (uint li = 0, i = gx; li < TOPK_IDX_THRD_LOAD; i+=gxStride, li++) {
-        if(i < elements) {
+    for (uint li = 0, i = gx; li < TOPK_IDX_THRD_LOAD; i += gxStride, li++) {
+        if (i < elements) {
             keys[li] = kdata[i];
             vals[li] = (READ_INDEX) ? idata[i] : i;
         } else {
@@ -84,7 +75,7 @@ kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
         BlockRadixSortT(smem).SortBlockedToStriped(keys, vals);
     }
 
-    if(threadIdx.x < k) {
+    if (threadIdx.x < k) {
         int oidx   = threadIdx.x + blockIdx.x * k;
         ores[oidx] = keys[0];
         ires[oidx] = vals[0];
@@ -92,8 +83,8 @@ kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
 }
 
 template<typename T>
-void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
-              const int k, const af::topkFunction order) {
+void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals, const int k,
+              const af::topkFunction order) {
     const dim3 threads(TOPK_THRDS_PER_BLK, 1);
     const int thrdLoad = TOPK_IDX_THRD_LOAD;
 
@@ -106,7 +97,7 @@ void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
     // before the first iteration and reused for further iterations.
 
     // Temporary storage allocation for iterations
-    Array<T> tvals = createEmptyArray<T>(dim4());
+    Array<T> tvals    = createEmptyArray<T>(dim4());
     Array<uint> tidxs = createEmptyArray<uint>(dim4());
 
     if (numBlocksX > 1) {
@@ -118,25 +109,26 @@ void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
 
     int prevBlocksX = 1;
 
-    CParam<T> iivals = ivals;
+    CParam<T> iivals    = ivals;
     CParam<uint> iiidxs = tidxs;
 
-    int dims0 = tvals.dims()[0];
+    int dims0      = tvals.dims()[0];
     bool first_run = true;
     do {
-        if (blocks.x==1) {
+        if (blocks.x == 1) {
             tvals = createParamArray(ovals, false);
             tidxs = createParamArray(oidxs, false);
         }
 
-        if(first_run) {
-            // Launch topk which doesn't read the indice values from global memory
-            CUDA_LAUNCH((kerTopkDim0<T, false>), blocks, threads, tvals, tidxs, iivals,
-                        iiidxs, k, order, ivals.dims[1]);
+        if (first_run) {
+            // Launch topk which doesn't read the indice values from global
+            // memory
+            CUDA_LAUNCH((kerTopkDim0<T, false>), blocks, threads, tvals, tidxs,
+                        iivals, iiidxs, k, order, ivals.dims[1]);
             first_run = false;
         } else {
-            CUDA_LAUNCH((kerTopkDim0<T, true>), blocks, threads, tvals, tidxs, iivals,
-                        iiidxs, k, order, ivals.dims[1]);
+            CUDA_LAUNCH((kerTopkDim0<T, true>), blocks, threads, tvals, tidxs,
+                        iivals, iiidxs, k, order, ivals.dims[1]);
         }
 
         POST_LAUNCH_CHECK();
@@ -144,25 +136,23 @@ void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
         prevBlocksX = blocks.x;
         blocks.x    = divup(dims0, threads.x * thrdLoad);
 
-        //set output of current iteration as input for the next iteration
+        // set output of current iteration as input for the next iteration
         iivals = tvals;
         iiidxs = tidxs;
 
         dims0 = blocks.x * k;
 
-        tvals.setDataDims(dim4(dims0, tvals.elements()/(float)dims0));
-        tidxs.setDataDims(dim4(dims0, tidxs.elements()/(float)dims0));
-    } while (prevBlocksX>1);
+        tvals.setDataDims(dim4(dims0, tvals.elements() / (float)dims0));
+        tidxs.setDataDims(dim4(dims0, tidxs.elements() / (float)dims0));
+    } while (prevBlocksX > 1);
 }
 
 template<typename T>
-inline
-void topk(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
-          const int k, const int dim, const af::topkFunction order)
-{
+inline void topk(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
+                 const int k, const int dim, const af::topkFunction order) {
     assert(dim == 0);
-    //TODO Add switch statement when support for other dims is added
+    // TODO Add switch statement when support for other dims is added
     topkDim0<T>(ovals, oidxs, ivals, k, order);
 }
-}
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 70eba90fe9..291db28d1b 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -7,232 +7,227 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include "interp.hpp"
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 16;
-        static const unsigned TY = 16;
-        // Used for batching images
-        static const unsigned TI = 4;
-
-        __constant__ float c_tmat[3072]; // Allows 512 Affine Transforms and 340 Persp. Transforms
-
-        template <typename T>
-        __host__ __device__
-        void calc_transf_inverse(T *txo, const T *txi, const bool perspective)
-        {
-            if (perspective) {
-                txo[0] =   txi[4]*txi[8] - txi[5]*txi[7];
-                txo[1] = -(txi[1]*txi[8] - txi[2]*txi[7]);
-                txo[2] =   txi[1]*txi[5] - txi[2]*txi[4];
-
-                txo[3] = -(txi[3]*txi[8] - txi[5]*txi[6]);
-                txo[4] =   txi[0]*txi[8] - txi[2]*txi[6];
-                txo[5] = -(txi[0]*txi[5] - txi[2]*txi[3]);
-
-                txo[6] =   txi[3]*txi[7] - txi[4]*txi[6];
-                txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
-                txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
-
-                T det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
-
-                txo[0] /= det; txo[1] /= det; txo[2] /= det;
-                txo[3] /= det; txo[4] /= det; txo[5] /= det;
-                txo[6] /= det; txo[7] /= det; txo[8] /= det;
-                }
-            else {
-                T det = txi[0]*txi[4] - txi[1]*txi[3];
-
-                txo[0] = txi[4] / det;
-                txo[1] = txi[3] / det;
-                txo[3] = txi[1] / det;
-                txo[4] = txi[0] / det;
-
-                txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-                txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
-            }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX = 16;
+static const unsigned TY = 16;
+// Used for batching images
+static const unsigned TI = 4;
+
+__constant__ float
+    c_tmat[3072];  // Allows 512 Affine Transforms and 340 Persp. Transforms
+
+template<typename T>
+__host__ __device__ void calc_transf_inverse(T *txo, const T *txi,
+                                             const bool perspective) {
+    if (perspective) {
+        txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
+        txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
+        txo[2] = txi[1] * txi[5] - txi[2] * txi[4];
+
+        txo[3] = -(txi[3] * txi[8] - txi[5] * txi[6]);
+        txo[4] = txi[0] * txi[8] - txi[2] * txi[6];
+        txo[5] = -(txi[0] * txi[5] - txi[2] * txi[3]);
+
+        txo[6] = txi[3] * txi[7] - txi[4] * txi[6];
+        txo[7] = -(txi[0] * txi[7] - txi[1] * txi[6]);
+        txo[8] = txi[0] * txi[4] - txi[1] * txi[3];
+
+        T det = txi[0] * txo[0] + txi[1] * txo[3] + txi[2] * txo[6];
+
+        txo[0] /= det;
+        txo[1] /= det;
+        txo[2] /= det;
+        txo[3] /= det;
+        txo[4] /= det;
+        txo[5] /= det;
+        txo[6] /= det;
+        txo[7] /= det;
+        txo[8] /= det;
+    } else {
+        T det = txi[0] * txi[4] - txi[1] * txi[3];
+
+        txo[0] = txi[4] / det;
+        txo[1] = txi[3] / det;
+        txo[3] = txi[1] / det;
+        txo[4] = txi[0] / det;
+
+        txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+        txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+    }
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Transform Kernel
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool inverse, int order>
+__global__ static void transform_kernel(
+    Param<T> out, CParam<T> in, const int nImg2, const int nImg3,
+    const int nTfs2, const int nTfs3, const int batchImg2,
+    const int blocksXPerImage, const int blocksYPerImage,
+    const bool perspective, af_interp_type method) {
+    // Image Ids
+    const int imgId2 = blockIdx.x / blocksXPerImage;
+    const int imgId3 = blockIdx.y / blocksYPerImage;
+
+    // Block in local image
+    const int blockIdx_x = blockIdx.x - imgId2 * blocksXPerImage;
+    const int blockIdx_y = blockIdx.y - imgId3 * blocksYPerImage;
+
+    // Get thread indices in local image
+    const int xido = blockIdx_x * blockDim.x + threadIdx.x;
+    const int yido = blockIdx_y * blockDim.y + threadIdx.y;
+
+    // Image iteration loop count for image batching
+    int limages = min(max(out.dims[2] - imgId2 * nImg2, 1), batchImg2);
+
+    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
+
+    // Index of transform
+    const int eTfs2 = max((nTfs2 / nImg2), 1);
+    const int eTfs3 = max((nTfs3 / nImg3), 1);
+
+    int t_idx3        = -1;  // init
+    int t_idx2        = -1;  // init
+    int t_idx2_offset = 0;
+
+    if (nTfs3 == 1) {
+        t_idx3 = 0;  // Always 0 as only 1 transform defined
+    } else {
+        if (nTfs3 == nImg3) {
+            t_idx3 = imgId3;  // One to one batch with all transforms defined
+        } else {
+            t_idx3        = blockIdx.z / eTfs2;  // Transform batched, calculate
+            t_idx2_offset = t_idx3 * nTfs2;
         }
+    }
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Transform Kernel
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool inverse, int order>
-        __global__ static void
-        transform_kernel(Param<T> out, CParam<T> in,
-                         const int nImg2, const int nImg3, const int nTfs2, const int nTfs3,
-                         const int batchImg2,
-                         const int blocksXPerImage, const int blocksYPerImage,
-                         const bool perspective, af_interp_type method)
-        {
-            // Image Ids
-            const int imgId2 = blockIdx.x / blocksXPerImage;
-            const int imgId3 = blockIdx.y / blocksYPerImage;
-
-            // Block in local image
-            const int blockIdx_x = blockIdx.x - imgId2 * blocksXPerImage;
-            const int blockIdx_y = blockIdx.y - imgId3 * blocksYPerImage;
-
-            // Get thread indices in local image
-            const int xido = blockIdx_x * blockDim.x + threadIdx.x;
-            const int yido = blockIdx_y * blockDim.y + threadIdx.y;
-
-            // Image iteration loop count for image batching
-            int limages = min(max(out.dims[2] - imgId2 * nImg2, 1), batchImg2);
-
-            if(xido >= out.dims[0] || yido >= out.dims[1])
-                return;
-
-            // Index of transform
-            const int eTfs2 = max((nTfs2 / nImg2), 1);
-            const int eTfs3 = max((nTfs3 / nImg3), 1);
-
-            int t_idx3 = -1;    // init
-            int t_idx2 = -1;    // init
-            int t_idx2_offset = 0;
-
-            if(nTfs3 == 1) {
-                t_idx3 = 0;     // Always 0 as only 1 transform defined
-            } else {
-                if(nTfs3 == nImg3) {
-                    t_idx3 = imgId3;    // One to one batch with all transforms defined
-                } else {
-                    t_idx3 = blockIdx.z / eTfs2;    // Transform batched, calculate
-                    t_idx2_offset = t_idx3 * nTfs2;
-                }
-            }
-
-            if(nTfs2 == 1) {
-                t_idx2 = 0;     // Always 0 as only 1 transform defined
-            } else {
-                if(nTfs2 == nImg2) {
-                    t_idx2 = imgId2;    // One to one batch with all transforms defined
-                } else {
-                    t_idx2 = blockIdx.z - t_idx2_offset;   // Transform batched, calculate
-                }
-            }
-
-            // Linear transform index
-            const int t_idx = t_idx2 + t_idx3 * nTfs2;
-            int outoff = 0;
-
-            // Global offsets
-            const int inoff= imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
-            if(nImg2 == nTfs2 || nImg2 > 1) {   // One-to-One or Image on dim2
-                outoff += imgId2 * batchImg2 * out.strides[2];
-            } else {                            // Transform batched on dim2
-                outoff += t_idx2 * out.strides[2];
-            }
-
-            if(nImg3 == nTfs3 || nImg3 > 1) {   // One-to-One or Image on dim3
-                outoff += imgId3 * out.strides[3];
-            } else {                            // Transform batched on dim2
-                outoff += t_idx3 * out.strides[3];
-            }
-
-            // Transform is in constant memory.
-            const int transf_len = (perspective ? 9 : 6);
-            const float *tmat_ptr = c_tmat + t_idx * transf_len;
-            float tmat[9];
-
-            // We expect a inverse transform matrix by default
-            // If it is an forward transform, then we need its inverse
-            if(inverse) {
-                #pragma unroll 3
-                for(int i = 0; i < transf_len; i++)
-                    tmat[i] = tmat_ptr[i];
-            } else {
-                calc_transf_inverse(tmat, tmat_ptr, perspective);
-            }
-
-            const int loco = outoff + (yido * out.strides[1] + xido);
-
-            // Compute input index
-            typedef typename itype_t<T>::wtype WT;
-            WT xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
-            WT yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
-
-            if (perspective) {
-                const WT W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-                xidi /= W;
-                yidi /= W;
-            }
-
-            if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi || in.dims[1] <= yidi) {
-                for(int i = 0; i < limages; i++) {
-                    out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
-                }
-                return;
-            }
-
-            Interp2<T, WT, order> interp;
-            // FIXME: Nearest and lower do not do clamping, but other methods do
-            // Make it consistent
-            bool clamp = order != 1;
-            interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
+    if (nTfs2 == 1) {
+        t_idx2 = 0;  // Always 0 as only 1 transform defined
+    } else {
+        if (nTfs2 == nImg2) {
+            t_idx2 = imgId2;  // One to one batch with all transforms defined
+        } else {
+            t_idx2 =
+                blockIdx.z - t_idx2_offset;  // Transform batched, calculate
         }
+    }
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename T, int order>
-        void transform(Param<T> out, CParam<T> in, CParam<float> tf,
-                       const bool inverse, const bool perspective,
-                       af_interp_type method)
-        {
-            const int nImg2 = in.dims[2];
-            const int nImg3 = in.dims[3];
-            const int nTfs2 = tf.dims[2];
-            const int nTfs3 = tf.dims[3];
-
-            const int tf_len = (perspective) ? 9 : 6;
-
-            // Copy transform to constant memory.
-            CUDA_CHECK(cudaMemcpyToSymbolAsync(c_tmat, tf.ptr,
-                                               nTfs2 * nTfs3 * tf_len * sizeof(float),
-                                               0, cudaMemcpyDeviceToDevice,
-                                               cuda::getActiveStream()));
-
-            dim3 threads(TX, TY, 1);
-            dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
-
-            const int blocksXPerImage = blocks.x;
-            const int blocksYPerImage = blocks.y;
-
-            // Takes care of all types of batching
-            // One-to-one batching is only done on blocks.x
-            // TODO If dim2 is not one-to-one batched, then divide blocks.x by factor
-            int batchImg2 = 1;
-            if(nImg2 != nTfs2)
-                batchImg2 = min(nImg2, TI);
-
-            blocks.x *= (nImg2 / batchImg2);
-            blocks.y *= nImg3;
-
-            // Use blocks.z for transforms
-            blocks.z *= max((nTfs2 / nImg2), 1)
-                     *  max((nTfs3 / nImg3), 1);
-
-            if(inverse) {
-                CUDA_LAUNCH((transform_kernel<T, true, order>), blocks, threads, out, in,
-                            nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                            blocksXPerImage, blocksYPerImage,
-                            perspective, method);
-            } else {
-                CUDA_LAUNCH((transform_kernel<T, false, order>), blocks, threads, out, in,
-                            nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                            blocksXPerImage, blocksYPerImage,
-                            perspective, method);
-            }
-            POST_LAUNCH_CHECK();
+    // Linear transform index
+    const int t_idx = t_idx2 + t_idx3 * nTfs2;
+    int outoff      = 0;
+
+    // Global offsets
+    const int inoff =
+        imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
+    if (nImg2 == nTfs2 || nImg2 > 1) {  // One-to-One or Image on dim2
+        outoff += imgId2 * batchImg2 * out.strides[2];
+    } else {  // Transform batched on dim2
+        outoff += t_idx2 * out.strides[2];
+    }
+
+    if (nImg3 == nTfs3 || nImg3 > 1) {  // One-to-One or Image on dim3
+        outoff += imgId3 * out.strides[3];
+    } else {  // Transform batched on dim2
+        outoff += t_idx3 * out.strides[3];
+    }
+
+    // Transform is in constant memory.
+    const int transf_len  = (perspective ? 9 : 6);
+    const float *tmat_ptr = c_tmat + t_idx * transf_len;
+    float tmat[9];
+
+    // We expect a inverse transform matrix by default
+    // If it is an forward transform, then we need its inverse
+    if (inverse) {
+#pragma unroll 3
+        for (int i = 0; i < transf_len; i++) tmat[i] = tmat_ptr[i];
+    } else {
+        calc_transf_inverse(tmat, tmat_ptr, perspective);
+    }
+
+    const int loco = outoff + (yido * out.strides[1] + xido);
+
+    // Compute input index
+    typedef typename itype_t<T>::wtype WT;
+    WT xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
+    WT yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
+
+    if (perspective) {
+        const WT W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+        xidi /= W;
+        yidi /= W;
+    }
+
+    if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi ||
+        in.dims[1] <= yidi) {
+        for (int i = 0; i < limages; i++) {
+            out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
         }
+        return;
+    }
+
+    Interp2<T, WT, order> interp;
+    // FIXME: Nearest and lower do not do clamping, but other methods do
+    // Make it consistent
+    bool clamp = order != 1;
+    interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
+}
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, int order>
+void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
+               const bool perspective, af_interp_type method) {
+    const int nImg2 = in.dims[2];
+    const int nImg3 = in.dims[3];
+    const int nTfs2 = tf.dims[2];
+    const int nTfs3 = tf.dims[3];
+
+    const int tf_len = (perspective) ? 9 : 6;
+
+    // Copy transform to constant memory.
+    CUDA_CHECK(cudaMemcpyToSymbolAsync(
+        c_tmat, tf.ptr, nTfs2 * nTfs3 * tf_len * sizeof(float), 0,
+        cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+
+    dim3 threads(TX, TY, 1);
+    dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
+
+    const int blocksXPerImage = blocks.x;
+    const int blocksYPerImage = blocks.y;
+
+    // Takes care of all types of batching
+    // One-to-one batching is only done on blocks.x
+    // TODO If dim2 is not one-to-one batched, then divide blocks.x by factor
+    int batchImg2 = 1;
+    if (nImg2 != nTfs2) batchImg2 = min(nImg2, TI);
+
+    blocks.x *= (nImg2 / batchImg2);
+    blocks.y *= nImg3;
+
+    // Use blocks.z for transforms
+    blocks.z *= max((nTfs2 / nImg2), 1) * max((nTfs3 / nImg3), 1);
+
+    if (inverse) {
+        CUDA_LAUNCH((transform_kernel<T, true, order>), blocks, threads, out,
+                    in, nImg2, nImg3, nTfs2, nTfs3, batchImg2, blocksXPerImage,
+                    blocksYPerImage, perspective, method);
+    } else {
+        CUDA_LAUNCH((transform_kernel<T, false, order>), blocks, threads, out,
+                    in, nImg2, nImg3, nTfs2, nTfs3, batchImg2, blocksXPerImage,
+                    blocksYPerImage, perspective, method);
     }
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 7e80d049aa..8481115b90 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -7,114 +7,113 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
-    static const int TILE_DIM  = 32;
-    static const int THREADS_X = TILE_DIM;
-    static const int THREADS_Y = 256 / TILE_DIM;
+static const int TILE_DIM  = 32;
+static const int THREADS_X = TILE_DIM;
+static const int THREADS_Y = 256 / TILE_DIM;
 
-    template<typename T, bool conjugate>
-    __device__ T doOp(T in)
-    {
-        if (conjugate) return conj(in);
-        else return in;
-    }
+template<typename T, bool conjugate>
+__device__ T doOp(T in) {
+    if (conjugate)
+        return conj(in);
+    else
+        return in;
+}
 
-    // Kernel is going access original data in coaleasced format
-    template<typename T, bool conjugate, bool is32Multiple>
-    __global__
-    void transpose(Param<T> out, CParam<T> in,
-                   const int blocksPerMatX, const int blocksPerMatY)
-    {
-        __shared__ T shrdMem[TILE_DIM][TILE_DIM+1];
-        // create variables to hold output dimensions
-        const int oDim0 = out.dims[0];
-        const int oDim1 = out.dims[1];
-        const int iDim0 = in.dims[0];
-        const int iDim1 = in.dims[1];
+// Kernel is going access original data in coaleasced format
+template<typename T, bool conjugate, bool is32Multiple>
+__global__ void transpose(Param<T> out, CParam<T> in, const int blocksPerMatX,
+                          const int blocksPerMatY) {
+    __shared__ T shrdMem[TILE_DIM][TILE_DIM + 1];
+    // create variables to hold output dimensions
+    const int oDim0 = out.dims[0];
+    const int oDim1 = out.dims[1];
+    const int iDim0 = in.dims[0];
+    const int iDim1 = in.dims[1];
 
-        // calculate strides
-        const int oStride1 = out.strides[1];
-        const int iStride1 = in.strides[1];
+    // calculate strides
+    const int oStride1 = out.strides[1];
+    const int iStride1 = in.strides[1];
 
-        const int lx = threadIdx.x;
-        const int ly = threadIdx.y;
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
 
-        // batch based block Id
-        const int batchId_x = blockIdx.x / blocksPerMatX;
-        const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
+    // batch based block Id
+    const int batchId_x  = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
 
-        const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-        const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
+    const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
 
-        if(batchId_x >= in.dims[2] || batchId_y >= in.dims[3])
-            return;
+    if (batchId_x >= in.dims[2] || batchId_y >= in.dims[3]) return;
 
-        const int x0 = TILE_DIM * blockIdx_x;
-        const int y0 = TILE_DIM * blockIdx_y;
+    const int x0 = TILE_DIM * blockIdx_x;
+    const int y0 = TILE_DIM * blockIdx_y;
 
-        // calculate global indices
-        int gx = lx + x0;
-        int gy = ly + y0;
+    // calculate global indices
+    int gx = lx + x0;
+    int gy = ly + y0;
 
-        //offset in and out based on batch id
-        in.ptr  += batchId_x *  in.strides[2] + batchId_y *  in.strides[3];
-        out.ptr += batchId_x * out.strides[2] + batchId_y * out.strides[3];
+    // offset in and out based on batch id
+    in.ptr += batchId_x * in.strides[2] + batchId_y * in.strides[3];
+    out.ptr += batchId_x * out.strides[2] + batchId_y * out.strides[3];
 
 #pragma unroll
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy+repeat;
-            if (is32Multiple || (gx<iDim0 && gy_<iDim1))
-                shrdMem[ly + repeat][lx] = in.ptr[gy_ * iStride1 + gx];
-        }
-        __syncthreads();
+    for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+        int gy_ = gy + repeat;
+        if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+            shrdMem[ly + repeat][lx] = in.ptr[gy_ * iStride1 + gx];
+    }
+    __syncthreads();
 
-        gx = lx + y0;
-        gy = ly + x0;
+    gx = lx + y0;
+    gy = ly + x0;
 
 #pragma unroll
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy+repeat;
-            if (is32Multiple || (gx<oDim0 && gy_<oDim1))
-                out.ptr[gy_ * oStride1 + gx] = doOp<T, conjugate>(shrdMem[lx][ly + repeat]);
-        }
+    for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+        int gy_ = gy + repeat;
+        if (is32Multiple || (gx < oDim0 && gy_ < oDim1))
+            out.ptr[gy_ * oStride1 + gx] =
+                doOp<T, conjugate>(shrdMem[lx][ly + repeat]);
     }
+}
 
-    template<typename T, bool conjugate>
-    void transpose(Param<T> out, CParam<T> in)
-    {
-        // dimensions passed to this function should be input dimensions
-        // any necessary transformations and dimension related calculations are
-        // carried out here and inside the kernel
-        dim3 threads(kernel::THREADS_X,kernel::THREADS_Y);
-
-
-        int blk_x = divup(in.dims[0],TILE_DIM);
-        int blk_y = divup(in.dims[1],TILE_DIM);
-        // launch batch * blk_x blocks along x dimension
-        dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
-
-        if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0) {
-            CUDA_LAUNCH((transpose<T, conjugate, true >), blocks, threads, out, in, blk_x, blk_y);
-        } else {
-            CUDA_LAUNCH((transpose<T, conjugate, false>), blocks, threads, out, in, blk_x, blk_y);
-        }
-
-        POST_LAUNCH_CHECK();
+template<typename T, bool conjugate>
+void transpose(Param<T> out, CParam<T> in) {
+    // dimensions passed to this function should be input dimensions
+    // any necessary transformations and dimension related calculations are
+    // carried out here and inside the kernel
+    dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
+
+    int blk_x = divup(in.dims[0], TILE_DIM);
+    int blk_y = divup(in.dims[1], TILE_DIM);
+    // launch batch * blk_x blocks along x dimension
+    dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0) {
+        CUDA_LAUNCH((transpose<T, conjugate, true>), blocks, threads, out, in,
+                    blk_x, blk_y);
+    } else {
+        CUDA_LAUNCH((transpose<T, conjugate, false>), blocks, threads, out, in,
+                    blk_x, blk_y);
     }
-}
 
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index 26cf7b2205..f192d1c8d1 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -7,148 +7,147 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-namespace kernel
-{
+namespace kernel {
 
-    static const int TILE_DIM  = 32;
-    static const int THREADS_X = TILE_DIM;
-    static const int THREADS_Y = 256 / TILE_DIM;
+static const int TILE_DIM  = 32;
+static const int THREADS_X = TILE_DIM;
+static const int THREADS_Y = 256 / TILE_DIM;
 
-    template<typename T, bool conjugate>
-    __device__ T doOp(T in)
-    {
-        if (conjugate) return conj(in);
-        else return in;
-    }
+template<typename T, bool conjugate>
+__device__ T doOp(T in) {
+    if (conjugate)
+        return conj(in);
+    else
+        return in;
+}
 
-    // Hint from txbob
-    // https://devtalk.nvidia.com/default/topic/765696/efficient-in-place-transpose-of-multiple-square-float-matrices
-    //
-    // Kernel is going access original data in colleased format
-    template<typename T, bool conjugate, bool is32Multiple>
-    __global__
-    void transposeIP(Param<T> in, const int blocksPerMatX, const int blocksPerMatY)
-    {
-        __shared__ T shrdMem_s[TILE_DIM][TILE_DIM+1];
-        __shared__ T shrdMem_d[TILE_DIM][TILE_DIM+1];
+// Hint from txbob
+// https://devtalk.nvidia.com/default/topic/765696/efficient-in-place-transpose-of-multiple-square-float-matrices
+//
+// Kernel is going access original data in colleased format
+template<typename T, bool conjugate, bool is32Multiple>
+__global__ void transposeIP(Param<T> in, const int blocksPerMatX,
+                            const int blocksPerMatY) {
+    __shared__ T shrdMem_s[TILE_DIM][TILE_DIM + 1];
+    __shared__ T shrdMem_d[TILE_DIM][TILE_DIM + 1];
 
-        // create variables to hold output dimensions
-        const int iDim0 = in.dims[0];
-        const int iDim1 = in.dims[1];
+    // create variables to hold output dimensions
+    const int iDim0 = in.dims[0];
+    const int iDim1 = in.dims[1];
 
-        // calculate strides
-        const int iStride1 = in.strides[1];
+    // calculate strides
+    const int iStride1 = in.strides[1];
 
-        const int lx = threadIdx.x;
-        const int ly = threadIdx.y;
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
 
-        // batch based block Id
-        const int batchId_x = blockIdx.x / blocksPerMatX;
-        const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
+    // batch based block Id
+    const int batchId_x  = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
 
-        const int batchId_y = blockIdx.y / blocksPerMatY;
-        const int blockIdx_y = (blockIdx.y - batchId_y * blocksPerMatY);
+    const int batchId_y  = blockIdx.y / blocksPerMatY;
+    const int blockIdx_y = (blockIdx.y - batchId_y * blocksPerMatY);
 
-        const int x0 = TILE_DIM * blockIdx_x;
-        const int y0 = TILE_DIM * blockIdx_y;
+    const int x0 = TILE_DIM * blockIdx_x;
+    const int y0 = TILE_DIM * blockIdx_y;
 
-        // offset in and out based on batch id
-        T *iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
+    // offset in and out based on batch id
+    T *iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
 
-        if(blockIdx_y > blockIdx_x) {       // Off diagonal blocks
-            // calculate global indices
-            int gx      = lx + x0;
-            int gy      = ly + y0;
-            int dx      = lx + y0;
-            int dy      = ly + x0;
+    if (blockIdx_y > blockIdx_x) {  // Off diagonal blocks
+        // calculate global indices
+        int gx = lx + x0;
+        int gy = ly + y0;
+        int dx = lx + y0;
+        int dy = ly + x0;
 
-            // Copy to shared memory
+        // Copy to shared memory
 #pragma unroll
-            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-
-                int gy_ = gy + repeat;
-                if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                    shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
-
-                int dy_ = dy + repeat;
-                if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
-                    shrdMem_d[ly + repeat][lx] = iptr[dy_ * iStride1 + dx];
-            }
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
+
+            int dy_ = dy + repeat;
+            if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
+                shrdMem_d[ly + repeat][lx] = iptr[dy_ * iStride1 + dx];
+        }
 
-            __syncthreads();
+        __syncthreads();
 
-            // Copy from shared to global memory
+        // Copy from shared to global memory
 #pragma unroll
-            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-
-                int dy_ = dy + repeat;
-                if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
-                    iptr[dy_ * iStride1 + dx] = doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
-
-                int gy_ = gy + repeat;
-                if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                    iptr[gy_ * iStride1 + gx] = doOp<T, conjugate>(shrdMem_d[lx][ly + repeat]);
-            }
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int dy_ = dy + repeat;
+            if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
+                iptr[dy_ * iStride1 + dx] =
+                    doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
+
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                iptr[gy_ * iStride1 + gx] =
+                    doOp<T, conjugate>(shrdMem_d[lx][ly + repeat]);
+        }
 
-        } else if (blockIdx_y == blockIdx_x) {    // Diagonal blocks
-            // calculate global indices
-            int gx      = lx + x0;
-            int gy      = ly + y0;
+    } else if (blockIdx_y == blockIdx_x) {  // Diagonal blocks
+        // calculate global indices
+        int gx = lx + x0;
+        int gy = ly + y0;
 
-            // offset in and out based on batch id
-            iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
+        // offset in and out based on batch id
+        iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
 
-            // Copy to shared memory
+        // Copy to shared memory
 #pragma unroll
-            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-                int gy_ = gy + repeat;
-                if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                    shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
-            }
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
+        }
 
-            __syncthreads();
+        __syncthreads();
 
-            // Copy from shared to global memory
+        // Copy from shared to global memory
 #pragma unroll
-            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-                int gy_ = gy + repeat;
-                if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                    iptr[gy_ * iStride1 + gx] = doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
-            }
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                iptr[gy_ * iStride1 + gx] =
+                    doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
         }
     }
+}
 
-    template<typename T, bool conjugate>
-    void transpose_inplace(Param<T> in)
-    {
-        // dimensions passed to this function should be input dimensions
-        // any necessary transformations and dimension related calculations are
-        // carried out here and inside the kernel
-        dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
-
+template<typename T, bool conjugate>
+void transpose_inplace(Param<T> in) {
+    // dimensions passed to this function should be input dimensions
+    // any necessary transformations and dimension related calculations are
+    // carried out here and inside the kernel
+    dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
-        int blk_x = divup(in.dims[0],TILE_DIM);
-        int blk_y = divup(in.dims[1],TILE_DIM);
+    int blk_x = divup(in.dims[0], TILE_DIM);
+    int blk_y = divup(in.dims[1], TILE_DIM);
 
-        // launch batch * blk_x blocks along x dimension
-        dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
+    // launch batch * blk_x blocks along x dimension
+    dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
-        if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0)
-            CUDA_LAUNCH((transposeIP<T, conjugate, true >), blocks, threads, in, blk_x, blk_y);
-        else
-            CUDA_LAUNCH((transposeIP<T, conjugate, false>), blocks, threads, in, blk_x, blk_y);
+    if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0)
+        CUDA_LAUNCH((transposeIP<T, conjugate, true>), blocks, threads, in,
+                    blk_x, blk_y);
+    else
+        CUDA_LAUNCH((transposeIP<T, conjugate, false>), blocks, threads, in,
+                    blk_x, blk_y);
 
-        POST_LAUNCH_CHECK();
-    }
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 22f7cada81..73bd145623 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -7,90 +7,85 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
-namespace cuda
-{
-    namespace kernel
-    {
-        // Kernel Launch Config Values
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned TILEX = 128;
-        static const unsigned TILEY = 32;
-
-        template<typename T, bool is_upper, bool is_unit_diag>
-        __global__
-        void triangle_kernel(Param<T> r, CParam<T> in,
-                             const int blocksPerMatX, const int blocksPerMatY)
-        {
-            const int oz = blockIdx.x / blocksPerMatX;
-            const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-            const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-            const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-            const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-            const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-            const int incy = blocksPerMatY * blockDim.y;
-            const int incx = blocksPerMatX * blockDim.x;
-
-            T *d_r = r.ptr;
-            const T *d_i = in.ptr;
-
-            const T one  = scalar<T>(1);
-            const T zero = scalar<T>(0);
-
-            if(oz < r.dims[2] && ow < r.dims[3]) {
-                d_i = d_i + oz * in.strides[2]    + ow * in.strides[3];
-                d_r = d_r + oz * r.strides[2] + ow * r.strides[3];
-
-                for (int oy = yy; oy < r.dims[1]; oy += incy) {
-                    const T *Yd_i = d_i + oy * in.strides[1];
-                    T *Yd_r = d_r +  oy * r.strides[1];
-
-                    for (int ox = xx; ox < r.dims[0]; ox += incx) {
-
-                        bool cond = is_upper ? (oy >= ox) : (oy <= ox);
-                        bool do_unit_diag  = is_unit_diag && (ox == oy);
-                        if(cond) {
-                            // Change made because of compute 53 failing tests
-                            Yd_r[ox] = do_unit_diag ? one : Yd_i[ox];
-                        } else {
-                            Yd_r[ox] = zero;
-                        }
-                    }
+namespace cuda {
+namespace kernel {
+// Kernel Launch Config Values
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
+static const unsigned TILEX = 128;
+static const unsigned TILEY = 32;
+
+template<typename T, bool is_upper, bool is_unit_diag>
+__global__ void triangle_kernel(Param<T> r, CParam<T> in,
+                                const int blocksPerMatX,
+                                const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    T *d_r       = r.ptr;
+    const T *d_i = in.ptr;
+
+    const T one  = scalar<T>(1);
+    const T zero = scalar<T>(0);
+
+    if (oz < r.dims[2] && ow < r.dims[3]) {
+        d_i = d_i + oz * in.strides[2] + ow * in.strides[3];
+        d_r = d_r + oz * r.strides[2] + ow * r.strides[3];
+
+        for (int oy = yy; oy < r.dims[1]; oy += incy) {
+            const T *Yd_i = d_i + oy * in.strides[1];
+            T *Yd_r       = d_r + oy * r.strides[1];
+
+            for (int ox = xx; ox < r.dims[0]; ox += incx) {
+                bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
+                bool do_unit_diag = is_unit_diag && (ox == oy);
+                if (cond) {
+                    // Change made because of compute 53 failing tests
+                    Yd_r[ox] = do_unit_diag ? one : Yd_i[ox];
+                } else {
+                    Yd_r[ox] = zero;
                 }
             }
         }
+    }
+}
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool is_upper, bool is_unit_diag>
-        void triangle(Param<T> r, CParam<T> in)
-        {
-            dim3 threads(TX, TY, 1);
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool is_upper, bool is_unit_diag>
+void triangle(Param<T> r, CParam<T> in) {
+    dim3 threads(TX, TY, 1);
 
-            int blocksPerMatX = divup(r.dims[0], TILEX);
-            int blocksPerMatY = divup(r.dims[1], TILEY);
-            dim3 blocks(blocksPerMatX * r.dims[2],
-                        blocksPerMatY * r.dims[3],
-                        1);
+    int blocksPerMatX = divup(r.dims[0], TILEX);
+    int blocksPerMatY = divup(r.dims[1], TILEY);
+    dim3 blocks(blocksPerMatX * r.dims[2], blocksPerMatY * r.dims[3], 1);
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            CUDA_LAUNCH((triangle_kernel<T, is_upper, is_unit_diag>), blocks, threads,
-                    r, in, blocksPerMatX, blocksPerMatY);
+    CUDA_LAUNCH((triangle_kernel<T, is_upper, is_unit_diag>), blocks, threads,
+                r, in, blocksPerMatX, blocksPerMatY);
 
-            POST_LAUNCH_CHECK();
-        }
-    }
+    POST_LAUNCH_CHECK();
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 42678d8f0d..e0bf4616cc 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -7,146 +7,139 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <math.hpp>
 #include "config.hpp"
 
-namespace cuda
-{
-    namespace kernel
-    {
-        ///////////////////////////////////////////////////////////////////////////
-        // Unwrap Kernel
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool is_column>
-        __global__
-        void unwrap_kernel(Param<T> out, CParam<T> in,
-                           const int wx, const int wy, const int sx, const int sy,
-                           const int px, const int py, const int nx, int reps)
-        {
-            // Compute channel and volume
-            const int w = (blockIdx.y + blockIdx.z * gridDim.y) / in.dims[2];
-            const int z = (blockIdx.y + blockIdx.z * gridDim.y) % in.dims[2];
-
-            if(w >= in.dims[3] || z >= in.dims[2])
-                return;
-
-            // Compute offset for channel and volume
-            const int cOut = w * out.strides[3] + z * out.strides[2];
-            const int cIn  = w *  in.strides[3] + z *  in.strides[2];
-
-            // Compute the output column index
-            const int id = is_column ?
-                (blockIdx.x * blockDim.y + threadIdx.y) :
-                (blockIdx.x * blockDim.x + threadIdx.x);
-
-            if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
-
-            // Compute the starting index of window in x and y of input
-            const int startx = (id % nx) * sx;
-            const int starty = (id / nx) * sy;
-
-            const int spx = startx - px;
-            const int spy = starty - py;
-
-            // Offset the global pointers to the respective starting indices
-            T* optr = out.ptr + cOut + id * (is_column ? out.strides[1] : 1);
-            const T* iptr = in.ptr  + cIn;
-
-            bool cond = (spx >= 0 && spx + wx < in.dims[0] && spy >= 0 && spy + wy < in.dims[1]);
-
-            for(int i = 0; i < reps; i++) {
-
-                // Compute output index local to column
-                const int outIdx = is_column ?
-                    (i * blockDim.x + threadIdx.x) :
-                    (i * blockDim.y + threadIdx.y);
-
-                if(outIdx >= (is_column ? out.dims[0] : out.dims[1]))
-                    return;
-
-                // Compute input index local to window
-                const int x = outIdx % wx;
-                const int y = outIdx / wx;
-
-                const int xpad = spx + x;
-                const int ypad = spy + y;
-
-                // Copy
-                T val = scalar<T>(0.0);
-                if(cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 && ypad < in.dims[1])) {
-                    const int inIdx = ypad * in.strides[1] + xpad;
-                    val = iptr[inIdx];
-                }
-
-                if (is_column) {
-                    optr[outIdx] = val;
-                } else {
-                    optr[outIdx * out.strides[1]] = val;
-                }
-            }
+namespace cuda {
+namespace kernel {
+///////////////////////////////////////////////////////////////////////////
+// Unwrap Kernel
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool is_column>
+__global__ void unwrap_kernel(Param<T> out, CParam<T> in, const int wx,
+                              const int wy, const int sx, const int sy,
+                              const int px, const int py, const int nx,
+                              int reps) {
+    // Compute channel and volume
+    const int w = (blockIdx.y + blockIdx.z * gridDim.y) / in.dims[2];
+    const int z = (blockIdx.y + blockIdx.z * gridDim.y) % in.dims[2];
+
+    if (w >= in.dims[3] || z >= in.dims[2]) return;
+
+    // Compute offset for channel and volume
+    const int cOut = w * out.strides[3] + z * out.strides[2];
+    const int cIn  = w * in.strides[3] + z * in.strides[2];
+
+    // Compute the output column index
+    const int id = is_column ? (blockIdx.x * blockDim.y + threadIdx.y)
+                             : (blockIdx.x * blockDim.x + threadIdx.x);
+
+    if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
+
+    // Compute the starting index of window in x and y of input
+    const int startx = (id % nx) * sx;
+    const int starty = (id / nx) * sy;
+
+    const int spx = startx - px;
+    const int spy = starty - py;
+
+    // Offset the global pointers to the respective starting indices
+    T* optr       = out.ptr + cOut + id * (is_column ? out.strides[1] : 1);
+    const T* iptr = in.ptr + cIn;
+
+    bool cond = (spx >= 0 && spx + wx < in.dims[0] && spy >= 0 &&
+                 spy + wy < in.dims[1]);
+
+    for (int i = 0; i < reps; i++) {
+        // Compute output index local to column
+        const int outIdx = is_column ? (i * blockDim.x + threadIdx.x)
+                                     : (i * blockDim.y + threadIdx.y);
+
+        if (outIdx >= (is_column ? out.dims[0] : out.dims[1])) return;
+
+        // Compute input index local to window
+        const int x = outIdx % wx;
+        const int y = outIdx / wx;
+
+        const int xpad = spx + x;
+        const int ypad = spy + y;
+
+        // Copy
+        T val = scalar<T>(0.0);
+        if (cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 &&
+                     ypad < in.dims[1])) {
+            const int inIdx = ypad * in.strides[1] + xpad;
+            val             = iptr[inIdx];
         }
 
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename T>
-        void unwrap_col(Param<T> out, CParam<T> in, const int wx, const int wy,
-                        const int sx, const int sy,
-                        const int px, const int py, const int nx)
-        {
-            int TX = std::min(THREADS_PER_BLOCK, nextpow2(out.dims[0]));
+        if (is_column) {
+            optr[outIdx] = val;
+        } else {
+            optr[outIdx * out.strides[1]] = val;
+        }
+    }
+}
 
-            dim3 threads(TX, THREADS_PER_BLOCK / TX);
-            dim3 blocks(divup(out.dims[1], threads.y), out.dims[2] * out.dims[3]);
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void unwrap_col(Param<T> out, CParam<T> in, const int wx, const int wy,
+                const int sx, const int sy, const int px, const int py,
+                const int nx) {
+    int TX = std::min(THREADS_PER_BLOCK, nextpow2(out.dims[0]));
 
-            int reps = divup((wx * wy), threads.x); // is > 1 only when TX == 256 && wx * wy > 256
+    dim3 threads(TX, THREADS_PER_BLOCK / TX);
+    dim3 blocks(divup(out.dims[1], threads.y), out.dims[2] * out.dims[3]);
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    int reps = divup((wx * wy),
+                     threads.x);  // is > 1 only when TX == 256 && wx * wy > 256
 
-            CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads,
-                        out, in, wx, wy, sx, sy, px, py, nx, reps);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            POST_LAUNCH_CHECK();
-        }
+    CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads, out, in, wx, wy, sx,
+                sy, px, py, nx, reps);
 
-        template<typename T>
-        void unwrap_row(Param<T> out, CParam<T> in, const int wx, const int wy,
-                        const int sx, const int sy,
-                        const int px, const int py, const int nx)
-        {
-            dim3 threads(THREADS_X, THREADS_Y);
-            dim3 blocks(divup(out.dims[0], threads.x), out.dims[2] * out.dims[3]);
+    POST_LAUNCH_CHECK();
+}
 
-            int reps = divup((wx * wy), threads.y);
+template<typename T>
+void unwrap_row(Param<T> out, CParam<T> in, const int wx, const int wy,
+                const int sx, const int sy, const int px, const int py,
+                const int nx) {
+    dim3 threads(THREADS_X, THREADS_Y);
+    dim3 blocks(divup(out.dims[0], threads.x), out.dims[2] * out.dims[3]);
 
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    int reps = divup((wx * wy), threads.y);
 
-            CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads,
-                        out, in, wx, wy, sx, sy, px, py, nx, reps);
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
 
-            POST_LAUNCH_CHECK();
-        }
+    CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads, out, in, wx, wy, sx,
+                sy, px, py, nx, reps);
 
-        template <typename T>
-        void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
-                    const int sx, const int sy,
-                    const int px, const int py, const int nx, const bool is_column)
-        {
-
-            if (is_column) {
-                unwrap_col<T>(out, in, wx, wy, sx, sy, px, py, nx);
-            } else {
-                unwrap_row<T>(out, in, wx, wy, sx, sy, px, py, nx);
-            }
-        }
+    POST_LAUNCH_CHECK();
+}
 
+template<typename T>
+void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
+            const int sx, const int sy, const int px, const int py,
+            const int nx, const bool is_column) {
+    if (is_column) {
+        unwrap_col<T>(out, in, wx, wy, sx, sy, px, py, nx);
+    } else {
+        unwrap_row<T>(out, in, wx, wy, sx, sy, px, py, nx);
     }
 }
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 8ecadb6bd8..f971c96ae0 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -7,146 +7,139 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
-#include <backend.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <ops.hpp>
 #include "config.hpp"
 #include "scan_first.hpp"
 
-namespace cuda
-{
-namespace kernel
-{
-
-    template<typename T>
-    __global__
-    static void get_out_idx(uint *optr,
-                            CParam<uint> otmp,
-                            CParam<uint> rtmp,
-                            CParam<T> in,
-                            uint blocks_x,
-                            uint blocks_y,
-                            uint lim)
-    {
-        const uint tidx = threadIdx.x;
-        const uint tidy = threadIdx.y;
-
-        const uint zid = blockIdx.x / blocks_x;
-        const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-        const uint blockIdx_x = blockIdx.x - (blocks_x) * zid;
-        const uint blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y) * wid;
-        const uint xid = blockIdx_x * blockDim.x * lim + tidx;
-        const uint yid = blockIdx_y * blockDim.y + tidy;
-
-        const uint *otptr = otmp.ptr;
-        const uint *rtptr = rtmp.ptr;
-        const T *iptr = in.ptr;
-
-        const uint off = wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
-        const uint bid = wid * rtmp.strides[3] + zid * rtmp.strides[2] + yid * rtmp.strides[1] + blockIdx_x;
-
-        otptr += wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
-        iptr  += wid *   in.strides[3] + zid *   in.strides[2] + yid *   in.strides[1];
-
-        bool cond = (yid < otmp.dims[1]) && (zid < otmp.dims[2]) && (wid < otmp.dims[3]);
-        T zero = scalar<T>(0);
-
-        if (!cond) return;
-
-        uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
-
-        for (uint k = 0, id = xid;
-             k < lim && id < otmp.dims[0];
-             k++, id += blockDim.x) {
-
-            uint idx = otptr[id] + accum;
-            if (iptr[id] != zero) optr[idx - 1] = (off + id);
-        }
+namespace cuda {
+namespace kernel {
+
+template<typename T>
+__global__ static void get_out_idx(uint *optr, CParam<uint> otmp,
+                                   CParam<uint> rtmp, CParam<T> in,
+                                   uint blocks_x, uint blocks_y, uint lim) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint xid = blockIdx_x * blockDim.x * lim + tidx;
+    const uint yid = blockIdx_y * blockDim.y + tidy;
+
+    const uint *otptr = otmp.ptr;
+    const uint *rtptr = rtmp.ptr;
+    const T *iptr     = in.ptr;
+
+    const uint off =
+        wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
+    const uint bid = wid * rtmp.strides[3] + zid * rtmp.strides[2] +
+                     yid * rtmp.strides[1] + blockIdx_x;
+
+    otptr +=
+        wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+
+    bool cond =
+        (yid < otmp.dims[1]) && (zid < otmp.dims[2]) && (wid < otmp.dims[3]);
+    T zero = scalar<T>(0);
+
+    if (!cond) return;
+
+    uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
+
+    for (uint k = 0, id = xid; k < lim && id < otmp.dims[0];
+         k++, id += blockDim.x) {
+        uint idx = otptr[id] + accum;
+        if (iptr[id] != zero) optr[idx - 1] = (off + id);
+    }
+}
+
+template<typename T>
+static void where(Param<uint> &out, CParam<T> in) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.dims[1], threads_y);
+
+    Param<uint> rtmp;
+    Param<uint> otmp;
+    rtmp.dims[0]    = blocks_x;
+    otmp.dims[0]    = in.dims[0];
+    rtmp.strides[0] = 1;
+    otmp.strides[0] = 1;
+
+    for (int k = 1; k < 4; k++) {
+        rtmp.dims[k]    = in.dims[k];
+        rtmp.strides[k] = rtmp.strides[k - 1] * rtmp.dims[k - 1];
+
+        otmp.dims[k]    = in.dims[k];
+        otmp.strides[k] = otmp.strides[k - 1] * otmp.dims[k - 1];
     }
 
-    template<typename T>
-    static void where(Param<uint> &out, CParam<T> in)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(in.dims[1], threads_y);
-
-        Param<uint> rtmp;
-        Param<uint> otmp;
-        rtmp.dims[0] = blocks_x;
-        otmp.dims[0] = in.dims[0];
-        rtmp.strides[0] = 1;
-        otmp.strides[0] = 1;
-
-        for (int k = 1; k < 4; k++) {
-            rtmp.dims[k] = in.dims[k];
-            rtmp.strides[k] = rtmp.strides[k - 1] * rtmp.dims[k - 1];
-
-            otmp.dims[k] = in.dims[k];
-            otmp.strides[k] = otmp.strides[k - 1] * otmp.dims[k - 1];
-        }
-
-        int rtmp_elements = rtmp.strides[3] * rtmp.dims[3];
-        int otmp_elements = otmp.strides[3] * otmp.dims[3];
-        auto rtmp_alloc = memAlloc<uint>(rtmp_elements);
-        auto otmp_alloc = memAlloc<uint>(otmp_elements);
-        rtmp.ptr = rtmp_alloc.get();
-        otmp.ptr = otmp_alloc.get();
-
-        scan_first_launcher<T, uint, af_notzero_t, false, true>(otmp, rtmp, in,
-                                                          blocks_x, blocks_y,
-                                                          threads_x);
-
-        // Linearize the dimensions and perform scan
-        Param<uint> ltmp = rtmp;
-        ltmp.dims[0] = rtmp_elements;
-        for (int k = 1; k < 4; k++) {
-            ltmp.dims[k] = 1;
-            ltmp.strides[k] = rtmp_elements;
-        }
-
-        scan_first<uint, uint, af_add_t, true>(ltmp, ltmp);
-
-        // Get output size and allocate output
-        uint total;
-        CUDA_CHECK(cudaMemcpyAsync(&total, rtmp.ptr + rtmp_elements - 1,
-                              sizeof(uint), cudaMemcpyDeviceToHost,
-                              cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-        auto out_alloc = memAlloc<uint>(total);
-        out.ptr = out_alloc.get();
-
-        out.dims[0] = total;
-        out.strides[0] = 1;
-        for (int k = 1; k < 4; k++) {
-            out.dims[k] = 1;
-            out.strides[k] = total;
-        }
-
-        dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
-        dim3 blocks(blocks_x * in.dims[2],
-                    blocks_y * in.dims[3]);
-
-        uint lim = divup(otmp.dims[0], (threads_x * blocks_x));
-
-        const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-        blocks.z = divup(blocks.y, maxBlocksY);
-        blocks.y = divup(blocks.y, blocks.z);
-
-        CUDA_LAUNCH((get_out_idx<T>), blocks, threads,
-                out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
-        POST_LAUNCH_CHECK();
-
-        out_alloc.release();
+    int rtmp_elements = rtmp.strides[3] * rtmp.dims[3];
+    int otmp_elements = otmp.strides[3] * otmp.dims[3];
+    auto rtmp_alloc   = memAlloc<uint>(rtmp_elements);
+    auto otmp_alloc   = memAlloc<uint>(otmp_elements);
+    rtmp.ptr          = rtmp_alloc.get();
+    otmp.ptr          = otmp_alloc.get();
+
+    scan_first_launcher<T, uint, af_notzero_t, false, true>(
+        otmp, rtmp, in, blocks_x, blocks_y, threads_x);
+
+    // Linearize the dimensions and perform scan
+    Param<uint> ltmp = rtmp;
+    ltmp.dims[0]     = rtmp_elements;
+    for (int k = 1; k < 4; k++) {
+        ltmp.dims[k]    = 1;
+        ltmp.strides[k] = rtmp_elements;
     }
+
+    scan_first<uint, uint, af_add_t, true>(ltmp, ltmp);
+
+    // Get output size and allocate output
+    uint total;
+    CUDA_CHECK(cudaMemcpyAsync(&total, rtmp.ptr + rtmp_elements - 1,
+                               sizeof(uint), cudaMemcpyDeviceToHost,
+                               cuda::getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+
+    auto out_alloc = memAlloc<uint>(total);
+    out.ptr        = out_alloc.get();
+
+    out.dims[0]    = total;
+    out.strides[0] = 1;
+    for (int k = 1; k < 4; k++) {
+        out.dims[k]    = 1;
+        out.strides[k] = total;
+    }
+
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
+
+    uint lim = divup(otmp.dims[0], (threads_x * blocks_x));
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    CUDA_LAUNCH((get_out_idx<T>), blocks, threads, out.ptr, otmp, rtmp, in,
+                blocks_x, blocks_y, lim);
+    POST_LAUNCH_CHECK();
+
+    out_alloc.release();
 }
-}
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 8a8b3d7a4a..036ea4310d 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -7,111 +7,102 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <err_cuda.hpp>
+#include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <math.hpp>
-#include "config.hpp"
 #include "atomics.hpp"
+#include "config.hpp"
 
-namespace cuda
-{
-    namespace kernel
-    {
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrap Kernel
-        ///////////////////////////////////////////////////////////////////////////
-        template<typename T, bool is_column>
-        __global__
-        void wrap_kernel(Param<T> out, CParam<T> in,
-                         const int wx, const int wy,
-                         const int sx, const int sy,
-                         const int px, const int py,
-                         const int nx, const int ny,
-                         int blocks_x,
-                         int blocks_y)
-        {
-            int idx2 = blockIdx.x / blocks_x;
-            int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-
-            int blockIdx_x = blockIdx.x - idx2 * blocks_x;
-            int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idx3 * blocks_y;
-
-            int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
-            int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
+namespace cuda {
+namespace kernel {
 
-                  T *optr = out.ptr + idx2 * out.strides[2] + idx3 * out.strides[3];
-            const T *iptr =  in.ptr + idx2 *  in.strides[2] + idx3 *  in.strides[3];
+///////////////////////////////////////////////////////////////////////////
+// Wrap Kernel
+///////////////////////////////////////////////////////////////////////////
+template<typename T, bool is_column>
+__global__ void wrap_kernel(Param<T> out, CParam<T> in, const int wx,
+                            const int wy, const int sx, const int sy,
+                            const int px, const int py, const int nx,
+                            const int ny, int blocks_x, int blocks_y) {
+    int idx2 = blockIdx.x / blocks_x;
+    int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
 
+    int blockIdx_x = blockIdx.x - idx2 * blocks_x;
+    int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idx3 * blocks_y;
 
-            if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1] || idx2 >= out.dims[2] || idx3 >= out.dims[3]) return;
+    int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
+    int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
 
-            int pidx0 = oidx0 + px;
-            int pidx1 = oidx1 + py;
+    T *optr       = out.ptr + idx2 * out.strides[2] + idx3 * out.strides[3];
+    const T *iptr = in.ptr + idx2 * in.strides[2] + idx3 * in.strides[3];
 
-            // The last time a value appears in the unwrapped index is padded_index / stride
-            // Each previous index has the value appear "stride" locations earlier
-            // We work our way back from the last index
+    if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1] || idx2 >= out.dims[2] ||
+        idx3 >= out.dims[3])
+        return;
 
-            const int x_end = min(pidx0 / sx, nx - 1);
-            const int y_end = min(pidx1 / sy, ny - 1);
+    int pidx0 = oidx0 + px;
+    int pidx1 = oidx1 + py;
 
-            const int x_off = pidx0 - sx * x_end;
-            const int y_off = pidx1 - sy * y_end;
+    // The last time a value appears in the unwrapped index is padded_index /
+    // stride Each previous index has the value appear "stride" locations
+    // earlier We work our way back from the last index
 
-            T val = scalar<T>(0);
-            int idx = 1;
+    const int x_end = min(pidx0 / sx, nx - 1);
+    const int y_end = min(pidx1 / sy, ny - 1);
 
-            for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
-                int win_end_y = yo * wx;
-                int dim_end_y = y * nx;
+    const int x_off = pidx0 - sx * x_end;
+    const int y_off = pidx1 - sy * y_end;
 
-                for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
+    T val   = scalar<T>(0);
+    int idx = 1;
 
-                    int win_end = win_end_y + xo;
-                    int dim_end = dim_end_y + x;
+    for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
+        int win_end_y = yo * wx;
+        int dim_end_y = y * nx;
 
-                    if (is_column) {
-                        idx = dim_end * in.strides[1] + win_end;
-                    } else {
-                        idx = dim_end + win_end * in.strides[1];
-                    }
+        for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
+            int win_end = win_end_y + xo;
+            int dim_end = dim_end_y + x;
 
-                    val = val + iptr[idx];
-                }
+            if (is_column) {
+                idx = dim_end * in.strides[1] + win_end;
+            } else {
+                idx = dim_end + win_end * in.strides[1];
             }
 
-            optr[oidx1 * out.strides[1] + oidx0] = val;
+            val = val + iptr[idx];
         }
+    }
 
-        template <typename T>
-        void wrap(Param<T> out, CParam<T> in, const int wx, const int wy,
-                  const int sx, const int sy,
-                  const int px, const int py,
-                  const bool is_column)
-        {
-            int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
-            int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
-
-            dim3 threads(THREADS_X, THREADS_Y);
-            int blocks_x = divup(out.dims[0], threads.x);
-            int blocks_y = divup(out.dims[1], threads.y);
-
-            dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
-
-            const int maxBlocksY = cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-            blocks.z = divup(blocks.y, maxBlocksY);
-            blocks.y = divup(blocks.y, blocks.z);
+    optr[oidx1 * out.strides[1] + oidx0] = val;
+}
 
-            if (is_column) {
-                CUDA_LAUNCH((wrap_kernel<T, true >), blocks, threads,
-                            out, in, wx, wy, sx, sy, px, py, nx, ny, blocks_x, blocks_y);
-            } else {
-                CUDA_LAUNCH((wrap_kernel<T, false>), blocks, threads,
-                            out, in, wx, wy, sx, sy, px, py, nx, ny, blocks_x, blocks_y);
-            }
-        }
+template<typename T>
+void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
+          const int sy, const int px, const int py, const bool is_column) {
+    int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
+    int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
+
+    dim3 threads(THREADS_X, THREADS_Y);
+    int blocks_x = divup(out.dims[0], threads.x);
+    int blocks_y = divup(out.dims[1], threads.y);
+
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    if (is_column) {
+        CUDA_LAUNCH((wrap_kernel<T, true>), blocks, threads, out, in, wx, wy,
+                    sx, sy, px, py, nx, ny, blocks_x, blocks_y);
+    } else {
+        CUDA_LAUNCH((wrap_kernel<T, false>), blocks, threads, out, in, wx, wy,
+                    sx, sy, px, py, nx, ny, blocks_x, blocks_y);
     }
 }
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/logic.hpp b/src/backend/cuda/logic.hpp
index 2c047ba8f8..1f044e8ee4 100644
--- a/src/backend/cuda/logic.hpp
+++ b/src/backend/cuda/logic.hpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <binary.hpp>
 #include <err_cuda.hpp>
+#include <optypes.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    template<typename T, af_op_t op>
-    Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<char, T, op>(lhs, rhs, odims);
-    }
+namespace cuda {
+template<typename T, af_op_t op>
+Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
+                    const af::dim4 &odims) {
+    return createBinaryNode<char, T, op>(lhs, rhs, odims);
+}
 
-    template<typename T, af_op_t op>
-    Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<T, T, op>(lhs, rhs, odims);
-    }
+template<typename T, af_op_t op>
+Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
+               const af::dim4 &odims) {
+    return createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/lookup.cu b/src/backend/cuda/lookup.cu
index 7849e3e366..725f238f50 100644
--- a/src/backend/cuda/lookup.cu
+++ b/src/backend/cuda/lookup.cu
@@ -7,57 +7,72 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <lookup.hpp>
 #include <err_cuda.hpp>
 #include <kernel/lookup.hpp>
+#include <lookup.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input,
-                   const Array<idx_t> &indices, const unsigned dim)
-{
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim) {
     const dim4 iDims = input.dims();
 
     dim4 oDims(1);
-    for (dim_t d=0; d<4; ++d)
-        oDims[d] = (d==dim ? indices.elements() : iDims[d]);
+    for (dim_t d = 0; d < 4; ++d)
+        oDims[d] = (d == dim ? indices.elements() : iDims[d]);
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
     dim_t nDims = iDims.ndims();
 
-    switch(dim) {
-        case 0: kernel::lookup<in_t, idx_t, 0>(out, input, indices, nDims); break;
-        case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices, nDims); break;
-        case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices, nDims); break;
-        case 3: kernel::lookup<in_t, idx_t, 3>(out, input, indices, nDims); break;
+    switch (dim) {
+        case 0:
+            kernel::lookup<in_t, idx_t, 0>(out, input, indices, nDims);
+            break;
+        case 1:
+            kernel::lookup<in_t, idx_t, 1>(out, input, indices, nDims);
+            break;
+        case 2:
+            kernel::lookup<in_t, idx_t, 2>(out, input, indices, nDims);
+            break;
+        case 3:
+            kernel::lookup<in_t, idx_t, 3>(out, input, indices, nDims);
+            break;
     }
 
     return out;
 }
 
-#define INSTANTIATE(T)  \
-template Array<T> lookup<T, float   >(const Array<T>&, const Array<float   >&, const unsigned); \
-template Array<T> lookup<T, double  >(const Array<T>&, const Array<double  >&, const unsigned); \
-template Array<T> lookup<T, int     >(const Array<T>&, const Array<int     >&, const unsigned); \
-template Array<T> lookup<T, unsigned>(const Array<T>&, const Array<unsigned>&, const unsigned); \
-template Array<T> lookup<T, short   >(const Array<T>&, const Array<short   >&, const unsigned); \
-template Array<T> lookup<T, ushort  >(const Array<T>&, const Array<ushort  >&, const unsigned); \
-template Array<T> lookup<T, intl    >(const Array<T>&, const Array<intl    >&, const unsigned); \
-template Array<T> lookup<T, uintl   >(const Array<T>&, const Array<uintl   >&, const unsigned); \
-template Array<T> lookup<T, uchar   >(const Array<T>&, const Array<uchar   >&, const unsigned);
-
-INSTANTIATE(float   );
-INSTANTIATE(cfloat  );
-INSTANTIATE(double  );
-INSTANTIATE(cdouble );
-INSTANTIATE(int     );
+#define INSTANTIATE(T)                                                         \
+    template Array<T> lookup<T, float>(const Array<T> &, const Array<float> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, double>(                                       \
+        const Array<T> &, const Array<double> &, const unsigned);              \
+    template Array<T> lookup<T, int>(const Array<T> &, const Array<int> &,     \
+                                     const unsigned);                          \
+    template Array<T> lookup<T, unsigned>(                                     \
+        const Array<T> &, const Array<unsigned> &, const unsigned);            \
+    template Array<T> lookup<T, short>(const Array<T> &, const Array<short> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, ushort>(                                       \
+        const Array<T> &, const Array<ushort> &, const unsigned);              \
+    template Array<T> lookup<T, intl>(const Array<T> &, const Array<intl> &,   \
+                                      const unsigned);                         \
+    template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
+                                       const unsigned);
+
+INSTANTIATE(float);
+INSTANTIATE(cfloat);
+INSTANTIATE(double);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
 INSTANTIATE(unsigned);
-INSTANTIATE(intl    );
-INSTANTIATE(uintl   );
-INSTANTIATE(uchar   );
-INSTANTIATE(char    );
-INSTANTIATE(short   );
-INSTANTIATE(ushort  );
-}
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(uchar);
+INSTANTIATE(char);
+INSTANTIATE(short);
+INSTANTIATE(ushort);
+}  // namespace cuda
diff --git a/src/backend/cuda/lookup.hpp b/src/backend/cuda/lookup.hpp
index d1ff6aa48f..0a3c25414a 100644
--- a/src/backend/cuda/lookup.hpp
+++ b/src/backend/cuda/lookup.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input,
-                   const Array<idx_t> &indices, const unsigned dim);
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim);
 }
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index 7aae836594..2fdf9bf45c 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -7,28 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <lu.hpp>
 #include <common/err_common.hpp>
+#include <lu.hpp>
 
-#include <platform.hpp>
-#include <memory.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
 #include <math.hpp>
-#include <common/err_common.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
 
 #include <kernel/lu_split.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-//cusolverStatus_t CUDENSEAPI cusolverDn<>getrf_bufferSize(
+// cusolverStatus_t CUDENSEAPI cusolverDn<>getrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
 //        <> *A,
 //        int lda, int *Lwork );
 //
 //
-//cusolverStatus_t CUDENSEAPI cusolverDn<>getrf(
+// cusolverStatus_t CUDENSEAPI cusolverDn<>getrf(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
 //        <> *A,
@@ -37,61 +36,54 @@ namespace cuda
 //        int *devIpiv, int *devInfo );
 
 template<typename T>
-struct getrf_func_def_t
-{
-    typedef cusolverStatus_t (*getrf_func_def) (
-                              cusolverDnHandle_t, int, int,
-                              T *, int,
-                              T *,
-                              int *, int *);
+struct getrf_func_def_t {
+    typedef cusolverStatus_t (*getrf_func_def)(cusolverDnHandle_t, int, int,
+                                               T *, int, T *, int *, int *);
 };
 
 template<typename T>
-struct getrf_buf_func_def_t
-{
-    typedef cusolverStatus_t (*getrf_buf_func_def) (
-                              cusolverDnHandle_t, int, int,
-                              T *, int, int *);
+struct getrf_buf_func_def_t {
+    typedef cusolverStatus_t (*getrf_buf_func_def)(cusolverDnHandle_t, int, int,
+                                                   T *, int, int *);
 };
 
-#define LU_FUNC_DEF( FUNC )                                                     \
-template<typename T>                                                            \
-typename FUNC##_func_def_t<T>::FUNC##_func_def                                  \
-FUNC##_func();                                                                  \
-                                                                                \
-template<typename T>                                                            \
-typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def                          \
-FUNC##_buf_func();
-
-
-#define LU_FUNC( FUNC, TYPE, PREFIX )                                                   \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def                            \
-FUNC##_func<TYPE>()                                                                     \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusolverDn##PREFIX##FUNC; }         \
-                                                                                        \
-template<> typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def                    \
-FUNC##_buf_func<TYPE>()                                                                 \
-{ return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def)& cusolverDn##PREFIX##FUNC##_bufferSize; }
-
-LU_FUNC_DEF( getrf )
-LU_FUNC(getrf , float  , S)
-LU_FUNC(getrf , double , D)
-LU_FUNC(getrf , cfloat , C)
-LU_FUNC(getrf , cdouble, Z)
-
-void convertPivot(Array<int> &pivot, int out_sz)
-{
+#define LU_FUNC_DEF(FUNC)                                         \
+    template<typename T>                                          \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func(); \
+                                                                  \
+    template<typename T>                                          \
+    typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def FUNC##_buf_func();
+
+#define LU_FUNC(FUNC, TYPE, PREFIX)                                         \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusolverDn##PREFIX##FUNC;                                    \
+    }                                                                       \
+                                                                            \
+    template<>                                                              \
+    typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def               \
+        FUNC##_buf_func<TYPE>() {                                           \
+        return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def) &         \
+               cusolverDn##PREFIX##FUNC##_bufferSize;                       \
+    }
+
+LU_FUNC_DEF(getrf)
+LU_FUNC(getrf, float, S)
+LU_FUNC(getrf, double, D)
+LU_FUNC(getrf, cfloat, C)
+LU_FUNC(getrf, cdouble, Z)
+
+void convertPivot(Array<int> &pivot, int out_sz) {
     dim_t d0 = pivot.dims()[0];
 
     std::vector<int> d_po(out_sz);
-    for(int i = 0; i < out_sz; i++) {
-        d_po[i] = i;
-    }
+    for (int i = 0; i < out_sz; i++) { d_po[i] = i; }
 
     std::vector<int> d_pi(d0);
     copyData(&d_pi[0], pivot);
 
-    for(int j = 0; j < d0; j++) {
+    for (int j = 0; j < d0; j++) {
         // 1 indexed in pivot
         std::swap(d_po[j], d_po[d_pi[j] - 1]);
     }
@@ -99,16 +91,15 @@ void convertPivot(Array<int> &pivot, int out_sz)
     pivot = createHostDataArray<int>(out_sz, &d_po[0]);
 }
 
-
 template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<T> in_copy = copyArray<T>(in);
-    pivot = lu_inplace(in_copy);
+    pivot            = lu_inplace(in_copy);
 
     // SPLIT into lower and upper
     dim4 ldims(M, min(M, N));
@@ -119,48 +110,40 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 }
 
 template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(getrf_buf_func<T>()(solverDnHandle(),
-                                       M, N,
-                                       in.get(), in.strides()[1],
-                                       &lwork));
+    CUSOLVER_CHECK(getrf_buf_func<T>()(solverDnHandle(), M, N, in.get(),
+                                       in.strides()[1], &lwork));
 
     auto workspace = memAlloc<T>(lwork);
-    auto info = memAlloc<int>(1);
-
-    CUSOLVER_CHECK(getrf_func<T>()(solverDnHandle(),
-                                   M, N,
-                                   in.get(), in.strides()[1],
-                                   workspace.get(),
-                                   pivot.get(),
-                                   info.get()));
+    auto info      = memAlloc<int>(1);
 
-    if(convert_pivot) convertPivot(pivot, M);
+    CUSOLVER_CHECK(getrf_func<T>()(solverDnHandle(), M, N, in.get(),
+                                   in.strides()[1], workspace.get(),
+                                   pivot.get(), info.get()));
 
+    if (convert_pivot) convertPivot(pivot, M);
 
     return pivot;
 }
 
-bool isLAPACKAvailable()
-{
-    return true;
-}
+bool isLAPACKAvailable() { return true; }
 
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
 
 INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/lu.hpp b/src/backend/cuda/lu.hpp
index 507564ff23..335d6b3376 100644
--- a/src/backend/cuda/lu.hpp
+++ b/src/backend/cuda/lu.hpp
@@ -9,13 +9,13 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+namespace cuda {
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in);
 
-    template<typename T>
-    Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
-    bool isLAPACKAvailable();
-}
+bool isLAPACKAvailable();
+}  // namespace cuda
diff --git a/src/backend/cuda/match_template.cu b/src/backend/cuda/match_template.cu
index 7307e1969a..d13cd5d6b9 100644
--- a/src/backend/cuda/match_template.cu
+++ b/src/backend/cuda/match_template.cu
@@ -7,25 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <match_template.hpp>
 #include <kernel/match_template.hpp>
+#include <match_template.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename inType, typename outType, af_match_type mType>
-Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg)
-{
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg) {
     Array<outType> out = createEmptyArray<outType>(sImg.dims());
 
-    bool needMean = mType==AF_ZSAD || mType==AF_LSAD ||
-                    mType==AF_ZSSD || mType==AF_LSSD ||
-                    mType==AF_ZNCC;
+    bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
+                    mType == AF_LSSD || mType == AF_ZNCC;
 
     if (needMean)
         kernel::matchTemplate<inType, outType, mType, true>(out, sImg, tImg);
@@ -35,24 +33,33 @@ Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tI
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)\
-    template Array<out_t> match_template<in_t, out_t, AF_SAD >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_LSAD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_SSD >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_LSSD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_NCC >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_SHD >(const Array<in_t> &sImg, const Array<in_t> &tImg);
+#define INSTANTIATE(in_t, out_t)                                \
+    template Array<out_t> match_template<in_t, out_t, AF_SAD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_LSAD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_SSD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_LSSD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_NCC>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_SHD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float ,  float)
-INSTANTIATE(char  ,  float)
-INSTANTIATE(int   ,  float)
-INSTANTIATE(uint  ,  float)
-INSTANTIATE(uchar ,  float)
-INSTANTIATE(short ,  float)
-INSTANTIATE(ushort,  float)
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/match_template.hpp b/src/backend/cuda/match_template.hpp
index 7803d90e23..b6308c91ed 100644
--- a/src/backend/cuda/match_template.hpp
+++ b/src/backend/cuda/match_template.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename inType, typename outType, af_match_type mType>
-Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg);
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg);
 
 }
diff --git a/src/backend/cuda/math.cpp b/src/backend/cuda/math.cpp
index 928ad7ea80..e6d8c90d7d 100644
--- a/src/backend/cuda/math.cpp
+++ b/src/backend/cuda/math.cpp
@@ -9,21 +9,18 @@
 
 #include <math.hpp>
 
-namespace cuda
-{
-    cfloat division(cfloat lhs, double rhs)
-    {
-        cfloat retVal;
-        retVal.x = real(lhs) / rhs;
-        retVal.y = imag(lhs) / rhs;
-        return retVal;
-    }
+namespace cuda {
+cfloat division(cfloat lhs, double rhs) {
+    cfloat retVal;
+    retVal.x = real(lhs) / rhs;
+    retVal.y = imag(lhs) / rhs;
+    return retVal;
+}
 
-    cdouble division(cdouble lhs, double rhs)
-    {
-        cdouble retVal;
-        retVal.x = real(lhs) / rhs;
-        retVal.y = imag(lhs) / rhs;
-        return retVal;
-    }
+cdouble division(cdouble lhs, double rhs) {
+    cdouble retVal;
+    retVal.x = real(lhs) / rhs;
+    retVal.y = imag(lhs) / rhs;
+    return retVal;
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index f549b8a063..bbf64726d3 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <common/defines.hpp>
+#include <af/defines.h>
 #include "backend.hpp"
 #include "types.hpp"
 
@@ -18,217 +18,321 @@
 #include <math_constants.h>
 #endif
 
-#include <limits>
 #include <algorithm>
+#include <limits>
 
-namespace cuda
-{
-    template<typename T> static inline __DH__ T abs(T val)  { return abs(val); }
-    static inline __DH__ int  abs(int  val) { return (val>0? val : -val); }
-    static inline __DH__ char  abs(char  val) { return (val>0? val : -val); }
-    static inline __DH__ float  abs(float  val) { return fabsf(val); }
-    static inline __DH__ double abs(double val) { return fabs (val); }
-    static inline __DH__ float  abs(cfloat  cval) { return cuCabsf(cval); }
-    static inline __DH__ double abs(cdouble cval) { return cuCabs (cval); }
-
-    static inline __DH__ size_t min(size_t lhs, size_t rhs) { return lhs < rhs ? lhs : rhs; }
-    static inline __DH__ size_t max(size_t lhs, size_t rhs) { return lhs > rhs ? lhs : rhs; }
+namespace cuda {
+template<typename T>
+static inline __DH__ T abs(T val) {
+    return abs(val);
+}
+static inline __DH__ int abs(int val) { return (val > 0 ? val : -val); }
+static inline __DH__ char abs(char val) { return (val > 0 ? val : -val); }
+static inline __DH__ float abs(float val) { return fabsf(val); }
+static inline __DH__ double abs(double val) { return fabs(val); }
+static inline __DH__ float abs(cfloat cval) { return cuCabsf(cval); }
+static inline __DH__ double abs(cdouble cval) { return cuCabs(cval); }
+
+static inline __DH__ size_t min(size_t lhs, size_t rhs) {
+    return lhs < rhs ? lhs : rhs;
+}
+static inline __DH__ size_t max(size_t lhs, size_t rhs) {
+    return lhs > rhs ? lhs : rhs;
+}
 
 #ifndef __CUDA_ARCH__
-    template<typename T> static inline __DH__ T min(T lhs, T rhs) { return std::min(lhs, rhs);}
-    template<typename T> static inline __DH__ T max(T lhs, T rhs) { return std::max(lhs, rhs);}
+template<typename T>
+static inline __DH__ T min(T lhs, T rhs) {
+    return std::min(lhs, rhs);
+}
+template<typename T>
+static inline __DH__ T max(T lhs, T rhs) {
+    return std::max(lhs, rhs);
+}
 #else
-    template<typename T> static inline __DH__ T min(T lhs, T rhs) { return ::min(lhs, rhs);}
-    template<typename T> static inline __DH__ T max(T lhs, T rhs) { return ::max(lhs, rhs);}
+template<typename T>
+static inline __DH__ T min(T lhs, T rhs) {
+    return ::min(lhs, rhs);
+}
+template<typename T>
+static inline __DH__ T max(T lhs, T rhs) {
+    return ::max(lhs, rhs);
+}
 #endif
 
-    template<> __DH__
-    STATIC_ cfloat  max<cfloat >(cfloat lhs, cfloat rhs)
-    {
-        return abs(lhs) > abs(rhs) ? lhs : rhs;
-    }
-
-    template<> __DH__
-    STATIC_ cdouble max<cdouble>(cdouble lhs, cdouble rhs)
-    {
-        return abs(lhs) > abs(rhs) ? lhs : rhs;
-    }
-
-    template<> __DH__
-    STATIC_ cfloat  min<cfloat >(cfloat lhs, cfloat rhs)
-    {
-        return abs(lhs) < abs(rhs) ? lhs :  rhs;
-    }
-
-    template<> __DH__
-    STATIC_ cdouble min<cdouble>(cdouble lhs, cdouble rhs)
-    {
-        return abs(lhs) < abs(rhs) ? lhs :  rhs;
-    }
-
-    template<typename T> __DH__
-    static T scalar(double val)
-    {
-        return (T)(val);
-    }
-
-    template<> __DH__
-    STATIC_ cfloat scalar<cfloat >(double val)
-    {
-        cfloat  cval = {(float)val, 0};
-        return cval;
-    }
-
-    template<> __DH__
-    STATIC_ cdouble scalar<cdouble >(double val)
-    {
-        cdouble  cval = {val, 0};
-        return cval;
-    }
-
-    template<typename To, typename Ti> __DH__
-    static To scalar(Ti real, Ti imag)
-    {
-        To  cval = {real, imag};
-        return cval;
-    }
+template<>
+__DH__ STATIC_ cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+template<>
+__DH__ STATIC_ cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+template<>
+__DH__ STATIC_ cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+template<>
+__DH__ STATIC_ cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+template<typename T>
+__DH__ static T scalar(double val) {
+    return (T)(val);
+}
+
+template<>
+__DH__ STATIC_ cfloat scalar<cfloat>(double val) {
+    cfloat cval = {(float)val, 0};
+    return cval;
+}
+
+template<>
+__DH__ STATIC_ cdouble scalar<cdouble>(double val) {
+    cdouble cval = {val, 0};
+    return cval;
+}
+
+template<typename To, typename Ti>
+__DH__ static To scalar(Ti real, Ti imag) {
+    To cval = {real, imag};
+    return cval;
+}
 
 #ifndef __CUDA_ARCH__
-    template <typename T> STATIC_ T      maxval() { return  std::numeric_limits<T     >::max();      }
-    template <typename T> STATIC_ T      minval() { return  std::numeric_limits<T     >::min();      }
-    template <>           STATIC_ float  maxval() { return  std::numeric_limits<float >::infinity(); }
-    template <>           STATIC_ double maxval() { return  std::numeric_limits<double>::infinity(); }
-    template <>           STATIC_ float  minval() { return -std::numeric_limits<float >::infinity(); }
-    template <>           STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
+template<typename T>
+STATIC_ T maxval() {
+    return std::numeric_limits<T>::max();
+}
+template<typename T>
+STATIC_ T minval() {
+    return std::numeric_limits<T>::min();
+}
+template<>
+STATIC_ float maxval() {
+    return std::numeric_limits<float>::infinity();
+}
+template<>
+STATIC_ double maxval() {
+    return std::numeric_limits<double>::infinity();
+}
+template<>
+STATIC_ float minval() {
+    return -std::numeric_limits<float>::infinity();
+}
+template<>
+STATIC_ double minval() {
+    return -std::numeric_limits<double>::infinity();
+}
 #else
-    template <typename T> __device__ T maxval() { return 1u << (8 * sizeof(T) - 1); }
-    template <typename T> __device__ T minval() { return scalar<T>(0); }
-
-    template<> __device__  int    maxval<int>()    { return 0x7fffffff; }
-    template<> __device__  int    minval<int>()    { return 0x80000000; }
-    template<> __device__  intl   maxval<intl>()   { return 0x7fffffffffffffff; }
-    template<> __device__  intl   minval<intl>()   { return 0x8000000000000000; }
-    template<> __device__  uintl  maxval<uintl>()  { return 1ULL << (8 * sizeof(uintl) - 1); }
-    template<> __device__  char   maxval<char>()   { return 0x7f; }
-    template<> __device__  char   minval<char>()   { return 0x80; }
-    template<> __device__  float  maxval<float>()  { return  CUDART_INF_F; }
-    template<> __device__  float  minval<float>()  { return -CUDART_INF_F; }
-    template<> __device__  double maxval<double>() { return  CUDART_INF; }
-    template<> __device__  double minval<double>() { return -CUDART_INF; }
-    template<> __device__  short  maxval<short>()  { return 0x7fff; }
-    template<> __device__  short  minval<short>()  { return 0x8000; }
-    template<> __device__  ushort maxval<ushort>() { return ((ushort)1) << (8 * sizeof(ushort) - 1); }
+template<typename T>
+__device__ T maxval() {
+    return 1u << (8 * sizeof(T) - 1);
+}
+template<typename T>
+__device__ T minval() {
+    return scalar<T>(0);
+}
+
+template<>
+__device__ int maxval<int>() {
+    return 0x7fffffff;
+}
+template<>
+__device__ int minval<int>() {
+    return 0x80000000;
+}
+template<>
+__device__ intl maxval<intl>() {
+    return 0x7fffffffffffffff;
+}
+template<>
+__device__ intl minval<intl>() {
+    return 0x8000000000000000;
+}
+template<>
+__device__ uintl maxval<uintl>() {
+    return 1ULL << (8 * sizeof(uintl) - 1);
+}
+template<>
+__device__ char maxval<char>() {
+    return 0x7f;
+}
+template<>
+__device__ char minval<char>() {
+    return 0x80;
+}
+template<>
+__device__ float maxval<float>() {
+    return CUDART_INF_F;
+}
+template<>
+__device__ float minval<float>() {
+    return -CUDART_INF_F;
+}
+template<>
+__device__ double maxval<double>() {
+    return CUDART_INF;
+}
+template<>
+__device__ double minval<double>() {
+    return -CUDART_INF;
+}
+template<>
+__device__ short maxval<short>() {
+    return 0x7fff;
+}
+template<>
+__device__ short minval<short>() {
+    return 0x8000;
+}
+template<>
+__device__ ushort maxval<ushort>() {
+    return ((ushort)1) << (8 * sizeof(ushort) - 1);
+}
 #endif
 
 #define upcast cuComplexFloatToDouble
 #define downcast cuComplexDoubleToFloat
 
 #ifdef __GNUC__
-//This suprresses unused function warnings in gcc
-//FIXME: Check if the warnings exist in other compilers
+// This suprresses unused function warnings in gcc
+// FIXME: Check if the warnings exist in other compilers
 #define __SDH__ static __DH__ __attribute__((unused))
 #else
 #define __SDH__ static __DH__
 #endif
-__SDH__ float  real(cfloat  c) { return cuCrealf(c); }
-__SDH__ double real(cdouble c) { return cuCreal(c);  }
+__SDH__ float real(cfloat c) { return cuCrealf(c); }
+__SDH__ double real(cdouble c) { return cuCreal(c); }
 
-__SDH__ float  imag(cfloat  c) { return cuCimagf(c); }
-__SDH__ double imag(cdouble c) { return cuCimag(c);  }
+__SDH__ float imag(cfloat c) { return cuCimagf(c); }
+__SDH__ double imag(cdouble c) { return cuCimag(c); }
 
-template<typename T> T
-__SDH__  conj(T  x) { return x; }
-__SDH__ cfloat  conj(cfloat  c) { return cuConjf(c);}
+template<typename T>
+T __SDH__ conj(T x) {
+    return x;
+}
+__SDH__ cfloat conj(cfloat c) { return cuConjf(c); }
 __SDH__ cdouble conj(cdouble c) { return cuConj(c); }
 
-__SDH__ cfloat make_cfloat(bool     x) { return make_cuComplex(static_cast<float>(x),0);     }
-__SDH__ cfloat make_cfloat(int      x) { return make_cuComplex(static_cast<float>(x),0);     }
-__SDH__ cfloat make_cfloat(unsigned x) { return make_cuComplex(static_cast<float>(x),0);     }
-__SDH__ cfloat make_cfloat(short    x) { return make_cuComplex(static_cast<float>(x),0);     }
-__SDH__ cfloat make_cfloat(ushort   x) { return make_cuComplex(static_cast<float>(x),0);     }
-__SDH__ cfloat make_cfloat(float    x) { return make_cuComplex(static_cast<float>(x),0);     }
-  __SDH__ cfloat make_cfloat(double   x) { return make_cuComplex(static_cast<float>(x),0);     }
-  __SDH__ cfloat make_cfloat(cfloat   x) { return x;                    }
-  __SDH__ cfloat make_cfloat(cdouble  c) { return make_cuComplex(c.x,c.y); }
-
-__SDH__ cdouble make_cdouble(bool      x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(int       x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(unsigned  x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(short     x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(ushort    x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(float     x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(double    x) { return make_cuDoubleComplex(static_cast<double>(x),0);       }
-__SDH__ cdouble make_cdouble(cdouble   x) { return x;                       }
-__SDH__ cdouble make_cdouble(cfloat    c) { return make_cuDoubleComplex(static_cast<double>(c.x),c.y);   }
-
-__SDH__ cfloat make_cfloat(float x, float y) { return make_cuComplex(x, y); }
-__SDH__ cdouble make_cdouble(double x, double y) { return make_cuDoubleComplex(x, y); }
+__SDH__ cfloat make_cfloat(bool x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(int x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(unsigned x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(short x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(ushort x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(float x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(double x) {
+    return make_cuComplex(static_cast<float>(x), 0);
+}
+__SDH__ cfloat make_cfloat(cfloat x) { return x; }
+__SDH__ cfloat make_cfloat(cdouble c) { return make_cuComplex(c.x, c.y); }
 
+__SDH__ cdouble make_cdouble(bool x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(int x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(unsigned x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(short x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(ushort x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(float x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(double x) {
+    return make_cuDoubleComplex(static_cast<double>(x), 0);
+}
+__SDH__ cdouble make_cdouble(cdouble x) { return x; }
+__SDH__ cdouble make_cdouble(cfloat c) {
+    return make_cuDoubleComplex(static_cast<double>(c.x), c.y);
+}
 
-#define BINOP(OP, cfn, zfn)                                             \
-    __SDH__ cfloat   operator OP(cfloat  a, cfloat  b)                  \
-    { return cfn(a,b); }                                                \
-    __SDH__ cdouble  operator OP(cdouble a, cfloat  b)                  \
-    { return zfn(a,upcast(b)); }                                        \
-    __SDH__ cdouble  operator OP(cfloat  a, cdouble b)                  \
-    { return zfn(upcast(a),b); }                                        \
-    __SDH__ cdouble  operator OP(cdouble a, cdouble b)                  \
-    { return zfn(a,b); }                                                \
-                                                                        \
+__SDH__ cfloat make_cfloat(float x, float y) { return make_cuComplex(x, y); }
+__SDH__ cdouble make_cdouble(double x, double y) {
+    return make_cuDoubleComplex(x, y);
+}
 
-    BINOP(+, cuCaddf, cuCadd)
-    BINOP(-, cuCsubf, cuCsub)
-    BINOP(*, cuCmulf, cuCmul)
-    BINOP(/, cuCdivf, cuCdiv)
+#define BINOP(OP, cfn, zfn)                                              \
+    __SDH__ cfloat operator OP(cfloat a, cfloat b) { return cfn(a, b); } \
+    __SDH__ cdouble operator OP(cdouble a, cfloat b) {                   \
+        return zfn(a, upcast(b));                                        \
+    }                                                                    \
+    __SDH__ cdouble operator OP(cfloat a, cdouble b) {                   \
+        return zfn(upcast(a), b);                                        \
+    }                                                                    \
+    __SDH__ cdouble operator OP(cdouble a, cdouble b) { return zfn(a, b); }
+
+BINOP(+, cuCaddf, cuCadd)
+BINOP(-, cuCsubf, cuCsub)
+BINOP(*, cuCmulf, cuCmul)
+BINOP(/, cuCdivf, cuCdiv)
 
 #undef BINOP
 
-#define BINOP_SCALAR(T, TR, R)                  \
-    __SDH__ R operator *(TR a, T b)             \
-    { return make_##R(a * b.x, a * b.y); }      \
-                                                \
-    __SDH__ R operator *(T a, TR b)             \
-    { return make_##R(a.x * b,  a.y * b); }     \
-                                                \
-    __SDH__ R operator +(TR a, T b)             \
-    { return make_##R(a + b.x, a + b.y); }      \
-                                                \
-    __SDH__ R operator +(T a, TR b)             \
-    { return make_##R(a.x + b,  a.y + b); }     \
-                                                \
-    __SDH__ R operator -(TR a, T b)             \
-    { return make_##R(a - b.x, a - b.y); }      \
-                                                \
-    __SDH__ R operator -(T a, TR b)             \
-    { return make_##R(a.x - b,  a.y - b); }     \
-                                                \
-    __SDH__ R operator /(T a, TR b)             \
-    { return make_##R(a.x / b, a.y / b); }      \
-                                                \
-    __SDH__ R operator /(TR a, T b)             \
-    { return make_##R(a) / b; }                 \
-                                                \
-
-    BINOP_SCALAR(cfloat, float, cfloat)
-    BINOP_SCALAR(cfloat, double, cdouble)
-    BINOP_SCALAR(cdouble, float, cdouble)
-    BINOP_SCALAR(cdouble, double, cdouble)
+#define BINOP_SCALAR(T, TR, R)                                            \
+    __SDH__ R operator*(TR a, T b) { return make_##R(a * b.x, a * b.y); } \
+                                                                          \
+    __SDH__ R operator*(T a, TR b) { return make_##R(a.x * b, a.y * b); } \
+                                                                          \
+    __SDH__ R operator+(TR a, T b) { return make_##R(a + b.x, a + b.y); } \
+                                                                          \
+    __SDH__ R operator+(T a, TR b) { return make_##R(a.x + b, a.y + b); } \
+                                                                          \
+    __SDH__ R operator-(TR a, T b) { return make_##R(a - b.x, a - b.y); } \
+                                                                          \
+    __SDH__ R operator-(T a, TR b) { return make_##R(a.x - b, a.y - b); } \
+                                                                          \
+    __SDH__ R operator/(T a, TR b) { return make_##R(a.x / b, a.y / b); } \
+                                                                          \
+    __SDH__ R operator/(TR a, T b) { return make_##R(a) / b; }
+
+BINOP_SCALAR(cfloat, float, cfloat)
+BINOP_SCALAR(cfloat, double, cdouble)
+BINOP_SCALAR(cdouble, float, cdouble)
+BINOP_SCALAR(cdouble, double, cdouble)
 
 #undef BINOP_SCALAR
 
-__SDH__ bool operator ==(cfloat a, cfloat b) { return (a.x == b.x) && (a.y == b.y); }
-__SDH__ bool operator !=(cfloat a, cfloat b) { return !(a == b); }
-__SDH__ bool operator ==(cdouble a, cdouble b) { return (a.x == b.x) && (a.y == b.y); }
-__SDH__ bool operator !=(cdouble a, cdouble b) { return !(a == b); }
+__SDH__ bool operator==(cfloat a, cfloat b) {
+    return (a.x == b.x) && (a.y == b.y);
+}
+__SDH__ bool operator!=(cfloat a, cfloat b) { return !(a == b); }
+__SDH__ bool operator==(cdouble a, cdouble b) {
+    return (a.x == b.x) && (a.y == b.y);
+}
+__SDH__ bool operator!=(cdouble a, cdouble b) { return !(a == b); }
 
-    template<typename T> static inline T division(T lhs, double rhs) { return lhs / rhs; }
-    cfloat division(cfloat lhs, double rhs);
-    cdouble division(cdouble lhs, double rhs);
+template<typename T>
+static inline T division(T lhs, double rhs) {
+    return lhs / rhs;
+}
+cfloat division(cfloat lhs, double rhs);
+cdouble division(cdouble lhs, double rhs);
 
-    template<typename T>
-    static inline __DH__
-    T clamp(const T value, const T lo, const T hi)
-    {
-        return max(lo, min(value, hi));
-    }
+template<typename T>
+static inline __DH__ T clamp(const T value, const T lo, const T hi) {
+    return max(lo, min(value, hi));
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/max.cu b/src/backend/cuda/max.cu
index c910beaad6..c74fc46cf5 100644
--- a/src/backend/cuda/max.cu
+++ b/src/backend/cuda/max.cu
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    //max
-    INSTANTIATE(af_max_t, float  , float  )
-    INSTANTIATE(af_max_t, double , double )
-    INSTANTIATE(af_max_t, cfloat , cfloat )
-    INSTANTIATE(af_max_t, cdouble, cdouble)
-    INSTANTIATE(af_max_t, int    , int    )
-    INSTANTIATE(af_max_t, uint   , uint   )
-    INSTANTIATE(af_max_t, intl   , intl   )
-    INSTANTIATE(af_max_t, uintl  , uintl  )
-    INSTANTIATE(af_max_t, char   , char   )
-    INSTANTIATE(af_max_t, uchar  , uchar  )
-    INSTANTIATE(af_max_t, short  , short  )
-    INSTANTIATE(af_max_t, ushort , ushort )
-}
+namespace cuda {
+// max
+INSTANTIATE(af_max_t, float, float)
+INSTANTIATE(af_max_t, double, double)
+INSTANTIATE(af_max_t, cfloat, cfloat)
+INSTANTIATE(af_max_t, cdouble, cdouble)
+INSTANTIATE(af_max_t, int, int)
+INSTANTIATE(af_max_t, uint, uint)
+INSTANTIATE(af_max_t, intl, intl)
+INSTANTIATE(af_max_t, uintl, uintl)
+INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, uchar, uchar)
+INSTANTIATE(af_max_t, short, short)
+INSTANTIATE(af_max_t, ushort, ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/mean.cu b/src/backend/cuda/mean.cu
index e42a6071e5..ecc649cc44 100644
--- a/src/backend/cuda/mean.cu
+++ b/src/backend/cuda/mean.cu
@@ -7,75 +7,71 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <af/dim4.hpp>
 
 #undef _GLIBCXX_USE_INT128
-#include <complex>
-#include <mean.hpp>
-#include <kernel/mean.hpp>
 #include <err_cuda.hpp>
+#include <kernel/mean.hpp>
+#include <mean.hpp>
+#include <complex>
 
-using std::swap;
 using af::dim4;
-namespace cuda
-{
-    template<typename Ti, typename Tw, typename To>
-    To mean(const Array<Ti>& in)
-    {
-        return kernel::mean_all<Ti, Tw, To>(in);
-    }
+using std::swap;
+namespace cuda {
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in) {
+    return kernel::mean_all<Ti, Tw, To>(in);
+}
 
-    template<typename T, typename Tw>
-    T mean(const Array<T>& in, const Array<Tw>& wts)
-    {
-        return kernel::mean_all_weighted<T, Tw>(in, wts);
-    }
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts) {
+    return kernel::mean_all_weighted<T, Tw>(in, wts);
+}
 
-    template<typename Ti, typename Tw, typename To>
-    Array<To> mean(const Array<Ti>& in, const int dim)
-    {
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        Array<To> out = createEmptyArray<To>(odims);
-        kernel::mean<Ti, Tw, To>(out, in, dim);
-        return out;
-    }
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim) {
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    kernel::mean<Ti, Tw, To>(out, in, dim);
+    return out;
+}
 
-    template<typename T, typename Tw>
-    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim)
-    {
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        Array<T> out = createEmptyArray<T>(odims);
-        kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
-        return out;
-    }
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim) {
+    dim4 odims   = in.dims();
+    odims[dim]   = 1;
+    Array<T> out = createEmptyArray<T>(odims);
+    kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
+    return out;
+}
 
-    #define INSTANTIATE(Ti, Tw, To)                                         \
-        template To mean<Ti, Tw, To>(const Array<Ti> &in); \
-        template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim); \
+#define INSTANTIATE(Ti, Tw, To)                        \
+    template To mean<Ti, Tw, To>(const Array<Ti>& in); \
+    template Array<To> mean<Ti, Tw, To>(const Array<Ti>& in, const int dim);
 
-    INSTANTIATE(double  , double,  double);
-    INSTANTIATE(float   , float ,  float );
-    INSTANTIATE(int     , float ,  float );
-    INSTANTIATE(unsigned, float ,  float );
-    INSTANTIATE(intl    , double,  double);
-    INSTANTIATE(uintl   , double,  double);
-    INSTANTIATE(short   , float ,  float );
-    INSTANTIATE(ushort  , float ,  float );
-    INSTANTIATE(uchar   , float ,  float );
-    INSTANTIATE(char    , float ,  float );
-    INSTANTIATE(cfloat  , float ,  cfloat);
-    INSTANTIATE(cdouble , double, cdouble);
+INSTANTIATE(double, double, double);
+INSTANTIATE(float, float, float);
+INSTANTIATE(int, float, float);
+INSTANTIATE(unsigned, float, float);
+INSTANTIATE(intl, double, double);
+INSTANTIATE(uintl, double, double);
+INSTANTIATE(short, float, float);
+INSTANTIATE(ushort, float, float);
+INSTANTIATE(uchar, float, float);
+INSTANTIATE(char, float, float);
+INSTANTIATE(cfloat, float, cfloat);
+INSTANTIATE(cdouble, double, cdouble);
 
-    #define INSTANTIATE_WGT(T, Tw)                                         \
-        template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts); \
-        template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, const int dim); \
+#define INSTANTIATE_WGT(T, Tw)                                              \
+    template T mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts);       \
+    template Array<T> mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts, \
+                                  const int dim);
 
-    INSTANTIATE_WGT(double , double);
-    INSTANTIATE_WGT(float  , float );
-    INSTANTIATE_WGT(cfloat , float );
-    INSTANTIATE_WGT(cdouble, double);
+INSTANTIATE_WGT(double, double);
+INSTANTIATE_WGT(float, float);
+INSTANTIATE_WGT(cfloat, float);
+INSTANTIATE_WGT(cdouble, double);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/mean.hpp b/src/backend/cuda/mean.hpp
index ec989a1989..c97e78c896 100644
--- a/src/backend/cuda/mean.hpp
+++ b/src/backend/cuda/mean.hpp
@@ -11,18 +11,17 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-    template<typename Ti, typename Tw, typename To>
-    To mean(const Array<Ti>& in);
+namespace cuda {
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in);
 
-    template<typename T, typename Tw>
-    T mean(const Array<T>& in, const Array<Tw>& wts);
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts);
 
-    template<typename Ti, typename Tw, typename To>
-    Array<To> mean(const Array<Ti>& in, const int dim);
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim);
 
-    template<typename T, typename Tw>
-    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/meanshift.cu b/src/backend/cuda/meanshift.cu
index f7fc36421f..fcc9075bdc 100644
--- a/src/backend/cuda/meanshift.cu
+++ b/src/backend/cuda/meanshift.cu
@@ -7,44 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <meanshift.hpp>
-#include <kernel/meanshift.hpp>
 #include <err_cuda.hpp>
+#include <kernel/meanshift.hpp>
+#include <meanshift.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-Array<T>  meanshift(const Array<T> &in,
-        const float &spatialSigma, const float &chromaticSigma,
-        const unsigned& numIterations, const bool& isColor)
-{
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor) {
     const dim4 dims = in.dims();
 
-    Array<T> out   = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
     if (isColor)
-        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma, numIterations);
+        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma,
+                                   numIterations);
     else
-        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma, numIterations);
+        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma,
+                                    numIterations);
 
     return out;
 }
 
-#define INSTANTIATE(T) \
-    template Array<T> meanshift<T>(const Array<T>&, const float&, const float&, const unsigned&, const bool&);
+#define INSTANTIATE(T)                                              \
+    template Array<T> meanshift<T>(const Array<T> &, const float &, \
+                                   const float &, const unsigned &, \
+                                   const bool &);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
-INSTANTIATE(intl  )
-INSTANTIATE(uintl )
-}
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace cuda
diff --git a/src/backend/cuda/meanshift.hpp b/src/backend/cuda/meanshift.hpp
index 13d46a2560..d27ff71279 100644
--- a/src/backend/cuda/meanshift.hpp
+++ b/src/backend/cuda/meanshift.hpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-Array<T>  meanshift(const Array<T> &in,
-                    const float &spatialSigma, const float &chromaticSigma,
-                    const unsigned& numIterations, const bool& isColor);
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor);
 }
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cu
index c36edca4b0..ed0b8a75d3 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cu
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <medfilt.hpp>
-#include <kernel/medfilt.hpp>
 #include <err_cuda.hpp>
+#include <kernel/medfilt.hpp>
+#include <medfilt.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, af_border_type pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
-{
-    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER1_LEN));
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
+    ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
     const dim4 dims = in.dims();
 
-    Array<T> out    = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
     kernel::medfilt1<T, pad>(out, in, w_wid);
 
@@ -34,33 +32,36 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 }
 
 template<typename T, af_border_type pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
-{
-    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER2_LEN));
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
+    ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
     ARG_ASSERT(2, (w_len % 2 != 0));
 
-    const dim4 dims   = in.dims();
+    const dim4 dims = in.dims();
 
-    Array<T> out      = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
     kernel::medfilt2<T, pad>(out, in, w_len, w_wid);
 
     return out;
 }
 
-#define INSTANTIATE(T)                                                                          \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);                \
-    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);                \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);   \
-    template Array<T> medfilt2<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_len, dim_t w_wid);
-
-INSTANTIATE(float )
+#define INSTANTIATE(T)                                                         \
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in,             \
+                                               dim_t w_wid);                   \
+    template Array<T> medfilt1<T, AF_PAD_SYM>(const Array<T> &in,              \
+                                              dim_t w_wid);                    \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in,             \
+                                               dim_t w_len, dim_t w_wid);      \
+    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, \
+                                              dim_t w_wid);
+
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/medfilt.hpp b/src/backend/cuda/medfilt.hpp
index 663c819012..b6fa31176a 100644
--- a/src/backend/cuda/medfilt.hpp
+++ b/src/backend/cuda/medfilt.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
@@ -18,4 +17,4 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 8bcafb9597..0f8bfe130d 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -36,209 +36,155 @@ template class common::MemoryManager<cuda::MemoryManagerPinned>;
 
 using common::bytesToString;
 
+using std::function;
 using std::lock_guard;
 using std::recursive_mutex;
-using std::function;
 using std::unique_ptr;
 
-namespace cuda
-{
-void setMemStepSize(size_t step_bytes)
-{
+namespace cuda {
+void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
-size_t getMemStepSize(void)
-{
-    return memoryManager().getMemStepSize();
-}
+size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
 
-size_t getMaxBytes()
-{
-    return memoryManager().getMaxBytes();
-}
+size_t getMaxBytes() { return memoryManager().getMaxBytes(); }
 
-unsigned getMaxBuffers()
-{
-    return memoryManager().getMaxBuffers();
-}
+unsigned getMaxBuffers() { return memoryManager().getMaxBuffers(); }
 
-void garbageCollect()
-{
-    memoryManager().garbageCollect();
-}
+void garbageCollect() { memoryManager().garbageCollect(); }
 
-void printMemInfo(const char *msg, const int device)
-{
+void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
 }
 
 template<typename T>
-uptr<T>
-memAlloc(const size_t &elements)
-{
+uptr<T> memAlloc(const size_t &elements) {
     size_t size = elements * sizeof(T);
-    return uptr<T>(static_cast<T*>(memoryManager().alloc(size, false)),
+    return uptr<T>(static_cast<T *>(memoryManager().alloc(size, false)),
                    memFree<T>);
 }
 
-void* memAllocUser(const size_t &bytes)
-{
+void *memAllocUser(const size_t &bytes) {
     return memoryManager().alloc(bytes, true);
 }
 template<typename T>
-void memFree(T *ptr)
-{
+void memFree(T *ptr) {
     memoryManager().unlock((void *)ptr, false);
 }
 
-void memFreeUser(void *ptr)
-{
-    memoryManager().unlock((void *)ptr, true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
 
-void memLock(const void *ptr)
-{
-    memoryManager().userLock((void *)ptr);
-}
+void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
-void memUnlock(const void *ptr)
-{
-    memoryManager().userUnlock((void *)ptr);
-}
+void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
 
-bool isLocked(const void *ptr)
-{
+bool isLocked(const void *ptr) {
     return memoryManager().isUserLocked((void *)ptr);
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                      size_t *lock_bytes,  size_t *lock_buffers)
-{
-    memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
-                                  lock_bytes,  lock_buffers);
+                      size_t *lock_bytes, size_t *lock_buffers) {
+    memoryManager().bufferInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                               lock_buffers);
 }
 
 template<typename T>
-T* pinnedAlloc(const size_t &elements)
-{
+T *pinnedAlloc(const size_t &elements) {
     return (T *)pinnedMemoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
-void pinnedFree(T* ptr)
-{
+void pinnedFree(T *ptr) {
     return pinnedMemoryManager().unlock((void *)ptr, false);
 }
 
-bool checkMemoryLimit()
-{
-    return memoryManager().checkMemoryLimit();
-}
+bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
 
 #define INSTANTIATE(T)                                 \
     template uptr<T> memAlloc(const size_t &elements); \
-    template void memFree(T* ptr);                     \
-    template T* pinnedAlloc(const size_t &elements);   \
-    template void pinnedFree(T* ptr);
-
-    INSTANTIATE(float)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(double)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(void *)
+    template void memFree(T *ptr);                     \
+    template T *pinnedAlloc(const size_t &elements);   \
+    template void pinnedFree(T *ptr);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(void *)
 
 MemoryManager::MemoryManager()
-    : common::MemoryManager<cuda::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
-                                                 AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{
+    : common::MemoryManager<cuda::MemoryManager>(
+          getDeviceCount(), common::MAX_BUFFERS,
+          AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG) {
     this->setMaxMemorySize();
 }
 
-MemoryManager::~MemoryManager()
-{
+MemoryManager::~MemoryManager() {
     for (int n = 0; n < cuda::getDeviceCount(); n++) {
         try {
             cuda::setDevice(n);
             garbageCollect();
-        } catch(AfError err) {
-            continue; // Do not throw any errors while shutting down
+        } catch (AfError err) {
+            continue;  // Do not throw any errors while shutting down
         }
     }
 }
 
-int MemoryManager::getActiveDeviceId()
-{
-    return cuda::getActiveDeviceId();
-}
+int MemoryManager::getActiveDeviceId() { return cuda::getActiveDeviceId(); }
 
-size_t MemoryManager::getMaxMemorySize(int id)
-{
+size_t MemoryManager::getMaxMemorySize(int id) {
     return cuda::getDeviceMemorySize(id);
 }
 
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
+void *MemoryManager::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
     CUDA_CHECK(cudaMalloc(&ptr, bytes));
     AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
-void MemoryManager::nativeFree(void *ptr)
-{
+void MemoryManager::nativeFree(void *ptr) {
     AF_TRACE("nativeFree:          {}", ptr);
     cudaError_t err = cudaFree(ptr);
-    if (err != cudaErrorCudartUnloading) {
-        CUDA_CHECK(err);
-    }
+    if (err != cudaErrorCudartUnloading) { CUDA_CHECK(err); }
 }
 
 MemoryManagerPinned::MemoryManagerPinned()
-    : common::MemoryManager<MemoryManagerPinned>(1, common::MAX_BUFFERS,
-                                                 AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG)
-{
+    : common::MemoryManager<MemoryManagerPinned>(
+          1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG) {
     this->setMaxMemorySize();
 }
 
-MemoryManagerPinned::~MemoryManagerPinned()
-{
-    garbageCollect();
-}
+MemoryManagerPinned::~MemoryManagerPinned() { garbageCollect(); }
 
-int MemoryManagerPinned::getActiveDeviceId()
-{
-    return 0; // pinned uses a single vector
+int MemoryManagerPinned::getActiveDeviceId() {
+    return 0;  // pinned uses a single vector
 }
 
-size_t MemoryManagerPinned::getMaxMemorySize(int id)
-{
+size_t MemoryManagerPinned::getMaxMemorySize(int id) {
     UNUSED(id);
     return cuda::getHostMemorySize();
 }
 
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
-{
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes) {
     void *ptr;
     CUDA_CHECK(cudaMallocHost(&ptr, bytes));
     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
-void MemoryManagerPinned::nativeFree(void *ptr)
-{
+void MemoryManagerPinned::nativeFree(void *ptr) {
     AF_TRACE("Pinned::nativeFree:          {}", ptr);
     cudaError_t err = cudaFreeHost(ptr);
-    if (err != cudaErrorCudartUnloading) {
-        CUDA_CHECK(err);
-    }
-}
+    if (err != cudaErrorCudartUnloading) { CUDA_CHECK(err); }
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index d33813a7aa..4fd6afeb9d 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -8,14 +8,14 @@
  ********************************************************/
 #pragma once
 
-#include <cstdlib>
 #include <common/MemoryManager.hpp>
+#include <cstdlib>
 
 #include <functional>
 #include <memory>
-namespace cuda
-{
-template<typename T> void memFree(T* ptr);
+namespace cuda {
+template<typename T>
+void memFree(T *ptr);
 
 template<typename T>
 using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
@@ -29,20 +29,22 @@ void *memAllocUser(const size_t &bytes);
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
 
-void memFreeUser(void* ptr);
+void memFreeUser(void *ptr);
 
 void memLock(const void *ptr);
 void memUnlock(const void *ptr);
 bool isLocked(const void *ptr);
 
-template<typename T> T* pinnedAlloc(const size_t &elements);
-template<typename T> void pinnedFree(T* ptr);
+template<typename T>
+T *pinnedAlloc(const size_t &elements);
+template<typename T>
+void pinnedFree(T *ptr);
 
 size_t getMaxBytes();
 unsigned getMaxBuffers();
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                      size_t *lock_bytes,  size_t *lock_buffers);
+                      size_t *lock_bytes, size_t *lock_buffers);
 void garbageCollect();
 void pinnedGarbageCollect();
 
@@ -53,29 +55,27 @@ size_t getMemStepSize(void);
 
 bool checkMemoryLimit();
 
-class MemoryManager : public common::MemoryManager<cuda::MemoryManager>
-{
-    public:
-        MemoryManager();
-        ~MemoryManager();
-        int getActiveDeviceId();
-        size_t getMaxMemorySize(int id);
-        void *nativeAlloc(const size_t bytes);
-        void nativeFree(void *ptr);
+class MemoryManager : public common::MemoryManager<cuda::MemoryManager> {
+   public:
+    MemoryManager();
+    ~MemoryManager();
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
 };
 
 // CUDA Pinned Memory does not depend on device
 // So we pass 1 as numDevices to the constructor so that it creates 1 vector
 // of memory_info
 // When allocating and freeing, it doesn't really matter which device is active
-class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned>
-{
-    public:
-        MemoryManagerPinned();
-        ~MemoryManagerPinned();
-        int getActiveDeviceId();
-        size_t getMaxMemorySize(int id);
-        void *nativeAlloc(const size_t bytes);
-        void nativeFree(void *ptr);
+class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned> {
+   public:
+    MemoryManagerPinned();
+    ~MemoryManagerPinned();
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/min.cu b/src/backend/cuda/min.cu
index 26719de468..14721080a5 100644
--- a/src/backend/cuda/min.cu
+++ b/src/backend/cuda/min.cu
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    //min
-    INSTANTIATE(af_min_t, float  , float  )
-    INSTANTIATE(af_min_t, double , double )
-    INSTANTIATE(af_min_t, cfloat , cfloat )
-    INSTANTIATE(af_min_t, cdouble, cdouble)
-    INSTANTIATE(af_min_t, int    , int    )
-    INSTANTIATE(af_min_t, uint   , uint   )
-    INSTANTIATE(af_min_t, intl   , intl   )
-    INSTANTIATE(af_min_t, uintl  , uintl  )
-    INSTANTIATE(af_min_t, char   , char   )
-    INSTANTIATE(af_min_t, uchar  , uchar  )
-    INSTANTIATE(af_min_t, short  , short  )
-    INSTANTIATE(af_min_t, ushort , ushort )
-}
+namespace cuda {
+// min
+INSTANTIATE(af_min_t, float, float)
+INSTANTIATE(af_min_t, double, double)
+INSTANTIATE(af_min_t, cfloat, cfloat)
+INSTANTIATE(af_min_t, cdouble, cdouble)
+INSTANTIATE(af_min_t, int, int)
+INSTANTIATE(af_min_t, uint, uint)
+INSTANTIATE(af_min_t, intl, intl)
+INSTANTIATE(af_min_t, uintl, uintl)
+INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, uchar, uchar)
+INSTANTIATE(af_min_t, short, short)
+INSTANTIATE(af_min_t, ushort, ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/moments.cu b/src/backend/cuda/moments.cu
index 2314b1cc97..0f88a53c5f 100644
--- a/src/backend/cuda/moments.cu
+++ b/src/backend/cuda/moments.cu
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <err_cuda.hpp>
 #include <debug_cuda.hpp>
+#include <err_cuda.hpp>
 #include <kernel/moments.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 static inline int bitCount(int v) {
     v = v - ((v >> 1) & 0x55555555);
@@ -24,8 +23,7 @@ static inline int bitCount(int v) {
 using af::dim4;
 
 template<typename T>
-Array<float> moments(const Array<T> &in, const af_moment_type moment)
-{
+Array<float> moments(const Array<T> &in, const af_moment_type moment) {
     in.eval();
     dim4 odims, idims = in.dims();
     dim_t moments_dim = bitCount(moment);
@@ -42,8 +40,9 @@ Array<float> moments(const Array<T> &in, const af_moment_type moment)
     return out;
 }
 
-#define INSTANTIATE(T)                                                          \
-    template Array<float>  moments<T>(const Array<T> &in, const af_moment_type moment);
+#define INSTANTIATE(T)                                   \
+    template Array<float> moments<T>(const Array<T> &in, \
+                                     const af_moment_type moment);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -54,4 +53,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/moments.hpp b/src/backend/cuda/moments.hpp
index 78142e0c18..d8361d8896 100644
--- a/src/backend/cuda/moments.hpp
+++ b/src/backend/cuda/moments.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<float> moments(const Array<T> &in, const af_moment_type moment);
+namespace cuda {
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment);
 }
diff --git a/src/backend/cuda/morph.hpp b/src/backend/cuda/morph.hpp
index 54eef63967..45abac1c95 100644
--- a/src/backend/cuda/morph.hpp
+++ b/src/backend/cuda/morph.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask);
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index c283302d3a..667114dc60 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -7,42 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <morph.hpp>
-#include <kernel/morph.hpp>
 #include <err_cuda.hpp>
+#include <kernel/morph.hpp>
+#include <morph.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
-{
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
     const dim4 mdims = mask.dims();
 
     if (mdims[0] != mdims[1] || mdims[0] != mdims[2])
         CUDA_NOT_SUPPORTED("Only cubic masks are supported");
 
-    if (mdims[0] > 7)
-        CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported");
+    if (mdims[0] > 7) CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported");
 
-    Array<T> out       = createEmptyArray<T>(in.dims());
+    Array<T> out = createEmptyArray<T>(in.dims());
 
-    CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter, mask.get(),
-                                  mdims[0] * mdims[1] *mdims[2] * sizeof(T),
-                                  0, cudaMemcpyDeviceToDevice,
-                                  cuda::getActiveStream()));
+    CUDA_CHECK(cudaMemcpyToSymbolAsync(
+        kernel::cFilter, mask.get(), mdims[0] * mdims[1] * mdims[2] * sizeof(T),
+        0, cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
     if (isDilation)
-        kernel::morph3d<T, true >(out, in, mdims[0]);
+        kernel::morph3d<T, true>(out, in, mdims[0]);
     else
         kernel::morph3d<T, false>(out, in, mdims[0]);
 
     return out;
 }
 
-#define INSTANTIATE(T, ISDILATE)                                        \
-    template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
-}
+#define INSTANTIATE(T, ISDILATE)                               \
+    template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, \
+                                           const Array<T> &mask);
+}  // namespace cuda
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index 8fd04d576f..e811a1d4a6 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -7,42 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <morph.hpp>
-#include <kernel/morph.hpp>
 #include <err_cuda.hpp>
+#include <kernel/morph.hpp>
+#include <morph.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 template<typename T, bool isDilation>
-Array<T>  morph(const Array<T> &in, const Array<T> &mask)
-{
+Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     const dim4 mdims = mask.dims();
 
     if (mdims[0] != mdims[1])
         CUDA_NOT_SUPPORTED("Rectangular masks are not supported");
 
-    if (mdims[0] > 19)
-        CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
+    if (mdims[0] > 19) CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
 
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    CUDA_CHECK(cudaMemcpyToSymbolAsync(kernel::cFilter, mask.get(),
-                                  mdims[0] * mdims[1] * sizeof(T),
-                                  0, cudaMemcpyDeviceToDevice,
-                                  cuda::getActiveStream()));
+    CUDA_CHECK(cudaMemcpyToSymbolAsync(
+        kernel::cFilter, mask.get(), mdims[0] * mdims[1] * sizeof(T), 0,
+        cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
     if (isDilation)
-        kernel::morph<T, true >(out, in, mdims[0]);
+        kernel::morph<T, true>(out, in, mdims[0]);
     else
         kernel::morph<T, false>(out, in, mdims[0]);
 
     return out;
 }
 
-#define INSTANTIATE(T, ISDILATE)                                        \
-    template Array<T> morph  <T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
-}
+#define INSTANTIATE(T, ISDILATE)                             \
+    template Array<T> morph<T, ISDILATE>(const Array<T> &in, \
+                                         const Array<T> &mask);
+}  // namespace cuda
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index 2aebba471d..53e22a29fc 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -7,26 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
+#include <math.hpp>
 #include <topk.hpp>
 #include <transpose.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename To>
-void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
-                     const Array<T>& query, const Array<T>& train,
-                     const uint dist_dim, const uint n_dist,
-                     const af_match_type dist_type)
-{
-    uint  sample_dim = (dist_dim == 0) ? 1 : 0;
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist, const af_match_type dist_type) {
+    uint sample_dim  = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
 
@@ -41,35 +38,38 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
     Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
     Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
-    switch(dist_type) {
-        case AF_SAD: kernel::all_distances<T, To, AF_SAD>(tmp_dists, queryT, trainT, 1);
-                     break;
-        case AF_SSD: kernel::all_distances<T, To, AF_SSD>(tmp_dists, queryT, trainT, 1);
-                     break;
-        case AF_SHD: kernel::all_distances<T, To, AF_SHD>(tmp_dists, queryT, trainT, 1);
-                     break;
+    switch (dist_type) {
+        case AF_SAD:
+            kernel::all_distances<T, To, AF_SAD>(tmp_dists, queryT, trainT, 1);
+            break;
+        case AF_SSD:
+            kernel::all_distances<T, To, AF_SSD>(tmp_dists, queryT, trainT, 1);
+            break;
+        case AF_SHD:
+            kernel::all_distances<T, To, AF_SHD>(tmp_dists, queryT, trainT, 1);
+            break;
         default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
 
     topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
 }
 
-#define INSTANTIATE(T, To)                                                              \
-    template void nearest_neighbour<T, To>(Array<uint>& idx, Array<To>& dist,           \
-                                         const Array<T>& query, const Array<T>& train,  \
-                                         const uint dist_dim, const uint n_dist,        \
-                                         const af_match_type dist_type);
+#define INSTANTIATE(T, To)                                             \
+    template void nearest_neighbour<T, To>(                            \
+        Array<uint> & idx, Array<To> & dist, const Array<T>& query,    \
+        const Array<T>& train, const uint dist_dim, const uint n_dist, \
+        const af_match_type dist_type);
 
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
-INSTANTIATE(int   , int)
-INSTANTIATE(uint  , uint)
-INSTANTIATE(intl  , intl)
-INSTANTIATE(uintl , uintl)
-INSTANTIATE(uchar , uint)
-INSTANTIATE(short , int)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(uchar, uint)
+INSTANTIATE(short, int)
 INSTANTIATE(ushort, uint)
 
-INSTANTIATE(uintl, uint)    // For Hamming
+INSTANTIATE(uintl, uint)  // For Hamming
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/nearest_neighbour.hpp b/src/backend/cuda/nearest_neighbour.hpp
index 443f97242b..8de98e6924 100644
--- a/src/backend/cuda/nearest_neighbour.hpp
+++ b/src/backend/cuda/nearest_neighbour.hpp
@@ -8,16 +8,16 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename To>
-void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
-                       const Array<T>& query, const Array<T>& train,
-                       const uint dist_dim, const uint n_dist,
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
 
 }
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index 8479da443c..541df50d20 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -7,30 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <fast_pyramid.hpp>
 #include <kernel/orb.hpp>
 #include <kernel/orb_patch.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename convAccT>
-unsigned orb(Array<float> &x, Array<float> &y,
-             Array<float> &score, Array<float> &ori,
-             Array<float> &size, Array<uint> &desc,
-             const Array<T>& image,
-             const float fast_thr, const unsigned max_feat,
-             const float scl_fctr, const unsigned levels,
-             const bool blur_img)
-{
+unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
+             Array<float> &ori, Array<float> &size, Array<uint> &desc,
+             const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img) {
     std::vector<unsigned> feat_pyr, lvl_best;
     std::vector<float> lvl_scl;
-    std::vector<float*> d_x_pyr, d_y_pyr;
+    std::vector<float *> d_x_pyr, d_y_pyr;
     std::vector<Array<T>> img_pyr;
 
     fast_pyramid<T>(feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
@@ -44,14 +40,14 @@ unsigned orb(Array<float> &x, Array<float> &y,
     float *size_out;
     unsigned *desc_out;
 
-    kernel::orb<T, convAccT>(&nfeat_out, &x_out, &y_out, &score_out, &orientation_out, &size_out,
-                              &desc_out, feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
-                              fast_thr, max_feat, scl_fctr, levels, blur_img);
+    kernel::orb<T, convAccT>(&nfeat_out, &x_out, &y_out, &score_out,
+                             &orientation_out, &size_out, &desc_out, feat_pyr,
+                             d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
+                             fast_thr, max_feat, scl_fctr, levels, blur_img);
 
     if (nfeat_out > 0) {
-
-        if (x_out == NULL || y_out == NULL || score_out == NULL || orientation_out == NULL ||
-            size_out == NULL || desc_out == NULL) {
+        if (x_out == NULL || y_out == NULL || score_out == NULL ||
+            orientation_out == NULL || size_out == NULL || desc_out == NULL) {
             AF_ERROR("orb_descriptor: feature array is null.", AF_ERR_SIZE);
         }
 
@@ -64,22 +60,19 @@ unsigned orb(Array<float> &x, Array<float> &y,
         ori   = createDeviceDataArray<float>(feat_dims, orientation_out);
         size  = createDeviceDataArray<float>(feat_dims, size_out);
         desc  = createDeviceDataArray<unsigned>(desc_dims, desc_out);
-
     }
 
     return nfeat_out;
 }
 
-#define INSTANTIATE(T, convAccT)                                                        \
-    template unsigned orb<T, convAccT>(Array<float> &x, Array<float> &y,                \
-                                       Array<float> &score, Array<float> &ori,          \
-                                       Array<float> &size, Array<uint> &desc,           \
-                                       const Array<T>& image,                           \
-                                       const float fast_thr, const unsigned max_feat,   \
-                                       const float scl_fctr, const unsigned levels,     \
-                                       const bool blur_img);
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned orb<T, convAccT>(                                       \
+        Array<float> & x, Array<float> & y, Array<float> & score,             \
+        Array<float> & ori, Array<float> & size, Array<uint> & desc,          \
+        const Array<T> &image, const float fast_thr, const unsigned max_feat, \
+        const float scl_fctr, const unsigned levels, const bool blur_img);
 
-INSTANTIATE(float , float )
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/orb.hpp b/src/backend/cuda/orb.hpp
index c0c61c906a..e7a03ad9e1 100644
--- a/src/backend/cuda/orb.hpp
+++ b/src/backend/cuda/orb.hpp
@@ -7,21 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename convAccT>
 unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              Array<float> &orientation, Array<float> &size,
-             Array<unsigned> &desc,
-             const Array<T>& image,
-             const float fast_thr, const unsigned max_feat,
-             const float scl_fctr, const unsigned levels,
-             const bool blur_img);
+             Array<unsigned> &desc, const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img);
 
 }
diff --git a/src/backend/cuda/pad_array_borders.cu b/src/backend/cuda/pad_array_borders.cu
index 7df417a73b..0986731f59 100644
--- a/src/backend/cuda/pad_array_borders.cu
+++ b/src/backend/cuda/pad_array_borders.cu
@@ -7,20 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <copy.hpp>
 #include <err_cuda.hpp>
 #include <kernel/pad_array_borders.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
-Array<T> padArrayBorders(Array<T> const& in,
-                         dim4 const& lowerBoundPadding,
+Array<T> padArrayBorders(Array<T> const& in, dim4 const& lowerBoundPadding,
                          dim4 const& upperBoundPadding,
-                         const af::borderType btype)
-{
+                         const af::borderType btype) {
     const dim4& iDims = in.dims();
 
     dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
@@ -35,20 +32,20 @@ Array<T> padArrayBorders(Array<T> const& in,
     return ret;
 }
 
-#define INSTANTIATE_PAD_ARRAY_BORDERS(T)                    \
-    template Array<T> padArrayBorders<T>(Array<T> const&,   \
-            dim4 const &, dim4 const &, const af::borderType);
+#define INSTANTIATE_PAD_ARRAY_BORDERS(T)                               \
+    template Array<T> padArrayBorders<T>(Array<T> const&, dim4 const&, \
+                                         dim4 const&, const af::borderType);
 
-INSTANTIATE_PAD_ARRAY_BORDERS(cfloat )
+INSTANTIATE_PAD_ARRAY_BORDERS(cfloat)
 INSTANTIATE_PAD_ARRAY_BORDERS(cdouble)
-INSTANTIATE_PAD_ARRAY_BORDERS(float  )
-INSTANTIATE_PAD_ARRAY_BORDERS(double )
-INSTANTIATE_PAD_ARRAY_BORDERS(int    )
-INSTANTIATE_PAD_ARRAY_BORDERS(uint   )
-INSTANTIATE_PAD_ARRAY_BORDERS(intl   )
-INSTANTIATE_PAD_ARRAY_BORDERS(uintl  )
-INSTANTIATE_PAD_ARRAY_BORDERS(uchar  )
-INSTANTIATE_PAD_ARRAY_BORDERS(char   )
-INSTANTIATE_PAD_ARRAY_BORDERS(ushort )
-INSTANTIATE_PAD_ARRAY_BORDERS(short  )
-}
+INSTANTIATE_PAD_ARRAY_BORDERS(float)
+INSTANTIATE_PAD_ARRAY_BORDERS(double)
+INSTANTIATE_PAD_ARRAY_BORDERS(int)
+INSTANTIATE_PAD_ARRAY_BORDERS(uint)
+INSTANTIATE_PAD_ARRAY_BORDERS(intl)
+INSTANTIATE_PAD_ARRAY_BORDERS(uintl)
+INSTANTIATE_PAD_ARRAY_BORDERS(uchar)
+INSTANTIATE_PAD_ARRAY_BORDERS(char)
+INSTANTIATE_PAD_ARRAY_BORDERS(ushort)
+INSTANTIATE_PAD_ARRAY_BORDERS(short)
+}  // namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 5bd93c90f5..89131ea37b 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -11,19 +11,18 @@
 #include <windows.h>
 #endif
 
-#include <af/version.h>
-#include <af/cuda.h>
-#include <platform.hpp>
 #include <common/defines.hpp>
+#include <common/host_memory.hpp>
 #include <common/util.hpp>
-#include <version.hpp>
 #include <driver.h>
 #include <err_cuda.hpp>
-#include <common/util.hpp>
-#include <common/host_memory.hpp>
+#include <platform.hpp>
+#include <version.hpp>
+#include <af/cuda.h>
+#include <af/version.h>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <common/graphics_common.hpp>
-#define __gl_h_ //FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#define __gl_h_  // FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
 #include <cuda_gl_interop.h>
 
 #include <algorithm>
@@ -38,55 +37,39 @@
 
 using namespace std;
 
-namespace cuda
-{
+namespace cuda {
 ///////////////////////////////////////////////////////////////////////////
 // HELPERS
 ///////////////////////////////////////////////////////////////////////////
 // pulled from CUTIL from CUDA SDK
-static inline int compute2cores(int major, int minor)
-{
+static inline int compute2cores(int major, int minor) {
     struct {
-        int compute; // 0xMm (hex), M = major version, m = minor version
+        int compute;  // 0xMm (hex), M = major version, m = minor version
         int cores;
     } gpus[] = {
-        { 0x10,  8 },
-        { 0x11,  8 },
-        { 0x12,  8 },
-        { 0x13,  8 },
-        { 0x20, 32 },
-        { 0x21, 48 },
-        { 0x30, 192 },
-        { 0x32, 192 },
-        { 0x35, 192 },
-        { 0x37, 192 },
-        { 0x50, 128 },
-        { 0x52, 128 },
-        { 0x53, 128 },
-        { 0x60, 128 },
-        { 0x61, 64  },
-        { 0x62, 128 },
-        {   -1, -1  },
+        {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
+        {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
+        {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 128}, {0x61, 64},
+        {0x62, 128}, {-1, -1},
     };
 
     for (int i = 0; gpus[i].compute != -1; ++i) {
-        if (gpus[i].compute == (major << 4) + minor)
-            return gpus[i].cores;
+        if (gpus[i].compute == (major << 4) + minor) return gpus[i].cores;
     }
     return 0;
 }
 
 // Return true if greater, false if lesser.
 // if equal, it continues to next comparison
-#define COMPARE(a,b,f) do {                     \
-        if ((a)->f > (b)->f) return true;       \
-        if ((a)->f < (b)->f) return false;      \
-        break;                                  \
+#define COMPARE(a, b, f)                   \
+    do {                                   \
+        if ((a)->f > (b)->f) return true;  \
+        if ((a)->f < (b)->f) return false; \
+        break;                             \
     } while (0)
 
-
-static inline bool card_compare_compute(const cudaDevice_t &l, const cudaDevice_t &r)
-{
+static inline bool card_compare_compute(const cudaDevice_t &l,
+                                        const cudaDevice_t &r) {
     const cudaDevice_t *lc = &l;
     const cudaDevice_t *rc = &r;
 
@@ -98,8 +81,8 @@ static inline bool card_compare_compute(const cudaDevice_t &l, const cudaDevice_
     return false;
 }
 
-static inline bool card_compare_flops(const cudaDevice_t &l, const cudaDevice_t &r)
-{
+static inline bool card_compare_flops(const cudaDevice_t &l,
+                                      const cudaDevice_t &r) {
     const cudaDevice_t *lc = &l;
     const cudaDevice_t *rc = &r;
 
@@ -111,8 +94,8 @@ static inline bool card_compare_flops(const cudaDevice_t &l, const cudaDevice_t
     return false;
 }
 
-static inline bool card_compare_mem(const cudaDevice_t &l, const cudaDevice_t &r)
-{
+static inline bool card_compare_mem(const cudaDevice_t &l,
+                                    const cudaDevice_t &r) {
     const cudaDevice_t *lc = &l;
     const cudaDevice_t *rc = &r;
 
@@ -124,8 +107,8 @@ static inline bool card_compare_mem(const cudaDevice_t &l, const cudaDevice_t &r
     return false;
 }
 
-static inline bool card_compare_num(const cudaDevice_t &l, const cudaDevice_t &r)
-{
+static inline bool card_compare_num(const cudaDevice_t &l,
+                                    const cudaDevice_t &r) {
     const cudaDevice_t *lc = &l;
     const cudaDevice_t *rc = &r;
 
@@ -133,89 +116,77 @@ static inline bool card_compare_num(const cudaDevice_t &l, const cudaDevice_t &r
     return false;
 }
 
-static const std::string get_system(void)
-{
+static const std::string get_system(void) {
     std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
 #if defined(OS_LNX)
-    "Linux";
+           "Linux";
 #elif defined(OS_WIN)
-    "Windows";
+           "Windows";
 #elif defined(OS_MAC)
-    "Mac OSX";
+           "Mac OSX";
 #endif
 }
 
-template <typename T>
-static inline string toString(T val)
-{
+template<typename T>
+static inline string toString(T val) {
     stringstream s;
     s << val;
     return s.str();
 }
 
-static inline
-int getMinSupportedCompute(int cudaMajorVer)
-{
+static inline int getMinSupportedCompute(int cudaMajorVer) {
     // Vector of minimum supported compute versions
     // for CUDA toolkit (i+1).* where i is the index
     // of the vector
     static const std::array<int, 10> minSV{1, 1, 1, 1, 1, 1, 2, 2, 3, 3};
 
     int CVSize = static_cast<int>(minSV.size());
-    return (cudaMajorVer > CVSize ? minSV[CVSize-1] : minSV[cudaMajorVer-1]);
+    return (cudaMajorVer > CVSize ? minSV[CVSize - 1]
+                                  : minSV[cudaMajorVer - 1]);
 }
 
 ///////////////////////////////////////////////////////////////////////////
 // Wrapper Functions
 ///////////////////////////////////////////////////////////////////////////
-int getBackend()
-{
-    return AF_BACKEND_CUDA;
-}
+int getBackend() { return AF_BACKEND_CUDA; }
 
-string getDeviceInfo(int device)
-{
+string getDeviceInfo(int device) {
     cudaDeviceProp dev = getDeviceProp(device);
 
     size_t mem_gpu_total = dev.totalGlobalMem;
-    //double cc = double(dev.major) + double(dev.minor) / 10;
+    // double cc = double(dev.major) + double(dev.minor) / 10;
 
     bool show_braces = getActiveDeviceId() == device;
 
     string id = (show_braces ? string("[") : "-") + toString(device) +
                 (show_braces ? string("]") : "-");
     string name(dev.name);
-    string memory = toString((mem_gpu_total / (1024 * 1024))
-                          + !!(mem_gpu_total % (1024 * 1024)))
-                    + string(" MB");
-    string compute = string("CUDA Compute ") + toString(dev.major) + string(".") + toString(dev.minor);
-
-    string info = id + string(" ")  +
-                name + string(", ") +
-              memory + string(", ") +
-             compute + string("\n");
+    string memory = toString((mem_gpu_total / (1024 * 1024)) +
+                             !!(mem_gpu_total % (1024 * 1024))) +
+                    string(" MB");
+    string compute = string("CUDA Compute ") + toString(dev.major) +
+                     string(".") + toString(dev.minor);
+
+    string info = id + string(" ") + name + string(", ") + memory +
+                  string(", ") + compute + string("\n");
     return info;
 }
 
-string getDeviceInfo()
-{
+string getDeviceInfo() {
     ostringstream info;
-    info << "ArrayFire v" << AF_VERSION
-         << " (CUDA, " << get_system() << ", build " << AF_REVISION << ")" << std::endl;
+    info << "ArrayFire v" << AF_VERSION << " (CUDA, " << get_system()
+         << ", build " << AF_REVISION << ")" << std::endl;
     info << getPlatformInfo();
-    for (int i = 0; i < getDeviceCount(); ++i) {
-        info << getDeviceInfo(i);
-    }
+    for (int i = 0; i < getDeviceCount(); ++i) { info << getDeviceInfo(i); }
     return info.str();
 }
 
-string getPlatformInfo()
-{
-    string driverVersion = getDriverVersion();
+string getPlatformInfo() {
+    string driverVersion    = getDriverVersion();
     std::string cudaRuntime = getCUDARuntimeVersion();
-    string platform = "Platform: CUDA Toolkit " + cudaRuntime;
+    string platform         = "Platform: CUDA Toolkit " + cudaRuntime;
     if (!driverVersion.empty()) {
         platform.append(", Driver: ");
         platform.append(driverVersion);
@@ -224,14 +195,12 @@ string getPlatformInfo()
     return platform;
 }
 
-bool isDoubleSupported(int device)
-{
+bool isDoubleSupported(int device) {
     UNUSED(device);
     return true;
 }
 
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-{
+void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
     if (getDeviceCount() <= 0) {
         printf("No CUDA-capable devices detected.\n");
         return;
@@ -242,7 +211,7 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     // Name
     snprintf(d_name, 256, "%s", dev.name);
 
-    //Platform
+    // Platform
     std::string cudaRuntime = getCUDARuntimeVersion();
     snprintf(d_platform, 10, "CUDA");
     snprintf(d_toolkit, 64, "v%s", cudaRuntime.c_str());
@@ -253,21 +222,25 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
     // Sanitize input
     for (int i = 0; i < 256; i++) {
         if (d_name[i] == ' ') {
-            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ') d_name[i] = 0;
-            else d_name[i] = '_';
+            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ')
+                d_name[i] = 0;
+            else
+                d_name[i] = '_';
         }
     }
 }
 
-string getDriverVersion()
-{
-    char driverVersion[1024] = {" ",};
+string getDriverVersion() {
+    char driverVersion[1024] = {
+        " ",
+    };
     int x = nvDriverVersion(driverVersion, sizeof(driverVersion));
     if (x != 1) {
-        // Windows, OSX, Tegra Need a new way to fetch driver
-        #if !defined(OS_WIN) && !defined(OS_MAC) && !defined(__arm__) && !defined(__aarch64__)
+// Windows, OSX, Tegra Need a new way to fetch driver
+#if !defined(OS_WIN) && !defined(OS_MAC) && !defined(__arm__) && \
+    !defined(__aarch64__)
         throw runtime_error("Invalid driver");
-        #endif
+#endif
         int driver = 0;
         CUDA_CHECK(cudaDriverGetVersion(&driver));
         return string("CUDA Driver Version: ") + toString(driver);
@@ -276,19 +249,16 @@ string getDriverVersion()
     }
 }
 
-string getCUDARuntimeVersion()
-{
+string getCUDARuntimeVersion() {
     int runtime = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
-    if(runtime / 100.f > 0)
-        return toString((runtime / 1000) + (runtime % 1000)/ 100.);
+    if (runtime / 100.f > 0)
+        return toString((runtime / 1000) + (runtime % 1000) / 100.);
     else
         return toString(runtime / 1000) + string(".0");
-
 }
 
-unsigned getMaxJitSize()
-{
+unsigned getMaxJitSize() {
     const int MAX_JIT_LEN = 100;
 
     thread_local int length = 0;
@@ -304,181 +274,168 @@ unsigned getMaxJitSize()
     return length;
 }
 
-int& tlocalActiveDeviceId()
-{
+int &tlocalActiveDeviceId() {
     thread_local int activeDeviceId = 0;
 
     return activeDeviceId;
 }
 
-int getDeviceCount()
-{
-    return DeviceManager::getInstance().nDevices;
-}
+int getDeviceCount() { return DeviceManager::getInstance().nDevices; }
 
-int getActiveDeviceId()
-{
-    return tlocalActiveDeviceId();
-}
+int getActiveDeviceId() { return tlocalActiveDeviceId(); }
 
-int getDeviceNativeId(int device)
-{
-    if(device < (int)DeviceManager::getInstance().cuDevices.size())
+int getDeviceNativeId(int device) {
+    if (device < (int)DeviceManager::getInstance().cuDevices.size())
         return DeviceManager::getInstance().cuDevices[device].nativeId;
     return -1;
 }
 
-int getDeviceIdFromNativeId(int nativeId)
-{
-    DeviceManager& mngr = DeviceManager::getInstance();
+int getDeviceIdFromNativeId(int nativeId) {
+    DeviceManager &mngr = DeviceManager::getInstance();
 
     int devId = 0;
-    for(devId = 0; devId < mngr.nDevices; ++devId) {
-        if (nativeId == mngr.cuDevices[devId].nativeId)
-            break;
+    for (devId = 0; devId < mngr.nDevices; ++devId) {
+        if (nativeId == mngr.cuDevices[devId].nativeId) break;
     }
     return devId;
 }
 
-cudaStream_t getStream(int device)
-{
+cudaStream_t getStream(int device) {
     static std::once_flag streamInitFlags[DeviceManager::MAX_DEVICES];
 
-    std::call_once(streamInitFlags[device],
-            [device]() {
-                DeviceManager& inst = DeviceManager::getInstance();
-                CUDA_CHECK(cudaStreamCreate( & (inst.streams[device]) ));
-            });
+    std::call_once(streamInitFlags[device], [device]() {
+        DeviceManager &inst = DeviceManager::getInstance();
+        CUDA_CHECK(cudaStreamCreate(&(inst.streams[device])));
+    });
 
     return DeviceManager::getInstance().streams[device];
 }
 
-cudaStream_t getActiveStream()
-{
-    return getStream(getActiveDeviceId());
-}
+cudaStream_t getActiveStream() { return getStream(getActiveDeviceId()); }
 
-size_t getDeviceMemorySize(int device)
-{
+size_t getDeviceMemorySize(int device) {
     return getDeviceProp(device).totalGlobalMem;
 }
 
-size_t getHostMemorySize()
-{
-    return common::getHostMemorySize();
-}
+size_t getHostMemorySize() { return common::getHostMemorySize(); }
 
-int setDevice(int device)
-{
+int setDevice(int device) {
     return DeviceManager::getInstance().setActiveDevice(device);
 }
 
-cudaDeviceProp getDeviceProp(int device)
-{
-    if(device < (int)DeviceManager::getInstance().cuDevices.size())
+cudaDeviceProp getDeviceProp(int device) {
+    if (device < (int)DeviceManager::getInstance().cuDevices.size())
         return DeviceManager::getInstance().cuDevices[device].prop;
     return DeviceManager::getInstance().cuDevices[0].prop;
 }
 
-bool DeviceManager::checkGraphicsInteropCapability()
-{
+bool DeviceManager::checkGraphicsInteropCapability() {
     static std::once_flag checkInteropFlag;
-    thread_local bool capable  = true;
-
-    std::call_once(checkInteropFlag, [](){
-            unsigned int pCudaEnabledDeviceCount = 0;
-            int pCudaGraphicsEnabledDeviceIds = 0;
-            cudaGetLastError(); // Reset Errors
-            cudaError_t err = cudaGLGetDevices(&pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds, getDeviceCount(), cudaGLDeviceListAll);
-            if(err == 63) { // OS Support Failure - Happens when devices are only Tesla
-                capable = false;
-                printf("Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL Interop will use CPU fallback.\n");
-                printf("Corresponding CUDA Error (%d): %s.\n", err, cudaGetErrorString(err));
-                printf("This may happen if all CUDA Devices are in TCC Mode and/or not connected to a display.\n");
-            }
-            cudaGetLastError(); // Reset Errors
-        });
+    thread_local bool capable = true;
+
+    std::call_once(checkInteropFlag, []() {
+        unsigned int pCudaEnabledDeviceCount = 0;
+        int pCudaGraphicsEnabledDeviceIds    = 0;
+        cudaGetLastError();  // Reset Errors
+        cudaError_t err = cudaGLGetDevices(
+            &pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds,
+            getDeviceCount(), cudaGLDeviceListAll);
+        if (err ==
+            63) {  // OS Support Failure - Happens when devices are only Tesla
+            capable = false;
+            printf(
+                "Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL "
+                "Interop will use CPU fallback.\n");
+            printf("Corresponding CUDA Error (%d): %s.\n", err,
+                   cudaGetErrorString(err));
+            printf(
+                "This may happen if all CUDA Devices are in TCC Mode and/or "
+                "not connected to a display.\n");
+        }
+        cudaGetLastError();  // Reset Errors
+    });
 
     return capable;
 }
 
-DeviceManager& DeviceManager::getInstance()
-{
+DeviceManager &DeviceManager::getInstance() {
     static DeviceManager *my_instance = new DeviceManager();
     return *my_instance;
 }
 
-MemoryManager& memoryManager()
-{
+MemoryManager &memoryManager() {
     static std::once_flag flag;
 
-    DeviceManager& inst = DeviceManager::getInstance();
+    DeviceManager &inst = DeviceManager::getInstance();
 
     std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
 
     return *(inst.memManager.get());
 }
 
-MemoryManagerPinned& pinnedMemoryManager()
-{
+MemoryManagerPinned &pinnedMemoryManager() {
     static std::once_flag flag;
 
-    DeviceManager& inst = DeviceManager::getInstance();
+    DeviceManager &inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
+    std::call_once(flag, [&]() {
+        inst.pinnedMemManager.reset(new MemoryManagerPinned());
+    });
 
     return *(inst.pinnedMemManager.get());
 }
 
-graphics::ForgeManager& forgeManager()
-{
+graphics::ForgeManager &forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
-GraphicsResourceManager& interopManager()
-{
+GraphicsResourceManager &interopManager() {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = getActiveDeviceId();
 
-    DeviceManager& inst = DeviceManager::getInstance();
+    DeviceManager &inst = DeviceManager::getInstance();
 
-    std::call_once(initFlags[id], [&]{ inst.gfxManagers[id].reset(new GraphicsResourceManager()); });
+    std::call_once(initFlags[id], [&] {
+        inst.gfxManagers[id].reset(new GraphicsResourceManager());
+    });
 
     return *(inst.gfxManagers[id].get());
 }
 
-PlanCache& fftManager()
-{
+PlanCache &fftManager() {
     thread_local PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
 
     return cufftManagers[getActiveDeviceId()];
 }
 
-BlasHandle blasHandle()
-{
-    thread_local std::unique_ptr<cublasHandle> cublasHandles[DeviceManager::MAX_DEVICES];
+BlasHandle blasHandle() {
+    thread_local std::unique_ptr<cublasHandle>
+        cublasHandles[DeviceManager::MAX_DEVICES];
     thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    std::call_once(initFlags[id], [&]{ cublasHandles[id].reset(new cublasHandle()); });
+    std::call_once(initFlags[id],
+                   [&] { cublasHandles[id].reset(new cublasHandle()); });
 
-    CUBLAS_CHECK(cublasSetStream(cublasHandles[id].get()->get(), cuda::getStream(id)));
+    CUBLAS_CHECK(
+        cublasSetStream(cublasHandles[id].get()->get(), cuda::getStream(id)));
 
     return cublasHandles[id].get()->get();
 }
 
-SolveHandle solverDnHandle()
-{
-    thread_local std::unique_ptr<cusolverDnHandle> cusolverHandles[DeviceManager::MAX_DEVICES];
+SolveHandle solverDnHandle() {
+    thread_local std::unique_ptr<cusolverDnHandle>
+        cusolverHandles[DeviceManager::MAX_DEVICES];
     thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    std::call_once(initFlags[id], [&]{ cusolverHandles[id].reset(new cusolverDnHandle()); });
+    std::call_once(initFlags[id],
+                   [&] { cusolverHandles[id].reset(new cusolverDnHandle()); });
 
-    //FIXME
+    // FIXME
     // This is not an ideal case. It's just a hack.
     // The correct way to do is to use
     // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
@@ -496,33 +453,33 @@ SolveHandle solverDnHandle()
     return cusolverHandles[id].get()->get();
 }
 
-SparseHandle sparseHandle()
-{
-    thread_local std::unique_ptr<cusparseHandle> cusparseHandles[DeviceManager::MAX_DEVICES];
+SparseHandle sparseHandle() {
+    thread_local std::unique_ptr<cusparseHandle>
+        cusparseHandles[DeviceManager::MAX_DEVICES];
     thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = cuda::getActiveDeviceId();
 
-    std::call_once(initFlags[id], [&]{ cusparseHandles[id].reset(new cusparseHandle()); });
+    std::call_once(initFlags[id],
+                   [&] { cusparseHandles[id].reset(new cusparseHandle()); });
 
-    CUSPARSE_CHECK(cusparseSetStream(cusparseHandles[id].get()->get(), cuda::getStream(id)));
+    CUSPARSE_CHECK(cusparseSetStream(cusparseHandles[id].get()->get(),
+                                     cuda::getStream(id)));
 
     return cusparseHandles[id].get()->get();
 }
 
 DeviceManager::DeviceManager()
-    : cuDevices(0), nDevices(0), fgMngr(new graphics::ForgeManager())
-{
+    : cuDevices(0), nDevices(0), fgMngr(new graphics::ForgeManager()) {
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
-    if (nDevices == 0)
-        throw runtime_error("No CUDA-Capable devices found");
+    if (nDevices == 0) throw runtime_error("No CUDA-Capable devices found");
     cuDevices.reserve(nDevices);
 
     int cudaRtVer = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&cudaRtVer));
     int cudaMajorVer = cudaRtVer / 1000;
 
-    for(int i = 0; i < nDevices; i++) {
+    for (int i = 0; i < nDevices; i++) {
         cudaDevice_t dev;
         cudaGetDeviceProperties(&dev.prop, i);
         if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
@@ -541,17 +498,16 @@ DeviceManager::DeviceManager()
 
     // Initialize all streams to 0.
     // Streams will be created in setActiveDevice()
-    for(int i = 0; i < (int)MAX_DEVICES; i++)
-        streams[i] = (cudaStream_t)0;
+    for (int i = 0; i < (int)MAX_DEVICES; i++) streams[i] = (cudaStream_t)0;
 
     std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
-    if(deviceENV.empty()) {
+    if (deviceENV.empty()) {
         setActiveDevice(0, cuDevices[0].nativeId);
     } else {
         stringstream s(deviceENV);
         int def_device = -1;
         s >> def_device;
-        if(def_device < 0 || def_device >= nDevices) {
+        if (def_device < 0 || def_device >= nDevices) {
             printf("WARNING: AF_CUDA_DEFAULT_DEVICE is out of range\n");
             printf("Setting default device as 0\n");
             setActiveDevice(0, cuDevices[0].nativeId);
@@ -561,37 +517,38 @@ DeviceManager::DeviceManager()
     }
 }
 
-void DeviceManager::sortDevices(sort_mode mode)
-{
-    switch(mode) {
-        case memory :
-            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_mem);
+void DeviceManager::sortDevices(sort_mode mode) {
+    switch (mode) {
+        case memory:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_mem);
             break;
-        case flops :
-            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_flops);
+        case flops:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_flops);
             break;
-        case compute :
-            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_compute);
+        case compute:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_compute);
             break;
-        case none : default :
-            std::stable_sort(cuDevices.begin(), cuDevices.end(), card_compare_num);
+        case none:
+        default:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_num);
             break;
     }
 }
 
-int DeviceManager::setActiveDevice(int device, int nId)
-{
+int DeviceManager::setActiveDevice(int device, int nId) {
     thread_local bool retryFlag = true;
 
     int numDevices = cuDevices.size();
 
-    if (device >= numDevices)
-        return -1;
+    if (device >= numDevices) return -1;
 
     int old = getActiveDeviceId();
 
-    if(nId == -1)
-        nId = getDeviceNativeId(device);
+    if (nId == -1) nId = getDeviceNativeId(device);
 
     cudaError_t err = cudaSetDevice(nId);
 
@@ -611,7 +568,7 @@ int DeviceManager::setActiveDevice(int device, int nId)
     // Comes only when retryFlag is true. Set it to false
     retryFlag = false;
 
-    while(true) {
+    while (true) {
         // Check for errors other than DevicesUnavailable
         // If success, return. Else throw error
         // If DevicesUnavailable, try other devices (while loop below)
@@ -620,9 +577,11 @@ int DeviceManager::setActiveDevice(int device, int nId)
             tlocalActiveDeviceId() = device;
             return old;
         }
-        cudaGetLastError(); // Reset error stack
+        cudaGetLastError();  // Reset error stack
 #ifndef NDEBUG
-        printf("Warning: Device %d is unavailable. Incrementing to next device \n", device);
+        printf(
+            "Warning: Device %d is unavailable. Incrementing to next device \n",
+            device);
 #endif
         // Comes here is the device is in exclusive mode or
         // otherwise fails streamCreate with this error.
@@ -630,7 +589,8 @@ int DeviceManager::setActiveDevice(int device, int nId)
         device++;
         if (device >= numDevices) break;
 
-        // Can't call getNativeId here as it will cause an infinite loop with the constructor
+        // Can't call getNativeId here as it will cause an infinite loop with
+        // the constructor
         nId = cuDevices[device].nativeId;
 
         err = cudaSetDevice(nId);
@@ -642,47 +602,44 @@ int DeviceManager::setActiveDevice(int device, int nId)
     return old;
 }
 
-void sync(int device)
-{
+void sync(int device) {
     int currDevice = getActiveDeviceId();
     setDevice(device);
     CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
     setDevice(currDevice);
 }
 
-bool synchronize_calls()
-{
+bool synchronize_calls() {
     static const bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
 }
 
-bool& evalFlag()
-{
+bool &evalFlag() {
     thread_local bool flag = true;
     return flag;
 }
-}
+}  // namespace cuda
 
-af_err afcu_get_stream(cudaStream_t* stream, int id)
-{
-    try{
+af_err afcu_get_stream(cudaStream_t *stream, int id) {
+    try {
         *stream = cuda::getStream(id);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcu_get_native_id(int* nativeid, int id)
-{
+af_err afcu_get_native_id(int *nativeid, int id) {
     try {
         *nativeid = cuda::getDeviceNativeId(id);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcu_set_native_id(int nativeid)
-{
+af_err afcu_set_native_id(int nativeid) {
     try {
         cuda::setDevice(cuda::getDeviceIdFromNativeId(nativeid));
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index f3b38440a1..22d4a6f3c2 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -9,30 +9,28 @@
 
 #pragma once
 
+#include <GraphicsResourceManager.hpp>
+#include <cublas.hpp>
 #include <cuda.h>
 #include <cuda_runtime.h>
-#include <memory.hpp>
-#include <GraphicsResourceManager.hpp>
 #include <cufft.hpp>
-#include <cublas.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
-
+#include <memory.hpp>
 
 #include <memory>
 #include <string>
 #include <vector>
 
 namespace spdlog {
-  class logger;
+class logger;
 }
 
 namespace graphics {
-  class ForgeManager;
+class ForgeManager;
 }
 
-namespace cuda
-{
+namespace cuda {
 int getBackend();
 
 std::string getDeviceInfo();
@@ -46,7 +44,7 @@ std::string getCUDARuntimeVersion();
 
 bool isDoubleSupported(int device);
 
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
 
@@ -103,73 +101,72 @@ SparseHandle sparseHandle();
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
-class DeviceManager
-{
-    public:
-        static const unsigned MAX_DEVICES = 16;
+class DeviceManager {
+   public:
+    static const unsigned MAX_DEVICES = 16;
 
-        static bool checkGraphicsInteropCapability();
+    static bool checkGraphicsInteropCapability();
 
-        static DeviceManager& getInstance();
+    static DeviceManager& getInstance();
 
-        friend MemoryManager& memoryManager();
+    friend MemoryManager& memoryManager();
 
-        friend MemoryManagerPinned& pinnedMemoryManager();
+    friend MemoryManagerPinned& pinnedMemoryManager();
 
-        friend graphics::ForgeManager& forgeManager();
+    friend graphics::ForgeManager& forgeManager();
 
-        friend GraphicsResourceManager& interopManager();
+    friend GraphicsResourceManager& interopManager();
 
-        friend std::string getDeviceInfo(int device);
+    friend std::string getDeviceInfo(int device);
 
-        friend std::string getPlatformInfo();
+    friend std::string getPlatformInfo();
 
-        friend std::string getDriverVersion();
+    friend std::string getDriverVersion();
 
-        friend std::string getCUDARuntimeVersion();
+    friend std::string getCUDARuntimeVersion();
 
-        friend std::string getDeviceInfo();
+    friend std::string getDeviceInfo();
 
-        friend int getDeviceCount();
+    friend int getDeviceCount();
 
-        friend int getDeviceNativeId(int device);
+    friend int getDeviceNativeId(int device);
 
-        friend int getDeviceIdFromNativeId(int nativeId);
+    friend int getDeviceIdFromNativeId(int nativeId);
 
-        friend cudaStream_t getStream(int device);
+    friend cudaStream_t getStream(int device);
 
-        friend int setDevice(int device);
+    friend int setDevice(int device);
 
-        friend cudaDeviceProp getDeviceProp(int device);
+    friend cudaDeviceProp getDeviceProp(int device);
 
-    private:
-        DeviceManager();
+   private:
+    DeviceManager();
 
-        // Following two declarations are required to
-        // avoid copying accidental copy/assignment
-        // of instance returned by getInstance to other
-        // variables
-        DeviceManager(DeviceManager const&);
-        void operator=(DeviceManager const&);
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&);
+    void operator=(DeviceManager const&);
 
-        // Attributes
-        std::vector<cudaDevice_t> cuDevices;
+    // Attributes
+    std::vector<cudaDevice_t> cuDevices;
 
-        enum sort_mode {flops = 0, memory = 1, compute = 2, none = 3};
+    enum sort_mode { flops = 0, memory = 1, compute = 2, none = 3 };
 
-        void sortDevices(sort_mode mode = flops);
+    void sortDevices(sort_mode mode = flops);
 
-        int setActiveDevice(int device, int native = -1);
+    int setActiveDevice(int device, int native = -1);
 
-        int nDevices;
-        cudaStream_t streams[MAX_DEVICES];
+    int nDevices;
+    cudaStream_t streams[MAX_DEVICES];
 
-        std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
 
-        std::unique_ptr<MemoryManager> memManager;
+    std::unique_ptr<MemoryManager> memManager;
 
-        std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
-        std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
 };
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index d77f7dbf2b..ea7dce05ae 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -8,29 +8,28 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <plot.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
+#include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <plot.hpp>
 
 using af::dim4;
 
 namespace cuda {
 
 template<typename T>
-void copy_plot(const Array<T> &P, fg_plot plot)
-{
+void copy_plot(const Array<T> &P, fg_plot plot) {
     auto stream = cuda::getActiveStream();
-    if(DeviceManager::checkGraphicsInteropCapability()) {
+    if (DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
         auto res = interopManager().getPlotResources(plot);
 
         size_t bytes = 0;
-        T* d_vbo = NULL;
+        T *d_vbo     = NULL;
         cudaGraphicsMapResources(1, res[0].get(), stream);
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo,
-                                             &bytes, *(res[0].get()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
+                                             *(res[0].get()));
         cudaMemcpyAsync(d_vbo, d_P, bytes, cudaMemcpyDeviceToDevice, stream);
         cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
@@ -38,14 +37,14 @@ void copy_plot(const Array<T> &P, fg_plot plot)
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule &_ = graphics::forgePlugin();
         unsigned bytes = 0, buffer = 0;
         FG_CHECK(_.fg_get_plot_vertex_buffer(&buffer, plot));
         FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
@@ -57,8 +56,7 @@ void copy_plot(const Array<T> &P, fg_plot plot)
     }
 }
 
-#define INSTANTIATE(T)  \
-template void copy_plot<T>(const Array<T> &, fg_plot);
+#define INSTANTIATE(T) template void copy_plot<T>(const Array<T> &, fg_plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -68,4 +66,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/print.hpp b/src/backend/cuda/print.hpp
index cdd08a0277..a61811a478 100644
--- a/src/backend/cuda/print.hpp
+++ b/src/backend/cuda/print.hpp
@@ -8,22 +8,17 @@
  ********************************************************/
 
 #pragma once
-#include <ostream>
 #include <backend.hpp>
+#include <ostream>
 
-namespace cuda
-{
-    static std::ostream&
-    operator<<(std::ostream &out, const cfloat& var)
-    {
-        out << "(" << var.x << "," << var.y << ")";
-        return out;
-    }
+namespace cuda {
+static std::ostream& operator<<(std::ostream& out, const cfloat& var) {
+    out << "(" << var.x << "," << var.y << ")";
+    return out;
+}
 
-    static std::ostream&
-    operator<<(std::ostream &out, const cdouble& var)
-    {
-        out << "(" << var.x << "," << var.y << ")";
-        return out;
-    }
+static std::ostream& operator<<(std::ostream& out, const cdouble& var) {
+    out << "(" << var.x << "," << var.y << ")";
+    return out;
 }
+}  // namespace cuda
diff --git a/src/backend/cuda/product.cu b/src/backend/cuda/product.cu
index d00e140f49..532f983ce2 100644
--- a/src/backend/cuda/product.cu
+++ b/src/backend/cuda/product.cu
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    //mul
-    INSTANTIATE(af_mul_t, float  , float  )
-    INSTANTIATE(af_mul_t, double , double )
-    INSTANTIATE(af_mul_t, cfloat , cfloat )
-    INSTANTIATE(af_mul_t, cdouble, cdouble)
-    INSTANTIATE(af_mul_t, int    , int    )
-    INSTANTIATE(af_mul_t, uint   , uint   )
-    INSTANTIATE(af_mul_t, intl   , intl   )
-    INSTANTIATE(af_mul_t, uintl  , uintl  )
-    INSTANTIATE(af_mul_t, char   , int    )
-    INSTANTIATE(af_mul_t, uchar  , uint   )
-    INSTANTIATE(af_mul_t, short  , int    )
-    INSTANTIATE(af_mul_t, ushort , uint   )
-}
+namespace cuda {
+// mul
+INSTANTIATE(af_mul_t, float, float)
+INSTANTIATE(af_mul_t, double, double)
+INSTANTIATE(af_mul_t, cfloat, cfloat)
+INSTANTIATE(af_mul_t, cdouble, cdouble)
+INSTANTIATE(af_mul_t, int, int)
+INSTANTIATE(af_mul_t, uint, uint)
+INSTANTIATE(af_mul_t, intl, intl)
+INSTANTIATE(af_mul_t, uintl, uintl)
+INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, uchar, uint)
+INSTANTIATE(af_mul_t, short, int)
+INSTANTIATE(af_mul_t, ushort, uint)
+}  // namespace cuda
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index 600e9a8bcb..336e1350a2 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -7,31 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <qr.hpp>
 #include <common/err_common.hpp>
+#include <qr.hpp>
 
-#include <platform.hpp>
+#include <copy.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
 #include <memory.hpp>
-#include <copy.hpp>
+#include <platform.hpp>
 
-#include <math.hpp>
 #include <common/err_common.hpp>
+#include <math.hpp>
 
 #include <kernel/triangle.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
-//cusolverStatus_t cusolverDn<>geqrf_bufferSize(
+// cusolverStatus_t cusolverDn<>geqrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
 //        <> *A,
 //        int lda,
 //        int *Lwork );
 //
-//cusolverStatus_t cusolverDn<>geqrf(
+// cusolverStatus_t cusolverDn<>geqrf(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
 //        <> *A, int lda,
@@ -39,7 +38,7 @@ namespace cuda
 //        <> *Workspace,
 //        int Lwork, int *devInfo );
 //
-//cusolverStatus_t cusolverDn<>mqr(
+// cusolverStatus_t cusolverDn<>mqr(
 //        cusolverDnHandle_t handle,
 //        cublasSideMode_t side, cublasOperation_t trans,
 //        int m, int n, int k,
@@ -50,102 +49,91 @@ namespace cuda
 //        int lwork, int *devInfo);
 
 template<typename T>
-struct geqrf_func_def_t
-{
-    typedef cusolverStatus_t (*geqrf_func_def) (
-                              cusolverDnHandle_t, int, int,
-                              T *, int,
-                              T *,
-                              T *,
-                              int, int *);
+struct geqrf_func_def_t {
+    typedef cusolverStatus_t (*geqrf_func_def)(cusolverDnHandle_t, int, int,
+                                               T *, int, T *, T *, int, int *);
 };
 
 template<typename T>
-struct geqrf_buf_func_def_t
-{
-    typedef cusolverStatus_t (*geqrf_buf_func_def) (
-                              cusolverDnHandle_t, int, int,
-                              T *, int, int *);
+struct geqrf_buf_func_def_t {
+    typedef cusolverStatus_t (*geqrf_buf_func_def)(cusolverDnHandle_t, int, int,
+                                                   T *, int, int *);
 };
 
 template<typename T>
-struct mqr_func_def_t
-{
-    typedef cusolverStatus_t (*mqr_func_def) (
-                              cusolverDnHandle_t,
-                              cublasSideMode_t, cublasOperation_t,
-                              int, int, int,
-                              const T *, int,
-                              const T *,
-                              T *, int,
-                              T *, int,
-                              int *);
+struct mqr_func_def_t {
+    typedef cusolverStatus_t (*mqr_func_def)(cusolverDnHandle_t,
+                                             cublasSideMode_t,
+                                             cublasOperation_t, int, int, int,
+                                             const T *, int, const T *, T *,
+                                             int, T *, int, int *);
 };
 
-#define QR_FUNC_DEF( FUNC )                                                     \
-template<typename T>                                                            \
-typename FUNC##_func_def_t<T>::FUNC##_func_def                                  \
-FUNC##_func();                                                                  \
-                                                                                \
-template<typename T>                                                            \
-typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def                          \
-FUNC##_buf_func();                                                              \
-
-#define QR_FUNC( FUNC, TYPE, PREFIX )                                                           \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>()                \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusolverDn##PREFIX##FUNC; }                 \
-                                                                                                \
-template<> typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def FUNC##_buf_func<TYPE>()    \
-{ return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def)& cusolverDn##PREFIX##FUNC##_bufferSize; }
-
-QR_FUNC_DEF( geqrf )
-QR_FUNC(geqrf , float  , S)
-QR_FUNC(geqrf , double , D)
-QR_FUNC(geqrf , cfloat , C)
-QR_FUNC(geqrf , cdouble, Z)
-
-#define MQR_FUNC_DEF( FUNC )                                                        \
-template<typename T>                                                                \
-typename FUNC##_func_def_t<T>::FUNC##_func_def                                      \
-FUNC##_func();
-
-#define MQR_FUNC( FUNC, TYPE, PREFIX )                                              \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>()    \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusolverDn##PREFIX; }
-
-MQR_FUNC_DEF( mqr )
-MQR_FUNC(mqr , float  , Sormqr)
-MQR_FUNC(mqr , double , Dormqr)
-MQR_FUNC(mqr , cfloat , Cunmqr)
-MQR_FUNC(mqr , cdouble, Zunmqr)
+#define QR_FUNC_DEF(FUNC)                                         \
+    template<typename T>                                          \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func(); \
+                                                                  \
+    template<typename T>                                          \
+    typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def FUNC##_buf_func();
+
+#define QR_FUNC(FUNC, TYPE, PREFIX)                                         \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusolverDn##PREFIX##FUNC;                                    \
+    }                                                                       \
+                                                                            \
+    template<>                                                              \
+    typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def               \
+        FUNC##_buf_func<TYPE>() {                                           \
+        return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def) &         \
+               cusolverDn##PREFIX##FUNC##_bufferSize;                       \
+    }
+
+QR_FUNC_DEF(geqrf)
+QR_FUNC(geqrf, float, S)
+QR_FUNC(geqrf, double, D)
+QR_FUNC(geqrf, cfloat, C)
+QR_FUNC(geqrf, cdouble, Z)
+
+#define MQR_FUNC_DEF(FUNC) \
+    template<typename T>   \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
+
+#define MQR_FUNC(FUNC, TYPE, PREFIX)                                        \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusolverDn##PREFIX;                                          \
+    }
+
+MQR_FUNC_DEF(mqr)
+MQR_FUNC(mqr, float, Sormqr)
+MQR_FUNC(mqr, double, Dormqr)
+MQR_FUNC(mqr, cfloat, Cunmqr)
+MQR_FUNC(mqr, cdouble, Zunmqr)
 
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<T> in_copy = copyArray<T>(in);
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(solverDnHandle(),
-                                       M, N,
-                                       in_copy.get(), in_copy.strides()[1],
-                                       &lwork));
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(solverDnHandle(), M, N, in_copy.get(),
+                                       in_copy.strides()[1], &lwork));
 
     auto workspace = memAlloc<T>(lwork);
 
-    t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
+    t         = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
     auto info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(),
-                                   M, N,
-                                   in_copy.get(), in_copy.strides()[1],
-                                   t.get(),
-                                   workspace.get(),
-                                   lwork, info.get()));
+    CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(), M, N, in_copy.get(),
+                                   in_copy.strides()[1], t.get(),
+                                   workspace.get(), lwork, info.get()));
 
     // SPLIT into q and r
     dim4 rdims(M, N);
@@ -157,56 +145,44 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     dim4 qdims(M, mn);
     q = identity<T>(qdims);
 
-    CUSOLVER_CHECK(mqr_func<T>()(solverDnHandle(),
-                                 CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
-                                 q.dims()[0],
-                                 q.dims()[1],
-                                 min(M, N),
-                                 in_copy.get(), in_copy.strides()[1],
-                                 t.get(),
-                                 q.get(), q.strides()[1],
-                                 workspace.get(), lwork,
-                                 info.get()));
+    CUSOLVER_CHECK(mqr_func<T>()(
+        solverDnHandle(), CUBLAS_SIDE_LEFT, CUBLAS_OP_N, q.dims()[0],
+        q.dims()[1], min(M, N), in_copy.get(), in_copy.strides()[1], t.get(),
+        q.get(), q.strides()[1], workspace.get(), lwork, info.get()));
 
     q.resetDims(dim4(M, M));
-
 }
 
 template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
+Array<T> qr_inplace(Array<T> &in) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
     int lwork = 0;
 
-    CUSOLVER_CHECK(geqrf_buf_func<T>()(solverDnHandle(),
-                                       M, N,
-                                       in.get(), in.strides()[1],
-                                       &lwork));
+    CUSOLVER_CHECK(geqrf_buf_func<T>()(solverDnHandle(), M, N, in.get(),
+                                       in.strides()[1], &lwork));
 
     auto workspace = memAlloc<T>(lwork);
-    auto info = memAlloc<int>(1);
+    auto info      = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(),
-                                   M, N,
-                                   in.get(), in.strides()[1],
-                                   t.get(),
-                                   workspace.get(), lwork,
-                                   info.get()));
+    CUSOLVER_CHECK(geqrf_func<T>()(solverDnHandle(), M, N, in.get(),
+                                   in.strides()[1], t.get(), workspace.get(),
+                                   lwork, info.get()));
 
     return t;
 }
 
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
 
 INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/qr.hpp b/src/backend/cuda/qr.hpp
index dc0f56a6dc..450a3555a6 100644
--- a/src/backend/cuda/qr.hpp
+++ b/src/backend/cuda/qr.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+namespace cuda {
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
 
-    template<typename T>
-    Array<T> qr_inplace(Array<T> &in);
-}
+template<typename T>
+Array<T> qr_inplace(Array<T> &in);
+}  // namespace cuda
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 1200801880..8cbb61d4ed 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -7,152 +7,149 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
+#include <af/dim4.hpp>
 #include <cassert>
 
-namespace cuda
-{
-    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
-    {
-        kernel::initMersenneState(state.get(), tbl.get(), seed);
-    }
+namespace cuda {
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> tbl) {
+    kernel::initMersenneState(state.get(), tbl.get(), seed);
+}
 
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistributionCBRNG<T>(out.get(), out.elements(), type, seed, counter);
-        return out;
-    }
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::uniformDistributionCBRNG<T>(out.get(), out.elements(), type, seed,
+                                        counter);
+    return out;
+}
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistributionCBRNG<T>(out.get(), out.elements(), type, seed, counter);
-        return out;
-    }
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl &seed,
+                            uintl &counter) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::normalDistributionCBRNG<T>(out.get(), out.elements(), type, seed,
+                                       counter);
+    return out;
+}
 
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistributionMT<T>(
-              out.get(), out.elements(),
-              state.get(), pos.get(),
-              sh1.get(), sh2.get(),
-              mask, recursion_table.get(),
-              temper_table.get());
-        return out;
-    }
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::uniformDistributionMT<T>(out.get(), out.elements(), state.get(),
+                                     pos.get(), sh1.get(), sh2.get(), mask,
+                                     recursion_table.get(), temper_table.get());
+    return out;
+}
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistributionMT<T>(
-              out.get(), out.elements(),
-              state.get(), pos.get(),
-              sh1.get(), sh2.get(),
-              mask, recursion_table.get(),
-              temper_table.get());
-        return out;
-    }
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::normalDistributionMT<T>(out.get(), out.elements(), state.get(),
+                                    pos.get(), sh1.get(), sh2.get(), mask,
+                                    recursion_table.get(), temper_table.get());
+    return out;
+}
 
-#define INSTANTIATE_UNIFORM(T)                                                                                                  \
-    template                                                                                                                    \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter); \
-    template                                                                                                                    \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+#define INSTANTIATE_UNIFORM(T)                                   \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl &seed, uintl &counter);                      \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
 
-#define INSTANTIATE_NORMAL(T)                                                                                                   \
-    template                                                                                                                    \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);  \
-    template                                                                                                                    \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+#define INSTANTIATE_NORMAL(T)                                    \
+    template Array<T> normalDistribution<T>(                     \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl &seed, uintl &counter);                      \
+    template Array<T> normalDistribution<T>(                     \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
 
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                     \
-    template<>                                                                                                                  \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)  \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        TR *outPtr = (TR*)out.get();                                                                                            \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                            \
-        return out;                                                                                                             \
-    }                                                                                                                           \
-    template<>                                                                                                                  \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        TR *outPtr = (TR*)out.get();                                                                                            \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::uniformDistributionMT<TR>(                                                                                      \
-              outPtr, elements,                                                                                                 \
-              state.get(), pos.get(),                                                                                           \
-              sh1.get(), sh2.get(),                                                                                             \
-              mask, recursion_table.get(),                                                                                      \
-              temper_table.get());                                                                                              \
-        return out;                                                                                                             \
-    }                                                                                                                           \
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                 \
+    template<>                                                              \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                   \
+                                    const af_random_engine_type type,       \
+                                    const uintl &seed, uintl &counter) {    \
+        Array<T> out    = createEmptyArray<T>(dims);                        \
+        TR *outPtr      = (TR *)out.get();                                  \
+        size_t elements = out.elements() * 2;                               \
+        kernel::uniformDistributionCBRNG<TR>(outPtr, elements, type, seed,  \
+                                             counter);                      \
+        return out;                                                         \
+    }                                                                       \
+    template<>                                                              \
+    Array<T> uniformDistribution<T>(                                        \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,             \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,            \
+        Array<uint> temper_table, Array<uint> state) {                      \
+        Array<T> out    = createEmptyArray<T>(dims);                        \
+        TR *outPtr      = (TR *)out.get();                                  \
+        size_t elements = out.elements() * 2;                               \
+        kernel::uniformDistributionMT<TR>(                                  \
+            outPtr, elements, state.get(), pos.get(), sh1.get(), sh2.get(), \
+            mask, recursion_table.get(), temper_table.get());               \
+        return out;                                                         \
+    }
 
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                      \
-    template<>                                                                                                                  \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)   \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        TR *outPtr = (TR*)out.get();                                                                                            \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed, counter);                                             \
-        return out;                                                                                                             \
-    }                                                                                                                           \
-    template<>                                                                                                                  \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        TR *outPtr = (TR*)out.get();                                                                                            \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::normalDistributionMT<TR>(                                                                                       \
-              outPtr, elements,                                                                                                 \
-              state.get(), pos.get(),                                                                                           \
-              sh1.get(), sh2.get(),                                                                                             \
-              mask, recursion_table.get(),                                                                                      \
-              temper_table.get());                                                                                              \
-        return out;                                                                                                             \
-    }                                                                                                                           \
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                  \
+    template<>                                                              \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                    \
+                                   const af_random_engine_type type,        \
+                                   const uintl &seed, uintl &counter) {     \
+        Array<T> out    = createEmptyArray<T>(dims);                        \
+        TR *outPtr      = (TR *)out.get();                                  \
+        size_t elements = out.elements() * 2;                               \
+        kernel::normalDistributionCBRNG<TR>(outPtr, elements, type, seed,   \
+                                            counter);                       \
+        return out;                                                         \
+    }                                                                       \
+    template<>                                                              \
+    Array<T> normalDistribution<T>(                                         \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,             \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,            \
+        Array<uint> temper_table, Array<uint> state) {                      \
+        Array<T> out    = createEmptyArray<T>(dims);                        \
+        TR *outPtr      = (TR *)out.get();                                  \
+        size_t elements = out.elements() * 2;                               \
+        kernel::normalDistributionMT<TR>(                                   \
+            outPtr, elements, state.get(), pos.get(), sh1.get(), sh2.get(), \
+            mask, recursion_table.get(), temper_table.get());               \
+        return out;                                                         \
+    }
 
-    INSTANTIATE_UNIFORM(float )
-    INSTANTIATE_UNIFORM(double)
-    INSTANTIATE_UNIFORM(int   )
-    INSTANTIATE_UNIFORM(uint  )
-    INSTANTIATE_UNIFORM(intl  )
-    INSTANTIATE_UNIFORM(uintl )
-    INSTANTIATE_UNIFORM(char  )
-    INSTANTIATE_UNIFORM(uchar )
-    INSTANTIATE_UNIFORM(short )
-    INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(float)
+INSTANTIATE_UNIFORM(double)
+INSTANTIATE_UNIFORM(int)
+INSTANTIATE_UNIFORM(uint)
+INSTANTIATE_UNIFORM(intl)
+INSTANTIATE_UNIFORM(uintl)
+INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(uchar)
+INSTANTIATE_UNIFORM(short)
+INSTANTIATE_UNIFORM(ushort)
 
-    INSTANTIATE_NORMAL(float )
-    INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(float)
+INSTANTIATE_NORMAL(double)
 
-    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
-    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index f33ba590df..a5047d3429 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -10,28 +10,34 @@
 #pragma once
 
 #include <Array.hpp>
-#include <af/defines.h>
 #include <backend.hpp>
+#include <af/defines.h>
 
-namespace cuda
-{
-    Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
-
-    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
-
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
-
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
-}
+namespace cuda {
+Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
+
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> tbl);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl &seed,
+                            uintl &counter);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state);
+}  // namespace cuda
diff --git a/src/backend/cuda/range.cu b/src/backend/cuda/range.cu
index ace3b1c49d..39b3bcb980 100644
--- a/src/backend/cuda/range.cu
+++ b/src/backend/cuda/range.cu
@@ -8,43 +8,41 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <range.hpp>
+#include <err_cuda.hpp>
 #include <kernel/range.hpp>
 #include <math.hpp>
+#include <range.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> range(const dim4& dim, const int seq_dim)
-    {
-        // Set dimension along which the sequence should be
-        // Other dimensions are simply tiled
-        int _seq_dim = seq_dim;
-        if(seq_dim < 0) {
-            _seq_dim = 0;   // column wise sequence
-        }
+namespace cuda {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim) {
+    // Set dimension along which the sequence should be
+    // Other dimensions are simply tiled
+    int _seq_dim = seq_dim;
+    if (seq_dim < 0) {
+        _seq_dim = 0;  // column wise sequence
+    }
 
-        if(_seq_dim < 0 || _seq_dim > 3)
-            AF_ERROR("Invalid rep selection", AF_ERR_ARG);
+    if (_seq_dim < 0 || _seq_dim > 3)
+        AF_ERROR("Invalid rep selection", AF_ERR_ARG);
 
-        Array<T> out = createEmptyArray<T>(dim);
-        kernel::range<T>(out, _seq_dim);
+    Array<T> out = createEmptyArray<T>(dim);
+    kernel::range<T>(out, _seq_dim);
 
-        return out;
-    }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> range<T>(const af::dim4 &dims, const int seq_dim);    \
+#define INSTANTIATE(T) \
+    template Array<T> range<T>(const af::dim4& dims, const int seq_dim);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/range.hpp b/src/backend/cuda/range.hpp
index b6cf0c1393..904fe139a9 100644
--- a/src/backend/cuda/range.hpp
+++ b/src/backend/cuda/range.hpp
@@ -10,8 +10,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> range(const dim4& dim, const int seq_dim = -1);
+namespace cuda {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim = -1);
 }
diff --git a/src/backend/cuda/reduce.hpp b/src/backend/cuda/reduce.hpp
index d3189cd9d3..af47866e8f 100644
--- a/src/backend/cuda/reduce.hpp
+++ b/src/backend/cuda/reduce.hpp
@@ -10,11 +10,11 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan=false, double nanval=0);
+namespace cuda {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
+                 double nanval = 0);
 
-    template<af_op_t op, typename Ti, typename To>
-    To reduce_all(const Array<Ti> &in, bool change_nan=false, double nanval=0);
-}
+template<af_op_t op, typename Ti, typename To>
+To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
+}  // namespace cuda
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 6b400286f3..9ed2ddb60d 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -7,38 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <af/dim4.hpp>
 
 #undef _GLIBCXX_USE_INT128
+#include <err_cuda.hpp>
+#include <kernel/reduce.hpp>
 #include <reduce.hpp>
 #include <complex>
-#include <kernel/reduce.hpp>
-#include <err_cuda.hpp>
 
-using std::swap;
 using af::dim4;
-namespace cuda
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nanval)
-    {
-
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        Array<To> out = createEmptyArray<To>(odims);
-        kernel::reduce<Ti, To, op>(out, in, dim, change_nan, nanval);
-        return out;
-    }
+using std::swap;
+namespace cuda {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
+                 double nanval) {
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    kernel::reduce<Ti, To, op>(out, in, dim, change_nan, nanval);
+    return out;
+}
 
-    template<af_op_t op, typename Ti, typename To>
-    To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
-    {
-        return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
-    }
+template<af_op_t op, typename Ti, typename To>
+To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+    return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
 }
+}  // namespace cuda
 
-#define INSTANTIATE(Op, Ti, To)                                         \
+#define INSTANTIATE(Op, Ti, To)                                               \
     template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim, \
-                                          bool change_nan, double nanval); \
-    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan, double nanval);
+                                          bool change_nan, double nanval);    \
+    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,  \
+                                       double nanval);
diff --git a/src/backend/cuda/regions.cu b/src/backend/cuda/regions.cu
index ee4dc3273b..a79717a5bf 100644
--- a/src/backend/cuda/regions.cu
+++ b/src/backend/cuda/regions.cu
@@ -7,32 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <regions.hpp>
-#include <kernel/regions.hpp>
 #include <err_cuda.hpp>
+#include <kernel/regions.hpp>
+#include <regions.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T>  regions(const Array<char> &in, af_connectivity connectivity)
-{
+Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
     const dim4 dims = in.dims();
 
-    Array<T>  out  = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
     // Create bindless texture object for the equiv map.
     cudaTextureObject_t tex = 0;
 
-    //Use texture objects with compute 3.0 or higher
-    if (!std::is_same<T,double>::value) {
+    // Use texture objects with compute 3.0 or higher
+    if (!std::is_same<T, double>::value) {
         cudaResourceDesc resDesc;
         memset(&resDesc, 0, sizeof(resDesc));
-        resDesc.resType = cudaResourceTypeLinear;
+        resDesc.resType           = cudaResourceTypeLinear;
         resDesc.res.linear.devPtr = out.get();
 
         if (std::is_signed<T>::value)
@@ -42,7 +40,7 @@ Array<T>  regions(const Array<char> &in, af_connectivity connectivity)
         else
             resDesc.res.linear.desc.f = cudaChannelFormatKindFloat;
 
-        resDesc.res.linear.desc.x = sizeof(T)*8; // bits per channel
+        resDesc.res.linear.desc.x      = sizeof(T) * 8;  // bits per channel
         resDesc.res.linear.sizeInBytes = dims[0] * dims[1] * sizeof(T);
         cudaTextureDesc texDesc;
         memset(&texDesc, 0, sizeof(texDesc));
@@ -50,31 +48,28 @@ Array<T>  regions(const Array<char> &in, af_connectivity connectivity)
         CUDA_CHECK(cudaCreateTextureObject(&tex, &resDesc, &texDesc, NULL));
     }
 
-    switch(connectivity) {
-        case AF_CONNECTIVITY_4:
-            ::regions<T, false, 2>(out, in, tex);
-            break;
-        case AF_CONNECTIVITY_8:
-            ::regions<T, true,  2>(out, in, tex);
-            break;
+    switch (connectivity) {
+        case AF_CONNECTIVITY_4: ::regions<T, false, 2>(out, in, tex); break;
+        case AF_CONNECTIVITY_8: ::regions<T, true, 2>(out, in, tex); break;
     }
 
-    //Iterative procedure(while loop) in kernel::regions
-    //does stream synchronization towards loop end. So, it is
-    //safe to destroy the texture object
+    // Iterative procedure(while loop) in kernel::regions
+    // does stream synchronization towards loop end. So, it is
+    // safe to destroy the texture object
     CUDA_CHECK(cudaDestroyTextureObject(tex));
 
     return out;
 }
 
-#define INSTANTIATE(T)\
-    template Array<T>  regions<T>(const Array<char> &in, af_connectivity connectivity);
+#define INSTANTIATE(T)                                  \
+    template Array<T> regions<T>(const Array<char> &in, \
+                                 af_connectivity connectivity);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(short )
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/regions.hpp b/src/backend/cuda/regions.hpp
index ac6550122f..f94b2f7f79 100644
--- a/src/backend/cuda/regions.hpp
+++ b/src/backend/cuda/regions.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
diff --git a/src/backend/cuda/reorder.cu b/src/backend/cuda/reorder.cu
index 7292fcd6a0..1bb7e5a932 100644
--- a/src/backend/cuda/reorder.cu
+++ b/src/backend/cuda/reorder.cu
@@ -8,42 +8,39 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <reorder.hpp>
+#include <err_cuda.hpp>
 #include <kernel/reorder.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims(0);
-        for(int i = 0; i < 4; i++)
-            oDims[i] = iDims[rdims[i]];
-
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        kernel::reorder<T>(out, in, rdims.get());
-
-        return out;
-    }
-
-#define INSTANTIATE(T)                                                         \
-    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);  \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+namespace cuda {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims(0);
+    for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
 
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    kernel::reorder<T>(out, in, rdims.get());
+
+    return out;
 }
+
+#define INSTANTIATE(T) \
+    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/reorder.hpp b/src/backend/cuda/reorder.hpp
index 8d58189971..525b50001f 100644
--- a/src/backend/cuda/reorder.hpp
+++ b/src/backend/cuda/reorder.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
+namespace cuda {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
 }
diff --git a/src/backend/cuda/resize.cu b/src/backend/cuda/resize.cu
index 02d34999e8..901a617ee1 100644
--- a/src/backend/cuda/resize.cu
+++ b/src/backend/cuda/resize.cu
@@ -8,55 +8,51 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <resize.hpp>
+#include <err_cuda.hpp>
 #include <kernel/resize.hpp>
+#include <resize.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                     const af_interp_type method)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
-
-        Array<T> out = createEmptyArray<T>(oDims);
 
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::resize<T, AF_INTERP_NEAREST >(out, in);
-                break;
-            case AF_INTERP_BILINEAR:
-                kernel::resize<T, AF_INTERP_BILINEAR>(out, in);
-                break;
-            case AF_INTERP_LOWER:
-                kernel::resize<T, AF_INTERP_LOWER>(out, in);
-                break;
-            default:
-                break;
-        }
-
-        return out;
+namespace cuda {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
+
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    switch (method) {
+        case AF_INTERP_NEAREST:
+            kernel::resize<T, AF_INTERP_NEAREST>(out, in);
+            break;
+        case AF_INTERP_BILINEAR:
+            kernel::resize<T, AF_INTERP_BILINEAR>(out, in);
+            break;
+        case AF_INTERP_LOWER:
+            kernel::resize<T, AF_INTERP_LOWER>(out, in);
+            break;
+        default: break;
     }
 
-
-#define INSTANTIATE(T)                                                                            \
-    template Array<T> resize<T> (const Array<T> &in, const dim_t odim0, const dim_t odim1, \
-                                 const af_interp_type method);
-
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return out;
 }
+
+#define INSTANTIATE(T)                                                 \
+    template Array<T> resize<T>(const Array<T> &in, const dim_t odim0, \
+                                const dim_t odim1,                     \
+                                const af_interp_type method);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/resize.hpp b/src/backend/cuda/resize.hpp
index 2b2f97cf6f..602a071b24 100644
--- a/src/backend/cuda/resize.hpp
+++ b/src/backend/cuda/resize.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                    const af_interp_type method);
+namespace cuda {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method);
 }
diff --git a/src/backend/cuda/rotate.cu b/src/backend/cuda/rotate.cu
index dc0ec4dc55..828a189d89 100644
--- a/src/backend/cuda/rotate.cu
+++ b/src/backend/cuda/rotate.cu
@@ -8,19 +8,17 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <rotate.hpp>
 #include <kernel/rotate.hpp>
+#include <rotate.hpp>
 #include <stdexcept>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                     const af_interp_type method)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
+namespace cuda {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method) {
+    Array<T> out = createEmptyArray<T>(odims);
 
-        switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
             kernel::rotate<T, 1>(out, in, theta, method);
@@ -33,29 +31,27 @@ namespace cuda
         case AF_INTERP_BICUBIC_SPLINE:
             kernel::rotate<T, 3>(out, in, theta, method);
             break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-        }
-
-        return out;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
 
-
-#define INSTANTIATE(T)                                                              \
-    template Array<T> rotate(const Array<T> &in, const float theta,                 \
-                             const af::dim4 &odims, const af_interp_type method);
-
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return out;
 }
+
+#define INSTANTIATE(T)                                              \
+    template Array<T> rotate(const Array<T> &in, const float theta, \
+                             const af::dim4 &odims,                 \
+                             const af_interp_type method);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/rotate.hpp b/src/backend/cuda/rotate.hpp
index 4ca7bac527..0686fd40bd 100644
--- a/src/backend/cuda/rotate.hpp
+++ b/src/backend/cuda/rotate.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                    const af_interp_type method);
+namespace cuda {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method);
 }
diff --git a/src/backend/cuda/scalar.hpp b/src/backend/cuda/scalar.hpp
index 4a23315679..9b7f6b2b8b 100644
--- a/src/backend/cuda/scalar.hpp
+++ b/src/backend/cuda/scalar.hpp
@@ -8,18 +8,17 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <optypes.hpp>
-#include <math.hpp>
 #include <common/jit/ScalarNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
 #include <memory>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> createScalarNode(const dim4 &size, const T val)
-{
-    return createNodeArray<T>(size, std::make_shared<common::ScalarNode<T>>(val));
+Array<T> createScalarNode(const dim4 &size, const T val) {
+    return createNodeArray<T>(size,
+                              std::make_shared<common::ScalarNode<T>>(val));
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/scan.cu b/src/backend/cuda/scan.cu
index d6f6d8908b..d3aa13eb8c 100644
--- a/src/backend/cuda/scan.cu
+++ b/src/backend/cuda/scan.cu
@@ -7,63 +7,62 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
+#include <af/dim4.hpp>
 
 #undef _GLIBCXX_USE_INT128
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
 #include <scan.hpp>
 #include <complex>
-#include <kernel/scan_first.hpp>
-#include <kernel/scan_dim.hpp>
 
-namespace cuda
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        Array<To> out = createEmptyArray<To>(in.dims());
+namespace cuda {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
+    Array<To> out = createEmptyArray<To>(in.dims());
 
-        if (inclusive_scan) {
-            switch (dim) {
-            case 0: kernel::scan_first<Ti, To, op   , true>(out, in); break;
-            case 1: kernel::scan_dim  <Ti, To, op, 1, true>(out, in); break;
-            case 2: kernel::scan_dim  <Ti, To, op, 2, true>(out, in); break;
-            case 3: kernel::scan_dim  <Ti, To, op, 3, true>(out, in); break;
-            }
-        } else {
-            switch (dim) {
-            case 0: kernel::scan_first<Ti, To, op   , false>(out, in); break;
-            case 1: kernel::scan_dim  <Ti, To, op, 1, false>(out, in); break;
-            case 2: kernel::scan_dim  <Ti, To, op, 2, false>(out, in); break;
-            case 3: kernel::scan_dim  <Ti, To, op, 3, false>(out, in); break;
-            }
+    if (inclusive_scan) {
+        switch (dim) {
+            case 0: kernel::scan_first<Ti, To, op, true>(out, in); break;
+            case 1: kernel::scan_dim<Ti, To, op, 1, true>(out, in); break;
+            case 2: kernel::scan_dim<Ti, To, op, 2, true>(out, in); break;
+            case 3: kernel::scan_dim<Ti, To, op, 3, true>(out, in); break;
+        }
+    } else {
+        switch (dim) {
+            case 0: kernel::scan_first<Ti, To, op, false>(out, in); break;
+            case 1: kernel::scan_dim<Ti, To, op, 1, false>(out, in); break;
+            case 2: kernel::scan_dim<Ti, To, op, 2, false>(out, in); break;
+            case 3: kernel::scan_dim<Ti, To, op, 3, false>(out, in); break;
         }
-
-        return out;
     }
 
-#define INSTANTIATE_SCAN(ROp, Ti, To)\
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
+    return out;
+}
 
-#define INSTANTIATE_SCAN_ALL(ROp)                       \
-    INSTANTIATE_SCAN(ROp, float  , float  )             \
-    INSTANTIATE_SCAN(ROp, double , double )             \
-    INSTANTIATE_SCAN(ROp, cfloat , cfloat )             \
-    INSTANTIATE_SCAN(ROp, cdouble, cdouble)             \
-    INSTANTIATE_SCAN(ROp, int    , int    )             \
-    INSTANTIATE_SCAN(ROp, uint   , uint   )             \
-    INSTANTIATE_SCAN(ROp, intl   , intl   )             \
-    INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
-    INSTANTIATE_SCAN(ROp, char   , int    )             \
-    INSTANTIATE_SCAN(ROp, char   , uint   )             \
-    INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
-    INSTANTIATE_SCAN(ROp, short  , int    )             \
-    INSTANTIATE_SCAN(ROp, ushort , uint   )
+#define INSTANTIATE_SCAN(ROp, Ti, To)                                        \
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti>& in, const int dim, \
+                                         bool inclusive_scan);
 
-    INSTANTIATE_SCAN(af_notzero_t, char, uint)
-    INSTANTIATE_SCAN_ALL(af_add_t)
-    INSTANTIATE_SCAN_ALL(af_mul_t)
-    INSTANTIATE_SCAN_ALL(af_min_t)
-    INSTANTIATE_SCAN_ALL(af_max_t)
-}
+#define INSTANTIATE_SCAN_ALL(ROp)           \
+    INSTANTIATE_SCAN(ROp, float, float)     \
+    INSTANTIATE_SCAN(ROp, double, double)   \
+    INSTANTIATE_SCAN(ROp, cfloat, cfloat)   \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble) \
+    INSTANTIATE_SCAN(ROp, int, int)         \
+    INSTANTIATE_SCAN(ROp, uint, uint)       \
+    INSTANTIATE_SCAN(ROp, intl, intl)       \
+    INSTANTIATE_SCAN(ROp, uintl, uintl)     \
+    INSTANTIATE_SCAN(ROp, char, int)        \
+    INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, uchar, uint)      \
+    INSTANTIATE_SCAN(ROp, short, int)       \
+    INSTANTIATE_SCAN(ROp, ushort, uint)
+
+INSTANTIATE_SCAN(af_notzero_t, char, uint)
+INSTANTIATE_SCAN_ALL(af_add_t)
+INSTANTIATE_SCAN_ALL(af_mul_t)
+INSTANTIATE_SCAN_ALL(af_min_t)
+INSTANTIATE_SCAN_ALL(af_max_t)
+}  // namespace cuda
diff --git a/src/backend/cuda/scan.hpp b/src/backend/cuda/scan.hpp
index 490adf14c7..523e0ce432 100644
--- a/src/backend/cuda/scan.hpp
+++ b/src/backend/cuda/scan.hpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+namespace cuda {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/cuda/scan_by_key.cu b/src/backend/cuda/scan_by_key.cu
index 93d43c17ed..715a719c3a 100644
--- a/src/backend/cuda/scan_by_key.cu
+++ b/src/backend/cuda/scan_by_key.cu
@@ -11,47 +11,49 @@
 #include <ops.hpp>
 
 #undef _GLIBCXX_USE_INT128
+#include <kernel/scan_dim_by_key.hpp>
+#include <kernel/scan_first_by_key.hpp>
 #include <scan_by_key.hpp>
 #include <complex>
-#include <kernel/scan_first_by_key.hpp>
-#include <kernel/scan_dim_by_key.hpp>
 
-namespace cuda
-{
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        Array<To> out = createEmptyArray<To>(in.dims());
-
-        if (dim == 0) {
-            kernel::scan_first_by_key<Ti, Tk, To, op>(out, in, key, inclusive_scan);
-        } else {
-            kernel::scan_dim_by_key  <Ti, Tk, To, op>(out, in, key, dim, inclusive_scan);
-        }
-        return out;
+namespace cuda {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan) {
+    Array<To> out = createEmptyArray<To>(in.dims());
+
+    if (dim == 0) {
+        kernel::scan_first_by_key<Ti, Tk, To, op>(out, in, key, inclusive_scan);
+    } else {
+        kernel::scan_dim_by_key<Ti, Tk, To, op>(out, in, key, dim,
+                                                inclusive_scan);
     }
+    return out;
+}
 
-#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
-    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
-
-#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
-    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-
-#define INSTANTIATE_SCAN_OP(ROp)            \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint ) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl ) \
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)                  \
+    template Array<To> scan<ROp, Ti, Tk, To>(                     \
+        const Array<Tk>& key, const Array<Ti>& in, const int dim, \
+        bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)           \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl, Tk, uintl)
+
+#define INSTANTIATE_SCAN_OP(ROp)           \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl) \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
 
-    INSTANTIATE_SCAN_OP(af_add_t)
-    INSTANTIATE_SCAN_OP(af_mul_t)
-    INSTANTIATE_SCAN_OP(af_min_t)
-    INSTANTIATE_SCAN_OP(af_max_t)
-}
+INSTANTIATE_SCAN_OP(af_add_t)
+INSTANTIATE_SCAN_OP(af_mul_t)
+INSTANTIATE_SCAN_OP(af_min_t)
+INSTANTIATE_SCAN_OP(af_max_t)
+}  // namespace cuda
diff --git a/src/backend/cuda/scan_by_key.hpp b/src/backend/cuda/scan_by_key.hpp
index d876332f17..ffb2945a81 100644
--- a/src/backend/cuda/scan_by_key.hpp
+++ b/src/backend/cuda/scan_by_key.hpp
@@ -10,8 +10,8 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace cuda
-{
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+namespace cuda {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan);
 }
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index aca530d5bd..e3d15eed48 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -7,108 +7,93 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <Array.hpp>
-#include <select.hpp>
-#include <scalar.hpp>
+#include <common/jit/NaryNode.hpp>
 #include <err_cuda.hpp>
 #include <kernel/select.hpp>
-#include <common/jit/NaryNode.hpp>
+#include <scalar.hpp>
+#include <select.hpp>
 
 using common::NaryNode;
 using common::Node_ptr;
 
-namespace cuda
-{
-    template<typename T>
-    void select(Array<T> &out,
-                const Array<char> &cond,
-                const Array<T> &a, const Array<T> &b)
-    {
-        kernel::select<T>(out, cond, a, b, out.ndims());
-    }
+namespace cuda {
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b) {
+    kernel::select<T>(out, cond, a, b, out.ndims());
+}
 
-    template<typename T, bool flip>
-    void select_scalar(Array<T> &out,
-                       const Array<char> &cond,
-                       const Array<T> &a, const double &b)
-    {
-        kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
-    }
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const double &b) {
+    kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
+}
 
-    template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const Array<T> &b,
-                              const af::dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        auto b_node = b.getNode();
-        int height = std::max(a_node->getHeight(), b_node->getHeight());
-        height = std::max(height, cond_node->getHeight()) + 1;
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const af::dim4 &odims) {
+    auto cond_node = cond.getNode();
+    auto a_node    = a.getNode();
+    auto b_node    = b.getNode();
+    int height     = std::max(a_node->getHeight(), b_node->getHeight());
+    height         = std::max(height, cond_node->getHeight()) + 1;
 
-        NaryNode *node = new NaryNode(getFullName<T>(), shortname<T>(true),
-                                      "__select", 3, {{cond_node, a_node, b_node}},
-                                      (int)af_select_t, height);
+    NaryNode *node =
+        new NaryNode(getFullName<T>(), shortname<T>(true), "__select", 3,
+                     {{cond_node, a_node, b_node}}, (int)af_select_t, height);
 
-        Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
-        return out;
-    }
+    Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
+    return out;
+}
 
-    template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const double &b_val,
-                              const af::dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
-        auto b_node = b.getNode();
-        int height = std::max(a_node->getHeight(), b_node->getHeight());
-        height = std::max(height, cond_node->getHeight()) + 1;
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const double &b_val, const af::dim4 &odims) {
+    auto cond_node = cond.getNode();
+    auto a_node    = a.getNode();
+    Array<T> b     = createScalarNode<T>(odims, scalar<T>(b_val));
+    auto b_node    = b.getNode();
+    int height     = std::max(a_node->getHeight(), b_node->getHeight());
+    height         = std::max(height, cond_node->getHeight()) + 1;
 
-        NaryNode *node = new NaryNode(getFullName<T>(), shortname<T>(true),
-                                      flip ? "__not_select" : "__select",
-                                      3, {{cond_node, a_node, b_node}},
-                                      (int)(flip ? af_not_select_t : af_select_t),
-                                      height);
+    NaryNode *node = new NaryNode(
+        getFullName<T>(), shortname<T>(true),
+        flip ? "__not_select" : "__select", 3, {{cond_node, a_node, b_node}},
+        (int)(flip ? af_not_select_t : af_select_t), height);
 
-        Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
-        return out;
-    }
+    Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
+    return out;
+}
 
-#define INSTANTIATE(T)                                                    \
-    template                                                              \
-    Array<T> createSelectNode<T>(const Array<char> &cond,                 \
-                                 const Array<T> &a, const Array<T> &b,    \
-                                 const af::dim4 &odims);                  \
-    template                                                              \
-    Array<T> createSelectNode<T, true>(const Array<char> &cond,           \
-                                       const Array<T> &a, const double &b_val, \
-                                       const af::dim4 &odims);            \
-    template                                                              \
-    Array<T> createSelectNode<T, false>(const Array<char> &cond,          \
-                                        const Array<T> &a, const double &b_val, \
-                                        const af::dim4 &odims);           \
-    template void select<T>(Array<T> &out, const Array<char> &cond, \
-                            const Array<T> &a, const Array<T> &b);  \
-    template void select_scalar<T, true >(Array<T> &out,            \
-                                          const Array<char> &cond,  \
-                                          const Array<T> &a,        \
-                                          const double &b);         \
-    template void select_scalar<T, false>(Array<T> &out, const      \
-                                          Array<char> &cond,        \
-                                          const Array<T> &a,        \
-                                          const double &b)
+#define INSTANTIATE(T)                                                        \
+    template Array<T> createSelectNode<T>(                                    \
+        const Array<char> &cond, const Array<T> &a, const Array<T> &b,        \
+        const af::dim4 &odims);                                               \
+    template Array<T> createSelectNode<T, true>(                              \
+        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
+        const af::dim4 &odims);                                               \
+    template Array<T> createSelectNode<T, false>(                             \
+        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
+        const af::dim4 &odims);                                               \
+    template void select<T>(Array<T> & out, const Array<char> &cond,          \
+                            const Array<T> &a, const Array<T> &b);            \
+    template void select_scalar<T, true>(Array<T> & out,                      \
+                                         const Array<char> &cond,             \
+                                         const Array<T> &a, const double &b); \
+    template void select_scalar<T, false>(Array<T> & out,                     \
+                                          const Array<char> &cond,            \
+                                          const Array<T> &a, const double &b)
 
-    INSTANTIATE(float  );
-    INSTANTIATE(double );
-    INSTANTIATE(cfloat );
-    INSTANTIATE(cdouble);
-    INSTANTIATE(int    );
-    INSTANTIATE(uint   );
-    INSTANTIATE(intl   );
-    INSTANTIATE(uintl  );
-    INSTANTIATE(char   );
-    INSTANTIATE(uchar  );
-    INSTANTIATE(short  );
-    INSTANTIATE(ushort );
-}
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(cfloat);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
+INSTANTIATE(uint);
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(char);
+INSTANTIATE(uchar);
+INSTANTIATE(short);
+INSTANTIATE(ushort);
+}  // namespace cuda
diff --git a/src/backend/cuda/select.hpp b/src/backend/cuda/select.hpp
index cd15509d51..edd51a93bb 100644
--- a/src/backend/cuda/select.hpp
+++ b/src/backend/cuda/select.hpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <af/dim4.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b);
+namespace cuda {
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b);
 
-    template<typename T, bool flip>
-    void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const double &b);
 
-    template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const Array<T> &b,
-                              const af::dim4 &odims);
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const af::dim4 &odims);
 
-    template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const double &b_val,
-                              const af::dim4 &odims);
-}
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const double &b_val, const af::dim4 &odims);
+}  // namespace cuda
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index eede44d403..5e9446b27a 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -7,118 +7,122 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <set.hpp>
 #include <copy.hpp>
-#include <sort.hpp>
 #include <debug_cuda.hpp>
+#include <set.hpp>
+#include <sort.hpp>
+#include <af/dim4.hpp>
 
 #include <algorithm>
 
 #include <thrust/device_ptr.h>
+#include <thrust/set_operations.h>
 #include <thrust/sort.h>
 #include <thrust/unique.h>
-#include <thrust/set_operations.h>
 
-namespace cuda
-{
-    using af::dim4;
+namespace cuda {
+using af::dim4;
 
-    template<typename T>
-    Array<T> setUnique(const Array<T> &in,
-                        const bool is_sorted)
-    {
-        Array<T> out = copyArray<T>(in);
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
+    Array<T> out = copyArray<T>(in);
 
-        thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
-        thrust::device_ptr<T> out_ptr_end = out_ptr + out.elements();
+    thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
+    thrust::device_ptr<T> out_ptr_end = out_ptr + out.elements();
 
-        if(!is_sorted) THRUST_SELECT(thrust::sort, out_ptr, out_ptr_end);
-        thrust::device_ptr<T> out_ptr_last;
-        THRUST_SELECT_OUT(out_ptr_last, thrust::unique, out_ptr, out_ptr_end);
+    if (!is_sorted) THRUST_SELECT(thrust::sort, out_ptr, out_ptr_end);
+    thrust::device_ptr<T> out_ptr_last;
+    THRUST_SELECT_OUT(out_ptr_last, thrust::unique, out_ptr, out_ptr_end);
 
-        out.resetDims(dim4(thrust::distance(out_ptr, out_ptr_last)));
-        return out;
-    }
+    out.resetDims(dim4(thrust::distance(out_ptr, out_ptr_last)));
+    return out;
+}
 
-    template<typename T>
-    Array<T> setUnion(const Array<T> &first,
-                       const Array<T> &second,
-                       const bool is_unique)
-    {
-        Array<T> unique_first = first;
-        Array<T> unique_second = second;
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique) {
+    Array<T> unique_first  = first;
+    Array<T> unique_second = second;
 
-        if (!is_unique) {
-            unique_first = setUnique(first, false);
-            unique_second = setUnique(second, false);
-        }
+    if (!is_unique) {
+        unique_first  = setUnique(first, false);
+        unique_second = setUnique(second, false);
+    }
 
-        dim_t out_size = unique_first.elements() + unique_second.elements();
-        Array<T> out = createEmptyArray<T>(dim4(out_size));
+    dim_t out_size = unique_first.elements() + unique_second.elements();
+    Array<T> out   = createEmptyArray<T>(dim4(out_size));
 
-        thrust::device_ptr<T> first_ptr = thrust::device_pointer_cast<T>(unique_first.get());
-        thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.elements();
+    thrust::device_ptr<T> first_ptr =
+        thrust::device_pointer_cast<T>(unique_first.get());
+    thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.elements();
 
-        thrust::device_ptr<T> second_ptr = thrust::device_pointer_cast<T>(unique_second.get());
-        thrust::device_ptr<T> second_ptr_end = second_ptr + unique_second.elements();
+    thrust::device_ptr<T> second_ptr =
+        thrust::device_pointer_cast<T>(unique_second.get());
+    thrust::device_ptr<T> second_ptr_end =
+        second_ptr + unique_second.elements();
 
-        thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
+    thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
 
-        thrust::device_ptr<T> out_ptr_last;
-        THRUST_SELECT_OUT(out_ptr_last, thrust::set_union, first_ptr, first_ptr_end, second_ptr, second_ptr_end, out_ptr);
+    thrust::device_ptr<T> out_ptr_last;
+    THRUST_SELECT_OUT(out_ptr_last, thrust::set_union, first_ptr, first_ptr_end,
+                      second_ptr, second_ptr_end, out_ptr);
 
-        out.resetDims(dim4(thrust::distance(out_ptr, out_ptr_last)));
+    out.resetDims(dim4(thrust::distance(out_ptr, out_ptr_last)));
 
-        return out;
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T> setIntersect(const Array<T> &first,
-                           const Array<T> &second,
-                           const bool is_unique)
-    {
-        Array<T> unique_first = first;
-        Array<T> unique_second = second;
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique) {
+    Array<T> unique_first  = first;
+    Array<T> unique_second = second;
 
-        if (!is_unique) {
-            unique_first = setUnique(first, false);
-            unique_second = setUnique(second, false);
-        }
+    if (!is_unique) {
+        unique_first  = setUnique(first, false);
+        unique_second = setUnique(second, false);
+    }
 
-        dim_t out_size = std::max(unique_first.elements(), unique_second.elements());
-        Array<T> out = createEmptyArray<T>(dim4(out_size));
+    dim_t out_size =
+        std::max(unique_first.elements(), unique_second.elements());
+    Array<T> out = createEmptyArray<T>(dim4(out_size));
 
-        thrust::device_ptr<T> first_ptr = thrust::device_pointer_cast<T>(unique_first.get());
-        thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.elements();
+    thrust::device_ptr<T> first_ptr =
+        thrust::device_pointer_cast<T>(unique_first.get());
+    thrust::device_ptr<T> first_ptr_end = first_ptr + unique_first.elements();
 
-        thrust::device_ptr<T> second_ptr = thrust::device_pointer_cast<T>(unique_second.get());
-        thrust::device_ptr<T> second_ptr_end = second_ptr + unique_second.elements();
+    thrust::device_ptr<T> second_ptr =
+        thrust::device_pointer_cast<T>(unique_second.get());
+    thrust::device_ptr<T> second_ptr_end =
+        second_ptr + unique_second.elements();
 
-        thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
+    thrust::device_ptr<T> out_ptr = thrust::device_pointer_cast<T>(out.get());
 
-        thrust::device_ptr<T> out_ptr_last;
-        THRUST_SELECT_OUT(out_ptr_last, thrust::set_intersection, first_ptr, first_ptr_end, second_ptr, second_ptr_end, out_ptr);
+    thrust::device_ptr<T> out_ptr_last;
+    THRUST_SELECT_OUT(out_ptr_last, thrust::set_intersection, first_ptr,
+                      first_ptr_end, second_ptr, second_ptr_end, out_ptr);
 
-        out.resetDims(dim4(thrust::distance(out_ptr, out_ptr_last)));
+    out.resetDims(dim4(thrust::distance(out_ptr, out_ptr_last)));
 
-        return out;
-    }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                  \
+#define INSTANTIATE(T)                                                        \
     template Array<T> setUnique<T>(const Array<T> &in, const bool is_sorted); \
-    template Array<T> setUnion<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
-    template Array<T> setIntersect<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-}
+    template Array<T> setUnion<T>(                                            \
+        const Array<T> &first, const Array<T> &second, const bool is_unique); \
+    template Array<T> setIntersect<T>(                                        \
+        const Array<T> &first, const Array<T> &second, const bool is_unique);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace cuda
diff --git a/src/backend/cuda/set.hpp b/src/backend/cuda/set.hpp
index 5c77106983..7b72447bcf 100644
--- a/src/backend/cuda/set.hpp
+++ b/src/backend/cuda/set.hpp
@@ -9,16 +9,15 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T> Array<T> setUnique(const Array<T> &in,
-                                            const bool is_sorted);
+namespace cuda {
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted);
 
-    template<typename T> Array<T> setUnion(const Array<T> &first,
-                                           const Array<T> &second,
-                                           const bool is_unique);
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique);
 
-    template<typename T> Array<T> setIntersect(const Array<T> &first,
-                                               const Array<T> &second,
-                                               const bool is_unique);
-}
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique);
+}  // namespace cuda
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index 59a5ff73af..c5ab83248e 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <jit/BufferNode.hpp>
-#include <common/jit/ShiftNodeBase.hpp>
 #include <Array.hpp>
-#include <shift.hpp>
+#include <common/jit/ShiftNodeBase.hpp>
 #include <err_cuda.hpp>
+#include <jit/BufferNode.hpp>
+#include <shift.hpp>
 
 #include <memory>
 
@@ -27,51 +27,49 @@ using std::make_shared;
 using std::static_pointer_cast;
 using std::string;
 
-namespace cuda
-{
-    template<typename T>
-    using ShiftNode = ShiftNodeBase<BufferNode<T>>;
+namespace cuda {
+template<typename T>
+using ShiftNode = ShiftNodeBase<BufferNode<T>>;
 
-    template<typename T>
-    Array<T> shift(const Array<T> &in, const int sdims[4])
-    {
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]) {
+    // Shift should only be the first node in the JIT tree.
+    // Force input to be evaluated so that in is always a buffer.
+    in.eval();
 
-        // Shift should only be the first node in the JIT tree.
-        // Force input to be evaluated so that in is always a buffer.
-        in.eval();
+    string name_str("Sh");
+    name_str += shortname<T>(true);
+    const dim4 iDims = in.dims();
+    dim4 oDims       = iDims;
 
-        string name_str("Sh");
-        name_str += shortname<T>(true);
-        const dim4 iDims = in.dims();
-        dim4 oDims = iDims;
-
-        array<int, 4> shifts;
-        for(int i = 0; i < 4; i++) {
-            // sdims_[i] will always be positive and always [0, oDims[i]].
-            // Negative shifts are converted to position by going the other way round
-            shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
-            assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
-        }
-
-        auto node = make_shared<ShiftNode<T>>(getFullName<T>(), name_str.c_str(),
-                                              static_pointer_cast<BufferNode<T>>(in.getNode()),
-                                              shifts);
-        return createNodeArray<T>(oDims, Node_ptr(node));
+    array<int, 4> shifts;
+    for (int i = 0; i < 4; i++) {
+        // sdims_[i] will always be positive and always [0, oDims[i]].
+        // Negative shifts are converted to position by going the other way
+        // round
+        shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+        assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
     }
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]); \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    auto node = make_shared<ShiftNode<T>>(
+        getFullName<T>(), name_str.c_str(),
+        static_pointer_cast<BufferNode<T>>(in.getNode()), shifts);
+    return createNodeArray<T>(oDims, Node_ptr(node));
 }
+
+#define INSTANTIATE(T) \
+    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/shift.hpp b/src/backend/cuda/shift.hpp
index b08db93f7a..e651c2b0d3 100644
--- a/src/backend/cuda/shift.hpp
+++ b/src/backend/cuda/shift.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> shift(const Array<T> &in, const int sdims[4]);
+namespace cuda {
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]);
 }
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index ebcae8e1e8..9df00c9e03 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 #ifdef AF_WITH_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
@@ -19,8 +19,7 @@
 using af::dim4;
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename convAccT>
 unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
@@ -29,8 +28,7 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float contrast_thr, const float edge_thr,
               const float init_sigma, const bool double_input,
               const float img_scale, const float feature_ratio,
-              const bool compute_GLOH)
-{
+              const bool compute_GLOH) {
 #ifdef AF_WITH_NONFREE_SIFT
     unsigned nfeat_out;
     unsigned desc_len;
@@ -41,16 +39,14 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
     float* size_out;
     float* desc_out;
 
-    kernel::sift<T, convAccT>(&nfeat_out, &desc_len, &x_out, &y_out, &score_out,
-                              &orientation_out, &size_out, &desc_out,
-                              in, n_layers, contrast_thr, edge_thr,
-                              init_sigma, double_input, img_scale, feature_ratio,
-                              compute_GLOH);
+    kernel::sift<T, convAccT>(
+        &nfeat_out, &desc_len, &x_out, &y_out, &score_out, &orientation_out,
+        &size_out, &desc_out, in, n_layers, contrast_thr, edge_thr, init_sigma,
+        double_input, img_scale, feature_ratio, compute_GLOH);
 
     if (nfeat_out > 0) {
         if (x_out == NULL || y_out == NULL || score_out == NULL ||
-            orientation_out == NULL || size_out == NULL ||
-            desc_out == NULL) {
+            orientation_out == NULL || size_out == NULL || desc_out == NULL) {
             AF_ERROR("sift: feature array is null.", AF_ERR_SIZE);
         }
 
@@ -82,23 +78,26 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
     UNUSED(img_scale);
     UNUSED(feature_ratio);
     if (compute_GLOH)
-        AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, GLOH disabled\n",
+            AF_ERR_NONFREE);
     else
-        AF_ERROR("ArrayFire was not built with nonfree support, SIFT disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, SIFT disabled\n",
+            AF_ERR_NONFREE);
 #endif
 }
 
-#define INSTANTIATE(T, convAccT)\
-    template unsigned sift<T, convAccT>(Array<float>& x, Array<float>& y,                   \
-                                        Array<float>& score, Array<float>& ori,             \
-                                        Array<float>& size, Array<float>& desc,             \
-                                        const Array<T>& in, const unsigned n_layers,        \
-                                        const float contrast_thr, const float edge_thr,     \
-                                        const float init_sigma, const bool double_input,    \
-                                        const float img_scale, const float feature_ratio,   \
-                                        const bool compute_GLOH);
+#define INSTANTIATE(T, convAccT)                                               \
+    template unsigned sift<T, convAccT>(                                       \
+        Array<float> & x, Array<float> & y, Array<float> & score,              \
+        Array<float> & ori, Array<float> & size, Array<float> & desc,          \
+        const Array<T>& in, const unsigned n_layers, const float contrast_thr, \
+        const float edge_thr, const float init_sigma, const bool double_input, \
+        const float img_scale, const float feature_ratio,                      \
+        const bool compute_GLOH);
 
-INSTANTIATE(float , float )
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sift.hpp b/src/backend/cuda/sift.hpp
index 28b887929a..1ec8638b41 100644
--- a/src/backend/cuda/sift.hpp
+++ b/src/backend/cuda/sift.hpp
@@ -7,13 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, typename convAccT>
 unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
diff --git a/src/backend/cuda/sobel.cu b/src/backend/cuda/sobel.cu
index a86d8e497f..c58bb17974 100644
--- a/src/backend/cuda/sobel.cu
+++ b/src/backend/cuda/sobel.cu
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <sobel.hpp>
-#include <kernel/sobel.hpp>
 #include <err_cuda.hpp>
+#include <kernel/sobel.hpp>
+#include <sobel.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename Ti, typename To>
-std::pair< Array<To>, Array<To> >
-sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
-{
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size) {
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
@@ -30,17 +28,17 @@ sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
     return std::make_pair(dx, dy);
 }
 
-#define INSTANTIATE(Ti, To)                                             \
-    template std::pair< Array<To>, Array<To> >                          \
-    sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
+#define INSTANTIATE(Ti, To)                                    \
+    template std::pair<Array<To>, Array<To>> sobelDerivatives( \
+        const Array<Ti> &img, const unsigned &ker_size);
 
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
-INSTANTIATE(int   , int)
-INSTANTIATE(uint  , int)
-INSTANTIATE(char  , int)
-INSTANTIATE(uchar , int)
-INSTANTIATE(short , int)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, int)
+INSTANTIATE(char, int)
+INSTANTIATE(uchar, int)
+INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sobel.hpp b/src/backend/cuda/sobel.hpp
index 096930f4c9..4cba95b4cf 100644
--- a/src/backend/cuda/sobel.hpp
+++ b/src/backend/cuda/sobel.hpp
@@ -10,11 +10,10 @@
 #include <Array.hpp>
 #include <utility>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename Ti, typename To>
-std::pair< Array<To>, Array<To> >
-sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size);
 
 }
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 5a69c2c84d..901367eaa1 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -10,15 +10,15 @@
 #include <common/err_common.hpp>
 #include <solve.hpp>
 
-#include <platform.hpp>
+#include <copy.hpp>
 #include <cublas_v2.h>
 #include <identity.hpp>
 #include <memory.hpp>
-#include <copy.hpp>
+#include <platform.hpp>
 #include <transpose.hpp>
 
-#include <math.hpp>
 #include <common/err_common.hpp>
+#include <math.hpp>
 
 #include <blas.hpp>
 #include <lu.hpp>
@@ -26,10 +26,9 @@
 
 #include <cstdio>
 
-namespace cuda
-{
+namespace cuda {
 
-//cusolverStatus_t cusolverDn<>getrs(
+// cusolverStatus_t cusolverDn<>getrs(
 //    cusolverDnHandle_t handle,
 //    cublasOperation_t trans,
 //    int n, int nrhs,
@@ -39,41 +38,38 @@ namespace cuda
 //    int *devInfo );
 
 template<typename T>
-struct getrs_func_def_t
-{
-    typedef cusolverStatus_t (*getrs_func_def) (
-                              cusolverDnHandle_t,
-                              cublasOperation_t,
-                              int, int,
-                              const T *, int,
-                              const int *,
-                              T *, int,
-                              int *);
+struct getrs_func_def_t {
+    typedef cusolverStatus_t (*getrs_func_def)(cusolverDnHandle_t,
+                                               cublasOperation_t, int, int,
+                                               const T *, int, const int *, T *,
+                                               int, int *);
 };
 
-#define SOLVE_FUNC_DEF( FUNC )                                                  \
-template<typename T>                                                            \
-typename FUNC##_func_def_t<T>::FUNC##_func_def                                  \
-FUNC##_func();
+#define SOLVE_FUNC_DEF(FUNC) \
+    template<typename T>     \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
 
-#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                                                    \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>()            \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusolverDn##PREFIX##FUNC; }             \
+#define SOLVE_FUNC(FUNC, TYPE, PREFIX)                                      \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusolverDn##PREFIX##FUNC;                                    \
+    }
 
-SOLVE_FUNC_DEF( getrs )
-SOLVE_FUNC(getrs , float  , S)
-SOLVE_FUNC(getrs , double , D)
-SOLVE_FUNC(getrs , cfloat , C)
-SOLVE_FUNC(getrs , cdouble, Z)
+SOLVE_FUNC_DEF(getrs)
+SOLVE_FUNC(getrs, float, S)
+SOLVE_FUNC(getrs, double, D)
+SOLVE_FUNC(getrs, cfloat, C)
+SOLVE_FUNC(getrs, cdouble, Z)
 
-//cusolverStatus_t cusolverDn<>geqrf_bufferSize(
+// cusolverStatus_t cusolverDn<>geqrf_bufferSize(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
 //        <> *A,
 //        int lda,
 //        int *Lwork );
 //
-//cusolverStatus_t cusolverDn<>geqrf(
+// cusolverStatus_t cusolverDn<>geqrf(
 //        cusolverDnHandle_t handle,
 //        int m, int n,
 //        <> *A, int lda,
@@ -81,7 +77,7 @@ SOLVE_FUNC(getrs , cdouble, Z)
 //        <> *Workspace,
 //        int Lwork, int *devInfo );
 //
-//cusolverStatus_t cusolverDn<>mqr(
+// cusolverStatus_t cusolverDn<>mqr(
 //        cusolverDnHandle_t handle,
 //        cublasSideMode_t side, cublasOperation_t trans,
 //        int m, int n, int k,
@@ -92,131 +88,127 @@ SOLVE_FUNC(getrs , cdouble, Z)
 //        int lwork, int *devInfo);
 
 template<typename T>
-struct geqrf_solve_func_def_t
-{
-    typedef cusolverStatus_t (*geqrf_solve_func_def) (
-                              cusolverDnHandle_t, int, int,
-                              T *, int,
-                              T *,
-                              T *,
-                              int, int *);
+struct geqrf_solve_func_def_t {
+    typedef cusolverStatus_t (*geqrf_solve_func_def)(cusolverDnHandle_t, int,
+                                                     int, T *, int, T *, T *,
+                                                     int, int *);
 };
 
 template<typename T>
-struct geqrf_solve_buf_func_def_t
-{
-    typedef cusolverStatus_t (*geqrf_solve_buf_func_def) (
-                              cusolverDnHandle_t, int, int,
-                              T *, int, int *);
+struct geqrf_solve_buf_func_def_t {
+    typedef cusolverStatus_t (*geqrf_solve_buf_func_def)(cusolverDnHandle_t,
+                                                         int, int, T *, int,
+                                                         int *);
 };
 
 template<typename T>
-struct mqr_solve_func_def_t
-{
-    typedef cusolverStatus_t (*mqr_solve_func_def) (
-                              cusolverDnHandle_t,
-                              cublasSideMode_t, cublasOperation_t,
-                              int, int, int,
-                              const T *, int,
-                              const T *,
-                              T *, int,
-                              T *, int,
-                              int *);
+struct mqr_solve_func_def_t {
+    typedef cusolverStatus_t (*mqr_solve_func_def)(
+        cusolverDnHandle_t, cublasSideMode_t, cublasOperation_t, int, int, int,
+        const T *, int, const T *, T *, int, T *, int, int *);
 };
 
-#define QR_FUNC_DEF( FUNC )                                                     \
-template<typename T>                                                            \
-static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def               \
-FUNC##_solve_func();                                                            \
-                                                                                \
-template<typename T>                                                            \
-static typename FUNC##_solve_buf_func_def_t<T>::FUNC##_solve_buf_func_def       \
-FUNC##_solve_buf_func();                                                        \
-
-#define QR_FUNC( FUNC, TYPE, PREFIX )                                                                               \
-template<> typename FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def FUNC##_solve_func<TYPE>()                  \
-{ return (FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def)&cusolverDn##PREFIX##FUNC; }                         \
-                                                                                                                    \
-template<> typename FUNC##_solve_buf_func_def_t<TYPE>::FUNC##_solve_buf_func_def FUNC##_solve_buf_func<TYPE>()      \
-{ return (FUNC##_solve_buf_func_def_t<TYPE>::FUNC##_solve_buf_func_def)& cusolverDn##PREFIX##FUNC##_bufferSize; }
-
-QR_FUNC_DEF( geqrf )
-QR_FUNC(geqrf , float  , S)
-QR_FUNC(geqrf , double , D)
-QR_FUNC(geqrf , cfloat , C)
-QR_FUNC(geqrf , cdouble, Z)
-
-#define MQR_FUNC_DEF( FUNC )                                                            \
-template<typename T>                                                                    \
-static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def                       \
-FUNC##_solve_func();
-
-#define MQR_FUNC( FUNC, TYPE, PREFIX )                                                  \
-template<> typename FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def                \
-FUNC##_solve_func<TYPE>()                                                               \
-{ return (FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def)&cusolverDn##PREFIX; }   \
-
-MQR_FUNC_DEF( mqr )
-MQR_FUNC(mqr , float  , Sormqr)
-MQR_FUNC(mqr , double , Dormqr)
-MQR_FUNC(mqr , cfloat , Cunmqr)
-MQR_FUNC(mqr , cdouble, Zunmqr)
+#define QR_FUNC_DEF(FUNC)                                                     \
+    template<typename T>                                                      \
+    static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def         \
+        FUNC##_solve_func();                                                  \
+                                                                              \
+    template<typename T>                                                      \
+    static typename FUNC##_solve_buf_func_def_t<T>::FUNC##_solve_buf_func_def \
+        FUNC##_solve_buf_func();
+
+#define QR_FUNC(FUNC, TYPE, PREFIX)                                       \
+    template<>                                                            \
+    typename FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def         \
+        FUNC##_solve_func<TYPE>() {                                       \
+        return (FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def) &   \
+               cusolverDn##PREFIX##FUNC;                                  \
+    }                                                                     \
+                                                                          \
+    template<>                                                            \
+    typename FUNC##_solve_buf_func_def_t<TYPE>::FUNC##_solve_buf_func_def \
+        FUNC##_solve_buf_func<TYPE>() {                                   \
+        return (FUNC##_solve_buf_func_def_t<                              \
+                   TYPE>::FUNC##_solve_buf_func_def) &                    \
+               cusolverDn##PREFIX##FUNC##_bufferSize;                     \
+    }
+
+QR_FUNC_DEF(geqrf)
+QR_FUNC(geqrf, float, S)
+QR_FUNC(geqrf, double, D)
+QR_FUNC(geqrf, cfloat, C)
+QR_FUNC(geqrf, cdouble, Z)
+
+#define MQR_FUNC_DEF(FUNC)                                            \
+    template<typename T>                                              \
+    static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def \
+        FUNC##_solve_func();
+
+#define MQR_FUNC(FUNC, TYPE, PREFIX)                                    \
+    template<>                                                          \
+    typename FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def       \
+        FUNC##_solve_func<TYPE>() {                                     \
+        return (FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def) & \
+               cusolverDn##PREFIX;                                      \
+    }
+
+MQR_FUNC_DEF(mqr)
+MQR_FUNC(mqr, float, Sormqr)
+MQR_FUNC(mqr, double, Dormqr)
+MQR_FUNC(mqr, cfloat, Cunmqr)
+MQR_FUNC(mqr, cdouble, Zunmqr)
 
 template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
     UNUSED(options);
-    int N = A.dims()[0];
+    int N    = A.dims()[0];
     int NRHS = b.dims()[1];
 
-    Array< T > B = copyArray<T>(b);
+    Array<T> B = copyArray<T>(b);
 
     auto info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(),
-                                   CUBLAS_OP_N,
-                                   N, NRHS,
-                                   A.get(), A.strides()[1],
-                                   pivot.get(),
-                                   B.get(), B.strides()[1],
-                                   info.get()));
+    CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(), CUBLAS_OP_N, N, NRHS,
+                                   A.get(), A.strides()[1], pivot.get(),
+                                   B.get(), B.strides()[1], info.get()));
 
     return B;
 }
 
 template<typename T>
-Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
-{
+Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
     int M = a.dims()[0];
     int N = a.dims()[1];
     int K = b.dims()[1];
 
-    Array<T> A = copyArray<T>(a);
-    Array<T> B = copyArray<T>(b);
+    Array<T> A       = copyArray<T>(a);
+    Array<T> B       = copyArray<T>(b);
     Array<int> pivot = lu_inplace(A, false);
 
     auto info = memAlloc<int>(1);
 
-    CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(),
-                                   CUBLAS_OP_N,
-                                   N, K,
-                                   A.get(), A.strides()[1],
-                                   pivot.get(),
-                                   B.get(), B.strides()[1],
-                                   info.get()));
+    CUSOLVER_CHECK(getrs_func<T>()(solverDnHandle(), CUBLAS_OP_N, N, K, A.get(),
+                                   A.strides()[1], pivot.get(), B.get(),
+                                   B.strides()[1], info.get()));
     return B;
 }
 
 template<typename T>
-cublasOperation_t trans() { return CUBLAS_OP_T; }
-template<> cublasOperation_t trans<cfloat>() { return CUBLAS_OP_C; }
-template<> cublasOperation_t trans<cdouble>() { return CUBLAS_OP_C; }
-
+cublasOperation_t trans() {
+    return CUBLAS_OP_T;
+}
+template<>
+cublasOperation_t trans<cfloat>() {
+    return CUBLAS_OP_C;
+}
+template<>
+cublasOperation_t trans<cdouble>() {
+    return CUBLAS_OP_C;
+}
 
 template<typename T>
-Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
-{
+Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
     int M = a.dims()[0];
     int N = a.dims()[1];
     int K = b.dims()[1];
@@ -224,7 +216,6 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
     Array<T> B = createEmptyArray<T>(dim4());
 
     if (M < N) {
-
         // Least squres for this case is solved using the following
         // solve(A, B) == matmul(Q, Xpad);
         // Where:
@@ -235,27 +226,23 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
 
         // QR is performed on the transpose of A
         Array<T> A = transpose<T>(a, true);
-        B = padArray<T, T>(b, dim4(N, K), scalar<T>(0));
+        B          = padArray<T, T>(b, dim4(N, K), scalar<T>(0));
 
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(solverDnHandle(),
-                                                 A.dims()[0], A.dims()[1],
-                                                 A.get(), A.strides()[1],
-                                                 &lwork));
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(solverDnHandle(), A.dims()[0],
+                                                 A.dims()[1], A.get(),
+                                                 A.strides()[1], &lwork));
 
         auto workspace = memAlloc<T>(lwork);
-        Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
-        auto info = memAlloc<int>(1);
+        Array<T> t     = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
+        auto info      = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(solverDnHandle(),
-                                             A.dims()[0], A.dims()[1],
-                                             A.get(), A.strides()[1],
-                                             t.get(),
-                                             workspace.get(), lwork,
-                                             info.get()));
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(
+            solverDnHandle(), A.dims()[0], A.dims()[1], A.get(), A.strides()[1],
+            t.get(), workspace.get(), lwork, info.get()));
 
         // R1 = R(seq(M), seq(M));
         A.resetDims(dim4(M, M));
@@ -268,19 +255,12 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(solverDnHandle(),
-                                           CUBLAS_SIDE_LEFT, CUBLAS_OP_N,
-                                           B.dims()[0],
-                                           B.dims()[1],
-                                           A.dims()[0],
-                                           A.get(), A.strides()[1],
-                                           t.get(),
-                                           B.get(), B.strides()[1],
-                                           workspace.get(), lwork,
-                                           info.get()));
+        CUSOLVER_CHECK(mqr_solve_func<T>()(
+            solverDnHandle(), CUBLAS_SIDE_LEFT, CUBLAS_OP_N, B.dims()[0],
+            B.dims()[1], A.dims()[0], A.get(), A.strides()[1], t.get(), B.get(),
+            B.strides()[1], workspace.get(), lwork, info.get()));
 
     } else if (M > N) {
-
         // Least squres for this case is solved using the following
         // solve(A, B) == tri_solve(R1, Bt);
         // Where:
@@ -290,83 +270,73 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         // A  == matmul(Q, R);
 
         Array<T> A = copyArray<T>(a);
-        B = copyArray(b);
+        B          = copyArray(b);
 
         int lwork = 0;
 
         // Get workspace needed for QR
-        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(solverDnHandle(),
-                                                 A.dims()[0], A.dims()[1],
-                                                 A.get(), A.strides()[1],
-                                                 &lwork));
+        CUSOLVER_CHECK(geqrf_solve_buf_func<T>()(solverDnHandle(), A.dims()[0],
+                                                 A.dims()[1], A.get(),
+                                                 A.strides()[1], &lwork));
 
         auto workspace = memAlloc<T>(lwork);
-        Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
-        auto info = memAlloc<int>(1);
+        Array<T> t     = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
+        auto info      = memAlloc<int>(1);
 
         // In place Perform in place QR
-        CUSOLVER_CHECK(geqrf_solve_func<T>()(solverDnHandle(),
-                                             A.dims()[0], A.dims()[1],
-                                             A.get(), A.strides()[1],
-                                             t.get(),
-                                             workspace.get(), lwork,
-                                             info.get()));
+        CUSOLVER_CHECK(geqrf_solve_func<T>()(
+            solverDnHandle(), A.dims()[0], A.dims()[1], A.get(), A.strides()[1],
+            t.get(), workspace.get(), lwork, info.get()));
 
         // matmul(Q1, B)
-        CUSOLVER_CHECK(mqr_solve_func<T>()(solverDnHandle(),
-                                           CUBLAS_SIDE_LEFT,
-                                           trans<T>(),
-                                           M, K, N,
-                                           A.get(), A.strides()[1],
-                                           t.get(),
-                                           B.get(), B.strides()[1],
-                                           workspace.get(), lwork,
-                                           info.get()));
+        CUSOLVER_CHECK(mqr_solve_func<T>()(
+            solverDnHandle(), CUBLAS_SIDE_LEFT, trans<T>(), M, K, N, A.get(),
+            A.strides()[1], t.get(), B.get(), B.strides()[1], workspace.get(),
+            lwork, info.get()));
         // tri_solve(R1, Bt)
         A.resetDims(dim4(N, N));
         B.resetDims(dim4(N, K));
         trsm(A, B, AF_MAT_NONE, true, true, false);
-
     }
     return B;
 }
 
 template<typename T>
-Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
-{
+Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
+                       const af_mat_prop options) {
     Array<T> B = copyArray<T>(b);
     trsm(A, B,
-         AF_MAT_NONE, // transpose flag
+         AF_MAT_NONE,  // transpose flag
          options & AF_MAT_UPPER ? true : false,
-         true, // is_left
+         true,  // is_left
          options & AF_MAT_DIAG_UNIT ? true : false);
     return B;
 }
 
 template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-    if (options & AF_MAT_UPPER ||
-        options & AF_MAT_LOWER) {
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
+    if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
     }
 
-    if(a.dims()[0] == a.dims()[1]) {
+    if (a.dims()[0] == a.dims()[1]) {
         return generalSolve<T>(a, b);
     } else {
         return leastSquares<T>(a, b);
     }
 }
 
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
     template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
 
 INSTANTIATE_SOLVE(float)
 INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/solve.hpp b/src/backend/cuda/solve.hpp
index 43933ec19d..72c80000d0 100644
--- a/src/backend/cuda/solve.hpp
+++ b/src/backend/cuda/solve.hpp
@@ -9,12 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
+namespace cuda {
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options = AF_MAT_NONE);
 
-    template<typename T>
-    Array<T> solveLU(const Array<T> &a, const Array<int> &pivot,
-                     const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
-}
+template<typename T>
+Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options = AF_MAT_NONE);
+}  // namespace cuda
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index f49dc45dcd..8596c3b894 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -9,54 +9,53 @@
 
 #include <Array.hpp>
 #include <copy.hpp>
-#include <sort.hpp>
+#include <err_cuda.hpp>
 #include <kernel/sort.hpp>
 #include <math.hpp>
 #include <reorder.hpp>
+#include <sort.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
-    {
-        Array<T> out = copyArray<T>(in);
-        switch(dim) {
-            case 0: kernel::sort0<T>(out, isAscending); break;
-            case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
-            case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
-            case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
 
-        if(dim != 0) {
-            af::dim4 preorderDims = out.dims();
-            af::dim4 reorderDims(0, 1, 2, 3);
-            reorderDims[dim] = 0;
-            preorderDims[0] = out.dims()[dim];
-            for(int i = 1; i <= (int)dim; i++) {
-                reorderDims[i - 1] = i;
-                preorderDims[i] = out.dims()[i - 1];
-            }
+namespace cuda {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
+    Array<T> out = copyArray<T>(in);
+    switch (dim) {
+        case 0: kernel::sort0<T>(out, isAscending); break;
+        case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+        case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+        case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 
-            out.setDataDims(preorderDims);
-            out = reorder<T>(out, reorderDims);
+    if (dim != 0) {
+        af::dim4 preorderDims = out.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0]  = out.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i]    = out.dims()[i - 1];
         }
-        return out;
+
+        out.setDataDims(preorderDims);
+        out = reorder<T>(out, reorderDims);
     }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
+#define INSTANTIATE(T)                                                \
+    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, \
+                              bool isAscending);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace cuda
diff --git a/src/backend/cuda/sort.hpp b/src/backend/cuda/sort.hpp
index 5ea6309868..74473bb981 100644
--- a/src/backend/cuda/sort.hpp
+++ b/src/backend/cuda/sort.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
+namespace cuda {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cuda/sort_by_key.cu b/src/backend/cuda/sort_by_key.cu
index 86f5b23a18..4cc64e2aed 100644
--- a/src/backend/cuda/sort_by_key.cu
+++ b/src/backend/cuda/sort_by_key.cu
@@ -9,76 +9,76 @@
 
 #include <Array.hpp>
 #include <copy.hpp>
-#include <sort_by_key.hpp>
+#include <err_cuda.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <reorder.hpp>
+#include <sort_by_key.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
 
-namespace cuda
-{
-    template<typename Tk, typename Tv>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-                     const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim, bool isAscending)
-    {
-        okey = copyArray<Tk>(ikey);
-        oval = copyArray<Tv>(ival);
+namespace cuda {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const uint dim, bool isAscending) {
+    okey = copyArray<Tk>(ikey);
+    oval = copyArray<Tv>(ival);
 
-        switch(dim) {
-            case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
-            case 1:
-            case 2:
-            case 3: kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
+    switch (dim) {
+        case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
+        case 1:
+        case 2:
+        case 3:
+            kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending);
+            break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 
-        if(dim != 0) {
-            af::dim4 preorderDims = okey.dims();
-            af::dim4 reorderDims(0, 1, 2, 3);
-            reorderDims[dim] = 0;
-            preorderDims[0] = okey.dims()[dim];
-            for(int i = 1; i <= (int)dim; i++) {
-                reorderDims[i - 1] = i;
-                preorderDims[i] = okey.dims()[i - 1];
-            }
+    if (dim != 0) {
+        af::dim4 preorderDims = okey.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0]  = okey.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i]    = okey.dims()[i - 1];
+        }
 
-            okey.setDataDims(preorderDims);
-            oval.setDataDims(preorderDims);
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
 
-            okey = reorder<Tk>(okey, reorderDims);
-            oval = reorder<Tv>(oval, reorderDims);
-        }
+        okey = reorder<Tk>(okey, reorderDims);
+        oval = reorder<Tv>(oval, reorderDims);
     }
+}
 
-#define INSTANTIATE(Tk, Tv)                                                         \
-    template void sort_by_key<Tk, Tv>(Array<Tk> &okey, Array<Tv> &oval,      \
-                                      const Array<Tk> &ikey, const Array<Tv> &ival, const uint dim, bool);
+#define INSTANTIATE(Tk, Tv)                                        \
+    template void sort_by_key<Tk, Tv>(                             \
+        Array<Tk> & okey, Array<Tv> & oval, const Array<Tk> &ikey, \
+        const Array<Tv> &ival, const uint dim, bool);
 
-#define INSTANTIATE1(Tk    ) \
-    INSTANTIATE(Tk, float  ) \
-    INSTANTIATE(Tk, double ) \
-    INSTANTIATE(Tk, cfloat ) \
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
     INSTANTIATE(Tk, cdouble) \
-    INSTANTIATE(Tk, int    ) \
-    INSTANTIATE(Tk, uint   ) \
-    INSTANTIATE(Tk, short  ) \
-    INSTANTIATE(Tk, ushort ) \
-    INSTANTIATE(Tk, char   ) \
-    INSTANTIATE(Tk, uchar  ) \
-    INSTANTIATE(Tk, intl   ) \
-    INSTANTIATE(Tk, uintl  )
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
 
-
-INSTANTIATE1(float )
+INSTANTIATE1(float)
 INSTANTIATE1(double)
-INSTANTIATE1(int   )
-INSTANTIATE1(uint  )
-INSTANTIATE1(short )
+INSTANTIATE1(int)
+INSTANTIATE1(uint)
+INSTANTIATE1(short)
 INSTANTIATE1(ushort)
-INSTANTIATE1(char  )
-INSTANTIATE1(uchar )
-INSTANTIATE1(intl  )
-INSTANTIATE1(uintl )
+INSTANTIATE1(char)
+INSTANTIATE1(uchar)
+INSTANTIATE1(intl)
+INSTANTIATE1(uintl)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sort_by_key.hpp b/src/backend/cuda/sort_by_key.hpp
index ac3840bea6..5eb7c1e716 100644
--- a/src/backend/cuda/sort_by_key.hpp
+++ b/src/backend/cuda/sort_by_key.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename Tk, typename Tv>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending);
+namespace cuda {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 707b428b35..ea176789d7 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -8,63 +8,65 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <sort_index.hpp>
-#include <kernel/sort_by_key.hpp>
 #include <copy.hpp>
-#include <math.hpp>
-#include <stdexcept>
 #include <err_cuda.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <math.hpp>
 #include <range.hpp>
 #include <reorder.hpp>
+#include <sort_index.hpp>
+#include <stdexcept>
 
-namespace cuda
-{
-    template<typename T>
-    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim, bool isAscending)
-    {
-        okey = copyArray<T>(in);
-        oval = range<uint>(in.dims(), dim);
-        oval.eval();
+namespace cuda {
+template<typename T>
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
+                const uint dim, bool isAscending) {
+    okey = copyArray<T>(in);
+    oval = range<uint>(in.dims(), dim);
+    oval.eval();
 
-        switch(dim) {
-            case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
-            case 1:
-            case 2:
-            case 3: kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
+    switch (dim) {
+        case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
+        case 1:
+        case 2:
+        case 3:
+            kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending);
+            break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 
-        if(dim != 0) {
-            af::dim4 preorderDims = okey.dims();
-            af::dim4 reorderDims(0, 1, 2, 3);
-            reorderDims[dim] = 0;
-            preorderDims[0] = okey.dims()[dim];
-            for(int i = 1; i <= (int)dim; i++) {
-                reorderDims[i - 1] = i;
-                preorderDims[i] = okey.dims()[i - 1];
-            }
+    if (dim != 0) {
+        af::dim4 preorderDims = okey.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0]  = okey.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i]    = okey.dims()[i - 1];
+        }
 
-            okey.setDataDims(preorderDims);
-            oval.setDataDims(preorderDims);
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
 
-            okey = reorder<T>(okey, reorderDims);
-            oval = reorder<uint>(oval, reorderDims);
-        }
+        okey = reorder<T>(okey, reorderDims);
+        oval = reorder<uint>(oval, reorderDims);
     }
+}
 
-#define INSTANTIATE(T)                                                  \
-    template void sort_index<T>(Array<T> &val, Array<uint> &idx, const Array<T> &in, \
-                                      const uint dim,bool isAscending);
+#define INSTANTIATE(T)                                              \
+    template void sort_index<T>(Array<T> & val, Array<uint> & idx,  \
+                                const Array<T> &in, const uint dim, \
+                                bool isAscending);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sort_index.hpp b/src/backend/cuda/sort_index.hpp
index 5520014b02..970e7c9b48 100644
--- a/src/backend/cuda/sort_index.hpp
+++ b/src/backend/cuda/sort_index.hpp
@@ -9,8 +9,8 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim, bool isAscending);
+namespace cuda {
+template<typename T>
+void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in,
+                const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 6912d75ff3..7c82e02a6e 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -7,49 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse.hpp>
 #include <kernel/sparse.hpp>
+#include <sparse.hpp>
 
 #include <stdexcept>
 #include <string>
 
 #include <arith.hpp>
 #include <cast.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <where.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 using namespace common;
 using namespace std;
 
-//cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
 //                                  int m, int n, int nnz,
 //                                  const cuDoubleComplex *csrSortedVal,
-//                                  const int *csrSortedRowPtr, const int *csrSortedColInd,
-//                                  cuDoubleComplex *cscSortedVal,
-//                                  int *cscSortedRowInd, int *cscSortedColPtr,
-//                                  cusparseAction_t copyValues,
-//                                  cusparseIndexBase_t idxBase);
+//                                  const int *csrSortedRowPtr, const int
+//                                  *csrSortedColInd, cuDoubleComplex
+//                                  *cscSortedVal, int *cscSortedRowInd, int
+//                                  *cscSortedColPtr, cusparseAction_t
+//                                  copyValues, cusparseIndexBase_t idxBase);
 
 template<typename T>
-struct csr2csc_func_def_t
-{
-    typedef cusparseStatus_t (*csr2csc_func_def)( cusparseHandle_t,
-                                                  int, int, int,
-                                                  const T *, const int *, const int *,
-                                                  T *, int *, int *,
-                                                  cusparseAction_t,
-                                                  cusparseIndexBase_t);
+struct csr2csc_func_def_t {
+    typedef cusparseStatus_t (*csr2csc_func_def)(cusparseHandle_t, int, int,
+                                                 int, const T *, const int *,
+                                                 const int *, T *, int *, int *,
+                                                 cusparseAction_t,
+                                                 cusparseIndexBase_t);
 };
 
-//cusparseStatus_t cusparseZdense2csr(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZdense2csr(cusparseHandle_t handle,
 //                                    int m, int n,
 //                                    const cusparseMatDescr_t descrA,
 //                                    const cuDoubleComplex *A, int lda,
@@ -57,18 +54,14 @@ struct csr2csc_func_def_t
 //                                    cuDoubleComplex *csrValA,
 //                                    int *csrRowPtrA, int *csrColIndA)
 template<typename T>
-struct dense2csr_func_def_t
-{
-    typedef cusparseStatus_t (*dense2csr_func_def)( cusparseHandle_t,
-                                                    int, int,
-                                                    const cusparseMatDescr_t,
-                                                    const T *, int,
-                                                    const int *,
-                                                    T *,
-                                                    int *, int *);
+struct dense2csr_func_def_t {
+    typedef cusparseStatus_t (*dense2csr_func_def)(cusparseHandle_t, int, int,
+                                                   const cusparseMatDescr_t,
+                                                   const T *, int, const int *,
+                                                   T *, int *, int *);
 };
 
-//cusparseStatus_t cusparseZdense2csc(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZdense2csc(cusparseHandle_t handle,
 //                                    int m, int n,
 //                                    const cusparseMatDescr_t descrA,
 //                                    const cuDoubleComplex *A, int lda,
@@ -76,18 +69,14 @@ struct dense2csr_func_def_t
 //                                    cuDoubleComplex *cscValA,
 //                                    int *cscRowIndA, int *cscColPtrA)
 template<typename T>
-struct dense2csc_func_def_t
-{
-    typedef cusparseStatus_t (*dense2csc_func_def)( cusparseHandle_t,
-                                                    int, int,
-                                                    const cusparseMatDescr_t,
-                                                    const T *, int,
-                                                    const int *,
-                                                    T *,
-                                                    int *, int *);
+struct dense2csc_func_def_t {
+    typedef cusparseStatus_t (*dense2csc_func_def)(cusparseHandle_t, int, int,
+                                                   const cusparseMatDescr_t,
+                                                   const T *, int, const int *,
+                                                   T *, int *, int *);
 };
 
-//cusparseStatus_t cusparseZcsr2dense(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZcsr2dense(cusparseHandle_t handle,
 //                                    int m, int n,
 //                                    const cusparseMatDescr_t descrA,
 //                                    const cuDoubleComplex *csrValA,
@@ -95,18 +84,14 @@ struct dense2csc_func_def_t
 //                                    const int *csrColIndA,
 //                                    cuDoubleComplex *A, int lda)
 template<typename T>
-struct csr2dense_func_def_t
-{
-    typedef cusparseStatus_t (*csr2dense_func_def)( cusparseHandle_t,
-                                                    int, int,
-                                                    const cusparseMatDescr_t,
-                                                    const T *,
-                                                    const int *,
-                                                    const int *,
-                                                    T *, int);
+struct csr2dense_func_def_t {
+    typedef cusparseStatus_t (*csr2dense_func_def)(cusparseHandle_t, int, int,
+                                                   const cusparseMatDescr_t,
+                                                   const T *, const int *,
+                                                   const int *, T *, int);
 };
 
-//cusparseStatus_t cusparseZcsc2dense(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZcsc2dense(cusparseHandle_t handle,
 //                                    int m, int n,
 //                                    const cusparseMatDescr_t descrA,
 //                                    const cuDoubleComplex *cscValA,
@@ -114,18 +99,14 @@ struct csr2dense_func_def_t
 //                                    const int *cscColPtrA,
 //                                    cuDoubleComplex *A, int lda)
 template<typename T>
-struct csc2dense_func_def_t
-{
-    typedef cusparseStatus_t (*csc2dense_func_def)( cusparseHandle_t,
-                                                    int, int,
-                                                    const cusparseMatDescr_t,
-                                                    const T *,
-                                                    const int *,
-                                                    const int *,
-                                                    T *, int);
+struct csc2dense_func_def_t {
+    typedef cusparseStatus_t (*csc2dense_func_def)(cusparseHandle_t, int, int,
+                                                   const cusparseMatDescr_t,
+                                                   const T *, const int *,
+                                                   const int *, T *, int);
 };
 
-//cusparseStatus_t cusparseZnnz(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZnnz(cusparseHandle_t handle,
 //                              cusparseDirection_t dirA,
 //                              int m, int n,
 //                              const cusparseMatDescr_t descrA,
@@ -133,82 +114,77 @@ struct csc2dense_func_def_t
 //                              int *nnzPerRowColumn,
 //                              int *nnzTotalDevHostPtr)
 template<typename T>
-struct nnz_func_def_t
-{
-    typedef cusparseStatus_t (*nnz_func_def)( cusparseHandle_t,
-                                              cusparseDirection_t,
-                                              int, int,
-                                              const cusparseMatDescr_t,
-                                              const T *, int,
-                                              int *, int *);
+struct nnz_func_def_t {
+    typedef cusparseStatus_t (*nnz_func_def)(cusparseHandle_t,
+                                             cusparseDirection_t, int, int,
+                                             const cusparseMatDescr_t,
+                                             const T *, int, int *, int *);
 };
 
-//cusparseStatus_t cusparseZgthr(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZgthr(cusparseHandle_t handle,
 //                               int nnz,
 //                               const cuDoubleComplex *y,
 //                               cuDoubleComplex *xVal, const int *xInd,
 //                               cusparseIndexBase_t idxBase)
 template<typename T>
-struct gthr_func_def_t
-{
-    typedef cusparseStatus_t (*gthr_func_def)(cusparseHandle_t,
-                                              int,
-                                              const T *,
-                                              T*, const int *,
+struct gthr_func_def_t {
+    typedef cusparseStatus_t (*gthr_func_def)(cusparseHandle_t, int, const T *,
+                                              T *, const int *,
                                               cusparseIndexBase_t);
 };
 
-#define SPARSE_FUNC_DEF( FUNC )                     \
-template<typename T>                                \
-typename FUNC##_func_def_t<T>::FUNC##_func_def      \
-FUNC##_func();
+#define SPARSE_FUNC_DEF(FUNC) \
+    template<typename T>      \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
 
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def        \
-FUNC##_func<TYPE>()                                                 \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusparse##PREFIX##FUNC; }
+#define SPARSE_FUNC(FUNC, TYPE, PREFIX)                                     \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusparse##PREFIX##FUNC;                                      \
+    }
 
 SPARSE_FUNC_DEF(csr2csc)
-SPARSE_FUNC(csr2csc, float,  S)
+SPARSE_FUNC(csr2csc, float, S)
 SPARSE_FUNC(csr2csc, double, D)
 SPARSE_FUNC(csr2csc, cfloat, C)
-SPARSE_FUNC(csr2csc, cdouble,Z)
+SPARSE_FUNC(csr2csc, cdouble, Z)
 
 SPARSE_FUNC_DEF(dense2csr)
-SPARSE_FUNC(dense2csr, float,  S)
+SPARSE_FUNC(dense2csr, float, S)
 SPARSE_FUNC(dense2csr, double, D)
 SPARSE_FUNC(dense2csr, cfloat, C)
-SPARSE_FUNC(dense2csr, cdouble,Z)
+SPARSE_FUNC(dense2csr, cdouble, Z)
 
 SPARSE_FUNC_DEF(dense2csc)
-SPARSE_FUNC(dense2csc, float,  S)
+SPARSE_FUNC(dense2csc, float, S)
 SPARSE_FUNC(dense2csc, double, D)
 SPARSE_FUNC(dense2csc, cfloat, C)
-SPARSE_FUNC(dense2csc, cdouble,Z)
+SPARSE_FUNC(dense2csc, cdouble, Z)
 
 SPARSE_FUNC_DEF(csr2dense)
-SPARSE_FUNC(csr2dense, float,  S)
+SPARSE_FUNC(csr2dense, float, S)
 SPARSE_FUNC(csr2dense, double, D)
 SPARSE_FUNC(csr2dense, cfloat, C)
-SPARSE_FUNC(csr2dense, cdouble,Z)
+SPARSE_FUNC(csr2dense, cdouble, Z)
 
 SPARSE_FUNC_DEF(csc2dense)
-SPARSE_FUNC(csc2dense, float,  S)
+SPARSE_FUNC(csc2dense, float, S)
 SPARSE_FUNC(csc2dense, double, D)
 SPARSE_FUNC(csc2dense, cfloat, C)
-SPARSE_FUNC(csc2dense, cdouble,Z)
+SPARSE_FUNC(csc2dense, cdouble, Z)
 
 SPARSE_FUNC_DEF(nnz)
-SPARSE_FUNC(nnz, float,  S)
+SPARSE_FUNC(nnz, float, S)
 SPARSE_FUNC(nnz, double, D)
 SPARSE_FUNC(nnz, cfloat, C)
-SPARSE_FUNC(nnz, cdouble,Z)
+SPARSE_FUNC(nnz, cdouble, Z)
 
 SPARSE_FUNC_DEF(gthr)
-SPARSE_FUNC(gthr, float,  S)
+SPARSE_FUNC(gthr, float, S)
 SPARSE_FUNC(gthr, double, D)
 SPARSE_FUNC(gthr, cfloat, C)
-SPARSE_FUNC(gthr, cdouble,Z)
+SPARSE_FUNC(gthr, cdouble, Z)
 
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
@@ -216,28 +192,29 @@ SPARSE_FUNC(gthr, cdouble,Z)
 // Partial template specialization of sparseConvertDenseToStorage for COO
 // However, template specialization is not allowed
 template<typename T>
-SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
-{
+SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in) {
     Array<uint> nonZeroIdx_ = where<T>(in);
-    Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
+    Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
 
     dim_t nNZ = nonZeroIdx.elements();
 
     Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
 
-    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
-    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> rowIdx =
+        arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx =
+        arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
     Array<T> values = copyArray<T>(in);
     values.modDims(dim4(values.elements()));
     values = lookup<T, int>(values, nonZeroIdx, 0);
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+                                         AF_STORAGE_COO);
 }
 
 template<typename T, af_storage stype>
-SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
-{
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     const int M = in.dims()[0];
     const int N = in.dims()[1];
 
@@ -247,71 +224,57 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in)
     cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
     cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
 
-    int d = -1;
+    int d                   = -1;
     cusparseDirection_t dir = CUSPARSE_DIRECTION_ROW;
 
-    if(stype == AF_STORAGE_CSR) {
-        d = M;
+    if (stype == AF_STORAGE_CSR) {
+        d   = M;
         dir = CUSPARSE_DIRECTION_ROW;
     } else {
-        d = N;
+        d   = N;
         dir = CUSPARSE_DIRECTION_COLUMN;
     }
     Array<int> nnzPerDir = createEmptyArray<int>(dim4(d));
 
     int nNZ = -1;
-    CUSPARSE_CHECK(nnz_func<T>()(
-                        sparseHandle(),
-                        dir,
-                        M, N,
-                        descr,
-                        in.get(), in.strides()[1],
-                        nnzPerDir.get(), &nNZ));
+    CUSPARSE_CHECK(nnz_func<T>()(sparseHandle(), dir, M, N, descr, in.get(),
+                                 in.strides()[1], nnzPerDir.get(), &nNZ));
 
     Array<int> rowIdx = createEmptyArray<int>(dim4());
     Array<int> colIdx = createEmptyArray<int>(dim4());
 
-    if(stype == AF_STORAGE_CSR) {
-        rowIdx = createEmptyArray<int>(dim4(M+1));
+    if (stype == AF_STORAGE_CSR) {
+        rowIdx = createEmptyArray<int>(dim4(M + 1));
         colIdx = createEmptyArray<int>(dim4(nNZ));
     } else {
         rowIdx = createEmptyArray<int>(dim4(nNZ));
-        colIdx = createEmptyArray<int>(dim4(N+1));
+        colIdx = createEmptyArray<int>(dim4(N + 1));
     }
     Array<T> values = createEmptyArray<T>(dim4(nNZ));
 
-    if(stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR)
         CUSPARSE_CHECK(dense2csr_func<T>()(
-                        sparseHandle(),
-                        M, N,
-                        descr,
-                        in.get(), in.strides()[1],
-                        nnzPerDir.get(),
-                        values.get(), rowIdx.get(), colIdx.get()));
+            sparseHandle(), M, N, descr, in.get(), in.strides()[1],
+            nnzPerDir.get(), values.get(), rowIdx.get(), colIdx.get()));
     else
         CUSPARSE_CHECK(dense2csc_func<T>()(
-                        sparseHandle(),
-                        M, N,
-                        descr,
-                        in.get(), in.strides()[1],
-                        nnzPerDir.get(),
-                        values.get(), rowIdx.get(), colIdx.get()));
+            sparseHandle(), M, N, descr, in.get(), in.strides()[1],
+            nnzPerDir.get(), values.get(), rowIdx.get(), colIdx.get()));
 
     // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, stype);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+                                         stype);
 }
 
-
 // Partial template specialization of sparseConvertStorageToDense for COO
 // However, template specialization is not allowed
 template<typename T>
-Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
-{
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
 
-    const Array<T>   values = in.getValues();
+    const Array<T> values   = in.getValues();
     const Array<int> rowIdx = in.getRowIdx();
     const Array<int> colIdx = in.getColIdx();
 
@@ -321,37 +284,28 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
 }
 
 template<typename T, af_storage stype>
-Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
-{
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
     cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
     cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
 
-    int M = in.dims()[0];
-    int N = in.dims()[1];
+    int M          = in.dims()[0];
+    int N          = in.dims()[1];
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
     int d_strides1 = dense.strides()[1];
 
-    if(stype == AF_STORAGE_CSR)
-        CUSPARSE_CHECK(csr2dense_func<T>()(
-                        sparseHandle(),
-                        M, N,
-                        descr,
-                        in.getValues().get(),
-                        in.getRowIdx().get(),
-                        in.getColIdx().get(),
-                        dense.get(), d_strides1));
+    if (stype == AF_STORAGE_CSR)
+        CUSPARSE_CHECK(
+            csr2dense_func<T>()(sparseHandle(), M, N, descr,
+                                in.getValues().get(), in.getRowIdx().get(),
+                                in.getColIdx().get(), dense.get(), d_strides1));
     else
-        CUSPARSE_CHECK(csc2dense_func<T>()(
-                        sparseHandle(),
-                        M, N,
-                        descr,
-                        in.getValues().get(),
-                        in.getRowIdx().get(),
-                        in.getColIdx().get(),
-                        dense.get(), d_strides1));
+        CUSPARSE_CHECK(
+            csc2dense_func<T>()(sparseHandle(), M, N, descr,
+                                in.getValues().get(), in.getRowIdx().get(),
+                                in.getColIdx().get(), dense.get(), d_strides1));
 
     // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
@@ -360,52 +314,46 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in)
 }
 
 template<typename T, af_storage dest, af_storage src>
-SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
-{
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     using std::shared_ptr;
     in.eval();
 
-    int nNZ = in.getNNZ();
+    int nNZ                  = in.getNNZ();
     SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), nNZ, dest);
 
-    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+    if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
         // Copy colIdx as is
-        CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
-                                   in.getColIdx().elements() * sizeof(int),
-                                   cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
+        CUDA_CHECK(
+            cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
+                            in.getColIdx().elements() * sizeof(int),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         // cusparse function to expand compressed row into coordinate
         CUSPARSE_CHECK(cusparseXcsr2coo(
-                        sparseHandle(),
-                        in.getRowIdx().get(),
-                        nNZ, in.dims()[0],
-                        converted.getRowIdx().get(),
-                        CUSPARSE_INDEX_BASE_ZERO));
+            sparseHandle(), in.getRowIdx().get(), nNZ, in.dims()[0],
+            converted.getRowIdx().get(), CUSPARSE_INDEX_BASE_ZERO));
 
         // Call sort
         size_t pBufferSizeInBytes = 0;
         CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                        sparseHandle(),
-                        in.dims()[0], in.dims()[1], nNZ,
-                        converted.getRowIdx().get(), converted.getColIdx().get(),
-                        &pBufferSizeInBytes));
-        shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
+            sparseHandle(), in.dims()[0], in.dims()[1], nNZ,
+            converted.getRowIdx().get(), converted.getColIdx().get(),
+            &pBufferSizeInBytes));
+        shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes).release(),
+                                 memFree<char>);
 
         shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
-        CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
+        CUSPARSE_CHECK(
+            cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
         CUSPARSE_CHECK(cusparseXcoosortByColumn(
-                       sparseHandle(),
-                       in.dims()[0], in.dims()[1], nNZ,
-                       converted.getRowIdx().get(), converted.getColIdx().get(),
-                       P.get(), (void*)pBuffer.get()));
+            sparseHandle(), in.dims()[0], in.dims()[1], nNZ,
+            converted.getRowIdx().get(), converted.getColIdx().get(), P.get(),
+            (void *)pBuffer.get()));
 
-        CUSPARSE_CHECK(gthr_func<T>()(
-                       sparseHandle(), nNZ,
-                       in.getValues().get(),
-                       converted.getValues().get(),
-                       P.get(), CUSPARSE_INDEX_BASE_ZERO));
+        CUSPARSE_CHECK(gthr_func<T>()(sparseHandle(), nNZ, in.getValues().get(),
+                                      converted.getValues().get(), P.get(),
+                                      CUSPARSE_INDEX_BASE_ZERO));
 
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
         // The cusparse csr sort function is not behaving correctly.
@@ -413,89 +361,99 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
         // convert it to CSR
 
         // Deep copy input into temporary COO Row Major
-        SparseArray<T> cooT = createArrayDataSparseArray<T>(in.dims(), in.getValues(),
-                                                            in.getRowIdx(), in.getColIdx(),
-                                                            in.getStorage(), true);
+        SparseArray<T> cooT = createArrayDataSparseArray<T>(
+            in.dims(), in.getValues(), in.getRowIdx(), in.getColIdx(),
+            in.getStorage(), true);
 
         // Call sort to convert column major to row major
         {
             size_t pBufferSizeInBytes = 0;
             CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
-                            sparseHandle(),
-                            cooT.dims()[0], cooT.dims()[1], nNZ,
-                            cooT.getRowIdx().get(), cooT.getColIdx().get(),
-                            &pBufferSizeInBytes));
-            shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
+                sparseHandle(), cooT.dims()[0], cooT.dims()[1], nNZ,
+                cooT.getRowIdx().get(), cooT.getColIdx().get(),
+                &pBufferSizeInBytes));
+            shared_ptr<char> pBuffer(
+                memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
 
             shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
-            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
+            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(),
+                                                             nNZ, P.get()));
 
             CUSPARSE_CHECK(cusparseXcoosortByRow(
-                           sparseHandle(),
-                           cooT.dims()[0], cooT.dims()[1], nNZ,
-                           cooT.getRowIdx().get(), cooT.getColIdx().get(),
-                           P.get(), (void*)pBuffer.get()));
+                sparseHandle(), cooT.dims()[0], cooT.dims()[1], nNZ,
+                cooT.getRowIdx().get(), cooT.getColIdx().get(), P.get(),
+                (void *)pBuffer.get()));
 
             CUSPARSE_CHECK(gthr_func<T>()(
-                           sparseHandle(), nNZ,
-                           in.getValues().get(),
-                           cooT.getValues().get(),
-                           P.get(), CUSPARSE_INDEX_BASE_ZERO));
-
+                sparseHandle(), nNZ, in.getValues().get(),
+                cooT.getValues().get(), P.get(), CUSPARSE_INDEX_BASE_ZERO));
         }
 
         // Copy values and colIdx as is
-        CUDA_CHECK(cudaMemcpyAsync(converted.getValues().get(), cooT.getValues().get(),
-                                   cooT.getValues().elements() * sizeof(T),
-                                   cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(converted.getColIdx().get(), cooT.getColIdx().get(),
-                                   cooT.getColIdx().elements() * sizeof(int),
-                                   cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
+        CUDA_CHECK(
+            cudaMemcpyAsync(converted.getValues().get(), cooT.getValues().get(),
+                            cooT.getValues().elements() * sizeof(T),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(
+            cudaMemcpyAsync(converted.getColIdx().get(), cooT.getColIdx().get(),
+                            cooT.getColIdx().elements() * sizeof(int),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(cusparseXcoo2csr(
-                        sparseHandle(),
-                        cooT.getRowIdx().get(),
-                        nNZ, cooT.dims()[0],
-                        converted.getRowIdx().get(),
-                        CUSPARSE_INDEX_BASE_ZERO));
+            sparseHandle(), cooT.getRowIdx().get(), nNZ, cooT.dims()[0],
+            converted.getRowIdx().get(), CUSPARSE_INDEX_BASE_ZERO));
 
         // No need to call CSRSORT
 
     } else {
         // Should never come here
-        AF_ERROR("CUDA Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("CUDA Backend invalid conversion combination",
+                 AF_ERR_NOT_SUPPORTED);
     }
 
     return converted;
 }
 
-#define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T> &in);  \
-
-#define INSTANTIATE_COO_SPECIAL(T)                                                                      \
-    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in)        \
-    { return sparseConvertDenseToCOO<T>(in); }                                                          \
-    template<> Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in)        \
-    { return sparseConvertCOOToDense<T>(in); }                                                          \
-
-#define INSTANTIATE_SPARSE(T)                                                                           \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in);         \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in);         \
-                                                                                                        \
-    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in);         \
-    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in);         \
-                                                                                                        \
-    INSTANTIATE_COO_SPECIAL(T)                                                                          \
-                                                                                                        \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                           \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                           \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                           \
+#define INSTANTIATE_TO_STORAGE(T, S)                     \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>( \
+        const SparseArray<T> &in);                       \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>( \
+        const SparseArray<T> &in);                       \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>( \
+        const SparseArray<T> &in);
+
+#define INSTANTIATE_COO_SPECIAL(T)                                 \
+    template<>                                                     \
+    SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>( \
+        const Array<T> &in) {                                      \
+        return sparseConvertDenseToCOO<T>(in);                     \
+    }                                                              \
+    template<>                                                     \
+    Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(       \
+        const SparseArray<T> &in) {                                \
+        return sparseConvertCOOToDense<T>(in);                     \
+    }
 
+#define INSTANTIATE_SPARSE(T)                                               \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>( \
+        const Array<T> &in);                                                \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>( \
+        const Array<T> &in);                                                \
+                                                                            \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(       \
+        const SparseArray<T> &in);                                          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(       \
+        const SparseArray<T> &in);                                          \
+                                                                            \
+    INSTANTIATE_COO_SPECIAL(T)                                              \
+                                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
@@ -506,4 +464,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_COO_SPECIAL
 #undef INSTANTIATE_SPARSE
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
index 23f2d9d6a8..5b571d4eb9 100644
--- a/src/backend/cuda/sparse.hpp
+++ b/src/backend/cuda/sparse.hpp
@@ -12,8 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
@@ -22,6 +21,7 @@ template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
 template<typename T, af_storage dest, af_storage src>
-common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
+common::SparseArray<T> sparseConvertStorageToStorage(
+    const common::SparseArray<T> &in);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 76ec4e9333..7003a8f836 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -7,125 +7,135 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse.hpp>
 #include <kernel/sparse_arith.hpp>
+#include <sparse.hpp>
 
 #include <stdexcept>
 #include <string>
 
 #include <arith.hpp>
 #include <cast.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <where.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 using namespace common;
 using namespace std;
 
 template<typename T>
-T getInf()
-{
+T getInf() {
     return scalar<T>(std::numeric_limits<T>::infinity());
 }
 
 template<>
-cfloat getInf()
-{
-    return scalar<cfloat, float>(NAN, NAN); // Matches behavior of complex division by 0 in CUDA
+cfloat getInf() {
+    return scalar<cfloat, float>(
+        NAN, NAN);  // Matches behavior of complex division by 0 in CUDA
 }
 
 template<>
-cdouble getInf()
-{
-    return scalar<cdouble, double>(NAN, NAN); // Matches behavior of complex division by 0 in CUDA
+cdouble getInf() {
+    return scalar<cdouble, double>(
+        NAN, NAN);  // Matches behavior of complex division by 0 in CUDA
 }
 
 template<typename T, af_op_t op>
-Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
-{
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
+                  const bool reverse) {
     lhs.eval();
     rhs.eval();
 
-    Array<T> out = createEmptyArray<T>(dim4(0));
+    Array<T> out  = createEmptyArray<T>(dim4(0));
     Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
-    switch(op) {
+    switch (op) {
         case af_add_t: out = copyArray<T>(rhs); break;
-        case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
-        default      : out = copyArray<T>(rhs);
+        case af_sub_t:
+            out = reverse ? copyArray<T>(rhs)
+                          : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
+            break;
+        default: out = copyArray<T>(rhs);
     }
     out.eval();
-    switch(lhs.getStorage()) {
+    switch (lhs.getStorage()) {
         case AF_STORAGE_CSR:
-            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
+                                            lhs.getRowIdx(), lhs.getColIdx(),
                                             rhs, reverse);
             break;
         case AF_STORAGE_COO:
-            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
+                                            lhs.getRowIdx(), lhs.getColIdx(),
                                             rhs, reverse);
             break;
         default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
     }
 
     return out;
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
-{
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
+                       const bool reverse) {
     lhs.eval();
     rhs.eval();
 
-    SparseArray<T> out = createArrayDataSparseArray<T>(lhs.dims(), lhs.getValues(),
-                                                       lhs.getRowIdx(), lhs.getColIdx(),
-                                                       lhs.getStorage(), true);
+    SparseArray<T> out = createArrayDataSparseArray<T>(
+        lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+        lhs.getStorage(), true);
     out.eval();
-    switch(lhs.getStorage()) {
+    switch (lhs.getStorage()) {
         case AF_STORAGE_CSR:
-            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
-                                            rhs, reverse);
+            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
+                                            out.getColIdx(), rhs, reverse);
             break;
         case AF_STORAGE_COO:
-            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
-                                            rhs, reverse);
+            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
+                                            out.getColIdx(), rhs, reverse);
             break;
         default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
     }
 
     return out;
 }
 
 template<typename T>
-using csrgeam_def = cusparseStatus_t (*)(cusparseHandle_t, int, int,
-        const T*, const cusparseMatDescr_t, int, const T*, const int*, const int*,
-        const T*, const cusparseMatDescr_t, int, const T*, const int*, const int*,
-        const cusparseMatDescr_t, T*, int*, int*);
-
-#define SPARSE_ARITH_OP_FUNC_DEF( FUNC )            \
-template<typename T> FUNC##_def<T> FUNC##_func();
-
-SPARSE_ARITH_OP_FUNC_DEF( csrgeam );
-
-#define SPARSE_ARITH_OP_FUNC( FUNC, TYPE, INFIX )   \
-template<> FUNC##_def<TYPE> FUNC##_func<TYPE>()     \
-{ return cusparse##INFIX##FUNC; }
+using csrgeam_def = cusparseStatus_t (*)(cusparseHandle_t, int, int, const T *,
+                                         const cusparseMatDescr_t, int,
+                                         const T *, const int *, const int *,
+                                         const T *, const cusparseMatDescr_t,
+                                         int, const T *, const int *,
+                                         const int *, const cusparseMatDescr_t,
+                                         T *, int *, int *);
+
+#define SPARSE_ARITH_OP_FUNC_DEF(FUNC) \
+    template<typename T>               \
+    FUNC##_def<T> FUNC##_func();
+
+SPARSE_ARITH_OP_FUNC_DEF(csrgeam);
+
+#define SPARSE_ARITH_OP_FUNC(FUNC, TYPE, INFIX) \
+    template<>                                  \
+    FUNC##_def<TYPE> FUNC##_func<TYPE>() {      \
+        return cusparse##INFIX##FUNC;           \
+    }
 
-SPARSE_ARITH_OP_FUNC(csrgeam, float  , S);
-SPARSE_ARITH_OP_FUNC(csrgeam, double , D);
-SPARSE_ARITH_OP_FUNC(csrgeam, cfloat , C);
+SPARSE_ARITH_OP_FUNC(csrgeam, float, S);
+SPARSE_ARITH_OP_FUNC(csrgeam, double, D);
+SPARSE_ARITH_OP_FUNC(csrgeam, cfloat, C);
 SPARSE_ARITH_OP_FUNC(csrgeam, cdouble, Z);
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
-{
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     lhs.eval();
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
@@ -141,28 +151,27 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
     const dim_t nnzA = lhs.getNNZ();
     const dim_t nnzB = rhs.getNNZ();
 
-    const int* csrRowPtrA = lhs.getRowIdx().get();
-    const int* csrColPtrA = lhs.getColIdx().get();
-    const int* csrRowPtrB = rhs.getRowIdx().get();
-    const int* csrColPtrB = rhs.getColIdx().get();
+    const int *csrRowPtrA = lhs.getRowIdx().get();
+    const int *csrColPtrA = lhs.getColIdx().get();
+    const int *csrRowPtrB = rhs.getRowIdx().get();
+    const int *csrColPtrB = rhs.getColIdx().get();
 
-    auto outRowIdx = createEmptyArray<int>(dim4(M+1));
+    auto outRowIdx = createEmptyArray<int>(dim4(M + 1));
 
-    int* csrRowPtrC = outRowIdx.get();
+    int *csrRowPtrC = outRowIdx.get();
     int baseC, nnzC;
-    int* nnzcDevHostPtr = &nnzC;
+    int *nnzcDevHostPtr = &nnzC;
 
-    cusparseXcsrgeamNnz(sparseHandle(), M, N,
-                        desc, nnzA, csrRowPtrA, csrColPtrA,
-                        desc, nnzB, csrRowPtrB, csrColPtrB,
-                        desc, csrRowPtrC, nnzcDevHostPtr);
+    cusparseXcsrgeamNnz(sparseHandle(), M, N, desc, nnzA, csrRowPtrA,
+                        csrColPtrA, desc, nnzB, csrRowPtrB, csrColPtrB, desc,
+                        csrRowPtrC, nnzcDevHostPtr);
     if (NULL != nnzcDevHostPtr) {
         nnzC = *nnzcDevHostPtr;
     } else {
-        cudaMemcpyAsync(&nnzC, csrRowPtrC+M, sizeof(int),
-                        cudaMemcpyDeviceToHost, cuda::getActiveStream());
-        cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int),
+        cudaMemcpyAsync(&nnzC, csrRowPtrC + M, sizeof(int),
                         cudaMemcpyDeviceToHost, cuda::getActiveStream());
+        cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int), cudaMemcpyDeviceToHost,
+                        cuda::getActiveStream());
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         nnzC -= baseC;
     }
@@ -173,49 +182,45 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
     T alpha = scalar<T>(1);
     T beta  = op == af_sub_t ? scalar<T>(-1) : alpha;
 
-    csrgeam_func<T>()(sparseHandle(), M, N,
-                      &alpha, desc, nnzA,
-                      lhs.getValues().get(), csrRowPtrA, csrColPtrA,
-                      &beta,  desc, nnzB,
-                      rhs.getValues().get(), csrRowPtrB, csrColPtrB,
+    csrgeam_func<T>()(sparseHandle(), M, N, &alpha, desc, nnzA,
+                      lhs.getValues().get(), csrRowPtrA, csrColPtrA, &beta,
+                      desc, nnzB, rhs.getValues().get(), csrRowPtrB, csrColPtrB,
                       desc, outValues.get(), csrRowPtrC, outColIdx.get());
 
-    SparseArray<T> retVal = createArrayDataSparseArray(ldims,
-                                 outValues, outRowIdx, outColIdx,
-                                 sfmt);
+    SparseArray<T> retVal = createArrayDataSparseArray(
+        ldims, outValues, outRowIdx, outColIdx, sfmt);
     return retVal;
 }
 
-#define INSTANTIATE(T)                                                                              \
-    template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template SparseArray<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_sub_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_mul_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_div_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);
-
-INSTANTIATE(float  )
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
+#define INSTANTIATE(T)                                                         \
+    template Array<T> arithOpD<T, af_add_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_sub_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_mul_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_div_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_mul_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_div_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_mul_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_div_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
-
+}  // namespace cuda
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
index bbdf18e541..bd1839d058 100644
--- a/src/backend/cuda/sparse_arith.hpp
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -9,23 +9,22 @@
 
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
-#include <sparse.hpp>
 #include <optypes.hpp>
+#include <sparse.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 // These two functions cannot be overloaded by return type.
 // So have to give them separate names.
 template<typename T, af_op_t op>
 Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                 const bool reverse = false);
+                  const bool reverse = false);
 
 template<typename T, af_op_t op>
-common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                               const bool reverse = false);
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const Array<T> &rhs, const bool reverse = false);
 
 template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 588776c732..725a742e0e 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -7,139 +7,124 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse_blas.hpp>
 #include <cuda_runtime.h>
 #include <platform.hpp>
+#include <sparse_blas.hpp>
 
-#include <stdexcept>
-#include <string>
 #include <common/err_common.hpp>
-#include <math.hpp>
 #include <complex.hpp>
+#include <math.hpp>
+#include <stdexcept>
+#include <string>
 
-namespace cuda
-{
+namespace cuda {
 
 using namespace std;
 
-cusparseOperation_t
-toCusparseTranspose(af_mat_prop opt)
-{
+cusparseOperation_t toCusparseTranspose(af_mat_prop opt) {
     cusparseOperation_t out = CUSPARSE_OPERATION_NON_TRANSPOSE;
-    switch(opt) {
-        case AF_MAT_NONE        : out = CUSPARSE_OPERATION_NON_TRANSPOSE;       break;
-        case AF_MAT_TRANS       : out = CUSPARSE_OPERATION_TRANSPOSE;           break;
-        case AF_MAT_CTRANS      : out = CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE; break;
-        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    switch (opt) {
+        case AF_MAT_NONE: out = CUSPARSE_OPERATION_NON_TRANSPOSE; break;
+        case AF_MAT_TRANS: out = CUSPARSE_OPERATION_TRANSPOSE; break;
+        case AF_MAT_CTRANS: out = CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE; break;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
 }
 
-//cusparseStatus_t cusparseZcsrmm(  cusparseHandle_t handle,
+// cusparseStatus_t cusparseZcsrmm(  cusparseHandle_t handle,
 //                                  cusparseOperation_t transA,
 //                                  int m, int n, int k, int nnz,
 //                                  const cuDoubleComplex *alpha,
 //                                  const cusparseMatDescr_t descrA,
 //                                  const cuDoubleComplex *csrValA,
-//                                  const int *csrRowPtrA, const int *csrColIndA,
-//                                  const cuDoubleComplex *B, int ldb,
-//                                  const cuDoubleComplex *beta,
+//                                  const int *csrRowPtrA, const int
+//                                  *csrColIndA, const cuDoubleComplex *B, int
+//                                  ldb, const cuDoubleComplex *beta,
 //                                  cuDoubleComplex *C, int ldc);
 
 template<typename T>
-struct csrmm_func_def_t
-{
-    typedef cusparseStatus_t (*csrmm_func_def)( cusparseHandle_t,
-                                                cusparseOperation_t,
-                                                int, int, int, int,
-                                                const T *,
-                                                const cusparseMatDescr_t,
-                                                const T *, const int *, const int *,
-                                                const T *, int,
-                                                const T *,
-                                                T *, int);
+struct csrmm_func_def_t {
+    typedef cusparseStatus_t (*csrmm_func_def)(
+        cusparseHandle_t, cusparseOperation_t, int, int, int, int, const T *,
+        const cusparseMatDescr_t, const T *, const int *, const int *,
+        const T *, int, const T *, T *, int);
 };
 
-//cusparseStatus_t cusparseZcsrmv(  cusparseHandle_t handle,
+// cusparseStatus_t cusparseZcsrmv(  cusparseHandle_t handle,
 //                                  cusparseOperation_t transA,
 //                                  int m, int n, int nnz,
 //                                  const cuDoubleComplex *alpha,
 //                                  const cusparseMatDescr_t descrA,
 //                                  const cuDoubleComplex *csrValA,
-//                                  const int *csrRowPtrA, const int *csrColIndA,
-//                                  const cuDoubleComplex *x,
-//                                  const cuDoubleComplex *beta,
-//                                  cuDoubleComplex *y)
+//                                  const int *csrRowPtrA, const int
+//                                  *csrColIndA, const cuDoubleComplex *x, const
+//                                  cuDoubleComplex *beta, cuDoubleComplex *y)
 
 template<typename T>
-struct csrmv_func_def_t
-{
-    typedef cusparseStatus_t (*csrmv_func_def)( cusparseHandle_t,
-                                                cusparseOperation_t,
-                                                int, int, int,
-                                                const T *,
-                                                const cusparseMatDescr_t,
-                                                const T *, const int *, const int *,
-                                                const T *,
-                                                const T *,
-                                                T *);
+struct csrmv_func_def_t {
+    typedef cusparseStatus_t (*csrmv_func_def)(
+        cusparseHandle_t, cusparseOperation_t, int, int, int, const T *,
+        const cusparseMatDescr_t, const T *, const int *, const int *,
+        const T *, const T *, T *);
 };
 
-//cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
+// cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
 //                                  int m, int n, int nnz,
 //                                  const cuDoubleComplex *csrSortedVal,
-//                                  const int *csrSortedRowPtr, const int *csrSortedColInd,
-//                                  cuDoubleComplex *cscSortedVal,
-//                                  int *cscSortedRowInd, int *cscSortedColPtr,
-//                                  cusparseAction_t copyValues,
-//                                  cusparseIndexBase_t idxBase);
-
-#define SPARSE_FUNC_DEF( FUNC )                     \
-template<typename T>                                \
-typename FUNC##_func_def_t<T>::FUNC##_func_def      \
-FUNC##_func();
-
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> typename FUNC##_func_def_t<TYPE>::FUNC##_func_def        \
-FUNC##_func<TYPE>()                                                 \
-{ return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)&cusparse##PREFIX##FUNC; }
+//                                  const int *csrSortedRowPtr, const int
+//                                  *csrSortedColInd, cuDoubleComplex
+//                                  *cscSortedVal, int *cscSortedRowInd, int
+//                                  *cscSortedColPtr, cusparseAction_t
+//                                  copyValues, cusparseIndexBase_t idxBase);
+
+#define SPARSE_FUNC_DEF(FUNC) \
+    template<typename T>      \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
+
+#define SPARSE_FUNC(FUNC, TYPE, PREFIX)                                     \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cusparse##PREFIX##FUNC;                                      \
+    }
 
 SPARSE_FUNC_DEF(csrmm)
-SPARSE_FUNC(csrmm, float,  S)
+SPARSE_FUNC(csrmm, float, S)
 SPARSE_FUNC(csrmm, double, D)
 SPARSE_FUNC(csrmm, cfloat, C)
-SPARSE_FUNC(csrmm, cdouble,Z)
+SPARSE_FUNC(csrmm, cdouble, Z)
 
 SPARSE_FUNC_DEF(csrmv)
-SPARSE_FUNC(csrmv, float,  S)
+SPARSE_FUNC(csrmv, float, S)
 SPARSE_FUNC(csrmv, double, D)
 SPARSE_FUNC(csrmv, cfloat, C)
-SPARSE_FUNC(csrmv, cdouble,Z)
+SPARSE_FUNC(csrmv, cdouble, Z)
 
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                af_mat_prop optLhs, af_mat_prop optRhs) {
     UNUSED(optRhs);
     // Similar Operations to GEMM
     cusparseOperation_t lOpts = toCusparseTranspose(optLhs);
 
     int lRowDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
-    //int lColDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
-    static const int rColDim = 1; //Unsupported : (rOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    // int lColDim = (lOpts == CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    static const int rColDim = 1;  // Unsupported : (rOpts ==
+                                   // CUSPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
-    int M = lDims[lRowDim];
-    int N = rDims[rColDim];
-    //int K = lDims[lColDim];
+    int M      = lDims[lRowDim];
+    int N      = rDims[rColDim];
+    // int K = lDims[lColDim];
 
     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
-    T alpha = scalar<T>(1);
-    T beta  = scalar<T>(0);
+    T alpha      = scalar<T>(1);
+    T beta       = scalar<T>(0);
 
     dim4 rStrides = rhs.strides();
 
@@ -154,29 +139,17 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     // Do not use M, N, K here. Use lDims and rDims instead.
     // This is because the function wants row/col of A
     // and not OP(A) (gemm wants row/col of OP(A)).
-    if(rDims[rColDim] == 1) {
+    if (rDims[rColDim] == 1) {
         CUSPARSE_CHECK(csrmv_func<T>()(
-                       sparseHandle(),
-                       lOpts,
-                       lDims[0], lDims[1], lhs.getNNZ(),
-                       &alpha,
-                       descr, lhs.getValues().get(),
-                       lhs.getRowIdx().get(), lhs.getColIdx().get(),
-                       rhs.get(),
-                       &beta,
-                       out.get()));
+            sparseHandle(), lOpts, lDims[0], lDims[1], lhs.getNNZ(), &alpha,
+            descr, lhs.getValues().get(), lhs.getRowIdx().get(),
+            lhs.getColIdx().get(), rhs.get(), &beta, out.get()));
     } else {
         CUSPARSE_CHECK(csrmm_func<T>()(
-                       sparseHandle(),
-                       lOpts,
-                       lDims[0], rDims[rColDim], lDims[1], lhs.getNNZ(),
-                       &alpha,
-                       descr, lhs.getValues().get(),
-                       lhs.getRowIdx().get(), lhs.getColIdx().get(),
-                       rhs.get(), rStrides[1],
-                       &beta,
-                       out.get(),
-                       out.dims()[0]));
+            sparseHandle(), lOpts, lDims[0], rDims[rColDim], lDims[1],
+            lhs.getNNZ(), &alpha, descr, lhs.getValues().get(),
+            lhs.getRowIdx().get(), lhs.getColIdx().get(), rhs.get(),
+            rStrides[1], &beta, out.get(), out.dims()[0]));
     }
 
     // Destory Sparse Matrix Descriptor
@@ -185,14 +158,14 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     return out;
 }
 
-#define INSTANTIATE_SPARSE(T)                                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
-                                af_mat_prop optLhs, af_mat_prop optRhs);                \
-
+#define INSTANTIATE_SPARSE(T)                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
+                                const Array<T> rhs, af_mat_prop optLhs, \
+                                af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
 INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/sparse_blas.hpp b/src/backend/cuda/sparse_blas.hpp
index b873e8aa73..9b012400d7 100644
--- a/src/backend/cuda/sparse_blas.hpp
+++ b/src/backend/cuda/sparse_blas.hpp
@@ -10,12 +10,10 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
-
diff --git a/src/backend/cuda/sum.cu b/src/backend/cuda/sum.cu
index 863cf9a7da..adc93c9b79 100644
--- a/src/backend/cuda/sum.cu
+++ b/src/backend/cuda/sum.cu
@@ -9,27 +9,26 @@
 
 #include "reduce_impl.hpp"
 
-namespace cuda
-{
-    //sum
-    INSTANTIATE(af_add_t, float  , float  )
-    INSTANTIATE(af_add_t, double , double )
-    INSTANTIATE(af_add_t, cfloat , cfloat )
-    INSTANTIATE(af_add_t, cdouble, cdouble)
-    INSTANTIATE(af_add_t, int    , int    )
-    INSTANTIATE(af_add_t, int    , float  )
-    INSTANTIATE(af_add_t, uint   , uint   )
-    INSTANTIATE(af_add_t, uint   , float  )
-    INSTANTIATE(af_add_t, intl   , intl   )
-    INSTANTIATE(af_add_t, intl   , double )
-    INSTANTIATE(af_add_t, uintl  , uintl  )
-    INSTANTIATE(af_add_t, uintl  , double )
-    INSTANTIATE(af_add_t, char   , int    )
-    INSTANTIATE(af_add_t, char   , float  )
-    INSTANTIATE(af_add_t, uchar  , uint   )
-    INSTANTIATE(af_add_t, uchar  , float  )
-    INSTANTIATE(af_add_t, short  , int    )
-    INSTANTIATE(af_add_t, short  , float  )
-    INSTANTIATE(af_add_t, ushort , uint   )
-    INSTANTIATE(af_add_t, ushort , float  )
-}
+namespace cuda {
+// sum
+INSTANTIATE(af_add_t, float, float)
+INSTANTIATE(af_add_t, double, double)
+INSTANTIATE(af_add_t, cfloat, cfloat)
+INSTANTIATE(af_add_t, cdouble, cdouble)
+INSTANTIATE(af_add_t, int, int)
+INSTANTIATE(af_add_t, int, float)
+INSTANTIATE(af_add_t, uint, uint)
+INSTANTIATE(af_add_t, uint, float)
+INSTANTIATE(af_add_t, intl, intl)
+INSTANTIATE(af_add_t, intl, double)
+INSTANTIATE(af_add_t, uintl, uintl)
+INSTANTIATE(af_add_t, uintl, double)
+INSTANTIATE(af_add_t, char, int)
+INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, uchar, uint)
+INSTANTIATE(af_add_t, uchar, float)
+INSTANTIATE(af_add_t, short, int)
+INSTANTIATE(af_add_t, short, float)
+INSTANTIATE(af_add_t, ushort, uint)
+INSTANTIATE(af_add_t, ushort, float)
+}  // namespace cuda
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index c7c52bd9c3..ed6cab0e63 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -8,29 +8,28 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <surface.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
+#include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <surface.hpp>
 
 using af::dim4;
 
 namespace cuda {
 
 template<typename T>
-void copy_surface(const Array<T> &P, fg_surface surface)
-{
+void copy_surface(const Array<T> &P, fg_surface surface) {
     auto stream = cuda::getActiveStream();
-    if(DeviceManager::checkGraphicsInteropCapability()) {
+    if (DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
         auto res = interopManager().getSurfaceResources(surface);
 
         size_t bytes = 0;
-        T* d_vbo = NULL;
+        T *d_vbo     = NULL;
         cudaGraphicsMapResources(1, res[0].get(), stream);
-        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo,
-                                             &bytes, *(res[0].get()));
+        cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
+                                             *(res[0].get()));
         cudaMemcpyAsync(d_vbo, d_P, bytes, cudaMemcpyDeviceToDevice, stream);
         cudaGraphicsUnmapResources(1, res[0].get(), stream);
 
@@ -38,14 +37,14 @@ void copy_surface(const Array<T> &P, fg_surface surface)
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule &_ = graphics::forgePlugin();
         unsigned bytes = 0, buffer = 0;
         FG_CHECK(_.fg_get_surface_vertex_buffer(&buffer, surface));
         FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
@@ -57,8 +56,8 @@ void copy_surface(const Array<T> &P, fg_surface surface)
     }
 }
 
-#define INSTANTIATE(T)  \
-template void copy_surface<T>(const Array<T> &, fg_surface);
+#define INSTANTIATE(T) \
+    template void copy_surface<T>(const Array<T> &, fg_surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -68,4 +67,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/susan.cu b/src/backend/cuda/susan.cu
index 4f2a094223..17bea453fb 100644
--- a/src/backend/cuda/susan.cu
+++ b/src/backend/cuda/susan.cu
@@ -7,37 +7,37 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <susan.hpp>
 #include <kernel/susan.hpp>
+#include <susan.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
 unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
-               const Array<T> &in,
-               const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge)
-{
+               const Array<T> &in, const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge) {
     dim4 idims = in.dims();
 
     const unsigned corner_lim = in.elements() * feature_ratio;
-    auto x_corners          = memAlloc<float>(corner_lim);
-    auto y_corners          = memAlloc<float>(corner_lim);
-    auto resp_corners       = memAlloc<float>(corner_lim);
+    auto x_corners            = memAlloc<float>(corner_lim);
+    auto y_corners            = memAlloc<float>(corner_lim);
+    auto resp_corners         = memAlloc<float>(corner_lim);
 
-    auto resp = memAlloc<T>(in.elements());
+    auto resp              = memAlloc<T>(in.elements());
     unsigned corners_found = 0;
 
-    kernel::susan_responses<T>(resp.get(), in.get(), idims[0], idims[1], radius, diff_thr, geom_thr, edge);
+    kernel::susan_responses<T>(resp.get(), in.get(), idims[0], idims[1], radius,
+                               diff_thr, geom_thr, edge);
 
-    kernel::nonMaximal<T>(x_corners.get(), y_corners.get(), resp_corners.get(), &corners_found,
-                          idims[0], idims[1], resp.get(), edge, corner_lim);
+    kernel::nonMaximal<T>(x_corners.get(), y_corners.get(), resp_corners.get(),
+                          &corners_found, idims[0], idims[1], resp.get(), edge,
+                          corner_lim);
 
     const unsigned corners_out = min(corners_found, corner_lim);
     if (corners_out == 0) {
@@ -46,28 +46,32 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
         resp_out = createEmptyArray<float>(dim4());
         return 0;
     } else {
-        x_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)x_corners.get());
-        y_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)y_corners.get());
-        resp_out = createDeviceDataArray<float>(dim4(corners_out), (void*)resp_corners.get());
+        x_out    = createDeviceDataArray<float>(dim4(corners_out),
+                                             (void *)x_corners.get());
+        y_out    = createDeviceDataArray<float>(dim4(corners_out),
+                                             (void *)y_corners.get());
+        resp_out = createDeviceDataArray<float>(dim4(corners_out),
+                                                (void *)resp_corners.get());
         x_corners.release();
         y_corners.release();
-        resp_corners.release(); 
+        resp_corners.release();
         return corners_out;
     }
 }
 
-#define INSTANTIATE(T) \
-template unsigned susan<T>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,   \
-                           const Array<T> &in, const unsigned radius, const float diff_thr,     \
-                           const float geom_thr, const float feature_ratio, const unsigned edge);
+#define INSTANTIATE(T)                                                        \
+    template unsigned susan<T>(                                               \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned radius, const float diff_thr,      \
+        const float geom_thr, const float feature_ratio, const unsigned edge);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/susan.hpp b/src/backend/cuda/susan.hpp
index 86977f8c27..1d50a846be 100644
--- a/src/backend/cuda/susan.hpp
+++ b/src/backend/cuda/susan.hpp
@@ -7,18 +7,18 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-               const Array<T> &in,
-               const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge);
+unsigned susan(Array<float> &x_out, Array<float> &y_out,
+               Array<float> &score_out, const Array<T> &in,
+               const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge);
 
 }
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cu
index ed5ebfaf1d..012c04ece6 100644
--- a/src/backend/cuda/svd.cu
+++ b/src/backend/cuda/svd.cu
@@ -7,123 +7,109 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <svd.hpp>
 #include <common/err_common.hpp>
+#include <svd.hpp>
 
-#include <platform.hpp>
-#include "transpose.hpp"
-#include <memory.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
 #include <math.hpp>
-#include <common/err_common.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include "transpose.hpp"
 
 #include <cusolverDn.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    cusolverStatus_t gesvd_buf_func(cusolverDnHandle_t handle, int m, int n, int *Lwork)
-    {
-        return CUSOLVER_STATUS_ARCH_MISMATCH;
-    }
+namespace cuda {
+template<typename T>
+cusolverStatus_t gesvd_buf_func(cusolverDnHandle_t handle, int m, int n,
+                                int *Lwork) {
+    return CUSOLVER_STATUS_ARCH_MISMATCH;
+}
+
+template<typename T, typename Tr>
+cusolverStatus_t gesvd_func(cusolverDnHandle_t handle, char jobu, char jobvt,
+                            int m, int n, T *A, int lda, Tr *S, T *U, int ldu,
+                            T *VT, int ldvt, T *Work, int Lwork, Tr *rwork,
+                            int *devInfo) {
+    return CUSOLVER_STATUS_ARCH_MISMATCH;
+}
 
-    template<typename T, typename Tr>
-    cusolverStatus_t gesvd_func(cusolverDnHandle_t handle, char jobu, char jobvt,
-                                int m, int n,
-                                T *A, int lda,
-                                Tr *S,
-                                T *U, int ldu,
-                                T *VT, int ldvt,
-                                T *Work, int Lwork,
-                                Tr *rwork, int *devInfo)
-    {
-        return CUSOLVER_STATUS_ARCH_MISMATCH;
+#define SVD_SPECIALIZE(T, Tr, X)                                         \
+    template<>                                                           \
+    cusolverStatus_t gesvd_buf_func<T>(cusolverDnHandle_t handle, int m, \
+                                       int n, int *Lwork) {              \
+        return cusolverDn##X##gesvd_bufferSize(handle, m, n, Lwork);     \
     }
 
-#define SVD_SPECIALIZE(T, Tr, X)                                        \
-    template<> cusolverStatus_t                                         \
-    gesvd_buf_func<T>(cusolverDnHandle_t handle,                        \
-                      int m, int n, int *Lwork)                         \
-    {                                                                   \
-        return cusolverDn##X##gesvd_bufferSize(handle, m, n, Lwork);    \
-    }                                                                   \
-
-SVD_SPECIALIZE(float  , float , S);
-SVD_SPECIALIZE(double , double, D);
-SVD_SPECIALIZE(cfloat , float , C);
+SVD_SPECIALIZE(float, float, S);
+SVD_SPECIALIZE(double, double, D);
+SVD_SPECIALIZE(cfloat, float, C);
 SVD_SPECIALIZE(cdouble, double, Z);
 
 #undef SVD_SPECIALIZE
 
-#define SVD_SPECIALIZE(T, Tr, X)                                        \
-    template<> cusolverStatus_t                                         \
-    gesvd_func<T, Tr>(cusolverDnHandle_t handle,                        \
-                      char jobu, char jobvt,                            \
-                      int m, int n,                                     \
-                      T *A, int lda,                                    \
-                      Tr *S,                                            \
-                      T *U, int ldu,                                    \
-                      T *VT, int ldvt,                                  \
-                      T *Work, int Lwork,                               \
-                      Tr *rwork, int *devInfo)                          \
-    {                                                                   \
-        return cusolverDn##X##gesvd(handle, jobu, jobvt,                \
-                                    m, n, A, lda, S, U, ldu, VT, ldvt,  \
-                                    Work, Lwork, rwork, devInfo);       \
-    }                                                                   \
-
-SVD_SPECIALIZE(float  , float , S);
-SVD_SPECIALIZE(double , double, D);
-SVD_SPECIALIZE(cfloat , float , C);
-SVD_SPECIALIZE(cdouble, double, Z);
+#define SVD_SPECIALIZE(T, Tr, X)                                              \
+    template<>                                                                \
+    cusolverStatus_t gesvd_func<T, Tr>(                                       \
+        cusolverDnHandle_t handle, char jobu, char jobvt, int m, int n, T *A, \
+        int lda, Tr *S, T *U, int ldu, T *VT, int ldvt, T *Work, int Lwork,   \
+        Tr *rwork, int *devInfo) {                                            \
+        return cusolverDn##X##gesvd(handle, jobu, jobvt, m, n, A, lda, S, U,  \
+                                    ldu, VT, ldvt, Work, Lwork, rwork,        \
+                                    devInfo);                                 \
+    }
 
-    template <typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-    {
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
+SVD_SPECIALIZE(float, float, S);
+SVD_SPECIALIZE(double, double, D);
+SVD_SPECIALIZE(cfloat, float, C);
+SVD_SPECIALIZE(cdouble, double, Z);
 
-        int lwork = 0;
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
+    dim4 iDims = in.dims();
+    int M      = iDims[0];
+    int N      = iDims[1];
 
-        CUSOLVER_CHECK(gesvd_buf_func<T>(solverDnHandle(), M, N, &lwork));
+    int lwork = 0;
 
-        auto lWorkspace = memAlloc<T >(lwork);
-        auto rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
+    CUSOLVER_CHECK(gesvd_buf_func<T>(solverDnHandle(), M, N, &lwork));
 
-        auto info = memAlloc<int>(1);
+    auto lWorkspace = memAlloc<T>(lwork);
+    auto rWorkspace = memAlloc<Tr>(5 * std::min(M, N));
 
-        gesvd_func<T, Tr>(solverDnHandle(), 'A', 'A', M, N, in.get(),
-                          M, s.get(), u.get(), M, vt.get(), N,
-                          lWorkspace.get(), lwork, rWorkspace.get(), info.get());
+    auto info = memAlloc<int>(1);
 
-    }
+    gesvd_func<T, Tr>(solverDnHandle(), 'A', 'A', M, N, in.get(), M, s.get(),
+                      u.get(), M, vt.get(), N, lWorkspace.get(), lwork,
+                      rWorkspace.get(), info.get());
+}
 
-    template <typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-    {
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
-
-        if (M >= N) {
-            Array<T> in_copy = copyArray(in);
-            svdInPlace(s, u, vt, in_copy);
-        } else {
-            Array<T> in_trans = transpose(in, true);
-            svdInPlace(s, vt, u, in_trans);
-            transpose_inplace(vt, true);
-            transpose_inplace(u, true);
-        }
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
+    dim4 iDims = in.dims();
+    int M      = iDims[0];
+    int N      = iDims[1];
+
+    if (M >= N) {
+        Array<T> in_copy = copyArray(in);
+        svdInPlace(s, u, vt, in_copy);
+    } else {
+        Array<T> in_trans = transpose(in, true);
+        svdInPlace(s, vt, u, in_trans);
+        transpose_inplace(vt, true);
+        transpose_inplace(u, true);
     }
+}
 
-#define INSTANTIATE(T, Tr)                                              \
-    template void svd<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in); \
-    template void svdInPlace<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+#define INSTANTIATE(T, Tr)                                               \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
 
 INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/svd.hpp b/src/backend/cuda/svd.hpp
index 5713adcef6..39192f95bb 100644
--- a/src/backend/cuda/svd.hpp
+++ b/src/backend/cuda/svd.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
+namespace cuda {
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 
-    template<typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
-}
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+}  // namespace cuda
diff --git a/src/backend/cuda/tile.cu b/src/backend/cuda/tile.cu
index f15fd87039..541601e8a0 100644
--- a/src/backend/cuda/tile.cu
+++ b/src/backend/cuda/tile.cu
@@ -8,45 +8,43 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <tile.hpp>
+#include <err_cuda.hpp>
 #include <kernel/tile.hpp>
+#include <tile.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
-        oDims *= tileDims;
 
-        if(iDims.elements() == 0 || oDims.elements() == 0) {
-            AF_ERROR("Elements are 0", AF_ERR_SIZE);
-        }
+namespace cuda {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims       = iDims;
+    oDims *= tileDims;
 
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        kernel::tile<T>(out, in);
-
-        return out;
+    if (iDims.elements() == 0 || oDims.elements() == 0) {
+        AF_ERROR("Elements are 0", AF_ERR_SIZE);
     }
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);  \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    Array<T> out = createEmptyArray<T>(oDims);
 
+    kernel::tile<T>(out, in);
+
+    return out;
 }
+
+#define INSTANTIATE(T) \
+    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/tile.hpp b/src/backend/cuda/tile.hpp
index 0cfc0efd12..d58795a629 100644
--- a/src/backend/cuda/tile.hpp
+++ b/src/backend/cuda/tile.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
+namespace cuda {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
 }
diff --git a/src/backend/cuda/topk.cu b/src/backend/cuda/topk.cu
index 8d44076516..e6c5c0b366 100644
--- a/src/backend/cuda/topk.cu
+++ b/src/backend/cuda/topk.cu
@@ -7,13 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Array.hpp>
 #include <kernel/topk.hpp>
 #include <topk.hpp>
-#include <Array.hpp>
 #include <af/dim4.hpp>
 
-namespace cuda
-{
+namespace cuda {
 template<typename T>
 void topk(Array<T>& ovals, Array<uint>& oidxs, const Array<T>& ivals,
           const int k, const int dim, const af::topkFunction order) {
@@ -26,14 +25,14 @@ void topk(Array<T>& ovals, Array<uint>& oidxs, const Array<T>& ivals,
     kernel::topk<T>(ovals, oidxs, ivals, k, dim, order);
 }
 
-#define INSTANTIATE(T)\
-template void topk<T>(Array<T>&, Array<uint>&, const Array<T>&, \
-                      const int, const int, const af::topkFunction);
+#define INSTANTIATE(T)                                                         \
+    template void topk<T>(Array<T>&, Array<uint>&, const Array<T>&, const int, \
+                          const int, const af::topkFunction);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
+INSTANTIATE(int)
+INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/topk.hpp b/src/backend/cuda/topk.hpp
index 8fbc298e6d..3b87427eb3 100644
--- a/src/backend/cuda/topk.hpp
+++ b/src/backend/cuda/topk.hpp
@@ -8,8 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-namespace cuda
-{
+namespace cuda {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
diff --git a/src/backend/cuda/traits.hpp b/src/backend/cuda/traits.hpp
index 5d293febab..ffabcf0a66 100644
--- a/src/backend/cuda/traits.hpp
+++ b/src/backend/cuda/traits.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#include <af/traits.hpp>
 #include <cuComplex.h>
+#include <af/traits.hpp>
 
 namespace af {
 
@@ -28,6 +28,6 @@ struct dtype_traits<cuDoubleComplex> {
     static const char* getName() { return "cuDoubleComplex"; }
 };
 
-}
+}  // namespace af
 
 using af::dtype_traits;
diff --git a/src/backend/cuda/transform.cu b/src/backend/cuda/transform.cu
index 63745b304e..afea4a3856 100644
--- a/src/backend/cuda/transform.cu
+++ b/src/backend/cuda/transform.cu
@@ -8,19 +8,18 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <transform.hpp>
 #include <kernel/transform.hpp>
+#include <transform.hpp>
 #include <stdexcept>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse, const bool perspective)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
+namespace cuda {
+template<typename T>
+Array<T> transform(const Array<T> &in, const Array<float> &tf,
+                   const af::dim4 &odims, const af_interp_type method,
+                   const bool inverse, const bool perspective) {
+    Array<T> out = createEmptyArray<T>(odims);
 
-        switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
             kernel::transform<T, 1>(out, in, tf, inverse, perspective, method);
@@ -33,29 +32,28 @@ namespace cuda
         case AF_INTERP_BICUBIC_SPLINE:
             kernel::transform<T, 3>(out, in, tf, inverse, perspective, method);
             break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-        }
-
-        return out;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
 
+    return out;
+}
 
-#define INSTANTIATE(T)                                                                      \
-    template Array<T> transform(const Array<T> &in, const Array<float> &tf,                 \
-                                const af::dim4 &odims, const af_interp_type method,         \
+#define INSTANTIATE(T)                                                      \
+    template Array<T> transform(const Array<T> &in, const Array<float> &tf, \
+                                const af::dim4 &odims,                      \
+                                const af_interp_type method,                \
                                 const bool inverse, const bool perspective);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/transform.hpp b/src/backend/cuda/transform.hpp
index 29ae83640c..e814ee85b0 100644
--- a/src/backend/cuda/transform.hpp
+++ b/src/backend/cuda/transform.hpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                       const af_interp_type method, const bool inverse,
-                       const bool perspective);
+namespace cuda {
+template<typename T>
+Array<T> transform(const Array<T> &in, const Array<float> &tf,
+                   const af::dim4 &odims, const af_interp_type method,
+                   const bool inverse, const bool perspective);
 }
diff --git a/src/backend/cuda/transpose.cu b/src/backend/cuda/transpose.cu
index ff9fa4b9fd..e9e33ca957 100644
--- a/src/backend/cuda/transpose.cu
+++ b/src/backend/cuda/transpose.cu
@@ -7,45 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <transpose.hpp>
 #include <kernel/transpose.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T> transpose(const Array<T> &in, const bool conjugate)
-{
-    const dim4 inDims   = in.dims();
+Array<T> transpose(const Array<T> &in, const bool conjugate) {
+    const dim4 inDims = in.dims();
 
-    dim4 outDims  = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
+    dim4 outDims = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
 
-    Array<T> out  = createEmptyArray<T>(outDims);
+    Array<T> out = createEmptyArray<T>(outDims);
 
-    if(conjugate)   { kernel::transpose<T, true>(out, in); }
-    else            { kernel::transpose<T, false>(out, in);}
+    if (conjugate) {
+        kernel::transpose<T, true>(out, in);
+    } else {
+        kernel::transpose<T, false>(out, in);
+    }
 
     return out;
 }
 
-#define INSTANTIATE(T)                                                              \
+#define INSTANTIATE(T) \
     template Array<T> transpose(const Array<T> &in, const bool conjugate);
 
-INSTANTIATE(float  )
-INSTANTIATE(cfloat )
-INSTANTIATE(double )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
 INSTANTIATE(cdouble)
-INSTANTIATE(char   )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/transpose.hpp b/src/backend/cuda/transpose.hpp
index 48089a0aa2..5a26aa8b14 100644
--- a/src/backend/cuda/transpose.hpp
+++ b/src/backend/cuda/transpose.hpp
@@ -9,13 +9,12 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-Array<T>  transpose(const Array<T> &in, const bool conjugate);
+Array<T> transpose(const Array<T> &in, const bool conjugate);
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/transpose_inplace.cu b/src/backend/cuda/transpose_inplace.cu
index 1d34580d3e..fc2c723d02 100644
--- a/src/backend/cuda/transpose_inplace.cu
+++ b/src/backend/cuda/transpose_inplace.cu
@@ -7,38 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <transpose.hpp>
 #include <kernel/transpose_inplace.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T>
-void transpose_inplace(Array<T> &in, const bool conjugate)
-{
-    if(conjugate)   { kernel::transpose_inplace<T, true >(in); }
-    else            { kernel::transpose_inplace<T, false>(in); }
+void transpose_inplace(Array<T> &in, const bool conjugate) {
+    if (conjugate) {
+        kernel::transpose_inplace<T, true>(in);
+    } else {
+        kernel::transpose_inplace<T, false>(in);
+    }
 }
 
-#define INSTANTIATE(T)                                                              \
+#define INSTANTIATE(T) \
     template void transpose_inplace(Array<T> &in, const bool conjugate);
 
-INSTANTIATE(float  )
-INSTANTIATE(cfloat )
-INSTANTIATE(double )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
 INSTANTIATE(cdouble)
-INSTANTIATE(char   )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
-
-}
-
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/triangle.cu b/src/backend/cuda/triangle.cu
index e92b1d5f65..25b2d22858 100644
--- a/src/backend/cuda/triangle.cu
+++ b/src/backend/cuda/triangle.cu
@@ -7,51 +7,50 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <triangle.hpp>
 #include <kernel/triangle.hpp>
+#include <triangle.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace cuda
-{
+namespace cuda {
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in)
-{
+void triangle(Array<T> &out, const Array<T> &in) {
     kernel::triangle<T, is_upper, is_unit_diag>(out, in);
 }
 
-
 template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in)
-{
+Array<T> triangle(const Array<T> &in) {
     Array<T> out = createEmptyArray<T>(in.dims());
     triangle<T, is_upper, is_unit_diag>(out, in);
     return out;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template void triangle<T, true ,  true>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, false,  true>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, true , false>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, false, false>(Array<T> &out, const Array<T> &in); \
-    template Array<T> triangle<T, true ,  true>(const Array<T> &in);    \
-    template Array<T> triangle<T, false,  true>(const Array<T> &in);    \
-    template Array<T> triangle<T, true , false>(const Array<T> &in);    \
-    template Array<T> triangle<T, false, false>(const Array<T> &in);    \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+#define INSTANTIATE(T)                                                         \
+    template void triangle<T, true, true>(Array<T> & out, const Array<T> &in); \
+    template void triangle<T, false, true>(Array<T> & out,                     \
+                                           const Array<T> &in);                \
+    template void triangle<T, true, false>(Array<T> & out,                     \
+                                           const Array<T> &in);                \
+    template void triangle<T, false, false>(Array<T> & out,                    \
+                                            const Array<T> &in);               \
+    template Array<T> triangle<T, true, true>(const Array<T> &in);             \
+    template Array<T> triangle<T, false, true>(const Array<T> &in);            \
+    template Array<T> triangle<T, true, false>(const Array<T> &in);            \
+    template Array<T> triangle<T, false, false>(const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/triangle.hpp b/src/backend/cuda/triangle.hpp
index 539e70e081..ddd7af6aa0 100644
--- a/src/backend/cuda/triangle.hpp
+++ b/src/backend/cuda/triangle.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T, bool is_upper, bool is_unit_diag>
-    void triangle(Array<T> &out, const Array<T> &in);
+namespace cuda {
+template<typename T, bool is_upper, bool is_unit_diag>
+void triangle(Array<T> &out, const Array<T> &in);
 
-    template<typename T, bool is_upper, bool is_unit_diag>
-    Array<T> triangle(const Array<T> &in);
-}
+template<typename T, bool is_upper, bool is_unit_diag>
+Array<T> triangle(const Array<T> &in);
+}  // namespace cuda
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 3b5321702f..91d2df224e 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -8,11 +8,10 @@
  ********************************************************/
 
 #pragma once
-#include <af/traits.hpp>
 #include <cuComplex.h>
+#include <af/traits.hpp>
 
-namespace cuda
-{
+namespace cuda {
 using cdouble = cuDoubleComplex;
 using cfloat  = cuFloatComplex;
 using intl    = long long;
@@ -22,39 +21,82 @@ using uintl   = unsigned long long;
 using ushort  = unsigned short;
 
 namespace {
-template<typename T> const char *shortname(bool caps = false) { return caps ?  "Q" : "q"; }
-template<> const char *shortname<float   >(bool caps) { return caps ?  "S" : "s"; }
-template<> const char *shortname<double  >(bool caps) { return caps ?  "D" : "d"; }
-template<> const char *shortname<cfloat  >(bool caps) { return caps ?  "C" : "c"; }
-template<> const char *shortname<cdouble >(bool caps) { return caps ?  "Z" : "z"; }
-template<> const char *shortname<int     >(bool caps) { return caps ?  "I" : "i"; }
-template<> const char *shortname<uint    >(bool caps) { return caps ?  "U" : "u"; }
-template<> const char *shortname<char    >(bool caps) { return caps ?  "J" : "j"; }
-template<> const char *shortname<uchar   >(bool caps) { return caps ?  "V" : "v"; }
-template<> const char *shortname<intl    >(bool caps) { return caps ?  "X" : "x"; }
-template<> const char *shortname<uintl   >(bool caps) { return caps ?  "Y" : "y"; }
-template<> const char *shortname<short   >(bool caps) { return caps ?  "P" : "p"; }
-template<> const char *shortname<ushort  >(bool caps) { return caps ?  "Q" : "q"; }
-
-template<typename T> const char *getFullName();
-
-#define SPECIALIZE(T)                                      \
-    template<> const char *getFullName<T>() { return #T; }
-
-    SPECIALIZE(float)
-    SPECIALIZE(double)
-    SPECIALIZE(cfloat)
-    SPECIALIZE(cdouble)
-    SPECIALIZE(char)
-    SPECIALIZE(unsigned char)
-    SPECIALIZE(short)
-    SPECIALIZE(unsigned short)
-    SPECIALIZE(int)
-    SPECIALIZE(unsigned int)
-    SPECIALIZE(unsigned long long)
-    SPECIALIZE(long long)
+template<typename T>
+const char *shortname(bool caps = false) {
+    return caps ? "Q" : "q";
+}
+template<>
+const char *shortname<float>(bool caps) {
+    return caps ? "S" : "s";
+}
+template<>
+const char *shortname<double>(bool caps) {
+    return caps ? "D" : "d";
+}
+template<>
+const char *shortname<cfloat>(bool caps) {
+    return caps ? "C" : "c";
+}
+template<>
+const char *shortname<cdouble>(bool caps) {
+    return caps ? "Z" : "z";
+}
+template<>
+const char *shortname<int>(bool caps) {
+    return caps ? "I" : "i";
+}
+template<>
+const char *shortname<uint>(bool caps) {
+    return caps ? "U" : "u";
+}
+template<>
+const char *shortname<char>(bool caps) {
+    return caps ? "J" : "j";
+}
+template<>
+const char *shortname<uchar>(bool caps) {
+    return caps ? "V" : "v";
+}
+template<>
+const char *shortname<intl>(bool caps) {
+    return caps ? "X" : "x";
+}
+template<>
+const char *shortname<uintl>(bool caps) {
+    return caps ? "Y" : "y";
+}
+template<>
+const char *shortname<short>(bool caps) {
+    return caps ? "P" : "p";
+}
+template<>
+const char *shortname<ushort>(bool caps) {
+    return caps ? "Q" : "q";
+}
+
+template<typename T>
+const char *getFullName();
+
+#define SPECIALIZE(T)              \
+    template<>                     \
+    const char *getFullName<T>() { \
+        return #T;                 \
+    }
+
+SPECIALIZE(float)
+SPECIALIZE(double)
+SPECIALIZE(cfloat)
+SPECIALIZE(cdouble)
+SPECIALIZE(char)
+SPECIALIZE(unsigned char)
+SPECIALIZE(short)
+SPECIALIZE(unsigned short)
+SPECIALIZE(int)
+SPECIALIZE(unsigned int)
+SPECIALIZE(unsigned long long)
+SPECIALIZE(long long)
 
 #undef SPECIALIZE
-}
+}  // namespace
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 0081f99019..5f30b99f0b 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -8,22 +8,22 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <optypes.hpp>
-#include <math.hpp>
 #include <common/jit/UnaryNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
 
-namespace cuda
-{
+namespace cuda {
 
 template<af_op_t op>
-static const char *unaryName() { return "__noop"; }
+static const char *unaryName() {
+    return "__noop";
+}
 
-#define UNARY_DECL(OP, FNAME)                   \
-    template<> STATIC_                          \
-    const char *unaryName<af_##OP##_t>()        \
-    {                                           \
-        return FNAME;                           \
-    }                                           \
+#define UNARY_DECL(OP, FNAME)                      \
+    template<>                                     \
+    STATIC_ const char *unaryName<af_##OP##_t>() { \
+        return FNAME;                              \
+    }
 
 #define UNARY_FN(OP) UNARY_DECL(OP, #OP)
 
@@ -74,29 +74,24 @@ UNARY_FN(iszero)
 #undef UNARY_FN
 
 template<typename T, af_op_t op>
-Array<T> unaryOp(const Array<T> &in)
-{
+Array<T> unaryOp(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
 
-    common::UnaryNode *node = new common::UnaryNode(getFullName<T>(),
-                                                    shortname<T>(true),
-                                                    unaryName<op>(),
-                                                    in_node, op);
+    common::UnaryNode *node = new common::UnaryNode(
+        getFullName<T>(), shortname<T>(true), unaryName<op>(), in_node, op);
 
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
-Array<char> checkOp(const Array<T> &in)
-{
+Array<char> checkOp(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
 
-    common::UnaryNode *node = new common::UnaryNode(getFullName<char>(),
-                                                    shortname<char>(true),
-                                                    unaryName<op>(),
-                                                    in_node, op);
+    common::UnaryNode *node =
+        new common::UnaryNode(getFullName<char>(), shortname<char>(true),
+                              unaryName<op>(), in_node, op);
 
     return createNodeArray<char>(in.dims(), common::Node_ptr(node));
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/unwrap.cu b/src/backend/cuda/unwrap.cu
index a61aba487e..605bf09a67 100644
--- a/src/backend/cuda/unwrap.cu
+++ b/src/backend/cuda/unwrap.cu
@@ -8,52 +8,50 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <unwrap.hpp>
+#include <err_cuda.hpp>
 #include <kernel/unwrap.hpp>
+#include <unwrap.hpp>
 #include <stdexcept>
-#include <err_cuda.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
-    {
-        af::dim4 idims = in.dims();
-
-        dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-        dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
-
-        af::dim4 odims;
-
-        if (is_column) {
-            odims = dim4(wx * wy, nx * ny, idims[2], idims[3]);
-        } else {
-            odims = dim4(nx * ny, wx * wy, idims[2], idims[3]);
-        }
-
-        // Create output placeholder
-        Array<T> outArray = createEmptyArray<T>(odims);
-        kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, nx, is_column);
-        return outArray;
-    }
+namespace cuda {
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const bool is_column) {
+    af::dim4 idims = in.dims();
 
+    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
+    dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
 
-#define INSTANTIATE(T)                                                                  \
-    template Array<T> unwrap<T> (const Array<T> &in, const dim_t wx, const dim_t wy,    \
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+    af::dim4 odims;
 
+    if (is_column) {
+        odims = dim4(wx * wy, nx * ny, idims[2], idims[3]);
+    } else {
+        odims = dim4(nx * ny, wx * wy, idims[2], idims[3]);
+    }
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    // Create output placeholder
+    Array<T> outArray = createEmptyArray<T>(odims);
+    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, nx, is_column);
+    return outArray;
 }
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> unwrap<T>(                                            \
+        const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
+        const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/unwrap.hpp b/src/backend/cuda/unwrap.hpp
index 7105585ec0..a03b4a2e39 100644
--- a/src/backend/cuda/unwrap.hpp
+++ b/src/backend/cuda/unwrap.hpp
@@ -9,9 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+namespace cuda {
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const bool is_column);
 }
diff --git a/src/backend/cuda/utility.hpp b/src/backend/cuda/utility.hpp
index 1f01b6ddf7..7133da542a 100644
--- a/src/backend/cuda/utility.hpp
+++ b/src/backend/cuda/utility.hpp
@@ -11,20 +11,18 @@
 #include <af/defines.h>
 #include "backend.hpp"
 
-namespace cuda
-{
+namespace cuda {
 
-static __DH__ dim_t trimIndex(const int &idx, const dim_t &len)
-{
+static __DH__ dim_t trimIndex(const int &idx, const dim_t &len) {
     int ret_val = idx;
-    if (ret_val<0) {
-        int offset  = (abs(ret_val)-1)%len;
-        ret_val = offset;
-    } else if (ret_val>=len) {
-        int offset  = abs(ret_val)%len;
-        ret_val = len-offset-1;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
     }
     return ret_val;
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index fc2ac458da..9f0ecd5783 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <vector_field.hpp>
-#include <err_cuda.hpp>
-#include <debug_cuda.hpp>
 #include <GraphicsResourceManager.hpp>
+#include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <vector_field.hpp>
 
 using af::dim4;
 
@@ -19,10 +19,9 @@ namespace cuda {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vfield)
-{
+                       fg_vector_field vfield) {
     auto stream = cuda::getActiveStream();
-    if(DeviceManager::checkGraphicsInteropCapability()) {
+    if (DeviceManager::checkGraphicsInteropCapability()) {
         auto res = interopManager().getVectorFieldResources(vfield);
         cudaGraphicsResource_t resources[2] = {*res[0].get(), *res[1].get()};
 
@@ -32,19 +31,21 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
         {
             const T *ptr = points.get();
             size_t bytes = 0;
-            T* d_vbo = NULL;
+            T *d_vbo     = NULL;
             cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
                                                  resources[0]);
-            cudaMemcpyAsync(d_vbo, ptr, bytes, cudaMemcpyDeviceToDevice, stream);
+            cudaMemcpyAsync(d_vbo, ptr, bytes, cudaMemcpyDeviceToDevice,
+                            stream);
         }
         // Directions
         {
             const T *ptr = directions.get();
             size_t bytes = 0;
-            T* d_vbo = NULL;
+            T *d_vbo     = NULL;
             cudaGraphicsResourceGetMappedPointer((void **)&d_vbo, &bytes,
                                                  resources[1]);
-            cudaMemcpyAsync(d_vbo, ptr, bytes, cudaMemcpyDeviceToDevice, stream);
+            cudaMemcpyAsync(d_vbo, ptr, bytes, cudaMemcpyDeviceToDevice,
+                            stream);
         }
         cudaGraphicsUnmapResources(2, resources, stream);
 
@@ -52,7 +53,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule &_ = graphics::forgePlugin();
         CheckGL("Begin CUDA fallback-resource copy");
         unsigned size1 = 0, size2 = 0;
         unsigned buff1 = 0, buff2 = 0;
@@ -63,7 +64,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Points
         glBindBuffer(GL_ARRAY_BUFFER, buff1);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, points.get(), size1,
                                        cudaMemcpyDeviceToHost, stream));
@@ -74,7 +75,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Directions
         glBindBuffer(GL_ARRAY_BUFFER, buff2);
-        ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, directions.get(), size2,
                                        cudaMemcpyDeviceToHost, stream));
@@ -87,9 +88,9 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
     }
 }
 
-#define INSTANTIATE(T)                                                  \
-template void copy_vector_field<T>(const Array<T> &, const Array<T> &,  \
-                                   fg_vector_field);
+#define INSTANTIATE(T)                                                     \
+    template void copy_vector_field<T>(const Array<T> &, const Array<T> &, \
+                                       fg_vector_field);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -99,4 +100,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/where.cu b/src/backend/cuda/where.cu
index 9c53266e41..fd39c88eb6 100644
--- a/src/backend/cuda/where.cu
+++ b/src/backend/cuda/where.cu
@@ -7,40 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_cuda.hpp>
+#include <af/dim4.hpp>
 
 #undef _GLIBCXX_USE_INT128
+#include <kernel/where.hpp>
 #include <where.hpp>
 #include <complex>
-#include <kernel/where.hpp>
-
-namespace cuda
-{
-    template<typename T>
-    Array<uint> where(const Array<T> &in)
-    {
-        Param<uint> out;
-        kernel::where<T>(out, in);
-        return createParamArray<uint>(out, true);
-    }
 
+namespace cuda {
+template<typename T>
+Array<uint> where(const Array<T> &in) {
+    Param<uint> out;
+    kernel::where<T>(out, in);
+    return createParamArray<uint>(out, true);
+}
 
-#define INSTANTIATE(T)                                  \
-    template Array<uint> where<T>(const Array<T> &in);    \
+#define INSTANTIATE(T) template Array<uint> where<T>(const Array<T> &in);
 
-    INSTANTIATE(float  )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(double )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(char   )
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/where.hpp b/src/backend/cuda/where.hpp
index 1e955522b6..6a2069f344 100644
--- a/src/backend/cuda/where.hpp
+++ b/src/backend/cuda/where.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<uint> where(const Array<T>& in);
+namespace cuda {
+template<typename T>
+Array<uint> where(const Array<T>& in);
 }
diff --git a/src/backend/cuda/wrap.cu b/src/backend/cuda/wrap.cu
index 095bd976ce..13fc2aded1 100644
--- a/src/backend/cuda/wrap.cu
+++ b/src/backend/cuda/wrap.cu
@@ -8,52 +8,43 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <wrap.hpp>
-#include <stdexcept>
-#include <err_cuda.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
+#include <err_cuda.hpp>
 #include <kernel/wrap.hpp>
+#include <math.hpp>
+#include <wrap.hpp>
+#include <stdexcept>
 
-namespace cuda
-{
-
-    template<typename T>
-    Array<T> wrap(const Array<T> &in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column)
-    {
-        af::dim4 idims = in.dims();
-        af::dim4 odims(ox, oy, idims[2], idims[3]);
-        Array<T> out = createValueArray<T>(odims, scalar<T>(0));
-
-        kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
-        return out;
-    }
-
-
-#define INSTANTIATE(T)                                          \
-    template Array<T> wrap<T> (const Array<T> &in,              \
-                               const dim_t ox, const dim_t oy,  \
-                               const dim_t wx, const dim_t wy,  \
-                               const dim_t sx, const dim_t sy,  \
-                               const dim_t px, const dim_t py,  \
-                               const bool is_column);
+namespace cuda {
 
+template<typename T>
+Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py, const bool is_column) {
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
+    return out;
 }
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> wrap<T>(const Array<T> &in, const dim_t ox,             \
+                              const dim_t oy, const dim_t wx, const dim_t wy, \
+                              const dim_t sx, const dim_t sy, const dim_t px, \
+                              const dim_t py, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace cuda
diff --git a/src/backend/cuda/wrap.hpp b/src/backend/cuda/wrap.hpp
index 300b06122a..4beeb4fb5c 100644
--- a/src/backend/cuda/wrap.hpp
+++ b/src/backend/cuda/wrap.hpp
@@ -9,13 +9,9 @@
 
 #include <Array.hpp>
 
-namespace cuda
-{
-    template<typename T>
-    Array<T> wrap(const Array<T> &in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column);
+namespace cuda {
+template<typename T>
+Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py, const bool is_column);
 }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index e104edc654..b127baf768 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -8,16 +8,16 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <jit/BufferNode.hpp>
-#include <af/dim4.hpp>
-#include <af/opencl.h>
 #include <common/jit/NodeIterator.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
+#include <jit/BufferNode.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <scalar.hpp>
+#include <af/dim4.hpp>
+#include <af/opencl.h>
 
 #include <cstddef>
 #include <numeric>
@@ -26,460 +26,423 @@ using af::dim4;
 
 using cl::Buffer;
 
-using common::NodeIterator;
-using opencl::jit::BufferNode;
 using common::Node;
 using common::Node_ptr;
+using common::NodeIterator;
+using opencl::jit::BufferNode;
 
 using std::accumulate;
 using std::is_standard_layout;
 using std::make_shared;
 using std::vector;
 
-namespace opencl
-{
-    template<typename T>
-    Node_ptr bufferNodePtr()
-    {
-        return make_shared<BufferNode>(dtype_traits<T>::getName(), shortname<T>(true));
-    }
-
-    template<typename T>
-    Array<T>::Array(dim4 dims) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(bufferAlloc(info.elements() * sizeof(T)), bufferFree),
-        data_dims(dims),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
-    {
-    }
-
-    template<typename T>
-    Array<T>::Array(dim4 dims, Node_ptr n) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(),
-        data_dims(dims),
-        node(n), ready(false), owner(true)
-    {
-    }
-
-    template<typename T>
-    Array<T>::Array(dim4 dims, const T * const in_data) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(bufferAlloc(info.elements()*sizeof(T)), bufferFree),
-        data_dims(dims),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
-    {
-        static_assert(is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
-        static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
-        getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T)*info.elements(), in_data);
-    }
-
-    template<typename T>
-    Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy) :
-        info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
-        data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new Buffer(mem), bufferFree),
-        data_dims(dims),
-        node(bufferNodePtr<T>()), ready(true), owner(true)
-    {
-        if (copy) {
-            clRetainMemObject(mem);
-            Buffer src_buf = Buffer((cl_mem)(mem));
-            getQueue().enqueueCopyBuffer(src_buf, *data.get(),
-                                         src_offset, 0,
-                                         sizeof(T) * info.elements());
-        }
-    }
-
-    template<typename T>
-    Array<T>::Array(const Array<T>& parent, const dim4 &dims, const dim_t &offset_, const dim4 &stride) :
-        info(parent.getDevId(), dims, offset_, stride, (af_dtype)dtype_traits<T>::af_type),
-        data(parent.getData()),
-        data_dims(parent.getDataDims()),
-        node(bufferNodePtr<T>()),
-        ready(true),
-        owner(false)
-    {
-    }
+namespace opencl {
+template<typename T>
+Node_ptr bufferNodePtr() {
+    return make_shared<BufferNode>(dtype_traits<T>::getName(),
+                                   shortname<T>(true));
+}
 
+template<typename T>
+Array<T>::Array(dim4 dims)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(bufferAlloc(info.elements() * sizeof(T)), bufferFree)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {}
+
+template<typename T>
+Array<T>::Array(dim4 dims, Node_ptr n)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data()
+    , data_dims(dims)
+    , node(n)
+    , ready(false)
+    , owner(true) {}
+
+template<typename T>
+Array<T>::Array(dim4 dims, const T *const in_data)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(bufferAlloc(info.elements() * sizeof(T)), bufferFree)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
+    static_assert(is_standard_layout<Array<T>>::value,
+                  "Array<T> must be a standard layout type");
+    static_assert(
+        offsetof(Array<T>, info) == 0,
+        "Array<T>::info must be the first member variable of Array<T>");
+    getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
+                                  sizeof(T) * info.elements(), in_data);
+}
 
-    template<typename T>
-    Array<T>::Array(Param &tmp, bool owner_) :
-        info(getActiveDeviceId(),
-             dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3]),
-             0,
-             dim4(tmp.info.strides[0], tmp.info.strides[1],
-                      tmp.info.strides[2], tmp.info.strides[3]),
-             (af_dtype)dtype_traits<T>::af_type),
-        data(tmp.data, owner_ ? bufferFree : [] (Buffer*) {}),
-        data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2], tmp.info.dims[3])),
-        node(bufferNodePtr<T>()), ready(true), owner(owner_)
-    {
+template<typename T>
+Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new Buffer(mem),
+           bufferFree)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
+    if (copy) {
+        clRetainMemObject(mem);
+        Buffer src_buf = Buffer((cl_mem)(mem));
+        getQueue().enqueueCopyBuffer(src_buf, *data.get(), src_offset, 0,
+                                     sizeof(T) * info.elements());
     }
+}
 
-    template<typename T>
-    Array<T>::Array(dim4 dims, dim4 strides, dim_t offset_,
-                    const T * const in_data, bool is_device) :
-        info(getActiveDeviceId(), dims, offset_, strides, (af_dtype)dtype_traits<T>::af_type),
-        data(is_device ?
-             (new Buffer((cl_mem)in_data)) :
-             (bufferAlloc(info.total() * sizeof(T))), bufferFree),
-        data_dims(dims),
-        node(bufferNodePtr<T>()),
-        ready(true),
-        owner(true)
-    {
-        if (!is_device) {
-            getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0, sizeof(T) * info.total(), in_data);
-        }
+template<typename T>
+Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
+                const dim4 &stride)
+    : info(parent.getDevId(), dims, offset_, stride,
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(parent.getData())
+    , data_dims(parent.getDataDims())
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(false) {}
+
+template<typename T>
+Array<T>::Array(Param &tmp, bool owner_)
+    : info(getActiveDeviceId(),
+           dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
+                tmp.info.dims[3]),
+           0,
+           dim4(tmp.info.strides[0], tmp.info.strides[1], tmp.info.strides[2],
+                tmp.info.strides[3]),
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(tmp.data, owner_ ? bufferFree : [](Buffer *) {})
+    , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
+                     tmp.info.dims[3]))
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(owner_) {}
+
+template<typename T>
+Array<T>::Array(dim4 dims, dim4 strides, dim_t offset_, const T *const in_data,
+                bool is_device)
+    : info(getActiveDeviceId(), dims, offset_, strides,
+           (af_dtype)dtype_traits<T>::af_type)
+    , data(is_device ? (new Buffer((cl_mem)in_data))
+                     : (bufferAlloc(info.total() * sizeof(T))),
+           bufferFree)
+    , data_dims(dims)
+    , node(bufferNodePtr<T>())
+    , ready(true)
+    , owner(true) {
+    if (!is_device) {
+        getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
+                                      sizeof(T) * info.total(), in_data);
     }
+}
 
-    template<typename T>
-    void Array<T>::eval()
-    {
-        if (isReady()) return;
+template<typename T>
+void Array<T>::eval() {
+    if (isReady()) return;
 
-        this->setId(getActiveDeviceId());
-        data = Buffer_ptr(bufferAlloc(elements() * sizeof(T)), bufferFree);
+    this->setId(getActiveDeviceId());
+    data = Buffer_ptr(bufferAlloc(elements() * sizeof(T)), bufferFree);
 
-        // Do not replace this with cast operator
-        KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
-                       {strides()[0], strides()[1], strides()[2], strides()[3]},
-                       0};
+    // Do not replace this with cast operator
+    KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
+                   {strides()[0], strides()[1], strides()[2], strides()[3]},
+                   0};
 
-        Param res = {data.get(), info};
+    Param res = {data.get(), info};
 
-        evalNodes(res, node.get());
-        ready = true;
-        node = bufferNodePtr<T>();
-    }
+    evalNodes(res, node.get());
+    ready = true;
+    node  = bufferNodePtr<T>();
+}
 
-    template<typename T>
-    void Array<T>::eval() const
-    {
-        if (isReady()) return;
-        const_cast<Array<T> *>(this)->eval();
-    }
+template<typename T>
+void Array<T>::eval() const {
+    if (isReady()) return;
+    const_cast<Array<T> *>(this)->eval();
+}
 
-    template<typename T>
-    Buffer* Array<T>::device()
-    {
-        if (!isOwner() || getOffset() || data.use_count() > 1) {
-            *this = copyArray<T>(*this);
-        }
-        return this->get();
+template<typename T>
+Buffer *Array<T>::device() {
+    if (!isOwner() || getOffset() || data.use_count() > 1) {
+        *this = copyArray<T>(*this);
     }
+    return this->get();
+}
 
-    template<typename T>
-    void evalMultiple(vector<Array<T>*> arrays)
-    {
-        vector<Param> outputs;
-        vector<Array<T> *> output_arrays;
-        vector<Node *> nodes;
+template<typename T>
+void evalMultiple(vector<Array<T> *> arrays) {
+    vector<Param> outputs;
+    vector<Array<T> *> output_arrays;
+    vector<Node *> nodes;
 
-        for (Array<T>* array : arrays) {
-            if (array->isReady()) {
-                continue;
-            }
+    for (Array<T> *array : arrays) {
+        if (array->isReady()) { continue; }
 
-            const ArrayInfo info = array->info;
+        const ArrayInfo info = array->info;
 
-            array->ready = true;
-            array->setId(getActiveDeviceId());
-            array->data = Buffer_ptr(bufferAlloc(info.elements() * sizeof(T)), bufferFree);
+        array->ready = true;
+        array->setId(getActiveDeviceId());
+        array->data =
+            Buffer_ptr(bufferAlloc(info.elements() * sizeof(T)), bufferFree);
 
-            // Do not replace this with cast operator
-            KParam kInfo = {{info.dims()[0], info.dims()[1], info.dims()[2], info.dims()[3]},
-                            {info.strides()[0], info.strides()[1],
-                             info.strides()[2], info.strides()[3]},
-                            0};
+        // Do not replace this with cast operator
+        KParam kInfo = {
+            {info.dims()[0], info.dims()[1], info.dims()[2], info.dims()[3]},
+            {info.strides()[0], info.strides()[1], info.strides()[2],
+             info.strides()[3]},
+            0};
 
-            Param res = {array->data.get(), kInfo};
+        Param res = {array->data.get(), kInfo};
 
-            outputs.push_back(res);
-            output_arrays.push_back(array);
-            nodes.push_back(array->node.get());
-        }
-        evalNodes(outputs, nodes);
-        for (Array<T>* array : output_arrays) {
-            array->node = bufferNodePtr<T>();
-        }
+        outputs.push_back(res);
+        output_arrays.push_back(array);
+        nodes.push_back(array->node.get());
     }
+    evalNodes(outputs, nodes);
+    for (Array<T> *array : output_arrays) { array->node = bufferNodePtr<T>(); }
+}
 
-    template<typename T>
-    Array<T>::~Array()
-    {
-    }
+template<typename T>
+Array<T>::~Array() {}
 
-    template<typename T>
-    Node_ptr Array<T>::getNode()
-    {
-        if (node->isBuffer()) {
-            KParam kinfo = *this;
-            BufferNode *bufNode = reinterpret_cast<BufferNode *>(node.get());
-            unsigned bytes = this->getDataDims().elements() * sizeof(T);
-            bufNode->setData(kinfo, data, bytes, isLinear());
-        }
-        return node;
+template<typename T>
+Node_ptr Array<T>::getNode() {
+    if (node->isBuffer()) {
+        KParam kinfo        = *this;
+        BufferNode *bufNode = reinterpret_cast<BufferNode *>(node.get());
+        unsigned bytes      = this->getDataDims().elements() * sizeof(T);
+        bufNode->setData(kinfo, data, bytes, isLinear());
     }
+    return node;
+}
 
-    template<typename T>
-    Node_ptr Array<T>::getNode() const
-    {
-        if (node->isBuffer()) {
-            return const_cast<Array<T> *>(this)->getNode();
-        }
-        return node;
-    }
+template<typename T>
+Node_ptr Array<T>::getNode() const {
+    if (node->isBuffer()) { return const_cast<Array<T> *>(this)->getNode(); }
+    return node;
+}
 
-    template<typename T>
-    Array<T> createNodeArray(const dim4 &dims, Node_ptr node)
-    {
-        verifyDoubleSupport<T>();
-        Array<T> out =  Array<T>(dims, node);
-
-        if (evalFlag()) {
-
-            if (node->getHeight() >= (int)getMaxJitSize()) {
-                out.eval();
-            } else {
-
-                size_t alloc_bytes, alloc_buffers;
-                size_t lock_bytes, lock_buffers;
-
-                deviceMemoryInfo(&alloc_bytes, &alloc_buffers,
-                                 &lock_bytes, &lock_buffers);
-
-                bool isBufferLimit =
-                    lock_bytes > getMaxBytes() ||
-                    lock_buffers > getMaxBuffers();
-
-
-                bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA;
-                // We eval in the following cases.
-                // 1. Too many bytes are locked up by JIT causing memory pressure.
-                // Too many bytes is assumed to be half of all bytes allocated so far.
-                // 2. Too many buffers in a nonlinear kernel cause param space overflow.
-                // Too many buffers comes out to be about 48 (49 including output).
-                // Too many buffers can occur in a tree of size 24 in the worst case scenario.
-                // This error only happens on nvidia devices.
-                // TODO: Find better solution than the following emperical solution.
-                bool isParamLimit = (isNvidia && node->getHeight() > 24);
-                if (isParamLimit || isBufferLimit) {
-                    // This is the maximum non-linear buffers that are allowed in
-                    // the parameter list
-                    constexpr int max_nonlinear_buffer_count = 48;
-
-                    Node *n = node.get();
-
-                    struct  tree_info {
-                        size_t buffer_size;
-                        int num_buffers;
-                        bool is_linear;
-                    };
-                    NodeIterator<> it(n);
-                    dim4 outdim = out.dims();
-                    tree_info info = accumulate(it, NodeIterator<>(),
-                                                tree_info{0, 0, true},
-                                                [=](tree_info& prev, Node& n) {
-                                                    if(n.isBuffer()) {
-                                                        auto& buf_node = static_cast<BufferNode&>(n);
-                                                        prev.buffer_size += buf_node.getBytes();
-                                                        prev.num_buffers++;
-                                                        prev.is_linear &= buf_node.isLinear((dim_t*)outdim.get());
-                                                    }
-                                                    // getBytes returns the size of the data Array. Sub arrays will
-                                                    // be represented by their parent size.
-                                                    return prev;
-                                                });
-                    isBufferLimit = 2 * info.buffer_size > lock_bytes;
-                    isParamLimit = isNvidia &&
-                                   !info.is_linear &&
-                                   info.num_buffers >= max_nonlinear_buffer_count;
-
-                    if (isBufferLimit || isParamLimit) {
-                        out.eval();
-                    }
-                }
+template<typename T>
+Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    verifyDoubleSupport<T>();
+    Array<T> out = Array<T>(dims, node);
+
+    if (evalFlag()) {
+        if (node->getHeight() >= (int)getMaxJitSize()) {
+            out.eval();
+        } else {
+            size_t alloc_bytes, alloc_buffers;
+            size_t lock_bytes, lock_buffers;
+
+            deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
+                             &lock_buffers);
+
+            bool isBufferLimit =
+                lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
+
+            bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA;
+            // We eval in the following cases.
+            // 1. Too many bytes are locked up by JIT causing memory pressure.
+            // Too many bytes is assumed to be half of all bytes allocated so
+            // far.
+            // 2. Too many buffers in a nonlinear kernel cause param space
+            // overflow. Too many buffers comes out to be about 48 (49 including
+            // output). Too many buffers can occur in a tree of size 24 in the
+            // worst case scenario. This error only happens on nvidia devices.
+            // TODO: Find better solution than the following emperical solution.
+            bool isParamLimit = (isNvidia && node->getHeight() > 24);
+            if (isParamLimit || isBufferLimit) {
+                // This is the maximum non-linear buffers that are allowed in
+                // the parameter list
+                constexpr int max_nonlinear_buffer_count = 48;
+
+                Node *n = node.get();
+
+                struct tree_info {
+                    size_t buffer_size;
+                    int num_buffers;
+                    bool is_linear;
+                };
+                NodeIterator<> it(n);
+                dim4 outdim    = out.dims();
+                tree_info info = accumulate(
+                    it, NodeIterator<>(), tree_info{0, 0, true},
+                    [=](tree_info &prev, Node &n) {
+                        if (n.isBuffer()) {
+                            auto &buf_node = static_cast<BufferNode &>(n);
+                            prev.buffer_size += buf_node.getBytes();
+                            prev.num_buffers++;
+                            prev.is_linear &=
+                                buf_node.isLinear((dim_t *)outdim.get());
+                        }
+                        // getBytes returns the size of the data Array. Sub
+                        // arrays will be represented by their parent size.
+                        return prev;
+                    });
+                isBufferLimit = 2 * info.buffer_size > lock_bytes;
+                isParamLimit  = isNvidia && !info.is_linear &&
+                               info.num_buffers >= max_nonlinear_buffer_count;
+
+                if (isBufferLimit || isParamLimit) { out.eval(); }
             }
         }
-
-        return out;
     }
 
-    template<typename T>
-    Array<T> createSubArray(const Array<T>& parent,
-                            const vector<af_seq> &index,
-                            bool copy)
-    {
-        parent.eval();
-
-        dim4 dDims = parent.getDataDims();
-        dim4 dStrides = calcStrides(dDims);
-        dim4 parent_strides = parent.strides();
+    return out;
+}
 
-        if (dStrides != parent_strides) {
-            const Array<T> parentCopy = copyArray(parent);
-            return createSubArray(parentCopy, index, copy);
-        }
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
+                        bool copy) {
+    parent.eval();
 
-        dim4 pDims = parent.dims();
+    dim4 dDims          = parent.getDataDims();
+    dim4 dStrides       = calcStrides(dDims);
+    dim4 parent_strides = parent.strides();
 
-        dim4 dims    = toDims  (index, pDims);
-        dim4 strides = toStride (index, dDims);
+    if (dStrides != parent_strides) {
+        const Array<T> parentCopy = copyArray(parent);
+        return createSubArray(parentCopy, index, copy);
+    }
 
-        // Find total offsets after indexing
-        dim4 offsets = toOffset(index, pDims);
-        dim_t offset = parent.getOffset();
-        for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+    dim4 pDims = parent.dims();
 
-        Array<T> out = Array<T>(parent, dims, offset, strides);
+    dim4 dims    = toDims(index, pDims);
+    dim4 strides = toStride(index, dDims);
 
-        if (!copy) return out;
+    // Find total offsets after indexing
+    dim4 offsets = toOffset(index, pDims);
+    dim_t offset = parent.getOffset();
+    for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
 
-        if (strides[0] != 1 ||
-            strides[1] <  0 ||
-            strides[2] <  0 ||
-            strides[3] <  0) {
+    Array<T> out = Array<T>(parent, dims, offset, strides);
 
-            out = copyArray(out);
-        }
+    if (!copy) return out;
 
-        return out;
+    if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
+        out = copyArray(out);
     }
 
-    template<typename T>
-    Array<T>
-    createHostDataArray(const dim4 &size, const T * const data)
-    {
-        verifyDoubleSupport<T>();
-        return Array<T>(size, data);
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T>
-    createDeviceDataArray(const dim4 &size, const void *data, bool copy)
-    {
-        verifyDoubleSupport<T>();
+template<typename T>
+Array<T> createHostDataArray(const dim4 &size, const T *const data) {
+    verifyDoubleSupport<T>();
+    return Array<T>(size, data);
+}
 
-        return Array<T>(size, (cl_mem)(data), 0, copy);
-    }
+template<typename T>
+Array<T> createDeviceDataArray(const dim4 &size, const void *data, bool copy) {
+    verifyDoubleSupport<T>();
 
-    template<typename T>
-    Array<T>
-    createValueArray(const dim4 &size, const T& value)
-    {
-        verifyDoubleSupport<T>();
-        return createScalarNode<T>(size, value);
-    }
-
-    template<typename T>
-    Array<T>
-    createEmptyArray(const dim4 &size)
-    {
-        verifyDoubleSupport<T>();
-        return Array<T>(size);
-    }
+    return Array<T>(size, (cl_mem)(data), 0, copy);
+}
 
-    template<typename T>
-    Array<T>
-    createParamArray(Param &tmp, bool owner)
-    {
-        verifyDoubleSupport<T>();
-        return Array<T>(tmp, owner);
-    }
+template<typename T>
+Array<T> createValueArray(const dim4 &size, const T &value) {
+    verifyDoubleSupport<T>();
+    return createScalarNode<T>(size, value);
+}
 
-    template<typename T>
-    void
-    destroyArray(Array<T> *A)
-    {
-        delete A;
-    }
+template<typename T>
+Array<T> createEmptyArray(const dim4 &size) {
+    verifyDoubleSupport<T>();
+    return Array<T>(size);
+}
 
-    template<typename T>
-    void
-    writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes)
-    {
-        if (!arr.isOwner()) {
-            arr = copyArray<T>(arr);
-        }
+template<typename T>
+Array<T> createParamArray(Param &tmp, bool owner) {
+    verifyDoubleSupport<T>();
+    return Array<T>(tmp, owner);
+}
 
-        getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE,
-                                      arr.getOffset(),
-                                      bytes,
-                                      data);
+template<typename T>
+void destroyArray(Array<T> *A) {
+    delete A;
+}
 
-        return;
-    }
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data,
+                        const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
 
-    template<typename T>
-    void
-    writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes)
-    {
-        if (!arr.isOwner()) {
-            arr = copyArray<T>(arr);
-        }
+    getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(), bytes,
+                                  data);
 
-        Buffer& buf = *arr.get();
+    return;
+}
 
-        clRetainMemObject((cl_mem)(data));
-        Buffer data_buf = Buffer((cl_mem)(data));
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
 
-        getQueue().enqueueCopyBuffer(data_buf, buf,
-                                     0, (size_t)arr.getOffset(),
-                                     bytes);
+    Buffer &buf = *arr.get();
 
-        return;
-    }
+    clRetainMemObject((cl_mem)(data));
+    Buffer data_buf = Buffer((cl_mem)(data));
 
-    template<typename T>
-    void
-    Array<T>::setDataDims(const dim4 &new_dims)
-    {
-        modDims(new_dims);
-        data_dims = new_dims;
-        if (node->isBuffer()) {
-            node = bufferNodePtr<T>();
-        }
-    }
+    getQueue().enqueueCopyBuffer(data_buf, buf, 0, (size_t)arr.getOffset(),
+                                 bytes);
 
-#define INSTANTIATE(T)                                                  \
-    template       Array<T>  createHostDataArray<T>   (const dim4 &size, const T * const data); \
-    template       Array<T>  createDeviceDataArray<T> (const dim4 &size, const void *data, bool copy); \
-    template       Array<T>  createValueArray<T>      (const dim4 &size, const T &value); \
-    template       Array<T>  createEmptyArray<T>      (const dim4 &size); \
-    template       Array<T>  createParamArray<T>      (Param &tmp, bool owner);    \
-    template       Array<T>  createSubArray<T>        (const Array<T> &parent, \
-                                                       const vector<af_seq> &index, \
-                                                       bool copy);      \
-    template       void      destroyArray<T>          (Array<T> *A);    \
-    template       Array<T>  createNodeArray<T>       (const dim4 &size, Node_ptr node); \
-    template       Array<T>::Array(dim4 dims, dim4 strides, dim_t offset, \
-                                   const T * const in_data,             \
-                                   bool is_device);                     \
-    template       Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy); \
-    template       Array<T>::~Array        ();                          \
-    template       Node_ptr Array<T>::getNode() const;                  \
-    template       void Array<T>::eval();                               \
-    template       void Array<T>::eval() const;                         \
-    template       Buffer* Array<T>::device();                      \
-    template       void      writeHostDataArray<T>    (Array<T> &arr, const T * const data, \
-                                                       const size_t bytes); \
-    template       void      writeDeviceDataArray<T>  (Array<T> &arr, const void * const data, \
-                                                       const size_t bytes); \
-    template       void      evalMultiple<T>     (vector<Array<T>*> arrays); \
-    template       void Array<T>::setDataDims(const dim4 &new_dims);    \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return;
+}
 
+template<typename T>
+void Array<T>::setDataDims(const dim4 &new_dims) {
+    modDims(new_dims);
+    data_dims = new_dims;
+    if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
+
+#define INSTANTIATE(T)                                                       \
+    template Array<T> createHostDataArray<T>(const dim4 &size,               \
+                                             const T *const data);           \
+    template Array<T> createDeviceDataArray<T>(const dim4 &size,             \
+                                               const void *data, bool copy); \
+    template Array<T> createValueArray<T>(const dim4 &size, const T &value); \
+    template Array<T> createEmptyArray<T>(const dim4 &size);                 \
+    template Array<T> createParamArray<T>(Param & tmp, bool owner);          \
+    template Array<T> createSubArray<T>(                                     \
+        const Array<T> &parent, const vector<af_seq> &index, bool copy);     \
+    template void destroyArray<T>(Array<T> * A);                             \
+    template Array<T> createNodeArray<T>(const dim4 &size, Node_ptr node);   \
+    template Array<T>::Array(dim4 dims, dim4 strides, dim_t offset,          \
+                             const T *const in_data, bool is_device);        \
+    template Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset,       \
+                             bool copy);                                     \
+    template Array<T>::~Array();                                             \
+    template Node_ptr Array<T>::getNode() const;                             \
+    template void Array<T>::eval();                                          \
+    template void Array<T>::eval() const;                                    \
+    template Buffer *Array<T>::device();                                     \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data, \
+                                        const size_t bytes);                 \
+    template void writeDeviceDataArray<T>(                                   \
+        Array<T> & arr, const void *const data, const size_t bytes);         \
+    template void evalMultiple<T>(vector<Array<T> *> arrays);                \
+    template void Array<T>::setDataDims(const dim4 &new_dims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index a8a27271be..04a58c8082 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -8,289 +8,274 @@
  ********************************************************/
 
 #pragma once
-#include <platform.hpp>
-#include <af/dim4.hpp>
-#include <common/ArrayInfo.hpp>
-#include <traits.hpp>
-#include <backend.hpp>
-#include <types.hpp>
-#include <traits.hpp>
 #include <Param.hpp>
+#include <backend.hpp>
+#include <common/ArrayInfo.hpp>
 #include <common/jit/Node.hpp>
+#include <err_opencl.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <af/dim4.hpp>
 #include <memory>
-#include <err_opencl.hpp>
-
-namespace opencl
-{
-    typedef std::shared_ptr<cl::Buffer> Buffer_ptr;
-    using af::dim4;
-    template<typename T> class Array;
 
-    template<typename T>
-    void evalMultiple(std::vector<Array<T> *> arrays);
+namespace opencl {
+typedef std::shared_ptr<cl::Buffer> Buffer_ptr;
+using af::dim4;
+template<typename T>
+class Array;
 
-    void evalNodes(Param &out, common::Node *node);
-    void evalNodes(std::vector<Param> &outputs, std::vector<common::Node *> nodes);
+template<typename T>
+void evalMultiple(std::vector<Array<T> *> arrays);
 
-    /// Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
+void evalNodes(Param &out, common::Node *node);
+void evalNodes(std::vector<Param> &outputs, std::vector<common::Node *> nodes);
 
-    /// Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    Array<T> createValueArray(const af::dim4 &size, const T& value);
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
 
-    /// Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    Array<T> createHostDataArray(const af::dim4 &size, const T * const data);
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createValueArray(const af::dim4 &size, const T &value);
 
-    template<typename T>
-    Array<T> createDeviceDataArray(const af::dim4 &size, const void *data, bool copy = false);
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createHostDataArray(const af::dim4 &size, const T *const data);
 
-    template<typename T>
-    Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
-                                const T * const in_data, bool is_device) {
-        return Array<T>(dims, strides, offset, in_data, is_device);
-    }
+template<typename T>
+Array<T> createDeviceDataArray(const af::dim4 &size, const void *data,
+                               bool copy = false);
 
-    /// Copies data to an existing Array object from a host pointer
-    template<typename T>
-    void writeHostDataArray(Array<T> &arr, const T * const data, const size_t bytes);
-
-    /// Copies data to an existing Array object from a device pointer
-    template<typename T>
-    void writeDeviceDataArray(Array<T> &arr, const void * const data, const size_t bytes);
-
-    /// Creates an empty array of a given size. No data is initialized
-    ///
-    /// \param[in] size The dimension of the output array
-    template<typename T>
-    Array<T> createEmptyArray(const af::dim4 &size);
-
-    /// Create an Array object from Param object.
-    ///
-    /// \param[in] in    The Param array that is created.
-    /// \param[in] owner If true, the new Array<T> object is the owner of the data. If false
-    ///                  the Array<T> will not delete the object on destruction
-    template<typename T>
-    Array<T> createParamArray(Param &tmp, bool owner);
-
-    template<typename T>
-    Array<T> createSubArray(const Array<T>& parent,
-                            const std::vector<af_seq> &index,
-                            bool copy=true);
-
-    /// Creates a new Array object on the heap and returns a reference to it.
-    template<typename T>
-    void destroyArray(Array<T> *A);
-
-    template<typename T>
-    void *getDevicePtr(const Array<T>& arr)
-    {
-        const cl::Buffer *buf = arr.device();
-        if (!buf) return NULL;
-        memLock((T *)buf);
-        cl_mem mem = (*buf)();
-        return (void *)mem;
-    }
+template<typename T>
+Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
+                            const T *const in_data, bool is_device) {
+    return Array<T>(dims, strides, offset, in_data, is_device);
+}
 
-    template<typename T>
-    void *getRawPtr(const Array<T>& arr)
-    {
-        const cl::Buffer *buf = arr.get();
-        if (!buf) return NULL;
-        cl_mem mem = (*buf)();
-        return (void *)mem;
-    }
+/// Copies data to an existing Array object from a host pointer
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data, const size_t bytes);
+
+/// Copies data to an existing Array object from a device pointer
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes);
+
+/// Creates an empty array of a given size. No data is initialized
+///
+/// \param[in] size The dimension of the output array
+template<typename T>
+Array<T> createEmptyArray(const af::dim4 &size);
+
+/// Create an Array object from Param object.
+///
+/// \param[in] in    The Param array that is created.
+/// \param[in] owner If true, the new Array<T> object is the owner of the data.
+/// If false
+///                  the Array<T> will not delete the object on destruction
+template<typename T>
+Array<T> createParamArray(Param &tmp, bool owner);
+
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent,
+                        const std::vector<af_seq> &index, bool copy = true);
+
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+void destroyArray(Array<T> *A);
+
+template<typename T>
+void *getDevicePtr(const Array<T> &arr) {
+    const cl::Buffer *buf = arr.device();
+    if (!buf) return NULL;
+    memLock((T *)buf);
+    cl_mem mem = (*buf)();
+    return (void *)mem;
+}
 
-    template<typename T>
-    class Array
-    {
-        ArrayInfo info; // This must be the first element of Array<T>
-        Buffer_ptr  data;
-        af::dim4 data_dims;
+template<typename T>
+void *getRawPtr(const Array<T> &arr) {
+    const cl::Buffer *buf = arr.get();
+    if (!buf) return NULL;
+    cl_mem mem = (*buf)();
+    return (void *)mem;
+}
 
-        common::Node_ptr node;
-        bool ready;
-        bool owner;
+template<typename T>
+class Array {
+    ArrayInfo info;  // This must be the first element of Array<T>
+    Buffer_ptr data;
+    af::dim4 data_dims;
 
-        Array(af::dim4 dims);
+    common::Node_ptr node;
+    bool ready;
+    bool owner;
 
-        Array(const Array<T>& parnt, const dim4 &dims, const dim_t &offset, const dim4 &stride);
-        Array(Param &tmp, bool owner);
-        explicit Array(af::dim4 dims, common::Node_ptr n);
-        explicit Array(af::dim4 dims, const T * const in_data);
-        explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
+    Array(af::dim4 dims);
 
-    public:
+    Array(const Array<T> &parnt, const dim4 &dims, const dim_t &offset,
+          const dim4 &stride);
+    Array(Param &tmp, bool owner);
+    explicit Array(af::dim4 dims, common::Node_ptr n);
+    explicit Array(af::dim4 dims, const T *const in_data);
+    explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
 
-        Array(af::dim4 dims, af::dim4 strides, dim_t offset,
-              const T * const in_data, bool is_device = false);
+   public:
+    Array(af::dim4 dims, af::dim4 strides, dim_t offset, const T *const in_data,
+          bool is_device = false);
 
-        void resetInfo(const af::dim4& dims)        { info.resetInfo(dims);         }
-        void resetDims(const af::dim4& dims)        { info.resetDims(dims);         }
-        void modDims(const af::dim4 &newDims)       { info.modDims(newDims);        }
-        void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides);  }
-        void setId(int id)                          { info.setId(id);               }
+    void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
+    void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
+    void modDims(const af::dim4 &newDims) { info.modDims(newDims); }
+    void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides); }
+    void setId(int id) { info.setId(id); }
 
-#define INFO_FUNC(RET_TYPE, NAME)   \
+#define INFO_FUNC(RET_TYPE, NAME) \
     RET_TYPE NAME() const { return info.NAME(); }
 
-        INFO_FUNC(const af_dtype& ,getType)
-        INFO_FUNC(const af::dim4& ,strides)
-        INFO_FUNC(size_t          ,elements)
-        INFO_FUNC(size_t          ,ndims)
-        INFO_FUNC(const af::dim4& ,dims )
-        INFO_FUNC(int             ,getDevId)
+    INFO_FUNC(const af_dtype &, getType)
+    INFO_FUNC(const af::dim4 &, strides)
+    INFO_FUNC(size_t, elements)
+    INFO_FUNC(size_t, ndims)
+    INFO_FUNC(const af::dim4 &, dims)
+    INFO_FUNC(int, getDevId)
 
 #undef INFO_FUNC
 
-#define INFO_IS_FUNC(NAME)\
-    bool NAME () const { return info.NAME(); }
-
-        INFO_IS_FUNC(isEmpty);
-        INFO_IS_FUNC(isScalar);
-        INFO_IS_FUNC(isRow);
-        INFO_IS_FUNC(isColumn);
-        INFO_IS_FUNC(isVector);
-        INFO_IS_FUNC(isComplex);
-        INFO_IS_FUNC(isReal);
-        INFO_IS_FUNC(isDouble);
-        INFO_IS_FUNC(isSingle);
-        INFO_IS_FUNC(isRealFloating);
-        INFO_IS_FUNC(isFloating);
-        INFO_IS_FUNC(isInteger);
-        INFO_IS_FUNC(isBool);
-        INFO_IS_FUNC(isLinear);
-        INFO_IS_FUNC(isSparse);
+#define INFO_IS_FUNC(NAME) \
+    bool NAME() const { return info.NAME(); }
+
+    INFO_IS_FUNC(isEmpty);
+    INFO_IS_FUNC(isScalar);
+    INFO_IS_FUNC(isRow);
+    INFO_IS_FUNC(isColumn);
+    INFO_IS_FUNC(isVector);
+    INFO_IS_FUNC(isComplex);
+    INFO_IS_FUNC(isReal);
+    INFO_IS_FUNC(isDouble);
+    INFO_IS_FUNC(isSingle);
+    INFO_IS_FUNC(isRealFloating);
+    INFO_IS_FUNC(isFloating);
+    INFO_IS_FUNC(isInteger);
+    INFO_IS_FUNC(isBool);
+    INFO_IS_FUNC(isLinear);
+    INFO_IS_FUNC(isSparse);
 
 #undef INFO_IS_FUNC
-        ~Array();
+    ~Array();
 
-        bool isReady() const { return ready; }
-        bool isOwner() const { return owner; }
+    bool isReady() const { return ready; }
+    bool isOwner() const { return owner; }
 
-        void eval();
-        void eval() const;
+    void eval();
+    void eval() const;
 
-        cl::Buffer* device();
-        cl::Buffer* device() const
-        {
-            return const_cast<Array<T>*>(this)->device();
-        }
+    cl::Buffer *device();
+    cl::Buffer *device() const {
+        return const_cast<Array<T> *>(this)->device();
+    }
 
-        //FIXME: This should do a copy if it is not owner. You do not want to overwrite parents data
-        cl::Buffer *get()
-        {
-            if (!isReady()) eval();
-            return data.get();
-        }
+    // FIXME: This should do a copy if it is not owner. You do not want to
+    // overwrite parents data
+    cl::Buffer *get() {
+        if (!isReady()) eval();
+        return data.get();
+    }
 
-        const cl::Buffer *get() const
-        {
-            if (!isReady()) eval();
-            return data.get();
-        }
+    const cl::Buffer *get() const {
+        if (!isReady()) eval();
+        return data.get();
+    }
 
-        int useCount() const
-        {
-            if (!isReady()) eval();
-            return data.use_count();
-        }
+    int useCount() const {
+        if (!isReady()) eval();
+        return data.use_count();
+    }
 
-        dim_t getOffset() const
-        {
-            return info.getOffset();
-        }
+    dim_t getOffset() const { return info.getOffset(); }
 
-        Buffer_ptr getData() const
-        {
-            return data;
-        }
+    Buffer_ptr getData() const { return data; }
 
-        dim4 getDataDims() const
-        {
-            return data_dims;
-        }
+    dim4 getDataDims() const { return data_dims; }
 
-        void setDataDims(const dim4 &new_dims);
+    void setDataDims(const dim4 &new_dims);
 
-        size_t getAllocatedBytes() const
-        {
-            if (!isReady()) return 0;
-            size_t bytes = memoryManager().allocated(data.get());
-            // External device poitner
-            if (bytes == 0 && data.get()) {
-                return data_dims.elements() * sizeof(T);
-            }
-            return  bytes;
+    size_t getAllocatedBytes() const {
+        if (!isReady()) return 0;
+        size_t bytes = memoryManager().allocated(data.get());
+        // External device poitner
+        if (bytes == 0 && data.get()) {
+            return data_dims.elements() * sizeof(T);
         }
+        return bytes;
+    }
 
-        operator Param() const
-        {
-            KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
-                           {strides()[0], strides()[1], strides()[2], strides()[3]},
-                           getOffset()};
+    operator Param() const {
+        KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
+                       {strides()[0], strides()[1], strides()[2], strides()[3]},
+                       getOffset()};
 
-            Param out{(cl::Buffer *)this->get(), info};
-            return out;
-        }
+        Param out{(cl::Buffer *)this->get(), info};
+        return out;
+    }
 
-        operator KParam() const
-        {
-            KParam kinfo = {{dims()[0], dims()[1], dims()[2], dims()[3]},
-                            {strides()[0], strides()[1], strides()[2], strides()[3]},
-                            getOffset()};
+    operator KParam() const {
+        KParam kinfo = {
+            {dims()[0], dims()[1], dims()[2], dims()[3]},
+            {strides()[0], strides()[1], strides()[2], strides()[3]},
+            getOffset()};
 
-            return kinfo;
-        }
+        return kinfo;
+    }
 
-        common::Node_ptr getNode() const;
-        common::Node_ptr getNode();
-
-    public:
-        std::shared_ptr<T> getMappedPtr() const
-        {
-            auto func = [=] (void* ptr) {
-                if(ptr != nullptr) {
-                    getQueue().enqueueUnmapMemObject(*data, ptr);
-                    ptr = nullptr;
-                }
-            };
-
-            T *ptr = nullptr;
-            if(ptr == nullptr) {
-                ptr = (T*)getQueue().enqueueMapBuffer(*const_cast<cl::Buffer*>(get()),
-                                                      true, CL_MAP_READ|CL_MAP_WRITE,
-                                                      getOffset() * sizeof(T),
-                                                      (getDataDims().elements() - getOffset())
-                                                      * sizeof(T));
-            }
+    common::Node_ptr getNode() const;
+    common::Node_ptr getNode();
 
-            return std::shared_ptr<T>(ptr, func);
+   public:
+    std::shared_ptr<T> getMappedPtr() const {
+        auto func = [=](void *ptr) {
+            if (ptr != nullptr) {
+                getQueue().enqueueUnmapMemObject(*data, ptr);
+                ptr = nullptr;
+            }
+        };
+
+        T *ptr = nullptr;
+        if (ptr == nullptr) {
+            ptr = (T *)getQueue().enqueueMapBuffer(
+                *const_cast<cl::Buffer *>(get()), true,
+                CL_MAP_READ | CL_MAP_WRITE, getOffset() * sizeof(T),
+                (getDataDims().elements() - getOffset()) * sizeof(T));
         }
 
+        return std::shared_ptr<T>(ptr, func);
+    }
 
-        friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
+    friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
-        friend Array<T> createValueArray<T>(const af::dim4 &size, const T& value);
-        friend Array<T> createHostDataArray<T>(const af::dim4 &size, const T * const data);
-        friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, const void *data, bool copy);
-        friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides, dim_t offset,
-                                              const T * const in_data, bool is_device);
+    friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
+    friend Array<T> createHostDataArray<T>(const af::dim4 &size,
+                                           const T *const data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size,
+                                             const void *data, bool copy);
+    friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
+                                          dim_t offset, const T *const in_data,
+                                          bool is_device);
 
-        friend Array<T> createEmptyArray<T>(const af::dim4 &size);
-        friend Array<T> createParamArray<T>(Param &tmp, bool owner);
-        friend Array<T> createNodeArray<T>(const af::dim4 &dims, common::Node_ptr node);
+    friend Array<T> createEmptyArray<T>(const af::dim4 &size);
+    friend Array<T> createParamArray<T>(Param &tmp, bool owner);
+    friend Array<T> createNodeArray<T>(const af::dim4 &dims,
+                                       common::Node_ptr node);
 
-        friend Array<T> createSubArray<T>(const Array<T>& parent,
-                                          const std::vector<af_seq> &index,
-                                          bool copy);
+    friend Array<T> createSubArray<T>(const Array<T> &parent,
+                                      const std::vector<af_seq> &index,
+                                      bool copy);
 
-        friend void destroyArray<T>(Array<T> *arr);
-        friend void *getDevicePtr<T>(const Array<T>& arr);
-        friend void *getRawPtr<T>(const Array<T>& arr);
-    };
+    friend void destroyArray<T>(Array<T> *arr);
+    friend void *getDevicePtr<T>(const Array<T> &arr);
+    friend void *getRawPtr<T>(const Array<T> &arr);
+};
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
index fe20fcf210..954e9e2b6b 100644
--- a/src/backend/opencl/GraphicsResourceManager.cpp
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <platform.hpp>
 #include <GraphicsResourceManager.hpp>
+#include <platform.hpp>
 
 namespace opencl {
 GraphicsResourceManager::ShrdResVector
-GraphicsResourceManager::registerResources(std::vector<uint32_t> resources)
-{
+GraphicsResourceManager::registerResources(std::vector<uint32_t> resources) {
     ShrdResVector output;
 
-    for (auto id: resources)
-        output.emplace_back(new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, id, NULL));
+    for (auto id : resources)
+        output.emplace_back(
+            new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, id, NULL));
 
     return output;
 }
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index fdf5dce3b4..8924661572 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -14,23 +14,21 @@
 #include <map>
 #include <vector>
 
-namespace cl
-{
+namespace cl {
 class Buffer;
 }
 
 namespace opencl {
-class GraphicsResourceManager :
-    public common::InteropManager<GraphicsResourceManager, cl::Buffer>
-{
-    public:
-        using ShrdResVector = std::vector< std::shared_ptr<cl::Buffer> >;
+class GraphicsResourceManager
+    : public common::InteropManager<GraphicsResourceManager, cl::Buffer> {
+   public:
+    using ShrdResVector = std::vector<std::shared_ptr<cl::Buffer>>;
 
-        GraphicsResourceManager() {}
-        ShrdResVector registerResources(std::vector<uint32_t> resources);
+    GraphicsResourceManager() {}
+    ShrdResVector registerResources(std::vector<uint32_t> resources);
 
-    protected:
-        GraphicsResourceManager(GraphicsResourceManager const&);
-        void operator=(GraphicsResourceManager const&);
+   protected:
+    GraphicsResourceManager(GraphicsResourceManager const&);
+    void operator=(GraphicsResourceManager const&);
 };
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index 60d8febaff..6be8d546ab 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <Param.hpp>
-#include <platform.hpp>
 #include <kernel/KParam.hpp>
+#include <platform.hpp>
+#include <af/defines.h>
 
-namespace opencl
-{
-    Param::Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
-    Param::Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_){}
+namespace opencl {
+Param::Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
+Param::Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_) {}
 
-    Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]) {
-         Param out;
-         out.data = new cl::Buffer(mem);
-         out.info.offset = off;
-         for (int i = 0; i < 4; i++) {
-             out.info.dims[i] = dims[i];
-             out.info.strides[i] = strides[i];
-         }
-         return out;
+Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]) {
+    Param out;
+    out.data        = new cl::Buffer(mem);
+    out.info.offset = off;
+    for (int i = 0; i < 4; i++) {
+        out.info.dims[i]    = dims[i];
+        out.info.strides[i] = strides[i];
     }
+    return out;
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 0a671a42b8..484ef71030 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -8,27 +8,25 @@
  ********************************************************/
 
 #pragma once
-#include <platform.hpp>
 #include <kernel/KParam.hpp>
+#include <platform.hpp>
 
-namespace opencl
-{
-
-    struct Param
-    {
-        cl::Buffer *data;
-        KParam info;
-        Param& operator=(const Param& other) = default;
-        Param(const Param& other) = default;
-        Param(Param&& other) = default;
+namespace opencl {
 
-        // AF_DEPRECATED("Use Array<T>")
-        Param();
-        // AF_DEPRECATED("Use Array<T>")
-        Param(cl::Buffer *data_, KParam info_);
-        ~Param() = default;
-    };
+struct Param {
+    cl::Buffer* data;
+    KParam info;
+    Param& operator=(const Param& other) = default;
+    Param(const Param& other)            = default;
+    Param(Param&& other)                 = default;
 
     // AF_DEPRECATED("Use Array<T>")
-    Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]);
-}
+    Param();
+    // AF_DEPRECATED("Use Array<T>")
+    Param(cl::Buffer* data_, KParam info_);
+    ~Param() = default;
+};
+
+// AF_DEPRECATED("Use Array<T>")
+Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]);
+}  // namespace opencl
diff --git a/src/backend/opencl/all.cpp b/src/backend/opencl/all.cpp
index 3c9513db4c..271ca86499 100644
--- a/src/backend/opencl/all.cpp
+++ b/src/backend/opencl/all.cpp
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    //alltrue
-    INSTANTIATE(af_and_t, float  , char)
-    INSTANTIATE(af_and_t, double , char)
-    INSTANTIATE(af_and_t, cfloat , char)
-    INSTANTIATE(af_and_t, cdouble, char)
-    INSTANTIATE(af_and_t, int    , char)
-    INSTANTIATE(af_and_t, uint   , char)
-    INSTANTIATE(af_and_t, intl   , char)
-    INSTANTIATE(af_and_t, uintl  , char)
-    INSTANTIATE(af_and_t, char   , char)
-    INSTANTIATE(af_and_t, uchar  , char)
-    INSTANTIATE(af_and_t, short  , char)
-    INSTANTIATE(af_and_t, ushort , char)
-}
+namespace opencl {
+// alltrue
+INSTANTIATE(af_and_t, float, char)
+INSTANTIATE(af_and_t, double, char)
+INSTANTIATE(af_and_t, cfloat, char)
+INSTANTIATE(af_and_t, cdouble, char)
+INSTANTIATE(af_and_t, int, char)
+INSTANTIATE(af_and_t, uint, char)
+INSTANTIATE(af_and_t, intl, char)
+INSTANTIATE(af_and_t, uintl, char)
+INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, uchar, char)
+INSTANTIATE(af_and_t, short, char)
+INSTANTIATE(af_and_t, ushort, char)
+}  // namespace opencl
diff --git a/src/backend/opencl/anisotropic_diffusion.cpp b/src/backend/opencl/anisotropic_diffusion.cpp
index 676a421ae3..b5ce054750 100644
--- a/src/backend/opencl/anisotropic_diffusion.cpp
+++ b/src/backend/opencl/anisotropic_diffusion.cpp
@@ -7,29 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <copy.hpp>
 #include <anisotropic_diffusion.hpp>
+#include <copy.hpp>
 #include <kernel/anisotropic_diffusion.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
+namespace opencl {
 template<typename T>
-void anisotropicDiffusion(Array<T>& inout, const float dt,
-                          const float mct, const af::fluxFunction fftype,
-                          const af::diffusionEq eq)
-{
-    if (eq==AF_DIFFUSION_MCDE)
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
+                          const af::diffusionEq eq) {
+    if (eq == AF_DIFFUSION_MCDE)
         kernel::anisotropicDiffusion<T, true>(inout, dt, mct, fftype);
     else
         kernel::anisotropicDiffusion<T, false>(inout, dt, mct, fftype);
 }
 
-#define INSTANTIATE(T)\
-template void anisotropicDiffusion<T>(Array<T> &inout, const float dt, const float mct,\
-                                      const af::fluxFunction fftype, const af::diffusionEq eq);
+#define INSTANTIATE(T)                                     \
+    template void anisotropicDiffusion<T>(                 \
+        Array<T> & inout, const float dt, const float mct, \
+        const af::fluxFunction fftype, const af::diffusionEq eq);
 
 INSTANTIATE(double)
-INSTANTIATE( float)
-}
+INSTANTIATE(float)
+}  // namespace opencl
diff --git a/src/backend/opencl/anisotropic_diffusion.hpp b/src/backend/opencl/anisotropic_diffusion.hpp
index 7fb714eb8a..816cae3359 100644
--- a/src/backend/opencl/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/anisotropic_diffusion.hpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 template<typename T>
-void anisotropicDiffusion(Array<T>& inout, const float dt,
-                          const float mct, const af::fluxFunction fftype,
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
                           const af::diffusionEq eq);
 }
diff --git a/src/backend/opencl/any.cpp b/src/backend/opencl/any.cpp
index e8c6de51ed..2636a8c26f 100644
--- a/src/backend/opencl/any.cpp
+++ b/src/backend/opencl/any.cpp
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    //anytrue
-    INSTANTIATE(af_or_t, float  , char)
-    INSTANTIATE(af_or_t, double , char)
-    INSTANTIATE(af_or_t, cfloat , char)
-    INSTANTIATE(af_or_t, cdouble, char)
-    INSTANTIATE(af_or_t, int    , char)
-    INSTANTIATE(af_or_t, uint   , char)
-    INSTANTIATE(af_or_t, intl   , char)
-    INSTANTIATE(af_or_t, uintl  , char)
-    INSTANTIATE(af_or_t, char   , char)
-    INSTANTIATE(af_or_t, uchar  , char)
-    INSTANTIATE(af_or_t, short  , char)
-    INSTANTIATE(af_or_t, ushort , char)
-}
+namespace opencl {
+// anytrue
+INSTANTIATE(af_or_t, float, char)
+INSTANTIATE(af_or_t, double, char)
+INSTANTIATE(af_or_t, cfloat, char)
+INSTANTIATE(af_or_t, cdouble, char)
+INSTANTIATE(af_or_t, int, char)
+INSTANTIATE(af_or_t, uint, char)
+INSTANTIATE(af_or_t, intl, char)
+INSTANTIATE(af_or_t, uintl, char)
+INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, uchar, char)
+INSTANTIATE(af_or_t, short, char)
+INSTANTIATE(af_or_t, ushort, char)
+}  // namespace opencl
diff --git a/src/backend/opencl/api.cpp b/src/backend/opencl/api.cpp
index 1508308c98..ef8b9f9894 100644
--- a/src/backend/opencl/api.cpp
+++ b/src/backend/opencl/api.cpp
@@ -2,11 +2,12 @@
 #include <af/opencl.h>
 
 namespace af {
-    template<> AFAPI cl_mem *array::device() const
-    {
-        cl_mem *mem_ptr = new cl_mem;
-        af_err err = af_get_device_ptr((void **)mem_ptr, get());
-        if (err != AF_SUCCESS) throw af::exception("Failed to get cl_mem from array object");
-        return mem_ptr;
-    }
+template<>
+AFAPI cl_mem *array::device() const {
+    cl_mem *mem_ptr = new cl_mem;
+    af_err err      = af_get_device_ptr((void **)mem_ptr, get());
+    if (err != AF_SUCCESS)
+        throw af::exception("Failed to get cl_mem from array object");
+    return mem_ptr;
 }
+}  // namespace af
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index edaffd6245..f425377e52 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -7,108 +7,89 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <approx.hpp>
 #include <kernel/approx.hpp>
+#include <af/dim4.hpp>
 #include <stdexcept>
 
-namespace opencl
-{
-    template<typename Ty, typename Tp>
-    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
-                 const Array<Tp> &xo, const int xdim,
-                 const Tp &xi_beg, const Tp &xi_step,
-                 const af_interp_type method, const float offGrid)
-    {
-        switch(method) {
+namespace opencl {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp, 1> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
+            kernel::approx1<Ty, Tp, 1>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp, 2> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
+            kernel::approx1<Ty, Tp, 2>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp, 3> (yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method);
-            break;
-        default:
+            kernel::approx1<Ty, Tp, 3>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
-        }
+        default: break;
     }
+}
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &zi,
-                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                      const af_interp_type method, const float offGrid)
-    {
-        af::dim4 odims = zi.dims();
-        odims[xdim] = xo.dims()[xdim];
-        odims[ydim] = xo.dims()[ydim];
+template<typename Ty, typename Tp>
+Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
+                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                  const af_interp_type method, const float offGrid) {
+    af::dim4 odims = zi.dims();
+    odims[xdim]    = xo.dims()[xdim];
+    odims[ydim]    = xo.dims()[ydim];
 
-        // Create output placeholder
-        Array<Ty> zo = createEmptyArray<Ty>(odims);
+    // Create output placeholder
+    Array<Ty> zo = createEmptyArray<Ty>(odims);
 
-        switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp, 1> (zo, zi,
-                                        xo, xdim, xi_beg, xi_step,
-                                        yo, ydim, yi_beg, yi_step,
-                                        offGrid, method);
+            kernel::approx2<Ty, Tp, 1>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp, 2> (zo, zi,
-                                        xo, xdim, xi_beg, xi_step,
-                                        yo, ydim, yi_beg, yi_step,
-                                        offGrid, method);
+            kernel::approx2<Ty, Tp, 2>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp, 3> (zo, zi,
-                                        xo, xdim, xi_beg, xi_step,
-                                        yo, ydim, yi_beg, yi_step,
-                                        offGrid, method);
-            break;
-        default:
+            kernel::approx2<Ty, Tp, 3>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
-        }
-
-        return zo;
+        default: break;
     }
 
-#define INSTANTIATE(Ty, Tp)                                         \
-    template void approx1<Ty, Tp>(Array<Ty> &yo,                    \
-                                  const Array<Ty> &yi,              \
-                                  const Array<Tp> &xo,              \
-                                  const int xdim,                   \
-                                  const Tp &xi_beg,                 \
-                                  const Tp &xi_step,                \
-                                  const af_interp_type method,      \
-                                  const float offGrid);             \
-    template Array<Ty> approx2<Ty, Tp>(const Array<Ty> &zi,         \
-                                       const Array<Tp> &xo,         \
-                                       const int xdim,              \
-                                       const Tp &xi_beg,            \
-                                       const Tp &xi_step,           \
-                                       const Array<Tp> &yo,         \
-                                       const int ydim,              \
-                                       const Tp &yi_beg,            \
-                                       const Tp &yi_step,           \
-                                       const af_interp_type method, \
-                                       const float offGrid);        \
+    return zo;
+}
+
+#define INSTANTIATE(Ty, Tp)                                       \
+    template void approx1<Ty, Tp>(                                \
+        Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const af_interp_type method, const float offGrid);        \
+    template Array<Ty> approx2<Ty, Tp>(                           \
+        const Array<Ty> &zi, const Array<Tp> &xo, const int xdim, \
+        const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo, \
+        const int ydim, const Tp &yi_beg, const Tp &yi_step,      \
+        const af_interp_type method, const float offGrid);
 
-    INSTANTIATE(float  , float )
-    INSTANTIATE(double , double)
-    INSTANTIATE(cfloat , float )
-    INSTANTIATE(cdouble, double)
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/approx.hpp b/src/backend/opencl/approx.hpp
index db26c4151a..4ae6362d64 100644
--- a/src/backend/opencl/approx.hpp
+++ b/src/backend/opencl/approx.hpp
@@ -9,17 +9,15 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename Ty, typename Tp>
-    void approx1(Array<Ty> &yo, const Array<Ty> &yi,
-                 const Array<Tp> &xo, const int xdim,
-                 const Tp &xi_beg, const Tp &xi_step,
-                 const af_interp_type method, const float offGrid);
+namespace opencl {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid);
 
-    template<typename Ty, typename Tp>
-    Array<Ty> approx2(const Array<Ty> &zi,
-                      const Array<Tp> &xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-                      const Array<Tp> &yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                      const af_interp_type method, const float offGrid);
-}
+template<typename Ty, typename Tp>
+Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
+                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
+                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
+                  const af_interp_type method, const float offGrid);
+}  // namespace opencl
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index 1d80db80de..3c1e68d7e5 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -7,16 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <binary.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-    template<typename T, af_op_t op>
-    Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<T, T, op>(lhs, rhs, odims);
-    }
+namespace opencl {
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
+                 const af::dim4 &odims) {
+    return createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 998947514a..11fd915e30 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -7,40 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <handle.hpp>
 #include <assign.hpp>
-#include <kernel/assign.hpp>
 #include <err_opencl.hpp>
+#include <handle.hpp>
+#include <kernel/assign.hpp>
 #include <memory.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
-{
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
     kernel::AssignKernelParam_t p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (dim_t x=0; x<4; ++x) {
-        if (idxrs[x].isSeq) {
-            seqs[x] = idxrs[x].idx.seq;
-        }
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
     }
 
     // retrieve dimensions, strides and offsets
     dim4 dDims = out.dims();
     // retrieve dimensions & strides for array
     // to which rhs is being copied to
-    dim4 dstOffs = toOffset(seqs, dDims);
-    dim4 dstStrds= toStride(seqs, dDims);
+    dim4 dstOffs  = toOffset(seqs, dDims);
+    dim4 dstStrds = toStride(seqs, dDims);
 
-    for (dim_t i=0; i<4; ++i) {
+    for (dim_t i = 0; i < 4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
         p.offs[i]  = dstOffs[i];
         p.strds[i] = dstStrds[i];
@@ -48,15 +44,14 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 
     Buffer* bPtrs[4];
 
-    std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         // set index pointers were applicable
         if (!p.isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
-            bPtrs[x] = idxArrs[x].get();
-        }
-        else {
+            bPtrs[x]   = idxArrs[x].get();
+        } else {
             // alloc an 1-element buffer to avoid OpenCL from failing
             bPtrs[x] = bufferAlloc(sizeof(uint));
         }
@@ -64,25 +59,26 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
 
     kernel::assign<T>(out, rhs, p, bPtrs);
 
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         if (p.isSeq[x]) bufferFree(bPtrs[x]);
     }
 }
 
-#define INSTANTIATE(T) \
-    template void assign<T>(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
+#define INSTANTIATE(T)                                                \
+    template void assign<T>(Array<T> & out, const af_index_t idxrs[], \
+                            const Array<T>& rhs);
 
 INSTANTIATE(cdouble)
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
-INSTANTIATE(float  )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/assign.hpp b/src/backend/opencl/assign.hpp
index b4f2db0340..4dd07541d5 100644
--- a/src/backend/opencl/assign.hpp
+++ b/src/backend/opencl/assign.hpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/index.h>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index 37d1808695..523e32f1c9 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -7,35 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <bilateral.hpp>
 #include <kernel/bilateral.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma)
-{
-    Array<outType> out       = createEmptyArray<outType>(in.dims());
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
+                         const float &c_sigma) {
+    Array<outType> out = createEmptyArray<outType>(in.dims());
     kernel::bilateral<inType, outType, isColor>(out, in, s_sigma, c_sigma);
     return out;
 }
 
-#define INSTANTIATE(inT, outT)\
-template Array<outT> bilateral<inT, outT,true >(const Array<inT> &in, const float &s_sigma, const float &c_sigma);\
-template Array<outT> bilateral<inT, outT,false>(const Array<inT> &in, const float &s_sigma, const float &c_sigma);
+#define INSTANTIATE(inT, outT)                                             \
+    template Array<outT> bilateral<inT, outT, true>(                       \
+        const Array<inT> &in, const float &s_sigma, const float &c_sigma); \
+    template Array<outT> bilateral<inT, outT, false>(                      \
+        const Array<inT> &in, const float &s_sigma, const float &c_sigma);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float ,  float)
-INSTANTIATE(char  ,  float)
-INSTANTIATE(int   ,  float)
-INSTANTIATE(uint  ,  float)
-INSTANTIATE(uchar ,  float)
-INSTANTIATE(short ,  float)
-INSTANTIATE(ushort,  float)
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/bilateral.hpp b/src/backend/opencl/bilateral.hpp
index d28e7b1249..ce587dca17 100644
--- a/src/backend/opencl/bilateral.hpp
+++ b/src/backend/opencl/bilateral.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma, const float &c_sigma);
+Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
+                         const float &c_sigma);
 
 }
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index b3ff5b5b5a..2e910a0432 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -8,52 +8,34 @@
  ********************************************************/
 
 #pragma once
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
-#include <math.hpp>
 #include <common/jit/BinaryNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-
-    template<typename To, typename Ti, af_op_t op>
-    struct BinOp
-    {
-        const char *name()
-        {
-            return "__invalid";
-        }
-    };
+namespace opencl {
 
-#define BINARY_TYPE_1(fn)                      \
-    template<typename To, typename Ti>          \
-    struct BinOp<To, Ti, af_##fn##_t>           \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__"#fn;                     \
-        }                                       \
-    };                                          \
-                                                \
-    template<typename To>                       \
-    struct BinOp<To, cfloat, af_##fn##_t>       \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn"f";                 \
-        }                                       \
-    };                                          \
-                                                \
-    template<typename To>                       \
-    struct BinOp<To, cdouble, af_##fn##_t>      \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn;                    \
-        }                                       \
-    };                                          \
+template<typename To, typename Ti, af_op_t op>
+struct BinOp {
+    const char *name() { return "__invalid"; }
+};
 
+#define BINARY_TYPE_1(fn)                            \
+    template<typename To, typename Ti>               \
+    struct BinOp<To, Ti, af_##fn##_t> {              \
+        const char *name() { return "__" #fn; }      \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cfloat, af_##fn##_t> {          \
+        const char *name() { return "__c" #fn "f"; } \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cdouble, af_##fn##_t> {         \
+        const char *name() { return "__c" #fn; }     \
+    };
 
 BINARY_TYPE_1(eq)
 BINARY_TYPE_1(neq)
@@ -75,49 +57,28 @@ BINARY_TYPE_1(bitshiftr)
 
 #undef BINARY_TYPE_1
 
-#define BINARY_TYPE_2(fn)                       \
-    template<typename To, typename Ti>          \
-    struct BinOp<To, Ti, af_##fn##_t>           \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__"#fn;                     \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, float, af_##fn##_t>        \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "f"#fn;                      \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, double, af_##fn##_t>       \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "f"#fn;                      \
-        }                                       \
-    };                                          \
-    template<typename To>                       \
-    struct BinOp<To, cfloat, af_##fn##_t>       \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn"f";                 \
-        }                                       \
-    };                                          \
-                                                \
-    template<typename To>                       \
-    struct BinOp<To, cdouble, af_##fn##_t>      \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__c"#fn;                    \
-        }                                       \
-    };                                          \
-
+#define BINARY_TYPE_2(fn)                            \
+    template<typename To, typename Ti>               \
+    struct BinOp<To, Ti, af_##fn##_t> {              \
+        const char *name() { return "__" #fn; }      \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, float, af_##fn##_t> {           \
+        const char *name() { return "f" #fn; }       \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, double, af_##fn##_t> {          \
+        const char *name() { return "f" #fn; }       \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, cfloat, af_##fn##_t> {          \
+        const char *name() { return "__c" #fn "f"; } \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cdouble, af_##fn##_t> {         \
+        const char *name() { return "__c" #fn; }     \
+    };
 
 BINARY_TYPE_2(min)
 BINARY_TYPE_2(max)
@@ -129,80 +90,58 @@ struct BinOp<To, Ti, af_pow_t> {
     const char *name() { return "__pow"; }
 };
 
-#define POW_BINARY_OP(INTYPE, OPNAME)       \
-template<typename To>                       \
-struct BinOp<To, INTYPE, af_pow_t> {        \
-    const char *name() { return OPNAME; }   \
-};
+#define POW_BINARY_OP(INTYPE, OPNAME)         \
+    template<typename To>                     \
+    struct BinOp<To, INTYPE, af_pow_t> {      \
+        const char *name() { return OPNAME; } \
+    };
 
-POW_BINARY_OP(double, "pow"    )
-POW_BINARY_OP( float, "pow"    )
-POW_BINARY_OP(  intl, "__powll")
-POW_BINARY_OP( uintl, "__powul")
-POW_BINARY_OP(  uint, "__powui")
-POW_BINARY_OP(   int, "__powsi")
+POW_BINARY_OP(double, "pow")
+POW_BINARY_OP(float, "pow")
+POW_BINARY_OP(intl, "__powll")
+POW_BINARY_OP(uintl, "__powul")
+POW_BINARY_OP(uint, "__powui")
+POW_BINARY_OP(int, "__powsi")
 
 #undef POW_BINARY_OP
 
 template<typename Ti>
-struct BinOp<cfloat, Ti, af_cplx2_t>
-{
-    const char *name()
-    {
-        return "__cplx2f";
-    }
+struct BinOp<cfloat, Ti, af_cplx2_t> {
+    const char *name() { return "__cplx2f"; }
 };
 
 template<typename Ti>
-struct BinOp<cdouble, Ti, af_cplx2_t>
-{
-    const char *name()
-    {
-        return "__cplx2";
-    }
+struct BinOp<cdouble, Ti, af_cplx2_t> {
+    const char *name() { return "__cplx2"; }
 };
 
 template<typename To, typename Ti>
-struct BinOp<To, Ti, af_cplx2_t>
-{
-    const char *name()
-    {
-        return "noop";
-    }
+struct BinOp<To, Ti, af_cplx2_t> {
+    const char *name() { return "noop"; }
 };
 
 template<typename To, typename Ti>
-struct BinOp<To, Ti, af_atan2_t>
-{
-    const char *name()
-    {
-        return "atan2";
-    }
+struct BinOp<To, Ti, af_atan2_t> {
+    const char *name() { return "atan2"; }
 };
 
 template<typename To, typename Ti>
-struct BinOp<To, Ti, af_hypot_t>
-{
-    const char *name()
-    {
-        return "hypot";
-    }
+struct BinOp<To, Ti, af_hypot_t> {
+    const char *name() { return "hypot"; }
 };
 
 template<typename To, typename Ti, af_op_t op>
-Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
-{
+Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
+                           const af::dim4 &odims) {
     BinOp<To, Ti, op> bop;
 
     common::Node_ptr lhs_node = lhs.getNode();
     common::Node_ptr rhs_node = rhs.getNode();
-    common::BinaryNode *node = new common::BinaryNode(dtype_traits<To>::getName(),
-                                                      shortname<To>(true),
-                                                      bop.name(),
-                                                      lhs_node,
-                                                      rhs_node, (int)(op));
+    common::BinaryNode *node =
+        new common::BinaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+                               bop.name(), lhs_node, rhs_node, (int)(op));
 
     return createNodeArray<To>(odims, common::Node_ptr(node));
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index d8a6f73a57..436cbb95ef 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -9,14 +9,14 @@
 
 #include <complex>
 
-#include <blas.hpp>
 #include <Array.hpp>
+#include <arith.hpp>
+#include <blas.hpp>
+#include <complex.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
-#include <transpose.hpp>
-#include <arith.hpp>
 #include <reduce.hpp>
-#include <complex.hpp>
+#include <transpose.hpp>
 
 // Includes one of the supported OpenCL BLAS back-ends (e.g. clBLAS, CLBlast)
 #include <magma/magma_blas.h>
@@ -25,37 +25,30 @@
 #include <cpu/cpu_blas.hpp>
 #endif
 
-namespace opencl
-{
+namespace opencl {
 
-void initBlas()
-{
-    gpu_blas_init();
-}
+void initBlas() { gpu_blas_init(); }
 
-void deInitBlas()
-{
-    gpu_blas_deinit();
-}
+void deInitBlas() { gpu_blas_deinit(); }
 
-// Converts an af_mat_prop options to a transpose type for one of the OpenCL BLAS back-ends
+// Converts an af_mat_prop options to a transpose type for one of the OpenCL
+// BLAS back-ends
 OPENCL_BLAS_TRANS_T
-toBlasTranspose(af_mat_prop opt)
-{
-    switch(opt) {
-        case AF_MAT_NONE    : return OPENCL_BLAS_NO_TRANS;
-        case AF_MAT_TRANS   : return OPENCL_BLAS_TRANS;
-        case AF_MAT_CTRANS  : return OPENCL_BLAS_CONJ_TRANS;
-        default             : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+toBlasTranspose(af_mat_prop opt) {
+    switch (opt) {
+        case AF_MAT_NONE: return OPENCL_BLAS_NO_TRANS;
+        case AF_MAT_TRANS: return OPENCL_BLAS_TRANS;
+        case AF_MAT_CTRANS: return OPENCL_BLAS_CONJ_TRANS;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
 }
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
 #if defined(WITH_LINEAR_ALGEBRA)
-    if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
+    if (OpenCLCPUOffload(
+            false)) {  // Do not force offload gemm on OSX Intel devices
         return cpu::matmul(lhs, rhs, optLhs, optRhs);
     }
 #endif
@@ -68,13 +61,13 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
     const dim4 lDims = lhs.dims();
     const dim4 rDims = rhs.dims();
-    const int M = lDims[aRowDim];
-    const int N = rDims[bColDim];
-    const int K = lDims[aColDim];
+    const int M      = lDims[aRowDim];
+    const int N      = rDims[bColDim];
+    const int K      = lDims[aColDim];
 
-    dim_t d2 = std::max(lDims[2], rDims[2]);
-    dim_t d3 = std::max(lDims[3], rDims[3]);
-    dim4 oDims = af::dim4(M, N, d2, d3);
+    dim_t d2     = std::max(lDims[2], rDims[2]);
+    dim_t d3     = std::max(lDims[3], rDims[3]);
+    dim4 oDims   = af::dim4(M, N, d2, d3);
     Array<T> out = createEmptyArray<T>(oDims);
 
     const auto alpha = scalar<T>(1);
@@ -95,37 +88,31 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
         int w = n / oDims[2];
         int z = n - w * oDims[2];
 
-        int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
-        int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+        int loff = z * (is_l_d2_batched * lStrides[2]) +
+                   w * (is_l_d3_batched * lStrides[3]);
+        int roff = z * (is_r_d2_batched * rStrides[2]) +
+                   w * (is_r_d3_batched * rStrides[3]);
 
         dim_t lOffset = lhs.getOffset() + loff;
         dim_t rOffset = rhs.getOffset() + roff;
         dim_t oOffset = out.getOffset() + z * oStrides[2] + w * oStrides[3];
 
         cl::Event event;
-        if(rDims[bColDim] == 1) {
+        if (rDims[bColDim] == 1) {
             dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
             gpu_blas_gemv_func<T> gemv;
-            OPENCL_BLAS_CHECK(
-                gemv(lOpts, lDims[0], lDims[1],
-                     alpha,
-                     (*lhs.get())(), lOffset, lStrides[1],
-                     (*rhs.get())(), rOffset, incr,
-                     beta,
-                     (*out.get())(), oOffset, 1,
-                     1, &getQueue()(), 0, nullptr, &event())
-                );
+            OPENCL_BLAS_CHECK(gemv(lOpts, lDims[0], lDims[1], alpha,
+                                   (*lhs.get())(), lOffset, lStrides[1],
+                                   (*rhs.get())(), rOffset, incr, beta,
+                                   (*out.get())(), oOffset, 1, 1, &getQueue()(),
+                                   0, nullptr, &event()));
         } else {
             gpu_blas_gemm_func<T> gemm;
-            OPENCL_BLAS_CHECK(
-                gemm(lOpts, rOpts, M, N, K,
-                     alpha,
-                     (*lhs.get())(), lOffset, lStrides[1],
-                     (*rhs.get())(), rOffset, rStrides[1],
-                     beta,
-                     (*out.get())(), oOffset, out.dims()[0],
-                     1, &getQueue()(), 0, nullptr, &event())
-                );
+            OPENCL_BLAS_CHECK(gemm(lOpts, rOpts, M, N, K, alpha, (*lhs.get())(),
+                                   lOffset, lStrides[1], (*rhs.get())(),
+                                   rOffset, rStrides[1], beta, (*out.get())(),
+                                   oOffset, out.dims()[0], 1, &getQueue()(), 0,
+                                   nullptr, &event()));
         }
     }
 
@@ -133,9 +120,8 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 }
 
 template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs) {
     const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
     const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
 
@@ -143,22 +129,24 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
     return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
-#define INSTANTIATE_BLAS(TYPE)                                                          \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
-                    af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_BLAS(TYPE)                                \
+    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
+                                      const Array<TYPE> &rhs, \
+                                      af_mat_prop optLhs, af_mat_prop optRhs);
 
 INSTANTIATE_BLAS(float)
 INSTANTIATE_BLAS(cfloat)
 INSTANTIATE_BLAS(double)
 INSTANTIATE_BLAS(cdouble)
 
-#define INSTANTIATE_DOT(TYPE)                                                       \
-    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,  \
-                                   af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_DOT(TYPE)                                                  \
+    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
+                                   const Array<TYPE> &rhs, af_mat_prop optLhs, \
+                                   af_mat_prop optRhs);
 
 INSTANTIATE_DOT(float)
 INSTANTIATE_DOT(double)
 INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index 6946f38166..c034607d29 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -14,16 +14,15 @@
 // functions. They can be implemented in different back-ends,
 // such as CLBlast or clBLAS.
 
-namespace opencl
-{
+namespace opencl {
 void initBlas();
 void deInitBlas();
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs);
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs);
 
 template<typename T>
-Array<T> dot(const Array<T> &lhs, const Array<T> &rhs,
-             af_mat_prop optLhs, af_mat_prop optRhs);
-}
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs);
+}  // namespace opencl
diff --git a/src/backend/opencl/cache.hpp b/src/backend/opencl/cache.hpp
index 2283838a66..1b870a68c4 100644
--- a/src/backend/opencl/cache.hpp
+++ b/src/backend/opencl/cache.hpp
@@ -13,16 +13,15 @@
 #include <string>
 
 namespace cl {
-  class Program;
-  class Kernel;
-}
+class Program;
+class Kernel;
+}  // namespace cl
 
-namespace opencl
-{
-    typedef struct {
-        cl::Program* prog;
-        cl::Kernel* ker;
-    } kc_entry_t;
+namespace opencl {
+struct kc_entry_t {
+    cl::Program* prog;
+    cl::Kernel* ker;
+};
 
-    typedef std::map<std::string, kc_entry_t> kc_t;
-}
+typedef std::map<std::string, kc_entry_t> kc_t;
+}  // namespace opencl
diff --git a/src/backend/opencl/canny.cpp b/src/backend/opencl/canny.cpp
index 601422703f..ab2ec78c2f 100644
--- a/src/backend/opencl/canny.cpp
+++ b/src/backend/opencl/canny.cpp
@@ -14,11 +14,10 @@
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
-                                   const Array<float>& gx, const Array<float>& gy)
-{
+                                   const Array<float>& gx,
+                                   const Array<float>& gy) {
     Array<float> out = createValueArray<float>(mag.dims(), 0);
 
     kernel::nonMaxSuppression<float>(out, mag, gx, gy);
@@ -26,12 +25,12 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
     return out;
 }
 
-Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak)
-{
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak) {
     Array<char> out = createValueArray<char>(strong.dims(), 0);
 
     kernel::edgeTrackingHysteresis<char>(out, strong, weak);
 
     return out;
 }
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/canny.hpp b/src/backend/opencl/canny.hpp
index d24919ce2f..173937b521 100644
--- a/src/backend/opencl/canny.hpp
+++ b/src/backend/opencl/canny.hpp
@@ -9,10 +9,11 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
-                                   const Array<float>& gx, const Array<float>& gy);
+                                   const Array<float>& gx,
+                                   const Array<float>& gy);
 
-Array<char> edgeTrackingByHysteresis(const Array<char>& strong, const Array<char>& weak);
-}
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak);
+}  // namespace opencl
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index 1adf84fddc..a1817bfaff 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -8,35 +8,26 @@
  ********************************************************/
 
 #pragma once
-#include <af/dim4.hpp>
-#include <complex>
+#include <Array.hpp>
+#include <common/jit/UnaryNode.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
-#include <common/jit/UnaryNode.hpp>
 #include <types.hpp>
-#include <Array.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename To, typename Ti>
-struct CastOp
-{
-    const char *name()
-    {
-        return "";
-    }
+struct CastOp {
+    const char *name() { return ""; }
 };
 
-#define CAST_FN(TYPE)                           \
-    template<typename Ti>                       \
-    struct CastOp<TYPE, Ti>                     \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "convert_"#TYPE;             \
-        }                                       \
+#define CAST_FN(TYPE)                                   \
+    template<typename Ti>                               \
+    struct CastOp<TYPE, Ti> {                           \
+        const char *name() { return "convert_" #TYPE; } \
     };
 
 CAST_FN(int)
@@ -45,14 +36,10 @@ CAST_FN(uchar)
 CAST_FN(float)
 CAST_FN(double)
 
-#define CAST_CFN(TYPE)                          \
-    template<typename Ti>                       \
-    struct CastOp<TYPE, Ti>                     \
-    {                                           \
-        const char *name()                      \
-        {                                       \
-            return "__convert_"#TYPE;           \
-        }                                       \
+#define CAST_CFN(TYPE)                                    \
+    template<typename Ti>                                 \
+    struct CastOp<TYPE, Ti> {                             \
+        const char *name() { return "__convert_" #TYPE; } \
     };
 
 CAST_CFN(cfloat)
@@ -60,73 +47,49 @@ CAST_CFN(cdouble)
 CAST_CFN(char)
 
 template<>
-struct CastOp<cfloat, cdouble>
-{
-    const char *name()
-    {
-        return "__convert_z2c";
-    }
+struct CastOp<cfloat, cdouble> {
+    const char *name() { return "__convert_z2c"; }
 };
 
 template<>
-struct CastOp<cdouble, cfloat>
-{
-    const char *name()
-    {
-        return "__convert_c2z";
-    }
+struct CastOp<cdouble, cfloat> {
+    const char *name() { return "__convert_c2z"; }
 };
 
 template<>
-struct CastOp<cfloat, cfloat>
-{
-    const char *name()
-    {
-        return "__convert_c2c";
-    }
+struct CastOp<cfloat, cfloat> {
+    const char *name() { return "__convert_c2c"; }
 };
 
 template<>
-struct CastOp<cdouble, cdouble>
-{
-    const char *name()
-    {
-        return "__convert_z2z";
-    }
+struct CastOp<cdouble, cdouble> {
+    const char *name() { return "__convert_z2z"; }
 };
 
 #undef CAST_FN
 #undef CAST_CFN
 
 template<typename To, typename Ti>
-struct CastWrapper
-{
-    Array<To> operator()(const Array<Ti> &in)
-    {
+struct CastWrapper {
+    Array<To> operator()(const Array<Ti> &in) {
         CastOp<To, Ti> cop;
         common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
-                                                        shortname<To>(true),
-                                                        cop.name(),
-                                                        in_node, af_cast_t);
+        common::UnaryNode *node  = new common::UnaryNode(
+            dtype_traits<To>::getName(), shortname<To>(true), cop.name(),
+            in_node, af_cast_t);
         return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 };
 
 template<typename T>
-struct CastWrapper<T, T>
-{
-    Array<T> operator()(const Array<T> &in)
-    {
-        return in;
-    }
+struct CastWrapper<T, T> {
+    Array<T> operator()(const Array<T> &in) { return in; }
 };
 
 template<typename To, typename Ti>
-Array<To> cast(const Array<Ti> &in)
-{
+Array<To> cast(const Array<Ti> &in) {
     CastWrapper<To, Ti> cast_op;
     return cast_op(in);
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index 3e100391e7..963cf2299e 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -7,95 +7,86 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cholesky.hpp>
-#include <err_opencl.hpp>
 #include <blas.hpp>
+#include <cholesky.hpp>
 #include <copy.hpp>
+#include <err_opencl.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <cpu/cpu_cholesky.hpp>
 #include <magma/magma.h>
-#include <triangle.hpp>
 #include <platform.hpp>
-#include <cpu/cpu_cholesky.hpp>
+#include <triangle.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::cholesky_inplace(in, is_upper);
-    }
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
+    if (OpenCLCPUOffload()) { return cpu::cholesky_inplace(in, is_upper); }
 
     dim4 iDims = in.dims();
-    int N = iDims[0];
+    int N      = iDims[0];
 
     magma_uplo_t uplo = is_upper ? MagmaUpper : MagmaLower;
 
-    int info = 0;
+    int info           = 0;
     cl::Buffer *in_buf = in.get();
-    magma_potrf_gpu<T>(uplo, N,
-                        (*in_buf)(), in.getOffset(),  in.strides()[1],
-                        getQueue()(), &info);
+    magma_potrf_gpu<T>(uplo, N, (*in_buf)(), in.getOffset(), in.strides()[1],
+                       getQueue()(), &info);
     return info;
 }
 
 template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::cholesky(info, in, is_upper);
-    }
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
+    if (OpenCLCPUOffload()) { return cpu::cholesky(info, in, is_upper); }
 
     Array<T> out = copyArray<T>(in);
-    *info = cholesky_inplace(out, is_upper);
+    *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper) triangle<T, true , false>(out, out);
-    else          triangle<T, false, false>(out, out);
+    if (is_upper)
+        triangle<T, true, false>(out, out);
+    else
+        triangle<T, false, false>(out, out);
 
     return out;
 }
 
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
 
 INSTANTIATE_CH(float)
 INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
-}
+}  // namespace opencl
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
 
 INSTANTIATE_CH(float)
 INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
-}
+}  // namespace opencl
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cholesky.hpp b/src/backend/opencl/cholesky.hpp
index 34f774e6bd..aa4e56bf29 100644
--- a/src/backend/opencl/cholesky.hpp
+++ b/src/backend/opencl/cholesky.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
+namespace opencl {
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 
-    template<typename T>
-    int cholesky_inplace(Array<T> &in, const bool is_upper);
-}
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper);
+}  // namespace opencl
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index 82927001e6..49fd0fb430 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -7,31 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <common/err_common.hpp>
 #include <clfft.hpp>
+#include <common/err_common.hpp>
 #include <platform.hpp>
+#include <af/defines.h>
 #include <string>
 
 using std::string;
 
-namespace opencl
-{
-const char * _clfftGetResultString(clfftStatus st)
-{
-    switch (st)
-    {
+namespace opencl {
+const char *_clfftGetResultString(clfftStatus st) {
+    switch (st) {
         case CLFFT_SUCCESS: return "Success";
         case CLFFT_DEVICE_NOT_FOUND: return "Device Not Found";
         case CLFFT_DEVICE_NOT_AVAILABLE: return "Device Not Available";
         case CLFFT_COMPILER_NOT_AVAILABLE: return "Compiler Not Available";
-        case CLFFT_MEM_OBJECT_ALLOCATION_FAILURE: return "Memory Object Allocation Failure";
+        case CLFFT_MEM_OBJECT_ALLOCATION_FAILURE:
+            return "Memory Object Allocation Failure";
         case CLFFT_OUT_OF_RESOURCES: return "Out of Resources";
         case CLFFT_OUT_OF_HOST_MEMORY: return "Out of Host Memory";
-        case CLFFT_PROFILING_INFO_NOT_AVAILABLE: return "Profiling Information Not Available";
+        case CLFFT_PROFILING_INFO_NOT_AVAILABLE:
+            return "Profiling Information Not Available";
         case CLFFT_MEM_COPY_OVERLAP: return "Memory Copy Overlap";
         case CLFFT_IMAGE_FORMAT_MISMATCH: return "Image Format Mismatch";
-        case CLFFT_IMAGE_FORMAT_NOT_SUPPORTED: return "Image Format Not Supported";
+        case CLFFT_IMAGE_FORMAT_NOT_SUPPORTED:
+            return "Image Format Not Supported";
         case CLFFT_BUILD_PROGRAM_FAILURE: return "Build Program Failure";
         case CLFFT_MAP_FAILURE: return "Map Failure";
         case CLFFT_INVALID_VALUE: return "Invalid Value";
@@ -43,15 +43,18 @@ const char * _clfftGetResultString(clfftStatus st)
         case CLFFT_INVALID_COMMAND_QUEUE: return "Invalid Command Queue";
         case CLFFT_INVALID_HOST_PTR: return "Invalid Host Pointer";
         case CLFFT_INVALID_MEM_OBJECT: return "Invalid Memory Object";
-        case CLFFT_INVALID_IMAGE_FORMAT_DESCRIPTOR: return "Invalid Image Format Descriptor";
+        case CLFFT_INVALID_IMAGE_FORMAT_DESCRIPTOR:
+            return "Invalid Image Format Descriptor";
         case CLFFT_INVALID_IMAGE_SIZE: return "Invalid Image Size";
         case CLFFT_INVALID_SAMPLER: return "Invalid Sampler";
         case CLFFT_INVALID_BINARY: return "Invalid Binary";
         case CLFFT_INVALID_BUILD_OPTIONS: return "Invalid Build Options";
         case CLFFT_INVALID_PROGRAM: return "Invalid Program";
-        case CLFFT_INVALID_PROGRAM_EXECUTABLE: return "Invalid Program Executable";
+        case CLFFT_INVALID_PROGRAM_EXECUTABLE:
+            return "Invalid Program Executable";
         case CLFFT_INVALID_KERNEL_NAME: return "Invalid Kernel Name";
-        case CLFFT_INVALID_KERNEL_DEFINITION: return "Invalid Kernel Definition";
+        case CLFFT_INVALID_KERNEL_DEFINITION:
+            return "Invalid Kernel Definition";
         case CLFFT_INVALID_KERNEL: return "Invalid Kernel";
         case CLFFT_INVALID_ARG_INDEX: return "Invalid Argument Index";
         case CLFFT_INVALID_ARG_VALUE: return "Invalid Argument Value";
@@ -70,12 +73,14 @@ const char * _clfftGetResultString(clfftStatus st)
         case CLFFT_INVALID_GLOBAL_WORK_SIZE: return "Invalid Global Work Size";
         case CLFFT_BUGCHECK: return "Bugcheck";
         case CLFFT_NOTIMPLEMENTED: return "Not implemented";
-        case CLFFT_TRANSPOSED_NOTIMPLEMENTED: return "Transpose not implemented for this transformation";
+        case CLFFT_TRANSPOSED_NOTIMPLEMENTED:
+            return "Transpose not implemented for this transformation";
         case CLFFT_FILE_NOT_FOUND: return "File not found";
         case CLFFT_FILE_CREATE_FAILURE: return "File creation failed";
         case CLFFT_VERSION_MISMATCH: return "Version mismatch";
         case CLFFT_INVALID_PLAN: return "Invalid plan";
-        case CLFFT_DEVICE_NO_DOUBLE: return "Device does not support double precision";
+        case CLFFT_DEVICE_NO_DOUBLE:
+            return "Device does not support double precision";
         case CLFFT_DEVICE_MISMATCH: return "Plan device mismatch";
         case CLFFT_ENDSTATUS: return "End status";
     }
@@ -83,12 +88,10 @@ const char * _clfftGetResultString(clfftStatus st)
     return "Unknown error";
 }
 
-SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
-                    clfftDim rank, size_t *clLengths,
-                    size_t *istrides, size_t idist,
-                    size_t *ostrides, size_t odist,
-                    clfftPrecision precision, size_t batch)
-{
+SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
+                    size_t *clLengths, size_t *istrides, size_t idist,
+                    size_t *ostrides, size_t odist, clfftPrecision precision,
+                    size_t batch) {
     // create the key string
     char key_str_temp[64];
     sprintf(key_str_temp, "%d:%d:%d:", iLayout, oLayout, rank);
@@ -96,13 +99,13 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
     string key_string(key_str_temp);
 
     /* WARNING: DO NOT CHANGE sprintf format specifier */
-    for(int r=0; r<rank; ++r) {
+    for (int r = 0; r < rank; ++r) {
         sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", clLengths[r]);
         key_string.append(std::string(key_str_temp));
     }
 
-    if(istrides!=NULL) {
-        for(int r=0; r<rank; ++r) {
+    if (istrides != NULL) {
+        for (int r = 0; r < rank; ++r) {
             sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", istrides[r]);
             key_string.append(std::string(key_str_temp));
         }
@@ -110,8 +113,8 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
         key_string.append(std::string(key_str_temp));
     }
 
-    if (ostrides!=NULL) {
-        for(int r=0; r<rank; ++r) {
+    if (ostrides != NULL) {
+        for (int r = 0; r < rank; ++r) {
             sprintf(key_str_temp, SIZE_T_FRMT_SPECIFIER ":", ostrides[r]);
             key_string.append(std::string(key_str_temp));
         }
@@ -123,16 +126,16 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
     key_string.append(std::string(key_str_temp));
 
     PlanCache &planner = opencl::fftManager();
-    SharedPlan retVal = planner.find(key_string);
+    SharedPlan retVal  = planner.find(key_string);
 
-    if (retVal)
-        return retVal;
+    if (retVal) return retVal;
 
-    PlanType* temp = (PlanType*)malloc(sizeof(PlanType));
+    PlanType *temp = (PlanType *)malloc(sizeof(PlanType));
 
     // getContext() returns object of type Context
     // Context() returns the actual cl_context handle
-    CLFFT_CHECK(clfftCreateDefaultPlan(temp, opencl::getContext()(), rank, clLengths));
+    CLFFT_CHECK(
+        clfftCreateDefaultPlan(temp, opencl::getContext()(), rank, clLengths));
 
     // complex to complex
     if (iLayout == oLayout) {
@@ -153,7 +156,7 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
     // CommandQueue() returns the actual cl_command_queue handle
     CLFFT_CHECK(clfftBakePlan(*temp, 1, &(opencl::getQueue()()), NULL, NULL));
 
-    retVal.reset(temp, [](PlanType* p) {
+    retVal.reset(temp, [](PlanType *p) {
 #ifndef OS_WIN
         // On Windows the resources that are released after the main function
         // have exited cause "Pure Virtual Function Called" errors. It seems
@@ -170,4 +173,4 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
 
     return retVal;
 }
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
index d4c7426d08..eaf67f6c31 100644
--- a/src/backend/opencl/clfft.hpp
+++ b/src/backend/opencl/clfft.hpp
@@ -15,45 +15,39 @@
 
 #include <cstdio>
 
-namespace opencl
-{
+namespace opencl {
 typedef clfftPlanHandle PlanType;
 typedef std::shared_ptr<PlanType> SharedPlan;
 
-const char * _clfftGetResultString(clfftStatus st);
+const char *_clfftGetResultString(clfftStatus st);
 
-SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
-                    clfftDim rank, size_t *clLengths,
-                    size_t *istrides, size_t idist,
-                    size_t *ostrides, size_t odist,
-                    clfftPrecision precision, size_t batch);
+SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
+                    size_t *clLengths, size_t *istrides, size_t idist,
+                    size_t *ostrides, size_t odist, clfftPrecision precision,
+                    size_t batch);
 
-class PlanCache : public common::FFTPlanCache<PlanCache, PlanType>
-{
+class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
     friend SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout,
                                clfftDim rank, size_t *clLengths,
-                               size_t *istrides, size_t idist,
-                               size_t *ostrides, size_t odist,
-                               clfftPrecision precision, size_t batch);
+                               size_t *istrides, size_t idist, size_t *ostrides,
+                               size_t odist, clfftPrecision precision,
+                               size_t batch);
 };
-}
-
-#define CLFFT_CHECK(fn) do {                        \
-        clfftStatus _clfft_st = fn;                 \
-        if (_clfft_st != CLFFT_SUCCESS) {           \
-            opencl::garbageCollect();               \
-            _clfft_st = (fn);                       \
-        }                                           \
-        if (_clfft_st != CLFFT_SUCCESS) {           \
-            char clfft_st_msg[1024];                \
-            snprintf(clfft_st_msg,                  \
-                     sizeof(clfft_st_msg),          \
-                     "clFFT Error (%d): %s\n",      \
-                     (int)(_clfft_st),              \
-                     opencl::_clfftGetResultString( \
-                         _clfft_st));               \
-                                                    \
-            AF_ERROR(clfft_st_msg,                  \
-                     AF_ERR_INTERNAL);              \
-        }                                           \
-    } while(0)
+}  // namespace opencl
+
+#define CLFFT_CHECK(fn)                                          \
+    do {                                                         \
+        clfftStatus _clfft_st = fn;                              \
+        if (_clfft_st != CLFFT_SUCCESS) {                        \
+            opencl::garbageCollect();                            \
+            _clfft_st = (fn);                                    \
+        }                                                        \
+        if (_clfft_st != CLFFT_SUCCESS) {                        \
+            char clfft_st_msg[1024];                             \
+            snprintf(clfft_st_msg, sizeof(clfft_st_msg),         \
+                     "clFFT Error (%d): %s\n", (int)(_clfft_st), \
+                     opencl::_clfftGetResultString(_clfft_st));  \
+                                                                 \
+            AF_ERROR(clfft_st_msg, AF_ERR_INTERNAL);             \
+        }                                                        \
+    } while (0)
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index 0d92b1cd74..a17f0506bb 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -7,73 +7,82 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <binary.hpp>
 #include <common/jit/UnaryNode.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-    template<typename To, typename Ti>
-    Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
-    }
+namespace opencl {
+template<typename To, typename Ti>
+Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
+               const af::dim4 &odims) {
+    return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
+}
 
-    template<typename To, typename Ti>
-    Array<To> real(const Array<Ti> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
-                                                        shortname<To>(true),
-                                                        "__creal",
-                                                        in_node, af_real_t);
+template<typename To, typename Ti>
+Array<To> real(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+                              "__creal", in_node, af_real_t);
 
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
 
-    template<typename To, typename Ti>
-    Array<To> imag(const Array<Ti> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
-                                                        shortname<To>(true),
-                                                        "__cimag",
-                                                        in_node, af_imag_t);
+template<typename To, typename Ti>
+Array<To> imag(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+                              "__cimag", in_node, af_imag_t);
 
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
 
-    template<typename T> static const char *abs_name() { return "fabs"; }
-    template<> STATIC_ const char *abs_name<cfloat>() { return "__cabsf"; }
-    template<> STATIC_ const char *abs_name<cdouble>() { return "__cabs"; }
+template<typename T>
+static const char *abs_name() {
+    return "fabs";
+}
+template<>
+STATIC_ const char *abs_name<cfloat>() {
+    return "__cabsf";
+}
+template<>
+STATIC_ const char *abs_name<cdouble>() {
+    return "__cabs";
+}
 
-    template<typename To, typename Ti>
-    Array<To> abs(const Array<Ti> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(dtype_traits<To>::getName(),
-                                                        shortname<To>(true),
-                                                        abs_name<Ti>(),
-                                                        in_node, af_abs_t);
+template<typename To, typename Ti>
+Array<To> abs(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+                              abs_name<Ti>(), in_node, af_abs_t);
 
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
 
-    template<typename T> static const char *conj_name() { return "__noop"; }
-    template<> STATIC_ const char *conj_name<cfloat>() { return "__cconjf"; }
-    template<> STATIC_ const char *conj_name<cdouble>() { return "__cconj"; }
+template<typename T>
+static const char *conj_name() {
+    return "__noop";
+}
+template<>
+STATIC_ const char *conj_name<cfloat>() {
+    return "__cconjf";
+}
+template<>
+STATIC_ const char *conj_name<cdouble>() {
+    return "__cconj";
+}
 
-    template<typename T>
-    Array<T> conj(const Array<T> &in)
-    {
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node = new common::UnaryNode(dtype_traits<T>::getName(),
-                                                        shortname<T>(true),
-                                                        conj_name<T>(),
-                                                        in_node, af_conj_t);
+template<typename T>
+Array<T> conj(const Array<T> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(dtype_traits<T>::getName(), shortname<T>(true),
+                              conj_name<T>(), in_node, af_conj_t);
 
-        return createNodeArray<T>(in.dims(), common::Node_ptr(node));
-    }
+    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 2feede31b0..2cfdb7c159 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -7,55 +7,61 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
-#include <kernel/convolve.hpp>
 #include <err_opencl.hpp>
+#include <kernel/convolve.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind)
-{
-    const dim4 sDims    = signal.dims();
-    const dim4 fDims    = filter.dims();
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+                  AF_BATCH_KIND kind) {
+    const dim4 sDims = signal.dims();
+    const dim4 fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
-                oDims[d] = sDims[d]+fDims[d]-1;
+        for (dim_t d = 0; d < 4; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
+                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_RHS) {
-            for (dim_t i=baseDim; i<4; ++i)
-                oDims[i] = fDims[i];
+        if (kind == AF_BATCH_RHS) {
+            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
         }
     }
 
-    Array<T> out   = createEmptyArray<T>(oDims);
+    Array<T> out    = createEmptyArray<T>(oDims);
     bool callKernel = true;
 
     dim_t MCFL2 = kernel::MAX_CONV2_FILTER_LEN;
     dim_t MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
-    switch(baseDim) {
-        case 1: if (fDims[0]>kernel::MAX_CONV1_FILTER_LEN) callKernel = false; break;
-        case 2: if ((fDims[0]*fDims[1]) > (MCFL2 * MCFL2)) callKernel = false; break;
-        case 3: if ((fDims[0]*fDims[1]*fDims[2]) > (MCFL3 * MCFL3 * MCFL3)) callKernel = false; break;
+    switch (baseDim) {
+        case 1:
+            if (fDims[0] > kernel::MAX_CONV1_FILTER_LEN) callKernel = false;
+            break;
+        case 2:
+            if ((fDims[0] * fDims[1]) > (MCFL2 * MCFL2)) callKernel = false;
+            break;
+        case 3:
+            if ((fDims[0] * fDims[1] * fDims[2]) > (MCFL3 * MCFL3 * MCFL3))
+                callKernel = false;
+            break;
     }
 
-    if(!callKernel) {
+    if (!callKernel) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nOpenCL N Dimensional Convolution doesn't support %llux%llux%llu kernel\n",
+                 "\nOpenCL N Dimensional Convolution doesn't support "
+                 "%llux%llux%llu kernel\n",
                  fDims[0], fDims[1], fDims[2]);
         OPENCL_NOT_SUPPORTED(errMessage);
     }
@@ -65,25 +71,37 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KI
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                            \
-    template Array<T> convolve <T, accT, 1, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 1, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 2, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 2, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 3, true >(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
-    template Array<T> convolve <T, accT, 3, false>(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind); \
+#define INSTANTIATE(T, accT)                                                 \
+    template Array<T> convolve<T, accT, 1, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 1, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve<T, accT, 2, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 2, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);       \
+    template Array<T> convolve<T, accT, 3, true>(Array<T> const& signal,     \
+                                                 Array<accT> const& filter,  \
+                                                 AF_BATCH_KIND kind);        \
+    template Array<T> convolve<T, accT, 3, false>(Array<T> const& signal,    \
+                                                  Array<accT> const& filter, \
+                                                  AF_BATCH_KIND kind);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/convolve.hpp b/src/backend/opencl/convolve.hpp
index 285f848a5a..7216ee1663 100644
--- a/src/backend/opencl/convolve.hpp
+++ b/src/backend/opencl/convolve.hpp
@@ -9,13 +9,14 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter, AF_BATCH_KIND kind);
+Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+                  AF_BATCH_KIND kind);
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index b6d68be213..08c5f57841 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -7,20 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <convolve.hpp>
-#include <kernel/convolve_separable.hpp>
 #include <err_opencl.hpp>
+#include <kernel/convolve_separable.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter)
-{
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter) {
     const dim_t cflen = (dim_t)c_filter.elements();
     const dim_t rflen = (dim_t)r_filter.elements();
 
@@ -29,14 +28,15 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
         // TODO call upon fft
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                "\nOpenCL Separable convolution doesn't support %lld(coloumn) %lld(row) filters\n",
+                 "\nOpenCL Separable convolution doesn't support %lld(coloumn) "
+                 "%lld(row) filters\n",
                  cflen, rflen);
         OPENCL_NOT_SUPPORTED(errMessage);
     }
 
     const dim4 sDims = signal.dims();
-    dim4 tDims = sDims;
-    dim4 oDims = sDims;
+    dim4 tDims       = sDims;
+    dim4 oDims       = sDims;
 
     if (expand) {
         tDims[0] += cflen - 1;
@@ -44,30 +44,34 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<ac
         oDims[1] += rflen - 1;
     }
 
-    Array<T> temp= createEmptyArray<T>(tDims);
-    Array<T> out = createEmptyArray<T>(oDims);
+    Array<T> temp = createEmptyArray<T>(tDims);
+    Array<T> out  = createEmptyArray<T>(oDims);
 
     kernel::convSep<T, accT, 0, expand>(temp, signal, c_filter);
-    kernel::convSep<T, accT, 1, expand>( out,   temp, r_filter);
+    kernel::convSep<T, accT, 1, expand>(out, temp, r_filter);
 
     return out;
 }
 
-#define INSTANTIATE(T, accT)  \
-    template Array<T> convolve2<T, accT, true >(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);  \
-    template Array<T> convolve2<T, accT, false>(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter);
+#define INSTANTIATE(T, accT)                                                 \
+    template Array<T> convolve2<T, accT, true>(Array<T> const& signal,       \
+                                               Array<accT> const& c_filter,  \
+                                               Array<accT> const& r_filter); \
+    template Array<T> convolve2<T, accT, false>(Array<T> const& signal,      \
+                                                Array<accT> const& c_filter, \
+                                                Array<accT> const& r_filter);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(intl   ,   float)
-INSTANTIATE(uintl  ,   float)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(intl, float)
+INSTANTIATE(uintl, float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index a1f66d35f3..aa9a1da287 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -8,211 +8,244 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/complex.hpp>
 #include <copy.hpp>
-#include <kernel/memcopy.hpp>
 #include <err_opencl.hpp>
+#include <kernel/memcopy.hpp>
 #include <math.hpp>
-#include <common/complex.hpp>
 
 using common::is_complex;
 
-namespace opencl
-{
-
-    template<typename T>
-    void copyData(T *data, const Array<T> &A)
-    {
-        // FIXME: Merge this with copyArray
-        A.eval();
-
-        dim_t offset = 0;
-        cl::Buffer buf;
-        Array<T> out = A;
+namespace opencl {
+
+template<typename T>
+void copyData(T *data, const Array<T> &A) {
+    // FIXME: Merge this with copyArray
+    A.eval();
+
+    dim_t offset = 0;
+    cl::Buffer buf;
+    Array<T> out = A;
+
+    if (A.isLinear() ||  // No offsets, No strides
+        A.ndims() == 1   // Simple offset, no strides.
+    ) {
+        buf    = *A.get();
+        offset = A.getOffset();
+    } else {
+        // FIXME: Think about implementing eval
+        out    = copyArray(A);
+        buf    = *out.get();
+        offset = 0;
+    }
 
-        if (A.isLinear() || // No offsets, No strides
-            A.ndims() == 1 // Simple offset, no strides.
-            ) {
-            buf = *A.get();
-            offset = A.getOffset();
-        } else {
-            //FIXME: Think about implementing eval
-            out = copyArray(A);
-            buf = *out.get();
-            offset = 0;
-        }
+    // FIXME: Add checks
+    getQueue().enqueueReadBuffer(buf, CL_TRUE, sizeof(T) * offset,
+                                 sizeof(T) * A.elements(), data);
+    return;
+}
 
-        //FIXME: Add checks
-        getQueue().enqueueReadBuffer(buf, CL_TRUE,
-                                     sizeof(T) * offset,
-                                     sizeof(T) * A.elements(),
-                                     data);
-        return;
+template<typename T>
+Array<T> copyArray(const Array<T> &A) {
+    Array<T> out = createEmptyArray<T>(A.dims());
+    dim_t offset = A.getOffset();
+
+    if (A.isLinear()) {
+        // FIXME: Add checks
+        getQueue().enqueueCopyBuffer(*A.get(), *out.get(), sizeof(T) * offset,
+                                     0, A.elements() * sizeof(T));
+    } else {
+        kernel::memcopy<T>(*out.get(), out.strides().get(), *A.get(),
+                           A.dims().get(), A.strides().get(), offset,
+                           (uint)A.ndims());
     }
+    return out;
+}
 
-    template<typename T>
-    Array<T> copyArray(const Array<T> &A)
-    {
-        Array<T> out = createEmptyArray<T>(A.dims());
-        dim_t offset = A.getOffset();
-
-        if (A.isLinear()) {
-            // FIXME: Add checks
-            getQueue().enqueueCopyBuffer(*A.get(), *out.get(),
-                                         sizeof(T) * offset, 0,
-                                         A.elements() * sizeof(T));
-        } else {
-            kernel::memcopy<T>(*out.get(), out.strides().get(), *A.get(), A.dims().get(),
-                               A.strides().get(), offset, (uint)A.ndims());
-        }
-        return out;
-    }
+template<typename inType, typename outType>
+Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                        outType default_value, double factor) {
+    Array<outType> ret = createEmptyArray<outType>(dims);
+
+    if (in.dims() == dims)
+        kernel::copy<inType, outType, true>(ret, in, in.ndims(), default_value,
+                                            factor);
+    else
+        kernel::copy<inType, outType, false>(ret, in, in.ndims(), default_value,
+                                             factor);
+    return ret;
+}
 
-    template<typename inType, typename outType>
-    Array<outType> padArray(Array<inType> const &in, dim4 const &dims, outType default_value, double factor)
-    {
-        Array<outType> ret = createEmptyArray<outType>(dims);
+template<typename T>
+void multiply_inplace(Array<T> &in, double val) {
+    kernel::copy<T, T, true>(in, in, in.ndims(), scalar<T>(0), val);
+}
 
-        if (in.dims() == dims)
-            kernel::copy<inType, outType, true >(ret, in, in.ndims(), default_value, factor);
+template<typename inType, typename outType>
+struct copyWrapper {
+    void operator()(Array<outType> &out, Array<inType> const &in) {
+        if (in.dims() == out.dims())
+            kernel::copy<inType, outType, true>(out, in, in.ndims(),
+                                                scalar<outType>(0), 1);
         else
-            kernel::copy<inType, outType, false>(ret, in, in.ndims(), default_value, factor);
-        return ret;
+            kernel::copy<inType, outType, false>(out, in, in.ndims(),
+                                                 scalar<outType>(0), 1);
     }
-
-    template<typename T>
-    void multiply_inplace(Array<T> &in, double val)
-    {
-        kernel::copy<T, T, true>(in, in, in.ndims(), scalar<T>(0), val);
-    }
-
-    template<typename inType, typename outType>
-    struct copyWrapper {
-        void operator()(Array<outType> &out, Array<inType> const &in)
-        {
+};
+
+template<typename T>
+struct copyWrapper<T, T> {
+    void operator()(Array<T> &out, Array<T> const &in) {
+        if (out.isLinear() && in.isLinear() &&
+            out.elements() == in.elements()) {
+            dim_t in_offset  = in.getOffset() * sizeof(T);
+            dim_t out_offset = out.getOffset() * sizeof(T);
+
+            getQueue().enqueueCopyBuffer(*in.get(), *out.get(), in_offset,
+                                         out_offset, in.elements() * sizeof(T));
+        } else {
             if (in.dims() == out.dims())
-                kernel::copy<inType, outType, true >(out, in, in.ndims(), scalar<outType>(0), 1);
+                kernel::copy<T, T, true>(out, in, in.ndims(), scalar<T>(0), 1);
             else
-                kernel::copy<inType, outType, false>(out, in, in.ndims(), scalar<outType>(0), 1);
-        }
-    };
-
-    template<typename T>
-    struct copyWrapper<T, T> {
-        void operator()(Array<T> &out, Array<T> const &in)
-        {
-            if (out.isLinear() &&
-                in.isLinear() &&
-                out.elements() == in.elements())
-            {
-                dim_t in_offset = in.getOffset() * sizeof(T);
-                dim_t out_offset = out.getOffset() * sizeof(T);
-
-                getQueue().enqueueCopyBuffer(*in.get(), *out.get(),
-                                             in_offset, out_offset,
-                                             in.elements() * sizeof(T));
-            } else {
-                if (in.dims() == out.dims())
-                    kernel::copy<T, T, true >(out, in, in.ndims(), scalar<T>(0), 1);
-                else
-                    kernel::copy<T, T, false>(out, in, in.ndims(), scalar<T>(0), 1);
-            }
+                kernel::copy<T, T, false>(out, in, in.ndims(), scalar<T>(0), 1);
         }
-    };
-
-    template<typename inType, typename outType>
-    void copyArray(Array<outType> &out, Array<inType> const &in)
-    {
-        static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
-                      "Cannot copy from complex value to a non complex value");
-        copyWrapper<inType, outType> copyFn;
-        copyFn(out, in);
-    }
-
-#define INSTANTIATE(T)                                              \
-    template void      copyData<T> (T *data, const Array<T> &from); \
-    template Array<T>  copyArray<T>(const Array<T> &A);             \
-    template void      multiply_inplace<T> (Array<T> &in, double norm); \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-
-    #define INSTANTIATE_PAD_ARRAY(SRC_T)                                    \
-    template Array<float  > padArray<SRC_T, float  >(Array<SRC_T> const &src, dim4 const &dims, float   default_value, double factor); \
-    template Array<double > padArray<SRC_T, double >(Array<SRC_T> const &src, dim4 const &dims, double  default_value, double factor); \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
-    template Array<int    > padArray<SRC_T, int    >(Array<SRC_T> const &src, dim4 const &dims, int     default_value, double factor); \
-    template Array<uint   > padArray<SRC_T, uint   >(Array<SRC_T> const &src, dim4 const &dims, uint    default_value, double factor); \
-    template Array<intl   > padArray<SRC_T, intl   >(Array<SRC_T> const &src, dim4 const &dims, intl    default_value, double factor); \
-    template Array<uintl  > padArray<SRC_T, uintl  >(Array<SRC_T> const &src, dim4 const &dims, uintl   default_value, double factor); \
-    template Array<short  > padArray<SRC_T, short  >(Array<SRC_T> const &src, dim4 const &dims, short   default_value, double factor); \
-    template Array<ushort > padArray<SRC_T, ushort >(Array<SRC_T> const &src, dim4 const &dims, ushort  default_value, double factor); \
-    template Array<uchar  > padArray<SRC_T, uchar  >(Array<SRC_T> const &src, dim4 const &dims, uchar   default_value, double factor); \
-    template Array<char   > padArray<SRC_T, char   >(Array<SRC_T> const &src, dim4 const &dims, char    default_value, double factor); \
-    template void copyArray<SRC_T, float  >(Array<float  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, double >(Array<double > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, cfloat >(Array<cfloat > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, int    >(Array<int    > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, uint   >(Array<uint   > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, intl   >(Array<intl   > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, uintl  >(Array<uintl  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, short  >(Array<short  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, ushort >(Array<ushort > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, uchar  >(Array<uchar  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, char   >(Array<char   > &dst, Array<SRC_T> const &src);
-
-    INSTANTIATE_PAD_ARRAY(float )
-    INSTANTIATE_PAD_ARRAY(double)
-    INSTANTIATE_PAD_ARRAY(int   )
-    INSTANTIATE_PAD_ARRAY(uint  )
-    INSTANTIATE_PAD_ARRAY(intl  )
-    INSTANTIATE_PAD_ARRAY(uintl )
-    INSTANTIATE_PAD_ARRAY(uchar )
-    INSTANTIATE_PAD_ARRAY(char  )
-    INSTANTIATE_PAD_ARRAY(short )
-    INSTANTIATE_PAD_ARRAY(ushort)
-
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                            \
-    template Array<cfloat > padArray<SRC_T, cfloat >(Array<SRC_T> const &src, dim4 const &dims, cfloat  default_value, double factor); \
-    template Array<cdouble> padArray<SRC_T, cdouble>(Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, double factor); \
-    template void copyArray<SRC_T, cfloat  >(Array<cfloat  > &dst, Array<SRC_T> const &src); \
-    template void copyArray<SRC_T, cdouble   >(Array<cdouble > &dst, Array<SRC_T> const &src);
-
-    INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat )
-    INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
-
-    template<typename T>
-    T getScalar(const Array<T> &in)
-    {
-        T retVal;
-        getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, sizeof(T) * in.getOffset(), sizeof(T), &retVal);
-        return retVal;
     }
+};
+
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, Array<inType> const &in) {
+    static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
+                  "Cannot copy from complex value to a non complex value");
+    copyWrapper<inType, outType> copyFn;
+    copyFn(out, in);
+}
 
-#define INSTANTIATE_GETSCALAR(T) \
-    template T getScalar(const Array<T> &in);
-
-    INSTANTIATE_GETSCALAR(float  )
-    INSTANTIATE_GETSCALAR(double )
-    INSTANTIATE_GETSCALAR(cfloat )
-    INSTANTIATE_GETSCALAR(cdouble)
-    INSTANTIATE_GETSCALAR(int    )
-    INSTANTIATE_GETSCALAR(uint   )
-    INSTANTIATE_GETSCALAR(uchar  )
-    INSTANTIATE_GETSCALAR(char   )
-    INSTANTIATE_GETSCALAR(intl   )
-    INSTANTIATE_GETSCALAR(uintl  )
-    INSTANTIATE_GETSCALAR(short  )
-    INSTANTIATE_GETSCALAR(ushort )
+#define INSTANTIATE(T)                                         \
+    template void copyData<T>(T * data, const Array<T> &from); \
+    template Array<T> copyArray<T>(const Array<T> &A);         \
+    template void multiply_inplace<T>(Array<T> & in, double norm);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+#define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
+    template Array<float> padArray<SRC_T, float>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, float default_value,   \
+        double factor);                                                   \
+    template Array<double> padArray<SRC_T, double>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, double default_value,  \
+        double factor);                                                   \
+    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
+        double factor);                                                   \
+    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
+        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
+        double factor);                                                   \
+    template Array<int> padArray<SRC_T, int>(                             \
+        Array<SRC_T> const &src, dim4 const &dims, int default_value,     \
+        double factor);                                                   \
+    template Array<uint> padArray<SRC_T, uint>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, uint default_value,    \
+        double factor);                                                   \
+    template Array<intl> padArray<SRC_T, intl>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, intl default_value,    \
+        double factor);                                                   \
+    template Array<uintl> padArray<SRC_T, uintl>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, uintl default_value,   \
+        double factor);                                                   \
+    template Array<short> padArray<SRC_T, short>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, short default_value,   \
+        double factor);                                                   \
+    template Array<ushort> padArray<SRC_T, ushort>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, ushort default_value,  \
+        double factor);                                                   \
+    template Array<uchar> padArray<SRC_T, uchar>(                         \
+        Array<SRC_T> const &src, dim4 const &dims, uchar default_value,   \
+        double factor);                                                   \
+    template Array<char> padArray<SRC_T, char>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, char default_value,    \
+        double factor);                                                   \
+    template void copyArray<SRC_T, float>(Array<float> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, double>(Array<double> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
+                                            Array<SRC_T> const &src);     \
+    template void copyArray<SRC_T, int>(Array<int> & dst,                 \
+                                        Array<SRC_T> const &src);         \
+    template void copyArray<SRC_T, uint>(Array<uint> & dst,               \
+                                         Array<SRC_T> const &src);        \
+    template void copyArray<SRC_T, intl>(Array<intl> & dst,               \
+                                         Array<SRC_T> const &src);        \
+    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, short>(Array<short> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,             \
+                                          Array<SRC_T> const &src);       \
+    template void copyArray<SRC_T, char>(Array<char> & dst,               \
+                                         Array<SRC_T> const &src);
+
+INSTANTIATE_PAD_ARRAY(float)
+INSTANTIATE_PAD_ARRAY(double)
+INSTANTIATE_PAD_ARRAY(int)
+INSTANTIATE_PAD_ARRAY(uint)
+INSTANTIATE_PAD_ARRAY(intl)
+INSTANTIATE_PAD_ARRAY(uintl)
+INSTANTIATE_PAD_ARRAY(uchar)
+INSTANTIATE_PAD_ARRAY(char)
+INSTANTIATE_PAD_ARRAY(short)
+INSTANTIATE_PAD_ARRAY(ushort)
+
+#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
+    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
+        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
+        double factor);                                                   \
+    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
+        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
+        double factor);                                                   \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
+                                           Array<SRC_T> const &src);      \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
+                                            Array<SRC_T> const &src);
+
+INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
+INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+
+template<typename T>
+T getScalar(const Array<T> &in) {
+    T retVal;
+    getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, sizeof(T) * in.getOffset(),
+                                 sizeof(T), &retVal);
+    return retVal;
 }
+
+#define INSTANTIATE_GETSCALAR(T) template T getScalar(const Array<T> &in);
+
+INSTANTIATE_GETSCALAR(float)
+INSTANTIATE_GETSCALAR(double)
+INSTANTIATE_GETSCALAR(cfloat)
+INSTANTIATE_GETSCALAR(cdouble)
+INSTANTIATE_GETSCALAR(int)
+INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(uchar)
+INSTANTIATE_GETSCALAR(char)
+INSTANTIATE_GETSCALAR(intl)
+INSTANTIATE_GETSCALAR(uintl)
+INSTANTIATE_GETSCALAR(short)
+INSTANTIATE_GETSCALAR(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index 4a4e0dffa2..fdf32fc1ec 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -11,57 +11,52 @@
 #include <Array.hpp>
 #include <kernel/pad_array_borders.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void copyData(T *data, const Array<T> &A);
-
-    template<typename T>
-    Array<T> copyArray(const Array<T> &A);
-
-    template<typename inType, typename outType>
-    void copyArray(Array<outType> &out, const Array<inType> &in);
-
-    template<typename inType, typename outType>
-    Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                            outType default_value, double factor=1.0);
-
-    template<typename T>
-    Array<T> padArrayBorders(Array<T> const& in,
-                             dim4 const& lowerBoundPadding,
-                             dim4 const& upperBoundPadding,
-                             const af::borderType btype)
-    {
-        auto iDims = in.dims();
-
-        dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
-                lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
-                lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
-                lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
-
-        auto ret = createEmptyArray<T>(oDims);
-
-        switch(btype)
-        {
-            case AF_PAD_SYM:
-                kernel::padBorders<T, AF_PAD_SYM>(ret, in, lowerBoundPadding);
-                break;
-            case AF_PAD_CLAMP_TO_EDGE:
-                kernel::padBorders<T, AF_PAD_CLAMP_TO_EDGE>(ret, in,
-                        lowerBoundPadding);
-                break;
-            default:
-                kernel::padBorders<T, AF_PAD_ZERO>(ret, in, lowerBoundPadding);
-                break;
-        }
-
-        return ret;
+namespace opencl {
+template<typename T>
+void copyData(T *data, const Array<T> &A);
+
+template<typename T>
+Array<T> copyArray(const Array<T> &A);
+
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, const Array<inType> &in);
+
+template<typename inType, typename outType>
+Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
+                        outType default_value, double factor = 1.0);
+
+template<typename T>
+Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
+                         dim4 const &upperBoundPadding,
+                         const af::borderType btype) {
+    auto iDims = in.dims();
+
+    dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
+               lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
+               lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
+               lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
+
+    auto ret = createEmptyArray<T>(oDims);
+
+    switch (btype) {
+        case AF_PAD_SYM:
+            kernel::padBorders<T, AF_PAD_SYM>(ret, in, lowerBoundPadding);
+            break;
+        case AF_PAD_CLAMP_TO_EDGE:
+            kernel::padBorders<T, AF_PAD_CLAMP_TO_EDGE>(ret, in,
+                                                        lowerBoundPadding);
+            break;
+        default:
+            kernel::padBorders<T, AF_PAD_ZERO>(ret, in, lowerBoundPadding);
+            break;
     }
 
+    return ret;
+}
 
-    template<typename T>
-    void multiply_inplace(Array<T> &in, double val);
+template<typename T>
+void multiply_inplace(Array<T> &in, double val);
 
-    template<typename T>
-    T getScalar(const Array<T> &in);
-}
+template<typename T>
+T getScalar(const Array<T> &in);
+}  // namespace opencl
diff --git a/src/backend/opencl/count.cpp b/src/backend/opencl/count.cpp
index c1162954ad..c8ae0bf692 100644
--- a/src/backend/opencl/count.cpp
+++ b/src/backend/opencl/count.cpp
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    // count
-    INSTANTIATE(af_notzero_t, float  , uint)
-    INSTANTIATE(af_notzero_t, double , uint)
-    INSTANTIATE(af_notzero_t, cfloat , uint)
-    INSTANTIATE(af_notzero_t, cdouble, uint)
-    INSTANTIATE(af_notzero_t, int    , uint)
-    INSTANTIATE(af_notzero_t, uint   , uint)
-    INSTANTIATE(af_notzero_t, intl   , uint)
-    INSTANTIATE(af_notzero_t, uintl  , uint)
-    INSTANTIATE(af_notzero_t, char   , uint)
-    INSTANTIATE(af_notzero_t, uchar  , uint)
-    INSTANTIATE(af_notzero_t, short  , uint)
-    INSTANTIATE(af_notzero_t, ushort , uint)
-}
+namespace opencl {
+// count
+INSTANTIATE(af_notzero_t, float, uint)
+INSTANTIATE(af_notzero_t, double, uint)
+INSTANTIATE(af_notzero_t, cfloat, uint)
+INSTANTIATE(af_notzero_t, cdouble, uint)
+INSTANTIATE(af_notzero_t, int, uint)
+INSTANTIATE(af_notzero_t, uint, uint)
+INSTANTIATE(af_notzero_t, intl, uint)
+INSTANTIATE(af_notzero_t, uintl, uint)
+INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, uchar, uint)
+INSTANTIATE(af_notzero_t, short, uint)
+INSTANTIATE(af_notzero_t, ushort, uint)
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 51b5123ee3..11b2451d4e 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -8,59 +8,64 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <cpu/cpu_helper.hpp>
-#include <cpu/cpu_blas.hpp>
-#include <math.hpp>
 #include <common/blas_headers.hpp>
 #include <common/complex.hpp>
+#include <cpu/cpu_blas.hpp>
+#include <cpu/cpu_helper.hpp>
+#include <math.hpp>
 
 using common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
+using std::conditional;
 using std::enable_if;
 using std::is_floating_point;
 using std::remove_const;
-using std::conditional;
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
-// Some implementations of BLAS require void* for complex pointers while others use float*/double*
+// Some implementations of BLAS require void* for complex pointers while others
+// use float*/double*
 //
 // Sample cgemm API
 // OpenBLAS
-// void cblas_cgemm(OPENBLAS_CONST enum CBLAS_ORDER Order, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransA, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransB,
-//                  OPENBLAS_CONST blasint M, OPENBLAS_CONST blasint N, OPENBLAS_CONST blasint K,
-//                  OPENBLAS_CONST float *alpha, OPENBLAS_CONST float *A, OPENBLAS_CONST blasint lda,
-//                  OPENBLAS_CONST float *B, OPENBLAS_CONST blasint ldb, OPENBLAS_CONST float *beta,
-//                  float *C, OPENBLAS_CONST blasint ldc);
+// void cblas_cgemm(OPENBLAS_CONST enum CBLAS_ORDER Order, OPENBLAS_CONST enum
+// CBLAS_TRANSPOSE TransA, OPENBLAS_CONST enum CBLAS_TRANSPOSE TransB,
+//                  OPENBLAS_CONST blasint M, OPENBLAS_CONST blasint N,
+//                  OPENBLAS_CONST blasint K, OPENBLAS_CONST float *alpha,
+//                  OPENBLAS_CONST float *A, OPENBLAS_CONST blasint lda,
+//                  OPENBLAS_CONST float *B, OPENBLAS_CONST blasint ldb,
+//                  OPENBLAS_CONST float *beta, float *C, OPENBLAS_CONST blasint
+//                  ldc);
 //
 // MKL
-// void cblas_cgemm(const  CBLAS_LAYOUT Layout, const CBLAS_TRANSPOSE TransA, const  CBLAS_TRANSPOSE TransB,
+// void cblas_cgemm(const  CBLAS_LAYOUT Layout, const CBLAS_TRANSPOSE TransA,
+// const  CBLAS_TRANSPOSE TransB,
 //                  const MKL_INT M, const MKL_INT N, const MKL_INT K,
 //                  const void *alpha, const void *A, const MKL_INT lda,
 //                  const void *B, const MKL_INT ldb, const void *beta,
 //                  void *C, const MKL_INT ldc);
 // atlas cblas
-// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE TransA,
-//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-//                  const void *alpha, const void *A, const int lda,
-//                  const void *B, const int ldb, const void *beta,
-//                  void *C, const int ldc);
+// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE
+// TransA,
+//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N,
+//                  const int K, const void *alpha, const void *A, const int
+//                  lda, const void *B, const int ldb, const void *beta, void
+//                  *C, const int ldc);
 //
 // LAPACKE
-// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE TransA,
-//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-//                  const void *alpha, const void *A, const int lda,
-//                  const void *B, const int ldb, const void *beta,
-//                  void *C, const int ldc);
+// void cblas_cgemm(const enum CBLAS_ORDER Order, const enum CBLAS_TRANSPOSE
+// TransA,
+//                  const enum CBLAS_TRANSPOSE TransB, const int M, const int N,
+//                  const int K, const void *alpha, const void *A, const int
+//                  lda, const void *B, const int ldb, const void *beta, void
+//                  *C, const int ldc);
 #if defined(IS_OPENBLAS)
-    static const bool cplx_void_ptr = false;
+static const bool cplx_void_ptr = false;
 #else
-    static const bool cplx_void_ptr = true;
+static const bool cplx_void_ptr = true;
 #endif
 
 template<typename T, class Enable = void>
@@ -69,86 +74,87 @@ struct blas_base {
 };
 
 template<typename T>
-struct blas_base <T, typename enable_if<is_complex<T>::value && cplx_void_ptr>::type> {
+struct blas_base<
+    T, typename enable_if<is_complex<T>::value && cplx_void_ptr>::type> {
     using type = void;
 };
 
-
 template<typename T>
-using cptr_type     =   typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T*>::type;
+using cptr_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type *, const T *>::type;
 template<typename T>
-using ptr_type     =    typename conditional<   is_complex<T>::value,
-                                                typename blas_base<T>::type *,
-                                                T*>::type;
+using ptr_type = typename conditional<is_complex<T>::value,
+                                      typename blas_base<T>::type *, T *>::type;
 template<typename T>
-using scale_type   =    typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T>::type;
+using scale_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type *, const T>::type;
 
 template<typename T>
-using gemm_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE, const CBLAS_TRANSPOSE,
-                                const blasint, const blasint, const blasint,
-                                scale_type<T>, cptr_type<T>, const blasint,
-                                cptr_type<T>, const blasint,
-                                scale_type<T>, ptr_type<T>, const blasint);
+using gemm_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
+                               const CBLAS_TRANSPOSE, const blasint,
+                               const blasint, const blasint, scale_type<T>,
+                               cptr_type<T>, const blasint, cptr_type<T>,
+                               const blasint, scale_type<T>, ptr_type<T>,
+                               const blasint);
 
 template<typename T>
-using gemv_func_def = void (*)( const CBLAS_ORDER, const CBLAS_TRANSPOSE,
-                                const blasint, const blasint,
-                                scale_type<T>, cptr_type<T>, const blasint,
-                                cptr_type<T>, const blasint,
-                                scale_type<T>, ptr_type<T>, const blasint);
-
-#define BLAS_FUNC_DEF( FUNC )                           \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define BLAS_FUNC( FUNC, TYPE, PREFIX )                 \
-  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &cblas_##PREFIX##FUNC; }
+using gemv_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
+                               const blasint, const blasint, scale_type<T>,
+                               cptr_type<T>, const blasint, cptr_type<T>,
+                               const blasint, scale_type<T>, ptr_type<T>,
+                               const blasint);
+
+#define BLAS_FUNC_DEF(FUNC) \
+    template<typename T>    \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define BLAS_FUNC(FUNC, TYPE, PREFIX)           \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &cblas_##PREFIX##FUNC;           \
+    }
 
-BLAS_FUNC_DEF( gemm )
-BLAS_FUNC(gemm , float   , s)
-BLAS_FUNC(gemm , double  , d)
-BLAS_FUNC(gemm , cfloat  , c)
-BLAS_FUNC(gemm , cdouble , z)
+BLAS_FUNC_DEF(gemm)
+BLAS_FUNC(gemm, float, s)
+BLAS_FUNC(gemm, double, d)
+BLAS_FUNC(gemm, cfloat, c)
+BLAS_FUNC(gemm, cdouble, z)
 
 BLAS_FUNC_DEF(gemv)
-BLAS_FUNC(gemv , float   , s)
-BLAS_FUNC(gemv , double  , d)
-BLAS_FUNC(gemv , cfloat  , c)
-BLAS_FUNC(gemv , cdouble , z)
+BLAS_FUNC(gemv, float, s)
+BLAS_FUNC(gemv, double, d)
+BLAS_FUNC(gemv, cfloat, c)
+BLAS_FUNC(gemv, cdouble, z)
 
 template<typename T, int value>
 typename enable_if<is_floating_point<T>::value, scale_type<T>>::type
-getScale() { return T(value); }
+getScale() {
+    return T(value);
+}
 
 template<typename T, int value>
-typename enable_if<is_complex<T>::value, scale_type<T>>::type
-getScale()
-{
+typename enable_if<is_complex<T>::value, scale_type<T>>::type getScale() {
     thread_local T val = scalar<T>(value);
     return (const typename blas_base<T>::type *)&val;
 }
 
 CBLAS_TRANSPOSE
-toCblasTranspose(af_mat_prop opt)
-{
+toCblasTranspose(af_mat_prop opt) {
     CBLAS_TRANSPOSE out = CblasNoTrans;
-    switch(opt) {
-        case AF_MAT_NONE        : out = CblasNoTrans;   break;
-        case AF_MAT_TRANS       : out = CblasTrans;     break;
-        case AF_MAT_CTRANS      : out = CblasConjTrans; break;
-        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    switch (opt) {
+        case AF_MAT_NONE: out = CblasNoTrans; break;
+        case AF_MAT_TRANS: out = CblasTrans; break;
+        case AF_MAT_CTRANS: out = CblasConjTrans; break;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
 }
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
     CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
     CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
 
@@ -158,23 +164,23 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
-    int M = lDims[aRowDim];
-    int N = rDims[bColDim];
-    int K = lDims[aColDim];
-    dim_t d2 = std::max(lDims[2], rDims[2]);
-    dim_t d3 = std::max(lDims[3], rDims[3]);
+    int M      = lDims[aRowDim];
+    int N      = rDims[bColDim];
+    int K      = lDims[aColDim];
+    dim_t d2   = std::max(lDims[2], rDims[2]);
+    dim_t d3   = std::max(lDims[3], rDims[3]);
     dim4 oDims = af::dim4(M, N, d2, d3);
 
-    //FIXME: Leaks on errors.
+    // FIXME: Leaks on errors.
     Array<T> out = createValueArray<T>(oDims, scalar<T>(0));
-    auto alpha = getScale<T, 1>();
-    auto beta  = getScale<T, 0>();
+    auto alpha   = getScale<T, 1>();
+    auto beta    = getScale<T, 0>();
 
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
     dim4 oStrides = out.strides();
 
-    using BT  =       typename blas_base<T>::type;
+    using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
     int batchSize = oDims[2] * oDims[3];
@@ -184,50 +190,42 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
     bool is_r_d2_batched = (oDims[2] == rDims[2]);
     bool is_r_d3_batched = (oDims[3] == rDims[3]);
 
-    for(int n = 0; n < batchSize; ++n) {
+    for (int n = 0; n < batchSize; ++n) {
         int w = n / rDims[2];
         int z = n - w * rDims[2];
 
-        int loff = z * (is_l_d2_batched * lStrides[2]) + w * (is_l_d3_batched * lStrides[3]);
-        int roff = z * (is_r_d2_batched * rStrides[2]) + w * (is_r_d3_batched * rStrides[3]);
+        int loff = z * (is_l_d2_batched * lStrides[2]) +
+                   w * (is_l_d3_batched * lStrides[3]);
+        int roff = z * (is_r_d2_batched * rStrides[2]) +
+                   w * (is_r_d3_batched * rStrides[3]);
 
         // get host pointers from mapped memory
         auto lPtr = lhs.getMappedPtr();
         auto rPtr = rhs.getMappedPtr();
         auto oPtr = out.getMappedPtr();
 
-        CBT *lptr = (CBT*)(lPtr.get() + loff);
-        CBT *rptr = (CBT*)(rPtr.get() + roff);
-        BT  *optr = (BT*)(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
+        CBT *lptr = (CBT *)(lPtr.get() + loff);
+        CBT *rptr = (CBT *)(rPtr.get() + roff);
+        BT *optr  = (BT *)(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
 
-        if(rDims[bColDim] == 1) {
+        if (rDims[bColDim] == 1) {
             dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
-            N = lDims[aColDim];
-            gemv_func<T>()(
-                CblasColMajor, lOpts,
-                lDims[0], lDims[1],
-                alpha,
-                lptr, lStrides[1],
-                rptr, incr,
-                beta,
-                optr, 1);
+            N          = lDims[aColDim];
+            gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
+                           lptr, lStrides[1], rptr, incr, beta, optr, 1);
         } else {
-            gemm_func<T>()(
-                CblasColMajor, lOpts, rOpts,
-                M, N, K,
-                alpha,
-                lptr, lStrides[1],
-                rptr, rStrides[1],
-                beta,
-                optr, out.dims()[0]);
+            gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha, lptr,
+                           lStrides[1], rptr, rStrides[1], beta, optr,
+                           out.dims()[0]);
         }
     }
 
     return out;
 }
 
-#define INSTANTIATE_BLAS(TYPE)                                                          \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, const Array<TYPE> &rhs,   \
+#define INSTANTIATE_BLAS(TYPE)                                \
+    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
+                                      const Array<TYPE> &rhs, \
                                       af_mat_prop optLhs, af_mat_prop optRhs);
 
 INSTANTIATE_BLAS(float)
@@ -235,6 +233,6 @@ INSTANTIATE_BLAS(cfloat)
 INSTANTIATE_BLAS(double)
 INSTANTIATE_BLAS(cdouble)
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
index 908742471d..2aafe0dc90 100644
--- a/src/backend/opencl/cpu/cpu_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -9,12 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs,
-                    af_mat_prop optLhs, af_mat_prop optRhs);
-}
+namespace opencl {
+namespace cpu {
+template<typename T>
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs);
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index 98fc48c335..68d8415f18 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -8,77 +8,76 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <cpu/cpu_helper.hpp>
+#include <copy.hpp>
 #include <cpu/cpu_cholesky.hpp>
+#include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_triangle.hpp>
-#include <copy.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T>
-using potrf_func_def = int (*)(ORDER_TYPE, char,
-                               int,
-                               T*, int);
+using potrf_func_def = int (*)(ORDER_TYPE, char, int, T *, int);
 
-#define CH_FUNC_DEF( FUNC )                                     \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+#define CH_FUNC_DEF(FUNC) \
+    template<typename T>  \
+    FUNC##_func_def<T> FUNC##_func();
 
+#define CH_FUNC(FUNC, TYPE, PREFIX)             \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-#define CH_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-CH_FUNC_DEF( potrf )
-CH_FUNC(potrf , float  , s)
-CH_FUNC(potrf , double , d)
-CH_FUNC(potrf , cfloat , c)
-CH_FUNC(potrf , cdouble, z)
+CH_FUNC_DEF(potrf)
+CH_FUNC(potrf, float, s)
+CH_FUNC(potrf, double, d)
+CH_FUNC(potrf, cfloat, c)
+CH_FUNC(potrf, cdouble, z)
 
 template<typename T>
-Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
-{
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
-    *info = cholesky_inplace(out, is_upper);
+    *info        = cholesky_inplace(out, is_upper);
 
     std::shared_ptr<T> oPtr = out.getMappedPtr();
 
-    if (is_upper) triangle<T, true , false>(oPtr.get(), oPtr.get(), out.dims(), out.strides(), out.strides());
-    else          triangle<T, false, false>(oPtr.get(), oPtr.get(), out.dims(), out.strides(), out.strides());
+    if (is_upper)
+        triangle<T, true, false>(oPtr.get(), oPtr.get(), out.dims(),
+                                 out.strides(), out.strides());
+    else
+        triangle<T, false, false>(oPtr.get(), oPtr.get(), out.dims(),
+                                  out.strides(), out.strides());
 
     return out;
 }
 
 template<typename T>
-int cholesky_inplace(Array<T> &in, const bool is_upper)
-{
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
     dim4 iDims = in.dims();
-    int N = iDims[0];
+    int N      = iDims[0];
 
     char uplo = 'L';
-    if(is_upper)
-        uplo = 'U';
+    if (is_upper) uplo = 'U';
 
     std::shared_ptr<T> inPtr = in.getMappedPtr();
 
-    int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo,
-                               N, inPtr.get(), in.strides()[1]);
+    int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, inPtr.get(),
+                               in.strides()[1]);
 
     return info;
 }
 
-#define INSTANTIATE_CH(T)                                                                   \
-    template int cholesky_inplace<T>(Array<T> &in, const bool is_upper);                    \
-    template Array<T> cholesky<T>   (int *info, const Array<T> &in, const bool is_upper);   \
-
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
 
 INSTANTIATE_CH(float)
 INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_cholesky.hpp b/src/backend/opencl/cpu/cpu_cholesky.hpp
index 041e93980e..3fdecfcd4a 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.hpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.hpp
@@ -9,14 +9,12 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
+namespace opencl {
+namespace cpu {
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 
-    template<typename T>
-    int cholesky_inplace(Array<T> &in, const bool is_upper);
-}
-}
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper);
+}  // namespace cpu
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index 1bf19fc986..8ca6a4928c 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -12,8 +12,8 @@
 
 #include <Array.hpp>
 #include <memory.hpp>
-#include <types.hpp>
 #include <platform.hpp>
+#include <types.hpp>
 
 //********************************************************/
 // LAPACK
@@ -28,16 +28,16 @@
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
 #ifdef USE_MKL
-    #include<mkl_lapacke.h>
+#include <mkl_lapacke.h>
 #else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-        #include <common/lapacke.hpp>
-        #undef AF_LAPACK_COL_MAJOR
-        #define AF_LAPACK_COL_MAJOR 0
-    #else // NETLIB LAPACKE
-        #include<lapacke.h>
-    #endif
+#ifdef __APPLE__
+#include <Accelerate/Accelerate.h>
+#include <common/lapacke.hpp>
+#undef AF_LAPACK_COL_MAJOR
+#define AF_LAPACK_COL_MAJOR 0
+#else  // NETLIB LAPACKE
+#include <lapacke.h>
+#endif
 #endif
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index 9dba95048d..e7815659ba 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -8,69 +8,64 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_inverse.hpp>
 #include <cpu/cpu_lu.hpp>
-#include <copy.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T>
-using getri_func_def = int (*)(ORDER_TYPE, int,
-                               T *, int,
-                               const int *);
+using getri_func_def = int (*)(ORDER_TYPE, int, T *, int, const int *);
 
-#define INV_FUNC_DEF( FUNC )                                        \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+#define INV_FUNC_DEF(FUNC) \
+    template<typename T>   \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define INV_FUNC( FUNC, TYPE, PREFIX )                              \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
+#define INV_FUNC(FUNC, TYPE, PREFIX)            \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-INV_FUNC_DEF( getri )
-INV_FUNC(getri , float  , s)
-INV_FUNC(getri , double , d)
-INV_FUNC(getri , cfloat , c)
-INV_FUNC(getri , cdouble, z)
+INV_FUNC_DEF(getri)
+INV_FUNC(getri, float, s)
+INV_FUNC(getri, double, d)
+INV_FUNC(getri, cfloat, c)
+INV_FUNC(getri, cdouble, z)
 
 template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
+Array<T> inverse(const Array<T> &in) {
     int M = in.dims()[0];
-    //int N = in.dims()[1];
+    // int N = in.dims()[1];
 
     // This condition is already handled in opencl/inverse.cpp
-    //if (M != N) {
-        //Array<T> I = identity<T>(in.dims());
-        //return solve(in, I);
+    // if (M != N) {
+    // Array<T> I = identity<T>(in.dims());
+    // return solve(in, I);
     //}
 
     Array<T> A = copyArray<T>(in);
 
     Array<int> pivot = cpu::lu_inplace<T>(A, false);
 
-
-    std::shared_ptr<T>   aPtr = A.getMappedPtr();
+    std::shared_ptr<T> aPtr   = A.getMappedPtr();
     std::shared_ptr<int> pPtr = pivot.getMappedPtr();
 
-    getri_func<T>()(AF_LAPACK_COL_MAJOR, M,
-                    aPtr.get(), A.strides()[1],
+    getri_func<T>()(AF_LAPACK_COL_MAJOR, M, aPtr.get(), A.strides()[1],
                     pPtr.get());
 
     return A;
 }
 
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_inverse.hpp b/src/backend/opencl/cpu/cpu_inverse.hpp
index 38581a1906..b5be9e1ee0 100644
--- a/src/backend/opencl/cpu/cpu_inverse.hpp
+++ b/src/backend/opencl/cpu/cpu_inverse.hpp
@@ -9,11 +9,9 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> inverse(const Array<T> &in);
-}
+namespace opencl {
+namespace cpu {
+template<typename T>
+Array<T> inverse(const Array<T> &in);
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index c496b09ca9..39706c0b6a 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -8,39 +8,36 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_lu.hpp>
 #include <math.hpp>
-#include <copy.hpp>
 #include <range.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T>
-using getrf_func_def = int (*)(ORDER_TYPE, int, int,
-                               T*, int,
-                               int*);
-
-#define LU_FUNC_DEF( FUNC )                                     \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
+using getrf_func_def = int (*)(ORDER_TYPE, int, int, T *, int, int *);
 
+#define LU_FUNC_DEF(FUNC) \
+    template<typename T>  \
+    FUNC##_func_def<T> FUNC##_func();
 
-#define LU_FUNC( FUNC, TYPE, PREFIX )                           \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()        \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
+#define LU_FUNC(FUNC, TYPE, PREFIX)             \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-LU_FUNC_DEF( getrf )
-LU_FUNC(getrf , float  , s)
-LU_FUNC(getrf , double , d)
-LU_FUNC(getrf , cfloat , c)
-LU_FUNC(getrf , cdouble, z)
+LU_FUNC_DEF(getrf)
+LU_FUNC(getrf, float, s)
+LU_FUNC(getrf, double, d)
+LU_FUNC(getrf, cfloat, c)
+LU_FUNC(getrf, cdouble, z)
 
 template<typename T>
-void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
-{
+void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in) {
     std::shared_ptr<T> ls = lower.getMappedPtr();
     std::shared_ptr<T> us = upper.getMappedPtr();
     std::shared_ptr<T> is = in.getMappedPtr();
@@ -57,40 +54,34 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
     dim4 ust = upper.strides();
     dim4 ist = in.strides();
 
-    for(dim_t ow = 0; ow < idm[3]; ow++) {
+    for (dim_t ow = 0; ow < idm[3]; ow++) {
         const dim_t lW = ow * lst[3];
         const dim_t uW = ow * ust[3];
         const dim_t iW = ow * ist[3];
 
-        for(dim_t oz = 0; oz < idm[2]; oz++) {
+        for (dim_t oz = 0; oz < idm[2]; oz++) {
             const dim_t lZW = lW + oz * lst[2];
             const dim_t uZW = uW + oz * ust[2];
             const dim_t iZW = iW + oz * ist[2];
 
-            for(dim_t oy = 0; oy < idm[1]; oy++) {
+            for (dim_t oy = 0; oy < idm[1]; oy++) {
                 const dim_t lYZW = lZW + oy * lst[1];
                 const dim_t uYZW = uZW + oy * ust[1];
                 const dim_t iYZW = iZW + oy * ist[1];
 
-                for(dim_t ox = 0; ox < idm[0]; ox++) {
+                for (dim_t ox = 0; ox < idm[0]; ox++) {
                     const dim_t lMem = lYZW + ox;
                     const dim_t uMem = uYZW + ox;
                     const dim_t iMem = iYZW + ox;
-                    if(ox > oy) {
-                        if(oy < ldm[1])
-                            l[lMem] = i[iMem];
-                        if(ox < udm[0])
-                            u[uMem] = scalar<T>(0);
+                    if (ox > oy) {
+                        if (oy < ldm[1]) l[lMem] = i[iMem];
+                        if (ox < udm[0]) u[uMem] = scalar<T>(0);
                     } else if (oy > ox) {
-                        if(oy < ldm[1])
-                            l[lMem] = scalar<T>(0);
-                        if(ox < udm[0])
-                            u[uMem] = i[iMem];
-                    } else if(ox == oy) {
-                        if(oy < ldm[1])
-                            l[lMem] = scalar<T>(1.0);
-                        if(ox < udm[0])
-                            u[uMem] = i[iMem];
+                        if (oy < ldm[1]) l[lMem] = scalar<T>(0);
+                        if (ox < udm[0]) u[uMem] = i[iMem];
+                    } else if (ox == oy) {
+                        if (oy < ldm[1]) l[lMem] = scalar<T>(1.0);
+                        if (ox < udm[0]) u[uMem] = i[iMem];
                     }
                 }
             }
@@ -98,9 +89,8 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in)
     }
 }
 
-void convertPivot(Array<int> &pivot, int out_sz)
-{
-    Array<int> p = range<int>(dim4(out_sz), 0); // Runs opencl
+void convertPivot(Array<int> &pivot, int out_sz) {
+    Array<int> p = range<int>(dim4(out_sz), 0);  // Runs opencl
 
     std::shared_ptr<int> pi = pivot.getMappedPtr();
     std::shared_ptr<int> po = p.getMappedPtr();
@@ -110,7 +100,7 @@ void convertPivot(Array<int> &pivot, int out_sz)
 
     dim_t d0 = pivot.dims()[0];
 
-    for(int j = 0; j < (int)d0; j++) {
+    for (int j = 0; j < (int)d0; j++) {
         // 1 indexed in pivot
         std::swap(d_po[j], d_po[d_pi[j] - 1]);
     }
@@ -122,14 +112,14 @@ void convertPivot(Array<int> &pivot, int out_sz)
 }
 
 template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<T> in_copy = copyArray<T>(in);
-    pivot = lu_inplace(in_copy);
+    pivot            = lu_inplace(in_copy);
 
     // SPLIT into lower and upper
     dim4 ldims(M, min(M, N));
@@ -141,38 +131,38 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
 }
 
 template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
 
-    std::shared_ptr<T>   inPtr = in.getMappedPtr();
+    std::shared_ptr<T> inPtr   = in.getMappedPtr();
     std::shared_ptr<int> piPtr = pivot.getMappedPtr();
 
-    getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    inPtr.get(), in.strides()[1],
+    getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, inPtr.get(), in.strides()[1],
                     piPtr.get());
 
     inPtr.reset();
     piPtr.reset();
 
-    if(convert_pivot) convertPivot(pivot, M);
+    if (convert_pivot) convertPivot(pivot, M);
 
     return pivot;
 }
 
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
 
 INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_lu.hpp b/src/backend/opencl/cpu/cpu_lu.hpp
index 6c038f20c7..f3cf4aaa1d 100644
--- a/src/backend/opencl/cpu/cpu_lu.hpp
+++ b/src/backend/opencl/cpu/cpu_lu.hpp
@@ -9,14 +9,13 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T>
-    void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+namespace opencl {
+namespace cpu {
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in);
 
-    template<typename T>
-    Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
-}
-}
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+}  // namespace cpu
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index f52a18f6c2..199747e4e9 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -8,59 +8,57 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_qr.hpp>
 #include <cpu/cpu_triangle.hpp>
-#include <copy.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T>
-using geqrf_func_def = int (*)(ORDER_TYPE, int, int,
-                               T*, int,
-                               T*);
+using geqrf_func_def = int (*)(ORDER_TYPE, int, int, T *, int, T *);
 
 template<typename T>
-using gqr_func_def = int (*)(ORDER_TYPE, int, int, int,
-                             T*, int,
-                             const T*);
-
-#define QR_FUNC_DEF( FUNC )                                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define QR_FUNC( FUNC, TYPE, PREFIX )                               \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
-
-QR_FUNC_DEF( geqrf )
-QR_FUNC(geqrf , float  , s)
-QR_FUNC(geqrf , double , d)
-QR_FUNC(geqrf , cfloat , c)
-QR_FUNC(geqrf , cdouble, z)
-
-#define GQR_FUNC_DEF( FUNC )                                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define GQR_FUNC( FUNC, TYPE, PREFIX )                               \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()             \
-{ return & LAPACK_NAME(PREFIX); }
-
-GQR_FUNC_DEF( gqr )
-GQR_FUNC(gqr , float  , sorgqr)
-GQR_FUNC(gqr , double , dorgqr)
-GQR_FUNC(gqr , cfloat , cungqr)
-GQR_FUNC(gqr , cdouble, zungqr)
+using gqr_func_def = int (*)(ORDER_TYPE, int, int, int, T *, int, const T *);
+
+#define QR_FUNC_DEF(FUNC) \
+    template<typename T>  \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define QR_FUNC(FUNC, TYPE, PREFIX)             \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
+
+QR_FUNC_DEF(geqrf)
+QR_FUNC(geqrf, float, s)
+QR_FUNC(geqrf, double, d)
+QR_FUNC(geqrf, cfloat, c)
+QR_FUNC(geqrf, cdouble, z)
+
+#define GQR_FUNC_DEF(FUNC) \
+    template<typename T>   \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define GQR_FUNC(FUNC, TYPE, PREFIX)            \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX);            \
+    }
+
+GQR_FUNC_DEF(gqr)
+GQR_FUNC(gqr, float, sorgqr)
+GQR_FUNC(gqr, double, dorgqr)
+GQR_FUNC(gqr, cfloat, cungqr)
+GQR_FUNC(gqr, cdouble, zungqr)
 
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     dim4 padDims(M, max(M, N));
     q = padArray<T, T>(in, padDims, scalar<T>(0));
@@ -75,44 +73,42 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
     std::shared_ptr<T> rPtr = r.getMappedPtr();
     std::shared_ptr<T> tPtr = t.getMappedPtr();
 
-    triangle<T, true, false>(rPtr.get(), qPtr.get(), rdims, r.strides(), q.strides());
+    triangle<T, true, false>(rPtr.get(), qPtr.get(), rdims, r.strides(),
+                             q.strides());
 
-    gqr_func<T>()(AF_LAPACK_COL_MAJOR,
-                  M, M, min(M, N),
-                  qPtr.get(), q.strides()[1],
-                  tPtr.get());
+    gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), qPtr.get(),
+                  q.strides()[1], tPtr.get());
 
     q.resetDims(dim4(M, M));
 }
 
 template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
+Array<T> qr_inplace(Array<T> &in) {
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
     std::shared_ptr<T> iPtr = in.getMappedPtr();
     std::shared_ptr<T> tPtr = t.getMappedPtr();
 
-    geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N,
-                    iPtr.get(), in.strides()[1],
+    geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, iPtr.get(), in.strides()[1],
                     tPtr.get());
 
     return t;
 }
 
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
 
 INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_qr.hpp b/src/backend/opencl/cpu/cpu_qr.hpp
index c499b9d03b..5d755dbd0b 100644
--- a/src/backend/opencl/cpu/cpu_qr.hpp
+++ b/src/backend/opencl/cpu/cpu_qr.hpp
@@ -9,14 +9,12 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T>
-    void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+namespace opencl {
+namespace cpu {
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
 
-    template<typename T>
-    Array<T> qr_inplace(Array<T> &in);
-}
-}
+template<typename T>
+Array<T> qr_inplace(Array<T> &in);
+}  // namespace cpu
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 1886b4cbc9..7ed2371b45 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -8,126 +8,104 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
-#include <copy.hpp>
 #include <math.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T>
-using gesv_func_def = int (*)(ORDER_TYPE, int, int,
-                              T *, int,
-                              int *,
-                              T *, int);
+using gesv_func_def = int (*)(ORDER_TYPE, int, int, T *, int, int *, T *, int);
 
 template<typename T>
-using gels_func_def = int (*)(ORDER_TYPE, char,
-                              int, int, int,
-                              T *, int,
-                              T *, int);
+using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int, T *, int, T *,
+                              int);
 
 template<typename T>
-using getrs_func_def = int (*)(ORDER_TYPE, char,
-                               int, int,
-                               const T *, int,
-                               const int *,
-                               T *, int);
+using getrs_func_def = int (*)(ORDER_TYPE, char, int, int, const T *, int,
+                               const int *, T *, int);
 
 template<typename T>
-using trtrs_func_def = int (*)(ORDER_TYPE,
-                               char, char, char,
-                               int, int,
-                               const T *, int,
-                               T *, int);
-
-
-#define SOLVE_FUNC_DEF( FUNC )                                      \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-
-#define SOLVE_FUNC( FUNC, TYPE, PREFIX )                            \
-template<> FUNC##_func_def<TYPE>     FUNC##_func<TYPE>()            \
-{ return & LAPACK_NAME(PREFIX##FUNC); }
+using trtrs_func_def = int (*)(ORDER_TYPE, char, char, char, int, int,
+                               const T *, int, T *, int);
 
-SOLVE_FUNC_DEF( gesv )
-SOLVE_FUNC(gesv , float  , s)
-SOLVE_FUNC(gesv , double , d)
-SOLVE_FUNC(gesv , cfloat , c)
-SOLVE_FUNC(gesv , cdouble, z)
+#define SOLVE_FUNC_DEF(FUNC) \
+    template<typename T>     \
+    FUNC##_func_def<T> FUNC##_func();
 
-SOLVE_FUNC_DEF( gels )
-SOLVE_FUNC(gels , float  , s)
-SOLVE_FUNC(gels , double , d)
-SOLVE_FUNC(gels , cfloat , c)
-SOLVE_FUNC(gels , cdouble, z)
-
-SOLVE_FUNC_DEF( getrs )
-SOLVE_FUNC(getrs , float  , s)
-SOLVE_FUNC(getrs , double , d)
-SOLVE_FUNC(getrs , cfloat , c)
-SOLVE_FUNC(getrs , cdouble, z)
+#define SOLVE_FUNC(FUNC, TYPE, PREFIX)          \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);      \
+    }
 
-SOLVE_FUNC_DEF( trtrs )
-SOLVE_FUNC(trtrs , float  , s)
-SOLVE_FUNC(trtrs , double , d)
-SOLVE_FUNC(trtrs , cfloat , c)
-SOLVE_FUNC(trtrs , cdouble, z)
+SOLVE_FUNC_DEF(gesv)
+SOLVE_FUNC(gesv, float, s)
+SOLVE_FUNC(gesv, double, d)
+SOLVE_FUNC(gesv, cfloat, c)
+SOLVE_FUNC(gesv, cdouble, z)
+
+SOLVE_FUNC_DEF(gels)
+SOLVE_FUNC(gels, float, s)
+SOLVE_FUNC(gels, double, d)
+SOLVE_FUNC(gels, cfloat, c)
+SOLVE_FUNC(gels, cdouble, z)
+
+SOLVE_FUNC_DEF(getrs)
+SOLVE_FUNC(getrs, float, s)
+SOLVE_FUNC(getrs, double, d)
+SOLVE_FUNC(getrs, cfloat, c)
+SOLVE_FUNC(getrs, cdouble, z)
+
+SOLVE_FUNC_DEF(trtrs)
+SOLVE_FUNC(trtrs, float, s)
+SOLVE_FUNC(trtrs, double, d)
+SOLVE_FUNC(trtrs, cfloat, c)
+SOLVE_FUNC(trtrs, cdouble, z)
 
 template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
     UNUSED(options);
-    int N = A.dims()[0];
+    int N    = A.dims()[0];
     int NRHS = b.dims()[1];
 
     Array<T> B = copyArray<T>(b);
 
-    std::shared_ptr<T  > aPtr = A.getMappedPtr();
-    std::shared_ptr<T  > bPtr = B.getMappedPtr();
+    std::shared_ptr<T> aPtr   = A.getMappedPtr();
+    std::shared_ptr<T> bPtr   = B.getMappedPtr();
     std::shared_ptr<int> pPtr = pivot.getMappedPtr();
 
-    getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                    N, NRHS,
-                    aPtr.get(), A.strides()[1],
-                    pPtr.get(),
-                    bPtr.get(), B.strides()[1]);
+    getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N', N, NRHS, aPtr.get(),
+                    A.strides()[1], pPtr.get(), bPtr.get(), B.strides()[1]);
 
     return B;
 }
 
 template<typename T>
-Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
-{
+Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
+                       const af_mat_prop options) {
     Array<T> B = copyArray<T>(b);
-    int N = B.dims()[0];
-    int NRHS = B.dims()[1];
+    int N      = B.dims()[0];
+    int NRHS   = B.dims()[1];
 
     std::shared_ptr<T> aPtr = A.getMappedPtr();
     std::shared_ptr<T> bPtr = B.getMappedPtr();
 
-    trtrs_func<T>()(AF_LAPACK_COL_MAJOR,
-                    options & AF_MAT_UPPER ? 'U' : 'L',
-                    'N', // transpose flag
-                    options & AF_MAT_DIAG_UNIT ? 'U' : 'N',
-                    N, NRHS,
-                    aPtr.get(), A.strides()[1],
-                    bPtr.get(), B.strides()[1]);
+    trtrs_func<T>()(AF_LAPACK_COL_MAJOR, options & AF_MAT_UPPER ? 'U' : 'L',
+                    'N',  // transpose flag
+                    options & AF_MAT_DIAG_UNIT ? 'U' : 'N', N, NRHS, aPtr.get(),
+                    A.strides()[1], bPtr.get(), B.strides()[1]);
 
     return B;
 }
 
-
 template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-
-    if (options & AF_MAT_UPPER ||
-        options & AF_MAT_LOWER) {
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
+    if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
     }
 
@@ -141,37 +119,34 @@ Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
     std::shared_ptr<T> aPtr = A.getMappedPtr();
     std::shared_ptr<T> bPtr = B.getMappedPtr();
 
-    if(M == N) {
+    if (M == N) {
         std::vector<int> pivot(N);
-        gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K,
-                       aPtr.get(), A.strides()[1],
-                       &pivot.front(),
-                       bPtr.get(), B.strides()[1]);
+        gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, aPtr.get(), A.strides()[1],
+                       &pivot.front(), bPtr.get(), B.strides()[1]);
     } else {
         int sM = a.strides()[1];
         int sN = a.strides()[2] / sM;
 
-        gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
-                       M, N, K,
-                       aPtr.get(), A.strides()[1],
-                       bPtr.get(), max(sM, sN));
+        gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N', M, N, K, aPtr.get(),
+                       A.strides()[1], bPtr.get(), max(sM, sN));
         B.resetDims(dim4(N, K));
     }
 
     return B;
 }
 
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
     template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
 
 INSTANTIATE_SOLVE(float)
 INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_solve.hpp b/src/backend/opencl/cpu/cpu_solve.hpp
index 6c3de642ad..9ef13caa8f 100644
--- a/src/backend/opencl/cpu/cpu_solve.hpp
+++ b/src/backend/opencl/cpu/cpu_solve.hpp
@@ -9,15 +9,14 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T>
-    Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
+namespace opencl {
+namespace cpu {
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options = AF_MAT_NONE);
 
-    template<typename T>
-    Array<T> solveLU(const Array<T> &a, const Array<int> &pivot,
-                     const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
-}
-}
+template<typename T>
+Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options = AF_MAT_NONE);
+}  // namespace cpu
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 626f48dd0e..dd5031bc24 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -10,12 +10,12 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_sparse_blas.hpp>
 
-#include <af/dim4.hpp>
 #include <common/complex.hpp>
 #include <complex.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <af/dim4.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -24,11 +24,11 @@ using common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
+using std::conditional;
 using std::enable_if;
 using std::is_floating_point;
-using std::remove_const;
-using std::conditional;
 using std::is_same;
+using std::remove_const;
 
 namespace opencl {
 namespace cpu {
@@ -39,28 +39,25 @@ struct blas_base {
 };
 
 template<typename T>
-struct blas_base <T, typename enable_if<is_complex<T>::value>::type> {
-    using type = typename conditional<is_same<T, cdouble>::value,
-                                      sp_cdouble, sp_cfloat>
-                                     ::type;
+struct blas_base<T, typename enable_if<is_complex<T>::value>::type> {
+    using type = typename conditional<is_same<T, cdouble>::value, sp_cdouble,
+                                      sp_cfloat>::type;
 };
 
 template<typename T>
-using cptr_type     =   typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const T*>::type;
+using cptr_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type *, const T *>::type;
 template<typename T>
-using ptr_type     =    typename conditional<   is_complex<T>::value,
-                                                typename blas_base<T>::type *,
-                                                T*>::type;
+using ptr_type = typename conditional<is_complex<T>::value,
+                                      typename blas_base<T>::type *, T *>::type;
 template<typename T>
-using scale_type   =    typename conditional<   is_complex<T>::value,
-                                                const typename blas_base<T>::type,
-                                                const T>::type;
+using scale_type =
+    typename conditional<is_complex<T>::value,
+                         const typename blas_base<T>::type, const T>::type;
 
 template<typename To, typename Ti>
-To getScaleValue(Ti val)
-{
+To getScaleValue(Ti val) {
     return (To)(val);
 }
 
@@ -97,66 +94,57 @@ To getScaleValue(Ti val)
 //                 MKL_INT ldy);
 
 template<typename T>
-using create_csr_func_def = sparse_status_t (*)
-                           (sparse_matrix_t *,
-                            sparse_index_base_t,
-                            int, int,
-                            int *, int *, int*,
-                            ptr_type<T>);
+using create_csr_func_def = sparse_status_t (*)(sparse_matrix_t *,
+                                                sparse_index_base_t, int, int,
+                                                int *, int *, int *,
+                                                ptr_type<T>);
 
 template<typename T>
-using mv_func_def         = sparse_status_t (*)
-                           (sparse_operation_t,
-                            scale_type<T>,
-                            const sparse_matrix_t,
-                            struct matrix_descr,
-                            cptr_type<T>,
-                            scale_type<T>,
-                            ptr_type<T>);
+using mv_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
+                                        const sparse_matrix_t,
+                                        struct matrix_descr, cptr_type<T>,
+                                        scale_type<T>, ptr_type<T>);
 
 template<typename T>
-using mm_func_def         = sparse_status_t (*)
-                           (sparse_operation_t,
-                            scale_type<T>,
-                            const sparse_matrix_t,
-                            struct matrix_descr,
-                            sparse_layout_t,
-                            cptr_type<T>,
-                            int, int,
-                            scale_type<T>,
-                            ptr_type<T>, int);
-
-#define SPARSE_FUNC_DEF( FUNC )                         \
-template<typename T> FUNC##_func_def<T> FUNC##_func();
-
-#define SPARSE_FUNC( FUNC, TYPE, PREFIX )               \
-  template<> FUNC##_func_def<TYPE> FUNC##_func<TYPE>()  \
-{ return &mkl_sparse_##PREFIX##_##FUNC; }
-
-SPARSE_FUNC_DEF( create_csr )
-SPARSE_FUNC(create_csr , float   , s)
-SPARSE_FUNC(create_csr , double  , d)
-SPARSE_FUNC(create_csr , cfloat  , c)
-SPARSE_FUNC(create_csr , cdouble , z)
-
-SPARSE_FUNC_DEF( mv )
-SPARSE_FUNC(mv , float   , s)
-SPARSE_FUNC(mv , double  , d)
-SPARSE_FUNC(mv , cfloat  , c)
-SPARSE_FUNC(mv , cdouble , z)
-
-SPARSE_FUNC_DEF( mm )
-SPARSE_FUNC(mm , float   , s)
-SPARSE_FUNC(mm , double  , d)
-SPARSE_FUNC(mm , cfloat  , c)
-SPARSE_FUNC(mm , cdouble , z)
+using mm_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
+                                        const sparse_matrix_t,
+                                        struct matrix_descr, sparse_layout_t,
+                                        cptr_type<T>, int, int, scale_type<T>,
+                                        ptr_type<T>, int);
+
+#define SPARSE_FUNC_DEF(FUNC) \
+    template<typename T>      \
+    FUNC##_func_def<T> FUNC##_func();
+
+#define SPARSE_FUNC(FUNC, TYPE, PREFIX)         \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &mkl_sparse_##PREFIX##_##FUNC;   \
+    }
+
+SPARSE_FUNC_DEF(create_csr)
+SPARSE_FUNC(create_csr, float, s)
+SPARSE_FUNC(create_csr, double, d)
+SPARSE_FUNC(create_csr, cfloat, c)
+SPARSE_FUNC(create_csr, cdouble, z)
+
+SPARSE_FUNC_DEF(mv)
+SPARSE_FUNC(mv, float, s)
+SPARSE_FUNC(mv, double, d)
+SPARSE_FUNC(mv, cfloat, c)
+SPARSE_FUNC(mv, cdouble, z)
+
+SPARSE_FUNC_DEF(mm)
+SPARSE_FUNC(mm, float, s)
+SPARSE_FUNC(mm, double, d)
+SPARSE_FUNC(mm, cfloat, c)
+SPARSE_FUNC(mm, cdouble, z)
 
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
 
 template<>
-const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
-{
+const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
     sp_cfloat ret;
     ret.real = val.s[0];
     ret.imag = val.s[1];
@@ -164,53 +152,47 @@ const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val)
 }
 
 template<>
-const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val)
-{
+const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val) {
     sp_cdouble ret;
     ret.real = val.s[0];
     ret.imag = val.s[1];
     return ret;
 }
 
-#else   // USE_MKL
+#else  // USE_MKL
 
 // From mkl_spblas.h
-typedef enum
-{
-    SPARSE_OPERATION_NON_TRANSPOSE          = 10,
-    SPARSE_OPERATION_TRANSPOSE              = 11,
-    SPARSE_OPERATION_CONJUGATE_TRANSPOSE    = 12,
+typedef enum {
+    SPARSE_OPERATION_NON_TRANSPOSE       = 10,
+    SPARSE_OPERATION_TRANSPOSE           = 11,
+    SPARSE_OPERATION_CONJUGATE_TRANSPOSE = 12,
 } sparse_operation_t;
 
 #endif  // USE_MKL
 
-sparse_operation_t
-toSparseTranspose(af_mat_prop opt)
-{
+sparse_operation_t toSparseTranspose(af_mat_prop opt) {
     sparse_operation_t out = SPARSE_OPERATION_NON_TRANSPOSE;
-    switch(opt) {
-        case AF_MAT_NONE        : out = SPARSE_OPERATION_NON_TRANSPOSE;         break;
-        case AF_MAT_TRANS       : out = SPARSE_OPERATION_TRANSPOSE;             break;
-        case AF_MAT_CTRANS      : out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE;   break;
-        default                 : AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    switch (opt) {
+        case AF_MAT_NONE: out = SPARSE_OPERATION_NON_TRANSPOSE; break;
+        case AF_MAT_TRANS: out = SPARSE_OPERATION_TRANSPOSE; break;
+        case AF_MAT_CTRANS: out = SPARSE_OPERATION_CONJUGATE_TRANSPOSE; break;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
     }
     return out;
 }
 
 template<typename T, int value>
-scale_type<T> getScale()
-{
+scale_type<T> getScale() {
     thread_local T val = scalar<T>(value);
     return getScaleValue<scale_type<T>, T>(val);
 }
 
 ////////////////////////////////////////////////////////////////////////////////
-#ifdef USE_MKL // Implementation using MKL
+#ifdef USE_MKL  // Implementation using MKL
 ////////////////////////////////////////////////////////////////////////////////
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                af_mat_prop optLhs, af_mat_prop optRhs) {
     // MKL: CSRMM Does not support optRhs
     UNUSED(optRhs);
 
@@ -221,16 +203,16 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     sparse_operation_t lOpts = toSparseTranspose(optLhs);
 
     int lRowDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 0 : 1;
-    //int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
+    // int lColDim = (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) ? 1 : 0;
 
-    //Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
+    // Unsupported : (rOpts == SPARSE_OPERATION_NON_TRANSPOSE;) ? 1 : 0;
     static const int rColDim = 1;
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
-    int M = lDims[lRowDim];
-    int N = rDims[rColDim];
-    //int K = lDims[lColDim];
+    int M      = lDims[lRowDim];
+    int N      = rDims[rColDim];
+    // int K = lDims[lColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
@@ -245,19 +227,19 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     auto rhsPtr = rhs.getMappedPtr();
     auto outPtr = out.getMappedPtr();
 
-    Array<T  > values = lhs.getValues();
+    Array<T> values   = lhs.getValues();
     Array<int> rowIdx = lhs.getRowIdx();
     Array<int> colIdx = lhs.getColIdx();
 
     auto vPtr = values.getMappedPtr();
     auto rPtr = rowIdx.getMappedPtr();
     auto cPtr = colIdx.getMappedPtr();
-    int* pB   = rPtr.get();
-    int* pE   = rPtr.get() + 1;
+    int *pB   = rPtr.get();
+    int *pE   = rPtr.get() + 1;
 
     sparse_matrix_t csrLhs;
-    create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, lhs.dims()[0], lhs.dims()[1],
-                         pB, pE, cPtr.get(),
+    create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, lhs.dims()[0],
+                         lhs.dims()[1], pB, pE, cPtr.get(),
                          reinterpret_cast<ptr_type<T>>(vPtr.get()));
 
     struct matrix_descr descrLhs;
@@ -265,22 +247,17 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     mkl_sparse_optimize(csrLhs);
 
-    if(rDims[rColDim] == 1) {
+    if (rDims[rColDim] == 1) {
         mkl_sparse_set_mv_hint(csrLhs, lOpts, descrLhs, 1);
-        mv_func<T>()(
-            lOpts, alpha,
-            csrLhs, descrLhs,
-            reinterpret_cast<cptr_type<T>>(rhsPtr.get()),
-            beta,
-            reinterpret_cast<ptr_type<T>>(outPtr.get()));
+        mv_func<T>()(lOpts, alpha, csrLhs, descrLhs,
+                     reinterpret_cast<cptr_type<T>>(rhsPtr.get()), beta,
+                     reinterpret_cast<ptr_type<T>>(outPtr.get()));
     } else {
-        mkl_sparse_set_mm_hint(csrLhs, lOpts, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR, N, 1);
-        mm_func<T>()(
-            lOpts, alpha,
-            csrLhs, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR,
-            reinterpret_cast<cptr_type<T>>(rhsPtr.get()),
-            N, ldb, beta,
-            reinterpret_cast<ptr_type<T>>(outPtr.get()), ldc);
+        mkl_sparse_set_mm_hint(csrLhs, lOpts, descrLhs,
+                               SPARSE_LAYOUT_COLUMN_MAJOR, N, 1);
+        mm_func<T>()(lOpts, alpha, csrLhs, descrLhs, SPARSE_LAYOUT_COLUMN_MAJOR,
+                     reinterpret_cast<cptr_type<T>>(rhsPtr.get()), N, ldb, beta,
+                     reinterpret_cast<ptr_type<T>>(outPtr.get()), ldc);
     }
     mkl_sparse_destroy(csrLhs);
 
@@ -288,61 +265,54 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 }
 
 ////////////////////////////////////////////////////////////////////////////////
-#else // Implementation without using MKL
+#else  // Implementation without using MKL
 ////////////////////////////////////////////////////////////////////////////////
 
 template<typename T>
-T getConjugate(const T &in)
-{
+T getConjugate(const T &in) {
     // For non-complex types return same
     return in;
 }
 
 template<>
-cfloat getConjugate(const cfloat &in)
-{
+cfloat getConjugate(const cfloat &in) {
     cfloat val;
-    val.s[0] =  in.s[0];
+    val.s[0] = in.s[0];
     val.s[1] = -in.s[1];
     return val;
 }
 
 template<>
-cdouble getConjugate(const cdouble &in)
-{
+cdouble getConjugate(const cdouble &in) {
     cdouble val;
-    val.s[0] =  in.s[0];
+    val.s[0] = in.s[0];
     val.s[1] = -in.s[1];
     return val;
 }
 
 template<typename T, bool conjugate>
-void mv(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
-        int M)
-{
+void mv(Array<T> output, const Array<T> values, const Array<int> rowIdx,
+        const Array<int> colIdx, const Array<T> right, int M) {
     UNUSED(M);
     auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right .getMappedPtr();
+    auto rhtPtr = right.getMappedPtr();
     auto vPtr   = values.getMappedPtr();
     auto rPtr   = rowIdx.getMappedPtr();
     auto cPtr   = colIdx.getMappedPtr();
 
-    T   const * const valPtr = vPtr.get();
-    int const * const rowPtr = rPtr.get();
-    int const * const colPtr = cPtr.get();
-    T   const * const rhsPtr = rhtPtr.get();
-    T         * const outPtr = oPtr.get();
+    T const *const valPtr   = vPtr.get();
+    int const *const rowPtr = rPtr.get();
+    int const *const colPtr = cPtr.get();
+    T const *const rhsPtr   = rhtPtr.get();
+    T *const outPtr         = oPtr.get();
 
-    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+    for (int i = 0; i < rowIdx.dims()[0] - 1; ++i) {
         outPtr[i] = scalar<T>(0);
-        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-            //If stride[0] of right is not 1 then rhsPtr[colPtr[j]*stride]
+        for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+            // If stride[0] of right is not 1 then rhsPtr[colPtr[j]*stride]
             if (conjugate) {
-                outPtr[i] = outPtr[i] + getConjugate(valPtr[j]) * rhsPtr[colPtr[j]];
+                outPtr[i] =
+                    outPtr[i] + getConjugate(valPtr[j]) * rhsPtr[colPtr[j]];
             } else {
                 outPtr[i] = outPtr[i] + valPtr[j] * rhsPtr[colPtr[j]];
             }
@@ -351,34 +321,28 @@ void mv(Array<T> output,
 }
 
 template<typename T, bool conjugate>
-void mtv(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
-        int M)
-{
+void mtv(Array<T> output, const Array<T> values, const Array<int> rowIdx,
+         const Array<int> colIdx, const Array<T> right, int M) {
     auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right .getMappedPtr();
+    auto rhtPtr = right.getMappedPtr();
     auto vPtr   = values.getMappedPtr();
     auto rPtr   = rowIdx.getMappedPtr();
     auto cPtr   = colIdx.getMappedPtr();
 
-    T   const * const valPtr = vPtr.get();
-    int const * const rowPtr = rPtr.get();
-    int const * const colPtr = cPtr.get();
-    T   const * const rhsPtr = rhtPtr.get();
-    T         * const outPtr = oPtr.get();
+    T const *const valPtr   = vPtr.get();
+    int const *const rowPtr = rPtr.get();
+    int const *const colPtr = cPtr.get();
+    T const *const rhsPtr   = rhtPtr.get();
+    T *const outPtr         = oPtr.get();
 
-    for (int i = 0; i < M; ++i) {
-        outPtr[i] = scalar<T>(0);
-    }
+    for (int i = 0; i < M; ++i) { outPtr[i] = scalar<T>(0); }
 
-    for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
-        for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-            //If stride[0] of right is not 1 then rhsPtr[i*stride]
+    for (int i = 0; i < rowIdx.dims()[0] - 1; ++i) {
+        for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+            // If stride[0] of right is not 1 then rhsPtr[i*stride]
             if (conjugate) {
-                outPtr[colPtr[j]] = outPtr[colPtr[j]] + getConjugate(valPtr[j]) * rhsPtr[i];
+                outPtr[colPtr[j]] =
+                    outPtr[colPtr[j]] + getConjugate(valPtr[j]) * rhsPtr[i];
             } else {
                 outPtr[colPtr[j]] = outPtr[colPtr[j]] + valPtr[j] * rhsPtr[i];
             }
@@ -387,34 +351,30 @@ void mtv(Array<T> output,
 }
 
 template<typename T, bool conjugate>
-void mm(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
-        int M, int N,
-        int ldb, int ldc)
-{
+void mm(Array<T> output, const Array<T> values, const Array<int> rowIdx,
+        const Array<int> colIdx, const Array<T> right, int M, int N, int ldb,
+        int ldc) {
     UNUSED(M);
     auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right .getMappedPtr();
+    auto rhtPtr = right.getMappedPtr();
     auto vPtr   = values.getMappedPtr();
     auto rPtr   = rowIdx.getMappedPtr();
     auto cPtr   = colIdx.getMappedPtr();
 
-    T   const * const valPtr = vPtr.get();
-    int const * const rowPtr = rPtr.get();
-    int const * const colPtr = cPtr.get();
-    T   const *       rhsPtr = rhtPtr.get();
-    T         *       outPtr = oPtr.get();
+    T const *const valPtr   = vPtr.get();
+    int const *const rowPtr = rPtr.get();
+    int const *const colPtr = cPtr.get();
+    T const *rhsPtr         = rhtPtr.get();
+    T *outPtr               = oPtr.get();
 
     for (int o = 0; o < N; ++o) {
-        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
+        for (int i = 0; i < rowIdx.dims()[0] - 1; ++i) {
             outPtr[i] = scalar<T>(0);
-            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-                //If stride[0] of right is not 1 then rhsPtr[colPtr[j]*stride]
+            for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+                // If stride[0] of right is not 1 then rhsPtr[colPtr[j]*stride]
                 if (conjugate) {
-                    outPtr[i] = outPtr[i] + getConjugate(valPtr[j]) * rhsPtr[colPtr[j]];
+                    outPtr[i] =
+                        outPtr[i] + getConjugate(valPtr[j]) * rhsPtr[colPtr[j]];
                 } else {
                     outPtr[i] = outPtr[i] + valPtr[j] * rhsPtr[colPtr[j]];
                 }
@@ -426,38 +386,33 @@ void mm(Array<T> output,
 }
 
 template<typename T, bool conjugate>
-void mtm(Array<T> output,
-        const Array<T> values,
-        const Array<int> rowIdx,
-        const Array<int> colIdx,
-        const Array<T> right,
-        int M, int N,
-        int ldb, int ldc)
-{
+void mtm(Array<T> output, const Array<T> values, const Array<int> rowIdx,
+         const Array<int> colIdx, const Array<T> right, int M, int N, int ldb,
+         int ldc) {
     auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right .getMappedPtr();
+    auto rhtPtr = right.getMappedPtr();
     auto vPtr   = values.getMappedPtr();
     auto rPtr   = rowIdx.getMappedPtr();
     auto cPtr   = colIdx.getMappedPtr();
 
-    T   const * const valPtr = vPtr.get();
-    int const * const rowPtr = rPtr.get();
-    int const * const colPtr = cPtr.get();
-    T   const *       rhsPtr = rhtPtr.get();
-    T         *       outPtr = oPtr.get();
+    T const *const valPtr   = vPtr.get();
+    int const *const rowPtr = rPtr.get();
+    int const *const colPtr = cPtr.get();
+    T const *rhsPtr         = rhtPtr.get();
+    T *outPtr               = oPtr.get();
 
     for (int o = 0; o < N; ++o) {
-        for (int i = 0; i < M; ++i) {
-            outPtr[i] = scalar<T>(0);
-        }
+        for (int i = 0; i < M; ++i) { outPtr[i] = scalar<T>(0); }
 
-        for (int i = 0; i < rowIdx.dims()[0]-1; ++i) {
-            for (int j = rowPtr[i]; j < rowPtr[i+1]; ++j) {
-                //If stride[0] of right is not 1 then rhsPtr[i*stride]
+        for (int i = 0; i < rowIdx.dims()[0] - 1; ++i) {
+            for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
+                // If stride[0] of right is not 1 then rhsPtr[i*stride]
                 if (conjugate) {
-                    outPtr[colPtr[j]] = outPtr[colPtr[j]] + getConjugate(valPtr[j]) * rhsPtr[i];
+                    outPtr[colPtr[j]] =
+                        outPtr[colPtr[j]] + getConjugate(valPtr[j]) * rhsPtr[i];
                 } else {
-                    outPtr[colPtr[j]] = outPtr[colPtr[j]] + valPtr[j] * rhsPtr[i];
+                    outPtr[colPtr[j]] =
+                        outPtr[colPtr[j]] + valPtr[j] * rhsPtr[i];
                 }
             }
         }
@@ -467,8 +422,7 @@ void mtm(Array<T> output,
 }
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                af_mat_prop optLhs, af_mat_prop optRhs) {
     UNUSED(optRhs);
     lhs.eval();
     rhs.eval();
@@ -482,8 +436,8 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhs.dims();
-    int M = lDims[lRowDim];
-    int N = rDims[rColDim];
+    int M      = lDims[lRowDim];
+    int N      = rDims[rColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
     out.eval();
@@ -491,11 +445,11 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     int ldb = rhs.strides()[1];
     int ldc = out.strides()[1];
 
-    Array<T  > values = lhs.getValues();
+    Array<T> values   = lhs.getValues();
     Array<int> rowIdx = lhs.getRowIdx();
     Array<int> colIdx = lhs.getColIdx();
 
-    if(rDims[rColDim] == 1) {
+    if (rDims[rColDim] == 1) {
         if (lOpts == SPARSE_OPERATION_NON_TRANSPOSE) {
             mv<T, false>(out, values, rowIdx, colIdx, rhs, M);
         } else if (lOpts == SPARSE_OPERATION_TRANSPOSE) {
@@ -520,10 +474,10 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 #endif
 ////////////////////////////////////////////////////////////////////////////////
 
-#define INSTANTIATE_SPARSE(T)                                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
-                                af_mat_prop optLhs, af_mat_prop optRhs);                \
-
+#define INSTANTIATE_SPARSE(T)                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
+                                const Array<T> rhs, af_mat_prop optLhs, \
+                                af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
@@ -532,6 +486,6 @@ INSTANTIATE_SPARSE(cdouble)
 
 #undef INSTANTIATE_SPARSE
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.hpp b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
index 01df836839..90e53e30d6 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
@@ -15,21 +15,19 @@
 #endif
 
 #ifdef USE_MKL
-using sp_cfloat = MKL_Complex8;
+using sp_cfloat  = MKL_Complex8;
 using sp_cdouble = MKL_Complex16;
 #else
-using sp_cfloat = opencl::cfloat;
+using sp_cfloat  = opencl::cfloat;
 using sp_cdouble = opencl::cdouble;
 #endif
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index 353dd7681a..a0f07a32d8 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -8,105 +8,89 @@
  ********************************************************/
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_svd.hpp>
-#include <copy.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
-#define SVD_FUNC_DEF( FUNC )                                            \
-    template<typename T,typename Tr> svd_func_def<T, Tr> svd_func();
+#define SVD_FUNC_DEF(FUNC)            \
+    template<typename T, typename Tr> \
+    svd_func_def<T, Tr> svd_func();
 
-#define SVD_FUNC( FUNC, T, Tr, PREFIX )                     \
-    template<> svd_func_def<T, Tr>     svd_func<T, Tr>()    \
-    { return & LAPACK_NAME(PREFIX##FUNC); }
+#define SVD_FUNC(FUNC, T, Tr, PREFIX)       \
+    template<>                              \
+    svd_func_def<T, Tr> svd_func<T, Tr>() { \
+        return &LAPACK_NAME(PREFIX##FUNC);  \
+    }
 
 #if defined(USE_MKL) || defined(__APPLE__)
 
-    template<typename T, typename Tr>
-    using svd_func_def = int (*)(ORDER_TYPE,
-                                 char jobz,
-                                 int m, int n,
-                                 T* in, int ldin,
-                                 Tr* s,
-                                 T* u, int ldu,
-                                 T* vt, int ldvt);
-
-    SVD_FUNC_DEF( gesdd )
-    SVD_FUNC(gesdd, float  , float , s)
-    SVD_FUNC(gesdd, double , double, d)
-    SVD_FUNC(gesdd, cfloat , float , c)
-    SVD_FUNC(gesdd, cdouble, double, z)
-
-#else   // Atlas causes memory freeing issues with using gesdd
-
-    template<typename T, typename Tr>
-    using svd_func_def = int (*)(ORDER_TYPE,
-                                 char jobu, char jobvt,
-                                 int m, int n,
-                                 T* in, int ldin,
-                                 Tr* s,
-                                 T* u, int ldu,
-                                 T* vt, int ldvt,
-                                 Tr *superb);
-
-    SVD_FUNC_DEF( gesvd )
-    SVD_FUNC(gesvd, float  , float , s)
-    SVD_FUNC(gesvd, double , double, d)
-    SVD_FUNC(gesvd, cfloat , float , c)
-    SVD_FUNC(gesvd, cdouble, double, z)
+template<typename T, typename Tr>
+using svd_func_def = int (*)(ORDER_TYPE, char jobz, int m, int n, T *in,
+                             int ldin, Tr *s, T *u, int ldu, T *vt, int ldvt);
 
-#endif
+SVD_FUNC_DEF(gesdd)
+SVD_FUNC(gesdd, float, float, s)
+SVD_FUNC(gesdd, double, double, d)
+SVD_FUNC(gesdd, cfloat, float, c)
+SVD_FUNC(gesdd, cdouble, double, z)
 
-    template <typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-    {
-        dim4 iDims = in.dims();
-        int M = iDims[0];
-        int N = iDims[1];
+#else  // Atlas causes memory freeing issues with using gesdd
 
-        std::shared_ptr<Tr> sPtr = s.getMappedPtr();
-        std::shared_ptr<T > uPtr = u.getMappedPtr();
-        std::shared_ptr<T > vPtr = vt.getMappedPtr();
-        std::shared_ptr<T > iPtr = in.getMappedPtr();
+template<typename T, typename Tr>
+using svd_func_def = int (*)(ORDER_TYPE, char jobu, char jobvt, int m, int n,
+                             T *in, int ldin, Tr *s, T *u, int ldu, T *vt,
+                             int ldvt, Tr *superb);
+
+SVD_FUNC_DEF(gesvd)
+SVD_FUNC(gesvd, float, float, s)
+SVD_FUNC(gesvd, double, double, d)
+SVD_FUNC(gesvd, cfloat, float, c)
+SVD_FUNC(gesvd, cdouble, double, z)
 
-#if defined(USE_MKL) || defined(__APPLE__)
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A',
-                          M, N,
-                          iPtr.get(), in.strides()[1],
-                          sPtr.get(),
-                          uPtr.get(), u.strides()[1],
-                          vPtr.get(), vt.strides()[1]);
-#else
-        std::vector<Tr> superb(std::min(M, N));
-        svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A',
-                          M, N,
-                          iPtr.get(), in.strides()[1],
-                          sPtr.get(),
-                          uPtr.get(), u.strides()[1],
-                          vPtr.get(), vt.strides()[1],
-                          &superb[0]);
 #endif
-    }
 
-    template <typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-    {
-        Array<T> in_copy = copyArray<T>(in);
-        svdInPlace(s, u, vt, in_copy);
-    }
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
+    dim4 iDims = in.dims();
+    int M      = iDims[0];
+    int N      = iDims[1];
 
-#define INSTANTIATE_SVD(T, Tr)                                          \
-    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, const Array<T> &in); \
-    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, Array<T> &in);
+    std::shared_ptr<Tr> sPtr = s.getMappedPtr();
+    std::shared_ptr<T> uPtr  = u.getMappedPtr();
+    std::shared_ptr<T> vPtr  = vt.getMappedPtr();
+    std::shared_ptr<T> iPtr  = in.getMappedPtr();
 
-    INSTANTIATE_SVD(float  , float )
-    INSTANTIATE_SVD(double , double)
-    INSTANTIATE_SVD(cfloat , float )
-    INSTANTIATE_SVD(cdouble, double)
+#if defined(USE_MKL) || defined(__APPLE__)
+    svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, iPtr.get(),
+                      in.strides()[1], sPtr.get(), uPtr.get(), u.strides()[1],
+                      vPtr.get(), vt.strides()[1]);
+#else
+    std::vector<Tr> superb(std::min(M, N));
+    svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', 'A', M, N, iPtr.get(),
+                      in.strides()[1], sPtr.get(), uPtr.get(), u.strides()[1],
+                      vPtr.get(), vt.strides()[1], &superb[0]);
+#endif
 }
+
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
+    Array<T> in_copy = copyArray<T>(in);
+    svdInPlace(s, u, vt, in_copy);
 }
+
+#define INSTANTIATE_SVD(T, Tr)                                           \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
+
+INSTANTIATE_SVD(float, float)
+INSTANTIATE_SVD(double, double)
+INSTANTIATE_SVD(cfloat, float)
+INSTANTIATE_SVD(cdouble, double)
+}  // namespace cpu
+}  // namespace opencl
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_svd.hpp b/src/backend/opencl/cpu/cpu_svd.hpp
index 4f271af8b9..783c1664fe 100644
--- a/src/backend/opencl/cpu/cpu_svd.hpp
+++ b/src/backend/opencl/cpu/cpu_svd.hpp
@@ -9,14 +9,12 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
-    template<typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
+namespace opencl {
+namespace cpu {
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 
-    template<typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
-}
-}
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+}  // namespace cpu
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_triangle.hpp b/src/backend/opencl/cpu/cpu_triangle.hpp
index 630d865205..51bc242428 100644
--- a/src/backend/opencl/cpu/cpu_triangle.hpp
+++ b/src/backend/opencl/cpu/cpu_triangle.hpp
@@ -13,33 +13,31 @@
 
 #include <math.hpp>
 
-namespace opencl
-{
-namespace cpu
-{
+namespace opencl {
+namespace cpu {
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(T *o, const T *i, const dim4 odm, const dim4 ost, const dim4 ist)
-{
-    for(dim_t ow = 0; ow < odm[3]; ow++) {
+void triangle(T *o, const T *i, const dim4 odm, const dim4 ost,
+              const dim4 ist) {
+    for (dim_t ow = 0; ow < odm[3]; ow++) {
         const dim_t oW = ow * ost[3];
         const dim_t iW = ow * ist[3];
 
-        for(dim_t oz = 0; oz < odm[2]; oz++) {
+        for (dim_t oz = 0; oz < odm[2]; oz++) {
             const dim_t oZW = oW + oz * ost[2];
             const dim_t iZW = iW + oz * ist[2];
 
-            for(dim_t oy = 0; oy < odm[1]; oy++) {
+            for (dim_t oy = 0; oy < odm[1]; oy++) {
                 const dim_t oYZW = oZW + oy * ost[1];
                 const dim_t iYZW = iZW + oy * ist[1];
 
-                for(dim_t ox = 0; ox < odm[0]; ox++) {
+                for (dim_t ox = 0; ox < odm[0]; ox++) {
                     const dim_t oMem = oYZW + ox;
                     const dim_t iMem = iYZW + ox;
 
-                    bool cond = is_upper ? (oy >= ox) : (oy <= ox);
+                    bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
                     bool do_unit_diag = (is_unit_diag && ox == oy);
-                    if(cond) {
+                    if (cond) {
                         o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
                     } else {
                         o[oMem] = scalar<T>(0);
@@ -50,8 +48,8 @@ void triangle(T *o, const T *i, const dim4 odm, const dim4 ost, const dim4 ist)
     }
 }
 
-}
-}
+}  // namespace cpu
+}  // namespace opencl
 
 #endif
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/debug_opencl.hpp b/src/backend/opencl/debug_opencl.hpp
index 19680a65ac..e2e808d160 100644
--- a/src/backend/opencl/debug_opencl.hpp
+++ b/src/backend/opencl/debug_opencl.hpp
@@ -9,16 +9,14 @@
 
 #pragma once
 #include <err_opencl.hpp>
-#include <stdio.h>
 #include <errorcodes.hpp>
+#include <stdio.h>
 
 #ifndef NDEBUG
 #define CL_DEBUG_FINISH(Q) Q.finish()
 #else
-#define CL_DEBUG_FINISH(Q)                      \
-    do {                                        \
-        if(synchronize_calls()) {               \
-            Q.finish();                         \
-        }                                       \
+#define CL_DEBUG_FINISH(Q)                       \
+    do {                                         \
+        if (synchronize_calls()) { Q.finish(); } \
     } while (false);
 #endif
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 70e80ec5a2..198e22f349 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -7,55 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <diagonal.hpp>
-#include <math.hpp>
 #include <err_opencl.hpp>
 #include <kernel/diagonal.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> diagCreate(const Array<T> &in, const int num)
-    {
-        int size = in.dims()[0] + std::abs(num);
-        int batch = in.dims()[1];
-        Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
-
-        kernel::diagCreate<T>(out, in, num);
+namespace opencl {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num) {
+    int size     = in.dims()[0] + std::abs(num);
+    int batch    = in.dims()[1];
+    Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
-        return out;
-    }
+    kernel::diagCreate<T>(out, in, num);
 
-    template<typename T>
-    Array<T> diagExtract(const Array<T> &in, const int num)
-    {
-        const dim_t *idims = in.dims().get();
-        dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
-        Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
+    return out;
+}
 
-        kernel::diagExtract<T>(out, in, num);
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num) {
+    const dim_t *idims = in.dims().get();
+    dim_t size         = std::min(idims[0], idims[1]) - std::abs(num);
+    Array<T> out       = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
-        return out;
+    kernel::diagExtract<T>(out, in, num);
 
-    }
+    return out;
+}
 
 #define INSTANTIATE_DIAGONAL(T)                                          \
-    template Array<T>  diagExtract<T>    (const Array<T> &in, const int num); \
-    template Array<T>  diagCreate <T>    (const Array<T> &in, const int num);
-
-    INSTANTIATE_DIAGONAL(float)
-    INSTANTIATE_DIAGONAL(double)
-    INSTANTIATE_DIAGONAL(cfloat)
-    INSTANTIATE_DIAGONAL(cdouble)
-    INSTANTIATE_DIAGONAL(int)
-    INSTANTIATE_DIAGONAL(uint)
-    INSTANTIATE_DIAGONAL(intl)
-    INSTANTIATE_DIAGONAL(uintl)
-    INSTANTIATE_DIAGONAL(char)
-    INSTANTIATE_DIAGONAL(uchar)
-    INSTANTIATE_DIAGONAL(short)
-    INSTANTIATE_DIAGONAL(ushort)
-
-}
+    template Array<T> diagExtract<T>(const Array<T> &in, const int num); \
+    template Array<T> diagCreate<T>(const Array<T> &in, const int num);
+
+INSTANTIATE_DIAGONAL(float)
+INSTANTIATE_DIAGONAL(double)
+INSTANTIATE_DIAGONAL(cfloat)
+INSTANTIATE_DIAGONAL(cdouble)
+INSTANTIATE_DIAGONAL(int)
+INSTANTIATE_DIAGONAL(uint)
+INSTANTIATE_DIAGONAL(intl)
+INSTANTIATE_DIAGONAL(uintl)
+INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(uchar)
+INSTANTIATE_DIAGONAL(short)
+INSTANTIATE_DIAGONAL(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/diagonal.hpp b/src/backend/opencl/diagonal.hpp
index 5244fe098a..df2a4d4ff9 100644
--- a/src/backend/opencl/diagonal.hpp
+++ b/src/backend/opencl/diagonal.hpp
@@ -10,11 +10,10 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> diagCreate(const Array<T> &in, const int num);
+namespace opencl {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num);
 
-    template<typename T>
-    Array<T> diagExtract(const Array<T> &in, const int num);
-}
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num);
+}  // namespace opencl
diff --git a/src/backend/opencl/diff.cpp b/src/backend/opencl/diff.cpp
index 7e95692584..2a556052da 100644
--- a/src/backend/opencl/diff.cpp
+++ b/src/backend/opencl/diff.cpp
@@ -7,72 +7,62 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <diff.hpp>
 #include <kernel/diff.hpp>
+#include <af/dim4.hpp>
 #include <stdexcept>
 
-namespace opencl
-{
-    template<typename T, bool isDiff2>
-    static Array<T> diff(const Array<T> &in, const int dim)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
-        oDims[dim] -= (isDiff2 + 1);
-
-        if(iDims.elements() == 0 || oDims.elements() == 0) {
-            throw std::runtime_error("Elements are 0");
-        }
+namespace opencl {
+template<typename T, bool isDiff2>
+static Array<T> diff(const Array<T> &in, const int dim) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims       = iDims;
+    oDims[dim] -= (isDiff2 + 1);
 
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        switch (dim) {
+    if (iDims.elements() == 0 || oDims.elements() == 0) {
+        throw std::runtime_error("Elements are 0");
+    }
 
-            case (0):    kernel::diff<T, 0, isDiff2>(out, in, in.ndims());
-                         break;
+    Array<T> out = createEmptyArray<T>(oDims);
 
-            case (1):    kernel::diff<T, 1, isDiff2>(out, in, in.ndims());
-                         break;
+    switch (dim) {
+        case (0): kernel::diff<T, 0, isDiff2>(out, in, in.ndims()); break;
 
-            case (2):    kernel::diff<T, 2, isDiff2>(out, in, in.ndims());
-                         break;
+        case (1): kernel::diff<T, 1, isDiff2>(out, in, in.ndims()); break;
 
-            case (3):    kernel::diff<T, 3, isDiff2>(out, in, in.ndims());
-                         break;
-        }
+        case (2): kernel::diff<T, 2, isDiff2>(out, in, in.ndims()); break;
 
-        return out;
+        case (3): kernel::diff<T, 3, isDiff2>(out, in, in.ndims()); break;
     }
 
-    template<typename T>
-    Array<T> diff1(const Array<T> &in, const int dim)
-    {
-        return diff<T, false>(in, dim);
-    }
+    return out;
+}
 
-    template<typename T>
-    Array<T> diff2(const Array<T> &in, const int dim)
-    {
-        return diff<T, true>(in, dim);
-    }
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim) {
+    return diff<T, false>(in, dim);
+}
 
-#define INSTANTIATE(T)                                                 \
-    template Array<T> diff1<T>  (const Array<T> &in, const int dim);   \
-    template Array<T> diff2<T>  (const Array<T> &in, const int dim);   \
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim) {
+    return diff<T, true>(in, dim);
+}
 
+#define INSTANTIATE(T)                                             \
+    template Array<T> diff1<T>(const Array<T> &in, const int dim); \
+    template Array<T> diff2<T>(const Array<T> &in, const int dim);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(char)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(char)
+}  // namespace opencl
diff --git a/src/backend/opencl/diff.hpp b/src/backend/opencl/diff.hpp
index 81ef63a855..d670ebcf33 100644
--- a/src/backend/opencl/diff.hpp
+++ b/src/backend/opencl/diff.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> diff1(const Array<T> &in, const int dim);
+namespace opencl {
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim);
 
-    template<typename T>
-    Array<T> diff2(const Array<T> &in, const int dim);
-}
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim);
+}  // namespace opencl
diff --git a/src/backend/opencl/dilate.cpp b/src/backend/opencl/dilate.cpp
index fff9f99887..64a538ee76 100644
--- a/src/backend/opencl/dilate.cpp
+++ b/src/backend/opencl/dilate.cpp
@@ -9,16 +9,15 @@
 
 #include "morph_impl.hpp"
 
-namespace opencl
-{
+namespace opencl {
 
-INSTANTIATE(float , true)
+INSTANTIATE(float, true)
 INSTANTIATE(double, true)
-INSTANTIATE(char  , true)
-INSTANTIATE(int   , true)
-INSTANTIATE(uint  , true)
-INSTANTIATE(uchar , true)
-INSTANTIATE(short , true)
+INSTANTIATE(char, true)
+INSTANTIATE(int, true)
+INSTANTIATE(uint, true)
+INSTANTIATE(uchar, true)
+INSTANTIATE(short, true)
 INSTANTIATE(ushort, true)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/dilate3d.cpp b/src/backend/opencl/dilate3d.cpp
index d519957a63..522fcbdc2b 100644
--- a/src/backend/opencl/dilate3d.cpp
+++ b/src/backend/opencl/dilate3d.cpp
@@ -9,16 +9,15 @@
 
 #include "morph3d_impl.hpp"
 
-namespace opencl
-{
+namespace opencl {
 
-INSTANTIATE(float , true)
+INSTANTIATE(float, true)
 INSTANTIATE(double, true)
-INSTANTIATE(char  , true)
-INSTANTIATE(int   , true)
-INSTANTIATE(uint  , true)
-INSTANTIATE(uchar , true)
-INSTANTIATE(short , true)
+INSTANTIATE(char, true)
+INSTANTIATE(int, true)
+INSTANTIATE(uint, true)
+INSTANTIATE(uchar, true)
+INSTANTIATE(short, true)
 INSTANTIATE(ushort, true)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/erode.cpp b/src/backend/opencl/erode.cpp
index 1618802575..c5d6d84b84 100644
--- a/src/backend/opencl/erode.cpp
+++ b/src/backend/opencl/erode.cpp
@@ -9,16 +9,15 @@
 
 #include "morph_impl.hpp"
 
-namespace opencl
-{
+namespace opencl {
 
-INSTANTIATE(float , false)
+INSTANTIATE(float, false)
 INSTANTIATE(double, false)
-INSTANTIATE(char  , false)
-INSTANTIATE(int   , false)
-INSTANTIATE(uint  , false)
-INSTANTIATE(uchar , false)
-INSTANTIATE(short , false)
+INSTANTIATE(char, false)
+INSTANTIATE(int, false)
+INSTANTIATE(uint, false)
+INSTANTIATE(uchar, false)
+INSTANTIATE(short, false)
 INSTANTIATE(ushort, false)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/erode3d.cpp b/src/backend/opencl/erode3d.cpp
index 7ffb423687..73043c653d 100644
--- a/src/backend/opencl/erode3d.cpp
+++ b/src/backend/opencl/erode3d.cpp
@@ -9,16 +9,15 @@
 
 #include "morph3d_impl.hpp"
 
-namespace opencl
-{
+namespace opencl {
 
-INSTANTIATE(float , false)
+INSTANTIATE(float, false)
 INSTANTIATE(double, false)
-INSTANTIATE(char  , false)
-INSTANTIATE(int   , false)
-INSTANTIATE(uint  , false)
-INSTANTIATE(uchar , false)
-INSTANTIATE(short , false)
+INSTANTIATE(char, false)
+INSTANTIATE(int, false)
+INSTANTIATE(uint, false)
+INSTANTIATE(uchar, false)
+INSTANTIATE(short, false)
 INSTANTIATE(ushort, false)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/err_clblas.hpp b/src/backend/opencl/err_clblas.hpp
index 1440582e2c..f01d272adb 100644
--- a/src/backend/opencl/err_clblas.hpp
+++ b/src/backend/opencl/err_clblas.hpp
@@ -8,45 +8,48 @@
  ********************************************************/
 
 #pragma once
-#include <stdio.h>
-#include <err_opencl.hpp>
 #include <clBLAS.h>
+#include <err_opencl.hpp>
+#include <stdio.h>
 #include <mutex>
 
-static const char * _clblasGetResultString(clblasStatus st)
-{
-    switch (st)
-    {
-    case clblasSuccess:              return "Success";
-    case clblasInvalidValue:         return "Invalid value";
-    case clblasInvalidCommandQueue:  return "Invalid queue";
-    case clblasInvalidContext:       return "Invalid context";
-    case clblasInvalidMemObject:     return "Invalid memory object";
-    case clblasInvalidDevice:        return "Invalid device";
-    case clblasInvalidEventWaitList: return "Invalid event list";
-    case clblasOutOfResources:       return "Out of resources";
-    case clblasOutOfHostMemory:      return "Out of host memory";
-    case clblasInvalidOperation:     return "Invalid operation";
-    case clblasCompilerNotAvailable: return "Compiler not available";
-    case clblasBuildProgramFailure:  return "Build program failure";
-    case clblasNotImplemented:       return "Not implemented";
-    case clblasNotInitialized:       return "CLBLAS Not initialized";
-    case clblasInvalidMatA:          return "Invalid matrix A";
-    case clblasInvalidMatB:          return "Invalid matrix B";
-    case clblasInvalidMatC:          return "Invalid matrix C";
-    case clblasInvalidVecX:          return "Invalid vector X";
-    case clblasInvalidVecY:          return "Invalid vector Y";
-    case clblasInvalidDim:           return "Invalid dimension";
-    case clblasInvalidLeadDimA:      return "Invalid lda";
-    case clblasInvalidLeadDimB:      return "Invalid ldb";
-    case clblasInvalidLeadDimC:      return "Invalid ldc";
-    case clblasInvalidIncX:          return "Invalid incx";
-    case clblasInvalidIncY:          return "Invalid incy";
-    case clblasInsufficientMemMatA:  return  "Insufficient Memory for Matrix A";
-    case clblasInsufficientMemMatB:  return  "Insufficient Memory for Matrix B";
-    case clblasInsufficientMemMatC:  return  "Insufficient Memory for Matrix C";
-    case clblasInsufficientMemVecX:  return  "Insufficient Memory for Vector X";
-    case clblasInsufficientMemVecY:  return  "Insufficient Memory for Vector Y";
+static const char* _clblasGetResultString(clblasStatus st) {
+    switch (st) {
+        case clblasSuccess: return "Success";
+        case clblasInvalidValue: return "Invalid value";
+        case clblasInvalidCommandQueue: return "Invalid queue";
+        case clblasInvalidContext: return "Invalid context";
+        case clblasInvalidMemObject: return "Invalid memory object";
+        case clblasInvalidDevice: return "Invalid device";
+        case clblasInvalidEventWaitList: return "Invalid event list";
+        case clblasOutOfResources: return "Out of resources";
+        case clblasOutOfHostMemory: return "Out of host memory";
+        case clblasInvalidOperation: return "Invalid operation";
+        case clblasCompilerNotAvailable: return "Compiler not available";
+        case clblasBuildProgramFailure: return "Build program failure";
+        case clblasNotImplemented: return "Not implemented";
+        case clblasNotInitialized: return "CLBLAS Not initialized";
+        case clblasInvalidMatA: return "Invalid matrix A";
+        case clblasInvalidMatB: return "Invalid matrix B";
+        case clblasInvalidMatC: return "Invalid matrix C";
+        case clblasInvalidVecX: return "Invalid vector X";
+        case clblasInvalidVecY: return "Invalid vector Y";
+        case clblasInvalidDim: return "Invalid dimension";
+        case clblasInvalidLeadDimA: return "Invalid lda";
+        case clblasInvalidLeadDimB: return "Invalid ldb";
+        case clblasInvalidLeadDimC: return "Invalid ldc";
+        case clblasInvalidIncX: return "Invalid incx";
+        case clblasInvalidIncY: return "Invalid incy";
+        case clblasInsufficientMemMatA:
+            return "Insufficient Memory for Matrix A";
+        case clblasInsufficientMemMatB:
+            return "Insufficient Memory for Matrix B";
+        case clblasInsufficientMemMatC:
+            return "Insufficient Memory for Matrix C";
+        case clblasInsufficientMemVecX:
+            return "Insufficient Memory for Vector X";
+        case clblasInsufficientMemVecY:
+            return "Insufficient Memory for Vector Y";
     }
 
     return "Unknown error";
@@ -54,20 +57,17 @@ static const char * _clblasGetResultString(clblasStatus st)
 
 static std::recursive_mutex gCLBlasMutex;
 
-#define CLBLAS_CHECK(fn) do {                   \
-        gCLBlasMutex.lock();                    \
-        clblasStatus _clblas_st = fn;           \
-        gCLBlasMutex.unlock();                  \
-        if (_clblas_st != clblasSuccess) {      \
-            char clblas_st_msg[1024];           \
-            snprintf(clblas_st_msg,             \
-                     sizeof(clblas_st_msg),     \
-                     "clblas Error (%d): %s\n", \
-                     (int)(_clblas_st),         \
-                     _clblasGetResultString(    \
-                         _clblas_st));          \
-                                                \
-            AF_ERROR(clblas_st_msg,             \
-                     AF_ERR_INTERNAL);          \
-        }                                       \
-    } while(0)
+#define CLBLAS_CHECK(fn)                                           \
+    do {                                                           \
+        gCLBlasMutex.lock();                                       \
+        clblasStatus _clblas_st = fn;                              \
+        gCLBlasMutex.unlock();                                     \
+        if (_clblas_st != clblasSuccess) {                         \
+            char clblas_st_msg[1024];                              \
+            snprintf(clblas_st_msg, sizeof(clblas_st_msg),         \
+                     "clblas Error (%d): %s\n", (int)(_clblas_st), \
+                     _clblasGetResultString(_clblas_st));          \
+                                                                   \
+            AF_ERROR(clblas_st_msg, AF_ERR_INTERNAL);              \
+        }                                                          \
+    } while (0)
diff --git a/src/backend/opencl/err_clblast.hpp b/src/backend/opencl/err_clblast.hpp
index 522935499e..c4201d175f 100644
--- a/src/backend/opencl/err_clblast.hpp
+++ b/src/backend/opencl/err_clblast.hpp
@@ -8,79 +8,128 @@
  ********************************************************/
 
 #pragma once
-#include <mutex>
-#include <stdio.h>
-#include <common/err_common.hpp>
 #include <clblast.h>
+#include <common/err_common.hpp>
+#include <stdio.h>
 #include <mutex>
 
-static const char * _clblastGetResultString(clblast::StatusCode st)
-{
-    switch (st)
-    {
-    // Status codes in common with the OpenCL standard
-    case clblast::StatusCode::kSuccess:                    return "CL_SUCCESS";
-    case clblast::StatusCode::kOpenCLCompilerNotAvailable: return "CL_COMPILER_NOT_AVAILABLE";
-    case clblast::StatusCode::kTempBufferAllocFailure:     return "CL_MEM_OBJECT_ALLOCATION_FAILURE";
-    case clblast::StatusCode::kOpenCLOutOfResources:       return "CL_OUT_OF_RESOURCES";
-    case clblast::StatusCode::kOpenCLOutOfHostMemory:      return "CL_OUT_OF_HOST_MEMORY";
-    case clblast::StatusCode::kOpenCLBuildProgramFailure:  return "CL_BUILD_PROGRAM_FAILURE: OpenCL compilation error";
-    case clblast::StatusCode::kInvalidValue:               return "CL_INVALID_VALUE";
-    case clblast::StatusCode::kInvalidCommandQueue:        return "CL_INVALID_COMMAND_QUEUE";
-    case clblast::StatusCode::kInvalidMemObject:           return "CL_INVALID_MEM_OBJECT";
-    case clblast::StatusCode::kInvalidBinary:              return "CL_INVALID_BINARY";
-    case clblast::StatusCode::kInvalidBuildOptions:        return "CL_INVALID_BUILD_OPTIONS";
-    case clblast::StatusCode::kInvalidProgram:             return "CL_INVALID_PROGRAM";
-    case clblast::StatusCode::kInvalidProgramExecutable:   return "CL_INVALID_PROGRAM_EXECUTABLE";
-    case clblast::StatusCode::kInvalidKernelName:          return "CL_INVALID_KERNEL_NAME";
-    case clblast::StatusCode::kInvalidKernelDefinition:    return "CL_INVALID_KERNEL_DEFINITION";
-    case clblast::StatusCode::kInvalidKernel:              return "CL_INVALID_KERNEL";
-    case clblast::StatusCode::kInvalidArgIndex:            return "CL_INVALID_ARG_INDEX";
-    case clblast::StatusCode::kInvalidArgValue:            return "CL_INVALID_ARG_VALUE";
-    case clblast::StatusCode::kInvalidArgSize:             return "CL_INVALID_ARG_SIZE";
-    case clblast::StatusCode::kInvalidKernelArgs:          return "CL_INVALID_KERNEL_ARGS";
-    case clblast::StatusCode::kInvalidLocalNumDimensions:  return "CL_INVALID_WORK_DIMENSION: Too many thread dimensions";
-    case clblast::StatusCode::kInvalidLocalThreadsTotal:   return "CL_INVALID_WORK_GROUP_SIZE: Too many threads in total";
-    case clblast::StatusCode::kInvalidLocalThreadsDim:     return "CL_INVALID_WORK_ITEM_SIZE: ... or for a specific dimension";
-    case clblast::StatusCode::kInvalidGlobalOffset:        return "CL_INVALID_GLOBAL_OFFSET";
-    case clblast::StatusCode::kInvalidEventWaitList:       return "CL_INVALID_EVENT_WAIT_LIST";
-    case clblast::StatusCode::kInvalidEvent:               return "CL_INVALID_EVENT";
-    case clblast::StatusCode::kInvalidOperation:           return "CL_INVALID_OPERATION";
-    case clblast::StatusCode::kInvalidBufferSize:          return "CL_INVALID_BUFFER_SIZE";
-    case clblast::StatusCode::kInvalidGlobalWorkSize:      return "CL_INVALID_GLOBAL_WORK_SIZE";
+static const char* _clblastGetResultString(clblast::StatusCode st) {
+    switch (st) {
+        // Status codes in common with the OpenCL standard
+        case clblast::StatusCode::kSuccess: return "CL_SUCCESS";
+        case clblast::StatusCode::kOpenCLCompilerNotAvailable:
+            return "CL_COMPILER_NOT_AVAILABLE";
+        case clblast::StatusCode::kTempBufferAllocFailure:
+            return "CL_MEM_OBJECT_ALLOCATION_FAILURE";
+        case clblast::StatusCode::kOpenCLOutOfResources:
+            return "CL_OUT_OF_RESOURCES";
+        case clblast::StatusCode::kOpenCLOutOfHostMemory:
+            return "CL_OUT_OF_HOST_MEMORY";
+        case clblast::StatusCode::kOpenCLBuildProgramFailure:
+            return "CL_BUILD_PROGRAM_FAILURE: OpenCL compilation error";
+        case clblast::StatusCode::kInvalidValue: return "CL_INVALID_VALUE";
+        case clblast::StatusCode::kInvalidCommandQueue:
+            return "CL_INVALID_COMMAND_QUEUE";
+        case clblast::StatusCode::kInvalidMemObject:
+            return "CL_INVALID_MEM_OBJECT";
+        case clblast::StatusCode::kInvalidBinary: return "CL_INVALID_BINARY";
+        case clblast::StatusCode::kInvalidBuildOptions:
+            return "CL_INVALID_BUILD_OPTIONS";
+        case clblast::StatusCode::kInvalidProgram: return "CL_INVALID_PROGRAM";
+        case clblast::StatusCode::kInvalidProgramExecutable:
+            return "CL_INVALID_PROGRAM_EXECUTABLE";
+        case clblast::StatusCode::kInvalidKernelName:
+            return "CL_INVALID_KERNEL_NAME";
+        case clblast::StatusCode::kInvalidKernelDefinition:
+            return "CL_INVALID_KERNEL_DEFINITION";
+        case clblast::StatusCode::kInvalidKernel: return "CL_INVALID_KERNEL";
+        case clblast::StatusCode::kInvalidArgIndex:
+            return "CL_INVALID_ARG_INDEX";
+        case clblast::StatusCode::kInvalidArgValue:
+            return "CL_INVALID_ARG_VALUE";
+        case clblast::StatusCode::kInvalidArgSize: return "CL_INVALID_ARG_SIZE";
+        case clblast::StatusCode::kInvalidKernelArgs:
+            return "CL_INVALID_KERNEL_ARGS";
+        case clblast::StatusCode::kInvalidLocalNumDimensions:
+            return "CL_INVALID_WORK_DIMENSION: Too many thread dimensions";
+        case clblast::StatusCode::kInvalidLocalThreadsTotal:
+            return "CL_INVALID_WORK_GROUP_SIZE: Too many threads in total";
+        case clblast::StatusCode::kInvalidLocalThreadsDim:
+            return "CL_INVALID_WORK_ITEM_SIZE: ... or for a specific dimension";
+        case clblast::StatusCode::kInvalidGlobalOffset:
+            return "CL_INVALID_GLOBAL_OFFSET";
+        case clblast::StatusCode::kInvalidEventWaitList:
+            return "CL_INVALID_EVENT_WAIT_LIST";
+        case clblast::StatusCode::kInvalidEvent: return "CL_INVALID_EVENT";
+        case clblast::StatusCode::kInvalidOperation:
+            return "CL_INVALID_OPERATION";
+        case clblast::StatusCode::kInvalidBufferSize:
+            return "CL_INVALID_BUFFER_SIZE";
+        case clblast::StatusCode::kInvalidGlobalWorkSize:
+            return "CL_INVALID_GLOBAL_WORK_SIZE";
 
-    // Status codes in common with the clBLAS library
-    case clblast::StatusCode::kNotImplemented:             return "Routine or functionality not implemented yet";
-    case clblast::StatusCode::kInvalidMatrixA:             return "Matrix A is not a valid OpenCL buffer";
-    case clblast::StatusCode::kInvalidMatrixB:             return "Matrix B is not a valid OpenCL buffer";
-    case clblast::StatusCode::kInvalidMatrixC:             return "Matrix C is not a valid OpenCL buffer";
-    case clblast::StatusCode::kInvalidVectorX:             return "Vector X is not a valid OpenCL buffer";
-    case clblast::StatusCode::kInvalidVectorY:             return "Vector Y is not a valid OpenCL buffer";
-    case clblast::StatusCode::kInvalidDimension:           return "Dimensions M, N, and K have to be larger than zero";
-    case clblast::StatusCode::kInvalidLeadDimA:            return "LD of A is smaller than the matrix's first dimension";
-    case clblast::StatusCode::kInvalidLeadDimB:            return "LD of B is smaller than the matrix's first dimension";
-    case clblast::StatusCode::kInvalidLeadDimC:            return "LD of C is smaller than the matrix's first dimension";
-    case clblast::StatusCode::kInvalidIncrementX:          return "Increment of vector X cannot be zero";
-    case clblast::StatusCode::kInvalidIncrementY:          return "Increment of vector Y cannot be zero";
-    case clblast::StatusCode::kInsufficientMemoryA:        return "Matrix A's OpenCL buffer is too small";
-    case clblast::StatusCode::kInsufficientMemoryB:        return "Matrix B's OpenCL buffer is too small";
-    case clblast::StatusCode::kInsufficientMemoryC:        return "Matrix C's OpenCL buffer is too small";
-    case clblast::StatusCode::kInsufficientMemoryX:        return "Vector X's OpenCL buffer is too small";
-    case clblast::StatusCode::kInsufficientMemoryY:        return "Vector Y's OpenCL buffer is too small";
+        // Status codes in common with the clBLAS library
+        case clblast::StatusCode::kNotImplemented:
+            return "Routine or functionality not implemented yet";
+        case clblast::StatusCode::kInvalidMatrixA:
+            return "Matrix A is not a valid OpenCL buffer";
+        case clblast::StatusCode::kInvalidMatrixB:
+            return "Matrix B is not a valid OpenCL buffer";
+        case clblast::StatusCode::kInvalidMatrixC:
+            return "Matrix C is not a valid OpenCL buffer";
+        case clblast::StatusCode::kInvalidVectorX:
+            return "Vector X is not a valid OpenCL buffer";
+        case clblast::StatusCode::kInvalidVectorY:
+            return "Vector Y is not a valid OpenCL buffer";
+        case clblast::StatusCode::kInvalidDimension:
+            return "Dimensions M, N, and K have to be larger than zero";
+        case clblast::StatusCode::kInvalidLeadDimA:
+            return "LD of A is smaller than the matrix's first dimension";
+        case clblast::StatusCode::kInvalidLeadDimB:
+            return "LD of B is smaller than the matrix's first dimension";
+        case clblast::StatusCode::kInvalidLeadDimC:
+            return "LD of C is smaller than the matrix's first dimension";
+        case clblast::StatusCode::kInvalidIncrementX:
+            return "Increment of vector X cannot be zero";
+        case clblast::StatusCode::kInvalidIncrementY:
+            return "Increment of vector Y cannot be zero";
+        case clblast::StatusCode::kInsufficientMemoryA:
+            return "Matrix A's OpenCL buffer is too small";
+        case clblast::StatusCode::kInsufficientMemoryB:
+            return "Matrix B's OpenCL buffer is too small";
+        case clblast::StatusCode::kInsufficientMemoryC:
+            return "Matrix C's OpenCL buffer is too small";
+        case clblast::StatusCode::kInsufficientMemoryX:
+            return "Vector X's OpenCL buffer is too small";
+        case clblast::StatusCode::kInsufficientMemoryY:
+            return "Vector Y's OpenCL buffer is too small";
 
-    // Custom additional status codes for CLBlast
-    case clblast::StatusCode::kInsufficientMemoryTemp:     return "Temporary buffer provided to GEMM routine is too small";
-    case clblast::StatusCode::kInvalidBatchCount:          return "The batch count needs to be positive";
-    case clblast::StatusCode::kInvalidOverrideKernel:      return "Trying to override parameters for an invalid kernel";
-    case clblast::StatusCode::kMissingOverrideParameter:   return "Missing override parameter(s) for the target kernel";
-    case clblast::StatusCode::kInvalidLocalMemUsage:       return "Not enough local memory available on this device";
-    case clblast::StatusCode::kNoHalfPrecision:            return "Half precision (16-bits) not supported by the device";
-    case clblast::StatusCode::kNoDoublePrecision:          return "Double precision (64-bits) not supported by the device";
-    case clblast::StatusCode::kInvalidVectorScalar:        return "The unit-sized vector is not a valid OpenCL buffer";
-    case clblast::StatusCode::kInsufficientMemoryScalar:   return "The unit-sized vector's OpenCL buffer is too small";
-    case clblast::StatusCode::kDatabaseError:              return "Entry for the device was not found in the database";
-    case clblast::StatusCode::kUnknownError:               return "A catch-all error code representing an unspecified error";
-    case clblast::StatusCode::kUnexpectedError:            return "A catch-all error code representing an unexpected exception";
+        // Custom additional status codes for CLBlast
+        case clblast::StatusCode::kInsufficientMemoryTemp:
+            return "Temporary buffer provided to GEMM routine is too small";
+        case clblast::StatusCode::kInvalidBatchCount:
+            return "The batch count needs to be positive";
+        case clblast::StatusCode::kInvalidOverrideKernel:
+            return "Trying to override parameters for an invalid kernel";
+        case clblast::StatusCode::kMissingOverrideParameter:
+            return "Missing override parameter(s) for the target kernel";
+        case clblast::StatusCode::kInvalidLocalMemUsage:
+            return "Not enough local memory available on this device";
+        case clblast::StatusCode::kNoHalfPrecision:
+            return "Half precision (16-bits) not supported by the device";
+        case clblast::StatusCode::kNoDoublePrecision:
+            return "Double precision (64-bits) not supported by the device";
+        case clblast::StatusCode::kInvalidVectorScalar:
+            return "The unit-sized vector is not a valid OpenCL buffer";
+        case clblast::StatusCode::kInsufficientMemoryScalar:
+            return "The unit-sized vector's OpenCL buffer is too small";
+        case clblast::StatusCode::kDatabaseError:
+            return "Entry for the device was not found in the database";
+        case clblast::StatusCode::kUnknownError:
+            return "A catch-all error code representing an unspecified error";
+        case clblast::StatusCode::kUnexpectedError:
+            return "A catch-all error code representing an unexpected "
+                   "exception";
     }
 
     return "Unknown error";
@@ -88,20 +137,17 @@ static const char * _clblastGetResultString(clblast::StatusCode st)
 
 static std::recursive_mutex gCLBlastMutex;
 
-#define CLBLAST_CHECK(fn) do {                              \
-        gCLBlastMutex.lock();                               \
-        clblast::StatusCode _clblast_st = fn;               \
-        gCLBlastMutex.unlock();                             \
-        if (_clblast_st != clblast::StatusCode::kSuccess) { \
-            char clblast_st_msg[1024];                      \
-            snprintf(clblast_st_msg,                        \
-                     sizeof(clblast_st_msg),                \
-                     "CLBlast Error (%d): %s\n",            \
-                     (int)(_clblast_st),                    \
-                     _clblastGetResultString(               \
-                         _clblast_st));                     \
-                                                            \
-            AF_ERROR(clblast_st_msg,                        \
-                     AF_ERR_INTERNAL);                      \
-        }                                                   \
-    } while(0)
+#define CLBLAST_CHECK(fn)                                            \
+    do {                                                             \
+        gCLBlastMutex.lock();                                        \
+        clblast::StatusCode _clblast_st = fn;                        \
+        gCLBlastMutex.unlock();                                      \
+        if (_clblast_st != clblast::StatusCode::kSuccess) {          \
+            char clblast_st_msg[1024];                               \
+            snprintf(clblast_st_msg, sizeof(clblast_st_msg),         \
+                     "CLBlast Error (%d): %s\n", (int)(_clblast_st), \
+                     _clblastGetResultString(_clblast_st));          \
+                                                                     \
+            AF_ERROR(clblast_st_msg, AF_ERR_INTERNAL);               \
+        }                                                            \
+    } while (0)
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index c330675932..2d11178056 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -8,26 +8,24 @@
  ********************************************************/
 
 #pragma once
-#include <cstdio>
-#include <errorcodes.hpp>
 #include <common/err_common.hpp>
+#include <errorcodes.hpp>
 #include <platform.hpp>
 #include <types.hpp>
+#include <cstdio>
 
-#define OPENCL_NOT_SUPPORTED(message) do {              \
-        throw SupportError(__PRETTY_FUNCTION__,         \
-                __AF_FILENAME__, __LINE__, message);    \
-    } while(0)
+#define OPENCL_NOT_SUPPORTED(message)                                      \
+    do {                                                                   \
+        throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
+                           message);                                       \
+    } while (0)
 
-namespace opencl
-{
-    template <typename T>
-    void verifyDoubleSupport()
-    {
-        if ((std::is_same<T, double>::value ||
-             std::is_same<T, cdouble>::value) &&
-            !isDoubleSupported(getActiveDeviceId())) {
-            AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
-        }
+namespace opencl {
+template<typename T>
+void verifyDoubleSupport() {
+    if ((std::is_same<T, double>::value || std::is_same<T, cdouble>::value) &&
+        !isDoubleSupported(getActiveDeviceId())) {
+        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
     }
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/errorcodes.cpp b/src/backend/opencl/errorcodes.cpp
index eb12c0e4c2..31ae6f6de0 100644
--- a/src/backend/opencl/errorcodes.cpp
+++ b/src/backend/opencl/errorcodes.cpp
@@ -11,7 +11,6 @@
 
 #include <boost/compute/exception/opencl_error.hpp>
 
-std::string getErrorMessage(int error_code)
-{
+std::string getErrorMessage(int error_code) {
     return boost::compute::opencl_error::to_string(error_code);
 }
diff --git a/src/backend/opencl/exampleFunction.cpp b/src/backend/opencl/exampleFunction.cpp
index 6c477d6e77..fd0f7c3e18 100644
--- a/src/backend/opencl/exampleFunction.cpp
+++ b/src/backend/opencl/exampleFunction.cpp
@@ -7,48 +7,47 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>                    // header with opencl backend specific
-                                        // Array class implementation that inherits
-                                        // ArrayInfo base class
+#include <Array.hpp>  // header with opencl backend specific
+                      // Array class implementation that inherits
+                      // ArrayInfo base class
 
-#include <exampleFunction.hpp>          // opencl backend function header
+#include <exampleFunction.hpp>  // opencl backend function header
 
-#include <err_opencl.hpp>               // error check functions and Macros
-                                        // specific to opencl backend
+#include <err_opencl.hpp>  // error check functions and Macros
+                           // specific to opencl backend
 
-#include <kernel/exampleFunction.hpp>   // this header under the folder src/opencl/kernel
-                                        // defines the OpenCL kernel wrapper
-                                        // function to which the main computation of your
-                                        // algorithm should be relayed to
+#include <kernel/exampleFunction.hpp>  // this header under the folder src/opencl/kernel
+                                       // defines the OpenCL kernel wrapper
+// function to which the main computation of your
+// algorithm should be relayed to
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method)
-{
-    dim4 outputDims;                    // this should be '= in.dims();' in most cases
-                                        // but would definitely depend on the type of
-                                        // algorithm you are implementing.
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method) {
+    dim4 outputDims;  // this should be '= in.dims();' in most cases
+                      // but would definitely depend on the type of
+                      // algorithm you are implementing.
 
     Array<T> out = createEmptyArray<T>(outputDims);
-                                        // Please use the create***Array<T> helper
-                                        // functions defined in Array.hpp to create
-                                        // different types of Arrays. Please check the
-                                        // file to know what are the different types you
-                                        // can create.
+    // Please use the create***Array<T> helper
+    // functions defined in Array.hpp to create
+    // different types of Arrays. Please check the
+    // file to know what are the different types you
+    // can create.
 
     // Relay the actual computation to OpenCL kernel wrapper
     kernel::exampleFunc<T>(out, a, b, method);
 
-    return out;                         // return the result
+    return out;  // return the result
 }
 
-
-#define INSTANTIATE(T)  \
-    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
+#define INSTANTIATE(T)                                                         \
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, \
+                                         const af_someenum_t method);
 
 // INSTANTIATIONS for all the types which
 // are present in the switch case statement
@@ -62,4 +61,4 @@ INSTANTIATE(char)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/exampleFunction.hpp b/src/backend/opencl/exampleFunction.hpp
index ee72c1cd74..2ee89e8f42 100644
--- a/src/backend/opencl/exampleFunction.hpp
+++ b/src/backend/opencl/exampleFunction.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> exampleFunction(const Array<T> &a, const Array<T> &b, const af_someenum_t method);
+namespace opencl {
+template<typename T>
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method);
 }
-
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index fe61874ae3..f24bcced3f 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -7,54 +7,53 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <kernel/fast.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 using af::dim4;
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
-              const bool non_max, const float feature_ratio, const unsigned edge)
-{
+              const bool non_max, const float feature_ratio,
+              const unsigned edge) {
     unsigned nfeat;
 
     Param x;
     Param y;
     Param score;
 
-    kernel::fast_dispatch<T>(arc_length, non_max,
-                             &nfeat, x, y, score, in,
-                             thr, feature_ratio, edge);
+    kernel::fast_dispatch<T>(arc_length, non_max, &nfeat, x, y, score, in, thr,
+                             feature_ratio, edge);
 
     if (nfeat > 0) {
-        x_out = createParamArray<float>(x, true);
-        y_out = createParamArray<float>(y, true);
+        x_out     = createParamArray<float>(x, true);
+        y_out     = createParamArray<float>(y, true);
         score_out = createParamArray<float>(score, true);
     }
 
     return nfeat;
 }
 
-#define INSTANTIATE(T)                                                                              \
-    template unsigned fast<T>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,    \
-                              const Array<T> &in, const float thr, const unsigned arc_length,       \
-                              const bool nonmax, const float feature_ratio, const unsigned edge);
+#define INSTANTIATE(T)                                                        \
+    template unsigned fast<T>(                                                \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const float thr, const unsigned arc_length,       \
+        const bool nonmax, const float feature_ratio, const unsigned edge);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/fast.hpp b/src/backend/opencl/fast.hpp
index 5d11e8fdcb..2eda909eb1 100644
--- a/src/backend/opencl/fast.hpp
+++ b/src/backend/opencl/fast.hpp
@@ -7,17 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
-              const bool non_max, const float feature_ratio, const unsigned edge);
+              const bool non_max, const float feature_ratio,
+              const unsigned edge);
 
 }
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index b882b21cdf..d0ae97d98b 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -7,53 +7,53 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <clfft.hpp>
 #include <copy.hpp>
-#include <fft.hpp>
 #include <err_opencl.hpp>
+#include <fft.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <clfft.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using std::string;
 
-namespace opencl
-{
+namespace opencl {
 
-void setFFTPlanCacheSize(size_t numPlans)
-{
+void setFFTPlanCacheSize(size_t numPlans) {
     fftManager().setMaxCacheSize(numPlans);
 }
 
-template<typename T> struct Precision;
-template<> struct Precision<cfloat > { enum {type = CLFFT_SINGLE}; };
-template<> struct Precision<cdouble> { enum {type = CLFFT_DOUBLE}; };
-
-static void computeDims(size_t rdims[4], const dim4 &idims)
-{
-    for (int i = 0; i < 4; i++) {
-        rdims[i] = (size_t)idims[i];
-    }
+template<typename T>
+struct Precision;
+template<>
+struct Precision<cfloat> {
+    enum { type = CLFFT_SINGLE };
+};
+template<>
+struct Precision<cdouble> {
+    enum { type = CLFFT_DOUBLE };
+};
+
+static void computeDims(size_t rdims[4], const dim4 &idims) {
+    for (int i = 0; i < 4; i++) { rdims[i] = (size_t)idims[i]; }
 }
 
 //(currently) true is in clFFT if length is a power of 2,3,5
-inline bool isSupLen(dim_t length)
-{
-    while( length > 1 )
-    {
-        if( length % 2 == 0 )
+inline bool isSupLen(dim_t length) {
+    while (length > 1) {
+        if (length % 2 == 0)
             length /= 2;
-        else if( length % 3  == 0 )
+        else if (length % 3 == 0)
             length /= 3;
-        else if( length % 5  == 0 )
+        else if (length % 5 == 0)
             length /= 5;
-        else if( length % 7  == 0 )
+        else if (length % 7 == 0)
             length /= 7;
-        else if( length % 11 == 0 )
+        else if (length % 11 == 0)
             length /= 11;
-        else if( length % 13 == 0 )
+        else if (length % 13 == 0)
             length /= 13;
         else
             return false;
@@ -62,44 +62,36 @@ inline bool isSupLen(dim_t length)
 }
 
 template<int rank>
-void verifySupported(const dim4 dims)
-{
-    for (int i = 0; i < rank; i++) {
-        ARG_ASSERT(1, isSupLen(dims[i]));
-    }
+void verifySupported(const dim4 dims) {
+    for (int i = 0; i < rank; i++) { ARG_ASSERT(1, isSupLen(dims[i])); }
 }
 
 template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in)
-{
+void fft_inplace(Array<T> &in) {
     verifySupported<rank>(in.dims());
     size_t tdims[4], istrides[4];
 
-    computeDims(tdims   , in.dims());
+    computeDims(tdims, in.dims());
     computeDims(istrides, in.strides());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= tdims[i];
-    }
+    for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
 
-    SharedPlan plan = findPlan(CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
-                               (clfftDim)rank, tdims,
-                               istrides, istrides[rank], istrides, istrides[rank],
-                               (clfftPrecision)Precision<T>::type, batch);
+    SharedPlan plan =
+        findPlan(CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
+                 (clfftDim)rank, tdims, istrides, istrides[rank], istrides,
+                 istrides[rank], (clfftPrecision)Precision<T>::type, batch);
 
-    cl_mem imem = (*in.get())();
+    cl_mem imem            = (*in.get())();
     cl_command_queue queue = getQueue()();
 
-    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(),
-                                      direction ? CLFFT_FORWARD : CLFFT_BACKWARD,
-                                      1, &queue, 0, NULL, NULL,
-                                      &imem, &imem, NULL));
+    CLFFT_CHECK(clfftEnqueueTransform(
+        *plan.get(), direction ? CLFFT_FORWARD : CLFFT_BACKWARD, 1, &queue, 0,
+        NULL, NULL, &imem, &imem, NULL));
 }
 
 template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in)
-{
+Array<Tc> fft_r2c(const Array<Tr> &in) {
     dim4 odims = in.dims();
 
     odims[0] = odims[0] / 2 + 1;
@@ -109,85 +101,79 @@ Array<Tc> fft_r2c(const Array<Tr> &in)
     verifySupported<rank>(in.dims());
     size_t tdims[4], istrides[4], ostrides[4];
 
-    computeDims(tdims   ,  in.dims());
-    computeDims(istrides,  in.strides());
+    computeDims(tdims, in.dims());
+    computeDims(istrides, in.strides());
     computeDims(ostrides, out.strides());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= tdims[i];
-    }
+    for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
 
-    SharedPlan plan = findPlan(CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED,
-                               (clfftDim)rank, tdims,
-                               istrides, istrides[rank], ostrides, ostrides[rank],
-                               (clfftPrecision)Precision<Tc>::type, batch);
+    SharedPlan plan =
+        findPlan(CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED, (clfftDim)rank, tdims,
+                 istrides, istrides[rank], ostrides, ostrides[rank],
+                 (clfftPrecision)Precision<Tc>::type, batch);
 
-    cl_mem imem = (*in.get())();
-    cl_mem omem = (*out.get())();
+    cl_mem imem            = (*in.get())();
+    cl_mem omem            = (*out.get())();
     cl_command_queue queue = getQueue()();
 
-    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(),
-                                      CLFFT_FORWARD,
-                                      1, &queue, 0, NULL, NULL,
-                                      &imem, &omem, NULL));
+    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(), CLFFT_FORWARD, 1, &queue, 0,
+                                      NULL, NULL, &imem, &omem, NULL));
 
     return out;
 }
 
 template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims)
-{
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     Array<Tr> out = createEmptyArray<Tr>(odims);
 
     verifySupported<rank>(odims);
     size_t tdims[4], istrides[4], ostrides[4];
 
-    computeDims(tdims   ,  odims);
-    computeDims(istrides,  in.strides());
+    computeDims(tdims, odims);
+    computeDims(istrides, in.strides());
     computeDims(ostrides, out.strides());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) {
-        batch *= tdims[i];
-    }
+    for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
 
-    SharedPlan plan = findPlan(CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL,
-                               (clfftDim)rank, tdims,
-                               istrides, istrides[rank], ostrides, ostrides[rank],
-                               (clfftPrecision)Precision<Tc>::type, batch);
+    SharedPlan plan =
+        findPlan(CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL, (clfftDim)rank, tdims,
+                 istrides, istrides[rank], ostrides, ostrides[rank],
+                 (clfftPrecision)Precision<Tc>::type, batch);
 
-    cl_mem imem = (*in.get())();
-    cl_mem omem = (*out.get())();
+    cl_mem imem            = (*in.get())();
+    cl_mem omem            = (*out.get())();
     cl_command_queue queue = getQueue()();
 
-    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(),
-                                      CLFFT_BACKWARD,
-                                      1, &queue, 0, NULL, NULL,
-                                      &imem, &omem, NULL));
+    CLFFT_CHECK(clfftEnqueueTransform(*plan.get(), CLFFT_BACKWARD, 1, &queue, 0,
+                                      NULL, NULL, &imem, &omem, NULL));
 
     return out;
 }
 
-#define INSTANTIATE(T)                                      \
-    template void fft_inplace<T, 1, true >(Array<T> &in);   \
-    template void fft_inplace<T, 2, true >(Array<T> &in);   \
-    template void fft_inplace<T, 3, true >(Array<T> &in);   \
-    template void fft_inplace<T, 1, false>(Array<T> &in);   \
-    template void fft_inplace<T, 2, false>(Array<T> &in);   \
-    template void fft_inplace<T, 3, false>(Array<T> &in);   \
-
-    INSTANTIATE(cfloat )
-    INSTANTIATE(cdouble)
-
-#define INSTANTIATE_REAL(Tr, Tc)                                        \
-    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in);         \
-    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in);         \
-    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in);         \
-    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in, const dim4 &odims); \
-    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in, const dim4 &odims); \
-    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in, const dim4 &odims); \
-
-    INSTANTIATE_REAL(float , cfloat )
-    INSTANTIATE_REAL(double, cdouble)
-}
+#define INSTANTIATE(T)                                     \
+    template void fft_inplace<T, 1, true>(Array<T> & in);  \
+    template void fft_inplace<T, 2, true>(Array<T> & in);  \
+    template void fft_inplace<T, 3, true>(Array<T> & in);  \
+    template void fft_inplace<T, 1, false>(Array<T> & in); \
+    template void fft_inplace<T, 2, false>(Array<T> & in); \
+    template void fft_inplace<T, 3, false>(Array<T> & in);
+
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+
+#define INSTANTIATE_REAL(Tr, Tc)                                \
+    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in); \
+    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in); \
+    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in); \
+    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in,  \
+                                          const dim4 &odims);   \
+    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in,  \
+                                          const dim4 &odims);   \
+    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in,  \
+                                          const dim4 &odims);
+
+INSTANTIATE_REAL(float, cfloat)
+INSTANTIATE_REAL(double, cdouble)
+}  // namespace opencl
diff --git a/src/backend/opencl/fft.hpp b/src/backend/opencl/fft.hpp
index b6155b9987..5c29588602 100644
--- a/src/backend/opencl/fft.hpp
+++ b/src/backend/opencl/fft.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 void setFFTPlanCacheSize(size_t numPlans);
 
@@ -23,4 +22,4 @@ Array<Tc> fft_r2c(const Array<Tr> &in);
 template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 98cb981f18..e4b1e607d8 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -7,23 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <fftconvolve.hpp>
-#include <kernel/fftconvolve.hpp>
 #include <err_opencl.hpp>
 #include <fft.hpp>
+#include <fftconvolve.hpp>
+#include <kernel/fftconvolve.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-static const dim4 calcPackedSize(Array<T> const& i1,
-                                 Array<T> const& i2,
-                                 const dim_t baseDim)
-{
+static const dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
+                                 const dim_t baseDim) {
     const dim4 i1d = i1.dims();
     const dim4 i2d = i2.dims();
 
@@ -48,60 +45,61 @@ static const dim4 calcPackedSize(Array<T> const& i1,
     return dim4(pd[0], pd[1], pd[2], pd[3]);
 }
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind)
-{
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind) {
     const dim4 sDims = signal.dims();
     const dim4 fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for(dim_t d=0; d<4; ++d) {
-            if (kind==AF_BATCH_NONE || kind==AF_BATCH_RHS) {
-                oDims[d] = sDims[d]+fDims[d]-1;
+        for (dim_t d = 0; d < 4; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d<baseDim ? sDims[d]+fDims[d]-1 : sDims[d]);
+                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
-        if (kind==AF_BATCH_RHS) {
-            for (dim_t i=baseDim; i<4; ++i)
-                oDims[i] = fDims[i];
+        if (kind == AF_BATCH_RHS) {
+            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
         }
     }
 
     const dim4 pDims = calcPackedSize<T>(signal, filter, baseDim);
     Array<cT> packed = createEmptyArray<cT>(pDims);
 
-    kernel::packDataHelper<cT, T, isDouble, convT>(packed, signal, filter, baseDim, kind);
+    kernel::packDataHelper<cT, T, isDouble, convT>(packed, signal, filter,
+                                                   baseDim, kind);
 
     fft_inplace<cT, baseDim, true>(packed);
 
-    kernel::complexMultiplyHelper<cT, T, isDouble, convT>(packed, signal, filter, baseDim, kind);
+    kernel::complexMultiplyHelper<cT, T, isDouble, convT>(
+        packed, signal, filter, baseDim, kind);
 
     // Compute inverse FFT only on complex-multiplied data
     if (kind == AF_BATCH_RHS) {
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim)
-                seqs.push_back({0., static_cast<double>(pDims[k]-1), 1.});
+                seqs.push_back({0., static_cast<double>(pDims[k] - 1), 1.});
             else if (k == baseDim)
-                seqs.push_back({1., static_cast<double>(pDims[k]-1), 1.});
+                seqs.push_back({1., static_cast<double>(pDims[k] - 1), 1.});
             else
                 seqs.push_back({0., 0., 1.});
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
         fft_inplace<cT, baseDim, false>(subPacked);
-    }
-    else {
+    } else {
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim)
-                seqs.push_back({0., (double)pDims[k]-1, 1.});
+                seqs.push_back({0., (double)pDims[k] - 1, 1.});
             else if (k == baseDim)
-                seqs.push_back({0., static_cast<double>(pDims[k]-2), 1.});
+                seqs.push_back({0., static_cast<double>(pDims[k] - 2), 1.});
             else
                 seqs.push_back({0., 0., 1.});
         }
@@ -113,30 +111,35 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool
     Array<T> out = createEmptyArray<T>(oDims);
 
     if (expand)
-        kernel::reorderOutputHelper<T, cT, isDouble, roundOut, true , convT>(out, packed, signal, filter, baseDim, kind);
+        kernel::reorderOutputHelper<T, cT, isDouble, roundOut, true, convT>(
+            out, packed, signal, filter, baseDim, kind);
     else
-        kernel::reorderOutputHelper<T, cT, isDouble, roundOut, false, convT>(out, packed, signal, filter, baseDim, kind);
+        kernel::reorderOutputHelper<T, cT, isDouble, roundOut, false, convT>(
+            out, packed, signal, filter, baseDim, kind);
 
     return out;
 }
 
-#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                                                   \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 1>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 2>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);        \
-    template Array<T> fftconvolve <T, convT, cT, isDouble, roundOut, 3>                                 \
-        (Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
-
-INSTANTIATE(double, double, cdouble, true , false)
-INSTANTIATE(float , float,  cfloat,  false, false)
-INSTANTIATE(uint  , float,  cfloat,  false, true)
-INSTANTIATE(int   , float,  cfloat,  false, true)
-INSTANTIATE(uchar , float,  cfloat,  false, true)
-INSTANTIATE(char  , float,  cfloat,  false, true)
-INSTANTIATE(ushort, float,  cfloat,  false, true)
-INSTANTIATE(short , float,  cfloat,  false, true)
-INSTANTIATE(uintl , float,  cfloat,  false, true)
-INSTANTIATE(intl  , float,  cfloat,  false, true)
-
-}
+#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                      \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 1>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);                                               \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 2>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);                                               \
+    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 3>(    \
+        Array<T> const& signal, Array<T> const& filter, const bool expand, \
+        AF_BATCH_KIND kind);
+
+INSTANTIATE(double, double, cdouble, true, false)
+INSTANTIATE(float, float, cfloat, false, false)
+INSTANTIATE(uint, float, cfloat, false, true)
+INSTANTIATE(int, float, cfloat, false, true)
+INSTANTIATE(uchar, float, cfloat, false, true)
+INSTANTIATE(char, float, cfloat, false, true)
+INSTANTIATE(ushort, float, cfloat, false, true)
+INSTANTIATE(short, float, cfloat, false, true)
+INSTANTIATE(uintl, float, cfloat, false, true)
+INSTANTIATE(intl, float, cfloat, false, true)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/fftconvolve.hpp b/src/backend/opencl/fftconvolve.hpp
index b32abf973f..ca3d9defa0 100644
--- a/src/backend/opencl/fftconvolve.hpp
+++ b/src/backend/opencl/fftconvolve.hpp
@@ -9,10 +9,11 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut, dim_t baseDim>
-Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter, const bool expand, AF_BATCH_KIND kind);
+template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
+         dim_t baseDim>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind);
 
 }
diff --git a/src/backend/opencl/gradient.cpp b/src/backend/opencl/gradient.cpp
index a4ef700be9..0ecf94f06b 100644
--- a/src/backend/opencl/gradient.cpp
+++ b/src/backend/opencl/gradient.cpp
@@ -9,23 +9,22 @@
 
 #include <Array.hpp>
 #include <gradient.hpp>
-#include <math.hpp>
 #include <kernel/gradient.hpp>
+#include <math.hpp>
 #include <stdexcept>
 
-namespace opencl
-{
-    template<typename T>
-    void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in)
-    {
-        kernel::gradient<T>(grad0, grad1, in);
-    }
+namespace opencl {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
+    kernel::gradient<T>(grad0, grad1, in);
+}
 
-#define INSTANTIATE(T)                                                                  \
-    template void gradient<T>(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);    \
+#define INSTANTIATE(T)                                            \
+    template void gradient<T>(Array<T> & grad0, Array<T> & grad1, \
+                              const Array<T> &in);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+}  // namespace opencl
diff --git a/src/backend/opencl/gradient.hpp b/src/backend/opencl/gradient.hpp
index c6bb5a4b24..c5108ae93f 100644
--- a/src/backend/opencl/gradient.hpp
+++ b/src/backend/opencl/gradient.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
+namespace opencl {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
 }
diff --git a/src/backend/opencl/harris.cpp b/src/backend/opencl/harris.cpp
index 27f6a3a03d..eedb054add 100644
--- a/src/backend/opencl/harris.cpp
+++ b/src/backend/opencl/harris.cpp
@@ -7,48 +7,49 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <kernel/harris.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 using af::dim4;
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename convAccT>
-unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-                const Array<T> &in, const unsigned max_corners, const float min_response,
-                const float sigma, const unsigned filter_len, const float k_thr)
-{
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &score_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr) {
     unsigned nfeat;
 
     Param x;
     Param y;
     Param score;
 
-    kernel::harris<T, convAccT>(&nfeat, x, y, score, in,
-                                max_corners, min_response,
-                                sigma, filter_len, k_thr);
+    kernel::harris<T, convAccT>(&nfeat, x, y, score, in, max_corners,
+                                min_response, sigma, filter_len, k_thr);
 
     if (nfeat > 0) {
-        x_out = createParamArray<float>(x, true);
-        y_out = createParamArray<float>(y, true);
+        x_out     = createParamArray<float>(x, true);
+        y_out     = createParamArray<float>(y, true);
         score_out = createParamArray<float>(score, true);
     }
 
     return nfeat;
 }
 
-#define INSTANTIATE(T, convAccT)                                                                                    \
-    template unsigned harris<T, convAccT>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,        \
-                                          const Array<T> &in, const unsigned max_corners, const float min_response, \
-                                          const float sigma, const unsigned filter_len, const float k_thr);
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned harris<T, convAccT>(                                    \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned max_corners,                       \
+        const float min_response, const float sigma,                          \
+        const unsigned filter_len, const float k_thr);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/harris.hpp b/src/backend/opencl/harris.hpp
index e65053a329..b68dfbf098 100644
--- a/src/backend/opencl/harris.hpp
+++ b/src/backend/opencl/harris.hpp
@@ -7,17 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename convAccT>
-unsigned harris(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-                const Array<T> &in, const unsigned max_corners, const float min_response,
-                const float sigma, const unsigned filter_len, const float k_thr);
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &score_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr);
 
 }
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index c0874e570c..b83a73274f 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -8,21 +8,20 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <hist_graphics.hpp>
-#include <GraphicsResourceManager.hpp>
 
 namespace opencl {
 
 template<typename T>
-void copy_histogram(const Array<T> &data, fg_histogram hist)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_histogram(const Array<T> &data, fg_histogram hist) {
+    ForgeModule &_ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = data.get();
-        unsigned bytes = 0;
+        unsigned bytes        = 0;
         FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
 
         auto res = interopManager().getHistogramResources(hist);
@@ -38,7 +37,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+                                     &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
@@ -51,7 +51,7 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             getQueue().enqueueReadBuffer(*data.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
@@ -61,8 +61,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist)
     }
 }
 
-#define INSTANTIATE(T)  \
-template void copy_histogram<T>(const Array<T> &, fg_histogram);
+#define INSTANTIATE(T) \
+    template void copy_histogram<T>(const Array<T> &, fg_histogram);
 
 INSTANTIATE(float)
 INSTANTIATE(int)
@@ -71,4 +71,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/hist_graphics.hpp b/src/backend/opencl/hist_graphics.hpp
index d891aa7a2e..fa49bfe43f 100644
--- a/src/backend/opencl/hist_graphics.hpp
+++ b/src/backend/opencl/hist_graphics.hpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/graphics_common.hpp>
 #include <Array.hpp>
+#include <common/graphics_common.hpp>
 
 namespace opencl {
 
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 2bde97c965..7735803519 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -7,44 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
-#include <err_opencl.hpp>
+#include <af/dim4.hpp>
 #include <vector>
 
 using af::dim4;
 using std::vector;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval)
-{
-    const dim4 dims     = in.dims();
-    dim4 outDims        = dim4(nbins, 1, dims[2], dims[3]);
+Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
+                         const double &minval, const double &maxval) {
+    const dim4 dims    = in.dims();
+    dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
-    kernel::histogram<inType, outType, isLinear>(out, in, nbins, minval, maxval);
+    kernel::histogram<inType, outType, isLinear>(out, in, nbins, minval,
+                                                 maxval);
 
     return out;
 }
 
-#define INSTANTIATE(in_t,out_t)\
-template Array<out_t> histogram<in_t, out_t, true>(const Array<in_t> &in, const unsigned &nbins, const double &minval, const double &maxval); \
-template Array<out_t> histogram<in_t, out_t, false>(const Array<in_t> &in, const unsigned &nbins, const double &minval, const double &maxval);
+#define INSTANTIATE(in_t, out_t)                                            \
+    template Array<out_t> histogram<in_t, out_t, true>(                     \
+        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
+        const double &maxval);                                              \
+    template Array<out_t> histogram<in_t, out_t, false>(                    \
+        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
+        const double &maxval);
 
-INSTANTIATE(float , uint)
+INSTANTIATE(float, uint)
 INSTANTIATE(double, uint)
-INSTANTIATE(char  , uint)
-INSTANTIATE(int   , uint)
-INSTANTIATE(uint  , uint)
-INSTANTIATE(uchar , uint)
-INSTANTIATE(short , uint)
+INSTANTIATE(char, uint)
+INSTANTIATE(int, uint)
+INSTANTIATE(uint, uint)
+INSTANTIATE(uchar, uint)
+INSTANTIATE(short, uint)
 INSTANTIATE(ushort, uint)
-INSTANTIATE(intl  , uint)
-INSTANTIATE(uintl , uint)
+INSTANTIATE(intl, uint)
+INSTANTIATE(uintl, uint)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/histogram.hpp b/src/backend/opencl/histogram.hpp
index 17b46f26d0..aaa64038a5 100644
--- a/src/backend/opencl/histogram.hpp
+++ b/src/backend/opencl/histogram.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins, const double &minval, const double &maxval);
+Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
+                         const double &minval, const double &maxval);
 
 }
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 69c40a98a4..8eaa3bf394 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -7,45 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <err_opencl.hpp>
 #include <arith.hpp>
+#include <err_opencl.hpp>
 #include <kernel/homography.hpp>
+#include <af/dim4.hpp>
 #include <algorithm>
 
 #include <cfloat>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 #define RANSACConfidence 0.99f
 #define LMEDSConfidence 0.99f
 #define LMEDSOutlierRatio 0.4f
 
 template<typename T>
-int homography(Array<T> &bestH,
-               const Array<float> &x_src,
-               const Array<float> &y_src,
-               const Array<float> &x_dst,
-               const Array<float> &y_dst,
-               const Array<float> &initial,
-               const af_homography_type htype,
-               const float inlier_thr,
-               const unsigned iterations)
-{
-    const af::dim4 idims = x_src.dims();
+int homography(Array<T> &bestH, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
+               const af_homography_type htype, const float inlier_thr,
+               const unsigned iterations) {
+    const af::dim4 idims    = x_src.dims();
     const unsigned nsamples = idims[0];
 
-    unsigned iter = iterations;
+    unsigned iter    = iterations;
     Array<float> err = createEmptyArray<float>(af::dim4());
     if (htype == AF_HOMOGRAPHY_LMEDS) {
-        iter = ::std::min(iter, (unsigned)(log(1.f - LMEDSConfidence) / log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
+        iter = ::std::min(
+            iter, (unsigned)(log(1.f - LMEDSConfidence) /
+                             log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
         err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
-    }
-    else {
+    } else {
         // Avoid passing "null" cl_mem object to kernels
         err = createEmptyArray<float>(af::dim4(1));
     }
@@ -54,37 +49,34 @@ int homography(Array<T> &bestH,
 
     af::dim4 rdims(4, iter_sz);
     Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
+    Array<float> rnd  = arithOp<float, af_mul_t>(initial, fctr, rdims);
 
     Array<T> tmpH = createValueArray<T>(af::dim4(9, iter_sz), (T)0);
 
     bestH = createValueArray<T>(af::dim4(3, 3), (T)0);
     switch (htype) {
-    case AF_HOMOGRAPHY_RANSAC:
-        return kernel::computeH<T, AF_HOMOGRAPHY_RANSAC>(bestH, tmpH, err,
-                                              x_src, y_src, x_dst, y_dst,
-                                              rnd, iter, nsamples, inlier_thr);
-        break;
-    case AF_HOMOGRAPHY_LMEDS:
-        return kernel::computeH<T, AF_HOMOGRAPHY_LMEDS> (bestH, tmpH, err,
-                                              x_src, y_src, x_dst, y_dst,
-                                              rnd, iter, nsamples, inlier_thr);
-        break;
-    default:
-        return -1;
-        break;
+        case AF_HOMOGRAPHY_RANSAC:
+            return kernel::computeH<T, AF_HOMOGRAPHY_RANSAC>(
+                bestH, tmpH, err, x_src, y_src, x_dst, y_dst, rnd, iter,
+                nsamples, inlier_thr);
+            break;
+        case AF_HOMOGRAPHY_LMEDS:
+            return kernel::computeH<T, AF_HOMOGRAPHY_LMEDS>(
+                bestH, tmpH, err, x_src, y_src, x_dst, y_dst, rnd, iter,
+                nsamples, inlier_thr);
+            break;
+        default: return -1; break;
     }
 }
 
-#define INSTANTIATE(T)                                                              \
-    template int homography(Array<T> &H,                                            \
-                            const Array<float> &x_src, const Array<float> &y_src,   \
-                            const Array<float> &x_dst, const Array<float> &y_dst,   \
-                            const Array<float> &initial,                            \
-                            const af_homography_type htype, const float inlier_thr, \
-                            const unsigned iterations);
+#define INSTANTIATE(T)                                                     \
+    template int homography(                                               \
+        Array<T> &H, const Array<float> &x_src, const Array<float> &y_src, \
+        const Array<float> &x_dst, const Array<float> &y_dst,              \
+        const Array<float> &initial, const af_homography_type htype,       \
+        const float inlier_thr, const unsigned iterations);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/homography.hpp b/src/backend/opencl/homography.hpp
index 492d64d448..3453abc11f 100644
--- a/src/backend/opencl/homography.hpp
+++ b/src/backend/opencl/homography.hpp
@@ -9,14 +9,12 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-int homography(Array<T> &H,
-               const Array<float> &x_src, const Array<float> &y_src,
-               const Array<float> &x_dst, const Array<float> &y_dst,
-               const Array<float> &initial,
+int homography(Array<T> &H, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
diff --git a/src/backend/opencl/hsv_rgb.cpp b/src/backend/opencl/hsv_rgb.cpp
index 41fc69c1d8..4af64ee10f 100644
--- a/src/backend/opencl/hsv_rgb.cpp
+++ b/src/backend/opencl/hsv_rgb.cpp
@@ -1,27 +1,25 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <hsv_rgb.hpp>
 #include <kernel/hsv_rgb.hpp>
-#include <err_opencl.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> hsv2rgb(const Array<T>& in)
-{
-    Array<T> out   = createEmptyArray<T>(in.dims());
+Array<T> hsv2rgb(const Array<T>& in) {
+    Array<T> out = createEmptyArray<T>(in.dims());
 
     kernel::hsv2rgb_convert<T, true>(out, in);
 
@@ -29,20 +27,19 @@ Array<T> hsv2rgb(const Array<T>& in)
 }
 
 template<typename T>
-Array<T> rgb2hsv(const Array<T>& in)
-{
-    Array<T> out   = createEmptyArray<T>(in.dims());
+Array<T> rgb2hsv(const Array<T>& in) {
+    Array<T> out = createEmptyArray<T>(in.dims());
 
     kernel::hsv2rgb_convert<T, false>(out, in);
 
     return out;
 }
 
-#define INSTANTIATE(T)  \
+#define INSTANTIATE(T)                                \
     template Array<T> hsv2rgb<T>(const Array<T>& in); \
-    template Array<T> rgb2hsv<T>(const Array<T>& in); \
+    template Array<T> rgb2hsv<T>(const Array<T>& in);
 
 INSTANTIATE(double)
-INSTANTIATE(float )
+INSTANTIATE(float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/hsv_rgb.hpp b/src/backend/opencl/hsv_rgb.hpp
index d08490993e..fbbaf66569 100644
--- a/src/backend/opencl/hsv_rgb.hpp
+++ b/src/backend/opencl/hsv_rgb.hpp
@@ -1,16 +1,15 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in);
@@ -18,4 +17,4 @@ Array<T> hsv2rgb(const Array<T>& in);
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/identity.cpp b/src/backend/opencl/identity.cpp
index e94c25cb33..16c144d12f 100644
--- a/src/backend/opencl/identity.cpp
+++ b/src/backend/opencl/identity.cpp
@@ -7,36 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <identity.hpp>
 #include <debug_opencl.hpp>
+#include <identity.hpp>
 #include <kernel/identity.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> identity(const dim4& dims)
-    {
-        Array<T> out  = createEmptyArray<T>(dims);
-        kernel::identity<T>(out);
-        return out;
-    }
+namespace opencl {
+template<typename T>
+Array<T> identity(const dim4& dims) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::identity<T>(out);
+    return out;
+}
 
-#define INSTANTIATE_IDENTITY(T)                              \
-    template Array<T>  identity<T>    (const af::dim4 &dims);
+#define INSTANTIATE_IDENTITY(T) \
+    template Array<T> identity<T>(const af::dim4& dims);
 
-    INSTANTIATE_IDENTITY(float)
-    INSTANTIATE_IDENTITY(double)
-    INSTANTIATE_IDENTITY(cfloat)
-    INSTANTIATE_IDENTITY(cdouble)
-    INSTANTIATE_IDENTITY(int)
-    INSTANTIATE_IDENTITY(uint)
-    INSTANTIATE_IDENTITY(intl)
-    INSTANTIATE_IDENTITY(uintl)
-    INSTANTIATE_IDENTITY(char)
-    INSTANTIATE_IDENTITY(uchar)
-    INSTANTIATE_IDENTITY(short)
-    INSTANTIATE_IDENTITY(ushort)
+INSTANTIATE_IDENTITY(float)
+INSTANTIATE_IDENTITY(double)
+INSTANTIATE_IDENTITY(cfloat)
+INSTANTIATE_IDENTITY(cdouble)
+INSTANTIATE_IDENTITY(int)
+INSTANTIATE_IDENTITY(uint)
+INSTANTIATE_IDENTITY(intl)
+INSTANTIATE_IDENTITY(uintl)
+INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(uchar)
+INSTANTIATE_IDENTITY(short)
+INSTANTIATE_IDENTITY(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/identity.hpp b/src/backend/opencl/identity.hpp
index 542db7a0fb..cb5512d1b5 100644
--- a/src/backend/opencl/identity.hpp
+++ b/src/backend/opencl/identity.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> identity(const dim4& dim);
+namespace opencl {
+template<typename T>
+Array<T> identity(const dim4& dim);
 }
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 098d44f5ba..b2b7843459 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -7,56 +7,53 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <iir.hpp>
-#include <err_opencl.hpp>
-#include <math.hpp>
 #include <arith.hpp>
 #include <convolve.hpp>
+#include <err_opencl.hpp>
+#include <iir.hpp>
 #include <kernel/iir.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x)
-    {
-        AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
-        if (x.ndims() != b.ndims()) {
-            type = (x.ndims() < b.ndims()) ?  AF_BATCH_RHS  : AF_BATCH_LHS;
-        }
-
-        // Extract the first N elements
-        Array<T> c = convolve<T, T, 1, true>(x, b, type);
-        dim4 cdims = c.dims();
-        cdims[0] = x.dims()[0];
-        c.resetDims(cdims);
+namespace opencl {
+template<typename T>
+Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
+    AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
+    if (x.ndims() != b.ndims()) {
+        type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
+    }
 
-        int num_a = a.dims()[0];
+    // Extract the first N elements
+    Array<T> c = convolve<T, T, 1, true>(x, b, type);
+    dim4 cdims = c.dims();
+    cdims[0]   = x.dims()[0];
+    c.resetDims(cdims);
 
-        if (num_a == 1) return c;
+    int num_a = a.dims()[0];
 
-        dim4 ydims = c.dims();
-        Array<T> y = createEmptyArray<T>(ydims);
+    if (num_a == 1) return c;
 
-        if (a.ndims() > 1) {
-            kernel::iir<T,  true>(y, c, a);
-        } else {
-            kernel::iir<T, false>(y, c, a);
-        }
+    dim4 ydims = c.dims();
+    Array<T> y = createEmptyArray<T>(ydims);
 
-        return y;
+    if (a.ndims() > 1) {
+        kernel::iir<T, true>(y, c, a);
+    } else {
+        kernel::iir<T, false>(y, c, a);
     }
 
-#define INSTANTIATE(T)                          \
-    template Array<T> iir(const Array<T> &b,    \
-                          const Array<T> &a,    \
-                          const Array<T> &x);   \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
+    return y;
 }
+
+#define INSTANTIATE(T)                                          \
+    template Array<T> iir(const Array<T> &b, const Array<T> &a, \
+                          const Array<T> &x);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+}  // namespace opencl
diff --git a/src/backend/opencl/iir.hpp b/src/backend/opencl/iir.hpp
index 6fb69459c4..c278a86b05 100644
--- a/src/backend/opencl/iir.hpp
+++ b/src/backend/opencl/iir.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 86418db1e9..f441f0d37f 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <image.hpp>
-#include <GraphicsResourceManager.hpp>
 
 #include <stdexcept>
 #include <vector>
@@ -19,9 +19,8 @@
 namespace opencl {
 
 template<typename T>
-void copy_image(const Array<T> &in, fg_image image)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_image(const Array<T> &in, fg_image image) {
+    ForgeModule &_ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
 
@@ -43,7 +42,8 @@ void copy_image(const Array<T> &in, fg_image image)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_X, *(res[0].get()), 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_X, *(res[0].get()), 0, 0, bytes, NULL,
+                                     &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
@@ -57,7 +57,8 @@ void copy_image(const Array<T> &in, fg_image image)
 
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
         glBufferData(GL_PIXEL_UNPACK_BUFFER, bytes, 0, GL_STREAM_DRAW);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr =
+            (GLubyte *)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
@@ -67,8 +68,7 @@ void copy_image(const Array<T> &in, fg_image image)
     }
 }
 
-#define INSTANTIATE(T)      \
-template void copy_image<T>(const Array<T> &, fg_image);
+#define INSTANTIATE(T) template void copy_image<T>(const Array<T> &, fg_image);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -79,4 +79,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index acc715aa78..b153abc9e2 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -7,38 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <handle.hpp>
 #include <index.hpp>
 #include <kernel/index.hpp>
-#include <err_opencl.hpp>
 #include <memory.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> index(const Array<T>& in, const af_index_t idxrs[])
-{
+Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     kernel::IndexKernelParam_t p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
-    for (dim_t x=0; x<4; ++x) {
-        if (idxrs[x].isSeq) {
-            seqs[x] = idxrs[x].idx.seq;
-        }
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
     }
 
     // retrieve dimensions, strides and offsets
-    dim4 iDims = in.dims();
-    dim4 dDims = in.getDataDims();
-    dim4 oDims = toDims  (seqs, iDims);
-    dim4 iOffs = toOffset(seqs, dDims);
-    dim4 iStrds= in.strides();
+    dim4 iDims  = in.dims();
+    dim4 dDims  = in.getDataDims();
+    dim4 oDims  = toDims(seqs, iDims);
+    dim4 iOffs  = toOffset(seqs, dDims);
+    dim4 iStrds = in.strides();
 
-    for (dim_t i=0; i<4; ++i) {
+    for (dim_t i = 0; i < 4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
         p.offs[i]  = iOffs[i];
         p.strds[i] = iStrds[i];
@@ -46,28 +42,27 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
 
     Buffer* bPtrs[4];
 
-    std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
+    std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         // set index pointers were applicable
         if (!p.isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
-            bPtrs[x] = idxArrs[x].get();
+            bPtrs[x]   = idxArrs[x].get();
             // set output array ith dimension value
             oDims[x] = idxArrs[x].elements();
-        }
-        else {
+        } else {
             // alloc an 1-element buffer to avoid OpenCL from failing
             bPtrs[x] = bufferAlloc(sizeof(uint));
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
-    if(oDims.elements() == 0) { return out; }
+    if (oDims.elements() == 0) { return out; }
 
     kernel::index<T>(out, in, p, bPtrs);
 
-    for (dim_t x=0; x<4; ++x) {
+    for (dim_t x = 0; x < 4; ++x) {
         if (p.isSeq[x]) bufferFree(bPtrs[x]);
     }
 
@@ -78,16 +73,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[])
     template Array<T> index<T>(const Array<T>& in, const af_index_t idxrs[]);
 
 INSTANTIATE(cdouble)
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
-INSTANTIATE(float  )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(uchar  )
-INSTANTIATE(char   )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/index.hpp b/src/backend/opencl/index.hpp
index 2d3ad6bc5a..b0d933a4f3 100644
--- a/src/backend/opencl/index.hpp
+++ b/src/backend/opencl/index.hpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <af/index.h>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
diff --git a/src/backend/opencl/inverse.cpp b/src/backend/opencl/inverse.cpp
index 71072e3067..a6f141385b 100644
--- a/src/backend/opencl/inverse.cpp
+++ b/src/backend/opencl/inverse.cpp
@@ -8,56 +8,49 @@
  ********************************************************/
 
 #include <err_opencl.hpp>
-#include <solve.hpp>
 #include <identity.hpp>
+#include <solve.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <platform.hpp>
 #include <cpu/cpu_inverse.hpp>
+#include <platform.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
-    if(OpenCLCPUOffload()) {
-        if (in.dims()[0] == in.dims()[1])
-            return cpu::inverse(in);
+Array<T> inverse(const Array<T> &in) {
+    if (OpenCLCPUOffload()) {
+        if (in.dims()[0] == in.dims()[1]) return cpu::inverse(in);
     }
     Array<T> I = identity<T>(in.dims());
     return solve<T>(in, I);
 }
 
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace opencl
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> inverse(const Array<T> &in)
-{
+Array<T> inverse(const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE(T)                                                                   \
-    template Array<T> inverse<T> (const Array<T> &in);
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
 INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace opencl
 
 #endif
diff --git a/src/backend/opencl/inverse.hpp b/src/backend/opencl/inverse.hpp
index 753e3d232c..9316532a1a 100644
--- a/src/backend/opencl/inverse.hpp
+++ b/src/backend/opencl/inverse.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> inverse(const Array<T> &in);
+namespace opencl {
+template<typename T>
+Array<T> inverse(const Array<T> &in);
 }
diff --git a/src/backend/opencl/iota.cpp b/src/backend/opencl/iota.cpp
index c570856fa5..6582a4b952 100644
--- a/src/backend/opencl/iota.cpp
+++ b/src/backend/opencl/iota.cpp
@@ -8,35 +8,33 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <iota.hpp>
 #include <kernel/iota.hpp>
 #include <math.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> iota(const dim4 &dims, const dim4 &tile_dims)
-    {
-        dim4 outdims = dims * tile_dims;
+namespace opencl {
+template<typename T>
+Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
+    dim4 outdims = dims * tile_dims;
 
-        Array<T> out = createEmptyArray<T>(outdims);
-        kernel::iota<T>(out, dims);
+    Array<T> out = createEmptyArray<T>(outdims);
+    kernel::iota<T>(out, dims);
 
-        return out;
-    }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                          \
-    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims); \
+#define INSTANTIATE(T) \
+    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/iota.hpp b/src/backend/opencl/iota.hpp
index 87e1f4c734..5552e63332 100644
--- a/src/backend/opencl/iota.hpp
+++ b/src/backend/opencl/iota.hpp
@@ -10,10 +10,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
+namespace opencl {
+template<typename T>
+Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
 }
-
-
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index 529bbe6b52..01077f7174 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -7,62 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <ireduce.hpp>
-#include <ops.hpp>
 #include <kernel/ireduce.hpp>
-#include <err_opencl.hpp>
+#include <ops.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
-    template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc,
-                 const Array<T> &in, const int dim)
-    {
-        kernel::ireduce<T, op>(out, loc.get(), in, dim);
-    }
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim) {
+    kernel::ireduce<T, op>(out, loc.get(), in, dim);
+}
 
-    template<af_op_t op, typename T>
-    T ireduce_all(unsigned *loc, const Array<T> &in)
-    {
-        return kernel::ireduce_all<T, op>(loc, in);
-    }
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in) {
+    return kernel::ireduce_all<T, op>(loc, in);
+}
 
-#define INSTANTIATE(ROp, T)                                             \
-    template void ireduce<ROp, T>(Array<T> &out, Array<uint> &loc,      \
-                                       const Array<T> &in, const int dim); \
-    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);  \
+#define INSTANTIATE(ROp, T)                                           \
+    template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim); \
+    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
 
-    //min
-    INSTANTIATE(af_min_t, float  )
-    INSTANTIATE(af_min_t, double )
-    INSTANTIATE(af_min_t, cfloat )
-    INSTANTIATE(af_min_t, cdouble)
-    INSTANTIATE(af_min_t, int    )
-    INSTANTIATE(af_min_t, uint   )
-    INSTANTIATE(af_min_t, intl   )
-    INSTANTIATE(af_min_t, uintl  )
-    INSTANTIATE(af_min_t, char   )
-    INSTANTIATE(af_min_t, uchar  )
-    INSTANTIATE(af_min_t, short  )
-    INSTANTIATE(af_min_t, ushort )
+// min
+INSTANTIATE(af_min_t, float)
+INSTANTIATE(af_min_t, double)
+INSTANTIATE(af_min_t, cfloat)
+INSTANTIATE(af_min_t, cdouble)
+INSTANTIATE(af_min_t, int)
+INSTANTIATE(af_min_t, uint)
+INSTANTIATE(af_min_t, intl)
+INSTANTIATE(af_min_t, uintl)
+INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, uchar)
+INSTANTIATE(af_min_t, short)
+INSTANTIATE(af_min_t, ushort)
 
-    //max
-    INSTANTIATE(af_max_t, float  )
-    INSTANTIATE(af_max_t, double )
-    INSTANTIATE(af_max_t, cfloat )
-    INSTANTIATE(af_max_t, cdouble)
-    INSTANTIATE(af_max_t, int    )
-    INSTANTIATE(af_max_t, uint   )
-    INSTANTIATE(af_max_t, intl   )
-    INSTANTIATE(af_max_t, uintl  )
-    INSTANTIATE(af_max_t, char   )
-    INSTANTIATE(af_max_t, uchar  )
-    INSTANTIATE(af_max_t, short  )
-    INSTANTIATE(af_max_t, ushort )
-}
+// max
+INSTANTIATE(af_max_t, float)
+INSTANTIATE(af_max_t, double)
+INSTANTIATE(af_max_t, cfloat)
+INSTANTIATE(af_max_t, cdouble)
+INSTANTIATE(af_max_t, int)
+INSTANTIATE(af_max_t, uint)
+INSTANTIATE(af_max_t, intl)
+INSTANTIATE(af_max_t, uintl)
+INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, uchar)
+INSTANTIATE(af_max_t, short)
+INSTANTIATE(af_max_t, ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/ireduce.hpp b/src/backend/opencl/ireduce.hpp
index 75d097cefd..5af4b15001 100644
--- a/src/backend/opencl/ireduce.hpp
+++ b/src/backend/opencl/ireduce.hpp
@@ -10,12 +10,11 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace opencl
-{
-    template<af_op_t op, typename T>
-    void ireduce(Array<T> &out, Array<uint> &loc,
-                 const Array<T> &in, const int dim);
+namespace opencl {
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim);
 
-    template<af_op_t op, typename T>
-    T ireduce_all(unsigned *loc, const Array<T> &in);
-}
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in);
+}  // namespace opencl
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 2a3a4eb9e0..9dfc8cb8ad 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -7,14 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <copy.hpp>
+#include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
+#include <copy.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/jit.hpp>
 #include <program.hpp>
-#include <common/dispatch.hpp>
-#include <err_opencl.hpp>
+#include <af/dim4.hpp>
 #include <af/opencl.h>
 
 #include <functional>
@@ -34,17 +34,15 @@ using cl::NullRange;
 using cl::Program;
 
 using std::hash;
-using std::string; using std::stringstream;
+using std::string;
+using std::stringstream;
 using std::vector;
 
-namespace opencl
-{
+namespace opencl {
 
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
-                          const vector<Node_ids> &full_ids,
-                          bool is_linear)
-{
+                          const vector<Node_ids> &full_ids, bool is_linear) {
     stringstream hashName;
     stringstream funcName;
 
@@ -54,9 +52,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
         funcName << "G_";
     }
 
-    for (auto node :  output_nodes) {
-        funcName << node->getNameStr() << "_";
-    }
+    for (auto node : output_nodes) { funcName << node->getNameStr() << "_"; }
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
         full_nodes[i]->genKerName(funcName, full_ids[i]);
@@ -70,17 +66,15 @@ static string getFuncName(const vector<Node *> &output_nodes,
 static string getKernelString(const string funcName,
                               const vector<const Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
-                              const vector<int> &output_ids,
-                              bool is_linear)
-{
-
+                              const vector<int> &output_ids, bool is_linear) {
     // Common OpenCL code
     // This part of the code does not change with the kernel.
 
     static const char *kernelVoid = "__kernel void\n";
-    static const char *dimParams = "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
+    static const char *dimParams =
+        "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
     static const char *blockStart = "{\n\n";
-    static const char *blockEnd = "\n\n}";
+    static const char *blockEnd   = "\n\n}";
 
     static const char *linearIndex = R"JIT(
         uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
@@ -126,7 +120,7 @@ static string getKernelString(const string funcName,
     stringstream opsStream;
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
-        const auto &node = full_nodes[i];
+        const auto &node     = full_nodes[i];
         const auto &ids_curr = full_ids[i];
         // Generate input parameters, only needs current id
         node->genParams(inParamStream, ids_curr.id, is_linear);
@@ -139,7 +133,8 @@ static string getKernelString(const string funcName,
     for (int i = 0; i < (int)output_ids.size(); i++) {
         int id = output_ids[i];
         // Generate output parameters
-        outParamStream << "__global " << full_nodes[id]->getTypeStr() << " *out" << id << ", \n";
+        outParamStream << "__global " << full_nodes[id]->getTypeStr() << " *out"
+                       << id << ", \n";
         // Generate code to write the output
         outWriteStream << "out" << id << "[idx] = val" << id << ";\n";
     }
@@ -171,25 +166,27 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
                         const vector<int> &output_ids,
                         const vector<const Node *> &full_nodes,
                         const vector<Node_ids> &full_ids,
-                        const bool is_linear)
-{
-    string funcName = getFuncName(output_nodes, full_nodes, full_ids, is_linear);
+                        const bool is_linear) {
+    string funcName =
+        getFuncName(output_nodes, full_nodes, full_ids, is_linear);
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, funcName);
 
-    if (entry.prog==0 && entry.ker==0) {
-        string jit_ker = getKernelString(funcName, full_nodes, full_ids, output_ids, is_linear);
+    if (entry.prog == 0 && entry.ker == 0) {
+        string jit_ker = getKernelString(funcName, full_nodes, full_ids,
+                                         output_ids, is_linear);
 
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
-        const int ker_lens[] = {jit_cl_len, (int)jit_ker.size()};
+        const int ker_lens[]   = {jit_cl_len, (int)jit_ker.size()};
 
         Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens,
-                     isDoubleSupported(device) ? string(" -D USE_DOUBLE") :  string(""));
+        buildProgram(
+            prog, 2, ker_strs, ker_lens,
+            isDoubleSupported(device) ? string(" -D USE_DOUBLE") : string(""));
 
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, funcName.c_str());
+        entry.ker  = new Kernel(*entry.prog, funcName.c_str());
 
         addKernelToCache(device, funcName, entry);
     }
@@ -197,12 +194,11 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
     return *entry.ker;
 }
 
-void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
-{
+void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
     if (outputs.size() == 0) return;
 
     // Assume all ouputs are of same size
-    //FIXME: Add assert to check if all outputs are same size?
+    // FIXME: Add assert to check if all outputs are same size?
     KParam out_info = outputs[0].info;
 
     // Use thread local to reuse the memory every time you are here.
@@ -229,36 +225,38 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
         is_linear &= node->isLinear(outputs[0].info.dims);
     }
 
-    Kernel ker = getKernel(output_nodes, output_ids,
-                           full_nodes, full_ids,
-                           is_linear);
+    Kernel ker =
+        getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
 
-    uint local_0 = 1;
-    uint local_1 = 1;
-    uint global_0 = 1;
-    uint global_1 = 1;
-    uint groups_0 = 1;
-    uint groups_1 = 1;
+    uint local_0   = 1;
+    uint local_1   = 1;
+    uint global_0  = 1;
+    uint global_1  = 1;
+    uint groups_0  = 1;
+    uint groups_1  = 1;
     uint num_odims = 4;
 
     // CPUs seem to perform better with work group size 1024
-    const int work_group_size = (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
+    const int work_group_size =
+        (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
 
     while (num_odims >= 1) {
-        if (out_info.dims[num_odims - 1] == 1) num_odims--;
-        else break;
+        if (out_info.dims[num_odims - 1] == 1)
+            num_odims--;
+        else
+            break;
     }
 
     if (is_linear) {
-        local_0 = work_group_size;
+        local_0           = work_group_size;
         uint out_elements = out_info.dims[3] * out_info.strides[3];
-        uint groups = divup(out_elements, local_0);
+        uint groups       = divup(out_elements, local_0);
 
-        global_1 = divup(groups,     1000) * local_1;
+        global_1 = divup(groups, 1000) * local_1;
         global_0 = divup(groups, global_1) * local_0;
 
     } else {
-        local_1 =  4;
+        local_1 = 4;
         local_0 = work_group_size / local_1;
 
         groups_0 = divup(out_info.dims[0], local_0);
@@ -274,7 +272,7 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     int nargs = 0;
     for (const auto &node : full_nodes) {
         nargs = node->setArgs(nargs, is_linear,
-                              [&] (int id, const void* ptr, size_t arg_size) {
+                              [&](int id, const void *ptr, size_t arg_size) {
                                   ker.setArg(id, arg_size, ptr);
                               });
     }
@@ -288,10 +286,10 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
-    ker.setArg(nargs + 0,  out_info);
-    ker.setArg(nargs + 1,  groups_0);
-    ker.setArg(nargs + 2,  groups_1);
-    ker.setArg(nargs + 3,  num_odims);
+    ker.setArg(nargs + 0, out_info);
+    ker.setArg(nargs + 1, groups_0);
+    ker.setArg(nargs + 2, groups_1);
+    ker.setArg(nargs + 3, num_odims);
 
     getQueue().enqueueNDRangeKernel(ker, NullRange, global, local);
 
@@ -302,11 +300,10 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes)
     full_ids.clear();
 }
 
-void evalNodes(Param &out, Node *node)
-{
-    vector<Param>  outputs{out};
+void evalNodes(Param &out, Node *node) {
+    vector<Param> outputs{out};
     vector<Node *> nodes{node};
     return evalNodes(outputs, nodes);
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/jit/BufferNode.hpp b/src/backend/opencl/jit/BufferNode.hpp
index 7eb164a600..84ca574965 100644
--- a/src/backend/opencl/jit/BufferNode.hpp
+++ b/src/backend/opencl/jit/BufferNode.hpp
@@ -8,17 +8,15 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
-#include "../kernel/KParam.hpp"
-#include <common/jit/Node.hpp>
 #include <common/jit/BufferNodeBase.hpp>
+#include <common/jit/Node.hpp>
+#include <af/defines.h>
 #include <iomanip>
 #include <mutex>
+#include "../kernel/KParam.hpp"
 
-namespace opencl
-{
-namespace jit
-{
-    using BufferNode = common::BufferNodeBase<std::shared_ptr<cl::Buffer>, KParam>;
-}
+namespace opencl {
+namespace jit {
+using BufferNode = common::BufferNodeBase<std::shared_ptr<cl::Buffer>, KParam>;
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 853293011a..473b3d2c80 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -8,16 +8,16 @@
  ********************************************************/
 
 #pragma once
-#include <string>
 #include <sstream>
+#include <string>
 
 namespace opencl {
 
 namespace {
 
-  /// Creates a string that will be used to declare the parameter of kernel
-  void generateParamDeclaration(std::stringstream& kerStream, int id, bool is_linear,
-                                const std::string& m_type_str) {
+/// Creates a string that will be used to declare the parameter of kernel
+void generateParamDeclaration(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& m_type_str) {
     if (is_linear) {
         kerStream << "__global " << m_type_str << " *in" << id
                   << ", dim_t iInfo" << id << "_offset, \n";
@@ -25,78 +25,83 @@ namespace {
         kerStream << "__global " << m_type_str << " *in" << id
                   << ", KParam iInfo" << id << ", \n";
     }
-  }
-
-  /// Calls the setArg function to set the arguments for a kernel call
-  int setKernelArguments(int start_id, bool is_linear,
-                         std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-                         const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
-      setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()), sizeof(cl_mem));
-      if (is_linear) {
-          setArg(start_id + 1, static_cast<const void*>(&info.offset), sizeof(dim_t));
-      } else {
-          setArg(start_id + 1,  static_cast<const void*>(&info), sizeof(KParam));
-      }
-      return start_id + 2;
-  }
-
-
-  /// Generates the code to calculate the offsets for a buffer
-  void generateBufferOffsets(std::stringstream &kerStream, int id, bool is_linear, const std::string& type_str) {
-      UNUSED(type_str);
-      std::string idx_str = std::string("int idx") + std::to_string(id);
-      std::string info_str = std::string("iInfo") + std::to_string(id);
+}
 
-      if (is_linear) {
-          kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
-      } else {
-          kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                    << info_str << ".strides[3] * id3 + (id2 < " << info_str << ".dims[2]) * "
-                    << info_str << ".strides[2] * id2 + (id1 < " << info_str << ".dims[1]) * "
-                    << info_str << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * id0 + "
-                    << info_str << ".offset;\n";
-      }
-  }
+/// Calls the setArg function to set the arguments for a kernel call
+int setKernelArguments(
+    int start_id, bool is_linear,
+    std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+    const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
+    setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
+           sizeof(cl_mem));
+    if (is_linear) {
+        setArg(start_id + 1, static_cast<const void*>(&info.offset),
+               sizeof(dim_t));
+    } else {
+        setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam));
+    }
+    return start_id + 2;
+}
 
-  /// Generates the code to read a buffer and store it in a local variable
-  void generateBufferRead(std::stringstream &kerStream, int id, const std::string& type_str)
-  {
-      kerStream << type_str << " val" << id << " = in" << id << "[idx" << id << "];\n";
-  }
+/// Generates the code to calculate the offsets for a buffer
+void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
+                           const std::string& type_str) {
+    UNUSED(type_str);
+    std::string idx_str  = std::string("int idx") + std::to_string(id);
+    std::string info_str = std::string("iInfo") + std::to_string(id);
 
+    if (is_linear) {
+        kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
+    } else {
+        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
+                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
+                  << info_str << ".dims[1]) * " << info_str
+                  << ".strides[1] * id1 + (id0 < " << info_str
+                  << ".dims[0]) * id0 + " << info_str << ".offset;\n";
+    }
+}
 
-  inline
-  void generateShiftNodeOffsets(std::stringstream &kerStream, int id,
-                                bool is_linear, const std::string& type_str) {
-      UNUSED(is_linear);
-      UNUSED(type_str);
-      std::string idx_str = std::string("idx") + std::to_string(id);
-      std::string info_str = std::string("iInfo") + std::to_string(id);
-      std::string id_str = std::string("sh_id_") + std::to_string(id) + "_";
-      std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+/// Generates the code to read a buffer and store it in a local variable
+void generateBufferRead(std::stringstream& kerStream, int id,
+                        const std::string& type_str) {
+    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
+              << "];\n";
+}
 
-      for (int i = 0; i < 4; i++) {
-          kerStream << "int " << id_str << i
-                    << " = __circular_mod(id" << i
-                    << " + " << shift_str << i
-                    << ", " << info_str << ".dims[" << i << "]);\n";
-      }
+inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& type_str) {
+    UNUSED(is_linear);
+    UNUSED(type_str);
+    std::string idx_str   = std::string("idx") + std::to_string(id);
+    std::string info_str  = std::string("iInfo") + std::to_string(id);
+    std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
+    std::string shift_str = std::string("shift") + std::to_string(id) + "_";
 
-      kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str << ".dims[3]) * "
-                << info_str << ".strides[3] * " << id_str << "3;\n";
-      kerStream << idx_str  << " += (" << id_str << "2 < " << info_str << ".dims[2]) * "
-                << info_str << ".strides[2] * " << id_str << "2;\n";
-      kerStream << idx_str  << " += (" << id_str << "1 < " << info_str << ".dims[1]) * "
-                << info_str << ".strides[1] * " << id_str << "1;\n";
-      kerStream << idx_str  << " += (" << id_str << "0 < " << info_str << ".dims[0]) * "
-                << id_str << "0 + " << info_str << ".offset;\n";
-  }
+    for (int i = 0; i < 4; i++) {
+        kerStream << "int " << id_str << i << " = __circular_mod(id" << i
+                  << " + " << shift_str << i << ", " << info_str << ".dims["
+                  << i << "]);\n";
+    }
 
-  inline
-  void generateShiftNodeRead(std::stringstream &kerStream, int id,
-                             const std::string& type_str) {
-      kerStream << type_str << " val" << id
-                << " = in" << id << "[idx" << id << "];\n";
-  }
+    kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str
+              << ".dims[3]) * " << info_str << ".strides[3] * " << id_str
+              << "3;\n";
+    kerStream << idx_str << " += (" << id_str << "2 < " << info_str
+              << ".dims[2]) * " << info_str << ".strides[2] * " << id_str
+              << "2;\n";
+    kerStream << idx_str << " += (" << id_str << "1 < " << info_str
+              << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
+              << "1;\n";
+    kerStream << idx_str << " += (" << id_str << "0 < " << info_str
+              << ".dims[0]) * " << id_str << "0 + " << info_str << ".offset;\n";
 }
+
+inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
+                                  const std::string& type_str) {
+    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
+              << "];\n";
 }
+}  // namespace
+}  // namespace opencl
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 64a8aaafdf..8dcf24048f 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -8,199 +8,176 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
-
-namespace opencl
-{
-    template<int dim>
-    af::dim4 calcOffset(const af::dim4 dims)
-    {
-        af::dim4 offset;
-        offset[0] = (dim == 0) ? dims[0] : 0;
-        offset[1] = (dim == 1) ? dims[1] : 0;
-        offset[2] = (dim == 2) ? dims[2] : 0;
-        offset[3] = (dim == 3) ? dims[3] : 0;
-        return offset;
-    }
 
-    template<typename Tx, typename Ty>
-    Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second)
-    {
-        // All dimensions except join dimension must be equal
-        // Compute output dims
-        af::dim4 odims;
-        af::dim4 fdims = first.dims();
-        af::dim4 sdims = second.dims();
-
-        for(int i = 0; i < 4; i++) {
-            if(i == dim) {
-                odims[i] = fdims[i] + sdims[i];
-            } else {
-                odims[i] = fdims[i];
-            }
-        }
+namespace opencl {
+template<int dim>
+af::dim4 calcOffset(const af::dim4 dims) {
+    af::dim4 offset;
+    offset[0] = (dim == 0) ? dims[0] : 0;
+    offset[1] = (dim == 1) ? dims[1] : 0;
+    offset[2] = (dim == 2) ? dims[2] : 0;
+    offset[3] = (dim == 3) ? dims[3] : 0;
+    return offset;
+}
 
-        Array<Tx> out = createEmptyArray<Tx>(odims);
-
-        af::dim4 zero(0,0,0,0);
-
-        switch(dim) {
-            case 0:
-                kernel::join<Tx, Tx, 0>(out, first,  zero);
-                kernel::join<Tx, Ty, 0>(out, second, calcOffset<0>(fdims));
-                break;
-            case 1:
-                kernel::join<Tx, Tx, 1>(out, first,  zero);
-                kernel::join<Tx, Ty, 1>(out, second, calcOffset<1>(fdims));
-                break;
-            case 2:
-                kernel::join<Tx, Tx, 2>(out, first,  zero);
-                kernel::join<Tx, Ty, 2>(out, second, calcOffset<2>(fdims));
-                break;
-            case 3:
-                kernel::join<Tx, Tx, 3>(out, first,  zero);
-                kernel::join<Tx, Ty, 3>(out, second, calcOffset<3>(fdims));
-                break;
+template<typename Tx, typename Ty>
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
+    // All dimensions except join dimension must be equal
+    // Compute output dims
+    af::dim4 odims;
+    af::dim4 fdims = first.dims();
+    af::dim4 sdims = second.dims();
+
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
+            odims[i] = fdims[i] + sdims[i];
+        } else {
+            odims[i] = fdims[i];
         }
-
-        return out;
     }
 
-    template<typename T, int n_arrays>
-    void join_wrapper(const int dim, Array<T> &out, const std::vector<Array<T> > &inputs)
-    {
-        af::dim4 zero(0,0,0,0);
-        af::dim4 d = zero;
-
-        switch(dim) {
-            case 0:
-                kernel::join<T, T, 0>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 0>(out, inputs[i], calcOffset<0>(d));
-                }
-                break;
-            case 1:
-                kernel::join<T, T, 1>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 1>(out, inputs[i], calcOffset<1>(d));
-                }
-                break;
-            case 2:
-                kernel::join<T, T, 1>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 2>(out, inputs[i], calcOffset<2>(d));
-                }
-                break;
-            case 3:
-                kernel::join<T, T, 3>(out, inputs[0], zero);
-                for(int i = 1; i < n_arrays; i++) {
-                    d += inputs[i - 1].dims();
-                    kernel::join<T, T, 3>(out, inputs[i], calcOffset<3>(d));
-                }
-                break;
-        }
+    Array<Tx> out = createEmptyArray<Tx>(odims);
+
+    af::dim4 zero(0, 0, 0, 0);
+
+    switch (dim) {
+        case 0:
+            kernel::join<Tx, Tx, 0>(out, first, zero);
+            kernel::join<Tx, Ty, 0>(out, second, calcOffset<0>(fdims));
+            break;
+        case 1:
+            kernel::join<Tx, Tx, 1>(out, first, zero);
+            kernel::join<Tx, Ty, 1>(out, second, calcOffset<1>(fdims));
+            break;
+        case 2:
+            kernel::join<Tx, Tx, 2>(out, first, zero);
+            kernel::join<Tx, Ty, 2>(out, second, calcOffset<2>(fdims));
+            break;
+        case 3:
+            kernel::join<Tx, Tx, 3>(out, first, zero);
+            kernel::join<Tx, Ty, 3>(out, second, calcOffset<3>(fdims));
+            break;
     }
 
-    template<typename T>
-    Array<T> join(const int dim, const std::vector<Array<T> > &inputs)
-    {
+    return out;
+}
 
-        // All dimensions except join dimension must be equal
-        // Compute output dims
-        af::dim4 odims;
-        const dim_t n_arrays = inputs.size();
-        std::vector<af::dim4> idims(n_arrays);
+template<typename T, int n_arrays>
+void join_wrapper(const int dim, Array<T> &out,
+                  const std::vector<Array<T>> &inputs) {
+    af::dim4 zero(0, 0, 0, 0);
+    af::dim4 d = zero;
+
+    switch (dim) {
+        case 0:
+            kernel::join<T, T, 0>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 0>(out, inputs[i], calcOffset<0>(d));
+            }
+            break;
+        case 1:
+            kernel::join<T, T, 1>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 1>(out, inputs[i], calcOffset<1>(d));
+            }
+            break;
+        case 2:
+            kernel::join<T, T, 1>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 2>(out, inputs[i], calcOffset<2>(d));
+            }
+            break;
+        case 3:
+            kernel::join<T, T, 3>(out, inputs[0], zero);
+            for (int i = 1; i < n_arrays; i++) {
+                d += inputs[i - 1].dims();
+                kernel::join<T, T, 3>(out, inputs[i], calcOffset<3>(d));
+            }
+            break;
+    }
+}
 
-        dim_t dim_size = 0;
-        for(int i = 0; i < (int)idims.size(); i++) {
-            idims[i] = inputs[i].dims();
-            dim_size += idims[i][dim];
-        }
+template<typename T>
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
+    // All dimensions except join dimension must be equal
+    // Compute output dims
+    af::dim4 odims;
+    const dim_t n_arrays = inputs.size();
+    std::vector<af::dim4> idims(n_arrays);
+
+    dim_t dim_size = 0;
+    for (int i = 0; i < (int)idims.size(); i++) {
+        idims[i] = inputs[i].dims();
+        dim_size += idims[i][dim];
+    }
 
-        for(int i = 0; i < 4; i++) {
-            if(i == dim) {
-                odims[i] = dim_size;
-            } else {
-                odims[i] = idims[0][i];
-            }
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
+            odims[i] = dim_size;
+        } else {
+            odims[i] = idims[0][i];
         }
+    }
 
-        Array<T> out = createEmptyArray<T>(odims);
-
-        switch(n_arrays) {
-            case 1:
-                join_wrapper<T, 1>(dim, out, inputs);
-                break;
-            case 2:
-                join_wrapper<T, 2>(dim, out, inputs);
-                break;
-            case 3:
-                join_wrapper<T, 3>(dim, out, inputs);
-                break;
-            case 4:
-                join_wrapper<T, 4>(dim, out, inputs);
-                break;
-            case 5:
-                join_wrapper<T, 5>(dim, out, inputs);
-                break;
-            case 6:
-                join_wrapper<T, 6>(dim, out, inputs);
-                break;
-            case 7:
-                join_wrapper<T, 7>(dim, out, inputs);
-                break;
-            case 8:
-                join_wrapper<T, 8>(dim, out, inputs);
-                break;
-            case 9:
-                join_wrapper<T, 9>(dim, out, inputs);
-                break;
-            case 10:
-                join_wrapper<T,10>(dim, out, inputs);
-                break;
-        }
-        return out;
+    Array<T> out = createEmptyArray<T>(odims);
+
+    switch (n_arrays) {
+        case 1: join_wrapper<T, 1>(dim, out, inputs); break;
+        case 2: join_wrapper<T, 2>(dim, out, inputs); break;
+        case 3: join_wrapper<T, 3>(dim, out, inputs); break;
+        case 4: join_wrapper<T, 4>(dim, out, inputs); break;
+        case 5: join_wrapper<T, 5>(dim, out, inputs); break;
+        case 6: join_wrapper<T, 6>(dim, out, inputs); break;
+        case 7: join_wrapper<T, 7>(dim, out, inputs); break;
+        case 8: join_wrapper<T, 8>(dim, out, inputs); break;
+        case 9: join_wrapper<T, 9>(dim, out, inputs); break;
+        case 10: join_wrapper<T, 10>(dim, out, inputs); break;
     }
+    return out;
+}
 
-#define INSTANTIATE(Tx, Ty)                                                                             \
-    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, const Array<Ty> &second);   \
-
-    INSTANTIATE(float,   float)
-    INSTANTIATE(double,  double)
-    INSTANTIATE(cfloat,  cfloat)
-    INSTANTIATE(cdouble, cdouble)
-    INSTANTIATE(int,     int)
-    INSTANTIATE(uint,    uint)
-    INSTANTIATE(intl,    intl)
-    INSTANTIATE(uintl,   uintl)
-    INSTANTIATE(short,   short)
-    INSTANTIATE(ushort,  ushort)
-    INSTANTIATE(uchar,   uchar)
-    INSTANTIATE(char,    char)
+#define INSTANTIATE(Tx, Ty)                                                \
+    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, \
+                                    const Array<Ty> &second);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(short, short)
+INSTANTIATE(ushort, ushort)
+INSTANTIATE(uchar, uchar)
+INSTANTIATE(char, char)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                                                                              \
-    template Array<T> join<T>(const int dim, const std::vector<Array<T> > &inputs);
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
+#define INSTANTIATE(T)                       \
+    template Array<T> join<T>(const int dim, \
+                              const std::vector<Array<T>> &inputs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
 
 #undef INSTANTIATE
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/join.hpp b/src/backend/opencl/join.hpp
index 398a36c98e..63bd65b891 100644
--- a/src/backend/opencl/join.hpp
+++ b/src/backend/opencl/join.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename Tx, typename Ty>
-    Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+namespace opencl {
+template<typename Tx, typename Ty>
+Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
 
-    template<typename T>
-    Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
-}
+template<typename T>
+Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/KParam.hpp b/src/backend/opencl/kernel/KParam.hpp
index 6ca6aa4c97..38a3752760 100644
--- a/src/backend/opencl/kernel/KParam.hpp
+++ b/src/backend/opencl/kernel/KParam.hpp
@@ -10,8 +10,14 @@
 #ifndef __KPARAM_H
 #define __KPARAM_H
 
-typedef struct
-{
+#ifndef __OPENCL_VERSION__
+// Only define dim_t in host code. dim_t is defined when setting the program
+// options in program.cpp
+#include <af/defines.h>
+#endif
+
+// Defines the size and shape of the data in the OpenCL buffer
+typedef struct {
     dim_t dims[4];
     dim_t strides[4];
     dim_t offset;
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.cl b/src/backend/opencl/kernel/anisotropic_diffusion.cl
index b336623e27..be867684b1 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.cl
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.cl
@@ -7,81 +7,74 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-int lIndex(const int j, const int i)
-{
-    return j*SHRD_MEM_WIDTH + i;
-}
+int lIndex(const int j, const int i) { return j * SHRD_MEM_WIDTH + i; }
 
-int gIndex(const int x, const int y,
-           const int dim0, const int dim1,
-           const int stride0, const int stride1)
-{
-    return clamp(x, 0, dim0-1)*stride0 + clamp(y, 0, dim1-1)*stride1;
+int gIndex(const int x, const int y, const int dim0, const int dim1,
+           const int stride0, const int stride1) {
+    return clamp(x, 0, dim0 - 1) * stride0 + clamp(y, 0, dim1 - 1) * stride1;
 }
 
-float quadratic(const float value)
-{
-    return 1.0f/(1.0f+value);
-}
+float quadratic(const float value) { return 1.0f / (1.0f + value); }
 
-float computeGradientBasedUpdate(const float mct, const float C,
-                                 const float  S, const float  N, const float  W, const float  E,
-                                 const float SE, const float SW, const float NE, const float NW,
-                                 const int FLUX_FN)
-{
+float computeGradientBasedUpdate(const float mct, const float C, const float S,
+                                 const float N, const float W, const float E,
+                                 const float SE, const float SW, const float NE,
+                                 const float NW, const int FLUX_FN) {
     float delta = 0;
 
     float dx, dy, df, db, cx, cxd;
 
     // centralized derivatives
-    dx = (E-W)*0.5f;
-    dy = (S-N)*0.5f;
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
 
     // half-d's and conductance along first dimension
-    df  = E - C;
-    db  = C - W;
+    df = E - C;
+    db = C - W;
 
-    if (FLUX_FN==2) {
-        cx  = exp( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2)) * mct );
-        cxd = exp( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2)) * mct );
+    if (FLUX_FN == 2) {
+        cx  = exp((df * df + 0.25f * pow(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd = exp((db * db + 0.25f * pow(dy + 0.5f * (SW - NW), 2)) * mct);
     } else {
-        cx  = quadratic( (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2)) * mct );
-        cxd = quadratic( (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2)) * mct );
+        cx = quadratic((df * df + 0.25f * pow(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd =
+            quadratic((db * db + 0.25f * pow(dy + 0.5f * (SW - NW), 2)) * mct);
     }
 
-    delta += (cx*df - cxd*db);
+    delta += (cx * df - cxd * db);
 
     // half-d's and conductance along second dimension
-    df  = S - C;
-    db  = C - N;
+    df = S - C;
+    db = C - N;
 
-    if (FLUX_FN==2) {
-        cx  = exp( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2)) * mct );
-        cxd = exp( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2)) * mct );
+    if (FLUX_FN == 2) {
+        cx  = exp((df * df + 0.25f * pow(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd = exp((db * db + 0.25f * pow(dx + 0.5f * (NE - NW), 2)) * mct);
     } else {
-        cx  = quadratic( (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2)) * mct );
-        cxd = quadratic( (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2)) * mct );
+        cx = quadratic((df * df + 0.25f * pow(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd =
+            quadratic((db * db + 0.25f * pow(dx + 0.5f * (NE - NW), 2)) * mct);
     }
 
-    delta += (cx*df - cxd*db);
+    delta += (cx * df - cxd * db);
 
     return delta;
 }
 
-float computeCurvatureBasedUpdate(const float mct, const float C,
-                                  const  float  S, const float  N, const float  W, const float  E,
-                                  const  float SE, const float SW, const float NE, const float NW,
-                                  const int FLUX_FN)
-{
-    float delta = 0;
+float computeCurvatureBasedUpdate(const float mct, const float C, const float S,
+                                  const float N, const float W, const float E,
+                                  const float SE, const float SW,
+                                  const float NE, const float NW,
+                                  const int FLUX_FN) {
+    float delta     = 0;
     float prop_grad = 0;
 
     float df0, db0;
     float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
 
     // centralized derivatives
-    dx = (E-W)*0.5f;
-    dy = (S-N)*0.5f;
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
 
     // half-d's and conductance along first dimension
     df  = E - C;
@@ -89,47 +82,46 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     df0 = df;
     db0 = db;
 
-    gmsqf = (df*df + 0.25f*pow(dy+0.5f*(SE - NE), 2));
-    gmsqb = (db*db + 0.25f*pow(dy+0.5f*(SW - NW), 2));
+    gmsqf = (df * df + 0.25f * pow(dy + 0.5f * (SE - NE), 2));
+    gmsqb = (db * db + 0.25f * pow(dy + 0.5f * (SW - NW), 2));
 
     gmf = sqrt(1.0e-10f + gmsqf);
     gmb = sqrt(1.0e-10f + gmsqb);
 
-    cx  = exp( gmsqf * mct );
-    cxd = exp( gmsqb * mct );
+    cx  = exp(gmsqf * mct);
+    cxd = exp(gmsqb * mct);
 
-    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
 
     // half-d's and conductance along second dimension
-    df  = S - C;
-    db  = C - N;
+    df = S - C;
+    db = C - N;
 
-    gmsqf = (df*df + 0.25f*pow(dx+0.5f*(SE - SW), 2));
-    gmsqb = (db*db + 0.25f*pow(dx+0.5f*(NE - NW), 2));
+    gmsqf = (df * df + 0.25f * pow(dx + 0.5f * (SE - SW), 2));
+    gmsqb = (db * db + 0.25f * pow(dx + 0.5f * (NE - NW), 2));
 
     gmf = sqrt(1.0e-10 + gmsqf);
     gmb = sqrt(1.0e-10 + gmsqb);
 
-    cx  = exp( gmsqf * mct );
-    cxd = exp( gmsqb * mct );
+    cx  = exp(gmsqf * mct);
+    cxd = exp(gmsqb * mct);
 
-    delta += ((df/gmf)*cx - (db/gmb)*cxd);
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
 
-    if (delta>0) {
-        prop_grad += (pow(min(db0, 0.0f),2.0f) + pow(max(df0, 0.0f), 2.0f));
-        prop_grad += (pow(min( db, 0.0f),2.0f) + pow(max( df, 0.0f), 2.0f));
+    if (delta > 0) {
+        prop_grad += (pow(min(db0, 0.0f), 2.0f) + pow(max(df0, 0.0f), 2.0f));
+        prop_grad += (pow(min(db, 0.0f), 2.0f) + pow(max(df, 0.0f), 2.0f));
     } else {
-        prop_grad += (pow(max(db0, 0.0f),2.0f) + pow(min(df0, 0.0f), 2.0f));
-        prop_grad += (pow(max( db, 0.0f),2.0f) + pow(min( df, 0.0f), 2.0f));
+        prop_grad += (pow(max(db0, 0.0f), 2.0f) + pow(min(df0, 0.0f), 2.0f));
+        prop_grad += (pow(max(db, 0.0f), 2.0f) + pow(min(df, 0.0f), 2.0f));
     }
 
-    return sqrt(prop_grad)*delta;
+    return sqrt(prop_grad) * delta;
 }
 
-kernel
-void diffUpdate(global T* inout, KParam info, const float dt,
-                const float mct, const int FLUX_FN, unsigned blkX, unsigned blkY)
-{
+kernel void diffUpdate(global T* inout, KParam info, const float dt,
+                       const float mct, const int FLUX_FN, unsigned blkX,
+                       unsigned blkY) {
     // Beware of the integer value of FLUX_FN
 
     local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
@@ -140,42 +132,42 @@ void diffUpdate(global T* inout, KParam info, const float dt,
     const unsigned b2 = get_group_id(0) / blkX;
     const unsigned b3 = get_group_id(1) / blkY;
 
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*blkX) + lx;
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*blkY) + ly;
+    const int gx = get_local_size(0) * (get_group_id(0) - b2 * blkX) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1) - b3 * blkY) + ly;
 
-    global T* img = inout + (b3 * info.strides[3] + b2 * info.strides[2]) + info.offset;
+    global T* img =
+        inout + (b3 * info.strides[3] + b2 * info.strides[2]) + info.offset;
 
-    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=get_local_size(1), gy2+=get_local_size(1))
-    {
-        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=get_local_size(0), gx2+=get_local_size(0))
-        {
-            int idx = gIndex(gx2-1, gy2-1, info.dims[0], info.dims[1],
-                            info.strides[0], info.strides[1]);
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            int idx = gIndex(gx2 - 1, gy2 - 1, info.dims[0], info.dims[1],
+                             info.strides[0], info.strides[1]);
             localMem[b][a] = img[idx];
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (gx<info.dims[0] && gy<info.dims[1])
-    {
+    if (gx < info.dims[0] && gy < info.dims[1]) {
         int i       = lx + 1;
         int j       = ly + 1;
         float C     = localMem[j][i];
         float delta = 0;
 
-#if IS_MCDE==1
+#if IS_MCDE == 1
         delta = computeCurvatureBasedUpdate(
-                mct, C, localMem[j][i+1], localMem[j][i-1], localMem[j-1][i], localMem[j+1][i],
-                localMem[j+1][i+1], localMem[j-1][i+1], localMem[j+1][i-1], localMem[j-1][i-1],
-                FLUX_FN);
+            mct, C, localMem[j][i + 1], localMem[j][i - 1], localMem[j - 1][i],
+            localMem[j + 1][i], localMem[j + 1][i + 1], localMem[j - 1][i + 1],
+            localMem[j + 1][i - 1], localMem[j - 1][i - 1], FLUX_FN);
 #else
         delta = computeGradientBasedUpdate(
-                mct, C, localMem[j][i+1], localMem[j][i-1], localMem[j-1][i], localMem[j+1][i],
-                localMem[j+1][i+1], localMem[j-1][i+1], localMem[j+1][i-1], localMem[j-1][i-1],
-                FLUX_FN);
+            mct, C, localMem[j][i + 1], localMem[j][i - 1], localMem[j - 1][i],
+            localMem[j + 1][i], localMem[j + 1][i + 1], localMem[j - 1][i + 1],
+            localMem[j + 1][i - 1], localMem[j - 1][i - 1], FLUX_FN);
 #endif
 
-        img[gx*info.strides[0] + gy*info.strides[1]] = (T)(C + delta*dt);
+        img[gx * info.strides[0] + gy * info.strides[1]] = (T)(C + delta * dt);
     }
 }
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index 93411089f4..26baecbf86 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -8,61 +8,58 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/anisotropic_diffusion.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
-#include <memory.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/anisotropic_diffusion.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <type_util.hpp>
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool isMCDE>
-void anisotropicDiffusion(Param inout, const float dt, const float mct, const int fluxFnCode)
-{
+void anisotropicDiffusion(Param inout, const float dt, const float mct,
+                          const int fluxFnCode) {
     using cl::Buffer;
-    using cl::Program;
+    using cl::EnqueueArgs;
     using cl::Kernel;
     using cl::KernelFunctor;
-    using cl::EnqueueArgs;
     using cl::NDRange;
+    using cl::Program;
 
     std::string kerKeyStr = std::string("anisotropic_diffusion_") +
-        std::string(dtype_traits<T>::getName()) +
-        "_" +
-        std::to_string(isMCDE);
+                            std::string(dtype_traits<T>::getName()) + "_" +
+                            std::to_string(isMCDE);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, kerKeyStr);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="               << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X+2)
-                << " -D SHRD_MEM_WIDTH="  << (THREADS_Y+2)
-                << " -D IS_MCDE="         << isMCDE;
-        if (std::is_same<T, double>::value)
-            options << " -D USE_DOUBLE";
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
+                << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
+                << " -D IS_MCDE=" << isMCDE;
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {anisotropic_diffusion_cl};
-        const int   ker_lens[] = {anisotropic_diffusion_cl_len};
+        const int ker_lens[]   = {anisotropic_diffusion_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "diffUpdate");
+        entry.ker  = new Kernel(*entry.prog, "diffUpdate");
         addKernelToCache(device, kerKeyStr, entry);
     }
 
-    auto diffUpdateOp = KernelFunctor<Buffer, KParam, float,
-                                      float, int, unsigned, unsigned>(*entry.ker);
+    auto diffUpdateOp =
+        KernelFunctor<Buffer, KParam, float, float, int, unsigned, unsigned>(
+            *entry.ker);
 
     NDRange threads(THREADS_X, THREADS_Y, 1);
 
@@ -72,10 +69,10 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct, const in
     NDRange global(threads[0] * blkX * inout.info.dims[2],
                    threads[1] * blkY * inout.info.dims[3], 1);
 
-    diffUpdateOp(EnqueueArgs(getQueue(), global, threads),
-            *inout.data, inout.info, dt, mct, fluxFnCode, blkX, blkY);
+    diffUpdateOp(EnqueueArgs(getQueue(), global, threads), *inout.data,
+                 inout.info, dt, mct, fluxFnCode, blkX, blkY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 0cf29fc780..9d4731d610 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -8,58 +8,54 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/approx1.hpp>
 #include <kernel_headers/approx2.hpp>
 #include <kernel_headers/interp.hpp>
+#include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <math.hpp>
+#include <string>
 #include "config.hpp"
 #include "interp.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int TX = 16;
 static const int TY = 16;
 
 static const int THREADS = 256;
 
 template<typename Ty, typename Tp, int order>
-std::string generateOptionsString()
-{
+std::string generateOptionsString() {
     ToNumStr<Ty> toNumStr;
 
     std::ostringstream options;
-    options << " -D Ty="          << dtype_traits<Ty>::getName()
-        << " -D Tp="          << dtype_traits<Tp>::getName()
-        << " -D InterpInTy="  << dtype_traits<Ty>::getName()
-        << " -D InterpValTy=" << dtype_traits<Ty>::getName()
-        << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-        << " -D ZERO="        << toNumStr(scalar<Ty>(0));
-
-    if((af_dtype) dtype_traits<Ty>::af_type == c32 ||
-            (af_dtype) dtype_traits<Ty>::af_type == c64) {
+    options << " -D Ty=" << dtype_traits<Ty>::getName()
+            << " -D Tp=" << dtype_traits<Tp>::getName()
+            << " -D InterpInTy=" << dtype_traits<Ty>::getName()
+            << " -D InterpValTy=" << dtype_traits<Ty>::getName()
+            << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
+            << " -D ZERO=" << toNumStr(scalar<Ty>(0));
+
+    if ((af_dtype)dtype_traits<Ty>::af_type == c32 ||
+        (af_dtype)dtype_traits<Ty>::af_type == c64) {
         options << " -D IS_CPLX=1";
     } else {
         options << " -D IS_CPLX=0";
     }
-    if (std::is_same<Ty, double>::value ||
-            std::is_same<Ty, cdouble>::value) {
+    if (std::is_same<Ty, double>::value || std::is_same<Ty, cdouble>::value) {
         options << " -D USE_DOUBLE";
     }
 
@@ -72,24 +68,23 @@ std::string generateOptionsString()
 ///////////////////////////////////////////////////////////////////////////
 // Wrapper functions
 ///////////////////////////////////////////////////////////////////////////
-template <typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp, int order>
 void approx1(Param yo, const Param yi, const Param xo, const int xdim,
              const Tp xi_beg, const Tp xi_step, const float offGrid,
-             af_interp_type method)
-{
+             af_interp_type method) {
     std::string refName = std::string("approx1_kernel_") +
-        std::string(dtype_traits<Ty>::getName()) +
-        std::string(dtype_traits<Tp>::getName()) +
-        std::to_string(order);
+                          std::string(dtype_traits<Ty>::getName()) +
+                          std::string(dtype_traits<Tp>::getName()) +
+                          std::to_string(order);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::string options = generateOptionsString<Ty, Tp, order>();
 
         const char *ker_strs[] = {interp_cl, approx1_cl};
-        const int   ker_lens[] = {interp_cl_len, approx1_cl_len};
+        const int ker_lens[]   = {interp_cl_len, approx1_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options);
         entry.prog = new Program(prog);
@@ -98,10 +93,10 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
         addKernelToCache(device, refName, entry);
     }
 
-    auto approx1Op = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
-                                    const Buffer, const KParam, const int,
-                                    const Tp, const Tp, const Ty,
-                                    const int, const int, const int >(*entry.ker);
+    auto approx1Op =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const Buffer, const KParam, const int, const Tp, const Tp,
+                      const Ty, const int, const int, const int>(*entry.ker);
 
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
@@ -109,36 +104,34 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
                    yo.info.dims[2] * yo.info.dims[3] * local[1]);
 
     // Passing bools to opencl kernels is not allowed
-    bool batch = !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
+    bool batch =
+        !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
-    approx1Op(EnqueueArgs(getQueue(), global, local),
-              *yo.data, yo.info, *yi.data, yi.info,
-              *xo.data, xo.info, xdim, xi_beg, xi_step,
-              scalar<Ty>(offGrid),
-              blocksPerMat, (int)batch, (int)method);
+    approx1Op(EnqueueArgs(getQueue(), global, local), *yo.data, yo.info,
+              *yi.data, yi.info, *xo.data, xo.info, xdim, xi_beg, xi_step,
+              scalar<Ty>(offGrid), blocksPerMat, (int)batch, (int)method);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Ty, typename Tp, int order>
-void approx2(Param zo, const Param zi,
-             const Param xo, const int xdim, const Tp &xi_beg, const Tp &xi_step,
-             const Param yo, const int ydim, const Tp &yi_beg, const Tp &yi_step,
-             const float offGrid, af_interp_type method)
-{
+template<typename Ty, typename Tp, int order>
+void approx2(Param zo, const Param zi, const Param xo, const int xdim,
+             const Tp &xi_beg, const Tp &xi_step, const Param yo,
+             const int ydim, const Tp &yi_beg, const Tp &yi_step,
+             const float offGrid, af_interp_type method) {
     std::string refName = std::string("approx2_kernel_") +
-        std::string(dtype_traits<Ty>::getName()) +
-        std::string(dtype_traits<Tp>::getName()) +
-        std::to_string(order);
+                          std::string(dtype_traits<Ty>::getName()) +
+                          std::string(dtype_traits<Tp>::getName()) +
+                          std::to_string(order);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::string options = generateOptionsString<Ty, Tp, order>();
 
         const char *ker_strs[] = {interp_cl, approx2_cl};
-        const int   ker_lens[] = {interp_cl_len, approx2_cl_len};
+        const int ker_lens[]   = {interp_cl_len, approx2_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options);
         entry.prog = new Program(prog);
@@ -147,12 +140,12 @@ void approx2(Param zo, const Param zi,
         addKernelToCache(device, refName, entry);
     }
 
-    auto approx2Op = KernelFunctor< Buffer, const KParam,
-                                    const Buffer, const KParam,
-                                    const Buffer, const KParam, const int,
-                                    const Buffer, const KParam, const int,
-                                    const Tp, const Tp, const Tp, const Tp,
-                                    const Ty, const int, const int, const int, const int >(*entry.ker);
+    auto approx2Op =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const Buffer, const KParam, const int, const Buffer,
+                      const KParam, const int, const Tp, const Tp, const Tp,
+                      const Tp, const Ty, const int, const int, const int,
+                      const int>(*entry.ker);
 
     NDRange local(TX, TY, 1);
     dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
@@ -163,14 +156,12 @@ void approx2(Param zo, const Param zi,
     // Passing bools to opencl kernels is not allowed
     bool batch = !(xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
-    approx2Op(EnqueueArgs(getQueue(), global, local),
-              *zo.data, zo.info, *zi.data, zi.info,
-              *xo.data, xo.info, xdim,
-              *yo.data, yo.info, ydim,
-              xi_beg, xi_step, yi_beg, yi_step,
-              scalar<Ty>(offGrid), blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
+    approx2Op(EnqueueArgs(getQueue(), global, local), *zo.data, zo.info,
+              *zi.data, zi.info, *xo.data, xo.info, xdim, *yo.data, yo.info,
+              ydim, xi_beg, xi_step, yi_beg, yi_step, scalar<Ty>(offGrid),
+              blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index be95771dce..1e7da75f18 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -7,31 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void approx1_kernel(__global       Ty *d_yo, const KParam yo,
-                    __global const Ty *d_yi,  const KParam yi,
-                    __global const Tp *d_xo, const KParam xo, const int xdim,
-                    const Tp xi_beg, const Tp xi_step,
-                    const Ty offGrid, const int blocksMatX, const int batch, const int method)
-{
+__kernel void approx1_kernel(__global Ty *d_yo, const KParam yo,
+                             __global const Ty *d_yi, const KParam yi,
+                             __global const Tp *d_xo, const KParam xo,
+                             const int xdim, const Tp xi_beg, const Tp xi_step,
+                             const Ty offGrid, const int blocksMatX,
+                             const int batch, const int method) {
     const int idw = get_group_id(1) / yo.dims[2];
-    const int idz = get_group_id(1)  - idw * yo.dims[2];
+    const int idz = get_group_id(1) - idw * yo.dims[2];
 
-    const int idy = get_group_id(0) / blocksMatX;
+    const int idy        = get_group_id(0) / blocksMatX;
     const int blockIdx_x = get_group_id(0) - idy * blocksMatX;
-    const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
+    const int idx        = get_local_id(0) + blockIdx_x * get_local_size(0);
 
-    if(idx >= yo.dims[0] ||
-       idy >= yo.dims[1] ||
-       idz >= yo.dims[2] ||
-       idw >= yo.dims[3])
+    if (idx >= yo.dims[0] || idy >= yo.dims[1] || idz >= yo.dims[2] ||
+        idw >= yo.dims[3])
         return;
 
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                        xo.dims[3] > 1};
     bool is_yi_off[] = {true, true, true, true};
-    is_yi_off[xdim] = false;
+    is_yi_off[xdim]  = false;
 
-    const int yo_idx = idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx + yo.offset;
+    const int yo_idx = idw * yo.strides[3] + idz * yo.strides[2] +
+                       idy * yo.strides[1] + idx + yo.offset;
 
     int xo_idx = idx * is_xo_off[0] + xo.offset;
     xo_idx += idw * xo.strides[3] * is_xo_off[3];
@@ -39,7 +38,7 @@ void approx1_kernel(__global       Ty *d_yo, const KParam yo,
     xo_idx += idy * xo.strides[1] * is_xo_off[1];
 
     const Tp x = (d_xo[xo_idx] - xi_beg) / xi_step;
-    if (x < 0 || yi.dims[xdim] < x+1) {
+    if (x < 0 || yi.dims[xdim] < x + 1) {
         d_yo[yo_idx] = offGrid;
         return;
     }
@@ -54,7 +53,5 @@ void approx1_kernel(__global       Ty *d_yo, const KParam yo,
     // Not changing the behavior because tests will fail
     bool clamp = INTERP_ORDER == 3;
 
-    interp1_dim(d_yo, yo, yo_idx,
-                d_yi, yi, yi_idx,
-                x, method, 1, clamp, xdim);
+    interp1_dim(d_yo, yo, yo_idx, d_yi, yi, yi_idx, x, method, 1, clamp, xdim);
 }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index 0da4e2bda7..b22e6f9c04 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -7,16 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void approx2_kernel(__global       Ty *d_zo, const KParam zo,
-                    __global const Ty *d_zi,  const KParam zi,
-                    __global const Tp *d_xo, const KParam xo, const int xdim,
-                    __global const Tp *d_yo, const KParam yo, const int ydim,
-                    const Tp xi_beg, const Tp xi_step,
-                    const Tp yi_beg, const Tp yi_step,
-                    const Ty offGrid, const int blocksMatX, const int blocksMatY,
-                    const int batch, int method)
-{
+__kernel void approx2_kernel(
+    __global Ty *d_zo, const KParam zo, __global const Ty *d_zi,
+    const KParam zi, __global const Tp *d_xo, const KParam xo, const int xdim,
+    __global const Tp *d_yo, const KParam yo, const int ydim, const Tp xi_beg,
+    const Tp xi_step, const Tp yi_beg, const Tp yi_step, const Ty offGrid,
+    const int blocksMatX, const int blocksMatY, const int batch, int method) {
     const int idz = get_group_id(0) / blocksMatX;
     const int idw = get_group_id(1) / blocksMatY;
 
@@ -26,40 +22,44 @@ void approx2_kernel(__global       Ty *d_zo, const KParam zo,
     const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int idy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(idx >= zo.dims[0] ||
-       idy >= zo.dims[1] ||
-       idz >= zo.dims[2] ||
-       idw >= zo.dims[3])
+    if (idx >= zo.dims[0] || idy >= zo.dims[1] || idz >= zo.dims[2] ||
+        idw >= zo.dims[3])
         return;
 
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1, xo.dims[3] > 1};
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                        xo.dims[3] > 1};
     bool is_zi_off[] = {true, true, true, true};
-    is_zi_off[xdim] = false;
-    is_zi_off[ydim] = false;
+    is_zi_off[xdim]  = false;
+    is_zi_off[ydim]  = false;
 
-    const int zo_idx = idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx + zo.offset;
-    int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + xo.offset;
-    int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + yo.offset;
-    xo_idx += idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2]  * is_xo_off[2];
-    yo_idx += idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2]  * is_xo_off[2];
+    const int zo_idx = idw * zo.strides[3] + idz * zo.strides[2] +
+                       idy * zo.strides[1] + idx + zo.offset;
+    int xo_idx =
+        idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + xo.offset;
+    int yo_idx =
+        idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + yo.offset;
+    xo_idx +=
+        idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2] * is_xo_off[2];
+    yo_idx +=
+        idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2] * is_xo_off[2];
 
     const Tp x = (d_xo[xo_idx] - xi_beg) / xi_step;
     const Tp y = (d_yo[yo_idx] - yi_beg) / yi_step;
-    if (x < 0 || y < 0 || zi.dims[xdim] < x+1 || zi.dims[ydim] < y+1) {
+    if (x < 0 || y < 0 || zi.dims[xdim] < x + 1 || zi.dims[ydim] < y + 1) {
         d_zo[zo_idx] = offGrid;
         return;
     }
 
-    int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0] + zi.offset;
-    zi_idx += idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
+    int zi_idx =
+        idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0] + zi.offset;
+    zi_idx +=
+        idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
 
     // FIXME: Only cubic interpolation is doing clamping
     // We need to make it consistent across all methods
     // Not changing the behavior because tests will fail
     bool clamp = INTERP_ORDER == 3;
 
-    interp2_dim(d_zo, zo, zo_idx,
-                d_zi,  zi, zi_idx,
-                x, y, method, 1, clamp,
+    interp2_dim(d_zo, zo, zo_idx, d_zi, zi, zi_idx, x, y, method, 1, clamp,
                 xdim, ydim);
 }
diff --git a/src/backend/opencl/kernel/assign.cl b/src/backend/opencl/kernel/assign.cl
index e24c258efa..90bb5fd789 100644
--- a/src/backend/opencl/kernel/assign.cl
+++ b/src/backend/opencl/kernel/assign.cl
@@ -8,54 +8,58 @@
  ********************************************************/
 
 typedef struct {
-    int  offs[4];
+    int offs[4];
     int strds[4];
-    char     isSeq[4];
+    char isSeq[4];
 } AssignKernelParam_t;
 
-int trimIndex(int idx, const int len)
-{
+int trimIndex(int idx, const int len) {
     int ret_val = idx;
-    if (ret_val<0) {
-        int offset  = (abs(ret_val)-1)%len;
-        ret_val = offset;
-    } else if (ret_val>=len) {
-        int offset  = abs(ret_val)%len;
-        ret_val = len-offset-1;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
     }
     return ret_val;
 }
 
-kernel
-void assignKernel(global T * optr, KParam oInfo, global const T * iptr, KParam iInfo,
-                 const AssignKernelParam_t p, global const uint* ptr0,
-                 global const uint* ptr1, global const uint* ptr2,
-                 global const uint* ptr3, const int nBBS0, const int nBBS1)
-{
+kernel void assignKernel(global T* optr, KParam oInfo, global const T* iptr,
+                         KParam iInfo, const AssignKernelParam_t p,
+                         global const uint* ptr0, global const uint* ptr1,
+                         global const uint* ptr2, global const uint* ptr3,
+                         const int nBBS0, const int nBBS1) {
     // retrive booleans that tell us which index to use
     const bool s0 = p.isSeq[0];
     const bool s1 = p.isSeq[1];
     const bool s2 = p.isSeq[2];
     const bool s3 = p.isSeq[3];
 
-    const int gz = get_group_id(0)/nBBS0;
-    const int gw = get_group_id(1)/nBBS1;
-    const int gx = get_local_size(0) * (get_group_id(0) - gz*nBBS0) + get_local_id(0);
-    const int gy = get_local_size(1) * (get_group_id(1) - gw*nBBS1) + get_local_id(1);
+    const int gz = get_group_id(0) / nBBS0;
+    const int gw = get_group_id(1) / nBBS1;
+    const int gx =
+        get_local_size(0) * (get_group_id(0) - gz * nBBS0) + get_local_id(0);
+    const int gy =
+        get_local_size(1) * (get_group_id(1) - gw * nBBS1) + get_local_id(1);
 
-    if (gx<iInfo.dims[0] && gy<iInfo.dims[1] && gz<iInfo.dims[2] && gw<iInfo.dims[3]) {
+    if (gx < iInfo.dims[0] && gy < iInfo.dims[1] && gz < iInfo.dims[2] &&
+        gw < iInfo.dims[3]) {
         // calculate pointer offsets for input
-        int i = p.strds[0] * trimIndex(s0 ? gx+p.offs[0] : ptr0[gx], oInfo.dims[0]);
-        int j = p.strds[1] * trimIndex(s1 ? gy+p.offs[1] : ptr1[gy], oInfo.dims[1]);
-        int k = p.strds[2] * trimIndex(s2 ? gz+p.offs[2] : ptr2[gz], oInfo.dims[2]);
-        int l = p.strds[3] * trimIndex(s3 ? gw+p.offs[3] : ptr3[gw], oInfo.dims[3]);
+        int i = p.strds[0] *
+                trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], oInfo.dims[0]);
+        int j = p.strds[1] *
+                trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], oInfo.dims[1]);
+        int k = p.strds[2] *
+                trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], oInfo.dims[2]);
+        int l = p.strds[3] *
+                trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], oInfo.dims[3]);
         // offset input and output pointers
-        global const T *src = iptr + (gx*iInfo.strides[0]+
-                                      gy*iInfo.strides[1]+
-                                      gz*iInfo.strides[2]+
-                                      gw*iInfo.strides[3]+
-                                      iInfo.offset);
-        global T *dst = optr + (i+j+k+l) + oInfo.offset;
+        global const T* src =
+            iptr +
+            (gx * iInfo.strides[0] + gy * iInfo.strides[1] +
+             gz * iInfo.strides[2] + gw * iInfo.strides[3] + iInfo.offset);
+        global T* dst = optr + (i + j + k + l) + oInfo.offset;
         // set the output
         dst[0] = src[0];
     }
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 864dda2597..0caee37fd8 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -8,52 +8,51 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/assign.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 32;
-static const int THREADS_Y =  8;
+static const int THREADS_Y = 8;
 
 typedef struct {
-    int  offs[4];
+    int offs[4];
     int strds[4];
-    char     isSeq[4];
+    char isSeq[4];
 } AssignKernelParam_t;
 
 template<typename T>
-void assign(Param out, const Param in, const AssignKernelParam_t& p, Buffer *bPtr[4])
-{
-    std::string refName = std::string("assignKernel_") + std::string(dtype_traits<T>::getName());
+void assign(Param out, const Param in, const AssignKernelParam_t& p,
+            Buffer* bPtr[4]) {
+    std::string refName =
+        std::string("assignKernel_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {assign_cl};
-        const int   ker_lens[] = {assign_cl_len};
+        const int ker_lens[]   = {assign_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -67,16 +66,18 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p, Buffer *bPt
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X, blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                   blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto assignOp = KernelFunctor< Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
-                                   Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
+    auto assignOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
+                      Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
 
-    assignOp(EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *in.data, in.info, p,
-             *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
+    assignOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3],
+             blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index 46416412a3..e435d15b0f 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -13,60 +13,53 @@
 #define EXP exp
 #endif
 
-int lIdx(int x, int y,
-        int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
+int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
 }
 
-void load2LocalMem(__local outType *  shrd,
-        __global const inType *      in,
-        int lx, int ly, int shrdStride,
-        int dim0, int dim1,
-        int gx, int gy,
-        int inStride1, int inStride0)
-{
-    int gx_  = clamp(gx, 0, dim0-1);
-    int gy_  = clamp(gy, 0, dim1-1);
-    shrd[ lIdx(lx, ly, shrdStride, 1) ] = (outType)in[ lIdx(gx_, gy_, inStride1, inStride0) ];
+void load2LocalMem(__local outType* shrd, __global const inType* in, int lx,
+                   int ly, int shrdStride, int dim0, int dim1, int gx, int gy,
+                   int inStride1, int inStride0) {
+    int gx_ = clamp(gx, 0, dim0 - 1);
+    int gy_ = clamp(gy, 0, dim1 - 1);
+    shrd[lIdx(lx, ly, shrdStride, 1)] =
+        (outType)in[lIdx(gx_, gy_, inStride1, inStride0)];
 }
 
-__kernel
-void bilateral(__global outType *        d_dst,
-               KParam                    oInfo,
-               __global const inType *   d_src,
-               KParam                    iInfo,
-               __local outType *         localMem,
-               __local outType *         gauss2d,
-               float sigma_space, float sigma_color,
-               int gaussOff, int nBBS0, int nBBS1)
-{
-    const int radius      = max((int)(sigma_space * 1.5f), 1);
-    const int padding     = 2 * radius;
-    const int window_size = padding + 1;
-    const int shrdLen     = get_local_size(0) + padding;
-    const float variance_range = sigma_color * sigma_color;
-    const float variance_space = sigma_space * sigma_space;
-    const float variance_space_neg2 = -2.0 * variance_space;
+__kernel void bilateral(__global outType* d_dst, KParam oInfo,
+                        __global const inType* d_src, KParam iInfo,
+                        __local outType* localMem, __local outType* gauss2d,
+                        float sigma_space, float sigma_color, int gaussOff,
+                        int nBBS0, int nBBS1) {
+    const int radius                    = max((int)(sigma_space * 1.5f), 1);
+    const int padding                   = 2 * radius;
+    const int window_size               = padding + 1;
+    const int shrdLen                   = get_local_size(0) + padding;
+    const float variance_range          = sigma_color * sigma_color;
+    const float variance_space          = sigma_space * sigma_space;
+    const float variance_space_neg2     = -2.0 * variance_space;
     const float inv_variance_range_neg2 = -0.5 / (variance_range);
 
     // gfor batch offsets
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
-    __global const inType* in = d_src + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
-    __global outType* out     = d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+    __global const inType* in =
+        d_src + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
+    __global outType* out =
+        d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     int lx = get_local_id(0);
     int ly = get_local_id(1);
 
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    const int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
     // generate gauss2d spatial variance values for block
-    if (lx<window_size && ly<window_size) {
+    if (lx < window_size && ly < window_size) {
         int x = lx - radius;
         int y = ly - radius;
-        gauss2d[ly*window_size+lx] = EXP(((x*x) + (y*y)) / variance_space_neg2);
+        gauss2d[ly * window_size + lx] =
+            EXP(((x * x) + (y * y)) / variance_space_neg2);
     }
 
     int s0 = iInfo.strides[0];
@@ -74,39 +67,42 @@ void bilateral(__global outType *        d_dst,
     int d0 = iInfo.dims[0];
     int d1 = iInfo.dims[1];
     // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen; b+=get_local_size(1), gy2+=get_local_size(1)) {
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
         // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-            load2LocalMem(localMem, in, a, b, shrdLen, d0, d1, gx2-radius, gy2-radius, s1, s0);
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            load2LocalMem(localMem, in, a, b, shrdLen, d0, d1, gx2 - radius,
+                          gy2 - radius, s1, s0);
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (gx<iInfo.dims[0] && gy<iInfo.dims[1]) {
+    if (gx < iInfo.dims[0] && gy < iInfo.dims[1]) {
         lx += radius;
         ly += radius;
-        outType center_color = localMem[ly*shrdLen+lx];
-        outType res  = 0;
-        outType norm = 0;
+        outType center_color = localMem[ly * shrdLen + lx];
+        outType res          = 0;
+        outType norm         = 0;
 
-        int joff = (ly - radius)*shrdLen + (lx-radius);
+        int joff = (ly - radius) * shrdLen + (lx - radius);
         int goff = 0;
 
 #pragma unroll
-        for(int wj=0; wj<window_size; ++wj) {
+        for (int wj = 0; wj < window_size; ++wj) {
 #pragma unroll
-            for(int wi=0; wi<window_size; ++wi) {
-                outType tmp_color   = localMem[joff+wi];
-                const outType c = center_color - tmp_color;
+            for (int wi = 0; wi < window_size; ++wi) {
+                outType tmp_color   = localMem[joff + wi];
+                const outType c     = center_color - tmp_color;
                 outType gauss_range = EXP(c * c * inv_variance_range_neg2);
-                outType weight      = gauss2d[goff+wi] * gauss_range;
+                outType weight      = gauss2d[goff + wi] * gauss_range;
                 norm += weight;
-                res  += tmp_color * weight;
+                res += tmp_color * weight;
             }
             joff += shrdLen;
             goff += window_size;
         }
-        out[gy*oInfo.strides[1] + gx] = res / norm;
+        out[gy * oInfo.strides[1] + gx] = res / norm;
     }
 }
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 6c4f2b2504..7aab2a5588 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -8,57 +8,54 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/bilateral.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
-#include <algorithm>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/bilateral.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <af/opencl.h>
+#include <algorithm>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename inType, typename outType, bool isColor>
-void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
-{
+void bilateral(Param out, const Param in, float s_sigma, float c_sigma) {
     std::string refName = std::string("bilateral_") +
-        std::string(dtype_traits<inType>::getName()) +
-        std::string(dtype_traits<outType>::getName()) +
-        std::to_string(isColor);
+                          std::string(dtype_traits<inType>::getName()) +
+                          std::string(dtype_traits<outType>::getName()) +
+                          std::to_string(isColor);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D inType=" << dtype_traits<inType>::getName()
-            << " -D outType=" << dtype_traits<outType>::getName();
+                << " -D outType=" << dtype_traits<outType>::getName();
         if (std::is_same<inType, double>::value ||
-                std::is_same<inType, cdouble>::value) {
+            std::is_same<inType, cdouble>::value) {
             options << " -D USE_DOUBLE";
         } else {
             options << " -D USE_NATIVE_EXP";
         }
 
         const char* ker_strs[] = {bilateral_cl};
-        const int   ker_lens[] = {bilateral_cl_len};
+        const int ker_lens[]   = {bilateral_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -67,36 +64,39 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma)
         addKernelToCache(device, refName, entry);
     }
 
-    auto bilateralOp = KernelFunctor< Buffer, KParam, Buffer, KParam, LocalSpaceArg, LocalSpaceArg,
-                                      float, float, int, int, int >(*entry.ker);
+    auto bilateralOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, LocalSpaceArg,
+                      LocalSpaceArg, float, float, int, int, int>(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x*in.info.dims[2]*THREADS_X, blk_y*in.info.dims[3]*THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                   blk_y * in.info.dims[3] * THREADS_Y);
 
     // calculate local memory size
-    int radius = (int)std::max(s_sigma * 1.5f, 1.f);
-    int num_shrd_elems    = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
-    int num_gauss_elems   = (2*radius+1)*(2*radius+1);
-    size_t localMemSize   = (num_shrd_elems + num_gauss_elems)*sizeof(outType);
-    size_t MaxLocalSize   = getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
-    if (localMemSize>MaxLocalSize) {
+    int radius          = (int)std::max(s_sigma * 1.5f, 1.f);
+    int num_shrd_elems  = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
+    int num_gauss_elems = (2 * radius + 1) * (2 * radius + 1);
+    size_t localMemSize = (num_shrd_elems + num_gauss_elems) * sizeof(outType);
+    size_t MaxLocalSize =
+        getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
+    if (localMemSize > MaxLocalSize) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nOpenCL Bilateral filter doesn't support %f spatial sigma\n", s_sigma);
+                 "\nOpenCL Bilateral filter doesn't support %f spatial sigma\n",
+                 s_sigma);
         OPENCL_NOT_SUPPORTED(errMessage);
     }
 
-    bilateralOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info,
-                cl::Local(num_shrd_elems*sizeof(outType)),
-                cl::Local(num_gauss_elems*sizeof(outType)),
-                s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y);
+    bilateralOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *in.data, in.info, cl::Local(num_shrd_elems * sizeof(outType)),
+                cl::Local(num_gauss_elems * sizeof(outType)), s_sigma, c_sigma,
+                num_shrd_elems, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 322ab7c035..d9dd2c6f3c 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -8,228 +8,227 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/trace_edge.hpp>
-#include <kernel_headers/nonmax_suppression.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
-#include <memory.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/nonmax_suppression.hpp>
+#include <kernel_headers/trace_edge.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <type_util.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T>
-void nonMaxSuppression(Param output, const Param magnitude, const Param dx, const Param dy)
-{
-    std::string refName = std::string("non_max_suppression_") + std::string(dtype_traits<T>::getName());
+void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
+                       const Param dy) {
+    std::string refName = std::string("non_max_suppression_") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="           << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT="       << (THREADS_X+2)
-                << " -D SHRD_MEM_WIDTH="        << (THREADS_Y+2)
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
+                << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
                 << " -D NON_MAX_SUPPRESSION";
-        if (std::is_same<T, double>::value)
-            options << " -D USE_DOUBLE";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {nonmax_suppression_cl};
-        const int   ker_lens[] = {nonmax_suppression_cl_len};
+        const int ker_lens[]   = {nonmax_suppression_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "nonMaxSuppressionKernel");
+        entry.ker  = new Kernel(*entry.prog, "nonMaxSuppressionKernel");
         addKernelToCache(device, refName, entry);
     }
 
-    auto nonMaxOp = KernelFunctor<Buffer, const KParam,
-                                  const Buffer, const KParam,
-                                  const Buffer, const KParam,
-                                  const Buffer, const KParam,
-                                  const unsigned, const unsigned>(*entry.ker);
+    auto nonMaxOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const Buffer, const KParam, const Buffer, const KParam,
+                      const unsigned, const unsigned>(*entry.ker);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
     // Launch only threads to process non-border pixels
-    int blk_x = divup(magnitude.info.dims[0]-2, threads[0]);
-    int blk_y = divup(magnitude.info.dims[1]-2, threads[1]);
+    int blk_x = divup(magnitude.info.dims[0] - 2, threads[0]);
+    int blk_y = divup(magnitude.info.dims[1] - 2, threads[1]);
 
     // launch batch * blk_x blocks along x dimension
     NDRange global(blk_x * magnitude.info.dims[2] * threads[0],
                    blk_y * magnitude.info.dims[3] * threads[1], 1);
 
-    nonMaxOp(EnqueueArgs(getQueue(), global, threads),
-             *output.data, output.info, *magnitude.data, magnitude.info,
-             *dx.data, dx.info, *dy.data, dy.info, blk_x, blk_y);
+    nonMaxOp(EnqueueArgs(getQueue(), global, threads), *output.data,
+             output.info, *magnitude.data, magnitude.info, *dx.data, dx.info,
+             *dy.data, dy.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-void initEdgeOut(Param output, const Param strong, const Param weak)
-{
-    std::string refName = std::string("init_edge_out_") + std::string(dtype_traits<T>::getName());
+void initEdgeOut(Param output, const Param strong, const Param weak) {
+    std::string refName =
+        std::string("init_edge_out_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="           << dtype_traits<T>::getName()
+        options << " -D T=" << dtype_traits<T>::getName()
                 << " -D INIT_EDGE_OUT";
-        if (std::is_same<T, double>::value)
-            options << " -D USE_DOUBLE";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {trace_edge_cl};
-        const int   ker_lens[] = {trace_edge_cl_len};
+        const int ker_lens[]   = {trace_edge_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "initEdgeOutKernel");
+        entry.ker  = new Kernel(*entry.prog, "initEdgeOutKernel");
         addKernelToCache(device, refName, entry);
     }
 
-    auto initOp = KernelFunctor<Buffer, const KParam,
-                                const Buffer, const KParam,
-                                const Buffer, const KParam,
+    auto initOp = KernelFunctor<Buffer, const KParam, const Buffer,
+                                const KParam, const Buffer, const KParam,
                                 const unsigned, const unsigned>(*entry.ker);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
     // Launch only threads to process non-border pixels
-    int blk_x = divup(strong.info.dims[0]-2, threads[0]);
-    int blk_y = divup(strong.info.dims[1]-2, threads[1]);
+    int blk_x = divup(strong.info.dims[0] - 2, threads[0]);
+    int blk_y = divup(strong.info.dims[1] - 2, threads[1]);
 
     // launch batch * blk_x blocks along x dimension
     NDRange global(blk_x * strong.info.dims[2] * threads[0],
                    blk_y * strong.info.dims[3] * threads[1], 1);
 
-    initOp(EnqueueArgs(getQueue(), global, threads),
-           *output.data, output.info, *strong.data, strong.info, *weak.data, weak.info, blk_x, blk_y);
+    initOp(EnqueueArgs(getQueue(), global, threads), *output.data, output.info,
+           *strong.data, strong.info, *weak.data, weak.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-void suppressLeftOver(Param output)
-{
-    std::string refName = std::string("suppress_left_over_") + std::string(dtype_traits<T>::getName());
+void suppressLeftOver(Param output) {
+    std::string refName = std::string("suppress_left_over_") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="           << dtype_traits<T>::getName()
+        options << " -D T=" << dtype_traits<T>::getName()
                 << " -D SUPPRESS_LEFT_OVER";
-        if (std::is_same<T, double>::value)
-            options << " -D USE_DOUBLE";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {trace_edge_cl};
-        const int   ker_lens[] = {trace_edge_cl_len};
+        const int ker_lens[]   = {trace_edge_cl_len};
 
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "suppressLeftOverKernel");
+        entry.ker  = new Kernel(*entry.prog, "suppressLeftOverKernel");
         addKernelToCache(device, refName, entry);
     }
 
-    auto finalOp = KernelFunctor<Buffer, const KParam, const unsigned, const unsigned>(*entry.ker);
+    auto finalOp =
+        KernelFunctor<Buffer, const KParam, const unsigned, const unsigned>(
+            *entry.ker);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
     // Launch only threads to process non-border pixels
-    int blk_x = divup(output.info.dims[0]-2, threads[0]);
-    int blk_y = divup(output.info.dims[1]-2, threads[1]);
+    int blk_x = divup(output.info.dims[0] - 2, threads[0]);
+    int blk_y = divup(output.info.dims[1] - 2, threads[1]);
 
     // launch batch * blk_x blocks along x dimension
     NDRange global(blk_x * output.info.dims[2] * threads[0],
                    blk_y * output.info.dims[3] * threads[1], 1);
 
-    finalOp(EnqueueArgs(getQueue(), global, threads), *output.data, output.info, blk_x, blk_y);
+    finalOp(EnqueueArgs(getQueue(), global, threads), *output.data, output.info,
+            blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-void edgeTrackingHysteresis(Param output, const Param strong, const Param weak)
-{
-    std::string refName = std::string("edge_track_") + std::string(dtype_traits<T>::getName());
+void edgeTrackingHysteresis(Param output, const Param strong,
+                            const Param weak) {
+    std::string refName =
+        std::string("edge_track_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="           << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT="       << (THREADS_X+2)
-                << " -D SHRD_MEM_WIDTH="        << (THREADS_Y+2)
-                << " -D TOTAL_NUM_THREADS="     << (THREADS_X*THREADS_Y)
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
+                << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
+                << " -D TOTAL_NUM_THREADS=" << (THREADS_X * THREADS_Y)
                 << " -D EDGE_TRACER";
-        if (std::is_same<T, double>::value)
-            options << " -D USE_DOUBLE";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {trace_edge_cl};
-        const int   ker_lens[] = {trace_edge_cl_len};
+        const int ker_lens[]   = {trace_edge_cl_len};
 
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "edgeTrackKernel");
+        entry.ker  = new Kernel(*entry.prog, "edgeTrackKernel");
         addKernelToCache(device, refName, entry);
     }
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     // Launch only threads to process non-border pixels
-    int blk_x = divup(weak.info.dims[0]-2, threads[0]);
-    int blk_y = divup(weak.info.dims[1]-2, threads[1]);
+    int blk_x = divup(weak.info.dims[0] - 2, threads[0]);
+    int blk_y = divup(weak.info.dims[1] - 2, threads[1]);
 
     // launch batch * blk_x blocks along x dimension
     NDRange global(blk_x * weak.info.dims[2] * threads[0],
                    blk_y * weak.info.dims[3] * threads[1], 1);
 
-    auto edgeTraceOp = KernelFunctor<Buffer, const KParam,
-                                     const unsigned, const unsigned, Buffer>(*entry.ker);
+    auto edgeTraceOp = KernelFunctor<Buffer, const KParam, const unsigned,
+                                     const unsigned, Buffer>(*entry.ker);
 
     initEdgeOut<T>(output, strong, weak);
 
-    int notFinished = 1;
+    int notFinished        = 1;
     cl::Buffer *d_continue = bufferAlloc(sizeof(int));
 
-    while(notFinished) {
+    while (notFinished) {
         notFinished = 0;
-        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &notFinished);
+        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
+                                      &notFinished);
 
-        edgeTraceOp(EnqueueArgs(getQueue(), global, threads),
-                    *output.data, output.info, blk_x, blk_y, *d_continue);
+        edgeTraceOp(EnqueueArgs(getQueue(), global, threads), *output.data,
+                    output.info, blk_x, blk_y, *d_continue);
         CL_DEBUG_FINISH(getQueue());
 
-        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &notFinished);
+        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
+                                     &notFinished);
     }
 
     bufferFree(d_continue);
 
     suppressLeftOver<T>(output);
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/config.cpp b/src/backend/opencl/kernel/config.cpp
index 322f514443..97d91c510a 100644
--- a/src/backend/opencl/kernel/config.cpp
+++ b/src/backend/opencl/kernel/config.cpp
@@ -8,23 +8,17 @@
  ********************************************************/
 
 #include "config.hpp"
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 
-    std::ostream&
-    operator<<(std::ostream &out, const cfloat& var)
-    {
-        out << "{" << var.s[0] << "," << var.s[1] << "}";
-        return out;
-    }
-
-    std::ostream&
-    operator<<(std::ostream &out, const cdouble& var)
-    {
-        out << "{" << var.s[0] << "," << var.s[1] << "}";
-        return out;
-    }
+std::ostream& operator<<(std::ostream& out, const cfloat& var) {
+    out << "{" << var.s[0] << "," << var.s[1] << "}";
+    return out;
 }
+
+std::ostream& operator<<(std::ostream& out, const cdouble& var) {
+    out << "{" << var.s[0] << "," << var.s[1] << "}";
+    return out;
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/config.hpp b/src/backend/opencl/kernel/config.hpp
index cbe92c4023..38a47399a4 100644
--- a/src/backend/opencl/kernel/config.hpp
+++ b/src/backend/opencl/kernel/config.hpp
@@ -8,23 +8,19 @@
  ********************************************************/
 
 #pragma once
-#include <ostream>
 #include <types.hpp>
+#include <ostream>
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 
-    std::ostream&
-    operator<<(std::ostream &out, const cfloat& var);
+std::ostream& operator<<(std::ostream& out, const cfloat& var);
 
-    std::ostream&
-    operator<<(std::ostream &out, const cdouble& var);
+std::ostream& operator<<(std::ostream& out, const cdouble& var);
 
-    static const uint THREADS_PER_GROUP = 256;
-    static const uint THREADS_X = 32;
-    static const uint THREADS_Y = THREADS_PER_GROUP / THREADS_X;
-    static const uint REPEAT    = 32;
-}
-}
+static const uint THREADS_PER_GROUP = 256;
+static const uint THREADS_X         = 32;
+static const uint THREADS_Y         = THREADS_PER_GROUP / THREADS_X;
+static const uint REPEAT            = 32;
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve.cl b/src/backend/opencl/kernel/convolve.cl
index 0d13a0eee2..9bb8cd68d3 100644
--- a/src/backend/opencl/kernel/convolve.cl
+++ b/src/backend/opencl/kernel/convolve.cl
@@ -7,94 +7,101 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-int index(int i, int j, int k, int jstride, int kstride)
-{
-    return i+j*jstride+k*kstride;
+int index(int i, int j, int k, int jstride, int kstride) {
+    return i + j * jstride + k * kstride;
 }
 
-#if BASE_DIM==1
-kernel
-void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
-              local T *localMem, constant accType const *impulse, KParam fInfo,
-              int nBBS0, int nBBS1, int ostep1, int ostep2,
-              int ostep3, int sstep1, int sstep2, int sstep3)
-{
-    int fLen     = fInfo.dims[0];
-    int padding  = fLen-1;
-    int shrdLen  = get_local_size(0) + 2*padding;
-    const unsigned b1 = get_group_id(0)/nBBS0;
-    const unsigned b0 = get_group_id(0)-nBBS0*b1;
-    const unsigned b3 = get_group_id(1)/nBBS1;
-    const unsigned b2 = get_group_id(1)-nBBS1*b3;
-
-    global T *dst = out + (b1 * oInfo.strides[1] +  /* activated with batched input signal */
-                       ostep1 * oInfo.strides[1] +  /* activated with batched input filter */
-                           b2 * oInfo.strides[2] +  /* activated with batched input signal */
-                       ostep2 * oInfo.strides[2] +  /* activated with batched input filter */
-                           b3 * oInfo.strides[3] +  /* activated with batched input signal */
-                       ostep3 * oInfo.strides[3]);  /* activated with batched input filter */
-
-    global T const *src = signal + sInfo.offset + (b1 * sInfo.strides[1] + /* activated with batched input signal */
-                                               sstep1 * sInfo.strides[1] + /* activated with batched input filter */
-                                                   b2 * sInfo.strides[2] + /* activated with batched input signal */
-                                               sstep2 * sInfo.strides[2] + /* activated with batched input filter */
-                                                   b3 * sInfo.strides[3] + /* activated with batched input signal */
-                                               sstep3 * sInfo.strides[3]); /* activated with batched input filter */
-
-    int gx  = get_local_size(0)*b0;
-
-    for (int i=get_local_id(0); i<shrdLen; i+=get_local_size(0)) {
-        int idx = gx-padding + i;
-        localMem[i]  = (idx>=0 && idx<sInfo.dims[0]) ? src[idx*sInfo.strides[0]] : (T)(0);
+#if BASE_DIM == 1
+kernel void convolve(global T *out, KParam oInfo, global T const *signal,
+                     KParam sInfo, local T *localMem,
+                     constant accType const *impulse, KParam fInfo, int nBBS0,
+                     int nBBS1, int ostep1, int ostep2, int ostep3, int sstep1,
+                     int sstep2, int sstep3) {
+    int fLen          = fInfo.dims[0];
+    int padding       = fLen - 1;
+    int shrdLen       = get_local_size(0) + 2 * padding;
+    const unsigned b1 = get_group_id(0) / nBBS0;
+    const unsigned b0 = get_group_id(0) - nBBS0 * b1;
+    const unsigned b3 = get_group_id(1) / nBBS1;
+    const unsigned b2 = get_group_id(1) - nBBS1 * b3;
+
+    global T *dst =
+        out +
+        (b1 * oInfo.strides[1] +     /* activated with batched input signal */
+         ostep1 * oInfo.strides[1] + /* activated with batched input filter */
+         b2 * oInfo.strides[2] +     /* activated with batched input signal */
+         ostep2 * oInfo.strides[2] + /* activated with batched input filter */
+         b3 * oInfo.strides[3] +     /* activated with batched input signal */
+         ostep3 * oInfo.strides[3]); /* activated with batched input filter */
+
+    global T const *src =
+        signal + sInfo.offset +
+        (b1 * sInfo.strides[1] +     /* activated with batched input signal */
+         sstep1 * sInfo.strides[1] + /* activated with batched input filter */
+         b2 * sInfo.strides[2] +     /* activated with batched input signal */
+         sstep2 * sInfo.strides[2] + /* activated with batched input filter */
+         b3 * sInfo.strides[3] +     /* activated with batched input signal */
+         sstep3 * sInfo.strides[3]); /* activated with batched input filter */
+
+    int gx = get_local_size(0) * b0;
+
+    for (int i = get_local_id(0); i < shrdLen; i += get_local_size(0)) {
+        int idx     = gx - padding + i;
+        localMem[i] = (idx >= 0 && idx < sInfo.dims[0])
+                          ? src[idx * sInfo.strides[0]]
+                          : (T)(0);
     }
     barrier(CLK_LOCAL_MEM_FENCE);
     gx += get_local_id(0);
 
-    if (gx>=0 && gx<oInfo.dims[0]) {
-        int lx   = get_local_id(0) + padding + (EXPAND ? 0 : fLen>>1);
+    if (gx >= 0 && gx < oInfo.dims[0]) {
+        int lx        = get_local_id(0) + padding + (EXPAND ? 0 : fLen >> 1);
         accType accum = (accType)(0);
-        for(int f=0; f<fLen; ++f) {
-            //binOp will do MUL_OP for convolution operation
-            accum = accum + binOp((accType)localMem[lx-f], (accType)impulse[f]);
+        for (int f = 0; f < fLen; ++f) {
+            // binOp will do MUL_OP for convolution operation
+            accum =
+                accum + binOp((accType)localMem[lx - f], (accType)impulse[f]);
         }
         dst[gx] = (T)accum;
     }
 }
 #endif
 
-#if BASE_DIM==2
-kernel
-void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
-              constant accType const *impulse, KParam fInfo,
-              int nBBS0, int nBBS1, int ostep2,
-              int ostep3, int sstep2, int sstep3)
-{
+#if BASE_DIM == 2
+kernel void convolve(global T *out, KParam oInfo, global T const *signal,
+                     KParam sInfo, constant accType const *impulse,
+                     KParam fInfo, int nBBS0, int nBBS1, int ostep2, int ostep3,
+                     int sstep2, int sstep3) {
     local T localMem[C_SIZE];
 
-    int radius0  = FLEN0-1;
-    int radius1  = FLEN1-1;
-    int padding0 = 2*radius0;
-    int padding1 = 2*radius1;
+    int radius0  = FLEN0 - 1;
+    int radius1  = FLEN1 - 1;
+    int padding0 = 2 * radius0;
+    int padding1 = 2 * radius1;
     int shrdLen0 = get_local_size(0) + padding0;
     int shrdLen1 = get_local_size(1) + padding1;
 
-    unsigned b0  = get_group_id(0)/nBBS0;
-    unsigned b1  = get_group_id(1)/nBBS1;
+    unsigned b0 = get_group_id(0) / nBBS0;
+    unsigned b1 = get_group_id(1) / nBBS1;
 
-    global T *dst = out + (b0 * oInfo.strides[2] + /* activated with batched input signal */
-                       ostep2 * oInfo.strides[2] + /* activated with batched input filter */
-                           b1 * oInfo.strides[3] + /* activated with batched input signal */
-                       ostep3 * oInfo.strides[3]); /* activated with batched input filter */
+    global T *dst =
+        out +
+        (b0 * oInfo.strides[2] +     /* activated with batched input signal */
+         ostep2 * oInfo.strides[2] + /* activated with batched input filter */
+         b1 * oInfo.strides[3] +     /* activated with batched input signal */
+         ostep3 * oInfo.strides[3]); /* activated with batched input filter */
 
-    global const T *src = signal + sInfo.offset + (b0 * sInfo.strides[2] + /* activated with batched input signal */
-                                               sstep2 * sInfo.strides[2] + /* activated with batched input filter */
-                                                   b1 * sInfo.strides[3] + /* activated with batched input signal */
-                                               sstep3 * sInfo.strides[3]); /* activated with batched input filter */
+    global const T *src =
+        signal + sInfo.offset +
+        (b0 * sInfo.strides[2] +     /* activated with batched input signal */
+         sstep2 * sInfo.strides[2] + /* activated with batched input filter */
+         b1 * sInfo.strides[3] +     /* activated with batched input signal */
+         sstep3 * sInfo.strides[3]); /* activated with batched input filter */
 
     int lx = get_local_id(0);
     int ly = get_local_id(1);
-    int gx = get_local_size(0) * (get_group_id(0)-b0*nBBS0) + lx;
-    int gy = get_local_size(1) * (get_group_id(1)-b1*nBBS1) + ly;
+    int gx = get_local_size(0) * (get_group_id(0) - b0 * nBBS0) + lx;
+    int gy = get_local_size(1) * (get_group_id(1) - b1 * nBBS1) + ly;
 
     // below loops are traditional loops, they only run multiple
     // times filter length is more than launch size
@@ -102,67 +109,73 @@ void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
     int s1 = sInfo.strides[1];
     int d0 = sInfo.dims[0];
     int d1 = sInfo.dims[1];
-    for (int b=ly, gy2=gy; b<shrdLen1; b+=get_local_size(1), gy2+=get_local_size(1)) {
-        int j = gy2-radius1;
-        bool is_j  = j>=0 && j<d1;
+    for (int b = ly, gy2 = gy; b < shrdLen1;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
+        int j     = gy2 - radius1;
+        bool is_j = j >= 0 && j < d1;
         // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen0; a+=get_local_size(0), gx2+=get_local_size(0)) {
-            int i = gx2-radius0;
-            bool is_i  = i>=0 && i<d0;
-            localMem[b*shrdLen0+a] = (is_i && is_j ? src[i*s0+j*s1] : (T)(0));
+        for (int a = lx, gx2 = gx; a < shrdLen0;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            int i     = gx2 - radius0;
+            bool is_i = i >= 0 && i < d0;
+            localMem[b * shrdLen0 + a] =
+                (is_i && is_j ? src[i * s0 + j * s1] : (T)(0));
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (gx<oInfo.dims[0] && gy<oInfo.dims[1]) {
-        int ci = lx + radius0 + (EXPAND ? 0 : FLEN0>>1);
-        int cj = ly + radius1 + (EXPAND ? 0 : FLEN1>>1);
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1]) {
+        int ci = lx + radius0 + (EXPAND ? 0 : FLEN0 >> 1);
+        int cj = ly + radius1 + (EXPAND ? 0 : FLEN1 >> 1);
 
         accType accum = (accType)(0);
-        for(int fj=0; fj<FLEN1; ++fj) {
-            for(int fi=0; fi<FLEN0; ++fi) {
-                accType f_val = impulse[fj*FLEN0+fi];
-                T s_val = localMem[(cj-fj)*shrdLen0+(ci-fi)];
+        for (int fj = 0; fj < FLEN1; ++fj) {
+            for (int fi = 0; fi < FLEN0; ++fi) {
+                accType f_val = impulse[fj * FLEN0 + fi];
+                T s_val       = localMem[(cj - fj) * shrdLen0 + (ci - fi)];
 
-                //binOp will do MUL_OP for convolution operation
+                // binOp will do MUL_OP for convolution operation
                 accum = accum + binOp((accType)s_val, (accType)f_val);
             }
         }
-        dst[gy*oInfo.strides[1]+gx] = (T)accum;
+        dst[gy * oInfo.strides[1] + gx] = (T)accum;
     }
 }
 #endif
 
-#if BASE_DIM==3
-kernel
-void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
-              local T *localMem, constant accType const *impulse, KParam fInfo,
-              int nBBS0, int nBBS1, int o1, int ostep2,
-              int ostep3, int sstep1, int sstep2, int sstep3)
-{
+#if BASE_DIM == 3
+kernel void convolve(global T *out, KParam oInfo, global T const *signal,
+                     KParam sInfo, local T *localMem,
+                     constant accType const *impulse, KParam fInfo, int nBBS0,
+                     int nBBS1, int o1, int ostep2, int ostep3, int sstep1,
+                     int sstep2, int sstep3) {
     int fLen0    = fInfo.dims[0];
     int fLen1    = fInfo.dims[1];
     int fLen2    = fInfo.dims[2];
-    int radius0  = fLen0-1;
-    int radius1  = fLen1-1;
-    int radius2  = fLen2-1;
-    int shrdLen0 = get_local_size(0) + 2*radius0;
-    int shrdLen1 = get_local_size(1) + 2*radius1;
-    int shrdLen2 = get_local_size(2) + 2*radius2;
+    int radius0  = fLen0 - 1;
+    int radius1  = fLen1 - 1;
+    int radius2  = fLen2 - 1;
+    int shrdLen0 = get_local_size(0) + 2 * radius0;
+    int shrdLen1 = get_local_size(1) + 2 * radius1;
+    int shrdLen2 = get_local_size(2) + 2 * radius2;
     int skStride = shrdLen0 * shrdLen1;
     int fStride  = fLen0 * fLen1;
-    unsigned b2  = get_group_id(0)/nBBS0;
+    unsigned b2  = get_group_id(0) / nBBS0;
 
-    global T *dst = out + (b2 * oInfo.strides[3] + /* activated with batched input signal */
-                       ostep3 * oInfo.strides[3]); /* activated with batched input filter */
+    global T *dst =
+        out +
+        (b2 * oInfo.strides[3] +     /* activated with batched input signal */
+         ostep3 * oInfo.strides[3]); /* activated with batched input filter */
 
-    global const T *src = signal + sInfo.offset + (b2 * sInfo.strides[3] + /* activated with batched input signal */
-                                               sstep3 * sInfo.strides[3]); /* activated with batched input filter */
+    global const T *src =
+        signal + sInfo.offset +
+        (b2 * sInfo.strides[3] +     /* activated with batched input signal */
+         sstep3 * sInfo.strides[3]); /* activated with batched input filter */
 
     int lx  = get_local_id(0);
     int ly  = get_local_id(1);
     int lz  = get_local_id(2);
-    int gx  = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
+    int gx  = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
     int gy  = get_local_size(1) * get_group_id(1) + ly;
     int gz  = get_local_size(2) * get_group_id(2) + lz;
     int lx2 = lx + get_local_size(0);
@@ -179,34 +192,40 @@ void convolve(global T *out, KParam oInfo, global T const *signal, KParam sInfo,
     int d1 = sInfo.dims[1];
     int d2 = sInfo.dims[2];
 
-    for (int c=lz, gz2=gz; c<shrdLen2; c+=get_local_size(2), gz2+=get_local_size(2)) {
-        int k = gz2-radius2;
-        bool is_k  = k>=0 && k<d2;
-        for (int b=ly, gy2=gy; b<shrdLen1; b+=get_local_size(1), gy2+=get_local_size(1)) {
-            int j = gy2-radius1;
-            bool is_j  = j>=0 && j<d1;
-            for (int a=lx, gx2=gx; a<shrdLen0; a+=get_local_size(0), gx2+=get_local_size(0)) {
-                int i = gx2-radius0;
-                bool is_i  = i>=0 && i<d0;
-                localMem[c*skStride+b*shrdLen0+a] = (is_i && is_j && is_k ? src[i*s0+j*s1+k*s2] : (T)(0));
+    for (int c = lz, gz2 = gz; c < shrdLen2;
+         c += get_local_size(2), gz2 += get_local_size(2)) {
+        int k     = gz2 - radius2;
+        bool is_k = k >= 0 && k < d2;
+        for (int b = ly, gy2 = gy; b < shrdLen1;
+             b += get_local_size(1), gy2 += get_local_size(1)) {
+            int j     = gy2 - radius1;
+            bool is_j = j >= 0 && j < d1;
+            for (int a = lx, gx2 = gx; a < shrdLen0;
+                 a += get_local_size(0), gx2 += get_local_size(0)) {
+                int i     = gx2 - radius0;
+                bool is_i = i >= 0 && i < d0;
+                localMem[c * skStride + b * shrdLen0 + a] =
+                    (is_i && is_j && is_k ? src[i * s0 + j * s1 + k * s2]
+                                          : (T)(0));
             }
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (gx<oInfo.dims[0] && gy<oInfo.dims[1] && gz<oInfo.dims[2]) {
-        int ci = lx + radius0 + (EXPAND ? 0 : fLen0>>1);
-        int cj = ly + radius1 + (EXPAND ? 0 : fLen1>>1);
-        int ck = lz + radius2 + (EXPAND ? 0 : fLen2>>1);
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1] && gz < oInfo.dims[2]) {
+        int ci = lx + radius0 + (EXPAND ? 0 : fLen0 >> 1);
+        int cj = ly + radius1 + (EXPAND ? 0 : fLen1 >> 1);
+        int ck = lz + radius2 + (EXPAND ? 0 : fLen2 >> 1);
 
         accType accum = (accType)(0);
-        for(int fk=0; fk<fLen2; ++fk) {
-            for(int fj=0; fj<fLen1; ++fj) {
-                for(int fi=0; fi<fLen0; ++fi) {
+        for (int fk = 0; fk < fLen2; ++fk) {
+            for (int fj = 0; fj < fLen1; ++fj) {
+                for (int fi = 0; fi < fLen0; ++fi) {
                     accType f_val = impulse[index(fi, fj, fk, fLen0, fStride)];
-                    T s_val = localMem[index(ci-fi, cj-fj, ck-fk, shrdLen0, skStride)];
+                    T s_val       = localMem[index(ci - fi, cj - fj, ck - fk,
+                                             shrdLen0, skStride)];
 
-                    //binOp will do MUL_OP for convolution operation
+                    // binOp will do MUL_OP for convolution operation
                     accum = accum + binOp((accType)s_val, (accType)f_val);
                 }
             }
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index 879c0c7e20..bd01a2eac2 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -10,11 +10,9 @@
 #pragma once
 #include <kernel/convolve/conv_common.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 // below shared MAX_*_LEN's are calculated based on
 // a maximum shared memory configuration of 48KB per block
@@ -32,21 +30,21 @@ static const int MAX_CONV3_FILTER_LEN = 5;
  * written in corresponding conv[1|2|3].cpp files under the same folder.
  */
 template<typename T, typename accType, int baseDim, bool expand>
-void convolve_nd(Param out, const Param signal, const Param filter, AF_BATCH_KIND kind)
-{
+void convolve_nd(Param out, const Param signal, const Param filter,
+                 AF_BATCH_KIND kind) {
     conv_kparam_t param;
 
-    for (int i=0; i<3; ++i) {
+    for (int i = 0; i < 3; ++i) {
         param.o[i] = 0;
         param.s[i] = 0;
     }
-    param.launchMoreBlocks = kind==AF_BATCH_SAME || kind==AF_BATCH_RHS;
-    param.outHasNoOffset   = kind==AF_BATCH_LHS  || kind==AF_BATCH_NONE;
-    param.inHasNoOffset    = kind!=AF_BATCH_SAME;
+    param.launchMoreBlocks = kind == AF_BATCH_SAME || kind == AF_BATCH_RHS;
+    param.outHasNoOffset   = kind == AF_BATCH_LHS || kind == AF_BATCH_NONE;
+    param.inHasNoOffset    = kind != AF_BATCH_SAME;
 
     prepareKernelArgs<T>(param, out.info.dims, filter.info.dims, baseDim);
 
-    switch(baseDim) {
+    switch (baseDim) {
         case 1: conv1<T, accType, expand>(param, out, signal, filter); break;
         case 2: conv2<T, accType, expand>(param, out, signal, filter); break;
         case 3: conv3<T, accType, expand>(param, out, signal, filter); break;
@@ -56,6 +54,6 @@ void convolve_nd(Param out, const Param signal, const Param filter, AF_BATCH_KIN
     bufferFree(param.impulse);
 }
 
-}
+}  // namespace kernel
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv1.cpp b/src/backend/opencl/kernel/convolve/conv1.cpp
index 86329c3c50..7a3b434c10 100644
--- a/src/backend/opencl/kernel/convolve/conv1.cpp
+++ b/src/backend/opencl/kernel/convolve/conv1.cpp
@@ -9,33 +9,30 @@
 
 #include <kernel/convolve/conv_common.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T, typename aT, bool expand>
-void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt)
-{
+void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
     size_t se_size = filt.info.dims[0] * sizeof(aT);
-    p.impulse = bufferAlloc(se_size);
-    int f0Off = filt.info.offset;
+    p.impulse      = bufferAlloc(se_size);
+    int f0Off      = filt.info.offset;
 
-    for (int b3=0; b3<filt.info.dims[3]; ++b3) {
+    for (int b3 = 0; b3 < filt.info.dims[3]; ++b3) {
         int f3Off = b3 * filt.info.strides[3];
 
-        for (int b2=0; b2<filt.info.dims[2]; ++b2) {
+        for (int b2 = 0; b2 < filt.info.dims[2]; ++b2) {
             int f2Off = b2 * filt.info.strides[2];
 
-            for (int b1=0; b1<filt.info.dims[1]; ++b1) {
+            for (int b1 = 0; b1 < filt.info.dims[1]; ++b1) {
                 int f1Off = b1 * filt.info.strides[1];
 
                 // FIXME: if the filter array is strided, direct copy of symbols
                 // might cause issues
-                getQueue().enqueueCopyBuffer(*filt.data, *p.impulse,
-                                            (f0Off+f1Off+f2Off+f3Off)*sizeof(aT)
-                                             , 0, se_size);
+                getQueue().enqueueCopyBuffer(
+                    *filt.data, *p.impulse,
+                    (f0Off + f1Off + f2Off + f3Off) * sizeof(aT), 0, se_size);
 
                 p.o[0] = (p.outHasNoOffset ? 0 : b1);
                 p.o[1] = (p.outHasNoOffset ? 0 : b2);
@@ -50,23 +47,25 @@ void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt)
     }
 }
 
-#define INSTANTIATE(T, accT)  \
-    template void conv1<T, accT, true >(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt); \
-    template void conv1<T, accT, false>(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt); \
+#define INSTANTIATE(T, accT)                                                 \
+    template void conv1<T, accT, true>(conv_kparam_t & p, Param & out,       \
+                                       const Param& sig, const Param& filt); \
+    template void conv1<T, accT, false>(conv_kparam_t & p, Param & out,      \
+                                        const Param& sig, const Param& filt);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
 
-}
+}  // namespace kernel
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_b8.cpp b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
index 2a7252294b..2ddd478faf 100644
--- a/src/backend/opencl/kernel/convolve/conv2_b8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
@@ -9,15 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(char, float)
 
 }
 
-}
-
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_c32.cpp b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
index 91ad98862d..253aeef4cb 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(cfloat, cfloat)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_c64.cpp b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
index 80754c4e08..9ba2ce1844 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(cdouble, cdouble)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_f32.cpp b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
index 887bb2a299..b1567ac9d8 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(float, float)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_f64.cpp b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
index 3482c4dde4..aff172d7db 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(double, double)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index e61a1c46c4..7df69c2f60 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -7,54 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/convolve/conv_common.hpp>
 #include <cache.hpp>
+#include <kernel/convolve/conv_common.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T, typename aT, bool expand>
-void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, const Param filter)
-{
+void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
+                 const Param filter) {
     int f0 = filter.info.dims[0];
     int f1 = filter.info.dims[1];
 
     std::string ref_name =
-        std::string("conv2_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::string("_") +
-        std::string(dtype_traits<aT>::getName()) +
-        std::string("_") +
-        std::to_string(expand) +
-        std::string("_") +
-        std::to_string(f0) +
-        std::string("_") +
-        std::to_string(f1);
+        std::string("conv2_") + std::string(dtype_traits<T>::getName()) +
+        std::string("_") + std::string(dtype_traits<aT>::getName()) +
+        std::string("_") + std::to_string(expand) + std::string("_") +
+        std::to_string(f0) + std::string("_") + std::to_string(f1);
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
-        size_t LOC_SIZE = (THREADS_X+2*(f0-1))*(THREADS_Y+2*(f1-1));
+    if (entry.prog == 0 && entry.ker == 0) {
+        size_t LOC_SIZE =
+            (THREADS_X + 2 * (f0 - 1)) * (THREADS_Y + 2 * (f1 - 1));
 
         std::ostringstream options;
-        options << " -D T="         << dtype_traits<T>::getName()
-                << " -D Ti="        << dtype_traits<T>::getName()
-                << " -D To="        << dtype_traits<aT>::getName()
-                << " -D accType="   << dtype_traits<aT>::getName()
-                << " -D BASE_DIM="  << 2 /* hard constant specific to this convolution type */
-                << " -D FLEN0="     << f0
-                << " -D FLEN1="     << f1
-                << " -D EXPAND="    << expand
-                << " -D C_SIZE="    << LOC_SIZE
-                << " -D "           << binOpName<af_mul_t>();
-
-        if((af_dtype) dtype_traits<T>::af_type == c32 ||
-            (af_dtype) dtype_traits<T>::af_type == c64) {
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D Ti=" << dtype_traits<T>::getName()
+                << " -D To=" << dtype_traits<aT>::getName()
+                << " -D accType=" << dtype_traits<aT>::getName()
+                << " -D BASE_DIM="
+                << 2 /* hard constant specific to this convolution type */
+                << " -D FLEN0=" << f0 << " -D FLEN1=" << f1
+                << " -D EXPAND=" << expand << " -D C_SIZE=" << LOC_SIZE
+                << " -D " << binOpName<af_mul_t>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
@@ -62,46 +54,43 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal, cons
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char *ker_strs[] = {ops_cl, convolve_cl};
-        const int   ker_lens[] = {ops_cl_len, convolve_cl_len};
+        const char* ker_strs[] = {ops_cl, convolve_cl};
+        const int ker_lens[]   = {ops_cl_len, convolve_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-        entry.prog   = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "convolve");
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "convolve");
 
         addKernelToCache(device, ref_name, entry);
     }
 
-    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
-                              Buffer, KParam, int, int,
-                              int, int,
-                              int, int
-                              >(*entry.ker);
+    auto convOp =
+        cl::KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int,
+                          int, int, int, int, int>(*entry.ker);
 
-    convOp(EnqueueArgs(getQueue(), param.global, param.local),
-            *out.data, out.info, *signal.data, signal.info,
-            *param.impulse, filter.info, param.nBBS0, param.nBBS1,
-            param.o[1], param.o[2], param.s[1], param.s[2]);
+    convOp(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
+           out.info, *signal.data, signal.info, *param.impulse, filter.info,
+           param.nBBS0, param.nBBS1, param.o[1], param.o[2], param.s[1],
+           param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
-void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt)
-{
+void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
     size_t se_size = filt.info.dims[0] * filt.info.dims[1] * sizeof(aT);
-    p.impulse = bufferAlloc(se_size);
-    int f0Off = filt.info.offset;
+    p.impulse      = bufferAlloc(se_size);
+    int f0Off      = filt.info.offset;
 
-    for (int b3=0; b3<filt.info.dims[3]; ++b3) {
+    for (int b3 = 0; b3 < filt.info.dims[3]; ++b3) {
         int f3Off = b3 * filt.info.strides[3];
 
-        for (int b2=0; b2<filt.info.dims[2]; ++b2) {
+        for (int b2 = 0; b2 < filt.info.dims[2]; ++b2) {
             int f2Off = b2 * filt.info.strides[2];
 
             // FIXME: if the filter array is strided, direct copy of symbols
             // might cause issues
             getQueue().enqueueCopyBuffer(*filt.data, *p.impulse,
-                                         (f2Off+f3Off+f0Off)*sizeof(aT),
+                                         (f2Off + f3Off + f0Off) * sizeof(aT),
                                          0, se_size);
 
             p.o[1] = (p.outHasNoOffset ? 0 : b2);
@@ -114,10 +103,12 @@ void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt)
     }
 }
 
-#define INSTANTIATE(T, accT)  \
-    template void conv2<T, accT, true >(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt); \
-    template void conv2<T, accT, false>(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt); \
+#define INSTANTIATE(T, accT)                                                 \
+    template void conv2<T, accT, true>(conv_kparam_t & p, Param & out,       \
+                                       const Param& sig, const Param& filt); \
+    template void conv2<T, accT, false>(conv_kparam_t & p, Param & out,      \
+                                        const Param& sig, const Param& filt);
 
-}
+}  // namespace kernel
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s16.cpp b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
index 66b6527e68..d8b7f33af0 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
@@ -9,15 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(short, float)
 
 }
 
-}
-
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s32.cpp b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
index 431c85d838..7b73459ec2 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(int, float)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s64.cpp b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
index 1bd4b53a42..39a06ae060 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
@@ -9,15 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(intl, float)
 
 }
 
-}
-
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u16.cpp b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
index 419e1a64b4..8404825a23 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
@@ -9,15 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(ushort, float)
 
 }
 
-}
-
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u32.cpp b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
index 332b3fe70e..2dd7dfe3a4 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(uint, float)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u64.cpp b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
index 62fe737cb5..7c40aac13f 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
@@ -9,15 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(uintl, float)
 
 }
 
-}
-
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u8.cpp b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
index 39cedd4acf..4c0d2580a5 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
@@ -9,14 +9,12 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 INSTANTIATE(uchar, float)
 
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv3.cpp b/src/backend/opencl/kernel/convolve/conv3.cpp
index 3c9645d32e..961d9f5ace 100644
--- a/src/backend/opencl/kernel/convolve/conv3.cpp
+++ b/src/backend/opencl/kernel/convolve/conv3.cpp
@@ -9,24 +9,23 @@
 
 #include <kernel/convolve/conv_common.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T, typename aT, bool expand>
-void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt)
-{
-    size_t se_size = filt.info.dims[0] * filt.info.dims[1] * filt.info.dims[2] * sizeof(aT);
+void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
+    size_t se_size =
+        filt.info.dims[0] * filt.info.dims[1] * filt.info.dims[2] * sizeof(aT);
     p.impulse = bufferAlloc(se_size);
     int f0Off = filt.info.offset;
 
-    for (int b3=0; b3<filt.info.dims[3]; ++b3) {
+    for (int b3 = 0; b3 < filt.info.dims[3]; ++b3) {
         int f3Off = b3 * filt.info.strides[3];
         // FIXME: if the filter array is strided, direct copy of symbols
         // might cause issues
-        getQueue().enqueueCopyBuffer(*filt.data, *p.impulse, (f0Off + f3Off)*sizeof(aT), 0, se_size);
+        getQueue().enqueueCopyBuffer(*filt.data, *p.impulse,
+                                     (f0Off + f3Off) * sizeof(aT), 0, se_size);
 
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
         p.s[2] = (p.inHasNoOffset ? 0 : b3);
@@ -35,23 +34,25 @@ void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt)
     }
 }
 
-#define INSTANTIATE(T, accT)  \
-    template void conv3<T, accT, true >(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt); \
-    template void conv3<T, accT, false>(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt); \
+#define INSTANTIATE(T, accT)                                                 \
+    template void conv3<T, accT, true>(conv_kparam_t & p, Param & out,       \
+                                       const Param& sig, const Param& filt); \
+    template void conv3<T, accT, false>(conv_kparam_t & p, Param & out,      \
+                                        const Param& sig, const Param& filt);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
-
-}
-
-}
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace kernel
+
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 219944fffe..f71f5ee0e1 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -10,112 +10,110 @@
 #pragma once
 #include <af/defines.h>
 
-#include <kernel_headers/ops.hpp>
 #include <kernel_headers/convolve.hpp>
+#include <kernel_headers/ops.hpp>
 
-#include <string>
 #include <Param.hpp>
-#include <types.hpp>
-#include <traits.hpp>
-#include <memory.hpp>
-#include <program.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <platform.hpp>
 #include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <kernel/names.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
 using cl::EnqueueArgs;
+using cl::Kernel;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-static const int THREADS   = 256;
+namespace opencl {
+namespace kernel {
+static const int THREADS = 256;
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-static const int CUBE_X    =  8;
-static const int CUBE_Y    =  8;
-static const int CUBE_Z    =  4;
+static const int CUBE_X = 8;
+static const int CUBE_Y = 8;
+static const int CUBE_Z = 4;
 
 struct conv_kparam_t {
-    NDRange         global;
-    NDRange          local;
-    size_t        loc_size;
-    int         nBBS0;
-    int         nBBS1;
-    bool    outHasNoOffset;
-    bool     inHasNoOffset;
-    bool  launchMoreBlocks;
-    int          o[3];
-    int          s[3];
-    cl::Buffer*    impulse;
+    NDRange global;
+    NDRange local;
+    size_t loc_size;
+    int nBBS0;
+    int nBBS1;
+    bool outHasNoOffset;
+    bool inHasNoOffset;
+    bool launchMoreBlocks;
+    int o[3];
+    int s[3];
+    cl::Buffer* impulse;
 };
 
 template<typename T>
-void prepareKernelArgs(conv_kparam_t& param, dim_t *oDims,
-                       const dim_t *fDims, int baseDim)
-{
+void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
+                       int baseDim) {
     int batchDims[4] = {1, 1, 1, 1};
-    for(int i=baseDim; i<4; ++i) {
+    for (int i = baseDim; i < 4; ++i) {
         batchDims[i] = (param.launchMoreBlocks ? 1 : oDims[i]);
     }
 
-    if (baseDim==1) {
+    if (baseDim == 1) {
         param.local    = NDRange(THREADS, 1);
         param.nBBS0    = divup(oDims[0], THREADS);
         param.nBBS1    = batchDims[2];
-        param.global   = NDRange(param.nBBS0 * THREADS * batchDims[1], param.nBBS1 * batchDims[3]);
-        param.loc_size = (THREADS+2*(fDims[0]-1)) * sizeof(T);
-    } else if (baseDim==2) {
-        param.local    = NDRange(THREADS_X, THREADS_Y);
-        param.nBBS0    = divup(oDims[0], THREADS_X);
-        param.nBBS1    = divup(oDims[1], THREADS_Y);
-        param.global   = NDRange(param.nBBS0*THREADS_X*batchDims[2],
-                                 param.nBBS1*THREADS_Y*batchDims[3]);
-    } else if (baseDim==3) {
+        param.global   = NDRange(param.nBBS0 * THREADS * batchDims[1],
+                               param.nBBS1 * batchDims[3]);
+        param.loc_size = (THREADS + 2 * (fDims[0] - 1)) * sizeof(T);
+    } else if (baseDim == 2) {
+        param.local  = NDRange(THREADS_X, THREADS_Y);
+        param.nBBS0  = divup(oDims[0], THREADS_X);
+        param.nBBS1  = divup(oDims[1], THREADS_Y);
+        param.global = NDRange(param.nBBS0 * THREADS_X * batchDims[2],
+                               param.nBBS1 * THREADS_Y * batchDims[3]);
+    } else if (baseDim == 3) {
         param.local    = NDRange(CUBE_X, CUBE_Y, CUBE_Z);
         param.nBBS0    = divup(oDims[0], CUBE_X);
         param.nBBS1    = divup(oDims[1], CUBE_Y);
-        int blk_z = divup(oDims[2], CUBE_Z);
+        int blk_z      = divup(oDims[2], CUBE_Z);
         param.global   = NDRange(param.nBBS0 * CUBE_X * batchDims[3],
-                                 param.nBBS1 * CUBE_Y,
-                                 blk_z * CUBE_Z);
-        param.loc_size = (CUBE_X+2*(fDims[0]-1)) * (CUBE_Y+2*(fDims[1]-1)) *
-                         (CUBE_Z+2*(fDims[2]-1)) * sizeof(T);
+                               param.nBBS1 * CUBE_Y, blk_z * CUBE_Z);
+        param.loc_size = (CUBE_X + 2 * (fDims[0] - 1)) *
+                         (CUBE_Y + 2 * (fDims[1] - 1)) *
+                         (CUBE_Z + 2 * (fDims[2] - 1)) * sizeof(T);
     }
 }
 
 template<typename T, typename aT, int bDim, bool expand>
-void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, const Param& filter)
-{
+void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
+                 const Param& filter) {
     std::string ref_name = std::string("convolveND_") +
-        std::string(dtype_traits<T>::getName()) + std::string(dtype_traits<aT>::getName()) +
-        std::to_string(bDim) + std::to_string(expand);
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string(dtype_traits<aT>::getName()) +
+                           std::to_string(bDim) + std::to_string(expand);
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="         << dtype_traits<T>::getName()
-                << " -D Ti="        << dtype_traits<T>::getName()
-                << " -D To="        << dtype_traits<aT>::getName()
-                << " -D accType="   << dtype_traits<aT>::getName()
-                << " -D BASE_DIM="  << bDim
-                << " -D EXPAND="    << expand
-                << " -D "           << binOpName<af_mul_t>();
-
-        if((af_dtype) dtype_traits<T>::af_type == c32 ||
-            (af_dtype) dtype_traits<T>::af_type == c64) {
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D Ti=" << dtype_traits<T>::getName()
+                << " -D To=" << dtype_traits<aT>::getName()
+                << " -D accType=" << dtype_traits<aT>::getName()
+                << " -D BASE_DIM=" << bDim << " -D EXPAND=" << expand << " -D "
+                << binOpName<af_mul_t>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
@@ -123,24 +121,25 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, co
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char *ker_strs[] = {ops_cl, convolve_cl};
-        const int   ker_lens[] = {ops_cl_len, convolve_cl_len};
+        const char* ker_strs[] = {ops_cl, convolve_cl};
+        const int ker_lens[]   = {ops_cl_len, convolve_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-        entry.prog   = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "convolve");
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "convolve");
 
         addKernelToCache(device, ref_name, entry);
     }
 
-    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, Buffer, KParam,
-                                    int, int, int, int, int, int, int, int >(*entry.ker);
+    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                    cl::LocalSpaceArg, Buffer, KParam, int, int,
+                                    int, int, int, int, int, int>(*entry.ker);
 
-    convOp(EnqueueArgs(getQueue(), param.global, param.local),
-           *out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
-           *param.impulse, filter.info, param.nBBS0, param.nBBS1,
-           param.o[0], param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
+    convOp(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
+           out.info, *signal.data, signal.info, cl::Local(param.loc_size),
+           *param.impulse, filter.info, param.nBBS0, param.nBBS1, param.o[0],
+           param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
@@ -151,5 +150,5 @@ void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
 
 template<typename T, typename aT, bool expand>
 void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve_separable.cl b/src/backend/opencl/kernel/convolve_separable.cl
index 02aadb14c5..02e5d53d41 100644
--- a/src/backend/opencl/kernel/convolve_separable.cl
+++ b/src/backend/opencl/kernel/convolve_separable.cl
@@ -7,74 +7,81 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-kernel
-void convolve(global T *out, KParam oInfo, global T const *signal,
-              KParam sInfo, constant accType const *impulse,
-              int nBBS0, int nBBS1)
-{
+kernel void convolve(global T *out, KParam oInfo, global T const *signal,
+                     KParam sInfo, constant accType const *impulse, int nBBS0,
+                     int nBBS1) {
     local T localMem[LOCAL_MEM_SIZE];
 
-    const int radius  = FLEN-1;
-    const int padding = 2*radius;
+    const int radius  = FLEN - 1;
+    const int padding = 2 * radius;
     const int s0      = sInfo.strides[0];
     const int s1      = sInfo.strides[1];
     const int d0      = sInfo.dims[0];
     const int d1      = sInfo.dims[1];
-    const int shrdLen = get_local_size(0) + (CONV_DIM==0 ? padding : 0);
+    const int shrdLen = get_local_size(0) + (CONV_DIM == 0 ? padding : 0);
 
-    unsigned b2  = get_group_id(0)/nBBS0;
-    unsigned b3  = get_group_id(1)/nBBS1;
-    global T *dst = out + (b2*oInfo.strides[2] + b3*oInfo.strides[3]);
-    global const T *src = signal + (b2*sInfo.strides[2] + b3*sInfo.strides[3]) + sInfo.offset;
+    unsigned b2   = get_group_id(0) / nBBS0;
+    unsigned b3   = get_group_id(1) / nBBS1;
+    global T *dst = out + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+    global const T *src =
+        signal + (b2 * sInfo.strides[2] + b3 * sInfo.strides[3]) + sInfo.offset;
 
     int lx = get_local_id(0);
     int ly = get_local_id(1);
-    int ox = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    int oy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    int ox = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    int oy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
     int gx = ox;
     int gy = oy;
 
-    // below if-else statement is based on MACRO value passed while kernel compilation
-    if (CONV_DIM==0) {
-        gx += (EXPAND ? 0 : FLEN>>1);
-        int endX = ((FLEN-1)<<1) + get_local_size(0);
+    // below if-else statement is based on MACRO value passed while kernel
+    // compilation
+    if (CONV_DIM == 0) {
+        gx += (EXPAND ? 0 : FLEN >> 1);
+        int endX = ((FLEN - 1) << 1) + get_local_size(0);
 #pragma unroll
-        for(int lx = get_local_id(0), glb_x = gx; lx<endX; lx += get_local_size(0), glb_x += get_local_size(0)) {
-            int i = glb_x - radius;
-            int j = gy;
-            bool is_i  = i>=0 && i<d0;
-            bool is_j  = j>=0 && j<d1;
-            localMem[ly*shrdLen+lx] = (is_i && is_j ? src[i*s0 + j*s1] : (T)(0));
+        for (int lx = get_local_id(0), glb_x = gx; lx < endX;
+             lx += get_local_size(0), glb_x += get_local_size(0)) {
+            int i     = glb_x - radius;
+            int j     = gy;
+            bool is_i = i >= 0 && i < d0;
+            bool is_j = j >= 0 && j < d1;
+            localMem[ly * shrdLen + lx] =
+                (is_i && is_j ? src[i * s0 + j * s1] : (T)(0));
         }
 
-    } else if (CONV_DIM==1) {
-        gy += (EXPAND ? 0 : FLEN>>1);
-        int endY = ((FLEN-1)<<1) + get_local_size(1);
+    } else if (CONV_DIM == 1) {
+        gy += (EXPAND ? 0 : FLEN >> 1);
+        int endY = ((FLEN - 1) << 1) + get_local_size(1);
 #pragma unroll
-        for(int ly = get_local_id(1), glb_y = gy; ly<endY; ly += get_local_size(1), glb_y += get_local_size(1)) {
-            int i = gx;
-            int j = glb_y - radius;
-            bool is_i  = i>=0 && i<d0;
-            bool is_j  = j>=0 && j<d1;
-            localMem[ly*shrdLen+lx] = (is_i && is_j ? src[i*s0 + j*s1] : (T)(0));
+        for (int ly = get_local_id(1), glb_y = gy; ly < endY;
+             ly += get_local_size(1), glb_y += get_local_size(1)) {
+            int i     = gx;
+            int j     = glb_y - radius;
+            bool is_i = i >= 0 && i < d0;
+            bool is_j = j >= 0 && j < d1;
+            localMem[ly * shrdLen + lx] =
+                (is_i && is_j ? src[i * s0 + j * s1] : (T)(0));
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (ox<oInfo.dims[0] && oy<oInfo.dims[1]) {
-        // below conditional statement is based on MACRO value passed while kernel compilation
-        int i  = (CONV_DIM==0 ? lx : ly) + radius;
+    if (ox < oInfo.dims[0] && oy < oInfo.dims[1]) {
+        // below conditional statement is based on MACRO value passed while
+        // kernel compilation
+        int i         = (CONV_DIM == 0 ? lx : ly) + radius;
         accType accum = (accType)(0);
 #pragma unroll
-        for(int f=0; f<FLEN; ++f) {
+        for (int f = 0; f < FLEN; ++f) {
             accType f_val = impulse[f];
-            // below conditional statement is based on MACRO value passed while kernel compilation
-            int s_idx = (CONV_DIM==0 ? (ly*shrdLen+(i-f)) : ((i-f)*shrdLen+lx));
-            T s_val = localMem[s_idx];
+            // below conditional statement is based on MACRO value passed while
+            // kernel compilation
+            int s_idx = (CONV_DIM == 0 ? (ly * shrdLen + (i - f))
+                                       : ((i - f) * shrdLen + lx));
+            T s_val   = localMem[s_idx];
 
-            //binOp will do MUL_OP for convolution operation
+            // binOp will do MUL_OP for convolution operation
             accum = accum + binOp((accType)s_val, (accType)f_val);
         }
-        dst[oy*oInfo.strides[1]+ox] = (T)accum;
+        dst[oy * oInfo.strides[1] + ox] = (T)accum;
     }
 }
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index de2a4140d0..e5b051f12e 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -7,78 +7,67 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel_headers/ops.hpp>
 #include <kernel_headers/convolve_separable.hpp>
+#include <kernel_headers/ops.hpp>
 
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <memory.hpp>
 #include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel/names.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <map>
+#include <mutex>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, typename accType, int conv_dim, bool expand>
-void convSep(Param out, const Param signal, const Param filter)
-{
+void convSep(Param out, const Param signal, const Param filter) {
     const int fLen = filter.info.dims[0] * filter.info.dims[1];
 
     std::string ref_name =
-        std::string("convsep_") +
-        std::to_string(conv_dim) +
-        std::string("_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::string("_") +
-        std::string(dtype_traits<accType>::getName()) +
-        std::string("_") +
-        std::to_string(expand) +
-        std::string("_") +
-        std::to_string(fLen);
+        std::string("convsep_") + std::to_string(conv_dim) + std::string("_") +
+        std::string(dtype_traits<T>::getName()) + std::string("_") +
+        std::string(dtype_traits<accType>::getName()) + std::string("_") +
+        std::to_string(expand) + std::string("_") + std::to_string(fLen);
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
-        const size_t C0_SIZE  = (THREADS_X+2*(fLen-1))* THREADS_Y;
-        const size_t C1_SIZE  = (THREADS_Y+2*(fLen-1))* THREADS_X;
+    if (entry.prog == 0 && entry.ker == 0) {
+        const size_t C0_SIZE = (THREADS_X + 2 * (fLen - 1)) * THREADS_Y;
+        const size_t C1_SIZE = (THREADS_Y + 2 * (fLen - 1)) * THREADS_X;
 
-        size_t locSize = (conv_dim==0 ? C0_SIZE : C1_SIZE);
+        size_t locSize = (conv_dim == 0 ? C0_SIZE : C1_SIZE);
 
         std::ostringstream options;
-        options << " -D T="             << dtype_traits<T>::getName()
-                << " -D Ti="            << dtype_traits<T>::getName()
-                << " -D To="            << dtype_traits<accType>::getName()
-                << " -D accType="       << dtype_traits<accType>::getName()
-                << " -D CONV_DIM="      << conv_dim
-                << " -D EXPAND="        << expand
-                << " -D FLEN="          << fLen
-                << " -D LOCAL_MEM_SIZE="<<locSize
-                << " -D "               << binOpName<af_mul_t>();
-
-        if((af_dtype) dtype_traits<T>::af_type == c32 ||
-            (af_dtype) dtype_traits<T>::af_type == c64) {
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D Ti=" << dtype_traits<T>::getName()
+                << " -D To=" << dtype_traits<accType>::getName()
+                << " -D accType=" << dtype_traits<accType>::getName()
+                << " -D CONV_DIM=" << conv_dim << " -D EXPAND=" << expand
+                << " -D FLEN=" << fLen << " -D LOCAL_MEM_SIZE=" << locSize
+                << " -D " << binOpName<af_mul_t>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
@@ -88,56 +77,62 @@ void convSep(Param out, const Param signal, const Param filter)
         }
 
         const char *ker_strs[] = {ops_cl, convolve_separable_cl};
-        const int   ker_lens[] = {ops_cl_len, convolve_separable_cl_len};
+        const int ker_lens[]   = {ops_cl_len, convolve_separable_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-        entry.prog   = new Program(prog);
+        entry.prog = new Program(prog);
         entry.ker  = new Kernel(*entry.prog, "convolve");
 
         addKernelToCache(device, ref_name, entry);
     }
 
-    auto convOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                              int, int>(*entry.ker);
+    auto convOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, int, int>(
+            *entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x*signal.info.dims[2]*THREADS_X,
-                    blk_y*signal.info.dims[3]*THREADS_Y);
+    NDRange global(blk_x * signal.info.dims[2] * THREADS_X,
+                   blk_y * signal.info.dims[3] * THREADS_Y);
 
-    cl::Buffer *mBuff = bufferAlloc(fLen*sizeof(accType));
+    cl::Buffer *mBuff = bufferAlloc(fLen * sizeof(accType));
     // FIX ME: if the filter array is strided, direct might cause issues
-    getQueue().enqueueCopyBuffer(*filter.data, *mBuff, 0, 0, fLen*sizeof(accType));
+    getQueue().enqueueCopyBuffer(*filter.data, *mBuff, 0, 0,
+                                 fLen * sizeof(accType));
 
-    convOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *signal.data, signal.info, *mBuff, blk_x, blk_y);
+    convOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *signal.data, signal.info, *mBuff, blk_x, blk_y);
 
     bufferFree(mBuff);
 }
 
-#define INSTANTIATE(T, accT)  \
-    template void convSep<T, accT, 0, true >(Param out, const Param sig, const Param filt); \
-    template void convSep<T, accT, 1, true >(Param out, const Param sig, const Param filt); \
-    template void convSep<T, accT, 0, false>(Param out, const Param sig, const Param filt); \
-    template void convSep<T, accT, 1, false>(Param out, const Param sig, const Param filt);
+#define INSTANTIATE(T, accT)                                             \
+    template void convSep<T, accT, 0, true>(Param out, const Param sig,  \
+                                            const Param filt);           \
+    template void convSep<T, accT, 1, true>(Param out, const Param sig,  \
+                                            const Param filt);           \
+    template void convSep<T, accT, 0, false>(Param out, const Param sig, \
+                                             const Param filt);          \
+    template void convSep<T, accT, 1, false>(Param out, const Param sig, \
+                                             const Param filt);
 
 INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat ,  cfloat)
-INSTANTIATE(double ,  double)
-INSTANTIATE(float  ,   float)
-INSTANTIATE(uint   ,   float)
-INSTANTIATE(int    ,   float)
-INSTANTIATE(uchar  ,   float)
-INSTANTIATE(char   ,   float)
-INSTANTIATE(ushort ,   float)
-INSTANTIATE(short  ,   float)
-INSTANTIATE(uintl  ,   float)
-INSTANTIATE(intl   ,   float)
-
-}
-
-}
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace kernel
+
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve_separable.hpp b/src/backend/opencl/kernel/convolve_separable.hpp
index 265dc6e6e8..7794d830d0 100644
--- a/src/backend/opencl/kernel/convolve_separable.hpp
+++ b/src/backend/opencl/kernel/convolve_separable.hpp
@@ -10,11 +10,9 @@
 #pragma once
 #include <Param.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 // below shared MAX_*_LEN's are calculated based on
 // a maximum shared memory configuration of 48KB per block
@@ -24,6 +22,6 @@ static const int MAX_SCONV_FILTER_LEN = 31;
 template<typename T, typename accT, int cDim, bool expand>
 void convSep(Param out, const Param sig, const Param filt);
 
-}
+}  // namespace kernel
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/coo2dense.cl b/src/backend/opencl/kernel/coo2dense.cl
index fb86ebd82b..12580c027b 100644
--- a/src/backend/opencl/kernel/coo2dense.cl
+++ b/src/backend/opencl/kernel/coo2dense.cl
@@ -7,24 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void coo2dense_kernel(__global T *oPtr, const KParam output,
-                      __global const T *vPtr, const KParam values,
-                      __global const int *rPtr, const KParam rowIdx,
-                      __global const int *cPtr, const KParam colIdx)
-{
+__kernel void coo2dense_kernel(__global T *oPtr, const KParam output,
+                               __global const T *vPtr, const KParam values,
+                               __global const int *rPtr, const KParam rowIdx,
+                               __global const int *cPtr, const KParam colIdx) {
     const int id = get_group_id(0) * get_local_size(0) * reps + get_local_id(0);
 
-    if(id >= values.dims[0])
-        return;
+    if (id >= values.dims[0]) return;
 
     const int dimSize = get_local_size(0);
 
-    for(int i = get_local_id(0); i < reps * dimSize; i += dimSize) {
-        if(i >= values.dims[0])
-            return;
+    for (int i = get_local_id(0); i < reps * dimSize; i += dimSize) {
+        if (i >= values.dims[0]) return;
 
-        T   v = vPtr[i];
+        T v   = vPtr[i];
         int r = rPtr[i];
         int c = cPtr[i];
 
diff --git a/src/backend/opencl/kernel/copy.cl b/src/backend/opencl/kernel/copy.cl
index 84f77e0b61..3c4e883d51 100644
--- a/src/backend/opencl/kernel/copy.cl
+++ b/src/backend/opencl/kernel/copy.cl
@@ -11,12 +11,11 @@ typedef struct {
     dim_t dim[4];
 } dims_t;
 
-inType scale(inType value, float factor)
-{
+inType scale(inType value, float factor) {
 #ifdef inType_float2
-    return (inType)(value.s0*factor, value.s1*factor);
+    return (inType)(value.s0 * factor, value.s1 * factor);
 #else
-    return (inType)(value*factor);
+    return (inType)(value * factor);
 #endif
 }
 
@@ -48,27 +47,26 @@ inType scale(inType value, float factor)
 
 #endif
 
-__kernel
-void copy(__global outType * dst,
-          KParam oInfo,
-          __global const inType * src,
-          KParam iInfo,
-          outType default_value,
-          float factor, dims_t trgt,
-          int blk_x, int blk_y)
-{
+__kernel void copy(__global outType *dst, KParam oInfo,
+                   __global const inType *src, KParam iInfo,
+                   outType default_value, float factor, dims_t trgt, int blk_x,
+                   int blk_y) {
     uint lx = get_local_id(0);
     uint ly = get_local_id(1);
 
-    uint gz = get_group_id(0) / blk_x;
-    uint gw = get_group_id(1) / blk_y;
-    uint blockIdx_x = get_group_id(0) - (blk_x) * gz;
-    uint blockIdx_y = get_group_id(1) - (blk_y) * gw;
-    uint gx = blockIdx_x * get_local_size(0) + lx;
-    uint gy = blockIdx_y * get_local_size(1) + ly;
+    uint gz         = get_group_id(0) / blk_x;
+    uint gw         = get_group_id(1) / blk_y;
+    uint blockIdx_x = get_group_id(0) - (blk_x)*gz;
+    uint blockIdx_y = get_group_id(1) - (blk_y)*gw;
+    uint gx         = blockIdx_x * get_local_size(0) + lx;
+    uint gy         = blockIdx_y * get_local_size(1) + ly;
 
-    __global const inType *in = src + (gw * iInfo.strides[3] + gz * iInfo.strides[2] + gy * iInfo.strides[1] + iInfo.offset);
-    __global outType *out     = dst + (gw * oInfo.strides[3] + gz * oInfo.strides[2] + gy * oInfo.strides[1] + oInfo.offset);
+    __global const inType *in =
+        src + (gw * iInfo.strides[3] + gz * iInfo.strides[2] +
+               gy * iInfo.strides[1] + iInfo.offset);
+    __global outType *out =
+        dst + (gw * oInfo.strides[3] + gz * oInfo.strides[2] +
+               gy * oInfo.strides[1] + oInfo.offset);
 
     uint istride0 = iInfo.strides[0];
     uint ostride0 = oInfo.strides[0];
@@ -76,16 +74,16 @@ void copy(__global outType * dst,
     if (gy < oInfo.dims[1] && gz < oInfo.dims[2] && gw < oInfo.dims[3]) {
         int loop_offset = get_local_size(0) * blk_x;
         bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
-        for(int rep=gx; rep<oInfo.dims[0]; rep+=loop_offset) {
-            outType temp  = default_value;
+        for (int rep = gx; rep < oInfo.dims[0]; rep += loop_offset) {
+            outType temp = default_value;
 #if SAME_DIMS
-            temp = CONVERT(scale(in[rep*istride0], factor));
+            temp = CONVERT(scale(in[rep * istride0], factor));
 #else
             if (rep < trgt.dim[0] && cond) {
-                temp = CONVERT(scale(in[rep*istride0], factor));
+                temp = CONVERT(scale(in[rep * istride0], factor));
             }
 #endif
-            out[rep*ostride0] = temp;
+            out[rep * ostride0] = temp;
         }
     }
 }
diff --git a/src/backend/opencl/kernel/cscmm.cl b/src/backend/opencl/kernel/cscmm.cl
index 6e72414c27..5d038e7506 100644
--- a/src/backend/opencl/kernel/cscmm.cl
+++ b/src/backend/opencl/kernel/cscmm.cl
@@ -8,16 +8,14 @@
  ********************************************************/
 
 #if IS_CPLX
-T __cmul(T lhs, T rhs)
-{
+T __cmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-T __ccmul(T lhs, T rhs)
-{
+T __ccmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x + lhs.y * rhs.y;
     out.y = lhs.x * rhs.y - lhs.y * rhs.x;
@@ -37,8 +35,7 @@ T __ccmul(T lhs, T rhs)
 #define CMUL(a, b) (a) * (b)
 #endif
 
-int binary_search(__global const int *ptr, int len, int val)
-{
+int binary_search(__global const int *ptr, int len, int val) {
     int start = 0;
     int end   = len;
     while (end > start) {
@@ -55,21 +52,17 @@ int binary_search(__global const int *ptr, int len, int val)
 }
 
 // Each group computes an output of size ROWS_PER_GROUP x COLS_PER_GROUP
-// Each thread in a group maintains the partial outputs of size ROWS_PER_GROUP x COLS_PER_GROUP
-// The outputs from each thread are added up to generate the final result.
-__kernel void
-cscmm_nn(__global T *output,
-         __global const T *values,
-         __global const int *colidx,  // rowidx from csr is colidx in csc
-         __global const int *rowidx,  // colidx from csr is rowidx in csc
-         const int M,                 // K from csr is M in csc
-         const int K,                 // M from csr is K in csc
-         const int N,                 // N is number of columns in dense matrix
-         __global const T *rhs,
-         const KParam rinfo,
-         const T alpha,
-         const T beta)
-{
+// Each thread in a group maintains the partial outputs of size ROWS_PER_GROUP x
+// COLS_PER_GROUP The outputs from each thread are added up to generate the
+// final result.
+__kernel void cscmm_nn(
+    __global T *output, __global const T *values,
+    __global const int *colidx,  // rowidx from csr is colidx in csc
+    __global const int *rowidx,  // colidx from csr is rowidx in csc
+    const int M,                 // K from csr is M in csc
+    const int K,                 // M from csr is K in csc
+    const int N,                 // N is number of columns in dense matrix
+    __global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
     int lid = get_local_id(0);
 
     // Get the row offset for the current group in the uncompressed matrix
@@ -86,29 +79,27 @@ cscmm_nn(__global T *output,
     // Initialize partial output to 0
     T l_outvals[COLS_PER_GROUP][ROWS_PER_GROUP];
     for (int j = 0; j < colLim; j++) {
-        for (int i = 0; i < rowLim; i++) {
-            l_outvals[j][i] = 0;
-        }
+        for (int i = 0; i < rowLim; i++) { l_outvals[j][i] = 0; }
     }
 
     // Dot requires you to traverse the entire inner dimension
     for (int colId = lid; colId < K; colId += THREADS) {
-
-        int rowStart = colidx[colId];
-        int rowEnd   = colidx[colId + 1];
+        int rowStart     = colidx[colId];
+        int rowEnd       = colidx[colId + 1];
         int nonZeroCount = rowEnd - rowStart;
 
         // Find the location of the next non zero element after rowOff
-        int rowPos   = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
+        int rowPos = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
 
-        // Read the rhs values from all the columns as they can be reused for all rows
+        // Read the rhs values from all the columns as they can be reused for
+        // all rows
         T rhsvals[COLS_PER_GROUP];
         for (int j = 0; j < colLim; j++) {
             rhsvals[j] = rhs[colId + j * rinfo.strides[1]];
         }
 
         // Traversing through nonzero elements in the current chunk
-        for (int id =  rowPos + rowStart; id < rowEnd; id++) {
+        for (int id = rowPos + rowStart; id < rowEnd; id++) {
             int rowId = rowidx[id];
 
             // Exit if going past current chunk
@@ -124,10 +115,10 @@ cscmm_nn(__global T *output,
 
     __local T s_outvals[THREADS];
 
-    // For each row and col of output, copy registers to local memory, add results, write to output.
+    // For each row and col of output, copy registers to local memory, add
+    // results, write to output.
     for (int j = 0; j < colLim; j++) {
         for (int i = 0; i < rowLim; i++) {
-
             // Copying to local memory
             s_outvals[lid] = l_outvals[j][i];
             barrier(CLK_LOCAL_MEM_FENCE);
@@ -147,7 +138,8 @@ cscmm_nn(__global T *output,
 #endif
 
 #if USE_BETA
-                output[j * M + rowOff + i] = outval + MUL(beta, output[j * M + rowOff + i]);
+                output[j * M + rowOff + i] =
+                    outval + MUL(beta, output[j * M + rowOff + i]);
 #else
                 output[j * M + rowOff + i] = outval;
 #endif
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 12e9df25d6..44e1e1a5e5 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -9,121 +9,105 @@
 
 #pragma once
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/cscmm.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <type_util.hpp>
-#include "scan_dim.hpp"
+#include <af/opencl.h>
+#include <map>
+#include <mutex>
+#include <string>
+#include "config.hpp"
 #include "reduce.hpp"
+#include "scan_dim.hpp"
 #include "scan_first.hpp"
-#include "config.hpp"
-#include <af/opencl.h>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T>
-        void cscmm_nn(Param out,
-                      const Param &values, const Param &colIdx, const Param &rowIdx,
-                      const Param &rhs, const T alpha, const T beta, bool is_conj)
-        {
-            bool use_alpha = (alpha != scalar<T>(1.0));
-            bool use_beta = (beta != scalar<T>(0.0));
-
-            int threads = 256;
-            // TODO: Find a better way to tune these parameters
-            int rows_per_group = 8;
-            int cols_per_group = 8;
-
-            std::string ref_name =
-                std::string("cscmm_nn_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(use_alpha) +
-                std::string("_") +
-                std::to_string(use_beta) +
-                std::string("_") +
-                std::to_string(is_conj) +
-                std::string("_") +
-                std::to_string(rows_per_group) +
-                std::string("_") +
-                std::to_string(cols_per_group) +
-                std::string("_") +
-                std::to_string(threads);
-
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                options << " -D USE_ALPHA=" << use_alpha;
-                options << " -D USE_BETA=" << use_beta;
-                options << " -D IS_CONJ=" << is_conj;
-                options << " -D THREADS=" << threads;
-                options << " -D ROWS_PER_GROUP=" << rows_per_group;
-                options << " -D COLS_PER_GROUP=" << cols_per_group;
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                if (std::is_same<T, cfloat>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-
-                const char *ker_strs[] = {cscmm_cl};
-                const int   ker_lens[] = {cscmm_cl_len};
-
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto cscmm_kernel = *entry.ker;
-            auto cscmm_func = KernelFunctor<Buffer,
-                                            Buffer, Buffer, Buffer,
-                                            int, int, int,
-                                            Buffer, KParam, T, T>(cscmm_kernel);
-
-            NDRange local(threads, 1);
-            int M = out.info.dims[0];
-            int N = out.info.dims[1];
-            int K = colIdx.info.dims[0] - 1;
-
-            int groups_x = divup(M, rows_per_group);
-            int groups_y = divup(N, cols_per_group);
-            NDRange global(local[0] * groups_x, local[1] * groups_y);
-
-            cscmm_func(EnqueueArgs(getQueue(), global, local),
-                        *out.data, *values.data, *colIdx.data, *rowIdx.data,
-                        M, K, N, *rhs.data, rhs.info, alpha, beta);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace opencl {
+namespace kernel {
+template<typename T>
+void cscmm_nn(Param out, const Param &values, const Param &colIdx,
+              const Param &rowIdx, const Param &rhs, const T alpha,
+              const T beta, bool is_conj) {
+    bool use_alpha = (alpha != scalar<T>(1.0));
+    bool use_beta  = (beta != scalar<T>(0.0));
+
+    int threads = 256;
+    // TODO: Find a better way to tune these parameters
+    int rows_per_group = 8;
+    int cols_per_group = 8;
+
+    std::string ref_name =
+        std::string("cscmm_nn_") + std::string(dtype_traits<T>::getName()) +
+        std::string("_") + std::to_string(use_alpha) + std::string("_") +
+        std::to_string(use_beta) + std::string("_") + std::to_string(is_conj) +
+        std::string("_") + std::to_string(rows_per_group) + std::string("_") +
+        std::to_string(cols_per_group) + std::string("_") +
+        std::to_string(threads);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D USE_ALPHA=" << use_alpha;
+        options << " -D USE_BETA=" << use_beta;
+        options << " -D IS_CONJ=" << is_conj;
+        options << " -D THREADS=" << threads;
+        options << " -D ROWS_PER_GROUP=" << rows_per_group;
+        options << " -D COLS_PER_GROUP=" << cols_per_group;
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
         }
+
+        const char *ker_strs[] = {cscmm_cl};
+        const int ker_lens[]   = {cscmm_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    auto cscmm_kernel = *entry.ker;
+    auto cscmm_func   = KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, int,
+                                    int, Buffer, KParam, T, T>(cscmm_kernel);
+
+    NDRange local(threads, 1);
+    int M = out.info.dims[0];
+    int N = out.info.dims[1];
+    int K = colIdx.info.dims[0] - 1;
+
+    int groups_x = divup(M, rows_per_group);
+    int groups_y = divup(N, cols_per_group);
+    NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+    cscmm_func(EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
+               *colIdx.data, *rowIdx.data, M, K, N, *rhs.data, rhs.info, alpha,
+               beta);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/cscmv.cl b/src/backend/opencl/kernel/cscmv.cl
index b60956d9f5..cd698115c5 100644
--- a/src/backend/opencl/kernel/cscmv.cl
+++ b/src/backend/opencl/kernel/cscmv.cl
@@ -8,16 +8,14 @@
  ********************************************************/
 
 #if IS_CPLX
-T __cmul(T lhs, T rhs)
-{
+T __cmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-T __ccmul(T lhs, T rhs)
-{
+T __ccmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x + lhs.y * rhs.y;
     out.y = lhs.x * rhs.y - lhs.y * rhs.x;
@@ -37,8 +35,7 @@ T __ccmul(T lhs, T rhs)
 #define CMUL(a, b) (a) * (b)
 #endif
 
-int binary_search(__global const int *ptr, int len, int val)
-{
+int binary_search(__global const int *ptr, int len, int val) {
     int start = 0;
     int end   = len;
     while (end > start) {
@@ -54,21 +51,17 @@ int binary_search(__global const int *ptr, int len, int val)
     return start;
 }
 
-// Each thread performs Matrix Vector multiplications for ROWS_PER_GROUP rows and (K / THREAD) columns.
-// This generates a local output buffer of size ROWS_PER_THREAD for each thread.
-// The outputs from each thread are added up to generate the final result.
-__kernel void
-cscmv_block(__global T *output,
-            __global const T *values,
-            __global const int *colidx,  // rowidx from csr is colidx in csc
-            __global const int *rowidx,  // colidx from csr is rowidx in csc
-            const int M,                 // K from csr is M in csc
-            const int K,                 // M from csr is K in csc
-            __global const T *rhs,
-            const KParam rinfo,
-            const T alpha,
-            const T beta)
-{
+// Each thread performs Matrix Vector multiplications for ROWS_PER_GROUP rows
+// and (K / THREAD) columns. This generates a local output buffer of size
+// ROWS_PER_THREAD for each thread. The outputs from each thread are added up to
+// generate the final result.
+__kernel void cscmv_block(
+    __global T *output, __global const T *values,
+    __global const int *colidx,  // rowidx from csr is colidx in csc
+    __global const int *rowidx,  // colidx from csr is rowidx in csc
+    const int M,                 // K from csr is M in csc
+    const int K,                 // M from csr is K in csc
+    __global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
     int lid = get_local_id(0);
 
     // Get the row offset for the current group in the uncompressed matrix
@@ -77,22 +70,19 @@ cscmv_block(__global T *output,
     rhs += rinfo.offset;
 
     T l_outvals[ROWS_PER_GROUP];
-    for (int i = 0; i < rowLim; i++) {
-        l_outvals[i] = 0;
-    }
+    for (int i = 0; i < rowLim; i++) { l_outvals[i] = 0; }
 
     for (int colId = lid; colId < K; colId += THREADS) {
-
-        int rowStart = colidx[colId];
-        int rowEnd   = colidx[colId + 1];
+        int rowStart     = colidx[colId];
+        int rowEnd       = colidx[colId + 1];
         int nonZeroCount = rowEnd - rowStart;
 
         // Find the location of the next non zero element after rowOff
-        int rowPos   = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
-        T rhsval = rhs[colId];
+        int rowPos = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
+        T rhsval   = rhs[colId];
 
         // Traversing through nonzero elements in the current chunk
-        for (int id =  rowPos + rowStart; id < rowEnd; id++) {
+        for (int id = rowPos + rowStart; id < rowEnd; id++) {
             int rowId = rowidx[id];
 
             // Exit if moving past current chunk
@@ -108,9 +98,9 @@ cscmv_block(__global T *output,
     // s_output is used to store the final output into local memory
     __local T s_output[ROWS_PER_GROUP];
 
-    // For each row of output, copy registers to local memory, add results, write to output.
+    // For each row of output, copy registers to local memory, add results,
+    // write to output.
     for (int i = 0; i < rowLim; i++) {
-
         // Copying to local memory
         s_outvals[lid] = l_outvals[i];
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -121,10 +111,9 @@ cscmv_block(__global T *output,
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
-        // Store to another local buffer so it can be written in a coalesced manner later
-        if (lid == 0) {
-            s_output[i] = s_outvals[0];
-        }
+        // Store to another local buffer so it can be written in a coalesced
+        // manner later
+        if (lid == 0) { s_output[i] = s_outvals[0]; }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index ed9a7bbae8..0ac76a7bcd 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -9,114 +9,100 @@
 
 #pragma once
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/cscmv.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <type_util.hpp>
-#include "scan_dim.hpp"
+#include <af/opencl.h>
+#include <map>
+#include <mutex>
+#include <string>
+#include "config.hpp"
 #include "reduce.hpp"
+#include "scan_dim.hpp"
 #include "scan_first.hpp"
-#include "config.hpp"
-#include <af/opencl.h>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T>
-        void cscmv(Param out,
-                   const Param &values, const Param &colIdx, const Param &rowIdx,
-                   const Param &rhs, const T alpha, const T beta, bool is_conj)
-        {
-            bool use_alpha = (alpha != scalar<T>(1.0));
-            bool use_beta = (beta != scalar<T>(0.0));
-
-            int threads = 256;
-            //TODO: rows_per_group limited by register pressure. Find better way to handle this.
-            int rows_per_group = 64;
-
-            std::string ref_name =
-                std::string("cscmv_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(use_alpha) +
-                std::string("_") +
-                std::to_string(use_beta) +
-                std::string("_") +
-                std::to_string(is_conj) +
-                std::string("_") +
-                std::to_string(rows_per_group) +
-                std::string("_") +
-                std::to_string(threads);
-
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                options << " -D USE_ALPHA=" << use_alpha;
-                options << " -D USE_BETA=" << use_beta;
-                options << " -D IS_CONJ=" << is_conj;
-                options << " -D THREADS=" << threads;
-                options << " -D ROWS_PER_GROUP=" << rows_per_group;
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                if (std::is_same<T, cfloat>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-
-                const char *ker_strs[] = {cscmv_cl};
-                const int   ker_lens[] = {cscmv_cl_len};
-
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "cscmv_block");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto cscmv_kernel = *entry.ker;
-            auto cscmv_func = KernelFunctor<Buffer,
-                                            Buffer, Buffer, Buffer,
-                                            int, int,
-                                            Buffer, KParam, T, T>(cscmv_kernel);
-
-            NDRange local(threads);
-            int K = colIdx.info.dims[0] - 1;
-            int M = out.info.dims[0];
-            int groups_x = divup(M, rows_per_group);
-            NDRange global(local[0] * groups_x, 1);
-
-            cscmv_func(EnqueueArgs(getQueue(), global, local),
-                        *out.data, *values.data, *colIdx.data, *rowIdx.data,
-                        M, K, *rhs.data, rhs.info, alpha, beta);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace opencl {
+namespace kernel {
+template<typename T>
+void cscmv(Param out, const Param &values, const Param &colIdx,
+           const Param &rowIdx, const Param &rhs, const T alpha, const T beta,
+           bool is_conj) {
+    bool use_alpha = (alpha != scalar<T>(1.0));
+    bool use_beta  = (beta != scalar<T>(0.0));
+
+    int threads = 256;
+    // TODO: rows_per_group limited by register pressure. Find better way to
+    // handle this.
+    int rows_per_group = 64;
+
+    std::string ref_name =
+        std::string("cscmv_") + std::string(dtype_traits<T>::getName()) +
+        std::string("_") + std::to_string(use_alpha) + std::string("_") +
+        std::to_string(use_beta) + std::string("_") + std::to_string(is_conj) +
+        std::string("_") + std::to_string(rows_per_group) + std::string("_") +
+        std::to_string(threads);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D USE_ALPHA=" << use_alpha;
+        options << " -D USE_BETA=" << use_beta;
+        options << " -D IS_CONJ=" << is_conj;
+        options << " -D THREADS=" << threads;
+        options << " -D ROWS_PER_GROUP=" << rows_per_group;
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
+        }
+
+        const char *ker_strs[] = {cscmv_cl};
+        const int ker_lens[]   = {cscmv_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "cscmv_block");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    auto cscmv_kernel = *entry.ker;
+    auto cscmv_func   = KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, int,
+                                    Buffer, KParam, T, T>(cscmv_kernel);
+
+    NDRange local(threads);
+    int K        = colIdx.info.dims[0] - 1;
+    int M        = out.info.dims[0];
+    int groups_x = divup(M, rows_per_group);
+    NDRange global(local[0] * groups_x, 1);
+
+    cscmv_func(EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
+               *colIdx.data, *rowIdx.data, M, K, *rhs.data, rhs.info, alpha,
+               beta);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/csr2coo.cl b/src/backend/opencl/kernel/csr2coo.cl
index 862b64d9bb..3268c8245b 100644
--- a/src/backend/opencl/kernel/csr2coo.cl
+++ b/src/backend/opencl/kernel/csr2coo.cl
@@ -7,34 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void csr2coo(__global       int *orowidx,
-             __global       int *ocolidx,
-             __global const int *irowidx,
-             __global const int *icolidx,
-             const int M)
-{
+__kernel void csr2coo(__global int *orowidx, __global int *ocolidx,
+                      __global const int *irowidx, __global const int *icolidx,
+                      const int M) {
     int lid = get_local_id(0);
     for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
         int colStart = irowidx[rowId];
         int colEnd   = irowidx[rowId + 1];
-        for (int colId = colStart + lid;  colId < colEnd; colId += get_local_size(0)) {
+        for (int colId = colStart + lid; colId < colEnd;
+             colId += get_local_size(0)) {
             orowidx[colId] = rowId;
             ocolidx[colId] = icolidx[colId];
         }
     }
 }
 
-__kernel
-void swapIndex_kernel(__global       T   *ovalues,
-                      __global       int *oindex,
-                      __global const T   *ivalues,
-                      __global const int *iindex,
-                      __global const int *swapIdx,
-                      const int nNZ)
-{
+__kernel void swapIndex_kernel(__global T *ovalues, __global int *oindex,
+                               __global const T *ivalues,
+                               __global const int *iindex,
+                               __global const int *swapIdx, const int nNZ) {
     int id = get_global_id(0);
-    if(id >= nNZ) return;
+    if (id >= nNZ) return;
 
     int idx = swapIdx[id];
 
@@ -42,39 +35,33 @@ void swapIndex_kernel(__global       T   *ovalues,
     oindex[id]  = iindex[idx];
 }
 
-__kernel
-void csrReduce_kernel(__global       int *orowIdx,
-                      __global const int *irowIdx,
-                      const int M, const int nNZ)
-{
+__kernel void csrReduce_kernel(__global int *orowIdx,
+                               __global const int *irowIdx, const int M,
+                               const int nNZ) {
     int id = get_global_id(0);
 
-    if(id >= nNZ) return;
+    if (id >= nNZ) return;
 
     // Read COO row indices
     int iRId  = irowIdx[id];
     int iRId1 = 0;
-    if(id > 0) iRId1 = irowIdx[id - 1];
+    if (id > 0) iRId1 = irowIdx[id - 1];
 
     // If id is 0, then mark the edge cases of csrRow[0] and csrRow[M]
-    if(id == 0) {
+    if (id == 0) {
         orowIdx[id] = 0;
         orowIdx[M]  = nNZ;
-    } else if(iRId1 != iRId) {
+    } else if (iRId1 != iRId) {
         // If iRId1 and iRId are not same, that means the row has incremented
         // For example, if iRId is 5 and iRId1 is 4, that means row 4 has
         // ended and row 5 has begun at index id.
         // We use the for-loop because there can be any number of empty rows
         // between iRId1 and iRId, all of which should be marked by id
-        for(int i = iRId1 + 1; i <= iRId; i++)
-            orowIdx[i] = id;
+        for (int i = iRId1 + 1; i <= iRId; i++) orowIdx[i] = id;
     }
 
     // The last X rows are corner cases if they dont have any values
-    if(id < M) {
-        if(id > irowIdx[nNZ - 1] && orowIdx[id] == 0) {
-            orowIdx[id] = nNZ;
-        }
+    if (id < M) {
+        if (id > irowIdx[nNZ - 1] && orowIdx[id] == 0) { orowIdx[id] = nNZ; }
     }
 }
-
diff --git a/src/backend/opencl/kernel/csr2dense.cl b/src/backend/opencl/kernel/csr2dense.cl
index a23d121cb6..acd2ef454a 100644
--- a/src/backend/opencl/kernel/csr2dense.cl
+++ b/src/backend/opencl/kernel/csr2dense.cl
@@ -7,18 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void csr2dense(__global T *output,
-               __global const T *values,
-               __global const int *rowidx,
-               __global const int *colidx,
-               const int M)
-{
+__kernel void csr2dense(__global T *output, __global const T *values,
+                        __global const int *rowidx, __global const int *colidx,
+                        const int M) {
     int lid = get_local_id(0);
     for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
         int colStart = rowidx[rowId];
         int colEnd   = rowidx[rowId + 1];
-        for (int colId = colStart + lid;  colId < colEnd; colId += THREADS) {
+        for (int colId = colStart + lid; colId < colEnd; colId += THREADS) {
             output[rowId + colidx[colId] * M] = values[colId];
         }
     }
diff --git a/src/backend/opencl/kernel/csrmm.cl b/src/backend/opencl/kernel/csrmm.cl
index 90cd74bebe..1dd7d75972 100644
--- a/src/backend/opencl/kernel/csrmm.cl
+++ b/src/backend/opencl/kernel/csrmm.cl
@@ -8,16 +8,14 @@
  ********************************************************/
 
 #if IS_CPLX
-T __cmul(T lhs, T rhs)
-{
+T __cmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-T __ccmul(T lhs, T rhs)
-{
+T __ccmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x + lhs.y * rhs.y;
     out.y = lhs.x * rhs.y - lhs.y * rhs.x;
@@ -37,27 +35,21 @@ T __ccmul(T lhs, T rhs)
 #define CMUL(a, b) (a) * (b)
 #endif
 
-// This kernel expects the dense matrix to be transpose of column major (aka non transpose row major).
+// This kernel expects the dense matrix to be transpose of column major (aka non
+// transpose row major).
 
 // In this kernel, each block performs multiple "dot" operations.
-// In each outer facing iteration, the group performs a "dot" on (one sparse row, `THREADS_PER_GROUP` dense columns).
-// The threads in the block load the sparse row into local memmory and then perform individual "dot" operations.
-
-__kernel void
-csrmm_nt(__global T *output,
-         __global const T *values,
-         __global const int *rowidx,
-         __global const int *colidx,
-         const int M,
-         const int N,
-         __global const T *rhs,
-         const KParam rinfo,
-         const T alpha,
-         const T beta,
-         __global int *counter)
-{
+// In each outer facing iteration, the group performs a "dot" on (one sparse
+// row, `THREADS_PER_GROUP` dense columns). The threads in the block load the
+// sparse row into local memmory and then perform individual "dot" operations.
+
+__kernel void csrmm_nt(__global T *output, __global const T *values,
+                       __global const int *rowidx, __global const int *colidx,
+                       const int M, const int N, __global const T *rhs,
+                       const KParam rinfo, const T alpha, const T beta,
+                       __global int *counter) {
     int gidx = get_global_id(0);
-    int lid = get_local_id(0);
+    int lid  = get_local_id(0);
 
     rhs += gidx + rinfo.offset;
     output += gidx * M;
@@ -70,13 +62,13 @@ csrmm_nt(__global T *output,
     int rowNext = get_group_id(1);
     __local int s_rowId;
 
-    // Each iteration writes `THREADS_PER_GROUP` columns from one row of the output
-    while(true) {
+    // Each iteration writes `THREADS_PER_GROUP` columns from one row of the
+    // output
+    while (true) {
 #if USE_GREEDY
-        // If the hardware has decent atomic operation support, greediy get the next available row
-        if (lid == 0) {
-            s_rowId = atomic_inc(counter + get_group_id(0));
-        }
+        // If the hardware has decent atomic operation support, greediy get the
+        // next available row
+        if (lid == 0) { s_rowId = atomic_inc(counter + get_group_id(0)); }
         barrier(CLK_LOCAL_MEM_FENCE);
         int rowId = s_rowId;
 #else
@@ -91,18 +83,20 @@ csrmm_nt(__global T *output,
         const int colEnd   = rowidx[rowId + 1];
 
         T outval = 0;
-        // Since the number of nonzero elements might be greater than local memory available,
-        // Load only part of the row into local memory, perform partial dot, repeat until done.
+        // Since the number of nonzero elements might be greater than local
+        // memory available, Load only part of the row into local memory,
+        // perform partial dot, repeat until done.
         for (int id = colStart; id < colEnd; id += THREADS_PER_GROUP) {
             // Load the current chunk of the row into local memory
-            int lim = min(colEnd - id, THREADS_PER_GROUP);
+            int lim       = min(colEnd - id, THREADS_PER_GROUP);
             s_values[lid] = lid < lim ? values[id + lid] : 0;
             s_colidx[lid] = lid < lim ? colidx[id + lid] : -1;
             barrier(CLK_LOCAL_MEM_FENCE);
 
             // Perform partial "dot" operation for each thread
             for (int idy = 0; within_N && idy < lim; idy++) {
-                outval += CMUL(s_values[idy], rhs[rinfo.strides[1] * s_colidx[idy]]);
+                outval +=
+                    CMUL(s_values[idy], rhs[rinfo.strides[1] * s_colidx[idy]]);
             }
             barrier(CLK_LOCAL_MEM_FENCE);
         }
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 2e308ac0d1..69ea435524 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -9,121 +9,108 @@
 
 #pragma once
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/csrmm.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <type_util.hpp>
-#include "scan_dim.hpp"
+#include <map>
+#include <mutex>
+#include <string>
+#include "config.hpp"
 #include "reduce.hpp"
+#include "scan_dim.hpp"
 #include "scan_first.hpp"
-#include "config.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        static const int MAX_CSRMM_GROUPS = 4096;
-        template<typename T>
-        void csrmm_nt(Param out,
-                      const Param &values, const Param &rowIdx, const Param &colIdx,
-                      const Param &rhs, const T alpha, const T beta)
-        {
-            bool use_alpha = (alpha != scalar<T>(1.0));
-            bool use_beta = (beta != scalar<T>(0.0));
-
-            // Using greedy indexing is causing performance issues on many platforms
-            // FIXME: Figure out why
-            bool use_greedy = false;
-
-            std::string ref_name =
-                std::string("csrmm_nt_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(use_alpha) +
-                std::string("_") +
-                std::to_string(use_beta) +
-                std::string("_") +
-                std::to_string(use_greedy);
-
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                options << " -D USE_ALPHA=" << use_alpha;
-                options << " -D USE_BETA=" << use_beta;
-                options << " -D USE_GREEDY=" << use_greedy;
-                options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                if (std::is_same<T, cfloat>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-
-                const char *ker_strs[] = {csrmm_cl};
-                const int   ker_lens[] = {csrmm_cl_len};
-
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel[2];
-                entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
-                // FIXME: Change this after adding another kernel
-                entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto csrmm_nt_kernel = entry.ker[0];
-            auto csrmm_nt_func = KernelFunctor<Buffer,
-                                                Buffer, Buffer, Buffer,
-                                                int, int,
-                                                Buffer, KParam, T, T, Buffer>(csrmm_nt_kernel);
-            NDRange local(THREADS_PER_GROUP, 1);
-            int M = rowIdx.info.dims[0] - 1;
-            int N = rhs.info.dims[0];
-
-            int groups_x = divup(N, local[0]);
-            int groups_y = divup(M, REPEAT);
-            groups_y = std::min(groups_y, MAX_CSRMM_GROUPS);
-            NDRange global(local[0] * groups_x, local[1] * groups_y);
-
-            std::vector<int> count(groups_x);
-            cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
-            getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
-                                          0,
-                                          count.size() * sizeof(int),
-                                          (void *)count.data());
-
-            csrmm_nt_func(EnqueueArgs(getQueue(), global, local),
-                          *out.data, *values.data, *rowIdx.data, *colIdx.data,
-                          M, N, *rhs.data, rhs.info, alpha, beta, *counter);
-
-            bufferFree(counter);
+namespace opencl {
+namespace kernel {
+static const int MAX_CSRMM_GROUPS = 4096;
+template<typename T>
+void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
+              const Param &colIdx, const Param &rhs, const T alpha,
+              const T beta) {
+    bool use_alpha = (alpha != scalar<T>(1.0));
+    bool use_beta  = (beta != scalar<T>(0.0));
+
+    // Using greedy indexing is causing performance issues on many platforms
+    // FIXME: Figure out why
+    bool use_greedy = false;
+
+    std::string ref_name = std::string("csrmm_nt_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(use_alpha) +
+                           std::string("_") + std::to_string(use_beta) +
+                           std::string("_") + std::to_string(use_greedy);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D USE_ALPHA=" << use_alpha;
+        options << " -D USE_BETA=" << use_beta;
+        options << " -D USE_GREEDY=" << use_greedy;
+        options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
         }
+
+        const char *ker_strs[] = {csrmm_cl};
+        const int ker_lens[]   = {csrmm_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog   = new Program(prog);
+        entry.ker    = new Kernel[2];
+        entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
+        // FIXME: Change this after adding another kernel
+        entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    auto csrmm_nt_kernel = entry.ker[0];
+    auto csrmm_nt_func =
+        KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, int, Buffer, KParam,
+                      T, T, Buffer>(csrmm_nt_kernel);
+    NDRange local(THREADS_PER_GROUP, 1);
+    int M = rowIdx.info.dims[0] - 1;
+    int N = rhs.info.dims[0];
+
+    int groups_x = divup(N, local[0]);
+    int groups_y = divup(M, REPEAT);
+    groups_y     = std::min(groups_y, MAX_CSRMM_GROUPS);
+    NDRange global(local[0] * groups_x, local[1] * groups_y);
+
+    std::vector<int> count(groups_x);
+    cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
+    getQueue().enqueueWriteBuffer(
+        *counter, CL_TRUE, 0, count.size() * sizeof(int), (void *)count.data());
+
+    csrmm_nt_func(EnqueueArgs(getQueue(), global, local), *out.data,
+                  *values.data, *rowIdx.data, *colIdx.data, M, N, *rhs.data,
+                  rhs.info, alpha, beta, *counter);
+
+    bufferFree(counter);
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index 222b04e3df..552912a7b3 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -8,16 +8,14 @@
  ********************************************************/
 
 #if IS_CPLX
-T __cmul(T lhs, T rhs)
-{
+T __cmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-T __ccmul(T lhs, T rhs)
-{
+T __ccmul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x + lhs.y * rhs.y;
     out.y = lhs.x * rhs.y - lhs.y * rhs.x;
@@ -37,32 +35,24 @@ T __ccmul(T lhs, T rhs)
 #define CMUL(a, b) (a) * (b)
 #endif
 
-
-// In this kernel, each thread performs one "dot" operation by reading nonzero elements from one row
-// and multiplying with the corresponding elements from the dense vector to produce a single output value.
-// This kernel should be used when the number of nonzero elements per block is fairly small
-__kernel void
-csrmv_thread(__global T *output,
-             __global const T *values,
-             __global const int *rowidx,
-             __global const int *colidx,
-             const int M,
-             __global const T *rhs,
-             const KParam rinfo,
-             const T alpha,
-             const T beta,
-             __global int *counter)
-{
-
+// In this kernel, each thread performs one "dot" operation by reading nonzero
+// elements from one row and multiplying with the corresponding elements from
+// the dense vector to produce a single output value. This kernel should be used
+// when the number of nonzero elements per block is fairly small
+__kernel void csrmv_thread(__global T *output, __global const T *values,
+                           __global const int *rowidx,
+                           __global const int *colidx, const int M,
+                           __global const T *rhs, const KParam rinfo,
+                           const T alpha, const T beta, __global int *counter) {
     rhs += rinfo.offset;
     int rowNext = get_global_id(0);
 
     while (true) {
-
         // Each thread performs multiple "dot" operations
 #if USE_GREEDY
-        // Considering that the number of non zero elements per row can be uneven a greedy approach may be useful.
-        // This acheived by getting the next available row to perform the "dot" operation on.
+        // Considering that the number of non zero elements per row can be
+        // uneven a greedy approach may be useful. This acheived by getting the
+        // next available row to perform the "dot" operation on.
         int rowId = atomic_inc(counter);
 #else
         // Unfortunately atomic operations are costly on some architectures.
@@ -96,37 +86,30 @@ csrmv_thread(__global T *output,
     }
 }
 
-// In this kernel, each block performs one "dot" operation by having each thread read a nonzero element from a row
-// and multiplying with the corresponding elements from dense vector to produce a local output values.
-// Then the block performs a reduction operation to produce a single output value.
-// This kernel should be used when the number of nonzero elements per block is large
-__kernel void
-csrmv_block(__global T *output,
-            __global const T *values,
-            __global const int *rowidx,
-            __global const int *colidx,
-            const int M,
-            __global const T *rhs,
-            const KParam rinfo,
-            const T alpha,
-            const T beta,
-            __global int *counter)
-{
+// In this kernel, each block performs one "dot" operation by having each thread
+// read a nonzero element from a row and multiplying with the corresponding
+// elements from dense vector to produce a local output values. Then the block
+// performs a reduction operation to produce a single output value. This kernel
+// should be used when the number of nonzero elements per block is large
+__kernel void csrmv_block(__global T *output, __global const T *values,
+                          __global const int *rowidx,
+                          __global const int *colidx, const int M,
+                          __global const T *rhs, const KParam rinfo,
+                          const T alpha, const T beta, __global int *counter) {
     rhs += rinfo.offset;
-    int lid = get_local_id(0);
+    int lid     = get_local_id(0);
     int rowNext = get_group_id(0);
     __local int s_rowId;
 
     // Each groups performs multiple "dot" operations
     while (true) {
-
 #if USE_GREEDY
-        // Considering that the number of non zero elements per row can be uneven a greedy approach may be useful.
-        // This acheived by getting the next available row to perform the "dot" operation on.
-        // Since the rowId needs is the same across the block, only one thread needs to increment the counter.
-        if (lid == 0) {
-            s_rowId = atomic_inc(counter);
-        }
+        // Considering that the number of non zero elements per row can be
+        // uneven a greedy approach may be useful. This acheived by getting the
+        // next available row to perform the "dot" operation on. Since the rowId
+        // needs is the same across the block, only one thread needs to
+        // increment the counter.
+        if (lid == 0) { s_rowId = atomic_inc(counter); }
         barrier(CLK_LOCAL_MEM_FENCE);
         int rowId = s_rowId;
 #else
@@ -142,9 +125,10 @@ csrmv_block(__global T *output,
 
         int colStart = rowidx[rowId];
         int colEnd   = rowidx[rowId + 1];
-        T outval = 0;
+        T outval     = 0;
 
-        // Each thread performs "dot" on num_nonzero_elements / THREADS for a given row
+        // Each thread performs "dot" on num_nonzero_elements / THREADS for a
+        // given row
         for (int id = colStart + lid; id < colEnd; id += THREADS) {
             int cid = colidx[id];
             outval += MUL(values[id], rhs[cid]);
@@ -163,7 +147,7 @@ csrmv_block(__global T *output,
 #if USE_ALPHA
             outval = MUL(alpha, s_outval[0]);
 #else
-            outval = s_outval[0];
+            outval        = s_outval[0];
 #endif
 
 #if USE_BETA
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 6a37cbfbfe..e4c06ad39d 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -9,128 +9,113 @@
 
 #pragma once
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/csrmv.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <type_util.hpp>
-#include "scan_dim.hpp"
+#include <af/opencl.h>
+#include <map>
+#include <mutex>
+#include <string>
+#include "config.hpp"
 #include "reduce.hpp"
+#include "scan_dim.hpp"
 #include "scan_first.hpp"
-#include "config.hpp"
-#include <af/opencl.h>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        static const int MAX_CSRMV_GROUPS = 4096;
-        template<typename T>
-        void csrmv(Param out,
-                   const Param &values, const Param &rowIdx, const Param &colIdx,
-                   const Param &rhs, const T alpha, const T beta)
-        {
-            bool use_alpha = (alpha != scalar<T>(1.0));
-            bool use_beta = (beta != scalar<T>(0.0));
-
-            // Using greedy indexing is causing performance issues on many platforms
-            // FIXME: Figure out why
-            bool use_greedy = false;
-
-            // FIXME: Find a better number based on average non zeros per row
-            int threads = 64;
-
-            std::string ref_name =
-                std::string("csrmv_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(use_alpha) +
-                std::string("_") +
-                std::to_string(use_beta) +
-                std::string("_") +
-                std::to_string(use_greedy) +
-                std::string("_") +
-                std::to_string(threads);
-
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                options << " -D USE_ALPHA=" << use_alpha;
-                options << " -D USE_BETA=" << use_beta;
-                options << " -D USE_GREEDY=" << use_greedy;
-                options << " -D THREADS=" << threads;
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                if (std::is_same<T, cfloat>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-
-                const char *ker_strs[] = {csrmv_cl};
-                const int   ker_lens[] = {csrmv_cl_len};
-
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel[2];
-                entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
-                entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            int count = 0;
-            cl::Buffer *counter = bufferAlloc(sizeof(int));
-            getQueue().enqueueWriteBuffer(*counter, CL_TRUE,
-                                          0,
-                                          sizeof(int),
-                                          (void *)&count);
-
-            // TODO: Figure out the proper way to choose either csrmv_thread or csrmv_block
-            bool is_csrmv_block = true;
-            auto csrmv_kernel = is_csrmv_block ? entry.ker[1] : entry.ker[0];
-            auto csrmv_func = KernelFunctor<Buffer,
-                                            Buffer, Buffer, Buffer,
-                                            int,
-                                            Buffer, KParam, T, T, Buffer>(csrmv_kernel);
-
-            NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
-            int M = rowIdx.info.dims[0] - 1;
-
-            int groups_x = is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
-            groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
-            NDRange global(local[0] * groups_x, 1);
-
-            csrmv_func(EnqueueArgs(getQueue(), global, local),
-                        *out.data, *values.data, *rowIdx.data, *colIdx.data,
-                        M, *rhs.data, rhs.info, alpha, beta, *counter);
-
-            CL_DEBUG_FINISH(getQueue());
-            bufferFree(counter);
+namespace opencl {
+namespace kernel {
+static const int MAX_CSRMV_GROUPS = 4096;
+template<typename T>
+void csrmv(Param out, const Param &values, const Param &rowIdx,
+           const Param &colIdx, const Param &rhs, const T alpha, const T beta) {
+    bool use_alpha = (alpha != scalar<T>(1.0));
+    bool use_beta  = (beta != scalar<T>(0.0));
+
+    // Using greedy indexing is causing performance issues on many platforms
+    // FIXME: Figure out why
+    bool use_greedy = false;
+
+    // FIXME: Find a better number based on average non zeros per row
+    int threads = 64;
+
+    std::string ref_name =
+        std::string("csrmv_") + std::string(dtype_traits<T>::getName()) +
+        std::string("_") + std::to_string(use_alpha) + std::string("_") +
+        std::to_string(use_beta) + std::string("_") +
+        std::to_string(use_greedy) + std::string("_") + std::to_string(threads);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D USE_ALPHA=" << use_alpha;
+        options << " -D USE_BETA=" << use_beta;
+        options << " -D USE_GREEDY=" << use_greedy;
+        options << " -D THREADS=" << threads;
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
+        }
+
+        const char *ker_strs[] = {csrmv_cl};
+        const int ker_lens[]   = {csrmv_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog   = new Program(prog);
+        entry.ker    = new Kernel[2];
+        entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
+        entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    int count           = 0;
+    cl::Buffer *counter = bufferAlloc(sizeof(int));
+    getQueue().enqueueWriteBuffer(*counter, CL_TRUE, 0, sizeof(int),
+                                  (void *)&count);
+
+    // TODO: Figure out the proper way to choose either csrmv_thread or
+    // csrmv_block
+    bool is_csrmv_block = true;
+    auto csrmv_kernel   = is_csrmv_block ? entry.ker[1] : entry.ker[0];
+    auto csrmv_func = KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, Buffer,
+                                    KParam, T, T, Buffer>(csrmv_kernel);
+
+    NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
+    int M = rowIdx.info.dims[0] - 1;
+
+    int groups_x =
+        is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
+    groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
+    NDRange global(local[0] * groups_x, 1);
+
+    csrmv_func(EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
+               *rowIdx.data, *colIdx.data, M, *rhs.data, rhs.info, alpha, beta,
+               *counter);
+
+    CL_DEBUG_FINISH(getQueue());
+    bufferFree(counter);
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/dense2csr.cl b/src/backend/opencl/kernel/dense2csr.cl
index 3843707392..c2ad83cc7e 100644
--- a/src/backend/opencl/kernel/dense2csr.cl
+++ b/src/backend/opencl/kernel/dense2csr.cl
@@ -13,15 +13,12 @@
 #define IS_ZERO(val) (val == 0)
 #endif
 
-__kernel
-void dense2csr_split_kernel(__global T *svalptr,
-                            __global int *scolptr,
-                            __global const T *dvalptr,
-                            const KParam valinfo,
-                            __global const int *dcolptr,
-                            const KParam colinfo,
-                            __global const int *rowptr)
-{
+__kernel void dense2csr_split_kernel(__global T *svalptr, __global int *scolptr,
+                                     __global const T *dvalptr,
+                                     const KParam valinfo,
+                                     __global const int *dcolptr,
+                                     const KParam colinfo,
+                                     __global const int *rowptr) {
     int gidx = get_global_id(0);
     int gidy = get_global_id(1);
 
@@ -36,10 +33,10 @@ void dense2csr_split_kernel(__global T *svalptr,
     dcolptr += colinfo.offset;
 
     int idx = gidx + gidy * valinfo.strides[1];
-    T val = dvalptr[gidx + gidy * valinfo.strides[1]];
+    T val   = dvalptr[gidx + gidy * valinfo.strides[1]];
     if (IS_ZERO(val)) return;
 
-    int oloc = dcolptr[gidx + gidy * colinfo.strides[1]];
+    int oloc          = dcolptr[gidx + gidy * colinfo.strides[1]];
     svalptr[oloc - 1] = val;
-    scolptr[oloc - 1]  = gidy;
+    scolptr[oloc - 1] = gidy;
 }
diff --git a/src/backend/opencl/kernel/diag_create.cl b/src/backend/opencl/kernel/diag_create.cl
index 179c59c455..3eb16ce3cc 100644
--- a/src/backend/opencl/kernel/diag_create.cl
+++ b/src/backend/opencl/kernel/diag_create.cl
@@ -7,24 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void diagCreateKernel(__global T *oData, KParam oInfo,
-                 const __global T *iData, KParam iInfo,
-                 int num, int groups_x)
-{
-    unsigned idz = get_group_id(0) / groups_x;
+__kernel void diagCreateKernel(__global T *oData, KParam oInfo,
+                               const __global T *iData, KParam iInfo, int num,
+                               int groups_x) {
+    unsigned idz       = get_group_id(0) / groups_x;
     unsigned groupId_x = get_group_id(0) - idz * groups_x;
 
     unsigned idx = get_local_id(0) + groupId_x * get_local_size(0);
     unsigned idy = get_global_id(1);
 
-    if (idx >= oInfo.dims[0] ||
-        idy >= oInfo.dims[1] ||
-        idz >= oInfo.dims[2]) return;
+    if (idx >= oInfo.dims[0] || idy >= oInfo.dims[1] || idz >= oInfo.dims[2])
+        return;
 
-
-    __global T *optr = oData + idz * oInfo.strides[2] + idy * oInfo.strides[1] + idx;
-    const __global T *iptr = iData  + idz *  iInfo.strides[1] + ((num > 0) ? idx : idy) + iInfo.offset;
+    __global T *optr =
+        oData + idz * oInfo.strides[2] + idy * oInfo.strides[1] + idx;
+    const __global T *iptr =
+        iData + idz * iInfo.strides[1] + ((num > 0) ? idx : idy) + iInfo.offset;
 
     T val = (idx == (idy - num)) ? *iptr : ZERO;
     *optr = val;
diff --git a/src/backend/opencl/kernel/diag_extract.cl b/src/backend/opencl/kernel/diag_extract.cl
index 2c7b2561d2..c663923fd6 100644
--- a/src/backend/opencl/kernel/diag_extract.cl
+++ b/src/backend/opencl/kernel/diag_extract.cl
@@ -7,32 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void diagExtractKernel(__global T *oData, KParam oInfo,
-                  const __global T *iData, KParam iInfo,
-                  int num, int groups_z)
-{
+__kernel void diagExtractKernel(__global T *oData, KParam oInfo,
+                                const __global T *iData, KParam iInfo, int num,
+                                int groups_z) {
     unsigned idw = get_group_id(1) / groups_z;
     unsigned idz = get_group_id(1) - idw * groups_z;
 
     unsigned idx = get_global_id(0);
 
-    if (idx >= oInfo.dims[0] ||
-        idz >= oInfo.dims[2] ||
-        idw >= oInfo.dims[3]) return;
+    if (idx >= oInfo.dims[0] || idz >= oInfo.dims[2] || idw >= oInfo.dims[3])
+        return;
 
-    __global T *optr = oData + idz * oInfo.strides[2] + idw * oInfo.strides[3] + idx;
+    __global T *optr =
+        oData + idz * oInfo.strides[2] + idw * oInfo.strides[3] + idx;
 
     if (idx >= iInfo.dims[0] || idx >= iInfo.dims[1]) {
         *optr = ZERO;
         return;
     }
 
-    int i_off = (num > 0) ? (num * iInfo.strides[1] + idx) : (idx - num) + iInfo.offset;
+    int i_off =
+        (num > 0) ? (num * iInfo.strides[1] + idx) : (idx - num) + iInfo.offset;
 
-    const __global T *iptr = iData  +
-        idz *  iInfo.strides[2] +
-        idw *  iInfo.strides[3] + i_off;
+    const __global T *iptr =
+        iData + idz * iInfo.strides[2] + idw * iInfo.strides[3] + i_off;
 
     *optr = iptr[idx * iInfo.strides[1]];
 }
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index a459596df2..afb860691a 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -7,36 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/diag_create.hpp>
 #include <kernel_headers/diag_extract.hpp>
+#include <math.hpp>
 #include <program.hpp>
 #include "../traits.hpp"
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
-#include <math.hpp>
 #include "config.hpp"
 
+using af::scalar_to_option;
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
-using af::scalar_to_option;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 template<typename T>
-std::string generateOptionsString()
-{
+std::string generateOptionsString() {
     std::ostringstream options;
-    options << " -D T="    << dtype_traits<T>::getName()
-            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+    options << " -D T=" << dtype_traits<T>::getName() << " -D ZERO=(T)("
+            << scalar_to_option(scalar<T>(0)) << ")";
     if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
         options << " -D USE_DOUBLE";
     }
@@ -44,17 +41,17 @@ std::string generateOptionsString()
 }
 
 template<typename T>
-static void diagCreate(Param out, Param in, int num)
-{
-    std::string refName = std::string("diagCreateKernel_") + std::string(dtype_traits<T>::getName());
+static void diagCreate(Param out, Param in, int num) {
+    std::string refName = std::string("diagCreateKernel_") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
-        std::string options = generateOptionsString<T>();
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::string options    = generateOptionsString<T>();
         const char* ker_strs[] = {diag_create_cl};
-        const int   ker_lens[] = {diag_create_cl_len};
+        const int ker_lens[]   = {diag_create_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options);
         entry.prog = new Program(prog);
@@ -68,27 +65,28 @@ static void diagCreate(Param out, Param in, int num)
     int groups_y = divup(out.info.dims[1], local[1]);
     NDRange global(groups_x * local[0] * out.info.dims[2], groups_y * local[1]);
 
-    auto diagCreateOp = KernelFunctor< Buffer, const KParam, Buffer, const KParam,
-                                       int, int > (*entry.ker);
+    auto diagCreateOp =
+        KernelFunctor<Buffer, const KParam, Buffer, const KParam, int, int>(
+            *entry.ker);
 
-    diagCreateOp(EnqueueArgs(getQueue(), global, local),
-                 *(out.data), out.info, *(in.data), in.info, num, groups_x);
+    diagCreateOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info,
+                 *(in.data), in.info, num, groups_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-static void diagExtract(Param out, Param in, int num)
-{
-    std::string refName = std::string("diagExtractKernel_") + std::string(dtype_traits<T>::getName());
+static void diagExtract(Param out, Param in, int num) {
+    std::string refName = std::string("diagExtractKernel_") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
-        std::string options = generateOptionsString<T>();
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::string options    = generateOptionsString<T>();
         const char* ker_strs[] = {diag_extract_cl};
-        const int   ker_lens[] = {diag_extract_cl_len};
+        const int ker_lens[]   = {diag_extract_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options);
         entry.prog = new Program(prog);
@@ -102,14 +100,14 @@ static void diagExtract(Param out, Param in, int num)
     int groups_z = out.info.dims[2];
     NDRange global(groups_x * local[0], groups_z * local[1] * out.info.dims[3]);
 
-    auto diagExtractOp = KernelFunctor< Buffer, const KParam, Buffer, const KParam,
-                                        int, int > (*entry.ker);
+    auto diagExtractOp =
+        KernelFunctor<Buffer, const KParam, Buffer, const KParam, int, int>(
+            *entry.ker);
 
-    diagExtractOp(EnqueueArgs(getQueue(), global, local),
-                  *(out.data), out.info, *(in.data), in.info, num, groups_z);
+    diagExtractOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info,
+                  *(in.data), in.info, num, groups_z);
 
     CL_DEBUG_FINISH(getQueue());
-
-}
-}
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/diff.cl b/src/backend/opencl/kernel/diff.cl
index 0d00a77bee..89da8abd2c 100644
--- a/src/backend/opencl/kernel/diff.cl
+++ b/src/backend/opencl/kernel/diff.cl
@@ -8,20 +8,17 @@
  ********************************************************/
 
 void diff_this(__global T* out, __global const T* in, const int oMem,
-               const int iMem0, const int iMem1, const int iMem2)
-{
-    if(isDiff2 == 0) {
+               const int iMem0, const int iMem1, const int iMem2) {
+    if (isDiff2 == 0) {
         out[oMem] = in[iMem1] - in[iMem0];
     } else {
         out[oMem] = in[iMem2] - in[iMem1] - in[iMem1] + in[iMem0];
     }
 }
 
-__kernel
-void diff_kernel(__global T *out, __global const T *in,
-                 const KParam op, const KParam ip, const int oElem,
-                 const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void diff_kernel(__global T* out, __global const T* in,
+                          const KParam op, const KParam ip, const int oElem,
+                          const int blocksPerMatX, const int blocksPerMatY) {
     const int idz = get_group_id(0) / blocksPerMatX;
     const int idw = get_group_id(1) / blocksPerMatY;
 
@@ -31,17 +28,17 @@ void diff_kernel(__global T *out, __global const T *in,
     const int idx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int idy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(idx >= op.dims[0] ||
-       idy >= op.dims[1] ||
-       idz >= op.dims[2] ||
-       idw >= op.dims[3])
+    if (idx >= op.dims[0] || idy >= op.dims[1] || idz >= op.dims[2] ||
+        idw >= op.dims[3])
         return;
 
-    int iMem0 = idw * ip.strides[3] + idz * ip.strides[2] + idy * ip.strides[1] + idx;
+    int iMem0 =
+        idw * ip.strides[3] + idz * ip.strides[2] + idy * ip.strides[1] + idx;
     int iMem1 = iMem0 + ip.strides[DIM];
     int iMem2 = iMem1 + ip.strides[DIM];
 
-    int oMem = idw * op.strides[3] + idz * op.strides[2] + idy * op.strides[1] + idx;
+    int oMem =
+        idw * op.strides[3] + idz * op.strides[2] + idy * op.strides[1] + idx;
 
     iMem2 *= isDiff2;
 
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 2c3a81091e..6fbf41a5c4 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -8,53 +8,47 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/diff.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int TX = 16;
 static const int TY = 16;
 
 template<typename T, unsigned dim, bool isDiff2>
-void diff(Param out, const Param in, const unsigned indims)
-{
+void diff(Param out, const Param in, const unsigned indims) {
     std::string refName = std::string("diff_kernel_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(dim) +
-        std::to_string(isDiff2);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(dim) + std::to_string(isDiff2);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="        << dtype_traits<T>::getName()
-                << " -D DIM="      << dim
+        options << " -D T=" << dtype_traits<T>::getName() << " -D DIM=" << dim
                 << " -D isDiff2=" << isDiff2;
-        if (std::is_same<T, double>::value ||
-            std::is_same<T, cdouble>::value) {
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {diff_cl};
-        const int   ker_lens[] = {diff_cl_len};
+        const int ker_lens[]   = {diff_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -63,25 +57,25 @@ void diff(Param out, const Param in, const unsigned indims)
         addKernelToCache(device, refName, entry);
     }
 
-    auto diffOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
-                                 const int, const int, const int> (*entry.ker);
+    auto diffOp =
+        KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
+                      const int, const int, const int>(*entry.ker);
 
     NDRange local(TX, TY, 1);
-    if(dim == 0 && indims == 1) {
-        local = NDRange(TX * TY, 1, 1);
-    }
+    if (dim == 0 && indims == 1) { local = NDRange(TX * TY, 1, 1); }
 
     int blocksPerMatX = divup(out.info.dims[0], local[0]);
     int blocksPerMatY = divup(out.info.dims[1], local[1]);
     NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                    local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-    const int oElem = out.info.dims[0] * out.info.dims[1] * out.info.dims[2] * out.info.dims[3];
+    const int oElem = out.info.dims[0] * out.info.dims[1] * out.info.dims[2] *
+                      out.info.dims[3];
 
-    diffOp(EnqueueArgs(getQueue(), global, local),
-           *out.data, *in.data, out.info, in.info, oElem, blocksPerMatX, blocksPerMatY);
+    diffOp(EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
+           out.info, in.info, oElem, blocksPerMatX, blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/example.cl b/src/backend/opencl/kernel/example.cl
index 94edd711b3..32be1bdd39 100644
--- a/src/backend/opencl/kernel/example.cl
+++ b/src/backend/opencl/kernel/example.cl
@@ -7,24 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void example(__global T *       d_dst,
-             KParam             oInfo,
-             __global const T * d_src1,
-             KParam             iInfo1,
-             __global const T * d_src2,
-             KParam             iInfo2,
-             int                method);
+__kernel void example(__global T* d_dst, KParam oInfo, __global const T* d_src1,
+                      KParam iInfo1, __global const T* d_src2, KParam iInfo2,
+                      int method);
 {
     // get current thread global identifiers along required dimensions
     int i = get_global_id(0);
     int j = get_global_id(1);
 
-    if ( i<iInfo1.dims[0] && j<iInfo1.dims[1] ) {
+    if (i < iInfo1.dims[0] && j < iInfo1.dims[1]) {
         // if needed use strides array to compute linear index of arrays
-        int src1Idx = i + j*iInfo1.strides[1];
-        int src2Idx = i + j*iInfo2.strides[1];
-        int dstIdx  = i + j* oInfo.strides[1];
+        int src1Idx = i + j * iInfo1.strides[1];
+        int src2Idx = i + j * iInfo2.strides[1];
+        int dstIdx  = i + j * oInfo.strides[1];
 
         // kernel algorithm goes here
         d_dst[dstIdx] = d_src1[src1Idx] + d_src2[src2Idx];
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index c6375d6709..c2c32c00bb 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/example.hpp>   // This is the header that gets auto-generated
-                                        // from the .cl file you will create. We pre-process
-                                        // cl files to obfuscate code.
+#include <kernel_headers/example.hpp>  // This is the header that gets auto-generated
+          // from the .cl file you will create. We pre-process
+          // cl files to obfuscate code.
 
 #include <program.hpp>
 #include <traits.hpp>
@@ -19,56 +19,52 @@
 // OpenCL cl::Kernel & cl::Program objects
 #include <string>
 
-#include <cache.hpp>                    // Has the definitions of functions such as the following
-                                        // used in caching and fetching kernels.
-                                        // * kernelCache - used to fetch existing kernel from cache
-                                        // if any
-                                        // * addKernelToCache - push new kernels into cache
+#include <cache.hpp>  // Has the definitions of functions such as the following
+                      // used in caching and fetching kernels.
+          // * kernelCache - used to fetch existing kernel from cache
+          // if any
+          // * addKernelToCache - push new kernels into cache
 
-#include <common/dispatch.hpp>          // common utility header for CUDA & OpenCL backends
-                                        // has the divup macro
+#include <common/dispatch.hpp>  // common utility header for CUDA & OpenCL backends
+                                // has the divup macro
 
-#include <Param.hpp>                    // This header has the declaration of structures
-                                        // that are passed onto kernel. Operator overloads
-                                        // for creating Param objects from opencl::Array<T>
-                                        // objects is automatic, no special work is needed.
-                                        // Hence, the OpenCL kernel wrapper function takes in
-                                        // Param instead of opencl::Array<T>
+#include <Param.hpp>  // This header has the declaration of structures
+                      // that are passed onto kernel. Operator overloads
+                      // for creating Param objects from opencl::Array<T>
+                      // objects is automatic, no special work is needed.
+                      // Hence, the OpenCL kernel wrapper function takes in
+                      // Param instead of opencl::Array<T>
 
-#include <debug_opencl.hpp>             // For Debug only related OpenCL validations
+#include <debug_opencl.hpp>  // For Debug only related OpenCL validations
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T>
-void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
-{
-    std::string refName =
-        std::string("example_") + //<kernel_function_name>_
-        std::string(dtype_traits<T>::getName());
-        // std::string("encode template parameters one after one");
-        // If you have numericals, you can use std::to_string to convert
-        // them into std::strings
-
-    int device = getActiveDeviceId();
+void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
+    std::string refName = std::string("example_") +  //<kernel_function_name>_
+                          std::string(dtype_traits<T>::getName());
+    // std::string("encode template parameters one after one");
+    // If you have numericals, you can use std::to_string to convert
+    // them into std::strings
+
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     // Make sure OpenCL kernel isn't already available before
     // compiling for given device and combination of template
     // parameters to this kernel wrapper function 'exampleFunc<T>'
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         // You can pass any template parameters as compile options
@@ -78,17 +74,16 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
         // The following option is passed to kernel compilation
         // if template parameter T is double or complex double
         // to enable FP64 extension
-        if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char *ker_strs[] = {example_cl};
-        const int   ker_lens[] = {example_cl_len};
+        const int ker_lens[]   = {example_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "example");
+        entry.ker  = new Kernel(*entry.prog, "example");
 
         addKernelToCache(device, refName, entry);
     }
@@ -105,16 +100,17 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
     // create a kernel functor from the cl::Kernel object
     // corresponding to the device on which current execution
     // is happending.
-    auto exampleFuncOp = KernelFunctor< Buffer, KParam, Buffer, KParam,
-                                        Buffer, KParam, int>(*entry.ker);
+    auto exampleFuncOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int>(
+            *entry.ker);
 
     // launch the kernel
-    exampleFuncOp(EnqueueArgs(getQueue(), global, local),
-                  *c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
+    exampleFuncOp(EnqueueArgs(getQueue(), global, local), *c.data, c.info,
+                  *a.data, a.info, *b.data, b.info, (int)p);
 
     // Below Macro activates validations ONLY in DEBUG
     // mode as its name indicates
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/fast.cl b/src/backend/opencl/kernel/fast.cl
index cd207f3324..3b34735e69 100644
--- a/src/backend/opencl/kernel/fast.cl
+++ b/src/backend/opencl/kernel/fast.cl
@@ -7,36 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define MAX_VAL(A,B) (A<B) ? (B) : (A)
+#define MAX_VAL(A, B) (A < B) ? (B) : (A)
 
-inline int idx_y(const int i)
-{
+inline int idx_y(const int i) {
     int j = i - 4;
     int k = min(j, 8 - j);
     return clamp(k, -3, 3);
 }
 
-inline int idx_x(const int i)
-{
-    return idx_y((i + 4) & 15);
-}
+inline int idx_x(const int i) { return idx_y((i + 4) & 15); }
 
-inline int idx(const int x, const int y)
-{
-    return ((get_local_id(0) + 3 + x) + (get_local_size(0) + 6) * (get_local_id(1) + 3 + y));
+inline int idx(const int x, const int y) {
+    return ((get_local_id(0) + 3 + x) +
+            (get_local_size(0) + 6) * (get_local_id(1) + 3 + y));
 }
 
 // test_greater()
 // Tests if a pixel x > p + thr
-inline int test_greater(const float x, const float p, const float thr)
-{
+inline int test_greater(const float x, const float p, const float thr) {
     return (x > p + thr);
 }
 
 // test_smaller()
 // Tests if a pixel x < p - thr
-inline int test_smaller(const float x, const float p, const float thr)
-{
+inline int test_smaller(const float x, const float p, const float thr) {
     return (x < p - thr);
 }
 
@@ -44,41 +38,42 @@ inline int test_smaller(const float x, const float p, const float thr)
 // Returns -1 when x < p - thr
 // Returns  0 when x >= p - thr && x <= p + thr
 // Returns  1 when x > p + thr
-inline int test_pixel(__local T* local_image, const float p, const float thr, const int x, const int y)
-{
-    return -test_smaller((float)local_image[idx(x,y)], p, thr) + test_greater((float)local_image[idx(x,y)], p, thr);
+inline int test_pixel(__local T* local_image, const float p, const float thr,
+                      const int x, const int y) {
+    return -test_smaller((float)local_image[idx(x, y)], p, thr) +
+           test_greater((float)local_image[idx(x, y)], p, thr);
 }
 
-void locate_features_core(
-    __local T* local_image,
-    __global float* score,
-    KParam iInfo,
-    const float thr,
-    int x, int y,
-    const unsigned edge)
-{
+void locate_features_core(__local T* local_image, __global float* score,
+                          KParam iInfo, const float thr, int x, int y,
+                          const unsigned edge) {
     if (x >= iInfo.dims[0] - edge || y >= iInfo.dims[1] - edge) return;
 
-    float p = local_image[idx( 0, 0)];
+    float p = local_image[idx(0, 0)];
 
     // Start by testing opposite pixels of the circle that will result in
     // a non-kepoint
-    int d = test_pixel(local_image, p, thr, -3,  0) | test_pixel(local_image, p, thr, 3,  0);
-    if (d == 0)
-        return;
-
-    d &= test_pixel(local_image, p, thr, -2,  2) | test_pixel(local_image, p, thr,  2, -2);
-    d &= test_pixel(local_image, p, thr,  0,  3) | test_pixel(local_image, p, thr,  0, -3);
-    d &= test_pixel(local_image, p, thr,  2,  2) | test_pixel(local_image, p, thr, -2, -2);
-    if (d == 0)
-        return;
-
-    d &= test_pixel(local_image, p, thr, -3,  1) | test_pixel(local_image, p, thr,  3, -1);
-    d &= test_pixel(local_image, p, thr, -1,  3) | test_pixel(local_image, p, thr,  1, -3);
-    d &= test_pixel(local_image, p, thr,  1,  3) | test_pixel(local_image, p, thr, -1, -3);
-    d &= test_pixel(local_image, p, thr,  3,  1) | test_pixel(local_image, p, thr, -3, -1);
-    if (d == 0)
-        return;
+    int d = test_pixel(local_image, p, thr, -3, 0) |
+            test_pixel(local_image, p, thr, 3, 0);
+    if (d == 0) return;
+
+    d &= test_pixel(local_image, p, thr, -2, 2) |
+         test_pixel(local_image, p, thr, 2, -2);
+    d &= test_pixel(local_image, p, thr, 0, 3) |
+         test_pixel(local_image, p, thr, 0, -3);
+    d &= test_pixel(local_image, p, thr, 2, 2) |
+         test_pixel(local_image, p, thr, -2, -2);
+    if (d == 0) return;
+
+    d &= test_pixel(local_image, p, thr, -3, 1) |
+         test_pixel(local_image, p, thr, 3, -1);
+    d &= test_pixel(local_image, p, thr, -1, 3) |
+         test_pixel(local_image, p, thr, 1, -3);
+    d &= test_pixel(local_image, p, thr, 1, 3) |
+         test_pixel(local_image, p, thr, -1, -3);
+    d &= test_pixel(local_image, p, thr, 3, 1) |
+         test_pixel(local_image, p, thr, -3, -1);
+    if (d == 0) return;
 
     int sum = 0;
 
@@ -94,7 +89,8 @@ void locate_features_core(
 
     // Sum responses and test the remaining 16-ARC_LENGTH pixels of the circle
     for (int i = ARC_LENGTH; i < 16; i++) {
-        sum -= test_pixel(local_image, p, thr, idx_x(i-ARC_LENGTH), idx_y(i-ARC_LENGTH));
+        sum -= test_pixel(local_image, p, thr, idx_x(i - ARC_LENGTH),
+                          idx_y(i - ARC_LENGTH));
         sum += test_pixel(local_image, p, thr, idx_x(i), idx_y(i));
         max_sum = max(max_sum, sum);
         min_sum = min(min_sum, sum);
@@ -102,8 +98,9 @@ void locate_features_core(
 
     // To completely test all possible segments, it's necessary to test
     // segments that include the top junction of the circle
-    for (int i = 0; i < ARC_LENGTH-1; i++) {
-        sum -= test_pixel(local_image, p, thr, idx_x(16-ARC_LENGTH+i), idx_y(16-ARC_LENGTH+i));
+    for (int i = 0; i < ARC_LENGTH - 1; i++) {
+        sum -= test_pixel(local_image, p, thr, idx_x(16 - ARC_LENGTH + i),
+                          idx_y(16 - ARC_LENGTH + i));
         sum += test_pixel(local_image, p, thr, idx_x(i), idx_y(i));
         max_sum = max(max_sum, sum);
         min_sum = min(min_sum, sum);
@@ -119,71 +116,59 @@ void locate_features_core(
             float p_x    = local_image[idx(idx_x(i), idx_y(i))];
             float weight = fabs((float)p_x - (float)p) - thr;
             s_bright += test_greater(p_x, p, thr) * weight;
-            s_dark   += test_smaller(p_x, p, thr) * weight;
+            s_dark += test_smaller(p_x, p, thr) * weight;
         }
 
         score[x + iInfo.dims[0] * y] = MAX_VAL(s_bright, s_dark);
     }
 }
 
-void load_shared_image(
-    __global const T *in,
-    KParam iInfo,
-    __local T *local_image,
-    unsigned ix, unsigned iy,
-    unsigned bx, unsigned by,
-    unsigned  x, unsigned  y,
-    unsigned lx, unsigned ly)
-{
+void load_shared_image(__global const T* in, KParam iInfo,
+                       __local T* local_image, unsigned ix, unsigned iy,
+                       unsigned bx, unsigned by, unsigned x, unsigned y,
+                       unsigned lx, unsigned ly) {
     // Copy an image patch to shared memory, with a 3-pixel edge
     if (ix < lx && iy < ly && x - 3 < iInfo.dims[0] && y - 3 < iInfo.dims[1]) {
-        local_image[(ix)      + (bx+6) * (iy)]    = in[(x-3)    + iInfo.dims[0] * (y-3)];
+        local_image[(ix) + (bx + 6) * (iy)] =
+            in[(x - 3) + iInfo.dims[0] * (y - 3)];
         if (x + lx - 3 < iInfo.dims[0])
-            local_image[(ix + lx) + (bx+6) * (iy)]    = in[(x+lx-3) + iInfo.dims[0] * (y-3)];
+            local_image[(ix + lx) + (bx + 6) * (iy)] =
+                in[(x + lx - 3) + iInfo.dims[0] * (y - 3)];
         if (y + ly - 3 < iInfo.dims[1])
-            local_image[(ix)      + (bx+6) * (iy+ly)] = in[(x-3)    + iInfo.dims[0] * (y+ly-3)];
+            local_image[(ix) + (bx + 6) * (iy + ly)] =
+                in[(x - 3) + iInfo.dims[0] * (y + ly - 3)];
         if (x + lx - 3 < iInfo.dims[0] && y + ly - 3 < iInfo.dims[1])
-            local_image[(ix + lx) + (bx+6) * (iy+ly)] = in[(x+lx-3) + iInfo.dims[0] * (y+ly-3)];
+            local_image[(ix + lx) + (bx + 6) * (iy + ly)] =
+                in[(x + lx - 3) + iInfo.dims[0] * (y + ly - 3)];
     }
 }
 
-__kernel
-void locate_features(
-    __global const T* in,
-    KParam          iInfo,
-    __global float* score,
-    const float thr,
-    const unsigned edge,
-    __local T* local_image)
-{
+__kernel void locate_features(__global const T* in, KParam iInfo,
+                              __global float* score, const float thr,
+                              const unsigned edge, __local T* local_image) {
     unsigned ix = get_local_id(0);
     unsigned iy = get_local_id(1);
     unsigned bx = get_local_size(0);
     unsigned by = get_local_size(1);
-    unsigned x = bx * get_group_id(0) + ix + edge;
-    unsigned y = by * get_group_id(1) + iy + edge;
+    unsigned x  = bx * get_group_id(0) + ix + edge;
+    unsigned y  = by * get_group_id(1) + iy + edge;
     unsigned lx = bx / 2 + 3;
     unsigned ly = by / 2 + 3;
 
-    load_shared_image(in + iInfo.offset, iInfo, local_image, ix, iy, bx, by, x, y, lx, ly);
+    load_shared_image(in + iInfo.offset, iInfo, local_image, ix, iy, bx, by, x,
+                      y, lx, ly);
     barrier(CLK_LOCAL_MEM_FENCE);
-    locate_features_core(local_image, score,
-                         iInfo, thr, x, y, edge);
+    locate_features_core(local_image, score, iInfo, thr, x, y, edge);
 }
 
-__kernel
-void non_max_counts(
-    __global unsigned *d_counts,
-    __global unsigned *d_offsets,
-    __global unsigned *d_total,
-    __global float *flags,
-    __global const float* score,
-    KParam iInfo,
-    const unsigned edge)
-{
+__kernel void non_max_counts(__global unsigned* d_counts,
+                             __global unsigned* d_offsets,
+                             __global unsigned* d_total, __global float* flags,
+                             __global const float* score, KParam iInfo,
+                             const unsigned edge) {
     __local unsigned s_counts[256];
 
-    const int yid = get_group_id(1) * get_local_size(1) * 8 + get_local_id(1);
+    const int yid  = get_group_id(1) * get_local_size(1) * 8 + get_local_id(1);
     const int yend = (get_group_id(1) + 1) * get_local_size(1) * 8;
     const int yoff = get_local_size(1);
 
@@ -191,14 +176,15 @@ void non_max_counts(
 
     const int max1 = (int)iInfo.dims[1] - edge - 1;
     for (int y = yid; y < yend; y += yoff) {
-        if (y >= max1 || y <= (int)(edge+1)) continue;
+        if (y >= max1 || y <= (int)(edge + 1)) continue;
 
-        const int xid = get_group_id(0) * get_local_size(0) * 2 + get_local_id(0);
+        const int xid =
+            get_group_id(0) * get_local_size(0) * 2 + get_local_id(0);
         const int xend = (get_group_id(0) + 1) * get_local_size(0) * 2;
 
         const int max0 = (int)iInfo.dims[0] - edge - 1;
         for (int x = xid; x < xend; x += get_local_size(0)) {
-            if (x >= max0 || x <= (int)(edge+1)) continue;
+            if (x >= max0 || x <= (int)(edge + 1)) continue;
 
             float v = score[y * iInfo.dims[0] + x];
             if (v == 0) {
@@ -209,18 +195,19 @@ void non_max_counts(
             }
 
 #if NONMAX
-                float max_v = v;
-                max_v = MAX_VAL(score[x-1 + iInfo.dims[0] * (y-1)], score[x-1 + iInfo.dims[0] * y]);
-                max_v = MAX_VAL(max_v, score[x-1 + iInfo.dims[0] * (y+1)]);
-                max_v = MAX_VAL(max_v, score[x   + iInfo.dims[0] * (y-1)]);
-                max_v = MAX_VAL(max_v, score[x   + iInfo.dims[0] * (y+1)]);
-                max_v = MAX_VAL(max_v, score[x+1 + iInfo.dims[0] * (y-1)]);
-                max_v = MAX_VAL(max_v, score[x+1 + iInfo.dims[0] * (y)  ]);
-                max_v = MAX_VAL(max_v, score[x+1 + iInfo.dims[0] * (y+1)]);
-
-                v = (v > max_v) ? v : 0;
-                flags[y * iInfo.dims[0] + x] = v;
-                if (v == 0) continue;
+            float max_v = v;
+            max_v       = MAX_VAL(score[x - 1 + iInfo.dims[0] * (y - 1)],
+                            score[x - 1 + iInfo.dims[0] * y]);
+            max_v = MAX_VAL(max_v, score[x - 1 + iInfo.dims[0] * (y + 1)]);
+            max_v = MAX_VAL(max_v, score[x + iInfo.dims[0] * (y - 1)]);
+            max_v = MAX_VAL(max_v, score[x + iInfo.dims[0] * (y + 1)]);
+            max_v = MAX_VAL(max_v, score[x + 1 + iInfo.dims[0] * (y - 1)]);
+            max_v = MAX_VAL(max_v, score[x + 1 + iInfo.dims[0] * (y)]);
+            max_v = MAX_VAL(max_v, score[x + 1 + iInfo.dims[0] * (y + 1)]);
+
+            v                            = (v > max_v) ? v : 0;
+            flags[y * iInfo.dims[0] + x] = v;
+            if (v == 0) continue;
 #endif
 
             count++;
@@ -232,34 +219,37 @@ void non_max_counts(
     s_counts[tid] = count;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (tid < 128) s_counts[tid] += s_counts[tid + 128]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <  64) s_counts[tid] += s_counts[tid +  64]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <  32) s_counts[tid] += s_counts[tid +  32]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <  16) s_counts[tid] += s_counts[tid +  16]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <   8) s_counts[tid] += s_counts[tid +   8]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <   4) s_counts[tid] += s_counts[tid +   4]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <   2) s_counts[tid] += s_counts[tid +   2]; barrier(CLK_LOCAL_MEM_FENCE);
-    if (tid <   1) s_counts[tid] += s_counts[tid +   1]; barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 128) s_counts[tid] += s_counts[tid + 128];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 64) s_counts[tid] += s_counts[tid + 64];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 32) s_counts[tid] += s_counts[tid + 32];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 16) s_counts[tid] += s_counts[tid + 16];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 8) s_counts[tid] += s_counts[tid + 8];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 4) s_counts[tid] += s_counts[tid + 4];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 2) s_counts[tid] += s_counts[tid + 2];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (tid < 1) s_counts[tid] += s_counts[tid + 1];
+    barrier(CLK_LOCAL_MEM_FENCE);
 
     if (tid == 0) {
-        const int bid = get_group_id(1) * get_num_groups(0) + get_group_id(0);
+        const int bid  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
         unsigned total = s_counts[0] ? atomic_add(d_total, s_counts[0]) : 0;
-        d_counts [bid] = s_counts[0];
+        d_counts[bid]  = s_counts[0];
         d_offsets[bid] = total;
     }
 }
 
-__kernel void get_features(
-    __global float* x_out,
-    __global float* y_out,
-    __global float* score_out,
-    __global const float* flags,
-    __global const unsigned* d_counts,
-    __global const unsigned* d_offsets,
-    KParam iInfo,
-    const unsigned total,
-    const unsigned edge)
-{
+__kernel void get_features(__global float* x_out, __global float* y_out,
+                           __global float* score_out,
+                           __global const float* flags,
+                           __global const unsigned* d_counts,
+                           __global const unsigned* d_offsets, KParam iInfo,
+                           const unsigned total, const unsigned edge) {
     const int xid = get_group_id(0) * get_local_size(0) * 2 + get_local_id(0);
     const int yid = get_group_id(1) * get_local_size(1) * 8 + get_local_id(1);
     const int tid = get_local_size(0) * get_local_id(1) + get_local_id(0);
@@ -276,25 +266,25 @@ __kernel void get_features(
     __local unsigned s_idx;
 
     if (tid == 0) {
-        s_count  = d_counts [bid];
-        s_idx    = d_offsets[bid];
+        s_count = d_counts[bid];
+        s_idx   = d_offsets[bid];
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     // Blocks that are empty, please bail
     if (s_count == 0) return;
     for (int y = yid; y < yend; y += yoff) {
-        if (y >= iInfo.dims[1] - edge - 1 || y <= edge+1) continue;
+        if (y >= iInfo.dims[1] - edge - 1 || y <= edge + 1) continue;
         for (int x = xid; x < xend; x += xoff) {
-            if (x >= iInfo.dims[0] - edge - 1 || x <= edge+1) continue;
+            if (x >= iInfo.dims[0] - edge - 1 || x <= edge + 1) continue;
 
             float v = flags[y * iInfo.dims[0] + x];
             if (v == 0) continue;
 
             unsigned id = atomic_inc(&s_idx);
             if (id < total) {
-                y_out[id] = x;
-                x_out[id] = y;
+                y_out[id]     = x;
+                x_out[id]     = y;
                 score_out[id] = v;
             }
         }
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 844c3b9fee..434452c8e9 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -7,74 +7,60 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <program.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
-#include <cache.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/fast.hpp>
 #include <memory.hpp>
+#include <program.hpp>
+#include <af/defines.h>
 #include <map>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
-static const int FAST_THREADS_X = 16;
-static const int FAST_THREADS_Y = 16;
+static const int FAST_THREADS_X        = 16;
+static const int FAST_THREADS_Y        = 16;
 static const int FAST_THREADS_NONMAX_X = 32;
 static const int FAST_THREADS_NONMAX_Y = 8;
 
 template<typename T, const bool nonmax>
-void fast(const unsigned arc_length,
-          unsigned* out_feat,
-          Param &x_out,
-          Param &y_out,
-          Param &score_out,
-          Param in,
-          const float thr,
-          const float feature_ratio,
-          const unsigned edge)
-{
-    std::string ref_name =
-        std::string("fast_") +
-        std::to_string(arc_length) +
-        std::string("_") +
-        std::to_string(nonmax) +
-        std::string("_") +
-        std::string(dtype_traits<T>::getName());
+void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
+          Param &y_out, Param &score_out, Param in, const float thr,
+          const float feature_ratio, const unsigned edge) {
+    std::string ref_name = std::string("fast_") + std::to_string(arc_length) +
+                           std::string("_") + std::to_string(nonmax) +
+                           std::string("_") +
+                           std::string(dtype_traits<T>::getName());
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
-
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-            << " -D ARC_LENGTH=" << arc_length
-            << " -D NONMAX=" << static_cast<unsigned>(nonmax);
+                << " -D ARC_LENGTH=" << arc_length
+                << " -D NONMAX=" << static_cast<unsigned>(nonmax);
 
-        if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         cl::Program prog;
         buildProgram(prog, fast_cl, fast_cl_len, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel[3];
+        entry.ker  = new Kernel[3];
 
         entry.ker[0] = Kernel(*entry.prog, "locate_features");
         entry.ker[1] = Kernel(*entry.prog, "non_max_counts");
@@ -83,33 +69,37 @@ void fast(const unsigned arc_length,
         addKernelToCache(device, ref_name, entry);
     }
 
-    const unsigned max_feat = ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
+    const unsigned max_feat =
+        ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
 
     // Matrix containing scores for detected features, scores are stored in the
     // same coordinates as features, dimensions should be equal to in.
-    cl::Buffer *d_score = bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
+    cl::Buffer *d_score =
+        bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
     std::vector<float> score_init(in.info.dims[0] * in.info.dims[1], (float)0);
-    getQueue().enqueueWriteBuffer(*d_score, CL_TRUE, 0, in.info.dims[0] * in.info.dims[1] * sizeof(float), &score_init[0]);
+    getQueue().enqueueWriteBuffer(
+        *d_score, CL_TRUE, 0, in.info.dims[0] * in.info.dims[1] * sizeof(float),
+        &score_init[0]);
 
     cl::Buffer *d_flags = d_score;
     if (nonmax) {
-        d_flags = bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
+        d_flags =
+            bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
     }
 
-    const int blk_x = divup(in.info.dims[0]-edge*2, FAST_THREADS_X);
-    const int blk_y = divup(in.info.dims[1]-edge*2, FAST_THREADS_Y);
+    const int blk_x = divup(in.info.dims[0] - edge * 2, FAST_THREADS_X);
+    const int blk_y = divup(in.info.dims[1] - edge * 2, FAST_THREADS_Y);
 
     // Locate features kernel sizes
     const NDRange local(FAST_THREADS_X, FAST_THREADS_Y);
     const NDRange global(blk_x * FAST_THREADS_X, blk_y * FAST_THREADS_Y);
 
-    auto lfOp = KernelFunctor<Buffer, KParam,
-                            Buffer, const float, const unsigned,
-                            LocalSpaceArg> (entry.ker[0]);
+    auto lfOp = KernelFunctor<Buffer, KParam, Buffer, const float,
+                              const unsigned, LocalSpaceArg>(entry.ker[0]);
 
-    lfOp(EnqueueArgs(getQueue(), global, local),
-          *in.data, in.info, *d_score, thr, edge,
-          cl::Local((FAST_THREADS_X + 6) * (FAST_THREADS_Y + 6) * sizeof(T)));
+    lfOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, *d_score,
+         thr, edge,
+         cl::Local((FAST_THREADS_X + 6) * (FAST_THREADS_Y + 6) * sizeof(T)));
     CL_DEBUG_FINISH(getQueue());
 
     const int blk_nonmax_x = divup(in.info.dims[0], 64);
@@ -117,60 +107,61 @@ void fast(const unsigned arc_length,
 
     // Nonmax kernel sizes
     const NDRange local_nonmax(FAST_THREADS_NONMAX_X, FAST_THREADS_NONMAX_Y);
-    const NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X, blk_nonmax_y * FAST_THREADS_NONMAX_Y);
+    const NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X,
+                                blk_nonmax_y * FAST_THREADS_NONMAX_Y);
 
     unsigned count_init = 0;
     cl::Buffer *d_total = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned), &count_init);
+    getQueue().enqueueWriteBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned),
+                                  &count_init);
 
-    //size_t *global_nonmax_dims = global_nonmax();
-    size_t blocks_sz = blk_nonmax_x * FAST_THREADS_NONMAX_X * blk_nonmax_y * FAST_THREADS_NONMAX_Y * sizeof(unsigned);
+    // size_t *global_nonmax_dims = global_nonmax();
+    size_t blocks_sz = blk_nonmax_x * FAST_THREADS_NONMAX_X * blk_nonmax_y *
+                       FAST_THREADS_NONMAX_Y * sizeof(unsigned);
     cl::Buffer *d_counts  = bufferAlloc(blocks_sz);
     cl::Buffer *d_offsets = bufferAlloc(blocks_sz);
 
-    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer,
-                            Buffer, Buffer,
-                            KParam, const unsigned> (entry.ker[1]);
-    nmOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
-                      *d_counts, *d_offsets, *d_total, *d_flags, *d_score, in.info, edge);
+    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, KParam,
+                              const unsigned>(entry.ker[1]);
+    nmOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax), *d_counts,
+         *d_offsets, *d_total, *d_flags, *d_score, in.info, edge);
     CL_DEBUG_FINISH(getQueue());
 
     unsigned total;
-    getQueue().enqueueReadBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned), &total);
+    getQueue().enqueueReadBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned),
+                                 &total);
     total = total < max_feat ? total : max_feat;
 
     if (total > 0) {
-        size_t out_sz = total * sizeof(float);
-        x_out.data = bufferAlloc(out_sz);
-        y_out.data = bufferAlloc(out_sz);
+        size_t out_sz  = total * sizeof(float);
+        x_out.data     = bufferAlloc(out_sz);
+        y_out.data     = bufferAlloc(out_sz);
         score_out.data = bufferAlloc(out_sz);
 
-        auto gfOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer,
-                                KParam, const unsigned,
-                                const unsigned> (entry.ker[2]);
-        gfOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
-                          *x_out.data, *y_out.data, *score_out.data,
-                          *d_flags, *d_counts, *d_offsets,
-                          in.info, total, edge);
+        auto gfOp =
+            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                          KParam, const unsigned, const unsigned>(entry.ker[2]);
+        gfOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax), *x_out.data,
+             *y_out.data, *score_out.data, *d_flags, *d_counts, *d_offsets,
+             in.info, total, edge);
         CL_DEBUG_FINISH(getQueue());
     }
 
     *out_feat = total;
 
-    x_out.info.dims[0] = total;
-    x_out.info.strides[0] = 1;
-    y_out.info.dims[0] = total;
-    y_out.info.strides[0] = 1;
-    score_out.info.dims[0] = total;
+    x_out.info.dims[0]        = total;
+    x_out.info.strides[0]     = 1;
+    y_out.info.dims[0]        = total;
+    y_out.info.strides[0]     = 1;
+    score_out.info.dims[0]    = total;
     score_out.info.strides[0] = 1;
 
     for (int k = 1; k < 4; k++) {
-        x_out.info.dims[k] = 1;
-        x_out.info.strides[k] = total;
-        y_out.info.dims[k] = 1;
-        y_out.info.strides[k] = total;
-        score_out.info.dims[k] = 1;
+        x_out.info.dims[k]        = 1;
+        x_out.info.strides[k]     = total;
+        y_out.info.dims[k]        = 1;
+        y_out.info.strides[k]     = total;
+        score_out.info.dims[k]    = 1;
         score_out.info.strides[k] = total;
     }
 
@@ -183,24 +174,18 @@ void fast(const unsigned arc_length,
 
 template<typename T>
 void fast_dispatch(const unsigned arc_length, const bool nonmax,
-                   unsigned* out_feat,
-                   Param &x_out,
-                   Param &y_out,
-                   Param &score_out,
-                   Param in,
-                   const float thr,
-                   const float feature_ratio,
-                   const unsigned edge)
-{
+                   unsigned *out_feat, Param &x_out, Param &y_out,
+                   Param &score_out, Param in, const float thr,
+                   const float feature_ratio, const unsigned edge) {
     if (!nonmax) {
-        fast<T, 0>(arc_length, out_feat, x_out, y_out, score_out, in,
-                   thr, feature_ratio, edge);
+        fast<T, 0>(arc_length, out_feat, x_out, y_out, score_out, in, thr,
+                   feature_ratio, edge);
     } else {
-        fast<T, 1>(arc_length, out_feat, x_out, y_out, score_out, in,
-                   thr, feature_ratio, edge);
+        fast<T, 1>(arc_length, out_feat, x_out, y_out, score_out, in, thr,
+                   feature_ratio, edge);
     }
 }
 
-} //namespace kernel
+}  // namespace kernel
 
-} //namespace opencl
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 0c906b91e3..ac24c432d3 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -7,40 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
 #include <backend.hpp>
-#include <program.hpp>
-#include <common/dispatch.hpp>
 #include <cache.hpp>
-#include <err_opencl.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
-#include <kernel_headers/fftconvolve_pack.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/fftconvolve_multiply.hpp>
+#include <kernel_headers/fftconvolve_pack.hpp>
 #include <kernel_headers/fftconvolve_reorder.hpp>
 #include <memory.hpp>
+#include <program.hpp>
+#include <af/defines.h>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS = 256;
 
-void calcParamSizes(Param& sig_tmp,
-                    Param& filter_tmp,
-                    Param& packed,
-                    Param& sig,
-                    Param& filter,
-                    const int baseDim,
-                    AF_BATCH_KIND kind)
-{
+void calcParamSizes(Param& sig_tmp, Param& filter_tmp, Param& packed,
+                    Param& sig, Param& filter, const int baseDim,
+                    AF_BATCH_KIND kind) {
     sig_tmp.info.dims[0] = filter_tmp.info.dims[0] = packed.info.dims[0];
     sig_tmp.info.strides[0] = filter_tmp.info.strides[0] = 1;
 
@@ -48,61 +41,57 @@ void calcParamSizes(Param& sig_tmp,
         if (k < baseDim) {
             sig_tmp.info.dims[k]    = packed.info.dims[k];
             filter_tmp.info.dims[k] = packed.info.dims[k];
-        }
-        else {
+        } else {
             sig_tmp.info.dims[k]    = sig.info.dims[k];
             filter_tmp.info.dims[k] = filter.info.dims[k];
         }
 
-        sig_tmp.info.strides[k]    = sig_tmp.info.strides[k - 1] * sig_tmp.info.dims[k - 1];
-        filter_tmp.info.strides[k] = filter_tmp.info.strides[k - 1] * filter_tmp.info.dims[k - 1];
+        sig_tmp.info.strides[k] =
+            sig_tmp.info.strides[k - 1] * sig_tmp.info.dims[k - 1];
+        filter_tmp.info.strides[k] =
+            filter_tmp.info.strides[k - 1] * filter_tmp.info.dims[k - 1];
     }
 
     // Calculate memory offsets for packed signal and filter
-    sig_tmp.data = packed.data;
+    sig_tmp.data    = packed.data;
     filter_tmp.data = packed.data;
 
     if (kind == AF_BATCH_RHS) {
         filter_tmp.info.offset = 0;
-        sig_tmp.info.offset = filter_tmp.info.strides[3] * filter_tmp.info.dims[3] * 2;
-    }
-    else {
+        sig_tmp.info.offset =
+            filter_tmp.info.strides[3] * filter_tmp.info.dims[3] * 2;
+    } else {
         sig_tmp.info.offset = 0;
-        filter_tmp.info.offset = sig_tmp.info.strides[3] * sig_tmp.info.dims[3] * 2;
+        filter_tmp.info.offset =
+            sig_tmp.info.strides[3] * sig_tmp.info.dims[3] * 2;
     }
 }
 
 template<typename convT, typename T, bool isDouble, typename printT>
-void packDataHelper(Param packed,
-                    Param sig,
-                    Param filter,
-                    const int baseDim,
-                    AF_BATCH_KIND kind)
-{
-    std::string refName =
-        std::string("pack_data_") +
-        std::string(dtype_traits<convT>::getName()) +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDouble);
-
-    int device = getActiveDeviceId();
+void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
+                    AF_BATCH_KIND kind) {
+    std::string refName = std::string("pack_data_") +
+                          std::string(dtype_traits<convT>::getName()) +
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(isDouble);
+
+    int device          = getActiveDeviceId();
     kc_entry_t pdkEntry = kernelCache(device, refName);
 
-    if (pdkEntry.prog==0 && pdkEntry.ker==0) {
+    if (pdkEntry.prog == 0 && pdkEntry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
 
-        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
             options << " -D CONVT=float";
-        }
-        else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
             options << " -D CONVT=double"
-                << " -D USE_DOUBLE";
+                    << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {fftconvolve_pack_cl};
-        const int   ker_lens[] = {fftconvolve_pack_cl_len};
+        const int ker_lens[]   = {fftconvolve_pack_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         pdkEntry.prog = new Program(prog);
@@ -115,10 +104,11 @@ void packDataHelper(Param packed,
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
 
     int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
-    int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+    int filter_packed_elem =
+        filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
 
     // Number of packed complex elements in dimension 0
-    int sig_half_d0 = divup(sig.info.dims[0], 2);
+    int sig_half_d0     = divup(sig.info.dims[0], 2);
     int sig_half_d0_odd = sig.info.dims[0] % 2;
 
     int blocks = divup(sig_packed_elem, THREADS);
@@ -129,36 +119,36 @@ void packDataHelper(Param packed,
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    auto pdOp = KernelFunctor< Buffer, KParam, Buffer, KParam, const int, const int > (*pdkEntry.ker);
+    auto pdOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, const int, const int>(
+            *pdkEntry.ker);
 
-    pdOp(EnqueueArgs(getQueue(), global, local),
-         *sig_tmp.data, sig_tmp.info, *sig.data, sig.info, sig_half_d0, sig_half_d0_odd);
+    pdOp(EnqueueArgs(getQueue(), global, local), *sig_tmp.data, sig_tmp.info,
+         *sig.data, sig.info, sig_half_d0, sig_half_d0_odd);
 
     CL_DEBUG_FINISH(getQueue());
 
-    refName =
-        std::string("pack_array_") +
-        std::string(dtype_traits<convT>::getName()) +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDouble);
+    refName = std::string("pack_array_") +
+              std::string(dtype_traits<convT>::getName()) +
+              std::string(dtype_traits<T>::getName()) +
+              std::to_string(isDouble);
 
     kc_entry_t pakEntry = kernelCache(device, refName);
 
-    if (pakEntry.prog==0 && pakEntry.ker==0) {
+    if (pakEntry.prog == 0 && pakEntry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
 
-        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
             options << " -D CONVT=float";
-        }
-        else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
             options << " -D CONVT=double"
-                << " -D USE_DOUBLE";
+                    << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {fftconvolve_pack_cl};
-        const int   ker_lens[] = {fftconvolve_pack_cl_len};
+        const int ker_lens[]   = {fftconvolve_pack_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         pakEntry.prog = new Program(prog);
@@ -171,48 +161,43 @@ void packDataHelper(Param packed,
     global = NDRange(blocks * THREADS);
 
     // Pad filter array with 0s
-    auto paOp = KernelFunctor< Buffer, KParam, Buffer, KParam > (*pakEntry.ker);
+    auto paOp = KernelFunctor<Buffer, KParam, Buffer, KParam>(*pakEntry.ker);
 
-    paOp(EnqueueArgs(getQueue(), global, local),
-         *filter_tmp.data, filter_tmp.info, *filter.data, filter.info);
+    paOp(EnqueueArgs(getQueue(), global, local), *filter_tmp.data,
+         filter_tmp.info, *filter.data, filter.info);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename convT, typename T, bool isDouble, typename printT>
-void complexMultiplyHelper(Param packed,
-                           Param sig,
-                           Param filter,
-                           const int baseDim,
-                           AF_BATCH_KIND kind)
-{
-    std::string refName =
-        std::string("complex_multiply_") +
-        std::string(dtype_traits<convT>::getName()) +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDouble);
-
-    int device = getActiveDeviceId();
+void complexMultiplyHelper(Param packed, Param sig, Param filter,
+                           const int baseDim, AF_BATCH_KIND kind) {
+    std::string refName = std::string("complex_multiply_") +
+                          std::string(dtype_traits<convT>::getName()) +
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(isDouble);
+
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName()
-            << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
-            << " -D AF_BATCH_LHS="  << (int)AF_BATCH_LHS
-            << " -D AF_BATCH_RHS="  << (int)AF_BATCH_RHS
-            << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
+                << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
+                << " -D AF_BATCH_LHS=" << (int)AF_BATCH_LHS
+                << " -D AF_BATCH_RHS=" << (int)AF_BATCH_RHS
+                << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
 
-        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
             options << " -D CONVT=float";
-        } else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
             options << " -D CONVT=double"
                     << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {fftconvolve_multiply_cl};
-        const int   ker_lens[] = {fftconvolve_multiply_cl_len};
+        const int ker_lens[]   = {fftconvolve_multiply_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -225,9 +210,10 @@ void complexMultiplyHelper(Param packed,
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
 
     int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
-    int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
-    int mul_elem = (sig_packed_elem < filter_packed_elem) ?
-                        filter_packed_elem : sig_packed_elem;
+    int filter_packed_elem =
+        filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+    int mul_elem = (sig_packed_elem < filter_packed_elem) ? filter_packed_elem
+                                                          : sig_packed_elem;
 
     int blocks = divup(mul_elem, THREADS);
 
@@ -235,51 +221,45 @@ void complexMultiplyHelper(Param packed,
     NDRange global(blocks * THREADS);
 
     // Multiply filter and signal FFT arrays
-    auto cmOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                               const int, const int > (*entry.ker);
+    auto cmOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                              const int, const int>(*entry.ker);
 
-    cmOp(EnqueueArgs(getQueue(), global, local),
-         *packed.data, packed.info, *sig_tmp.data, sig_tmp.info,
-         *filter_tmp.data, filter_tmp.info, mul_elem, (int)kind);
+    cmOp(EnqueueArgs(getQueue(), global, local), *packed.data, packed.info,
+         *sig_tmp.data, sig_tmp.info, *filter_tmp.data, filter_tmp.info,
+         mul_elem, (int)kind);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename T, typename convT, bool isDouble, bool roundOut, bool expand, typename printT>
-void reorderOutputHelper(Param out,
-                         Param packed,
-                         Param sig,
-                         Param filter,
-                         const int baseDim,
-                         AF_BATCH_KIND kind)
-{
-    std::string refName =
-        std::string("reorder_output_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::string(dtype_traits<convT>::getName()) +
-        std::to_string(isDouble) +
-        std::to_string(roundOut) +
-        std::to_string(expand);
-
-    int device = getActiveDeviceId();
+template<typename T, typename convT, bool isDouble, bool roundOut, bool expand,
+         typename printT>
+void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
+                         const int baseDim, AF_BATCH_KIND kind) {
+    std::string refName = std::string("reorder_output_") +
+                          std::string(dtype_traits<T>::getName()) +
+                          std::string(dtype_traits<convT>::getName()) +
+                          std::to_string(isDouble) + std::to_string(roundOut) +
+                          std::to_string(expand);
+
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName()
-            << " -D ROUND_OUT=" << (int)roundOut
-            << " -D EXPAND=" << (int)expand;
+                << " -D ROUND_OUT=" << (int)roundOut
+                << " -D EXPAND=" << (int)expand;
 
-        if ((af_dtype) dtype_traits<convT>::af_type == c32) {
+        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
             options << " -D CONVT=float";
-        } else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
             options << " -D CONVT=double"
-                << " -D USE_DOUBLE";
+                    << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {fftconvolve_reorder_cl};
-        const int   ker_lens[] = {fftconvolve_reorder_cl_len};
+        const int ker_lens[]   = {fftconvolve_reorder_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -291,8 +271,7 @@ void reorderOutputHelper(Param out,
     int fftScale = 1;
 
     // Calculate the scale by which to divide clFFT results
-    for (int k = 0; k < baseDim; k++)
-        fftScale *= packed.info.dims[k];
+    for (int k = 0; k < baseDim; k++) fftScale *= packed.info.dims[k];
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -305,18 +284,20 @@ void reorderOutputHelper(Param out,
     NDRange local(THREADS);
     NDRange global(blocks * THREADS);
 
-    auto roOp = KernelFunctor< Buffer, KParam, Buffer, KParam, KParam, const int,
-                               const int, const int > (*entry.ker);
+    auto roOp = KernelFunctor<Buffer, KParam, Buffer, KParam, KParam, const int,
+                              const int, const int>(*entry.ker);
 
     if (kind == AF_BATCH_RHS) {
         roOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *filter_tmp.data, filter_tmp.info, filter.info, sig_half_d0, baseDim, fftScale);
+             *filter_tmp.data, filter_tmp.info, filter.info, sig_half_d0,
+             baseDim, fftScale);
     } else {
         roOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, baseDim, fftScale);
+             *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, baseDim,
+             fftScale);
     }
 
     CL_DEBUG_FINISH(getQueue());
 }
-} // namespace kernel
-} // namespace opencl
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/fftconvolve_multiply.cl b/src/backend/opencl/kernel/fftconvolve_multiply.cl
index 6ff7a1162d..f824b9ddc6 100644
--- a/src/backend/opencl/kernel/fftconvolve_multiply.cl
+++ b/src/backend/opencl/kernel/fftconvolve_multiply.cl
@@ -7,21 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void complex_multiply(
-    __global CONVT       *d_out,
-    KParam                oInfo,
-    __global const CONVT *d_in1,
-    KParam                i1Info,
-    __global const CONVT *d_in2,
-    KParam                i2Info,
-    const int        nelem,
-    const int             kind)
-{
+__kernel void complex_multiply(__global CONVT *d_out, KParam oInfo,
+                               __global const CONVT *d_in1, KParam i1Info,
+                               __global const CONVT *d_in2, KParam i2Info,
+                               const int nelem, const int kind) {
     const int t = get_global_id(0);
 
-    if (t >= nelem)
-        return;
+    if (t >= nelem) return;
 
     if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
         // Complex multiply each signal to equivalent filter
@@ -33,10 +25,9 @@ void complex_multiply(
         CONVT c = d_in2[i2Info.offset + ridx];
         CONVT d = d_in2[i2Info.offset + iidx];
 
-        d_out[oInfo.offset + ridx] = a*c - b*d;
-        d_out[oInfo.offset + iidx] = a*d + b*c;
-    }
-    else if (kind == AF_BATCH_LHS) {
+        d_out[oInfo.offset + ridx] = a * c - b * d;
+        d_out[oInfo.offset + iidx] = a * d + b * c;
+    } else if (kind == AF_BATCH_LHS) {
         // Complex multiply all signals to filter
         const int ridx1 = t * 2;
         const int iidx1 = t * 2 + 1;
@@ -51,10 +42,9 @@ void complex_multiply(
         CONVT c = d_in2[i2Info.offset + ridx2];
         CONVT d = d_in2[i2Info.offset + iidx2];
 
-        d_out[oInfo.offset + ridx1] = a*c - b*d;
-        d_out[oInfo.offset + iidx1] = a*d + b*c;
-    }
-    else if (kind == AF_BATCH_RHS) {
+        d_out[oInfo.offset + ridx1] = a * c - b * d;
+        d_out[oInfo.offset + iidx1] = a * d + b * c;
+    } else if (kind == AF_BATCH_RHS) {
         // Complex multiply signal to all filters
         const int ridx2 = t * 2;
         const int iidx2 = t * 2 + 1;
@@ -69,7 +59,7 @@ void complex_multiply(
         CONVT c = d_in2[i2Info.offset + ridx2];
         CONVT d = d_in2[i2Info.offset + iidx2];
 
-        d_out[oInfo.offset + ridx2] = a*c - b*d;
-        d_out[oInfo.offset + iidx2] = a*d + b*c;
+        d_out[oInfo.offset + ridx2] = a * c - b * d;
+        d_out[oInfo.offset + iidx2] = a * d + b * c;
     }
 }
diff --git a/src/backend/opencl/kernel/fftconvolve_pack.cl b/src/backend/opencl/kernel/fftconvolve_pack.cl
index 981f4b8709..99af5b592d 100644
--- a/src/backend/opencl/kernel/fftconvolve_pack.cl
+++ b/src/backend/opencl/kernel/fftconvolve_pack.cl
@@ -7,21 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void pack_data(
-    __global CONVT   *d_out,
-    KParam            oInfo,
-    __global const T *d_in,
-    KParam            iInfo,
-    const int    di0_half,
-    const int         odd_di0)
-{
+__kernel void pack_data(__global CONVT *d_out, KParam oInfo,
+                        __global const T *d_in, KParam iInfo,
+                        const int di0_half, const int odd_di0) {
     const int t = get_global_id(0);
 
     const int tMax = oInfo.strides[3] * oInfo.dims[3];
 
-    if (t >= tMax)
-        return;
+    if (t >= tMax) return;
 
     const int do0 = oInfo.dims[0];
     const int do1 = oInfo.dims[1];
@@ -54,36 +47,30 @@ void pack_data(
 
     // Treating complex output array as real-only array,
     // thus, multiply strides by 2
-    const int oidx1 = oInfo.offset + to3*so3*2 + to2*so2*2 + to1*so1*2 + to0*2;
+    const int oidx1 =
+        oInfo.offset + to3 * so3 * 2 + to2 * so2 * 2 + to1 * so1 * 2 + to0 * 2;
     const int oidx2 = oidx1 + 1;
 
     if (to0 < di0_half && to1 < di1 && to2 < di2) {
         d_out[oidx1] = (CONVT)d_in[iidx1];
-        if (ti0 == di0_half-1 && odd_di0 == 1)
+        if (ti0 == di0_half - 1 && odd_di0 == 1)
             d_out[oidx2] = (CONVT)0;
         else
             d_out[oidx2] = (CONVT)d_in[iidx2];
-    }
-    else {
+    } else {
         // Pad remaining elements with 0s
         d_out[oidx1] = (CONVT)0;
         d_out[oidx2] = (CONVT)0;
     }
 }
 
-__kernel
-void pad_array(
-    __global CONVT   *d_out,
-    KParam            oInfo,
-    __global const T *d_in,
-    KParam            iInfo)
-{
+__kernel void pad_array(__global CONVT *d_out, KParam oInfo,
+                        __global const T *d_in, KParam iInfo) {
     const int t = get_global_id(0);
 
     const int tMax = oInfo.strides[3] * oInfo.dims[3];
 
-    if (t >= tMax)
-        return;
+    if (t >= tMax) return;
 
     const int do0 = oInfo.dims[0];
     const int do1 = oInfo.dims[1];
@@ -114,16 +101,15 @@ void pad_array(
 
     const int iidx = iInfo.offset + ti3 + ti2 + ti1 + ti0;
 
-    const int oidx = oInfo.offset + t*2;
+    const int oidx = oInfo.offset + t * 2;
 
     if (to0 < di0 && to1 < di1 && to2 < di2 && to3 < di3) {
         // Copy input elements to real elements, set imaginary elements to 0
-        d_out[oidx]   = (CONVT)d_in[iidx];
-        d_out[oidx+1] = (CONVT)0;
-    }
-    else {
+        d_out[oidx]     = (CONVT)d_in[iidx];
+        d_out[oidx + 1] = (CONVT)0;
+    } else {
         // Pad remaining of the matrix to 0s
-        d_out[oidx]   = (CONVT)0;
-        d_out[oidx+1] = (CONVT)0;
+        d_out[oidx]     = (CONVT)0;
+        d_out[oidx + 1] = (CONVT)0;
     }
 }
diff --git a/src/backend/opencl/kernel/fftconvolve_reorder.cl b/src/backend/opencl/kernel/fftconvolve_reorder.cl
index 1eb7cde729..5ccfa75855 100644
--- a/src/backend/opencl/kernel/fftconvolve_reorder.cl
+++ b/src/backend/opencl/kernel/fftconvolve_reorder.cl
@@ -7,23 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void reorder_output(
-    __global T           *d_out,
-    KParam                oInfo,
-    __global const CONVT *d_in,
-    KParam                iInfo,
-    KParam                fInfo,
-    const int        half_di0,
-    const int             baseDim,
-    const int             fftScale)
-{
+__kernel void reorder_output(__global T *d_out, KParam oInfo,
+                             __global const CONVT *d_in, KParam iInfo,
+                             KParam fInfo, const int half_di0,
+                             const int baseDim, const int fftScale) {
     const int t = get_global_id(0);
 
     const int tMax = oInfo.strides[3] * oInfo.dims[3];
 
-    if (t >= tMax)
-        return;
+    if (t >= tMax) return;
 
     const int do0 = oInfo.dims[0];
     const int do1 = oInfo.dims[1];
@@ -48,7 +40,7 @@ void reorder_output(
     const int to2 = (t / so2) % do2;
     const int to3 = (t / so3);
 
-    int oidx = to3*so3 + to2*so2 + to1*so1 + to0;
+    int oidx = to3 * so3 + to2 * so2 + to1 * so1 + to0;
 
     int ti0, ti1, ti2, ti3;
 #if EXPAND == 1
@@ -57,9 +49,9 @@ void reorder_output(
     ti2 = to2 * si2;
     ti3 = to3 * si3;
 #else
-    ti0 = to0 + fInfo.dims[0]/2;
-    ti1 = (to1 + (baseDim > 1)*(fInfo.dims[1]/2)) * si1;
-    ti2 = (to2 + (baseDim > 2)*(fInfo.dims[2]/2)) * si2;
+    ti0 = to0 + fInfo.dims[0] / 2;
+    ti1 = (to1 + (baseDim > 1) * (fInfo.dims[1] / 2)) * si1;
+    ti2 = (to2 + (baseDim > 2) * (fInfo.dims[2] / 2)) * si2;
     ti3 = to3 * si3;
 #endif
 
@@ -73,8 +65,7 @@ void reorder_output(
 #else
         d_out[oidx] = (T)(d_in[iidx] / fftScale);
 #endif
-    }
-    else if (ti0 < half_di0 + fInfo.dims[0] - 1) {
+    } else if (ti0 < half_di0 + fInfo.dims[0] - 1) {
         // Add central elements
         int iidx1 = iInfo.offset + ti3 + ti2 + ti1 + ti0 * 2;
         int iidx2 = iInfo.offset + ti3 + ti2 + ti1 + (ti0 - half_di0) * 2 + 1;
@@ -83,10 +74,10 @@ void reorder_output(
 #else
         d_out[oidx] = (T)((d_in[iidx1] + d_in[iidx2]) / fftScale);
 #endif
-    }
-    else {
+    } else {
         // Copy bottom elements
-        const int iidx = iInfo.offset + ti3 + ti2 + ti1 + (ti0 - half_di0) * 2 + 1;
+        const int iidx =
+            iInfo.offset + ti3 + ti2 + ti1 + (ti0 - half_di0) * 2 + 1;
 #if ROUND_OUT == 1
         d_out[oidx] = (T)round(d_in[iidx] / fftScale);
 #else
diff --git a/src/backend/opencl/kernel/gradient.cl b/src/backend/opencl/kernel/gradient.cl
index bbd4f3b8df..a378c84e2f 100644
--- a/src/backend/opencl/kernel/gradient.cl
+++ b/src/backend/opencl/kernel/gradient.cl
@@ -9,10 +9,11 @@
 
 #if CPLX
 #define set(a, b) a = b
-#define set_scalar(a, b) do {                   \
-        a.x = b;                                \
-        a.y = 0;                                \
-    } while(0)
+#define set_scalar(a, b) \
+    do {                 \
+        a.x = b;         \
+        a.y = 0;         \
+    } while (0)
 
 #else
 
@@ -23,12 +24,11 @@
 
 #define sidx(y, x) scratch[((y + 1) * (TX + 2)) + (x + 1)]
 
-__kernel
-void gradient_kernel(__global T *d_grad0, const KParam grad0,
-                     __global T *d_grad1, const KParam grad1,
-                     __global const T* d_in, const KParam in,
-                     const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void gradient_kernel(__global T *d_grad0, const KParam grad0,
+                              __global T *d_grad1, const KParam grad1,
+                              __global const T *d_in, const KParam in,
+                              const int blocksPerMatX,
+                              const int blocksPerMatY) {
     const int idz = get_group_id(0) / blocksPerMatX;
     const int idw = get_group_id(1) / blocksPerMatY;
 
@@ -50,14 +50,14 @@ void gradient_kernel(__global T *d_grad0, const KParam grad0,
     int xmax = (TX > (in.dims[0] - xB)) ? (in.dims[0] - xB) : TX;
     int ymax = (TY > (in.dims[1] - yB)) ? (in.dims[1] - yB) : TY;
 
-    int iIdx = in.offset + idw * in.strides[3] + idz * in.strides[2]
-                              + idy * in.strides[1] + idx;
+    int iIdx = in.offset + idw * in.strides[3] + idz * in.strides[2] +
+               idy * in.strides[1] + idx;
 
-    int g0dx = idw * grad0.strides[3] + idz * grad0.strides[2]
-                  + idy * grad0.strides[1] + idx;
+    int g0dx = idw * grad0.strides[3] + idz * grad0.strides[2] +
+               idy * grad0.strides[1] + idx;
 
-    int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2]
-                  + idy * grad1.strides[1] + idx;
+    int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2] +
+               idy * grad1.strides[1] + idx;
 
     __local T scratch[(TY + 2) * (TX + 2)];
 
@@ -67,7 +67,7 @@ void gradient_kernel(__global T *d_grad0, const KParam grad0,
 
     // Copy data to scratch space
     T zero = ZERO;
-    if(cond) {
+    if (cond) {
         sidx(ty, tx) = zero;
     } else {
         sidx(ty, tx) = d_in[iIdx];
@@ -77,26 +77,27 @@ void gradient_kernel(__global T *d_grad0, const KParam grad0,
 
     // Copy buffer zone data. Corner (0,0) etc, are not used.
     // Cols
-    if(ty == 0) {
+    if (ty == 0) {
         // Y-1
-        sidx(-1, tx) = (cond || idy == 0) ?
-                        sidx(0, tx) : d_in[iIdx - in.strides[1]];
-        sidx(ymax, tx) = (cond || (idy + ymax) >= in.dims[1]) ?
-                          sidx(ymax - 1, tx) : d_in[iIdx + ymax * in.strides[1]];
+        sidx(-1, tx) =
+            (cond || idy == 0) ? sidx(0, tx) : d_in[iIdx - in.strides[1]];
+        sidx(ymax, tx) = (cond || (idy + ymax) >= in.dims[1])
+                             ? sidx(ymax - 1, tx)
+                             : d_in[iIdx + ymax * in.strides[1]];
     }
     // Rows
-    if(tx == 0) {
-        sidx(ty, -1) = (cond || idx == 0) ?
-                        sidx(ty, 0) : d_in[iIdx - 1];
-        sidx(ty, xmax) = (cond || (idx + xmax) >= in.dims[0]) ?
-                          sidx(ty, xmax - 1) : d_in[iIdx + xmax];
+    if (tx == 0) {
+        sidx(ty, -1)   = (cond || idx == 0) ? sidx(ty, 0) : d_in[iIdx - 1];
+        sidx(ty, xmax) = (cond || (idx + xmax) >= in.dims[0])
+                             ? sidx(ty, xmax - 1)
+                             : d_in[iIdx + xmax];
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (cond) return;
 
-    //set_scalar(d_grad0[iIdx], sidx(ty, tx));
-    d_grad0[g0dx] = xf * (sidx(ty, tx + 1) -  sidx(ty, tx - 1));
-    d_grad1[g1dx] = yf * (sidx(ty + 1, tx) -  sidx(ty - 1, tx));
+    // set_scalar(d_grad0[iIdx], sidx(ty, tx));
+    d_grad0[g0dx] = xf * (sidx(ty, tx + 1) - sidx(ty, tx - 1));
+    d_grad1[g1dx] = yf * (sidx(ty + 1, tx) - sidx(ty - 1, tx));
 }
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 9aec3898d0..0fd5473937 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -8,63 +8,58 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/gradient.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <type_util.hpp>
+#include <kernel_headers/gradient.hpp>
 #include <math.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <type_util.hpp>
+#include <string>
 #include "config.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
 static const int TX = 32;
 static const int TY = 8;
 
 template<typename T>
-void gradient(Param grad0, Param grad1, const Param in)
-{
-    std::string refName = std::string("gradient_kernel_") + std::string(dtype_traits<T>::getName());
+void gradient(Param grad0, Param grad1, const Param in) {
+    std::string refName = std::string("gradient_kernel_") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         ToNumStr<T> toNumStr;
         std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D TX=" << TX
-                << " -D TY=" << TY
-                << " -D ZERO=" << toNumStr(scalar<T>(0));
+        options << " -D T=" << dtype_traits<T>::getName() << " -D TX=" << TX
+                << " -D TY=" << TY << " -D ZERO=" << toNumStr(scalar<T>(0));
 
-        if((af_dtype) dtype_traits<T>::af_type == c32 ||
-            (af_dtype) dtype_traits<T>::af_type == c64) {
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
         }
-        if (std::is_same<T, double>::value ||
-            std::is_same<T, cdouble>::value) {
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {gradient_cl};
-        const int   ker_lens[] = {gradient_cl_len};
+        const int ker_lens[]   = {gradient_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -73,8 +68,9 @@ void gradient(Param grad0, Param grad1, const Param in)
         addKernelToCache(device, refName, entry);
     }
 
-    auto gradOp = KernelFunctor< Buffer, const KParam, Buffer, const KParam,
-                                 const Buffer, const KParam, const int, const int >(*entry.ker);
+    auto gradOp =
+        KernelFunctor<Buffer, const KParam, Buffer, const KParam, const Buffer,
+                      const KParam, const int, const int>(*entry.ker);
 
     NDRange local(TX, TY, 1);
 
@@ -83,11 +79,11 @@ void gradient(Param grad0, Param grad1, const Param in)
     NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
                    local[1] * blocksPerMatY * in.info.dims[3], 1);
 
-    gradOp(EnqueueArgs(getQueue(), global, local),
-           *grad0.data, grad0.info, *grad1.data, grad1.info,
-           *in.data, in.info, blocksPerMatX, blocksPerMatY);
+    gradOp(EnqueueArgs(getQueue(), global, local), *grad0.data, grad0.info,
+           *grad1.data, grad1.info, *in.data, in.info, blocksPerMatX,
+           blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/harris.cl b/src/backend/opencl/kernel/harris.cl
index 2582b14f27..1c84a168b8 100644
--- a/src/backend/opencl/kernel/harris.cl
+++ b/src/backend/opencl/kernel/harris.cl
@@ -7,16 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define MAX_VAL(A,B) (A) < (B) ? (B) : (A)
+#define MAX_VAL(A, B) (A) < (B) ? (B) : (A)
 
-__kernel void second_order_deriv(
-    __global T* ixx_out,
-    __global T* ixy_out,
-    __global T* iyy_out,
-    const unsigned in_len,
-    __global const T* ix_in,
-    __global const T* iy_in)
-{
+__kernel void second_order_deriv(__global T* ixx_out, __global T* ixy_out,
+                                 __global T* iyy_out, const unsigned in_len,
+                                 __global const T* ix_in,
+                                 __global const T* iy_in) {
     const unsigned x = get_global_id(0);
 
     if (x < in_len) {
@@ -26,16 +22,11 @@ __kernel void second_order_deriv(
     }
 }
 
-__kernel void harris_responses(
-    __global T* resp_out,
-    const unsigned idim0,
-    const unsigned idim1,
-    __global const T* ixx_in,
-    __global const T* ixy_in,
-    __global const T* iyy_in,
-    const float k_thr,
-    const unsigned border_len)
-{
+__kernel void harris_responses(__global T* resp_out, const unsigned idim0,
+                               const unsigned idim1, __global const T* ixx_in,
+                               __global const T* ixy_in,
+                               __global const T* iyy_in, const float k_thr,
+                               const unsigned border_len) {
     const unsigned r = border_len;
 
     const unsigned x = get_global_id(0) + r;
@@ -45,26 +36,20 @@ __kernel void harris_responses(
         const unsigned idx = x * idim0 + y;
 
         // Calculates matrix trace and determinant
-        T tr = ixx_in[idx] + iyy_in[idx];
+        T tr  = ixx_in[idx] + iyy_in[idx];
         T det = ixx_in[idx] * iyy_in[idx] - ixy_in[idx] * ixy_in[idx];
 
         // Calculates local Harris response
-        resp_out[idx] = det - k_thr * (tr*tr);
+        resp_out[idx] = det - k_thr * (tr * tr);
     }
 }
 
-__kernel void non_maximal(
-    __global float* x_out,
-    __global float* y_out,
-    __global float* resp_out,
-    __global unsigned* count,
-    __global const T* resp_in,
-    const unsigned idim0,
-    const unsigned idim1,
-    const float min_resp,
-    const unsigned border_len,
-    const unsigned max_corners)
-{
+__kernel void non_maximal(__global float* x_out, __global float* y_out,
+                          __global float* resp_out, __global unsigned* count,
+                          __global const T* resp_in, const unsigned idim0,
+                          const unsigned idim1, const float min_resp,
+                          const unsigned border_len,
+                          const unsigned max_corners) {
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
 
@@ -76,13 +61,14 @@ __kernel void non_maximal(
 
         // Find maximum neighborhood response
         T max_v;
-        max_v = MAX_VAL(resp_in[(x-1) * idim0 + y-1], resp_in[x * idim0 + y-1]);
-        max_v = MAX_VAL(max_v, resp_in[(x+1) * idim0 + y-1]);
-        max_v = MAX_VAL(max_v, resp_in[(x-1) * idim0 + y  ]);
-        max_v = MAX_VAL(max_v, resp_in[(x+1) * idim0 + y  ]);
-        max_v = MAX_VAL(max_v, resp_in[(x-1) * idim0 + y+1]);
-        max_v = MAX_VAL(max_v, resp_in[(x)   * idim0 + y+1]);
-        max_v = MAX_VAL(max_v, resp_in[(x+1) * idim0 + y+1]);
+        max_v = MAX_VAL(resp_in[(x - 1) * idim0 + y - 1],
+                        resp_in[x * idim0 + y - 1]);
+        max_v = MAX_VAL(max_v, resp_in[(x + 1) * idim0 + y - 1]);
+        max_v = MAX_VAL(max_v, resp_in[(x - 1) * idim0 + y]);
+        max_v = MAX_VAL(max_v, resp_in[(x + 1) * idim0 + y]);
+        max_v = MAX_VAL(max_v, resp_in[(x - 1) * idim0 + y + 1]);
+        max_v = MAX_VAL(max_v, resp_in[(x)*idim0 + y + 1]);
+        max_v = MAX_VAL(max_v, resp_in[(x + 1) * idim0 + y + 1]);
 
         // Stores corner to {x,y,resp}_out if it's response is maximum compared
         // to its 8-neighborhood and greater or equal minimum response
@@ -97,21 +83,18 @@ __kernel void non_maximal(
     }
 }
 
-__kernel void keep_corners(
-    __global float* x_out,
-    __global float* y_out,
-    __global float* score_out,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const float* score_in,
-    __global const unsigned* score_idx,
-    const unsigned n_feat)
-{
+__kernel void keep_corners(__global float* x_out, __global float* y_out,
+                           __global float* score_out,
+                           __global const float* x_in,
+                           __global const float* y_in,
+                           __global const float* score_in,
+                           __global const unsigned* score_idx,
+                           const unsigned n_feat) {
     unsigned f = get_global_id(0);
 
     if (f < n_feat) {
-        x_out[f] = x_in[score_idx[f]];
-        y_out[f] = y_in[score_idx[f]];
+        x_out[f]     = x_in[score_idx[f]];
+        y_out[f]     = y_in[score_idx[f]];
         score_out[f] = score_in[f];
     }
 }
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index a92463d532..026bb5150c 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -7,52 +7,49 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <af/constants.h>
-#include <program.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/gradient.hpp>
-#include <kernel/sort_by_key.hpp>
 #include <kernel/range.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
-#include <cache.hpp>
+#include <program.hpp>
+#include <af/constants.h>
+#include <af/defines.h>
 
 #include <tuple>
 #include <vector>
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const unsigned HARRIS_THREADS_PER_GROUP = 256;
-static const unsigned HARRIS_THREADS_X = 16;
-static const unsigned HARRIS_THREADS_Y = HARRIS_THREADS_PER_GROUP / HARRIS_THREADS_X;
+static const unsigned HARRIS_THREADS_X         = 16;
+static const unsigned HARRIS_THREADS_Y =
+    HARRIS_THREADS_PER_GROUP / HARRIS_THREADS_X;
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T *out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * af::Pi * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * af::Pi * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
 template<typename T, typename convAccT>
-void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy, Array<convAccT> &filter)
-{
+void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy,
+                 Array<convAccT> &filter) {
     Array<convAccT> ixx_tmp = createEmptyArray<convAccT>(ixx.dims());
     Array<convAccT> ixy_tmp = createEmptyArray<convAccT>(ixy.dims());
     Array<convAccT> iyy_tmp = createEmptyArray<convAccT>(iyy.dims());
@@ -66,69 +63,59 @@ void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy, Array<convAccT> &f
 }
 
 template<typename T>
-std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
-getHarrisKernels()
-{
-    using cl::Program;
+std::tuple<cl::Kernel *, cl::Kernel *, cl::Kernel *, cl::Kernel *>
+getHarrisKernels() {
     using cl::Kernel;
-    static const char* kernelNames[4] =
-        {"second_order_deriv", "keep_corners", "harris_responses", "non_maximal"};
+    using cl::Program;
+    static const char *kernelNames[4] = {"second_order_deriv", "keep_corners",
+                                         "harris_responses", "non_maximal"};
 
     kc_entry_t entries[4];
 
     int device = getActiveDeviceId();
 
-    std::string checkName = kernelNames[0] + std::string("_") + std::string(dtype_traits<T>::getName());
+    std::string checkName = kernelNames[0] + std::string("_") +
+                            std::string(dtype_traits<T>::getName());
 
     entries[0] = kernelCache(device, checkName);
 
-    if (entries[0].prog==0 && entries[0].ker==0)
-    {
+    if (entries[0].prog == 0 && entries[0].ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char* ker_strs[] = {harris_cl};
-        const int   ker_lens[] = {harris_cl_len};
+        const char *ker_strs[] = {harris_cl};
+        const int ker_lens[]   = {harris_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
 
-        for (int i=0; i<4; ++i)
-        {
+        for (int i = 0; i < 4; ++i) {
             entries[i].prog = new Program(prog);
             entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
-            std::string name = kernelNames[i] +
-                std::string("_") + std::string(dtype_traits<T>::getName());
+            std::string name = kernelNames[i] + std::string("_") +
+                               std::string(dtype_traits<T>::getName());
 
             addKernelToCache(device, name, entries[i]);
         }
     } else {
-        for (int i=1; i<4; ++i) {
-            std::string name = kernelNames[i] +
-                std::string("_") + std::string(dtype_traits<T>::getName());
+        for (int i = 1; i < 4; ++i) {
+            std::string name = kernelNames[i] + std::string("_") +
+                               std::string(dtype_traits<T>::getName());
 
             entries[i] = kernelCache(device, name);
         }
     }
 
-    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker, entries[3].ker);
+    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker,
+                           entries[3].ker);
 }
 
 template<typename T, typename convAccT>
-void
-harris(unsigned* corners_out,
-            Param &x_out,
-            Param &y_out,
-            Param &resp_out,
-            Param in,
-            const unsigned max_corners,
-            const float min_response,
-            const float sigma,
-            const unsigned filter_len,
-            const float k_thr)
-{
+void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
+            Param in, const unsigned max_corners, const float min_response,
+            const float sigma, const unsigned filter_len, const float k_thr) {
     auto kernels = getHarrisKernels<T>();
     using cl::Buffer;
     using cl::EnqueueArgs;
@@ -147,7 +134,8 @@ harris(unsigned* corners_out,
     const unsigned border_len = filter_len / 2 + 1;
 
     // Copy filter to device object
-    Array<convAccT> filter = createHostDataArray<convAccT>(filter_len, h_filter.data());
+    Array<convAccT> filter =
+        createHostDataArray<convAccT>(filter_len, h_filter.data());
     Array<T> ix = createEmptyArray<T>(dim4(4, in.info.dims));
     Array<T> iy = createEmptyArray<T>(dim4(4, in.info.dims));
 
@@ -159,80 +147,93 @@ harris(unsigned* corners_out,
     Array<T> iyy = createEmptyArray<T>(dim4(4, in.info.dims));
 
     // Second order-derivatives kernel sizes
-    const unsigned blk_x_so = divup(in.info.dims[3] * in.info.strides[3], HARRIS_THREADS_PER_GROUP);
+    const unsigned blk_x_so =
+        divup(in.info.dims[3] * in.info.strides[3], HARRIS_THREADS_PER_GROUP);
     const NDRange local_so(HARRIS_THREADS_PER_GROUP, 1);
     const NDRange global_so(blk_x_so * HARRIS_THREADS_PER_GROUP, 1);
 
-    auto soOp = KernelFunctor< Buffer, Buffer, Buffer,
-                               unsigned, Buffer, Buffer > (*std::get<0>(kernels));
+    auto soOp = KernelFunctor<Buffer, Buffer, Buffer, unsigned, Buffer, Buffer>(
+        *std::get<0>(kernels));
 
     // Compute second-order derivatives
-    soOp(EnqueueArgs(getQueue(), global_so, local_so),
-         *ixx.get(), *ixy.get(), *iyy.get(),
-         in.info.dims[3] * in.info.strides[3], *ix.get(), *iy.get());
+    soOp(EnqueueArgs(getQueue(), global_so, local_so), *ixx.get(), *ixy.get(),
+         *iyy.get(), in.info.dims[3] * in.info.strides[3], *ix.get(),
+         *iy.get());
     CL_DEBUG_FINISH(getQueue());
 
     // Convolve second order derivatives with proper window filter
     conv_helper<T, convAccT>(ixx, ixy, iyy, filter);
 
-    cl::Buffer *d_responses = bufferAlloc(in.info.dims[3] * in.info.strides[3] * sizeof(T));
+    cl::Buffer *d_responses =
+        bufferAlloc(in.info.dims[3] * in.info.strides[3] * sizeof(T));
 
     // Harris responses kernel sizes
-    unsigned blk_x_hr = divup(in.info.dims[0] - border_len*2, HARRIS_THREADS_X);
-    unsigned blk_y_hr = divup(in.info.dims[1] - border_len*2, HARRIS_THREADS_Y);
+    unsigned blk_x_hr =
+        divup(in.info.dims[0] - border_len * 2, HARRIS_THREADS_X);
+    unsigned blk_y_hr =
+        divup(in.info.dims[1] - border_len * 2, HARRIS_THREADS_Y);
     const NDRange local_hr(HARRIS_THREADS_X, HARRIS_THREADS_Y);
-    const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X, blk_y_hr * HARRIS_THREADS_Y);
+    const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X,
+                            blk_y_hr * HARRIS_THREADS_Y);
 
-    auto hrOp = KernelFunctor< Buffer, unsigned, unsigned, Buffer, Buffer, Buffer,
-                               float, unsigned> (*std::get<2>(kernels));
+    auto hrOp = KernelFunctor<Buffer, unsigned, unsigned, Buffer, Buffer,
+                              Buffer, float, unsigned>(*std::get<2>(kernels));
 
     // Calculate Harris responses for all pixels
-    hrOp(EnqueueArgs(getQueue(), global_hr, local_hr),
-         *d_responses, in.info.dims[0], in.info.dims[1],
-         *ixx.get(), *ixy.get(), *iyy.get(), k_thr, border_len);
+    hrOp(EnqueueArgs(getQueue(), global_hr, local_hr), *d_responses,
+         in.info.dims[0], in.info.dims[1], *ixx.get(), *ixy.get(), *iyy.get(),
+         k_thr, border_len);
     CL_DEBUG_FINISH(getQueue());
 
     // Number of corners is not known a priori, limit maximum number of corners
     // according to image dimensions
     unsigned corner_lim = in.info.dims[3] * in.info.strides[3] * 0.2f;
 
-    unsigned corners_found = 0;
+    unsigned corners_found      = 0;
     cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0,
+                                  sizeof(unsigned), &corners_found);
 
-    cl::Buffer *d_x_corners = bufferAlloc(corner_lim * sizeof(float));
-    cl::Buffer *d_y_corners = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *d_x_corners    = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *d_y_corners    = bufferAlloc(corner_lim * sizeof(float));
     cl::Buffer *d_resp_corners = bufferAlloc(corner_lim * sizeof(float));
 
     const float min_r = (max_corners > 0) ? 0.f : min_response;
 
-    auto nmOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, Buffer, unsigned, unsigned,
-                            float, unsigned, unsigned> (*std::get<3>(kernels));
+    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
+                              unsigned, float, unsigned, unsigned>(
+        *std::get<3>(kernels));
 
     // Perform non-maximal suppression
-    nmOp(EnqueueArgs(getQueue(), global_hr, local_hr),
-         *d_x_corners, *d_y_corners, *d_resp_corners, *d_corners_found,
-         *d_responses, in.info.dims[0], in.info.dims[1],
-         min_r, border_len, corner_lim);
+    nmOp(EnqueueArgs(getQueue(), global_hr, local_hr), *d_x_corners,
+         *d_y_corners, *d_resp_corners, *d_corners_found, *d_responses,
+         in.info.dims[0], in.info.dims[1], min_r, border_len, corner_lim);
     CL_DEBUG_FINISH(getQueue());
 
-    getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned),
+                                 &corners_found);
 
     bufferFree(d_responses);
     bufferFree(d_corners_found);
 
-    *corners_out = min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
+    *corners_out =
+        min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
     if (*corners_out == 0) return;
 
     // Set output Param info
-    x_out.info.dims[0] = y_out.info.dims[0] = resp_out.info.dims[0] = *corners_out;
-    x_out.info.strides[0] = y_out.info.strides[0] = resp_out.info.strides[0] = 1;
+    x_out.info.dims[0] = y_out.info.dims[0] = resp_out.info.dims[0] =
+        *corners_out;
+    x_out.info.strides[0] = y_out.info.strides[0] = resp_out.info.strides[0] =
+        1;
     x_out.info.offset = y_out.info.offset = resp_out.info.offset = 0;
     for (int k = 1; k < 4; k++) {
-        x_out.info.dims[k] = y_out.info.dims[k] = resp_out.info.dims[k] =  1;
-        x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
-        y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
-        resp_out.info.strides[k] = resp_out.info.dims[k - 1] * resp_out.info.strides[k - 1];
+        x_out.info.dims[k] = y_out.info.dims[k] = resp_out.info.dims[k] = 1;
+        x_out.info.strides[k] =
+            x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
+        y_out.info.strides[k] =
+            y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
+        resp_out.info.strides[k] =
+            resp_out.info.dims[k - 1] * resp_out.info.strides[k - 1];
     }
 
     if (max_corners > 0 && corners_found > *corners_out) {
@@ -244,9 +245,11 @@ harris(unsigned* corners_out,
 
         for (int k = 1; k < 4; k++) {
             harris_resp.info.dims[k] = 1;
-            harris_resp.info.strides[k] = harris_resp.info.dims[k - 1] * harris_resp.info.strides[k - 1];
+            harris_resp.info.strides[k] =
+                harris_resp.info.dims[k - 1] * harris_resp.info.strides[k - 1];
             harris_idx.info.dims[k] = 1;
-            harris_idx.info.strides[k] = harris_idx.info.dims[k - 1] * harris_idx.info.strides[k - 1];
+            harris_idx.info.strides[k] =
+                harris_idx.info.dims[k - 1] * harris_idx.info.strides[k - 1];
         }
 
         int sort_elem = harris_resp.info.strides[3] * harris_resp.info.dims[3];
@@ -258,8 +261,8 @@ harris(unsigned* corners_out,
         // Sort Harris responses
         kernel::sort0ByKey<float, uint>(harris_resp, harris_idx, false);
 
-        x_out.data = bufferAlloc(*corners_out * sizeof(float));
-        y_out.data = bufferAlloc(*corners_out * sizeof(float));
+        x_out.data    = bufferAlloc(*corners_out * sizeof(float));
+        y_out.data    = bufferAlloc(*corners_out * sizeof(float));
         resp_out.data = bufferAlloc(*corners_out * sizeof(float));
 
         // Keep corners kernel sizes
@@ -267,39 +270,40 @@ harris(unsigned* corners_out,
         const NDRange local_kc(HARRIS_THREADS_PER_GROUP, 1);
         const NDRange global_kc(blk_x_kc * HARRIS_THREADS_PER_GROUP, 1);
 
-        auto kcOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                                   unsigned> (*std::get<1>(kernels));
+        auto kcOp =
+            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                          Buffer, unsigned>(*std::get<1>(kernels));
 
         // Keep only the first corners_to_keep corners with higher Harris
         // responses
-        kcOp(EnqueueArgs(getQueue(), global_kc, local_kc),
-             *x_out.data, *y_out.data, *resp_out.data,
-             *d_x_corners, *d_y_corners, *harris_resp.data, *harris_idx.data,
-             *corners_out);
+        kcOp(EnqueueArgs(getQueue(), global_kc, local_kc), *x_out.data,
+             *y_out.data, *resp_out.data, *d_x_corners, *d_y_corners,
+             *harris_resp.data, *harris_idx.data, *corners_out);
         CL_DEBUG_FINISH(getQueue());
 
         bufferFree(d_x_corners);
         bufferFree(d_y_corners);
         bufferFree(harris_resp.data);
         bufferFree(harris_idx.data);
-    }
-    else if (max_corners == 0 && corners_found < corner_lim) {
-        x_out.data = bufferAlloc(*corners_out * sizeof(float));
-        y_out.data = bufferAlloc(*corners_out * sizeof(float));
+    } else if (max_corners == 0 && corners_found < corner_lim) {
+        x_out.data    = bufferAlloc(*corners_out * sizeof(float));
+        y_out.data    = bufferAlloc(*corners_out * sizeof(float));
         resp_out.data = bufferAlloc(*corners_out * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_x_corners, *x_out.data, 0, 0, *corners_out * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_y_corners, *y_out.data, 0, 0, *corners_out * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_resp_corners, *resp_out.data, 0, 0, *corners_out * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_x_corners, *x_out.data, 0, 0,
+                                     *corners_out * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_y_corners, *y_out.data, 0, 0,
+                                     *corners_out * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_resp_corners, *resp_out.data, 0, 0,
+                                     *corners_out * sizeof(float));
 
         bufferFree(d_x_corners);
         bufferFree(d_y_corners);
         bufferFree(d_resp_corners);
-    }
-    else {
-        x_out.data = d_x_corners;
-        y_out.data = d_y_corners;
+    } else {
+        x_out.data    = d_x_corners;
+        y_out.data    = d_y_corners;
         resp_out.data = d_resp_corners;
     }
 }
-} //namespace kernel
-} //namespace opencl
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/histogram.cl b/src/backend/opencl/kernel/histogram.cl
index 7754590afd..3821b985bf 100644
--- a/src/backend/opencl/kernel/histogram.cl
+++ b/src/backend/opencl/kernel/histogram.cl
@@ -7,23 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void histogram(__global outType *         d_dst,
-               KParam                     oInfo,
-               __global const inType *    d_src,
-               KParam                     iInfo,
-               __local outType *          localMem,
-               int len, int nbins, float minval, float maxval, int nBBS)
-{
-    unsigned b2    = get_group_id(0)/nBBS;
-    int start = (get_group_id(0)-b2*nBBS) * THRD_LOAD * get_local_size(0) + get_local_id(0);
-    int end   = min((int)(start + THRD_LOAD * get_local_size(0)), len);
+__kernel void histogram(__global outType *d_dst, KParam oInfo,
+                        __global const inType *d_src, KParam iInfo,
+                        __local outType *localMem, int len, int nbins,
+                        float minval, float maxval, int nBBS) {
+    unsigned b2 = get_group_id(0) / nBBS;
+    int start = (get_group_id(0) - b2 * nBBS) * THRD_LOAD * get_local_size(0) +
+                get_local_id(0);
+    int end = min((int)(start + THRD_LOAD * get_local_size(0)), len);
 
     // offset input and output to account for batch ops
-    __global const inType *in = d_src + b2 * iInfo.strides[2] + get_group_id(1) * iInfo.strides[3] + iInfo.offset;
-    __global outType * out    = d_dst + b2 * oInfo.strides[2] + get_group_id(1) * oInfo.strides[3];
+    __global const inType *in = d_src + b2 * iInfo.strides[2] +
+                                get_group_id(1) * iInfo.strides[3] +
+                                iInfo.offset;
+    __global outType *out =
+        d_dst + b2 * oInfo.strides[2] + get_group_id(1) * oInfo.strides[3];
 
-    float dx = (maxval-minval)/(float)nbins;
+    float dx = (maxval - minval) / (float)nbins;
 
     bool use_global = nbins > MAX_BINS;
 
@@ -37,20 +37,19 @@ void histogram(__global outType *         d_dst,
 #if defined(IS_LINEAR)
         int idx = row;
 #else
-        int i0 = row % iInfo.dims[0];
-        int i1 = row / iInfo.dims[0];
-        int idx= i0+i1*iInfo.strides[1];
+        int i0  = row % iInfo.dims[0];
+        int i1  = row / iInfo.dims[0];
+        int idx = i0 + i1 * iInfo.strides[1];
 #endif
         int bin = (int)(((float)in[idx] - minval) / dx);
         bin     = max(bin, 0);
-        bin     = min(bin, (int)nbins-1);
+        bin     = min(bin, (int)nbins - 1);
 
         if (use_global) {
             atomic_inc((out + bin));
         } else {
             atomic_inc((localMem + bin));
         }
-
     }
 
     if (!use_global) {
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index f0d56d8273..43d18d7335 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -8,14 +8,14 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/histogram.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
 using cl::EnqueueArgs;
@@ -24,40 +24,36 @@ using cl::KernelFunctor;
 using cl::NDRange;
 using cl::Program;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 constexpr int MAX_BINS  = 4000;
-constexpr int THREADS_X =  256;
-constexpr int THRD_LOAD =   16;
+constexpr int THREADS_X = 256;
+constexpr int THRD_LOAD = 16;
 
 template<typename inType, typename outType, bool isLinear>
-void histogram(Param out, const Param in, int nbins, float minval, float maxval)
-{
+void histogram(Param out, const Param in, int nbins, float minval,
+               float maxval) {
     std::string refName = std::string("histogram_") +
-        std::string(dtype_traits<inType>::getName()) +
-        std::string(dtype_traits<inType>::getName()) +
-        std::to_string(isLinear);
+                          std::string(dtype_traits<inType>::getName()) +
+                          std::string(dtype_traits<inType>::getName()) +
+                          std::to_string(isLinear);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D inType=" << dtype_traits<inType>::getName()
-            << " -D outType=" << dtype_traits<outType>::getName()
-            << " -D THRD_LOAD=" << THRD_LOAD
-            << " -D MAX_BINS=" << MAX_BINS;
-        if (isLinear)
-            options << " -D IS_LINEAR";
+                << " -D outType=" << dtype_traits<outType>::getName()
+                << " -D THRD_LOAD=" << THRD_LOAD << " -D MAX_BINS=" << MAX_BINS;
+        if (isLinear) options << " -D IS_LINEAR";
         if (std::is_same<inType, double>::value ||
-                std::is_same<inType, cdouble>::value) {
+            std::is_same<inType, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {histogram_cl};
-        const int   ker_lens[] = {histogram_cl_len};
+        const int ker_lens[]   = {histogram_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -66,21 +62,22 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval)
         addKernelToCache(device, refName, entry);
     }
 
-    auto histogramOp = KernelFunctor< Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg,
-                                      int, int, float, float, int >(*entry.ker);
+    auto histogramOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, int,
+                      int, float, float, int>(*entry.ker);
 
-    int nElems = in.info.dims[0]*in.info.dims[1];
-    int blk_x  = divup(nElems, THRD_LOAD*THREADS_X);
+    int nElems  = in.info.dims[0] * in.info.dims[1];
+    int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
     int locSize = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 1;
 
     NDRange local(THREADS_X, 1);
-    NDRange global(blk_x*in.info.dims[2]*THREADS_X, in.info.dims[3]);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X, in.info.dims[3]);
 
-    histogramOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info,
-                cl::Local(locSize), nElems, nbins, minval, maxval, blk_x);
+    histogramOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *in.data, in.info, cl::Local(locSize), nElems, nbins, minval,
+                maxval, blk_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/homography.cl b/src/backend/opencl/kernel/homography.cl
index 3ae68dadac..fe01a3f926 100644
--- a/src/backend/opencl/kernel/homography.cl
+++ b/src/backend/opencl/kernel/homography.cl
@@ -7,14 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-inline T sq(T a)
-{
-    return a * a;
-}
+inline T sq(T a) { return a * a; }
 
 inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
-                       __local T* l_acc1, __local T* l_acc2, int m, int n)
-{
+                       __local T* l_acc1, __local T* l_acc2, int m, int n) {
     const int iterations = 30;
 
     int tid_x = get_local_id(0);
@@ -22,14 +18,14 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
     int tid_y = get_local_id(1);
     int gid_y = get_global_id(1);
 
-    int doff = tid_y *  n;
+    int doff = tid_y * n;
     int soff = tid_y * 81;
 
     if (tid_x < n) {
         T acc1 = 0;
         for (int i = 0; i < m; i++) {
             int stid = soff + tid_x * m + i;
-            T t = l_S[stid];
+            T t      = l_S[stid];
             acc1 += t * t;
             l_V[stid] = (tid_x == i) ? 1 : 0;
         }
@@ -41,26 +37,24 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
     // All threads do the same work
     // FIXME: Figure out why code below doesnt work
     int tst = 0, toff = 1, tcond = tid_x == 0;
-#define BARRIER //nothing
+#define BARRIER  // nothing
 #else
     // Split work across subgroup
     int tst = tid_x, toff = bsz_x, tcond = 1;
 #define BARRIER barrier(CLK_LOCAL_MEM_FENCE)
 #endif
 
-
     for (int it = 0; tcond && it < iterations; it++) {
-        for (int i = 0; i < n-1; i++) {
-            for (int j = i+1; j < n; j++) {
-                __local T* Si = l_S + soff + i*m;
-                __local T* Sj = l_S + soff + j*m;
+        for (int i = 0; i < n - 1; i++) {
+            for (int j = i + 1; j < n; j++) {
+                __local T* Si = l_S + soff + i * m;
+                __local T* Sj = l_S + soff + j * m;
 
-                __local T* Vi = l_V + soff + i*n;
-                __local T* Vj = l_V + soff + j*n;
+                __local T* Vi = l_V + soff + i * n;
+                __local T* Vj = l_V + soff + j * n;
 
                 T p = (T)0;
-                for (int k = 0; k < m; k++)
-                    p += Si[k]*Sj[k];
+                for (int k = 0; k < m; k++) p += Si[k] * Sj[k];
 
                 T di = l_d[doff + i];
                 T dj = l_d[doff + j];
@@ -68,27 +62,26 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
 
                 T c = 0, s = 0;
                 T t0 = 0, t1 = 0;
-                int cond = (fabs(p) > m*EPS*sqrt(di * dj));
+                int cond = (fabs(p) > m * EPS * sqrt(di * dj));
                 T a = 0, b = 0;
 
                 if (cond) {
-                    T y = di - dj;
-                    T r = hypot(p*2, y);
-                    T r2 = r*2;
+                    T y  = di - dj;
+                    T r  = hypot(p * 2, y);
+                    T r2 = r * 2;
                     if (y >= 0) {
                         c = sqrt((r + y) / r2);
-                        s = p / (r2*c);
-                    }
-                    else {
+                        s = p / (r2 * c);
+                    } else {
                         s = sqrt((r - y) / r2);
-                        c = p / (r2*s);
+                        c = p / (r2 * s);
                     }
 
-                    for (int k = tst; k < m; k+=toff) {
-                        t0 = c*Si[k] + s*Sj[k];
-                        t1 = c*Sj[k] - s*Si[k];
-                        Si[k] = t0;
-                        Sj[k] = t1;
+                    for (int k = tst; k < m; k += toff) {
+                        t0                        = c * Si[k] + s * Sj[k];
+                        t1                        = c * Sj[k] - s * Si[k];
+                        Si[k]                     = t0;
+                        Sj[k]                     = t1;
                         l_acc1[tid_y * bsz_x + k] = t0 * t0;
                         l_acc2[tid_y * bsz_x + k] = t1 * t1;
                     }
@@ -122,35 +115,23 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
     }
 }
 
-inline int compute_mean_scale(
-    float* x_src_mean,
-    float* y_src_mean,
-    float* x_dst_mean,
-    float* y_dst_mean,
-    float* src_scale,
-    float* dst_scale,
-    float* src_pt_x,
-    float* src_pt_y,
-    float* dst_pt_x,
-    float* dst_pt_y,
-    __global const float* x_src,
-    __global const float* y_src,
-    __global const float* x_dst,
-    __global const float* y_dst,
-    __global const float* rnd,
-    KParam rInfo,
-    int i)
-{
+inline int compute_mean_scale(float* x_src_mean, float* y_src_mean,
+                              float* x_dst_mean, float* y_dst_mean,
+                              float* src_scale, float* dst_scale,
+                              float* src_pt_x, float* src_pt_y, float* dst_pt_x,
+                              float* dst_pt_y, __global const float* x_src,
+                              __global const float* y_src,
+                              __global const float* x_dst,
+                              __global const float* y_dst,
+                              __global const float* rnd, KParam rInfo, int i) {
     const unsigned ridx = rInfo.dims[0] * i;
-    unsigned r[4] = { (unsigned)rnd[ridx],
-                      (unsigned)rnd[ridx+1],
-                      (unsigned)rnd[ridx+2],
-                      (unsigned)rnd[ridx+3] };
+    unsigned r[4]       = {(unsigned)rnd[ridx], (unsigned)rnd[ridx + 1],
+                     (unsigned)rnd[ridx + 2], (unsigned)rnd[ridx + 3]};
 
     // If one of the points is repeated, it's a bad samples, will still
     // compute homography to ensure all threads pass barrier()
-    int bad = (r[0] == r[1] || r[0] == r[2] || r[0] == r[3] ||
-               r[1] == r[2] || r[1] == r[3] || r[2] == r[3]);
+    int bad = (r[0] == r[1] || r[0] == r[2] || r[0] == r[3] || r[1] == r[2] ||
+               r[1] == r[3] || r[2] == r[3]);
 
     for (unsigned j = 0; j < 4; j++) {
         src_pt_x[j] = x_src[r[j]];
@@ -166,8 +147,10 @@ inline int compute_mean_scale(
 
     float src_var = 0.0f, dst_var = 0.0f;
     for (unsigned j = 0; j < 4; j++) {
-        src_var += sq(src_pt_x[j] - *x_src_mean) + sq(src_pt_y[j] - *y_src_mean);
-        dst_var += sq(dst_pt_x[j] - *x_dst_mean) + sq(dst_pt_y[j] - *y_dst_mean);
+        src_var +=
+            sq(src_pt_x[j] - *x_src_mean) + sq(src_pt_y[j] - *y_src_mean);
+        dst_var +=
+            sq(dst_pt_x[j] - *x_dst_mean) + sq(dst_pt_y[j] - *y_dst_mean);
     }
 
     src_var /= 4.f;
@@ -179,20 +162,16 @@ inline int compute_mean_scale(
     return bad;
 }
 
-#define LSPTR(Z, Y, X) (l_S[(Z) * 81 + (Y) * 9 + (X)])
-
-__kernel void compute_homography(
-    __global T* H,
-    KParam HInfo,
-    __global const float* x_src,
-    __global const float* y_src,
-    __global const float* x_dst,
-    __global const float* y_dst,
-    __global const float* rnd,
-    KParam rInfo,
-    const unsigned iterations)
-{
-    unsigned i = get_global_id(1);
+#define LSPTR(Z, Y, X) (l_S[(Z)*81 + (Y)*9 + (X)])
+
+__kernel void compute_homography(__global T* H, KParam HInfo,
+                                 __global const float* x_src,
+                                 __global const float* y_src,
+                                 __global const float* x_dst,
+                                 __global const float* y_dst,
+                                 __global const float* rnd, KParam rInfo,
+                                 const unsigned iterations) {
+    unsigned i     = get_global_id(1);
     unsigned tid_y = get_local_id(1);
     unsigned tid_x = get_local_id(0);
 
@@ -201,20 +180,17 @@ __kernel void compute_homography(
     float src_scale, dst_scale;
     float src_pt_x[4], src_pt_y[4], dst_pt_x[4], dst_pt_y[4];
 
-    int bad = compute_mean_scale(&x_src_mean, &y_src_mean,
-                                 &x_dst_mean, &y_dst_mean,
-                                 &src_scale, &dst_scale,
-                                 src_pt_x, src_pt_y,
-                                 dst_pt_x, dst_pt_y,
-                                 x_src, y_src, x_dst, y_dst,
-                                 rnd, rInfo, i);
+    int bad =
+        compute_mean_scale(&x_src_mean, &y_src_mean, &x_dst_mean, &y_dst_mean,
+                           &src_scale, &dst_scale, src_pt_x, src_pt_y, dst_pt_x,
+                           dst_pt_y, x_src, y_src, x_dst, y_dst, rnd, rInfo, i);
 
     __local T l_acc1[256];
     __local T l_acc2[256];
 
-    __local T l_S[16*81];
-    __local T l_V[16*81];
-    __local T l_d[16*9];
+    __local T l_S[16 * 81];
+    __local T l_V[16 * 81];
+    __local T l_d[16 * 9];
 
     // Compute input matrix
     if (tid_x < 4) {
@@ -223,25 +199,25 @@ __kernel void compute_homography(
         float dstx = (dst_pt_x[tid_x] - x_dst_mean) * dst_scale;
         float dsty = (dst_pt_y[tid_x] - y_dst_mean) * dst_scale;
 
-        LSPTR(tid_y, 0, tid_x*2) = 0.0f;
-        LSPTR(tid_y, 1, tid_x*2) = 0.0f;
-        LSPTR(tid_y, 2, tid_x*2) = 0.0f;
-        LSPTR(tid_y, 3, tid_x*2) = -srcx;
-        LSPTR(tid_y, 4, tid_x*2) = -srcy;
-        LSPTR(tid_y, 5, tid_x*2) = -1.0f;
-        LSPTR(tid_y, 6, tid_x*2) = dsty*srcx;
-        LSPTR(tid_y, 7, tid_x*2) = dsty*srcy;
-        LSPTR(tid_y, 8, tid_x*2) = dsty;
-
-        LSPTR(tid_y, 0, tid_x*2+1) = srcx;
-        LSPTR(tid_y, 1, tid_x*2+1) = srcy;
-        LSPTR(tid_y, 2, tid_x*2+1) = 1.0f;
-        LSPTR(tid_y, 3, tid_x*2+1) = 0.0f;
-        LSPTR(tid_y, 4, tid_x*2+1) = 0.0f;
-        LSPTR(tid_y, 5, tid_x*2+1) = 0.0f;
-        LSPTR(tid_y, 6, tid_x*2+1) = -dstx*srcx;
-        LSPTR(tid_y, 7, tid_x*2+1) = -dstx*srcy;
-        LSPTR(tid_y, 8, tid_x*2+1) = -dstx;
+        LSPTR(tid_y, 0, tid_x * 2) = 0.0f;
+        LSPTR(tid_y, 1, tid_x * 2) = 0.0f;
+        LSPTR(tid_y, 2, tid_x * 2) = 0.0f;
+        LSPTR(tid_y, 3, tid_x * 2) = -srcx;
+        LSPTR(tid_y, 4, tid_x * 2) = -srcy;
+        LSPTR(tid_y, 5, tid_x * 2) = -1.0f;
+        LSPTR(tid_y, 6, tid_x * 2) = dsty * srcx;
+        LSPTR(tid_y, 7, tid_x * 2) = dsty * srcy;
+        LSPTR(tid_y, 8, tid_x * 2) = dsty;
+
+        LSPTR(tid_y, 0, tid_x * 2 + 1) = srcx;
+        LSPTR(tid_y, 1, tid_x * 2 + 1) = srcy;
+        LSPTR(tid_y, 2, tid_x * 2 + 1) = 1.0f;
+        LSPTR(tid_y, 3, tid_x * 2 + 1) = 0.0f;
+        LSPTR(tid_y, 4, tid_x * 2 + 1) = 0.0f;
+        LSPTR(tid_y, 5, tid_x * 2 + 1) = 0.0f;
+        LSPTR(tid_y, 6, tid_x * 2 + 1) = -dstx * srcx;
+        LSPTR(tid_y, 7, tid_x * 2 + 1) = -dstx * srcy;
+        LSPTR(tid_y, 8, tid_x * 2 + 1) = -dstx;
 
         if (tid_x == 4) {
             LSPTR(tid_y, 0, 8) = 0.0f;
@@ -261,51 +237,52 @@ __kernel void compute_homography(
 
     if (i < HInfo.dims[1] && tid_x == 0) {
         T vH[9], H_tmp[9];
-        for (unsigned j = 0; j < 9; j++)
-            vH[j] = l_V[tid_y * 81 + 8 * 9 + j];
-
-        H_tmp[0] = src_scale*x_dst_mean*vH[6] + src_scale*vH[0]/dst_scale;
-        H_tmp[1] = src_scale*x_dst_mean*vH[7] + src_scale*vH[1]/dst_scale;
-        H_tmp[2] = x_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-            (vH[2] - src_scale*y_src_mean*vH[1] - src_scale*x_src_mean*vH[0])/dst_scale;
-
-        H_tmp[3] = src_scale*y_dst_mean*vH[6] + src_scale*vH[3]/dst_scale;
-        H_tmp[4] = src_scale*y_dst_mean*vH[7] + src_scale*vH[4]/dst_scale;
-        H_tmp[5] = y_dst_mean*(vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6]) +
-            (vH[5] - src_scale*y_src_mean*vH[4] - src_scale*x_src_mean*vH[3])/dst_scale;
-
-        H_tmp[6] = src_scale*vH[6];
-        H_tmp[7] = src_scale*vH[7];
-        H_tmp[8] = vH[8] - src_scale*y_src_mean*vH[7] - src_scale*x_src_mean*vH[6];
+        for (unsigned j = 0; j < 9; j++) vH[j] = l_V[tid_y * 81 + 8 * 9 + j];
+
+        H_tmp[0] =
+            src_scale * x_dst_mean * vH[6] + src_scale * vH[0] / dst_scale;
+        H_tmp[1] =
+            src_scale * x_dst_mean * vH[7] + src_scale * vH[1] / dst_scale;
+        H_tmp[2] = x_dst_mean * (vH[8] - src_scale * y_src_mean * vH[7] -
+                                 src_scale * x_src_mean * vH[6]) +
+                   (vH[2] - src_scale * y_src_mean * vH[1] -
+                    src_scale * x_src_mean * vH[0]) /
+                       dst_scale;
+
+        H_tmp[3] =
+            src_scale * y_dst_mean * vH[6] + src_scale * vH[3] / dst_scale;
+        H_tmp[4] =
+            src_scale * y_dst_mean * vH[7] + src_scale * vH[4] / dst_scale;
+        H_tmp[5] = y_dst_mean * (vH[8] - src_scale * y_src_mean * vH[7] -
+                                 src_scale * x_src_mean * vH[6]) +
+                   (vH[5] - src_scale * y_src_mean * vH[4] -
+                    src_scale * x_src_mean * vH[3]) /
+                       dst_scale;
+
+        H_tmp[6] = src_scale * vH[6];
+        H_tmp[7] = src_scale * vH[7];
+        H_tmp[8] = vH[8] - src_scale * y_src_mean * vH[7] -
+                   src_scale * x_src_mean * vH[6];
 
         const unsigned Hidx = HInfo.dims[0] * i;
-        __global T* H_ptr = H + Hidx;
-        for (int h = 0; h < 9; h++)
-            H_ptr[h] = bad ? 0 : H_tmp[h];
+        __global T* H_ptr   = H + Hidx;
+        for (int h = 0; h < 9; h++) H_ptr[h] = bad ? 0 : H_tmp[h];
     }
 }
 
 #undef APTR
 
-// LMedS: http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
+// LMedS:
+// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
 __kernel void eval_homography(
-    __global unsigned* inliers,
-    __global unsigned* idx,
-    __global T* H,
-    KParam HInfo,
-    __global float* err,
-    KParam eInfo,
-    __global const float* x_src,
-    __global const float* y_src,
-    __global const float* x_dst,
-    __global const float* y_dst,
-    __global const float* rnd,
-    const unsigned iterations,
-    const unsigned nsamples,
-    const float inlier_thr)
-{
+    __global unsigned* inliers, __global unsigned* idx, __global T* H,
+    KParam HInfo, __global float* err, KParam eInfo,
+    __global const float* x_src, __global const float* y_src,
+    __global const float* x_dst, __global const float* y_dst,
+    __global const float* rnd, const unsigned iterations,
+    const unsigned nsamples, const float inlier_thr) {
     unsigned tid_x = get_local_id(0);
-    unsigned i = get_global_id(0);
+    unsigned i     = get_global_id(0);
 
     __local unsigned l_inliers[256];
     __local unsigned l_idx[256];
@@ -316,22 +293,22 @@ __kernel void eval_homography(
 
     if (i < iterations) {
         const unsigned Hidx = HInfo.dims[0] * i;
-        __global T* H_ptr = H + Hidx;
+        __global T* H_ptr   = H + Hidx;
         T H_tmp[9];
-        for (int h = 0; h < 9; h++)
-            H_tmp[h] = H_ptr[h];
+        for (int h = 0; h < 9; h++) H_tmp[h] = H_ptr[h];
 
 #ifdef RANSAC
         // Compute inliers
         unsigned inliers_count = 0;
         for (unsigned j = 0; j < nsamples; j++) {
-            float z =  H_tmp[6]*x_src[j] + H_tmp[7]*y_src[j] + H_tmp[8];
-            float x = (H_tmp[0]*x_src[j] + H_tmp[1]*y_src[j] + H_tmp[2]) / z;
-            float y = (H_tmp[3]*x_src[j] + H_tmp[4]*y_src[j] + H_tmp[5]) / z;
+            float z = H_tmp[6] * x_src[j] + H_tmp[7] * y_src[j] + H_tmp[8];
+            float x =
+                (H_tmp[0] * x_src[j] + H_tmp[1] * y_src[j] + H_tmp[2]) / z;
+            float y =
+                (H_tmp[3] * x_src[j] + H_tmp[4] * y_src[j] + H_tmp[5]) / z;
 
             float dist = sq(x_dst[j] - x) + sq(y_dst[j] - y);
-            if (dist < inlier_thr*inlier_thr)
-                inliers_count++;
+            if (dist < inlier_thr * inlier_thr) inliers_count++;
         }
 
         l_inliers[tid_x] = inliers_count;
@@ -340,12 +317,14 @@ __kernel void eval_homography(
 #ifdef LMEDS
         // Compute error
         for (unsigned j = 0; j < nsamples; j++) {
-            float z =  H_tmp[6]*x_src[j] + H_tmp[7]*y_src[j] + H_tmp[8];
-            float x = (H_tmp[0]*x_src[j] + H_tmp[1]*y_src[j] + H_tmp[2]) / z;
-            float y = (H_tmp[3]*x_src[j] + H_tmp[4]*y_src[j] + H_tmp[5]) / z;
-
-            float dist = sq(x_dst[j] - x) + sq(y_dst[j] - y);
-            err[i*eInfo.dims[0] + j] = sqrt(dist);
+            float z = H_tmp[6] * x_src[j] + H_tmp[7] * y_src[j] + H_tmp[8];
+            float x =
+                (H_tmp[0] * x_src[j] + H_tmp[1] * y_src[j] + H_tmp[2]) / z;
+            float y =
+                (H_tmp[3] * x_src[j] + H_tmp[4] * y_src[j] + H_tmp[5]) / z;
+
+            float dist                 = sq(x_dst[j] - x) + sq(y_dst[j] - y);
+            err[i * eInfo.dims[0] + j] = sqrt(dist);
         }
 #endif
     }
@@ -372,30 +351,26 @@ __kernel void eval_homography(
 #endif
 }
 
-__kernel void compute_median(
-    __global float* median,
-    __global unsigned* idx,
-    __global const float* err,
-    KParam eInfo,
-    const unsigned iterations)
-{
+__kernel void compute_median(__global float* median, __global unsigned* idx,
+                             __global const float* err, KParam eInfo,
+                             const unsigned iterations) {
     const unsigned tid = get_local_id(0);
     const unsigned bid = get_group_id(0);
-    const unsigned i = get_global_id(0);
+    const unsigned i   = get_global_id(0);
 
     __local float l_median[256];
     __local unsigned l_idx[256];
 
     l_median[tid] = FLT_MAX;
-    l_idx[tid] = 0;
+    l_idx[tid]    = 0;
 
     if (i < iterations) {
         const int nsamples = eInfo.dims[0];
-        float m = err[i*nsamples + nsamples / 2];
+        float m            = err[i * nsamples + nsamples / 2];
         if (nsamples % 2 == 0)
-            m = (m + err[i*nsamples + nsamples / 2 - 1]) * 0.5f;
+            m = (m + err[i * nsamples + nsamples / 2 - 1]) * 0.5f;
 
-        l_idx[tid] = i;
+        l_idx[tid]    = i;
         l_median[tid] = m;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
@@ -411,25 +386,22 @@ __kernel void compute_median(
     }
 
     median[bid] = l_median[0];
-    idx[bid] = l_idx[0];
+    idx[bid]    = l_idx[0];
 }
 
-#define DIVUP(A, B) (((A) + (B) - 1) / (B))
+#define DIVUP(A, B) (((A) + (B)-1) / (B))
 
-__kernel void find_min_median(
-    __global float* minMedian,
-    __global unsigned* minIdx,
-    __global const float* median,
-    KParam mInfo,
-    __global const unsigned* idx)
-{
+__kernel void find_min_median(__global float* minMedian,
+                              __global unsigned* minIdx,
+                              __global const float* median, KParam mInfo,
+                              __global const unsigned* idx) {
     const unsigned tid = get_local_id(0);
 
     __local float l_minMedian[256];
     __local unsigned l_minIdx[256];
 
     l_minMedian[tid] = FLT_MAX;
-    l_minIdx[tid] = 0;
+    l_minIdx[tid]    = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     const int loop = DIVUP(mInfo.dims[0], get_local_size(0));
@@ -438,7 +410,7 @@ __kernel void find_min_median(
         int j = i * get_local_size(0) + tid;
         if (j < mInfo.dims[0] && median[j] < l_minMedian[tid]) {
             l_minMedian[tid] = median[j];
-            l_minIdx[tid] = idx[j];
+            l_minIdx[tid]    = idx[j];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
@@ -454,24 +426,19 @@ __kernel void find_min_median(
     }
 
     *minMedian = l_minMedian[0];
-    *minIdx = l_minIdx[0];
+    *minIdx    = l_minIdx[0];
 }
 
 #undef DIVUP
 
 __kernel void compute_lmeds_inliers(
-    __global unsigned* inliers,
-    __global const T* H,
-    __global const float* x_src,
-    __global const float* y_src,
-    __global const float* x_dst,
-    __global const float* y_dst,
-    const float minMedian,
-    const unsigned nsamples)
-{
+    __global unsigned* inliers, __global const T* H,
+    __global const float* x_src, __global const float* y_src,
+    __global const float* x_dst, __global const float* y_dst,
+    const float minMedian, const unsigned nsamples) {
     unsigned tid = get_local_id(0);
     unsigned bid = get_group_id(0);
-    unsigned i = get_global_id(0);
+    unsigned i   = get_global_id(0);
 
     __local T l_H[9];
     __local unsigned l_inliers[256];
@@ -479,27 +446,25 @@ __kernel void compute_lmeds_inliers(
     l_inliers[tid] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (tid < 9)
-        l_H[tid] = H[tid];
+    if (tid < 9) l_H[tid] = H[tid];
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    float sigma = fmax(1.4826f * (1 + 5.f/(nsamples - 4)) * (float)sqrt(minMedian), 1e-6f);
+    float sigma = fmax(
+        1.4826f * (1 + 5.f / (nsamples - 4)) * (float)sqrt(minMedian), 1e-6f);
     float dist_thr = sq(2.5f * sigma);
 
     if (i < nsamples) {
-        float z =  l_H[6]*x_src[i] + l_H[7]*y_src[i] + l_H[8];
-        float x = (l_H[0]*x_src[i] + l_H[1]*y_src[i] + l_H[2]) / z;
-        float y = (l_H[3]*x_src[i] + l_H[4]*y_src[i] + l_H[5]) / z;
+        float z = l_H[6] * x_src[i] + l_H[7] * y_src[i] + l_H[8];
+        float x = (l_H[0] * x_src[i] + l_H[1] * y_src[i] + l_H[2]) / z;
+        float y = (l_H[3] * x_src[i] + l_H[4] * y_src[i] + l_H[5]) / z;
 
         float dist = sq(x_dst[i] - x) + sq(y_dst[i] - y);
-        if (dist <= dist_thr)
-            l_inliers[tid] = 1;
+        if (dist <= dist_thr) l_inliers[tid] = 1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     for (unsigned t = 128; t > 0; t >>= 1) {
-        if (tid < t)
-            l_inliers[tid] += l_inliers[tid + t];
+        if (tid < t) l_inliers[tid] += l_inliers[tid + t];
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 54dafb258e..63a3e7213d 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -7,54 +7,50 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
-#include <memory.hpp>
-#include <kernel_headers/homography.hpp>
+#include <err_opencl.hpp>
 #include <kernel/ireduce.hpp>
 #include <kernel/reduce.hpp>
 #include <kernel/sort.hpp>
+#include <kernel_headers/homography.hpp>
+#include <memory.hpp>
+#include <af/defines.h>
 #include <cfloat>
-#include <cache.hpp>
 
 using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
 using cl::EnqueueArgs;
+using cl::Kernel;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 using std::vector;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 const int HG_THREADS_X = 16;
 const int HG_THREADS_Y = 16;
 const int HG_THREADS   = 256;
 
 template<typename T, af_homography_type htype>
-std::array<cl::Kernel*, 5> getHomographyKernels()
-{
-    static const unsigned NUM_KERNELS = 5;
-    static const char* kernelNames[NUM_KERNELS] =
-        {"compute_homography", "eval_homography", "compute_median",
-         "find_min_median", "compute_lmeds_inliers"};
+std::array<cl::Kernel*, 5> getHomographyKernels() {
+    static const unsigned NUM_KERNELS           = 5;
+    static const char* kernelNames[NUM_KERNELS] = {
+        "compute_homography", "eval_homography", "compute_median",
+        "find_min_median", "compute_lmeds_inliers"};
 
     kc_entry_t entries[NUM_KERNELS];
 
     int device = getActiveDeviceId();
 
     std::string checkName = kernelNames[0] + std::string("_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(htype);
+                            std::string(dtype_traits<T>::getName()) +
+                            std::to_string(htype);
 
     entries[0] = kernelCache(device, checkName);
 
-    if (entries[0].prog==0 && entries[0].ker==0)
-    {
+    if (entries[0].prog == 0 && entries[0].ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
 
@@ -76,30 +72,28 @@ std::array<cl::Kernel*, 5> getHomographyKernels()
         cl::Program prog;
         buildProgram(prog, homography_cl, homography_cl_len, options.str());
 
-        for (unsigned i=0; i<NUM_KERNELS; ++i)
-        {
+        for (unsigned i = 0; i < NUM_KERNELS; ++i) {
             entries[i].prog = new Program(prog);
             entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] + std::string("_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::to_string(htype);
+                               std::string(dtype_traits<T>::getName()) +
+                               std::to_string(htype);
 
             addKernelToCache(device, name, entries[i]);
         }
     } else {
-        for (unsigned i=1; i<NUM_KERNELS; ++i) {
+        for (unsigned i = 1; i < NUM_KERNELS; ++i) {
             std::string name = kernelNames[i] + std::string("_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::to_string(htype);
+                               std::string(dtype_traits<T>::getName()) +
+                               std::to_string(htype);
 
             entries[i] = kernelCache(device, name);
         }
     }
 
     std::array<cl::Kernel*, NUM_KERNELS> retVal;
-    for (unsigned i=0; i<NUM_KERNELS; ++i)
-        retVal[i] = entries[i].ker;
+    for (unsigned i = 0; i < NUM_KERNELS; ++i) retVal[i] = entries[i].ker;
 
     return retVal;
 }
@@ -107,9 +101,7 @@ std::array<cl::Kernel*, 5> getHomographyKernels()
 template<typename T, af_homography_type htype>
 int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
              Param x_dst, Param y_dst, Param rnd, const unsigned iterations,
-             const unsigned nsamples,
-             const float inlier_thr)
-{
+             const unsigned nsamples, const float inlier_thr) {
     auto kernels = getHomographyKernels<T, htype>();
 
     const int blk_x_ch = 1;
@@ -118,11 +110,12 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
     const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
 
     // Build linear system and solve SVD
-    auto chOp = KernelFunctor< Buffer, KParam, Buffer, Buffer, Buffer, Buffer,
-                               Buffer, KParam, unsigned>(*kernels[0]);
+    auto chOp = KernelFunctor<Buffer, KParam, Buffer, Buffer, Buffer, Buffer,
+                              Buffer, KParam, unsigned>(*kernels[0]);
 
     chOp(EnqueueArgs(getQueue(), global_ch, local_ch), *H.data, H.info,
-         *x_src.data, *y_src.data, *x_dst.data, *y_dst.data, *rnd.data, rnd.info, iterations);
+         *x_src.data, *y_src.data, *x_dst.data, *y_dst.data, *rnd.data,
+         rnd.info, iterations);
 
     CL_DEBUG_FINISH(getQueue());
 
@@ -133,31 +126,38 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
     // Allocate some temporary buffers
     Param inliers, idx, median;
     inliers.info.offset = idx.info.offset = median.info.offset = 0;
-    inliers.info.dims[0] = (htype == AF_HOMOGRAPHY_RANSAC) ? blk_x_eh : divup(nsamples, HG_THREADS);
+    inliers.info.dims[0] = (htype == AF_HOMOGRAPHY_RANSAC)
+                               ? blk_x_eh
+                               : divup(nsamples, HG_THREADS);
     inliers.info.strides[0] = 1;
     idx.info.dims[0] = median.info.dims[0] = blk_x_eh;
     idx.info.strides[0] = median.info.strides[0] = 1;
     for (int k = 1; k < 4; k++) {
         inliers.info.dims[k] = 1;
-        inliers.info.strides[k] = inliers.info.dims[k-1] * inliers.info.strides[k-1];
+        inliers.info.strides[k] =
+            inliers.info.dims[k - 1] * inliers.info.strides[k - 1];
         idx.info.dims[k] = median.info.dims[k] = 1;
-        idx.info.strides[k] = median.info.strides[k] = idx.info.dims[k-1] * idx.info.strides[k-1];
+        idx.info.strides[k]                    = median.info.strides[k] =
+            idx.info.dims[k - 1] * idx.info.strides[k - 1];
     }
-    idx.data = bufferAlloc(idx.info.dims[3] * idx.info.strides[3] * sizeof(unsigned));
-    inliers.data = bufferAlloc(inliers.info.dims[3] * inliers.info.strides[3] * sizeof(unsigned));
+    idx.data =
+        bufferAlloc(idx.info.dims[3] * idx.info.strides[3] * sizeof(unsigned));
+    inliers.data = bufferAlloc(inliers.info.dims[3] * inliers.info.strides[3] *
+                               sizeof(unsigned));
     if (htype == AF_HOMOGRAPHY_LMEDS)
-        median.data = bufferAlloc(median.info.dims[3] * median.info.strides[3] * sizeof(float));
+        median.data = bufferAlloc(median.info.dims[3] * median.info.strides[3] *
+                                  sizeof(float));
     else
         median.data = bufferAlloc(sizeof(float));
 
     // Compute (and for RANSAC, evaluate) homographies
-    auto ehOp = KernelFunctor< Buffer, Buffer, Buffer, KParam, Buffer, KParam,
-                               Buffer, Buffer, Buffer, Buffer,
-                               Buffer, unsigned, unsigned, float>(*kernels[1]);
+    auto ehOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer, KParam,
+                              Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
+                              unsigned, float>(*kernels[1]);
 
-    ehOp(EnqueueArgs(getQueue(), global_eh, local_eh), *inliers.data, *idx.data, *H.data, H.info,
-         *err.data, err.info, *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
-         *rnd.data, iterations, nsamples, inlier_thr);
+    ehOp(EnqueueArgs(getQueue(), global_eh, local_eh), *inliers.data, *idx.data,
+         *H.data, H.info, *err.data, err.info, *x_src.data, *y_src.data,
+         *x_dst.data, *y_dst.data, *rnd.data, iterations, nsamples, inlier_thr);
 
     CL_DEBUG_FINISH(getQueue());
 
@@ -171,10 +171,11 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
         float minMedian;
 
         // Compute median of every iteration
-        auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, unsigned>(*kernels[2]);
+        auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, unsigned>(
+            *kernels[2]);
 
-        cmOp(EnqueueArgs(getQueue(), global_eh, local_eh),
-             *median.data, *idx.data, *err.data, err.info, iterations);
+        cmOp(EnqueueArgs(getQueue(), global_eh, local_eh), *median.data,
+             *idx.data, *err.data, err.info, iterations);
 
         CL_DEBUG_FINISH(getQueue());
 
@@ -184,38 +185,44 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
             const NDRange global_fm(HG_THREADS);
 
             cl::Buffer* finalMedian = bufferAlloc(sizeof(float));
-            cl::Buffer* finalIdx = bufferAlloc(sizeof(unsigned));
+            cl::Buffer* finalIdx    = bufferAlloc(sizeof(unsigned));
 
-            auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer>(*kernels[3]);
+            auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer>(
+                *kernels[3]);
 
-            fmOp(EnqueueArgs(getQueue(), global_fm, local_fm),
-                 *finalMedian, *finalIdx, *median.data, median.info, *idx.data);
+            fmOp(EnqueueArgs(getQueue(), global_fm, local_fm), *finalMedian,
+                 *finalIdx, *median.data, median.info, *idx.data);
 
             CL_DEBUG_FINISH(getQueue());
 
-            getQueue().enqueueReadBuffer(*finalMedian, CL_TRUE, 0, sizeof(float), &minMedian);
-            getQueue().enqueueReadBuffer(*finalIdx, CL_TRUE, 0, sizeof(unsigned), &minIdx);
+            getQueue().enqueueReadBuffer(*finalMedian, CL_TRUE, 0,
+                                         sizeof(float), &minMedian);
+            getQueue().enqueueReadBuffer(*finalIdx, CL_TRUE, 0,
+                                         sizeof(unsigned), &minIdx);
 
             bufferFree(finalMedian);
             bufferFree(finalIdx);
-        }
-        else {
-            getQueue().enqueueReadBuffer(*median.data, CL_TRUE, 0, sizeof(float), &minMedian);
-            getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, 0, sizeof(unsigned), &minIdx);
+        } else {
+            getQueue().enqueueReadBuffer(*median.data, CL_TRUE, 0,
+                                         sizeof(float), &minMedian);
+            getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, 0,
+                                         sizeof(unsigned), &minIdx);
         }
 
         // Copy best homography to output
-        getQueue().enqueueCopyBuffer(*H.data, *bestH.data, minIdx*9*sizeof(T), 0, 9*sizeof(T));
+        getQueue().enqueueCopyBuffer(*H.data, *bestH.data,
+                                     minIdx * 9 * sizeof(T), 0, 9 * sizeof(T));
 
         const int blk_x_cl = divup(nsamples, HG_THREADS);
         const NDRange local_cl(HG_THREADS);
         const NDRange global_cl(blk_x_cl * HG_THREADS);
 
-        auto clOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                                   float, unsigned >(*kernels[4]);
+        auto clOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer,
+                                  Buffer, float, unsigned>(*kernels[4]);
 
-        clOp(EnqueueArgs(getQueue(), global_cl, local_cl), *inliers.data, *bestH.data,
-             *x_src.data, *y_src.data, *x_dst.data, *y_dst.data, minMedian, nsamples);
+        clOp(EnqueueArgs(getQueue(), global_cl, local_cl), *inliers.data,
+             *bestH.data, *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
+             minMedian, nsamples);
 
         CL_DEBUG_FINISH(getQueue());
 
@@ -226,9 +233,11 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
             totalInliers.info.dims[k] = totalInliers.info.strides[k] = 1;
         totalInliers.data = bufferAlloc(sizeof(unsigned));
 
-        kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0, false, 0.0);
+        kernel::reduce<unsigned, unsigned, af_add_t>(totalInliers, inliers, 0,
+                                                     false, 0.0);
 
-        getQueue().enqueueReadBuffer(*totalInliers.data, CL_TRUE, 0, sizeof(unsigned), &inliersH);
+        getQueue().enqueueReadBuffer(*totalInliers.data, CL_TRUE, 0,
+                                     sizeof(unsigned), &inliersH);
 
         bufferFree(totalInliers.data);
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
@@ -236,9 +245,11 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
         inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
 
         // Copies back index and number of inliers of best homography estimation
-        getQueue().enqueueReadBuffer(*idx.data, CL_TRUE, blockIdx*sizeof(unsigned),
-                                      sizeof(unsigned), &idxH);
-        getQueue().enqueueCopyBuffer(*H.data, *bestH.data, idxH*9*sizeof(T), 0, 9*sizeof(T));
+        getQueue().enqueueReadBuffer(*idx.data, CL_TRUE,
+                                     blockIdx * sizeof(unsigned),
+                                     sizeof(unsigned), &idxH);
+        getQueue().enqueueCopyBuffer(*H.data, *bestH.data, idxH * 9 * sizeof(T),
+                                     0, 9 * sizeof(T));
     }
 
     bufferFree(inliers.data);
@@ -247,5 +258,5 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
 
     return (int)inliersH;
 }
-} // namespace kernel
-} // namespace cuda
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/hsv_rgb.cl b/src/backend/opencl/kernel/hsv_rgb.cl
index d3095256e4..d5308903c2 100644
--- a/src/backend/opencl/kernel/hsv_rgb.cl
+++ b/src/backend/opencl/kernel/hsv_rgb.cl
@@ -7,24 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-kernel
-void convert(global T * out, KParam oInfo, global const T * in, KParam iInfo, int nBBS)
-{
+kernel void convert(global T* out, KParam oInfo, global const T* in,
+                    KParam iInfo, int nBBS) {
     // batch offsets
-    unsigned batchId = get_group_id(0) / nBBS;
-    global const T* src =  in + (batchId * iInfo.strides[3]);
-    global T*       dst = out + (batchId * oInfo.strides[3]);
+    unsigned batchId    = get_group_id(0) / nBBS;
+    global const T* src = in + (batchId * iInfo.strides[3]);
+    global T* dst       = out + (batchId * oInfo.strides[3]);
     // global indices
-    int gx = get_local_size(0) * (get_group_id(0)-batchId*nBBS) + get_local_id(0);
+    int gx = get_local_size(0) * (get_group_id(0) - batchId * nBBS) +
+             get_local_id(0);
     int gy = get_local_size(1) * get_group_id(1) + get_local_id(1);
 
     if (gx < oInfo.dims[0] && gy < oInfo.dims[1]) {
-
         int oIdx0 = gx + gy * oInfo.strides[1];
         int oIdx1 = oIdx0 + oInfo.strides[2];
         int oIdx2 = oIdx1 + oInfo.strides[2];
 
-        int iIdx0 = gx * iInfo.strides[0] + gy * iInfo.strides[1] + iInfo.offset;
+        int iIdx0 =
+            gx * iInfo.strides[0] + gy * iInfo.strides[1] + iInfo.offset;
         int iIdx1 = iIdx0 + iInfo.strides[2];
         int iIdx2 = iIdx1 + iInfo.strides[2];
 
@@ -36,11 +36,11 @@ void convert(global T * out, KParam oInfo, global const T * in, KParam iInfo, in
         T R, G, B;
         R = G = B = 0;
 
-        int   i = (int)(H * 6);
-        T f = H * 6 - i;
-        T p = V * (1 - S);
-        T q = V * (1 - f * S);
-        T t = V * (1 - (1 - f) * S);
+        int i = (int)(H * 6);
+        T f   = H * 6 - i;
+        T p   = V * (1 - S);
+        T q   = V * (1 - f * S);
+        T t   = V * (1 - (1 - f) * S);
 
         switch (i % 6) {
             case 0: R = V, G = t, B = p; break;
@@ -55,24 +55,24 @@ void convert(global T * out, KParam oInfo, global const T * in, KParam iInfo, in
         dst[oIdx1] = G;
         dst[oIdx2] = B;
 #else
-        T R = src[iIdx0];
-        T G = src[iIdx1];
-        T B = src[iIdx2];
-        T Cmax = fmax(fmax(R, G), B);
-        T Cmin = fmin(fmin(R, G), B);
-        T delta= Cmax-Cmin;
+        T R     = src[iIdx0];
+        T G     = src[iIdx1];
+        T B     = src[iIdx2];
+        T Cmax  = fmax(fmax(R, G), B);
+        T Cmin  = fmin(fmin(R, G), B);
+        T delta = Cmax - Cmin;
 
         T H = 0;
 
-        if (Cmax!=Cmin) {
-            if (Cmax==R) H = (G-B)/delta + (G<B ? 6 : 0);
-            if (Cmax==G) H = (B-R)/delta + 2;
-            if (Cmax==B) H = (R-G)/delta + 4;
+        if (Cmax != Cmin) {
+            if (Cmax == R) H = (G - B) / delta + (G < B ? 6 : 0);
+            if (Cmax == G) H = (B - R) / delta + 2;
+            if (Cmax == B) H = (R - G) / delta + 4;
             H = H / 6.0f;
         }
 
         dst[oIdx0] = H;
-        dst[oIdx1] = Cmax==0.0f ? 0 : delta/Cmax;
+        dst[oIdx1] = Cmax == 0.0f ? 0 : delta / Cmax;
         dst[oIdx2] = Cmax;
 #endif
     }
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 6b539c90ce..abff64a6e7 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -1,55 +1,53 @@
 /*******************************************************
-* Copyright (c) 2014, ArrayFire
-* All rights reserved.
-*
-* This file is distributed under 3-clause BSD license.
-* The complete license agreement can be obtained at:
-* http://arrayfire.com/licenses/BSD-3-Clause
-********************************************************/
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/hsv_rgb.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool isHSV2RGB>
-void hsv2rgb_convert(Param out, const Param in)
-{
+void hsv2rgb_convert(Param out, const Param in) {
     std::string refName = std::string("hsvrgb_convert_") +
-        std::string(dtype_traits<T>::getName()) + std::to_string(isHSV2RGB);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(isHSV2RGB);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
 
-        if(isHSV2RGB) options << " -D isHSV2RGB";
+        if (isHSV2RGB) options << " -D isHSV2RGB";
         if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {hsv_rgb_cl};
-        const int   ker_lens[] = {hsv_rgb_cl_len};
+        const int ker_lens[]   = {hsv_rgb_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -67,11 +65,13 @@ void hsv2rgb_convert(Param out, const Param in)
     // parameter would be along 4th dimension
     NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
 
-    auto hsvrgbOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int> (*entry.ker);
+    auto hsvrgbOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, int>(*entry.ker);
 
-    hsvrgbOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info, blk_x);
+    hsvrgbOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, blk_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/identity.cl b/src/backend/opencl/kernel/identity.cl
index 0c71099a9b..0c0144c31f 100644
--- a/src/backend/opencl/kernel/identity.cl
+++ b/src/backend/opencl/kernel/identity.cl
@@ -7,10 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void identity_kernel(__global T *oData, KParam oInfo, int groups_x, int groups_y)
-{
-
+__kernel void identity_kernel(__global T *oData, KParam oInfo, int groups_x,
+                              int groups_y) {
     unsigned idz = get_group_id(0) / groups_x;
     unsigned idw = get_group_id(1) / groups_y;
 
@@ -20,13 +18,11 @@ void identity_kernel(__global T *oData, KParam oInfo, int groups_x, int groups_y
     unsigned idx = get_local_id(0) + groupId_x * get_local_size(0);
     unsigned idy = get_local_id(1) + groupId_y * get_local_size(1);
 
-    if(idx >= oInfo.dims[0] ||
-       idy >= oInfo.dims[1] ||
-       idz >= oInfo.dims[2] ||
-       idw >= oInfo.dims[3])
+    if (idx >= oInfo.dims[0] || idy >= oInfo.dims[1] || idz >= oInfo.dims[2] ||
+        idw >= oInfo.dims[3])
         return;
 
     __global T *ptr = oData + idz * oInfo.strides[2] + idw * oInfo.strides[3];
-    T val = (idx == idy) ? ONE : ZERO;
+    T val           = (idx == idy) ? ONE : ZERO;
     ptr[idx + idy * oInfo.strides[1]] = val;
 }
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 26621ab274..72e3071d77 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -7,50 +7,47 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel_headers/identity.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
+#include <kernel_headers/identity.hpp>
 #include <math.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include "config.hpp"
 
+using af::scalar_to_option;
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
-using std::string;
+using cl::Program;
 using std::ostringstream;
-using af::scalar_to_option;
+using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 template<typename T>
-static void identity(Param out)
-{
-    std::string refName = std::string("identity_kernel") + std::string(dtype_traits<T>::getName());
+static void identity(Param out) {
+    std::string refName = std::string("identity_kernel") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="    << dtype_traits<T>::getName()
-            << " -D ONE=(T)("  << scalar_to_option(scalar<T>(1)) << ")"
-            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-        if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
+        options << " -D T=" << dtype_traits<T>::getName() << " -D ONE=(T)("
+                << scalar_to_option(scalar<T>(1)) << ")"
+                << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {identity_cl};
-        const int   ker_lens[] = {identity_cl_len};
+        const int ker_lens[]   = {identity_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -62,13 +59,15 @@ static void identity(Param out)
     NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
     int groups_y = divup(out.info.dims[1], local[1]);
-    NDRange global(groups_x * out.info.dims[2] * local[0], groups_y * out.info.dims[3] * local[1]);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
 
-    auto identityOp = KernelFunctor<Buffer, const KParam, int, int> (*entry.ker);
+    auto identityOp = KernelFunctor<Buffer, const KParam, int, int>(*entry.ker);
 
-    identityOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info, groups_x, groups_y);
+    identityOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info,
+               groups_x, groups_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/iir.cl b/src/backend/opencl/kernel/iir.cl
index b189065ed2..6a941c2e10 100644
--- a/src/backend/opencl/kernel/iir.cl
+++ b/src/backend/opencl/kernel/iir.cl
@@ -14,26 +14,23 @@
 #endif
 
 #if CPLX
-T __mul(T lhs, T rhs)
-{
+T __mul(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-T __cconjf(T in)
-{
+T __cconjf(T in) {
     T out = {in.x, -in.y};
     return out;
 }
 
 // FIXME: overflow / underflow issues
-T __div(T lhs, T rhs)
-{
+T __div(T lhs, T rhs) {
     T out;
     TR den = (rhs.x * rhs.x + rhs.y * rhs.y);
-    T num = __mul(lhs, __cconjf(rhs));
+    T num  = __mul(lhs, __cconjf(rhs));
 
     out.x = num.x / den;
     out.y = num.y / den;
@@ -41,16 +38,14 @@ T __div(T lhs, T rhs)
     return out;
 }
 #else
-#define __mul(lhs, rhs) ((lhs)*(rhs))
-#define __div(lhs, rhs) ((lhs)/(rhs))
+#define __mul(lhs, rhs) ((lhs) * (rhs))
+#define __div(lhs, rhs) ((lhs) / (rhs))
 #endif
 
-__kernel
-void iir_kernel(      __global T *yptr, const KParam yinfo,
-                const __global T *cptr, const KParam cinfo,
-                const __global T *aptr, const KParam ainfo,
-                const int groups_y)
-{
+__kernel void iir_kernel(__global T *yptr, const KParam yinfo,
+                         const __global T *cptr, const KParam cinfo,
+                         const __global T *aptr, const KParam ainfo,
+                         const int groups_y) {
     __local T s_z[MAX_A_SIZE];
     __local T s_a[MAX_A_SIZE];
     __local T s_y;
@@ -59,34 +54,36 @@ void iir_kernel(      __global T *yptr, const KParam yinfo,
     const int idw = get_group_id(1) / groups_y;
     const int idy = get_group_id(1) - idw * groups_y;
 
-    const int tx = get_local_id(0);
+    const int tx    = get_local_id(0);
     const int num_a = ainfo.dims[0];
 
-    int y_off = idw * yinfo.strides[3] + idz * yinfo.strides[2] + idy * yinfo.strides[1];
-    int c_off = idw * cinfo.strides[3] + idz * cinfo.strides[2] + idy * cinfo.strides[1];
+    int y_off = idw * yinfo.strides[3] + idz * yinfo.strides[2] +
+                idy * yinfo.strides[1];
+    int c_off = idw * cinfo.strides[3] + idz * cinfo.strides[2] +
+                idy * cinfo.strides[1];
 
 #if BATCH_A
-    int a_off = idw * ainfo.strides[3] + idz * ainfo.strides[2] + idy * ainfo.strides[1];
+    int a_off = idw * ainfo.strides[3] + idz * ainfo.strides[2] +
+                idy * ainfo.strides[1];
 #else
     int a_off = 0;
 #endif
 
-    __global T *d_y = yptr + y_off;
+    __global T *d_y       = yptr + y_off;
     const __global T *d_c = cptr + c_off + cinfo.offset;
     const __global T *d_a = aptr + a_off + ainfo.offset;
-    const int repeat = (num_a + get_local_size(0) - 1) / get_local_size(0);
+    const int repeat      = (num_a + get_local_size(0) - 1) / get_local_size(0);
 
     for (int ii = 0; ii < MAX_A_SIZE / get_local_size(0); ii++) {
-        int id = ii * get_local_size(0) + tx;
+        int id  = ii * get_local_size(0) + tx;
         s_z[id] = ZERO;
         s_a[id] = (id < num_a) ? d_a[id] : ZERO;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
-
     for (int i = 0; i < yinfo.dims[0]; i++) {
         if (tx == 0) {
-            s_y = __div((d_c[i] + s_z[0]), s_a[0]);
+            s_y    = __div((d_c[i] + s_z[0]), s_a[0]);
             d_y[i] = s_y;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -94,7 +91,7 @@ void iir_kernel(      __global T *yptr, const KParam yinfo,
         for (int ii = 0; ii < repeat; ii++) {
             int id = ii * get_local_size(0) + tx + 1;
 
-            T z = s_z[id] - __mul(s_a[id],  s_y);
+            T z = s_z[id] - __mul(s_a[id], s_y);
             barrier(CLK_LOCAL_MEM_FENCE);
 
             s_z[id - 1] = z;
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 2d64d2ebf3..c594fd3bc3 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -8,53 +8,51 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/iir.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/iir.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <types.hpp>
+#include <string>
 
+using af::scalar_to_option;
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
-using af::scalar_to_option;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 template<typename T, bool batch_a>
-void iir(Param y, Param c, Param a)
-{
-    //FIXME: This is a temporary fix. Ideally the local memory should be allocted outside
+void iir(Param y, Param c, Param a) {
+    // FIXME: This is a temporary fix. Ideally the local memory should be
+    // allocted outside
     static const int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
 
     std::string refName = std::string("iir_kernel_") +
-        std::string(dtype_traits<T>::getName()) + std::to_string(batch_a);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(batch_a);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D MAX_A_SIZE=" << MAX_A_SIZE
-            << " -D BATCH_A=" << batch_a
-            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-            << " -D T=" << dtype_traits<T>::getName();
+        options << " -D MAX_A_SIZE=" << MAX_A_SIZE << " -D BATCH_A=" << batch_a
+                << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+                << " -D T=" << dtype_traits<T>::getName();
 
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {iir_cl};
-        const int   ker_lens[] = {iir_cl_len};
+        const int ker_lens[]   = {iir_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -72,16 +70,18 @@ void iir(Param y, Param c, Param a)
     NDRange local(threads, 1);
     NDRange global(groups_x * local[0], groups_y * y.info.dims[3] * local[1]);
 
-    auto iirOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int>(*entry.ker);
+    auto iirOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int>(
+            *entry.ker);
 
     try {
-        iirOp(EnqueueArgs(getQueue(), global, local),
-              *y.data, y.info, *c.data, c.info, *a.data, a.info, groups_y);
-    } catch(cl::Error &clerr) {
+        iirOp(EnqueueArgs(getQueue(), global, local), *y.data, y.info, *c.data,
+              c.info, *a.data, a.info, groups_y);
+    } catch (cl::Error& clerr) {
         AF_ERROR("Size of a too big for this datatype", AF_ERR_SIZE);
     }
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/index.cl b/src/backend/opencl/kernel/index.cl
index 6b44938dd1..85e6e10cc0 100644
--- a/src/backend/opencl/kernel/index.cl
+++ b/src/backend/opencl/kernel/index.cl
@@ -8,54 +8,57 @@
  ********************************************************/
 
 typedef struct {
-    int  offs[4];
+    int offs[4];
     int strds[4];
-    char     isSeq[4];
+    char isSeq[4];
 } IndexKernelParam_t;
 
-int trimIndex(int idx, const int len)
-{
+int trimIndex(int idx, const int len) {
     int ret_val = idx;
-    int offset  = abs(ret_val)%len;
-    if (ret_val<0) {
-        int offset  = (abs(ret_val)-1)%len;
-        ret_val = offset;
-    } else if (ret_val>=len) {
-        int offset  = abs(ret_val)%len;
-        ret_val = len-offset-1;
+    int offset  = abs(ret_val) % len;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
     }
     return ret_val;
 }
 
-kernel
-void indexKernel(global T * optr, KParam oInfo, global const T * iptr, KParam iInfo,
-                 const IndexKernelParam_t p, global const uint* ptr0,
-                 global const uint* ptr1, global const uint* ptr2,
-                 global const uint* ptr3, const int nBBS0, const int nBBS1)
-{
+kernel void indexKernel(global T* optr, KParam oInfo, global const T* iptr,
+                        KParam iInfo, const IndexKernelParam_t p,
+                        global const uint* ptr0, global const uint* ptr1,
+                        global const uint* ptr2, global const uint* ptr3,
+                        const int nBBS0, const int nBBS1) {
     // retrive booleans that tell us which index to use
     const bool s0 = p.isSeq[0];
     const bool s1 = p.isSeq[1];
     const bool s2 = p.isSeq[2];
     const bool s3 = p.isSeq[3];
 
-    const int gz = get_group_id(0)/nBBS0;
-    const int gw = get_group_id(1)/nBBS1;
-    const int gx = get_local_size(0) * (get_group_id(0) - gz*nBBS0) + get_local_id(0);
-    const int gy = get_local_size(1) * (get_group_id(1) - gw*nBBS1) + get_local_id(1);
+    const int gz = get_group_id(0) / nBBS0;
+    const int gw = get_group_id(1) / nBBS1;
+    const int gx =
+        get_local_size(0) * (get_group_id(0) - gz * nBBS0) + get_local_id(0);
+    const int gy =
+        get_local_size(1) * (get_group_id(1) - gw * nBBS1) + get_local_id(1);
 
-    if (gx<oInfo.dims[0] && gy<oInfo.dims[1] && gz<oInfo.dims[2] && gw<oInfo.dims[3]) {
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1] && gz < oInfo.dims[2] &&
+        gw < oInfo.dims[3]) {
         // calculate pointer offsets for input
-        int i = p.strds[0] * trimIndex(s0 ? gx+p.offs[0] : ptr0[gx], iInfo.dims[0]);
-        int j = p.strds[1] * trimIndex(s1 ? gy+p.offs[1] : ptr1[gy], iInfo.dims[1]);
-        int k = p.strds[2] * trimIndex(s2 ? gz+p.offs[2] : ptr2[gz], iInfo.dims[2]);
-        int l = p.strds[3] * trimIndex(s3 ? gw+p.offs[3] : ptr3[gw], iInfo.dims[3]);
+        int i = p.strds[0] *
+                trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], iInfo.dims[0]);
+        int j = p.strds[1] *
+                trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], iInfo.dims[1]);
+        int k = p.strds[2] *
+                trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], iInfo.dims[2]);
+        int l = p.strds[3] *
+                trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], iInfo.dims[3]);
         // offset input and output pointers
-        global const T *src = iptr + (i+j+k+l) + iInfo.offset;
-        global T *dst = optr + (gx*oInfo.strides[0]+
-                                gy*oInfo.strides[1]+
-                                gz*oInfo.strides[2]+
-                                gw*oInfo.strides[3]+
+        global const T* src = iptr + (i + j + k + l) + iInfo.offset;
+        global T* dst = optr + (gx * oInfo.strides[0] + gy * oInfo.strides[1] +
+                                gz * oInfo.strides[2] + gw * oInfo.strides[3] +
                                 oInfo.offset);
         // set the output
         dst[0] = src[0];
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index ea8c34646d..0f22da66cc 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -8,45 +8,44 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/index.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 32;
-static const int THREADS_Y =  8;
+static const int THREADS_Y = 8;
 
 typedef struct {
-    int  offs[4];
+    int offs[4];
     int strds[4];
-    char     isSeq[4];
+    char isSeq[4];
 } IndexKernelParam_t;
 
 template<typename T>
-void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[4])
-{
-    std::string refName = std::string("indexKernel_") + std::string(dtype_traits<T>::getName());
+void index(Param out, const Param in, const IndexKernelParam_t& p,
+           Buffer* bPtr[4]) {
+    std::string refName =
+        std::string("indexKernel_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
@@ -54,7 +53,7 @@ void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {index_cl};
-        const int   ker_lens[] = {index_cl_len};
+        const int ker_lens[]   = {index_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -68,16 +67,18 @@ void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * out.info.dims[2] * THREADS_X, blk_y * out.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
+                   blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto indexOp = KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
-                                 Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
+    auto indexOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
+                      Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
 
-    indexOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *in.data, in.info, p,
-            *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
+    indexOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x,
+            blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index 9cb435adc8..aa9c77ffde 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -13,8 +13,7 @@ typedef double ScalarTy;
 #else
 typedef float ScalarTy;
 #endif
-InterpInTy __mulrc(ScalarTy s, InterpInTy v)
-{
+InterpInTy __mulrc(ScalarTy s, InterpInTy v) {
     InterpInTy out = {s * v.x, s * v.y};
     return out;
 }
@@ -25,38 +24,31 @@ InterpInTy __mulrc(ScalarTy s, InterpInTy v)
 #define MULCR(a, b) (a) * (b)
 #endif
 
-InterpValTy linearInterpFunc(InterpValTy val[2], InterpPosTy ratio)
-{
+InterpValTy linearInterpFunc(InterpValTy val[2], InterpPosTy ratio) {
     return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
 }
 
-InterpValTy bilinearInterpFunc(InterpValTy val[2][2], InterpPosTy xratio, InterpPosTy yratio)
-{
+InterpValTy bilinearInterpFunc(InterpValTy val[2][2], InterpPosTy xratio,
+                               InterpPosTy yratio) {
     InterpValTy res[2];
     res[0] = linearInterpFunc(val[0], xratio);
     res[1] = linearInterpFunc(val[1], xratio);
     return linearInterpFunc(res, yratio);
 }
 
-InterpValTy cubicInterpFunc(InterpValTy val[4], InterpPosTy xratio, bool spline)
-{
+InterpValTy cubicInterpFunc(InterpValTy val[4], InterpPosTy xratio,
+                            bool spline) {
     InterpValTy a0, a1, a2, a3;
     if (spline) {
-        a0 =
-            MULRC((InterpPosTy)-0.5, val[0]) +
-            MULRC((InterpPosTy) 1.5, val[1]) +
-            MULRC((InterpPosTy)-1.5, val[2]) +
-            MULRC((InterpPosTy) 0.5, val[3]);
-
-        a1 =
-            MULRC((InterpPosTy) 1.0, val[0]) +
-            MULRC((InterpPosTy)-2.5, val[1]) +
-            MULRC((InterpPosTy) 2.0, val[2]) +
-            MULRC((InterpPosTy)-0.5, val[3]);
-
-        a2 =
-            MULRC((InterpPosTy)-0.5, val[0]) +
-            MULRC((InterpPosTy) 0.5, val[2]);
+        a0 = MULRC((InterpPosTy)-0.5, val[0]) +
+             MULRC((InterpPosTy)1.5, val[1]) +
+             MULRC((InterpPosTy)-1.5, val[2]) + MULRC((InterpPosTy)0.5, val[3]);
+
+        a1 = MULRC((InterpPosTy)1.0, val[0]) +
+             MULRC((InterpPosTy)-2.5, val[1]) +
+             MULRC((InterpPosTy)2.0, val[2]) + MULRC((InterpPosTy)-0.5, val[3]);
+
+        a2 = MULRC((InterpPosTy)-0.5, val[0]) + MULRC((InterpPosTy)0.5, val[2]);
 
         a3 = val[1];
     } else {
@@ -72,8 +64,8 @@ InterpValTy cubicInterpFunc(InterpValTy val[4], InterpPosTy xratio, bool spline)
     return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
 }
 
-InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio, InterpPosTy yratio, bool spline)
-{
+InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio,
+                              InterpPosTy yratio, bool spline) {
     InterpValTy res[4];
     res[0] = cubicInterpFunc(val[0], xratio, spline);
     res[1] = cubicInterpFunc(val[1], xratio, spline);
@@ -83,20 +75,16 @@ InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio, InterpP
 }
 
 #if INTERP_ORDER == 1
-void interp1_general(
-    __global InterpInTy *d_out,
-    KParam out, int ooff,
-    __global const InterpInTy *d_in,
-    KParam in, int ioff, InterpPosTy x,
-    int method, int batch, bool clamp,
-    int xdim, int batch_dim)
-{
+void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
+                     __global const InterpInTy *d_in, KParam in, int ioff,
+                     InterpPosTy x, int method, int batch, bool clamp, int xdim,
+                     int batch_dim) {
     InterpInTy zero = ZERO;
 
-    const int x_lim = in.dims[xdim];
+    const int x_lim    = in.dims[xdim];
     const int x_stride = in.strides[xdim];
 
-    int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+    int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
     bool cond = xid >= 0 && xid < x_lim;
     if (clamp) xid = max(0, min(xid, x_lim));
 
@@ -104,59 +92,55 @@ void interp1_general(
 
     for (int n = 0; n < batch; n++) {
         int idx_n = idx + n * in.strides[batch_dim];
-        d_out[ooff + n * out.strides[batch_dim]] = (clamp || cond) ? d_in[idx_n] : zero;
+        d_out[ooff + n * out.strides[batch_dim]] =
+            (clamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-void interp1_general(
-    __global InterpInTy *d_out,
-    KParam out, int ooff,
-    __global const InterpInTy *d_in,
-    KParam in, int ioff, InterpPosTy x,
-    int method, int batch, bool clamp,
-    int xdim, int batch_dim)
-{
-    const int grid_x = floor(x);    // nearest grid
-    const InterpPosTy off_x = x - grid_x;    // fractional offset
-
-    const int x_lim = in.dims[xdim];
+void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
+                     __global const InterpInTy *d_in, KParam in, int ioff,
+                     InterpPosTy x, int method, int batch, bool clamp, int xdim,
+                     int batch_dim) {
+    const int grid_x        = floor(x);    // nearest grid
+    const InterpPosTy off_x = x - grid_x;  // fractional offset
+
+    const int x_lim    = in.dims[xdim];
     const int x_stride = in.strides[xdim];
-    const int idx = ioff + grid_x * x_stride;
+    const int idx      = ioff + grid_x * x_stride;
 
-    InterpValTy zero = ZERO;
-    bool cond[2] = {true, grid_x + 1 < x_lim};
-    int  offx[2]  = {0, cond[1] ? 1 : 0};
+    InterpValTy zero  = ZERO;
+    bool cond[2]      = {true, grid_x + 1 < x_lim};
+    int offx[2]       = {0, cond[1] ? 1 : 0};
     InterpPosTy ratio = off_x;
     if (method == AF_INTERP_LINEAR_COSINE) {
-        ratio = (1 - cos(ratio * (InterpPosTy)M_PI))/2;
+        ratio = (1 - cos(ratio * (InterpPosTy)M_PI)) / 2;
     }
 
     for (int n = 0; n < batch; n++) {
-        int idx_n = idx + n * in.strides[batch_dim];
-        InterpValTy val[2] = {(clamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
-                              (clamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
+        int idx_n          = idx + n * in.strides[batch_dim];
+        InterpValTy val[2] = {
+            (clamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
+            (clamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
 
         d_out[ooff + n * out.strides[batch_dim]] = linearInterpFunc(val, ratio);
     }
 }
 #elif INTERP_ORDER == 3
-void interp1_general(
-    __global InterpInTy *d_out,
-    KParam out, int ooff,
-    __global const InterpInTy *d_in,
-    KParam in, int ioff, InterpPosTy x,
-    int method, int batch, bool clamp,
-    int xdim, int batch_dim)
-{
-    const int grid_x = floor(x);    // nearest grid
-    const InterpPosTy off_x = x - grid_x;    // fractional offset
-
-    const int x_lim = in.dims[xdim];
+void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
+                     __global const InterpInTy *d_in, KParam in, int ioff,
+                     InterpPosTy x, int method, int batch, bool clamp, int xdim,
+                     int batch_dim) {
+    const int grid_x        = floor(x);    // nearest grid
+    const InterpPosTy off_x = x - grid_x;  // fractional offset
+
+    const int x_lim    = in.dims[xdim];
     const int x_stride = in.strides[xdim];
-    const int idx = ioff + grid_x * x_stride;
+    const int idx      = ioff + grid_x * x_stride;
 
-    bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
-    int  off[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0, cond[3] ? 2 : (cond[2] ? 1 : 0)};
+    bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                    grid_x + 2 < x_lim};
+    int off[4]   = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
+                  cond[3] ? 2 : (cond[2] ? 1 : 0)};
 
     InterpValTy zero = ZERO;
 
@@ -164,28 +148,27 @@ void interp1_general(
         InterpValTy val[4];
         int idx_n = idx + n * in.strides[batch_dim];
         for (int i = 0; i < 4; i++) {
-            val[i] = (clamp || cond[i]) ? d_in[idx_n + off[i] * x_stride] : zero;
+            val[i] =
+                (clamp || cond[i]) ? d_in[idx_n + off[i] * x_stride] : zero;
         }
         bool spline = method == AF_INTERP_CUBIC_SPLINE;
-        d_out[ooff + n * out.strides[batch_dim]] = cubicInterpFunc(val, off_x, spline);;
+        d_out[ooff + n * out.strides[batch_dim]] =
+            cubicInterpFunc(val, off_x, spline);
+        ;
     }
 }
 #endif
 
 #if INTERP_ORDER == 1
-void interp2_general(
-    __global InterpInTy *d_out,
-    KParam out, int ooff,
-    __global const InterpInTy *d_in,
-    KParam in, int ioff, InterpPosTy x, InterpPosTy y,
-    int method, int batch, bool clamp,
-    int xdim, int ydim, int batch_dim)
-{
+void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
+                     __global const InterpInTy *d_in, KParam in, int ioff,
+                     InterpPosTy x, InterpPosTy y, int method, int batch,
+                     bool clamp, int xdim, int ydim, int batch_dim) {
     int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
     int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
 
-    const int x_lim = in.dims[xdim];
-    const int y_lim = in.dims[ydim];
+    const int x_lim    = in.dims[xdim];
+    const int y_lim    = in.dims[ydim];
     const int x_stride = in.strides[xdim];
     const int y_stride = in.strides[ydim];
 
@@ -199,42 +182,39 @@ void interp2_general(
     bool condY = yid >= 0 && yid < y_lim;
 
     InterpInTy zero = ZERO;
-    bool cond = condX && condY;
+    bool cond       = condX && condY;
     for (int n = 0; n < batch; n++) {
         int idx_n = idx + n * in.strides[batch_dim];
-        d_out[ooff + n * out.strides[batch_dim]] =  (clamp || cond) ? d_in[idx_n] : zero;
+        d_out[ooff + n * out.strides[batch_dim]] =
+            (clamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-void interp2_general(
-    __global InterpInTy *d_out,
-    KParam out, int ooff,
-    __global const InterpInTy *d_in,
-    KParam in, int ioff, InterpPosTy x, InterpPosTy y,
-    int method, int batch, bool clamp,
-    int xdim, int ydim, int batch_dim)
-{
-    const int grid_x = floor(x);
+void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
+                     __global const InterpInTy *d_in, KParam in, int ioff,
+                     InterpPosTy x, InterpPosTy y, int method, int batch,
+                     bool clamp, int xdim, int ydim, int batch_dim) {
+    const int grid_x        = floor(x);
     const InterpPosTy off_x = x - grid_x;
 
-    const int grid_y = floor(y);
+    const int grid_y        = floor(y);
     const InterpPosTy off_y = y - grid_y;
 
-    const int x_lim = in.dims[xdim];
-    const int y_lim = in.dims[ydim];
+    const int x_lim    = in.dims[xdim];
+    const int y_lim    = in.dims[ydim];
     const int x_stride = in.strides[xdim];
     const int y_stride = in.strides[ydim];
-    const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
+    const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
     bool condX[2] = {true, x + 1 < x_lim};
     bool condY[2] = {true, y + 1 < y_lim};
-    int  offx[2]  = {0, condX[1] ? 1 : 0};
-    int  offy[2]  = {0, condY[1] ? 1 : 0};
+    int offx[2]   = {0, condX[1] ? 1 : 0};
+    int offy[2]   = {0, condY[1] ? 1 : 0};
 
     InterpPosTy xratio = off_x, yratio = off_y;
     if (method == AF_INTERP_LINEAR_COSINE) {
-        xratio = (1 - cos(xratio * (InterpPosTy)M_PI))/2;
-        yratio = (1 - cos(yratio * (InterpPosTy)M_PI))/2;
+        xratio = (1 - cos(xratio * (InterpPosTy)M_PI)) / 2;
+        yratio = (1 - cos(yratio * (InterpPosTy)M_PI)) / 2;
     }
 
     InterpValTy zero = ZERO;
@@ -248,40 +228,41 @@ void interp2_general(
                 val[j][i] = cond ? d_in[off_y + offx[i] * x_stride] : zero;
             }
         }
-        d_out[ooff + n * out.strides[batch_dim]] =  bilinearInterpFunc(val, xratio, yratio);
+        d_out[ooff + n * out.strides[batch_dim]] =
+            bilinearInterpFunc(val, xratio, yratio);
     }
 }
 #elif INTERP_ORDER == 3
-void interp2_general(
-    __global InterpInTy *d_out,
-    KParam out, int ooff,
-    __global const InterpInTy *d_in,
-    KParam in, int ioff, InterpPosTy x, InterpPosTy y,
-    int method, int batch, bool clamp,
-    int xdim, int ydim, int batch_dim)
-{
-    const int grid_x = floor(x);
+void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
+                     __global const InterpInTy *d_in, KParam in, int ioff,
+                     InterpPosTy x, InterpPosTy y, int method, int batch,
+                     bool clamp, int xdim, int ydim, int batch_dim) {
+    const int grid_x        = floor(x);
     const InterpPosTy off_x = x - grid_x;
 
-    const int grid_y = floor(y);
+    const int grid_y        = floor(y);
     const InterpPosTy off_y = y - grid_y;
 
-    const int x_lim = in.dims[xdim];
-    const int y_lim = in.dims[ydim];
+    const int x_lim    = in.dims[xdim];
+    const int y_lim    = in.dims[ydim];
     const int x_stride = in.strides[xdim];
     const int y_stride = in.strides[ydim];
-    const int idx = ioff + grid_y * y_stride + grid_x * x_stride;
+    const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
     // used for setting values at boundaries
-    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim, grid_x + 2 < x_lim};
-    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim, grid_y + 2 < y_lim};
-    int  offX[4]  = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0 , condX[3] ? 2 : (condX[2] ? 1 : 0)};
-    int  offY[4]  = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0 , condY[3] ? 2 : (condY[2] ? 1 : 0)};
+    bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                     grid_x + 2 < x_lim};
+    bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim,
+                     grid_y + 2 < y_lim};
+    int offX[4]   = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0,
+                   condX[3] ? 2 : (condX[2] ? 1 : 0)};
+    int offY[4]   = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0,
+                   condY[3] ? 2 : (condY[2] ? 1 : 0)};
 
     InterpValTy zero = ZERO;
     for (int n = 0; n < batch; n++) {
         int idx_n = idx + n * in.strides[batch_dim];
-        //for bicubic interpolation, work with 4x4 val at a time
+        // for bicubic interpolation, work with 4x4 val at a time
         InterpValTy val[4][4];
 #pragma unroll
         for (int j = 0; j < 4; j++) {
@@ -292,50 +273,27 @@ void interp2_general(
                 val[j][i] = cond ? d_in[ioff_j + offX[i] * x_stride] : zero;
             }
         }
-        bool spline  = method == AF_INTERP_CUBIC_SPLINE || method == AF_INTERP_BICUBIC_SPLINE;
-        d_out[ooff +  n * out.strides[batch_dim]] = bicubicInterpFunc(val, off_x, off_y, spline);
+        bool spline = method == AF_INTERP_CUBIC_SPLINE ||
+                      method == AF_INTERP_BICUBIC_SPLINE;
+        d_out[ooff + n * out.strides[batch_dim]] =
+            bicubicInterpFunc(val, off_x, off_y, spline);
     }
 }
 #endif
 
-#define interp1_dim(d_out,                      \
-                    out, ooff, d_in,            \
-                    in, ioff, x,                \
-                    method, batch, clamp,       \
-                    xdim)                       \
-    interp1_general(d_out,                      \
-                    out, ooff, d_in,            \
-                    in, ioff, x,                \
-                    method, batch, clamp,       \
-                    xdim, 1)                    \
-
-#define interp1(d_out,                          \
-                out, ooff, d_in,                \
-                in, ioff, x,                    \
-                method, batch, clamp)           \
-    interp1_dim(d_out,                          \
-                out, ooff, d_in,                \
-                in, ioff, x,                    \
-                method, batch, clamp,           \
-                0)                              \
-
-#define interp2_dim(d_out,                      \
-                    out, ooff, d_in,            \
-                    in, ioff, x, y,             \
-                    method, batch, clamp,       \
-                    xdim, ydim)                 \
-    interp2_general(d_out,                      \
-                    out, ooff, d_in,            \
-                    in, ioff, x, y,             \
-                    method, batch, clamp,       \
-                    xdim, ydim, 2)              \
-
-#define interp2(d_out,                          \
-                out, ooff, d_in,                \
-                in, ioff, x, y,                 \
-                method, batch, clamp)           \
-    interp2_dim(d_out,                          \
-                out, ooff, d_in,                \
-                in, ioff, x, y,                 \
-                method, batch, clamp,           \
-                0, 1)                           \
+#define interp1_dim(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp, \
+                    xdim)                                                      \
+    interp1_general(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp, \
+                    xdim, 1)
+
+#define interp1(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp) \
+    interp1_dim(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp, 0)
+
+#define interp2_dim(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, \
+                    clamp, xdim, ydim)                                     \
+    interp2_general(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, \
+                    clamp, xdim, ydim, 2)
+
+#define interp2(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, clamp) \
+    interp2_dim(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, clamp, \
+                0, 1)\
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
index 9bb23d9abf..7b71d9395c 100644
--- a/src/backend/opencl/kernel/interp.hpp
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -11,25 +11,23 @@
 #include <af/defines.h>
 #include <string>
 
-#define ADD_ENUM_OPTION(options, name) do {      \
-        options << " -D " #name "=" << name;     \
-    } while(0)
+#define ADD_ENUM_OPTION(options, name) \
+    do { options << " -D " #name "=" << name; } while (0)
 
 namespace opencl {
-    namespace kernel {
+namespace kernel {
 
-        static void addInterpEnumOptions(std::ostringstream &options)
-        {
-            ADD_ENUM_OPTION(options, AF_INTERP_NEAREST);
-            ADD_ENUM_OPTION(options, AF_INTERP_LINEAR);
-            ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR);
-            ADD_ENUM_OPTION(options, AF_INTERP_CUBIC);
-            ADD_ENUM_OPTION(options, AF_INTERP_LOWER);
-            ADD_ENUM_OPTION(options, AF_INTERP_LINEAR_COSINE);
-            ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR_COSINE);
-            ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC);
-            ADD_ENUM_OPTION(options, AF_INTERP_CUBIC_SPLINE);
-            ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC_SPLINE);
-        }
-    }
+static void addInterpEnumOptions(std::ostringstream &options) {
+    ADD_ENUM_OPTION(options, AF_INTERP_NEAREST);
+    ADD_ENUM_OPTION(options, AF_INTERP_LINEAR);
+    ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR);
+    ADD_ENUM_OPTION(options, AF_INTERP_CUBIC);
+    ADD_ENUM_OPTION(options, AF_INTERP_LOWER);
+    ADD_ENUM_OPTION(options, AF_INTERP_LINEAR_COSINE);
+    ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR_COSINE);
+    ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC);
+    ADD_ENUM_OPTION(options, AF_INTERP_CUBIC_SPLINE);
+    ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC_SPLINE);
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/iops.cl b/src/backend/opencl/kernel/iops.cl
index 000848b0af..06a606abbe 100644
--- a/src/backend/opencl/kernel/iops.cl
+++ b/src/backend/opencl/kernel/iops.cl
@@ -14,46 +14,40 @@ inline bool is_nan(T in) { return (in != in); }
 #endif
 
 #if CPLX
-#define sabs(in) ((in.x)*(in.x) + (in.y)*(in.y))
+#define sabs(in) ((in.x) * (in.x) + (in.y) * (in.y))
 #ifdef MIN_OP
-void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
-{
+void binOp(T *lhs, uint *lidx, T rhs, uint ridx) {
     if (((sabs(lhs[0]) > sabs(rhs)) ||
          (sabs(lhs[0]) == sabs(rhs) && *lidx < ridx))) {
-        *lhs = rhs;
+        *lhs  = rhs;
         *lidx = ridx;
     }
 }
 #endif
 
 #ifdef MAX_OP
-void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
-{
+void binOp(T *lhs, uint *lidx, T rhs, uint ridx) {
     if (((sabs(lhs[0]) < sabs(rhs)) ||
          (sabs(lhs[0]) == sabs(rhs) && *lidx > ridx))) {
-        *lhs = rhs;
+        *lhs  = rhs;
         *lidx = ridx;
     }
 }
 #endif
 #else
 #ifdef MIN_OP
-void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
-{
-    if (((*lhs > rhs) ||
-         (*lhs == rhs && *lidx < ridx))) {
-        *lhs = rhs;
+void binOp(T *lhs, uint *lidx, T rhs, uint ridx) {
+    if (((*lhs > rhs) || (*lhs == rhs && *lidx < ridx))) {
+        *lhs  = rhs;
         *lidx = ridx;
     }
 }
 #endif
 
 #ifdef MAX_OP
-void binOp(T *lhs, uint *lidx, T rhs, uint ridx)
-{
-    if (((*lhs < rhs) ||
-         (*lhs == rhs && *lidx > ridx))) {
-        *lhs = rhs;
+void binOp(T *lhs, uint *lidx, T rhs, uint ridx) {
+    if (((*lhs < rhs) || (*lhs == rhs && *lidx > ridx))) {
+        *lhs  = rhs;
         *lidx = ridx;
     }
 }
diff --git a/src/backend/opencl/kernel/iota.cl b/src/backend/opencl/kernel/iota.cl
index f00335e0af..ef8ac16819 100644
--- a/src/backend/opencl/kernel/iota.cl
+++ b/src/backend/opencl/kernel/iota.cl
@@ -7,11 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void iota_kernel(__global T *out, const KParam op,
-                 const int s0, const int s1, const int s2, const int s3,
-                 const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void iota_kernel(__global T *out, const KParam op, const int s0,
+                          const int s1, const int s2, const int s3,
+                          const int blocksPerMatX, const int blocksPerMatY) {
     const int oz = get_group_id(0) / blocksPerMatX;
     const int ow = get_group_id(1) / blocksPerMatY;
 
@@ -21,24 +19,22 @@ void iota_kernel(__global T *out, const KParam op,
     const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(xx >= op.dims[0] ||
-       yy >= op.dims[1] ||
-       oz >= op.dims[2] ||
-       ow >= op.dims[3])
+    if (xx >= op.dims[0] || yy >= op.dims[1] || oz >= op.dims[2] ||
+        ow >= op.dims[3])
         return;
 
     const int ozw = ow * op.strides[3] + oz * op.strides[2];
 
     T val = (ow % s3) * s2 * s1 * s0;
-    val  += (oz % s2) * s1 * s0;
+    val += (oz % s2) * s1 * s0;
 
     const int incy = blocksPerMatY * get_local_size(1);
     const int incx = blocksPerMatX * get_local_size(0);
 
-    for(int oy = yy; oy < op.dims[1]; oy += incy) {
-        T valY = val + (oy % s1) * s0;
+    for (int oy = yy; oy < op.dims[1]; oy += incy) {
+        T valY   = val + (oy % s1) * s0;
         int oyzw = ozw + oy * op.strides[1];
-        for(int ox = xx; ox < op.dims[0]; ox += incx) {
+        for (int ox = xx; ox < op.dims[0]; ox += incx) {
             int oidx = oyzw + ox;
 
             out[oidx] = valY + (ox % s0);
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index ee01154da8..e214813fae 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -8,43 +8,41 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/iota.hpp>
-#include <af/dim4.hpp>
 #include <program.hpp>
 #include <traits.hpp>
+#include <af/dim4.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
 static const int IOTA_TX = 32;
 static const int IOTA_TY = 8;
-static const int TILEX = 512;
-static const int TILEY = 32;
+static const int TILEX   = 512;
+static const int TILEY   = 32;
 
 template<typename T>
-void iota(Param out, const af::dim4 &sdims)
-{
-    std::string refName = std::string("iota_kernel_") + std::string(dtype_traits<T>::getName());
+void iota(Param out, const af::dim4& sdims) {
+    std::string refName =
+        std::string("iota_kernel_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
@@ -52,7 +50,7 @@ void iota(Param out, const af::dim4 &sdims)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {iota_cl};
-        const int   ker_lens[] = {iota_cl_len};
+        const int ker_lens[]   = {iota_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -61,9 +59,9 @@ void iota(Param out, const af::dim4 &sdims)
         addKernelToCache(device, refName, entry);
     }
 
-    auto iotaOp = KernelFunctor<Buffer, const KParam,
-                                const int, const int, const int, const int,
-                                const int, const int> (*entry.ker);
+    auto iotaOp =
+        KernelFunctor<Buffer, const KParam, const int, const int, const int,
+                      const int, const int, const int>(*entry.ker);
 
     NDRange local(IOTA_TX, IOTA_TY, 1);
 
@@ -72,11 +70,11 @@ void iota(Param out, const af::dim4 &sdims)
     NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                    local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-    iotaOp(EnqueueArgs(getQueue(), global, local),
-           *out.data, out.info, sdims[0], sdims[1], sdims[2], sdims[3],
-           blocksPerMatX, blocksPerMatY);
+    iotaOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           sdims[0], sdims[1], sdims[2], sdims[3], blocksPerMatX,
+           blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index af76439d46..4994a006b5 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -8,412 +8,361 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <memory>
-#include <kernel_headers/ireduce_first.hpp>
-#include <kernel_headers/ireduce_dim.hpp>
-#include <kernel_headers/iops.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/iops.hpp>
+#include <kernel_headers/ireduce_dim.hpp>
+#include <kernel_headers/ireduce_first.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <type_util.hpp>
-#include "names.hpp"
+#include <map>
+#include <memory>
+#include <mutex>
+#include <string>
 #include "config.hpp"
-#include <memory.hpp>
+#include "names.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 using std::unique_ptr;
 
-namespace opencl
-{
-
-namespace kernel
-{
-
-    template<typename T, af_op_t op>
-    void ireduce_dim_launcher(Param out, cl::Buffer *oidx,
-                              Param in, cl::Buffer *iidx,
-                              const int dim,
-                              const int threads_y,
-                              const bool is_first,
-                              const uint groups_all[4])
-    {
-        std::string ref_name =
-            std::string("ireduce_") +
-            std::to_string(dim) +
-            std::string("_") +
-            std::string(dtype_traits<T>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(is_first) +
-            std::string("_") +
-            std::to_string(threads_y);
-
-        int device = getActiveDeviceId();
-
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-
-            ToNumStr<T> toNumStr;
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                << " -D dim=" << dim
-                << " -D DIMY=" << threads_y
-                << " -D THREADS_X=" << THREADS_X
-                << " -D init=" << toNumStr(Binary<T, op>::init())
-                << " -D " << binOpName<op>()
-                << " -D CPLX=" << af::iscplx<T>()
-                << " -D IS_FIRST=" << is_first;
+namespace opencl {
 
-            if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+namespace kernel {
 
-            const char *ker_strs[] = {iops_cl, ireduce_dim_cl};
-            const int   ker_lens[] = {iops_cl_len, ireduce_dim_cl_len};
-            Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel(*entry.prog, "ireduce_dim_kernel");
+template<typename T, af_op_t op>
+void ireduce_dim_launcher(Param out, cl::Buffer *oidx, Param in,
+                          cl::Buffer *iidx, const int dim, const int threads_y,
+                          const bool is_first, const uint groups_all[4]) {
+    std::string ref_name =
+        std::string("ireduce_") + std::to_string(dim) + std::string("_") +
+        std::string(dtype_traits<T>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(is_first) +
+        std::string("_") + std::to_string(threads_y);
 
-            addKernelToCache(device, ref_name, entry);
-        }
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                       groups_all[1] * groups_all[3] * local[1]);
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<T> toNumStr;
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName() << " -D dim=" << dim
+                << " -D DIMY=" << threads_y << " -D THREADS_X=" << THREADS_X
+                << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
+                << " -D IS_FIRST=" << is_first;
 
-        auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer,
-                                     Buffer, KParam, Buffer,
-                                     uint, uint, uint>(*entry.ker);
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-        ireduceOp(EnqueueArgs(getQueue(), global, local),
-                  *out.data, out.info, *oidx,
-                  *in.data, in.info, *iidx,
-                  groups_all[0],
-                  groups_all[1],
-                  groups_all[dim]);
+        const char *ker_strs[] = {iops_cl, ireduce_dim_cl};
+        const int ker_lens[]   = {iops_cl_len, ireduce_dim_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "ireduce_dim_kernel");
 
-        CL_DEBUG_FINISH(getQueue());
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename T, af_op_t op>
-    void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
-        uint threads_x = THREADS_X;
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        uint groups_all[] = {(uint)divup(in.info.dims[0], threads_x),
-                             (uint)in.info.dims[1],
-                             (uint)in.info.dims[2],
-                             (uint)in.info.dims[3]};
+    auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam,
+                                   Buffer, uint, uint, uint>(*entry.ker);
 
-        groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
+    ireduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *oidx, *in.data, in.info, *iidx, groups_all[0], groups_all[1],
+              groups_all[dim]);
 
-        Param tmp = out;
-        cl::Buffer *tidx = oidx;
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        int tmp_elements = 1;
-        if (groups_all[dim] > 1) {
-            tmp.info.dims[dim] = groups_all[dim];
+template<typename T, af_op_t op>
+void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = THREADS_X;
 
-            for (int k = 0; k < 4; k++) tmp_elements *= tmp.info.dims[k];
+    uint groups_all[] = {(uint)divup(in.info.dims[0], threads_x),
+                         (uint)in.info.dims[1], (uint)in.info.dims[2],
+                         (uint)in.info.dims[3]};
 
-            tmp.data = bufferAlloc(tmp_elements * sizeof(T));
-            tidx = bufferAlloc(tmp_elements * sizeof(uint));
+    groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
 
-            for (int k = dim + 1; k < 4; k++) tmp.info.strides[k] *= groups_all[dim];
-        }
+    Param tmp        = out;
+    cl::Buffer *tidx = oidx;
 
-        ireduce_dim_launcher<T, op>(tmp, tidx, in, tidx, dim, threads_y, true, groups_all);
+    int tmp_elements = 1;
+    if (groups_all[dim] > 1) {
+        tmp.info.dims[dim] = groups_all[dim];
 
-        if (groups_all[dim] > 1) {
-            groups_all[dim] = 1;
+        for (int k = 0; k < 4; k++) tmp_elements *= tmp.info.dims[k];
 
-            ireduce_dim_launcher<T, op>(out, oidx, tmp, tidx, dim, threads_y, false, groups_all);
-            bufferFree(tmp.data);
-            bufferFree(tidx);
-        }
+        tmp.data = bufferAlloc(tmp_elements * sizeof(T));
+        tidx     = bufferAlloc(tmp_elements * sizeof(uint));
 
+        for (int k = dim + 1; k < 4; k++)
+            tmp.info.strides[k] *= groups_all[dim];
     }
 
-    template<typename T, af_op_t op>
-    void ireduce_first_launcher(Param out, cl::Buffer *oidx,
-                                Param in, cl::Buffer *iidx,
-                                const int threads_x,
-                                const bool is_first,
-                                const uint groups_x,
-                                const uint groups_y)
-    {
-        std::string ref_name =
-            std::string("ireduce_0_") +
-            std::string(dtype_traits<T>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(is_first) +
-            std::string("_") +
-            std::to_string(threads_x);
-
-        int device = getActiveDeviceId();
-
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-
-            ToNumStr<T> toNumStr;
-
-            std::ostringstream options;
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D DIMX=" << threads_x
-                    << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                    << " -D init=" << toNumStr(Binary<T, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<T>()
-                    << " -D IS_FIRST=" << is_first;
-
-            if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+    ireduce_dim_launcher<T, op>(tmp, tidx, in, tidx, dim, threads_y, true,
+                                groups_all);
 
-            const char *ker_strs[] = {iops_cl, ireduce_first_cl};
-            const int   ker_lens[] = {iops_cl_len, ireduce_first_cl_len};
-            Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel(*entry.prog, "ireduce_first_kernel");
+    if (groups_all[dim] > 1) {
+        groups_all[dim] = 1;
 
-            addKernelToCache(device, ref_name, entry);
-        }
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * in.info.dims[2] * local[0],
-                       groups_y * in.info.dims[3] * local[1]);
+        ireduce_dim_launcher<T, op>(out, oidx, tmp, tidx, dim, threads_y, false,
+                                    groups_all);
+        bufferFree(tmp.data);
+        bufferFree(tidx);
+    }
+}
 
-        uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
+template<typename T, af_op_t op>
+void ireduce_first_launcher(Param out, cl::Buffer *oidx, Param in,
+                            cl::Buffer *iidx, const int threads_x,
+                            const bool is_first, const uint groups_x,
+                            const uint groups_y) {
+    std::string ref_name =
+        std::string("ireduce_0_") + std::string(dtype_traits<T>::getName()) +
+        std::string("_") + std::to_string(op) + std::string("_") +
+        std::to_string(is_first) + std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<T> toNumStr;
+
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D DIMX=" << threads_x
+                << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
+                << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
+                << " -D IS_FIRST=" << is_first;
 
-        auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer,
-                                     Buffer, KParam, Buffer,
-                                     uint, uint, uint>(*entry.ker);
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-        ireduceOp(EnqueueArgs(getQueue(), global, local),
-                  *out.data, out.info, *oidx,
-                  *in.data, in.info, *iidx,
-                  groups_x, groups_y, repeat);
+        const char *ker_strs[] = {iops_cl, ireduce_first_cl};
+        const int ker_lens[]   = {iops_cl_len, ireduce_first_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "ireduce_first_kernel");
 
-        CL_DEBUG_FINISH(getQueue());
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename T, af_op_t op>
-    void ireduce_first(Param out, cl::Buffer *oidx, Param in)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * in.info.dims[2] * local[0],
+                   groups_y * in.info.dims[3] * local[1]);
 
-        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(in.info.dims[1], threads_y);
+    uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-        Param tmp = out;
-        cl::Buffer *tidx = oidx;
+    auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam,
+                                   Buffer, uint, uint, uint>(*entry.ker);
 
-        if (groups_x > 1) {
+    ireduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *oidx, *in.data, in.info, *iidx, groups_x, groups_y, repeat);
 
-            tmp.data = bufferAlloc(groups_x *
-                                in.info.dims[1] *
-                                in.info.dims[2] *
-                                in.info.dims[3] *
-                                sizeof(T));
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            tidx = bufferAlloc(groups_x *
-                               in.info.dims[1] *
-                               in.info.dims[2] *
-                               in.info.dims[3] *
-                               sizeof(uint));
+template<typename T, af_op_t op>
+void ireduce_first(Param out, cl::Buffer *oidx, Param in) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
 
+    uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(in.info.dims[1], threads_y);
 
-            tmp.info.dims[0] = groups_x;
-            for (int k = 1; k < 4; k++) tmp.info.strides[k] *= groups_x;
-        }
+    Param tmp        = out;
+    cl::Buffer *tidx = oidx;
 
-        ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x, groups_y);
+    if (groups_x > 1) {
+        tmp.data = bufferAlloc(groups_x * in.info.dims[1] * in.info.dims[2] *
+                               in.info.dims[3] * sizeof(T));
 
-        if (groups_x > 1) {
-            ireduce_first_launcher<T, op>(out, oidx, tmp, tidx, threads_x, false, 1, groups_y);
+        tidx = bufferAlloc(groups_x * in.info.dims[1] * in.info.dims[2] *
+                           in.info.dims[3] * sizeof(uint));
 
-            bufferFree(tmp.data);
-            bufferFree(tidx);
-        }
+        tmp.info.dims[0] = groups_x;
+        for (int k = 1; k < 4; k++) tmp.info.strides[k] *= groups_x;
     }
 
-    template<typename T, af_op_t op>
-    void ireduce(Param out, cl::Buffer *oidx, Param in, int dim)
-    {
-        if (dim == 0)
-            return ireduce_first<T, op>(out, oidx, in);
-        else
-            return ireduce_dim  <T, op>(out, oidx, in, dim);
+    ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
+                                  groups_x, groups_y);
+
+    if (groups_x > 1) {
+        ireduce_first_launcher<T, op>(out, oidx, tmp, tidx, threads_x, false, 1,
+                                      groups_y);
+
+        bufferFree(tmp.data);
+        bufferFree(tidx);
     }
+}
+
+template<typename T, af_op_t op>
+void ireduce(Param out, cl::Buffer *oidx, Param in, int dim) {
+    if (dim == 0)
+        return ireduce_first<T, op>(out, oidx, in);
+    else
+        return ireduce_dim<T, op>(out, oidx, in, dim);
+}
 
 #if defined(__GNUC__) || defined(__GNUG__)
-    /* GCC/G++, Clang/LLVM, Intel ICC */
-    #pragma GCC diagnostic push
-    #pragma GCC diagnostic ignored "-Wunused-function"
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
 #else
-    /* Other */
+/* Other */
 #endif
 
-    template<typename T> double cabs(const T in) { return (double)in; }
-    static double cabs(const cfloat in) { return (double)abs(in); }
-    static double cabs(const cdouble in) { return (double)abs(in); }
-
-    template<af_op_t op, typename T>
-    struct MinMaxOp
-    {
-        T m_val;
-        uint m_idx;
-        MinMaxOp(T val, uint idx) :
-            m_val(val), m_idx(idx)
-        {
-        }
-
-        void operator()(T val, uint idx)
-        {
-            if (cabs(val) < cabs(m_val) ||
-                (cabs(val) == cabs(m_val) &&
-                 idx > m_idx)) {
-                m_val = val;
-                m_idx = idx;
-            }
-        }
-    };
-
-    template<typename T>
-    struct MinMaxOp<af_max_t, T>
-    {
-        T m_val;
-        uint m_idx;
-        MinMaxOp(T val, uint idx) :
-            m_val(val), m_idx(idx)
-        {
+template<typename T>
+double cabs(const T in) {
+    return (double)in;
+}
+static double cabs(const cfloat in) { return (double)abs(in); }
+static double cabs(const cdouble in) { return (double)abs(in); }
+
+template<af_op_t op, typename T>
+struct MinMaxOp {
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {}
+
+    void operator()(T val, uint idx) {
+        if (cabs(val) < cabs(m_val) ||
+            (cabs(val) == cabs(m_val) && idx > m_idx)) {
+            m_val = val;
+            m_idx = idx;
         }
-
-        void operator()(T val, uint idx)
-        {
-            if (cabs(val) > cabs(m_val) ||
-                (cabs(val) == cabs(m_val) &&
-                 idx <= m_idx)) {
-                m_val = val;
-                m_idx = idx;
-            }
+    }
+};
+
+template<typename T>
+struct MinMaxOp<af_max_t, T> {
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {}
+
+    void operator()(T val, uint idx) {
+        if (cabs(val) > cabs(m_val) ||
+            (cabs(val) == cabs(m_val) && idx <= m_idx)) {
+            m_val = val;
+            m_idx = idx;
         }
-    };
+    }
+};
 
 #if defined(__GNUC__) || defined(__GNUG__)
-    /* GCC/G++, Clang/LLVM, Intel ICC */
-    #pragma GCC diagnostic pop
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic pop
 #else
-    /* Other */
+/* Other */
 #endif
 
-    template<typename T, af_op_t op>
-    T ireduce_all(uint *loc, Param in)
-    {
-        int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
-
-        // FIXME: Use better heuristics to get to the optimum number
-        if (in_elements > 4096) {
+template<typename T, af_op_t op>
+T ireduce_all(uint *loc, Param in) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+        bool is_linear = (in.info.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            is_linear &= (in.info.strides[k] ==
+                          (in.info.strides[k - 1] * in.info.dims[k - 1]));
+        }
 
-            bool is_linear = (in.info.strides[0] == 1);
+        if (is_linear) {
+            in.info.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
-                is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
-            }
-
-            if (is_linear) {
-                in.info.dims[0] = in_elements;
-                for (int k = 1; k < 4; k++) {
-                    in.info.dims[k] = 1;
-                    in.info.strides[k] = in_elements;
-                }
+                in.info.dims[k]    = 1;
+                in.info.strides[k] = in_elements;
             }
+        }
 
-            uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_GROUP);
-            uint threads_y = THREADS_PER_GROUP / threads_x;
-
-            uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-            uint groups_y = divup(in.info.dims[1], threads_y);
-            Array<T> tmp = createEmptyArray<T>({groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
-
-            int tmp_elements = tmp.elements();
-            cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
-
-            ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x, groups_y);
-
-            unique_ptr<T[]> h_ptr(new T[tmp_elements]);
-            unique_ptr<uint[]> h_iptr(new uint[tmp_elements]);
-
-            getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0, sizeof(T) * tmp_elements, h_ptr.get());
-            getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0, sizeof(uint) * tmp_elements, h_iptr.get());
-
-            T* h_ptr_raw = h_ptr.get();
-            uint* h_iptr_raw = h_iptr.get();
-
-            if (!is_linear) {
-                // Converting n-d index into a linear index
-                // in is of size   [   dims0, dims1, dims2, dims3]
-                // tidx is of size [groups_x, dims1, dims2, dims3]
-                // i / groups_x gives you the batch number "N"
-                // "N * dims0 + i" gives the linear index
-                for (int i = 0; i < tmp_elements; i++) {
-                    h_iptr_raw[i] += (i / groups_x) * in.info.dims[0];
-                }
-            }
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
 
-            MinMaxOp<op, T> Op(h_ptr_raw[0], h_iptr_raw[0]);
-            for (int i = 1; i < (int)tmp_elements; i++) {
-                Op(h_ptr_raw[i], h_iptr_raw[i]);
+        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(in.info.dims[1], threads_y);
+        Array<T> tmp  = createEmptyArray<T>(
+            {groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+
+        int tmp_elements = tmp.elements();
+        cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
+
+        ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
+                                      groups_x, groups_y);
+
+        unique_ptr<T[]> h_ptr(new T[tmp_elements]);
+        unique_ptr<uint[]> h_iptr(new uint[tmp_elements]);
+
+        getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
+                                     sizeof(T) * tmp_elements, h_ptr.get());
+        getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0,
+                                     sizeof(uint) * tmp_elements, h_iptr.get());
+
+        T *h_ptr_raw     = h_ptr.get();
+        uint *h_iptr_raw = h_iptr.get();
+
+        if (!is_linear) {
+            // Converting n-d index into a linear index
+            // in is of size   [   dims0, dims1, dims2, dims3]
+            // tidx is of size [groups_x, dims1, dims2, dims3]
+            // i / groups_x gives you the batch number "N"
+            // "N * dims0 + i" gives the linear index
+            for (int i = 0; i < tmp_elements; i++) {
+                h_iptr_raw[i] += (i / groups_x) * in.info.dims[0];
             }
+        }
 
-            bufferFree(tidx);
-
-            *loc = Op.m_idx;
-            return Op.m_val;
+        MinMaxOp<op, T> Op(h_ptr_raw[0], h_iptr_raw[0]);
+        for (int i = 1; i < (int)tmp_elements; i++) {
+            Op(h_ptr_raw[i], h_iptr_raw[i]);
+        }
 
-        } else {
+        bufferFree(tidx);
 
-            unique_ptr<T[]> h_ptr(new T[in_elements]);
-            T* h_ptr_raw = h_ptr.get();
+        *loc = Op.m_idx;
+        return Op.m_val;
 
-            getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
-                                          sizeof(T) * in_elements, h_ptr_raw);
+    } else {
+        unique_ptr<T[]> h_ptr(new T[in_elements]);
+        T *h_ptr_raw = h_ptr.get();
 
+        getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
+                                     sizeof(T) * in.info.offset,
+                                     sizeof(T) * in_elements, h_ptr_raw);
 
-            MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
-            for (int i = 1; i < (int)in_elements; i++) {
-                Op(h_ptr_raw[i], i);
-            }
+        MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
+        for (int i = 1; i < (int)in_elements; i++) { Op(h_ptr_raw[i], i); }
 
-            *loc = Op.m_idx;
-            return Op.m_val;
-        }
+        *loc = Op.m_idx;
+        return Op.m_val;
     }
 }
+}  // namespace kernel
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/ireduce_dim.cl b/src/backend/opencl/kernel/ireduce_dim.cl
index 333af887fc..35d29ea8f2 100644
--- a/src/backend/opencl/kernel/ireduce_dim.cl
+++ b/src/backend/opencl/kernel/ireduce_dim.cl
@@ -7,60 +7,53 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void ireduce_dim_kernel(__global T *oData,
-                        KParam oInfo,
-                        __global uint *olData,
-                        const __global T *iData,
-                        KParam iInfo,
-                        const __global uint *ilData,
-                        uint groups_x, uint groups_y, uint group_dim)
-{
+__kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
+                                 __global uint *olData, const __global T *iData,
+                                 KParam iInfo, const __global uint *ilData,
+                                 uint groups_x, uint groups_y, uint group_dim) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * THREADS_X + lidx;
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) + lidx;
-    const uint yid = groupId_y;
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) + lidx;
+    const uint yid       = groupId_y;
 
     uint ids[4] = {xid, yid, zid, wid};
 
     // There is only one element per group for out
     // There are get_local_size(1) elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
-        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+             ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
     olData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
-        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
     const uint id_dim_out = ids[dim];
 
     ids[dim] = ids[dim] * get_local_size(1) + lidy;
 
-    iData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-        ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+    iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+             ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
 
     if (!IS_FIRST) {
-        ilData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-            ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+        ilData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+                  ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
     }
 
-    const uint id_dim_in = ids[dim];
+    const uint id_dim_in   = ids[dim];
     const uint istride_dim = iInfo.strides[dim];
 
-    bool is_valid =
-        (ids[0] < iInfo.dims[0]) &&
-        (ids[1] < iInfo.dims[1]) &&
-        (ids[2] < iInfo.dims[2]) &&
-        (ids[3] < iInfo.dims[3]);
+    bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
+                    (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
 
     __local T s_val[THREADS_X * DIMY];
     __local uint s_idx[THREADS_X * DIMY];
 
-    T out_val = init;
+    T out_val    = init;
     uint out_idx = id_dim_in;
 
     if (is_valid && id_dim_in < iInfo.dims[dim]) {
@@ -72,7 +65,6 @@ void ireduce_dim_kernel(__global T *oData,
 
     for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
          id += group_dim * get_local_size(1)) {
-
         iData = iData + group_dim * get_local_size(1) * istride_dim;
 
 #if IS_FIRST
@@ -86,14 +78,14 @@ void ireduce_dim_kernel(__global T *oData,
     s_val[lid] = out_val;
     s_idx[lid] = out_idx;
 
-    __local T *s_vptr = s_val + lid;
+    __local T *s_vptr    = s_val + lid;
     __local uint *s_iptr = s_idx + lid;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (DIMY == 8) {
         if (lidy < 4) {
-            binOp(&out_val, &out_idx,
-                  s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
+            binOp(&out_val, &out_idx, s_vptr[THREADS_X * 4],
+                  s_iptr[THREADS_X * 4]);
             *s_vptr = out_val;
             *s_iptr = out_idx;
         }
@@ -102,8 +94,8 @@ void ireduce_dim_kernel(__global T *oData,
 
     if (DIMY >= 4) {
         if (lidy < 2) {
-            binOp(&out_val, &out_idx,
-                  s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
+            binOp(&out_val, &out_idx, s_vptr[THREADS_X * 2],
+                  s_iptr[THREADS_X * 2]);
             *s_vptr = out_val;
             *s_iptr = out_idx;
         }
@@ -112,18 +104,16 @@ void ireduce_dim_kernel(__global T *oData,
 
     if (DIMY >= 2) {
         if (lidy < 1) {
-            binOp(&out_val, &out_idx,
-                  s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
+            binOp(&out_val, &out_idx, s_vptr[THREADS_X * 1],
+                  s_iptr[THREADS_X * 1]);
             *s_vptr = out_val;
             *s_iptr = out_idx;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidy == 0 && is_valid &&
-        (id_dim_out < oInfo.dims[dim])) {
-        *oData = *s_vptr;
+    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[dim])) {
+        *oData  = *s_vptr;
         *olData = *s_iptr;
     }
-
 }
diff --git a/src/backend/opencl/kernel/ireduce_first.cl b/src/backend/opencl/kernel/ireduce_first.cl
index 9c9453c2e2..48f8826be5 100644
--- a/src/backend/opencl/kernel/ireduce_first.cl
+++ b/src/backend/opencl/kernel/ireduce_first.cl
@@ -7,48 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void ireduce_first_kernel(__global T *oData,
-                          KParam oInfo,
-                          __global uint *olData,
-                          const __global T *iData,
-                          KParam iInfo,
-                          const __global uint *ilData,
-                          uint groups_x, uint groups_y, uint repeat)
-{
+__kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
+                                   __global uint *olData,
+                                   const __global T *iData, KParam iInfo,
+                                   const __global uint *ilData, uint groups_x,
+                                   uint groups_y, uint repeat) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * get_local_size(0) + lidx;
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) * repeat + lidx;
-    const uint yid = groupId_y * get_local_size(1) + lidy;
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) * repeat + lidx;
+    const uint yid       = groupId_y * get_local_size(1) + lidy;
 
     iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-        yid * iInfo.strides[1] + iInfo.offset;
+             yid * iInfo.strides[1] + iInfo.offset;
 
     if (!IS_FIRST) {
         ilData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-            yid * iInfo.strides[1] + iInfo.offset;
+                  yid * iInfo.strides[1] + iInfo.offset;
     }
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1] + oInfo.offset;
 
     olData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+              yid * oInfo.strides[1] + oInfo.offset;
 
-    bool cond = (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
+    bool cond =
+        (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
     __local T s_val[THREADS_PER_GROUP];
     __local uint s_idx[THREADS_PER_GROUP];
 
-    int last = (xid + repeat * DIMX);
-    int lim = last > iInfo.dims[0] ? iInfo.dims[0] : last;
-    T out_val = init;
+    int last     = (xid + repeat * DIMX);
+    int lim      = last > iInfo.dims[0] ? iInfo.dims[0] : last;
+    T out_val    = init;
     uint out_idx = xid;
 
     if (cond && xid < lim && !is_nan(iData[xid])) {
@@ -68,13 +65,12 @@ void ireduce_first_kernel(__global T *oData,
     s_idx[lid] = out_idx;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local T *s_vptr = s_val + lidy * DIMX;
+    __local T *s_vptr    = s_val + lidy * DIMX;
     __local uint *s_iptr = s_idx + lidy * DIMX;
 
     if (DIMX == 256) {
         if (lidx < 128) {
-            binOp(&out_val, &out_idx,
-                  s_vptr[lidx + 128], s_iptr[lidx + 128]);
+            binOp(&out_val, &out_idx, s_vptr[lidx + 128], s_iptr[lidx + 128]);
             s_vptr[lidx] = out_val;
             s_iptr[lidx] = out_idx;
         }
@@ -82,64 +78,57 @@ void ireduce_first_kernel(__global T *oData,
     }
 
     if (DIMX >= 128) {
-        if (lidx <  64) {
-            binOp(&out_val, &out_idx,
-                  s_vptr[lidx +  64], s_iptr[lidx +  64]);
+        if (lidx < 64) {
+            binOp(&out_val, &out_idx, s_vptr[lidx + 64], s_iptr[lidx + 64]);
             s_vptr[lidx] = out_val;
             s_iptr[lidx] = out_idx;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (DIMX >=  64) {
-        if (lidx <  32) {
-            binOp(&out_val, &out_idx,
-                  s_vptr[lidx +  32], s_iptr[lidx +  32]);
+    if (DIMX >= 64) {
+        if (lidx < 32) {
+            binOp(&out_val, &out_idx, s_vptr[lidx + 32], s_iptr[lidx + 32]);
             s_vptr[lidx] = out_val;
             s_iptr[lidx] = out_idx;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidx <  16) {
-        binOp(&out_val, &out_idx,
-              s_vptr[lidx +  16], s_iptr[lidx +  16]);
+    if (lidx < 16) {
+        binOp(&out_val, &out_idx, s_vptr[lidx + 16], s_iptr[lidx + 16]);
         s_vptr[lidx] = out_val;
         s_iptr[lidx] = out_idx;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   8) {
-        binOp(&out_val, &out_idx,
-              s_vptr[lidx +   8], s_iptr[lidx +   8]);
+    if (lidx < 8) {
+        binOp(&out_val, &out_idx, s_vptr[lidx + 8], s_iptr[lidx + 8]);
         s_vptr[lidx] = out_val;
         s_iptr[lidx] = out_idx;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   4) {
-        binOp(&out_val, &out_idx,
-              s_vptr[lidx +   4], s_iptr[lidx +   4]);
+    if (lidx < 4) {
+        binOp(&out_val, &out_idx, s_vptr[lidx + 4], s_iptr[lidx + 4]);
         s_vptr[lidx] = out_val;
         s_iptr[lidx] = out_idx;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   2) {
-        binOp(&out_val, &out_idx,
-              s_vptr[lidx +   2], s_iptr[lidx +   2]);
+    if (lidx < 2) {
+        binOp(&out_val, &out_idx, s_vptr[lidx + 2], s_iptr[lidx + 2]);
         s_vptr[lidx] = out_val;
         s_iptr[lidx] = out_idx;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   1) {
-        binOp(&out_val, &out_idx,
-              s_vptr[lidx +   1], s_iptr[lidx +   1]);
+    if (lidx < 1) {
+        binOp(&out_val, &out_idx, s_vptr[lidx + 1], s_iptr[lidx + 1]);
         s_vptr[lidx] = out_val;
         s_iptr[lidx] = out_idx;
     }
@@ -147,7 +136,7 @@ void ireduce_first_kernel(__global T *oData,
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (cond && lidx == 0) {
-        oData[groupId_x] = s_vptr[0];
+        oData[groupId_x]  = s_vptr[0];
         olData[groupId_x] = s_iptr[0];
     }
 }
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index 1ab81f16ac..ec6da04b6c 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -27,8 +27,8 @@
 #define __neq(lhs, rhs) (lhs) != (rhs)
 
 #define __conj(in) (in)
-#define __real(in) (in)
-#define __imag(in) (0)
+#define __real(in)(in)
+#define __imag(in)(0)
 #define __abs(in) abs(in)
 
 #define __crealf(in) ((in).x)
@@ -38,28 +38,24 @@
 #define __creal(in) ((in).x)
 #define __cimag(in) ((in).y)
 #define __cabs(in) hypot((in).x, (in).y)
-#define __sigmoid(in) (1.0/(1 + exp(-(in))))
+#define __sigmoid(in) (1.0 / (1 + exp(-(in))))
 
-float2 __cconjf(float2 in)
-{
+float2 __cconjf(float2 in) {
     float2 out = {in.x, -in.y};
     return out;
 }
 
-float2 __caddf(float2 lhs, float2 rhs)
-{
+float2 __caddf(float2 lhs, float2 rhs) {
     float2 out = {lhs.x + rhs.x, lhs.y + rhs.y};
     return out;
 }
 
-float2 __csubf(float2 lhs, float2 rhs)
-{
+float2 __csubf(float2 lhs, float2 rhs) {
     float2 out = {lhs.x - rhs.x, lhs.y - rhs.y};
     return out;
 }
 
-float2 __cmulf(float2 lhs, float2 rhs)
-{
+float2 __cmulf(float2 lhs, float2 rhs) {
     float2 out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
@@ -67,15 +63,13 @@ float2 __cmulf(float2 lhs, float2 rhs)
 }
 
 // FIXME: overflow / underflow issues
-float2 __cdivf(float2 lhs, float2 rhs)
-{
+float2 __cdivf(float2 lhs, float2 rhs) {
     // Normalize by absolute value and multiply
-    float rhs_abs = __cabsf(rhs);
+    float rhs_abs     = __cabsf(rhs);
     float inv_rhs_abs = 1.0f / rhs_abs;
-    float rhs_x = inv_rhs_abs * rhs.x;
-    float rhs_y = inv_rhs_abs * rhs.y;
-    float2 out = {lhs.x * rhs_x + lhs.y * rhs_y,
-                  lhs.y * rhs_x - lhs.x * rhs_y};
+    float rhs_x       = inv_rhs_abs * rhs.x;
+    float rhs_y       = inv_rhs_abs * rhs.y;
+    float2 out = {lhs.x * rhs_x + lhs.y * rhs_y, lhs.y * rhs_x - lhs.x * rhs_y};
     out.x *= inv_rhs_abs;
     out.y *= inv_rhs_abs;
     return out;
@@ -112,36 +106,39 @@ float2 __cdivf(float2 lhs, float2 rhs)
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
 
-#define __pow(lhs, rhs) convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
-#define __powll(lhs, rhs) convert_long_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
-#define __powul(lhs, rhs) convert_ulong_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#define __pow(lhs, rhs) \
+    convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#define __powll(lhs, rhs) \
+    convert_long_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#define __powul(lhs, rhs) \
+    convert_ulong_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
 
 #ifdef USE_DOUBLE
-#define __powui(lhs, rhs) convert_uint_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
-#define __powsi(lhs, rhs) convert_int_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#define __powui(lhs, rhs) \
+    convert_uint_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#define __powsi(lhs, rhs) \
+    convert_int_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
 #else
-#define __powui(lhs, rhs) convert_uint_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
-#define __powsi(lhs, rhs) convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#define __powui(lhs, rhs) \
+    convert_uint_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#define __powsi(lhs, rhs) \
+    convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
 #endif
 
-float2 __cminf(float2 lhs, float2 rhs)
-{
+float2 __cminf(float2 lhs, float2 rhs) {
     return __cabsf(lhs) < __cabsf(rhs) ? lhs : rhs;
 }
 
-float2 __cmaxf(float2 lhs, float2 rhs)
-{
+float2 __cmaxf(float2 lhs, float2 rhs) {
     return __cabsf(lhs) > __cabsf(rhs) ? lhs : rhs;
 }
 
-float2 __cplx2f(float lhs, float rhs)
-{
+float2 __cplx2f(float lhs, float rhs) {
     float2 out = {lhs, rhs};
     return out;
 }
 
-float2 __convert_cfloat(float in)
-{
+float2 __convert_cfloat(float in) {
     float2 out = {in, 0};
     return out;
 }
@@ -152,76 +149,73 @@ float2 __convert_cfloat(float in)
 
 #define iszero(a) ((a) == 0)
 
-float2  __convert_c2c(float2 in) { return in; }
+float2 __convert_c2c(float2 in) { return in; }
 
 #ifdef USE_DOUBLE
 
-float2  __convert_z2c(double2 in) { float2  out = {in.x, in.y}; return out; }
+float2 __convert_z2c(double2 in) {
+    float2 out = {in.x, in.y};
+    return out;
+}
 
-double2 __cconj(double2 in)
-{
+double2 __cconj(double2 in) {
     double2 out = {in.x, -in.y};
     return out;
 }
 
-double2 __cadd(double2 lhs, double2 rhs)
-{
+double2 __cadd(double2 lhs, double2 rhs) {
     double2 out = {lhs.x + rhs.x, lhs.y + rhs.y};
     return out;
 }
 
-double2 __csub(double2 lhs, double2 rhs)
-{
+double2 __csub(double2 lhs, double2 rhs) {
     double2 out = {lhs.x - rhs.x, lhs.y - rhs.y};
     return out;
 }
 
-double2 __cmul(double2 lhs, double2 rhs)
-{
+double2 __cmul(double2 lhs, double2 rhs) {
     double2 out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 
-double2 __cdiv(double2 lhs, double2 rhs)
-{
+double2 __cdiv(double2 lhs, double2 rhs) {
     // Normalize by absolute value and multiply
-    double rhs_abs = __cabs(rhs);
+    double rhs_abs     = __cabs(rhs);
     double inv_rhs_abs = 1.0 / rhs_abs;
-    double rhs_x = inv_rhs_abs * rhs.x;
-    double rhs_y = inv_rhs_abs * rhs.y;
-    double2 out = {lhs.x * rhs_x + lhs.y * rhs_y,
+    double rhs_x       = inv_rhs_abs * rhs.x;
+    double rhs_y       = inv_rhs_abs * rhs.y;
+    double2 out        = {lhs.x * rhs_x + lhs.y * rhs_y,
                    lhs.y * rhs_x - lhs.x * rhs_y};
     out.x *= inv_rhs_abs;
     out.y *= inv_rhs_abs;
     return out;
 }
 
-double2 __cmin(double2 lhs, double2 rhs)
-{
+double2 __cmin(double2 lhs, double2 rhs) {
     return __cabs(lhs) < __cabs(rhs) ? lhs : rhs;
 }
 
-double2 __cmax(double2 lhs, double2 rhs)
-{
+double2 __cmax(double2 lhs, double2 rhs) {
     return __cabs(lhs) > __cabs(rhs) ? lhs : rhs;
 }
 
-double2 __cplx2(double lhs, double rhs)
-{
+double2 __cplx2(double lhs, double rhs) {
     double2 out = {lhs, rhs};
     return out;
 }
 
-double2 __convert_cdouble(double in)
-{
+double2 __convert_cdouble(double in) {
     double2 out = {in, 0};
     return out;
 }
 
-double2 __convert_c2z(float2  in) { double2 out = {in.x, in.y}; return out; }
+double2 __convert_c2z(float2 in) {
+    double2 out = {in.x, in.y};
+    return out;
+}
 
 double2 __convert_z2z(double2 in) { return in; }
 
-#endif // USE_DOUBLE
+#endif  // USE_DOUBLE
diff --git a/src/backend/opencl/kernel/join.cl b/src/backend/opencl/kernel/join.cl
index 6145574fa1..71a1e16db7 100644
--- a/src/backend/opencl/kernel/join.cl
+++ b/src/backend/opencl/kernel/join.cl
@@ -7,12 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void join_kernel(__global To *d_out, const KParam out,
-                 __global const Ti *d_in, const KParam in,
-                 const int o0, const int o1, const int o2, const int o3,
-                 const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void join_kernel(__global To *d_out, const KParam out,
+                          __global const Ti *d_in, const KParam in,
+                          const int o0, const int o1, const int o2,
+                          const int o3, const int blocksPerMatX,
+                          const int blocksPerMatY) {
     const int iz = get_group_id(0) / blocksPerMatX;
     const int iw = get_group_id(1) / blocksPerMatY;
 
@@ -29,10 +28,10 @@ void join_kernel(__global To *d_out, const KParam out,
 
     if (iz < in.dims[2] && iw < in.dims[3]) {
         d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
-        d_in = d_in + iz * in.strides[2] + iw * in.strides[3];
+        d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
 
         for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            __global Ti *d_in_ = d_in + iy * in.strides[1];
+            __global Ti *d_in_  = d_in + iy * in.strides[1];
             __global To *d_out_ = d_out + (iy + o1) * out.strides[1];
 
             for (int ix = xx; ix < in.dims[0]; ix += incx) {
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index acca8ab749..7a4837a73e 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -8,59 +8,57 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/join.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
 #include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
+#include <mutex>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
-static const int TX = 32;
-static const int TY = 8;
+static const int TX    = 32;
+static const int TY    = 8;
 static const int TILEX = 256;
 static const int TILEY = 32;
 
 template<typename To, typename Ti, int dim>
-void join(Param out, const Param in, const af::dim4 offset)
-{
-    std::string refName = std::string("join_kernel_") +
-        std::string(dtype_traits<To>::getName()) +
-        std::string(dtype_traits<Ti>::getName()) +
-        std::to_string(dim);
+void join(Param out, const Param in, const af::dim4 offset) {
+    std::string refName =
+        std::string("join_kernel_") + std::string(dtype_traits<To>::getName()) +
+        std::string(dtype_traits<Ti>::getName()) + std::to_string(dim);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Ti=" << dtype_traits<Ti>::getName()
                 << " -D dim=" << dim;
 
-        if (std::is_same<To, double>::value || std::is_same<To, cdouble>::value) {
+        if (std::is_same<To, double>::value ||
+            std::is_same<To, cdouble>::value) {
             options << " -D USE_DOUBLE";
-        } else if (std::is_same<Ti, double>::value || std::is_same<Ti, cdouble>::value) {
+        } else if (std::is_same<Ti, double>::value ||
+                   std::is_same<Ti, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {join_cl};
-        const int   ker_lens[] = {join_cl_len};
+        const int ker_lens[]   = {join_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -69,9 +67,9 @@ void join(Param out, const Param in, const af::dim4 offset)
         addKernelToCache(device, refName, entry);
     }
 
-    auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                                const int, const int, const int, const int,
-                                const int, const int> (*entry.ker);
+    auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer,
+                                const KParam, const int, const int, const int,
+                                const int, const int, const int>(*entry.ker);
 
     NDRange local(TX, TY, 1);
 
@@ -80,10 +78,11 @@ void join(Param out, const Param in, const af::dim4 offset)
     NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
                    local[1] * blocksPerMatY * in.info.dims[3], 1);
 
-    joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info,
-           offset[0], offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
+    joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, offset[0], offset[1], offset[2], offset[3],
+           blocksPerMatX, blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/laset.cl b/src/backend/opencl/kernel/laset.cl
index 91ab50a408..40c5933503 100644
--- a/src/backend/opencl/kernel/laset.cl
+++ b/src/backend/opencl/kernel/laset.cl
@@ -35,22 +35,22 @@
  * * Redistributions  of  source  code  must  retain  the above copyright
  *   notice,  this  list  of  conditions  and  the  following  disclaimer.
  * * Redistributions  in  binary  form must reproduce the above copyright
- *   notice,  this list of conditions and the following disclaimer in the 
+ *   notice,  this list of conditions and the following disclaimer in the
  *   documentation  and/or other materials provided with the distribution.
- * * Neither  the  name of the University of Tennessee, Knoxville nor the 
+ * * Neither  the  name of the University of Tennessee, Knoxville nor the
  *   names of its contributors may be used to endorse or promote products
  *   derived from this software without specific prior written permission.
  *
  * THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
- * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+ * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  * A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  * HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT 
+ * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
  * LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 
+ * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  * THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+ * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  * OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *
  **********************************************************************/
@@ -69,117 +69,97 @@
 #define IS_EQUAL(lhs, rhs) ((rhs == lhs))
 #endif
 
-__kernel
-void laset_full(
-    int m, int n,
-    T offdiag, T diag,
-    __global T *A, unsigned long A_offset, int lda )
-{
+__kernel void laset_full(int m, int n, T offdiag, T diag, __global T *A,
+                         unsigned long A_offset, int lda) {
     A += A_offset;
 
-    int ind = get_group_id(0)*BLK_X + get_local_id(0);
-    int iby = get_group_id(1)*BLK_Y;
-    /* check if full block-column && (below diag || above diag || offdiag == diag) */
-    bool full = (iby + BLK_Y <= n && (ind >= iby + BLK_Y || ind + BLK_X <= iby || IS_EQUAL(offdiag, diag)));
+    int ind = get_group_id(0) * BLK_X + get_local_id(0);
+    int iby = get_group_id(1) * BLK_Y;
+    /* check if full block-column && (below diag || above diag || offdiag ==
+     * diag) */
+    bool full =
+        (iby + BLK_Y <= n &&
+         (ind >= iby + BLK_Y || ind + BLK_X <= iby || IS_EQUAL(offdiag, diag)));
     /* do only rows inside matrix */
-    if ( ind < m ) {
-        A += ind + iby*lda;
-        if ( full ) {
-            // full block-column, off-diagonal block or offdiag == diag
-            #pragma unroll
-            for( int j=0; j < BLK_Y; ++j ) {
-                A[j*lda] = offdiag;
-            }
-        }
-        else {
+    if (ind < m) {
+        A += ind + iby * lda;
+        if (full) {
+// full block-column, off-diagonal block or offdiag == diag
+#pragma unroll
+            for (int j = 0; j < BLK_Y; ++j) { A[j * lda] = offdiag; }
+        } else {
             // either partial block-column or diagonal block
-            for( int j=0; j < BLK_Y && iby+j < n; ++j ) {
-                if ( iby+j == ind )
-                    A[j*lda] = diag;
+            for (int j = 0; j < BLK_Y && iby + j < n; ++j) {
+                if (iby + j == ind)
+                    A[j * lda] = diag;
                 else
-                    A[j*lda] = offdiag;
+                    A[j * lda] = offdiag;
             }
         }
     }
 }
 
-
 /*
     Similar to zlaset_full, but updates only the diagonal and below.
     Blocks that are fully above the diagonal exit immediately.
 
     Code similar to zlacpy, zlat2c, clat2z.
 */
-__kernel
-void laset_lower(
-    int m, int n,
-    T offdiag, T diag,
-    __global T *A, unsigned long A_offset, int lda )
-{
+__kernel void laset_lower(int m, int n, T offdiag, T diag, __global T *A,
+                          unsigned long A_offset, int lda) {
     A += A_offset;
 
-    int ind = get_group_id(0)*BLK_X + get_local_id(0);
-    int iby = get_group_id(1)*BLK_Y;
+    int ind = get_group_id(0) * BLK_X + get_local_id(0);
+    int iby = get_group_id(1) * BLK_Y;
     /* check if full block-column && (below diag) */
     bool full = (iby + BLK_Y <= n && (ind >= iby + BLK_Y));
     /* do only rows inside matrix, and blocks not above diag */
-    if ( ind < m && ind + BLK_X > iby ) {
-        A += ind + iby*lda;
-        if ( full ) {
-            // full block-column, off-diagonal block
-            #pragma unroll
-            for( int j=0; j < BLK_Y; ++j ) {
-                A[j*lda] = offdiag;
-            }
-        }
-        else {
+    if (ind < m && ind + BLK_X > iby) {
+        A += ind + iby * lda;
+        if (full) {
+// full block-column, off-diagonal block
+#pragma unroll
+            for (int j = 0; j < BLK_Y; ++j) { A[j * lda] = offdiag; }
+        } else {
             // either partial block-column or diagonal block
-            for( int j=0; j < BLK_Y && iby+j < n; ++j ) {
-                if ( iby+j == ind )
-                    A[j*lda] = diag;
-                else if ( ind > iby+j )
-                    A[j*lda] = offdiag;
+            for (int j = 0; j < BLK_Y && iby + j < n; ++j) {
+                if (iby + j == ind)
+                    A[j * lda] = diag;
+                else if (ind > iby + j)
+                    A[j * lda] = offdiag;
             }
         }
     }
 }
 
-
 /*
     Similar to zlaset_full, but updates only the diagonal and above.
     Blocks that are fully below the diagonal exit immediately.
 
     Code similar to zlacpy, zlat2c, clat2z.
 */
-__kernel
-void laset_upper(
-    int m, int n,
-    T offdiag, T diag,
-    __global T *A, unsigned long A_offset, int lda )
-{
+__kernel void laset_upper(int m, int n, T offdiag, T diag, __global T *A,
+                          unsigned long A_offset, int lda) {
     A += A_offset;
 
-    int ind = get_group_id(0)*BLK_X + get_local_id(0);
-    int iby = get_group_id(1)*BLK_Y;
+    int ind = get_group_id(0) * BLK_X + get_local_id(0);
+    int iby = get_group_id(1) * BLK_Y;
     /* check if full block-column && (above diag) */
     bool full = (iby + BLK_Y <= n && (ind + BLK_X <= iby));
     /* do only rows inside matrix, and blocks not below diag */
-    if ( ind < m && ind < iby + BLK_Y ) {
-        A += ind + iby*lda;
-        if ( full ) {
-            // full block-column, off-diagonal block
-            #pragma unroll
-            for( int j=0; j < BLK_Y; ++j ) {
-                A[j*lda] = offdiag;
-            }
-        }
-        else {
+    if (ind < m && ind < iby + BLK_Y) {
+        A += ind + iby * lda;
+        if (full) {
+// full block-column, off-diagonal block
+#pragma unroll
+            for (int j = 0; j < BLK_Y; ++j) { A[j * lda] = offdiag; }
+        } else {
             // either partial block-column or diagonal block
-            for( int j=0; j < BLK_Y && iby+j < n; ++j ) {
-                if ( iby+j == ind )
-                    A[j*lda] = diag;
-                else if ( ind < iby+j )
-                    A[j*lda] = offdiag;
+            for (int j = 0; j < BLK_Y && iby + j < n; ++j) {
+                if (iby + j == ind)
+                    A[j * lda] = diag;
+                else if (ind < iby + j)
+                    A[j * lda] = offdiag;
             }
         }
     }
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index d209598c13..dec5615df9 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -8,62 +8,68 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/laset.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <types.hpp>
+#include <kernel_headers/laset.hpp>
+#include <program.hpp>
 #include <traits.hpp>
+#include <types.hpp>
+#include <string>
+#include <magma_types.h>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int BLK_X = 64;
 static const int BLK_Y = 32;
 
 template<int num>
-const char *laset_name() { return "laset_none"; }
-template<> const char *laset_name<0>() { return "laset_full"; }
-template<> const char *laset_name<1>() { return "laset_lower"; }
-template<> const char *laset_name<2>() { return "laset_upper"; }
+const char *laset_name() {
+    return "laset_none";
+}
+template<>
+const char *laset_name<0>() {
+    return "laset_full";
+}
+template<>
+const char *laset_name<1>() {
+    return "laset_lower";
+}
+template<>
+const char *laset_name<2>() {
+    return "laset_upper";
+}
 
 template<typename T, int uplo>
-void laset(int m, int  n,
-           T offdiag, T diag,
-           cl_mem dA, size_t dA_offset, magma_int_t ldda, cl_command_queue queue)
-{
+void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
+           magma_int_t ldda, cl_command_queue queue) {
     std::string refName = laset_name<uplo>() + std::string("_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(uplo);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(uplo);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-                << " -D BLK_X=" << BLK_X
-                << " -D BLK_Y=" << BLK_Y
+                << " -D BLK_X=" << BLK_X << " -D BLK_Y=" << BLK_Y
                 << " -D IS_CPLX=" << af::iscplx<T>();
 
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char* ker_strs[] = {laset_cl};
-        const int   ker_lens[] = {laset_cl_len};
+        const char *ker_strs[] = {laset_cl};
+        const int ker_lens[]   = {laset_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -81,10 +87,13 @@ void laset(int m, int  n,
     // retain the cl_mem object during cl::Buffer creation
     cl::Buffer dAObj(dA, true);
 
-    auto lasetOp = KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(*entry.ker);
+    auto lasetOp =
+        KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(
+            *entry.ker);
 
     cl::CommandQueue q(queue);
-    lasetOp(EnqueueArgs(q, global, local), m, n, offdiag, diag, dAObj, dA_offset, ldda);
-}
-}
+    lasetOp(EnqueueArgs(q, global, local), m, n, offdiag, diag, dAObj,
+            dA_offset, ldda);
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/laset_band.cl b/src/backend/opencl/kernel/laset_band.cl
index b69d9484ce..01e3a6dacd 100644
--- a/src/backend/opencl/kernel/laset_band.cl
+++ b/src/backend/opencl/kernel/laset_band.cl
@@ -37,28 +37,24 @@
                       |     3 2   => skip below matrix
                               3   => skip below matrix
 
-    Thread assignment for m=10, n=12, k=4, nb=8. Each column is done in parallel.
+    Thread assignment for m=10, n=12, k=4, nb=8. Each column is done in
+ parallel.
 */
-__kernel
-void laset_band_upper(
-    int m, int n,
-    T offdiag, T diag,
-    __global T *A, unsigned long off, int lda)
-{
+__kernel void laset_band_upper(int m, int n, T offdiag, T diag, __global T *A,
+                               unsigned long off, int lda) {
     int k   = get_local_size(0);
     int ibx = get_group_id(0) * NB;
     int ind = ibx + get_local_id(0) - k + 1;
 
-    A += ind + ibx*lda + off;
+    A += ind + ibx * lda + off;
 
     T value = offdiag;
-    if (get_local_id(0) == k-1)
-        value = diag;
+    if (get_local_id(0) == k - 1) value = diag;
 
-    #pragma unroll
-    for (int j=0; j < NB; j++) {
+#pragma unroll
+    for (int j = 0; j < NB; j++) {
         if (ibx + j < n && ind + j >= 0 && ind + j < m) {
-            A[j*(lda+1)] = value;
+            A[j * (lda + 1)] = value;
         }
     }
 }
@@ -88,29 +84,23 @@ void laset_band_upper(
                             3 2   => skip below matrix
                               3   => skip below matrix
 
-    Thread assignment for m=13, n=12, k=4, nb=8. Each column is done in parallel.
+    Thread assignment for m=13, n=12, k=4, nb=8. Each column is done in
+ parallel.
 */
 
-__kernel
-void laset_band_lower(
-    int m, int n,
-    T offdiag, T diag,
-    __global T *A, unsigned long off, int lda)
-{
-    //int k   = get_local_size(0);
+__kernel void laset_band_lower(int m, int n, T offdiag, T diag, __global T *A,
+                               unsigned long off, int lda) {
+    // int k   = get_local_size(0);
     int ibx = get_group_id(0) * NB;
     int ind = ibx + get_local_id(0);
 
-    A += ind + ibx*lda + off;
+    A += ind + ibx * lda + off;
 
     T value = offdiag;
-    if (get_local_id(0) == 0)
-        value = diag;
+    if (get_local_id(0) == 0) value = diag;
 
-    #pragma unroll
-    for (int j=0; j < NB; j++) {
-        if (ibx + j < n && ind + j < m) {
-            A[j*(lda+1)] = value;
-        }
+#pragma unroll
+    for (int j = 0; j < NB; j++) {
+        if (ibx + j < n && ind + j < m) { A[j * (lda + 1)] = value; }
     }
 }
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 9dc99d78fa..e1e031705d 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -8,30 +8,27 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/laset_band.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <types.hpp>
+#include <kernel_headers/laset_band.hpp>
+#include <program.hpp>
 #include <traits.hpp>
+#include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-#if 0 // Needs to be enabled when unmqr2 is enabled
+namespace opencl {
+namespace kernel {
+#if 0  // Needs to be enabled when unmqr2 is enabled
 static const int NB = 64;
 template<int num>
 const char *laset_band_name() { return "laset_none"; }
@@ -88,5 +85,5 @@ void laset_band(int m, int  n, int k,
     lasetBandOp(EnqueueArgs(getQueue(), global, local), m, n, offdiag, diag, dA, dA_offset, ldda);
 }
 #endif
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/laswp.cl b/src/backend/opencl/kernel/laswp.cl
index e052e24001..101fc39ab7 100644
--- a/src/backend/opencl/kernel/laswp.cl
+++ b/src/backend/opencl/kernel/laswp.cl
@@ -38,22 +38,22 @@
  * * Redistributions  of  source  code  must  retain  the above copyright
  *   notice,  this  list  of  conditions  and  the  following  disclaimer.
  * * Redistributions  in  binary  form must reproduce the above copyright
- *   notice,  this list of conditions and the following disclaimer in the 
+ *   notice,  this list of conditions and the following disclaimer in the
  *   documentation  and/or other materials provided with the distribution.
- * * Neither  the  name of the University of Tennessee, Knoxville nor the 
+ * * Neither  the  name of the University of Tennessee, Knoxville nor the
  *   names of its contributors may be used to endorse or promote products
  *   derived from this software without specific prior written permission.
  *
  * THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
- * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+ * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  * A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  * HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT 
+ * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
  * LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 
+ * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  * THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+ * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  * OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *
  **********************************************************************/
@@ -69,22 +69,21 @@ typedef struct {
 // Each GPU block processes one block-column of A.
 // Each thread goes down a column of A,
 // swapping rows according to pivots stored in params.
-__kernel void laswp(int n, __global T *dAT, unsigned long dAT_offset,
-                    int ldda, zlaswp_params_t params )
-{
+__kernel void laswp(int n, __global T *dAT, unsigned long dAT_offset, int ldda,
+                    zlaswp_params_t params) {
     dAT += dAT_offset;
 
-    int tid = get_local_id(0) + get_local_size(0)*get_group_id(0);
-    if ( tid < n ) {
+    int tid = get_local_id(0) + get_local_size(0) * get_group_id(0);
+    if (tid < n) {
         dAT += tid;
-        __global T *A1  = dAT;
+        __global T *A1 = dAT;
 
-        for( int i1 = 0; i1 < params.npivots; ++i1 ) {
-            int i2 = params.ipiv[i1];
-            __global T *A2 = dAT + i2*ldda;
-            T temp = *A1;
-            *A1 = *A2;
-            *A2 = temp;
+        for (int i1 = 0; i1 < params.npivots; ++i1) {
+            int i2         = params.ipiv[i1];
+            __global T *A2 = dAT + i2 * ldda;
+            T temp         = *A1;
+            *A1            = *A2;
+            *A2            = temp;
             A1 += ldda;  // A1 = dA + i1*ldx
         }
     }
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 77f70238a7..0a83f6b339 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -8,30 +8,28 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/laswp.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/laswp.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-static const int NTHREADS = 256;
-static const int MAX_PIVOTS =  32;
+namespace opencl {
+namespace kernel {
+static const int NTHREADS   = 256;
+static const int MAX_PIVOTS = 32;
 
 typedef struct {
     int npivots;
@@ -39,14 +37,15 @@ typedef struct {
 } zlaswp_params_t;
 
 template<typename T>
-void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int *ipiv, int inci, cl::CommandQueue &queue)
-{
-    std::string refName = std::string("laswp_") + std::string(dtype_traits<T>::getName());
+void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
+           const int *ipiv, int inci, cl::CommandQueue &queue) {
+    std::string refName =
+        std::string("laswp_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D MAX_PIVOTS=" << MAX_PIVOTS;
@@ -54,8 +53,8 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char* ker_strs[] = {laswp_cl};
-        const int   ker_lens[] = {laswp_cl_len};
+        const char *ker_strs[] = {laswp_cl};
+        const int ker_lens[]   = {laswp_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -69,23 +68,26 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2, const int
     NDRange global(groups * local[0]);
     zlaswp_params_t params;
 
-    //retain the cl_mem object during cl::Buffer creation
+    // retain the cl_mem object during cl::Buffer creation
     cl::Buffer inObj(in, true);
 
-    auto laswpOp = KernelFunctor<int, Buffer, unsigned long long, int, zlaswp_params_t>(*entry.ker);
+    auto laswpOp =
+        KernelFunctor<int, Buffer, unsigned long long, int, zlaswp_params_t>(
+            *entry.ker);
 
-    for( int k = k1-1; k < k2; k += MAX_PIVOTS ) {
-        int pivots_left = k2-k;
+    for (int k = k1 - 1; k < k2; k += MAX_PIVOTS) {
+        int pivots_left = k2 - k;
 
         params.npivots = pivots_left > MAX_PIVOTS ? MAX_PIVOTS : pivots_left;
 
-        for( int j = 0; j < params.npivots; ++j )
-            params.ipiv[j] = ipiv[(k+j)*inci] - k - 1;
+        for (int j = 0; j < params.npivots; ++j)
+            params.ipiv[j] = ipiv[(k + j) * inci] - k - 1;
 
-        unsigned long long k_offset = offset + k*ldda;
+        unsigned long long k_offset = offset + k * ldda;
 
-        laswpOp(EnqueueArgs(queue, global, local), n, inObj, k_offset, ldda, params);
+        laswpOp(EnqueueArgs(queue, global, local), n, inObj, k_offset, ldda,
+                params);
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/lookup.cl b/src/backend/opencl/kernel/lookup.cl
index 686cf48f7a..622a47e8f6 100644
--- a/src/backend/opencl/kernel/lookup.cl
+++ b/src/backend/opencl/kernel/lookup.cl
@@ -7,51 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-int trimIndex(int idx, const int len)
-{
+int trimIndex(int idx, const int len) {
     int ret_val = idx;
-    if (ret_val<0) {
-        int offset  = (abs(ret_val)-1)%len;
-        ret_val = offset;
-    } else if (ret_val>=len) {
-        int offset  = abs(ret_val)%len;
-        ret_val = len-offset-1;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
     }
     return ret_val;
 }
 
-kernel
-void lookupND(global in_t * out,
-                  KParam oInfo,
-                  global const in_t * in,
-                  KParam iInfo,
-                  global const idx_t * indices,
-                  KParam idxInfo,
-                  int nBBS0,
-                  int nBBS1)
-{
+kernel void lookupND(global in_t *out, KParam oInfo, global const in_t *in,
+                     KParam iInfo, global const idx_t *indices, KParam idxInfo,
+                     int nBBS0, int nBBS1) {
     int lx = get_local_id(0);
     int ly = get_local_id(1);
 
-    int gz = get_group_id(0)/nBBS0;
-    int gw = get_group_id(1)/nBBS1;
+    int gz = get_group_id(0) / nBBS0;
+    int gw = get_group_id(1) / nBBS1;
 
-    int gx = get_local_size(0) * (get_group_id(0) - gz*nBBS0) + lx;
-    int gy = get_local_size(1) * (get_group_id(1) - gw*nBBS1) + ly;
+    int gx = get_local_size(0) * (get_group_id(0) - gz * nBBS0) + lx;
+    int gy = get_local_size(1) * (get_group_id(1) - gw * nBBS1) + ly;
 
     global const idx_t *idxPtr = indices;
 
-    int i = iInfo.strides[0]*(DIM==0 ? trimIndex((int)idxPtr[gx], iInfo.dims[0]): gx);
-    int j = iInfo.strides[1]*(DIM==1 ? trimIndex((int)idxPtr[gy], iInfo.dims[1]): gy);
-    int k = iInfo.strides[2]*(DIM==2 ? trimIndex((int)idxPtr[gz], iInfo.dims[2]): gz);
-    int l = iInfo.strides[3]*(DIM==3 ? trimIndex((int)idxPtr[gw], iInfo.dims[3]): gw);
-
-    global const in_t *inPtr = in + (i+j+k+l) + iInfo.offset;
-    global in_t *outPtr = out + (gx*oInfo.strides[0]+gy*oInfo.strides[1]+
-                                 gz*oInfo.strides[2]+gw*oInfo.strides[3]+
-                                 oInfo.offset);
-
-    if (gx<oInfo.dims[0] && gy<oInfo.dims[1] && gz<oInfo.dims[2] && gw<oInfo.dims[3]) {
+    int i = iInfo.strides[0] *
+            (DIM == 0 ? trimIndex((int)idxPtr[gx], iInfo.dims[0]) : gx);
+    int j = iInfo.strides[1] *
+            (DIM == 1 ? trimIndex((int)idxPtr[gy], iInfo.dims[1]) : gy);
+    int k = iInfo.strides[2] *
+            (DIM == 2 ? trimIndex((int)idxPtr[gz], iInfo.dims[2]) : gz);
+    int l = iInfo.strides[3] *
+            (DIM == 3 ? trimIndex((int)idxPtr[gw], iInfo.dims[3]) : gw);
+
+    global const in_t *inPtr = in + (i + j + k + l) + iInfo.offset;
+    global in_t *outPtr =
+        out + (gx * oInfo.strides[0] + gy * oInfo.strides[1] +
+               gz * oInfo.strides[2] + gw * oInfo.strides[3] + oInfo.offset);
+
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1] && gz < oInfo.dims[2] &&
+        gw < oInfo.dims[3]) {
         outPtr[0] = inPtr[0];
     }
 }
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 263c4ed571..05fec3bced 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -8,55 +8,51 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/lookup.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
 
 template<typename in_t, typename idx_t, unsigned dim>
-void lookup(Param out, const Param in, const Param indices)
-{
-    std::string refName = std::string("lookupND_") +
-        std::string(dtype_traits<in_t>::getName()) +
-        std::string(dtype_traits<idx_t>::getName()) +
-        std::to_string(dim);
+void lookup(Param out, const Param in, const Param indices) {
+    std::string refName =
+        std::string("lookupND_") + std::string(dtype_traits<in_t>::getName()) +
+        std::string(dtype_traits<idx_t>::getName()) + std::to_string(dim);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D in_t=" << dtype_traits<in_t>::getName()
                 << " -D idx_t=" << dtype_traits<idx_t>::getName()
-                << " -D DIM=" <<dim;
+                << " -D DIM=" << dim;
 
         if (std::is_same<in_t, double>::value ||
-                std::is_same<in_t, cdouble>::value ||
-                std::is_same<idx_t, double>::value) {
+            std::is_same<in_t, cdouble>::value ||
+            std::is_same<idx_t, double>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char* ker_strs[] = {lookup_cl};
-        const int   ker_lens[] = {lookup_cl_len};
+        const int ker_lens[]   = {lookup_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -70,14 +66,17 @@ void lookup(Param out, const Param in, const Param indices)
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * out.info.dims[2] * THREADS_X, blk_y * out.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
+                   blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int, int>(*entry.ker);
+    auto arrIdxOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int, int>(
+            *entry.ker);
 
-    arrIdxOp(EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
+    arrIdxOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/lu_split.cl b/src/backend/opencl/kernel/lu_split.cl
index 856a9e2ad7..3a70ee668c 100644
--- a/src/backend/opencl/kernel/lu_split.cl
+++ b/src/backend/opencl/kernel/lu_split.cl
@@ -7,12 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void lu_split_kernel(__global T *lptr, KParam linfo,
-                     __global T *uptr, KParam uinfo,
-                     const __global T *iptr, KParam iinfo,
-                     const int groups_x, const int groups_y)
-{
+__kernel void lu_split_kernel(__global T *lptr, KParam linfo, __global T *uptr,
+                              KParam uinfo, const __global T *iptr,
+                              KParam iinfo, const int groups_x,
+                              const int groups_y) {
     const int oz = get_group_id(0) / groups_x;
     const int ow = get_group_id(1) / groups_y;
 
@@ -29,31 +27,25 @@ void lu_split_kernel(__global T *lptr, KParam linfo,
     __global T *d_u = uptr;
     __global T *d_i = iptr;
 
-    if(oz < iinfo.dims[2] && ow < iinfo.dims[3]) {
-        d_i = d_i + oz * iinfo.strides[2]    + ow * iinfo.strides[3];
+    if (oz < iinfo.dims[2] && ow < iinfo.dims[3]) {
+        d_i = d_i + oz * iinfo.strides[2] + ow * iinfo.strides[3];
         d_l = d_l + oz * linfo.strides[2] + ow * linfo.strides[3];
         d_u = d_u + oz * uinfo.strides[2] + ow * uinfo.strides[3];
 
         for (int oy = yy; oy < iinfo.dims[1]; oy += incy) {
             __global T *Yd_i = d_i + oy * iinfo.strides[1];
-            __global T *Yd_l = d_l +  oy * linfo.strides[1];
-            __global T *Yd_u = d_u +  oy * uinfo.strides[1];
+            __global T *Yd_l = d_l + oy * linfo.strides[1];
+            __global T *Yd_u = d_u + oy * uinfo.strides[1];
             for (int ox = xx; ox < iinfo.dims[0]; ox += incx) {
-                if(ox > oy) {
-                    if(same_dims || oy < linfo.dims[1])
-                        Yd_l[ox] = Yd_i[ox];
-                    if(!same_dims || ox < uinfo.dims[0])
-                        Yd_u[ox] = ZERO;
+                if (ox > oy) {
+                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = Yd_i[ox];
+                    if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = ZERO;
                 } else if (oy > ox) {
-                    if(same_dims || oy < linfo.dims[1])
-                        Yd_l[ox] = ZERO;
-                    if(!same_dims || ox < uinfo.dims[0])
-                        Yd_u[ox] = Yd_i[ox];
-                } else if(ox == oy) {
-                    if(same_dims || oy < linfo.dims[1])
-                        Yd_l[ox] = ONE;
-                    if(!same_dims || ox < uinfo.dims[0])
-                        Yd_u[ox] = Yd_i[ox];
+                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = ZERO;
+                    if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = Yd_i[ox];
+                } else if (ox == oy) {
+                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = ONE;
+                    if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = Yd_i[ox];
                 }
             }
         }
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index ca15c0fedd..83c5395fd7 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -8,58 +8,55 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/lu_split.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <types.hpp>
+#include <kernel_headers/lu_split.hpp>
 #include <math.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <string>
 
+using af::scalar_to_option;
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
-using af::scalar_to_option;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
-static const unsigned TX = 32;
-static const unsigned TY = 8;
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
 static const unsigned TILEX = 128;
 static const unsigned TILEY = 32;
 
 template<typename T, bool same_dims>
-void lu_split_launcher(Param lower, Param upper, const Param in)
-{
+void lu_split_launcher(Param lower, Param upper, const Param in) {
     std::string refName = std::string("lu_split_kernel_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(same_dims);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(same_dims);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-            << " -D same_dims=" << same_dims
-            << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-            << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
+                << " -D same_dims=" << same_dims << " -D ZERO=(T)("
+                << scalar_to_option(scalar<T>(0)) << ")"
+                << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
 
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {lu_split_cl};
-        const int   ker_lens[] = {lu_split_cl_len};
+        const int ker_lens[]   = {lu_split_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -73,30 +70,29 @@ void lu_split_launcher(Param lower, Param upper, const Param in)
     int groups_x = divup(in.info.dims[0], TILEX);
     int groups_y = divup(in.info.dims[1], TILEY);
 
-    NDRange global(groups_x * local[0] * in.info.dims[2], groups_y * local[1] * in.info.dims[3]);
+    NDRange global(groups_x * local[0] * in.info.dims[2],
+                   groups_y * local[1] * in.info.dims[3]);
 
-    auto lu_split_op = KernelFunctor<Buffer, const KParam, Buffer, const KParam,
-                                    const Buffer, const KParam, const int, const int> (*entry.ker);
+    auto lu_split_op =
+        KernelFunctor<Buffer, const KParam, Buffer, const KParam, const Buffer,
+                      const KParam, const int, const int>(*entry.ker);
 
-    lu_split_op(EnqueueArgs(getQueue(), global, local),
-                *lower.data, lower.info, *upper.data, upper.info,
-                *in.data, in.info, groups_x, groups_y);
+    lu_split_op(EnqueueArgs(getQueue(), global, local), *lower.data, lower.info,
+                *upper.data, upper.info, *in.data, in.info, groups_x, groups_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-void lu_split(Param lower, Param upper, const Param in)
-{
-    bool same_dims =
-        (lower.info.dims[0] == in.info.dims[0]) &&
-        (lower.info.dims[1] == in.info.dims[1]);
+void lu_split(Param lower, Param upper, const Param in) {
+    bool same_dims = (lower.info.dims[0] == in.info.dims[0]) &&
+                     (lower.info.dims[1] == in.info.dims[1]);
 
     if (same_dims) {
-        lu_split_launcher<T, true >(lower, upper, in);
+        lu_split_launcher<T, true>(lower, upper, in);
     } else {
         lu_split_launcher<T, false>(lower, upper, in);
     }
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/matchTemplate.cl b/src/backend/opencl/kernel/matchTemplate.cl
index c80c9ce731..2a42a77619 100644
--- a/src/backend/opencl/kernel/matchTemplate.cl
+++ b/src/backend/opencl/kernel/matchTemplate.cl
@@ -7,56 +7,56 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-kernel
-void matchTemplate(global outType * out,
-                    KParam          oInfo,
-                    global const inType * srch,
-                    KParam          sInfo,
-                    global const inType * tmplt,
-                    KParam          tInfo,
-                    int        nBBS0,
-                    int        nBBS1)
-{
+kernel void matchTemplate(global outType* out, KParam oInfo,
+                          global const inType* srch, KParam sInfo,
+                          global const inType* tmplt, KParam tInfo, int nBBS0,
+                          int nBBS1) {
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
 
-    int gx = get_local_id(0) + (get_group_id(0) - b2*nBBS0) * get_local_size(0);
-    int gy = get_local_id(1) + (get_group_id(1) - b3*nBBS1)* get_local_size(1);
+    int gx =
+        get_local_id(0) + (get_group_id(0) - b2 * nBBS0) * get_local_size(0);
+    int gy =
+        get_local_id(1) + (get_group_id(1) - b3 * nBBS1) * get_local_size(1);
 
     if (gx < sInfo.dims[0] && gy < sInfo.dims[1]) {
-
         const int tDim0 = tInfo.dims[0];
         const int tDim1 = tInfo.dims[1];
         const int sDim0 = sInfo.dims[0];
         const int sDim1 = sInfo.dims[1];
-        int winNumElems = tDim0*tDim1;
+        int winNumElems = tDim0 * tDim1;
 
         global const inType* tptr = tmplt + tInfo.offset;
 
         outType tImgMean = (outType)0;
         if (NEEDMEAN) {
-            for(int tj=0; tj<tDim1; tj++) {
-                int tjStride = tj*tInfo.strides[1];
+            for (int tj = 0; tj < tDim1; tj++) {
+                int tjStride = tj * tInfo.strides[1];
 
-                for(int ti=0; ti<tDim0; ti++) {
-                    tImgMean += (outType)tptr[ tjStride + ti*tInfo.strides[0] ];
+                for (int ti = 0; ti < tDim0; ti++) {
+                    tImgMean += (outType)tptr[tjStride + ti * tInfo.strides[0]];
                 }
             }
             tImgMean /= winNumElems;
         }
 
-        global const inType* sptr  = srch + (b2 * sInfo.strides[2] + b3 * sInfo.strides[3] + sInfo.offset);
-        global outType* optr       = out  + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+        global const inType* sptr =
+            srch +
+            (b2 * sInfo.strides[2] + b3 * sInfo.strides[3] + sInfo.offset);
+        global outType* optr =
+            out + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
         // mean for window
         // this variable will be used based on MATCH_T value
         outType wImgMean = (outType)0;
         if (NEEDMEAN) {
-            for(int tj=0,j=gy; tj<tDim1; tj++, j++) {
-                int jStride = j*sInfo.strides[1];
+            for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
+                int jStride = j * sInfo.strides[1];
 
-                for(int ti=0, i=gx; ti<tDim0; ti++, i++) {
-                    inType sVal = ((j<sDim1 && i<sDim0) ? sptr[jStride + i*sInfo.strides[0]] : (inType)0);
+                for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
+                    inType sVal = ((j < sDim1 && i < sDim0)
+                                       ? sptr[jStride + i * sInfo.strides[0]]
+                                       : (inType)0);
                     wImgMean += (outType)sVal;
                 }
             }
@@ -66,52 +66,55 @@ void matchTemplate(global outType * out,
         // run the window match metric
         outType disparity = (outType)0;
 
-        for(int tj=0,j=gy; tj<tDim1; tj++, j++) {
-
-            int jStride  = j*sInfo.strides[1];
-            int tjStride = tj*tInfo.strides[1];
-
-            for(int ti=0, i=gx; ti<tDim0; ti++, i++) {
+        for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
+            int jStride  = j * sInfo.strides[1];
+            int tjStride = tj * tInfo.strides[1];
 
-                inType sVal = ((j<sDim1 && i<sDim0) ? sptr[jStride + i*sInfo.strides[0]] : (inType)0);
-                inType tVal = tptr[ tjStride + ti*tInfo.strides[0] ];
+            for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
+                inType sVal = ((j < sDim1 && i < sDim0)
+                                   ? sptr[jStride + i * sInfo.strides[0]]
+                                   : (inType)0);
+                inType tVal = tptr[tjStride + ti * tInfo.strides[0]];
 
                 outType temp;
-                switch(MATCH_T) {
+                switch (MATCH_T) {
                     case AF_SAD:
-                        disparity += fabs((outType)sVal-(outType)tVal);
+                        disparity += fabs((outType)sVal - (outType)tVal);
                         break;
                     case AF_ZSAD:
                         disparity += fabs((outType)sVal - wImgMean -
-                                (outType)tVal + tImgMean);
+                                          (outType)tVal + tImgMean);
                         break;
                     case AF_LSAD:
-                        disparity += fabs((outType)sVal-(wImgMean/tImgMean)*tVal);
+                        disparity +=
+                            fabs((outType)sVal - (wImgMean / tImgMean) * tVal);
                         break;
                     case AF_SSD:
-                        disparity += ((outType)sVal-(outType)tVal)*((outType)sVal-(outType)tVal);
+                        disparity += ((outType)sVal - (outType)tVal) *
+                                     ((outType)sVal - (outType)tVal);
                         break;
                     case AF_ZSSD:
-                        temp = ((outType)sVal - wImgMean - (outType)tVal + tImgMean);
-                        disparity += temp*temp;
+                        temp = ((outType)sVal - wImgMean - (outType)tVal +
+                                tImgMean);
+                        disparity += temp * temp;
                         break;
                     case AF_LSSD:
-                        temp = ((outType)sVal-(wImgMean/tImgMean)*tVal);
-                        disparity += temp*temp;
+                        temp = ((outType)sVal - (wImgMean / tImgMean) * tVal);
+                        disparity += temp * temp;
                         break;
                     case AF_NCC:
-                        //TODO: furture implementation
+                        // TODO: furture implementation
                         break;
                     case AF_ZNCC:
-                        //TODO: furture implementation
+                        // TODO: furture implementation
                         break;
                     case AF_SHD:
-                        //TODO: furture implementation
+                        // TODO: furture implementation
                         break;
                 }
             }
         }
 
-        optr[gy*oInfo.strides[1]+gx] = disparity;
+        optr[gy * oInfo.strides[1] + gx] = disparity;
     }
 }
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index eaede72bbd..d0c5f7b003 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -8,61 +8,52 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/matchTemplate.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename inType, typename outType, af_match_type mType, bool needMean>
-void matchTemplate(Param out, const Param srch, const Param tmplt)
-{
+void matchTemplate(Param out, const Param srch, const Param tmplt) {
     std::string refName = std::string("matchTemplate_") +
-        std::string(dtype_traits<inType>::getName()) +
-        std::string(dtype_traits<outType>::getName()) +
-        std::to_string(mType) + std::to_string(needMean);
+                          std::string(dtype_traits<inType>::getName()) +
+                          std::string(dtype_traits<outType>::getName()) +
+                          std::to_string(mType) + std::to_string(needMean);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D inType="  << dtype_traits<inType>::getName()
+        options << " -D inType=" << dtype_traits<inType>::getName()
                 << " -D outType=" << dtype_traits<outType>::getName()
-                << " -D MATCH_T=" << mType
-                << " -D NEEDMEAN="<< needMean
-                << " -D AF_SAD="  << AF_SAD
-                << " -D AF_ZSAD=" << AF_ZSAD
-                << " -D AF_LSAD=" << AF_LSAD
-                << " -D AF_SSD="  << AF_SSD
-                << " -D AF_ZSSD=" << AF_ZSSD
-                << " -D AF_LSSD=" << AF_LSSD
-                << " -D AF_NCC="  << AF_NCC
-                << " -D AF_ZNCC=" << AF_ZNCC
-                << " -D AF_SHD="  << AF_SHD;
-        if (std::is_same<outType, double>::value)
-            options << " -D USE_DOUBLE";
+                << " -D MATCH_T=" << mType << " -D NEEDMEAN=" << needMean
+                << " -D AF_SAD=" << AF_SAD << " -D AF_ZSAD=" << AF_ZSAD
+                << " -D AF_LSAD=" << AF_LSAD << " -D AF_SSD=" << AF_SSD
+                << " -D AF_ZSSD=" << AF_ZSSD << " -D AF_LSSD=" << AF_LSSD
+                << " -D AF_NCC=" << AF_NCC << " -D AF_ZNCC=" << AF_ZNCC
+                << " -D AF_SHD=" << AF_SHD;
+        if (std::is_same<outType, double>::value) options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {matchTemplate_cl};
-        const int   ker_lens[] = {matchTemplate_cl_len};
+        const int ker_lens[]   = {matchTemplate_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -76,15 +67,17 @@ void matchTemplate(Param out, const Param srch, const Param tmplt)
     int blk_x = divup(srch.info.dims[0], THREADS_X);
     int blk_y = divup(srch.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * srch.info.dims[2] * THREADS_X, blk_y * srch.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * srch.info.dims[2] * THREADS_X,
+                   blk_y * srch.info.dims[3] * THREADS_Y);
 
-    auto matchImgOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                    int, int> (*entry.ker);
+    auto matchImgOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int, int>(
+            *entry.ker);
 
-    matchImgOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info, *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
+    matchImgOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index c4d20d928f..dfde1e850e 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -8,412 +8,320 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/mean_first.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/mean_dim.hpp>
+#include <kernel_headers/mean_first.hpp>
 #include <kernel_headers/mean_ops.hpp>
+#include <memory.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <cache.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include "names.hpp"
 #include "config.hpp"
-#include <memory.hpp>
+#include "names.hpp"
 
-#include <string>
-#include <mutex>
 #include <map>
+#include <mutex>
+#include <string>
 #include <vector>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 using std::vector;
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 template<typename T, typename Tw>
-struct MeanOp
-{
+struct MeanOp {
     T runningMean;
     Tw runningCount;
-    MeanOp(T mean, Tw count) :
-        runningMean(mean), runningCount(count)
-    {
-    }
+    MeanOp(T mean, Tw count) : runningMean(mean), runningCount(count) {}
 
-    void operator()(T newMean, Tw newCount)
-    {
+    void operator()(T newMean, Tw newCount) {
         if ((newCount != 0) || (runningCount != 0)) {
             Tw runningScale = runningCount;
-            Tw newScale = newCount;
+            Tw newScale     = newCount;
             runningCount += newCount;
-            runningScale = runningScale/runningCount;
-            newScale = newScale/(Tw)runningCount;
-            runningMean = (runningScale*runningMean) + (newScale*newMean);
+            runningScale = runningScale / runningCount;
+            newScale     = newScale / (Tw)runningCount;
+            runningMean  = (runningScale * runningMean) + (newScale * newMean);
         }
     }
 };
 
 template<>
-struct MeanOp<cfloat, float>
-{
+struct MeanOp<cfloat, float> {
     cfloat runningMean;
     float runningCount;
-    MeanOp(cfloat mean, float count) :
-        runningMean(mean), runningCount(count)
-    {
-    }
+    MeanOp(cfloat mean, float count) : runningMean(mean), runningCount(count) {}
 
-    void operator()(cfloat newMean, float newCount)
-    {
+    void operator()(cfloat newMean, float newCount) {
         if ((newCount != 0) || (runningCount != 0)) {
             float runningScale = runningCount;
-            float newScale = newCount;
+            float newScale     = newCount;
             runningCount += newCount;
-            runningScale = runningScale/runningCount;
-            newScale = newScale/(float)runningCount;
-            runningMean.s[0] = (runningScale*runningMean.s[0]) + (newScale*newMean.s[0]);
-            runningMean.s[1] = (runningScale*runningMean.s[1]) + (newScale*newMean.s[1]);
+            runningScale = runningScale / runningCount;
+            newScale     = newScale / (float)runningCount;
+            runningMean.s[0] =
+                (runningScale * runningMean.s[0]) + (newScale * newMean.s[0]);
+            runningMean.s[1] =
+                (runningScale * runningMean.s[1]) + (newScale * newMean.s[1]);
         }
     }
 };
 
 template<>
-struct MeanOp<cdouble, double>
-{
+struct MeanOp<cdouble, double> {
     cdouble runningMean;
     double runningCount;
-    MeanOp(cdouble mean, double count) :
-        runningMean(mean), runningCount(count)
-    {
-    }
+    MeanOp(cdouble mean, double count)
+        : runningMean(mean), runningCount(count) {}
 
-    void operator()(cdouble newMean, double newCount)
-    {
+    void operator()(cdouble newMean, double newCount) {
         if ((newCount != 0) || (runningCount != 0)) {
             double runningScale = runningCount;
-            double newScale = newCount;
+            double newScale     = newCount;
             runningCount += newCount;
-            runningScale = runningScale/runningCount;
-            newScale = newScale/(double)runningCount;
-            runningMean.s[0] = (runningScale*runningMean.s[0]) + (newScale*newMean.s[0]);
-            runningMean.s[1] = (runningScale*runningMean.s[1]) + (newScale*newMean.s[1]);
+            runningScale = runningScale / runningCount;
+            newScale     = newScale / (double)runningCount;
+            runningMean.s[0] =
+                (runningScale * runningMean.s[0]) + (newScale * newMean.s[0]);
+            runningMean.s[1] =
+                (runningScale * runningMean.s[1]) + (newScale * newMean.s[1]);
         }
     }
 };
 
 template<typename Ti, typename Tw, typename To>
-void mean_dim_launcher(Param out, Param owt,
-        Param in, Param inWeight,
-        const int dim,
-        const int threads_y,
-        const uint groups_all[4])
-{
-    bool input_weight = ((
-            inWeight.info.dims[0] *
-            inWeight.info.dims[1] *
-            inWeight.info.dims[2] *
-            inWeight.info.dims[3]) != 0);
-
-    bool output_weight = ((
-            owt.info.dims[0] *
-            owt.info.dims[1] *
-            owt.info.dims[2] *
-            owt.info.dims[3]) != 0);
+void mean_dim_launcher(Param out, Param owt, Param in, Param inWeight,
+                       const int dim, const int threads_y,
+                       const uint groups_all[4]) {
+    bool input_weight = ((inWeight.info.dims[0] * inWeight.info.dims[1] *
+                          inWeight.info.dims[2] * inWeight.info.dims[3]) != 0);
+
+    bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
+                           owt.info.dims[2] * owt.info.dims[3]) != 0);
 
     std::string ref_name =
-        std::string("mean_") +
-        std::to_string(dim) +
-        std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) +
-        std::string("_") +
-        std::string(dtype_traits<Tw>::getName()) +
-        std::string("_") +
-        std::string(dtype_traits<To>::getName()) +
-        std::string("_") +
-        std::to_string(threads_y) +
-        std::string("_") +
-        std::to_string(input_weight) +
-        std::string("_") +
+        std::string("mean_") + std::to_string(dim) + std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<Tw>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(threads_y) + std::string("_") +
+        std::to_string(input_weight) + std::string("_") +
         std::to_string(output_weight);
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
-
+    if (entry.prog == 0 && entry.ker == 0) {
         ToNumStr<To> toNumStr;
         ToNumStr<Tw> twNumStr;
         Transform<uint, Tw, af_add_t> transform_weight;
 
         std::ostringstream options;
         options << " -D Ti=" << dtype_traits<Ti>::getName()
-            << " -D Tw=" << dtype_traits<Tw>::getName()
-            << " -D To=" << dtype_traits<To>::getName()
-            << " -D dim=" << dim
-            << " -D DIMY=" << threads_y
-            << " -D THREADS_X=" << THREADS_X
-            << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
-            << " -D init_Tw=" << twNumStr(transform_weight(0))
-            << " -D one_Tw=" << twNumStr(transform_weight(1));
+                << " -D Tw=" << dtype_traits<Tw>::getName()
+                << " -D To=" << dtype_traits<To>::getName() << " -D dim=" << dim
+                << " -D DIMY=" << threads_y << " -D THREADS_X=" << THREADS_X
+                << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
+                << " -D init_Tw=" << twNumStr(transform_weight(0))
+                << " -D one_Tw=" << twNumStr(transform_weight(1));
 
         if (input_weight) { options << " -D INPUT_WEIGHT"; }
         if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
 
         if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value ||
-                std::is_same<To, double>::value) {
+            std::is_same<Ti, cdouble>::value ||
+            std::is_same<To, double>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char *ker_strs[] = {mean_ops_cl, mean_dim_cl};
-        const int   ker_lens[] = {mean_ops_cl_len, mean_dim_cl_len};
+        const int ker_lens[]   = {mean_ops_cl_len, mean_dim_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "mean_dim_kernel");
+        entry.ker  = new Kernel(*entry.prog, "mean_dim_kernel");
 
         addKernelToCache(device, ref_name, entry);
     }
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
-            groups_all[1] * groups_all[3] * local[1]);
+                   groups_all[1] * groups_all[3] * local[1]);
 
     if (input_weight && output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *owt.data, owt.info,
-                *in.data, in.info,
-                *inWeight.data, inWeight.info,
-                groups_all[0],
-                groups_all[1],
-                groups_all[dim]);
+        auto meanOp =
+            KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                          Buffer, KParam, uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *owt.data, owt.info, *in.data, in.info, *inWeight.data,
+               inWeight.info, groups_all[0], groups_all[1], groups_all[dim]);
     } else if (!input_weight && !output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *in.data, in.info,
-                groups_all[0],
-                groups_all[1],
-                groups_all[dim]);
-    } else if ( input_weight && !output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *in.data, in.info,
-                *inWeight.data, inWeight.info,
-                groups_all[0],
-                groups_all[1],
-                groups_all[dim]);
-    } else if (!input_weight &&  output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *owt.data, owt.info,
-                *in.data, in.info,
-                groups_all[0],
-                groups_all[1],
-                groups_all[dim]);
+        auto meanOp =
+            KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint>(
+                *entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *in.data, in.info, groups_all[0], groups_all[1],
+               groups_all[dim]);
+    } else if (input_weight && !output_weight) {
+        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                    KParam, uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *in.data, in.info, *inWeight.data, inWeight.info, groups_all[0],
+               groups_all[1], groups_all[dim]);
+    } else if (!input_weight && output_weight) {
+        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                    KParam, uint, uint, uint>(*entry.ker);
+
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *owt.data, owt.info, *in.data, in.info, groups_all[0],
+               groups_all[1], groups_all[dim]);
     }
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_dim(Param out, Param in, Param inWeight, int dim)
-{
+void mean_dim(Param out, Param in, Param inWeight, int dim) {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
     uint threads_x = THREADS_X;
 
     uint groups_all[] = {(uint)divup(in.info.dims[0], threads_x),
-        (uint)in.info.dims[1],
-        (uint)in.info.dims[2],
-        (uint)in.info.dims[3]};
+                         (uint)in.info.dims[1], (uint)in.info.dims[2],
+                         (uint)in.info.dims[3]};
 
     groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
 
     if (groups_all[dim] > 1) {
         dim4 d(4, out.info.dims);
-        d[dim] = groups_all[dim];
-        Array<To> tmpOut = createEmptyArray<To>(d);
+        d[dim]              = groups_all[dim];
+        Array<To> tmpOut    = createEmptyArray<To>(d);
         Array<Tw> tmpWeight = createEmptyArray<Tw>(d);
-        mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, dim, threads_y, groups_all);
+        mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, dim,
+                                      threads_y, groups_all);
 
         Param owt;
         groups_all[dim] = 1;
-        mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWeight, dim, threads_y, groups_all);
+        mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWeight, dim,
+                                      threads_y, groups_all);
     } else {
         Param tmpWeight;
-        mean_dim_launcher<Ti, Tw, To>(out, tmpWeight, in, inWeight, dim, threads_y, groups_all);
+        mean_dim_launcher<Ti, Tw, To>(out, tmpWeight, in, inWeight, dim,
+                                      threads_y, groups_all);
     }
-
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_first_launcher(Param out, Param owt,
-        Param in, Param inWeight,
-        const int threads_x,
-        const uint groups_x,
-        const uint groups_y)
-{
-
-    bool input_weight = ((inWeight.info.dims[0] *
-                          inWeight.info.dims[1] *
-                          inWeight.info.dims[2] *
-                          inWeight.info.dims[3]) != 0);
-
-    bool output_weight = (( owt.info.dims[0] *
-                            owt.info.dims[1] *
-                            owt.info.dims[2] *
-                            owt.info.dims[3]) != 0);
+void mean_first_launcher(Param out, Param owt, Param in, Param inWeight,
+                         const int threads_x, const uint groups_x,
+                         const uint groups_y) {
+    bool input_weight = ((inWeight.info.dims[0] * inWeight.info.dims[1] *
+                          inWeight.info.dims[2] * inWeight.info.dims[3]) != 0);
+
+    bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
+                           owt.info.dims[2] * owt.info.dims[3]) != 0);
 
     std::string ref_name =
-        std::string("mean_0_") +
-        std::string(dtype_traits<Ti>::getName()) +
-        std::string("_") +
-        std::string(dtype_traits<Tw>::getName()) +
-        std::string("_") +
-        std::string(dtype_traits<To>::getName()) +
-        std::string("_") +
-        std::to_string(threads_x) +
-        std::string("_") +
-        std::to_string(input_weight) +
-        std::string("_") +
+        std::string("mean_0_") + std::string(dtype_traits<Ti>::getName()) +
+        std::string("_") + std::string(dtype_traits<Tw>::getName()) +
+        std::string("_") + std::string(dtype_traits<To>::getName()) +
+        std::string("_") + std::to_string(threads_x) + std::string("_") +
+        std::to_string(input_weight) + std::string("_") +
         std::to_string(output_weight);
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
-
+    if (entry.prog == 0 && entry.ker == 0) {
         ToNumStr<To> toNumStr;
         ToNumStr<Tw> twNumStr;
         Transform<uint, Tw, af_add_t> transform_weight;
 
         std::ostringstream options;
         options << " -D Ti=" << dtype_traits<Ti>::getName()
-            << " -D Tw=" << dtype_traits<Tw>::getName()
-            << " -D To=" << dtype_traits<To>::getName()
-            << " -D DIMX=" << threads_x
-            << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-            << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
-            << " -D init_Tw=" << twNumStr(transform_weight(0))
-            << " -D one_Tw=" << twNumStr(transform_weight(1));
+                << " -D Tw=" << dtype_traits<Tw>::getName()
+                << " -D To=" << dtype_traits<To>::getName()
+                << " -D DIMX=" << threads_x
+                << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
+                << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
+                << " -D init_Tw=" << twNumStr(transform_weight(0))
+                << " -D one_Tw=" << twNumStr(transform_weight(1));
 
         if (input_weight) { options << " -D INPUT_WEIGHT"; }
         if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
 
         if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value ||
-                std::is_same<To, double>::value) {
+            std::is_same<Ti, cdouble>::value ||
+            std::is_same<To, double>::value) {
             options << " -D USE_DOUBLE";
         }
 
         const char *ker_strs[] = {mean_ops_cl, mean_first_cl};
-        const int   ker_lens[] = {mean_ops_cl_len, mean_first_cl_len};
+        const int ker_lens[]   = {mean_ops_cl_len, mean_first_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
-        entry.ker = new Kernel(*entry.prog, "mean_first_kernel");
+        entry.ker  = new Kernel(*entry.prog, "mean_first_kernel");
 
         addKernelToCache(device, ref_name, entry);
     }
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * in.info.dims[2] * local[0],
-            groups_y * in.info.dims[3] * local[1]);
+                   groups_y * in.info.dims[3] * local[1]);
 
     uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
     if (input_weight && output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *owt.data, owt.info,
-                *in.data, in.info,
-                *inWeight.data, inWeight.info,
-                groups_x, groups_y, repeat);
+        auto meanOp =
+            KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                          Buffer, KParam, uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *owt.data, owt.info, *in.data, in.info, *inWeight.data,
+               inWeight.info, groups_x, groups_y, repeat);
     } else if (!input_weight && !output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *in.data, in.info,
-                groups_x, groups_y, repeat);
-    } else if ( input_weight && !output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *in.data, in.info,
-                *inWeight.data, inWeight.info,
-                groups_x, groups_y, repeat);
-    } else if (!input_weight &&  output_weight) {
-        auto meanOp = KernelFunctor<
-            Buffer, KParam,
-            Buffer, KParam,
-            Buffer, KParam,
-            uint, uint, uint>(*entry.ker);
-        meanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *owt.data, owt.info,
-                *in.data, in.info,
-                groups_x, groups_y, repeat);
+        auto meanOp =
+            KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint>(
+                *entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *in.data, in.info, groups_x, groups_y, repeat);
+    } else if (input_weight && !output_weight) {
+        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                    KParam, uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *in.data, in.info, *inWeight.data, inWeight.info, groups_x,
+               groups_y, repeat);
+    } else if (!input_weight && output_weight) {
+        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                    KParam, uint, uint, uint>(*entry.ker);
+        meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *owt.data, owt.info, *in.data, in.info, groups_x, groups_y,
+               repeat);
     }
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_first(Param out, Param in, Param inWeight)
-{
+void mean_first(Param out, Param in, Param inWeight) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-    threads_x = std::min(threads_x, THREADS_PER_GROUP);
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
 
     uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
@@ -423,39 +331,35 @@ void mean_first(Param out, Param in, Param inWeight)
     Param noWeight;
     noWeight.info.offset = 0;
     for (int k = 0; k < 4; ++k) {
-        noWeight.info.dims[k] = 0;
+        noWeight.info.dims[k]    = 0;
         noWeight.info.strides[k] = 0;
     }
-    // Does not matter what the value is it will not be used. Just needs to be valid.
+    // Does not matter what the value is it will not be used. Just needs to be
+    // valid.
     noWeight.data = inWeight.data;
 
     Param tmpWeight = noWeight;
 
     if (groups_x > 1) {
+        tmpOut.data = bufferAlloc(groups_x * in.info.dims[1] * in.info.dims[2] *
+                                  in.info.dims[3] * sizeof(To));
 
-        tmpOut.data = bufferAlloc(groups_x *
-                in.info.dims[1] *
-                in.info.dims[2] *
-                in.info.dims[3] *
-                sizeof(To));
-
-        tmpWeight.data = bufferAlloc(groups_x *
-                in.info.dims[1] *
-                in.info.dims[2] *
-                in.info.dims[3] *
-                sizeof(Tw));
-
+        tmpWeight.data =
+            bufferAlloc(groups_x * in.info.dims[1] * in.info.dims[2] *
+                        in.info.dims[3] * sizeof(Tw));
 
         tmpOut.info.dims[0] = groups_x;
         for (int k = 1; k < 4; k++) tmpOut.info.strides[k] *= groups_x;
         tmpWeight.info = tmpOut.info;
     }
 
-    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, threads_x, groups_x, groups_y);
+    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, threads_x,
+                                    groups_x, groups_y);
 
     if (groups_x > 1) {
         // No Weight is needed when writing out the output.
-        mean_first_launcher<Ti, Tw, To>(out, noWeight, tmpOut, tmpWeight, threads_x, 1, groups_y);
+        mean_first_launcher<Ti, Tw, To>(out, noWeight, tmpOut, tmpWeight,
+                                        threads_x, 1, groups_y);
 
         bufferFree(tmpOut.data);
         bufferFree(tmpWeight.data);
@@ -463,62 +367,67 @@ void mean_first(Param out, Param in, Param inWeight)
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_weighted(Param out, Param in, Param inWeight, int dim)
-{
+void mean_weighted(Param out, Param in, Param inWeight, int dim) {
     if (dim == 0)
         return mean_first<Ti, Tw, To>(out, in, inWeight);
     else
-        return mean_dim  <Ti, Tw, To>(out, in, inWeight, dim);
+        return mean_dim<Ti, Tw, To>(out, in, inWeight, dim);
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean(Param out, Param in, int dim)
-{
+void mean(Param out, Param in, int dim) {
     Param noWeight;
     mean_weighted<Ti, Tw, To>(out, in, noWeight, dim);
 }
 
 template<typename T, typename Tw>
-T mean_all_weighted(Param in, Param inWeight)
-{
-    int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+T mean_all_weighted(Param in, Param inWeight) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
     // FIXME: Use better heuristics to get to the optimum number
     if (in_elements > 4096) {
-
         bool in_is_linear = (in.info.strides[0] == 1);
         bool wt_is_linear = (in.info.strides[0] == 1);
         for (int k = 1; k < 4; k++) {
-            in_is_linear &= ( in.info.strides[k] == ( in.info.strides[k - 1] *  in.info.dims[k - 1]));
-            wt_is_linear &= (inWeight.info.strides[k] == (inWeight.info.strides[k - 1] * inWeight.info.dims[k - 1]));
+            in_is_linear &= (in.info.strides[k] ==
+                             (in.info.strides[k - 1] * in.info.dims[k - 1]));
+            wt_is_linear &=
+                (inWeight.info.strides[k] ==
+                 (inWeight.info.strides[k - 1] * inWeight.info.dims[k - 1]));
         }
 
         if (in_is_linear && wt_is_linear) {
             in.info.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
-                in.info.dims[k] = 1;
+                in.info.dims[k]    = 1;
                 in.info.strides[k] = in_elements;
             }
             inWeight.info = in.info;
         }
 
         uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        threads_x      = std::min(threads_x, THREADS_PER_GROUP);
         uint threads_y = THREADS_PER_GROUP / threads_x;
 
         uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
         uint groups_y = divup(in.info.dims[1], threads_y);
 
-        Array<T> tmpOut = createEmptyArray<T>(groups_x);
+        Array<T> tmpOut     = createEmptyArray<T>(groups_x);
         Array<Tw> tmpWeight = createEmptyArray<Tw>(groups_x);
 
-        mean_first_launcher<T, Tw, T>(tmpOut, tmpWeight, in, inWeight, threads_x, groups_x, groups_y);
+        mean_first_launcher<T, Tw, T>(tmpOut, tmpWeight, in, inWeight,
+                                      threads_x, groups_x, groups_y);
 
         vector<T> h_ptr(tmpOut.elements());
         vector<Tw> h_wptr(tmpWeight.elements());
 
-        getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0, sizeof(T) * tmpOut.elements(), h_ptr.data());
-        getQueue().enqueueReadBuffer(*tmpWeight.get(),  CL_TRUE, 0, sizeof(Tw) * tmpWeight.elements(), h_wptr.data());
+        getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0,
+                                     sizeof(T) * tmpOut.elements(),
+                                     h_ptr.data());
+        getQueue().enqueueReadBuffer(*tmpWeight.get(), CL_TRUE, 0,
+                                     sizeof(Tw) * tmpWeight.elements(),
+                                     h_wptr.data());
 
         MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
         for (int i = 1; i < (int)tmpOut.elements(); i++) {
@@ -528,76 +437,80 @@ T mean_all_weighted(Param in, Param inWeight)
         return Op.runningMean;
 
     } else {
-
         vector<T> h_ptr(in_elements);
         vector<Tw> h_wptr(in_elements);
 
-        getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(T) * in.info.offset,
+        getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
+                                     sizeof(T) * in.info.offset,
                                      sizeof(T) * in_elements, h_ptr.data());
-        getQueue().enqueueReadBuffer(*inWeight.data, CL_TRUE, sizeof(Tw) * inWeight.info.offset,
+        getQueue().enqueueReadBuffer(*inWeight.data, CL_TRUE,
+                                     sizeof(Tw) * inWeight.info.offset,
                                      sizeof(Tw) * in_elements, h_wptr.data());
 
         MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
-        for (int i = 1; i < (int)in_elements; i++) {
-            Op(h_ptr[i], h_wptr[i]);
-        }
+        for (int i = 1; i < (int)in_elements; i++) { Op(h_ptr[i], h_wptr[i]); }
 
         return Op.runningMean;
     }
 }
 
 template<typename Ti, typename Tw, typename To>
-To mean_all(Param in)
-{
-    int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+To mean_all(Param in) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
     // FIXME: Use better heuristics to get to the optimum number
     if (in_elements > 4096) {
         bool is_linear = (in.info.strides[0] == 1);
         for (int k = 1; k < 4; k++) {
-            is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
+            is_linear &= (in.info.strides[k] ==
+                          (in.info.strides[k - 1] * in.info.dims[k - 1]));
         }
 
         if (is_linear) {
             in.info.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
-                in.info.dims[k] = 1;
+                in.info.dims[k]    = 1;
                 in.info.strides[k] = in_elements;
             }
         }
 
         uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
+        threads_x      = std::min(threads_x, THREADS_PER_GROUP);
         uint threads_y = THREADS_PER_GROUP / threads_x;
 
         uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
         uint groups_y = divup(in.info.dims[1], threads_y);
 
         Array<To> tmpOut = createEmptyArray<To>(groups_x);
-        Array<Tw> tmpCt = createEmptyArray<Tw>(groups_x);
+        Array<Tw> tmpCt  = createEmptyArray<Tw>(groups_x);
         Param iWt;
 
-        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x, groups_x, groups_y);
+        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x,
+                                        groups_x, groups_y);
 
         vector<To> h_ptr(tmpOut.elements());
         vector<Tw> h_cptr(tmpOut.elements());
 
-        getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0, sizeof(To) * tmpOut.elements(), h_ptr.data());
-        getQueue().enqueueReadBuffer(*tmpCt.get(),  CL_TRUE, 0, sizeof(Tw) * tmpCt.elements(), h_cptr.data());
+        getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0,
+                                     sizeof(To) * tmpOut.elements(),
+                                     h_ptr.data());
+        getQueue().enqueueReadBuffer(*tmpCt.get(), CL_TRUE, 0,
+                                     sizeof(Tw) * tmpCt.elements(),
+                                     h_cptr.data());
 
         MeanOp<To, Tw> Op(h_ptr[0], h_cptr[0]);
-        for (int i = 1; i < (int)h_ptr.size(); i++) {
-            Op(h_ptr[i], h_cptr[i]);
-        }
+        for (int i = 1; i < (int)h_ptr.size(); i++) { Op(h_ptr[i], h_cptr[i]); }
 
         return Op.runningMean;
     } else {
         vector<Ti> h_ptr(in_elements);
 
-        getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
+        getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
+                                     sizeof(Ti) * in.info.offset,
                                      sizeof(Ti) * in_elements, h_ptr.data());
 
-        //TODO : MeanOp with (Tw)1
+        // TODO : MeanOp with (Tw)1
         Transform<Ti, To, af_add_t> transform;
         Transform<uint, Tw, af_add_t> transform_weight;
         MeanOp<To, Tw> Op(transform(h_ptr[0]), transform_weight(1));
@@ -608,6 +521,6 @@ To mean_all(Param in)
         return Op.runningMean;
     }
 }
-}
+}  // namespace kernel
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/mean_dim.cl b/src/backend/opencl/kernel/mean_dim.cl
index 29b8ae0d3f..59dfe7757a 100644
--- a/src/backend/opencl/kernel/mean_dim.cl
+++ b/src/backend/opencl/kernel/mean_dim.cl
@@ -7,70 +7,62 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void mean_dim_kernel(__global To *oData,
-                        KParam oInfo,
+__kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
 #ifdef OUTPUT_WEIGHT
-                        __global Tw *owData,
-                        KParam owInfo,
+                              __global Tw *owData, KParam owInfo,
 #endif
-                        const __global Ti *iData,
-                        KParam iInfo,
+                              const __global Ti *iData, KParam iInfo,
 #ifdef INPUT_WEIGHT
-                        const __global Tw *iwData,
-                        KParam iwInfo,
+                              const __global Tw *iwData, KParam iwInfo,
 #endif
-                        uint groups_x, uint groups_y, uint group_dim)
-{
+                              uint groups_x, uint groups_y, uint group_dim) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * THREADS_X + lidx;
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) + lidx;
-    const uint yid = groupId_y;
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) + lidx;
+    const uint yid       = groupId_y;
 
     uint ids[4] = {xid, yid, zid, wid};
 
     // There is only one element per group for out
     // There are get_local_size(1) elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
-        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+             ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
 
 #ifdef OUTPUT_WEIGHT
     owData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
-        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
 #endif
     const uint id_dim_out = ids[dim];
 
     ids[dim] = ids[dim] * get_local_size(1) + lidy;
 
-    iData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-        ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+    iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+             ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
 
 #ifdef INPUT_WEIGHT
-    iwData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-        ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+    iwData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+              ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
 #endif
 
-    const uint id_dim_in = ids[dim];
+    const uint id_dim_in   = ids[dim];
     const uint istride_dim = iInfo.strides[dim];
 
-    bool is_valid =
-        (ids[0] < iInfo.dims[0]) &&
-        (ids[1] < iInfo.dims[1]) &&
-        (ids[2] < iInfo.dims[2]) &&
-        (ids[3] < iInfo.dims[3]);
+    bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
+                    (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
 
     __local To s_val[THREADS_X * DIMY];
     __local Tw s_wt[THREADS_X * DIMY];
 
     To out_val = init_To;
-    Tw out_wt = init_Tw;
+    Tw out_wt  = init_Tw;
 
     if (is_valid && id_dim_in < iInfo.dims[dim]) {
         out_val = transform(*iData);
@@ -85,23 +77,21 @@ void mean_dim_kernel(__global To *oData,
 
 #ifdef INPUT_WEIGHT
     for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
-            id += group_dim * get_local_size(1)) {
-
-        iData = iData + group_dim * get_local_size(1) * istride_dim;
+         id += group_dim * get_local_size(1)) {
+        iData  = iData + group_dim * get_local_size(1) * istride_dim;
         iwData = iwData + group_dim * get_local_size(1) * istride_dim;
         binOp(&out_val, &out_wt, transform(*iData), *iwData);
     }
 #else
     for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
-            id += group_dim * get_local_size(1)) {
-
+         id += group_dim * get_local_size(1)) {
         iData = iData + group_dim * get_local_size(1) * istride_dim;
         binOp(&out_val, &out_wt, transform(*iData), one_Tw);
     }
 #endif
 
     s_val[lid] = out_val;
-    s_wt[lid] = out_wt;
+    s_wt[lid]  = out_wt;
 
     __local To *s_vptr = s_val + lid;
     __local Tw *s_wptr = s_wt + lid;
@@ -109,8 +99,8 @@ void mean_dim_kernel(__global To *oData,
 
     if (DIMY == 8) {
         if (lidy < 4) {
-            binOp(&out_val, &out_wt,
-                    s_vptr[THREADS_X * 4], s_wptr[THREADS_X * 4]);
+            binOp(&out_val, &out_wt, s_vptr[THREADS_X * 4],
+                  s_wptr[THREADS_X * 4]);
             *s_vptr = out_val;
             *s_wptr = out_wt;
         }
@@ -119,8 +109,8 @@ void mean_dim_kernel(__global To *oData,
 
     if (DIMY >= 4) {
         if (lidy < 2) {
-            binOp(&out_val, &out_wt,
-                    s_vptr[THREADS_X * 2], s_wptr[THREADS_X * 2]);
+            binOp(&out_val, &out_wt, s_vptr[THREADS_X * 2],
+                  s_wptr[THREADS_X * 2]);
             *s_vptr = out_val;
             *s_wptr = out_wt;
         }
@@ -129,20 +119,18 @@ void mean_dim_kernel(__global To *oData,
 
     if (DIMY >= 2) {
         if (lidy < 1) {
-            binOp(&out_val, &out_wt,
-                    s_vptr[THREADS_X * 1], s_wptr[THREADS_X * 1]);
+            binOp(&out_val, &out_wt, s_vptr[THREADS_X * 1],
+                  s_wptr[THREADS_X * 1]);
             *s_vptr = out_val;
             *s_wptr = out_wt;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidy == 0 && is_valid &&
-            (id_dim_out < oInfo.dims[dim])) {
+    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[dim])) {
         *oData = *s_vptr;
 #ifdef OUTPUT_WEIGHT
         *owData = *s_wptr;
 #endif
     }
-
 }
diff --git a/src/backend/opencl/kernel/mean_first.cl b/src/backend/opencl/kernel/mean_first.cl
index 266ee7bfb8..dbef188298 100644
--- a/src/backend/opencl/kernel/mean_first.cl
+++ b/src/backend/opencl/kernel/mean_first.cl
@@ -7,57 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void mean_first_kernel(__global To *oData,
-                          KParam oInfo,
+__kernel void mean_first_kernel(__global To *oData, KParam oInfo,
 #ifdef OUTPUT_WEIGHT
-                          __global Tw *owData,
-                          KParam owInfo,
+                                __global Tw *owData, KParam owInfo,
 #endif
-                          const __global Ti *iData,
-                          KParam iInfo,
+                                const __global Ti *iData, KParam iInfo,
 #ifdef INPUT_WEIGHT
-                          const __global Tw *iwData,
-                          KParam iwInfo,
+                                const __global Tw *iwData, KParam iwInfo,
 #endif
-                          uint groups_x, uint groups_y, uint repeat)
-{
+                                uint groups_x, uint groups_y, uint repeat) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * get_local_size(0) + lidx;
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) * repeat + lidx;
-    const uint yid = groupId_y * get_local_size(1) + lidy;
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) * repeat + lidx;
+    const uint yid       = groupId_y * get_local_size(1) + lidy;
 
     iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-        yid * iInfo.strides[1] + iInfo.offset;
+             yid * iInfo.strides[1] + iInfo.offset;
 
 #ifdef INPUT_WEIGHT
     iwData += wid * iwInfo.strides[3] + zid * iwInfo.strides[2] +
-        yid * iwInfo.strides[1] + iwInfo.offset;
+              yid * iwInfo.strides[1] + iwInfo.offset;
 #endif
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1] + oInfo.offset;
 
 #ifdef OUTPUT_WEIGHT
     owData += wid * owInfo.strides[3] + zid * owInfo.strides[2] +
-        yid * owInfo.strides[1] + owInfo.offset;
+              yid * owInfo.strides[1] + owInfo.offset;
 #endif
 
-    bool cond = (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
+    bool cond =
+        (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
     __local To s_val[THREADS_PER_GROUP];
     __local Tw s_wt[THREADS_PER_GROUP];
 
-    int last = (xid + repeat * DIMX);
-    int lim = last > iInfo.dims[0] ? iInfo.dims[0] : last;
+    int last   = (xid + repeat * DIMX);
+    int lim    = last > iInfo.dims[0] ? iInfo.dims[0] : last;
     To out_val = init_To;
-    Tw out_wt = init_Tw;
+    Tw out_wt  = init_Tw;
 
     if (cond && xid < lim) {
         out_val = transform(iData[xid]);
@@ -79,7 +74,7 @@ void mean_first_kernel(__global To *oData,
 #endif
 
     s_val[lid] = out_val;
-    s_wt[lid] = out_wt;
+    s_wt[lid]  = out_wt;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     __local To *s_vptr = s_val + lidy * DIMX;
@@ -87,8 +82,7 @@ void mean_first_kernel(__global To *oData,
 
     if (DIMX == 256) {
         if (lidx < 128) {
-            binOp(&out_val, &out_wt,
-                    s_vptr[lidx + 128], s_wptr[lidx + 128]);
+            binOp(&out_val, &out_wt, s_vptr[lidx + 128], s_wptr[lidx + 128]);
             s_vptr[lidx] = out_val;
             s_wptr[lidx] = out_wt;
         }
@@ -96,64 +90,57 @@ void mean_first_kernel(__global To *oData,
     }
 
     if (DIMX >= 128) {
-        if (lidx <  64) {
-            binOp(&out_val, &out_wt,
-                    s_vptr[lidx +  64], s_wptr[lidx +  64]);
+        if (lidx < 64) {
+            binOp(&out_val, &out_wt, s_vptr[lidx + 64], s_wptr[lidx + 64]);
             s_vptr[lidx] = out_val;
             s_wptr[lidx] = out_wt;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (DIMX >=  64) {
-        if (lidx <  32) {
-            binOp(&out_val, &out_wt,
-                    s_vptr[lidx +  32], s_wptr[lidx +  32]);
+    if (DIMX >= 64) {
+        if (lidx < 32) {
+            binOp(&out_val, &out_wt, s_vptr[lidx + 32], s_wptr[lidx + 32]);
             s_vptr[lidx] = out_val;
             s_wptr[lidx] = out_wt;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidx <  16) {
-        binOp(&out_val, &out_wt,
-                s_vptr[lidx +  16], s_wptr[lidx +  16]);
+    if (lidx < 16) {
+        binOp(&out_val, &out_wt, s_vptr[lidx + 16], s_wptr[lidx + 16]);
         s_vptr[lidx] = out_val;
         s_wptr[lidx] = out_wt;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   8) {
-        binOp(&out_val, &out_wt,
-                s_vptr[lidx +   8], s_wptr[lidx +   8]);
+    if (lidx < 8) {
+        binOp(&out_val, &out_wt, s_vptr[lidx + 8], s_wptr[lidx + 8]);
         s_vptr[lidx] = out_val;
         s_wptr[lidx] = out_wt;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   4) {
-        binOp(&out_val, &out_wt,
-                s_vptr[lidx +   4], s_wptr[lidx +   4]);
+    if (lidx < 4) {
+        binOp(&out_val, &out_wt, s_vptr[lidx + 4], s_wptr[lidx + 4]);
         s_vptr[lidx] = out_val;
         s_wptr[lidx] = out_wt;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   2) {
-        binOp(&out_val, &out_wt,
-                s_vptr[lidx +   2], s_wptr[lidx +   2]);
+    if (lidx < 2) {
+        binOp(&out_val, &out_wt, s_vptr[lidx + 2], s_wptr[lidx + 2]);
         s_vptr[lidx] = out_val;
         s_wptr[lidx] = out_wt;
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <   1) {
-        binOp(&out_val, &out_wt,
-                s_vptr[lidx +   1], s_wptr[lidx +   1]);
+    if (lidx < 1) {
+        binOp(&out_val, &out_wt, s_vptr[lidx + 1], s_wptr[lidx + 1]);
         s_vptr[lidx] = out_val;
         s_wptr[lidx] = out_wt;
     }
diff --git a/src/backend/opencl/kernel/mean_ops.cl b/src/backend/opencl/kernel/mean_ops.cl
index aa10242a4f..b4f104f6ba 100644
--- a/src/backend/opencl/kernel/mean_ops.cl
+++ b/src/backend/opencl/kernel/mean_ops.cl
@@ -7,19 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-To transform(Ti in)
-{
-    return (To)(in);
-}
+To transform(Ti in) { return (To)(in); }
 
-void binOp(To *lhs, Tw *l_wt, To rhs, Tw r_wt)
-{
+void binOp(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
     if (((*l_wt) != 0) || (r_wt != 0)) {
         Tw l_scale = (*l_wt);
         (*l_wt) += r_wt;
-        l_scale = l_scale/(*l_wt);
+        l_scale = l_scale / (*l_wt);
 
-        Tw r_scale = r_wt/(*l_wt);
-        (*lhs) = (l_scale * (*lhs)) + (r_scale * rhs);
+        Tw r_scale = r_wt / (*l_wt);
+        (*lhs)     = (l_scale * (*lhs)) + (r_scale * rhs);
     }
 }
diff --git a/src/backend/opencl/kernel/meanshift.cl b/src/backend/opencl/kernel/meanshift.cl
index f776cfe69f..0f8ae9355d 100644
--- a/src/backend/opencl/kernel/meanshift.cl
+++ b/src/backend/opencl/kernel/meanshift.cl
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void meanshift(__global T *       d_dst,
-               KParam             oInfo,
-               __global const T * d_src,
-               KParam             iInfo,
-               int radius, float cvar, unsigned numIters,
-               int nBBS0, int nBBS1)
-{
-    unsigned b2  = get_group_id(0) / nBBS0;
-    unsigned b3  = get_group_id(1) / nBBS1;
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + get_local_id(0);
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + get_local_id(1);
-
-    if (gx<iInfo.dims[0] && gy<iInfo.dims[1])
-    {
-        __global const T* iptr = d_src + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
-        __global T*       optr = d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
-
-        int meanPosI  = gx;
-        int meanPosJ  = gy;
+__kernel void meanshift(__global T* d_dst, KParam oInfo,
+                        __global const T* d_src, KParam iInfo, int radius,
+                        float cvar, unsigned numIters, int nBBS0, int nBBS1) {
+    unsigned b2 = get_group_id(0) / nBBS0;
+    unsigned b3 = get_group_id(1) / nBBS1;
+    const int gx =
+        get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + get_local_id(0);
+    const int gy =
+        get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + get_local_id(1);
+
+    if (gx < iInfo.dims[0] && gy < iInfo.dims[1]) {
+        __global const T* iptr = d_src + (b2 * iInfo.strides[2] +
+                                          b3 * iInfo.strides[3] + iInfo.offset);
+        __global T* optr =
+            d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+
+        int meanPosI = gx;
+        int meanPosJ = gy;
 
         T currentCenterColors[MAX_CHANNELS];
         T tempColors[MAX_CHANNELS];
@@ -34,15 +32,16 @@ void meanshift(__global T *       d_dst,
         AccType currentMeanColors[MAX_CHANNELS];
 
 #pragma unroll
-        for(int ch=0; ch<MAX_CHANNELS; ++ch)
-            currentCenterColors[ch] = iptr[ gx*iInfo.strides[0]+gy*iInfo.strides[1]+ch*iInfo.strides[2] ];
+        for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+            currentCenterColors[ch] =
+                iptr[gx * iInfo.strides[0] + gy * iInfo.strides[1] +
+                     ch * iInfo.strides[2]];
 
-        const int dim0LenLmt = iInfo.dims[0]-1;
-        const int dim1LenLmt = iInfo.dims[1]-1;
+        const int dim0LenLmt = iInfo.dims[0] - 1;
+        const int dim1LenLmt = iInfo.dims[1] - 1;
 
         // scope of meanshift iterationd begin
-        for(uint it=0; it<numIters; ++it) {
-
+        for (uint it = 0; it < numIters; ++it) {
             int oldMeanPosJ = meanPosJ;
             int oldMeanPosI = meanPosI;
             unsigned count  = 0;
@@ -50,73 +49,79 @@ void meanshift(__global T *       d_dst,
             int shift_x = 0;
             int shift_y = 0;
 
-            for (int ch=0; ch<MAX_CHANNELS; ++ch)
-                currentMeanColors[ch] = 0;
+            for (int ch = 0; ch < MAX_CHANNELS; ++ch) currentMeanColors[ch] = 0;
 
-            for(int wj=-radius; wj<=radius; ++wj) {
+            for (int wj = -radius; wj <= radius; ++wj) {
                 int hit_count = 0;
-                int tj = meanPosJ + wj;
-
-                if (tj<0 || tj>dim1LenLmt) continue;
+                int tj        = meanPosJ + wj;
 
-                for(int wi=-radius; wi<=radius; ++wi) {
+                if (tj < 0 || tj > dim1LenLmt) continue;
 
+                for (int wi = -radius; wi <= radius; ++wi) {
                     int ti = meanPosI + wi;
 
-                    if (ti<0 || ti>dim0LenLmt) continue;
+                    if (ti < 0 || ti > dim0LenLmt) continue;
 
                     AccType norm = 0;
 #pragma unroll
-                    for(int ch=0; ch<MAX_CHANNELS; ++ch) {
-                        unsigned idx = ti*iInfo.strides[0] + tj*iInfo.strides[1] + ch*iInfo.strides[2];
+                    for (int ch = 0; ch < MAX_CHANNELS; ++ch) {
+                        unsigned idx = ti * iInfo.strides[0] +
+                                       tj * iInfo.strides[1] +
+                                       ch * iInfo.strides[2];
                         tempColors[ch] = iptr[idx];
-                        AccType diff = (AccType)currentCenterColors[ch] - (AccType)tempColors[ch];
+                        AccType diff   = (AccType)currentCenterColors[ch] -
+                                       (AccType)tempColors[ch];
                         norm += (diff * diff);
                     }
 
-                    if (norm<= cvar) {
+                    if (norm <= cvar) {
 #pragma unroll
-                        for(int ch=0; ch<MAX_CHANNELS; ++ch)
+                        for (int ch = 0; ch < MAX_CHANNELS; ++ch)
                             currentMeanColors[ch] += (AccType)tempColors[ch];
 
                         shift_x += ti;
                         ++hit_count;
                     }
                 }
-                count+= hit_count;
-                shift_y += tj*hit_count;
+                count += hit_count;
+                shift_y += tj * hit_count;
             }
 
-            if (count==0) break;
+            if (count == 0) break;
 
-            const AccType fcount = 1/(AccType)count;
+            const AccType fcount = 1 / (AccType)count;
 
-            meanPosI = convert_int_rtz(shift_x*fcount);
-            meanPosJ = convert_int_rtz(shift_y*fcount);
+            meanPosI = convert_int_rtz(shift_x * fcount);
+            meanPosJ = convert_int_rtz(shift_y * fcount);
 
 #pragma unroll
-            for(int ch=0; ch<MAX_CHANNELS; ++ch)
-                currentMeanColors[ch] = convert_int_rtz(currentMeanColors[ch]*fcount);
+            for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                currentMeanColors[ch] =
+                    convert_int_rtz(currentMeanColors[ch] * fcount);
 
             AccType norm = 0;
 #pragma unroll
-            for(int ch=0; ch<MAX_CHANNELS; ++ch) {
-                AccType diff = (AccType)currentCenterColors[ch] - currentMeanColors[ch];
-                norm += (diff*diff);
+            for (int ch = 0; ch < MAX_CHANNELS; ++ch) {
+                AccType diff =
+                    (AccType)currentCenterColors[ch] - currentMeanColors[ch];
+                norm += (diff * diff);
             }
 
-            bool stop = (meanPosJ==oldMeanPosJ && meanPosI==oldMeanPosI) ||
-                        ((abs(oldMeanPosJ-meanPosJ)+abs(oldMeanPosI-meanPosI)) + norm) <= 1;
+            bool stop =
+                (meanPosJ == oldMeanPosJ && meanPosI == oldMeanPosI) ||
+                ((abs(oldMeanPosJ - meanPosJ) + abs(oldMeanPosI - meanPosI)) +
+                 norm) <= 1;
 
 #pragma unroll
-            for(int ch=0; ch<MAX_CHANNELS; ++ch)
+            for (int ch = 0; ch < MAX_CHANNELS; ++ch)
                 currentCenterColors[ch] = (T)(currentMeanColors[ch]);
 
             if (stop) break;
-        } // scope of meanshift iterations end
+        }  // scope of meanshift iterations end
 
 #pragma unroll
-        for(int ch=0; ch<MAX_CHANNELS; ++ch)
-            optr[gx*oInfo.strides[0] + gy*oInfo.strides[1] + ch*oInfo.strides[2]] = currentCenterColors[ch];
+        for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+            optr[gx * oInfo.strides[0] + gy * oInfo.strides[1] +
+                 ch * oInfo.strides[2]] = currentCenterColors[ch];
     }
 }
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index 175483c7ea..534480d107 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -8,45 +8,44 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/meanshift.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <cache.hpp>
 #include <algorithm>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool is_color>
-void meanshift(Param out, const Param in,
-        const float spatialSigma, const float chromaticSigma, const uint numIters)
-{
-    typedef typename std::conditional< std::is_same<T, double>::value, double, float >::type AccType;
+void meanshift(Param out, const Param in, const float spatialSigma,
+               const float chromaticSigma, const uint numIters) {
+    typedef typename std::conditional<std::is_same<T, double>::value, double,
+                                      float>::type AccType;
 
     std::string refName = std::string("meanshift_") +
-        std::string(dtype_traits<T>::getName()) + std::to_string(is_color);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(is_color);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D AccType=" << dtype_traits<AccType>::getName()
@@ -55,7 +54,7 @@ void meanshift(Param out, const Param in,
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {meanshift_cl};
-        const int   ker_lens[] = {meanshift_cl_len};
+        const int ker_lens[]   = {meanshift_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -64,28 +63,28 @@ void meanshift(Param out, const Param in,
         addKernelToCache(device, refName, entry);
     }
 
-    auto meanshiftOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                    int, float, unsigned, int, int >(*entry.ker);
+    auto meanshiftOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int, float,
+                                     unsigned, int, int>(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    const int bCount   = (is_color ? 1 : in.info.dims[2]);
+    const int bCount = (is_color ? 1 : in.info.dims[2]);
 
-    NDRange global(bCount*blk_x*THREADS_X, in.info.dims[3]*blk_y*THREADS_Y);
+    NDRange global(bCount * blk_x * THREADS_X,
+                   in.info.dims[3] * blk_y * THREADS_Y);
 
     // clamp spatical and chromatic sigma's
-    int radius   = std::max( (int)(spatialSigma * 1.5f), 1 );
+    int radius = std::max((int)(spatialSigma * 1.5f), 1);
 
-    const float cvar = chromaticSigma*chromaticSigma;
+    const float cvar = chromaticSigma * chromaticSigma;
 
-    meanshiftOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info,
-                radius, cvar, numIters, blk_x, blk_y);
+    meanshiftOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *in.data, in.info, radius, cvar, numIters, blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index e0390244c1..81f69b082c 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -8,28 +8,26 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/medfilt2.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/medfilt1.hpp>
+#include <kernel_headers/medfilt2.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int MAX_MEDFILTER2_LEN = 15;
 static const int MAX_MEDFILTER1_LEN = 121;
 
@@ -37,29 +35,27 @@ static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, af_border_type pad>
-void medfilt1(Param out, const Param in, unsigned w_wid)
-{
+void medfilt1(Param out, const Param in, unsigned w_wid) {
     std::string refName = std::string("medfilt1_") +
-        std::string(dtype_traits<T>::getName()) + std::to_string(pad);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(pad);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
-        const int ARR_SIZE = (w_wid-w_wid/2) + 1;
+    if (entry.prog == 0 && entry.ker == 0) {
+        const int ARR_SIZE = (w_wid - w_wid / 2) + 1;
 
         std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D pad="<< pad
-                << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
-                << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                << " -D ARR_SIZE="<< ARR_SIZE
-                << " -D w_wid=" << w_wid;
+        options << " -D T=" << dtype_traits<T>::getName() << " -D pad=" << pad
+                << " -D AF_PAD_ZERO=" << AF_PAD_ZERO
+                << " -D AF_PAD_SYM=" << AF_PAD_SYM
+                << " -D ARR_SIZE=" << ARR_SIZE << " -D w_wid=" << w_wid;
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {medfilt1_cl};
-        const int   ker_lens[] = {medfilt1_cl_len};
+        const int ker_lens[]   = {medfilt1_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -72,44 +68,44 @@ void medfilt1(Param out, const Param in, unsigned w_wid)
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
 
-    NDRange global(blk_x * in.info.dims[1] * THREADS_X, in.info.dims[2], in.info.dims[3]);
+    NDRange global(blk_x * in.info.dims[1] * THREADS_X, in.info.dims[2],
+                   in.info.dims[3]);
 
-    auto medfiltOp = KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, int> (*entry.ker);
+    auto medfiltOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, int>(
+            *entry.ker);
 
-    size_t loc_size = (THREADS_X+w_wid-1)*sizeof(T);
+    size_t loc_size = (THREADS_X + w_wid - 1) * sizeof(T);
 
-    medfiltOp(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x);
+    medfiltOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *in.data, in.info, cl::Local(loc_size), blk_x);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
-void medfilt2(Param out, const Param in)
-{
-    std::string refName = std::string("medfilt2_") +
-        std::string(dtype_traits<T>::getName()) +
+void medfilt2(Param out, const Param in) {
+    std::string refName =
+        std::string("medfilt2_") + std::string(dtype_traits<T>::getName()) +
         std::to_string(pad) + std::to_string(w_len) + std::to_string(w_wid);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
-        const int ARR_SIZE = w_len * (w_wid-w_wid/2);
+    if (entry.prog == 0 && entry.ker == 0) {
+        const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
 
         std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D pad="<< pad
-                << " -D AF_PAD_ZERO="<< AF_PAD_ZERO
-                << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                << " -D ARR_SIZE="<< ARR_SIZE
-                << " -D w_len="<< w_len
+        options << " -D T=" << dtype_traits<T>::getName() << " -D pad=" << pad
+                << " -D AF_PAD_ZERO=" << AF_PAD_ZERO
+                << " -D AF_PAD_SYM=" << AF_PAD_SYM
+                << " -D ARR_SIZE=" << ARR_SIZE << " -D w_len=" << w_len
                 << " -D w_wid=" << w_wid;
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {medfilt2_cl};
-        const int   ker_lens[] = {medfilt2_cl_len};
+        const int ker_lens[]   = {medfilt2_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -123,17 +119,19 @@ void medfilt2(Param out, const Param in)
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X, blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                   blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto medfiltOp = KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg,
-                                   int, int> (*entry.ker);
+    auto medfiltOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
+                                   cl::LocalSpaceArg, int, int>(*entry.ker);
 
-    size_t loc_size = (THREADS_X+w_len-1)*(THREADS_Y+w_wid-1)*sizeof(T);
+    size_t loc_size =
+        (THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1) * sizeof(T);
 
-    medfiltOp(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+    medfiltOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/medfilt1.cl b/src/backend/opencl/kernel/medfilt1.cl
index 0459f69957..1720da0d63 100644
--- a/src/backend/opencl/kernel/medfilt1.cl
+++ b/src/backend/opencl/kernel/medfilt1.cl
@@ -8,58 +8,59 @@
  ********************************************************/
 
 // Exchange trick: Morgan McGuire, ShaderX 2008
-#define swap(a,b)    { T tmp = a; a = min(a,b); b = max(tmp,b); }
-
-void load2ShrdMem_1d(__local T *      shrd,
-                     __global const T * in,
-                                    int lx,
-                                  int dim0,
-                                    int gx,
-                             int inStride0)
-{
-    if (pad==AF_PAD_ZERO) {
-        if (gx<0 || gx>=dim0)
+#define swap(a, b)           \
+    {                        \
+        T tmp = a;           \
+        a     = min(a, b);   \
+        b     = max(tmp, b); \
+    }
+
+void load2ShrdMem_1d(__local T* shrd, __global const T* in, int lx, int dim0,
+                     int gx, int inStride0) {
+    if (pad == AF_PAD_ZERO) {
+        if (gx < 0 || gx >= dim0)
             shrd[lx] = (T)0;
         else
             shrd[lx] = in[gx];
-    } else if (pad==AF_PAD_SYM) {
-        if (gx<0) gx *= -1;
-        if (gx>=dim0) gx = 2*(dim0-1) - gx;
+    } else if (pad == AF_PAD_SYM) {
+        if (gx < 0) gx *= -1;
+        if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
         shrd[lx] = in[gx];
     }
 }
 
-__kernel
-void medfilt1(__global T *       out,
-                KParam             oInfo,
-                __global const T * in,
-                KParam             iInfo,
-                __local T *        localMem,
-                int                nBBS0)
-{
+__kernel void medfilt1(__global T* out, KParam oInfo, __global const T* in,
+                       KParam iInfo, __local T* localMem, int nBBS0) {
     // calculate necessary offset and window parameters
-    const int padding = w_wid-1;
-    const int halo    = padding/2;
+    const int padding = w_wid - 1;
+    const int halo    = padding / 2;
     const int shrdLen = get_local_size(0) + padding;
 
     // batch offsets
-    unsigned b1 = get_group_id(0) / nBBS0;
-    unsigned b0 = get_group_id(0) - b1 * nBBS0;
-    unsigned b2 = get_group_id(1);
-    unsigned b3 = get_group_id(2);
-    __global const T* iptr =  in + (b1 * iInfo.strides[1] + b2 * iInfo.strides[2] + b3 * iInfo.strides[3]) + iInfo.offset;
-    __global T*       optr = out + (b1 * oInfo.strides[1] + b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) + oInfo.offset;
+    unsigned b1            = get_group_id(0) / nBBS0;
+    unsigned b0            = get_group_id(0) - b1 * nBBS0;
+    unsigned b2            = get_group_id(1);
+    unsigned b3            = get_group_id(2);
+    __global const T* iptr = in +
+                             (b1 * iInfo.strides[1] + b2 * iInfo.strides[2] +
+                              b3 * iInfo.strides[3]) +
+                             iInfo.offset;
+    __global T* optr = out +
+                       (b1 * oInfo.strides[1] + b2 * oInfo.strides[2] +
+                        b3 * oInfo.strides[3]) +
+                       oInfo.offset;
 
     // local neighborhood indices
     int lx = get_local_id(0);
 
     // global indices
-    int gx = get_local_size(0) * b0  + lx;
+    int gx = get_local_size(0) * b0 + lx;
 
     int s0 = iInfo.strides[0];
     int d0 = iInfo.dims[0];
-    for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-        load2ShrdMem_1d(localMem, iptr, a, d0, gx2-halo, s0);
+    for (int a = lx, gx2 = gx; a < shrdLen;
+         a += get_local_size(0), gx2 += get_local_size(0)) {
+        load2ShrdMem_1d(localMem, iptr, a, d0, gx2 - halo, s0);
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
@@ -70,46 +71,40 @@ void medfilt1(__global T *       out,
         T v[ARR_SIZE];
 
 #pragma unroll
-        for(int k = 0; k <= w_wid/2 + 1; k++) {
-            v[k] = localMem[lx+k];
-        }
+        for (int k = 0; k <= w_wid / 2 + 1; k++) { v[k] = localMem[lx + k]; }
 
         // with each pass, remove min and max values and add new value
         // initial sort
         // ensure min in first half, max in second half
 #pragma unroll
-        for(int i = 0; i < ARR_SIZE/2; i++) {
-            swap(v[i], v[ARR_SIZE-1-i]);
+        for (int i = 0; i < ARR_SIZE / 2; i++) {
+            swap(v[i], v[ARR_SIZE - 1 - i]);
         }
         // move min in first half to first pos
 #pragma unroll
-        for(int i = 1; i < (ARR_SIZE+1)/2; i++) {
-            swap(v[0], v[i]);
-        }
+        for (int i = 1; i < (ARR_SIZE + 1) / 2; i++) { swap(v[0], v[i]); }
         // move max in second half to last pos
 #pragma unroll
-        for(int i = ARR_SIZE-2; i >= ARR_SIZE/2; i--) {
-            swap(v[i], v[ARR_SIZE-1]);
+        for (int i = ARR_SIZE - 2; i >= ARR_SIZE / 2; i--) {
+            swap(v[i], v[ARR_SIZE - 1]);
         }
 
-        int last = ARR_SIZE-1;
+        int last = ARR_SIZE - 1;
 
-        for(int k = w_wid/2 + 2; k < w_wid; k++) {
+        for (int k = w_wid / 2 + 2; k < w_wid; k++) {
             // add new contestant to first position in array
-            v[0] = localMem[lx+k];
+            v[0] = localMem[lx + k];
 
             last--;
 
             // place max in last half, min in first half
-            for(int i = 0; i < (last+1)/2; i++) {
-                swap(v[i], v[last-i]);
+            for (int i = 0; i < (last + 1) / 2; i++) {
+                swap(v[i], v[last - i]);
             }
             // now perform swaps on each half such that
             // max is in last pos, min is in first pos
-            for(int i = 1; i <= last/2; i++) {
-                swap(v[0], v[i]);
-            }
-            for(int i = last-1; i >= (last+1)/2; i--) {
+            for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
+            for (int i = last - 1; i >= (last + 1) / 2; i--) {
                 swap(v[i], v[last]);
             }
         }
@@ -117,22 +112,20 @@ void medfilt1(__global T *       out,
         // no more new contestants
         // may still have to sort the last row
         // each outer loop drops the min and max
-        for(int k = 0; k < last; k++) {
+        for (int k = 0; k < last; k++) {
             // move max/min into respective halves
-            for(int i = k; i < ARR_SIZE/2; i++) {
-                swap(v[i], v[ARR_SIZE-1-i]);
+            for (int i = k; i < ARR_SIZE / 2; i++) {
+                swap(v[i], v[ARR_SIZE - 1 - i]);
             }
             // move min into first pos
-            for(int i = k+1; i <= ARR_SIZE/2; i++) {
-                swap(v[k], v[i]);
-            }
+            for (int i = k + 1; i <= ARR_SIZE / 2; i++) { swap(v[k], v[i]); }
             // move max into last pos
-            for(int i = ARR_SIZE-k-2; i >= ARR_SIZE/2; i--) {
-                swap(v[i], v[ARR_SIZE-1-k]);
+            for (int i = ARR_SIZE - k - 2; i >= ARR_SIZE / 2; i--) {
+                swap(v[i], v[ARR_SIZE - 1 - k]);
             }
         }
 
         // pick the middle element of the first row
-        optr[gx*oInfo.strides[0]] = v[last/2];
+        optr[gx * oInfo.strides[0]] = v[last / 2];
     }
 }
diff --git a/src/backend/opencl/kernel/medfilt2.cl b/src/backend/opencl/kernel/medfilt2.cl
index 0fbf186969..87dd490381 100644
--- a/src/backend/opencl/kernel/medfilt2.cl
+++ b/src/backend/opencl/kernel/medfilt2.cl
@@ -8,71 +8,71 @@
  ********************************************************/
 
 // Exchange trick: Morgan McGuire, ShaderX 2008
-#define swap(a,b)    { T tmp = a; a = min(a,b); b = max(tmp,b); }
+#define swap(a, b)           \
+    {                        \
+        T tmp = a;           \
+        a     = min(a, b);   \
+        b     = max(tmp, b); \
+    }
 
-int lIdx(int x, int y, int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
+int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
 }
 
-void load2ShrdMem(__local T *           shrd,
-                  __global const T *    in,
-                  int lx, int ly, int shrdStride,
-                  int dim0, int dim1,
-                  int gx, int gy,
-                  int inStride1, int inStride0)
-{
-    if (pad==AF_PAD_ZERO) {
-        if (gx<0 || gx>=dim0 || gy<0 || gy>=dim1)
+void load2ShrdMem(__local T* shrd, __global const T* in, int lx, int ly,
+                  int shrdStride, int dim0, int dim1, int gx, int gy,
+                  int inStride1, int inStride0) {
+    if (pad == AF_PAD_ZERO) {
+        if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
             shrd[lIdx(lx, ly, shrdStride, 1)] = (T)0;
         else
-            shrd[lIdx(lx, ly, shrdStride, 1)] = in[lIdx(gx, gy, inStride1, inStride0)];
-    } else if (pad==AF_PAD_SYM) {
-        if (gx<0) gx *= -1;
-        if (gy<0) gy *= -1;
-        if (gx>=dim0) gx = 2*(dim0-1) - gx;
-        if (gy>=dim1) gy = 2*(dim1-1) - gy;
-        shrd[lIdx(lx, ly, shrdStride, 1)] = in[lIdx(gx, gy, inStride1, inStride0)];
+            shrd[lIdx(lx, ly, shrdStride, 1)] =
+                in[lIdx(gx, gy, inStride1, inStride0)];
+    } else if (pad == AF_PAD_SYM) {
+        if (gx < 0) gx *= -1;
+        if (gy < 0) gy *= -1;
+        if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
+        if (gy >= dim1) gy = 2 * (dim1 - 1) - gy;
+        shrd[lIdx(lx, ly, shrdStride, 1)] =
+            in[lIdx(gx, gy, inStride1, inStride0)];
     }
 }
 
-__kernel
-void medfilt2(__global T *       out,
-              KParam             oInfo,
-              __global const T * in,
-              KParam             iInfo,
-              __local T *        localMem,
-              int           nBBS0,
-              int           nBBS1)
-{
+__kernel void medfilt2(__global T* out, KParam oInfo, __global const T* in,
+                       KParam iInfo, __local T* localMem, int nBBS0,
+                       int nBBS1) {
     // calculate necessary offset and window parameters
-    const int padding = w_len-1;
-    const int halo    = padding/2;
+    const int padding = w_len - 1;
+    const int halo    = padding / 2;
     const int shrdLen = get_local_size(0) + padding;
 
     // batch offsets
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
-    __global const T* iptr =  in + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
-    __global T*       optr = out + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+    __global const T* iptr =
+        in + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
+    __global T* optr = out + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     // local neighborhood indices
     int lx = get_local_id(0);
     int ly = get_local_id(1);
 
     // global indices
-    int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
     int s0 = iInfo.strides[0];
     int s1 = iInfo.strides[1];
     int d0 = iInfo.dims[0];
     int d1 = iInfo.dims[1];
     // pull image to local memory
-    for (int b=ly, gy2=gy; b<shrdLen; b+=get_local_size(1), gy2+=get_local_size(1)) {
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
         // move row_set get_local_size(1) along coloumns
-        for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-            load2ShrdMem(localMem, iptr, a, b, shrdLen, d0, d1, gx2-halo, gy2-halo, s1, s0);
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            load2ShrdMem(localMem, iptr, a, b, shrdLen, d0, d1, gx2 - halo,
+                         gy2 - halo, s1, s0);
         }
     }
 
@@ -80,14 +80,13 @@ void medfilt2(__global T *       out,
 
     // Only continue if we're at a valid location
     if (gx < iInfo.dims[0] && gy < iInfo.dims[1]) {
-
         // pull top half from shared memory into local memory
         T v[ARR_SIZE];
 #pragma unroll
-        for(int k = 0; k <= w_wid/2; k++) {
+        for (int k = 0; k <= w_wid / 2; k++) {
 #pragma unroll
-            for(int i = 0; i < w_len; i++) {
-                v[w_len*k + i] = localMem[lIdx(lx+i,ly+k,shrdLen,1)];
+            for (int i = 0; i < w_len; i++) {
+                v[w_len * k + i] = localMem[lIdx(lx + i, ly + k, shrdLen, 1)];
             }
         }
 
@@ -95,41 +94,35 @@ void medfilt2(__global T *       out,
         // initial sort
         // ensure min in first half, max in second half
 #pragma unroll
-        for(int i = 0; i < ARR_SIZE/2; i++) {
-            swap(v[i], v[ARR_SIZE-1-i]);
+        for (int i = 0; i < ARR_SIZE / 2; i++) {
+            swap(v[i], v[ARR_SIZE - 1 - i]);
         }
         // move min in first half to first pos
 #pragma unroll
-        for(int i = 1; i < (ARR_SIZE+1)/2; i++) {
-            swap(v[0], v[i]);
-        }
+        for (int i = 1; i < (ARR_SIZE + 1) / 2; i++) { swap(v[0], v[i]); }
         // move max in second half to last pos
 #pragma unroll
-        for(int i = ARR_SIZE-2; i >= ARR_SIZE/2; i--) {
-            swap(v[i], v[ARR_SIZE-1]);
+        for (int i = ARR_SIZE - 2; i >= ARR_SIZE / 2; i--) {
+            swap(v[i], v[ARR_SIZE - 1]);
         }
 
-        int last = ARR_SIZE-1;
-
-        for(int k = 1+w_wid/2; k < w_wid; k++) {
-
-            for(int j = 0; j < w_len; j++) {
+        int last = ARR_SIZE - 1;
 
+        for (int k = 1 + w_wid / 2; k < w_wid; k++) {
+            for (int j = 0; j < w_len; j++) {
                 // add new contestant to first position in array
-                v[0] = localMem[lIdx(lx+j, ly+k, shrdLen, 1)];
+                v[0] = localMem[lIdx(lx + j, ly + k, shrdLen, 1)];
 
                 last--;
 
                 // place max in last half, min in first half
-                for(int i = 0; i < (last+1)/2; i++) {
-                    swap(v[i], v[last-i]);
+                for (int i = 0; i < (last + 1) / 2; i++) {
+                    swap(v[i], v[last - i]);
                 }
                 // now perform swaps on each half such that
                 // max is in last pos, min is in first pos
-                for(int i = 1; i <= last/2; i++) {
-                    swap(v[0], v[i]);
-                }
-                for(int i = last-1; i >= (last+1)/2; i--) {
+                for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
+                for (int i = last - 1; i >= (last + 1) / 2; i--) {
                     swap(v[i], v[last]);
                 }
             }
@@ -138,22 +131,20 @@ void medfilt2(__global T *       out,
         // no more new contestants
         // may still have to sort the last row
         // each outer loop drops the min and max
-        for(int k = 1; k < w_len/2; k++) {
+        for (int k = 1; k < w_len / 2; k++) {
             // move max/min into respective halves
-            for(int i = k; i < w_len/2; i++) {
-                swap(v[i], v[w_len-1-i]);
+            for (int i = k; i < w_len / 2; i++) {
+                swap(v[i], v[w_len - 1 - i]);
             }
             // move min into first pos
-            for(int i = k+1; i <= w_len/2; i++) {
-                swap(v[k], v[i]);
-            }
+            for (int i = k + 1; i <= w_len / 2; i++) { swap(v[k], v[i]); }
             // move max into last pos
-            for(int i = w_len-k-2; i >= w_len/2; i--) {
-                swap(v[i], v[w_len-1-k]);
+            for (int i = w_len - k - 2; i >= w_len / 2; i--) {
+                swap(v[i], v[w_len - 1 - k]);
             }
         }
 
         // pick the middle element of the first row
-        optr[gy*oInfo.strides[1]+gx*oInfo.strides[0]] = v[w_len/2];
+        optr[gy * oInfo.strides[1] + gx * oInfo.strides[0]] = v[w_len / 2];
     }
 }
diff --git a/src/backend/opencl/kernel/memcopy.cl b/src/backend/opencl/kernel/memcopy.cl
index 942c127ef2..8219c8f211 100644
--- a/src/backend/opencl/kernel/memcopy.cl
+++ b/src/backend/opencl/kernel/memcopy.cl
@@ -11,32 +11,29 @@ typedef struct {
     dim_t dim[4];
 } dims_t;
 
-__kernel
-void memcopy_kernel(__global T *out, dims_t ostrides,
-                    __global const T *in, dims_t idims,
-                    dims_t istrides, int offset,
-                    int groups_0, int groups_1)
-{
+__kernel void memcopy_kernel(__global T *out, dims_t ostrides,
+                             __global const T *in, dims_t idims,
+                             dims_t istrides, int offset, int groups_0,
+                             int groups_1) {
     const int lid0 = get_local_id(0);
     const int lid1 = get_local_id(1);
 
-    const int id2 = get_group_id(0) / groups_0;
-    const int id3 = get_group_id(1) / groups_1;
+    const int id2        = get_group_id(0) / groups_0;
+    const int id3        = get_group_id(1) / groups_1;
     const int group_id_0 = get_group_id(0) - groups_0 * id2;
     const int group_id_1 = get_group_id(1) - groups_1 * id3;
-    const int id0 = group_id_0 * get_local_size(0) + lid0;
-    const int id1 = group_id_1 * get_local_size(1) + lid1;
+    const int id0        = group_id_0 * get_local_size(0) + lid0;
+    const int id1        = group_id_1 * get_local_size(1) + lid1;
 
     in += offset;
 
     // FIXME: Do more work per work group
-    out += id3 * ostrides.dim[3] + id2 * ostrides.dim[2] + id1 * ostrides.dim[1];
-    in  += id3 * istrides.dim[3] + id2 * istrides.dim[2] + id1 * istrides.dim[1];
+    out +=
+        id3 * ostrides.dim[3] + id2 * ostrides.dim[2] + id1 * ostrides.dim[1];
+    in += id3 * istrides.dim[3] + id2 * istrides.dim[2] + id1 * istrides.dim[1];
 
     int istride0 = istrides.dim[0];
-    if (id0 < idims.dim[0] &&
-        id1 < idims.dim[1] &&
-        id2 < idims.dim[2] &&
+    if (id0 < idims.dim[0] && id1 < idims.dim[1] && id2 < idims.dim[2] &&
         id3 < idims.dim[3]) {
         out[id0] = in[id0 * istride0];
     }
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index f112ee91c9..fed2f17b4d 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -8,17 +8,17 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/memcopy.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/copy.hpp>
+#include <kernel_headers/memcopy.hpp>
 #include <program.hpp>
 #include <traits.hpp>
+#include <algorithm>
 #include <sstream>
 #include <string>
-#include <algorithm>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
 using cl::EnqueueArgs;
@@ -29,37 +29,34 @@ using cl::Program;
 
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-typedef struct
-{
+namespace opencl {
+namespace kernel {
+typedef struct {
     dim_t dim[4];
 } dims_t;
 
 static const uint DIM0 = 32;
-static const uint DIM1 =  8;
+static const uint DIM1 = 8;
 
 template<typename T>
-void memcopy(cl::Buffer out, const dim_t *ostrides,
-             const cl::Buffer in, const dim_t *idims,
-             const dim_t *istrides, int offset, uint ndims)
-{
-    std::string refName = std::string("memcopy_") + std::string(dtype_traits<T>::getName());
+void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
+             const dim_t *idims, const dim_t *istrides, int offset,
+             uint ndims) {
+    std::string refName =
+        std::string("memcopy_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char* ker_strs[] = {memcopy_cl};
-        const int   ker_lens[] = {memcopy_cl_len};
+        const char *ker_strs[] = {memcopy_cl};
+        const int ker_lens[]   = {memcopy_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -70,56 +67,59 @@ void memcopy(cl::Buffer out, const dim_t *ostrides,
 
     dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
     dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
-    dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
+    dims_t _idims    = {{idims[0], idims[1], idims[2], idims[3]}};
 
     size_t local_size[2] = {DIM0, DIM1};
     if (ndims == 1) {
         local_size[0] *= local_size[1];
-        local_size[1]  = 1;
+        local_size[1] = 1;
     }
 
     int groups_0 = divup(idims[0], local_size[0]);
     int groups_1 = divup(idims[1], local_size[1]);
 
     NDRange local(local_size[0], local_size[1]);
-    NDRange global(groups_0 * idims[2] * local_size[0], groups_1 * idims[3] * local_size[1]);
+    NDRange global(groups_0 * idims[2] * local_size[0],
+                   groups_1 * idims[3] * local_size[1]);
 
-    auto memCpyOp = KernelFunctor< Buffer, dims_t, Buffer, dims_t,
-                                   dims_t, int, int, int >(*entry.ker);
+    auto memCpyOp =
+        KernelFunctor<Buffer, dims_t, Buffer, dims_t, dims_t, int, int, int>(
+            *entry.ker);
 
-    memCpyOp(EnqueueArgs(getQueue(), global, local),
-             out, _ostrides, in, _idims, _istrides, offset, groups_0, groups_1);
+    memCpyOp(EnqueueArgs(getQueue(), global, local), out, _ostrides, in, _idims,
+             _istrides, offset, groups_0, groups_1);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename inType, typename outType, bool same_dims>
-void copy(Param dst, const Param src, int ndims, outType default_value, double factor)
-{
-    std::string refName =
-        std::string("copy_") +
-        std::string(dtype_traits<inType>::getName()) +
-        std::string(dtype_traits<outType>::getName()) +
-        std::to_string(same_dims);
-
-    int device = getActiveDeviceId();
+void copy(Param dst, const Param src, int ndims, outType default_value,
+          double factor) {
+    std::string refName = std::string("copy_") +
+                          std::string(dtype_traits<inType>::getName()) +
+                          std::string(dtype_traits<outType>::getName()) +
+                          std::to_string(same_dims);
+
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
-        options << " -D inType="    << dtype_traits<inType>::getName()
-                << " -D outType="   << dtype_traits<outType>::getName()
-                << " -D inType_"    << dtype_traits<inType>::getName()
-                << " -D outType_"   << dtype_traits<outType>::getName()
+        options << " -D inType=" << dtype_traits<inType>::getName()
+                << " -D outType=" << dtype_traits<outType>::getName()
+                << " -D inType_" << dtype_traits<inType>::getName()
+                << " -D outType_" << dtype_traits<outType>::getName()
                 << " -D SAME_DIMS=" << same_dims;
 
-        if (std::is_same<inType, double>::value  || std::is_same<inType, cdouble>::value ||
-            std::is_same<outType, double>::value || std::is_same<outType, cdouble>::value)
+        if (std::is_same<inType, double>::value ||
+            std::is_same<inType, cdouble>::value ||
+            std::is_same<outType, double>::value ||
+            std::is_same<outType, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char* ker_strs[] = {copy_cl};
-        const int   ker_lens[] = {copy_cl_len};
+        const char *ker_strs[] = {copy_cl};
+        const int ker_lens[]   = {copy_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -132,34 +132,34 @@ void copy(Param dst, const Param src, int ndims, outType default_value, double f
     size_t local_size[] = {DIM0, DIM1};
 
     local_size[0] *= local_size[1];
-    if (ndims == 1) {
-        local_size[1] = 1;
-    }
+    if (ndims == 1) { local_size[1] = 1; }
 
     int blk_x = divup(dst.info.dims[0], local_size[0]);
     int blk_y = divup(dst.info.dims[1], local_size[1]);
 
-    NDRange global(blk_x * dst.info.dims[2] * DIM0, blk_y * dst.info.dims[3] * DIM1);
+    NDRange global(blk_x * dst.info.dims[2] * DIM0,
+                   blk_y * dst.info.dims[3] * DIM1);
 
     dims_t trgt_dims;
     if (same_dims) {
-        trgt_dims= {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2], dst.info.dims[3]}};
+        trgt_dims = {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2],
+                      dst.info.dims[3]}};
     } else {
         dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
         dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
         dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
         dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
-        trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
+        trgt_dims    = {{trgt_i, trgt_j, trgt_k, trgt_l}};
     }
 
-    auto copyOp = KernelFunctor< Buffer, KParam, Buffer, KParam,
-                                 outType, float, dims_t, int, int >(*entry.ker);
+    auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam, outType, float,
+                                dims_t, int, int>(*entry.ker);
 
-    copyOp(EnqueueArgs(getQueue(), global, local),
-           *dst.data, dst.info, *src.data, src.info,
-           default_value, (float)factor, trgt_dims, blk_x, blk_y);
+    copyOp(EnqueueArgs(getQueue(), global, local), *dst.data, dst.info,
+           *src.data, src.info, default_value, (float)factor, trgt_dims, blk_x,
+           blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/moments.cl b/src/backend/opencl/kernel/moments.cl
index ea19371729..1afbaa2b0e 100644
--- a/src/backend/opencl/kernel/moments.cl
+++ b/src/backend/opencl/kernel/moments.cl
@@ -25,11 +25,13 @@ inline void fatomic_add_l(volatile __local float *source, const float operand) {
     do {
         expVal.floatVal = prevVal.floatVal;
         newVal.floatVal = expVal.floatVal + operand;
-        prevVal.intVal  = atomic_cmpxchg((volatile __local unsigned int *)source, expVal.intVal, newVal.intVal);
+        prevVal.intVal = atomic_cmpxchg((volatile __local unsigned int *)source,
+                                        expVal.intVal, newVal.intVal);
     } while (expVal.intVal != prevVal.intVal);
 }
 
-inline void fatomic_add_g(volatile __global float *source, const float operand) {
+inline void fatomic_add_g(volatile __global float *source,
+                          const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -39,60 +41,53 @@ inline void fatomic_add_g(volatile __global float *source, const float operand)
     do {
         expVal.floatVal = prevVal.floatVal;
         newVal.floatVal = expVal.floatVal + operand;
-        prevVal.intVal  = atomic_cmpxchg((volatile __global unsigned int *)source, expVal.intVal, newVal.intVal);
+        prevVal.intVal =
+            atomic_cmpxchg((volatile __global unsigned int *)source,
+                           expVal.intVal, newVal.intVal);
     } while (expVal.intVal != prevVal.intVal);
 }
 
-
-__kernel
-void moments_kernel(__global  float *d_out, const KParam out,
-                    __global const T *d_in,  const KParam in,
-                    const int moment, const int pBatch)
-{
+__kernel void moments_kernel(__global float *d_out, const KParam out,
+                             __global const T *d_in, const KParam in,
+                             const int moment, const int pBatch) {
     const dim_t idw = get_group_id(1) / in.dims[2];
-    const dim_t idz = get_group_id(1)  - idw * in.dims[2];
+    const dim_t idz = get_group_id(1) - idw * in.dims[2];
 
     const dim_t idy = get_group_id(0);
-    dim_t idx = get_local_id(0);
+    dim_t idx       = get_local_id(0);
 
-    if(idy >= in.dims[1] ||
-       idz >= in.dims[2] ||
-       idw >= in.dims[3] )
-        return;
+    if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3]) return;
 
     __local float wkg_moment_sum[MOMENTS_SZ];
-    if(get_local_id(0) < MOMENTS_SZ) {
-         wkg_moment_sum[get_local_id(0)] = 0.f;
-    }
+    if (get_local_id(0) < MOMENTS_SZ) { wkg_moment_sum[get_local_id(0)] = 0.f; }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     int mId = idy * in.strides[1] + idx;
-    if(pBatch) {
-       mId += idw * in.strides[3] + idz * in.strides[2];
-    }
+    if (pBatch) { mId += idw * in.strides[3] + idz * in.strides[2]; }
 
-    for(; idx<in.dims[0]; idx+=get_local_size(0)) {
+    for (; idx < in.dims[0]; idx += get_local_size(0)) {
         dim_t m_off = 0;
-        float val = d_in[mId];
+        float val   = d_in[mId];
         mId += get_local_size(0);
 
-        if((moment & AF_MOMENT_M00) > 0) {
+        if ((moment & AF_MOMENT_M00) > 0) {
             fatomic_add_l(wkg_moment_sum + m_off++, val);
         }
-        if((moment & AF_MOMENT_M01) > 0) {
+        if ((moment & AF_MOMENT_M01) > 0) {
             fatomic_add_l(wkg_moment_sum + m_off++, idx * val);
         }
-        if((moment & AF_MOMENT_M10) > 0) {
+        if ((moment & AF_MOMENT_M10) > 0) {
             fatomic_add_l(wkg_moment_sum + m_off++, idy * val);
         }
-        if((moment & AF_MOMENT_M11) > 0) {
+        if ((moment & AF_MOMENT_M11) > 0) {
             fatomic_add_l(wkg_moment_sum + m_off, idx * idy * val);
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if(get_local_id(0) < out.dims[0])
-        fatomic_add_g(d_out + (idw * out.strides[3] + idz * out.strides[2]) + get_local_id(0), wkg_moment_sum[get_local_id(0)]);
-
+    if (get_local_id(0) < out.dims[0])
+        fatomic_add_g(d_out + (idw * out.strides[3] + idz * out.strides[2]) +
+                          get_local_id(0),
+                      wkg_moment_sum[get_local_id(0)]);
 }
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index f1b2f7bca1..a64aa813c7 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -8,85 +8,76 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/moments.hpp>
+#include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <math.hpp>
-#include <cache.hpp>
+#include <map>
+#include <mutex>
+#include <string>
 #include "config.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        static const int THREADS = 128;
-
-        ///////////////////////////////////////////////////////////////////////////
-        // Wrapper functions
-        ///////////////////////////////////////////////////////////////////////////
-        template <typename T>
-        void moments(Param out, const Param in, af_moment_type moment)
-        {
-            std::string ref_name =
-            std::string("moments_") +
-            std::string(dtype_traits<T>::getName()) +
-            std::string("_") +
-            std::to_string(out.info.dims[0]);
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName();
-                options << " -D MOMENTS_SZ=" << out.info.dims[0];
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-
-                Program prog;
-                buildProgram(prog, moments_cl, moments_cl_len, options.str());
+namespace opencl {
+namespace kernel {
+static const int THREADS = 128;
+
+///////////////////////////////////////////////////////////////////////////
+// Wrapper functions
+///////////////////////////////////////////////////////////////////////////
+template<typename T>
+void moments(Param out, const Param in, af_moment_type moment) {
+    std::string ref_name = std::string("moments_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(out.info.dims[0]);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D MOMENTS_SZ=" << out.info.dims[0];
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "moments_kernel");
+        Program prog;
+        buildProgram(prog, moments_cl, moments_cl_len, options.str());
 
-                addKernelToCache(device, ref_name, entry);
-            }
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "moments_kernel");
 
+        addKernelToCache(device, ref_name, entry);
+    }
 
-            auto momentsp = KernelFunctor<Buffer, const KParam,
-                                          const Buffer, const KParam,
-                                          const int, const int>(*entry.ker);
+    auto momentsp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const int, const int>(*entry.ker);
 
-            NDRange local(THREADS, 1, 1);
-            NDRange global(in.info.dims[1] * local[0] ,
-                            in.info.dims[2] * in.info.dims[3] * local[1] );
+    NDRange local(THREADS, 1, 1);
+    NDRange global(in.info.dims[1] * local[0],
+                   in.info.dims[2] * in.info.dims[3] * local[1]);
 
-            bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
+    bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
 
-            momentsp(EnqueueArgs(getQueue(), global, local),
-                      *out.data, out.info, *in.data, in.info,
-                      (int)moment, (int)pBatch);
+    momentsp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, (int)moment, (int)pBatch);
 
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index 59985a6b47..22db54f0fa 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -7,46 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-int lIdx(int x, int y,
-        int stride1, int stride0)
-{
-    return (y*stride1 + x*stride0);
+int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
 }
 
-void load2LocalMem(__local T *  shrd,
-        __global const T *      in,
-        int lx, int ly, int shrdStride,
-        int dim0, int dim1,
-        int gx, int gy,
-        int inStride1, int inStride0)
-{
-    T val = gx>=0 && gx<dim0 && gy>=0 && gy<dim1 ? in[ lIdx(gx, gy, inStride1, inStride0) ] : init;
-    shrd[ lIdx(lx, ly, shrdStride, 1) ] = val;
+void load2LocalMem(__local T* shrd, __global const T* in, int lx, int ly,
+                   int shrdStride, int dim0, int dim1, int gx, int gy,
+                   int inStride1, int inStride0) {
+    T val = gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1
+                ? in[lIdx(gx, gy, inStride1, inStride0)]
+                : init;
+    shrd[lIdx(lx, ly, shrdStride, 1)] = val;
 }
 
-//kernel assumes four dimensions
-//doing this to reduce one uneccesary parameter
-__kernel
-void morph(__global T *              out,
-           KParam                  oInfo,
-           __global const T *         in,
-           KParam                  iInfo,
-           __constant const T *   d_filt,
-           __local T *          localMem,
-           int nBBS0, int nBBS1, int windLen)
-{
-    if (SeLength>0)
-        windLen = SeLength;
-
-    const int halo     = windLen/2;
-    const int padding  = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
+// kernel assumes four dimensions
+// doing this to reduce one uneccesary parameter
+__kernel void morph(__global T* out, KParam oInfo, __global const T* in,
+                    KParam iInfo, __constant const T* d_filt,
+                    __local T* localMem, int nBBS0, int nBBS1, int windLen) {
+    if (SeLength > 0) windLen = SeLength;
+
+    const int halo = windLen / 2;
+    const int padding =
+        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
     const int shrdLen  = get_local_size(0) + padding + 1;
     const int shrdLen1 = get_local_size(1) + padding;
 
     // gfor batch offsets
     int b2 = get_group_id(0) / nBBS0;
     int b3 = get_group_id(1) / nBBS1;
-    in  += (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
+    in += (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
     out += (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     // local neighborhood indices
@@ -54,16 +44,19 @@ void morph(__global T *              out,
     const int ly = get_local_id(1);
 
     // global indices
-    int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
     int s0 = iInfo.strides[0];
     int s1 = iInfo.strides[1];
     int d0 = iInfo.dims[0];
     int d1 = iInfo.dims[1];
-    for (int b=ly, gy2=gy; b<shrdLen1; b+=get_local_size(1), gy2+=get_local_size(1)) {
-        for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-            load2LocalMem(localMem, in, a, b, shrdLen, d0, d1, gx2-halo, gy2-halo, s1, s0);
+    for (int b = ly, gy2 = gy; b < shrdLen1;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            load2LocalMem(localMem, in, a, b, shrdLen, d0, d1, gx2 - halo,
+                          gy2 - halo, s1, s0);
         }
     }
 
@@ -73,13 +66,13 @@ void morph(__global T *              out,
 
     T acc = init;
 #pragma unroll
-    for(int wj=0; wj<windLen; ++wj) {
-        int joff   = wj*windLen;
-        int w_joff = (j+wj-halo)*shrdLen;
+    for (int wj = 0; wj < windLen; ++wj) {
+        int joff   = wj * windLen;
+        int w_joff = (j + wj - halo) * shrdLen;
 #pragma unroll
-        for(int wi=0; wi<windLen; ++wi) {
-            T cur  = localMem[w_joff+i+wi-halo];
-            if (d_filt[joff+wi]) {
+        for (int wi = 0; wi < windLen; ++wi) {
+            T cur = localMem[w_joff + i + wi - halo];
+            if (d_filt[joff + wi]) {
                 if (isDilation)
                     acc = max(acc, cur);
                 else
@@ -88,64 +81,51 @@ void morph(__global T *              out,
         }
     }
 
-    if (gx<oInfo.dims[0] && gy<oInfo.dims[1]) {
-        int outIdx = lIdx(gx, gy, oInfo.strides[1], oInfo.strides[0]);
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1]) {
+        int outIdx  = lIdx(gx, gy, oInfo.strides[1], oInfo.strides[0]);
         out[outIdx] = acc;
     }
 }
 
-
-
-int lIdx3D(int x, int y, int z,
-        int stride2, int stride1, int stride0)
-{
-    return (z*stride2 + y*stride1 + x*stride0);
+int lIdx3D(int x, int y, int z, int stride2, int stride1, int stride0) {
+    return (z * stride2 + y * stride1 + x * stride0);
 }
 
-void load2LocVolume(__local T * shrd,
-        __global const T * in,
-        int lx, int ly, int lz,
-        int shrdStride1, int shrdStride2,
-        int dim0, int dim1, int dim2,
-        int gx, int gy, int gz,
-        int inStride2, int inStride1, int inStride0)
-{
+void load2LocVolume(__local T* shrd, __global const T* in, int lx, int ly,
+                    int lz, int shrdStride1, int shrdStride2, int dim0,
+                    int dim1, int dim2, int gx, int gy, int gz, int inStride2,
+                    int inStride1, int inStride0) {
     T val = (T)0;
-    if (gx>=0 && gx<dim0 && gy>=0 && gy<dim1 && gz>=0 && gz<dim2)
-       val = in[gx*inStride0 + gy*inStride1 + gz*inStride2];
+    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1 && gz >= 0 && gz < dim2)
+        val = in[gx * inStride0 + gy * inStride1 + gz * inStride2];
     else
-       val = init;
+        val = init;
 
-    shrd[ lx + ly*shrdStride1 + lz*shrdStride2 ] = val;
+    shrd[lx + ly * shrdStride1 + lz * shrdStride2] = val;
 }
 
-__kernel
-void morph3d(__global T *         out,
-             KParam               oInfo,
-             __global const T *   in,
-             KParam               iInfo,
-             __constant const T * d_filt,
-             __local T *          localMem,
-             int             nBBS)
-{
-    const int halo      = SeLength/2;
-    const int padding   = (SeLength%2==0 ? (SeLength-1) : (2*(SeLength/2)));
-    const int se_area   = SeLength*SeLength;
-    const int shrdLen   = get_local_size(0) + padding + 1;
-    const int shrdLen1  = get_local_size(1) + padding;
-    const int shrdLen2  = get_local_size(2) + padding;
-    const int shrdArea  = shrdLen * shrdLen1;
+__kernel void morph3d(__global T* out, KParam oInfo, __global const T* in,
+                      KParam iInfo, __constant const T* d_filt,
+                      __local T* localMem, int nBBS) {
+    const int halo = SeLength / 2;
+    const int padding =
+        (SeLength % 2 == 0 ? (SeLength - 1) : (2 * (SeLength / 2)));
+    const int se_area  = SeLength * SeLength;
+    const int shrdLen  = get_local_size(0) + padding + 1;
+    const int shrdLen1 = get_local_size(1) + padding;
+    const int shrdLen2 = get_local_size(2) + padding;
+    const int shrdArea = shrdLen * shrdLen1;
 
     // gfor batch offsets
-    int batchId    = get_group_id(0) / nBBS;
-    in  += (batchId * iInfo.strides[3] + iInfo.offset);
+    int batchId = get_group_id(0) / nBBS;
+    in += (batchId * iInfo.strides[3] + iInfo.offset);
     out += (batchId * oInfo.strides[3]);
 
     const int lx = get_local_id(0);
     const int ly = get_local_id(1);
     const int lz = get_local_id(2);
 
-    const int gx = get_local_size(0) * (get_group_id(0)-batchId*nBBS) + lx;
+    const int gx = get_local_size(0) * (get_group_id(0) - batchId * nBBS) + lx;
     const int gy = get_local_size(1) * get_group_id(1) + ly;
     const int gz = get_local_size(2) * get_group_id(2) + lz;
 
@@ -156,34 +136,38 @@ void morph3d(__global T *         out,
     int d1 = iInfo.dims[1];
     int d2 = iInfo.dims[2];
 
-    for (int c=lz, gz2=gz; c<shrdLen2; c+=get_local_size(2), gz2+=get_local_size(2)) {
-        for (int b=ly, gy2=gy; b<shrdLen1; b+=get_local_size(1), gy2+=get_local_size(1)) {
-            for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-                load2LocVolume(localMem, in, a, b, c, shrdLen, shrdArea, d0, d1, d2,
-                               gx2-halo, gy2-halo, gz2-halo, s2, s1, s0);
+    for (int c = lz, gz2 = gz; c < shrdLen2;
+         c += get_local_size(2), gz2 += get_local_size(2)) {
+        for (int b = ly, gy2 = gy; b < shrdLen1;
+             b += get_local_size(1), gy2 += get_local_size(1)) {
+            for (int a = lx, gx2 = gx; a < shrdLen;
+                 a += get_local_size(0), gx2 += get_local_size(0)) {
+                load2LocVolume(localMem, in, a, b, c, shrdLen, shrdArea, d0, d1,
+                               d2, gx2 - halo, gy2 - halo, gz2 - halo, s2, s1,
+                               s0);
             }
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    int i  = lx + halo;
-    int j  = ly + halo;
-    int k  = lz + halo;
+    int i = lx + halo;
+    int j = ly + halo;
+    int k = lz + halo;
 
     T acc = init;
 #pragma unroll
-    for(int wk=0; wk<SeLength; ++wk) {
-        int koff   = wk*se_area;
-        int w_koff = (k+wk-halo)*shrdArea;
+    for (int wk = 0; wk < SeLength; ++wk) {
+        int koff   = wk * se_area;
+        int w_koff = (k + wk - halo) * shrdArea;
 #pragma unroll
-        for(int wj=0; wj<SeLength; ++wj) {
-        int joff   = wj*SeLength;
-        int w_joff = (j+wj-halo)*shrdLen;
+        for (int wj = 0; wj < SeLength; ++wj) {
+            int joff   = wj * SeLength;
+            int w_joff = (j + wj - halo) * shrdLen;
 #pragma unroll
-            for(int wi=0; wi<SeLength; ++wi) {
-                T cur  = localMem[w_koff+w_joff + i+wi-halo];
-                if (d_filt[koff+joff+wi]) {
+            for (int wi = 0; wi < SeLength; ++wi) {
+                T cur = localMem[w_koff + w_joff + i + wi - halo];
+                if (d_filt[koff + joff + wi]) {
                     if (isDilation)
                         acc = max(acc, cur);
                     else
@@ -193,10 +177,9 @@ void morph3d(__global T *         out,
         }
     }
 
-    if (gx<oInfo.dims[0] && gy<oInfo.dims[1] && gz<oInfo.dims[2]) {
-        int outIdx = gz * oInfo.strides[2] +
-            gy * oInfo.strides[1] +
-            gx * oInfo.strides[0];
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1] && gz < oInfo.dims[2]) {
+        int outIdx = gz * oInfo.strides[2] + gy * oInfo.strides[1] +
+                     gx * oInfo.strides[0];
         out[outIdx] = acc;
     }
 }
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index fb201e7155..a50c1e3fb8 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -8,69 +8,65 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/morph.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/morph.hpp>
 #include <memory.hpp>
 #include <ops.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <type_util.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-static const int CUBE_X    =  8;
-static const int CUBE_Y    =  8;
-static const int CUBE_Z    =  4;
+static const int CUBE_X = 8;
+static const int CUBE_Y = 8;
+static const int CUBE_Z = 4;
 
 template<typename T, bool isDilation, int SeLength>
-std::string generateOptionsString()
-{
+std::string generateOptionsString() {
     ToNumStr<T> toNumStr;
-    T init = isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T init =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
     std::ostringstream options;
     options << " -D T=" << dtype_traits<T>::getName()
-        << " -D isDilation="<< isDilation
-        << " -D init=" << toNumStr(init)
-        << " -D SeLength=" << SeLength;
+            << " -D isDilation=" << isDilation << " -D init=" << toNumStr(init)
+            << " -D SeLength=" << SeLength;
     if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
         options << " -D USE_DOUBLE";
     return options.str();
 }
 
-template<typename T, bool isDilation, int SeLength=0>
-void morph(Param out, const Param in, const Param mask, int windLen=0)
-{
+template<typename T, bool isDilation, int SeLength = 0>
+void morph(Param out, const Param in, const Param mask, int windLen = 0) {
     std::string refName = std::string("morph_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDilation) + std::to_string(SeLength);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(isDilation) + std::to_string(SeLength);
 
-    windLen = (SeLength>0 ? SeLength : windLen);
+    windLen = (SeLength > 0 ? SeLength : windLen);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::string options = generateOptionsString<T, isDilation, SeLength>();
         const char* ker_strs[] = {morph_cl};
-        const int   ker_lens[] = {morph_cl_len};
+        const int ker_lens[]   = {morph_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options);
         entry.prog = new Program(prog);
@@ -78,49 +74,48 @@ void morph(Param out, const Param in, const Param mask, int windLen=0)
         addKernelToCache(device, refName, entry);
     }
 
-    auto morphOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, cl::LocalSpaceArg,
-                                  int, int, int >(*entry.ker);
+    auto morphOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                 cl::LocalSpaceArg, int, int, int>(*entry.ker);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
     // launch batch * blk_x blocks along x dimension
-    NDRange global(blk_x * THREADS_X * in.info.dims[2], blk_y * THREADS_Y * in.info.dims[3]);
+    NDRange global(blk_x * THREADS_X * in.info.dims[2],
+                   blk_y * THREADS_Y * in.info.dims[3]);
 
     // copy mask/filter to constant memory
-    cl_int se_size   = sizeof(T)*windLen*windLen;
-    auto mBuff = memAlloc<T>(windLen*windLen);
+    cl_int se_size = sizeof(T) * windLen * windLen;
+    auto mBuff     = memAlloc<T>(windLen * windLen);
     getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
 
     // calculate shared memory size
-    const int padding = (windLen%2==0 ? (windLen-1) : (2*(windLen/2)));
+    const int padding =
+        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
     const int locLen  = THREADS_X + padding + 1;
-    const int locSize = locLen * (THREADS_Y+padding);
+    const int locSize = locLen * (THREADS_Y + padding);
 
-    morphOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *in.data, in.info, *mBuff,
-            cl::Local(locSize*sizeof(T)), blk_x, blk_y, windLen);
+    morphOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *in.data, in.info, *mBuff, cl::Local(locSize * sizeof(T)), blk_x,
+            blk_y, windLen);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, bool isDilation, int SeLength>
-void morph3d(Param       out,
-        const Param      in,
-        const Param      mask)
-{
+void morph3d(Param out, const Param in, const Param mask) {
     std::string refName = std::string("morph3d_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(isDilation) + std::to_string(SeLength);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(isDilation) + std::to_string(SeLength);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::string options = generateOptionsString<T, isDilation, SeLength>();
         const char* ker_strs[] = {morph_cl};
-        const int   ker_lens[] = {morph_cl_len};
+        const int ker_lens[]   = {morph_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options);
         entry.prog = new Program(prog);
@@ -128,8 +123,8 @@ void morph3d(Param       out,
         addKernelToCache(device, refName, entry);
     }
 
-    auto morphOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer,
-                                  cl::LocalSpaceArg, int >(*entry.ker);
+    auto morphOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                 cl::LocalSpaceArg, int>(*entry.ker);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
@@ -137,25 +132,26 @@ void morph3d(Param       out,
     int blk_y = divup(in.info.dims[1], CUBE_Y);
     int blk_z = divup(in.info.dims[2], CUBE_Z);
     // launch batch * blk_x blocks along x dimension
-    NDRange global(blk_x * CUBE_X * in.info.dims[3], blk_y * CUBE_Y, blk_z * CUBE_Z);
+    NDRange global(blk_x * CUBE_X * in.info.dims[3], blk_y * CUBE_Y,
+                   blk_z * CUBE_Z);
 
     // copy mask/filter to constant memory
-    cl_int se_size   = sizeof(T)*SeLength*SeLength*SeLength;
-    cl::Buffer *mBuff = bufferAlloc(se_size);
+    cl_int se_size    = sizeof(T) * SeLength * SeLength * SeLength;
+    cl::Buffer* mBuff = bufferAlloc(se_size);
     getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
 
     // calculate shared memory size
-    const int padding = (SeLength%2==0 ? (SeLength-1) : (2*(SeLength/2)));
-    const int locLen  = CUBE_X+padding+1;
-    const int locArea = locLen *(CUBE_Y+padding);
-    const int locSize = locArea*(CUBE_Z+padding);
+    const int padding =
+        (SeLength % 2 == 0 ? (SeLength - 1) : (2 * (SeLength / 2)));
+    const int locLen  = CUBE_X + padding + 1;
+    const int locArea = locLen * (CUBE_Y + padding);
+    const int locSize = locArea * (CUBE_Z + padding);
 
-    morphOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, *in.data, in.info,
-            *mBuff, cl::Local(locSize*sizeof(T)), blk_x);
+    morphOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *in.data, in.info, *mBuff, cl::Local(locSize * sizeof(T)), blk_x);
 
     bufferFree(mBuff);
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/names.hpp b/src/backend/opencl/kernel/names.hpp
index 1602f79482..acafade34c 100644
--- a/src/backend/opencl/kernel/names.hpp
+++ b/src/backend/opencl/kernel/names.hpp
@@ -9,12 +9,36 @@
 
 #pragma once
 #include <ops.hpp>
-template<af_op_t T> static const char *binOpName() { return "ADD_OP"; }
+template<af_op_t T>
+static const char *binOpName() {
+    return "ADD_OP";
+}
 
-template<> STATIC_ const char *binOpName<af_add_t>() { return "ADD_OP"; }
-template<> STATIC_ const char *binOpName<af_mul_t>() { return "MUL_OP"; }
-template<> STATIC_ const char *binOpName<af_and_t>() { return "AND_OP"; }
-template<> STATIC_ const char *binOpName<af_or_t >() { return "OR_OP" ; }
-template<> STATIC_ const char *binOpName<af_min_t>() { return "MIN_OP"; }
-template<> STATIC_ const char *binOpName<af_max_t>() { return "MAX_OP"; }
-template<> STATIC_ const char *binOpName<af_notzero_t>() { return "NOTZERO_OP"; }
+template<>
+STATIC_ const char *binOpName<af_add_t>() {
+    return "ADD_OP";
+}
+template<>
+STATIC_ const char *binOpName<af_mul_t>() {
+    return "MUL_OP";
+}
+template<>
+STATIC_ const char *binOpName<af_and_t>() {
+    return "AND_OP";
+}
+template<>
+STATIC_ const char *binOpName<af_or_t>() {
+    return "OR_OP";
+}
+template<>
+STATIC_ const char *binOpName<af_min_t>() {
+    return "MIN_OP";
+}
+template<>
+STATIC_ const char *binOpName<af_max_t>() {
+    return "MAX_OP";
+}
+template<>
+STATIC_ const char *binOpName<af_notzero_t>() {
+    return "NOTZERO_OP";
+}
diff --git a/src/backend/opencl/kernel/nearest_neighbour.cl b/src/backend/opencl/kernel/nearest_neighbour.cl
index a8b039490d..8de72a611d 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.cl
+++ b/src/backend/opencl/kernel/nearest_neighbour.cl
@@ -9,8 +9,7 @@
 
 // OpenCL < 1.2 compatibility
 #if !defined(__OPENCL_VERSION__) || __OPENCL_VERSION__ < 120
-__inline unsigned popcount(unsigned x)
-{
+__inline unsigned popcount(unsigned x) {
     x = x - ((x >> 1) & 0x55555555);
     x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
     x = (x + (x >> 4)) & 0x0F0F0F0F;
@@ -21,46 +20,26 @@ __inline unsigned popcount(unsigned x)
 #endif
 
 #ifdef USE_DOUBLE
-To _sad_(T v1, T v2)
-{
-    return fabs(v1 - v2);
-}
+To _sad_(T v1, T v2) { return fabs(v1 - v2); }
 #else
-To _sad_(T v1, T v2)
-{
-    return fabs((float)v1 - (float)v2);
-}
+To _sad_(T v1, T v2) { return fabs((float)v1 - (float)v2); }
 #endif
 
-To _ssd_(T v1, T v2)
-{
-    return (v1 - v2) * (v1 - v2);
-}
+To _ssd_(T v1, T v2) { return (v1 - v2) * (v1 - v2); }
 
 #ifdef __SHD__
-unsigned _shd_(T v1, T v2)
-{
-    return popcount(v1 ^ v2);
-}
+unsigned _shd_(T v1, T v2) { return popcount(v1 ^ v2); }
 #endif
 
-__kernel
-void all_distances(
-    __global To* out_dist,
-    __global const T* query,
-    KParam qInfo,
-    __global const T* train,
-    KParam tInfo,
-    const To max_dist,
-    const unsigned feat_len,
-    const unsigned max_feat_len,
-    const unsigned feat_offset,
-    __local T* lmem)
-{
+__kernel void all_distances(__global To* out_dist, __global const T* query,
+                            KParam qInfo, __global const T* train, KParam tInfo,
+                            const To max_dist, const unsigned feat_len,
+                            const unsigned max_feat_len,
+                            const unsigned feat_offset, __local T* lmem) {
     unsigned nquery = qInfo.dims[0];
     unsigned ntrain = tInfo.dims[0];
 
-    unsigned f = get_global_id(0);
+    unsigned f   = get_global_id(0);
     unsigned tid = get_local_id(0);
 
     __local To l_dist[THREADS];
@@ -77,7 +56,8 @@ void all_distances(
         // Copy local_size(0) training features to shared memory
         unsigned end_feat = min(feat_offset + max_feat_len, feat_len);
         for (unsigned i = feat_offset; i < feat_len; i++) {
-            l_train[(i - feat_offset) * get_local_size(0) + tid] = train[i * ntrain + f + tInfo.offset];
+            l_train[(i - feat_offset) * get_local_size(0) + tid] =
+                train[i * ntrain + f + tInfo.offset];
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
@@ -89,7 +69,8 @@ void all_distances(
         // Load one query feature that will be tested against all training
         // features in current block
         if (tid < max_feat_len) {
-            l_query[tid] = query[(tid + feat_offset) * nquery + j + qInfo.offset];
+            l_query[tid] =
+                query[(tid + feat_offset) * nquery + j + qInfo.offset];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
 
@@ -100,28 +81,28 @@ void all_distances(
                 // Calculate Hamming distance for 32-bits of descriptor and
                 // accumulates to dist
 #ifdef USE_LOCAL_MEM
-                dist += DISTOP(l_train[(k - feat_offset) * get_local_size(0) + tid], l_query[k - feat_offset]);
+                dist +=
+                    DISTOP(l_train[(k - feat_offset) * get_local_size(0) + tid],
+                           l_query[k - feat_offset]);
 #else
-                dist += DISTOP(train[k * ntrain + f + tInfo.offset], l_query[k - feat_offset]);
+                dist += DISTOP(train[k * ntrain + f + tInfo.offset],
+                               l_query[k - feat_offset]);
 #endif
             }
         }
 
         // Only stores the feature index and distance if it's smaller
         // than the best match found so far
-        if (valid_feat) {
-            l_dist[tid] = dist;
-        }
+        if (valid_feat) { l_dist[tid] = dist; }
         barrier(CLK_LOCAL_MEM_FENCE);
 
         // Store best match in training features from block to the current
         // query feature
         if (valid_feat) {
-            if(feat_offset == 0)
+            if (feat_offset == 0)
                 out_dist[j * ntrain + f] = l_dist[tid];
             else
                 out_dist[j * ntrain + f] += l_dist[tid];
-
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 3d913f0b98..795e08b3fc 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -7,99 +7,83 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <program.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/nearest_neighbour.hpp>
-#include <memory.hpp>
 #include <math.hpp>
-#include <common/dispatch.hpp>
-#include <cache.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <af/defines.h>
 
 using cl::Buffer;
 using cl::EnqueueArgs;
-using cl::KernelFunctor;
 using cl::Kernel;
+using cl::KernelFunctor;
+using cl::LocalSpaceArg;
 using cl::NDRange;
 using cl::Program;
-using cl::LocalSpaceArg;
 
-namespace opencl
-{
+namespace opencl {
 
-namespace kernel
-{
+namespace kernel {
 
 static const unsigned THREADS = 256;
 
 template<typename T, typename To, af_match_type dist_type>
-void all_distances(Param dist,
-                   Param query,
-                   Param train,
-                   const dim_t dist_dim)
-{
+void all_distances(Param dist, Param query, Param train, const dim_t dist_dim) {
     const dim_t feat_len = query.info.dims[dist_dim];
-    const unsigned max_kern_feat_len = min(THREADS, static_cast<unsigned>(feat_len));
+    const unsigned max_kern_feat_len =
+        min(THREADS, static_cast<unsigned>(feat_len));
     const To max_dist = maxval<To>();
 
     // Determine maximum feat_len capable of using shared memory (faster)
     cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
-    size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + max_kern_feat_len * sizeof(T);
+    size_t lmem_predef =
+        2 * THREADS * sizeof(unsigned) + max_kern_feat_len * sizeof(T);
     size_t ltrain_sz = THREADS * max_kern_feat_len * sizeof(T);
-    bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
-    size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
+    bool use_lmem    = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
+    size_t lmem_sz   = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
 
     unsigned unroll_len = nextpow2(feat_len);
     if (unroll_len != feat_len) unroll_len = 0;
 
-    std::string ref_name =
-        std::string("knn_") +
-        std::to_string(dist_type) +
-        std::string("_") +
-        std::to_string(use_lmem) +
-        std::string("_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::string("_") +
-        std::to_string(unroll_len);
+    std::string ref_name = std::string("knn_") + std::to_string(dist_type) +
+                           std::string("_") + std::to_string(use_lmem) +
+                           std::string("_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(unroll_len);
 
     int device = getActiveDeviceId();
 
     kc_entry_t entry = kernelCache(device, ref_name);
 
-    if (entry.prog==0 && entry.ker==0) {
-
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-            << " -D To=" << dtype_traits<To>::getName()
-            << " -D THREADS=" << THREADS
-            << " -D FEAT_LEN=" << unroll_len;
-
-        switch(dist_type) {
-            case AF_SAD: options <<" -D DISTOP=_sad_"; break;
-            case AF_SSD: options <<" -D DISTOP=_ssd_"; break;
-            case AF_SHD: options <<" -D DISTOP=_shd_ -D __SHD__";
-                         break;
+                << " -D To=" << dtype_traits<To>::getName()
+                << " -D THREADS=" << THREADS << " -D FEAT_LEN=" << unroll_len;
+
+        switch (dist_type) {
+            case AF_SAD: options << " -D DISTOP=_sad_"; break;
+            case AF_SSD: options << " -D DISTOP=_ssd_"; break;
+            case AF_SHD: options << " -D DISTOP=_shd_ -D __SHD__"; break;
             default: break;
         }
 
-        if (std::is_same<T, double>::value ||
-                std::is_same<T, cdouble>::value) {
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
-        if (use_lmem)
-            options << " -D USE_LOCAL_MEM";
+        if (use_lmem) options << " -D USE_LOCAL_MEM";
 
         cl::Program prog;
-        buildProgram(prog,
-                nearest_neighbour_cl,
-                nearest_neighbour_cl_len,
-                options.str());
+        buildProgram(prog, nearest_neighbour_cl, nearest_neighbour_cl_len,
+                     options.str());
 
         entry.prog = new Program(prog);
-        entry.ker = new Kernel;
+        entry.ker  = new Kernel;
 
         *entry.ker = Kernel(*entry.prog, "all_distances");
 
@@ -116,22 +100,19 @@ void all_distances(Param dist,
 
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
-    auto hmOp = KernelFunctor<Buffer,
-                              Buffer, KParam,
-                              Buffer, KParam,
-                              const To, const unsigned,
-                              const unsigned,const unsigned,
-                              LocalSpaceArg> (*entry.ker);
-
-    for(dim_t feat_offset=0; feat_offset<feat_len; feat_offset+=THREADS) {
-        hmOp(EnqueueArgs(getQueue(), global, local),
-              *dist.data,
-              *query.data, query.info, *train.data, train.info,
-              max_dist, feat_len, max_kern_feat_len, feat_offset, cl::Local(lmem_sz));
+    auto hmOp = KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, const To,
+                              const unsigned, const unsigned, const unsigned,
+                              LocalSpaceArg>(*entry.ker);
+
+    for (dim_t feat_offset = 0; feat_offset < feat_len;
+         feat_offset += THREADS) {
+        hmOp(EnqueueArgs(getQueue(), global, local), *dist.data, *query.data,
+             query.info, *train.data, train.info, max_dist, feat_len,
+             max_kern_feat_len, feat_offset, cl::Local(lmem_sz));
         CL_DEBUG_FINISH(getQueue());
     }
 }
 
-} // namespace kernel
+}  // namespace kernel
 
-} // namespace opencl
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/nonmax_suppression.cl b/src/backend/opencl/kernel/nonmax_suppression.cl
index 02b599542f..1b5a627454 100644
--- a/src/backend/opencl/kernel/nonmax_suppression.cl
+++ b/src/backend/opencl/kernel/nonmax_suppression.cl
@@ -7,13 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void nonMaxSuppressionKernel(__global     T* output, KParam   oInfo,
-                             __global const T*   in, KParam  inInfo,
-                             __global const T*   dx, KParam  dxInfo,
-                             __global const T*   dy, KParam  dyInfo,
-                             unsigned nBBS0, unsigned nBBS1)
-{
+__kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
+                                      __global const T* in, KParam inInfo,
+                                      __global const T* dx, KParam dxInfo,
+                                      __global const T* dy, KParam dyInfo,
+                                      unsigned nBBS0, unsigned nBBS1) {
     // local thread indices
     const int lx = get_local_id(0);
     const int ly = get_local_id(1);
@@ -23,40 +21,39 @@ void nonMaxSuppressionKernel(__global     T* output, KParam   oInfo,
     const unsigned b3 = get_group_id(1) / nBBS1;
 
     // global indices
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    const int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
     __local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
-    __global const T* mag = in +
-        (b2 * inInfo.strides[2] + b3 * inInfo.strides[3] + inInfo.offset) +
+    __global const T* mag =
+        in + (b2 * inInfo.strides[2] + b3 * inInfo.strides[3] + inInfo.offset) +
         inInfo.strides[1] + 1;
-    __global const T* dX  = dx +
-        (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3] + dxInfo.offset) +
+    __global const T* dX =
+        dx + (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3] + dxInfo.offset) +
         dxInfo.strides[1] + 1;
-    __global const T* dY  = dy +
-        (b2 * dyInfo.strides[2] + b3 * dyInfo.strides[3] + dyInfo.offset) +
+    __global const T* dY =
+        dy + (b2 * dyInfo.strides[2] + b3 * dyInfo.strides[3] + dyInfo.offset) +
         dyInfo.strides[1] + 1;
-    __global     T*   out = output +
-        (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
-        oInfo.strides[1] + 1;
+    __global T* out = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
+                      oInfo.strides[1] + 1;
 
 #pragma unroll
-    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=get_local_size(1), gy2+=get_local_size(1))
-    {
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
 #pragma unroll
-        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=get_local_size(0), gx2+=get_local_size(0))
-        {
-            localMem[b][a] = mag[ (gx2-1)*inInfo.strides[0] + (gy2-1)*inInfo.strides[1] ];
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            localMem[b][a] = mag[(gx2 - 1) * inInfo.strides[0] +
+                                 (gy2 - 1) * inInfo.strides[1]];
         }
     }
     int i = lx + 1;
     int j = ly + 1;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (gx<inInfo.dims[0]-2 && gy<inInfo.dims[1]-2)
-    {
-        int idx = gx*inInfo.strides[0]+gy*inInfo.strides[1];
+    if (gx < inInfo.dims[0] - 2 && gy < inInfo.dims[1] - 2) {
+        int idx = gx * inInfo.strides[0] + gy * inInfo.strides[1];
 
         const float cmag = localMem[j][i];
 
@@ -65,59 +62,59 @@ void nonMaxSuppressionKernel(__global     T* output, KParam   oInfo,
         else {
             const float dx = dX[idx];
             const float dy = dY[idx];
-            const float se = localMem[j+1][i+1];
-            const float nw = localMem[j-1][i-1];
-            const float ea = localMem[j  ][i+1];
-            const float we = localMem[j  ][i-1];
-            const float ne = localMem[j-1][i+1];
-            const float sw = localMem[j+1][i-1];
-            const float no = localMem[j-1][i  ];
-            const float so = localMem[j+1][i  ];
+            const float se = localMem[j + 1][i + 1];
+            const float nw = localMem[j - 1][i - 1];
+            const float ea = localMem[j][i + 1];
+            const float we = localMem[j][i - 1];
+            const float ne = localMem[j - 1][i + 1];
+            const float sw = localMem[j + 1][i - 1];
+            const float no = localMem[j - 1][i];
+            const float so = localMem[j + 1][i];
 
             float a1, a2, b1, b2, alpha;
 
-            if (dx>=0) {
-                if (dy>=0) {
-                    const bool isTrue = (dx-dy)>=0;
+            if (dx >= 0) {
+                if (dy >= 0) {
+                    const bool isTrue = (dx - dy) >= 0;
 
                     a1    = isTrue ? ea : so;
                     a2    = isTrue ? we : no;
                     b1    = se;
                     b2    = nw;
-                    alpha = isTrue ? dy/dx : dx/dy;
+                    alpha = isTrue ? dy / dx : dx / dy;
                 } else {
-                    const bool isTrue = (dx+dy)>=0;
+                    const bool isTrue = (dx + dy) >= 0;
 
                     a1    = isTrue ? ea : no;
                     a2    = isTrue ? we : so;
                     b1    = ne;
                     b2    = sw;
-                    alpha = isTrue ? -dy/dx : dx/-dy;
+                    alpha = isTrue ? -dy / dx : dx / -dy;
                 }
             } else {
-                if (dy>=0) {
-                    const bool isTrue = (dx+dy)>=0;
+                if (dy >= 0) {
+                    const bool isTrue = (dx + dy) >= 0;
 
                     a1    = isTrue ? so : we;
                     a2    = isTrue ? no : ea;
                     b1    = sw;
                     b2    = ne;
-                    alpha = isTrue ? -dx/dy : dy/-dx;
+                    alpha = isTrue ? -dx / dy : dy / -dx;
                 } else {
-                    const bool isTrue = (-dx+dy)>=0;
+                    const bool isTrue = (-dx + dy) >= 0;
 
                     a1    = isTrue ? we : no;
                     a2    = isTrue ? ea : so;
                     b1    = nw;
                     b2    = se;
-                    alpha = isTrue ? -dy/dx : dx/-dy;
+                    alpha = isTrue ? -dy / dx : dx / -dy;
                 }
             }
 
-            float mag1 = (1-alpha)*a1 + alpha*b1;
-            float mag2 = (1-alpha)*a2 + alpha*b2;
+            float mag1 = (1 - alpha) * a1 + alpha * b1;
+            float mag2 = (1 - alpha) * a2 + alpha * b2;
 
-            if (cmag>mag1 && cmag>mag2) {
+            if (cmag > mag1 && cmag > mag2) {
                 out[idx] = cmag;
             } else {
                 out[idx] = (T)0;
diff --git a/src/backend/opencl/kernel/ops.cl b/src/backend/opencl/kernel/ops.cl
index 90e2548762..a15c934fc4 100644
--- a/src/backend/opencl/kernel/ops.cl
+++ b/src/backend/opencl/kernel/ops.cl
@@ -10,140 +10,92 @@
 #define IS_NAN(in) !((in) == (in))
 
 #ifdef ADD_OP
-T binOp(T lhs, T rhs)
-{
-    return lhs + rhs;
-}
+T binOp(T lhs, T rhs) { return lhs + rhs; }
 
-To transform(Ti in)
-{
-    return(To)(in);
-}
+To transform(Ti in) { return (To)(in); }
 #endif
 
 #ifdef MUL_OP
 #if CPLX
-T binOp(T lhs, T rhs)
-{
+T binOp(T lhs, T rhs) {
     T out;
     out.x = lhs.x * rhs.x - lhs.y * rhs.y;
     out.y = lhs.x * rhs.y + lhs.y * rhs.x;
     return out;
 }
 #else
-T binOp(T lhs, T rhs)
-{
-    return lhs * rhs;
-}
+T binOp(T lhs, T rhs) { return lhs * rhs; }
 #endif
 
-To transform(Ti in)
-{
-    return(To)(in);
-}
+To transform(Ti in) { return (To)(in); }
 #endif
 
 #ifdef OR_OP
-uchar binOp(uchar lhs, uchar rhs)
-{
-    return lhs || rhs;
-}
+uchar binOp(uchar lhs, uchar rhs) { return lhs || rhs; }
 
 #if CPLX
-uchar transform(Ti in)
-{
-    return (in.x != 0) || (in.y != 0);
-}
+uchar transform(Ti in) { return (in.x != 0) || (in.y != 0); }
 #else
-uchar transform(Ti in)
-{
-    return (in != 0);
-}
+uchar transform(Ti in) { return (in != 0); }
 #endif
 #endif
 
 #ifdef AND_OP
-uchar binOp(uchar lhs, uchar rhs)
-{
-    return lhs && rhs;
-}
+uchar binOp(uchar lhs, uchar rhs) { return lhs && rhs; }
 
 #if CPLX
-uchar transform(Ti in)
-{
-    return (in.x != 0) || (in.y != 0);
-}
+uchar transform(Ti in) { return (in.x != 0) || (in.y != 0); }
 #else
-uchar transform(Ti in)
-{
-    return (in != 0);
-}
+uchar transform(Ti in) { return (in != 0); }
 #endif
 #endif
 
 #ifdef NOTZERO_OP
-uint binOp(uint lhs, uint rhs)
-{
-    return lhs + rhs;
-}
+uint binOp(uint lhs, uint rhs) { return lhs + rhs; }
 
 #if CPLX
-uint transform(Ti in)
-{
-    return (in.x != 0) || (in.y != 0);
-}
+uint transform(Ti in) { return (in.x != 0) || (in.y != 0); }
 #else
-uint transform(Ti in)
-{
-    return (in != 0);
-}
+uint transform(Ti in) { return (in != 0); }
 #endif
 #endif
 
 #ifdef MIN_OP
 
 #if CPLX
-    #define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
+#define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
 #endif
 
-T transform(T in)
-{
+T transform(T in) {
     T val = init;
     return IS_NAN(in) ? (val) : (in);
 }
 
 #if CPLX
-#define sabs(in) ((in.x)*(in.x) + (in.y)*(in.y))
+#define sabs(in) ((in.x) * (in.x) + (in.y) * (in.y))
 #else
 #define sabs(in) in
 #endif
 
-T binOp(T lhs, T rhs)
-{
-    return sabs(lhs) < sabs(rhs) ? lhs : rhs;
-}
+T binOp(T lhs, T rhs) { return sabs(lhs) < sabs(rhs) ? lhs : rhs; }
 #endif
 
 #ifdef MAX_OP
 
 #if CPLX
-    #define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
+#define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
 #endif
 
-T transform(T in)
-{
+T transform(T in) {
     T val = init;
     return IS_NAN(in) ? (val) : (in);
 }
 
 #if CPLX
-#define sabs(in) ((in.x)*(in.x) + (in.y)*(in.y))
+#define sabs(in) ((in.x) * (in.x) + (in.y) * (in.y))
 #else
 #define sabs(in) in
 #endif
 
-T binOp(T lhs, T rhs)
-{
-    return sabs(lhs) > sabs(rhs) ? lhs : rhs;
-}
+T binOp(T lhs, T rhs) { return sabs(lhs) > sabs(rhs) ? lhs : rhs; }
 #endif
diff --git a/src/backend/opencl/kernel/orb.cl b/src/backend/opencl/kernel/orb.cl
index 5c5739b383..0026f1410c 100644
--- a/src/backend/opencl/kernel/orb.cl
+++ b/src/backend/opencl/kernel/orb.cl
@@ -11,284 +11,91 @@
 // original ORB paper
 #define REF_PAT_SAMPLES 256
 #define REF_PAT_COORDS 4
-#define REF_PAT_LENGTH (REF_PAT_SAMPLES*REF_PAT_COORDS)
+#define REF_PAT_LENGTH (REF_PAT_SAMPLES * REF_PAT_COORDS)
 
 // Current reference pattern was borrowed from OpenCV, a randomly generated
 // pattern will not achieve same quality as it must be trained like described
 // in sections 4.2 and 4.3 of the original ORB paper.
 __constant int ref_pat[] = {
-    8,-3, 9,5,
-    4,2, 7,-12,
-    -11,9, -8,2,
-    7,-12, 12,-13,
-    2,-13, 2,12,
-    1,-7, 1,6,
-    -2,-10, -2,-4,
-    -13,-13, -11,-8,
-    -13,-3, -12,-9,
-    10,4, 11,9,
-    -13,-8, -8,-9,
-    -11,7, -9,12,
-    7,7, 12,6,
-    -4,-5, -3,0,
-    -13,2, -12,-3,
-    -9,0, -7,5,
-    12,-6, 12,-1,
-    -3,6, -2,12,
-    -6,-13, -4,-8,
-    11,-13, 12,-8,
-    4,7, 5,1,
-    5,-3, 10,-3,
-    3,-7, 6,12,
-    -8,-7, -6,-2,
-    -2,11, -1,-10,
-    -13,12, -8,10,
-    -7,3, -5,-3,
-    -4,2, -3,7,
-    -10,-12, -6,11,
-    5,-12, 6,-7,
-    5,-6, 7,-1,
-    1,0, 4,-5,
-    9,11, 11,-13,
-    4,7, 4,12,
-    2,-1, 4,4,
-    -4,-12, -2,7,
-    -8,-5, -7,-10,
-    4,11, 9,12,
-    0,-8, 1,-13,
-    -13,-2, -8,2,
-    -3,-2, -2,3,
-    -6,9, -4,-9,
-    8,12, 10,7,
-    0,9, 1,3,
-    7,-5, 11,-10,
-    -13,-6, -11,0,
-    10,7, 12,1,
-    -6,-3, -6,12,
-    10,-9, 12,-4,
-    -13,8, -8,-12,
-    -13,0, -8,-4,
-    3,3, 7,8,
-    5,7, 10,-7,
-    -1,7, 1,-12,
-    3,-10, 5,6,
-    2,-4, 3,-10,
-    -13,0, -13,5,
-    -13,-7, -12,12,
-    -13,3, -11,8,
-    -7,12, -4,7,
-    6,-10, 12,8,
-    -9,-1, -7,-6,
-    -2,-5, 0,12,
-    -12,5, -7,5,
-    3,-10, 8,-13,
-    -7,-7, -4,5,
-    -3,-2, -1,-7,
-    2,9, 5,-11,
-    -11,-13, -5,-13,
-    -1,6, 0,-1,
-    5,-3, 5,2,
-    -4,-13, -4,12,
-    -9,-6, -9,6,
-    -12,-10, -8,-4,
-    10,2, 12,-3,
-    7,12, 12,12,
-    -7,-13, -6,5,
-    -4,9, -3,4,
-    7,-1, 12,2,
-    -7,6, -5,1,
-    -13,11, -12,5,
-    -3,7, -2,-6,
-    7,-8, 12,-7,
-    -13,-7, -11,-12,
-    1,-3, 12,12,
-    2,-6, 3,0,
-    -4,3, -2,-13,
-    -1,-13, 1,9,
-    7,1, 8,-6,
-    1,-1, 3,12,
-    9,1, 12,6,
-    -1,-9, -1,3,
-    -13,-13, -10,5,
-    7,7, 10,12,
-    12,-5, 12,9,
-    6,3, 7,11,
-    5,-13, 6,10,
-    2,-12, 2,3,
-    3,8, 4,-6,
-    2,6, 12,-13,
-    9,-12, 10,3,
-    -8,4, -7,9,
-    -11,12, -4,-6,
-    1,12, 2,-8,
-    6,-9, 7,-4,
-    2,3, 3,-2,
-    6,3, 11,0,
-    3,-3, 8,-8,
-    7,8, 9,3,
-    -11,-5, -6,-4,
-    -10,11, -5,10,
-    -5,-8, -3,12,
-    -10,5, -9,0,
-    8,-1, 12,-6,
-    4,-6, 6,-11,
-    -10,12, -8,7,
-    4,-2, 6,7,
-    -2,0, -2,12,
-    -5,-8, -5,2,
-    7,-6, 10,12,
-    -9,-13, -8,-8,
-    -5,-13, -5,-2,
-    8,-8, 9,-13,
-    -9,-11, -9,0,
-    1,-8, 1,-2,
-    7,-4, 9,1,
-    -2,1, -1,-4,
-    11,-6, 12,-11,
-    -12,-9, -6,4,
-    3,7, 7,12,
-    5,5, 10,8,
-    0,-4, 2,8,
-    -9,12, -5,-13,
-    0,7, 2,12,
-    -1,2, 1,7,
-    5,11, 7,-9,
-    3,5, 6,-8,
-    -13,-4, -8,9,
-    -5,9, -3,-3,
-    -4,-7, -3,-12,
-    6,5, 8,0,
-    -7,6, -6,12,
-    -13,6, -5,-2,
-    1,-10, 3,10,
-    4,1, 8,-4,
-    -2,-2, 2,-13,
-    2,-12, 12,12,
-    -2,-13, 0,-6,
-    4,1, 9,3,
-    -6,-10, -3,-5,
-    -3,-13, -1,1,
-    7,5, 12,-11,
-    4,-2, 5,-7,
-    -13,9, -9,-5,
-    7,1, 8,6,
-    7,-8, 7,6,
-    -7,-4, -7,1,
-    -8,11, -7,-8,
-    -13,6, -12,-8,
-    2,4, 3,9,
-    10,-5, 12,3,
-    -6,-5, -6,7,
-    8,-3, 9,-8,
-    2,-12, 2,8,
-    -11,-2, -10,3,
-    -12,-13, -7,-9,
-    -11,0, -10,-5,
-    5,-3, 11,8,
-    -2,-13, -1,12,
-    -1,-8, 0,9,
-    -13,-11, -12,-5,
-    -10,-2, -10,11,
-    -3,9, -2,-13,
-    2,-3, 3,2,
-    -9,-13, -4,0,
-    -4,6, -3,-10,
-    -4,12, -2,-7,
-    -6,-11, -4,9,
-    6,-3, 6,11,
-    -13,11, -5,5,
-    11,11, 12,6,
-    7,-5, 12,-2,
-    -1,12, 0,7,
-    -4,-8, -3,-2,
-    -7,1, -6,7,
-    -13,-12, -8,-13,
-    -7,-2, -6,-8,
-    -8,5, -6,-9,
-    -5,-1, -4,5,
-    -13,7, -8,10,
-    1,5, 5,-13,
-    1,0, 10,-13,
-    9,12, 10,-1,
-    5,-8, 10,-9,
-    -1,11, 1,-13,
-    -9,-3, -6,2,
-    -1,-10, 1,12,
-    -13,1, -8,-10,
-    8,-11, 10,-6,
-    2,-13, 3,-6,
-    7,-13, 12,-9,
-    -10,-10, -5,-7,
-    -10,-8, -8,-13,
-    4,-6, 8,5,
-    3,12, 8,-13,
-    -4,2, -3,-3,
-    5,-13, 10,-12,
-    4,-13, 5,-1,
-    -9,9, -4,3,
-    0,3, 3,-9,
-    -12,1, -6,1,
-    3,2, 4,-8,
-    -10,-10, -10,9,
-    8,-13, 12,12,
-    -8,-12, -6,-5,
-    2,2, 3,7,
-    10,6, 11,-8,
-    6,8, 8,-12,
-    -7,10, -6,5,
-    -3,-9, -3,9,
-    -1,-13, -1,5,
-    -3,-7, -3,4,
-    -8,-2, -8,3,
-    4,2, 12,12,
-    2,-5, 3,11,
-    6,-9, 11,-13,
-    3,-1, 7,12,
-    11,-1, 12,4,
-    -3,0, -3,6,
-    4,-11, 4,12,
-    2,-4, 2,1,
-    -10,-6, -8,1,
-    -13,7, -11,1,
-    -13,12, -11,-13,
-    6,0, 11,-13,
-    0,-1, 1,4,
-    -13,3, -9,-2,
-    -9,8, -6,-3,
-    -13,-6, -8,-2,
-    5,-9, 8,10,
-    2,7, 3,-9,
-    -1,-6, -1,-1,
-    9,5, 11,-2,
-    11,-3, 12,-8,
-    3,0, 3,5,
-    -1,4, 0,10,
-    3,-6, 4,5,
-    -13,0, -10,5,
-    5,8, 12,11,
-    8,9, 9,-6,
-    7,-4, 8,-12,
-    -10,4, -10,9,
-    7,3, 12,4,
-    9,-7, 10,-2,
-    7,0, 12,-2,
-    -1,-6, 0,-11,
+    8,   -3,  9,   5,   4,   2,   7,   -12, -11, 9,   -8,  2,   7,   -12, 12,
+    -13, 2,   -13, 2,   12,  1,   -7,  1,   6,   -2,  -10, -2,  -4,  -13, -13,
+    -11, -8,  -13, -3,  -12, -9,  10,  4,   11,  9,   -13, -8,  -8,  -9,  -11,
+    7,   -9,  12,  7,   7,   12,  6,   -4,  -5,  -3,  0,   -13, 2,   -12, -3,
+    -9,  0,   -7,  5,   12,  -6,  12,  -1,  -3,  6,   -2,  12,  -6,  -13, -4,
+    -8,  11,  -13, 12,  -8,  4,   7,   5,   1,   5,   -3,  10,  -3,  3,   -7,
+    6,   12,  -8,  -7,  -6,  -2,  -2,  11,  -1,  -10, -13, 12,  -8,  10,  -7,
+    3,   -5,  -3,  -4,  2,   -3,  7,   -10, -12, -6,  11,  5,   -12, 6,   -7,
+    5,   -6,  7,   -1,  1,   0,   4,   -5,  9,   11,  11,  -13, 4,   7,   4,
+    12,  2,   -1,  4,   4,   -4,  -12, -2,  7,   -8,  -5,  -7,  -10, 4,   11,
+    9,   12,  0,   -8,  1,   -13, -13, -2,  -8,  2,   -3,  -2,  -2,  3,   -6,
+    9,   -4,  -9,  8,   12,  10,  7,   0,   9,   1,   3,   7,   -5,  11,  -10,
+    -13, -6,  -11, 0,   10,  7,   12,  1,   -6,  -3,  -6,  12,  10,  -9,  12,
+    -4,  -13, 8,   -8,  -12, -13, 0,   -8,  -4,  3,   3,   7,   8,   5,   7,
+    10,  -7,  -1,  7,   1,   -12, 3,   -10, 5,   6,   2,   -4,  3,   -10, -13,
+    0,   -13, 5,   -13, -7,  -12, 12,  -13, 3,   -11, 8,   -7,  12,  -4,  7,
+    6,   -10, 12,  8,   -9,  -1,  -7,  -6,  -2,  -5,  0,   12,  -12, 5,   -7,
+    5,   3,   -10, 8,   -13, -7,  -7,  -4,  5,   -3,  -2,  -1,  -7,  2,   9,
+    5,   -11, -11, -13, -5,  -13, -1,  6,   0,   -1,  5,   -3,  5,   2,   -4,
+    -13, -4,  12,  -9,  -6,  -9,  6,   -12, -10, -8,  -4,  10,  2,   12,  -3,
+    7,   12,  12,  12,  -7,  -13, -6,  5,   -4,  9,   -3,  4,   7,   -1,  12,
+    2,   -7,  6,   -5,  1,   -13, 11,  -12, 5,   -3,  7,   -2,  -6,  7,   -8,
+    12,  -7,  -13, -7,  -11, -12, 1,   -3,  12,  12,  2,   -6,  3,   0,   -4,
+    3,   -2,  -13, -1,  -13, 1,   9,   7,   1,   8,   -6,  1,   -1,  3,   12,
+    9,   1,   12,  6,   -1,  -9,  -1,  3,   -13, -13, -10, 5,   7,   7,   10,
+    12,  12,  -5,  12,  9,   6,   3,   7,   11,  5,   -13, 6,   10,  2,   -12,
+    2,   3,   3,   8,   4,   -6,  2,   6,   12,  -13, 9,   -12, 10,  3,   -8,
+    4,   -7,  9,   -11, 12,  -4,  -6,  1,   12,  2,   -8,  6,   -9,  7,   -4,
+    2,   3,   3,   -2,  6,   3,   11,  0,   3,   -3,  8,   -8,  7,   8,   9,
+    3,   -11, -5,  -6,  -4,  -10, 11,  -5,  10,  -5,  -8,  -3,  12,  -10, 5,
+    -9,  0,   8,   -1,  12,  -6,  4,   -6,  6,   -11, -10, 12,  -8,  7,   4,
+    -2,  6,   7,   -2,  0,   -2,  12,  -5,  -8,  -5,  2,   7,   -6,  10,  12,
+    -9,  -13, -8,  -8,  -5,  -13, -5,  -2,  8,   -8,  9,   -13, -9,  -11, -9,
+    0,   1,   -8,  1,   -2,  7,   -4,  9,   1,   -2,  1,   -1,  -4,  11,  -6,
+    12,  -11, -12, -9,  -6,  4,   3,   7,   7,   12,  5,   5,   10,  8,   0,
+    -4,  2,   8,   -9,  12,  -5,  -13, 0,   7,   2,   12,  -1,  2,   1,   7,
+    5,   11,  7,   -9,  3,   5,   6,   -8,  -13, -4,  -8,  9,   -5,  9,   -3,
+    -3,  -4,  -7,  -3,  -12, 6,   5,   8,   0,   -7,  6,   -6,  12,  -13, 6,
+    -5,  -2,  1,   -10, 3,   10,  4,   1,   8,   -4,  -2,  -2,  2,   -13, 2,
+    -12, 12,  12,  -2,  -13, 0,   -6,  4,   1,   9,   3,   -6,  -10, -3,  -5,
+    -3,  -13, -1,  1,   7,   5,   12,  -11, 4,   -2,  5,   -7,  -13, 9,   -9,
+    -5,  7,   1,   8,   6,   7,   -8,  7,   6,   -7,  -4,  -7,  1,   -8,  11,
+    -7,  -8,  -13, 6,   -12, -8,  2,   4,   3,   9,   10,  -5,  12,  3,   -6,
+    -5,  -6,  7,   8,   -3,  9,   -8,  2,   -12, 2,   8,   -11, -2,  -10, 3,
+    -12, -13, -7,  -9,  -11, 0,   -10, -5,  5,   -3,  11,  8,   -2,  -13, -1,
+    12,  -1,  -8,  0,   9,   -13, -11, -12, -5,  -10, -2,  -10, 11,  -3,  9,
+    -2,  -13, 2,   -3,  3,   2,   -9,  -13, -4,  0,   -4,  6,   -3,  -10, -4,
+    12,  -2,  -7,  -6,  -11, -4,  9,   6,   -3,  6,   11,  -13, 11,  -5,  5,
+    11,  11,  12,  6,   7,   -5,  12,  -2,  -1,  12,  0,   7,   -4,  -8,  -3,
+    -2,  -7,  1,   -6,  7,   -13, -12, -8,  -13, -7,  -2,  -6,  -8,  -8,  5,
+    -6,  -9,  -5,  -1,  -4,  5,   -13, 7,   -8,  10,  1,   5,   5,   -13, 1,
+    0,   10,  -13, 9,   12,  10,  -1,  5,   -8,  10,  -9,  -1,  11,  1,   -13,
+    -9,  -3,  -6,  2,   -1,  -10, 1,   12,  -13, 1,   -8,  -10, 8,   -11, 10,
+    -6,  2,   -13, 3,   -6,  7,   -13, 12,  -9,  -10, -10, -5,  -7,  -10, -8,
+    -8,  -13, 4,   -6,  8,   5,   3,   12,  8,   -13, -4,  2,   -3,  -3,  5,
+    -13, 10,  -12, 4,   -13, 5,   -1,  -9,  9,   -4,  3,   0,   3,   3,   -9,
+    -12, 1,   -6,  1,   3,   2,   4,   -8,  -10, -10, -10, 9,   8,   -13, 12,
+    12,  -8,  -12, -6,  -5,  2,   2,   3,   7,   10,  6,   11,  -8,  6,   8,
+    8,   -12, -7,  10,  -6,  5,   -3,  -9,  -3,  9,   -1,  -13, -1,  5,   -3,
+    -7,  -3,  4,   -8,  -2,  -8,  3,   4,   2,   12,  12,  2,   -5,  3,   11,
+    6,   -9,  11,  -13, 3,   -1,  7,   12,  11,  -1,  12,  4,   -3,  0,   -3,
+    6,   4,   -11, 4,   12,  2,   -4,  2,   1,   -10, -6,  -8,  1,   -13, 7,
+    -11, 1,   -13, 12,  -11, -13, 6,   0,   11,  -13, 0,   -1,  1,   4,   -13,
+    3,   -9,  -2,  -9,  8,   -6,  -3,  -13, -6,  -8,  -2,  5,   -9,  8,   10,
+    2,   7,   3,   -9,  -1,  -6,  -1,  -1,  9,   5,   11,  -2,  11,  -3,  12,
+    -8,  3,   0,   3,   5,   -1,  4,   0,   10,  3,   -6,  4,   5,   -13, 0,
+    -10, 5,   5,   8,   12,  11,  8,   9,   9,   -6,  7,   -4,  8,   -12, -10,
+    4,   -10, 9,   7,   3,   12,  4,   9,   -7,  10,  -2,  7,   0,   12,  -2,
+    -1,  -6,  0,   -11,
 };
 
-
-float block_reduce_sum(float val, __local float *data)
-{
+float block_reduce_sum(float val, __local float* data) {
     unsigned idx = get_local_id(0) * get_local_size(0) + get_local_id(1);
 
     data[idx] = val;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    for (unsigned i = get_local_size(1) / 2; i > 0; i >>= 1)
-    {
-        if (get_local_id(1) < i)
-        {
-            data[idx] += data[idx + i];
-        }
+    for (unsigned i = get_local_size(1) / 2; i > 0; i >>= 1) {
+        if (get_local_id(1) < i) { data[idx] += data[idx + i]; }
 
         barrier(CLK_LOCAL_MEM_FENCE);
     }
@@ -296,40 +103,29 @@ float block_reduce_sum(float val, __local float *data)
     return data[get_local_id(0) * get_local_size(0)];
 }
 
-__kernel void keep_features(
-    __global float* x_out,
-    __global float* y_out,
-    __global float* score_out,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const float* score_in,
-    __global const unsigned* score_idx,
-    const unsigned n_feat)
-{
+__kernel void keep_features(__global float* x_out, __global float* y_out,
+                            __global float* score_out,
+                            __global const float* x_in,
+                            __global const float* y_in,
+                            __global const float* score_in,
+                            __global const unsigned* score_idx,
+                            const unsigned n_feat) {
     unsigned f = get_global_id(0);
 
     if (f < n_feat) {
-        x_out[f] = x_in[score_idx[f]];
-        y_out[f] = y_in[score_idx[f]];
+        x_out[f]     = x_in[score_idx[f]];
+        y_out[f]     = y_in[score_idx[f]];
         score_out[f] = score_in[f];
     }
 }
 
 __kernel void harris_response(
-    __global float* x_out,
-    __global float* y_out,
-    __global float* score_out,
-    __global const float* x_in,
-    __global const float* y_in,
-    const unsigned total_feat,
-    __global unsigned* usable_feat,
-    __global const T* image,
-    KParam            iInfo,
-    const unsigned block_size,
-    const float k_thr,
-    const unsigned patch_size)
-{
-    __local float data[BLOCK_SIZE*BLOCK_SIZE];
+    __global float* x_out, __global float* y_out, __global float* score_out,
+    __global const float* x_in, __global const float* y_in,
+    const unsigned total_feat, __global unsigned* usable_feat,
+    __global const T* image, KParam iInfo, const unsigned block_size,
+    const float k_thr, const unsigned patch_size) {
+    __local float data[BLOCK_SIZE * BLOCK_SIZE];
 
     unsigned f = get_global_id(0);
 
@@ -348,22 +144,26 @@ __kernel void harris_response(
         // represents widest case possible
         unsigned patch_r = ceil(size * sqrt(2.f) / 2.f);
 
-        if (x >= patch_r && y >= patch_r && x < iInfo.dims[1] - patch_r && y < iInfo.dims[0] - patch_r) {
+        if (x >= patch_r && y >= patch_r && x < iInfo.dims[1] - patch_r &&
+            y < iInfo.dims[0] - patch_r) {
             unsigned r = block_size / 2;
 
             unsigned block_size_sq = block_size * block_size;
-            for (unsigned k = get_local_id(1); k < block_size_sq; k += get_local_size(1)) {
+            for (unsigned k = get_local_id(1); k < block_size_sq;
+                 k += get_local_size(1)) {
                 int i = k / block_size - r;
                 int j = k % block_size - r;
 
                 // Calculate local x and y derivatives
-                float ix = image[(x+i+1) * iInfo.dims[0] + y+j] - image[(x+i-1) * iInfo.dims[0] + y+j];
-                float iy = image[(x+i) * iInfo.dims[0] + y+j+1] - image[(x+i) * iInfo.dims[0] + y+j-1];
+                float ix = image[(x + i + 1) * iInfo.dims[0] + y + j] -
+                           image[(x + i - 1) * iInfo.dims[0] + y + j];
+                float iy = image[(x + i) * iInfo.dims[0] + y + j + 1] -
+                           image[(x + i) * iInfo.dims[0] + y + j - 1];
 
                 // Accumulate second order derivatives
-                ixx += ix*ix;
-                iyy += iy*iy;
-                ixy += ix*iy;
+                ixx += ix * ix;
+                iyy += iy * iy;
+                ixy += ix * iy;
             }
         }
     }
@@ -376,34 +176,30 @@ __kernel void harris_response(
     if (f < total_feat && get_local_id(1) == 0) {
         unsigned idx = atomic_inc(usable_feat);
         if (idx < total_feat) {
-            float tr = ixx + iyy;
-            float det = ixx*iyy - ixy*ixy;
+            float tr  = ixx + iyy;
+            float det = ixx * iyy - ixy * ixy;
 
             // Calculate Harris responses
-            float resp = det - k_thr * (tr*tr);
+            float resp = det - k_thr * (tr * tr);
 
             // Scale factor
             // TODO: improve scaling for responses
             float rscale = 0.001f;
-            rscale = rscale * rscale * rscale * rscale;
+            rscale       = rscale * rscale * rscale * rscale;
 
-            x_out[idx] = x;
-            y_out[idx] = y;
+            x_out[idx]     = x;
+            y_out[idx]     = y;
             score_out[idx] = resp * rscale;
         }
     }
 }
 
-__kernel void centroid_angle(
-    __global const float* x_in,
-    __global const float* y_in,
-    __global float* orientation_out,
-    const unsigned total_feat,
-    __global const T* image,
-    KParam            iInfo,
-    const unsigned patch_size)
-{
-    __local float data[BLOCK_SIZE*BLOCK_SIZE];
+__kernel void centroid_angle(__global const float* x_in,
+                             __global const float* y_in,
+                             __global float* orientation_out,
+                             const unsigned total_feat, __global const T* image,
+                             KParam iInfo, const unsigned patch_size) {
+    __local float data[BLOCK_SIZE * BLOCK_SIZE];
     unsigned f = get_global_id(0);
 
     T m01 = (T)0, m10 = (T)0;
@@ -414,14 +210,16 @@ __kernel void centroid_angle(
 
         unsigned r = patch_size / 2;
 
-        if (x >= r && y >= r && x <= iInfo.dims[1] - r && y <= iInfo.dims[0] - r) {
+        if (x >= r && y >= r && x <= iInfo.dims[1] - r &&
+            y <= iInfo.dims[0] - r) {
             unsigned patch_size_sq = patch_size * patch_size;
-            for (unsigned k = get_local_id(1); k < patch_size_sq; k += get_local_size(1)) {
+            for (unsigned k = get_local_id(1); k < patch_size_sq;
+                 k += get_local_size(1)) {
                 int i = k / patch_size - r;
                 int j = k % patch_size - r;
 
                 // Calculate first order moments
-                T p = image[(x+i) * iInfo.dims[0] + y+j];
+                T p = image[(x + i) * iInfo.dims[0] + y + j];
                 m01 += j * p;
                 m10 += i * p;
             }
@@ -433,24 +231,16 @@ __kernel void centroid_angle(
     m10 = block_reduce_sum(m10, data);
 
     if (f < total_feat && get_local_id(1) == 0) {
-        float angle = atan2(m01, m10);
+        float angle        = atan2(m01, m10);
         orientation_out[f] = angle;
     }
 }
 
-inline T get_pixel(
-    unsigned x,
-    unsigned y,
-    const float ori,
-    const unsigned size,
-    const int dist_x,
-    const int dist_y,
-    __global const T* image,
-    KParam            iInfo,
-    const unsigned patch_size)
-{
-    float ori_sin = sin(ori);
-    float ori_cos = cos(ori);
+inline T get_pixel(unsigned x, unsigned y, const float ori, const unsigned size,
+                   const int dist_x, const int dist_y, __global const T* image,
+                   KParam iInfo, const unsigned patch_size) {
+    float ori_sin   = sin(ori);
+    float ori_cos   = cos(ori);
     float patch_scl = (float)size / (float)patch_size;
 
     x += round(dist_x * patch_scl * ori_cos - dist_y * patch_scl * ori_sin);
@@ -459,57 +249,54 @@ inline T get_pixel(
     return image[x * iInfo.dims[0] + y];
 }
 
-__kernel void extract_orb(
-    __global unsigned* desc_out,
-    const unsigned n_feat,
-    __global float* x_in,
-    __global float* y_in,
-    __global float* ori_in,
-    __global float* size_out,
-    __global const T* image,
-    KParam            iInfo,
-    const float scl,
-    const unsigned patch_size)
-{
+__kernel void extract_orb(__global unsigned* desc_out, const unsigned n_feat,
+                          __global float* x_in, __global float* y_in,
+                          __global float* ori_in, __global float* size_out,
+                          __global const T* image, KParam iInfo,
+                          const float scl, const unsigned patch_size) {
     unsigned f = get_global_id(0);
 
     unsigned x, y;
 
     if (f < n_feat) {
-        x = (unsigned)round(x_in[f]);
-        y = (unsigned)round(y_in[f]);
-        float ori = ori_in[f];
+        x             = (unsigned)round(x_in[f]);
+        y             = (unsigned)round(y_in[f]);
+        float ori     = ori_in[f];
         unsigned size = patch_size;
 
         unsigned r = ceil(patch_size * sqrt(2.f) / 2.f);
 
-        if (x >= r && y >= r && x < iInfo.dims[1] - r && y < iInfo.dims[0] - r) {
-           // Descriptor fixed at 256 bits for now
+        if (x >= r && y >= r && x < iInfo.dims[1] - r &&
+            y < iInfo.dims[0] - r) {
+            // Descriptor fixed at 256 bits for now
             for (unsigned i = get_local_id(1); i < 16; i += get_local_size(1)) {
                 unsigned v = 0;
 
                 for (unsigned j = 0; j < 16; j++) {
-                    int dist_x = ref_pat[i*16*4 + j*4];
-                    int dist_y = ref_pat[i*16*4 + j*4+1];
-                    T p1 = get_pixel(x, y, ori, size, dist_x, dist_y, image, iInfo, patch_size);
-
-                    dist_x = ref_pat[i*16*4 + j*4+2];
-                    dist_y = ref_pat[i*16*4 + j*4+3];
-                    T p2 = get_pixel(x, y, ori, size, dist_x, dist_y, image, iInfo, patch_size);
-
-                    // Calculate bit based on p1 and p2 and shifts it to correct position
-                    v |= (p1 < p2) << (j + 16*(i % 2));
+                    int dist_x = ref_pat[i * 16 * 4 + j * 4];
+                    int dist_y = ref_pat[i * 16 * 4 + j * 4 + 1];
+                    T p1 = get_pixel(x, y, ori, size, dist_x, dist_y, image,
+                                     iInfo, patch_size);
+
+                    dist_x = ref_pat[i * 16 * 4 + j * 4 + 2];
+                    dist_y = ref_pat[i * 16 * 4 + j * 4 + 3];
+                    T p2   = get_pixel(x, y, ori, size, dist_x, dist_y, image,
+                                     iInfo, patch_size);
+
+                    // Calculate bit based on p1 and p2 and shifts it to correct
+                    // position
+                    v |= (p1 < p2) << (j + 16 * (i % 2));
                 }
                 // Store 16 bits of descriptor
-                atomic_add(&desc_out[f * 8 + i/2], v);
+                atomic_add(&desc_out[f * 8 + i / 2], v);
             }
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (f < n_feat && get_local_id(1) == 0) {
-        x_in[f] = round(x * scl);
-        y_in[f] = round(y * scl);
+        x_in[f]     = round(x * scl);
+        y_in[f]     = round(y * scl);
         size_out[f] = patch_size * scl;
     }
 }
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index fa6f211fc9..f19202027b 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -7,52 +7,50 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <program.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
+#include <kernel/range.hpp>
 #include <kernel/resize.hpp>
 #include <kernel/sort_by_key.hpp>
-#include <kernel/range.hpp>
 #include <kernel_headers/orb.hpp>
 #include <memory.hpp>
+#include <program.hpp>
+#include <af/defines.h>
 #include <vector>
-#include <cache.hpp>
 
 using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
 using cl::EnqueueArgs;
+using cl::Kernel;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 using std::vector;
 
 #if defined(__clang__)
-    /* Clang/LLVM */
-    #pragma clang diagnostic push
-    #pragma clang diagnostic ignored "-Wsometimes-uninitialized"
+/* Clang/LLVM */
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wsometimes-uninitialized"
 #elif defined(__ICC) || defined(__INTEL_COMPILER)
-    /* Intel ICC/ICPC */
-    // Fix the warning code here, if any
+/* Intel ICC/ICPC */
+// Fix the warning code here, if any
 #elif defined(__GNUC__) || defined(__GNUG__)
-    /* GNU GCC/G++ */
-    #pragma GCC diagnostic push
-    #pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+/* GNU GCC/G++ */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
 #elif defined(_MSC_VER)
-    /* Microsoft Visual Studio */
-    #pragma warning( push )
-    #pragma warning( disable : 4700 )
+/* Microsoft Visual Studio */
+#pragma warning(push)
+#pragma warning(disable : 4700)
 #else
-    /* Other */
+/* Other */
 #endif
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int ORB_THREADS   = 256;
 static const int ORB_THREADS_X = 16;
 static const int ORB_THREADS_Y = 16;
@@ -64,44 +62,39 @@ static const float PI_VAL = 3.14159265358979323846f;
 #define REF_PAT_SIZE 31
 #define REF_PAT_SAMPLES 256
 #define REF_PAT_COORDS 4
-#define REF_PAT_LENGTH (REF_PAT_SAMPLES*REF_PAT_COORDS)
-
+#define REF_PAT_LENGTH (REF_PAT_SAMPLES * REF_PAT_COORDS)
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * PI_VAL * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
 template<typename T>
-std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*>
-getOrbKernels()
-{
-    static const char* kernelNames[4] =
-        {"harris_response", "keep_features", "centroid_angle", "extract_orb"};
+std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*> getOrbKernels() {
+    static const char* kernelNames[4] = {"harris_response", "keep_features",
+                                         "centroid_angle", "extract_orb"};
 
     kc_entry_t entries[4];
 
     int device = getActiveDeviceId();
 
-    std::string checkName = kernelNames[0] + std::string("_") + std::string(dtype_traits<T>::getName());
+    std::string checkName = kernelNames[0] + std::string("_") +
+                            std::string(dtype_traits<T>::getName());
 
     entries[0] = kernelCache(device, checkName);
 
-    if (entries[0].prog==0 && entries[0].ker==0)
-    {
+    if (entries[0].prog == 0 && entries[0].ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D BLOCK_SIZE=" << ORB_THREADS_X;
@@ -110,45 +103,44 @@ getOrbKernels()
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {orb_cl};
-        const int   ker_lens[] = {orb_cl_len};
+        const int ker_lens[]   = {orb_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
 
-        for (int i=0; i<4; ++i)
-        {
+        for (int i = 0; i < 4; ++i) {
             entries[i].prog = new Program(prog);
             entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
-            std::string name = kernelNames[i] +
-                std::string("_") + std::string(dtype_traits<T>::getName());
+            std::string name = kernelNames[i] + std::string("_") +
+                               std::string(dtype_traits<T>::getName());
 
             addKernelToCache(device, name, entries[i]);
         }
     } else {
-        for (int i=1; i<4; ++i) {
-            std::string name = kernelNames[i] +
-                std::string("_") + std::string(dtype_traits<T>::getName());
+        for (int i = 1; i < 4; ++i) {
+            std::string name = kernelNames[i] + std::string("_") +
+                               std::string(dtype_traits<T>::getName());
 
             entries[i] = kernelCache(device, name);
         }
     }
 
-    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker, entries[3].ker);
+    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker,
+                           entries[3].ker);
 }
 
 template<typename T, typename convAccT>
 void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
          Param& ori_out, Param& size_out, Param& desc_out, Param image,
          const float fast_thr, const unsigned max_feat, const float scl_fctr,
-         const unsigned levels, const bool blur_img)
-{
+         const unsigned levels, const bool blur_img) {
     auto kernels = getOrbKernels<T>();
 
     unsigned patch_size = REF_PAT_SIZE;
 
-    unsigned min_side = std::min(image.info.dims[0], image.info.dims[1]);
+    unsigned min_side   = std::min(image.info.dims[0], image.info.dims[1]);
     unsigned max_levels = 0;
-    float scl_sum = 0.f;
+    float scl_sum       = 0.f;
     for (unsigned i = 0; i < levels; i++) {
         min_side /= scl_fctr;
 
@@ -156,7 +148,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         if (min_side < patch_size || max_levels == levels) break;
 
         max_levels++;
-        scl_sum += 1.f / (float)pow(scl_fctr,(float)i);
+        scl_sum += 1.f / (float)pow(scl_fctr, (float)i);
     }
 
     vector<cl::Buffer*> d_x_pyr(max_levels);
@@ -172,32 +164,31 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
     // Compute number of features to keep for each level
     vector<unsigned> lvl_best(max_levels);
     unsigned feat_sum = 0;
-    for (unsigned i = 0; i < max_levels-1; i++) {
-        float lvl_scl = (float)pow(scl_fctr,(float)i);
-        lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl);
+    for (unsigned i = 0; i < max_levels - 1; i++) {
+        float lvl_scl = (float)pow(scl_fctr, (float)i);
+        lvl_best[i]   = ceil((max_feat / scl_sum) / lvl_scl);
         feat_sum += lvl_best[i];
     }
-    lvl_best[max_levels-1] = max_feat - feat_sum;
+    lvl_best[max_levels - 1] = max_feat - feat_sum;
 
     // Maintain a reference to previous level image
     Param prev_img;
     Param lvl_img;
 
     const unsigned gauss_len = 9;
-    T* h_gauss = nullptr;
+    T* h_gauss               = nullptr;
     Param gauss_filter;
     gauss_filter.data = nullptr;
 
     for (unsigned i = 0; i < max_levels; i++) {
-        const float lvl_scl = (float)pow(scl_fctr,(float)i);
+        const float lvl_scl = (float)pow(scl_fctr, (float)i);
 
         if (i == 0) {
             // First level is used in its original size
             lvl_img = image;
 
             prev_img = image;
-        }
-        else if (i > 0) {
+        } else if (i > 0) {
             // Resize previous level image to current level dimensions
             lvl_img.info.dims[0] = round(image.info.dims[0] / lvl_scl);
             lvl_img.info.dims[1] = round(image.info.dims[1] / lvl_scl);
@@ -207,16 +198,17 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
             for (int k = 2; k < 4; k++) {
                 lvl_img.info.dims[k] = 1;
-                lvl_img.info.strides[k] = lvl_img.info.dims[k - 1] * lvl_img.info.strides[k - 1];
+                lvl_img.info.strides[k] =
+                    lvl_img.info.dims[k - 1] * lvl_img.info.strides[k - 1];
             }
 
             lvl_img.info.offset = 0;
-            lvl_img.data = bufferAlloc(lvl_img.info.dims[3] * lvl_img.info.strides[3] * sizeof(T));
+            lvl_img.data        = bufferAlloc(lvl_img.info.dims[3] *
+                                       lvl_img.info.strides[3] * sizeof(T));
 
             resize<T, AF_INTERP_BILINEAR>(lvl_img, prev_img);
 
-            if (i > 1)
-                bufferFree(prev_img.data);
+            if (i > 1) bufferFree(prev_img.data);
             prev_img = lvl_img;
         }
 
@@ -232,26 +224,26 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         unsigned edge = ceil(size * sqrt(2.f) / 2.f);
 
         // Detect FAST features
-        fast<T, true>(9, &lvl_feat, d_x_feat, d_y_feat, d_score_feat,
-                      lvl_img, fast_thr, 0.15f, edge);
+        fast<T, true>(9, &lvl_feat, d_x_feat, d_y_feat, d_score_feat, lvl_img,
+                      fast_thr, 0.15f, edge);
 
         if (lvl_feat == 0) {
             feat_pyr[i] = 0;
 
-            if (i > 0 && i == max_levels-1)
-                bufferFree(lvl_img.data);
+            if (i > 0 && i == max_levels - 1) bufferFree(lvl_img.data);
 
             continue;
         }
 
         bufferFree(d_score_feat.data);
 
-        unsigned usable_feat = 0;
+        unsigned usable_feat      = 0;
         cl::Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
-        getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
+        getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0,
+                                      sizeof(unsigned), &usable_feat);
 
-        cl::Buffer* d_x_harris = bufferAlloc(lvl_feat * sizeof(float));
-        cl::Buffer* d_y_harris = bufferAlloc(lvl_feat * sizeof(float));
+        cl::Buffer* d_x_harris     = bufferAlloc(lvl_feat * sizeof(float));
+        cl::Buffer* d_y_harris     = bufferAlloc(lvl_feat * sizeof(float));
         cl::Buffer* d_score_harris = bufferAlloc(lvl_feat * sizeof(float));
 
         // Calculate Harris responses
@@ -261,23 +253,23 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         const NDRange global(blk_x * ORB_THREADS_X, ORB_THREADS_Y);
 
         unsigned block_size = 7;
-        float k_thr = 0.04f;
-
-        auto hrOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                Buffer, Buffer, const unsigned,
-                                Buffer, Buffer, KParam,
-                                const unsigned, const float, const unsigned> (*std::get<0>(kernels));
-
-        hrOp(EnqueueArgs(getQueue(), global, local),
-              *d_x_harris, *d_y_harris, *d_score_harris,
-              *d_x_feat.data, *d_y_feat.data, lvl_feat,
-              *d_usable_feat, *lvl_img.data, lvl_img.info,
-              block_size, k_thr, patch_size);
+        float k_thr         = 0.04f;
+
+        auto hrOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer,
+                                  const unsigned, Buffer, Buffer, KParam,
+                                  const unsigned, const float, const unsigned>(
+            *std::get<0>(kernels));
+
+        hrOp(EnqueueArgs(getQueue(), global, local), *d_x_harris, *d_y_harris,
+             *d_score_harris, *d_x_feat.data, *d_y_feat.data, lvl_feat,
+             *d_usable_feat, *lvl_img.data, lvl_img.info, block_size, k_thr,
+             patch_size);
         CL_DEBUG_FINISH(getQueue());
 
-        getQueue().enqueueReadBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
+        getQueue().enqueueReadBuffer(*d_usable_feat, CL_TRUE, 0,
+                                     sizeof(unsigned), &usable_feat);
 
-        if (lvl_feat > 0) { //This is just to supress warnings
+        if (lvl_feat > 0) {  // This is just to supress warnings
             bufferFree(d_x_feat.data);
             bufferFree(d_y_feat.data);
             bufferFree(d_usable_feat);
@@ -290,8 +282,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
             bufferFree(d_y_harris);
             bufferFree(d_score_harris);
 
-            if (i > 0 && i == max_levels-1)
-                bufferFree(lvl_img.data);
+            if (i > 0 && i == max_levels - 1) bufferFree(lvl_img.data);
 
             continue;
         }
@@ -300,46 +291,49 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         Param d_harris_sorted;
         Param d_harris_idx;
 
-        d_harris_sorted.info.dims[0] = usable_feat;
-        d_harris_idx.info.dims[0] = usable_feat;
+        d_harris_sorted.info.dims[0]    = usable_feat;
+        d_harris_idx.info.dims[0]       = usable_feat;
         d_harris_sorted.info.strides[0] = 1;
-        d_harris_idx.info.strides[0] = 1;
+        d_harris_idx.info.strides[0]    = 1;
 
         for (int k = 1; k < 4; k++) {
             d_harris_sorted.info.dims[k] = 1;
-            d_harris_idx.info.dims[k] = 1;
-            d_harris_sorted.info.strides[k] = d_harris_sorted.info.dims[k - 1] * d_harris_sorted.info.strides[k - 1];
-            d_harris_idx.info.strides[k] = d_harris_idx.info.dims[k - 1] * d_harris_idx.info.strides[k - 1];
+            d_harris_idx.info.dims[k]    = 1;
+            d_harris_sorted.info.strides[k] =
+                d_harris_sorted.info.dims[k - 1] *
+                d_harris_sorted.info.strides[k - 1];
+            d_harris_idx.info.strides[k] = d_harris_idx.info.dims[k - 1] *
+                                           d_harris_idx.info.strides[k - 1];
         }
 
         d_harris_sorted.info.offset = 0;
-        d_harris_idx.info.offset = 0;
-        d_harris_sorted.data = d_score_harris;
+        d_harris_idx.info.offset    = 0;
+        d_harris_sorted.data        = d_score_harris;
         // Create indices using range
-        d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
+        d_harris_idx.data =
+            bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
         kernel::range<uint>(d_harris_idx, 0);
 
         kernel::sort0ByKey<float, uint>(d_harris_sorted, d_harris_idx, false);
 
-        cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
-        cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
+        cl::Buffer* d_x_lvl     = bufferAlloc(usable_feat * sizeof(float));
+        cl::Buffer* d_y_lvl     = bufferAlloc(usable_feat * sizeof(float));
         cl::Buffer* d_score_lvl = bufferAlloc(usable_feat * sizeof(float));
 
-        usable_feat = min(usable_feat, lvl_best[i]);
+        usable_feat = std::min(usable_feat, lvl_best[i]);
 
         // Keep only features with higher Harris responses
         const int keep_blk = divup(usable_feat, ORB_THREADS);
         const NDRange local_keep(ORB_THREADS, 1);
         const NDRange global_keep(keep_blk * ORB_THREADS, 1);
 
-        auto kfOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer, Buffer,
-                                const unsigned> (*std::get<1>(kernels));
+        auto kfOp =
+            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                          Buffer, const unsigned>(*std::get<1>(kernels));
 
-        kfOp(EnqueueArgs(getQueue(), global_keep, local_keep),
-              *d_x_lvl, *d_y_lvl, *d_score_lvl,
-              *d_x_harris, *d_y_harris, *d_harris_sorted.data, *d_harris_idx.data,
-              usable_feat);
+        kfOp(EnqueueArgs(getQueue(), global_keep, local_keep), *d_x_lvl,
+             *d_y_lvl, *d_score_lvl, *d_x_harris, *d_y_harris,
+             *d_harris_sorted.data, *d_harris_idx.data, usable_feat);
         CL_DEBUG_FINISH(getQueue());
 
         bufferFree(d_x_harris);
@@ -347,22 +341,22 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         bufferFree(d_harris_sorted.data);
         bufferFree(d_harris_idx.data);
 
-        cl::Buffer* d_ori_lvl = bufferAlloc(usable_feat * sizeof(float));
+        cl::Buffer* d_ori_lvl  = bufferAlloc(usable_feat * sizeof(float));
         cl::Buffer* d_size_lvl = bufferAlloc(usable_feat * sizeof(float));
 
         // Compute orientation of features
         const int centroid_blk_x = divup(usable_feat, ORB_THREADS_X);
         const NDRange local_centroid(ORB_THREADS_X, ORB_THREADS_Y);
-        const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X, ORB_THREADS_Y);
+        const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X,
+                                      ORB_THREADS_Y);
 
-        auto caOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                const unsigned, Buffer, KParam,
-                                const unsigned> (*std::get<2>(kernels));
+        auto caOp =
+            KernelFunctor<Buffer, Buffer, Buffer, const unsigned, Buffer,
+                          KParam, const unsigned>(*std::get<2>(kernels));
 
-        caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
-              *d_x_lvl, *d_y_lvl, *d_ori_lvl,
-              usable_feat, *lvl_img.data, lvl_img.info,
-              patch_size);
+        caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid), *d_x_lvl,
+             *d_y_lvl, *d_ori_lvl, usable_feat, *lvl_img.data, lvl_img.info,
+             patch_size);
         CL_DEBUG_FINISH(getQueue());
 
         Param lvl_filt;
@@ -370,29 +364,36 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         if (blur_img) {
             lvl_filt = lvl_img;
-            lvl_tmp = lvl_img;
+            lvl_tmp  = lvl_img;
 
-            lvl_filt.data = bufferAlloc(lvl_filt.info.dims[0] * lvl_filt.info.dims[1] * sizeof(T));
-            lvl_tmp.data = bufferAlloc(lvl_tmp.info.dims[0] * lvl_tmp.info.dims[1] * sizeof(T));
+            lvl_filt.data = bufferAlloc(lvl_filt.info.dims[0] *
+                                        lvl_filt.info.dims[1] * sizeof(T));
+            lvl_tmp.data  = bufferAlloc(lvl_tmp.info.dims[0] *
+                                       lvl_tmp.info.dims[1] * sizeof(T));
 
             // Calculate a separable Gaussian kernel
             if (h_gauss == nullptr) {
                 h_gauss = new T[gauss_len];
                 gaussian1D(h_gauss, gauss_len, 2.f);
-                gauss_filter.info.dims[0] = gauss_len;
+                gauss_filter.info.dims[0]    = gauss_len;
                 gauss_filter.info.strides[0] = 1;
 
                 for (int k = 1; k < 4; k++) {
                     gauss_filter.info.dims[k] = 1;
-                    gauss_filter.info.strides[k] = gauss_filter.info.dims[k - 1] * gauss_filter.info.strides[k - 1];
+                    gauss_filter.info.strides[k] =
+                        gauss_filter.info.dims[k - 1] *
+                        gauss_filter.info.strides[k - 1];
                 }
 
-                int gauss_elem = gauss_filter.info.strides[3] * gauss_filter.info.dims[3];
+                int gauss_elem =
+                    gauss_filter.info.strides[3] * gauss_filter.info.dims[3];
                 gauss_filter.data = bufferAlloc(gauss_elem * sizeof(T));
-                getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0, gauss_elem * sizeof(T), h_gauss);
+                getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0,
+                                              gauss_elem * sizeof(T), h_gauss);
             }
 
-            // Filter level image with Gaussian kernel to reduce noise sensitivity
+            // Filter level image with Gaussian kernel to reduce noise
+            // sensitivity
             convSep<T, convAccT, 0, false>(lvl_tmp, lvl_img, gauss_filter);
             convSep<T, convAccT, 1, false>(lvl_filt, lvl_tmp, gauss_filter);
 
@@ -400,54 +401,50 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         }
 
         // Compute ORB descriptors
-        cl::Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
+        cl::Buffer* d_desc_lvl =
+            bufferAlloc(usable_feat * 8 * sizeof(unsigned));
         {
             vector<unsigned> h_desc_lvl(usable_feat * 8);
-            getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0, usable_feat * 8 * sizeof(unsigned), h_desc_lvl.data());
+            getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0,
+                                          usable_feat * 8 * sizeof(unsigned),
+                                          h_desc_lvl.data());
         }
 
-        auto eoOp = KernelFunctor<Buffer, const unsigned,
-                                Buffer, Buffer, Buffer, Buffer,
-                                Buffer, KParam,
-                                const float, const unsigned> (*std::get<3>(kernels));
+        auto eoOp =
+            KernelFunctor<Buffer, const unsigned, Buffer, Buffer, Buffer,
+                          Buffer, Buffer, KParam, const float, const unsigned>(
+                *std::get<3>(kernels));
 
         if (blur_img) {
             eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
-                  *d_desc_lvl, usable_feat,
-                  *d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
-                  *lvl_filt.data, lvl_filt.info,
-                  lvl_scl, patch_size);
+                 *d_desc_lvl, usable_feat, *d_x_lvl, *d_y_lvl, *d_ori_lvl,
+                 *d_size_lvl, *lvl_filt.data, lvl_filt.info, lvl_scl,
+                 patch_size);
             CL_DEBUG_FINISH(getQueue());
 
             bufferFree(lvl_filt.data);
-        }
-        else {
+        } else {
             eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
-                  *d_desc_lvl, usable_feat,
-                  *d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
-                  *lvl_img.data, lvl_img.info,
-                  lvl_scl, patch_size);
+                 *d_desc_lvl, usable_feat, *d_x_lvl, *d_y_lvl, *d_ori_lvl,
+                 *d_size_lvl, *lvl_img.data, lvl_img.info, lvl_scl, patch_size);
             CL_DEBUG_FINISH(getQueue());
         }
 
         // Store results to pyramids
         total_feat += usable_feat;
-        feat_pyr[i] = usable_feat;
-        d_x_pyr[i] = d_x_lvl;
-        d_y_pyr[i] = d_y_lvl;
+        feat_pyr[i]    = usable_feat;
+        d_x_pyr[i]     = d_x_lvl;
+        d_y_pyr[i]     = d_y_lvl;
         d_score_pyr[i] = d_score_lvl;
-        d_ori_pyr[i] = d_ori_lvl;
-        d_size_pyr[i] = d_size_lvl;
-        d_desc_pyr[i] = d_desc_lvl;
+        d_ori_pyr[i]   = d_ori_lvl;
+        d_size_pyr[i]  = d_size_lvl;
+        d_desc_pyr[i]  = d_desc_lvl;
 
-        if (i > 0 && i == max_levels-1)
-            bufferFree(lvl_img.data);
+        if (i > 0 && i == max_levels - 1) bufferFree(lvl_img.data);
     }
 
-    if (gauss_filter.data != nullptr)
-        bufferFree(gauss_filter.data);
-    if (h_gauss != nullptr)
-        delete[] h_gauss;
+    if (gauss_filter.data != nullptr) bufferFree(gauss_filter.data);
+    if (h_gauss != nullptr) delete[] h_gauss;
 
     // If no features are found, set found features to 0 and return
     if (total_feat == 0) {
@@ -456,36 +453,42 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
     }
 
     // Allocate output memory
-    x_out.info.dims[0] = total_feat;
-    x_out.info.strides[0] = 1;
-    y_out.info.dims[0] = total_feat;
-    y_out.info.strides[0] = 1;
-    score_out.info.dims[0] = total_feat;
+    x_out.info.dims[0]        = total_feat;
+    x_out.info.strides[0]     = 1;
+    y_out.info.dims[0]        = total_feat;
+    y_out.info.strides[0]     = 1;
+    score_out.info.dims[0]    = total_feat;
     score_out.info.strides[0] = 1;
-    ori_out.info.dims[0] = total_feat;
-    ori_out.info.strides[0] = 1;
-    size_out.info.dims[0] = total_feat;
-    size_out.info.strides[0] = 1;
+    ori_out.info.dims[0]      = total_feat;
+    ori_out.info.strides[0]   = 1;
+    size_out.info.dims[0]     = total_feat;
+    size_out.info.strides[0]  = 1;
 
-    desc_out.info.dims[0] = 8;
+    desc_out.info.dims[0]    = 8;
     desc_out.info.strides[0] = 1;
-    desc_out.info.dims[1] = total_feat;
+    desc_out.info.dims[1]    = total_feat;
     desc_out.info.strides[1] = desc_out.info.dims[0];
 
     for (int k = 1; k < 4; k++) {
         x_out.info.dims[k] = 1;
-        x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
+        x_out.info.strides[k] =
+            x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
         y_out.info.dims[k] = 1;
-        y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
+        y_out.info.strides[k] =
+            y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
         score_out.info.dims[k] = 1;
-        score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
+        score_out.info.strides[k] =
+            score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
         ori_out.info.dims[k] = 1;
-        ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
+        ori_out.info.strides[k] =
+            ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
         size_out.info.dims[k] = 1;
-        size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
+        size_out.info.strides[k] =
+            size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
         if (k > 1) {
             desc_out.info.dims[k] = 1;
-            desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
+            desc_out.info.strides[k] =
+                desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
         }
     }
 
@@ -503,18 +506,28 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
     unsigned offset = 0;
     for (unsigned i = 0; i < max_levels; i++) {
-        if (feat_pyr[i] == 0)
-            continue;
-
-        if (i > 0)
-            offset += feat_pyr[i-1];
-
-        getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_score_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*8*sizeof(unsigned), feat_pyr[i] * 8 * sizeof(unsigned));
+        if (feat_pyr[i] == 0) continue;
+
+        if (i > 0) offset += feat_pyr[i - 1];
+
+        getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_score_pyr[i], *score_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0,
+                                     offset * 8 * sizeof(unsigned),
+                                     feat_pyr[i] * 8 * sizeof(unsigned));
 
         bufferFree(d_x_pyr[i]);
         bufferFree(d_y_pyr[i]);
@@ -527,21 +540,21 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
     // Sets number of output features
     *out_feat = total_feat;
 }
-} //namespace kernel
-} //namespace opencl
+}  // namespace kernel
+}  // namespace opencl
 
 #if defined(__clang__)
-    /* Clang/LLVM */
-    #pragma clang diagnostic pop
+/* Clang/LLVM */
+#pragma clang diagnostic pop
 #elif defined(__ICC) || defined(__INTEL_COMPILER)
-    /* Intel ICC/ICPC */
-    // Fix the warning code here, if any
+/* Intel ICC/ICPC */
+// Fix the warning code here, if any
 #elif defined(__GNUC__) || defined(__GNUG__)
-    /* GNU GCC/G++ */
-    #pragma GCC diagnostic pop
+/* GNU GCC/G++ */
+#pragma GCC diagnostic pop
 #elif defined(_MSC_VER)
-    /* Microsoft Visual Studio */
-    #pragma warning( pop )
+/* Microsoft Visual Studio */
+#pragma warning(pop)
 #else
-    /* Other */
+/* Other */
 #endif
diff --git a/src/backend/opencl/kernel/pad_array_borders.cl b/src/backend/opencl/kernel/pad_array_borders.cl
index e1c28c0700..766810b030 100644
--- a/src/backend/opencl/kernel/pad_array_borders.cl
+++ b/src/backend/opencl/kernel/pad_array_borders.cl
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if AF_BORDER_TYPE==AF_PAD_SYM
+#if AF_BORDER_TYPE == AF_PAD_SYM
 
-int idxByndEdge(const int i, const int lb, const int len)
-{
-    if (i < lb || i>= (lb+len)) {
-        return (len-1) - ((i-lb)%len);
+int idxByndEdge(const int i, const int lb, const int len) {
+    if (i < lb || i >= (lb + len)) {
+        return (len - 1) - ((i - lb) % len);
     } else
         return i - lb;
 }
 
-#elif AF_BORDER_TYPE==AF_PAD_CLAMP_TO_EDGE
+#elif AF_BORDER_TYPE == AF_PAD_CLAMP_TO_EDGE
 
-int idxByndEdge(const int i, const int lb, const int len)
-{
-    return clamp(i-lb, 0, len-1);
+int idxByndEdge(const int i, const int lb, const int len) {
+    return clamp(i - lb, 0, len - 1);
 }
 
 #else
@@ -30,23 +28,18 @@ int idxByndEdge(const int i, const int lb, const int len)
 
 #endif
 
-__kernel
-void padBorders(__global T *       out,
-                KParam             oInfo,
-                __global const T * in,
-                KParam             iInfo,
-                int l0, int l1, int l2, int l3,
-                unsigned blk_x, unsigned blk_y)
-{
+__kernel void padBorders(__global T* out, KParam oInfo, __global const T* in,
+                         KParam iInfo, int l0, int l1, int l2, int l3,
+                         unsigned blk_x, unsigned blk_y) {
     const int lx = get_local_id(0);
     const int ly = get_local_id(1);
     const int k  = get_group_id(0) / blk_x;
     const int l  = get_group_id(1) / blk_y;
 
-    const int blockIdx_x = get_group_id(0) - (blk_x) * k;
-    const int blockIdx_y = get_group_id(1) - (blk_y) * l;
-    const int i = blockIdx_x * get_local_size(0) + lx;
-    const int j = blockIdx_y * get_local_size(1) + ly;
+    const int blockIdx_x = get_group_id(0) - (blk_x)*k;
+    const int blockIdx_y = get_group_id(1) - (blk_y)*l;
+    const int i          = blockIdx_x * get_local_size(0) + lx;
+    const int j          = blockIdx_y * get_local_size(1) + ly;
 
     const int d0 = iInfo.dims[0];
     const int d1 = iInfo.dims[1];
@@ -57,13 +50,12 @@ void padBorders(__global T *       out,
     const int s2 = iInfo.strides[2];
     const int s3 = iInfo.strides[3];
 
-    __global const T * src = in + iInfo.offset;
-    __global       T * dst = out;
+    __global const T* src = in + iInfo.offset;
+    __global T* dst       = out;
 
-    bool isNotPadding = ( l>=l3 && l<(d3+l3) ) &&
-                        ( k>=l2 && k<(d2+l2) ) &&
-                        ( j>=l1 && j<(d1+l1) ) &&
-                        ( i>=l0 && i<(d0+l0) );
+    bool isNotPadding =
+        (l >= l3 && l < (d3 + l3)) && (k >= l2 && k < (d2 + l2)) &&
+        (j >= l1 && j < (d1 + l1)) && (i >= l0 && i < (d0 + l0));
     T value = (T)0;
 
     if (isNotPadding) {
@@ -72,7 +64,7 @@ void padBorders(__global T *       out,
         unsigned iJOff = (j - l1) * s1;
         unsigned iIOff = (i - l0) * s0;
 
-        value = src[ iLOff + iKOff + iJOff + iIOff ];
+        value = src[iLOff + iKOff + iJOff + iIOff];
     } else {
 #if !defined(DEFAULT_BORDER)
         unsigned iLOff = idxByndEdge(l, l3, d3) * s3;
@@ -80,14 +72,14 @@ void padBorders(__global T *       out,
         unsigned iJOff = idxByndEdge(j, l1, d1) * s1;
         unsigned iIOff = idxByndEdge(i, l0, d0) * s0;
 
-        value = src[ iLOff + iKOff + iJOff + iIOff ];
+        value = src[iLOff + iKOff + iJOff + iIOff];
 #endif
     }
 
-    if (i<oInfo.dims[0] && j<oInfo.dims[1] &&
-        k<oInfo.dims[2] && l<oInfo.dims[3]) {
-        unsigned offset = l*oInfo.strides[3] + k*oInfo.strides[2] +
-                          j*oInfo.strides[1] + i*oInfo.strides[0];
-        dst[ offset ] = value;
+    if (i < oInfo.dims[0] && j < oInfo.dims[1] && k < oInfo.dims[2] &&
+        l < oInfo.dims[3]) {
+        unsigned offset = l * oInfo.strides[3] + k * oInfo.strides[2] +
+                          j * oInfo.strides[1] + i * oInfo.strides[0];
+        dst[offset] = value;
     }
 }
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index e710fe0cc4..cc82da65f0 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -8,51 +8,48 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/pad_array_borders.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int PADB_THREADS_X = 16;
 static const int PADB_THREADS_Y = 16;
 
 template<typename T, af_border_type BType>
-void padBorders(Param out, const Param in, dim4 const& lBPadding)
-{
+void padBorders(Param out, const Param in, dim4 const& lBPadding) {
     std::string refName = std::string("padBorders_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(BType);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(BType);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-                << " -D AF_BORDER_TYPE="<< BType
-                << " -D AF_PAD_SYM="<< AF_PAD_SYM
-                << " -D AF_PAD_CLAMP_TO_EDGE="<< AF_PAD_CLAMP_TO_EDGE;
+                << " -D AF_BORDER_TYPE=" << BType
+                << " -D AF_PAD_SYM=" << AF_PAD_SYM
+                << " -D AF_PAD_CLAMP_TO_EDGE=" << AF_PAD_CLAMP_TO_EDGE;
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {pad_array_borders_cl};
-        const int   ker_lens[] = {pad_array_borders_cl_len};
+        const int ker_lens[]   = {pad_array_borders_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -69,15 +66,15 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding)
     NDRange global(blk_x * out.info.dims[2] * local[0],
                    blk_y * out.info.dims[3] * local[1]);
 
-    auto padOP = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                               unsigned, unsigned, unsigned,
-                               unsigned, int, int> (*entry.ker);
+    auto padOP =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, unsigned, unsigned,
+                      unsigned, unsigned, int, int>(*entry.ker);
 
-    padOP(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-            *in.data, in.info, lBPadding[0], lBPadding[1],
-            lBPadding[2], lBPadding[3], blk_x, blk_y);
+    padOP(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+          in.info, lBPadding[0], lBPadding[1], lBPadding[2], lBPadding[3],
+          blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 41d16a3fc2..29dc16891c 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -9,221 +9,223 @@
 
 #pragma once
 
-#include <platform.hpp>
-#include <af/defines.h>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/random_engine_philox.hpp>
 #include <kernel_headers/random_engine_threefry.hpp>
 #include <kernel_headers/random_engine_write.hpp>
+#include <platform.hpp>
+#include <program.hpp>
+#include <random_engine.hpp>
 #include <traits.hpp>
+#include <type_util.hpp>
+#include <af/defines.h>
 #include <sstream>
 #include <string>
-#include <common/dispatch.hpp>
-#include <err_opencl.hpp>
-#include <debug_opencl.hpp>
-#include <program.hpp>
-#include <type_util.hpp>
-#include <cache.hpp>
-#include <random_engine.hpp>
 #include "config.hpp"
 
-#include <kernel_headers/random_engine_mersenne_init.hpp>
 #include <kernel_headers/random_engine_mersenne.hpp>
+#include <kernel_headers/random_engine_mersenne_init.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-static const int N = 351;
+static const int N          = 351;
 static const int TABLE_SIZE = 16;
 static const int MAX_BLOCKS = 32;
-static const int STATE_SIZE = (256*3);
-
-namespace opencl
-{
-    namespace kernel
-    {
-        static const uint THREADS = 256;
-
-        template <typename T>
-        static Kernel get_random_engine_kernel(const af_random_engine_type type, const int kerIdx, const uint elementsPerBlock)
-        {
-            using std::string;
-            using std::to_string;
-            string engineName;
-            const char *ker_strs[2];
-            int ker_lens[2];
-            ker_strs[0] = random_engine_write_cl;
-            ker_lens[0] = random_engine_write_cl_len;
-            switch (type) {
-                case AF_RANDOM_ENGINE_PHILOX_4X32_10   :
-                    engineName = "Philox";
-                    ker_strs[1] = random_engine_philox_cl;
-                    ker_lens[1] = random_engine_philox_cl_len;
-                    break;
-                case AF_RANDOM_ENGINE_THREEFRY_2X32_16 :
-                    engineName = "Threefry";
-                    ker_strs[1] = random_engine_threefry_cl;
-                    ker_lens[1] = random_engine_threefry_cl_len;
-                    break;
-                case AF_RANDOM_ENGINE_MERSENNE_GP11213 :
-                    engineName = "Mersenne";
-                    ker_strs[1] = random_engine_mersenne_cl;
-                    ker_lens[1] = random_engine_mersenne_cl_len;
-                    break;
-                default :
-                    AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
-            }
-
-            string ref_name =
-                "random_engine_kernel_" + engineName +
-                "_" + string(dtype_traits<T>::getName()) +
-                "_" + to_string(kerIdx);
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName()
-                        << " -D THREADS=" << THREADS
-                        << " -D RAND_DIST=" << kerIdx;
-                if (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-                    options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
-                }
-                if (std::is_same<T, double>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-#if defined(OS_MAC) // Because apple is "special"
-                options << " -D IS_APPLE"
-                        << " -D log10_val=" << std::log(10.0);
-#endif
-                cl::Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "generate");
+static const int STATE_SIZE = (256 * 3);
+
+namespace opencl {
+namespace kernel {
+static const uint THREADS = 256;
+
+template<typename T>
+static Kernel get_random_engine_kernel(const af_random_engine_type type,
+                                       const int kerIdx,
+                                       const uint elementsPerBlock) {
+    using std::string;
+    using std::to_string;
+    string engineName;
+    const char *ker_strs[2];
+    int ker_lens[2];
+    ker_strs[0] = random_engine_write_cl;
+    ker_lens[0] = random_engine_write_cl_len;
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            engineName  = "Philox";
+            ker_strs[1] = random_engine_philox_cl;
+            ker_lens[1] = random_engine_philox_cl_len;
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            engineName  = "Threefry";
+            ker_strs[1] = random_engine_threefry_cl;
+            ker_lens[1] = random_engine_threefry_cl_len;
+            break;
+        case AF_RANDOM_ENGINE_MERSENNE_GP11213:
+            engineName  = "Mersenne";
+            ker_strs[1] = random_engine_mersenne_cl;
+            ker_lens[1] = random_engine_mersenne_cl_len;
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 
-                addKernelToCache(device, ref_name, entry);
-            }
+    string ref_name = "random_engine_kernel_" + engineName + "_" +
+                      string(dtype_traits<T>::getName()) + "_" +
+                      to_string(kerIdx);
+    int device = getActiveDeviceId();
 
-            return *entry.ker;
-        }
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-        static Kernel get_mersenne_init_kernel(void)
-        {
-            using std::string;
-            using std::to_string;
-            string engineName;
-            const char *ker_str = random_engine_mersenne_init_cl;
-            int ker_len = random_engine_mersenne_init_cl_len;
-            string ref_name = "mersenne_init";
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                std::string emptyOptionString;
-                cl::Program prog;
-                buildProgram(prog, 1, &ker_str, &ker_len, emptyOptionString);
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "initState");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            return *entry.ker;
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D THREADS=" << THREADS << " -D RAND_DIST=" << kerIdx;
+        if (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) {
+            options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
         }
+        if (std::is_same<T, double>::value) { options << " -D USE_DOUBLE"; }
+#if defined(OS_MAC)  // Because apple is "special"
+        options << " -D IS_APPLE"
+                << " -D log10_val=" << std::log(10.0);
+#endif
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "generate");
 
-        template <typename T>
-        static void randomDistribution(cl::Buffer out, const size_t elements,
-                const af_random_engine_type type, const uintl &seed, uintl &counter, int kerIdx)
-        {
-            uint elementsPerBlock = THREADS*4*sizeof(uint)/sizeof(T);
-            uint groups = divup(elements, elementsPerBlock);
-
-            uint hi = seed>>32;
-            uint lo = seed;
-	    uint hic = counter>>32;
-	    uint loc = counter;
-
-            NDRange local(THREADS, 1);
-            NDRange global(THREADS * groups, 1);
-
-            if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) || (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
-                Kernel ker = get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
-                auto randomEngineOp = KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
-                randomEngineOp(EnqueueArgs(getQueue(), global, local),
-			       out, elements, hic, loc, hi, lo);
-            }
-
-            counter += elements;
-            CL_DEBUG_FINISH(getQueue());
-        }
+        addKernelToCache(device, ref_name, entry);
+    }
 
-        template <typename T>
-        void randomDistribution(cl::Buffer out, const size_t elements,
-                cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
-                const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table,
-                int kerIdx)
-        {
-            int threads = THREADS;
-            int min_elements_per_block = 32*THREADS*4*sizeof(uint)/sizeof(T);
-            int blocks = divup(elements, min_elements_per_block);
-            blocks = (blocks > MAX_BLOCKS)? MAX_BLOCKS : blocks;
-            int elementsPerBlock = divup(elements, blocks);
-
-            NDRange local(threads, 1);
-            NDRange global(threads * blocks, 1);
-            Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213, kerIdx, elementsPerBlock);
-            auto randomEngineOp = KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
-                  uint, cl::Buffer, cl::Buffer, uint, uint>(ker);
-            randomEngineOp(EnqueueArgs(getQueue(), global, local),
-                    out, state, pos, sh1, sh2, mask, recursion_table, temper_table, elementsPerBlock, elements);
-            CL_DEBUG_FINISH(getQueue());
-        }
+    return *entry.ker;
+}
 
-        template <typename T>
-        void uniformDistributionCBRNG(cl::Buffer out, const size_t elements,
-                const af_random_engine_type type, const uintl &seed, uintl &counter)
-        {
-            randomDistribution<T>(out, elements, type, seed, counter, 0);
-        }
+static Kernel get_mersenne_init_kernel(void) {
+    using std::string;
+    using std::to_string;
+    string engineName;
+    const char *ker_str = random_engine_mersenne_init_cl;
+    int ker_len         = random_engine_mersenne_init_cl_len;
+    string ref_name     = "mersenne_init";
+    int device          = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::string emptyOptionString;
+        cl::Program prog;
+        buildProgram(prog, 1, &ker_str, &ker_len, emptyOptionString);
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "initState");
+
+        addKernelToCache(device, ref_name, entry);
+    }
 
-        template <typename T>
-        void normalDistributionCBRNG(cl::Buffer out, const size_t elements,
-                const af_random_engine_type type, const uintl &seed, uintl &counter)
-        {
-            randomDistribution<T>(out, elements, type, seed, counter, 1);
-        }
+    return *entry.ker;
+}
 
-        template <typename T>
-        void uniformDistributionMT(cl::Buffer out, const size_t elements,
-                cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
-                const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table)
-        {
-            randomDistribution<T>(out, elements, state, pos, sh1, sh2, mask, recursion_table, temper_table, 0);
-        }
+template<typename T>
+static void randomDistribution(cl::Buffer out, const size_t elements,
+                               const af_random_engine_type type,
+                               const uintl &seed, uintl &counter, int kerIdx) {
+    uint elementsPerBlock = THREADS * 4 * sizeof(uint) / sizeof(T);
+    uint groups           = divup(elements, elementsPerBlock);
+
+    uint hi  = seed >> 32;
+    uint lo  = seed;
+    uint hic = counter >> 32;
+    uint loc = counter;
+
+    NDRange local(THREADS, 1);
+    NDRange global(THREADS * groups, 1);
+
+    if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) ||
+        (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
+        Kernel ker =
+            get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
+        auto randomEngineOp =
+            KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
+        randomEngineOp(EnqueueArgs(getQueue(), global, local), out, elements,
+                       hic, loc, hi, lo);
+    }
 
-        template <typename T>
-        void normalDistributionMT(cl::Buffer out, const size_t elements,
-                cl::Buffer state, cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
-                const uint mask, cl::Buffer recursion_table, cl::Buffer temper_table)
-        {
-            randomDistribution<T>(out, elements, state, pos, sh1, sh2, mask, recursion_table, temper_table, 1);
-        }
+    counter += elements;
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed)
-        {
-            NDRange local(THREADS_PER_GROUP, 1);
-            NDRange global(local[0] * MAX_BLOCKS, 1);
+template<typename T>
+void randomDistribution(cl::Buffer out, const size_t elements, cl::Buffer state,
+                        cl::Buffer pos, cl::Buffer sh1, cl::Buffer sh2,
+                        const uint mask, cl::Buffer recursion_table,
+                        cl::Buffer temper_table, int kerIdx) {
+    int threads                = THREADS;
+    int min_elements_per_block = 32 * THREADS * 4 * sizeof(uint) / sizeof(T);
+    int blocks                 = divup(elements, min_elements_per_block);
+    blocks                     = (blocks > MAX_BLOCKS) ? MAX_BLOCKS : blocks;
+    int elementsPerBlock       = divup(elements, blocks);
+
+    NDRange local(threads, 1);
+    NDRange global(threads * blocks, 1);
+    Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213,
+                                             kerIdx, elementsPerBlock);
+    auto randomEngineOp =
+        KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
+                      cl::Buffer, uint, cl::Buffer, cl::Buffer, uint, uint>(
+            ker);
+    randomEngineOp(EnqueueArgs(getQueue(), global, local), out, state, pos, sh1,
+                   sh2, mask, recursion_table, temper_table, elementsPerBlock,
+                   elements);
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            Kernel ker = get_mersenne_init_kernel();
-            auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
-            initOp(EnqueueArgs(getQueue(), global, local), state, table, seed);
-            CL_DEBUG_FINISH(getQueue());
-        }
-    }
+template<typename T>
+void uniformDistributionCBRNG(cl::Buffer out, const size_t elements,
+                              const af_random_engine_type type,
+                              const uintl &seed, uintl &counter) {
+    randomDistribution<T>(out, elements, type, seed, counter, 0);
+}
+
+template<typename T>
+void normalDistributionCBRNG(cl::Buffer out, const size_t elements,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter) {
+    randomDistribution<T>(out, elements, type, seed, counter, 1);
+}
+
+template<typename T>
+void uniformDistributionMT(cl::Buffer out, const size_t elements,
+                           cl::Buffer state, cl::Buffer pos, cl::Buffer sh1,
+                           cl::Buffer sh2, const uint mask,
+                           cl::Buffer recursion_table,
+                           cl::Buffer temper_table) {
+    randomDistribution<T>(out, elements, state, pos, sh1, sh2, mask,
+                          recursion_table, temper_table, 0);
+}
+
+template<typename T>
+void normalDistributionMT(cl::Buffer out, const size_t elements,
+                          cl::Buffer state, cl::Buffer pos, cl::Buffer sh1,
+                          cl::Buffer sh2, const uint mask,
+                          cl::Buffer recursion_table, cl::Buffer temper_table) {
+    randomDistribution<T>(out, elements, state, pos, sh1, sh2, mask,
+                          recursion_table, temper_table, 1);
+}
+
+void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
+    NDRange local(THREADS_PER_GROUP, 1);
+    NDRange global(local[0] * MAX_BLOCKS, 1);
+
+    Kernel ker  = get_mersenne_init_kernel();
+    auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
+    initOp(EnqueueArgs(getQueue(), global, local), state, table, seed);
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index 05a328f25b..24be51e47d 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -44,112 +44,112 @@
 
 #define N 351
 #define TABLE_SIZE 16
-#define STATE_SIZE (256*3)
+#define STATE_SIZE (256 * 3)
 
-#define divup(NUM, DEN) (((NUM) + (DEN) - 1)/(DEN));
+#define divup(NUM, DEN) (((NUM) + (DEN)-1) / (DEN));
 
-void read_table(__local uint * const localTable, __global const uint * const table)
-{
-    __global const uint * const t = table + (get_group_id(0) * TABLE_SIZE);
+void read_table(__local uint *const localTable,
+                __global const uint *const table) {
+    __global const uint *const t = table + (get_group_id(0) * TABLE_SIZE);
     if (get_local_id(0) < TABLE_SIZE) {
         localTable[get_local_id(0)] = t[get_local_id(0)];
     }
 }
 
-void state_read(__local uint * const localState, __global const uint * const state)
-{
-    __global const uint * const g = state + (get_group_id(0) * N);
+void state_read(__local uint *const localState,
+                __global const uint *const state) {
+    __global const uint *const g = state + (get_group_id(0) * N);
     localState[STATE_SIZE - N + get_local_id(0)] = g[get_local_id(0)];
     if (get_local_id(0) < N - THREADS) {
-        localState[STATE_SIZE - N + THREADS + get_local_id(0)] = g[THREADS + get_local_id(0)];
+        localState[STATE_SIZE - N + THREADS + get_local_id(0)] =
+            g[THREADS + get_local_id(0)];
     }
 }
 
-void state_write(__global uint * const state, __local const uint * const localState)
-{
-    __global uint * const g = state + (get_group_id(0) * N);
-    g[get_local_id(0)] = localState[STATE_SIZE - N + get_local_id(0)];
+void state_write(__global uint *const state,
+                 __local const uint *const localState) {
+    __global uint *const g = state + (get_group_id(0) * N);
+    g[get_local_id(0)]     = localState[STATE_SIZE - N + get_local_id(0)];
     if (get_local_id(0) < N - THREADS) {
-        g[THREADS + get_local_id(0)] = localState[STATE_SIZE - N + THREADS + get_local_id(0)];
+        g[THREADS + get_local_id(0)] =
+            localState[STATE_SIZE - N + THREADS + get_local_id(0)];
     }
 }
 
-uint recursion(__local const uint * const recursion_table, const uint mask,
-        const uint sh1, const uint sh2, const uint x1, const uint x2, uint y)
-{
+uint recursion(__local const uint *const recursion_table, const uint mask,
+               const uint sh1, const uint sh2, const uint x1, const uint x2,
+               uint y) {
     uint x = (x1 & mask) ^ x2;
     x ^= x << sh1;
-    y = x ^ (y >> sh2);
+    y        = x ^ (y >> sh2);
     uint mat = recursion_table[y & 0x0f];
     return y ^ mat;
 }
 
-uint temper(__local const uint * const temper_table, const uint v, uint t)
-{
-     t ^= t >> 16;
-     t ^= t >> 8;
-     uint mat = temper_table[t & 0x0f];
-     return v ^ mat;
+uint temper(__local const uint *const temper_table, const uint v, uint t) {
+    t ^= t >> 16;
+    t ^= t >> 8;
+    uint mat = temper_table[t & 0x0f];
+    return v ^ mat;
 }
 
-__kernel void generate(__global T *output,
-        __global uint * const state,
-        __global const uint * const pos_tbl,
-        __global const uint * const sh1_tbl,
-        __global const uint * const sh2_tbl,
-        uint mask,
-        __global const uint * const recursion_table,
-        __global const uint * const temper_table,
-        uint elements_per_block, uint elements)
-{
+__kernel void generate(__global T *output, __global uint *const state,
+                       __global const uint *const pos_tbl,
+                       __global const uint *const sh1_tbl,
+                       __global const uint *const sh2_tbl, uint mask,
+                       __global const uint *const recursion_table,
+                       __global const uint *const temper_table,
+                       uint elements_per_block, uint elements) {
     __local uint l_state[STATE_SIZE];
     __local uint l_recursion_table[TABLE_SIZE];
     __local uint l_temper_table[TABLE_SIZE];
-    uint start = get_group_id(0)*elements_per_block;
-    uint end = start + elements_per_block;
-    end = (end > elements)? elements : end;
-    int iter = divup((end - start)*sizeof(T), THREADS*4*sizeof(uint));
-    uint pos = pos_tbl[get_group_id(0)];
-    uint sh1 = sh1_tbl[get_group_id(0)];
-    uint sh2 = sh2_tbl[get_group_id(0)];
+    uint start = get_group_id(0) * elements_per_block;
+    uint end   = start + elements_per_block;
+    end        = (end > elements) ? elements : end;
+    int iter   = divup((end - start) * sizeof(T), THREADS * 4 * sizeof(uint));
+    uint pos   = pos_tbl[get_group_id(0)];
+    uint sh1   = sh1_tbl[get_group_id(0)];
+    uint sh2   = sh2_tbl[get_group_id(0)];
 
     state_read(l_state, state);
     read_table(l_recursion_table, recursion_table);
     read_table(l_temper_table, temper_table);
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    uint index = start;
-    int elementsPerBlockIteration = THREADS*4*sizeof(uint)/sizeof(T);
+    uint index                    = start;
+    int elementsPerBlockIteration = THREADS * 4 * sizeof(uint) / sizeof(T);
     uint o[4];
-    int offsetX1 = (STATE_SIZE - N + get_local_id(0)          ) % STATE_SIZE;
-    int offsetX2 = (STATE_SIZE - N + get_local_id(0) + 1      ) % STATE_SIZE;
-    int offsetY  = (STATE_SIZE - N + get_local_id(0) + pos    ) % STATE_SIZE;
+    int offsetX1 = (STATE_SIZE - N + get_local_id(0)) % STATE_SIZE;
+    int offsetX2 = (STATE_SIZE - N + get_local_id(0) + 1) % STATE_SIZE;
+    int offsetY  = (STATE_SIZE - N + get_local_id(0) + pos) % STATE_SIZE;
     int offsetT  = (STATE_SIZE - N + get_local_id(0) + pos - 1) % STATE_SIZE;
     int offsetO  = get_local_id(0);
 
     for (int i = 0; i < iter; ++i) {
         for (int ii = 0; ii < 4; ++ii) {
-            uint r = recursion(l_recursion_table, mask, sh1, sh2,
-                    l_state[offsetX1],
-                    l_state[offsetX2],
-                    l_state[offsetY ]);
+            uint r =
+                recursion(l_recursion_table, mask, sh1, sh2, l_state[offsetX1],
+                          l_state[offsetX2], l_state[offsetY]);
             l_state[offsetO] = r;
-            o[ii] = temper(l_temper_table, r, l_state[offsetT]);
+            o[ii]            = temper(l_temper_table, r, l_state[offsetT]);
             offsetX1 += THREADS;
             offsetX2 += THREADS;
-            offsetY  += THREADS;
-            offsetT  += THREADS;
-            offsetO  += THREADS;
-            offsetX1 = (offsetX1 >= STATE_SIZE)? offsetX1 - STATE_SIZE : offsetX1;
-            offsetX2 = (offsetX2 >= STATE_SIZE)? offsetX2 - STATE_SIZE : offsetX2;
-            offsetY  = (offsetY  >= STATE_SIZE)? offsetY  - STATE_SIZE : offsetY ;
-            offsetT  = (offsetT  >= STATE_SIZE)? offsetT  - STATE_SIZE : offsetT ;
-            offsetO  = (offsetO  >= STATE_SIZE)? offsetO  - STATE_SIZE : offsetO ;
+            offsetY += THREADS;
+            offsetT += THREADS;
+            offsetO += THREADS;
+            offsetX1 =
+                (offsetX1 >= STATE_SIZE) ? offsetX1 - STATE_SIZE : offsetX1;
+            offsetX2 =
+                (offsetX2 >= STATE_SIZE) ? offsetX2 - STATE_SIZE : offsetX2;
+            offsetY = (offsetY >= STATE_SIZE) ? offsetY - STATE_SIZE : offsetY;
+            offsetT = (offsetT >= STATE_SIZE) ? offsetT - STATE_SIZE : offsetT;
+            offsetO = (offsetO >= STATE_SIZE) ? offsetO - STATE_SIZE : offsetO;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
         uint writeIndex = index + get_local_id(0);
         if (i == iter - 1) {
-            PARTIAL_WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3], &elements);
+            PARTIAL_WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3],
+                          &elements);
         } else {
             WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3]);
         }
@@ -157,4 +157,3 @@ __kernel void generate(__global T *output,
     }
     state_write(state, l_state);
 }
-
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index 9b931e474f..de4db1a03e 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -45,36 +45,35 @@
 #define N 351
 #define TABLE_SIZE 16
 
-__kernel void initState(__global uint *state, __global uint *tbl, ulong seed)
-{
-    int tid = get_local_id(0);
+__kernel void initState(__global uint *state, __global uint *tbl, ulong seed) {
+    int tid      = get_local_id(0);
     int nthreads = get_local_size(0);
-    int gid = get_group_id(0);
+    int gid      = get_group_id(0);
     __local uint lstate[N];
-    const __global uint *ltbl = tbl + (TABLE_SIZE*gid);
-    uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
-    uint tmp = hidden_seed;
+    const __global uint *ltbl = tbl + (TABLE_SIZE * gid);
+    uint hidden_seed          = ltbl[4] ^ (ltbl[8] << 16);
+    uint tmp                  = hidden_seed;
     tmp += tmp >> 16;
     tmp += tmp >> 8;
     tmp &= 0xff;
     tmp |= tmp << 8;
     tmp |= tmp << 16;
 
-    for (int id = tid; id < N; id += nthreads) {
-        lstate[id] = tmp;
-    }
+    for (int id = tid; id < N; id += nthreads) { lstate[id] = tmp; }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (tid == 0) {
         lstate[0] = seed;
         lstate[1] = hidden_seed;
         for (int i = 1; i < N; ++i) {
-            lstate[i] ^= (uint)(1812433253) * (lstate[i-1] ^ (lstate[i-1] >> 30)) + i;
+            lstate[i] ^=
+                (uint)(1812433253) * (lstate[i - 1] ^ (lstate[i - 1] >> 30)) +
+                i;
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     for (int id = tid; id < N; id += nthreads) {
-        state[N*gid + id] = lstate[id];
+        state[N * gid + id] = lstate[id];
     }
 }
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index 7e67309eb3..46bd9964cf 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -45,59 +45,63 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
-//Utils
-//Source of these constants :
-//github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
 
 #define m4x32_0 0xD2511F53
 #define m4x32_1 0xCD9E8D57
 #define w32_0 0x9E3779B9
 #define w32_1 0xBB67AE85
 
-void mulhilo(const uint a, const uint b, uint * const hi, uint * const lo)
-{
+void mulhilo(const uint a, const uint b, uint *const hi, uint *const lo) {
     *hi = mul_hi(a, b);
-    *lo = a*b;
+    *lo = a * b;
 }
 
-void philoxBump(uint k[2])
-{
+void philoxBump(uint k[2]) {
     k[0] += w32_0;
     k[1] += w32_1;
 }
 
-void philoxRound(const uint k[2], uint c[4])
-{
+void philoxRound(const uint k[2], uint c[4]) {
     uint hi0, lo0, hi1, lo1;
     mulhilo(m4x32_0, c[0], &hi0, &lo0);
     mulhilo(m4x32_1, c[2], &hi1, &lo1);
-    c[0] = hi1^c[1]^k[0];
+    c[0] = hi1 ^ c[1] ^ k[0];
     c[1] = lo1;
-    c[2] = hi0^c[3]^k[1];
+    c[2] = hi0 ^ c[3] ^ k[1];
     c[3] = lo0;
 }
 
-void philox(uint key[2], uint ctr[4])
-{
-    //10 Rounds
-                       philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
-    philoxBump(key);   philoxRound(key, ctr);
+void philox(uint key[2], uint ctr[4]) {
+    // 10 Rounds
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
+    philoxBump(key);
+    philoxRound(key, ctr);
 }
 
-__kernel void generate(__global T *output, unsigned elements,
-        unsigned hic, unsigned loc, unsigned hi, unsigned lo)
-{
-    unsigned gid = get_group_id(0);
-    unsigned off = get_local_size(0);
-    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
+__kernel void generate(__global T *output, unsigned elements, unsigned hic,
+                       unsigned loc, unsigned hi, unsigned lo) {
+    unsigned gid   = get_group_id(0);
+    unsigned off   = get_local_size(0);
+    unsigned index = gid * ELEMENTS_PER_BLOCK + get_local_id(0);
 
     uint key[2] = {lo, hi};
     uint ctr[4] = {loc, hic, 0, 0};
@@ -110,7 +114,7 @@ __kernel void generate(__global T *output, unsigned elements,
     if (gid != get_num_groups(0) - 1) {
         WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3]);
     } else {
-        PARTIAL_WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3], &elements);
+        PARTIAL_WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3],
+                      &elements);
     }
 }
-
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index 1c48837869..6482b4b92e 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -45,83 +45,117 @@
  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *********************************************************/
 
-//Utils
-//Source of these constants :
-//github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
 
 #define SKEIN_KS_PARITY 0x1BD11BDA
 
 #define R0 13
 #define R1 15
 #define R2 26
-#define R3  6
+#define R3 6
 #define R4 17
 #define R5 29
 #define R6 16
 #define R7 24
 
-inline uint rotL(uint x, uint N)
-{
-    return (x << (N & 31)) | (x >> ((32-N) & 31));
+inline uint rotL(uint x, uint N) {
+    return (x << (N & 31)) | (x >> ((32 - N) & 31));
 }
 
-inline void threefry(uint k[2], uint c[2], uint X[2])
-{
+inline void threefry(uint k[2], uint c[2], uint X[2]) {
     uint ks[3];
 
     ks[2] = SKEIN_KS_PARITY;
     ks[0] = k[0];
-    X[0] = c[0];
+    X[0]  = c[0];
     ks[2] ^= k[0];
     ks[1] = k[1];
-    X[1] = c[1];
+    X[1]  = c[1];
     ks[2] ^= k[1];
 
-    X[0] += ks[0]; X[1] += ks[1];
-
-    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+    X[0] += ks[0];
+    X[1] += ks[1];
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
 
     /* InjectKey(r=1) */
-    X[0] += ks[1]; X[1] += ks[2];
-    X[1] += 1;     /* X[2-1] += r  */
-
-    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 1; /* X[2-1] += r  */
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
 
     /* InjectKey(r=2) */
-    X[0] += ks[2]; X[1] += ks[0];
+    X[0] += ks[2];
+    X[1] += ks[0];
     X[1] += 2;
 
-    X[0] += X[1]; X[1] = rotL(X[1],R0); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R1); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R2); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R3); X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
 
     /* InjectKey(r=3) */
-    X[0] += ks[0]; X[1] += ks[1];
+    X[0] += ks[0];
+    X[1] += ks[1];
     X[1] += 3;
 
-    X[0] += X[1]; X[1] = rotL(X[1],R4); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R5); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R6); X[1] ^= X[0];
-    X[0] += X[1]; X[1] = rotL(X[1],R7); X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
 
     /* InjectKey(r=4) */
-    X[0] += ks[1]; X[1] += ks[2];
+    X[0] += ks[1];
+    X[1] += ks[2];
     X[1] += 4;
 }
 
-__kernel void generate(__global T *output, unsigned elements,
-        unsigned hic, unsigned loc, unsigned hi, unsigned lo)
-{
-    unsigned gid = get_group_id(0);
-    unsigned off = get_local_size(0);
-    unsigned index =  gid * ELEMENTS_PER_BLOCK + get_local_id(0);
+__kernel void generate(__global T *output, unsigned elements, unsigned hic,
+                       unsigned loc, unsigned hi, unsigned lo) {
+    unsigned gid   = get_group_id(0);
+    unsigned off   = get_local_size(0);
+    unsigned index = gid * ELEMENTS_PER_BLOCK + get_local_id(0);
 
     uint key[2] = {lo, hi};
     uint ctr[2] = {loc, hic};
@@ -134,7 +168,7 @@ __kernel void generate(__global T *output, unsigned elements,
     uint step = ELEMENTS_PER_BLOCK / 2;
     ctr[0] += step;
     ctr[1] += (ctr[0] < step);
-    threefry(key, ctr, o+2);
+    threefry(key, ctr, o + 2);
 
     if (gid != get_num_groups(0) - 1) {
         WRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
@@ -142,4 +176,3 @@ __kernel void generate(__global T *output, unsigned elements,
         PARTIAL_WRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
     }
 }
-
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index dd07e61946..22288cac1a 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -7,355 +7,440 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define PI_VAL 3.1415926535897932384626433832795028841971693993751058209749445923078164
+#define PI_VAL \
+    3.1415926535897932384626433832795028841971693993751058209749445923078164
 
-//Conversion to floats adapted from Random123
+// Conversion to floats adapted from Random123
 #define UINTMAX 0xffffffff
-#define FLT_FACTOR ((1.0f)/(UINTMAX + (1.0f)))
-#define HALF_FLT_FACTOR ((0.5f)*FLT_FACTOR)
+#define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
+#define HALF_FLT_FACTOR ((0.5f) * FLT_FACTOR)
 
-//Generates rationals in (0, 1]
-float getFloat(const uint * const num)
-{
-    return ((*num)*FLT_FACTOR + HALF_FLT_FACTOR);
+// Generates rationals in (0, 1]
+float getFloat(const uint *const num) {
+    return ((*num) * FLT_FACTOR + HALF_FLT_FACTOR);
 }
 
-//Writes without boundary checking
-
-void writeOut128Bytes_uchar(__global uchar *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]              =     *r1;
-    out[*index +    THREADS] =  *r1>>8;
-    out[*index +  2*THREADS] = *r1>>16;
-    out[*index +  3*THREADS] = *r1>>24;
-    out[*index +  4*THREADS] =     *r2;
-    out[*index +  5*THREADS] =  *r2>>8;
-    out[*index +  6*THREADS] = *r2>>16;
-    out[*index +  7*THREADS] = *r2>>24;
-    out[*index +  8*THREADS] =     *r3;
-    out[*index +  9*THREADS] =  *r3>>8;
-    out[*index + 10*THREADS] = *r3>>16;
-    out[*index + 11*THREADS] = *r3>>24;
-    out[*index + 12*THREADS] =     *r4;
-    out[*index + 13*THREADS] =  *r4>>8;
-    out[*index + 14*THREADS] = *r4>>16;
-    out[*index + 15*THREADS] = *r4>>24;
+// Writes without boundary checking
+
+void writeOut128Bytes_uchar(__global uchar *out, const uint *const index,
+                            const uint *const r1, const uint *const r2,
+                            const uint *const r3, const uint *const r4) {
+    out[*index]                = *r1;
+    out[*index + THREADS]      = *r1 >> 8;
+    out[*index + 2 * THREADS]  = *r1 >> 16;
+    out[*index + 3 * THREADS]  = *r1 >> 24;
+    out[*index + 4 * THREADS]  = *r2;
+    out[*index + 5 * THREADS]  = *r2 >> 8;
+    out[*index + 6 * THREADS]  = *r2 >> 16;
+    out[*index + 7 * THREADS]  = *r2 >> 24;
+    out[*index + 8 * THREADS]  = *r3;
+    out[*index + 9 * THREADS]  = *r3 >> 8;
+    out[*index + 10 * THREADS] = *r3 >> 16;
+    out[*index + 11 * THREADS] = *r3 >> 24;
+    out[*index + 12 * THREADS] = *r4;
+    out[*index + 13 * THREADS] = *r4 >> 8;
+    out[*index + 14 * THREADS] = *r4 >> 16;
+    out[*index + 15 * THREADS] = *r4 >> 24;
 }
 
-void writeOut128Bytes_char(__global char *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]              = (*r1   )&0x1;
-    out[*index +    THREADS] = (*r1>>1)&0x1;
-    out[*index +  2*THREADS] = (*r1>>2)&0x1;
-    out[*index +  3*THREADS] = (*r1>>3)&0x1;
-    out[*index +  4*THREADS] = (*r2   )&0x1;
-    out[*index +  5*THREADS] = (*r2>>1)&0x1;
-    out[*index +  6*THREADS] = (*r2>>2)&0x1;
-    out[*index +  7*THREADS] = (*r2>>3)&0x1;
-    out[*index +  8*THREADS] = (*r3   )&0x1;
-    out[*index +  9*THREADS] = (*r3>>1)&0x1;
-    out[*index + 10*THREADS] = (*r3>>2)&0x1;
-    out[*index + 11*THREADS] = (*r3>>3)&0x1;
-    out[*index + 12*THREADS] = (*r4   )&0x1;
-    out[*index + 13*THREADS] = (*r4>>1)&0x1;
-    out[*index + 14*THREADS] = (*r4>>2)&0x1;
-    out[*index + 15*THREADS] = (*r4>>3)&0x1;
+void writeOut128Bytes_char(__global char *out, const uint *const index,
+                           const uint *const r1, const uint *const r2,
+                           const uint *const r3, const uint *const r4) {
+    out[*index]                = (*r1) & 0x1;
+    out[*index + THREADS]      = (*r1 >> 1) & 0x1;
+    out[*index + 2 * THREADS]  = (*r1 >> 2) & 0x1;
+    out[*index + 3 * THREADS]  = (*r1 >> 3) & 0x1;
+    out[*index + 4 * THREADS]  = (*r2) & 0x1;
+    out[*index + 5 * THREADS]  = (*r2 >> 1) & 0x1;
+    out[*index + 6 * THREADS]  = (*r2 >> 2) & 0x1;
+    out[*index + 7 * THREADS]  = (*r2 >> 3) & 0x1;
+    out[*index + 8 * THREADS]  = (*r3) & 0x1;
+    out[*index + 9 * THREADS]  = (*r3 >> 1) & 0x1;
+    out[*index + 10 * THREADS] = (*r3 >> 2) & 0x1;
+    out[*index + 11 * THREADS] = (*r3 >> 3) & 0x1;
+    out[*index + 12 * THREADS] = (*r4) & 0x1;
+    out[*index + 13 * THREADS] = (*r4 >> 1) & 0x1;
+    out[*index + 14 * THREADS] = (*r4 >> 2) & 0x1;
+    out[*index + 15 * THREADS] = (*r4 >> 3) & 0x1;
 }
 
-void writeOut128Bytes_short(__global short *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]             =     *r1;
-    out[*index +   THREADS] = *r1>>16;
-    out[*index + 2*THREADS] =     *r2;
-    out[*index + 3*THREADS] = *r2>>16;
-    out[*index + 4*THREADS] =     *r3;
-    out[*index + 5*THREADS] = *r3>>16;
-    out[*index + 6*THREADS] =     *r4;
-    out[*index + 7*THREADS] = *r4>>16;
+void writeOut128Bytes_short(__global short *out, const uint *const index,
+                            const uint *const r1, const uint *const r2,
+                            const uint *const r3, const uint *const r4) {
+    out[*index]               = *r1;
+    out[*index + THREADS]     = *r1 >> 16;
+    out[*index + 2 * THREADS] = *r2;
+    out[*index + 3 * THREADS] = *r2 >> 16;
+    out[*index + 4 * THREADS] = *r3;
+    out[*index + 5 * THREADS] = *r3 >> 16;
+    out[*index + 6 * THREADS] = *r4;
+    out[*index + 7 * THREADS] = *r4 >> 16;
 }
 
-void writeOut128Bytes_ushort(__global ushort *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]             =     *r1;
-    out[*index +   THREADS] = *r1>>16;
-    out[*index + 2*THREADS] =     *r2;
-    out[*index + 3*THREADS] = *r2>>16;
-    out[*index + 4*THREADS] =     *r3;
-    out[*index + 5*THREADS] = *r3>>16;
-    out[*index + 6*THREADS] =     *r4;
-    out[*index + 7*THREADS] = *r4>>16;
+void writeOut128Bytes_ushort(__global ushort *out, const uint *const index,
+                             const uint *const r1, const uint *const r2,
+                             const uint *const r3, const uint *const r4) {
+    out[*index]               = *r1;
+    out[*index + THREADS]     = *r1 >> 16;
+    out[*index + 2 * THREADS] = *r2;
+    out[*index + 3 * THREADS] = *r2 >> 16;
+    out[*index + 4 * THREADS] = *r3;
+    out[*index + 5 * THREADS] = *r3 >> 16;
+    out[*index + 6 * THREADS] = *r4;
+    out[*index + 7 * THREADS] = *r4 >> 16;
 }
 
-void writeOut128Bytes_int(__global int *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]             = *r1;
-    out[*index +   THREADS] = *r2;
-    out[*index + 2*THREADS] = *r3;
-    out[*index + 3*THREADS] = *r4;
+void writeOut128Bytes_int(__global int *out, const uint *const index,
+                          const uint *const r1, const uint *const r2,
+                          const uint *const r3, const uint *const r4) {
+    out[*index]               = *r1;
+    out[*index + THREADS]     = *r2;
+    out[*index + 2 * THREADS] = *r3;
+    out[*index + 3 * THREADS] = *r4;
 }
 
-void writeOut128Bytes_uint(__global uint *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]             = *r1;
-    out[*index +   THREADS] = *r2;
-    out[*index + 2*THREADS] = *r3;
-    out[*index + 3*THREADS] = *r4;
+void writeOut128Bytes_uint(__global uint *out, const uint *const index,
+                           const uint *const r1, const uint *const r2,
+                           const uint *const r3, const uint *const r4) {
+    out[*index]               = *r1;
+    out[*index + THREADS]     = *r2;
+    out[*index + 2 * THREADS] = *r3;
+    out[*index + 3 * THREADS] = *r4;
 }
 
-void writeOut128Bytes_long(__global long *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    long c1 = *r2;
-    c1 = (c1<<32) | *r1;
-    long c2 = *r4;
-    c2 = (c2<<32) | *r3;
+void writeOut128Bytes_long(__global long *out, const uint *const index,
+                           const uint *const r1, const uint *const r2,
+                           const uint *const r3, const uint *const r4) {
+    long c1               = *r2;
+    c1                    = (c1 << 32) | *r1;
+    long c2               = *r4;
+    c2                    = (c2 << 32) | *r3;
     out[*index]           = c1;
     out[*index + THREADS] = c2;
 }
 
-void writeOut128Bytes_ulong(__global ulong *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    long c1 = *r2;
-    c1 = (c1<<32) | *r1;
-    long c2 = *r4;
-    c2 = (c2<<32) | *r3;
+void writeOut128Bytes_ulong(__global ulong *out, const uint *const index,
+                            const uint *const r1, const uint *const r2,
+                            const uint *const r3, const uint *const r4) {
+    long c1               = *r2;
+    c1                    = (c1 << 32) | *r1;
+    long c2               = *r4;
+    c2                    = (c2 << 32) | *r3;
     out[*index]           = c1;
     out[*index + THREADS] = c2;
 }
 
-void writeOut128Bytes_float(__global float *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
-    out[*index]             = 1.f - getFloat(r1);
-    out[*index +   THREADS] = 1.f - getFloat(r2);
-    out[*index + 2*THREADS] = 1.f - getFloat(r3);
-    out[*index + 3*THREADS] = 1.f - getFloat(r4);
+void writeOut128Bytes_float(__global float *out, const uint *const index,
+                            const uint *const r1, const uint *const r2,
+                            const uint *const r3, const uint *const r4) {
+    out[*index]               = 1.f - getFloat(r1);
+    out[*index + THREADS]     = 1.f - getFloat(r2);
+    out[*index + 2 * THREADS] = 1.f - getFloat(r3);
+    out[*index + 3 * THREADS] = 1.f - getFloat(r4);
 }
 
-
 #if RAND_DIST == 1
 #endif
 
-//Writes with boundary checking
-
-void partialWriteOut128Bytes_uchar(__global uchar *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index              < *elements) {out[*index]              =     *r1;}
-    if (*index +    THREADS < *elements) {out[*index +    THREADS] =  *r1>>8;}
-    if (*index +  2*THREADS < *elements) {out[*index +  2*THREADS] = *r1>>16;}
-    if (*index +  3*THREADS < *elements) {out[*index +  3*THREADS] = *r1>>24;}
-    if (*index +  4*THREADS < *elements) {out[*index +  4*THREADS] =     *r2;}
-    if (*index +  5*THREADS < *elements) {out[*index +  5*THREADS] =  *r2>>8;}
-    if (*index +  6*THREADS < *elements) {out[*index +  6*THREADS] = *r2>>16;}
-    if (*index +  7*THREADS < *elements) {out[*index +  7*THREADS] = *r2>>24;}
-    if (*index +  8*THREADS < *elements) {out[*index +  8*THREADS] =     *r3;}
-    if (*index +  9*THREADS < *elements) {out[*index +  9*THREADS] =  *r3>>8;}
-    if (*index + 10*THREADS < *elements) {out[*index + 10*THREADS] = *r3>>16;}
-    if (*index + 11*THREADS < *elements) {out[*index + 11*THREADS] = *r3>>24;}
-    if (*index + 12*THREADS < *elements) {out[*index + 12*THREADS] =     *r4;}
-    if (*index + 13*THREADS < *elements) {out[*index + 13*THREADS] =  *r4>>8;}
-    if (*index + 14*THREADS < *elements) {out[*index + 14*THREADS] = *r4>>16;}
-    if (*index + 15*THREADS < *elements) {out[*index + 15*THREADS] = *r4>>24;}
+// Writes with boundary checking
+
+void partialWriteOut128Bytes_uchar(__global uchar *out, const uint *const index,
+                                   const uint *const r1, const uint *const r2,
+                                   const uint *const r3, const uint *const r4,
+                                   const uint *const elements) {
+    if (*index < *elements) { out[*index] = *r1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = *r1 >> 8; }
+    if (*index + 2 * THREADS < *elements) {
+        out[*index + 2 * THREADS] = *r1 >> 16;
+    }
+    if (*index + 3 * THREADS < *elements) {
+        out[*index + 3 * THREADS] = *r1 >> 24;
+    }
+    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = *r2; }
+    if (*index + 5 * THREADS < *elements) {
+        out[*index + 5 * THREADS] = *r2 >> 8;
+    }
+    if (*index + 6 * THREADS < *elements) {
+        out[*index + 6 * THREADS] = *r2 >> 16;
+    }
+    if (*index + 7 * THREADS < *elements) {
+        out[*index + 7 * THREADS] = *r2 >> 24;
+    }
+    if (*index + 8 * THREADS < *elements) { out[*index + 8 * THREADS] = *r3; }
+    if (*index + 9 * THREADS < *elements) {
+        out[*index + 9 * THREADS] = *r3 >> 8;
+    }
+    if (*index + 10 * THREADS < *elements) {
+        out[*index + 10 * THREADS] = *r3 >> 16;
+    }
+    if (*index + 11 * THREADS < *elements) {
+        out[*index + 11 * THREADS] = *r3 >> 24;
+    }
+    if (*index + 12 * THREADS < *elements) { out[*index + 12 * THREADS] = *r4; }
+    if (*index + 13 * THREADS < *elements) {
+        out[*index + 13 * THREADS] = *r4 >> 8;
+    }
+    if (*index + 14 * THREADS < *elements) {
+        out[*index + 14 * THREADS] = *r4 >> 16;
+    }
+    if (*index + 15 * THREADS < *elements) {
+        out[*index + 15 * THREADS] = *r4 >> 24;
+    }
 }
 
-void partialWriteOut128Bytes_char(__global char *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index              < *elements) {out[*index]              = (*r1   )&0x1;}
-    if (*index +    THREADS < *elements) {out[*index +    THREADS] = (*r1>>1)&0x1;}
-    if (*index +  2*THREADS < *elements) {out[*index +  2*THREADS] = (*r1>>2)&0x1;}
-    if (*index +  3*THREADS < *elements) {out[*index +  3*THREADS] = (*r1>>3)&0x1;}
-    if (*index +  4*THREADS < *elements) {out[*index +  4*THREADS] = (*r2   )&0x1;}
-    if (*index +  5*THREADS < *elements) {out[*index +  5*THREADS] = (*r2>>1)&0x1;}
-    if (*index +  6*THREADS < *elements) {out[*index +  6*THREADS] = (*r2>>2)&0x1;}
-    if (*index +  7*THREADS < *elements) {out[*index +  7*THREADS] = (*r2>>3)&0x1;}
-    if (*index +  8*THREADS < *elements) {out[*index +  8*THREADS] = (*r3   )&0x1;}
-    if (*index +  9*THREADS < *elements) {out[*index +  9*THREADS] = (*r3>>1)&0x1;}
-    if (*index + 10*THREADS < *elements) {out[*index + 10*THREADS] = (*r3>>2)&0x1;}
-    if (*index + 11*THREADS < *elements) {out[*index + 11*THREADS] = (*r3>>3)&0x1;}
-    if (*index + 12*THREADS < *elements) {out[*index + 12*THREADS] = (*r4   )&0x1;}
-    if (*index + 13*THREADS < *elements) {out[*index + 13*THREADS] = (*r4>>1)&0x1;}
-    if (*index + 14*THREADS < *elements) {out[*index + 14*THREADS] = (*r4>>2)&0x1;}
-    if (*index + 15*THREADS < *elements) {out[*index + 15*THREADS] = (*r4>>3)&0x1;}
+void partialWriteOut128Bytes_char(__global char *out, const uint *const index,
+                                  const uint *const r1, const uint *const r2,
+                                  const uint *const r3, const uint *const r4,
+                                  const uint *const elements) {
+    if (*index < *elements) { out[*index] = (*r1) & 0x1; }
+    if (*index + THREADS < *elements) {
+        out[*index + THREADS] = (*r1 >> 1) & 0x1;
+    }
+    if (*index + 2 * THREADS < *elements) {
+        out[*index + 2 * THREADS] = (*r1 >> 2) & 0x1;
+    }
+    if (*index + 3 * THREADS < *elements) {
+        out[*index + 3 * THREADS] = (*r1 >> 3) & 0x1;
+    }
+    if (*index + 4 * THREADS < *elements) {
+        out[*index + 4 * THREADS] = (*r2) & 0x1;
+    }
+    if (*index + 5 * THREADS < *elements) {
+        out[*index + 5 * THREADS] = (*r2 >> 1) & 0x1;
+    }
+    if (*index + 6 * THREADS < *elements) {
+        out[*index + 6 * THREADS] = (*r2 >> 2) & 0x1;
+    }
+    if (*index + 7 * THREADS < *elements) {
+        out[*index + 7 * THREADS] = (*r2 >> 3) & 0x1;
+    }
+    if (*index + 8 * THREADS < *elements) {
+        out[*index + 8 * THREADS] = (*r3) & 0x1;
+    }
+    if (*index + 9 * THREADS < *elements) {
+        out[*index + 9 * THREADS] = (*r3 >> 1) & 0x1;
+    }
+    if (*index + 10 * THREADS < *elements) {
+        out[*index + 10 * THREADS] = (*r3 >> 2) & 0x1;
+    }
+    if (*index + 11 * THREADS < *elements) {
+        out[*index + 11 * THREADS] = (*r3 >> 3) & 0x1;
+    }
+    if (*index + 12 * THREADS < *elements) {
+        out[*index + 12 * THREADS] = (*r4) & 0x1;
+    }
+    if (*index + 13 * THREADS < *elements) {
+        out[*index + 13 * THREADS] = (*r4 >> 1) & 0x1;
+    }
+    if (*index + 14 * THREADS < *elements) {
+        out[*index + 14 * THREADS] = (*r4 >> 2) & 0x1;
+    }
+    if (*index + 15 * THREADS < *elements) {
+        out[*index + 15 * THREADS] = (*r4 >> 3) & 0x1;
+    }
 }
 
-void partialWriteOut128Bytes_short(__global short *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index             < *elements) {out[*index]             =     *r1;}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r1>>16;}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] =     *r2;}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r2>>16;}
-    if (*index + 4*THREADS < *elements) {out[*index + 4*THREADS] =     *r3;}
-    if (*index + 5*THREADS < *elements) {out[*index + 5*THREADS] = *r3>>16;}
-    if (*index + 6*THREADS < *elements) {out[*index + 6*THREADS] =     *r4;}
-    if (*index + 7*THREADS < *elements) {out[*index + 7*THREADS] = *r4>>16;}
+void partialWriteOut128Bytes_short(__global short *out, const uint *const index,
+                                   const uint *const r1, const uint *const r2,
+                                   const uint *const r3, const uint *const r4,
+                                   const uint *const elements) {
+    if (*index < *elements) { out[*index] = *r1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = *r1 >> 16; }
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r2; }
+    if (*index + 3 * THREADS < *elements) {
+        out[*index + 3 * THREADS] = *r2 >> 16;
+    }
+    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = *r3; }
+    if (*index + 5 * THREADS < *elements) {
+        out[*index + 5 * THREADS] = *r3 >> 16;
+    }
+    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = *r4; }
+    if (*index + 7 * THREADS < *elements) {
+        out[*index + 7 * THREADS] = *r4 >> 16;
+    }
 }
 
-void partialWriteOut128Bytes_ushort(__global ushort *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index             < *elements) {out[*index]             =     *r1;}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r1>>16;}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] =     *r2;}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r2>>16;}
-    if (*index + 4*THREADS < *elements) {out[*index + 4*THREADS] =     *r3;}
-    if (*index + 5*THREADS < *elements) {out[*index + 5*THREADS] = *r3>>16;}
-    if (*index + 6*THREADS < *elements) {out[*index + 6*THREADS] =     *r4;}
-    if (*index + 7*THREADS < *elements) {out[*index + 7*THREADS] = *r4>>16;}
+void partialWriteOut128Bytes_ushort(__global ushort *out,
+                                    const uint *const index,
+                                    const uint *const r1, const uint *const r2,
+                                    const uint *const r3, const uint *const r4,
+                                    const uint *const elements) {
+    if (*index < *elements) { out[*index] = *r1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = *r1 >> 16; }
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r2; }
+    if (*index + 3 * THREADS < *elements) {
+        out[*index + 3 * THREADS] = *r2 >> 16;
+    }
+    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = *r3; }
+    if (*index + 5 * THREADS < *elements) {
+        out[*index + 5 * THREADS] = *r3 >> 16;
+    }
+    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = *r4; }
+    if (*index + 7 * THREADS < *elements) {
+        out[*index + 7 * THREADS] = *r4 >> 16;
+    }
 }
 
-void partialWriteOut128Bytes_int(__global int *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index             < *elements) {out[*index]             = *r1;}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r2;}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = *r3;}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
+void partialWriteOut128Bytes_int(__global int *out, const uint *const index,
+                                 const uint *const r1, const uint *const r2,
+                                 const uint *const r3, const uint *const r4,
+                                 const uint *const elements) {
+    if (*index < *elements) { out[*index] = *r1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = *r2; }
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r3; }
+    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = *r4; }
 }
 
-void partialWriteOut128Bytes_uint(__global uint *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index             < *elements) {out[*index]             = *r1;}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = *r2;}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = *r3;}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = *r4;}
+void partialWriteOut128Bytes_uint(__global uint *out, const uint *const index,
+                                  const uint *const r1, const uint *const r2,
+                                  const uint *const r3, const uint *const r4,
+                                  const uint *const elements) {
+    if (*index < *elements) { out[*index] = *r1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = *r2; }
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r3; }
+    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = *r4; }
 }
 
-void partialWriteOut128Bytes_long(__global long *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
+void partialWriteOut128Bytes_long(__global long *out, const uint *const index,
+                                  const uint *const r1, const uint *const r2,
+                                  const uint *const r3, const uint *const r4,
+                                  const uint *const elements) {
     long c1 = *r2;
-    c1 = (c1<<32) | *r1;
+    c1      = (c1 << 32) | *r1;
     long c2 = *r4;
-    c2 = (c2<<32) | *r3;
-    if (*index           < *elements) {out[*index]           = c1;}
-    if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
+    c2      = (c2 << 32) | *r3;
+    if (*index < *elements) { out[*index] = c1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = c2; }
 }
 
-void partialWriteOut128Bytes_ulong(__global ulong *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
+void partialWriteOut128Bytes_ulong(__global ulong *out, const uint *const index,
+                                   const uint *const r1, const uint *const r2,
+                                   const uint *const r3, const uint *const r4,
+                                   const uint *const elements) {
     long c1 = *r2;
-    c1 = (c1<<32) | *r1;
+    c1      = (c1 << 32) | *r1;
     long c2 = *r4;
-    c2 = (c2<<32) | *r3;
-    if (*index           < *elements) {out[*index]           = c1;}
-    if (*index + THREADS < *elements) {out[*index + THREADS] = c2;}
+    c2      = (c2 << 32) | *r3;
+    if (*index < *elements) { out[*index] = c1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = c2; }
 }
 
-void partialWriteOut128Bytes_float(__global float *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index             < *elements) {out[*index]             = 1.f - getFloat(r1);}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = 1.f - getFloat(r2);}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = 1.f - getFloat(r3);}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = 1.f - getFloat(r4);}
+void partialWriteOut128Bytes_float(__global float *out, const uint *const index,
+                                   const uint *const r1, const uint *const r2,
+                                   const uint *const r3, const uint *const r4,
+                                   const uint *const elements) {
+    if (*index < *elements) { out[*index] = 1.f - getFloat(r1); }
+    if (*index + THREADS < *elements) {
+        out[*index + THREADS] = 1.f - getFloat(r2);
+    }
+    if (*index + 2 * THREADS < *elements) {
+        out[*index + 2 * THREADS] = 1.f - getFloat(r3);
+    }
+    if (*index + 3 * THREADS < *elements) {
+        out[*index + 3 * THREADS] = 1.f - getFloat(r4);
+    }
 }
 
 #if RAND_DIST == 1
-void boxMullerTransform(T * const out1, T * const out2, const T r1, const T r2)
-{
+void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
-#if defined(IS_APPLE) // Because Apple is.. "special"
+#if defined(IS_APPLE)  // Because Apple is.. "special"
     T r = sqrt((T)(-2.0) * log10(r1) * (T)log10_val);
 #else
     T r = sqrt((T)(-2.0) * log(r1));
 #endif
     T theta = 2 * (T)PI_VAL * (r2);
-    *out1 = r*sin(theta);
-    *out2 = r*cos(theta);
+    *out1   = r * sin(theta);
+    *out2   = r * cos(theta);
 }
 
-//BoxMuller writes without boundary checking
-void boxMullerWriteOut128Bytes_float(__global float *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
+// BoxMuller writes without boundary checking
+void boxMullerWriteOut128Bytes_float(__global float *out,
+                                     const uint *const index,
+                                     const uint *const r1, const uint *const r2,
+                                     const uint *const r3,
+                                     const uint *const r4) {
     float n1, n2, n3, n4;
     boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
     boxMullerTransform(&n3, &n4, getFloat(r1), getFloat(r2));
-    out[*index]             = n1;
-    out[*index +   THREADS] = n2;
-    out[*index + 2*THREADS] = n3;
-    out[*index + 3*THREADS] = n4;
+    out[*index]               = n1;
+    out[*index + THREADS]     = n2;
+    out[*index + 2 * THREADS] = n3;
+    out[*index + 3 * THREADS] = n4;
 }
 
-//BoxMuller writes with boundary checking
-void partialBoxMullerWriteOut128Bytes_float(__global float *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
+// BoxMuller writes with boundary checking
+void partialBoxMullerWriteOut128Bytes_float(
+    __global float *out, const uint *const index, const uint *const r1,
+    const uint *const r2, const uint *const r3, const uint *const r4,
+    const uint *const elements) {
     float n1, n2, n3, n4;
     boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
     boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
-    if (*index             < *elements) {out[*index]             = n1;}
-    if (*index +   THREADS < *elements) {out[*index +   THREADS] = n2;}
-    if (*index + 2*THREADS < *elements) {out[*index + 2*THREADS] = n3;}
-    if (*index + 3*THREADS < *elements) {out[*index + 3*THREADS] = n4;}
+    if (*index < *elements) { out[*index] = n1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = n3; }
+    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = n4; }
 }
 #endif
 
 #ifdef USE_DOUBLE
 
-//Conversion to floats adapted from Random123
+// Conversion to floats adapted from Random123
 #define UINTLMAX 0xffffffffffffffff
-#define DBL_FACTOR ((1.0)/(UINTLMAX + (1.0)))
-#define HALF_DBL_FACTOR ((0.5)*DBL_FACTOR)
-
-//Generates rationals in (0, 1]
-double getDouble(const uint * const num1, const uint * const num2)
-{
-    ulong num = (((ulong)*num1)<<32) | ((ulong)*num2);
-    return (num*DBL_FACTOR + HALF_DBL_FACTOR);
+#define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
+#define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
+
+// Generates rationals in (0, 1]
+double getDouble(const uint *const num1, const uint *const num2) {
+    ulong num = (((ulong)*num1) << 32) | ((ulong)*num2);
+    return (num * DBL_FACTOR + HALF_DBL_FACTOR);
 }
 
-void writeOut128Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
+void writeOut128Bytes_double(__global double *out, const uint *const index,
+                             const uint *const r1, const uint *const r2,
+                             const uint *const r3, const uint *const r4) {
     out[*index]           = 1.0 - getDouble(r1, r2);
     out[*index + THREADS] = 1.0 - getDouble(r3, r4);
 }
 
-void partialWriteOut128Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
-    if (*index           < *elements) {out[*index]           = 1.0 - getDouble(r1, r2);}
-    if (*index + THREADS < *elements) {out[*index + THREADS] = 1.0 - getDouble(r3, r4);}
+void partialWriteOut128Bytes_double(__global double *out,
+                                    const uint *const index,
+                                    const uint *const r1, const uint *const r2,
+                                    const uint *const r3, const uint *const r4,
+                                    const uint *const elements) {
+    if (*index < *elements) { out[*index] = 1.0 - getDouble(r1, r2); }
+    if (*index + THREADS < *elements) {
+        out[*index + THREADS] = 1.0 - getDouble(r3, r4);
+    }
 }
 
 #if RAND_DIST == 1
-void boxMullerWriteOut128Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4)
-{
+void boxMullerWriteOut128Bytes_double(
+    __global double *out, const uint *const index, const uint *const r1,
+    const uint *const r2, const uint *const r3, const uint *const r4) {
     double n1, n2;
     boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
     out[*index]           = n1;
     out[*index + THREADS] = n2;
 }
 
-void partialBoxMullerWriteOut128Bytes_double(__global double *out, const uint * const index,
-        const uint * const r1, const uint * const r2, const uint * const r3, const uint * const r4, const uint * const elements)
-{
+void partialBoxMullerWriteOut128Bytes_double(
+    __global double *out, const uint *const index, const uint *const r1,
+    const uint *const r2, const uint *const r3, const uint *const r4,
+    const uint *const elements) {
     double n1, n2;
     boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
-    if (*index           < *elements) {out[*index]           = n1;}
-    if (*index + THREADS < *elements) {out[*index + THREADS] = n2;}
+    if (*index < *elements) { out[*index] = n1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
 }
 #endif
 #endif
 
-#define PASTER(x,y) x ## _ ## y
-#define EVALUATOR(x,y) PASTER(x,y)
+#define PASTER(x, y) x##_##y
+#define EVALUATOR(x, y) PASTER(x, y)
 #define EVALUATE_T(function) EVALUATOR(function, T)
 #define UNIFORM_WRITE EVALUATE_T(writeOut128Bytes)
 #define UNIFORM_PARTIAL_WRITE EVALUATE_T(partialWriteOut128Bytes)
diff --git a/src/backend/opencl/kernel/range.cl b/src/backend/opencl/kernel/range.cl
index b3ba67762c..102cda92cf 100644
--- a/src/backend/opencl/kernel/range.cl
+++ b/src/backend/opencl/kernel/range.cl
@@ -7,10 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void range_kernel(__global T *out, const KParam op, const int dim,
-                 const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void range_kernel(__global T *out, const KParam op, const int dim,
+                           const int blocksPerMatX, const int blocksPerMatY) {
     const int mul0 = (dim == 0);
     const int mul1 = (dim == 1);
     const int mul2 = (dim == 2);
@@ -25,10 +23,8 @@ void range_kernel(__global T *out, const KParam op, const int dim,
     const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(xx >= op.dims[0] ||
-       yy >= op.dims[1] ||
-       oz >= op.dims[2] ||
-       ow >= op.dims[3])
+    if (xx >= op.dims[0] || yy >= op.dims[1] || oz >= op.dims[2] ||
+        ow >= op.dims[3])
         return;
 
     const int ozw = ow * op.strides[3] + oz * op.strides[2];
@@ -38,12 +34,12 @@ void range_kernel(__global T *out, const KParam op, const int dim,
 
     T valZW = (mul3 * ow) + (mul2 * oz);
 
-    for(int oy = yy; oy < op.dims[1]; oy += incy) {
+    for (int oy = yy; oy < op.dims[1]; oy += incy) {
         T valYZW = valZW + (mul1 * oy);
         int oyzw = ozw + oy * op.strides[1];
-        for(int ox = xx; ox < op.dims[0]; ox += incx) {
+        for (int ox = xx; ox < op.dims[0]; ox += incx) {
             int oidx = oyzw + ox;
-            T val = valYZW + (mul0 * ox);
+            T val    = valYZW + (mul0 * ox);
 
             out[oidx] = val;
         }
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 2896307dac..b3f4af8527 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -8,49 +8,47 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/range.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
-static const int RANGE_TX = 32;
-static const int RANGE_TY = 8;
+static const int RANGE_TX    = 32;
+static const int RANGE_TY    = 8;
 static const int RANGE_TILEX = 512;
 static const int RANGE_TILEY = 32;
 
 template<typename T>
-void range(Param out, const int dim)
-{
-    std::string refName = std::string("range_kernel_") + std::string(dtype_traits<T>::getName());
+void range(Param out, const int dim) {
+    std::string refName =
+        std::string("range_kernel_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {range_cl};
-        const int   ker_lens[] = {range_cl_len};
+        const int ker_lens[]   = {range_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -59,7 +57,9 @@ void range(Param out, const int dim)
         addKernelToCache(device, refName, entry);
     }
 
-    auto rangeOp = KernelFunctor< Buffer, const KParam, const int, const int, const int > (*entry.ker);
+    auto rangeOp =
+        KernelFunctor<Buffer, const KParam, const int, const int, const int>(
+            *entry.ker);
 
     NDRange local(RANGE_TX, RANGE_TY, 1);
 
@@ -68,10 +68,10 @@ void range(Param out, const int dim)
     NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                    local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-    rangeOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, out.info, dim, blocksPerMatX, blocksPerMatY);
+    rangeOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, dim,
+            blocksPerMatX, blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index fe8ecc62eb..be7ecf0d98 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -8,334 +8,299 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/reduce_first.hpp>
-#include <kernel_headers/reduce_dim.hpp>
+#include <Param.hpp>
+#include <Array.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
+#include <kernel_headers/reduce_dim.hpp>
+#include <kernel_headers/reduce_first.hpp>
+#include <memory.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
-#include "config.hpp"
-#include <memory.hpp>
+#include <map>
 #include <memory>
+#include <mutex>
+#include <string>
+#include "config.hpp"
+#include "names.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 using std::unique_ptr;
 
-namespace opencl
-{
-
-namespace kernel
-{
-
-    template<typename Ti, typename To, af_op_t op>
-    void reduce_dim_launcher(Param out, Param in,
-                             const int dim,
-                             const uint threads_y,
-                             const uint groups_all[4],
-                             int change_nan, double nanval)
-    {
-        std::string ref_name =
-            std::string("reduce_") +
-            std::to_string(dim) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_y);
-
-        int device = getActiveDeviceId();
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-            ToNumStr<To> toNumStr;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D T=To"
-                    << " -D dim=" << dim
-                    << " -D DIMY=" << threads_y
-                    << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNumStr(Binary<To, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>();
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-
-            }
+namespace opencl {
+
+namespace kernel {
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_dim_launcher(Param out, Param in, const int dim,
+                         const uint threads_y, const uint groups_all[4],
+                         int change_nan, double nanval) {
+    std::string ref_name =
+        std::string("reduce_") + std::to_string(dim) + std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_y);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
+                << " -D dim=" << dim << " -D DIMY=" << threads_y
+                << " -D THREADS_X=" << THREADS_X
+                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-            const char *ker_strs[] = {ops_cl, reduce_dim_cl};
-            const int   ker_lens[] = {ops_cl_len, reduce_dim_cl_len};
-            Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        const char *ker_strs[] = {ops_cl, reduce_dim_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_dim_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel(*entry.prog, "reduce_dim_kernel");
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "reduce_dim_kernel");
 
-            addKernelToCache(device, ref_name, entry);
-        }
-
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                       groups_all[1] * groups_all[3] * local[1]);
-
-        auto reduceOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint, uint,
-                                    int, To>(*entry.ker);
-
-        reduceOp(EnqueueArgs(getQueue(), global, local),
-                 *out.data, out.info,
-                 *in.data, in.info,
-                 groups_all[0],
-                 groups_all[1],
-                 groups_all[dim],
-                 change_nan,
-                 scalar<To>(nanval));
-
-        CL_DEBUG_FINISH(getQueue());
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce_dim(Param out, Param in, int change_nan, double nanval, int dim)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
-        uint threads_x = THREADS_X;
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        uint groups_all[] = {(uint)divup(in.info.dims[0], threads_x),
-                             (uint)in.info.dims[1],
-                             (uint)in.info.dims[2],
-                             (uint)in.info.dims[3]};
+    auto reduceOp = KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint,
+                                  uint, int, To>(*entry.ker);
 
-        groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
+    reduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, groups_all[0], groups_all[1], groups_all[dim],
+             change_nan, scalar<To>(nanval));
 
-        Param tmp = out;
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        int tmp_elements = 1;
-        if (groups_all[dim] > 1) {
-            tmp.info.dims[dim] = groups_all[dim];
+template<typename Ti, typename To, af_op_t op>
+void reduce_dim(Param out, Param in, int change_nan, double nanval, int dim) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = THREADS_X;
 
-            for (int k = 0; k < 4; k++) tmp_elements *= tmp.info.dims[k];
+    uint groups_all[] = {(uint)divup(in.info.dims[0], threads_x),
+                         (uint)in.info.dims[1], (uint)in.info.dims[2],
+                         (uint)in.info.dims[3]};
 
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+    groups_all[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
 
-            for (int k = dim + 1; k < 4; k++) tmp.info.strides[k] *= groups_all[dim];
-        }
+    Param tmp = out;
 
-        reduce_dim_launcher<Ti, To, op>(tmp, in, dim, threads_y, groups_all, change_nan, nanval);
+    int tmp_elements = 1;
+    if (groups_all[dim] > 1) {
+        tmp.info.dims[dim] = groups_all[dim];
 
-        if (groups_all[dim] > 1) {
-            groups_all[dim] = 1;
+        for (int k = 0; k < 4; k++) tmp_elements *= tmp.info.dims[k];
 
-            if (op == af_notzero_t) {
-                reduce_dim_launcher<To, To, af_add_t>(out, tmp, dim, threads_y, groups_all,
-                                                      change_nan, nanval);
-            } else {
-                reduce_dim_launcher<To, To,       op>(out, tmp, dim, threads_y, groups_all,
-                                                      change_nan, nanval);
-            }
-            bufferFree(tmp.data);
-        }
+        tmp.data = bufferAlloc(tmp_elements * sizeof(To));
 
+        for (int k = dim + 1; k < 4; k++)
+            tmp.info.strides[k] *= groups_all[dim];
     }
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce_first_launcher(Param out, Param in,
-                               const uint groups_x,
-                               const uint groups_y,
-                               const uint threads_x,
-                               int change_nan, double nanval)
-    {
-        std::string ref_name =
-            std::string("reduce_0_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_x);
-
-        int device = getActiveDeviceId();
-
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-
-            ToNumStr<To> toNumStr;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D T=To"
-                    << " -D DIMX=" << threads_x
-                    << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                    << " -D init=" << toNumStr(Binary<To, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>();
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
+    reduce_dim_launcher<Ti, To, op>(tmp, in, dim, threads_y, groups_all,
+                                    change_nan, nanval);
 
-            const char *ker_strs[] = {ops_cl, reduce_first_cl};
-            const int   ker_lens[] = {ops_cl_len, reduce_first_cl_len};
-            Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+    if (groups_all[dim] > 1) {
+        groups_all[dim] = 1;
 
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel(*entry.prog, "reduce_first_kernel");
-
-            addKernelToCache(device, ref_name, entry);
+        if (op == af_notzero_t) {
+            reduce_dim_launcher<To, To, af_add_t>(
+                out, tmp, dim, threads_y, groups_all, change_nan, nanval);
+        } else {
+            reduce_dim_launcher<To, To, op>(out, tmp, dim, threads_y,
+                                            groups_all, change_nan, nanval);
         }
+        bufferFree(tmp.data);
+    }
+}
 
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * in.info.dims[2] * local[0],
-                       groups_y * in.info.dims[3] * local[1]);
-
-        uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
+template<typename Ti, typename To, af_op_t op>
+void reduce_first_launcher(Param out, Param in, const uint groups_x,
+                           const uint groups_y, const uint threads_x,
+                           int change_nan, double nanval) {
+    std::string ref_name =
+        std::string("reduce_0_") + std::string(dtype_traits<Ti>::getName()) +
+        std::string("_") + std::string(dtype_traits<To>::getName()) +
+        std::string("_") + std::to_string(op) + std::string("_") +
+        std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x
+                << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
+                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-        auto reduceOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint, uint,
-                                    int, To>(*entry.ker);
+        const char *ker_strs[] = {ops_cl, reduce_first_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_first_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-        reduceOp(EnqueueArgs(getQueue(), global, local),
-                 *out.data, out.info,
-                 *in.data, in.info, groups_x, groups_y, repeat, change_nan, scalar<To>(nanval));
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "reduce_first_kernel");
 
-        CL_DEBUG_FINISH(getQueue());
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce_first(Param out, Param in, int change_nan, double nanval)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * in.info.dims[2] * local[0],
+                   groups_y * in.info.dims[3] * local[1]);
 
-        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(in.info.dims[1], threads_y);
+    uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-        Param tmp = out;
+    auto reduceOp = KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint,
+                                  uint, int, To>(*entry.ker);
 
-        if (groups_x > 1) {
-            tmp.data = bufferAlloc(groups_x *
-                                in.info.dims[1] *
-                                in.info.dims[2] *
-                                in.info.dims[3] *
-                                sizeof(To));
+    reduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, groups_x, groups_y, repeat, change_nan,
+             scalar<To>(nanval));
 
-            tmp.info.dims[0] = groups_x;
-            for (int k = 1; k < 4; k++) tmp.info.strides[k] *= groups_x;
-        }
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x, change_nan, nanval);
+template<typename Ti, typename To, af_op_t op>
+void reduce_first(Param out, Param in, int change_nan, double nanval) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
 
-        if (groups_x > 1) {
+    uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(in.info.dims[1], threads_y);
 
-            //FIXME: Is there an alternative to the if condition ?
-            if (op == af_notzero_t) {
-                reduce_first_launcher<To, To, af_add_t>(out, tmp, 1, groups_y, threads_x, change_nan, nanval);
-            } else {
-                reduce_first_launcher<To, To,       op>(out, tmp, 1, groups_y, threads_x, change_nan, nanval);
-            }
+    Param tmp = out;
 
-            bufferFree(tmp.data);
-        }
-    }
+    if (groups_x > 1) {
+        tmp.data = bufferAlloc(groups_x * in.info.dims[1] * in.info.dims[2] *
+                               in.info.dims[3] * sizeof(To));
 
-    template<typename Ti, typename To, af_op_t op>
-    void reduce(Param out, Param in, int dim, int change_nan, double nanval)
-    {
-        if (dim == 0)
-            return reduce_first<Ti, To, op>(out, in, change_nan, nanval);
-        else
-            return reduce_dim  <Ti, To, op>(out, in, change_nan, nanval, dim);
+        tmp.info.dims[0] = groups_x;
+        for (int k = 1; k < 4; k++) tmp.info.strides[k] *= groups_x;
     }
 
-    template<typename Ti, typename To, af_op_t op>
-    To reduce_all(Param in, int change_nan, double nanval)
-    {
-        int in_elements = in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+    reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x,
+                                      change_nan, nanval);
 
-        bool is_linear = (in.info.strides[0] == 1);
-        for (int k = 1; k < 4; k++) {
-            is_linear &= (in.info.strides[k] == (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    if (groups_x > 1) {
+        // FIXME: Is there an alternative to the if condition ?
+        if (op == af_notzero_t) {
+            reduce_first_launcher<To, To, af_add_t>(
+                out, tmp, 1, groups_y, threads_x, change_nan, nanval);
+        } else {
+            reduce_first_launcher<To, To, op>(out, tmp, 1, groups_y, threads_x,
+                                              change_nan, nanval);
         }
 
-        // FIXME: Use better heuristics to get to the optimum number
-        if (in_elements > 4096 || !is_linear) {
-
-            if (is_linear) {
-                in.info.dims[0] = in_elements;
-                for (int k = 1; k < 4; k++) {
-                    in.info.dims[k] = 1;
-                    in.info.strides[k] = in_elements;
-                }
-            }
+        bufferFree(tmp.data);
+    }
+}
 
-            uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-            threads_x = std::min(threads_x, THREADS_PER_GROUP);
-            uint threads_y = THREADS_PER_GROUP / threads_x;
+template<typename Ti, typename To, af_op_t op>
+void reduce(Param out, Param in, int dim, int change_nan, double nanval) {
+    if (dim == 0)
+        return reduce_first<Ti, To, op>(out, in, change_nan, nanval);
+    else
+        return reduce_dim<Ti, To, op>(out, in, change_nan, nanval, dim);
+}
 
-            uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-            uint groups_y = divup(in.info.dims[1], threads_y);
-            Array<To> tmp = createEmptyArray<To>({groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+template<typename Ti, typename To, af_op_t op>
+To reduce_all(Param in, int change_nan, double nanval) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
-            int tmp_elements = tmp.elements();
+    bool is_linear = (in.info.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.info.strides[k] ==
+                      (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    }
 
-            reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x, change_nan, nanval);
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096 || !is_linear) {
+        if (is_linear) {
+            in.info.dims[0] = in_elements;
+            for (int k = 1; k < 4; k++) {
+                in.info.dims[k]    = 1;
+                in.info.strides[k] = in_elements;
+            }
+        }
 
-            std::vector<To> h_ptr(tmp_elements);
-            getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0, sizeof(To) * tmp_elements, h_ptr.data());
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+        uint threads_y = THREADS_PER_GROUP / threads_x;
 
-            Binary<To, op> reduce;
-            To out = Binary<To, op>::init();
-            for (int i = 0; i < (int)tmp_elements; i++) {
-                out = reduce(out, h_ptr[i]);
-            }
-            return out;
-        } else {
+        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint groups_y = divup(in.info.dims[1], threads_y);
+        Array<To> tmp = createEmptyArray<To>(
+            {groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
 
-            std::vector<Ti> h_ptr(in_elements);
-            getQueue().enqueueReadBuffer(*in.data, CL_TRUE, sizeof(Ti) * in.info.offset,
-                                          sizeof(Ti) * in_elements, h_ptr.data());
+        int tmp_elements = tmp.elements();
 
-            Transform<Ti, To, op> transform;
-            Binary<To, op> reduce;
-            To out = Binary<To, op>::init();
-            To nanval_to = scalar<To>(nanval);
+        reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y,
+                                          threads_x, change_nan, nanval);
 
-            for (int i = 0; i < (int)in_elements; i++) {
-                To in_val = transform(h_ptr[i]);
-                if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
-                out = reduce(out, in_val);
-            }
+        std::vector<To> h_ptr(tmp_elements);
+        getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
+                                     sizeof(To) * tmp_elements, h_ptr.data());
 
-            return out;
+        Binary<To, op> reduce;
+        To out = Binary<To, op>::init();
+        for (int i = 0; i < (int)tmp_elements; i++) {
+            out = reduce(out, h_ptr[i]);
+        }
+        return out;
+    } else {
+        std::vector<Ti> h_ptr(in_elements);
+        getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
+                                     sizeof(Ti) * in.info.offset,
+                                     sizeof(Ti) * in_elements, h_ptr.data());
+
+        Transform<Ti, To, op> transform;
+        Binary<To, op> reduce;
+        To out       = Binary<To, op>::init();
+        To nanval_to = scalar<To>(nanval);
+
+        for (int i = 0; i < (int)in_elements; i++) {
+            To in_val = transform(h_ptr[i]);
+            if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
+            out = reduce(out, in_val);
         }
-    }
-
 
+        return out;
+    }
 }
 
-}
+}  // namespace kernel
+
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/reduce_dim.cl b/src/backend/opencl/kernel/reduce_dim.cl
index 012661ca4e..f2bbba5aa6 100644
--- a/src/backend/opencl/kernel/reduce_dim.cl
+++ b/src/backend/opencl/kernel/reduce_dim.cl
@@ -7,57 +7,50 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void reduce_dim_kernel(__global To *oData,
-                       KParam oInfo,
-                       const __global Ti *iData,
-                       KParam iInfo,
-                       uint groups_x, uint groups_y, uint group_dim,
-                       int change_nan, To nanval)
-{
+__kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
+                                const __global Ti *iData, KParam iInfo,
+                                uint groups_x, uint groups_y, uint group_dim,
+                                int change_nan, To nanval) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * THREADS_X + lidx;
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) + lidx;
-    const uint yid = groupId_y;
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) + lidx;
+    const uint yid       = groupId_y;
 
     uint ids[4] = {xid, yid, zid, wid};
 
     // There is only one element per group for out
     // There are get_local_size(1) elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
-        ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+             ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
     const uint id_dim_out = ids[dim];
 
     ids[dim] = ids[dim] * get_local_size(1) + lidy;
-    iData  += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-        ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
+    iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+             ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
     const uint id_dim_in = ids[dim];
 
     const uint istride_dim = iInfo.strides[dim];
 
-    bool is_valid =
-        (ids[0] < iInfo.dims[0]) &&
-        (ids[1] < iInfo.dims[1]) &&
-        (ids[2] < iInfo.dims[2]) &&
-        (ids[3] < iInfo.dims[3]);
+    bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
+                    (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
 
     __local To s_val[THREADS_X * DIMY];
 
     To out_val = init;
     for (int id = id_dim_in; is_valid && (id < iInfo.dims[dim]);
          id += group_dim * get_local_size(1)) {
-
         To in_val = transform(*iData);
         if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
         out_val = binOp(in_val, out_val);
-        iData = iData + group_dim * get_local_size(1) * istride_dim;
+        iData   = iData + group_dim * get_local_size(1) * istride_dim;
     }
 
     s_val[lid] = out_val;
@@ -80,9 +73,7 @@ void reduce_dim_kernel(__global To *oData,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidy == 0 && is_valid &&
-        (id_dim_out < oInfo.dims[dim])) {
+    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[dim])) {
         *oData = *s_ptr;
     }
-
 }
diff --git a/src/backend/opencl/kernel/reduce_first.cl b/src/backend/opencl/kernel/reduce_first.cl
index 16dcf9d6d5..06edf09b38 100644
--- a/src/backend/opencl/kernel/reduce_first.cl
+++ b/src/backend/opencl/kernel/reduce_first.cl
@@ -7,36 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void reduce_first_kernel(__global To *oData,
-                         KParam oInfo,
-                         const __global Ti *iData,
-                         KParam iInfo,
-                         uint groups_x, uint groups_y, uint repeat,
-                         int change_nan, To nanval)
-{
+__kernel void reduce_first_kernel(__global To *oData, KParam oInfo,
+                                  const __global Ti *iData, KParam iInfo,
+                                  uint groups_x, uint groups_y, uint repeat,
+                                  int change_nan, To nanval) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * get_local_size(0) + lidx;
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) * repeat + lidx;
-    const uint yid = groupId_y * get_local_size(1) + lidy;
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) * repeat + lidx;
+    const uint yid       = groupId_y * get_local_size(1) + lidy;
 
     iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-        yid * iInfo.strides[1] + iInfo.offset;
+             yid * iInfo.strides[1] + iInfo.offset;
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1] + oInfo.offset;
 
-    bool cond = (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
+    bool cond =
+        (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
     __local To s_val[THREADS_PER_GROUP];
 
-    int last = (xid + repeat * DIMX);
-    int lim = last > iInfo.dims[0] ? iInfo.dims[0] : last;
+    int last   = (xid + repeat * DIMX);
+    int lim    = last > iInfo.dims[0] ? iInfo.dims[0] : last;
     To out_val = init;
 
     for (int id = xid; cond && id < lim; id += DIMX) {
@@ -55,31 +52,29 @@ void reduce_first_kernel(__global To *oData,
     }
 
     if (DIMX >= 128) {
-        if (lidx <  64) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx +  64]);
+        if (lidx < 64) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 64]);
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (DIMX >=  64) {
-        if (lidx <  32) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx +  32]);
+    if (DIMX >= 64) {
+        if (lidx < 32) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 32]);
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
     if (lidx < 16) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 16]);
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <  8) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx +  8]);
+    if (lidx < 8) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 8]);
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <  4) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx +  4]);
+    if (lidx < 4) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 4]);
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <  2) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx +  2]);
+    if (lidx < 2) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 2]);
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lidx <  1) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx +  1]);
+    if (lidx < 1) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 1]);
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (cond && lidx == 0) {
-        oData[groupId_x] = s_ptr[0];
-    }
+    if (cond && lidx == 0) { oData[groupId_x] = s_ptr[0]; }
 }
diff --git a/src/backend/opencl/kernel/regions.cl b/src/backend/opencl/kernel/regions.cl
index b0ee96ca95..0183696382 100644
--- a/src/backend/opencl/kernel/regions.cl
+++ b/src/backend/opencl/kernel/regions.cl
@@ -9,20 +9,18 @@
 
 // The initial label kernel distinguishes between valid (nonzero)
 // pixels and "background" (zero) pixels.
-__kernel
-void initial_label(global    T * equiv_map,
-                   KParam        eInfo,
-                   global char * bin_,
-                   KParam        bInfo)
-{
-    global char *bin = bin_ + bInfo.offset;
-    const int base_x = (get_group_id(0) * get_local_size(0) * N_PER_THREAD) + get_local_id(0);
-    const int base_y = (get_group_id(1) * get_local_size(1) * N_PER_THREAD) + get_local_id(1);
-
-    // If in bounds and a valid pixel, set the initial label.
-    #pragma unroll
+__kernel void initial_label(global T* equiv_map, KParam eInfo,
+                            global char* bin_, KParam bInfo) {
+    global char* bin = bin_ + bInfo.offset;
+    const int base_x =
+        (get_group_id(0) * get_local_size(0) * N_PER_THREAD) + get_local_id(0);
+    const int base_y =
+        (get_group_id(1) * get_local_size(1) * N_PER_THREAD) + get_local_id(1);
+
+// If in bounds and a valid pixel, set the initial label.
+#pragma unroll
     for (int xb = 0; xb < N_PER_THREAD; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < N_PER_THREAD; ++yb) {
             const int x = base_x + (xb * get_local_size(0));
             const int y = base_y + (yb * get_local_size(1));
@@ -34,27 +32,26 @@ void initial_label(global    T * equiv_map,
     }
 }
 
-__kernel
-void final_relabel(global       T    * equiv_map,
-                   KParam              eInfo,
-                   global       char * bin_,
-                   KParam              bInfo,
-                   global const T    * d_tmp)
-{
-    global char *bin = bin_ + bInfo.offset;
-    const int base_x = (get_group_id(0) * get_local_size(0) * N_PER_THREAD) + get_local_id(0);
-    const int base_y = (get_group_id(1) * get_local_size(1) * N_PER_THREAD) + get_local_id(1);
-
-    // If in bounds and a valid pixel, set the initial label.
-    #pragma unroll
+__kernel void final_relabel(global T* equiv_map, KParam eInfo,
+                            global char* bin_, KParam bInfo,
+                            global const T* d_tmp) {
+    global char* bin = bin_ + bInfo.offset;
+    const int base_x =
+        (get_group_id(0) * get_local_size(0) * N_PER_THREAD) + get_local_id(0);
+    const int base_y =
+        (get_group_id(1) * get_local_size(1) * N_PER_THREAD) + get_local_id(1);
+
+// If in bounds and a valid pixel, set the initial label.
+#pragma unroll
     for (int xb = 0; xb < N_PER_THREAD; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < N_PER_THREAD; ++yb) {
             const int x = base_x + (xb * get_local_size(0));
             const int y = base_y + (yb * get_local_size(1));
             const int n = y * bInfo.dims[0] + x;
             if (x < bInfo.dims[0] && y < bInfo.dims[1]) {
-                equiv_map[n] = (bin[n] > (char)0) ? d_tmp[(int)equiv_map[n]] : (T)0;
+                equiv_map[n] =
+                    (bin[n] > (char)0) ? d_tmp[(int)equiv_map[n]] : (T)0;
             }
         }
     }
@@ -63,8 +60,7 @@ void final_relabel(global       T    * equiv_map,
 // When two labels are equivalent, choose the lower label, but
 // do not choose zero, which indicates invalid.
 //#if T == double
-static inline T relabel(const T a, const T b)
-{
+static inline T relabel(const T a, const T b) {
     T aa = (a == 0) ? LIMIT_MAX : a;
     T bb = (b == 0) ? LIMIT_MAX : b;
     return min(aa, bb);
@@ -79,30 +75,29 @@ static inline T relabel(const T a, const T b)
 // NUM_WARPS = 8; // (Could compute this from block dim)
 // Number of elements to handle per thread in each dimension
 // N_PER_THREAD = 2; // 2x2 per thread = 4 total elems per thread
-__kernel
-void update_equiv(global T*   equiv_map,
-                  KParam      eInfo,
-                  global int* continue_flag)
-{
+__kernel void update_equiv(global T* equiv_map, KParam eInfo,
+                           global int* continue_flag) {
     // Basic coordinates
-    const int base_x = (get_group_id(0) * get_local_size(0) * N_PER_THREAD) + get_local_id(0);
-    const int base_y = (get_group_id(1) * get_local_size(1) * N_PER_THREAD) + get_local_id(1);
+    const int base_x =
+        (get_group_id(0) * get_local_size(0) * N_PER_THREAD) + get_local_id(0);
+    const int base_y =
+        (get_group_id(1) * get_local_size(1) * N_PER_THREAD) + get_local_id(1);
 
     const int width  = eInfo.dims[0];
     const int height = eInfo.dims[1];
 
     // Per element write flags and label, initially 0
-    char      write[N_PER_THREAD * N_PER_THREAD];
-    T    best_label[N_PER_THREAD * N_PER_THREAD];
+    char write[N_PER_THREAD * N_PER_THREAD];
+    T best_label[N_PER_THREAD * N_PER_THREAD];
 
-    #pragma unroll
+#pragma unroll
     for (int i = 0; i < N_PER_THREAD * N_PER_THREAD; ++i) {
         write[i]      = (char)0;
         best_label[i] = (T)0;
     }
 
     // Cached tile of the equivalency map
-    __local T s_tile[N_PER_THREAD*BLOCK_DIM][(N_PER_THREAD*BLOCK_DIM)];
+    __local T s_tile[N_PER_THREAD * BLOCK_DIM][(N_PER_THREAD * BLOCK_DIM)];
 
     // Space to track ballot funcs to track convergence
     __local int s_changed[NUM_WARPS];
@@ -110,7 +105,7 @@ void update_equiv(global T*   equiv_map,
     const int tn = (get_local_id(1) * get_local_size(0)) + get_local_id(0);
 
     const int warpSize = 32;
-    const int warpIdx = tn / warpSize;
+    const int warpIdx  = tn / warpSize;
     s_changed[warpIdx] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
@@ -118,22 +113,21 @@ void update_equiv(global T*   equiv_map,
     tid_changed[warpIdx] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    #pragma unroll
+#pragma unroll
     for (int xb = 0; xb < N_PER_THREAD; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < N_PER_THREAD; ++yb) {
-
             // Indexing variables
-            const int x = base_x + (xb * get_local_size(0));
-            const int y = base_y + (yb * get_local_size(1));
-            const int tx = get_local_id(0) + (xb * get_local_size(0));
-            const int ty = get_local_id(1) + (yb * get_local_size(1));
+            const int x     = base_x + (xb * get_local_size(0));
+            const int y     = base_y + (yb * get_local_size(1));
+            const int tx    = get_local_id(0) + (xb * get_local_size(0));
+            const int ty    = get_local_id(1) + (yb * get_local_size(1));
             const int tid_i = xb * N_PER_THREAD + yb;
-            const int n = y * width + x;
+            const int n     = y * width + x;
 
             // Get the label for this pixel if we're  in bounds
-            const T orig_label = (x < width && y < height) ?
-                equiv_map[n] : (T)0;
+            const T orig_label =
+                (x < width && y < height) ? equiv_map[n] : (T)0;
             s_tile[ty][tx] = orig_label;
 
             // Find the lowest label of the nearest valid pixel
@@ -141,46 +135,45 @@ void update_equiv(global T*   equiv_map,
             best_label[tid_i] = orig_label;
 
             if (orig_label != (T)0) {
-
-                const int south_y = min(y, height-2) + 1;
+                const int south_y = min(y, height - 2) + 1;
                 const int north_y = max(y, 1) - 1;
-                const int east_x = min(x, width-2) + 1;
-                const int west_x = max(x, 1) - 1;
+                const int east_x  = min(x, width - 2) + 1;
+                const int west_x  = max(x, 1) - 1;
 
                 // Check bottom
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[(south_y) * width + x]);
+                best_label[tid_i] =
+                    relabel(best_label[tid_i], equiv_map[(south_y)*width + x]);
 
                 // Check right neighbor
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[y * width + east_x]);
+                best_label[tid_i] =
+                    relabel(best_label[tid_i], equiv_map[y * width + east_x]);
 
                 // Check left neighbor
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[y * width + west_x]);
+                best_label[tid_i] =
+                    relabel(best_label[tid_i], equiv_map[y * width + west_x]);
 
                 // Check top neighbor
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[(north_y) * width + x]);
+                best_label[tid_i] =
+                    relabel(best_label[tid_i], equiv_map[(north_y)*width + x]);
 
 #ifdef FULL_CONN
                 // Check NW corner
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[(north_y) * width + west_x]);
+                best_label[tid_i] = relabel(
+                    best_label[tid_i], equiv_map[(north_y)*width + west_x]);
 
                 // Check NE corner
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[(north_y) * width + east_x]);
+                best_label[tid_i] = relabel(
+                    best_label[tid_i], equiv_map[(north_y)*width + east_x]);
 
                 // Check SW corner
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[(south_y) * width + west_x]);
+                best_label[tid_i] = relabel(
+                    best_label[tid_i], equiv_map[(south_y)*width + west_x]);
 
                 // Check SE corner
-                best_label[tid_i] = relabel(best_label[tid_i],
-                        equiv_map[(south_y) * width + east_x]);
-#endif // if connectivity == 8
-            } // if orig_label != 0
+                best_label[tid_i] = relabel(
+                    best_label[tid_i], equiv_map[(south_y)*width + east_x]);
+#endif         // if connectivity == 8
+            }  // if orig_label != 0
 
             // Process the equivalency list.
             T last_label = orig_label;
@@ -188,13 +181,13 @@ void update_equiv(global T*   equiv_map,
 
             while (best_label[tid_i] != (T)0 && new_label < last_label) {
                 last_label = new_label;
-                new_label = equiv_map[(int)new_label - 1];
+                new_label  = equiv_map[(int)new_label - 1];
             }
 
             if (orig_label != new_label) {
                 tid_changed[warpIdx] = 1;
-                s_tile[ty][tx] = new_label;
-                write[tid_i] = (char)1;
+                s_tile[ty][tx]       = new_label;
+                write[tid_i]         = (char)1;
             }
             best_label[tid_i] = new_label;
         }
@@ -203,70 +196,69 @@ void update_equiv(global T*   equiv_map,
 
     // Determine if any pixel changed
     unsigned int continue_iter = 0;
-    s_changed[warpIdx] = tid_changed[warpIdx];
+    s_changed[warpIdx]         = tid_changed[warpIdx];
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    #pragma unroll
+#pragma unroll
     for (int i = 0; i < NUM_WARPS; i++)
         continue_iter = continue_iter || (s_changed[i] != 0);
 
     // Iterate until no pixel in the tile changes
     while (continue_iter != 0) {
-
         // Reset whether or not this thread's pixels have changed.
         tid_changed[warpIdx] = 0;
 
-        #pragma unroll
+#pragma unroll
         for (int xb = 0; xb < N_PER_THREAD; ++xb) {
-            #pragma unroll
+#pragma unroll
             for (int yb = 0; yb < N_PER_THREAD; ++yb) {
-
                 // Indexing
-                const int tx = get_local_id(0) + (xb * get_local_size(0));
-                const int ty = get_local_id(1) + (yb * get_local_size(1));
+                const int tx    = get_local_id(0) + (xb * get_local_size(0));
+                const int ty    = get_local_id(1) + (yb * get_local_size(1));
                 const int tid_i = xb * N_PER_THREAD + yb;
 
                 T last_label = best_label[tid_i];
 
                 if (best_label[tid_i] != 0) {
-
-                    const int north_y   = max(ty, 1) - 1;
-                    const int south_y   = min(ty, N_PER_THREAD*BLOCK_DIM - 2) + 1;
-                    const int east_x    = min(tx, N_PER_THREAD*BLOCK_DIM - 2) + 1;
-                    const int west_x    = max(tx, 1) - 1;
+                    const int north_y = max(ty, 1) - 1;
+                    const int south_y =
+                        min(ty, N_PER_THREAD * BLOCK_DIM - 2) + 1;
+                    const int east_x =
+                        min(tx, N_PER_THREAD * BLOCK_DIM - 2) + 1;
+                    const int west_x = max(tx, 1) - 1;
 
                     // Check bottom
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[south_y][tx]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[south_y][tx]);
 
                     // Check right neighbor
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[ty][east_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[ty][east_x]);
 
                     // Check left neighbor
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[ty][west_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[ty][west_x]);
 
                     // Check top neighbor
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[north_y][tx]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[north_y][tx]);
 
 #ifdef FULL_CONN
                     // Check NW corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[north_y][west_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[north_y][west_x]);
 
                     // Check NE corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[north_y][east_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[north_y][east_x]);
 
                     // Check SW corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[south_y][west_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[south_y][west_x]);
 
                     // Check SE corner
-                    best_label[tid_i] = relabel(best_label[tid_i],
-                                                s_tile[south_y][east_x]);
+                    best_label[tid_i] =
+                        relabel(best_label[tid_i], s_tile[south_y][east_x]);
 #endif
                 }
                 // This thread's value changed  this iteration if the
@@ -283,16 +275,16 @@ void update_equiv(global T*   equiv_map,
         s_changed[warpIdx] = tid_changed[warpIdx];
         barrier(CLK_LOCAL_MEM_FENCE);
         continue_iter = 0;
-        #pragma unroll
+#pragma unroll
         for (int i = 0; i < NUM_WARPS; i++)
             continue_iter |= (s_changed[i] != 0);
 
         // If we have to continue iterating, update the tile of the
         // equiv map in shared memory
         if (continue_iter != 0) {
-            #pragma unroll
+#pragma unroll
             for (int xb = 0; xb < N_PER_THREAD; ++xb) {
-                #pragma unroll
+#pragma unroll
                 for (int yb = 0; yb < N_PER_THREAD; ++yb) {
                     const int tx = get_local_id(0) + (xb * get_local_size(0));
                     const int ty = get_local_id(1) + (yb * get_local_size(1));
@@ -303,19 +295,19 @@ void update_equiv(global T*   equiv_map,
             }
             barrier(CLK_LOCAL_MEM_FENCE);
         }
-    } // while (continue_iter)
+    }  // while (continue_iter)
 
-    // Write out equiv_map
-    #pragma unroll
+// Write out equiv_map
+#pragma unroll
     for (int xb = 0; xb < N_PER_THREAD; ++xb) {
-        #pragma unroll
+#pragma unroll
         for (int yb = 0; yb < N_PER_THREAD; ++yb) {
-            const int x = base_x + (xb * get_local_size(0));
-            const int y = base_y + (yb * get_local_size(1));
-            const int n = y * width + x;
+            const int x     = base_x + (xb * get_local_size(0));
+            const int y     = base_y + (yb * get_local_size(1));
+            const int n     = y * width + x;
             const int tid_i = xb * N_PER_THREAD + yb;
             if (x < width && y < height && write[tid_i]) {
-                equiv_map[n]  = best_label[tid_i];
+                equiv_map[n]   = best_label[tid_i];
                 *continue_flag = 1;
             }
         }
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 26e74b828e..d30800a615 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -8,68 +8,64 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/regions.hpp>
-#include <program.hpp>
-#include <af/defines.h>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <kernel_headers/regions.hpp>
 #include <math.hpp>
-#include <stdio.h>
-#include <cache.hpp>
 #include <memory.hpp>
+#include <program.hpp>
+#include <stdio.h>
+#include <af/defines.h>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
-#include <boost/compute/container/vector.hpp>
 #include <boost/compute/algorithm/adjacent_difference.hpp>
-#include <boost/compute/algorithm/sort.hpp>
-#include <boost/compute/iterator/counting_iterator.hpp>
 #include <boost/compute/algorithm/exclusive_scan.hpp>
+#include <boost/compute/algorithm/sort.hpp>
 #include <boost/compute/algorithm/transform.hpp>
-#include <boost/compute/lambda/placeholders.hpp>
+#include <boost/compute/container/vector.hpp>
+#include <boost/compute/iterator/counting_iterator.hpp>
 #include <boost/compute/lambda.hpp>
+#include <boost/compute/lambda/placeholders.hpp>
 
 #pragma GCC diagnostic pop
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 namespace compute = boost::compute;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename T, bool full_conn, int n_per_thread>
-std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*>
-getRegionsKernels()
-{
-    static const int block_dim = 16;
-    static const int num_warps = 8;
-    static const unsigned NUM_KERNELS = 3;
-    static const char* kernelNames[NUM_KERNELS] =
-        {"initial_label", "final_relabel", "update_equiv"};
+std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*> getRegionsKernels() {
+    static const int block_dim                  = 16;
+    static const int num_warps                  = 8;
+    static const unsigned NUM_KERNELS           = 3;
+    static const char* kernelNames[NUM_KERNELS] = {
+        "initial_label", "final_relabel", "update_equiv"};
 
     kc_entry_t entries[NUM_KERNELS];
 
     int device = getActiveDeviceId();
 
     std::string checkName = kernelNames[0] + std::string("_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(full_conn) + std::to_string(n_per_thread);
+                            std::string(dtype_traits<T>::getName()) +
+                            std::to_string(full_conn) +
+                            std::to_string(n_per_thread);
 
     entries[0] = kernelCache(device, checkName);
 
-    if (entries[0].prog==0 && entries[0].ker==0)
-    {
+    if (entries[0].prog == 0 && entries[0].ker == 0) {
         ToNumStr<T> toNumStr;
         std::ostringstream options;
         if (full_conn) {
@@ -79,8 +75,7 @@ getRegionsKernels()
                     << " -D N_PER_THREAD=" << n_per_thread
                     << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
                     << " -D FULL_CONN";
-        }
-        else {
+        } else {
             options << " -D T=" << dtype_traits<T>::getName()
                     << " -D BLOCK_DIM=" << block_dim
                     << " -D NUM_WARPS=" << num_warps
@@ -91,26 +86,27 @@ getRegionsKernels()
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {regions_cl};
-        const int   ker_lens[] = {regions_cl_len};
+        const int ker_lens[]   = {regions_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
 
-        for (unsigned i=0; i<NUM_KERNELS; ++i)
-        {
+        for (unsigned i = 0; i < NUM_KERNELS; ++i) {
             entries[i].prog = new Program(prog);
             entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] + std::string("_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::to_string(full_conn) + std::to_string(n_per_thread);
+                               std::string(dtype_traits<T>::getName()) +
+                               std::to_string(full_conn) +
+                               std::to_string(n_per_thread);
 
             addKernelToCache(device, name, entries[i]);
         }
     } else {
-        for (unsigned i=1; i<NUM_KERNELS; ++i) {
+        for (unsigned i = 1; i < NUM_KERNELS; ++i) {
             std::string name = kernelNames[i] + std::string("_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::to_string(full_conn) + std::to_string(n_per_thread);
+                               std::string(dtype_traits<T>::getName()) +
+                               std::to_string(full_conn) +
+                               std::to_string(n_per_thread);
 
             entries[i] = kernelCache(device, name);
         }
@@ -120,37 +116,41 @@ getRegionsKernels()
 }
 
 template<typename T, bool full_conn, int n_per_thread>
-void regions(Param out, Param in)
-{
+void regions(Param out, Param in) {
     auto kernels = getRegionsKernels<T, full_conn, n_per_thread>();
 
     const NDRange local(THREADS_X, THREADS_Y);
 
-    const int blk_x = divup(in.info.dims[0], THREADS_X*2);
-    const int blk_y = divup(in.info.dims[1], THREADS_Y*2);
+    const int blk_x = divup(in.info.dims[0], THREADS_X * 2);
+    const int blk_y = divup(in.info.dims[1], THREADS_Y * 2);
 
     const NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
 
-    auto ilOp = KernelFunctor<Buffer, KParam, Buffer, KParam> (*std::get<0>(kernels));
+    auto ilOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam>(*std::get<0>(kernels));
 
-    ilOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info);
+    ilOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+         in.info);
 
     CL_DEBUG_FINISH(getQueue());
 
-    int h_continue = 1;
-    cl::Buffer *d_continue = bufferAlloc(sizeof(int));
+    int h_continue         = 1;
+    cl::Buffer* d_continue = bufferAlloc(sizeof(int));
 
     while (h_continue) {
         h_continue = 0;
-        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
+        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
+                                      &h_continue);
 
-        auto ueOp = KernelFunctor<Buffer, KParam, Buffer> (*std::get<2>(kernels));
+        auto ueOp =
+            KernelFunctor<Buffer, KParam, Buffer>(*std::get<2>(kernels));
 
-        ueOp(EnqueueArgs(getQueue(), global, local),
-              *out.data, out.info, *d_continue);
+        ueOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *d_continue);
         CL_DEBUG_FINISH(getQueue());
 
-        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int), &h_continue);
+        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
+                                     &h_continue);
     }
 
     bufferFree(d_continue);
@@ -166,7 +166,8 @@ void regions(Param out, Param in)
     // Wrap raw device ptr
     compute::context context(getContext()());
     compute::vector<T> tmp(size, context);
-    clEnqueueCopyBuffer(getQueue()(), (*out.data)(), tmp.get_buffer().get(), 0, 0, size * sizeof(T), 0, NULL, NULL);
+    clEnqueueCopyBuffer(getQueue()(), (*out.data)(), tmp.get_buffer().get(), 0,
+                        0, size * sizeof(T), 0, NULL, NULL);
 
     // Sort the copy
     compute::sort(tmp.begin(), tmp.end(), c_queue);
@@ -175,7 +176,8 @@ void regions(Param out, Param in)
     // of label assignments to compute.
     T last_label;
     clEnqueueReadBuffer(getQueue()(), tmp.get_buffer().get(), CL_TRUE,
-                        (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL, NULL);
+                        (size - 1) * sizeof(T), sizeof(T), &last_label, 0, NULL,
+                        NULL);
     const int num_bins = (int)last_label + 1;
 
     // If the number of label assignments is two,
@@ -183,30 +185,26 @@ void regions(Param out, Param in)
     // component(1's) or it has only one component other than
     // background(0's). Either way, no further
     // post-processing of labels is required.
-    if (num_bins<=2)
-        return;
+    if (num_bins <= 2) return;
 
     Buffer labels(getContext(), CL_MEM_READ_WRITE, num_bins * sizeof(T));
     compute::buffer c_labels(labels());
-    compute::buffer_iterator<T> labels_begin = compute::make_buffer_iterator<T>(c_labels, 0);
-    compute::buffer_iterator<T> labels_end   = compute::make_buffer_iterator<T>(c_labels, num_bins);
+    compute::buffer_iterator<T> labels_begin =
+        compute::make_buffer_iterator<T>(c_labels, 0);
+    compute::buffer_iterator<T> labels_end =
+        compute::make_buffer_iterator<T>(c_labels, num_bins);
 
     // Find the end of each section of values
     compute::counting_iterator<T> search_begin(0);
 
     int tmp_size = size;
-    BOOST_COMPUTE_CLOSURE(int, upper_bound_closure, (int v), (tmp, tmp_size),
-    {
+    BOOST_COMPUTE_CLOSURE(int, upper_bound_closure, (int v), (tmp, tmp_size), {
         int start = 0, n = tmp_size, i;
-        while(start < n)
-        {
+        while (start < n) {
             i = (start + n) / 2;
-            if(v < tmp[i])
-            {
+            if (v < tmp[i]) {
                 n = i;
-            }
-            else
-            {
+            } else {
                 start = i + 1;
             }
         }
@@ -214,27 +212,29 @@ void regions(Param out, Param in)
     });
 
     BOOST_COMPUTE_FUNCTION(int, clamp_to_one, (int i),
-    {
-        return (i >= 1) ? 1 : i;
-    });
+                           { return (i >= 1) ? 1 : i; });
 
-    compute::transform(search_begin, search_begin + num_bins,
-                       labels_begin, upper_bound_closure, c_queue);
-    compute::adjacent_difference(labels_begin, labels_end, labels_begin, c_queue);
+    compute::transform(search_begin, search_begin + num_bins, labels_begin,
+                       upper_bound_closure, c_queue);
+    compute::adjacent_difference(labels_begin, labels_end, labels_begin,
+                                 c_queue);
 
     // Perform the scan -- this can computes the correct labels for each
     // component
-    compute::transform(labels_begin, labels_end, labels_begin, clamp_to_one, c_queue);
+    compute::transform(labels_begin, labels_end, labels_begin, clamp_to_one,
+                       c_queue);
 
     compute::exclusive_scan(labels_begin, labels_end, labels_begin, c_queue);
 
     // Apply the correct labels to the equivalency map
-    auto frOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer> (*std::get<1>(kernels));
+    auto frOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer>(
+        *std::get<1>(kernels));
 
-    //Buffer labels_buf(tmp.get_buffer().get());
-    frOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data, in.info, labels);
+    // Buffer labels_buf(tmp.get_buffer().get());
+    frOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+         in.info, labels);
 
     CL_DEBUG_FINISH(getQueue());
 }
-} //namespace kernel
-} //namespace opencl
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/reorder.cl b/src/backend/opencl/kernel/reorder.cl
index 8153341e94..52a1bfdff5 100644
--- a/src/backend/opencl/kernel/reorder.cl
+++ b/src/backend/opencl/kernel/reorder.cl
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void reorder_kernel(__global T *out, __global const T *in, const KParam op, const KParam ip,
-                    const int d0, const int d1, const int d2, const int d3,
-                    const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void reorder_kernel(__global T *out, __global const T *in,
+                             const KParam op, const KParam ip, const int d0,
+                             const int d1, const int d2, const int d3,
+                             const int blocksPerMatX, const int blocksPerMatY) {
     const int oz = get_group_id(0) / blocksPerMatX;
     const int ow = get_group_id(1) / blocksPerMatY;
 
@@ -21,10 +20,8 @@ void reorder_kernel(__global T *out, __global const T *in, const KParam op, cons
     const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(xx >= op.dims[0] ||
-       yy >= op.dims[1] ||
-       oz >= op.dims[2] ||
-       ow >= op.dims[3])
+    if (xx >= op.dims[0] || yy >= op.dims[1] || oz >= op.dims[2] ||
+        ow >= op.dims[3])
         return;
 
     const int incy = blocksPerMatY * get_local_size(1);
@@ -32,21 +29,21 @@ void reorder_kernel(__global T *out, __global const T *in, const KParam op, cons
 
     const int o_off   = ow * op.strides[3] + oz * op.strides[2];
     const int rdims[] = {d0, d1, d2, d3};
-          int ods[]   = {xx, yy, oz, ow};
-          int ids[4]  = {0};
+    int ods[]         = {xx, yy, oz, ow};
+    int ids[4]        = {0};
 
     ids[rdims[3]] = ow;
     ids[rdims[2]] = oz;
 
-    for(int oy = yy; oy < op.dims[1]; oy += incy) {
+    for (int oy = yy; oy < op.dims[1]; oy += incy) {
         ids[rdims[1]] = oy;
-        for(int ox = xx; ox < op.dims[0]; ox += incx) {
+        for (int ox = xx; ox < op.dims[0]; ox += incx) {
             ids[rdims[0]] = ox;
 
             const int oIdx = o_off + oy * op.strides[1] + ox;
 
             const int iIdx = ids[3] * ip.strides[3] + ids[2] * ip.strides[2] +
-                                  ids[1] * ip.strides[1] + ids[0];
+                             ids[1] * ip.strides[1] + ids[0];
 
             out[oIdx] = in[ip.offset + iIdx];
         }
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index a15939daa1..d7ef354238 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -8,49 +8,47 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/reorder.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
-static const int TX = 32;
-static const int TY = 8;
+static const int TX    = 32;
+static const int TY    = 8;
 static const int TILEX = 512;
 static const int TILEY = 32;
 
 template<typename T>
-void reorder(Param out, const Param in, const dim_t *rdims)
-{
-    std::string refName = std::string("reorder_kernel_") + std::string(dtype_traits<T>::getName());
+void reorder(Param out, const Param in, const dim_t* rdims) {
+    std::string refName = std::string("reorder_kernel_") +
+                          std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {reorder_cl};
-        const int   ker_lens[] = {reorder_cl_len};
+        const int ker_lens[]   = {reorder_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -59,9 +57,10 @@ void reorder(Param out, const Param in, const dim_t *rdims)
         addKernelToCache(device, refName, entry);
     }
 
-    auto reorderOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
-                                    const int, const int, const int, const int,
-                                    const int, const int >(*entry.ker);
+    auto reorderOp =
+        KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
+                      const int, const int, const int, const int, const int,
+                      const int>(*entry.ker);
 
     NDRange local(TX, TY, 1);
 
@@ -70,12 +69,11 @@ void reorder(Param out, const Param in, const dim_t *rdims)
     NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                    local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-    reorderOp(EnqueueArgs(getQueue(), global, local),
-              *out.data, *in.data, out.info, in.info,
-              rdims[0], rdims[1], rdims[2], rdims[3],
+    reorderOp(EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
+              out.info, in.info, rdims[0], rdims[1], rdims[2], rdims[3],
               blocksPerMatX, blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/resize.cl b/src/backend/opencl/kernel/resize.cl
index f4eaf6f2f2..e69e53a50b 100644
--- a/src/backend/opencl/kernel/resize.cl
+++ b/src/backend/opencl/kernel/resize.cl
@@ -9,10 +9,11 @@
 
 #if CPLX
 #define set(a, b) a = b
-#define set_scalar(a, b) do {                   \
-        a.x = b;                                \
-        a.y = 0;                                \
-    } while(0)
+#define set_scalar(a, b) \
+    do {                 \
+        a.x = b;         \
+        a.y = 0;         \
+    } while (0)
 
 #else
 
@@ -27,45 +28,41 @@
 
 ////////////////////////////////////////////////////////////////////////////////////
 // nearest-neighbor resampling
-void resize_n_(__global T* d_out, const KParam out,
-               __global const T* d_in, const KParam in,
-               const int blockIdx_x, const int blockIdx_y,
-               const float xf, const float yf)
-{
+void resize_n_(__global T* d_out, const KParam out, __global const T* d_in,
+               const KParam in, const int blockIdx_x, const int blockIdx_y,
+               const float xf, const float yf) {
     int const ox = get_local_id(0) + blockIdx_x * get_local_size(0);
     int const oy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    //int ix = convert_int_rtp(ox * xf);
-    //int iy = convert_int_rtp(oy * yf);
+    // int ix = convert_int_rtp(ox * xf);
+    // int iy = convert_int_rtp(oy * yf);
     int ix = round(ox * xf);
     int iy = round(oy * yf);
 
     if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-    if (ix >=  in.dims[0]) { ix = in.dims[0] - 1; }
-    if (iy >=  in.dims[1]) { iy = in.dims[1] - 1; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
 
     d_out[ox + oy * out.strides[1]] = d_in[ix + iy * in.strides[1]];
 }
 
 ////////////////////////////////////////////////////////////////////////////////////
 // bilinear resampling
-void resize_b_(__global T* d_out, const KParam out,
-               __global const T* d_in, const KParam in,
-               const int blockIdx_x, const int blockIdx_y,
-               const float xf_, const float yf_)
-{
+void resize_b_(__global T* d_out, const KParam out, __global const T* d_in,
+               const KParam in, const int blockIdx_x, const int blockIdx_y,
+               const float xf_, const float yf_) {
     int const ox = get_local_id(0) + blockIdx_x * get_local_size(0);
     int const oy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
     float xf = ox * xf_;
     float yf = oy * yf_;
 
-    int ix   = floor(xf);
-    int iy   = floor(yf);
+    int ix = floor(xf);
+    int iy = floor(yf);
 
     if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-    if (ix >=  in.dims[0]) { ix = in.dims[0] - 1; }
-    if (iy >=  in.dims[1]) { iy = in.dims[1] - 1; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
 
     float b = xf - ix;
     float a = yf - iy;
@@ -73,26 +70,21 @@ void resize_b_(__global T* d_out, const KParam out,
     const int ix2 = (ix + 1) < in.dims[0] ? (ix + 1) : ix;
     const int iy2 = (iy + 1) < in.dims[1] ? (iy + 1) : iy;
 
-    const VT p1 = d_in[ix  + in.strides[1] * iy ];
-    const VT p2 = d_in[ix  + in.strides[1] * iy2];
-    const VT p3 = d_in[ix2 + in.strides[1] * iy ];
+    const VT p1 = d_in[ix + in.strides[1] * iy];
+    const VT p2 = d_in[ix + in.strides[1] * iy2];
+    const VT p3 = d_in[ix2 + in.strides[1] * iy];
     const VT p4 = d_in[ix2 + in.strides[1] * iy2];
 
     d_out[ox + oy * out.strides[1]] =
-             (((1.0f-a) * (1.0f-b)) * p1) +
-             (((a)      * (1.0f-b)) * p2) +
-             (((1.0f-a) * (b)     ) * p3) +
-             (((a)      * (b)     ) * p4);
-
+        (((1.0f - a) * (1.0f - b)) * p1) + (((a) * (1.0f - b)) * p2) +
+        (((1.0f - a) * (b)) * p3) + (((a) * (b)) * p4);
 }
 
 ////////////////////////////////////////////////////////////////////////////////////
 // lower resampling
-void resize_l_(__global T* d_out, const KParam out,
-               __global const T* d_in, const KParam in,
-               const int blockIdx_x, const int blockIdx_y,
-               const float xf, const float yf)
-{
+void resize_l_(__global T* d_out, const KParam out, __global const T* d_in,
+               const KParam in, const int blockIdx_x, const int blockIdx_y,
+               const float xf, const float yf) {
     int const ox = get_local_id(0) + blockIdx_x * get_local_size(0);
     int const oy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
@@ -100,26 +92,26 @@ void resize_l_(__global T* d_out, const KParam out,
     int iy = (oy * yf);
 
     if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-    if (ix >=  in.dims[0]) { ix = in.dims[0] - 1; }
-    if (iy >=  in.dims[1]) { iy = in.dims[1] - 1; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
 
     d_out[ox + oy * out.strides[1]] = d_in[ix + iy * in.strides[1]];
 }
 
 ////////////////////////////////////////////////////////////////////////////////////
 // Wrapper Kernel
-__kernel
-void resize_kernel(__global T *d_out, const KParam out,
-                   __global const T *d_in, const KParam in,
-                   const int b0, const int b1, const float xf, const float yf)
-{
+__kernel void resize_kernel(__global T* d_out, const KParam out,
+                            __global const T* d_in, const KParam in,
+                            const int b0, const int b1, const float xf,
+                            const float yf) {
     int bIdx = get_group_id(0) / b0;
     int bIdy = get_group_id(1) / b1;
     // batch adjustment
-    int i_off = bIdy *  in.strides[3] + bIdx *  in.strides[2] + in.offset;
-    int o_off = bIdy * out.strides[3] + bIdx * out.strides[2];
-    int blockIdx_x =  get_group_id(0) - bIdx * b0;
-    int blockIdx_y =  get_group_id(1) - bIdy * b1;
+    int i_off      = bIdy * in.strides[3] + bIdx * in.strides[2] + in.offset;
+    int o_off      = bIdy * out.strides[3] + bIdx * out.strides[2];
+    int blockIdx_x = get_group_id(0) - bIdx * b0;
+    int blockIdx_y = get_group_id(1) - bIdy * b1;
 
-    INTERP(d_out + o_off, out, d_in + i_off, in, blockIdx_x, blockIdx_y, xf, yf);
+    INTERP(d_out + o_off, out, d_in + i_off, in, blockIdx_x, blockIdx_y, xf,
+           yf);
 }
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 83fa03a388..3095eb562e 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -8,56 +8,55 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/resize.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <common/complex.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int RESIZE_TX = 16;
 static const int RESIZE_TY = 16;
 
 template<typename T>
-using wtype_t = typename std::conditional<std::is_same<T, double>::value, double, float>::type;
+using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                          double, float>::type;
 
 template<typename T>
-using vtype_t = typename std::conditional<common::is_complex<T>::value, T, wtype_t<T> >::type;
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
+                                          wtype_t<T>>::type;
 
 template<typename T, af_interp_type method>
-void resize(Param out, const Param in)
-{
+void resize(Param out, const Param in) {
     typedef typename dtype_traits<T>::base_type BT;
 
     std::string refName = std::string("reorder_kernel_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(method);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(method);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D T="        << dtype_traits<T>::getName();
-        options << " -D VT="       << dtype_traits<vtype_t<T>>::getName();
-        options << " -D WT="       << dtype_traits<wtype_t<BT>>::getName();
-
-        switch(method) {
-            case AF_INTERP_NEAREST:  options <<" -D INTERP=NEAREST" ; break;
-            case AF_INTERP_BILINEAR: options <<" -D INTERP=BILINEAR"; break;
-            case AF_INTERP_LOWER:    options <<" -D INTERP=LOWER"   ; break;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D VT=" << dtype_traits<vtype_t<T>>::getName();
+        options << " -D WT=" << dtype_traits<wtype_t<BT>>::getName();
+
+        switch (method) {
+            case AF_INTERP_NEAREST: options << " -D INTERP=NEAREST"; break;
+            case AF_INTERP_BILINEAR: options << " -D INTERP=BILINEAR"; break;
+            case AF_INTERP_LOWER: options << " -D INTERP=LOWER"; break;
             default: break;
         }
 
-        if((af_dtype) dtype_traits<T>::af_type == c32 ||
-            (af_dtype) dtype_traits<T>::af_type == c64) {
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
             options << " -D CPLX=1";
             options << " -D TB=" << dtype_traits<BT>::getName();
         } else {
@@ -68,7 +67,7 @@ void resize(Param out, const Param in)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {resize_cl};
-        const int   ker_lens[] = {resize_cl_len};
+        const int ker_lens[]   = {resize_cl_len};
         cl::Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new cl::Program(prog);
@@ -77,8 +76,10 @@ void resize(Param out, const Param in)
         addKernelToCache(device, refName, entry);
     }
 
-    auto resizeOp = cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer, const KParam,
-                                      const int, const int, const float, const float > (*entry.ker);
+    auto resizeOp =
+        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
+                          const KParam, const int, const int, const float,
+                          const float>(*entry.ker);
 
     cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
@@ -92,10 +93,10 @@ void resize(Param out, const Param in)
 
     float xf = (float)xd, yf = (float)yd;
 
-    resizeOp(cl::EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
+    resizeOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/rotate.cl b/src/backend/opencl/kernel/rotate.cl
index e77c367ce3..835ce0c5ae 100644
--- a/src/backend/opencl/kernel/rotate.cl
+++ b/src/backend/opencl/kernel/rotate.cl
@@ -15,17 +15,16 @@ typedef struct {
     float tmat[6];
 } tmat_t;
 
-__kernel
-void rotate_kernel(__global T *d_out, const KParam out,
-                   __global const T *d_in, const KParam in,
-                   const tmat_t t, const int nimages, const int batches,
-                   const int blocksXPerImage, const int blocksYPerImage, int method)
-{
+__kernel void rotate_kernel(__global T *d_out, const KParam out,
+                            __global const T *d_in, const KParam in,
+                            const tmat_t t, const int nimages,
+                            const int batches, const int blocksXPerImage,
+                            const int blocksYPerImage, int method) {
     // Compute which image set
-    const int setId = get_group_id(0) / blocksXPerImage;
+    const int setId      = get_group_id(0) / blocksXPerImage;
     const int blockIdx_x = get_group_id(0) - setId * blocksXPerImage;
 
-    const int batch = get_group_id(1) / blocksYPerImage;
+    const int batch      = get_group_id(1) / blocksYPerImage;
     const int blockIdx_y = get_group_id(1) - batch * blocksYPerImage;
 
     // Get thread indices
@@ -34,27 +33,27 @@ void rotate_kernel(__global T *d_out, const KParam out,
 
     const int limages = min((int)out.dims[2] - setId * nimages, nimages);
 
-    if(xido >= out.dims[0] || yido >= out.dims[1])
-        return;
+    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
 
     InterpPosTy xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
     InterpPosTy yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
 
-    int outoff = out.offset + setId * nimages * out.strides[2] + batch * out.strides[3];
-    int inoff = in.offset + setId * nimages * in.strides[2] + batch * in.strides[3];
+    int outoff =
+        out.offset + setId * nimages * out.strides[2] + batch * out.strides[3];
+    int inoff =
+        in.offset + setId * nimages * in.strides[2] + batch * in.strides[3];
 
     const int loco = outoff + (yido * out.strides[1] + xido);
 
     InterpInTy zero = ZERO;
     if (INTERP_ORDER > 1) {
         // Special conditions to deal with boundaries for bilinear and bicubic
-        // FIXME: Ideally this condition should be removed or be present for all methods
-        // But tests are expecting a different behavior for bilinear and nearest
-        if (xidi < (InterpPosTy)-0.0001 ||
-            yidi < (InterpPosTy)-0.0001 ||
-            in.dims[0] <= xidi ||
-            in.dims[1] <= yidi) {
-            for(int i = 0; i < nimages; i++) {
+        // FIXME: Ideally this condition should be removed or be present for all
+        // methods But tests are expecting a different behavior for bilinear and
+        // nearest
+        if (xidi < (InterpPosTy)-0.0001 || yidi < (InterpPosTy)-0.0001 ||
+            in.dims[0] <= xidi || in.dims[1] <= yidi) {
+            for (int i = 0; i < nimages; i++) {
                 d_out[loco + i * out.strides[2]] = zero;
             }
             return;
@@ -64,7 +63,6 @@ void rotate_kernel(__global T *d_out, const KParam out,
     // FIXME: Nearest and lower do not do clamping, but other methods do
     // Make it consistent
     bool clamp = INTERP_ORDER != 1;
-    interp2(d_out, out, loco,
-            d_in,  in, inoff,
-            xidi, yidi, method, limages, clamp);
+    interp2(d_out, out, loco, d_in, in, inoff, xidi, yidi, method, limages,
+            clamp);
 }
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 4d947d012a..c69c9fa502 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -8,25 +8,23 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/interp.hpp>
 #include <kernel_headers/rotate.hpp>
+#include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <common/complex.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <math.hpp>
+#include <string>
 #include "config.hpp"
 #include "interp.hpp"
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int TX = 16;
 static const int TY = 16;
 // Used for batching images
@@ -37,34 +35,36 @@ typedef struct {
 } tmat_t;
 
 template<typename T>
-using wtype_t = typename std::conditional<std::is_same<T, double>::value, double, float>::type;
+using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                          double, float>::type;
 
 template<typename T>
-using vtype_t = typename std::conditional<common::is_complex<T>::value, T, wtype_t<T> >::type;
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
+                                          wtype_t<T>>::type;
 
 template<typename T, int order>
-void rotate(Param out, const Param in, const float theta, af_interp_type method)
-{
+void rotate(Param out, const Param in, const float theta,
+            af_interp_type method) {
     typedef typename dtype_traits<T>::base_type BT;
 
     std::string refName = std::string("rotate_kernel_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(order);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(order);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         ToNumStr<T> toNumStr;
         std::ostringstream options;
-        options << " -D T="        << dtype_traits<T>::getName();
-        options << " -D ZERO="      << toNumStr(scalar<T>(0));
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D ZERO=" << toNumStr(scalar<T>(0));
         options << " -D InterpInTy=" << dtype_traits<T>::getName();
-        options << " -D InterpValTy="  << dtype_traits<vtype_t<T>>::getName();
+        options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
         options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
 
-        if((af_dtype) dtype_traits<T>::af_type == c32 ||
-            (af_dtype) dtype_traits<T>::af_type == c64) {
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
             options << " -D IS_CPLX=1";
             options << " -D TB=" << dtype_traits<BT>::getName();
         } else {
@@ -77,7 +77,7 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method)
         addInterpEnumOptions(options);
 
         const char *ker_strs[] = {interp_cl, rotate_cl};
-        const int   ker_lens[] = {interp_cl_len, rotate_cl_len};
+        const int ker_lens[]   = {interp_cl_len, rotate_cl_len};
         cl::Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
         entry.prog = new cl::Program(prog);
@@ -86,9 +86,10 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method)
         addKernelToCache(device, refName, entry);
     }
 
-    auto rotateOp = cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer, const KParam,
-                                      const tmat_t, const int, const int,
-                                      const int, const int, const int>(*entry.ker);
+    auto rotateOp =
+        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
+                          const KParam, const tmat_t, const int, const int,
+                          const int, const int, const int>(*entry.ker);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
@@ -97,45 +98,44 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method)
         const float ny = 0.5 * (in.info.dims[1] - 1);
         const float mx = 0.5 * (out.info.dims[0] - 1);
         const float my = 0.5 * (out.info.dims[1] - 1);
-        const float sx = (mx * c + my *-s);
+        const float sx = (mx * c + my * -s);
         const float sy = (mx * s + my * c);
-        tx = -(sx - nx);
-        ty = -(sy - ny);
+        tx             = -(sx - nx);
+        ty             = -(sy - ny);
     }
 
     // Rounding error. Anything more than 3 decimal points wont make a diff
     tmat_t t;
-    t.tmat[0] = round( c * 1000) / 1000.0f;
+    t.tmat[0] = round(c * 1000) / 1000.0f;
     t.tmat[1] = round(-s * 1000) / 1000.0f;
     t.tmat[2] = round(tx * 1000) / 1000.0f;
-    t.tmat[3] = round( s * 1000) / 1000.0f;
-    t.tmat[4] = round( c * 1000) / 1000.0f;
+    t.tmat[3] = round(s * 1000) / 1000.0f;
+    t.tmat[4] = round(c * 1000) / 1000.0f;
     t.tmat[5] = round(ty * 1000) / 1000.0f;
 
-
     cl::NDRange local(TX, TY, 1);
 
-    int nimages  = in.info.dims[2];
-    int nbatches = in.info.dims[3];
-    int global_x = local[0] * divup(out.info.dims[0], local[0]);
-    int global_y = local[1] * divup(out.info.dims[1], local[1]);
+    int nimages               = in.info.dims[2];
+    int nbatches              = in.info.dims[3];
+    int global_x              = local[0] * divup(out.info.dims[0], local[0]);
+    int global_y              = local[1] * divup(out.info.dims[1], local[1]);
     const int blocksXPerImage = global_x / local[0];
     const int blocksYPerImage = global_y / local[1];
 
-    if(nimages > TI) {
+    if (nimages > TI) {
         int tile_images = divup(nimages, TI);
-        nimages = TI;
-        global_x = global_x * tile_images;
+        nimages         = TI;
+        global_x        = global_x * tile_images;
     }
     global_y *= nbatches;
 
     cl::NDRange global(global_x, global_y, 1);
 
-    rotateOp(cl::EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *in.data, in.info, t, nimages, nbatches,
-             blocksXPerImage, blocksYPerImage, (int)method);
+    rotateOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, t, nimages, nbatches, blocksXPerImage,
+             blocksYPerImage, (int)method);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
index dd5f9e1382..3cead6f2bb 100644
--- a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
@@ -7,20 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <ops.hpp>
 #include <backend.hpp>
-#include <kernel/scan_first_by_key_impl.hpp>
 #include <kernel/scan_dim_by_key_impl.hpp>
+#include <kernel/scan_first_by_key_impl.hpp>
+#include <ops.hpp>
 
 // This file instantiates scan_dim_by_key as separate object files from CMake
 // The line below is read by CMake to determenine the instantiations
 // SBK_BINARY_OPS:af_add_t af_mul_t af_max_t af_min_t
 
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE_SCAN_FIRST_BY_KEY_OP(TYPE)
-    INSTANTIATE_SCAN_DIM_BY_KEY_OP(TYPE)
-}
-}
+namespace opencl {
+namespace kernel {
+INSTANTIATE_SCAN_FIRST_BY_KEY_OP(TYPE)
+INSTANTIATE_SCAN_DIM_BY_KEY_OP(TYPE)
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_dim.cl b/src/backend/opencl/kernel/scan_dim.cl
index 8b379407d7..53977f8d6c 100644
--- a/src/backend/opencl/kernel/scan_dim.cl
+++ b/src/backend/opencl/kernel/scan_dim.cl
@@ -7,76 +7,71 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void scan_dim_kernel(__global To *oData, KParam oInfo,
-                     __global To *tData, KParam tInfo,
-                     const __global Ti *iData, KParam iInfo,
-                     uint groups_x,
-                     uint groups_y,
-                     uint groups_dim,
-                     uint lim)
-{
+__kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
+                              __global To *tData, KParam tInfo,
+                              const __global Ti *iData, KParam iInfo,
+                              uint groups_x, uint groups_y, uint groups_dim,
+                              uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) + lidx;
-    const int yid = groupId_y;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) + lidx;
+    const int yid       = groupId_y;
 
     int ids[4] = {xid, yid, zid, wid};
 
     // There is only one element per group for out
     // There are DIMY elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting in
-    tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] + ids[1] * tInfo.strides[1] + ids[0];
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
+             ids[1] * tInfo.strides[1] + ids[0];
     const int groupId_dim = ids[dim];
 
     ids[dim] = ids[dim] * DIMY * lim + lidy;
-    oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
-    iData  += ids[3] *  iInfo.strides[3] + ids[2] *  iInfo.strides[2] + ids[1] *  iInfo.strides[1] + ids[0];
-    iData  += iInfo.offset;
+    oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+             ids[1] * oInfo.strides[1] + ids[0];
+    iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+             ids[1] * iInfo.strides[1] + ids[0];
+    iData += iInfo.offset;
 
-    int id_dim = ids[dim];
+    int id_dim        = ids[dim];
     const int out_dim = oInfo.dims[dim];
 
-    bool is_valid =
-        (ids[0] < oInfo.dims[0]) &&
-        (ids[1] < oInfo.dims[1]) &&
-        (ids[2] < oInfo.dims[2]) &&
-        (ids[3] < oInfo.dims[3]);
+    bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
+                    (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
     const int ostride_dim = oInfo.strides[dim];
-    const int istride_dim =  iInfo.strides[dim];
+    const int istride_dim = iInfo.strides[dim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
     __local To *l_val = l_val0;
     __local To l_tmp[THREADS_X];
 
-    bool flip = 0;
-    const To init_val  = init;
-    To val = init_val;
+    bool flip         = 0;
+    const To init_val = init;
+    To val            = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
     for (int k = 0; k < lim; k++) {
-
         if (isLast) l_tmp[lidx] = val;
 
-        bool cond = (is_valid) && (id_dim < out_dim);
-        val = cond ? transform(*iData) : init_val;
+        bool cond  = (is_valid) && (id_dim < out_dim);
+        val        = cond ? transform(*iData) : init_val;
         l_val[lid] = val;
         barrier(CLK_LOCAL_MEM_FENCE);
 
         for (int off = 1; off < DIMY; off *= 2) {
-
             if (lidy >= off) val = binOp(val, l_val[lid - off * THREADS_X]);
 
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
+            flip       = 1 - flip;
+            l_val      = flip ? l_val1 : l_val0;
             l_val[lid] = val;
 
             barrier(CLK_LOCAL_MEM_FENCE);
@@ -85,13 +80,10 @@ void scan_dim_kernel(__global To *oData, KParam oInfo,
         val = binOp(val, l_tmp[lidx]);
 
         if (inclusive_scan != 0) {
-            if (cond) {
-                *oData = val;
-            }
-        }
-        else if (is_valid) {
+            if (cond) { *oData = val; }
+        } else if (is_valid) {
             if (id_dim == (out_dim - 1)) {
-                *(oData - (id_dim*ostride_dim)) = init_val;
+                *(oData - (id_dim * ostride_dim)) = init_val;
             } else if (id_dim < (out_dim - 1)) {
                 *(oData + ostride_dim) = val;
             }
@@ -103,69 +95,58 @@ void scan_dim_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (!isFinalPass &&
-        is_valid &&
-        (groupId_dim < tInfo.dims[dim]) &&
-        isLast) {
+    if (!isFinalPass && is_valid && (groupId_dim < tInfo.dims[dim]) && isLast) {
         *tData = val;
     }
 }
 
-__kernel
-void bcast_dim_kernel(__global To *oData, KParam oInfo,
-                      const __global To *tData, KParam tInfo,
-                      uint groups_x,
-                      uint groups_y,
-                      uint groups_dim,
-                      uint lim)
-{
+__kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
+                               const __global To *tData, KParam tInfo,
+                               uint groups_x, uint groups_y, uint groups_dim,
+                               uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) + lidx;
-    const int yid = groupId_y;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) + lidx;
+    const int yid       = groupId_y;
 
-    int ids[4] = {xid, yid, zid, wid};
+    int ids[4]            = {xid, yid, zid, wid};
     const int groupId_dim = ids[dim];
 
     if (groupId_dim != 0) {
-
         // There is only one element per group for out
         // There are DIMY elements per group for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] + ids[1] * tInfo.strides[1] + ids[0];
+        // Hence increment ids[dim] just after offseting out and before
+        // offsetting in
+        tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
+                 ids[1] * tInfo.strides[1] + ids[0];
 
         ids[dim] = ids[dim] * DIMY * lim + lidy;
-        oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+        oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+                 ids[1] * oInfo.strides[1] + ids[0];
 
         // Shift broadcast one step to the right for exclusive scan (#2366)
         int offset = inclusive_scan ? 0 : oInfo.strides[dim];
         oData += offset;
 
-        const int id_dim = ids[dim];
+        const int id_dim  = ids[dim];
         const int out_dim = oInfo.dims[dim];
 
-        bool is_valid =
-            (ids[0] < oInfo.dims[0]) &&
-            (ids[1] < oInfo.dims[1]) &&
-            (ids[2] < oInfo.dims[2]) &&
-            (ids[3] < oInfo.dims[3]);
+        bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
+                        (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
         if (is_valid) {
-
             To accum = *(tData - tInfo.strides[dim]);
 
             const int ostride_dim = oInfo.strides[dim];
 
-            for (int k = 0, id = id_dim;
-                 is_valid && k < lim && (id < out_dim);
+            for (int k = 0, id = id_dim; is_valid && k < lim && (id < out_dim);
                  k++, id += DIMY) {
-
                 *oData = binOp(*oData, accum);
                 oData += DIMY * ostride_dim;
             }
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 7b65f2feaf..db7ca5d839 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -8,209 +8,173 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/scan_dim.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
+#include <kernel_headers/scan_dim.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
+#include <map>
+#include <mutex>
+#include <string>
 #include "config.hpp"
+#include "names.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool isFinalPass, uint threads_y)
-    {
-        std::string ref_name =
-            std::string("scan_") +
-            std::to_string(dim) +
-            std::string("_") +
-            std::to_string(isFinalPass) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_y) +
-            std::string("_") +
-            std::to_string(int(inclusive_scan));
-
-        int device = getActiveDeviceId();
-
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-            ToNumStr<To> toNumStr;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D T=To"
-                    << " -D dim=" << dim
-                    << " -D DIMY=" << threads_y
-                    << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNumStr(Binary<To, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D isFinalPass=" << (int)(isFinalPass)
-                    << " -D inclusive_scan=" << inclusive_scan;
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            const char *ker_strs[] = {ops_cl, scan_dim_cl};
-            const int   ker_lens[] = {ops_cl_len, scan_dim_cl_len};
-            cl::Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[2];
-
-            entry.ker[0] = Kernel(*entry.prog, "scan_dim_kernel");
-            entry.ker[1] = Kernel(*entry.prog, "bcast_dim_kernel");
-
-
-            addKernelToCache(device, ref_name, entry);
+namespace opencl {
+namespace kernel {
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
+static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool isFinalPass,
+                                   uint threads_y) {
+    std::string ref_name =
+        std::string("scan_") + std::to_string(dim) + std::string("_") +
+        std::to_string(isFinalPass) + std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_y) +
+        std::string("_") + std::to_string(int(inclusive_scan));
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
+                << " -D dim=" << dim << " -D DIMY=" << threads_y
+                << " -D THREADS_X=" << THREADS_X
+                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
+                << " -D isFinalPass=" << (int)(isFinalPass)
+                << " -D inclusive_scan=" << inclusive_scan;
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        return entry.ker[kerIdx];
+        const char *ker_strs[] = {ops_cl, scan_dim_cl};
+        const int ker_lens[]   = {ops_cl_len, scan_dim_cl_len};
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel[2];
+
+        entry.ker[0] = Kernel(*entry.prog, "scan_dim_kernel");
+        entry.ker[1] = Kernel(*entry.prog, "bcast_dim_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_launcher(Param out,
-                                  Param tmp,
-                                  const Param in,
-                                  int dim, bool isFinalPass, uint threads_y,
-                                  const uint groups_all[4])
-    {
-        Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(0, dim, isFinalPass, threads_y);
+    return entry.ker[kerIdx];
+}
 
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                        groups_all[1] * groups_all[3] * local[1]);
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
+static void scan_dim_launcher(Param out, Param tmp, const Param in, int dim,
+                              bool isFinalPass, uint threads_y,
+                              const uint groups_all[4]) {
+    Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(
+        0, dim, isFinalPass, threads_y);
 
-        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        auto scanOp = KernelFunctor<Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  uint, uint,
-                                  uint, uint>(ker);
+    uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
+    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                uint, uint, uint, uint>(ker);
 
-        scanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *tmp.data, tmp.info, *in.data, in.info,
-                groups_all[0], groups_all[1], groups_all[dim], lim);
+    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *tmp.data, tmp.info, *in.data, in.info, groups_all[0], groups_all[1],
+           groups_all[dim], lim);
 
-        CL_DEBUG_FINISH(getQueue());
-    }
+    CL_DEBUG_FINISH(getQueue());
+}
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_dim_launcher(Param out,
-                                   Param tmp,
-                                   int dim, bool isFinalPass, uint threads_y,
-                                   const uint groups_all[4])
-    {
-        Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(1, dim, isFinalPass, threads_y);
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
+static void bcast_dim_launcher(Param out, Param tmp, int dim, bool isFinalPass,
+                               uint threads_y, const uint groups_all[4]) {
+    Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(
+        1, dim, isFinalPass, threads_y);
 
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                        groups_all[1] * groups_all[3] * local[1]);
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+    uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-        auto bcastOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint,
-                                    uint, uint>(ker);
+    auto bcastOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint, uint>(
+            ker);
 
-        bcastOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *tmp.data, tmp.info,
-                groups_all[0], groups_all[1], groups_all[dim], lim);
+    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *tmp.data, tmp.info, groups_all[0], groups_all[1], groups_all[dim],
+            lim);
 
-        CL_DEBUG_FINISH(getQueue());
-    }
+    CL_DEBUG_FINISH(getQueue());
+}
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_dim(Param out, const Param in, int dim)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
-        uint threads_x = THREADS_X;
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
+static void scan_dim(Param out, const Param in, int dim) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+    uint threads_x = THREADS_X;
 
-        uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
-                              (uint)out.info.dims[1],
-                              (uint)out.info.dims[2],
-                              (uint)out.info.dims[3]};
+    uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
+                         (uint)out.info.dims[1], (uint)out.info.dims[2],
+                         (uint)out.info.dims[3]};
 
-        groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+    groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
 
-        if (groups_all[dim] == 1) {
+    if (groups_all[dim] == 1) {
+        scan_dim_launcher<Ti, To, op, inclusive_scan>(out, out, in, dim, true,
+                                                      threads_y, groups_all);
+    } else {
+        Param tmp = out;
 
-            scan_dim_launcher<Ti, To, op, inclusive_scan>(out, out, in,
-                                          dim, true,
-                                          threads_y,
-                                          groups_all);
-        } else {
+        tmp.info.dims[dim]  = groups_all[dim];
+        tmp.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++) {
+            tmp.info.strides[k] =
+                tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+        }
+
+        int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+        // FIXME: Do I need to free this ?
+        tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+
+        scan_dim_launcher<Ti, To, op, inclusive_scan>(out, tmp, in, dim, false,
+                                                      threads_y, groups_all);
 
-            Param tmp = out;
-
-            tmp.info.dims[dim] = groups_all[dim];
-            tmp.info.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
-            }
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-            // FIXME: Do I need to free this ?
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-
-            scan_dim_launcher<Ti, To, op, inclusive_scan>(out, tmp, in,
-                                          dim, false,
-                                          threads_y,
-                                          groups_all);
-
-            int gdim = groups_all[dim];
-            groups_all[dim] = 1;
-
-            if (op == af_notzero_t) {
-                scan_dim_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
-                                                    dim, true,
-                                                    threads_y,
-                                                    groups_all);
-            } else {
-                scan_dim_launcher<To, To,       op, true>(tmp, tmp, tmp,
-                                                    dim, true,
-                                                    threads_y,
-                                                    groups_all);
-            }
-
-            groups_all[dim] = gdim;
-            bcast_dim_launcher<To, To, op, inclusive_scan>(out, tmp,
-                                            dim, true,
-                                            threads_y,
-                                            groups_all);
-            bufferFree(tmp.data);
+        int gdim        = groups_all[dim];
+        groups_all[dim] = 1;
+
+        if (op == af_notzero_t) {
+            scan_dim_launcher<To, To, af_add_t, true>(tmp, tmp, tmp, dim, true,
+                                                      threads_y, groups_all);
+        } else {
+            scan_dim_launcher<To, To, op, true>(tmp, tmp, tmp, dim, true,
+                                                threads_y, groups_all);
         }
+
+        groups_all[dim] = gdim;
+        bcast_dim_launcher<To, To, op, inclusive_scan>(out, tmp, dim, true,
+                                                       threads_y, groups_all);
+        bufferFree(tmp.data);
     }
 }
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index 9ea1f1cd2b..fbb5fe4ba2 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -7,87 +7,82 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride)
-{
-    return (id == 0)? 1 : ((*kptr) != (*(kptr - stride)));
+char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride) {
+    return (id == 0) ? 1 : ((*kptr) != (*(kptr - stride)));
 }
 
-__kernel
-void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
-                     __global To *tData, KParam tInfo,
-                     __global char *tfData, KParam tfInfo,
-                     __global int *tiData, KParam tiInfo,
-                     const __global Ti *iData, KParam iInfo,
-                     const __global Tk *kData, KParam kInfo,
-                     uint groups_x,
-                     uint groups_y,
-                     uint groups_dim,
-                     uint lim)
-{
+__kernel void scan_dim_by_key_nonfinal_kernel(
+    __global To *oData, KParam oInfo, __global To *tData, KParam tInfo,
+    __global char *tfData, KParam tfInfo, __global int *tiData, KParam tiInfo,
+    const __global Ti *iData, KParam iInfo, const __global Tk *kData,
+    KParam kInfo, uint groups_x, uint groups_y, uint groups_dim, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) + lidx;
-    const int yid = groupId_y;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) + lidx;
+    const int yid       = groupId_y;
 
     int ids[4] = {xid, yid, zid, wid};
 
     // There is only one element per group for out
     // There are DIMY elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting in
-    tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] + ids[1] * tInfo.strides[1] + ids[0];
-    tfData += ids[3] * tfInfo.strides[3] + ids[2] * tfInfo.strides[2] + ids[1] * tfInfo.strides[1] + ids[0];
-    tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] + ids[1] * tiInfo.strides[1] + ids[0];
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
+             ids[1] * tInfo.strides[1] + ids[0];
+    tfData += ids[3] * tfInfo.strides[3] + ids[2] * tfInfo.strides[2] +
+              ids[1] * tfInfo.strides[1] + ids[0];
+    tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] +
+              ids[1] * tiInfo.strides[1] + ids[0];
     const int groupId_dim = ids[dim];
 
     ids[dim] = ids[dim] * DIMY * lim + lidy;
-    oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
-    iData  += ids[3] *  iInfo.strides[3] + ids[2] *  iInfo.strides[2] + ids[1] *  iInfo.strides[1] + ids[0];
-    kData  += ids[3] *  kInfo.strides[3] + ids[2] *  kInfo.strides[2] + ids[1] *  kInfo.strides[1] + ids[0];
-    iData  += iInfo.offset;
-
-    int id_dim = ids[dim];
+    oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+             ids[1] * oInfo.strides[1] + ids[0];
+    iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+             ids[1] * iInfo.strides[1] + ids[0];
+    kData += ids[3] * kInfo.strides[3] + ids[2] * kInfo.strides[2] +
+             ids[1] * kInfo.strides[1] + ids[0];
+    iData += iInfo.offset;
+
+    int id_dim        = ids[dim];
     const int out_dim = oInfo.dims[dim];
 
-    bool is_valid =
-        (ids[0] < oInfo.dims[0]) &&
-        (ids[1] < oInfo.dims[1]) &&
-        (ids[2] < oInfo.dims[2]) &&
-        (ids[3] < oInfo.dims[3]);
+    bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
+                    (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
     const int ostride_dim = oInfo.strides[dim];
-    const int istride_dim =  iInfo.strides[dim];
+    const int istride_dim = iInfo.strides[dim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
     __local char l_flg0[THREADS_X * DIMY];
     __local char l_flg1[THREADS_X * DIMY];
-    __local To *l_val = l_val0;
+    __local To *l_val   = l_val0;
     __local char *l_flg = l_flg0;
     __local To l_tmp[THREADS_X];
     __local char l_ftmp[THREADS_X];
     __local int boundaryid[THREADS_X];
 
-    bool flip = 0;
-    const To init_val  = init;
-    To val = init_val;
+    bool flip         = 0;
+    const To init_val = init;
+    To val            = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
     if (isLast) {
-        l_tmp[lidx] = val;
-        l_ftmp[lidx] = 0;
+        l_tmp[lidx]      = val;
+        l_ftmp[lidx]     = 0;
         boundaryid[lidx] = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     char flag = 0;
     for (int k = 0; k < lim; k++) {
-
         bool cond = (is_valid) && (id_dim < out_dim);
 
         if (cond) {
@@ -96,7 +91,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             flag = 0;
         }
 
-        //Load val from global in
+        // Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -111,33 +106,33 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             }
         }
 
-        //Add partial result from last iteration before scan operation
+        // Add partial result from last iteration before scan operation
         if ((lidy == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidx]);
+            val  = binOp(val, l_tmp[lidx]);
             flag = l_ftmp[lidx];
         }
 
-        //Write to shared memory
+        // Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        //Segmented Scan
+        // Segmented Scan
         for (int off = 1; off < DIMY; off *= 2) {
-
             if (lidy >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
+                val =
+                    l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
                 flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
             }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
+            flip       = 1 - flip;
+            l_val      = flip ? l_val1 : l_val0;
+            l_flg      = flip ? l_flg1 : l_flg0;
             l_val[lid] = val;
             l_flg[lid] = flag;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
-        //Identify segment boundary
+        // Identify segment boundary
         if (lidy == 0) {
             if ((l_ftmp[lidx] == 0) && (l_flg[lid] == 1)) {
                 boundaryid[lidx] = id_dim;
@@ -151,7 +146,7 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if (cond) *oData = val;
         if (isLast) {
-            l_tmp[lidx] = val;
+            l_tmp[lidx]  = val;
             l_ftmp[lidx] = flag;
         }
         id_dim += DIMY;
@@ -161,89 +156,83 @@ void scan_dim_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (is_valid &&
-        (groupId_dim < tInfo.dims[dim]) &&
-        isLast) {
-        *tData = val;
-        *tfData = flag;
+    if (is_valid && (groupId_dim < tInfo.dims[dim]) && isLast) {
+        *tData       = val;
+        *tfData      = flag;
         int boundary = boundaryid[lidx];
-        *tiData = (boundary == -1)? id_dim : boundary;
+        *tiData      = (boundary == -1) ? id_dim : boundary;
     }
 }
 
-__kernel
-void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
-                     const __global Ti *iData, KParam iInfo,
-                     const __global Tk *kData, KParam kInfo,
-                     uint groups_x,
-                     uint groups_y,
-                     uint groups_dim,
-                     uint lim)
-{
+__kernel void scan_dim_by_key_final_kernel(
+    __global To *oData, KParam oInfo, const __global Ti *iData, KParam iInfo,
+    const __global Tk *kData, KParam kInfo, uint groups_x, uint groups_y,
+    uint groups_dim, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) + lidx;
-    const int yid = groupId_y;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) + lidx;
+    const int yid       = groupId_y;
 
     int ids[4] = {xid, yid, zid, wid};
 
     // There is only one element per group for out
     // There are DIMY elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
     const int groupId_dim = ids[dim];
 
     ids[dim] = ids[dim] * DIMY * lim + lidy;
-    oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
-    iData  += ids[3] *  iInfo.strides[3] + ids[2] *  iInfo.strides[2] + ids[1] *  iInfo.strides[1] + ids[0];
-    kData  += ids[3] *  kInfo.strides[3] + ids[2] *  kInfo.strides[2] + ids[1] *  kInfo.strides[1] + ids[0];
-    iData  += iInfo.offset;
-
-    int id_dim = ids[dim];
+    oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+             ids[1] * oInfo.strides[1] + ids[0];
+    iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
+             ids[1] * iInfo.strides[1] + ids[0];
+    kData += ids[3] * kInfo.strides[3] + ids[2] * kInfo.strides[2] +
+             ids[1] * kInfo.strides[1] + ids[0];
+    iData += iInfo.offset;
+
+    int id_dim        = ids[dim];
     const int out_dim = oInfo.dims[dim];
 
-    bool is_valid =
-        (ids[0] < oInfo.dims[0]) &&
-        (ids[1] < oInfo.dims[1]) &&
-        (ids[2] < oInfo.dims[2]) &&
-        (ids[3] < oInfo.dims[3]);
+    bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
+                    (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
     const int ostride_dim = oInfo.strides[dim];
-    const int istride_dim =  iInfo.strides[dim];
+    const int istride_dim = iInfo.strides[dim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
     __local char l_flg0[THREADS_X * DIMY];
     __local char l_flg1[THREADS_X * DIMY];
-    __local To *l_val = l_val0;
+    __local To *l_val   = l_val0;
     __local char *l_flg = l_flg0;
     __local To l_tmp[THREADS_X];
     __local char l_ftmp[THREADS_X];
 
-    bool flip = 0;
-    const To init_val  = init;
-    To val = init_val;
+    bool flip         = 0;
+    const To init_val = init;
+    To val            = init_val;
     const bool isLast = (lidy == (DIMY - 1));
 
     if (isLast) {
-        l_tmp[lidx] = val;
+        l_tmp[lidx]  = val;
         l_ftmp[lidx] = 0;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     char flag = 0;
     for (int k = 0; k < lim; k++) {
-
         bool cond = (is_valid) && (id_dim < out_dim);
 
         if (calculateFlags) {
             if (cond) {
-                flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
+                flag =
+                    calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
             } else {
                 flag = 0;
             }
@@ -251,7 +240,7 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
             flag = *kData;
         }
 
-        //Load val from global in
+        // Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -266,27 +255,27 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
             }
         }
 
-        //Add partial result from last iteration before scan operation
+        // Add partial result from last iteration before scan operation
         if ((lidy == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidx]);
+            val  = binOp(val, l_tmp[lidx]);
             flag = l_ftmp[lidx];
         }
 
-        //Write to shared memory
+        // Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        //Segmented Scan
+        // Segmented Scan
         for (int off = 1; off < DIMY; off *= 2) {
-
             if (lidy >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
+                val =
+                    l_flg[lid] ? val : binOp(val, l_val[lid - off * THREADS_X]);
                 flag = l_flg[lid] | l_flg[lid - off * THREADS_X];
             }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
+            flip       = 1 - flip;
+            l_val      = flip ? l_val1 : l_val0;
+            l_flg      = flip ? l_flg1 : l_flg0;
             l_val[lid] = val;
             l_flg[lid] = flag;
             barrier(CLK_LOCAL_MEM_FENCE);
@@ -294,7 +283,7 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
 
         if (cond) *oData = val;
         if (isLast) {
-            l_tmp[lidx] = val;
+            l_tmp[lidx]  = val;
             l_ftmp[lidx] = flag;
         }
         id_dim += DIMY;
@@ -305,59 +294,52 @@ void scan_dim_by_key_final_kernel(__global To *oData, KParam oInfo,
     }
 }
 
-__kernel
-void bcast_dim_kernel(__global To *oData, KParam oInfo,
-                      const __global To *tData, KParam tInfo,
-                      const __global int *tiData, KParam tiInfo,
-                      uint groups_x,
-                      uint groups_y,
-                      uint groups_dim,
-                      uint lim)
-{
+__kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
+                               const __global To *tData, KParam tInfo,
+                               const __global int *tiData, KParam tiInfo,
+                               uint groups_x, uint groups_y, uint groups_dim,
+                               uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) + lidx;
-    const int yid = groupId_y;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) + lidx;
+    const int yid       = groupId_y;
 
-    int ids[4] = {xid, yid, zid, wid};
+    int ids[4]            = {xid, yid, zid, wid};
     const int groupId_dim = ids[dim];
 
     if (groupId_dim != 0) {
-
         // There is only one element per group for out
         // There are DIMY elements per group for in
-        // Hence increment ids[dim] just after offseting out and before offsetting in
-        tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] + ids[1] * tiInfo.strides[1] + ids[0];
-        tData  += ids[3] * tInfo.strides[3]  + ids[2] * tInfo.strides[2]  + ids[1] * tInfo.strides[1]  + ids[0];
+        // Hence increment ids[dim] just after offseting out and before
+        // offsetting in
+        tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] +
+                  ids[1] * tiInfo.strides[1] + ids[0];
+        tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
+                 ids[1] * tInfo.strides[1] + ids[0];
 
         ids[dim] = ids[dim] * DIMY * lim + lidy;
-        oData  += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] + ids[1] * oInfo.strides[1] + ids[0];
+        oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
+                 ids[1] * oInfo.strides[1] + ids[0];
 
         const int id_dim = ids[dim];
 
-        bool is_valid =
-            (ids[0] < oInfo.dims[0]) &&
-            (ids[1] < oInfo.dims[1]) &&
-            (ids[2] < oInfo.dims[2]) &&
-            (ids[3] < oInfo.dims[3]);
+        bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
+                        (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
         if (is_valid) {
-
             int boundary = *tiData;
-            To accum = *(tData - tInfo.strides[dim]);
+            To accum     = *(tData - tInfo.strides[dim]);
 
             const int ostride_dim = oInfo.strides[dim];
 
-            for (int k = 0, id = id_dim;
-                 is_valid && k < lim && (id < boundary);
+            for (int k = 0, id = id_dim; is_valid && k < lim && (id < boundary);
                  k++, id += DIMY) {
-
                 *oData = binOp(*oData, accum);
                 oData += DIMY * ostride_dim;
             }
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
index 2f84509d1c..3f441192cb 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -8,15 +8,13 @@
  ********************************************************/
 
 #pragma once
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
-namespace opencl
-{
-namespace kernel
-{
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_dim(Param out, const Param in, const Param key, int dim);
-}
+#include <traits.hpp>
+namespace opencl {
+namespace kernel {
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+void scan_dim(Param out, const Param in, const Param key, int dim);
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index f46815a606..42486369c9 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -8,279 +8,230 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/scan_dim_by_key.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
+#include <kernel_headers/scan_dim_by_key.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
-#include "config.hpp"
 #include <types.hpp>
+#include <map>
+#include <mutex>
+#include <string>
+#include "config.hpp"
+#include "names.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags, uint threads_y)
-    {
-        std::string ref_name =
-            std::string("scan_") +
-            std::to_string(dim) +
-            std::string("_") +
-            std::to_string(calculateFlags) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<Tk>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_y) +
-            std::string("_") +
-            std::to_string(int(inclusive_scan));
-
-        int device = getActiveDeviceId();
-
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-
-            ToNumStr<To> toNumStr;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D Tk=" << dtype_traits<Tk>::getName()
-                    << " -D T=To"
-                    << " -D dim=" << dim
-                    << " -D DIMY=" << threads_y
-                    << " -D THREADS_X=" << THREADS_X
-                    << " -D init=" << toNumStr(Binary<To, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D calculateFlags=" << calculateFlags
-                    << " -D inclusive_scan=" << inclusive_scan;
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
-            const int   ker_lens[] = {ops_cl_len, scan_dim_by_key_cl_len};
-            cl::Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[3];
-
-            entry.ker[0] = Kernel(*entry.prog, "scan_dim_by_key_final_kernel");
-            entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
-            entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
-
-            addKernelToCache(device, ref_name, entry);
+namespace opencl {
+namespace kernel {
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags,
+                                   uint threads_y) {
+    std::string ref_name =
+        std::string("scan_") + std::to_string(dim) + std::string("_") +
+        std::to_string(calculateFlags) + std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_y) +
+        std::string("_") + std::to_string(int(inclusive_scan));
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
+                << " -D dim=" << dim << " -D DIMY=" << threads_y
+                << " -D THREADS_X=" << THREADS_X
+                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
+                << " -D calculateFlags=" << calculateFlags
+                << " -D inclusive_scan=" << inclusive_scan;
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        return entry.ker[kerIdx];
-    }
+        const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
+        const int ker_lens[]   = {ops_cl_len, scan_dim_by_key_cl_len};
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_nonfinal_launcher(Param out,
-                                  Param tmp,
-                                  Param tmpflg,
-                                  Param tmpid,
-                                  const Param in,
-                                  const Param key,
-                                  int dim, uint threads_y,
-                                  const uint groups_all[4])
-    {
-        Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(1, dim, false, threads_y);
-
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                        groups_all[1] * groups_all[3] * local[1]);
-
-        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
-
-        auto scanOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint,
-                                    uint, uint>(ker);
-
-        scanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info,
-                *tmp.data, tmp.info,
-                *tmpflg.data, tmpflg.info,
-                *tmpid.data, tmpid.info,
-                *in.data, in.info, *key.data, key.info,
-                groups_all[0], groups_all[1], groups_all[dim], lim);
-
-        CL_DEBUG_FINISH(getQueue());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel[3];
+
+        entry.ker[0] = Kernel(*entry.prog, "scan_dim_by_key_final_kernel");
+        entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
+        entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void scan_dim_final_launcher(Param out,
-                                  const Param in,
-                                  const Param key,
-                                  int dim, const bool calculateFlags, uint threads_y,
-                                  const uint groups_all[4])
-    {
-        Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(0, dim, calculateFlags, threads_y);
+    return entry.ker[kerIdx];
+}
 
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                        groups_all[1] * groups_all[3] * local[1]);
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+static void scan_dim_nonfinal_launcher(Param out, Param tmp, Param tmpflg,
+                                       Param tmpid, const Param in,
+                                       const Param key, int dim, uint threads_y,
+                                       const uint groups_all[4]) {
+    Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(
+        1, dim, false, threads_y);
 
-        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        auto scanOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint,
-                                    uint, uint>(ker);
+    uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-        scanOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *in.data, in.info, *key.data, key.info,
-                groups_all[0], groups_all[1], groups_all[dim], lim);
+    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                uint, uint, uint, uint>(ker);
 
-        CL_DEBUG_FINISH(getQueue());
-    }
+    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *tmp.data, tmp.info, *tmpflg.data, tmpflg.info, *tmpid.data,
+           tmpid.info, *in.data, in.info, *key.data, key.info, groups_all[0],
+           groups_all[1], groups_all[dim], lim);
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_dim_launcher(Param out,
-                                   Param tmp,
-                                   Param tmpid,
-                                   int dim, uint threads_y,
-                                   const uint groups_all[4])
-    {
-        Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(2, dim, false, threads_y);
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        NDRange local(THREADS_X, threads_y);
-        NDRange global(groups_all[0] * groups_all[2] * local[0],
-                        groups_all[1] * groups_all[3] * local[1]);
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+static void scan_dim_final_launcher(Param out, const Param in, const Param key,
+                                    int dim, const bool calculateFlags,
+                                    uint threads_y, const uint groups_all[4]) {
+    Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(
+        0, dim, calculateFlags, threads_y);
 
-        uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        auto bcastOp = KernelFunctor<Buffer, KParam,
-                                      Buffer, KParam,
-                                      Buffer, KParam,
-                                      uint, uint,
-                                      uint, uint>(ker);
+    uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-        bcastOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
-                groups_all[0], groups_all[1], groups_all[dim], lim);
+    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                uint, uint, uint, uint>(ker);
 
-        CL_DEBUG_FINISH(getQueue());
-    }
+    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, *key.data, key.info, groups_all[0], groups_all[1],
+           groups_all[dim], lim);
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_dim(Param out, const Param in, const Param key, int dim)
-    {
-        uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
-        uint threads_x = THREADS_X;
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
-                              (uint)out.info.dims[1],
-                              (uint)out.info.dims[2],
-                              (uint)out.info.dims[3]};
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+static void bcast_dim_launcher(Param out, Param tmp, Param tmpid, int dim,
+                               uint threads_y, const uint groups_all[4]) {
+    Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(
+        2, dim, false, threads_y);
 
-        groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+    NDRange local(THREADS_X, threads_y);
+    NDRange global(groups_all[0] * groups_all[2] * local[0],
+                   groups_all[1] * groups_all[3] * local[1]);
 
-        if (groups_all[dim] == 1) {
+    uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-            scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
-                                          dim, true,
-                                          threads_y,
-                                          groups_all);
-        } else {
+    auto bcastOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                 uint, uint, uint, uint>(ker);
+
+    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *tmp.data, tmp.info, *tmpid.data, tmpid.info, groups_all[0],
+            groups_all[1], groups_all[dim], lim);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+void scan_dim(Param out, const Param in, const Param key, int dim) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+    uint threads_x = THREADS_X;
 
-            Param tmp = out;
-
-            tmp.info.dims[dim] = groups_all[dim];
-            tmp.info.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
-            }
-            Param tmpflg = tmp;
-            Param tmpid = tmp;
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-            // FIXME: Do I need to free this ?
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-            tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
-            tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
-
-            scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
-                                          dim,
-                                          threads_y,
-                                          groups_all);
-
-            int gdim = groups_all[dim];
-            groups_all[dim] = 1;
-
-            if (op == af_notzero_t) {
-                scan_dim_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
-                                                    dim, false,
-                                                    threads_y,
-                                                    groups_all);
-            } else {
-                scan_dim_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
-                                                    dim, false,
-                                                    threads_y,
-                                                    groups_all);
-            }
-
-            groups_all[dim] = gdim;
-            bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
-                                            dim,
-                                            threads_y,
-                                            groups_all);
-            bufferFree(tmp.data);
-            bufferFree(tmpflg.data);
-            bufferFree(tmpid.data);
+    uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
+                         (uint)out.info.dims[1], (uint)out.info.dims[2],
+                         (uint)out.info.dims[3]};
+
+    groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+
+    if (groups_all[dim] == 1) {
+        scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(
+            out, in, key, dim, true, threads_y, groups_all);
+    } else {
+        Param tmp = out;
+
+        tmp.info.dims[dim]  = groups_all[dim];
+        tmp.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++) {
+            tmp.info.strides[k] =
+                tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+        }
+        Param tmpflg = tmp;
+        Param tmpid  = tmp;
+
+        int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+        // FIXME: Do I need to free this ?
+        tmp.data    = bufferAlloc(tmp_elements * sizeof(To));
+        tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+        tmpid.data  = bufferAlloc(tmp_elements * sizeof(int));
+
+        scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(
+            out, tmp, tmpflg, tmpid, in, key, dim, threads_y, groups_all);
+
+        int gdim        = groups_all[dim];
+        groups_all[dim] = 1;
+
+        if (op == af_notzero_t) {
+            scan_dim_final_launcher<To, char, To, af_add_t, true>(
+                tmp, tmp, tmpflg, dim, false, threads_y, groups_all);
+        } else {
+            scan_dim_final_launcher<To, char, To, op, true>(
+                tmp, tmp, tmpflg, dim, false, threads_y, groups_all);
         }
+
+        groups_all[dim] = gdim;
+        bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(
+            out, tmp, tmpid, dim, threads_y, groups_all);
+        bufferFree(tmp.data);
+        bufferFree(tmpflg.data);
+        bufferFree(tmpid.data);
     }
 }
-
-#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)\
-    template void scan_dim<Ti, Tk, To, ROp,  true>(Param out, const Param in, const Param key, int dim);\
-    template void scan_dim<Ti, Tk, To, ROp, false>(Param out, const Param in, const Param key, int dim);
-
-#define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)          \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl  , Tk, uintl  )  \
-
-#define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)         \
-    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int  )   \
-    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uint )   \
-    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl )   \
+}  // namespace kernel
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)                          \
+    template void scan_dim<Ti, Tk, To, ROp, true>(Param out, const Param in,  \
+                                                  const Param key, int dim);  \
+    template void scan_dim<Ti, Tk, To, ROp, false>(Param out, const Param in, \
+                                                   const Param key, int dim);
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)         \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_DIM_BY_KEY(ROp, uintl, Tk, uintl)
+
+#define INSTANTIATE_SCAN_DIM_BY_KEY_OP(ROp)      \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, int)  \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uint) \
+    INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uintl)
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_first.cl b/src/backend/opencl/kernel/scan_first.cl
index 48bd975eea..3d4da2e0fd 100644
--- a/src/backend/opencl/kernel/scan_first.cl
+++ b/src/backend/opencl/kernel/scan_first.cl
@@ -7,34 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void scan_first_kernel(__global To *oData, KParam oInfo,
-                       __global To *tData, KParam tInfo,
-                       const __global Ti *iData, KParam iInfo,
-                       uint groups_x, uint groups_y,
-                       uint lim)
-{
+__kernel void scan_first_kernel(__global To *oData, KParam oInfo,
+                                __global To *tData, KParam tInfo,
+                                const __global Ti *iData, KParam iInfo,
+                                uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) * lim + lidx;
-    const int yid = groupId_y * get_local_size(1) + lidy;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid       = groupId_y * get_local_size(1) + lidy;
 
-    bool cond_yzw = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+    bool cond_yzw =
+        (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
 
     iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-        yid * iInfo.strides[1] + iInfo.offset;
+             yid * iInfo.strides[1] + iInfo.offset;
 
     tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
-        yid * tInfo.strides[1] + tInfo.offset;
+             yid * tInfo.strides[1] + tInfo.offset;
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1] + oInfo.offset;
 
     __local To l_val0[SHARED_MEM_SIZE];
     __local To l_val1[SHARED_MEM_SIZE];
@@ -44,36 +42,32 @@ void scan_first_kernel(__global To *oData, KParam oInfo,
     bool flip = 0;
 
     const To init_val = init;
-    int id = xid;
-    To val = init_val;
+    int id            = xid;
+    To val            = init_val;
 
     const bool isLast = (lidx == (DIMX - 1));
 
     for (int k = 0; k < lim; k++) {
-
         if (isLast) l_tmp[lidy] = val;
 
-        bool cond = ((id < iInfo.dims[0]) && cond_yzw);
-        val = cond ? transform(iData[id]) : init_val;
+        bool cond  = ((id < iInfo.dims[0]) && cond_yzw);
+        val        = cond ? transform(iData[id]) : init_val;
         l_val[lid] = val;
         barrier(CLK_LOCAL_MEM_FENCE);
 
         for (int off = 1; off < DIMX; off *= 2) {
             if (lidx >= off) val = binOp(val, l_val[lid - off]);
 
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
+            flip       = 1 - flip;
+            l_val      = flip ? l_val1 : l_val0;
             l_val[lid] = val;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
         val = binOp(val, l_tmp[lidy]);
         if (inclusive_scan != 0) {
-            if (cond) {
-                oData[id] = val;
-            }
-        }
-        else {
+            if (cond) { oData[id] = val; }
+        } else {
             if (id == (oInfo.dims[0] - 1)) {
                 oData[0] = init_val;
             } else if (id < (oInfo.dims[0] - 1)) {
@@ -84,46 +78,40 @@ void scan_first_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (!isFinalPass && isLast && cond_yzw) {
-        tData[groupId_x] = val;
-    }
+    if (!isFinalPass && isLast && cond_yzw) { tData[groupId_x] = val; }
 }
 
-__kernel
-void bcast_first_kernel(__global To *oData, KParam oInfo,
-                        const __global To *tData, KParam tInfo,
-                        uint groups_x, uint groups_y, uint lim)
-{
+__kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
+                                 const __global To *tData, KParam tInfo,
+                                 uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) * lim + lidx;
-    const int yid = groupId_y * get_local_size(1) + lidy;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid       = groupId_y * get_local_size(1) + lidy;
 
     if (groupId_x != 0) {
-        bool cond = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+        bool cond = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) &&
+                    (wid < oInfo.dims[3]);
 
         if (cond) {
-
             tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
-                yid * tInfo.strides[1] + tInfo.offset;
+                     yid * tInfo.strides[1] + tInfo.offset;
 
             oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-                yid * oInfo.strides[1] + oInfo.offset;
+                     yid * oInfo.strides[1] + oInfo.offset;
 
             To accum = tData[groupId_x - 1];
 
             // Shift broadcast one step to the right for exclusive scan (#2366)
             int offset = !inclusive_scan;
-            for (int k = 0, id = xid + offset;
-                 k < lim && id < oInfo.dims[0];
+            for (int k = 0, id = xid + offset; k < lim && id < oInfo.dims[0];
                  k++, id += DIMX) {
-
                 oData[id] = binOp(accum, oData[id]);
             }
         }
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index 7356c72bbf..a4e753aaac 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -8,211 +8,172 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/scan_first.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
+#include <kernel_headers/scan_first.hpp>
+#include <memory.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
+#include <map>
+#include <mutex>
+#include <string>
 #include "config.hpp"
-#include <memory.hpp>
+#include "names.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static Kernel get_scan_first_kernels(int kerIdx, bool isFinalPass, uint threads_x)
-    {
-        std::string ref_name =
-            std::string("scan_0_") +
-            std::string("_") +
-            std::to_string(isFinalPass) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_x) +
-            std::string("_") +
-            std::to_string(int(inclusive_scan));
-
-        int device = getActiveDeviceId();
-
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-            const uint threads_y = THREADS_PER_GROUP / threads_x;
-            const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
-
-            ToNumStr<To> toNumStr;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D T=To"
-                    << " -D DIMX=" << threads_x
-                    << " -D DIMY=" << threads_y
-                    << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNumStr(Binary<To, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D isFinalPass=" << (int)(isFinalPass)
-                    << " -D inclusive_scan=" << inclusive_scan;
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            const char *ker_strs[] = {ops_cl, scan_first_cl};
-            const int   ker_lens[] = {ops_cl_len, scan_first_cl_len};
-            cl::Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[2];
-
-            entry.ker[0] = Kernel(*entry.prog, "scan_first_kernel");
-            entry.ker[1] = Kernel(*entry.prog, "bcast_first_kernel");
-
-            addKernelToCache(device, ref_name, entry);
+namespace opencl {
+namespace kernel {
+
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
+static Kernel get_scan_first_kernels(int kerIdx, bool isFinalPass,
+                                     uint threads_x) {
+    std::string ref_name =
+        std::string("scan_0_") + std::string("_") +
+        std::to_string(isFinalPass) + std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_x) +
+        std::string("_") + std::to_string(int(inclusive_scan));
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        const uint threads_y       = THREADS_PER_GROUP / threads_x;
+        const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
+
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x << " -D DIMY=" << threads_y
+                << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
+                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
+                << " -D isFinalPass=" << (int)(isFinalPass)
+                << " -D inclusive_scan=" << inclusive_scan;
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        return entry.ker[kerIdx];
-    }
+        const char *ker_strs[] = {ops_cl, scan_first_cl};
+        const int ker_lens[]   = {ops_cl_len, scan_first_cl_len};
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first_launcher(Param &out,
-                                    Param &tmp,
-                                    const Param &in,
-                                    const bool isFinalPass,
-                                    const uint groups_x,
-                                    const uint groups_y,
-                                    const uint threads_x)
-    {
-        Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(0, isFinalPass, threads_x);
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
-
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
-
-        auto scanOp = KernelFunctor<Buffer, KParam,
-                                  Buffer, KParam,
-                                  Buffer, KParam,
-                                  uint, uint, uint>(ker);
-
-        scanOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info, *tmp.data, tmp.info, *in.data, in.info,
-               groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel[2];
+
+        entry.ker[0] = Kernel(*entry.prog, "scan_first_kernel");
+        entry.ker[1] = Kernel(*entry.prog, "bcast_first_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_first_launcher(Param &out,
-                                     Param &tmp,
-                                     const bool isFinalPass,
-                                     const uint groups_x,
-                                     const uint groups_y,
-                                     const uint threads_x)
-    {
+    return entry.ker[kerIdx];
+}
 
-        Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(1, isFinalPass, threads_x);
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
+static void scan_first_launcher(Param &out, Param &tmp, const Param &in,
+                                const bool isFinalPass, const uint groups_x,
+                                const uint groups_y, const uint threads_x) {
+    Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(
+        0, isFinalPass, threads_x);
 
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
 
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        auto bcastOp = KernelFunctor<Buffer, KParam,
-                                   Buffer, KParam,
-                                   uint, uint, uint>(ker);
+    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                uint, uint, uint>(ker);
 
-        bcastOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *tmp.data, tmp.info,
-                groups_x, groups_y, lim);
+    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *tmp.data, tmp.info, *in.data, in.info, groups_x, groups_y, lim);
 
-        CL_DEBUG_FINISH(getQueue());
-    }
+    CL_DEBUG_FINISH(getQueue());
+}
 
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
+static void bcast_first_launcher(Param &out, Param &tmp, const bool isFinalPass,
+                                 const uint groups_x, const uint groups_y,
+                                 const uint threads_x) {
+    Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(
+        1, isFinalPass, threads_x);
 
-    template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first(Param &out, const Param &in)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
 
-        uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(out.info.dims[1], threads_y);
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        if (groups_x == 1) {
-            scan_first_launcher<Ti, To, op, inclusive_scan>(out, out, in,
-                                            true,
-                                            groups_x, groups_y,
-                                            threads_x);
+    auto bcastOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint>(ker);
 
-        } else {
+    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *tmp.data, tmp.info, groups_x, groups_y, lim);
 
-            Param tmp = out;
-            tmp.info.dims[0] = groups_x;
-            tmp.info.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims   [k - 1];
-            }
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-
-            scan_first_launcher<Ti, To, op, inclusive_scan>(out, tmp, in,
-                                            false,
-                                            groups_x, groups_y,
-                                            threads_x);
-
-            if (op == af_notzero_t) {
-                scan_first_launcher<To, To, af_add_t, true>(tmp, tmp, tmp,
-                                                      true,
-                                                      1, groups_y,
-                                                      threads_x);
-            } else {
-                scan_first_launcher<To, To,       op, true>(tmp, tmp, tmp,
-                                                      true,
-                                                      1, groups_y,
-                                                      threads_x);
-            }
-
-            bcast_first_launcher<To, To, op, inclusive_scan>(out, tmp,
-                                             true,
-                                             groups_x,
-                                             groups_y,
-                                             threads_x);
-
-            bufferFree(tmp.data);
+    CL_DEBUG_FINISH(getQueue());
+}
 
+template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
+static void scan_first(Param &out, const Param &in) {
+    uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
+
+    uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(out.info.dims[1], threads_y);
+
+    if (groups_x == 1) {
+        scan_first_launcher<Ti, To, op, inclusive_scan>(
+            out, out, in, true, groups_x, groups_y, threads_x);
+
+    } else {
+        Param tmp           = out;
+        tmp.info.dims[0]    = groups_x;
+        tmp.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++) {
+            tmp.info.strides[k] =
+                tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
         }
-    }
 
+        int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+
+        tmp.data = bufferAlloc(tmp_elements * sizeof(To));
+
+        scan_first_launcher<Ti, To, op, inclusive_scan>(
+            out, tmp, in, false, groups_x, groups_y, threads_x);
+
+        if (op == af_notzero_t) {
+            scan_first_launcher<To, To, af_add_t, true>(tmp, tmp, tmp, true, 1,
+                                                        groups_y, threads_x);
+        } else {
+            scan_first_launcher<To, To, op, true>(tmp, tmp, tmp, true, 1,
+                                                  groups_y, threads_x);
+        }
+
+        bcast_first_launcher<To, To, op, inclusive_scan>(
+            out, tmp, true, groups_x, groups_y, threads_x);
+
+        bufferFree(tmp.data);
+    }
 }
-}
+
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index ac843d8c1e..bce1eb8f9e 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -7,57 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-char calculate_head_flags(const __global Tk *kptr, int id, int previd)
-{
-    return (id == 0)? 1 : (kptr[id] != kptr[previd]);
+char calculate_head_flags(const __global Tk *kptr, int id, int previd) {
+    return (id == 0) ? 1 : (kptr[id] != kptr[previd]);
 }
 
-__kernel
-void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
-                       __global To *tData, KParam tInfo,
-                       __global char *tfData, KParam tfInfo,
-                       __global int *tiData, KParam tiInfo,
-                       const __global Ti *iData, KParam iInfo,
-                       const __global Tk *kData, KParam kInfo,
-                       uint groups_x, uint groups_y,
-                       uint lim)
-{
+__kernel void scan_first_by_key_nonfinal_kernel(
+    __global To *oData, KParam oInfo, __global To *tData, KParam tInfo,
+    __global char *tfData, KParam tfInfo, __global int *tiData, KParam tiInfo,
+    const __global Ti *iData, KParam iInfo, const __global Tk *kData,
+    KParam kInfo, uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) * lim + lidx;
-    const int yid = groupId_y * get_local_size(1) + lidy;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid       = groupId_y * get_local_size(1) + lidy;
 
-    bool cond_yzw = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+    bool cond_yzw =
+        (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
 
     iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-        yid * iInfo.strides[1] + iInfo.offset;
+             yid * iInfo.strides[1] + iInfo.offset;
 
     kData += wid * kInfo.strides[3] + zid * kInfo.strides[2] +
-        yid * kInfo.strides[1] + kInfo.offset;
+             yid * kInfo.strides[1] + kInfo.offset;
 
     tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
-        yid * tInfo.strides[1] + tInfo.offset;
+             yid * tInfo.strides[1] + tInfo.offset;
 
     tfData += wid * tfInfo.strides[3] + zid * tfInfo.strides[2] +
-        yid * tfInfo.strides[1] + tfInfo.offset;
+              yid * tfInfo.strides[1] + tfInfo.offset;
 
     tiData += wid * tiInfo.strides[3] + zid * tiInfo.strides[2] +
-        yid * tiInfo.strides[1] + tiInfo.offset;
+              yid * tiInfo.strides[1] + tiInfo.offset;
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1] + oInfo.offset;
 
     __local To l_val0[SHARED_MEM_SIZE];
     __local To l_val1[SHARED_MEM_SIZE];
     __local char l_flg0[SHARED_MEM_SIZE];
     __local char l_flg1[SHARED_MEM_SIZE];
-    __local To *l_val = l_val0;
+    __local To *l_val   = l_val0;
     __local char *l_flg = l_flg0;
     __local To l_tmp[DIMY];
     __local char l_ftmp[DIMY];
@@ -66,21 +61,20 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     bool flip = 0;
 
     const To init_val = init;
-    int id = xid;
-    To val = init_val;
+    int id            = xid;
+    To val            = init_val;
 
     const bool isLast = (lidx == (DIMX - 1));
 
     if (isLast) {
-        l_tmp[lidy] = val;
-        l_ftmp[lidy] = 0;
+        l_tmp[lidy]      = val;
+        l_ftmp[lidy]     = 0;
         boundaryid[lidy] = -1;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     char flag = 0;
     for (int k = 0; k < lim; k++) {
-
         bool cond = ((id < oInfo.dims[0]) && cond_yzw);
 
         if (cond) {
@@ -89,7 +83,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             flag = 0;
         }
 
-        //Load val from global in
+        // Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -104,38 +98,38 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
             }
         }
 
-        //Add partial result from last iteration before scan operation
+        // Add partial result from last iteration before scan operation
         if ((lidx == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidy]);
+            val  = binOp(val, l_tmp[lidy]);
             flag = l_ftmp[lidy];
         }
 
-        //Write to shared memory
+        // Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        //Segmented Scan
+        // Segmented Scan
         for (int off = 1; off < DIMX; off *= 2) {
             if (lidx >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
+                val  = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
                 flag = l_flg[lid] | l_flg[lid - off];
             }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
+            flip       = 1 - flip;
+            l_val      = flip ? l_val1 : l_val0;
+            l_flg      = flip ? l_flg1 : l_flg0;
             l_val[lid] = val;
             l_flg[lid] = flag;
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
-        //Identify segment boundary
+        // Identify segment boundary
         if (lidx == 0) {
             if ((l_ftmp[lidy] == 0) && (l_flg[lid] == 1)) {
                 boundaryid[lidy] = id;
             }
         } else {
-            if ((l_flg[lid-1] == 0) && (l_flg[lid] == 1)) {
+            if ((l_flg[lid - 1] == 0) && (l_flg[lid] == 1)) {
                 boundaryid[lidy] = id;
             }
         }
@@ -143,7 +137,7 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
 
         if (cond) oData[id] = val;
         if (isLast) {
-            l_tmp[lidy] = val;
+            l_tmp[lidy]  = val;
             l_ftmp[lidy] = flag;
         }
         id += DIMX;
@@ -151,47 +145,47 @@ void scan_first_by_key_nonfinal_kernel(__global To *oData, KParam oInfo,
     }
 
     if (isLast && cond_yzw) {
-        tData[groupId_x] = val;
+        tData[groupId_x]  = val;
         tfData[groupId_x] = flag;
-        int boundary = boundaryid[lidy];
-        tiData[groupId_x] = (boundary == -1)? id : boundary;
+        int boundary      = boundaryid[lidy];
+        tiData[groupId_x] = (boundary == -1) ? id : boundary;
     }
 }
 
-__kernel
-void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
-                       const __global Ti *iData, KParam iInfo,
-                       const __global Tk *kData, KParam kInfo,
-                       uint groups_x, uint groups_y,
-                       uint lim)
-{
+__kernel void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
+                                             const __global Ti *iData,
+                                             KParam iInfo,
+                                             const __global Tk *kData,
+                                             KParam kInfo, uint groups_x,
+                                             uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) * lim + lidx;
-    const int yid = groupId_y * get_local_size(1) + lidy;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid       = groupId_y * get_local_size(1) + lidy;
 
-    bool cond_yzw = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+    bool cond_yzw =
+        (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
 
     iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
-        yid * iInfo.strides[1] + iInfo.offset;
+             yid * iInfo.strides[1] + iInfo.offset;
 
     kData += wid * kInfo.strides[3] + zid * kInfo.strides[2] +
-        yid * kInfo.strides[1] + kInfo.offset;
+             yid * kInfo.strides[1] + kInfo.offset;
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-        yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1] + oInfo.offset;
 
     __local To l_val0[SHARED_MEM_SIZE];
     __local To l_val1[SHARED_MEM_SIZE];
     __local char l_flg0[SHARED_MEM_SIZE];
     __local char l_flg1[SHARED_MEM_SIZE];
-    __local To *l_val = l_val0;
+    __local To *l_val   = l_val0;
     __local char *l_flg = l_flg0;
     __local To l_tmp[DIMY];
     __local char l_ftmp[DIMY];
@@ -199,8 +193,8 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     bool flip = 0;
 
     const To init_val = init;
-    int id = xid;
-    To val = init_val;
+    int id            = xid;
+    To val            = init_val;
 
     const bool isLast = (lidx == (DIMX - 1));
 
@@ -219,7 +213,7 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
             flag = kData[id];
         }
 
-        //Load val from global in
+        // Load val from global in
         if (inclusive_scan) {
             if (!cond) {
                 val = init_val;
@@ -234,26 +228,26 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
             }
         }
 
-        //Add partial result from last iteration before scan operation
+        // Add partial result from last iteration before scan operation
         if ((lidx == 0) && (flag == 0)) {
-            val = binOp(val, l_tmp[lidy]);
+            val  = binOp(val, l_tmp[lidy]);
             flag = flag | l_ftmp[lidy];
         }
 
-        //Write to shared memory
+        // Write to shared memory
         l_val[lid] = val;
         l_flg[lid] = flag;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        //Write to shared memory
+        // Write to shared memory
         for (int off = 1; off < DIMX; off *= 2) {
             if (lidx >= off) {
-                val = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
+                val  = l_flg[lid] ? val : binOp(val, l_val[lid - off]);
                 flag = l_flg[lid] | l_flg[lid - off];
             }
-            flip = 1 - flip;
-            l_val = flip ? l_val1 : l_val0;
-            l_flg = flip ? l_flg1 : l_flg0;
+            flip       = 1 - flip;
+            l_val      = flip ? l_val1 : l_val0;
+            l_flg      = flip ? l_flg1 : l_flg0;
             l_val[lid] = val;
             l_flg[lid] = flag;
             barrier(CLK_LOCAL_MEM_FENCE);
@@ -261,7 +255,7 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
 
         if (cond) oData[id] = val;
         if (isLast) {
-            l_tmp[lidy] = val;
+            l_tmp[lidy]  = val;
             l_ftmp[lidy] = flag;
         }
         id += DIMX;
@@ -269,44 +263,40 @@ void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     }
 }
 
-__kernel
-void bcast_first_kernel(__global To *oData, KParam oInfo,
-                        const __global To *tData, KParam tInfo,
-                        const __global int *tiData, KParam tiInfo,
-                        uint groups_x, uint groups_y, uint lim)
-{
+__kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
+                                 const __global To *tData, KParam tInfo,
+                                 const __global int *tiData, KParam tiInfo,
+                                 uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
 
-    const int zid = get_group_id(0) / groups_x;
-    const int wid = get_group_id(1) / groups_y;
-    const int groupId_x = get_group_id(0) - (groups_x) * zid;
-    const int groupId_y = get_group_id(1) - (groups_y) * wid;
-    const int xid = groupId_x * get_local_size(0) * lim + lidx;
-    const int yid = groupId_y * get_local_size(1) + lidy;
+    const int zid       = get_group_id(0) / groups_x;
+    const int wid       = get_group_id(1) / groups_y;
+    const int groupId_x = get_group_id(0) - (groups_x)*zid;
+    const int groupId_y = get_group_id(1) - (groups_y)*wid;
+    const int xid       = groupId_x * get_local_size(0) * lim + lidx;
+    const int yid       = groupId_y * get_local_size(1) + lidy;
 
     if (groupId_x != 0) {
-        bool cond = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) && (wid < oInfo.dims[3]);
+        bool cond = (yid < oInfo.dims[1]) && (zid < oInfo.dims[2]) &&
+                    (wid < oInfo.dims[3]);
 
         if (cond) {
-
             tiData += wid * tiInfo.strides[3] + zid * tiInfo.strides[2] +
-                yid * tiInfo.strides[1] + tiInfo.offset;
+                      yid * tiInfo.strides[1] + tiInfo.offset;
 
             tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
-                yid * tInfo.strides[1] + tInfo.offset;
+                     yid * tInfo.strides[1] + tInfo.offset;
 
             oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-                yid * oInfo.strides[1] + oInfo.offset;
+                     yid * oInfo.strides[1] + oInfo.offset;
 
             int boundary = tiData[groupId_x];
-            To accum = tData[groupId_x - 1];
+            To accum     = tData[groupId_x - 1];
 
-            for (int k = 0, id = xid;
-                 k < lim && id < boundary;
+            for (int k = 0, id = xid; k < lim && id < boundary;
                  k++, id += DIMX) {
-
                 oData[id] = binOp(accum, oData[id]);
             }
         }
diff --git a/src/backend/opencl/kernel/scan_first_by_key.hpp b/src/backend/opencl/kernel/scan_first_by_key.hpp
index 3eaa5c8356..c94e22a526 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key.hpp
@@ -8,16 +8,14 @@
  ********************************************************/
 
 #pragma once
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <traits.hpp>
 
-namespace opencl
-{
-namespace kernel
-{
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_first(Param &out, const Param &in, const Param &key);
-}
+namespace opencl {
+namespace kernel {
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+void scan_first(Param &out, const Param &in, const Param &key);
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index f419760f44..90bc212c24 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -8,277 +8,235 @@
  ********************************************************/
 
 #pragma once
-#include <string>
-#include <mutex>
-#include <map>
-#include <kernel_headers/scan_first_by_key.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
+#include <kernel_headers/scan_first_by_key.hpp>
+#include <memory.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include <cache.hpp>
-#include "names.hpp"
+#include <map>
+#include <mutex>
+#include <string>
 #include "config.hpp"
-#include <memory.hpp>
+#include "names.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags, uint threads_x)
-    {
-        std::string ref_name =
-            std::string("scan_0_") +
-            std::string("_") +
-            std::to_string(calculateFlags) +
-            std::string("_") +
-            std::string(dtype_traits<Ti>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<Tk>::getName()) +
-            std::string("_") +
-            std::string(dtype_traits<To>::getName()) +
-            std::string("_") +
-            std::to_string(op) +
-            std::string("_") +
-            std::to_string(threads_x) +
-            std::string("_") +
-            std::to_string(int(inclusive_scan));
-
-        int device = getActiveDeviceId();
-        kc_entry_t entry = kernelCache(device, ref_name);
-
-        if (entry.prog==0 && entry.ker==0) {
-
-            const uint threads_y = THREADS_PER_GROUP / threads_x;
-            const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
-
-            ToNumStr<To> toNumStr;
-
-            std::ostringstream options;
-            options << " -D To=" << dtype_traits<To>::getName()
-                    << " -D Ti=" << dtype_traits<Ti>::getName()
-                    << " -D Tk=" << dtype_traits<Tk>::getName()
-                    << " -D T=To"
-                    << " -D DIMX=" << threads_x
-                    << " -D DIMY=" << threads_y
-                    << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                    << " -D init=" << toNumStr(Binary<To, op>::init())
-                    << " -D " << binOpName<op>()
-                    << " -D CPLX=" << af::iscplx<Ti>()
-                    << " -D calculateFlags=" << calculateFlags
-                    << " -D inclusive_scan=" << inclusive_scan;
-            if (std::is_same<Ti, double>::value ||
-                std::is_same<Ti, cdouble>::value) {
-                options << " -D USE_DOUBLE";
-            }
-
-            const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
-            const int   ker_lens[] = {ops_cl_len, scan_first_by_key_cl_len};
-            cl::Program prog;
-            buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-            entry.prog = new Program(prog);
-            entry.ker = new Kernel[3];
-
-            entry.ker[0] = Kernel(*entry.prog, "scan_first_by_key_final_kernel");
-            entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
-            entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
-
-            addKernelToCache(device, ref_name, entry);
+namespace opencl {
+namespace kernel {
+
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags,
+                                     uint threads_x) {
+    std::string ref_name =
+        std::string("scan_0_") + std::string("_") +
+        std::to_string(calculateFlags) + std::string("_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_x) +
+        std::string("_") + std::to_string(int(inclusive_scan));
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        const uint threads_y       = THREADS_PER_GROUP / threads_x;
+        const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
+
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x << " -D DIMY=" << threads_y
+                << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
+                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
+                << " -D calculateFlags=" << calculateFlags
+                << " -D inclusive_scan=" << inclusive_scan;
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        return entry.ker[kerIdx];
-    }
+        const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
+        const int ker_lens[]   = {ops_cl_len, scan_first_by_key_cl_len};
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first_nonfinal_launcher(Param &out,
-                                    Param &tmp,
-                                    Param &tmpflg,
-                                    Param &tmpid,
-                                    const Param &in,
-                                    const Param &key,
-                                    const uint groups_x,
-                                    const uint groups_y,
-                                    const uint threads_x)
-    {
-        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(1, false, threads_x);
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
-
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
-
-        auto scanOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint, uint>(ker);
-
-        scanOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info,
-               *tmp.data, tmp.info,
-               *tmpflg.data, tmpflg.info,
-               *tmpid.data, tmpid.info,
-               *in.data, in.info, *key.data, key.info,
-               groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
-    }
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel[3];
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan = true>
-    static void scan_first_final_launcher(Param &out,
-                                    const Param &in,
-                                    const Param &key,
-                                    const bool calculateFlags,
-                                    const uint groups_x,
-                                    const uint groups_y,
-                                    const uint threads_x)
-    {
-        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(0, calculateFlags, threads_x);
-
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
-
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
-
-        auto scanOp = KernelFunctor<Buffer, KParam,
-                                    Buffer, KParam,
-                                    Buffer, KParam,
-                                    uint, uint, uint>(ker);
-
-        scanOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info, *in.data, in.info, *key.data, key.info,
-               groups_x, groups_y, lim);
-
-        CL_DEBUG_FINISH(getQueue());
+        entry.ker[0] = Kernel(*entry.prog, "scan_first_by_key_final_kernel");
+        entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
+        entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    static void bcast_first_launcher(Param &out,
-                                     Param &tmp,
-                                     Param &tmpid,
-                                     const uint groups_x,
-                                     const uint groups_y,
-                                     const uint threads_x)
-    {
+    return entry.ker[kerIdx];
+}
 
-        Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(2, false, threads_x);
+template<typename Ti, typename Tk, typename To, af_op_t op,
+         bool inclusive_scan = true>
+static void scan_first_nonfinal_launcher(Param &out, Param &tmp, Param &tmpflg,
+                                         Param &tmpid, const Param &in,
+                                         const Param &key, const uint groups_x,
+                                         const uint groups_y,
+                                         const uint threads_x) {
+    Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(
+        1, false, threads_x);
+
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
+
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+    auto scanOp =
+        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, Buffer,
+                      KParam, Buffer, KParam, Buffer, KParam, uint, uint, uint>(
+            ker);
+
+    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *tmp.data, tmp.info, *tmpflg.data, tmpflg.info, *tmpid.data,
+           tmpid.info, *in.data, in.info, *key.data, key.info, groups_x,
+           groups_y, lim);
+
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-        NDRange global(groups_x * out.info.dims[2] * local[0],
-                       groups_y * out.info.dims[3] * local[1]);
+template<typename Ti, typename Tk, typename To, af_op_t op,
+         bool inclusive_scan = true>
+static void scan_first_final_launcher(Param &out, const Param &in,
+                                      const Param &key,
+                                      const bool calculateFlags,
+                                      const uint groups_x, const uint groups_y,
+                                      const uint threads_x) {
+    Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(
+        0, calculateFlags, threads_x);
 
-        uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
 
-        auto bcastOp = KernelFunctor<Buffer, KParam,
-                                     Buffer, KParam,
-                                     Buffer, KParam,
-                                     uint, uint, uint>(ker);
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        bcastOp(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, *tmp.data, tmp.info, *tmpid.data, tmpid.info,
-                groups_x, groups_y, lim);
+    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                uint, uint, uint>(ker);
 
-        CL_DEBUG_FINISH(getQueue());
-    }
+    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, *key.data, key.info, groups_x, groups_y, lim);
 
+    CL_DEBUG_FINISH(getQueue());
+}
 
-    template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-    void scan_first(Param &out, const Param &in, const Param &key)
-    {
-        uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
-        threads_x = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+static void bcast_first_launcher(Param &out, Param &tmp, Param &tmpid,
+                                 const uint groups_x, const uint groups_y,
+                                 const uint threads_x) {
+    Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(
+        2, false, threads_x);
 
-        uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(out.info.dims[1], threads_y);
+    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
 
-        if (groups_x == 1) {
-            scan_first_final_launcher<Ti, Tk, To, op, inclusive_scan>(out, in, key,
-                                            true,
-                                            groups_x, groups_y,
-                                            threads_x);
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-        } else {
+    auto bcastOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                 uint, uint, uint>(ker);
+
+    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *tmp.data, tmp.info, *tmpid.data, tmpid.info, groups_x, groups_y,
+            lim);
+
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            Param tmp = out;
-            tmp.info.dims[0] = groups_x;
-            tmp.info.strides[0] = 1;
-            for (int k = 1; k < 4; k++) {
-                tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims   [k - 1];
-            }
-            Param tmpflg = tmp;
-            Param tmpid = tmp;
-
-            int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
-
-            tmp.data = bufferAlloc(tmp_elements * sizeof(To));
-            tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
-            tmpid.data = bufferAlloc(tmp_elements * sizeof(int));
-
-            scan_first_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(out, tmp, tmpflg, tmpid, in, key,
-                                            groups_x, groups_y,
-                                            threads_x);
-
-            if (op == af_notzero_t) {
-                scan_first_final_launcher<To, char, To, af_add_t, true>(tmp, tmp, tmpflg,
-                                                      false,
-                                                      1, groups_y,
-                                                      threads_x);
-            } else {
-                scan_first_final_launcher<To, char, To,       op, true>(tmp, tmp, tmpflg,
-                                                      false,
-                                                      1, groups_y,
-                                                      threads_x);
-            }
-
-            bcast_first_launcher<To, Tk, To, op, inclusive_scan>(out, tmp, tmpid,
-                                             groups_x,
-                                             groups_y,
-                                             threads_x);
-
-            bufferFree(tmp.data);
-            bufferFree(tmpflg.data);
-            bufferFree(tmpid.data);
+template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
+void scan_first(Param &out, const Param &in, const Param &key) {
+    uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
+
+    uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(out.info.dims[1], threads_y);
+
+    if (groups_x == 1) {
+        scan_first_final_launcher<Ti, Tk, To, op, inclusive_scan>(
+            out, in, key, true, groups_x, groups_y, threads_x);
+
+    } else {
+        Param tmp           = out;
+        tmp.info.dims[0]    = groups_x;
+        tmp.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++) {
+            tmp.info.strides[k] =
+                tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
         }
-    }
+        Param tmpflg = tmp;
+        Param tmpid  = tmp;
+
+        int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
 
+        tmp.data    = bufferAlloc(tmp_elements * sizeof(To));
+        tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
+        tmpid.data  = bufferAlloc(tmp_elements * sizeof(int));
+
+        scan_first_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(
+            out, tmp, tmpflg, tmpid, in, key, groups_x, groups_y, threads_x);
+
+        if (op == af_notzero_t) {
+            scan_first_final_launcher<To, char, To, af_add_t, true>(
+                tmp, tmp, tmpflg, false, 1, groups_y, threads_x);
+        } else {
+            scan_first_final_launcher<To, char, To, op, true>(
+                tmp, tmp, tmpflg, false, 1, groups_y, threads_x);
+        }
+
+        bcast_first_launcher<To, Tk, To, op, inclusive_scan>(
+            out, tmp, tmpid, groups_x, groups_y, threads_x);
+
+        bufferFree(tmp.data);
+        bufferFree(tmpflg.data);
+        bufferFree(tmpid.data);
+    }
 }
 
-#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)                                                  \
-    template void scan_first<Ti, Tk, To, ROp,  true>(Param &out, const Param &in, const Param &key);    \
-    template void scan_first<Ti, Tk, To, ROp, false>(Param &out, const Param &in, const Param &key);
-
-#define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)        \
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float  , Tk, float  )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, double , Tk, double )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cfloat , Tk, cfloat )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cdouble, Tk, cdouble)\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, int    , Tk, int    )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint   , Tk, uint   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl   , Tk, intl   )\
-    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl  , Tk, uintl  )\
-
-#define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)       \
-    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int  ) \
-    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uint ) \
-    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl ) \
+}  // namespace kernel
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)   \
+    template void scan_first<Ti, Tk, To, ROp, true>(     \
+        Param & out, const Param &in, const Param &key); \
+    template void scan_first<Ti, Tk, To, ROp, false>(    \
+        Param & out, const Param &in, const Param &key);
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)         \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, uintl, Tk, uintl)
+
+#define INSTANTIATE_SCAN_FIRST_BY_KEY_OP(ROp)      \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, int)  \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uint) \
+    INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uintl)
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/select.cl b/src/backend/opencl/kernel/select.cl
index a16a7b4ee3..e498aafbf5 100644
--- a/src/backend/opencl/kernel/select.cl
+++ b/src/backend/opencl/kernel/select.cl
@@ -15,8 +15,7 @@
 #define is_same 0
 #endif
 
-int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4])
-{
+int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4]) {
     int off = 0;
     off += ids[3] * (dims[3] == refdims[3]) * strides[3];
     off += ids[2] * (dims[2] == refdims[2]) * strides[2];
@@ -24,17 +23,13 @@ int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4])
     return off;
 }
 
-__kernel
-void select_kernel(__global T *optr, KParam oinfo,
-                   __global char *cptr_, KParam cinfo,
-                   __global T *aptr_, KParam ainfo,
-                   __global T *bptr_, KParam binfo,
-                   int groups_0,
-                   int groups_1)
-{
+__kernel void select_kernel(__global T *optr, KParam oinfo,
+                            __global char *cptr_, KParam cinfo,
+                            __global T *aptr_, KParam ainfo, __global T *bptr_,
+                            KParam binfo, int groups_0, int groups_1) {
     __global char *cptr = cptr_ + cinfo.offset;
-    __global T *aptr = aptr_ + ainfo.offset;
-    __global T *bptr = bptr_ + binfo.offset;
+    __global T *aptr    = aptr_ + ainfo.offset;
+    __global T *bptr    = bptr_ + binfo.offset;
 
     const int idz = get_group_id(0) / groups_0;
     const int idw = get_group_id(1) / groups_1;
@@ -43,13 +38,12 @@ void select_kernel(__global T *optr, KParam oinfo,
     const int group_id_1 = get_group_id(1) - idw * groups_1;
 
     const int idx0 = group_id_0 * get_local_size(0) + get_local_id(0);
-    const int idy = group_id_1 * get_local_size(1) + get_local_id(1);
+    const int idy  = group_id_1 * get_local_size(1) + get_local_id(1);
 
-    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1];
+    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] +
+                    idy * oinfo.strides[1];
 
-    if (idw >= oinfo.dims[3] ||
-        idz >= oinfo.dims[2] ||
-        idy >= oinfo.dims[1])  {
+    if (idw >= oinfo.dims[3] || idz >= oinfo.dims[2] || idy >= oinfo.dims[1]) {
         return;
     }
 
@@ -61,29 +55,28 @@ void select_kernel(__global T *optr, KParam oinfo,
     cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims, ids);
 
     if (is_same) {
-        for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
+        for (int idx = idx0; idx < oinfo.dims[0];
+             idx += get_local_size(0) * groups_0) {
             optr[idx] = (cptr[idx]) ? aptr[idx] : bptr[idx];
         }
     } else {
         bool csame = cinfo.dims[0] == oinfo.dims[0];
         bool asame = ainfo.dims[0] == oinfo.dims[0];
         bool bsame = binfo.dims[0] == oinfo.dims[0];
-        for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
-            optr[idx] = (cptr[csame * idx]) ? aptr[asame * idx] : bptr[bsame * idx];
+        for (int idx = idx0; idx < oinfo.dims[0];
+             idx += get_local_size(0) * groups_0) {
+            optr[idx] =
+                (cptr[csame * idx]) ? aptr[asame * idx] : bptr[bsame * idx];
         }
     }
 }
 
-__kernel
-void select_scalar_kernel(__global T *optr, KParam oinfo,
-                          __global char *cptr_, KParam cinfo,
-                          __global T *aptr_, KParam ainfo,
-                          T b,
-                          int groups_0,
-                          int groups_1)
-{
+__kernel void select_scalar_kernel(__global T *optr, KParam oinfo,
+                                   __global char *cptr_, KParam cinfo,
+                                   __global T *aptr_, KParam ainfo, T b,
+                                   int groups_0, int groups_1) {
     __global char *cptr = cptr_ + cinfo.offset;
-    __global T *aptr = aptr_ + ainfo.offset;
+    __global T *aptr    = aptr_ + ainfo.offset;
 
     const int idz = get_group_id(0) / groups_0;
     const int idw = get_group_id(1) / groups_1;
@@ -94,20 +87,20 @@ void select_scalar_kernel(__global T *optr, KParam oinfo,
     const int idx0 = group_id_0 * get_local_size(0) + get_local_id(0);
     const int idy  = group_id_1 * get_local_size(1) + get_local_id(1);
 
-    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] + idy * oinfo.strides[1];
+    const int off = idw * oinfo.strides[3] + idz * oinfo.strides[2] +
+                    idy * oinfo.strides[1];
 
     int ids[] = {idx0, idy, idz, idw};
     optr += off;
     aptr += getOffset(ainfo.dims, ainfo.strides, oinfo.dims, ids);
     cptr += getOffset(cinfo.dims, cinfo.strides, oinfo.dims, ids);
 
-    if (idw >= oinfo.dims[3] ||
-        idz >= oinfo.dims[2] ||
-        idy >= oinfo.dims[1])  {
+    if (idw >= oinfo.dims[3] || idz >= oinfo.dims[2] || idy >= oinfo.dims[1]) {
         return;
     }
 
-    for (int idx = idx0; idx < oinfo.dims[0]; idx += get_local_size(0) * groups_0) {
+    for (int idx = idx0; idx < oinfo.dims[0];
+         idx += get_local_size(0) * groups_0) {
         optr[idx] = (cptr[idx] ^ flip) ? aptr[idx] : b;
     }
 }
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index fe3f1daf76..019fb80ac7 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -8,50 +8,49 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/select.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <types.hpp>
+#include <kernel_headers/select.hpp>
 #include <math.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
-static const uint DIMX = 32;
-static const uint DIMY =  8;
+namespace opencl {
+namespace kernel {
+static const uint DIMX  = 32;
+static const uint DIMY  = 8;
 static const int REPEAT = 64;
 
 template<typename T, bool is_same>
-void select_launcher(Param out, Param cond, Param a, Param b, int ndims)
-{
+void select_launcher(Param out, Param cond, Param a, Param b, int ndims) {
     std::string refName = std::string("select_kernel_") +
-        std::string(dtype_traits<T>::getName()) + std::to_string(is_same);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(is_same);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D is_same=" << is_same << " -D T=" << dtype_traits<T>::getName();
+        options << " -D is_same=" << is_same
+                << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {select_cl};
-        const int   ker_lens[] = {select_cl_len};
+        const int ker_lens[]   = {select_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -72,49 +71,49 @@ void select_launcher(Param out, Param cond, Param a, Param b, int ndims)
     int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
     int groups_1 = divup(out.info.dims[1], local[1]);
 
-    NDRange global(groups_0 * out.info.dims[2] * local[0], groups_1 * out.info.dims[3] * local[1]);
+    NDRange global(groups_0 * out.info.dims[2] * local[0],
+                   groups_1 * out.info.dims[3] * local[1]);
 
-    auto selectOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                   Buffer, KParam, int, int>(*entry.ker);
+    auto selectOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                  KParam, Buffer, KParam, int, int>(*entry.ker);
 
-    selectOp(EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *cond.data, cond.info, *a.data, a.info,
-             *b.data, b.info, groups_0, groups_1);
+    selectOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *cond.data, cond.info, *a.data, a.info, *b.data, b.info, groups_0,
+             groups_1);
 }
 
 template<typename T>
-void select(Param out, Param cond, Param a, Param b, int ndims)
-{
+void select(Param out, Param cond, Param a, Param b, int ndims) {
     bool is_same = true;
     for (int i = 0; i < 4; i++) {
         is_same &= (a.info.dims[i] == b.info.dims[i]);
     }
 
     if (is_same) {
-        select_launcher<T, true >(out, cond, a, b, ndims);
+        select_launcher<T, true>(out, cond, a, b, ndims);
     } else {
         select_launcher<T, false>(out, cond, a, b, ndims);
     }
 }
 
 template<typename T, bool flip>
-void select_scalar(Param out, Param cond, Param a, const double b, int ndims)
-{
+void select_scalar(Param out, Param cond, Param a, const double b, int ndims) {
     std::string refName = std::string("select_scalar_kernel_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(flip);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(flip);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D flip=" << flip << " -D T=" << dtype_traits<T>::getName();
+        options << " -D flip=" << flip
+                << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {select_cl};
-        const int   ker_lens[] = {select_cl_len};
+        const int ker_lens[]   = {select_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -135,14 +134,15 @@ void select_scalar(Param out, Param cond, Param a, const double b, int ndims)
     int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
     int groups_1 = divup(out.info.dims[1], local[1]);
 
-    NDRange global(groups_0 * out.info.dims[2] * local[0], groups_1 * out.info.dims[3] * local[1]);
+    NDRange global(groups_0 * out.info.dims[2] * local[0],
+                   groups_1 * out.info.dims[3] * local[1]);
 
-    auto selectOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                   T, int, int>(*entry.ker);
+    auto selectOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
+                                  KParam, T, int, int>(*entry.ker);
 
-    selectOp(EnqueueArgs(getQueue(), global, local),
-             *out.data, out.info, *cond.data, cond.info,
-             *a.data, a.info, scalar<T>(b), groups_0, groups_1);
-}
-}
+    selectOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *cond.data, cond.info, *a.data, a.info, scalar<T>(b), groups_0,
+             groups_1);
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sift_nonfree.cl b/src/backend/opencl/kernel/sift_nonfree.cl
index dc968d4f4d..c31f3bf6af 100644
--- a/src/backend/opencl/kernel/sift_nonfree.cl
+++ b/src/backend/opencl/kernel/sift_nonfree.cl
@@ -104,35 +104,31 @@ __constant float GLOHRadii[3] = {6.f, 11.f, 15.f};
 
 #define PI_VAL 3.14159265358979323846f
 
-void gaussianElimination(float* A, float* b, float* x, const int n)
-{
+void gaussianElimination(float* A, float* b, float* x, const int n) {
     // forward elimination
-    for (int i = 0; i < n-1; i++) {
-        for (int j = i+1; j < n; j++) {
-            float s = A[j*n+i] / A[i*n+i];
+    for (int i = 0; i < n - 1; i++) {
+        for (int j = i + 1; j < n; j++) {
+            float s = A[j * n + i] / A[i * n + i];
 
-            //for (int k = i+1; k < n; k++)
-            for (int k = i; k < n; k++)
-                A[j*n+k] -= s * A[i*n+k];
+            // for (int k = i+1; k < n; k++)
+            for (int k = i; k < n; k++) A[j * n + k] -= s * A[i * n + k];
 
             b[j] -= s * b[i];
         }
     }
 
-    for (int i = 0; i < n; i++)
-        x[i] = 0;
+    for (int i = 0; i < n; i++) x[i] = 0;
 
     // backward substitution
     float sum = 0;
-    for (int i = 0; i <= n-2; i++) {
+    for (int i = 0; i <= n - 2; i++) {
         sum = b[i];
-        for (int j = i+1; j < n; j++)
-            sum -= A[i*n+j] * x[j];
-        x[i] = sum / A[i*n+i];
+        for (int j = i + 1; j < n; j++) sum -= A[i * n + j] * x[j];
+        x[i] = sum / A[i * n + i];
     }
 }
 
-inline void fatomic_add(volatile __local float *source, const float operand) {
+inline void fatomic_add(volatile __local float* source, const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -143,214 +139,201 @@ inline void fatomic_add(volatile __local float *source, const float operand) {
     } prevVal;
     do {
         prevVal.floatVal = *source;
-        newVal.floatVal = prevVal.floatVal + operand;
-    } while (atomic_cmpxchg((volatile __local unsigned int *)source, prevVal.intVal, newVal.intVal) != prevVal.intVal);
+        newVal.floatVal  = prevVal.floatVal + operand;
+    } while (atomic_cmpxchg((volatile __local unsigned int*)source,
+                            prevVal.intVal, newVal.intVal) != prevVal.intVal);
 }
 
-inline void normalizeDesc(
-    __local float* desc,
-    __local float* accum,
-    const int histlen,
-    int lid_x,
-    int lid_y,
-    int lsz_x)
-{
+inline void normalizeDesc(__local float* desc, __local float* accum,
+                          const int histlen, int lid_x, int lid_y, int lsz_x) {
     for (int i = lid_x; i < histlen; i += lsz_x)
-        accum[i] = desc[lid_y*histlen+i]*desc[lid_y*histlen+i];
+        accum[i] = desc[lid_y * histlen + i] * desc[lid_y * histlen + i];
     barrier(CLK_LOCAL_MEM_FENCE);
 
     float sum = 0.0f;
     for (int i = 0; i < histlen; i++)
-        sum += desc[lid_y*histlen+i]*desc[lid_y*histlen+i];
+        sum += desc[lid_y * histlen + i] * desc[lid_y * histlen + i];
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lid_x < 64)
-        accum[lid_x] += accum[lid_x+64];
+    if (lid_x < 64) accum[lid_x] += accum[lid_x + 64];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 32)
-        accum[lid_x] += accum[lid_x+32];
+    if (lid_x < 32) accum[lid_x] += accum[lid_x + 32];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 16)
-        accum[lid_x] += accum[lid_x+16];
+    if (lid_x < 16) accum[lid_x] += accum[lid_x + 16];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 8)
-        accum[lid_x] += accum[lid_x+8];
+    if (lid_x < 8) accum[lid_x] += accum[lid_x + 8];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 4)
-        accum[lid_x] += accum[lid_x+4];
+    if (lid_x < 4) accum[lid_x] += accum[lid_x + 4];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 2)
-        accum[lid_x] += accum[lid_x+2];
+    if (lid_x < 2) accum[lid_x] += accum[lid_x + 2];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 1)
-        accum[lid_x] += accum[lid_x+1];
+    if (lid_x < 1) accum[lid_x] += accum[lid_x + 1];
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    float len_sq = accum[0];
+    float len_sq  = accum[0];
     float len_inv = 1.0f / sqrt(len_sq);
 
     for (int i = lid_x; i < histlen; i += lsz_x) {
-        desc[lid_y*histlen+i] *= len_inv;
+        desc[lid_y * histlen + i] *= len_inv;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 }
 
-inline void normalizeGLOHDesc(
-    __local float* desc,
-    __local float* accum,
-    const int histlen,
-    int lid_x,
-    int lid_y,
-    int lsz_x)
-{
+inline void normalizeGLOHDesc(__local float* desc, __local float* accum,
+                              const int histlen, int lid_x, int lid_y,
+                              int lsz_x) {
     for (int i = lid_x; i < histlen; i += lsz_x)
-        accum[i] = desc[lid_y*histlen+i]*desc[lid_y*histlen+i];
+        accum[i] = desc[lid_y * histlen + i] * desc[lid_y * histlen + i];
     barrier(CLK_LOCAL_MEM_FENCE);
 
     float sum = 0.0f;
     for (int i = 0; i < histlen; i++)
-        sum += desc[lid_y*histlen+i]*desc[lid_y*histlen+i];
+        sum += desc[lid_y * histlen + i] * desc[lid_y * histlen + i];
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (lid_x < 128)
-        accum[lid_x] += accum[lid_x+128];
+    if (lid_x < 128) accum[lid_x] += accum[lid_x + 128];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 64)
-        accum[lid_x] += accum[lid_x+64];
+    if (lid_x < 64) accum[lid_x] += accum[lid_x + 64];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 32)
-        accum[lid_x] += accum[lid_x+32];
+    if (lid_x < 32) accum[lid_x] += accum[lid_x + 32];
     barrier(CLK_LOCAL_MEM_FENCE);
     if (lid_x < 16)
         // GLOH is 272-dimensional, accumulating last 16 descriptors
-        accum[lid_x] += accum[lid_x+16] + accum[lid_x+256];
+        accum[lid_x] += accum[lid_x + 16] + accum[lid_x + 256];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 8)
-        accum[lid_x] += accum[lid_x+8];
+    if (lid_x < 8) accum[lid_x] += accum[lid_x + 8];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 4)
-        accum[lid_x] += accum[lid_x+4];
+    if (lid_x < 4) accum[lid_x] += accum[lid_x + 4];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 2)
-        accum[lid_x] += accum[lid_x+2];
+    if (lid_x < 2) accum[lid_x] += accum[lid_x + 2];
     barrier(CLK_LOCAL_MEM_FENCE);
-    if (lid_x < 1)
-        accum[lid_x] += accum[lid_x+1];
+    if (lid_x < 1) accum[lid_x] += accum[lid_x + 1];
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    float len_sq = accum[0];
+    float len_sq  = accum[0];
     float len_inv = 1.0f / sqrt(len_sq);
 
     for (int i = lid_x; i < histlen; i += lsz_x) {
-        desc[lid_y*histlen+i] *= len_inv;
+        desc[lid_y * histlen + i] *= len_inv;
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 }
 
-__kernel void sub(
-    __global T* out,
-    __global const T* in,
-    unsigned nel,
-    unsigned n_layers)
-{
+__kernel void sub(__global T* out, __global const T* in, unsigned nel,
+                  unsigned n_layers) {
     unsigned i = get_global_id(0);
 
     if (i < nel) {
         for (unsigned l = 0; l < n_layers; l++)
-            out[l*nel + i] = in[l*nel + i] - in[(l+1)*nel + i];
+            out[l * nel + i] = in[l * nel + i] - in[(l + 1) * nel + i];
     }
 }
 
-#define LCPTR(Y, X) (l_center[(Y) * l_i + (X)])
-#define LPPTR(Y, X) (l_prev[(Y) * l_i + (X)])
-#define LNPTR(Y, X) (l_next[(Y) * l_i + (X)])
+#define LCPTR(Y, X) (l_center[(Y)*l_i + (X)])
+#define LPPTR(Y, X) (l_prev[(Y)*l_i + (X)])
+#define LNPTR(Y, X) (l_next[(Y)*l_i + (X)])
 
 // Determines whether a pixel is a scale-space extremum by comparing it to its
 // 3x3x3 pixel neighborhood.
-__kernel void detectExtrema(
-    __global float* x_out,
-    __global float* y_out,
-    __global unsigned* layer_out,
-    __global unsigned* counter,
-    __global const T* dog,
-    KParam iDoG,
-    const unsigned max_feat,
-    const float threshold,
-    __local float* l_mem)
-{
+__kernel void detectExtrema(__global float* x_out, __global float* y_out,
+                            __global unsigned* layer_out,
+                            __global unsigned* counter, __global const T* dog,
+                            KParam iDoG, const unsigned max_feat,
+                            const float threshold, __local float* l_mem) {
     const int dim0 = iDoG.dims[0];
     const int dim1 = iDoG.dims[1];
-    const int imel = iDoG.dims[0]*iDoG.dims[1];
+    const int imel = iDoG.dims[0] * iDoG.dims[1];
 
     const int lid_i = get_local_id(0);
     const int lid_j = get_local_id(1);
     const int lsz_i = get_local_size(0);
     const int lsz_j = get_local_size(1);
-    const int i = get_group_id(0) * lsz_i + lid_i+IMG_BORDER;
-    const int j = get_group_id(1) * lsz_j + lid_j+IMG_BORDER;
+    const int i     = get_group_id(0) * lsz_i + lid_i + IMG_BORDER;
+    const int j     = get_group_id(1) * lsz_j + lid_j + IMG_BORDER;
 
     // One pixel border for each side
-    const int l_i = lsz_i+2;
-    const int l_j = lsz_j+2;
+    const int l_i = lsz_i + 2;
+    const int l_j = lsz_j + 2;
 
     __local float* l_prev   = l_mem;
     __local float* l_center = l_mem + l_i * l_j;
     __local float* l_next   = l_mem + l_i * l_j * 2;
 
-    const int x = lid_i+1;
-    const int y = lid_j+1;
-
-    for (int l = 1; l < iDoG.dims[2]-1; l++) {
-        const int l_i_half = l_i/2;
-        const int l_j_half = l_j/2;
-        if (lid_i < l_i_half && lid_j < l_j_half && i < dim0-IMG_BORDER+1 && j < dim1-IMG_BORDER+1) {
-                l_next  [lid_j*l_i + lid_i] = (float)dog[(l+1)*imel+(j-1)*dim0+i-1];
-                l_center[lid_j*l_i + lid_i] = (float)dog[(l  )*imel+(j-1)*dim0+i-1];
-                l_prev  [lid_j*l_i + lid_i] = (float)dog[(l-1)*imel+(j-1)*dim0+i-1];
-
-                l_next  [lid_j*l_i + lid_i+l_i_half] = (float)dog[(l+1)*imel+(j-1)*dim0+i-1+l_i_half];
-                l_center[lid_j*l_i + lid_i+l_i_half] = (float)dog[(l  )*imel+(j-1)*dim0+i-1+l_i_half];
-                l_prev  [lid_j*l_i + lid_i+l_i_half] = (float)dog[(l-1)*imel+(j-1)*dim0+i-1+l_i_half];
-
-                l_next  [(lid_j+l_j_half)*l_i + lid_i] = (float)dog[(l+1)*imel+(j-1+l_j_half)*dim0+i-1];
-                l_center[(lid_j+l_j_half)*l_i + lid_i] = (float)dog[(l  )*imel+(j-1+l_j_half)*dim0+i-1];
-                l_prev  [(lid_j+l_j_half)*l_i + lid_i] = (float)dog[(l-1)*imel+(j-1+l_j_half)*dim0+i-1];
-
-                l_next  [(lid_j+l_j_half)*l_i + lid_i+l_i_half] = (float)dog[(l+1)*imel+(j-1+l_j_half)*dim0+i-1+l_i_half];
-                l_center[(lid_j+l_j_half)*l_i + lid_i+l_i_half] = (float)dog[(l  )*imel+(j-1+l_j_half)*dim0+i-1+l_i_half];
-                l_prev  [(lid_j+l_j_half)*l_i + lid_i+l_i_half] = (float)dog[(l-1)*imel+(j-1+l_j_half)*dim0+i-1+l_i_half];
+    const int x = lid_i + 1;
+    const int y = lid_j + 1;
+
+    for (int l = 1; l < iDoG.dims[2] - 1; l++) {
+        const int l_i_half = l_i / 2;
+        const int l_j_half = l_j / 2;
+        if (lid_i < l_i_half && lid_j < l_j_half && i < dim0 - IMG_BORDER + 1 &&
+            j < dim1 - IMG_BORDER + 1) {
+            l_next[lid_j * l_i + lid_i] =
+                (float)dog[(l + 1) * imel + (j - 1) * dim0 + i - 1];
+            l_center[lid_j * l_i + lid_i] =
+                (float)dog[(l)*imel + (j - 1) * dim0 + i - 1];
+            l_prev[lid_j * l_i + lid_i] =
+                (float)dog[(l - 1) * imel + (j - 1) * dim0 + i - 1];
+
+            l_next[lid_j * l_i + lid_i + l_i_half] =
+                (float)dog[(l + 1) * imel + (j - 1) * dim0 + i - 1 + l_i_half];
+            l_center[lid_j * l_i + lid_i + l_i_half] =
+                (float)dog[(l)*imel + (j - 1) * dim0 + i - 1 + l_i_half];
+            l_prev[lid_j * l_i + lid_i + l_i_half] =
+                (float)dog[(l - 1) * imel + (j - 1) * dim0 + i - 1 + l_i_half];
+
+            l_next[(lid_j + l_j_half) * l_i + lid_i] =
+                (float)dog[(l + 1) * imel + (j - 1 + l_j_half) * dim0 + i - 1];
+            l_center[(lid_j + l_j_half) * l_i + lid_i] =
+                (float)dog[(l)*imel + (j - 1 + l_j_half) * dim0 + i - 1];
+            l_prev[(lid_j + l_j_half) * l_i + lid_i] =
+                (float)dog[(l - 1) * imel + (j - 1 + l_j_half) * dim0 + i - 1];
+
+            l_next[(lid_j + l_j_half) * l_i + lid_i + l_i_half] =
+                (float)dog[(l + 1) * imel + (j - 1 + l_j_half) * dim0 + i - 1 +
+                           l_i_half];
+            l_center[(lid_j + l_j_half) * l_i + lid_i + l_i_half] = (float)
+                dog[(l)*imel + (j - 1 + l_j_half) * dim0 + i - 1 + l_i_half];
+            l_prev[(lid_j + l_j_half) * l_i + lid_i + l_i_half] =
+                (float)dog[(l - 1) * imel + (j - 1 + l_j_half) * dim0 + i - 1 +
+                           l_i_half];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        if (i < dim0-IMG_BORDER && j < dim1-IMG_BORDER) {
-            const int l_i_half = l_i/2;
-            float p = l_center[y*l_i + x];
+        if (i < dim0 - IMG_BORDER && j < dim1 - IMG_BORDER) {
+            const int l_i_half = l_i / 2;
+            float p            = l_center[y * l_i + x];
 
             if (fabs((float)p) > threshold &&
-                ((p > 0 && p > LCPTR(y-1, x-1) && p > LCPTR(y-1, x) &&
-                  p > LCPTR(y-1, x+1) && p > LCPTR(y, x-1) && p > LCPTR(y,   x+1)  &&
-                  p > LCPTR(y+1, x-1) && p > LCPTR(y+1, x) && p > LCPTR(y+1, x+1)  &&
-                  p > LPPTR(y-1, x-1) && p > LPPTR(y-1, x) && p > LPPTR(y-1, x+1)  &&
-                  p > LPPTR(y,   x-1) && p > LPPTR(y  , x) && p > LPPTR(y,   x+1)  &&
-                  p > LPPTR(y+1, x-1) && p > LPPTR(y+1, x) && p > LPPTR(y+1, x+1)  &&
-                  p > LNPTR(y-1, x-1) && p > LNPTR(y-1, x) && p > LNPTR(y-1, x+1)  &&
-                  p > LNPTR(y,   x-1) && p > LNPTR(y  , x) && p > LNPTR(y,   x+1)  &&
-                  p > LNPTR(y+1, x-1) && p > LNPTR(y+1, x) && p > LNPTR(y+1, x+1)) ||
-                 (p < 0 && p < LCPTR(y-1, x-1) && p < LCPTR(y-1, x) &&
-                  p < LCPTR(y-1, x+1) && p < LCPTR(y, x-1) && p < LCPTR(y,   x+1)  &&
-                  p < LCPTR(y+1, x-1) && p < LCPTR(y+1, x) && p < LCPTR(y+1, x+1)  &&
-                  p < LPPTR(y-1, x-1) && p < LPPTR(y-1, x) && p < LPPTR(y-1, x+1)  &&
-                  p < LPPTR(y,   x-1) && p < LPPTR(y  , x) && p < LPPTR(y,   x+1)  &&
-                  p < LPPTR(y+1, x-1) && p < LPPTR(y+1, x) && p < LPPTR(y+1, x+1)  &&
-                  p < LNPTR(y-1, x-1) && p < LNPTR(y-1, x) && p < LNPTR(y-1, x+1)  &&
-                  p < LNPTR(y,   x-1) && p < LNPTR(y  , x) && p < LNPTR(y,   x+1)  &&
-                  p < LNPTR(y+1, x-1) && p < LNPTR(y+1, x) && p < LNPTR(y+1, x+1)))) {
-
+                ((p > 0 && p > LCPTR(y - 1, x - 1) && p > LCPTR(y - 1, x) &&
+                  p > LCPTR(y - 1, x + 1) && p > LCPTR(y, x - 1) &&
+                  p > LCPTR(y, x + 1) && p > LCPTR(y + 1, x - 1) &&
+                  p > LCPTR(y + 1, x) && p > LCPTR(y + 1, x + 1) &&
+                  p > LPPTR(y - 1, x - 1) && p > LPPTR(y - 1, x) &&
+                  p > LPPTR(y - 1, x + 1) && p > LPPTR(y, x - 1) &&
+                  p > LPPTR(y, x) && p > LPPTR(y, x + 1) &&
+                  p > LPPTR(y + 1, x - 1) && p > LPPTR(y + 1, x) &&
+                  p > LPPTR(y + 1, x + 1) && p > LNPTR(y - 1, x - 1) &&
+                  p > LNPTR(y - 1, x) && p > LNPTR(y - 1, x + 1) &&
+                  p > LNPTR(y, x - 1) && p > LNPTR(y, x) &&
+                  p > LNPTR(y, x + 1) && p > LNPTR(y + 1, x - 1) &&
+                  p > LNPTR(y + 1, x) && p > LNPTR(y + 1, x + 1)) ||
+                 (p < 0 && p < LCPTR(y - 1, x - 1) && p < LCPTR(y - 1, x) &&
+                  p < LCPTR(y - 1, x + 1) && p < LCPTR(y, x - 1) &&
+                  p < LCPTR(y, x + 1) && p < LCPTR(y + 1, x - 1) &&
+                  p < LCPTR(y + 1, x) && p < LCPTR(y + 1, x + 1) &&
+                  p < LPPTR(y - 1, x - 1) && p < LPPTR(y - 1, x) &&
+                  p < LPPTR(y - 1, x + 1) && p < LPPTR(y, x - 1) &&
+                  p < LPPTR(y, x) && p < LPPTR(y, x + 1) &&
+                  p < LPPTR(y + 1, x - 1) && p < LPPTR(y + 1, x) &&
+                  p < LPPTR(y + 1, x + 1) && p < LNPTR(y - 1, x - 1) &&
+                  p < LNPTR(y - 1, x) && p < LNPTR(y - 1, x + 1) &&
+                  p < LNPTR(y, x - 1) && p < LNPTR(y, x) &&
+                  p < LNPTR(y, x + 1) && p < LNPTR(y + 1, x - 1) &&
+                  p < LNPTR(y + 1, x) && p < LNPTR(y + 1, x + 1)))) {
                 unsigned idx = atomic_inc(counter);
-                if (idx < max_feat)
-                {
-                    x_out[idx] = (float)j;
-                    y_out[idx] = (float)i;
+                if (idx < max_feat) {
+                    x_out[idx]     = (float)j;
+                    y_out[idx]     = (float)i;
                     layer_out[idx] = l;
                 }
             }
@@ -362,76 +345,67 @@ __kernel void detectExtrema(
 #undef LCPTR
 #undef LPPTR
 #undef LNPTR
-#define CPTR(Y, X) (center[(Y) * dim0 + (X)])
-#define PPTR(Y, X) (prev[(Y) * dim0 + (X)])
-#define NPTR(Y, X) (next[(Y) * dim0 + (X)])
+#define CPTR(Y, X) (center[(Y)*dim0 + (X)])
+#define PPTR(Y, X) (prev[(Y)*dim0 + (X)])
+#define NPTR(Y, X) (next[(Y)*dim0 + (X)])
 
 // Interpolates a scale-space extremum's location and scale to subpixel
 // accuracy to form an image feature. Rejects features with low contrast.
 // Based on Section 4 of Lowe's paper.
 __kernel void interpolateExtrema(
-    __global float* x_out,
-    __global float* y_out,
-    __global unsigned* layer_out,
-    __global float* response_out,
-    __global float* size_out,
-    __global unsigned* counter,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const unsigned* layer_in,
-    const unsigned extrema_feat,
-    __global const T* dog_octave,
-    KParam iDoG,
-    const unsigned max_feat,
-    const unsigned octave,
-    const unsigned n_layers,
-    const float contrast_thr,
-    const float edge_thr,
-    const float sigma,
-    const float img_scale)
-{
+    __global float* x_out, __global float* y_out, __global unsigned* layer_out,
+    __global float* response_out, __global float* size_out,
+    __global unsigned* counter, __global const float* x_in,
+    __global const float* y_in, __global const unsigned* layer_in,
+    const unsigned extrema_feat, __global const T* dog_octave, KParam iDoG,
+    const unsigned max_feat, const unsigned octave, const unsigned n_layers,
+    const float contrast_thr, const float edge_thr, const float sigma,
+    const float img_scale) {
     const unsigned f = get_global_id(0);
 
-    if (f < extrema_feat)
-    {
-        const float first_deriv_scale = img_scale*0.5f;
+    if (f < extrema_feat) {
+        const float first_deriv_scale  = img_scale * 0.5f;
         const float second_deriv_scale = img_scale;
-        const float cross_deriv_scale = img_scale*0.25f;
+        const float cross_deriv_scale  = img_scale * 0.25f;
 
         float xl = 0, xy = 0, xx = 0, contr = 0;
         int i = 0;
 
-        unsigned x = x_in[f];
-        unsigned y = y_in[f];
+        unsigned x     = x_in[f];
+        unsigned y     = y_in[f];
         unsigned layer = layer_in[f];
 
         const int dim0 = iDoG.dims[0];
         const int dim1 = iDoG.dims[1];
         const int imel = dim0 * dim1;
 
-        __global const T* prev   = dog_octave + (int)((layer-1)*imel);
-        __global const T* center = dog_octave + (int)((layer  )*imel);
-        __global const T* next   = dog_octave + (int)((layer+1)*imel);
-
-        for(i = 0; i < MAX_INTERP_STEPS; i++) {
-            float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                           (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                           (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
-
-            float d2  = CPTR(x, y) * 2.f;
-            float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-            float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-            float dss = (NPTR(x, y  ) + PPTR(x, y  ) - d2) * second_deriv_scale;
-            float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                         CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
-            float dxs = (NPTR(x+1, y) - NPTR(x-1, y) -
-                         PPTR(x+1, y) + PPTR(x-1, y)) * cross_deriv_scale;
-            float dys = (NPTR(x, y+1) - NPTR(x-1, y-1) -
-                         PPTR(x, y-1) + PPTR(x-1, y-1)) * cross_deriv_scale;
-
-            float H[9] = {dxx, dxy, dxs,
-                          dxy, dyy, dys,
-                          dxs, dys, dss};
+        __global const T* prev   = dog_octave + (int)((layer - 1) * imel);
+        __global const T* center = dog_octave + (int)((layer)*imel);
+        __global const T* next   = dog_octave + (int)((layer + 1) * imel);
+
+        for (i = 0; i < MAX_INTERP_STEPS; i++) {
+            float dD[3] = {
+                (float)(CPTR(x + 1, y) - CPTR(x - 1, y)) * first_deriv_scale,
+                (float)(CPTR(x, y + 1) - CPTR(x, y - 1)) * first_deriv_scale,
+                (float)(NPTR(x, y) - PPTR(x, y)) * first_deriv_scale};
+
+            float d2 = CPTR(x, y) * 2.f;
+            float dxx =
+                (CPTR(x + 1, y) + CPTR(x - 1, y) - d2) * second_deriv_scale;
+            float dyy =
+                (CPTR(x, y + 1) + CPTR(x, y - 1) - d2) * second_deriv_scale;
+            float dss = (NPTR(x, y) + PPTR(x, y) - d2) * second_deriv_scale;
+            float dxy = (CPTR(x + 1, y + 1) - CPTR(x - 1, y + 1) -
+                         CPTR(x + 1, y - 1) + CPTR(x - 1, y - 1)) *
+                        cross_deriv_scale;
+            float dxs = (NPTR(x + 1, y) - NPTR(x - 1, y) - PPTR(x + 1, y) +
+                         PPTR(x - 1, y)) *
+                        cross_deriv_scale;
+            float dys = (NPTR(x, y + 1) - NPTR(x - 1, y - 1) - PPTR(x, y - 1) +
+                         PPTR(x - 1, y - 1)) *
+                        cross_deriv_scale;
+
+            float H[9] = {dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss};
 
             float X[3];
             gaussianElimination(H, dD, X, 3);
@@ -440,57 +414,57 @@ __kernel void interpolateExtrema(
             xy = -X[1];
             xx = -X[0];
 
-            if(fabs(xl) < 0.5f && fabs(xy) < 0.5f && fabs(xx) < 0.5f)
-                break;
+            if (fabs(xl) < 0.5f && fabs(xy) < 0.5f && fabs(xx) < 0.5f) break;
 
             x += round(xx);
             y += round(xy);
             layer += round(xl);
 
-            if(layer < 1 || layer > n_layers ||
-               x < IMG_BORDER || x >= dim1 - IMG_BORDER ||
-               y < IMG_BORDER || y >= dim0 - IMG_BORDER)
+            if (layer < 1 || layer > n_layers || x < IMG_BORDER ||
+                x >= dim1 - IMG_BORDER || y < IMG_BORDER ||
+                y >= dim0 - IMG_BORDER)
                 return;
         }
 
         // ensure convergence of interpolation
-        if (i >= MAX_INTERP_STEPS)
-            return;
+        if (i >= MAX_INTERP_STEPS) return;
 
-        float dD[3] = {(float)(CPTR(x+1, y) - CPTR(x-1, y)) * first_deriv_scale,
-                       (float)(CPTR(x, y+1) - CPTR(x, y-1)) * first_deriv_scale,
-                       (float)(NPTR(x, y)   - PPTR(x, y))   * first_deriv_scale};
+        float dD[3] = {
+            (float)(CPTR(x + 1, y) - CPTR(x - 1, y)) * first_deriv_scale,
+            (float)(CPTR(x, y + 1) - CPTR(x, y - 1)) * first_deriv_scale,
+            (float)(NPTR(x, y) - PPTR(x, y)) * first_deriv_scale};
         float X[3] = {xx, xy, xl};
 
-        float P = dD[0]*X[0] + dD[1]*X[1] + dD[2]*X[2];
+        float P = dD[0] * X[0] + dD[1] * X[1] + dD[2] * X[2];
 
-        contr = center[x*dim0+y]*img_scale + P * 0.5f;
-        if(fabs(contr) < (contrast_thr / n_layers))
-            return;
+        contr = center[x * dim0 + y] * img_scale + P * 0.5f;
+        if (fabs(contr) < (contrast_thr / n_layers)) return;
 
         // principal curvatures are computed using the trace and det of Hessian
         float d2  = CPTR(x, y) * 2.f;
-        float dxx = (CPTR(x+1, y) + CPTR(x-1, y) - d2) * second_deriv_scale;
-        float dyy = (CPTR(x, y+1) + CPTR(x, y-1) - d2) * second_deriv_scale;
-        float dxy = (CPTR(x+1, y+1) - CPTR(x-1, y+1) -
-                     CPTR(x+1, y-1) + CPTR(x-1, y-1)) * cross_deriv_scale;
+        float dxx = (CPTR(x + 1, y) + CPTR(x - 1, y) - d2) * second_deriv_scale;
+        float dyy = (CPTR(x, y + 1) + CPTR(x, y - 1) - d2) * second_deriv_scale;
+        float dxy = (CPTR(x + 1, y + 1) - CPTR(x - 1, y + 1) -
+                     CPTR(x + 1, y - 1) + CPTR(x - 1, y - 1)) *
+                    cross_deriv_scale;
 
-        float tr = dxx + dyy;
+        float tr  = dxx + dyy;
         float det = dxx * dyy - dxy * dxy;
 
         // add FLT_EPSILON for double-precision compatibility
-        if (det <= 0 || tr*tr*edge_thr >= (edge_thr + 1)*(edge_thr + 1)*det+FLT_EPSILON)
+        if (det <= 0 || tr * tr * edge_thr >=
+                            (edge_thr + 1) * (edge_thr + 1) * det + FLT_EPSILON)
             return;
 
         unsigned ridx = atomic_inc(counter);
 
-        if (ridx < max_feat)
-        {
-            x_out[ridx] = (x + xx) * (1 << octave);
-            y_out[ridx] = (y + xy) * (1 << octave);
-            layer_out[ridx] = layer;
+        if (ridx < max_feat) {
+            x_out[ridx]        = (x + xx) * (1 << octave);
+            y_out[ridx]        = (y + xy) * (1 << octave);
+            layer_out[ridx]    = layer;
             response_out[ridx] = fabs(contr);
-            size_out[ridx] = sigma*pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
+            size_out[ridx] =
+                sigma * pow(2.f, octave + (layer + xl) / n_layers) * 2.f;
         }
     }
 }
@@ -501,71 +475,55 @@ __kernel void interpolateExtrema(
 
 // Remove duplicate keypoints
 __kernel void removeDuplicates(
-    __global float* x_out,
-    __global float* y_out,
-    __global unsigned* layer_out,
-    __global float* response_out,
-    __global float* size_out,
-    __global unsigned* counter,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const unsigned* layer_in,
-    __global const float* response_in,
-    __global const float* size_in,
-    const unsigned total_feat)
-{
+    __global float* x_out, __global float* y_out, __global unsigned* layer_out,
+    __global float* response_out, __global float* size_out,
+    __global unsigned* counter, __global const float* x_in,
+    __global const float* y_in, __global const unsigned* layer_in,
+    __global const float* response_in, __global const float* size_in,
+    const unsigned total_feat) {
     const unsigned f = get_global_id(0);
 
     if (f < total_feat) {
         const float prec_fctr = 1e4f;
 
-        bool cond = (f < total_feat-1)
-                    ? !(round(x_in[f]*prec_fctr) == round(x_in[f+1]*prec_fctr) &&
-                        round(y_in[f]*prec_fctr) == round(y_in[f+1]*prec_fctr) &&
-                        layer_in[f] == layer_in[f+1] &&
-                        round(response_in[f]*prec_fctr) == round(response_in[f+1]*prec_fctr) &&
-                        round(size_in[f]*prec_fctr) == round(size_in[f+1]*prec_fctr))
-                    : true;
+        bool cond = (f < total_feat - 1)
+                        ? !(round(x_in[f] * prec_fctr) ==
+                                round(x_in[f + 1] * prec_fctr) &&
+                            round(y_in[f] * prec_fctr) ==
+                                round(y_in[f + 1] * prec_fctr) &&
+                            layer_in[f] == layer_in[f + 1] &&
+                            round(response_in[f] * prec_fctr) ==
+                                round(response_in[f + 1] * prec_fctr) &&
+                            round(size_in[f] * prec_fctr) ==
+                                round(size_in[f + 1] * prec_fctr))
+                        : true;
 
         if (cond) {
             unsigned idx = atomic_inc(counter);
 
-            x_out[idx] = x_in[f];
-            y_out[idx] = y_in[f];
-            layer_out[idx] = layer_in[f];
+            x_out[idx]        = x_in[f];
+            y_out[idx]        = y_in[f];
+            layer_out[idx]    = layer_in[f];
             response_out[idx] = response_in[f];
-            size_out[idx] = size_in[f];
+            size_out[idx]     = size_in[f];
         }
     }
-
 }
 
-#define IPTR(Y, X) (img[(Y) * dim0 + X])
+#define IPTR(Y, X) (img[(Y)*dim0 + X])
 
 // Computes a canonical orientation for each image feature in an array.  Based
 // on Section 5 of Lowe's paper.  This function adds features to the array when
 // there is more than one dominant orientation at a given feature location.
 __kernel void calcOrientation(
-    __global float* x_out,
-    __global float* y_out,
-    __global unsigned* layer_out,
-    __global float* response_out,
-    __global float* size_out,
-    __global float* ori_out,
-    __global unsigned* counter,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const unsigned* layer_in,
-    __global const float* response_in,
-    __global const float* size_in,
-    const unsigned total_feat,
-    __global const T* gauss_octave,
-    KParam iGauss,
-    const unsigned max_feat,
-    const unsigned octave,
-    const int double_input,
-    __local float* l_mem)
-{
+    __global float* x_out, __global float* y_out, __global unsigned* layer_out,
+    __global float* response_out, __global float* size_out,
+    __global float* ori_out, __global unsigned* counter,
+    __global const float* x_in, __global const float* y_in,
+    __global const unsigned* layer_in, __global const float* response_in,
+    __global const float* size_in, const unsigned total_feat,
+    __global const T* gauss_octave, KParam iGauss, const unsigned max_feat,
+    const unsigned octave, const int double_input, __local float* l_mem) {
     const int lid_x = get_local_id(0);
     const int lid_y = get_local_id(1);
     const int lsz_x = get_local_size(0);
@@ -574,13 +532,11 @@ __kernel void calcOrientation(
 
     const int n = ORI_HIST_BINS;
 
-    __local float* hist = l_mem;
-    __local float* temphist = l_mem + n*8;
+    __local float* hist     = l_mem;
+    __local float* temphist = l_mem + n * 8;
 
     // Initialize temporary histogram
-    for (int i = lid_x; i < n; i += lsz_x) {
-        hist[lid_y*n + i] = 0.f;
-    }
+    for (int i = lid_x; i < n; i += lsz_x) { hist[lid_y * n + i] = 0.f; }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     float real_x, real_y, response, size;
@@ -588,20 +544,20 @@ __kernel void calcOrientation(
 
     if (f < total_feat) {
         // Load keypoint information
-        real_x = x_in[f];
-        real_y = y_in[f];
-        layer = layer_in[f];
+        real_x   = x_in[f];
+        real_y   = y_in[f];
+        layer    = layer_in[f];
         response = response_in[f];
-        size = size_in[f];
+        size     = size_in[f];
 
         const int pt_x = (int)round(real_x / (1 << octave));
         const int pt_y = (int)round(real_y / (1 << octave));
 
         // Calculate auxiliary parameters
-        const float scl_octv = size*0.5f / (1 << octave);
-        const int radius = (int)round(ORI_RADIUS * scl_octv);
-        const float sigma = ORI_SIG_FCTR * scl_octv;
-        const int len = (radius*2+1);
+        const float scl_octv  = size * 0.5f / (1 << octave);
+        const int radius      = (int)round(ORI_RADIUS * scl_octv);
+        const float sigma     = ORI_SIG_FCTR * scl_octv;
+        const int len         = (radius * 2 + 1);
         const float exp_denom = 2.f * sigma * sigma;
 
         const int dim0 = iGauss.dims[0];
@@ -609,92 +565,102 @@ __kernel void calcOrientation(
 
         // Calculate layer offset
         const int layer_offset = layer * dim0 * dim1;
-        __global const T* img = gauss_octave + layer_offset;
+        __global const T* img  = gauss_octave + layer_offset;
 
         // Calculate orientation histogram
-        for (int l = lid_x; l < len*len; l += lsz_x) {
+        for (int l = lid_x; l < len * len; l += lsz_x) {
             int i = l / len - radius;
             int j = l % len - radius;
 
             int y = pt_y + i;
             int x = pt_x + j;
-            if (y < 1 || y >= dim0 - 1 ||
-                x < 1 || x >= dim1 - 1)
-                continue;
+            if (y < 1 || y >= dim0 - 1 || x < 1 || x >= dim1 - 1) continue;
 
-            float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-            float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+            float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
 
-            float mag = sqrt(dx*dx+dy*dy);
-            float ori = atan2(dy,dx);
-            float w = exp(-(i*i + j*j)/exp_denom);
+            float mag = sqrt(dx * dx + dy * dy);
+            float ori = atan2(dy, dx);
+            float w   = exp(-(i * i + j * j) / exp_denom);
 
-            int bin = round(n*(ori+PI_VAL)/(2.f*PI_VAL));
-            bin = bin < n ? bin : 0;
-            bin = (bin < 0) ? 0 : (bin >= n) ? n-1 : bin;
+            int bin = round(n * (ori + PI_VAL) / (2.f * PI_VAL));
+            bin     = bin < n ? bin : 0;
+            bin     = (bin < 0) ? 0 : (bin >= n) ? n - 1 : bin;
 
-            fatomic_add(&hist[lid_y*n+bin], w*mag);
+            fatomic_add(&hist[lid_y * n + bin], w * mag);
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     for (int i = 0; i < SMOOTH_ORI_PASSES; i++) {
         for (int j = lid_x; j < n; j += lsz_x) {
-            temphist[lid_y*n+j] = hist[lid_y*n+j];
+            temphist[lid_y * n + j] = hist[lid_y * n + j];
         }
         barrier(CLK_LOCAL_MEM_FENCE);
         for (int j = lid_x; j < n; j += lsz_x) {
-            float prev = (j == 0) ? temphist[lid_y*n+n-1] : temphist[lid_y*n+j-1];
-            float next = (j+1 == n) ? temphist[lid_y*n] : temphist[lid_y*n+j+1];
-            hist[lid_y*n+j] = 0.25f * prev + 0.5f * temphist[lid_y*n+j] + 0.25f * next;
+            float prev = (j == 0) ? temphist[lid_y * n + n - 1]
+                                  : temphist[lid_y * n + j - 1];
+            float next = (j + 1 == n) ? temphist[lid_y * n]
+                                      : temphist[lid_y * n + j + 1];
+            hist[lid_y * n + j] =
+                0.25f * prev + 0.5f * temphist[lid_y * n + j] + 0.25f * next;
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
     for (int i = lid_x; i < n; i += lsz_x)
-        temphist[lid_y*n+i] = hist[lid_y*n+i];
+        temphist[lid_y * n + i] = hist[lid_y * n + i];
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (lid_x < 16)
-        temphist[lid_y*n+lid_x] = fmax(hist[lid_y*n+lid_x], hist[lid_y*n+lid_x+16]);
+        temphist[lid_y * n + lid_x] =
+            fmax(hist[lid_y * n + lid_x], hist[lid_y * n + lid_x + 16]);
     barrier(CLK_LOCAL_MEM_FENCE);
     if (lid_x < 8)
-        temphist[lid_y*n+lid_x] = fmax(temphist[lid_y*n+lid_x], temphist[lid_y*n+lid_x+8]);
+        temphist[lid_y * n + lid_x] =
+            fmax(temphist[lid_y * n + lid_x], temphist[lid_y * n + lid_x + 8]);
     barrier(CLK_LOCAL_MEM_FENCE);
     if (lid_x < 4) {
-        temphist[lid_y*n+lid_x] = fmax(temphist[lid_y*n+lid_x], hist[lid_y*n+lid_x+32]);
-        temphist[lid_y*n+lid_x] = fmax(temphist[lid_y*n+lid_x], temphist[lid_y*n+lid_x+4]);
+        temphist[lid_y * n + lid_x] =
+            fmax(temphist[lid_y * n + lid_x], hist[lid_y * n + lid_x + 32]);
+        temphist[lid_y * n + lid_x] =
+            fmax(temphist[lid_y * n + lid_x], temphist[lid_y * n + lid_x + 4]);
     }
     barrier(CLK_LOCAL_MEM_FENCE);
     if (lid_x < 2)
-        temphist[lid_y*n+lid_x] = fmax(temphist[lid_y*n+lid_x], temphist[lid_y*n+lid_x+2]);
+        temphist[lid_y * n + lid_x] =
+            fmax(temphist[lid_y * n + lid_x], temphist[lid_y * n + lid_x + 2]);
     barrier(CLK_LOCAL_MEM_FENCE);
     if (lid_x < 1)
-        temphist[lid_y*n+lid_x] = fmax(temphist[lid_y*n+lid_x], temphist[lid_y*n+lid_x+1]);
+        temphist[lid_y * n + lid_x] =
+            fmax(temphist[lid_y * n + lid_x], temphist[lid_y * n + lid_x + 1]);
     barrier(CLK_LOCAL_MEM_FENCE);
-    float omax = temphist[lid_y*n];
+    float omax = temphist[lid_y * n];
 
     if (f < total_feat) {
         float mag_thr = (float)(omax * ORI_PEAK_RATIO);
         int l, r;
         float bin;
-        for (int j = lid_x; j < n; j+=lsz_x) {
+        for (int j = lid_x; j < n; j += lsz_x) {
             l = (j == 0) ? n - 1 : j - 1;
             r = (j + 1) % n;
-            if (hist[lid_y*n+j] > hist[lid_y*n+l] &&
-                hist[lid_y*n+j] > hist[lid_y*n+r] &&
-                hist[lid_y*n+j] >= mag_thr) {
+            if (hist[lid_y * n + j] > hist[lid_y * n + l] &&
+                hist[lid_y * n + j] > hist[lid_y * n + r] &&
+                hist[lid_y * n + j] >= mag_thr) {
                 unsigned idx = atomic_inc(counter);
 
                 if (idx < max_feat) {
-                    float bin = j + 0.5f * (hist[lid_y*n+l] - hist[lid_y*n+r]) /
-                                (hist[lid_y*n+l] - 2.0f*hist[lid_y*n+j] + hist[lid_y*n+r]);
+                    float bin =
+                        j +
+                        0.5f * (hist[lid_y * n + l] - hist[lid_y * n + r]) /
+                            (hist[lid_y * n + l] - 2.0f * hist[lid_y * n + j] +
+                             hist[lid_y * n + r]);
                     bin = (bin < 0.0f) ? bin + n : (bin >= n) ? bin - n : bin;
-                    float ori = 360.f - ((360.f/n) * bin);
+                    float ori = 360.f - ((360.f / n) * bin);
 
                     float new_real_x = real_x;
                     float new_real_y = real_y;
-                    float new_size = size;
+                    float new_size   = size;
 
                     if (double_input != 0) {
                         float scale = 0.5f;
@@ -703,12 +669,12 @@ __kernel void calcOrientation(
                         new_size *= scale;
                     }
 
-                    x_out[idx] = new_real_x;
-                    y_out[idx] = new_real_y;
-                    layer_out[idx] = layer;
+                    x_out[idx]        = new_real_x;
+                    y_out[idx]        = new_real_y;
+                    layer_out[idx]    = layer;
                     response_out[idx] = response;
-                    size_out[idx] = new_size;
-                    ori_out[idx] = ori;
+                    size_out[idx]     = new_size;
+                    ori_out[idx]      = ori;
                 }
             }
         }
@@ -718,62 +684,51 @@ __kernel void calcOrientation(
 // Computes feature descriptors for features in an array.  Based on Section 6
 // of Lowe's paper.
 __kernel void computeDescriptor(
-    __global float* desc_out,
-    const unsigned desc_len,
-    const unsigned histsz,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const unsigned* layer_in,
-    __global const float* response_in,
-    __global const float* size_in,
-    __global const float* ori_in,
-    const unsigned total_feat,
-    __global const T* gauss_octave,
-    KParam iGauss,
-    const int d,
-    const int n,
-    const float scale,
-    const int n_layers,
-    __local float* l_mem)
-{
+    __global float* desc_out, const unsigned desc_len, const unsigned histsz,
+    __global const float* x_in, __global const float* y_in,
+    __global const unsigned* layer_in, __global const float* response_in,
+    __global const float* size_in, __global const float* ori_in,
+    const unsigned total_feat, __global const T* gauss_octave, KParam iGauss,
+    const int d, const int n, const float scale, const int n_layers,
+    __local float* l_mem) {
     const int lid_x = get_local_id(0);
     const int lid_y = get_local_id(1);
     const int lsz_x = get_local_size(0);
 
     const int f = get_global_id(1);
 
-    __local float* desc = l_mem;
+    __local float* desc  = l_mem;
     __local float* accum = l_mem + desc_len * histsz;
 
-    for (int i = lid_x; i < desc_len*histsz; i += lsz_x)
-        desc[lid_y*desc_len+i] = 0.f;
+    for (int i = lid_x; i < desc_len * histsz; i += lsz_x)
+        desc[lid_y * desc_len + i] = 0.f;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (f < total_feat) {
         const unsigned layer = layer_in[f];
-        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-        const float size = size_in[f];
-        const int fx = round(x_in[f] * scale);
-        const int fy = round(y_in[f] * scale);
+        float ori            = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori                  = (ori > PI_VAL) ? ori - PI_VAL * 2 : ori;
+        const float size     = size_in[f];
+        const int fx         = round(x_in[f] * scale);
+        const int fy         = round(y_in[f] * scale);
 
         // Points img to correct Gaussian pyramid layer
-        const int dim0 = iGauss.dims[0];
-        const int dim1 = iGauss.dims[1];
+        const int dim0        = iGauss.dims[0];
+        const int dim1        = iGauss.dims[1];
         __global const T* img = gauss_octave + (layer * dim0 * dim1);
 
-        float cos_t = cos(ori);
-        float sin_t = sin(ori);
+        float cos_t        = cos(ori);
+        float sin_t        = sin(ori);
         float bins_per_rad = n / (PI_VAL * 2.f);
-        float exp_denom = d * d * 0.5f;
-        float hist_width = DESCR_SCL_FCTR * size * scale * 0.5f;
-        int radius = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
+        float exp_denom    = d * d * 0.5f;
+        float hist_width   = DESCR_SCL_FCTR * size * scale * 0.5f;
+        int radius         = hist_width * sqrt(2.f) * (d + 1.f) * 0.5f + 0.5f;
 
-        int len = radius*2+1;
+        int len            = radius * 2 + 1;
         const int hist_off = (lid_x % histsz) * desc_len;
 
         // Calculate orientation histogram
-        for (int l = lid_x; l < len*len; l += lsz_x) {
+        for (int l = lid_x; l < len * len; l += lsz_x) {
             int i = l / len - radius;
             int j = l % len - radius;
 
@@ -782,24 +737,22 @@ __kernel void computeDescriptor(
 
             float x_rot = (j * cos_t - i * sin_t) / hist_width;
             float y_rot = (j * sin_t + i * cos_t) / hist_width;
-            float xbin = x_rot + d/2 - 0.5f;
-            float ybin = y_rot + d/2 - 0.5f;
+            float xbin  = x_rot + d / 2 - 0.5f;
+            float ybin  = y_rot + d / 2 - 0.5f;
 
-            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d &&
-                y > 0 && y < dim0 - 1 && x > 0 && x < dim1 - 1) {
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
+            if (ybin > -1.0f && ybin < d && xbin > -1.0f && xbin < d && y > 0 &&
+                y < dim0 - 1 && x > 0 && x < dim1 - 1) {
+                float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+                float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
 
-                float grad_mag = sqrt(dx*dx + dy*dy);
+                float grad_mag = sqrt(dx * dx + dy * dy);
                 float grad_ori = atan2(dy, dx) - ori;
-                while (grad_ori < 0.0f)
-                    grad_ori += PI_VAL*2;
-                while (grad_ori >= PI_VAL*2)
-                    grad_ori -= PI_VAL*2;
+                while (grad_ori < 0.0f) grad_ori += PI_VAL * 2;
+                while (grad_ori >= PI_VAL * 2) grad_ori -= PI_VAL * 2;
 
-                float w = exp(-(x_rot*x_rot + y_rot*y_rot) / exp_denom);
+                float w    = exp(-(x_rot * x_rot + y_rot * y_rot) / exp_denom);
                 float obin = grad_ori * bins_per_rad;
-                float mag = grad_mag*w;
+                float mag  = grad_mag * w;
 
                 int x0 = floor(xbin);
                 int y0 = floor(ybin);
@@ -811,19 +764,24 @@ __kernel void computeDescriptor(
                 for (int yl = 0; yl <= 1; yl++) {
                     int yb = y0 + yl;
                     if (yb >= 0 && yb < d) {
-	                    float v_y = mag * ((yl == 0) ? 1.0f - ybin : ybin);
-	                    for (int xl = 0; xl <= 1; xl++) {
-	                        int xb = x0 + xl;
-	                        if (xb >= 0 && xb < d) {
-		                        float v_x = v_y * ((xl == 0) ? 1.0f - xbin : xbin);
-		                        for (int ol = 0; ol <= 1; ol++) {
-		                            int ob = (o0 + ol) % n;
-		                            float v_o = v_x * ((ol == 0) ? 1.0f - obin : obin);
-		                            fatomic_add(&desc[hist_off + lid_y*desc_len + (yb*d + xb)*n + ob], v_o);
-		                        }
-		                    }
-	                    }
-	                }
+                        float v_y = mag * ((yl == 0) ? 1.0f - ybin : ybin);
+                        for (int xl = 0; xl <= 1; xl++) {
+                            int xb = x0 + xl;
+                            if (xb >= 0 && xb < d) {
+                                float v_x =
+                                    v_y * ((xl == 0) ? 1.0f - xbin : xbin);
+                                for (int ol = 0; ol <= 1; ol++) {
+                                    int ob = (o0 + ol) % n;
+                                    float v_o =
+                                        v_x * ((ol == 0) ? 1.0f - obin : obin);
+                                    fatomic_add(
+                                        &desc[hist_off + lid_y * desc_len +
+                                              (yb * d + xb) * n + ob],
+                                        v_o);
+                                }
+                            }
+                        }
+                    }
                 }
             }
         }
@@ -831,83 +789,71 @@ __kernel void computeDescriptor(
     barrier(CLK_LOCAL_MEM_FENCE);
 
     // Combine histograms (reduces previous atomicAdd overhead)
-    for (int l = lid_x; l < desc_len*4; l += lsz_x)
-        desc[l] += desc[l+4*desc_len];
+    for (int l = lid_x; l < desc_len * 4; l += lsz_x)
+        desc[l] += desc[l + 4 * desc_len];
     barrier(CLK_LOCAL_MEM_FENCE);
-    for (int l = lid_x; l < desc_len*2; l += lsz_x)
-        desc[l    ] += desc[l+2*desc_len];
+    for (int l = lid_x; l < desc_len * 2; l += lsz_x)
+        desc[l] += desc[l + 2 * desc_len];
     barrier(CLK_LOCAL_MEM_FENCE);
-    for (int l = lid_x; l < desc_len; l += lsz_x)
-        desc[l] += desc[l+desc_len];
+    for (int l = lid_x; l < desc_len; l += lsz_x) desc[l] += desc[l + desc_len];
     barrier(CLK_LOCAL_MEM_FENCE);
 
     normalizeDesc(desc, accum, desc_len, lid_x, lid_y, lsz_x);
 
-    for (int i = lid_x; i < d*d*n; i += lsz_x)
-        desc[lid_y*desc_len+i] = min(desc[lid_y*desc_len+i], DESCR_MAG_THR);
+    for (int i = lid_x; i < d * d * n; i += lsz_x)
+        desc[lid_y * desc_len + i] =
+            min(desc[lid_y * desc_len + i], DESCR_MAG_THR);
     barrier(CLK_LOCAL_MEM_FENCE);
 
     normalizeDesc(desc, accum, desc_len, lid_x, lid_y, lsz_x);
 
     if (f < total_feat) {
         // Calculate final descriptor values
-        for (int k = lid_x; k < d*d*n; k += lsz_x)
-            desc_out[f*desc_len+k] = round(min(255.f, desc[lid_y*desc_len+k] * INT_DESCR_FCTR));
+        for (int k = lid_x; k < d * d * n; k += lsz_x)
+            desc_out[f * desc_len + k] =
+                round(min(255.f, desc[lid_y * desc_len + k] * INT_DESCR_FCTR));
     }
 }
 
 __kernel void computeGLOHDescriptor(
-    __global float* desc_out,
-    const unsigned desc_len,
-    const unsigned histsz,
-    __global const float* x_in,
-    __global const float* y_in,
-    __global const unsigned* layer_in,
-    __global const float* response_in,
-    __global const float* size_in,
-    __global const float* ori_in,
-    const unsigned total_feat,
-    __global const T* gauss_octave,
-    KParam iGauss,
-    const int d,
-    const unsigned rb,
-    const unsigned ab,
-    const unsigned hb,
-    const float scale,
-    const int n_layers,
-    __local float* l_mem)
-{
+    __global float* desc_out, const unsigned desc_len, const unsigned histsz,
+    __global const float* x_in, __global const float* y_in,
+    __global const unsigned* layer_in, __global const float* response_in,
+    __global const float* size_in, __global const float* ori_in,
+    const unsigned total_feat, __global const T* gauss_octave, KParam iGauss,
+    const int d, const unsigned rb, const unsigned ab, const unsigned hb,
+    const float scale, const int n_layers, __local float* l_mem) {
     const int lid_x = get_local_id(0);
     const int lid_y = get_local_id(1);
     const int lsz_x = get_local_size(0);
 
     const int f = get_global_id(1);
 
-    __local float* desc = l_mem;
+    __local float* desc  = l_mem;
     __local float* accum = l_mem + desc_len * histsz;
 
-    for (int i = lid_x; i < desc_len*histsz; i += lsz_x)
-        desc[lid_y*desc_len+i] = 0.f;
+    for (int i = lid_x; i < desc_len * histsz; i += lsz_x)
+        desc[lid_y * desc_len + i] = 0.f;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (f < total_feat) {
         const unsigned layer = layer_in[f];
-        float ori = (360.f - ori_in[f]) * PI_VAL / 180.f;
-        ori = (ori > PI_VAL) ? ori - PI_VAL*2 : ori;
-        const float size = size_in[f];
-        const int fx = round(x_in[f] * scale);
-        const int fy = round(y_in[f] * scale);
+        float ori            = (360.f - ori_in[f]) * PI_VAL / 180.f;
+        ori                  = (ori > PI_VAL) ? ori - PI_VAL * 2 : ori;
+        const float size     = size_in[f];
+        const int fx         = round(x_in[f] * scale);
+        const int fy         = round(y_in[f] * scale);
 
         // Points img to correct Gaussian pyramid layer
-        const int dim0 = iGauss.dims[0];
-        const int dim1 = iGauss.dims[1];
+        const int dim0        = iGauss.dims[0];
+        const int dim1        = iGauss.dims[1];
         __global const T* img = gauss_octave + (layer * dim0 * dim1);
 
-        float cos_t = cos(ori);
-        float sin_t = sin(ori);
-        float hist_bins_per_rad = hb / (PI_VAL * 2.f);
+        float cos_t              = cos(ori);
+        float sin_t              = sin(ori);
+        float hist_bins_per_rad  = hb / (PI_VAL * 2.f);
         float polar_bins_per_rad = ab / (PI_VAL * 2.f);
-        float exp_denom = GLOHRadii[rb-1] * 0.5f;
+        float exp_denom          = GLOHRadii[rb - 1] * 0.5f;
 
         float hist_width = DESCR_SCL_FCTR * size * scale * 0.5f;
 
@@ -918,14 +864,14 @@ __kernel void computeGLOHDescriptor(
         // (rw) in the range of 0.25f-0.75f gives different results,
         // increasing it tends to show a better recall rate but with a
         // smaller amount of correct matches
-        //float rw = 0.5f;
-        //int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
+        // float rw = 0.5f;
+        // int radius = hist_width * GLOHRadii[rb-1] * rw + 0.5f;
 
-        int len = radius*2+1;
+        int len            = radius * 2 + 1;
         const int hist_off = (lid_x % histsz) * desc_len;
 
         // Calculate orientation histogram
-        for (int l = lid_x; l < len*len; l += lsz_x) {
+        for (int l = lid_x; l < len * len; l += lsz_x) {
             int i = l / len - radius;
             int j = l % len - radius;
 
@@ -935,33 +881,36 @@ __kernel void computeGLOHDescriptor(
             float x_rot = (j * cos_t - i * sin_t);
             float y_rot = (j * sin_t + i * cos_t);
 
-            float r = sqrt(x_rot*x_rot + y_rot*y_rot) / radius * GLOHRadii[rb-1];
+            float r = sqrt(x_rot * x_rot + y_rot * y_rot) / radius *
+                      GLOHRadii[rb - 1];
             float theta = atan2(y_rot, x_rot);
-            while (theta < 0.0f)
-                theta += PI_VAL*2;
-            while (theta >= PI_VAL*2)
-                theta -= PI_VAL*2;
+            while (theta < 0.0f) theta += PI_VAL * 2;
+            while (theta >= PI_VAL * 2) theta -= PI_VAL * 2;
 
             float tbin = theta * polar_bins_per_rad;
-            float rbin = (r < GLOHRadii[0]) ? r / GLOHRadii[0] :
-                         ((r < GLOHRadii[1]) ? 1 + (r - GLOHRadii[0]) / (float)(GLOHRadii[1] - GLOHRadii[0]) :
-                         min(2 + (r - GLOHRadii[1]) / (float)(GLOHRadii[2] - GLOHRadii[1]), 3.f-FLT_EPSILON));
-
-            if (r <= GLOHRadii[rb-1] &&
-                y > 0 && y < dim0 - 1 && x > 0 && x < dim1 - 1) {
-                float dx = (float)(IPTR(x+1, y) - IPTR(x-1, y));
-                float dy = (float)(IPTR(x, y-1) - IPTR(x, y+1));
-
-                float grad_mag = sqrt(dx*dx + dy*dy);
+            float rbin =
+                (r < GLOHRadii[0])
+                    ? r / GLOHRadii[0]
+                    : ((r < GLOHRadii[1])
+                           ? 1 + (r - GLOHRadii[0]) /
+                                     (float)(GLOHRadii[1] - GLOHRadii[0])
+                           : min(2 + (r - GLOHRadii[1]) /
+                                         (float)(GLOHRadii[2] - GLOHRadii[1]),
+                                 3.f - FLT_EPSILON));
+
+            if (r <= GLOHRadii[rb - 1] && y > 0 && y < dim0 - 1 && x > 0 &&
+                x < dim1 - 1) {
+                float dx = (float)(IPTR(x + 1, y) - IPTR(x - 1, y));
+                float dy = (float)(IPTR(x, y - 1) - IPTR(x, y + 1));
+
+                float grad_mag = sqrt(dx * dx + dy * dy);
                 float grad_ori = atan2(dy, dx) - ori;
-                while (grad_ori < 0.0f)
-                    grad_ori += PI_VAL*2;
-                while (grad_ori >= PI_VAL*2)
-                    grad_ori -= PI_VAL*2;
+                while (grad_ori < 0.0f) grad_ori += PI_VAL * 2;
+                while (grad_ori >= PI_VAL * 2) grad_ori -= PI_VAL * 2;
 
-                float w = exp(-r / exp_denom);
+                float w    = exp(-r / exp_denom);
                 float obin = grad_ori * hist_bins_per_rad;
-                float mag = grad_mag*w;
+                float mag  = grad_mag * w;
 
                 int t0 = floor(tbin);
                 int r0 = floor(rbin);
@@ -971,17 +920,23 @@ __kernel void computeGLOHDescriptor(
                 obin -= o0;
 
                 for (int rl = 0; rl <= 1; rl++) {
-                    int rb = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
+                    int rb    = (rbin > 0.5f) ? (r0 + rl) : (r0 - rl);
                     float v_r = mag * ((rl == 0) ? 1.0f - rbin : rbin);
                     if (rb >= 0 && rb <= 2) {
                         for (int tl = 0; tl <= 1; tl++) {
-                            int tb = (t0 + tl) % ab;
+                            int tb    = (t0 + tl) % ab;
                             float v_t = v_r * ((tl == 0) ? 1.0f - tbin : tbin);
                             for (int ol = 0; ol <= 1; ol++) {
                                 int ob = (o0 + ol) % hb;
-                                float v_o = v_t * ((ol == 0) ? 1.0f - obin : obin);
-                                unsigned idx = (rb > 0) * (hb + ((rb-1) * ab + tb)*hb) + ob;
-		                        fatomic_add(&desc[hist_off + lid_y*desc_len + idx], v_o);
+                                float v_o =
+                                    v_t * ((ol == 0) ? 1.0f - obin : obin);
+                                unsigned idx =
+                                    (rb > 0) *
+                                        (hb + ((rb - 1) * ab + tb) * hb) +
+                                    ob;
+                                fatomic_add(
+                                    &desc[hist_off + lid_y * desc_len + idx],
+                                    v_o);
                             }
                         }
                     }
@@ -992,20 +947,20 @@ __kernel void computeGLOHDescriptor(
     barrier(CLK_LOCAL_MEM_FENCE);
 
     // Combine histograms (reduces previous atomicAdd overhead)
-    for (int l = lid_x; l < desc_len*4; l += lsz_x)
-        desc[l] += desc[l+4*desc_len];
+    for (int l = lid_x; l < desc_len * 4; l += lsz_x)
+        desc[l] += desc[l + 4 * desc_len];
     barrier(CLK_LOCAL_MEM_FENCE);
-    for (int l = lid_x; l < desc_len*2; l += lsz_x)
-        desc[l    ] += desc[l+2*desc_len];
+    for (int l = lid_x; l < desc_len * 2; l += lsz_x)
+        desc[l] += desc[l + 2 * desc_len];
     barrier(CLK_LOCAL_MEM_FENCE);
-    for (int l = lid_x; l < desc_len; l += lsz_x)
-        desc[l] += desc[l+desc_len];
+    for (int l = lid_x; l < desc_len; l += lsz_x) desc[l] += desc[l + desc_len];
     barrier(CLK_LOCAL_MEM_FENCE);
 
     normalizeGLOHDesc(desc, accum, desc_len, lid_x, lid_y, lsz_x);
 
     for (int i = lid_x; i < desc_len; i += lsz_x)
-        desc[lid_y*desc_len+i] = min(desc[lid_y*desc_len+i], DESCR_MAG_THR);
+        desc[lid_y * desc_len + i] =
+            min(desc[lid_y * desc_len + i], DESCR_MAG_THR);
     barrier(CLK_LOCAL_MEM_FENCE);
 
     normalizeGLOHDesc(desc, accum, desc_len, lid_x, lid_y, lsz_x);
@@ -1013,7 +968,8 @@ __kernel void computeGLOHDescriptor(
     if (f < total_feat) {
         // Calculate final descriptor values
         for (int k = lid_x; k < desc_len; k += lsz_x)
-            desc_out[f*desc_len+k] = round(min(255.f, desc[lid_y*desc_len+k] * INT_DESCR_FCTR));
+            desc_out[f * desc_len + k] =
+                round(min(255.f, desc[lid_y * desc_len + k] * INT_DESCR_FCTR));
     }
 }
 
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index 72c63f7cba..17f3e064ee 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -70,46 +70,43 @@
 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-#include <af/defines.h>
-#include <program.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
-
+#include <err_opencl.hpp>
+#include <program.hpp>
+#include <af/defines.h>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
-#include <boost/compute/core.hpp>
-#include <boost/compute/iterator/buffer_iterator.hpp>
 #include <boost/compute/algorithm/gather.hpp>
 #include <boost/compute/algorithm/iota.hpp>
 #include <boost/compute/algorithm/sort_by_key.hpp>
+#include <boost/compute/core.hpp>
+#include <boost/compute/iterator/buffer_iterator.hpp>
 
 #pragma GCC diagnostic pop
 
+#include <cache.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/resize.hpp>
 #include <kernel_headers/sift_nonfree.hpp>
 #include <memory.hpp>
-#include <cache.hpp>
 #include <vector>
 
 namespace compute = boost::compute;
 
 using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
 using cl::EnqueueArgs;
+using cl::Kernel;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 using std::vector;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int SIFT_THREADS   = 256;
 static const int SIFT_THREADS_X = 32;
 static const int SIFT_THREADS_Y = 8;
@@ -141,26 +138,23 @@ static const unsigned GLOHHistBins = 16;
 static const float PI_VAL = 3.14159265358979323846f;
 
 template<typename T>
-void gaussian1D(T* out, const int dim, double sigma=0.0)
-{
-    if(!(sigma>0)) sigma = 0.25*dim;
+void gaussian1D(T* out, const int dim, double sigma = 0.0) {
+    if (!(sigma > 0)) sigma = 0.25 * dim;
 
     T sum = (T)0;
-    for(int i=0;i<dim;i++)
-    {
-        int x = i-(dim-1)/2;
-        T el = 1. / sqrt(2 * PI_VAL * sigma*sigma) * exp(-((x*x)/(2*(sigma*sigma))));
+    for (int i = 0; i < dim; i++) {
+        int x = i - (dim - 1) / 2;
+        T el  = 1. / sqrt(2 * PI_VAL * sigma * sigma) *
+               exp(-((x * x) / (2 * (sigma * sigma))));
         out[i] = el;
-        sum   += el;
+        sum += el;
     }
 
-    for(int k=0;k<dim;k++)
-        out[k] /= sum;
+    for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 
 template<typename T>
-Param gaussFilter(float sigma)
-{
+Param gaussFilter(float sigma) {
     // Using 6-sigma rule
     unsigned gauss_len = std::min((unsigned)round(sigma * 6 + 1) | 1, 31u);
 
@@ -168,18 +162,20 @@ Param gaussFilter(float sigma)
     gaussian1D(h_gauss, gauss_len, sigma);
 
     Param gauss_filter;
-    gauss_filter.info.offset = 0;
-    gauss_filter.info.dims[0] = gauss_len;
+    gauss_filter.info.offset     = 0;
+    gauss_filter.info.dims[0]    = gauss_len;
     gauss_filter.info.strides[0] = 1;
 
     for (int k = 1; k < 4; k++) {
         gauss_filter.info.dims[k] = 1;
-        gauss_filter.info.strides[k] = gauss_filter.info.dims[k-1] * gauss_filter.info.strides[k-1];
+        gauss_filter.info.strides[k] =
+            gauss_filter.info.dims[k - 1] * gauss_filter.info.strides[k - 1];
     }
 
     dim_t gauss_elem = gauss_filter.info.strides[3] * gauss_filter.info.dims[3];
     gauss_filter.data = bufferAlloc(gauss_elem * sizeof(T));
-    getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0, gauss_elem * sizeof(T), h_gauss);
+    getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0,
+                                  gauss_elem * sizeof(T), h_gauss);
 
     delete[] h_gauss;
 
@@ -187,17 +183,16 @@ Param gaussFilter(float sigma)
 }
 
 template<typename T, typename convAccT>
-void convSepFull(Param& dst, Param src, Param filter)
-{
+void convSepFull(Param& dst, Param src, Param filter) {
     Param tmp;
     tmp.info.offset = 0;
     for (int k = 0; k < 4; k++) {
-        tmp.info.dims[k] = src.info.dims[k];
+        tmp.info.dims[k]    = src.info.dims[k];
         tmp.info.strides[k] = src.info.strides[k];
     }
 
     const dim_t src_el = src.info.dims[3] * src.info.strides[3];
-    tmp.data = bufferAlloc(src_el * sizeof(T));
+    tmp.data           = bufferAlloc(src_el * sizeof(T));
 
     convSep<T, convAccT, 0, false>(tmp, src, filter);
     convSep<T, convAccT, 1, false>(dst, tmp, filter);
@@ -206,33 +201,37 @@ void convSepFull(Param& dst, Param src, Param filter)
 }
 
 template<typename T, typename convAccT>
-Param createInitialImage(
-    Param img,
-    const float init_sigma,
-    const bool double_input)
-{
+Param createInitialImage(Param img, const float init_sigma,
+                         const bool double_input) {
     Param init_img;
     init_img.info.offset = 0;
-    init_img.info.dims[0] = (double_input) ? img.info.dims[0] * 2 : img.info.dims[0];
-    init_img.info.dims[1] = (double_input) ? img.info.dims[1] * 2 : img.info.dims[1];
+    init_img.info.dims[0] =
+        (double_input) ? img.info.dims[0] * 2 : img.info.dims[0];
+    init_img.info.dims[1] =
+        (double_input) ? img.info.dims[1] * 2 : img.info.dims[1];
     init_img.info.strides[0] = 1;
     init_img.info.strides[1] = init_img.info.dims[0];
 
     for (int k = 2; k < 4; k++) {
         init_img.info.dims[k] = 1;
-        init_img.info.strides[k] = init_img.info.dims[k-1] * init_img.info.strides[k-1];
+        init_img.info.strides[k] =
+            init_img.info.dims[k - 1] * init_img.info.strides[k - 1];
     }
 
     dim_t init_img_el = init_img.info.strides[3] * init_img.info.dims[3];
-    init_img.data = bufferAlloc(init_img_el * sizeof(T));
+    init_img.data     = bufferAlloc(init_img_el * sizeof(T));
 
-    float s = (double_input) ? std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma * 4.f), 0.1f)
-                             : std::max((float)sqrt(init_sigma * init_sigma - InitSigma * InitSigma), 0.1f);
+    float s = (double_input)
+                  ? std::max((float)sqrt(init_sigma * init_sigma -
+                                         InitSigma * InitSigma * 4.f),
+                             0.1f)
+                  : std::max((float)sqrt(init_sigma * init_sigma -
+                                         InitSigma * InitSigma),
+                             0.1f);
 
     const Param filter = gaussFilter<convAccT>(s);
 
-    if (double_input)
-        resize<T, AF_INTERP_BILINEAR>(init_img, img);
+    if (double_input) resize<T, AF_INTERP_BILINEAR>(init_img, img);
 
     convSepFull<T, convAccT>(init_img, (double_input) ? init_img : img, filter);
 
@@ -242,54 +241,59 @@ Param createInitialImage(
 }
 
 template<typename T, typename convAccT>
-std::vector<Param> buildGaussPyr(
-    Param init_img,
-    const unsigned n_octaves,
-    const unsigned n_layers,
-    const float init_sigma)
-{
+std::vector<Param> buildGaussPyr(Param init_img, const unsigned n_octaves,
+                                 const unsigned n_layers,
+                                 const float init_sigma) {
     // Precompute Gaussian sigmas using the following formula:
     // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
     std::vector<float> sig_layers(n_layers + 3);
     sig_layers[0] = init_sigma;
-    float k = std::pow(2.0f, 1.0f / n_layers);
+    float k       = std::pow(2.0f, 1.0f / n_layers);
     for (unsigned i = 1; i < n_layers + 3; i++) {
-        float sig_prev = std::pow(k, i-1) * init_sigma;
+        float sig_prev  = std::pow(k, i - 1) * init_sigma;
         float sig_total = sig_prev * k;
-        sig_layers[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);
+        sig_layers[i] = std::sqrt(sig_total * sig_total - sig_prev * sig_prev);
     }
 
     // Gaussian Pyramid
     std::vector<Param> gauss_pyr(n_octaves);
-    std::vector<Param> tmp_pyr(n_octaves * (n_layers+3));
+    std::vector<Param> tmp_pyr(n_octaves * (n_layers + 3));
     for (unsigned o = 0; o < n_octaves; o++) {
-        gauss_pyr[o].info.offset = 0;
-        gauss_pyr[o].info.dims[0] = (o == 0) ? init_img.info.dims[0] : gauss_pyr[o-1].info.dims[0] / 2;
-        gauss_pyr[o].info.dims[1] = (o == 0) ? init_img.info.dims[1] : gauss_pyr[o-1].info.dims[1] / 2;
-        gauss_pyr[o].info.dims[2] = n_layers+3;
+        gauss_pyr[o].info.offset  = 0;
+        gauss_pyr[o].info.dims[0] = (o == 0)
+                                        ? init_img.info.dims[0]
+                                        : gauss_pyr[o - 1].info.dims[0] / 2;
+        gauss_pyr[o].info.dims[1] = (o == 0)
+                                        ? init_img.info.dims[1]
+                                        : gauss_pyr[o - 1].info.dims[1] / 2;
+        gauss_pyr[o].info.dims[2] = n_layers + 3;
         gauss_pyr[o].info.dims[3] = 1;
 
         gauss_pyr[o].info.strides[0] = 1;
-        gauss_pyr[o].info.strides[1] = gauss_pyr[o].info.dims[0] * gauss_pyr[o].info.strides[0];
-        gauss_pyr[o].info.strides[2] = gauss_pyr[o].info.dims[1] * gauss_pyr[o].info.strides[1];
-        gauss_pyr[o].info.strides[3] = gauss_pyr[o].info.dims[2] * gauss_pyr[o].info.strides[2];
-
-        const unsigned nel = gauss_pyr[o].info.dims[3] * gauss_pyr[o].info.strides[3];
+        gauss_pyr[o].info.strides[1] =
+            gauss_pyr[o].info.dims[0] * gauss_pyr[o].info.strides[0];
+        gauss_pyr[o].info.strides[2] =
+            gauss_pyr[o].info.dims[1] * gauss_pyr[o].info.strides[1];
+        gauss_pyr[o].info.strides[3] =
+            gauss_pyr[o].info.dims[2] * gauss_pyr[o].info.strides[2];
+
+        const unsigned nel =
+            gauss_pyr[o].info.dims[3] * gauss_pyr[o].info.strides[3];
         gauss_pyr[o].data = bufferAlloc(nel * sizeof(T));
 
-        for (unsigned l = 0; l < n_layers+3; l++) {
-            unsigned src_idx = (l == 0) ? (o-1)*(n_layers+3) + n_layers : o*(n_layers+3) + l-1;
-            unsigned idx = o*(n_layers+3) + l;
+        for (unsigned l = 0; l < n_layers + 3; l++) {
+            unsigned src_idx = (l == 0) ? (o - 1) * (n_layers + 3) + n_layers
+                                        : o * (n_layers + 3) + l - 1;
+            unsigned idx = o * (n_layers + 3) + l;
 
             tmp_pyr[o].info.offset = 0;
             if (o == 0 && l == 0) {
                 for (int k = 0; k < 4; k++) {
-                    tmp_pyr[idx].info.dims[k] = init_img.info.dims[k];
+                    tmp_pyr[idx].info.dims[k]    = init_img.info.dims[k];
                     tmp_pyr[idx].info.strides[k] = init_img.info.strides[k];
                 }
                 tmp_pyr[idx].data = init_img.data;
-            }
-            else if (l == 0) {
+            } else if (l == 0) {
                 tmp_pyr[idx].info.dims[0] = tmp_pyr[src_idx].info.dims[0] / 2;
                 tmp_pyr[idx].info.dims[1] = tmp_pyr[src_idx].info.dims[1] / 2;
                 tmp_pyr[idx].info.strides[0] = 1;
@@ -297,39 +301,47 @@ std::vector<Param> buildGaussPyr(
 
                 for (int k = 2; k < 4; k++) {
                     tmp_pyr[idx].info.dims[k] = 1;
-                    tmp_pyr[idx].info.strides[k] = tmp_pyr[idx].info.dims[k-1] * tmp_pyr[idx].info.strides[k-1];
+                    tmp_pyr[idx].info.strides[k] =
+                        tmp_pyr[idx].info.dims[k - 1] *
+                        tmp_pyr[idx].info.strides[k - 1];
                 }
 
-                dim_t lvl_el = tmp_pyr[idx].info.strides[3] * tmp_pyr[idx].info.dims[3];
+                dim_t lvl_el =
+                    tmp_pyr[idx].info.strides[3] * tmp_pyr[idx].info.dims[3];
                 tmp_pyr[idx].data = bufferAlloc(lvl_el * sizeof(T));
 
                 resize<T, AF_INTERP_BILINEAR>(tmp_pyr[idx], tmp_pyr[src_idx]);
-            }
-            else {
+            } else {
                 for (int k = 0; k < 4; k++) {
                     tmp_pyr[idx].info.dims[k] = tmp_pyr[src_idx].info.dims[k];
-                    tmp_pyr[idx].info.strides[k] = tmp_pyr[src_idx].info.strides[k];
+                    tmp_pyr[idx].info.strides[k] =
+                        tmp_pyr[src_idx].info.strides[k];
                 }
-                dim_t lvl_el = tmp_pyr[idx].info.strides[3] * tmp_pyr[idx].info.dims[3];
+                dim_t lvl_el =
+                    tmp_pyr[idx].info.strides[3] * tmp_pyr[idx].info.dims[3];
                 tmp_pyr[idx].data = bufferAlloc(lvl_el * sizeof(T));
 
                 Param filter = gaussFilter<convAccT>(sig_layers[l]);
 
-                convSepFull<T, convAccT>(tmp_pyr[idx], tmp_pyr[src_idx], filter);
+                convSepFull<T, convAccT>(tmp_pyr[idx], tmp_pyr[src_idx],
+                                         filter);
 
                 bufferFree(filter.data);
             }
 
-            const unsigned imel = tmp_pyr[idx].info.dims[3] * tmp_pyr[idx].info.strides[3];
+            const unsigned imel =
+                tmp_pyr[idx].info.dims[3] * tmp_pyr[idx].info.strides[3];
             const unsigned offset = imel * l;
 
-            getQueue().enqueueCopyBuffer(*tmp_pyr[idx].data, *gauss_pyr[o].data, 0, offset*sizeof(T), imel * sizeof(T));
+            getQueue().enqueueCopyBuffer(*tmp_pyr[idx].data, *gauss_pyr[o].data,
+                                         0, offset * sizeof(T),
+                                         imel * sizeof(T));
         }
     }
 
     for (unsigned o = 0; o < n_octaves; o++) {
-        for (unsigned l = 0; l < n_layers+3; l++) {
-            unsigned idx = o*(n_layers+3) + l;
+        for (unsigned l = 0; l < n_layers + 3; l++) {
+            unsigned idx = o * (n_layers + 3) + l;
             bufferFree(tmp_pyr[idx].data);
         }
     }
@@ -338,81 +350,92 @@ std::vector<Param> buildGaussPyr(
 }
 
 template<typename T>
-std::vector<Param> buildDoGPyr(
-    std::vector<Param> gauss_pyr,
-    const unsigned n_octaves,
-    const unsigned n_layers,
-    Kernel* suKernel)
-{
+std::vector<Param> buildDoGPyr(std::vector<Param> gauss_pyr,
+                               const unsigned n_octaves,
+                               const unsigned n_layers, Kernel* suKernel) {
     // DoG Pyramid
     std::vector<Param> dog_pyr(n_octaves);
     for (unsigned o = 0; o < n_octaves; o++) {
         for (int k = 0; k < 4; k++) {
-            dog_pyr[o].info.dims[k] = (k == 2) ? gauss_pyr[o].info.dims[k]-1 : gauss_pyr[o].info.dims[k];
-            dog_pyr[o].info.strides[k] = (k == 0) ? 1 : dog_pyr[o].info.dims[k-1] * dog_pyr[o].info.strides[k-1];
+            dog_pyr[o].info.dims[k] = (k == 2) ? gauss_pyr[o].info.dims[k] - 1
+                                               : gauss_pyr[o].info.dims[k];
+            dog_pyr[o].info.strides[k] =
+                (k == 0) ? 1
+                         : dog_pyr[o].info.dims[k - 1] *
+                               dog_pyr[o].info.strides[k - 1];
         }
         dog_pyr[o].info.offset = 0;
 
-        dog_pyr[o].data = bufferAlloc(dog_pyr[o].info.dims[3] * dog_pyr[o].info.strides[3] * sizeof(T));
+        dog_pyr[o].data = bufferAlloc(dog_pyr[o].info.dims[3] *
+                                      dog_pyr[o].info.strides[3] * sizeof(T));
 
-        const unsigned nel = dog_pyr[o].info.dims[1] * dog_pyr[o].info.strides[1];
-        const unsigned dog_layers = n_layers+2;
+        const unsigned nel =
+            dog_pyr[o].info.dims[1] * dog_pyr[o].info.strides[1];
+        const unsigned dog_layers = n_layers + 2;
 
         const int blk_x = divup(nel, SIFT_THREADS);
         const NDRange local(SIFT_THREADS, 1);
         const NDRange global(blk_x * SIFT_THREADS, 1);
 
-        auto suOp = KernelFunctor<Buffer, Buffer, unsigned, unsigned> (*suKernel);
+        auto suOp =
+            KernelFunctor<Buffer, Buffer, unsigned, unsigned>(*suKernel);
 
-        suOp(EnqueueArgs(getQueue(), global, local),
-             *dog_pyr[o].data, *gauss_pyr[o].data, nel, dog_layers);
+        suOp(EnqueueArgs(getQueue(), global, local), *dog_pyr[o].data,
+             *gauss_pyr[o].data, nel, dog_layers);
         CL_DEBUG_FINISH(getQueue());
     }
 
     return dog_pyr;
 }
 
-template <typename T>
-void update_permutation(compute::buffer_iterator<T>& keys, compute::vector<int>& permutation, compute::command_queue& queue)
-{
+template<typename T>
+void update_permutation(compute::buffer_iterator<T>& keys,
+                        compute::vector<int>& permutation,
+                        compute::command_queue& queue) {
     // temporary storage for keys
     compute::vector<T> temp(permutation.size(), 0, queue);
 
     // permute the keys with the current reordering
-    compute::gather(permutation.begin(), permutation.end(), keys, temp.begin(), queue);
+    compute::gather(permutation.begin(), permutation.end(), keys, temp.begin(),
+                    queue);
 
     // stable_sort the permuted keys and update the permutation
     compute::sort_by_key(temp.begin(), temp.end(), permutation.begin(), queue);
 }
 
-template <typename T>
-void apply_permutation(compute::buffer_iterator<T>& keys, compute::vector<int>& permutation, compute::command_queue& queue)
-{
+template<typename T>
+void apply_permutation(compute::buffer_iterator<T>& keys,
+                       compute::vector<int>& permutation,
+                       compute::command_queue& queue) {
     // copy keys to temporary vector
-    compute::vector<T> temp(keys, keys+permutation.size(), queue);
+    compute::vector<T> temp(keys, keys + permutation.size(), queue);
 
     // permute the keys
-    compute::gather(permutation.begin(), permutation.end(), temp.begin(), keys, queue);
+    compute::gather(permutation.begin(), permutation.end(), temp.begin(), keys,
+                    queue);
 }
 
 template<typename T>
-std::array<cl::Kernel*, 7> getSiftKernels()
-{
-    static const unsigned NUM_KERNELS = 7;
-    static const char* kernelNames[NUM_KERNELS] =
-        {"sub", "detectExtrema", "interpolateExtrema", "calcOrientation", "removeDuplicates",
-         "computeDescriptor", "computeGLOHDescriptor"};
+std::array<cl::Kernel*, 7> getSiftKernels() {
+    static const unsigned NUM_KERNELS           = 7;
+    static const char* kernelNames[NUM_KERNELS] = {"sub",
+                                                   "detectExtrema",
+                                                   "interpolateExtrema",
+                                                   "calcOrientation",
+                                                   "removeDuplicates",
+                                                   "computeDescriptor",
+                                                   "computeGLOHDescriptor"};
 
     kc_entry_t entries[NUM_KERNELS];
 
     int device = getActiveDeviceId();
 
-    std::string checkName = kernelNames[0] + std::string("_") + std::string(dtype_traits<T>::getName());
+    std::string checkName = kernelNames[0] + std::string("_") +
+                            std::string(dtype_traits<T>::getName());
 
     entries[0] = kernelCache(device, checkName);
 
-    if (entries[0].prog==0 && entries[0].ker==0)
-    {
+    if (entries[0].prog == 0 && entries[0].ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
@@ -421,28 +444,26 @@ std::array<cl::Kernel*, 7> getSiftKernels()
         cl::Program prog;
         buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
 
-        for (unsigned i=0; i<NUM_KERNELS; ++i)
-        {
+        for (unsigned i = 0; i < NUM_KERNELS; ++i) {
             entries[i].prog = new Program(prog);
             entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
 
             std::string name = kernelNames[i] + std::string("_") +
-                std::string(dtype_traits<T>::getName());
+                               std::string(dtype_traits<T>::getName());
 
             addKernelToCache(device, name, entries[i]);
         }
     } else {
-        for (unsigned i=1; i<NUM_KERNELS; ++i) {
+        for (unsigned i = 1; i < NUM_KERNELS; ++i) {
             std::string name = kernelNames[i] + std::string("_") +
-                std::string(dtype_traits<T>::getName());
+                               std::string(dtype_traits<T>::getName());
 
             entries[i] = kernelCache(device, name);
         }
     }
 
     std::array<cl::Kernel*, NUM_KERNELS> retVal;
-    for (unsigned i=0; i<NUM_KERNELS; ++i)
-        retVal[i] = entries[i].ker;
+    for (unsigned i = 0; i < NUM_KERNELS; ++i) retVal[i] = entries[i].ker;
 
     return retVal;
 }
@@ -450,10 +471,10 @@ std::array<cl::Kernel*, 7> getSiftKernels()
 template<typename T, typename convAccT>
 void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
           Param& score_out, Param& ori_out, Param& size_out, Param& desc_out,
-          Param img, const unsigned n_layers, const float contrast_thr, const float edge_thr,
-          const float init_sigma, const bool double_input, const float img_scale,
-          const float feature_ratio, const bool compute_GLOH)
-{
+          Param img, const unsigned n_layers, const float contrast_thr,
+          const float edge_thr, const float init_sigma, const bool double_input,
+          const float img_scale, const float feature_ratio,
+          const bool compute_GLOH) {
     auto kernels = getSiftKernels<T>();
 
     unsigned min_dim = min(img.info.dims[0], img.info.dims[1]);
@@ -461,11 +482,14 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
 
-    Param init_img = createInitialImage<T, convAccT>(img, init_sigma, double_input);
+    Param init_img =
+        createInitialImage<T, convAccT>(img, init_sigma, double_input);
 
-    std::vector<Param> gauss_pyr = buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
+    std::vector<Param> gauss_pyr =
+        buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-    std::vector<Param> dog_pyr = buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, kernels[0]);
+    std::vector<Param> dog_pyr =
+        buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, kernels[0]);
 
     std::vector<cl::Buffer*> d_x_pyr(n_octaves, NULL);
     std::vector<cl::Buffer*> d_y_pyr(n_octaves, NULL);
@@ -476,18 +500,19 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     std::vector<unsigned> feat_pyr(n_octaves, 0);
     unsigned total_feat = 0;
 
-    const unsigned d = DescrWidth;
-    const unsigned n = DescrHistBins;
+    const unsigned d  = DescrWidth;
+    const unsigned n  = DescrHistBins;
     const unsigned rb = GLOHRadialBins;
     const unsigned ab = GLOHAngularBins;
     const unsigned hb = GLOHHistBins;
-    const unsigned desc_len = (compute_GLOH) ? (1 + (rb-1) * ab) * hb : d*d*n;
+    const unsigned desc_len =
+        (compute_GLOH) ? (1 + (rb - 1) * ab) * hb : d * d * n;
 
     cl::Buffer* d_count = bufferAlloc(sizeof(unsigned));
 
     for (unsigned o = 0; o < n_octaves; o++) {
-        if (dog_pyr[o].info.dims[0]-2*ImgBorder < 1 ||
-            dog_pyr[o].info.dims[1]-2*ImgBorder < 1)
+        if (dog_pyr[o].info.dims[0] - 2 * ImgBorder < 1 ||
+            dog_pyr[o].info.dims[1] - 2 * ImgBorder < 1)
             continue;
 
         const unsigned imel = dog_pyr[o].info.dims[0] * dog_pyr[o].info.dims[1];
@@ -498,29 +523,32 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         cl::Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
 
         unsigned extrema_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &extrema_feat);
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                      &extrema_feat);
 
         int dim0 = dog_pyr[o].info.dims[0];
         int dim1 = dog_pyr[o].info.dims[1];
 
-        const int blk_x = divup(dim0-2*ImgBorder, SIFT_THREADS_X);
-        const int blk_y = divup(dim1-2*ImgBorder, SIFT_THREADS_Y);
+        const int blk_x = divup(dim0 - 2 * ImgBorder, SIFT_THREADS_X);
+        const int blk_y = divup(dim1 - 2 * ImgBorder, SIFT_THREADS_Y);
         const NDRange local(SIFT_THREADS_X, SIFT_THREADS_Y);
         const NDRange global(blk_x * SIFT_THREADS_X, blk_y * SIFT_THREADS_Y);
 
         float extrema_thr = 0.5f * contrast_thr / n_layers;
 
-        auto deOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
-                                Buffer, KParam, unsigned, float,
-                                LocalSpaceArg> (*kernels[1]);
+        auto deOp =
+            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, KParam,
+                          unsigned, float, LocalSpaceArg>(*kernels[1]);
 
-        deOp(EnqueueArgs(getQueue(), global, local),
-              *d_extrema_x, *d_extrema_y, *d_extrema_layer, *d_count,
-              *dog_pyr[o].data, dog_pyr[o].info, max_feat, extrema_thr,
-              cl::Local((SIFT_THREADS_X+2) * (SIFT_THREADS_Y+2) * 3 * sizeof(float)));
+        deOp(EnqueueArgs(getQueue(), global, local), *d_extrema_x, *d_extrema_y,
+             *d_extrema_layer, *d_count, *dog_pyr[o].data, dog_pyr[o].info,
+             max_feat, extrema_thr,
+             cl::Local((SIFT_THREADS_X + 2) * (SIFT_THREADS_Y + 2) * 3 *
+                       sizeof(float)));
         CL_DEBUG_FINISH(getQueue());
 
-        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &extrema_feat);
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                     &extrema_feat);
         extrema_feat = min(extrema_feat, max_feat);
 
         if (extrema_feat == 0) {
@@ -532,37 +560,39 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         }
 
         unsigned interp_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &interp_feat);
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                      &interp_feat);
 
         cl::Buffer* d_interp_x = bufferAlloc(extrema_feat * sizeof(float));
         cl::Buffer* d_interp_y = bufferAlloc(extrema_feat * sizeof(float));
-        cl::Buffer* d_interp_layer = bufferAlloc(extrema_feat * sizeof(unsigned));
-        cl::Buffer* d_interp_response = bufferAlloc(extrema_feat * sizeof(float));
+        cl::Buffer* d_interp_layer =
+            bufferAlloc(extrema_feat * sizeof(unsigned));
+        cl::Buffer* d_interp_response =
+            bufferAlloc(extrema_feat * sizeof(float));
         cl::Buffer* d_interp_size = bufferAlloc(extrema_feat * sizeof(float));
 
         const int blk_x_interp = divup(extrema_feat, SIFT_THREADS);
         const NDRange local_interp(SIFT_THREADS, 1);
         const NDRange global_interp(blk_x_interp * SIFT_THREADS, 1);
 
-        auto ieOp = KernelFunctor<Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer, unsigned,
-                                Buffer, KParam, unsigned, unsigned, unsigned,
-                                float, float, float, float> (*kernels[2]);
-
-        ieOp(EnqueueArgs(getQueue(), global_interp, local_interp),
-              *d_interp_x, *d_interp_y, *d_interp_layer,
-              *d_interp_response, *d_interp_size, *d_count,
-              *d_extrema_x, *d_extrema_y, *d_extrema_layer, extrema_feat,
-              *dog_pyr[o].data, dog_pyr[o].info, extrema_feat, o, n_layers,
-              contrast_thr, edge_thr, init_sigma, img_scale);
+        auto ieOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer,
+                                  Buffer, Buffer, Buffer, Buffer, unsigned,
+                                  Buffer, KParam, unsigned, unsigned, unsigned,
+                                  float, float, float, float>(*kernels[2]);
+
+        ieOp(EnqueueArgs(getQueue(), global_interp, local_interp), *d_interp_x,
+             *d_interp_y, *d_interp_layer, *d_interp_response, *d_interp_size,
+             *d_count, *d_extrema_x, *d_extrema_y, *d_extrema_layer,
+             extrema_feat, *dog_pyr[o].data, dog_pyr[o].info, extrema_feat, o,
+             n_layers, contrast_thr, edge_thr, init_sigma, img_scale);
         CL_DEBUG_FINISH(getQueue());
 
         bufferFree(d_extrema_x);
         bufferFree(d_extrema_y);
         bufferFree(d_extrema_layer);
 
-        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &interp_feat);
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                     &interp_feat);
         interp_feat = min(interp_feat, extrema_feat);
 
         if (interp_feat == 0) {
@@ -584,11 +614,16 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         compute::buffer buf_interp_response((*d_interp_response)(), true);
         compute::buffer buf_interp_size((*d_interp_size)(), true);
 
-        compute::buffer_iterator<float> interp_x_begin = compute::make_buffer_iterator<float>(buf_interp_x, 0);
-        compute::buffer_iterator<float> interp_y_begin = compute::make_buffer_iterator<float>(buf_interp_y, 0);
-        compute::buffer_iterator<unsigned> interp_layer_begin = compute::make_buffer_iterator<unsigned>(buf_interp_layer, 0);
-        compute::buffer_iterator<float> interp_response_begin = compute::make_buffer_iterator<float>(buf_interp_response, 0);
-        compute::buffer_iterator<float> interp_size_begin = compute::make_buffer_iterator<float>(buf_interp_size, 0);
+        compute::buffer_iterator<float> interp_x_begin =
+            compute::make_buffer_iterator<float>(buf_interp_x, 0);
+        compute::buffer_iterator<float> interp_y_begin =
+            compute::make_buffer_iterator<float>(buf_interp_y, 0);
+        compute::buffer_iterator<unsigned> interp_layer_begin =
+            compute::make_buffer_iterator<unsigned>(buf_interp_layer, 0);
+        compute::buffer_iterator<float> interp_response_begin =
+            compute::make_buffer_iterator<float>(buf_interp_response, 0);
+        compute::buffer_iterator<float> interp_size_begin =
+            compute::make_buffer_iterator<float>(buf_interp_size, 0);
 
         compute::vector<int> permutation(interp_feat, context);
         compute::iota(permutation.begin(), permutation.end(), 0, queue);
@@ -606,30 +641,32 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         apply_permutation<float>(interp_size_begin, permutation, queue);
 
         unsigned nodup_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &nodup_feat);
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                      &nodup_feat);
 
-        cl::Buffer* d_nodup_x = bufferAlloc(interp_feat * sizeof(float));
-        cl::Buffer* d_nodup_y = bufferAlloc(interp_feat * sizeof(float));
+        cl::Buffer* d_nodup_x     = bufferAlloc(interp_feat * sizeof(float));
+        cl::Buffer* d_nodup_y     = bufferAlloc(interp_feat * sizeof(float));
         cl::Buffer* d_nodup_layer = bufferAlloc(interp_feat * sizeof(unsigned));
         cl::Buffer* d_nodup_response = bufferAlloc(interp_feat * sizeof(float));
-        cl::Buffer* d_nodup_size = bufferAlloc(interp_feat * sizeof(float));
+        cl::Buffer* d_nodup_size     = bufferAlloc(interp_feat * sizeof(float));
 
         const int blk_x_nodup = divup(extrema_feat, SIFT_THREADS);
         const NDRange local_nodup(SIFT_THREADS, 1);
         const NDRange global_nodup(blk_x_nodup * SIFT_THREADS, 1);
 
-        auto rdOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer, Buffer, Buffer,
-                                unsigned> (*kernels[4]);
+        auto rdOp =
+            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                          Buffer, Buffer, Buffer, Buffer, Buffer, unsigned>(
+                *kernels[4]);
 
-        rdOp(EnqueueArgs(getQueue(), global_nodup, local_nodup),
-              *d_nodup_x, *d_nodup_y, *d_nodup_layer,
-              *d_nodup_response, *d_nodup_size, *d_count,
-              *d_interp_x, *d_interp_y, *d_interp_layer,
-              *d_interp_response, *d_interp_size, interp_feat);
+        rdOp(EnqueueArgs(getQueue(), global_nodup, local_nodup), *d_nodup_x,
+             *d_nodup_y, *d_nodup_layer, *d_nodup_response, *d_nodup_size,
+             *d_count, *d_interp_x, *d_interp_y, *d_interp_layer,
+             *d_interp_response, *d_interp_size, interp_feat);
         CL_DEBUG_FINISH(getQueue());
 
-        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &nodup_feat);
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                     &nodup_feat);
         nodup_feat = min(nodup_feat, interp_feat);
 
         bufferFree(d_interp_x);
@@ -639,32 +676,40 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         bufferFree(d_interp_size);
 
         unsigned oriented_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &oriented_feat);
+        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                      &oriented_feat);
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        cl::Buffer* d_oriented_x        = bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_y        = bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_layer    = bufferAlloc(max_oriented_feat * sizeof(unsigned));
-        cl::Buffer* d_oriented_response = bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_size     = bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_ori      = bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_x =
+            bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_y =
+            bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_layer =
+            bufferAlloc(max_oriented_feat * sizeof(unsigned));
+        cl::Buffer* d_oriented_response =
+            bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_size =
+            bufferAlloc(max_oriented_feat * sizeof(float));
+        cl::Buffer* d_oriented_ori =
+            bufferAlloc(max_oriented_feat * sizeof(float));
 
         const int blk_x_ori = divup(nodup_feat, SIFT_THREADS_Y);
         const NDRange local_ori(SIFT_THREADS_X, SIFT_THREADS_Y);
         const NDRange global_ori(SIFT_THREADS_X, blk_x_ori * SIFT_THREADS_Y);
 
-        auto coOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                                Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                                Buffer, KParam, unsigned, unsigned, int,
-                                LocalSpaceArg> (*kernels[3]);
-
-        coOp(EnqueueArgs(getQueue(), global_ori, local_ori),
-              *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-              *d_oriented_response, *d_oriented_size, *d_oriented_ori, *d_count,
-              *d_nodup_x, *d_nodup_y, *d_nodup_layer,
-              *d_nodup_response, *d_nodup_size, nodup_feat,
-              *gauss_pyr[o].data, gauss_pyr[o].info, max_oriented_feat, o, (int)double_input,
-              cl::Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
+        auto coOp =
+            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                          Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
+                          unsigned, Buffer, KParam, unsigned, unsigned, int,
+                          LocalSpaceArg>(*kernels[3]);
+
+        coOp(EnqueueArgs(getQueue(), global_ori, local_ori), *d_oriented_x,
+             *d_oriented_y, *d_oriented_layer, *d_oriented_response,
+             *d_oriented_size, *d_oriented_ori, *d_count, *d_nodup_x,
+             *d_nodup_y, *d_nodup_layer, *d_nodup_response, *d_nodup_size,
+             nodup_feat, *gauss_pyr[o].data, gauss_pyr[o].info,
+             max_oriented_feat, o, (int)double_input,
+             cl::Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
         CL_DEBUG_FINISH(getQueue());
 
         bufferFree(d_nodup_x);
@@ -673,7 +718,8 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         bufferFree(d_nodup_response);
         bufferFree(d_nodup_size);
 
-        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned), &oriented_feat);
+        getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+                                     &oriented_feat);
         oriented_feat = min(oriented_feat, max_oriented_feat);
 
         if (oriented_feat == 0) {
@@ -686,9 +732,10 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
             continue;
         }
 
-        cl::Buffer* d_desc = bufferAlloc(oriented_feat * desc_len * sizeof(float));
+        cl::Buffer* d_desc =
+            bufferAlloc(oriented_feat * desc_len * sizeof(float));
 
-        float scale = 1.f/(1 << o);
+        float scale = 1.f / (1 << o);
         if (double_input) scale *= 2.f;
 
         const int blk_x_desc = divup(oriented_feat, 1);
@@ -698,30 +745,31 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         const unsigned histsz = 8;
 
         if (compute_GLOH) {
-            auto cgOp = KernelFunctor<Buffer, unsigned, unsigned,
-                                    Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                                    Buffer, KParam, int, unsigned, unsigned, unsigned, float, int,
-                                    LocalSpaceArg> (*kernels[6]);
-
-            cgOp(EnqueueArgs(getQueue(), global_desc, local_desc),
-                 *d_desc, desc_len, histsz,
-                 *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-                 *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
-                 *gauss_pyr[o].data, gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
-                 cl::Local(desc_len * (histsz+1) * sizeof(float)));
-        }
-        else {
-            auto cdOp = KernelFunctor<Buffer, unsigned, unsigned,
-                                    Buffer, Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                                    Buffer, KParam, int, int, float, int,
-                                    LocalSpaceArg> (*kernels[5]);
-
-            cdOp(EnqueueArgs(getQueue(), global_desc, local_desc),
-                  *d_desc, desc_len, histsz,
-                  *d_oriented_x, *d_oriented_y, *d_oriented_layer,
-                  *d_oriented_response, *d_oriented_size, *d_oriented_ori, oriented_feat,
-                  *gauss_pyr[o].data, gauss_pyr[o].info, d, n, scale, n_layers,
-                  cl::Local(desc_len * (histsz+1) * sizeof(float)));
+            auto cgOp =
+                KernelFunctor<Buffer, unsigned, unsigned, Buffer, Buffer,
+                              Buffer, Buffer, Buffer, Buffer, unsigned, Buffer,
+                              KParam, int, unsigned, unsigned, unsigned, float,
+                              int, LocalSpaceArg>(*kernels[6]);
+
+            cgOp(EnqueueArgs(getQueue(), global_desc, local_desc), *d_desc,
+                 desc_len, histsz, *d_oriented_x, *d_oriented_y,
+                 *d_oriented_layer, *d_oriented_response, *d_oriented_size,
+                 *d_oriented_ori, oriented_feat, *gauss_pyr[o].data,
+                 gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
+                 cl::Local(desc_len * (histsz + 1) * sizeof(float)));
+        } else {
+            auto cdOp =
+                KernelFunctor<Buffer, unsigned, unsigned, Buffer, Buffer,
+                              Buffer, Buffer, Buffer, Buffer, unsigned, Buffer,
+                              KParam, int, int, float, int, LocalSpaceArg>(
+                    *kernels[5]);
+
+            cdOp(EnqueueArgs(getQueue(), global_desc, local_desc), *d_desc,
+                 desc_len, histsz, *d_oriented_x, *d_oriented_y,
+                 *d_oriented_layer, *d_oriented_response, *d_oriented_size,
+                 *d_oriented_ori, oriented_feat, *gauss_pyr[o].data,
+                 gauss_pyr[o].info, d, n, scale, n_layers,
+                 cl::Local(desc_len * (histsz + 1) * sizeof(float)));
         }
         CL_DEBUG_FINISH(getQueue());
 
@@ -729,21 +777,19 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         feat_pyr[o] = oriented_feat;
 
         if (oriented_feat > 0) {
-            d_x_pyr[o] = d_oriented_x;
-            d_y_pyr[o] = d_oriented_y;
+            d_x_pyr[o]        = d_oriented_x;
+            d_y_pyr[o]        = d_oriented_y;
             d_response_pyr[o] = d_oriented_response;
-            d_ori_pyr[o] = d_oriented_ori;
-            d_size_pyr[o] = d_oriented_size;
-            d_desc_pyr[o] = d_desc;
+            d_ori_pyr[o]      = d_oriented_ori;
+            d_size_pyr[o]     = d_oriented_size;
+            d_desc_pyr[o]     = d_desc;
         }
     }
 
     bufferFree(d_count);
 
-    for (size_t i = 0; i < gauss_pyr.size(); i++)
-        bufferFree(gauss_pyr[i].data);
-    for (size_t i = 0; i < dog_pyr.size(); i++)
-        bufferFree(dog_pyr[i].data);
+    for (size_t i = 0; i < gauss_pyr.size(); i++) bufferFree(gauss_pyr[i].data);
+    for (size_t i = 0; i < dog_pyr.size(); i++) bufferFree(dog_pyr[i].data);
 
     // If no features are found, set found features to 0 and return
     if (total_feat == 0) {
@@ -752,36 +798,42 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     }
 
     // Allocate output memory
-    x_out.info.dims[0] = total_feat;
-    x_out.info.strides[0] = 1;
-    y_out.info.dims[0] = total_feat;
-    y_out.info.strides[0] = 1;
-    score_out.info.dims[0] = total_feat;
+    x_out.info.dims[0]        = total_feat;
+    x_out.info.strides[0]     = 1;
+    y_out.info.dims[0]        = total_feat;
+    y_out.info.strides[0]     = 1;
+    score_out.info.dims[0]    = total_feat;
     score_out.info.strides[0] = 1;
-    ori_out.info.dims[0] = total_feat;
-    ori_out.info.strides[0] = 1;
-    size_out.info.dims[0] = total_feat;
-    size_out.info.strides[0] = 1;
+    ori_out.info.dims[0]      = total_feat;
+    ori_out.info.strides[0]   = 1;
+    size_out.info.dims[0]     = total_feat;
+    size_out.info.strides[0]  = 1;
 
-    desc_out.info.dims[0] = desc_len;
+    desc_out.info.dims[0]    = desc_len;
     desc_out.info.strides[0] = 1;
-    desc_out.info.dims[1] = total_feat;
+    desc_out.info.dims[1]    = total_feat;
     desc_out.info.strides[1] = desc_out.info.dims[0];
 
     for (int k = 1; k < 4; k++) {
         x_out.info.dims[k] = 1;
-        x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
+        x_out.info.strides[k] =
+            x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
         y_out.info.dims[k] = 1;
-        y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
+        y_out.info.strides[k] =
+            y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
         score_out.info.dims[k] = 1;
-        score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
+        score_out.info.strides[k] =
+            score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
         ori_out.info.dims[k] = 1;
-        ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
+        ori_out.info.strides[k] =
+            ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
         size_out.info.dims[k] = 1;
-        size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
+        size_out.info.strides[k] =
+            size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
         if (k > 1) {
             desc_out.info.dims[k] = 1;
-            desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
+            desc_out.info.strides[k] =
+                desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
         }
     }
 
@@ -799,15 +851,26 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
     unsigned offset = 0;
     for (unsigned i = 0; i < n_octaves; i++) {
-        if (feat_pyr[i] == 0)
-            continue;
-
-        getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_response_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
-        getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*desc_len*sizeof(unsigned), feat_pyr[i] * desc_len * sizeof(unsigned));
+        if (feat_pyr[i] == 0) continue;
+
+        getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_response_pyr[i], *score_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0,
+                                     offset * sizeof(float),
+                                     feat_pyr[i] * sizeof(float));
+        getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0,
+                                     offset * desc_len * sizeof(unsigned),
+                                     feat_pyr[i] * desc_len * sizeof(unsigned));
 
         bufferFree(d_x_pyr[i]);
         bufferFree(d_y_pyr[i]);
@@ -823,5 +886,5 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     *out_feat = total_feat;
     *out_dlen = desc_len;
 }
-} //namespace kernel
-} //namespace opencl
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sobel.cl b/src/backend/opencl/kernel/sobel.cl
index 0e83035ffa..9a85a15b9f 100644
--- a/src/backend/opencl/kernel/sobel.cl
+++ b/src/backend/opencl/kernel/sobel.cl
@@ -7,72 +7,70 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-Ti load2LocalMem(global const Ti * in,
-               int dim0, int dim1,
-               int gx, int gy,
-               int inStride1, int inStride0)
-{
-    if (gx<0 || gx>=dim0 || gy<0 || gy>=dim1)
+Ti load2LocalMem(global const Ti* in, int dim0, int dim1, int gx, int gy,
+                 int inStride1, int inStride0) {
+    if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
         return (Ti)0;
     else
-        return in[gx*inStride0+gy*inStride1];
+        return in[gx * inStride0 + gy * inStride1];
 }
 
-kernel
-void sobel3x3(global To * dx, KParam dxInfo,
-              global To * dy, KParam dyInfo,
-              global const Ti * in, KParam iInfo,
-              local        Ti * localMem,
-              int nBBS0, int nBBS1)
-{
+kernel void sobel3x3(global To* dx, KParam dxInfo, global To* dy, KParam dyInfo,
+                     global const Ti* in, KParam iInfo, local Ti* localMem,
+                     int nBBS0, int nBBS1) {
     const int radius  = 1;
-    const int padding = 2*radius;
+    const int padding = 2 * radius;
     const int shrdLen = get_local_size(0) + padding;
 
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
-    global const Ti* iptr = in + (b2 * iInfo.strides[2]  + b3 * iInfo.strides[3] + iInfo.offset);
-    global To*      dxptr = dx + (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3]);
-    global To*      dyptr = dy + (b2 * dyInfo.strides[2] + b3 * dyInfo.strides[3]);
+    global const Ti* iptr =
+        in + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
+    global To* dxptr = dx + (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3]);
+    global To* dyptr = dy + (b2 * dyInfo.strides[2] + b3 * dyInfo.strides[3]);
 
     int lx = get_local_id(0);
     int ly = get_local_id(1);
 
-    int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
     int s0 = iInfo.strides[0];
     int s1 = iInfo.strides[1];
     int d0 = iInfo.dims[0];
     int d1 = iInfo.dims[1];
-    for (int b=ly, gy2=gy; b<shrdLen; b+=get_local_size(1), gy2+=get_local_size(1)) {
-        for (int a=lx, gx2=gx; a<shrdLen; a+=get_local_size(0), gx2+=get_local_size(0)) {
-            localMem[a+shrdLen*b] = load2LocalMem(iptr, d0, d1, gx2-radius, gy2-radius, s1, s0);
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            localMem[a + shrdLen * b] =
+                load2LocalMem(iptr, d0, d1, gx2 - radius, gy2 - radius, s1, s0);
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (gx < iInfo.dims[0] && gy < iInfo.dims[1]) {
-        int i = lx + radius;
-        int j = ly + radius;
-        int _i = i-1;
-        int i_ = i+1;
-        int _j = j-1;
-        int j_ = j+1;
+        int i  = lx + radius;
+        int j  = ly + radius;
+        int _i = i - 1;
+        int i_ = i + 1;
+        int _j = j - 1;
+        int j_ = j + 1;
 
-        float NW = localMem[_i+shrdLen*_j];
-        float SW = localMem[i_+shrdLen*_j];
-        float NE = localMem[_i+shrdLen*j_];
-        float SE = localMem[i_+shrdLen*j_];
+        float NW = localMem[_i + shrdLen * _j];
+        float SW = localMem[i_ + shrdLen * _j];
+        float NE = localMem[_i + shrdLen * j_];
+        float SE = localMem[i_ + shrdLen * j_];
 
-        float t1 = localMem[i+shrdLen*_j];
-        float t2 = localMem[i+shrdLen*j_];
-        dxptr[gy*dxInfo.strides[1]+gx] = (NW+SW - (NE+SE) + 2*(t1-t2));
-
-        t1 = localMem[_i+shrdLen*j];
-        t2 = localMem[i_+shrdLen*j];
-        dyptr[gy*dyInfo.strides[1]+gx] = (NW+NE - (SW+SE) + 2*(t1-t2));
+        float t1 = localMem[i + shrdLen * _j];
+        float t2 = localMem[i + shrdLen * j_];
+        dxptr[gy * dxInfo.strides[1] + gx] =
+            (NW + SW - (NE + SE) + 2 * (t1 - t2));
 
+        t1 = localMem[_i + shrdLen * j];
+        t2 = localMem[i_ + shrdLen * j];
+        dyptr[gy * dyInfo.strides[1] + gx] =
+            (NW + NE - (SW + SE) + 2 * (t1 - t2));
     }
 }
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 5a4dbb68ca..b2a085b81c 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -8,49 +8,46 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/sobel.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
 template<typename Ti, typename To, unsigned ker_size>
-void sobel(Param dx, Param dy, const Param in)
-{
-    std::string refName = std::string("sobel3x3_") + std::string(dtype_traits<Ti>::getName()) +
+void sobel(Param dx, Param dy, const Param in) {
+    std::string refName =
+        std::string("sobel3x3_") + std::string(dtype_traits<Ti>::getName()) +
         std::string(dtype_traits<To>::getName()) + std::to_string(ker_size);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D Ti=" << dtype_traits<Ti>::getName()
                 << " -D To=" << dtype_traits<To>::getName()
-                << " -D KER_SIZE="<< ker_size;
-        if (std::is_same<Ti, double>::value)
-            options << " -D USE_DOUBLE";
+                << " -D KER_SIZE=" << ker_size;
+        if (std::is_same<Ti, double>::value) options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {sobel_cl};
-        const int   ker_lens[] = {sobel_cl_len};
+        const int ker_lens[]   = {sobel_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -64,18 +61,19 @@ void sobel(Param dx, Param dy, const Param in)
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X, blk_y * in.info.dims[3] * THREADS_Y);
+    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                   blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto sobelOp = KernelFunctor< Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                  cl::LocalSpaceArg, int, int> (*entry.ker);
+    auto sobelOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                                 cl::LocalSpaceArg, int, int>(*entry.ker);
 
-    size_t loc_size = (THREADS_X+ker_size-1)*(THREADS_Y+ker_size-1)*sizeof(Ti);
+    size_t loc_size =
+        (THREADS_X + ker_size - 1) * (THREADS_Y + ker_size - 1) * sizeof(Ti);
 
-    sobelOp(EnqueueArgs(getQueue(), global, local),
-            *dx.data, dx.info, *dy.data, dy.info,
-            *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
+    sobelOp(EnqueueArgs(getQueue(), global, local), *dx.data, dx.info, *dy.data,
+            dy.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 87730ea35c..8fed30aa41 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -8,133 +8,133 @@
  ********************************************************/
 
 #pragma once
-#include <program.hpp>
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
-#include <kernel/sort_helper.hpp>
 #include <iota.hpp>
+#include <kernel/sort_helper.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
-#include <boost/compute/core.hpp>
 #include <boost/compute/algorithm/sort.hpp>
 #include <boost/compute/algorithm/sort_by_key.hpp>
+#include <boost/compute/core.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
 namespace compute = boost::compute;
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T>
-        void sort0Iterative(Param val, bool isAscending)
-        {
-            compute::command_queue c_queue(getQueue()());
-
-            compute::buffer val_buf((*val.data)());
-
-            for(int w = 0; w < val.info.dims[3]; w++) {
-                int valW = w * val.info.strides[3];
-                for(int z = 0; z < val.info.dims[2]; z++) {
-                    int valWZ = valW + z * val.info.strides[2];
-                    for(int y = 0; y < val.info.dims[1]; y++) {
-
-                        int valOffset = valWZ + y * val.info.strides[1];
-
-                        if(isAscending) {
-                            compute::sort(
-                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                    compute::less< type_t<T> >(), c_queue);
-                        } else {
-                            compute::sort(
-                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset),
-                                    compute::make_buffer_iterator< type_t<T> >(val_buf, valOffset + val.info.dims[0]),
-                                    compute::greater< type_t<T> >(), c_queue);
-                        }
-                    }
+namespace opencl {
+namespace kernel {
+template<typename T>
+void sort0Iterative(Param val, bool isAscending) {
+    compute::command_queue c_queue(getQueue()());
+
+    compute::buffer val_buf((*val.data)());
+
+    for (int w = 0; w < val.info.dims[3]; w++) {
+        int valW = w * val.info.strides[3];
+        for (int z = 0; z < val.info.dims[2]; z++) {
+            int valWZ = valW + z * val.info.strides[2];
+            for (int y = 0; y < val.info.dims[1]; y++) {
+                int valOffset = valWZ + y * val.info.strides[1];
+
+                if (isAscending) {
+                    compute::sort(compute::make_buffer_iterator<type_t<T>>(
+                                      val_buf, valOffset),
+                                  compute::make_buffer_iterator<type_t<T>>(
+                                      val_buf, valOffset + val.info.dims[0]),
+                                  compute::less<type_t<T>>(), c_queue);
+                } else {
+                    compute::sort(compute::make_buffer_iterator<type_t<T>>(
+                                      val_buf, valOffset),
+                                  compute::make_buffer_iterator<type_t<T>>(
+                                      val_buf, valOffset + val.info.dims[0]),
+                                  compute::greater<type_t<T>>(), c_queue);
                 }
             }
-
-            CL_DEBUG_FINISH(getQueue());
         }
+    }
 
-        template<typename T>
-        void sortBatched(Param pVal, int dim, bool isAscending)
-        {
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pVal.info.dims[i];
-
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
-
-            // Create/call iota
-            //Array<uint> pKey = createEmptyArray<uint>(inDims);
-            Array<uint> pKey = iota<uint>(seqDims, tileDims);
-
-            pKey.setDataDims(inDims.elements());
-
-            // Flat
-            pVal.info.dims[0] = inDims.elements();
-            pVal.info.strides[0] = 1;
-            for(int i = 1; i < 4; i++) {
-                pVal.info.dims[i] = 1;
-                pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
-            }
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            // Sort indices
-            // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
-            //kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
-            compute::command_queue c_queue(getQueue()());
-
-            compute::buffer pKey_buf((*pKey.get())());
-            compute::buffer pVal_buf((*pVal.data)());
-
-            compute::buffer_iterator<type_t<T> > val0 = compute::make_buffer_iterator<type_t<T> >(pVal_buf, 0);
-            compute::buffer_iterator<type_t<T> > valN = compute::make_buffer_iterator<type_t<T> >(pVal_buf,+ pVal.info.dims[0]);
-            compute::buffer_iterator<uint      > key0 = compute::make_buffer_iterator<uint>(pKey_buf, 0);
-            compute::buffer_iterator<uint      > keyN = compute::make_buffer_iterator<uint>(pKey_buf, pKey.dims()[0]);
-            if(isAscending) {
-                compute::sort_by_key(val0, valN, key0, c_queue);
-            } else {
-                compute::sort_by_key(val0, valN, key0, compute::greater< type_t<T> >(), c_queue);
-            }
+template<typename T>
+void sortBatched(Param pVal, int dim, bool isAscending) {
+    af::dim4 inDims;
+    for (int i = 0; i < 4; i++) inDims[i] = pVal.info.dims[i];
+
+    // Sort dimension
+    // tileDims * seqDims = inDims
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
+
+    // Create/call iota
+    // Array<uint> pKey = createEmptyArray<uint>(inDims);
+    Array<uint> pKey = iota<uint>(seqDims, tileDims);
+
+    pKey.setDataDims(inDims.elements());
+
+    // Flat
+    pVal.info.dims[0]    = inDims.elements();
+    pVal.info.strides[0] = 1;
+    for (int i = 1; i < 4; i++) {
+        pVal.info.dims[i]    = 1;
+        pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
+    }
 
-            // Needs to be ascending (true) in order to maintain the indices properly
-            //kernel::sort0_by_key<uint, T, true>(pKey, pVal);
-            compute::sort_by_key(key0, keyN, val0, c_queue);
+    // Sort indices
+    // sort_by_key<T, uint, isAscending>(*resVal, *resKey, val, key, 0);
+    // kernel::sort0_by_key<T, uint, isAscending>(pVal, pKey);
+    compute::command_queue c_queue(getQueue()());
+
+    compute::buffer pKey_buf((*pKey.get())());
+    compute::buffer pVal_buf((*pVal.data)());
+
+    compute::buffer_iterator<type_t<T>> val0 =
+        compute::make_buffer_iterator<type_t<T>>(pVal_buf, 0);
+    compute::buffer_iterator<type_t<T>> valN =
+        compute::make_buffer_iterator<type_t<T>>(pVal_buf, +pVal.info.dims[0]);
+    compute::buffer_iterator<uint> key0 =
+        compute::make_buffer_iterator<uint>(pKey_buf, 0);
+    compute::buffer_iterator<uint> keyN =
+        compute::make_buffer_iterator<uint>(pKey_buf, pKey.dims()[0]);
+    if (isAscending) {
+        compute::sort_by_key(val0, valN, key0, c_queue);
+    } else {
+        compute::sort_by_key(val0, valN, key0, compute::greater<type_t<T>>(),
+                             c_queue);
+    }
 
-            CL_DEBUG_FINISH(getQueue());
-        }
+    // Needs to be ascending (true) in order to maintain the indices properly
+    // kernel::sort0_by_key<uint, T, true>(pKey, pVal);
+    compute::sort_by_key(key0, keyN, val0, c_queue);
 
-        template<typename T>
-        void sort0(Param val, bool isAscending)
-        {
-            int higherDims =  val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
-            // TODO Make a better heurisitic
-            if(higherDims > 10)
-                sortBatched<T>(val, 0, isAscending);
-            else
-                kernel::sort0Iterative<T>(val, isAscending);
-        }
-    }
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void sort0(Param val, bool isAscending) {
+    int higherDims = val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
+    // TODO Make a better heurisitic
+    if (higherDims > 10)
+        sortBatched<T>(val, 0, isAscending);
+    else
+        kernel::sort0Iterative<T>(val, isAscending);
 }
+}  // namespace kernel
+}  // namespace opencl
 
 #pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index b3bc500d4b..7a25662667 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -8,22 +8,20 @@
  ********************************************************/
 
 #pragma once
-#include <traits.hpp>
-#include <common/dispatch.hpp>
 #include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <traits.hpp>
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename Tk, typename Tv>
-        void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending);
+namespace opencl {
+namespace kernel {
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending);
 
-        template<typename Tk_, typename Tv_>
-        void sortByKeyBatched(Param pKey, Param pVal, const int dim, bool isAscending);
+template<typename Tk_, typename Tv_>
+void sortByKeyBatched(Param pKey, Param pVal, const int dim, bool isAscending);
 
-        template<typename Tk, typename Tv>
-        void sort0ByKey(Param pKey, Param pVal, bool isAscending);
-    }
-}
+template<typename Tk, typename Tv>
+void sort0ByKey(Param pKey, Param pVal, bool isAscending);
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index 43732771cd..2a64f05b0a 100644
--- a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -11,10 +11,8 @@
 
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar
 
-namespace opencl
-{
-namespace kernel
-{
-    INSTANTIATE1(TYPE)
-}
+namespace opencl {
+namespace kernel {
+INSTANTIATE1(TYPE)
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 7f5cd9f73f..076b359ea8 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -9,91 +9,76 @@
 
 #pragma once
 #include <Array.hpp>
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
+#include <iota.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <kernel/sort_helper.hpp>
-#include <program.hpp>
+#include <math.hpp>
 #include <memory.hpp>
+#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 #include <mutex>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <math.hpp>
-#include <iota.hpp>
+#include <string>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 
-#include <boost/compute/core.hpp>
-#include <boost/compute/algorithm/sort_by_key.hpp>
-#include <boost/compute/functional/operator.hpp>
-#include <boost/compute/iterator/buffer_iterator.hpp>
 #include <boost/compute/algorithm/copy.hpp>
+#include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/algorithm/transform.hpp>
 #include <boost/compute/container/vector.hpp>
-#include <boost/compute/functional/get.hpp>
+#include <boost/compute/core.hpp>
 #include <boost/compute/functional/field.hpp>
+#include <boost/compute/functional/get.hpp>
+#include <boost/compute/functional/operator.hpp>
+#include <boost/compute/iterator/buffer_iterator.hpp>
 #include <boost/compute/types/pair.hpp>
 
 namespace compute = boost::compute;
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
 template<typename Tk, typename Tv, bool isAscending>
-inline
-boost::compute::function<bool(const std::pair<Tk, Tv>, const std::pair<Tk, Tv>)>
-makeCompareFunction()
-{
+inline boost::compute::function<bool(const std::pair<Tk, Tv>,
+                                     const std::pair<Tk, Tv>)>
+makeCompareFunction() {
     // Cannot use isAscending in BOOST_COMPUTE_FUNCTION
-    if(isAscending) {
-        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
-            {
-                return lhs.first < rhs.first;
-            }
-        );
+    if (isAscending) {
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare,
+                               (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+                               { return lhs.first < rhs.first; });
         return IPCompare;
     } else {
-        BOOST_COMPUTE_FUNCTION(bool, IPCompare, (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
-            {
-                return lhs.first > rhs.first;
-            }
-        );
+        BOOST_COMPUTE_FUNCTION(bool, IPCompare,
+                               (std::pair<Tk, Tv> lhs, std::pair<Tk, Tv> rhs),
+                               { return lhs.first > rhs.first; });
         return IPCompare;
     }
 }
 
 template<typename Tk>
-inline boost::compute::function<Tk(Tk)>
-flipFunction()
-{
-    BOOST_COMPUTE_FUNCTION(Tk, negateFn, (const Tk x),
-        {
-            return -x;
-        }
-    );
+inline boost::compute::function<Tk(Tk)> flipFunction() {
+    BOOST_COMPUTE_FUNCTION(Tk, negateFn, (const Tk x), { return -x; });
 
     return negateFn;
 }
 
-#define INSTANTIATE_FLIP(TY, XMAX)                              \
-template<> inline boost::compute::function<TY(TY)>              \
-flipFunction<TY>()                                              \
-{                                                               \
-    BOOST_COMPUTE_FUNCTION(TY, negateFn, (const TY x),          \
-       {                                                        \
-            return XMAX - x;                                    \
-        }                                                       \
-    );                                                          \
-                                                                \
-    return negateFn;                                            \
-}
+#define INSTANTIATE_FLIP(TY, XMAX)                               \
+    template<>                                                   \
+    inline boost::compute::function<TY(TY)> flipFunction<TY>() { \
+        BOOST_COMPUTE_FUNCTION(TY, negateFn, (const TY x),       \
+                               { return XMAX - x; });            \
+                                                                 \
+        return negateFn;                                         \
+    }
 
 INSTANTIATE_FLIP(unsigned, UINT_MAX)
 INSTANTIATE_FLIP(unsigned short, USHRT_MAX)
@@ -102,162 +87,177 @@ INSTANTIATE_FLIP(cl_ulong, ULONG_MAX)
 
 #undef INSTANTIATE_FLIP
 
-namespace opencl
-{
-    namespace kernel
-    {
-        static const int copyPairIter = 4;
-
-        template<typename Tk, typename Tv>
-        void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending)
-        {
-            compute::command_queue c_queue(getQueue()());
-
-            compute::buffer pKey_buf((*pKey.data)());
-            compute::buffer pVal_buf((*pVal.data)());
-
-            for(int w = 0; w < pKey.info.dims[3]; w++) {
-                int pKeyW = w * pKey.info.strides[3];
-                int pValW = w * pVal.info.strides[3];
-                for(int z = 0; z < pKey.info.dims[2]; z++) {
-                    int pKeyWZ = pKeyW + z * pKey.info.strides[2];
-                    int pValWZ = pValW + z * pVal.info.strides[2];
-                    for(int y = 0; y < pKey.info.dims[1]; y++) {
-
-                        int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
-                        int pValOffset = pValWZ + y * pVal.info.strides[1];
-
-                        compute::buffer_iterator< type_t<Tk> > start= compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset);
-                        compute::buffer_iterator< type_t<Tk> > end  = compute::make_buffer_iterator< type_t<Tk> >(pKey_buf, pKeyOffset + pKey.info.dims[0]);
-                        compute::buffer_iterator< type_t<Tv> > vals = compute::make_buffer_iterator< type_t<Tv> >(pVal_buf, pValOffset);
-                        if(isAscending) {
-                            compute::sort_by_key(start, end, vals, c_queue);
-                        } else {
-                            compute::sort_by_key(start, end, vals,
-                                                  compute::greater< type_t<Tk> >(), c_queue);
-                        }
-                    }
+namespace opencl {
+namespace kernel {
+static const int copyPairIter = 4;
+
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Param pKey, Param pVal, bool isAscending) {
+    compute::command_queue c_queue(getQueue()());
+
+    compute::buffer pKey_buf((*pKey.data)());
+    compute::buffer pVal_buf((*pVal.data)());
+
+    for (int w = 0; w < pKey.info.dims[3]; w++) {
+        int pKeyW = w * pKey.info.strides[3];
+        int pValW = w * pVal.info.strides[3];
+        for (int z = 0; z < pKey.info.dims[2]; z++) {
+            int pKeyWZ = pKeyW + z * pKey.info.strides[2];
+            int pValWZ = pValW + z * pVal.info.strides[2];
+            for (int y = 0; y < pKey.info.dims[1]; y++) {
+                int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
+                int pValOffset = pValWZ + y * pVal.info.strides[1];
+
+                compute::buffer_iterator<type_t<Tk>> start =
+                    compute::make_buffer_iterator<type_t<Tk>>(pKey_buf,
+                                                              pKeyOffset);
+                compute::buffer_iterator<type_t<Tk>> end =
+                    compute::make_buffer_iterator<type_t<Tk>>(
+                        pKey_buf, pKeyOffset + pKey.info.dims[0]);
+                compute::buffer_iterator<type_t<Tv>> vals =
+                    compute::make_buffer_iterator<type_t<Tv>>(pVal_buf,
+                                                              pValOffset);
+                if (isAscending) {
+                    compute::sort_by_key(start, end, vals, c_queue);
+                } else {
+                    compute::sort_by_key(start, end, vals,
+                                         compute::greater<type_t<Tk>>(),
+                                         c_queue);
                 }
             }
-
-            CL_DEBUG_FINISH(getQueue());
         }
+    }
 
-        template<typename Tk_, typename Tv_>
-        void sortByKeyBatched(Param pKey, Param pVal, const int dim, bool isAscending)
-        {
-            typedef type_t<Tk_> Tk;
-            typedef type_t<Tv_> Tv;
-
-            af::dim4 inDims;
-            for(int i = 0; i < 4; i++)
-                inDims[i] = pKey.info.dims[i];
-
-            // Sort dimension
-            // tileDims * seqDims = inDims
-            af::dim4 tileDims(1);
-            af::dim4 seqDims = inDims;
-            tileDims[dim] = inDims[dim];
-            seqDims[dim] = 1;
-
-            // Create/call iota
-            Array<unsigned> pSeq = iota<unsigned>(seqDims, tileDims);
-
-            int elements = inDims.elements();
-
-            // Flat - Not required since inplace and both are continuous
-            //val.modDims(inDims.elements());
-            //key.modDims(inDims.elements());
-
-            // Sort indices
-            // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
-            //kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
-            compute::command_queue c_queue(getQueue()());
-            compute::context c_context(getContext()());
-
-            // Create buffer iterators for seq
-            compute::buffer pSeq_buf((*pSeq.get())());
-            compute::buffer_iterator<unsigned> seq0 = compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
-            compute::buffer_iterator<unsigned> seqN = compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
-            // Create buffer iterators for key and val
-            compute::buffer pKey_buf((*pKey.data)());
-            compute::buffer pVal_buf((*pVal.data)());
-            compute::buffer_iterator<Tk> key0 = compute::make_buffer_iterator<Tk>(pKey_buf, 0);
-            compute::buffer_iterator<Tk> keyN = compute::make_buffer_iterator<Tk>(pKey_buf, elements);
-            compute::buffer_iterator<Tv> val0 = compute::make_buffer_iterator<Tv>(pVal_buf, 0);
-            compute::buffer_iterator<Tv> valN = compute::make_buffer_iterator<Tv>(pVal_buf, elements);
-
-            // Sort By Key for descending is stable in the reverse
-            // (greater) order. Sorting in ascending with negated values
-            // will give the right result
-            if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
-
-            // Create a copy of the pKey buffer
-            cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
-            compute::buffer cKey_buf((*cKey)());
-            compute::buffer_iterator<Tk> cKey0 = compute::make_buffer_iterator<Tk>(cKey_buf, 0);
-            compute::buffer_iterator<Tk> cKeyN = compute::make_buffer_iterator<Tk>(cKey_buf, elements);
-            compute::copy(key0, keyN, cKey0, c_queue);
-
-            // FIRST SORT
-            compute::sort_by_key(key0, keyN, seq0, c_queue);
-            compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
-
-            // Create a copy of the seq buffer after first sort
-            cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
-            compute::buffer cSeq_buf((*cSeq)());
-            compute::buffer_iterator<unsigned> cSeq0 = compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
-            compute::buffer_iterator<unsigned> cSeqN = compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
-            compute::copy(seq0, seqN, cSeq0, c_queue);
-
-            // SECOND SORT
-            // First call will sort key, second sort will sort val
-            // Needs to be ascending (true) in order to maintain the indices properly
-            //kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
-            compute::sort_by_key(seq0, seqN, key0, c_queue);
-            compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
-
-            // If descending, flip it back
-            if(!isAscending) compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
-
-            CL_DEBUG_FINISH(getQueue());
-            bufferFree(cSeq);
-            bufferFree(cKey);
-        }
+    CL_DEBUG_FINISH(getQueue());
+}
 
-        template<typename Tk, typename Tv>
-        void sort0ByKey(Param pKey, Param pVal, bool isAscending)
-        {
-            int higherDims =  pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
-            // Batced sort performs 4x sort by keys
-            // But this is only useful before GPU is saturated
-            // The GPU is saturated at around 1000,000 integers
-            // Call batched sort only if both conditions are met
-            if(higherDims > 4 && pKey.info.dims[0] < 1000000)
-                kernel::sortByKeyBatched<Tk, Tv>(pKey, pVal, 0, isAscending);
-            else
-                kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
-        }
+template<typename Tk_, typename Tv_>
+void sortByKeyBatched(Param pKey, Param pVal, const int dim, bool isAscending) {
+    typedef type_t<Tk_> Tk;
+    typedef type_t<Tv_> Tv;
+
+    af::dim4 inDims;
+    for (int i = 0; i < 4; i++) inDims[i] = pKey.info.dims[i];
+
+    // Sort dimension
+    // tileDims * seqDims = inDims
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
+
+    // Create/call iota
+    Array<unsigned> pSeq = iota<unsigned>(seqDims, tileDims);
+
+    int elements = inDims.elements();
+
+    // Flat - Not required since inplace and both are continuous
+    // val.modDims(inDims.elements());
+    // key.modDims(inDims.elements());
+
+    // Sort indices
+    // sort_by_key<T, Tv, isAscending>(*resVal, *resKey, val, key, 0);
+    // kernel::sort0_by_key<T, Tv, isAscending>(pVal, pKey);
+    compute::command_queue c_queue(getQueue()());
+    compute::context c_context(getContext()());
+
+    // Create buffer iterators for seq
+    compute::buffer pSeq_buf((*pSeq.get())());
+    compute::buffer_iterator<unsigned> seq0 =
+        compute::make_buffer_iterator<unsigned>(pSeq_buf, 0);
+    compute::buffer_iterator<unsigned> seqN =
+        compute::make_buffer_iterator<unsigned>(pSeq_buf, elements);
+    // Create buffer iterators for key and val
+    compute::buffer pKey_buf((*pKey.data)());
+    compute::buffer pVal_buf((*pVal.data)());
+    compute::buffer_iterator<Tk> key0 =
+        compute::make_buffer_iterator<Tk>(pKey_buf, 0);
+    compute::buffer_iterator<Tk> keyN =
+        compute::make_buffer_iterator<Tk>(pKey_buf, elements);
+    compute::buffer_iterator<Tv> val0 =
+        compute::make_buffer_iterator<Tv>(pVal_buf, 0);
+    compute::buffer_iterator<Tv> valN =
+        compute::make_buffer_iterator<Tv>(pVal_buf, elements);
+
+    // Sort By Key for descending is stable in the reverse
+    // (greater) order. Sorting in ascending with negated values
+    // will give the right result
+    if (!isAscending)
+        compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+    // Create a copy of the pKey buffer
+    cl::Buffer* cKey = bufferAlloc(elements * sizeof(Tk));
+    compute::buffer cKey_buf((*cKey)());
+    compute::buffer_iterator<Tk> cKey0 =
+        compute::make_buffer_iterator<Tk>(cKey_buf, 0);
+    compute::buffer_iterator<Tk> cKeyN =
+        compute::make_buffer_iterator<Tk>(cKey_buf, elements);
+    compute::copy(key0, keyN, cKey0, c_queue);
+
+    // FIRST SORT
+    compute::sort_by_key(key0, keyN, seq0, c_queue);
+    compute::sort_by_key(cKey0, cKeyN, val0, c_queue);
+
+    // Create a copy of the seq buffer after first sort
+    cl::Buffer* cSeq = bufferAlloc(elements * sizeof(unsigned));
+    compute::buffer cSeq_buf((*cSeq)());
+    compute::buffer_iterator<unsigned> cSeq0 =
+        compute::make_buffer_iterator<unsigned>(cSeq_buf, 0);
+    compute::buffer_iterator<unsigned> cSeqN =
+        compute::make_buffer_iterator<unsigned>(cSeq_buf, elements);
+    compute::copy(seq0, seqN, cSeq0, c_queue);
+
+    // SECOND SORT
+    // First call will sort key, second sort will sort val
+    // Needs to be ascending (true) in order to maintain the indices properly
+    // kernel::sort0_by_key<Tv, T, true>(pKey, pVal);
+    compute::sort_by_key(seq0, seqN, key0, c_queue);
+    compute::sort_by_key(cSeq0, cSeqN, val0, c_queue);
+
+    // If descending, flip it back
+    if (!isAscending)
+        compute::transform(key0, keyN, key0, flipFunction<Tk>(), c_queue);
+
+    CL_DEBUG_FINISH(getQueue());
+    bufferFree(cSeq);
+    bufferFree(cKey);
+}
 
-#define INSTANTIATE(Tk, Tv)                                                                        \
-  template void sort0ByKey<Tk, Tv>(Param okey, Param oval, bool isAscending);                      \
-  template void sort0ByKeyIterative<Tk, Tv>(Param okey, Param oval, bool isAscending);             \
-  template void sortByKeyBatched<Tk, Tv>(Param okey, Param oval, const int dim, bool isAscending);
+template<typename Tk, typename Tv>
+void sort0ByKey(Param pKey, Param pVal, bool isAscending) {
+    int higherDims = pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
+    // Batced sort performs 4x sort by keys
+    // But this is only useful before GPU is saturated
+    // The GPU is saturated at around 1000,000 integers
+    // Call batched sort only if both conditions are met
+    if (higherDims > 4 && pKey.info.dims[0] < 1000000)
+        kernel::sortByKeyBatched<Tk, Tv>(pKey, pVal, 0, isAscending);
+    else
+        kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
+}
 
-#define INSTANTIATE1(Tk    ) \
-    INSTANTIATE(Tk, float  ) \
-    INSTANTIATE(Tk, double ) \
-    INSTANTIATE(Tk, cfloat ) \
+#define INSTANTIATE(Tk, Tv)                                           \
+    template void sort0ByKey<Tk, Tv>(Param okey, Param oval,          \
+                                     bool isAscending);               \
+    template void sort0ByKeyIterative<Tk, Tv>(Param okey, Param oval, \
+                                              bool isAscending);      \
+    template void sortByKeyBatched<Tk, Tv>(Param okey, Param oval,    \
+                                           const int dim, bool isAscending);
+
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
     INSTANTIATE(Tk, cdouble) \
-    INSTANTIATE(Tk, int    ) \
-    INSTANTIATE(Tk, uint   ) \
-    INSTANTIATE(Tk, short  ) \
-    INSTANTIATE(Tk, ushort ) \
-    INSTANTIATE(Tk, char   ) \
-    INSTANTIATE(Tk, uchar  ) \
-    INSTANTIATE(Tk, intl   ) \
-    INSTANTIATE(Tk, uintl  )
-    }
-}
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
+}  // namespace kernel
+}  // namespace opencl
 
 #pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 5ec0a099d1..7908801593 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -8,41 +8,34 @@
  ********************************************************/
 
 #pragma once
-#include <common/dispatch.hpp>
 #include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <type_traits>
 
-namespace opencl
-{
-    namespace kernel
-    {
-        using std::conditional;
-        using std::is_same;
-
-        // If type is cdouble, return std::complex<double>, else return T
-        template<typename T>
-        using ztype_t = typename conditional<is_same<T, cdouble>::value,
-                                             std::complex<double>, T
-                                            >::type;
+namespace opencl {
+namespace kernel {
+using std::conditional;
+using std::is_same;
 
-        // If type is cfloat, return std::complex<float>, else return ztype_t
-        template<typename T>
-        using ctype_t = typename conditional<is_same<T, cfloat>::value,
-                                             std::complex<float>, ztype_t<T>
-                                            >::type;
+// If type is cdouble, return std::complex<double>, else return T
+template<typename T>
+using ztype_t = typename conditional<is_same<T, cdouble>::value,
+                                     std::complex<double>, T>::type;
 
-        // If type is intl, return cl_long, else return ctype_t
-        template<typename T>
-        using ltype_t = typename conditional<is_same<T, intl>::value,
-                                             cl_long, ctype_t<T>
-                                            >::type;
+// If type is cfloat, return std::complex<float>, else return ztype_t
+template<typename T>
+using ctype_t = typename conditional<is_same<T, cfloat>::value,
+                                     std::complex<float>, ztype_t<T>>::type;
 
-        // If type is uintl, return cl_ulong, else return ltype_t
-        template<typename T>
-        using type_t = typename conditional<is_same<T, uintl>::value,
-                                            cl_ulong, ltype_t<T>
-                                           >::type;
-    }
-}
+// If type is intl, return cl_long, else return ctype_t
+template<typename T>
+using ltype_t =
+    typename conditional<is_same<T, intl>::value, cl_long, ctype_t<T>>::type;
 
+// If type is uintl, return cl_ulong, else return ltype_t
+template<typename T>
+using type_t =
+    typename conditional<is_same<T, uintl>::value, cl_ulong, ltype_t<T>>::type;
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sp_sp_arith_csr.cl b/src/backend/opencl/kernel/sp_sp_arith_csr.cl
index 684beef7bf..df589ee0f4 100644
--- a/src/backend/opencl/kernel/sp_sp_arith_csr.cl
+++ b/src/backend/opencl/kernel/sp_sp_arith_csr.cl
@@ -7,26 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-//TODO_PERF(pradeep) More performance improvements are possible
-__attribute__((reqd_work_group_size(256, 1, 1)))
-kernel
-void ssarith_csr_kernel(global T* oVals, global int* oColIdx,
-                        global const int* oRowIdx,
-                        uint M, uint N,
-                        uint nnza, global const T *lVals,
-                        global const int *lRowIdx, global const int *lColIdx,
-                        uint nnzb, global const T *rVals,
-                        global const int *rRowIdx, global const int *rColIdx)
-{
-    const uint row     = get_global_id(0);
+// TODO_PERF(pradeep) More performance improvements are possible
+__attribute__((reqd_work_group_size(256, 1, 1))) kernel void ssarith_csr_kernel(
+    global T *oVals, global int *oColIdx, global const int *oRowIdx, uint M,
+    uint N, uint nnza, global const T *lVals, global const int *lRowIdx,
+    global const int *lColIdx, uint nnzb, global const T *rVals,
+    global const int *rRowIdx, global const int *rColIdx) {
+    const uint row = get_global_id(0);
 
     const bool valid = row < M;
 
-    const uint lEnd    = (valid ? lRowIdx[row+1] : 0);
-    const uint rEnd    = (valid ? rRowIdx[row+1] : 0);
-    const uint offset  = (valid ? oRowIdx[row]   : 0);
+    const uint lEnd   = (valid ? lRowIdx[row + 1] : 0);
+    const uint rEnd   = (valid ? rRowIdx[row + 1] : 0);
+    const uint offset = (valid ? oRowIdx[row] : 0);
 
-    global   T *ovPtr = oVals + offset;
+    global T *ovPtr   = oVals + offset;
     global int *ocPtr = oColIdx + offset;
 
     uint l = (valid ? lRowIdx[row] : 0);
@@ -40,8 +35,8 @@ void ssarith_csr_kernel(global T* oVals, global int* oColIdx,
         T lhs = (lci <= rci ? lVals[l] : IDENTITY_VALUE);
         T rhs = (lci >= rci ? rVals[r] : IDENTITY_VALUE);
 
-        ovPtr[ nnz ] = OP(lhs, rhs);
-        ocPtr[ nnz ] = (lci <= rci) ? lci : rci;
+        ovPtr[nnz] = OP(lhs, rhs);
+        ocPtr[nnz] = (lci <= rci) ? lci : rci;
 
         l += (lci <= rci);
         r += (lci >= rci);
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 4ec35eceb9..dc9a5c2430 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -8,364 +8,328 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/coo2dense.hpp>
+#include <kernel_headers/csr2coo.hpp>
 #include <kernel_headers/csr2dense.hpp>
 #include <kernel_headers/dense2csr.hpp>
-#include <kernel_headers/csr2coo.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <type_util.hpp>
-#include "scan_dim.hpp"
+#include <map>
+#include <mutex>
+#include <string>
+#include "config.hpp"
 #include "reduce.hpp"
+#include "scan_dim.hpp"
 #include "scan_first.hpp"
 #include "sort_by_key.hpp"
-#include "config.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T>
-        void coo2dense(Param out, const Param values, const Param rowIdx, const Param colIdx)
-        {
-            std::string ref_name =
-                std::string("coo2dense_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(REPEAT);
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName()
-                        << " -D reps="     << REPEAT
-                        ;
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
-                entry.prog   = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "coo2dense_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            };
-
-            auto coo2denseOp = KernelFunctor<Buffer, const KParam,
-                                        const Buffer, const KParam,
-                                        const Buffer, const KParam,
-                                        const Buffer, const KParam>
-                                      (*entry.ker);
-
-            NDRange local(THREADS_PER_GROUP, 1, 1);
-
-            NDRange global(divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
-
-            coo2denseOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info,
-                    *values.data, values.info,
-                    *rowIdx.data, rowIdx.info,
-                    *colIdx.data, colIdx.info);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace opencl {
+namespace kernel {
+template<typename T>
+void coo2dense(Param out, const Param values, const Param rowIdx,
+               const Param colIdx) {
+    std::string ref_name = std::string("coo2dense_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(REPEAT);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D reps=" << REPEAT;
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        template<typename T>
-        void csr2dense(Param output, const Param values, const Param rowIdx, const Param colIdx)
-        {
-            const int MAX_GROUPS = 4096;
-            int M = rowIdx.info.dims[0] - 1;
-            //FIXME: This needs to be based non nonzeros per row
-            int threads = 64;
-
-            std::string ref_name =
-                std::string("csr2dense_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(threads);
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                options << " -D THREADS=" << threads;
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                const char *ker_strs[] = {csr2dense_cl};
-                const int   ker_lens[] = {csr2dense_cl_len};
-
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "csr2dense");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            NDRange local(threads, 1);
-            int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
-            NDRange global(local[0] * groups_x, 1);
-            auto csr2dense_kernel = *entry.ker;
-            auto csr2dense_func = KernelFunctor<Buffer,
-                                                Buffer, Buffer, Buffer,
-                                                int> (csr2dense_kernel);
-
-            csr2dense_func(EnqueueArgs(getQueue(), global, local),
-                            *output.data, *values.data, *rowIdx.data, *colIdx.data, M);
-
-            CL_DEBUG_FINISH(getQueue());
-        }
+        Program prog;
+        buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "coo2dense_kernel");
 
-        template<typename T>
-        void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense)
-        {
-            int num_rows = dense.info.dims[0];
-            int num_cols = dense.info.dims[1];
-
-            // sd1 contains output of scan along dim 1 of dense
-            Array<int> sd1 = createEmptyArray<int>(dim4(num_rows, num_cols));
-            // rd1 contains output of nonzero count along dim 1 along dense
-            Array<int> rd1 = createEmptyArray<int>(num_rows);
-
-            scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
-            reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
-            scan_first<int, int, af_add_t, false>(rowIdx, rd1);
-
-            int nnz = values.info.dims[0];
-            getQueue().enqueueWriteBuffer(*rowIdx.data, CL_TRUE,
-                                          rowIdx.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
-                                          sizeof(int),
-                                          (void *)&nnz);
-
-            std::string ref_name =
-                std::string("dense2csr_") +
-                std::string(dtype_traits<T>::getName());
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-                if (std::is_same<T, cfloat>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-
-                const char *ker_strs[] = {dense2csr_cl};
-                const int   ker_lens[] = {dense2csr_cl_len};
-
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            NDRange local(THREADS_X, THREADS_Y);
-            int groups_x = divup(dense.info.dims[0], local[0]);
-            int groups_y = divup(dense.info.dims[1], local[1]);
-            NDRange global(groups_x * local[0], groups_y * local[1]);
-            auto dense2csr_split = KernelFunctor<Buffer, Buffer,
-                                                  Buffer, KParam,
-                                                  Buffer, KParam,
-                                                  Buffer>(*entry.ker);
-
-            dense2csr_split(EnqueueArgs(getQueue(), global, local),
-                            *values.data, *colIdx.data,
-                            *dense.data, dense.info,
-                            *sd1.get(), sd1,
-                            *rowIdx.data);
-
-            CL_DEBUG_FINISH(getQueue());
-        }
+        addKernelToCache(device, ref_name, entry);
+    };
+
+    auto coo2denseOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const Buffer, const KParam, const Buffer, const KParam>(
+            *entry.ker);
+
+    NDRange local(THREADS_PER_GROUP, 1, 1);
+
+    NDRange global(
+        divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
+
+    coo2denseOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *values.data, values.info, *rowIdx.data, rowIdx.info,
+                *colIdx.data, colIdx.info);
 
-        template<typename T>
-        void swapIndex(Param ovalues, Param oindex,
-                       const Param ivalues, const cl::Buffer *iindex,
-                       const Param swapIdx)
-        {
-            std::string ref_name =
-                std::string("swapIndex_kernel_") +
-                std::string(dtype_traits<T>::getName());
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void csr2dense(Param output, const Param values, const Param rowIdx,
+               const Param colIdx) {
+    const int MAX_GROUPS = 4096;
+    int M                = rowIdx.info.dims[0] - 1;
+    // FIXME: This needs to be based non nonzeros per row
+    int threads = 64;
+
+    std::string ref_name = std::string("csr2dense_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(threads);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D THREADS=" << threads;
 
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+        const char *ker_strs[] = {csr2dense_cl};
+        const int ker_lens[]   = {csr2dense_cl_len};
 
-                Program prog;
-                buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-                entry.prog   = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "swapIndex_kernel");
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "csr2dense");
 
-                addKernelToCache(device, ref_name, entry);
-            };
+        addKernelToCache(device, ref_name, entry);
+    }
 
-            auto swapIndexOp = KernelFunctor<Buffer, Buffer,
-                                              const Buffer, const Buffer, const Buffer,
-                                              const int> (*entry.ker);
+    NDRange local(threads, 1);
+    int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+    NDRange global(local[0] * groups_x, 1);
+    auto csr2dense_kernel = *entry.ker;
+    auto csr2dense_func =
+        KernelFunctor<Buffer, Buffer, Buffer, Buffer, int>(csr2dense_kernel);
 
-            NDRange global(ovalues.info.dims[0], 1, 1);
+    csr2dense_func(EnqueueArgs(getQueue(), global, local), *output.data,
+                   *values.data, *rowIdx.data, *colIdx.data, M);
 
-            swapIndexOp(EnqueueArgs(getQueue(), global),
-                        *ovalues.data, *oindex.data,
-                        *ivalues.data, *iindex,
-                        *swapIdx.data, ovalues.info.dims[0]);
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            CL_DEBUG_FINISH(getQueue());
+template<typename T>
+void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
+    int num_rows = dense.info.dims[0];
+    int num_cols = dense.info.dims[1];
+
+    // sd1 contains output of scan along dim 1 of dense
+    Array<int> sd1 = createEmptyArray<int>(dim4(num_rows, num_cols));
+    // rd1 contains output of nonzero count along dim 1 along dense
+    Array<int> rd1 = createEmptyArray<int>(num_rows);
+
+    scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
+    reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
+    scan_first<int, int, af_add_t, false>(rowIdx, rd1);
+
+    int nnz = values.info.dims[0];
+    getQueue().enqueueWriteBuffer(
+        *rowIdx.data, CL_TRUE,
+        rowIdx.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
+        sizeof(int), (void *)&nnz);
+
+    std::string ref_name =
+        std::string("dense2csr_") + std::string(dtype_traits<T>::getName());
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
         }
 
-        template<typename T>
-        void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx,
-                     const Param ivalues, const Param irowIdx, const Param icolIdx,
-                     Param index)
-        {
-            const int MAX_GROUPS = 4096;
-            int M = irowIdx.info.dims[0] - 1;
-            //FIXME: This needs to be based non nonzeros per row
-            int threads = 64;
+        const char *ker_strs[] = {dense2csr_cl};
+        const int ker_lens[]   = {dense2csr_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(THREADS_X, THREADS_Y);
+    int groups_x = divup(dense.info.dims[0], local[0]);
+    int groups_y = divup(dense.info.dims[1], local[1]);
+    NDRange global(groups_x * local[0], groups_y * local[1]);
+    auto dense2csr_split =
+        KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer, KParam, Buffer>(
+            *entry.ker);
+
+    dense2csr_split(EnqueueArgs(getQueue(), global, local), *values.data,
+                    *colIdx.data, *dense.data, dense.info, *sd1.get(), sd1,
+                    *rowIdx.data);
+
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            std::string ref_name =
-                std::string("csr2coo_") +
-                std::string(dtype_traits<T>::getName());
+template<typename T>
+void swapIndex(Param ovalues, Param oindex, const Param ivalues,
+               const cl::Buffer *iindex, const Param swapIdx) {
+    std::string ref_name = std::string("swapIndex_kernel_") +
+                           std::string(dtype_traits<T>::getName());
 
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-            if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
 
-                std::ostringstream options;
-                options << " -D T=" << dtype_traits<T>::getName();
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+        Program prog;
+        buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "swapIndex_kernel");
 
-                const char *ker_strs[] = {csr2coo_cl};
-                const int   ker_lens[] = {csr2coo_cl_len};
+        addKernelToCache(device, ref_name, entry);
+    };
 
-                Program prog;
-                buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-                entry.prog = new Program(prog);
-                entry.ker  = new Kernel(*entry.prog, "csr2coo");
+    auto swapIndexOp = KernelFunctor<Buffer, Buffer, const Buffer, const Buffer,
+                                     const Buffer, const int>(*entry.ker);
 
-                addKernelToCache(device, ref_name, entry);
-            }
+    NDRange global(ovalues.info.dims[0], 1, 1);
 
-            cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
+    swapIndexOp(EnqueueArgs(getQueue(), global), *ovalues.data, *oindex.data,
+                *ivalues.data, *iindex, *swapIdx.data, ovalues.info.dims[0]);
 
-            NDRange local(threads, 1);
-            int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
-            NDRange global(local[0] * groups_x, 1);
-            auto csr2coo_kernel = *entry.ker;
-            auto csr2coo_func = KernelFunctor<Buffer, Buffer,
-                                              const Buffer, const Buffer,
-                                              int> (csr2coo_kernel);
+    CL_DEBUG_FINISH(getQueue());
+}
 
-            csr2coo_func(EnqueueArgs(getQueue(), global, local),
-                            *scratch, *ocolIdx.data,
-                            *irowIdx.data, *icolIdx.data, M);
+template<typename T>
+void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
+             const Param irowIdx, const Param icolIdx, Param index) {
+    const int MAX_GROUPS = 4096;
+    int M                = irowIdx.info.dims[0] - 1;
+    // FIXME: This needs to be based non nonzeros per row
+    int threads = 64;
 
-            // Now we need to sort this into column major
-            kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
+    std::string ref_name =
+        std::string("csr2coo_") + std::string(dtype_traits<T>::getName());
 
-            // Now use index to sort values and rows
-            kernel::swapIndex<T>(ovalues, orowIdx, ivalues, scratch, index);
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-            CL_DEBUG_FINISH(getQueue());
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
 
-            bufferFree(scratch);
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        template<typename T>
-        void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx,
-                     const Param ivalues, const Param irowIdx, const Param icolIdx,
-                     Param index, Param rowCopy, const int M)
-        {
-            // Now we need to sort this into column major
-            kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
+        const char *ker_strs[] = {csr2coo_cl};
+        const int ker_lens[]   = {csr2coo_cl_len};
+
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "csr2coo");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
 
-            // Now use index to sort values and rows
-            kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, icolIdx.data, index);
+    NDRange local(threads, 1);
+    int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+    NDRange global(local[0] * groups_x, 1);
+    auto csr2coo_kernel = *entry.ker;
+    auto csr2coo_func =
+        KernelFunctor<Buffer, Buffer, const Buffer, const Buffer, int>(
+            csr2coo_kernel);
 
-            CL_DEBUG_FINISH(getQueue());
+    csr2coo_func(EnqueueArgs(getQueue(), global, local), *scratch,
+                 *ocolIdx.data, *irowIdx.data, *icolIdx.data, M);
 
-            std::string ref_name =
-                std::string("csrReduce_kernel_") +
-                std::string(dtype_traits<T>::getName());
+    // Now we need to sort this into column major
+    kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
 
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
+    // Now use index to sort values and rows
+    kernel::swapIndex<T>(ovalues, orowIdx, ivalues, scratch, index);
 
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T="        << dtype_traits<T>::getName();
+    CL_DEBUG_FINISH(getQueue());
+
+    bufferFree(scratch);
+}
 
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
+template<typename T>
+void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
+             const Param irowIdx, const Param icolIdx, Param index,
+             Param rowCopy, const int M) {
+    // Now we need to sort this into column major
+    kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
 
-                Program prog;
-                buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-                entry.prog   = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "csrReduce_kernel");
+    // Now use index to sort values and rows
+    kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, icolIdx.data, index);
 
-                addKernelToCache(device, ref_name, entry);
-            };
+    CL_DEBUG_FINISH(getQueue());
 
-            auto csrReduceOp = KernelFunctor<Buffer, const Buffer, const int, const int> (*entry.ker);
+    std::string ref_name = std::string("csrReduce_kernel_") +
+                           std::string(dtype_traits<T>::getName());
 
-            NDRange global(irowIdx.info.dims[0], 1, 1);
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
 
-            csrReduceOp(EnqueueArgs(getQueue(), global),
-                        *orowIdx.data, *rowCopy.data, M, ovalues.info.dims[0]);
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
 
-            CL_DEBUG_FINISH(getQueue());
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
-    }
+
+        Program prog;
+        buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "csrReduce_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    };
+
+    auto csrReduceOp =
+        KernelFunctor<Buffer, const Buffer, const int, const int>(*entry.ker);
+
+    NDRange global(irowIdx.info.dims[0], 1, 1);
+
+    csrReduceOp(EnqueueArgs(getQueue(), global), *orowIdx.data, *rowCopy.data,
+                M, ovalues.info.dims[0]);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 6539d4e73f..b8593aae9b 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -8,365 +8,343 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/sparse_arith_csr.hpp>
-#include <kernel_headers/sparse_arith_coo.hpp>
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
+#include <kernel_headers/sp_sp_arith_csr.hpp>
 #include <kernel_headers/sparse_arith_common.hpp>
+#include <kernel_headers/sparse_arith_coo.hpp>
+#include <kernel_headers/sparse_arith_csr.hpp>
 #include <kernel_headers/ssarith_calc_out_nnz.hpp>
-#include <kernel_headers/sp_sp_arith_csr.hpp>
+#include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
-#include <mutex>
-#include <map>
-#include <common/complex.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <cache.hpp>
 #include <type_util.hpp>
 #include <types.hpp>
-#include <math.hpp>
+#include <map>
+#include <mutex>
+#include <string>
 
-namespace opencl
-{
-    namespace kernel
-    {
-        static const unsigned TX = 32;
-        static const unsigned TY = 8;
-        static const unsigned THREADS = TX * TY;
-
-        template<af_op_t op>
-        std::string getOpString()
-        {
-            switch(op) {
-                case af_add_t : return "ADD";
-                case af_sub_t : return "SUB";
-                case af_mul_t : return "MUL";
-                case af_div_t : return "DIV";
-                default       : return ""; // kernel will fail to compile
-            }
-            return "";
-        }
+namespace opencl {
+namespace kernel {
+static const unsigned TX      = 32;
+static const unsigned TY      = 8;
+static const unsigned THREADS = TX * TY;
+
+template<af_op_t op>
+std::string getOpString() {
+    switch (op) {
+        case af_add_t: return "ADD";
+        case af_sub_t: return "SUB";
+        case af_mul_t: return "MUL";
+        case af_div_t: return "DIV";
+        default: return "";  // kernel will fail to compile
+    }
+    return "";
+}
 
-        template<typename T, af_op_t op>
-        void sparseArithOpCSR(Param out, const Param values, const Param rowIdx, const Param colIdx,
-                const Param rhs, const bool reverse)
-        {
-            std::string ref_name =
-                std::string("sparseArithOpCSR_") +
-                getOpString<op>() + std::string("_") +
-                std::string(dtype_traits<T>::getName());
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T="  << dtype_traits<T>::getName();
-                options << " -D OP=" << getOpString<op>();
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                        (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
-                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
-
-                cl::Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new cl::Program(prog);
-                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto sparseArithCSROp = cl::KernelFunctor<cl::Buffer, const KParam,
-                 const cl::Buffer, const cl::Buffer, const cl::Buffer,
-                 const int,
-                 const cl::Buffer, const KParam,
-                 const int>(*entry.ker);
-
-            cl::NDRange local(TX, TY, 1);
-            cl::NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
-
-            sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info,
-                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                    *rhs.data, rhs.info, reverse);
-
-            CL_DEBUG_FINISH(getQueue());
+template<typename T, af_op_t op>
+void sparseArithOpCSR(Param out, const Param values, const Param rowIdx,
+                      const Param colIdx, const Param rhs, const bool reverse) {
+    std::string ref_name = std::string("sparseArithOpCSR_") +
+                           getOpString<op>() + std::string("_") +
+                           std::string(dtype_traits<T>::getName());
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D OP=" << getOpString<op>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
         }
-
-        template<typename T, af_op_t op>
-        void sparseArithOpCOO(Param out, const Param values, const Param rowIdx, const Param colIdx,
-                const Param rhs, const bool reverse)
-        {
-            std::string ref_name =
-                std::string("sparseArithOpCOO_") +
-                getOpString<op>() + std::string("_") +
-                std::string(dtype_traits<T>::getName());
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T="  << dtype_traits<T>::getName();
-                options << " -D OP=" << getOpString<op>();
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                        (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
-                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
-
-                cl::Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new cl::Program(prog);
-                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto sparseArithCOOOp = cl::KernelFunctor<cl::Buffer, const KParam,
-                 const cl::Buffer, const cl::Buffer, const cl::Buffer,
-                 const int,
-                 const cl::Buffer, const KParam,
-                 const int>(*entry.ker);
-
-            cl::NDRange local(THREADS, 1, 1);
-            cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
-
-            sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info,
-                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                    *rhs.data, rhs.info, reverse);
-
-            CL_DEBUG_FINISH(getQueue());
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        template<typename T, af_op_t op>
-        void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx,
-                const Param rhs, const bool reverse)
-        {
-            std::string ref_name =
-                std::string("sparseArithOpSCSR_") +
-                getOpString<op>() + std::string("_") +
-                std::string(dtype_traits<T>::getName());
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                std::ostringstream options;
-                options << " -D T="  << dtype_traits<T>::getName();
-                options << " -D OP=" << getOpString<op>();
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                        (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_csr_cl};
-                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_csr_cl_len};
-
-                cl::Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new cl::Program(prog);
-                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto sparseArithCSROp = cl::KernelFunctor<const cl::Buffer, const cl::Buffer, const cl::Buffer,
-                 const int,
-                 const cl::Buffer, const KParam,
-                 const int>(*entry.ker);
-
-            cl::NDRange local(TX, TY, 1);
-            cl::NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
-
-            sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local),
-                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                    *rhs.data, rhs.info, reverse);
-
-            CL_DEBUG_FINISH(getQueue());
+        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_csr_cl};
+        const int ker_lens[]   = {sparse_arith_common_cl_len,
+                                sparse_arith_csr_cl_len};
+
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    auto sparseArithCSROp =
+        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
+                          const cl::Buffer, const cl::Buffer, const int,
+                          const cl::Buffer, const KParam, const int>(
+            *entry.ker);
+
+    cl::NDRange local(TX, TY, 1);
+    cl::NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
+
+    sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                     out.info, *values.data, *rowIdx.data, *colIdx.data,
+                     values.info.dims[0], *rhs.data, rhs.info, reverse);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCOO(Param out, const Param values, const Param rowIdx,
+                      const Param colIdx, const Param rhs, const bool reverse) {
+    std::string ref_name = std::string("sparseArithOpCOO_") +
+                           getOpString<op>() + std::string("_") +
+                           std::string(dtype_traits<T>::getName());
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D OP=" << getOpString<op>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
+        }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        template<typename T, af_op_t op>
-        void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx,
-                const Param rhs, const bool reverse)
-        {
-            std::string ref_name =
-                std::string("sparseArithOpSCOO_") +
-                getOpString<op>() + std::string("_") +
-                std::string(dtype_traits<T>::getName());
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                std::ostringstream options;
-                options << " -D T="  << dtype_traits<T>::getName();
-                options << " -D OP=" << getOpString<op>();
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                        (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                const char *ker_strs[] = {sparse_arith_common_cl    , sparse_arith_coo_cl};
-                const int   ker_lens[] = {sparse_arith_common_cl_len, sparse_arith_coo_cl_len};
-
-                cl::Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new cl::Program(prog);
-                entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto sparseArithCOOOp = cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer,
-                 const int,
-                 const cl::Buffer, const KParam,
-                 const int>(*entry.ker);
-
-            cl::NDRange local(THREADS, 1, 1);
-            cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
-
-            sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local),
-                    *values.data, *rowIdx.data, *colIdx.data, values.info.dims[0],
-                    *rhs.data, rhs.info, reverse);
-
-            CL_DEBUG_FINISH(getQueue());
+        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_coo_cl};
+        const int ker_lens[]   = {sparse_arith_common_cl_len,
+                                sparse_arith_coo_cl_len};
+
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    auto sparseArithCOOOp =
+        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
+                          const cl::Buffer, const cl::Buffer, const int,
+                          const cl::Buffer, const KParam, const int>(
+            *entry.ker);
+
+    cl::NDRange local(THREADS, 1, 1);
+    cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+
+    sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                     out.info, *values.data, *rowIdx.data, *colIdx.data,
+                     values.info.dims[0], *rhs.data, rhs.info, reverse);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx, const Param rhs,
+                      const bool reverse) {
+    std::string ref_name = std::string("sparseArithOpSCSR_") +
+                           getOpString<op>() + std::string("_") +
+                           std::string(dtype_traits<T>::getName());
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D OP=" << getOpString<op>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
+        }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        static
-        void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC,
-                const uint M, const uint N,
-                uint nnzA, const Param lrowIdx, const Param lcolIdx,
-                uint nnzB, const Param rrowIdx, const Param rcolIdx)
-        {
-            std::string refName = std::string("csr_calc_output_NNZ");
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, refName);
-
-            if (entry.prog==0 && entry.ker==0) {
-                const char *kerStrs[] = { ssarith_calc_out_nnz_cl };
-                const int kerLens[] = { ssarith_calc_out_nnz_cl_len };
-
-                cl::Program prog;
-                buildProgram(prog, 1, kerStrs, kerLens, std::string(""));
-                entry.prog = new cl::Program(prog);
-                entry.ker = new cl::Kernel(*entry.prog, "csr_calc_out_nnz");
-
-                addKernelToCache(device, refName, entry);
-            }
-            auto calcNNZop = cl::KernelFunctor<cl::Buffer, cl::Buffer, unsigned,
-                 const cl::Buffer, const cl::Buffer,
-                 const cl::Buffer, const cl::Buffer,
-                 cl::LocalSpaceArg>(*entry.ker);
-
-            cl::NDRange local(256, 1);
-            cl::NDRange global(divup(M, local[0])*local[0], 1, 1);
-
-            nnzC = 0;
-            cl::Buffer* out = bufferAlloc(sizeof(unsigned));
-            getQueue().enqueueWriteBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
-
-            calcNNZop(cl::EnqueueArgs(getQueue(), global, local),
-                      *out, *outRowIdx.data, M,
-                      *lrowIdx.data, *lcolIdx.data,
-                      *rrowIdx.data, *rcolIdx.data,
-                      cl::Local(local[0]*sizeof(unsigned int)));
-            getQueue().enqueueReadBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
-
-            CL_DEBUG_FINISH(getQueue());
+        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_csr_cl};
+        const int ker_lens[]   = {sparse_arith_common_cl_len,
+                                sparse_arith_csr_cl_len};
+
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    auto sparseArithCSROp =
+        cl::KernelFunctor<const cl::Buffer, const cl::Buffer, const cl::Buffer,
+                          const int, const cl::Buffer, const KParam, const int>(
+            *entry.ker);
+
+    cl::NDRange local(TX, TY, 1);
+    cl::NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
+
+    sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local), *values.data,
+                     *rowIdx.data, *colIdx.data, values.info.dims[0], *rhs.data,
+                     rhs.info, reverse);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx, const Param rhs,
+                      const bool reverse) {
+    std::string ref_name = std::string("sparseArithOpSCOO_") +
+                           getOpString<op>() + std::string("_") +
+                           std::string(dtype_traits<T>::getName());
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName();
+        options << " -D OP=" << getOpString<op>();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
+            options << " -D IS_CPLX=1";
+        } else {
+            options << " -D IS_CPLX=0";
+        }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
 
-        template<typename T, af_op_t op>
-        void ssArithCSR(Param oVals, Param oColIdx,
-                const Param oRowIdx, const uint M, const uint N,
-                unsigned nnzA, const Param lVals, const Param lRowIdx, const Param lColIdx,
-                unsigned nnzB, const Param rVals, const Param rRowIdx, const Param rColIdx)
-        {
-            std::string refName = std::string("ss_arith_csr_") +
-                                  getOpString<op>() + "_" +
-                                  std::string(dtype_traits<T>::getName());
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, refName);
-
-            if (entry.prog==0 && entry.ker==0) {
-                const T iden_val = (op == af_mul_t || op == af_div_t ?
-                                      scalar<T>(1) : scalar<T>(0));
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                options << " -D T="  << dtype_traits<T>::getName()
-                        << " -D OP=" << getOpString<op>()
-                        << " -D IDENTITY_VALUE=(T)(" << af::scalar_to_option(iden_val) << ")";
-
-                options << " -D IS_CPLX=" << common::is_complex<T>::value;
-                if (std::is_same<T, double>::value ||
-                        std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                const char *kerStrs[] = { sparse_arith_common_cl, sp_sp_arith_csr_cl };
-                const int kerLens[] = { sparse_arith_common_cl_len, sp_sp_arith_csr_cl_len };
-
-                cl::Program prog;
-                buildProgram(prog, 2, kerStrs, kerLens, options.str());
-                entry.prog = new cl::Program(prog);
-                entry.ker = new cl::Kernel(*entry.prog, "ssarith_csr_kernel");
-
-                addKernelToCache(device, refName, entry);
-            }
-            auto arithOp = cl::KernelFunctor<cl::Buffer, cl::Buffer,
-                 cl::Buffer, unsigned, unsigned,
-                 unsigned, const cl::Buffer,
-                 const cl::Buffer, const cl::Buffer,
-                 unsigned, const cl::Buffer,
-                 const cl::Buffer, const cl::Buffer>(*entry.ker);
-
-            cl::NDRange local(256, 1);
-            cl::NDRange global(divup(M, local[0])*local[0], 1, 1);
-
-            arithOp(cl::EnqueueArgs(getQueue(), global, local),
-                      *oVals.data, *oColIdx.data,
-                      *oRowIdx.data, M, N,
-                      nnzA, *lVals.data, *lRowIdx.data, *lColIdx.data,
-                      nnzB, *rVals.data, *rRowIdx.data, *rColIdx.data);
-
-            CL_DEBUG_FINISH(getQueue());
+        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_coo_cl};
+        const int ker_lens[]   = {sparse_arith_common_cl_len,
+                                sparse_arith_coo_cl_len};
+
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    auto sparseArithCOOOp =
+        cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, const int,
+                          const cl::Buffer, const KParam, const int>(
+            *entry.ker);
+
+    cl::NDRange local(THREADS, 1, 1);
+    cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
+
+    sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local), *values.data,
+                     *rowIdx.data, *colIdx.data, values.info.dims[0], *rhs.data,
+                     rhs.info, reverse);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
+                          const uint N, uint nnzA, const Param lrowIdx,
+                          const Param lcolIdx, uint nnzB, const Param rrowIdx,
+                          const Param rcolIdx) {
+    std::string refName = std::string("csr_calc_output_NNZ");
+    int device          = getActiveDeviceId();
+    kc_entry_t entry    = kernelCache(device, refName);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        const char *kerStrs[] = {ssarith_calc_out_nnz_cl};
+        const int kerLens[]   = {ssarith_calc_out_nnz_cl_len};
+
+        cl::Program prog;
+        buildProgram(prog, 1, kerStrs, kerLens, std::string(""));
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "csr_calc_out_nnz");
+
+        addKernelToCache(device, refName, entry);
+    }
+    auto calcNNZop =
+        cl::KernelFunctor<cl::Buffer, cl::Buffer, unsigned, const cl::Buffer,
+                          const cl::Buffer, const cl::Buffer, const cl::Buffer,
+                          cl::LocalSpaceArg>(*entry.ker);
+
+    cl::NDRange local(256, 1);
+    cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
+
+    nnzC            = 0;
+    cl::Buffer *out = bufferAlloc(sizeof(unsigned));
+    getQueue().enqueueWriteBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
+
+    calcNNZop(cl::EnqueueArgs(getQueue(), global, local), *out, *outRowIdx.data,
+              M, *lrowIdx.data, *lcolIdx.data, *rrowIdx.data, *rcolIdx.data,
+              cl::Local(local[0] * sizeof(unsigned int)));
+    getQueue().enqueueReadBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
+                const uint N, unsigned nnzA, const Param lVals,
+                const Param lRowIdx, const Param lColIdx, unsigned nnzB,
+                const Param rVals, const Param rRowIdx, const Param rColIdx) {
+    std::string refName = std::string("ss_arith_csr_") + getOpString<op>() +
+                          "_" + std::string(dtype_traits<T>::getName());
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        const T iden_val =
+            (op == af_mul_t || op == af_div_t ? scalar<T>(1) : scalar<T>(0));
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D OP=" << getOpString<op>() << " -D IDENTITY_VALUE=(T)("
+                << af::scalar_to_option(iden_val) << ")";
+
+        options << " -D IS_CPLX=" << common::is_complex<T>::value;
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+
+        const char *kerStrs[] = {sparse_arith_common_cl, sp_sp_arith_csr_cl};
+        const int kerLens[]   = {sparse_arith_common_cl_len,
+                               sp_sp_arith_csr_cl_len};
+
+        cl::Program prog;
+        buildProgram(prog, 2, kerStrs, kerLens, options.str());
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "ssarith_csr_kernel");
+
+        addKernelToCache(device, refName, entry);
     }
+    auto arithOp =
+        cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, unsigned,
+                          unsigned, unsigned, const cl::Buffer,
+                          const cl::Buffer, const cl::Buffer, unsigned,
+                          const cl::Buffer, const cl::Buffer, const cl::Buffer>(
+            *entry.ker);
+
+    cl::NDRange local(256, 1);
+    cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
+
+    arithOp(cl::EnqueueArgs(getQueue(), global, local), *oVals.data,
+            *oColIdx.data, *oRowIdx.data, M, N, nnzA, *lVals.data,
+            *lRowIdx.data, *lColIdx.data, nnzB, *rVals.data, *rRowIdx.data,
+            *rColIdx.data);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/sparse_arith_common.cl b/src/backend/opencl/kernel/sparse_arith_common.cl
index 0a6058e86f..e89f223b4a 100644
--- a/src/backend/opencl/kernel/sparse_arith_common.cl
+++ b/src/backend/opencl/kernel/sparse_arith_common.cl
@@ -7,45 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-T _add_(T v1, T v2)
-{
-    return v1 + v2;
-}
+T _add_(T v1, T v2) { return v1 + v2; }
 
-T _sub_(T v1, T v2)
-{
-    return v1 - v2;
-}
+T _sub_(T v1, T v2) { return v1 - v2; }
 
 #if IS_CPLX
-T _mul_(T v1, T v2)
-{
+T _mul_(T v1, T v2) {
     T out;
     out.x = v1.x * v2.x - v1.y * v2.y;
     out.y = v1.x * v2.y + v1.y * v2.x;
     return out;
 }
 
-T _div_(T v1, T v2)
-{
+T _div_(T v1, T v2) {
     T out;
     out.x = (v1.x * v2.x + v1.y * v2.y) / (v2.x * v2.x + v2.y * v2.y);
     out.y = (v1.y * v2.x - v1.x * v2.y) / (v2.x * v2.x + v2.y * v2.y);
     return out;
 }
 #else
-T _mul_(T v1, T v2)
-{
-    return v1 * v2;
-}
+T _mul_(T v1, T v2) { return v1 * v2; }
 
-T _div_(T v1, T v2)
-{
-    return v1 / v2;
-}
+T _div_(T v1, T v2) { return v1 / v2; }
 #endif
 
-
 #define ADD _add_
 #define SUB _sub_
 #define MUL _mul_
diff --git a/src/backend/opencl/kernel/sparse_arith_coo.cl b/src/backend/opencl/kernel/sparse_arith_coo.cl
index 1dc18422dc..7d6c084a1d 100644
--- a/src/backend/opencl/kernel/sparse_arith_coo.cl
+++ b/src/backend/opencl/kernel/sparse_arith_coo.cl
@@ -7,57 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void sparse_arith_coo_kernel(__global T *oPtr,
-                             const KParam out,
-                             __global const T *values,
-                             __global const int *rowIdx,
-                             __global const int *colIdx,
-                             const int nNZ,
-                             __global const T *rPtr,
-                             const KParam rhs,
-                             const int reverse)
-{
+__kernel void sparse_arith_coo_kernel(__global T *oPtr, const KParam out,
+                                      __global const T *values,
+                                      __global const int *rowIdx,
+                                      __global const int *colIdx, const int nNZ,
+                                      __global const T *rPtr, const KParam rhs,
+                                      const int reverse) {
     const int idx = get_global_id(0);
 
-    if(idx >= nNZ) return;
+    if (idx >= nNZ) return;
 
     const int row = rowIdx[idx];
     const int col = colIdx[idx];
 
-    if(row >= out.dims[0] || col >= out.dims[1]) return;    // Bad indices
+    if (row >= out.dims[0] || col >= out.dims[1]) return;  // Bad indices
 
     // Get Values
     const T val  = values[idx];
     const T rval = rPtr[col * rhs.strides[1] + row];
 
     const int offset = col * out.strides[1] + row;
-    if(reverse) oPtr[offset] = OP(rval, val);
-    else        oPtr[offset] = OP(val, rval);
+    if (reverse)
+        oPtr[offset] = OP(rval, val);
+    else
+        oPtr[offset] = OP(val, rval);
 }
 
-__kernel
-void sparse_arith_coo_kernel_S(__global T *values,
-                               __global int *rowIdx,
-                               __global int *colIdx,
-                               const int nNZ,
-                               __global const T *rPtr,
-                               const KParam rhs,
-                               const int reverse)
-{
+__kernel void sparse_arith_coo_kernel_S(__global T *values,
+                                        __global int *rowIdx,
+                                        __global int *colIdx, const int nNZ,
+                                        __global const T *rPtr,
+                                        const KParam rhs, const int reverse) {
     const int idx = get_global_id(0);
 
-    if(idx >= nNZ) return;
+    if (idx >= nNZ) return;
 
     const int row = rowIdx[idx];
     const int col = colIdx[idx];
 
-    if(row >= rhs.dims[0] || col >= rhs.dims[1]) return;    // Bad indices
+    if (row >= rhs.dims[0] || col >= rhs.dims[1]) return;  // Bad indices
 
     // Get Values
     const T val  = values[idx];
     const T rval = rPtr[col * rhs.strides[1] + row];
 
-    if(reverse) values[idx] = OP(rval, val);
-    else        values[idx] = OP(val, rval);
+    if (reverse)
+        values[idx] = OP(rval, val);
+    else
+        values[idx] = OP(val, rval);
 }
diff --git a/src/backend/opencl/kernel/sparse_arith_csr.cl b/src/backend/opencl/kernel/sparse_arith_csr.cl
index 1bea95b5e6..80255cc462 100644
--- a/src/backend/opencl/kernel/sparse_arith_csr.cl
+++ b/src/backend/opencl/kernel/sparse_arith_csr.cl
@@ -7,67 +7,64 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void sparse_arith_csr_kernel(__global T *oPtr,
-                             const KParam out,
-                             __global const T *values,
-                             __global const int *rowIdx,
-                             __global const int *colIdx,
-                             const int nNZ,
-                             __global const T *rPtr,
-                             const KParam rhs,
-                             const int reverse)
-{
+__kernel void sparse_arith_csr_kernel(__global T *oPtr, const KParam out,
+                                      __global const T *values,
+                                      __global const int *rowIdx,
+                                      __global const int *colIdx, const int nNZ,
+                                      __global const T *rPtr, const KParam rhs,
+                                      const int reverse) {
     const int row = get_group_id(0) * get_local_size(1) + get_local_id(1);
 
-    if(row >= out.dims[0]) return;
+    if (row >= out.dims[0]) return;
 
-    const int rowStartIdx = rowIdx[row  ];
-    const int rowEndIdx   = rowIdx[row+1];
+    const int rowStartIdx = rowIdx[row];
+    const int rowEndIdx   = rowIdx[row + 1];
 
     // Repeat loop until all values in the row are computed
-    for(int idx = rowStartIdx + get_local_id(0); idx < rowEndIdx; idx += get_local_size(0)) {
+    for (int idx = rowStartIdx + get_local_id(0); idx < rowEndIdx;
+         idx += get_local_size(0)) {
         const int col = colIdx[idx];
 
-        if(row >= out.dims[0] || col >= out.dims[1]) continue;    // Bad indices
+        if (row >= out.dims[0] || col >= out.dims[1]) continue;  // Bad indices
 
         // Get Values
         const T val  = values[idx];
         const T rval = rPtr[col * rhs.strides[1] + row];
 
         const int offset = col * out.strides[1] + row;
-        if(reverse) oPtr[offset] = OP(rval, val);
-        else        oPtr[offset] = OP(val, rval);
+        if (reverse)
+            oPtr[offset] = OP(rval, val);
+        else
+            oPtr[offset] = OP(val, rval);
     }
 }
 
-__kernel
-void sparse_arith_csr_kernel_S(__global T *values,
-                               __global int *rowIdx,
-                               __global int *colIdx,
-                               const int nNZ,
-                               __global const T *rPtr,
-                               const KParam rhs,
-                               const int reverse)
-{
+__kernel void sparse_arith_csr_kernel_S(__global T *values,
+                                        __global int *rowIdx,
+                                        __global int *colIdx, const int nNZ,
+                                        __global const T *rPtr,
+                                        const KParam rhs, const int reverse) {
     const int row = get_group_id(0) * get_local_size(1) + get_local_id(1);
 
-    if(row >= rhs.dims[0]) return;
+    if (row >= rhs.dims[0]) return;
 
-    const int rowStartIdx = rowIdx[row  ];
-    const int rowEndIdx   = rowIdx[row+1];
+    const int rowStartIdx = rowIdx[row];
+    const int rowEndIdx   = rowIdx[row + 1];
 
     // Repeat loop until all values in the row are computed
-    for(int idx = rowStartIdx + get_local_id(0); idx < rowEndIdx; idx += get_local_size(0)) {
+    for (int idx = rowStartIdx + get_local_id(0); idx < rowEndIdx;
+         idx += get_local_size(0)) {
         const int col = colIdx[idx];
 
-        if(row >= rhs.dims[0] || col >= rhs.dims[1]) continue;    // Bad indices
+        if (row >= rhs.dims[0] || col >= rhs.dims[1]) continue;  // Bad indices
 
         // Get Values
         const T val  = values[idx];
         const T rval = rPtr[col * rhs.strides[1] + row];
 
-        if(reverse) values[idx] = OP(rval, val);
-        else        values[idx] = OP(val, rval);
+        if (reverse)
+            values[idx] = OP(rval, val);
+        else
+            values[idx] = OP(val, rval);
     }
 }
diff --git a/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl b/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl
index 6b162cb239..f395a1807d 100644
--- a/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl
+++ b/src/backend/opencl/kernel/ssarith_calc_out_nnz.cl
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-kernel
-void csr_calc_out_nnz(global unsigned* nnzc,
-                      global int* oRowIdx, uint M,
-                      global const int *lRowIdx, global const int *lColIdx,
-                      global const int *rRowIdx, global const int *rColIdx,
-                      local uint* blkNnz)
-{
-    const uint row  = get_global_id(0);
-    const uint tid  = get_local_id(0);
+kernel void csr_calc_out_nnz(global unsigned *nnzc, global int *oRowIdx, uint M,
+                             global const int *lRowIdx,
+                             global const int *lColIdx,
+                             global const int *rRowIdx,
+                             global const int *rColIdx, local uint *blkNnz) {
+    const uint row = get_global_id(0);
+    const uint tid = get_local_id(0);
 
     const bool valid = row < M;
 
-    const uint lEnd = (valid ? lRowIdx[row+1] : 0);
-    const uint rEnd = (valid ? rRowIdx[row+1] : 0);
+    const uint lEnd = (valid ? lRowIdx[row + 1] : 0);
+    const uint rEnd = (valid ? rRowIdx[row + 1] : 0);
 
     blkNnz[tid] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    uint l = (valid ? lRowIdx[row] : 0);
-    uint r = (valid ? rRowIdx[row] : 0);
+    uint l   = (valid ? lRowIdx[row] : 0);
+    uint r   = (valid ? rRowIdx[row] : 0);
     uint nnz = 0;
     while (l < lEnd && r < rEnd) {
         uint lci = lColIdx[l];
@@ -35,19 +33,16 @@ void csr_calc_out_nnz(global unsigned* nnzc,
         r += (lci >= rci);
         nnz++;
     }
-    nnz += (lEnd-l);
-    nnz += (rEnd-r);
+    nnz += (lEnd - l);
+    nnz += (rEnd - r);
 
     blkNnz[tid] = nnz;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (valid)
-        oRowIdx[row+1] = nnz;
+    if (valid) oRowIdx[row + 1] = nnz;
 
-    for(uint s=get_local_size(0)/2; s>0; s>>=1) {
-        if (tid < s) {
-            blkNnz[tid] += blkNnz[tid + s];
-        }
+    for (uint s = get_local_size(0) / 2; s > 0; s >>= 1) {
+        if (tid < s) { blkNnz[tid] += blkNnz[tid + s]; }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
diff --git a/src/backend/opencl/kernel/susan.cl b/src/backend/opencl/kernel/susan.cl
index 97dc8d652d..fa4e9f892d 100644
--- a/src/backend/opencl/kernel/susan.cl
+++ b/src/backend/opencl/kernel/susan.cl
@@ -7,21 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define MAX_VAL(A,B) (A) < (B) ? (B) : (A)
+#define MAX_VAL(A, B) (A) < (B) ? (B) : (A)
 
 #ifdef RESPONSE
-kernel
-void susan_responses(global T* out, global const T* in_,
-                     const unsigned in_off,
-                     const unsigned idim0, const unsigned idim1,
-                     const float t, const float g,
-                     const unsigned edge)
-{
+kernel void susan_responses(global T* out, global const T* in_,
+                            const unsigned in_off, const unsigned idim0,
+                            const unsigned idim1, const float t, const float g,
+                            const unsigned edge) {
     global const T* in = in_ + in_off;
 
-    const int rSqrd   = RADIUS*RADIUS;
-    const int windLen = 2*RADIUS+1;
-    const int shrdLen = BLOCK_X + windLen-1;
+    const int rSqrd   = RADIUS * RADIUS;
+    const int windLen = 2 * RADIUS + 1;
+    const int shrdLen = BLOCK_X + windLen - 1;
     local T localMem[LOCAL_MEM_SIZE];
 
     const unsigned lx = get_local_id(0);
@@ -29,43 +26,43 @@ void susan_responses(global T* out, global const T* in_,
     const unsigned gx = get_global_id(0) + edge;
     const unsigned gy = get_global_id(1) + edge;
 
-    const unsigned nucleusIdx = (ly+RADIUS)*shrdLen + lx+RADIUS;
-    if (gx<idim0 && gy<idim1)
-        localMem[nucleusIdx] = in[gy*idim0+gx];
+    const unsigned nucleusIdx = (ly + RADIUS) * shrdLen + lx + RADIUS;
+    if (gx < idim0 && gy < idim1)
+        localMem[nucleusIdx] = in[gy * idim0 + gx];
     else
         localMem[nucleusIdx] = 0;
     T m_0 = localMem[nucleusIdx];
 
 #pragma unroll
-    for (int b=ly, gy2=gy; b<shrdLen; b+=BLOCK_Y, gy2+=BLOCK_Y) {
-        int j = gy2-RADIUS;
+    for (int b = ly, gy2 = gy; b < shrdLen; b += BLOCK_Y, gy2 += BLOCK_Y) {
+        int j = gy2 - RADIUS;
 #pragma unroll
-        for (int a=lx, gx2=gx; a<shrdLen; a+=BLOCK_X, gx2+=BLOCK_X) {
-            int i = gx2-RADIUS;
-            if (i<idim0 && j<idim1)
-                localMem[b*shrdLen+a] = in[i+idim0*j];
+        for (int a = lx, gx2 = gx; a < shrdLen; a += BLOCK_X, gx2 += BLOCK_X) {
+            int i = gx2 - RADIUS;
+            if (i < idim0 && j < idim1)
+                localMem[b * shrdLen + a] = in[i + idim0 * j];
             else
-                localMem[b*shrdLen+a] = m_0;
+                localMem[b * shrdLen + a] = m_0;
         }
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (gx < idim0 - edge && gy < idim1 - edge) {
         unsigned idx = gx + idim0 * gy;
-        float nM  = 0.0f;
+        float nM     = 0.0f;
 #pragma unroll
-        for (int p=0; p<windLen; ++p) {
+        for (int p = 0; p < windLen; ++p) {
 #pragma unroll
-            for (int q=0; q<windLen; ++q) {
+            for (int q = 0; q < windLen; ++q) {
                 int i = p - RADIUS;
                 int j = q - RADIUS;
                 int a = lx + RADIUS + i;
                 int b = ly + RADIUS + j;
-                if (i*i + j*j < rSqrd) {
-                    float c = m_0;
-                    float m = localMem[b * shrdLen + a];
-                    float exp_pow = pow((m - c)/t, 6.0f);
-                    float cM = exp(-exp_pow);
+                if (i * i + j * j < rSqrd) {
+                    float c       = m_0;
+                    float m       = localMem[b * shrdLen + a];
+                    float exp_pow = pow((m - c) / t, 6.0f);
+                    float cM      = exp(-exp_pow);
                     nM += cM;
                 }
             }
@@ -76,12 +73,11 @@ void susan_responses(global T* out, global const T* in_,
 #endif
 
 #ifdef NONMAX
-kernel
-void non_maximal(global float* x_out, global float* y_out,
-                 global float* resp_out, global unsigned* count,
-                 const unsigned idim0, const unsigned idim1, global const T* resp_in,
-                 const unsigned edge, const unsigned max_corners)
-{
+kernel void non_maximal(global float* x_out, global float* y_out,
+                        global float* resp_out, global unsigned* count,
+                        const unsigned idim0, const unsigned idim1,
+                        global const T* resp_in, const unsigned edge,
+                        const unsigned max_corners) {
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = edge + 1;
 
@@ -93,13 +89,14 @@ void non_maximal(global float* x_out, global float* y_out,
 
         // Find maximum neighborhood response
         T max_v;
-        max_v = MAX_VAL(resp_in[(gy-1) * idim0 + gx-1], resp_in[gy * idim0 + gx-1]);
-        max_v = MAX_VAL(max_v, resp_in[(gy+1) * idim0 + gx-1]);
-        max_v = MAX_VAL(max_v, resp_in[(gy-1) * idim0 + gx  ]);
-        max_v = MAX_VAL(max_v, resp_in[(gy+1) * idim0 + gx  ]);
-        max_v = MAX_VAL(max_v, resp_in[(gy-1) * idim0 + gx+1]);
-        max_v = MAX_VAL(max_v, resp_in[(gy)   * idim0 + gx+1]);
-        max_v = MAX_VAL(max_v, resp_in[(gy+1) * idim0 + gx+1]);
+        max_v = MAX_VAL(resp_in[(gy - 1) * idim0 + gx - 1],
+                        resp_in[gy * idim0 + gx - 1]);
+        max_v = MAX_VAL(max_v, resp_in[(gy + 1) * idim0 + gx - 1]);
+        max_v = MAX_VAL(max_v, resp_in[(gy - 1) * idim0 + gx]);
+        max_v = MAX_VAL(max_v, resp_in[(gy + 1) * idim0 + gx]);
+        max_v = MAX_VAL(max_v, resp_in[(gy - 1) * idim0 + gx + 1]);
+        max_v = MAX_VAL(max_v, resp_in[(gy)*idim0 + gx + 1]);
+        max_v = MAX_VAL(max_v, resp_in[(gy + 1) * idim0 + gx + 1]);
 
         // Stores corner to {x,y,resp}_out if it's response is maximum compared
         // to its 8-neighborhood and greater or equal minimum response
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index b78f573021..96105f1ca4 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -7,58 +7,55 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <program.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/susan.hpp>
 #include <memory.hpp>
-#include <cache.hpp>
+#include <program.hpp>
+#include <af/defines.h>
 #include "config.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::LocalSpaceArg;
 using cl::NDRange;
+using cl::Program;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const unsigned THREADS_PER_BLOCK = 256;
-static const unsigned SUSAN_THREADS_X = 16;
-static const unsigned SUSAN_THREADS_Y = 16;
+static const unsigned SUSAN_THREADS_X   = 16;
+static const unsigned SUSAN_THREADS_Y   = 16;
 
 template<typename T, unsigned radius>
-void susan(cl::Buffer* out, const cl::Buffer* in,
-           const unsigned in_off,
-           const unsigned idim0, const unsigned idim1,
-           const float t, const float g, const unsigned edge)
-{
+void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
+           const unsigned idim0, const unsigned idim1, const float t,
+           const float g, const unsigned edge) {
     std::string refName = std::string("susan_responses_") +
-        std::string(dtype_traits<T>::getName()) + std::to_string(radius);
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(radius);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
-        const size_t LOCAL_MEM_SIZE = (SUSAN_THREADS_X+2*radius)*(SUSAN_THREADS_Y+2*radius);
+    if (entry.prog == 0 && entry.ker == 0) {
+        const size_t LOCAL_MEM_SIZE =
+            (SUSAN_THREADS_X + 2 * radius) * (SUSAN_THREADS_Y + 2 * radius);
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D LOCAL_MEM_SIZE=" << LOCAL_MEM_SIZE
-                << " -D BLOCK_X="<< SUSAN_THREADS_X
-                << " -D BLOCK_Y="<< SUSAN_THREADS_Y
-                << " -D RADIUS="<< radius
+                << " -D BLOCK_X=" << SUSAN_THREADS_X
+                << " -D BLOCK_Y=" << SUSAN_THREADS_Y << " -D RADIUS=" << radius
                 << " -D RESPONSE";
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {susan_cl};
-        const int   ker_lens[] = {susan_cl_len};
+        const int ker_lens[]   = {susan_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -67,35 +64,38 @@ void susan(cl::Buffer* out, const cl::Buffer* in,
         addKernelToCache(device, refName, entry);
     }
 
-    auto susanOp = KernelFunctor< Buffer, Buffer, unsigned, unsigned, unsigned,
-                                  float, float, unsigned >(*entry.ker);
+    auto susanOp = KernelFunctor<Buffer, Buffer, unsigned, unsigned, unsigned,
+                                 float, float, unsigned>(*entry.ker);
 
     NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-    NDRange global(divup(idim0-2*edge, local[0])*local[0], divup(idim1-2*edge, local[1])*local[1]);
+    NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
+                   divup(idim1 - 2 * edge, local[1]) * local[1]);
 
-    susanOp(EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0, idim1, t, g, edge);
+    susanOp(EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0,
+            idim1, t, g, edge);
 }
 
 template<typename T>
 unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
-                    const unsigned idim0, const unsigned idim1, const cl::Buffer* resp_in,
-                    const unsigned edge, const unsigned max_corners)
-{
+                    const unsigned idim0, const unsigned idim1,
+                    const cl::Buffer* resp_in, const unsigned edge,
+                    const unsigned max_corners) {
     unsigned corners_found = 0;
 
-    std::string refName = std::string("non_maximal_") + std::string(dtype_traits<T>::getName());
+    std::string refName =
+        std::string("non_maximal_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName() << " -D NONMAX";
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {susan_cl};
-        const int   ker_lens[] = {susan_cl_len};
+        const int ker_lens[]   = {susan_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -104,23 +104,27 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
         addKernelToCache(device, refName, entry);
     }
 
-    cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    cl::Buffer* d_corners_found = bufferAlloc(sizeof(unsigned));
+    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0,
+                                  sizeof(unsigned), &corners_found);
 
-    auto nonMaximalOp = KernelFunctor< Buffer, Buffer, Buffer, Buffer, unsigned, unsigned, Buffer,
-                                       unsigned, unsigned >(*entry.ker);
+    auto nonMaximalOp =
+        KernelFunctor<Buffer, Buffer, Buffer, Buffer, unsigned, unsigned,
+                      Buffer, unsigned, unsigned>(*entry.ker);
 
     NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-    NDRange global(divup(idim0-2*edge, local[0])*local[0], divup(idim1-2*edge, local[1])*local[1]);
+    NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
+                   divup(idim1 - 2 * edge, local[1]) * local[1]);
 
-    nonMaximalOp(EnqueueArgs(getQueue(), global, local),
-                 *x_out, *y_out, *resp_out, *d_corners_found,
-                 idim0, idim1, *resp_in, edge, max_corners);
+    nonMaximalOp(EnqueueArgs(getQueue(), global, local), *x_out, *y_out,
+                 *resp_out, *d_corners_found, idim0, idim1, *resp_in, edge,
+                 max_corners);
 
-    getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned), &corners_found);
+    getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned),
+                                 &corners_found);
     bufferFree(d_corners_found);
 
     return corners_found;
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/swapdblk.cl b/src/backend/opencl/kernel/swapdblk.cl
index c1bbf87dd8..f4be35a9b8 100644
--- a/src/backend/opencl/kernel/swapdblk.cl
+++ b/src/backend/opencl/kernel/swapdblk.cl
@@ -29,31 +29,29 @@
  * * Redistributions  of  source  code  must  retain  the above copyright
  *   notice,  this  list  of  conditions  and  the  following  disclaimer.
  * * Redistributions  in  binary  form must reproduce the above copyright
- *   notice,  this list of conditions and the following disclaimer in the 
+ *   notice,  this list of conditions and the following disclaimer in the
  *   documentation  and/or other materials provided with the distribution.
- * * Neither  the  name of the University of Tennessee, Knoxville nor the 
+ * * Neither  the  name of the University of Tennessee, Knoxville nor the
  *   names of its contributors may be used to endorse or promote products
  *   derived from this software without specific prior written permission.
  *
  * THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
- * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+ * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  * A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  * HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT 
+ * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
  * LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 
+ * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  * THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+ * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  * OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *
  **********************************************************************/
 
-__kernel void
-swapdblk(int nb,
-         __global T *dA, unsigned long dA_offset, int ldda, int inca,
-         __global T *dB, unsigned long dB_offset, int lddb, int incb)
-{
+__kernel void swapdblk(int nb, __global T *dA, unsigned long dA_offset,
+                       int ldda, int inca, __global T *dB,
+                       unsigned long dB_offset, int lddb, int incb) {
     const int tx = get_local_id(0);
     const int bx = get_group_id(0);
 
@@ -62,10 +60,10 @@ swapdblk(int nb,
 
     T tmp;
 
-    #pragma unroll
-    for( int i = 0; i < nb; i++ ){
-        tmp        = dA[i*ldda];
-        dA[i*ldda] = dB[i*lddb];
-        dB[i*lddb] = tmp;
+#pragma unroll
+    for (int i = 0; i < nb; i++) {
+        tmp          = dA[i * ldda];
+        dA[i * ldda] = dB[i * lddb];
+        dB[i * lddb] = tmp;
     }
 }
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index a653b47b17..b396423371 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -8,40 +8,37 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/swapdblk.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/swapdblk.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 template<typename T>
-void swapdblk(int n, int nb,
-              cl_mem dA, size_t dA_offset, int ldda, int inca,
+void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
               cl_mem dB, size_t dB_offset, int lddb, int incb,
-              cl_command_queue queue)
-{
-    std::string refName = std::string("swapdblk_") + std::string(dtype_traits<T>::getName());
+              cl_command_queue queue) {
+    std::string refName =
+        std::string("swapdblk_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
@@ -49,7 +46,7 @@ void swapdblk(int n, int nb,
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {swapdblk_cl};
-        const int   ker_lens[] = {swapdblk_cl_len};
+        const int ker_lens[]   = {swapdblk_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -60,19 +57,18 @@ void swapdblk(int n, int nb,
 
     int nblocks = n / nb;
 
-    if(nblocks == 0)
-        return;
+    if (nblocks == 0) return;
 
     int info = 0;
     if (n < 0) {
         info = -1;
     } else if (nb < 1 || nb > 1024) {
         info = -2;
-    } else if (ldda < (nblocks-1)*nb*inca + nb) {
+    } else if (ldda < (nblocks - 1) * nb * inca + nb) {
         info = -4;
     } else if (inca < 0) {
         info = -5;
-    } else if (lddb < (nblocks-1)*nb*incb + nb) {
+    } else if (lddb < (nblocks - 1) * nb * incb + nb) {
         info = -7;
     } else if (incb < 0) {
         info = -8;
@@ -89,12 +85,13 @@ void swapdblk(int n, int nb,
     cl::Buffer dAObj(dA, true);
     cl::Buffer dBObj(dB, true);
 
-    auto swapdOp = KernelFunctor<int, Buffer, unsigned long long, int, int,
-                               Buffer, unsigned long long, int, int>(*entry.ker);
+    auto swapdOp =
+        KernelFunctor<int, Buffer, unsigned long long, int, int, Buffer,
+                      unsigned long long, int, int>(*entry.ker);
 
     cl::CommandQueue q(queue);
-    swapdOp(EnqueueArgs(q, global, local),
-            nb, dAObj, dA_offset, ldda, inca, dBObj, dB_offset, lddb, incb);
-}
-}
+    swapdOp(EnqueueArgs(q, global, local), nb, dAObj, dA_offset, ldda, inca,
+            dBObj, dB_offset, lddb, incb);
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/tile.cl b/src/backend/opencl/kernel/tile.cl
index 37fbe63de2..3ecf2a1396 100644
--- a/src/backend/opencl/kernel/tile.cl
+++ b/src/backend/opencl/kernel/tile.cl
@@ -7,10 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void tile_kernel(__global T *out, __global const T *in, const KParam op, const KParam ip,
-                 const int blocksPerMatX, const int blocksPerMatY)
-{
+__kernel void tile_kernel(__global T *out, __global const T *in,
+                          const KParam op, const KParam ip,
+                          const int blocksPerMatX, const int blocksPerMatY) {
     const int oz = get_group_id(0) / blocksPerMatX;
     const int ow = get_group_id(1) / blocksPerMatY;
 
@@ -20,23 +19,21 @@ void tile_kernel(__global T *out, __global const T *in, const KParam op, const K
     const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
     const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
 
-    if(xx >= op.dims[0] ||
-       yy >= op.dims[1] ||
-       oz >= op.dims[2] ||
-       ow >= op.dims[3])
+    if (xx >= op.dims[0] || yy >= op.dims[1] || oz >= op.dims[2] ||
+        ow >= op.dims[3])
         return;
 
-    const int iz = oz % ip.dims[2];
-    const int iw = ow % ip.dims[3];
+    const int iz  = oz % ip.dims[2];
+    const int iw  = ow % ip.dims[3];
     const int izw = iw * ip.strides[3] + iz * ip.strides[2];
     const int ozw = ow * op.strides[3] + oz * op.strides[2];
 
     const int incy = blocksPerMatY * get_local_size(1);
     const int incx = blocksPerMatX * get_local_size(0);
 
-    for(int oy = yy; oy < op.dims[1]; oy += incy) {
+    for (int oy = yy; oy < op.dims[1]; oy += incy) {
         const int iy = oy % ip.dims[1];
-        for(int ox = xx; ox < op.dims[0]; ox += incx) {
+        for (int ox = xx; ox < op.dims[0]; ox += incx) {
             const int ix = ox % ip.dims[0];
 
             int iMem = izw + iy * ip.strides[1] + ix;
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index 66fe5c88c4..d0e8467d26 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -8,49 +8,47 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/tile.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
-#include <cache.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
-static const int TX = 32;
-static const int TY = 8;
+static const int TX    = 32;
+static const int TY    = 8;
 static const int TILEX = 512;
 static const int TILEY = 32;
 
 template<typename T>
-void tile(Param out, const Param in)
-{
-    std::string refName = std::string("tile_kernel_") + std::string(dtype_traits<T>::getName());
+void tile(Param out, const Param in) {
+    std::string refName =
+        std::string("tile_kernel_") + std::string(dtype_traits<T>::getName());
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {tile_cl};
-        const int   ker_lens[] = {tile_cl_len};
+        const int ker_lens[]   = {tile_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -59,8 +57,8 @@ void tile(Param out, const Param in)
         addKernelToCache(device, refName, entry);
     }
 
-    auto tileOp = KernelFunctor< Buffer, const Buffer, const KParam, const KParam,
-                                 const int, const int> (*entry.ker);
+    auto tileOp = KernelFunctor<Buffer, const Buffer, const KParam,
+                                const KParam, const int, const int>(*entry.ker);
 
     NDRange local(TX, TY, 1);
 
@@ -69,10 +67,10 @@ void tile(Param out, const Param in)
     NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                    local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-    tileOp(EnqueueArgs(getQueue(), global, local),
-            *out.data, *in.data, out.info, in.info, blocksPerMatX, blocksPerMatY);
+    tileOp(EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
+           out.info, in.info, blocksPerMatX, blocksPerMatY);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
index 4cec56d7d5..a72bfd554e 100644
--- a/src/backend/opencl/kernel/trace_edge.cl
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -12,50 +12,55 @@ __constant int WEAK   = 2;
 __constant int NOEDGE = 0;
 
 #if defined(INIT_EDGE_OUT)
-__kernel
-void initEdgeOutKernel(__global T*       output, KParam oInfo,
-                       __global const T* strong, KParam sInfo,
-                       __global const T*   weak, KParam wInfo,
-                       unsigned nBBS0, unsigned nBBS1)
-{
+__kernel void initEdgeOutKernel(__global T* output, KParam oInfo,
+                                __global const T* strong, KParam sInfo,
+                                __global const T* weak, KParam wInfo,
+                                unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = get_group_id(0) / nBBS0;
     const unsigned b3 = get_group_id(1) / nBBS1;
 
     // global indices
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + get_local_id(0);
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + get_local_id(1);
+    const int gx =
+        get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + get_local_id(0);
+    const int gy =
+        get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + get_local_id(1);
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    __global const T* wPtr = weak   +
-        (b2 * wInfo.strides[2] + b3 * wInfo.strides[3] + wInfo.offset) + wInfo.strides[1] + 1;
-
-    __global const T* sPtr = strong +
-        (b2 * sInfo.strides[2] + b3 * sInfo.strides[3] + sInfo.offset) + sInfo.strides[1] + 1;
-
-    __global       T* oPtr = output +
-        (b2 * oInfo.strides[2] + b3 * oInfo.strides[3] + oInfo.offset) + oInfo.strides[1] + 1;
-
-    if (gx<(oInfo.dims[0]-2) && gy<(oInfo.dims[1]-2))
-    {
-        int idx   = gx*oInfo.strides[0] + gy*oInfo.strides[1];
+    __global const T* wPtr =
+        weak + (b2 * wInfo.strides[2] + b3 * wInfo.strides[3] + wInfo.offset) +
+        wInfo.strides[1] + 1;
+
+    __global const T* sPtr =
+        strong +
+        (b2 * sInfo.strides[2] + b3 * sInfo.strides[3] + sInfo.offset) +
+        sInfo.strides[1] + 1;
+
+    __global T* oPtr =
+        output +
+        (b2 * oInfo.strides[2] + b3 * oInfo.strides[3] + oInfo.offset) +
+        oInfo.strides[1] + 1;
+
+    if (gx < (oInfo.dims[0] - 2) && gy < (oInfo.dims[1] - 2)) {
+        int idx   = gx * oInfo.strides[0] + gy * oInfo.strides[1];
         oPtr[idx] = (sPtr[idx] > 0 ? STRONG : (wPtr[idx] > 0 ? WEAK : NOEDGE));
     }
 }
 #endif
 
-#define VALID_BLOCK_IDX(j, i) ( (j)>0 && (j)<(SHRD_MEM_HEIGHT-1) && (i)>0 && (i)<(SHRD_MEM_WIDTH-1) )
+#define VALID_BLOCK_IDX(j, i)                             \
+    ((j) > 0 && (j) < (SHRD_MEM_HEIGHT - 1) && (i) > 0 && \
+     (i) < (SHRD_MEM_WIDTH - 1))
 
 #if defined(EDGE_TRACER)
-__kernel
-void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned nBBS1,
-                    __global volatile int* hasChanged)
-{
+__kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
+                              unsigned nBBS1,
+                              __global volatile int* hasChanged) {
     // shared memory with 1 pixel border
     // strong and weak images are binary(char) images thus,
     // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
-    __local int outMem  [ SHRD_MEM_HEIGHT ] [ SHRD_MEM_WIDTH ];
+    __local int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
     __local int predicates[TOTAL_NUM_THREADS];
 
     // local thread indices
@@ -67,24 +72,27 @@ void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned
     const unsigned b3 = get_group_id(1) / nBBS1;
 
     // global indices
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + lx;
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + ly;
+    const int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
+    const int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    __global T* oPtr = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) + oInfo.strides[1] + 1;
+    __global T* oPtr = output +
+                       (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
+                       oInfo.strides[1] + 1;
 
     // pull image to local memory
 #pragma unroll
-    for (int b=ly, gy2=gy; b<SHRD_MEM_HEIGHT; b+=get_local_size(1), gy2+=get_local_size(1))
-    {
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
 #pragma unroll
-        for (int a=lx, gx2=gx; a<SHRD_MEM_WIDTH; a+=get_local_size(0), gx2+=get_local_size(0))
-        {
-            int x = gx2-1;
-            int y = gy2-1;
-            if (x>=0 && x<oInfo.dims[0] && y>=0 && y<oInfo.dims[1])
-                outMem[b][a] = oPtr[ x*oInfo.strides[0] + y*oInfo.strides[1] ];
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            int x = gx2 - 1;
+            int y = gy2 - 1;
+            if (x >= 0 && x < oInfo.dims[0] && y >= 0 && y < oInfo.dims[1])
+                outMem[b][a] =
+                    oPtr[x * oInfo.strides[0] + y * oInfo.strides[1]];
             else
                 outMem[b][a] = NOEDGE;
         }
@@ -99,47 +107,50 @@ void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned
 
     int continueIter = 1;
 
-    while (continueIter)
-    {
+    while (continueIter) {
         int cu = outMem[j][i];
-        int nw = outMem[j-1][i-1];
-        int no = outMem[j-1][i  ];
-        int ne = outMem[j-1][i+1];
-        int ea = outMem[j  ][i+1];
-        int se = outMem[j+1][i+1];
-        int so = outMem[j+1][i  ];
-        int sw = outMem[j+1][i-1];
-        int we = outMem[j  ][i-1];
+        int nw = outMem[j - 1][i - 1];
+        int no = outMem[j - 1][i];
+        int ne = outMem[j - 1][i + 1];
+        int ea = outMem[j][i + 1];
+        int se = outMem[j + 1][i + 1];
+        int so = outMem[j + 1][i];
+        int sw = outMem[j + 1][i - 1];
+        int we = outMem[j][i - 1];
 
-        bool hasStrongNeighbour = nw==STRONG || no==STRONG || ne==STRONG || ea==STRONG ||
-                                  se==STRONG || so==STRONG || sw==STRONG || we==STRONG;
+        bool hasStrongNeighbour =
+            nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
+            se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
 
-        if (cu==WEAK && hasStrongNeighbour)
-            outMem[j][i] = STRONG;
+        if (cu == WEAK && hasStrongNeighbour) outMem[j][i] = STRONG;
 
         barrier(CLK_LOCAL_MEM_FENCE);
 
         cu = outMem[j][i];
 
-        bool _nw = outMem[j-1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i-1);
-        bool _no = outMem[j-1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j-1, i  );
-        bool _ne = outMem[j-1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j-1, i+1);
-        bool _ea = outMem[j  ][i+1] == WEAK  &&  VALID_BLOCK_IDX(j  , i+1);
-        bool _se = outMem[j+1][i+1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i+1);
-        bool _so = outMem[j+1][i  ] == WEAK  &&  VALID_BLOCK_IDX(j+1, i  );
-        bool _sw = outMem[j+1][i-1] == WEAK  &&  VALID_BLOCK_IDX(j+1, i-1);
-        bool _we = outMem[j  ][i-1] == WEAK  &&  VALID_BLOCK_IDX(j  , i-1);
-
-        bool hasWeakNeighbour = _nw || _no || _ne || _ea || _se || _so || _sw || _we;
+        bool _nw =
+            outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
+        bool _no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+        bool _ne =
+            outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
+        bool _ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+        bool _se =
+            outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
+        bool _so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+        bool _sw =
+            outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
+        bool _we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+
+        bool hasWeakNeighbour =
+            _nw || _no || _ne || _ea || _se || _so || _sw || _we;
 
         // Following Block is equivalent of __syncthreads_or in CUDA
-        predicates[tid] = cu==STRONG && hasWeakNeighbour;
+        predicates[tid] = cu == STRONG && hasWeakNeighbour;
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        for (int nt = TOTAL_NUM_THREADS/2;  nt>0; nt>>=1)
-        {
+        for (int nt = TOTAL_NUM_THREADS / 2; nt > 0; nt >>= 1) {
             if (tid < nt)
-                predicates[tid] = predicates[tid] || predicates[tid+nt];
+                predicates[tid] = predicates[tid] || predicates[tid + nt];
             barrier(CLK_LOCAL_MEM_FENCE);
         }
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -151,63 +162,62 @@ void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned
     // has weak pixels with strong candidates
     // within the main region, then increment hasChanged.
     int cu = outMem[j][i];
-    int nw = outMem[j-1][i-1];
-    int no = outMem[j-1][i  ];
-    int ne = outMem[j-1][i+1];
-    int ea = outMem[j  ][i+1];
-    int se = outMem[j+1][i+1];
-    int so = outMem[j+1][i  ];
-    int sw = outMem[j+1][i-1];
-    int we = outMem[j  ][i-1];
-
-    bool hasWeakNeighbour = nw==WEAK || no==WEAK || ne==WEAK || ea==WEAK ||
-                            se==WEAK || so==WEAK || sw==WEAK || we==WEAK;
+    int nw = outMem[j - 1][i - 1];
+    int no = outMem[j - 1][i];
+    int ne = outMem[j - 1][i + 1];
+    int ea = outMem[j][i + 1];
+    int se = outMem[j + 1][i + 1];
+    int so = outMem[j + 1][i];
+    int sw = outMem[j + 1][i - 1];
+    int we = outMem[j][i - 1];
+
+    bool hasWeakNeighbour = nw == WEAK || no == WEAK || ne == WEAK ||
+                            ea == WEAK || se == WEAK || so == WEAK ||
+                            sw == WEAK || we == WEAK;
 
     // Following Block is equivalent of __syncthreads_or in CUDA
-    predicates[tid] = cu==STRONG && hasWeakNeighbour;
+    predicates[tid] = cu == STRONG && hasWeakNeighbour;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    for (int nt = TOTAL_NUM_THREADS/2;  nt>0; nt>>=1)
-    {
-        if (tid < nt)
-            predicates[tid] = predicates[tid] || predicates[tid+nt];
+    for (int nt = TOTAL_NUM_THREADS / 2; nt > 0; nt >>= 1) {
+        if (tid < nt) predicates[tid] = predicates[tid] || predicates[tid + nt];
         barrier(CLK_LOCAL_MEM_FENCE);
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
     continueIter = predicates[0];
 
-    if (continueIter>0 && lx==0 && ly==0)
-        atomic_add(hasChanged, 1);
+    if (continueIter > 0 && lx == 0 && ly == 0) atomic_add(hasChanged, 1);
 
     // Update output with shared memory result
-    if (gx<(oInfo.dims[0]-2) && gy<(oInfo.dims[1]-2))
-        oPtr[ gx*oInfo.strides[0] + gy*oInfo.strides[1] ] = outMem[j][i];
+    if (gx < (oInfo.dims[0] - 2) && gy < (oInfo.dims[1] - 2))
+        oPtr[gx * oInfo.strides[0] + gy * oInfo.strides[1]] = outMem[j][i];
 }
 #endif
 
 #if defined(SUPPRESS_LEFT_OVER)
-__kernel
-void suppressLeftOverKernel(__global T* output, KParam oInfo, unsigned nBBS0, unsigned nBBS1)
-{
+__kernel void suppressLeftOverKernel(__global T* output, KParam oInfo,
+                                     unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = get_group_id(0) / nBBS0;
     const unsigned b3 = get_group_id(1) / nBBS1;
 
     // global indices
-    const int gx = get_local_size(0) * (get_group_id(0)-b2*nBBS0) + get_local_id(0);
-    const int gy = get_local_size(1) * (get_group_id(1)-b3*nBBS1) + get_local_id(1);
+    const int gx =
+        get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + get_local_id(0);
+    const int gy =
+        get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + get_local_id(1);
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    __global T* oPtr = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) + oInfo.strides[1] + 1;
+    __global T* oPtr = output +
+                       (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
+                       oInfo.strides[1] + 1;
 
-    if (gx<(oInfo.dims[0]-2) && gy<(oInfo.dims[1]-2))
-    {
-        int idx = gx*oInfo.strides[0]+gy*oInfo.strides[1];
+    if (gx < (oInfo.dims[0] - 2) && gy < (oInfo.dims[1] - 2)) {
+        int idx = gx * oInfo.strides[0] + gy * oInfo.strides[1];
         T val   = oPtr[idx];
-        if (val==WEAK)
-            oPtr[idx] = NOEDGE;
+        if (val == WEAK) oPtr[idx] = NOEDGE;
     }
 }
 #endif
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 3f50335ed7..2e4cc7a2a7 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -11,50 +11,51 @@
 #define BILINEAR transform_b
 #define LOWER transform_l
 
-void calc_transf_inverse(float* txo, __global const float* txi)
-{
+void calc_transf_inverse(float *txo, __global const float *txi) {
 #if PERSPECTIVE
-    txo[0] =   txi[4]*txi[8] - txi[5]*txi[7];
-    txo[1] = -(txi[1]*txi[8] - txi[2]*txi[7]);
-    txo[2] =   txi[1]*txi[5] - txi[2]*txi[4];
-
-    txo[3] = -(txi[3]*txi[8] - txi[5]*txi[6]);
-    txo[4] =   txi[0]*txi[8] - txi[2]*txi[6];
-    txo[5] = -(txi[0]*txi[5] - txi[2]*txi[3]);
-
-    txo[6] =   txi[3]*txi[7] - txi[4]*txi[6];
-    txo[7] = -(txi[0]*txi[7] - txi[1]*txi[6]);
-    txo[8] =   txi[0]*txi[4] - txi[1]*txi[3];
-
-    float det = txi[0]*txo[0] + txi[1]*txo[3] + txi[2]*txo[6];
-
-    txo[0] /= det; txo[1] /= det; txo[2] /= det;
-    txo[3] /= det; txo[4] /= det; txo[5] /= det;
-    txo[6] /= det; txo[7] /= det; txo[8] /= det;
+    txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
+    txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
+    txo[2] = txi[1] * txi[5] - txi[2] * txi[4];
+
+    txo[3] = -(txi[3] * txi[8] - txi[5] * txi[6]);
+    txo[4] = txi[0] * txi[8] - txi[2] * txi[6];
+    txo[5] = -(txi[0] * txi[5] - txi[2] * txi[3]);
+
+    txo[6] = txi[3] * txi[7] - txi[4] * txi[6];
+    txo[7] = -(txi[0] * txi[7] - txi[1] * txi[6]);
+    txo[8] = txi[0] * txi[4] - txi[1] * txi[3];
+
+    float det = txi[0] * txo[0] + txi[1] * txo[3] + txi[2] * txo[6];
+
+    txo[0] /= det;
+    txo[1] /= det;
+    txo[2] /= det;
+    txo[3] /= det;
+    txo[4] /= det;
+    txo[5] /= det;
+    txo[6] /= det;
+    txo[7] /= det;
+    txo[8] /= det;
 #else
-    float det = txi[0]*txi[4] - txi[1]*txi[3];
+    float det = txi[0] * txi[4] - txi[1] * txi[3];
 
     txo[0] = txi[4] / det;
     txo[1] = txi[3] / det;
     txo[3] = txi[1] / det;
     txo[4] = txi[0] / det;
 
-    txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-    txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+    txo[2]               = txi[2] * -txo[0] + txi[5] * -txo[1];
+    txo[5]               = txi[2] * -txo[3] + txi[5] * -txo[4];
 #endif
 }
 
-__kernel
-void transform_kernel(__global T *d_out, const KParam out,
-                      __global const T *d_in, const KParam in,
-                      __global const float *c_tmat, const KParam tf,
-                      const int nImg2, const int nImg3,
-                      const int nTfs2, const int nTfs3,
-                      const int batchImg2,
-                      const int blocksXPerImage,
-                      const int blocksYPerImage,
-                      const int method)
-{
+__kernel void transform_kernel(__global T *d_out, const KParam out,
+                               __global const T *d_in, const KParam in,
+                               __global const float *c_tmat, const KParam tf,
+                               const int nImg2, const int nImg3,
+                               const int nTfs2, const int nTfs3,
+                               const int batchImg2, const int blocksXPerImage,
+                               const int blocksYPerImage, const int method) {
     // Image Ids
     const int imgId2 = get_group_id(0) / blocksXPerImage;
     const int imgId3 = get_group_id(1) / blocksYPerImage;
@@ -70,37 +71,37 @@ void transform_kernel(__global T *d_out, const KParam out,
     // Image iteration loop count for image batching
     int limages = min(max((int)(out.dims[2] - imgId2 * nImg2), 1), batchImg2);
 
-    if(xido >= out.dims[0] || yido >= out.dims[1])
-        return;
+    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
 
     // Index of transform
     const int eTfs2 = max((nTfs2 / nImg2), 1);
     const int eTfs3 = max((nTfs3 / nImg3), 1);
 
-    int t_idx3 = -1;    // init
-    int t_idx2 = -1;    // init
+    int t_idx3        = -1;  // init
+    int t_idx2        = -1;  // init
     int t_idx2_offset = 0;
 
     const int blockIdx_z = get_group_id(2);
 
-    if(nTfs3 == 1) {
-        t_idx3 = 0;     // Always 0 as only 1 transform defined
+    if (nTfs3 == 1) {
+        t_idx3 = 0;  // Always 0 as only 1 transform defined
     } else {
-        if(nTfs3 == nImg3) {
-            t_idx3 = imgId3;    // One to one batch with all transforms defined
+        if (nTfs3 == nImg3) {
+            t_idx3 = imgId3;  // One to one batch with all transforms defined
         } else {
-            t_idx3 = blockIdx_z / eTfs2;    // Transform batched, calculate
+            t_idx3        = blockIdx_z / eTfs2;  // Transform batched, calculate
             t_idx2_offset = t_idx3 * nTfs2;
         }
     }
 
-    if(nTfs2 == 1) {
-        t_idx2 = 0;     // Always 0 as only 1 transform defined
+    if (nTfs2 == 1) {
+        t_idx2 = 0;  // Always 0 as only 1 transform defined
     } else {
-        if(nTfs2 == nImg2) {
-            t_idx2 = imgId2;    // One to one batch with all transforms defined
+        if (nTfs2 == nImg2) {
+            t_idx2 = imgId2;  // One to one batch with all transforms defined
         } else {
-            t_idx2 = blockIdx_z - t_idx2_offset;   // Transform batched, calculate
+            t_idx2 =
+                blockIdx_z - t_idx2_offset;  // Transform batched, calculate
         }
     }
 
@@ -109,17 +110,18 @@ void transform_kernel(__global T *d_out, const KParam out,
 
     // Global outoff
     int outoff = out.offset;
-    int inoff = imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3] + in.offset;
-    if(nImg2 == nTfs2 || nImg2 > 1) {   // One-to-One or Image on dim2
-          outoff += imgId2 * batchImg2 * out.strides[2];
-    } else {                            // Transform batched on dim2
-          outoff += t_idx2 * out.strides[2];
+    int inoff =
+        imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3] + in.offset;
+    if (nImg2 == nTfs2 || nImg2 > 1) {  // One-to-One or Image on dim2
+        outoff += imgId2 * batchImg2 * out.strides[2];
+    } else {  // Transform batched on dim2
+        outoff += t_idx2 * out.strides[2];
     }
 
-    if(nImg3 == nTfs3 || nImg3 > 1) {   // One-to-One or Image on dim3
-          outoff += imgId3 * out.strides[3];
-    } else {                            // Transform batched on dim2
-          outoff += t_idx3 * out.strides[3];
+    if (nImg3 == nTfs3 || nImg3 > 1) {  // One-to-One or Image on dim3
+        outoff += imgId3 * out.strides[3];
+    } else {  // Transform batched on dim2
+        outoff += t_idx3 * out.strides[3];
     }
 
     // Transform is in global memory.
@@ -135,10 +137,9 @@ void transform_kernel(__global T *d_out, const KParam out,
 
     // We expect a inverse transform matrix by default
     // If it is an forward transform, then we need its inverse
-    if(INVERSE == 1) {
-        #pragma unroll 3
-        for(int i = 0; i < transf_len; i++)
-            tmat[i] = tmat_ptr[i];
+    if (INVERSE == 1) {
+#pragma unroll 3
+        for (int i = 0; i < transf_len; i++) tmat[i] = tmat_ptr[i];
     } else {
         calc_transf_inverse(tmat, tmat_ptr);
     }
@@ -147,9 +148,9 @@ void transform_kernel(__global T *d_out, const KParam out,
     InterpPosTy yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
 
 #if PERSPECTIVE
-        const InterpPosTy W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-        xidi /= W;
-        yidi /= W;
+    const InterpPosTy W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+    xidi /= W;
+    yidi /= W;
 #endif
     const int loco = outoff + (yido * out.strides[1] + xido);
     // FIXME: Nearest and lower do not do clamping, but other methods do
@@ -157,17 +158,14 @@ void transform_kernel(__global T *d_out, const KParam out,
     bool clamp = INTERP_ORDER != 1;
 
     T zero = ZERO;
-    if (xidi < (InterpPosTy)-0.0001 ||
-        yidi < (InterpPosTy)-0.0001 ||
-        in.dims[0] <= xidi ||
-        in.dims[1] <= yidi) {
-        for(int n = 0; n < limages; n++) {
+    if (xidi < (InterpPosTy)-0.0001 || yidi < (InterpPosTy)-0.0001 ||
+        in.dims[0] <= xidi || in.dims[1] <= yidi) {
+        for (int n = 0; n < limages; n++) {
             d_out[loco + n * out.strides[2]] = zero;
         }
         return;
     }
 
-    interp2(d_out, out, loco,
-             d_in,  in, inoff,
-            xidi, yidi, method, limages, clamp);
+    interp2(d_out, out, loco, d_in, in, inoff, xidi, yidi, method, limages,
+            clamp);
 }
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 92821f0940..9adc9d08ba 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -8,136 +8,118 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/transform.hpp>
 #include <kernel_headers/interp.hpp>
+#include <kernel_headers/transform.hpp>
 
-#include "config.hpp"
-#include "interp.hpp"
 #include <Param.hpp>
 #include <cache.hpp>
-#include <common/dispatch.hpp>
 #include <common/complex.hpp>
+#include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <type_util.hpp>
+#include "config.hpp"
+#include "interp.hpp"
 
 #include <string>
 
-namespace opencl
-{
-    namespace kernel
-    {
-        static const int TX = 16;
-        static const int TY = 16;
-        // Used for batching images
-        static const int TI = 4;
-
-        template<typename T>
-        using wtype_t = typename std::conditional<std::is_same<T, double>::value,
-                                                  double, float>::type;
-
-        template<typename T>
-        using vtype_t = typename std::conditional<common::is_complex<T>::value,
-                                                  T, wtype_t<T>>::type;
-
-
-        template<typename T, int order>
-        void transform(Param out, const Param in,
-                       const Param tf, bool isInverse,
-                       bool isPerspective, af_interp_type method)
-        {
-
-            using BT = typename dtype_traits<T>::base_type;
-
-            std::string ref_name =
-                std::string("transform_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(isInverse) +
-                std::string("_") +
-                std::to_string(isPerspective) +
-                std::string("_") +
-                std::to_string(order);
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                options << " -D T="           << dtype_traits<T>::getName()
-                        << " -D INVERSE="     << (isInverse ? 1 : 0)
-                        << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
-                        << " -D ZERO="        << toNumStr(scalar<T>(0));
-                options << " -D InterpInTy="  << dtype_traits<T>::getName();
-                options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
-                options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
-
-                if((af_dtype) dtype_traits<T>::af_type == c32 ||
-                    (af_dtype) dtype_traits<T>::af_type == c64) {
-                    options << " -D IS_CPLX=1";
-                    options << " -D TB=" << dtype_traits<BT>::getName();
-                } else {
-                    options << " -D IS_CPLX=0";
-                }
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                options << " -D INTERP_ORDER=" << order;
-                addInterpEnumOptions(options);
-
-                const char *ker_strs[] = {interp_cl, transform_cl};
-                const int   ker_lens[] = {interp_cl_len, transform_cl_len};
-                cl::Program prog;
-                buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-                entry.prog = new cl::Program(prog);
-                entry.ker = new cl::Kernel(*entry.prog, "transform_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            auto transformOp = cl::KernelFunctor<cl::Buffer, const KParam,
-                                                 const cl::Buffer, const KParam,
-                                                 const cl::Buffer, const KParam,
-                                                 const int, const int, const int, const int,
-                                                 const int, const int, const int, const int>(*entry.ker);
-
-            const int nImg2 = in.info.dims[2];
-            const int nImg3 = in.info.dims[3];
-            const int nTfs2 = tf.info.dims[2];
-            const int nTfs3 = tf.info.dims[3];
-
-            cl::NDRange local(TX, TY, 1);
-
-            int batchImg2 = 1;
-            if(nImg2 != nTfs2)
-                batchImg2 = min(nImg2, TI);
-
-            const int blocksXPerImage = divup(out.info.dims[0], local[0]);
-            const int blocksYPerImage = divup(out.info.dims[1], local[1]);
-
-            int global_x = local[0]
-                          * blocksXPerImage
-                          * (nImg2 / batchImg2);
-            int global_y = local[1]
-                          * blocksYPerImage
-                          * nImg3;
-            int global_z = local[2]
-                          * max((nTfs2 / nImg2), 1)
-                          * max((nTfs3 / nImg3), 1);
-
-            cl::NDRange global(global_x, global_y, global_z);
-
-            transformOp(cl::EnqueueArgs(getQueue(), global, local),
-                        *out.data, out.info, *in.data, in.info, *tf.data, tf.info,
-                        nImg2, nImg3, nTfs2, nTfs3, batchImg2,
-                        blocksXPerImage, blocksYPerImage, (int)method);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace opencl {
+namespace kernel {
+static const int TX = 16;
+static const int TY = 16;
+// Used for batching images
+static const int TI = 4;
+
+template<typename T>
+using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                          double, float>::type;
+
+template<typename T>
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
+                                          wtype_t<T>>::type;
+
+template<typename T, int order>
+void transform(Param out, const Param in, const Param tf, bool isInverse,
+               bool isPerspective, af_interp_type method) {
+    using BT = typename dtype_traits<T>::base_type;
+
+    std::string ref_name = std::string("transform_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(isInverse) +
+                           std::string("_") + std::to_string(isPerspective) +
+                           std::string("_") + std::to_string(order);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D INVERSE=" << (isInverse ? 1 : 0)
+                << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
+                << " -D ZERO=" << toNumStr(scalar<T>(0));
+        options << " -D InterpInTy=" << dtype_traits<T>::getName();
+        options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
+        options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
+
+        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+            (af_dtype)dtype_traits<T>::af_type == c64) {
+            options << " -D IS_CPLX=1";
+            options << " -D TB=" << dtype_traits<BT>::getName();
+        } else {
+            options << " -D IS_CPLX=0";
+        }
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+
+        options << " -D INTERP_ORDER=" << order;
+        addInterpEnumOptions(options);
+
+        const char *ker_strs[] = {interp_cl, transform_cl};
+        const int ker_lens[]   = {interp_cl_len, transform_cl_len};
+        cl::Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "transform_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    auto transformOp =
+        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
+                          const KParam, const cl::Buffer, const KParam,
+                          const int, const int, const int, const int, const int,
+                          const int, const int, const int>(*entry.ker);
+
+    const int nImg2 = in.info.dims[2];
+    const int nImg3 = in.info.dims[3];
+    const int nTfs2 = tf.info.dims[2];
+    const int nTfs3 = tf.info.dims[3];
+
+    cl::NDRange local(TX, TY, 1);
+
+    int batchImg2 = 1;
+    if (nImg2 != nTfs2) batchImg2 = min(nImg2, TI);
+
+    const int blocksXPerImage = divup(out.info.dims[0], local[0]);
+    const int blocksYPerImage = divup(out.info.dims[1], local[1]);
+
+    int global_x = local[0] * blocksXPerImage * (nImg2 / batchImg2);
+    int global_y = local[1] * blocksYPerImage * nImg3;
+    int global_z = local[2] * max((nTfs2 / nImg2), 1) * max((nTfs3 / nImg3), 1);
+
+    cl::NDRange global(global_x, global_y, global_z);
+
+    transformOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *in.data, in.info, *tf.data, tf.info, nImg2, nImg3, nTfs2,
+                nTfs3, batchImg2, blocksXPerImage, blocksYPerImage,
+                (int)method);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/transpose.cl b/src/backend/opencl/kernel/transpose.cl
index 0a6554e4c5..7b486f49fc 100644
--- a/src/backend/opencl/kernel/transpose.cl
+++ b/src/backend/opencl/kernel/transpose.cl
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #if DOCONJUGATE
-T doOp(T in)
-{
+T doOp(T in) {
     T out = {in.x, -in.y};
     return out;
 }
@@ -16,14 +15,12 @@ T doOp(T in)
 #define doOp(in) in
 #endif
 
-__kernel
-void transpose(__global T *oData, const KParam out,
-               const __global T *iData, const KParam in,
-               const int blocksPerMatX, const int blocksPerMatY)
-{
-    __local T shrdMem[TILE_DIM*(TILE_DIM+1)];
+__kernel void transpose(__global T *oData, const KParam out,
+                        const __global T *iData, const KParam in,
+                        const int blocksPerMatX, const int blocksPerMatY) {
+    __local T shrdMem[TILE_DIM * (TILE_DIM + 1)];
 
-    const int shrdStride = TILE_DIM+1;
+    const int shrdStride = TILE_DIM + 1;
     // create variables to hold output dimensions
     const int oDim0 = out.dims[0];
     const int oDim1 = out.dims[1];
@@ -53,13 +50,15 @@ void transpose(__global T *oData, const KParam out,
 
     // offset in and out based on batch id
     // also add the subBuffer offsets
-    iData += batchId_x *  in.strides[2] + batchId_y *  in.strides[3] +  in.offset;
-    oData += batchId_x * out.strides[2] + batchId_y * out.strides[3] + out.offset;
+    iData += batchId_x * in.strides[2] + batchId_y * in.strides[3] + in.offset;
+    oData +=
+        batchId_x * out.strides[2] + batchId_y * out.strides[3] + out.offset;
 
     for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
         int gy_ = gy + repeat;
-        if (IS32MULTIPLE || (gx < iDim0 && gy_< iDim1))
-            shrdMem[(ly + repeat) * shrdStride + lx] = iData[gy_ * iStride1 + gx];
+        if (IS32MULTIPLE || (gx < iDim0 && gy_ < iDim1))
+            shrdMem[(ly + repeat) * shrdStride + lx] =
+                iData[gy_ * iStride1 + gx];
     }
     barrier(CLK_LOCAL_MEM_FENCE);
 
@@ -69,7 +68,8 @@ void transpose(__global T *oData, const KParam out,
     for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
         int gy_ = gy + repeat;
         if (IS32MULTIPLE || (gx < oDim0 && gy_ < oDim1)) {
-            oData[gy_ * oStride1 + gx] = doOp(shrdMem[lx * shrdStride + ly + repeat]);
+            oData[gy_ * oStride1 + gx] =
+                doOp(shrdMem[lx * shrdStride + ly + repeat]);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index f69738aad6..9f643db75a 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -8,45 +8,42 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/transpose.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/transpose.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int TILE_DIM  = 32;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
 template<typename T, bool conjugate, bool IS32MULTIPLE>
-void transpose(Param out, const Param in, cl::CommandQueue queue)
-{
-    std::string refName = std::string("transpose_") + std::string(dtype_traits<T>::getName()) +
+void transpose(Param out, const Param in, cl::CommandQueue queue) {
+    std::string refName =
+        std::string("transpose_") + std::string(dtype_traits<T>::getName()) +
         std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D TILE_DIM=" << TILE_DIM
-                << " -D THREADS_Y=" << THREADS_Y
+        options << " -D TILE_DIM=" << TILE_DIM << " -D THREADS_Y=" << THREADS_Y
                 << " -D IS32MULTIPLE=" << IS32MULTIPLE
                 << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
                 << " -D T=" << dtype_traits<T>::getName();
@@ -55,7 +52,7 @@ void transpose(Param out, const Param in, cl::CommandQueue queue)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {transpose_cl};
-        const int   ker_lens[] = {transpose_cl_len};
+        const int ker_lens[]   = {transpose_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -71,15 +68,16 @@ void transpose(Param out, const Param in, cl::CommandQueue queue)
 
     // launch batch * blk_x blocks along x dimension
     NDRange global(blk_x * local[0] * in.info.dims[2],
-                    blk_y * local[1] * in.info.dims[3]);
+                   blk_y * local[1] * in.info.dims[3]);
 
-    auto transposeOp = KernelFunctor< Buffer, const KParam, const Buffer, const KParam,
-                                      const int, const int> (*entry.ker);
+    auto transposeOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const int, const int>(*entry.ker);
 
-    transposeOp(EnqueueArgs(queue, global, local),
-                *out.data, out.info, *in.data, in.info, blk_x, blk_y);
+    transposeOp(EnqueueArgs(queue, global, local), *out.data, out.info,
+                *in.data, in.info, blk_x, blk_y);
 
     CL_DEBUG_FINISH(queue);
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/transpose_inplace.cl b/src/backend/opencl/kernel/transpose_inplace.cl
index 074f242351..ee9c7edf3a 100644
--- a/src/backend/opencl/kernel/transpose_inplace.cl
+++ b/src/backend/opencl/kernel/transpose_inplace.cl
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #if DOCONJUGATE
-T doOp(T in)
-{
+T doOp(T in) {
     T out = {in.x, -in.y};
     return out;
 }
@@ -16,14 +15,13 @@ T doOp(T in)
 #define doOp(in) in
 #endif
 
-__kernel
-void transpose_inplace(__global T *iData, const KParam in,
-                       const int blocksPerMatX, const int blocksPerMatY)
-{
-    __local T shrdMem_s[TILE_DIM*(TILE_DIM+1)];
-    __local T shrdMem_d[TILE_DIM*(TILE_DIM+1)];
+__kernel void transpose_inplace(__global T *iData, const KParam in,
+                                const int blocksPerMatX,
+                                const int blocksPerMatY) {
+    __local T shrdMem_s[TILE_DIM * (TILE_DIM + 1)];
+    __local T shrdMem_d[TILE_DIM * (TILE_DIM + 1)];
 
-    const int shrdStride = TILE_DIM+1;
+    const int shrdStride = TILE_DIM + 1;
 
     // create variables to hold output dimensions
     const int iDim0 = in.dims[0];
@@ -45,9 +43,10 @@ void transpose_inplace(__global T *iData, const KParam in,
     const int x0 = TILE_DIM * blockIdx_x;
     const int y0 = TILE_DIM * blockIdx_y;
 
-    __global T *iptr = iData + batchId_x * in.strides[2] + batchId_y * in.strides[3] + in.offset;
+    __global T *iptr = iData + batchId_x * in.strides[2] +
+                       batchId_y * in.strides[3] + in.offset;
 
-    if(blockIdx_y > blockIdx_x) {
+    if (blockIdx_y > blockIdx_x) {
         // calculate global indices
         int gx = lx + x0;
         int gy = ly + y0;
@@ -56,28 +55,30 @@ void transpose_inplace(__global T *iData, const KParam in,
 
         // Copy to shared memory
         for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-
             int gy_ = gy + repeat;
-            if (IS32MULTIPLE || (gx < iDim0 && gy_< iDim1))
-                shrdMem_s[(ly + repeat) * shrdStride + lx] = iptr[gy_ * iStride1 + gx];
+            if (IS32MULTIPLE || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s[(ly + repeat) * shrdStride + lx] =
+                    iptr[gy_ * iStride1 + gx];
 
             int dy_ = dy + repeat;
-            if (IS32MULTIPLE || (dx < iDim0 && dy_< iDim1))
-                shrdMem_d[(ly + repeat) * shrdStride + lx] = iptr[dy_ * iStride1 + dx];
+            if (IS32MULTIPLE || (dx < iDim0 && dy_ < iDim1))
+                shrdMem_d[(ly + repeat) * shrdStride + lx] =
+                    iptr[dy_ * iStride1 + dx];
         }
 
         barrier(CLK_LOCAL_MEM_FENCE);
 
         // Copy from shared memory to global memory
         for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-
             int dy_ = dy + repeat;
-            if (IS32MULTIPLE || (dx < iDim0 && dy_< iDim1))
-                iptr[dy_ * iStride1 + dx] = doOp(shrdMem_s[(ly + repeat) + (shrdStride * lx)]);
+            if (IS32MULTIPLE || (dx < iDim0 && dy_ < iDim1))
+                iptr[dy_ * iStride1 + dx] =
+                    doOp(shrdMem_s[(ly + repeat) + (shrdStride * lx)]);
 
             int gy_ = gy + repeat;
-            if (IS32MULTIPLE || (gx < iDim0 && gy_< iDim1))
-                iptr[gy_ * iStride1 + gx] = doOp(shrdMem_d[(ly + repeat) + (shrdStride * lx)]);
+            if (IS32MULTIPLE || (gx < iDim0 && gy_ < iDim1))
+                iptr[gy_ * iStride1 + gx] =
+                    doOp(shrdMem_d[(ly + repeat) + (shrdStride * lx)]);
         }
 
     } else if (blockIdx_y == blockIdx_x) {
@@ -87,21 +88,20 @@ void transpose_inplace(__global T *iData, const KParam in,
 
         // Copy to shared memory
         for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-
             int gy_ = gy + repeat;
-            if (IS32MULTIPLE || (gx < iDim0 && gy_< iDim1))
-                shrdMem_s[(ly + repeat) * shrdStride + lx] = iptr[gy_ * iStride1 + gx];
-
+            if (IS32MULTIPLE || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s[(ly + repeat) * shrdStride + lx] =
+                    iptr[gy_ * iStride1 + gx];
         }
 
         barrier(CLK_LOCAL_MEM_FENCE);
 
         // Copy from shared memory to global memory
         for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-
             int gy_ = gy + repeat;
-            if (IS32MULTIPLE || (gx < iDim0 && gy_< iDim1))
-                iptr[gy_ * iStride1 + gx] = doOp(shrdMem_s[(ly + repeat) + (shrdStride * lx)]);
+            if (IS32MULTIPLE || (gx < iDim0 && gy_ < iDim1))
+                iptr[gy_ * iStride1 + gx] =
+                    doOp(shrdMem_s[(ly + repeat) + (shrdStride * lx)]);
         }
     }
 }
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index c1ebb71a6c..761cd01335 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -8,45 +8,43 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/transpose_inplace.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
+#include <kernel_headers/transpose_inplace.hpp>
+#include <program.hpp>
+#include <traits.hpp>
 #include <types.hpp>
+#include <string>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 static const int TILE_DIM  = 16;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
 template<typename T, bool conjugate, bool IS32MULTIPLE>
-void transpose_inplace(Param in, cl::CommandQueue &queue)
-{
-    std::string refName = std::string("transpose_inplace_") + std::string(dtype_traits<T>::getName()) +
-        std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
+void transpose_inplace(Param in, cl::CommandQueue& queue) {
+    std::string refName = std::string("transpose_inplace_") +
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(conjugate) +
+                          std::to_string(IS32MULTIPLE);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D TILE_DIM=" << TILE_DIM
-                << " -D THREADS_Y=" << THREADS_Y
+        options << " -D TILE_DIM=" << TILE_DIM << " -D THREADS_Y=" << THREADS_Y
                 << " -D IS32MULTIPLE=" << IS32MULTIPLE
                 << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
                 << " -D T=" << dtype_traits<T>::getName();
@@ -55,7 +53,7 @@ void transpose_inplace(Param in, cl::CommandQueue &queue)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {transpose_inplace_cl};
-        const int   ker_lens[] = {transpose_inplace_cl_len};
+        const int ker_lens[]   = {transpose_inplace_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -70,13 +68,16 @@ void transpose_inplace(Param in, cl::CommandQueue &queue)
     int blk_y = divup(in.info.dims[1], TILE_DIM);
 
     // launch batch * blk_x blocks along x dimension
-    NDRange global(blk_x * local[0] * in.info.dims[2], blk_y * local[1] * in.info.dims[3]);
+    NDRange global(blk_x * local[0] * in.info.dims[2],
+                   blk_y * local[1] * in.info.dims[3]);
 
-    auto transposeOp = KernelFunctor<Buffer, const KParam, const int, const int> (*entry.ker);
+    auto transposeOp =
+        KernelFunctor<Buffer, const KParam, const int, const int>(*entry.ker);
 
-    transposeOp(EnqueueArgs(queue, global, local), *in.data, in.info, blk_x, blk_y);
+    transposeOp(EnqueueArgs(queue, global, local), *in.data, in.info, blk_x,
+                blk_y);
 
     CL_DEBUG_FINISH(queue);
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/triangle.cl b/src/backend/opencl/kernel/triangle.cl
index cb0d2ce84d..c3dddffd44 100644
--- a/src/backend/opencl/kernel/triangle.cl
+++ b/src/backend/opencl/kernel/triangle.cl
@@ -7,11 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void triangle_kernel(__global T *rptr, KParam rinfo,
-                     const __global T *iptr, KParam iinfo,
-                     const int groups_x, const int groups_y)
-{
+__kernel void triangle_kernel(__global T *rptr, KParam rinfo,
+                              const __global T *iptr, KParam iinfo,
+                              const int groups_x, const int groups_y) {
     const int oz = get_group_id(0) / groups_x;
     const int ow = get_group_id(1) / groups_y;
 
@@ -24,22 +22,21 @@ void triangle_kernel(__global T *rptr, KParam rinfo,
     const int incy = groups_y * get_local_size(1);
     const int incx = groups_x * get_local_size(0);
 
-    __global T *d_r = rptr;
+    __global T *d_r       = rptr;
     const __global T *d_i = iptr + iinfo.offset;
 
-    if(oz < rinfo.dims[2] && ow < rinfo.dims[3]) {
+    if (oz < rinfo.dims[2] && ow < rinfo.dims[3]) {
         d_i = d_i + oz * iinfo.strides[2] + ow * iinfo.strides[3];
         d_r = d_r + oz * rinfo.strides[2] + ow * rinfo.strides[3];
 
         for (int oy = yy; oy < rinfo.dims[1]; oy += incy) {
             const __global T *Yd_i = d_i + oy * iinfo.strides[1];
-            __global T *Yd_r = d_r +  oy * rinfo.strides[1];
+            __global T *Yd_r       = d_r + oy * rinfo.strides[1];
 
             for (int ox = xx; ox < rinfo.dims[0]; ox += incx) {
-
-                bool cond = is_upper ? (oy >= ox) : (oy <= ox);
+                bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
                 bool do_unit_diag = is_unit_diag && (oy == ox);
-                if(cond) {
+                if (cond) {
                     Yd_r[ox] = do_unit_diag ? ONE : Yd_i[ox];
                 } else {
                     Yd_r[ox] = ZERO;
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 122d75afb1..6bedf6e723 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -8,57 +8,56 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/triangle.hpp>
-#include <program.hpp>
-#include <traits.hpp>
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
-#include <Param.hpp>
 #include <debug_opencl.hpp>
-#include <types.hpp>
+#include <kernel_headers/triangle.hpp>
 #include <math.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <string>
 
+using af::scalar_to_option;
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
-using af::scalar_to_option;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 // Kernel Launch Config Values
-static const unsigned TX = 32;
-static const unsigned TY = 8;
+static const unsigned TX    = 32;
+static const unsigned TY    = 8;
 static const unsigned TILEX = 128;
 static const unsigned TILEY = 32;
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Param out, const Param in)
-{
-    std::string refName = std::string("triangle_kernel_") + std::string(dtype_traits<T>::getName()) +
-        std::to_string(is_upper) + std::to_string(is_unit_diag);
+void triangle(Param out, const Param in) {
+    std::string refName = std::string("triangle_kernel_") +
+                          std::string(dtype_traits<T>::getName()) +
+                          std::to_string(is_upper) +
+                          std::to_string(is_unit_diag);
 
-    int device = getActiveDeviceId();
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D is_upper=" << is_upper
-                << " -D is_unit_diag=" << is_unit_diag
-                << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
+                << " -D is_unit_diag=" << is_unit_diag << " -D ZERO=(T)("
+                << scalar_to_option(scalar<T>(0)) << ")"
                 << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
         const char* ker_strs[] = {triangle_cl};
-        const int   ker_lens[] = {triangle_cl_len};
+        const int ker_lens[]   = {triangle_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -72,15 +71,16 @@ void triangle(Param out, const Param in)
     int groups_x = divup(out.info.dims[0], TILEX);
     int groups_y = divup(out.info.dims[1], TILEY);
 
-    NDRange global(groups_x * out.info.dims[2] * local[0], groups_y * out.info.dims[3] * local[1]);
+    NDRange global(groups_x * out.info.dims[2] * local[0],
+                   groups_y * out.info.dims[3] * local[1]);
 
-    auto triangleOp = KernelFunctor< Buffer, KParam, const Buffer, KParam,
-                                     const int, const int >(*entry.ker);
+    auto triangleOp = KernelFunctor<Buffer, KParam, const Buffer, KParam,
+                                    const int, const int>(*entry.ker);
 
-    triangleOp(EnqueueArgs(getQueue(), global, local),
-               *out.data, out.info, *in.data, in.info, groups_x, groups_y);
+    triangleOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *in.data, in.info, groups_x, groups_y);
 
     CL_DEBUG_FINISH(getQueue());
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/unwrap.cl b/src/backend/opencl/kernel/unwrap.cl
index ddd990f1a5..09a4216329 100644
--- a/src/backend/opencl/kernel/unwrap.cl
+++ b/src/backend/opencl/kernel/unwrap.cl
@@ -7,27 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void unwrap_kernel(__global T *d_out, const KParam out,
-                   __global const T *d_in, const KParam in,
-                   const int wx, const int wy, const int sx, const int sy,
-                   const int px, const int py, const int nx, const int reps)
-{
+__kernel void unwrap_kernel(__global T* d_out, const KParam out,
+                            __global const T* d_in, const KParam in,
+                            const int wx, const int wy, const int sx,
+                            const int sy, const int px, const int py,
+                            const int nx, const int reps) {
     // Compute channel and volume
     const int w = get_group_id(1) / in.dims[2];
-    const int z = get_group_id(1) - w * in.dims[2]; // get_group_id(1) % in.dims[2];
+    const int z =
+        get_group_id(1) - w * in.dims[2];  // get_group_id(1) % in.dims[2];
 
-    if(w >= in.dims[3] || z >= in.dims[2])
-        return;
+    if (w >= in.dims[3] || z >= in.dims[2]) return;
 
     // Compute offset for channel and volume
     const int cOut = w * out.strides[3] + z * out.strides[2];
-    const int cIn  = w *  in.strides[3] + z *  in.strides[2];
+    const int cIn  = w * in.strides[3] + z * in.strides[2];
 
     // Compute the output column index
-    const int id = is_column ?
-        (get_group_id(0) * get_local_size(1) + get_local_id(1)) :
-        get_global_id(0);
+    const int id = is_column
+                       ? (get_group_id(0) * get_local_size(1) + get_local_id(1))
+                       : get_global_id(0);
 
     if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
 
@@ -39,20 +38,19 @@ void unwrap_kernel(__global T *d_out, const KParam out,
     const int spy = starty - py;
 
     // Offset the global pointers to the respective starting indices
-    __global       T* optr = d_out + cOut + id * (is_column ? out.strides[1] : 1);
-    __global const T* iptr = d_in  + cIn + in.offset;
+    __global T* optr = d_out + cOut + id * (is_column ? out.strides[1] : 1);
+    __global const T* iptr = d_in + cIn + in.offset;
 
-    bool cond = (spx >= 0 && spx + wx < in.dims[0] && spy >= 0 && spy + wy < in.dims[1]);
-
-    for(int i = 0; i < reps; i++) {
+    bool cond = (spx >= 0 && spx + wx < in.dims[0] && spy >= 0 &&
+                 spy + wy < in.dims[1]);
 
+    for (int i = 0; i < reps; i++) {
         // Compute output index local to column
-        const int outIdx = is_column ?
-            (i * get_local_size(0) + get_local_id(0)) :
-            (i * get_local_size(1) + get_local_id(1));
+        const int outIdx = is_column
+                               ? (i * get_local_size(0) + get_local_id(0))
+                               : (i * get_local_size(1) + get_local_id(1));
 
-        if(outIdx >= (is_column ? out.dims[0] : out.dims[1]))
-            return;
+        if (outIdx >= (is_column ? out.dims[0] : out.dims[1])) return;
 
         // Compute input index local to window
         const int y = outIdx / wx;
@@ -63,9 +61,10 @@ void unwrap_kernel(__global T *d_out, const KParam out,
 
         // Copy
         T val = ZERO;
-        if(cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 && ypad < in.dims[1])) {
+        if (cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 &&
+                     ypad < in.dims[1])) {
             const int inIdx = ypad * in.strides[1] + xpad * in.strides[0];
-            val = iptr[inIdx];
+            val             = iptr[inIdx];
         }
 
         if (is_column) {
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 7c4dead472..89c6052b95 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -8,102 +8,91 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/unwrap.hpp>
+#include <math.hpp>
 #include <program.hpp>
-#include <cache.hpp>
 #include <traits.hpp>
-#include <string>
+#include <type_util.hpp>
 #include <map>
 #include <mutex>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <type_util.hpp>
-#include <math.hpp>
+#include <string>
 #include "config.hpp"
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T>
-        void unwrap(Param out, const Param in,
-                    const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy,
-                    const dim_t px, const dim_t py,
-                    const dim_t nx, const bool is_column)
-        {
-            std::string ref_name =
-                std::string("unwrap_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(is_column);
-
-            int device = getActiveDeviceId();
-
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                options << " -D is_column=" << is_column
-                        << " -D ZERO=" << toNumStr(scalar<T>(0))
-                        << " -D T="    << dtype_traits<T>::getName();
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                buildProgram(prog, unwrap_cl, unwrap_cl_len, options.str());
-
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "unwrap_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            dim_t TX = 1, TY = 1;
-            dim_t BX = 1;
-            const dim_t BY = out.info.dims[2] * out.info.dims[3];
-            dim_t reps = 1;
-
-            if (is_column) {
-                TX = std::min(THREADS_PER_GROUP, nextpow2(out.info.dims[0]));
-                TY = THREADS_PER_GROUP / TX;
-                BX = divup(out.info.dims[1], TY);
-                reps = divup((wx * wy), TX);
-            } else {
-                TX = THREADS_X;
-                TY = THREADS_Y;
-                BX = divup(out.info.dims[0], TX);
-                reps = divup((wx * wy), TY);
-            }
-
-            NDRange local(TX, TY);
-            NDRange global(local[0] * BX,
-                            local[1] * BY);
-
-            auto unwrapOp = KernelFunctor<Buffer, const KParam,
-                                        const Buffer, const KParam,
-                                        const int, const int,
-                                        const int, const int,
-                                        const int, const int,
-                                        const int, const int> (*entry.ker);
-
-            unwrapOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info, wx, wy, sx, sy, px, py, nx, reps);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace opencl {
+namespace kernel {
+template<typename T>
+void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
+            const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+            const dim_t nx, const bool is_column) {
+    std::string ref_name = std::string("unwrap_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(is_column);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D is_column=" << is_column
+                << " -D ZERO=" << toNumStr(scalar<T>(0))
+                << " -D T=" << dtype_traits<T>::getName();
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+
+        Program prog;
+        buildProgram(prog, unwrap_cl, unwrap_cl_len, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "unwrap_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    dim_t TX = 1, TY = 1;
+    dim_t BX       = 1;
+    const dim_t BY = out.info.dims[2] * out.info.dims[3];
+    dim_t reps     = 1;
+
+    if (is_column) {
+        TX   = std::min(THREADS_PER_GROUP, nextpow2(out.info.dims[0]));
+        TY   = THREADS_PER_GROUP / TX;
+        BX   = divup(out.info.dims[1], TY);
+        reps = divup((wx * wy), TX);
+    } else {
+        TX   = THREADS_X;
+        TY   = THREADS_Y;
+        BX   = divup(out.info.dims[0], TX);
+        reps = divup((wx * wy), TY);
+    }
+
+    NDRange local(TX, TY);
+    NDRange global(local[0] * BX, local[1] * BY);
+
+    auto unwrapOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const int, const int, const int, const int, const int,
+                      const int, const int, const int>(*entry.ker);
+
+    unwrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, wx, wy, sx, sy, px, py, nx, reps);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/where.cl b/src/backend/opencl/kernel/where.cl
index 08c47310ec..f3d5091916 100644
--- a/src/backend/opencl/kernel/where.cl
+++ b/src/backend/opencl/kernel/where.cl
@@ -8,53 +8,47 @@
  ********************************************************/
 
 #if CPLX
-#define isZero(val) ((val.x ==0) && (val.y == 0))
+#define isZero(val) ((val.x == 0) && (val.y == 0))
 #else
 #define isZero(val) ((val == 0))
 #endif
 
-__kernel
-void get_out_idx_kernel(__global uint *oData,
-                        __global uint *otData,
-                        KParam otInfo,
-                        __global uint *rtData,
-                        KParam rtInfo,
-                        __global T *iData,
-                        KParam iInfo,
-                        uint groups_x,
-                        uint groups_y,
-                        uint lim)
-{
-
+__kernel void get_out_idx_kernel(__global uint *oData, __global uint *otData,
+                                 KParam otInfo, __global uint *rtData,
+                                 KParam rtInfo, __global T *iData, KParam iInfo,
+                                 uint groups_x, uint groups_y, uint lim) {
     T Zero = zero;
 
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
 
-    const uint zid = get_group_id(0) / groups_x;
-    const uint wid = get_group_id(1) / groups_y;
-    const uint groupId_x = get_group_id(0) - (groups_x) * zid;
-    const uint groupId_y = get_group_id(1) - (groups_y) * wid;
-    const uint xid = groupId_x * get_local_size(0) * lim + lidx;
-    const uint yid = groupId_y * get_local_size(1) + lidy;
-
-    const uint off = wid * otInfo.strides[3] + zid * otInfo.strides[2] + yid * otInfo.strides[1];
-    const uint gid = wid * rtInfo.strides[3] + zid * rtInfo.strides[2] + yid * rtInfo.strides[1] + groupId_x;
-
-    otData += wid * otInfo.strides[3] + zid * otInfo.strides[2] + yid * otInfo.strides[1];
-    iData  += wid *  iInfo.strides[3] + zid *  iInfo.strides[2] + yid *  iInfo.strides[1] + iInfo.offset;
-
-    bool cond = (yid < otInfo.dims[1]) && (zid < otInfo.dims[2]) && (wid < otInfo.dims[3]);
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint xid       = groupId_x * get_local_size(0) * lim + lidx;
+    const uint yid       = groupId_y * get_local_size(1) + lidy;
+
+    const uint off = wid * otInfo.strides[3] + zid * otInfo.strides[2] +
+                     yid * otInfo.strides[1];
+    const uint gid = wid * rtInfo.strides[3] + zid * rtInfo.strides[2] +
+                     yid * rtInfo.strides[1] + groupId_x;
+
+    otData += wid * otInfo.strides[3] + zid * otInfo.strides[2] +
+              yid * otInfo.strides[1];
+    iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
+             yid * iInfo.strides[1] + iInfo.offset;
+
+    bool cond = (yid < otInfo.dims[1]) && (zid < otInfo.dims[2]) &&
+                (wid < otInfo.dims[3]);
     if (!cond) return;
 
     uint accum = (gid == 0) ? 0 : rtData[gid - 1];
 
-    for (uint k = 0, id = xid;
-         k < lim && id < otInfo.dims[0];
+    for (uint k = 0, id = xid; k < lim && id < otInfo.dims[0];
          k++, id += get_local_size(0)) {
-
         uint idx = otData[id] + accum;
-        T ival = iData[id];
+        T ival   = iData[id];
         if (!isZero(ival)) oData[idx - 1] = (off + id);
     }
 }
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index fe618a911c..3ae2339d91 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -8,44 +8,40 @@
  ********************************************************/
 
 #pragma once
-#include <string>
+#include <Param.hpp>
 #include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/where.hpp>
+#include <memory.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
 #include <type_util.hpp>
-#include "names.hpp"
+#include <string>
 #include "config.hpp"
+#include "names.hpp"
 #include "scan_first.hpp"
-#include <memory.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-namespace kernel
-{
+namespace opencl {
+namespace kernel {
 template<typename T>
-static void get_out_idx(Buffer *out_data,
-                        Param &otmp, Param &rtmp,
-                        Param &in, uint threads_x,
-                        uint groups_x, uint groups_y)
-{
-    std::string refName = std::string("get_out_idx_kernel_") + std::string(dtype_traits<T>::getName());
-
-    int device = getActiveDeviceId();
+static void get_out_idx(Buffer *out_data, Param &otmp, Param &rtmp, Param &in,
+                        uint threads_x, uint groups_x, uint groups_y) {
+    std::string refName = std::string("get_out_idx_kernel_") +
+                          std::string(dtype_traits<T>::getName());
+
+    int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog==0 && entry.ker==0) {
+    if (entry.prog == 0 && entry.ker == 0) {
         ToNumStr<T> toNumStr;
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
@@ -54,8 +50,8 @@ static void get_out_idx(Buffer *out_data,
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        const char* ker_strs[] = {where_cl};
-        const int   ker_lens[] = {where_cl_len};
+        const char *ker_strs[] = {where_cl};
+        const int ker_lens[]   = {where_cl_len};
         Program prog;
         buildProgram(prog, 1, ker_strs, ker_lens, options.str());
         entry.prog = new Program(prog);
@@ -65,26 +61,25 @@ static void get_out_idx(Buffer *out_data,
     }
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-    NDRange global(local[0] * groups_x * in.info.dims[2], local[1] * groups_y * in.info.dims[3]);
+    NDRange global(local[0] * groups_x * in.info.dims[2],
+                   local[1] * groups_y * in.info.dims[3]);
 
     uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
 
-    auto whereOp = KernelFunctor< Buffer, Buffer, KParam, Buffer, KParam,
-                                  Buffer, KParam, uint, uint, uint>(*entry.ker);
+    auto whereOp = KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer,
+                                 KParam, uint, uint, uint>(*entry.ker);
 
-    whereOp(EnqueueArgs(getQueue(), global, local),
-            *out_data, *otmp.data, otmp.info,
-            *rtmp.data, rtmp.info, *in.data, in.info,
-            groups_x, groups_y, lim);
+    whereOp(EnqueueArgs(getQueue(), global, local), *out_data, *otmp.data,
+            otmp.info, *rtmp.data, rtmp.info, *in.data, in.info, groups_x,
+            groups_y, lim);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-static void where(Param &out, Param &in)
-{
+static void where(Param &out, Param &in) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-    threads_x = std::min(threads_x, THREADS_PER_GROUP);
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
 
     uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
@@ -103,27 +98,28 @@ static void where(Param &out, Param &in)
     otmp.info.offset = 0;
 
     for (int k = 1; k < 4; k++) {
-        rtmp.info.dims[k] = in.info.dims[k];
+        rtmp.info.dims[k]    = in.info.dims[k];
         rtmp.info.strides[k] = rtmp.info.strides[k - 1] * rtmp.info.dims[k - 1];
 
-        otmp.info.dims[k] = in.info.dims[k];
+        otmp.info.dims[k]    = in.info.dims[k];
         otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
     }
 
     int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
-    rtmp.data = bufferAlloc(rtmp_elements * sizeof(uint));
+    rtmp.data         = bufferAlloc(rtmp_elements * sizeof(uint));
 
     int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
-    otmp.data = bufferAlloc(otmp_elements * sizeof(uint));
+    otmp.data         = bufferAlloc(otmp_elements * sizeof(uint));
 
-    scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in, false, groups_x, groups_y, threads_x);
+    scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in, false, groups_x,
+                                               groups_y, threads_x);
 
     // Linearize the dimensions and perform scan
-    Param ltmp = rtmp;
-    ltmp.info.offset = 0;
+    Param ltmp        = rtmp;
+    ltmp.info.offset  = 0;
     ltmp.info.dims[0] = rtmp_elements;
     for (int k = 1; k < 4; k++) {
-        ltmp.info.dims[k] = 1;
+        ltmp.info.dims[k]    = 1;
         ltmp.info.strides[k] = rtmp_elements;
     }
 
@@ -132,16 +128,15 @@ static void where(Param &out, Param &in)
     // Get output size and allocate output
     uint total;
     getQueue().enqueueReadBuffer(*rtmp.data, CL_TRUE,
-                                  sizeof(uint) * (rtmp_elements - 1),
-                                  sizeof(uint),
-                                  &total);
+                                 sizeof(uint) * (rtmp_elements - 1),
+                                 sizeof(uint), &total);
 
     out.data = bufferAlloc(total * sizeof(uint));
 
-    out.info.dims[0] = total;
+    out.info.dims[0]    = total;
     out.info.strides[0] = 1;
     for (int k = 1; k < 4; k++) {
-        out.info.dims[k] = 1;
+        out.info.dims[k]    = 1;
         out.info.strides[k] = total;
     }
 
@@ -151,5 +146,5 @@ static void where(Param &out, Param &in)
     bufferFree(rtmp.data);
     bufferFree(otmp.data);
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/wrap.cl b/src/backend/opencl/kernel/wrap.cl
index 88e14e26cf..238eb28892 100644
--- a/src/backend/opencl/kernel/wrap.cl
+++ b/src/backend/opencl/kernel/wrap.cl
@@ -7,16 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel
-void wrap_kernel(__global T *optr, KParam out,
-                 __global T *iptr, KParam in,
-                 const int wx, const int wy,
-                 const int sx, const int sy,
-                 const int px, const int py,
-                 const int nx, const int ny,
-                 int groups_x,
-                 int groups_y)
-{
+__kernel void wrap_kernel(__global T *optr, KParam out, __global T *iptr,
+                          KParam in, const int wx, const int wy, const int sx,
+                          const int sy, const int px, const int py,
+                          const int nx, const int ny, int groups_x,
+                          int groups_y) {
     int idx2 = get_group_id(0) / groups_x;
     int idx3 = get_group_id(1) / groups_y;
 
@@ -27,17 +22,16 @@ void wrap_kernel(__global T *optr, KParam out,
     int oidx1 = get_local_id(1) + get_local_size(1) * groupId_y;
 
     optr += idx2 * out.strides[2] + idx3 * out.strides[3];
-    iptr += idx2 *  in.strides[2] + idx3 *  in.strides[3] + in.offset;
-
+    iptr += idx2 * in.strides[2] + idx3 * in.strides[3] + in.offset;
 
     if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1]) return;
 
     int pidx0 = oidx0 + px;
     int pidx1 = oidx1 + py;
 
-    // The last time a value appears in the unwrapped index is padded_index / stride
-    // Each previous index has the value appear "stride" locations earlier
-    // We work our way back from the last index
+    // The last time a value appears in the unwrapped index is padded_index /
+    // stride Each previous index has the value appear "stride" locations
+    // earlier We work our way back from the last index
 
     const int x_end = min(pidx0 / sx, nx - 1);
     const int y_end = min(pidx1 / sy, ny - 1);
@@ -45,7 +39,7 @@ void wrap_kernel(__global T *optr, KParam out,
     const int x_off = pidx0 - sx * x_end;
     const int y_off = pidx1 - sy * y_end;
 
-    T val = ZERO;
+    T val   = ZERO;
     int idx = 1;
 
     for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
@@ -53,7 +47,6 @@ void wrap_kernel(__global T *optr, KParam out,
         int dim_end_y = y * nx;
 
         for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
-
             int win_end = win_end_y + xo;
             int dim_end = dim_end_y + x;
 
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index d0136e3e5c..fd1787939f 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -8,95 +8,83 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
 #include <kernel_headers/wrap.hpp>
+#include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <string>
+#include <type_util.hpp>
 #include <map>
 #include <mutex>
-#include <common/dispatch.hpp>
-#include <Param.hpp>
-#include <debug_opencl.hpp>
-#include <type_util.hpp>
-#include <math.hpp>
+#include <string>
 #include "config.hpp"
-#include <cache.hpp>
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    namespace kernel
-    {
-        template<typename T>
-        void wrap(Param out, const Param in,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column)
-        {
-            std::string ref_name =
-                std::string("wrap_") +
-                std::string(dtype_traits<T>::getName()) +
-                std::string("_") +
-                std::to_string(is_column);
-
-            int device = getActiveDeviceId();
-            kc_entry_t entry = kernelCache(device, ref_name);
-
-            if (entry.prog==0 && entry.ker==0) {
-
-                ToNumStr<T> toNumStr;
-                std::ostringstream options;
-                options << " -D is_column=" << is_column
-                        << " -D ZERO=" << toNumStr(scalar<T>(0))
-                        << " -D T="    << dtype_traits<T>::getName();
-
-                if (std::is_same<T, double>::value ||
-                    std::is_same<T, cdouble>::value) {
-                    options << " -D USE_DOUBLE";
-                }
-
-                Program prog;
-                buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
-
-                entry.prog = new Program(prog);
-                entry.ker = new Kernel(*entry.prog, "wrap_kernel");
-
-                addKernelToCache(device, ref_name, entry);
-            }
-
-            dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
-            dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
-
-            NDRange local(THREADS_X, THREADS_Y);
-
-            dim_t groups_x = divup(out.info.dims[0], local[0]);
-            dim_t groups_y = divup(out.info.dims[1], local[1]);
-
-            NDRange global(local[0] * groups_x * out.info.dims[2],
-                            local[1] * groups_y * out.info.dims[3]);
-
-
-            auto wrapOp = KernelFunctor<Buffer, const KParam,
-                                      const Buffer, const KParam,
-                                      const int, const int,
-                                      const int, const int,
-                                      const int, const int,
-                                      const int, const int,
-                                      const int, const int> (*entry.ker);
-
-            wrapOp(EnqueueArgs(getQueue(), global, local),
-                    *out.data, out.info, *in.data, in.info,
-                    wx, wy, sx, sy, px, py, nx, ny, groups_x, groups_y);
-
-            CL_DEBUG_FINISH(getQueue());
+namespace opencl {
+namespace kernel {
+template<typename T>
+void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column) {
+    std::string ref_name = std::string("wrap_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(is_column);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D is_column=" << is_column
+                << " -D ZERO=" << toNumStr(scalar<T>(0))
+                << " -D T=" << dtype_traits<T>::getName();
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
         }
+
+        Program prog;
+        buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "wrap_kernel");
+
+        addKernelToCache(device, ref_name, entry);
     }
+
+    dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
+    dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    dim_t groups_x = divup(out.info.dims[0], local[0]);
+    dim_t groups_y = divup(out.info.dims[1], local[1]);
+
+    NDRange global(local[0] * groups_x * out.info.dims[2],
+                   local[1] * groups_y * out.info.dims[3]);
+
+    auto wrapOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const int, const int, const int, const int, const int,
+                      const int, const int, const int, const int, const int>(
+            *entry.ker);
+
+    wrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, wx, wy, sx, sy, px, py, nx, ny, groups_x,
+           groups_y);
+
+    CL_DEBUG_FINISH(getQueue());
 }
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/logic.hpp b/src/backend/opencl/logic.hpp
index 90f241c038..61f10e038f 100644
--- a/src/backend/opencl/logic.hpp
+++ b/src/backend/opencl/logic.hpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/defines.h>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <binary.hpp>
 #include <err_opencl.hpp>
+#include <optypes.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-    template<typename T, af_op_t op>
-    Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<char, T, op>(lhs, rhs, odims);
-    }
+namespace opencl {
+template<typename T, af_op_t op>
+Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
+                    const af::dim4 &odims) {
+    return createBinaryNode<char, T, op>(lhs, rhs, odims);
+}
 
-    template<typename T, af_op_t op>
-    Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs, const af::dim4 &odims)
-    {
-        return createBinaryNode<T, T, op>(lhs, rhs, odims);
-    }
+template<typename T, af_op_t op>
+Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
+               const af::dim4 &odims) {
+    return createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index a3354e16b7..0e5d756bc1 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -7,27 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <kernel/lookup.hpp>
 #include <lookup.hpp>
-#include <err_opencl.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
+namespace opencl {
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input,
-                   const Array<idx_t> &indices, const unsigned dim)
-{
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim) {
     const dim4 iDims = input.dims();
 
     dim4 oDims(1);
-    for (int d=0; d<4; ++d)
-        oDims[d] = (d==int(dim) ? indices.elements() : iDims[d]);
+    for (int d = 0; d < 4; ++d)
+        oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    switch(dim) {
+    switch (dim) {
         case 0: kernel::lookup<in_t, idx_t, 0>(out, input, indices); break;
         case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices); break;
         case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices); break;
@@ -37,27 +35,36 @@ Array<in_t> lookup(const Array<in_t> &input,
     return out;
 }
 
-#define INSTANTIATE(T)  \
-template Array<T> lookup<T, float   >(const Array<T>&, const Array<float   >&, const unsigned); \
-template Array<T> lookup<T, double  >(const Array<T>&, const Array<double  >&, const unsigned); \
-template Array<T> lookup<T, int     >(const Array<T>&, const Array<int     >&, const unsigned); \
-template Array<T> lookup<T, unsigned>(const Array<T>&, const Array<unsigned>&, const unsigned); \
-template Array<T> lookup<T, short   >(const Array<T>&, const Array<short   >&, const unsigned); \
-template Array<T> lookup<T, ushort  >(const Array<T>&, const Array<ushort  >&, const unsigned); \
-template Array<T> lookup<T, intl    >(const Array<T>&, const Array<intl    >&, const unsigned); \
-template Array<T> lookup<T, uintl   >(const Array<T>&, const Array<uintl   >&, const unsigned); \
-template Array<T> lookup<T, uchar   >(const Array<T>&, const Array<uchar   >&, const unsigned);
-
-INSTANTIATE(float   );
-INSTANTIATE(cfloat  );
-INSTANTIATE(double  );
-INSTANTIATE(cdouble );
-INSTANTIATE(int     );
+#define INSTANTIATE(T)                                                         \
+    template Array<T> lookup<T, float>(const Array<T> &, const Array<float> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, double>(                                       \
+        const Array<T> &, const Array<double> &, const unsigned);              \
+    template Array<T> lookup<T, int>(const Array<T> &, const Array<int> &,     \
+                                     const unsigned);                          \
+    template Array<T> lookup<T, unsigned>(                                     \
+        const Array<T> &, const Array<unsigned> &, const unsigned);            \
+    template Array<T> lookup<T, short>(const Array<T> &, const Array<short> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, ushort>(                                       \
+        const Array<T> &, const Array<ushort> &, const unsigned);              \
+    template Array<T> lookup<T, intl>(const Array<T> &, const Array<intl> &,   \
+                                      const unsigned);                         \
+    template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
+                                       const unsigned);
+
+INSTANTIATE(float);
+INSTANTIATE(cfloat);
+INSTANTIATE(double);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
 INSTANTIATE(unsigned);
-INSTANTIATE(intl    );
-INSTANTIATE(uintl   );
-INSTANTIATE(uchar   );
-INSTANTIATE(char    );
-INSTANTIATE(ushort  );
-INSTANTIATE(short   );
-}
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(uchar);
+INSTANTIATE(char);
+INSTANTIATE(ushort);
+INSTANTIATE(short);
+}  // namespace opencl
diff --git a/src/backend/opencl/lookup.hpp b/src/backend/opencl/lookup.hpp
index 8c1e939815..5164648cfa 100644
--- a/src/backend/opencl/lookup.hpp
+++ b/src/backend/opencl/lookup.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 template<typename in_t, typename idx_t>
-Array<in_t> lookup(const Array<in_t> &input,
-                   const Array<idx_t> &indices, const unsigned dim);
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim);
 }
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index 02da58893f..3c99dfd392 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -7,30 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <lu.hpp>
 #include <err_opencl.hpp>
+#include <lu.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <kernel/lu_split.hpp>
-#include <copy.hpp>
 #include <blas.hpp>
-#include <platform.hpp>
-#include <magma/magma.h>
+#include <copy.hpp>
 #include <cpu/cpu_lu.hpp>
+#include <kernel/lu_split.hpp>
+#include <magma/magma.h>
+#include <platform.hpp>
 
-namespace opencl
-{
-
-Array<int> convertPivot(int *ipiv, int in_sz, int out_sz)
-{
+namespace opencl {
 
+Array<int> convertPivot(int *ipiv, int in_sz, int out_sz) {
     std::vector<int> out(out_sz);
 
-    for (int i = 0; i < out_sz; i++) {
-        out[i] = i;
-    }
+    for (int i = 0; i < out_sz; i++) { out[i] = i; }
 
-    for(int j = 0; j < in_sz; j++) {
+    for (int j = 0; j < in_sz; j++) {
         // 1 indexed in pivot
         std::swap(out[j], out[ipiv[j] - 1]);
     }
@@ -41,19 +36,17 @@ Array<int> convertPivot(int *ipiv, int in_sz, int out_sz)
 }
 
 template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::lu(lower, upper, pivot, in);
-    }
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
+    if (OpenCLCPUOffload()) { return cpu::lu(lower, upper, pivot, in); }
 
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-    int MN = std::min(M, N);
+    int M      = iDims[0];
+    int N      = iDims[1];
+    int MN     = std::min(M, N);
 
     Array<T> in_copy = copyArray<T>(in);
-    pivot = lu_inplace(in_copy);
+    pivot            = lu_inplace(in_copy);
 
     // SPLIT into lower and upper
     dim4 ldims(M, MN);
@@ -61,26 +54,22 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
     kernel::lu_split<T>(lower, upper, in_copy);
-
 }
 
 template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::lu_inplace(in, convert_pivot);
-    }
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
+    if (OpenCLCPUOffload()) { return cpu::lu_inplace(in, convert_pivot); }
 
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-    int MN = std::min(M, N);
+    int M      = iDims[0];
+    int N      = iDims[1];
+    int MN     = std::min(M, N);
     std::vector<int> ipiv(MN);
 
     cl::Buffer *in_buf = in.get();
-    int info = 0;
+    int info           = 0;
     magma_getrf_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
-                        &ipiv[0], getQueue()(), &info);
+                       &ipiv[0], getQueue()(), &info);
 
     if (!convert_pivot) return createHostDataArray<int>(dim4(MN), &ipiv[0]);
 
@@ -88,53 +77,49 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
     return pivot;
 }
 
-bool isLAPACKAvailable()
-{
-    return true;
-}
+bool isLAPACKAvailable() { return true; }
 
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
 
 INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
-}
+}  // namespace opencl
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in)
-{
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-Array<int> lu_inplace(Array<T> &in, const bool convert_pivot)
-{
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
-bool isLAPACKAvailable()
-{
-    return false;
-}
+bool isLAPACKAvailable() { return false; }
 
-#define INSTANTIATE_LU(T)                                                                           \
-    template Array<int> lu_inplace<T>(Array<T> &in, const bool convert_pivot);                      \
-    template void lu<T>(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
 
 INSTANTIATE_LU(float)
 INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
-}
+}  // namespace opencl
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/lu.hpp b/src/backend/opencl/lu.hpp
index 3eab168d9a..6ba417baa7 100644
--- a/src/backend/opencl/lu.hpp
+++ b/src/backend/opencl/lu.hpp
@@ -9,13 +9,13 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot, const Array<T> &in);
+namespace opencl {
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in);
 
-    template<typename T>
-    Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
-    bool isLAPACKAvailable();
-}
+bool isLAPACKAvailable();
+}  // namespace opencl
diff --git a/src/backend/opencl/magma/gebrd.cpp b/src/backend/opencl/magma/gebrd.cpp
index b287b70a5d..57bd505c31 100644
--- a/src/backend/opencl/magma/gebrd.cpp
+++ b/src/backend/opencl/magma/gebrd.cpp
@@ -51,157 +51,150 @@
  *
  **********************************************************************/
 
+#include <traits.hpp>
+#include <types.hpp>
 #include "magma.h"
 #include "magma_blas.h"
-#include "magma_data.h"
-#include "magma_cpu_lapack.h"
 #include "magma_cpu_blas.h"
+#include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
-#include <traits.hpp>
-#include <types.hpp>
 
 #include <algorithm>
 
 // produces pointer and offset as two args to magmaBLAS routines
-#define dA(i,j)  da, ((da_offset) + (i) + (j)*ldda)
+#define dA(i, j) da, ((da_offset) + (i) + (j)*ldda)
 // produces pointer as single arg to BLAS routines
-#define A(i,j)  &a[ (i) + (j)*lda ]
+#define A(i, j) &a[(i) + (j)*lda]
 
 template<typename Ty>
-magma_int_t
-magma_gebrd_hybrid(
-    magma_int_t m, magma_int_t n,
-    Ty *a, magma_int_t lda,
-    cl_mem da, size_t da_offset, magma_int_t ldda,
-    void *_d, void *_e,
-    Ty *tauq, Ty *taup,
-    Ty *work, magma_int_t lwork,
-    magma_queue_t queue,
-    magma_int_t *info,
-    bool copy)
-{
-/*  -- MAGMA (version 1.1) --
-    Univ. of Tennessee, Knoxville
-    Univ. of California, Berkeley
-    Univ. of Colorado, Denver
-    @date
-
-    Purpose
-    =======
-    ZGEBRD reduces a general complex M-by-N matrix A to upper or lower
-    bidiagonal form B by an orthogonal transformation: Q**H * A * P = B.
-
-    If m >= n, B is upper bidiagonal; if m < n, B is lower bidiagonal.
-
-    Arguments
-    =========
-    M       (input) INTEGER
-    The number of rows in the matrix A.  M >= 0.
-
-    N       (input) INTEGER
-    The number of columns in the matrix A.  N >= 0.
-
-    A       (input/output) COMPLEX_16 array, dimension (LDA,N)
-    On entry, the M-by-N general matrix to be reduced.
-    On exit,
-    if m >= n, the diagonal and the first superdiagonal are
-    overwritten with the upper bidiagonal matrix B; the
-    elements below the diagonal, with the array TAUQ, represent
-    the orthogonal matrix Q as a product of elementary
-    reflectors, and the elements above the first superdiagonal,
-    with the array TAUP, represent the orthogonal matrix P as
-    a product of elementary reflectors;
-    if m < n, the diagonal and the first subdiagonal are
-    overwritten with the lower bidiagonal matrix B; the
-    elements below the first subdiagonal, with the array TAUQ,
-    represent the orthogonal matrix Q as a product of
-    elementary reflectors, and the elements above the diagonal,
-    with the array TAUP, represent the orthogonal matrix P as
-    a product of elementary reflectors.
-    See Further Details.
-
-    LDA     (input) INTEGER
-    The leading dimension of the array A.  LDA >= max(1,M).
-
-    D       (output) double precision array, dimension (min(M,N))
-    The diagonal elements of the bidiagonal matrix B:
-    D(i) = A(i,i).
-
-    E       (output) double precision array, dimension (min(M,N)-1)
-    The off-diagonal elements of the bidiagonal matrix B:
-    if m >= n, E(i) = A(i,i+1) for i = 1,2,...,n-1;
-    if m < n, E(i) = A(i+1,i) for i = 1,2,...,m-1.
-
-    TAUQ    (output) COMPLEX_16 array dimension (min(M,N))
-    The scalar factors of the elementary reflectors which
-    represent the orthogonal matrix Q. See Further Details.
-
-    TAUP    (output) COMPLEX_16 array, dimension (min(M,N))
-    The scalar factors of the elementary reflectors which
-    represent the orthogonal matrix P. See Further Details.
-
-    WORK    (workspace/output) COMPLEX_16 array, dimension (MAX(1,LWORK))
-    On exit, if INFO = 0, WORK[0] returns the optimal LWORK.
-
-    LWORK   (input) INTEGER
-    The length of the array WORK. LWORK >= (M+N)*NB, where NB
-    is the optimal blocksize.
-
-    If LWORK = -1, then a workspace query is assumed; the routine
-    only calculates the optimal size of the WORK array, returns
-    this value as the first entry of the WORK array, and no error
-    message related to LWORK is issued by XERBLA.
-
-    INFO    (output) INTEGER
-    = 0:  successful exit
-    < 0:  if INFO = -i, the i-th argument had an illegal value.
-
-    Further Details
-    ===============
-    The matrices Q and P are represented as products of elementary
-    reflectors:
-
-    If m >= n,
-    Q = H(1) H(2) . . . H(n)  and  P = G(1) G(2) . . . G(n-1)
-    Each H(i) and G(i) has the form:
-    H(i) = I - tauq * v * v'  and G(i) = I - taup * u * u'
-    where tauq and taup are complex scalars, and v and u are complex vectors;
-    v(1:i-1) = 0, v(i) = 1, and v(i+1:m) is stored on exit in A(i+1:m,i);
-    u(1:i) = 0, u(i+1) = 1, and u(i+2:n) is stored on exit in A(i,i+2:n);
-    tauq is stored in TAUQ(i) and taup in TAUP(i).
-
-    If m < n,
-    Q = H(1) H(2) . . . H(m-1)  and  P = G(1) G(2) . . . G(m)
-    Each H(i) and G(i) has the form:
-    H(i) = I - tauq * v * v'  and G(i) = I - taup * u * u'
-    where tauq and taup are complex scalars, and v and u are complex vectors;
-    v(1:i) = 0, v(i+1) = 1, and v(i+2:m) is stored on exit in A(i+2:m,i);
-    u(1:i-1) = 0, u(i) = 1, and u(i+1:n) is stored on exit in A(i,i+1:n);
-    tauq is stored in TAUQ(i) and taup in TAUP(i).
-
-    The contents of A on exit are illustrated by the following examples:
-
-    m = 6 and n = 5 (m > n):          m = 5 and n = 6 (m < n):
-
-    ( d   e   u1  u1  u1)           ( d   u1  u1  u1  u1  u1)
-    ( v1  d   e   u2  u2)           ( e   d   u2  u2  u2  u2)
-    ( v1  v2  d   e   u3)           ( v1  e   d   u3  u3  u3)
-    ( v1  v2  v3  d   e )           ( v1  v2  e   d   u4  u4)
-    ( v1  v2  v3  v4  d )           ( v1  v2  v3  e   d   u5)
-    ( v1  v2  v3  v4  v5)
-
-    where d and e denote diagonal and off-diagonal elements of B, vi
-    denotes an element of the vector defining H(i), and ui an element of
-    the vector defining G(i).
-    =====================================================================    */
+magma_int_t magma_gebrd_hybrid(magma_int_t m, magma_int_t n, Ty *a,
+                               magma_int_t lda, cl_mem da, size_t da_offset,
+                               magma_int_t ldda, void *_d, void *_e, Ty *tauq,
+                               Ty *taup, Ty *work, magma_int_t lwork,
+                               magma_queue_t queue, magma_int_t *info,
+                               bool copy) {
+    /*  -- MAGMA (version 1.1) --
+        Univ. of Tennessee, Knoxville
+        Univ. of California, Berkeley
+        Univ. of Colorado, Denver
+        @date
+
+        Purpose
+        =======
+        ZGEBRD reduces a general complex M-by-N matrix A to upper or lower
+        bidiagonal form B by an orthogonal transformation: Q**H * A * P = B.
+
+        If m >= n, B is upper bidiagonal; if m < n, B is lower bidiagonal.
+
+        Arguments
+        =========
+        M       (input) INTEGER
+        The number of rows in the matrix A.  M >= 0.
+
+        N       (input) INTEGER
+        The number of columns in the matrix A.  N >= 0.
+
+        A       (input/output) COMPLEX_16 array, dimension (LDA,N)
+        On entry, the M-by-N general matrix to be reduced.
+        On exit,
+        if m >= n, the diagonal and the first superdiagonal are
+        overwritten with the upper bidiagonal matrix B; the
+        elements below the diagonal, with the array TAUQ, represent
+        the orthogonal matrix Q as a product of elementary
+        reflectors, and the elements above the first superdiagonal,
+        with the array TAUP, represent the orthogonal matrix P as
+        a product of elementary reflectors;
+        if m < n, the diagonal and the first subdiagonal are
+        overwritten with the lower bidiagonal matrix B; the
+        elements below the first subdiagonal, with the array TAUQ,
+        represent the orthogonal matrix Q as a product of
+        elementary reflectors, and the elements above the diagonal,
+        with the array TAUP, represent the orthogonal matrix P as
+        a product of elementary reflectors.
+        See Further Details.
+
+        LDA     (input) INTEGER
+        The leading dimension of the array A.  LDA >= max(1,M).
+
+        D       (output) double precision array, dimension (min(M,N))
+        The diagonal elements of the bidiagonal matrix B:
+        D(i) = A(i,i).
+
+        E       (output) double precision array, dimension (min(M,N)-1)
+        The off-diagonal elements of the bidiagonal matrix B:
+        if m >= n, E(i) = A(i,i+1) for i = 1,2,...,n-1;
+        if m < n, E(i) = A(i+1,i) for i = 1,2,...,m-1.
+
+        TAUQ    (output) COMPLEX_16 array dimension (min(M,N))
+        The scalar factors of the elementary reflectors which
+        represent the orthogonal matrix Q. See Further Details.
+
+        TAUP    (output) COMPLEX_16 array, dimension (min(M,N))
+        The scalar factors of the elementary reflectors which
+        represent the orthogonal matrix P. See Further Details.
+
+        WORK    (workspace/output) COMPLEX_16 array, dimension (MAX(1,LWORK))
+        On exit, if INFO = 0, WORK[0] returns the optimal LWORK.
+
+        LWORK   (input) INTEGER
+        The length of the array WORK. LWORK >= (M+N)*NB, where NB
+        is the optimal blocksize.
+
+        If LWORK = -1, then a workspace query is assumed; the routine
+        only calculates the optimal size of the WORK array, returns
+        this value as the first entry of the WORK array, and no error
+        message related to LWORK is issued by XERBLA.
+
+        INFO    (output) INTEGER
+        = 0:  successful exit
+        < 0:  if INFO = -i, the i-th argument had an illegal value.
+
+        Further Details
+        ===============
+        The matrices Q and P are represented as products of elementary
+        reflectors:
+
+        If m >= n,
+        Q = H(1) H(2) . . . H(n)  and  P = G(1) G(2) . . . G(n-1)
+        Each H(i) and G(i) has the form:
+        H(i) = I - tauq * v * v'  and G(i) = I - taup * u * u'
+        where tauq and taup are complex scalars, and v and u are complex
+       vectors; v(1:i-1) = 0, v(i) = 1, and v(i+1:m) is stored on exit in
+       A(i+1:m,i); u(1:i) = 0, u(i+1) = 1, and u(i+2:n) is stored on exit in
+       A(i,i+2:n); tauq is stored in TAUQ(i) and taup in TAUP(i).
+
+        If m < n,
+        Q = H(1) H(2) . . . H(m-1)  and  P = G(1) G(2) . . . G(m)
+        Each H(i) and G(i) has the form:
+        H(i) = I - tauq * v * v'  and G(i) = I - taup * u * u'
+        where tauq and taup are complex scalars, and v and u are complex
+       vectors; v(1:i) = 0, v(i+1) = 1, and v(i+2:m) is stored on exit in
+       A(i+2:m,i); u(1:i-1) = 0, u(i) = 1, and u(i+1:n) is stored on exit in
+       A(i,i+1:n); tauq is stored in TAUQ(i) and taup in TAUP(i).
+
+        The contents of A on exit are illustrated by the following examples:
+
+        m = 6 and n = 5 (m > n):          m = 5 and n = 6 (m < n):
+
+        ( d   e   u1  u1  u1)           ( d   u1  u1  u1  u1  u1)
+        ( v1  d   e   u2  u2)           ( e   d   u2  u2  u2  u2)
+        ( v1  v2  d   e   u3)           ( v1  e   d   u3  u3  u3)
+        ( v1  v2  v3  d   e )           ( v1  v2  e   d   u4  u4)
+        ( v1  v2  v3  v4  d )           ( v1  v2  v3  e   d   u5)
+        ( v1  v2  v3  v4  v5)
+
+        where d and e denote diagonal and off-diagonal elements of B, vi
+        denotes an element of the vector defining H(i), and ui an element of
+        the vector defining G(i).
+        ===================================================================== */
 
     typedef typename af::dtype_traits<Ty>::base_type Tr;
 
     Tr *d = (Tr *)_d;
     Tr *e = (Tr *)_e;
 
-
     Ty c_neg_one = magma_neg_one<Ty>();
     Ty c_one     = magma_one<Ty>();
     cl_mem dwork;
@@ -209,17 +202,17 @@ magma_gebrd_hybrid(
     magma_int_t ncol, nrow, jmax, nb;
 
     magma_int_t i, j, nx;
-    //magma_int_t iinfo;
+    // magma_int_t iinfo;
 
     magma_int_t minmn;
     magma_int_t ldwrkx, ldwrky, lwkopt;
     magma_int_t lquery;
 
-    nb   = magma_get_gebrd_nb<Ty>(n);
+    nb = magma_get_gebrd_nb<Ty>(n);
 
-    lwkopt = (m + n) * nb;
+    lwkopt  = (m + n) * nb;
     work[0] = magma_make<Ty>(lwkopt, 0.);
-    lquery = (lwork == -1);
+    lquery  = (lwork == -1);
 
     /* Check arguments */
     *info = 0;
@@ -227,32 +220,31 @@ magma_gebrd_hybrid(
         *info = -1;
     } else if (n < 0) {
         *info = -2;
-    } else if (lda < std::max(1,m)) {
+    } else if (lda < std::max(1, m)) {
         *info = -4;
-    } else if (lwork < lwkopt && (! lquery)) {
+    } else if (lwork < lwkopt && (!lquery)) {
         *info = -10;
     }
     if (*info < 0) {
-        //magma_xerbla(__func__, -(*info));
+        // magma_xerbla(__func__, -(*info));
         return *info;
-    }
-    else if (lquery)
+    } else if (lquery)
         return *info;
 
     /* Quick return if possible */
-    minmn = std::min(m,n);
+    minmn = std::min(m, n);
     if (minmn == 0) {
         work[0] = c_one;
         return *info;
     }
 
-    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dwork, (m + n)*nb)) {
+    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dwork, (m + n) * nb)) {
         *info = MAGMA_ERR_DEVICE_ALLOC;
         return *info;
     }
     size_t dwork_offset = 0;
 
-    cl_event event =  0;
+    cl_event event = 0;
 
     ldwrkx = m;
     ldwrky = n;
@@ -268,7 +260,7 @@ magma_gebrd_hybrid(
     gpu_blas_gemm_func<Ty> gpu_blas_gemm;
     cpu_lapack_gebrd_work_func<Ty> cpu_lapack_gebrd_work;
 
-    for (i=0; i< (minmn - nx); i += nb) {
+    for (i = 0; i < (minmn - nx); i += nb) {
         /*  Reduce rows and columns i:i+nb-1 to bidiagonal form and return
             the matrices X and Y which are needed to update the unreduced
             part of the matrix */
@@ -278,17 +270,15 @@ magma_gebrd_hybrid(
         /*   Get the current panel (no need for the 1st iteration) */
         if (i > 0) {
             magma_getmatrix<Ty>(nrow, nb, dA(i, i), ldda, A(i, i), lda, queue);
-            magma_getmatrix<Ty>(nb, ncol - nb,
-                                dA(i, i+nb), ldda,
-                                A(i, i+nb), lda, queue);
+            magma_getmatrix<Ty>(nb, ncol - nb, dA(i, i + nb), ldda,
+                                A(i, i + nb), lda, queue);
         }
 
-        magma_labrd_gpu<Ty>(nrow, ncol, nb,
-                            A(i, i),          lda,
-                            dA(i, i),          ldda,
-                            d+i, e+i, tauq+i, taup+i,
-                            work,             ldwrkx, dwork, dwork_offset,             ldwrkx,  // x, dx
-                            work+(ldwrkx*nb), ldwrky, dwork, dwork_offset+(ldwrkx*nb), ldwrky,  // y, dy
+        magma_labrd_gpu<Ty>(nrow, ncol, nb, A(i, i), lda, dA(i, i), ldda, d + i,
+                            e + i, tauq + i, taup + i, work, ldwrkx, dwork,
+                            dwork_offset, ldwrkx,  // x, dx
+                            work + (ldwrkx * nb), ldwrky, dwork,
+                            dwork_offset + (ldwrkx * nb), ldwrky,  // y, dy
                             queue);
 
         /*  Update the trailing submatrix A(i+nb:m,i+nb:n), using an update
@@ -297,37 +287,34 @@ magma_gebrd_hybrid(
         ncol = n - i - nb;
 
         // Send Y back to the GPU
-        magma_setmatrix<Ty>(nrow, nb, work+nb, ldwrkx, dwork, dwork_offset+nb, ldwrkx, queue);
-        magma_setmatrix<Ty>(ncol, nb,
-                            work  +               (ldwrkx+1)*nb, ldwrky,
-                            dwork, dwork_offset + (ldwrkx+1)*nb, ldwrky, queue);
-
-        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_CONJ_TRANS,
-                                        nrow, ncol, nb,
-                                        c_neg_one, dA(i+nb, i  ),      ldda,
-                                        dwork, dwork_offset+(ldwrkx+1)*nb, ldwrky,
-                                        c_one,     dA(i+nb, i+nb), ldda,
-                                        1, &queue, 0, nullptr, &event));
-
-        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
-                                        nrow, ncol, nb,
-                                        c_neg_one, dwork, dwork_offset+nb, ldwrkx,
-                                        dA(i,    i+nb), ldda,
-                                        c_one,     dA(i+nb, i+nb), ldda,
-                                        1, &queue, 0, nullptr, &event));
+        magma_setmatrix<Ty>(nrow, nb, work + nb, ldwrkx, dwork,
+                            dwork_offset + nb, ldwrkx, queue);
+        magma_setmatrix<Ty>(ncol, nb, work + (ldwrkx + 1) * nb, ldwrky, dwork,
+                            dwork_offset + (ldwrkx + 1) * nb, ldwrky, queue);
+
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(
+            OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_CONJ_TRANS, nrow, ncol, nb,
+            c_neg_one, dA(i + nb, i), ldda, dwork,
+            dwork_offset + (ldwrkx + 1) * nb, ldwrky, c_one, dA(i + nb, i + nb),
+            ldda, 1, &queue, 0, nullptr, &event));
+
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(
+            OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS, nrow, ncol, nb,
+            c_neg_one, dwork, dwork_offset + nb, ldwrkx, dA(i, i + nb), ldda,
+            c_one, dA(i + nb, i + nb), ldda, 1, &queue, 0, nullptr, &event));
 
         /* Copy diagonal and off-diagonal elements of B back into A */
         if (m >= n) {
             jmax = i + nb;
             for (j = i; j < jmax; ++j) {
-                *A(j, j ) = magma_make<Ty>(d[j], 0.);
-                *A(j, j+1) = magma_make<Ty>(e[j], 0.);
+                *A(j, j)     = magma_make<Ty>(d[j], 0.);
+                *A(j, j + 1) = magma_make<Ty>(e[j], 0.);
             }
         } else {
             jmax = i + nb;
             for (j = i; j < jmax; ++j) {
-                *A(j,   j) = magma_make<Ty>(d[j], 0.);
-                *A(j+1, j) = magma_make<Ty>(e[j], 0.);
+                *A(j, j)     = magma_make<Ty>(d[j], 0.);
+                *A(j + 1, j) = magma_make<Ty>(e[j], 0.);
             }
         }
     }
@@ -340,9 +327,8 @@ magma_gebrd_hybrid(
         magma_getmatrix<Ty>(nrow, ncol, dA(i, i), ldda, A(i, i), lda, queue);
     }
 
-    LAPACKE_CHECK(cpu_lapack_gebrd_work(nrow, ncol,
-                                        A(i, i), lda, d+i, e+i,
-                                        tauq+i, taup+i, work, lwork));
+    LAPACKE_CHECK(cpu_lapack_gebrd_work(nrow, ncol, A(i, i), lda, d + i, e + i,
+                                        tauq + i, taup + i, work, lwork));
     work[0] = magma_make<Ty>(lwkopt, 0.);
 
     magma_free(dwork);
@@ -350,17 +336,12 @@ magma_gebrd_hybrid(
     return 0;
 } /* magma_zgebrd */
 
-#define INSTANTIATE(Ty)                                 \
-    template magma_int_t                                \
-    magma_gebrd_hybrid<Ty>(                             \
-        magma_int_t m, magma_int_t n,                   \
-        Ty *a, magma_int_t lda,                         \
-        cl_mem da, size_t da_offset, magma_int_t ldda,  \
-        void *_d, void *_e,                             \
-        Ty *tauq, Ty *taup,                             \
-        Ty *work, magma_int_t lwork,                    \
-        magma_queue_t queue,                            \
-        magma_int_t *info, bool copy);                  \
+#define INSTANTIATE(Ty)                                                   \
+    template magma_int_t magma_gebrd_hybrid<Ty>(                          \
+        magma_int_t m, magma_int_t n, Ty * a, magma_int_t lda, cl_mem da, \
+        size_t da_offset, magma_int_t ldda, void *_d, void *_e, Ty *tauq, \
+        Ty *taup, Ty *work, magma_int_t lwork, magma_queue_t queue,       \
+        magma_int_t *info, bool copy);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/geqrf2.cpp b/src/backend/opencl/magma/geqrf2.cpp
index 3191954b85..29dc4cf94c 100644
--- a/src/backend/opencl/magma/geqrf2.cpp
+++ b/src/backend/opencl/magma/geqrf2.cpp
@@ -51,74 +51,71 @@
  *
  **********************************************************************/
 
+#include "../platform.hpp"
 #include "magma.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
-#include "../platform.hpp"
 
 #include <algorithm>
 
 template<typename Ty>
-void panel_to_q(magma_uplo_t uplo, magma_int_t ib, Ty *A, magma_int_t lda, Ty *work)
-{
+void panel_to_q(magma_uplo_t uplo, magma_int_t ib, Ty *A, magma_int_t lda,
+                Ty *work) {
     magma_int_t i, j, k = 0;
     Ty *col;
     static const Ty c_zero = magma_zero<Ty>();
     static const Ty c_one  = magma_one<Ty>();
 
     if (uplo == MagmaUpper) {
-        for(i = 0; i < ib; ++i) {
-            col = A + i*lda;
-            for(j = 0; j < i; ++j) {
+        for (i = 0; i < ib; ++i) {
+            col = A + i * lda;
+            for (j = 0; j < i; ++j) {
                 work[k] = col[j];
-                col [j] = c_zero;
+                col[j]  = c_zero;
                 ++k;
             }
 
             work[k] = col[i];
-            col [j] = c_one;
+            col[j]  = c_one;
             ++k;
         }
-    }
-    else {
-        for(i=0; i<ib; ++i) {
-            col = A + i*lda;
+    } else {
+        for (i = 0; i < ib; ++i) {
+            col     = A + i * lda;
             work[k] = col[i];
-            col [i] = c_one;
+            col[i]  = c_one;
             ++k;
-            for(j=i+1; j<ib; ++j) {
+            for (j = i + 1; j < ib; ++j) {
                 work[k] = col[j];
-                col [j] = c_zero;
+                col[j]  = c_zero;
                 ++k;
             }
         }
     }
 }
 
-
 // -------------------------
 // Restores a panel, after call to panel_to_q.
 template<typename Ty>
-void q_to_panel(magma_uplo_t uplo, magma_int_t ib, Ty *A, magma_int_t lda, Ty *work)
-{
+void q_to_panel(magma_uplo_t uplo, magma_int_t ib, Ty *A, magma_int_t lda,
+                Ty *work) {
     magma_int_t i, j, k = 0;
     Ty *col;
 
     if (uplo == MagmaUpper) {
-        for(i = 0; i < ib; ++i) {
-            col = A + i*lda;
-            for(j = 0; j <= i; ++j) {
+        for (i = 0; i < ib; ++i) {
+            col = A + i * lda;
+            for (j = 0; j <= i; ++j) {
                 col[j] = work[k];
                 ++k;
             }
         }
-    }
-    else {
-        for(i = 0; i < ib; ++i) {
-            col = A + i*lda;
-            for(j = i; j < ib; ++j) {
+    } else {
+        for (i = 0; i < ib; ++i) {
+            col = A + i * lda;
+            for (j = i; j < ib; ++j) {
                 col[j] = work[k];
                 ++k;
             }
@@ -126,77 +123,74 @@ void q_to_panel(magma_uplo_t uplo, magma_int_t ib, Ty *A, magma_int_t lda, Ty *w
     }
 }
 
-template<typename Ty> magma_int_t
-magma_geqrf2_gpu(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    Ty *tau,
-    magma_queue_t* queue,
-    magma_int_t *info)
-{
-/*  -- clMAGMA (version 0.1) --
-       Univ. of Tennessee, Knoxville
-       Univ. of California, Berkeley
-       Univ. of Colorado, Denver
-       @date
-
-    Purpose
-    =======
-    ZGEQRF computes a QR factorization of a complex M-by-N matrix A:
-    A = Q * R.
-
-    Arguments
-    =========
-    M       (input) INTEGER
-            The number of rows of the matrix A.  M >= 0.
-
-    N       (input) INTEGER
-            The number of columns of the matrix A.  N >= 0.
-
-    dA      (input/output) COMPLEX_16 array on the GPU, dimension (LDDA,N)
-            On entry, the M-by-N matrix A.
-            On exit, the elements on and above the diagonal of the array
-            contain the min(M,N)-by-N upper trapezoidal matrix R (R is
-            upper triangular if m >= n); the elements below the diagonal,
-            with the array TAU, represent the orthogonal matrix Q as a
-            product of min(m,n) elementary reflectors (see Further
-            Details).
-
-    LDDA    (input) INTEGER
-            The leading dimension of the array dA.  LDDA >= max(1,M).
-            To benefit from coalescent memory accesses LDDA must be
-            divisible by 16.
-
-    TAU     (output) COMPLEX_16 array, dimension (min(M,N))
-            The scalar factors of the elementary reflectors (see Further
-            Details).
-
-    INFO    (output) INTEGER
-            = 0:  successful exit
-            < 0:  if INFO = -i, the i-th argument had an illegal value
-                  or another error occured, such as memory allocation failed.
-
-    Further Details
-    ===============
-    The matrix Q is represented as a product of elementary reflectors
-
-        Q = H(1) H(2) . . . H(k), where k = min(m,n).
-
-    Each H(i) has the form
-
-        H(i) = I - tau * v * v'
-
-    where tau is a complex scalar, and v is a complex vector with
-    v(1:i-1) = 0 and v(i) = 1; v(i+1:m) is stored on exit in A(i+1:m,i),
-    and tau in TAU(i).
-    =====================================================================    */
-
-    #define dA(a_1,a_2)    dA, (dA_offset + (a_1) + (a_2)*(ldda))
-    #define work(a_1)      ( work + (a_1))
-    #define hwork          ( work + (nb)*(m))
+template<typename Ty>
+magma_int_t magma_geqrf2_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
+                             size_t dA_offset, magma_int_t ldda, Ty *tau,
+                             magma_queue_t *queue, magma_int_t *info) {
+    /*  -- clMAGMA (version 0.1) --
+           Univ. of Tennessee, Knoxville
+           Univ. of California, Berkeley
+           Univ. of Colorado, Denver
+           @date
+
+        Purpose
+        =======
+        ZGEQRF computes a QR factorization of a complex M-by-N matrix A:
+        A = Q * R.
+
+        Arguments
+        =========
+        M       (input) INTEGER
+                The number of rows of the matrix A.  M >= 0.
+
+        N       (input) INTEGER
+                The number of columns of the matrix A.  N >= 0.
+
+        dA      (input/output) COMPLEX_16 array on the GPU, dimension (LDDA,N)
+                On entry, the M-by-N matrix A.
+                On exit, the elements on and above the diagonal of the array
+                contain the min(M,N)-by-N upper trapezoidal matrix R (R is
+                upper triangular if m >= n); the elements below the diagonal,
+                with the array TAU, represent the orthogonal matrix Q as a
+                product of min(m,n) elementary reflectors (see Further
+                Details).
+
+        LDDA    (input) INTEGER
+                The leading dimension of the array dA.  LDDA >= max(1,M).
+                To benefit from coalescent memory accesses LDDA must be
+                divisible by 16.
+
+        TAU     (output) COMPLEX_16 array, dimension (min(M,N))
+                The scalar factors of the elementary reflectors (see Further
+                Details).
+
+        INFO    (output) INTEGER
+                = 0:  successful exit
+                < 0:  if INFO = -i, the i-th argument had an illegal value
+                      or another error occured, such as memory allocation
+       failed.
+
+        Further Details
+        ===============
+        The matrix Q is represented as a product of elementary reflectors
+
+            Q = H(1) H(2) . . . H(k), where k = min(m,n).
+
+        Each H(i) has the form
+
+            H(i) = I - tau * v * v'
+
+        where tau is a complex scalar, and v is a complex vector with
+        v(1:i-1) = 0 and v(i) = 1; v(i+1:m) is stored on exit in A(i+1:m,i),
+        and tau in TAU(i).
+        ===================================================================== */
+
+#define dA(a_1, a_2) dA, (dA_offset + (a_1) + (a_2) * (ldda))
+#define work(a_1) (work + (a_1))
+#define hwork (work + (nb) * (m))
 
     cl_mem dwork;
-    Ty  *work;
+    Ty *work;
 
     magma_int_t i, k, ldwork, lddwork, old_i, old_ib, rows;
     magma_int_t nbmin, nx, ib, nb;
@@ -207,24 +201,23 @@ magma_geqrf2_gpu(
         *info = -1;
     } else if (n < 0) {
         *info = -2;
-    } else if (ldda < std::max(1,m)) {
+    } else if (ldda < std::max(1, m)) {
         *info = -4;
     }
     if (*info != 0) {
-        //magma_xerbla( __func__, -(*info) );
+        // magma_xerbla( __func__, -(*info) );
         return *info;
     }
 
-    k = std::min(m,n);
-    if (k == 0)
-        return *info;
+    k = std::min(m, n);
+    if (k == 0) return *info;
 
     nb = magma_get_geqrf_nb<Ty>(m);
 
-    lwork  = (m+n) * nb;
+    lwork  = (m + n) * nb;
     lhwork = lwork - (m)*nb;
 
-    if ( MAGMA_SUCCESS != magma_malloc<Ty>( &dwork, n*nb )) {
+    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dwork, n * nb)) {
         *info = MAGMA_ERR_DEVICE_ALLOC;
         return *info;
     }
@@ -237,78 +230,87 @@ magma_geqrf2_gpu(
     }
     */
 
-    cl_mem buffer = clCreateBuffer(opencl::getContext()(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR,
-                                   sizeof(Ty)*lwork, NULL, NULL);
-    work = (Ty*)clEnqueueMapBuffer(queue[0], buffer, CL_TRUE,
-                                   CL_MAP_READ | CL_MAP_WRITE,
-                                   0, lwork*sizeof(Ty),
-                                   0, NULL, NULL, NULL);
+    cl_mem buffer = clCreateBuffer(opencl::getContext()(),
+                                   CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR,
+                                   sizeof(Ty) * lwork, NULL, NULL);
+    work          = (Ty *)clEnqueueMapBuffer(queue[0], buffer, CL_TRUE,
+                                    CL_MAP_READ | CL_MAP_WRITE, 0,
+                                    lwork * sizeof(Ty), 0, NULL, NULL, NULL);
 
     cpu_lapack_geqrf_work_func<Ty> cpu_lapack_geqrf;
     cpu_lapack_larft_func<Ty> cpu_lapack_larft;
 
-    nbmin = 2;
-    nx    = nb;
-    ldwork = m;
-    lddwork= n;
+    nbmin   = 2;
+    nx      = nb;
+    ldwork  = m;
+    lddwork = n;
 
     if (nb >= nbmin && nb < k && nx < k) {
         /* Use blocked code initially */
-        old_i = 0; old_ib = nb;
-        for (i = 0; i < k-nx; i += nb) {
-            ib = std::min(k-i, nb);
-            rows = m -i;
+        old_i  = 0;
+        old_ib = nb;
+        for (i = 0; i < k - nx; i += nb) {
+            ib   = std::min(k - i, nb);
+            rows = m - i;
 
-            magma_queue_sync( queue[1] );
-            magma_getmatrix_async<Ty>(rows, ib, dA(i, i), ldda, work(i), ldwork, queue[0], NULL);
+            magma_queue_sync(queue[1]);
+            magma_getmatrix_async<Ty>(rows, ib, dA(i, i), ldda, work(i), ldwork,
+                                      queue[0], NULL);
 
             if (i > 0) {
                 /* Apply H' to A(i:m,i+2*ib:n) from the left */
-                magma_larfb_gpu<Ty>( MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
-                                     m-old_i, n-old_i-2*old_ib, old_ib,
-                                     dA(old_i, old_i         ), ldda, dwork,0,      lddwork,
-                                     dA(old_i, old_i+2*old_ib), ldda, dwork,old_ib, lddwork, queue[1]);
-
-                magma_setmatrix_async<Ty>( old_ib, old_ib, work(old_i), ldwork,
-                                           dA(old_i, old_i), ldda, queue[1], NULL);
+                magma_larfb_gpu<Ty>(
+                    MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
+                    m - old_i, n - old_i - 2 * old_ib, old_ib, dA(old_i, old_i),
+                    ldda, dwork, 0, lddwork, dA(old_i, old_i + 2 * old_ib),
+                    ldda, dwork, old_ib, lddwork, queue[1]);
+
+                magma_setmatrix_async<Ty>(old_ib, old_ib, work(old_i), ldwork,
+                                          dA(old_i, old_i), ldda, queue[1],
+                                          NULL);
             }
 
             magma_queue_sync(queue[0]);
-            LAPACKE_CHECK(cpu_lapack_geqrf( rows, ib, work(i), ldwork, tau+i, hwork, lhwork));
+            LAPACKE_CHECK(cpu_lapack_geqrf(rows, ib, work(i), ldwork, tau + i,
+                                           hwork, lhwork));
 
             /* Form the triangular factor of the block reflector
                H = H(i) H(i+1) . . . H(i+ib-1) */
-            LAPACKE_CHECK(cpu_lapack_larft(
-                              *MagmaForwardStr, *MagmaColumnwiseStr,
-                              rows, ib,
-                              work(i), ldwork, tau+i, hwork, ib));
+            LAPACKE_CHECK(
+                cpu_lapack_larft(*MagmaForwardStr, *MagmaColumnwiseStr, rows,
+                                 ib, work(i), ldwork, tau + i, hwork, ib));
 
-            panel_to_q<Ty>( MagmaUpper, ib, work(i), ldwork, hwork+ib*ib );
+            panel_to_q<Ty>(MagmaUpper, ib, work(i), ldwork, hwork + ib * ib);
 
             /* download the i-th V matrix */
-            magma_setmatrix_async<Ty>(rows, ib, work(i), ldwork, dA(i,i), ldda, queue[0], NULL);
+            magma_setmatrix_async<Ty>(rows, ib, work(i), ldwork, dA(i, i), ldda,
+                                      queue[0], NULL);
 
             /* download the T matrix */
-            magma_queue_sync( queue[1] );
-            magma_setmatrix_async<Ty>( ib, ib, hwork, ib, dwork, 0, lddwork, queue[0], NULL);
-            magma_queue_sync( queue[0] );
+            magma_queue_sync(queue[1]);
+            magma_setmatrix_async<Ty>(ib, ib, hwork, ib, dwork, 0, lddwork,
+                                      queue[0], NULL);
+            magma_queue_sync(queue[0]);
 
             if (i + ib < n) {
-                if (i+nb < k-nx) {
+                if (i + nb < k - nx) {
                     /* Apply H' to A(i:m,i+ib:i+2*ib) from the left */
-                    magma_larfb_gpu<Ty>( MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
-                                         rows, ib, ib,
-                                         dA(i, i   ), ldda, dwork,0,  lddwork,
-                                         dA(i, i+ib), ldda, dwork,ib, lddwork, queue[1]);
-                    q_to_panel<Ty>( MagmaUpper, ib, work(i), ldwork, hwork+ib*ib );
-                }
-                else {
-                    magma_larfb_gpu<Ty>( MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
-                                         rows, n-i-ib, ib,
-                                         dA(i, i   ), ldda, dwork,0,  lddwork,
-                                         dA(i, i+ib), ldda, dwork,ib, lddwork, queue[1]);
-                    q_to_panel<Ty>( MagmaUpper, ib, work(i), ldwork, hwork+ib*ib );
-                    magma_setmatrix_async<Ty>(ib, ib, work(i), ldwork, dA(i,i), ldda, queue[1], NULL);
+                    magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward,
+                                        MagmaColumnwise, rows, ib, ib, dA(i, i),
+                                        ldda, dwork, 0, lddwork, dA(i, i + ib),
+                                        ldda, dwork, ib, lddwork, queue[1]);
+                    q_to_panel<Ty>(MagmaUpper, ib, work(i), ldwork,
+                                   hwork + ib * ib);
+                } else {
+                    magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward,
+                                        MagmaColumnwise, rows, n - i - ib, ib,
+                                        dA(i, i), ldda, dwork, 0, lddwork,
+                                        dA(i, i + ib), ldda, dwork, ib, lddwork,
+                                        queue[1]);
+                    q_to_panel<Ty>(MagmaUpper, ib, work(i), ldwork,
+                                   hwork + ib * ib);
+                    magma_setmatrix_async<Ty>(ib, ib, work(i), ldwork, dA(i, i),
+                                              ldda, queue[1], NULL);
                 }
                 old_i  = i;
                 old_ib = ib;
@@ -322,15 +324,18 @@ magma_geqrf2_gpu(
 
     /* Use unblocked code to factor the last or only block. */
     if (i < k) {
-        ib   = n-i;
-        rows = m-i;
-        magma_getmatrix_async<Ty>(rows, ib, dA(i, i), ldda, work, rows, queue[1], NULL);
+        ib   = n - i;
+        rows = m - i;
+        magma_getmatrix_async<Ty>(rows, ib, dA(i, i), ldda, work, rows,
+                                  queue[1], NULL);
         magma_queue_sync(queue[1]);
 
-        lhwork = lwork - rows*ib;
-        LAPACKE_CHECK(cpu_lapack_geqrf( rows, ib, work, rows, tau+i, work+ib*rows, lhwork));
+        lhwork = lwork - rows * ib;
+        LAPACKE_CHECK(cpu_lapack_geqrf(rows, ib, work, rows, tau + i,
+                                       work + ib * rows, lhwork));
 
-        magma_setmatrix_async<Ty>(rows, ib, work, rows, dA(i, i), ldda, queue[1], NULL);
+        magma_setmatrix_async<Ty>(rows, ib, work, rows, dA(i, i), ldda,
+                                  queue[1], NULL);
     }
 
     magma_queue_sync(queue[0]);
@@ -343,14 +348,11 @@ magma_geqrf2_gpu(
     return *info;
 } /* magma_zgeqrf2_gpu */
 
-#define INSTANTIATE(Ty)                                 \
-    template magma_int_t                                \
-    magma_geqrf2_gpu<Ty>(                               \
-        magma_int_t m, magma_int_t n,                   \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
-        Ty *tau,                                        \
-        magma_queue_t* queue,                           \
-        magma_int_t *info);                             \
+#define INSTANTIATE(Ty)                                            \
+    template magma_int_t magma_geqrf2_gpu<Ty>(                     \
+        magma_int_t m, magma_int_t n, cl_mem dA, size_t dA_offset, \
+        magma_int_t ldda, Ty * tau, magma_queue_t * queue,         \
+        magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/geqrf3.cpp b/src/backend/opencl/magma/geqrf3.cpp
index 8a6a05ff0b..40bfd875db 100644
--- a/src/backend/opencl/magma/geqrf3.cpp
+++ b/src/backend/opencl/magma/geqrf3.cpp
@@ -52,8 +52,8 @@
  **********************************************************************/
 
 #include "magma.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
 
@@ -70,17 +70,16 @@
  */
 
 template<typename Ty>
-void split_diag_block(magma_int_t ib, Ty *a, magma_int_t lda, Ty *work)
-{
+void split_diag_block(magma_int_t ib, Ty *a, magma_int_t lda, Ty *work) {
     magma_int_t i, j;
     Ty *cola, *colw;
     static const Ty c_zero = magma_zero<Ty>();
     static const Ty c_one  = magma_one<Ty>();
 
-    for(i=0; i<ib; i++){
-        cola = a    + i*lda;
-        colw = work + i*ib;
-        for(j=0; j<i; j++){
+    for (i = 0; i < ib; i++) {
+        cola = a + i * lda;
+        colw = work + i * ib;
+        for (j = 0; j < i; j++) {
             colw[j] = cola[j];
             cola[j] = c_zero;
         }
@@ -89,91 +88,90 @@ void split_diag_block(magma_int_t ib, Ty *a, magma_int_t lda, Ty *work)
     }
 }
 
-template<typename Ty> magma_int_t
-magma_geqrf3_gpu(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA, size_t dA_offset,  magma_int_t ldda,
-    Ty *tau, cl_mem dT, size_t dT_offset,
-    magma_queue_t queue,
-    magma_int_t *info)
-{
-/*  -- clMAGMA (version 0.1) --
-       Univ. of Tennessee, Knoxville
-       Univ. of California, Berkeley
-       Univ. of Colorado, Denver
-       @date
-
-    Purpose
-    =======
-    ZGEQRF computes a QR factorization of a complex M-by-N matrix A:
-    A = Q * R.
-
-    This version stores the triangular dT matrices used in
-    the block QR factorization so that they can be applied directly (i.e.,
-    without being recomputed) later. As a result, the application
-    of Q is much faster. Also, the upper triangular matrices for V have 0s
-    in them. The corresponding parts of the upper triangular R are inverted
-    and stored separately in dT.
-
-    Arguments
-    =========
-    M       (input) INTEGER
-            The number of rows of the matrix A.  M >= 0.
-
-    N       (input) INTEGER
-            The number of columns of the matrix A.  N >= 0.
-
-    dA      (input/output) COMPLEX_16 array on the GPU, dimension (LDDA,N)
-            On entry, the M-by-N matrix A.
-            On exit, the elements on and above the diagonal of the array
-            contain the min(M,N)-by-N upper trapezoidal matrix R (R is
-            upper triangular if m >= n); the elements below the diagonal,
-            with the array TAU, represent the orthogonal matrix Q as a
-            product of min(m,n) elementary reflectors (see Further
-            Details).
-
-    LDDA     (input) INTEGER
-            The leading dimension of the array dA.  LDDA >= max(1,M).
-            To benefit from coalescent memory accesses LDDA must be
-            divisible by 16.
-
-    TAU     (output) COMPLEX_16 array, dimension (min(M,N))
-            The scalar factors of the elementary reflectors (see Further
-            Details).
-
-    dT      (workspace/output)  COMPLEX_16 array on the GPU,
-            dimension (2*MIN(M, N) + (N+31)/32*32 )*NB,
-            where NB can be obtained through magma_get_zgeqrf_nb(M).
-            It starts with MIN(M,N)*NB block that store the triangular T
-            matrices, followed by the MIN(M,N)*NB block of the diagonal
-            inverses for the R matrix. The rest of the array is used as workspace.
-
-    INFO    (output) INTEGER
-            = 0:  successful exit
-            < 0:  if INFO = -i, the i-th argument had an illegal value
-                  or another error occured, such as memory allocation failed.
-
-    Further Details
-    ===============
-    The matrix Q is represented as a product of elementary reflectors
-
-       Q = H(1) H(2) . . . H(k), where k = min(m,n).
-
-    Each H(i) has the form
-
-       H(i) = I - tau * v * v'
-
-    where tau is a complex scalar, and v is a complex vector with
-    v(1:i-1) = 0 and v(i) = 1; v(i+1:m) is stored on exit in A(i+1:m,i),
-    and tau in TAU(i).
-    =====================================================================    */
-
-    #define a_ref(a_1,a_2) dA, (dA_offset + (a_1) + (a_2)*(ldda))
-    #define t_ref(a_1)     dT, (dT_offset + (a_1)*nb)
-    #define d_ref(a_1)     dT, (dT_offset + (minmn + (a_1))*nb)
-    #define dd_ref(a_1)    dT, (dT_offset + (2*minmn+(a_1))*nb)
-    #define work_ref(a_1)  ( work + (a_1))
-    #define hwork          ( work + (nb)*(m))
+template<typename Ty>
+magma_int_t magma_geqrf3_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
+                             size_t dA_offset, magma_int_t ldda, Ty *tau,
+                             cl_mem dT, size_t dT_offset, magma_queue_t queue,
+                             magma_int_t *info) {
+    /*  -- clMAGMA (version 0.1) --
+           Univ. of Tennessee, Knoxville
+           Univ. of California, Berkeley
+           Univ. of Colorado, Denver
+           @date
+
+        Purpose
+        =======
+        ZGEQRF computes a QR factorization of a complex M-by-N matrix A:
+        A = Q * R.
+
+        This version stores the triangular dT matrices used in
+        the block QR factorization so that they can be applied directly (i.e.,
+        without being recomputed) later. As a result, the application
+        of Q is much faster. Also, the upper triangular matrices for V have 0s
+        in them. The corresponding parts of the upper triangular R are inverted
+        and stored separately in dT.
+
+        Arguments
+        =========
+        M       (input) INTEGER
+                The number of rows of the matrix A.  M >= 0.
+
+        N       (input) INTEGER
+                The number of columns of the matrix A.  N >= 0.
+
+        dA      (input/output) COMPLEX_16 array on the GPU, dimension (LDDA,N)
+                On entry, the M-by-N matrix A.
+                On exit, the elements on and above the diagonal of the array
+                contain the min(M,N)-by-N upper trapezoidal matrix R (R is
+                upper triangular if m >= n); the elements below the diagonal,
+                with the array TAU, represent the orthogonal matrix Q as a
+                product of min(m,n) elementary reflectors (see Further
+                Details).
+
+        LDDA     (input) INTEGER
+                The leading dimension of the array dA.  LDDA >= max(1,M).
+                To benefit from coalescent memory accesses LDDA must be
+                divisible by 16.
+
+        TAU     (output) COMPLEX_16 array, dimension (min(M,N))
+                The scalar factors of the elementary reflectors (see Further
+                Details).
+
+        dT      (workspace/output)  COMPLEX_16 array on the GPU,
+                dimension (2*MIN(M, N) + (N+31)/32*32 )*NB,
+                where NB can be obtained through magma_get_zgeqrf_nb(M).
+                It starts with MIN(M,N)*NB block that store the triangular T
+                matrices, followed by the MIN(M,N)*NB block of the diagonal
+                inverses for the R matrix. The rest of the array is used as
+       workspace.
+
+        INFO    (output) INTEGER
+                = 0:  successful exit
+                < 0:  if INFO = -i, the i-th argument had an illegal value
+                      or another error occured, such as memory allocation
+       failed.
+
+        Further Details
+        ===============
+        The matrix Q is represented as a product of elementary reflectors
+
+           Q = H(1) H(2) . . . H(k), where k = min(m,n).
+
+        Each H(i) has the form
+
+           H(i) = I - tau * v * v'
+
+        where tau is a complex scalar, and v is a complex vector with
+        v(1:i-1) = 0 and v(i) = 1; v(i+1:m) is stored on exit in A(i+1:m,i),
+        and tau in TAU(i).
+        ===================================================================== */
+
+#define a_ref(a_1, a_2) dA, (dA_offset + (a_1) + (a_2) * (ldda))
+#define t_ref(a_1) dT, (dT_offset + (a_1)*nb)
+#define d_ref(a_1) dT, (dT_offset + (minmn + (a_1)) * nb)
+#define dd_ref(a_1) dT, (dT_offset + (2 * minmn + (a_1)) * nb)
+#define work_ref(a_1) (work + (a_1))
+#define hwork (work + (nb) * (m))
 
     magma_int_t i, k, minmn, old_i, old_ib, rows, cols;
     magma_int_t ib, nb;
@@ -186,99 +184,100 @@ magma_geqrf3_gpu(
         *info = -1;
     } else if (n < 0) {
         *info = -2;
-    } else if (ldda < std::max(1,m)) {
+    } else if (ldda < std::max(1, m)) {
         *info = -4;
     }
     if (*info != 0) {
-        //magma_xerbla( __func__, -(*info) );
+        // magma_xerbla( __func__, -(*info) );
         return *info;
     }
 
-    k = minmn = std::min(m,n);
-    if (k == 0)
-        return *info;
+    k = minmn = std::min(m, n);
+    if (k == 0) return *info;
 
     nb = magma_get_geqrf_nb<Ty>(m);
 
-    lwork  = (m + n + nb)*nb;
-    lhwork = lwork - m*nb;
+    lwork  = (m + n + nb) * nb;
+    lhwork = lwork - m * nb;
 
-    if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>( &work, lwork )) {
+    if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>(&work, lwork)) {
         *info = MAGMA_ERR_HOST_ALLOC;
         return *info;
     }
 
-    ut = hwork+nb*(n);
-    memset(ut, 0, nb*nb*sizeof(Ty));
+    ut = hwork + nb * (n);
+    memset(ut, 0, nb * nb * sizeof(Ty));
 
     magma_event_t event[2] = {NULL, NULL};
 
-    ldwork = m;
-    lddwork= n;
+    ldwork  = m;
+    lddwork = n;
 
     cpu_lapack_geqrf_work_func<Ty> cpu_lapack_geqrf;
     cpu_lapack_larft_func<Ty> cpu_lapack_larft;
 
-    if ( (nb > 1) && (nb < k) ) {
+    if ((nb > 1) && (nb < k)) {
         /* Use blocked code initially */
-        old_i = 0; old_ib = nb;
-        for (i = 0; i < k-nb; i += nb) {
-            ib = std::min(k-i, nb);
-            rows = m -i;
-            magma_getmatrix_async<Ty>(rows, ib,
-                                     a_ref(i,i),  ldda,
-                                     work_ref(i), ldwork, queue, &event[1]);
-            if (i>0){
+        old_i  = 0;
+        old_ib = nb;
+        for (i = 0; i < k - nb; i += nb) {
+            ib   = std::min(k - i, nb);
+            rows = m - i;
+            magma_getmatrix_async<Ty>(rows, ib, a_ref(i, i), ldda, work_ref(i),
+                                      ldwork, queue, &event[1]);
+            if (i > 0) {
                 /* Apply H' to A(i:m,i+2*ib:n) from the left */
-                cols = n-old_i-2*old_ib;
-                magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
-                                   m-old_i, cols, old_ib,
-                                   a_ref(old_i, old_i         ), ldda, t_ref(old_i), nb,
-                                   a_ref(old_i, old_i+2*old_ib), ldda, dd_ref(0),    lddwork, queue);
+                cols = n - old_i - 2 * old_ib;
+                magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward,
+                                    MagmaColumnwise, m - old_i, cols, old_ib,
+                                    a_ref(old_i, old_i), ldda, t_ref(old_i), nb,
+                                    a_ref(old_i, old_i + 2 * old_ib), ldda,
+                                    dd_ref(0), lddwork, queue);
 
                 /* store the diagonal */
-                magma_setmatrix_async<Ty>(old_ib, old_ib,
-                                         ut, old_ib,
-                                         d_ref(old_i), old_ib, queue, &event[0]);
+                magma_setmatrix_async<Ty>(old_ib, old_ib, ut, old_ib,
+                                          d_ref(old_i), old_ib, queue,
+                                          &event[0]);
             }
 
             magma_event_sync(event[1]);
-            LAPACKE_CHECK(cpu_lapack_geqrf( rows, ib, work_ref(i), ldwork, tau+i, hwork, lhwork));
+            LAPACKE_CHECK(cpu_lapack_geqrf(rows, ib, work_ref(i), ldwork,
+                                           tau + i, hwork, lhwork));
 
             /* Form the triangular factor of the block reflector
                H = H(i) H(i+1) . . . H(i+ib-1) */
-            LAPACKE_CHECK(cpu_lapack_larft(
-                              *MagmaForwardStr, *MagmaColumnwiseStr,
-                              rows, ib,
-                              work_ref(i), ldwork,
-                              tau+i, hwork, ib));
+            LAPACKE_CHECK(
+                cpu_lapack_larft(*MagmaForwardStr, *MagmaColumnwiseStr, rows,
+                                 ib, work_ref(i), ldwork, tau + i, hwork, ib));
 
             /* Put 0s in the upper triangular part of a panel (and 1s on the
                diagonal); copy the upper triangular in ut and invert it. */
             if (i > 0) magma_event_sync(event[0]);
-            //Change me
+            // Change me
             split_diag_block<Ty>(ib, work_ref(i), ldwork, ut);
-            magma_setmatrix<Ty>(rows, ib, work_ref(i), ldwork, a_ref(i,i), ldda, queue);
+            magma_setmatrix<Ty>(rows, ib, work_ref(i), ldwork, a_ref(i, i),
+                                ldda, queue);
 
             if (i + ib < n) {
                 /* Send the triangular factor T to the GPU */
                 magma_setmatrix<Ty>(ib, ib, hwork, ib, t_ref(i), nb, queue);
 
-                if (i+nb < k-nb){
+                if (i + nb < k - nb) {
                     /* Apply H' to A(i:m,i+ib:i+2*ib) from the left */
-                    magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
-                                       rows, ib, ib,
-                                       a_ref(i, i   ), ldda, t_ref(i),  nb,
-                                       a_ref(i, i+ib), ldda, dd_ref(0), lddwork, queue);
-                }
-                else {
-                    cols = n-i-ib;
-                    magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward, MagmaColumnwise,
-                                       rows, cols, ib,
-                                       a_ref(i, i   ), ldda, t_ref(i),  nb,
-                                       a_ref(i, i+ib), ldda, dd_ref(0), lddwork, queue);
+                    magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward,
+                                        MagmaColumnwise, rows, ib, ib,
+                                        a_ref(i, i), ldda, t_ref(i), nb,
+                                        a_ref(i, i + ib), ldda, dd_ref(0),
+                                        lddwork, queue);
+                } else {
+                    cols = n - i - ib;
+                    magma_larfb_gpu<Ty>(MagmaLeft, MagmaConjTrans, MagmaForward,
+                                        MagmaColumnwise, rows, cols, ib,
+                                        a_ref(i, i), ldda, t_ref(i), nb,
+                                        a_ref(i, i + ib), ldda, dd_ref(0),
+                                        lddwork, queue);
                     /* Fix the diagonal block */
-                    magma_setmatrix<Ty>( ib, ib, ut, ib, d_ref(i), ib , queue);
+                    magma_setmatrix<Ty>(ib, ib, ut, ib, d_ref(i), ib, queue);
                 }
                 old_i  = i;
                 old_ib = ib;
@@ -290,17 +289,18 @@ magma_geqrf3_gpu(
 
     /* Use unblocked code to factor the last or only block. */
     if (i < k) {
-        ib   = n-i;
-        rows = m-i;
-        magma_getmatrix<Ty>( rows, ib, a_ref(i, i), ldda, work, rows, queue );
+        ib   = n - i;
+        rows = m - i;
+        magma_getmatrix<Ty>(rows, ib, a_ref(i, i), ldda, work, rows, queue);
 
-        lhwork = lwork - rows*ib;
-        LAPACKE_CHECK(cpu_lapack_geqrf( rows, ib, work, rows, tau+i, work+ib*rows, lhwork));
+        lhwork = lwork - rows * ib;
+        LAPACKE_CHECK(cpu_lapack_geqrf(rows, ib, work, rows, tau + i,
+                                       work + ib * rows, lhwork));
 
-        magma_setmatrix<Ty>( rows, ib, work, rows, a_ref(i, i), ldda, queue );
+        magma_setmatrix<Ty>(rows, ib, work, rows, a_ref(i, i), ldda, queue);
     }
 
-    magma_free_cpu( work );
+    magma_free_cpu(work);
     return *info;
 } /* magma_zgeqrf_gpu */
 
@@ -309,14 +309,11 @@ magma_geqrf3_gpu(
 #undef d_ref
 #undef work_ref
 
-#define INSTANTIATE(T)                                  \
-    template magma_int_t                                \
-    magma_geqrf3_gpu<T>(                                 \
-        magma_int_t m, magma_int_t n,                   \
-        cl_mem dA, size_t dA_offset,  magma_int_t ldda, \
-        T *tau, cl_mem dT, size_t dT_offset,            \
-        magma_queue_t queue,                            \
-        magma_int_t *info);                             \
+#define INSTANTIATE(T)                                             \
+    template magma_int_t magma_geqrf3_gpu<T>(                      \
+        magma_int_t m, magma_int_t n, cl_mem dA, size_t dA_offset, \
+        magma_int_t ldda, T * tau, cl_mem dT, size_t dT_offset,    \
+        magma_queue_t queue, magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/getrf.cpp b/src/backend/opencl/magma/getrf.cpp
index c57ea32893..f8b756e61b 100644
--- a/src/backend/opencl/magma/getrf.cpp
+++ b/src/backend/opencl/magma/getrf.cpp
@@ -53,73 +53,70 @@
 
 #include "magma.h"
 #include "magma_blas.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 
 #include <algorithm>
 
 template<typename Ty>
-magma_int_t magma_getrf_gpu(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    magma_int_t *ipiv,
-    magma_queue_t queue,
-    magma_int_t *info)
-{
-/*  -- clMAGMA (version 0.1) --
-    Univ. of Tennessee, Knoxville
-    Univ. of California, Berkeley
-    Univ. of Colorado, Denver
-    @date
-
-    Purpose
-    =======
-    GETRF computes an LU factorization of a general M-by-N matrix A
-    using partial pivoting with row interchanges.
-
-    The factorization has the form
-    A = P * L * U
-    where P is a permutation matrix, L is lower triangular with unit
-    diagonal elements (lower trapezoidal if m > n), and U is upper
-    triangular (upper trapezoidal if m < n).
-
-    This is the right-looking Level 3 BLAS version of the algorithm.
-
-    Arguments
-    =========
-    M       (input) INTEGER
-    The number of rows of the matrix A.  M >= 0.
-
-    N       (input) INTEGER
-    The number of columns of the matrix A.  N >= 0.
-
-    A       (input/output) an array on the GPU, dimension (LDDA,N).
-    On entry, the M-by-N matrix to be factored.
-    On exit, the factors L and U from the factorization
-    A = P*L*U; the unit diagonal elements of L are not stored.
-
-    LDDA     (input) INTEGER
-    The leading dimension of the array A.  LDDA >= max(1,M).
-
-    IPIV    (output) INTEGER array, dimension (min(M,N))
-    The pivot indices; for 1 <= i <= min(M,N), row i of the
-    matrix was interchanged with row IPIV(i).
-
-    INFO    (output) INTEGER
-    = 0:  successful exit
-    < 0:  if INFO = -i, the i-th argument had an illegal value
-    or another error occured, such as memory allocation failed.
-    > 0:  if INFO = i, U(i,i) is exactly zero. The factorization
-    has been completed, but the factor U is exactly
-    singular, and division by zero will occur if it is used
-    to solve a system of equations.
-    =====================================================================    */
-
-#define  dA(i_, j_) dA,   dA_offset  + (i_)*nb       + (j_)*nb*ldda
-#define dAT(i_, j_) dAT,  dAT_offset + (i_)*nb*lddat + (j_)*nb
-#define dAP(i_, j_) dAP,               (i_)          + (j_)*maxm
-#define work(i_)   (work + (i_))
+magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
+                            size_t dA_offset, magma_int_t ldda,
+                            magma_int_t *ipiv, magma_queue_t queue,
+                            magma_int_t *info) {
+    /*  -- clMAGMA (version 0.1) --
+        Univ. of Tennessee, Knoxville
+        Univ. of California, Berkeley
+        Univ. of Colorado, Denver
+        @date
+
+        Purpose
+        =======
+        GETRF computes an LU factorization of a general M-by-N matrix A
+        using partial pivoting with row interchanges.
+
+        The factorization has the form
+        A = P * L * U
+        where P is a permutation matrix, L is lower triangular with unit
+        diagonal elements (lower trapezoidal if m > n), and U is upper
+        triangular (upper trapezoidal if m < n).
+
+        This is the right-looking Level 3 BLAS version of the algorithm.
+
+        Arguments
+        =========
+        M       (input) INTEGER
+        The number of rows of the matrix A.  M >= 0.
+
+        N       (input) INTEGER
+        The number of columns of the matrix A.  N >= 0.
+
+        A       (input/output) an array on the GPU, dimension (LDDA,N).
+        On entry, the M-by-N matrix to be factored.
+        On exit, the factors L and U from the factorization
+        A = P*L*U; the unit diagonal elements of L are not stored.
+
+        LDDA     (input) INTEGER
+        The leading dimension of the array A.  LDDA >= max(1,M).
+
+        IPIV    (output) INTEGER array, dimension (min(M,N))
+        The pivot indices; for 1 <= i <= min(M,N), row i of the
+        matrix was interchanged with row IPIV(i).
+
+        INFO    (output) INTEGER
+        = 0:  successful exit
+        < 0:  if INFO = -i, the i-th argument had an illegal value
+        or another error occured, such as memory allocation failed.
+        > 0:  if INFO = i, U(i,i) is exactly zero. The factorization
+        has been completed, but the factor U is exactly
+        singular, and division by zero will occur if it is used
+        to solve a system of equations.
+        ===================================================================== */
+
+#define dA(i_, j_) dA, dA_offset + (i_)*nb + (j_)*nb *ldda
+#define dAT(i_, j_) dAT, dAT_offset + (i_)*nb *lddat + (j_)*nb
+#define dAP(i_, j_) dAP, (i_) + (j_)*maxm
+#define work(i_) (work + (i_))
 
     static const Ty c_one     = magma_one<Ty>();
     static const Ty c_neg_one = magma_neg_one<Ty>();
@@ -137,17 +134,16 @@ magma_int_t magma_getrf_gpu(
         *info = -1;
     else if (n < 0)
         *info = -2;
-    else if (ldda < std::max(1,m))
+    else if (ldda < std::max(1, m))
         *info = -4;
 
     if (*info != 0) {
-        //magma_xerbla(__func__, -(*info));
+        // magma_xerbla(__func__, -(*info));
         return *info;
     }
 
     /* Quick return if possible */
-    if (m == 0 || n == 0)
-        return *info;
+    if (m == 0 || n == 0) return *info;
 
     gpu_blas_gemm_func<Ty> gpu_blas_gemm;
     gpu_blas_trsm_func<Ty> gpu_blas_trsm;
@@ -158,23 +154,22 @@ magma_int_t magma_getrf_gpu(
     nb     = magma_get_getrf_nb<Ty>(m);
     s      = mindim / nb;
 
-    if (nb <= 1 || nb >= std::min(m,n)) {
+    if (nb <= 1 || nb >= std::min(m, n)) {
         /* Use CPU code. */
-        if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>(&work, m*n)) {
+        if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>(&work, m * n)) {
             *info = MAGMA_ERR_HOST_ALLOC;
             return *info;
         }
-        magma_getmatrix<Ty>(m, n, dA(0,0), ldda, work(0), m, queue);
-        LAPACKE_CHECK(cpu_lapack_getrf( m, n, work, m, ipiv));
-        magma_setmatrix<Ty>(m, n, work(0), m, dA(0,0), ldda, queue);
+        magma_getmatrix<Ty>(m, n, dA(0, 0), ldda, work(0), m, queue);
+        LAPACKE_CHECK(cpu_lapack_getrf(m, n, work, m, ipiv));
+        magma_setmatrix<Ty>(m, n, work(0), m, dA(0, 0), ldda, queue);
         magma_free_cpu(work);
-    }
-    else {
+    } else {
         /* Use hybrid blocked code. */
-        maxm = ((m + 31)/32)*32;
-        maxn = ((n + 31)/32)*32;
+        maxm = ((m + 31) / 32) * 32;
+        maxn = ((n + 31) / 32) * 32;
 
-        if (MAGMA_SUCCESS != magma_malloc<Ty>(&dAP, nb*maxm)) {
+        if (MAGMA_SUCCESS != magma_malloc<Ty>(&dAP, nb * maxm)) {
             *info = MAGMA_ERR_DEVICE_ALLOC;
             return *info;
         }
@@ -182,27 +177,26 @@ magma_int_t magma_getrf_gpu(
         // square matrices can be done in place;
         // rectangular requires copy to transpose
         if (m == n) {
-            dAT = dA;
+            dAT        = dA;
             dAT_offset = dA_offset;
-            lddat = ldda;
-            magmablas_transpose_inplace<Ty>(m, dAT(0,0), lddat, queue);
-        }
-        else {
-            lddat = maxn;  // N-by-M
+            lddat      = ldda;
+            magmablas_transpose_inplace<Ty>(m, dAT(0, 0), lddat, queue);
+        } else {
+            lddat      = maxn;  // N-by-M
             dAT_offset = 0;
-            if (MAGMA_SUCCESS != magma_malloc<Ty>(&dAT, lddat*maxm)) {
+            if (MAGMA_SUCCESS != magma_malloc<Ty>(&dAT, lddat * maxm)) {
                 magma_free(dAP);
                 *info = MAGMA_ERR_DEVICE_ALLOC;
                 return *info;
             }
-            magmablas_transpose<Ty>(m, n, dA(0,0), ldda, dAT(0,0), lddat, queue);
+            magmablas_transpose<Ty>(m, n, dA(0, 0), ldda, dAT(0, 0), lddat,
+                                    queue);
         }
 
         ldwork = maxm;
-        if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>(&work, ldwork*nb)) {
+        if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>(&work, ldwork * nb)) {
             magma_free(dAP);
-            if (dA != dAT)
-                magma_free(dAT);
+            if (dA != dAT) magma_free(dAT);
 
             *info = MAGMA_ERR_HOST_ALLOC;
             return *info;
@@ -210,132 +204,120 @@ magma_int_t magma_getrf_gpu(
 
         cl_event event = 0;
 
-
-        for(j=0; j < s; j++) {
-
+        for (j = 0; j < s; j++) {
             // download j-th panel
-            magmablas_transpose<Ty>(nb, m-j*nb, dAT(j,j), lddat, dAP(0,0), maxm, queue);
+            magmablas_transpose<Ty>(nb, m - j * nb, dAT(j, j), lddat, dAP(0, 0),
+                                    maxm, queue);
 
-            magma_getmatrix<Ty>(m-j*nb, nb, dAP(0,0), maxm, work(0), ldwork, queue);
+            magma_getmatrix<Ty>(m - j * nb, nb, dAP(0, 0), maxm, work(0),
+                                ldwork, queue);
 
             if (j > 0 && n > (j + 1) * nb) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
-                                                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
-                                                n - (j+1)*nb, nb,
-                                                c_one,
-                                                dAT(j-1,j-1), lddat,
-                                                dAT(j-1,j+1), lddat,
-                                                1, &queue, 0, nullptr, &event));
-
-                if (m > j * nb)  {
-                    OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
-                                                    n-(j+1)*nb, m-j*nb, nb,
-                                                    c_neg_one,
-                                                    dAT(j-1,j+1), lddat,
-                                                    dAT(j,  j-1), lddat,
-                                                    c_one,
-                                                    dAT(j,  j+1), lddat,
-                                                    1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                    n - (j + 1) * nb, nb, c_one, dAT(j - 1, j - 1), lddat,
+                    dAT(j - 1, j + 1), lddat, 1, &queue, 0, nullptr, &event));
+
+                if (m > j * nb) {
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(
+                        OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
+                        n - (j + 1) * nb, m - j * nb, nb, c_neg_one,
+                        dAT(j - 1, j + 1), lddat, dAT(j, j - 1), lddat, c_one,
+                        dAT(j, j + 1), lddat, 1, &queue, 0, nullptr, &event));
                 }
             }
 
             // do the cpu part
-            rows = m - j*nb;
-            LAPACKE_CHECK(cpu_lapack_getrf( rows, nb, work, ldwork, ipiv+j*nb));
-            if (*info == 0 && iinfo > 0)
-                *info = iinfo + j*nb;
+            rows = m - j * nb;
+            LAPACKE_CHECK(
+                cpu_lapack_getrf(rows, nb, work, ldwork, ipiv + j * nb));
+            if (*info == 0 && iinfo > 0) *info = iinfo + j * nb;
 
-            for(i=j*nb; i < j*nb + nb; ++i) {
-                ipiv[i] += j*nb;
-            }
-            magmablas_laswp<Ty>(n, dAT(0,0), lddat, j*nb + 1, j*nb + nb, ipiv, 1, queue);
+            for (i = j * nb; i < j * nb + nb; ++i) { ipiv[i] += j * nb; }
+            magmablas_laswp<Ty>(n, dAT(0, 0), lddat, j * nb + 1, j * nb + nb,
+                                ipiv, 1, queue);
 
             // upload j-th panel
-            magma_setmatrix<Ty>(m-j*nb, nb, work(0), ldwork, dAP(0,0), maxm, queue);
+            magma_setmatrix<Ty>(m - j * nb, nb, work(0), ldwork, dAP(0, 0),
+                                maxm, queue);
 
-            magmablas_transpose<Ty>(m-j*nb, nb, dAP(0,0), maxm, dAT(j,j), lddat, queue);
+            magmablas_transpose<Ty>(m - j * nb, nb, dAP(0, 0), maxm, dAT(j, j),
+                                    lddat, queue);
 
             // do the small non-parallel computations (next panel update)
-            if (s > (j+1)) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
-                                                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
-                                                nb, nb,
-                                                c_one,
-                                                dAT(j, j  ), lddat,
-                                                dAT(j, j+1), lddat,
-                                                1, &queue, 0, nullptr, &event));
-
-
-                OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
-                                                nb, m-(j+1)*nb, nb,
-                                                c_neg_one,
-                                                dAT(j,   j+1), lddat,
-                                                dAT(j+1, j  ), lddat,
-                                                c_one,
-                                                dAT(j+1, j+1), lddat,
-                                                1, &queue, 0, nullptr, &event));
-            }
-            else {
+            if (s > (j + 1)) {
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL, nb, nb,
+                    c_one, dAT(j, j), lddat, dAT(j, j + 1), lddat, 1, &queue, 0,
+                    nullptr, &event));
+
+                OPENCL_BLAS_CHECK(gpu_blas_gemm(
+                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS, nb,
+                    m - (j + 1) * nb, nb, c_neg_one, dAT(j, j + 1), lddat,
+                    dAT(j + 1, j), lddat, c_one, dAT(j + 1, j + 1), lddat, 1,
+                    &queue, 0, nullptr, &event));
+            } else {
                 if (n > s * nb) {
-                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
-                                                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
-                                                    n-s*nb, nb,
-                                                    c_one,
-                                                    dAT(j, j  ), lddat,
-                                                    dAT(j, j+1), lddat,
-                                                    1, &queue, 0, nullptr, &event));
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                        OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                        OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                        n - s * nb, nb, c_one, dAT(j, j), lddat, dAT(j, j + 1),
+                        lddat, 1, &queue, 0, nullptr, &event));
                 }
 
-                if ((n > (j+1) * nb) && (m > (j+1) * nb)) {
-                    OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
-                                                    n-(j+1)*nb, m-(j+1)*nb, nb,
-                                                    c_neg_one,
-                                                    dAT(j,   j+1), lddat,
-                                                    dAT(j+1, j  ), lddat,
-                                                    c_one,
-                                                    dAT(j+1, j+1), lddat,
-                                                    1, &queue, 0, nullptr, &event));
+                if ((n > (j + 1) * nb) && (m > (j + 1) * nb)) {
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(
+                        OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NO_TRANS,
+                        n - (j + 1) * nb, m - (j + 1) * nb, nb, c_neg_one,
+                        dAT(j, j + 1), lddat, dAT(j + 1, j), lddat, c_one,
+                        dAT(j + 1, j + 1), lddat, 1, &queue, 0, nullptr,
+                        &event));
                 }
             }
         }
 
-        magma_int_t nb0 = std::min(m - s*nb, n - s*nb);
+        magma_int_t nb0 = std::min(m - s * nb, n - s * nb);
 
         if (nb0 > 0 && m > s * nb) {
-            rows = m - s*nb;
+            rows = m - s * nb;
 
-            magmablas_transpose<Ty>(nb0, rows, dAT(s,s), lddat, dAP(0,0), maxm, queue);
-            magma_getmatrix<Ty>(rows, nb0, dAP(0,0), maxm, work(0), ldwork, queue);
+            magmablas_transpose<Ty>(nb0, rows, dAT(s, s), lddat, dAP(0, 0),
+                                    maxm, queue);
+            magma_getmatrix<Ty>(rows, nb0, dAP(0, 0), maxm, work(0), ldwork,
+                                queue);
 
             // do the cpu part
-            LAPACKE_CHECK(cpu_lapack_getrf( rows, nb0, work, ldwork, ipiv+s*nb));
-            if (*info == 0 && iinfo > 0)
-                *info = iinfo + s*nb;
+            LAPACKE_CHECK(
+                cpu_lapack_getrf(rows, nb0, work, ldwork, ipiv + s * nb));
+            if (*info == 0 && iinfo > 0) *info = iinfo + s * nb;
 
-            for(i=s*nb; i < s*nb + nb0; ++i) {
-                ipiv[i] += s*nb;
-            }
-            magmablas_laswp<Ty>(n, dAT(0,0), lddat, s*nb + 1, s*nb + nb0, ipiv, 1, queue);
+            for (i = s * nb; i < s * nb + nb0; ++i) { ipiv[i] += s * nb; }
+            magmablas_laswp<Ty>(n, dAT(0, 0), lddat, s * nb + 1, s * nb + nb0,
+                                ipiv, 1, queue);
 
             // upload j-th panel
-            magma_setmatrix<Ty>(rows, nb0, work(0), ldwork, dAP(0,0), maxm, queue);
-            magmablas_transpose<Ty>(rows, nb0, dAP(0,0), maxm, dAT(s,s), lddat, queue);
+            magma_setmatrix<Ty>(rows, nb0, work(0), ldwork, dAP(0, 0), maxm,
+                                queue);
+            magmablas_transpose<Ty>(rows, nb0, dAP(0, 0), maxm, dAT(s, s),
+                                    lddat, queue);
 
             if (n > s * nb + nb0) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
-                                                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
-                                                n-s*nb-nb0, nb0,
-                                                c_one, dAT(s,s),     lddat,
-                                                dAT(s,s)+nb0, lddat, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_UPPER,
+                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                    n - s * nb - nb0, nb0, c_one, dAT(s, s), lddat,
+                    dAT(s, s) + nb0, lddat, 1, &queue, 0, nullptr, &event));
             }
         }
 
         // undo transpose
         if (dA == dAT) {
-            magmablas_transpose_inplace<Ty>(m, dAT(0,0), lddat, queue);
-        }
-        else {
-            magmablas_transpose<Ty>(n, m, dAT(0,0), lddat, dA(0,0), ldda, queue);
+            magmablas_transpose_inplace<Ty>(m, dAT(0, 0), lddat, queue);
+        } else {
+            magmablas_transpose<Ty>(n, m, dAT(0, 0), lddat, dA(0, 0), ldda,
+                                    queue);
             magma_free(dAT);
         }
 
@@ -348,13 +330,11 @@ magma_int_t magma_getrf_gpu(
 
 #undef dAT
 
-#define INSTANTIATE(T)                                  \
-    template magma_int_t magma_getrf_gpu<T>(            \
-        magma_int_t m, magma_int_t n,                   \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
-        magma_int_t *ipiv,                              \
-        magma_queue_t queue,                            \
-        magma_int_t *info);                             \
+#define INSTANTIATE(T)                                             \
+    template magma_int_t magma_getrf_gpu<T>(                       \
+        magma_int_t m, magma_int_t n, cl_mem dA, size_t dA_offset, \
+        magma_int_t ldda, magma_int_t * ipiv, magma_queue_t queue, \
+        magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index 096eddadba..829b909d2d 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -53,106 +53,99 @@
 
 #include "magma.h"
 #include "magma_blas.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
 
 #include <platform.hpp>
+#include <af/opencl.h>
 #include <algorithm>
 #include <string>
-#include <af/opencl.h>
 
-template<typename Ty>  magma_int_t
-magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
-                cl_mem dA, size_t dA_offset, magma_int_t ldda,
-                magma_int_t *ipiv,
-                cl_mem dB, size_t dB_offset, magma_int_t lddb,
-                magma_queue_t queue,
-                magma_int_t *info)
-{
-/*  -- clMagma (version 0.1) --
-       Univ. of Tennessee, Knoxville
-       Univ. of California, Berkeley
-       Univ. of Colorado, Denver
-       @date
-
-    Purpose
-    =======
-    Solves a system of linear equations
-      A * X = B  or  A' * X = B
-    with a general N-by-N matrix A using the LU factorization computed by ZGETRF_GPU.
-
-    Arguments
-    =========
-    TRANS   (input) CHARACTER*1
-            Specifies the form of the system of equations:
-            = 'N':  A * X = B  (No transpose)
-            = 'T':  A'* X = B  (Transpose)
-            = 'C':  A'* X = B  (Conjugate transpose = Transpose)
-
-    N       (input) INTEGER
-            The order of the matrix A.  N >= 0.
-
-    NRHS    (input) INTEGER
-            The number of right hand sides, i.e., the number of columns
-            of the matrix B.  NRHS >= 0.
-
-    A       (input) COMPLEX_16 array on the GPU, dimension (LDA,N)
-            The factors L and U from the factorization A = P*L*U as computed
-            by ZGETRF_GPU.
-
-    LDA     (input) INTEGER
-            The leading dimension of the array A.  LDA >= max(1,N).
-
-    IPIV    (input) INTEGER array, dimension (N)
-            The pivot indices from ZGETRF; for 1<=i<=N, row i of the
-            matrix was interchanged with row IPIV(i).
-
-    B       (input/output) COMPLEX_16 array on the GPU, dimension (LDB,NRHS)
-            On entry, the right hand side matrix B.
-            On exit, the solution matrix X.
-
-    LDB     (input) INTEGER
-            The leading dimension of the array B.  LDB >= max(1,N).
-
-    INFO    (output) INTEGER
-            = 0:  successful exit
-            < 0:  if INFO = -i, the i-th argument had an illegal value
-
-    HWORK   (workspace) COMPLEX_16 array, dimension N*NRHS
-    =====================================================================    */
+template<typename Ty>
+magma_int_t magma_getrs_gpu(magma_trans_t trans, magma_int_t n,
+                            magma_int_t nrhs, cl_mem dA, size_t dA_offset,
+                            magma_int_t ldda, magma_int_t *ipiv, cl_mem dB,
+                            size_t dB_offset, magma_int_t lddb,
+                            magma_queue_t queue, magma_int_t *info) {
+    /*  -- clMagma (version 0.1) --
+           Univ. of Tennessee, Knoxville
+           Univ. of California, Berkeley
+           Univ. of Colorado, Denver
+           @date
+
+        Purpose
+        =======
+        Solves a system of linear equations
+          A * X = B  or  A' * X = B
+        with a general N-by-N matrix A using the LU factorization computed by
+       ZGETRF_GPU.
+
+        Arguments
+        =========
+        TRANS   (input) CHARACTER*1
+                Specifies the form of the system of equations:
+                = 'N':  A * X = B  (No transpose)
+                = 'T':  A'* X = B  (Transpose)
+                = 'C':  A'* X = B  (Conjugate transpose = Transpose)
+
+        N       (input) INTEGER
+                The order of the matrix A.  N >= 0.
+
+        NRHS    (input) INTEGER
+                The number of right hand sides, i.e., the number of columns
+                of the matrix B.  NRHS >= 0.
+
+        A       (input) COMPLEX_16 array on the GPU, dimension (LDA,N)
+                The factors L and U from the factorization A = P*L*U as computed
+                by ZGETRF_GPU.
+
+        LDA     (input) INTEGER
+                The leading dimension of the array A.  LDA >= max(1,N).
+
+        IPIV    (input) INTEGER array, dimension (N)
+                The pivot indices from ZGETRF; for 1<=i<=N, row i of the
+                matrix was interchanged with row IPIV(i).
+
+        B       (input/output) COMPLEX_16 array on the GPU, dimension (LDB,NRHS)
+                On entry, the right hand side matrix B.
+                On exit, the solution matrix X.
+
+        LDB     (input) INTEGER
+                The leading dimension of the array B.  LDB >= max(1,N).
+
+        INFO    (output) INTEGER
+                = 0:  successful exit
+                < 0:  if INFO = -i, the i-th argument had an illegal value
+
+        HWORK   (workspace) COMPLEX_16 array, dimension N*NRHS
+        ===================================================================== */
 
     static const Ty c_one = magma_one<Ty>();
-    Ty *work = NULL;
-    int notran = (trans == MagmaNoTrans);
+    Ty *work              = NULL;
+    int notran            = (trans == MagmaNoTrans);
     magma_int_t i1, i2, inc;
 
     *info = 0;
-    if ( (! notran) &&
-         (trans != MagmaTrans) &&
-         (trans != MagmaConjTrans) ) {
+    if ((!notran) && (trans != MagmaTrans) && (trans != MagmaConjTrans)) {
         *info = -1;
     } else if (n < 0) {
         *info = -2;
     } else if (nrhs < 0) {
         *info = -3;
-    } else if (ldda < std::max(1,n)) {
+    } else if (ldda < std::max(1, n)) {
         *info = -5;
-    } else if (lddb < std::max(1,n)) {
+    } else if (lddb < std::max(1, n)) {
         *info = -8;
     }
-    if (*info != 0) {
-        return *info;
-    }
+    if (*info != 0) { return *info; }
 
     /* Quick return if possible */
-    if (n == 0 || nrhs == 0) {
-        return *info;
-    }
+    if (n == 0 || nrhs == 0) { return *info; }
 
-    magma_malloc_cpu<Ty>( &work, n*nrhs );
-    if ( work == NULL ) {
+    magma_malloc_cpu<Ty>(&work, n * nrhs);
+    if (work == NULL) {
         *info = MAGMA_ERR_HOST_ALLOC;
         return *info;
     }
@@ -166,10 +159,13 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
 
     cl_event event = NULL;
 
-    OPENCL_BLAS_TRANS_T cltrans =(trans == MagmaNoTrans) ? OPENCL_BLAS_NO_TRANS :
-        (trans == MagmaTrans ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS);
+    OPENCL_BLAS_TRANS_T cltrans =
+        (trans == MagmaNoTrans)
+            ? OPENCL_BLAS_NO_TRANS
+            : (trans == MagmaTrans ? OPENCL_BLAS_TRANS
+                                   : OPENCL_BLAS_CONJ_TRANS);
 
-    bool cond = opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
+    bool cond  = opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
     cl_mem dAT = 0;
     if (nrhs > 1 && cond) {
         magma_malloc<Ty>(&dAT, n * n);
@@ -179,39 +175,74 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
         inc = 1;
 
         /* Solve A * X = B. */
-        magma_getmatrix<Ty>( n, nrhs, dB, dB_offset, lddb, work, n, queue );
-        LAPACKE_CHECK(cpu_lapack_laswp( nrhs, work, n, i1, i2, ipiv, inc));
-        magma_setmatrix<Ty>( n, nrhs, work, n, dB, dB_offset, lddb, queue );
-        if ( nrhs == 1) {
-            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
-            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+        magma_getmatrix<Ty>(n, nrhs, dB, dB_offset, lddb, work, n, queue);
+        LAPACKE_CHECK(cpu_lapack_laswp(nrhs, work, n, i1, i2, ipiv, inc));
+        magma_setmatrix<Ty>(n, nrhs, work, n, dB, dB_offset, lddb, queue);
+        if (nrhs == 1) {
+            OPENCL_BLAS_CHECK(
+                gpu_blas_trsv(OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS,
+                              OPENCL_BLAS_UNIT_DIAGONAL, n, dA, dA_offset, ldda,
+                              dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsv(
+                OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS,
+                OPENCL_BLAS_NON_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB,
+                dB_offset, 1, 1, &queue, 0, nullptr, &event));
         } else {
-            OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(
+                gpu_blas_trsm(OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+                              OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_UNIT_DIAGONAL,
+                              n, nrhs, c_one, dA, dA_offset, ldda, dB,
+                              dB_offset, lddb, 1, &queue, 0, nullptr, &event));
 
-            if(cond) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+            if (cond) {
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+                    OPENCL_BLAS_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs,
+                    c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0,
+                    nullptr, &event));
             } else {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n,
+                    nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1,
+                    &queue, 0, nullptr, &event));
             }
         }
     } else {
         inc = -1;
 
         /* Solve A' * X = B. */
-        if ( nrhs == 1) {
-            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_UPPER, cltrans, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
-            OPENCL_BLAS_CHECK(gpu_blas_trsv( OPENCL_BLAS_TRIANGLE_LOWER, cltrans, OPENCL_BLAS_UNIT_DIAGONAL, n, dA, dA_offset, ldda, dB, dB_offset, 1, 1, &queue, 0, nullptr, &event));
+        if (nrhs == 1) {
+            OPENCL_BLAS_CHECK(gpu_blas_trsv(OPENCL_BLAS_TRIANGLE_UPPER, cltrans,
+                                            OPENCL_BLAS_NON_UNIT_DIAGONAL, n,
+                                            dA, dA_offset, ldda, dB, dB_offset,
+                                            1, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsv(OPENCL_BLAS_TRIANGLE_LOWER, cltrans,
+                                            OPENCL_BLAS_UNIT_DIAGONAL, n, dA,
+                                            dA_offset, ldda, dB, dB_offset, 1,
+                                            1, &queue, 0, nullptr, &event));
         } else {
-            if(cond) {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+            if (cond) {
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+                    OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, n,
+                    nrhs, c_one, dAT, 0, n, dB, dB_offset, lddb, 1, &queue, 0,
+                    nullptr, &event));
             } else {
-                OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER, cltrans, OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                    OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER, cltrans,
+                    OPENCL_BLAS_NON_UNIT_DIAGONAL, n, nrhs, c_one, dA,
+                    dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr,
+                    &event));
             }
-            OPENCL_BLAS_CHECK(gpu_blas_trsm( OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, cltrans, OPENCL_BLAS_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda, dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER, cltrans,
+                OPENCL_BLAS_UNIT_DIAGONAL, n, nrhs, c_one, dA, dA_offset, ldda,
+                dB, dB_offset, lddb, 1, &queue, 0, nullptr, &event));
         }
-        magma_getmatrix<Ty>( n, nrhs, dB, dB_offset, lddb, work, n, queue );
-        LAPACKE_CHECK(cpu_lapack_laswp( nrhs, work, n, i1, i2, ipiv, inc));
-        magma_setmatrix<Ty>( n, nrhs, work, n, dB, dB_offset, lddb, queue );
+        magma_getmatrix<Ty>(n, nrhs, dB, dB_offset, lddb, work, n, queue);
+        LAPACKE_CHECK(cpu_lapack_laswp(nrhs, work, n, i1, i2, ipiv, inc));
+        magma_setmatrix<Ty>(n, nrhs, work, n, dB, dB_offset, lddb, queue);
     }
 
     if (nrhs > 1 && dAT != 0) magma_free(dAT);
@@ -219,14 +250,12 @@ magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
     return *info;
 }
 
-#define INSTANTIATE(T)                                                  \
-    template  magma_int_t                                               \
-    magma_getrs_gpu<T>(magma_trans_t trans, magma_int_t n, magma_int_t nrhs, \
-                       cl_mem dA, size_t dA_offset, magma_int_t ldda,   \
-                       magma_int_t *ipiv,                               \
-                       cl_mem dB, size_t dB_offset, magma_int_t lddb,   \
-                       magma_queue_t queue,                             \
-                       magma_int_t *info);                              \
+#define INSTANTIATE(T)                                                     \
+    template magma_int_t magma_getrs_gpu<T>(                               \
+        magma_trans_t trans, magma_int_t n, magma_int_t nrhs, cl_mem dA,   \
+        size_t dA_offset, magma_int_t ldda, magma_int_t * ipiv, cl_mem dB, \
+        size_t dB_offset, magma_int_t lddb, magma_queue_t queue,           \
+        magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/labrd.cpp b/src/backend/opencl/magma/labrd.cpp
index 61bc58b84a..ed566f7956 100644
--- a/src/backend/opencl/magma/labrd.cpp
+++ b/src/backend/opencl/magma/labrd.cpp
@@ -51,159 +51,155 @@
  *
  **********************************************************************/
 
-
+#include <common/complex.hpp>
+#include <traits.hpp>
 #include "magma.h"
 #include "magma_blas.h"
-#include "magma_data.h"
-#include "magma_cpu_lapack.h"
 #include "magma_cpu_blas.h"
+#include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
-#include <traits.hpp>
-#include <common/complex.hpp>
 
 #include <algorithm>
 
-#define cpu_blas_gemv_macro(_trans, _m, _n, _alpha, _aptr, _lda, _xptr, _incx, _beta, _yptr, _incy) \
-    cpu_blas_gemv(_trans, _m, _n,                                       \
-                  cblas_scalar(_alpha), cblas_ptr(_aptr), _lda,         \
-                  cblas_ptr(_xptr), _incx,                              \
-                  cblas_scalar(_beta), cblas_ptr(_yptr), _incy)
-
-template<typename Ty>  magma_int_t
-magma_labrd_gpu(
-    magma_int_t m, magma_int_t n, magma_int_t nb,
-    Ty *a, magma_int_t lda,
-    cl_mem da, size_t da_offset, magma_int_t ldda,
-    void *_d, void *_e, Ty *tauq, Ty *taup,
-    Ty *x, magma_int_t ldx,
-    cl_mem dx, size_t dx_offset, magma_int_t lddx,
-    Ty *y, magma_int_t ldy,
-    cl_mem dy, size_t dy_offset, magma_int_t lddy,
-    magma_queue_t queue)
-{
-/*  -- MAGMA (version 1.1) --
-    Univ. of Tennessee, Knoxville
-    Univ. of California, Berkeley
-    Univ. of Colorado, Denver
-    @date
-
-    Purpose
-    =======
-    ZLABRD reduces the first NB rows and columns of a complex general
-    m by n matrix A to upper or lower bidiagonal form by an orthogonal
-    transformation Q' * A * P, and returns the matrices X and Y which
-    are needed to apply the transformation to the unreduced part of A.
-
-    If m >= n, A is reduced to upper bidiagonal form; if m < n, to lower
-    bidiagonal form.
-
-    This is an auxiliary routine called by SGEBRD
-
-    Arguments
-    =========
-    M       (input) INTEGER
-    The number of rows in the matrix A.
-
-    N       (input) INTEGER
-    The number of columns in the matrix A.
-
-    NB      (input) INTEGER
-    The number of leading rows and columns of A to be reduced.
-
-    A       (input/output) COMPLEX_16 array, dimension (LDA,N)
-    On entry, the m by n general matrix to be reduced.
-    On exit, the first NB rows and columns of the matrix are
-    overwritten; the rest of the array is unchanged.
-    If m >= n, elements on and below the diagonal in the first NB
-    columns, with the array TAUQ, represent the orthogonal
-    matrix Q as a product of elementary reflectors; and
-    elements above the diagonal in the first NB rows, with the
-    array TAUP, represent the orthogonal matrix P as a product
-    of elementary reflectors.
-    If m < n, elements below the diagonal in the first NB
-    columns, with the array TAUQ, represent the orthogonal
-    matrix Q as a product of elementary reflectors, and
-    elements on and above the diagonal in the first NB rows,
-    with the array TAUP, represent the orthogonal matrix P as
-    a product of elementary reflectors.
-    See Further Details.
-
-    LDA     (input) INTEGER
-    The leading dimension of the array A.  LDA >= max(1,M).
-
-    D       (output) COMPLEX_16 array, dimension (NB)
-    The diagonal elements of the first NB rows and columns of
-    the reduced matrix.  D(i) = A(i,i).
-
-    E       (output) COMPLEX_16 array, dimension (NB)
-    The off-diagonal elements of the first NB rows and columns of
-    the reduced matrix.
-
-    TAUQ    (output) COMPLEX_16 array dimension (NB)
-    The scalar factors of the elementary reflectors which
-    represent the orthogonal matrix Q. See Further Details.
-
-    TAUP    (output) COMPLEX_16 array, dimension (NB)
-    The scalar factors of the elementary reflectors which
-    represent the orthogonal matrix P. See Further Details.
-
-    X       (output) COMPLEX_16 array, dimension (LDX,NB)
-    The m-by-nb matrix X required to update the unreduced part
-    of A.
-
-    LDX     (input) INTEGER
-    The leading dimension of the array X. LDX >= M.
-
-    Y       (output) COMPLEX_16 array, dimension (LDY,NB)
-    The n-by-nb matrix Y required to update the unreduced part
-    of A.
-
-    LDY     (input) INTEGER
-    The leading dimension of the array Y. LDY >= N.
-
-    Further Details
-    ===============
-    The matrices Q and P are represented as products of elementary
-    reflectors:
-
-    Q = H(1) H(2) . . . H(nb)  and  P = G(1) G(2) . . . G(nb)
-
-    Each H(i) and G(i) has the form:
-
-    H(i) = I - tauq * v * v'  and G(i) = I - taup * u * u'
-
-    where tauq and taup are complex scalars, and v and u are complex vectors.
-
-    If m >= n, v(1:i-1) = 0, v(i) = 1, and v(i:m) is stored on exit in
-    A(i:m,i); u(1:i) = 0, u(i+1) = 1, and u(i+1:n) is stored on exit in
-    A(i,i+1:n); tauq is stored in TAUQ(i) and taup in TAUP(i).
-
-    If m < n, v(1:i) = 0, v(i+1) = 1, and v(i+1:m) is stored on exit in
-    A(i+2:m,i); u(1:i-1) = 0, u(i) = 1, and u(i:n) is stored on exit in
-    A(i,i+1:n); tauq is stored in TAUQ(i) and taup in TAUP(i).
-
-    The elements of the vectors v and u together form the m-by-nb matrix
-    V and the nb-by-n matrix U' which are needed, with X and Y, to apply
-    the transformation to the unreduced part of the matrix, using a block
-    update of the form:  A := A - V*Y' - X*U'.
+#define cpu_blas_gemv_macro(_trans, _m, _n, _alpha, _aptr, _lda, _xptr, _incx, \
+                            _beta, _yptr, _incy)                               \
+    cpu_blas_gemv(_trans, _m, _n, cblas_scalar(_alpha), cblas_ptr(_aptr),      \
+                  _lda, cblas_ptr(_xptr), _incx, cblas_scalar(_beta),          \
+                  cblas_ptr(_yptr), _incy)
+
+template<typename Ty>
+magma_int_t magma_labrd_gpu(magma_int_t m, magma_int_t n, magma_int_t nb, Ty *a,
+                            magma_int_t lda, cl_mem da, size_t da_offset,
+                            magma_int_t ldda, void *_d, void *_e, Ty *tauq,
+                            Ty *taup, Ty *x, magma_int_t ldx, cl_mem dx,
+                            size_t dx_offset, magma_int_t lddx, Ty *y,
+                            magma_int_t ldy, cl_mem dy, size_t dy_offset,
+                            magma_int_t lddy, magma_queue_t queue) {
+    /*  -- MAGMA (version 1.1) --
+        Univ. of Tennessee, Knoxville
+        Univ. of California, Berkeley
+        Univ. of Colorado, Denver
+        @date
+
+        Purpose
+        =======
+        ZLABRD reduces the first NB rows and columns of a complex general
+        m by n matrix A to upper or lower bidiagonal form by an orthogonal
+        transformation Q' * A * P, and returns the matrices X and Y which
+        are needed to apply the transformation to the unreduced part of A.
+
+        If m >= n, A is reduced to upper bidiagonal form; if m < n, to lower
+        bidiagonal form.
+
+        This is an auxiliary routine called by SGEBRD
+
+        Arguments
+        =========
+        M       (input) INTEGER
+        The number of rows in the matrix A.
+
+        N       (input) INTEGER
+        The number of columns in the matrix A.
+
+        NB      (input) INTEGER
+        The number of leading rows and columns of A to be reduced.
+
+        A       (input/output) COMPLEX_16 array, dimension (LDA,N)
+        On entry, the m by n general matrix to be reduced.
+        On exit, the first NB rows and columns of the matrix are
+        overwritten; the rest of the array is unchanged.
+        If m >= n, elements on and below the diagonal in the first NB
+        columns, with the array TAUQ, represent the orthogonal
+        matrix Q as a product of elementary reflectors; and
+        elements above the diagonal in the first NB rows, with the
+        array TAUP, represent the orthogonal matrix P as a product
+        of elementary reflectors.
+        If m < n, elements below the diagonal in the first NB
+        columns, with the array TAUQ, represent the orthogonal
+        matrix Q as a product of elementary reflectors, and
+        elements on and above the diagonal in the first NB rows,
+        with the array TAUP, represent the orthogonal matrix P as
+        a product of elementary reflectors.
+        See Further Details.
+
+        LDA     (input) INTEGER
+        The leading dimension of the array A.  LDA >= max(1,M).
+
+        D       (output) COMPLEX_16 array, dimension (NB)
+        The diagonal elements of the first NB rows and columns of
+        the reduced matrix.  D(i) = A(i,i).
+
+        E       (output) COMPLEX_16 array, dimension (NB)
+        The off-diagonal elements of the first NB rows and columns of
+        the reduced matrix.
+
+        TAUQ    (output) COMPLEX_16 array dimension (NB)
+        The scalar factors of the elementary reflectors which
+        represent the orthogonal matrix Q. See Further Details.
+
+        TAUP    (output) COMPLEX_16 array, dimension (NB)
+        The scalar factors of the elementary reflectors which
+        represent the orthogonal matrix P. See Further Details.
+
+        X       (output) COMPLEX_16 array, dimension (LDX,NB)
+        The m-by-nb matrix X required to update the unreduced part
+        of A.
+
+        LDX     (input) INTEGER
+        The leading dimension of the array X. LDX >= M.
+
+        Y       (output) COMPLEX_16 array, dimension (LDY,NB)
+        The n-by-nb matrix Y required to update the unreduced part
+        of A.
+
+        LDY     (input) INTEGER
+        The leading dimension of the array Y. LDY >= N.
+
+        Further Details
+        ===============
+        The matrices Q and P are represented as products of elementary
+        reflectors:
+
+        Q = H(1) H(2) . . . H(nb)  and  P = G(1) G(2) . . . G(nb)
+
+        Each H(i) and G(i) has the form:
+
+        H(i) = I - tauq * v * v'  and G(i) = I - taup * u * u'
+
+        where tauq and taup are complex scalars, and v and u are complex
+       vectors.
+
+        If m >= n, v(1:i-1) = 0, v(i) = 1, and v(i:m) is stored on exit in
+        A(i:m,i); u(1:i) = 0, u(i+1) = 1, and u(i+1:n) is stored on exit in
+        A(i,i+1:n); tauq is stored in TAUQ(i) and taup in TAUP(i).
+
+        If m < n, v(1:i) = 0, v(i+1) = 1, and v(i+1:m) is stored on exit in
+        A(i+2:m,i); u(1:i-1) = 0, u(i) = 1, and u(i:n) is stored on exit in
+        A(i,i+1:n); tauq is stored in TAUQ(i) and taup in TAUP(i).
+
+        The elements of the vectors v and u together form the m-by-nb matrix
+        V and the nb-by-n matrix U' which are needed, with X and Y, to apply
+        the transformation to the unreduced part of the matrix, using a block
+        update of the form:  A := A - V*Y' - X*U'.
+
+        The contents of A on exit are illustrated by the following examples
+        with nb = 2:
 
-    The contents of A on exit are illustrated by the following examples
-    with nb = 2:
+        m = 6 and n = 5 (m > n):          m = 5 and n = 6 (m < n):
 
-    m = 6 and n = 5 (m > n):          m = 5 and n = 6 (m < n):
-
-    ( 1   1   u1  u1  u1)           ( 1   u1  u1  u1  u1  u1)
-    ( v1  1   1   u2  u2)           ( 1   1   u2  u2  u2  u2)
-    ( v1  v2  a   a   a )           ( v1  1   a   a   a   a )
-    ( v1  v2  a   a   a )           ( v1  v2  a   a   a   a )
-    ( v1  v2  a   a   a )           ( v1  v2  a   a   a   a )
-    ( v1  v2  a   a   a )
-
-    where a denotes an element of the original matrix which is unchanged,
-    vi denotes an element of the vector defining H(i), and ui an element
-    of the vector defining G(i).
-    =====================================================================    */
+        ( 1   1   u1  u1  u1)           ( 1   u1  u1  u1  u1  u1)
+        ( v1  1   1   u2  u2)           ( 1   1   u2  u2  u2  u2)
+        ( v1  v2  a   a   a )           ( v1  1   a   a   a   a )
+        ( v1  v2  a   a   a )           ( v1  v2  a   a   a   a )
+        ( v1  v2  a   a   a )           ( v1  v2  a   a   a   a )
+        ( v1  v2  a   a   a )
+
+        where a denotes an element of the original matrix which is unchanged,
+        vi denotes an element of the vector defining H(i), and ui an element
+        of the vector defining G(i).
+        ===================================================================== */
 
     typedef typename af::dtype_traits<Ty>::base_type Tr;
 
@@ -212,16 +208,17 @@ magma_labrd_gpu(
     Tr *d = (Tr *)_d;
     Tr *e = (Tr *)_e;
 
-    Ty c_neg_one = magma_neg_one<Ty>();
-    Ty c_one = magma_one<Ty>();
-    Ty c_zero = magma_zero<Ty>();
+    Ty c_neg_one     = magma_neg_one<Ty>();
+    Ty c_one         = magma_one<Ty>();
+    Ty c_zero        = magma_zero<Ty>();
     magma_int_t c__1 = 1;
 
-    magma_int_t a_dim1, a_offset, x_dim1, x_offset, y_dim1, y_offset, i__2, i__3;
+    magma_int_t a_dim1, a_offset, x_dim1, x_offset, y_dim1, y_offset, i__2,
+        i__3;
     magma_int_t i__;
     Ty alpha;
 
-    a_dim1 = lda;
+    a_dim1   = lda;
     a_offset = 1 + a_dim1;
     a -= a_offset;
     --d;
@@ -229,23 +226,21 @@ magma_labrd_gpu(
     --tauq;
     --taup;
 
-    x_dim1 = ldx;
+    x_dim1   = ldx;
     x_offset = 1 + x_dim1;
     x -= x_offset;
     dx_offset -= 1 + lddx;
 
-    y_dim1 = ldy;
+    y_dim1   = ldy;
     y_offset = 1 + y_dim1;
     y -= y_offset;
     dy_offset -= 1 + lddy;
 
     /* Quick return if possible */
-    if (m <= 0 || n <= 0) {
-        return 0;
-    }
+    if (m <= 0 || n <= 0) { return 0; }
 
     Ty *f;
-    magma_malloc_cpu<Ty>(&f, std::max(n,m));
+    magma_malloc_cpu<Ty>(&f, std::max(n, m));
     assert(f != NULL);  // TODO return error, or allocate outside zlatrd
 
     magma_event_t event = NULL;
@@ -267,27 +262,29 @@ magma_labrd_gpu(
             i__3 = i__ - 1;
 
             if (is_cplx) {
-                LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__+y_dim1], ldy));
+                LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
             }
 
-            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &a[i__ + a_dim1], lda,
-                          &y[i__+y_dim1], ldy, (&c_one), &a[i__ + i__ * a_dim1], c__1);
+            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                                &a[i__ + a_dim1], lda, &y[i__ + y_dim1], ldy,
+                                (&c_one), &a[i__ + i__ * a_dim1], c__1);
 
             if (is_cplx) {
-                LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__+y_dim1], ldy));
+                LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
             }
 
-            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &x[i__ + x_dim1], ldx,
-                          &a[i__*a_dim1+1], c__1, (&c_one), &a[i__+i__*a_dim1], c__1);
+            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                                &x[i__ + x_dim1], ldx, &a[i__ * a_dim1 + 1],
+                                c__1, (&c_one), &a[i__ + i__ * a_dim1], c__1);
 
             /* Generate reflection Q(i) to annihilate A(i+1:m,i) */
             alpha = a[i__ + i__ * a_dim1];
-            i__2 = m - i__ + 1;
-            i__3 = i__ + 1;
+            i__2  = m - i__ + 1;
+            i__3  = i__ + 1;
 
             LAPACKE_CHECK(cpu_lapack_larfg(i__2, &alpha,
-                                          &a[std::min(i__3,m) + i__ * a_dim1],
-                                          c__1, &tauq[i__]));
+                                           &a[std::min(i__3, m) + i__ * a_dim1],
+                                           c__1, &tauq[i__]));
 
             d[i__] = magma_real<Ty>(alpha);
             if (i__ < n) {
@@ -298,180 +295,199 @@ magma_labrd_gpu(
                 i__3 = n - i__;
 
                 // 1. Send the block reflector  A(i+1:m,i) to the GPU ------
-                magma_setvector<Ty>(i__2,
-                                    a + i__   + i__   * a_dim1, 1,
-                                    da, da_offset + (i__-1)+(i__-1)* (ldda), 1,
-                                    queue);
+                magma_setvector<Ty>(i__2, a + i__ + i__ * a_dim1, 1, da,
+                                    da_offset + (i__ - 1) + (i__ - 1) * (ldda),
+                                    1, queue);
                 // 2. Multiply ---------------------------------------------
-                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_CONJ_TRANS, i__2, i__3, c_one,
-                                                da, da_offset + (i__-1) + ((i__-1) + 1) * (ldda), ldda,
-                                                da, da_offset + (i__-1) + (i__-1) * (ldda), c__1, c_zero,
-                                                dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1,
-                                                1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(
+                    OPENCL_BLAS_CONJ_TRANS, i__2, i__3, c_one, da,
+                    da_offset + (i__ - 1) + ((i__ - 1) + 1) * (ldda), ldda, da,
+                    da_offset + (i__ - 1) + (i__ - 1) * (ldda), c__1, c_zero,
+                    dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1, 1, &queue, 0,
+                    nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
-                magma_getmatrix_async<Ty>(i__3, 1,
-                                          dy, dy_offset + i__+1+i__*y_dim1, y_dim1,
-                                          y+i__+1+i__*y_dim1, y_dim1,
-                                          queue, &event);
+                magma_getmatrix_async<Ty>(
+                    i__3, 1, dy, dy_offset + i__ + 1 + i__ * y_dim1, y_dim1,
+                    y + i__ + 1 + i__ * y_dim1, y_dim1, queue, &event);
                 i__2 = m - i__ + 1;
                 i__3 = i__ - 1;
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &a[i__ + a_dim1],
-                              lda, &a[i__ + i__ * a_dim1], c__1, (&c_zero),
-                              &y[i__ * y_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one),
+                                    &a[i__ + a_dim1], lda,
+                                    &a[i__ + i__ * a_dim1], c__1, (&c_zero),
+                                    &y[i__ * y_dim1 + 1], c__1);
 
                 i__2 = n - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &y[i__ + 1 +y_dim1], ldy,
-                              &y[i__ * y_dim1 + 1], c__1,
-                              (&c_zero), f, c__1);
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                                    &y[i__ + 1 + y_dim1], ldy,
+                                    &y[i__ * y_dim1 + 1], c__1, (&c_zero), f,
+                                    c__1);
                 i__2 = m - i__ + 1;
                 i__3 = i__ - 1;
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &x[i__ + x_dim1],
-                              ldx, &a[i__ + i__ * a_dim1], c__1, (&c_zero),
-                              &y[i__ * y_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one),
+                                    &x[i__ + x_dim1], ldx,
+                                    &a[i__ + i__ * a_dim1], c__1, (&c_zero),
+                                    &y[i__ * y_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
 
-                if (i__3 != 0){
+                if (i__3 != 0) {
                     i__2 = n - i__;
-                    cpu_blas_axpy(i__2, cblas_scalar(&c_one),
-                                  cblas_ptr(f),c__1, cblas_ptr(&y[i__+1+i__*y_dim1]), c__1);
+                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),
+                                  c__1, cblas_ptr(&y[i__ + 1 + i__ * y_dim1]),
+                                  c__1);
                 }
 
                 i__2 = i__ - 1;
                 i__3 = n - i__;
                 cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one),
-                              &a[(i__ + 1) * a_dim1 + 1], lda, &y[i__ * y_dim1 + 1], c__1, (&c_one),
-                              &y[i__ + 1 + i__ * y_dim1], c__1);
+                                    &a[(i__ + 1) * a_dim1 + 1], lda,
+                                    &y[i__ * y_dim1 + 1], c__1, (&c_one),
+                                    &y[i__ + 1 + i__ * y_dim1], c__1);
                 i__2 = n - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]), cblas_ptr(&y[i__ + 1 + i__ * y_dim1]), c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]),
+                              cblas_ptr(&y[i__ + 1 + i__ * y_dim1]), c__1);
 
                 /* Update A(i,i+1:n) */
                 i__2 = n - i__;
                 if (is_cplx) {
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &a[i__+(i__+1)*a_dim1], lda));
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__,  &a[i__+a_dim1], lda));
+                    LAPACKE_CHECK(cpu_lapack_lacgv(
+                        i__2, &a[i__ + (i__ + 1) * a_dim1], lda));
+                    LAPACKE_CHECK(cpu_lapack_lacgv(i__, &a[i__ + a_dim1], lda));
                 }
 
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__, (&c_neg_one),
-                              &y[i__ + 1 + y_dim1], ldy, &a[i__ + a_dim1], lda,
-                              (&c_one), &a[i__ + (i__ + 1) * a_dim1], lda);
+                                    &y[i__ + 1 + y_dim1], ldy, &a[i__ + a_dim1],
+                                    lda, (&c_one), &a[i__ + (i__ + 1) * a_dim1],
+                                    lda);
                 i__2 = i__ - 1;
                 i__3 = n - i__;
 
                 if (is_cplx) {
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__,  &a[i__+a_dim1], lda));
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &x[i__+x_dim1], ldx));
+                    LAPACKE_CHECK(cpu_lapack_lacgv(i__, &a[i__ + a_dim1], lda));
+                    LAPACKE_CHECK(
+                        cpu_lapack_lacgv(i__2, &x[i__ + x_dim1], ldx));
                 }
 
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one), &a[(i__ + 1) *
-                                                                              a_dim1 + 1], lda, &x[i__ + x_dim1], ldx, (&c_one), &a[
-                                                                                  i__ + (i__ + 1) * a_dim1], lda);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one),
+                                    &a[(i__ + 1) * a_dim1 + 1], lda,
+                                    &x[i__ + x_dim1], ldx, (&c_one),
+                                    &a[i__ + (i__ + 1) * a_dim1], lda);
                 if (is_cplx) {
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &x[i__+x_dim1], ldx));
+                    LAPACKE_CHECK(
+                        cpu_lapack_lacgv(i__2, &x[i__ + x_dim1], ldx));
                 }
 
                 /* Generate reflection P(i) to annihilate A(i,i+2:n) */
                 i__2 = n - i__;
                 /* Computing MIN */
-                i__3 = i__ + 2;
+                i__3  = i__ + 2;
                 alpha = a[i__ + (i__ + 1) * a_dim1];
-                LAPACKE_CHECK(cpu_lapack_larfg(i__2, &alpha,
-                                              &a[i__ + std::min(i__3,n) * a_dim1],
-                                              lda, &taup[i__]));
-                e[i__] = magma_real<Ty>(alpha);
+                LAPACKE_CHECK(cpu_lapack_larfg(
+                    i__2, &alpha, &a[i__ + std::min(i__3, n) * a_dim1], lda,
+                    &taup[i__]));
+                e[i__]                      = magma_real<Ty>(alpha);
                 a[i__ + (i__ + 1) * a_dim1] = c_one;
 
                 /* Compute X(i+1:m,i) */
                 i__2 = m - i__;
                 i__3 = n - i__;
                 // 1. Send the block reflector  A(i+1:m,i) to the GPU ------
-                magma_setvector<Ty>(i__3,
-                                    a + i__   + (i__   +1)* a_dim1, lda,
-                                    da, da_offset + (i__-1)+((i__-1)+1)*(ldda), ldda,
-                                    queue);
+                magma_setvector<Ty>(
+                    i__3, a + i__ + (i__ + 1) * a_dim1, lda, da,
+                    da_offset + (i__ - 1) + ((i__ - 1) + 1) * (ldda), ldda,
+                    queue);
                 // 2. Multiply ---------------------------------------------
-                //magma_zcopy(i__3, da+(i__-1)+((i__-1)+1)*(ldda), ldda,
+                // magma_zcopy(i__3, da+(i__-1)+((i__-1)+1)*(ldda), ldda,
                 //            dy + 1 + lddy, 1);
-                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_NO_TRANS, i__2, i__3, c_one,
-                                                da, da_offset + (i__-1)+1+ ((i__-1)+1) * (ldda), ldda,
-                                                da, da_offset + (i__-1) +  ((i__-1)+1) * (ldda), ldda,
-                                                //dy + 1 + lddy, 1,
-                                                c_zero, dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1,
-                                                1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(
+                    OPENCL_BLAS_NO_TRANS, i__2, i__3, c_one, da,
+                    da_offset + (i__ - 1) + 1 + ((i__ - 1) + 1) * (ldda), ldda,
+                    da, da_offset + (i__ - 1) + ((i__ - 1) + 1) * (ldda), ldda,
+                    // dy + 1 + lddy, 1,
+                    c_zero, dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1, 1,
+                    &queue, 0, nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
-                magma_getmatrix_async<Ty>(i__2, 1,
-                                          dx, dx_offset + i__+1+i__*x_dim1, x_dim1,
-                                          x+i__+1+i__*x_dim1, x_dim1,
-                                          queue, &event);
+                magma_getmatrix_async<Ty>(
+                    i__2, 1, dx, dx_offset + i__ + 1 + i__ * x_dim1, x_dim1,
+                    x + i__ + 1 + i__ * x_dim1, x_dim1, queue, &event);
 
                 i__2 = n - i__;
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__, (&c_one), &y[i__ + 1 + y_dim1],
-                              ldy, &a[i__ + (i__ + 1) * a_dim1], lda, (&c_zero), &x[
-                                  i__ * x_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__, (&c_one),
+                                    &y[i__ + 1 + y_dim1], ldy,
+                                    &a[i__ + (i__ + 1) * a_dim1], lda,
+                                    (&c_zero), &x[i__ * x_dim1 + 1], c__1);
 
                 i__2 = m - i__;
-                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__, (&c_neg_one), &a[i__ + 1 + a_dim1], lda,
-                              &x[i__ * x_dim1 + 1], c__1, (&c_zero), f, c__1);
+                cpu_blas_gemv_macro(
+                    CblasNoTrans, i__2, i__, (&c_neg_one), &a[i__ + 1 + a_dim1],
+                    lda, &x[i__ * x_dim1 + 1], c__1, (&c_zero), f, c__1);
                 i__2 = i__ - 1;
                 i__3 = n - i__;
-                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_one), &a[(i__ + 1) * a_dim1 + 1],
-                              lda, &a[i__ + (i__ + 1) * a_dim1], lda,
-                              (&c_zero), &x[i__ * x_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_one),
+                                    &a[(i__ + 1) * a_dim1 + 1], lda,
+                                    &a[i__ + (i__ + 1) * a_dim1], lda,
+                                    (&c_zero), &x[i__ * x_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
 
-                if (i__!=0){
+                if (i__ != 0) {
                     i__2 = m - i__;
-                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),c__1, cblas_ptr(&x[i__+1+i__*x_dim1]),c__1);
+                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),
+                                  c__1, cblas_ptr(&x[i__ + 1 + i__ * x_dim1]),
+                                  c__1);
                 }
 
-
                 i__2 = m - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &x[i__ + 1 +
-                                                                           x_dim1], ldx, &x[i__ * x_dim1 + 1], c__1, (&c_one), &x[
-                                                                               i__ + 1 + i__ * x_dim1], c__1);
+                cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                                    &x[i__ + 1 + x_dim1], ldx,
+                                    &x[i__ * x_dim1 + 1], c__1, (&c_one),
+                                    &x[i__ + 1 + i__ * x_dim1], c__1);
                 i__2 = m - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]), cblas_ptr(&x[i__ + 1 + i__ * x_dim1]), c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]),
+                              cblas_ptr(&x[i__ + 1 + i__ * x_dim1]), c__1);
 
                 if (is_cplx) {
                     i__2 = n - i__;
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__2,  &a[i__+(i__+1)*a_dim1], lda));
-                    // 4. Send the block reflector  A(i+1:m,i) to the GPU after ZLACGV()
-                    magma_setvector<Ty>(i__2,
-                                        a + i__   + (i__   +1)* a_dim1, lda,
-                                        da, da_offset + (i__-1)+((i__-1)+1)*(ldda), ldda,
-                                        queue);
+                    LAPACKE_CHECK(cpu_lapack_lacgv(
+                        i__2, &a[i__ + (i__ + 1) * a_dim1], lda));
+                    // 4. Send the block reflector  A(i+1:m,i) to the GPU after
+                    // ZLACGV()
+                    magma_setvector<Ty>(
+                        i__2, a + i__ + (i__ + 1) * a_dim1, lda, da,
+                        da_offset + (i__ - 1) + ((i__ - 1) + 1) * (ldda), ldda,
+                        queue);
                 }
             }
         }
-    }
-    else {
+    } else {
         /* Reduce to lower bidiagonal form */
         for (i__ = 1; i__ <= nb; ++i__) {
-
             /* Update A(i,i:n) */
             i__2 = n - i__ + 1;
             i__3 = i__ - 1;
             if (is_cplx) {
-                LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
+                LAPACKE_CHECK(
+                    cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &a[i__ + a_dim1], lda));
             }
-            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one), &y[i__ + y_dim1], ldy,
-                          &a[i__ + a_dim1], lda, (&c_one), &a[i__ + i__ * a_dim1], lda);
+            cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
+                                &y[i__ + y_dim1], ldy, &a[i__ + a_dim1], lda,
+                                (&c_one), &a[i__ + i__ * a_dim1], lda);
             i__2 = i__ - 1;
             if (is_cplx) {
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &a[i__ + a_dim1], lda));
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &x[i__ + x_dim1], ldx));
             }
             i__3 = n - i__ + 1;
-            cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one), &a[i__ * a_dim1 + 1],
-                          lda, &x[i__ + x_dim1], ldx, (&c_one), &a[i__ + i__ * a_dim1], lda);
+            cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_neg_one),
+                                &a[i__ * a_dim1 + 1], lda, &x[i__ + x_dim1],
+                                ldx, (&c_one), &a[i__ + i__ * a_dim1], lda);
             if (is_cplx) {
                 LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &x[i__ + x_dim1], ldx));
             }
@@ -479,10 +495,11 @@ magma_labrd_gpu(
             /* Generate reflection P(i) to annihilate A(i,i+1:n) */
             i__2 = n - i__ + 1;
             /* Computing MIN */
-            i__3 = i__ + 1;
+            i__3  = i__ + 1;
             alpha = a[i__ + i__ * a_dim1];
             LAPACKE_CHECK(cpu_lapack_larfg(i__2, &alpha,
-                                           &a[i__ + std::min(i__3,n) * a_dim1], lda, &taup[i__]));
+                                           &a[i__ + std::min(i__3, n) * a_dim1],
+                                           lda, &taup[i__]));
             d[i__] = magma_real<Ty>(alpha);
             if (i__ < m) {
                 a[i__ + i__ * a_dim1] = c_one;
@@ -492,68 +509,73 @@ magma_labrd_gpu(
                 i__3 = n - i__ + 1;
 
                 // 1. Send the block reflector  A(i,i+1:n) to the GPU ------
-                magma_setvector<Ty>(i__3,
-                                    a + i__   + i__   * a_dim1, lda,
-                                    da, da_offset + (i__-1)+(i__-1)* (ldda), ldda,
-                                    queue);
+                magma_setvector<Ty>(i__3, a + i__ + i__ * a_dim1, lda, da,
+                                    da_offset + (i__ - 1) + (i__ - 1) * (ldda),
+                                    ldda, queue);
 
                 // 2. Multiply ---------------------------------------------
-                //magma_zcopy(i__3, da+(i__-1)+(i__-1)*(ldda), ldda,
+                // magma_zcopy(i__3, da+(i__-1)+(i__-1)*(ldda), ldda,
                 //            dy + 1 + lddy, 1);
-                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_NO_TRANS, i__2, i__3, c_one,
-                                                da, da_offset + (i__-1)+1 + (i__-1) * ldda, ldda,
-                                                da, da_offset + (i__-1)   + (i__-1) * ldda, ldda,
-                                                // dy + 1 + lddy, 1,
-                                                c_zero,
-                                                dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1,
-                                                1, &queue, 0, nullptr, &event));
-
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(
+                    OPENCL_BLAS_NO_TRANS, i__2, i__3, c_one, da,
+                    da_offset + (i__ - 1) + 1 + (i__ - 1) * ldda, ldda, da,
+                    da_offset + (i__ - 1) + (i__ - 1) * ldda, ldda,
+                    // dy + 1 + lddy, 1,
+                    c_zero, dx, dx_offset + i__ + 1 + i__ * x_dim1, c__1, 1,
+                    &queue, 0, nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
-                magma_getmatrix_async<Ty>(i__2, 1,
-                                          dx, dx_offset + i__+1+i__*x_dim1, x_dim1,
-                                          x+i__+1+i__*x_dim1, x_dim1,
-                                          queue, &event);
+                magma_getmatrix_async<Ty>(
+                    i__2, 1, dx, dx_offset + i__ + 1 + i__ * x_dim1, x_dim1,
+                    x + i__ + 1 + i__ * x_dim1, x_dim1, queue, &event);
 
                 i__2 = n - i__ + 1;
                 i__3 = i__ - 1;
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &y[i__ + y_dim1],
-                              ldy, &a[i__ + i__ * a_dim1], lda, (&c_zero),
-                              &x[i__ *  x_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one),
+                                    &y[i__ + y_dim1], ldy,
+                                    &a[i__ + i__ * a_dim1], lda, (&c_zero),
+                                    &x[i__ * x_dim1 + 1], c__1);
                 i__2 = m - i__;
                 i__3 = i__ - 1;
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
-                              &a[i__ + 1 + a_dim1], lda, &x[i__ * x_dim1 + 1], c__1, (&c_zero),
-                              f, c__1);
+                                    &a[i__ + 1 + a_dim1], lda,
+                                    &x[i__ * x_dim1 + 1], c__1, (&c_zero), f,
+                                    c__1);
 
                 i__2 = i__ - 1;
                 i__3 = n - i__ + 1;
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_one),
-                              &a[i__ * a_dim1 + 1], lda, &a[i__ + i__ * a_dim1], lda, (&c_zero),
-                              &x[i__ * x_dim1 + 1], c__1);
+                                    &a[i__ * a_dim1 + 1], lda,
+                                    &a[i__ + i__ * a_dim1], lda, (&c_zero),
+                                    &x[i__ * x_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
-                if (i__2 != 0){
+                if (i__2 != 0) {
                     i__3 = m - i__;
-                    cpu_blas_axpy(i__3, cblas_scalar(&c_one), cblas_ptr(f),c__1, cblas_ptr(&x[i__+1+i__*x_dim1]),c__1);
+                    cpu_blas_axpy(i__3, cblas_scalar(&c_one), cblas_ptr(f),
+                                  c__1, cblas_ptr(&x[i__ + 1 + i__ * x_dim1]),
+                                  c__1);
                 }
 
                 i__2 = m - i__;
                 i__3 = i__ - 1;
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
-                              &x[i__ + 1 + x_dim1], ldx, &x[i__ * x_dim1 + 1], c__1, (&c_one),
-                              &x[i__ + 1 + i__ * x_dim1], c__1);
+                                    &x[i__ + 1 + x_dim1], ldx,
+                                    &x[i__ * x_dim1 + 1], c__1, (&c_one),
+                                    &x[i__ + 1 + i__ * x_dim1], c__1);
                 i__2 = m - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]), cblas_ptr(&x[i__ + 1 + i__ * x_dim1]), c__1);
+                cpu_blas_scal(i__2, cblas_scalar(&taup[i__]),
+                              cblas_ptr(&x[i__ + 1 + i__ * x_dim1]), c__1);
                 i__2 = n - i__ + 1;
 
                 if (is_cplx) {
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
-                    magma_setvector<Ty>(i__2,
-                                        a + i__   + (i__ )* a_dim1, lda,
-                                        da, da_offset + (i__-1)+ (i__-1)*(ldda), ldda,
-                                        queue);
+                    LAPACKE_CHECK(
+                        cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
+                    magma_setvector<Ty>(
+                        i__2, a + i__ + (i__)*a_dim1, lda, da,
+                        da_offset + (i__ - 1) + (i__ - 1) * (ldda), ldda,
+                        queue);
                 }
 
                 /* Update A(i+1:m,i) */
@@ -561,28 +583,32 @@ magma_labrd_gpu(
                 i__3 = i__ - 1;
 
                 if (is_cplx) {
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
+                    LAPACKE_CHECK(
+                        cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
                 }
 
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
-                              &a[i__ + 1 + a_dim1], lda, &y[i__ + y_dim1], ldy, (&c_one),
-                              &a[i__ + 1 + i__ * a_dim1], c__1);
+                                    &a[i__ + 1 + a_dim1], lda, &y[i__ + y_dim1],
+                                    ldy, (&c_one), &a[i__ + 1 + i__ * a_dim1],
+                                    c__1);
                 i__2 = m - i__;
                 if (is_cplx) {
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
+                    LAPACKE_CHECK(
+                        cpu_lapack_lacgv(i__3, &y[i__ + y_dim1], ldy));
                 }
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__, (&c_neg_one),
-                              &x[i__ + 1 + x_dim1], ldx, &a[i__ * a_dim1 + 1], c__1, (&c_one),
-                              &a[i__ + 1 + i__ * a_dim1], c__1);
+                                    &x[i__ + 1 + x_dim1], ldx,
+                                    &a[i__ * a_dim1 + 1], c__1, (&c_one),
+                                    &a[i__ + 1 + i__ * a_dim1], c__1);
 
                 /* Generate reflection Q(i) to annihilate A(i+2:m,i) */
-                i__2 = m - i__;
-                i__3 = i__ + 2;
+                i__2  = m - i__;
+                i__3  = i__ + 2;
                 alpha = a[i__ + 1 + i__ * a_dim1];
-                LAPACKE_CHECK(cpu_lapack_larfg(i__2, &alpha,
-                                               &a[std::min(i__3,m) + i__ * a_dim1],
-                                               c__1, &tauq[i__]));
-                e[i__] = magma_real<Ty>(alpha);
+                LAPACKE_CHECK(cpu_lapack_larfg(
+                    i__2, &alpha, &a[std::min(i__3, m) + i__ * a_dim1], c__1,
+                    &tauq[i__]));
+                e[i__]                    = magma_real<Ty>(alpha);
                 a[i__ + 1 + i__ * a_dim1] = c_one;
 
                 /* Compute Y(i+1:n,i) */
@@ -590,61 +616,67 @@ magma_labrd_gpu(
                 i__3 = n - i__;
 
                 // 1. Send the block reflector  A(i+1:m,i) to the GPU ------
-                magma_setvector<Ty>(i__2,
-                                    a + i__   +1+  i__   * a_dim1, 1,
-                                    da, da_offset + (i__-1)+1+ (i__-1)*(ldda),  1,
-                                    queue);
+                magma_setvector<Ty>(
+                    i__2, a + i__ + 1 + i__ * a_dim1, 1, da,
+                    da_offset + (i__ - 1) + 1 + (i__ - 1) * (ldda), 1, queue);
                 // 2. Multiply ---------------------------------------------
-                OPENCL_BLAS_CHECK(gpu_blas_gemv(OPENCL_BLAS_CONJ_TRANS, i__2, i__3, c_one,
-                                                da, da_offset + (i__-1)+1+ ((i__-1)+1) * ldda, ldda,
-                                                da, da_offset + (i__-1)+1+  (i__-1)    * ldda, c__1,
-                                                c_zero, dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1,
-                                                1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_CHECK(gpu_blas_gemv(
+                    OPENCL_BLAS_CONJ_TRANS, i__2, i__3, c_one, da,
+                    da_offset + (i__ - 1) + 1 + ((i__ - 1) + 1) * ldda, ldda,
+                    da, da_offset + (i__ - 1) + 1 + (i__ - 1) * ldda, c__1,
+                    c_zero, dy, dy_offset + i__ + 1 + i__ * y_dim1, c__1, 1,
+                    &queue, 0, nullptr, &event));
 
                 // 3. Put the result back ----------------------------------
-                magma_getmatrix_async<Ty>(i__3, 1,
-                                          dy, dy_offset + i__+1+i__*y_dim1, y_dim1,
-                                          y+i__+1+i__*y_dim1, y_dim1,
-                                          queue, &event);
+                magma_getmatrix_async<Ty>(
+                    i__3, 1, dy, dy_offset + i__ + 1 + i__ * y_dim1, y_dim1,
+                    y + i__ + 1 + i__ * y_dim1, y_dim1, queue, &event);
 
                 i__2 = m - i__;
                 i__3 = i__ - 1;
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one), &a[i__ + 1 + a_dim1],
-                              lda, &a[i__ + 1 + i__ * a_dim1], c__1, (&c_zero),
-                              &y[ i__ * y_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__3, (&c_one),
+                                    &a[i__ + 1 + a_dim1], lda,
+                                    &a[i__ + 1 + i__ * a_dim1], c__1, (&c_zero),
+                                    &y[i__ * y_dim1 + 1], c__1);
                 i__2 = n - i__;
                 i__3 = i__ - 1;
                 cpu_blas_gemv_macro(CblasNoTrans, i__2, i__3, (&c_neg_one),
-                              &y[i__ + 1 + y_dim1], ldy, &y[i__ * y_dim1 + 1], c__1,
-                              (&c_zero), f, c__1);
+                                    &y[i__ + 1 + y_dim1], ldy,
+                                    &y[i__ * y_dim1 + 1], c__1, (&c_zero), f,
+                                    c__1);
 
                 i__2 = m - i__;
-                cpu_blas_gemv_macro(CblasTransParam, i__2, i__, (&c_one), &x[i__ + 1 + x_dim1],
-                              ldx, &a[i__ + 1 + i__ * a_dim1], c__1, (&c_zero),
-                              &y[i__ * y_dim1 + 1], c__1);
+                cpu_blas_gemv_macro(CblasTransParam, i__2, i__, (&c_one),
+                                    &x[i__ + 1 + x_dim1], ldx,
+                                    &a[i__ + 1 + i__ * a_dim1], c__1, (&c_zero),
+                                    &y[i__ * y_dim1 + 1], c__1);
 
                 // 4. Synch to make sure the result is back ----------------
                 magma_event_sync(event);
-                if (i__3 != 0){
+                if (i__3 != 0) {
                     i__2 = n - i__;
-                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),c__1, cblas_ptr(&y[i__+1+i__*y_dim1]),c__1);
+                    cpu_blas_axpy(i__2, cblas_scalar(&c_one), cblas_ptr(f),
+                                  c__1, cblas_ptr(&y[i__ + 1 + i__ * y_dim1]),
+                                  c__1);
                 }
 
                 i__2 = n - i__;
                 cpu_blas_gemv_macro(CblasTransParam, i__, i__2, (&c_neg_one),
-                              &a[(i__ + 1) * a_dim1 + 1], lda, &y[i__ * y_dim1 + 1],
-                              c__1, (&c_one), &y[i__ + 1 + i__ * y_dim1], c__1);
+                                    &a[(i__ + 1) * a_dim1 + 1], lda,
+                                    &y[i__ * y_dim1 + 1], c__1, (&c_one),
+                                    &y[i__ + 1 + i__ * y_dim1], c__1);
                 i__2 = n - i__;
-                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]), cblas_ptr(&y[i__ + 1 + i__ * y_dim1]), c__1);
-            }
-            else {
+                cpu_blas_scal(i__2, cblas_scalar(&tauq[i__]),
+                              cblas_ptr(&y[i__ + 1 + i__ * y_dim1]), c__1);
+            } else {
                 if (is_cplx) {
                     i__2 = n - i__ + 1;
-                    LAPACKE_CHECK(cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
-                    magma_setvector<Ty>(i__2,
-                                        a + i__   + (i__ )* a_dim1, lda,
-                                        da, da_offset + (i__-1)+ (i__-1)*(ldda), ldda,
-                                        queue);
+                    LAPACKE_CHECK(
+                        cpu_lapack_lacgv(i__2, &a[i__ + i__ * a_dim1], lda));
+                    magma_setvector<Ty>(
+                        i__2, a + i__ + (i__)*a_dim1, lda, da,
+                        da_offset + (i__ - 1) + (i__ - 1) * (ldda), ldda,
+                        queue);
                 }
             }
         }
@@ -656,18 +688,13 @@ magma_labrd_gpu(
     return MAGMA_SUCCESS;
 }
 
-#define INSTANTIATE(Ty)                                 \
-    template  magma_int_t                               \
-    magma_labrd_gpu<Ty>(                                \
-        magma_int_t m, magma_int_t n, magma_int_t nb,   \
-        Ty *a, magma_int_t lda,                         \
-        cl_mem da, size_t da_offset, magma_int_t ldda,  \
-        void *_d, void *_e, Ty *tauq, Ty *taup,         \
-        Ty *x, magma_int_t ldx,                         \
-        cl_mem dx, size_t dx_offset, magma_int_t lddx,  \
-        Ty *y, magma_int_t ldy,                         \
-        cl_mem dy, size_t dy_offset, magma_int_t lddy,  \
-        magma_queue_t queue);
+#define INSTANTIATE(Ty)                                                        \
+    template magma_int_t magma_labrd_gpu<Ty>(                                  \
+        magma_int_t m, magma_int_t n, magma_int_t nb, Ty * a, magma_int_t lda, \
+        cl_mem da, size_t da_offset, magma_int_t ldda, void *_d, void *_e,     \
+        Ty *tauq, Ty *taup, Ty *x, magma_int_t ldx, cl_mem dx,                 \
+        size_t dx_offset, magma_int_t lddx, Ty *y, magma_int_t ldy, cl_mem dy, \
+        size_t dy_offset, magma_int_t lddy, magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/larfb.cpp b/src/backend/opencl/magma/larfb.cpp
index e4800e1580..abb8d7a60f 100644
--- a/src/backend/opencl/magma/larfb.cpp
+++ b/src/backend/opencl/magma/larfb.cpp
@@ -174,25 +174,25 @@
 
     @ingroup magma_zaux3
     ********************************************************************/
-template<typename Ty> magma_int_t
-magma_larfb_gpu(
-    magma_side_t side, magma_trans_t trans, magma_direct_t direct, magma_storev_t storev,
-    magma_int_t m, magma_int_t n, magma_int_t k,
-    cl_mem dV   , size_t dV_offset,    magma_int_t lddv,
-    cl_mem dT   , size_t dT_offset,    magma_int_t lddt,
-    cl_mem dC   , size_t dC_offset,    magma_int_t lddc,
-    cl_mem dwork, size_t dwork_offset, magma_int_t ldwork,
-    magma_queue_t queue )
-{
-    #define dV(i_,j_)  dV,     (dV_offset    + (i_) + (j_)*lddv)
-    #define dT(i_,j_)  dT,     (dT_offset    + (i_) + (j_)*lddt)
-    #define dC(i_,j_)  dC,     (dC_offset    + (i_) + (j_)*lddc)
-    #define dwork(i_)  dwork,  (dwork_offset + (i_))
+template<typename Ty>
+magma_int_t magma_larfb_gpu(magma_side_t side, magma_trans_t trans,
+                            magma_direct_t direct, magma_storev_t storev,
+                            magma_int_t m, magma_int_t n, magma_int_t k,
+                            cl_mem dV, size_t dV_offset, magma_int_t lddv,
+                            cl_mem dT, size_t dT_offset, magma_int_t lddt,
+                            cl_mem dC, size_t dC_offset, magma_int_t lddc,
+                            cl_mem dwork, size_t dwork_offset,
+                            magma_int_t ldwork, magma_queue_t queue) {
+#define dV(i_, j_) dV, (dV_offset + (i_) + (j_)*lddv)
+#define dT(i_, j_) dT, (dT_offset + (i_) + (j_)*lddt)
+#define dC(i_, j_) dC, (dC_offset + (i_) + (j_)*lddc)
+#define dwork(i_) dwork, (dwork_offset + (i_))
 
     static const Ty c_zero    = magma_zero<Ty>();
     static const Ty c_one     = magma_one<Ty>();
     static const Ty c_neg_one = magma_neg_one<Ty>();
-    static const OPENCL_BLAS_TRANS_T transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
+    static const OPENCL_BLAS_TRANS_T transType =
+        magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
 
     /* Check input arguments */
     magma_int_t info = 0;
@@ -202,37 +202,36 @@ magma_larfb_gpu(
         info = -6;
     } else if (k < 0) {
         info = -7;
-    } else if ( ((storev == MagmaColumnwise) && (side == MagmaLeft) && lddv < std::max(1,m)) ||
-                ((storev == MagmaColumnwise) && (side == MagmaRight) && lddv < std::max(1,n)) ||
-                ((storev == MagmaRowwise) && lddv < k) ) {
+    } else if (((storev == MagmaColumnwise) && (side == MagmaLeft) &&
+                lddv < std::max(1, m)) ||
+               ((storev == MagmaColumnwise) && (side == MagmaRight) &&
+                lddv < std::max(1, n)) ||
+               ((storev == MagmaRowwise) && lddv < k)) {
         info = -9;
     } else if (lddt < k) {
         info = -11;
-    } else if (lddc < std::max(1,m)) {
+    } else if (lddc < std::max(1, m)) {
         info = -13;
-    } else if ( ((side == MagmaLeft) && ldwork < std::max(1,n)) ||
-                ((side == MagmaRight) && ldwork < std::max(1,m)) ) {
+    } else if (((side == MagmaLeft) && ldwork < std::max(1, n)) ||
+               ((side == MagmaRight) && ldwork < std::max(1, m))) {
         info = -15;
     }
     if (info != 0) {
-        //magma_xerbla( __func__, -(info) );
+        // magma_xerbla( __func__, -(info) );
         return info;
     }
 
     /* Function Body */
-    if (m <= 0 || n <= 0) {
-        return info;
-    }
+    if (m <= 0 || n <= 0) { return info; }
 
     // opposite of trans
     OPENCL_BLAS_TRANS_T transt;
     OPENCL_BLAS_TRANS_T cltrans;
     if (trans == MagmaNoTrans) {
-        transt = transType;
+        transt  = transType;
         cltrans = OPENCL_BLAS_NO_TRANS;
-    }
-    else {
-        transt = OPENCL_BLAS_NO_TRANS;
+    } else {
+        transt  = OPENCL_BLAS_NO_TRANS;
         cltrans = transType;
     }
 
@@ -248,8 +247,7 @@ magma_larfb_gpu(
     if (storev == MagmaColumnwise) {
         notransV = OPENCL_BLAS_NO_TRANS;
         transV   = transType;
-    }
-    else {
+    } else {
         notransV = transType;
         transV   = OPENCL_BLAS_NO_TRANS;
     }
@@ -259,87 +257,59 @@ magma_larfb_gpu(
 
     cl_event event = NULL;
 
-    if ( side == MagmaLeft ) {
+    if (side == MagmaLeft) {
         // Form H C or H^H C
-        // Comments assume H C. When forming H^H C, T gets transposed via transt.
+        // Comments assume H C. When forming H^H C, T gets transposed via
+        // transt.
 
         // W = C^H V
-        OPENCL_BLAS_CHECK(gpu_blas_gemm(transType, notransV,
-                                        n, k, m,
-                                        c_one,
-                                        dC(0,0),  lddc,
-                                        dV(0,0),  lddv,
-                                        c_zero,
-                                        dwork(0), ldwork,
-                                        1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(
+            transType, notransV, n, k, m, c_one, dC(0, 0), lddc, dV(0, 0), lddv,
+            c_zero, dwork(0), ldwork, 1, &queue, 0, nullptr, &event));
 
         // W = W T^H = C^H V T^H
-        OPENCL_BLAS_CHECK(gpu_blas_trmm(OPENCL_BLAS_SIDE_RIGHT,
-                                        uplo, transt, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                                        n, k,
-                                        c_one,
-                                        dT(0,0) , lddt,
-                                        dwork(0), ldwork,
+        OPENCL_BLAS_CHECK(gpu_blas_trmm(OPENCL_BLAS_SIDE_RIGHT, uplo, transt,
+                                        OPENCL_BLAS_NON_UNIT_DIAGONAL, n, k,
+                                        c_one, dT(0, 0), lddt, dwork(0), ldwork,
                                         1, &queue, 0, nullptr, &event));
-                        // C = C - V W^H = C - V T V^H C = (I - V T V^H) C = H C
-        OPENCL_BLAS_CHECK(gpu_blas_gemm(notransV, transType,
-                                        m, n, k,
-                                        c_neg_one,
-                                        dV(0,0),  lddv,
-                                        dwork(0), ldwork,
-                                        c_one,
-                                        dC(0,0),  lddc,
-                                        1, &queue, 0, nullptr, &event));
-    }
-    else {
+        // C = C - V W^H = C - V T V^H C = (I - V T V^H) C = H C
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(
+            notransV, transType, m, n, k, c_neg_one, dV(0, 0), lddv, dwork(0),
+            ldwork, c_one, dC(0, 0), lddc, 1, &queue, 0, nullptr, &event));
+    } else {
         // Form C H or C H^H
         // Comments assume C H. When forming C H^H, T gets transposed via trans.
 
         // W = C V
-        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, notransV,
-                                        m, k, n,
-                                        c_one,
-                                        dC(0,0),  lddc,
-                                        dV(0,0),  lddv,
-                                        c_zero,
-                                        dwork(0), ldwork,
-                                        1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, notransV, m, k, n,
+                                        c_one, dC(0, 0), lddc, dV(0, 0), lddv,
+                                        c_zero, dwork(0), ldwork, 1, &queue, 0,
+                                        nullptr, &event));
 
         // W = W T = C V T
-        OPENCL_BLAS_CHECK(gpu_blas_trmm(OPENCL_BLAS_SIDE_RIGHT, uplo,
-                                        cltrans,
-                                        OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                                        m, k,
-                                        c_one,
-                                        dT(0,0),  lddt,
-                                        dwork(0), ldwork,
+        OPENCL_BLAS_CHECK(gpu_blas_trmm(OPENCL_BLAS_SIDE_RIGHT, uplo, cltrans,
+                                        OPENCL_BLAS_NON_UNIT_DIAGONAL, m, k,
+                                        c_one, dT(0, 0), lddt, dwork(0), ldwork,
                                         1, &queue, 0, nullptr, &event));
 
         // C = C - W V^H = C - C V T V^H = C (I - V T V^H) = C H
-        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, transV,
-                                        m, n, k,
-                                        c_neg_one,
-                                        dwork(0), ldwork,
-                                        dV(0,0),  lddv,
-                                        c_one,
-                                        dC(0,0),  lddc,
-                                        1, &queue, 0, nullptr, &event));
+        OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, transV, m, n, k,
+                                        c_neg_one, dwork(0), ldwork, dV(0, 0),
+                                        lddv, c_one, dC(0, 0), lddc, 1, &queue,
+                                        0, nullptr, &event));
     }
 
     return info;
 } /* magma_zlarfb */
 
-#define INSTANTIATE(T)                                          \
-    template magma_int_t                                        \
-    magma_larfb_gpu<T>(                                         \
-        magma_side_t side, magma_trans_t trans,                 \
-        magma_direct_t direct, magma_storev_t storev,           \
-        magma_int_t m, magma_int_t n, magma_int_t k,            \
-        cl_mem dV   , size_t dV_offset,    magma_int_t lddv,    \
-        cl_mem dT   , size_t dT_offset,    magma_int_t lddt,    \
-        cl_mem dC   , size_t dC_offset,    magma_int_t lddc,    \
-        cl_mem dwork, size_t dwork_offset, magma_int_t ldwork,  \
-        magma_queue_t queue );                                  \
+#define INSTANTIATE(T)                                                      \
+    template magma_int_t magma_larfb_gpu<T>(                                \
+        magma_side_t side, magma_trans_t trans, magma_direct_t direct,      \
+        magma_storev_t storev, magma_int_t m, magma_int_t n, magma_int_t k, \
+        cl_mem dV, size_t dV_offset, magma_int_t lddv, cl_mem dT,           \
+        size_t dT_offset, magma_int_t lddt, cl_mem dC, size_t dC_offset,    \
+        magma_int_t lddc, cl_mem dwork, size_t dwork_offset,                \
+        magma_int_t ldwork, magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/laset.cpp b/src/backend/opencl/magma/laset.cpp
index bcbf5e2ec3..5af6d859e7 100644
--- a/src/backend/opencl/magma/laset.cpp
+++ b/src/backend/opencl/magma/laset.cpp
@@ -51,51 +51,49 @@
  *
  **********************************************************************/
 
-#include "magma_data.h"
 #include "kernel/laset.hpp"
+#include "magma_data.h"
 
 #include <algorithm>
 
-template<typename T> void
-magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n,
-                T offdiag, T diag,
-                cl_mem dA, size_t dA_offset, magma_int_t ldda,
-                magma_queue_t queue)
-{
+template<typename T>
+void magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n, T offdiag,
+                     T diag, cl_mem dA, size_t dA_offset, magma_int_t ldda,
+                     magma_queue_t queue) {
     magma_int_t info = 0;
-    if ( uplo != MagmaLower && uplo != MagmaUpper && uplo != MagmaFull )
+    if (uplo != MagmaLower && uplo != MagmaUpper && uplo != MagmaFull)
         info = -1;
-    else if ( m < 0 )
+    else if (m < 0)
         info = -2;
-    else if ( n < 0 )
+    else if (n < 0)
         info = -3;
-    else if ( ldda < std::max(1,m) )
+    else if (ldda < std::max(1, m))
         info = -7;
 
     if (info != 0) {
-        return;  //info;
-    }
-
-    if ( m == 0 || n == 0 ) {
-        return;
+        return;  // info;
     }
 
+    if (m == 0 || n == 0) { return; }
 
     switch (uplo) {
-    case MagmaFull : return opencl::kernel::laset<T, 0>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
-    case MagmaLower: return opencl::kernel::laset<T, 1>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
-    case MagmaUpper: return opencl::kernel::laset<T, 2>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
-    default: return;
+        case MagmaFull:
+            return opencl::kernel::laset<T, 0>(m, n, offdiag, diag, dA,
+                                               dA_offset, ldda, queue);
+        case MagmaLower:
+            return opencl::kernel::laset<T, 1>(m, n, offdiag, diag, dA,
+                                               dA_offset, ldda, queue);
+        case MagmaUpper:
+            return opencl::kernel::laset<T, 2>(m, n, offdiag, diag, dA,
+                                               dA_offset, ldda, queue);
+        default: return;
     }
-
 }
 
-#define INSTANTIATE(T)                                      \
-    template void magmablas_laset<T>(                       \
-        magma_uplo_t uplo, magma_int_t m, magma_int_t n,    \
-        T offdiag, T diag,                                  \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,      \
-        magma_queue_t queue);                               \
+#define INSTANTIATE(T)                                                      \
+    template void magmablas_laset<T>(                                       \
+        magma_uplo_t uplo, magma_int_t m, magma_int_t n, T offdiag, T diag, \
+        cl_mem dA, size_t dA_offset, magma_int_t ldda, magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/laset_band.cpp b/src/backend/opencl/magma/laset_band.cpp
index 89e944acc5..ba4e35360e 100644
--- a/src/backend/opencl/magma/laset_band.cpp
+++ b/src/backend/opencl/magma/laset_band.cpp
@@ -52,8 +52,8 @@
  **********************************************************************/
 
 #if 0  // Needs to be enabled when unmqr2 is enabled
-#include "magma_data.h"
 #include "kernel/laset_band.hpp"
+#include "magma_data.h"
 
 #include <algorithm>
 
@@ -96,12 +96,10 @@ magmablas_laset_band(magma_uplo_t uplo, magma_int_t m, magma_int_t n, magma_int_
 
 }
 
-#define INSTANTIATE(T)                                  \
-    template void magmablas_laset_band<T>(              \
-        magma_uplo_t uplo,                              \
-        magma_int_t m, magma_int_t n, magma_int_t k,    \
-        T offdiag, T diag,                              \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
+#define INSTANTIATE(T)                                                    \
+    template void magmablas_laset_band<T>(                                \
+        magma_uplo_t uplo, magma_int_t m, magma_int_t n, magma_int_t k,   \
+        T offdiag, T diag, cl_mem dA, size_t dA_offset, magma_int_t ldda, \
         magma_queue_t queue);                           \
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/magma/laswp.cpp b/src/backend/opencl/magma/laswp.cpp
index b6bf3b6a9a..62fdaff9c5 100644
--- a/src/backend/opencl/magma/laswp.cpp
+++ b/src/backend/opencl/magma/laswp.cpp
@@ -51,47 +51,40 @@
  *
  **********************************************************************/
 
-#include "magma_data.h"
 #include "kernel/laswp.hpp"
+#include "magma_data.h"
 
 #include <algorithm>
 
-template<typename T> void
-magmablas_laswp(
-    magma_int_t n,
-    cl_mem dAT, size_t dAT_offset, magma_int_t ldda,
-    magma_int_t k1, magma_int_t k2,
-    const magma_int_t *ipiv, magma_int_t inci,
-    magma_queue_t queue)
-{
+template<typename T>
+void magmablas_laswp(magma_int_t n, cl_mem dAT, size_t dAT_offset,
+                     magma_int_t ldda, magma_int_t k1, magma_int_t k2,
+                     const magma_int_t *ipiv, magma_int_t inci,
+                     magma_queue_t queue) {
     magma_int_t info = 0;
-    if ( n < 0 )
+    if (n < 0)
         info = -1;
-    else if ( k1 < 1 )
+    else if (k1 < 1)
         info = -4;
-    else if ( k2 < 1 )
+    else if (k2 < 1)
         info = -5;
-    else if ( inci <= 0 )
+    else if (inci <= 0)
         info = -7;
 
     if (info != 0) {
-        //magma_xerbla( __func__, -(info) );
-        return;  //info;
+        // magma_xerbla( __func__, -(info) );
+        return;  // info;
     }
 
     cl::CommandQueue q(queue, true);
     opencl::kernel::laswp<T>(n, dAT, dAT_offset, ldda, k1, k2, ipiv, inci, q);
 }
 
-
-#define INSTANTIATE(T)                                      \
-    template void magmablas_laswp<T>(                       \
-        magma_int_t n,                                      \
-        cl_mem dAT, size_t dAT_offset, magma_int_t ldda,    \
-        magma_int_t k1, magma_int_t k2,                     \
-        const magma_int_t *ipiv, magma_int_t inci,          \
-        magma_queue_t queue);
-
+#define INSTANTIATE(T)                                                  \
+    template void magmablas_laswp<T>(                                   \
+        magma_int_t n, cl_mem dAT, size_t dAT_offset, magma_int_t ldda, \
+        magma_int_t k1, magma_int_t k2, const magma_int_t *ipiv,        \
+        magma_int_t inci, magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/magma_blas_clblas.h b/src/backend/opencl/magma/magma_blas_clblas.h
index 3c1e1a9a2d..b2e1680bc2 100644
--- a/src/backend/opencl/magma/magma_blas_clblas.h
+++ b/src/backend/opencl/magma/magma_blas_clblas.h
@@ -16,23 +16,23 @@
 #include <mutex>  // for std::once_flag
 
 // Convert MAGMA constants to clBLAS constants
-clblasOrder          clblas_order_const( magma_order_t order );
-clblasTranspose      clblas_trans_const( magma_trans_t trans );
-clblasUplo           clblas_uplo_const ( magma_uplo_t  uplo  );
-clblasDiag           clblas_diag_const ( magma_diag_t  diag  );
-clblasSide           clblas_side_const ( magma_side_t  side  );
+clblasOrder clblas_order_const(magma_order_t order);
+clblasTranspose clblas_trans_const(magma_trans_t trans);
+clblasUplo clblas_uplo_const(magma_uplo_t uplo);
+clblasDiag clblas_diag_const(magma_diag_t diag);
+clblasSide clblas_side_const(magma_side_t side);
 
 // Error checking
 #define OPENCL_BLAS_CHECK CLBLAS_CHECK
 
 // Transposing
-#define OPENCL_BLAS_TRANS_T clblasTranspose // the type
+#define OPENCL_BLAS_TRANS_T clblasTranspose  // the type
 #define OPENCL_BLAS_NO_TRANS clblasNoTrans
 #define OPENCL_BLAS_TRANS clblasTrans
 #define OPENCL_BLAS_CONJ_TRANS clblasConjTrans
 
 // Triangles
-#define OPENCL_BLAS_TRIANGLE_T clblasUplo // the type
+#define OPENCL_BLAS_TRIANGLE_T clblasUplo  // the type
 #define OPENCL_BLAS_TRIANGLE_UPPER clblasUpper
 #define OPENCL_BLAS_TRIANGLE_LOWER clblasLower
 
@@ -48,17 +48,13 @@ clblasSide           clblas_side_const ( magma_side_t  side  );
 // Only meant to be once and from constructor
 // of DeviceManager singleton
 // DONT'T CALL FROM ANY OTHER LOCATION
-inline void gpu_blas_init()
-{
-    clblasSetup();
-}
+inline void gpu_blas_init() { clblasSetup(); }
 
 // tear down of the OpenCL BLAS library
 // Only meant to be called from destructor
 // of DeviceManager singleton
 // DONT'T CALL FROM ANY OTHER LOCATION
-inline void gpu_blas_deinit()
-{
+inline void gpu_blas_deinit() {
 #ifndef OS_WIN
     // FIXME:
     // clblasTeardown() causes a "Pure Virtual Function Called" crash on
@@ -70,24 +66,20 @@ inline void gpu_blas_deinit()
 #define clblasSherk(...) clblasSsyrk(__VA_ARGS__)
 #define clblasDherk(...) clblasDsyrk(__VA_ARGS__)
 
-#define BLAS_FUNC(NAME, TYPE, PREFIX)                       \
-    template<>                                              \
-    struct gpu_blas_##NAME##_func<TYPE>                     \
-    {                                                       \
-        template<typename... Args>                          \
-            clblasStatus                                    \
-            operator() (Args... args)                       \
-        {                                                   \
-            return clblas##PREFIX##NAME(clblasColumnMajor,  \
-                                        args...);           \
-        }                                                   \
+#define BLAS_FUNC(NAME, TYPE, PREFIX)                                \
+    template<>                                                       \
+    struct gpu_blas_##NAME##_func<TYPE> {                            \
+        template<typename... Args>                                   \
+        clblasStatus operator()(Args... args) {                      \
+            return clblas##PREFIX##NAME(clblasColumnMajor, args...); \
+        }                                                            \
     };
 
-#define BLAS_FUNC_DECL(NAME)                    \
-    BLAS_FUNC(NAME, float,      S)              \
-    BLAS_FUNC(NAME, double,     D)              \
-    BLAS_FUNC(NAME, cfloat,     C)              \
-    BLAS_FUNC(NAME, cdouble,    Z)              \
+#define BLAS_FUNC_DECL(NAME)   \
+    BLAS_FUNC(NAME, float, S)  \
+    BLAS_FUNC(NAME, double, D) \
+    BLAS_FUNC(NAME, cfloat, C) \
+    BLAS_FUNC(NAME, cdouble, Z)
 
 BLAS_FUNC_DECL(gemm)
 BLAS_FUNC_DECL(gemv)
diff --git a/src/backend/opencl/magma/magma_cpu_blas.h b/src/backend/opencl/magma/magma_cpu_blas.h
index 87bc65aef3..608ddc29aa 100644
--- a/src/backend/opencl/magma/magma_cpu_blas.h
+++ b/src/backend/opencl/magma/magma_cpu_blas.h
@@ -9,63 +9,60 @@
 
 #ifndef MAGMA_CPU_BLAS
 #define MAGMA_CPU_BLAS
-#include <err_opencl.hpp>
+#include <common/blas_headers.hpp>
 #include <common/defines.hpp>
+#include <err_opencl.hpp>
 #include "magma_types.h"
-#include <common/blas_headers.hpp>
-
 
-#define CPU_BLAS_FUNC_DEF(NAME)                 \
-    template<typename T>                        \
+#define CPU_BLAS_FUNC_DEF(NAME) \
+    template<typename T>        \
     struct cpu_blas_##NAME##_func;
 
-#define CPU_BLAS_FUNC1(NAME, TYPE, X)                   \
-    template<>                                          \
-    struct cpu_blas_##NAME##_func<TYPE>                 \
-    {                                                   \
-        template<typename... Args>                      \
-            void                                        \
-            operator() (Args... args)                   \
-        { cblas_##X##NAME(CblasColMajor, args...); }    \
+#define CPU_BLAS_FUNC1(NAME, TYPE, X)                \
+    template<>                                       \
+    struct cpu_blas_##NAME##_func<TYPE> {            \
+        template<typename... Args>                   \
+        void operator()(Args... args) {              \
+            cblas_##X##NAME(CblasColMajor, args...); \
+        }                                            \
     };
 
-#define CPU_BLAS_FUNC2(NAME, TYPE, X)           \
-    template<>                                  \
-    struct cpu_blas_##NAME##_func<TYPE>         \
-    {                                           \
-        template<typename... Args>              \
-            void                                \
-            operator() (Args... args)           \
-        {  cblas_##X##NAME(args...); }          \
+#define CPU_BLAS_FUNC2(NAME, TYPE, X)     \
+    template<>                            \
+    struct cpu_blas_##NAME##_func<TYPE> { \
+        template<typename... Args>        \
+        void operator()(Args... args) {   \
+            cblas_##X##NAME(args...);     \
+        }                                 \
     };
 
-#define CPU_BLAS_DECL1(NAME)                        \
-    CPU_BLAS_FUNC_DEF(NAME)                         \
-    CPU_BLAS_FUNC1(NAME, float,      s)             \
-    CPU_BLAS_FUNC1(NAME, double,     d)             \
-    CPU_BLAS_FUNC1(NAME, magmaFloatComplex,     c)  \
-    CPU_BLAS_FUNC1(NAME, magmaDoubleComplex,    z)  \
+#define CPU_BLAS_DECL1(NAME)                   \
+    CPU_BLAS_FUNC_DEF(NAME)                    \
+    CPU_BLAS_FUNC1(NAME, float, s)             \
+    CPU_BLAS_FUNC1(NAME, double, d)            \
+    CPU_BLAS_FUNC1(NAME, magmaFloatComplex, c) \
+    CPU_BLAS_FUNC1(NAME, magmaDoubleComplex, z)
 
-#define CPU_BLAS_DECL2(NAME)                        \
-    CPU_BLAS_FUNC_DEF(NAME)                         \
-    CPU_BLAS_FUNC2(NAME, float,      s)             \
-    CPU_BLAS_FUNC2(NAME, double,     d)             \
-    CPU_BLAS_FUNC2(NAME, magmaFloatComplex,     c)  \
-    CPU_BLAS_FUNC2(NAME, magmaDoubleComplex,    z)  \
+#define CPU_BLAS_DECL2(NAME)                   \
+    CPU_BLAS_FUNC_DEF(NAME)                    \
+    CPU_BLAS_FUNC2(NAME, float, s)             \
+    CPU_BLAS_FUNC2(NAME, double, d)            \
+    CPU_BLAS_FUNC2(NAME, magmaFloatComplex, c) \
+    CPU_BLAS_FUNC2(NAME, magmaDoubleComplex, z)
 
 CPU_BLAS_DECL1(gemv)
 CPU_BLAS_DECL2(scal)
 CPU_BLAS_DECL2(axpy)
 
-inline float * cblas_ptr(float *in) { return in; }
-inline double * cblas_ptr(double *in) { return in; }
+inline float *cblas_ptr(float *in) { return in; }
+inline double *cblas_ptr(double *in) { return in; }
 
 #if defined(IS_OPENBLAS)
-inline float * cblas_ptr(magmaFloatComplex *in) { return (float *)in; }
-inline double * cblas_ptr(magmaDoubleComplex *in) { return (double *)in; }
+inline float *cblas_ptr(magmaFloatComplex *in) { return (float *)in; }
+inline double *cblas_ptr(magmaDoubleComplex *in) { return (double *)in; }
 #else
-inline void * cblas_ptr(magmaFloatComplex *in) { return (void *)in; }
-inline void * cblas_ptr(magmaDoubleComplex *in) { return (void *)in; }
+inline void *cblas_ptr(magmaFloatComplex *in) { return (void *)in; }
+inline void *cblas_ptr(magmaDoubleComplex *in) { return (void *)in; }
 #endif
 
 inline float cblas_scalar(float *in) { return *in; }
diff --git a/src/backend/opencl/magma/magma_cpu_lapack.h b/src/backend/opencl/magma/magma_cpu_lapack.h
index 2529ad5291..5bba77d0cb 100644
--- a/src/backend/opencl/magma/magma_cpu_lapack.h
+++ b/src/backend/opencl/magma/magma_cpu_lapack.h
@@ -10,8 +10,8 @@
 #ifndef MAGMA_CPU_LAPACK
 #define MAGMA_CPU_LAPACK
 
-#include <err_opencl.hpp>
 #include <common/defines.hpp>
+#include <err_opencl.hpp>
 #include "magma_types.h"
 
 #define LAPACKE_sunmqr_work(...) LAPACKE_sormqr_work(__VA_ARGS__)
@@ -22,16 +22,24 @@
 #define LAPACKE_dungbr_work(...) LAPACKE_dorgbr_work(__VA_ARGS__)
 
 template<typename... Args>
-int LAPACKE_slacgv(Args... /*args*/) { return 0; }
+int LAPACKE_slacgv(Args... /*args*/) {
+    return 0;
+}
 
 template<typename... Args>
-int LAPACKE_dlacgv(Args... /*args*/) { return 0; }
+int LAPACKE_dlacgv(Args... /*args*/) {
+    return 0;
+}
 
 template<typename... Args>
-int LAPACKE_slacgv_work(Args... /*args*/) { return 0; }
+int LAPACKE_slacgv_work(Args... /*args*/) {
+    return 0;
+}
 
 template<typename... Args>
-int LAPACKE_dlacgv_work(Args... /*args*/) { return 0; }
+int LAPACKE_dlacgv_work(Args... /*args*/) {
+    return 0;
+}
 
 #define lapack_complex_float magmaFloatComplex
 #define lapack_complex_double magmaDoubleComplex
@@ -40,90 +48,80 @@ int LAPACKE_dlacgv_work(Args... /*args*/) { return 0; }
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
 #ifdef USE_MKL
-    #include<mkl_lapacke.h>
+#include <mkl_lapacke.h>
 #else
-    #ifdef __APPLE__
-        #include <Accelerate/Accelerate.h>
-        #include <common/lapacke.hpp>
-        #undef LAPACK_COL_MAJOR
-        #define LAPACK_COL_MAJOR 102
-        #undef AF_LAPACK_COL_MAJOR
-        #define AF_LAPACK_COL_MAJOR 0
-    #else // NETLIB LAPACKE
-        #include<lapacke.h>
-    #endif
+#ifdef __APPLE__
+#include <Accelerate/Accelerate.h>
+#include <common/lapacke.hpp>
+#undef LAPACK_COL_MAJOR
+#define LAPACK_COL_MAJOR 102
+#undef AF_LAPACK_COL_MAJOR
+#define AF_LAPACK_COL_MAJOR 0
+#else  // NETLIB LAPACKE
+#include <lapacke.h>
+#endif
 #endif
 
-#define LAPACKE_CHECK(fn) do {                  \
-        int __info = fn;                        \
-        if (__info != 0) {                      \
-            char lapacke_st_msg[32];            \
-            snprintf(lapacke_st_msg,            \
-                     sizeof(lapacke_st_msg),    \
-                     "LAPACKE Error (%d)",      \
-                     (int)(__info));            \
-            AF_ERROR(lapacke_st_msg,            \
-                     AF_ERR_INTERNAL);          \
-        }                                       \
-    } while(0)
-
-#define CPU_LAPACK_FUNC_DEF(NAME)               \
-    template<typename T>                        \
+#define LAPACKE_CHECK(fn)                                    \
+    do {                                                     \
+        int __info = fn;                                     \
+        if (__info != 0) {                                   \
+            char lapacke_st_msg[32];                         \
+            snprintf(lapacke_st_msg, sizeof(lapacke_st_msg), \
+                     "LAPACKE Error (%d)", (int)(__info));   \
+            AF_ERROR(lapacke_st_msg, AF_ERR_INTERNAL);       \
+        }                                                    \
+    } while (0)
+
+#define CPU_LAPACK_FUNC_DEF(NAME) \
+    template<typename T>          \
     struct cpu_lapack_##NAME##_func;
 
-#define CPU_LAPACK_FUNC1(NAME, TYPE, X)                     \
-    template<>                                              \
-    struct cpu_lapack_##NAME##_func<TYPE>                   \
-    {                                                       \
-        template<typename... Args>                          \
-            int                                             \
-            operator() (Args... args)                       \
-        {                                                   \
-            return LAPACK_NAME(X##NAME)(LAPACK_COL_MAJOR,   \
-                                        args...);           \
-        }                                                   \
+#define CPU_LAPACK_FUNC1(NAME, TYPE, X)                             \
+    template<>                                                      \
+    struct cpu_lapack_##NAME##_func<TYPE> {                         \
+        template<typename... Args>                                  \
+        int operator()(Args... args) {                              \
+            return LAPACK_NAME(X##NAME)(LAPACK_COL_MAJOR, args...); \
+        }                                                           \
     };
 
-#define CPU_LAPACK_FUNC2(NAME, TYPE, X)             \
-    template<>                                      \
-    struct cpu_lapack_##NAME##_func<TYPE>           \
-    {                                               \
-        template<typename... Args>                  \
-            int                                     \
-            operator() (Args... args)               \
-        {                                           \
-            return LAPACK_NAME(X##NAME)(args...);   \
-        }                                           \
+#define CPU_LAPACK_FUNC2(NAME, TYPE, X)           \
+    template<>                                    \
+    struct cpu_lapack_##NAME##_func<TYPE> {       \
+        template<typename... Args>                \
+        int operator()(Args... args) {            \
+            return LAPACK_NAME(X##NAME)(args...); \
+        }                                         \
     };
 
-#define CPU_LAPACK_FUNC3(NAME, TYPE, X)             \
-    template<>                                      \
-    struct cpu_lapack_##NAME##_func<TYPE>           \
-    {                                               \
-        template<typename... Args>                  \
-            double                                  \
-            operator() (Args... args)               \
-        { return LAPACK_NAME(X##NAME)(args...); }   \
+#define CPU_LAPACK_FUNC3(NAME, TYPE, X)           \
+    template<>                                    \
+    struct cpu_lapack_##NAME##_func<TYPE> {       \
+        template<typename... Args>                \
+        double operator()(Args... args) {         \
+            return LAPACK_NAME(X##NAME)(args...); \
+        }                                         \
     };
 
-#define CPU_LAPACK_DECL1(NAME)                          \
-    CPU_LAPACK_FUNC_DEF(NAME)                           \
-    CPU_LAPACK_FUNC1(NAME, float,      s)               \
-    CPU_LAPACK_FUNC1(NAME, double,     d)               \
-    CPU_LAPACK_FUNC1(NAME, magmaFloatComplex,     c)    \
-    CPU_LAPACK_FUNC1(NAME, magmaDoubleComplex,    z)    \
-
-#define CPU_LAPACK_DECL2(NAME)                          \
-    CPU_LAPACK_FUNC_DEF(NAME)                           \
-    CPU_LAPACK_FUNC2(NAME, float,      s)               \
-    CPU_LAPACK_FUNC2(NAME, double,     d)               \
-    CPU_LAPACK_FUNC2(NAME, magmaFloatComplex,     c)    \
-    CPU_LAPACK_FUNC2(NAME, magmaDoubleComplex,    z)    \
-
-#define CPU_LAPACK_DECL3(NAME)                  \
-    CPU_LAPACK_FUNC_DEF(NAME)                   \
-    CPU_LAPACK_FUNC3(NAME, float,      s)       \
-    CPU_LAPACK_FUNC3(NAME, double,     d)       \
+#define CPU_LAPACK_DECL1(NAME)                   \
+    CPU_LAPACK_FUNC_DEF(NAME)                    \
+    CPU_LAPACK_FUNC1(NAME, float, s)             \
+    CPU_LAPACK_FUNC1(NAME, double, d)            \
+    CPU_LAPACK_FUNC1(NAME, magmaFloatComplex, c) \
+    CPU_LAPACK_FUNC1(NAME, magmaDoubleComplex, z)
+
+#define CPU_LAPACK_DECL2(NAME)                   \
+    CPU_LAPACK_FUNC_DEF(NAME)                    \
+    CPU_LAPACK_FUNC2(NAME, float, s)             \
+    CPU_LAPACK_FUNC2(NAME, double, d)            \
+    CPU_LAPACK_FUNC2(NAME, magmaFloatComplex, c) \
+    CPU_LAPACK_FUNC2(NAME, magmaDoubleComplex, z)
+
+#define CPU_LAPACK_DECL3(NAME)       \
+    CPU_LAPACK_FUNC_DEF(NAME)        \
+    CPU_LAPACK_FUNC3(NAME, float, s) \
+    CPU_LAPACK_FUNC3(NAME, double, d)
 
 CPU_LAPACK_DECL1(getrf)
 CPU_LAPACK_DECL1(gebrd_work)
diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 19d83df841..38470a5f76 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -52,42 +52,36 @@
  *
  **********************************************************************/
 
-
 #ifndef MAGMA_DATA_H
 #define MAGMA_DATA_H
 
 #include <platform.hpp>
 #include "magma_types.h"
 
-#define check_error( err ) if (err != CL_SUCCESS) throw cl::Error(err);
+#define check_error(err) \
+    if (err != CL_SUCCESS) throw cl::Error(err);
 
 // ========================================
 // memory allocation
 // Allocate size bytes on GPU, returning pointer in ptrPtr.
-template<typename T> static magma_int_t
-magma_malloc( magma_ptr* ptrPtr, int num)
-{
+template<typename T>
+static magma_int_t magma_malloc(magma_ptr* ptrPtr, int num) {
     size_t size = num * sizeof(T);
-    // malloc and free sometimes don't work for size=0, so allocate some minimal size
-    if ( size == 0 )
-        size = sizeof(T);
+    // malloc and free sometimes don't work for size=0, so allocate some minimal
+    // size
+    if (size == 0) size = sizeof(T);
     cl_int err;
-    *ptrPtr = clCreateBuffer(opencl::getContext()(), CL_MEM_READ_WRITE, size, NULL, &err );
-    if ( err != CL_SUCCESS ) {
-        return MAGMA_ERR_DEVICE_ALLOC;
-    }
+    *ptrPtr = clCreateBuffer(opencl::getContext()(), CL_MEM_READ_WRITE, size,
+                             NULL, &err);
+    if (err != CL_SUCCESS) { return MAGMA_ERR_DEVICE_ALLOC; }
     return MAGMA_SUCCESS;
 }
 
 // --------------------
 // Free GPU memory allocated by magma_malloc.
-static inline magma_int_t
-magma_free(cl_mem ptr)
-{
-    cl_int err = clReleaseMemObject( ptr );
-    if ( err != CL_SUCCESS ) {
-        return MAGMA_ERR_INVALID_PTR;
-    }
+static inline magma_int_t magma_free(cl_mem ptr) {
+    cl_int err = clReleaseMemObject(ptr);
+    if (err != CL_SUCCESS) { return MAGMA_ERR_INVALID_PTR; }
     return MAGMA_SUCCESS;
 }
 
@@ -99,395 +93,304 @@ magma_free(cl_mem ptr)
 // to align memory to a 32 byte boundary.
 // Use magma_free_cpu() to free this memory.
 
-template<typename T> static magma_int_t
-magma_malloc_cpu(T** ptrPtr, int num)
-{
+template<typename T>
+static magma_int_t magma_malloc_cpu(T** ptrPtr, int num) {
     size_t size = num * sizeof(T);
-    // malloc and free sometimes don't work for size=0, so allocate some minimal size
-    if ( size == 0 )
-        size = sizeof(T);
+    // malloc and free sometimes don't work for size=0, so allocate some minimal
+    // size
+    if (size == 0) size = sizeof(T);
 #if 1
-    #if defined( _WIN32 ) || defined( _WIN64 )
-    *ptrPtr = (T *)_aligned_malloc( size, 32 );
-    if ( *ptrPtr == NULL ) {
-        return MAGMA_ERR_HOST_ALLOC;
-    }
-    #else
-    int err = posix_memalign((void **)ptrPtr, 32, size );
-    if ( err != 0 ) {
+#if defined(_WIN32) || defined(_WIN64)
+    *ptrPtr = (T*)_aligned_malloc(size, 32);
+    if (*ptrPtr == NULL) { return MAGMA_ERR_HOST_ALLOC; }
+#else
+    int err = posix_memalign((void**)ptrPtr, 32, size);
+    if (err != 0) {
         *ptrPtr = NULL;
         return MAGMA_ERR_HOST_ALLOC;
     }
-    #endif
+#endif
 #else
-    *ptrPtr = malloc( size );
-    if ( *ptrPtr == NULL ) {
-        return MAGMA_ERR_HOST_ALLOC;
-    }
+    *ptrPtr = malloc(size);
+    if (*ptrPtr == NULL) { return MAGMA_ERR_HOST_ALLOC; }
 #endif
     return MAGMA_SUCCESS;
 }
 
 // --------------------
 // Free CPU pinned memory previously allocated by magma_malloc_pinned.
-// The default implementation uses free(), which works for both malloc and posix_memalign.
-// For Windows, _aligned_free() is used.
-template<typename T> static magma_int_t
-magma_free_cpu(T* ptr )
-{
-#if defined( _WIN32 ) || defined( _WIN64 )
-    _aligned_free( ptr );
+// The default implementation uses free(), which works for both malloc and
+// posix_memalign. For Windows, _aligned_free() is used.
+template<typename T>
+static magma_int_t magma_free_cpu(T* ptr) {
+#if defined(_WIN32) || defined(_WIN64)
+    _aligned_free(ptr);
 #else
-    free( ptr );
+    free(ptr);
 #endif
     return MAGMA_SUCCESS;
 }
 
 // ========================================
 // copying vectors
-template<typename T> static void
-magma_setvector(
-    magma_int_t n,
-    T const* hx_src,                   magma_int_t incx,
-    cl_mem   dy_dst, size_t dy_offset, magma_int_t incy,
-    magma_queue_t queue )
-{
-    if (n <= 0)
-        return;
+template<typename T>
+static void magma_setvector(magma_int_t n, T const* hx_src, magma_int_t incx,
+                            cl_mem dy_dst, size_t dy_offset, magma_int_t incy,
+                            magma_queue_t queue) {
+    if (n <= 0) return;
 
     if (incx == 1 && incy == 1) {
-        cl_int err = clEnqueueWriteBuffer(
-            queue, dy_dst, CL_TRUE,
-            dy_offset*sizeof(T), n*sizeof(T),
-            hx_src, 0, NULL, NULL);
-        check_error( err );
-    }
-    else {
+        cl_int err =
+            clEnqueueWriteBuffer(queue, dy_dst, CL_TRUE, dy_offset * sizeof(T),
+                                 n * sizeof(T), hx_src, 0, NULL, NULL);
+        check_error(err);
+    } else {
         magma_int_t ldha = incx;
         magma_int_t lddb = incy;
-        magma_setmatrix( 1, n,
-            hx_src,            ldha,
-            dy_dst, dy_offset, lddb,
-            queue);
+        magma_setmatrix(1, n, hx_src, ldha, dy_dst, dy_offset, lddb, queue);
     }
 }
 
 // --------------------
-template<typename T> static void
-magma_setvector_async(
-    magma_int_t n,
-    T const* hx_src,                   magma_int_t incx,
-    cl_mem   dy_dst, size_t dy_offset, magma_int_t incy,
-    magma_queue_t queue, magma_event_t *event )
-{
-    if (n <= 0)
-        return;
+template<typename T>
+static void magma_setvector_async(magma_int_t n, T const* hx_src,
+                                  magma_int_t incx, cl_mem dy_dst,
+                                  size_t dy_offset, magma_int_t incy,
+                                  magma_queue_t queue, magma_event_t* event) {
+    if (n <= 0) return;
 
     if (incx == 1 && incy == 1) {
-        cl_int err = clEnqueueWriteBuffer(
-            queue, dy_dst, CL_FALSE,
-            dy_offset*sizeof(T), n*sizeof(T),
-            hx_src, 0, NULL, event);
-        check_error( err );
-    }
-    else {
+        cl_int err =
+            clEnqueueWriteBuffer(queue, dy_dst, CL_FALSE, dy_offset * sizeof(T),
+                                 n * sizeof(T), hx_src, 0, NULL, event);
+        check_error(err);
+    } else {
         magma_int_t ldha = incx;
         magma_int_t lddb = incy;
-        magma_setmatrix_async( 1, n,
-            hx_src,            ldha,
-            dy_dst, dy_offset, lddb,
-            queue, event);
+        magma_setmatrix_async(1, n, hx_src, ldha, dy_dst, dy_offset, lddb,
+                              queue, event);
     }
 }
 
 // --------------------
-template<typename T> static void
-magma_getvector(
-    magma_int_t n,
-    cl_mem dx_src, size_t dx_offset, magma_int_t incx,
-    T*     hy_dst,                   magma_int_t incy,
-    magma_queue_t queue )
-{
-    if (n <= 0)
-        return;
+template<typename T>
+static void magma_getvector(magma_int_t n, cl_mem dx_src, size_t dx_offset,
+                            magma_int_t incx, T* hy_dst, magma_int_t incy,
+                            magma_queue_t queue) {
+    if (n <= 0) return;
 
     if (incx == 1 && incy == 1) {
-        cl_int err = clEnqueueReadBuffer(
-            queue, dx_src, CL_TRUE,
-            dx_offset*sizeof(T), n*sizeof(T),
-            hy_dst, 0, NULL, NULL);
-        check_error( err );
-    }
-    else {
+        cl_int err =
+            clEnqueueReadBuffer(queue, dx_src, CL_TRUE, dx_offset * sizeof(T),
+                                n * sizeof(T), hy_dst, 0, NULL, NULL);
+        check_error(err);
+    } else {
         magma_int_t ldda = incx;
         magma_int_t ldhb = incy;
-        magma_getmatrix( 1, n,
-            dx_src, dx_offset, ldda,
-            hy_dst,            ldhb,
-            queue);
+        magma_getmatrix(1, n, dx_src, dx_offset, ldda, hy_dst, ldhb, queue);
     }
 }
 
 // --------------------
-template<typename T> static void
-magma_getvector_async(
-    magma_int_t n,
-    cl_mem dx_src, size_t dx_offset, magma_int_t incx,
-    T*     hy_dst,                   magma_int_t incy,
-    magma_queue_t queue, magma_event_t *event )
-{
-    if (n <= 0)
-        return;
+template<typename T>
+static void magma_getvector_async(magma_int_t n, cl_mem dx_src,
+                                  size_t dx_offset, magma_int_t incx, T* hy_dst,
+                                  magma_int_t incy, magma_queue_t queue,
+                                  magma_event_t* event) {
+    if (n <= 0) return;
 
     if (incx == 1 && incy == 1) {
-        cl_int err = clEnqueueReadBuffer(
-            queue, dx_src, CL_FALSE,
-            dx_offset*sizeof(T), n*sizeof(T),
-            hy_dst, 0, NULL, event);
-        check_error( err );
-    }
-    else {
+        cl_int err =
+            clEnqueueReadBuffer(queue, dx_src, CL_FALSE, dx_offset * sizeof(T),
+                                n * sizeof(T), hy_dst, 0, NULL, event);
+        check_error(err);
+    } else {
         magma_int_t ldda = incx;
         magma_int_t ldhb = incy;
-        magma_getmatrix_async( 1, n,
-            dx_src, dx_offset, ldda,
-            hy_dst,            ldhb,
-            queue, event);
+        magma_getmatrix_async(1, n, dx_src, dx_offset, ldda, hy_dst, ldhb,
+                              queue, event);
     }
 }
 
 // --------------------
-template<typename T> static void
-magma_copymatrix(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA_src, size_t dA_offset, magma_int_t ldda,
-    cl_mem dB_dst, size_t dB_offset, magma_int_t lddb,
-    magma_queue_t queue )
-{
-    if (m <= 0 || n <= 0)
-        return;
-
-    size_t src_origin[3] = { dA_offset*sizeof(T), 0, 0 };
-    size_t dst_orig[3]   = { dB_offset*sizeof(T), 0, 0 };
-    size_t region[3]     = { m*sizeof(T), static_cast<size_t>(n), 1 };
-    cl_int err = clEnqueueCopyBufferRect(
-        queue, dA_src, dB_dst,
-        src_origin, dst_orig, region,
-        ldda*sizeof(T), 0,
-        lddb*sizeof(T), 0,
-        0, NULL, NULL );
-    check_error( err );
+template<typename T>
+static void magma_copymatrix(magma_int_t m, magma_int_t n, cl_mem dA_src,
+                             size_t dA_offset, magma_int_t ldda, cl_mem dB_dst,
+                             size_t dB_offset, magma_int_t lddb,
+                             magma_queue_t queue) {
+    if (m <= 0 || n <= 0) return;
+
+    size_t src_origin[3] = {dA_offset * sizeof(T), 0, 0};
+    size_t dst_orig[3]   = {dB_offset * sizeof(T), 0, 0};
+    size_t region[3]     = {m * sizeof(T), static_cast<size_t>(n), 1};
+    cl_int err = clEnqueueCopyBufferRect(queue, dA_src, dB_dst, src_origin,
+                                         dst_orig, region, ldda * sizeof(T), 0,
+                                         lddb * sizeof(T), 0, 0, NULL, NULL);
+    check_error(err);
 }
 
 // --------------------
-template<typename T> static void
-magma_copymatrix_async(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA_src, size_t dA_offset, magma_int_t ldda,
-    cl_mem dB_dst, size_t dB_offset, magma_int_t lddb,
-    magma_queue_t queue, magma_event_t *event )
-{
-    if (m <= 0 || n <= 0)
-        return;
+template<typename T>
+static void magma_copymatrix_async(magma_int_t m, magma_int_t n, cl_mem dA_src,
+                                   size_t dA_offset, magma_int_t ldda,
+                                   cl_mem dB_dst, size_t dB_offset,
+                                   magma_int_t lddb, magma_queue_t queue,
+                                   magma_event_t* event) {
+    if (m <= 0 || n <= 0) return;
 
     // TODO how to make non-blocking?
-    size_t src_origin[3] = { dA_offset*sizeof(T), 0, 0 };
-    size_t dst_orig[3]   = { dB_offset*sizeof(T), 0, 0 };
-    size_t region[3]     = { m*sizeof(T), static_cast<size_t>(n), 1 };
-    cl_int err = clEnqueueCopyBufferRect(
-        queue, dA_src, dB_dst,
-        src_origin, dst_orig, region,
-        ldda*sizeof(T), 0,
-        lddb*sizeof(T), 0,
-        0, NULL, event );
-    check_error( err );
+    size_t src_origin[3] = {dA_offset * sizeof(T), 0, 0};
+    size_t dst_orig[3]   = {dB_offset * sizeof(T), 0, 0};
+    size_t region[3]     = {m * sizeof(T), static_cast<size_t>(n), 1};
+    cl_int err = clEnqueueCopyBufferRect(queue, dA_src, dB_dst, src_origin,
+                                         dst_orig, region, ldda * sizeof(T), 0,
+                                         lddb * sizeof(T), 0, 0, NULL, event);
+    check_error(err);
 }
 
 // --------------------
-template<typename T> static void
-magma_copyvector(
-    magma_int_t n,
-    cl_mem dx_src, size_t dx_offset, magma_int_t incx,
-    cl_mem dy_dst, size_t dy_offset, magma_int_t incy,
-    magma_queue_t queue )
-{
-    if (n <= 0)
-        return;
+template<typename T>
+static void magma_copyvector(magma_int_t n, cl_mem dx_src, size_t dx_offset,
+                             magma_int_t incx, cl_mem dy_dst, size_t dy_offset,
+                             magma_int_t incy, magma_queue_t queue) {
+    if (n <= 0) return;
 
     if (incx == 1 && incy == 1) {
         cl_int err = clEnqueueReadBuffer(
-            queue, dx_src, CL_TRUE,
-            dx_offset*sizeof(T), n*sizeof(T),
-            dy_dst, dy_offset*sizeof(T), NULL, NULL);
-        check_error( err );
-    }
-    else {
+            queue, dx_src, CL_TRUE, dx_offset * sizeof(T), n * sizeof(T),
+            dy_dst, dy_offset * sizeof(T), NULL, NULL);
+        check_error(err);
+    } else {
         magma_int_t ldda = incx;
         magma_int_t lddb = incy;
-        magma_copymatrix<T>( 1, n,
-            dx_src, dx_offset, ldda,
-            dy_dst, dy_offset, lddb,
-            queue);
+        magma_copymatrix<T>(1, n, dx_src, dx_offset, ldda, dy_dst, dy_offset,
+                            lddb, queue);
     }
 }
 
 // --------------------
-template<typename T> static void
-magma_copyvector_async(
-    magma_int_t n,
-    cl_mem dx_src, size_t dx_offset, magma_int_t incx,
-    cl_mem dy_dst, size_t dy_offset, magma_int_t incy,
-    magma_queue_t queue, magma_event_t *event )
-{
-    if (n <= 0)
-        return;
+template<typename T>
+static void magma_copyvector_async(magma_int_t n, cl_mem dx_src,
+                                   size_t dx_offset, magma_int_t incx,
+                                   cl_mem dy_dst, size_t dy_offset,
+                                   magma_int_t incy, magma_queue_t queue,
+                                   magma_event_t* event) {
+    if (n <= 0) return;
 
     if (incx == 1 && incy == 1) {
         cl_int err = clEnqueueReadBuffer(
-            queue, dx_src, CL_FALSE,
-            dx_offset*sizeof(T), n*sizeof(T),
-            dy_dst, dy_offset*sizeof(T), NULL, event);
-        check_error( err );
-    }
-    else {
+            queue, dx_src, CL_FALSE, dx_offset * sizeof(T), n * sizeof(T),
+            dy_dst, dy_offset * sizeof(T), NULL, event);
+        check_error(err);
+    } else {
         magma_int_t ldda = incx;
         magma_int_t lddb = incy;
-        magma_copymatrix_async<T>( 1, n,
-            dx_src, dx_offset, ldda,
-            dy_dst, dy_offset, lddb,
-            queue, event);
+        magma_copymatrix_async<T>(1, n, dx_src, dx_offset, ldda, dy_dst,
+                                  dy_offset, lddb, queue, event);
     }
 }
 
-
 // ========================================
 // copying sub-matrices (contiguous columns)
 // OpenCL takes queue even for blocking transfers, oddly.
-template<typename T> static void
-magma_setmatrix(
-    magma_int_t m, magma_int_t n,
-    T const* hA_src,                   magma_int_t ldha,
-    cl_mem   dB_dst, size_t dB_offset, magma_int_t lddb,
-    magma_queue_t queue )
-{
-    if (m <= 0 || n <= 0)
-        return;
-
-    size_t buffer_origin[3] = { dB_offset*sizeof(T), 0, 0 };
-    size_t host_orig[3]     = { 0, 0, 0 };
-    size_t region[3]        = { m*sizeof(T), (size_t)n, 1 };
-    cl_int err = clEnqueueWriteBufferRect(
-        queue, dB_dst, CL_TRUE,  // blocking
-        buffer_origin, host_orig, region,
-        lddb*sizeof(T), 0,
-        ldha*sizeof(T), 0,
-        hA_src, 0, NULL, NULL );
-    check_error( err );
+template<typename T>
+static void magma_setmatrix(magma_int_t m, magma_int_t n, T const* hA_src,
+                            magma_int_t ldha, cl_mem dB_dst, size_t dB_offset,
+                            magma_int_t lddb, magma_queue_t queue) {
+    if (m <= 0 || n <= 0) return;
+
+    size_t buffer_origin[3] = {dB_offset * sizeof(T), 0, 0};
+    size_t host_orig[3]     = {0, 0, 0};
+    size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
+    cl_int err = clEnqueueWriteBufferRect(queue, dB_dst, CL_TRUE,  // blocking
+                                          buffer_origin, host_orig, region,
+                                          lddb * sizeof(T), 0, ldha * sizeof(T),
+                                          0, hA_src, 0, NULL, NULL);
+    check_error(err);
 }
 
 // --------------------
-template<typename T> static void
-magma_setmatrix_async(
-    magma_int_t m, magma_int_t n,
-    T const* hA_src,                   magma_int_t ldha,
-    cl_mem   dB_dst, size_t dB_offset, magma_int_t lddb,
-    magma_queue_t queue, magma_event_t *event )
-{
-    if (m <= 0 || n <= 0)
-        return;
-
-    size_t buffer_origin[3] = { dB_offset*sizeof(T), 0, 0 };
-    size_t host_orig[3]     = { 0, 0, 0 };
-    size_t region[3]        = { m*sizeof(T), (size_t)n, 1 };
-    cl_int err = clEnqueueWriteBufferRect(
+template<typename T>
+static void magma_setmatrix_async(magma_int_t m, magma_int_t n, T const* hA_src,
+                                  magma_int_t ldha, cl_mem dB_dst,
+                                  size_t dB_offset, magma_int_t lddb,
+                                  magma_queue_t queue, magma_event_t* event) {
+    if (m <= 0 || n <= 0) return;
+
+    size_t buffer_origin[3] = {dB_offset * sizeof(T), 0, 0};
+    size_t host_orig[3]     = {0, 0, 0};
+    size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
+    cl_int err              = clEnqueueWriteBufferRect(
         queue, dB_dst, CL_FALSE,  // non-blocking
-        buffer_origin, host_orig, region,
-        lddb*sizeof(T), 0,
-        ldha*sizeof(T), 0,
-        hA_src, 0, NULL, event );
+        buffer_origin, host_orig, region, lddb * sizeof(T), 0, ldha * sizeof(T),
+        0, hA_src, 0, NULL, event);
     clFlush(queue);
-    check_error( err );
+    check_error(err);
 }
 
 // --------------------
-template<typename T> static void
-magma_getmatrix(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA_src, size_t dA_offset, magma_int_t ldda,
-    T*     hB_dst,                   magma_int_t ldhb,
-    magma_queue_t queue )
-{
-    if (m <= 0 || n <= 0)
-       return;
-
-    size_t buffer_origin[3] = { dA_offset*sizeof(T), 0, 0 };
-    size_t host_orig[3]     = { 0, 0, 0 };
-    size_t region[3]        = { m*sizeof(T), (size_t)n, 1 };
-    cl_int err = clEnqueueReadBufferRect(
-        queue, dA_src, CL_TRUE,  // blocking
-        buffer_origin, host_orig, region,
-        ldda*sizeof(T), 0,
-        ldhb*sizeof(T), 0,
-        hB_dst, 0, NULL, NULL );
-    check_error( err );
+template<typename T>
+static void magma_getmatrix(magma_int_t m, magma_int_t n, cl_mem dA_src,
+                            size_t dA_offset, magma_int_t ldda, T* hB_dst,
+                            magma_int_t ldhb, magma_queue_t queue) {
+    if (m <= 0 || n <= 0) return;
+
+    size_t buffer_origin[3] = {dA_offset * sizeof(T), 0, 0};
+    size_t host_orig[3]     = {0, 0, 0};
+    size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
+    cl_int err = clEnqueueReadBufferRect(queue, dA_src, CL_TRUE,  // blocking
+                                         buffer_origin, host_orig, region,
+                                         ldda * sizeof(T), 0, ldhb * sizeof(T),
+                                         0, hB_dst, 0, NULL, NULL);
+    check_error(err);
 }
 
 // --------------------
-template<typename T> static void
-magma_getmatrix_async(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA_src, size_t dA_offset, magma_int_t ldda,
-    T*     hB_dst,                   magma_int_t ldhb,
-    magma_queue_t queue, magma_event_t *event )
-{
-    if (m <= 0 || n <= 0)
-        return;
-
-    size_t buffer_origin[3] = { dA_offset*sizeof(T), 0, 0 };
-    size_t host_orig[3]     = { 0, 0, 0 };
-    size_t region[3]        = { m*sizeof(T), (size_t)n, 1 };
-    cl_int err = clEnqueueReadBufferRect(
+template<typename T>
+static void magma_getmatrix_async(magma_int_t m, magma_int_t n, cl_mem dA_src,
+                                  size_t dA_offset, magma_int_t ldda, T* hB_dst,
+                                  magma_int_t ldhb, magma_queue_t queue,
+                                  magma_event_t* event) {
+    if (m <= 0 || n <= 0) return;
+
+    size_t buffer_origin[3] = {dA_offset * sizeof(T), 0, 0};
+    size_t host_orig[3]     = {0, 0, 0};
+    size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
+    cl_int err              = clEnqueueReadBufferRect(
         queue, dA_src, CL_FALSE,  // non-blocking
-        buffer_origin, host_orig, region,
-        ldda*sizeof(T), 0,
-        ldhb*sizeof(T), 0,
-        hB_dst, 0, NULL, event );
+        buffer_origin, host_orig, region, ldda * sizeof(T), 0, ldhb * sizeof(T),
+        0, hB_dst, 0, NULL, event);
     clFlush(queue);
-    check_error( err );
+    check_error(err);
 }
 
-template<typename T> void
-magmablas_transpose_inplace(
-    magma_int_t n,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    magma_queue_t queue);
+template<typename T>
+void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
+                                 magma_int_t ldda, magma_queue_t queue);
 
-template<typename T> void
-magmablas_transpose(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA,  size_t dA_offset,  magma_int_t ldda,
-    cl_mem dAT, size_t dAT_offset, magma_int_t lddat,
-    magma_queue_t queue);
+template<typename T>
+void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
+                         size_t dA_offset, magma_int_t ldda, cl_mem dAT,
+                         size_t dAT_offset, magma_int_t lddat,
+                         magma_queue_t queue);
 
-template<typename T> void
-magmablas_laswp(
-    magma_int_t n,
-    cl_mem dAT, size_t dAT_offset, magma_int_t ldda,
-    magma_int_t k1, magma_int_t k2,
-    const magma_int_t *ipiv, magma_int_t inci,
-    magma_queue_t queue);
+template<typename T>
+void magmablas_laswp(magma_int_t n, cl_mem dAT, size_t dAT_offset,
+                     magma_int_t ldda, magma_int_t k1, magma_int_t k2,
+                     const magma_int_t* ipiv, magma_int_t inci,
+                     magma_queue_t queue);
 
-template<typename T> void
-magmablas_swapdblk(magma_int_t n, magma_int_t nb,
-                   cl_mem dA, magma_int_t dA_offset, magma_int_t ldda, magma_int_t inca,
-                   cl_mem dB, magma_int_t dB_offset, magma_int_t lddb, magma_int_t incb,
-                   magma_queue_t queue);
+template<typename T>
+void magmablas_swapdblk(magma_int_t n, magma_int_t nb, cl_mem dA,
+                        magma_int_t dA_offset, magma_int_t ldda,
+                        magma_int_t inca, cl_mem dB, magma_int_t dB_offset,
+                        magma_int_t lddb, magma_int_t incb,
+                        magma_queue_t queue);
 
-template<typename T> void
-magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n,
-                T offdiag, T diag,
-                cl_mem dA, size_t dA_offset, magma_int_t ldda,
-                magma_queue_t queue);
+template<typename T>
+void magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n, T offdiag,
+                     T diag, cl_mem dA, size_t dA_offset, magma_int_t ldda,
+                     magma_queue_t queue);
 
 #if 0  // Needs to be enabled when unmqr2 is enabled
 template<typename T> void
diff --git a/src/backend/opencl/magma/magma_helper.cpp b/src/backend/opencl/magma/magma_helper.cpp
index 116df3933a..a05d1d0fe9 100644
--- a/src/backend/opencl/magma/magma_helper.cpp
+++ b/src/backend/opencl/magma/magma_helper.cpp
@@ -7,133 +7,168 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "magma_common.h"
 #include "common/defines.hpp"
+#include "magma_common.h"
 
-template<typename T> T magma_one() { return (T)1.0; }
-template<typename T> T magma_neg_one() { return (T)-1.0; }
-template<typename T> T magma_zero() { return (T)0; }
+template<typename T>
+T magma_one() {
+    return (T)1.0;
+}
+template<typename T>
+T magma_neg_one() {
+    return (T)-1.0;
+}
+template<typename T>
+T magma_zero() {
+    return (T)0;
+}
 
-#define INSTANTIATE_REAL(func, T)          \
-    template T func<T>();
+#define INSTANTIATE_REAL(func, T) template T func<T>();
 
-INSTANTIATE_REAL(magma_one    , float )
-INSTANTIATE_REAL(magma_neg_one, float )
-INSTANTIATE_REAL(magma_zero   , float )
-INSTANTIATE_REAL(magma_one    , double)
+INSTANTIATE_REAL(magma_one, float)
+INSTANTIATE_REAL(magma_neg_one, float)
+INSTANTIATE_REAL(magma_zero, float)
+INSTANTIATE_REAL(magma_one, double)
 INSTANTIATE_REAL(magma_neg_one, double)
-INSTANTIATE_REAL(magma_zero   , double)
-
-#define INSTANTIATE_CPLX(func, T, val)          \
-    template<> T func<T>()                      \
-    {                                           \
-        T res;                                  \
-        res.s[0] = val;                         \
-        res.s[1] = 0;                           \
-        return res;                             \
-    }                                           \
-
-INSTANTIATE_CPLX(magma_one    , magmaFloatComplex ,  1.0)
-INSTANTIATE_CPLX(magma_neg_one, magmaFloatComplex , -1.0)
-INSTANTIATE_CPLX(magma_zero   , magmaFloatComplex ,  0.0)
-INSTANTIATE_CPLX(magma_one    , magmaDoubleComplex,  1.0)
+INSTANTIATE_REAL(magma_zero, double)
+
+#define INSTANTIATE_CPLX(func, T, val) \
+    template<>                         \
+    T func<T>() {                      \
+        T res;                         \
+        res.s[0] = val;                \
+        res.s[1] = 0;                  \
+        return res;                    \
+    }
+
+INSTANTIATE_CPLX(magma_one, magmaFloatComplex, 1.0)
+INSTANTIATE_CPLX(magma_neg_one, magmaFloatComplex, -1.0)
+INSTANTIATE_CPLX(magma_zero, magmaFloatComplex, 0.0)
+INSTANTIATE_CPLX(magma_one, magmaDoubleComplex, 1.0)
 INSTANTIATE_CPLX(magma_neg_one, magmaDoubleComplex, -1.0)
-INSTANTIATE_CPLX(magma_zero   , magmaDoubleComplex,  0.0)
+INSTANTIATE_CPLX(magma_zero, magmaDoubleComplex, 0.0)
 
-template<typename T> T magma_scalar(double val) { return (T)val; }
+template<typename T>
+T magma_scalar(double val) {
+    return (T)val;
+}
 template float magma_scalar<float>(double val);
 template double magma_scalar<double>(double val);
 
-template<typename T> double  magma_real(T val) { return (double)val; }
+template<typename T>
+double magma_real(T val) {
+    return (double)val;
+}
 template double magma_real<float>(float val);
 template double magma_real<double>(double val);
-template<> double magma_real<magmaFloatComplex>(magmaFloatComplex val) { return (double)val.s[0]; }
-template<> double magma_real<magmaDoubleComplex>(magmaDoubleComplex val) { return (double)val.s[0]; }
-
-#define INSTANTIATE_CPLX_SCALAR(T)              \
-    template<> T magma_scalar<T>(double val)    \
-    {                                           \
-        T res;                                  \
-        res.s[0] = val;                         \
-        res.s[1] = 0;                           \
-        return res;                             \
-    }                                           \
+template<>
+double magma_real<magmaFloatComplex>(magmaFloatComplex val) {
+    return (double)val.s[0];
+}
+template<>
+double magma_real<magmaDoubleComplex>(magmaDoubleComplex val) {
+    return (double)val.s[0];
+}
+
+#define INSTANTIATE_CPLX_SCALAR(T)  \
+    template<>                      \
+    T magma_scalar<T>(double val) { \
+        T res;                      \
+        res.s[0] = val;             \
+        res.s[1] = 0;               \
+        return res;                 \
+    }
 
 INSTANTIATE_CPLX_SCALAR(magmaFloatComplex);
 INSTANTIATE_CPLX_SCALAR(magmaDoubleComplex);
 
-template<typename T> bool magma_is_real() { return true; }
+template<typename T>
+bool magma_is_real() {
+    return true;
+}
 template bool magma_is_real<float>();
 template bool magma_is_real<double>();
-template<> bool magma_is_real<magmaFloatComplex>() { return false; }
-template<> bool magma_is_real<magmaDoubleComplex>() { return false; }
+template<>
+bool magma_is_real<magmaFloatComplex>() {
+    return false;
+}
+template<>
+bool magma_is_real<magmaDoubleComplex>() {
+    return false;
+}
 
 template<typename T>
-magma_int_t magma_get_getrf_nb(magma_int_t m )
-{
-    if      (m <= 3200) return 128;
-    else if (m <  9000) return 256;
-    else                return 320;
+magma_int_t magma_get_getrf_nb(magma_int_t m) {
+    if (m <= 3200)
+        return 128;
+    else if (m < 9000)
+        return 256;
+    else
+        return 320;
 }
 
 template magma_int_t magma_get_getrf_nb<float>(magma_int_t m);
 
 template<>
-magma_int_t magma_get_getrf_nb<double>( magma_int_t m )
-{
-    if      (m <= 2048) return 64;
-    else if (m <  7200) return 192;
-    else                return 256;
+magma_int_t magma_get_getrf_nb<double>(magma_int_t m) {
+    if (m <= 2048)
+        return 64;
+    else if (m < 7200)
+        return 192;
+    else
+        return 256;
 }
 
 template<>
-magma_int_t magma_get_getrf_nb<magmaFloatComplex>( magma_int_t m )
-{
-    if      (m <= 2048) return 64;
-    else                return 128;
+magma_int_t magma_get_getrf_nb<magmaFloatComplex>(magma_int_t m) {
+    if (m <= 2048)
+        return 64;
+    else
+        return 128;
 }
 
 template<>
-magma_int_t magma_get_getrf_nb<magmaDoubleComplex>( magma_int_t m )
-{
-    if      (m <= 3072) return 32;
-    else if (m <= 9024) return 64;
-    else                return 128;
+magma_int_t magma_get_getrf_nb<magmaDoubleComplex>(magma_int_t m) {
+    if (m <= 3072)
+        return 32;
+    else if (m <= 9024)
+        return 64;
+    else
+        return 128;
 }
 
 template<typename T>
-magma_int_t magma_get_potrf_nb(magma_int_t m )
-{
-    if      (m <= 1024) return 128;
-    else                return 320;
+magma_int_t magma_get_potrf_nb(magma_int_t m) {
+    if (m <= 1024)
+        return 128;
+    else
+        return 320;
 }
 
 template magma_int_t magma_get_potrf_nb<float>(magma_int_t m);
 
 template<>
-magma_int_t magma_get_potrf_nb<double>(magma_int_t m)
-{
-    if      (m <= 4256) return 128;
-    else                return 256;
+magma_int_t magma_get_potrf_nb<double>(magma_int_t m) {
+    if (m <= 4256)
+        return 128;
+    else
+        return 256;
 }
 
 template<>
-magma_int_t magma_get_potrf_nb<magmaFloatComplex>(magma_int_t m)
-{
+magma_int_t magma_get_potrf_nb<magmaFloatComplex>(magma_int_t m) {
     UNUSED(m);
     return 128;
 }
 
 template<>
-magma_int_t magma_get_potrf_nb<magmaDoubleComplex>(magma_int_t m)
-{
+magma_int_t magma_get_potrf_nb<magmaDoubleComplex>(magma_int_t m) {
     UNUSED(m);
-    return  64;
+    return 64;
 }
 
 template<typename T>
-magma_int_t magma_get_geqrf_nb(magma_int_t m )
-{
+magma_int_t magma_get_geqrf_nb(magma_int_t m) {
     UNUSED(m);
     return 128;
 }
@@ -141,53 +176,60 @@ magma_int_t magma_get_geqrf_nb(magma_int_t m )
 template magma_int_t magma_get_geqrf_nb<float>(magma_int_t m);
 
 template<>
-magma_int_t magma_get_geqrf_nb<double>( magma_int_t m )
-{
-    if      (m <= 2048) return 64;
+magma_int_t magma_get_geqrf_nb<double>(magma_int_t m) {
+    if (m <= 2048) return 64;
     return 128;
 }
 
 template<>
-magma_int_t magma_get_geqrf_nb<magmaFloatComplex>( magma_int_t m )
-{
-    if      (m <= 2048) return 32;
-    else if (m <= 4032) return 64;
-    else                return 128;
+magma_int_t magma_get_geqrf_nb<magmaFloatComplex>(magma_int_t m) {
+    if (m <= 2048)
+        return 32;
+    else if (m <= 4032)
+        return 64;
+    else
+        return 128;
 }
 
 template<>
-magma_int_t magma_get_geqrf_nb<magmaDoubleComplex>( magma_int_t m )
-{
-    if      (m <= 2048) return 32;
-    else if (m <= 4032) return 64;
-    else                return 128;
+magma_int_t magma_get_geqrf_nb<magmaDoubleComplex>(magma_int_t m) {
+    if (m <= 2048)
+        return 32;
+    else if (m <= 4032)
+        return 64;
+    else
+        return 128;
 }
 
 #if defined(__GNUC__) || defined(__GNUG__)
-    /* GCC/G++, Clang/LLVM, Intel ICC */
-    #pragma GCC diagnostic push
-    #pragma GCC diagnostic ignored "-Wmissing-braces"
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wmissing-braces"
 #else
-    /* Other */
+/* Other */
 #endif
 
-template<typename T> T magma_make(double r, double i) { UNUSED(i); return (T) r; }
+template<typename T>
+T magma_make(double r, double i) {
+    UNUSED(i);
+    return (T)r;
+}
 template float magma_make<float>(double r, double i);
 template double magma_make<double>(double r, double i);
-template<> magmaFloatComplex magma_make<magmaFloatComplex>(double r, double i)
-{
+template<>
+magmaFloatComplex magma_make<magmaFloatComplex>(double r, double i) {
     magmaFloatComplex tmp = {(float)r, (float)i};
     return tmp;
 }
-template<> magmaDoubleComplex magma_make<magmaDoubleComplex>(double r, double i)
-{
+template<>
+magmaDoubleComplex magma_make<magmaDoubleComplex>(double r, double i) {
     magmaDoubleComplex tmp = {r, i};
     return tmp;
 }
 
 #if defined(__GNUC__) || defined(__GNUG__)
-    /* GCC/G++, Clang/LLVM, Intel ICC */
-    #pragma GCC diagnostic pop
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic pop
 #else
-    /* Other */
+/* Other */
 #endif
diff --git a/src/backend/opencl/magma/magma_sync.h b/src/backend/opencl/magma/magma_sync.h
index 6221cb0b63..220af8acc4 100644
--- a/src/backend/opencl/magma/magma_sync.h
+++ b/src/backend/opencl/magma/magma_sync.h
@@ -11,22 +11,22 @@
 #define MAGMA_SYNC_H
 
 #ifndef check_error
-#define check_error( err ) if (err != CL_SUCCESS) { printf ("OpenCL err: %d\n", err); throw cl::Error(err); }
+#define check_error(err)                 \
+    if (err != CL_SUCCESS) {             \
+        printf("OpenCL err: %d\n", err); \
+        throw cl::Error(err);            \
+    }
 #endif
 
-static inline void
-magma_event_sync( magma_event_t event )
-{
+static inline void magma_event_sync(magma_event_t event) {
     cl_int err = clWaitForEvents(1, &event);
     check_error(err);
 }
 
-static inline void
-magma_queue_sync( magma_queue_t queue )
-{
-    cl_int err = clFinish( queue );
+static inline void magma_queue_sync(magma_queue_t queue) {
+    cl_int err = clFinish(queue);
     check_error(err);
-    err = clFlush( queue );
+    err = clFlush(queue);
     check_error(err)
 }
 
diff --git a/src/backend/opencl/magma/potrf.cpp b/src/backend/opencl/magma/potrf.cpp
index e457f0187b..68909fe8f9 100644
--- a/src/backend/opencl/magma/potrf.cpp
+++ b/src/backend/opencl/magma/potrf.cpp
@@ -53,20 +53,17 @@
 
 #include "magma.h"
 #include "magma_blas.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
 
 #include <algorithm>
 
 template<typename Ty>
-magma_int_t magma_potrf_gpu(
-    magma_uplo_t   uplo, magma_int_t    n,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    magma_queue_t queue,
-    magma_int_t*   info)
-{
+magma_int_t magma_potrf_gpu(magma_uplo_t uplo, magma_int_t n, cl_mem dA,
+                            size_t dA_offset, magma_int_t ldda,
+                            magma_queue_t queue, magma_int_t* info) {
 /*  -- clMAGMA (version 0.1) --
     Univ. of Tennessee, Knoxville
     Univ. of California, Berkeley
@@ -120,18 +117,19 @@ magma_int_t magma_potrf_gpu(
     =====================================================================   */
 
 // produces pointer and offset as two args to magmaBLAS routines
-#define dA(i,j)  dA, ((dA_offset) + (i) + (j)*ldda)
+#define dA(i, j) dA, ((dA_offset) + (i) + (j)*ldda)
 
 // produces pointer as single arg to BLAS routines
-#define A(i,j)  &A[ (i) + (j)*lda ]
+#define A(i, j) &A[(i) + (j)*lda]
 
     magma_int_t j, jb, nb;
-    static const Ty  z_one = magma_one<Ty>();
-    static const Ty mz_one = magma_neg_one<Ty>();
-    static const double    one =  1.0;
-    static const double  m_one = -1.0;
+    static const Ty z_one     = magma_one<Ty>();
+    static const Ty mz_one    = magma_neg_one<Ty>();
+    static const double one   = 1.0;
+    static const double m_one = -1.0;
 
-    static const OPENCL_BLAS_TRANS_T transType = magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
+    static const OPENCL_BLAS_TRANS_T transType =
+        magma_is_real<Ty>() ? OPENCL_BLAS_TRANS : OPENCL_BLAS_CONJ_TRANS;
 
     Ty* work;
     magma_int_t err;
@@ -141,11 +139,11 @@ magma_int_t magma_potrf_gpu(
         *info = -1;
     } else if (n < 0) {
         *info = -2;
-    } else if (ldda < std::max(1,n)) {
+    } else if (ldda < std::max(1, n)) {
         *info = -4;
     }
     if (*info != 0) {
-        //magma_xerbla(__func__, -(*info));
+        // magma_xerbla(__func__, -(*info));
         return *info;
     }
 
@@ -156,8 +154,7 @@ magma_int_t magma_potrf_gpu(
     gpu_blas_herk_func<Ty> gpu_blas_herk;
     cpu_lapack_potrf_func<Ty> cpu_lapack_potrf;
 
-
-    err = magma_malloc_cpu<Ty>( &work, nb*nb);
+    err = magma_malloc_cpu<Ty>(&work, nb * nb);
     if (err != MAGMA_SUCCESS) {
         *info = MAGMA_ERR_HOST_ALLOC;
         return *info;
@@ -171,48 +168,42 @@ magma_int_t magma_potrf_gpu(
         magma_getmatrix<Ty>(n, n, dA, dA_offset, ldda, work, n, queue);
 
         LAPACKE_CHECK(cpu_lapack_potrf(
-                          uplo == MagmaUpper ? *MagmaUpperStr : *MagmaLowerStr,
-                          n, work, n));
+            uplo == MagmaUpper ? *MagmaUpperStr : *MagmaLowerStr, n, work, n));
 
         magma_setmatrix<Ty>(n, n, work, n, dA, dA_offset, ldda, queue);
-    }
-    else {
+    } else {
         if (uplo == MagmaUpper) {
             // --------------------
             // compute Cholesky factorization A = U'*U
             // using the left looking algorithm
-            for(j = 0; j < n; j += nb) {
+            for (j = 0; j < n; j += nb) {
                 // apply all previous updates to diagonal block
-                jb = std::min(nb, n-j);
+                jb = std::min(nb, n - j);
                 if (j > 0) {
-                    OPENCL_BLAS_CHECK(gpu_blas_herk(OPENCL_BLAS_TRIANGLE_UPPER, transType,
-                                                    jb, j,
-                                                    m_one,
-                                                    dA(0,j), ldda,
-                                                    one,
-                                                    dA(j,j), ldda,
-                                                    1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_herk(
+                        OPENCL_BLAS_TRIANGLE_UPPER, transType, jb, j, m_one,
+                        dA(0, j), ldda, one, dA(j, j), ldda, 1, &queue, 0,
+                        nullptr, &blas_event));
                 }
 
                 // start asynchronous data transfer
-                magma_getmatrix_async<Ty>(jb, jb, dA(j,j), ldda, work, jb, queue, &event);
-
-                // apply all previous updates to block row right of diagonal block
-                if (j+jb < n && j > 0) {
-                    OPENCL_BLAS_CHECK(gpu_blas_gemm(transType, OPENCL_BLAS_NO_TRANS,
-                                                    jb, n-j-jb, j,
-                                                    mz_one,
-                                                    dA(0, j   ), ldda,
-                                                    dA(0, j+jb), ldda,
-                                                    z_one,
-                                                    dA(j, j+jb), ldda,
-                                                    1, &queue, 0, nullptr, &blas_event));
+                magma_getmatrix_async<Ty>(jb, jb, dA(j, j), ldda, work, jb,
+                                          queue, &event);
+
+                // apply all previous updates to block row right of diagonal
+                // block
+                if (j + jb < n && j > 0) {
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(
+                        transType, OPENCL_BLAS_NO_TRANS, jb, n - j - jb, j,
+                        mz_one, dA(0, j), ldda, dA(0, j + jb), ldda, z_one,
+                        dA(j, j + jb), ldda, 1, &queue, 0, nullptr,
+                        &blas_event));
                 }
 
                 // simultaneous with above zgemm, transfer data, factor
                 // diagonal block on CPU, and test for positive definiteness
                 magma_event_sync(event);
-                LAPACKE_CHECK(cpu_lapack_potrf( *MagmaUpperStr, jb, work, jb));
+                LAPACKE_CHECK(cpu_lapack_potrf(*MagmaUpperStr, jb, work, jb));
 
                 if (*info != 0) {
                     assert(*info > 0);
@@ -220,73 +211,67 @@ magma_int_t magma_potrf_gpu(
                     break;
                 }
 
-                magma_setmatrix_async<Ty>(jb, jb, work, jb, dA(j,j), ldda, queue, &event);
+                magma_setmatrix_async<Ty>(jb, jb, work, jb, dA(j, j), ldda,
+                                          queue, &event);
 
                 // apply diagonal block to block row right of diagonal block
-                if (j+jb < n) {
+                if (j + jb < n) {
                     magma_event_sync(event);
-                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
-                                                    transType, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                                                    jb, n-j-jb,
-                                                    z_one,
-                                                    dA(j, j   ), ldda,
-                                                    dA(j, j+jb), ldda,
-                                                    1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                        OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                        transType, OPENCL_BLAS_NON_UNIT_DIAGONAL, jb,
+                        n - j - jb, z_one, dA(j, j), ldda, dA(j, j + jb), ldda,
+                        1, &queue, 0, nullptr, &blas_event));
                 }
             }
-        }
-        else {
+        } else {
             // --------------------
             // compute Cholesky factorization A = L*L'
             // using the left looking algorithm
-            for(j = 0; j < n; j += nb) {
+            for (j = 0; j < n; j += nb) {
                 // apply all previous updates to diagonal block
-                jb = std::min(nb, n-j);
-                if (j>0) {
-                    OPENCL_BLAS_CHECK(gpu_blas_herk(OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, jb, j,
-                                                    m_one,
-                                                    dA(j, 0), ldda,
-                                                    one,
-                                                    dA(j, j), ldda,
-                                                    1, &queue, 0, nullptr, &blas_event));
+                jb = std::min(nb, n - j);
+                if (j > 0) {
+                    OPENCL_BLAS_CHECK(gpu_blas_herk(
+                        OPENCL_BLAS_TRIANGLE_LOWER, OPENCL_BLAS_NO_TRANS, jb, j,
+                        m_one, dA(j, 0), ldda, one, dA(j, j), ldda, 1, &queue,
+                        0, nullptr, &blas_event));
                 }
 
                 // start asynchronous data transfer
-                magma_getmatrix_async<Ty>(jb, jb, dA(j,j), ldda, work, jb, queue, &event);
-
-                // apply all previous updates to block column below diagonal block
-                if (j+jb < n && j > 0) {
-                    OPENCL_BLAS_CHECK(gpu_blas_gemm(OPENCL_BLAS_NO_TRANS, transType,
-                                                    n-j-jb, jb, j,
-                                                    mz_one,
-                                                    dA(j+jb, 0), ldda,
-                                                    dA(j,    0), ldda,
-                                                    z_one,
-                                                    dA(j+jb, j), ldda,
-                                                    1, &queue, 0, nullptr, &blas_event));
+                magma_getmatrix_async<Ty>(jb, jb, dA(j, j), ldda, work, jb,
+                                          queue, &event);
+
+                // apply all previous updates to block column below diagonal
+                // block
+                if (j + jb < n && j > 0) {
+                    OPENCL_BLAS_CHECK(gpu_blas_gemm(
+                        OPENCL_BLAS_NO_TRANS, transType, n - j - jb, jb, j,
+                        mz_one, dA(j + jb, 0), ldda, dA(j, 0), ldda, z_one,
+                        dA(j + jb, j), ldda, 1, &queue, 0, nullptr,
+                        &blas_event));
                 }
 
                 // simultaneous with above zgemm, transfer data, factor
                 // diagonal block on CPU, and test for positive definiteness
                 magma_event_sync(event);
-                LAPACKE_CHECK(cpu_lapack_potrf(
-                                  *MagmaLowerStr, jb, work, jb));
+                LAPACKE_CHECK(cpu_lapack_potrf(*MagmaLowerStr, jb, work, jb));
                 if (*info != 0) {
                     assert(*info > 0);
                     *info += j;
                     break;
                 }
-                magma_setmatrix_async<Ty>(jb, jb, work, jb, dA(j,j), ldda, queue, &event);
+                magma_setmatrix_async<Ty>(jb, jb, work, jb, dA(j, j), ldda,
+                                          queue, &event);
 
                 // apply diagonal block to block column below diagonal
-                if (j+jb < n) {
+                if (j + jb < n) {
                     magma_event_sync(event);
-                    OPENCL_BLAS_CHECK(gpu_blas_trsm(OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_LOWER, transType, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                                                    n-j-jb, jb,
-                                                    z_one,
-                                                    dA(j   , j), ldda,
-                                                    dA(j+jb, j), ldda,
-                                                    1, &queue, 0, nullptr, &blas_event));
+                    OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                        OPENCL_BLAS_SIDE_RIGHT, OPENCL_BLAS_TRIANGLE_LOWER,
+                        transType, OPENCL_BLAS_NON_UNIT_DIAGONAL, n - j - jb,
+                        jb, z_one, dA(j, j), ldda, dA(j + jb, j), ldda, 1,
+                        &queue, 0, nullptr, &blas_event));
                 }
             }
         }
@@ -298,12 +283,10 @@ magma_int_t magma_potrf_gpu(
     return *info;
 }
 
-#define INSTANTIATE(T)                                  \
-    template magma_int_t magma_potrf_gpu<T>(            \
-        magma_uplo_t   uplo, magma_int_t    n,          \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
-        magma_queue_t queue,                            \
-        magma_int_t*   info);                           \
+#define INSTANTIATE(T)                                                 \
+    template magma_int_t magma_potrf_gpu<T>(                           \
+        magma_uplo_t uplo, magma_int_t n, cl_mem dA, size_t dA_offset, \
+        magma_int_t ldda, magma_queue_t queue, magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/swapdblk.cpp b/src/backend/opencl/magma/swapdblk.cpp
index a33eea4304..d6751b2c0f 100644
--- a/src/backend/opencl/magma/swapdblk.cpp
+++ b/src/backend/opencl/magma/swapdblk.cpp
@@ -7,28 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "magma_data.h"
 #include "kernel/swapdblk.hpp"
+#include "magma_data.h"
 
-template<typename T> void
-magmablas_swapdblk(magma_int_t n, magma_int_t nb,
-                   cl_mem dA, magma_int_t dA_offset, magma_int_t ldda, magma_int_t inca,
-                   cl_mem dB, magma_int_t dB_offset, magma_int_t lddb, magma_int_t incb,
-                   magma_queue_t queue)
-{
-    opencl::kernel::swapdblk<T>(n, nb,
-                                dA, dA_offset, ldda, inca,
-                                dB, dB_offset, lddb, incb, queue);
+template<typename T>
+void magmablas_swapdblk(magma_int_t n, magma_int_t nb, cl_mem dA,
+                        magma_int_t dA_offset, magma_int_t ldda,
+                        magma_int_t inca, cl_mem dB, magma_int_t dB_offset,
+                        magma_int_t lddb, magma_int_t incb,
+                        magma_queue_t queue) {
+    opencl::kernel::swapdblk<T>(n, nb, dA, dA_offset, ldda, inca, dB, dB_offset,
+                                lddb, incb, queue);
 }
 
-
-#define INSTANTIATE(T)                                                  \
-    template void magmablas_swapdblk<T>(magma_int_t n, magma_int_t nb,  \
-                                        cl_mem dA, magma_int_t dA_offset, \
-                                        magma_int_t ldda, magma_int_t inca, \
-                                        cl_mem dB, magma_int_t dB_offset, \
-                                        magma_int_t lddb, magma_int_t incb, \
-                                        magma_queue_t queue);           \
+#define INSTANTIATE(T)                                                        \
+    template void magmablas_swapdblk<T>(                                      \
+        magma_int_t n, magma_int_t nb, cl_mem dA, magma_int_t dA_offset,      \
+        magma_int_t ldda, magma_int_t inca, cl_mem dB, magma_int_t dB_offset, \
+        magma_int_t lddb, magma_int_t incb, magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/transpose.cpp b/src/backend/opencl/magma/transpose.cpp
index ce5fbf3edb..5ccc6c3cbe 100644
--- a/src/backend/opencl/magma/transpose.cpp
+++ b/src/backend/opencl/magma/transpose.cpp
@@ -51,37 +51,34 @@
  *
  **********************************************************************/
 
-#include "magma_data.h"
 #include "kernel/transpose.hpp"
+#include "magma_data.h"
 
-template<typename T> void
-magmablas_transpose(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA,  size_t dA_offset,  magma_int_t ldda,
-    cl_mem dAT, size_t dAT_offset, magma_int_t lddat,
-    magma_queue_t queue)
-{
+template<typename T>
+void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
+                         size_t dA_offset, magma_int_t ldda, cl_mem dAT,
+                         size_t dAT_offset, magma_int_t lddat,
+                         magma_queue_t queue) {
     magma_int_t info = 0;
-    if ( m < 0 )
+    if (m < 0)
         info = -1;
-    else if ( n < 0 )
+    else if (n < 0)
         info = -2;
-    else if ( ldda < m )
+    else if (ldda < m)
         info = -4;
-    else if ( lddat < n )
+    else if (lddat < n)
         info = -6;
 
-    if ( info != 0 ) {
-        //magma_xerbla( __func__, -(info) );
-        return;  //info;
+    if (info != 0) {
+        // magma_xerbla( __func__, -(info) );
+        return;  // info;
     }
 
     /* Quick return */
-    if ( (m == 0) || (n == 0) )
-        return;
+    if ((m == 0) || (n == 0)) return;
 
-    int idims[] = {m, n, 1, 1};
-    int odims[] = {n, m, 1, 1};
+    int idims[]    = {m, n, 1, 1};
+    int odims[]    = {n, m, 1, 1};
     int istrides[] = {1, ldda, ldda * n, ldda * n};
     int ostrides[] = {1, lddat, lddat * m, lddat * m};
 
@@ -89,19 +86,20 @@ magmablas_transpose(
 
     cl::CommandQueue q(queue, true);
     if (m % 32 == 0 && n % 32 == 0) {
-        kernel::transpose<T, false, true >(makeParam(dAT, dAT_offset, odims, ostrides),
-                                           makeParam(dA , dA_offset , idims, istrides), q);
+        kernel::transpose<T, false, true>(
+            makeParam(dAT, dAT_offset, odims, ostrides),
+            makeParam(dA, dA_offset, idims, istrides), q);
     } else {
-        kernel::transpose<T, false, false>(makeParam(dAT, dAT_offset, odims, ostrides),
-                                           makeParam(dA , dA_offset , idims, istrides), q);
+        kernel::transpose<T, false, false>(
+            makeParam(dAT, dAT_offset, odims, ostrides),
+            makeParam(dA, dA_offset, idims, istrides), q);
     }
 }
 
-#define INSTANTIATE(T)                                      \
-    template void magmablas_transpose<T>(                   \
-        magma_int_t m, magma_int_t n,                       \
-        cl_mem dA,  size_t dA_offset,  magma_int_t ldda,    \
-        cl_mem dAT, size_t dAT_offset, magma_int_t lddat,   \
+#define INSTANTIATE(T)                                                      \
+    template void magmablas_transpose<T>(                                   \
+        magma_int_t m, magma_int_t n, cl_mem dA, size_t dA_offset,          \
+        magma_int_t ldda, cl_mem dAT, size_t dAT_offset, magma_int_t lddat, \
         magma_queue_t queue);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/magma/transpose_inplace.cpp b/src/backend/opencl/magma/transpose_inplace.cpp
index 8dc9cabc79..d99d727927 100644
--- a/src/backend/opencl/magma/transpose_inplace.cpp
+++ b/src/backend/opencl/magma/transpose_inplace.cpp
@@ -51,47 +51,44 @@
  *
  **********************************************************************/
 
-#include "magma_data.h"
 #include "kernel/transpose_inplace.hpp"
+#include "magma_data.h"
 
-template<typename T> void
-magmablas_transpose_inplace(
-    magma_int_t n,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    magma_queue_t queue)
-{
+template<typename T>
+void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
+                                 magma_int_t ldda, magma_queue_t queue) {
     magma_int_t info = 0;
-    if ( n < 0 )
+    if (n < 0)
         info = -1;
-    else if ( ldda < n )
+    else if (ldda < n)
         info = -3;
 
-    if ( info != 0 ) {
-        //magma_xerbla( __func__, -(info) );
-        return;  //info;
+    if (info != 0) {
+        // magma_xerbla( __func__, -(info) );
+        return;  // info;
     }
 
     if (n == 0) return;
 
-    int dims[] = {n, n, 1, 1};
+    int dims[]    = {n, n, 1, 1};
     int strides[] = {1, ldda, ldda * n, ldda * n};
 
     using namespace opencl;
 
     cl::CommandQueue q(queue, true);
     if (n % 32 == 0) {
-        kernel::transpose_inplace<T, false, true >(makeParam(dA , dA_offset , dims, strides), q);
+        kernel::transpose_inplace<T, false, true>(
+            makeParam(dA, dA_offset, dims, strides), q);
     } else {
-        kernel::transpose_inplace<T, false, false>(makeParam(dA , dA_offset , dims, strides), q);
+        kernel::transpose_inplace<T, false, false>(
+            makeParam(dA, dA_offset, dims, strides), q);
     }
 }
 
-#define INSTANTIATE(T)                                  \
-    template void magmablas_transpose_inplace<T>(       \
-        magma_int_t n,                                  \
-        cl_mem dA, size_t dA_offset,                    \
-        magma_int_t ldda, magma_queue_t queue);
-
+#define INSTANTIATE(T)                                                \
+    template void magmablas_transpose_inplace<T>(                     \
+        magma_int_t n, cl_mem dA, size_t dA_offset, magma_int_t ldda, \
+        magma_queue_t queue);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/ungqr.cpp b/src/backend/opencl/magma/ungqr.cpp
index 88fd9a5c5f..8976758786 100644
--- a/src/backend/opencl/magma/ungqr.cpp
+++ b/src/backend/opencl/magma/ungqr.cpp
@@ -52,24 +52,21 @@
  **********************************************************************/
 
 #include "magma.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
 
 #include <algorithm>
 
-template<typename Ty>  magma_int_t
-magma_ungqr_gpu(
-    magma_int_t m, magma_int_t n, magma_int_t k,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    Ty *tau,
-    cl_mem dT, size_t dT_offset, magma_int_t nb,
-    magma_queue_t queue,
-    magma_int_t *info)
-{
-#define dA(i,j) (dA),  (dA_offset + ((i) + (j)*ldda))
-#define dT(j)   (dT),  (dT_offset + ((j)*nb))
+template<typename Ty>
+magma_int_t magma_ungqr_gpu(magma_int_t m, magma_int_t n, magma_int_t k,
+                            cl_mem dA, size_t dA_offset, magma_int_t ldda,
+                            Ty *tau, cl_mem dT, size_t dT_offset,
+                            magma_int_t nb, magma_queue_t queue,
+                            magma_int_t *info) {
+#define dA(i, j) (dA), (dA_offset + ((i) + (j)*ldda))
+#define dT(j) (dT), (dT_offset + ((j)*nb))
 
     static const Ty c_zero = magma_zero<Ty>();
     static const Ty c_one  = magma_one<Ty>();
@@ -88,23 +85,21 @@ magma_ungqr_gpu(
         *info = -2;
     } else if ((k < 0) || (k > n)) {
         *info = -3;
-    } else if (ldda < std::max(1,m)) {
+    } else if (ldda < std::max(1, m)) {
         *info = -5;
     }
     if (*info != 0) {
-        //magma_xerbla( __func__, -(*info));
+        // magma_xerbla( __func__, -(*info));
         return *info;
     }
 
-    if (n <= 0) {
-        return *info;
-    }
+    if (n <= 0) { return *info; }
 
     // first kk columns are handled by blocked method.
     // ki is start of 2nd-to-last block
     if ((nb > 1) && (nb < k)) {
         ki = (k - nb - 1) / nb * nb;
-        kk = std::min(k, ki+nb);
+        kk = std::min(k, ki + nb);
     } else {
         ki = 0;
         kk = 0;
@@ -113,8 +108,8 @@ magma_ungqr_gpu(
     // Allocate CPU work space
     // n*nb for zungqr workspace
     // (m - kk)*(n - kk) for last block's panel
-    lwork = n*nb;
-    lpanel = (m - kk)*(n - kk);
+    lwork  = n * nb;
+    lpanel = (m - kk) * (n - kk);
     magma_malloc_cpu<Ty>(&work, lwork + lpanel);
     if (work == NULL) {
         *info = MAGMA_ERR_HOST_ALLOC;
@@ -123,7 +118,7 @@ magma_ungqr_gpu(
     panel = work + lwork;
 
     // Allocate work space on GPU
-    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dV, ldda*nb)) {
+    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dV, ldda * nb)) {
         magma_free_cpu(work);
         *info = MAGMA_ERR_DEVICE_ALLOC;
         return *info;
@@ -132,9 +127,9 @@ magma_ungqr_gpu(
     // dT workspace has:
     // 2*std::min(m,n)*nb      for T and R^{-1} matrices from geqrf
     // ((n+31)/32*32)*nb for dW larfb workspace.
-    lddwork = std::min(m,n);
+    lddwork = std::min(m, n);
     cl_mem dW;
-    magma_malloc<Ty>(&dW, (((n+31)/32)*32)*nb);
+    magma_malloc<Ty>(&dW, (((n + 31) / 32) * 32) * nb);
 
     cpu_lapack_ungqr_work_func<Ty> cpu_lapack_ungqr;
 
@@ -143,19 +138,16 @@ magma_ungqr_gpu(
         m_kk = m - kk;
         n_kk = n - kk;
         k_kk = k - kk;
-        magma_getmatrix<Ty>(m_kk, k_kk,
-                            dA(kk, kk), ldda, panel, m_kk, queue);
+        magma_getmatrix<Ty>(m_kk, k_kk, dA(kk, kk), ldda, panel, m_kk, queue);
 
-        LAPACKE_CHECK(cpu_lapack_ungqr(
-                          m_kk, n_kk, k_kk,
-                          panel, m_kk,
-                          &tau[kk], work, lwork));
+        LAPACKE_CHECK(cpu_lapack_ungqr(m_kk, n_kk, k_kk, panel, m_kk, &tau[kk],
+                                       work, lwork));
 
-        magma_setmatrix<Ty>(m_kk, n_kk,
-                            panel, m_kk, dA(kk, kk), ldda, queue);
+        magma_setmatrix<Ty>(m_kk, n_kk, panel, m_kk, dA(kk, kk), ldda, queue);
 
         // Set A(1:kk,kk+1:n) to zero.
-        magmablas_laset<Ty>(MagmaFull, kk, n - kk, c_zero, c_zero, dA(0, kk), ldda, queue);
+        magmablas_laset<Ty>(MagmaFull, kk, n - kk, c_zero, c_zero, dA(0, kk),
+                            ldda, queue);
     }
 
     if (kk > 0) {
@@ -164,25 +156,24 @@ magma_ungqr_gpu(
         // CPU has no computation
 
         for (i = ki; i >= 0; i -= nb) {
-            ib = std::min(nb, k-i);
+            ib = std::min(nb, k - i);
             mi = m - i;
 
             // Copy current panel on the GPU from dA to dV
-            magma_copymatrix<Ty>(mi, ib,
-                                 dA(i,i), ldda,
-                                 dV, 0,   ldda, queue);
+            magma_copymatrix<Ty>(mi, ib, dA(i, i), ldda, dV, 0, ldda, queue);
 
             // set panel to identity
-            magmablas_laset<Ty>(MagmaFull, i,  ib, c_zero, c_zero, dA(0, i), ldda, queue);
-            magmablas_laset<Ty>(MagmaFull, mi, ib, c_zero, c_one,  dA(i, i), ldda, queue);
+            magmablas_laset<Ty>(MagmaFull, i, ib, c_zero, c_zero, dA(0, i),
+                                ldda, queue);
+            magmablas_laset<Ty>(MagmaFull, mi, ib, c_zero, c_one, dA(i, i),
+                                ldda, queue);
 
             if (i < n) {
-
                 // Apply H to A(i:m,i:n) from the left
-                magma_larfb_gpu<Ty>(MagmaLeft, MagmaNoTrans, MagmaForward, MagmaColumnwise,
-                                    mi, n-i, ib,
-                                    dV, 0,    ldda, dT(i), nb,
-                                    dA(i, i), ldda, dW, 0, lddwork, queue);
+                magma_larfb_gpu<Ty>(MagmaLeft, MagmaNoTrans, MagmaForward,
+                                    MagmaColumnwise, mi, n - i, ib, dV, 0, ldda,
+                                    dT(i), nb, dA(i, i), ldda, dW, 0, lddwork,
+                                    queue);
             }
         }
     }
@@ -191,17 +182,14 @@ magma_ungqr_gpu(
     magma_free(dW);
     magma_free_cpu(work);
     return *info;
-
 }
 
-#define INSTANTIATE(T)                                                  \
-    template  magma_int_t                                               \
-    magma_ungqr_gpu<T>(magma_int_t m, magma_int_t n, magma_int_t k,     \
-                       cl_mem dA, size_t dA_offset, magma_int_t ldda,   \
-                       T *tau,                                          \
-                       cl_mem dT, size_t dT_offset, magma_int_t nb,     \
-                       magma_queue_t queue,                             \
-                       magma_int_t *info);                              \
+#define INSTANTIATE(T)                                          \
+    template magma_int_t magma_ungqr_gpu<T>(                    \
+        magma_int_t m, magma_int_t n, magma_int_t k, cl_mem dA, \
+        size_t dA_offset, magma_int_t ldda, T * tau, cl_mem dT, \
+        size_t dT_offset, magma_int_t nb, magma_queue_t queue,  \
+        magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/unmqr.cpp b/src/backend/opencl/magma/unmqr.cpp
index 366810e2b4..420c5a3572 100644
--- a/src/backend/opencl/magma/unmqr.cpp
+++ b/src/backend/opencl/magma/unmqr.cpp
@@ -52,120 +52,116 @@
  **********************************************************************/
 
 #include "magma.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
 
 #include <algorithm>
 
-template<typename Ty>  magma_int_t
-magma_unmqr_gpu(
-    magma_side_t side, magma_trans_t trans,
-    magma_int_t m, magma_int_t n, magma_int_t k,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    Ty *tau,
-    cl_mem dC, size_t dC_offset, magma_int_t lddc,
-    Ty *hwork, magma_int_t lwork,
-    cl_mem dT, size_t dT_offset, magma_int_t nb,
-    magma_queue_t queue,
-    magma_int_t *info)
-{
-/*  -- clMAGMA (version 0.1) --
-       Univ. of Tennessee, Knoxville
-       Univ. of California, Berkeley
-       Univ. of Colorado, Denver
-       @date
-
-    Purpose
-    =======
-    ZUNMQR_GPU overwrites the general complex M-by-N matrix C with
-
-                    SIDE = 'L'     SIDE = 'R'
-    TRANS = 'N':      Q * C          C * Q
-    TRANS = 'T':      Q**H * C       C * Q**H
-
-    where Q is a complex orthogonal matrix defined as the product of k
-    elementary reflectors
-
-          Q = H(1) H(2) . . . H(k)
-
-    as returned by ZGEQRF. Q is of order M if SIDE = 'L' and of order N
-    if SIDE = 'R'.
-
-    Arguments
-    =========
-    SIDE    (input) CHARACTER*1
-            = 'L': apply Q or Q**H from the Left;
-            = 'R': apply Q or Q**H from the Right.
-
-    TRANS   (input) CHARACTER*1
-            = 'N':  No transpose, apply Q;
-            = 'T':  Transpose, apply Q**H.
-
-    M       (input) INTEGER
-            The number of rows of the matrix C. M >= 0.
-
-    N       (input) INTEGER
-            The number of columns of the matrix C. N >= 0.
-
-    K       (input) INTEGER
-            The number of elementary reflectors whose product defines
-            the matrix Q.
-            If SIDE = 'L', M >= K >= 0;
-            if SIDE = 'R', N >= K >= 0.
-
-    DA      (input) COMPLEX_16 array on the GPU, dimension (LDDA,K)
-            The i-th column must contain the vector which defines the
-            elementary reflector H(i), for i = 1,2,...,k, as returned by
-            ZGEQRF in the first k columns of its array argument DA.
-            DA is modified by the routine but restored on exit.
-
-    LDDA    (input) INTEGER
-            The leading dimension of the array DA.
-            If SIDE = 'L', LDDA >= max(1,M);
-            if SIDE = 'R', LDDA >= max(1,N).
-
-    TAU     (input) COMPLEX_16 array, dimension (K)
-            TAU(i) must contain the scalar factor of the elementary
-            reflector H(i), as returned by ZGEQRF.
-
-    DC      (input/output) COMPLEX_16 array on the GPU, dimension (LDDC,N)
-            On entry, the M-by-N matrix C.
-            On exit, C is overwritten by Q*C or Q**H * C or C * Q**H or C*Q.
-
-    LDDC     (input) INTEGER
-            The leading dimension of the array DC. LDDC >= max(1,M).
-
-    HWORK    (workspace/output) COMPLEX_16 array, dimension (MAX(1,LWORK))
-            On exit, if INFO = 0, HWORK(1) returns the optimal LWORK.
-
-    LWORK   (input) INTEGER
-            The dimension of the array HWORK.
-            LWORK >= (M-K+NB)*(N+2*NB) if SIDE = 'L',
-            and LWORK >= (N-K+NB)*(M+2*NB) if SIDE = 'R', where NB is the
-            optimal blocksize.
-
-            If LWORK = -1, then a workspace query is assumed; the routine
-            only calculates the optimal size of the HWORK array, returns
-            this value as the first entry of the HWORK array, and no error
-            message related to LWORK is issued by XERBLA.
-
-    DT      (input) COMPLEX_16 array on the GPU that is the output
-            (the 9th argument) of magma_zgeqrf_gpu.
-
-    NB      (input) INTEGER
-            This is the blocking size that was used in pre-computing DT, e.g.,
-            the blocking size used in magma_zgeqrf_gpu.
-
-    INFO    (output) INTEGER
-            = 0:  successful exit
-            < 0:  if INFO = -i, the i-th argument had an illegal value
-    =====================================================================   */
-
-    #define a_ref(a_1,a_2) dA, (dA_offset+(a_1)+(a_2)*(ldda))
-    #define c_ref(a_1,a_2) dC, (dC_offset+(a_1)+(a_2)*(lddc))
-    #define t_ref(a_1)     dT, (dT_offset+(a_1)*nb)
+template<typename Ty>
+magma_int_t magma_unmqr_gpu(magma_side_t side, magma_trans_t trans,
+                            magma_int_t m, magma_int_t n, magma_int_t k,
+                            cl_mem dA, size_t dA_offset, magma_int_t ldda,
+                            Ty* tau, cl_mem dC, size_t dC_offset,
+                            magma_int_t lddc, Ty* hwork, magma_int_t lwork,
+                            cl_mem dT, size_t dT_offset, magma_int_t nb,
+                            magma_queue_t queue, magma_int_t* info) {
+    /*  -- clMAGMA (version 0.1) --
+           Univ. of Tennessee, Knoxville
+           Univ. of California, Berkeley
+           Univ. of Colorado, Denver
+           @date
+
+        Purpose
+        =======
+        ZUNMQR_GPU overwrites the general complex M-by-N matrix C with
+
+                        SIDE = 'L'     SIDE = 'R'
+        TRANS = 'N':      Q * C          C * Q
+        TRANS = 'T':      Q**H * C       C * Q**H
+
+        where Q is a complex orthogonal matrix defined as the product of k
+        elementary reflectors
+
+              Q = H(1) H(2) . . . H(k)
+
+        as returned by ZGEQRF. Q is of order M if SIDE = 'L' and of order N
+        if SIDE = 'R'.
+
+        Arguments
+        =========
+        SIDE    (input) CHARACTER*1
+                = 'L': apply Q or Q**H from the Left;
+                = 'R': apply Q or Q**H from the Right.
+
+        TRANS   (input) CHARACTER*1
+                = 'N':  No transpose, apply Q;
+                = 'T':  Transpose, apply Q**H.
+
+        M       (input) INTEGER
+                The number of rows of the matrix C. M >= 0.
+
+        N       (input) INTEGER
+                The number of columns of the matrix C. N >= 0.
+
+        K       (input) INTEGER
+                The number of elementary reflectors whose product defines
+                the matrix Q.
+                If SIDE = 'L', M >= K >= 0;
+                if SIDE = 'R', N >= K >= 0.
+
+        DA      (input) COMPLEX_16 array on the GPU, dimension (LDDA,K)
+                The i-th column must contain the vector which defines the
+                elementary reflector H(i), for i = 1,2,...,k, as returned by
+                ZGEQRF in the first k columns of its array argument DA.
+                DA is modified by the routine but restored on exit.
+
+        LDDA    (input) INTEGER
+                The leading dimension of the array DA.
+                If SIDE = 'L', LDDA >= max(1,M);
+                if SIDE = 'R', LDDA >= max(1,N).
+
+        TAU     (input) COMPLEX_16 array, dimension (K)
+                TAU(i) must contain the scalar factor of the elementary
+                reflector H(i), as returned by ZGEQRF.
+
+        DC      (input/output) COMPLEX_16 array on the GPU, dimension (LDDC,N)
+                On entry, the M-by-N matrix C.
+                On exit, C is overwritten by Q*C or Q**H * C or C * Q**H or C*Q.
+
+        LDDC     (input) INTEGER
+                The leading dimension of the array DC. LDDC >= max(1,M).
+
+        HWORK    (workspace/output) COMPLEX_16 array, dimension (MAX(1,LWORK))
+                On exit, if INFO = 0, HWORK(1) returns the optimal LWORK.
+
+        LWORK   (input) INTEGER
+                The dimension of the array HWORK.
+                LWORK >= (M-K+NB)*(N+2*NB) if SIDE = 'L',
+                and LWORK >= (N-K+NB)*(M+2*NB) if SIDE = 'R', where NB is the
+                optimal blocksize.
+
+                If LWORK = -1, then a workspace query is assumed; the routine
+                only calculates the optimal size of the HWORK array, returns
+                this value as the first entry of the HWORK array, and no error
+                message related to LWORK is issued by XERBLA.
+
+        DT      (input) COMPLEX_16 array on the GPU that is the output
+                (the 9th argument) of magma_zgeqrf_gpu.
+
+        NB      (input) INTEGER
+                This is the blocking size that was used in pre-computing DT,
+       e.g., the blocking size used in magma_zgeqrf_gpu.
+
+        INFO    (output) INTEGER
+                = 0:  successful exit
+                < 0:  if INFO = -i, the i-th argument had an illegal value
+        ===================================================================== */
+
+#define a_ref(a_1, a_2) dA, (dA_offset + (a_1) + (a_2) * (ldda))
+#define c_ref(a_1, a_2) dC, (dC_offset + (a_1) + (a_2) * (lddc))
+#define t_ref(a_1) dT, (dT_offset + (a_1)*nb)
 
     static const Ty c_one = magma_one<Ty>();
 
@@ -176,7 +172,7 @@ magma_unmqr_gpu(
     int left, notran, lquery;
     magma_int_t lwkopt;
 
-    *info = 0;
+    *info  = 0;
     left   = (side == MagmaLeft);
     notran = (trans == MagmaNoTrans);
     lquery = (lwork == -1);
@@ -189,9 +185,9 @@ magma_unmqr_gpu(
         nq = n;
         nw = m;
     }
-    if ( (!left) && (side != MagmaRight) ) {
+    if ((!left) && (side != MagmaRight)) {
         *info = -1;
-    } else if ( (!notran) && (trans != MagmaConjTrans) ) {
+    } else if ((!notran) && (trans != MagmaConjTrans)) {
         *info = -2;
     } else if (m < 0) {
         *info = -3;
@@ -199,22 +195,21 @@ magma_unmqr_gpu(
         *info = -4;
     } else if (k < 0 || k > nq) {
         *info = -5;
-    } else if (ldda < std::max(1,nq)) {
+    } else if (ldda < std::max(1, nq)) {
         *info = -7;
-    } else if (lddc < std::max(1,m)) {
+    } else if (lddc < std::max(1, m)) {
         *info = -10;
-    } else if (lwork < std::max(1,nw) && ! lquery) {
+    } else if (lwork < std::max(1, nw) && !lquery) {
         *info = -12;
     }
 
-    lwkopt = (m-k+nb)*(n+2*nb);
+    lwkopt   = (m - k + nb) * (n + 2 * nb);
     hwork[0] = magma_scalar<Ty>(lwkopt);
 
     if (*info != 0) {
-        //magma_xerbla( __func__, -(*info) );
+        // magma_xerbla( __func__, -(*info) );
         return *info;
-    }
-    else if (lquery) {
+    } else if (lquery) {
         return *info;
     }
 
@@ -224,17 +219,17 @@ magma_unmqr_gpu(
         return *info;
     }
 
-    magma_malloc<Ty>(&dwork, (((n+31)/32)*32)*nb);
+    magma_malloc<Ty>(&dwork, (((n + 31) / 32) * 32) * nb);
 
     cpu_lapack_unmqr_work_func<Ty> cpu_lapack_unmqr;
 
-    if ( (left && (! notran)) || ( (!left) && notran ) ) {
-        i1 = 0;
-        i2 = k-nb;
+    if ((left && (!notran)) || ((!left) && notran)) {
+        i1   = 0;
+        i2   = k - nb;
         step = nb;
     } else {
-        i1 = (k - 1 - nb) / nb * nb;
-        i2 = 0;
+        i1   = (k - 1 - nb) / nb * nb;
+        i2   = 0;
         step = -nb;
     }
 
@@ -251,112 +246,96 @@ magma_unmqr_gpu(
 
     static const bool is_real = magma_is_real<Ty>();
 
-    /* Use unblocked code to multiply last or only block (cases Q*C or C*Q^T). */
-    // workspace left:  A(mi*nb) + C(mi*ni) + work(ni*nb_la) = (m-k-nb)*nb + (m-k-nb)*n + n*nb
-    // workspace right: A(ni*nb) + C(mi*ni) + work(mi*nb_la) = (n-k-nb)*nb + m*(n-k-nb) + m*nb
-    if ( step < 0 ) {
+    /* Use unblocked code to multiply last or only block (cases Q*C or C*Q^T).
+     */
+    // workspace left:  A(mi*nb) + C(mi*ni) + work(ni*nb_la) = (m-k-nb)*nb +
+    // (m-k-nb)*n + n*nb workspace right: A(ni*nb) + C(mi*ni) + work(mi*nb_la) =
+    // (n-k-nb)*nb + m*(n-k-nb) + m*nb
+    if (step < 0) {
         // i is beginning of last block
         i = i1 - step;
-        if ( i >= k ) {
-            i = i1;
-        }
+        if (i >= k) { i = i1; }
         ib = k - i;
         if (left) {
             // ni=n, jc=0, H or H^T is applied to C(i:m-1,0:n-1)
             mi = m - i;
             ma = mi;
             ic = i;
-        }
-        else {
+        } else {
             // mi=m, ic=0, H or H^T is applied to C(0:m-1,i:n-1)
             ni = n - i;
             ma = ni;
             jc = i;
         }
 
-        Ty* hA = hwork;
-        Ty* hC = hwork + ma*ib;
-        Ty* hW = hwork + ma*ib + mi*ni;
-        magma_int_t lhwork = lwork - (ma*ib + mi*ni);
+        Ty* hA             = hwork;
+        Ty* hC             = hwork + ma * ib;
+        Ty* hW             = hwork + ma * ib + mi * ni;
+        magma_int_t lhwork = lwork - (ma * ib + mi * ni);
 
-        magma_getmatrix<Ty>(ma, ib, a_ref(i,  i ), ldda, hA, ma, queue);
+        magma_getmatrix<Ty>(ma, ib, a_ref(i, i), ldda, hA, ma, queue);
         magma_getmatrix<Ty>(mi, ni, c_ref(ic, jc), lddc, hC, mi, queue);
 
-        LAPACKE_CHECK(cpu_lapack_unmqr(
-                          side == MagmaRight ? 'R' : 'L',
-                          notran ? 'N' : (is_real ? 'T' : 'C'),
-                          mi, ni, ib,
-                          hA, ma, tau+i,
-                          hC, mi,
-                          hW, lhwork));
+        LAPACKE_CHECK(cpu_lapack_unmqr(side == MagmaRight ? 'R' : 'L',
+                                       notran ? 'N' : (is_real ? 'T' : 'C'), mi,
+                                       ni, ib, hA, ma, tau + i, hC, mi, hW,
+                                       lhwork));
 
         // send the updated part of C back to the GPU
-        magma_setmatrix<Ty>( mi, ni, hC, mi, c_ref(ic, jc), lddc, queue);
+        magma_setmatrix<Ty>(mi, ni, hC, mi, c_ref(ic, jc), lddc, queue);
     }
 
-
-    if (nb < k)
-    {
-        for (i=i1; step<0 ? i>i2 : i<i2; i+=step)
-        {
+    if (nb < k) {
+        for (i = i1; step < 0 ? i > i2 : i < i2; i += step) {
             ib = std::min(nb, k - i);
-            if (left){
+            if (left) {
                 mi = m - i;
                 ic = i;
-            }
-            else {
+            } else {
                 ni = n - i;
                 jc = i;
             }
 
             if (mi == 0 || ni == 0) break;
 
-            ret = magma_larfb_gpu<Ty>(MagmaLeft,
-                                      is_real ? MagmaTrans : MagmaConjTrans,
-                                      MagmaForward, MagmaColumnwise,
-                                      mi, ni, ib,
-                                      a_ref(i,  i ), ldda, t_ref(i), nb,
-                                      c_ref(ic, jc), lddc, dwork, 0, nw, queue);
-            if ( ret != MAGMA_SUCCESS )
-              return ret;
+            ret = magma_larfb_gpu<Ty>(
+                MagmaLeft, is_real ? MagmaTrans : MagmaConjTrans, MagmaForward,
+                MagmaColumnwise, mi, ni, ib, a_ref(i, i), ldda, t_ref(i), nb,
+                c_ref(ic, jc), lddc, dwork, 0, nw, queue);
+            if (ret != MAGMA_SUCCESS) return ret;
         }
-    }
-    else
-    {
+    } else {
         i = i1;
     }
 
-    /* Use unblocked code to multiply the last or only block (cases Q^T*C or C*Q). */
-    if ( step > 0 ) {
-        ib = k-i;
+    /* Use unblocked code to multiply the last or only block (cases Q^T*C or
+     * C*Q). */
+    if (step > 0) {
+        ib = k - i;
         if (left) {
             // ni=n, jc=0, H or H^T is applied to C(i:m-1,0:n-1)
             mi = m - i;
             ma = mi;
             ic = i;
-        }
-        else {
+        } else {
             // mi=m, ic=0, H or H^T is applied to C(0:m-1,i:n-1)
             ni = n - i;
             ma = ni;
             jc = i;
         }
 
-        Ty* hA = hwork;
-        Ty* hC = hwork + ma*ib;
-        Ty* hW = hwork + ma*ib + mi*ni;
-        magma_int_t lhwork = lwork - (ma*ib + mi*ni);
+        Ty* hA             = hwork;
+        Ty* hC             = hwork + ma * ib;
+        Ty* hW             = hwork + ma * ib + mi * ni;
+        magma_int_t lhwork = lwork - (ma * ib + mi * ni);
 
-        magma_getmatrix<Ty>(ma, ib, a_ref(i,  i ), ldda, hA, ma, queue);
+        magma_getmatrix<Ty>(ma, ib, a_ref(i, i), ldda, hA, ma, queue);
         magma_getmatrix<Ty>(mi, ni, c_ref(ic, jc), lddc, hC, mi, queue);
 
-        LAPACKE_CHECK(cpu_lapack_unmqr(
-                          side == MagmaRight ? 'R' : 'L',
-                          notran ? 'N' : (is_real ? 'T' : 'C'),
-                          mi, ni, ib,
-                          hA, ma, tau+i,
-                          hC, mi,
-                          hW, lhwork));
+        LAPACKE_CHECK(cpu_lapack_unmqr(side == MagmaRight ? 'R' : 'L',
+                                       notran ? 'N' : (is_real ? 'T' : 'C'), mi,
+                                       ni, ib, hA, ma, tau + i, hC, mi, hW,
+                                       lhwork));
 
         // send the updated part of C back to the GPU
         magma_setmatrix<Ty>(mi, ni, hC, mi, c_ref(ic, jc), lddc, queue);
@@ -368,18 +347,13 @@ magma_unmqr_gpu(
     /* End of MAGMA_ZUNMQR_GPU */
 }
 
-#define INSTANTIATE(T)                                  \
-    template  magma_int_t                               \
-    magma_unmqr_gpu<T>(                                 \
-        magma_side_t side, magma_trans_t trans,         \
-        magma_int_t m, magma_int_t n, magma_int_t k,    \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
-        T *tau,                                         \
-        cl_mem dC, size_t dC_offset, magma_int_t lddc,  \
-        T *hwork, magma_int_t lwork,                    \
-        cl_mem dT, size_t dT_offset, magma_int_t nb,    \
-        magma_queue_t queue,                            \
-        magma_int_t *info);                             \
+#define INSTANTIATE(T)                                                         \
+    template magma_int_t magma_unmqr_gpu<T>(                                   \
+        magma_side_t side, magma_trans_t trans, magma_int_t m, magma_int_t n,  \
+        magma_int_t k, cl_mem dA, size_t dA_offset, magma_int_t ldda, T * tau, \
+        cl_mem dC, size_t dC_offset, magma_int_t lddc, T * hwork,              \
+        magma_int_t lwork, cl_mem dT, size_t dT_offset, magma_int_t nb,        \
+        magma_queue_t queue, magma_int_t * info);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/magma/unmqr2.cpp b/src/backend/opencl/magma/unmqr2.cpp
index 0cfc2757fa..11d753fb80 100644
--- a/src/backend/opencl/magma/unmqr2.cpp
+++ b/src/backend/opencl/magma/unmqr2.cpp
@@ -54,8 +54,8 @@
 #if 0  // Needs hetrd to be enabled
 #include "magma.h"
 #include "magma_blas.h"
-#include "magma_data.h"
 #include "magma_cpu_lapack.h"
+#include "magma_data.h"
 #include "magma_helper.h"
 #include "magma_sync.h"
 
@@ -164,9 +164,9 @@ magma_unmqr2_gpu(
     magma_queue_t queue,
     magma_int_t *info)
 {
-    #define dA(i_,j_) (dA) , ((i_) + (j_)*ldda) + dA_offset
-    #define dC(i_,j_) (dC) , ((i_) + (j_)*lddc) + dC_offset
-    #define wA(i_,j_) (wA + (i_) + (j_)*ldwa)
+#define dA(i_, j_) (dA), ((i_) + (j_)*ldda) + dA_offset
+#define dC(i_, j_) (dC), ((i_) + (j_)*lddc) + dC_offset
+#define wA(i_, j_) (wA + (i_) + (j_)*ldwa)
 
     /* Allocate work space on the GPU */
     cl_mem dwork;
@@ -301,18 +301,12 @@ magma_unmqr2_gpu(
     return *info;
 } /* magma_zunmqr */
 
-
-#define INSTANTIATE(Ty)                                 \
-    template magma_int_t                                \
-    magma_unmqr2_gpu<Ty>(                               \
-        magma_side_t side, magma_trans_t trans,         \
-        magma_int_t m, magma_int_t n, magma_int_t k,    \
-        cl_mem dA, size_t dA_offset, magma_int_t ldda,  \
-        Ty    *tau,                                     \
-        cl_mem dC, size_t dC_offset, magma_int_t lddc,  \
-        Ty    *wA, magma_int_t ldwa,                    \
-        magma_queue_t queue,                            \
-        magma_int_t *info);                             \
+#define INSTANTIATE(Ty)                                                       \
+    template magma_int_t magma_unmqr2_gpu<Ty>(                                \
+        magma_side_t side, magma_trans_t trans, magma_int_t m, magma_int_t n, \
+        magma_int_t k, cl_mem dA, size_t dA_offset, magma_int_t ldda,         \
+        Ty * tau, cl_mem dC, size_t dC_offset, magma_int_t lddc, Ty * wA,     \
+        magma_int_t ldwa, magma_queue_t queue, magma_int_t * info);                             \
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index 98b07c49c3..c94b42770f 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -7,53 +7,60 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/defines.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <match_template.hpp>
 #include <kernel/match_template.hpp>
+#include <match_template.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename inType, typename outType, af_match_type mType>
-Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg)
-{
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg) {
     Array<outType> out = createEmptyArray<outType>(sImg.dims());
 
-    bool needMean = mType==AF_ZSAD || mType==AF_LSAD ||
-                    mType==AF_ZSSD || mType==AF_LSSD ||
-                    mType==AF_ZNCC;
+    bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
+                    mType == AF_LSSD || mType == AF_ZNCC;
 
     if (needMean)
-        kernel::matchTemplate<inType, outType, mType, true >(out, sImg, tImg);
+        kernel::matchTemplate<inType, outType, mType, true>(out, sImg, tImg);
     else
         kernel::matchTemplate<inType, outType, mType, false>(out, sImg, tImg);
 
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)\
-    template Array<out_t> match_template<in_t, out_t, AF_SAD >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_LSAD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_SSD >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_LSSD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_NCC >(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>(const Array<in_t> &sImg, const Array<in_t> &tImg); \
-    template Array<out_t> match_template<in_t, out_t, AF_SHD >(const Array<in_t> &sImg, const Array<in_t> &tImg);
+#define INSTANTIATE(in_t, out_t)                                \
+    template Array<out_t> match_template<in_t, out_t, AF_SAD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_LSAD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_SSD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_LSSD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_NCC>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>( \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
+    template Array<out_t> match_template<in_t, out_t, AF_SHD>(  \
+        const Array<in_t> &sImg, const Array<in_t> &tImg);
 
 INSTANTIATE(double, double)
-INSTANTIATE(float ,  float)
-INSTANTIATE(char  ,  float)
-INSTANTIATE(int   ,  float)
-INSTANTIATE(uint  ,  float)
-INSTANTIATE(uchar ,  float)
-INSTANTIATE(short ,  float)
-INSTANTIATE(ushort,  float)
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/match_template.hpp b/src/backend/opencl/match_template.hpp
index 3d599f2d91..2b82aeac03 100644
--- a/src/backend/opencl/match_template.hpp
+++ b/src/backend/opencl/match_template.hpp
@@ -9,10 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename inType, typename outType, af_match_type mType>
-Array<outType> match_template(const Array<inType> &sImg, const Array<inType> &tImg);
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg);
 
 }
diff --git a/src/backend/opencl/math.cpp b/src/backend/opencl/math.cpp
index 8aeb5c49aa..80ffd3f66a 100644
--- a/src/backend/opencl/math.cpp
+++ b/src/backend/opencl/math.cpp
@@ -9,54 +9,51 @@
 
 #include "math.hpp"
 
-namespace opencl
-{
-    bool operator ==(cfloat a, cfloat b) { return (a.s[0] == b.s[0]) && (a.s[1] == b.s[1]); }
-    bool operator !=(cfloat a, cfloat b) { return !(a == b); }
-    bool operator ==(cdouble a, cdouble b) { return (a.s[0] == b.s[0]) && (a.s[1] == b.s[1]); }
-    bool operator !=(cdouble a, cdouble b) { return !(a == b); }
-
-    cfloat operator +(cfloat a, cfloat b)
-    {
-        cfloat res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
-        return res;
-    }
-
-    cdouble operator +(cdouble a, cdouble b)
-    {
-        cdouble res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
-        return res;
-    }
-
-    cfloat operator *(cfloat lhs, cfloat rhs)
-    {
-        cfloat out;
-        out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
-        out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
-        return out;
-    }
-
-    cdouble operator *(cdouble lhs, cdouble rhs)
-    {
-        cdouble out;
-        out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
-        out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
-        return out;
-    }
-
-    cfloat division(cfloat lhs, double rhs)
-    {
-        cfloat retVal;
-        retVal.s[0] = real(lhs) / rhs;
-        retVal.s[1] = imag(lhs) / rhs;
-        return retVal;
-    }
-
-    cdouble division(cdouble lhs, double rhs)
-    {
-        cdouble retVal;
-        retVal.s[0] = real(lhs) / rhs;
-        retVal.s[1] = imag(lhs) / rhs;
-        return retVal;
-    }
+namespace opencl {
+bool operator==(cfloat a, cfloat b) {
+    return (a.s[0] == b.s[0]) && (a.s[1] == b.s[1]);
 }
+bool operator!=(cfloat a, cfloat b) { return !(a == b); }
+bool operator==(cdouble a, cdouble b) {
+    return (a.s[0] == b.s[0]) && (a.s[1] == b.s[1]);
+}
+bool operator!=(cdouble a, cdouble b) { return !(a == b); }
+
+cfloat operator+(cfloat a, cfloat b) {
+    cfloat res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
+    return res;
+}
+
+cdouble operator+(cdouble a, cdouble b) {
+    cdouble res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
+    return res;
+}
+
+cfloat operator*(cfloat lhs, cfloat rhs) {
+    cfloat out;
+    out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
+    out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
+    return out;
+}
+
+cdouble operator*(cdouble lhs, cdouble rhs) {
+    cdouble out;
+    out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
+    out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
+    return out;
+}
+
+cfloat division(cfloat lhs, double rhs) {
+    cfloat retVal;
+    retVal.s[0] = real(lhs) / rhs;
+    retVal.s[1] = imag(lhs) / rhs;
+    return retVal;
+}
+
+cdouble division(cdouble lhs, double rhs) {
+    cdouble retVal;
+    retVal.s[0] = real(lhs) / rhs;
+    retVal.s[1] = imag(lhs) / rhs;
+    return retVal;
+}
+}  // namespace opencl
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index e3dcbe245f..9b2cd80630 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -9,134 +9,147 @@
 
 #pragma once
 
-#include <af/defines.h>
 #include <common/defines.hpp>
+#include <af/defines.h>
 
 #include <backend.hpp>
 #include <types.hpp>
 
+#include <algorithm>
 #include <complex>
 #include <limits>
-#include <algorithm>
 
 #if defined(__GNUC__) || defined(__GNUG__)
-    /* GCC/G++, Clang/LLVM, Intel ICC */
-    #pragma GCC diagnostic push
-    #pragma GCC diagnostic ignored "-Wunused-function"
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
 #else
-    /* Other */
+/* Other */
 #endif
 
-namespace opencl
-{
-
-    template<typename T> static inline T abs(T val)  { return std::abs(val); }
-    template<typename T> static inline T min(T lhs, T rhs) { return std::min(lhs, rhs); }
-    template<typename T> static inline T max(T lhs, T rhs) { return std::max(lhs, rhs); }
-
-    static inline float  abs(cfloat  cval) { return std::sqrt(cval.s[0]*cval.s[0] + cval.s[1]*cval.s[1]); }
-    static inline double abs(cdouble cval) { return std::sqrt(cval.s[0]*cval.s[0] + cval.s[1]*cval.s[1]); }
-
-    template<typename T> static inline T division(T lhs, double rhs) { return lhs / rhs; }
-    cfloat division(cfloat lhs, double rhs);
-    cdouble division(cdouble lhs, double rhs);
-
-    template<> STATIC_
-    cfloat max<cfloat>(cfloat lhs, cfloat rhs)
-    {
-        return abs(lhs) > abs(rhs) ? lhs : rhs;
-    }
-
-    template<> STATIC_
-    cdouble max<cdouble>(cdouble lhs, cdouble rhs)
-    {
-        return abs(lhs) > abs(rhs) ? lhs : rhs;
-    }
-
-    template<> STATIC_
-    cfloat min<cfloat>(cfloat lhs, cfloat rhs)
-    {
-        return abs(lhs) < abs(rhs) ? lhs :  rhs;
-    }
-
-    template<> STATIC_
-    cdouble min<cdouble>(cdouble lhs, cdouble rhs)
-    {
-        return abs(lhs) < abs(rhs) ? lhs :  rhs;
-    }
-
-    template<typename T>
-    static T scalar(double val)
-    {
-        return (T)(val);
-    }
-
-    template<> STATIC_
-    cfloat  scalar<cfloat >(double val)
-    {
-        cfloat  cval;
-        cval.s[0]= (float)val;
-        cval.s[1] = 0;
-        return cval;
-    }
-
-    template<> STATIC_
-    cdouble scalar<cdouble >(double val)
-    {
-        cdouble cval;
-        cval.s[0]= val;
-        cval.s[1] = 0;
-        return cval;
-    }
-
-    template<typename To, typename Ti>
-    static To scalar(Ti real, Ti imag)
-    {
-        To  cval;
-        cval.s[0] = real;
-        cval.s[1] = imag;
-        return cval;
-    }
-
-    template <typename T> STATIC_ T      maxval() { return  std::numeric_limits<T     >::max();      }
-    template <typename T> STATIC_ T      minval() { return  std::numeric_limits<T     >::min();      }
-    template <>           STATIC_ float  maxval() { return  std::numeric_limits<float >::infinity(); }
-    template <>           STATIC_ double maxval() { return  std::numeric_limits<double>::infinity(); }
-    template <>           STATIC_ float  minval() { return -std::numeric_limits<float >::infinity(); }
-    template <>           STATIC_ double minval() { return -std::numeric_limits<double>::infinity(); }
-
-    static inline double real(cdouble in)
-    {
-        return in.s[0];
-    }
-    static inline float real(cfloat in)
-    {
-        return in.s[0];
-    }
-    static inline double imag(cdouble in)
-    {
-        return in.s[1];
-    }
-    static inline float imag(cfloat in)
-    {
-        return in.s[1];
-    }
-
-    bool operator ==(cfloat a, cfloat b);
-    bool operator !=(cfloat a, cfloat b);
-    bool operator ==(cdouble a, cdouble b);
-    bool operator !=(cdouble a, cdouble b);
-    cfloat operator +(cfloat a, cfloat b);
-    cfloat operator +(cfloat a);
-    cdouble operator +(cdouble a, cdouble b);
-    cdouble operator +(cdouble a);
-    cfloat operator *(cfloat a, cfloat b);
-    cdouble operator *(cdouble a, cdouble b);
+namespace opencl {
+
+template<typename T>
+static inline T abs(T val) {
+    return std::abs(val);
+}
+template<typename T>
+static inline T min(T lhs, T rhs) {
+    return std::min(lhs, rhs);
+}
+template<typename T>
+static inline T max(T lhs, T rhs) {
+    return std::max(lhs, rhs);
+}
+
+static inline float abs(cfloat cval) {
+    return std::sqrt(cval.s[0] * cval.s[0] + cval.s[1] * cval.s[1]);
+}
+static inline double abs(cdouble cval) {
+    return std::sqrt(cval.s[0] * cval.s[0] + cval.s[1] * cval.s[1]);
+}
+
+template<typename T>
+static inline T division(T lhs, double rhs) {
+    return lhs / rhs;
+}
+cfloat division(cfloat lhs, double rhs);
+cdouble division(cdouble lhs, double rhs);
+
+template<>
+STATIC_ cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+template<>
+STATIC_ cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+template<>
+STATIC_ cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
+template<>
+STATIC_ cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+template<typename T>
+static T scalar(double val) {
+    return (T)(val);
+}
+
+template<>
+STATIC_ cfloat scalar<cfloat>(double val) {
+    cfloat cval;
+    cval.s[0] = (float)val;
+    cval.s[1] = 0;
+    return cval;
+}
+
+template<>
+STATIC_ cdouble scalar<cdouble>(double val) {
+    cdouble cval;
+    cval.s[0] = val;
+    cval.s[1] = 0;
+    return cval;
+}
+
+template<typename To, typename Ti>
+static To scalar(Ti real, Ti imag) {
+    To cval;
+    cval.s[0] = real;
+    cval.s[1] = imag;
+    return cval;
+}
+
+template<typename T>
+STATIC_ T maxval() {
+    return std::numeric_limits<T>::max();
+}
+template<typename T>
+STATIC_ T minval() {
+    return std::numeric_limits<T>::min();
+}
+template<>
+STATIC_ float maxval() {
+    return std::numeric_limits<float>::infinity();
+}
+template<>
+STATIC_ double maxval() {
+    return std::numeric_limits<double>::infinity();
+}
+template<>
+STATIC_ float minval() {
+    return -std::numeric_limits<float>::infinity();
+}
+template<>
+STATIC_ double minval() {
+    return -std::numeric_limits<double>::infinity();
+}
+
+static inline double real(cdouble in) { return in.s[0]; }
+static inline float real(cfloat in) { return in.s[0]; }
+static inline double imag(cdouble in) { return in.s[1]; }
+static inline float imag(cfloat in) { return in.s[1]; }
+
+bool operator==(cfloat a, cfloat b);
+bool operator!=(cfloat a, cfloat b);
+bool operator==(cdouble a, cdouble b);
+bool operator!=(cdouble a, cdouble b);
+cfloat operator+(cfloat a, cfloat b);
+cfloat operator+(cfloat a);
+cdouble operator+(cdouble a, cdouble b);
+cdouble operator+(cdouble a);
+cfloat operator*(cfloat a, cfloat b);
+cdouble operator*(cdouble a, cdouble b);
+}  // namespace opencl
+
 #if defined(__GNUC__) || defined(__GNUG__)
-    /* GCC/G++, Clang/LLVM, Intel ICC */
-    #pragma GCC diagnostic pop
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic pop
 #else
-    /* Other */
+/* Other */
 #endif
diff --git a/src/backend/opencl/max.cpp b/src/backend/opencl/max.cpp
index 2ac2ed2833..eaaba7ee11 100644
--- a/src/backend/opencl/max.cpp
+++ b/src/backend/opencl/max.cpp
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    //max
-    INSTANTIATE(af_max_t, float  , float  )
-    INSTANTIATE(af_max_t, double , double )
-    INSTANTIATE(af_max_t, cfloat , cfloat )
-    INSTANTIATE(af_max_t, cdouble, cdouble)
-    INSTANTIATE(af_max_t, int    , int    )
-    INSTANTIATE(af_max_t, uint   , uint   )
-    INSTANTIATE(af_max_t, intl   , intl   )
-    INSTANTIATE(af_max_t, uintl  , uintl  )
-    INSTANTIATE(af_max_t, char   , char   )
-    INSTANTIATE(af_max_t, uchar  , uchar  )
-    INSTANTIATE(af_max_t, short  , short  )
-    INSTANTIATE(af_max_t, ushort , ushort )
-}
+namespace opencl {
+// max
+INSTANTIATE(af_max_t, float, float)
+INSTANTIATE(af_max_t, double, double)
+INSTANTIATE(af_max_t, cfloat, cfloat)
+INSTANTIATE(af_max_t, cdouble, cdouble)
+INSTANTIATE(af_max_t, int, int)
+INSTANTIATE(af_max_t, uint, uint)
+INSTANTIATE(af_max_t, intl, intl)
+INSTANTIATE(af_max_t, uintl, uintl)
+INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, uchar, uchar)
+INSTANTIATE(af_max_t, short, short)
+INSTANTIATE(af_max_t, ushort, ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
index e4578f10c6..1f9cbbdcd6 100644
--- a/src/backend/opencl/mean.cpp
+++ b/src/backend/opencl/mean.cpp
@@ -7,74 +7,70 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <af/dim4.hpp>
 
-#include <complex>
-#include <mean.hpp>
-#include <kernel/mean.hpp>
 #include <err_opencl.hpp>
+#include <kernel/mean.hpp>
+#include <mean.hpp>
+#include <complex>
 
-using std::swap;
 using af::dim4;
-namespace opencl
-{
-    template<typename Ti, typename Tw, typename To>
-    To mean(const Array<Ti>& in)
-    {
-        return kernel::mean_all<Ti, Tw, To>(in);
-    }
+using std::swap;
+namespace opencl {
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in) {
+    return kernel::mean_all<Ti, Tw, To>(in);
+}
 
-    template<typename T, typename Tw>
-    T mean(const Array<T>& in, const Array<Tw>& wts)
-    {
-        return kernel::mean_all_weighted<T, Tw>(in, wts);
-    }
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts) {
+    return kernel::mean_all_weighted<T, Tw>(in, wts);
+}
 
-    template<typename Ti, typename Tw, typename To>
-    Array<To> mean(const Array<Ti>& in, const int dim)
-    {
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        Array<To> out = createEmptyArray<To>(odims);
-        kernel::mean<Ti, Tw, To>(out, in, dim);
-        return out;
-    }
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim) {
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    kernel::mean<Ti, Tw, To>(out, in, dim);
+    return out;
+}
 
-    template<typename T, typename Tw>
-    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim)
-    {
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        Array<T> out = createEmptyArray<T>(odims);
-        kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
-        return out;
-    }
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim) {
+    dim4 odims   = in.dims();
+    odims[dim]   = 1;
+    Array<T> out = createEmptyArray<T>(odims);
+    kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
+    return out;
+}
 
-    #define INSTANTIATE(Ti, Tw, To)                                         \
-        template To mean<Ti, Tw, To>(const Array<Ti> &in); \
-        template Array<To> mean<Ti, Tw, To>(const Array<Ti> &in, const int dim); \
+#define INSTANTIATE(Ti, Tw, To)                        \
+    template To mean<Ti, Tw, To>(const Array<Ti>& in); \
+    template Array<To> mean<Ti, Tw, To>(const Array<Ti>& in, const int dim);
 
-    INSTANTIATE(double  , double,  double);
-    INSTANTIATE(float   , float ,  float );
-    INSTANTIATE(int     , float ,  float );
-    INSTANTIATE(unsigned, float ,  float );
-    INSTANTIATE(intl    , double,  double);
-    INSTANTIATE(uintl   , double,  double);
-    INSTANTIATE(short   , float ,  float );
-    INSTANTIATE(ushort  , float ,  float );
-    INSTANTIATE(uchar   , float ,  float );
-    INSTANTIATE(char    , float ,  float );
-    INSTANTIATE(cfloat  , float ,  cfloat);
-    INSTANTIATE(cdouble , double, cdouble);
+INSTANTIATE(double, double, double);
+INSTANTIATE(float, float, float);
+INSTANTIATE(int, float, float);
+INSTANTIATE(unsigned, float, float);
+INSTANTIATE(intl, double, double);
+INSTANTIATE(uintl, double, double);
+INSTANTIATE(short, float, float);
+INSTANTIATE(ushort, float, float);
+INSTANTIATE(uchar, float, float);
+INSTANTIATE(char, float, float);
+INSTANTIATE(cfloat, float, cfloat);
+INSTANTIATE(cdouble, double, cdouble);
 
-    #define INSTANTIATE_WGT(T, Tw)                                         \
-        template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts); \
-        template Array<T> mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts, const int dim); \
+#define INSTANTIATE_WGT(T, Tw)                                              \
+    template T mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts);       \
+    template Array<T> mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts, \
+                                  const int dim);
 
-    INSTANTIATE_WGT(double , double);
-    INSTANTIATE_WGT(float  , float );
-    INSTANTIATE_WGT(cfloat , float );
-    INSTANTIATE_WGT(cdouble, double);
+INSTANTIATE_WGT(double, double);
+INSTANTIATE_WGT(float, float);
+INSTANTIATE_WGT(cfloat, float);
+INSTANTIATE_WGT(cdouble, double);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/mean.hpp b/src/backend/opencl/mean.hpp
index 91c718af8b..60a03e297c 100644
--- a/src/backend/opencl/mean.hpp
+++ b/src/backend/opencl/mean.hpp
@@ -11,18 +11,17 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace opencl
-{
-    template<typename Ti, typename Tw, typename To>
-    To mean(const Array<Ti>& in);
+namespace opencl {
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in);
 
-    template<typename T, typename Tw>
-    T mean(const Array<T>& in, const Array<Tw>& wts);
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts);
 
-    template<typename Ti, typename Tw, typename To>
-    Array<To> mean(const Array<Ti>& in, const int dim);
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim);
 
-    template<typename T, typename Tw>
-    Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index 6480deca82..5ab1d0ddc1 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -7,41 +7,43 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <meanshift.hpp>
-#include <kernel/meanshift.hpp>
 #include <err_opencl.hpp>
+#include <kernel/meanshift.hpp>
+#include <meanshift.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 template<typename T>
-Array<T>  meanshift(const Array<T> &in,
-                    const float &spatialSigma, const float &chromaticSigma,
-                    const unsigned& numIterations,const bool& isColor)
-{
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor) {
     const dim4 dims = in.dims();
-    Array<T> out   = createEmptyArray<T>(dims);
+    Array<T> out    = createEmptyArray<T>(dims);
     if (isColor)
-        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma, numIterations);
+        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma,
+                                   numIterations);
     else
-        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma, numIterations);
+        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma,
+                                    numIterations);
     return out;
 }
 
-#define INSTANTIATE(T) \
-    template Array<T> meanshift<T>(const Array<T>&, const float&, const float&, const unsigned&, const bool&);
+#define INSTANTIATE(T)                                              \
+    template Array<T> meanshift<T>(const Array<T> &, const float &, \
+                                   const float &, const unsigned &, \
+                                   const bool &);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
-INSTANTIATE(intl  )
-INSTANTIATE(uintl )
-}
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace opencl
diff --git a/src/backend/opencl/meanshift.hpp b/src/backend/opencl/meanshift.hpp
index 3bf08bb259..eafd6dbd93 100644
--- a/src/backend/opencl/meanshift.hpp
+++ b/src/backend/opencl/meanshift.hpp
@@ -9,10 +9,9 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 template<typename T>
-Array<T>  meanshift(const Array<T> &in,
-                    const float &spatialSigma, const float &chromaticSigma,
-                    const unsigned& numIterations, const bool& isColor);
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor);
 }
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index f16f9cd564..72600dcb59 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <medfilt.hpp>
-#include <kernel/medfilt.hpp>
 #include <err_opencl.hpp>
+#include <kernel/medfilt.hpp>
+#include <medfilt.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, af_border_type pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
-{
-    ARG_ASSERT(2, (w_wid<=kernel::MAX_MEDFILTER1_LEN));
+Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
+    ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
     const dim4 dims = in.dims();
 
-    Array<T> out    = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
     kernel::medfilt1<T, pad>(out, in, w_wid);
 
@@ -34,21 +32,20 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid)
 }
 
 template<typename T, af_border_type pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
-{
+Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     UNUSED(w_wid);
-    ARG_ASSERT(2, (w_len<=kernel::MAX_MEDFILTER2_LEN));
+    ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
     ARG_ASSERT(2, (w_len % 2 != 0));
 
-    const dim4 dims     = in.dims();
+    const dim4 dims = in.dims();
 
-    Array<T> out      = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
-    switch(w_len) {
-        case  3: kernel::medfilt2<T, pad,  3,  3>(out, in); break;
-        case  5: kernel::medfilt2<T, pad,  5,  5>(out, in); break;
-        case  7: kernel::medfilt2<T, pad,  7,  7>(out, in); break;
-        case  9: kernel::medfilt2<T, pad,  9,  9>(out, in); break;
+    switch (w_len) {
+        case 3: kernel::medfilt2<T, pad, 3, 3>(out, in); break;
+        case 5: kernel::medfilt2<T, pad, 5, 5>(out, in); break;
+        case 7: kernel::medfilt2<T, pad, 7, 7>(out, in); break;
+        case 9: kernel::medfilt2<T, pad, 9, 9>(out, in); break;
         case 11: kernel::medfilt2<T, pad, 11, 11>(out, in); break;
         case 13: kernel::medfilt2<T, pad, 13, 13>(out, in); break;
         case 15: kernel::medfilt2<T, pad, 15, 15>(out, in); break;
@@ -56,19 +53,23 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid)
     return out;
 }
 
-#define INSTANTIATE(T)                                                                          \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_wid);                \
-    template Array<T> medfilt1<T, AF_PAD_SYM >(const Array<T> &in, dim_t w_wid);                \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in, dim_t w_len, dim_t w_wid);   \
-    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, dim_t w_wid);
+#define INSTANTIATE(T)                                                         \
+    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in,             \
+                                               dim_t w_wid);                   \
+    template Array<T> medfilt1<T, AF_PAD_SYM>(const Array<T> &in,              \
+                                              dim_t w_wid);                    \
+    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in,             \
+                                               dim_t w_len, dim_t w_wid);      \
+    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, \
+                                              dim_t w_wid);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/medfilt.hpp b/src/backend/opencl/medfilt.hpp
index f3d13259d0..355dbbcebb 100644
--- a/src/backend/opencl/medfilt.hpp
+++ b/src/backend/opencl/medfilt.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
@@ -18,4 +17,4 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
 template<typename T, af_border_type edge_pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 01b1bffdb6..4accb8fb16 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <err_opencl.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <types.hpp>
-#include <err_opencl.hpp>
 
 #include <common/Logger.hpp>
 #include <spdlog/spdlog.h>
@@ -29,184 +29,143 @@ template class common::MemoryManager<opencl::MemoryManagerPinned>;
 
 using common::bytesToString;
 
-using std::unique_ptr;
 using std::function;
+using std::unique_ptr;
 
-namespace opencl
-{
-void setMemStepSize(size_t step_bytes)
-{
+namespace opencl {
+void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
-size_t getMemStepSize(void)
-{
-    return memoryManager().getMemStepSize();
-}
+size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
 
-size_t getMaxBytes()
-{
-    return memoryManager().getMaxBytes();
-}
+size_t getMaxBytes() { return memoryManager().getMaxBytes(); }
 
-unsigned getMaxBuffers()
-{
-    return memoryManager().getMaxBuffers();
-}
+unsigned getMaxBuffers() { return memoryManager().getMaxBuffers(); }
 
-void garbageCollect()
-{
-    memoryManager().garbageCollect();
-}
+void garbageCollect() { memoryManager().garbageCollect(); }
 
-void printMemInfo(const char *msg, const int device)
-{
+void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
 }
 
 template<typename T>
-unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>
-memAlloc(const size_t &elements)
-{
-    cl::Buffer* ptr = static_cast<cl::Buffer *>(memoryManager().alloc(elements * sizeof(T), false));
-    return unique_ptr<cl::Buffer, function<void(cl::Buffer* )>>(ptr, bufferFree);
+unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
+    const size_t &elements) {
+    cl::Buffer *ptr = static_cast<cl::Buffer *>(
+        memoryManager().alloc(elements * sizeof(T), false));
+    return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(ptr,
+                                                                bufferFree);
 }
 
-void* memAllocUser(const size_t &bytes)
-{
+void *memAllocUser(const size_t &bytes) {
     return memoryManager().alloc(bytes, true);
 }
 template<typename T>
-void memFree(T *ptr)
-{
+void memFree(T *ptr) {
     return memoryManager().unlock((void *)ptr, false);
 }
 
-void memFreeUser(void *ptr)
-{
-    memoryManager().unlock((void *)ptr, true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
 
-cl::Buffer *bufferAlloc(const size_t &bytes)
-{
+cl::Buffer *bufferAlloc(const size_t &bytes) {
     return (cl::Buffer *)memoryManager().alloc(bytes, false);
 }
 
-void bufferFree(cl::Buffer *buf)
-{
+void bufferFree(cl::Buffer *buf) {
     return memoryManager().unlock((void *)buf, false);
 }
 
-void memLock(const void *ptr)
-{
-    memoryManager().userLock((void *)ptr);
-}
+void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
-void memUnlock(const void *ptr)
-{
-    memoryManager().userUnlock((void *)ptr);
-}
+void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
 
-bool isLocked(const void *ptr)
-{
+bool isLocked(const void *ptr) {
     return memoryManager().isUserLocked((void *)ptr);
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                      size_t *lock_bytes,  size_t *lock_buffers)
-{
-    memoryManager().bufferInfo(alloc_bytes, alloc_buffers,
-                                  lock_bytes,  lock_buffers);
+                      size_t *lock_bytes, size_t *lock_buffers) {
+    memoryManager().bufferInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                               lock_buffers);
 }
 
 template<typename T>
-T* pinnedAlloc(const size_t &elements)
-{
+T *pinnedAlloc(const size_t &elements) {
     return (T *)pinnedMemoryManager().alloc(elements * sizeof(T), false);
 }
 
 template<typename T>
-void pinnedFree(T* ptr)
-{
+void pinnedFree(T *ptr) {
     return pinnedMemoryManager().unlock((void *)ptr, false);
 }
 
-bool checkMemoryLimit()
-{
-    return memoryManager().checkMemoryLimit();
-}
-
-#define INSTANTIATE(T)                                                                                 \
-    template unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc<T>(const size_t &elements); \
-    template void memFree(T* ptr);                                                                     \
-    template T* pinnedAlloc(const size_t &elements);                                                   \
-    template void pinnedFree(T* ptr);                                                                  \
-
-    INSTANTIATE(float)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(double)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
+
+#define INSTANTIATE(T)                                                         \
+    template unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc<T>( \
+        const size_t &elements);                                               \
+    template void memFree(T *ptr);                                             \
+    template T *pinnedAlloc(const size_t &elements);                           \
+    template void pinnedFree(T *ptr);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
 MemoryManager::MemoryManager()
-    : common::MemoryManager<opencl::MemoryManager>(getDeviceCount(), common::MAX_BUFFERS,
-                                                   AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
-{
+    : common::MemoryManager<opencl::MemoryManager>(
+          getDeviceCount(), common::MAX_BUFFERS,
+          AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG) {
     this->setMaxMemorySize();
 }
 
-MemoryManager::~MemoryManager()
-{
+MemoryManager::~MemoryManager() {
     for (int n = 0; n < opencl::getDeviceCount(); n++) {
         try {
             opencl::setDevice(n);
             this->garbageCollect();
-        } catch(AfError err) {
-            continue; // Do not throw any errors while shutting down
+        } catch (AfError err) {
+            continue;  // Do not throw any errors while shutting down
         }
     }
 }
 
-int MemoryManager::getActiveDeviceId()
-{
-    return opencl::getActiveDeviceId();
-}
+int MemoryManager::getActiveDeviceId() { return opencl::getActiveDeviceId(); }
 
-size_t MemoryManager::getMaxMemorySize(int id)
-{
+size_t MemoryManager::getMaxMemorySize(int id) {
     return opencl::getDeviceMemorySize(id);
 }
 
-void *MemoryManager::nativeAlloc(const size_t bytes)
-{
+void *MemoryManager::nativeAlloc(const size_t bytes) {
     auto ptr = (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
     AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
-void MemoryManager::nativeFree(void *ptr)
-{
+void MemoryManager::nativeFree(void *ptr) {
     AF_TRACE("nativeFree:          {}", ptr);
     delete (cl::Buffer *)ptr;
 }
 
 MemoryManagerPinned::MemoryManagerPinned()
-    : common::MemoryManager<MemoryManagerPinned>(getDeviceCount(), common::MAX_BUFFERS,
-                                                 AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG),
-    pinnedMaps(getDeviceCount())
-{
+    : common::MemoryManager<MemoryManagerPinned>(
+          getDeviceCount(), common::MAX_BUFFERS,
+          AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
+    , pinnedMaps(getDeviceCount()) {
     this->setMaxMemorySize();
 }
 
-MemoryManagerPinned::~MemoryManagerPinned()
-{
+MemoryManagerPinned::~MemoryManagerPinned() {
     for (int n = 0; n < opencl::getDeviceCount(); n++) {
         opencl::setDevice(n);
         this->garbageCollect();
@@ -218,38 +177,36 @@ MemoryManagerPinned::~MemoryManagerPinned()
     }
 }
 
-int MemoryManagerPinned::getActiveDeviceId()
-{
+int MemoryManagerPinned::getActiveDeviceId() {
     return opencl::getActiveDeviceId();
 }
 
-size_t MemoryManagerPinned::getMaxMemorySize(int id)
-{
+size_t MemoryManagerPinned::getMaxMemorySize(int id) {
     return opencl::getDeviceMemorySize(id);
 }
 
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes)
-{
+void *MemoryManagerPinned::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
-    cl::Buffer* buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
-    ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ | CL_MAP_WRITE, 0, bytes);
+    cl::Buffer *buf =
+        new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
+    ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ | CL_MAP_WRITE, 0,
+                                      bytes);
     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, buf);
     return ptr;
 }
 
-void MemoryManagerPinned::nativeFree(void *ptr)
-{
+void MemoryManagerPinned::nativeFree(void *ptr) {
     AF_TRACE("Pinned::nativeFree:          {}", ptr);
-    int n = opencl::getActiveDeviceId();
-    auto map = pinnedMaps[n];
+    int n     = opencl::getActiveDeviceId();
+    auto map  = pinnedMaps[n];
     auto iter = map.find(ptr);
 
     if (iter != map.end()) {
-        cl::Buffer* buf = map[ptr];
+        cl::Buffer *buf = map[ptr];
         getQueue().enqueueUnmapMemObject(*buf, ptr);
         delete buf;
         map.erase(iter);
     }
 }
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 10b25b73e5..c4298b1404 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -16,38 +16,40 @@
 #include <memory>
 #include <vector>
 
-namespace cl
-{
-class Buffer; //Forward declaration of cl::Buffer from CL/cl2.hpp
+namespace cl {
+class Buffer;  // Forward declaration of cl::Buffer from CL/cl2.hpp
 }
 
-namespace opencl
-{
+namespace opencl {
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
 
-template<typename T> std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>>
-      memAlloc(const size_t &elements);
+template<typename T>
+std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>> memAlloc(
+    const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-template<typename T> void memFree(T* ptr);
-void memFreeUser(void* ptr);
+template<typename T>
+void memFree(T *ptr);
+void memFreeUser(void *ptr);
 
 void memLock(const void *ptr);
 void memUnlock(const void *ptr);
 bool isLocked(const void *ptr);
 
-template<typename T> T* pinnedAlloc(const size_t &elements);
-template<typename T> void pinnedFree(T* ptr);
+template<typename T>
+T *pinnedAlloc(const size_t &elements);
+template<typename T>
+void pinnedFree(T *ptr);
 
 size_t getMaxBytes();
 unsigned getMaxBuffers();
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                      size_t *lock_bytes,  size_t *lock_buffers);
+                      size_t *lock_bytes, size_t *lock_buffers);
 void garbageCollect();
 void pinnedGarbageCollect();
 
@@ -57,27 +59,26 @@ void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 bool checkMemoryLimit();
 
-class MemoryManager : public common::MemoryManager<opencl::MemoryManager>
-{
-    public:
-        MemoryManager();
-        ~MemoryManager();
-        int getActiveDeviceId();
-        size_t getMaxMemorySize(int id);
-        void *nativeAlloc(const size_t bytes);
-        void nativeFree(void *ptr);
+class MemoryManager : public common::MemoryManager<opencl::MemoryManager> {
+   public:
+    MemoryManager();
+    ~MemoryManager();
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
 };
 
-class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned>
-{
-    public:
-        MemoryManagerPinned();
-        ~MemoryManagerPinned();
-        int getActiveDeviceId();
-        size_t getMaxMemorySize(int id);
-        void *nativeAlloc(const size_t bytes);
-        void nativeFree(void *ptr);
-    private:
-        std::vector< std::map<void *, cl::Buffer*> > pinnedMaps;
+class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned> {
+   public:
+    MemoryManagerPinned();
+    ~MemoryManagerPinned();
+    int getActiveDeviceId();
+    size_t getMaxMemorySize(int id);
+    void *nativeAlloc(const size_t bytes);
+    void nativeFree(void *ptr);
+
+   private:
+    std::vector<std::map<void *, cl::Buffer *>> pinnedMaps;
 };
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/min.cpp b/src/backend/opencl/min.cpp
index 3dd770264f..b1eb210175 100644
--- a/src/backend/opencl/min.cpp
+++ b/src/backend/opencl/min.cpp
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    //min
-    INSTANTIATE(af_min_t, float  , float  )
-    INSTANTIATE(af_min_t, double , double )
-    INSTANTIATE(af_min_t, cfloat , cfloat )
-    INSTANTIATE(af_min_t, cdouble, cdouble)
-    INSTANTIATE(af_min_t, int    , int    )
-    INSTANTIATE(af_min_t, uint   , uint   )
-    INSTANTIATE(af_min_t, intl   , intl   )
-    INSTANTIATE(af_min_t, uintl  , uintl  )
-    INSTANTIATE(af_min_t, char   , char   )
-    INSTANTIATE(af_min_t, uchar  , uchar  )
-    INSTANTIATE(af_min_t, short  , short  )
-    INSTANTIATE(af_min_t, ushort , ushort )
-}
+namespace opencl {
+// min
+INSTANTIATE(af_min_t, float, float)
+INSTANTIATE(af_min_t, double, double)
+INSTANTIATE(af_min_t, cfloat, cfloat)
+INSTANTIATE(af_min_t, cdouble, cdouble)
+INSTANTIATE(af_min_t, int, int)
+INSTANTIATE(af_min_t, uint, uint)
+INSTANTIATE(af_min_t, intl, intl)
+INSTANTIATE(af_min_t, uintl, uintl)
+INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, uchar, uchar)
+INSTANTIATE(af_min_t, short, short)
+INSTANTIATE(af_min_t, ushort, ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index 950c36de87..8074c3ed4e 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <kernel/moments.hpp>
-#include <err_opencl.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
+#include <kernel/moments.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 static inline int bitCount(int v) {
     v = v - ((v >> 1) & 0x55555555);
@@ -22,8 +21,7 @@ static inline int bitCount(int v) {
 }
 
 template<typename T>
-Array<float> moments(const Array<T> &in, const af_moment_type moment)
-{
+Array<float> moments(const Array<T> &in, const af_moment_type moment) {
     in.eval();
     dim4 odims, idims = in.dims();
     dim_t moments_dim = bitCount(moment);
@@ -40,8 +38,9 @@ Array<float> moments(const Array<T> &in, const af_moment_type moment)
     return out;
 }
 
-#define INSTANTIATE(T)                                                              \
-    template Array<float> moments<T>(const Array<T> &in, const af_moment_type moment);
+#define INSTANTIATE(T)                                   \
+    template Array<float> moments<T>(const Array<T> &in, \
+                                     const af_moment_type moment);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -52,4 +51,4 @@ INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/moments.hpp b/src/backend/opencl/moments.hpp
index e2ad2e2a01..90666f710a 100644
--- a/src/backend/opencl/moments.hpp
+++ b/src/backend/opencl/moments.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<float> moments(const Array<T> &in, const af_moment_type moment);
+namespace opencl {
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment);
 }
diff --git a/src/backend/opencl/morph.hpp b/src/backend/opencl/morph.hpp
index 4d3d74206d..17b539d5e7 100644
--- a/src/backend/opencl/morph.hpp
+++ b/src/backend/opencl/morph.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask);
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
index 4771b31190..ae7171ee27 100644
--- a/src/backend/opencl/morph3d_impl.hpp
+++ b/src/backend/opencl/morph3d_impl.hpp
@@ -7,44 +7,44 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
+#include <kernel/morph.hpp>
 #include <math.hpp>
 #include <morph.hpp>
-#include <kernel/morph.hpp>
-#include <err_opencl.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask)
-{
-    const dim4 mdims    = mask.dims();
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
+    const dim4 mdims = mask.dims();
 
-    if (mdims[0]!=mdims[1] || mdims[0]!=mdims[2])
+    if (mdims[0] != mdims[1] || mdims[0] != mdims[2])
         OPENCL_NOT_SUPPORTED("Only cubic masks are supported");
 
-    if (mdims[0]>7)
+    if (mdims[0] > 7)
         OPENCL_NOT_SUPPORTED("Kernels > 7x7x7 masks are not supported");
 
-    const dim4 dims= in.dims();
-    Array<T> out   = createEmptyArray<T>(dims);
-
-    switch(mdims[0]) {
-        case  2: kernel::morph3d<T, isDilation,  2>(out, in, mask); break;
-        case  3: kernel::morph3d<T, isDilation,  3>(out, in, mask); break;
-        case  4: kernel::morph3d<T, isDilation,  4>(out, in, mask); break;
-        case  5: kernel::morph3d<T, isDilation,  5>(out, in, mask); break;
-        case  6: kernel::morph3d<T, isDilation,  6>(out, in, mask); break;
-        case  7: kernel::morph3d<T, isDilation,  7>(out, in, mask); break;
-        default: assert(mdims[0] < 7 && "Kernel size should be haandled above.");
+    const dim4 dims = in.dims();
+    Array<T> out    = createEmptyArray<T>(dims);
+
+    switch (mdims[0]) {
+        case 2: kernel::morph3d<T, isDilation, 2>(out, in, mask); break;
+        case 3: kernel::morph3d<T, isDilation, 3>(out, in, mask); break;
+        case 4: kernel::morph3d<T, isDilation, 4>(out, in, mask); break;
+        case 5: kernel::morph3d<T, isDilation, 5>(out, in, mask); break;
+        case 6: kernel::morph3d<T, isDilation, 6>(out, in, mask); break;
+        case 7: kernel::morph3d<T, isDilation, 7>(out, in, mask); break;
+        default:
+            assert(mdims[0] < 7 && "Kernel size should be haandled above.");
     }
 
     return out;
 }
 
-#define INSTANTIATE(T, ISDILATE)                                                 \
-    template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
-}
+#define INSTANTIATE(T, ISDILATE)                               \
+    template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, \
+                                           const Array<T> &mask);
+}  // namespace opencl
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
index 553ff05d1f..1a79f6b338 100644
--- a/src/backend/opencl/morph_impl.hpp
+++ b/src/backend/opencl/morph_impl.hpp
@@ -7,40 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
+#include <kernel/morph.hpp>
 #include <math.hpp>
 #include <morph.hpp>
-#include <kernel/morph.hpp>
-#include <err_opencl.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask)
-{
-    const dim4 mdims    = mask.dims();
+Array<T> morph(const Array<T> &in, const Array<T> &mask) {
+    const dim4 mdims = mask.dims();
 
-    if (mdims[0]!=mdims[1])
+    if (mdims[0] != mdims[1])
         OPENCL_NOT_SUPPORTED("Rectangular masks are not suported");
 
-    if (mdims[0]>19)
+    if (mdims[0] > 19)
         OPENCL_NOT_SUPPORTED("Kernels > 19x19 are not supported");
 
     const dim4 dims = in.dims();
-    Array<T> out   = createEmptyArray<T>(dims);
-
-    switch(mdims[0]) {
-        case  2: kernel::morph<T, isDilation,  2>(out, in, mask); break;
-        case  3: kernel::morph<T, isDilation,  3>(out, in, mask); break;
-        case  4: kernel::morph<T, isDilation,  4>(out, in, mask); break;
-        case  5: kernel::morph<T, isDilation,  5>(out, in, mask); break;
-        case  6: kernel::morph<T, isDilation,  6>(out, in, mask); break;
-        case  7: kernel::morph<T, isDilation,  7>(out, in, mask); break;
-        case  8: kernel::morph<T, isDilation,  8>(out, in, mask); break;
-        case  9: kernel::morph<T, isDilation,  9>(out, in, mask); break;
+    Array<T> out    = createEmptyArray<T>(dims);
+
+    switch (mdims[0]) {
+        case 2: kernel::morph<T, isDilation, 2>(out, in, mask); break;
+        case 3: kernel::morph<T, isDilation, 3>(out, in, mask); break;
+        case 4: kernel::morph<T, isDilation, 4>(out, in, mask); break;
+        case 5: kernel::morph<T, isDilation, 5>(out, in, mask); break;
+        case 6: kernel::morph<T, isDilation, 6>(out, in, mask); break;
+        case 7: kernel::morph<T, isDilation, 7>(out, in, mask); break;
+        case 8: kernel::morph<T, isDilation, 8>(out, in, mask); break;
+        case 9: kernel::morph<T, isDilation, 9>(out, in, mask); break;
         case 10: kernel::morph<T, isDilation, 10>(out, in, mask); break;
         default: kernel::morph<T, isDilation>(out, in, mask, mdims[0]); break;
     }
@@ -48,6 +46,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask)
     return out;
 }
 
-#define INSTANTIATE(T, ISDILATE)                                                 \
-    template Array<T> morph  <T, ISDILATE>(const Array<T> &in, const Array<T> &mask);
-}
+#define INSTANTIATE(T, ISDILATE)                             \
+    template Array<T> morph<T, ISDILATE>(const Array<T> &in, \
+                                         const Array<T> &mask);
+}  // namespace opencl
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 18ec62133e..f51a7336a1 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -7,26 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <math.hpp>
 #include <kernel/nearest_neighbour.hpp>
+#include <math.hpp>
 #include <topk.hpp>
 #include <transpose.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using cl::Device;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename To, af_match_type dist_type>
 void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
                         const Array<T>& query, const Array<T>& train,
-                        const uint dist_dim, const uint n_dist)
-{
-    uint sample_dim = (dist_dim == 0) ? 1 : 0;
+                        const uint dist_dim, const uint n_dist) {
+    uint sample_dim  = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
 
@@ -47,35 +45,42 @@ void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
 }
 
 template<typename T, typename To>
-void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
-                       const Array<T>& query, const Array<T>& train,
-                       const uint dist_dim, const uint n_dist,
-                       const af_match_type dist_type)
-{
-    switch(dist_type) {
-        case AF_SAD: nearest_neighbour_<T, To, AF_SAD>(idx, dist, query, train, dist_dim, n_dist); break;
-        case AF_SSD: nearest_neighbour_<T, To, AF_SSD>(idx, dist, query, train, dist_dim, n_dist); break;
-        case AF_SHD: nearest_neighbour_<T, To, AF_SHD>(idx, dist, query, train, dist_dim, n_dist); break;
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist, const af_match_type dist_type) {
+    switch (dist_type) {
+        case AF_SAD:
+            nearest_neighbour_<T, To, AF_SAD>(idx, dist, query, train, dist_dim,
+                                              n_dist);
+            break;
+        case AF_SSD:
+            nearest_neighbour_<T, To, AF_SSD>(idx, dist, query, train, dist_dim,
+                                              n_dist);
+            break;
+        case AF_SHD:
+            nearest_neighbour_<T, To, AF_SHD>(idx, dist, query, train, dist_dim,
+                                              n_dist);
+            break;
         default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
     }
 }
 
-#define INSTANTIATE(T, To)                                                              \
-    template void nearest_neighbour<T, To>(Array<uint>& idx, Array<To>& dist,           \
-                                         const Array<T>& query, const Array<T>& train,  \
-                                         const uint dist_dim, const uint n_dist,        \
-                                         const af_match_type dist_type);
+#define INSTANTIATE(T, To)                                             \
+    template void nearest_neighbour<T, To>(                            \
+        Array<uint> & idx, Array<To> & dist, const Array<T>& query,    \
+        const Array<T>& train, const uint dist_dim, const uint n_dist, \
+        const af_match_type dist_type);
 
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
-INSTANTIATE(int   , int)
-INSTANTIATE(uint  , uint)
-INSTANTIATE(intl  , intl)
-INSTANTIATE(uintl , uintl)
-INSTANTIATE(short , int)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(short, int)
 INSTANTIATE(ushort, uint)
-INSTANTIATE(uchar , uint)
+INSTANTIATE(uchar, uint)
 
-INSTANTIATE(uintl, uint)    // For Hamming
+INSTANTIATE(uintl, uint)  // For Hamming
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/nearest_neighbour.hpp b/src/backend/opencl/nearest_neighbour.hpp
index 787e4a4794..2f64436874 100644
--- a/src/backend/opencl/nearest_neighbour.hpp
+++ b/src/backend/opencl/nearest_neighbour.hpp
@@ -8,16 +8,16 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename To>
-void nearest_neighbour(Array<uint>& idx, Array<To>& dist,
-                       const Array<T>& query, const Array<T>& train,
-                       const uint dist_dim, const uint n_dist,
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
 
 }
diff --git a/src/backend/opencl/orb.cpp b/src/backend/opencl/orb.cpp
index e0320c44f4..44971f9d02 100644
--- a/src/backend/opencl/orb.cpp
+++ b/src/backend/opencl/orb.cpp
@@ -7,28 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
-#include <math.hpp>
 #include <kernel/orb.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 using af::dim4;
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename convAccT>
-unsigned orb(Array<float> &x_out, Array<float> &y_out,
-             Array<float> &score_out, Array<float> &ori_out,
-             Array<float> &size_out, Array<uint> &desc_out,
-             const Array<T>& image,
-             const float fast_thr, const unsigned max_feat,
-             const float scl_fctr, const unsigned levels,
-             const bool blur_img)
-{
+unsigned orb(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
+             Array<float> &ori_out, Array<float> &size_out,
+             Array<uint> &desc_out, const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img) {
     unsigned nfeat;
 
     Param x;
@@ -38,9 +34,8 @@ unsigned orb(Array<float> &x_out, Array<float> &y_out,
     Param size;
     Param desc;
 
-    kernel::orb<T,convAccT>(&nfeat, x, y, score, ori, size, desc,
-                            image, fast_thr, max_feat, scl_fctr,
-                            levels, blur_img);
+    kernel::orb<T, convAccT>(&nfeat, x, y, score, ori, size, desc, image,
+                             fast_thr, max_feat, scl_fctr, levels, blur_img);
 
     if (nfeat > 0) {
         const dim4 out_dims(nfeat);
@@ -57,17 +52,14 @@ unsigned orb(Array<float> &x_out, Array<float> &y_out,
     return nfeat;
 }
 
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned orb<T, convAccT>(                                       \
+        Array<float> & x, Array<float> & y, Array<float> & score,             \
+        Array<float> & ori, Array<float> & size, Array<uint> & desc,          \
+        const Array<T> &image, const float fast_thr, const unsigned max_feat, \
+        const float scl_fctr, const unsigned levels, const bool blur_img);
 
-#define INSTANTIATE(T, convAccT)                                                        \
-    template unsigned orb<T, convAccT>(Array<float> &x, Array<float> &y,                \
-                                       Array<float> &score, Array<float> &ori,          \
-                                       Array<float> &size, Array<uint> &desc,           \
-                                       const Array<T>& image,                           \
-                                       const float fast_thr, const unsigned max_feat,   \
-                                       const float scl_fctr, const unsigned levels,     \
-                                       const bool blur_img);
-
-INSTANTIATE(float , float )
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/orb.hpp b/src/backend/opencl/orb.hpp
index 47c477428a..6b5906ae18 100644
--- a/src/backend/opencl/orb.hpp
+++ b/src/backend/opencl/orb.hpp
@@ -7,21 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename convAccT>
 unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              Array<float> &orientation, Array<float> &size,
-             Array<unsigned> &desc,
-             const Array<T>& image,
-             const float fast_thr, const unsigned max_feat,
-             const float scl_fctr, const unsigned levels,
-             const bool blur_img);
+             Array<unsigned> &desc, const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img);
 
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 32a8f7fc17..76a2aae494 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -11,17 +11,17 @@
 // Causes conflict between system cl.hpp and opencl/cl.hpp
 #include <common/graphics_common.hpp>
 
-#include <af/version.h>
-#include <af/opencl.h>
+#include <blas.hpp>
+#include <clfft.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
-#include <blas.hpp>
-#include <clfft.hpp>
-#include <errorcodes.hpp>
 #include <err_opencl.hpp>
+#include <errorcodes.hpp>
 #include <platform.hpp>
 #include <version.hpp>
+#include <af/opencl.h>
+#include <af/version.h>
 
 #ifdef OS_MAC
 #include <OpenGL/CGLCurrent.h>
@@ -43,51 +43,44 @@
 #include <utility>
 #include <vector>
 
-using std::string;
-using std::vector;
 using std::ostringstream;
 using std::runtime_error;
+using std::string;
+using std::vector;
 
-using cl::Platform;
-using cl::Context;
 using cl::CommandQueue;
+using cl::Context;
 using cl::Device;
+using cl::Platform;
 
-namespace opencl
-{
+namespace opencl {
 
-#if defined (OS_MAC)
+#if defined(OS_MAC)
 static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 #else
 static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
 #endif
 
-static const std::string get_system(void)
-{
-    std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
+static const std::string get_system(void) {
+    std::string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
 #if defined(OS_LNX)
-    "Linux";
+           "Linux";
 #elif defined(OS_WIN)
-    "Windows";
+           "Windows";
 #elif defined(OS_MAC)
-    "Mac OSX";
+           "Mac OSX";
 #endif
 }
 
-int getBackend()
-{
-    return AF_BACKEND_OPENCL;
-}
+int getBackend() { return AF_BACKEND_OPENCL; }
 
-static inline bool verify_present(std::string pname, const char *ref)
-{
+static inline bool verify_present(std::string pname, const char* ref) {
     return pname.find(ref) != std::string::npos;
 }
 
-static inline bool compare_default(const Device *ldev, const Device *rdev)
-{
+static inline bool compare_default(const Device* ldev, const Device* rdev) {
     const cl_device_type device_types[] = {CL_DEVICE_TYPE_GPU,
                                            CL_DEVICE_TYPE_ACCELERATOR};
 
@@ -99,16 +92,16 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
         auto is_l_curr_type = l_dev_type == current_type;
         auto is_r_curr_type = r_dev_type == current_type;
 
-        if ( is_l_curr_type && !is_r_curr_type) return true;
-        if (!is_l_curr_type &&  is_r_curr_type) return false;
+        if (is_l_curr_type && !is_r_curr_type) return true;
+        if (!is_l_curr_type && is_r_curr_type) return false;
     }
 
     // For GPUs, this ensures discrete > integrated
     auto is_l_integrated = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
     auto is_r_integrated = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
 
-    if (!is_l_integrated &&  is_r_integrated) return true;
-    if ( is_l_integrated && !is_r_integrated) return false;
+    if (!is_l_integrated && is_r_integrated) return true;
+    if (is_l_integrated && !is_r_integrated) return false;
 
     // At this point, the devices are of same type.
     // Sort based on emperical evidence of preferred platforms
@@ -117,45 +110,50 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
     std::string lPlatName = getPlatformName(*ldev);
     std::string rPlatName = getPlatformName(*rdev);
 
-    if (l_dev_type == CL_DEVICE_TYPE_GPU &&
-        r_dev_type == CL_DEVICE_TYPE_GPU ) {
+    if (l_dev_type == CL_DEVICE_TYPE_GPU && r_dev_type == CL_DEVICE_TYPE_GPU) {
         // If GPU, prefer AMD > NVIDIA > Beignet / Intel > APPLE
-        const char *platforms[] = {"AMD", "NVIDIA", "APPLE", "INTEL", "BEIGNET"};
+        const char* platforms[] = {"AMD", "NVIDIA", "APPLE", "INTEL",
+                                   "BEIGNET"};
 
         for (auto ref_name : platforms) {
-            if ( verify_present(lPlatName, ref_name) &&
-                !verify_present(rPlatName, ref_name)) return true;
+            if (verify_present(lPlatName, ref_name) &&
+                !verify_present(rPlatName, ref_name))
+                return true;
 
             if (!verify_present(lPlatName, ref_name) &&
-                 verify_present(rPlatName, ref_name)) return false;
+                verify_present(rPlatName, ref_name))
+                return false;
         }
 
         // Intel falls back to compare based on memory
     } else {
         // If CPU, prefer Intel > AMD > POCL > APPLE
-        const char *platforms[] = {"INTEL", "AMD", "POCL", "APPLE"};
+        const char* platforms[] = {"INTEL", "AMD", "POCL", "APPLE"};
 
         for (auto ref_name : platforms) {
-            if ( verify_present(lPlatName, ref_name) &&
-                !verify_present(rPlatName, ref_name)) return true;
+            if (verify_present(lPlatName, ref_name) &&
+                !verify_present(rPlatName, ref_name))
+                return true;
 
             if (!verify_present(lPlatName, ref_name) &&
-                 verify_present(rPlatName, ref_name)) return false;
+                verify_present(rPlatName, ref_name))
+                return false;
         }
     }
 
-
     // Compare device compute versions
 
     {
         // Check Device OpenCL Version
-        auto lversion =  ldev->getInfo<CL_DEVICE_VERSION>();
-        auto rversion =  rdev->getInfo<CL_DEVICE_VERSION>();
+        auto lversion = ldev->getInfo<CL_DEVICE_VERSION>();
+        auto rversion = rdev->getInfo<CL_DEVICE_VERSION>();
 
-        bool lres = (lversion[7] > rversion[7]) ||
+        bool lres =
+            (lversion[7] > rversion[7]) ||
             ((lversion[7] == rversion[7]) && (lversion[9] > rversion[9]));
 
-        bool rres = (lversion[7] < rversion[7]) ||
+        bool rres =
+            (lversion[7] < rversion[7]) ||
             ((lversion[7] == rversion[7]) && (lversion[9] < rversion[9]));
 
         if (lres) return true;
@@ -169,13 +167,11 @@ static inline bool compare_default(const Device *ldev, const Device *rdev)
     return l_mem >= r_mem;
 }
 
-static afcl::deviceType getDeviceTypeEnum(cl::Device dev)
-{
+static afcl::deviceType getDeviceTypeEnum(cl::Device dev) {
     return (afcl::deviceType)dev.getInfo<CL_DEVICE_TYPE>();
 }
 
-static afcl::platform getPlatformEnum(cl::Device dev)
-{
+static afcl::platform getPlatformEnum(cl::Device dev) {
     std::string pname = getPlatformName(dev);
     if (verify_present(pname, "AMD")) return AFCL_PLATFORM_AMD;
     if (verify_present(pname, "NVIDIA")) return AFCL_PLATFORM_NVIDIA;
@@ -188,16 +184,14 @@ static afcl::platform getPlatformEnum(cl::Device dev)
 
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
-static inline std::string &ltrim(std::string &s)
-{
-    s.erase(s.begin(), std::find_if(s.begin(), s.end(),
-                                    std::not1(std::ptr_fun<int, int>(std::isspace))));
+static inline std::string& ltrim(std::string& s) {
+    s.erase(s.begin(),
+            std::find_if(s.begin(), s.end(),
+                         std::not1(std::ptr_fun<int, int>(std::isspace))));
     return s;
 }
 
-static std::string platformMap(std::string &platStr)
-{
-
+static std::string platformMap(std::string& platStr) {
     typedef std::map<std::string, std::string> strmap_t;
     static const strmap_t platMap = {
         std::make_pair("NVIDIA CUDA", "NVIDIA"),
@@ -217,31 +211,29 @@ static std::string platformMap(std::string &platStr)
     }
 }
 
-std::string getDeviceInfo()
-{
+std::string getDeviceInfo() {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     vector<cl::Device*> devices;
     {
-      common::lock_guard_t lock(devMngr.deviceMutex);
-      devices = devMngr.mDevices;
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        devices = devMngr.mDevices;
     }
 
     ostringstream info;
-    info << "ArrayFire v" << AF_VERSION
-         << " (OpenCL, " << get_system() << ", build " << AF_REVISION << ")\n";
+    info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
+         << ", build " << AF_REVISION << ")\n";
 
     unsigned nDevices = 0;
-    for(auto device: devices) {
+    for (auto device : devices) {
         const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
-        string dstr = device->getInfo<CL_DEVICE_NAME>();
+        string dstr      = device->getInfo<CL_DEVICE_NAME>();
         bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
 
-        string id =
-            (show_braces ? string("[") : "-") +
-            std::to_string(nDevices) +
-            (show_braces ? string("]") : "-");
+        string id = (show_braces ? string("[") : "-") +
+                    std::to_string(nDevices) +
+                    (show_braces ? string("]") : "-");
 
         size_t msize = device->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
         info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
@@ -253,10 +245,11 @@ std::string getDeviceInfo()
         info << devVersion;
         info << " -- Device driver " << driVersion;
         info << " -- FP64 Support: "
-             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0 ? "True" : "False");
+             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0
+                     ? "True"
+                     : "False");
         info << " -- Unified Memory ("
-             << (isHostUnifiedMemory(*device) ? "True" : "False")
-             << ")";
+             << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif
         info << std::endl;
 
@@ -265,8 +258,7 @@ std::string getDeviceInfo()
     return info.str();
 }
 
-std::string getPlatformName(const cl::Device &device)
-{
+std::string getPlatformName(const cl::Device& device) {
     const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
     std::string platStr = platform.getInfo<CL_PLATFORM_NAME>();
     return platformMap(platStr);
@@ -274,8 +266,7 @@ std::string getPlatformName(const cl::Device &device)
 
 typedef std::pair<unsigned, unsigned> device_id_t;
 
-std::pair<unsigned, unsigned>& tlocalActiveDeviceId()
-{
+std::pair<unsigned, unsigned>& tlocalActiveDeviceId() {
     // First element is active context id
     // Second element is active queue id
     thread_local device_id_t activeDeviceId(0, 0);
@@ -283,13 +274,11 @@ std::pair<unsigned, unsigned>& tlocalActiveDeviceId()
     return activeDeviceId;
 }
 
-void setActiveContext(int device)
-{
+void setActiveContext(int device) {
     tlocalActiveDeviceId() = std::make_pair(device, device);
 }
 
-int getDeviceCount()
-{
+int getDeviceCount() {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
@@ -297,31 +286,27 @@ int getDeviceCount()
     return devMngr.mQueues.size();
 }
 
-int getActiveDeviceId()
-{
+int getActiveDeviceId() {
     // Second element is the queue id, which is
     // what we mean by active device id in opencl backend
     return std::get<1>(tlocalActiveDeviceId());
 }
 
-int getDeviceIdFromNativeId(cl_device_id id)
-{
+int getDeviceIdFromNativeId(cl_device_id id) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
     int nDevices = devMngr.mDevices.size();
-    int devId = 0;
-    for (devId=0; devId<nDevices; ++devId) {
-        if (id == devMngr.mDevices[devId]->operator()())
-            break;
+    int devId    = 0;
+    for (devId = 0; devId < nDevices; ++devId) {
+        if (id == devMngr.mDevices[devId]->operator()()) break;
     }
 
     return devId;
 }
 
-int getActiveDeviceType()
-{
+int getActiveDeviceType() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -331,8 +316,7 @@ int getActiveDeviceType()
     return devMngr.mDeviceTypes[std::get<1>(devId)];
 }
 
-int getActivePlatform()
-{
+int getActivePlatform() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -341,8 +325,7 @@ int getActivePlatform()
 
     return devMngr.mPlatforms[std::get<1>(devId)];
 }
-const Context& getContext()
-{
+const Context& getContext() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -352,8 +335,7 @@ const Context& getContext()
     return *(devMngr.mContexts[std::get<0>(devId)]);
 }
 
-CommandQueue& getQueue()
-{
+CommandQueue& getQueue() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -363,12 +345,10 @@ CommandQueue& getQueue()
     return *(devMngr.mQueues[std::get<1>(devId)]);
 }
 
-const cl::Device& getDevice(int id)
-{
+const cl::Device& getDevice(int id) {
     device_id_t& devId = tlocalActiveDeviceId();
 
-    if (id == -1)
-        id = std::get<1>(devId);
+    if (id == -1) id = std::get<1>(devId);
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -376,42 +356,35 @@ const cl::Device& getDevice(int id)
     return *(devMngr.mDevices[id]);
 }
 
-size_t getDeviceMemorySize(int device)
-{
+size_t getDeviceMemorySize(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     cl::Device dev;
     {
-      common::lock_guard_t lock(devMngr.deviceMutex);
-      // Assuming devices don't deallocate or are invalidated during execution
-      dev = *devMngr.mDevices[device];
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        // Assuming devices don't deallocate or are invalidated during execution
+        dev = *devMngr.mDevices[device];
     }
     size_t msize = dev.getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
     return msize;
 }
 
-size_t getHostMemorySize()
-{
-    return common::getHostMemorySize();
-}
+size_t getHostMemorySize() { return common::getHostMemorySize(); }
 
-cl_device_type getDeviceType()
-{
-    cl::Device device = getDevice();
+cl_device_type getDeviceType() {
+    cl::Device device   = getDevice();
     cl_device_type type = device.getInfo<CL_DEVICE_TYPE>();
     return type;
 }
 
-bool isHostUnifiedMemory(const cl::Device &device)
-{
+bool isHostUnifiedMemory(const cl::Device& device) {
     return device.getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
 }
 
-bool OpenCLCPUOffload(bool forceOffloadOSX)
-{
+bool OpenCLCPUOffload(bool forceOffloadOSX) {
     static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") != "0";
-    bool offload = false;
-    if(offloadEnv) offload = isHostUnifiedMemory(getDevice());
+    bool offload                 = false;
+    if (offloadEnv) offload = isHostUnifiedMemory(getDevice());
 #if OS_MAC
     // FORCED OFFLOAD FOR LAPACK FUNCTIONS ON OSX UNIFIED MEMORY DEVICES
     //
@@ -432,8 +405,7 @@ bool OpenCLCPUOffload(bool forceOffloadOSX)
     return offload;
 }
 
-bool isGLSharingSupported()
-{
+bool isGLSharingSupported() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -443,37 +415,35 @@ bool isGLSharingSupported()
     return devMngr.mIsGLSharingOn[std::get<1>(devId)];
 }
 
-bool isDoubleSupported(int device)
-{
+bool isDoubleSupported(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     cl::Device dev;
     {
-      common::lock_guard_t lock(devMngr.deviceMutex);
-      dev = *devMngr.mDevices[device];
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        dev = *devMngr.mDevices[device];
     }
 
     return (dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0);
 }
 
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
-{
-    unsigned nDevices = 0;
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
+    unsigned nDevices        = 0;
     unsigned currActiveDevId = (unsigned)getActiveDeviceId();
-    bool devset = false;
+    bool devset              = false;
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     vector<cl::Context*> contexts;
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
-        contexts = devMngr.mContexts; // NOTE: copy, not a reference
+        contexts = devMngr.mContexts;  // NOTE: copy, not a reference
     }
 
     for (auto context : contexts) {
         vector<Device> devices = context->getInfo<CL_CONTEXT_DEVICES>();
 
-        for (auto &device : devices) {
+        for (auto& device : devices) {
             const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
             string platStr = platform.getInfo<CL_PLATFORM_NAME>();
 
@@ -481,14 +451,14 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
                 string dev_str;
                 device.getInfo(CL_DEVICE_NAME, &dev_str);
                 string com_str = device.getInfo<CL_DEVICE_VERSION>();
-                com_str = com_str.substr(7, 3);
+                com_str        = com_str.substr(7, 3);
 
                 // strip out whitespace from the device string:
                 const std::string& whitespace = " \t";
                 const auto strBegin = dev_str.find_first_not_of(whitespace);
-                const auto strEnd = dev_str.find_last_not_of(whitespace);
+                const auto strEnd   = dev_str.find_last_not_of(whitespace);
                 const auto strRange = strEnd - strBegin + 1;
-                dev_str = dev_str.substr(strBegin, strRange);
+                dev_str             = dev_str.substr(strBegin, strRange);
 
                 // copy to output
                 snprintf(d_name, 64, "%s", dev_str.c_str());
@@ -497,23 +467,24 @@ void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute)
                 snprintf(d_compute, 10, "%s", com_str.c_str());
                 devset = true;
             }
-            if(devset) break;
+            if (devset) break;
             nDevices++;
         }
-        if(devset) break;
+        if (devset) break;
     }
 
     // Sanitize input
     for (int i = 0; i < 31; i++) {
         if (d_name[i] == ' ') {
-            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ') d_name[i] = 0;
-            else d_name[i] = '_';
+            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ')
+                d_name[i] = 0;
+            else
+                d_name[i] = '_';
         }
     }
 }
 
-int setDevice(int device)
-{
+int setDevice(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
@@ -528,23 +499,21 @@ int setDevice(int device)
     }
 }
 
-void sync(int device)
-{
+void sync(int device) {
     int currDevice = getActiveDeviceId();
     setDevice(device);
     getQueue().finish();
     setDevice(currDevice);
 }
 
-bool checkExtnAvailability(const Device &pDevice, std::string pName)
-{
+bool checkExtnAvailability(const Device& pDevice, std::string pName) {
     bool ret_val = false;
     // find the extension required
     std::string exts = pDevice.getInfo<CL_DEVICE_EXTENSIONS>();
     std::stringstream ss(exts);
     std::string item;
-    while (std::getline(ss,item,' ')) {
-        if (item==pName) {
+    while (std::getline(ss, item, ' ')) {
+        if (item == pName) {
             ret_val = true;
             break;
         }
@@ -552,34 +521,34 @@ bool checkExtnAvailability(const Device &pDevice, std::string pName)
     return ret_val;
 }
 
-void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
-{
+void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
     clRetainDevice(dev);
     clRetainContext(ctx);
     clRetainCommandQueue(que);
 
-    DeviceManager& devMngr   = DeviceManager::getInstance();
+    DeviceManager& devMngr = DeviceManager::getInstance();
 
     int nDevices = 0;
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
 
-        cl::Device* tDevice      = new cl::Device(dev);
-        cl::Context* tContext    = new cl::Context(ctx);
-        cl::CommandQueue* tQueue = (que==NULL ?
-                new cl::CommandQueue(*tContext, *tDevice) : new cl::CommandQueue(que));
+        cl::Device* tDevice   = new cl::Device(dev);
+        cl::Context* tContext = new cl::Context(ctx);
+        cl::CommandQueue* tQueue =
+            (que == NULL ? new cl::CommandQueue(*tContext, *tDevice)
+                         : new cl::CommandQueue(que));
         devMngr.mDevices.push_back(tDevice);
         devMngr.mContexts.push_back(tContext);
         devMngr.mQueues.push_back(tQueue);
         devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
-        nDevices = devMngr.mDevices.size()-1;
+        nDevices = devMngr.mDevices.size() - 1;
 
-        //cache the boost program_cache object, clean up done on program exit
-        //not during removeDeviceContext
+        // cache the boost program_cache object, clean up done on program exit
+        // not during removeDeviceContext
         namespace compute = boost::compute;
-        using BPCache = DeviceManager::BoostProgCache;
+        using BPCache     = DeviceManager::BoostProgCache;
         compute::context c(ctx);
         BPCache currCache = compute::program_cache::get_global_cache(c);
         devMngr.mBoostProgCacheVector.emplace_back(new BPCache(currCache));
@@ -589,17 +558,16 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que)
     memoryManager().addMemoryManagement(nDevices);
 }
 
-void setDeviceContext(cl_device_id dev, cl_context ctx)
-{
+void setDeviceContext(cl_device_id dev, cl_context ctx) {
     // FIXME: add OpenGL Interop for user provided contexts later
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
     const int dCount = devMngr.mDevices.size();
-    for (int i=0; i<dCount; ++i) {
-        if(devMngr.mDevices[i]->operator()()==dev &&
-           devMngr.mContexts[i]->operator()()==ctx) {
+    for (int i = 0; i < dCount; ++i) {
+        if (devMngr.mDevices[i]->operator()() == dev &&
+            devMngr.mContexts[i]->operator()() == ctx) {
             setActiveContext(i);
             return;
         }
@@ -607,9 +575,8 @@ void setDeviceContext(cl_device_id dev, cl_context ctx)
     AF_ERROR("No matching device found", AF_ERR_ARG);
 }
 
-void removeDeviceContext(cl_device_id dev, cl_context ctx)
-{
-    if (getDevice()() == dev && getContext()()==ctx) {
+void removeDeviceContext(cl_device_id dev, cl_context ctx) {
+    if (getDevice()() == dev && getContext()() == ctx) {
         AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
     }
 
@@ -620,9 +587,9 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
         common::lock_guard_t lock(devMngr.deviceMutex);
 
         const int dCount = devMngr.mDevices.size();
-        for (int i = 0; i<dCount; ++i) {
-            if(devMngr.mDevices[i]->operator()()==dev &&
-               devMngr.mContexts[i]->operator()()==ctx) {
+        for (int i = 0; i < dCount; ++i) {
+            if (devMngr.mDevices[i]->operator()() == dev &&
+                devMngr.mContexts[i]->operator()() == ctx) {
                 deleteIdx = i;
                 break;
             }
@@ -634,7 +601,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
     } else if (deleteIdx == -1) {
         AF_ERROR("No matching device found", AF_ERR_ARG);
     } else {
-        //remove memory management for device added by user outside of the lock
+        // remove memory management for device added by user outside of the lock
         memoryManager().removeMemoryManagement(deleteIdx);
 
         common::lock_guard_t lock(devMngr.deviceMutex);
@@ -649,36 +616,36 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx)
         // that lies ahead of the device that has been requested
         // to be removed. We just pop the entries from pool since it
         // has no side effects.
-        devMngr.mDevices.erase(devMngr.mDevices.begin()+deleteIdx);
-        devMngr.mContexts.erase(devMngr.mContexts.begin()+deleteIdx);
-        devMngr.mQueues.erase(devMngr.mQueues.begin()+deleteIdx);
-        devMngr.mPlatforms.erase(devMngr.mPlatforms.begin()+deleteIdx);
+        devMngr.mDevices.erase(devMngr.mDevices.begin() + deleteIdx);
+        devMngr.mContexts.erase(devMngr.mContexts.begin() + deleteIdx);
+        devMngr.mQueues.erase(devMngr.mQueues.begin() + deleteIdx);
+        devMngr.mPlatforms.erase(devMngr.mPlatforms.begin() + deleteIdx);
 
         // FIXME: add OpenGL Interop for user provided contexts later
-        devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin()+deleteIdx);
+        devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin() +
+                                     deleteIdx);
 
         // OTHERWISE, update(decrement) the thread local active device ids
         device_id_t& devId = tlocalActiveDeviceId();
 
         if (deleteIdx < (int)devId.first) {
-            device_id_t newVals = std::make_pair(devId.first-1, devId.second-1);
+            device_id_t newVals =
+                std::make_pair(devId.first - 1, devId.second - 1);
             devId = newVals;
         }
     }
 }
 
-bool synchronize_calls()
-{
+bool synchronize_calls() {
     static const bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
 }
 
-unsigned getMaxJitSize()
-{
+unsigned getMaxJitSize() {
 #if defined(OS_MAC)
     const int MAX_JIT_LEN = 50;
 #else
-    const int MAX_JIT_LEN = 100;
+    const int MAX_JIT_LEN       = 100;
 #endif
 
     thread_local int length = 0;
@@ -693,98 +660,90 @@ unsigned getMaxJitSize()
     return length;
 }
 
-bool& evalFlag()
-{
+bool& evalFlag() {
     thread_local bool flag = true;
     return flag;
 }
 
-MemoryManager& memoryManager()
-{
+MemoryManager& memoryManager() {
     static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]{ inst.memManager.reset(new MemoryManager()); });
+    std::call_once(flag, [&] { inst.memManager.reset(new MemoryManager()); });
 
     return *(inst.memManager.get());
 }
 
-MemoryManagerPinned& pinnedMemoryManager()
-{
+MemoryManagerPinned& pinnedMemoryManager() {
     static std::once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]{ inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
+    std::call_once(
+        flag, [&] { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
 
     return *(inst.pinnedMemManager.get());
 }
 
-graphics::ForgeManager& forgeManager()
-{
+graphics::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
-GraphicsResourceManager& interopManager()
-{
+GraphicsResourceManager& interopManager() {
     static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = getActiveDeviceId();
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(initFlags[id], [&]{ inst.gfxManagers[id].reset(new GraphicsResourceManager()); });
+    std::call_once(initFlags[id], [&] {
+        inst.gfxManagers[id].reset(new GraphicsResourceManager());
+    });
 
     return *(inst.gfxManagers[id].get());
 }
 
-PlanCache& fftManager()
-{
+PlanCache& fftManager() {
     thread_local PlanCache clfftManagers[DeviceManager::MAX_DEVICES];
 
     return clfftManagers[getActiveDeviceId()];
 }
 
-kc_t& getKernelCache(int device)
-{
+kc_t& getKernelCache(int device) {
     thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
     return kernelCaches[device];
 }
 
-void addKernelToCache(int device, const std::string& key, const kc_entry_t entry)
-{
+void addKernelToCache(int device, const std::string& key,
+                      const kc_entry_t entry) {
     getKernelCache(device).emplace(key, entry);
 }
 
-void removeKernelFromCache(int device, const std::string& key)
-{
+void removeKernelFromCache(int device, const std::string& key) {
     getKernelCache(device).erase(key);
 }
 
-kc_entry_t kernelCache(int device, const std::string& key)
-{
+kc_entry_t kernelCache(int device, const std::string& key) {
     kc_t& cache = getKernelCache(device);
 
     kc_t::iterator iter = cache.find(key);
 
-    return (iter==cache.end() ? kc_entry_t{0, 0} : iter->second);
+    return (iter == cache.end() ? kc_entry_t{0, 0} : iter->second);
 }
 
-DeviceManager& DeviceManager::getInstance()
-{
+DeviceManager& DeviceManager::getInstance() {
     static DeviceManager* my_instance = new DeviceManager();
     return *my_instance;
 }
 
-DeviceManager::~DeviceManager()
-{
-    for (int i=0; i<getDeviceCount(); ++i) {
+DeviceManager::~DeviceManager() {
+    for (int i = 0; i < getDeviceCount(); ++i) {
         delete gfxManagers[i].release();
     }
 #ifndef OS_WIN
-    //TODO: FIXME:
+    // TODO: FIXME:
     // clfftTeardown() causes a "Pure Virtual Function Called" crash on
     // Windows for Intel devices. This causes tests to fail.
     clfftTeardown();
@@ -795,14 +754,13 @@ DeviceManager::~DeviceManager()
     // deCache Boost program_cache
 #ifndef OS_WIN
     namespace compute = boost::compute;
-    for (auto bCache : mBoostProgCacheVector)
-        delete bCache;
+    for (auto bCache : mBoostProgCacheVector) delete bCache;
 #endif
 
     delete memManager.release();
     delete pinnedMemManager.release();
 
-    //TODO: FIXME:
+    // TODO: FIXME:
     // OpenCL libs on Windows platforms
     // are crashing the application at program exit
     // most probably a reference counting issue based
@@ -810,18 +768,17 @@ DeviceManager::~DeviceManager()
     // doesn't seem to happen on Linux or MacOSX.
     // So, clean up OpenCL resources on non-Windows platforms
 #ifndef OS_WIN
-    for (auto q: mQueues) delete q;
-    for (auto c: mContexts) delete c;
-    for (auto d: mDevices) delete d;
+    for (auto q : mQueues) delete q;
+    for (auto c : mContexts) delete c;
+    for (auto d : mDevices) delete d;
 #endif
 }
 
 DeviceManager::DeviceManager()
-    : mUserDeviceOffset(0),
-      fgMngr(new graphics::ForgeManager()),
-      mFFTSetup(new clfftSetupData)
-{
-    std::vector<cl::Platform>   platforms;
+    : mUserDeviceOffset(0)
+    , fgMngr(new graphics::ForgeManager())
+    , mFFTSetup(new clfftSetupData) {
+    std::vector<cl::Platform> platforms;
     Platform::get(&platforms);
 
     // This is all we need because the sort takes care of the order of devices
@@ -842,15 +799,13 @@ DeviceManager::DeviceManager()
     }
 
     // Iterate through platforms, get all available devices and store them
-    for (auto &platform : platforms) {
+    for (auto& platform : platforms) {
         std::vector<Device> current_devices;
 
         try {
             platform.getDevices(DEVICE_TYPES, &current_devices);
-        } catch(const cl::Error &err) {
-            if (err.err() != CL_DEVICE_NOT_FOUND) {
-                throw;
-            }
+        } catch (const cl::Error& err) {
+            if (err.err() != CL_DEVICE_NOT_FOUND) { throw; }
         }
         for (auto dev : current_devices) {
             mDevices.push_back(new Device(dev));
@@ -866,13 +821,13 @@ DeviceManager::DeviceManager()
 
     // Create contexts and queues once the sort is done
     for (int i = 0; i < nDevices; i++) {
-        cl_platform_id device_platform = mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
-        cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM,
-                                        (cl_context_properties)(device_platform),
-                                        0};
+        cl_platform_id device_platform =
+            mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
+        cl_context_properties cps[3] = {
+            CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
 
-        Context *ctx = new Context(*mDevices[i], cps);
-        CommandQueue *cq = new CommandQueue(*ctx, *mDevices[i]);
+        Context* ctx     = new Context(*mDevices[i], cps);
+        CommandQueue* cq = new CommandQueue(*ctx, *mDevices[i]);
         mContexts.push_back(ctx);
         mQueues.push_back(cq);
         mIsGLSharingOn.push_back(false);
@@ -881,12 +836,12 @@ DeviceManager::DeviceManager()
     }
 
     bool default_device_set = false;
-    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
-    if(!deviceENV.empty()) {
+    deviceENV               = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
+    if (!deviceENV.empty()) {
         std::stringstream s(deviceENV);
         int def_device = -1;
         s >> def_device;
-        if(def_device < 0 || def_device >= (int)nDevices) {
+        if (def_device < 0 || def_device >= (int)nDevices) {
             printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
             printf("Setting default device as 0\n");
         } else {
@@ -896,8 +851,7 @@ DeviceManager::DeviceManager()
     }
 
     deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
-    if (!default_device_set && !deviceENV.empty())
-    {
+    if (!default_device_set && !deviceENV.empty()) {
         cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
         if (deviceENV.compare("CPU") == 0) {
             default_device_type = CL_DEVICE_TYPE_CPU;
@@ -914,8 +868,9 @@ DeviceManager::DeviceManager()
             }
         }
         if (!default_device_set) {
-            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
-                    deviceENV.c_str());
+            printf(
+                "WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
+                deviceENV.c_str());
             printf("Using default device as 0\n");
         }
     }
@@ -928,20 +883,18 @@ DeviceManager::DeviceManager()
         try {
             /* loop over devices and replace contexts with
              * OpenGL shared contexts whereever applicable */
-            int devCount = mDevices.size();
+            int devCount      = mDevices.size();
             fg_window wHandle = fgMngr->getMainWindow();
-            for(int i=0; i<devCount; ++i)
-                markDeviceForInterop(i, wHandle);
-        } catch (...) {
-        }
+            for (int i = 0; i < devCount; ++i) markDeviceForInterop(i, wHandle);
+        } catch (...) {}
     }
 
     mUserDeviceOffset = mDevices.size();
-    //Initialize FFT setup data structure
+    // Initialize FFT setup data structure
     CLFFT_CHECK(clfftInitSetupData(mFFTSetup.get()));
     CLFFT_CHECK(clfftSetup(mFFTSetup.get()));
 
-    //Initialize clBlas library
+    // Initialize clBlas library
     initBlas();
 
     // Cache Boost program_cache
@@ -953,20 +906,22 @@ DeviceManager::DeviceManager()
     }
 }
 
-void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
-{
+void DeviceManager::markDeviceForInterop(const int device,
+                                         const void* wHandle) {
     try {
         if (device >= (int)mQueues.size() ||
-                device>= (int)DeviceManager::MAX_DEVICES) {
-            throw cl::Error(CL_INVALID_DEVICE, "Invalid device passed for CL-GL Interop");
+            device >= (int)DeviceManager::MAX_DEVICES) {
+            throw cl::Error(CL_INVALID_DEVICE,
+                            "Invalid device passed for CL-GL Interop");
         } else {
             mQueues[device]->finish();
 
             // check if the device has CL_GL sharing extension enabled
-            bool temp = checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
+            bool temp =
+                checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
             if (!temp) {
-                /* return silently if given device has not OpenGL sharing extension
-                 * enabled so that regular queue is used for it */
+                /* return silently if given device has not OpenGL sharing
+                 * extension enabled so that regular queue is used for it */
                 return;
             }
 
@@ -974,27 +929,31 @@ void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
             cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
 
             long long wnd_ctx, wnd_dsp;
-            fgMngr->plugin().fg_get_window_context_handle(&wnd_ctx,
-                                                          const_cast<fg_window>(wHandle));
-            fgMngr->plugin().fg_get_window_display_handle(&wnd_dsp,
-                                                          const_cast<fg_window>(wHandle));
+            fgMngr->plugin().fg_get_window_context_handle(
+                &wnd_ctx, const_cast<fg_window>(wHandle));
+            fgMngr->plugin().fg_get_window_display_handle(
+                &wnd_dsp, const_cast<fg_window>(wHandle));
 #ifdef OS_MAC
             CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
-            CGLShareGroupObj cgl_share_group = CGLGetShareGroup(cgl_current_ctx);
+            CGLShareGroupObj cgl_share_group =
+                CGLGetShareGroup(cgl_current_ctx);
 
             cl_context_properties cps[] = {
-                CL_CONTEXT_PROPERTY_USE_CGL_SHAREGROUP_APPLE, (cl_context_properties)cgl_share_group,
-                0
-            };
+                CL_CONTEXT_PROPERTY_USE_CGL_SHAREGROUP_APPLE,
+                (cl_context_properties)cgl_share_group, 0};
 #else
             cl_context_properties cps[] = {
-                CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
+                CL_GL_CONTEXT_KHR,
+                (cl_context_properties)wnd_ctx,
 #if defined(_WIN32) || defined(_MSC_VER)
-                CL_WGL_HDC_KHR, (cl_context_properties)wnd_dsp,
+                CL_WGL_HDC_KHR,
+                (cl_context_properties)wnd_dsp,
 #else
-                CL_GLX_DISPLAY_KHR, (cl_context_properties)wnd_dsp,
+                CL_GLX_DISPLAY_KHR,
+                (cl_context_properties)wnd_dsp,
 #endif
-                CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
+                CL_CONTEXT_PLATFORM,
+                (cl_context_properties)plat(),
                 0
             };
 
@@ -1002,15 +961,15 @@ void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
             {
                 cl_context_properties test_cps[] = {
                     CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
-                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(),
-                    0
-                };
+                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(), 0};
 
                 // Load the extension
-                // If cl_khr_gl_sharing is available, this function should be present
-                // This has been checked earlier, it comes to this point only if it is found
+                // If cl_khr_gl_sharing is available, this function should be
+                // present This has been checked earlier, it comes to this point
+                // only if it is found
                 auto func = (clGetGLContextInfoKHR_fn)
-                    clGetExtensionFunctionAddressForPlatform(plat(), "clGetGLContextInfoKHR");
+                    clGetExtensionFunctionAddressForPlatform(
+                        plat(), "clGetGLContextInfoKHR");
 
                 // If the function doesn't load, bail early
                 if (!func) return;
@@ -1018,19 +977,16 @@ void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
                 // Get all devices associated with opengl context
                 std::vector<cl_device_id> devices(16);
                 size_t ret = 0;
-                cl_int err = func(test_cps,
-                                  CL_DEVICES_FOR_GL_CONTEXT_KHR,
+                cl_int err = func(test_cps, CL_DEVICES_FOR_GL_CONTEXT_KHR,
                                   devices.size() * sizeof(cl_device_id),
-                                  &devices[0],
-                                  &ret);
+                                  &devices[0], &ret);
                 if (err != CL_SUCCESS) return;
                 int num = ret / sizeof(cl_device_id);
                 devices.resize(num);
 
                 // Check if current device is present in the associated devices
                 cl_device_id current_device = (*mDevices[device])();
-                auto res = std::find(std::begin(devices),
-                                     std::end(devices),
+                auto res = std::find(std::begin(devices), std::end(devices),
                                      current_device);
 
                 if (res == std::end(devices)) return;
@@ -1038,8 +994,8 @@ void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
 #endif
 
             // Change current device to use GL sharing
-            Context * ctx = new Context(*mDevices[device], cps);
-            CommandQueue * cq = new CommandQueue(*ctx, *mDevices[device]);
+            Context* ctx     = new Context(*mDevices[device], cps);
+            CommandQueue* cq = new CommandQueue(*ctx, *mDevices[device]);
 
             // May be fixes the AMD GL issues we see on windows?
 #if !defined(_WIN32) && !defined(_MSC_VER)
@@ -1047,92 +1003,92 @@ void DeviceManager::markDeviceForInterop(const int device, const void* wHandle)
             delete mQueues[device];
 #endif
 
-            mContexts[device] = ctx;
-            mQueues[device] = cq;
+            mContexts[device]      = ctx;
+            mQueues[device]        = cq;
             mIsGLSharingOn[device] = true;
         }
-    } catch (const cl::Error &ex) {
+    } catch (const cl::Error& ex) {
         /* If replacing the original context with GL shared context
          * failes, don't throw an error and instead fall back to
          * original context and use copy via host to support graphics
          * on that particular OpenCL device. So mark it as no GL sharing */
     }
 }
-}
+}  // namespace opencl
 
 using namespace opencl;
 
-af_err afcl_get_device_type(afcl_device_type *res)
-{
+af_err afcl_get_device_type(afcl_device_type* res) {
     try {
         *res = (afcl_device_type)getActiveDeviceType();
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_get_platform(afcl_platform *res)
-{
+af_err afcl_get_platform(afcl_platform* res) {
     try {
         *res = (afcl_platform)getActivePlatform();
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_get_context(cl_context *ctx, const bool retain)
-{
+af_err afcl_get_context(cl_context* ctx, const bool retain) {
     try {
         *ctx = getContext()();
         if (retain) clRetainContext(*ctx);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-
-af_err afcl_get_queue(cl_command_queue *queue, const bool retain)
-{
+af_err afcl_get_queue(cl_command_queue* queue, const bool retain) {
     try {
         *queue = getQueue()();
         if (retain) clRetainCommandQueue(*queue);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_get_device_id(cl_device_id *id)
-{
+af_err afcl_get_device_id(cl_device_id* id) {
     try {
         *id = getDevice()();
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_set_device_id(cl_device_id id)
-{
+af_err afcl_set_device_id(cl_device_id id) {
     try {
         setDevice(getDeviceIdFromNativeId(id));
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que)
-{
+af_err afcl_add_device_context(cl_device_id dev, cl_context ctx,
+                               cl_command_queue que) {
     try {
         addDeviceContext(dev, ctx, que);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_set_device_context(cl_device_id dev, cl_context ctx)
-{
+af_err afcl_set_device_context(cl_device_id dev, cl_context ctx) {
     try {
         setDeviceContext(dev, ctx);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
 
-af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx)
-{
+af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx) {
     try {
         removeDeviceContext(dev, ctx);
-    } CATCHALL;
+    }
+    CATCHALL;
     return AF_SUCCESS;
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 79bd82b6b6..cde9c04b13 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -21,34 +21,35 @@
 #pragma GCC diagnostic pop
 
 #include <memory>
-#include <vector>
 #include <string>
+#include <vector>
 
+#include <GraphicsResourceManager.hpp>
 #include <cache.hpp>
 #include <memory.hpp>
-#include <GraphicsResourceManager.hpp>
 
 namespace boost {
-  template<typename T> class shared_ptr;
+template<typename T>
+class shared_ptr;
 
-  namespace compute {
-    class program_cache;
-  }
+namespace compute {
+class program_cache;
 }
+}  // namespace boost
 
 namespace graphics {
-  class ForgeManager;
+class ForgeManager;
 }
 
 // Forward declaration from clFFT.h
 struct clfftSetupData_;
 typedef clfftSetupData_ clfftSetupData;
 
-namespace opencl
-{
+namespace opencl {
 // Forward declaration from clfft.hpp
 class PlanCache;
 
+struct kc_entry_t;
 int getBackend();
 
 std::string getDeviceInfo();
@@ -71,7 +72,7 @@ size_t getHostMemorySize();
 
 cl_device_type getDeviceType();
 
-bool isHostUnifiedMemory(const cl::Device &device);
+bool isHostUnifiedMemory(const cl::Device& device);
 
 bool OpenCLCPUOffload(bool forceOffloadOSX = true);
 
@@ -79,9 +80,9 @@ bool isGLSharingSupported();
 
 bool isDoubleSupported(int device);
 
-void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
-std::string getPlatformName(const cl::Device &device);
+std::string getPlatformName(const cl::Device& device);
 
 int setDevice(int device);
 
@@ -112,7 +113,8 @@ GraphicsResourceManager& interopManager();
 
 PlanCache& fftManager();
 
-void addKernelToCache(int device, const std::string& key, const kc_entry_t entry);
+void addKernelToCache(int device, const std::string& key,
+                      const opencl::kc_entry_t entry);
 
 void removeKernelFromCache(int device, const std::string& key);
 
@@ -120,8 +122,7 @@ kc_entry_t kernelCache(int device, const std::string& key);
 //
 ///////////////////////// END Sub-Managers /////////////////////
 
-class DeviceManager
-{
+class DeviceManager {
     friend MemoryManager& memoryManager();
 
     friend MemoryManagerPinned& pinnedMemoryManager();
@@ -132,7 +133,8 @@ class DeviceManager
 
     friend PlanCache& fftManager();
 
-    friend void addKernelToCache(int device, const std::string& key, const kc_entry_t entry);
+    friend void addKernelToCache(int device, const std::string& key,
+                                 const kc_entry_t entry);
 
     friend void removeKernelFromCache(int device, const std::string& key);
 
@@ -156,11 +158,13 @@ class DeviceManager
 
     friend bool isDoubleSupported(int device);
 
-    friend void devprop(char* d_name, char* d_platform, char *d_toolkit, char* d_compute);
+    friend void devprop(char* d_name, char* d_platform, char* d_toolkit,
+                        char* d_compute);
 
     friend int setDevice(int device);
 
-    friend void addDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+    friend void addDeviceContext(cl_device_id dev, cl_context cxt,
+                                 cl_command_queue que);
 
     friend void setDeviceContext(cl_device_id dev, cl_context cxt);
 
@@ -170,42 +174,42 @@ class DeviceManager
 
     friend int getActivePlatform();
 
-    public:
-        static const unsigned MAX_DEVICES = 32;
-
-        static DeviceManager& getInstance();
-
-        ~DeviceManager();
-
-    protected:
-        DeviceManager();
-
-        // Following two declarations are required to
-        // avoid copying accidental copy/assignment
-        // of instance returned by getInstance to other
-        // variables
-        DeviceManager(DeviceManager const&);
-        void operator=(DeviceManager const&);
-        void markDeviceForInterop(const int device, const void* wHandle);
-
-    private:
-        // Attributes
-        common::mutex_t deviceMutex;
-        std::vector<cl::Device*>       mDevices;
-        std::vector<cl::Context*>     mContexts;
-        std::vector<cl::CommandQueue*>  mQueues;
-        std::vector<bool>        mIsGLSharingOn;
-        std::vector<int>         mDeviceTypes;
-        std::vector<int>         mPlatforms;
-        unsigned mUserDeviceOffset;
-
-        std::unique_ptr<graphics::ForgeManager> fgMngr;
-        std::unique_ptr<MemoryManager> memManager;
-        std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
-        std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
-        std::unique_ptr<clfftSetupData> mFFTSetup;
-
-        using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
-        std::vector<BoostProgCache*> mBoostProgCacheVector;
+   public:
+    static const unsigned MAX_DEVICES = 32;
+
+    static DeviceManager& getInstance();
+
+    ~DeviceManager();
+
+   protected:
+    DeviceManager();
+
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&);
+    void operator=(DeviceManager const&);
+    void markDeviceForInterop(const int device, const void* wHandle);
+
+   private:
+    // Attributes
+    common::mutex_t deviceMutex;
+    std::vector<cl::Device*> mDevices;
+    std::vector<cl::Context*> mContexts;
+    std::vector<cl::CommandQueue*> mQueues;
+    std::vector<bool> mIsGLSharingOn;
+    std::vector<int> mDeviceTypes;
+    std::vector<int> mPlatforms;
+    unsigned mUserDeviceOffset;
+
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<MemoryManager> memManager;
+    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+    std::unique_ptr<clfftSetupData> mFFTSetup;
+
+    using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
+    std::vector<BoostProgCache*> mBoostProgCacheVector;
 };
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 6f8db54664..00da7e2bde 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <GraphicsResourceManager.hpp>
 #include <plot.hpp>
 
 using af::dim4;
@@ -18,13 +18,12 @@ using af::dim4;
 namespace opencl {
 
 template<typename T>
-void copy_plot(const Array<T> &P, fg_plot plot)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_plot(const Array<T> &P, fg_plot plot) {
+    ForgeModule &_ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
-        unsigned bytes = 0;
+        unsigned bytes        = 0;
         FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
 
         auto res = interopManager().getPlotResources(plot);
@@ -40,7 +39,8 @@ void copy_plot(const Array<T> &P, fg_plot plot)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+                                     &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
@@ -53,7 +53,7 @@ void copy_plot(const Array<T> &P, fg_plot plot)
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
@@ -63,8 +63,7 @@ void copy_plot(const Array<T> &P, fg_plot plot)
     }
 }
 
-#define INSTANTIATE(T)  \
-template void copy_plot<T>(const Array<T> &, fg_plot);
+#define INSTANTIATE(T) template void copy_plot<T>(const Array<T> &, fg_plot);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -74,4 +73,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/print.hpp b/src/backend/opencl/print.hpp
index 8525c5f67f..d78e1a36a2 100644
--- a/src/backend/opencl/print.hpp
+++ b/src/backend/opencl/print.hpp
@@ -11,19 +11,14 @@
 #include <backend.hpp>
 #include <ostream>
 
-namespace opencl
-{
-    static std::ostream&
-    operator<<(std::ostream &out, const cfloat& var)
-    {
-        out << "(" << var.s[0] << "," << var.s[1] << ")";
-        return out;
-    }
+namespace opencl {
+static std::ostream& operator<<(std::ostream& out, const cfloat& var) {
+    out << "(" << var.s[0] << "," << var.s[1] << ")";
+    return out;
+}
 
-    static std::ostream&
-    operator<<(std::ostream &out, const cdouble& var)
-    {
-        out << "(" << var.s[0] << "," << var.s[1] << ")";
-        return out;
-    }
+static std::ostream& operator<<(std::ostream& out, const cdouble& var) {
+    out << "(" << var.s[0] << "," << var.s[1] << ")";
+    return out;
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/product.cpp b/src/backend/opencl/product.cpp
index d9019ba973..01e131c092 100644
--- a/src/backend/opencl/product.cpp
+++ b/src/backend/opencl/product.cpp
@@ -9,19 +9,18 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    //sum
-    INSTANTIATE(af_mul_t, float  , float  )
-    INSTANTIATE(af_mul_t, double , double )
-    INSTANTIATE(af_mul_t, cfloat , cfloat )
-    INSTANTIATE(af_mul_t, cdouble, cdouble)
-    INSTANTIATE(af_mul_t, int    , int    )
-    INSTANTIATE(af_mul_t, uint   , uint   )
-    INSTANTIATE(af_mul_t, intl   , intl   )
-    INSTANTIATE(af_mul_t, uintl  , uintl  )
-    INSTANTIATE(af_mul_t, char   , int    )
-    INSTANTIATE(af_mul_t, uchar  , uint   )
-    INSTANTIATE(af_mul_t, short  , int    )
-    INSTANTIATE(af_mul_t, ushort , uint   )
-}
+namespace opencl {
+// sum
+INSTANTIATE(af_mul_t, float, float)
+INSTANTIATE(af_mul_t, double, double)
+INSTANTIATE(af_mul_t, cfloat, cfloat)
+INSTANTIATE(af_mul_t, cdouble, cdouble)
+INSTANTIATE(af_mul_t, int, int)
+INSTANTIATE(af_mul_t, uint, uint)
+INSTANTIATE(af_mul_t, intl, intl)
+INSTANTIATE(af_mul_t, uintl, uintl)
+INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, uchar, uint)
+INSTANTIATE(af_mul_t, short, int)
+INSTANTIATE(af_mul_t, ushort, uint)
+}  // namespace opencl
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 154d20d091..2f19c4a8e1 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -7,21 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <debug_opencl.hpp>
+#include <kernel_headers/KParam.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <kernel_headers/KParam.hpp>
-#include <debug_opencl.hpp>
 
 using cl::Buffer;
-using cl::Program;
-using cl::Kernel;
 using cl::EnqueueArgs;
+using cl::Kernel;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 
-namespace opencl
-{
-    const static std::string DEFAULT_MACROS_STR("\n\
+namespace opencl {
+const static std::string DEFAULT_MACROS_STR(
+    "\n\
                                            #ifdef USE_DOUBLE\n\
                                            #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
                                            #endif\n                     \
@@ -29,41 +29,40 @@ namespace opencl
                                            #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
                                            #endif\n                     \
                                            ");
-    void buildProgram(cl::Program &prog,
-                      const char *ker_str, const int ker_len, std::string options)
-    {
-        buildProgram(prog, 1, &ker_str, &ker_len, options);
-    }
+void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
+                  std::string options) {
+    buildProgram(prog, 1, &ker_str, &ker_len, options);
+}
 
-    void buildProgram(cl::Program &prog, const int num_files,
-                      const char **ker_strs, const int *ker_lens, std::string options)
-    {
-        try {
-            Program::Sources setSrc;
-            setSrc.emplace_back(DEFAULT_MACROS_STR.c_str(), DEFAULT_MACROS_STR.length());
-            setSrc.emplace_back(KParam_hpp, KParam_hpp_len);
+void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
+                  const int *ker_lens, std::string options) {
+    try {
+        Program::Sources setSrc;
+        setSrc.emplace_back(DEFAULT_MACROS_STR.c_str(),
+                            DEFAULT_MACROS_STR.length());
+        setSrc.emplace_back(KParam_hpp, KParam_hpp_len);
 
-            for (int i = 0; i < num_files; i++) {
-                setSrc.emplace_back(ker_strs[i], ker_lens[i]);
-            }
+        for (int i = 0; i < num_files; i++) {
+            setSrc.emplace_back(ker_strs[i], ker_lens[i]);
+        }
 
-            const std::string defaults =
-                std::string(" -D dim_t=") +
-                std::string(dtype_traits<dim_t>::getName());
+        const std::string defaults =
+            std::string(" -D dim_t=") +
+            std::string(dtype_traits<dim_t>::getName());
 
-            prog = cl::Program(getContext(), setSrc);
-            auto device = getDevice();
+        prog        = cl::Program(getContext(), setSrc);
+        auto device = getDevice();
 
-            std::string cl_std =
-                std::string(" -cl-std=CL") +
-                device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
+        std::string cl_std =
+            std::string(" -cl-std=CL") +
+            device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
 
-            // Braces needed to list initialize the vector for the first argument
-            prog.build({device}, (cl_std + defaults + options).c_str());
+        // Braces needed to list initialize the vector for the first argument
+        prog.build({device}, (cl_std + defaults + options).c_str());
 
-        } catch (...) {
-            SHOW_BUILD_INFO(prog);
-            throw;
-        }
+    } catch (...) {
+        SHOW_BUILD_INFO(prog);
+        throw;
     }
 }
+}  // namespace opencl
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 6b2c8e1fec..34eef3b8db 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -8,46 +8,45 @@
  ********************************************************/
 
 #pragma once
-#include <platform.hpp>
 #include <common/util.hpp>
+#include <platform.hpp>
 
 #include <cstdio>
 #include <string>
 
-#define SHOW_DEBUG_BUILD_INFO(PROG) do {                                \
-        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();    \
-        for (unsigned int i = 0; i<numDevices; ++i) {                   \
-          printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
-                      PROG.getInfo<CL_PROGRAM_DEVICES>()[i]).c_str());  \
-          printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
-                      PROG.getInfo<CL_PROGRAM_DEVICES>()[i]).c_str());  \
-        }                                                               \
-    } while(0)                                                          \
-
+#define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
+    do {                                                                  \
+        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();      \
+        for (unsigned int i = 0; i < numDevices; ++i) {                   \
+            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
+                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                 \
+            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
+                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                 \
+        }                                                                 \
+    } while (0)
 
 #if defined(NDEBUG)
 
-#define SHOW_BUILD_INFO(PROG) do {                                  \
-        std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");  \
-        if (!info.empty() && info != "0") {                         \
-            SHOW_DEBUG_BUILD_INFO(prog);                            \
-        }                                                           \
-    } while(0)
+#define SHOW_BUILD_INFO(PROG)                                              \
+    do {                                                                   \
+        std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");         \
+        if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(prog); } \
+    } while (0)
 
 #else
 #define SHOW_BUILD_INFO(PROG) SHOW_DEBUG_BUILD_INFO(PROG)
 #endif
 
 namespace cl {
-  class Program;
+class Program;
 }
 
-namespace opencl
-{
-    void buildProgram(cl::Program &prog,
-                      const char *ker_str, const int ker_len, std::string options);
+namespace opencl {
+void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
+                  std::string options);
 
-    void buildProgram(cl::Program &prog,
-                      const int num_files,
-                      const char **ker_str, const int *ker_len, std::string options);
-}
+void buildProgram(cl::Program &prog, const int num_files, const char **ker_str,
+                  const int *ker_len, std::string options);
+}  // namespace opencl
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 0615a005fc..3c6130d8e2 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -7,143 +7,131 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <qr.hpp>
-#include <err_opencl.hpp>
 #include <blas.hpp>
 #include <copy.hpp>
+#include <err_opencl.hpp>
+#include <qr.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
 
+#include <cpu/cpu_qr.hpp>
+#include <identity.hpp>
+#include <kernel/triangle.hpp>
 #include <magma/magma.h>
-#include <magma/magma_helper.h>
 #include <magma/magma_data.h>
-#include <kernel/triangle.hpp>
+#include <magma/magma_helper.h>
 #include <platform.hpp>
-#include <identity.hpp>
-#include <cpu/cpu_qr.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::qr(q, r, t, orig);
-    }
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig) {
+    if (OpenCLCPUOffload()) { return cpu::qr(q, r, t, orig); }
 
     dim4 iDims = orig.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     dim4 pDims(M, std::max(M, N));
-    Array<T> in = padArray<T, T>(orig, pDims, scalar<T>(0));  //copyArray<T>(orig);
+    Array<T> in =
+        padArray<T, T>(orig, pDims, scalar<T>(0));  // copyArray<T>(orig);
     in.resetDims(iDims);
 
     int MN = std::min(M, N);
     int NB = magma_get_geqrf_nb<T>(M);
 
-    int NUM = (2*MN + ((N+31)/32)*32)*NB;
+    int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
     Array<T> tmp = createEmptyArray<T>(dim4(NUM));
 
     std::vector<T> h_tau(MN);
 
-    int info = 0;
+    int info           = 0;
     cl::Buffer *in_buf = in.get();
-    cl::Buffer *dT = tmp.get();
+    cl::Buffer *dT     = tmp.get();
 
-    magma_geqrf3_gpu<T>(M, N,
-                        (*in_buf)(), in.getOffset(), in.strides()[1],
-                        &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
+    magma_geqrf3_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
+                        &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
+                        &info);
 
     r = createEmptyArray<T>(in.dims());
     kernel::triangle<T, true, false>(r, in);
 
     cl::Buffer *r_buf = r.get();
-    magmablas_swapdblk<T>(MN - 1, NB,
-                          ( *r_buf)(), r.getOffset(),
-                          r.strides()[1], 1,
-                          (*dT)(), tmp.getOffset() + MN * NB,
-                          NB, 0, getQueue()());
+    magmablas_swapdblk<T>(MN - 1, NB, (*r_buf)(), r.getOffset(), r.strides()[1],
+                          1, (*dT)(), tmp.getOffset() + MN * NB, NB, 0,
+                          getQueue()());
 
-    q = in; // No need to copy
+    q = in;  // No need to copy
     q.resetDims(dim4(M, M));
     cl::Buffer *q_buf = q.get();
 
-    magma_ungqr_gpu<T>(q.dims()[0], q.dims()[1], std::min(M, N),
-                        (*q_buf)(), q.getOffset(), q.strides()[1],
-                        &h_tau[0],
-                        (*dT)(), tmp.getOffset(), NB, getQueue()(), &info);
+    magma_ungqr_gpu<T>(q.dims()[0], q.dims()[1], std::min(M, N), (*q_buf)(),
+                       q.getOffset(), q.strides()[1], &h_tau[0], (*dT)(),
+                       tmp.getOffset(), NB, getQueue()(), &info);
 
     t = createHostDataArray(dim4(MN), &h_tau[0]);
 }
 
 template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::qr_inplace(in);
-    }
+Array<T> qr_inplace(Array<T> &in) {
+    if (OpenCLCPUOffload()) { return cpu::qr_inplace(in); }
 
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-    int MN = std::min(M, N);
+    int M      = iDims[0];
+    int N      = iDims[1];
+    int MN     = std::min(M, N);
 
-    getQueue().finish(); // FIXME: Does this need to be here?
+    getQueue().finish();  // FIXME: Does this need to be here?
     cl::CommandQueue Queue2(getContext(), getDevice());
     cl_command_queue queues[] = {getQueue()(), Queue2()};
 
-
     std::vector<T> h_tau(MN);
     cl::Buffer *in_buf = in.get();
 
     int info = 0;
-    magma_geqrf2_gpu<T>(M, N, (*in_buf)(),
-                        in.getOffset(), in.strides()[1],
+    magma_geqrf2_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
                         &h_tau[0], queues, &info);
 
     Array<T> t = createHostDataArray(dim4(MN), &h_tau[0]);
     return t;
 }
 
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
 
 INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
-}
+}  // namespace opencl
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in)
-{
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-Array<T> qr_inplace(Array<T> &in)
-{
+Array<T> qr_inplace(Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE_QR(T)                                                                           \
-    template Array<T> qr_inplace<T>(Array<T> &in);                                                \
-    template void qr<T>(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
 
 INSTANTIATE_QR(float)
 INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
-}
+}  // namespace opencl
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/qr.hpp b/src/backend/opencl/qr.hpp
index 72bf669f2f..26a877ba5a 100644
--- a/src/backend/opencl/qr.hpp
+++ b/src/backend/opencl/qr.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+namespace opencl {
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
 
-    template<typename T>
-    Array<T> qr_inplace(Array<T> &in);
-}
+template<typename T>
+Array<T> qr_inplace(Array<T> &in);
+}  // namespace opencl
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 500ba904a2..0208c8d2a1 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -7,148 +7,145 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <kernel/random_engine.hpp>
+#include <af/dim4.hpp>
 #include <cassert>
 
-namespace opencl
-{
-    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl)
-    {
-        kernel::initMersenneState(*state.get(), *tbl.get(), seed);
-    }
+namespace opencl {
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> tbl) {
+    kernel::initMersenneState(*state.get(), *tbl.get(), seed);
+}
 
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed, counter);
-        return out;
-    }
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,
+                                        counter);
+    return out;
+}
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistributionCBRNG<T>(*out.get(), out.elements(), type, seed, counter);
-        return out;
-    }
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl &seed,
+                            uintl &counter) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::normalDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,
+                                       counter);
+    return out;
+}
 
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::uniformDistributionMT<T>(
-              *out.get(), out.elements(),
-              *state.get(), *pos.get(),
-              *sh1.get(), *sh2.get(),
-              mask, *recursion_table.get(),
-              *temper_table.get());
-        return out;
-    }
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::uniformDistributionMT<T>(
+        *out.get(), out.elements(), *state.get(), *pos.get(), *sh1.get(),
+        *sh2.get(), mask, *recursion_table.get(), *temper_table.get());
+    return out;
+}
 
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)
-    {
-        Array<T> out = createEmptyArray<T>(dims);
-        kernel::normalDistributionMT<T>(
-              *out.get(), out.elements(),
-              *state.get(), *pos.get(),
-              *sh1.get(), *sh2.get(),
-              mask, *recursion_table.get(),
-              *temper_table.get());
-        return out;
-    }
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state) {
+    Array<T> out = createEmptyArray<T>(dims);
+    kernel::normalDistributionMT<T>(
+        *out.get(), out.elements(), *state.get(), *pos.get(), *sh1.get(),
+        *sh2.get(), mask, *recursion_table.get(), *temper_table.get());
+    return out;
+}
 
-#define INSTANTIATE_UNIFORM(T)                                                                                                  \
-    template                                                                                                                    \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter); \
-    template                                                                                                                    \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+#define INSTANTIATE_UNIFORM(T)                                   \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl &seed, uintl &counter);                      \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
 
-#define INSTANTIATE_NORMAL(T)                                                                                                   \
-    template                                                                                                                    \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);  \
-    template                                                                                                                    \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);                                          \
+#define INSTANTIATE_NORMAL(T)                                    \
+    template Array<T> normalDistribution<T>(                     \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl &seed, uintl &counter);                      \
+    template Array<T> normalDistribution<T>(                     \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
 
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                                                                     \
-    template<>                                                                                                                  \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)  \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                        \
-        return out;                                                                                                             \
-    }                                                                                                                           \
-    template<>                                                                                                                  \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                                                                       \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::uniformDistributionMT<TR>(                                                                                      \
-              *out.get(), elements,                                                                                             \
-              *state.get(), *pos.get(),                                                                                         \
-              *sh1.get(), *sh2.get(),                                                                                           \
-              mask, *recursion_table.get(),                                                                                     \
-              *temper_table.get());                                                                                             \
-        return out;                                                                                                             \
-    }                                                                                                                           \
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                    \
+    template<>                                                                 \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                      \
+                                    const af_random_engine_type type,          \
+                                    const uintl &seed, uintl &counter) {       \
+        Array<T> out    = createEmptyArray<T>(dims);                           \
+        size_t elements = out.elements() * 2;                                  \
+        kernel::uniformDistributionCBRNG<TR>(*out.get(), elements, type, seed, \
+                                             counter);                         \
+        return out;                                                            \
+    }                                                                          \
+    template<>                                                                 \
+    Array<T> uniformDistribution<T>(                                           \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,                \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,               \
+        Array<uint> temper_table, Array<uint> state) {                         \
+        Array<T> out    = createEmptyArray<T>(dims);                           \
+        size_t elements = out.elements() * 2;                                  \
+        kernel::uniformDistributionMT<TR>(                                     \
+            *out.get(), elements, *state.get(), *pos.get(), *sh1.get(),        \
+            *sh2.get(), mask, *recursion_table.get(), *temper_table.get());    \
+        return out;                                                            \
+    }
 
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                                                                      \
-    template<>                                                                                                                  \
-    Array<T> normalDistribution<T>(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter)   \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, counter);                                         \
-        return out;                                                                                                             \
-    }                                                                                                                           \
-    template<>                                                                                                                  \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                                                                        \
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,                                                       \
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state)                                           \
-    {                                                                                                                           \
-        Array<T> out = createEmptyArray<T>(dims);                                                                               \
-        size_t elements = out.elements()*2;                                                                                     \
-        kernel::normalDistributionMT<TR>(                                                                                       \
-              *out.get(), elements,                                                                                             \
-              *state.get(), *pos.get(),                                                                                         \
-              *sh1.get(), *sh2.get(),                                                                                           \
-              mask, *recursion_table.get(),                                                                                     \
-              *temper_table.get());                                                                                             \
-        return out;                                                                                                             \
-    }                                                                                                                           \
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                    \
+    template<>                                                                \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                      \
+                                   const af_random_engine_type type,          \
+                                   const uintl &seed, uintl &counter) {       \
+        Array<T> out    = createEmptyArray<T>(dims);                          \
+        size_t elements = out.elements() * 2;                                 \
+        kernel::normalDistributionCBRNG<TR>(*out.get(), elements, type, seed, \
+                                            counter);                         \
+        return out;                                                           \
+    }                                                                         \
+    template<>                                                                \
+    Array<T> normalDistribution<T>(                                           \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,               \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,              \
+        Array<uint> temper_table, Array<uint> state) {                        \
+        Array<T> out    = createEmptyArray<T>(dims);                          \
+        size_t elements = out.elements() * 2;                                 \
+        kernel::normalDistributionMT<TR>(                                     \
+            *out.get(), elements, *state.get(), *pos.get(), *sh1.get(),       \
+            *sh2.get(), mask, *recursion_table.get(), *temper_table.get());   \
+        return out;                                                           \
+    }
 
-    INSTANTIATE_UNIFORM(float )
-    INSTANTIATE_UNIFORM(double)
-    INSTANTIATE_UNIFORM(int   )
-    INSTANTIATE_UNIFORM(uint  )
-    INSTANTIATE_UNIFORM(intl  )
-    INSTANTIATE_UNIFORM(uintl )
-    INSTANTIATE_UNIFORM(char  )
-    INSTANTIATE_UNIFORM(uchar )
-    INSTANTIATE_UNIFORM(short )
-    INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(float)
+INSTANTIATE_UNIFORM(double)
+INSTANTIATE_UNIFORM(int)
+INSTANTIATE_UNIFORM(uint)
+INSTANTIATE_UNIFORM(intl)
+INSTANTIATE_UNIFORM(uintl)
+INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(uchar)
+INSTANTIATE_UNIFORM(short)
+INSTANTIATE_UNIFORM(ushort)
 
-    INSTANTIATE_NORMAL(float )
-    INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(float)
+INSTANTIATE_NORMAL(double)
 
-    COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-    COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
 
-    COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-    COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index edd74c4558..c3a692ec0b 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -10,28 +10,34 @@
 #pragma once
 
 #include <Array.hpp>
-#include <af/defines.h>
 #include <backend.hpp>
+#include <af/defines.h>
 
-namespace opencl
-{
-    Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
-
-    void initMersenneState(Array<uint> &state, const uintl seed, const Array<uint> tbl);
-
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims, const af_random_engine_type type, const uintl &seed, uintl &counter);
-
-    template<typename T>
-    Array<T> uniformDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
-
-    template<typename T>
-    Array<T> normalDistribution(const af::dim4 &dims,
-            Array<uint> pos, Array<uint> sh1, Array<uint> sh2, uint mask,
-            Array<uint> recursion_table, Array<uint> temper_table, Array<uint> state);
-}
+namespace opencl {
+Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
+
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> tbl);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl &seed,
+                            uintl &counter);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state);
+}  // namespace opencl
diff --git a/src/backend/opencl/range.cpp b/src/backend/opencl/range.cpp
index 61bba9c613..848f6d8ea0 100644
--- a/src/backend/opencl/range.cpp
+++ b/src/backend/opencl/range.cpp
@@ -8,43 +8,41 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <range.hpp>
+#include <err_opencl.hpp>
 #include <kernel/range.hpp>
 #include <math.hpp>
+#include <range.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> range(const dim4& dim, const int seq_dim)
-    {
-        // Set dimension along which the sequence should be
-        // Other dimensions are simply tiled
-        int _seq_dim = seq_dim;
-        if(seq_dim < 0) {
-            _seq_dim = 0;   // column wise sequence
-        }
+namespace opencl {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim) {
+    // Set dimension along which the sequence should be
+    // Other dimensions are simply tiled
+    int _seq_dim = seq_dim;
+    if (seq_dim < 0) {
+        _seq_dim = 0;  // column wise sequence
+    }
 
-        if(_seq_dim < 0 || _seq_dim > 3)
-            AF_ERROR("Invalid rep selection", AF_ERR_ARG);
+    if (_seq_dim < 0 || _seq_dim > 3)
+        AF_ERROR("Invalid rep selection", AF_ERR_ARG);
 
-        Array<T> out = createEmptyArray<T>(dim);
-        kernel::range<T>(out, _seq_dim);
+    Array<T> out = createEmptyArray<T>(dim);
+    kernel::range<T>(out, _seq_dim);
 
-        return out;
-    }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> range<T>(const af::dim4 &dims, const int seq_dims);   \
+#define INSTANTIATE(T) \
+    template Array<T> range<T>(const af::dim4& dims, const int seq_dims);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/range.hpp b/src/backend/opencl/range.hpp
index 88ffba2373..610d31933f 100644
--- a/src/backend/opencl/range.hpp
+++ b/src/backend/opencl/range.hpp
@@ -10,8 +10,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> range(const dim4& dim, const int seq_dim = -1);
+namespace opencl {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim = -1);
 }
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 88e0193614..389038ca2e 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -11,11 +11,11 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace opencl
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan=false, double nanval=0);
+namespace opencl {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
+                 double nanval = 0);
 
-    template<af_op_t op, typename Ti, typename To>
-    To reduce_all(const Array<Ti> &in, bool change_nan=false, double nanval=0);
-}
+template<af_op_t op, typename Ti, typename To>
+To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
+}  // namespace opencl
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index 12148cc240..b7301912c6 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -7,35 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <reduce.hpp>
-#include <kernel/reduce.hpp>
 #include <err_opencl.hpp>
+#include <kernel/reduce.hpp>
+#include <reduce.hpp>
+#include <af/dim4.hpp>
+#include <complex>
 
-using std::swap;
 using af::dim4;
-namespace opencl
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan, double nanval)
-    {
-        dim4 odims = in.dims();
-        odims[dim] = 1;
-        Array<To> out = createEmptyArray<To>(odims);
-        kernel::reduce<Ti, To, op>(out, in, dim, change_nan, nanval);
-        return out;
-    }
+using std::swap;
+namespace opencl {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
+                 double nanval) {
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    kernel::reduce<Ti, To, op>(out, in, dim, change_nan, nanval);
+    return out;
+}
 
-    template<af_op_t op, typename Ti, typename To>
-    To reduce_all(const Array<Ti> &in, bool change_nan, double nanval)
-    {
-        return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
-    }
+template<af_op_t op, typename Ti, typename To>
+To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+    return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
 }
+}  // namespace opencl
 
-#define INSTANTIATE(Op, Ti, To)                                         \
+#define INSTANTIATE(Op, Ti, To)                                               \
     template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim, \
-                                          bool change_nan, double nanval); \
-    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan, double nanval);
+                                          bool change_nan, double nanval);    \
+    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,  \
+                                       double nanval);
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index d8c4d16cd0..9229d0005e 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -7,44 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <regions.hpp>
-#include <kernel/regions.hpp>
 #include <err_opencl.hpp>
+#include <kernel/regions.hpp>
+#include <regions.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> regions(const Array<char> &in, af_connectivity connectivity)
-{
+Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
     const af::dim4 dims = in.dims();
 
-    Array<T> out  = createEmptyArray<T>(dims);
+    Array<T> out = createEmptyArray<T>(dims);
 
-    switch(connectivity) {
-        case AF_CONNECTIVITY_4:
-            kernel::regions<T, false, 2>(out, in);
-            break;
-        case AF_CONNECTIVITY_8:
-            kernel::regions<T, true,  2>(out, in);
-            break;
+    switch (connectivity) {
+        case AF_CONNECTIVITY_4: kernel::regions<T, false, 2>(out, in); break;
+        case AF_CONNECTIVITY_8: kernel::regions<T, true, 2>(out, in); break;
     }
 
     return out;
 }
 
-#define INSTANTIATE(T)                                                                  \
-    template Array<T> regions<T>(const Array<char> &in, af_connectivity connectivity);
+#define INSTANTIATE(T)                                  \
+    template Array<T> regions<T>(const Array<char> &in, \
+                                 af_connectivity connectivity);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
+INSTANTIATE(int)
+INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/regions.hpp b/src/backend/opencl/regions.hpp
index a645f69f9c..89eab2714c 100644
--- a/src/backend/opencl/regions.hpp
+++ b/src/backend/opencl/regions.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
diff --git a/src/backend/opencl/reorder.cpp b/src/backend/opencl/reorder.cpp
index c10472df75..6786e6e82a 100644
--- a/src/backend/opencl/reorder.cpp
+++ b/src/backend/opencl/reorder.cpp
@@ -8,41 +8,38 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <reorder.hpp>
+#include <err_opencl.hpp>
 #include <kernel/reorder.hpp>
+#include <reorder.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims(0);
-        for(int i = 0; i < 4; i++)
-            oDims[i] = iDims[rdims[i]];
+namespace opencl {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims(0);
+    for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
 
-        Array<T> out = createEmptyArray<T>(oDims);
+    Array<T> out = createEmptyArray<T>(oDims);
 
-        kernel::reorder<T>(out, in, rdims.get());
+    kernel::reorder<T>(out, in, rdims.get());
 
-        return out;
-    }
+    return out;
+}
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);  \
+#define INSTANTIATE(T) \
+    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/reorder.hpp b/src/backend/opencl/reorder.hpp
index 057f601c55..bd49a074f9 100644
--- a/src/backend/opencl/reorder.hpp
+++ b/src/backend/opencl/reorder.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
+namespace opencl {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
 }
diff --git a/src/backend/opencl/resize.cpp b/src/backend/opencl/resize.cpp
index 9c246d128d..4bb68a6a64 100644
--- a/src/backend/opencl/resize.cpp
+++ b/src/backend/opencl/resize.cpp
@@ -7,56 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <resize.hpp>
 #include <kernel/resize.hpp>
+#include <resize.hpp>
+#include <af/dim4.hpp>
 #include <stdexcept>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                    const af_interp_type method)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
-
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        switch(method) {
-            case AF_INTERP_NEAREST:
-                kernel::resize<T, AF_INTERP_NEAREST> (out, in);
-                break;
-            case AF_INTERP_BILINEAR:
-                kernel::resize<T, AF_INTERP_BILINEAR>(out, in);
-                break;
-            case AF_INTERP_LOWER:
-                kernel::resize<T, AF_INTERP_LOWER>(out, in);
-                break;
-            default:
-                break;
-        }
-        return out;
+namespace opencl {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
+
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    switch (method) {
+        case AF_INTERP_NEAREST:
+            kernel::resize<T, AF_INTERP_NEAREST>(out, in);
+            break;
+        case AF_INTERP_BILINEAR:
+            kernel::resize<T, AF_INTERP_BILINEAR>(out, in);
+            break;
+        case AF_INTERP_LOWER:
+            kernel::resize<T, AF_INTERP_LOWER>(out, in);
+            break;
+        default: break;
     }
-
-
-#define INSTANTIATE(T)                                                  \
-    template Array<T> resize<T> (const Array<T> &in,                    \
-                                 const dim_t odim0, const dim_t odim1, \
-                                 const af_interp_type method);
-
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return out;
 }
+
+#define INSTANTIATE(T)                                                 \
+    template Array<T> resize<T>(const Array<T> &in, const dim_t odim0, \
+                                const dim_t odim1,                     \
+                                const af_interp_type method);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/resize.hpp b/src/backend/opencl/resize.hpp
index b42ca024b5..0741be36b5 100644
--- a/src/backend/opencl/resize.hpp
+++ b/src/backend/opencl/resize.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
-                    const af_interp_type method);
+namespace opencl {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method);
 }
diff --git a/src/backend/opencl/rotate.cpp b/src/backend/opencl/rotate.cpp
index c57b8208e4..210a14e292 100644
--- a/src/backend/opencl/rotate.cpp
+++ b/src/backend/opencl/rotate.cpp
@@ -8,20 +8,18 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <rotate.hpp>
+#include <err_opencl.hpp>
 #include <kernel/rotate.hpp>
+#include <rotate.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                     const af_interp_type method)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
+namespace opencl {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method) {
+    Array<T> out = createEmptyArray<T>(odims);
 
-        switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
             kernel::rotate<T, 1>(out, in, theta, method);
@@ -34,28 +32,27 @@ namespace opencl
         case AF_INTERP_BICUBIC_SPLINE:
             kernel::rotate<T, 3>(out, in, theta, method);
             break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-        }
-
-        return out;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
 
+    return out;
+}
 
-#define INSTANTIATE(T)                                                              \
-    template Array<T> rotate(const Array<T> &in, const float theta,                 \
-                             const af::dim4 &odims, const af_interp_type method);
+#define INSTANTIATE(T)                                              \
+    template Array<T> rotate(const Array<T> &in, const float theta, \
+                             const af::dim4 &odims,                 \
+                             const af_interp_type method);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/rotate.hpp b/src/backend/opencl/rotate.hpp
index 3c5d40dfcd..94916e7441 100644
--- a/src/backend/opencl/rotate.hpp
+++ b/src/backend/opencl/rotate.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
-                    const af_interp_type method);
+namespace opencl {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method);
 }
diff --git a/src/backend/opencl/scalar.hpp b/src/backend/opencl/scalar.hpp
index 720da5e969..f52b22cf7a 100644
--- a/src/backend/opencl/scalar.hpp
+++ b/src/backend/opencl/scalar.hpp
@@ -8,17 +8,16 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <optypes.hpp>
-#include <math.hpp>
 #include <common/jit/ScalarNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> createScalarNode(const dim4 &size, const T val)
-{
-    return createNodeArray<T>(size, common::Node_ptr(new common::ScalarNode<T>(val)));
+Array<T> createScalarNode(const dim4 &size, const T val) {
+    return createNodeArray<T>(size,
+                              common::Node_ptr(new common::ScalarNode<T>(val)));
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 0bc82e0a2d..6b75549773 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -7,60 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <scan.hpp>
+#include <af/dim4.hpp>
 #include <complex>
-#include <err_opencl.hpp>
 
-#include <kernel/scan_first.hpp>
 #include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
 
-namespace opencl
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        Array<To> out = createEmptyArray<To>(in.dims());
-
-        Param Out = out;
-        Param In  =   in;
+namespace opencl {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
+    Array<To> out = createEmptyArray<To>(in.dims());
 
-        if (inclusive_scan) {
-            if (dim == 0)
-                kernel::scan_first<Ti, To, op, true>(Out, In);
-            else
-                kernel::scan_dim  <Ti, To, op, true>(Out, In, dim);
-        } else {
-            if (dim == 0)
-                kernel::scan_first<Ti, To, op, false>(Out, In);
-            else
-                kernel::scan_dim  <Ti, To, op, false>(Out, In, dim);
-        }
+    Param Out = out;
+    Param In  = in;
 
-        return out;
+    if (inclusive_scan) {
+        if (dim == 0)
+            kernel::scan_first<Ti, To, op, true>(Out, In);
+        else
+            kernel::scan_dim<Ti, To, op, true>(Out, In, dim);
+    } else {
+        if (dim == 0)
+            kernel::scan_first<Ti, To, op, false>(Out, In);
+        else
+            kernel::scan_dim<Ti, To, op, false>(Out, In, dim);
     }
 
-#define INSTANTIATE_SCAN(ROp, Ti, To)\
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti> &in, const int dim, bool inclusive_scan);
+    return out;
+}
 
-#define INSTANTIATE_SCAN_ALL(ROp)                       \
-    INSTANTIATE_SCAN(ROp, float  , float  )             \
-    INSTANTIATE_SCAN(ROp, double , double )             \
-    INSTANTIATE_SCAN(ROp, cfloat , cfloat )             \
-    INSTANTIATE_SCAN(ROp, cdouble, cdouble)             \
-    INSTANTIATE_SCAN(ROp, int    , int    )             \
-    INSTANTIATE_SCAN(ROp, uint   , uint   )             \
-    INSTANTIATE_SCAN(ROp, intl   , intl   )             \
-    INSTANTIATE_SCAN(ROp, uintl  , uintl  )             \
-    INSTANTIATE_SCAN(ROp, char   , uint   )             \
-    INSTANTIATE_SCAN(ROp, uchar  , uint   )             \
-    INSTANTIATE_SCAN(ROp, short  , int    )             \
-    INSTANTIATE_SCAN(ROp, ushort , uint   )
+#define INSTANTIATE_SCAN(ROp, Ti, To)                                        \
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti>& in, const int dim, \
+                                         bool inclusive_scan);
 
-    INSTANTIATE_SCAN(af_notzero_t, char, uint)
-    INSTANTIATE_SCAN_ALL(af_add_t)
-    INSTANTIATE_SCAN_ALL(af_mul_t)
-    INSTANTIATE_SCAN_ALL(af_min_t)
-    INSTANTIATE_SCAN_ALL(af_max_t)
-}
+#define INSTANTIATE_SCAN_ALL(ROp)           \
+    INSTANTIATE_SCAN(ROp, float, float)     \
+    INSTANTIATE_SCAN(ROp, double, double)   \
+    INSTANTIATE_SCAN(ROp, cfloat, cfloat)   \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble) \
+    INSTANTIATE_SCAN(ROp, int, int)         \
+    INSTANTIATE_SCAN(ROp, uint, uint)       \
+    INSTANTIATE_SCAN(ROp, intl, intl)       \
+    INSTANTIATE_SCAN(ROp, uintl, uintl)     \
+    INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, uchar, uint)      \
+    INSTANTIATE_SCAN(ROp, short, int)       \
+    INSTANTIATE_SCAN(ROp, ushort, uint)
+
+INSTANTIATE_SCAN(af_notzero_t, char, uint)
+INSTANTIATE_SCAN_ALL(af_add_t)
+INSTANTIATE_SCAN_ALL(af_mul_t)
+INSTANTIATE_SCAN_ALL(af_min_t)
+INSTANTIATE_SCAN_ALL(af_max_t)
+}  // namespace opencl
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index c8a62ff547..9e6a71763e 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace opencl
-{
-    template<af_op_t op, typename Ti, typename To>
-    Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+namespace opencl {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
 }
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index 0556caa073..0e63e52651 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -7,61 +7,62 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <err_opencl.hpp>
 #include <scan.hpp>
+#include <af/dim4.hpp>
 #include <complex>
-#include <err_opencl.hpp>
 
-#include <kernel/scan_first_by_key.hpp>
 #include <kernel/scan_dim_by_key.hpp>
+#include <kernel/scan_first_by_key.hpp>
 
-namespace opencl
-{
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan)
-    {
-        Array<To> out = createEmptyArray<To>(in.dims());
+namespace opencl {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan) {
+    Array<To> out = createEmptyArray<To>(in.dims());
 
-        Param Out = out;
-        Param Key = key;
-        Param In  =   in;
+    Param Out = out;
+    Param Key = key;
+    Param In  = in;
 
-        if (inclusive_scan) {
-            if (dim == 0)
-                kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
-            else
-                kernel::scan_dim  <Ti, Tk, To, op, true>(Out, In, Key, dim);
-        } else {
-            if (dim == 0)
-                kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
-            else
-                kernel::scan_dim  <Ti, Tk, To, op, false>(Out, In, Key, dim);
-        }
-        return out;
+    if (inclusive_scan) {
+        if (dim == 0)
+            kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
+        else
+            kernel::scan_dim<Ti, Tk, To, op, true>(Out, In, Key, dim);
+    } else {
+        if (dim == 0)
+            kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
+        else
+            kernel::scan_dim<Ti, Tk, To, op, false>(Out, In, Key, dim);
     }
+    return out;
+}
 
-#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)\
-    template Array<To> scan<ROp, Ti, Tk, To>(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan);
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)                  \
+    template Array<To> scan<ROp, Ti, Tk, To>(                     \
+        const Array<Tk>& key, const Array<Ti>& in, const int dim, \
+        bool inclusive_scan);
 
-#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)            \
-    INSTANTIATE_SCAN_BY_KEY(ROp, float  , Tk, float  )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, double , Tk, double )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat , Tk, cfloat )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble)  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, int    , Tk, int    )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uint   , Tk, uint   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, intl   , Tk, intl   )  \
-    INSTANTIATE_SCAN_BY_KEY(ROp, uintl  , Tk, uintl  )  \
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)           \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl, Tk, uintl)
 
-#define INSTANTIATE_SCAN_BY_KEY_OP(ROp)     \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int  ) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint ) \
-    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl ) \
+#define INSTANTIATE_SCAN_BY_KEY_OP(ROp)    \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl) \
     INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
 
-    INSTANTIATE_SCAN_BY_KEY_OP(af_add_t)
-    INSTANTIATE_SCAN_BY_KEY_OP(af_mul_t)
-    INSTANTIATE_SCAN_BY_KEY_OP(af_min_t)
-    INSTANTIATE_SCAN_BY_KEY_OP(af_max_t)
-}
+INSTANTIATE_SCAN_BY_KEY_OP(af_add_t)
+INSTANTIATE_SCAN_BY_KEY_OP(af_mul_t)
+INSTANTIATE_SCAN_BY_KEY_OP(af_min_t)
+INSTANTIATE_SCAN_BY_KEY_OP(af_max_t)
+}  // namespace opencl
diff --git a/src/backend/opencl/scan_by_key.hpp b/src/backend/opencl/scan_by_key.hpp
index 9bce51bf1e..5a4b449312 100644
--- a/src/backend/opencl/scan_by_key.hpp
+++ b/src/backend/opencl/scan_by_key.hpp
@@ -10,8 +10,8 @@
 #include <Array.hpp>
 #include <ops.hpp>
 
-namespace opencl
-{
-    template<af_op_t op, typename Ti, typename Tk, typename To>
-    Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+namespace opencl {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan = true);
 }
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index f8c3294033..b6e512b975 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -6,8 +6,8 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <select.hpp>
 #include <kernel/select.hpp>
+#include <select.hpp>
 
 #include <Array.hpp>
 #include <common/jit/NaryNode.hpp>
@@ -23,85 +23,72 @@ using common::NaryNode;
 using std::make_shared;
 using std::max;
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const Array<T> &b,
-                              const dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        auto b_node = b.getNode();
-        int height = max(a_node->getHeight(), b_node->getHeight());
-        height = max(height, cond_node->getHeight()) + 1;
-        auto node = make_shared<NaryNode>(NaryNode(dtype_traits<T>::getName(),
-                                                   shortname<T>(true), "__select",
-                                                   3, {{cond_node, a_node, b_node}},
-                                                   (int)af_select_t, height));
+namespace opencl {
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const dim4 &odims) {
+    auto cond_node = cond.getNode();
+    auto a_node    = a.getNode();
+    auto b_node    = b.getNode();
+    int height     = max(a_node->getHeight(), b_node->getHeight());
+    height         = max(height, cond_node->getHeight()) + 1;
+    auto node      = make_shared<NaryNode>(
+        NaryNode(dtype_traits<T>::getName(), shortname<T>(true), "__select", 3,
+                 {{cond_node, a_node, b_node}}, (int)af_select_t, height));
 
-        Array<T> out = createNodeArray<T>(odims, node);
-        return out;
-    }
+    Array<T> out = createNodeArray<T>(odims, node);
+    return out;
+}
 
-    template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const double &b_val,
-                              const dim4 &odims)
-    {
-        auto cond_node = cond.getNode();
-        auto a_node = a.getNode();
-        Array<T> b = createScalarNode<T>(odims, scalar<T>(b_val));
-        auto b_node = b.getNode();
-        int height = max(a_node->getHeight(), b_node->getHeight());
-        height = max(height, cond_node->getHeight()) + 1;
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const double &b_val, const dim4 &odims) {
+    auto cond_node = cond.getNode();
+    auto a_node    = a.getNode();
+    Array<T> b     = createScalarNode<T>(odims, scalar<T>(b_val));
+    auto b_node    = b.getNode();
+    int height     = max(a_node->getHeight(), b_node->getHeight());
+    height         = max(height, cond_node->getHeight()) + 1;
 
-        auto node = make_shared<NaryNode>(NaryNode(dtype_traits<T>::getName(),
-                                                   shortname<T>(true),
-                                                   (flip ? "__not_select" : "__select"),
-                                                   3, {{cond_node, a_node, b_node}},
-                                                   (int)(flip ? af_not_select_t : af_select_t),
-                                                   height));
+    auto node = make_shared<NaryNode>(NaryNode(
+        dtype_traits<T>::getName(), shortname<T>(true),
+        (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
+        (int)(flip ? af_not_select_t : af_select_t), height));
 
-        Array<T> out = createNodeArray<T>(odims, node);
-        return out;
-    }
+    Array<T> out = createNodeArray<T>(odims, node);
+    return out;
+}
 
-    template<typename T>
-    void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b)
-    {
-        kernel::select<T>(out, cond, a, b, out.ndims());
-    }
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b) {
+    kernel::select<T>(out, cond, a, b, out.ndims());
+}
 
-    template<typename T, bool flip>
-    void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b)
-    {
-        kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
-    }
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const double &b) {
+    kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
+}
 
-#define INSTANTIATE(T)                                                  \
-  template                                                              \
-  Array<T> createSelectNode<T>(const Array<char> &cond,                 \
-                               const Array<T> &a, const Array<T> &b,    \
-                               const af::dim4 &odims);                  \
-  template                                                              \
-  Array<T> createSelectNode<T, true>(const Array<char> &cond,           \
-                                     const Array<T> &a, const double &b_val, \
-                                     const af::dim4 &odims);            \
-  template                                                              \
-  Array<T> createSelectNode<T, false>(const Array<char> &cond,          \
-                                      const Array<T> &a, const double &b_val, \
-                                      const af::dim4 &odims);           \
-  template void select<T>(Array<T> &out, const Array<char> &cond,       \
-                          const Array<T> &a, const Array<T> &b);        \
-  template void select_scalar<T, true >(Array<T> &out,                  \
-                                        const Array<char> &cond,        \
-                                        const Array<T> &a,              \
-                                        const double &b);               \
-  template void select_scalar<T, false>(Array<T> &out, const            \
-                                        Array<char> &cond,              \
-                                        const Array<T> &a,              \
-                                        const double &b)
+#define INSTANTIATE(T)                                                        \
+    template Array<T> createSelectNode<T>(                                    \
+        const Array<char> &cond, const Array<T> &a, const Array<T> &b,        \
+        const af::dim4 &odims);                                               \
+    template Array<T> createSelectNode<T, true>(                              \
+        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
+        const af::dim4 &odims);                                               \
+    template Array<T> createSelectNode<T, false>(                             \
+        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
+        const af::dim4 &odims);                                               \
+    template void select<T>(Array<T> & out, const Array<char> &cond,          \
+                            const Array<T> &a, const Array<T> &b);            \
+    template void select_scalar<T, true>(Array<T> & out,                      \
+                                         const Array<char> &cond,             \
+                                         const Array<T> &a, const double &b); \
+    template void select_scalar<T, false>(Array<T> & out,                     \
+                                          const Array<char> &cond,            \
+                                          const Array<T> &a, const double &b)
 
 INSTANTIATE(float);
 INSTANTIATE(double);
@@ -117,4 +104,4 @@ INSTANTIATE(short);
 INSTANTIATE(ushort);
 
 #undef INSTANTIATE
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/select.hpp b/src/backend/opencl/select.hpp
index 66614cdc8f..01b99ae554 100644
--- a/src/backend/opencl/select.hpp
+++ b/src/backend/opencl/select.hpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <af/dim4.hpp>
 #include <Array.hpp>
+#include <af/dim4.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void select(Array<T> &out, const Array<char> &cond, const Array<T> &a, const Array<T> &b);
+namespace opencl {
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b);
 
-    template<typename T, bool flip>
-    void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a, const double &b);
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const double &b);
 
-    template<typename T>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const Array<T> &b,
-                              const af::dim4 &odims);
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const af::dim4 &odims);
 
-    template<typename T, bool flip>
-    Array<T> createSelectNode(const Array<char> &cond,
-                              const Array<T> &a, const double &b_val,
-                              const af::dim4 &odims);
-}
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const double &b_val, const af::dim4 &odims);
+}  // namespace opencl
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index 9d4f2aa999..7afb23d95e 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <set.hpp>
 #include <copy.hpp>
-#include <sort.hpp>
 #include <err_opencl.hpp>
+#include <set.hpp>
+#include <sort.hpp>
+#include <af/dim4.hpp>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -25,144 +25,133 @@
 
 namespace compute = boost::compute;
 
-namespace opencl
-{
-    using af::dim4;
+namespace opencl {
+using af::dim4;
 
-    using std::conditional;
-    using std::is_same;
-    template<typename T>
-    using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
+using std::conditional;
+using std::is_same;
+template<typename T>
+using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
 
-    template<typename T>
-    using type_t = typename conditional<is_same<T, uintl>::value,
-                                        cl_ulong, ltype_t<T>
-                                       >::type;
+template<typename T>
+using type_t =
+    typename conditional<is_same<T, uintl>::value, cl_ulong, ltype_t<T>>::type;
 
-    template<typename T>
-    Array<T> setUnique(const Array<T> &in,
-                       const bool is_sorted)
-    {
-        try {
-            Array<T> out = copyArray<T>(in);
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
+    try {
+        Array<T> out = copyArray<T>(in);
 
-            compute::command_queue queue(getQueue()());
+        compute::command_queue queue(getQueue()());
 
-            compute::buffer out_data((*out.get())());
+        compute::buffer out_data((*out.get())());
 
-            compute::buffer_iterator< type_t<T> > begin(out_data, 0);
-            compute::buffer_iterator< type_t<T> > end(out_data, out.elements());
+        compute::buffer_iterator<type_t<T>> begin(out_data, 0);
+        compute::buffer_iterator<type_t<T>> end(out_data, out.elements());
 
-            if (!is_sorted) {
-                compute::sort(begin, end, queue);
-            }
+        if (!is_sorted) { compute::sort(begin, end, queue); }
 
-            end = compute::unique(begin, end, queue);
+        end = compute::unique(begin, end, queue);
 
-            out.resetDims(dim4(std::distance(begin, end), 1, 1, 1));
+        out.resetDims(dim4(std::distance(begin, end), 1, 1, 1));
 
-            return out;
-        } catch (std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
-        }
-    }
-
-    template<typename T>
-    Array<T> setUnion(const Array<T> &first,
-                      const Array<T> &second,
-                      const bool is_unique)
-    {
-        try {
-            Array<T> unique_first = first;
-            Array<T> unique_second = second;
-
-            if (!is_unique) {
-                unique_first  = setUnique(first, false);
-                unique_second = setUnique(second, false);
-            }
-
-            size_t out_size = unique_first.elements() + unique_second.elements();
-            Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
-
-            compute::command_queue queue(getQueue()());
-
-            compute::buffer first_data((*unique_first.get())());
-            compute::buffer second_data((*unique_second.get())());
-            compute::buffer out_data((*out.get())());
-
-            compute::buffer_iterator< type_t<T> > first_begin(first_data, 0);
-            compute::buffer_iterator< type_t<T> > first_end(first_data, unique_first.elements());
-            compute::buffer_iterator< type_t<T> > second_begin(second_data, 0);
-            compute::buffer_iterator< type_t<T> > second_end(second_data, unique_second.elements());
-            compute::buffer_iterator< type_t<T> > out_begin(out_data, 0);
-
-            compute::buffer_iterator< type_t<T> > out_end = compute::set_union(
-                first_begin, first_end, second_begin, second_end, out_begin, queue
-                );
-
-            out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
-            return out;
-
-        } catch (std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+        return out;
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique) {
+    try {
+        Array<T> unique_first  = first;
+        Array<T> unique_second = second;
+
+        if (!is_unique) {
+            unique_first  = setUnique(first, false);
+            unique_second = setUnique(second, false);
         }
-    }
-
-    template<typename T>
-    Array<T> setIntersect(const Array<T> &first,
-                          const Array<T> &second,
-                          const bool is_unique)
-    {
-        try {
-            Array<T> unique_first = first;
-            Array<T> unique_second = second;
-
-            if (!is_unique) {
-                unique_first  = setUnique(first, false);
-                unique_second = setUnique(second, false);
-            }
-
-            size_t out_size = std::max(unique_first.elements(), unique_second.elements());
-            Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
-
-            compute::command_queue queue(getQueue()());
-
-            compute::buffer first_data((*unique_first.get())());
-            compute::buffer second_data((*unique_second.get())());
-            compute::buffer out_data((*out.get())());
-
-            compute::buffer_iterator< type_t<T> > first_begin(first_data, 0);
-            compute::buffer_iterator< type_t<T> > first_end(first_data, unique_first.elements());
-            compute::buffer_iterator< type_t<T> > second_begin(second_data, 0);
-            compute::buffer_iterator< type_t<T> > second_end(second_data, unique_second.elements());
-            compute::buffer_iterator< type_t<T> > out_begin(out_data, 0);
-
-            compute::buffer_iterator< type_t<T> > out_end = compute::set_intersection(
-                first_begin, first_end, second_begin, second_end, out_begin, queue
-                );
-
-            out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
-            return out;
-        } catch (std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+
+        size_t out_size = unique_first.elements() + unique_second.elements();
+        Array<T> out    = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
+
+        compute::command_queue queue(getQueue()());
+
+        compute::buffer first_data((*unique_first.get())());
+        compute::buffer second_data((*unique_second.get())());
+        compute::buffer out_data((*out.get())());
+
+        compute::buffer_iterator<type_t<T>> first_begin(first_data, 0);
+        compute::buffer_iterator<type_t<T>> first_end(first_data,
+                                                      unique_first.elements());
+        compute::buffer_iterator<type_t<T>> second_begin(second_data, 0);
+        compute::buffer_iterator<type_t<T>> second_end(
+            second_data, unique_second.elements());
+        compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
+
+        compute::buffer_iterator<type_t<T>> out_end = compute::set_union(
+            first_begin, first_end, second_begin, second_end, out_begin, queue);
+
+        out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
+        return out;
+
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique) {
+    try {
+        Array<T> unique_first  = first;
+        Array<T> unique_second = second;
+
+        if (!is_unique) {
+            unique_first  = setUnique(first, false);
+            unique_second = setUnique(second, false);
         }
-    }
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> setUnique<T>(const Array<T> &in, const bool is_sorted); \
-    template Array<T> setUnion<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
-    template Array<T> setIntersect<T>(const Array<T> &first, const Array<T> &second, const bool is_unique); \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+        size_t out_size =
+            std::max(unique_first.elements(), unique_second.elements());
+        Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
+
+        compute::command_queue queue(getQueue()());
+
+        compute::buffer first_data((*unique_first.get())());
+        compute::buffer second_data((*unique_second.get())());
+        compute::buffer out_data((*out.get())());
+
+        compute::buffer_iterator<type_t<T>> first_begin(first_data, 0);
+        compute::buffer_iterator<type_t<T>> first_end(first_data,
+                                                      unique_first.elements());
+        compute::buffer_iterator<type_t<T>> second_begin(second_data, 0);
+        compute::buffer_iterator<type_t<T>> second_end(
+            second_data, unique_second.elements());
+        compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
+
+        compute::buffer_iterator<type_t<T>> out_end = compute::set_intersection(
+            first_begin, first_end, second_begin, second_end, out_begin, queue);
+
+        out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
+        return out;
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
 }
 
+#define INSTANTIATE(T)                                                        \
+    template Array<T> setUnique<T>(const Array<T> &in, const bool is_sorted); \
+    template Array<T> setUnion<T>(                                            \
+        const Array<T> &first, const Array<T> &second, const bool is_unique); \
+    template Array<T> setIntersect<T>(                                        \
+        const Array<T> &first, const Array<T> &second, const bool is_unique);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace opencl
+
 #pragma GCC diagnostic pop
diff --git a/src/backend/opencl/set.hpp b/src/backend/opencl/set.hpp
index 592489d539..e67acc1ffd 100644
--- a/src/backend/opencl/set.hpp
+++ b/src/backend/opencl/set.hpp
@@ -9,16 +9,15 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T> Array<T> setUnique(const Array<T> &in,
-                                            const bool is_sorted);
+namespace opencl {
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted);
 
-    template<typename T> Array<T> setUnion(const Array<T> &first,
-                                           const Array<T> &second,
-                                           const bool is_unique);
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique);
 
-    template<typename T> Array<T> setIntersect(const Array<T> &first,
-                                               const Array<T> &second,
-                                               const bool is_unique);
-}
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique);
+}  // namespace opencl
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index 6531c3e130..da86c46cdf 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -8,67 +8,66 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <shift.hpp>
-#include <err_opencl.hpp>
 #include <common/jit/ShiftNodeBase.hpp>
+#include <err_opencl.hpp>
+#include <shift.hpp>
 
 #include <memory>
 #include <stdexcept>
 
 using af::dim4;
 
-using opencl::jit::BufferNode;
 using common::Node_ptr;
 using common::ShiftNodeBase;
+using opencl::jit::BufferNode;
 
 using std::array;
 using std::make_shared;
 using std::static_pointer_cast;
 using std::string;
 
-namespace opencl
-{
-    using ShiftNode = ShiftNodeBase<BufferNode>;
-
-    template<typename T>
-    Array<T> shift(const Array<T> &in, const int sdims[4])
-    {
-        // Shift should only be the first node in the JIT tree.
-        // Force input to be evaluated so that in is always a buffer.
-        in.eval();
+namespace opencl {
+using ShiftNode = ShiftNodeBase<BufferNode>;
 
-        string name_str("Sh");
-        name_str += shortname<T>(true);
-        const dim4 iDims = in.dims();
-        dim4 oDims = iDims;
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]) {
+    // Shift should only be the first node in the JIT tree.
+    // Force input to be evaluated so that in is always a buffer.
+    in.eval();
 
-        array<int, 4> shifts;
-        for(int i = 0; i < 4; i++) {
-            // sdims_[i] will always be positive and always [0, oDims[i]].
-            // Negative shifts are converted to position by going the other way round
-            shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
-            assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
-        }
+    string name_str("Sh");
+    name_str += shortname<T>(true);
+    const dim4 iDims = in.dims();
+    dim4 oDims       = iDims;
 
-        auto node = make_shared<ShiftNode>(dtype_traits<T>::getName(), name_str.c_str(),
-                                           static_pointer_cast<BufferNode>(in.getNode()),
-                                           shifts);
-        return createNodeArray<T>(oDims, common::Node_ptr(node));
+    array<int, 4> shifts;
+    for (int i = 0; i < 4; i++) {
+        // sdims_[i] will always be positive and always [0, oDims[i]].
+        // Negative shifts are converted to position by going the other way
+        // round
+        shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+        assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
     }
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]);     \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    auto node = make_shared<ShiftNode>(
+        dtype_traits<T>::getName(), name_str.c_str(),
+        static_pointer_cast<BufferNode>(in.getNode()), shifts);
+    return createNodeArray<T>(oDims, common::Node_ptr(node));
 }
+
+#define INSTANTIATE(T) \
+    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/shift.hpp b/src/backend/opencl/shift.hpp
index d93a4c9ae6..5ee21f063c 100644
--- a/src/backend/opencl/shift.hpp
+++ b/src/backend/opencl/shift.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> shift(const Array<T> &in, const int sdims[4]);
+namespace opencl {
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]);
 }
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index f79e0ba7fd..35289495e1 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
 
 #ifdef AF_WITH_NONFREE_SIFT
 #include <kernel/sift_nonfree.hpp>
@@ -20,18 +20,16 @@
 using af::dim4;
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename convAccT>
 unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
-              Array<float>& ori_out, Array<float>& size_out, Array<float>& desc_out,
-              const Array<T>& in, const unsigned n_layers,
-              const float contrast_thr, const float edge_thr,
-              const float init_sigma, const bool double_input,
-              const float img_scale, const float feature_ratio,
-              const bool compute_GLOH)
-{
+              Array<float>& ori_out, Array<float>& size_out,
+              Array<float>& desc_out, const Array<T>& in,
+              const unsigned n_layers, const float contrast_thr,
+              const float edge_thr, const float init_sigma,
+              const bool double_input, const float img_scale,
+              const float feature_ratio, const bool compute_GLOH) {
 #ifdef AF_WITH_NONFREE_SIFT
     unsigned nfeat_out;
     unsigned desc_len;
@@ -43,9 +41,10 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
     Param size;
     Param desc;
 
-    kernel::sift<T,convAccT>(&nfeat_out, &desc_len, x, y, score, ori, size, desc,
-                             in, n_layers, contrast_thr, edge_thr, init_sigma,
-                             double_input, img_scale, feature_ratio, compute_GLOH);
+    kernel::sift<T, convAccT>(&nfeat_out, &desc_len, x, y, score, ori, size,
+                              desc, in, n_layers, contrast_thr, edge_thr,
+                              init_sigma, double_input, img_scale,
+                              feature_ratio, compute_GLOH);
 
     if (nfeat_out > 0) {
         const dim4 out_dims(nfeat_out);
@@ -76,24 +75,27 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
     UNUSED(img_scale);
     UNUSED(feature_ratio);
     if (compute_GLOH)
-        AF_ERROR("ArrayFire was not built with nonfree support, GLOH disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, GLOH disabled\n",
+            AF_ERR_NONFREE);
     else
-        AF_ERROR("ArrayFire was not built with nonfree support, SIFT disabled\n", AF_ERR_NONFREE);
+        AF_ERROR(
+            "ArrayFire was not built with nonfree support, SIFT disabled\n",
+            AF_ERR_NONFREE);
 #endif
 }
 
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned sift<T, convAccT>(                                      \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        Array<float> & ori_out, Array<float> & size_out,                      \
+        Array<float> & desc_out, const Array<T>& in, const unsigned n_layers, \
+        const float contrast_thr, const float edge_thr,                       \
+        const float init_sigma, const bool double_input,                      \
+        const float img_scale, const float feature_ratio,                     \
+        const bool compute_GLOH);
 
-#define INSTANTIATE(T, convAccT)                                                            \
-    template unsigned sift<T, convAccT>(Array<float>& x_out, Array<float>& y_out,           \
-                                        Array<float>& score_out, Array<float>& ori_out,     \
-                                        Array<float>& size_out, Array<float>& desc_out,     \
-                                        const Array<T>& in, const unsigned n_layers,        \
-                                        const float contrast_thr, const float edge_thr,     \
-                                        const float init_sigma, const bool double_input,    \
-                                        const float img_scale, const float feature_ratio,   \
-                                        const bool compute_GLOH);
-
-INSTANTIATE(float , float )
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sift.hpp b/src/backend/opencl/sift.hpp
index 1587fc9655..3544405315 100644
--- a/src/backend/opencl/sift.hpp
+++ b/src/backend/opencl/sift.hpp
@@ -7,13 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename convAccT>
 unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
diff --git a/src/backend/opencl/sobel.cpp b/src/backend/opencl/sobel.cpp
index b8ac4d710d..9716140019 100644
--- a/src/backend/opencl/sobel.cpp
+++ b/src/backend/opencl/sobel.cpp
@@ -7,42 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <sobel.hpp>
-#include <kernel/sobel.hpp>
 #include <err_opencl.hpp>
+#include <kernel/sobel.hpp>
+#include <sobel.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename Ti, typename To>
-std::pair< Array<To>, Array<To> >
-sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size)
-{
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size) {
     Array<To> dx = createEmptyArray<To>(img.dims());
     Array<To> dy = createEmptyArray<To>(img.dims());
 
-    switch(ker_size) {
+    switch (ker_size) {
         case 3: kernel::sobel<Ti, To, 3>(dx, dy, img); break;
     }
 
     return std::make_pair(dx, dy);
 }
 
-#define INSTANTIATE(Ti, To)                                             \
-    template std::pair< Array<To>, Array<To> >                            \
-    sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
+#define INSTANTIATE(Ti, To)                                    \
+    template std::pair<Array<To>, Array<To>> sobelDerivatives( \
+        const Array<Ti> &img, const unsigned &ker_size);
 
-INSTANTIATE(float , float)
+INSTANTIATE(float, float)
 INSTANTIATE(double, double)
-INSTANTIATE(int   , int)
-INSTANTIATE(uint  , int)
-INSTANTIATE(char  , int)
-INSTANTIATE(uchar , int)
-INSTANTIATE(short , int)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, int)
+INSTANTIATE(char, int)
+INSTANTIATE(uchar, int)
+INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sobel.hpp b/src/backend/opencl/sobel.hpp
index 1145d9b9a8..63b25bd316 100644
--- a/src/backend/opencl/sobel.hpp
+++ b/src/backend/opencl/sobel.hpp
@@ -10,11 +10,10 @@
 #include <Array.hpp>
 #include <utility>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename Ti, typename To>
-std::pair< Array<To>, Array<To> >
-sobelDerivatives(const Array<Ti> &img, const unsigned &ker_size);
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size);
 
 }
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 13d1101d44..1ba3ec56e8 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -11,90 +11,79 @@
 #include <solve.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <blas.hpp>
+#include <copy.hpp>
+#include <err_opencl.hpp>
+#include <lu.hpp>
 #include <magma/magma.h>
 #include <magma/magma_blas.h>
 #include <magma/magma_data.h>
 #include <magma/magma_helper.h>
-#include <lu.hpp>
-#include <copy.hpp>
-#include <err_opencl.hpp>
-#include <blas.hpp>
-#include <transpose.hpp>
 #include <math.hpp>
+#include <transpose.hpp>
 #include <af/opencl.h>
 
 #include <algorithm>
 #include <string>
 
-#include <platform.hpp>
 #include <cpu/cpu_solve.hpp>
+#include <platform.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::solveLU(A, pivot, b, options);
-    }
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
+    if (OpenCLCPUOffload()) { return cpu::solveLU(A, pivot, b, options); }
 
-    int N = A.dims()[0];
+    int N    = A.dims()[0];
     int NRHS = b.dims()[1];
 
     std::vector<int> ipiv(N);
     copyData(&ipiv[0], pivot);
 
-    Array< T > B = copyArray<T>(b);
+    Array<T> B = copyArray<T>(b);
 
     const cl::Buffer *A_buf = A.get();
-    cl::Buffer *B_buf = B.get();
+    cl::Buffer *B_buf       = B.get();
 
     int info = 0;
-    magma_getrs_gpu<T>(MagmaNoTrans, N, NRHS,
-                       (*A_buf)(), A.getOffset(), A.strides()[1],
-                       &ipiv[0],
-                       (*B_buf)(), B.getOffset(), B.strides()[1],
-                       getQueue()(), &info);
+    magma_getrs_gpu<T>(MagmaNoTrans, N, NRHS, (*A_buf)(), A.getOffset(),
+                       A.strides()[1], &ipiv[0], (*B_buf)(), B.getOffset(),
+                       B.strides()[1], getQueue()(), &info);
     return B;
 }
 
 template<typename T>
-Array<T> generalSolve(const Array<T> &a, const Array<T> &b)
-{
-
+Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
     dim4 iDims = a.dims();
-    int M = iDims[0];
-    int N = iDims[1];
-    int MN = std::min(M, N);
+    int M      = iDims[0];
+    int N      = iDims[1];
+    int MN     = std::min(M, N);
     std::vector<int> ipiv(MN);
 
     Array<T> A = copyArray<T>(a);
     Array<T> B = copyArray<T>(b);
 
-    cl::Buffer *A_buf = A.get();
-    int info = 0;
+    cl::Buffer *A_buf  = A.get();
+    int info           = 0;
     cl_command_queue q = getQueue()();
     magma_getrf_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
                        &ipiv[0], q, &info);
 
     cl::Buffer *B_buf = B.get();
-    int K = B.dims()[1];
-    magma_getrs_gpu<T>(MagmaNoTrans, M, K,
-                       (*A_buf)(), A.getOffset(), A.strides()[1],
-                       &ipiv[0],
-                       (*B_buf)(), B.getOffset(), B.strides()[1],
-                       q, &info);
+    int K             = B.dims()[1];
+    magma_getrs_gpu<T>(MagmaNoTrans, M, K, (*A_buf)(), A.getOffset(),
+                       A.strides()[1], &ipiv[0], (*B_buf)(), B.getOffset(),
+                       B.strides()[1], q, &info);
     return B;
 }
 
 template<typename T>
-Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
-{
-    int M = a.dims()[0];
-    int N = a.dims()[1];
-    int K = b.dims()[1];
+Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
+    int M  = a.dims()[0];
+    int N  = a.dims()[1];
+    int K  = b.dims()[1];
     int MN = std::min(M, N);
 
     Array<T> B = createEmptyArray<T>(dim4());
@@ -104,8 +93,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
     cl_command_queue queue = getQueue()();
 
     if (M < N) {
-
-#define UNMQR 0 // FIXME: UNMQR == 1 should be faster but does not work
+#define UNMQR 0  // FIXME: UNMQR == 1 should be faster but does not work
 
         // Least squres for this case is solved using the following
         // solve(A, B) == matmul(Q, Xpad);
@@ -125,57 +113,52 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         B = copyArray<T>(b);
 #endif
 
-        int NB = magma_get_geqrf_nb<T>(A.dims()[1]);
-        int NUM = (2*MN + ((M+31)/32)*32)*NB;
+        int NB       = magma_get_geqrf_nb<T>(A.dims()[1]);
+        int NUM      = (2 * MN + ((M + 31) / 32) * 32) * NB;
         Array<T> tmp = createEmptyArray<T>(dim4(NUM));
 
         std::vector<T> h_tau(MN);
 
-        int info = 0;
+        int info       = 0;
         cl::Buffer *dA = A.get();
         cl::Buffer *dT = tmp.get();
         cl::Buffer *dB = B.get();
 
-        magma_geqrf3_gpu<T>(A.dims()[0], A.dims()[1],
-                           (*dA)(), A.getOffset(), A.strides()[1],
-                           &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
+        magma_geqrf3_gpu<T>(A.dims()[0], A.dims()[1], (*dA)(), A.getOffset(),
+                            A.strides()[1], &h_tau[0], (*dT)(), tmp.getOffset(),
+                            getQueue()(), &info);
 
         A.resetDims(dim4(M, M));
 
-        magmablas_swapdblk<T>(MN-1, NB,
-                              (*dA)(), A.getOffset(), A.strides()[1], 1,
-                              (*dT)(), tmp.getOffset() + MN * NB, NB, 0, queue);
+        magmablas_swapdblk<T>(MN - 1, NB, (*dA)(), A.getOffset(),
+                              A.strides()[1], 1, (*dT)(),
+                              tmp.getOffset() + MN * NB, NB, 0, queue);
 
-        OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                         OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
-                         OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                         B.dims()[0], B.dims()[1],
-                         scalar<T>(1),
-                         (*dA)(), A.getOffset(), A.strides()[1],
-                         (*dB)(), B.getOffset(), B.strides()[1],
-                         1, &queue, 0, nullptr, &event));
-
-        magmablas_swapdblk<T>(MN - 1, NB,
-                              (*dT)(), tmp.getOffset() + MN * NB, NB, 0,
-                              (*dA)(), A.getOffset(), A.strides()[1], 1, queue);
+        OPENCL_BLAS_CHECK(
+            gpu_blas_trsm(OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                          OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
+                          B.dims()[0], B.dims()[1], scalar<T>(1), (*dA)(),
+                          A.getOffset(), A.strides()[1], (*dB)(), B.getOffset(),
+                          B.strides()[1], 1, &queue, 0, nullptr, &event));
+
+        magmablas_swapdblk<T>(MN - 1, NB, (*dT)(), tmp.getOffset() + MN * NB,
+                              NB, 0, (*dA)(), A.getOffset(), A.strides()[1], 1,
+                              queue);
 
 #if UNMQR
-        int lwork = (B.dims()[0]-A.dims()[0]+NB)*(B.dims()[1]+2*NB);
+        int lwork = (B.dims()[0] - A.dims()[0] + NB) * (B.dims()[1] + 2 * NB);
         std::vector<T> h_work(lwork);
         B.resetDims(dim4(N, K));
-        magma_unmqr_gpu<T>(MagmaLeft, MagmaNoTrans,
-                           B.dims()[0], B.dims()[1], A.dims()[0],
-                           (*dA)(), A.getOffset(), A.strides()[1],
-                           &h_tau[0],
-                           (*dB)(), B.getOffset(), B.strides()[1],
-                           &h_work[0], lwork,
-                           (*dT)(), tmp.getOffset(), NB, queue, &info);
+        magma_unmqr_gpu<T>(MagmaLeft, MagmaNoTrans, B.dims()[0], B.dims()[1],
+                           A.dims()[0], (*dA)(), A.getOffset(), A.strides()[1],
+                           &h_tau[0], (*dB)(), B.getOffset(), B.strides()[1],
+                           &h_work[0], lwork, (*dT)(), tmp.getOffset(), NB,
+                           queue, &info);
 #else
         A.resetDims(dim4(N, M));
-        magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], std::min(M, N),
-                           (*dA)(), A.getOffset(), A.strides()[1],
-                           &h_tau[0],
-                           (*dT)(), tmp.getOffset(), NB, queue, &info);
+        magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], std::min(M, N), (*dA)(),
+                           A.getOffset(), A.strides()[1], &h_tau[0], (*dT)(),
+                           tmp.getOffset(), NB, queue, &info);
 
         B = matmul(A, B, AF_MAT_NONE, AF_MAT_NONE);
 #endif
@@ -189,64 +172,56 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
         // A  == matmul(Q, R);
 
         Array<T> A = copyArray<T>(a);
-        B = copyArray(b);
+        B          = copyArray(b);
 
         int MN = std::min(M, N);
         int NB = magma_get_geqrf_nb<T>(M);
 
-        int NUM = (2*MN + ((N+31)/32)*32)*NB;
+        int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
         Array<T> tmp = createEmptyArray<T>(dim4(NUM));
 
         std::vector<T> h_tau(NUM);
 
-        int info = 0;
+        int info          = 0;
         cl::Buffer *A_buf = A.get();
         cl::Buffer *B_buf = B.get();
-        cl::Buffer *dT = tmp.get();
+        cl::Buffer *dT    = tmp.get();
 
-        magma_geqrf3_gpu<T>(M, N,
-                           (*A_buf)(), A.getOffset(), A.strides()[1],
-                           &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
+        magma_geqrf3_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
+                            &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
+                            &info);
 
-        int NRHS = B.dims()[1];
+        int NRHS   = B.dims()[1];
         int lhwork = (M - N + NB) * (NRHS + NB) + NRHS * NB;
 
         std::vector<T> h_work(lhwork);
         h_work[0] = scalar<T>(lhwork);
 
-        magma_unmqr_gpu<T>(MagmaLeft, MagmaConjTrans,
-                           M, NRHS, N,
-                           (*A_buf)(), A.getOffset(), A.strides()[1],
-                           &h_tau[0],
-                           (*B_buf)(), B.getOffset(), B.strides()[1],
-                           &h_work[0], lhwork,
-                           (*dT)(), tmp.getOffset(), NB,
-                           queue, &info);
+        magma_unmqr_gpu<T>(MagmaLeft, MagmaConjTrans, M, NRHS, N, (*A_buf)(),
+                           A.getOffset(), A.strides()[1], &h_tau[0], (*B_buf)(),
+                           B.getOffset(), B.strides()[1], &h_work[0], lhwork,
+                           (*dT)(), tmp.getOffset(), NB, queue, &info);
 
-        magmablas_swapdblk<T>(MN - 1, NB,
-                              (*A_buf)(), A.getOffset(), A.strides()[1], 1,
-                              (*dT)(), tmp.getOffset() + NB * MN,
-                              NB, 0, queue);
+        magmablas_swapdblk<T>(MN - 1, NB, (*A_buf)(), A.getOffset(),
+                              A.strides()[1], 1, (*dT)(),
+                              tmp.getOffset() + NB * MN, NB, 0, queue);
 
-        if(getActivePlatform() == AFCL_PLATFORM_NVIDIA)
-        {
-            Array<T> AT = transpose<T>(A, true);
-            cl::Buffer* AT_buf = AT.get();
+        if (getActivePlatform() == AFCL_PLATFORM_NVIDIA) {
+            Array<T> AT        = transpose<T>(A, true);
+            cl::Buffer *AT_buf = AT.get();
             OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                             OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
-                             OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                             N, NRHS, scalar<T>(1),
-                             (*AT_buf)(), AT.getOffset(), AT.strides()[1],
-                             (*B_buf)(), B.getOffset(), B.strides()[1],
-                             1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+                OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
+                scalar<T>(1), (*AT_buf)(), AT.getOffset(), AT.strides()[1],
+                (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0,
+                nullptr, &event));
         } else {
             OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                             OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
-                             OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                             N, NRHS, scalar<T>(1),
-                             (*A_buf)(), A.getOffset(), A.strides()[1],
-                             (*B_buf)(), B.getOffset(), B.strides()[1],
-                             1, &queue, 0, nullptr, &event));
+                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
+                scalar<T>(1), (*A_buf)(), A.getOffset(), A.strides()[1],
+                (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0,
+                nullptr, &event));
         }
         B.resetDims(dim4(N, K));
     }
@@ -255,113 +230,107 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
 }
 
 template<typename T>
-Array<T> triangleSolve(const Array<T> &A, const Array<T> &b, const af_mat_prop options)
-{
+Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
+                       const af_mat_prop options) {
     gpu_blas_trsm_func<T> gpu_blas_trsm;
 
     Array<T> B = copyArray<T>(b);
 
-    int N = B.dims()[0];
+    int N    = B.dims()[0];
     int NRHS = B.dims()[1];
 
-    const cl::Buffer* A_buf = A.get();
-    cl::Buffer* B_buf = B.get();
+    const cl::Buffer *A_buf = A.get();
+    cl::Buffer *B_buf       = B.get();
 
-    cl_event event = 0;
+    cl_event event         = 0;
     cl_command_queue queue = getQueue()();
 
-    if(getActivePlatform() == AFCL_PLATFORM_NVIDIA && (options & AF_MAT_UPPER))
-    {
+    if (getActivePlatform() == AFCL_PLATFORM_NVIDIA &&
+        (options & AF_MAT_UPPER)) {
         Array<T> AT = transpose<T>(A, true);
 
-        cl::Buffer* AT_buf = AT.get();
+        cl::Buffer *AT_buf = AT.get();
         OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                         OPENCL_BLAS_SIDE_LEFT,
-                         OPENCL_BLAS_TRIANGLE_LOWER,
-                         OPENCL_BLAS_CONJ_TRANS,
-                         options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL : OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                         N, NRHS, scalar<T>(1),
-                         (*AT_buf)(), AT.getOffset(), AT.strides()[1],
-                         (*B_buf)(), B.getOffset(), B.strides()[1],
-                         1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+            OPENCL_BLAS_CONJ_TRANS,
+            options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL
+                                       : OPENCL_BLAS_NON_UNIT_DIAGONAL,
+            N, NRHS, scalar<T>(1), (*AT_buf)(), AT.getOffset(), AT.strides()[1],
+            (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0, nullptr,
+            &event));
     } else {
         OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                         OPENCL_BLAS_SIDE_LEFT,
-                         options & AF_MAT_LOWER ? OPENCL_BLAS_TRIANGLE_LOWER : OPENCL_BLAS_TRIANGLE_UPPER,
-                         OPENCL_BLAS_NO_TRANS,
-                         options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL : OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                         N, NRHS, scalar<T>(1),
-                         (*A_buf)(), A.getOffset(), A.strides()[1],
-                         (*B_buf)(), B.getOffset(), B.strides()[1],
-                         1, &queue, 0, nullptr, &event));
+            OPENCL_BLAS_SIDE_LEFT,
+            options & AF_MAT_LOWER ? OPENCL_BLAS_TRIANGLE_LOWER
+                                   : OPENCL_BLAS_TRIANGLE_UPPER,
+            OPENCL_BLAS_NO_TRANS,
+            options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL
+                                       : OPENCL_BLAS_NON_UNIT_DIAGONAL,
+            N, NRHS, scalar<T>(1), (*A_buf)(), A.getOffset(), A.strides()[1],
+            (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0, nullptr,
+            &event));
     }
 
     return B;
 }
 
-
 template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::solve(a, b, options);
-    }
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
+    if (OpenCLCPUOffload()) { return cpu::solve(a, b, options); }
 
-    if (options & AF_MAT_UPPER ||
-        options & AF_MAT_LOWER) {
+    if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
     }
 
-    if(a.dims()[0] == a.dims()[1]) {
+    if (a.dims()[0] == a.dims()[1]) {
         return generalSolve<T>(a, b);
     } else {
         return leastSquares<T>(a, b);
     }
 }
 
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
     template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
 
 INSTANTIATE_SOLVE(float)
 INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
-}
+}  // namespace opencl
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> solveLU(const Array<T> &A, const Array<int> &pivot,
-                 const Array<T> &b, const af_mat_prop options)
-{
-    AF_ERROR("Linear Algebra is disabled on OpenCL",
-             AF_ERR_NOT_CONFIGURED);
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
+    AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
-Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
-{
-    AF_ERROR("Linear Algebra is disabled on OpenCL",
-              AF_ERR_NOT_CONFIGURED);
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
+    AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE_SOLVE(T)                                            \
-    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,    \
-                               const af_mat_prop options);              \
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
     template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
-                                 const Array<T> &b, const af_mat_prop options); \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
 
 INSTANTIATE_SOLVE(float)
 INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
-}
+}  // namespace opencl
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/solve.hpp b/src/backend/opencl/solve.hpp
index d3c7bd29c4..c2b22810e4 100644
--- a/src/backend/opencl/solve.hpp
+++ b/src/backend/opencl/solve.hpp
@@ -9,12 +9,12 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
+namespace opencl {
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options = AF_MAT_NONE);
 
-    template<typename T>
-    Array<T> solveLU(const Array<T> &a, const Array<int> &pivot,
-                     const Array<T> &b, const af_mat_prop options = AF_MAT_NONE);
-}
+template<typename T>
+Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options = AF_MAT_NONE);
+}  // namespace opencl
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 9a2288a8b1..08f51faeaf 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -8,60 +8,57 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <sort.hpp>
 #include <copy.hpp>
+#include <err_opencl.hpp>
 #include <kernel/sort.hpp>
 #include <math.hpp>
 #include <reorder.hpp>
+#include <sort.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
-
-namespace opencl
-{
-    template<typename T>
-    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending)
-    {
-        try {
-            Array<T> out = copyArray<T>(in);
-            switch(dim) {
-                case 0: kernel::sort0<T>(out, isAscending); break;
-                case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
-                case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
-                case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
-                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-            }
 
-            if(dim != 0) {
-                af::dim4 preorderDims = out.dims();
-                af::dim4 reorderDims(0, 1, 2, 3);
-                reorderDims[dim] = 0;
-                preorderDims[0] = out.dims()[dim];
-                for(int i = 1; i <= (int)dim; i++) {
-                    reorderDims[i - 1] = i;
-                    preorderDims[i] = out.dims()[i - 1];
-                }
+namespace opencl {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
+    try {
+        Array<T> out = copyArray<T>(in);
+        switch (dim) {
+            case 0: kernel::sort0<T>(out, isAscending); break;
+            case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+            case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+            case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
 
-                out.setDataDims(preorderDims);
-                out = reorder<T>(out, reorderDims);
+        if (dim != 0) {
+            af::dim4 preorderDims = out.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0]  = out.dims()[dim];
+            for (int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i]    = out.dims()[i - 1];
             }
-            return out;
-        } catch (std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+
+            out.setDataDims(preorderDims);
+            out = reorder<T>(out, reorderDims);
         }
-    }
+        return out;
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
 
-#define INSTANTIATE(T)                                                  \
-    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, bool isAscending);
+#define INSTANTIATE(T)                                                \
+    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, \
+                              bool isAscending);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sort.hpp b/src/backend/opencl/sort.hpp
index 82f6385e2e..91e57b560c 100644
--- a/src/backend/opencl/sort.hpp
+++ b/src/backend/opencl/sort.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
+namespace opencl {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
index 9452311d11..f6cbb6158c 100644
--- a/src/backend/opencl/sort_by_key.cpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -9,80 +9,77 @@
 
 #include <Array.hpp>
 #include <copy.hpp>
-#include <sort_by_key.hpp>
+#include <err_opencl.hpp>
 #include <kernel/sort_by_key.hpp>
-#include <reorder.hpp>
 #include <math.hpp>
+#include <reorder.hpp>
+#include <sort_by_key.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
 
-namespace opencl
-{
-    template<typename Tk, typename Tv>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending)
-    {
-        try {
-            okey = copyArray<Tk>(ikey);
-            oval = copyArray<Tv>(ival);
+namespace opencl {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const unsigned dim, bool isAscending) {
+    try {
+        okey = copyArray<Tk>(ikey);
+        oval = copyArray<Tv>(ival);
 
-            switch(dim) {
-                case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
-                case 1:
-                case 2:
-                case 3: kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending); break;
-                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-            }
+        switch (dim) {
+            case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
+            case 1:
+            case 2:
+            case 3:
+                kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending);
+                break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
 
-            if(dim != 0) {
-                af::dim4 preorderDims = okey.dims();
-                af::dim4 reorderDims(0, 1, 2, 3);
-                reorderDims[dim] = 0;
-                preorderDims[0] = okey.dims()[dim];
-                for(int i = 1; i <= (int)dim; i++) {
-                    reorderDims[i - 1] = i;
-                    preorderDims[i] = okey.dims()[i - 1];
-                }
+        if (dim != 0) {
+            af::dim4 preorderDims = okey.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0]  = okey.dims()[dim];
+            for (int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i]    = okey.dims()[i - 1];
+            }
 
-                okey.setDataDims(preorderDims);
-                oval.setDataDims(preorderDims);
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
 
-                okey = reorder<Tk>(okey, reorderDims);
-                oval = reorder<Tv>(oval, reorderDims);
-            }
-        } catch(std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+            okey = reorder<Tk>(okey, reorderDims);
+            oval = reorder<Tv>(oval, reorderDims);
         }
-    }
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
 
-#define INSTANTIATE(Tk, Tv)                                                         \
-    template void sort_by_key<Tk, Tv>(Array<Tk> &okey, Array<Tv> &oval,             \
-                                      const Array<Tk> &ikey, const Array<Tv> &ival, \
-                                      const uint dim, bool isAscending);
+#define INSTANTIATE(Tk, Tv)                                        \
+    template void sort_by_key<Tk, Tv>(                             \
+        Array<Tk> & okey, Array<Tv> & oval, const Array<Tk> &ikey, \
+        const Array<Tv> &ival, const uint dim, bool isAscending);
 
-#define INSTANTIATE1(Tk    ) \
-    INSTANTIATE(Tk, float  ) \
-    INSTANTIATE(Tk, double ) \
-    INSTANTIATE(Tk, cfloat ) \
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
     INSTANTIATE(Tk, cdouble) \
-    INSTANTIATE(Tk, int    ) \
-    INSTANTIATE(Tk, uint   ) \
-    INSTANTIATE(Tk, short  ) \
-    INSTANTIATE(Tk, ushort ) \
-    INSTANTIATE(Tk, char   ) \
-    INSTANTIATE(Tk, uchar  ) \
-    INSTANTIATE(Tk, intl   ) \
-    INSTANTIATE(Tk, uintl  )
-
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
 
-  INSTANTIATE1(float )
-  INSTANTIATE1(double)
-  INSTANTIATE1(int   )
-  INSTANTIATE1(uint  )
-  INSTANTIATE1(short )
-  INSTANTIATE1(ushort)
-  INSTANTIATE1(char  )
-  INSTANTIATE1(uchar )
-  INSTANTIATE1(intl  )
-  INSTANTIATE1(uintl )
-}
+INSTANTIATE1(float)
+INSTANTIATE1(double)
+INSTANTIATE1(int)
+INSTANTIATE1(uint)
+INSTANTIATE1(short)
+INSTANTIATE1(ushort)
+INSTANTIATE1(char)
+INSTANTIATE1(uchar)
+INSTANTIATE1(intl)
+INSTANTIATE1(uintl)
+}  // namespace opencl
diff --git a/src/backend/opencl/sort_by_key.hpp b/src/backend/opencl/sort_by_key.hpp
index 0b8577f1a3..a1e616c3e5 100644
--- a/src/backend/opencl/sort_by_key.hpp
+++ b/src/backend/opencl/sort_by_key.hpp
@@ -9,9 +9,8 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename Tk, typename Tv>
-    void sort_by_key(Array<Tk> &okey, Array<Tv> &oval,
-                     const Array<Tk> &ikey, const Array<Tv> &ival, const unsigned dim, bool isAscending);
+namespace opencl {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index 20a92e6d45..a595e97f30 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -8,69 +8,68 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <sort_index.hpp>
 #include <copy.hpp>
+#include <err_opencl.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <math.hpp>
-#include <stdexcept>
-#include <err_opencl.hpp>
-#include <reorder.hpp>
 #include <range.hpp>
+#include <reorder.hpp>
+#include <sort_index.hpp>
+#include <stdexcept>
 
-namespace opencl
-{
-    template<typename T>
-    void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in, const uint dim, bool isAscending)
-    {
-        try {
-            // okey contains values, oval contains indices
-            okey = copyArray<T>(in);
-            oval = range<uint>(in.dims(), dim);
-            oval.eval();
+namespace opencl {
+template<typename T>
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
+                const uint dim, bool isAscending) {
+    try {
+        // okey contains values, oval contains indices
+        okey = copyArray<T>(in);
+        oval = range<uint>(in.dims(), dim);
+        oval.eval();
 
-            switch(dim) {
-                case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
-                case 1:
-                case 2:
-                case 3: kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending); break;
-                default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-            }
+        switch (dim) {
+            case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
+            case 1:
+            case 2:
+            case 3:
+                kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending);
+                break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
 
-            if(dim != 0) {
-                af::dim4 preorderDims = okey.dims();
-                af::dim4 reorderDims(0, 1, 2, 3);
-                reorderDims[dim] = 0;
-                preorderDims[0] = okey.dims()[dim];
-                for(int i = 1; i <= (int)dim; i++) {
-                    reorderDims[i - 1] = i;
-                    preorderDims[i] = okey.dims()[i - 1];
-                }
+        if (dim != 0) {
+            af::dim4 preorderDims = okey.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0]  = okey.dims()[dim];
+            for (int i = 1; i <= (int)dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i]    = okey.dims()[i - 1];
+            }
 
-                okey.setDataDims(preorderDims);
-                oval.setDataDims(preorderDims);
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
 
-                okey = reorder<T>(okey, reorderDims);
-                oval = reorder<uint>(oval, reorderDims);
-            }
-        } catch (std::exception &ex) {
-            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+            okey = reorder<T>(okey, reorderDims);
+            oval = reorder<uint>(oval, reorderDims);
         }
-    }
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
 
-#define INSTANTIATE(T)                                                  \
-  template void sort_index<T>(Array<T> &val, Array<uint> &idx,          \
-                              const Array<T> &in, const uint dim,       \
-                              bool isAscending);
+#define INSTANTIATE(T)                                              \
+    template void sort_index<T>(Array<T> & val, Array<uint> & idx,  \
+                                const Array<T> &in, const uint dim, \
+                                bool isAscending);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sort_index.hpp b/src/backend/opencl/sort_index.hpp
index cfa3366906..5b9560439d 100644
--- a/src/backend/opencl/sort_index.hpp
+++ b/src/backend/opencl/sort_index.hpp
@@ -9,8 +9,8 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in, const unsigned dim, bool isAscending);
+namespace opencl {
+template<typename T>
+void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in,
+                const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 0372d81f83..c36e950ffe 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse.hpp>
 #include <kernel/sparse.hpp>
+#include <sparse.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -25,39 +25,39 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 using namespace common;
 
 // Partial template specialization of sparseConvertDenseToStorage for COO
 // However, template specialization is not allowed
 template<typename T>
-SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
-{
+SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in) {
     in.eval();
 
     Array<uint> nonZeroIdx_ = where<T>(in);
-    Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
+    Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
 
     dim_t nNZ = nonZeroIdx.elements();
 
     Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
     constDim.eval();
 
-    Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
-    Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> rowIdx =
+        arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx =
+        arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
     Array<T> values = copyArray<T>(in);
     values.modDims(dim4(values.elements()));
     values = lookup<T, int>(values, nonZeroIdx, 0);
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+                                         AF_STORAGE_COO);
 }
 
 template<typename T, af_storage stype>
-SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
-{
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_) {
     in_.eval();
 
     uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
@@ -65,7 +65,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
     sparse_.eval();
 
-    Array<T> &values = sparse_.getValues();
+    Array<T> &values   = sparse_.getValues();
     Array<int> &rowIdx = sparse_.getRowIdx();
     Array<int> &colIdx = sparse_.getColIdx();
 
@@ -77,14 +77,13 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_)
 // Partial template specialization of sparseConvertStorageToDense for COO
 // However, template specialization is not allowed
 template<typename T>
-Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
-{
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
     in.eval();
 
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
     dense.eval();
 
-    const Array<T>   values = in.getValues();
+    const Array<T> values   = in.getValues();
     const Array<int> rowIdx = in.getRowIdx();
     const Array<int> colIdx = in.getColIdx();
 
@@ -94,102 +93,116 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in)
 }
 
 template<typename T, af_storage stype>
-Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_)
-{
-    if(stype != AF_STORAGE_CSR)
-        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
+    if (stype != AF_STORAGE_CSR)
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
 
     in_.eval();
 
     Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
     dense_.eval();
 
-    const Array<T> &values = in_.getValues();
+    const Array<T> &values   = in_.getValues();
     const Array<int> &rowIdx = in_.getRowIdx();
     const Array<int> &colIdx = in_.getColIdx();
 
-    if(stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR)
         kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
     else
-        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
 
     return dense_;
 }
 
 template<typename T, af_storage dest, af_storage src>
-SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in)
-{
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     in.eval();
 
-    SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    SparseArray<T> converted =
+        createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
     converted.eval();
 
-    if(src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
-
+    if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
         Array<int> index = range<int>(in.getNNZ(), 0);
         index.eval();
 
-        Array<T>   &ovalues = converted.getValues();
-        Array<int> &orowIdx = converted.getRowIdx();
-        Array<int> &ocolIdx = converted.getColIdx();
-        const Array<T>   &ivalues = in.getValues();
+        Array<T> &ovalues         = converted.getValues();
+        Array<int> &orowIdx       = converted.getRowIdx();
+        Array<int> &ocolIdx       = converted.getColIdx();
+        const Array<T> &ivalues   = in.getValues();
         const Array<int> &irowIdx = in.getRowIdx();
         const Array<int> &icolIdx = in.getColIdx();
 
-        kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx, index);
+        kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+                           index);
 
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-
         Array<int> index = range<int>(in.getNNZ(), 0);
         index.eval();
 
-        Array<T>   &ovalues = converted.getValues();
-        Array<int> &orowIdx = converted.getRowIdx();
-        Array<int> &ocolIdx = converted.getColIdx();
-        const Array<T>   &ivalues = in.getValues();
+        Array<T> &ovalues         = converted.getValues();
+        Array<int> &orowIdx       = converted.getRowIdx();
+        Array<int> &ocolIdx       = converted.getColIdx();
+        const Array<T> &ivalues   = in.getValues();
         const Array<int> &irowIdx = in.getRowIdx();
         const Array<int> &icolIdx = in.getColIdx();
 
         Array<int> rowCopy = copyArray<int>(irowIdx);
         rowCopy.eval();
 
-        kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx,
-                           ivalues, irowIdx, icolIdx,
+        kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
                            index, rowCopy, in.dims()[0]);
 
     } else {
         // Should never come here
-        AF_ERROR("OpenCL Backend invalid conversion combination", AF_ERR_NOT_SUPPORTED);
+        AF_ERROR("OpenCL Backend invalid conversion combination",
+                 AF_ERR_NOT_SUPPORTED);
     }
 
     return converted;
 }
 
+#define INSTANTIATE_TO_STORAGE(T, S)                     \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>( \
+        const SparseArray<T> &in);                       \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>( \
+        const SparseArray<T> &in);                       \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>( \
+        const SparseArray<T> &in);
+
+#define INSTANTIATE_COO_SPECIAL(T)                                 \
+    template<>                                                     \
+    SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>( \
+        const Array<T> &in) {                                      \
+        return sparseConvertDenseToCOO<T>(in);                     \
+    }                                                              \
+    template<>                                                     \
+    Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(       \
+        const SparseArray<T> &in) {                                \
+        return sparseConvertCOOToDense<T>(in);                     \
+    }
 
-#define INSTANTIATE_TO_STORAGE(T, S)                                                                        \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>(const SparseArray<T> &in);  \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>(const SparseArray<T> &in);  \
-    template SparseArray<T> sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>(const SparseArray<T> &in);  \
-
-#define INSTANTIATE_COO_SPECIAL(T)                                                                      \
-    template<> SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>(const Array<T> &in)        \
-    { return sparseConvertDenseToCOO<T>(in); }                                                          \
-    template<> Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(const SparseArray<T> &in)        \
-    { return sparseConvertCOOToDense<T>(in); }                                                          \
-
-#define INSTANTIATE_SPARSE(T)                                                                           \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(const Array<T> &in);         \
-    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>(const Array<T> &in);         \
-                                                                                                        \
-    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(const SparseArray<T> &in);         \
-    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(const SparseArray<T> &in);         \
-                                                                                                        \
-    INSTANTIATE_COO_SPECIAL(T)                                                                          \
-                                                                                                        \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                                                           \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                                                           \
-    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)                                                           \
-
+#define INSTANTIATE_SPARSE(T)                                               \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>( \
+        const Array<T> &in);                                                \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>( \
+        const Array<T> &in);                                                \
+                                                                            \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(       \
+        const SparseArray<T> &in);                                          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(       \
+        const SparseArray<T> &in);                                          \
+                                                                            \
+    INSTANTIATE_COO_SPECIAL(T)                                              \
+                                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
@@ -200,4 +213,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_COO_SPECIAL
 #undef INSTANTIATE_SPARSE
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index d50141ac22..e8496a533e 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -12,8 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
@@ -22,6 +21,7 @@ template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
 
 template<typename T, af_storage dest, af_storage src>
-common::SparseArray<T> sparseConvertStorageToStorage(const common::SparseArray<T> &in);
+common::SparseArray<T> sparseConvertStorageToStorage(
+    const common::SparseArray<T> &in);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index ea36b384fa..5759608979 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -7,106 +7,110 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <sparse.hpp>
 #include <kernel/sparse_arith.hpp>
+#include <sparse.hpp>
 
 #include <stdexcept>
 #include <string>
 
 #include <arith.hpp>
 #include <cast.hpp>
+#include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
-#include <common/err_common.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <scan.hpp>
 #include <where.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 using namespace common;
 using namespace std;
 
 template<typename T>
-T getInf()
-{
+T getInf() {
     return scalar<T>(std::numeric_limits<T>::infinity());
 }
 
 template<>
-cfloat getInf()
-{
-    return scalar<cfloat, float>(NAN, NAN); // Matches behavior of complex division by 0 in OpenCL
+cfloat getInf() {
+    return scalar<cfloat, float>(
+        NAN, NAN);  // Matches behavior of complex division by 0 in OpenCL
 }
 
 template<>
-cdouble getInf()
-{
-    return scalar<cdouble, double>(NAN, NAN); // Matches behavior of complex division by 0 in OpenCL
+cdouble getInf() {
+    return scalar<cdouble, double>(
+        NAN, NAN);  // Matches behavior of complex division by 0 in OpenCL
 }
 
 template<typename T, af_op_t op>
-Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
-{
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
+                  const bool reverse) {
     lhs.eval();
     rhs.eval();
 
-    Array<T> out = createEmptyArray<T>(dim4(0));
+    Array<T> out  = createEmptyArray<T>(dim4(0));
     Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
-    switch(op) {
+    switch (op) {
         case af_add_t: out = copyArray<T>(rhs); break;
-        case af_sub_t: out = reverse ? copyArray<T>(rhs) : arithOp<T, af_sub_t>(zero, rhs, rhs.dims()); break;
-        default      : out = copyArray<T>(rhs);
+        case af_sub_t:
+            out = reverse ? copyArray<T>(rhs)
+                          : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
+            break;
+        default: out = copyArray<T>(rhs);
     }
     out.eval();
-    switch(lhs.getStorage()) {
+    switch (lhs.getStorage()) {
         case AF_STORAGE_CSR:
-            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
+                                            lhs.getRowIdx(), lhs.getColIdx(),
                                             rhs, reverse);
             break;
         case AF_STORAGE_COO:
-            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
+                                            lhs.getRowIdx(), lhs.getColIdx(),
                                             rhs, reverse);
             break;
         default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
     }
 
     return out;
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse)
-{
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
+                       const bool reverse) {
     lhs.eval();
     rhs.eval();
 
-    SparseArray<T> out = createArrayDataSparseArray<T>(lhs.dims(), lhs.getValues(),
-                                                       lhs.getRowIdx(), lhs.getColIdx(),
-                                                       lhs.getStorage(), true);
+    SparseArray<T> out = createArrayDataSparseArray<T>(
+        lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+        lhs.getStorage(), true);
     out.eval();
-    switch(lhs.getStorage()) {
+    switch (lhs.getStorage()) {
         case AF_STORAGE_CSR:
-            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
-                                            rhs, reverse);
+            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
+                                            out.getColIdx(), rhs, reverse);
             break;
         case AF_STORAGE_COO:
-            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(), out.getColIdx(),
-                                            rhs, reverse);
+            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
+                                            out.getColIdx(), rhs, reverse);
             break;
         default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO", AF_ERR_NOT_SUPPORTED);
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
     }
 
     return out;
 }
 
 template<typename T, af_op_t op>
-SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
-{
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     lhs.eval();
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
@@ -119,55 +123,54 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs)
     const dim_t nnzA = lhs.getNNZ();
     const dim_t nnzB = rhs.getNNZ();
 
-    auto temp = createValueArray<int>(dim4(M+1), scalar<int>(0));
+    auto temp = createValueArray<int>(dim4(M + 1), scalar<int>(0));
     temp.eval();
 
     unsigned nnzC = 0;
-    kernel::csrCalcOutNNZ(temp, nnzC, M, N,
-            nnzA, lhs.getRowIdx(), lhs.getColIdx(),
-            nnzB, rhs.getRowIdx(), rhs.getColIdx());
+    kernel::csrCalcOutNNZ(temp, nnzC, M, N, nnzA, lhs.getRowIdx(),
+                          lhs.getColIdx(), nnzB, rhs.getRowIdx(),
+                          rhs.getColIdx());
 
     auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
 
     auto outColIdx = createEmptyArray<int>(dim4(nnzC));
     auto outValues = createEmptyArray<T>(dim4(nnzC));
 
-    kernel::ssArithCSR<T, op>(outValues, outColIdx,
-            outRowIdx, M, N,
-            nnzA, lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-            nnzB, rhs.getValues(), rhs.getRowIdx(), rhs.getColIdx());
+    kernel::ssArithCSR<T, op>(outValues, outColIdx, outRowIdx, M, N, nnzA,
+                              lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                              nnzB, rhs.getValues(), rhs.getRowIdx(),
+                              rhs.getColIdx());
 
-    SparseArray<T> retVal = createArrayDataSparseArray(ldims,
-                                 outValues, outRowIdx, outColIdx,
-                                 sfmt);
+    SparseArray<T> retVal = createArrayDataSparseArray(
+        ldims, outValues, outRowIdx, outColIdx, sfmt);
     return retVal;
 }
 
-#define INSTANTIATE(T)                                                                              \
-    template Array<T> arithOpD<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template Array<T> arithOpD<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,         \
-                                            const bool reverse);                                    \
-    template SparseArray<T> arithOp<T, af_add_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                 const bool reverse);                               \
-    template SparseArray<T> arithOp<T, af_sub_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                 const bool reverse);                               \
-    template SparseArray<T> arithOp<T, af_mul_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                 const bool reverse);                               \
-    template SparseArray<T> arithOp<T, af_div_t>(const SparseArray<T> &lhs, const Array<T> &rhs,    \
-                                                  const bool reverse);                              \
-    template SparseArray<T> arithOp<T, af_add_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-    template SparseArray<T> arithOp<T, af_sub_t>(const common::SparseArray<T> &lhs,                 \
-                                                 const common::SparseArray<T> &rhs);                \
-
-INSTANTIATE(float  )
-INSTANTIATE(double )
-INSTANTIATE(cfloat )
+#define INSTANTIATE(T)                                                         \
+    template Array<T> arithOpD<T, af_add_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_sub_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_mul_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_div_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_mul_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_div_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
index a794f1e69d..c0ac32c180 100644
--- a/src/backend/opencl/sparse_arith.hpp
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -9,23 +9,22 @@
 
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
-#include <sparse.hpp>
 #include <optypes.hpp>
+#include <sparse.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 // These two functions cannot be overloaded by return type.
 // So have to give them separate names.
 template<typename T, af_op_t op>
 Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                 const bool reverse = false);
+                  const bool reverse = false);
 
 template<typename T, af_op_t op>
-common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs, const Array<T> &rhs,
-                               const bool reverse = false);
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const Array<T> &rhs, const bool reverse = false);
 
 template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 3c5a265468..b666d4bdcb 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -9,59 +9,60 @@
 
 #include <sparse_blas.hpp>
 
-#include <kernel/csrmv.hpp>
-#include <kernel/csrmm.hpp>
-#include <kernel/cscmv.hpp>
 #include <kernel/cscmm.hpp>
+#include <kernel/cscmv.hpp>
+#include <kernel/csrmm.hpp>
+#include <kernel/csrmv.hpp>
 
+#include <cassert>
 #include <stdexcept>
 #include <string>
-#include <cassert>
 
-#include <af/dim4.hpp>
-#include <complex.hpp>
 #include <common/err_common.hpp>
+#include <complex.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <transpose.hpp>
+#include <af/dim4.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_sparse_blas.hpp>
 #endif  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 using namespace common;
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
-                af_mat_prop optLhs, af_mat_prop optRhs)
-{
+                af_mat_prop optLhs, af_mat_prop optRhs) {
 #if defined(WITH_LINEAR_ALGEBRA)
-    if(OpenCLCPUOffload(false)) {   // Do not force offload gemm on OSX Intel devices
+    if (OpenCLCPUOffload(
+            false)) {  // Do not force offload gemm on OSX Intel devices
         return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
     }
 #endif
 
     int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
-    //int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
-    static const int rColDim = 1; //Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
+    // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+    static const int rColDim =
+        1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
 
     dim4 lDims = lhs.dims();
     dim4 rDims = rhsIn.dims();
-    int M = lDims[lRowDim];
-    int N = rDims[rColDim];
-    //int K = lDims[lColDim];
+    int M      = lDims[lRowDim];
+    int N      = rDims[rColDim];
+    // int K = lDims[lColDim];
 
-    const Array<T> rhs = (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) : rhsIn;
+    const Array<T> rhs =
+        (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) : rhsIn;
     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
 
     static const T alpha = scalar<T>(1.0);
     static const T beta  = scalar<T>(0.0);
 
-    const Array<T> &values = lhs.getValues();
+    const Array<T> &values   = lhs.getValues();
     const Array<int> &rowIdx = lhs.getRowIdx();
     const Array<int> &colIdx = lhs.getColIdx();
 
@@ -74,22 +75,24 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
     } else {
         // CSR transpose is a CSC matrix
         if (N == 1) {
-            kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta, optLhs == AF_MAT_CTRANS);
+            kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
+                          optLhs == AF_MAT_CTRANS);
         } else {
-            kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha, beta, optLhs == AF_MAT_CTRANS);
+            kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha, beta,
+                             optLhs == AF_MAT_CTRANS);
         }
     }
     return out;
 }
 
-#define INSTANTIATE_SPARSE(T)                                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs, const Array<T> rhs,   \
-                                af_mat_prop optLhs, af_mat_prop optRhs);                \
-
+#define INSTANTIATE_SPARSE(T)                                           \
+    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
+                                const Array<T> rhs, af_mat_prop optLhs, \
+                                af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
 INSTANTIATE_SPARSE(double)
 INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/sparse_blas.hpp b/src/backend/opencl/sparse_blas.hpp
index 91e9c48bf9..9849beb54a 100644
--- a/src/backend/opencl/sparse_blas.hpp
+++ b/src/backend/opencl/sparse_blas.hpp
@@ -11,12 +11,10 @@
 #include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
-
diff --git a/src/backend/opencl/sum.cpp b/src/backend/opencl/sum.cpp
index 9ae378fd6e..69bc820219 100644
--- a/src/backend/opencl/sum.cpp
+++ b/src/backend/opencl/sum.cpp
@@ -9,27 +9,26 @@
 
 #include "reduce_impl.hpp"
 
-namespace opencl
-{
-    //sum
-    INSTANTIATE(af_add_t, float  , float  )
-    INSTANTIATE(af_add_t, double , double )
-    INSTANTIATE(af_add_t, cfloat , cfloat )
-    INSTANTIATE(af_add_t, cdouble, cdouble)
-    INSTANTIATE(af_add_t, int    , int    )
-    INSTANTIATE(af_add_t, int    , float  )
-    INSTANTIATE(af_add_t, uint   , uint   )
-    INSTANTIATE(af_add_t, uint   , float  )
-    INSTANTIATE(af_add_t, intl   , intl   )
-    INSTANTIATE(af_add_t, intl   , double )
-    INSTANTIATE(af_add_t, uintl  , uintl  )
-    INSTANTIATE(af_add_t, uintl  , double )
-    INSTANTIATE(af_add_t, char   , int    )
-    INSTANTIATE(af_add_t, char   , float  )
-    INSTANTIATE(af_add_t, uchar  , uint   )
-    INSTANTIATE(af_add_t, uchar  , float  )
-    INSTANTIATE(af_add_t, short  , int    )
-    INSTANTIATE(af_add_t, short  , float  )
-    INSTANTIATE(af_add_t, ushort , uint   )
-    INSTANTIATE(af_add_t, ushort , float  )
-}
+namespace opencl {
+// sum
+INSTANTIATE(af_add_t, float, float)
+INSTANTIATE(af_add_t, double, double)
+INSTANTIATE(af_add_t, cfloat, cfloat)
+INSTANTIATE(af_add_t, cdouble, cdouble)
+INSTANTIATE(af_add_t, int, int)
+INSTANTIATE(af_add_t, int, float)
+INSTANTIATE(af_add_t, uint, uint)
+INSTANTIATE(af_add_t, uint, float)
+INSTANTIATE(af_add_t, intl, intl)
+INSTANTIATE(af_add_t, intl, double)
+INSTANTIATE(af_add_t, uintl, uintl)
+INSTANTIATE(af_add_t, uintl, double)
+INSTANTIATE(af_add_t, char, int)
+INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, uchar, uint)
+INSTANTIATE(af_add_t, uchar, float)
+INSTANTIATE(af_add_t, short, int)
+INSTANTIATE(af_add_t, short, float)
+INSTANTIATE(af_add_t, ushort, uint)
+INSTANTIATE(af_add_t, ushort, float)
+}  // namespace opencl
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 58ba8063ae..71a78589ab 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <GraphicsResourceManager.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
@@ -21,13 +21,12 @@ using af::dim4;
 namespace opencl {
 
 template<typename T>
-void copy_surface(const Array<T> &P, fg_surface surface)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+void copy_surface(const Array<T> &P, fg_surface surface) {
+    ForgeModule &_ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
-        unsigned bytes = 0;
+        unsigned bytes        = 0;
         FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
 
         auto res = interopManager().getSurfaceResources(surface);
@@ -43,7 +42,8 @@ void copy_surface(const Array<T> &P, fg_surface surface)
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+                                     &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
@@ -56,7 +56,7 @@ void copy_surface(const Array<T> &P, fg_surface surface)
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte* ptr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (ptr) {
             getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
@@ -66,8 +66,8 @@ void copy_surface(const Array<T> &P, fg_surface surface)
     }
 }
 
-#define INSTANTIATE(T)  \
-template void copy_surface<T>(const Array<T> &, fg_surface);
+#define INSTANTIATE(T) \
+    template void copy_surface<T>(const Array<T> &, fg_surface);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -77,4 +77,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/susan.cpp b/src/backend/opencl/susan.cpp
index b390566194..d481c6aaf1 100644
--- a/src/backend/opencl/susan.cpp
+++ b/src/backend/opencl/susan.cpp
@@ -7,47 +7,73 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <kernel/susan.hpp>
-#include <cmath>
+#include <af/features.h>
 #include <algorithm>
+#include <cmath>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
 unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
-               const Array<T> &in,
-               const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge)
-{
+               const Array<T> &in, const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge) {
     dim4 idims = in.dims();
 
     const unsigned corner_lim = in.elements() * feature_ratio;
-    cl::Buffer* x_corners     = bufferAlloc(corner_lim * sizeof(float));
-    cl::Buffer* y_corners     = bufferAlloc(corner_lim * sizeof(float));
-    cl::Buffer* resp_corners  = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *x_corners     = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *y_corners     = bufferAlloc(corner_lim * sizeof(float));
+    cl::Buffer *resp_corners  = bufferAlloc(corner_lim * sizeof(float));
 
-    cl::Buffer* resp = bufferAlloc(in.elements()*sizeof(float));
+    cl::Buffer *resp = bufferAlloc(in.elements() * sizeof(float));
 
-    switch(radius) {
-    case 1: kernel::susan<T, 1>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 2: kernel::susan<T, 2>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 3: kernel::susan<T, 3>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 4: kernel::susan<T, 4>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 5: kernel::susan<T, 5>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 6: kernel::susan<T, 6>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 7: kernel::susan<T, 7>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 8: kernel::susan<T, 8>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
-    case 9: kernel::susan<T, 9>(resp, in.get(), in.getOffset(), idims[0], idims[1], diff_thr, geom_thr, edge); break;
+    switch (radius) {
+        case 1:
+            kernel::susan<T, 1>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 2:
+            kernel::susan<T, 2>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 3:
+            kernel::susan<T, 3>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 4:
+            kernel::susan<T, 4>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 5:
+            kernel::susan<T, 5>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 6:
+            kernel::susan<T, 6>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 7:
+            kernel::susan<T, 7>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 8:
+            kernel::susan<T, 8>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
+        case 9:
+            kernel::susan<T, 9>(resp, in.get(), in.getOffset(), idims[0],
+                                idims[1], diff_thr, geom_thr, edge);
+            break;
     }
 
-    unsigned corners_found = kernel::nonMaximal<T>(x_corners, y_corners, resp_corners,
-                                                   idims[0], idims[1], resp, edge, corner_lim);
+    unsigned corners_found =
+        kernel::nonMaximal<T>(x_corners, y_corners, resp_corners, idims[0],
+                              idims[1], resp, edge, corner_lim);
     bufferFree(resp);
 
     const unsigned corners_out = std::min(corners_found, corner_lim);
@@ -60,25 +86,29 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
         resp_out = createEmptyArray<float>(dim4());
         return 0;
     } else {
-        x_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)((*x_corners)()));
-        y_out    = createDeviceDataArray<float>(dim4(corners_out), (void*)((*y_corners)()));
-        resp_out = createDeviceDataArray<float>(dim4(corners_out), (void*)((*resp_corners)()));
+        x_out    = createDeviceDataArray<float>(dim4(corners_out),
+                                             (void *)((*x_corners)()));
+        y_out    = createDeviceDataArray<float>(dim4(corners_out),
+                                             (void *)((*y_corners)()));
+        resp_out = createDeviceDataArray<float>(dim4(corners_out),
+                                                (void *)((*resp_corners)()));
         return corners_out;
     }
 }
 
-#define INSTANTIATE(T) \
-template unsigned susan<T>(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,   \
-                           const Array<T> &in, const unsigned radius, const float diff_thr,     \
-                           const float geom_thr, const float feature_ratio, const unsigned edge);
+#define INSTANTIATE(T)                                                        \
+    template unsigned susan<T>(                                               \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned radius, const float diff_thr,      \
+        const float geom_thr, const float feature_ratio, const unsigned edge);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(char  )
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
-INSTANTIATE(uchar )
-INSTANTIATE(short )
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/susan.hpp b/src/backend/opencl/susan.hpp
index 3fac35c672..a82fa4418b 100644
--- a/src/backend/opencl/susan.hpp
+++ b/src/backend/opencl/susan.hpp
@@ -7,18 +7,18 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
-#include <af/features.h>
 #include <Array.hpp>
+#include <af/features.h>
 
 using af::features;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
-               const Array<T> &in,
-               const unsigned radius, const float diff_thr, const float geom_thr,
-               const float feature_ratio, const unsigned edge);
+unsigned susan(Array<float> &x_out, Array<float> &y_out,
+               Array<float> &score_out, const Array<T> &in,
+               const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge);
 
 }
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index bc5c980686..ffdf69dfb3 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -8,28 +8,26 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <svd.hpp>               // opencl backend function header
-#include <err_opencl.hpp>        // error check functions and Macros
-#include <reduce.hpp>
-#include <copy.hpp>
 #include <blas.hpp>
+#include <copy.hpp>
+#include <err_opencl.hpp>  // error check functions and Macros
+#include <reduce.hpp>
+#include <svd.hpp>  // opencl backend function header
 #include <transpose.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
 
+#include <cpu/cpu_svd.hpp>
 #include <magma/magma.h>
 #include <magma/magma_cpu_lapack.h>
 #include <magma/magma_helper.h>
 #include <platform.hpp>
-#include <cpu/cpu_svd.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename Tr>
-Tr calc_scale(Tr From, Tr To)
-{
-    //FIXME: I am not sure this is correct, removing this for now
+Tr calc_scale(Tr From, Tr To) {
+    // FIXME: I am not sure this is correct, removing this for now
 #if 0
     //http://www.netlib.org/lapack/explore-3.1.1-html/dlascl.f.html
     cpu_lapack_lamch_func<Tr> cpu_lapack_lamch;
@@ -62,25 +60,20 @@ Tr calc_scale(Tr From, Tr To)
 }
 
 template<typename T, typename Tr>
-void svd(Array<T > &arrU,
-         Array<Tr> &arrS,
-         Array<T > &arrVT,
-         Array<T > &arrA,
-         bool want_vectors=true)
-{
-
-    dim4 idims = arrA.dims();
+void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
+         bool want_vectors = true) {
+    dim4 idims    = arrA.dims();
     dim4 istrides = arrA.strides();
 
-    const int m = (int)idims[0];
-    const int n = (int)idims[1];
-    const int ldda = (int)istrides[1];
-    const int lda = m;
+    const int m      = (int)idims[0];
+    const int n      = (int)idims[1];
+    const int ldda   = (int)istrides[1];
+    const int lda    = m;
     const int min_mn = std::min(m, n);
-    const int ldu = m;
-    const int ldvt = n;
+    const int ldu    = m;
+    const int ldvt   = n;
 
-    const int nb   = magma_get_gebrd_nb<T>(n);
+    const int nb    = magma_get_gebrd_nb<T>(n);
     const int lwork = (m + n) * nb;
 
     cpu_lapack_lacpy_func<T> cpu_lapack_lacpy;
@@ -89,46 +82,41 @@ void svd(Array<T > &arrU,
     cpu_lapack_lamch_func<Tr> cpu_lapack_lamch;
 
     // Get machine constants
-    static const double eps = cpu_lapack_lamch('P');
+    static const double eps    = cpu_lapack_lamch('P');
     static const double smlnum = std::sqrt(cpu_lapack_lamch('S')) / eps;
     static const double bignum = 1. / smlnum;
 
     Tr anrm = abs(reduce_all<af_max_t, T, T>(arrA));
 
-    T scale = scalar<T>(1);
+    T scale                = scalar<T>(1);
     static const int ione  = 1;
     static const int izero = 0;
 
-
     bool iscl = 0;
     if (anrm > 0. && anrm < smlnum) {
-        iscl = 1;
+        iscl  = 1;
         scale = scalar<T>(calc_scale<Tr>(anrm, smlnum));
     } else if (anrm > bignum) {
-        iscl = 1;
+        iscl  = 1;
         scale = scalar<T>(calc_scale<Tr>(anrm, bignum));
     }
 
-    if (iscl == 1) {
-        multiply_inplace(arrA, abs(scale));
-    }
+    if (iscl == 1) { multiply_inplace(arrA, abs(scale)); }
 
-    int nru = 0;
+    int nru  = 0;
     int ncvt = 0;
 
     // Instead of copying U, S, VT, and A to the host and copying the results
     // back to the device, create a pointer that's mapped to device memory where
     // the computation can directly happen
-    T *mappedA = (T*) getQueue().enqueueMapBuffer(*arrA.get(), CL_FALSE,
-                                                  CL_MAP_READ,
-                                                  sizeof(T) * arrA.getOffset(),
-                                                  sizeof(T) * arrA.elements());
+    T *mappedA = (T *)getQueue().enqueueMapBuffer(
+        *arrA.get(), CL_FALSE, CL_MAP_READ, sizeof(T) * arrA.getOffset(),
+        sizeof(T) * arrA.elements());
     std::vector<T> tauq(min_mn), taup(min_mn);
     std::vector<T> work(lwork);
-    Tr *mappedS0 = (Tr*) getQueue().enqueueMapBuffer(*arrS.get(), CL_TRUE,
-                                                     CL_MAP_WRITE,
-                                                     sizeof(Tr) * arrS.getOffset(),
-                                                     sizeof(Tr) * arrS.elements());
+    Tr *mappedS0 = (Tr *)getQueue().enqueueMapBuffer(
+        *arrS.get(), CL_TRUE, CL_MAP_WRITE, sizeof(Tr) * arrS.getOffset(),
+        sizeof(Tr) * arrS.elements());
     std::vector<Tr> s1(min_mn - 1);
     std::vector<Tr> rwork(5 * min_mn);
 
@@ -137,27 +125,21 @@ void svd(Array<T > &arrU,
     // Bidiagonalize A
     // (CWorkspace: need 2*N + M, prefer 2*N + (M + N)*NB)
     // (RWorkspace: need N)
-    magma_gebrd_hybrid<T>(m, n,
-                          mappedA, lda,
-                          (*arrA.get())(), arrA.getOffset(), ldda,
-                          (void *)mappedS0, (void *)&s1[0],
-                          &tauq[0], &taup[0],
-                          &work[0], lwork,
-                          getQueue()(), &info, false);
+    magma_gebrd_hybrid<T>(m, n, mappedA, lda, (*arrA.get())(), arrA.getOffset(),
+                          ldda, (void *)mappedS0, (void *)&s1[0], &tauq[0],
+                          &taup[0], &work[0], lwork, getQueue()(), &info,
+                          false);
 
     T *mappedU = nullptr, *mappedVT = nullptr;
     std::vector<T> cdummy(1);
 
     if (want_vectors) {
-
-        mappedU = (T*) getQueue().enqueueMapBuffer(*arrU.get(), CL_FALSE,
-                                                   CL_MAP_WRITE,
-                                                   sizeof(T) * arrU.getOffset(),
-                                                   sizeof(T) * arrU.elements());
-        mappedVT = (T*) getQueue().enqueueMapBuffer(*arrVT.get(), CL_TRUE,
-                                                    CL_MAP_WRITE,
-                                                    sizeof(T) * arrVT.getOffset(),
-                                                    sizeof(T) * arrVT.elements());
+        mappedU = (T *)getQueue().enqueueMapBuffer(
+            *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
+            sizeof(T) * arrU.elements());
+        mappedVT = (T *)getQueue().enqueueMapBuffer(
+            *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
+            sizeof(T) * arrVT.elements());
 
         // If left singular vectors desired in U, copy result to U
         // and generate left bidiagonalizing vectors in U
@@ -173,11 +155,12 @@ void svd(Array<T > &arrU,
         // VT and generate right bidiagonalizing vectors in VT
         // (CWorkspace: need 3*N-1, prefer 2*N + (N-1)*NB)
         // (RWorkspace: 0)
-        LAPACKE_CHECK(cpu_lapack_lacpy('U', n, n, mappedA, lda, mappedVT, ldvt));
+        LAPACKE_CHECK(
+            cpu_lapack_lacpy('U', n, n, mappedA, lda, mappedVT, ldvt));
         LAPACKE_CHECK(cpu_lapack_ungbr_work('P', n, n, n, mappedVT, ldvt,
                                             &taup[0], &work[0], lwork));
 
-        nru = m;
+        nru  = m;
         ncvt = n;
     }
     getQueue().enqueueUnmapMemObject(*arrA.get(), mappedA);
@@ -187,15 +170,13 @@ void svd(Array<T > &arrU,
     // vectors in VT
     // (CWorkspace: need 0)
     // (RWorkspace: need BDSPAC)
-    LAPACKE_CHECK(cpu_lapack_bdsqr_work('U', n, ncvt, nru, izero,
-                                        mappedS0, &s1[0], mappedVT,
-                                        ldvt, mappedU, ldu,
+    LAPACKE_CHECK(cpu_lapack_bdsqr_work('U', n, ncvt, nru, izero, mappedS0,
+                                        &s1[0], mappedVT, ldvt, mappedU, ldu,
                                         &cdummy[0], ione, &rwork[0]));
 
-
     if (want_vectors) {
-          getQueue().enqueueUnmapMemObject(*arrU.get(), mappedU);
-          getQueue().enqueueUnmapMemObject(*arrVT.get(), mappedVT);
+        getQueue().enqueueUnmapMemObject(*arrU.get(), mappedU);
+        getQueue().enqueueUnmapMemObject(*arrVT.get(), mappedVT);
     }
 
     getQueue().enqueueUnmapMemObject(*arrS.get(), mappedS0);
@@ -211,27 +192,20 @@ void svd(Array<T > &arrU,
     }
 }
 
-
 template<typename T, typename Tr>
-void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::svdInPlace(s, u, vt, in);
-    }
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
+    if (OpenCLCPUOffload()) { return cpu::svdInPlace(s, u, vt, in); }
 
     svd<T, Tr>(u, s, vt, in, true);
 }
 
 template<typename T, typename Tr>
-void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-{
-    if(OpenCLCPUOffload()) {
-        return cpu::svd(s, u, vt, in);
-    }
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
+    if (OpenCLCPUOffload()) { return cpu::svd(s, u, vt, in); }
 
     dim4 iDims = in.dims();
-    int M = iDims[0];
-    int N = iDims[1];
+    int M      = iDims[0];
+    int N      = iDims[1];
 
     if (M >= N) {
         Array<T> in_copy = copyArray(in);
@@ -244,43 +218,44 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
     }
 }
 
-#define INSTANTIATE(T, Tr)                                              \
-    template void svd<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in); \
-    template void svdInPlace<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+#define INSTANTIATE(T, Tr)                                               \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
 
 INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
-}
+}  // namespace opencl
 
 #else  // WITH_LINEAR_ALGEBRA
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, typename Tr>
-void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in)
-{
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T, typename Tr>
-void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in)
-{
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
 }
 
-#define INSTANTIATE(T, Tr)                                              \
-    template void svd<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in); \
-    template void svdInPlace<T, Tr>(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+#define INSTANTIATE(T, Tr)                                               \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
 
 INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
-}
+}  // namespace opencl
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/svd.hpp b/src/backend/opencl/svd.hpp
index 06f6901545..6dd4eb6dc6 100644
--- a/src/backend/opencl/svd.hpp
+++ b/src/backend/opencl/svd.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T, typename Tr>
-    void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
+namespace opencl {
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 
-    template<typename T, typename Tr>
-    void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
-}
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+}  // namespace opencl
diff --git a/src/backend/opencl/tile.cpp b/src/backend/opencl/tile.cpp
index 38902ad44d..4524f4fd68 100644
--- a/src/backend/opencl/tile.cpp
+++ b/src/backend/opencl/tile.cpp
@@ -8,40 +8,38 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <tile.hpp>
 #include <kernel/tile.hpp>
+#include <tile.hpp>
 #include <stdexcept>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> tile(const Array<T> &in, const af::dim4 &tileDims)
-    {
-        const af::dim4 iDims = in.dims();
-        af::dim4 oDims = iDims;
-        oDims *= tileDims;
-
-        Array<T> out = createEmptyArray<T>(oDims);
-
-        kernel::tile<T>(out, in);
-
-        return out;
-    }
-
-#define INSTANTIATE(T)                                                         \
-    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);  \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+namespace opencl {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
+    const af::dim4 iDims = in.dims();
+    af::dim4 oDims       = iDims;
+    oDims *= tileDims;
+
+    Array<T> out = createEmptyArray<T>(oDims);
 
+    kernel::tile<T>(out, in);
+
+    return out;
 }
+
+#define INSTANTIATE(T) \
+    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/tile.hpp b/src/backend/opencl/tile.hpp
index b61c8aec32..8326b034e2 100644
--- a/src/backend/opencl/tile.hpp
+++ b/src/backend/opencl/tile.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
+namespace opencl {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
 }
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index de0527e299..4f71d5260c 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <err_opencl.hpp>
+#include <index.hpp>
 #include <sort.hpp>
 #include <sort_index.hpp>
-#include <index.hpp>
-#include <err_opencl.hpp>
 
 #include <algorithm>
 #include <cmath>
@@ -27,18 +27,16 @@ using std::partial_sort_copy;
 using std::transform;
 using std::vector;
 
-namespace opencl
-{
-vector<af_index_t> indexForTopK(const int k)
-{
+namespace opencl {
+vector<af_index_t> indexForTopK(const int k) {
     af_index_t idx;
     idx.idx.seq = af_seq{0.0, (double)k - 1, 1.0};
-    idx.isSeq = true;
+    idx.isSeq   = true;
     idx.isBatch = false;
 
     af_index_t sp;
     sp.idx.seq = af_span;
-    sp.isSeq = true;
+    sp.isSeq   = true;
     sp.isBatch = false;
 
     return vector<af_index_t>({idx, sp, sp, sp});
@@ -46,10 +44,8 @@ vector<af_index_t> indexForTopK(const int k)
 
 template<typename T>
 void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
-          const int k, const int dim, const af::topkFunction order)
-{
-
-    if( getDeviceType() == CL_DEVICE_TYPE_CPU ) {
+          const int k, const int dim, const af::topkFunction order) {
+    if (getDeviceType() == CL_DEVICE_TYPE_CPU) {
         // This branch optimizes for CPU devices by first mapping the buffer
         // and calling partial sort on the buffer
 
@@ -59,7 +55,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         // and the same as the input dimension otherwise.
         dim4 out_dims(1);
         int ndims = in.dims().ndims();
-        for(int i = 0; i < ndims; i++) {
+        for (int i = 0; i < ndims; i++) {
             if (i == dim) {
                 out_dims[i] = min(k, (int)in.dims()[i]);
             } else {
@@ -67,29 +63,22 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
             }
         }
 
-        auto values  = createEmptyArray<T>(out_dims);
-        auto indices = createEmptyArray<unsigned>(out_dims);
-        const Buffer *in_buf = in.get();
-        Buffer *ibuf = indices.get();
-        Buffer *vbuf = values.get();
+        auto values          = createEmptyArray<T>(out_dims);
+        auto indices         = createEmptyArray<unsigned>(out_dims);
+        const Buffer* in_buf = in.get();
+        Buffer* ibuf         = indices.get();
+        Buffer* vbuf         = values.get();
 
         Event ev_in, ev_val, ev_ind;
 
-        T* ptr =
-          static_cast<T*>(getQueue().enqueueMapBuffer(*in_buf, CL_FALSE,
-                                                      CL_MAP_READ, 0,
-                                                      in.elements() * sizeof(T),
-                                                      nullptr, &ev_in));
-        uint* iptr =
-          static_cast<uint*>(getQueue().enqueueMapBuffer(*ibuf, CL_FALSE,
-                                                         CL_MAP_READ | CL_MAP_WRITE,
-                                                         0, k * sizeof(uint),
-                                                         nullptr, &ev_ind));
-        T* vptr =
-          static_cast<T*> (getQueue().enqueueMapBuffer(*vbuf, CL_FALSE,
-                                                       CL_MAP_WRITE, 0,
-                                                       k * sizeof(T),
-                                                       nullptr, &ev_val));
+        T* ptr     = static_cast<T*>(getQueue().enqueueMapBuffer(
+            *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
+            nullptr, &ev_in));
+        uint* iptr = static_cast<uint*>(getQueue().enqueueMapBuffer(
+            *ibuf, CL_FALSE, CL_MAP_READ | CL_MAP_WRITE, 0, k * sizeof(uint),
+            nullptr, &ev_ind));
+        T* vptr    = static_cast<T*>(getQueue().enqueueMapBuffer(
+            *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
 
         vector<uint> idx(in.elements());
 
@@ -98,28 +87,28 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         Event::waitForEvents({ev_in, ev_ind});
 
         int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
-        for(int i = 0; i < iter; i++) {
+        for (int i = 0; i < iter; i++) {
             auto idx_itr = begin(idx) + i * in.strides()[1];
-            auto kiptr = iptr + k * i;
+            auto kiptr   = iptr + k * i;
 
-            if(order == AF_TOPK_MIN) {
+            if (order == AF_TOPK_MIN) {
                 // Sort the top k values in each column
-                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
-                                  kiptr , kiptr + k,
-                                  [ptr](const uint lhs, const uint rhs) -> bool {
-                                      return ptr[lhs] < ptr[rhs];
-                                  });
+                partial_sort_copy(
+                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                    [ptr](const uint lhs, const uint rhs) -> bool {
+                        return ptr[lhs] < ptr[rhs];
+                    });
             } else {
-                partial_sort_copy(idx_itr , idx_itr + in.strides()[1],
-                                  kiptr , kiptr + k,
-                                  [ptr](const uint lhs, const uint rhs) -> bool {
-                                    return ptr[lhs] >= ptr[rhs];
-                                  });
+                partial_sort_copy(
+                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                    [ptr](const uint lhs, const uint rhs) -> bool {
+                        return ptr[lhs] >= ptr[rhs];
+                    });
             }
             ev_val.wait();
 
             auto kvptr = vptr + k * i;
-            for(int j = 0; j < k; j++) {
+            for (int j = 0; j < k; j++) {
                 // Update the value arrays with the original values
                 kvptr[j] = ptr[kiptr[j]];
                 // Convert linear indices back to column indices
@@ -134,23 +123,24 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         vals = values;
         idxs = indices;
     } else {
-      auto values  = createEmptyArray<T>(in.dims());
-      auto indices = createEmptyArray<unsigned>(in.dims());
-      sort_index(values, indices, in, dim, (order==AF_TOPK_MIN ? true : false));
-      auto indVec = indexForTopK(k);
-      vals = index<T>( values, indVec.data());
-      idxs = index<unsigned>(indices, indVec.data());
+        auto values  = createEmptyArray<T>(in.dims());
+        auto indices = createEmptyArray<unsigned>(in.dims());
+        sort_index(values, indices, in, dim,
+                   (order == AF_TOPK_MIN ? true : false));
+        auto indVec = indexForTopK(k);
+        vals        = index<T>(values, indVec.data());
+        idxs        = index<unsigned>(indices, indVec.data());
     }
-
 }
 
-#define INSTANTIATE(T)\
-template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, const int, const int, const af::topkFunction);
+#define INSTANTIATE(T)                                                  \
+    template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, \
+                          const int, const int, const af::topkFunction);
 
-INSTANTIATE(float )
+INSTANTIATE(float)
 INSTANTIATE(double)
-INSTANTIATE(int   )
-INSTANTIATE(uint  )
+INSTANTIATE(int)
+INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/topk.hpp b/src/backend/opencl/topk.hpp
index d35ebc98d4..5767d8a0d2 100644
--- a/src/backend/opencl/topk.hpp
+++ b/src/backend/opencl/topk.hpp
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-namespace opencl
-{
+namespace opencl {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index f65a41e552..be65c32a4c 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -12,52 +12,56 @@
 #include <common/defines.hpp>
 #include <types.hpp>
 #include <af/traits.hpp>
-#include <string>
 #include <sstream>
+#include <string>
 
-namespace af
-{
+namespace af {
 
 template<>
 struct dtype_traits<cl_float2> {
     enum { af_type = c32 };
     typedef float base_type;
-    static const char* getName() { return "float2"; }
+    static const char *getName() { return "float2"; }
 };
 
 template<>
 struct dtype_traits<cl_double2> {
     enum { af_type = c64 };
     typedef double base_type;
-    static const char* getName() { return "double2"; }
+    static const char *getName() { return "double2"; }
 };
 
-template<typename T> static bool iscplx() { return false; }
-template<> STATIC_ bool iscplx<cl_float2>() { return true; }
-template<> STATIC_ bool iscplx<cl_double2>() { return true; }
+template<typename T>
+static bool iscplx() {
+    return false;
+}
+template<>
+STATIC_ bool iscplx<cl_float2>() {
+    return true;
+}
+template<>
+STATIC_ bool iscplx<cl_double2>() {
+    return true;
+}
 
 template<typename T>
-STATIC_
-std::string scalar_to_option(const T &val)
-{
+STATIC_ std::string scalar_to_option(const T &val) {
     return std::to_string(+val);
 }
 
 template<>
-STATIC_
-std::string scalar_to_option<cl_float2>(const cl_float2 &val) {
+STATIC_ std::string scalar_to_option<cl_float2>(const cl_float2 &val) {
     std::ostringstream ss;
     ss << val.s[0] << "," << val.s[1];
     return ss.str();
 }
 
 template<>
-STATIC_
-std::string scalar_to_option<cl_double2>(const cl_double2 &val) {
+STATIC_ std::string scalar_to_option<cl_double2>(const cl_double2 &val) {
     std::ostringstream ss;
     ss << val.s[0] << "," << val.s[1];
     return ss.str();
 }
-}
+}  // namespace af
 
 using af::dtype_traits;
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 608128fd0b..8311a7f656 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -7,22 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <transform.hpp>
 #include <kernel/transform.hpp>
+#include <transform.hpp>
+#include <af/dim4.hpp>
 #include <stdexcept>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                       const af::dim4 &odims, const af_interp_type method,
-                       const bool inverse, const bool perspective)
-    {
-        Array<T> out = createEmptyArray<T>(odims);
+namespace opencl {
+template<typename T>
+Array<T> transform(const Array<T> &in, const Array<float> &tf,
+                   const af::dim4 &odims, const af_interp_type method,
+                   const bool inverse, const bool perspective) {
+    Array<T> out = createEmptyArray<T>(odims);
 
-        switch(method) {
+    switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
             kernel::transform<T, 1>(out, in, tf, inverse, perspective, method);
@@ -35,28 +33,27 @@ namespace opencl
         case AF_INTERP_BICUBIC_SPLINE:
             kernel::transform<T, 3>(out, in, tf, inverse, perspective, method);
             break;
-        default:
-            AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-        }
-        return out;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
+    return out;
+}
 
-
-#define INSTANTIATE(T)                                                                  \
-    template Array<T> transform(const Array<T> &in, const Array<float> &tf,             \
-                                const af::dim4 &odims, const af_interp_type method,     \
+#define INSTANTIATE(T)                                                      \
+    template Array<T> transform(const Array<T> &in, const Array<float> &tf, \
+                                const af::dim4 &odims,                      \
+                                const af_interp_type method,                \
                                 const bool inverse, const bool perspective);
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-}
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/transform.hpp b/src/backend/opencl/transform.hpp
index 03fc3074c0..bafd56175a 100644
--- a/src/backend/opencl/transform.hpp
+++ b/src/backend/opencl/transform.hpp
@@ -9,9 +9,9 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> transform(const Array<T> &in, const Array<float> &tf, const af::dim4 &odims,
-                        const af_interp_type method, const bool inverse, const bool perspective);
+namespace opencl {
+template<typename T>
+Array<T> transform(const Array<T> &in, const Array<float> &tf,
+                   const af::dim4 &odims, const af_interp_type method,
+                   const bool inverse, const bool perspective);
 }
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index be2832cb77..fc7b8b439d 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -7,30 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <transpose.hpp>
 #include <kernel/transpose.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T> transpose(const Array<T> &in, const bool conjugate)
-{
-    const dim4 inDims   = in.dims();
-    dim4 outDims  = dim4(inDims[1],inDims[0],inDims[2],inDims[3]);
-    Array<T> out  = createEmptyArray<T>(outDims);
-
-    if(conjugate) {
-        if(inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0)
+Array<T> transpose(const Array<T> &in, const bool conjugate) {
+    const dim4 inDims = in.dims();
+    dim4 outDims      = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
+    Array<T> out      = createEmptyArray<T>(outDims);
+
+    if (conjugate) {
+        if (inDims[0] % kernel::TILE_DIM == 0 &&
+            inDims[1] % kernel::TILE_DIM == 0)
             kernel::transpose<T, true, true>(out, in, getQueue());
         else
             kernel::transpose<T, true, false>(out, in, getQueue());
     } else {
-        if(inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0)
+        if (inDims[0] % kernel::TILE_DIM == 0 &&
+            inDims[1] % kernel::TILE_DIM == 0)
             kernel::transpose<T, false, true>(out, in, getQueue());
         else
             kernel::transpose<T, false, false>(out, in, getQueue());
@@ -38,20 +38,20 @@ Array<T> transpose(const Array<T> &in, const bool conjugate)
     return out;
 }
 
-#define INSTANTIATE(T)                                                          \
+#define INSTANTIATE(T) \
     template Array<T> transpose(const Array<T> &in, const bool conjugate);
 
-INSTANTIATE(float  )
-INSTANTIATE(cfloat )
-INSTANTIATE(double )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
 INSTANTIATE(cdouble)
-INSTANTIATE(char   )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
-
-}
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/transpose.hpp b/src/backend/opencl/transpose.hpp
index 58014bdc20..f9d363f11b 100644
--- a/src/backend/opencl/transpose.hpp
+++ b/src/backend/opencl/transpose.hpp
@@ -9,13 +9,12 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-Array<T>  transpose(const Array<T> &in, const bool conjugate);
+Array<T> transpose(const Array<T> &in, const bool conjugate);
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index 1f1e008f24..441d154244 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -7,48 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <transpose.hpp>
 #include <kernel/transpose_inplace.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T>
-void transpose_inplace(Array<T> &in, const bool conjugate)
-{
+void transpose_inplace(Array<T> &in, const bool conjugate) {
     dim4 iDims = in.dims();
 
-    if(conjugate) {
-        if(iDims[0] % kernel::TILE_DIM == 0 && iDims[1] % kernel::TILE_DIM == 0)
+    if (conjugate) {
+        if (iDims[0] % kernel::TILE_DIM == 0 &&
+            iDims[1] % kernel::TILE_DIM == 0)
             kernel::transpose_inplace<T, true, true>(in, getQueue());
         else
             kernel::transpose_inplace<T, true, false>(in, getQueue());
     } else {
-        if(iDims[0] % kernel::TILE_DIM == 0 && iDims[1] % kernel::TILE_DIM == 0)
+        if (iDims[0] % kernel::TILE_DIM == 0 &&
+            iDims[1] % kernel::TILE_DIM == 0)
             kernel::transpose_inplace<T, false, true>(in, getQueue());
         else
             kernel::transpose_inplace<T, false, false>(in, getQueue());
     }
 }
 
-#define INSTANTIATE(T)                                                          \
+#define INSTANTIATE(T) \
     template void transpose_inplace(Array<T> &in, const bool conjugate);
 
-INSTANTIATE(float  )
-INSTANTIATE(cfloat )
-INSTANTIATE(double )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
 INSTANTIATE(cdouble)
-INSTANTIATE(char   )
-INSTANTIATE(int    )
-INSTANTIATE(uint   )
-INSTANTIATE(uchar  )
-INSTANTIATE(intl   )
-INSTANTIATE(uintl  )
-INSTANTIATE(short  )
-INSTANTIATE(ushort )
-
-}
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index 0dd6357e08..13825bff6f 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -7,53 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
-#include <triangle.hpp>
 #include <kernel/triangle.hpp>
+#include <triangle.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
-namespace opencl
-{
+namespace opencl {
 
 template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in)
-{
+void triangle(Array<T> &out, const Array<T> &in) {
     kernel::triangle<T, is_upper, is_unit_diag>(out, in);
 }
 
-
 template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in)
-{
+Array<T> triangle(const Array<T> &in) {
     Array<T> out = createEmptyArray<T>(in.dims());
     triangle<T, is_upper, is_unit_diag>(out, in);
     return out;
 }
 
-
-#define INSTANTIATE(T)                                                  \
-    template void triangle<T, true ,  true>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, false,  true>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, true , false>(Array<T> &out, const Array<T> &in); \
-    template void triangle<T, false, false>(Array<T> &out, const Array<T> &in); \
-    template Array<T> triangle<T, true ,  true>(const Array<T> &in);    \
-    template Array<T> triangle<T, false,  true>(const Array<T> &in);    \
-    template Array<T> triangle<T, true , false>(const Array<T> &in);    \
-    template Array<T> triangle<T, false, false>(const Array<T> &in);    \
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(char)
-    INSTANTIATE(uchar)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
-
-}
+#define INSTANTIATE(T)                                                         \
+    template void triangle<T, true, true>(Array<T> & out, const Array<T> &in); \
+    template void triangle<T, false, true>(Array<T> & out,                     \
+                                           const Array<T> &in);                \
+    template void triangle<T, true, false>(Array<T> & out,                     \
+                                           const Array<T> &in);                \
+    template void triangle<T, false, false>(Array<T> & out,                    \
+                                            const Array<T> &in);               \
+    template Array<T> triangle<T, true, true>(const Array<T> &in);             \
+    template Array<T> triangle<T, false, true>(const Array<T> &in);            \
+    template Array<T> triangle<T, true, false>(const Array<T> &in);            \
+    template Array<T> triangle<T, false, false>(const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/triangle.hpp b/src/backend/opencl/triangle.hpp
index 28fd309226..f7d59e975f 100644
--- a/src/backend/opencl/triangle.hpp
+++ b/src/backend/opencl/triangle.hpp
@@ -9,11 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T, bool is_upper, bool is_unit_diag>
-    void triangle(Array<T> &out, const Array<T> &in);
+namespace opencl {
+template<typename T, bool is_upper, bool is_unit_diag>
+void triangle(Array<T> &out, const Array<T> &in);
 
-    template<typename T, bool is_upper, bool is_unit_diag>
-    Array<T> triangle(const Array<T> &in);
-}
+template<typename T, bool is_upper, bool is_unit_diag>
+Array<T> triangle(const Array<T> &in);
+}  // namespace opencl
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index d958c36aee..6b94af1501 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -19,12 +19,11 @@
 #include <type_util.hpp>
 #include <af/traits.hpp>
 
+#include <cmath>
 #include <sstream>
 #include <string>
-#include <cmath>
 
-namespace opencl
-{
+namespace opencl {
 using cdouble = cl_double2;
 using cfloat  = cl_float2;
 using intl    = long long;
@@ -34,48 +33,36 @@ using uintl   = unsigned long long;
 using ushort  = cl_ushort;
 
 template<typename T>
-struct ToNumStr
-{
-    inline std::string operator()(T val)
-    {
+struct ToNumStr {
+    inline std::string operator()(T val) {
         ToNum<T> toNum;
         return std::to_string(toNum(val));
     }
 };
 
 template<>
-struct ToNumStr<float>
-{
-    inline std::string operator()(float val)
-    {
-        static const char* PINF = "+INFINITY";
-        static const char* NINF = "-INFINITY";
-        if (std::isinf(val)) {
-            return val < 0 ? NINF : PINF;
-        }
+struct ToNumStr<float> {
+    inline std::string operator()(float val) {
+        static const char *PINF = "+INFINITY";
+        static const char *NINF = "-INFINITY";
+        if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
         return std::to_string(val);
     }
 };
 
 template<>
-struct ToNumStr<double>
-{
-    inline std::string operator()(double val)
-    {
-        static const char* PINF = "+INFINITY";
-        static const char* NINF = "-INFINITY";
-        if (std::isinf(val)) {
-            return val < 0 ? NINF : PINF;
-        }
+struct ToNumStr<double> {
+    inline std::string operator()(double val) {
+        static const char *PINF = "+INFINITY";
+        static const char *NINF = "-INFINITY";
+        if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
         return std::to_string(val);
     }
 };
 
 template<>
-struct ToNumStr<cfloat>
-{
-    inline std::string operator()(cfloat val)
-    {
+struct ToNumStr<cfloat> {
+    inline std::string operator()(cfloat val) {
         ToNumStr<float> realStr;
         std::stringstream s;
         s << "{";
@@ -88,10 +75,8 @@ struct ToNumStr<cfloat>
 };
 
 template<>
-struct ToNumStr<cdouble>
-{
-    inline std::string operator()(cdouble val)
-    {
+struct ToNumStr<cdouble> {
+    inline std::string operator()(cdouble val) {
         ToNumStr<double> realStr;
         std::stringstream s;
         s << "{";
@@ -104,26 +89,65 @@ struct ToNumStr<cdouble>
 };
 
 namespace {
-template<typename T> inline const char *shortname(bool caps) { return caps ? "X" : "x"; }
+template<typename T>
+inline const char *shortname(bool caps) {
+    return caps ? "X" : "x";
+}
 
-template<> inline const char *shortname<float   >(bool caps) { return caps ? "S" : "s"; }
-template<> inline const char *shortname<double  >(bool caps) { return caps ? "D" : "d"; }
-template<> inline const char *shortname<cfloat  >(bool caps) { return caps ? "C" : "c"; }
-template<> inline const char *shortname<cdouble >(bool caps) { return caps ? "Z" : "z"; }
-template<> inline const char *shortname<int     >(bool caps) { return caps ? "I" : "i"; }
-template<> inline const char *shortname<uint    >(bool caps) { return caps ? "U" : "u"; }
-template<> inline const char *shortname<char    >(bool caps) { return caps ? "J" : "j"; }
-template<> inline const char *shortname<uchar   >(bool caps) { return caps ? "V" : "v"; }
-template<> inline const char *shortname<intl    >(bool caps) { return caps ? "L" : "l"; }
-template<> inline const char *shortname<uintl   >(bool caps) { return caps ? "K" : "k"; }
-template<> inline const char *shortname<short   >(bool caps) { return caps ? "P" : "p"; }
-template<> inline const char *shortname<ushort  >(bool caps) { return caps ? "Q" : "q"; }
+template<>
+inline const char *shortname<float>(bool caps) {
+    return caps ? "S" : "s";
+}
+template<>
+inline const char *shortname<double>(bool caps) {
+    return caps ? "D" : "d";
+}
+template<>
+inline const char *shortname<cfloat>(bool caps) {
+    return caps ? "C" : "c";
+}
+template<>
+inline const char *shortname<cdouble>(bool caps) {
+    return caps ? "Z" : "z";
+}
+template<>
+inline const char *shortname<int>(bool caps) {
+    return caps ? "I" : "i";
+}
+template<>
+inline const char *shortname<uint>(bool caps) {
+    return caps ? "U" : "u";
+}
+template<>
+inline const char *shortname<char>(bool caps) {
+    return caps ? "J" : "j";
+}
+template<>
+inline const char *shortname<uchar>(bool caps) {
+    return caps ? "V" : "v";
+}
+template<>
+inline const char *shortname<intl>(bool caps) {
+    return caps ? "L" : "l";
+}
+template<>
+inline const char *shortname<uintl>(bool caps) {
+    return caps ? "K" : "k";
+}
+template<>
+inline const char *shortname<short>(bool caps) {
+    return caps ? "P" : "p";
+}
+template<>
+inline const char *shortname<ushort>(bool caps) {
+    return caps ? "Q" : "q";
+}
 
 template<typename T>
 const char *getFullName() {
     return af::dtype_traits<T>::getName();
 }
 
-}
+}  // namespace
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 84c35cc566..290f864bd7 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -8,22 +8,22 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <optypes.hpp>
-#include <math.hpp>
 #include <common/jit/UnaryNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
 
-namespace opencl
-{
+namespace opencl {
 
 template<af_op_t op>
-static const char *unaryName() { return "__noop"; }
+static const char *unaryName() {
+    return "__noop";
+}
 
-#define UNARY_DECL(OP, FNAME)                   \
-    template<> STATIC_                          \
-    const char *unaryName<af_##OP##_t>()        \
-    {                                           \
-        return FNAME;                           \
-    }                                           \
+#define UNARY_DECL(OP, FNAME)                      \
+    template<>                                     \
+    STATIC_ const char *unaryName<af_##OP##_t>() { \
+        return FNAME;                              \
+    }
 
 #define UNARY_FN(OP) UNARY_DECL(OP, #OP)
 
@@ -73,29 +73,25 @@ UNARY_FN(iszero)
 #undef UNARY_FN
 
 template<typename T, af_op_t op>
-Array<T> unaryOp(const Array<T> &in)
-{
+Array<T> unaryOp(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
 
-    common::UnaryNode *node = new common::UnaryNode(dtype_traits<T>::getName(),
-                                                    shortname<T>(true),
-                                                    unaryName<op>(),
-                                                    in_node, op);
+    common::UnaryNode *node =
+        new common::UnaryNode(dtype_traits<T>::getName(), shortname<T>(true),
+                              unaryName<op>(), in_node, op);
 
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
-Array<char> checkOp(const Array<T> &in)
-{
+Array<char> checkOp(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
 
-    common::UnaryNode *node = new common::UnaryNode(dtype_traits<char>::getName(),
-                                                    shortname<char>(true),
-                                                    unaryName<op>(),
-                                                    in_node, op);
+    common::UnaryNode *node = new common::UnaryNode(
+        dtype_traits<char>::getName(), shortname<char>(true), unaryName<op>(),
+        in_node, op);
 
     return createNodeArray<char>(in.dims(), common::Node_ptr(node));
 }
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/unwrap.cpp b/src/backend/opencl/unwrap.cpp
index 845b341699..01bd852513 100644
--- a/src/backend/opencl/unwrap.cpp
+++ b/src/backend/opencl/unwrap.cpp
@@ -8,51 +8,47 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <unwrap.hpp>
+#include <err_opencl.hpp>
 #include <kernel/unwrap.hpp>
+#include <unwrap.hpp>
 #include <stdexcept>
-#include <err_opencl.hpp>
-
-namespace opencl
-{
-    template<typename T>
-    Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column)
-    {
-        af::dim4 idims = in.dims();
 
-        dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-        dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
+namespace opencl {
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const bool is_column) {
+    af::dim4 idims = in.dims();
 
-        af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
+    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
+    dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
 
-        if (!is_column) {
-            std::swap(odims[0], odims[1]);
-        }
+    af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
 
-        // Create output placeholder
-        Array<T> outArray = createEmptyArray<T>(odims);
-        kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, nx, is_column);
+    if (!is_column) { std::swap(odims[0], odims[1]); }
 
-        return outArray;
-    }
+    // Create output placeholder
+    Array<T> outArray = createEmptyArray<T>(odims);
+    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, nx, is_column);
 
-
-#define INSTANTIATE(T)                                                                  \
-    template Array<T> unwrap<T> (const Array<T> &in, const dim_t wx, const dim_t wy,    \
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
-
-
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    return outArray;
 }
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> unwrap<T>(                                            \
+        const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
+        const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/unwrap.hpp b/src/backend/opencl/unwrap.hpp
index d8d3d55a5b..68a076b1d1 100644
--- a/src/backend/opencl/unwrap.hpp
+++ b/src/backend/opencl/unwrap.hpp
@@ -9,9 +9,9 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
-                    const dim_t sx, const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+namespace opencl {
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const bool is_column);
 }
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 8264fa33ee..b8e8cd0318 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <GraphicsResourceManager.hpp>
 #include <vector_field.hpp>
 
 using af::dim4;
@@ -19,15 +19,14 @@ namespace opencl {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vfield)
-{
-    ForgeModule& _ = graphics::forgePlugin();
+                       fg_vector_field vfield) {
+    ForgeModule &_ = graphics::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
-        const cl::Buffer *d_points      = points.get();
-        const cl::Buffer *d_directions  = directions.get();
-        unsigned pBytes = 0;
-        unsigned dBytes = 0;
+        const cl::Buffer *d_points     = points.get();
+        const cl::Buffer *d_directions = directions.get();
+        unsigned pBytes                = 0;
+        unsigned dBytes                = 0;
         FG_CHECK(_.fg_get_vector_field_vertex_buffer_size(&pBytes, vfield));
         FG_CHECK(_.fg_get_vector_field_direction_buffer_size(&dBytes, vfield));
 
@@ -45,8 +44,10 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
         event.wait();
-        getQueue().enqueueCopyBuffer(*d_points    , *(res[0].get()), 0, 0, pBytes, NULL, &event);
-        getQueue().enqueueCopyBuffer(*d_directions, *(res[1].get()), 0, 0, dBytes, NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_points, *(res[0].get()), 0, 0, pBytes,
+                                     NULL, &event);
+        getQueue().enqueueCopyBuffer(*d_directions, *(res[1].get()), 0, 0,
+                                     dBytes, NULL, &event);
         getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
         event.wait();
 
@@ -64,18 +65,20 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Points
         glBindBuffer(GL_ARRAY_BUFFER, buff1);
-        GLubyte* pPtr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *pPtr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (pPtr) {
-            getQueue().enqueueReadBuffer(*points.get(), CL_TRUE, 0, size1, pPtr);
+            getQueue().enqueueReadBuffer(*points.get(), CL_TRUE, 0, size1,
+                                         pPtr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
 
         // Directions
         glBindBuffer(GL_ARRAY_BUFFER, buff2);
-        GLubyte* dPtr = (GLubyte*)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        GLubyte *dPtr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
         if (dPtr) {
-            getQueue().enqueueReadBuffer(*directions.get(), CL_TRUE, 0, size2, dPtr);
+            getQueue().enqueueReadBuffer(*directions.get(), CL_TRUE, 0, size2,
+                                         dPtr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
         }
         glBindBuffer(GL_ARRAY_BUFFER, 0);
@@ -83,9 +86,9 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
     }
 }
 
-#define INSTANTIATE(T)                                                  \
-template void copy_vector_field<T>(const Array<T> &, const Array<T> &,  \
-                                   fg_vector_field);
+#define INSTANTIATE(T)                                                     \
+    template void copy_vector_field<T>(const Array<T> &, const Array<T> &, \
+                                       fg_vector_field);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -95,4 +98,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/where.cpp b/src/backend/opencl/where.cpp
index 6da39ce83d..4ad6a870d9 100644
--- a/src/backend/opencl/where.cpp
+++ b/src/backend/opencl/where.cpp
@@ -7,39 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/dim4.hpp>
 #include <Array.hpp>
 #include <err_opencl.hpp>
+#include <kernel/where.hpp>
 #include <where.hpp>
+#include <af/dim4.hpp>
 #include <complex>
-#include <kernel/where.hpp>
-
-namespace opencl
-{
-    template<typename T>
-    Array<uint> where(const Array<T> &in)
-    {
-        Param Out;
-        Param In = in;
-        kernel::where<T>(Out, In);
-        return createParamArray<uint>(Out, true);
-    }
 
+namespace opencl {
+template<typename T>
+Array<uint> where(const Array<T> &in) {
+    Param Out;
+    Param In = in;
+    kernel::where<T>(Out, In);
+    return createParamArray<uint>(Out, true);
+}
 
-#define INSTANTIATE(T)                                  \
-    template Array<uint> where<T>(const Array<T> &in);  \
+#define INSTANTIATE(T) template Array<uint> where<T>(const Array<T> &in);
 
-    INSTANTIATE(float  )
-    INSTANTIATE(cfloat )
-    INSTANTIATE(double )
-    INSTANTIATE(cdouble)
-    INSTANTIATE(char   )
-    INSTANTIATE(int    )
-    INSTANTIATE(uint   )
-    INSTANTIATE(intl   )
-    INSTANTIATE(uintl  )
-    INSTANTIATE(uchar  )
-    INSTANTIATE(short  )
-    INSTANTIATE(ushort )
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/where.hpp b/src/backend/opencl/where.hpp
index ea623e7159..c67a235e66 100644
--- a/src/backend/opencl/where.hpp
+++ b/src/backend/opencl/where.hpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<uint> where(const Array<T>& in);
+namespace opencl {
+template<typename T>
+Array<uint> where(const Array<T>& in);
 }
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 5fe949b2fc..dd44904f29 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -8,52 +8,43 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <wrap.hpp>
-#include <stdexcept>
-#include <err_opencl.hpp>
 #include <common/dispatch.hpp>
-#include <math.hpp>
+#include <err_opencl.hpp>
 #include <kernel/wrap.hpp>
+#include <math.hpp>
+#include <wrap.hpp>
+#include <stdexcept>
 
-namespace opencl
-{
-
-    template<typename T>
-    Array<T> wrap(const Array<T> &in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column)
-    {
-        af::dim4 idims = in.dims();
-        af::dim4 odims(ox, oy, idims[2], idims[3]);
-        Array<T> out = createValueArray<T>(odims, scalar<T>(0));
-
-        kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
-        return out;
-    }
-
-
-#define INSTANTIATE(T)                                          \
-    template Array<T> wrap<T> (const Array<T> &in,              \
-                               const dim_t ox, const dim_t oy,  \
-                               const dim_t wx, const dim_t wy,  \
-                               const dim_t sx, const dim_t sy,  \
-                               const dim_t px, const dim_t py,  \
-                               const bool is_column);
+namespace opencl {
 
+template<typename T>
+Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py, const bool is_column) {
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
 
-    INSTANTIATE(float)
-    INSTANTIATE(double)
-    INSTANTIATE(cfloat)
-    INSTANTIATE(cdouble)
-    INSTANTIATE(int)
-    INSTANTIATE(uint)
-    INSTANTIATE(intl)
-    INSTANTIATE(uintl)
-    INSTANTIATE(uchar)
-    INSTANTIATE(char)
-    INSTANTIATE(short)
-    INSTANTIATE(ushort)
+    kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
+    return out;
 }
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> wrap<T>(const Array<T> &in, const dim_t ox,             \
+                              const dim_t oy, const dim_t wx, const dim_t wy, \
+                              const dim_t sx, const dim_t sy, const dim_t px, \
+                              const dim_t py, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/opencl/wrap.hpp b/src/backend/opencl/wrap.hpp
index 1be64f4ea4..ee2f750a17 100644
--- a/src/backend/opencl/wrap.hpp
+++ b/src/backend/opencl/wrap.hpp
@@ -9,14 +9,10 @@
 
 #include <Array.hpp>
 
-namespace opencl
-{
-    template<typename T>
-    Array<T> wrap(const Array<T> &in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column);
+namespace opencl {
+template<typename T>
+Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
+              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+              const dim_t px, const dim_t py, const bool is_column);
 
 }
diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 4afbe8c16f..79c52d590f 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -7,99 +7,103 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::string;
-using std::vector;
-using std::abs;
 using af::array;
 using af::exception;
 using af::fluxFunction;
 using af::max;
 using af::min;
 using af::randu;
+using std::abs;
+using std::string;
+using std::vector;
 
 template<typename T>
-class AnisotropicDiffusion : public ::testing::Test
-{
-};
+class AnisotropicDiffusion : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, uchar, short, ushort>
+    TestTypes;
 
 TYPED_TEST_CASE(AnisotropicDiffusion, TestTypes);
 
 template<typename T>
-array normalize(const array &p_in)
-{
+array normalize(const array &p_in) {
     T mx = max<T>(p_in);
     T mn = min<T>(p_in);
-    return (p_in-mn)/(mx-mn);
+    return (p_in - mn) / (mx - mn);
 }
 
 template<typename T, bool isColor>
-void imageTest(string pTestFile, const float dt, const float K, const uint iters,
-               fluxFunction fluxKind, bool isCurvatureDiffusion=false)
-{
-    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
+void imageTest(string pTestFile, const float dt, const float K,
+               const uint iters, fluxFunction fluxKind,
+               bool isCurvatureDiffusion = false) {
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            OutType;
 
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
     using af::dim4;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
-    vector<dim_t>    outSizes;
-    vector<string>   outFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
+    vector<dim_t> outSizes;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-
+    for (size_t testId = 0; testId < testCount; ++testId) {
         if (isCurvatureDiffusion) {
             inFiles[testId].insert(0, string(TEST_DIR "/curvature_diffusion/"));
-            outFiles[testId].insert(0, string(TEST_DIR "/curvature_diffusion/"));
+            outFiles[testId].insert(0,
+                                    string(TEST_DIR "/curvature_diffusion/"));
         } else {
             inFiles[testId].insert(0, string(TEST_DIR "/gradient_diffusion/"));
             outFiles[testId].insert(0, string(TEST_DIR "/gradient_diffusion/"));
         }
 
-        af_array _inArray    = 0;
-        af_array inArray     = 0;
-        af_array _outArray   = 0;
-        af_array cstArray    = 0;
-        af_array minArray    = 0;
-        af_array numArray    = 0;
-        af_array denArray    = 0;
-        af_array divArray    = 0;
-        af_array outArray    = 0;
-        af_array goldArray   = 0;
-        af_array _goldArray  = 0;
-        dim_t nElems         = 0;
-
-        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        af_array _inArray   = 0;
+        af_array inArray    = 0;
+        af_array _outArray  = 0;
+        af_array cstArray   = 0;
+        af_array minArray   = 0;
+        af_array numArray   = 0;
+        af_array denArray   = 0;
+        af_array divArray   = 0;
+        af_array outArray   = 0;
+        af_array goldArray  = 0;
+        af_array _goldArray = 0;
+        dim_t nElems        = 0;
+
+        ASSERT_SUCCESS(
+            af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
 
-        ASSERT_SUCCESS(af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
         // af_load_image always returns float array, so convert to output type
         ASSERT_SUCCESS(conv_image<OutType>(&goldArray, _goldArray));
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         if (isCurvatureDiffusion) {
-            ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
-                                                           fluxKind, AF_DIFFUSION_MCDE));
+            ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K,
+                                                    iters, fluxKind,
+                                                    AF_DIFFUSION_MCDE));
         } else {
-            ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K, iters,
-                                                           fluxKind, AF_DIFFUSION_GRAD));
+            ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K,
+                                                    iters, fluxKind,
+                                                    AF_DIFFUSION_GRAD));
         }
 
         double maxima, minima, imag;
@@ -109,23 +113,26 @@ void imageTest(string pTestFile, const float dt, const float K, const uint iters
         unsigned ndims;
         dim_t dims[4];
         ASSERT_SUCCESS(af_get_numdims(&ndims, _outArray));
-        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
+        ASSERT_SUCCESS(
+            af_get_dims(dims, dims + 1, dims + 2, dims + 3, _outArray));
 
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
         ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, otype));
-        ASSERT_SUCCESS(af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_SUCCESS(
+            af_constant(&denArray, (maxima - minima), ndims, dims, otype));
         ASSERT_SUCCESS(af_constant(&minArray, minima, ndims, dims, otype));
         ASSERT_SUCCESS(af_sub(&numArray, _outArray, minArray, false));
         ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
         ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
         vector<OutType> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
         vector<OutType> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.025f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.025f));
 
         ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(_outArray));
@@ -141,54 +148,49 @@ void imageTest(string pTestFile, const float dt, const float K, const uint iters
     }
 }
 
-TYPED_TEST(AnisotropicDiffusion, GradientGrayscale)
-{
+TYPED_TEST(AnisotropicDiffusion, GradientGrayscale) {
     // Numeric values separated by underscore are arguments to fn being tested.
     // Divide first value by 1000 to get time step `dt`
     // Divide second value by 100 to get time step `K`
     // Divide third value stays as it is since it is iteration count
     // Fourth value is a 4-character string indicating the flux kind
-    imageTest<TypeParam, false>(string(TEST_DIR "/gradient_diffusion/gray_00125_100_2_exp.test"),
-                                0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
+    imageTest<TypeParam, false>(
+        string(TEST_DIR "/gradient_diffusion/gray_00125_100_2_exp.test"),
+        0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
 }
 
-TYPED_TEST(AnisotropicDiffusion, GradientColorImage)
-{
-    imageTest<TypeParam, true>(string(TEST_DIR "/gradient_diffusion/color_00125_100_2_exp.test"),
-                               0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
+TYPED_TEST(AnisotropicDiffusion, GradientColorImage) {
+    imageTest<TypeParam, true>(
+        string(TEST_DIR "/gradient_diffusion/color_00125_100_2_exp.test"),
+        0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
 }
 
-TEST(AnisotropicDiffusion, GradientInvalidInputArray)
-{
+TEST(AnisotropicDiffusion, GradientInvalidInputArray) {
     try {
-        array out = anisotropicDiffusion(randu(100), 0.125f, 0.2f, 10, AF_FLUX_QUADRATIC);
-    } catch (exception &exp) {
-        ASSERT_EQ(AF_ERR_SIZE, exp.err());
-    }
+        array out = anisotropicDiffusion(randu(100), 0.125f, 0.2f, 10,
+                                         AF_FLUX_QUADRATIC);
+    } catch (exception &exp) { ASSERT_EQ(AF_ERR_SIZE, exp.err()); }
 }
 
-TYPED_TEST(AnisotropicDiffusion, CurvatureGrayscale)
-{
+TYPED_TEST(AnisotropicDiffusion, CurvatureGrayscale) {
     // Numeric values separated by underscore are arguments to fn being tested.
     // Divide first value by 1000 to get time step `dt`
     // Divide second value by 100 to get time step `K`
     // Divide third value stays as it is since it is iteration count
     // Fourth value is a 4-character string indicating the flux kind
-    imageTest<TypeParam, false>(string(TEST_DIR "/curvature_diffusion/gray_00125_100_2_mcde.test"),
-                                0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
+    imageTest<TypeParam, false>(
+        string(TEST_DIR "/curvature_diffusion/gray_00125_100_2_mcde.test"),
+        0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
 }
 
-TYPED_TEST(AnisotropicDiffusion, CurvatureColorImage)
-{
-    imageTest<TypeParam, true>(string(TEST_DIR "/curvature_diffusion/color_00125_100_2_mcde.test"),
-                               0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
+TYPED_TEST(AnisotropicDiffusion, CurvatureColorImage) {
+    imageTest<TypeParam, true>(
+        string(TEST_DIR "/curvature_diffusion/color_00125_100_2_mcde.test"),
+        0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
 }
 
-TEST(AnisotropicDiffusion, CurvatureInvalidInputArray)
-{
+TEST(AnisotropicDiffusion, CurvatureInvalidInputArray) {
     try {
         array out = anisotropicDiffusion(randu(100), 0.125f, 0.2f, 10);
-    } catch (exception &exp) {
-        ASSERT_EQ(AF_ERR_SIZE, exp.err());
-    }
+    } catch (exception &exp) { ASSERT_EQ(AF_ERR_SIZE, exp.err()); }
 }
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 48a35b2f9c..bad97bb3c9 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -30,8 +30,8 @@ using af::dim4;
 using af::dtype_traits;
 using af::randu;
 using af::reorder;
-using af::span;
 using af::seq;
+using af::span;
 using af::sum;
 
 using std::abs;
@@ -40,15 +40,14 @@ using std::string;
 using std::vector;
 
 template<typename T>
-class Approx1 : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Approx1 : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // Create a list of types to be tested
@@ -58,35 +57,43 @@ typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(Approx1, TestTypes);
 
 template<typename T>
-void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void approx1Test(string pTestFile, const unsigned resultIdx,
+                 const af_interp_type method, bool isSubRef = false,
+                 const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
-    readTests<BT, T, float>(pTestFile,numDims,in,tests);
+    readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
 
-    af_array inArray = 0;
-    af_array posArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array posArray  = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
-    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
 
     ASSERT_SUCCESS(af_approx1(&outArray, inArray, posArray, method, 0));
 
@@ -96,61 +103,69 @@ void approx1Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
-    bool ret = true;
+    bool ret      = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                             << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(posArray  != 0) af_release_array(posArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (posArray != 0) af_release_array(posArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-TYPED_TEST(Approx1, Approx1Nearest)
-{
-    approx1Test<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 0, AF_INTERP_NEAREST);
+TYPED_TEST(Approx1, Approx1Nearest) {
+    approx1Test<TypeParam>(string(TEST_DIR "/approx/approx1.test"), 0,
+                           AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Approx1, Approx1Linear)
-{
-    approx1Test<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 1, AF_INTERP_LINEAR);
+TYPED_TEST(Approx1, Approx1Linear) {
+    approx1Test<TypeParam>(string(TEST_DIR "/approx/approx1.test"), 1,
+                           AF_INTERP_LINEAR);
 }
 
-
 template<typename T>
-void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void approx1CubicTest(string pTestFile, const unsigned resultIdx,
+                      const af_interp_type method, bool isSubRef = false,
+                      const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
-    readTests<BT, T, float>(pTestFile,numDims,in,tests);
+    readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
 
-    af_array inArray = 0;
-    af_array posArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array posArray  = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
-    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
     ASSERT_SUCCESS(af_approx1(&outArray, inArray, posArray, method, 0));
 
     // Get result
@@ -159,10 +174,10 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
-    bool ret = true;
+    bool ret      = true;
 
     float max = real(outData[0]), min = real(outData[0]);
-    for(int i=1; i < (int)nElems; ++i) {
+    for (int i = 1; i < (int)nElems; ++i) {
         min = (real(outData[i]) < min) ? real(outData[i]) : min;
         max = (real(outData[i]) > max) ? real(outData[i]) : max;
     }
@@ -172,42 +187,46 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx, const af_inter
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         double integral;
         // Test that control points are exact
-        if((std::modf(in[1][elIter], &integral) < 0.001) || (std::modf(in[1][elIter], &integral) > 0.999))  {
+        if ((std::modf(in[1][elIter], &integral) < 0.001) ||
+            (std::modf(in[1][elIter], &integral) > 0.999)) {
             ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001;
-            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                                 << outData[elIter] << "at: " << elIter << endl;
         } else {
             // Match intermediate values within a threshold
-            ret = abs(tests[resultIdx][elIter] - outData[elIter]) < 0.035 * range;
-            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+            ret =
+                abs(tests[resultIdx][elIter] - outData[elIter]) < 0.035 * range;
+            ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                                 << outData[elIter] << "at: " << elIter << endl;
         }
     }
 
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(posArray  != 0) af_release_array(posArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (posArray != 0) af_release_array(posArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-TYPED_TEST(Approx1, Approx1Cubic)
-{
-    approx1CubicTest<TypeParam>(string(TEST_DIR"/approx/approx1_cubic.test"), 0, AF_INTERP_CUBIC_SPLINE);
+TYPED_TEST(Approx1, Approx1Cubic) {
+    approx1CubicTest<TypeParam>(string(TEST_DIR "/approx/approx1_cubic.test"),
+                                0, AF_INTERP_CUBIC_SPLINE);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Test Argument Failure Cases
 ///////////////////////////////////////////////////////////////////////////////
 template<typename T>
-void approx1ArgsTest(string pTestFile, const af_interp_type method, const af_err err)
-{
+void approx1ArgsTest(string pTestFile, const af_interp_type method,
+                     const af_err err) {
     if (noDoubleTests<T>()) return;
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
-    readTests<BT, T, float>(pTestFile,numDims,in,tests);
+    readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
@@ -218,38 +237,42 @@ void approx1ArgsTest(string pTestFile, const af_interp_type method, const af_err
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(),
+                                   idims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
 
     ASSERT_EQ(err, af_approx1(&outArray, inArray, posArray, method, 0));
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(posArray  != 0) af_release_array(posArray);
-    if(outArray  != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (posArray != 0) af_release_array(posArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Approx1, Approx1NearestArgsPos2D)
-{
-    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), AF_INTERP_NEAREST, AF_ERR_SIZE);
+TYPED_TEST(Approx1, Approx1NearestArgsPos2D) {
+    approx1ArgsTest<TypeParam>(string(TEST_DIR "/approx/approx1_pos2d.test"),
+                               AF_INTERP_NEAREST, AF_ERR_SIZE);
 }
-TYPED_TEST(Approx1, Approx1LinearArgsPos2D)
-{
-    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1_pos2d.test"), AF_INTERP_LINEAR, AF_ERR_SIZE);
+TYPED_TEST(Approx1, Approx1LinearArgsPos2D) {
+    approx1ArgsTest<TypeParam>(string(TEST_DIR "/approx/approx1_pos2d.test"),
+                               AF_INTERP_LINEAR, AF_ERR_SIZE);
 }
-TYPED_TEST(Approx1, Approx1ArgsInterpBilinear)
-{
-    approx1ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx1.test"), AF_INTERP_BILINEAR, AF_ERR_ARG);
+TYPED_TEST(Approx1, Approx1ArgsInterpBilinear) {
+    approx1ArgsTest<TypeParam>(string(TEST_DIR "/approx/approx1.test"),
+                               AF_INTERP_BILINEAR, AF_ERR_ARG);
 }
 
 template<typename T>
-void approx1ArgsTestPrecision(string pTestFile, const unsigned , const af_interp_type method)
-{
+void approx1ArgsTestPrecision(string pTestFile, const unsigned,
+                              const af_interp_type method) {
     if (noDoubleTests<T>()) return;
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
@@ -260,48 +283,53 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned , const af_interp
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(),
+                                   idims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&posArray, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-       (af_dtype) dtype_traits<T>::af_type == c64) {
-        ASSERT_EQ(AF_ERR_ARG, af_approx1(&outArray, inArray, posArray, method, 0));
+    if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+        (af_dtype)dtype_traits<T>::af_type == c64) {
+        ASSERT_EQ(AF_ERR_ARG,
+                  af_approx1(&outArray, inArray, posArray, method, 0));
     } else {
         ASSERT_SUCCESS(af_approx1(&outArray, inArray, posArray, method, 0));
     }
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(posArray  != 0) af_release_array(posArray);
-    if(outArray  != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (posArray != 0) af_release_array(posArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Approx1, Approx1NearestArgsPrecision)
-{
-    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 0, AF_INTERP_NEAREST);
+TYPED_TEST(Approx1, Approx1NearestArgsPrecision) {
+    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR "/approx/approx1.test"),
+                                        0, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Approx1, Approx1LinearArgsPrecision)
-{
-    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/approx1.test"), 1, AF_INTERP_LINEAR);
+TYPED_TEST(Approx1, Approx1LinearArgsPrecision) {
+    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR "/approx/approx1.test"),
+                                        1, AF_INTERP_LINEAR);
 }
 
-TYPED_TEST(Approx1, Approx1CubicArgsPrecision)
-{
-    approx1ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/approx1_cubic.test"), 2, AF_INTERP_CUBIC_SPLINE);
+TYPED_TEST(Approx1, Approx1CubicArgsPrecision) {
+    approx1ArgsTestPrecision<TypeParam>(
+        string(TEST_DIR "/approx/approx1_cubic.test"), 2,
+        AF_INTERP_CUBIC_SPLINE);
 }
 
-
 //////////////////////////////////////// CPP //////////////////////////////////
 //
-TEST(Approx1, CPP)
-{
+TEST(Approx1, CPP) {
     const unsigned resultIdx = 1;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<float> > tests;
-    readTests<BT, float, float>(string(TEST_DIR"/approx/approx1.test"),numDims,in,tests);
+    readTests<BT, float, float>(string(TEST_DIR "/approx/approx1.test"),
+                                numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
@@ -309,7 +337,7 @@ TEST(Approx1, CPP)
     array input(idims, &(in[0].front()));
     array pos(pdims, &(in[1].front()));
     const af_interp_type method = AF_INTERP_LINEAR;
-    array output = approx1(input, pos, method, 0);
+    array output                = approx1(input, pos, method, 0);
 
     // Get result
     float* outData = new float[tests[resultIdx].size()];
@@ -317,10 +345,11 @@ TEST(Approx1, CPP)
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
-    bool ret = true;
+    bool ret      = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.0005);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                             << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -329,8 +358,7 @@ TEST(Approx1, CPP)
 #undef BT
 }
 
-TEST(Approx1, CPPNearestBatch)
-{
+TEST(Approx1, CPPNearestBatch) {
     array input = randu(600, 10);
     array pos   = input.dims(0) * randu(100, 10);
 
@@ -338,24 +366,21 @@ TEST(Approx1, CPPNearestBatch)
 
     array outSerial(pos.dims());
     for (int i = 0; i < pos.dims(1); i++) {
-        outSerial(span, i) = approx1(input(span, i),
-                                     pos(span, i),
-                                     AF_INTERP_NEAREST);
+        outSerial(span, i) =
+            approx1(input(span, i), pos(span, i), AF_INTERP_NEAREST);
     }
 
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(1)) {
-        outGFOR(span, i) = approx1(input(span, i),
-                                   pos(span, i),
-                                   AF_INTERP_NEAREST);
+        outGFOR(span, i) =
+            approx1(input(span, i), pos(span, i), AF_INTERP_NEAREST);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
-TEST(Approx1, CPPLinearBatch)
-{
+TEST(Approx1, CPPLinearBatch) {
     array input = iota(dim4(10000, 20), c32);
     array pos   = input.dims(0) * randu(10000, 20);
 
@@ -363,24 +388,21 @@ TEST(Approx1, CPPLinearBatch)
 
     array outSerial(pos.dims());
     for (int i = 0; i < pos.dims(1); i++) {
-        outSerial(span, i) = approx1(input(span, i),
-                                     pos(span, i),
-                                     AF_INTERP_LINEAR);
+        outSerial(span, i) =
+            approx1(input(span, i), pos(span, i), AF_INTERP_LINEAR);
     }
 
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(1)) {
-        outGFOR(span, i) = approx1(input(span, i),
-                                   pos(span, i),
-                                   AF_INTERP_LINEAR);
+        outGFOR(span, i) =
+            approx1(input(span, i), pos(span, i), AF_INTERP_LINEAR);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
-TEST(Approx1, CPPCubicBatch)
-{
+TEST(Approx1, CPPCubicBatch) {
     array input = iota(dim4(10000, 20), c32);
     array pos   = input.dims(0) * randu(10000, 20);
 
@@ -388,30 +410,27 @@ TEST(Approx1, CPPCubicBatch)
 
     array outSerial(pos.dims());
     for (int i = 0; i < pos.dims(1); i++) {
-        outSerial(span, i) = approx1(input(span, i),
-                                     pos(span, i),
-                                     AF_INTERP_CUBIC_SPLINE);
+        outSerial(span, i) =
+            approx1(input(span, i), pos(span, i), AF_INTERP_CUBIC_SPLINE);
     }
 
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(1)) {
-        outGFOR(span, i) = approx1(input(span, i),
-                                   pos(span, i),
-                                   AF_INTERP_CUBIC_SPLINE);
+        outGFOR(span, i) =
+            approx1(input(span, i), pos(span, i), AF_INTERP_CUBIC_SPLINE);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
-TEST(Approx1, CPPNearestMaxDims)
-{
+TEST(Approx1, CPPNearestMaxDims) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    array input = randu(1, largeDim);
-    array pos   = input.dims(0) * randu(1, largeDim);
-    array out   = approx1(input, pos, AF_INTERP_NEAREST);
+    array input           = randu(1, largeDim);
+    array pos             = input.dims(0) * randu(1, largeDim);
+    array out             = approx1(input, pos, AF_INTERP_NEAREST);
 
     input = randu(1, 1, largeDim);
     pos   = input.dims(0) * randu(1, 1, largeDim);
@@ -424,96 +443,97 @@ TEST(Approx1, CPPNearestMaxDims)
     SUCCEED();
 }
 
-TEST(Approx1, CPPLinearMaxDims)
-{
+TEST(Approx1, CPPLinearMaxDims) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    array input = iota(dim4(1, largeDim), c32);
-    array pos   = input.dims(0) * randu(1, largeDim);
-    array outBatch = approx1(input, pos, AF_INTERP_LINEAR);
+    array input           = iota(dim4(1, largeDim), c32);
+    array pos             = input.dims(0) * randu(1, largeDim);
+    array outBatch        = approx1(input, pos, AF_INTERP_LINEAR);
 
-    input = iota(dim4(1, 1, largeDim), c32);
-    pos   = input.dims(0) * randu(1, 1, largeDim);
+    input    = iota(dim4(1, 1, largeDim), c32);
+    pos      = input.dims(0) * randu(1, 1, largeDim);
     outBatch = approx1(input, pos, AF_INTERP_LINEAR);
 
-    input = iota(dim4(1, 1, 1, largeDim), c32);
-    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    input    = iota(dim4(1, 1, 1, largeDim), c32);
+    pos      = input.dims(0) * randu(1, 1, 1, largeDim);
     outBatch = approx1(input, pos, AF_INTERP_LINEAR);
 
     SUCCEED();
 }
 
-TEST(Approx1, CPPCubicMaxDims)
-{
+TEST(Approx1, CPPCubicMaxDims) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    array input = iota(dim4(1, largeDim), c32);
-    array pos   = input.dims(0) * randu(1, largeDim);
-    array outBatch = approx1(input, pos, AF_INTERP_CUBIC);
+    array input           = iota(dim4(1, largeDim), c32);
+    array pos             = input.dims(0) * randu(1, largeDim);
+    array outBatch        = approx1(input, pos, AF_INTERP_CUBIC);
 
-    input = iota(dim4(1, 1, largeDim), c32);
-    pos   = input.dims(0) * randu(1, 1, largeDim);
+    input    = iota(dim4(1, 1, largeDim), c32);
+    pos      = input.dims(0) * randu(1, 1, largeDim);
     outBatch = approx1(input, pos, AF_INTERP_CUBIC);
 
-    input = iota(dim4(1, 1, 1, largeDim), c32);
-    pos   = input.dims(0) * randu(1, 1, 1, largeDim);
+    input    = iota(dim4(1, 1, 1, largeDim), c32);
+    pos      = input.dims(0) * randu(1, 1, 1, largeDim);
     outBatch = approx1(input, pos, AF_INTERP_CUBIC);
 
     SUCCEED();
 }
 
-TEST(Approx1, OtherDimLinear)
-{
+TEST(Approx1, OtherDimLinear) {
     int start = 0;
-    int stop = 10000;
-    int step = 100;
-    int num = 1000;
-    array xi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
-    array yi = 4 * xi - 3;
-    array xo = af::round(step * randu(num, 2, 2, 2));
-    array yo = 4 * xo - 3;
+    int stop  = 10000;
+    int step  = 100;
+    int num   = 1000;
+    array xi  = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array yi  = 4 * xi - 3;
+    array xo  = af::round(step * randu(num, 2, 2, 2));
+    array yo  = 4 * xo - 3;
     for (int d = 1; d < 4; d++) {
-        dim4 rdims(0,1,2,3);
+        dim4 rdims(0, 1, 2, 3);
         rdims[0] = d;
         rdims[d] = 0;
 
-        array yi_reordered = reorder(yi, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array yo_reordered = approx1(yi_reordered, xo_reordered,
-                                     d, start, step, AF_INTERP_LINEAR);
-        array res = reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yi_reordered =
+            reorder(yi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered =
+            reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered = approx1(yi_reordered, xo_reordered, d, start, step,
+                                     AF_INTERP_LINEAR);
+        array res =
+            reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
         ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
     }
 }
 
-TEST(Approx1, OtherDimCubic)
-{
+TEST(Approx1, OtherDimCubic) {
     float start = 0;
-    float stop = 100;
-    float step = 0.01;
-    int num = 1000;
-    array xi = af::tile(af::seq(start, stop, step), 1, 2, 2, 2);
-    array yi = af::sin(xi);
-    array xo = af::round(step * af::randu(num, 2, 2, 2));
-    array yo = af::sin(xo);
+    float stop  = 100;
+    float step  = 0.01;
+    int num     = 1000;
+    array xi    = af::tile(af::seq(start, stop, step), 1, 2, 2, 2);
+    array yi    = af::sin(xi);
+    array xo    = af::round(step * af::randu(num, 2, 2, 2));
+    array yo    = af::sin(xo);
     for (int d = 1; d < 4; d++) {
-        dim4 rdims(0,1,2,3);
+        dim4 rdims(0, 1, 2, 3);
         rdims[0] = d;
         rdims[d] = 0;
 
-        array yi_reordered = reorder(yi, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array yo_reordered = approx1(yi_reordered, xo_reordered,
-                                     d, start, step, AF_INTERP_CUBIC);
-        array res = reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yi_reordered =
+            reorder(yi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered =
+            reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered = approx1(yi_reordered, xo_reordered, d, start, step,
+                                     AF_INTERP_CUBIC);
+        array res =
+            reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
         ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
     }
 }
 
-TEST(Approx1, CPPUsage)
-{
+TEST(Approx1, CPPUsage) {
     //! [ex_signal_approx1]
 
     // Input data array.
@@ -526,7 +546,7 @@ TEST(Approx1, CPPUsage)
 
     // Array of positions to be found along the first dimension.
     float pv[5] = {0.0f, 0.5, 1.0f, 1.5, 2.0f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
     // [5 1 1 1]
     //     0.0000
     //     0.5000
@@ -546,19 +566,15 @@ TEST(Approx1, CPPUsage)
     //! [ex_signal_approx1]
 
     float civ[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    array interp_gold(dim4(5,1), civ);
+    array interp_gold(dim4(5, 1), civ);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
-
 }
 
-
-TEST(Approx1, CPPUniformUsage)
-{
+TEST(Approx1, CPPUniformUsage) {
     //! [ex_signal_approx1_uniform]
 
-    float input_vals[9] = {10.0f, 20.0f, 30.0f,
-                           40.0f, 50.0f, 60.0f,
-                           70.0f, 80.0f, 90.0f};
+    float input_vals[9] = {10.0f, 20.0f, 30.0f, 40.0f, 50.0f,
+                           60.0f, 70.0f, 80.0f, 90.0f};
     array in(dim4(3, 3), input_vals);
     // [3 3 1 1]
     //     10.0000    40.0000    70.0000
@@ -568,7 +584,7 @@ TEST(Approx1, CPPUniformUsage)
     // Array of positions to be found along the interpolation
     // dimension, `interp_dim`.
     float pv[5] = {0.0f, 0.5, 1.0f, 1.5f, 2.0f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
     // [5 1 1 1]
     //     0.0000
     //     0.5000
@@ -579,10 +595,10 @@ TEST(Approx1, CPPUniformUsage)
     // Define range of indices with which the input values will
     // correspond along the interpolation dimension.
     const double idx_start = 0.0;
-    const double idx_step = 1.0;
+    const double idx_step  = 1.0;
 
     // Perform interpolation across dimension 0.
-    int interp_dim = 0;
+    int interp_dim         = 0;
     array col_major_interp = approx1(in, pos, interp_dim, idx_start, idx_step);
     // [5 3 1 1]
     //     10.0000    40.0000    70.0000
@@ -593,7 +609,8 @@ TEST(Approx1, CPPUniformUsage)
 
     // Perform interpolation across dimension 1.
     interp_dim = 1;
-    array row_major_interp = approx1(in, transpose(pos), interp_dim, idx_start, idx_step);
+    array row_major_interp =
+        approx1(in, transpose(pos), interp_dim, idx_start, idx_step);
     // [3 5 1 1]
     //     10.0000    25.0000    40.0000    55.0000    70.0000
     //     20.0000    35.0000    50.0000    65.0000    80.0000
@@ -601,178 +618,154 @@ TEST(Approx1, CPPUniformUsage)
 
     //! [ex_signal_approx1_uniform]
 
-    float civ[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
-                     40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
-                     70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
-    array interp_gold_col(dim4(5,3), civ);
+    float civ[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f, 40.0f, 45.0f, 50.0f,
+                     55.0f, 60.0f, 70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
+    array interp_gold_col(dim4(5, 3), civ);
     ASSERT_ARRAYS_EQ(col_major_interp, interp_gold_col);
 
-
-    float riv[15] = {10.0f, 20.0f, 30.0f,
-                     25.0f, 35.0f, 45.0f,
-                     40.0f, 50.0f, 60.0f,
-                     55.0f, 65.0f, 75.0f,
-                     70.0f, 80.0f, 90.0f};
-    array interp_gold_row(dim4(3,5), riv);
+    float riv[15] = {10.0f, 20.0f, 30.0f, 25.0f, 35.0f, 45.0f, 40.0f, 50.0f,
+                     60.0f, 55.0f, 65.0f, 75.0f, 70.0f, 80.0f, 90.0f};
+    array interp_gold_row(dim4(3, 5), riv);
     ASSERT_ARRAYS_EQ(row_major_interp, interp_gold_row);
 }
 
-TEST(Approx1, CPPDecimalStepRescaleGrid)
-{
+TEST(Approx1, CPPDecimalStepRescaleGrid) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     float pv[5] = {0.f, 0.25f, 0.5f, 0.75f, 1.0f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
 
-    const int interp_grid_start = 0;
+    const int interp_grid_start   = 0;
     const double interp_grid_step = 0.5;
-    const int interp_dim = 0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step);
+    const int interp_dim          = 0;
+    array interp =
+        approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
 
     float iv[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    array interp_gold(dim4(5,1), iv);
+    array interp_gold(dim4(5, 1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-TEST(Approx1, CPPRepeatPos)
-{
-    float inv[9] = {10.0f, 20.0f, 30.0f,
-                    40.0f, 50.0f, 60.0f,
-                    70.0f, 80.0f, 90.0f};
+TEST(Approx1, CPPRepeatPos) {
+    float inv[9] = {10.0f, 20.0f, 30.0f, 40.0f, 50.0f,
+                    60.0f, 70.0f, 80.0f, 90.0f};
     array in(dim4(3, 3), inv);
     float pv[5] = {0.0f, 0.5f, 0.5f, 1.5f, 1.5f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
 
-    const int interp_grid_start = 0;
+    const int interp_grid_start   = 0;
     const double interp_grid_step = 1.0;
-    const int interp_dim = 0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step);
-
-    float iv[15] = {10.0f, 15.0f, 15.0f, 25.0f, 25.0f,
-                    40.0f, 45.0f, 45.0f, 55.0f, 55.0f,
-                    70.0f, 75.0f, 75.0f, 85.0f, 85.0f};
-    array interp_gold(dim4(5,3), iv);
+    const int interp_dim          = 0;
+    array interp =
+        approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
+
+    float iv[15] = {10.0f, 15.0f, 15.0f, 25.0f, 25.0f, 40.0f, 45.0f, 45.0f,
+                    55.0f, 55.0f, 70.0f, 75.0f, 75.0f, 85.0f, 85.0f};
+    array interp_gold(dim4(5, 3), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-
-TEST(Approx1, CPPNonMonotonicPos)
-{
+TEST(Approx1, CPPNonMonotonicPos) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     float pv[5] = {0.5f, 1.0f, 1.5f, 0.0f, 2.0f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
 
-    const int interp_grid_start = 0;
+    const int interp_grid_start   = 0;
     const double interp_grid_step = 1.0;
-    const int interp_dim = 0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step);
+    const int interp_dim          = 0;
+    array interp =
+        approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
 
     float iv[5] = {15.0f, 20.0f, 25.0f, 10.0f, 30.0f};
-    array interp_gold(dim4(5,1), iv);
+    array interp_gold(dim4(5, 1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-TEST(Approx1, CPPMismatchingIndexingDim)
-{
+TEST(Approx1, CPPMismatchingIndexingDim) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     float pv[4] = {0.0f, 0.5f, 1.0f, 2.0f};
-    array pos(dim4(1,4), pv);
+    array pos(dim4(1, 4), pv);
 
-    const int interp_grid_start = 0;
+    const int interp_grid_start   = 0;
     const double interp_grid_step = 1.0;
-    const int interp_dim = 1;
-    const float off_grid = -1.0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step,
-                           AF_INTERP_LINEAR, off_grid);
-
-    float iv[12] = {10.0f, 20.0f, 30.0f,
-                   -1.0f, -1.0f, -1.0f,
-                   -1.0f, -1.0f, -1.0f,
-                   -1.0f, -1.0f, -1.0f};
-    array interp_gold(dim4(3,4), iv);
+    const int interp_dim          = 1;
+    const float off_grid          = -1.0;
+    array interp = approx1(in, pos, interp_dim, interp_grid_start,
+                           interp_grid_step, AF_INTERP_LINEAR, off_grid);
+
+    float iv[12] = {10.0f, 20.0f, 30.0f, -1.0f, -1.0f, -1.0f,
+                    -1.0f, -1.0f, -1.0f, -1.0f, -1.0f, -1.0f};
+    array interp_gold(dim4(3, 4), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-TEST(Approx1, CPPNegativeGridStart)
-{
+TEST(Approx1, CPPNegativeGridStart) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     float pv[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
 
-    const int interp_grid_start = -1;
+    const int interp_grid_start   = -1;
     const double interp_grid_step = 1;
-    const int interp_dim = 0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step);
+    const int interp_dim          = 0;
+    array interp =
+        approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
 
     float iv[5] = {20.0f, 25.0f, 30.0f, 0.0f, 0.0f};
-    array interp_gold(dim4(5,1), iv);
+    array interp_gold(dim4(5, 1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
-
 }
 
-TEST(Approx1, CPPInterpolateBackwards)
-{
+TEST(Approx1, CPPInterpolateBackwards) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     float pv[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    array pos(dim4(3,1), pv);
+    array pos(dim4(3, 1), pv);
 
-    const int interp_grid_start = in.elements()-1;
+    const int interp_grid_start   = in.elements() - 1;
     const double interp_grid_step = -1;
-    const int interp_dim = 0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step);
+    const int interp_dim          = 0;
+    array interp =
+        approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
 
     float iv[5] = {30.0f, 25.0f, 20.0f, 15.0f, 10.0f};
-    array interp_gold(dim4(3,1), iv);
+    array interp_gold(dim4(3, 1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-
-TEST(Approx1, CPPStartOffGridAndNegativeStep)
-{
+TEST(Approx1, CPPStartOffGridAndNegativeStep) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     float pv[5] = {0.0f, -0.5f, -1.0f, -1.5f, -2.0f};
-    array pos(dim4(5,1), pv);
+    array pos(dim4(5, 1), pv);
 
-    const int interp_grid_start = -1;
+    const int interp_grid_start   = -1;
     const double interp_grid_step = -1;
-    const int interp_dim = 0;
-    array interp = approx1(in,
-                           pos, interp_dim, interp_grid_start, interp_grid_step);
+    const int interp_dim          = 0;
+    array interp =
+        approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
 
     float iv[5] = {0.0f, 0.0f, 10.0f, 15.0f, 20.0f};
-    array interp_gold(dim4(5,1), iv);
+    array interp_gold(dim4(5, 1), iv);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-TEST(Approx1, CPPUniformInvalidStepSize)
-{
-    try
-    {
+TEST(Approx1, CPPUniformInvalidStepSize) {
+    try {
         float inv[3] = {10.0f, 20.0f, 30.0f};
-        array in(dim4(3,1), inv);
+        array in(dim4(3, 1), inv);
         float pv[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-        array pos(dim4(5,1), pv);
+        array pos(dim4(5, 1), pv);
 
-        const int interp_grid_start = 0;
+        const int interp_grid_start   = 0;
         const double interp_grid_step = 0;
-        const int interp_dim = 0;
-        array interp = approx1(in,
-                               pos, interp_dim, interp_grid_start, interp_grid_step);
+        const int interp_dim          = 0;
+        array interp =
+            approx1(in, pos, interp_dim, interp_grid_start, interp_grid_step);
         FAIL() << "Expected af::exception\n";
-    } catch (af::exception &ex) {
-        SUCCEED();
-    } catch(...) {
+    } catch (af::exception& ex) { SUCCEED(); } catch (...) {
         FAIL() << "Expected af::exception\n";
     }
 }
@@ -780,63 +773,60 @@ TEST(Approx1, CPPUniformInvalidStepSize)
 // Unless the sampling grid specifications - begin, step - are
 // specified by the user, ArrayFire will assume a regular grid with a
 // starting index of 0 and a step value of 1.
-TEST(Approx1, CPPInfCheck)
-{
+TEST(Approx1, CPPInfCheck) {
     array sampled(seq(0.0, 5.0, 0.5));
     sampled(0) = af::Inf;
     seq xo(0.0, 2.0, 0.25);
-    array interp = approx1(sampled, xo);
+    array interp           = approx1(sampled, xo);
     array interp_augmented = join(1, xo, interp);
 
     float goldv[9] = {static_cast<float>(af::Inf),
                       static_cast<float>(af::Inf),
                       static_cast<float>(af::Inf),
                       static_cast<float>(af::Inf),
-                      0.5f, 0.625f, 0.75f, 0.875f, 1.0f};
-    array gold(dim4(9,1), goldv);
+                      0.5f,
+                      0.625f,
+                      0.75f,
+                      0.875f,
+                      1.0f};
+    array gold(dim4(9, 1), goldv);
     interp(af::isInf(interp)) = 0;
-    gold(af::isInf(gold)) = 0;
+    gold(af::isInf(gold))     = 0;
     ASSERT_ARRAYS_EQ(interp, gold);
 }
 
-TEST(Approx1, CPPUniformInfCheck)
-{
+TEST(Approx1, CPPUniformInfCheck) {
     array sampled(seq(10.0, 50.0, 10.0));
     sampled(0) = af::Inf;
     seq xo(0.0, 8.0, 2.0);
-    array interp = approx1(sampled,
-                           xo, 0,
-                           0, 2);
+    array interp   = approx1(sampled, xo, 0, 0, 2);
     float goldv[5] = {static_cast<float>(af::Inf), 20.0f, 30.0f, 40.0f, 50.0f};
-    array gold(dim4(5,1), goldv);
+    array gold(dim4(5, 1), goldv);
     interp(af::isInf(interp)) = 0;
-    gold(af::isInf(gold)) = 0;
+    gold(af::isInf(gold))     = 0;
     ASSERT_ARRAYS_EQ(interp, gold);
 }
 
-TEST(Approx1, CPPEmptyPos)
-{
+TEST(Approx1, CPPEmptyPos) {
     float inv[3] = {10.0f, 20.0f, 30.0f};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     array pos;
     array interp = approx1(in, pos);
     ASSERT_TRUE(pos.isempty());
     ASSERT_TRUE(interp.isempty());
 }
 
-TEST(Approx1, CPPEmptyInput)
-{
+TEST(Approx1, CPPEmptyInput) {
     array in;
     float pv[3] = {0.0f, 1.0f, 2.0f};
-    array pos(dim4(3,1), pv);
+    array pos(dim4(3, 1), pv);
 
     array interp = approx1(in, pos);
     ASSERT_TRUE(in.isempty());
     ASSERT_TRUE(interp.isempty());
 }
 
-TEST(Approx1, CPPEmptyPosAndInput)
-{
+TEST(Approx1, CPPEmptyPosAndInput) {
     array in;
     array pos;
     array interp = approx1(in, pos);
@@ -847,9 +837,8 @@ TEST(Approx1, CPPEmptyPosAndInput)
 
 void testSpclOutArray(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
                       float* h_pos, dim4 pos_dims,
-                      TestOutputArrayType out_array_type)
-{
-    af_array in = 0;
+                      TestOutputArrayType out_array_type) {
+    af_array in  = 0;
     af_array pos = 0;
     ASSERT_SUCCESS(
         af_create_array(&in, h_in, in_dims.ndims(), in_dims.get(), f32));
@@ -863,14 +852,14 @@ void testSpclOutArray(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
     ASSERT_SUCCESS(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0));
 
     af_array gold = 0;
-    ASSERT_SUCCESS(
-        af_create_array(&gold, h_gold, gold_dims.ndims(), gold_dims.get(), f32));
+    ASSERT_SUCCESS(af_create_array(&gold, h_gold, gold_dims.ndims(),
+                                   gold_dims.get(), f32));
 
     ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
 
     if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
-    if (pos != 0)  { ASSERT_SUCCESS(af_release_array(pos)); }
-    if (in != 0)   { ASSERT_SUCCESS(af_release_array(in)); }
+    if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
+    if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
 }
 
 TEST(Approx1, UseNullOutputArray) {
@@ -884,8 +873,8 @@ TEST(Approx1, UseNullOutputArray) {
     dim4 gold_dims(5);
 
     SCOPED_TRACE("UseNullOutputArray");
-    testSpclOutArray(h_gold, gold_dims, h_in, in_dims,
-                     h_pos, pos_dims, NULL_ARRAY);
+    testSpclOutArray(h_gold, gold_dims, h_in, in_dims, h_pos, pos_dims,
+                     NULL_ARRAY);
 }
 
 TEST(Approx1, UseFullExistingOutputArray) {
@@ -899,8 +888,8 @@ TEST(Approx1, UseFullExistingOutputArray) {
     dim4 gold_dims(5);
 
     SCOPED_TRACE("UseFullExistingOutputArray");
-    testSpclOutArray(h_gold, gold_dims, h_in, in_dims,
-                     h_pos, pos_dims, FULL_ARRAY);
+    testSpclOutArray(h_gold, gold_dims, h_in, in_dims, h_pos, pos_dims,
+                     FULL_ARRAY);
 }
 
 TEST(Approx1, UseExistingOutputSubArray) {
@@ -908,32 +897,29 @@ TEST(Approx1, UseExistingOutputSubArray) {
     dim4 in_dims(3);
 
     float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims (5);
+    dim4 pos_dims(5);
 
     float h_gold_subarr[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
     dim4 gold_subarr_dims(5);
 
     SCOPED_TRACE("UseExistingOutputSubArray");
-    testSpclOutArray(h_gold_subarr, gold_subarr_dims, h_in, in_dims,
-                     h_pos, pos_dims, SUB_ARRAY);
+    testSpclOutArray(h_gold_subarr, gold_subarr_dims, h_in, in_dims, h_pos,
+                     pos_dims, SUB_ARRAY);
 }
 
 TEST(Approx1, UseReorderedOutputArray) {
-
-    float h_in[9] = {10.0f, 20.0f, 30.0f,
-                     40.0f, 50.0f, 60.0f,
-                     70.0f, 80.0f, 90.0f};
+    float h_in[9] = {10.0f, 20.0f, 30.0f, 40.0f, 50.0f,
+                     60.0f, 70.0f, 80.0f, 90.0f};
     dim4 in_dims(3, 3);
 
     float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
     dim4 pos_dims(5);
 
-    float h_gold[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
-                        40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
-                        70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
+    float h_gold[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f, 40.0f, 45.0f, 50.0f,
+                        55.0f, 60.0f, 70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
     dim4 gold_dims(5, 3);
 
     SCOPED_TRACE("UseReorderedOutputArray");
-    testSpclOutArray(h_gold, gold_dims, h_in, in_dims,
-                     h_pos, pos_dims, REORDERED_ARRAY);
+    testSpclOutArray(h_gold, gold_dims, h_in, in_dims, h_pos, pos_dims,
+                     REORDERED_ARRAY);
 }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 53b46b947e..10df492679 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -36,15 +36,14 @@ using std::string;
 using std::vector;
 
 template<typename T>
-class Approx2 : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Approx2 : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
@@ -54,39 +53,50 @@ typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(Approx2, TestTypes);
 
 template<typename T>
-void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void approx2Test(string pTestFile, const unsigned resultIdx,
+                 const af_interp_type method, bool isSubRef = false,
+                 const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
-    readTests<BT, T, float>(pTestFile,numDims,in,tests);
+    readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
     dim4 qdims = numDims[2];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array pos0Array = 0;
     af_array pos1Array = 0;
-    af_array outArray = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     vector<T> input(in[0].begin(), in[0].end());
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(input.front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
-    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
-    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(),
+                                   qdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
 
-    ASSERT_SUCCESS(af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
+    ASSERT_SUCCESS(
+        af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
@@ -94,159 +104,172 @@ void approx2Test(string pTestFile, const unsigned resultIdx, const af_interp_typ
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
-    bool ret = true;
+    bool ret      = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                             << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(pos0Array != 0) af_release_array(pos0Array);
-    if(pos1Array != 0) af_release_array(pos1Array);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (pos0Array != 0) af_release_array(pos0Array);
+    if (pos1Array != 0) af_release_array(pos1Array);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-TYPED_TEST(Approx2, Approx2Nearest)
-{
-    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2.test"), 0, AF_INTERP_NEAREST);
+TYPED_TEST(Approx2, Approx2Nearest) {
+    approx2Test<TypeParam>(string(TEST_DIR "/approx/approx2.test"), 0,
+                           AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Approx2, Approx2Linear)
-{
-    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2.test"), 1, AF_INTERP_LINEAR);
+TYPED_TEST(Approx2, Approx2Linear) {
+    approx2Test<TypeParam>(string(TEST_DIR "/approx/approx2.test"), 1,
+                           AF_INTERP_LINEAR);
 }
-TYPED_TEST(Approx2, NearestBatch)
-{
-    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2_batch.test"), 0, AF_INTERP_NEAREST);
+TYPED_TEST(Approx2, NearestBatch) {
+    approx2Test<TypeParam>(string(TEST_DIR "/approx/approx2_batch.test"), 0,
+                           AF_INTERP_NEAREST);
 }
-TYPED_TEST(Approx2, LinearBatch)
-{
-    approx2Test<TypeParam>(string(TEST_DIR"/approx/approx2_batch.test"), 1, AF_INTERP_LINEAR);
+TYPED_TEST(Approx2, LinearBatch) {
+    approx2Test<TypeParam>(string(TEST_DIR "/approx/approx2_batch.test"), 1,
+                           AF_INTERP_LINEAR);
 }
 
 // Test Argument Failure Cases
 template<typename T>
-void approx2ArgsTest(string pTestFile, const af_interp_type method, const af_err err)
-{
+void approx2ArgsTest(string pTestFile, const af_interp_type method,
+                     const af_err err) {
     if (noDoubleTests<T>()) return;
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<T> > tests;
-    readTests<BT, T, float>(pTestFile,numDims,in,tests);
+    readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
     dim4 qdims = numDims[2];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array pos0Array = 0;
     af_array pos1Array = 0;
-    af_array outArray = 0;
+    af_array outArray  = 0;
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(),
+                                   idims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<BT>::af_type));
-    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(),
+                                   qdims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
 
-    ASSERT_EQ(err, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
+    ASSERT_EQ(err,
+              af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(pos0Array != 0) af_release_array(pos0Array);
-    if(pos1Array != 0) af_release_array(pos1Array);
-    if(outArray  != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (pos0Array != 0) af_release_array(pos0Array);
+    if (pos1Array != 0) af_release_array(pos1Array);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Approx2, Approx2NearestArgsPos3D)
-{
-    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), AF_INTERP_NEAREST, AF_ERR_SIZE);
+TYPED_TEST(Approx2, Approx2NearestArgsPos3D) {
+    approx2ArgsTest<TypeParam>(string(TEST_DIR "/approx/approx2_pos3d.test"),
+                               AF_INTERP_NEAREST, AF_ERR_SIZE);
 }
 
-TYPED_TEST(Approx2, Approx2LinearArgsPos3D)
-{
-    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_pos3d.test"), AF_INTERP_LINEAR, AF_ERR_SIZE);
+TYPED_TEST(Approx2, Approx2LinearArgsPos3D) {
+    approx2ArgsTest<TypeParam>(string(TEST_DIR "/approx/approx2_pos3d.test"),
+                               AF_INTERP_LINEAR, AF_ERR_SIZE);
 }
 
-TYPED_TEST(Approx2, Approx2NearestArgsPosUnequal)
-{
-    approx2ArgsTest<TypeParam>(string(TEST_DIR"/approx/approx2_unequal.test"), AF_INTERP_NEAREST, AF_ERR_SIZE);
+TYPED_TEST(Approx2, Approx2NearestArgsPosUnequal) {
+    approx2ArgsTest<TypeParam>(string(TEST_DIR "/approx/approx2_unequal.test"),
+                               AF_INTERP_NEAREST, AF_ERR_SIZE);
 }
 
 template<typename T>
-void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx, const af_interp_type method)
-{
+void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx,
+                              const af_interp_type method) {
     UNUSED(resultIdx);
     if (noDoubleTests<T>()) return;
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
     dim4 qdims = numDims[2];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array pos0Array = 0;
     af_array pos1Array = 0;
-    af_array outArray = 0;
+    af_array outArray  = 0;
 
     vector<T> input(in[0].begin(), in[0].end());
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
-
-    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(), pdims.get(), (af_dtype) dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(), qdims.get(), (af_dtype) dtype_traits<T>::af_type));
-
-
-    if((af_dtype) dtype_traits<T>::af_type == c32 ||
-       (af_dtype) dtype_traits<T>::af_type == c64) {
-        ASSERT_EQ(AF_ERR_ARG, af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(input.front()), idims.ndims(),
+                                   idims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+
+    ASSERT_SUCCESS(af_create_array(&pos0Array, &(in[1].front()), pdims.ndims(),
+                                   pdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos1Array, &(in[2].front()), qdims.ndims(),
+                                   qdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+
+    if ((af_dtype)dtype_traits<T>::af_type == c32 ||
+        (af_dtype)dtype_traits<T>::af_type == c64) {
+        ASSERT_EQ(AF_ERR_ARG, af_approx2(&outArray, inArray, pos0Array,
+                                         pos1Array, method, 0));
     } else {
-        ASSERT_SUCCESS(af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
+        ASSERT_SUCCESS(
+            af_approx2(&outArray, inArray, pos0Array, pos1Array, method, 0));
     }
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(pos0Array != 0) af_release_array(pos0Array);
-    if(pos1Array != 0) af_release_array(pos1Array);
-    if(outArray  != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (pos0Array != 0) af_release_array(pos0Array);
+    if (pos1Array != 0) af_release_array(pos1Array);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-#define APPROX2_ARGSP(desc, file, resultIdx, method)                                    \
-    TYPED_TEST(Approx2, desc)                                                           \
-    {                                                                                   \
-        approx2ArgsTestPrecision<TypeParam>(string(TEST_DIR"/approx/"#file".test"),     \
-                                            resultIdx, method);                         \
+#define APPROX2_ARGSP(desc, file, resultIdx, method)                       \
+    TYPED_TEST(Approx2, desc) {                                            \
+        approx2ArgsTestPrecision<TypeParam>(                               \
+            string(TEST_DIR "/approx/" #file ".test"), resultIdx, method); \
     }
 
-    APPROX2_ARGSP(Approx2NearestArgsPrecision, approx2, 0, AF_INTERP_NEAREST);
-    APPROX2_ARGSP(Approx2LinearArgsPrecision, approx2, 1, AF_INTERP_LINEAR);
-
+APPROX2_ARGSP(Approx2NearestArgsPrecision, approx2, 0, AF_INTERP_NEAREST);
+APPROX2_ARGSP(Approx2LinearArgsPrecision, approx2, 1, AF_INTERP_LINEAR);
 
 //////////////////////////////////// CPP ////////////////////////////////////
 //
-TEST(Approx2, CPP)
-{
+TEST(Approx2, CPP) {
     const unsigned resultIdx = 1;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<float> > tests;
-    readTests<BT, float, float>(string(TEST_DIR"/approx/approx2.test"),numDims,in,tests);
+    readTests<BT, float, float>(string(TEST_DIR "/approx/approx2.test"),
+                                numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
     dim4 qdims = numDims[2];
 
-    array input(idims,&(in[0].front()));
-    array pos0(pdims,&(in[1].front()));
-    array pos1(qdims,&(in[2].front()));
+    array input(idims, &(in[0].front()));
+    array pos0(pdims, &(in[1].front()));
+    array pos1(qdims, &(in[2].front()));
     array output = approx2(input, pos0, pos1, AF_INTERP_LINEAR, 0);
 
     // Get result
@@ -255,10 +278,11 @@ TEST(Approx2, CPP)
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
-    bool ret = true;
+    bool ret      = true;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.001);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                             << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -267,25 +291,25 @@ TEST(Approx2, CPP)
 #undef BT
 }
 
-TEST(Approx2Cubic, CPP)
-{
+TEST(Approx2Cubic, CPP) {
     const unsigned resultIdx = 0;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
     vector<vector<BT> > in;
     vector<vector<float> > tests;
-    readTests<BT, float, float>(string(TEST_DIR"/approx/approx2_cubic.test"),numDims,in,tests);
+    readTests<BT, float, float>(string(TEST_DIR "/approx/approx2_cubic.test"),
+                                numDims, in, tests);
 
     dim4 idims = numDims[0];
     dim4 pdims = numDims[1];
     dim4 qdims = numDims[2];
 
-    array input(idims,&(in[0].front()));
+    array input(idims, &(in[0].front()));
     input = input.T();
-    array pos0(pdims,&(in[1].front()));
-    array pos1(qdims,&(in[2].front()));
-    pos0 = tile(pos0, 1, pos0.dims(0));
-    pos1 = tile(pos1.T(), pos1.dims(0));
+    array pos0(pdims, &(in[1].front()));
+    array pos1(qdims, &(in[2].front()));
+    pos0         = tile(pos0, 1, pos0.dims(0));
+    pos1         = tile(pos1.T(), pos1.dims(0));
     array output = approx2(input, pos0, pos1, AF_INTERP_BICUBIC_SPLINE, 0).T();
 
     // Get result
@@ -294,10 +318,10 @@ TEST(Approx2Cubic, CPP)
 
     // Compare result
     size_t nElems = tests[resultIdx].size();
-    bool ret = true;
+    bool ret      = true;
 
     float max = real(outData[0]), min = real(outData[0]);
-    for(int i=1; i < (int)nElems; ++i) {
+    for (int i = 1; i < (int)nElems; ++i) {
         min = (real(outData[i]) < min) ? real(outData[i]) : min;
         max = (real(outData[i]) > max) ? real(outData[i]) : max;
     }
@@ -305,8 +329,10 @@ TEST(Approx2Cubic, CPP)
     ASSERT_GT(range, 0.f);
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) < 0.01 * range);
-        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t" << outData[elIter] << "at: " << elIter << endl;
+        ret = (std::abs(tests[resultIdx][elIter] - outData[elIter]) <
+               0.01 * range);
+        ASSERT_EQ(true, ret) << tests[resultIdx][elIter] << "\t"
+                             << outData[elIter] << "at: " << elIter << endl;
     }
 
     // Delete
@@ -315,8 +341,7 @@ TEST(Approx2Cubic, CPP)
 #undef BT
 }
 
-TEST(Approx2, CPPNearestBatch)
-{
+TEST(Approx2, CPPNearestBatch) {
     array input = randu(200, 100, 10);
     array pos   = input.dims(0) * randu(100, 100, 10);
     array qos   = input.dims(1) * randu(100, 100, 10);
@@ -325,22 +350,23 @@ TEST(Approx2, CPPNearestBatch)
 
     array outSerial(pos.dims());
     for (int i = 0; i < pos.dims(2); i++) {
-        outSerial(span, span, i) = approx2(input(span, span, i),
-            pos(span, span, i), qos(span, span, i), AF_INTERP_NEAREST);
+        outSerial(span, span, i) =
+            approx2(input(span, span, i), pos(span, span, i),
+                    qos(span, span, i), AF_INTERP_NEAREST);
     }
 
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(2)) {
-        outGFOR(span, span, i) = approx2(input(span, span, i),
-            pos(span, span, i), qos(span, span, i), AF_INTERP_NEAREST);
+        outGFOR(span, span, i) =
+            approx2(input(span, span, i), pos(span, span, i),
+                    qos(span, span, i), AF_INTERP_NEAREST);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
-TEST(Approx2, CPPLinearBatch)
-{
+TEST(Approx2, CPPLinearBatch) {
     array input = randu(200, 100, 10);
     array pos   = input.dims(0) * randu(100, 100, 10);
     array qos   = input.dims(1) * randu(100, 100, 10);
@@ -349,22 +375,23 @@ TEST(Approx2, CPPLinearBatch)
 
     array outSerial(pos.dims());
     for (int i = 0; i < pos.dims(2); i++) {
-        outSerial(span, span, i) = approx2(input(span, span, i),
-            pos(span, span, i), qos(span, span, i), AF_INTERP_LINEAR);
+        outSerial(span, span, i) =
+            approx2(input(span, span, i), pos(span, span, i),
+                    qos(span, span, i), AF_INTERP_LINEAR);
     }
 
     array outGFOR(pos.dims());
     gfor(seq i, pos.dims(2)) {
-        outGFOR(span, span, i) = approx2(input(span, span, i),
-            pos(span, span, i), qos(span, span, i), AF_INTERP_LINEAR);
+        outGFOR(span, span, i) =
+            approx2(input(span, span, i), pos(span, span, i),
+                    qos(span, span, i), AF_INTERP_LINEAR);
     }
 
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outSerial)), 1e-3);
     ASSERT_NEAR(0, sum<float>(abs(outBatch - outGFOR)), 1e-3);
 }
 
-TEST(Approx2, CPPNearestMaxDims)
-{
+TEST(Approx2, CPPNearestMaxDims) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
@@ -387,8 +414,7 @@ TEST(Approx2, CPPNearestMaxDims)
     SUCCEED();
 }
 
-TEST(Approx2, CPPLinearMaxDims)
-{
+TEST(Approx2, CPPLinearMaxDims) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
@@ -411,8 +437,7 @@ TEST(Approx2, CPPLinearMaxDims)
     SUCCEED();
 }
 
-TEST(Approx2, CPPCubicMaxDims)
-{
+TEST(Approx2, CPPCubicMaxDims) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
@@ -435,76 +460,77 @@ TEST(Approx2, CPPCubicMaxDims)
     SUCCEED();
 }
 
-TEST(Approx2, OtherDimLinear)
-{
+TEST(Approx2, OtherDimLinear) {
     int start = 0;
-    int stop = 10000;
-    int step = 100;
-    int num = 1000;
-    array xi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
-    array yi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
-    array zi = 4 * xi * yi - 3 * xi;
-    array xo = af::round(step * randu(num, 2, 2, 2));
-    array yo = af::round(step * randu(num, 2, 2, 2));
-    array zo = 4 * xo * yo - 3 * xo;
+    int stop  = 10000;
+    int step  = 100;
+    int num   = 1000;
+    array xi  = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array yi  = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array zi  = 4 * xi * yi - 3 * xi;
+    array xo  = af::round(step * randu(num, 2, 2, 2));
+    array yo  = af::round(step * randu(num, 2, 2, 2));
+    array zo  = 4 * xo * yo - 3 * xo;
     for (int d = 1; d < 3; d++) {
-        dim4 rdims(0,1,2,3);
+        dim4 rdims(0, 1, 2, 3);
         rdims[0] = d;
         rdims[d] = 0;
 
-        array zi_reordered = reorder(zi, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array yo_reordered = reorder(yo, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array zo_reordered = approx2(zi_reordered,
-                                     xo_reordered, d, start, step,
-                                     yo_reordered, d + 1, start, step,
-                                     AF_INTERP_LINEAR);
+        array zi_reordered =
+            reorder(zi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered =
+            reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered =
+            reorder(yo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array zo_reordered =
+            approx2(zi_reordered, xo_reordered, d, start, step, yo_reordered,
+                    d + 1, start, step, AF_INTERP_LINEAR);
         rdims[d] = 0;
         rdims[0] = d;
-        array res = af::reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array res =
+            af::reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
         ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
     }
 }
 
-TEST(Approx2, OtherDimCubic)
-{
+TEST(Approx2, OtherDimCubic) {
     float start = 0;
-    float stop = 100;
-    float step = 0.01;
-    int num = 1000;
-    array xi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
-    array yi = af::tile(seq(start, stop, step), 1, 2, 2, 2);
-    array zi = 4 * sin(xi) * cos(yi);
-    array xo = af::round(step * randu(num, 2, 2, 2));
-    array yo = af::round(step * randu(num, 2, 2, 2));
-    array zo = 4 * sin(xo) * cos(yo);
+    float stop  = 100;
+    float step  = 0.01;
+    int num     = 1000;
+    array xi    = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array yi    = af::tile(seq(start, stop, step), 1, 2, 2, 2);
+    array zi    = 4 * sin(xi) * cos(yi);
+    array xo    = af::round(step * randu(num, 2, 2, 2));
+    array yo    = af::round(step * randu(num, 2, 2, 2));
+    array zo    = 4 * sin(xo) * cos(yo);
     for (int d = 1; d < 3; d++) {
-        dim4 rdims(0,1,2,3);
+        dim4 rdims(0, 1, 2, 3);
         rdims[0] = d;
         rdims[d] = 0;
 
-        array zi_reordered = reorder(zi, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array xo_reordered = reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array yo_reordered = reorder(yo, rdims[0], rdims[1], rdims[2], rdims[3]);
-        array zo_reordered = approx2(zi_reordered,
-                                     xo_reordered, d, start, step,
-                                     yo_reordered, d + 1, start, step,
-                                     AF_INTERP_CUBIC);
+        array zi_reordered =
+            reorder(zi, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array xo_reordered =
+            reorder(xo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array yo_reordered =
+            reorder(yo, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array zo_reordered =
+            approx2(zi_reordered, xo_reordered, d, start, step, yo_reordered,
+                    d + 1, start, step, AF_INTERP_CUBIC);
         rdims[d] = 0;
         rdims[0] = d;
-        array res = reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
+        array res =
+            reorder(yo_reordered, rdims[0], rdims[1], rdims[2], rdims[3]);
         ASSERT_NEAR(0, af::max<float>(af::abs(res - yo)), 1E-3);
     }
 }
 
-TEST(Approx2, CPPUsage)
-{
+TEST(Approx2, CPPUsage) {
     //! [ex_signal_approx2]
 
     // Input data array.
-    float input_vals[9] = {1.0, 1.0, 1.0,
-                           2.0, 2.0, 2.0,
-                           3.0, 3.0, 3.0};
+    float input_vals[9] = {1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0};
     array input(3, 3, input_vals);
     // [3 3 1 1]
     //     1.0000     2.0000     3.0000
@@ -533,21 +559,17 @@ TEST(Approx2, CPPUsage)
 
     //! [ex_signal_approx2]
 
-    float expected_interp[4] = {1.5, 1.5,
-                                2.5, 2.5};
+    float expected_interp[4] = {1.5, 1.5, 2.5, 2.5};
 
     array interp_gold(2, 2, expected_interp);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-TEST(Approx2, CPPUniformUsage)
-{
+TEST(Approx2, CPPUniformUsage) {
     //! [ex_signal_approx2_uniform]
 
     // Input data array.
-    float input_vals[9] = {1.0, 1.0, 1.0,
-                           2.0, 2.0, 2.0,
-                           3.0, 3.0, 3.0};
+    float input_vals[9] = {1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0};
     array input(3, 3, input_vals);
     // [3 3 1 1]
     //     1.0000     2.0000     3.0000
@@ -573,184 +595,163 @@ TEST(Approx2, CPPUniformUsage)
     // Define range of indices with which the input values will
     // correspond along both dimensions to be interpolated.
     const double idx_start_dim0 = 0.0;
-    const double idx_step_dim0 = 1.0;
-    const int interp_dim0 = 0;
-    const int interp_dim1 = 1;
-    array interp = approx2(input,
-                           pos0, interp_dim0, idx_start_dim0, idx_step_dim0,
-                           pos1, interp_dim1, idx_start_dim0, idx_step_dim0);
+    const double idx_step_dim0  = 1.0;
+    const int interp_dim0       = 0;
+    const int interp_dim1       = 1;
+    array interp =
+        approx2(input, pos0, interp_dim0, idx_start_dim0, idx_step_dim0, pos1,
+                interp_dim1, idx_start_dim0, idx_step_dim0);
     // [2 2 1 1]
     //     1.5000     2.5000
     //     1.5000     2.5000
 
     //! [ex_signal_approx2_uniform]
 
-    float expected_interp[4] = {1.5, 1.5,
-                                2.5, 2.5};
+    float expected_interp[4] = {1.5, 1.5, 2.5, 2.5};
 
     array interp_gold(2, 2, expected_interp);
     ASSERT_ARRAYS_EQ(interp, interp_gold);
 }
 
-TEST(Approx2, CPPUniformOneDimIndices)
-{
-    float inv[9] = {10.0, 20.0, 30.0,
-                    40.0, 50.0, 60.0,
-                    70.0, 80.0, 90.0};
-    array input(dim4(3,3), inv);
+TEST(Approx2, CPPUniformOneDimIndices) {
+    float inv[9] = {10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0};
+    array input(dim4(3, 3), inv);
 
     float p0[3] = {0.0, 1.0, 2.0};
     float p1[3] = {0.0, 1.0, 2.0};
-    array pos0(dim4(3,1), p0);
-    array pos1(dim4(3,1), p1);
+    array pos0(dim4(3, 1), p0);
+    array pos1(dim4(3, 1), p1);
 
-    const int pos0_interp_grid_start = 0;
+    const int pos0_interp_grid_start   = 0;
     const double pos0_interp_grid_step = 1;
-    array interpolated = approx2(input,
-                                 pos0, 0, pos0_interp_grid_start, pos0_interp_grid_step,
-                                 pos1, 1, pos0_interp_grid_start, pos0_interp_grid_step);
+    array interpolated =
+        approx2(input, pos0, 0, pos0_interp_grid_start, pos0_interp_grid_step,
+                pos1, 1, pos0_interp_grid_start, pos0_interp_grid_step);
 
     float expected_interp[3] = {10.0, 50.0, 90.0};
 
-
-    array interpolated_gold(dim4(3,1), expected_interp);
+    array interpolated_gold(dim4(3, 1), expected_interp);
     ASSERT_ARRAYS_EQ(interpolated, interpolated_gold);
 }
 
-TEST(Approx2, CPPUniformTwoDimIndices)
-{
-    float inv[9] = {10.0, 20.0, 30.0,
-                    40.0, 50.0, 60.0,
-                    70.0, 80.0, 90.0};
-    array input(dim4(3,3), inv);
+TEST(Approx2, CPPUniformTwoDimIndices) {
+    float inv[9] = {10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0};
+    array input(dim4(3, 3), inv);
 
     float p0[4] = {0, 2, 0, 2};
     float p1[4] = {0, 0, 2, 2};
-    array pos0(dim4(2,2), p0);
-    array pos1(dim4(2,2), p1);
-    const int pos0_interp_grid_start = 0;
+    array pos0(dim4(2, 2), p0);
+    array pos1(dim4(2, 2), p1);
+    const int pos0_interp_grid_start   = 0;
     const double pos0_interp_grid_step = 1;
-    const int pos0_interp_dim = 0;
-    const int pos1_interp_dim = 1;
+    const int pos0_interp_dim          = 0;
+    const int pos1_interp_dim          = 1;
 
-    array interpolated = approx2(input,
-                                 pos0, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
-                                 pos1, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+    array interpolated =
+        approx2(input, pos0, pos0_interp_dim, pos0_interp_grid_start,
+                pos0_interp_grid_step, pos1, pos1_interp_dim,
+                pos0_interp_grid_start, pos0_interp_grid_step);
 
     float expected_interp[4] = {10.0, 30.0, 70.0, 90.0};
-    array interpolated_gold(dim4(2,2), expected_interp);
+    array interpolated_gold(dim4(2, 2), expected_interp);
     ASSERT_ARRAYS_EQ(interpolated, interpolated_gold);
 }
 
-TEST(Approx2, CPPUniformInvalidStepSize)
-{
-    try
-    {
-        float inv[9] = {10.0, 20.0, 30.0,
-                        40.0, 50.0, 60.0,
-                        70.0, 80.0, 90.0};
-        array in(dim4(3,3), inv);
+TEST(Approx2, CPPUniformInvalidStepSize) {
+    try {
+        float inv[9] = {10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0};
+        array in(dim4(3, 3), inv);
         float pv[3] = {0.0, -1.0, -2.0};
-        array pos(dim4(3,1), pv);
-        const int pos0_interp_grid_start = -1;
+        array pos(dim4(3, 1), pv);
+        const int pos0_interp_grid_start   = -1;
         const double pos0_interp_grid_step = 0;
-        const int pos0_interp_dim = 0;
-        const int pos1_interp_dim = 1;
+        const int pos0_interp_dim          = 0;
+        const int pos1_interp_dim          = 1;
 
-        array interpolated = approx2(in,
-                                     pos, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
-                                     pos, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+        array interpolated =
+            approx2(in, pos, pos0_interp_dim, pos0_interp_grid_start,
+                    pos0_interp_grid_step, pos, pos1_interp_dim,
+                    pos0_interp_grid_start, pos0_interp_grid_step);
         FAIL() << "Expected af::exception\n";
-    } catch (af::exception &ex) {
-        SUCCEED();
-    } catch(...) {
+    } catch (af::exception& ex) { SUCCEED(); } catch (...) {
         FAIL() << "Expected af::exception\n";
     }
 }
 
-TEST(Approx2, CPPUniformColumnMajorInterpolation)
-{
-    float inv[9] = {10.0, 20.0, 30.0,
-                    40.0, 50.0, 60.0,
-                    70.0, 80.0, 90.0};
-    array input(dim4(3,3), inv);
+TEST(Approx2, CPPUniformColumnMajorInterpolation) {
+    float inv[9] = {10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0};
+    array input(dim4(3, 3), inv);
 
     float p0[4] = {0, 2, 0, 2};
     float p1[4] = {0, 0, 2, 2};
-    array pos0(dim4(2,2), p0);
-    array pos1(dim4(2,2), p1);
-    const int pos0_interp_dim = 0;
-    const int pos1_interp_dim = 1;
-    const int pos0_interp_grid_start = 0;
+    array pos0(dim4(2, 2), p0);
+    array pos1(dim4(2, 2), p1);
+    const int pos0_interp_dim          = 0;
+    const int pos1_interp_dim          = 1;
+    const int pos0_interp_grid_start   = 0;
     const double pos0_interp_grid_step = 1;
 
-    array first = approx2(input,
-                          pos0, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
-                          pos1, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+    array first = approx2(input, pos0, pos0_interp_dim, pos0_interp_grid_start,
+                          pos0_interp_grid_step, pos1, pos1_interp_dim,
+                          pos0_interp_grid_start, pos0_interp_grid_step);
 
-    array second = approx2(input,
-                           pos1, pos1_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step,
-                           pos0, pos0_interp_dim, pos0_interp_grid_start, pos0_interp_grid_step);
+    array second = approx2(input, pos1, pos1_interp_dim, pos0_interp_grid_start,
+                           pos0_interp_grid_step, pos0, pos0_interp_dim,
+                           pos0_interp_grid_start, pos0_interp_grid_step);
 
     // Verify.
     float expected_interp[4] = {10.0, 30.0, 70.0, 90.0};
-    array interpolated_gold(dim4(2,2), expected_interp);
+    array interpolated_gold(dim4(2, 2), expected_interp);
     ASSERT_ARRAYS_EQ(first, interpolated_gold);
     ASSERT_ARRAYS_EQ(first, second);
 }
 
-TEST(Approx2, CPPUniformRowMajorInterpolation)
-{
-    float inv[9] = {10.0, 20.0, 30.0,
-                    40.0, 50.0, 60.0,
-                    70.0, 80.0, 90.0};
-    array input(dim4(3,3), inv);
+TEST(Approx2, CPPUniformRowMajorInterpolation) {
+    float inv[9] = {10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0};
+    array input(dim4(3, 3), inv);
 
     float p0[4] = {0, 2, 0, 2};
     float p1[4] = {0, 0, 2, 2};
-    array pos0(dim4(2,2), p0);
-    array pos1(dim4(2,2), p1);
-    const int pos0_interp_grid_start = 0;
+    array pos0(dim4(2, 2), p0);
+    array pos1(dim4(2, 2), p1);
+    const int pos0_interp_grid_start   = 0;
     const double pos0_interp_grid_step = 1;
 
-    array first = approx2(input,
-                          pos0, 1, pos0_interp_grid_start, pos0_interp_grid_step,
-                          pos1, 0, pos0_interp_grid_start, pos0_interp_grid_step);
+    array first =
+        approx2(input, pos0, 1, pos0_interp_grid_start, pos0_interp_grid_step,
+                pos1, 0, pos0_interp_grid_start, pos0_interp_grid_step);
 
-    array second = approx2(input,
-                           pos1, 0, pos0_interp_grid_start, pos0_interp_grid_step,
-                           pos0, 1, pos0_interp_grid_start, pos0_interp_grid_step);
+    array second =
+        approx2(input, pos1, 0, pos0_interp_grid_start, pos0_interp_grid_step,
+                pos0, 1, pos0_interp_grid_start, pos0_interp_grid_step);
 
     // Verify.
     float expected_interp[4] = {10.0, 70.0, 30.0, 90.0};
-    array interpolated_gold(dim4(2,2), expected_interp);
+    array interpolated_gold(dim4(2, 2), expected_interp);
     ASSERT_ARRAYS_EQ(first, interpolated_gold);
     ASSERT_ARRAYS_EQ(first, second);
 }
 
-TEST(Approx2, CPPEmptyPos)
-{
+TEST(Approx2, CPPEmptyPos) {
     float inv[3] = {10.0, 20.0, 30.0};
-    array in(dim4(3,1), inv);
+    array in(dim4(3, 1), inv);
     array pos;
     array interpolated = approx2(in, pos, pos);
     ASSERT_TRUE(pos.isempty());
     ASSERT_TRUE(interpolated.isempty());
 }
 
-TEST(Approx2, CPPEmptyInput)
-{
+TEST(Approx2, CPPEmptyInput) {
     array in;
     float pv[3] = {0.0, 1.0, 2.0};
-    array pos(dim4(3,1), pv);
+    array pos(dim4(3, 1), pv);
 
     array interpolated = approx2(in, pos, pos);
     ASSERT_TRUE(in.isempty());
     ASSERT_TRUE(interpolated.isempty());
 }
 
-TEST(Approx2, CPPEmptyPosAndInput)
-{
+TEST(Approx2, CPPEmptyPosAndInput) {
     array in;
     array pos;
     array interpolated = approx2(in, pos, pos);
diff --git a/test/array.cpp b/test/array.cpp
index d977a97200..3ddf666370 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -7,220 +7,208 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cstddef>
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <cstddef>
 
 using namespace af;
 using std::vector;
 
 template<typename T>
-class Array : public ::testing::Test
-{
-
-};
+class Array : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char, int, uint, intl, uintl, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
+                         int, uint, intl, uintl, short, ushort>
+    TestTypes;
 
 TYPED_TEST_CASE(Array, TestTypes);
 
-TEST(Array, ConstructorDefault)
-{
+TEST(Array, ConstructorDefault) {
     array a;
-    EXPECT_EQ(0u,    a.numdims());
-    EXPECT_EQ(dim_t(0),    a.dims(0));
-    EXPECT_EQ(dim_t(0),    a.elements());
-    EXPECT_EQ(f32,  a.type());
-    EXPECT_EQ(0u,    a.bytes());
-    EXPECT_FALSE(   a.isrow());
-    EXPECT_FALSE(   a.iscomplex());
-    EXPECT_FALSE(   a.isdouble());
-    EXPECT_FALSE(   a.isbool());
-
-    EXPECT_FALSE(    a.isvector());
-    EXPECT_FALSE(    a.iscolumn());
-
-    EXPECT_TRUE(    a.isreal());
-    EXPECT_TRUE(    a.isempty());
-    EXPECT_TRUE(    a.issingle());
-    EXPECT_TRUE(    a.isfloating());
-    EXPECT_TRUE(    a.isrealfloating());
+    EXPECT_EQ(0u, a.numdims());
+    EXPECT_EQ(dim_t(0), a.dims(0));
+    EXPECT_EQ(dim_t(0), a.elements());
+    EXPECT_EQ(f32, a.type());
+    EXPECT_EQ(0u, a.bytes());
+    EXPECT_FALSE(a.isrow());
+    EXPECT_FALSE(a.iscomplex());
+    EXPECT_FALSE(a.isdouble());
+    EXPECT_FALSE(a.isbool());
+
+    EXPECT_FALSE(a.isvector());
+    EXPECT_FALSE(a.iscolumn());
+
+    EXPECT_TRUE(a.isreal());
+    EXPECT_TRUE(a.isempty());
+    EXPECT_TRUE(a.issingle());
+    EXPECT_TRUE(a.isfloating());
+    EXPECT_TRUE(a.isrealfloating());
 }
 
-TYPED_TEST(Array, ConstructorEmptyDim4)
-{
+TYPED_TEST(Array, ConstructorEmptyDim4) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     dim4 dims(3, 3, 3, 3);
     array a(dims, type);
-    EXPECT_EQ(4u,    a.numdims());
-    EXPECT_EQ(dim_t(3),    a.dims(0));
-    EXPECT_EQ(dim_t(3),    a.dims(1));
-    EXPECT_EQ(dim_t(3),    a.dims(2));
-    EXPECT_EQ(dim_t(3),    a.dims(3));
-    EXPECT_EQ(dim_t(81),   a.elements());
-    EXPECT_EQ(type,  a.type());
+    EXPECT_EQ(4u, a.numdims());
+    EXPECT_EQ(dim_t(3), a.dims(0));
+    EXPECT_EQ(dim_t(3), a.dims(1));
+    EXPECT_EQ(dim_t(3), a.dims(2));
+    EXPECT_EQ(dim_t(3), a.dims(3));
+    EXPECT_EQ(dim_t(81), a.elements());
+    EXPECT_EQ(type, a.type());
 }
 
-TYPED_TEST(Array, ConstructorEmpty1D)
-{
+TYPED_TEST(Array, ConstructorEmpty1D) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, type);
-    EXPECT_EQ(1u,    a.numdims());
-    EXPECT_EQ(dim_t(2),    a.dims(0));
-    EXPECT_EQ(dim_t(1),    a.dims(1));
-    EXPECT_EQ(dim_t(1),    a.dims(2));
-    EXPECT_EQ(dim_t(1),    a.dims(3));
-    EXPECT_EQ(dim_t(2),    a.elements());
-    EXPECT_EQ(type,  a.type());
+    EXPECT_EQ(1u, a.numdims());
+    EXPECT_EQ(dim_t(2), a.dims(0));
+    EXPECT_EQ(dim_t(1), a.dims(1));
+    EXPECT_EQ(dim_t(1), a.dims(2));
+    EXPECT_EQ(dim_t(1), a.dims(3));
+    EXPECT_EQ(dim_t(2), a.elements());
+    EXPECT_EQ(type, a.type());
 }
 
-TYPED_TEST(Array, ConstructorEmpty2D)
-{
+TYPED_TEST(Array, ConstructorEmpty2D) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, type);
-    EXPECT_EQ(2u,    a.numdims());
-    EXPECT_EQ(dim_t(2),    a.dims(0));
-    EXPECT_EQ(dim_t(2),    a.dims(1));
-    EXPECT_EQ(dim_t(1),    a.dims(2));
-    EXPECT_EQ(dim_t(1),    a.dims(3));
-    EXPECT_EQ(dim_t(4),    a.elements());
-    EXPECT_EQ(type,  a.type());
+    EXPECT_EQ(2u, a.numdims());
+    EXPECT_EQ(dim_t(2), a.dims(0));
+    EXPECT_EQ(dim_t(2), a.dims(1));
+    EXPECT_EQ(dim_t(1), a.dims(2));
+    EXPECT_EQ(dim_t(1), a.dims(3));
+    EXPECT_EQ(dim_t(4), a.elements());
+    EXPECT_EQ(type, a.type());
 }
 
-TYPED_TEST(Array, ConstructorEmpty3D)
-{
+TYPED_TEST(Array, ConstructorEmpty3D) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, 2, type);
-    EXPECT_EQ(3u,    a.numdims());
-    EXPECT_EQ(dim_t(2),    a.dims(0));
-    EXPECT_EQ(dim_t(2),    a.dims(1));
-    EXPECT_EQ(dim_t(2),    a.dims(2));
-    EXPECT_EQ(dim_t(1),    a.dims(3));
-    EXPECT_EQ(dim_t(8),    a.elements());
-    EXPECT_EQ(type,  a.type());
+    EXPECT_EQ(3u, a.numdims());
+    EXPECT_EQ(dim_t(2), a.dims(0));
+    EXPECT_EQ(dim_t(2), a.dims(1));
+    EXPECT_EQ(dim_t(2), a.dims(2));
+    EXPECT_EQ(dim_t(1), a.dims(3));
+    EXPECT_EQ(dim_t(8), a.elements());
+    EXPECT_EQ(type, a.type());
 }
 
-TYPED_TEST(Array, ConstructorEmpty4D)
-{
+TYPED_TEST(Array, ConstructorEmpty4D) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, 2, 2, type);
-    EXPECT_EQ(4u,    a.numdims());
-    EXPECT_EQ(dim_t(2),    a.dims(0));
-    EXPECT_EQ(dim_t(2),    a.dims(1));
-    EXPECT_EQ(dim_t(2),    a.dims(2));
-    EXPECT_EQ(dim_t(2),    a.dims(3));
-    EXPECT_EQ(dim_t(16),   a.elements());
+    EXPECT_EQ(4u, a.numdims());
+    EXPECT_EQ(dim_t(2), a.dims(0));
+    EXPECT_EQ(dim_t(2), a.dims(1));
+    EXPECT_EQ(dim_t(2), a.dims(2));
+    EXPECT_EQ(dim_t(2), a.dims(3));
+    EXPECT_EQ(dim_t(16), a.elements());
     EXPECT_EQ(type, a.type());
 }
 
-TYPED_TEST(Array, ConstructorHostPointer1D)
-{
+TYPED_TEST(Array, ConstructorHostPointer1D) {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
+    dtype type    = (dtype)dtype_traits<TypeParam>::af_type;
     size_t nelems = 10;
     vector<TypeParam> data(nelems, 4);
     array a(nelems, &data.front(), afHost);
-    EXPECT_EQ(1u,        a.numdims());
-    EXPECT_EQ(dim_t(nelems),   a.dims(0));
-    EXPECT_EQ(dim_t(1),        a.dims(1));
-    EXPECT_EQ(dim_t(1),        a.dims(2));
-    EXPECT_EQ(dim_t(1),        a.dims(3));
-    EXPECT_EQ(dim_t(nelems),   a.elements());
-    EXPECT_EQ(type,     a.type());
+    EXPECT_EQ(1u, a.numdims());
+    EXPECT_EQ(dim_t(nelems), a.dims(0));
+    EXPECT_EQ(dim_t(1), a.dims(1));
+    EXPECT_EQ(dim_t(1), a.dims(2));
+    EXPECT_EQ(dim_t(1), a.dims(3));
+    EXPECT_EQ(dim_t(nelems), a.elements());
+    EXPECT_EQ(type, a.type());
 
     vector<TypeParam> out(nelems);
     a.host(&out.front());
     ASSERT_TRUE(std::equal(data.begin(), data.end(), out.begin()));
 }
 
-TYPED_TEST(Array, ConstructorHostPointer2D)
-{
+TYPED_TEST(Array, ConstructorHostPointer2D) {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
+    dtype type      = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 2;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size;
     vector<TypeParam> data(nelems, 4);
     array a(dim_size, dim_size, &data.front(), afHost);
-    EXPECT_EQ(ndims,    a.numdims());
+    EXPECT_EQ(ndims, a.numdims());
     EXPECT_EQ(dim_t(dim_size), a.dims(0));
     EXPECT_EQ(dim_t(dim_size), a.dims(1));
-    EXPECT_EQ(dim_t(1),        a.dims(2));
-    EXPECT_EQ(dim_t(1),        a.dims(3));
-    EXPECT_EQ(dim_t(nelems),   a.elements());
-    EXPECT_EQ(type,     a.type());
+    EXPECT_EQ(dim_t(1), a.dims(2));
+    EXPECT_EQ(dim_t(1), a.dims(3));
+    EXPECT_EQ(dim_t(nelems), a.elements());
+    EXPECT_EQ(type, a.type());
 
     vector<TypeParam> out(nelems);
     a.host(&out.front());
     ASSERT_TRUE(std::equal(data.begin(), data.end(), out.begin()));
 }
 
-TYPED_TEST(Array, ConstructorHostPointer3D)
-{
+TYPED_TEST(Array, ConstructorHostPointer3D) {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
+    dtype type      = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 3;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size * dim_size;
     vector<TypeParam> data(nelems, 4);
     array a(dim_size, dim_size, dim_size, &data.front(), afHost);
-    EXPECT_EQ(ndims,    a.numdims());
+    EXPECT_EQ(ndims, a.numdims());
     EXPECT_EQ(dim_t(dim_size), a.dims(0));
     EXPECT_EQ(dim_t(dim_size), a.dims(1));
     EXPECT_EQ(dim_t(dim_size), a.dims(2));
-    EXPECT_EQ(dim_t(1),        a.dims(3));
-    EXPECT_EQ(dim_t(nelems),   a.elements());
-    EXPECT_EQ(type,     a.type());
+    EXPECT_EQ(dim_t(1), a.dims(3));
+    EXPECT_EQ(dim_t(nelems), a.elements());
+    EXPECT_EQ(type, a.type());
 
     vector<TypeParam> out(nelems);
     a.host(&out.front());
     ASSERT_TRUE(std::equal(data.begin(), data.end(), out.begin()));
 }
 
-TYPED_TEST(Array, ConstructorHostPointer4D)
-{
+TYPED_TEST(Array, ConstructorHostPointer4D) {
     if (noDoubleTests<TypeParam>()) return;
 
-    dtype type = (dtype)dtype_traits<TypeParam>::af_type;
+    dtype type      = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 4;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size * dim_size * dim_size;
     vector<TypeParam> data(nelems, 4);
     array a(dim_size, dim_size, dim_size, dim_size, &data.front(), afHost);
-    EXPECT_EQ(ndims,    a.numdims());
+    EXPECT_EQ(ndims, a.numdims());
     EXPECT_EQ(dim_t(dim_size), a.dims(0));
     EXPECT_EQ(dim_t(dim_size), a.dims(1));
     EXPECT_EQ(dim_t(dim_size), a.dims(2));
     EXPECT_EQ(dim_t(dim_size), a.dims(3));
-    EXPECT_EQ(dim_t(nelems),   a.elements());
-    EXPECT_EQ(type,     a.type());
+    EXPECT_EQ(dim_t(nelems), a.elements());
+    EXPECT_EQ(type, a.type());
 
     vector<TypeParam> out(nelems);
     a.host(&out.front());
     ASSERT_TRUE(std::equal(data.begin(), data.end(), out.begin()));
 }
 
-TYPED_TEST(Array, TypeAttributes)
-{
+TYPED_TEST(Array, TypeAttributes) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array one(10, type);
-    switch(type) {
+    switch (type) {
         case f32:
             EXPECT_TRUE(one.isfloating());
             EXPECT_FALSE(one.isdouble());
@@ -342,13 +330,10 @@ TYPED_TEST(Array, TypeAttributes)
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
             break;
-
     }
-
 }
 
-TEST(Array, ShapeAttributes)
-{
+TEST(Array, ShapeAttributes) {
     dim_t dim_size = 10;
     array scalar(1);
     array col(dim_size);
@@ -357,56 +342,55 @@ TEST(Array, ShapeAttributes)
     array volume(dim_size, dim_size, dim_size);
     array hypercube(dim_size, dim_size, dim_size, dim_size);
 
-    EXPECT_FALSE(scalar.    isempty());
-    EXPECT_FALSE(col.       isempty());
-    EXPECT_FALSE(row.       isempty());
-    EXPECT_FALSE(matrix.    isempty());
-    EXPECT_FALSE(volume.    isempty());
-    EXPECT_FALSE(hypercube. isempty());
-
-    EXPECT_TRUE(scalar.     isscalar());
-    EXPECT_FALSE(col.       isscalar());
-    EXPECT_FALSE(row.       isscalar());
-    EXPECT_FALSE(matrix.    isscalar());
-    EXPECT_FALSE(volume.    isscalar());
-    EXPECT_FALSE(hypercube. isscalar());
-
-    EXPECT_FALSE(scalar.    isvector());
-    EXPECT_TRUE(col.        isvector());
-    EXPECT_TRUE(row.        isvector());
-    EXPECT_FALSE(matrix.    isvector());
-    EXPECT_FALSE(volume.    isvector());
-    EXPECT_FALSE(hypercube. isvector());
-
-    EXPECT_FALSE(scalar.    isrow());
-    EXPECT_FALSE(col.       isrow());
-    EXPECT_TRUE(row.        isrow());
-    EXPECT_FALSE(matrix.    isrow());
-    EXPECT_FALSE(volume.    isrow());
-    EXPECT_FALSE(hypercube. isrow());
-
-    EXPECT_FALSE(scalar.    iscolumn());
-    EXPECT_TRUE(col.        iscolumn());
-    EXPECT_FALSE(row.       iscolumn());
-    EXPECT_FALSE(matrix.    iscolumn());
-    EXPECT_FALSE(volume.    iscolumn());
-    EXPECT_FALSE(hypercube. iscolumn());
+    EXPECT_FALSE(scalar.isempty());
+    EXPECT_FALSE(col.isempty());
+    EXPECT_FALSE(row.isempty());
+    EXPECT_FALSE(matrix.isempty());
+    EXPECT_FALSE(volume.isempty());
+    EXPECT_FALSE(hypercube.isempty());
+
+    EXPECT_TRUE(scalar.isscalar());
+    EXPECT_FALSE(col.isscalar());
+    EXPECT_FALSE(row.isscalar());
+    EXPECT_FALSE(matrix.isscalar());
+    EXPECT_FALSE(volume.isscalar());
+    EXPECT_FALSE(hypercube.isscalar());
+
+    EXPECT_FALSE(scalar.isvector());
+    EXPECT_TRUE(col.isvector());
+    EXPECT_TRUE(row.isvector());
+    EXPECT_FALSE(matrix.isvector());
+    EXPECT_FALSE(volume.isvector());
+    EXPECT_FALSE(hypercube.isvector());
+
+    EXPECT_FALSE(scalar.isrow());
+    EXPECT_FALSE(col.isrow());
+    EXPECT_TRUE(row.isrow());
+    EXPECT_FALSE(matrix.isrow());
+    EXPECT_FALSE(volume.isrow());
+    EXPECT_FALSE(hypercube.isrow());
+
+    EXPECT_FALSE(scalar.iscolumn());
+    EXPECT_TRUE(col.iscolumn());
+    EXPECT_FALSE(row.iscolumn());
+    EXPECT_FALSE(matrix.iscolumn());
+    EXPECT_FALSE(volume.iscolumn());
+    EXPECT_FALSE(hypercube.iscolumn());
 }
 
-TEST(Array, ISSUE_951)
-{
-// This works
-    //const array a(100, 100);
-    //array b = a.cols(0, 20);
-    //b = b.rows(10, 20);
+TEST(Array, ISSUE_951) {
+    // This works
+    // const array a(100, 100);
+    // array b = a.cols(0, 20);
+    // b = b.rows(10, 20);
 
-// This works
-    //array a(100, 100);
-    //array b = a.cols(0, 20).rows(10, 20);
+    // This works
+    // array a(100, 100);
+    // array b = a.cols(0, 20).rows(10, 20);
 
-// This fails with linking error
+    // This fails with linking error
     const array a = randu(100, 100);
-    array b = a.cols(0, 20).rows(10, 20);
+    array b       = a.cols(0, 20).rows(10, 20);
 }
 
 TEST(Array, CreateHandleInvalidNullDimsPointer) {
@@ -414,10 +398,8 @@ TEST(Array, CreateHandleInvalidNullDimsPointer) {
     EXPECT_EQ(AF_ERR_ARG, af_create_handle(&out, 1, NULL, f32));
 }
 
-
-TEST(Device, simple)
-{
-    array a = randu(5,5);
+TEST(Device, simple) {
+    array a = randu(5, 5);
     {
         float *ptr0 = a.device<float>();
         float *ptr1 = a.device<float>();
@@ -432,52 +414,48 @@ TEST(Device, simple)
     }
 }
 
-TEST(Device, index)
-{
-    array a = randu(5,5);
+TEST(Device, index) {
+    array a = randu(5, 5);
     array b = a(span, 0);
 
     ASSERT_NE(a.device<float>(), b.device<float>());
 }
 
-TEST(Device, unequal)
-{
+TEST(Device, unequal) {
     {
-        array a = randu(5,5);
+        array a    = randu(5, 5);
         float *ptr = a.device<float>();
-        array b = a;
+        array b    = a;
         ASSERT_NE(ptr, b.device<float>());
         ASSERT_EQ(ptr, a.device<float>());
     }
 
     {
-        array a = randu(5,5);
+        array a    = randu(5, 5);
         float *ptr = a.device<float>();
-        array b = a;
+        array b    = a;
         ASSERT_NE(ptr, a.device<float>());
         ASSERT_EQ(ptr, b.device<float>());
     }
 }
 
-TEST(DeviceId, Same)
-{
-    array a = randu(5,5);
+TEST(DeviceId, Same) {
+    array a = randu(5, 5);
     ASSERT_EQ(getDevice(), getDeviceId(a));
 }
 
-TEST(DeviceId, Different)
-{
+TEST(DeviceId, Different) {
     int ndevices = getDeviceCount();
     if (ndevices < 2) return;
     int id0 = getDevice();
     int id1 = (id0 + 1) % ndevices;
 
     {
-        array a = randu(5,5);
+        array a = randu(5, 5);
         ASSERT_EQ(getDeviceId(a), id0);
         setDevice(id1);
 
-        array b = randu(5,5);
+        array b = randu(5, 5);
 
         ASSERT_EQ(getDeviceId(a), id0);
         ASSERT_EQ(getDeviceId(b), id1);
@@ -495,34 +473,30 @@ TEST(DeviceId, Different)
     deviceGC();
 }
 
-TEST(Device, empty)
-{
+TEST(Device, empty) {
     array a = array();
     ASSERT_EQ(a.device<float>() == NULL, 1);
 }
 
-TEST(Device, JIT)
-{
+TEST(Device, JIT) {
     array a = constant(1, 5, 5);
     ASSERT_EQ(a.device<float>() != NULL, 1);
 }
 
-TYPED_TEST(Array, Scalar)
-{
+TYPED_TEST(Array, Scalar) {
     if (noDoubleTests<TypeParam>()) return;
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
-    array a = randu(dim4(1), type);
+    array a    = randu(dim4(1), type);
 
     vector<TypeParam> gold(a.elements());
 
-    a.host((void*)gold.data());
+    a.host((void *)gold.data());
 
-    EXPECT_EQ(true, gold[0]==a.scalar<TypeParam>());
+    EXPECT_EQ(true, gold[0] == a.scalar<TypeParam>());
 }
 
-TEST(Array, ScalarTypeMismatch)
-{
+TEST(Array, ScalarTypeMismatch) {
     array a = constant(1.0, dim4(1), f32);
 
     EXPECT_THROW(a.scalar<int>(), exception);
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
index e5a9d9609e..2f175977dd 100644
--- a/test/arrayio.cpp
+++ b/test/arrayio.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
 
 #include <testHelpers.hpp>
 
@@ -28,83 +28,88 @@ using std::string;
 using std::vector;
 
 struct type_params {
-  string name;
-  af_dtype type;
-  double real;
-  double imag;
-  type_params(string n, af_dtype t, double r, double i = 0.) : name(n), type(t), real(r), imag(i) {}
+    string name;
+    af_dtype type;
+    double real;
+    double imag;
+    type_params(string n, af_dtype t, double r, double i = 0.)
+        : name(n), type(t), real(r), imag(i) {}
 };
 
 class ArrayIOType : public ::testing::TestWithParam<type_params> {};
 
-string getTypeName( const ::testing::TestParamInfo<ArrayIOType::ParamType> info) {
+string getTypeName(
+    const ::testing::TestParamInfo<ArrayIOType::ParamType> info) {
     return info.param.name;
 }
 
-INSTANTIATE_TEST_CASE_P(Types,
-                        ArrayIOType,
-                        ::testing::Values(
-                                          type_params("f32", f32, 3.14f, 0),
-                                          type_params("f64", f64, 3.14, 0),
-                                          type_params("c32", c32, 3.0f, 4.5f),
-                                          type_params("c64", c64, 3.0, 4.5),
-                                          type_params("s32", s32, 11),
-                                          type_params("u32", u32, 12),
-                                          type_params("u8",  u8, 13),
-                                          type_params("b8",  b8, 1),
-                                          type_params("s64", s64, 15),
-                                          type_params("u64", u64, 16),
-                                          type_params("s16", s16, 17),
-                                          type_params("u16", u16, 18)),
-                        getTypeName);
+INSTANTIATE_TEST_CASE_P(
+    Types, ArrayIOType,
+    ::testing::Values(type_params("f32", f32, 3.14f, 0),
+                      type_params("f64", f64, 3.14, 0),
+                      type_params("c32", c32, 3.0f, 4.5f),
+                      type_params("c64", c64, 3.0, 4.5),
+                      type_params("s32", s32, 11), type_params("u32", u32, 12),
+                      type_params("u8", u8, 13), type_params("b8", b8, 1),
+                      type_params("s64", s64, 15), type_params("u64", u64, 16),
+                      type_params("s16", s16, 17), type_params("u16", u16, 18)),
+    getTypeName);
 
 TEST_P(ArrayIOType, ReadType) {
     type_params p = GetParam();
-    array arr = readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(), p.name.c_str());
+    array arr =
+        readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(),
+                  p.name.c_str());
 
     ASSERT_EQ(arr.type(), p.type);
 }
 
 TEST_P(ArrayIOType, ReadSize) {
     type_params p = GetParam();
-    array arr = readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(), p.name.c_str());
+    array arr =
+        readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(),
+                  p.name.c_str());
 
     ASSERT_EQ(arr.dims(), dim4(10, 10));
 }
 
 template<typename T>
 void checkVals(array arr, double r, double i, af_dtype t) {
-  vector<T> d(arr.elements());
-  arr.host(d.data());
-  int elements = arr.elements();
-  for(int ii = 0; ii < elements; ii++) {
-      if(t == c32 || t == c64) {
-          ASSERT_EQ(r, real<T>(d[ii])) << "at: " << ii;
-          ASSERT_EQ(i, imag<T>(d[ii])) << "at: " << ii;
-      } else {
-          ASSERT_EQ(real(r), real(d[ii])) << "at: " << ii;
-      }
-  }
+    vector<T> d(arr.elements());
+    arr.host(d.data());
+    int elements = arr.elements();
+    for (int ii = 0; ii < elements; ii++) {
+        if (t == c32 || t == c64) {
+            ASSERT_EQ(r, real<T>(d[ii])) << "at: " << ii;
+            ASSERT_EQ(i, imag<T>(d[ii])) << "at: " << ii;
+        } else {
+            ASSERT_EQ(real(r), real(d[ii])) << "at: " << ii;
+        }
+    }
 }
 
 TEST_P(ArrayIOType, ReadContent) {
     type_params p = GetParam();
-    array arr = readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(), p.name.c_str());
-
-    switch(arr.type()) {
-    case f32: checkVals<float>(arr, p.real, p.imag, p.type); break;
-    case f64: checkVals<double>(arr, p.real, p.imag, p.type); break;
-    case c32: checkVals<af::cfloat>(arr, p.real, p.imag, p.type); break;
-    case c64: checkVals<af::cdouble>(arr, p.real, p.imag, p.type); break;
-    case s32: checkVals<int>(arr, p.real, p.imag, p.type); break;
-    case u32: checkVals<unsigned>(arr, p.real, p.imag, p.type); break;
-    case u8: checkVals<unsigned char>(arr, p.real, p.imag, p.type); break;
-    case b8: checkVals<char>(arr, p.real, p.imag, p.type); break;
-    case s64: checkVals<long long>(arr, p.real, p.imag, p.type); break;
-    case u64: checkVals<unsigned long long>(arr, p.real, p.imag, p.type); break;
-    case s16: checkVals<short>(arr, p.real, p.imag, p.type); break;
-    case u16: checkVals<unsigned short>(arr, p.real, p.imag, p.type); break;
-    default: FAIL() << "Invalid type";
+    array arr =
+        readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(),
+                  p.name.c_str());
+
+    switch (arr.type()) {
+        case f32: checkVals<float>(arr, p.real, p.imag, p.type); break;
+        case f64: checkVals<double>(arr, p.real, p.imag, p.type); break;
+        case c32: checkVals<af::cfloat>(arr, p.real, p.imag, p.type); break;
+        case c64: checkVals<af::cdouble>(arr, p.real, p.imag, p.type); break;
+        case s32: checkVals<int>(arr, p.real, p.imag, p.type); break;
+        case u32: checkVals<unsigned>(arr, p.real, p.imag, p.type); break;
+        case u8: checkVals<unsigned char>(arr, p.real, p.imag, p.type); break;
+        case b8: checkVals<char>(arr, p.real, p.imag, p.type); break;
+        case s64: checkVals<long long>(arr, p.real, p.imag, p.type); break;
+        case u64:
+            checkVals<unsigned long long>(arr, p.real, p.imag, p.type);
+            break;
+        case s16: checkVals<short>(arr, p.real, p.imag, p.type); break;
+        case u16: checkVals<unsigned short>(arr, p.real, p.imag, p.type); break;
+        default: FAIL() << "Invalid type";
     }
 }
 
diff --git a/test/assign.cpp b/test/assign.cpp
index 35bac70300..3efaf3704d 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -7,18 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::cout;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
@@ -30,108 +26,115 @@ using af::exception;
 using af::randu;
 using af::seq;
 using af::span;
-
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class ArrayAssign : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat1D.push_back(af_make_seq(5,20,1));
-
-            subMat2D.push_back(af_make_seq(1,2,1));
-            subMat2D.push_back(af_make_seq(1,2,1));
-
-            subMat3D.push_back(af_make_seq(3,4,1));
-            subMat3D.push_back(af_make_seq(0,1,1));
-            subMat3D.push_back(af_make_seq(1,2,1));
-
-            subMat4D.push_back(af_make_seq(3,4,1));
-            subMat4D.push_back(af_make_seq(0,1,1));
-            subMat4D.push_back(af_make_seq(0,1,1));
-            subMat4D.push_back(af_make_seq(1,2,1));
-
-            subMat1D_to_2D.push_back(af_make_seq(1,2,1));
-            subMat1D_to_2D.push_back(af_make_seq(1,1,1));
-
-            subMat1D_to_3D.push_back(af_make_seq(5,20,1));
-            subMat1D_to_3D.push_back(af_make_seq(1,1,1));
-            subMat1D_to_3D.push_back(af_make_seq(2,2,1));
-
-            subMat2D_to_3D.push_back(af_make_seq(3,4,1));
-            subMat2D_to_3D.push_back(af_make_seq(0,1,1));
-            subMat2D_to_3D.push_back(af_make_seq(1,1,1));
-
-            subMat1D_to_4D.push_back(af_make_seq(3,4,1));
-            subMat1D_to_4D.push_back(af_make_seq(0,0,1));
-            subMat1D_to_4D.push_back(af_make_seq(0,0,1));
-            subMat1D_to_4D.push_back(af_make_seq(1,1,1));
-
-            subMat2D_to_4D.push_back(af_make_seq(3,4,1));
-            subMat2D_to_4D.push_back(af_make_seq(0,1,1));
-            subMat2D_to_4D.push_back(af_make_seq(0,0,1));
-            subMat2D_to_4D.push_back(af_make_seq(1,1,1));
-
-            subMat3D_to_4D.push_back(af_make_seq(3,4,1));
-            subMat3D_to_4D.push_back(af_make_seq(0,1,1));
-            subMat3D_to_4D.push_back(af_make_seq(0,1,1));
-            subMat3D_to_4D.push_back(af_make_seq(1,1,1));
-        }
-        vector<af_seq> subMat1D;
+class ArrayAssign : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat1D.push_back(af_make_seq(5, 20, 1));
+
+        subMat2D.push_back(af_make_seq(1, 2, 1));
+        subMat2D.push_back(af_make_seq(1, 2, 1));
+
+        subMat3D.push_back(af_make_seq(3, 4, 1));
+        subMat3D.push_back(af_make_seq(0, 1, 1));
+        subMat3D.push_back(af_make_seq(1, 2, 1));
+
+        subMat4D.push_back(af_make_seq(3, 4, 1));
+        subMat4D.push_back(af_make_seq(0, 1, 1));
+        subMat4D.push_back(af_make_seq(0, 1, 1));
+        subMat4D.push_back(af_make_seq(1, 2, 1));
+
+        subMat1D_to_2D.push_back(af_make_seq(1, 2, 1));
+        subMat1D_to_2D.push_back(af_make_seq(1, 1, 1));
+
+        subMat1D_to_3D.push_back(af_make_seq(5, 20, 1));
+        subMat1D_to_3D.push_back(af_make_seq(1, 1, 1));
+        subMat1D_to_3D.push_back(af_make_seq(2, 2, 1));
+
+        subMat2D_to_3D.push_back(af_make_seq(3, 4, 1));
+        subMat2D_to_3D.push_back(af_make_seq(0, 1, 1));
+        subMat2D_to_3D.push_back(af_make_seq(1, 1, 1));
+
+        subMat1D_to_4D.push_back(af_make_seq(3, 4, 1));
+        subMat1D_to_4D.push_back(af_make_seq(0, 0, 1));
+        subMat1D_to_4D.push_back(af_make_seq(0, 0, 1));
+        subMat1D_to_4D.push_back(af_make_seq(1, 1, 1));
+
+        subMat2D_to_4D.push_back(af_make_seq(3, 4, 1));
+        subMat2D_to_4D.push_back(af_make_seq(0, 1, 1));
+        subMat2D_to_4D.push_back(af_make_seq(0, 0, 1));
+        subMat2D_to_4D.push_back(af_make_seq(1, 1, 1));
+
+        subMat3D_to_4D.push_back(af_make_seq(3, 4, 1));
+        subMat3D_to_4D.push_back(af_make_seq(0, 1, 1));
+        subMat3D_to_4D.push_back(af_make_seq(0, 1, 1));
+        subMat3D_to_4D.push_back(af_make_seq(1, 1, 1));
+    }
+    vector<af_seq> subMat1D;
 
-        vector<af_seq> subMat2D;
-        vector<af_seq> subMat1D_to_2D;
+    vector<af_seq> subMat2D;
+    vector<af_seq> subMat1D_to_2D;
 
-        vector<af_seq> subMat3D;
-        vector<af_seq> subMat1D_to_3D;
-        vector<af_seq> subMat2D_to_3D;
+    vector<af_seq> subMat3D;
+    vector<af_seq> subMat1D_to_3D;
+    vector<af_seq> subMat2D_to_3D;
 
-        vector<af_seq> subMat4D;
-        vector<af_seq> subMat1D_to_4D;
-        vector<af_seq> subMat2D_to_4D;
-        vector<af_seq> subMat3D_to_4D;
+    vector<af_seq> subMat4D;
+    vector<af_seq> subMat1D_to_4D;
+    vector<af_seq> subMat2D_to_4D;
+    vector<af_seq> subMat3D_to_4D;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, uchar, intl, uintl, short, ushort> TestTypes;
+typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, uchar,
+                         intl, uintl, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(ArrayAssign, TestTypes);
 
 template<typename inType, typename outType>
-void assignTest(string pTestFile, const vector<af_seq> *seqv)
-{
+void assignTest(string pTestFile, const vector<af_seq> *seqv) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<inType> >      in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTests<inType, outType, int>(pTestFile, numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    af_array lhsArray  = 0;
-    af_array rhsArray  = 0;
-    af_array outArray  = 0;
+    dim4 dims0        = numDims[0];
+    dim4 dims1        = numDims[1];
+    af_array lhsArray = 0;
+    af_array rhsArray = 0;
+    af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<outType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<outType>::af_type));
 
-    ASSERT_SUCCESS(af_assign_seq(&outArray, lhsArray, seqv->size(), &seqv->front(), rhsArray));
+    ASSERT_SUCCESS(af_assign_seq(&outArray, lhsArray, seqv->size(),
+                                 &seqv->front(), rhsArray));
 
     outType *outData = new outType[dims1.elements()];
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
     vector<outType> currGoldBar = tests[0];
-    size_t nElems        = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    size_t nElems               = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     delete[] outData;
@@ -141,27 +144,26 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv)
 }
 
 template<typename T>
-void assignTestCPP(string pTestFile, const vector<af_seq> &seqv)
-{
+void assignTestCPP(string pTestFile, const vector<af_seq> &seqv) {
     if (noDoubleTests<T>()) return;
     try {
-        vector<dim4>  numDims;
-        vector<vector<T> >      in;
-        vector<vector<T> >   tests;
+        vector<dim4> numDims;
+        vector<vector<T> > in;
+        vector<vector<T> > tests;
 
         readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-        dim4 dims0     = numDims[0];
-        dim4 dims1     = numDims[1];
+        dim4 dims0 = numDims[0];
+        dim4 dims1 = numDims[1];
 
         array a(dims0, &(in[0].front()));
         array b(dims1, &(in[1].front()));
 
-        switch(seqv.size()) {
+        switch (seqv.size()) {
             case 1: b(seqv[0]) = a; break;
-            case 2: b(seqv[0],seqv[1]) = a; break;
-            case 3: b(seqv[0],seqv[1], seqv[2]) = a; break;
-            case 4: b(seqv[0],seqv[1], seqv[2], seqv[3]) = a; break;
+            case 2: b(seqv[0], seqv[1]) = a; break;
+            case 3: b(seqv[0], seqv[1], seqv[2]) = a; break;
+            case 4: b(seqv[0], seqv[1], seqv[2], seqv[3]) = a; break;
             default: assert(1 != 1 && "Does not compute");
         }
 
@@ -169,138 +171,137 @@ void assignTestCPP(string pTestFile, const vector<af_seq> &seqv)
         b.host(outData);
 
         vector<T> currGoldBar = tests[0];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            EXPECT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+        size_t nElems         = currGoldBar.size();
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            EXPECT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
         delete[] outData;
-    } catch(const exception &ex) {
+    } catch (const exception &ex) {
         FAIL() << "Exception thrown: " << ex.what();
     }
-
 }
 
-TYPED_TEST(ArrayAssign, Vector)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/1d_to_1d.test"), &(this->subMat1D));
+TYPED_TEST(ArrayAssign, Vector) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/1d_to_1d.test"),
+                                     &(this->subMat1D));
 }
 
-TYPED_TEST(ArrayAssign, VectorCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/1d_to_1d.test"), this->subMat1D);
+TYPED_TEST(ArrayAssign, VectorCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/1d_to_1d.test"),
+                             this->subMat1D);
 }
 
-TYPED_TEST(ArrayAssign, Matrix)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/2d_to_2d.test"), &(this->subMat2D));
+TYPED_TEST(ArrayAssign, Matrix) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/2d_to_2d.test"),
+                                     &(this->subMat2D));
 }
 
-TYPED_TEST(ArrayAssign, MatrixCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/2d_to_2d.test"), this->subMat2D);
+TYPED_TEST(ArrayAssign, MatrixCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/2d_to_2d.test"),
+                             this->subMat2D);
 }
 
-TYPED_TEST(ArrayAssign, Cube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/3d_to_3d.test"), &(this->subMat3D));
+TYPED_TEST(ArrayAssign, Cube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/3d_to_3d.test"),
+                                     &(this->subMat3D));
 }
 
-TYPED_TEST(ArrayAssign, CubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/3d_to_3d.test"), this->subMat3D);
+TYPED_TEST(ArrayAssign, CubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/3d_to_3d.test"),
+                             this->subMat3D);
 }
 
-TYPED_TEST(ArrayAssign, HyperCube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/4d_to_4d.test"), &(this->subMat4D));
+TYPED_TEST(ArrayAssign, HyperCube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/4d_to_4d.test"),
+                                     &(this->subMat4D));
 }
 
-TYPED_TEST(ArrayAssign, HyperCubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/4d_to_4d.test"), this->subMat4D);
+TYPED_TEST(ArrayAssign, HyperCubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/4d_to_4d.test"),
+                             this->subMat4D);
 }
 
-TYPED_TEST(ArrayAssign, Vector2Matrix)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/1d_to_2d.test"), &(this->subMat1D_to_2D));
+TYPED_TEST(ArrayAssign, Vector2Matrix) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/1d_to_2d.test"),
+                                     &(this->subMat1D_to_2D));
 }
 
-TYPED_TEST(ArrayAssign, Vector2MatrixCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/1d_to_2d.test"), this->subMat1D_to_2D);
+TYPED_TEST(ArrayAssign, Vector2MatrixCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/1d_to_2d.test"),
+                             this->subMat1D_to_2D);
 }
 
-TYPED_TEST(ArrayAssign, Vector2Cube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/1d_to_3d.test"), &(this->subMat1D_to_3D));
+TYPED_TEST(ArrayAssign, Vector2Cube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/1d_to_3d.test"),
+                                     &(this->subMat1D_to_3D));
 }
 
-TYPED_TEST(ArrayAssign, Vector2CubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/1d_to_3d.test"), this->subMat1D_to_3D);
+TYPED_TEST(ArrayAssign, Vector2CubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/1d_to_3d.test"),
+                             this->subMat1D_to_3D);
 }
 
-TYPED_TEST(ArrayAssign, Matrix2Cube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/2d_to_3d.test"), &(this->subMat2D_to_3D));
+TYPED_TEST(ArrayAssign, Matrix2Cube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/2d_to_3d.test"),
+                                     &(this->subMat2D_to_3D));
 }
 
-TYPED_TEST(ArrayAssign, Matrix2CubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/2d_to_3d.test"), this->subMat2D_to_3D);
+TYPED_TEST(ArrayAssign, Matrix2CubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/2d_to_3d.test"),
+                             this->subMat2D_to_3D);
 }
 
-TYPED_TEST(ArrayAssign, Vector2HyperCube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/1d_to_4d.test"), &(this->subMat1D_to_4D));
+TYPED_TEST(ArrayAssign, Vector2HyperCube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/1d_to_4d.test"),
+                                     &(this->subMat1D_to_4D));
 }
 
-TYPED_TEST(ArrayAssign, Vector2HyperCubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/1d_to_4d.test"), this->subMat1D_to_4D);
+TYPED_TEST(ArrayAssign, Vector2HyperCubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/1d_to_4d.test"),
+                             this->subMat1D_to_4D);
 }
 
-TYPED_TEST(ArrayAssign, Matrix2HyperCube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/2d_to_4d.test"), &(this->subMat2D_to_4D));
+TYPED_TEST(ArrayAssign, Matrix2HyperCube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/2d_to_4d.test"),
+                                     &(this->subMat2D_to_4D));
 }
 
-TYPED_TEST(ArrayAssign, Matrix2HyperCubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/2d_to_4d.test"), this->subMat2D_to_4D);
+TYPED_TEST(ArrayAssign, Matrix2HyperCubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/2d_to_4d.test"),
+                             this->subMat2D_to_4D);
 }
 
-TYPED_TEST(ArrayAssign, Cube2HyperCube)
-{
-    assignTest<TypeParam, TypeParam>(string(TEST_DIR"/assign/3d_to_4d.test"), &(this->subMat3D_to_4D));
+TYPED_TEST(ArrayAssign, Cube2HyperCube) {
+    assignTest<TypeParam, TypeParam>(string(TEST_DIR "/assign/3d_to_4d.test"),
+                                     &(this->subMat3D_to_4D));
 }
 
-TYPED_TEST(ArrayAssign, Cube2HyperCubeCPP)
-{
-    assignTestCPP<TypeParam>(string(TEST_DIR"/assign/3d_to_4d.test"), this->subMat3D_to_4D);
+TYPED_TEST(ArrayAssign, Cube2HyperCubeCPP) {
+    assignTestCPP<TypeParam>(string(TEST_DIR "/assign/3d_to_4d.test"),
+                             this->subMat3D_to_4D);
 }
 
 template<typename T>
-void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv)
-{
+void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv) {
     if (noDoubleTests<T>()) return;
     try {
-        vector<dim4>  numDims;
-        vector<vector<T> >      in;
-        vector<vector<T> >   tests;
+        vector<dim4> numDims;
+        vector<vector<T> > in;
+        vector<vector<T> > tests;
 
         readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-        dim4 dims1     = numDims[1];
+        dim4 dims1 = numDims[1];
 
         T a = in[0][0];
         array b(dims1, &(in[1].front()));
 
-        switch(seqv.size()) {
+        switch (seqv.size()) {
             case 1: b(seqv[0]) = a; break;
-            case 2: b(seqv[0],seqv[1]) = a; break;
-            case 3: b(seqv[0],seqv[1], seqv[2]) = a; break;
-            case 4: b(seqv[0],seqv[1], seqv[2], seqv[3]) = a; break;
+            case 2: b(seqv[0], seqv[1]) = a; break;
+            case 3: b(seqv[0], seqv[1], seqv[2]) = a; break;
+            case 4: b(seqv[0], seqv[1], seqv[2], seqv[3]) = a; break;
             default: assert(1 != 1 && "Does not compute");
         }
 
@@ -308,56 +309,58 @@ void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv)
         b.host(outData);
 
         vector<T> currGoldBar = tests[0];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            if(currGoldBar[elIter] != outData[elIter]){
-                switch(seqv.size()) {
+        size_t nElems         = currGoldBar.size();
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            if (currGoldBar[elIter] != outData[elIter]) {
+                switch (seqv.size()) {
                     case 1: printf("b(seqv[0]) = a\n"); break;
                     case 2: printf("b(seqv[0],seqv[1]) = a\n"); break;
                     case 3: printf("b(seqv[0],seqv[1], seqv[2]) = a\n"); break;
-                    case 4: printf("b(seqv[0],seqv[1], seqv[2], seqv[3]) = a\n"); break;
+                    case 4:
+                        printf("b(seqv[0],seqv[1], seqv[2], seqv[3]) = a\n");
+                        break;
                     default: assert(1 != 1 && "Does not compute");
                 }
                 cout << "a: " << a << endl;
                 af_print(b);
-                ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+                ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                    << "at: " << elIter << endl;
             }
         }
         delete[] outData;
-    } catch(const exception &ex) {
+    } catch (const exception &ex) {
         FAIL() << "Exception thrown: " << ex.what();
     }
 }
 
-TYPED_TEST(ArrayAssign, Scalar1DCPP)
-{
-    assignScalarCPP<TypeParam>(string(TEST_DIR"/assign/scalar_to_1d.test"), this->subMat1D);
+TYPED_TEST(ArrayAssign, Scalar1DCPP) {
+    assignScalarCPP<TypeParam>(string(TEST_DIR "/assign/scalar_to_1d.test"),
+                               this->subMat1D);
 }
 
-TYPED_TEST(ArrayAssign, Scalar2DCPP)
-{
-    assignScalarCPP<TypeParam>(string(TEST_DIR"/assign/scalar_to_2d.test"), this->subMat2D);
+TYPED_TEST(ArrayAssign, Scalar2DCPP) {
+    assignScalarCPP<TypeParam>(string(TEST_DIR "/assign/scalar_to_2d.test"),
+                               this->subMat2D);
 }
 
-TYPED_TEST(ArrayAssign, Scalar3DCPP)
-{
-    assignScalarCPP<TypeParam>(string(TEST_DIR"/assign/scalar_to_3d.test"), this->subMat3D);
+TYPED_TEST(ArrayAssign, Scalar3DCPP) {
+    assignScalarCPP<TypeParam>(string(TEST_DIR "/assign/scalar_to_3d.test"),
+                               this->subMat3D);
 }
 
-TYPED_TEST(ArrayAssign, Scalar4DCPP)
-{
-    assignScalarCPP<TypeParam>(string(TEST_DIR"/assign/scalar_to_4d.test"), this->subMat4D);
+TYPED_TEST(ArrayAssign, Scalar4DCPP) {
+    assignScalarCPP<TypeParam>(string(TEST_DIR "/assign/scalar_to_4d.test"),
+                               this->subMat4D);
 }
 
-TYPED_TEST(ArrayAssign, AssignRowCPP)
-{
+TYPED_TEST(ArrayAssign, AssignRowCPP) {
     if (noDoubleTests<TypeParam>()) return;
 
     const int dimsize = 10;
     vector<TypeParam> input(100, 1);
     vector<TypeParam> sq(dimsize);
     vector<int> arIdx(2);
-    for(int i = 0; i < (int)sq.size(); i++) sq[i] = i;
+    for (int i = 0; i < (int)sq.size(); i++) sq[i] = i;
     arIdx[0] = 5;
     arIdx[1] = 7;
 
@@ -366,42 +369,49 @@ TYPED_TEST(ArrayAssign, AssignRowCPP)
     array sarr(size, &sq.front(), afHost);
     array arrIdx(2, &arIdx.front(), afHost);
 
-    in.row(0)       = sarr;
-    in.row(2)       = 2;
-    in(arrIdx, span)= 8;
-    in.row(end) = 3;
-    in.rows(3, 4)   = 7;
+    in.row(0)        = sarr;
+    in.row(2)        = 2;
+    in(arrIdx, span) = 8;
+    in.row(end)      = 3;
+    in.rows(3, 4)    = 7;
 
     vector<TypeParam> out(100);
     in.host(&out.front());
 
-    for(int col = 0; col < dimsize; col++) {
-        for(int row = 0; row < dimsize; row++) {
-            if      (row == 0)              ASSERT_EQ(sq[col], out[col * dimsize + row])
-                << "Assigning array to indexed array using col";
-            else if (row == 2)              ASSERT_EQ(TypeParam(2), out[col * dimsize + row])
-                << "Assigning value to indexed array using col";
-            else if (row == dimsize-1)      ASSERT_EQ(TypeParam(3), out[col * dimsize + row])
-                << "Assigning value to array which is indexed using end.";
-            else if (row == 3 || row == 4)  ASSERT_EQ(TypeParam(7), out[col * dimsize + row])
-                << "Assigning value to an array which is indexed using an rows";
-            else if (row == 5 || row == 7)  ASSERT_EQ(TypeParam(8), out[col * dimsize + row])
-                << "Assigning value to an array which is indexed using an array (i.e. in(arrIdx, span) = 8);) using row";
-            else                            ASSERT_EQ(TypeParam(1),  out[col * dimsize + row])
-                << "Values written to incorrect location";
+    for (int col = 0; col < dimsize; col++) {
+        for (int row = 0; row < dimsize; row++) {
+            if (row == 0)
+                ASSERT_EQ(sq[col], out[col * dimsize + row])
+                    << "Assigning array to indexed array using col";
+            else if (row == 2)
+                ASSERT_EQ(TypeParam(2), out[col * dimsize + row])
+                    << "Assigning value to indexed array using col";
+            else if (row == dimsize - 1)
+                ASSERT_EQ(TypeParam(3), out[col * dimsize + row])
+                    << "Assigning value to array which is indexed using end.";
+            else if (row == 3 || row == 4)
+                ASSERT_EQ(TypeParam(7), out[col * dimsize + row])
+                    << "Assigning value to an array which is indexed using an "
+                       "rows";
+            else if (row == 5 || row == 7)
+                ASSERT_EQ(TypeParam(8), out[col * dimsize + row])
+                    << "Assigning value to an array which is indexed using an "
+                       "array (i.e. in(arrIdx, span) = 8);) using row";
+            else
+                ASSERT_EQ(TypeParam(1), out[col * dimsize + row])
+                    << "Values written to incorrect location";
         }
     }
 }
 
-TYPED_TEST(ArrayAssign, AssignColumnCPP)
-{
+TYPED_TEST(ArrayAssign, AssignColumnCPP) {
     if (noDoubleTests<TypeParam>()) return;
 
     const int dimsize = 10;
     vector<TypeParam> input(100, 1);
     vector<TypeParam> sq(dimsize);
     vector<int> arIdx(2);
-    for(int i = 0; i < (int)sq.size(); i++) sq[i] = i;
+    for (int i = 0; i < (int)sq.size(); i++) sq[i] = i;
     arIdx[0] = 5;
     arIdx[1] = 7;
 
@@ -410,41 +420,48 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP)
     array sarr(size, &sq.front(), afHost);
     array arrIdx(2, &arIdx.front(), afHost);
 
-    in.col(0)       = sarr;
-    in.col(2)       = 2;
-    in(span, arrIdx)= 8;
-    in.col(end) = 3;
-    in.cols(3, 4)   = 7;
+    in.col(0)        = sarr;
+    in.col(2)        = 2;
+    in(span, arrIdx) = 8;
+    in.col(end)      = 3;
+    in.cols(3, 4)    = 7;
 
     vector<TypeParam> out(100);
     in.host(&out.front());
 
-    for(int col = 0; col < dimsize; col++) {
-        for(int row = 0; row < dimsize; row++) {
-            if      (col == 0)              ASSERT_EQ(sq[row], out[col * dimsize + row])
-                << "Assigning array to indexed array using col";
-            else if (col == 2)              ASSERT_EQ(TypeParam(2), out[col * dimsize + row])
-                << "Assigning value to indexed array using col";
-            else if (col == dimsize-1)      ASSERT_EQ(TypeParam(3), out[col * dimsize + row])
-                << "Assigning value to array which is indexed using end.";
-            else if (col == 3 || col == 4)  ASSERT_EQ(TypeParam(7), out[col * dimsize + row])
-                << "Assigning value to an array which is indexed using an cols";
-            else if (col == 5 || col == 7)  ASSERT_EQ(TypeParam(8), out[col * dimsize + row])
-                << "Assigning value to an array which is indexed using an array (i.e. in(span, arrIdx) = 8);) using col";
-            else                            ASSERT_EQ(TypeParam(1),  out[col * dimsize + row])
-                << "Values written to incorrect location";
+    for (int col = 0; col < dimsize; col++) {
+        for (int row = 0; row < dimsize; row++) {
+            if (col == 0)
+                ASSERT_EQ(sq[row], out[col * dimsize + row])
+                    << "Assigning array to indexed array using col";
+            else if (col == 2)
+                ASSERT_EQ(TypeParam(2), out[col * dimsize + row])
+                    << "Assigning value to indexed array using col";
+            else if (col == dimsize - 1)
+                ASSERT_EQ(TypeParam(3), out[col * dimsize + row])
+                    << "Assigning value to array which is indexed using end.";
+            else if (col == 3 || col == 4)
+                ASSERT_EQ(TypeParam(7), out[col * dimsize + row])
+                    << "Assigning value to an array which is indexed using an "
+                       "cols";
+            else if (col == 5 || col == 7)
+                ASSERT_EQ(TypeParam(8), out[col * dimsize + row])
+                    << "Assigning value to an array which is indexed using an "
+                       "array (i.e. in(span, arrIdx) = 8);) using col";
+            else
+                ASSERT_EQ(TypeParam(1), out[col * dimsize + row])
+                    << "Values written to incorrect location";
         }
     }
 }
 
-TYPED_TEST(ArrayAssign, AssignSliceCPP)
-{
+TYPED_TEST(ArrayAssign, AssignSliceCPP) {
     if (noDoubleTests<TypeParam>()) return;
     const int dimsize = 10;
     vector<TypeParam> input(1000, 1);
     vector<TypeParam> sq(dimsize * dimsize);
     vector<int> arIdx(2);
-    for(int i = 0; i < (int)sq.size(); i++) sq[i] = i;
+    for (int i = 0; i < (int)sq.size(); i++) sq[i] = i;
     arIdx[0] = 5;
     arIdx[1] = 7;
 
@@ -453,39 +470,48 @@ TYPED_TEST(ArrayAssign, AssignSliceCPP)
     array sarr(size, &sq.front(), afHost);
     array arrIdx(2, &arIdx.front(), afHost);
 
-    in.slice(0)             = sarr;
-    in.slice(2)             = 2;
-    in(span, span, arrIdx)  = 8;
-    in.slice(end)           = 3;
-    in.slices(3, 4)         = 7;
+    in.slice(0)            = sarr;
+    in.slice(2)            = 2;
+    in(span, span, arrIdx) = 8;
+    in.slice(end)          = 3;
+    in.slices(3, 4)        = 7;
 
     vector<TypeParam> out(1000);
     in.host(&out.front());
 
-    for(int slice = 0; slice < dimsize; slice++) {
-        for(int col = 0; col < dimsize; col++) {
-            for(int row = 0; row < dimsize; row++) {
+    for (int slice = 0; slice < dimsize; slice++) {
+        for (int col = 0; col < dimsize; col++) {
+            for (int row = 0; row < dimsize; row++) {
                 int idx = slice * dimsize * dimsize + col * dimsize + row;
-                if      (slice == 0)              ASSERT_EQ(sq[col * dimsize + row], out[idx])
-                    << "Assigning array to indexed array using col";
-                else if (slice == 2)              ASSERT_EQ(TypeParam(2), out[idx])
-                    << "Assigning value to indexed array using col";
-                else if (slice == dimsize-1)      ASSERT_EQ(TypeParam(3), out[idx])
-                    << "Assigning value to array which is indexed using end.";
-                else if (slice == 3 || slice == 4)  ASSERT_EQ(TypeParam(7), out[idx])
-                    << "Assigning value to an array which is indexed using an slices";
-                else if (slice == 5 || slice == 7)  ASSERT_EQ(TypeParam(8), out[idx])
-                    << "Assigning value to an array which is indexed using an array (i.e. in(span, span, arrIdx) = 8);) using slice";
-                else                            ASSERT_EQ(TypeParam(1),  out[idx])
-                    << "Values written to incorrect location";
+                if (slice == 0)
+                    ASSERT_EQ(sq[col * dimsize + row], out[idx])
+                        << "Assigning array to indexed array using col";
+                else if (slice == 2)
+                    ASSERT_EQ(TypeParam(2), out[idx])
+                        << "Assigning value to indexed array using col";
+                else if (slice == dimsize - 1)
+                    ASSERT_EQ(TypeParam(3), out[idx])
+                        << "Assigning value to array which is indexed using "
+                           "end.";
+                else if (slice == 3 || slice == 4)
+                    ASSERT_EQ(TypeParam(7), out[idx])
+                        << "Assigning value to an array which is indexed using "
+                           "an slices";
+                else if (slice == 5 || slice == 7)
+                    ASSERT_EQ(TypeParam(8), out[idx])
+                        << "Assigning value to an array which is indexed using "
+                           "an array (i.e. in(span, span, arrIdx) = 8);) using "
+                           "slice";
+                else
+                    ASSERT_EQ(TypeParam(1), out[idx])
+                        << "Values written to incorrect location";
             }
         }
     }
 }
 
-TEST(ArrayAssign, InvalidArgs)
-{
-    vector<cfloat> in(100, cfloat(0,0));
+TEST(ArrayAssign, InvalidArgs) {
+    vector<cfloat> in(100, cfloat(0, 0));
     vector<float> tests(100, float(1));
 
     dim4 dims0(10, 1, 1, 1);
@@ -495,108 +521,102 @@ TEST(ArrayAssign, InvalidArgs)
     af_array outArray = 0;
 
     vector<af_seq> seqv;
-    seqv.push_back(af_make_seq(5,14,1));
+    seqv.push_back(af_make_seq(5, 14, 1));
 
-    ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray,
-                                    lhsArray, seqv.size(), &seqv.front(), rhsArray));
+    ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray, lhsArray, seqv.size(),
+                                        &seqv.front(), rhsArray));
 
-    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in.front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<cfloat>::af_type));
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in.front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<cfloat>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray,
-                                    lhsArray, seqv.size(), &seqv.front(), rhsArray));
+    ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray, lhsArray, seqv.size(),
+                                        &seqv.front(), rhsArray));
 
-    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in.front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in.front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_assign_seq(&outArray, lhsArray, 0, &seqv.front(), rhsArray));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_assign_seq(&outArray, lhsArray, 0, &seqv.front(), rhsArray));
 
-    ASSERT_EQ(AF_ERR_TYPE, af_assign_seq(&outArray,
-                                         lhsArray, seqv.size(), &seqv.front(), rhsArray));
+    ASSERT_EQ(AF_ERR_TYPE, af_assign_seq(&outArray, lhsArray, seqv.size(),
+                                         &seqv.front(), rhsArray));
 
     ASSERT_SUCCESS(af_release_array(rhsArray));
     ASSERT_SUCCESS(af_release_array(lhsArray));
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_TO_INDEXED)
-{
+TEST(ArrayAssign, CPP_ASSIGN_TO_INDEXED) {
     vector<int> in(20);
-    for(int i = 0; i < (int)in.size(); i++) in[i] = i;
+    for (int i = 0; i < (int)in.size(); i++) in[i] = i;
 
     array input(10, 2, &in.front(), afHost);
 
-    input(span, 0) = input(span, 1);// <-- Tests array_proxy to array_proxy assignment
+    input(span, 0) =
+        input(span, 1);  // <-- Tests array_proxy to array_proxy assignment
 
     vector<int> out(20);
     input.host(&out.front());
 
-    for(int i = 0; i < 10; i++)                 ASSERT_EQ(i + 10, out[i]);
-    for(int i = 10; i < (int)in.size(); i++)    ASSERT_EQ(i, out[i]);
+    for (int i = 0; i < 10; i++) ASSERT_EQ(i + 10, out[i]);
+    for (int i = 10; i < (int)in.size(); i++) ASSERT_EQ(i, out[i]);
 }
 
-TEST(ArrayAssign, CPP_END)
-{
-    const int n = 5;
-    const int m = 5;
+TEST(ArrayAssign, CPP_END) {
+    const int n       = 5;
+    const int m       = 5;
     const int end_off = 2;
 
-    array a = randu(n, m);
-    array b = randu(1, m);
+    array a                = randu(n, m);
+    array b                = randu(1, m);
     a(end - end_off, span) = b;
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
 
-    for (int i = 0; i < m; i++) {
-        ASSERT_EQ(hA[i * n + end_off], hB[i]);
-    }
+    for (int i = 0; i < m; i++) { ASSERT_EQ(hA[i * n + end_off], hB[i]); }
 
     af_free_host(hA);
     af_free_host(hB);
 }
 
-TEST(ArrayAssign, CPP_END_SEQ)
-{
-    const int num = 20;
+TEST(ArrayAssign, CPP_END_SEQ) {
+    const int num       = 20;
     const int end_begin = 10;
-    const int end_end = 0;
-    const int len = end_begin - end_end + 1;
+    const int end_end   = 0;
+    const int len       = end_begin - end_end + 1;
 
-    array a = randu(num);
-    array b = randu(len);
+    array a                                = randu(num);
+    array b                                = randu(len);
     a(seq(end - end_begin, end - end_end)) = b;
 
     float *hA = a.host<float>();
     float *hB = b.host<float>();
 
-    for (int i = 0; i < len; i++) {
-        ASSERT_EQ(hA[i + end_begin - 1], hB[i]);
-    }
+    for (int i = 0; i < len; i++) { ASSERT_EQ(hA[i + end_begin - 1], hB[i]); }
 
     af_free_host(hA);
     af_free_host(hB);
 }
 
-TEST(ArrayAssign, CPP_COPY_ON_WRITE)
-{
+TEST(ArrayAssign, CPP_COPY_ON_WRITE) {
     const int num = 20;
     const int len = 10;
 
-    array a = randu(num);
+    array a    = randu(num);
     float *hAO = a.host<float>();
 
     array a_copy = a;
-    array b = randu(len);
-    a(seq(len)) = b;
+    array b      = randu(len);
+    a(seq(len))  = b;
 
-    float *hA = a.host<float>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    float *hB  = b.host<float>();
     float *hAC = a_copy.host<float>();
 
     // first half should be from B
-    for (int i = 0; i < len; i++) {
-        ASSERT_EQ(hA[i], hB[i]);
-    }
+    for (int i = 0; i < len; i++) { ASSERT_EQ(hA[i], hB[i]); }
 
     // Second half should be same as original
     for (int i = 0; i < num - len; i++) {
@@ -604,9 +624,7 @@ TEST(ArrayAssign, CPP_COPY_ON_WRITE)
     }
 
     // hAC should not be modified, i.e. same as original
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hAO[i], hAC[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hAO[i], hAC[i]); }
 
     af_free_host(hA);
     af_free_host(hB);
@@ -614,26 +632,23 @@ TEST(ArrayAssign, CPP_COPY_ON_WRITE)
     af_free_host(hAO);
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_BINOP)
-{
+TEST(ArrayAssign, CPP_ASSIGN_BINOP) {
     const int num = 20;
     const int len = 10;
 
-    array a = randu(num);
+    array a    = randu(num);
     float *hAO = a.host<float>();
 
     array a_copy = a;
-    array b = randu(len);
+    array b      = randu(len);
     a(seq(len)) += b;
 
-    float *hA = a.host<float>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    float *hB  = b.host<float>();
     float *hAC = a_copy.host<float>();
 
     // first half should be hAO + hB
-    for (int i = 0; i < len; i++) {
-        ASSERT_EQ(hA[i], hAO[i] + hB[i]);
-    }
+    for (int i = 0; i < len; i++) { ASSERT_EQ(hA[i], hAO[i] + hB[i]); }
 
     // Second half should be same as original
     for (int i = 0; i < num - len; i++) {
@@ -641,9 +656,7 @@ TEST(ArrayAssign, CPP_ASSIGN_BINOP)
     }
 
     // hAC should not be modified, i.e. same as original
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hAO[i], hAC[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hAO[i], hAC[i]); }
 
     af_free_host(hA);
     af_free_host(hB);
@@ -651,8 +664,7 @@ TEST(ArrayAssign, CPP_ASSIGN_BINOP)
     af_free_host(hAO);
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_VECTOR)
-{
+TEST(ArrayAssign, CPP_ASSIGN_VECTOR) {
     const int num = 20;
 
     array a = randu(1, num);
@@ -663,44 +675,41 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR)
 
     a(idx) = c;
 
-    ASSERT_EQ(a.dims(0) , (dim_t)1);
-    ASSERT_EQ(a.dims(1) , (dim_t)num);
-    ASSERT_EQ(c.dims(0) , (dim_t)num);
+    ASSERT_EQ(a.dims(0), (dim_t)1);
+    ASSERT_EQ(a.dims(1), (dim_t)num);
+    ASSERT_EQ(c.dims(0), (dim_t)num);
 
     float *h_a = a.host<float>();
     float *h_b = b.host<float>();
 
-    for (int i =0; i < num; i++) {
-        ASSERT_EQ(h_a[i], h_b[i]) << "at " << i;
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(h_a[i], h_b[i]) << "at " << i; }
 
     af_free_host(h_a);
     af_free_host(h_b);
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ)
-{
+TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ) {
     const int num = 20;
     const int len = 10;
-    const int st = 3;
-    const int en = st + len - 1;
+    const int st  = 3;
+    const int en  = st + len - 1;
 
-    array a = randu(1, 1, num);
+    array a  = randu(1, 1, num);
     array a0 = a;
-    array b = randu(len);
+    array b  = randu(len);
 
     array idx = seq(st, en);
 
     a(seq(st, en)) = b;
 
-    ASSERT_EQ(a.dims(0) , (dim_t)1);
-    ASSERT_EQ(a.dims(1) , (dim_t)1);
-    ASSERT_EQ(a.dims(2) , (dim_t)num);
-    ASSERT_EQ(b.dims(0) , (dim_t)len);
+    ASSERT_EQ(a.dims(0), (dim_t)1);
+    ASSERT_EQ(a.dims(1), (dim_t)1);
+    ASSERT_EQ(a.dims(2), (dim_t)num);
+    ASSERT_EQ(b.dims(0), (dim_t)len);
 
     float *h_a0 = a0.host<float>();
-    float *h_a  =  a.host<float>();
-    float *h_b  =  b.host<float>();
+    float *h_a  = a.host<float>();
+    float *h_b  = b.host<float>();
 
     for (int i = 0; i < num; i++) {
         if (i >= st && i <= en) {
@@ -715,10 +724,9 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ)
     af_free_host(h_b);
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D)
-{
-    const int nx = 4;
-    const int ny = 5;
+TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D) {
+    const int nx  = 4;
+    const int ny  = 5;
     const int num = nx * ny;
 
     array a = randu(nx, ny);
@@ -729,44 +737,41 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_2D)
 
     a(idx) = c;
 
-    ASSERT_EQ(a.dims(0) , (dim_t)nx);
-    ASSERT_EQ(a.dims(1) , (dim_t)ny);
-    ASSERT_EQ(c.dims(0) , (dim_t)num);
+    ASSERT_EQ(a.dims(0), (dim_t)nx);
+    ASSERT_EQ(a.dims(1), (dim_t)ny);
+    ASSERT_EQ(c.dims(0), (dim_t)num);
 
     float *h_a = a.host<float>();
     float *h_b = b.host<float>();
 
-    for (int i =0; i < num; i++) {
-        ASSERT_EQ(h_a[i], h_b[i]) << "at " << i;
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(h_a[i], h_b[i]) << "at " << i; }
 
     af_free_host(h_a);
     af_free_host(h_b);
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
-{
-    const int nx = 4;
-    const int nz = 5;
+TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D) {
+    const int nx  = 4;
+    const int nz  = 5;
     const int num = nx * nz;
     const int len = 10;
-    const int st = 3;
-    const int en = st + len - 1;
+    const int st  = 3;
+    const int en  = st + len - 1;
 
-    array a = randu(nx, 1, nz);
+    array a  = randu(nx, 1, nz);
     array a0 = a;
-    array b = randu(len);
+    array b  = randu(len);
 
     a(seq(st, en)) = b;
 
-    ASSERT_EQ(a.dims(0) , (dim_t)nx);
-    ASSERT_EQ(a.dims(1) , (dim_t)1);
-    ASSERT_EQ(a.dims(2) , (dim_t)nz);
-    ASSERT_EQ(b.dims(0) , (dim_t)len);
+    ASSERT_EQ(a.dims(0), (dim_t)nx);
+    ASSERT_EQ(a.dims(1), (dim_t)1);
+    ASSERT_EQ(a.dims(2), (dim_t)nz);
+    ASSERT_EQ(b.dims(0), (dim_t)len);
 
     float *h_a0 = a0.host<float>();
-    float *h_a  =  a.host<float>();
-    float *h_b  =  b.host<float>();
+    float *h_a  = a.host<float>();
+    float *h_b  = b.host<float>();
 
     for (int i = 0; i < num; i++) {
         if (i >= st && i <= en) {
@@ -781,14 +786,13 @@ TEST(ArrayAssign, CPP_ASSIGN_VECTOR_SEQ_2D)
     af_free_host(h_b);
 }
 
-TEST(Assign, Copy)
-{
+TEST(Assign, Copy) {
     const int num = 20;
     const int len = 10;
-    const int st = 3;
-    const int en = st + len - 1;
+    const int st  = 3;
+    const int en  = st + len - 1;
 
-    array a = randu(num, 1);
+    array a     = randu(num, 1);
     float *h_a0 = a.host<float>();
 
     array b = randu(len);
@@ -799,8 +803,8 @@ TEST(Assign, Copy)
     // Ensure that a still has same device pointer
     ASSERT_EQ(d_ptr, a.device<float>());
 
-    float *h_a  =  a.host<float>();
-    float *h_b  =  b.host<float>();
+    float *h_a = a.host<float>();
+    float *h_b = b.host<float>();
 
     for (int i = 0; i < num; i++) {
         if (i >= st && i <= en) {
@@ -815,19 +819,18 @@ TEST(Assign, Copy)
     af_free_host(h_b);
 }
 
-TEST(Asssign, LinearCPP)
-{
-    const int nx = 5;
-    const int ny = 4;
+TEST(Asssign, LinearCPP) {
+    const int nx    = 5;
+    const int ny    = 4;
     const float val = 3;
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = randu(nx, ny);
-    array a_copy = a;
+    array a       = randu(nx, ny);
+    array a_copy  = a;
     af::index idx = seq(st, en);
-    a(idx) = 3;
+    a(idx)        = 3;
 
     ASSERT_EQ(a.dims(0), a_copy.dims(0));
     ASSERT_EQ(a.dims(1), a_copy.dims(1));
@@ -846,15 +849,14 @@ TEST(Asssign, LinearCPP)
     }
 }
 
-TEST(Asssign, LinearCPPMaxDim)
-{
+TEST(Asssign, LinearCPPMaxDim) {
     const size_t largeDim = 65535 * 32 + 2;
-    const float val = 3;
+    const float val       = 3;
 
-    array a = randu(1, 2 * largeDim);
-    array a_copy = a.copy();
-    af::index idx = array(seq(10, largeDim+10));
-    a(span, idx) = val;
+    array a       = randu(1, 2 * largeDim);
+    array a_copy  = a.copy();
+    af::index idx = array(seq(10, largeDim + 10));
+    a(span, idx)  = val;
 
     ASSERT_EQ(a.dims(0), a_copy.dims(0));
 
@@ -865,7 +867,7 @@ TEST(Asssign, LinearCPPMaxDim)
     a_copy.host(&ha_copy[0]);
 
     for (unsigned int i = 0; i < 2 * largeDim; i++) {
-        if(i >= 10 && i <= largeDim + 10) {
+        if (i >= 10 && i <= largeDim + 10) {
             ASSERT_EQ(ha[i], val) << "at " << i;
         } else {
             ASSERT_EQ(ha[i], ha_copy[i]) << "at " << i;
@@ -873,26 +875,24 @@ TEST(Asssign, LinearCPPMaxDim)
     }
 }
 
-TEST(Asssign, LinearAssignSeq)
-{
-    const int nx = 5;
-    const int ny = 4;
+TEST(Asssign, LinearAssignSeq) {
+    const int nx    = 5;
+    const int ny    = 4;
     const float val = 3;
     const array rhs = constant(val, 1, 1);
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = randu(nx, ny);
+    array a       = randu(nx, ny);
     af::index idx = seq(st, en);
 
-    af_array in_arr = a.get();
-    af_index_t ii = idx.get();
+    af_array in_arr  = a.get();
+    af_index_t ii    = idx.get();
     af_array rhs_arr = rhs.get();
     af_array out_arr;
 
-    ASSERT_SUCCESS(
-              af_assign_seq(&out_arr, in_arr, 1, &ii.idx.seq, rhs_arr));
+    ASSERT_SUCCESS(af_assign_seq(&out_arr, in_arr, 1, &ii.idx.seq, rhs_arr));
 
     array out(out_arr);
 
@@ -913,26 +913,24 @@ TEST(Asssign, LinearAssignSeq)
     }
 }
 
-TEST(Asssign, LinearAssignGenSeq)
-{
-    const int nx = 5;
-    const int ny = 4;
+TEST(Asssign, LinearAssignGenSeq) {
+    const int nx    = 5;
+    const int ny    = 4;
     const float val = 3;
     const array rhs = constant(val, 1, 1);
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = randu(nx, ny);
+    array a       = randu(nx, ny);
     af::index idx = seq(st, en);
 
-    af_array in_arr = a.get();
-    af_index_t ii = idx.get();
+    af_array in_arr  = a.get();
+    af_index_t ii    = idx.get();
     af_array rhs_arr = rhs.get();
     af_array out_arr;
 
-    ASSERT_SUCCESS(
-              af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
+    ASSERT_SUCCESS(af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
 
     array out(out_arr);
 
@@ -953,26 +951,24 @@ TEST(Asssign, LinearAssignGenSeq)
     }
 }
 
-TEST(Asssign, LinearAssignGenArr)
-{
-    const int nx = 5;
-    const int ny = 4;
+TEST(Asssign, LinearAssignGenArr) {
+    const int nx    = 5;
+    const int ny    = 4;
     const float val = 3;
     const array rhs = constant(val, 1, 1);
 
     const int st = nx - 2;
     const int en = nx * (ny - 1);
 
-    array a = randu(nx, ny);
+    array a       = randu(nx, ny);
     af::index idx = array(seq(st, en));
 
-    af_array in_arr = a.get();
-    af_index_t ii = idx.get();
+    af_array in_arr  = a.get();
+    af_index_t ii    = idx.get();
     af_array rhs_arr = rhs.get();
     af_array out_arr;
 
-    ASSERT_SUCCESS(
-              af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
+    ASSERT_SUCCESS(af_assign_gen(&out_arr, in_arr, 1, &ii, rhs_arr));
 
     array out(out_arr);
 
@@ -993,12 +989,11 @@ TEST(Asssign, LinearAssignGenArr)
     }
 }
 
-TEST(Assign, ISSUE_1764)
-{
-    int x = 2;
-    int y = 2;
-    int z = 2;
-    array a = randu(x,y,z);
+TEST(Assign, ISSUE_1764) {
+    int x   = 2;
+    int y   = 2;
+    int z   = 2;
+    array a = randu(x, y, z);
     vector<float> ha0(a.elements());
     a.host(&ha0[0]);
     a(0, span, span) = a(1, span, span);
@@ -1013,17 +1008,14 @@ TEST(Assign, ISSUE_1764)
     }
 }
 
-TEST(Assign, ISSUE_1677)
-{
+TEST(Assign, ISSUE_1677) {
     try {
-        dim_t sz = 1;
-        array a = constant(1.0f, 3, sz, f32);
-        array b = constant(2.0f, 3, sz, f32);
-        array cond = constant(0, sz, b8); // all false
+        dim_t sz      = 1;
+        array a       = constant(1.0f, 3, sz, f32);
+        array b       = constant(2.0f, 3, sz, f32);
+        array cond    = constant(0, sz, b8);  // all false
         a(span, cond) = b(span, cond);
-    } catch(exception &ex) {
+    } catch (exception &ex) {
         FAIL() << "ArrayFire exception: " << ex.what();
-    } catch(...) {
-        FAIL() << "Unknown exception thrown";
-    }
+    } catch (...) { FAIL() << "Unknown exception thrown"; }
 }
diff --git a/test/backend.cpp b/test/backend.cpp
index 78e2ff1f0b..c9d0abfa35 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -7,36 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
 #include <af/device.h>
 
-using std::string;
-using std::vector;
 using af::dtype_traits;
 using af::getAvailableBackends;
 using af::setBackend;
+using std::string;
+using std::vector;
 
-const char *getActiveBackendString(af_backend active)
-{
-    switch(active) {
-        case AF_BACKEND_CPU   : return "AF_BACKEND_CPU";
-        case AF_BACKEND_CUDA  : return "AF_BACKEND_CUDA";
+const char *getActiveBackendString(af_backend active) {
+    switch (active) {
+        case AF_BACKEND_CPU: return "AF_BACKEND_CPU";
+        case AF_BACKEND_CUDA: return "AF_BACKEND_CUDA";
         case AF_BACKEND_OPENCL: return "AF_BACKEND_OPENCL";
-        default               : return "AF_BACKEND_DEFAULT";
+        default: return "AF_BACKEND_DEFAULT";
     }
 }
 
 template<typename T>
-void testFunction()
-{
+void testFunction() {
     af_info();
 
     af_backend activeBackend = (af_backend)0;
@@ -45,8 +43,9 @@ void testFunction()
     printf("Active Backend Enum = %s\n", getActiveBackendString(activeBackend));
 
     af_array outArray = 0;
-    dim_t dims[] = {32, 32};
-    EXPECT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, (af_dtype) dtype_traits<T>::af_type));
+    dim_t dims[]      = {32, 32};
+    EXPECT_EQ(AF_SUCCESS,
+              af_randu(&outArray, 2, dims, (af_dtype)dtype_traits<T>::af_type));
 
     // Verify backends returned by array and by function are the same
     af_backend arrayBackend = (af_backend)0;
@@ -54,13 +53,10 @@ void testFunction()
     EXPECT_EQ(arrayBackend, activeBackend);
 
     // cleanup
-    if(outArray != 0) {
-        ASSERT_SUCCESS(af_release_array(outArray));
-    }
+    if (outArray != 0) { ASSERT_SUCCESS(af_release_array(outArray)); }
 }
 
-void backendTest()
-{
+void backendTest() {
     int backends = getAvailableBackends();
 
     ASSERT_NE(backends, 0);
@@ -72,26 +68,23 @@ void backendTest()
     printf("\nRunning Default Backend...\n");
     testFunction<float>();
 
-    if(cpu) {
+    if (cpu) {
         printf("\nRunning CPU Backend...\n");
         setBackend(AF_BACKEND_CPU);
         testFunction<float>();
     }
 
-    if(cuda) {
+    if (cuda) {
         printf("\nRunning CUDA Backend...\n");
         setBackend(AF_BACKEND_CUDA);
         testFunction<float>();
     }
 
-    if(opencl) {
+    if (opencl) {
         printf("\nRunning OpenCL Backend...\n");
         setBackend(AF_BACKEND_OPENCL);
         testFunction<float>();
     }
 }
 
-TEST(BACKEND_TEST, Basic)
-{
-    backendTest();
-}
+TEST(BACKEND_TEST, Basic) { backendTest(); }
diff --git a/test/basic.cpp b/test/basic.cpp
index 6379398f1c..7bbc3747d4 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -7,25 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/data.h>
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <af/data.h>
 
 #include <vector>
 
-using std::vector;
 using af::array;
 using af::constant;
 using af::dim4;
+using std::vector;
 
-TEST(BasicTests, constant1000x1000)
-{
+TEST(BasicTests, constant1000x1000) {
     if (noDoubleTests<float>()) return;
 
-    static const int ndims = 2;
+    static const int ndims    = 2;
     static const int dim_size = 1000;
-    dim_t d[ndims] = {dim_size, dim_size};
+    dim_t d[ndims]            = {dim_size, dim_size};
 
     double valA = 3.9;
     af_array a;
@@ -35,20 +34,17 @@ TEST(BasicTests, constant1000x1000)
     ASSERT_SUCCESS(af_get_data_ptr((void **)&h_a[0], a));
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
-        ASSERT_FLOAT_EQ(valA, h_a[i]);
-    }
+    for (size_t i = 0; i < elements; i++) { ASSERT_FLOAT_EQ(valA, h_a[i]); }
 
     ASSERT_SUCCESS(af_release_array(a));
 }
 
-TEST(BasicTests, constant10x10)
-{
+TEST(BasicTests, constant10x10) {
     if (noDoubleTests<float>()) return;
 
-    static const int ndims = 2;
+    static const int ndims    = 2;
     static const int dim_size = 10;
-    dim_t d[2] = {dim_size, dim_size};
+    dim_t d[2]                = {dim_size, dim_size};
 
     double valA = 3.9;
     af_array a;
@@ -58,20 +54,17 @@ TEST(BasicTests, constant10x10)
     ASSERT_SUCCESS(af_get_data_ptr((void **)&h_a[0], a));
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
-        ASSERT_FLOAT_EQ(valA, h_a[i]);
-    }
+    for (size_t i = 0; i < elements; i++) { ASSERT_FLOAT_EQ(valA, h_a[i]); }
 
     ASSERT_SUCCESS(af_release_array(a));
 }
 
-TEST(BasicTests, constant100x100)
-{
+TEST(BasicTests, constant100x100) {
     if (noDoubleTests<float>()) return;
 
-    static const int ndims = 2;
+    static const int ndims    = 2;
     static const int dim_size = 100;
-    dim_t d[2] = {dim_size, dim_size};
+    dim_t d[2]                = {dim_size, dim_size};
 
     double valA = 4.9;
     af_array a;
@@ -81,26 +74,23 @@ TEST(BasicTests, constant100x100)
     ASSERT_SUCCESS(af_get_data_ptr((void **)&h_a[0], a));
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
-        ASSERT_FLOAT_EQ(valA, h_a[i]);
-    }
+    for (size_t i = 0; i < elements; i++) { ASSERT_FLOAT_EQ(valA, h_a[i]); }
 
     ASSERT_SUCCESS(af_release_array(a));
 }
 
-//TODO: Test All The Types \o/
-TEST(BasicTests, AdditionSameType)
-{
+// TODO: Test All The Types \o/
+TEST(BasicTests, AdditionSameType) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<double>()) return;
 
-    static const int ndims = 2;
+    static const int ndims    = 2;
     static const int dim_size = 100;
-    dim_t d[ndims] = {dim_size, dim_size};
+    dim_t d[ndims]            = {dim_size, dim_size};
 
-    double valA = 3.9;
-    double valB = 5.7;
-    double  valCf = valA + valB;
+    double valA  = 3.9;
+    double valB  = 5.7;
+    double valCf = valA + valB;
 
     af_array af32, bf32, cf32;
     af_array af64, bf64, cf64;
@@ -114,18 +104,18 @@ TEST(BasicTests, AdditionSameType)
     ASSERT_SUCCESS(af_add(&cf32, af32, bf32, false));
     ASSERT_SUCCESS(af_add(&cf64, af64, bf64, false));
 
-    vector<float>  h_cf32 (dim_size * dim_size);
-    vector<double> h_cf64 (dim_size * dim_size);
+    vector<float> h_cf32(dim_size * dim_size);
+    vector<double> h_cf64(dim_size * dim_size);
     ASSERT_SUCCESS(af_get_data_ptr((void **)&h_cf32[0], cf32));
     ASSERT_SUCCESS(af_get_data_ptr((void **)&h_cf64[0], cf64));
 
     double err = 0;
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
+    for (size_t i = 0; i < elements; i++) {
         float df = h_cf32[i] - (valCf);
-        ASSERT_FLOAT_EQ(valCf,  h_cf32[i]);
-        ASSERT_FLOAT_EQ(valCf,  h_cf64[i]);
+        ASSERT_FLOAT_EQ(valCf, h_cf32[i]);
+        ASSERT_FLOAT_EQ(valCf, h_cf64[i]);
         err = err + df * df;
     }
     ASSERT_NEAR(0.0f, err, 1e-8);
@@ -138,13 +128,12 @@ TEST(BasicTests, AdditionSameType)
     ASSERT_SUCCESS(af_release_array(cf64));
 }
 
-TEST(BasicTests, Additionf64f64)
-{
+TEST(BasicTests, Additionf64f64) {
     if (noDoubleTests<double>()) return;
 
-    static const int ndims = 2;
+    static const int ndims    = 2;
     static const int dim_size = 100;
-    dim_t d[ndims] = {dim_size, dim_size};
+    dim_t d[ndims]            = {dim_size, dim_size};
 
     double valA = 3.9;
     double valB = 5.7;
@@ -162,7 +151,7 @@ TEST(BasicTests, Additionf64f64)
     double err = 0;
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
+    for (size_t i = 0; i < elements; i++) {
         double df = h_c[i] - (valC);
         ASSERT_FLOAT_EQ(valA + valB, h_c[i]);
         err = err + df * df;
@@ -172,17 +161,15 @@ TEST(BasicTests, Additionf64f64)
     ASSERT_SUCCESS(af_release_array(a));
     ASSERT_SUCCESS(af_release_array(b));
     ASSERT_SUCCESS(af_release_array(c));
-
 }
 
-TEST(BasicTests, Additionf32f64)
-{
+TEST(BasicTests, Additionf32f64) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<double>()) return;
 
-    static const int ndims = 2;
+    static const int ndims    = 2;
     static const int dim_size = 100;
-    dim_t d[ndims] = {dim_size, dim_size};
+    dim_t d[ndims]            = {dim_size, dim_size};
 
     double valA = 3.9;
     double valB = 5.7;
@@ -200,7 +187,7 @@ TEST(BasicTests, Additionf32f64)
     double err = 0;
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
+    for (size_t i = 0; i < elements; i++) {
         double df = h_c[i] - (valC);
         ASSERT_FLOAT_EQ(valA + valB, h_c[i]);
         err = err + df * df;
@@ -212,59 +199,53 @@ TEST(BasicTests, Additionf32f64)
     ASSERT_SUCCESS(af_release_array(c));
 }
 
-TEST(BasicArrayTests, constant10x10)
-{
+TEST(BasicArrayTests, constant10x10) {
     if (noDoubleTests<float>()) return;
 
     dim_t dim_size = 10;
-    double valA = 3.14;
-    array a = constant(valA, dim_size, dim_size, f32);
+    double valA    = 3.14;
+    array a        = constant(valA, dim_size, dim_size, f32);
 
     vector<float> h_a(dim_size * dim_size, 0);
     a.host(&h_a.front());
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
-        ASSERT_FLOAT_EQ(valA, h_a[i]);
-    }
+    for (size_t i = 0; i < elements; i++) { ASSERT_FLOAT_EQ(valA, h_a[i]); }
 }
 
-////////////////////////////////////// CPP Tests //////////////////////////////////
+////////////////////////////////////// CPP Tests
+/////////////////////////////////////
 using af::dim4;
 
-TEST(BasicTests, constant100x100_CPP)
-{
+TEST(BasicTests, constant100x100_CPP) {
     if (noDoubleTests<float>()) return;
 
     static const int dim_size = 100;
-    dim_t d[2] = {dim_size, dim_size};
+    dim_t d[2]                = {dim_size, dim_size};
 
     double valA = 4.9;
     dim4 dims(d[0], d[1]);
     array a = constant(valA, dims);
 
     vector<float> h_a(dim_size * dim_size, 0);
-    a.host((void**)&h_a[0]);
+    a.host((void **)&h_a[0]);
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
-        ASSERT_FLOAT_EQ(valA, h_a[i]);
-    }
+    for (size_t i = 0; i < elements; i++) { ASSERT_FLOAT_EQ(valA, h_a[i]); }
 }
 
-//TODO: Test All The Types \o/
-TEST(BasicTests, AdditionSameType_CPP)
-{
+// TODO: Test All The Types \o/
+TEST(BasicTests, AdditionSameType_CPP) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<double>()) return;
 
     static const int dim_size = 100;
-    dim_t d[2] = {dim_size, dim_size};
+    dim_t d[2]                = {dim_size, dim_size};
     dim4 dims(d[0], d[1]);
 
-    double valA = 3.9;
-    double valB = 5.7;
-    double  valCf = valA + valB;
+    double valA  = 3.9;
+    double valB  = 5.7;
+    double valCf = valA + valB;
 
     array a32 = constant(valA, dims, f32);
     array b32 = constant(valB, dims, f32);
@@ -274,31 +255,30 @@ TEST(BasicTests, AdditionSameType_CPP)
     array b64 = constant(valB, dims, f64);
     array c64 = a64 + b64;
 
-    vector<float>  h_cf32 (dim_size * dim_size);
-    vector<double> h_cf64 (dim_size * dim_size);
+    vector<float> h_cf32(dim_size * dim_size);
+    vector<double> h_cf64(dim_size * dim_size);
 
-    c32.host((void**)&h_cf32[0]);
-    c64.host((void**)&h_cf64[0]);
+    c32.host((void **)&h_cf32[0]);
+    c64.host((void **)&h_cf64[0]);
 
     double err = 0;
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
+    for (size_t i = 0; i < elements; i++) {
         float df = h_cf32[i] - (valCf);
-        ASSERT_FLOAT_EQ(valCf,  h_cf32[i]);
-        ASSERT_FLOAT_EQ(valCf,  h_cf64[i]);
+        ASSERT_FLOAT_EQ(valCf, h_cf32[i]);
+        ASSERT_FLOAT_EQ(valCf, h_cf64[i]);
         err = err + df * df;
     }
     ASSERT_NEAR(0.0f, err, 1e-8);
 }
 
-TEST(BasicTests, Additionf32f64_CPP)
-{
+TEST(BasicTests, Additionf32f64_CPP) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<double>()) return;
 
     static const int dim_size = 100;
-    dim_t d[2] = {dim_size, dim_size};
+    dim_t d[2]                = {dim_size, dim_size};
     dim4 dims(d[0], d[1]);
 
     double valA = 3.9;
@@ -310,12 +290,12 @@ TEST(BasicTests, Additionf32f64_CPP)
     array c = a + b;
 
     vector<double> h_c(dim_size * dim_size);
-    c.host((void**)&h_c[0]);
+    c.host((void **)&h_c[0]);
 
     double err = 0;
 
     size_t elements = dim_size * dim_size;
-    for(size_t i = 0; i < elements; i++) {
+    for (size_t i = 0; i < elements; i++) {
         double df = h_c[i] - (valC);
         ASSERT_FLOAT_EQ(valA + valB, h_c[i]);
         err = err + df * df;
@@ -325,7 +305,7 @@ TEST(BasicTests, Additionf32f64_CPP)
 
 TEST(Assert, TestEqualsCpp) {
     array gold = constant(1, 10, 10);
-    array out = constant(1, 10, 10);
+    array out  = constant(1, 10, 10);
 
     // Testing this macro
     // ASSERT_ARRAYS_EQ(gold, out);
@@ -334,8 +314,8 @@ TEST(Assert, TestEqualsCpp) {
 
 TEST(Assert, TestEqualsC) {
     af_array gold = 0;
-    af_array out = 0;
-    dim_t dims[] = {10, 10, 1, 1};
+    af_array out  = 0;
+    dim_t dims[]  = {10, 10, 1, 1};
     af_constant(&gold, 1.0, 4, dims, f32);
     af_constant(&out, 1.0, 4, dims, f32);
 
@@ -349,7 +329,7 @@ TEST(Assert, TestEqualsC) {
 
 TEST(Assert, TestEqualsDiffTypes) {
     array gold = constant(1, 10, 10, f64);
-    array out = constant(1, 10, 10);
+    array out  = constant(1, 10, 10);
 
     // Testing this macro
     // ASSERT_ARRAYS_EQ(gold, out);
@@ -358,7 +338,7 @@ TEST(Assert, TestEqualsDiffTypes) {
 
 TEST(Assert, TestEqualsDiffSizes) {
     array gold = constant(1, 10, 9);
-    array out = constant(1, 10, 10);
+    array out  = constant(1, 10, 10);
 
     // Testing this macro
     // ASSERT_ARRAYS_EQ(gold, out);
@@ -367,8 +347,8 @@ TEST(Assert, TestEqualsDiffSizes) {
 
 TEST(Assert, TestEqualsDiffValue) {
     array gold = constant(1, 3, 3);
-    array out = gold;
-    out(2, 2) = 2;
+    array out  = gold;
+    out(2, 2)  = 2;
 
     // Testing this macro
     // ASSERT_ARRAYS_EQ(gold, out);
@@ -377,8 +357,8 @@ TEST(Assert, TestEqualsDiffValue) {
 
 TEST(Assert, TestEqualsDiffComplexValue) {
     array gold = constant(af::cfloat(3.1f, 3.1f), 3, 3, c32);
-    array out = gold;
-    out(2, 2) = 2.2;
+    array out  = gold;
+    out(2, 2)  = 2.2;
 
     // Testing this macro
     // ASSERT_ARRAYS_EQ(gold, out);
@@ -394,8 +374,7 @@ TEST(Assert, TestVectorEquals) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
-    ASSERT_TRUE(assertArrayEq("gold", "goldDims", "out",
-                              gold, goldDims, out));
+    ASSERT_TRUE(assertArrayEq("gold", "goldDims", "out", gold, goldDims, out));
 }
 
 TEST(Assert, TestVectorDiffVecType) {
@@ -407,8 +386,7 @@ TEST(Assert, TestVectorDiffVecType) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
-    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
-                              gold, goldDims, out));
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out", gold, goldDims, out));
 }
 
 TEST(Assert, TestVectorDiffGoldSizeDims) {
@@ -420,8 +398,7 @@ TEST(Assert, TestVectorDiffGoldSizeDims) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
-    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
-                               gold, goldDims, out));
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out", gold, goldDims, out));
 }
 
 TEST(Assert, TestVectorDiffOutSizeGoldSize) {
@@ -433,8 +410,7 @@ TEST(Assert, TestVectorDiffOutSizeGoldSize) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_EQ(gold, goldDims, out);
-    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out",
-                               gold, goldDims, out));
+    ASSERT_FALSE(assertArrayEq("gold", "goldDims", "out", gold, goldDims, out));
 }
 
 TEST(Assert, TestVectorDiffDim4) {
@@ -461,8 +437,8 @@ TEST(Assert, TestVectorDiffVecSize) {
 
 TEST(Assert, TestArraysNearC) {
     af_array gold = 0;
-    af_array out = 0;
-    dim_t dims[] = {10, 10, 1, 1};
+    af_array out  = 0;
+    dim_t dims[]  = {10, 10, 1, 1};
     af_constant(&gold, 2.2345f, 4, dims, f32);
     af_constant(&out, 2.2346f, 4, dims, f32);
 
@@ -489,15 +465,15 @@ TEST(Assert, TestVecArrayNearC) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_NEAR(gold, goldDims, out, maxDiff);
-    ASSERT_TRUE(assertArrayNear("gold", "goldDims", "out", "maxDiff",
-                                gold, goldDims, out, maxDiff));
+    ASSERT_TRUE(assertArrayNear("gold", "goldDims", "out", "maxDiff", gold,
+                                goldDims, out, maxDiff));
 
     ASSERT_SUCCESS(af_release_array(out));
 }
 
 TEST(Assert, TestArraysNearWithinThresh) {
     array gold = constant(2.2345f, 3, 3);
-    array out = gold;
+    array out  = gold;
     out(2, 2) += 0.0001f;
     float maxDiff = 0.001f;
 
@@ -508,7 +484,7 @@ TEST(Assert, TestArraysNearWithinThresh) {
 
 TEST(Assert, TestArraysNearExceedThresh) {
     array gold = constant(2.2345f, 3, 3);
-    array out = gold;
+    array out  = gold;
     out(2, 2) += 0.002f;
     float maxDiff = 0.001f;
 
@@ -528,8 +504,8 @@ TEST(Assert, TestVecArrayNearWithinThresh) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_NEAR(gold, goldDims, out, maxDiff);
-    ASSERT_TRUE(assertArrayNear("gold", "goldDims", "out", "maxAbsDiff",
-                                 gold, goldDims, out, maxDiff));
+    ASSERT_TRUE(assertArrayNear("gold", "goldDims", "out", "maxAbsDiff", gold,
+                                goldDims, out, maxDiff));
 }
 
 TEST(Assert, TestVecArrayNearExceedThresh) {
@@ -543,6 +519,6 @@ TEST(Assert, TestVecArrayNearExceedThresh) {
 
     // Testing this macro
     // ASSERT_VEC_ARRAY_NEAR(gold, goldDims, out, maxDiff);
-    ASSERT_FALSE(assertArrayNear("gold", "goldDims", "out", "maxAbsDiff",
-                                 gold, goldDims, out, maxDiff));
+    ASSERT_FALSE(assertArrayNear("gold", "goldDims", "out", "maxAbsDiff", gold,
+                                 goldDims, out, maxDiff));
 }
diff --git a/test/basic_c.c b/test/basic_c.c
index aac34e142d..b6f3f39f13 100644
--- a/test/basic_c.c
+++ b/test/basic_c.c
@@ -9,11 +9,10 @@
 
 #include <arrayfire.h>
 
-int main()
-{
+int main() {
     af_array out = 0;
-    dim_t s[] = {10, 10, 1, 1};
-    af_err e = af_randu(&out, 4, s, f32);
-    if(out != 0) af_release_array(out);
+    dim_t s[]    = {10, 10, 1, 1};
+    af_err e     = af_randu(&out, 4, s, f32);
+    if (out != 0) af_release_array(out);
     return (AF_SUCCESS != e);
 }
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 775aaf1ea2..2c12d66aa8 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -7,51 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <cmath>
 #include <string>
 #include <vector>
-#include <cmath>
-#include <testHelpers.hpp>
 
-using std::string;
-using std::vector;
-using std::abs;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::string;
+using std::vector;
 
 template<typename T, bool isColor>
-void bilateralTest(string pTestFile)
-{
+void bilateralTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-
-        af_array inArray  = 0;
-        af_array outArray = 0;
-        af_array goldArray= 0;
-        dim_t nElems   = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray   = 0;
+        af_array outArray  = 0;
+        af_array goldArray = 0;
+        dim_t nElems       = 0;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/bilateral/"));
-        outFiles[testId].insert(0,string(TEST_DIR"/bilateral/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/bilateral/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/bilateral/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_SUCCESS(af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_SUCCESS(af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
+        ASSERT_SUCCESS(
+            af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
 
         vector<T> outData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
@@ -59,7 +60,8 @@ void bilateralTest(string pTestFile)
         vector<T> goldData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.02f));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(outArray));
@@ -67,46 +69,43 @@ void bilateralTest(string pTestFile)
     }
 }
 
-TEST(BilateralOnImage, Grayscale)
-{
-    bilateralTest<float, false>(string(TEST_DIR"/bilateral/gray.test"));
+TEST(BilateralOnImage, Grayscale) {
+    bilateralTest<float, false>(string(TEST_DIR "/bilateral/gray.test"));
 }
 
-TEST(BilateralOnImage, Color)
-{
-    bilateralTest<float, true>(string(TEST_DIR"/bilateral/color.test"));
+TEST(BilateralOnImage, Color) {
+    bilateralTest<float, true>(string(TEST_DIR "/bilateral/color.test"));
 }
 
-
 template<typename T>
-class BilateralOnData : public ::testing::Test
-{
-};
+class BilateralOnData : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> DataTestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+    DataTestTypes;
 
 // register the type list
 TYPED_TEST_CASE(BilateralOnData, DataTestTypes);
 
 template<typename inType>
-void bilateralDataTest(string pTestFile)
-{
+void bilateralDataTest(string pTestFile) {
     if (noDoubleTests<inType>()) return;
 
-    typedef typename cond_type<is_same_type<inType, double>::value, double, float>::type outType;
+    typedef typename cond_type<is_same_type<inType, double>::value, double,
+                               float>::type outType;
 
-    vector<dim4>        numDims;
-    vector<vector<inType> >       in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTests<inType, outType, float>(pTestFile, numDims, in, tests);
 
-    dim4 dims      = numDims[0];
-    af_array outArray  = 0;
-    af_array inArray   = 0;
+    dim4 dims         = numDims[0];
+    af_array outArray = 0;
+    af_array inArray  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
 
     ASSERT_SUCCESS(af_bilateral(&outArray, inArray, 2.25f, 25.56f, false));
 
@@ -114,10 +113,11 @@ void bilateralDataTest(string pTestFile)
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<outType> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
-        ASSERT_EQ(true, compareArraysRMSD(nElems, &currGoldBar.front(), outData.data(), 0.02f));
+        size_t nElems               = currGoldBar.size();
+        ASSERT_EQ(true, compareArraysRMSD(nElems, &currGoldBar.front(),
+                                          outData.data(), 0.02f));
     }
 
     // cleanup
@@ -125,30 +125,30 @@ void bilateralDataTest(string pTestFile)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(BilateralOnData, Rectangle)
-{
-    bilateralDataTest<TypeParam>(string(TEST_DIR"/bilateral/rectangle.test"));
+TYPED_TEST(BilateralOnData, Rectangle) {
+    bilateralDataTest<TypeParam>(string(TEST_DIR "/bilateral/rectangle.test"));
 }
 
-TYPED_TEST(BilateralOnData, Rectangle_Batch)
-{
-    bilateralDataTest<TypeParam>(string(TEST_DIR"/bilateral/rectangle_batch.test"));
+TYPED_TEST(BilateralOnData, Rectangle_Batch) {
+    bilateralDataTest<TypeParam>(
+        string(TEST_DIR "/bilateral/rectangle_batch.test"));
 }
 
-TYPED_TEST(BilateralOnData, InvalidArgs)
-{
+TYPED_TEST(BilateralOnData, InvalidArgs) {
     if (noDoubleTests<TypeParam>()) return;
 
-    vector<TypeParam>   in(100,1);
+    vector<TypeParam> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
     // check for color image bilateral
-    dim4 dims = dim4(100,1,1,1);
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<TypeParam>::af_type));
-    ASSERT_EQ(AF_ERR_SIZE, af_bilateral(&outArray, inArray, 0.12f, 0.34f, true));
+    dim4 dims = dim4(100, 1, 1, 1);
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<TypeParam>::af_type));
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_bilateral(&outArray, inArray, 0.12f, 0.34f, true));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
@@ -157,17 +157,17 @@ TYPED_TEST(BilateralOnData, InvalidArgs)
 using af::array;
 using af::bilateral;
 
-TEST(Bilateral, CPP)
-{
+TEST(Bilateral, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, float>(string(TEST_DIR"/bilateral/rectangle.test"), numDims, in, tests);
+    readTests<float, float, float>(string(TEST_DIR "/bilateral/rectangle.test"),
+                                   numDims, in, tests);
 
-    dim4 dims      = numDims[0];
+    dim4 dims = numDims[0];
 
     array a(dims, &(in[0].front()));
     array b = bilateral(a, 2.25f, 25.56f, false);
@@ -175,30 +175,28 @@ TEST(Bilateral, CPP)
     vector<float> outData(dims.elements());
     b.host(outData.data());
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
-        ASSERT_EQ(true, compareArraysRMSD(nElems, currGoldBar.data(), outData.data(), 0.02f));
+        size_t nElems             = currGoldBar.size();
+        ASSERT_EQ(true, compareArraysRMSD(nElems, currGoldBar.data(),
+                                          outData.data(), 0.02f));
     }
 }
 
-using af::iota;
 using af::constant;
+using af::iota;
 using af::max;
 using af::seq;
 using af::span;
 
-TEST(bilateral, GFOR)
-{
+TEST(bilateral, GFOR) {
     dim4 dims = dim4(10, 10, 3);
-    array A = iota(dims);
-    array B = constant(0, dims);
+    array A   = iota(dims);
+    array B   = constant(0, dims);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = bilateral(A(span, span, ii), 3, 5);
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = bilateral(A(span, span, ii), 3, 5); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = bilateral(A(span, span, ii), 3, 5);
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
diff --git a/test/binary.cpp b/test/binary.cpp
index 3bf483036d..4d7fbd91cd 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -9,10 +9,10 @@
 
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
 #include <cfenv>
 #include <cmath>
@@ -30,13 +30,12 @@ const int num = 10000;
 typedef std::complex<float> complex_float;
 typedef std::complex<double> complex_double;
 
-template<typename T> T mod(T a, T b)
-{
+template<typename T>
+T mod(T a, T b) {
     return std::fmod(a, b);
 }
 
-af::array randgen(const int num, dtype ty)
-{
+af::array randgen(const int num, dtype ty) {
     af::array tmp = round(1 + 2 * af::randu(num, f32)).as(ty);
     tmp.eval();
     return tmp;
@@ -44,127 +43,121 @@ af::array randgen(const int num, dtype ty)
 
 #define MY_ASSERT_NEAR(aa, bb, cc) ASSERT_NEAR(abs(aa), abs(bb), (cc))
 
-#define BINARY_TESTS(Ta, Tb, Tc, func)                                  \
-    TEST(BinaryTests, Test_##func##_##Ta##_##Tb)                        \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        af::array a = randgen(num, ta);                                 \
-        af::array b = randgen(num, tb);                                 \
-        af::array c = func(a, b);                                       \
-        Ta *h_a = a.host<Ta>();                                         \
-        Tb *h_b = b.host<Tb>();                                         \
-        Tc *h_c = c.host<Tc>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_c[i], func(h_a[i], h_b[i])) <<                  \
-                "for values: " << h_a[i]  << "," << h_b[i] << endl;     \
-        af_free_host(h_a);                                              \
-        af_free_host(h_b);                                              \
-        af_free_host(h_c);                                              \
-    }                                                                   \
-                                                                        \
-    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_left)                 \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af::array a = randgen(num, ta);                                 \
-        Tb h_b = 3.0;                                                   \
-        af::array c = func(a, h_b);                                     \
-        Ta *h_a = a.host<Ta>();                                         \
-        Ta *h_c = c.host<Ta>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_c[i], func(h_a[i], h_b)) <<                     \
-                "for values: " << h_a[i]  << "," << h_b << endl;   \
-        af_free_host(h_a);                                              \
-        af_free_host(h_c);                                              \
-    }                                                                   \
-                                                                        \
-    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_right)                \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-                                                                        \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        Ta h_a = 5.0;                                                   \
-        af::array b = randgen(num, tb);                                 \
-        af::array c = func(h_a, b);                                     \
-        Tb *h_b = b.host<Tb>();                                         \
-        Tb *h_c = c.host<Tb>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_c[i], func(h_a, h_b[i])) <<                     \
-                "for values: " << h_a  << "," << h_b[i] << endl;        \
-        af_free_host(h_b);                                              \
-        af_free_host(h_c);                                              \
-    }                                                                   \
-
-
-#define BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Td, Te,func, err)         \
-    TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb)                \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        af::array a = randgen(num, ta);                                 \
-        af::array b = randgen(num, tb);                                 \
-        af::array c = func(a, b);                                       \
-        Ta *h_a = a.host<Ta>();                                         \
-        Tb *h_b = b.host<Tb>();                                         \
-        Tc *h_c = c.host<Tc>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            MY_ASSERT_NEAR(h_c[i], func(h_a[i], h_b[i]), (err)) <<      \
-                "for values: " << h_a[i]  << "," << h_b[i] << endl;     \
-        af_free_host(h_a);                                              \
-        af_free_host(h_b);                                              \
-        af_free_host(h_c);                                              \
-    }                                                                   \
-                                                                        \
-    TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_left)         \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af::array a = randgen(num, ta);                                 \
-        Tb h_b = 0.3;                                                   \
-        af::array c = func(a, h_b);                                     \
-        Ta *h_a = a.host<Ta>();                                         \
-        Td *h_d = c.host<Td>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            MY_ASSERT_NEAR(h_d[i], func(h_a[i], h_b), err) <<           \
-                "for values: " << h_a[i]  << "," << h_b << endl;        \
-        af_free_host(h_a);                                              \
-        af_free_host(h_d);                                              \
-    }                                                                   \
-                                                                        \
-    TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_right)        \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        Ta h_a = 0.3;                                                   \
-        af::array b = randgen(num, tb);                                 \
-        af::array c = func(h_a, b);                                     \
-        Tb *h_b = b.host<Tb>();                                         \
-        Te *h_e = c.host<Te>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            MY_ASSERT_NEAR(h_e[i], func(h_a, h_b[i]), err) <<           \
-                "for values: " << h_a  << "," << h_b[i] << endl;        \
-        af_free_host(h_b);                                              \
-        af_free_host(h_e);                                              \
-    }                                                                   \
-
-#define BINARY_TESTS_NEAR(Ta, Tb, Tc, func, err) BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Ta, Tc, func, err)
+#define BINARY_TESTS(Ta, Tb, Tc, func)                                \
+    TEST(BinaryTests, Test_##func##_##Ta##_##Tb) {                    \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
+        af::array a = randgen(num, ta);                               \
+        af::array b = randgen(num, tb);                               \
+        af::array c = func(a, b);                                     \
+        Ta *h_a     = a.host<Ta>();                                   \
+        Tb *h_b     = b.host<Tb>();                                   \
+        Tc *h_c     = c.host<Tc>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], func(h_a[i], h_b[i]))                   \
+                << "for values: " << h_a[i] << "," << h_b[i] << endl; \
+        af_free_host(h_a);                                            \
+        af_free_host(h_b);                                            \
+        af_free_host(h_c);                                            \
+    }                                                                 \
+                                                                      \
+    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_left) {             \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+                                                                      \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
+        af::array a = randgen(num, ta);                               \
+        Tb h_b      = 3.0;                                            \
+        af::array c = func(a, h_b);                                   \
+        Ta *h_a     = a.host<Ta>();                                   \
+        Ta *h_c     = c.host<Ta>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], func(h_a[i], h_b))                      \
+                << "for values: " << h_a[i] << "," << h_b << endl;    \
+        af_free_host(h_a);                                            \
+        af_free_host(h_c);                                            \
+    }                                                                 \
+                                                                      \
+    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_right) {            \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+                                                                      \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
+        Ta h_a      = 5.0;                                            \
+        af::array b = randgen(num, tb);                               \
+        af::array c = func(h_a, b);                                   \
+        Tb *h_b     = b.host<Tb>();                                   \
+        Tb *h_c     = c.host<Tb>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], func(h_a, h_b[i]))                      \
+                << "for values: " << h_a << "," << h_b[i] << endl;    \
+        af_free_host(h_b);                                            \
+        af_free_host(h_c);                                            \
+    }
+
+#define BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Td, Te, func, err)      \
+    TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb) {            \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
+        af::array a = randgen(num, ta);                               \
+        af::array b = randgen(num, tb);                               \
+        af::array c = func(a, b);                                     \
+        Ta *h_a     = a.host<Ta>();                                   \
+        Tb *h_b     = b.host<Tb>();                                   \
+        Tc *h_c     = c.host<Tc>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            MY_ASSERT_NEAR(h_c[i], func(h_a[i], h_b[i]), (err))       \
+                << "for values: " << h_a[i] << "," << h_b[i] << endl; \
+        af_free_host(h_a);                                            \
+        af_free_host(h_b);                                            \
+        af_free_host(h_c);                                            \
+    }                                                                 \
+                                                                      \
+    TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_left) {     \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+                                                                      \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
+        af::array a = randgen(num, ta);                               \
+        Tb h_b      = 0.3;                                            \
+        af::array c = func(a, h_b);                                   \
+        Ta *h_a     = a.host<Ta>();                                   \
+        Td *h_d     = c.host<Td>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            MY_ASSERT_NEAR(h_d[i], func(h_a[i], h_b), err)            \
+                << "for values: " << h_a[i] << "," << h_b << endl;    \
+        af_free_host(h_a);                                            \
+        af_free_host(h_d);                                            \
+    }                                                                 \
+                                                                      \
+    TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_right) {    \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
+        Ta h_a      = 0.3;                                            \
+        af::array b = randgen(num, tb);                               \
+        af::array c = func(h_a, b);                                   \
+        Tb *h_b     = b.host<Tb>();                                   \
+        Te *h_e     = c.host<Te>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            MY_ASSERT_NEAR(h_e[i], func(h_a, h_b[i]), err)            \
+                << "for values: " << h_a << "," << h_b[i] << endl;    \
+        af_free_host(h_b);                                            \
+        af_free_host(h_e);                                            \
+    }
+
+#define BINARY_TESTS_NEAR(Ta, Tb, Tc, func, err) \
+    BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Ta, Tc, func, err)
 
 #define BINARY_TESTS_FLOAT(func) BINARY_TESTS(float, float, float, func)
 #define BINARY_TESTS_DOUBLE(func) BINARY_TESTS(double, double, double, func)
@@ -175,16 +168,18 @@ af::array randgen(const int num, dtype ty)
 #define BINARY_TESTS_UINT(func) BINARY_TESTS(uint, uint, uint, func)
 #define BINARY_TESTS_INTL(func) BINARY_TESTS(intl, intl, intl, func)
 #define BINARY_TESTS_UINTL(func) BINARY_TESTS(uintl, uintl, uintl, func)
-#define BINARY_TESTS_NEAR_FLOAT(func) BINARY_TESTS_NEAR(float, float, float, func, 1e-5)
-#define BINARY_TESTS_NEAR_DOUBLE(func) BINARY_TESTS_NEAR(double, double, double, func, 1e-10)
+#define BINARY_TESTS_NEAR_FLOAT(func) \
+    BINARY_TESTS_NEAR(float, float, float, func, 1e-5)
+#define BINARY_TESTS_NEAR_DOUBLE(func) \
+    BINARY_TESTS_NEAR(double, double, double, func, 1e-10)
 
 BINARY_TESTS_FLOAT(add)
 BINARY_TESTS_FLOAT(sub)
 BINARY_TESTS_FLOAT(mul)
-BINARY_TESTS_NEAR(float, float, float, div, 1e-3) // FIXME
+BINARY_TESTS_NEAR(float, float, float, div, 1e-3)  // FIXME
 BINARY_TESTS_FLOAT(min)
 BINARY_TESTS_FLOAT(max)
-BINARY_TESTS_NEAR(float, float, float, mod, 1e-5) // FIXME
+BINARY_TESTS_NEAR(float, float, float, mod, 1e-5)  // FIXME
 
 BINARY_TESTS_DOUBLE(add)
 BINARY_TESTS_DOUBLE(sub)
@@ -250,29 +245,26 @@ BINARY_TESTS_NEAR_GENERAL(cfloat, double, cdouble, cfloat, cdouble, sub, 1e-5)
 BINARY_TESTS_NEAR_GENERAL(cfloat, double, cdouble, cfloat, cdouble, mul, 1e-5)
 BINARY_TESTS_NEAR_GENERAL(cfloat, double, cdouble, cfloat, cdouble, div, 1e-5)
 
-
-#define BITOP(func, T, op)                                  \
-    TEST(BinaryTests, Test_##func##_##T)                    \
-    {                                                       \
-        af_dtype ty = (af_dtype)dtype_traits<T>::af_type;   \
-        const T vala = 4095;                                \
-        const T valb = 3;                                   \
-        const T valc = vala op valb;                        \
-        const int num = 10;                                 \
-        af::array a = af::constant(vala, num, ty);          \
-        af::array b = af::constant(valb, num, ty);          \
-        af::array c = a op b;                               \
-        T *h_a = a.host<T>();                               \
-        T *h_b = b.host<T>();                               \
-        T *h_c = c.host<T>();                               \
-        for (int i = 0; i < num; i++)                       \
-            ASSERT_EQ(h_c[i], valc) <<                      \
-                "for values: " << h_a[i]  <<                \
-                "," << h_b[i] << endl;                      \
-        af_free_host(h_a);                                  \
-        af_free_host(h_b);                                  \
-        af_free_host(h_c);                                  \
-    }                                                       \
+#define BITOP(func, T, op)                                            \
+    TEST(BinaryTests, Test_##func##_##T) {                            \
+        af_dtype ty   = (af_dtype)dtype_traits<T>::af_type;           \
+        const T vala  = 4095;                                         \
+        const T valb  = 3;                                            \
+        const T valc  = vala op valb;                                 \
+        const int num = 10;                                           \
+        af::array a   = af::constant(vala, num, ty);                  \
+        af::array b   = af::constant(valb, num, ty);                  \
+        af::array c   = a op b;                                       \
+        T *h_a        = a.host<T>();                                  \
+        T *h_b        = b.host<T>();                                  \
+        T *h_c        = c.host<T>();                                  \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], valc)                                   \
+                << "for values: " << h_a[i] << "," << h_b[i] << endl; \
+        af_free_host(h_a);                                            \
+        af_free_host(h_b);                                            \
+        af_free_host(h_c);                                            \
+    }
 
 BITOP(bitor, int, |)
 BITOP(bitand, int, &)
@@ -296,52 +288,47 @@ BITOP(bitxor, uintl, ^)
 BITOP(bitshiftl, uintl, <<)
 BITOP(bitshiftr, uintl, >>)
 
-TEST(BinaryTests, Test_pow_cfloat_float)
-{
-    af::array a = randgen(num, c32);
-    af::array b = randgen(num, f32);
-    af::array c = af::pow(a, b);
+TEST(BinaryTests, Test_pow_cfloat_float) {
+    af::array a        = randgen(num, c32);
+    af::array b        = randgen(num, f32);
+    af::array c        = af::pow(a, b);
     complex_float *h_a = (complex_float *)a.host<cfloat>();
-    float *h_b = b.host<float>();
+    float *h_b         = b.host<float>();
     complex_float *h_c = (complex_float *)c.host<cfloat>();
     for (int i = 0; i < num; i++) {
         complex_float res = std::pow(h_a[i], h_b[i]);
         ASSERT_NEAR(real(h_c[i]), real(res), 1E-5)
-            << "for real values of: " << h_a[i]  << "," << h_b[i] << endl;
+            << "for real values of: " << h_a[i] << "," << h_b[i] << endl;
         ASSERT_NEAR(imag(h_c[i]), imag(res), 1E-5)
-            << "for imag values of: " << h_a[i]  << "," << h_b[i] << endl;
-
+            << "for imag values of: " << h_a[i] << "," << h_b[i] << endl;
     }
     af_free_host(h_a);
     af_free_host(h_b);
     af_free_host(h_c);
 }
 
-TEST(BinaryTests, Test_pow_cdouble_cdouble)
-{
+TEST(BinaryTests, Test_pow_cdouble_cdouble) {
     if (noDoubleTests<cdouble>()) return;
-    af::array a = randgen(num, c64);
-    af::array b = randgen(num, c64);
-    af::array c = af::pow(a, b);
+    af::array a         = randgen(num, c64);
+    af::array b         = randgen(num, c64);
+    af::array c         = af::pow(a, b);
     complex_double *h_a = (complex_double *)a.host<cdouble>();
     complex_double *h_b = (complex_double *)b.host<cdouble>();
     complex_double *h_c = (complex_double *)c.host<cdouble>();
     for (int i = 0; i < num; i++) {
         complex_double res = std::pow(h_a[i], h_b[i]);
         ASSERT_NEAR(real(h_c[i]), real(res), 1E-10)
-            << "for real values of: " << h_a[i]  << "," << h_b[i] << endl;
+            << "for real values of: " << h_a[i] << "," << h_b[i] << endl;
         ASSERT_NEAR(imag(h_c[i]), imag(res), 1E-10)
-            << "for imag values of: " << h_a[i]  << "," << h_b[i] << endl;
-
+            << "for imag values of: " << h_a[i] << "," << h_b[i] << endl;
     }
     af_free_host(h_a);
     af_free_host(h_b);
     af_free_host(h_c);
 }
 
-TEST(BinaryTests, ISSUE_1762)
-{
-    af::array zero = af::constant(0, 5, f32);
+TEST(BinaryTests, ISSUE_1762) {
+    af::array zero   = af::constant(0, 5, f32);
     af::array result = af::pow(zero, 2);
     vector<complex_float> hres(result.elements());
     result.host(&hres[0]);
@@ -354,28 +341,27 @@ TEST(BinaryTests, ISSUE_1762)
 template<typename T>
 class PowPrecisionTest : public ::testing::TestWithParam<T> {};
 
-#define DEF_TEST(Sx, T)                                 \
-using PowPrecisionTest##Sx = PowPrecisionTest< T >;     \
-TEST_P(PowPrecisionTest##Sx, Issue2304)                 \
-{                                                       \
-    T param     = GetParam();                           \
-    auto dtype  = (af_dtype)dtype_traits< T >::af_type; \
-    af::array A = af::constant(param, 1, dtype);        \
-    af::array B = af::pow(A, 2);                        \
-    vector<T> hres(1, 0);                               \
-    B.host(&hres[0]);                                   \
-    std::fesetround(FE_TONEAREST);                      \
-    T gold = (T)std::rint(std::pow((double)param, 2.0));\
-    ASSERT_EQ(hres[0], gold);                           \
-}
+#define DEF_TEST(Sx, T)                                      \
+    using PowPrecisionTest##Sx = PowPrecisionTest<T>;        \
+    TEST_P(PowPrecisionTest##Sx, Issue2304) {                \
+        T param     = GetParam();                            \
+        auto dtype  = (af_dtype)dtype_traits<T>::af_type;    \
+        af::array A = af::constant(param, 1, dtype);         \
+        af::array B = af::pow(A, 2);                         \
+        vector<T> hres(1, 0);                                \
+        B.host(&hres[0]);                                    \
+        std::fesetround(FE_TONEAREST);                       \
+        T gold = (T)std::rint(std::pow((double)param, 2.0)); \
+        ASSERT_EQ(hres[0], gold);                            \
+    }
 
-DEF_TEST(ULong , unsigned long long)
-DEF_TEST(Long  , long long)
-DEF_TEST(UInt  , unsigned int)
-DEF_TEST(Int   , int)
+DEF_TEST(ULong, unsigned long long)
+DEF_TEST(Long, long long)
+DEF_TEST(UInt, unsigned int)
+DEF_TEST(Int, int)
 DEF_TEST(UShort, unsigned short)
-DEF_TEST(Short , short)
-DEF_TEST(UChar , unsigned char)
+DEF_TEST(Short, short)
+DEF_TEST(UChar, unsigned char)
 
 #undef DEF_TEST
 
@@ -397,6 +383,6 @@ INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUChar,
 INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestLong,
                         testing::Range<long long>(-1e7, 0, 1e6));
 INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestInt,
-                        testing::Range<int  >(-46340, 0, 10e3));
+                        testing::Range<int>(-46340, 0, 10e3));
 INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestShort,
                         testing::Range<short>(-180, 0, 50));
diff --git a/test/binary_ops.hpp b/test/binary_ops.hpp
index 3c9dbc0c24..b498f02094 100644
--- a/test/binary_ops.hpp
+++ b/test/binary_ops.hpp
@@ -1,140 +1,88 @@
 #include <af/defines.h>
 #include <algorithm>
-#include <numeric>
 #include <complex>
+#include <numeric>
 
-template<typename T> static inline T min(T lhs, T rhs) { return std::min(lhs, rhs); }
+template<typename T>
+static inline T min(T lhs, T rhs) {
+    return std::min(lhs, rhs);
+}
 std::complex<float> min(std::complex<float> lhs, std::complex<float> rhs);
 std::complex<double> min(std::complex<double> lhs, std::complex<double> rhs);
 
-template<typename T> static inline T max(T lhs, T rhs) { return std::max(lhs, rhs); }
+template<typename T>
+static inline T max(T lhs, T rhs) {
+    return std::max(lhs, rhs);
+}
 std::complex<float> max(std::complex<float> lhs, std::complex<float> rhs);
 std::complex<double> max(std::complex<double> lhs, std::complex<double> rhs);
 
 template<typename T, af_binary_op op>
-struct Binary
-{
-    T init()
-    {
-        return (T)(0);
-    }
-
-    T operator() (T lhs, T rhs)
-    {
-        return lhs + rhs;
-    }
+struct Binary {
+    T init() { return (T)(0); }
+
+    T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
-struct Binary<T, AF_BINARY_ADD>
-{
-    T init()
-    {
-        return (T)(0);
-    }
-
-    T operator() (T lhs, T rhs)
-    {
-        return lhs + rhs;
-    }
+struct Binary<T, AF_BINARY_ADD> {
+    T init() { return (T)(0); }
+
+    T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
-struct Binary<T, AF_BINARY_MUL>
-{
-    T init()
-    {
-        return (T)(1);
-    }
-
-    T operator() (T lhs, T rhs)
-    {
-        return lhs * rhs;
-    }
+struct Binary<T, AF_BINARY_MUL> {
+    T init() { return (T)(1); }
+
+    T operator()(T lhs, T rhs) { return lhs * rhs; }
 };
 
 template<typename T>
-struct Binary<T, AF_BINARY_MIN>
-{
-    T init()
-    {
-        return std::numeric_limits<T>::max();
-    }
-
-    T operator() (T lhs, T rhs)
-    {
-        return min(lhs, rhs);
-    }
+struct Binary<T, AF_BINARY_MIN> {
+    T init() { return std::numeric_limits<T>::max(); }
+
+    T operator()(T lhs, T rhs) { return min(lhs, rhs); }
 };
 
 template<typename T>
-struct Binary<T, AF_BINARY_MAX>
-{
-    T init()
-    {
-        return std::numeric_limits<T>::min();
-    }
-
-    T operator() (T lhs, T rhs)
-    {
-        return max(lhs, rhs);
-    }
+struct Binary<T, AF_BINARY_MAX> {
+    T init() { return std::numeric_limits<T>::min(); }
+
+    T operator()(T lhs, T rhs) { return max(lhs, rhs); }
 };
 
-#define SPECIALIZE_COMPLEX_MIN(T, Tr)               \
-    template<>                                      \
-    struct Binary<T, AF_BINARY_MIN>                 \
-    {                                               \
-        T init()                                    \
-        {                                           \
-            return                                  \
-            (T)(std::numeric_limits<Tr>::max());    \
-        }                                           \
-                                                    \
-        T operator() (T lhs, T rhs)                 \
-        {                                           \
-            return min(lhs, rhs);                   \
-        }                                           \
-    };                                              \
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)                            \
+    template<>                                                   \
+    struct Binary<T, AF_BINARY_MIN> {                            \
+        T init() { return (T)(std::numeric_limits<Tr>::max()); } \
+                                                                 \
+        T operator()(T lhs, T rhs) { return min(lhs, rhs); }     \
+    };
 
 SPECIALIZE_COMPLEX_MIN(std::complex<float>, float)
 SPECIALIZE_COMPLEX_MIN(std::complex<double>, double)
 #undef SPECIALIZE_COMPLEX_MIN
 
-#define SPECIALIZE_COMPLEX_MAX(T, Tr)               \
-    template<>                                      \
-    struct Binary<T, AF_BINARY_MAX>                 \
-    {                                               \
-        T init()                                    \
-        {                                           \
-            return (T)((Tr)(0));                    \
-        }                                           \
-                                                    \
-        T operator() (T lhs, T rhs)                 \
-        {                                           \
-            return max(lhs, rhs);                   \
-        }                                           \
-    };                                              \
+#define SPECIALIZE_COMPLEX_MAX(T, Tr)                        \
+    template<>                                               \
+    struct Binary<T, AF_BINARY_MAX> {                        \
+        T init() { return (T)((Tr)(0)); }                    \
+                                                             \
+        T operator()(T lhs, T rhs) { return max(lhs, rhs); } \
+    };
 
 SPECIALIZE_COMPLEX_MAX(std::complex<float>, float)
 SPECIALIZE_COMPLEX_MAX(std::complex<double>, double)
 #undef SPECIALIZE_COMPLEX_MAX
 
-#define SPECIALIZE_FLOATING_MAX(T, Tr)              \
-    template<>                                      \
-    struct Binary<T, AF_BINARY_MAX>                 \
-    {                                               \
-        T init()                                    \
-        {                                           \
-            return                                  \
-            (T)(-std::numeric_limits<Tr>::max());   \
-        }                                           \
-                                                    \
-        T operator() (T lhs, T rhs)                 \
-        {                                           \
-            return max(lhs, rhs);                   \
-        }                                           \
-    };                                              \
+#define SPECIALIZE_FLOATING_MAX(T, Tr)                            \
+    template<>                                                    \
+    struct Binary<T, AF_BINARY_MAX> {                             \
+        T init() { return (T)(-std::numeric_limits<Tr>::max()); } \
+                                                                  \
+        T operator()(T lhs, T rhs) { return max(lhs, rhs); }      \
+    };
 
 SPECIALIZE_FLOATING_MAX(float, float)
 SPECIALIZE_FLOATING_MAX(double, double)
diff --git a/test/blas.cpp b/test/blas.cpp
index 17c0911bbf..740284409d 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -7,21 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/blas.h>
-#include <af/traits.hpp>
 #include <af/defines.h>
-#include <testHelpers.hpp>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
 #include <string>
 
-using std::copy;
-using std::cout;
-using std::endl;
-using std::ostream_iterator;
-using std::string;
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
@@ -34,67 +28,68 @@ using af::max;
 using af::randu;
 using af::setDevice;
 using af::span;
+using std::copy;
+using std::cout;
+using std::endl;
+using std::ostream_iterator;
+using std::string;
+using std::vector;
 
 template<typename T>
-class MatrixMultiply : public ::testing::Test
-{
-
-};
+class MatrixMultiply : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(MatrixMultiply, TestTypes);
 
 template<typename T, bool isBVector>
-void MatMulCheck(string TestFile)
-{
+void MatMulCheck(string TestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
-    readTests<T,T,int>(TestFile, numDims, hData, tests);
+    readTests<T, T, int>(TestFile, numDims, hData, tests);
 
     af_array a, aT, b, bT;
-    ASSERT_SUCCESS(
-            af_create_array(&a, &hData[0].front(), numDims[0].ndims(), numDims[0].get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &hData[0].front(), numDims[0].ndims(),
+                                   numDims[0].get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
     dim4 atdims = numDims[0];
     {
-        dim_t f  =    atdims[0];
-        atdims[0]   =    atdims[1];
-        atdims[1]   =    f;
+        dim_t f   = atdims[0];
+        atdims[0] = atdims[1];
+        atdims[1] = f;
     }
-    ASSERT_SUCCESS(
-            af_moddims(&aT, a, atdims.ndims(), atdims.get()));
-    ASSERT_SUCCESS(
-            af_create_array(&b, &hData[1].front(), numDims[1].ndims(), numDims[1].get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_moddims(&aT, a, atdims.ndims(), atdims.get()));
+    ASSERT_SUCCESS(af_create_array(&b, &hData[1].front(), numDims[1].ndims(),
+                                   numDims[1].get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
     dim4 btdims = numDims[1];
     {
-        dim_t f = btdims[0];
+        dim_t f   = btdims[0];
         btdims[0] = btdims[1];
         btdims[1] = f;
     }
-    ASSERT_SUCCESS(
-            af_moddims(&bT, b, btdims.ndims(), btdims.get()));
+    ASSERT_SUCCESS(af_moddims(&bT, b, btdims.ndims(), btdims.get()));
 
     vector<af_array> out(tests.size(), 0);
-    if(isBVector) {
-        ASSERT_SUCCESS(af_matmul( &out[0] , aT, b,    AF_MAT_NONE,    AF_MAT_NONE));
-        ASSERT_SUCCESS(af_matmul( &out[1] , bT, a,   AF_MAT_NONE,    AF_MAT_NONE));
-        ASSERT_SUCCESS(af_matmul( &out[2] , b, a,    AF_MAT_TRANS,       AF_MAT_NONE));
-        ASSERT_SUCCESS(af_matmul( &out[3] , bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS));
-        ASSERT_SUCCESS(af_matmul( &out[4] , b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS));
-    }
-    else {
-        ASSERT_SUCCESS(af_matmul( &out[0] , a, b, AF_MAT_NONE,   AF_MAT_NONE));
-        ASSERT_SUCCESS(af_matmul( &out[1] , a, bT, AF_MAT_NONE,   AF_MAT_TRANS));
-        ASSERT_SUCCESS(af_matmul( &out[2] , a, bT, AF_MAT_TRANS,      AF_MAT_NONE));
-        ASSERT_SUCCESS(af_matmul( &out[3] , aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS));
+    if (isBVector) {
+        ASSERT_SUCCESS(af_matmul(&out[0], aT, b, AF_MAT_NONE, AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul(&out[1], bT, a, AF_MAT_NONE, AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul(&out[2], b, a, AF_MAT_TRANS, AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul(&out[3], bT, aT, AF_MAT_NONE, AF_MAT_TRANS));
+        ASSERT_SUCCESS(af_matmul(&out[4], b, aT, AF_MAT_TRANS, AF_MAT_TRANS));
+    } else {
+        ASSERT_SUCCESS(af_matmul(&out[0], a, b, AF_MAT_NONE, AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul(&out[1], a, bT, AF_MAT_NONE, AF_MAT_TRANS));
+        ASSERT_SUCCESS(af_matmul(&out[2], a, bT, AF_MAT_TRANS, AF_MAT_NONE));
+        ASSERT_SUCCESS(af_matmul(&out[3], aT, bT, AF_MAT_TRANS, AF_MAT_TRANS));
     }
 
-    for(size_t i = 0; i < tests.size(); i++) {
+    for (size_t i = 0; i < tests.size(); i++) {
         dim4 dd;
-        dim_t *d = dd.get();
+        dim_t* d = dd.get();
         af_get_dims(&d[0], &d[1], &d[2], &d[3], out[i]);
         ASSERT_VEC_ARRAY_EQ(tests[i], dd, out[i]);
     }
@@ -104,54 +99,49 @@ void MatMulCheck(string TestFile)
     ASSERT_SUCCESS(af_release_array(b));
     ASSERT_SUCCESS(af_release_array(bT));
 
-    for (size_t i = 0; i <  out.size(); i++) {
+    for (size_t i = 0; i < out.size(); i++) {
         ASSERT_SUCCESS(af_release_array(out[i]));
     }
 }
 
-TYPED_TEST(MatrixMultiply, Square)
-{
-    MatMulCheck<TypeParam, false>(TEST_DIR"/blas/Basic.test");
+TYPED_TEST(MatrixMultiply, Square) {
+    MatMulCheck<TypeParam, false>(TEST_DIR "/blas/Basic.test");
 }
 
-TYPED_TEST(MatrixMultiply, NonSquare)
-{
-    MatMulCheck<TypeParam, false>(TEST_DIR"/blas/NonSquare.test");
+TYPED_TEST(MatrixMultiply, NonSquare) {
+    MatMulCheck<TypeParam, false>(TEST_DIR "/blas/NonSquare.test");
 }
 
-TYPED_TEST(MatrixMultiply, SquareVector)
-{
-    MatMulCheck<TypeParam, true>(TEST_DIR"/blas/SquareVector.test");
+TYPED_TEST(MatrixMultiply, SquareVector) {
+    MatMulCheck<TypeParam, true>(TEST_DIR "/blas/SquareVector.test");
 }
 
-TYPED_TEST(MatrixMultiply, RectangleVector)
-{
-    MatMulCheck<TypeParam, true>(TEST_DIR"/blas/RectangleVector.test");
+TYPED_TEST(MatrixMultiply, RectangleVector) {
+    MatMulCheck<TypeParam, true>(TEST_DIR "/blas/RectangleVector.test");
 }
 
 template<typename T, bool isBVector>
-void cppMatMulCheck(string TestFile)
-{
+void cppMatMulCheck(string TestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
-    readTests<T,T,int>(TestFile, numDims, hData, tests);
+    readTests<T, T, int>(TestFile, numDims, hData, tests);
 
     array a(numDims[0], &hData[0].front());
     array b(numDims[1], &hData[1].front());
 
     dim4 atdims = numDims[0];
     {
-        dim_t f  =    atdims[0];
-        atdims[0]   =    atdims[1];
-        atdims[1]   =    f;
+        dim_t f   = atdims[0];
+        atdims[0] = atdims[1];
+        atdims[1] = f;
     }
     dim4 btdims = numDims[1];
     {
-        dim_t f = btdims[0];
+        dim_t f   = btdims[0];
         btdims[0] = btdims[1];
         btdims[1] = f;
     }
@@ -160,96 +150,90 @@ void cppMatMulCheck(string TestFile)
     array bT = moddims(b, btdims.ndims(), btdims.get());
 
     vector<array> out(tests.size());
-    if(isBVector) {
-        out[0] = matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
-        out[1] = matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
-        out[2] = matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
-        out[3] = matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
-        out[4] = matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
-    }
-    else {
-        out[0] = matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
-        out[1] = matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
-        out[2] = matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
-        out[3] = matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
+    if (isBVector) {
+        out[0] = matmul(aT, b, AF_MAT_NONE, AF_MAT_NONE);
+        out[1] = matmul(bT, a, AF_MAT_NONE, AF_MAT_NONE);
+        out[2] = matmul(b, a, AF_MAT_TRANS, AF_MAT_NONE);
+        out[3] = matmul(bT, aT, AF_MAT_NONE, AF_MAT_TRANS);
+        out[4] = matmul(b, aT, AF_MAT_TRANS, AF_MAT_TRANS);
+    } else {
+        out[0] = matmul(a, b, AF_MAT_NONE, AF_MAT_NONE);
+        out[1] = matmul(a, bT, AF_MAT_NONE, AF_MAT_TRANS);
+        out[2] = matmul(a, bT, AF_MAT_TRANS, AF_MAT_NONE);
+        out[3] = matmul(aT, bT, AF_MAT_TRANS, AF_MAT_TRANS);
     }
 
-    for(size_t i = 0; i < tests.size(); i++) {
+    for (size_t i = 0; i < tests.size(); i++) {
         dim_t elems = out[i].elements();
         vector<T> h_out(elems);
         out[i].host((void*)&h_out.front());
 
         if (false == equal(h_out.begin(), h_out.end(), tests[i].begin())) {
-
             cout << "Failed test " << i << "\nCalculated: " << endl;
             copy(h_out.begin(), h_out.end(), ostream_iterator<T>(cout, ", "));
             cout << "Expected: " << endl;
-            copy(tests[i].begin(), tests[i].end(), ostream_iterator<T>(cout, ", "));
+            copy(tests[i].begin(), tests[i].end(),
+                 ostream_iterator<T>(cout, ", "));
             FAIL();
         }
     }
 }
 
-TYPED_TEST(MatrixMultiply, Square_CPP)
-{
-    cppMatMulCheck<TypeParam, false>(TEST_DIR"/blas/Basic.test");
+TYPED_TEST(MatrixMultiply, Square_CPP) {
+    cppMatMulCheck<TypeParam, false>(TEST_DIR "/blas/Basic.test");
 }
 
-TYPED_TEST(MatrixMultiply, NonSquare_CPP)
-{
-    cppMatMulCheck<TypeParam, false>(TEST_DIR"/blas/NonSquare.test");
+TYPED_TEST(MatrixMultiply, NonSquare_CPP) {
+    cppMatMulCheck<TypeParam, false>(TEST_DIR "/blas/NonSquare.test");
 }
 
-TYPED_TEST(MatrixMultiply, SquareVector_CPP)
-{
-    cppMatMulCheck<TypeParam, true>(TEST_DIR"/blas/SquareVector.test");
+TYPED_TEST(MatrixMultiply, SquareVector_CPP) {
+    cppMatMulCheck<TypeParam, true>(TEST_DIR "/blas/SquareVector.test");
 }
 
-TYPED_TEST(MatrixMultiply, RectangleVector_CPP)
-{
-    cppMatMulCheck<TypeParam, true>(TEST_DIR"/blas/RectangleVector.test");
+TYPED_TEST(MatrixMultiply, RectangleVector_CPP) {
+    cppMatMulCheck<TypeParam, true>(TEST_DIR "/blas/RectangleVector.test");
 }
 
-#define DEVICE_ITERATE(func) do {                                       \
-    const char* ENV = getenv("AF_MULTI_GPU_TESTS");                     \
-    if(ENV && ENV[0] == '0') {                                          \
-        func;                                                           \
-    } else {                                                            \
-        int oldDevice = getDevice();                                    \
-        for(int i = 0; i < getDeviceCount(); i++) {                     \
-            setDevice(i);                                               \
-            func;                                                       \
-        }                                                               \
-        setDevice(oldDevice);                                           \
-    }                                                                   \
-} while(0);
-
-
-TYPED_TEST(MatrixMultiply, MultiGPUSquare_CPP)
-{
-    DEVICE_ITERATE((cppMatMulCheck<TypeParam, false>(TEST_DIR"/blas/Basic.test")));
+#define DEVICE_ITERATE(func)                             \
+    do {                                                 \
+        const char* ENV = getenv("AF_MULTI_GPU_TESTS");  \
+        if (ENV && ENV[0] == '0') {                      \
+            func;                                        \
+        } else {                                         \
+            int oldDevice = getDevice();                 \
+            for (int i = 0; i < getDeviceCount(); i++) { \
+                setDevice(i);                            \
+                func;                                    \
+            }                                            \
+            setDevice(oldDevice);                        \
+        }                                                \
+    } while (0);
+
+TYPED_TEST(MatrixMultiply, MultiGPUSquare_CPP) {
+    DEVICE_ITERATE(
+        (cppMatMulCheck<TypeParam, false>(TEST_DIR "/blas/Basic.test")));
 }
 
-TYPED_TEST(MatrixMultiply, MultiGPUNonSquare_CPP)
-{
-    DEVICE_ITERATE((cppMatMulCheck<TypeParam, false>(TEST_DIR"/blas/NonSquare.test")));
+TYPED_TEST(MatrixMultiply, MultiGPUNonSquare_CPP) {
+    DEVICE_ITERATE(
+        (cppMatMulCheck<TypeParam, false>(TEST_DIR "/blas/NonSquare.test")));
 }
 
-TYPED_TEST(MatrixMultiply, MultiGPUSquareVector_CPP)
-{
-    DEVICE_ITERATE((cppMatMulCheck<TypeParam, true>(TEST_DIR"/blas/SquareVector.test")));
+TYPED_TEST(MatrixMultiply, MultiGPUSquareVector_CPP) {
+    DEVICE_ITERATE(
+        (cppMatMulCheck<TypeParam, true>(TEST_DIR "/blas/SquareVector.test")));
 }
 
-TYPED_TEST(MatrixMultiply, MultiGPURectangleVector_CPP)
-{
-    DEVICE_ITERATE((cppMatMulCheck<TypeParam, true>(TEST_DIR"/blas/RectangleVector.test")));
+TYPED_TEST(MatrixMultiply, MultiGPURectangleVector_CPP) {
+    DEVICE_ITERATE((cppMatMulCheck<TypeParam, true>(
+        TEST_DIR "/blas/RectangleVector.test")));
 }
 
-TEST(MatrixMultiply, Batched)
-{
-    const int M = 512;
-    const int K = 512;
-    const int N = 10;
+TEST(MatrixMultiply, Batched) {
+    const int M  = 512;
+    const int K  = 512;
+    const int N  = 10;
     const int D2 = 2;
     const int D3 = 3;
     for (int d3 = 1; d3 <= D3; d3 *= D3) {
@@ -263,7 +247,7 @@ TEST(MatrixMultiply, Batched)
                     array a_ij = a(span, span, i, j);
                     array b_ij = b(span, span, i, j);
                     array c_ij = c(span, span, i, j);
-                    array res = matmul(a_ij, b_ij);
+                    array res  = matmul(a_ij, b_ij);
                     ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
                 }
             }
@@ -273,13 +257,12 @@ TEST(MatrixMultiply, Batched)
 
 #undef DEVICE_ITERATE
 
-TEST(MatrixMultiply, ISSUE_1882)
-{
+TEST(MatrixMultiply, ISSUE_1882) {
     const int m = 2;
     const int n = 3;
-    array A = randu(m, n);
-    array BB = randu(n, m);
-    array B = BB(0, span);
+    array A     = randu(m, n);
+    array BB    = randu(n, m);
+    array B     = BB(0, span);
 
     array res1 = matmul(A.T(), B.T());
     array res2 = matmulTT(A, B);
@@ -287,11 +270,10 @@ TEST(MatrixMultiply, ISSUE_1882)
     ASSERT_ARRAYS_NEAR(res1, res2, 1E-5);
 }
 
-TEST(MatrixMultiply, LhsBroadcastBatched)
-{
-    const int M = 512;
-    const int K = 512;
-    const int N = 10;
+TEST(MatrixMultiply, LhsBroadcastBatched) {
+    const int M  = 512;
+    const int K  = 512;
+    const int N  = 10;
     const int D2 = 2;
     const int D3 = 3;
 
@@ -305,7 +287,7 @@ TEST(MatrixMultiply, LhsBroadcastBatched)
                 for (int i = 0; i < d2; i++) {
                     array b_ij = b(span, span, i, j);
                     array c_ij = c(span, span, i, j);
-                    array res = matmul(a, b_ij);
+                    array res  = matmul(a, b_ij);
                     ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
                 }
             }
@@ -313,11 +295,10 @@ TEST(MatrixMultiply, LhsBroadcastBatched)
     }
 }
 
-TEST(MatrixMultiply, RhsBroadcastBatched)
-{
-    const int M = 512;
-    const int K = 512;
-    const int N = 10;
+TEST(MatrixMultiply, RhsBroadcastBatched) {
+    const int M  = 512;
+    const int K  = 512;
+    const int N  = 10;
     const int D2 = 2;
     const int D3 = 3;
 
@@ -331,7 +312,7 @@ TEST(MatrixMultiply, RhsBroadcastBatched)
                 for (int i = 0; i < d2; i++) {
                     array a_ij = a(span, span, i, j);
                     array c_ij = c(span, span, i, j);
-                    array res = matmul(a_ij, b);
+                    array res  = matmul(a_ij, b);
                     ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
                 }
             }
diff --git a/test/canny.cpp b/test/canny.cpp
index 7be356a5eb..1e15de68d6 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -7,41 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using af::dim4;
+using af::dtype_traits;
 using std::endl;
 using std::string;
 using std::vector;
-using af::dim4;
-using af::dtype_traits;
 
 template<typename T>
-class CannyEdgeDetector : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class CannyEdgeDetector : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, int, uint, short, ushort, uchar, double> TestTypes;
+typedef ::testing::Types<float, int, uint, short, ushort, uchar, double>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(CannyEdgeDetector, TestTypes);
 
 template<typename T>
-void cannyTest(string pTestFile)
-{
+void cannyTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<T> >      in;
-    vector<vector<char> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<char> > tests;
 
     readTests<T, char, int>(pTestFile, numDims, in, tests);
 
@@ -49,19 +48,22 @@ void cannyTest(string pTestFile)
     af_array outArray = 0;
     af_array sArray   = 0;
 
-    ASSERT_SUCCESS(af_create_array(&sArray, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&sArray, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL, 0.4147f, 0.8454f, 3, true));
+    ASSERT_SUCCESS(af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL,
+                            0.4147f, 0.8454f, 3, true));
 
     vector<char> outData(sDims.elements());
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<char> currGoldBar = tests[0];
-    size_t nElems        = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    size_t nElems            = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // cleanup
@@ -69,64 +71,65 @@ void cannyTest(string pTestFile)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(CannyEdgeDetector, ArraySizeLessThanBlockSize10x10)
-{
+TYPED_TEST(CannyEdgeDetector, ArraySizeLessThanBlockSize10x10) {
     cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast10x10.test"));
 }
 
-TYPED_TEST(CannyEdgeDetector, ArraySizeEqualBlockSize16x16)
-{
+TYPED_TEST(CannyEdgeDetector, ArraySizeEqualBlockSize16x16) {
     cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast16x16.test"));
 }
 
 template<typename T>
-void cannyImageOtsuTest(string pTestFile, bool isColor)
-{
+void cannyImageOtsuTest(string pTestFile, bool isColor) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
     using af::dim4;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
-    vector<dim_t>    outSizes;
-    vector<string>   outFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
+    vector<dim_t> outSizes;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-
-        af_array _inArray   = 0;
-        af_array inArray    = 0;
-        af_array _outArray  = 0;
-        af_array cstArray   = 0;
-        af_array mulArray   = 0;
-        af_array outArray   = 0;
-        af_array goldArray  = 0;
-        dim_t nElems        = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array _inArray  = 0;
+        af_array inArray   = 0;
+        af_array _outArray = 0;
+        af_array cstArray  = 0;
+        af_array mulArray  = 0;
+        af_array outArray  = 0;
+        af_array goldArray = 0;
+        dim_t nElems       = 0;
 
-        inFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
-        outFiles[testId].insert(0,string(TEST_DIR "/CannyEdgeDetector/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/CannyEdgeDetector/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/CannyEdgeDetector/"));
 
         af_dtype type = (af_dtype)dtype_traits<T>::af_type;
 
-        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
 
         ASSERT_SUCCESS(af_cast(&inArray, _inArray, type));
 
-        ASSERT_SUCCESS(af_load_image_native(&goldArray, outFiles[testId].c_str()));
+        ASSERT_SUCCESS(
+            af_load_image_native(&goldArray, outFiles[testId].c_str()));
 
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_SUCCESS(af_canny(&_outArray, inArray, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false));
+        ASSERT_SUCCESS(af_canny(&_outArray, inArray,
+                                AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3,
+                                false));
 
         unsigned ndims = 0;
         dim_t dims[4];
 
         ASSERT_SUCCESS(af_get_numdims(&ndims, _outArray));
-        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, _outArray));
+        ASSERT_SUCCESS(
+            af_get_dims(dims, dims + 1, dims + 2, dims + 3, _outArray));
 
         ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, f32));
 
@@ -139,7 +142,8 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
         vector<unsigned char> goldData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 1.0e-3));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 1.0e-3));
 
         ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(inArray));
@@ -151,58 +155,64 @@ void cannyImageOtsuTest(string pTestFile, bool isColor)
     }
 }
 
-TEST(CannyEdgeDetector, OtsuThreshold)
-{
-    cannyImageOtsuTest<float>(string(TEST_DIR "/CannyEdgeDetector/gray.test"), false);
+TEST(CannyEdgeDetector, OtsuThreshold) {
+    cannyImageOtsuTest<float>(string(TEST_DIR "/CannyEdgeDetector/gray.test"),
+                              false);
 }
 
-TEST(CannyEdgeDetector, InvalidSizeArray)
-{
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+TEST(CannyEdgeDetector, InvalidSizeArray) {
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<float>   in(100, 1);
+    vector<float> in(100, 1);
 
     dim4 sDims(100, 1, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24,
+                       0.72, 3, true));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TEST(CannyEdgeDetector, Array4x4_Invalid)
-{
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+TEST(CannyEdgeDetector, Array4x4_Invalid) {
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<float>   in(16, 1);
+    vector<float> in(16, 1);
 
     dim4 sDims(4, 4, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_SIZE, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 3, true));
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24,
+                       0.72, 3, true));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TEST(CannyEdgeDetector, Sobel5x5_Invalid)
-{
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+TEST(CannyEdgeDetector, Sobel5x5_Invalid) {
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<float>   in(25, 1);
+    vector<float> in(25, 1);
 
     dim4 sDims(5, 5, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24, 0.72, 5, true));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_canny(&outArray, inArray, AF_CANNY_THRESHOLD_MANUAL, 0.24,
+                       0.72, 5, true));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
diff --git a/test/cast.cpp b/test/cast.cpp
index 5fe4728eb1..e32fbea6ff 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -8,21 +8,20 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 
 const int num = 10;
 
-template <typename Ti, typename To>
-void cast_test()
-{
+template<typename Ti, typename To>
+void cast_test() {
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
@@ -37,30 +36,26 @@ void cast_test()
     ASSERT_SUCCESS(err);
 }
 
-#define REAL_TO_TESTS(Ti, To)                   \
-    TEST(CAST_TEST, Test_Real_##Ti##_##To)      \
-    {                                           \
-        cast_test<Ti, To>();                    \
-    }                                           \
-
-#define REAL_TEST_INVOKE(Ti)                    \
-    REAL_TO_TESTS(Ti, float);                   \
-    REAL_TO_TESTS(Ti, cfloat);                  \
-    REAL_TO_TESTS(Ti, double);                  \
-    REAL_TO_TESTS(Ti, cdouble);                 \
-    REAL_TO_TESTS(Ti, char);                    \
-    REAL_TO_TESTS(Ti, int);                     \
-    REAL_TO_TESTS(Ti, unsigned);                \
-    REAL_TO_TESTS(Ti, uchar);                   \
-    REAL_TO_TESTS(Ti, intl);                    \
-    REAL_TO_TESTS(Ti, uintl);                   \
-    REAL_TO_TESTS(Ti, short);                   \
-    REAL_TO_TESTS(Ti, ushort);                  \
+#define REAL_TO_TESTS(Ti, To) \
+    TEST(CAST_TEST, Test_Real_##Ti##_##To) { cast_test<Ti, To>(); }
 
-#define CPLX_TEST_INVOKE(Ti)                    \
-    REAL_TO_TESTS(Ti, cfloat);                  \
-    REAL_TO_TESTS(Ti, cdouble);                 \
+#define REAL_TEST_INVOKE(Ti)     \
+    REAL_TO_TESTS(Ti, float);    \
+    REAL_TO_TESTS(Ti, cfloat);   \
+    REAL_TO_TESTS(Ti, double);   \
+    REAL_TO_TESTS(Ti, cdouble);  \
+    REAL_TO_TESTS(Ti, char);     \
+    REAL_TO_TESTS(Ti, int);      \
+    REAL_TO_TESTS(Ti, unsigned); \
+    REAL_TO_TESTS(Ti, uchar);    \
+    REAL_TO_TESTS(Ti, intl);     \
+    REAL_TO_TESTS(Ti, uintl);    \
+    REAL_TO_TESTS(Ti, short);    \
+    REAL_TO_TESTS(Ti, ushort);
 
+#define CPLX_TEST_INVOKE(Ti)   \
+    REAL_TO_TESTS(Ti, cfloat); \
+    REAL_TO_TESTS(Ti, cdouble);
 
 REAL_TEST_INVOKE(float)
 REAL_TEST_INVOKE(double)
@@ -78,9 +73,8 @@ CPLX_TEST_INVOKE(cdouble)
 // Converting complex to real; expected to fail as this operation is
 // not allowed. Use functions abs, real, image, arg, etc to make the
 // conversion explicit.
-template <typename Ti, typename To>
-void cast_test_complex_real()
-{
+template<typename Ti, typename To>
+void cast_test_complex_real() {
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
@@ -95,10 +89,9 @@ void cast_test_complex_real()
 }
 
 #define COMPLEX_REAL_TESTS(Ti, To)                      \
-    TEST(CAST_TEST, Test_Complex_To_Real_##Ti##_##To)   \
-    {                                                   \
+    TEST(CAST_TEST, Test_Complex_To_Real_##Ti##_##To) { \
         cast_test_complex_real<Ti, To>();               \
-    }                                                   \
+    }
 
 COMPLEX_REAL_TESTS(cfloat, float)
 COMPLEX_REAL_TESTS(cfloat, double)
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index e41089f858..038c5ede95 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -7,33 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <complex>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::identity;
 using af::matmul;
 using af::max;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-void choleskyTester(const int n, double eps, bool is_upper)
-{
+void choleskyTester(const int n, double eps, bool is_upper) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -45,7 +44,7 @@ void choleskyTester(const int n, double eps, bool is_upper)
 #else
     array a = randu(n, n, ty);
 #endif
-    array b = 10 * n * identity(n, n, ty);
+    array b  = 10 * n * identity(n, n, ty);
     array in = matmul(a.H(), a) + b;
 
     //! [ex_chol_reg]
@@ -55,8 +54,10 @@ void choleskyTester(const int n, double eps, bool is_upper)
 
     array re = is_upper ? matmul(out.H(), out) : matmul(out, out.H());
 
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(in - re))), eps);
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(in - re))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(in - re))),
+                eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(in - re))),
+                eps);
 
     //! [ex_chol_inplace]
     array in2 = in.copy();
@@ -65,15 +66,16 @@ void choleskyTester(const int n, double eps, bool is_upper)
 
     array out2 = is_upper ? upper(in2) : lower(in2);
 
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(out2 - out))), eps);
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(out2 - out))), eps);
+    ASSERT_NEAR(0,
+                max<typename dtype_traits<T>::base_type>(abs(real(out2 - out))),
+                eps);
+    ASSERT_NEAR(0,
+                max<typename dtype_traits<T>::base_type>(abs(imag(out2 - out))),
+                eps);
 }
 
 template<typename T>
-class Cholesky : public ::testing::Test
-{
-
-};
+class Cholesky : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(Cholesky, TestTypes);
@@ -102,33 +104,33 @@ double eps<cdouble>() {
 }
 
 TYPED_TEST(Cholesky, Upper) {
-    choleskyTester<TypeParam>( 500, eps<TypeParam>(), true );
+    choleskyTester<TypeParam>(500, eps<TypeParam>(), true);
 }
 
 TYPED_TEST(Cholesky, UpperLarge) {
-    choleskyTester<TypeParam>( 1000, eps<TypeParam>(), true );
+    choleskyTester<TypeParam>(1000, eps<TypeParam>(), true);
 }
 
 TYPED_TEST(Cholesky, UpperMultipleOfTwo) {
-    choleskyTester<TypeParam>( 512, eps<TypeParam>(), true );
+    choleskyTester<TypeParam>(512, eps<TypeParam>(), true);
 }
 
 TYPED_TEST(Cholesky, UpperMultipleOfTwoLarge) {
-    choleskyTester<TypeParam>( 1024, eps<TypeParam>(), true );
+    choleskyTester<TypeParam>(1024, eps<TypeParam>(), true);
 }
 
 TYPED_TEST(Cholesky, Lower) {
-    choleskyTester<TypeParam>( 500, eps<TypeParam>(), false );
+    choleskyTester<TypeParam>(500, eps<TypeParam>(), false);
 }
 
 TYPED_TEST(Cholesky, LowerLarge) {
-    choleskyTester<TypeParam>( 1000, eps<TypeParam>(), false );
+    choleskyTester<TypeParam>(1000, eps<TypeParam>(), false);
 }
 
 TYPED_TEST(Cholesky, LowerMultipleOfTwo) {
-    choleskyTester<TypeParam>( 512, eps<TypeParam>(), false );
+    choleskyTester<TypeParam>(512, eps<TypeParam>(), false);
 }
 
 TYPED_TEST(Cholesky, LowerMultipleOfTwoLarge) {
-    choleskyTester<TypeParam>( 1024, eps<TypeParam>(), false );
+    choleskyTester<TypeParam>(1024, eps<TypeParam>(), false);
 }
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 90144b8430..747bd7ec2b 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -8,26 +8,24 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
-using std::abs;
-using std::vector;
 using af::array;
 using af::randu;
+using std::abs;
+using std::vector;
 
 const int num = 10000;
 
-TEST(ClampTests, FloatArrayArray)
-{
+TEST(ClampTests, FloatArrayArray) {
     array in = randu(num, f32);
-    array lo = randu(num, f32)/10;       // Ensure lo <= 0.1
-    array hi = 1.0 - randu(num, f32)/10; // Ensure hi >= 0.9
+    array lo = randu(num, f32) / 10;        // Ensure lo <= 0.1
+    array hi = 1.0 - randu(num, f32) / 10;  // Ensure hi >= 0.9
     eval(lo, hi);
 
-
     vector<float> hout(num), hin(num), hlo(num), hhi(num);
     array out = clamp(in, lo, hi);
     out.host(&hout[0]);
@@ -38,14 +36,14 @@ TEST(ClampTests, FloatArrayArray)
     for (int i = 0; i < num; i++) {
         ASSERT_LE(hout[i], hhi[i]);
         ASSERT_GE(hout[i], hlo[i]);
-        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == hlo[i] || hout[i] == hhi[i]);
+        ASSERT_EQ(true,
+                  hout[i] == hin[i] || hout[i] == hlo[i] || hout[i] == hhi[i]);
     }
 }
 
-TEST(ClampTests, FloatArrayScalar)
-{
+TEST(ClampTests, FloatArrayScalar) {
     array in = randu(num, f32);
-    array lo = randu(num, f32)/10; // Ensure lo <= 0.1
+    array lo = randu(num, f32) / 10;  // Ensure lo <= 0.1
     float hi = 0.9;
 
     vector<float> hout(num), hin(num), hlo(num);
@@ -58,15 +56,15 @@ TEST(ClampTests, FloatArrayScalar)
     for (int i = 0; i < num; i++) {
         ASSERT_LE(hout[i], hi);
         ASSERT_GE(hout[i], hlo[i]);
-        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == hlo[i] || hout[i] == hi);
+        ASSERT_EQ(true,
+                  hout[i] == hin[i] || hout[i] == hlo[i] || hout[i] == hi);
     }
 }
 
-TEST(ClampTests, FloatScalarArray)
-{
+TEST(ClampTests, FloatScalarArray) {
     array in = randu(num, f32);
     float lo = 0.1;
-    array hi = 1.0 - randu(num, f32)/10; // Ensure hi >= 0.9
+    array hi = 1.0 - randu(num, f32) / 10;  // Ensure hi >= 0.9
 
     vector<float> hout(num), hin(num), hhi(num);
     array out = clamp(in, lo, hi);
@@ -78,12 +76,12 @@ TEST(ClampTests, FloatScalarArray)
     for (int i = 0; i < num; i++) {
         ASSERT_LE(hout[i], hhi[i]);
         ASSERT_GE(hout[i], lo);
-        ASSERT_EQ(true, hout[i] == hin[i] || hout[i] == lo || hout[i] == hhi[i]);
+        ASSERT_EQ(true,
+                  hout[i] == hin[i] || hout[i] == lo || hout[i] == hhi[i]);
     }
 }
 
-TEST(ClampTests, FloatScalarScalar)
-{
+TEST(ClampTests, FloatScalarScalar) {
     array in = randu(num, f32);
     float lo = 0.1;
     float hi = 0.9;
diff --git a/test/compare.cpp b/test/compare.cpp
index a9915ad490..7533bf8430 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -8,44 +8,43 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
-using std::vector;
 using af::array;
 using af::dtype_traits;
 using af::randu;
+using std::vector;
 
 template<typename T>
-class Compare : public ::testing::Test
-{
-};
+class Compare : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, uint, int, intl, uintl, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, uint, int, intl, uintl, uchar, short,
+                         ushort>
+    TestTypes;
 TYPED_TEST_CASE(Compare, TestTypes);
 
-#define COMPARE(OP, Name)                                       \
-    TYPED_TEST(Compare, Test_##Name)                            \
-    {                                                           \
-        typedef TypeParam T;                                    \
-        if (noDoubleTests<T>()) return;                         \
-        const int num = 1 << 20;                                \
-        af_dtype ty = (af_dtype) dtype_traits<T>::af_type;      \
-        array a = randu(num, ty);                               \
-        array b = randu(num, ty);                               \
-        array c = a OP b;                                       \
-        vector<T> ha(num), hb(num);                             \
-        vector<char> hc(num);                                   \
-        a.host(&ha[0]);                                         \
-        b.host(&hb[0]);                                         \
-        c.host(&hc[0]);                                         \
-        for (int i = 0; i < num; i++) {                         \
-            char res = ha[i] OP hb[i];                          \
-            ASSERT_EQ((int)res, (int)hc[i]);                    \
-        }                                                       \
-    }                                                           \
+#define COMPARE(OP, Name)                                   \
+    TYPED_TEST(Compare, Test_##Name) {                      \
+        typedef TypeParam T;                                \
+        if (noDoubleTests<T>()) return;                     \
+        const int num = 1 << 20;                            \
+        af_dtype ty   = (af_dtype)dtype_traits<T>::af_type; \
+        array a       = randu(num, ty);                     \
+        array b       = randu(num, ty);                     \
+        array c       = a OP b;                             \
+        vector<T> ha(num), hb(num);                         \
+        vector<char> hc(num);                               \
+        a.host(&ha[0]);                                     \
+        b.host(&hb[0]);                                     \
+        c.host(&hc[0]);                                     \
+        for (int i = 0; i < num; i++) {                     \
+            char res = ha[i] OP hb[i];                      \
+            ASSERT_EQ((int)res, (int)hc[i]);                \
+        }                                                   \
+    }
 
 COMPARE(==, eq)
 COMPARE(!=, ne)
diff --git a/test/complex.cpp b/test/complex.cpp
index beef0be4c2..fbbc27b6ab 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -8,134 +8,126 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
 using std::endl;
 using namespace af;
 
 const int num = 10;
 
-#define CPLX(TYPE)  af_c##TYPE
-
-#define COMPLEX_TESTS(Ta, Tb, Tc)                                       \
-    TEST(ComplexTests, Test_##Ta##_##Tb)                                \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        array a = randu(num, ta);                                       \
-        array b = randu(num, tb);                                       \
-        array c = complex(a, b);                                        \
-        Ta *h_a = a.host<Ta>();                                         \
-        Tb *h_b = b.host<Tb>();                                         \
-        CPLX(Tc) *h_c = c.host< CPLX(Tc) >();                           \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_c[i], CPLX(Tc)(h_a[i], h_b[i])) <<              \
-                "for values: " << h_a[i]  << "," << h_b[i] << endl;     \
-        freeHost(h_a);                                                  \
-        freeHost(h_b);                                                  \
-        freeHost(h_c);                                                  \
-    }                                                                   \
-    TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_left)                    \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        array a = randu(num, ta);                                       \
-        Tb h_b = 0.3;                                                   \
-        array c = complex(a, h_b);                                      \
-        Ta *h_a = a.host<Ta>();                                         \
-        CPLX(Ta) *h_c = c.host<CPLX(Ta) >();                            \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_c[i], CPLX(Ta)(h_a[i], h_b)) <<                 \
-                "for values: " << h_a[i]  << "," << h_b << endl;        \
-        freeHost(h_a);                                                  \
-        freeHost(h_c);                                                  \
-    }                                                                   \
-                                                                        \
-    TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_right)                   \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-                                                                        \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        Ta h_a = 0.3;                                                   \
-        array b = randu(num, tb);                                       \
-        array c = complex(h_a, b);                                      \
-        Tb *h_b = b.host<Tb>();                                         \
-        CPLX(Tb) *h_c = c.host<CPLX(Tb) >();                            \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_c[i], CPLX(Tb)(h_a, h_b[i])) <<                 \
-                "for values: " << h_a  << "," << h_b[i] << endl;        \
-        freeHost(h_b);                                                  \
-        freeHost(h_c);                                                  \
-    }                                                                   \
-    TEST(ComplexTests, Test_##Ta##_##Tb##_Real)                         \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        array a = randu(num, ta);                                       \
-        array b = randu(num, tb);                                       \
-        array c = complex(a, b);                                        \
-        array d = real(c);                                              \
-        Ta *h_a = a.host<Ta>();                                         \
-        Tc *h_d = d.host<Tc>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_d[i], h_a[i]) << "at: " << i << endl;           \
-        freeHost(h_a);                                                  \
-        freeHost(h_d);                                                  \
-    }                                                                   \
-    TEST(ComplexTests, Test_##Ta##_##Tb##_Imag)                         \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        array a = randu(num, ta);                                       \
-        array b = randu(num, tb);                                       \
-        array c = complex(a, b);                                        \
-        array d = imag(c);                                              \
-        Tb *h_b = b.host<Tb>();                                         \
-        Tc *h_d = d.host<Tc>();                                         \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(h_d[i], h_b[i])  << "at: " << i << endl;          \
-        freeHost(h_b);                                                  \
-        freeHost(h_d);                                                  \
-    }                                                                   \
-    TEST(ComplexTests, Test_##Ta##_##Tb##_Conj)                         \
-    {                                                                   \
-        if (noDoubleTests<Ta>()) return;                                \
-        if (noDoubleTests<Tb>()) return;                                \
-        if (noDoubleTests<Tc>()) return;                                \
-                                                                        \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;              \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;              \
-        array a = randu(num, ta);                                       \
-        array b = randu(num, tb);                                       \
-        array c = complex(a, b);                                        \
-        array d = conjg(c);                                             \
-        CPLX(Tc) *h_c = c.host<CPLX(Tc) >();                            \
-        CPLX(Tc) *h_d = d.host<CPLX(Tc) >();                            \
-        for (int i = 0; i < num; i++)                                   \
-            ASSERT_EQ(conj(h_c[i]), h_d[i])                             \
-                << "at: " << i << endl;                                 \
-        freeHost(h_c);                                                  \
-        freeHost(h_d);                                                  \
-    }                                                                   \
+#define CPLX(TYPE) af_c##TYPE
 
+#define COMPLEX_TESTS(Ta, Tb, Tc)                                     \
+    TEST(ComplexTests, Test_##Ta##_##Tb) {                            \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype ta   = (af_dtype)dtype_traits<Ta>::af_type;          \
+        af_dtype tb   = (af_dtype)dtype_traits<Tb>::af_type;          \
+        array a       = randu(num, ta);                               \
+        array b       = randu(num, tb);                               \
+        array c       = complex(a, b);                                \
+        Ta *h_a       = a.host<Ta>();                                 \
+        Tb *h_b       = b.host<Tb>();                                 \
+        CPLX(Tc) *h_c = c.host<CPLX(Tc)>();                           \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], CPLX(Tc)(h_a[i], h_b[i]))               \
+                << "for values: " << h_a[i] << "," << h_b[i] << endl; \
+        freeHost(h_a);                                                \
+        freeHost(h_b);                                                \
+        freeHost(h_c);                                                \
+    }                                                                 \
+    TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_left) {                \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+                                                                      \
+        af_dtype ta   = (af_dtype)dtype_traits<Ta>::af_type;          \
+        array a       = randu(num, ta);                               \
+        Tb h_b        = 0.3;                                          \
+        array c       = complex(a, h_b);                              \
+        Ta *h_a       = a.host<Ta>();                                 \
+        CPLX(Ta) *h_c = c.host<CPLX(Ta)>();                           \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], CPLX(Ta)(h_a[i], h_b))                  \
+                << "for values: " << h_a[i] << "," << h_b << endl;    \
+        freeHost(h_a);                                                \
+        freeHost(h_c);                                                \
+    }                                                                 \
+                                                                      \
+    TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_right) {               \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+                                                                      \
+        af_dtype tb   = (af_dtype)dtype_traits<Tb>::af_type;          \
+        Ta h_a        = 0.3;                                          \
+        array b       = randu(num, tb);                               \
+        array c       = complex(h_a, b);                              \
+        Tb *h_b       = b.host<Tb>();                                 \
+        CPLX(Tb) *h_c = c.host<CPLX(Tb)>();                           \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_c[i], CPLX(Tb)(h_a, h_b[i]))                  \
+                << "for values: " << h_a << "," << h_b[i] << endl;    \
+        freeHost(h_b);                                                \
+        freeHost(h_c);                                                \
+    }                                                                 \
+    TEST(ComplexTests, Test_##Ta##_##Tb##_Real) {                     \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
+        array a     = randu(num, ta);                                 \
+        array b     = randu(num, tb);                                 \
+        array c     = complex(a, b);                                  \
+        array d     = real(c);                                        \
+        Ta *h_a     = a.host<Ta>();                                   \
+        Tc *h_d     = d.host<Tc>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_d[i], h_a[i]) << "at: " << i << endl;         \
+        freeHost(h_a);                                                \
+        freeHost(h_d);                                                \
+    }                                                                 \
+    TEST(ComplexTests, Test_##Ta##_##Tb##_Imag) {                     \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
+        array a     = randu(num, ta);                                 \
+        array b     = randu(num, tb);                                 \
+        array c     = complex(a, b);                                  \
+        array d     = imag(c);                                        \
+        Tb *h_b     = b.host<Tb>();                                   \
+        Tc *h_d     = d.host<Tc>();                                   \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(h_d[i], h_b[i]) << "at: " << i << endl;         \
+        freeHost(h_b);                                                \
+        freeHost(h_d);                                                \
+    }                                                                 \
+    TEST(ComplexTests, Test_##Ta##_##Tb##_Conj) {                     \
+        if (noDoubleTests<Ta>()) return;                              \
+        if (noDoubleTests<Tb>()) return;                              \
+        if (noDoubleTests<Tc>()) return;                              \
+                                                                      \
+        af_dtype ta   = (af_dtype)dtype_traits<Ta>::af_type;          \
+        af_dtype tb   = (af_dtype)dtype_traits<Tb>::af_type;          \
+        array a       = randu(num, ta);                               \
+        array b       = randu(num, tb);                               \
+        array c       = complex(a, b);                                \
+        array d       = conjg(c);                                     \
+        CPLX(Tc) *h_c = c.host<CPLX(Tc)>();                           \
+        CPLX(Tc) *h_d = d.host<CPLX(Tc)>();                           \
+        for (int i = 0; i < num; i++)                                 \
+            ASSERT_EQ(conj(h_c[i]), h_d[i]) << "at: " << i << endl;   \
+        freeHost(h_c);                                                \
+        freeHost(h_d);                                                \
+    }
 
 COMPLEX_TESTS(float, float, float)
 COMPLEX_TESTS(double, double, double)
diff --git a/test/constant.cpp b/test/constant.cpp
index eb09137eb4..f2ccd1af8c 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
@@ -23,11 +22,14 @@ using af::dtype_traits;
 using af::exception;
 using af::identity;
 using af::sum;
+using std::vector;
 
 template<typename T>
-class Constant : public ::testing::Test { };
+class Constant : public ::testing::Test {};
 
-typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char, uchar, uintl, intl, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
+                         uchar, uintl, intl, short, ushort>
+    TestTypes;
 TYPED_TEST_CASE(Constant, TestTypes);
 
 template<typename T>
@@ -35,16 +37,14 @@ void ConstantCPPCheck(T value) {
     if (noDoubleTests<T>()) return;
 
     const int num = 1000;
-    T val = value;
-    dtype dty = (dtype) dtype_traits<T>::af_type;
-    array in = constant(val, num, dty);
+    T val         = value;
+    dtype dty     = (dtype)dtype_traits<T>::af_type;
+    array in      = constant(val, num, dty);
 
     vector<T> h_in(num);
     in.host(&h_in.front());
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(h_in[i], val);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(h_in[i], val); }
 }
 
 template<typename T>
@@ -53,8 +53,8 @@ void ConstantCCheck(T value) {
 
     const int num = 1000;
     typedef typename dtype_traits<T>::base_type BT;
-    BT val = ::real(value);
-    dtype dty = (dtype) dtype_traits<T>::af_type;
+    BT val    = ::real(value);
+    dtype dty = (dtype)dtype_traits<T>::af_type;
     af_array out;
     dim_t dim[] = {(dim_t)num};
     ASSERT_SUCCESS(af_constant(&out, val, 1, dim, dty));
@@ -62,9 +62,7 @@ void ConstantCCheck(T value) {
     vector<T> h_in(num);
     af_get_data_ptr(&h_in.front(), out);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(::real(h_in[i]), val);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(::real(h_in[i]), val); }
     ASSERT_SUCCESS(af_release_array(out));
 }
 
@@ -72,16 +70,16 @@ template<typename T>
 void IdentityCPPCheck() {
     if (noDoubleTests<T>()) return;
 
-    int num = 1000;
-    dtype dty = (dtype) dtype_traits<T>::af_type;
+    int num   = 1000;
+    dtype dty = (dtype)dtype_traits<T>::af_type;
     array out = identity(num, num, dty);
 
-    vector<T> h_in(num*num);
+    vector<T> h_in(num * num);
     out.host(&h_in.front());
 
     for (int i = 0; i < num; i++) {
         for (int j = 0; j < num; j++) {
-            if(j == i)
+            if (j == i)
                 ASSERT_EQ(h_in[i * num + j], T(1));
             else
                 ASSERT_EQ(h_in[i * num + j], T(0));
@@ -91,18 +89,18 @@ void IdentityCPPCheck() {
     num = 100;
     out = identity(num, num, num, dty);
 
-    h_in.resize(num*num*num);
+    h_in.resize(num * num * num);
     out.host(&h_in.front());
 
     for (int h = 0; h < num; h++) {
-       for (int i = 0; i < num; i++) {
-           for (int j = 0; j < num; j++) {
-               if(j == i)
-                   ASSERT_EQ(h_in[i * num + j], T(1));
-               else
-                   ASSERT_EQ(h_in[i * num + j], T(0));
-           }
-       }
+        for (int i = 0; i < num; i++) {
+            for (int j = 0; j < num; j++) {
+                if (j == i)
+                    ASSERT_EQ(h_in[i * num + j], T(1));
+                else
+                    ASSERT_EQ(h_in[i * num + j], T(0));
+            }
+        }
     }
 }
 
@@ -112,7 +110,7 @@ void IdentityLargeDimCheck() {
 
     const size_t largeDim = 65535 * 8 + 1;
 
-    dtype dty = (dtype) dtype_traits<T>::af_type;
+    dtype dty = (dtype)dtype_traits<T>::af_type;
     array out = identity(largeDim, dty);
     ASSERT_EQ(1.f, sum<float>(out));
 
@@ -131,17 +129,17 @@ void IdentityCCheck() {
     if (noDoubleTests<T>()) return;
 
     static const int num = 1000;
-    dtype dty = (dtype) dtype_traits<T>::af_type;
+    dtype dty            = (dtype)dtype_traits<T>::af_type;
     af_array out;
     dim_t dim[] = {(dim_t)num, (dim_t)num};
     ASSERT_SUCCESS(af_identity(&out, 2, dim, dty));
 
-    vector<T> h_in(num*num);
+    vector<T> h_in(num * num);
     af_get_data_ptr(&h_in.front(), out);
 
     for (int i = 0; i < num; i++) {
         for (int j = 0; j < num; j++) {
-            if(j == i)
+            if (j == i)
                 ASSERT_EQ(h_in[i * num + j], T(1));
             else
                 ASSERT_EQ(h_in[i * num + j], T(0));
@@ -155,43 +153,24 @@ void IdentityCPPError() {
     if (noDoubleTests<T>()) return;
 
     static const int num = 1000;
-    dtype dty = (dtype) dtype_traits<T>::af_type;
+    dtype dty            = (dtype)dtype_traits<T>::af_type;
     try {
         array out = identity(num, 0, 10, dty);
-    }
-    catch(const exception &ex) {
+    } catch (const exception &ex) {
         FAIL() << "Incorrectly thrown 0-length exception";
         return;
     }
     SUCCEED();
 }
 
-TYPED_TEST(Constant, basicCPP)
-{
-    ConstantCPPCheck<TypeParam>(5);
-}
+TYPED_TEST(Constant, basicCPP) { ConstantCPPCheck<TypeParam>(5); }
 
-TYPED_TEST(Constant, basicC)
-{
-    ConstantCCheck<TypeParam>(5);
-}
+TYPED_TEST(Constant, basicC) { ConstantCCheck<TypeParam>(5); }
 
-TYPED_TEST(Constant, IdentityC)
-{
-    IdentityCCheck<TypeParam>();
-}
+TYPED_TEST(Constant, IdentityC) { IdentityCCheck<TypeParam>(); }
 
-TYPED_TEST(Constant, IdentityCPP)
-{
-    IdentityCPPCheck<TypeParam>();
-}
+TYPED_TEST(Constant, IdentityCPP) { IdentityCPPCheck<TypeParam>(); }
 
-TYPED_TEST(Constant, IdentityLargeDim)
-{
-    IdentityLargeDimCheck<TypeParam>();
-}
+TYPED_TEST(Constant, IdentityLargeDim) { IdentityLargeDimCheck<TypeParam>(); }
 
-TYPED_TEST(Constant, IdentityCPPError)
-{
-    IdentityCPPError<TypeParam>();
-}
+TYPED_TEST(Constant, IdentityCPPError) { IdentityCPPError<TypeParam>(); }
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 0e87c5841b..94edf3ec52 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -7,46 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <cmath>
 #include <string>
 #include <vector>
-#include <cmath>
-#include <testHelpers.hpp>
 
-using std::abs;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Convolve : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Convolve : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, char, uchar, short, ushort, intl, uintl> TestTypes;
+typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, char, uchar,
+                         short, ushort, intl, uintl>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Convolve, TestTypes);
 
 template<typename T>
-void convolveTest(string pTestFile, int baseDim, bool expand)
-{
+void convolveTest(string pTestFile, int baseDim, bool expand) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<T> >      in;
-    vector<vector<T> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -56,16 +56,27 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
     af_array filter   = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()),
-                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
-    switch(baseDim) {
-    case 1: ASSERT_SUCCESS(af_convolve1(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
-    case 2: ASSERT_SUCCESS(af_convolve2(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
-    case 3: ASSERT_SUCCESS(af_convolve3(&outArray, signal, filter, mode, AF_CONV_AUTO)); break;
+    switch (baseDim) {
+        case 1:
+            ASSERT_SUCCESS(
+                af_convolve1(&outArray, signal, filter, mode, AF_CONV_AUTO));
+            break;
+        case 2:
+            ASSERT_SUCCESS(
+                af_convolve2(&outArray, signal, filter, mode, AF_CONV_AUTO));
+            break;
+        case 3:
+            ASSERT_SUCCESS(
+                af_convolve3(&outArray, signal, filter, mode, AF_CONV_AUTO));
+            break;
     }
 
     vector<T> currGoldBar = tests[0];
@@ -74,8 +85,9 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(outArray));
@@ -83,134 +95,131 @@ void convolveTest(string pTestFile, int baseDim, bool expand)
     ASSERT_SUCCESS(af_release_array(filter));
 }
 
-TYPED_TEST(Convolve, Vector)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector.test"), 1, true);
+TYPED_TEST(Convolve, Vector) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/vector.test"), 1, true);
 }
 
-TYPED_TEST(Convolve, Rectangle)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle.test"), 2, true);
+TYPED_TEST(Convolve, Rectangle) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/rectangle.test"), 2,
+                            true);
 }
 
-TYPED_TEST(Convolve, Cuboid)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid.test"), 3, true);
+TYPED_TEST(Convolve, Cuboid) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/cuboid.test"), 3, true);
 }
 
-TYPED_TEST(Convolve, Vector_Many2One)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_many2one.test"), 1, true);
+TYPED_TEST(Convolve, Vector_Many2One) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/vector_many2one.test"),
+                            1, true);
 }
 
-TYPED_TEST(Convolve, Rectangle_Many2One)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_many2one.test"), 2, true);
+TYPED_TEST(Convolve, Rectangle_Many2One) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/rectangle_many2one.test"), 2, true);
 }
 
-TYPED_TEST(Convolve, Cuboid_Many2One)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_many2one.test"), 3, true);
+TYPED_TEST(Convolve, Cuboid_Many2One) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/cuboid_many2one.test"),
+                            3, true);
 }
 
-TYPED_TEST(Convolve, Vector_Many2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_many2many.test"), 1, true);
+TYPED_TEST(Convolve, Vector_Many2Many) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/vector_many2many.test"),
+                            1, true);
 }
 
-TYPED_TEST(Convolve, Rectangle_Many2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_many2many.test"), 2, true);
+TYPED_TEST(Convolve, Rectangle_Many2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/rectangle_many2many.test"), 2, true);
 }
 
-TYPED_TEST(Convolve, Cuboid_Many2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_many2many.test"), 3, true);
+TYPED_TEST(Convolve, Cuboid_Many2Many) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/cuboid_many2many.test"),
+                            3, true);
 }
 
-TYPED_TEST(Convolve, Vector_One2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_one2many.test"), 1, true);
+TYPED_TEST(Convolve, Vector_One2Many) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/vector_one2many.test"),
+                            1, true);
 }
 
-TYPED_TEST(Convolve, Rectangle_One2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_one2many.test"), 2, true);
+TYPED_TEST(Convolve, Rectangle_One2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/rectangle_one2many.test"), 2, true);
 }
 
-TYPED_TEST(Convolve, Cuboid_One2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_one2many.test"), 3, true);
+TYPED_TEST(Convolve, Cuboid_One2Many) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/cuboid_one2many.test"),
+                            3, true);
 }
 
-TYPED_TEST(Convolve, Same_Vector)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_same.test"), 1, false);
+TYPED_TEST(Convolve, Same_Vector) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/vector_same.test"), 1,
+                            false);
 }
 
-TYPED_TEST(Convolve, Same_Rectangle)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_same.test"), 2, false);
+TYPED_TEST(Convolve, Same_Rectangle) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/rectangle_same.test"), 2,
+                            false);
 }
 
-TYPED_TEST(Convolve, Same_Cuboid)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_same.test"), 3, false);
+TYPED_TEST(Convolve, Same_Cuboid) {
+    convolveTest<TypeParam>(string(TEST_DIR "/convolve/cuboid_same.test"), 3,
+                            false);
 }
 
-TYPED_TEST(Convolve, Same_Vector_Many2One)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_same_many2one.test"), 1, false);
+TYPED_TEST(Convolve, Same_Vector_Many2One) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/vector_same_many2one.test"), 1, false);
 }
 
-TYPED_TEST(Convolve, Same_Rectangle_Many2One)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_same_many2one.test"), 2, false);
+TYPED_TEST(Convolve, Same_Rectangle_Many2One) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/rectangle_same_many2one.test"), 2, false);
 }
 
-TYPED_TEST(Convolve, Same_Cuboid_Many2One)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_same_many2one.test"), 3, false);
+TYPED_TEST(Convolve, Same_Cuboid_Many2One) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/cuboid_same_many2one.test"), 3, false);
 }
 
-TYPED_TEST(Convolve, Same_Vector_Many2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_same_many2many.test"), 1, false);
+TYPED_TEST(Convolve, Same_Vector_Many2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/vector_same_many2many.test"), 1, false);
 }
 
-TYPED_TEST(Convolve, Same_Rectangle_Many2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_same_many2many.test"), 2, false);
+TYPED_TEST(Convolve, Same_Rectangle_Many2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/rectangle_same_many2many.test"), 2, false);
 }
 
-TYPED_TEST(Convolve, Same_Cuboid_Many2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_same_many2many.test"), 3, false);
+TYPED_TEST(Convolve, Same_Cuboid_Many2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/cuboid_same_many2many.test"), 3, false);
 }
 
-TYPED_TEST(Convolve, Same_Vector_One2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/vector_same_one2many.test"), 1, false);
+TYPED_TEST(Convolve, Same_Vector_One2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/vector_same_one2many.test"), 1, false);
 }
 
-TYPED_TEST(Convolve, Same_Rectangle_One2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/rectangle_same_one2many.test"), 2, false);
+TYPED_TEST(Convolve, Same_Rectangle_One2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/rectangle_same_one2many.test"), 2, false);
 }
 
-TYPED_TEST(Convolve, Same_Cuboid_One2Many)
-{
-    convolveTest<TypeParam>(string(TEST_DIR"/convolve/cuboid_same_one2many.test"), 3, false);
+TYPED_TEST(Convolve, Same_Cuboid_One2Many) {
+    convolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/cuboid_same_one2many.test"), 3, false);
 }
 
 template<typename T>
-void sepConvolveTest(string pTestFile, bool expand)
-{
+void sepConvolveTest(string pTestFile, bool expand) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<T> >      in;
-    vector<vector<T> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -222,15 +231,19 @@ void sepConvolveTest(string pTestFile, bool expand)
     af_array r_filter = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&c_filter, &(in[1].front()),
-                cfDims.ndims(), cfDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&r_filter, &(in[2].front()),
-                rfDims.ndims(), rfDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&c_filter, &(in[1].front()), cfDims.ndims(),
+                                   cfDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&r_filter, &(in[2].front()), rfDims.ndims(),
+                                   rfDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    af_conv_mode  mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
-    ASSERT_SUCCESS(af_convolve2_sep(&outArray, c_filter, r_filter, signal, mode));
+    af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
+    ASSERT_SUCCESS(
+        af_convolve2_sep(&outArray, c_filter, r_filter, signal, mode));
 
     vector<T> currGoldBar = tests[0];
     size_t nElems         = currGoldBar.size();
@@ -238,8 +251,9 @@ void sepConvolveTest(string pTestFile, bool expand)
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(outArray));
@@ -248,100 +262,110 @@ void sepConvolveTest(string pTestFile, bool expand)
     ASSERT_SUCCESS(af_release_array(r_filter));
 }
 
-TYPED_TEST(Convolve, Separable2D_Full)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_full.test"), true);
+TYPED_TEST(Convolve, Separable2D_Full) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_full.test"), true);
 }
 
-TYPED_TEST(Convolve, Separable2D_Full_Batch)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_full_batch.test"), true);
+TYPED_TEST(Convolve, Separable2D_Full_Batch) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_full_batch.test"), true);
 }
 
-TYPED_TEST(Convolve, Separable2D_Full_Rectangle)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_full_rectangle.test"), true);
+TYPED_TEST(Convolve, Separable2D_Full_Rectangle) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_full_rectangle.test"),
+        true);
 }
 
-TYPED_TEST(Convolve, Separable2D_Full_Rectangle_Batch)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_full_rectangle_batch.test"), true);
+TYPED_TEST(Convolve, Separable2D_Full_Rectangle_Batch) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_full_rectangle_batch.test"),
+        true);
 }
 
-TYPED_TEST(Convolve, Separable2D_Same)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_same.test"), false);
+TYPED_TEST(Convolve, Separable2D_Same) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_same.test"), false);
 }
 
-TYPED_TEST(Convolve, Separable2D_Same_Batch)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_same_batch.test"), false);
+TYPED_TEST(Convolve, Separable2D_Same_Batch) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_same_batch.test"), false);
 }
 
-TYPED_TEST(Convolve, Separable2D_Same_Rectangle)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_same_rectangle.test"), false);
+TYPED_TEST(Convolve, Separable2D_Same_Rectangle) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_same_rectangle.test"),
+        false);
 }
 
-TYPED_TEST(Convolve, Separable2D_Same_Rectangle_Batch)
-{
-    sepConvolveTest<TypeParam>(string(TEST_DIR"/convolve/separable_conv2d_same_rectangle_batch.test"), false);
+TYPED_TEST(Convolve, Separable2D_Same_Rectangle_Batch) {
+    sepConvolveTest<TypeParam>(
+        string(TEST_DIR "/convolve/separable_conv2d_same_rectangle_batch.test"),
+        false);
 }
 
-TEST(Convolve, Separable_TypeCheck)
-{
+TEST(Convolve, Separable_TypeCheck) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<int>()) return;
 
     dim4 sDims(10, 1, 1, 1);
     dim4 fDims(4, 1, 1, 1);
 
-    vector<float> in(10,1);
-    vector<int>   filt(4,1);
+    vector<float> in(10, 1);
+    vector<int> filt(4, 1);
 
     af_array signal   = 0;
     af_array c_filter = 0;
     af_array r_filter = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&signal, &(in.front()),
-                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<float>::af_type));
-    ASSERT_SUCCESS(af_create_array(&c_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
-    ASSERT_SUCCESS(af_create_array(&r_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
+    ASSERT_SUCCESS(af_create_array(&signal, &(in.front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&c_filter, &(filt.front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<int>::af_type));
+    ASSERT_SUCCESS(af_create_array(&r_filter, &(filt.front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<int>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter, signal, AF_CONV_EXPAND));
+    ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter,
+                                           signal, AF_CONV_EXPAND));
 
     ASSERT_SUCCESS(af_release_array(signal));
     ASSERT_SUCCESS(af_release_array(c_filter));
     ASSERT_SUCCESS(af_release_array(r_filter));
 }
 
-TEST(Convolve, Separable_DimCheck)
-{
+TEST(Convolve, Separable_DimCheck) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<int>()) return;
 
     dim4 sDims(10, 1, 1, 1);
     dim4 fDims(4, 1, 1, 1);
 
-    vector<float> in(10,1);
-    vector<int>   filt(4,1);
+    vector<float> in(10, 1);
+    vector<int> filt(4, 1);
 
     af_array signal   = 0;
     af_array c_filter = 0;
     af_array r_filter = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&signal, &(in.front()),
-                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<float>::af_type));
-    ASSERT_SUCCESS(af_create_array(&c_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
-    ASSERT_SUCCESS(af_create_array(&r_filter, &(filt.front()),
-                fDims.ndims(), fDims.get(), (af_dtype)dtype_traits<int>::af_type));
+    ASSERT_SUCCESS(af_create_array(&signal, &(in.front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&c_filter, &(filt.front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<int>::af_type));
+    ASSERT_SUCCESS(af_create_array(&r_filter, &(filt.front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<int>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter, signal, AF_CONV_EXPAND));
+    ASSERT_EQ(AF_ERR_ARG, af_convolve2_sep(&outArray, c_filter, r_filter,
+                                           signal, AF_CONV_EXPAND));
 
     ASSERT_SUCCESS(af_release_array(c_filter));
     ASSERT_SUCCESS(af_release_array(r_filter));
@@ -358,187 +382,193 @@ using af::seq;
 using af::span;
 using af::sum;
 
-TEST(Convolve1, CPP)
-{
+TEST(Convolve1, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/vector_same.test"), numDims, in, tests);
+    readTests<float, float, int>(string(TEST_DIR "/convolve/vector_same.test"),
+                                 numDims, in, tests);
 
     //![ex_image_convolve1]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [32 1 1 1]
+    // signal dims = [32 1 1 1]
     array filter(numDims[1], &(in[1].front()));
-    //filter dims = [4 1 1 1]
+    // filter dims = [4 1 1 1]
 
     array output = convolve1(signal, filter, AF_CONV_DEFAULT);
-    //output dims = [32 1 1 1] - same as input since expand(3rd argument is false)
-    //None of the dimensions > 1 has lenght > 1, so no batch mode is activated.
+    // output dims = [32 1 1 1] - same as input since expand(3rd argument is
+    // false) None of the dimensions > 1 has lenght > 1, so no batch mode is
+    // activated.
     //![ex_image_convolve1]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
     output.host(&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
-
 }
 
-TEST(Convolve2, CPP)
-{
+TEST(Convolve2, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/rectangle_same_one2many.test"), numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/convolve/rectangle_same_one2many.test"), numDims, in,
+        tests);
 
     //![ex_image_convolve2]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [15 17 1 1]
+    // signal dims = [15 17 1 1]
     array filter(numDims[1], &(in[1].front()));
-    //filter dims = [5 5 2 1]
+    // filter dims = [5 5 2 1]
 
     array output = convolve2(signal, filter, AF_CONV_DEFAULT);
-    //output dims = [15 17 1 1] - same as input since expand(3rd argument is false)
-    //however, notice that the 3rd dimension of filter is > 1.
-    //So, one to many batch mode will be activated automatically
-    //where the 2d input signal is convolved with each 2d filter
-    //and the result will written corresponding slice in the output 3d array
+    // output dims = [15 17 1 1] - same as input since expand(3rd argument is
+    // false) however, notice that the 3rd dimension of filter is > 1. So, one
+    // to many batch mode will be activated automatically where the 2d input
+    // signal is convolved with each 2d filter and the result will written
+    // corresponding slice in the output 3d array
     //![ex_image_convolve2]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
     output.host(&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
-
 }
 
-TEST(Convolve3, CPP)
-{
+TEST(Convolve3, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/cuboid_same_many2many.test"), numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/convolve/cuboid_same_many2many.test"), numDims, in,
+        tests);
 
     //![ex_image_convolve3]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [10 11 2 2]
+    // signal dims = [10 11 2 2]
     array filter(numDims[1], &(in[1].front()));
-    //filter dims = [4 2 3 2]
+    // filter dims = [4 2 3 2]
 
     array output = convolve3(signal, filter, AF_CONV_DEFAULT);
-    //output dims = [10 11 2 2] - same as input since expand(3rd argument is false)
-    //however, notice that the 4th dimension is > 1 for both signal
-    //and the filter, therefore many to many batch mode will be
-    //activated where each 3d signal is convolved with the corresponding 3d filter
+    // output dims = [10 11 2 2] - same as input since expand(3rd argument is
+    // false) however, notice that the 4th dimension is > 1 for both signal and
+    // the filter, therefore many to many batch mode will be activated where
+    // each 3d signal is convolved with the corresponding 3d filter
     //![ex_image_convolve3]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
     output.host(&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(Convolve, separable_CPP)
-{
+TEST(Convolve, separable_CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/separable_conv2d_same_rectangle_batch.test"),
-                                 numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/convolve/separable_conv2d_same_rectangle_batch.test"),
+        numDims, in, tests);
 
     //![ex_image_conv2_sep]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [3 4 2 1]
+    // signal dims = [3 4 2 1]
     array cFilter(numDims[1], &(in[1].front()));
-    //coloumn filter dims = [2 1 1 1]
+    // coloumn filter dims = [2 1 1 1]
     array rFilter(numDims[2], &(in[2].front()));
-    //row filter dims = [3 1 1 1]
+    // row filter dims = [3 1 1 1]
 
     array output = convolve(cFilter, rFilter, signal, AF_CONV_DEFAULT);
-    //output signal dims = [3 4 2 1] - same as input since 'expand = false'
-    //notice that the input signal is 3d array, therefore
-    //batch mode will be automatically activated.
-    //output will be 3d array with result of each 2d array convolution(with same filter)
-    //stacked along the 3rd dimension
+    // output signal dims = [3 4 2 1] - same as input since 'expand = false'
+    // notice that the input signal is 3d array, therefore
+    // batch mode will be automatically activated.
+    // output will be 3d array with result of each 2d array convolution(with
+    // same filter) stacked along the 3rd dimension
     //![ex_image_conv2_sep]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
 
     output.host((void*)&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(Convolve, Docs_Unified_Wrapper)
-{
+TEST(Convolve, Docs_Unified_Wrapper) {
     // This unit test doesn't necessarily need to function
     // accuracy as convolve is merely a wrapper to
     // convolve[1|2|3]
 
     //![ex_image_convolve_1d]
     array a = randu(10);
-    //af_print(a);
-    //a [10 1 1 1] = 0.0000 0.1315 0.7556 0.4587 0.5328 0.2190 0.0470 0.6789 0.6793 0.9347
+    // af_print(a);
+    // a [10 1 1 1] = 0.0000 0.1315 0.7556 0.4587 0.5328 0.2190 0.0470 0.6789
+    // 0.6793 0.9347
     array b = randu(4);
-    //af_print(b);
-    //b [4 1 1 1]  = 0.3835 0.5194 0.8310 0.0346
+    // af_print(b);
+    // b [4 1 1 1]  = 0.3835 0.5194 0.8310 0.0346
     array c = convolve(a, b);
-    //af_print(c);
-    //c [10 1 1 1] = 0.3581 0.6777 1.0750 0.7679 0.5903 0.4851 0.6598 1.2770 1.0734 0.8002
+    // af_print(c);
+    // c [10 1 1 1] = 0.3581 0.6777 1.0750 0.7679 0.5903 0.4851 0.6598
+    // 1.2770 1.0734 0.8002
     //![ex_image_convolve_1d]
 
     //![ex_image_convolve_2d]
     array d = constant(0.5, 5, 5);
-    //af_print(d);
-    //d [5 5 1 1]
+    // af_print(d);
+    // d [5 5 1 1]
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     array e = constant(1, 2, 2);
-    //af_print(e);
-    //e [2 2 1 1]
+    // af_print(e);
+    // e [2 2 1 1]
     //     1.0000     1.0000
     //     1.0000     1.0000
     array f = convolve(d, e);
-    //af_print(f);
-    //f [5 5 1 1]
+    // af_print(f);
+    // f [5 5 1 1]
     //     2.0000     2.0000     2.0000     2.0000     1.0000
     //     2.0000     2.0000     2.0000     2.0000     1.0000
     //     2.0000     2.0000     2.0000     2.0000     1.0000
@@ -548,8 +578,8 @@ TEST(Convolve, Docs_Unified_Wrapper)
 
     //![ex_image_convolve_3d]
     array g = constant(1, 4, 4, 4);
-    //af_print(g);
-    //g [4 4 4 1]
+    // af_print(g);
+    // g [4 4 4 1]
     //    1.0000     1.0000     1.0000     1.0000
     //    1.0000     1.0000     1.0000     1.0000
     //    1.0000     1.0000     1.0000     1.0000
@@ -570,8 +600,8 @@ TEST(Convolve, Docs_Unified_Wrapper)
     //    1.0000     1.0000     1.0000     1.0000
     //    1.0000     1.0000     1.0000     1.0000
     array h = constant(0.5, 2, 2, 2);
-    //af_print(h);
-    //h [2 2 2 1]
+    // af_print(h);
+    // h [2 2 2 1]
     //    0.5000     0.5000
     //    0.5000     0.5000
 
@@ -579,8 +609,8 @@ TEST(Convolve, Docs_Unified_Wrapper)
     //    0.5000     0.5000
 
     array i = convolve(g, h);
-    //af_print(i);
-    //i [4 4 4 1]
+    // af_print(i);
+    // i [4 4 4 1]
     //    4.0000     4.0000     4.0000     2.0000
     //    4.0000     4.0000     4.0000     2.0000
     //    4.0000     4.0000     4.0000     2.0000
@@ -603,15 +633,12 @@ TEST(Convolve, Docs_Unified_Wrapper)
     //![ex_image_convolve_3d]
 }
 
-TEST(GFOR, convolve2_MO)
-{
+TEST(GFOR, convolve2_MO) {
     array A = randu(5, 5, 3);
     array B = randu(5, 5, 3);
     array K = randu(3, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = convolve2(A(span, span, ii), K);
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = convolve2(A(span, span, ii), K); }
 
     for (int ii = 0; ii < 3; ii++) {
         array c_ii = convolve2(A(span, span, ii), K);
@@ -620,15 +647,12 @@ TEST(GFOR, convolve2_MO)
     }
 }
 
-TEST(GFOR, convolve2_OM)
-{
+TEST(GFOR, convolve2_OM) {
     array A = randu(5, 5);
     array B = randu(5, 5, 3);
     array K = randu(3, 3, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = convolve2(A, K(span, span, ii));
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = convolve2(A, K(span, span, ii)); }
 
     for (int ii = 0; ii < 3; ii++) {
         array c_ii = convolve2(A, K(span, span, ii));
@@ -637,8 +661,7 @@ TEST(GFOR, convolve2_OM)
     }
 }
 
-TEST(GFOR, convolve2_MM)
-{
+TEST(GFOR, convolve2_MM) {
     array A = randu(5, 5, 3);
     array B = randu(5, 5, 3);
     array K = randu(3, 3, 3);
@@ -654,108 +677,101 @@ TEST(GFOR, convolve2_MM)
     }
 }
 
-TEST(Convolve, 1D_C32)
-{
+TEST(Convolve, 1D_C32) {
     array A = randu(10, c32);
-    array B = randu( 3, c32);
+    array B = randu(3, c32);
 
     array out = convolve1(A, B);
     array gld = fftConvolve1(A, B);
 
-    cfloat acc = sum<cfloat>(out-gld);
+    cfloat acc = sum<cfloat>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
 }
 
-TEST(Convolve, 2D_C32)
-{
+TEST(Convolve, 2D_C32) {
     array A = randu(10, 10, c32);
-    array B = randu( 3,  3, c32);
+    array B = randu(3, 3, c32);
 
     array out = convolve2(A, B);
     array gld = fftConvolve2(A, B);
 
-    cfloat acc = sum<cfloat>(out-gld);
+    cfloat acc = sum<cfloat>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
 }
 
-TEST(Convolve, 3D_C32)
-{
+TEST(Convolve, 3D_C32) {
     array A = randu(10, 10, 3, c32);
-    array B = randu( 3,  3, 3, c32);
+    array B = randu(3, 3, 3, c32);
 
     array out = convolve3(A, B);
     array gld = fftConvolve3(A, B);
 
-    cfloat acc = sum<cfloat>(out-gld);
+    cfloat acc = sum<cfloat>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
 }
 
-TEST(Convolve, 1D_C64)
-{
+TEST(Convolve, 1D_C64) {
     if (noDoubleTests<double>()) return;
 
     array A = randu(10, c64);
-    array B = randu( 3, c64);
+    array B = randu(3, c64);
 
     array out = convolve1(A, B);
     array gld = fftConvolve1(A, B);
 
-    cdouble acc = sum<cdouble>(out-gld);
+    cdouble acc = sum<cdouble>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
 }
 
-TEST(Convolve, 2D_C64)
-{
+TEST(Convolve, 2D_C64) {
     if (noDoubleTests<double>()) return;
 
     array A = randu(10, 10, c64);
-    array B = randu( 3,  3, c64);
+    array B = randu(3, 3, c64);
 
     array out = convolve2(A, B);
     array gld = fftConvolve2(A, B);
 
-    cdouble acc = sum<cdouble>(out-gld);
+    cdouble acc = sum<cdouble>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
 }
 
-TEST(Convolve, 3D_C64)
-{
+TEST(Convolve, 3D_C64) {
     if (noDoubleTests<double>()) return;
 
     array A = randu(10, 10, 3, c64);
-    array B = randu( 3,  3, 3, c64);
+    array B = randu(3, 3, 3, c64);
 
     array out = convolve3(A, B);
     array gld = fftConvolve3(A, B);
 
-    cdouble acc = sum<cdouble>(out-gld);
+    cdouble acc = sum<cdouble>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc))< 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc))< 1E-3, true);
+    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
+    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
 }
 
-TEST(ConvolveLargeDim1D, CPP)
-{
+TEST(ConvolveLargeDim1D, CPP) {
     if (noDoubleTests<float>()) return;
 
-    const size_t n = 10;
+    const size_t n        = 10;
     const size_t largeDim = 65535 + 1;
 
     float h_filter[] = {0.f, 1.f, 0.f};
     array identity_filter(3, h_filter);
     array signal = constant(1, n, 1, largeDim);
 
-    array output = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
+    array output  = convolve1(signal, identity_filter, AF_CONV_DEFAULT);
     array output2 = output;
     ASSERT_EQ(largeDim * n, sum<float>(output2));
 
@@ -765,16 +781,13 @@ TEST(ConvolveLargeDim1D, CPP)
     ASSERT_EQ(largeDim * n, sum<float>(output));
 }
 
-TEST(ConvolveLargeDim2D, CPP)
-{
+TEST(ConvolveLargeDim2D, CPP) {
     if (noDoubleTests<float>()) return;
 
-    const size_t n = 10;
+    const size_t n        = 10;
     const size_t largeDim = 65535 + 1;
 
-    float h_filter[] = {0.f, 0.f, 0.f,
-                        0.f, 1.f, 0.f,
-                        0.f, 0.f, 0.f};
+    float h_filter[] = {0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f};
     array identity_filter(3, 3, h_filter);
     array signal = constant(1, n, n, largeDim);
 
@@ -787,24 +800,17 @@ TEST(ConvolveLargeDim2D, CPP)
     ASSERT_EQ(largeDim * n * n, sum<float>(output));
 }
 
-TEST(DISABLED_ConvolveLargeDim3D, CPP)
-{
+TEST(DISABLED_ConvolveLargeDim3D, CPP) {
     if (noDoubleTests<float>()) return;
 
-    const size_t n = 3;
+    const size_t n        = 3;
     const size_t largeDim = 65535 * 16 + 1;
 
-    float h_filter[] = {0.f, 0.f, 0.f,
-                        0.f, 0.f, 0.f,
-                        0.f, 0.f, 0.f,
+    float h_filter[] = {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
 
-                        0.f, 0.f, 0.f,
-                        0.f, 1.f, 0.f,
-                        0.f, 0.f, 0.f,
+                        0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f,
 
-                        0.f, 0.f, 0.f,
-                        0.f, 0.f, 0.f,
-                        0.f, 0.f, 0.f};
+                        0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f};
 
     array identity_filter(3, 3, 3, h_filter);
     array signal = constant(1, n, largeDim, n);
@@ -815,6 +821,6 @@ TEST(DISABLED_ConvolveLargeDim3D, CPP)
     signal = constant(1, n, n, largeDim);
 
     output = convolve3(signal, identity_filter, AF_CONV_EXPAND);
-    //TODO: fix product by indexing
-    //ASSERT_EQ(1.f, product<float>(output));
+    // TODO: fix product by indexing
+    // ASSERT_EQ(1.f, product<float>(output));
 }
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 24ac30a2db..21fb9aead2 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -7,83 +7,77 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <algorithm>
+#include <ctime>
 #include <string>
 #include <vector>
-#include <ctime>
-#include <algorithm>
-#include <testHelpers.hpp>
 
-using std::string;
-using std::vector;
 using af::array;
 using af::cfloat;
 using af::corrcoef;
 using af::dim4;
+using std::string;
+using std::vector;
 
 template<typename T>
-class CorrelationCoefficient : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class CorrelationCoefficient : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar> TestTypes;
+typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(CorrelationCoefficient, TestTypes);
 
 template<typename T>
 struct f32HelperType {
-   typedef typename cond_type<is_same_type<T, double>::value,
-                                             double,
-                                             float>::type type;
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            type;
 };
 
 template<typename T>
 struct c32HelperType {
-   typedef typename cond_type<is_same_type<T, cfloat>::value,
-                                             cfloat,
-                                             typename f32HelperType<T>::type >::type type;
+    typedef typename cond_type<is_same_type<T, cfloat>::value, cfloat,
+                               typename f32HelperType<T>::type>::type type;
 };
 
 template<typename T>
 struct elseType {
-   typedef typename cond_type< is_same_type<T, uintl>::value ||
-                               is_same_type<T, intl> ::value,
-                                              double,
-                                              T>::type type;
+    typedef typename cond_type<is_same_type<T, uintl>::value ||
+                                   is_same_type<T, intl>::value,
+                               double, T>::type type;
 };
 
 template<typename T>
 struct ccOutType {
-   typedef typename cond_type< is_same_type<T, float>   ::value ||
-                               is_same_type<T, int>     ::value ||
-                               is_same_type<T, uint>    ::value ||
-                               is_same_type<T, uchar>   ::value ||
-                               is_same_type<T, short>   ::value ||
-                               is_same_type<T, ushort>  ::value ||
-                               is_same_type<T, char>    ::value,
-                                              float,
-                              typename elseType<T>::type>::type type;
+    typedef typename cond_type<
+        is_same_type<T, float>::value || is_same_type<T, int>::value ||
+            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
+            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
-TYPED_TEST(CorrelationCoefficient, All)
-{
+TYPED_TEST(CorrelationCoefficient, All) {
     typedef typename ccOutType<TypeParam>::type outType;
     if (noDoubleTests<TypeParam>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>          numDims;
-    vector<vector<int> >       in;
-    vector<vector<float> >  tests;
+    vector<dim4> numDims;
+    vector<vector<int> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(string(TEST_DIR "/corrcoef/mat_10x10_scalar.test"),
-                                 numDims, in, tests);
+    readTestsFromFile<int, float>(
+        string(TEST_DIR "/corrcoef/mat_10x10_scalar.test"), numDims, in, tests);
 
     vector<TypeParam> input1(in[0].begin(), in[0].end());
     vector<TypeParam> input2(in[1].begin(), in[1].end());
diff --git a/test/covariance.cpp b/test/covariance.cpp
index c4ef41bd8e..d1ec20a2a3 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -7,86 +7,81 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <algorithm>
+#include <ctime>
 #include <string>
 #include <vector>
-#include <ctime>
-#include <algorithm>
-#include <testHelpers.hpp>
 
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
 using af::constant;
 using af::dim4;
 using af::exception;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Covariance : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Covariance : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short,
+                         ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Covariance, TestTypes);
 
 template<typename T>
 struct f32HelperType {
-   typedef typename cond_type<is_same_type<T, double>::value,
-                                             double,
-                                             float>::type type;
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            type;
 };
 
 template<typename T>
 struct c32HelperType {
-   typedef typename cond_type<is_same_type<T, cfloat>::value,
-                                             cfloat,
-                                             typename f32HelperType<T>::type >::type type;
+    typedef typename cond_type<is_same_type<T, cfloat>::value, cfloat,
+                               typename f32HelperType<T>::type>::type type;
 };
 
 template<typename T>
 struct elseType {
-   typedef typename cond_type< is_same_type<T, uintl>::value ||
-                               is_same_type<T, intl> ::value,
-                                              double,
-                                              T>::type type;
+    typedef typename cond_type<is_same_type<T, uintl>::value ||
+                                   is_same_type<T, intl>::value,
+                               double, T>::type type;
 };
 
 template<typename T>
 struct covOutType {
-   typedef typename cond_type< is_same_type<T, float>   ::value ||
-                               is_same_type<T, int>     ::value ||
-                               is_same_type<T, uint>    ::value ||
-                               is_same_type<T, uchar>   ::value ||
-                               is_same_type<T, short>   ::value ||
-                               is_same_type<T, ushort>  ::value ||
-                               is_same_type<T, char>    ::value,
-                                              float,
-                              typename elseType<T>::type>::type type;
+    typedef typename cond_type<
+        is_same_type<T, float>::value || is_same_type<T, int>::value ||
+            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
+            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
 template<typename T>
-void covTest(string pFileName, bool isbiased=false)
-{
+void covTest(string pFileName, bool isbiased = false) {
     typedef typename covOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<int> >        in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<int> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(pFileName, numDims, in, tests);
+    readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
     dim4 dims1 = numDims[0];
     dim4 dims2 = numDims[1];
@@ -100,36 +95,37 @@ void covTest(string pFileName, bool isbiased=false)
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
-    size_t nElems    = currGoldBar.size();
+    size_t nElems = currGoldBar.size();
     vector<outType> outData(nElems);
 
     c.host((void*)outData.data());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
-        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
+                    1.0e-3)
+            << "at: " << elIter << endl;
+        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
+                    1.0e-3)
+            << "at: " << elIter << endl;
     }
 }
 
-TYPED_TEST(Covariance, Vector)
-{
+TYPED_TEST(Covariance, Vector) {
     covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"), false);
 }
 
-TYPED_TEST(Covariance, Matrix)
-{
-    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"), false);
+TYPED_TEST(Covariance, Matrix) {
+    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"),
+                       false);
 }
 
-TEST(Covariance, c32)
-{
+TEST(Covariance, c32) {
     array a = constant(cfloat(1.0f, -1.0f), 10, c32);
     array b = constant(cfloat(2.0f, -1.0f), 10, c32);
     ASSERT_THROW(cov(a, b), exception);
 }
 
-TEST(Covariance, c64)
-{
+TEST(Covariance, c64) {
     if (noDoubleTests<double>()) return;
     array a = constant(cdouble(1.0, -1.0), 10, c64);
     array b = constant(cdouble(2.0, -1.0), 10, c64);
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index 180cfe2923..7bd93f0b07 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -7,13 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
-#include <arrayfire.h>
 
-using std::abs;
-using std::endl;
-using std::vector;
 using af::array;
 using af::constant;
 using af::deviceGC;
@@ -24,98 +21,86 @@ using af::max;
 using af::seq;
 using af::span;
 using af::sum;
+using std::abs;
+using std::endl;
+using std::vector;
 
 template<typename T>
-class Diagonal : public ::testing::Test
-{
+class Diagonal : public ::testing::Test {};
 
-};
-
-typedef ::testing::Types<float, double, int, uint, char, unsigned char> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, unsigned char>
+    TestTypes;
 TYPED_TEST_CASE(Diagonal, TestTypes);
 
-TYPED_TEST(Diagonal, Create)
-{
+TYPED_TEST(Diagonal, Create) {
     if (noDoubleTests<TypeParam>()) return;
     try {
-
         static const int size = 1000;
-        vector<TypeParam> input (size * size);
-        for(int i = 0; i < size; i++) {
-            input[i] = i;
-        }
-        for(int jj = 10; jj < size; jj+=100) {
+        vector<TypeParam> input(size * size);
+        for (int i = 0; i < size; i++) { input[i] = i; }
+        for (int jj = 10; jj < size; jj += 100) {
             array data(jj, &input.front(), afHost);
             array out = diag(data, 0, false);
 
             vector<TypeParam> h_out(out.elements());
             out.host(&h_out.front());
 
-            for(int i =0; i < (int)out.dims(0); i++) {
-                for(int j =0; j < (int)out.dims(1); j++) {
-                    if(i == j) ASSERT_EQ(input[i], h_out[i * out.dims(0) + j]);
-                    else       ASSERT_EQ(TypeParam(0), h_out[i * out.dims(0) + j]);
+            for (int i = 0; i < (int)out.dims(0); i++) {
+                for (int j = 0; j < (int)out.dims(1); j++) {
+                    if (i == j)
+                        ASSERT_EQ(input[i], h_out[i * out.dims(0) + j]);
+                    else
+                        ASSERT_EQ(TypeParam(0), h_out[i * out.dims(0) + j]);
                 }
             }
         }
-    } catch (const exception& ex) {
-        FAIL() << ex.what() << endl;
-    }
+    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
 }
 
-TYPED_TEST(Diagonal, DISABLED_CreateLargeDim)
-{
+TYPED_TEST(Diagonal, DISABLED_CreateLargeDim) {
     if (noDoubleTests<TypeParam>()) return;
     try {
         deviceGC();
         {
             static const size_t largeDim = 65535 + 1;
-            array diagvals = constant(1, largeDim);
-            array out = diag(diagvals,  0, false);
+            array diagvals               = constant(1, largeDim);
+            array out                    = diag(diagvals, 0, false);
 
             ASSERT_EQ(largeDim, sum<float>(out));
         }
-    } catch (const exception& ex) {
-        FAIL() << ex.what() << endl;
-    }
+    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
 }
 
-TYPED_TEST(Diagonal, Extract)
-{
+TYPED_TEST(Diagonal, Extract) {
     if (noDoubleTests<TypeParam>()) return;
 
     try {
         static const int size = 1000;
-        vector<TypeParam> input (size * size);
-        for(int i = 0; i < size * size; i++) {
-            input[i] = i;
-        }
-        for(int jj = 10; jj < size; jj+=100) {
+        vector<TypeParam> input(size * size);
+        for (int i = 0; i < size * size; i++) { input[i] = i; }
+        for (int jj = 10; jj < size; jj += 100) {
             array data(jj, jj, &input.front(), afHost);
             array out = diag(data, 0);
 
             vector<TypeParam> h_out(out.elements());
             out.host(&h_out.front());
 
-            for(int i =0; i < (int)out.dims(0); i++) {
+            for (int i = 0; i < (int)out.dims(0); i++) {
                 ASSERT_EQ(input[i * data.dims(0) + i], h_out[i]);
             }
         }
-    } catch (const exception& ex) {
-        FAIL() << ex.what() << endl;
-    }
+    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
 }
 
-TYPED_TEST(Diagonal, ExtractLargeDim)
-{
+TYPED_TEST(Diagonal, ExtractLargeDim) {
     if (noDoubleTests<TypeParam>()) return;
 
     try {
-        static const size_t n = 10;
+        static const size_t n        = 10;
         static const size_t largeDim = 65535 + 1;
 
         array largedata = constant(1, n, n, largeDim);
-        array out = diag(largedata, 0);
+        array out       = diag(largedata, 0);
 
         ASSERT_EQ(n * largeDim, sum<float>(out));
 
@@ -124,24 +109,19 @@ TYPED_TEST(Diagonal, ExtractLargeDim)
 
         ASSERT_EQ(n * largeDim, sum<float>(out1));
 
-    } catch (const exception& ex) {
-        FAIL() << ex.what() << endl;
-    }
+    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
 }
 
-TYPED_TEST(Diagonal, ExtractRect)
-{
+TYPED_TEST(Diagonal, ExtractRect) {
     if (noDoubleTests<TypeParam>()) return;
 
     try {
         static const int size0 = 1000, size1 = 900;
-        vector<TypeParam> input (size0 * size1);
-        for(int i = 0; i < size0 * size1; i++) {
-            input[i] = i;
-        }
+        vector<TypeParam> input(size0 * size1);
+        for (int i = 0; i < size0 * size1; i++) { input[i] = i; }
 
-        for(int jj = 10; jj < size0; jj += 100) {
-            for(int kk = 10; kk < size1; kk += 90) {
+        for (int jj = 10; jj < size0; jj += 100) {
+            for (int kk = 10; kk < size1; kk += 90) {
                 array data(jj, kk, &input.front(), afHost);
                 array out = diag(data, 0);
 
@@ -150,27 +130,22 @@ TYPED_TEST(Diagonal, ExtractRect)
 
                 ASSERT_EQ(out.dims(0), std::min(jj, kk));
 
-                for(int i =0; i < (int)out.dims(0); i++) {
+                for (int i = 0; i < (int)out.dims(0); i++) {
                     ASSERT_EQ(input[i * data.dims(0) + i], h_out[i]);
                 }
             }
         }
-    } catch (const exception& ex) {
-        FAIL() << ex.what() << endl;
-    }
+    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
 }
 
-TEST(Diagonal, ExtractGFOR)
-{
+TEST(Diagonal, ExtractGFOR) {
     dim4 dims = dim4(100, 100, 3);
-    array A = round(100 * randu(dims));
-    array B = constant(0, 100, 1, 3);
+    array A   = round(100 * randu(dims));
+    array B   = constant(0, 100, 1, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = diag(A(span, span, ii));
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = diag(A(span, span, ii)); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = diag(A(span, span, ii));
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 10a22adb9c..c261d0bbcb 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -7,73 +7,78 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Diff1 : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(1, 4, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-            subMat0.push_back(af_make_seq(0, 1, 1));
-
-            subMat1.push_back(af_make_seq(0, 4, 1));
-            subMat1.push_back(af_make_seq(1, 3, 1));
-            subMat1.push_back(af_make_seq(1, 3, 1));
-
-            subMat2.push_back(af_make_seq(1, 5, 1));
-            subMat2.push_back(af_make_seq(0, 3, 1));
-            subMat2.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
-        vector<af_seq> subMat1;
-        vector<af_seq> subMat2;
+class Diff1 : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(1, 4, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+        subMat0.push_back(af_make_seq(0, 1, 1));
+
+        subMat1.push_back(af_make_seq(0, 4, 1));
+        subMat1.push_back(af_make_seq(1, 3, 1));
+        subMat1.push_back(af_make_seq(1, 3, 1));
+
+        subMat2.push_back(af_make_seq(1, 5, 1));
+        subMat2.push_back(af_make_seq(0, 3, 1));
+        subMat2.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
+    vector<af_seq> subMat1;
+    vector<af_seq> subMat2;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
+                         uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Diff1, TestTypes);
 
 template<typename T>
-void diff1Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector<af_seq> *seqv=NULL)
-{
+void diff1Test(string pTestFile, unsigned dim, bool isSubRef = false,
+               const vector<af_seq> *seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     af_array inArray   = 0;
     af_array outArray  = 0;
     af_array tempArray = 0;
     // Get input array
     if (isSubRef) {
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       dims.ndims(), dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
-
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     // Run diff1
@@ -83,99 +88,90 @@ void diff1Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<T> currGoldBar = tests[testIter];
         dim4 goldDims;
-        ASSERT_SUCCESS(af_get_dims(&goldDims[0],
-                               &goldDims[1],
-                               &goldDims[2],
-                               &goldDims[3],
-                               inArray));
+        ASSERT_SUCCESS(af_get_dims(&goldDims[0], &goldDims[1], &goldDims[2],
+                                   &goldDims[3], inArray));
         goldDims[dim]--;
 
         ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
     }
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-TYPED_TEST(Diff1,Vector0)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/vector0.test"), 0);
+TYPED_TEST(Diff1, Vector0) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/vector0.test"), 0);
 }
 
-TYPED_TEST(Diff1,Matrix0)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/matrix0.test"), 0);
+TYPED_TEST(Diff1, Matrix0) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/matrix0.test"), 0);
 }
 
-TYPED_TEST(Diff1,Matrix1)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/matrix1.test"), 1);
+TYPED_TEST(Diff1, Matrix1) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/matrix1.test"), 1);
 }
 
 // Diff on 0 dimension
-TYPED_TEST(Diff1,Basic0)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/basic0.test"), 0);
+TYPED_TEST(Diff1, Basic0) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/basic0.test"), 0);
 }
 
 // Diff on 1 dimension
-TYPED_TEST(Diff1,Basic1)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/basic1.test"), 1);
+TYPED_TEST(Diff1, Basic1) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/basic1.test"), 1);
 }
 
 // Diff on 2 dimension
-TYPED_TEST(Diff1,Basic2)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/basic2.test"), 2);
+TYPED_TEST(Diff1, Basic2) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/basic2.test"), 2);
 }
 
 // Diff on 0 dimension subref
-TYPED_TEST(Diff1,Subref0)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/subref0.test"), 0,true,&(this->subMat0));
+TYPED_TEST(Diff1, Subref0) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/subref0.test"), 0, true,
+                         &(this->subMat0));
 }
 
 // Diff on 1 dimension subref
-TYPED_TEST(Diff1,Subref1)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/subref1.test"), 1,true,&(this->subMat1));
+TYPED_TEST(Diff1, Subref1) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/subref1.test"), 1, true,
+                         &(this->subMat1));
 }
 
 // Diff on 2 dimension subref
-TYPED_TEST(Diff1,Subref2)
-{
-    diff1Test<TypeParam>(string(TEST_DIR"/diff1/subref2.test"), 2,true,&(this->subMat2));
+TYPED_TEST(Diff1, Subref2) {
+    diff1Test<TypeParam>(string(TEST_DIR "/diff1/subref2.test"), 2, true,
+                         &(this->subMat2));
 }
 
 template<typename T>
-void diff1ArgsTest(string pTestFile)
-{
+void diff1ArgsTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray, -1));
-    ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray,  5));
+    ASSERT_EQ(AF_ERR_ARG, af_diff1(&outArray, inArray, 5));
 
-    if(inArray  != 0) af_release_array(inArray);
-    if(outArray != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Diff1,InvalidArgs)
-{
-    diff1ArgsTest<TypeParam>(string(TEST_DIR"/diff1/basic0.test"));
+TYPED_TEST(Diff1, InvalidArgs) {
+    diff1ArgsTest<TypeParam>(string(TEST_DIR "/diff1/basic0.test"));
 }
 
 ////////////////////////////////////// CPP ////////////////////////////////////
@@ -183,49 +179,48 @@ TYPED_TEST(Diff1,InvalidArgs)
 
 using af::array;
 using af::constant;
-using af::diff1;
 using af::deviceGC;
+using af::diff1;
 using af::sum;
 
-TEST(Diff1, DiffLargeDim)
-{
+TEST(Diff1, DiffLargeDim) {
     const size_t largeDim = 65535 * 32 + 1;
 
     deviceGC();
     {
-        array in = constant(1, largeDim);
+        array in   = constant(1, largeDim);
         array diff = diff1(in, 0);
-        float s = sum<float>(diff, 1);
+        float s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = constant(1, 1, largeDim);
+        in   = constant(1, 1, largeDim);
         diff = diff1(in, 1);
-        s = sum<float>(diff, 1);
+        s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = constant(1, 1, 1, largeDim);
+        in   = constant(1, 1, 1, largeDim);
         diff = diff1(in, 2);
-        s = sum<float>(diff, 1);
+        s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = constant(1, 1, 1, 1, largeDim);
+        in   = constant(1, 1, 1, 1, largeDim);
         diff = diff1(in, 3);
-        s = sum<float>(diff, 1);
+        s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
     }
 }
 
-TEST(Diff1, CPP)
-{
+TEST(Diff1, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned dim = 0;
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float,float,int>(string(TEST_DIR"/diff1/matrix0.test"),numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR "/diff1/matrix0.test"),
+                                 numDims, in, tests);
+    dim4 dims = numDims[0];
 
     array input(dims, &(in[0].front()));
     array output = diff1(input, dim);
@@ -233,10 +228,9 @@ TEST(Diff1, CPP)
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
-        dim4 goldDims = dims;
+        dim4 goldDims             = dims;
         goldDims[dim]--;
 
         ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
     }
 }
-
diff --git a/test/diff2.cpp b/test/diff2.cpp
index a5acafd04c..fd74c9efd2 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -7,78 +7,83 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::constant;
 using af::deviceGC;
 using af::diff2;
 using af::dim4;
 using af::dtype_traits;
 using af::sum;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Diff2 : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-            subMat0.push_back(af_make_seq(0, 1, 1));
-
-            subMat1.push_back(af_make_seq(1, 4, 1));
-            subMat1.push_back(af_make_seq(0, 2, 1));
-            subMat1.push_back(af_make_seq(0, 1, 1));
-
-            subMat2.push_back(af_make_seq(1, 4, 1));
-            subMat2.push_back(af_make_seq(0, 3, 1));
-            subMat2.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
-        vector<af_seq> subMat1;
-        vector<af_seq> subMat2;
+class Diff2 : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+        subMat0.push_back(af_make_seq(0, 1, 1));
+
+        subMat1.push_back(af_make_seq(1, 4, 1));
+        subMat1.push_back(af_make_seq(0, 2, 1));
+        subMat1.push_back(af_make_seq(0, 1, 1));
+
+        subMat2.push_back(af_make_seq(1, 4, 1));
+        subMat2.push_back(af_make_seq(0, 3, 1));
+        subMat2.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
+    vector<af_seq> subMat1;
+    vector<af_seq> subMat2;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
+                         uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Diff2, TestTypes);
 
 template<typename T>
-void diff2Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector<af_seq> *seqv=NULL)
-{
+void diff2Test(string pTestFile, unsigned dim, bool isSubRef = false,
+               const vector<af_seq> *seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     af_array inArray   = 0;
     af_array outArray  = 0;
     af_array tempArray = 0;
     // Get input array
     if (isSubRef) {
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       dims.ndims(), dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
-
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     // Run diff2
@@ -88,149 +93,138 @@ void diff2Test(string pTestFile, unsigned dim, bool isSubRef=false, const vector
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<T> currGoldBar = tests[testIter];
         dim4 goldDims;
-        ASSERT_SUCCESS(af_get_dims(&goldDims[0],
-                                   &goldDims[1],
-                                   &goldDims[2],
-                                   &goldDims[3],
-                                   inArray));
+        ASSERT_SUCCESS(af_get_dims(&goldDims[0], &goldDims[1], &goldDims[2],
+                                   &goldDims[3], inArray));
         goldDims[dim] -= 2;
 
         ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
     }
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-TYPED_TEST(Diff2,Vector0)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/vector0.test"), 0);
+TYPED_TEST(Diff2, Vector0) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/vector0.test"), 0);
 }
 
-TYPED_TEST(Diff2,Matrix0)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/matrix0.test"), 0);
+TYPED_TEST(Diff2, Matrix0) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/matrix0.test"), 0);
 }
 
-TYPED_TEST(Diff2,Matrix1)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/matrix1.test"), 1);
+TYPED_TEST(Diff2, Matrix1) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/matrix1.test"), 1);
 }
 
 // Diff on 0 dimension
-TYPED_TEST(Diff2,Basic0)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/basic0.test"), 0);
+TYPED_TEST(Diff2, Basic0) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/basic0.test"), 0);
 }
 
 // Diff on 1 dimension
-TYPED_TEST(Diff2,Basic1)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/basic1.test"), 1);
+TYPED_TEST(Diff2, Basic1) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/basic1.test"), 1);
 }
 
 // Diff on 2 dimension
-TYPED_TEST(Diff2,Basic2)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/basic2.test"), 2);
+TYPED_TEST(Diff2, Basic2) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/basic2.test"), 2);
 }
 
-TYPED_TEST(Diff2,Subref0)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/subref0.test"), 0,true,&(this->subMat0));
+TYPED_TEST(Diff2, Subref0) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/subref0.test"), 0, true,
+                         &(this->subMat0));
 }
 
-TYPED_TEST(Diff2,Subref1)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/subref1.test"), 1,true,&(this->subMat1));
+TYPED_TEST(Diff2, Subref1) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/subref1.test"), 1, true,
+                         &(this->subMat1));
 }
 
-TYPED_TEST(Diff2,Subref2)
-{
-    diff2Test<TypeParam>(string(TEST_DIR"/diff2/subref2.test"), 2,true,&(this->subMat2));
+TYPED_TEST(Diff2, Subref2) {
+    diff2Test<TypeParam>(string(TEST_DIR "/diff2/subref2.test"), 2, true,
+                         &(this->subMat2));
 }
 
 template<typename T>
-void diff2ArgsTest(string pTestFile)
-{
+void diff2ArgsTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray, -1));
-    ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray,  5));
+    ASSERT_EQ(AF_ERR_ARG, af_diff2(&outArray, inArray, 5));
 
-    if(inArray  != 0) af_release_array(inArray);
-    if(outArray != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Diff2,InvalidArgs)
-{
-    diff2ArgsTest<TypeParam>(string(TEST_DIR"/diff2/basic0.test"));
+TYPED_TEST(Diff2, InvalidArgs) {
+    diff2ArgsTest<TypeParam>(string(TEST_DIR "/diff2/basic0.test"));
 }
 
-TEST(Diff2, DiffLargeDim)
-{
+TEST(Diff2, DiffLargeDim) {
     const size_t largeDim = 65535 * 32 + 1;
 
     deviceGC();
     {
-        array in = constant(1, largeDim);
+        array in   = constant(1, largeDim);
         array diff = diff2(in, 0);
-        float s = sum<float>(diff, 1);
+        float s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = constant(1, 1, largeDim);
+        in   = constant(1, 1, largeDim);
         diff = diff2(in, 1);
-        s = sum<float>(diff, 1);
+        s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = constant(1, 1, 1, largeDim);
+        in   = constant(1, 1, 1, largeDim);
         diff = diff2(in, 2);
-        s = sum<float>(diff, 1);
+        s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
 
-        in = constant(1, 1, 1, 1, largeDim);
+        in   = constant(1, 1, 1, 1, largeDim);
         diff = diff2(in, 3);
-        s = sum<float>(diff, 1);
+        s    = sum<float>(diff, 1);
         ASSERT_EQ(s, 0.f);
     }
 }
 
 ////////////////////////////////// CPP ////////////////////////////////////////
 //
-TEST(Diff2, CPP)
-{
+TEST(Diff2, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned dim = 1;
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float,float,int>(string(TEST_DIR"/diff2/matrix1.test"),numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, int>(string(TEST_DIR "/diff2/matrix1.test"),
+                                 numDims, in, tests);
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = diff2(input, dim);
 
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
-        dim4 goldDims = input.dims();
+        dim4 goldDims             = input.dims();
         goldDims[dim] -= 2;
 
         ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
     }
 }
-
diff --git a/test/dog.cpp b/test/dog.cpp
index 86f780602e..183d521d53 100644
--- a/test/dog.cpp
+++ b/test/dog.cpp
@@ -7,41 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <af/vision.h>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
 using af::array;
+using af::convolve2;
 using af::dim4;
+using af::dog;
 using af::dtype_traits;
 using af::exception;
 using af::gaussianKernel;
-using af::convolve2;
-using af::dog;
 using af::randu;
 using af::sum;
 
 template<typename T>
-class DOG : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class DOG : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(DOG, TestTypes);
 
-
-TYPED_TEST(DOG, Basic)
-{
+TYPED_TEST(DOG, Basic) {
     if (noDoubleTests<TypeParam>()) return;
 
     dim4 iDims(512, 512, 1, 1);
@@ -53,14 +51,13 @@ TYPED_TEST(DOG, Basic)
     array smth2 = convolve2(in, k2);
     array diff  = smth1 - smth2;
     /* calcuate DOG using new function */
-    array out= dog(in, 3, 2);
+    array out = dog(in, 3, 2);
     /* compare both the values */
-    float accumErr = sum<float>(out-diff);
-    EXPECT_EQ(true, accumErr<1.0e-2);
+    float accumErr = sum<float>(out - diff);
+    EXPECT_EQ(true, accumErr < 1.0e-2);
 }
 
-TYPED_TEST(DOG, Batch)
-{
+TYPED_TEST(DOG, Batch) {
     if (noDoubleTests<TypeParam>()) return;
 
     dim4 iDims(512, 512, 3, 1);
@@ -72,15 +69,13 @@ TYPED_TEST(DOG, Batch)
     array smth2 = convolve2(in, k2);
     array diff  = smth1 - smth2;
     /* calcuate DOG using new function */
-    array out= dog(in, 3, 2);
+    array out = dog(in, 3, 2);
     /* compare both the values */
-    float accumErr = sum<float>(out-diff);
-    EXPECT_EQ(true, accumErr<1.0e-2);
+    float accumErr = sum<float>(out - diff);
+    EXPECT_EQ(true, accumErr < 1.0e-2);
 }
 
-TYPED_TEST(DOG, InvalidArray)
-{
+TYPED_TEST(DOG, InvalidArray) {
     array in = randu(512);
-    EXPECT_THROW(dog(in, 3, 2),
-                 exception);
+    EXPECT_THROW(dog(in, 3, 2), exception);
 }
diff --git a/test/dot.cpp b/test/dot.cpp
index 742ba35cb6..f8ad25a4ac 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -7,38 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <complex>
 #include <string>
 #include <vector>
-#include <complex>
-#include <testHelpers.hpp>
 
-using std::abs;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dot;
 using af::dtype_traits;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class DotF : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class DotF : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class DotC  : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class DotC : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create lists of types to be tested
@@ -46,32 +44,34 @@ typedef ::testing::Types<float, double> TestTypesF;
 typedef ::testing::Types<cfloat, cdouble> TestTypesC;
 
 // register the type list
-TYPED_TEST_CASE(DotF,  TestTypesF);
-TYPED_TEST_CASE(DotC,  TestTypesC);
+TYPED_TEST_CASE(DotF, TestTypesF);
+TYPED_TEST_CASE(DotC, TestTypesC);
 
 template<typename T>
 void dotTest(string pTestFile, const int resultIdx,
-        const af_mat_prop optLhs = AF_MAT_NONE, const af_mat_prop optRhs = AF_MAT_NONE)
-{
+             const af_mat_prop optLhs = AF_MAT_NONE,
+             const af_mat_prop optRhs = AF_MAT_NONE) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<T> >  in;
-    vector<vector<T> >  tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
     readTests<T, T, T>(pTestFile, numDims, in, tests);
 
-    dim4 aDims     = numDims[0];
-    dim4 bDims     = numDims[1];
+    dim4 aDims = numDims[0];
+    dim4 bDims = numDims[1];
 
-    af_array a = 0;
-    af_array b = 0;
+    af_array a   = 0;
+    af_array b   = 0;
     af_array out = 0;
 
-    ASSERT_SUCCESS(af_create_array(&a, &(in[0].front()),
-                aDims.ndims(), aDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&b, &(in[1].front()),
-                bDims.ndims(), bDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &(in[0].front()), aDims.ndims(),
+                                   aDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&b, &(in[1].front()), bDims.ndims(),
+                                   bDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_dot(&out, a, b, optLhs, optRhs));
 
@@ -81,8 +81,9 @@ void dotTest(string pTestFile, const int resultIdx,
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), out));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(a));
@@ -91,47 +92,46 @@ void dotTest(string pTestFile, const int resultIdx,
 }
 
 template<typename T>
-void compare(double rval, double /*ival*/, T gold)
-{
+void compare(double rval, double /*ival*/, T gold) {
     ASSERT_NEAR(gold, rval, 0.03);
 }
 
 template<>
-void compare<cfloat>(double rval, double ival, cfloat gold)
-{
+void compare<cfloat>(double rval, double ival, cfloat gold) {
     ASSERT_NEAR(gold.real, rval, 0.03);
     ASSERT_NEAR(gold.imag, ival, 0.03);
 }
 
 template<>
-void compare<cdouble>(double rval, double ival, cdouble gold)
-{
+void compare<cdouble>(double rval, double ival, cdouble gold) {
     ASSERT_NEAR(gold.real, rval, 0.03);
     ASSERT_NEAR(gold.imag, ival, 0.03);
 }
 
 template<typename T>
 void dotAllTest(string pTestFile, const int resultIdx,
-          const af_mat_prop optLhs = AF_MAT_NONE, const af_mat_prop optRhs = AF_MAT_NONE)
-{
+                const af_mat_prop optLhs = AF_MAT_NONE,
+                const af_mat_prop optRhs = AF_MAT_NONE) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<T> >  in;
-    vector<vector<T> >  tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
     readTests<T, T, T>(pTestFile, numDims, in, tests);
 
-    dim4 aDims     = numDims[0];
-    dim4 bDims     = numDims[1];
+    dim4 aDims = numDims[0];
+    dim4 bDims = numDims[1];
 
     af_array a = 0;
     af_array b = 0;
 
-    ASSERT_SUCCESS(af_create_array(&a, &(in[0].front()),
-                aDims.ndims(), aDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&b, &(in[1].front()),
-                bDims.ndims(), bDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &(in[0].front()), aDims.ndims(),
+                                   aDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&b, &(in[1].front()), bDims.ndims(),
+                                   bDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     double rval = 0, ival = 0;
     ASSERT_SUCCESS(af_dot_all(&rval, &ival, a, b, optLhs, optRhs));
@@ -144,57 +144,57 @@ void dotAllTest(string pTestFile, const int resultIdx,
     ASSERT_SUCCESS(af_release_array(b));
 }
 
+#define INSTANTIATEF(SIZE, FILENAME)                                           \
+    TYPED_TEST(DotF, DotF_##SIZE) {                                            \
+        dotTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 0);    \
+        dotAllTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 0); \
+    }
+
+#define INSTANTIATEC(SIZE, FILENAME)                                          \
+    TYPED_TEST(DotC, DotC_CC_##SIZE) {                                        \
+        dotTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 0,    \
+                           AF_MAT_CONJ, AF_MAT_CONJ);                         \
+        dotAllTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 0, \
+                              AF_MAT_CONJ, AF_MAT_CONJ);                      \
+    }                                                                         \
+    TYPED_TEST(DotC, DotC_UU_##SIZE) {                                        \
+        dotTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 1,    \
+                           AF_MAT_NONE, AF_MAT_NONE);                         \
+        dotAllTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 1, \
+                              AF_MAT_NONE, AF_MAT_NONE);                      \
+    }                                                                         \
+    TYPED_TEST(DotC, DotC_CU_##SIZE) {                                        \
+        dotTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 2,    \
+                           AF_MAT_CONJ, AF_MAT_NONE);                         \
+        dotAllTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 2, \
+                              AF_MAT_CONJ, AF_MAT_NONE);                      \
+    }                                                                         \
+    TYPED_TEST(DotC, DotC_UC_##SIZE) {                                        \
+        dotTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 3,    \
+                           AF_MAT_NONE, AF_MAT_CONJ);                         \
+        dotAllTest<TypeParam>(string(TEST_DIR "/blas/" #FILENAME ".test"), 3, \
+                              AF_MAT_NONE, AF_MAT_CONJ);                      \
+    }
 
-#define INSTANTIATEF(SIZE, FILENAME)                                                            \
-TYPED_TEST(DotF, DotF_##SIZE)                                                                   \
-{                                                                                               \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0);                            \
-    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0);                         \
-}                                                                                               \
-
-
-#define INSTANTIATEC(SIZE, FILENAME)                                                                \
-TYPED_TEST(DotC, DotC_CC_##SIZE)                                                                    \
-{                                                                                                   \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0, AF_MAT_CONJ, AF_MAT_CONJ);      \
-    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 0, AF_MAT_CONJ, AF_MAT_CONJ);   \
-}                                                                                                   \
-TYPED_TEST(DotC, DotC_UU_##SIZE)                                                                    \
-{                                                                                                   \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 1, AF_MAT_NONE, AF_MAT_NONE);      \
-    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 1, AF_MAT_NONE, AF_MAT_NONE);   \
-}                                                                                                   \
-TYPED_TEST(DotC, DotC_CU_##SIZE)                                                                    \
-{                                                                                                   \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 2, AF_MAT_CONJ, AF_MAT_NONE);      \
-    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 2, AF_MAT_CONJ, AF_MAT_NONE);   \
-}                                                                                                   \
-TYPED_TEST(DotC, DotC_UC_##SIZE)                                                                    \
-{                                                                                                   \
-    dotTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 3, AF_MAT_NONE, AF_MAT_CONJ);      \
-    dotAllTest<TypeParam>(string(TEST_DIR"/blas/"#FILENAME".test"), 3, AF_MAT_NONE, AF_MAT_CONJ);   \
-}                                                                                                   \
-
-
-INSTANTIATEF(1000   , dot_f_1000);
-INSTANTIATEF(10     , dot_f_10);
-INSTANTIATEF(25600  , dot_f_25600);
-INSTANTIATEC(1000   , dot_c_1000);
-INSTANTIATEC(10     , dot_c_10);
-INSTANTIATEC(25600  , dot_c_25600);
+INSTANTIATEF(1000, dot_f_1000);
+INSTANTIATEF(10, dot_f_10);
+INSTANTIATEF(25600, dot_f_25600);
+INSTANTIATEC(1000, dot_c_1000);
+INSTANTIATEC(10, dot_c_10);
+INSTANTIATEC(25600, dot_c_25600);
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(DotF, CPP)
-{
-    vector<dim4>         numDims;
-    vector<vector<float> >  in;
-    vector<vector<float> >  tests;
+TEST(DotF, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, float>(TEST_DIR"/blas/dot_f_1000.test", numDims, in, tests);
+    readTests<float, float, float>(TEST_DIR "/blas/dot_f_1000.test", numDims,
+                                   in, tests);
 
-    dim4 aDims     = numDims[0];
-    dim4 bDims     = numDims[1];
+    dim4 aDims = numDims[0];
+    dim4 bDims = numDims[1];
 
     array a(aDims, &(in[0].front()));
     array b(bDims, &(in[1].front()));
@@ -206,16 +206,16 @@ TEST(DotF, CPP)
     ASSERT_VEC_ARRAY_EQ(goldData, goldDims, out);
 }
 
-TEST(DotCCU, CPP)
-{
-    vector<dim4>         numDims;
-    vector<vector<cfloat> >  in;
-    vector<vector<cfloat> >  tests;
+TEST(DotCCU, CPP) {
+    vector<dim4> numDims;
+    vector<vector<cfloat> > in;
+    vector<vector<cfloat> > tests;
 
-    readTests<cfloat, cfloat, cfloat>(TEST_DIR"/blas/dot_c_1000.test", numDims, in, tests);
+    readTests<cfloat, cfloat, cfloat>(TEST_DIR "/blas/dot_c_1000.test", numDims,
+                                      in, tests);
 
-    dim4 aDims     = numDims[0];
-    dim4 bDims     = numDims[1];
+    dim4 aDims = numDims[0];
+    dim4 bDims = numDims[1];
 
     array a(aDims, &(in[0].front()));
     array b(bDims, &(in[1].front()));
@@ -227,16 +227,16 @@ TEST(DotCCU, CPP)
     ASSERT_VEC_ARRAY_EQ(goldData, goldDims, out);
 }
 
-TEST(DotAllF, CPP)
-{
-    vector<dim4>         numDims;
-    vector<vector<float> >  in;
-    vector<vector<float> >  tests;
+TEST(DotAllF, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, float>(TEST_DIR"/blas/dot_f_1000.test", numDims, in, tests);
+    readTests<float, float, float>(TEST_DIR "/blas/dot_f_1000.test", numDims,
+                                   in, tests);
 
-    dim4 aDims     = numDims[0];
-    dim4 bDims     = numDims[1];
+    dim4 aDims = numDims[0];
+    dim4 bDims = numDims[1];
 
     array a(aDims, &(in[0].front()));
     array b(bDims, &(in[1].front()));
@@ -248,16 +248,16 @@ TEST(DotAllF, CPP)
     ASSERT_EQ(goldData[0], out);
 }
 
-TEST(DotAllCCU, CPP)
-{
-    vector<dim4>         numDims;
-    vector<vector<cfloat> >  in;
-    vector<vector<cfloat> >  tests;
+TEST(DotAllCCU, CPP) {
+    vector<dim4> numDims;
+    vector<vector<cfloat> > in;
+    vector<vector<cfloat> > tests;
 
-    readTests<cfloat, cfloat, cfloat>(TEST_DIR"/blas/dot_c_1000.test", numDims, in, tests);
+    readTests<cfloat, cfloat, cfloat>(TEST_DIR "/blas/dot_c_1000.test", numDims,
+                                      in, tests);
 
-    dim4 aDims     = numDims[0];
-    dim4 bDims     = numDims[1];
+    dim4 aDims = numDims[0];
+    dim4 bDims = numDims[1];
 
     array a(aDims, &(in[0].front()));
     array b(bDims, &(in[1].front()));
diff --git a/test/empty.cpp b/test/empty.cpp
index f07bca2346..f38bb67eaf 100644
--- a/test/empty.cpp
+++ b/test/empty.cpp
@@ -17,38 +17,37 @@
 using namespace af;
 
 template<typename T>
-class Array : public ::testing::Test
-{
-
-};
+class Array : public ::testing::Test {};
 
 TEST(Array, TestEmptyAssignment) {
-    array A = randu(5, f32);
-    array C = constant(0,0);
-    array B = A(isNaN(A));
+    array A     = randu(5, f32);
+    array C     = constant(0, 0);
+    array B     = A(isNaN(A));
     A(isNaN(A)) = C;
     ASSERT_EQ(B.numdims(), 0u);
     ASSERT_EQ(A.numdims(), 1u);
-    ASSERT_EQ(lookup(constant(1,9), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(lookup(constant(1, 9), constant(0, 0)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptySigProc) {
-    ASSERT_EQ(convolve (constant(1,1), constant(0,0)).numdims(), 1u);
-    ASSERT_EQ(convolve (constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(convolve2(constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(convolve3(constant(0,0), constant(0,0)).numdims(), 0u);
-
-    ASSERT_EQ(iir(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0u);
-
-    ASSERT_EQ(approx1(constant(0,0), constant(0,0))                 .numdims(), 0u);
-    ASSERT_EQ(approx1(constant(0,0), seq(0,10))                     .numdims(), 0u);
-    ASSERT_EQ(approx2(constant(0,0), constant(0,0),  constant(0,0)) .numdims(), 0u);
-    ASSERT_EQ(approx2(constant(0,0), seq(0,10), seq(0,10))          .numdims(), 0u);
+    ASSERT_EQ(convolve(constant(1, 1), constant(0, 0)).numdims(), 1u);
+    ASSERT_EQ(convolve(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(convolve2(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(convolve3(constant(0, 0), constant(0, 0)).numdims(), 0u);
+
+    ASSERT_EQ(iir(constant(0, 0), constant(0, 0), constant(0, 0)).numdims(),
+              0u);
+
+    ASSERT_EQ(approx1(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(approx1(constant(0, 0), seq(0, 10)).numdims(), 0u);
+    ASSERT_EQ(approx2(constant(0, 0), constant(0, 0), constant(0, 0)).numdims(),
+              0u);
+    ASSERT_EQ(approx2(constant(0, 0), seq(0, 10), seq(0, 10)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptySet) {
-    ASSERT_EQ(setIntersect(constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(setUnique(constant(0,0))                  .numdims(), 0u);
+    ASSERT_EQ(setIntersect(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(setUnique(constant(0, 0)).numdims(), 0u);
 
     array A = randu(5, f32);
     array B = constant(0, 0);
@@ -58,34 +57,34 @@ TEST(Array, TestEmptySet) {
 }
 
 TEST(Array, TestEmptyOperators) {
-    ASSERT_EQ((constant(0,0) +  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) && constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) -  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) &  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) |  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) ^  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) << constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) >> constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) /  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) == constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) <= constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) >= constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) >  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) <  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(-constant(0,0)                  .numdims(), 0u);
-    ASSERT_EQ((!constant(0,0))                .numdims(), 0u);
-    ASSERT_EQ((constant(0,0) != constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) += 1)            .numdims(), 0u);
-    ASSERT_EQ((constant(0,0) -= 1)            .numdims(), 0u);
-    ASSERT_EQ((constant(0,0) *= 1)            .numdims(), 0u);
-    ASSERT_EQ((constant(0,0) /= 1)            .numdims(), 0u);
-    ASSERT_EQ((constant(0,0) || constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) %  constant(0,0)).numdims(), 0u);
-    ASSERT_EQ((constant(0,0) *  constant(0,0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) + constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) && constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) - constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) & constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) | constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) ^ constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) << constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) >> constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) / constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) == constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) <= constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) >= constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) > constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) < constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(-constant(0, 0).numdims(), 0u);
+    ASSERT_EQ((!constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) != constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) += 1).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) -= 1).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) *= 1).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) /= 1).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) || constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) % constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ((constant(0, 0) * constant(0, 0)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptyFFT) {
-    array arr = constant(0,0);
+    array arr = constant(0, 0);
     fftInPlace(arr);
     ASSERT_EQ(arr.numdims(), 0u);
     fft2InPlace(arr);
@@ -99,63 +98,64 @@ TEST(Array, TestEmptyFFT) {
     ifft3InPlace(arr);
     ASSERT_EQ(arr.numdims(), 0u);
 
-    ASSERT_EQ((fft(constant(0,0)))              .numdims(), 0u);
-    ASSERT_EQ((fftNorm(constant(0,0), 0.5))     .numdims(), 0u);
-    ASSERT_EQ((fft2(constant(0,0)))             .numdims(), 0u);
-    ASSERT_EQ((fft2Norm(constant(0,0), 0.5))    .numdims(), 0u);
-    ASSERT_EQ((fft3(constant(0,0)))             .numdims(), 0u);
-    ASSERT_EQ((fft3Norm(constant(0,0), 0.5))    .numdims(), 0u);
-    ASSERT_EQ((fftC2R<1>(constant(0,0)))        .numdims(), 0u);
-    ASSERT_EQ((fftR2C<1>(constant(0,0)))        .numdims(), 0u);
-    ASSERT_EQ((fftC2R<2>(constant(0,0)))        .numdims(), 0u);
-    ASSERT_EQ((fftR2C<2>(constant(0,0)))        .numdims(), 0u);
-    ASSERT_EQ((fftC2R<3>(constant(0,0)))        .numdims(), 0u);
-    ASSERT_EQ((fftR2C<3>(constant(0,0)))        .numdims(), 0u);
-    ASSERT_EQ((ifft(constant(0,0)))             .numdims(), 0u);
-    ASSERT_EQ((ifftNorm(constant(0,0), 0.5))    .numdims(), 0u);
-    ASSERT_EQ((ifft2(constant(0,0)))            .numdims(), 0u);
-    ASSERT_EQ((ifft2Norm(constant(0,0), 0.5))   .numdims(), 0u);
-    ASSERT_EQ((ifft3(constant(0,0)))            .numdims(), 0u);
-    ASSERT_EQ((ifft3Norm(constant(0,0), 0.5))   .numdims(), 0u);
+    ASSERT_EQ((fft(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fftNorm(constant(0, 0), 0.5)).numdims(), 0u);
+    ASSERT_EQ((fft2(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fft2Norm(constant(0, 0), 0.5)).numdims(), 0u);
+    ASSERT_EQ((fft3(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fft3Norm(constant(0, 0), 0.5)).numdims(), 0u);
+    ASSERT_EQ((fftC2R<1>(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fftR2C<1>(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fftC2R<2>(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fftR2C<2>(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fftC2R<3>(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((fftR2C<3>(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((ifft(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((ifftNorm(constant(0, 0), 0.5)).numdims(), 0u);
+    ASSERT_EQ((ifft2(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((ifft2Norm(constant(0, 0), 0.5)).numdims(), 0u);
+    ASSERT_EQ((ifft3(constant(0, 0))).numdims(), 0u);
+    ASSERT_EQ((ifft3Norm(constant(0, 0), 0.5)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptyDiff) {
-    ASSERT_EQ(diff1(constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(diff1(constant(1,1)).numdims(), 0u);
-    ASSERT_EQ(diff1(constant(1,2)).numdims(), 1u);
-    ASSERT_EQ(diff2(constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(diff2(constant(1,1)).numdims(), 0u);
-    ASSERT_EQ(diff2(constant(1,2)).numdims(), 0u);
-    ASSERT_EQ(diff2(constant(1,3)).numdims(), 1u);
+    ASSERT_EQ(diff1(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(diff1(constant(1, 1)).numdims(), 0u);
+    ASSERT_EQ(diff1(constant(1, 2)).numdims(), 1u);
+    ASSERT_EQ(diff2(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(diff2(constant(1, 1)).numdims(), 0u);
+    ASSERT_EQ(diff2(constant(1, 2)).numdims(), 0u);
+    ASSERT_EQ(diff2(constant(1, 3)).numdims(), 1u);
 }
 
 TEST(Array, TestEmptyLinAlg) {
-    ASSERT_EQ(det<float  >(constant(0,0)), 1);
-    ASSERT_EQ(det<cfloat >(constant(0,0)).real, 1);
-    ASSERT_EQ(det<cdouble>(constant(0,0)).real, 1);
-    ASSERT_EQ(norm(constant(0,0)), 0);
-    ASSERT_EQ(rank(constant(0,0)), 0u);
+    ASSERT_EQ(det<float>(constant(0, 0)), 1);
+    ASSERT_EQ(det<cfloat>(constant(0, 0)).real, 1);
+    ASSERT_EQ(det<cdouble>(constant(0, 0)).real, 1);
+    ASSERT_EQ(norm(constant(0, 0)), 0);
+    ASSERT_EQ(rank(constant(0, 0)), 0u);
 
-    array tau_qr, arr = constant(0,0);
+    array tau_qr, arr = constant(0, 0);
     qrInPlace(tau_qr, arr);
     ASSERT_EQ(tau_qr.numdims(), 0u);
 
     array out_qr;
-    qr(out_qr, tau_qr, constant(0,0));
+    qr(out_qr, tau_qr, constant(0, 0));
     ASSERT_EQ(out_qr.numdims(), 0u);
     ASSERT_EQ(tau_qr.numdims(), 0u);
-    ASSERT_EQ(solve(constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(solveLU(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(solve(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(solveLU(constant(0, 0), constant(0, 0), constant(0, 0)).numdims(),
+              0u);
 
     array out_lu, piv_lu;
-    lu(out_lu, piv_lu, constant(0,0));
+    lu(out_lu, piv_lu, constant(0, 0));
     ASSERT_EQ(out_lu.numdims(), 0u);
     ASSERT_EQ(piv_lu.numdims(), 0u);
 
     array low_lu, up_lu;
-    lu(low_lu, up_lu, piv_lu, constant(0,0));
+    lu(low_lu, up_lu, piv_lu, constant(0, 0));
     ASSERT_EQ(low_lu.numdims(), 0u);
-    ASSERT_EQ( up_lu.numdims(), 0u);
+    ASSERT_EQ(up_lu.numdims(), 0u);
     ASSERT_EQ(piv_lu.numdims(), 0u);
 
     luInPlace(piv_lu, arr, true);
@@ -163,129 +163,129 @@ TEST(Array, TestEmptyLinAlg) {
     ASSERT_EQ(arr.numdims(), 0u);
 
     array u, s, v;
-    svd(u,s,v, constant(0,0));
-    svdInPlace(u,s,v, arr);
-    ASSERT_EQ(dot(constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(transpose(constant(0,0)).numdims(), 0u);
+    svd(u, s, v, constant(0, 0));
+    svdInPlace(u, s, v, arr);
+    ASSERT_EQ(dot(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(transpose(constant(0, 0)).numdims(), 0u);
     choleskyInPlace(arr);
     ASSERT_EQ(arr.numdims(), 0u);
     array out;
-    cholesky(out, constant(0,0));
+    cholesky(out, constant(0, 0));
     ASSERT_EQ(out.numdims(), 0u);
 }
 
 TEST(Array, TestEmptyMath) {
-    ASSERT_EQ(acos  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(acosh (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(abs   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(asin  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(asinh (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(atan  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(atan2 (constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(atanh (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(cos   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(cosh  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(log   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(log10 (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(log1p (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(sin   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(sinh  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(tan   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(tanh  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(sqrt  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(real  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(imag  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(conjg (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(erf   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(erfc  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(exp   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(expm1 (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(cbrt  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(ceil  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(lgamma(constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(pow   (constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(root  (constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(tgamma(constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(arg   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(floor (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(hypot (constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(rem   (constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(round (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(sign  (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(trunc (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(factorial(constant(0,0)).numdims(), 0u);
-    //ASSERT_EQ(complex(constant(0,0), constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(acos(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(acosh(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(abs(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(asin(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(asinh(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(atan(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(atan2(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(atanh(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(cos(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(cosh(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(log(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(log10(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(log1p(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(sin(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(sinh(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(tan(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(tanh(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(sqrt(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(real(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(imag(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(conjg(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(erf(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(erfc(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(exp(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(expm1(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(cbrt(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(ceil(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(lgamma(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(pow(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(root(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(tgamma(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(arg(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(floor(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(hypot(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(rem(constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(round(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(sign(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(trunc(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(factorial(constant(0, 0)).numdims(), 0u);
+    // ASSERT_EQ(complex(constant(0,0), constant(0,0)).numdims(), 0u);
 }
 
 TEST(Array, TestEmptyVecOp) {
-    ASSERT_EQ(accum     (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(allTrue   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(anyTrue   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(count     (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(where     (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(max       (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(min       (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(product   (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(sum       (constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(sort      (constant(0,0)).numdims(), 0u);
+    ASSERT_EQ(accum(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(allTrue(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(anyTrue(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(count(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(where(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(max(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(min(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(product(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(sum(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(sort(constant(0, 0)).numdims(), 0u);
 
     array skeys, svals;
-    sort(skeys, svals, constant(0,0), constant(0,0));
+    sort(skeys, svals, constant(0, 0), constant(0, 0));
     ASSERT_EQ(skeys.numdims(), 0u);
     ASSERT_EQ(svals.numdims(), 0u);
 
     array sout, sind;
-    sort(sout, sind, constant(0,0));
+    sort(sout, sind, constant(0, 0));
     ASSERT_EQ(sout.numdims(), 0u);
     ASSERT_EQ(sind.numdims(), 0u);
 }
 
 TEST(Array, TestEmptyArrMod) {
-    ASSERT_EQ(diag  (constant(0,0))             .numdims(), 0u);
-    ASSERT_EQ(diag  (constant(0,0), true)       .numdims(), 0u);
-    ASSERT_EQ(identity(0)                   .numdims(), 0u);
-    ASSERT_EQ(iota(dim4(0))                     .numdims(), 0u);
-    ASSERT_EQ(lower(constant(0,0))              .numdims(), 0u);
-    ASSERT_EQ(upper(constant(0,0))              .numdims(), 0u);
-    ASSERT_EQ(constant(0,0).as(u8)              .numdims(), 0u);
-    ASSERT_EQ(isNaN(constant(0,0))              .numdims(), 0u);
-    ASSERT_EQ(isInf(constant(0,0))              .numdims(), 0u);
-    ASSERT_EQ(iszero(constant(0,0))             .numdims(), 0u);
-    ASSERT_EQ(flat(constant(0,0))               .numdims(), 0u);
-    ASSERT_EQ(flip(constant(0,0), 0)            .numdims(), 0u);
-    ASSERT_EQ(moddims(constant(0,0), dim4(0))   .numdims(), 0u);
-    ASSERT_EQ(reorder(constant(0,0),0)          .numdims(), 0u);
-    ASSERT_EQ(shift(constant(0,0), 1)           .numdims(), 0u);
-    ASSERT_EQ(tile(constant(0,0), 1)            .numdims(), 0u);
-
-    ASSERT_EQ(join(0, constant(0,0), constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(join(0, randu(3), constant(0,0)).elements(), 3);
-    ASSERT_EQ(join(0, constant(0,0), randn(3)).elements(), 3);
-
-    ASSERT_EQ(select(constant(0,0), constant(0,0), constant(0,0)).numdims(), 0u);
-
-    array arr = constant(0,0);
-    replace(arr, constant(0,0), constant(0,0));
+    ASSERT_EQ(diag(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(diag(constant(0, 0), true).numdims(), 0u);
+    ASSERT_EQ(identity(0).numdims(), 0u);
+    ASSERT_EQ(iota(dim4(0)).numdims(), 0u);
+    ASSERT_EQ(lower(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(upper(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(constant(0, 0).as(u8).numdims(), 0u);
+    ASSERT_EQ(isNaN(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(isInf(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(iszero(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(flat(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(flip(constant(0, 0), 0).numdims(), 0u);
+    ASSERT_EQ(moddims(constant(0, 0), dim4(0)).numdims(), 0u);
+    ASSERT_EQ(reorder(constant(0, 0), 0).numdims(), 0u);
+    ASSERT_EQ(shift(constant(0, 0), 1).numdims(), 0u);
+    ASSERT_EQ(tile(constant(0, 0), 1).numdims(), 0u);
+
+    ASSERT_EQ(join(0, constant(0, 0), constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(join(0, randu(3), constant(0, 0)).elements(), 3);
+    ASSERT_EQ(join(0, constant(0, 0), randn(3)).elements(), 3);
+
+    ASSERT_EQ(select(constant(0, 0), constant(0, 0), constant(0, 0)).numdims(),
+              0u);
+
+    array arr = constant(0, 0);
+    replace(arr, constant(0, 0), constant(0, 0));
     ASSERT_EQ(arr.numdims(), 0u);
-
 }
 
 TEST(Array, TestEmptyImage) {
-    ASSERT_EQ(histogram(constant(0,0) , 1).numdims(), 0u);
-    ASSERT_EQ(hsv2rgb(constant(0,0)) .numdims(), 0u);
-    ASSERT_EQ(gray2rgb(constant(0,0)).numdims(), 0u);
-    ASSERT_EQ(rotate(constant(0,0),0).numdims(), 0u);
+    ASSERT_EQ(histogram(constant(0, 0), 1).numdims(), 0u);
+    ASSERT_EQ(hsv2rgb(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(gray2rgb(constant(0, 0)).numdims(), 0u);
+    ASSERT_EQ(rotate(constant(0, 0), 0).numdims(), 0u);
 
     af_array h, hout;
     dim_t ds[1];
-    af_constant (&h, 0, 0, ds, f32);
+    af_constant(&h, 0, 0, ds, f32);
     af_histogram(&hout, h, 10, 0.0, 1.0);
 
-    unsigned nd; af_get_numdims(&nd, h);
+    unsigned nd;
+    af_get_numdims(&nd, h);
     ASSERT_EQ(nd, 0u);
     af_get_numdims(&nd, hout);
     ASSERT_EQ(nd, 0u);
     ASSERT_SUCCESS(af_release_array(h));
     ASSERT_SUCCESS(af_release_array(hout));
 }
-
diff --git a/test/fast.cpp b/test/fast.cpp
index cb5bca2e9c..8aedebdd62 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -7,39 +7,37 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
+using af::dim4;
 using std::abs;
 using std::endl;
 using std::string;
 using std::vector;
-using af::dim4;
 
-typedef struct
-{
+typedef struct {
     float f[5];
 } feat_t;
 
-static bool feat_cmp(feat_t i, feat_t j)
-{
+static bool feat_cmp(feat_t i, feat_t j) {
     for (int k = 0; k < 5; k++)
-        if (i.f[k] != j.f[k])
-            return (i.f[k] < j.f[k]);
+        if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
 
     return false;
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
-{
+static void array_to_feat(vector<feat_t> &feat, float *x, float *y,
+                          float *score, float *orientation, float *size,
+                          unsigned nfeat) {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -51,17 +49,15 @@ static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score
 }
 
 template<typename T>
-class FloatFAST : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class FloatFAST : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class FixedFAST : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class FixedFAST : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> FloatTestTypes;
@@ -71,28 +67,28 @@ TYPED_TEST_CASE(FloatFAST, FloatTestTypes);
 TYPED_TEST_CASE(FixedFAST, FixedTestTypes);
 
 template<typename T>
-void fastTest(string pTestFile, bool nonmax)
-{
+void fastTest(string pTestFile, bool nonmax) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>        inDims;
-    vector<string>     inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        dim_t nElems       = 0;
-        af_array inArray_f32  = 0;
-        af_array inArray      = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        dim_t nElems         = 0;
+        af_array inArray_f32 = 0;
+        af_array inArray     = 0;
         af_features out;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/fast/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/fast/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
 
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
@@ -108,35 +104,43 @@ void fastTest(string pTestFile, bool nonmax)
         ASSERT_SUCCESS(af_get_features_orientation(&orientation, out));
         ASSERT_SUCCESS(af_get_features_size(&size, out));
 
-
         ASSERT_SUCCESS(af_get_elements(&nElems, x));
 
-        float * outX           = new float[gold[0].size()];
-        float * outY           = new float[gold[1].size()];
-        float * outScore       = new float[gold[2].size()];
-        float * outOrientation = new float[gold[3].size()];
-        float * outSize        = new float[gold[4].size()];
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outOrientation, orientation));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outSize, size));
+        float *outX           = new float[gold[0].size()];
+        float *outY           = new float[gold[1].size()];
+        float *outScore       = new float[gold[2].size()];
+        float *outOrientation = new float[gold[3].size()];
+        float *outSize        = new float[gold[4].size()];
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outX, x));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outY, y));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outScore, score));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outOrientation, orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outSize, size));
 
         vector<feat_t> out_feat;
-        array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize, n);
+        array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize,
+                      n);
 
         vector<feat_t> gold_feat;
-        array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(), &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
+        array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(),
+                      &gold[2].front(), &gold[3].front(), &gold[4].front(),
+                      gold[0].size());
 
         std::sort(out_feat.begin(), out_feat.end(), feat_cmp);
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
         for (int elIter = 0; elIter < (int)nElems; elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4])
+                << "at: " << elIter << endl;
         }
 
         ASSERT_SUCCESS(af_release_array(inArray));
@@ -144,30 +148,30 @@ void fastTest(string pTestFile, bool nonmax)
 
         ASSERT_SUCCESS(af_release_features(out));
 
-        delete [] outX;
-        delete [] outY;
-        delete [] outScore;
-        delete [] outOrientation;
-        delete [] outSize;
+        delete[] outX;
+        delete[] outY;
+        delete[] outScore;
+        delete[] outOrientation;
+        delete[] outSize;
     }
 }
 
-#define FLOAT_FAST_INIT(desc, image, nonmax)                            \
-    TYPED_TEST(FloatFAST, desc)                                         \
-    {                                                                   \
-        fastTest<TypeParam>(string(TEST_DIR"/fast/"#image"_float.test"), nonmax); \
+#define FLOAT_FAST_INIT(desc, image, nonmax)                                \
+    TYPED_TEST(FloatFAST, desc) {                                           \
+        fastTest<TypeParam>(string(TEST_DIR "/fast/" #image "_float.test"), \
+                            nonmax);                                        \
     }
 
-#define FIXED_FAST_INIT(desc, image, nonmax)                            \
-    TYPED_TEST(FixedFAST, desc)                                         \
-    {                                                                   \
-        fastTest<TypeParam>(string(TEST_DIR"/fast/"#image"_fixed.test"), nonmax); \
+#define FIXED_FAST_INIT(desc, image, nonmax)                                \
+    TYPED_TEST(FixedFAST, desc) {                                           \
+        fastTest<TypeParam>(string(TEST_DIR "/fast/" #image "_fixed.test"), \
+                            nonmax);                                        \
     }
 
-    FLOAT_FAST_INIT(square, square, false);
-    FLOAT_FAST_INIT(square_nonmax, square_nonmax, true);
-    FIXED_FAST_INIT(square, square, false);
-    FIXED_FAST_INIT(square_nonmax, square_nonmax, true);
+FLOAT_FAST_INIT(square, square, false);
+FLOAT_FAST_INIT(square_nonmax, square_nonmax, true);
+FIXED_FAST_INIT(square, square, false);
+FIXED_FAST_INIT(square_nonmax, square_nonmax, true);
 
 /////////////////////////////////// CPP ////////////////////////////////
 
@@ -175,27 +179,27 @@ using af::array;
 using af::features;
 using af::loadImage;
 
-TEST(FloatFAST, CPP)
-{
+TEST(FloatFAST, CPP) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>        inDims;
-    vector<string>     inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
-    readImageTests(string(TEST_DIR"/fast/square_nonmax_float.test"), inDims, inFiles, gold);
-    inFiles[0].insert(0,string(TEST_DIR"/fast/"));
+    readImageTests(string(TEST_DIR "/fast/square_nonmax_float.test"), inDims,
+                   inFiles, gold);
+    inFiles[0].insert(0, string(TEST_DIR "/fast/"));
 
     array in = loadImage(inFiles[0].c_str(), false);
 
     features out = fast(in, 20.0f, 9, true, 0.05f, 3);
 
-    float * outX           = new float[gold[0].size()];
-    float * outY           = new float[gold[1].size()];
-    float * outScore       = new float[gold[2].size()];
-    float * outOrientation = new float[gold[3].size()];
-    float * outSize        = new float[gold[4].size()];
+    float *outX           = new float[gold[0].size()];
+    float *outY           = new float[gold[1].size()];
+    float *outScore       = new float[gold[2].size()];
+    float *outOrientation = new float[gold[3].size()];
+    float *outSize        = new float[gold[4].size()];
     out.getX().host(outX);
     out.getY().host(outY);
     out.getScore().host(outScore);
@@ -203,20 +207,28 @@ TEST(FloatFAST, CPP)
     out.getSize().host(outSize);
 
     vector<feat_t> out_feat;
-    array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize, out.getNumFeatures());
+    array_to_feat(out_feat, outX, outY, outScore, outOrientation, outSize,
+                  out.getNumFeatures());
 
     vector<feat_t> gold_feat;
-    array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(), &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
+    array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(),
+                  &gold[2].front(), &gold[3].front(), &gold[4].front(),
+                  gold[0].size());
 
     std::sort(out_feat.begin(), out_feat.end(), feat_cmp);
     std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
     for (unsigned elIter = 0; elIter < out.getNumFeatures(); elIter++) {
-        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3])
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4])
+            << "at: " << elIter << endl;
     }
 
     delete[] outX;
diff --git a/test/fft.cpp b/test/fft.cpp
index fbffc7646b..274d5fa467 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -7,179 +7,198 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <stdexcept>
 #include <string>
 #include <vector>
-#include <stdexcept>
-#include <testHelpers.hpp>
 
-using std::abs;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
 using af::constant;
 using af::dim4;
 using af::dtype_traits;
+using af::fft;
 using af::fft2;
 using af::fft2InPlace;
 using af::fft3;
 using af::fft3InPlace;
-using af::fft;
 using af::fftInPlace;
+using af::ifft;
 using af::ifft2;
 using af::ifft2InPlace;
 using af::ifft3;
 using af::ifft3InPlace;
-using af::ifft;
 using af::ifftInPlace;
 using af::moddims;
 using af::randu;
 using af::seq;
 using af::span;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
-TEST(fft, Invalid_Type)
-{
-    vector<char>   in(100,1);
+TEST(fft, Invalid_Type) {
+    vector<char> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
     dim4 dims(5 * 5 * 2 * 2);
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<char>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<char>::af_type));
 
     ASSERT_EQ(AF_ERR_TYPE, af_fft(&outArray, inArray, 1.0, 0));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TEST(fft2, Invalid_Array)
-{
+TEST(fft2, Invalid_Array) {
     if (noDoubleTests<float>()) return;
 
-    vector<float>   in(100,1);
+    vector<float> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
     dim4 dims(5 * 5 * 2 * 2);
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_fft2(&outArray, inArray, 1.0, 0, 0));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TEST(fft3, Invalid_Array)
-{
+TEST(fft3, Invalid_Array) {
     if (noDoubleTests<float>()) return;
 
-    vector<float>   in(100,1);
+    vector<float> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    dim4 dims(10,10,1,1);
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
+    dim4 dims(10, 10, 1, 1);
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_fft3(&outArray, inArray, 1.0, 0, 0, 0));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TEST(ifft2, Invalid_Array)
-{
+TEST(ifft2, Invalid_Array) {
     if (noDoubleTests<float>()) return;
 
-    vector<float>   in(100,1);
+    vector<float> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    dim4 dims(100,1,1,1);
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
+    dim4 dims(100, 1, 1, 1);
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_ifft2(&outArray, inArray, 0.01, 0, 0));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TEST(ifft3, Invalid_Array)
-{
+TEST(ifft3, Invalid_Array) {
     if (noDoubleTests<float>()) return;
 
-    vector<float>   in(100,1);
+    vector<float> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    dim4 dims(10,10,1,1);
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<float>::af_type));
+    dim4 dims(10, 10, 1, 1);
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in.front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_EQ(AF_ERR_SIZE, af_ifft3(&outArray, inArray, 0.01, 0, 0, 0));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename inType, typename outType, bool isInverse>
-void fftTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
-{
+void fftTest(string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0, dim_t pad2 = 0) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<inType> >       in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    dim4 dims       = numDims[0];
-    af_array outArray   = 0;
-    af_array inArray    = 0;
+    dim4 dims         = numDims[0];
+    af_array outArray = 0;
+    af_array inArray  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
 
-    if (isInverse){
+    if (isInverse) {
         switch (dims.ndims()) {
-            case 1 : ASSERT_SUCCESS(af_ifft (&outArray, inArray, 1.0, pad0));              break;
-            case 2 : ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
-            case 3 : ASSERT_SUCCESS(af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
-            default: throw std::runtime_error("This error shouldn't happen, pls check");
+            case 1:
+                ASSERT_SUCCESS(af_ifft(&outArray, inArray, 1.0, pad0));
+                break;
+            case 2:
+                ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));
+                break;
+            case 3:
+                ASSERT_SUCCESS(
+                    af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));
+                break;
+            default:
+                throw std::runtime_error(
+                    "This error shouldn't happen, pls check");
         }
     } else {
-        switch(dims.ndims()) {
-            case 1 : ASSERT_SUCCESS(af_fft (&outArray, inArray, 1.0, pad0));               break;
-            case 2 : ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
-            case 3 : ASSERT_SUCCESS(af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
-            default: throw std::runtime_error("This error shouldn't happen, pls check");
+        switch (dims.ndims()) {
+            case 1:
+                ASSERT_SUCCESS(af_fft(&outArray, inArray, 1.0, pad0));
+                break;
+            case 2:
+                ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));
+                break;
+            case 3:
+                ASSERT_SUCCESS(
+                    af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));
+                break;
+            default:
+                throw std::runtime_error(
+                    "This error shouldn't happen, pls check");
         }
     }
 
-    size_t out_size = tests[0].size();
-    outType *outData= new outType[out_size];
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+    size_t out_size  = tests[0].size();
+    outType *outData = new outType[out_size];
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
     vector<outType> goldBar(tests[0].begin(), tests[0].end());
 
     size_t test_size = 0;
-    switch(dims.ndims()) {
-        case 1  : test_size = dims[0]/2+1;                       break;
-        case 2  : test_size = dims[1] * (dims[0]/2+1);           break;
-        case 3  : test_size = dims[2] * dims[1] * (dims[0]/2+1); break;
-        default : test_size = dims[0]/2+1;                       break;
+    switch (dims.ndims()) {
+        case 1: test_size = dims[0] / 2 + 1; break;
+        case 2: test_size = dims[1] * (dims[0] / 2 + 1); break;
+        case 3: test_size = dims[2] * dims[1] * (dims[0] / 2 + 1); break;
+        default: test_size = dims[0] / 2 + 1; break;
     }
     outType output_scale = (outType)(isInverse ? test_size : 1);
-    for (size_t elIter=0; elIter<test_size; ++elIter) {
-        bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
-        ASSERT_EQ(true, isUnderTolerance)<<
-            "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < test_size; ++elIter) {
+        bool isUnderTolerance = abs(goldBar[elIter] - outData[elIter]) < 0.001;
+        ASSERT_EQ(true, isUnderTolerance)
+            << "Expected value=" << goldBar[elIter]
+            << "\t Actual Value=" << (output_scale * outData[elIter])
+            << " at: " << elIter << endl;
     }
 
     // cleanup
@@ -188,121 +207,173 @@ void fftTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-#define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, ...)  \
-    TEST(func, name)                                                \
-    {                                                               \
-        fftTest<in_t, out_t, is_inverse>(__VA_ARGS__);              \
-    }
+#define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, ...) \
+    TEST(func, name) { fftTest<in_t, out_t, is_inverse>(__VA_ARGS__); }
 
 // Real to complex transforms
-INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
-INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
-INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
-INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
-INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
-INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
+INSTANTIATE_TEST(fft, R2C_Float, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft_r2c.test"));
+INSTANTIATE_TEST(fft, R2C_Double, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft_r2c.test"));
+INSTANTIATE_TEST(fft2, R2C_Float, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft2_r2c.test"));
+INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft2_r2c.test"));
+INSTANTIATE_TEST(fft3, R2C_Float, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft3_r2c.test"));
+INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft3_r2c.test"));
 
 // complex to complex transforms
-INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
-INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
-INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
-INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
-INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
-INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
+INSTANTIATE_TEST(fft, C2C_Float, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft_c2c.test"));
+INSTANTIATE_TEST(fft, C2C_Double, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft_c2c.test"));
+INSTANTIATE_TEST(fft2, C2C_Float, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft2_c2c.test"));
+INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft2_c2c.test"));
+INSTANTIATE_TEST(fft3, C2C_Float, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft3_c2c.test"));
+INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft3_c2c.test"));
 
 // Factors 7, 11, 13
-INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
-INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
-
-INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
-INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+INSTANTIATE_TEST(fft, R2C_Float_7_11_13, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft_r2c_7_11_13.test"));
+INSTANTIATE_TEST(fft, R2C_Double_7_11_13, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft_r2c_7_11_13.test"));
+INSTANTIATE_TEST(fft2, R2C_Float_7_11_13, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft2_r2c_7_11_13.test"));
+INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft2_r2c_7_11_13.test"));
+INSTANTIATE_TEST(fft3, R2C_Float_7_11_13, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft3_r2c_7_11_13.test"));
+INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft3_r2c_7_11_13.test"));
+
+INSTANTIATE_TEST(fft, C2C_Float_7_11_13, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft_c2c_7_11_13.test"));
+INSTANTIATE_TEST(fft, C2C_Double_7_11_13, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft_c2c_7_11_13.test"));
+INSTANTIATE_TEST(fft2, C2C_Float_7_11_13, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft2_c2c_7_11_13.test"));
+INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft2_c2c_7_11_13.test"));
+INSTANTIATE_TEST(fft3, C2C_Float_7_11_13, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft3_c2c_7_11_13.test"));
+INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft3_c2c_7_11_13.test"));
 
 // transforms on padded and truncated arrays
-INSTANTIATE_TEST(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-INSTANTIATE_TEST(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+INSTANTIATE_TEST(fft2, R2C_Float_Trunc, false, float, cfloat,
+                 string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16, 16);
+INSTANTIATE_TEST(fft2, R2C_Double_Trunc, false, double, cdouble,
+                 string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16, 16);
 
-INSTANTIATE_TEST(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
-INSTANTIATE_TEST(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+INSTANTIATE_TEST(fft2, C2C_Float_Pad, false, cfloat, cfloat,
+                 string(TEST_DIR "/signal/fft2_c2c_pad.test"), 16, 16);
+INSTANTIATE_TEST(fft2, C2C_Double_Pad, false, cdouble, cdouble,
+                 string(TEST_DIR "/signal/fft2_c2c_pad.test"), 16, 16);
 
 // inverse transforms
 // complex to complex transforms
-INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
-INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
-INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
-INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
-INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
-INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
-
+INSTANTIATE_TEST(ifft, C2C_Float, true, cfloat, cfloat,
+                 string(TEST_DIR "/signal/ifft_c2c.test"));
+INSTANTIATE_TEST(ifft, C2C_Double, true, cdouble, cdouble,
+                 string(TEST_DIR "/signal/ifft_c2c.test"));
+INSTANTIATE_TEST(ifft2, C2C_Float, true, cfloat, cfloat,
+                 string(TEST_DIR "/signal/ifft2_c2c.test"));
+INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble,
+                 string(TEST_DIR "/signal/ifft2_c2c.test"));
+INSTANTIATE_TEST(ifft3, C2C_Float, true, cfloat, cfloat,
+                 string(TEST_DIR "/signal/ifft3_c2c.test"));
+INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble,
+                 string(TEST_DIR "/signal/ifft3_c2c.test"));
 
 template<typename inType, typename outType, int rank, bool isInverse>
-void fftBatchTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
-{
+void fftBatchTest(string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0,
+                  dim_t pad2 = 0) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<inType> >       in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    dim4 dims       = numDims[0];
-    af_array outArray   = 0;
-    af_array inArray    = 0;
-
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
-
-    if(isInverse) {
-        switch(rank) {
-            case 1 : ASSERT_SUCCESS(af_ifft (&outArray, inArray, 1.0, pad0));              break;
-            case 2 : ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
-            case 3 : ASSERT_SUCCESS(af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
-            default: throw std::runtime_error("This error shouldn't happen, pls check");
+    dim4 dims         = numDims[0];
+    af_array outArray = 0;
+    af_array inArray  = 0;
+
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
+
+    if (isInverse) {
+        switch (rank) {
+            case 1:
+                ASSERT_SUCCESS(af_ifft(&outArray, inArray, 1.0, pad0));
+                break;
+            case 2:
+                ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));
+                break;
+            case 3:
+                ASSERT_SUCCESS(
+                    af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));
+                break;
+            default:
+                throw std::runtime_error(
+                    "This error shouldn't happen, pls check");
         }
     } else {
-        switch(rank) {
-            case 1 : ASSERT_SUCCESS(af_fft (&outArray, inArray, 1.0, pad0));               break;
-            case 2 : ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
-            case 3 : ASSERT_SUCCESS(af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
-            default: throw std::runtime_error("This error shouldn't happen, pls check");
+        switch (rank) {
+            case 1:
+                ASSERT_SUCCESS(af_fft(&outArray, inArray, 1.0, pad0));
+                break;
+            case 2:
+                ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));
+                break;
+            case 3:
+                ASSERT_SUCCESS(
+                    af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));
+                break;
+            default:
+                throw std::runtime_error(
+                    "This error shouldn't happen, pls check");
         }
     }
 
-    size_t out_size = tests[0].size();
-    outType *outData= new outType[out_size];
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+    size_t out_size  = tests[0].size();
+    outType *outData = new outType[out_size];
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
     vector<outType> goldBar(tests[0].begin(), tests[0].end());
 
-    size_t test_size = 0;
+    size_t test_size   = 0;
     size_t batch_count = dims[rank];
-    switch(rank) {
-        case 1  : test_size = dims[0]/2+1;                       break;
-        case 2  : test_size = dims[1] * (dims[0]/2+1);           break;
-        case 3  : test_size = dims[2] * dims[1] * (dims[0]/2+1); break;
-        default : test_size = dims[0]/2+1;                       break;
+    switch (rank) {
+        case 1: test_size = dims[0] / 2 + 1; break;
+        case 2: test_size = dims[1] * (dims[0] / 2 + 1); break;
+        case 3: test_size = dims[2] * dims[1] * (dims[0] / 2 + 1); break;
+        default: test_size = dims[0] / 2 + 1; break;
     }
 
     size_t batch_stride = 1;
-    for(int i=0; i<rank; ++i) batch_stride *= dims[i];
+    for (int i = 0; i < rank; ++i) batch_stride *= dims[i];
 
     outType output_scale = (outType)(isInverse ? test_size : 1);
-    for(size_t batchId=0; batchId<batch_count; ++batchId) {
-        size_t off = batchId*batch_stride;
-        for (size_t elIter=0; elIter<test_size; ++elIter) {
-            bool isUnderTolerance = abs(goldBar[elIter+off]-outData[elIter+off])<0.001;
-            ASSERT_EQ(true, isUnderTolerance)<<"Batch id = "<<batchId<<
-                "; Expected value="<<goldBar[elIter+off] <<"\t Actual Value="<<
-                (output_scale*outData[elIter+off]) << " at: " << elIter<< endl;
+    for (size_t batchId = 0; batchId < batch_count; ++batchId) {
+        size_t off = batchId * batch_stride;
+        for (size_t elIter = 0; elIter < test_size; ++elIter) {
+            bool isUnderTolerance =
+                abs(goldBar[elIter + off] - outData[elIter + off]) < 0.001;
+            ASSERT_EQ(true, isUnderTolerance)
+                << "Batch id = " << batchId
+                << "; Expected value=" << goldBar[elIter + off]
+                << "\t Actual Value=" << (output_scale * outData[elIter + off])
+                << " at: " << elIter << endl;
         }
     }
 
@@ -313,45 +384,59 @@ void fftBatchTest(string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
 }
 
 #define INSTANTIATE_BATCH_TEST(func, name, rank, is_inverse, in_t, out_t, ...) \
-    TEST(func, name##_Batch)                                                   \
-    {                                                                          \
+    TEST(func, name##_Batch) {                                                 \
         fftBatchTest<in_t, out_t, rank, is_inverse>(__VA_ARGS__);              \
     }
 
 // real to complex transforms
-INSTANTIATE_BATCH_TEST(fft , R2C_Float, 1, false, float, cfloat, string(TEST_DIR"/signal/fft_r2c_batch.test") );
-INSTANTIATE_BATCH_TEST(fft2, R2C_Float, 2, false, float, cfloat, string(TEST_DIR"/signal/fft2_r2c_batch.test"));
-INSTANTIATE_BATCH_TEST(fft3, R2C_Float, 3, false, float, cfloat, string(TEST_DIR"/signal/fft3_r2c_batch.test"));
+INSTANTIATE_BATCH_TEST(fft, R2C_Float, 1, false, float, cfloat,
+                       string(TEST_DIR "/signal/fft_r2c_batch.test"));
+INSTANTIATE_BATCH_TEST(fft2, R2C_Float, 2, false, float, cfloat,
+                       string(TEST_DIR "/signal/fft2_r2c_batch.test"));
+INSTANTIATE_BATCH_TEST(fft3, R2C_Float, 3, false, float, cfloat,
+                       string(TEST_DIR "/signal/fft3_r2c_batch.test"));
 
 // complex to complex transforms
-INSTANTIATE_BATCH_TEST(fft , C2C_Float, 1, false, cfloat, cfloat, string(TEST_DIR"/signal/fft_c2c_batch.test") );
-INSTANTIATE_BATCH_TEST(fft2, C2C_Float, 2, false, cfloat, cfloat, string(TEST_DIR"/signal/fft2_c2c_batch.test"));
-INSTANTIATE_BATCH_TEST(fft3, C2C_Float, 3, false, cfloat, cfloat, string(TEST_DIR"/signal/fft3_c2c_batch.test"));
+INSTANTIATE_BATCH_TEST(fft, C2C_Float, 1, false, cfloat, cfloat,
+                       string(TEST_DIR "/signal/fft_c2c_batch.test"));
+INSTANTIATE_BATCH_TEST(fft2, C2C_Float, 2, false, cfloat, cfloat,
+                       string(TEST_DIR "/signal/fft2_c2c_batch.test"));
+INSTANTIATE_BATCH_TEST(fft3, C2C_Float, 3, false, cfloat, cfloat,
+                       string(TEST_DIR "/signal/fft3_c2c_batch.test"));
 
 // inverse transforms
 // complex to complex transforms
-INSTANTIATE_BATCH_TEST(ifft , C2C_Float, 1, true, cfloat, cfloat, string(TEST_DIR"/signal/ifft_c2c_batch.test") );
-INSTANTIATE_BATCH_TEST(ifft2, C2C_Float, 2, true, cfloat, cfloat, string(TEST_DIR"/signal/ifft2_c2c_batch.test"));
-INSTANTIATE_BATCH_TEST(ifft3, C2C_Float, 3, true, cfloat, cfloat, string(TEST_DIR"/signal/ifft3_c2c_batch.test"));
+INSTANTIATE_BATCH_TEST(ifft, C2C_Float, 1, true, cfloat, cfloat,
+                       string(TEST_DIR "/signal/ifft_c2c_batch.test"));
+INSTANTIATE_BATCH_TEST(ifft2, C2C_Float, 2, true, cfloat, cfloat,
+                       string(TEST_DIR "/signal/ifft2_c2c_batch.test"));
+INSTANTIATE_BATCH_TEST(ifft3, C2C_Float, 3, true, cfloat, cfloat,
+                       string(TEST_DIR "/signal/ifft3_c2c_batch.test"));
 
 // transforms on padded and truncated arrays
-INSTANTIATE_BATCH_TEST(fft2,  R2C_Float_Trunc, 2, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc_batch.test"), 16, 16);
-INSTANTIATE_BATCH_TEST(fft2, R2C_Double_Trunc, 2, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc_batch.test"), 16, 16);
-INSTANTIATE_BATCH_TEST(fft2,  C2C_Float_Pad, 2, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad_batch.test"), 16, 16);
-INSTANTIATE_BATCH_TEST(fft2, C2C_Double_Pad, 2, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad_batch.test"), 16, 16);
-
+INSTANTIATE_BATCH_TEST(fft2, R2C_Float_Trunc, 2, false, float, cfloat,
+                       string(TEST_DIR "/signal/fft2_r2c_trunc_batch.test"), 16,
+                       16);
+INSTANTIATE_BATCH_TEST(fft2, R2C_Double_Trunc, 2, false, double, cdouble,
+                       string(TEST_DIR "/signal/fft2_r2c_trunc_batch.test"), 16,
+                       16);
+INSTANTIATE_BATCH_TEST(fft2, C2C_Float_Pad, 2, false, cfloat, cfloat,
+                       string(TEST_DIR "/signal/fft2_c2c_pad_batch.test"), 16,
+                       16);
+INSTANTIATE_BATCH_TEST(fft2, C2C_Double_Pad, 2, false, cdouble, cdouble,
+                       string(TEST_DIR "/signal/fft2_c2c_pad_batch.test"), 16,
+                       16);
 
 /////////////////////////////////////// CPP ////////////////////////////////////
 //
 template<typename inType, typename outType, bool isInverse>
-void cppFFTTest(string pTestFile)
-{
+void cppFFTTest(string pTestFile) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<inType> >       in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
@@ -359,45 +444,45 @@ void cppFFTTest(string pTestFile)
     array signal(dims, &(in[0].front()));
     array output;
 
-    if (isInverse){
+    if (isInverse) {
         output = ifft3Norm(signal, 1.0);
     } else {
         output = fft3Norm(signal, 1.0);
     }
 
     size_t out_size = tests[0].size();
-    cfloat *outData= new cfloat[out_size];
-    output.host((void*)outData);
+    cfloat *outData = new cfloat[out_size];
+    output.host((void *)outData);
 
     vector<cfloat> goldBar(tests[0].begin(), tests[0].end());
 
     size_t test_size = 0;
-    switch(dims.ndims()) {
-        case 1  : test_size = dims[0]/2+1;                       break;
-        case 2  : test_size = dims[1] * (dims[0]/2+1);           break;
-        case 3  : test_size = dims[2] * dims[1] * (dims[0]/2+1); break;
-        default : test_size = dims[0]/2+1;                       break;
+    switch (dims.ndims()) {
+        case 1: test_size = dims[0] / 2 + 1; break;
+        case 2: test_size = dims[1] * (dims[0] / 2 + 1); break;
+        case 3: test_size = dims[2] * dims[1] * (dims[0] / 2 + 1); break;
+        default: test_size = dims[0] / 2 + 1; break;
     }
     outType output_scale = (outType)(isInverse ? test_size : 1);
-    for (size_t elIter=0; elIter<test_size; ++elIter) {
-        bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
-        ASSERT_EQ(true, isUnderTolerance)<<
-            "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < test_size; ++elIter) {
+        bool isUnderTolerance = abs(goldBar[elIter] - outData[elIter]) < 0.001;
+        ASSERT_EQ(true, isUnderTolerance)
+            << "Expected value=" << goldBar[elIter]
+            << "\t Actual Value=" << (output_scale * outData[elIter])
+            << " at: " << elIter << endl;
     }
     // cleanup
     delete[] outData;
 }
 
 template<typename inType, typename outType, bool isInverse>
-void cppDFTTest(string pTestFile)
-{
+void cppDFTTest(string pTestFile) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<inType> >       in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
@@ -405,80 +490,82 @@ void cppDFTTest(string pTestFile)
     array signal(dims, &(in[0].front()));
     array output;
 
-    if (isInverse){
+    if (isInverse) {
         output = idft(signal);
     } else {
         output = dft(signal);
     }
 
     size_t out_size = tests[0].size();
-    cfloat *outData= new cfloat[out_size];
-    output.host((void*)outData);
+    cfloat *outData = new cfloat[out_size];
+    output.host((void *)outData);
 
     vector<cfloat> goldBar(tests[0].begin(), tests[0].end());
 
     size_t test_size = 0;
-    switch(dims.ndims()) {
-        case 1  : test_size = dims[0]/2+1;                       break;
-        case 2  : test_size = dims[1] * (dims[0]/2+1);           break;
-        case 3  : test_size = dims[2] * dims[1] * (dims[0]/2+1); break;
-        default : test_size = dims[0]/2+1;                       break;
+    switch (dims.ndims()) {
+        case 1: test_size = dims[0] / 2 + 1; break;
+        case 2: test_size = dims[1] * (dims[0] / 2 + 1); break;
+        case 3: test_size = dims[2] * dims[1] * (dims[0] / 2 + 1); break;
+        default: test_size = dims[0] / 2 + 1; break;
     }
     outType output_scale = (outType)(isInverse ? test_size : 1);
-    for (size_t elIter=0; elIter<test_size; ++elIter) {
-        bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
-        ASSERT_EQ(true, isUnderTolerance)<<
-            "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < test_size; ++elIter) {
+        bool isUnderTolerance = abs(goldBar[elIter] - outData[elIter]) < 0.001;
+        ASSERT_EQ(true, isUnderTolerance)
+            << "Expected value=" << goldBar[elIter]
+            << "\t Actual Value=" << (output_scale * outData[elIter])
+            << " at: " << elIter << endl;
     }
     // cleanup
     delete[] outData;
 }
 
-
-TEST(fft3, CPP)
-{
-    cppFFTTest<cfloat, cfloat, false>(string(TEST_DIR"/signal/fft3_c2c.test"));
+TEST(fft3, CPP) {
+    cppFFTTest<cfloat, cfloat, false>(string(TEST_DIR "/signal/fft3_c2c.test"));
 }
 
-TEST(ifft3, CPP)
-{
-    cppFFTTest<cfloat, cfloat, true>(string(TEST_DIR"/signal/ifft3_c2c.test"));
+TEST(ifft3, CPP) {
+    cppFFTTest<cfloat, cfloat, true>(string(TEST_DIR "/signal/ifft3_c2c.test"));
 }
 
-TEST(fft3, RandomData)
-{
+TEST(fft3, RandomData) {
     array a = randu(31, 31, 31);
     array b = fft3(a, 64, 64, 64);
     array c = ifft3(b);
 
     dim4 aDims = a.dims();
     dim4 cDims = c.dims();
-    dim4 aStrides(1, aDims[0], aDims[0]*aDims[1], aDims[0]*aDims[1]*aDims[2]);
-    dim4 cStrides(1, cDims[0], cDims[0]*cDims[1], cDims[0]*cDims[1]*cDims[2]);
-
-    float* gold = new float[a.elements()];
-    float* out  = new float[2*c.elements()];
-
-    a.host((void*)gold);
-    c.host((void*)out);
-
-    for (int k=0; k<(int)aDims[2]; ++k) {
-        int gkOff = k*aStrides[2];
-        int okOff = k*cStrides[2];
-        for (int j=0; j<(int)aDims[1]; ++j) {
-            int gjOff = j*aStrides[1];
-            int ojOff = j*cStrides[1];
-            for (int i=0; i<(int)aDims[0]; ++i) {
-                int giOff = i*aStrides[0];
-                int oiOff = i*cStrides[0];
+    dim4 aStrides(1, aDims[0], aDims[0] * aDims[1],
+                  aDims[0] * aDims[1] * aDims[2]);
+    dim4 cStrides(1, cDims[0], cDims[0] * cDims[1],
+                  cDims[0] * cDims[1] * cDims[2]);
+
+    float *gold = new float[a.elements()];
+    float *out  = new float[2 * c.elements()];
+
+    a.host((void *)gold);
+    c.host((void *)out);
+
+    for (int k = 0; k < (int)aDims[2]; ++k) {
+        int gkOff = k * aStrides[2];
+        int okOff = k * cStrides[2];
+        for (int j = 0; j < (int)aDims[1]; ++j) {
+            int gjOff = j * aStrides[1];
+            int ojOff = j * cStrides[1];
+            for (int i = 0; i < (int)aDims[0]; ++i) {
+                int giOff = i * aStrides[0];
+                int oiOff = i * cStrides[0];
 
                 int gi = gkOff + gjOff + giOff;
                 int oi = okOff + ojOff + oiOff;
 
-                bool isUnderTolerance = std::abs(gold[gi]-out[2*oi])<0.001;
-                ASSERT_EQ(true, isUnderTolerance)<< "Expected value="<<
-                    gold[gi] <<"\t Actual Value="<< out[2*oi] << " at: " <<gi<< endl;
+                bool isUnderTolerance =
+                    std::abs(gold[gi] - out[2 * oi]) < 0.001;
+                ASSERT_EQ(true, isUnderTolerance)
+                    << "Expected value=" << gold[gi]
+                    << "\t Actual Value=" << out[2 * oi] << " at: " << gi
+                    << endl;
             }
         }
     }
@@ -487,38 +574,31 @@ TEST(fft3, RandomData)
     delete[] out;
 }
 
-TEST(dft, CPP)
-{
-    cppDFTTest<cfloat, cfloat, false>(string(TEST_DIR"/signal/fft_c2c.test"));
+TEST(dft, CPP) {
+    cppDFTTest<cfloat, cfloat, false>(string(TEST_DIR "/signal/fft_c2c.test"));
 }
 
-TEST(idft, CPP)
-{
-    cppDFTTest<cfloat, cfloat, true>(string(TEST_DIR"/signal/ifft_c2c.test"));
+TEST(idft, CPP) {
+    cppDFTTest<cfloat, cfloat, true>(string(TEST_DIR "/signal/ifft_c2c.test"));
 }
 
-TEST(dft2, CPP)
-{
-    cppDFTTest<cfloat, cfloat, false>(string(TEST_DIR"/signal/fft2_c2c.test"));
+TEST(dft2, CPP) {
+    cppDFTTest<cfloat, cfloat, false>(string(TEST_DIR "/signal/fft2_c2c.test"));
 }
 
-TEST(idft2, CPP)
-{
-    cppDFTTest<cfloat, cfloat, true>(string(TEST_DIR"/signal/ifft2_c2c.test"));
+TEST(idft2, CPP) {
+    cppDFTTest<cfloat, cfloat, true>(string(TEST_DIR "/signal/ifft2_c2c.test"));
 }
 
-TEST(dft3, CPP)
-{
-    cppDFTTest<cfloat, cfloat, false>(string(TEST_DIR"/signal/fft3_c2c.test"));
+TEST(dft3, CPP) {
+    cppDFTTest<cfloat, cfloat, false>(string(TEST_DIR "/signal/fft3_c2c.test"));
 }
 
-TEST(idft3, CPP)
-{
-    cppDFTTest<cfloat, cfloat, true>(string(TEST_DIR"/signal/ifft3_c2c.test"));
+TEST(idft3, CPP) {
+    cppDFTTest<cfloat, cfloat, true>(string(TEST_DIR "/signal/ifft3_c2c.test"));
 }
 
-TEST(fft, CPP_4D)
-{
+TEST(fft, CPP_4D) {
     array a = randu(1024, 1024);
     array b = fft(a);
 
@@ -536,8 +616,7 @@ TEST(fft, CPP_4D)
     freeHost(h_B);
 }
 
-TEST(ifft, CPP_4D)
-{
+TEST(ifft, CPP_4D) {
     array a = randu(1024, 1024, c32);
     array b = ifft(a);
 
@@ -555,15 +634,12 @@ TEST(ifft, CPP_4D)
     freeHost(h_B);
 }
 
-TEST(fft, GFOR)
-{
+TEST(fft, GFOR) {
     array a = randu(1024, 1024);
     array b = constant(0, 1024, 1024, c32);
     array c = fft(a);
 
-    gfor(seq ii, a.dims(1)) {
-        b(span, ii) = fft(a(span, ii));
-    }
+    gfor(seq ii, a.dims(1)) { b(span, ii) = fft(a(span, ii)); }
 
     cfloat *h_b = b.host<cfloat>();
     cfloat *h_c = c.host<cfloat>();
@@ -576,15 +652,12 @@ TEST(fft, GFOR)
     freeHost(h_c);
 }
 
-TEST(fft2, GFOR)
-{
+TEST(fft2, GFOR) {
     array a = randu(1024, 1024, 4);
     array b = constant(0, 1024, 1024, 4, c32);
     array c = fft2(a);
 
-    gfor(seq ii, a.dims(2)) {
-        b(span, span, ii) = fft2(a(span, span, ii));
-    }
+    gfor(seq ii, a.dims(2)) { b(span, span, ii) = fft2(a(span, span, ii)); }
 
     cfloat *h_b = b.host<cfloat>();
     cfloat *h_c = c.host<cfloat>();
@@ -597,8 +670,7 @@ TEST(fft2, GFOR)
     freeHost(h_c);
 }
 
-TEST(fft3, GFOR)
-{
+TEST(fft3, GFOR) {
     array a = randu(32, 32, 32, 4);
     array b = constant(0, 32, 32, 32, 4, c32);
     array c = fft3(a);
@@ -618,8 +690,7 @@ TEST(fft3, GFOR)
     freeHost(h_c);
 }
 
-TEST(fft, InPlace)
-{
+TEST(fft, InPlace) {
     array a = randu(1024, 1024, c32);
     array b = fft(a);
     fftInPlace(a);
@@ -627,8 +698,7 @@ TEST(fft, InPlace)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(ifft, InPlace)
-{
+TEST(ifft, InPlace) {
     array a = randu(1024, 1024, c32);
     array b = ifft(a);
     ifftInPlace(a);
@@ -639,8 +709,7 @@ TEST(ifft, InPlace)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(fft2, InPlace)
-{
+TEST(fft2, InPlace) {
     array a = randu(1024, 1024, c32);
     array b = fft2(a);
     fft2InPlace(a);
@@ -648,8 +717,7 @@ TEST(fft2, InPlace)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(ifft2, InPlace)
-{
+TEST(ifft2, InPlace) {
     array a = randu(1024, 1024, c32);
     array b = ifft2(a);
     ifft2InPlace(a);
@@ -657,8 +725,7 @@ TEST(ifft2, InPlace)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(fft3, InPlace)
-{
+TEST(fft3, InPlace) {
     array a = randu(32, 32, 32, c32);
     array b = fft3(a);
     fft3InPlace(a);
@@ -666,8 +733,7 @@ TEST(fft3, InPlace)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(ifft3, InPlace)
-{
+TEST(ifft3, InPlace) {
     array a = randu(32, 32, 32, c32);
     array b = ifft3(a);
     ifft3InPlace(a);
@@ -675,8 +741,7 @@ TEST(ifft3, InPlace)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-void fft2InPlaceFunc()
-{
+void fft2InPlaceFunc() {
     array a = randu(1024, 1024, c32);
     array b = fft2(a);
     fft2InPlace(a);
@@ -684,25 +749,23 @@ void fft2InPlaceFunc()
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-using af::setDevice;
 using af::getDevice;
 using af::getDeviceCount;
+using af::setDevice;
 
-#define DEVICE_ITERATE(func) do {                                           \
-    const char* ENV = getenv("AF_MULTI_GPU_TESTS");                         \
-    if(ENV && ENV[0] == '0') {                                              \
-        func;                                                               \
-    } else {                                                                \
-        int oldDevice = getDevice();                                        \
-        for(int i = 0; i < getDeviceCount(); i++) {                         \
-            setDevice(i);                                                   \
-            func;                                                           \
-        }                                                                   \
-        setDevice(oldDevice);                                               \
-    }                                                                       \
-} while(0);
-
-TEST(FFT2, MultiGPUInPlaceSquare_CPP)
-{
-    DEVICE_ITERATE((fft2InPlaceFunc()));
-}
+#define DEVICE_ITERATE(func)                             \
+    do {                                                 \
+        const char *ENV = getenv("AF_MULTI_GPU_TESTS");  \
+        if (ENV && ENV[0] == '0') {                      \
+            func;                                        \
+        } else {                                         \
+            int oldDevice = getDevice();                 \
+            for (int i = 0; i < getDeviceCount(); i++) { \
+                setDevice(i);                            \
+                func;                                    \
+            }                                            \
+            setDevice(oldDevice);                        \
+        }                                                \
+    } while (0);
+
+TEST(FFT2, MultiGPUInPlaceSquare_CPP) { DEVICE_ITERATE((fft2InPlaceFunc())); }
diff --git a/test/fft_large.cpp b/test/fft_large.cpp
index 2b85d61d3e..137d55b32a 100644
--- a/test/fft_large.cpp
+++ b/test/fft_large.cpp
@@ -7,27 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <stdexcept>
 #include <string>
 #include <vector>
-#include <stdexcept>
-#include <testHelpers.hpp>
 
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::cfloat;
 using af::fft2;
 using af::ifft2;
 using af::moddims;
 using af::randu;
+using std::endl;
+using std::string;
+using std::vector;
 
-TEST(fft2, CPP_4D)
-{
+TEST(fft2, CPP_4D) {
     array a = randu(1024, 1024, 32);
     array b = fft2(a);
 
@@ -45,8 +44,7 @@ TEST(fft2, CPP_4D)
     af_free_host(h_B);
 }
 
-TEST(ifft2, CPP_4D)
-{
+TEST(ifft2, CPP_4D) {
     array a = randu(1024, 1024, 32, c32);
     array b = ifft2(a);
 
diff --git a/test/fft_real.cpp b/test/fft_real.cpp
index 190b2d4f94..456d09ff40 100644
--- a/test/fft_real.cpp
+++ b/test/fft_real.cpp
@@ -7,61 +7,57 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <stdexcept>
 #include <string>
 #include <vector>
-#include <stdexcept>
-#include <testHelpers.hpp>
 
-using std::string;
-using std::vector;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::fft;
-using af::fftNorm;
 using af::fft2Norm;
 using af::fft3Norm;
 using af::fftC2R;
+using af::fftNorm;
 using af::fftR2C;
 using af::randu;
+using std::abs;
+using std::string;
+using std::vector;
 
 template<typename T>
-class FFT_REAL : public ::testing::Test
-{
-};
+class FFT_REAL : public ::testing::Test {};
 
 typedef ::testing::Types<cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(FFT_REAL, TestTypes);
 
 template<int rank>
-array fft(const array &in, double norm)
-{
-    switch(rank) {
-    case 1: return fftNorm(in, norm);
-    case 2: return fft2Norm(in, norm);
-    case 3: return fft3Norm(in, norm);
-    default: return in;
+array fft(const array &in, double norm) {
+    switch (rank) {
+        case 1: return fftNorm(in, norm);
+        case 2: return fft2Norm(in, norm);
+        case 3: return fft3Norm(in, norm);
+        default: return in;
     }
 }
 
 #define MY_ASSERT_NEAR(aa, bb, cc) ASSERT_NEAR(abs(aa), abs(bb), (cc))
 
 template<typename Tc, int rank>
-void fft_real(dim4 dims)
-{
+void fft_real(dim4 dims) {
     typedef typename dtype_traits<Tc>::base_type Tr;
     if (noDoubleTests<Tr>()) return;
 
     dtype ty = (dtype)dtype_traits<Tr>::af_type;
-    array a = randu(dims, ty);
+    array a  = randu(dims, ty);
 
     bool is_odd = dims[0] & 1;
 
@@ -69,12 +65,11 @@ void fft_real(dim4 dims)
 
     double norm = 1;
     for (int i = 0; i < rank; i++) norm *= dims[i];
-    norm = 1/norm;
+    norm = 1 / norm;
 
     array as = fftR2C<rank>(a, norm);
     array af = fft<rank>(a, norm);
 
-
     vector<Tc> has(as.elements());
     vector<Tc> haf(af.elements());
 
@@ -83,7 +78,8 @@ void fft_real(dim4 dims)
 
     for (int j = 0; j < a.elements() / dims[0]; j++) {
         for (int i = 0; i < dim0; i++) {
-            MY_ASSERT_NEAR(haf[j * dims[0] + i], has[j * dim0 + i], 1E-2) << "at " << j * dims[0] + i;
+            MY_ASSERT_NEAR(haf[j * dims[0] + i], has[j * dim0 + i], 1E-2)
+                << "at " << j * dims[0] + i;
         }
     }
 
@@ -95,37 +91,17 @@ void fft_real(dim4 dims)
     a.host(&ha[0]);
     b.host(&hb[0]);
 
-    for (int j = 0; j < a.elements(); j++) {
-        ASSERT_NEAR(ha[j], hb[j], 1E-2);
-    }
+    for (int j = 0; j < a.elements(); j++) { ASSERT_NEAR(ha[j], hb[j], 1E-2); }
 }
 
-TYPED_TEST(FFT_REAL, Even1D)
-{
-    fft_real<TypeParam, 1>(dim4(1024, 256));
-}
+TYPED_TEST(FFT_REAL, Even1D) { fft_real<TypeParam, 1>(dim4(1024, 256)); }
 
-TYPED_TEST(FFT_REAL, Odd1D)
-{
-    fft_real<TypeParam, 1>(dim4(625, 256));
-}
+TYPED_TEST(FFT_REAL, Odd1D) { fft_real<TypeParam, 1>(dim4(625, 256)); }
 
-TYPED_TEST(FFT_REAL, Even2D)
-{
-    fft_real<TypeParam, 2>(dim4(1024, 256));
-}
+TYPED_TEST(FFT_REAL, Even2D) { fft_real<TypeParam, 2>(dim4(1024, 256)); }
 
-TYPED_TEST(FFT_REAL, Odd2D)
-{
-    fft_real<TypeParam, 2>(dim4(625, 256));
-}
+TYPED_TEST(FFT_REAL, Odd2D) { fft_real<TypeParam, 2>(dim4(625, 256)); }
 
-TYPED_TEST(FFT_REAL, Even3D)
-{
-    fft_real<TypeParam, 3>(dim4(32, 32, 32));
-}
+TYPED_TEST(FFT_REAL, Even3D) { fft_real<TypeParam, 3>(dim4(32, 32, 32)); }
 
-TYPED_TEST(FFT_REAL, Odd3D)
-{
-    fft_real<TypeParam, 3>(dim4(25, 32, 32));
-}
+TYPED_TEST(FFT_REAL, Odd3D) { fft_real<TypeParam, 3>(dim4(25, 32, 32)); }
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index 64eb3a79c0..165d6db605 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -7,41 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::endl;
-using std::vector;
-using std::string;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::randu;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class FFTConvolve : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class FFTConvolve : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class FFTConvolveLarge : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class FFTConvolveLarge : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<cfloat, cdouble, float, double, int, uint, char, uchar, intl, uintl> TestTypes;
+typedef ::testing::Types<cfloat, cdouble, float, double, int, uint, char, uchar,
+                         intl, uintl>
+    TestTypes;
 typedef ::testing::Types<float, double> TestTypesLarge;
 
 // register the type list
@@ -49,13 +49,12 @@ TYPED_TEST_CASE(FFTConvolve, TestTypes);
 TYPED_TEST_CASE(FFTConvolveLarge, TestTypesLarge);
 
 template<typename T, int baseDim>
-void fftconvolveTest(string pTestFile, bool expand)
-{
+void fftconvolveTest(string pTestFile, bool expand) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<T> >      in;
-    vector<vector<T> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -64,18 +63,24 @@ void fftconvolveTest(string pTestFile, bool expand)
     af_array signal   = 0;
     af_array filter   = 0;
     af_array outArray = 0;
-    af_dtype in_type =(af_dtype)dtype_traits<T>::af_type;
+    af_dtype in_type  = (af_dtype)dtype_traits<T>::af_type;
 
-    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()),
-                                          sDims.ndims(), sDims.get(), in_type));
-    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()),
-                                          fDims.ndims(), fDims.get(), in_type));
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(), in_type));
+    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()), fDims.ndims(),
+                                   fDims.get(), in_type));
 
     af_conv_mode mode = expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT;
-    switch(baseDim) {
-        case 1: ASSERT_SUCCESS(af_fft_convolve1(&outArray, signal, filter, mode)); break;
-        case 2: ASSERT_SUCCESS(af_fft_convolve2(&outArray, signal, filter, mode)); break;
-        case 3: ASSERT_SUCCESS(af_fft_convolve3(&outArray, signal, filter, mode)); break;
+    switch (baseDim) {
+        case 1:
+            ASSERT_SUCCESS(af_fft_convolve1(&outArray, signal, filter, mode));
+            break;
+        case 2:
+            ASSERT_SUCCESS(af_fft_convolve2(&outArray, signal, filter, mode));
+            break;
+        case 3:
+            ASSERT_SUCCESS(af_fft_convolve3(&outArray, signal, filter, mode));
+            break;
     }
 
     vector<T> currGoldBar = tests[0];
@@ -89,11 +94,9 @@ void fftconvolveTest(string pTestFile, bool expand)
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(
-            real(currGoldBar[elIter]),
-            real(outData[elIter])
-            , 1e-2)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(real(currGoldBar[elIter]), real(outData[elIter]), 1e-2)
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(outArray));
@@ -102,8 +105,8 @@ void fftconvolveTest(string pTestFile, bool expand)
 }
 
 template<typename T, int baseDim>
-void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expand)
-{
+void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch,
+                          bool expand) {
     if (noDoubleTests<T>()) return;
 
     using af::seq;
@@ -116,12 +119,10 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
         if (k < baseDim) {
             sd[k] = sDim;
             fd[k] = fDim;
-        }
-        else if (k == baseDim) {
+        } else if (k == baseDim) {
             sd[k] = sBatch;
             fd[k] = fBatch;
-        }
-        else {
+        } else {
             sd[k] = 1;
             fd[k] = 1;
         }
@@ -130,336 +131,328 @@ void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch, bool expan
     const dim4 signalDims(sd[0], sd[1], sd[2], sd[3]);
     const dim4 filterDims(fd[0], fd[1], fd[2], fd[3]);
 
-    array signal = randu(signalDims, (af_dtype) dtype_traits<T>::af_type);
-    array filter = randu(filterDims, (af_dtype) dtype_traits<T>::af_type);
+    array signal = randu(signalDims, (af_dtype)dtype_traits<T>::af_type);
+    array filter = randu(filterDims, (af_dtype)dtype_traits<T>::af_type);
 
-    array out = fftConvolve(signal, filter, expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT);
+    array out =
+        fftConvolve(signal, filter, expand ? AF_CONV_EXPAND : AF_CONV_DEFAULT);
 
     array gold;
-    switch(baseDim) {
-    case 1:
-        gold = real(ifft(fft(signal, fftDim) * fft(filter, fftDim)));
-        break;
-    case 2:
-        gold = real(ifft2(fft2(signal, fftDim, fftDim) * fft2(filter, fftDim, fftDim)));
-        break;
-    case 3:
-        gold = real(ifft3(fft3(signal, fftDim, fftDim, fftDim) * fft3(filter, fftDim, fftDim, fftDim)));
-        break;
-    default:
-        ASSERT_LT(baseDim, 4);
+    switch (baseDim) {
+        case 1:
+            gold = real(ifft(fft(signal, fftDim) * fft(filter, fftDim)));
+            break;
+        case 2:
+            gold = real(ifft2(fft2(signal, fftDim, fftDim) *
+                              fft2(filter, fftDim, fftDim)));
+            break;
+        case 3:
+            gold = real(ifft3(fft3(signal, fftDim, fftDim, fftDim) *
+                              fft3(filter, fftDim, fftDim, fftDim)));
+            break;
+        default: ASSERT_LT(baseDim, 4);
     }
 
     int cropMin = 0, cropMax = 0;
     if (expand) {
         cropMin = 0;
         cropMax = outDim - 1;
-    }
-    else {
-        cropMin = fDim/2;
-        cropMax = outDim - fDim/2 - 1;
+    } else {
+        cropMin = fDim / 2;
+        cropMax = outDim - fDim / 2 - 1;
     }
 
-    switch(baseDim) {
-    case 1:
-        gold = gold(seq(cropMin, cropMax));
-        break;
-    case 2:
-        gold = gold(seq(cropMin, cropMax), seq(cropMin, cropMax));
-        break;
-    case 3:
-        gold = gold(seq(cropMin, cropMax), seq(cropMin, cropMax), seq(cropMin, cropMax));
-        break;
+    switch (baseDim) {
+        case 1: gold = gold(seq(cropMin, cropMax)); break;
+        case 2:
+            gold = gold(seq(cropMin, cropMax), seq(cropMin, cropMax));
+            break;
+        case 3:
+            gold = gold(seq(cropMin, cropMax), seq(cropMin, cropMax),
+                        seq(cropMin, cropMax));
+            break;
     }
 
     ASSERT_ARRAYS_NEAR(gold, out, 5e-2);
 }
 
-TYPED_TEST(FFTConvolveLarge, VectorLargeSignalSmallFilter)
-{
+TYPED_TEST(FFTConvolveLarge, VectorLargeSignalSmallFilter) {
     fftconvolveTestLarge<TypeParam, 1>(32768, 25, 1, 1, true);
 }
 
-TYPED_TEST(FFTConvolveLarge, VectorLargeSignalLargeFilter)
-{
+TYPED_TEST(FFTConvolveLarge, VectorLargeSignalLargeFilter) {
     fftconvolveTestLarge<TypeParam, 1>(32768, 4095, 1, 1, true);
 }
 
-TYPED_TEST(FFTConvolveLarge, SameVectorLargeSignalSmallFilter)
-{
+TYPED_TEST(FFTConvolveLarge, SameVectorLargeSignalSmallFilter) {
     fftconvolveTestLarge<TypeParam, 1>(32768, 25, 1, 1, false);
 }
 
-TYPED_TEST(FFTConvolveLarge, SameVectorLargeSignalLargeFilter)
-{
+TYPED_TEST(FFTConvolveLarge, SameVectorLargeSignalLargeFilter) {
     fftconvolveTestLarge<TypeParam, 1>(32768, 4095, 1, 1, false);
 }
 
-TYPED_TEST(FFTConvolveLarge, RectangleLargeSignalSmallFilter)
-{
+TYPED_TEST(FFTConvolveLarge, RectangleLargeSignalSmallFilter) {
     fftconvolveTestLarge<TypeParam, 2>(1024, 5, 1, 1, true);
 }
 
-TYPED_TEST(FFTConvolveLarge, RectangleLargeSignalLargeFilter)
-{
+TYPED_TEST(FFTConvolveLarge, RectangleLargeSignalLargeFilter) {
     fftconvolveTestLarge<TypeParam, 2>(1024, 511, 1, 1, true);
 }
 
-TYPED_TEST(FFTConvolveLarge, SameRectangleLargeSignalSmallFilter)
-{
+TYPED_TEST(FFTConvolveLarge, SameRectangleLargeSignalSmallFilter) {
     fftconvolveTestLarge<TypeParam, 2>(1024, 5, 1, 1, false);
 }
 
-TYPED_TEST(FFTConvolveLarge, SameRectangleLargeSignalLargeFilter)
-{
+TYPED_TEST(FFTConvolveLarge, SameRectangleLargeSignalLargeFilter) {
     fftconvolveTestLarge<TypeParam, 2>(1024, 511, 1, 1, false);
 }
 
-TYPED_TEST(FFTConvolveLarge, CuboidLargeSignalSmallFilter)
-{
+TYPED_TEST(FFTConvolveLarge, CuboidLargeSignalSmallFilter) {
     fftconvolveTestLarge<TypeParam, 3>(64, 5, 1, 1, true);
 }
 
-TYPED_TEST(FFTConvolveLarge, CuboidLargeSignalLargeFilter)
-{
+TYPED_TEST(FFTConvolveLarge, CuboidLargeSignalLargeFilter) {
     fftconvolveTestLarge<TypeParam, 3>(64, 31, 1, 1, true);
 }
 
-TYPED_TEST(FFTConvolveLarge, SameCuboidLargeSignalSmallFilter)
-{
+TYPED_TEST(FFTConvolveLarge, SameCuboidLargeSignalSmallFilter) {
     fftconvolveTestLarge<TypeParam, 3>(64, 5, 1, 1, false);
 }
 
-TYPED_TEST(FFTConvolveLarge, SameCuboidLargeSignalLargeFilter)
-{
+TYPED_TEST(FFTConvolveLarge, SameCuboidLargeSignalLargeFilter) {
     fftconvolveTestLarge<TypeParam, 2>(64, 31, 1, 1, false);
 }
 
-TYPED_TEST(FFTConvolve, Vector)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector.test"), true);
+TYPED_TEST(FFTConvolve, Vector) {
+    fftconvolveTest<TypeParam, 1>(string(TEST_DIR "/convolve/vector.test"),
+                                  true);
 }
 
-TYPED_TEST(FFTConvolve, Rectangle)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle.test"), true);
+TYPED_TEST(FFTConvolve, Rectangle) {
+    fftconvolveTest<TypeParam, 2>(string(TEST_DIR "/convolve/rectangle.test"),
+                                  true);
 }
 
-TYPED_TEST(FFTConvolve, Cuboid)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid.test"), true);
+TYPED_TEST(FFTConvolve, Cuboid) {
+    fftconvolveTest<TypeParam, 3>(string(TEST_DIR "/convolve/cuboid.test"),
+                                  true);
 }
 
-TYPED_TEST(FFTConvolve, Vector_Many2One)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_many2one.test"), true);
+TYPED_TEST(FFTConvolve, Vector_Many2One) {
+    fftconvolveTest<TypeParam, 1>(
+        string(TEST_DIR "/convolve/vector_many2one.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Rectangle_Many2One)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_many2one.test"), true);
+TYPED_TEST(FFTConvolve, Rectangle_Many2One) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_many2one.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Cuboid_Many2One)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_many2one.test"), true);
+TYPED_TEST(FFTConvolve, Cuboid_Many2One) {
+    fftconvolveTest<TypeParam, 3>(
+        string(TEST_DIR "/convolve/cuboid_many2one.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Vector_Many2Many)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_many2many.test"), true);
+TYPED_TEST(FFTConvolve, Vector_Many2Many) {
+    fftconvolveTest<TypeParam, 1>(
+        string(TEST_DIR "/convolve/vector_many2many.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Rectangle_Many2Many)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_many2many.test"), true);
+TYPED_TEST(FFTConvolve, Rectangle_Many2Many) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_many2many.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Cuboid_Many2Many)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_many2many.test"), true);
+TYPED_TEST(FFTConvolve, Cuboid_Many2Many) {
+    fftconvolveTest<TypeParam, 3>(
+        string(TEST_DIR "/convolve/cuboid_many2many.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Vector_One2Many)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_one2many.test"), true);
+TYPED_TEST(FFTConvolve, Vector_One2Many) {
+    fftconvolveTest<TypeParam, 1>(
+        string(TEST_DIR "/convolve/vector_one2many.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Rectangle_One2Many)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_one2many.test"), true);
+TYPED_TEST(FFTConvolve, Rectangle_One2Many) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_one2many.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Cuboid_One2Many)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_one2many.test"), true);
+TYPED_TEST(FFTConvolve, Cuboid_One2Many) {
+    fftconvolveTest<TypeParam, 3>(
+        string(TEST_DIR "/convolve/cuboid_one2many.test"), true);
 }
 
-TYPED_TEST(FFTConvolve, Same_Vector)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_same.test"), false);
+TYPED_TEST(FFTConvolve, Same_Vector) {
+    fftconvolveTest<TypeParam, 1>(string(TEST_DIR "/convolve/vector_same.test"),
+                                  false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Rectangle)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_same.test"), false);
+TYPED_TEST(FFTConvolve, Same_Rectangle) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_same.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Cuboid)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_same.test"), false);
+TYPED_TEST(FFTConvolve, Same_Cuboid) {
+    fftconvolveTest<TypeParam, 3>(string(TEST_DIR "/convolve/cuboid_same.test"),
+                                  false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Vector_Many2One)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_same_many2one.test"), false);
+TYPED_TEST(FFTConvolve, Same_Vector_Many2One) {
+    fftconvolveTest<TypeParam, 1>(
+        string(TEST_DIR "/convolve/vector_same_many2one.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Rectangle_Many2One)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_same_many2one.test"), false);
+TYPED_TEST(FFTConvolve, Same_Rectangle_Many2One) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_same_many2one.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Cuboid_Many2One)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_same_many2one.test"), false);
+TYPED_TEST(FFTConvolve, Same_Cuboid_Many2One) {
+    fftconvolveTest<TypeParam, 3>(
+        string(TEST_DIR "/convolve/cuboid_same_many2one.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Vector_Many2Many)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_same_many2many.test"), false);
+TYPED_TEST(FFTConvolve, Same_Vector_Many2Many) {
+    fftconvolveTest<TypeParam, 1>(
+        string(TEST_DIR "/convolve/vector_same_many2many.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Rectangle_Many2Many)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_same_many2many.test"), false);
+TYPED_TEST(FFTConvolve, Same_Rectangle_Many2Many) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_same_many2many.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Cuboid_Many2Many)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_same_many2many.test"), false);
+TYPED_TEST(FFTConvolve, Same_Cuboid_Many2Many) {
+    fftconvolveTest<TypeParam, 3>(
+        string(TEST_DIR "/convolve/cuboid_same_many2many.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Vector_One2Many)
-{
-    fftconvolveTest<TypeParam, 1>(string(TEST_DIR"/convolve/vector_same_one2many.test"), false);
+TYPED_TEST(FFTConvolve, Same_Vector_One2Many) {
+    fftconvolveTest<TypeParam, 1>(
+        string(TEST_DIR "/convolve/vector_same_one2many.test"), false);
 }
 
-TYPED_TEST(FFTConvolve, Same_Rectangle_One2Many)
-{
-    fftconvolveTest<TypeParam, 2>(string(TEST_DIR"/convolve/rectangle_same_one2many.test"), false);
+TYPED_TEST(FFTConvolve, Same_Rectangle_One2Many) {
+    fftconvolveTest<TypeParam, 2>(
+        string(TEST_DIR "/convolve/rectangle_same_one2many.test"), false);
 }
-TYPED_TEST(FFTConvolve, Same_Cuboid_One2Many)
-{
-    fftconvolveTest<TypeParam, 3>(string(TEST_DIR"/convolve/cuboid_same_one2many.test"), false);
+TYPED_TEST(FFTConvolve, Same_Cuboid_One2Many) {
+    fftconvolveTest<TypeParam, 3>(
+        string(TEST_DIR "/convolve/cuboid_same_one2many.test"), false);
 }
 
-TEST(FFTConvolve1, CPP)
-{
+TEST(FFTConvolve1, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/vector.test"), numDims, in, tests);
+    readTests<float, float, int>(string(TEST_DIR "/convolve/vector.test"),
+                                 numDims, in, tests);
 
     //![ex_image_convolve1]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [32 1 1 1]
+    // signal dims = [32 1 1 1]
     array filter(numDims[1], &(in[1].front()));
-    //filter dims = [4 1 1 1]
+    // filter dims = [4 1 1 1]
 
     array output = fftConvolve1(signal, filter, AF_CONV_EXPAND);
-    //output dims = [32 1 1 1] - same as input since expand(3rd argument is false)
-    //None of the dimensions > 1 has lenght > 1, so no batch mode is activated.
+    // output dims = [32 1 1 1] - same as input since expand(3rd argument is
+    // false) None of the dimensions > 1 has lenght > 1, so no batch mode is
+    // activated.
     //![ex_image_convolve1]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
     output.host(&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(FFTConvolve2, CPP)
-{
+TEST(FFTConvolve2, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/rectangle_one2many.test"), numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/convolve/rectangle_one2many.test"), numDims, in,
+        tests);
 
     //![ex_image_convolve2]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [15 17 1 1]
+    // signal dims = [15 17 1 1]
     array filter(numDims[1], &(in[1].front()));
-    //filter dims = [5 5 2 1]
+    // filter dims = [5 5 2 1]
 
     array output = fftConvolve2(signal, filter, AF_CONV_EXPAND);
-    //output dims = [15 17 1 1] - same as input since expand(3rd argument is false)
-    //however, notice that the 3rd dimension of filter is > 1.
-    //So, one to many batch mode will be activated automatically
-    //where the 2d input signal is convolved with each 2d filter
-    //and the result will written corresponding slice in the output 3d array
+    // output dims = [15 17 1 1] - same as input since expand(3rd argument is
+    // false) however, notice that the 3rd dimension of filter is > 1. So, one
+    // to many batch mode will be activated automatically where the 2d input
+    // signal is convolved with each 2d filter and the result will written
+    // corresponding slice in the output 3d array
     //![ex_image_convolve2]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
     output.host(&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(FFTConvolve3, CPP)
-{
+TEST(FFTConvolve3, CPP) {
     if (noDoubleTests<float>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/convolve/cuboid_many2many.test"), numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/convolve/cuboid_many2many.test"), numDims, in, tests);
 
     //![ex_image_convolve3]
-    //vector<dim4> numDims;
-    //vector<vector<float> > in;
+    // vector<dim4> numDims;
+    // vector<vector<float> > in;
     array signal(numDims[0], &(in[0].front()));
-    //signal dims = [10 11 2 2]
+    // signal dims = [10 11 2 2]
     array filter(numDims[1], &(in[1].front()));
-    //filter dims = [4 2 3 2]
+    // filter dims = [4 2 3 2]
 
     array output = fftConvolve3(signal, filter, AF_CONV_EXPAND);
-    //output dims = [10 11 2 2] - same as input since expand(3rd argument is false)
-    //however, notice that the 4th dimension is > 1 for both signal
-    //and the filter, therefore many to many batch mode will be
-    //activated where each 3d signal is convolved with the corresponding 3d filter
+    // output dims = [10 11 2 2] - same as input since expand(3rd argument is
+    // false) however, notice that the 4th dimension is > 1 for both signal and
+    // the filter, therefore many to many batch mode will be activated where
+    // each 3d signal is convolved with the corresponding 3d filter
     //![ex_image_convolve3]
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems  = output.elements();
+    size_t nElems             = output.elements();
     vector<float> outData(nElems);
     output.host(&outData.front());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1e-2)
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(FFTConvolve, Docs_Unified_Wrapper)
-{
+TEST(FFTConvolve, Docs_Unified_Wrapper) {
     // This unit test doesn't necessarily need to function
     // accuracy as convolve is merely a wrapper to
     // convolve[1|2|3]
@@ -468,33 +461,35 @@ TEST(FFTConvolve, Docs_Unified_Wrapper)
 
     //![ex_image_convolve_1d]
     array a = randu(10);
-    //af_print(a);
-    //a [10 1 1 1] = 0.0000 0.1315 0.7556 0.4587 0.5328 0.2190 0.0470 0.6789 0.6793 0.9347
+    // af_print(a);
+    // a [10 1 1 1] = 0.0000 0.1315 0.7556 0.4587 0.5328 0.2190 0.0470 0.6789
+    // 0.6793 0.9347
     array b = randu(4);
-    //af_print(b);
-    //b [4 1 1 1]  = 0.3835 0.5194 0.8310 0.0346
+    // af_print(b);
+    // b [4 1 1 1]  = 0.3835 0.5194 0.8310 0.0346
     array c = convolve(a, b);
-    //af_print(c);
-    //c [10 1 1 1] = 0.3581 0.6777 1.0750 0.7679 0.5903 0.4851 0.6598 1.2770 1.0734 0.8002
+    // af_print(c);
+    // c [10 1 1 1] = 0.3581 0.6777 1.0750 0.7679 0.5903 0.4851 0.6598
+    // 1.2770 1.0734 0.8002
     //![ex_image_convolve_1d]
 
     //![ex_image_convolve_2d]
     array d = constant(0.5, 5, 5);
-    //af_print(d);
-    //d [5 5 1 1]
+    // af_print(d);
+    // d [5 5 1 1]
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     //    0.5000     0.5000     0.5000     0.5000     0.5000
     array e = constant(1, 2, 2);
-    //af_print(e);
-    //e [2 2 1 1]
+    // af_print(e);
+    // e [2 2 1 1]
     //     1.0000     1.0000
     //     1.0000     1.0000
     array f = fftConvolve(d, e);
-    //af_print(f);
-    //f [5 5 1 1]
+    // af_print(f);
+    // f [5 5 1 1]
     //     2.0000     2.0000     2.0000     2.0000     1.0000
     //     2.0000     2.0000     2.0000     2.0000     1.0000
     //     2.0000     2.0000     2.0000     2.0000     1.0000
@@ -504,8 +499,8 @@ TEST(FFTConvolve, Docs_Unified_Wrapper)
 
     //![ex_image_convolve_3d]
     array g = constant(1, 4, 4, 4);
-    //af_print(g);
-    //g [4 4 4 1]
+    // af_print(g);
+    // g [4 4 4 1]
     //    1.0000     1.0000     1.0000     1.0000
     //    1.0000     1.0000     1.0000     1.0000
     //    1.0000     1.0000     1.0000     1.0000
@@ -526,8 +521,8 @@ TEST(FFTConvolve, Docs_Unified_Wrapper)
     //    1.0000     1.0000     1.0000     1.0000
     //    1.0000     1.0000     1.0000     1.0000
     array h = constant(0.5, 2, 2, 2);
-    //af_print(h);
-    //h [2 2 2 1]
+    // af_print(h);
+    // h [2 2 2 1]
     //    0.5000     0.5000
     //    0.5000     0.5000
 
@@ -535,8 +530,8 @@ TEST(FFTConvolve, Docs_Unified_Wrapper)
     //    0.5000     0.5000
 
     array i = fftConvolve(g, h);
-    //af_print(i);
-    //i [4 4 4 1]
+    // af_print(i);
+    // i [4 4 4 1]
     //    4.0000     4.0000     4.0000     2.0000
     //    4.0000     4.0000     4.0000     2.0000
     //    4.0000     4.0000     4.0000     2.0000
@@ -560,15 +555,12 @@ TEST(FFTConvolve, Docs_Unified_Wrapper)
 }
 using namespace af;
 
-TEST(GFOR, fftConvolve2_MO)
-{
+TEST(GFOR, fftConvolve2_MO) {
     array A = randu(5, 5, 3);
     array B = randu(5, 5, 3);
     array K = randu(3, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = fftConvolve2(A(span, span, ii), K);
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = fftConvolve2(A(span, span, ii), K); }
 
     for (int ii = 0; ii < 3; ii++) {
         array c_ii = fftConvolve2(A(span, span, ii), K);
@@ -577,15 +569,12 @@ TEST(GFOR, fftConvolve2_MO)
     }
 }
 
-TEST(GFOR, fftConvolve2_OM)
-{
+TEST(GFOR, fftConvolve2_OM) {
     array A = randu(5, 5);
     array B = randu(5, 5, 3);
     array K = randu(3, 3, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = fftConvolve2(A, K(span, span, ii));
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = fftConvolve2(A, K(span, span, ii)); }
 
     for (int ii = 0; ii < 3; ii++) {
         array c_ii = fftConvolve2(A, K(span, span, ii));
@@ -594,8 +583,7 @@ TEST(GFOR, fftConvolve2_OM)
     }
 }
 
-TEST(GFOR, fftConvolve2_MM)
-{
+TEST(GFOR, fftConvolve2_MM) {
     array A = randu(5, 5, 3);
     array B = randu(5, 5, 3);
     array K = randu(3, 3, 3);
@@ -611,8 +599,7 @@ TEST(GFOR, fftConvolve2_MM)
     }
 }
 
-TEST(Padding, fftConvolve2)
-{
+TEST(Padding, fftConvolve2) {
     for (int n = 5; n < 32; n++) {
         array a = randu(n, n);
         array b = randu(5, 5);
@@ -622,41 +609,38 @@ TEST(Padding, fftConvolve2)
     }
 }
 
-TEST(FFTConvolve1, Interleaved)
-{
+TEST(FFTConvolve1, Interleaved) {
     array a = randu(100, 1, 2);
     array b = randu(20, 3);
     array c = fftConvolve1(a, b);
 
     for (int ii = 0; ii < 2; ii++) {
         array c_ii = c(span, span, ii);
-        array d = fftConvolve1(a(span, 0, ii), b);
+        array d    = fftConvolve1(a(span, 0, ii), b);
         ASSERT_EQ(max<double>(abs(c_ii - d)) < 1E-5, true);
     }
 }
 
-TEST(FFTConvolve2, Interleaved)
-{
+TEST(FFTConvolve2, Interleaved) {
     array a = randu(100, 100, 2);
     array b = randu(5, 5, 1, 3);
     array c = fftConvolve2(a, b);
 
     for (int ii = 0; ii < 3; ii++) {
         array c_ii = c(span, span, span, ii);
-        array d = fftConvolve2(a, b(span, span, 0, ii));
+        array d    = fftConvolve2(a, b(span, span, 0, ii));
         ASSERT_EQ(max<double>(abs(c_ii - d)) < 1E-5, true);
     }
 }
 
-TEST(FFTConvolve2, Interleaved2)
-{
+TEST(FFTConvolve2, Interleaved2) {
     array a = randu(100, 100, 2);
     array b = randu(5, 5, 2, 3);
     array c = fftConvolve2(a, b);
 
     for (int ii = 0; ii < 3; ii++) {
         array c_ii = c(span, span, span, ii);
-        array d = fftConvolve2(a, b(span, span, span, ii));
+        array d    = fftConvolve2(a, b(span, span, span, ii));
         ASSERT_EQ(max<double>(abs(c_ii - d)) < 1E-5, true);
     }
 }
diff --git a/test/flat.cpp b/test/flat.cpp
index 7f622943b0..2d3745c135 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
 #include <vector>
 
@@ -25,70 +25,64 @@ using af::span;
 
 using std::vector;
 
-TEST(FlatTests, Test_flat_1D)
-{
+TEST(FlatTests, Test_flat_1D) {
     const int num = 10000;
-    array in = randu(num);
-    array out = flat(in);
+    array in      = randu(num);
+    array out     = flat(in);
 
     ASSERT_ARRAYS_EQ(in, out);
 }
 
-TEST(FlatTests, Test_flat_2D)
-{
+TEST(FlatTests, Test_flat_2D) {
     const int nx = 200;
     const int ny = 200;
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array out = flat(in);
 
     vector<float> h_in_flat(in.elements());
     in.host(h_in_flat.data());
-    dim4 h_in_flat_dims = dim4(nx*ny);
+    dim4 h_in_flat_dims = dim4(nx * ny);
     ASSERT_VEC_ARRAY_EQ(h_in_flat, h_in_flat_dims, out);
 }
 
-TEST(FlatTests, Test_flat_1D_index)
-{
+TEST(FlatTests, Test_flat_1D_index) {
     const int num = 10000;
-    const int st = 101;
-    const int en = 5000;
+    const int st  = 101;
+    const int en  = 5000;
 
-    array in = randu(num);
+    array in  = randu(num);
     array tmp = in(seq(st, en));
     array out = flat(tmp);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     // TODO: Use ASSERT_ARRAYS_EQUAL
-    for (int i = st; i <= en; i++) {
-        ASSERT_EQ(h_in[i], h_out[i - st]);
-    }
+    for (int i = st; i <= en; i++) { ASSERT_EQ(h_in[i], h_out[i - st]); }
 
     freeHost(h_in);
     freeHost(h_out);
 }
 
-TEST(FlatTests, Test_flat_2D_index0)
-{
-    const int nx = 200;
-    const int ny = 200;
-    const int st = 21;
-    const int en = 180;
+TEST(FlatTests, Test_flat_2D_index0) {
+    const int nx  = 200;
+    const int ny  = 200;
+    const int st  = 21;
+    const int en  = 180;
     const int nxo = (en - st + 1);
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array tmp = in(seq(st, en), span);
     array out = flat(tmp);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     // TODO: Use ASSERT_ARRAYS_EQUAL
     for (int j = 0; j < ny; j++) {
-        const int in_off = j * nx;
-        const int out_off =j * nxo;
+        const int in_off  = j * nx;
+        const int out_off = j * nxo;
         for (int i = st; i <= en; i++) {
             ASSERT_EQ(h_in[i + in_off], h_out[i - st + out_off])
                 << "at (" << i << "," << j << ")";
@@ -99,24 +93,22 @@ TEST(FlatTests, Test_flat_2D_index0)
     freeHost(h_out);
 }
 
-TEST(FlatTests, Test_flat_2D_index1)
-{
+TEST(FlatTests, Test_flat_2D_index1) {
     const int nx = 200;
     const int ny = 200;
     const int st = 21;
     const int en = 180;
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array tmp = in(span, seq(st, en));
     array out = flat(tmp);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     // TODO: Use ASSERT_ARRAYS_EQUAL
     for (int j = st; j <= en; j++) {
-
-        const int in_off = j * nx;
+        const int in_off  = j * nx;
         const int out_off = (j - st) * nx;
 
         for (int i = 0; i < nx; i++) {
diff --git a/test/flip.cpp b/test/flip.cpp
index 7b5461ba5a..f29dbfc643 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -8,47 +8,43 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
 #include <af/index.h>
-#include <testHelpers.hpp>
 
 using af::array;
-using af::randu;
 using af::flip;
 using af::freeHost;
 using af::randu;
 using af::seq;
 using af::span;
 
-TEST(FlipTests, Test_flip_1D)
-{
+TEST(FlipTests, Test_flip_1D) {
     const int num = 10000;
-    array in = randu(num);
-    array out = flip(in, 0);
+    array in      = randu(num);
+    array out     = flip(in, 0);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(h_in[num - i - 1], h_out[i])
-            << "at (" << i << ")";
+        ASSERT_EQ(h_in[num - i - 1], h_out[i]) << "at (" << i << ")";
     }
 
     freeHost(h_in);
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_2D0)
-{
+TEST(FlipTests, Test_flip_2D0) {
     const int nx = 200;
     const int ny = 200;
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array out = flip(in, 0);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int j = 0; j < ny; j++) {
@@ -56,7 +52,6 @@ TEST(FlipTests, Test_flip_2D0)
         for (int i = 0; i < nx; i++) {
             ASSERT_EQ(h_in[off + nx - 1 - i], h_out[off + i])
                 << "at (" << i << "," << j << ")";
-
         }
     }
 
@@ -64,15 +59,14 @@ TEST(FlipTests, Test_flip_2D0)
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_2D1)
-{
+TEST(FlipTests, Test_flip_2D1) {
     const int nx = 200;
     const int ny = 200;
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array out = flip(in, 1);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int j = 0; j < ny; j++) {
@@ -88,47 +82,43 @@ TEST(FlipTests, Test_flip_2D1)
     freeHost(h_out);
 }
 
-
-TEST(FlipTests, Test_flip_1D_index)
-{
+TEST(FlipTests, Test_flip_1D_index) {
     const int num = 10000;
-    const int st = 101;
-    const int en = 5000;
+    const int st  = 101;
+    const int en  = 5000;
 
-    array in = randu(num);
+    array in  = randu(num);
     array tmp = in(seq(st, en));
     array out = flip(tmp, 0);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int i = st; i <= en; i++) {
-        ASSERT_EQ(h_in[i], h_out[en - i])
-            << "at (" << i << ")";
+        ASSERT_EQ(h_in[i], h_out[en - i]) << "at (" << i << ")";
     }
 
     freeHost(h_in);
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_2D_index00)
-{
-    const int nx = 200;
-    const int ny = 200;
-    const int st = 21;
-    const int en = 180;
+TEST(FlipTests, Test_flip_2D_index00) {
+    const int nx  = 200;
+    const int ny  = 200;
+    const int st  = 21;
+    const int en  = 180;
     const int nxo = (en - st + 1);
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array tmp = in(seq(st, en), span);
     array out = flip(tmp, 0);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int j = 0; j < ny; j++) {
-        const int in_off = j * nx;
-        const int out_off =j * nxo;
+        const int in_off  = j * nx;
+        const int out_off = j * nxo;
         for (int i = st; i <= en; i++) {
             ASSERT_EQ(h_in[i + in_off], h_out[en - i + out_off])
                 << "at (" << i << "," << j << ")";
@@ -139,24 +129,23 @@ TEST(FlipTests, Test_flip_2D_index00)
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_2D_index01)
-{
-    const int nx = 200;
-    const int ny = 200;
-    const int st = 21;
-    const int en = 180;
+TEST(FlipTests, Test_flip_2D_index01) {
+    const int nx  = 200;
+    const int ny  = 200;
+    const int st  = 21;
+    const int en  = 180;
     const int nxo = (en - st + 1);
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array tmp = in(seq(st, en), span);
     array out = flip(tmp, 1);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int j = 0; j < ny; j++) {
-        const int in_off = (ny - 1 - j) * nx;
-        const int out_off =j * nxo;
+        const int in_off  = (ny - 1 - j) * nx;
+        const int out_off = j * nxo;
         for (int i = st; i <= en; i++) {
             ASSERT_EQ(h_in[i + in_off], h_out[i - st + out_off])
                 << "at (" << i << "," << j << ")";
@@ -167,23 +156,21 @@ TEST(FlipTests, Test_flip_2D_index01)
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_2D_index10)
-{
+TEST(FlipTests, Test_flip_2D_index10) {
     const int nx = 200;
     const int ny = 200;
     const int st = 21;
     const int en = 180;
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array tmp = in(span, seq(st, en));
     array out = flip(tmp, 0);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int j = st; j <= en; j++) {
-
-        const int in_off = j * nx;
+        const int in_off  = j * nx;
         const int out_off = (j - st) * nx;
 
         for (int i = 0; i < nx; i++) {
@@ -196,23 +183,21 @@ TEST(FlipTests, Test_flip_2D_index10)
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_2D_index11)
-{
+TEST(FlipTests, Test_flip_2D_index11) {
     const int nx = 200;
     const int ny = 200;
     const int st = 21;
     const int en = 180;
 
-    array in = randu(nx, ny);
+    array in  = randu(nx, ny);
     array tmp = in(span, seq(st, en));
     array out = flip(tmp, 1);
 
-    float *h_in = in.host<float>();
+    float *h_in  = in.host<float>();
     float *h_out = out.host<float>();
 
     for (int j = st; j <= en; j++) {
-
-        const int in_off = j * nx;
+        const int in_off  = j * nx;
         const int out_off = (en - j) * nx;
 
         for (int i = 0; i < nx; i++) {
diff --git a/test/gaussiankernel.cpp b/test/gaussiankernel.cpp
index 183ef77942..a16435de4a 100644
--- a/test/gaussiankernel.cpp
+++ b/test/gaussiankernel.cpp
@@ -7,24 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using af::dim4;
 using std::endl;
 using std::string;
 using std::vector;
-using af::dim4;
 
 template<typename T>
-class GaussianKernel : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class GaussianKernel : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
@@ -34,74 +33,75 @@ typedef ::testing::Types<float> TestTypes;
 TYPED_TEST_CASE(GaussianKernel, TestTypes);
 
 template<typename T>
-void gaussianKernelTest(string pFileName, double sigma)
-{
+void gaussianKernelTest(string pFileName, double sigma) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>     numDims;
+    vector<dim4> numDims;
     vector<vector<int> > in;
-    vector<vector<T> >   tests;
+    vector<vector<T> > tests;
 
-    readTestsFromFile<int,T>(pFileName, numDims, in, tests);
+    readTestsFromFile<int, T>(pFileName, numDims, in, tests);
 
-    af_array outArray  = 0;
+    af_array outArray = 0;
 
     vector<int> input(in[0].begin(), in[0].end());
 
-    ASSERT_SUCCESS(af_gaussian_kernel(&outArray, input[0], input[1], sigma, sigma));
+    ASSERT_SUCCESS(
+        af_gaussian_kernel(&outArray, input[0], input[1], sigma, sigma));
 
     dim_t outElems = 0;
     ASSERT_SUCCESS(af_get_elements(&outElems, outArray));
     T *outData = new T[outElems];
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
     vector<T> currGoldBar(tests[0].begin(), tests[0].end());
     size_t nElems = currGoldBar.size();
 
     ASSERT_EQ(outElems, (dim_t)nElems);
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)
+            << "at: " << elIter << endl;
     }
 
     delete[] outData;
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(GaussianKernel, Small1D)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss1_7.test"), 0.0);
+TYPED_TEST(GaussianKernel, Small1D) {
+    gaussianKernelTest<TypeParam>(string(TEST_DIR "/gaussian/gauss1_7.test"),
+                                  0.0);
 }
 
-TYPED_TEST(GaussianKernel, Large1D)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss1_15.test"), 0.0);
+TYPED_TEST(GaussianKernel, Large1D) {
+    gaussianKernelTest<TypeParam>(string(TEST_DIR "/gaussian/gauss1_15.test"),
+                                  0.0);
 }
 
-TYPED_TEST(GaussianKernel, Small1DWithSigma)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss1_7_sigma1.test"), 1.0);
+TYPED_TEST(GaussianKernel, Small1DWithSigma) {
+    gaussianKernelTest<TypeParam>(
+        string(TEST_DIR "/gaussian/gauss1_7_sigma1.test"), 1.0);
 }
 
-TYPED_TEST(GaussianKernel, SmallSmall2D)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss2_7x7.test"), 0.0);
+TYPED_TEST(GaussianKernel, SmallSmall2D) {
+    gaussianKernelTest<TypeParam>(string(TEST_DIR "/gaussian/gauss2_7x7.test"),
+                                  0.0);
 }
 
-TYPED_TEST(GaussianKernel, LargeSmall2D)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss2_15x7.test"), 0.0);
+TYPED_TEST(GaussianKernel, LargeSmall2D) {
+    gaussianKernelTest<TypeParam>(string(TEST_DIR "/gaussian/gauss2_15x7.test"),
+                                  0.0);
 }
 
-TYPED_TEST(GaussianKernel, LargeLarge2D)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss2_15x15.test"), 0.0);
+TYPED_TEST(GaussianKernel, LargeLarge2D) {
+    gaussianKernelTest<TypeParam>(
+        string(TEST_DIR "/gaussian/gauss2_15x15.test"), 0.0);
 }
 
-TYPED_TEST(GaussianKernel, SmallSmall2DWithSigma)
-{
-    gaussianKernelTest<TypeParam>(string(TEST_DIR"/gaussian/gauss2_7x7_sigma1.test"), 1.0);
+TYPED_TEST(GaussianKernel, SmallSmall2DWithSigma) {
+    gaussianKernelTest<TypeParam>(
+        string(TEST_DIR "/gaussian/gauss2_7x7_sigma1.test"), 1.0);
 }
 
 //////////////////////////////// CPP ////////////////////////////////////
@@ -112,13 +112,12 @@ TYPED_TEST(GaussianKernel, SmallSmall2DWithSigma)
 using af::array;
 using af::gaussianKernel;
 
-void gaussianKernelTestCPP(string pFileName, double sigma)
-{
-    vector<dim4>       numDims;
-    vector<vector<int> >   in;
+void gaussianKernelTestCPP(string pFileName, double sigma) {
+    vector<dim4> numDims;
+    vector<vector<int> > in;
     vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(pFileName, numDims, in, tests);
+    readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
     vector<int> input(in[0].begin(), in[0].end());
 
@@ -133,29 +132,28 @@ void gaussianKernelTestCPP(string pFileName, double sigma)
 
     ASSERT_EQ(outElems, (dim_t)nElems);
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)
+            << "at: " << elIter << endl;
     }
 
     delete[] outData;
 }
 
-TEST(GaussianKernel, Small1D_CPP)
-{
-    gaussianKernelTestCPP(string(TEST_DIR"/gaussian/gauss1_7.test"), 0.0);
+TEST(GaussianKernel, Small1D_CPP) {
+    gaussianKernelTestCPP(string(TEST_DIR "/gaussian/gauss1_7.test"), 0.0);
 }
 
-TEST(GaussianKernel, Small1DWithSigma_CPP)
-{
-    gaussianKernelTestCPP(string(TEST_DIR"/gaussian/gauss1_7_sigma1.test"), 1.0);
+TEST(GaussianKernel, Small1DWithSigma_CPP) {
+    gaussianKernelTestCPP(string(TEST_DIR "/gaussian/gauss1_7_sigma1.test"),
+                          1.0);
 }
 
-TEST(GaussianKernel, SmallSmall2D_CPP)
-{
-    gaussianKernelTestCPP(string(TEST_DIR"/gaussian/gauss2_7x7.test"), 0.0);
+TEST(GaussianKernel, SmallSmall2D_CPP) {
+    gaussianKernelTestCPP(string(TEST_DIR "/gaussian/gauss2_7x7.test"), 0.0);
 }
 
-TEST(GaussianKernel, SmallSmall2DWithSigma_CPP)
-{
-    gaussianKernelTestCPP(string(TEST_DIR"/gaussian/gauss2_7x7_sigma1.test"), 1.0);
+TEST(GaussianKernel, SmallSmall2DWithSigma_CPP) {
+    gaussianKernelTestCPP(string(TEST_DIR "/gaussian/gauss2_7x7_sigma1.test"),
+                          1.0);
 }
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 254ef5381f..716735740a 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -7,24 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <af/data.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/data.h>
 
-#include <vector>
+#include <testHelpers.hpp>
 #include <algorithm>
 #include <functional>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using std::ostream_iterator;
 using af::array;
 using af::dim4;
 using af::dtype_traits;
@@ -34,43 +30,51 @@ using af::randu;
 using af::seq;
 using af::span;
 using af::where;
+using std::endl;
+using std::ostream_iterator;
+using std::string;
+using std::vector;
 
-void testGeneralAssignOneArray(string pTestFile, const dim_t ndims, af_index_t* indexs, int arrayDim)
-{
-    vector<dim4>        numDims;
-    vector< vector<float> >      in;
-    vector< vector<float> >   tests;
+void testGeneralAssignOneArray(string pTestFile, const dim_t ndims,
+                               af_index_t *indexs, int arrayDim) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
     readTestsFromFile<float, float>(pTestFile, numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    dim4 dims2     = numDims[2];
-    af_array outArray  = 0;
-    af_array rhsArray  = 0;
-    af_array lhsArray  = 0;
-    af_array idxArray  = 0;
-
-    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
-
-    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
-
-    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[2].front()),
-                dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
+    dim4 dims0        = numDims[0];
+    dim4 dims1        = numDims[1];
+    dim4 dims2        = numDims[2];
+    af_array outArray = 0;
+    af_array rhsArray = 0;
+    af_array lhsArray = 0;
+    af_array idxArray = 0;
+
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+
+    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[2].front()), dims2.ndims(),
+                                   dims2.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
     indexs[arrayDim].idx.arr = idxArray;
 
     ASSERT_SUCCESS(af_assign_gen(&outArray, lhsArray, ndims, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
+    size_t nElems             = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(rhsArray));
@@ -79,62 +83,66 @@ void testGeneralAssignOneArray(string pTestFile, const dim_t ndims, af_index_t*
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TEST(GeneralAssign, ASSS)
-{
+TEST(GeneralAssign, ASSS) {
     af_index_t indexs[2];
     indexs[1].idx.seq = af_make_seq(0, 9, 1);
-    indexs[0].isSeq = false;
-    indexs[1].isSeq = true;
+    indexs[0].isSeq   = false;
+    indexs[1].isSeq   = true;
 
-    testGeneralAssignOneArray(string(TEST_DIR"/gen_assign/as0_9s0_ns0_n.test"), 2, indexs, 0);
+    testGeneralAssignOneArray(string(TEST_DIR "/gen_assign/as0_9s0_ns0_n.test"),
+                              2, indexs, 0);
 }
 
-TEST(GeneralAssign, SASS)
-{
+TEST(GeneralAssign, SASS) {
     af_index_t indexs[2];
     indexs[0].idx.seq = af_make_seq(10, 14, 1);
-    indexs[0].isSeq = true;
-    indexs[1].isSeq = false;
+    indexs[0].isSeq   = true;
+    indexs[1].isSeq   = false;
 
-    testGeneralAssignOneArray(string(TEST_DIR"/gen_assign/s10_14as0_ns0_n.test"), 2, indexs, 1);
+    testGeneralAssignOneArray(
+        string(TEST_DIR "/gen_assign/s10_14as0_ns0_n.test"), 2, indexs, 1);
 }
 
-TEST(GeneralAssign, SSSS)
-{
-    vector<dim4>        numDims;
-    vector< vector<float> >      in;
-    vector< vector<float> >   tests;
+TEST(GeneralAssign, SSSS) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float, float>(string(TEST_DIR"/gen_assign/s10_14s0_9s0_ns0_n.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(
+        string(TEST_DIR "/gen_assign/s10_14s0_9s0_ns0_n.test"), numDims, in,
+        tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    af_array outArray  = 0;
-    af_array rhsArray  = 0;
-    af_array lhsArray  = 0;
+    dim4 dims0        = numDims[0];
+    dim4 dims1        = numDims[1];
+    af_array outArray = 0;
+    af_array rhsArray = 0;
+    af_array lhsArray = 0;
 
     af_index_t indexs[2];
     indexs[0].idx.seq = af_make_seq(10, 14, 1);
     indexs[1].idx.seq = af_make_seq(0, 9, 1);
-    indexs[0].isSeq = true;
-    indexs[1].isSeq = true;
+    indexs[0].isSeq   = true;
+    indexs[1].isSeq   = true;
 
-    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
     ASSERT_SUCCESS(af_assign_gen(&outArray, lhsArray, 2, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
+    size_t nElems             = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(rhsArray));
@@ -142,20 +150,20 @@ TEST(GeneralAssign, SSSS)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TEST(GeneralAssign, AAAA)
-{
-    vector<dim4>        numDims;
-    vector< vector<float> >      in;
-    vector< vector<float> >   tests;
+TEST(GeneralAssign, AAAA) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float, float>(string(TEST_DIR"/gen_assign/aaaa.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(string(TEST_DIR "/gen_assign/aaaa.test"),
+                                    numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    dim4 dims2     = numDims[2];
-    dim4 dims3     = numDims[3];
-    dim4 dims4     = numDims[4];
-    dim4 dims5     = numDims[5];
+    dim4 dims0         = numDims[0];
+    dim4 dims1         = numDims[1];
+    dim4 dims2         = numDims[2];
+    dim4 dims3         = numDims[3];
+    dim4 dims4         = numDims[4];
+    dim4 dims5         = numDims[5];
     af_array outArray  = 0;
     af_array rhsArray  = 0;
     af_array lhsArray  = 0;
@@ -170,38 +178,45 @@ TEST(GeneralAssign, AAAA)
     indexs[2].isSeq = false;
     indexs[3].isSeq = false;
 
-    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&lhsArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&rhsArray, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[2].front()),
-                dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[2].front()), dims2.ndims(),
+                                   dims2.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
     indexs[0].idx.arr = idxArray0;
 
-    ASSERT_SUCCESS(af_create_array(&idxArray1, &(in[3].front()),
-                dims3.ndims(), dims3.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&idxArray1, &(in[3].front()), dims3.ndims(),
+                                   dims3.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
     indexs[1].idx.arr = idxArray1;
 
-    ASSERT_SUCCESS(af_create_array(&idxArray2, &(in[4].front()),
-                dims4.ndims(), dims4.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&idxArray2, &(in[4].front()), dims4.ndims(),
+                                   dims4.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
     indexs[2].idx.arr = idxArray2;
 
-    ASSERT_SUCCESS(af_create_array(&idxArray3, &(in[5].front()),
-                dims5.ndims(), dims5.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&idxArray3, &(in[5].front()), dims5.ndims(),
+                                   dims5.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
     indexs[3].idx.arr = idxArray3;
 
     ASSERT_SUCCESS(af_assign_gen(&outArray, lhsArray, 4, indexs, rhsArray));
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
+    size_t nElems             = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(rhsArray));
@@ -213,32 +228,28 @@ TEST(GeneralAssign, AAAA)
     ASSERT_SUCCESS(af_release_array(idxArray3));
 }
 
-
-TEST(ArrayAssign, CPP_ASSIGN_INDEX)
-{
+TEST(ArrayAssign, CPP_ASSIGN_INDEX) {
     using af::array;
 
     const int num = 20000;
 
-    array a = randu(num);
+    array a    = randu(num);
     float *hAO = a.host<float>();
 
-    array a_copy = a;
-    array idx = where(a < 0.5);
+    array a_copy  = a;
+    array idx     = where(a < 0.5);
     const int len = idx.elements();
-    array b = randu(len);
-    a(idx) = b;
+    array b       = randu(len);
+    a(idx)        = b;
 
-    float *hA = a.host<float>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    float *hB  = b.host<float>();
     float *hAC = a_copy.host<float>();
     uint *hIdx = idx.host<uint>();
 
     for (int i = 0; i < num; i++) {
-
         int j = 0;
-        while(j < len) {
-
+        while (j < len) {
             // If index found, value should match B
             if ((int)hIdx[j] == i) {
                 ASSERT_EQ(hA[i], hB[j]);
@@ -248,15 +259,11 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX)
         }
 
         // If index not found, value should match original
-        if (j >= len) {
-            ASSERT_EQ(hA[i], hAO[i]);
-        }
+        if (j >= len) { ASSERT_EQ(hA[i], hAO[i]); }
     }
 
     // hAC should not be modified, i.e. same as original
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hAO[i], hAC[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hAO[i], hAC[i]); }
 
     freeHost(hA);
     freeHost(hB);
@@ -265,32 +272,29 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX)
     freeHost(hIdx);
 }
 
-TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL)
-{
+TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL) {
     try {
         using af::array;
 
         const int num = 20000;
 
-        array a = randu(num);
+        array a    = randu(num);
         float *hAO = a.host<float>();
 
-        array a_copy = a;
-        array idx = where(a < 0.5);
+        array a_copy  = a;
+        array idx     = where(a < 0.5);
         const int len = idx.elements();
-        array b = randu(len);
-        a(a < 0.5) = b;
+        array b       = randu(len);
+        a(a < 0.5)    = b;
 
-        float *hA = a.host<float>();
-        float *hB = b.host<float>();
+        float *hA  = a.host<float>();
+        float *hB  = b.host<float>();
         float *hAC = a_copy.host<float>();
         uint *hIdx = idx.host<uint>();
 
         for (int i = 0; i < num; i++) {
-
             int j = 0;
-            while(j < len) {
-
+            while (j < len) {
                 // If index found, value should match B
                 if ((int)hIdx[j] == i) {
                     ASSERT_EQ(hA[i], hB[j]);
@@ -300,35 +304,27 @@ TEST(ArrayAssign, CPP_ASSIGN_INDEX_LOGICAL)
             }
 
             // If index not found, value should match original
-            if (j >= len) {
-                ASSERT_EQ(hA[i], hAO[i]);
-            }
+            if (j >= len) { ASSERT_EQ(hA[i], hAO[i]); }
         }
 
         // hAC should not be modified, i.e. same as original
-        for (int i = 0; i < num; i++) {
-            ASSERT_EQ(hAO[i], hAC[i]);
-        }
+        for (int i = 0; i < num; i++) { ASSERT_EQ(hAO[i], hAC[i]); }
 
         freeHost(hA);
         freeHost(hB);
         freeHost(hAC);
         freeHost(hAO);
         freeHost(hIdx);
-    } catch(exception &ex) {
-        FAIL() << ex.what() << endl;
-    }
+    } catch (exception &ex) { FAIL() << ex.what() << endl; }
 }
 
-
-TEST(GeneralAssign, CPP_ASNN)
-{
+TEST(GeneralAssign, CPP_ASNN) {
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
     const int en = 805;
 
-    array a = randu(nx, ny);
+    array a   = randu(nx, ny);
     array idx = where(randu(ny) > 0.5);
 
     const int nyb = (en - st) + 1;
@@ -338,17 +334,15 @@ TEST(GeneralAssign, CPP_ASNN)
 
     a(idx, seq(st, en)) = b;
 
-    float *hA = a.host<float>();
-    uint  *hIdx = idx.host<uint>();
-    float *hB = b.host<float>();
-
+    float *hA  = a.host<float>();
+    uint *hIdx = idx.host<uint>();
+    float *hB  = b.host<float>();
 
     for (int j = 0; j < nyb; j++) {
         float *hAt = hA + (st + j) * nx;
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
-            ASSERT_EQ(hAt[hIdx[i]], hBt[i])
-                << "at " << i << " " << j << endl;
+            ASSERT_EQ(hAt[hIdx[i]], hBt[i]) << "at " << i << " " << j << endl;
         }
     }
 
@@ -357,14 +351,13 @@ TEST(GeneralAssign, CPP_ASNN)
     freeHost(hIdx);
 }
 
-TEST(GeneralAssign, CPP_SANN)
-{
+TEST(GeneralAssign, CPP_SANN) {
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
     const int en = 805;
 
-    array a = randu(nx, ny);
+    array a   = randu(nx, ny);
     array idx = where(randu(ny) > 0.5);
 
     const int nxb = (en - st) + 1;
@@ -374,17 +367,16 @@ TEST(GeneralAssign, CPP_SANN)
 
     a(seq(st, en), idx) = b;
 
-    float *hA = a.host<float>();
-    uint  *hIdx = idx.host<uint>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    uint *hIdx = idx.host<uint>();
+    float *hB  = b.host<float>();
 
     for (int j = 0; j < nyb; j++) {
         float *hAt = hA + hIdx[j] * nx;
         float *hBt = hB + j * nxb;
 
         for (int i = 0; i < nxb; i++) {
-            ASSERT_EQ(hAt[i + st], hBt[i])
-            << "at " << i << " " << j << endl;
+            ASSERT_EQ(hAt[i + st], hBt[i]) << "at " << i << " " << j << endl;
         }
     }
 
@@ -393,27 +385,26 @@ TEST(GeneralAssign, CPP_SANN)
     freeHost(hIdx);
 }
 
-TEST(GeneralAssign, CPP_SSAN)
-{
+TEST(GeneralAssign, CPP_SSAN) {
     const int nx = 100;
     const int ny = 100;
     const int nz = 100;
     const int st = 20;
     const int en = 85;
 
-    array a = randu(nx, ny, nz);
+    array a   = randu(nx, ny, nz);
     array idx = where(randu(nz) > 0.5);
 
     const int nxb = (en - st) + 1;
     const int nyb = ny;
     const int nzb = idx.elements();
-    array b = randu(nxb, nyb, nzb);
+    array b       = randu(nxb, nyb, nzb);
 
     a(seq(st, en), span, idx) = b;
 
-    float *hA = a.host<float>();
-    uint  *hIdx = idx.host<uint>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    uint *hIdx = idx.host<uint>();
+    float *hB  = b.host<float>();
 
     for (int k = 0; k < nzb; k++) {
         float *hAt = hA + hIdx[k] * nx * ny;
@@ -421,7 +412,7 @@ TEST(GeneralAssign, CPP_SSAN)
 
         for (int j = 0; j < nyb; j++) {
             for (int i = 0; i < nxb; i++) {
-                ASSERT_EQ(hAt[j * nx  + i + st], hBt[j * nxb + i])
+                ASSERT_EQ(hAt[j * nx + i + st], hBt[j * nxb + i])
                     << "at " << i << " " << j << " " << k << endl;
             }
         }
@@ -432,32 +423,30 @@ TEST(GeneralAssign, CPP_SSAN)
     freeHost(hIdx);
 }
 
-TEST(GeneralAssign, CPP_AANN)
-{
+TEST(GeneralAssign, CPP_AANN) {
     const int nx = 1000;
     const int ny = 1000;
 
-    array a = randu(nx, ny);
+    array a    = randu(nx, ny);
     array idx0 = where(randu(nx) > 0.5);
     array idx1 = where(randu(ny) > 0.5);
 
     const int nxb = idx0.elements();
     const int nyb = idx1.elements();
-    array b = randu(nxb, nyb);
+    array b       = randu(nxb, nyb);
 
     a(idx0, idx1) = b;
 
-    float *hA = a.host<float>();
-    uint  *hIdx0 = idx0.host<uint>();
-    uint  *hIdx1 = idx1.host<uint>();
-    float *hB = b.host<float>();
+    float *hA   = a.host<float>();
+    uint *hIdx0 = idx0.host<uint>();
+    uint *hIdx1 = idx1.host<uint>();
+    float *hB   = b.host<float>();
 
     for (int j = 0; j < nyb; j++) {
         float *hAt = hA + hIdx1[j] * nx;
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
-            ASSERT_EQ(hAt[hIdx0[i]], hBt[i])
-                << "at " << i << " " << j << endl;
+            ASSERT_EQ(hAt[hIdx0[i]], hBt[i]) << "at " << i << " " << j << endl;
         }
     }
 
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index cffcf52835..14033ac4c2 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -7,58 +7,61 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <af/data.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/data.h>
 
-#include <vector>
+#include <testHelpers.hpp>
 #include <algorithm>
 #include <functional>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using std::ostream_iterator;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::ostream_iterator;
+using std::string;
+using std::vector;
 
-void testGeneralIndexOneArray(string pTestFile, const dim_t ndims, af_index_t* indexs, int arrayDim)
-{
-    vector<dim4>        numDims;
-    vector< vector<float> >      in;
-    vector< vector<float> >   tests;
+void testGeneralIndexOneArray(string pTestFile, const dim_t ndims,
+                              af_index_t *indexs, int arrayDim) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
     readTestsFromFile<float, float>(pTestFile, numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    af_array outArray  = 0;
-    af_array inArray   = 0;
-    af_array idxArray  = 0;
+    dim4 dims0        = numDims[0];
+    dim4 dims1        = numDims[1];
+    af_array outArray = 0;
+    af_array inArray  = 0;
+    af_array idxArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
     indexs[arrayDim].idx.arr = idxArray;
 
     ASSERT_SUCCESS(af_index_gen(&outArray, inArray, ndims, indexs));
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
+    size_t nElems             = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(inArray));
@@ -66,55 +69,55 @@ void testGeneralIndexOneArray(string pTestFile, const dim_t ndims, af_index_t* i
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TEST(GeneralIndex, SSSA)
-{
+TEST(GeneralIndex, SSSA) {
     af_index_t indexs[4];
     indexs[0].idx.seq = af_make_seq(0, 3, 1);
     indexs[1].idx.seq = af_make_seq(0, 1, 1);
     indexs[2].idx.seq = af_make_seq(1, 2, 1);
-    indexs[0].isSeq = true;
-    indexs[1].isSeq = true;
-    indexs[2].isSeq = true;
-    indexs[3].isSeq = false;
+    indexs[0].isSeq   = true;
+    indexs[1].isSeq   = true;
+    indexs[2].isSeq   = true;
+    indexs[3].isSeq   = false;
 
-    testGeneralIndexOneArray(string(TEST_DIR"/gen_index/s0_3s0_1s1_2a.test"), 4, indexs, 3);
+    testGeneralIndexOneArray(string(TEST_DIR "/gen_index/s0_3s0_1s1_2a.test"),
+                             4, indexs, 3);
 }
 
-TEST(GeneralIndex, ASSS)
-{
+TEST(GeneralIndex, ASSS) {
     af_index_t indexs[4];
     indexs[1].idx.seq = af_make_seq(0, 9, 1);
     indexs[2].idx.seq = af_span;
     indexs[3].idx.seq = af_span;
-    indexs[0].isSeq = false;
-    indexs[1].isSeq = true;
-    indexs[2].isSeq = true;
-    indexs[3].isSeq = true;
+    indexs[0].isSeq   = false;
+    indexs[1].isSeq   = true;
+    indexs[2].isSeq   = true;
+    indexs[3].isSeq   = true;
 
-    testGeneralIndexOneArray(string(TEST_DIR"/gen_index/as0_9s0_ns0_n.test"), 4, indexs, 0);
+    testGeneralIndexOneArray(string(TEST_DIR "/gen_index/as0_9s0_ns0_n.test"),
+                             4, indexs, 0);
 }
 
-TEST(GeneralIndex, SASS)
-{
+TEST(GeneralIndex, SASS) {
     af_index_t indexs[2];
     indexs[0].idx.seq = af_make_seq(10, 40, 1);
-    indexs[0].isSeq = true;
-    indexs[1].isSeq = false;
+    indexs[0].isSeq   = true;
+    indexs[1].isSeq   = false;
 
-    testGeneralIndexOneArray(string(TEST_DIR"/gen_index/s0_9as0_ns0_n.test"), 2, indexs, 1);
+    testGeneralIndexOneArray(string(TEST_DIR "/gen_index/s0_9as0_ns0_n.test"),
+                             2, indexs, 1);
 }
 
-TEST(GeneralIndex, AASS)
-{
-    vector<dim4>        numDims;
-    vector< vector<float> >      in;
-    vector< vector<float> >   tests;
+TEST(GeneralIndex, AASS) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float, float>(string(TEST_DIR"/gen_index/aas0_ns0_n.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(
+        string(TEST_DIR "/gen_index/aas0_ns0_n.test"), numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    dim4 dims2     = numDims[2];
+    dim4 dims0         = numDims[0];
+    dim4 dims1         = numDims[1];
+    dim4 dims2         = numDims[2];
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array idxArray0 = 0;
@@ -122,29 +125,33 @@ TEST(GeneralIndex, AASS)
 
     af_index_t indexs[2];
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<float>::af_type));
-    indexs[0].isSeq = false;
+    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+    indexs[0].isSeq   = false;
     indexs[0].idx.arr = idxArray0;
 
-    ASSERT_SUCCESS(af_create_array(&idxArray1, &(in[2].front()),
-                dims2.ndims(), dims2.get(), (af_dtype)dtype_traits<float>::af_type));
-    indexs[1].isSeq = false;
+    ASSERT_SUCCESS(af_create_array(&idxArray1, &(in[2].front()), dims2.ndims(),
+                                   dims2.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+    indexs[1].isSeq   = false;
     indexs[1].idx.arr = idxArray1;
 
     ASSERT_SUCCESS(af_index_gen(&outArray, inArray, 2, indexs));
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
+    size_t nElems             = currGoldBar.size();
     vector<float> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     ASSERT_SUCCESS(af_release_array(inArray));
@@ -160,31 +167,28 @@ using af::seq;
 using af::span;
 using af::where;
 
-TEST(GeneralIndex, CPP_ASNN)
-{
+TEST(GeneralIndex, CPP_ASNN) {
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
     const int en = 805;
 
-    array a = randu(nx, ny);
+    array a   = randu(nx, ny);
     array idx = where(randu(nx) > 0.5);
-    array b = a(idx, seq(st, en));
+    array b   = a(idx, seq(st, en));
 
     const int nxb = b.dims(0);
     const int nyb = b.dims(1);
 
-    float *hA = a.host<float>();
-    uint  *hIdx = idx.host<uint>();
-    float *hB = b.host<float>();
-
+    float *hA  = a.host<float>();
+    uint *hIdx = idx.host<uint>();
+    float *hB  = b.host<float>();
 
     for (int j = 0; j < nyb; j++) {
         float *hAt = hA + (st + j) * nx;
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
-            ASSERT_EQ(hAt[hIdx[i]], hBt[i])
-                << "at " << i << " " << j << endl;
+            ASSERT_EQ(hAt[hIdx[i]], hBt[i]) << "at " << i << " " << j << endl;
         }
     }
 
@@ -193,31 +197,29 @@ TEST(GeneralIndex, CPP_ASNN)
     freeHost(hIdx);
 }
 
-TEST(GeneralIndex, CPP_SANN)
-{
+TEST(GeneralIndex, CPP_SANN) {
     const int nx = 1000;
     const int ny = 1000;
     const int st = 200;
     const int en = 805;
 
-    array a = randu(nx, ny);
+    array a   = randu(nx, ny);
     array idx = where(randu(ny) > 0.5);
-    array b = a(seq(st, en), idx);
+    array b   = a(seq(st, en), idx);
 
     const int nxb = b.dims(0);
     const int nyb = b.dims(1);
 
-    float *hA = a.host<float>();
-    uint  *hIdx = idx.host<uint>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    uint *hIdx = idx.host<uint>();
+    float *hB  = b.host<float>();
 
     for (int j = 0; j < nyb; j++) {
         float *hAt = hA + hIdx[j] * nx;
         float *hBt = hB + j * nxb;
 
         for (int i = 0; i < nxb; i++) {
-            ASSERT_EQ(hAt[i + st], hBt[i])
-            << "at " << i << " " << j << endl;
+            ASSERT_EQ(hAt[i + st], hBt[i]) << "at " << i << " " << j << endl;
         }
     }
 
@@ -226,25 +228,24 @@ TEST(GeneralIndex, CPP_SANN)
     freeHost(hIdx);
 }
 
-TEST(GeneralIndex, CPP_SSAN)
-{
+TEST(GeneralIndex, CPP_SSAN) {
     const int nx = 100;
     const int ny = 100;
     const int nz = 100;
     const int st = 20;
     const int en = 85;
 
-    array a = randu(nx, ny, nz);
+    array a   = randu(nx, ny, nz);
     array idx = where(randu(nz) > 0.5);
-    array b = a(seq(st, en), span, idx);
+    array b   = a(seq(st, en), span, idx);
 
     const int nxb = b.dims(0);
     const int nyb = b.dims(1);
     const int nzb = b.dims(2);
 
-    float *hA = a.host<float>();
-    uint  *hIdx = idx.host<uint>();
-    float *hB = b.host<float>();
+    float *hA  = a.host<float>();
+    uint *hIdx = idx.host<uint>();
+    float *hB  = b.host<float>();
 
     for (int k = 0; k < nzb; k++) {
         float *hAt = hA + hIdx[k] * nx * ny;
@@ -252,7 +253,7 @@ TEST(GeneralIndex, CPP_SSAN)
 
         for (int j = 0; j < nyb; j++) {
             for (int i = 0; i < nxb; i++) {
-                ASSERT_EQ(hAt[j * nx  + i + st], hBt[j * nxb + i])
+                ASSERT_EQ(hAt[j * nx + i + st], hBt[j * nxb + i])
                     << "at " << i << " " << j << " " << k << endl;
             }
         }
@@ -263,31 +264,28 @@ TEST(GeneralIndex, CPP_SSAN)
     freeHost(hIdx);
 }
 
-TEST(GeneralIndex, CPP_AANN)
-{
+TEST(GeneralIndex, CPP_AANN) {
     const int nx = 1000;
     const int ny = 1000;
 
-    array a = randu(nx, ny);
+    array a    = randu(nx, ny);
     array idx0 = where(randu(nx) > 0.5);
     array idx1 = where(randu(ny) > 0.5);
-    array b = a(idx0, idx1);
+    array b    = a(idx0, idx1);
 
     const int nxb = b.dims(0);
     const int nyb = b.dims(1);
 
-    float *hA = a.host<float>();
-    uint  *hIdx0 = idx0.host<uint>();
-    uint  *hIdx1 = idx1.host<uint>();
-    float *hB = b.host<float>();
-
+    float *hA   = a.host<float>();
+    uint *hIdx0 = idx0.host<uint>();
+    uint *hIdx1 = idx1.host<uint>();
+    float *hB   = b.host<float>();
 
     for (int j = 0; j < nyb; j++) {
         float *hAt = hA + hIdx1[j] * nx;
         float *hBt = hB + j * nxb;
         for (int i = 0; i < nxb; i++) {
-            ASSERT_EQ(hAt[hIdx0[i]], hBt[i])
-                << "at " << i << " " << j << endl;
+            ASSERT_EQ(hAt[hIdx0[i]], hBt[i]) << "at " << i << " " << j << endl;
         }
     }
 
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index 35ad878364..ca148c3380 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -7,48 +7,46 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <vector>
-#include <complex>
+#include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <complex>
+#include <vector>
 
 using namespace af;
-using std::vector;
 using std::abs;
+using std::vector;
 
-TEST(GettingStarted, SNIPPET_getting_started_gen)
-{
-
+TEST(GettingStarted, SNIPPET_getting_started_gen) {
     //! [ex_getting_started_constructors]
     // Arrays may be created using the array constructor and dimensioned
     // as 1D, 2D, 3D; however, the values in these arrays will be undefined
-    array undefined_1D(100);        // 1D array with 100 elements
-    array undefined_2D(10, 100);    // 2D array of size 10 x 100
-    array undefined_3D(10, 10, 10); // 3D array of size 10 x 10 x 10
+    array undefined_1D(100);         // 1D array with 100 elements
+    array undefined_2D(10, 100);     // 2D array of size 10 x 100
+    array undefined_3D(10, 10, 10);  // 3D array of size 10 x 10 x 10
     //! [ex_getting_started_constructors]
 
     //! [ex_getting_started_gen]
     // Generate an array of size three filled with zeros.
     // If no data type is specified, ArrayFire defaults to f32.
     // The constant function generates the data on the device.
-    array zeros      = constant(0, 3);
+    array zeros = constant(0, 3);
 
     // Generate a 1x4 array of uniformly distributed [0,1] random numbers
     // The randu function generates the data on the device.
-    array rand1      = randu(1, 4);
+    array rand1 = randu(1, 4);
 
     // Generate a 2x2 array (or matrix, if you prefer) of random numbers
     // sampled from a normal distribution.
     // The randn function generates data on the device.
-    array rand2      = randn(2, 2);
+    array rand2 = randn(2, 2);
 
     // Generate a 3x3 identity matrix. The data is generated on the device.
-    array iden       = identity(3, 3);
+    array iden = identity(3, 3);
 
     // Lastly, create a 2x1 array (column vector) of uniformly distributed
     // 32-bit complex numbers (c32 data type):
-    array randcplx   = randu(2, 1, c32);
+    array randcplx = randu(2, 1, c32);
     //! [ex_getting_started_gen]
 
     {
@@ -56,30 +54,33 @@ TEST(GettingStarted, SNIPPET_getting_started_gen)
         output.resize(zeros.elements());
         zeros.host(&output.front());
         ASSERT_EQ(f32, zeros.type());
-        for(dim_t i = 0; i < zeros.elements(); i++) ASSERT_FLOAT_EQ(0, output[i]);
+        for (dim_t i = 0; i < zeros.elements(); i++)
+            ASSERT_FLOAT_EQ(0, output[i]);
     }
 
     if (!noDoubleTests<double>()) {
-        array ones       = constant(1, 3, 2, f64);
+        array ones = constant(1, 3, 2, f64);
         vector<double> output(ones.elements());
         ones.host(&output.front());
         ASSERT_EQ(f64, ones.type());
-        for(dim_t i = 0; i < ones.elements(); i++) ASSERT_FLOAT_EQ(1, output[i]);
+        for (dim_t i = 0; i < ones.elements(); i++)
+            ASSERT_FLOAT_EQ(1, output[i]);
     }
 
     {
         vector<float> output;
         output.resize(iden.elements());
         iden.host(&output.front());
-        for(dim_t i = 0; i < iden.dims(0); i++)
-            for(dim_t j = 0; j < iden.dims(1); j++)
-                if(i == j)  ASSERT_FLOAT_EQ(1, output[i * iden.dims(0) + j]);
-                else        ASSERT_FLOAT_EQ(0, output[i * iden.dims(0) + j]);
+        for (dim_t i = 0; i < iden.dims(0); i++)
+            for (dim_t j = 0; j < iden.dims(1); j++)
+                if (i == j)
+                    ASSERT_FLOAT_EQ(1, output[i * iden.dims(0) + j]);
+                else
+                    ASSERT_FLOAT_EQ(0, output[i * iden.dims(0) + j]);
     }
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_init)
-{
+TEST(GettingStarted, SNIPPET_getting_started_init) {
     //! [ex_getting_started_init]
     // Create a six-element array on the host
     float hA[] = {0, 1, 2, 3, 4, 5};
@@ -96,18 +97,18 @@ TEST(GettingStarted, SNIPPET_getting_started_init)
     // data (stored in {{real, imaginary}, {real, imaginary},  ... } format
     // as found in C's complex.h and C++'s <complex>.
     // Below we create a 3x1 column vector of complex data values:
-    array dB(3, 1, (cfloat*) hA); // 3x1 column vector of complex numbers
+    array dB(3, 1, (cfloat *)hA);  // 3x1 column vector of complex numbers
     af_print(dB);
 
     //! [ex_getting_started_init]
 
     vector<float> out(A.elements());
     A.host(&out.front());
-    for(unsigned int i = 0; i < out.size(); i++) ASSERT_FLOAT_EQ(hA[i], out[i]);
+    for (unsigned int i = 0; i < out.size(); i++)
+        ASSERT_FLOAT_EQ(hA[i], out[i]);
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_print)
-{
+TEST(GettingStarted, SNIPPET_getting_started_print) {
     //! [ex_getting_started_print]
     // Generate two arrays
     array a = randu(2, 2);
@@ -129,24 +130,24 @@ TEST(GettingStarted, SNIPPET_getting_started_print)
     b.host(&outb.front());
     result.host(&out.front());
 
-    for(unsigned i = 0; i < outb.size(); i++) ASSERT_FLOAT_EQ(outa[i] + outb[i] + 0.4, out[i]);
+    for (unsigned i = 0; i < outb.size(); i++)
+        ASSERT_FLOAT_EQ(outa[i] + outb[i] + 0.4, out[i]);
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_dims)
-{
+TEST(GettingStarted, SNIPPET_getting_started_dims) {
     //! [ex_getting_started_dims]
     // Create a 4x5x2 array of uniformly distributed random numbers
-    array a = randu(4,5,2);
+    array a = randu(4, 5, 2);
     // Determine the number of dimensions using the numdims() function:
-    printf("numdims(a)  %d\n",  a.numdims()); // 3
+    printf("numdims(a)  %d\n", a.numdims());  // 3
 
     // We can also find the size of the individual dimentions using either
     // the `dims` function:
-    printf("dims = [%lld %lld]\n", a.dims(0), a.dims(1)); // 4,5
+    printf("dims = [%lld %lld]\n", a.dims(0), a.dims(1));  // 4,5
 
     // Or the elements of a dim4 object:
     dim4 dims = a.dims();
-    printf("dims = [%lld %lld]\n", dims[0], dims[1]); // 4,5
+    printf("dims = [%lld %lld]\n", dims[0], dims[1]);  // 4,5
     //! [ex_getting_started_dims]
 
     //! [ex_getting_started_prop]
@@ -159,11 +160,13 @@ TEST(GettingStarted, SNIPPET_getting_started_dims)
     printf("is complex? %d    is real? %d\n", a.iscomplex(), a.isreal());
 
     // if it is a column or row vector
-    printf("is vector? %d  column? %d  row? %d\n", a.isvector(), a.iscolumn(), a.isrow());
+    printf("is vector? %d  column? %d  row? %d\n", a.isvector(), a.iscolumn(),
+           a.isrow());
 
     // and whether or not the array is empty and how much memory it takes on
     // the device:
-    printf("empty? %d  total elements: %lld  bytes: %zu\n", a.isempty(), a.elements(), a.bytes());
+    printf("empty? %d  total elements: %lld  bytes: %zu\n", a.isempty(),
+           a.elements(), a.bytes());
     //! [ex_getting_started_prop]
 
     ASSERT_EQ(f32, a.type());
@@ -184,8 +187,7 @@ TEST(GettingStarted, SNIPPET_getting_started_dims)
     ASSERT_EQ(5, a.dims(1));
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_arith)
-{
+TEST(GettingStarted, SNIPPET_getting_started_arith) {
     //! [ex_getting_started_arith]
     array R = randu(3, 3);
     af_print(constant(1, 3, 3) + complex(sin(R)));  // will be c32
@@ -202,30 +204,29 @@ TEST(GettingStarted, SNIPPET_getting_started_arith)
     //! [ex_getting_started_arith]
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_dev_ptr)
-{
+TEST(GettingStarted, SNIPPET_getting_started_dev_ptr) {
 #ifdef __CUDACC__
     //! [ex_getting_started_dev_ptr]
-    // Create an array on the host, copy it into an ArrayFire 2x3 ArrayFire array
-    float host_ptr[] = {0,1,2,3,4,5};
+    // Create an array on the host, copy it into an ArrayFire 2x3 ArrayFire
+    // array
+    float host_ptr[] = {0, 1, 2, 3, 4, 5};
     array a(2, 3, host_ptr);
 
     // Create a CUDA device pointer, populate it with data from the host
     float *device_ptr;
-    cudaMalloc((void**)&device_ptr, 6*sizeof(float));
-    cudaMemcpy(device_ptr, host_ptr, 6*sizeof(float), cudaMemcpyHostToDevice);
+    cudaMalloc((void **)&device_ptr, 6 * sizeof(float));
+    cudaMemcpy(device_ptr, host_ptr, 6 * sizeof(float), cudaMemcpyHostToDevice);
 
     // Convert the CUDA-allocated device memory into an ArrayFire array:
-    array b(2,3, device_ptr, afDevice); // Note: afDevice (default: afHost)
+    array b(2, 3, device_ptr, afDevice);  // Note: afDevice (default: afHost)
     // Note that ArrayFire takes ownership over `device_ptr`, so memory will
     // be freed when `b` id destructed. Do not call cudaFree(device_ptr)!
 
     //! [ex_getting_started_dev_ptr]
-#endif //__CUDACC__
+#endif  //__CUDACC__
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_ptr)
-{
+TEST(GettingStarted, SNIPPET_getting_started_ptr) {
 #ifdef __CUDACC__
     //! [ex_getting_started_ptr]
     // Create an array consisting of 3 random numbers
@@ -239,43 +240,44 @@ TEST(GettingStarted, SNIPPET_getting_started_ptr)
     freeHost(host_a);
 
     // Get access to the device memory for a CUDA kernel
-    float * d_cuda = a.device<float>();    // no need to free this
+    float *d_cuda = a.device<float>();  // no need to free this
     float value;
     cudaMemcpy(&value, d_cuda + 2, sizeof(float), cudaMemcpyDeviceToHost);
     printf("d_cuda[2] = %g\n", value);
-    a.unlock(); // unlock to allow garbage collection if necessary
+    a.unlock();  // unlock to allow garbage collection if necessary
 
     // Because OpenCL uses references rather than pointers, accessing memory
     // is similar, but has a somewhat clunky syntax. For the C-API
-    cl_mem d_opencl = (cl_mem) a.device<float>();
+    cl_mem d_opencl = (cl_mem)a.device<float>();
     // for the C++ API, you can just wrap this object into a cl::Buffer
     // after calling clRetainMemObject.
 
     //! [ex_getting_started_ptr]
-#endif //__CUDACC__
+#endif  //__CUDACC__
 }
 
-
-TEST(GettingStarted, SNIPPET_getting_started_scalar)
-{
+TEST(GettingStarted, SNIPPET_getting_started_scalar) {
     //! [ex_getting_started_scalar]
-    array a = randu(3);
+    array a   = randu(3);
     float val = a.scalar<float>();
     printf("scalar value: %g\n", val);
     //! [ex_getting_started_scalar]
 }
 
-TEST(GettingStarted, SNIPPET_getting_started_bit)
-{
+TEST(GettingStarted, SNIPPET_getting_started_bit) {
     //! [ex_getting_started_bit]
     int h_A[] = {1, 1, 0, 0, 4, 0, 0, 2, 0};
     int h_B[] = {1, 0, 1, 0, 1, 0, 1, 1, 1};
     array A = array(3, 3, h_A), B = array(3, 3, h_B);
-    af_print(A); af_print(B);
-
-    array A_and_B = A & B; af_print(A_and_B);
-    array  A_or_B = A | B; af_print(A_or_B);
-    array A_xor_B = A ^ B; af_print(A_xor_B);
+    af_print(A);
+    af_print(B);
+
+    array A_and_B = A & B;
+    af_print(A_and_B);
+    array A_or_B = A | B;
+    af_print(A_or_B);
+    array A_xor_B = A ^ B;
+    af_print(A_xor_B);
     //! [ex_getting_started_bit]
 
     vector<int> Andout(A_and_B.elements());
@@ -285,24 +287,23 @@ TEST(GettingStarted, SNIPPET_getting_started_bit)
     A_or_B.host(&Orout.front());
     A_xor_B.host(&Xorout.front());
 
-
-    for(unsigned int i = 0; i < Andout.size(); i++) ASSERT_FLOAT_EQ(h_A[i] & h_B[i], Andout[i]);
-    for(unsigned int i = 0; i < Orout.size(); i++)  ASSERT_FLOAT_EQ(h_A[i] | h_B[i], Orout[i]);
-    for(unsigned int i = 0; i < Xorout.size(); i++) ASSERT_FLOAT_EQ(h_A[i] ^ h_B[i], Xorout[i]);
+    for (unsigned int i = 0; i < Andout.size(); i++)
+        ASSERT_FLOAT_EQ(h_A[i] & h_B[i], Andout[i]);
+    for (unsigned int i = 0; i < Orout.size(); i++)
+        ASSERT_FLOAT_EQ(h_A[i] | h_B[i], Orout[i]);
+    for (unsigned int i = 0; i < Xorout.size(); i++)
+        ASSERT_FLOAT_EQ(h_A[i] ^ h_B[i], Xorout[i]);
 }
 
-
-TEST(GettingStarted, SNIPPET_getting_started_constants)
-{
+TEST(GettingStarted, SNIPPET_getting_started_constants) {
     //! [ex_getting_started_constants]
-    array A = randu(5,5);
+    array A          = randu(5, 5);
     A(where(A > .5)) = NaN;
 
     array x = randu(10e6), y = randu(10e6);
-    double pi_est = 4 * sum<float>(hypot(x,y) < 1) / 10e6;
+    double pi_est = 4 * sum<float>(hypot(x, y) < 1) / 10e6;
     printf("estimation error: %g\n", fabs(Pi - pi_est));
     //! [ex_getting_started_constants]
 
-    ASSERT_LE(fabs(Pi-pi_est), 0.005);
+    ASSERT_LE(fabs(Pi - pi_est), 0.005);
 }
-
diff --git a/test/gfor.cpp b/test/gfor.cpp
index 428cb37332..70d6f0addd 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -7,163 +7,145 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::constant;
 using af::freeHost;
 using af::gforSet;
+using af::randu;
 using af::seq;
 using af::span;
-using af::randu;
+using std::endl;
+using std::string;
+using std::vector;
 
-TEST(GFOR, Assign_Scalar_Span)
-{
-    const int num = 1000;
+TEST(GFOR, Assign_Scalar_Span) {
+    const int num   = 1000;
     const float val = 3;
-    array A = randu(num);
+    array A         = randu(num);
 
-    gfor(seq ii, num) {
-        A(ii) = val;
-    }
+    gfor(seq ii, num) { A(ii) = val; }
 
     float *hA = A.host<float>();
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hA[i], val);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hA[i], val); }
 
     freeHost(hA);
 }
 
-TEST(GFOR, Assign_Scalar_Seq)
-{
-    const int num = 1000;
-    const int st = 100;
-    const int en = 500;
+TEST(GFOR, Assign_Scalar_Seq) {
+    const int num   = 1000;
+    const int st    = 100;
+    const int en    = 500;
     const float val = 3;
-    array A = randu(num);
-    array B = A.copy();
+    array A         = randu(num);
+    array B         = A.copy();
 
-    gfor(seq ii, st, en) {
-        A(ii) = val;
-    }
+    gfor(seq ii, st, en) { A(ii) = val; }
 
     float *hA = A.host<float>();
     float *hB = B.host<float>();
 
     for (int i = 0; i < num; i++) {
-        if (i >= st && i <= en) ASSERT_EQ(hA[i], val);
-        else ASSERT_EQ(hA[i], hB[i]);
+        if (i >= st && i <= en)
+            ASSERT_EQ(hA[i], val);
+        else
+            ASSERT_EQ(hA[i], hB[i]);
     }
 
     freeHost(hA);
     freeHost(hB);
 }
 
-TEST(GFOR, Inc_Scalar_Span)
-{
-    const int num = 1000;
+TEST(GFOR, Inc_Scalar_Span) {
+    const int num   = 1000;
     const float val = 3;
-    array A = randu(num);
-    array B = A.copy();
+    array A         = randu(num);
+    array B         = A.copy();
 
-    gfor(seq ii, num) {
-        A(ii) += val;
-    }
+    gfor(seq ii, num) { A(ii) += val; }
 
     float *hA = A.host<float>();
     float *hB = B.host<float>();
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hA[i], val + hB[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hA[i], val + hB[i]); }
 
     freeHost(hA);
     freeHost(hB);
 }
 
-TEST(GFOR, Inc_Scalar_Seq)
-{
-    const int num = 1000;
-    const int st = 100;
-    const int en = 500;
+TEST(GFOR, Inc_Scalar_Seq) {
+    const int num   = 1000;
+    const int st    = 100;
+    const int en    = 500;
     const float val = 3;
-    array A = randu(num);
-    array B = A.copy();
+    array A         = randu(num);
+    array B         = A.copy();
 
-    gfor(seq ii, st, en) {
-        A(ii) += val;
-    }
+    gfor(seq ii, st, en) { A(ii) += val; }
 
     float *hA = A.host<float>();
     float *hB = B.host<float>();
 
     for (int i = 0; i < num; i++) {
-        if (i >= st && i <= en) ASSERT_EQ(hA[i], hB[i] + val);
-        else ASSERT_EQ(hA[i], hB[i]);
+        if (i >= st && i <= en)
+            ASSERT_EQ(hA[i], hB[i] + val);
+        else
+            ASSERT_EQ(hA[i], hB[i]);
     }
 
     freeHost(hA);
     freeHost(hB);
 }
 
-TEST(GFOR, Assign_Array_Span)
-{
+TEST(GFOR, Assign_Array_Span) {
     const int nx = 1000;
-    array A = randu(nx);
-    array B = randu(1, 1);
+    array A      = randu(nx);
+    array B      = randu(1, 1);
 
-    gfor(seq ii, nx) {
-        A(ii) = B;
-    }
+    gfor(seq ii, nx) { A(ii) = B; }
 
     float *hA = A.host<float>();
     float val = B.scalar<float>();
 
-    for (int i = 0; i < nx; i++) {
-        ASSERT_EQ(hA[i], val);
-    }
+    for (int i = 0; i < nx; i++) { ASSERT_EQ(hA[i], val); }
 
     freeHost(hA);
 }
 
-TEST(GFOR, Assign_Array_Seq)
-{
+TEST(GFOR, Assign_Array_Seq) {
     const int nx = 1000;
     const int ny = 25;
     const int st = 100;
     const int en = 500;
-    array A = randu(nx, ny);
-    array B = A.copy();
-    array C = randu(1, ny);
+    array A      = randu(nx, ny);
+    array B      = A.copy();
+    array C      = randu(1, ny);
 
-    gfor(seq ii, st, en) {
-        A(ii, span) = C;
-    }
+    gfor(seq ii, st, en) { A(ii, span) = C; }
 
     float *hA = A.host<float>();
     float *hB = B.host<float>();
     float *hC = C.host<float>();
 
     for (int j = 0; j < ny; j++) {
-        float val = hC[j];
+        float val     = hC[j];
         const int off = j * nx;
         for (int i = 0; i < nx; i++) {
-            if (i >= st && i <= en) ASSERT_EQ(hA[i + off], val);
-            else ASSERT_EQ(hA[i + off], hB[i + off]);
+            if (i >= st && i <= en)
+                ASSERT_EQ(hA[i + off], val);
+            else
+                ASSERT_EQ(hA[i + off], hB[i + off]);
         }
     }
 
@@ -172,53 +154,47 @@ TEST(GFOR, Assign_Array_Seq)
     freeHost(hC);
 }
 
-TEST(GFOR, Inc_Array_Span)
-{
+TEST(GFOR, Inc_Array_Span) {
     const int nx = 1000;
-    array A = randu(nx);
-    array B = A.copy();
-    array C = randu(1, 1);
+    array A      = randu(nx);
+    array B      = A.copy();
+    array C      = randu(1, 1);
 
-    gfor(seq ii, nx) {
-        A(ii) += C;
-    }
+    gfor(seq ii, nx) { A(ii) += C; }
 
     float *hA = A.host<float>();
     float *hB = B.host<float>();
     float val = C.scalar<float>();
 
-    for (int i = 0; i < nx; i++) {
-        ASSERT_EQ(hA[i], val + hB[i]);
-    }
+    for (int i = 0; i < nx; i++) { ASSERT_EQ(hA[i], val + hB[i]); }
 
     freeHost(hA);
     freeHost(hB);
 }
 
-TEST(GFOR, Inc_Array_Seq)
-{
+TEST(GFOR, Inc_Array_Seq) {
     const int nx = 1000;
     const int ny = 25;
     const int st = 100;
     const int en = 500;
-    array A = randu(nx, ny);
-    array B = A.copy();
-    array C = randu(1, ny);
+    array A      = randu(nx, ny);
+    array B      = A.copy();
+    array C      = randu(1, ny);
 
-    gfor(seq ii, st, en) {
-        A(ii, span) += C;
-    }
+    gfor(seq ii, st, en) { A(ii, span) += C; }
 
     float *hA = A.host<float>();
     float *hB = B.host<float>();
     float *hC = C.host<float>();
 
     for (int j = 0; j < ny; j++) {
-        float val = hC[j];
+        float val     = hC[j];
         const int off = j * nx;
         for (int i = 0; i < nx; i++) {
-            if (i >= st && i <= en) ASSERT_EQ(hA[i + off], val + hB[i + off]);
-            else ASSERT_EQ(hA[i + off], hB[i + off]);
+            if (i >= st && i <= en)
+                ASSERT_EQ(hA[i + off], val + hB[i + off]);
+            else
+                ASSERT_EQ(hA[i + off], hB[i + off]);
         }
     }
 
@@ -227,12 +203,11 @@ TEST(GFOR, Inc_Array_Seq)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 2D0)
-{
+TEST(BatchFunc, 2D0) {
     const int nx = 1000;
     const int ny = 10;
-    array A = randu(nx, ny);
-    array B = randu( 1, ny);
+    array A      = randu(nx, ny);
+    array B      = randu(1, ny);
 
     gforSet(true);
 
@@ -254,12 +229,11 @@ TEST(BatchFunc, 2D0)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 2D1)
-{
+TEST(BatchFunc, 2D1) {
     const int nx = 1000;
     const int ny = 10;
-    array A = randu(nx, ny);
-    array B = randu(nx, 1);
+    array A      = randu(nx, ny);
+    array B      = randu(nx, 1);
 
     gforSet(true);
 
@@ -281,13 +255,12 @@ TEST(BatchFunc, 2D1)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 3D0)
-{
+TEST(BatchFunc, 3D0) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
-    array A = randu(nx, ny, nz);
-    array B = randu( 1, ny, nz);
+    array A      = randu(nx, ny, nz);
+    array B      = randu(1, ny, nz);
 
     gforSet(true);
 
@@ -300,7 +273,8 @@ TEST(BatchFunc, 3D0)
     for (int k = 0; k < nz; k++) {
         for (int j = 0; j < ny; j++) {
             for (int i = 0; i < nx; i++) {
-                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[k * ny + j], hC[k * ny * nx + j * nx + i]);
+                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[k * ny + j],
+                          hC[k * ny * nx + j * nx + i]);
             }
         }
     }
@@ -311,13 +285,12 @@ TEST(BatchFunc, 3D0)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 3D1)
-{
+TEST(BatchFunc, 3D1) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
-    array A = randu(nx, ny, nz);
-    array B = randu(nx,  1, nz);
+    array A      = randu(nx, ny, nz);
+    array B      = randu(nx, 1, nz);
 
     gforSet(true);
 
@@ -330,7 +303,8 @@ TEST(BatchFunc, 3D1)
     for (int k = 0; k < nz; k++) {
         for (int j = 0; j < ny; j++) {
             for (int i = 0; i < nx; i++) {
-                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[k * nx + i], hC[k * ny * nx + j * nx + i]);
+                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[k * nx + i],
+                          hC[k * ny * nx + j * nx + i]);
             }
         }
     }
@@ -341,13 +315,12 @@ TEST(BatchFunc, 3D1)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 3D2)
-{
+TEST(BatchFunc, 3D2) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
-    array A = randu(nx, ny, nz);
-    array B = randu(nx, ny,  1);
+    array A      = randu(nx, ny, nz);
+    array B      = randu(nx, ny, 1);
 
     gforSet(true);
 
@@ -360,7 +333,8 @@ TEST(BatchFunc, 3D2)
     for (int k = 0; k < nz; k++) {
         for (int j = 0; j < ny; j++) {
             for (int i = 0; i < nx; i++) {
-                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[j * nx + i], hC[k * ny * nx + j * nx + i]);
+                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[j * nx + i],
+                          hC[k * ny * nx + j * nx + i]);
             }
         }
     }
@@ -371,13 +345,12 @@ TEST(BatchFunc, 3D2)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 3D01)
-{
+TEST(BatchFunc, 3D01) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
-    array A = randu(nx, ny, nz);
-    array B = randu( 1,  1, nz);
+    array A      = randu(nx, ny, nz);
+    array B      = randu(1, 1, nz);
 
     gforSet(true);
 
@@ -390,7 +363,8 @@ TEST(BatchFunc, 3D01)
     for (int k = 0; k < nz; k++) {
         for (int j = 0; j < ny; j++) {
             for (int i = 0; i < nx; i++) {
-                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[k], hC[k * ny * nx + j * nx + i]);
+                ASSERT_EQ(hA[k * ny * nx + j * nx + i] + hB[k],
+                          hC[k * ny * nx + j * nx + i]);
             }
         }
     }
@@ -401,13 +375,12 @@ TEST(BatchFunc, 3D01)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 3D_1_2)
-{
+TEST(BatchFunc, 3D_1_2) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
-    array A = randu(nx, ny,  1);
-    array B = randu(nx,  1, nz);
+    array A      = randu(nx, ny, 1);
+    array B      = randu(nx, 1, nz);
 
     gforSet(true);
 
@@ -420,7 +393,8 @@ TEST(BatchFunc, 3D_1_2)
     for (int k = 0; k < nz; k++) {
         for (int j = 0; j < ny; j++) {
             for (int i = 0; i < nx; i++) {
-                ASSERT_EQ(hA[j * nx + i] + hB[k * nx + i], hC[k * ny * nx + j * nx + i]);
+                ASSERT_EQ(hA[j * nx + i] + hB[k * nx + i],
+                          hC[k * ny * nx + j * nx + i]);
             }
         }
     }
@@ -431,14 +405,13 @@ TEST(BatchFunc, 3D_1_2)
     freeHost(hC);
 }
 
-TEST(BatchFunc, 4D3)
-{
+TEST(BatchFunc, 4D3) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
     const int nw = 2;
-    array A = randu(nx, ny, nz, nw);
-    array B = randu(nx, ny, nz,  1);
+    array A      = randu(nx, ny, nz, nw);
+    array B      = randu(nx, ny, nz, 1);
 
     gforSet(true);
 
@@ -453,7 +426,7 @@ TEST(BatchFunc, 4D3)
             for (int j = 0; j < ny; j++) {
                 for (int i = 0; i < nx; i++) {
                     ASSERT_EQ(hA[l * nz * ny * nx + k * ny * nx + j * nx + i] +
-                              hB[k * ny * nx + j * nx + i],
+                                  hB[k * ny * nx + j * nx + i],
                               hC[l * nz * ny * nx + k * ny * nx + j * nx + i]);
                 }
             }
@@ -466,15 +439,13 @@ TEST(BatchFunc, 4D3)
     freeHost(hC);
 }
 
-
-TEST(BatchFunc, 4D_2_3)
-{
+TEST(BatchFunc, 4D_2_3) {
     const int nx = 1000;
     const int ny = 10;
     const int nz = 3;
     const int nw = 2;
-    array A = randu(nx,  1, nz, nw);
-    array B = randu(nx, ny,  1,  1);
+    array A      = randu(nx, 1, nz, nw);
+    array B      = randu(nx, ny, 1, 1);
 
     gforSet(true);
 
@@ -488,8 +459,8 @@ TEST(BatchFunc, 4D_2_3)
         for (int k = 0; k < nz; k++) {
             for (int j = 0; j < ny; j++) {
                 for (int i = 0; i < nx; i++) {
-                    ASSERT_EQ(hA[l * nz * nx + k * nx + i] +
-                              hB[j * nx + i], hC[l * nz * ny * nx + k * ny * nx + j * nx + i]);
+                    ASSERT_EQ(hA[l * nz * nx + k * nx + i] + hB[j * nx + i],
+                              hC[l * nz * ny * nx + k * ny * nx + j * nx + i]);
                 }
             }
         }
@@ -501,27 +472,30 @@ TEST(BatchFunc, 4D_2_3)
     freeHost(hC);
 }
 
-TEST(ASSIGN, ISSUE_1127)
-{
-    array orig = randu(512, 768, 3);
-    array vert = randu(512, 768, 3);
+TEST(ASSIGN, ISSUE_1127) {
+    array orig  = randu(512, 768, 3);
+    array vert  = randu(512, 768, 3);
     array horiz = randu(512, 768, 3);
-    array diag = randu(512, 768, 3);
+    array diag  = randu(512, 768, 3);
 
     array out0 = constant(0, orig.dims(0) * 2, orig.dims(1) * 2, orig.dims(2));
     array out1 = constant(0, orig.dims(0) * 2, orig.dims(1) * 2, orig.dims(2));
     int rows = out0.dims(0), cols = out0.dims(1);
 
     gfor(seq chan, 3) {
-        out0(seq(0,rows-1,2), seq(0,cols-1,2), chan) = orig(span,span,chan);
-        out0(seq(1,rows-1,2), seq(0,cols-1,2), chan) = vert(span,span,chan);
-        out0(seq(0,rows-1,2), seq(1,cols-1,2), chan) = horiz(span,span,chan);
-        out0(seq(1,rows-1,2), seq(1,cols-1,2), chan) = diag(span,span,chan);
+        out0(seq(0, rows - 1, 2), seq(0, cols - 1, 2), chan) =
+            orig(span, span, chan);
+        out0(seq(1, rows - 1, 2), seq(0, cols - 1, 2), chan) =
+            vert(span, span, chan);
+        out0(seq(0, rows - 1, 2), seq(1, cols - 1, 2), chan) =
+            horiz(span, span, chan);
+        out0(seq(1, rows - 1, 2), seq(1, cols - 1, 2), chan) =
+            diag(span, span, chan);
     }
-    out1(seq(0,rows-1,2), seq(0,cols-1,2), span) = orig;
-    out1(seq(1,rows-1,2), seq(0,cols-1,2), span) = vert;
-    out1(seq(0,rows-1,2), seq(1,cols-1,2), span) = horiz;
-    out1(seq(1,rows-1,2), seq(1,cols-1,2), span) = diag;
+    out1(seq(0, rows - 1, 2), seq(0, cols - 1, 2), span) = orig;
+    out1(seq(1, rows - 1, 2), seq(0, cols - 1, 2), span) = vert;
+    out1(seq(0, rows - 1, 2), seq(1, cols - 1, 2), span) = horiz;
+    out1(seq(1, rows - 1, 2), seq(1, cols - 1, 2), span) = diag;
 
     ASSERT_ARRAYS_EQ(out0, out1);
 }
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 50b705e76e..b61040e2c5 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -7,55 +7,52 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
+using af::array;
+using af::dim4;
+using af::features;
+using af::loadImage;
 using std::abs;
 using std::cout;
 using std::endl;
 using std::string;
 using std::vector;
-using af::array;
-using af::dim4;
-using af::features;
-using af::loadImage;
 
-typedef struct
-{
+typedef struct {
     float f[5];
     unsigned d[272];
 } feat_desc_t;
 
-typedef struct
-{
+typedef struct {
     float f[5];
 } feat_t;
 
-typedef struct
-{
+typedef struct {
     float d[272];
 } desc_t;
 
-#ifdef  AF_WITH_NONFREE_SIFT
-static bool feat_cmp(feat_desc_t i, feat_desc_t j)
-{
+#ifdef AF_WITH_NONFREE_SIFT
+static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
     for (int k = 0; k < 5; k++)
-        if (round(i.f[k]*1e1f) != round(j.f[k]*1e1f))
-            return (round(i.f[k]*1e1f) < round(j.f[k]*1e1f));
+        if (round(i.f[k] * 1e1f) != round(j.f[k] * 1e1f))
+            return (round(i.f[k] * 1e1f) < round(j.f[k] * 1e1f));
 
     return true;
 }
 
-static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, float* desc, unsigned nfeat)
-{
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
+                               float* score, float* ori, float* size,
+                               float* desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -63,13 +60,13 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
         feat[i].f[2] = score[i];
         feat[i].f[3] = ori[i];
         feat[i].f[4] = size[i];
-        for (unsigned j = 0; j < 272; j++)
-            feat[i].d[j] = desc[i * 272 + j];
+        for (unsigned j = 0; j < 272; j++) feat[i].d[j] = desc[i * 272 + j];
     }
 }
 
-static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<float> >& desc, unsigned nfeat)
-{
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
+                               float* score, float* ori, float* size,
+                               vector<vector<float> >& desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -77,13 +74,12 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
         feat[i].f[2] = score[i];
         feat[i].f[3] = ori[i];
         feat[i].f[4] = size[i];
-        for (unsigned j = 0; j < 272; j++)
-            feat[i].d[j] = desc[i][j];
+        for (unsigned j = 0; j < 272; j++) feat[i].d[j] = desc[i][j];
     }
 }
 
-static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
-{
+static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f,
+                            vector<desc_t>& d) {
     f.resize(fd.size());
     d.resize(fd.size());
     for (size_t i = 0; i < fd.size(); i++) {
@@ -92,37 +88,38 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
         f[i].f[2] = fd[i].f[2];
         f[i].f[3] = fd[i].f[3];
         f[i].f[4] = fd[i].f[4];
-        for (unsigned j = 0; j < 272; j++)
-            d[i].d[j] = fd[i].d[j];
+        for (unsigned j = 0; j < 272; j++) d[i].d[j] = fd[i].d[j];
     }
 }
 
-static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
-{
-    bool ret = true;
+static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float* cpu,
+                             float* gpu, float unit_thr = 1.f,
+                             float euc_thr = 1.f) {
+    bool ret  = true;
     float sum = 0.0f;
 
     for (dim_t i = 0; i < ndesc; i++) {
         sum = 0.0f;
         for (dim_t l = 0; l < desc_len; l++) {
             dim_t idx = i * desc_len + l;
-            float x = (cpu[idx] - gpu[idx]);
-            sum += x*x;
+            float x   = (cpu[idx] - gpu[idx]);
+            sum += x * x;
             if (abs(x) > (float)unit_thr) {
                 ret = false;
-                cout<<endl<<"@compareEuclidean: unit mismatch."<<endl;
-                cout<<"(cpu,gpu,cpu-gpu)["<<i<<","<<l<<"] : {"<<cpu[idx]<<","<<gpu[idx]<<","<<cpu[idx]-gpu[idx]<<"}"<<endl;
-                cout<<endl;
+                cout << endl << "@compareEuclidean: unit mismatch." << endl;
+                cout << "(cpu,gpu,cpu-gpu)[" << i << "," << l << "] : {"
+                     << cpu[idx] << "," << gpu[idx] << ","
+                     << cpu[idx] - gpu[idx] << "}" << endl;
+                cout << endl;
                 break;
             }
         }
         if (sqrt(sum) > euc_thr) {
             ret = false;
-            cout<<endl<<"@compareEuclidean: distance mismatch."<<endl;
-            cout<<"Euclidean distance: "<<sqrt(sum)<<endl;
+            cout << endl << "@compareEuclidean: distance mismatch." << endl;
+            cout << "Euclidean distance: " << sqrt(sum) << endl;
         }
-        if (ret == false)
-            return ret;
+        if (ret == false) return ret;
     }
 
     return ret;
@@ -130,10 +127,9 @@ static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu
 #endif
 
 template<typename T>
-class GLOH : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class GLOH : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
@@ -141,33 +137,35 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(GLOH, TestTypes);
 
 template<typename T>
-void glohTest(string pTestFile)
-{
+void glohTest(string pTestFile) {
 #ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>           inDims;
-    vector<string>         inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > goldFeat;
     vector<vector<float> > goldDesc;
 
-    readImageFeaturesDescriptors<float>(pTestFile, inDims, inFiles, goldFeat, goldDesc);
+    readImageFeaturesDescriptors<float>(pTestFile, inDims, inFiles, goldFeat,
+                                        goldDesc);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        af_array inArray_f32  = 0;
-        af_array inArray      = 0;
-        af_array desc         = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray_f32 = 0;
+        af_array inArray     = 0;
+        af_array desc        = 0;
         af_features feat;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/gloh/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/gloh/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_gloh(&feat, &desc, inArray, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f));
+        ASSERT_SUCCESS(af_gloh(&feat, &desc, inArray, 3, 0.04f, 10.0f, 1.6f,
+                               true, 1.f / 256.f, 0.05f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
@@ -179,16 +177,17 @@ void glohTest(string pTestFile)
         ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
         ASSERT_SUCCESS(af_get_features_size(&size, feat));
 
-        float * outX           = new float[n];
-        float * outY           = new float[n];
-        float * outScore       = new float[n];
-        float * outOrientation = new float[n];
-        float * outSize        = new float[n];
+        float* outX           = new float[n];
+        float* outY           = new float[n];
+        float* outScore       = new float[n];
+        float* outOrientation = new float[n];
+        float* outSize        = new float[n];
         dim_t descSize;
         dim_t descDims[4];
         ASSERT_SUCCESS(af_get_elements(&descSize, desc));
-        ASSERT_SUCCESS(af_get_dims(&descDims[0], &descDims[1], &descDims[2], &descDims[3], desc));
-        float * outDesc     = new float[descSize];
+        ASSERT_SUCCESS(af_get_dims(&descDims[0], &descDims[1], &descDims[2],
+                                   &descDims[3], desc));
+        float* outDesc = new float[descSize];
         ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
         ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
         ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
@@ -197,13 +196,18 @@ void glohTest(string pTestFile)
         ASSERT_SUCCESS(af_get_data_ptr((void*)outDesc, desc));
 
         vector<feat_desc_t> out_feat_desc;
-        array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, n);
+        array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation,
+                           outSize, outDesc, n);
 
         vector<feat_desc_t> gold_feat_desc;
-        array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
+        array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(),
+                           &goldFeat[1].front(), &goldFeat[2].front(),
+                           &goldFeat[3].front(), &goldFeat[4].front(), goldDesc,
+                           goldFeat[0].size());
 
         std::stable_sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
-        std::stable_sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
+        std::stable_sort(gold_feat_desc.begin(), gold_feat_desc.end(),
+                         feat_cmp);
 
         vector<feat_t> out_feat;
         vector<desc_t> v_out_desc;
@@ -214,14 +218,26 @@ void glohTest(string pTestFile)
         split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
         for (int elIter = 0; elIter < (int)n; elIter++) {
-            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]),
+                      0.5f)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]),
+                      1e-3)
+                << "at: " << elIter << endl;
         }
 
-        EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 5.5f));
+        EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1],
+                                     (float*)&v_out_desc[0],
+                                     (float*)&v_gold_desc[0], 2.f, 5.5f));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
@@ -243,43 +259,42 @@ void glohTest(string pTestFile)
 #endif
 }
 
-#define GLOH_INIT(desc, image)                                        \
-    TYPED_TEST(GLOH, desc)                                            \
-    {                                                                 \
-        glohTest<TypeParam>(string(TEST_DIR"/gloh/"#image".test"));   \
+#define GLOH_INIT(desc, image)                                         \
+    TYPED_TEST(GLOH, desc) {                                           \
+        glohTest<TypeParam>(string(TEST_DIR "/gloh/" #image ".test")); \
     }
 
-    GLOH_INIT(man, man);
+GLOH_INIT(man, man);
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(GLOH, CPP)
-{
+TEST(GLOH, CPP) {
 #ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>           inDims;
-    vector<string>         inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > goldFeat;
     vector<vector<float> > goldDesc;
 
-    readImageFeaturesDescriptors<float>(string(TEST_DIR"/gloh/man.test"), inDims, inFiles, goldFeat, goldDesc);
-    inFiles[0].insert(0,string(TEST_DIR"/gloh/"));
+    readImageFeaturesDescriptors<float>(string(TEST_DIR "/gloh/man.test"),
+                                        inDims, inFiles, goldFeat, goldDesc);
+    inFiles[0].insert(0, string(TEST_DIR "/gloh/"));
 
     array in = loadImage(inFiles[0].c_str(), false);
 
     features feat;
     array desc;
-    gloh(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f);
-
-    float * outX           = new float[feat.getNumFeatures()];
-    float * outY           = new float[feat.getNumFeatures()];
-    float * outScore       = new float[feat.getNumFeatures()];
-    float * outOrientation = new float[feat.getNumFeatures()];
-    float * outSize        = new float[feat.getNumFeatures()];
-    float * outDesc        = new float[desc.elements()];
-    dim4 descDims = desc.dims();
+    gloh(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f / 256.f, 0.05f);
+
+    float* outX           = new float[feat.getNumFeatures()];
+    float* outY           = new float[feat.getNumFeatures()];
+    float* outScore       = new float[feat.getNumFeatures()];
+    float* outOrientation = new float[feat.getNumFeatures()];
+    float* outSize        = new float[feat.getNumFeatures()];
+    float* outDesc        = new float[desc.elements()];
+    dim4 descDims         = desc.dims();
     feat.getX().host(outX);
     feat.getY().host(outY);
     feat.getScore().host(outScore);
@@ -288,10 +303,14 @@ TEST(GLOH, CPP)
     desc.host(outDesc);
 
     vector<feat_desc_t> out_feat_desc;
-    array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, feat.getNumFeatures());
+    array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation,
+                       outSize, outDesc, feat.getNumFeatures());
 
     vector<feat_desc_t> gold_feat_desc;
-    array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
+    array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(),
+                       &goldFeat[1].front(), &goldFeat[2].front(),
+                       &goldFeat[3].front(), &goldFeat[4].front(), goldDesc,
+                       goldFeat[0].size());
 
     std::stable_sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
     std::stable_sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
@@ -305,14 +324,21 @@ TEST(GLOH, CPP)
     split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
     for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
-        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3)
+            << "at: " << elIter << endl;
     }
 
-    EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 5.5f));
+    EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1],
+                                 (float*)&v_out_desc[0],
+                                 (float*)&v_gold_desc[0], 2.f, 5.5f));
 
     delete[] outX;
     delete[] outY;
diff --git a/test/gradient.cpp b/test/gradient.cpp
index 3e74a1f0ed..3a02c5aa02 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -7,35 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Grad : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Grad : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
@@ -45,28 +44,34 @@ typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(Grad, TestTypes);
 
 template<typename T>
-void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned resultIdx1, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void gradTest(string pTestFile, const unsigned resultIdx0,
+              const unsigned resultIdx1, bool isSubRef = false,
+              const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array tempArray = 0;
-    af_array g0Array = 0;
-    af_array g1Array = 0;
+    af_array g0Array   = 0;
+    af_array g1Array   = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_gradient(&g0Array, &g1Array, inArray));
@@ -78,7 +83,8 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Get result
@@ -87,38 +93,37 @@ void gradTest(string pTestFile, const unsigned resultIdx0, const unsigned result
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter])
+            << "at: " << elIter << endl;
     }
 
-
     // Delete
     delete[] grad0Data;
     delete[] grad1Data;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(g0Array   != 0) af_release_array(g0Array);
-    if(g1Array   != 0) af_release_array(g1Array);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (g0Array != 0) af_release_array(g0Array);
+    if (g1Array != 0) af_release_array(g1Array);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define GRAD_INIT(desc, file, resultIdx0, resultIdx1)                                       \
-    TYPED_TEST(Grad, desc)                                                                  \
-    {                                                                                       \
-        gradTest<TypeParam>(string(TEST_DIR"/grad/"#file".test"), resultIdx0, resultIdx1);  \
+#define GRAD_INIT(desc, file, resultIdx0, resultIdx1)                \
+    TYPED_TEST(Grad, desc) {                                         \
+        gradTest<TypeParam>(string(TEST_DIR "/grad/" #file ".test"), \
+                            resultIdx0, resultIdx1);                 \
     }
 
-    GRAD_INIT(Grad0, grad, 0, 1);
-    GRAD_INIT(Grad1, grad2D, 0, 1);
-    GRAD_INIT(Grad2, grad3D, 0, 1);
-
+GRAD_INIT(Grad0, grad, 0, 1);
+GRAD_INIT(Grad1, grad2D, 0, 1);
+GRAD_INIT(Grad2, grad3D, 0, 1);
 
-/////////////////////////////////////// CPP ///////////////////////////////////////////
+/////////////////////////////////////// CPP
+//////////////////////////////////////////////
 //
 
 using af::array;
 
-TEST(Grad, CPP)
-{
+TEST(Grad, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx0 = 0;
@@ -127,7 +132,8 @@ TEST(Grad, CPP)
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, float>(string(TEST_DIR"/grad/grad3D.test"),numDims,in,tests);
+    readTests<float, float, float>(string(TEST_DIR "/grad/grad3D.test"),
+                                   numDims, in, tests);
 
     dim4 idims = numDims[0];
 
@@ -142,7 +148,8 @@ TEST(Grad, CPP)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], grad0Data[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Get result
@@ -151,7 +158,8 @@ TEST(Grad, CPP)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx1][elIter], grad1Data[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Delete
@@ -159,8 +167,7 @@ TEST(Grad, CPP)
     delete[] grad1Data;
 }
 
-TEST(Grad, MaxDim)
-{
+TEST(Grad, MaxDim) {
     using af::constant;
     using af::sum;
 
diff --git a/test/gray_rgb.cpp b/test/gray_rgb.cpp
index 81323860df..16a085fb80 100644
--- a/test/gray_rgb.cpp
+++ b/test/gray_rgb.cpp
@@ -7,20 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::vector;
 using af::array;
 using af::randu;
+using std::vector;
 
-TEST(rgb_gray, 32bit)
-{
-    array rgb = randu(10, 10, 3);
+TEST(rgb_gray, 32bit) {
+    array rgb  = randu(10, 10, 3);
     array gray = rgb2gray(rgb);
 
     vector<float> h_rgb(rgb.elements());
@@ -29,28 +28,25 @@ TEST(rgb_gray, 32bit)
     rgb.host(&h_rgb[0]);
     gray.host(&h_gray[0]);
 
-    int num = gray.elements();
+    int num  = gray.elements();
     int roff = 0;
     int goff = num;
     int boff = 2 * num;
 
-    const float rPercent=0.2126f;
-    const float gPercent=0.7152f;
-    const float bPercent=0.0722f;
+    const float rPercent = 0.2126f;
+    const float gPercent = 0.7152f;
+    const float bPercent = 0.0722f;
 
     for (int i = 0; i < num; i++) {
-        float res =
-            rPercent * h_rgb[i + roff] +
-            gPercent * h_rgb[i + goff] +
-            bPercent * h_rgb[i + boff];
+        float res = rPercent * h_rgb[i + roff] + gPercent * h_rgb[i + goff] +
+                    bPercent * h_rgb[i + boff];
 
         ASSERT_FLOAT_EQ(res, h_gray[i]);
     }
 }
 
-TEST(rgb_gray, 8bit)
-{
-    array rgb = randu(10, 10, 3, u8);
+TEST(rgb_gray, 8bit) {
+    array rgb  = randu(10, 10, 3, u8);
     array gray = rgb2gray(rgb);
 
     vector<uchar> h_rgb(rgb.elements());
@@ -59,38 +55,35 @@ TEST(rgb_gray, 8bit)
     rgb.host(&h_rgb[0]);
     gray.host(&h_gray[0]);
 
-    int num = gray.elements();
+    int num  = gray.elements();
     int roff = 0;
     int goff = num;
     int boff = 2 * num;
 
-    const float rPercent=0.2126f;
-    const float gPercent=0.7152f;
-    const float bPercent=0.0722f;
+    const float rPercent = 0.2126f;
+    const float gPercent = 0.7152f;
+    const float bPercent = 0.0722f;
 
     for (int i = 0; i < num; i++) {
-        float res =
-            rPercent * h_rgb[i + roff] +
-            gPercent * h_rgb[i + goff] +
-            bPercent * h_rgb[i + boff];
+        float res = rPercent * h_rgb[i + roff] + gPercent * h_rgb[i + goff] +
+                    bPercent * h_rgb[i + boff];
 
         ASSERT_FLOAT_EQ(res, h_gray[i]);
     }
 }
 
-TEST(gray_rgb, 32bit)
-{
+TEST(gray_rgb, 32bit) {
     array gray = randu(10, 10);
 
-    const float rPercent=0.33f;
-    const float gPercent=0.34f;
-    const float bPercent=0.33f;
+    const float rPercent = 0.33f;
+    const float gPercent = 0.34f;
+    const float bPercent = 0.33f;
 
     array rgb = gray2rgb(gray, rPercent, gPercent, bPercent);
     vector<float> h_rgb(rgb.elements());
     vector<float> h_gray(gray.elements());
 
-    int num = gray.elements();
+    int num  = gray.elements();
     int roff = 0;
     int goff = num;
     int boff = 2 * num;
@@ -108,11 +101,10 @@ TEST(gray_rgb, 32bit)
     }
 }
 
-TEST(rgb_gray, MaxDim)
-{
+TEST(rgb_gray, MaxDim) {
     size_t largeDim = 65535 * 32 + 1;
-    array rgb = randu(1, largeDim, 3, u8);
-    array gray = rgb2gray(rgb);
+    array rgb       = randu(1, largeDim, 3, u8);
+    array gray      = rgb2gray(rgb);
 
     vector<uchar> h_rgb(rgb.elements());
     vector<float> h_gray(gray.elements());
@@ -120,20 +112,18 @@ TEST(rgb_gray, MaxDim)
     rgb.host(&h_rgb[0]);
     gray.host(&h_gray[0]);
 
-    int num = gray.elements();
+    int num  = gray.elements();
     int roff = 0;
     int goff = num;
     int boff = 2 * num;
 
-    const float rPercent=0.2126f;
-    const float gPercent=0.7152f;
-    const float bPercent=0.0722f;
+    const float rPercent = 0.2126f;
+    const float gPercent = 0.7152f;
+    const float bPercent = 0.0722f;
 
     for (int i = 0; i < num; i++) {
-        float res =
-            rPercent * h_rgb[i + roff] +
-            gPercent * h_rgb[i + goff] +
-            bPercent * h_rgb[i + boff];
+        float res = rPercent * h_rgb[i + roff] + gPercent * h_rgb[i + goff] +
+                    bPercent * h_rgb[i + boff];
 
         ASSERT_FLOAT_EQ(res, h_gray[i]);
     }
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 131d68c0a4..14ca3b53d9 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -7,33 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::endl;
-using std::vector;
-using std::string;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dtype_traits;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class HammingMatcher8  : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class HammingMatcher8 : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class HammingMatcher32 : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class HammingMatcher32 : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create lists of types to be tested
@@ -41,25 +39,22 @@ typedef ::testing::Types<uchar, ushort> TestTypes8;
 typedef ::testing::Types<uint, uintl> TestTypes32;
 
 // register the type list
-TYPED_TEST_CASE(HammingMatcher8,  TestTypes8);
+TYPED_TEST_CASE(HammingMatcher8, TestTypes8);
 TYPED_TEST_CASE(HammingMatcher32, TestTypes32);
 
 template<typename T>
-void hammingMatcherTest(string pTestFile, int feat_dim)
-{
+void hammingMatcherTest(string pTestFile, int feat_dim) {
     using af::dim4;
 
-    vector<dim4>         numDims;
-    vector<vector<uint> >   in32;
-    vector<vector<uint> >  tests;
+    vector<dim4> numDims;
+    vector<vector<uint> > in32;
+    vector<vector<uint> > tests;
 
     readTests<uint, uint, uint>(pTestFile, numDims, in32, tests);
 
     vector<vector<T> > in(in32.size());
-    for (size_t i = 0; i < in32[0].size(); i++)
-        in[0].push_back((T)in32[0][i]);
-    for (size_t i = 0; i < in32[1].size(); i++)
-        in[1].push_back((T)in32[1][i]);
+    for (size_t i = 0; i < in32[0].size(); i++) in[0].push_back((T)in32[0][i]);
+    for (size_t i = 0; i < in32[1].size(); i++) in[1].push_back((T)in32[1][i]);
 
     dim4 qDims     = numDims[0];
     dim4 tDims     = numDims[1];
@@ -68,10 +63,12 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     af_array idx   = 0;
     af_array dist  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&query, &(in[0].front()),
-                qDims.ndims(), qDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&train, &(in[1].front()),
-                tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&query, &(in[0].front()), qDims.ndims(),
+                                   qDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&train, &(in[1].front()), tDims.ndims(),
+                                   tDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_hamming_matcher(&idx, &dist, query, train, feat_dim, 1));
 
@@ -81,11 +78,12 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     uint *outIdx          = new uint[nElems];
     uint *outDist         = new uint[nElems];
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outIdx,  idx));
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outDist, dist));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outIdx, idx));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outDist, dist));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])
+            << "at: " << elIter << endl;
     }
 
     delete[] outIdx;
@@ -96,41 +94,41 @@ void hammingMatcherTest(string pTestFile, int feat_dim)
     ASSERT_SUCCESS(af_release_array(dist));
 }
 
-TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim0)
-{
-    hammingMatcherTest<TypeParam>(string(TEST_DIR"/hamming/hamming_500_5000_dim0_u8.test"), 0);
+TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim0) {
+    hammingMatcherTest<TypeParam>(
+        string(TEST_DIR "/hamming/hamming_500_5000_dim0_u8.test"), 0);
 }
 
-TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim1)
-{
-    hammingMatcherTest<TypeParam>(string(TEST_DIR"/hamming/hamming_500_5000_dim1_u8.test"), 1);
+TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim1) {
+    hammingMatcherTest<TypeParam>(
+        string(TEST_DIR "/hamming/hamming_500_5000_dim1_u8.test"), 1);
 }
 
-TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim0)
-{
-    hammingMatcherTest<TypeParam>(string(TEST_DIR"/hamming/hamming_500_5000_dim0_u32.test"), 0);
+TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim0) {
+    hammingMatcherTest<TypeParam>(
+        string(TEST_DIR "/hamming/hamming_500_5000_dim0_u32.test"), 0);
 }
 
-TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim1)
-{
-    hammingMatcherTest<TypeParam>(string(TEST_DIR"/hamming/hamming_500_5000_dim1_u32.test"), 1);
+TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim1) {
+    hammingMatcherTest<TypeParam>(
+        string(TEST_DIR "/hamming/hamming_500_5000_dim1_u32.test"), 1);
 }
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(HammingMatcher, CPP)
-{
+TEST(HammingMatcher, CPP) {
     using af::array;
     using af::dim4;
 
-    vector<dim4>         numDims;
-    vector<vector<uint> >     in;
-    vector<vector<uint> >  tests;
+    vector<dim4> numDims;
+    vector<vector<uint> > in;
+    vector<vector<uint> > tests;
 
-    readTests<uint, uint, uint>(TEST_DIR"/hamming/hamming_500_5000_dim0_u32.test", numDims, in, tests);
+    readTests<uint, uint, uint>(
+        TEST_DIR "/hamming/hamming_500_5000_dim0_u32.test", numDims, in, tests);
 
-    dim4 qDims     = numDims[0];
-    dim4 tDims     = numDims[1];
+    dim4 qDims = numDims[0];
+    dim4 tDims = numDims[1];
 
     array query(qDims, &(in[0].front()));
     array train(tDims, &(in[1].front()));
@@ -147,8 +145,9 @@ TEST(HammingMatcher, CPP)
     idx.host(outIdx);
     dist.host(outDist);
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])
+            << "at: " << elIter << endl;
     }
 
     delete[] outIdx;
diff --git a/test/harris.cpp b/test/harris.cpp
index 754da00408..0014444085 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -7,39 +7,37 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
+using af::dim4;
+using std::abs;
 using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
-using af::dim4;
 
-typedef struct
-{
+typedef struct {
     float f[5];
 } feat_t;
 
-static bool feat_cmp(feat_t i, feat_t j)
-{
+static bool feat_cmp(feat_t i, feat_t j) {
     for (int k = 0; k < 5; k++)
-        if (i.f[k] != j.f[k])
-            return (i.f[k] < j.f[k]);
+        if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
 
     return false;
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
-{
+static void array_to_feat(vector<feat_t> &feat, float *x, float *y,
+                          float *score, float *orientation, float *size,
+                          unsigned nfeat) {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -51,10 +49,9 @@ static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score
 }
 
 template<typename T>
-class Harris : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Harris : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
@@ -62,32 +59,33 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(Harris, TestTypes);
 
 template<typename T>
-void harrisTest(string pTestFile, float sigma, unsigned block_size)
-{
+void harrisTest(string pTestFile, float sigma, unsigned block_size) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>        inDims;
-    vector<string>     inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        dim_t nElems       = 0;
-        af_array inArray_f32  = 0;
-        af_array inArray      = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        dim_t nElems         = 0;
+        af_array inArray_f32 = 0;
+        af_array inArray     = 0;
         af_features out;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/harris/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/harris/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
 
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_harris(&out, inArray, 500, 1e5f, sigma, block_size, 0.04f));
+        ASSERT_SUCCESS(
+            af_harris(&out, inArray, 500, 1e5f, sigma, block_size, 0.04f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
@@ -99,37 +97,43 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
         ASSERT_SUCCESS(af_get_features_orientation(&orientation, out));
         ASSERT_SUCCESS(af_get_features_size(&size, out));
 
-
         ASSERT_SUCCESS(af_get_elements(&nElems, x));
 
-        vector<float> outX           (gold[0].size());
-        vector<float> outY           (gold[1].size());
-        vector<float> outScore       (gold[2].size());
-        vector<float> outOrientation (gold[3].size());
-        vector<float> outSize        (gold[4].size());
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outX.front(), x));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outY.front(), y));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outScore.front(), score));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outOrientation.front(), orientation));
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outSize.front(), size));
+        vector<float> outX(gold[0].size());
+        vector<float> outY(gold[1].size());
+        vector<float> outScore(gold[2].size());
+        vector<float> outOrientation(gold[3].size());
+        vector<float> outSize(gold[4].size());
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outX.front(), x));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outY.front(), y));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outScore.front(), score));
+        ASSERT_SUCCESS(
+            af_get_data_ptr((void *)&outOrientation.front(), orientation));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outSize.front(), size));
 
         vector<feat_t> out_feat;
-        array_to_feat(out_feat, &outX.front(), &outY.front(),
-                      &outScore.front(), &outOrientation.front(), &outSize.front(), n);
+        array_to_feat(out_feat, &outX.front(), &outY.front(), &outScore.front(),
+                      &outOrientation.front(), &outSize.front(), n);
 
         vector<feat_t> gold_feat;
         array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(),
-                      &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
+                      &gold[2].front(), &gold[3].front(), &gold[4].front(),
+                      gold[0].size());
 
         std::sort(out_feat.begin(), out_feat.end(), feat_cmp);
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
         for (int elIter = 0; elIter < (int)nElems; elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2)
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4])
+                << "at: " << elIter << endl;
         }
 
         ASSERT_SUCCESS(af_release_array(inArray));
@@ -139,20 +143,21 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size)
     }
 }
 
-#define HARRIS_INIT(desc, image, sigma, block_size)                     \
-    TYPED_TEST(Harris, desc)                                            \
-    {                                                                   \
-        harrisTest<TypeParam>(string(TEST_DIR"/harris/"#image"_"#sigma"_"#block_size".test"), sigma, block_size); \
+#define HARRIS_INIT(desc, image, sigma, block_size)                        \
+    TYPED_TEST(Harris, desc) {                                             \
+        harrisTest<TypeParam>(string(TEST_DIR "/harris/" #image "_" #sigma \
+                                              "_" #block_size ".test"),    \
+                              sigma, block_size);                          \
     }
 
-    HARRIS_INIT(square_0_3, square, 0, 3);
-    HARRIS_INIT(square_0_7, square, 0, 7);
-    HARRIS_INIT(square_1_0, square, 1, 0);
-    HARRIS_INIT(square_5_0, square, 5, 0);
-    HARRIS_INIT(lena_0_3, lena, 0, 3);
-    HARRIS_INIT(lena_0_7, lena, 0, 7);
-    HARRIS_INIT(lena_1_0, lena, 1, 0);
-    HARRIS_INIT(lena_5_0, lena, 5, 0);
+HARRIS_INIT(square_0_3, square, 0, 3);
+HARRIS_INIT(square_0_7, square, 0, 7);
+HARRIS_INIT(square_1_0, square, 1, 0);
+HARRIS_INIT(square_5_0, square, 5, 0);
+HARRIS_INIT(lena_0_3, lena, 0, 3);
+HARRIS_INIT(lena_0_7, lena, 0, 7);
+HARRIS_INIT(lena_1_0, lena, 1, 0);
+HARRIS_INIT(lena_5_0, lena, 5, 0);
 
 /////////////////////////////////// CPP ////////////////////////////////
 
@@ -161,27 +166,27 @@ using af::features;
 using af::harris;
 using af::loadImage;
 
-TEST(FloatHarris, CPP)
-{
+TEST(FloatHarris, CPP) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>        inDims;
-    vector<string>     inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
-    readImageTests(string(TEST_DIR"/harris/square_0_3.test"), inDims, inFiles, gold);
-    inFiles[0].insert(0,string(TEST_DIR"/harris/"));
+    readImageTests(string(TEST_DIR "/harris/square_0_3.test"), inDims, inFiles,
+                   gold);
+    inFiles[0].insert(0, string(TEST_DIR "/harris/"));
 
     array in = loadImage(inFiles[0].c_str(), false);
 
     features out = harris(in, 500, 1e5f, 0.0f, 3, 0.04f);
 
-    vector<float> outX           (gold[0].size());
-    vector<float> outY           (gold[1].size());
-    vector<float> outScore       (gold[2].size());
-    vector<float> outOrientation (gold[3].size());
-    vector<float> outSize        (gold[4].size());
+    vector<float> outX(gold[0].size());
+    vector<float> outY(gold[1].size());
+    vector<float> outScore(gold[2].size());
+    vector<float> outOrientation(gold[3].size());
+    vector<float> outSize(gold[4].size());
     out.getX().host(&outX.front());
     out.getY().host(&outY.front());
     out.getScore().host(&outScore.front());
@@ -189,8 +194,8 @@ TEST(FloatHarris, CPP)
     out.getSize().host(&outSize.front());
 
     vector<feat_t> out_feat;
-    array_to_feat(out_feat, &outX.front(), &outY.front(),
-                  &outScore.front(), &outOrientation.front(), &outSize.front(),
+    array_to_feat(out_feat, &outX.front(), &outY.front(), &outScore.front(),
+                  &outOrientation.front(), &outSize.front(),
                   out.getNumFeatures());
 
     vector<feat_t> gold_feat;
@@ -202,10 +207,15 @@ TEST(FloatHarris, CPP)
     std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
     for (unsigned elIter = 0; elIter < out.getNumFeatures(); elIter++) {
-        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2)
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3])
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4])
+            << "at: " << elIter << endl;
     }
 }
diff --git a/test/histogram.cpp b/test/histogram.cpp
index 0d9258cfaf..1b4e52fea6 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -7,62 +7,64 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <iostream>
 #include <string>
 #include <vector>
-#include <iostream>
-#include <testHelpers.hpp>
 
+using af::dim4;
+using af::dtype_traits;
 using std::abs;
 using std::cout;
 using std::endl;
 using std::ostream_iterator;
 using std::string;
 using std::vector;
-using af::dim4;
-using af::dtype_traits;
 
 template<typename T>
-class Histogram : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Histogram : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort, intl, uintl> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort,
+                         intl, uintl>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Histogram, TestTypes);
 
 template<typename inType, typename outType>
-void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
-{
+void histTest(string pTestFile, unsigned nbins, double minval, double maxval) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<inType> >  in;
+    vector<vector<inType> > in;
     vector<vector<outType> > tests;
-    readTests<inType,uint,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    readTests<inType, uint, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
-    af_array outArray   = 0;
-    af_array inArray    = 0;
+    af_array outArray = 0;
+    af_array inArray  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
 
-    ASSERT_SUCCESS(af_histogram(&outArray,inArray,nbins,minval,maxval));
+    ASSERT_SUCCESS(af_histogram(&outArray, inArray, nbins, minval, maxval));
 
     vector<outType> outData(dims.elements());
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<outType> currGoldBar = tests[testIter];
 
         dim4 goldDims(nbins, 1, dims[2], dims[3]);
@@ -74,29 +76,29 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(Histogram,256Bins0min255max_ones)
-{
-    histTest<TypeParam,uint>(string(TEST_DIR"/histogram/256bin1min1max.test"),256,0,255);
+TYPED_TEST(Histogram, 256Bins0min255max_ones) {
+    histTest<TypeParam, uint>(string(TEST_DIR "/histogram/256bin1min1max.test"),
+                              256, 0, 255);
 }
 
-TYPED_TEST(Histogram,100Bins0min99max)
-{
-    histTest<TypeParam,uint>(string(TEST_DIR"/histogram/100bin0min99max.test"),100,0,99);
+TYPED_TEST(Histogram, 100Bins0min99max) {
+    histTest<TypeParam, uint>(
+        string(TEST_DIR "/histogram/100bin0min99max.test"), 100, 0, 99);
 }
 
-TYPED_TEST(Histogram,40Bins0min100max)
-{
-    histTest<TypeParam,uint>(string(TEST_DIR"/histogram/40bin0min100max.test"),40,0,100);
+TYPED_TEST(Histogram, 40Bins0min100max) {
+    histTest<TypeParam, uint>(
+        string(TEST_DIR "/histogram/40bin0min100max.test"), 40, 0, 100);
 }
 
-TYPED_TEST(Histogram,40Bins0min100max_Batch)
-{
-    histTest<TypeParam,uint>(string(TEST_DIR"/histogram/40bin0min100max_batch.test"),40,0,100);
+TYPED_TEST(Histogram, 40Bins0min100max_Batch) {
+    histTest<TypeParam, uint>(
+        string(TEST_DIR "/histogram/40bin0min100max_batch.test"), 40, 0, 100);
 }
 
-TYPED_TEST(Histogram,256Bins0min255max_zeros)
-{
-    histTest<TypeParam,uint>(string(TEST_DIR"/histogram/256bin0min0max.test"),256,0,255);
+TYPED_TEST(Histogram, 256Bins0min255max_zeros) {
+    histTest<TypeParam, uint>(string(TEST_DIR "/histogram/256bin0min0max.test"),
+                              256, 0, 255);
 }
 
 /////////////////////////////////// CPP //////////////////////////////////
@@ -111,50 +113,50 @@ using af::round;
 using af::seq;
 using af::span;
 
-TEST(Histogram, CPP)
-{
+TEST(Histogram, CPP) {
     if (noDoubleTests<float>()) return;
     if (noDoubleTests<int>()) return;
 
     const unsigned nbins = 100;
-    const double minval = 0.0;
-    const double maxval = 99.0;
+    const double minval  = 0.0;
+    const double maxval  = 99.0;
 
     vector<dim4> numDims;
 
-    vector<vector<float> >  in;
+    vector<vector<float> > in;
     vector<vector<uint> > tests;
-    readTests<float,uint,int>(string(TEST_DIR"/histogram/100bin0min99max.test"),numDims,in,tests);
+    readTests<float, uint, int>(
+        string(TEST_DIR "/histogram/100bin0min99max.test"), numDims, in, tests);
 
-//! [hist_nominmax]
+    //! [hist_nominmax]
     array input(numDims[0], &(in[0].front()));
     array output = histogram(input, nbins, minval, maxval);
-//! [hist_nominmax]
+    //! [hist_nominmax]
 
     vector<uint> outData(output.elements());
     output.host((void*)outData.data());
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<uint> currGoldBar = tests[testIter];
 
         dim4 goldDims = numDims[0];
-        goldDims[0] = nbins;
-        goldDims[1] = 1;
+        goldDims[0]   = nbins;
+        goldDims[1]   = 1;
         ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
     }
 }
 
-/////////////////////////////////// Documentation Snippets //////////////////////////////////
+/////////////////////////////////// Documentation Snippets
+/////////////////////////////////////
 //
-TEST(Histogram, SNIPPET_hist_nominmax)
-{
+TEST(Histogram, SNIPPET_hist_nominmax) {
     unsigned output[] = {3, 1, 2, 0, 0, 0, 0, 1, 1, 1};
 
     //! [ex_image_hist_nominmax]
-    float input[]  = {1, 2, 1, 1, 3, 6, 7, 8, 3};
-    int nbins = 10;
+    float input[] = {1, 2, 1, 1, 3, 6, 7, 8, 3};
+    int nbins     = 10;
 
-    size_t nElems = sizeof(input)/sizeof(float);
+    size_t nElems = sizeof(input) / sizeof(float);
     array hist_in(nElems, input);
 
     array hist_out = histogram(hist_in, nbins);
@@ -164,24 +166,24 @@ TEST(Histogram, SNIPPET_hist_nominmax)
     vector<unsigned> h_out(nbins);
     hist_out.host((void*)h_out.data());
 
-    if( false == equal(h_out.begin(), h_out.end(), output) ) {
+    if (false == equal(h_out.begin(), h_out.end(), output)) {
         cout << "Expected: ";
         copy(output, output + nbins, ostream_iterator<unsigned>(cout, ", "));
         cout << endl << "Actual: ";
-        copy(h_out.begin(), h_out.end(), ostream_iterator<unsigned>(cout, ", "));
+        copy(h_out.begin(), h_out.end(),
+             ostream_iterator<unsigned>(cout, ", "));
         FAIL() << "Output did not match";
     }
 }
 
-TEST(Histogram, SNIPPET_hist_minmax)
-{
+TEST(Histogram, SNIPPET_hist_minmax) {
     unsigned output[] = {0, 3, 1, 2, 0, 0, 1, 1, 1, 0};
 
     //! [ex_image_hist_minmax]
-    float input[]  = {1, 2, 1, 1, 3, 6, 7, 8, 3};
-    int nbins = 10;
+    float input[] = {1, 2, 1, 1, 3, 6, 7, 8, 3};
+    int nbins     = 10;
 
-    size_t nElems = sizeof(input)/sizeof(float);
+    size_t nElems = sizeof(input) / sizeof(float);
     array hist_in(nElems, input);
 
     array hist_out = histogram(hist_in, nbins, 0, 9);
@@ -191,24 +193,24 @@ TEST(Histogram, SNIPPET_hist_minmax)
     vector<unsigned> h_out(nbins);
     hist_out.host((void*)h_out.data());
 
-    if( false == equal(h_out.begin(), h_out.end(), output) ) {
+    if (false == equal(h_out.begin(), h_out.end(), output)) {
         cout << "Expected: ";
         copy(output, output + nbins, ostream_iterator<unsigned>(cout, ", "));
         cout << endl << "Actual: ";
-        copy(h_out.begin(), h_out.end(), ostream_iterator<unsigned>(cout, ", "));
+        copy(h_out.begin(), h_out.end(),
+             ostream_iterator<unsigned>(cout, ", "));
         FAIL() << "Output did not match";
     }
 }
 
-TEST(Histogram, SNIPPET_histequal)
-{
-    float output[] = { 1.5, 4.5,  1.5, 1.5, 4.5, 4.5, 6.0, 7.5, 4.5 };
+TEST(Histogram, SNIPPET_histequal) {
+    float output[] = {1.5, 4.5, 1.5, 1.5, 4.5, 4.5, 6.0, 7.5, 4.5};
 
     //! [ex_image_histequal]
-    float input[]  = {1, 2, 1, 1, 3, 6, 7, 8, 3};
-    int nbins = 10;
+    float input[] = {1, 2, 1, 1, 3, 6, 7, 8, 3};
+    int nbins     = 10;
 
-    size_t nElems = sizeof(input)/sizeof(float);
+    size_t nElems = sizeof(input) / sizeof(float);
     array hist_in(nElems, input);
 
     array hist_out = histogram(hist_in, nbins);
@@ -222,7 +224,7 @@ TEST(Histogram, SNIPPET_histequal)
     vector<float> h_out(nElems);
     eq_out.host((void*)h_out.data());
 
-    if( false == equal(h_out.begin(), h_out.end(), output) ) {
+    if (false == equal(h_out.begin(), h_out.end(), output)) {
         cout << "Expected: ";
         copy(output, output + nElems, ostream_iterator<float>(cout, ", "));
         cout << endl << "Actual: ";
@@ -231,47 +233,40 @@ TEST(Histogram, SNIPPET_histequal)
     }
 }
 
-TEST(histogram, GFOR)
-{
+TEST(histogram, GFOR) {
     dim4 dims = dim4(100, 100, 3);
-    array A = round(100 * randu(dims));
-    array B = constant(0, 100, 1, 3);
+    array A   = round(100 * randu(dims));
+    array B   = constant(0, 100, 1, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = histogram(A(span, span, ii), 100);
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = histogram(A(span, span, ii), 100); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = histogram(A(span, span, ii), 100);
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
 
-TEST(histogram, IndexedArray)
-{
+TEST(histogram, IndexedArray) {
     const dim_t LEN = 32;
-    array A = range(LEN, (dim_t)2);
-    for (int i=16; i<28; ++i) {
-        A(seq(i, i+3), span) = i/4 - 1;
-    }
+    array A         = range(LEN, (dim_t)2);
+    for (int i = 16; i < 28; ++i) { A(seq(i, i + 3), span) = i / 4 - 1; }
     array B = A(seq(20), span);
     array C = histogram(B, 4);
     unsigned out[4];
     C.host((void*)out);
     ASSERT_EQ(true, out[0] == 16);
-    ASSERT_EQ(true, out[1] ==  8);
-    ASSERT_EQ(true, out[2] ==  8);
-    ASSERT_EQ(true, out[3] ==  8);
+    ASSERT_EQ(true, out[1] == 8);
+    ASSERT_EQ(true, out[2] == 8);
+    ASSERT_EQ(true, out[3] == 8);
 }
 
-TEST(histogram, LargeBins)
-{
+TEST(histogram, LargeBins) {
     const int max_val = 20000;
     const int min_val = 0;
-    const int nbins = max_val / 2;
-    const int num = 1 << 20;
-    array A = round(max_val * randu(num) + min_val).as(u32);
+    const int nbins   = max_val / 2;
+    const int num     = 1 << 20;
+    array A           = round(max_val * randu(num) + min_val).as(u32);
     eval(A);
     array H = histogram(A, nbins, min_val, max_val);
 
@@ -284,11 +279,9 @@ TEST(histogram, LargeBins)
     int dx = (max_val - min_val) / nbins;
     for (int i = 0; i < num; i++) {
         int bin = (hA[i] - min_val) / dx;
-        bin = std::min(bin, nbins - 1);
+        bin     = std::min(bin, nbins - 1);
         hH[bin] -= 1;
     }
 
-    for (int i = 0; i < nbins; i++) {
-        ASSERT_EQ(hH[i], 0u);
-    }
+    for (int i = 0; i < nbins; i++) { ASSERT_EQ(hH[i], 0u); }
 }
diff --git a/test/homography.cpp b/test/homography.cpp
index b149fed18a..068e9fa6f5 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -7,29 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
+using af::array;
+using af::dim4;
+using std::abs;
 using std::endl;
 using std::string;
 using std::vector;
-using std::abs;
-using af::array;
-using af::dim4;
 
 template<typename T>
-class Homography : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Homography : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
@@ -37,8 +36,7 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(Homography, TestTypes);
 
 template<typename T>
-array perspectiveTransform(dim4 inDims, array H)
-{
+array perspectiveTransform(dim4 inDims, array H) {
     T d0 = (T)inDims[0];
     T d1 = (T)inDims[1];
     return transformCoordinates(H, d0, d1);
@@ -46,33 +44,33 @@ array perspectiveTransform(dim4 inDims, array H)
 
 template<typename T>
 void homographyTest(string pTestFile, const af_homography_type htype,
-                    const bool rotate, const float size_ratio)
-{
-    using af::Pi;
+                    const bool rotate, const float size_ratio) {
     using af::dtype_traits;
+    using af::Pi;
 
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>           inDims;
-    vector<string>         inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
-    inFiles[0].insert(0,string(TEST_DIR"/homography/"));
+    inFiles[0].insert(0, string(TEST_DIR "/homography/"));
 
-    af_array trainArray_f32   = 0;
-    af_array trainArray       = 0;
-    af_array train_desc       = 0;
-    af_array train_feat_x     = 0;
-    af_array train_feat_y     = 0;
+    af_array trainArray_f32 = 0;
+    af_array trainArray     = 0;
+    af_array train_desc     = 0;
+    af_array train_feat_x   = 0;
+    af_array train_feat_y   = 0;
     af_features train_feat;
 
     ASSERT_SUCCESS(af_load_image(&trainArray_f32, inFiles[0].c_str(), false));
     ASSERT_SUCCESS(conv_image<T>(&trainArray, trainArray_f32));
 
-    ASSERT_SUCCESS(af_orb(&train_feat, &train_desc, trainArray, 20.0f, 2000, 1.2f, 8, true));
+    ASSERT_SUCCESS(af_orb(&train_feat, &train_desc, trainArray, 20.0f, 2000,
+                          1.2f, 8, true));
 
     ASSERT_SUCCESS(af_get_features_xpos(&train_feat_x, train_feat));
     ASSERT_SUCCESS(af_get_features_ypos(&train_feat_y, train_feat));
@@ -94,31 +92,37 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     af_array query_feat_y_idx = 0;
     af_features query_feat;
 
-    const float theta = Pi * 0.5f;
+    const float theta   = Pi * 0.5f;
     const dim_t test_d0 = inDims[0][0] * size_ratio;
     const dim_t test_d1 = inDims[0][1] * size_ratio;
     const dim_t tDims[] = {test_d0, test_d1};
     if (rotate)
-        ASSERT_SUCCESS(af_rotate(&queryArray, trainArray, theta, false, AF_INTERP_NEAREST));
+        ASSERT_SUCCESS(af_rotate(&queryArray, trainArray, theta, false,
+                                 AF_INTERP_NEAREST));
     else
-        ASSERT_SUCCESS(af_resize(&queryArray, trainArray, test_d0, test_d1, AF_INTERP_BILINEAR));
+        ASSERT_SUCCESS(af_resize(&queryArray, trainArray, test_d0, test_d1,
+                                 AF_INTERP_BILINEAR));
 
-    ASSERT_SUCCESS(af_orb(&query_feat, &query_desc, queryArray, 20.0f, 2000, 1.2f, 8, true));
+    ASSERT_SUCCESS(af_orb(&query_feat, &query_desc, queryArray, 20.0f, 2000,
+                          1.2f, 8, true));
 
-    ASSERT_SUCCESS(af_hamming_matcher(&idx, &dist, train_desc, query_desc, 0, 1));
+    ASSERT_SUCCESS(
+        af_hamming_matcher(&idx, &dist, train_desc, query_desc, 0, 1));
 
     dim_t distDims[4];
-    ASSERT_SUCCESS(af_get_dims(&distDims[0], &distDims[1], &distDims[2], &distDims[3], dist));
+    ASSERT_SUCCESS(af_get_dims(&distDims[0], &distDims[1], &distDims[2],
+                               &distDims[3], dist));
 
     ASSERT_SUCCESS(af_constant(&const_50, 50, 2, distDims, u32));
     ASSERT_SUCCESS(af_lt(&dist_thr, dist, const_50, false));
     ASSERT_SUCCESS(af_where(&train_idx, dist_thr));
 
     dim_t tidxDims[4];
-    ASSERT_SUCCESS(af_get_dims(&tidxDims[0], &tidxDims[1], &tidxDims[2], &tidxDims[3], train_idx));
+    ASSERT_SUCCESS(af_get_dims(&tidxDims[0], &tidxDims[1], &tidxDims[2],
+                               &tidxDims[3], train_idx));
     af_index_t tindexs;
-    tindexs.isSeq = false;
-    tindexs.idx.seq = af_make_seq(0, tidxDims[0]-1, 1);
+    tindexs.isSeq   = false;
+    tindexs.idx.seq = af_make_seq(0, tidxDims[0] - 1, 1);
     tindexs.idx.arr = train_idx;
     ASSERT_SUCCESS(af_index_gen(&query_idx, idx, 1, &tindexs));
 
@@ -126,10 +130,11 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     ASSERT_SUCCESS(af_get_features_ypos(&query_feat_y, query_feat));
 
     dim_t qidxDims[4];
-    ASSERT_SUCCESS(af_get_dims(&qidxDims[0], &qidxDims[1], &qidxDims[2], &qidxDims[3], query_idx));
+    ASSERT_SUCCESS(af_get_dims(&qidxDims[0], &qidxDims[1], &qidxDims[2],
+                               &qidxDims[3], query_idx));
     af_index_t qindexs;
-    qindexs.isSeq = false;
-    qindexs.idx.seq = af_make_seq(0, qidxDims[0]-1, 1);
+    qindexs.isSeq   = false;
+    qindexs.idx.seq = af_make_seq(0, qidxDims[0] - 1, 1);
     qindexs.idx.arr = query_idx;
 
     ASSERT_SUCCESS(af_index_gen(&train_feat_x_idx, train_feat_x, 1, &tindexs));
@@ -138,17 +143,17 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     ASSERT_SUCCESS(af_index_gen(&query_feat_y_idx, query_feat_y, 1, &qindexs));
 
     int inliers = 0;
-    ASSERT_SUCCESS(af_homography(&H, &inliers, train_feat_x_idx, train_feat_y_idx,
-                                        query_feat_x_idx, query_feat_y_idx, htype,
-                                        3.0f, 1000, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_homography(&H, &inliers, train_feat_x_idx,
+                                 train_feat_y_idx, query_feat_x_idx,
+                                 query_feat_y_idx, htype, 3.0f, 1000,
+                                 (af_dtype)dtype_traits<T>::af_type));
 
     array HH(H);
 
     array t = perspectiveTransform<T>(inDims[0], HH);
 
     T* gold_t = new T[8];
-    for (int i = 0; i < 8; i++)
-        gold_t[i] = (T)0;
+    for (int i = 0; i < 8; i++) gold_t[i] = (T)0;
     if (rotate) {
         gold_t[1] = test_d0;
         gold_t[2] = test_d0;
@@ -165,7 +170,8 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     t.host(out_t);
 
     for (int elIter = 0; elIter < 8; elIter++) {
-        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.25f)
+        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1],
+                  0.25f)
             << "at: " << elIter << endl;
     }
 
@@ -193,19 +199,19 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     ASSERT_SUCCESS(af_release_array(train_desc));
 }
 
-#define HOMOGRAPHY_INIT(desc, image, htype, rotate, size_ratio)                 \
-    TYPED_TEST(Homography, desc)                                                \
-    {                                                                           \
-        homographyTest<TypeParam>(string(TEST_DIR"/homography/"#image".test"),  \
-                                  htype, rotate, size_ratio);                   \
+#define HOMOGRAPHY_INIT(desc, image, htype, rotate, size_ratio)            \
+    TYPED_TEST(Homography, desc) {                                         \
+        homographyTest<TypeParam>(                                         \
+            string(TEST_DIR "/homography/" #image ".test"), htype, rotate, \
+            size_ratio);                                                   \
     }
 
-    HOMOGRAPHY_INIT(Tux_RANSAC, tux, AF_HOMOGRAPHY_RANSAC, false, 1.0f);
-    HOMOGRAPHY_INIT(Tux_RANSAC_90degrees, tux, AF_HOMOGRAPHY_RANSAC, true, 1.0f);
-    HOMOGRAPHY_INIT(Tux_RANSAC_resize, tux, AF_HOMOGRAPHY_RANSAC, false, 1.5f);
-    //HOMOGRAPHY_INIT(Tux_LMedS, tux, AF_HOMOGRAPHY_LMEDS, false, 1.0f);
-    //HOMOGRAPHY_INIT(Tux_LMedS_90degrees, tux, AF_HOMOGRAPHY_LMEDS, true, 1.0f);
-    //HOMOGRAPHY_INIT(Tux_LMedS_resize, tux, AF_HOMOGRAPHY_LMEDS, false, 1.5f);
+HOMOGRAPHY_INIT(Tux_RANSAC, tux, AF_HOMOGRAPHY_RANSAC, false, 1.0f);
+HOMOGRAPHY_INIT(Tux_RANSAC_90degrees, tux, AF_HOMOGRAPHY_RANSAC, true, 1.0f);
+HOMOGRAPHY_INIT(Tux_RANSAC_resize, tux, AF_HOMOGRAPHY_RANSAC, false, 1.5f);
+// HOMOGRAPHY_INIT(Tux_LMedS, tux, AF_HOMOGRAPHY_LMEDS, false, 1.0f);
+// HOMOGRAPHY_INIT(Tux_LMedS_90degrees, tux, AF_HOMOGRAPHY_LMEDS, true, 1.0f);
+// HOMOGRAPHY_INIT(Tux_LMedS_resize, tux, AF_HOMOGRAPHY_LMEDS, false, 1.5f);
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
@@ -213,23 +219,23 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 using af::features;
 using af::loadImage;
 
-TEST(Homography, CPP)
-{
+TEST(Homography, CPP) {
     if (noImageIOTests()) return;
 
-    vector<dim4>           inDims;
-    vector<string>         inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
-    readImageTests(string(TEST_DIR"/homography/tux.test"), inDims, inFiles, gold);
+    readImageTests(string(TEST_DIR "/homography/tux.test"), inDims, inFiles,
+                   gold);
 
-    inFiles[0].insert(0,string(TEST_DIR"/homography/"));
+    inFiles[0].insert(0, string(TEST_DIR "/homography/"));
 
     const float size_ratio = 0.5f;
 
     array train_img = loadImage(inFiles[0].c_str(), false);
     array query_img = resize(size_ratio, train_img);
-    dim4 tDims = train_img.dims();
+    dim4 tDims      = train_img.dims();
 
     features feat_train, feat_query;
     array desc_train, desc_query;
@@ -242,24 +248,24 @@ TEST(Homography, CPP)
     array train_idx = where(dist < 30);
     array query_idx = idx(train_idx);
 
-    array feat_train_x = feat_train.getX()(train_idx);
-    array feat_train_y = feat_train.getY()(train_idx);
-    array feat_train_score = feat_train.getScore()(train_idx);
+    array feat_train_x           = feat_train.getX()(train_idx);
+    array feat_train_y           = feat_train.getY()(train_idx);
+    array feat_train_score       = feat_train.getScore()(train_idx);
     array feat_train_orientation = feat_train.getOrientation()(train_idx);
-    array feat_train_size = feat_train.getSize()(train_idx);
-    array feat_query_x = feat_query.getX()(query_idx);
-    array feat_query_y = feat_query.getY()(query_idx);
-    array feat_query_score = feat_query.getScore()(query_idx);
+    array feat_train_size        = feat_train.getSize()(train_idx);
+    array feat_query_x           = feat_query.getX()(query_idx);
+    array feat_query_y           = feat_query.getY()(query_idx);
+    array feat_query_score       = feat_query.getScore()(query_idx);
     array feat_query_orientation = feat_query.getOrientation()(query_idx);
-    array feat_query_size = feat_query.getSize()(query_idx);
+    array feat_query_size        = feat_query.getSize()(query_idx);
 
     array H;
     int inliers = 0;
-    homography(H, inliers, feat_train_x, feat_train_y, feat_query_x, feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
+    homography(H, inliers, feat_train_x, feat_train_y, feat_query_x,
+               feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
 
     float* gold_t = new float[8];
-    for (int i = 0; i < 8; i++)
-        gold_t[i] = 0.f;
+    for (int i = 0; i < 8; i++) gold_t[i] = 0.f;
     gold_t[2] = tDims[1] * size_ratio;
     gold_t[3] = tDims[1] * size_ratio;
     gold_t[5] = tDims[0] * size_ratio;
@@ -267,11 +273,12 @@ TEST(Homography, CPP)
 
     array t = perspectiveTransform<float>(train_img.dims(), H);
 
-    float* out_t = new float[4*2];
+    float* out_t = new float[4 * 2];
     t.host(out_t);
 
     for (int elIter = 0; elIter < 8; elIter++) {
-        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1], 0.1f)
+        ASSERT_LE(fabs(out_t[elIter] - gold_t[elIter]) / tDims[elIter & 1],
+                  0.1f)
             << "at: " << elIter << endl;
     }
 
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index 92f8292307..da484888c8 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -7,23 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::dim4;
 using af::exception;
 using af::hsv2rgb;
+using std::endl;
+using std::string;
+using std::vector;
 
-TEST(hsv_rgb, InvalidArray)
-{
+TEST(hsv_rgb, InvalidArray) {
     vector<float> in(100, 1);
 
     dim4 dims(100);
@@ -32,21 +31,21 @@ TEST(hsv_rgb, InvalidArray)
     try {
         array output = hsv2rgb(input);
         ASSERT_EQ(true, false);
-    } catch(exception) {
+    } catch (exception) {
         ASSERT_EQ(true, true);
         return;
     }
 }
 
-TEST(hsv2rgb, CPP)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(hsv2rgb, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/hsv2rgb.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/hsv2rgb.test"),
+                                    numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = hsv2rgb(input);
 
@@ -54,15 +53,15 @@ TEST(hsv2rgb, CPP)
     ASSERT_VEC_ARRAY_NEAR(currGoldBar, dims, output, 1.0e-3);
 }
 
-TEST(rgb2hsv, CPP)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(rgb2hsv, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/rgb2hsv.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/rgb2hsv.test"),
+                                    numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = rgb2hsv(input);
 
@@ -70,33 +69,36 @@ TEST(rgb2hsv, CPP)
     ASSERT_VEC_ARRAY_NEAR(currGoldBar, dims, output, 1.0e-3);
 }
 
-TEST(rgb2hsv, MaxDim)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(rgb2hsv, MaxDim) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/rgb2hsv.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/rgb2hsv.test"),
+                                    numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
-    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = tile(input, 1, ntile);
-    array output = rgb2hsv(input);
-    dim4 outDims = output.dims();
+    unsigned int ntile    = (largeDim + dims[1] - 1) / dims[1];
+    input                 = tile(input, 1, ntile);
+    array output          = rgb2hsv(input);
+    dim4 outDims          = output.dims();
 
     float *outData = new float[outDims.elements()];
-    output.host((void*)outData);
+    output.host((void *)outData);
 
     vector<float> currGoldBar = tests[0];
-    for(int z=0; z<outDims[2]; ++z) {
-        for(int y=0; y<outDims[1]; ++y) {
-            for(int x=0; x<outDims[0]; ++x) {
-                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
-                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
+    for (int z = 0; z < outDims[2]; ++z) {
+        for (int y = 0; y < outDims[1]; ++y) {
+            for (int x = 0; x < outDims[0]; ++x) {
+                int outIter =
+                    (z * outDims[1] * outDims[0]) + (y * outDims[0]) + x;
+                int goldIter =
+                    (z * dims[1] * dims[0]) + ((y % dims[1]) * dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)
+                    << "at: " << outIter << endl;
             }
         }
     }
@@ -105,33 +107,36 @@ TEST(rgb2hsv, MaxDim)
     delete[] outData;
 }
 
-TEST(hsv2rgb, MaxDim)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(hsv2rgb, MaxDim) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR"/hsv_rgb/hsv2rgb.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/hsv2rgb.test"),
+                                    numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
-    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = tile(input, 1, ntile);
-    array output = hsv2rgb(input);
-    dim4 outDims = output.dims();
+    unsigned int ntile    = (largeDim + dims[1] - 1) / dims[1];
+    input                 = tile(input, 1, ntile);
+    array output          = hsv2rgb(input);
+    dim4 outDims          = output.dims();
 
     float *outData = new float[outDims.elements()];
-    output.host((void*)outData);
+    output.host((void *)outData);
 
     vector<float> currGoldBar = tests[0];
-    for(int z=0; z<outDims[2]; ++z) {
-        for(int y=0; y<outDims[1]; ++y) {
-            for(int x=0; x<outDims[0]; ++x) {
-                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
-                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
+    for (int z = 0; z < outDims[2]; ++z) {
+        for (int y = 0; y < outDims[1]; ++y) {
+            for (int x = 0; x < outDims[0]; ++x) {
+                int outIter =
+                    (z * outDims[1] * outDims[0]) + (y * outDims[0]) + x;
+                int goldIter =
+                    (z * dims[1] * dims[0]) + ((y % dims[1]) * dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)
+                    << "at: " << outIter << endl;
             }
         }
     }
diff --git a/test/iir.cpp b/test/iir.cpp
index d6b1edce5b..f5fcc8c134 100644
--- a/test/iir.cpp
+++ b/test/iir.cpp
@@ -7,32 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::vector;
-using std::string;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::convolve1;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::exception;
-using af::iir;
 using af::fir;
+using af::iir;
 using af::randu;
+using std::string;
+using std::vector;
 
 template<typename T>
-class filter : public ::testing::Test
-{
-public:
+class filter : public ::testing::Test {
+   public:
     virtual void SetUp() {}
 };
 
@@ -40,15 +39,14 @@ class filter : public ::testing::Test
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(filter, TestTypes);
 
-
 template<typename T>
-void firTest(const int xrows, const int xcols, const int brows, const int bcols)
-{
+void firTest(const int xrows, const int xcols, const int brows,
+             const int bcols) {
     if (noDoubleTests<T>()) return;
     try {
         dtype ty = (dtype)dtype_traits<T>::af_type;
-        array x = randu(xrows, xcols, ty);
-        array b = randu(brows, bcols, ty);
+        array x  = randu(xrows, xcols, ty);
+        array b  = randu(brows, bcols, ty);
 
         array y = fir(b, x);
         array c = convolve1(x, b, AF_CONV_EXPAND);
@@ -65,44 +63,30 @@ void firTest(const int xrows, const int xcols, const int brows, const int bcols)
 
         for (int j = 0; j < ycols; j++) {
             for (int i = 0; i < yrows; i++) {
-                ASSERT_NEAR(real(hy[j * yrows + i]),
-                            real(hc[j * crows + i]), 0.01);
+                ASSERT_NEAR(real(hy[j * yrows + i]), real(hc[j * crows + i]),
+                            0.01);
             }
         }
-    } catch (exception &ex) {
-        FAIL() << ex.what();
-    }
+    } catch (exception &ex) { FAIL() << ex.what(); }
 }
 
-TYPED_TEST(filter, firVecVec)
-{
-    firTest<TypeParam>(10000, 1, 1000, 1);
-}
+TYPED_TEST(filter, firVecVec) { firTest<TypeParam>(10000, 1, 1000, 1); }
 
-TYPED_TEST(filter, firVecMat)
-{
-    firTest<TypeParam>(10000, 1, 50, 10);
-}
+TYPED_TEST(filter, firVecMat) { firTest<TypeParam>(10000, 1, 50, 10); }
 
-TYPED_TEST(filter, firMatVec)
-{
-    firTest<TypeParam>(5000, 10, 100, 1);
-}
+TYPED_TEST(filter, firMatVec) { firTest<TypeParam>(5000, 10, 100, 1); }
 
-TYPED_TEST(filter, firMatMat)
-{
-    firTest<TypeParam>(5000, 10, 50, 10);
-}
+TYPED_TEST(filter, firMatMat) { firTest<TypeParam>(5000, 10, 50, 10); }
 
 template<typename T>
-void iirA0Test(const int xrows, const int xcols, const int brows, const int bcols)
-{
+void iirA0Test(const int xrows, const int xcols, const int brows,
+               const int bcols) {
     if (noDoubleTests<T>()) return;
     try {
-        dtype ty = (dtype)dtype_traits<T>::af_type;
-        array x = randu(xrows, xcols, ty);
-        array b = randu(brows, bcols, ty);
-        array a = randu(    1, bcols, ty);
+        dtype ty    = (dtype)dtype_traits<T>::af_type;
+        array x     = randu(xrows, xcols, ty);
+        array b     = randu(brows, bcols, ty);
+        array a     = randu(1, bcols, ty);
         array bNorm = b / tile(a, brows);
 
         array y = iir(b, a, x);
@@ -120,77 +104,57 @@ void iirA0Test(const int xrows, const int xcols, const int brows, const int bcol
 
         for (int j = 0; j < ycols; j++) {
             for (int i = 0; i < yrows; i++) {
-                ASSERT_NEAR(real(hy[j * yrows + i]),
-                            real(hc[j * crows + i]), 0.01);
+                ASSERT_NEAR(real(hy[j * yrows + i]), real(hc[j * crows + i]),
+                            0.01);
             }
         }
-    } catch (exception &ex) {
-        FAIL() << ex.what();
-    }
+    } catch (exception &ex) { FAIL() << ex.what(); }
 }
 
-TYPED_TEST(filter, iirA0VecVec)
-{
-    iirA0Test<TypeParam>(10000, 1, 1000, 1);
-}
+TYPED_TEST(filter, iirA0VecVec) { iirA0Test<TypeParam>(10000, 1, 1000, 1); }
 
-TYPED_TEST(filter, iirA0VecMat)
-{
-    iirA0Test<TypeParam>(10000, 1, 50, 10);
-}
+TYPED_TEST(filter, iirA0VecMat) { iirA0Test<TypeParam>(10000, 1, 50, 10); }
 
-TYPED_TEST(filter, iirA0MatVec)
-{
-    iirA0Test<TypeParam>(5000, 10, 100, 1);
-}
+TYPED_TEST(filter, iirA0MatVec) { iirA0Test<TypeParam>(5000, 10, 100, 1); }
 
-TYPED_TEST(filter, iirA0MatMat)
-{
-    iirA0Test<TypeParam>(5000, 10, 50, 10);
-}
+TYPED_TEST(filter, iirA0MatMat) { iirA0Test<TypeParam>(5000, 10, 50, 10); }
 
 template<typename T>
-void iirTest(const char *testFile)
-{
+void iirTest(const char *testFile) {
     if (noDoubleTests<T>()) return;
     vector<dim4> inDims;
 
     vector<vector<T> > inputs;
     vector<vector<T> > outputs;
-    readTests<T, T, float> (testFile, inDims, inputs, outputs);
+    readTests<T, T, float>(testFile, inDims, inputs, outputs);
 
     try {
         array a = array(inDims[0], &inputs[0][0]);
         array b = array(inDims[1], &inputs[1][0]);
         array x = array(inDims[2], &inputs[2][0]);
 
-        array y = iir(b, a, x);
+        array y        = iir(b, a, x);
         vector<T> gold = outputs[0];
         ASSERT_EQ(gold.size(), (size_t)y.elements());
 
         vector<T> out(y.elements());
         y.host(&out[0]);
 
-        for(size_t i = 0; i < gold.size(); i++) {
+        for (size_t i = 0; i < gold.size(); i++) {
             ASSERT_NEAR(real(out[i]), real(gold[i]), 0.01) << "at: " << i;
         }
 
-    } catch (exception &ex) {
-        FAIL() << ex.what();
-    }
+    } catch (exception &ex) { FAIL() << ex.what(); }
 }
 
-TYPED_TEST(filter, iirVecVec)
-{
-    iirTest<TypeParam>(TEST_DIR"/iir/iir_vv.test");
+TYPED_TEST(filter, iirVecVec) {
+    iirTest<TypeParam>(TEST_DIR "/iir/iir_vv.test");
 }
 
-TYPED_TEST(filter, iirVecMat)
-{
-    iirTest<TypeParam>(TEST_DIR"/iir/iir_vm.test");
+TYPED_TEST(filter, iirVecMat) {
+    iirTest<TypeParam>(TEST_DIR "/iir/iir_vm.test");
 }
 
-TYPED_TEST(filter, iirMatMat)
-{
-    iirTest<TypeParam>(TEST_DIR"/iir/iir_mm.test");
+TYPED_TEST(filter, iirMatMat) {
+    iirTest<TypeParam>(TEST_DIR "/iir/iir_mm.test");
 }
diff --git a/test/imageio.cpp b/test/imageio.cpp
index e3e1168eee..78142da722 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -7,29 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::endl;
-using std::vector;
-using std::string;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class ImageIO : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class ImageIO : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float> TestTypes;
@@ -37,84 +35,82 @@ typedef ::testing::Types<float> TestTypes;
 // register the type list
 TYPED_TEST_CASE(ImageIO, TestTypes);
 
-void loadImageTest(string pTestFile, string pImageFile, const bool isColor)
-{
+void loadImageTest(string pTestFile, string pImageFile, const bool isColor) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     af_array imgArray = 0;
     ASSERT_SUCCESS(af_load_image(&imgArray, pImageFile.c_str(), isColor));
 
     // Get result
-    float *imgData = new float[dims.elements()];
-    ASSERT_SUCCESS(af_get_data_ptr((void*) imgData, imgArray));
+    float* imgData = new float[dims.elements()];
+    ASSERT_SUCCESS(af_get_data_ptr((void*)imgData, imgArray));
 
     bool isJPEG = false;
-    if(pImageFile.find(".jpg") != string::npos) {
-        isJPEG = true;
-    }
+    if (pImageFile.find(".jpg") != string::npos) { isJPEG = true; }
 
     // Compare result
     size_t nElems = in[0].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        if(isJPEG)  // Allow +- 1 because of compression when testing JPG
-            ASSERT_NEAR(in[0][elIter], imgData[elIter], 1) << "at: " << elIter << endl;
+        if (isJPEG)  // Allow +- 1 because of compression when testing JPG
+            ASSERT_NEAR(in[0][elIter], imgData[elIter], 1)
+                << "at: " << elIter << endl;
         else
-            ASSERT_EQ(in[0][elIter], imgData[elIter]) << "at: " << elIter << endl;
+            ASSERT_EQ(in[0][elIter], imgData[elIter])
+                << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] imgData;
 
-    if(imgArray != 0) af_release_array(imgArray);
+    if (imgArray != 0) af_release_array(imgArray);
 }
 
-TYPED_TEST(ImageIO, ColorSmall)
-{
-    loadImageTest(string(TEST_DIR"/imageio/color_small.test"), string(TEST_DIR"/imageio/color_small.png"), true);
+TYPED_TEST(ImageIO, ColorSmall) {
+    loadImageTest(string(TEST_DIR "/imageio/color_small.test"),
+                  string(TEST_DIR "/imageio/color_small.png"), true);
 }
 
-TYPED_TEST(ImageIO, GraySmall)
-{
-    loadImageTest(string(TEST_DIR"/imageio/gray_small.test"), string(TEST_DIR"/imageio/gray_small.jpg"), false);
+TYPED_TEST(ImageIO, GraySmall) {
+    loadImageTest(string(TEST_DIR "/imageio/gray_small.test"),
+                  string(TEST_DIR "/imageio/gray_small.jpg"), false);
 }
 
-TYPED_TEST(ImageIO, GraySeq)
-{
-    loadImageTest(string(TEST_DIR"/imageio/gray_seq.test"), string(TEST_DIR"/imageio/gray_seq.png"), false);
+TYPED_TEST(ImageIO, GraySeq) {
+    loadImageTest(string(TEST_DIR "/imageio/gray_seq.test"),
+                  string(TEST_DIR "/imageio/gray_seq.png"), false);
 }
 
-TYPED_TEST(ImageIO, ColorSeq)
-{
-    loadImageTest(string(TEST_DIR"/imageio/color_seq.test"), string(TEST_DIR"/imageio/color_seq.png"), true);
+TYPED_TEST(ImageIO, ColorSeq) {
+    loadImageTest(string(TEST_DIR "/imageio/color_seq.test"),
+                  string(TEST_DIR "/imageio/color_seq.png"), true);
 }
 
-void loadimageArgsTest(string pImageFile, const bool isColor, af_err err)
-{
+void loadimageArgsTest(string pImageFile, const bool isColor, af_err err) {
     if (noImageIOTests()) return;
 
     af_array imgArray = 0;
 
     ASSERT_EQ(err, af_load_image(&imgArray, pImageFile.c_str(), isColor));
 
-    if(imgArray != 0) af_release_array(imgArray);
+    if (imgArray != 0) af_release_array(imgArray);
 }
 
-TYPED_TEST(ImageIO,InvalidArgsMissingFile)
-{
-    loadimageArgsTest(string(TEST_DIR"/imageio/nofile.png"), false, AF_ERR_RUNTIME);
+TYPED_TEST(ImageIO, InvalidArgsMissingFile) {
+    loadimageArgsTest(string(TEST_DIR "/imageio/nofile.png"), false,
+                      AF_ERR_RUNTIME);
 }
 
-TYPED_TEST(ImageIO,InvalidArgsWrongExt)
-{
-    loadimageArgsTest(string(TEST_DIR"/imageio/image.wrongext"), true, AF_ERR_NOT_SUPPORTED);
+TYPED_TEST(ImageIO, InvalidArgsWrongExt) {
+    loadimageArgsTest(string(TEST_DIR "/imageio/image.wrongext"), true,
+                      AF_ERR_NOT_SUPPORTED);
 }
 
 ////////////////////////////////// CPP //////////////////////////////////////
@@ -126,22 +122,23 @@ using af::loadImageMem;
 using af::saveImageMem;
 using af::span;
 
-TEST(ImageIO, CPP)
-{
+TEST(ImageIO, CPP) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/imageio/color_small.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(string(TEST_DIR "/imageio/color_small.test"),
+                                   numDims, in, tests);
 
     dim4 dims = numDims[0];
-    array img = loadImage(string(TEST_DIR"/imageio/color_small.png").c_str(), true);
+    array img =
+        loadImage(string(TEST_DIR "/imageio/color_small.png").c_str(), true);
 
     // Get result
-    float *imgData = new float[dims.elements()];
+    float* imgData = new float[dims.elements()];
     img.host((void*)imgData);
 
     // Compare result
@@ -155,16 +152,15 @@ TEST(ImageIO, CPP)
 }
 
 TEST(ImageIO, SavePNGCPP) {
-
     if (noImageIOTests()) return;
 
     array input(10, 10, 3, f32);
 
     input(span, span, span) = 0;
-    input(0, 0, 0) = 255;
-    input(0, 9, 1) = 255;
-    input(9, 0, 2) = 255;
-    input(9, 9, span) = 255;
+    input(0, 0, 0)          = 255;
+    input(0, 9, 1)          = 255;
+    input(9, 0, 2)          = 255;
+    input(9, 9, span)       = 255;
 
     saveImage("SaveCPP.png", input);
     array out = loadImage("SaveCPP.png", true);
@@ -173,16 +169,15 @@ TEST(ImageIO, SavePNGCPP) {
 }
 
 TEST(ImageIO, SaveBMPCPP) {
-
     if (noImageIOTests()) return;
 
     array input(10, 10, 3, f32);
 
     input(span, span, span) = 0;
-    input(0, 0, 0) = 255;
-    input(0, 9, 1) = 255;
-    input(9, 0, 2) = 255;
-    input(9, 9, span) = 255;
+    input(0, 0, 0)          = 255;
+    input(0, 9, 1)          = 255;
+    input(9, 0, 2)          = 255;
+    input(9, 9, span)       = 255;
 
     saveImage("SaveCPP.bmp", input);
     array out = loadImage("SaveCPP.bmp", true);
@@ -190,12 +185,12 @@ TEST(ImageIO, SaveBMPCPP) {
     ASSERT_FALSE(anyTrue<bool>(out - input));
 }
 
-TEST(ImageMem, SaveMemPNG)
-{
+TEST(ImageMem, SaveMemPNG) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    array img = loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
+    array img =
+        loadImage(string(TEST_DIR "/imageio/color_seq.png").c_str(), true);
 
     void* savedMem = saveImageMem(img, AF_FIF_PNG);
 
@@ -206,48 +201,48 @@ TEST(ImageMem, SaveMemPNG)
     deleteImageMem(savedMem);
 }
 
-TEST(ImageMem, SaveMemJPG1)
-{
+TEST(ImageMem, SaveMemJPG1) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    array img = loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), false);
+    array img =
+        loadImage(string(TEST_DIR "/imageio/color_seq.png").c_str(), false);
     saveImage("color_seq1.jpg", img);
 
     void* savedMem = saveImageMem(img, AF_FIF_JPEG);
 
     array loadMem = loadImageMem(savedMem);
-    array imgJPG = loadImage("color_seq1.jpg", false);
+    array imgJPG  = loadImage("color_seq1.jpg", false);
 
     ASSERT_FALSE(anyTrue<bool>(imgJPG - loadMem));
 
     deleteImageMem(savedMem);
 }
 
-TEST(ImageMem, SaveMemJPG3)
-{
+TEST(ImageMem, SaveMemJPG3) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    array img = loadImage(string(TEST_DIR"/imageio/color_seq.png").c_str(), true);
+    array img =
+        loadImage(string(TEST_DIR "/imageio/color_seq.png").c_str(), true);
     saveImage("color_seq3.jpg", img);
 
     void* savedMem = saveImageMem(img, AF_FIF_JPEG);
 
     array loadMem = loadImageMem(savedMem);
-    array imgJPG = loadImage("color_seq3.jpg", true);
+    array imgJPG  = loadImage("color_seq3.jpg", true);
 
     ASSERT_FALSE(anyTrue<bool>(imgJPG - loadMem));
 
     deleteImageMem(savedMem);
 }
 
-TEST(ImageMem, SaveMemBMP)
-{
+TEST(ImageMem, SaveMemBMP) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    array img = loadImage(string(TEST_DIR"/imageio/color_rand.png").c_str(), true);
+    array img =
+        loadImage(string(TEST_DIR "/imageio/color_rand.png").c_str(), true);
 
     void* savedMem = saveImageMem(img, AF_FIF_BMP);
 
@@ -258,23 +253,24 @@ TEST(ImageMem, SaveMemBMP)
     deleteImageMem(savedMem);
 }
 
-TEST(ImageIO, LoadImage16CPP)
-{
+TEST(ImageIO, LoadImage16CPP) {
     if (noImageIOTests()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/imageio/color_seq_16.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(
+        string(TEST_DIR "/imageio/color_seq_16.test"), numDims, in, tests);
 
     dim4 dims = numDims[0];
 
-    array img = loadImage(string(TEST_DIR"/imageio/color_seq_16.png").c_str(), true);
-    ASSERT_EQ(img.type(), f32); // loadImage should always return float
+    array img =
+        loadImage(string(TEST_DIR "/imageio/color_seq_16.png").c_str(), true);
+    ASSERT_EQ(img.type(), f32);  // loadImage should always return float
 
     // Get result
-    float *imgData = new float[dims.elements()];
+    float* imgData = new float[dims.elements()];
     img.host((void*)imgData);
 
     // Compare result
@@ -287,19 +283,18 @@ TEST(ImageIO, LoadImage16CPP)
     delete[] imgData;
 }
 
-TEST(ImageIO, SaveImage16CPP)
-{
+TEST(ImageIO, SaveImage16CPP) {
     if (noImageIOTests()) return;
 
     dim4 dims(16, 24, 3);
 
-    array input = randu(dims, u16);
+    array input     = randu(dims, u16);
     array input_255 = (input / 257).as(u16);
 
     saveImage("saveImage16CPP.png", input);
 
     array img = loadImage("saveImage16CPP.png", true);
-    ASSERT_EQ(img.type(), f32); // loadImage should always return float
+    ASSERT_EQ(img.type(), f32);  // loadImage should always return float
 
     ASSERT_FALSE(anyTrue<bool>(abs(img - input_255)));
 }
@@ -313,22 +308,21 @@ using af::loadImageNative;
 using af::saveImageNative;
 
 template<typename T>
-void loadImageNativeCPPTest(string pTestFile, string pImageFile)
-{
+void loadImageNativeCPPTest(string pTestFile, string pImageFile) {
     if (noImageIOTests()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(pTestFile,numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
     array img = loadImageNative(pImageFile.c_str());
     ASSERT_EQ(img.type(), (af_dtype)dtype_traits<T>::af_type);
 
     // Get result
-    T *imgData = new T[dims.elements()];
+    T* imgData = new T[dims.elements()];
     img.host((void*)imgData);
 
     // Compare result
@@ -341,33 +335,30 @@ void loadImageNativeCPPTest(string pTestFile, string pImageFile)
     delete[] imgData;
 }
 
-TEST(ImageIONative, LoadImageNative8CPP)
-{
-    loadImageNativeCPPTest<uchar>(string(TEST_DIR"/imageio/color_small.test"),
-                                  string(TEST_DIR"/imageio/color_small.png"));
+TEST(ImageIONative, LoadImageNative8CPP) {
+    loadImageNativeCPPTest<uchar>(string(TEST_DIR "/imageio/color_small.test"),
+                                  string(TEST_DIR "/imageio/color_small.png"));
 }
 
-TEST(ImageIONative, LoadImageNative16SmallCPP)
-{
-    loadImageNativeCPPTest<ushort>(string(TEST_DIR"/imageio/color_small_16.test"),
-                                   string(TEST_DIR"/imageio/color_small_16.png"));
+TEST(ImageIONative, LoadImageNative16SmallCPP) {
+    loadImageNativeCPPTest<ushort>(
+        string(TEST_DIR "/imageio/color_small_16.test"),
+        string(TEST_DIR "/imageio/color_small_16.png"));
 }
 
-TEST(ImageIONative, LoadImageNative16ColorCPP)
-{
-    loadImageNativeCPPTest<ushort>(string(TEST_DIR"/imageio/color_seq_16.test"),
-                                   string(TEST_DIR"/imageio/color_seq_16.png"));
+TEST(ImageIONative, LoadImageNative16ColorCPP) {
+    loadImageNativeCPPTest<ushort>(
+        string(TEST_DIR "/imageio/color_seq_16.test"),
+        string(TEST_DIR "/imageio/color_seq_16.png"));
 }
 
-TEST(ImageIONative, LoadImageNative16GrayCPP)
-{
-    loadImageNativeCPPTest<ushort>(string(TEST_DIR"/imageio/gray_seq_16.test"),
-                                   string(TEST_DIR"/imageio/gray_seq_16.png"));
+TEST(ImageIONative, LoadImageNative16GrayCPP) {
+    loadImageNativeCPPTest<ushort>(string(TEST_DIR "/imageio/gray_seq_16.test"),
+                                   string(TEST_DIR "/imageio/gray_seq_16.png"));
 }
 
 template<typename T>
-void saveLoadImageNativeCPPTest(dim4 dims)
-{
+void saveLoadImageNativeCPPTest(dim4 dims) {
     if (noImageIOTests()) return;
 
     array input = randu(dims, (af_dtype)dtype_traits<T>::af_type);
@@ -380,22 +371,18 @@ void saveLoadImageNativeCPPTest(dim4 dims)
     ASSERT_FALSE(anyTrue<bool>(input - loaded));
 }
 
-TEST(ImageIONative, SaveLoadImageNative8CPP)
-{
+TEST(ImageIONative, SaveLoadImageNative8CPP) {
     saveLoadImageNativeCPPTest<uchar>(dim4(480, 720, 3, 1));
 }
 
-TEST(ImageIONative, SaveLoadImageNative16SmallCPP)
-{
+TEST(ImageIONative, SaveLoadImageNative16SmallCPP) {
     saveLoadImageNativeCPPTest<ushort>(dim4(8, 12, 3, 1));
 }
 
-TEST(ImageIONative, SaveLoadImageNative16ColorCPP)
-{
+TEST(ImageIONative, SaveLoadImageNative16ColorCPP) {
     saveLoadImageNativeCPPTest<ushort>(dim4(480, 720, 3, 1));
 }
 
-TEST(ImageIONative, SaveLoadImageNative16GrayCPP)
-{
+TEST(ImageIONative, SaveLoadImageNative16GrayCPP) {
     saveLoadImageNativeCPPTest<ushort>(dim4(24, 32, 1, 1));
 }
diff --git a/test/index.cpp b/test/index.cpp
index de7512c698..3cbedfdea2 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -7,78 +7,82 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <af/data.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/data.h>
 
-#include <vector>
+#include <testHelpers.hpp>
 #include <algorithm>
 #include <functional>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::ostream_iterator;
 using af::cdouble;
 using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::cout;
+using std::endl;
+using std::ostream_iterator;
+using std::string;
+using std::vector;
 
 template<typename T, typename OP>
-void
-checkValues(const af_seq &seq, const T* data, const T* indexed_data, OP compair_op) {
-    for(int i = 0, j = seq.begin; compair_op(j,(int)seq.end); j+= seq.step, i++) {
+void checkValues(const af_seq &seq, const T *data, const T *indexed_data,
+                 OP compair_op) {
+    for (int i = 0, j = seq.begin; compair_op(j, (int)seq.end);
+         j += seq.step, i++) {
         ASSERT_DOUBLE_EQ(real(data[j]), real(indexed_data[i]))
-        << "Where i = " << i << " and j = " << j;
+            << "Where i = " << i << " and j = " << j;
     }
 }
 
 template<typename T>
-void
-DimCheck(const vector<af_seq> &seqs) {
+void DimCheck(const vector<af_seq> &seqs) {
     if (noDoubleTests<T>()) return;
 
-    static const int ndims = 1;
+    static const int ndims   = 1;
     static const size_t dims = 100;
 
     dim_t d[1] = {dims};
 
     vector<T> hData(dims);
-    for(int i = 0; i < (int)dims; i++) { hData[i] = i; }
+    for (int i = 0; i < (int)dims; i++) { hData[i] = i; }
 
     af_array a = 0;
-    ASSERT_SUCCESS(af_create_array(&a, &hData.front(), ndims, d, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &hData.front(), ndims, d,
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     vector<af_array> indexed_array(seqs.size(), 0);
-    for(size_t i = 0; i < seqs.size(); i++) {
+    for (size_t i = 0; i < seqs.size(); i++) {
         ASSERT_SUCCESS(af_index(&(indexed_array[i]), a, ndims, &seqs[i]))
-            << "where seqs[i].begin == "    << seqs[i].begin
-            << " seqs[i].step == "          << seqs[i].step
-            << " seqs[i].end == "           << seqs[i].end;
+            << "where seqs[i].begin == " << seqs[i].begin
+            << " seqs[i].step == " << seqs[i].step
+            << " seqs[i].end == " << seqs[i].end;
     }
 
-    vector<T*> h_indexed(seqs.size());
-    for(size_t i = 0; i < seqs.size(); i++) {
+    vector<T *> h_indexed(seqs.size());
+    for (size_t i = 0; i < seqs.size(); i++) {
         dim_t elems;
         ASSERT_SUCCESS(af_get_elements(&elems, indexed_array[i]));
         h_indexed[i] = new T[elems];
-        ASSERT_SUCCESS(af_get_data_ptr((void *)(h_indexed[i]), indexed_array[i]));
+        ASSERT_SUCCESS(
+            af_get_data_ptr((void *)(h_indexed[i]), indexed_array[i]));
     }
 
-    for(size_t k = 0; k < seqs.size(); k++) {
-        if(seqs[k].step > 0)        {
-            checkValues(seqs[k], &hData.front(), h_indexed[k], std::less_equal<int>());
-        } else if (seqs[k].step < 0)  {
-            checkValues(seqs[k], &hData.front(), h_indexed[k], std::greater_equal<int>());
+    for (size_t k = 0; k < seqs.size(); k++) {
+        if (seqs[k].step > 0) {
+            checkValues(seqs[k], &hData.front(), h_indexed[k],
+                        std::less_equal<int>());
+        } else if (seqs[k].step < 0) {
+            checkValues(seqs[k], &hData.front(), h_indexed[k],
+                        std::greater_equal<int>());
         } else {
-            for(size_t i = 0; i <= seqs[k].end; i++) {
+            for (size_t i = 0; i <= seqs[k].end; i++) {
                 ASSERT_DOUBLE_EQ(real(hData[i]), real(h_indexed[k][i]))
                     << "Where i = " << i;
             }
@@ -93,32 +97,37 @@ DimCheck(const vector<af_seq> &seqs) {
 }
 
 template<typename T>
-class Indexing1D : public ::testing::Test
-{
-public:
+class Indexing1D : public ::testing::Test {
+   public:
     virtual void SetUp() {
-        continuous_seqs.push_back(af_make_seq(  0,    20,   1 )); // Begin Continious
-        continuous_seqs.push_back(af_make_seq(  80,   99,   1 )); // End Continious
-        continuous_seqs.push_back(af_make_seq(  10,   89,   1 )); // Mid Continious
-
-        continuous_reverse_seqs.push_back(af_make_seq(  20,   0,    -1 )); // Begin Reverse Continious
-        continuous_reverse_seqs.push_back(af_make_seq(  99,   80,   -1 )); // End Reverse Continious
-        continuous_reverse_seqs.push_back(af_make_seq(  89,   10,   -1 )); // Mid Reverse Continious
-
-        strided_seqs.push_back(af_make_seq(  5,    40,   2 )); // Two Step
-        strided_seqs.push_back(af_make_seq(  5,    40,   3 )); // Three Step
-        strided_seqs.push_back(af_make_seq(  5,    40,   4 )); // Four Step
-
-        strided_reverse_seqs.push_back(af_make_seq(  40,    5,   -2 )); // Reverse Two Step
-        strided_reverse_seqs.push_back(af_make_seq(  40,    5,   -3 )); // Reverse Three Step
-        strided_reverse_seqs.push_back(af_make_seq(  40,    5,   -4 )); // Reverse Four Step
+        continuous_seqs.push_back(af_make_seq(0, 20, 1));   // Begin Continious
+        continuous_seqs.push_back(af_make_seq(80, 99, 1));  // End Continious
+        continuous_seqs.push_back(af_make_seq(10, 89, 1));  // Mid Continious
+
+        continuous_reverse_seqs.push_back(
+            af_make_seq(20, 0, -1));  // Begin Reverse Continious
+        continuous_reverse_seqs.push_back(
+            af_make_seq(99, 80, -1));  // End Reverse Continious
+        continuous_reverse_seqs.push_back(
+            af_make_seq(89, 10, -1));  // Mid Reverse Continious
+
+        strided_seqs.push_back(af_make_seq(5, 40, 2));  // Two Step
+        strided_seqs.push_back(af_make_seq(5, 40, 3));  // Three Step
+        strided_seqs.push_back(af_make_seq(5, 40, 4));  // Four Step
+
+        strided_reverse_seqs.push_back(
+            af_make_seq(40, 5, -2));  // Reverse Two Step
+        strided_reverse_seqs.push_back(
+            af_make_seq(40, 5, -3));  // Reverse Three Step
+        strided_reverse_seqs.push_back(
+            af_make_seq(40, 5, -4));  // Reverse Four Step
 
         span_seqs.push_back(af_span);
     }
 
     virtual ~Indexing1D() {}
 
-    //virtual void TearDown() {}
+    // virtual void TearDown() {}
 
     vector<af_seq> continuous_seqs;
     vector<af_seq> continuous_reverse_seqs;
@@ -127,20 +136,26 @@ class Indexing1D : public ::testing::Test
     vector<af_seq> span_seqs;
 };
 
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned, unsigned char, intl, uintl, short, ushort> AllTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned,
+                         unsigned char, intl, uintl, short, ushort>
+    AllTypes;
 TYPED_TEST_CASE(Indexing1D, AllTypes);
 
-TYPED_TEST(Indexing1D, Continious)          { DimCheck<TypeParam>(this->continuous_seqs);           }
-TYPED_TEST(Indexing1D, ContiniousReverse)   { DimCheck<TypeParam>(this->continuous_reverse_seqs);   }
-TYPED_TEST(Indexing1D, Strided)             { DimCheck<TypeParam>(this->strided_seqs);              }
-TYPED_TEST(Indexing1D, StridedReverse)      { DimCheck<TypeParam>(this->strided_reverse_seqs);      }
-TYPED_TEST(Indexing1D, Span)                { DimCheck<TypeParam>(this->span_seqs);                 }
-
+TYPED_TEST(Indexing1D, Continious) {
+    DimCheck<TypeParam>(this->continuous_seqs);
+}
+TYPED_TEST(Indexing1D, ContiniousReverse) {
+    DimCheck<TypeParam>(this->continuous_reverse_seqs);
+}
+TYPED_TEST(Indexing1D, Strided) { DimCheck<TypeParam>(this->strided_seqs); }
+TYPED_TEST(Indexing1D, StridedReverse) {
+    DimCheck<TypeParam>(this->strided_reverse_seqs);
+}
+TYPED_TEST(Indexing1D, Span) { DimCheck<TypeParam>(this->span_seqs); }
 
 template<typename T>
-class Indexing2D : public ::testing::Test
-{
-public:
+class Indexing2D : public ::testing::Test {
+   public:
     vector<af_seq> make_vec(af_seq first, af_seq second) {
         vector<af_seq> out;
         out.push_back(first);
@@ -148,85 +163,139 @@ class Indexing2D : public ::testing::Test
         return out;
     }
     virtual void SetUp() {
-
-        column_continuous_seq.push_back(make_vec(af_span, af_make_seq(  0,  6,  1)));
-        column_continuous_seq.push_back(make_vec(af_span, af_make_seq(  4,  9,  1)));
-        column_continuous_seq.push_back(make_vec(af_span, af_make_seq(  3,  8,  1)));
-
-        column_continuous_reverse_seq.push_back(make_vec(af_span, af_make_seq(  6,  0,  -1)));
-        column_continuous_reverse_seq.push_back(make_vec(af_span, af_make_seq(  9,  4,  -1)));
-        column_continuous_reverse_seq.push_back(make_vec(af_span, af_make_seq(  8,  3,  -1)));
-
-        column_strided_seq.push_back(make_vec(af_span, af_make_seq(  0,    8,   2 ))); // Two Step
-        column_strided_seq.push_back(make_vec(af_span, af_make_seq(  2,    9,   3 ))); // Three Step
-        column_strided_seq.push_back(make_vec(af_span, af_make_seq(  0,    9,   4 ))); // Four Step
-
-        column_strided_reverse_seq.push_back(make_vec(af_span, af_make_seq(  8,   0,   -2 ))); // Two Step
-        column_strided_reverse_seq.push_back(make_vec(af_span, af_make_seq(  9,   2,   -3 ))); // Three Step
-        column_strided_reverse_seq.push_back(make_vec(af_span, af_make_seq(  9,   0,   -4 ))); // Four Step
-
-        row_continuous_seq.push_back(make_vec(af_make_seq(  0,  6,  1), af_span));
-        row_continuous_seq.push_back(make_vec(af_make_seq(  4,  9,  1), af_span));
-        row_continuous_seq.push_back(make_vec(af_make_seq(  3,  8,  1), af_span));
-
-        row_continuous_reverse_seq.push_back(make_vec(af_make_seq(  6,  0,  -1), af_span));
-        row_continuous_reverse_seq.push_back(make_vec(af_make_seq(  9,  4,  -1), af_span));
-        row_continuous_reverse_seq.push_back(make_vec(af_make_seq(  8,  3,  -1), af_span));
-
-        row_strided_seq.push_back(make_vec(af_make_seq(  0,    8,   2 ), af_span));
-        row_strided_seq.push_back(make_vec(af_make_seq(  2,    9,   3 ), af_span));
-        row_strided_seq.push_back(make_vec(af_make_seq(  0,    9,   4 ), af_span));
-
-        row_strided_reverse_seq.push_back(make_vec(af_make_seq(  8,   0,   -2 ), af_span));
-        row_strided_reverse_seq.push_back(make_vec(af_make_seq(  9,   2,   -3 ), af_span));
-        row_strided_reverse_seq.push_back(make_vec(af_make_seq(  9,   0,   -4 ), af_span));
-
-        continuous_continuous_seq.push_back(make_vec(af_make_seq(  1,  6,  1), af_make_seq(  0,  6,  1)));
-        continuous_continuous_seq.push_back(make_vec(af_make_seq(  3,  9,  1), af_make_seq(  4,  9,  1)));
-        continuous_continuous_seq.push_back(make_vec(af_make_seq(  5,  8,  1), af_make_seq(  3,  8,  1)));
-
-        continuous_reverse_seq.push_back(make_vec(af_make_seq(  1,  6,  1), af_make_seq(  6,  0,  -1)));
-        continuous_reverse_seq.push_back(make_vec(af_make_seq(  3,  9,  1), af_make_seq(  9,  4,  -1)));
-        continuous_reverse_seq.push_back(make_vec(af_make_seq(  5,  8,  1), af_make_seq(  8,  3,  -1)));
-
-        continuous_strided_seq.push_back(make_vec(af_make_seq(  1,  6,  1), af_make_seq(  0,  8,  2)));
-        continuous_strided_seq.push_back(make_vec(af_make_seq(  3,  9,  1), af_make_seq(  2,  9,  3)));
-        continuous_strided_seq.push_back(make_vec(af_make_seq(  5,  8,  1), af_make_seq(  1,  9,  4)));
-
-        continuous_strided_reverse_seq.push_back(make_vec(af_make_seq(  1,  6,  1), af_make_seq(  8,  0,  -2)));
-        continuous_strided_reverse_seq.push_back(make_vec(af_make_seq(  3,  9,  1), af_make_seq(  9,  2,  -3)));
-        continuous_strided_reverse_seq.push_back(make_vec(af_make_seq(  5,  8,  1), af_make_seq(  9,  1,  -4)));
-
-        reverse_continuous_seq.push_back(make_vec(af_make_seq(  6,  1,  -1), af_make_seq(  0,  6,  1)));
-        reverse_continuous_seq.push_back(make_vec(af_make_seq(  9,  3,  -1), af_make_seq(  4,  9,  1)));
-        reverse_continuous_seq.push_back(make_vec(af_make_seq(  8,  5,  -1), af_make_seq(  3,  8,  1)));
-
-        reverse_reverse_seq.push_back(make_vec(af_make_seq(  6,  1,  -1), af_make_seq(  6,  0,  -1)));
-        reverse_reverse_seq.push_back(make_vec(af_make_seq(  9,  3,  -1), af_make_seq(  9,  4,  -1)));
-        reverse_reverse_seq.push_back(make_vec(af_make_seq(  8,  5,  -1), af_make_seq(  8,  3,  -1)));
-
-        reverse_strided_seq.push_back(make_vec(af_make_seq(  6,  1,  -1), af_make_seq(  0,  8,  2)));
-        reverse_strided_seq.push_back(make_vec(af_make_seq(  9,  3,  -1), af_make_seq(  2,  9,  3)));
-        reverse_strided_seq.push_back(make_vec(af_make_seq(  8,  5,  -1), af_make_seq(  1,  9,  4)));
-
-        reverse_strided_reverse_seq.push_back(make_vec(af_make_seq(  6,  1,  -1), af_make_seq(  8,  0,  -2)));
-        reverse_strided_reverse_seq.push_back(make_vec(af_make_seq(  9,  3,  -1), af_make_seq(  9,  2,  -3)));
-        reverse_strided_reverse_seq.push_back(make_vec(af_make_seq(  8,  5,  -1), af_make_seq(  9,  1,  -4)));
-
-        strided_continuous_seq.push_back(make_vec(af_make_seq(  0,  8,  2), af_make_seq(  0,  6,  1)));
-        strided_continuous_seq.push_back(make_vec(af_make_seq(  2,  9,  3), af_make_seq(  4,  9,  1)));
-        strided_continuous_seq.push_back(make_vec(af_make_seq(  1,  9,  4), af_make_seq(  3,  8,  1)));
-
-        strided_strided_seq.push_back(make_vec(af_make_seq(  1,  6,  2), af_make_seq(  0,  8,  2)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  3,  9,  2), af_make_seq(  2,  9,  3)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  5,  8,  2), af_make_seq(  1,  9,  4)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  1,  6,  3), af_make_seq(  0,  8,  2)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  3,  9,  3), af_make_seq(  2,  9,  3)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  5,  8,  3), af_make_seq(  1,  9,  4)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  1,  6,  4), af_make_seq(  0,  8,  2)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  3,  9,  4), af_make_seq(  2,  9,  3)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  3,  8,  4), af_make_seq(  1,  9,  4)));
-        strided_strided_seq.push_back(make_vec(af_make_seq(  3,  6,  4), af_make_seq(  1,  9,  4)));
+        column_continuous_seq.push_back(
+            make_vec(af_span, af_make_seq(0, 6, 1)));
+        column_continuous_seq.push_back(
+            make_vec(af_span, af_make_seq(4, 9, 1)));
+        column_continuous_seq.push_back(
+            make_vec(af_span, af_make_seq(3, 8, 1)));
+
+        column_continuous_reverse_seq.push_back(
+            make_vec(af_span, af_make_seq(6, 0, -1)));
+        column_continuous_reverse_seq.push_back(
+            make_vec(af_span, af_make_seq(9, 4, -1)));
+        column_continuous_reverse_seq.push_back(
+            make_vec(af_span, af_make_seq(8, 3, -1)));
+
+        column_strided_seq.push_back(
+            make_vec(af_span, af_make_seq(0, 8, 2)));  // Two Step
+        column_strided_seq.push_back(
+            make_vec(af_span, af_make_seq(2, 9, 3)));  // Three Step
+        column_strided_seq.push_back(
+            make_vec(af_span, af_make_seq(0, 9, 4)));  // Four Step
+
+        column_strided_reverse_seq.push_back(
+            make_vec(af_span, af_make_seq(8, 0, -2)));  // Two Step
+        column_strided_reverse_seq.push_back(
+            make_vec(af_span, af_make_seq(9, 2, -3)));  // Three Step
+        column_strided_reverse_seq.push_back(
+            make_vec(af_span, af_make_seq(9, 0, -4)));  // Four Step
+
+        row_continuous_seq.push_back(make_vec(af_make_seq(0, 6, 1), af_span));
+        row_continuous_seq.push_back(make_vec(af_make_seq(4, 9, 1), af_span));
+        row_continuous_seq.push_back(make_vec(af_make_seq(3, 8, 1), af_span));
+
+        row_continuous_reverse_seq.push_back(
+            make_vec(af_make_seq(6, 0, -1), af_span));
+        row_continuous_reverse_seq.push_back(
+            make_vec(af_make_seq(9, 4, -1), af_span));
+        row_continuous_reverse_seq.push_back(
+            make_vec(af_make_seq(8, 3, -1), af_span));
+
+        row_strided_seq.push_back(make_vec(af_make_seq(0, 8, 2), af_span));
+        row_strided_seq.push_back(make_vec(af_make_seq(2, 9, 3), af_span));
+        row_strided_seq.push_back(make_vec(af_make_seq(0, 9, 4), af_span));
+
+        row_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(8, 0, -2), af_span));
+        row_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(9, 2, -3), af_span));
+        row_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(9, 0, -4), af_span));
+
+        continuous_continuous_seq.push_back(
+            make_vec(af_make_seq(1, 6, 1), af_make_seq(0, 6, 1)));
+        continuous_continuous_seq.push_back(
+            make_vec(af_make_seq(3, 9, 1), af_make_seq(4, 9, 1)));
+        continuous_continuous_seq.push_back(
+            make_vec(af_make_seq(5, 8, 1), af_make_seq(3, 8, 1)));
+
+        continuous_reverse_seq.push_back(
+            make_vec(af_make_seq(1, 6, 1), af_make_seq(6, 0, -1)));
+        continuous_reverse_seq.push_back(
+            make_vec(af_make_seq(3, 9, 1), af_make_seq(9, 4, -1)));
+        continuous_reverse_seq.push_back(
+            make_vec(af_make_seq(5, 8, 1), af_make_seq(8, 3, -1)));
+
+        continuous_strided_seq.push_back(
+            make_vec(af_make_seq(1, 6, 1), af_make_seq(0, 8, 2)));
+        continuous_strided_seq.push_back(
+            make_vec(af_make_seq(3, 9, 1), af_make_seq(2, 9, 3)));
+        continuous_strided_seq.push_back(
+            make_vec(af_make_seq(5, 8, 1), af_make_seq(1, 9, 4)));
+
+        continuous_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(1, 6, 1), af_make_seq(8, 0, -2)));
+        continuous_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(3, 9, 1), af_make_seq(9, 2, -3)));
+        continuous_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(5, 8, 1), af_make_seq(9, 1, -4)));
+
+        reverse_continuous_seq.push_back(
+            make_vec(af_make_seq(6, 1, -1), af_make_seq(0, 6, 1)));
+        reverse_continuous_seq.push_back(
+            make_vec(af_make_seq(9, 3, -1), af_make_seq(4, 9, 1)));
+        reverse_continuous_seq.push_back(
+            make_vec(af_make_seq(8, 5, -1), af_make_seq(3, 8, 1)));
+
+        reverse_reverse_seq.push_back(
+            make_vec(af_make_seq(6, 1, -1), af_make_seq(6, 0, -1)));
+        reverse_reverse_seq.push_back(
+            make_vec(af_make_seq(9, 3, -1), af_make_seq(9, 4, -1)));
+        reverse_reverse_seq.push_back(
+            make_vec(af_make_seq(8, 5, -1), af_make_seq(8, 3, -1)));
+
+        reverse_strided_seq.push_back(
+            make_vec(af_make_seq(6, 1, -1), af_make_seq(0, 8, 2)));
+        reverse_strided_seq.push_back(
+            make_vec(af_make_seq(9, 3, -1), af_make_seq(2, 9, 3)));
+        reverse_strided_seq.push_back(
+            make_vec(af_make_seq(8, 5, -1), af_make_seq(1, 9, 4)));
+
+        reverse_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(6, 1, -1), af_make_seq(8, 0, -2)));
+        reverse_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(9, 3, -1), af_make_seq(9, 2, -3)));
+        reverse_strided_reverse_seq.push_back(
+            make_vec(af_make_seq(8, 5, -1), af_make_seq(9, 1, -4)));
+
+        strided_continuous_seq.push_back(
+            make_vec(af_make_seq(0, 8, 2), af_make_seq(0, 6, 1)));
+        strided_continuous_seq.push_back(
+            make_vec(af_make_seq(2, 9, 3), af_make_seq(4, 9, 1)));
+        strided_continuous_seq.push_back(
+            make_vec(af_make_seq(1, 9, 4), af_make_seq(3, 8, 1)));
+
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(1, 6, 2), af_make_seq(0, 8, 2)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(3, 9, 2), af_make_seq(2, 9, 3)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(5, 8, 2), af_make_seq(1, 9, 4)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(1, 6, 3), af_make_seq(0, 8, 2)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(3, 9, 3), af_make_seq(2, 9, 3)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(5, 8, 3), af_make_seq(1, 9, 4)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(1, 6, 4), af_make_seq(0, 8, 2)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(3, 9, 4), af_make_seq(2, 9, 3)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(3, 8, 4), af_make_seq(1, 9, 4)));
+        strided_strided_seq.push_back(
+            make_vec(af_make_seq(3, 6, 4), af_make_seq(1, 9, 4)));
     }
 
     vector<vector<af_seq> > column_continuous_seq;
@@ -254,39 +323,43 @@ class Indexing2D : public ::testing::Test
 };
 
 template<typename T>
-void
-DimCheck2D(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDims)
-{
+void DimCheck2D(const vector<vector<af_seq> > &seqs, string TestFile,
+                size_t NDims) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
-    readTests<T,T,int>(TestFile, numDims, hData, tests);
+    readTests<T, T, int>(TestFile, numDims, hData, tests);
     dim4 dimensions = numDims[0];
 
     af_array a = 0;
-    ASSERT_SUCCESS(af_create_array(&a, &(hData[0].front()), NDims, dimensions.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&a, &(hData[0].front()), NDims,
+                                   dimensions.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     vector<af_array> indexed_arrays(seqs.size(), 0);
-    for(size_t i = 0; i < seqs.size(); i++) {
-        ASSERT_SUCCESS(af_index(&(indexed_arrays[i]), a, NDims, seqs[i].data()));
+    for (size_t i = 0; i < seqs.size(); i++) {
+        ASSERT_SUCCESS(
+            af_index(&(indexed_arrays[i]), a, NDims, seqs[i].data()));
     }
 
-    vector<T*> h_indexed(seqs.size(), NULL);
-    for(size_t i = 0; i < seqs.size(); i++) {
+    vector<T *> h_indexed(seqs.size(), NULL);
+    for (size_t i = 0; i < seqs.size(); i++) {
         dim_t elems;
         ASSERT_SUCCESS(af_get_elements(&elems, indexed_arrays[i]));
         h_indexed[i] = new T[elems];
-        ASSERT_SUCCESS(af_get_data_ptr((void *)h_indexed[i], indexed_arrays[i]));
+        ASSERT_SUCCESS(
+            af_get_data_ptr((void *)h_indexed[i], indexed_arrays[i]));
 
-        T* ptr = h_indexed[i];
-        if(false == equal(ptr, ptr + tests[i].size(), tests[i].begin())) {
+        T *ptr = h_indexed[i];
+        if (false == equal(ptr, ptr + tests[i].size(), tests[i].begin())) {
             cout << "index data: ";
             copy(ptr, ptr + tests[i].size(), ostream_iterator<T>(cout, ", "));
             cout << endl << "file data: ";
-            copy(tests[i].begin(), tests[i].end(), ostream_iterator<T>(cout, ", "));
+            copy(tests[i].begin(), tests[i].end(),
+                 ostream_iterator<T>(cout, ", "));
             FAIL() << "indexed_array[" << i << "] FAILED" << endl;
         }
         delete[] h_indexed[i];
@@ -300,94 +373,94 @@ DimCheck2D(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDims)
 
 TYPED_TEST_CASE(Indexing2D, AllTypes);
 
-TYPED_TEST(Indexing2D, ColumnContinious)
-{
-    DimCheck2D<TypeParam>(this->column_continuous_seq, TEST_DIR"/index/ColumnContinious.test", 2);
+TYPED_TEST(Indexing2D, ColumnContinious) {
+    DimCheck2D<TypeParam>(this->column_continuous_seq,
+                          TEST_DIR "/index/ColumnContinious.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ColumnContiniousReverse)
-{
-    DimCheck2D<TypeParam>(this->column_continuous_reverse_seq, TEST_DIR"/index/ColumnContiniousReverse.test", 2);
+TYPED_TEST(Indexing2D, ColumnContiniousReverse) {
+    DimCheck2D<TypeParam>(this->column_continuous_reverse_seq,
+                          TEST_DIR "/index/ColumnContiniousReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ColumnStrided)
-{
-    DimCheck2D<TypeParam>(this->column_strided_seq, TEST_DIR"/index/ColumnStrided.test", 2);
+TYPED_TEST(Indexing2D, ColumnStrided) {
+    DimCheck2D<TypeParam>(this->column_strided_seq,
+                          TEST_DIR "/index/ColumnStrided.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ColumnStridedReverse)
-{
-    DimCheck2D<TypeParam>(this->column_strided_reverse_seq, TEST_DIR"/index/ColumnStridedReverse.test", 2);
+TYPED_TEST(Indexing2D, ColumnStridedReverse) {
+    DimCheck2D<TypeParam>(this->column_strided_reverse_seq,
+                          TEST_DIR "/index/ColumnStridedReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, RowContinious)
-{
-    DimCheck2D<TypeParam>(this->row_continuous_seq, TEST_DIR"/index/RowContinious.test", 2);
+TYPED_TEST(Indexing2D, RowContinious) {
+    DimCheck2D<TypeParam>(this->row_continuous_seq,
+                          TEST_DIR "/index/RowContinious.test", 2);
 }
 
-TYPED_TEST(Indexing2D, RowContiniousReverse)
-{
-    DimCheck2D<TypeParam>(this->row_continuous_reverse_seq, TEST_DIR"/index/RowContiniousReverse.test", 2);
+TYPED_TEST(Indexing2D, RowContiniousReverse) {
+    DimCheck2D<TypeParam>(this->row_continuous_reverse_seq,
+                          TEST_DIR "/index/RowContiniousReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, RowStrided)
-{
-    DimCheck2D<TypeParam>(this->row_strided_seq, TEST_DIR"/index/RowStrided.test", 2);
+TYPED_TEST(Indexing2D, RowStrided) {
+    DimCheck2D<TypeParam>(this->row_strided_seq,
+                          TEST_DIR "/index/RowStrided.test", 2);
 }
 
-TYPED_TEST(Indexing2D, RowStridedReverse)
-{
-    DimCheck2D<TypeParam>(this->row_strided_reverse_seq, TEST_DIR"/index/RowStridedReverse.test", 2);
+TYPED_TEST(Indexing2D, RowStridedReverse) {
+    DimCheck2D<TypeParam>(this->row_strided_reverse_seq,
+                          TEST_DIR "/index/RowStridedReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ContiniousContinious)
-{
-    DimCheck2D<TypeParam>(this->continuous_continuous_seq, TEST_DIR"/index/ContiniousContinious.test", 2);
+TYPED_TEST(Indexing2D, ContiniousContinious) {
+    DimCheck2D<TypeParam>(this->continuous_continuous_seq,
+                          TEST_DIR "/index/ContiniousContinious.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ContiniousReverse)
-{
-    DimCheck2D<TypeParam>(this->continuous_reverse_seq, TEST_DIR"/index/ContiniousReverse.test", 2);
+TYPED_TEST(Indexing2D, ContiniousReverse) {
+    DimCheck2D<TypeParam>(this->continuous_reverse_seq,
+                          TEST_DIR "/index/ContiniousReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ContiniousStrided)
-{
-    DimCheck2D<TypeParam>(this->continuous_strided_seq, TEST_DIR"/index/ContiniousStrided.test", 2);
+TYPED_TEST(Indexing2D, ContiniousStrided) {
+    DimCheck2D<TypeParam>(this->continuous_strided_seq,
+                          TEST_DIR "/index/ContiniousStrided.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ContiniousStridedReverse)
-{
-    DimCheck2D<TypeParam>(this->continuous_strided_reverse_seq, TEST_DIR"/index/ContiniousStridedReverse.test", 2);
+TYPED_TEST(Indexing2D, ContiniousStridedReverse) {
+    DimCheck2D<TypeParam>(this->continuous_strided_reverse_seq,
+                          TEST_DIR "/index/ContiniousStridedReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ReverseContinious)
-{
-    DimCheck2D<TypeParam>(this->reverse_continuous_seq, TEST_DIR"/index/ReverseContinious.test", 2);
+TYPED_TEST(Indexing2D, ReverseContinious) {
+    DimCheck2D<TypeParam>(this->reverse_continuous_seq,
+                          TEST_DIR "/index/ReverseContinious.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ReverseReverse)
-{
-    DimCheck2D<TypeParam>(this->reverse_reverse_seq, TEST_DIR"/index/ReverseReverse.test", 2);
+TYPED_TEST(Indexing2D, ReverseReverse) {
+    DimCheck2D<TypeParam>(this->reverse_reverse_seq,
+                          TEST_DIR "/index/ReverseReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ReverseStrided)
-{
-    DimCheck2D<TypeParam>(this->reverse_strided_seq, TEST_DIR"/index/ReverseStrided.test", 2);
+TYPED_TEST(Indexing2D, ReverseStrided) {
+    DimCheck2D<TypeParam>(this->reverse_strided_seq,
+                          TEST_DIR "/index/ReverseStrided.test", 2);
 }
 
-TYPED_TEST(Indexing2D, ReverseStridedReverse)
-{
-    DimCheck2D<TypeParam>(this->reverse_strided_reverse_seq, TEST_DIR"/index/ReverseStridedReverse.test", 2);
+TYPED_TEST(Indexing2D, ReverseStridedReverse) {
+    DimCheck2D<TypeParam>(this->reverse_strided_reverse_seq,
+                          TEST_DIR "/index/ReverseStridedReverse.test", 2);
 }
 
-TYPED_TEST(Indexing2D, StridedContinious)
-{
-    DimCheck2D<TypeParam>(this->strided_continuous_seq, TEST_DIR"/index/StridedContinious.test", 2);
+TYPED_TEST(Indexing2D, StridedContinious) {
+    DimCheck2D<TypeParam>(this->strided_continuous_seq,
+                          TEST_DIR "/index/StridedContinious.test", 2);
 }
 
-TYPED_TEST(Indexing2D, StridedStrided)
-{
-    DimCheck2D<TypeParam>(this->strided_strided_seq, TEST_DIR"/index/StridedStrided.test", 2);
+TYPED_TEST(Indexing2D, StridedStrided) {
+    DimCheck2D<TypeParam>(this->strided_strided_seq,
+                          TEST_DIR "/index/StridedStrided.test", 2);
 }
 
 vector<af_seq> make_vec(af_seq first, af_seq second) {
@@ -397,10 +470,8 @@ vector<af_seq> make_vec(af_seq first, af_seq second) {
     return out;
 }
 
-
 template<typename T>
-class Indexing : public ::testing::Test
-{
+class Indexing : public ::testing::Test {
     vector<af_seq> make_vec3(af_seq first, af_seq second, af_seq third) {
         vector<af_seq> out;
         out.push_back(first);
@@ -409,7 +480,8 @@ class Indexing : public ::testing::Test
         return out;
     }
 
-    vector<af_seq> make_vec4(af_seq first, af_seq second, af_seq third, af_seq fourth) {
+    vector<af_seq> make_vec4(af_seq first, af_seq second, af_seq third,
+                             af_seq fourth) {
         vector<af_seq> out;
         out.push_back(first);
         out.push_back(second);
@@ -418,25 +490,40 @@ class Indexing : public ::testing::Test
         return out;
     }
 
-    public:
-
+   public:
     virtual void SetUp() {
-        continuous3d_to_3d.push_back(make_vec3(af_make_seq( 0, 4, 1), af_make_seq( 0,  6,  1), af_span));
-        continuous3d_to_3d.push_back(make_vec3(af_make_seq( 4, 8, 1), af_make_seq( 4,  9,  1), af_span));
-        continuous3d_to_3d.push_back(make_vec3(af_make_seq( 6, 9, 1), af_make_seq( 3,  8,  1), af_span));
-
-        continuous3d_to_2d.push_back(make_vec3(af_span, af_make_seq( 0,  6,  1), af_make_seq( 0, 0, 1)));
-        continuous3d_to_2d.push_back(make_vec3(af_span, af_make_seq( 4,  9,  1), af_make_seq( 1, 1, 1)));
-        continuous3d_to_2d.push_back(make_vec3(af_span, af_make_seq( 3,  8,  1), af_make_seq( 0, 0, 1)));
-
-        continuous3d_to_1d.push_back(make_vec3(af_span, af_make_seq( 0,  0,  1), af_make_seq( 0, 0, 1)));
-        continuous3d_to_1d.push_back(make_vec3(af_span, af_make_seq( 6,  6,  1), af_make_seq( 1, 1, 1)));
-        continuous3d_to_1d.push_back(make_vec3(af_span, af_make_seq( 9,  9,  1), af_make_seq( 0, 0, 1)));
-
-        continuous4d_to_4d.push_back(make_vec4(af_make_seq( 2, 6, 1), af_make_seq( 2,  6,  1), af_span, af_span));
-        continuous4d_to_3d.push_back(make_vec4(af_make_seq( 2, 6, 1), af_make_seq( 2,  6,  1), af_span, af_make_seq(0, 0, 1)));
-        continuous4d_to_2d.push_back(make_vec4(af_make_seq( 2, 6, 1), af_make_seq( 2,  6,  1), af_make_seq( 0, 0, 1), af_make_seq(0, 0, 1)));
-        continuous4d_to_1d.push_back(make_vec4(af_make_seq( 2, 6, 1), af_make_seq( 2,  2,  1), af_make_seq( 0, 0, 1), af_make_seq(0, 0, 1)));
+        continuous3d_to_3d.push_back(
+            make_vec3(af_make_seq(0, 4, 1), af_make_seq(0, 6, 1), af_span));
+        continuous3d_to_3d.push_back(
+            make_vec3(af_make_seq(4, 8, 1), af_make_seq(4, 9, 1), af_span));
+        continuous3d_to_3d.push_back(
+            make_vec3(af_make_seq(6, 9, 1), af_make_seq(3, 8, 1), af_span));
+
+        continuous3d_to_2d.push_back(
+            make_vec3(af_span, af_make_seq(0, 6, 1), af_make_seq(0, 0, 1)));
+        continuous3d_to_2d.push_back(
+            make_vec3(af_span, af_make_seq(4, 9, 1), af_make_seq(1, 1, 1)));
+        continuous3d_to_2d.push_back(
+            make_vec3(af_span, af_make_seq(3, 8, 1), af_make_seq(0, 0, 1)));
+
+        continuous3d_to_1d.push_back(
+            make_vec3(af_span, af_make_seq(0, 0, 1), af_make_seq(0, 0, 1)));
+        continuous3d_to_1d.push_back(
+            make_vec3(af_span, af_make_seq(6, 6, 1), af_make_seq(1, 1, 1)));
+        continuous3d_to_1d.push_back(
+            make_vec3(af_span, af_make_seq(9, 9, 1), af_make_seq(0, 0, 1)));
+
+        continuous4d_to_4d.push_back(make_vec4(
+            af_make_seq(2, 6, 1), af_make_seq(2, 6, 1), af_span, af_span));
+        continuous4d_to_3d.push_back(make_vec4(af_make_seq(2, 6, 1),
+                                               af_make_seq(2, 6, 1), af_span,
+                                               af_make_seq(0, 0, 1)));
+        continuous4d_to_2d.push_back(
+            make_vec4(af_make_seq(2, 6, 1), af_make_seq(2, 6, 1),
+                      af_make_seq(0, 0, 1), af_make_seq(0, 0, 1)));
+        continuous4d_to_1d.push_back(
+            make_vec4(af_make_seq(2, 6, 1), af_make_seq(2, 2, 1),
+                      af_make_seq(0, 0, 1), af_make_seq(0, 0, 1)));
     }
 
     vector<vector<af_seq> > continuous3d_to_3d;
@@ -450,8 +537,8 @@ class Indexing : public ::testing::Test
 };
 
 template<typename T>
-void DimCheckND(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDims)
-{
+void DimCheckND(const vector<vector<af_seq> > &seqs, string TestFile,
+                size_t NDims) {
     if (noDoubleTests<T>()) return;
 
     // DimCheck2D function is generalized enough
@@ -461,71 +548,72 @@ void DimCheckND(const vector<vector<af_seq> > &seqs,string TestFile, size_t NDim
 
 TYPED_TEST_CASE(Indexing, AllTypes);
 
-TYPED_TEST(Indexing, 4D_to_4D)
-{
-    DimCheckND<TypeParam>(this->continuous4d_to_4d, TEST_DIR"/index/Continuous4Dto4D.test", 4);
+TYPED_TEST(Indexing, 4D_to_4D) {
+    DimCheckND<TypeParam>(this->continuous4d_to_4d,
+                          TEST_DIR "/index/Continuous4Dto4D.test", 4);
 }
 
-TYPED_TEST(Indexing, 4D_to_3D)
-{
-    DimCheckND<TypeParam>(this->continuous4d_to_3d, TEST_DIR"/index/Continuous4Dto3D.test", 4);
+TYPED_TEST(Indexing, 4D_to_3D) {
+    DimCheckND<TypeParam>(this->continuous4d_to_3d,
+                          TEST_DIR "/index/Continuous4Dto3D.test", 4);
 }
 
-TYPED_TEST(Indexing, 4D_to_2D)
-{
-    DimCheckND<TypeParam>(this->continuous4d_to_2d, TEST_DIR"/index/Continuous4Dto2D.test", 4);
+TYPED_TEST(Indexing, 4D_to_2D) {
+    DimCheckND<TypeParam>(this->continuous4d_to_2d,
+                          TEST_DIR "/index/Continuous4Dto2D.test", 4);
 }
 
-TYPED_TEST(Indexing, 4D_to_1D)
-{
-    DimCheckND<TypeParam>(this->continuous4d_to_1d, TEST_DIR"/index/Continuous4Dto1D.test", 4);
+TYPED_TEST(Indexing, 4D_to_1D) {
+    DimCheckND<TypeParam>(this->continuous4d_to_1d,
+                          TEST_DIR "/index/Continuous4Dto1D.test", 4);
 }
 
-TYPED_TEST(Indexing, 3D_to_3D)
-{
-    DimCheckND<TypeParam>(this->continuous3d_to_3d, TEST_DIR"/index/Continuous3Dto3D.test", 3);
+TYPED_TEST(Indexing, 3D_to_3D) {
+    DimCheckND<TypeParam>(this->continuous3d_to_3d,
+                          TEST_DIR "/index/Continuous3Dto3D.test", 3);
 }
 
-TYPED_TEST(Indexing, 3D_to_2D)
-{
-    DimCheckND<TypeParam>(this->continuous3d_to_2d, TEST_DIR"/index/Continuous3Dto2D.test", 3);
+TYPED_TEST(Indexing, 3D_to_2D) {
+    DimCheckND<TypeParam>(this->continuous3d_to_2d,
+                          TEST_DIR "/index/Continuous3Dto2D.test", 3);
 }
 
-TYPED_TEST(Indexing, 3D_to_1D)
-{
-    DimCheckND<TypeParam>(this->continuous3d_to_1d, TEST_DIR"/index/Continuous3Dto1D.test", 3);
+TYPED_TEST(Indexing, 3D_to_1D) {
+    DimCheckND<TypeParam>(this->continuous3d_to_1d,
+                          TEST_DIR "/index/Continuous3Dto1D.test", 3);
 }
 
-
-TEST(Index, Docs_Util_C_API)
-{
+TEST(Index, Docs_Util_C_API) {
     //![ex_index_util_0]
-    af_index_t* indexers = 0;
-    af_err err = af_create_indexers(&indexers); // Memory is allocated on heap by the callee
-    // by default all the indexers span all the elements along the given dimension
+    af_index_t *indexers = 0;
+    af_err err           = af_create_indexers(
+        &indexers);  // Memory is allocated on heap by the callee
+    // by default all the indexers span all the elements along the given
+    // dimension
 
-    //Create array
+    // Create array
     af_array a;
     unsigned ndims = 2;
-    dim_t dim[] = {10,10};
+    dim_t dim[]    = {10, 10};
     af_randu(&a, ndims, dim, f32);
 
-    //Create index array
+    // Create index array
     af_array idx;
     unsigned n = 1;
-    dim_t d[] = {5};
+    dim_t d[]  = {5};
     af_range(&idx, n, d, 0, s32);
 
     af_print_array(a);
     af_print_array(idx);
 
-    //create array indexer
+    // create array indexer
     err = af_set_array_indexer(indexers, idx, 1);
 
-    //index with indexers
+    // index with indexers
     af_array out;
-    af_index_gen(&out, a, 2, indexers); // number of indexers should be two since
-                                        // we have set only second af_index_t
+    af_index_gen(&out, a, 2,
+                 indexers);  // number of indexers should be two since
+                             // we have set only second af_index_t
     if (err != AF_SUCCESS) {
         printf("Failed in af_index_gen: %d\n", err);
         throw;
@@ -553,7 +641,6 @@ TEST(Index, Docs_Util_C_API)
 
 //////////////////////////////// CPP ////////////////////////////////
 
-
 using af::allTrue;
 using af::array;
 using af::constant;
@@ -568,89 +655,92 @@ using af::seq;
 using af::span;
 using af::where;
 
-
-TEST(Indexing2D, ColumnContiniousCPP)
-{
+TEST(Indexing2D, ColumnContiniousCPP) {
     if (noDoubleTests<float>()) return;
 
     vector<vector<af_seq> > seqs;
 
-    seqs.push_back(make_vec(af_span, af_make_seq(  0,  6,  1)));
-    //seqs.push_back(make_vec(span, af_make_seq(  4,  9,  1)));
-    //seqs.push_back(make_vec(span, af_make_seq(  3,  8,  1)));
+    seqs.push_back(make_vec(af_span, af_make_seq(0, 6, 1)));
+    // seqs.push_back(make_vec(span, af_make_seq(  4,  9,  1)));
+    // seqs.push_back(make_vec(span, af_make_seq(  3,  8,  1)));
 
     vector<dim4> numDims;
 
     vector<vector<float> > hData;
     vector<vector<float> > tests;
-    readTests<float, float, int>(TEST_DIR"/index/ColumnContinious.test", numDims, hData, tests);
+    readTests<float, float, int>(TEST_DIR "/index/ColumnContinious.test",
+                                 numDims, hData, tests);
     dim4 dimensions = numDims[0];
 
-    array a(dimensions,&(hData[0].front()));
+    array a(dimensions, &(hData[0].front()));
 
     vector<array> sub;
-    for(size_t i = 0; i < seqs.size(); i++) {
+    for (size_t i = 0; i < seqs.size(); i++) {
         vector<af_seq> seq = seqs[i];
         sub.push_back(a(seq[0], seq[1]));
     }
 
-    for(size_t i = 0; i < seqs.size(); i++) {
+    for (size_t i = 0; i < seqs.size(); i++) {
         dim_t elems = sub[i].elements();
-        float *ptr = new float[elems];
+        float *ptr  = new float[elems];
         sub[i].host(ptr);
 
-        if(false == equal(ptr, ptr + tests[i].size(), tests[i].begin())) {
+        if (false == equal(ptr, ptr + tests[i].size(), tests[i].begin())) {
             cout << "index data: ";
-            copy(ptr, ptr + tests[i].size(), ostream_iterator<float>(cout, ", "));
+            copy(ptr, ptr + tests[i].size(),
+                 ostream_iterator<float>(cout, ", "));
             cout << endl << "file data: ";
-            copy(tests[i].begin(), tests[i].end(), ostream_iterator<float>(cout, ", "));
+            copy(tests[i].begin(), tests[i].end(),
+                 ostream_iterator<float>(cout, ", "));
             FAIL() << "indexed_array[" << i << "] FAILED" << endl;
         }
         delete[] ptr;
     }
 }
 
-/************************ Array Based indexing tests from here on ******************/
+/************************ Array Based indexing tests from here on
+ * ******************/
 
 template<typename T>
-class lookup : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class lookup : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
-typedef ::testing::Types<float, double, int, unsigned, unsigned char, short, ushort, intl, uintl> ArrIdxTestTypes;
+typedef ::testing::Types<float, double, int, unsigned, unsigned char, short,
+                         ushort, intl, uintl>
+    ArrIdxTestTypes;
 TYPED_TEST_CASE(lookup, ArrIdxTestTypes);
 
 template<typename T>
-void arrayIndexTest(string pTestFile, int dim)
-{
+void arrayIndexTest(string pTestFile, int dim) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<T> >      in;
-    vector<vector<T> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
-    af_array outArray  = 0;
-    af_array inArray   = 0;
-    af_array idxArray  = 0;
+    dim4 dims0        = numDims[0];
+    dim4 dims1        = numDims[1];
+    af_array outArray = 0;
+    af_array inArray  = 0;
+    af_array idxArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims0.ndims(), dims0.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[1].front()),
-                dims1.ndims(), dims1.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&idxArray, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_lookup(&outArray, inArray, idxArray, dim));
 
     vector<T> currGoldBar = tests[0];
-    dim4 goldDims = dims0;
-    goldDims[dim] = dims1[0];
+    dim4 goldDims         = dims0;
+    goldDims[dim]         = dims1[0];
 
     ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, outArray);
 
@@ -659,50 +749,45 @@ void arrayIndexTest(string pTestFile, int dim)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(lookup, Dim0)
-{
-    arrayIndexTest<TypeParam>(string(TEST_DIR"/arrayindex/dim0.test"), 0);
+TYPED_TEST(lookup, Dim0) {
+    arrayIndexTest<TypeParam>(string(TEST_DIR "/arrayindex/dim0.test"), 0);
 }
 
-TYPED_TEST(lookup, Dim1)
-{
-    arrayIndexTest<TypeParam>(string(TEST_DIR"/arrayindex/dim1.test"), 1);
+TYPED_TEST(lookup, Dim1) {
+    arrayIndexTest<TypeParam>(string(TEST_DIR "/arrayindex/dim1.test"), 1);
 }
 
-TYPED_TEST(lookup, Dim2)
-{
-    arrayIndexTest<TypeParam>(string(TEST_DIR"/arrayindex/dim2.test"), 2);
+TYPED_TEST(lookup, Dim2) {
+    arrayIndexTest<TypeParam>(string(TEST_DIR "/arrayindex/dim2.test"), 2);
 }
 
-TYPED_TEST(lookup, Dim3)
-{
-    arrayIndexTest<TypeParam>(string(TEST_DIR"/arrayindex/dim3.test"), 3);
+TYPED_TEST(lookup, Dim3) {
+    arrayIndexTest<TypeParam>(string(TEST_DIR "/arrayindex/dim3.test"), 3);
 }
 
-TEST(lookup, CPP)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(lookup, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/arrayindex/dim0.test"), numDims, in, tests);
+    readTests<float, float, int>(string(TEST_DIR "/arrayindex/dim0.test"),
+                                 numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
+    dim4 dims0 = numDims[0];
+    dim4 dims1 = numDims[1];
 
     array input(dims0, &(in[0].front()));
     array indices(dims1, &(in[1].front()));
     array output = af::lookup(input, indices, 0);
 
     vector<float> currGoldBar = tests[0];
-    dim4 goldDims = dims0;
-    goldDims[0] = dims1[0];
+    dim4 goldDims             = dims0;
+    goldDims[0]               = dims1[0];
 
     ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
 }
 
-TEST(lookup, largeDim)
-{
+TEST(lookup, largeDim) {
     const size_t largeDim = 65535 * 8 + 1;
 
     cleanSlate();
@@ -712,8 +797,7 @@ TEST(lookup, largeDim)
     array output = af::lookup(input, indices);
 }
 
-TEST(lookup, Issue2009)
-{
+TEST(lookup, Issue2009) {
     array a   = range(dim4(1000, 1));
     array idx = constant(0, 1, u32);
     array b   = af::lookup(a, idx, 1);
@@ -721,8 +805,7 @@ TEST(lookup, Issue2009)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(lookup, SNIPPET_lookup1d)
-{
+TEST(lookup, SNIPPET_lookup1d) {
     //! [ex_index_lookup1d]
 
     // input array
@@ -738,14 +821,13 @@ TEST(lookup, SNIPPET_lookup1d)
 
     //! [ex_index_lookup1d]
 
-    //indexing tests
-    float in_g[3] = {20, 40, 30 };
+    // indexing tests
+    float in_g[3] = {20, 40, 30};
     af::array indexed_gold(3, in_g);
     ASSERT_ARRAYS_NEAR(indexed, indexed_gold, 1e-5);
 }
 
-TEST(lookup, SNIPPET_lookup_oob)
-{
+TEST(lookup, SNIPPET_lookup_oob) {
     //! [ex_index_lookup_oob]
 
     // input array
@@ -753,7 +835,7 @@ TEST(lookup, SNIPPET_lookup_oob)
     af::array in(5, in_);
 
     // indexing past end of array
-    int idx_outofbounds_p_[8] = {4,  5,  6,  7,  8,  9, 10, 11};
+    int idx_outofbounds_p_[8] = {4, 5, 6, 7, 8, 9, 10, 11};
     af::array idx_outofbounds_p(8, idx_outofbounds_p_);
 
     // and indexing before beginning of array
@@ -768,34 +850,30 @@ TEST(lookup, SNIPPET_lookup_oob)
     //! [ex_index_lookup_oob]
 
     // out of bounds tests
-    float oob_p_g_[8] = { 50, 50, 40, 30, 20, 10, 50, 40 };
+    float oob_p_g_[8] = {50, 50, 40, 30, 20, 10, 50, 40};
     af::array oob_p_g(8, oob_p_g_);
     ASSERT_ARRAYS_NEAR(indexed_out_of_bounds_pos, oob_p_g, 1e-5);
-    float oob_n_g_[8] = { 10, 10, 20, 30, 40, 50, 10, 20 };
+    float oob_n_g_[8] = {10, 10, 20, 30, 40, 50, 10, 20};
     af::array oob_n_g(8, oob_n_g_);
     ASSERT_ARRAYS_NEAR(indexed_out_of_bounds_neg, oob_n_g, 1e-5);
 }
 
-TEST(lookup, SNIPPET_lookup2d)
-{
+TEST(lookup, SNIPPET_lookup2d) {
     //! [ex_index_lookup2d]
 
     // constant input data
-    float input_vals[9] = {10, 20, 30,
-                           11, 21, 31,
-                           12, 22, 32};
+    float input_vals[9] = {10, 20, 30, 11, 21, 31, 12, 22, 32};
     array input(3, 3, input_vals);
     // {{10 11 12},
     //  {20 21 22},
     //  {30 31 32}},
 
-
     // indices to lookup
     int idx_[6] = {0, 0, 1, 1, 2, 2};
     af::array idx(6, idx_);
 
-    //will look up all indices along specified dimension
-    af::array indexed = af::lookup(input, idx); //(dim = 0)
+    // will look up all indices along specified dimension
+    af::array indexed = af::lookup(input, idx);  //(dim = 0)
     // indexed == { 10, 11, 12,
     //              10, 11, 12,
     //              20, 21, 22,
@@ -810,29 +888,22 @@ TEST(lookup, SNIPPET_lookup2d)
 
     //! [ex_index_lookup2d]
 
-    float expected_indexed[18] = { 10, 10, 20, 20, 30, 30,
-                                   11, 11, 21, 21, 31, 31,
-                                   12, 12, 22, 22, 32, 32 };
+    float expected_indexed[18] = {10, 10, 20, 20, 30, 30, 11, 11, 21,
+                                  21, 31, 31, 12, 12, 22, 22, 32, 32};
 
     array indexed_gold(6, 3, expected_indexed);
     ASSERT_ARRAYS_NEAR(indexed, indexed_gold, 1e-5);
 
-    float expected_indexed_dim1[18] = { 10, 20, 30,
-                                        10, 20, 30,
-                                        11, 21, 31,
-                                        11, 21, 31,
-                                        12, 22, 32,
-                                        12, 22, 32 };
+    float expected_indexed_dim1[18] = {10, 20, 30, 10, 20, 30, 11, 21, 31,
+                                       11, 21, 31, 12, 22, 32, 12, 22, 32};
 
     array indexed_gold_dim1(3, 6, expected_indexed_dim1);
     ASSERT_ARRAYS_NEAR(indexed_dim1, indexed_gold_dim1, 1e-5);
-
 }
 
-TEST(SeqIndex, CPP_END)
-{
-    const int n = 5;
-    const int m = 5;
+TEST(SeqIndex, CPP_END) {
+    const int n       = 5;
+    const int m       = 5;
     const int end_off = 2;
 
     array a = randu(n, m);
@@ -841,21 +912,16 @@ TEST(SeqIndex, CPP_END)
     float *hA = a.host<float>();
     float *hB = b.host<float>();
 
-    for (int i = 0; i < m; i++) {
-        ASSERT_EQ(hA[i * n + end_off], hB[i]);
-    }
-
+    for (int i = 0; i < m; i++) { ASSERT_EQ(hA[i * n + end_off], hB[i]); }
 
     freeHost(hA);
     freeHost(hB);
 }
 
-
-TEST(SeqIndex, CPP_END_SEQ)
-{
-    const int num = 20;
+TEST(SeqIndex, CPP_END_SEQ) {
+    const int num       = 20;
     const int end_begin = 10;
-    const int end_end = 0;
+    const int end_end   = 0;
 
     array a = randu(num);
     array b = a(seq(end - end_begin, end - end_end));
@@ -871,42 +937,36 @@ TEST(SeqIndex, CPP_END_SEQ)
     freeHost(hB);
 }
 
-array cpp_scope_seq_test(const int num, const float val, const seq s)
-{
+array cpp_scope_seq_test(const int num, const float val, const seq s) {
     array a = constant(val, num);
     return a(s);
 }
 
-TEST(SeqIndex, CPP_SCOPE_SEQ)
-{
-    const int num = 20;
+TEST(SeqIndex, CPP_SCOPE_SEQ) {
+    const int num       = 20;
     const int seq_begin = 3;
-    const int seq_end = 10;
-    const float val = 133.33;
+    const int seq_end   = 10;
+    const float val     = 133.33;
 
-    array b = cpp_scope_seq_test(num, val, seq(seq_begin, seq_end));
+    array b   = cpp_scope_seq_test(num, val, seq(seq_begin, seq_end));
     float *hB = b.host<float>();
 
-    for (int i = 0; i < seq_end - seq_begin + 1; i++) {
-        ASSERT_EQ(hB[i], val);
-    }
+    for (int i = 0; i < seq_end - seq_begin + 1; i++) { ASSERT_EQ(hB[i], val); }
 
     freeHost(hB);
 }
 
-array cpp_scope_arr_test(const int num, const float val)
-{
-    array a = constant(val, num);
-    array idx = where(a > val/2);
+array cpp_scope_arr_test(const int num, const float val) {
+    array a   = constant(val, num);
+    array idx = where(a > val / 2);
     return a(idx) * (val - 1);
 }
 
-TEST(SeqIndex, CPP_SCOPE_ARR)
-{
-    const int num = 20;
+TEST(SeqIndex, CPP_SCOPE_ARR) {
+    const int num   = 20;
     const float val = 133.33;
 
-    array b = cpp_scope_arr_test(num, val);
+    array b   = cpp_scope_arr_test(num, val);
     float *hB = b.host<float>();
 
     for (int i = 0; i < (int)b.elements(); i++) {
@@ -916,48 +976,47 @@ TEST(SeqIndex, CPP_SCOPE_ARR)
     freeHost(hB);
 }
 
-TEST(SeqIndex, CPPLarge)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(SeqIndex, CPPLarge) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float, float, int>(string(TEST_DIR"/arrayindex/dim0Large.test"), numDims, in, tests);
+    readTests<float, float, int>(string(TEST_DIR "/arrayindex/dim0Large.test"),
+                                 numDims, in, tests);
 
-    dim4 dims0     = numDims[0];
-    dim4 dims1     = numDims[1];
+    dim4 dims0 = numDims[0];
+    dim4 dims1 = numDims[1];
 
     array input(dims0, &(in[0].front()));
     array indices(dims1, &(in[1].front()));
     array output = af::lookup(input, indices, 0);
 
     vector<float> currGoldBar = tests[0];
-    dim4 goldDims = dims0;
-    goldDims[0] = dims1[0];
+    dim4 goldDims             = dims0;
+    goldDims[0]               = dims1[0];
 
     ASSERT_VEC_ARRAY_EQ(currGoldBar, goldDims, output);
 }
 
-TEST(SeqIndex, Cascade00)
-{
+TEST(SeqIndex, Cascade00) {
     const int nx = 200;
     const int ny = 200;
 
     const int stb = 21;
     const int enb = 180;
 
-    const int stc = 3;   // Should be less than nx - stb
-    const int enc = 109; // Should be less than ny - enb
+    const int stc = 3;    // Should be less than nx - stb
+    const int enc = 109;  // Should be less than ny - enb
 
-    const int st = stb + stc;
-    const int en = stb + enc;
+    const int st  = stb + stc;
+    const int en  = stb + enc;
     const int nxc = en - st + 1;
 
     array a = randu(nx, ny);
     array b = a(seq(stb, enb), span);
     array c = b(seq(stc, enc), span);
 
-    ASSERT_EQ(c.dims(1), (dim_t)ny );
+    ASSERT_EQ(c.dims(1), (dim_t)ny);
     ASSERT_EQ(c.dims(0), (dim_t)nxc);
 
     float *h_a = a.host<float>();
@@ -965,13 +1024,11 @@ TEST(SeqIndex, Cascade00)
     float *h_c = c.host<float>();
 
     for (int j = 0; j < ny; j++) {
-
         int a_off = j * nx;
         int c_off = j * nxc;
 
         for (int i = st; i < en; i++) {
-            ASSERT_EQ(h_a[a_off + i],
-                      h_c[c_off + i - st])
+            ASSERT_EQ(h_a[a_off + i], h_c[c_off + i - st])
                 << "at (" << i << "," << j << ")";
         }
     }
@@ -981,8 +1038,7 @@ TEST(SeqIndex, Cascade00)
     freeHost(h_c);
 }
 
-TEST(SeqIndex, Cascade01)
-{
+TEST(SeqIndex, Cascade01) {
     const int nx = 200;
     const int ny = 200;
 
@@ -1007,14 +1063,11 @@ TEST(SeqIndex, Cascade01)
     float *h_c = c.host<float>();
 
     for (int j = stc; j < enc; j++) {
-
         int a_off = j * nx;
         int c_off = (j - stc) * nxc;
 
         for (int i = stb; i < enb; i++) {
-
-            ASSERT_EQ(h_a[a_off + i],
-                      h_c[c_off + i - stb])
+            ASSERT_EQ(h_a[a_off + i], h_c[c_off + i - stb])
                 << "at (" << i << "," << j << ")";
         }
     }
@@ -1024,8 +1077,7 @@ TEST(SeqIndex, Cascade01)
     freeHost(h_c);
 }
 
-TEST(SeqIndex, Cascade10)
-{
+TEST(SeqIndex, Cascade10) {
     const int nx = 200;
     const int ny = 200;
 
@@ -1050,14 +1102,11 @@ TEST(SeqIndex, Cascade10)
     float *h_c = c.host<float>();
 
     for (int j = stb; j < enb; j++) {
-
         int a_off = j * nx;
         int c_off = (j - stb) * nxc;
 
         for (int i = stc; i < enc; i++) {
-
-            ASSERT_EQ(h_a[a_off + i],
-                      h_c[c_off + i - stc])
+            ASSERT_EQ(h_a[a_off + i], h_c[c_off + i - stc])
                 << "at (" << i << "," << j << ")";
         }
     }
@@ -1067,19 +1116,18 @@ TEST(SeqIndex, Cascade10)
     freeHost(h_c);
 }
 
-TEST(SeqIndex, Cascade11)
-{
+TEST(SeqIndex, Cascade11) {
     const int nx = 200;
     const int ny = 200;
 
     const int stb = 50;
     const int enb = 150;
 
-    const int stc = 20; // Should be less than nx - stb
-    const int enc = 80; // Should be less than ny - enb
+    const int stc = 20;  // Should be less than nx - stb
+    const int enc = 80;  // Should be less than ny - enb
 
-    const int st = stb + stc;
-    const int en = stb + enc;
+    const int st  = stb + stc;
+    const int en  = stb + enc;
     const int nyc = en - st + 1;
 
     array a = randu(nx, ny);
@@ -1087,21 +1135,18 @@ TEST(SeqIndex, Cascade11)
     array c = b(span, seq(stc, enc));
 
     ASSERT_EQ(c.dims(1), nyc);
-    ASSERT_EQ(c.dims(0), nx );
+    ASSERT_EQ(c.dims(0), nx);
 
     float *h_a = a.host<float>();
     float *h_b = b.host<float>();
     float *h_c = c.host<float>();
 
     for (int j = st; j < en; j++) {
-
         int a_off = j * nx;
         int c_off = (j - st) * nx;
 
         for (int i = 0; i < nx; i++) {
-
-            ASSERT_EQ(h_a[a_off + i],
-                      h_c[c_off + i])
+            ASSERT_EQ(h_a[a_off + i], h_c[c_off + i])
                 << "at (" << i << "," << j << ")";
         }
     }
@@ -1111,9 +1156,8 @@ TEST(SeqIndex, Cascade11)
     freeHost(h_c);
 }
 
-TEST(ArrayIndex, CPP_INDEX_VECTOR)
-{
-    float h_inds[] = {0, 3, 2, 1}; // zero-based indexing
+TEST(ArrayIndex, CPP_INDEX_VECTOR) {
+    float h_inds[] = {0, 3, 2, 1};  // zero-based indexing
     array inds(1, 4, h_inds);
     array B = randu(1, 4);
     array C = B(inds);
@@ -1126,44 +1170,37 @@ TEST(ArrayIndex, CPP_INDEX_VECTOR)
     float *h_B = B.host<float>();
     float *h_C = C.host<float>();
 
-    for (int i = 0; i < 4; i++) {
-        ASSERT_EQ(h_C[i], h_B[(int)h_inds[i]]);
-    }
+    for (int i = 0; i < 4; i++) { ASSERT_EQ(h_C[i], h_B[(int)h_inds[i]]); }
 
     freeHost(h_B);
     freeHost(h_C);
 }
 
-TEST(SeqIndex, CPP_INDEX_VECTOR)
-{
+TEST(SeqIndex, CPP_INDEX_VECTOR) {
     const int num = 20;
     const int len = 10;
-    const int st  =  3;
+    const int st  = 3;
     const int en  = st + len - 1;
 
     array B = randu(1, 20);
     array C = B(seq(st, en));
 
-    ASSERT_EQ(1  , B.dims(0));
+    ASSERT_EQ(1, B.dims(0));
     ASSERT_EQ(num, B.dims(1));
-    ASSERT_EQ(1  , C.dims(0));
+    ASSERT_EQ(1, C.dims(0));
     ASSERT_EQ(len, C.dims(1));
 
     float *h_B = B.host<float>();
     float *h_C = C.host<float>();
 
-    for (int i = 0; i < len; i++) {
-        ASSERT_EQ(h_C[i], h_B[i + st]);
-    }
+    for (int i = 0; i < len; i++) { ASSERT_EQ(h_C[i], h_B[i + st]); }
 
     freeHost(h_B);
     freeHost(h_C);
 }
 
-
-TEST(ArrayIndex, CPP_INDEX_VECTOR_2D)
-{
-    float h_inds[] = {3, 5, 7, 2}; // zero-based indexing
+TEST(ArrayIndex, CPP_INDEX_VECTOR_2D) {
+    float h_inds[] = {3, 5, 7, 2};  // zero-based indexing
     array inds(1, 4, h_inds);
     array B = randu(4, 4);
     array C = B(inds);
@@ -1176,18 +1213,15 @@ TEST(ArrayIndex, CPP_INDEX_VECTOR_2D)
     float *h_B = B.host<float>();
     float *h_C = C.host<float>();
 
-    for (int i = 0; i < 4; i++) {
-        ASSERT_EQ(h_C[i], h_B[(int)h_inds[i]]);
-    }
+    for (int i = 0; i < 4; i++) { ASSERT_EQ(h_C[i], h_B[(int)h_inds[i]]); }
 
     freeHost(h_B);
     freeHost(h_C);
 }
 
-TEST(SeqIndex, CPP_INDEX_VECTOR_2D)
-{
-    const int nx = 4;
-    const int ny = 3 * nx;
+TEST(SeqIndex, CPP_INDEX_VECTOR_2D) {
+    const int nx  = 4;
+    const int ny  = 3 * nx;
     const int len = 2 * nx;
     const int st  = nx - 1;
     const int en  = st + len - 1;
@@ -1195,39 +1229,34 @@ TEST(SeqIndex, CPP_INDEX_VECTOR_2D)
     array B = randu(nx, ny);
     array C = B(seq(st, en));
 
-    ASSERT_EQ(nx , B.dims(0));
-    ASSERT_EQ(ny , B.dims(1));
+    ASSERT_EQ(nx, B.dims(0));
+    ASSERT_EQ(ny, B.dims(1));
     ASSERT_EQ(len, C.dims(0));
-    ASSERT_EQ(1  , C.dims(1));
+    ASSERT_EQ(1, C.dims(1));
 
     float *h_B = B.host<float>();
     float *h_C = C.host<float>();
 
-    for (int i = 0; i < len; i++) {
-        ASSERT_EQ(h_C[i], h_B[i + st]);
-    }
+    for (int i = 0; i < len; i++) { ASSERT_EQ(h_C[i], h_B[i + st]); }
 
     freeHost(h_B);
     freeHost(h_C);
 }
 
 template<typename T>
-class IndexedMembers : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class IndexedMembers : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 TYPED_TEST_CASE(IndexedMembers, AllTypes);
 
-TYPED_TEST(IndexedMembers, MemFuncs)
-{
+TYPED_TEST(IndexedMembers, MemFuncs) {
     if (noDoubleTests<TypeParam>()) return;
 
     const dim_t dimsize = 100;
     vector<TypeParam> in(dimsize * dimsize);
-    for(int i = 0; i < (int)in.size(); i++) in[i] = i;
+    for (int i = 0; i < (int)in.size(); i++) in[i] = i;
     array input(dimsize, dimsize, &in.front(), afHost);
 
     ASSERT_EQ(dimsize, input(span, 1).elements());
@@ -1247,71 +1276,68 @@ TYPED_TEST(IndexedMembers, MemFuncs)
     ASSERT_EQ(input.isinteger(), input(span, 1).isinteger());
     ASSERT_EQ(input.isbool(), input(span, 1).isbool());
     // TODO: Doesn't compile in cuda for cfloat and cdouble
-    //ASSERT_EQ(input.scalar<TypeParam>(), input(span, 0).scalar<TypeParam>());
+    // ASSERT_EQ(input.scalar<TypeParam>(), input(span, 0).scalar<TypeParam>());
 }
 
 #if 1
-TYPED_TEST(IndexedMembers, MemIndex)
-{
-    array a = range(dim4(10, 10));
-    array b = a(seq(1,7), span);
-    array brow = b.row(5);
+TYPED_TEST(IndexedMembers, MemIndex) {
+    array a     = range(dim4(10, 10));
+    array b     = a(seq(1, 7), span);
+    array brow  = b.row(5);
     array brows = b.rows(5, 6);
-    array bcol = b.col(5);
+    array bcol  = b.col(5);
     array bcols = b.cols(5, 6);
 
-    array out_row = a(seq(1,7), span).row(5);
-    array out_rows = a(seq(1,7), span).rows(5, 6);
-    array out_col = a(seq(1,7), span).col(5);
-    array out_cols = a(seq(1,7), span).cols(5, 6);
+    array out_row  = a(seq(1, 7), span).row(5);
+    array out_rows = a(seq(1, 7), span).rows(5, 6);
+    array out_col  = a(seq(1, 7), span).col(5);
+    array out_cols = a(seq(1, 7), span).cols(5, 6);
 
     ASSERT_EQ(0, where(brow != out_row).elements());
     ASSERT_EQ(0, where(brows != out_rows).elements());
     ASSERT_EQ(0, where(bcol != out_col).elements());
     ASSERT_EQ(0, where(bcols != out_cols).elements());
 
-    array avol = range(dim4(10, 10, 10));
-    array bvol = avol(seq(1, 7), span, span);
-    array bslice = bvol.slice(5);
+    array avol    = range(dim4(10, 10, 10));
+    array bvol    = avol(seq(1, 7), span, span);
+    array bslice  = bvol.slice(5);
     array bslices = bvol.slices(5, 6);
 
-    array out_slice = avol(seq(1,7), span, span).slice(5);
-    array out_slices = avol(seq(1,7), span, span).slices(5, 6);
+    array out_slice  = avol(seq(1, 7), span, span).slice(5);
+    array out_slices = avol(seq(1, 7), span, span).slices(5, 6);
 
     ASSERT_EQ(0, where(bslice != out_slice).elements());
     ASSERT_EQ(0, where(bslices != out_slices).elements());
 }
 #endif
 
-TEST(Indexing, SNIPPET_indexing_first)
-{
+TEST(Indexing, SNIPPET_indexing_first) {
     //! [ex_indexing_first]
-    array A = array(seq(1,9), 3, 3);
+    array A = array(seq(1, 9), 3, 3);
     af_print(A);
 
-    af_print(A(0));    // first element
-    af_print(A(0,1));  // first row, second column
+    af_print(A(0));     // first element
+    af_print(A(0, 1));  // first row, second column
 
-    af_print(A(end));   // last element
-    af_print(A(-1));    // also last element
-    af_print(A(end-1)); // second-to-last element
+    af_print(A(end));      // last element
+    af_print(A(-1));       // also last element
+    af_print(A(end - 1));  // second-to-last element
 
-    af_print(A(1,span));       // second row
+    af_print(A(1, span));      // second row
     af_print(A.row(end));      // last row
-    af_print(A.cols(1,end));   // all but first column
+    af_print(A.cols(1, end));  // all but first column
 
-    float b_host[] = {0,1,2,3,4,5,6,7,8,9};
+    float b_host[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
     array b(10, 1, b_host);
     af_print(b(seq(3)));
-    af_print(b(seq(1,7)));
-    af_print(b(seq(1,7,2)));
-    af_print(b(seq(0,end,2)));
+    af_print(b(seq(1, 7)));
+    af_print(b(seq(1, 7, 2)));
+    af_print(b(seq(0, end, 2)));
     //! [ex_indexing_first]
 
-
-    array lin_first = A(0);
-    array lin_last = A(end);
-    array lin_snd_last = A(end-1);
+    array lin_first    = A(0);
+    array lin_last     = A(end);
+    array lin_snd_last = A(end - 1);
 
     EXPECT_EQ(1, lin_first.dims(0));
     EXPECT_EQ(1, lin_first.elements());
@@ -1324,7 +1350,6 @@ TEST(Indexing, SNIPPET_indexing_first)
     EXPECT_FLOAT_EQ(9.0f, lin_last.scalar<float>());
     EXPECT_FLOAT_EQ(8.0f, lin_snd_last.scalar<float>());
 
-
     lin_last = A(-1);
     EXPECT_EQ(1, lin_last.dims(0));
     EXPECT_EQ(1, lin_last.elements());
@@ -1335,7 +1360,9 @@ TEST(Indexing, SNIPPET_indexing_first)
         ASSERT_EQ(3, out.elements());
         vector<float> hout(out.elements());
         out.host(&hout.front());
-        for(unsigned i = 0; i < hout.size(); i++) { ASSERT_FLOAT_EQ(b_host[i], hout[i]); }
+        for (unsigned i = 0; i < hout.size(); i++) {
+            ASSERT_FLOAT_EQ(b_host[i], hout[i]);
+        }
     }
 
     {
@@ -1343,7 +1370,9 @@ TEST(Indexing, SNIPPET_indexing_first)
         ASSERT_EQ(7, out.elements());
         vector<float> hout(out.elements());
         out.host(&hout.front());
-        for(unsigned i = 1; i < hout.size(); i++) { ASSERT_FLOAT_EQ(b_host[i], hout[i - 1]); }
+        for (unsigned i = 1; i < hout.size(); i++) {
+            ASSERT_FLOAT_EQ(b_host[i], hout[i - 1]);
+        }
     }
 
     {
@@ -1351,82 +1380,78 @@ TEST(Indexing, SNIPPET_indexing_first)
         ASSERT_EQ(4, out.elements());
         vector<float> hout(out.elements());
         out.host(&hout.front());
-        for(unsigned i = 0; i < hout.size(); i++) { ASSERT_FLOAT_EQ(b_host[i * 2 + 1], hout[i]); }
+        for (unsigned i = 0; i < hout.size(); i++) {
+            ASSERT_FLOAT_EQ(b_host[i * 2 + 1], hout[i]);
+        }
     }
 }
 
-TEST(Indexing, SNIPPET_indexing_set)
-{
+TEST(Indexing, SNIPPET_indexing_set) {
     //! [ex_indexing_set]
     array A = constant(0, 3, 3);
     af_print(A);
 
     // setting entries to a constant
-    A(span) = 4;        // fill entire array
+    A(span) = 4;  // fill entire array
     af_print(A);
 
-    A.row(0) = -1;      // first row
+    A.row(0) = -1;  // first row
     af_print(A);
 
-    A(seq(3)) = 3.1415; // first three elements
+    A(seq(3)) = 3.1415;  // first three elements
     af_print(A);
 
     // copy in another matrix
-    array B = constant(1, 4, 4, s32);
-    B.row(0) = randu(1, 4, f32); // set a row to random values (also upcast)
+    array B  = constant(1, 4, 4, s32);
+    B.row(0) = randu(1, 4, f32);  // set a row to random values (also upcast)
     //! [ex_indexing_set]
-    //TODO: Confirm the outputs are correct. see #697
+    // TODO: Confirm the outputs are correct. see #697
 }
 
-
-TEST(Indexing, SNIPPET_indexing_ref)
-{
+TEST(Indexing, SNIPPET_indexing_ref) {
     //! [ex_indexing_ref]
-    float h_inds[] = {0, 4, 2, 1}; // zero-based indexing
+    float h_inds[] = {0, 4, 2, 1};  // zero-based indexing
     array inds(1, 4, h_inds);
     af_print(inds);
 
     array B = randu(1, 4);
     af_print(B);
 
-    array c = B(inds);        // get
+    array c = B(inds);  // get
     af_print(c);
 
-    B(inds) = -1;             // set to scalar
-    B(inds) = constant(0, 4); // zero indices
+    B(inds) = -1;              // set to scalar
+    B(inds) = constant(0, 4);  // zero indices
     af_print(B);
     //! [ex_indexing_ref]
-    //TODO: Confirm the outputs are correct. see #697
+    // TODO: Confirm the outputs are correct. see #697
 }
 
-TEST(Indexing, SNIPPET_indexing_copy)
-{
-  array A = constant(0,1, s32);
-  af::index s1;
-  s1 = af::index(A);
-  // At exit both A and s1 will be destroyed
-  // but the underlying array should only be
-  // freed once.
+TEST(Indexing, SNIPPET_indexing_copy) {
+    array A = constant(0, 1, s32);
+    af::index s1;
+    s1 = af::index(A);
+    // At exit both A and s1 will be destroyed
+    // but the underlying array should only be
+    // freed once.
 }
 
-TEST(Assign, LinearIndexSeq)
-{
+TEST(Assign, LinearIndexSeq) {
     const int nx = 5;
     const int ny = 4;
 
-    const int st = nx - 2;
-    const int en = nx * (ny - 1);
+    const int st  = nx - 2;
+    const int en  = nx * (ny - 1);
     const int num = (en - st + 1);
 
-    array a = randu(nx, ny);
+    array a       = randu(nx, ny);
     af::index idx = seq(st, en);
 
     af_array in_arr = a.get();
-    af_index_t ii = idx.get();
+    af_index_t ii   = idx.get();
     af_array out_arr;
 
-    ASSERT_SUCCESS(
-              af_index(&out_arr, in_arr, 1, &ii.idx.seq));
+    ASSERT_SUCCESS(af_index(&out_arr, in_arr, 1, &ii.idx.seq));
 
     array out(out_arr);
 
@@ -1439,29 +1464,25 @@ TEST(Assign, LinearIndexSeq)
     a.host(&ha[0]);
     out.host(&hout[0]);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(ha[i + st], hout[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(ha[i + st], hout[i]); }
 }
 
-TEST(Assign, LinearIndexGenSeq)
-{
+TEST(Assign, LinearIndexGenSeq) {
     const int nx = 5;
     const int ny = 4;
 
-    const int st = nx - 2;
-    const int en = nx * (ny - 1);
+    const int st  = nx - 2;
+    const int en  = nx * (ny - 1);
     const int num = (en - st + 1);
 
-    array a = randu(nx, ny);
+    array a       = randu(nx, ny);
     af::index idx = seq(st, en);
 
     af_array in_arr = a.get();
-    af_index_t ii = idx.get();
+    af_index_t ii   = idx.get();
     af_array out_arr;
 
-    ASSERT_SUCCESS(
-              af_index_gen(&out_arr, in_arr, 1, &ii));
+    ASSERT_SUCCESS(af_index_gen(&out_arr, in_arr, 1, &ii));
 
     array out(out_arr);
 
@@ -1474,29 +1495,25 @@ TEST(Assign, LinearIndexGenSeq)
     a.host(&ha[0]);
     out.host(&hout[0]);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(ha[i + st], hout[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(ha[i + st], hout[i]); }
 }
 
-TEST(Assign, LinearIndexGenArr)
-{
+TEST(Assign, LinearIndexGenArr) {
     const int nx = 5;
     const int ny = 4;
 
-    const int st = nx - 2;
-    const int en = nx * (ny - 1);
+    const int st  = nx - 2;
+    const int en  = nx * (ny - 1);
     const int num = (en - st + 1);
 
-    array a = randu(nx, ny);
+    array a       = randu(nx, ny);
     af::index idx = array(seq(st, en));
 
     af_array in_arr = a.get();
-    af_index_t ii = idx.get();
+    af_index_t ii   = idx.get();
     af_array out_arr;
 
-    ASSERT_SUCCESS(
-              af_index_gen(&out_arr, in_arr, 1, &ii));
+    ASSERT_SUCCESS(af_index_gen(&out_arr, in_arr, 1, &ii));
 
     array out(out_arr);
 
@@ -1509,30 +1526,25 @@ TEST(Assign, LinearIndexGenArr)
     a.host(&ha[0]);
     out.host(&hout[0]);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(ha[i + st], hout[i]);
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(ha[i + st], hout[i]); }
 }
 
-TEST(Index, OutOfBounds)
-{
-    uint gold[7] = {0, 9, 49, 119, 149, 149, 148};
+TEST(Index, OutOfBounds) {
+    uint gold[7]  = {0, 9, 49, 119, 149, 149, 148};
     uint h_idx[7] = {0, 9, 49, 119, 149, 150, 151};
     uint output[7];
 
     array a = iota(dim4(50, 1, 3)).as(s32);
     array idx(7, h_idx);
     array b = a(idx);
-    b.host((void*)output);
+    b.host((void *)output);
 
-    for(int i=0; i<7; ++i)
-        ASSERT_EQ(gold[i], output[i]);
+    for (int i = 0; i < 7; ++i) ASSERT_EQ(gold[i], output[i]);
 }
 
-TEST(Index, ISSUE_1101_FULL)
-{
+TEST(Index, ISSUE_1101_FULL) {
     deviceGC();
-    array a = randu(5,5);
+    array a = randu(5, 5);
 
     size_t aby, abu, lby, lbu;
     deviceMemInfo(&aby, &abu, &lby, &lbu);
@@ -1550,13 +1562,12 @@ TEST(Index, ISSUE_1101_FULL)
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(Index, ISSUE_1101_COL0)
-{
+TEST(Index, ISSUE_1101_COL0) {
     deviceGC();
-    array a = randu(5,5);
+    array a = randu(5, 5);
     vector<float> ha(a.elements());
     a.host(ha.data());
-    vector<float> gold(ha.begin(), ha.begin()+5);
+    vector<float> gold(ha.begin(), ha.begin() + 5);
 
     size_t aby, abu, lby, lbu;
     deviceMemInfo(&aby, &abu, &lby, &lbu);
@@ -1574,20 +1585,19 @@ TEST(Index, ISSUE_1101_COL0)
     ASSERT_VEC_ARRAY_EQ(gold, dim4(a.dims()[0]), b);
 }
 
-TEST(Index, ISSUE_1101_MODDIMS)
-{
+TEST(Index, ISSUE_1101_MODDIMS) {
     deviceGC();
-    array a = randu(5,5);
+    array a = randu(5, 5);
     vector<float> ha(a.elements());
     a.host(&ha[0]);
 
     size_t aby, abu, lby, lbu;
     deviceMemInfo(&aby, &abu, &lby, &lbu);
 
-    int st = 0;
-    int en = 9;
-    int nx = 2;
-    int ny = 5;
+    int st  = 0;
+    int en  = 9;
+    int nx  = 2;
+    int ny  = 5;
     array b = a(seq(st, en));
     array c = moddims(b, nx, ny);
     size_t aby1, abu1, lby1, lbu1;
@@ -1600,19 +1610,14 @@ TEST(Index, ISSUE_1101_MODDIMS)
 
     vector<float> hb(b.elements());
     b.host(&hb[0]);
-    for (int i = 0; i < b.elements(); i++) {
-        ASSERT_EQ(ha[i + st], hb[i]);
-    }
+    for (int i = 0; i < b.elements(); i++) { ASSERT_EQ(ha[i + st], hb[i]); }
 
     vector<float> hc(c.elements());
     c.host(&hc[0]);
-    for (int i = 0; i < c.elements(); i++) {
-        ASSERT_EQ(ha[i + st], hc[i]);
-    }
+    for (int i = 0; i < c.elements(); i++) { ASSERT_EQ(ha[i + st], hc[i]); }
 }
 
-TEST(Index, Issue1846IndexStepCascade)
-{
+TEST(Index, Issue1846IndexStepCascade) {
     array a = randu(3, 12);
     array b = a(span, seq(0, end, 2));
     array c = b(span, seq(0, end, 3));
@@ -1620,57 +1625,50 @@ TEST(Index, Issue1846IndexStepCascade)
     EXPECT_EQ(allTrue<bool>(c == d), true);
 }
 
-TEST(Index, Issue1845IndexStepReorder)
-{
-    array a = randu(1,8,1);
-    array b = reorder(a,0,2,1);
-    array d = reorder(b(0,0,span),2,1,0);
+TEST(Index, Issue1845IndexStepReorder) {
+    array a = randu(1, 8, 1);
+    array b = reorder(a, 0, 2, 1);
+    array d = reorder(b(0, 0, span), 2, 1, 0);
     EXPECT_EQ(allTrue<bool>(a.T() == d), true);
 }
 
-TEST(Index, Issue1867ChainedIndexingLeak)
-{
+TEST(Index, Issue1867ChainedIndexingLeak) {
     using af::randn;
     using af::sync;
     {
         array lInput = randn(100, 100, f32);
-        array Q3 = lInput.rows(0, 3).cols(0, 3);
+        array Q3     = lInput.rows(0, 3).cols(0, 3);
         Q3.eval();
         sync();
     }
     size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
     ASSERT_EQ(0u, lock_buffers);
 }
 
-TEST(Index, InvalidSequence_SingleElementNegativeStep)
-{
-    EXPECT_THROW(af::seq(1,1,-1), af::exception);
+TEST(Index, InvalidSequence_SingleElementNegativeStep) {
+    EXPECT_THROW(af::seq(1, 1, -1), af::exception);
 }
-TEST(Index, InvalidSequence_PositiveRangeNegativeStep)
-{
-    EXPECT_THROW(af::seq(1,5,-1), af::exception);
+TEST(Index, InvalidSequence_PositiveRangeNegativeStep) {
+    EXPECT_THROW(af::seq(1, 5, -1), af::exception);
 }
 
-TEST(Index, InvalidSequence_NegativeRangePositiveStep)
-{
-    EXPECT_THROW(af::seq(-1,-5,1), af::exception);
+TEST(Index, InvalidSequence_NegativeRangePositiveStep) {
+    EXPECT_THROW(af::seq(-1, -5, 1), af::exception);
 }
 
 TEST(Index, ISSUE_2273) {
     int h_idx[2] = {1, 1};
     array idx(2, h_idx);
 
-    float h_input[12] = {0.f, 1.f, 2.f, 3.f, 4.f, 5.f,
+    float h_input[12] = {0.f, 1.f, 2.f, 3.f, 4.f,  5.f,
                          6.f, 7.f, 8.f, 9.f, 10.f, 11.f};
     array input(2, 3, 2, h_input);
     array input_reord = reorder(input, 0, 2, 1);
-    array output = input_reord(span, idx, span);
+    array output      = input_reord(span, idx, span);
 
-    float h_gold[12] = {6.f, 7.f, 6.f, 7.f,
-                        8.f, 9.f, 8.f, 9.f,
-                        10.f, 11.f, 10.f, 11.f};
+    float h_gold[12] = {6.f, 7.f, 6.f,  7.f,  8.f,  9.f,
+                        8.f, 9.f, 10.f, 11.f, 10.f, 11.f};
     array gold(2, 2, 3, h_gold);
 
     ASSERT_ARRAYS_EQ(gold, output);
@@ -1680,14 +1678,13 @@ TEST(Index, ISSUE_2273_Flipped) {
     int h_idx[2] = {1, 1};
     array idx(2, h_idx);
 
-    float h_input[12] = {0.f, 1.f, 6.f, 7.f,
-                         2.f, 3.f, 8.f, 9.f,
-                         4.f, 5.f, 10.f, 11.f};
+    float h_input[12] = {0.f, 1.f, 6.f, 7.f, 2.f,  3.f,
+                         8.f, 9.f, 4.f, 5.f, 10.f, 11.f};
     array input(2, 2, 3, h_input);
     array input_reord = reorder(input, 0, 2, 1);
     array input_slice = input_reord(span, span, idx);
 
-    array input_ref = iota(dim4(2, 3, 2));
+    array input_ref       = iota(dim4(2, 3, 2));
     array input_ref_slice = input_ref(span, span, idx);
 
     float h_gold[12] = {6.f, 7.f, 8.f, 9.f, 10.f, 11.f,
diff --git a/test/info.cpp b/test/info.cpp
index 51570f7a67..f1519d3380 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -7,51 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
 #include <af/device.h>
 
-using std::string;
-using std::vector;
 using af::dim4;
 using af::dtype_traits;
 using af::getDevice;
 using af::info;
 using af::setDevice;
+using std::string;
+using std::vector;
 
 template<typename T>
-void testFunction()
-{
+void testFunction() {
     info();
 
     af_array outArray = 0;
     dim4 dims(32, 32, 1, 1);
-    ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(),
+                            (af_dtype)dtype_traits<T>::af_type));
     // cleanup
-    if(outArray != 0) {
-        ASSERT_SUCCESS(af_release_array(outArray));
-    }
+    if (outArray != 0) { ASSERT_SUCCESS(af_release_array(outArray)); }
 }
 
-void infoTest()
-{
+void infoTest() {
     int nDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&nDevices));
-    ASSERT_EQ(true, nDevices>0);
+    ASSERT_EQ(true, nDevices > 0);
 
     const char* ENV = getenv("AF_MULTI_GPU_TESTS");
-    if(ENV && ENV[0] == '0') {
+    if (ENV && ENV[0] == '0') {
         testFunction<float>();
     } else {
         int oldDevice = getDevice();
-        for(int d = 0; d < nDevices; d++) {
+        for (int d = 0; d < nDevices; d++) {
             setDevice(d);
             testFunction<float>();
         }
@@ -59,7 +56,4 @@ void infoTest()
     }
 }
 
-TEST(Info, All)
-{
-    infoTest();
-}
+TEST(Info, All) { infoTest(); }
diff --git a/test/internal.cpp b/test/internal.cpp
index 0f8695f932..3540ff0ee0 100644
--- a/test/internal.cpp
+++ b/test/internal.cpp
@@ -7,48 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
-#include <af/traits.hpp>
 #include <af/internal.h>
+#include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::vector;
 using af::array;
 using af::dim4;
 using af::randu;
 using af::seq;
 using af::span;
+using std::vector;
+
+TEST(Internal, CreateStrided) {
+    float ha[] = {1,    101,  102,  103,  104,  105,  201,  202,  203,  204,
+                  205,  301,  302,  303,  304,  305,  401,  402,  403,  404,
+                  405,
 
-TEST(Internal, CreateStrided)
-{
-    float ha[] = {1,
-                  101, 102, 103, 104, 105,
-                  201, 202, 203, 204, 205,
-                  301, 302, 303, 304, 305,
-                  401, 402, 403, 404, 405,
-
-                  1010, 1020, 1030, 1040, 1050,
-                  2010, 2020, 2030, 2040, 2050,
-                  3010, 3020, 3030, 3040, 3050,
-                  4010, 4020, 4030, 4040, 4050};
-
-    dim_t offset = 1;
-    unsigned ndims = 3;
-    dim_t dims[] = {3, 3, 2};
+                  1010, 1020, 1030, 1040, 1050, 2010, 2020, 2030, 2040, 2050,
+                  3010, 3020, 3030, 3040, 3050, 4010, 4020, 4030, 4040, 4050};
+
+    dim_t offset    = 1;
+    unsigned ndims  = 3;
+    dim_t dims[]    = {3, 3, 2};
     dim_t strides[] = {1, 5, 20};
-    array a = createStridedArray((void *)ha,
-                                     offset,
-                                     dim4(ndims, dims),
-                                     dim4(ndims, strides),
-                                     f32,
-                                     afHost);
+    array a         = createStridedArray((void *)ha, offset, dim4(ndims, dims),
+                                 dim4(ndims, strides), f32, afHost);
 
     dim4 astrides = getStrides(a);
-    dim4 adims = a.dims();
+    dim4 adims    = a.dims();
 
     ASSERT_EQ(offset, getOffset(a));
     for (int i = 0; i < (int)ndims; i++) {
@@ -63,19 +54,16 @@ TEST(Internal, CreateStrided)
     for (int k = 0; k < dims[2]; k++) {
         for (int j = 0; j < dims[1]; j++) {
             for (int i = 0; i < dims[0]; i++) {
-                ASSERT_EQ(va[i + j * dims[0] + k * dims[0] * dims[1]],
-                          ha[i * strides[0] + j * strides[1] + k * strides[2] + o])
-                    << "at ("
-                    << i << ","
-                    << j << ","
-                    << k << ")";
+                ASSERT_EQ(
+                    va[i + j * dims[0] + k * dims[0] * dims[1]],
+                    ha[i * strides[0] + j * strides[1] + k * strides[2] + o])
+                    << "at (" << i << "," << j << "," << k << ")";
             }
         }
     }
 }
 
-TEST(Internal, CheckInfo)
-{
+TEST(Internal, CheckInfo) {
     const int xdim = 10;
     const int ydim = 8;
 
@@ -87,11 +75,10 @@ TEST(Internal, CheckInfo)
 
     array a = randu(10, 8);
 
-    array b = a(seq(xoff, xoff + xnum - 1),
-                    seq(yoff, yoff + ynum - 1));
+    array b = a(seq(xoff, xoff + xnum - 1), seq(yoff, yoff + ynum - 1));
 
     dim4 strides = getStrides(b);
-    dim4 dims = b.dims();
+    dim4 dims    = b.dims();
 
     dim_t offset = xoff + yoff * xdim;
 
@@ -107,8 +94,7 @@ TEST(Internal, CheckInfo)
     ASSERT_EQ(getRawPtr(a), getRawPtr(b));
 }
 
-TEST(Internal, Linear)
-{
+TEST(Internal, Linear) {
     array c;
     {
         array a = randu(10, 8);
@@ -125,16 +111,13 @@ TEST(Internal, Linear)
     }
 
     // Even though a and b are out of scope, c is still not an owner
-    {
-        ASSERT_EQ(isOwner(c), false);
-    }
+    { ASSERT_EQ(isOwner(c), false); }
 }
 
-TEST(Internal, Allocated)
-{
-    array a = randu(10, 8);
+TEST(Internal, Allocated) {
+    array a            = randu(10, 8);
     size_t a_allocated = a.allocated();
-    size_t a_bytes = a.bytes();
+    size_t a_bytes     = a.bytes();
 
     // b is just pointing to same underlying data
     // b is an owner;
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index 87094a8892..31c6373246 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -7,24 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using std::abs;
 using std::string;
 using std::vector;
-using std::abs;
 using namespace af;
 
 template<typename T>
-class InverseDeconvolution : public ::testing::Test
-{
-};
+class InverseDeconvolution : public ::testing::Test {};
 
 // create a list of types to be tested
 typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
@@ -33,26 +31,27 @@ typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
 TYPED_TEST_CASE(InverseDeconvolution, TestTypes);
 
 template<typename T, bool isColor>
-void invDeconvImageTest(string pTestFile, const float gamma, const af_inverse_deconv_algo algo)
-{
-    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
+void invDeconvImageTest(string pTestFile, const float gamma,
+                        const af_inverse_deconv_algo algo) {
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            OutType;
 
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
     using af::dim4;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId)
-    {
+    for (size_t testId = 0; testId < testCount; ++testId) {
         inFiles[testId].insert(0, string(TEST_DIR "/inverse_deconv/"));
         outFiles[testId].insert(0, string(TEST_DIR "/inverse_deconv/"));
 
@@ -74,25 +73,30 @@ void invDeconvImageTest(string pTestFile, const float gamma, const af_inverse_de
 
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
-        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
 
-        ASSERT_SUCCESS(af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(conv_image<OutType>(&goldArray, _goldArray));
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         unsigned ndims;
         dim_t dims[4];
         ASSERT_SUCCESS(af_get_numdims(&ndims, goldArray));
-        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
+        ASSERT_SUCCESS(
+            af_get_dims(dims, dims + 1, dims + 2, dims + 3, goldArray));
 
-        ASSERT_SUCCESS(af_inverse_deconv(&_outArray, inArray, kerArray, gamma, algo));
+        ASSERT_SUCCESS(
+            af_inverse_deconv(&_outArray, inArray, kerArray, gamma, algo));
 
         double maxima, minima, imag;
         ASSERT_SUCCESS(af_min_all(&minima, &imag, _outArray));
         ASSERT_SUCCESS(af_max_all(&maxima, &imag, _outArray));
         ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, otype));
-        ASSERT_SUCCESS(af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_SUCCESS(
+            af_constant(&denArray, (maxima - minima), ndims, dims, otype));
         ASSERT_SUCCESS(af_constant(&minArray, minima, ndims, dims, otype));
         ASSERT_SUCCESS(af_sub(&numArray, _outArray, minArray, false));
         ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
@@ -117,21 +121,24 @@ void invDeconvImageTest(string pTestFile, const float gamma, const af_inverse_de
         ASSERT_SUCCESS(af_release_array(_goldArray));
         ASSERT_SUCCESS(af_release_array(goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.03));
     }
 }
 
-TYPED_TEST(InverseDeconvolution, TikhonovOnGrayscale)
-{
-    // Test file name format: <colorspace>_<gamma with dots replaced by "_">_<inverse deconv algo>.test
-    invDeconvImageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_00_1_tikhonov.test"),
-                                00.1f, AF_INVERSE_DECONV_TIKHONOV);
+TYPED_TEST(InverseDeconvolution, TikhonovOnGrayscale) {
+    // Test file name format: <colorspace>_<gamma with dots replaced by
+    // "_">_<inverse deconv algo>.test
+    invDeconvImageTest<TypeParam, false>(
+        string(TEST_DIR "/inverse_deconv/gray_00_1_tikhonov.test"), 00.1f,
+        AF_INVERSE_DECONV_TIKHONOV);
 }
 
-TYPED_TEST(InverseDeconvolution, DISABLED_WienerOnGrayscale)
-{
-    // Test file name format: <colorspace>_<gamma with dots replaced by "_">_<inverse deconv algo>.test
-    invDeconvImageTest<TypeParam, false>(string(TEST_DIR "/inverse_deconv/gray_1_wiener.test"),
-                                1.0, AF_INVERSE_DECONV_DEFAULT);
-    //TODO(pradeep) change to wiener enum value
+TYPED_TEST(InverseDeconvolution, DISABLED_WienerOnGrayscale) {
+    // Test file name format: <colorspace>_<gamma with dots replaced by
+    // "_">_<inverse deconv algo>.test
+    invDeconvImageTest<TypeParam, false>(
+        string(TEST_DIR "/inverse_deconv/gray_1_wiener.test"), 1.0,
+        AF_INVERSE_DECONV_DEFAULT);
+    // TODO(pradeep) change to wiener enum value
 }
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index 0a78752a34..832e25fabc 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -11,76 +11,73 @@
 // backends for sizes larger than 128x128 or more. You can read more about it on
 // issue https://github.com/arrayfire/arrayfire/issues/1617
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <iostream>
 #include <complex>
-#include <testHelpers.hpp>
+#include <iostream>
 
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::identity;
 using af::matmul;
 using af::max;
+using std::abs;
 
 template<typename T>
-void inverseTester(const int m, const int n, double eps)
-{
+void inverseTester(const int m, const int n, double eps) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 #if 1
-    array A  = cpu_randu<T>(dim4(m, n));
+    array A = cpu_randu<T>(dim4(m, n));
 #else
-    array A  = randu(m, n, (dtype)dtype_traits<T>::af_type);
+    array A = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
     //! [ex_inverse]
     array IA = inverse(A);
-    array I = matmul(A, IA);
+    array I  = matmul(A, IA);
     //! [ex_inverse]
 
     array I2 = identity(m, n, (dtype)dtype_traits<T>::af_type);
 
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(I - I2))), eps);
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(I - I2))), eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(I - I2))),
+                eps);
+    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(I - I2))),
+                eps);
 }
 
-
 template<typename T>
-class Inverse : public ::testing::Test
-{
-
-};
+class Inverse : public ::testing::Test {};
 
 template<typename T>
 double eps();
 
 template<>
 double eps<float>() {
-  return 0.01f;
+    return 0.01f;
 }
 
 template<>
 double eps<double>() {
-  return 1e-5;
+    return 1e-5;
 }
 
 template<>
 double eps<cfloat>() {
-  return 0.01f;
+    return 0.01f;
 }
 
 template<>
 double eps<cdouble>() {
-  return 1e-5;
+    return 1e-5;
 }
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
diff --git a/test/iota.cpp b/test/iota.cpp
index f12ddff406..ffd88f05de 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -7,94 +7,93 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Iota : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Iota : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, unsigned int, intl, uintl, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
+                         unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Iota, TestTypes);
 
 template<typename T>
-void iotaTest(const dim4 idims, const dim4 tdims)
-{
+void iotaTest(const dim4 idims, const dim4 tdims) {
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_iota(&outArray, idims.ndims(), idims.get(),
-               tdims.ndims(), tdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_iota(&outArray, idims.ndims(), idims.get(), tdims.ndims(),
+                           tdims.get(), (af_dtype)dtype_traits<T>::af_type));
 
     af_array temp0 = 0, temp1 = 0, temp2 = 0;
     dim4 tempdims(idims.elements());
     dim4 fulldims;
-    for(unsigned i = 0; i < 4; i++) {
-        fulldims[i] = idims[i] * tdims[i];
-    }
-    ASSERT_SUCCESS(af_range(&temp2, tempdims.ndims(), tempdims.get(), 0, (af_dtype) dtype_traits<T>::af_type));
+    for (unsigned i = 0; i < 4; i++) { fulldims[i] = idims[i] * tdims[i]; }
+    ASSERT_SUCCESS(af_range(&temp2, tempdims.ndims(), tempdims.get(), 0,
+                            (af_dtype)dtype_traits<T>::af_type));
     ASSERT_SUCCESS(af_moddims(&temp1, temp2, idims.ndims(), idims.get()));
-    ASSERT_SUCCESS(af_tile(&temp0, temp1, tdims[0], tdims[1], tdims[2], tdims[3]));
+    ASSERT_SUCCESS(
+        af_tile(&temp0, temp1, tdims[0], tdims[1], tdims[2], tdims[3]));
 
     ASSERT_ARRAYS_EQ(temp0, outArray);
 
-    if(outArray  != 0) af_release_array(outArray);
-    if(temp0     != 0) af_release_array(temp0);
-    if(temp1     != 0) af_release_array(temp1);
-    if(temp2     != 0) af_release_array(temp2);
+    if (outArray != 0) af_release_array(outArray);
+    if (temp0 != 0) af_release_array(temp0);
+    if (temp1 != 0) af_release_array(temp1);
+    if (temp2 != 0) af_release_array(temp2);
 }
 
-#define IOTA_INIT(desc, x, y, z, w, a, b, c, d)                     \
-    TYPED_TEST(Iota, desc)                                          \
-    {                                                               \
-        iotaTest<TypeParam>(dim4(x, y, z, w), dim4(a, b, c, d));    \
+#define IOTA_INIT(desc, x, y, z, w, a, b, c, d)                  \
+    TYPED_TEST(Iota, desc) {                                     \
+        iotaTest<TypeParam>(dim4(x, y, z, w), dim4(a, b, c, d)); \
     }
 
-    IOTA_INIT(Iota1D0, 100,  1, 1, 1, 2, 3, 1, 1);
+IOTA_INIT(Iota1D0, 100, 1, 1, 1, 2, 3, 1, 1);
 
-    IOTA_INIT(Iota2D0,  10, 20, 1, 1, 3, 1, 2, 1);
-    IOTA_INIT(Iota2D1, 100,  5, 1, 1, 1, 2, 4, 2);
+IOTA_INIT(Iota2D0, 10, 20, 1, 1, 3, 1, 2, 1);
+IOTA_INIT(Iota2D1, 100, 5, 1, 1, 1, 2, 4, 2);
 
-    IOTA_INIT(Iota3D0,  20,  6, 3, 1, 1, 1, 1, 1);
-    IOTA_INIT(Iota3D1,  10, 12, 5, 1, 2, 3, 4, 5);
-    IOTA_INIT(Iota3D2,  25, 30, 2, 1, 1, 2, 2, 1);
+IOTA_INIT(Iota3D0, 20, 6, 3, 1, 1, 1, 1, 1);
+IOTA_INIT(Iota3D1, 10, 12, 5, 1, 2, 3, 4, 5);
+IOTA_INIT(Iota3D2, 25, 30, 2, 1, 1, 2, 2, 1);
 
-    IOTA_INIT(Iota4D0,  20,  6, 3, 2, 2, 3, 1, 2);
-    IOTA_INIT(Iota4D1,  10, 12, 5, 2, 1, 2, 2, 2);
-    IOTA_INIT(Iota4D2,  25, 30, 2, 2, 3, 2, 1, 1);
-    IOTA_INIT(Iota4D3,  25, 30, 2, 2, 4, 2, 4, 2);
+IOTA_INIT(Iota4D0, 20, 6, 3, 2, 2, 3, 1, 2);
+IOTA_INIT(Iota4D1, 10, 12, 5, 2, 1, 2, 2, 2);
+IOTA_INIT(Iota4D2, 25, 30, 2, 2, 3, 2, 1, 1);
+IOTA_INIT(Iota4D3, 25, 30, 2, 2, 4, 2, 4, 2);
 
-    IOTA_INIT(IotaMaxDimY,  1, 65535 * 32 + 1, 1, 1, 1, 1, 1, 1);
-    IOTA_INIT(IotaMaxDimZ,  1, 1, 65535 * 32 + 1, 1, 1, 1, 1, 1);
-    IOTA_INIT(IotaMaxDimW,  1, 1, 1, 65535 * 32 + 1, 1, 1, 1, 1);
+IOTA_INIT(IotaMaxDimY, 1, 65535 * 32 + 1, 1, 1, 1, 1, 1, 1);
+IOTA_INIT(IotaMaxDimZ, 1, 1, 65535 * 32 + 1, 1, 1, 1, 1, 1);
+IOTA_INIT(IotaMaxDimW, 1, 1, 1, 65535 * 32 + 1, 1, 1, 1, 1);
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
@@ -102,19 +101,17 @@ void iotaTest(const dim4 idims, const dim4 tdims)
 using af::array;
 using af::iota;
 
-TEST(Iota, CPP)
-{
+TEST(Iota, CPP) {
     if (noDoubleTests<float>()) return;
 
     dim4 idims(23, 15, 1, 1);
     dim4 tdims(2, 2, 1, 1);
     dim4 fulldims;
-    for(unsigned i = 0; i < 4; i++) {
-        fulldims[i] = idims[i] * tdims[i];
-    }
+    for (unsigned i = 0; i < 4; i++) { fulldims[i] = idims[i] * tdims[i]; }
 
     array output = iota(idims, tdims);
-    array tileArray = tile(moddims(range(dim4(idims.elements()), 0), idims), tdims);
+    array tileArray =
+        tile(moddims(range(dim4(idims.elements()), 0), idims), tdims);
 
     ASSERT_ARRAYS_EQ(tileArray, output);
 }
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 1b152cbfea..025bf6d69f 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -8,14 +8,12 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 #include <algorithm>
 
-using std::vector;
-using std::complex;
 using af::allTrue;
 using af::array;
 using af::constant;
@@ -26,74 +24,71 @@ using af::min;
 using af::randu;
 using af::seq;
 using af::span;
+using std::complex;
+using std::vector;
 
-#define MINMAXOP(fn, ty)                                \
-    TEST(IndexedReduce, fn##_##ty##_0)                  \
-    {                                                   \
-        if (noDoubleTests<ty>()) return;                \
-        dtype dty = (dtype)dtype_traits<ty>::af_type;   \
-        const int nx = 10000;                           \
-        const int ny = 100;                             \
-        array in = randu(nx, ny, dty);                  \
-        array val, idx;                                 \
-        fn(val, idx, in, 0);                            \
-                                                        \
-        ty *h_in = in.host<ty>();                       \
-        ty *h_in_st = h_in;                             \
-        ty *h_val = val.host<ty>();                     \
-        uint *h_idx = idx.host<uint>();                 \
-        for (int i = 0; i < ny; i++) {                  \
-            ty tmp = *std::fn##_element(h_in, h_in +nx);\
-            ASSERT_EQ(tmp, h_val[i])                    \
-                << "for index" << i;                    \
-            ASSERT_EQ(h_in[h_idx[i]], tmp)              \
-                << "for index" << i;                    \
-            h_in += nx;                                 \
-        }                                               \
-        af_free_host(h_in_st);                          \
-        af_free_host(h_val);                            \
-        af_free_host(h_idx);                            \
-    }                                                   \
-    TEST(IndexedReduce, fn##_##ty##_1)                  \
-    {                                                   \
-        if (noDoubleTests<ty>()) return;                \
-        dtype dty = (dtype)dtype_traits<ty>::af_type;   \
-        const int nx = 100;                             \
-        const int ny = 100;                             \
-        array in = randu(nx, ny, dty);                  \
-        array val, idx;                                 \
-        fn(val, idx, in, 1);                            \
-                                                        \
-        ty *h_in = in.host<ty>();                       \
-        ty *h_val = val.host<ty>();                     \
-        uint *h_idx = idx.host<uint>();                 \
-        for (int i = 0; i < nx; i++) {                  \
-            ty val = h_val[i];                          \
-            for (int j= 0; j < ny; j++) {               \
-                ty tmp = std::fn(val, h_in[j * nx + i]);\
-                ASSERT_EQ(tmp, val);                    \
-            }                                           \
-            ASSERT_EQ(val, h_in[h_idx[i] * nx + i]);    \
-        }                                               \
-        af_free_host(h_in);                             \
-        af_free_host(h_val);                            \
-        af_free_host(h_idx);                            \
-    }                                                   \
-    TEST(IndexedReduce, fn##_##ty##_all)                \
-    {                                                   \
-        if (noDoubleTests<ty>()) return;                \
-        dtype dty = (dtype)dtype_traits<ty>::af_type;   \
-        const int num = 100000;                         \
-        array in = randu(num, dty);                     \
-        ty val;                                         \
-        uint idx;                                       \
-        fn<ty>(&val, &idx, in);                         \
-        ty *h_in = in.host<ty>();                       \
-        ty tmp = *std::fn##_element(h_in, h_in + num);  \
-        ASSERT_EQ(tmp, val);                            \
-        ASSERT_EQ(tmp, h_in[idx]);                      \
-        af_free_host(h_in);                             \
-    }                                                   \
+#define MINMAXOP(fn, ty)                                        \
+    TEST(IndexedReduce, fn##_##ty##_0) {                        \
+        if (noDoubleTests<ty>()) return;                        \
+        dtype dty    = (dtype)dtype_traits<ty>::af_type;        \
+        const int nx = 10000;                                   \
+        const int ny = 100;                                     \
+        array in     = randu(nx, ny, dty);                      \
+        array val, idx;                                         \
+        fn(val, idx, in, 0);                                    \
+                                                                \
+        ty *h_in    = in.host<ty>();                            \
+        ty *h_in_st = h_in;                                     \
+        ty *h_val   = val.host<ty>();                           \
+        uint *h_idx = idx.host<uint>();                         \
+        for (int i = 0; i < ny; i++) {                          \
+            ty tmp = *std::fn##_element(h_in, h_in + nx);       \
+            ASSERT_EQ(tmp, h_val[i]) << "for index" << i;       \
+            ASSERT_EQ(h_in[h_idx[i]], tmp) << "for index" << i; \
+            h_in += nx;                                         \
+        }                                                       \
+        af_free_host(h_in_st);                                  \
+        af_free_host(h_val);                                    \
+        af_free_host(h_idx);                                    \
+    }                                                           \
+    TEST(IndexedReduce, fn##_##ty##_1) {                        \
+        if (noDoubleTests<ty>()) return;                        \
+        dtype dty    = (dtype)dtype_traits<ty>::af_type;        \
+        const int nx = 100;                                     \
+        const int ny = 100;                                     \
+        array in     = randu(nx, ny, dty);                      \
+        array val, idx;                                         \
+        fn(val, idx, in, 1);                                    \
+                                                                \
+        ty *h_in    = in.host<ty>();                            \
+        ty *h_val   = val.host<ty>();                           \
+        uint *h_idx = idx.host<uint>();                         \
+        for (int i = 0; i < nx; i++) {                          \
+            ty val = h_val[i];                                  \
+            for (int j = 0; j < ny; j++) {                      \
+                ty tmp = std::fn(val, h_in[j * nx + i]);        \
+                ASSERT_EQ(tmp, val);                            \
+            }                                                   \
+            ASSERT_EQ(val, h_in[h_idx[i] * nx + i]);            \
+        }                                                       \
+        af_free_host(h_in);                                     \
+        af_free_host(h_val);                                    \
+        af_free_host(h_idx);                                    \
+    }                                                           \
+    TEST(IndexedReduce, fn##_##ty##_all) {                      \
+        if (noDoubleTests<ty>()) return;                        \
+        dtype dty     = (dtype)dtype_traits<ty>::af_type;       \
+        const int num = 100000;                                 \
+        array in      = randu(num, dty);                        \
+        ty val;                                                 \
+        uint idx;                                               \
+        fn<ty>(&val, &idx, in);                                 \
+        ty *h_in = in.host<ty>();                               \
+        ty tmp   = *std::fn##_element(h_in, h_in + num);        \
+        ASSERT_EQ(tmp, val);                                    \
+        ASSERT_EQ(tmp, h_in[idx]);                              \
+        af_free_host(h_in);                                     \
+    }
 
 MINMAXOP(min, float)
 MINMAXOP(min, double)
@@ -109,12 +104,11 @@ MINMAXOP(max, uint)
 MINMAXOP(max, char)
 MINMAXOP(max, uchar)
 
-TEST(IndexedReduce, MaxIndexedSmall)
-{
+TEST(IndexedReduce, MaxIndexedSmall) {
     const int num = 1000;
-    const int st = 10;
-    const int en = num - 100;
-    array a = randu(num);
+    const int st  = 10;
+    const int en  = num - 100;
+    array a       = randu(num);
 
     float b;
     unsigned idx;
@@ -124,19 +118,16 @@ TEST(IndexedReduce, MaxIndexedSmall)
     a.host(&ha[0]);
 
     float res = ha[st];
-    for (int i = st; i <= en; i++) {
-        res = std::max(res, ha[i]);
-    }
+    for (int i = st; i <= en; i++) { res = std::max(res, ha[i]); }
 
     ASSERT_EQ(b, res);
 }
 
-TEST(IndexedReduce, MaxIndexedBig)
-{
+TEST(IndexedReduce, MaxIndexedBig) {
     const int num = 100000;
-    const int st = 1000;
-    const int en = num - 1000;
-    array a = randu(num);
+    const int st  = 1000;
+    const int en  = num - 1000;
+    array a       = randu(num);
 
     float b;
     unsigned idx;
@@ -146,22 +137,19 @@ TEST(IndexedReduce, MaxIndexedBig)
     a.host(&ha[0]);
 
     float res = ha[st];
-    for (int i = st; i <= en; i++) {
-        res = std::max(res, ha[i]);
-    }
+    for (int i = st; i <= en; i++) { res = std::max(res, ha[i]); }
 
     ASSERT_EQ(b, res);
 }
 
-TEST(IndexedReduce, BUG_FIX_1005)
-{
+TEST(IndexedReduce, BUG_FIX_1005) {
     const int m = 64;
     const int n = 100;
     const int b = 5;
 
     array in = constant(0, m, n, b);
     for (int i = 0; i < b; i++) {
-        array tmp = randu(m, n);
+        array tmp         = randu(m, n);
         in(span, span, i) = tmp;
 
         float val0, val1;
@@ -175,8 +163,7 @@ TEST(IndexedReduce, BUG_FIX_1005)
     }
 }
 
-TEST(IndexedReduce, MinReduceDimensionHasSingleValue)
-{
+TEST(IndexedReduce, MinReduceDimensionHasSingleValue) {
     array data = randu(10, 10, 1);
 
     array mm, indx;
@@ -186,8 +173,7 @@ TEST(IndexedReduce, MinReduceDimensionHasSingleValue)
     ASSERT_TRUE(allTrue<bool>(indx == 0));
 }
 
-TEST(IndexedReduce, MaxReduceDimensionHasSingleValue)
-{
+TEST(IndexedReduce, MaxReduceDimensionHasSingleValue) {
     array data = randu(10, 10, 1);
 
     array mm, indx;
@@ -197,15 +183,14 @@ TEST(IndexedReduce, MaxReduceDimensionHasSingleValue)
     ASSERT_TRUE(allTrue<bool>(indx == 0));
 }
 
-TEST(IndexedReduce, MinNaN)
-{
-    float test_data[] = { 1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f };
-    int rows = 4;
-    int cols = 2;
+TEST(IndexedReduce, MinNaN) {
+    float test_data[] = {1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f};
+    int rows          = 4;
+    int cols          = 2;
     array a(rows, cols, test_data);
 
-    float gold_min_val[] = { 0.1f, -0.5f };
-    int gold_min_idx[] = { 3, 1 };
+    float gold_min_val[] = {0.1f, -0.5f};
+    int gold_min_idx[]   = {3, 1};
 
     array min_val;
     array min_idx;
@@ -221,20 +206,17 @@ TEST(IndexedReduce, MinNaN)
         ASSERT_FLOAT_EQ(h_min_val[i], gold_min_val[i]);
     }
 
-    for (int i = 0; i < cols; i++) {
-        ASSERT_EQ(h_min_idx[i], gold_min_idx[i]);
-    }
+    for (int i = 0; i < cols; i++) { ASSERT_EQ(h_min_idx[i], gold_min_idx[i]); }
 }
 
-TEST(IndexedReduce, MaxNaN)
-{
-    float test_data[] = { 1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f };
-    int rows = 4;
-    int cols = 2;
+TEST(IndexedReduce, MaxNaN) {
+    float test_data[] = {1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f};
+    int rows          = 4;
+    int cols          = 2;
     array a(rows, cols, test_data);
 
-    float gold_max_val[] = { 5.0f, 0.f };
-    int gold_max_idx[] = { 2, 3 };
+    float gold_max_val[] = {5.0f, 0.f};
+    int gold_max_idx[]   = {2, 3};
 
     array max_val;
     array max_idx;
@@ -250,22 +232,15 @@ TEST(IndexedReduce, MaxNaN)
         ASSERT_FLOAT_EQ(h_max_val[i], gold_max_val[i]);
     }
 
-    for (int i = 0; i < cols; i++) {
-        ASSERT_EQ(h_max_idx[i], gold_max_idx[i]);
-    }
+    for (int i = 0; i < cols; i++) { ASSERT_EQ(h_max_idx[i], gold_max_idx[i]); }
 }
 
-TEST(IndexedReduce, MinCplxNaN)
-{
-    float real_wnan_data[] = {
-        0.005f, NAN, -6.3f, NAN, -0.5f,
-        NAN, NAN, 0.2f, -1205.4f, 8.9f
-    };
+TEST(IndexedReduce, MinCplxNaN) {
+    float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
+                              NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
-    float imag_wnan_data[] = {
-        NAN, NAN, -9.0f, -0.005f, -0.3f,
-        0.007f, NAN, 0.1f, NAN, 4.5f
-    };
+    float imag_wnan_data[] = {NAN,    NAN, -9.0f, -0.005f, -0.3f,
+                              0.007f, NAN, 0.1f,  NAN,     4.5f};
 
     int rows = 5;
     int cols = 2;
@@ -273,15 +248,15 @@ TEST(IndexedReduce, MinCplxNaN)
     array imag_wnan(rows, cols, imag_wnan_data);
     array a = af::complex(real_wnan, imag_wnan);
 
-    float gold_min_real[] = { -0.5f, 0.2f };
-    float gold_min_imag[] = { -0.3f, 0.1f };
-    int gold_min_idx[] = { 4, 2 };
+    float gold_min_real[] = {-0.5f, 0.2f};
+    float gold_min_imag[] = {-0.3f, 0.1f};
+    int gold_min_idx[]    = {4, 2};
 
     array min_val;
     array min_idx;
     af::min(min_val, min_idx, a);
 
-    vector< complex<float> > h_min_val(cols);
+    vector<complex<float> > h_min_val(cols);
     min_val.host(&h_min_val[0]);
 
     vector<int> h_min_idx(cols);
@@ -292,22 +267,15 @@ TEST(IndexedReduce, MinCplxNaN)
         ASSERT_FLOAT_EQ(h_min_val[i].imag(), gold_min_imag[i]);
     }
 
-    for (int i = 0; i < cols; i++) {
-        ASSERT_EQ(h_min_idx[i], gold_min_idx[i]);
-    }
+    for (int i = 0; i < cols; i++) { ASSERT_EQ(h_min_idx[i], gold_min_idx[i]); }
 }
 
-TEST(IndexedReduce, MaxCplxNaN)
-{
-    float real_wnan_data[] = {
-        0.005f, NAN, -6.3f, NAN, -0.5f,
-        NAN, NAN, 0.2f, -1205.4f, 8.9f
-    };
+TEST(IndexedReduce, MaxCplxNaN) {
+    float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
+                              NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
-    float imag_wnan_data[] = {
-        NAN, NAN, -9.0f, -0.005f, -0.3f,
-        0.007f, NAN, 0.1f, NAN, 4.5f
-    };
+    float imag_wnan_data[] = {NAN,    NAN, -9.0f, -0.005f, -0.3f,
+                              0.007f, NAN, 0.1f,  NAN,     4.5f};
 
     int rows = 5;
     int cols = 2;
@@ -315,15 +283,15 @@ TEST(IndexedReduce, MaxCplxNaN)
     array imag_wnan(rows, cols, imag_wnan_data);
     array a = af::complex(real_wnan, imag_wnan);
 
-    float gold_max_real[] = { -6.3f, 8.9f };
-    float gold_max_imag[] = { -9.0f, 4.5f };
-    int gold_max_idx[] = { 2, 4 };
+    float gold_max_real[] = {-6.3f, 8.9f};
+    float gold_max_imag[] = {-9.0f, 4.5f};
+    int gold_max_idx[]    = {2, 4};
 
     array max_val;
     array max_idx;
     af::max(max_val, max_idx, a);
 
-    vector< complex<float> > h_max_val(cols);
+    vector<complex<float> > h_max_val(cols);
     max_val.host(&h_max_val[0]);
 
     vector<int> h_max_idx(cols);
@@ -334,19 +302,16 @@ TEST(IndexedReduce, MaxCplxNaN)
         ASSERT_FLOAT_EQ(h_max_val[i].imag(), gold_max_imag[i]);
     }
 
-    for (int i = 0; i < cols; i++) {
-        ASSERT_EQ(h_max_idx[i], gold_max_idx[i]);
-    }
+    for (int i = 0; i < cols; i++) { ASSERT_EQ(h_max_idx[i], gold_max_idx[i]); }
 }
 
-TEST(IndexedReduce, MinPreferLargerIdxIfEqual)
-{
+TEST(IndexedReduce, MinPreferLargerIdxIfEqual) {
     float test_data[] = {0.f, 50.f, 50.f, 0.f};
-    int len = 4;
+    int len           = 4;
     array a(len, test_data);
 
     float gold_min_val = 0.f;
-    int gold_min_idx = 3;
+    int gold_min_idx   = 3;
 
     array min_val;
     array min_idx;
@@ -362,14 +327,13 @@ TEST(IndexedReduce, MinPreferLargerIdxIfEqual)
     ASSERT_EQ(h_min_idx[0], gold_min_idx);
 }
 
-TEST(IndexedReduce, MaxPreferSmallerIdxIfEqual)
-{
+TEST(IndexedReduce, MaxPreferSmallerIdxIfEqual) {
     float test_data[] = {0.f, 50.f, 50.f, 0.f};
-    int len = 4;
+    int len           = 4;
     array a(len, test_data);
 
     float gold_max_val = 50.f;
-    int gold_max_idx = 1;
+    int gold_max_idx   = 1;
 
     array max_val;
     array max_idx;
@@ -385,10 +349,9 @@ TEST(IndexedReduce, MaxPreferSmallerIdxIfEqual)
     ASSERT_EQ(h_max_idx[0], gold_max_idx);
 }
 
-TEST(IndexedReduce, MinCplxPreferLargerIdxIfEqual)
-{
-    float real_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
-    float imag_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
+TEST(IndexedReduce, MinCplxPreferLargerIdxIfEqual) {
+    float real_wnan_data[] = {0.f, 50.f, 50.f, 0.f};
+    float imag_wnan_data[] = {0.f, 50.f, 50.f, 0.f};
 
     int len = 4;
     array real_wnan(len, real_wnan_data);
@@ -397,13 +360,13 @@ TEST(IndexedReduce, MinCplxPreferLargerIdxIfEqual)
 
     float gold_min_real = 0.f;
     float gold_min_imag = 0.f;
-    int gold_min_idx = 3;
+    int gold_min_idx    = 3;
 
     array min_val;
     array min_idx;
     min(min_val, min_idx, a);
 
-    vector< complex<float> > h_min_val(1);
+    vector<complex<float> > h_min_val(1);
     min_val.host(&h_min_val[0]);
 
     vector<int> h_min_idx(1);
@@ -415,10 +378,9 @@ TEST(IndexedReduce, MinCplxPreferLargerIdxIfEqual)
     ASSERT_EQ(h_min_idx[0], gold_min_idx);
 }
 
-TEST(IndexedReduce, MaxCplxPreferSmallerIdxIfEqual)
-{
-    float real_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
-    float imag_wnan_data[] = { 0.f, 50.f, 50.f, 0.f };
+TEST(IndexedReduce, MaxCplxPreferSmallerIdxIfEqual) {
+    float real_wnan_data[] = {0.f, 50.f, 50.f, 0.f};
+    float imag_wnan_data[] = {0.f, 50.f, 50.f, 0.f};
 
     int len = 4;
     array real_wnan(len, real_wnan_data);
@@ -427,13 +389,13 @@ TEST(IndexedReduce, MaxCplxPreferSmallerIdxIfEqual)
 
     float gold_max_real = 50.f;
     float gold_max_imag = 50.f;
-    int gold_max_idx = 1;
+    int gold_max_idx    = 1;
 
     array max_val;
     array max_idx;
     max(max_val, max_idx, a);
 
-    vector< complex<float> > h_max_val(1);
+    vector<complex<float> > h_max_val(1);
     max_val.host(&h_max_val[0]);
 
     vector<int> h_max_idx(1);
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 95472baeec..29c9e39592 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -7,24 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using std::abs;
 using std::string;
 using std::vector;
-using std::abs;
 using namespace af;
 
 template<typename T>
-class IterativeDeconvolution : public ::testing::Test
-{
-};
+class IterativeDeconvolution : public ::testing::Test {};
 
 // create a list of types to be tested
 typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
@@ -34,26 +32,26 @@ TYPED_TEST_CASE(IterativeDeconvolution, TestTypes);
 
 template<typename T, bool isColor>
 void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
-                         const af::iterativeDeconvAlgo algo)
-{
-    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type OutType;
+                         const af::iterativeDeconvAlgo algo) {
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            OutType;
 
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
     using af::dim4;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId)
-    {
+    for (size_t testId = 0; testId < testCount; ++testId) {
         inFiles[testId].insert(0, string(TEST_DIR "/iterative_deconv/"));
         outFiles[testId].insert(0, string(TEST_DIR "/iterative_deconv/"));
 
@@ -75,25 +73,30 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
 
         af_dtype otype = (af_dtype)af::dtype_traits<OutType>::af_type;
 
-        ASSERT_SUCCESS(af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
 
-        ASSERT_SUCCESS(af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(conv_image<OutType>(&goldArray, _goldArray));
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         unsigned ndims;
         dim_t dims[4];
         ASSERT_SUCCESS(af_get_numdims(&ndims, goldArray));
-        ASSERT_SUCCESS(af_get_dims(dims, dims+1, dims+2, dims+3, goldArray));
+        ASSERT_SUCCESS(
+            af_get_dims(dims, dims + 1, dims + 2, dims + 3, goldArray));
 
-        ASSERT_SUCCESS(af_iterative_deconv(&_outArray, inArray, kerArray, iters, rf, algo));
+        ASSERT_SUCCESS(af_iterative_deconv(&_outArray, inArray, kerArray, iters,
+                                           rf, algo));
 
         double maxima, minima, imag;
         ASSERT_SUCCESS(af_min_all(&minima, &imag, _outArray));
         ASSERT_SUCCESS(af_max_all(&maxima, &imag, _outArray));
         ASSERT_SUCCESS(af_constant(&cstArray, 255.0, ndims, dims, otype));
-        ASSERT_SUCCESS(af_constant(&denArray, (maxima-minima), ndims, dims, otype));
+        ASSERT_SUCCESS(
+            af_constant(&denArray, (maxima - minima), ndims, dims, otype));
         ASSERT_SUCCESS(af_constant(&minArray, minima, ndims, dims, otype));
         ASSERT_SUCCESS(af_sub(&numArray, _outArray, minArray, false));
         ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
@@ -118,21 +121,24 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
         ASSERT_SUCCESS(af_release_array(_goldArray));
         ASSERT_SUCCESS(af_release_array(goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.03));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.03));
     }
 }
 
-TYPED_TEST(IterativeDeconvolution, LandweberOnGrayscale)
-{
-    // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation factor>_<algo>.test
-    iterDeconvImageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_landweber.test"),
-            100, 0.05, AF_ITERATIVE_DECONV_LANDWEBER);
+TYPED_TEST(IterativeDeconvolution, LandweberOnGrayscale) {
+    // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation
+    // factor>_<algo>.test
+    iterDeconvImageTest<TypeParam, false>(
+        string(TEST_DIR "/iterative_deconv/gray_100_50_landweber.test"), 100,
+        0.05, AF_ITERATIVE_DECONV_LANDWEBER);
 }
 
-TYPED_TEST(IterativeDeconvolution, RichardsonLucyOnGrayscale)
-{
-    // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation factor>_<algo>.test
-    // For RichardsonLucy algorithm, relaxation factor is not used.
-    iterDeconvImageTest<TypeParam, false>(string(TEST_DIR "/iterative_deconv/gray_100_50_lucy.test"),
-            100, 0.05, AF_ITERATIVE_DECONV_RICHARDSONLUCY);
+TYPED_TEST(IterativeDeconvolution, RichardsonLucyOnGrayscale) {
+    // Test file name format: <colorspace>_<iterations>_<number/1000:relaxation
+    // factor>_<algo>.test For RichardsonLucy algorithm, relaxation factor is
+    // not used.
+    iterDeconvImageTest<TypeParam, false>(
+        string(TEST_DIR "/iterative_deconv/gray_100_50_lucy.test"), 100, 0.05,
+        AF_ITERATIVE_DECONV_RICHARDSONLUCY);
 }
diff --git a/test/jit.cpp b/test/jit.cpp
index dd5a5045ce..4cfde76ee4 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -8,29 +8,28 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
-using std::vector;
 using af::array;
 using af::constant;
 using af::eval;
 using af::freeHost;
 using af::gforSet;
-using af::randu;
 using af::randn;
+using af::randu;
 using af::seq;
+using std::vector;
 
-TEST(JIT, CPP_JIT_HASH)
-{
-    const int num = 20;
-    const float valA = 3;
-    const float valB = 5;
-    const float valC = 2;
-    const float valD = valA + valB;
-    const float valE = valA + valC;
+TEST(JIT, CPP_JIT_HASH) {
+    const int num     = 20;
+    const float valA  = 3;
+    const float valB  = 5;
+    const float valC  = 2;
+    const float valD  = valA + valB;
+    const float valE  = valA + valC;
     const float valF1 = valD * valE - valE;
     const float valF2 = valD * valE - valD;
 
@@ -41,40 +40,34 @@ TEST(JIT, CPP_JIT_HASH)
     eval(b);
     eval(c);
 
-
     // Creating a kernel
     {
-        array d = a + b;
-        array e = a + c;
-        array f1 = d * e - e;
+        array d    = a + b;
+        array e    = a + c;
+        array f1   = d * e - e;
         float *hF1 = f1.host<float>();
 
-        for (int i = 0; i < num; i++) {
-            ASSERT_EQ(hF1[i], valF1);
-        }
+        for (int i = 0; i < num; i++) { ASSERT_EQ(hF1[i], valF1); }
 
         freeHost(hF1);
     }
 
     // Making sure a different kernel is generated
     {
-        array d = a + b;
-        array e = a + c;
-        array f2 = d * e - d;
+        array d    = a + b;
+        array e    = a + c;
+        array f2   = d * e - d;
         float *hF2 = f2.host<float>();
 
-        for (int i = 0; i < num; i++) {
-            ASSERT_EQ(hF2[i], valF2);
-        }
+        for (int i = 0; i < num; i++) { ASSERT_EQ(hF2[i], valF2); }
 
         freeHost(hF2);
     }
 }
 
-TEST(JIT, CPP_JIT_Reset_Binary)
-{
-    array a = constant(2, 5,5);
-    array b = constant(1, 5,5);
+TEST(JIT, CPP_JIT_Reset_Binary) {
+    array a = constant(2, 5, 5);
+    array b = constant(1, 5, 5);
     array c = a + b;
     array d = a - b;
     array e = c * d;
@@ -89,15 +82,12 @@ TEST(JIT, CPP_JIT_Reset_Binary)
     f.host(&hf[0]);
     g.host(&hg[0]);
 
-    for (int i = 0; i < (int)f.elements(); i++) {
-        ASSERT_EQ(hf[i], -hg[i]);
-    }
+    for (int i = 0; i < (int)f.elements(); i++) { ASSERT_EQ(hf[i], -hg[i]); }
 }
 
-TEST(JIT, CPP_JIT_Reset_Unary)
-{
-    array a = constant(2, 5,5);
-    array b = constant(1, 5,5);
+TEST(JIT, CPP_JIT_Reset_Unary) {
+    array a = constant(2, 5, 5);
+    array b = constant(1, 5, 5);
     array c = sin(a);
     array d = cos(b);
     array e = c * d;
@@ -112,18 +102,15 @@ TEST(JIT, CPP_JIT_Reset_Unary)
     f.host(&hf[0]);
     g.host(&hg[0]);
 
-    for (int i = 0; i < (int)f.elements(); i++) {
-        ASSERT_EQ(hf[i], -hg[i]);
-    }
+    for (int i = 0; i < (int)f.elements(); i++) { ASSERT_EQ(hf[i], -hg[i]); }
 }
 
-TEST(JIT, CPP_Multi_linear)
-{
+TEST(JIT, CPP_Multi_linear) {
     const int num = 1 << 16;
-    array a = randu(num, s32);
-    array b = randu(num, s32);
-    array x = a + b;
-    array y = a - b;
+    array a       = randu(num, s32);
+    array b       = randu(num, s32);
+    array x       = a + b;
+    array y       = a - b;
     eval(x, y);
 
     vector<int> ha(num);
@@ -142,8 +129,7 @@ TEST(JIT, CPP_Multi_linear)
     }
 }
 
-TEST(JIT, CPP_strided)
-{
+TEST(JIT, CPP_strided) {
     const int num = 1024;
     gforSet(true);
     array a = randu(num, 1, s32);
@@ -166,14 +152,13 @@ TEST(JIT, CPP_strided)
 
     for (int j = 0; j < num; j++) {
         for (int i = 0; i < num; i++) {
-            ASSERT_EQ((ha[i] + hb[j]), hx[j*num + i]);
-            ASSERT_EQ((ha[i] - hb[j]), hy[j*num + i]);
+            ASSERT_EQ((ha[i] + hb[j]), hx[j * num + i]);
+            ASSERT_EQ((ha[i] - hb[j]), hy[j * num + i]);
         }
     }
 }
 
-TEST(JIT, CPP_Multi_strided)
-{
+TEST(JIT, CPP_Multi_strided) {
     const int num = 1024;
     gforSet(true);
     array a = randu(num, 1, s32);
@@ -195,19 +180,18 @@ TEST(JIT, CPP_Multi_strided)
 
     for (int j = 0; j < num; j++) {
         for (int i = 0; i < num; i++) {
-            ASSERT_EQ((ha[i] + hb[j]), hx[j*num + i]);
-            ASSERT_EQ((ha[i] - hb[j]), hy[j*num + i]);
+            ASSERT_EQ((ha[i] + hb[j]), hx[j * num + i]);
+            ASSERT_EQ((ha[i] - hb[j]), hy[j * num + i]);
         }
     }
 }
 
-TEST(JIT, CPP_Multi_pre_eval)
-{
+TEST(JIT, CPP_Multi_pre_eval) {
     const int num = 1 << 16;
-    array a = randu(num, s32);
-    array b = randu(num, s32);
-    array x = a + b;
-    array y = a - b;
+    array a       = randu(num, s32);
+    array b       = randu(num, s32);
+    array x       = a + b;
+    array y       = a - b;
 
     eval(x);
 
@@ -234,8 +218,7 @@ TEST(JIT, CPP_Multi_pre_eval)
     }
 }
 
-TEST(JIT, CPP_common_node)
-{
+TEST(JIT, CPP_common_node) {
     array r = seq(-3, 3, 0.5);
 
     int n = r.dims(0);
@@ -245,7 +228,6 @@ TEST(JIT, CPP_common_node)
     x.eval();
     y.eval();
 
-
     vector<float> hx(x.elements());
     vector<float> hy(y.elements());
     vector<float> hr(r.elements());
@@ -262,8 +244,7 @@ TEST(JIT, CPP_common_node)
     }
 }
 
-TEST(JIT, ISSUE_1646)
-{
+TEST(JIT, ISSUE_1646) {
     array test1 = randn(10, 10);
     array test2 = randn(10);
     array test3 = randn(10);
@@ -276,13 +257,12 @@ TEST(JIT, ISSUE_1646)
     eval(test3);
 }
 
-TEST(JIT, NonLinearLargeY)
-{
+TEST(JIT, NonLinearLargeY) {
     const int d0 = 2;
     // This needs to be > 2 * (1 << 20) to properly check this.
     const int d1 = 3 * (1 << 20);
-    array a = randn(d0);
-    array b = randn(1, d1);
+    array a      = randn(d0);
+    array b      = randn(1, d1);
 
     // tile is jit-ted for both the operations
     array c = tile(a, 1, d1) + tile(b, d0, 1);
@@ -298,18 +278,18 @@ TEST(JIT, NonLinearLargeY)
 
     for (int j = 0; j < d1; j++) {
         for (int i = 0; i < d0; i++) {
-            ASSERT_EQ(hc[i + j * d0], ha[i] + hb[j]) << " at " << i << " , " << j;
+            ASSERT_EQ(hc[i + j * d0], ha[i] + hb[j])
+                << " at " << i << " , " << j;
         }
     }
 }
 
-TEST(JIT, NonLinearLargeX)
-{
+TEST(JIT, NonLinearLargeX) {
     af_array r, c, s;
     dim_t rdims[] = {1024000, 1, 3};
     dim_t cdims[] = {1, 1, 3};
     dim_t sdims[] = {1, 1, 1};
-    dim_t ndims = 3;
+    dim_t ndims   = 3;
 
     ASSERT_SUCCESS(af_randu(&r, ndims, rdims, f32));
     ASSERT_SUCCESS(af_constant(&c, 1, ndims, cdims, f32));
@@ -338,20 +318,16 @@ TEST(JIT, NonLinearLargeX)
     for (int k = 0; k < sdims[2]; k++) {
         for (int j = 0; j < sdims[1]; j++) {
             for (int i = 0; i < sdims[0]; i++) {
+                int sidx = i + j * sdims[0] + k * (sdims[0] * sdims[1]);
 
-                int sidx = i +
-                    j * sdims[0] +
-                    k * (sdims[0] * sdims[1]);
-
-                int ridx = (i % rdims[0]) +
-                    (j % rdims[1]) * rdims[0] +
-                    (k % rdims[2]) * rdims[0] * rdims[1];
+                int ridx = (i % rdims[0]) + (j % rdims[1]) * rdims[0] +
+                           (k % rdims[2]) * rdims[0] * rdims[1];
 
-                int cidx = (i % cdims[0]) +
-                    (j % cdims[1]) * cdims[0] +
-                    (k % cdims[2]) * cdims[0] * cdims[1];
+                int cidx = (i % cdims[0]) + (j % cdims[1]) * cdims[0] +
+                           (k % cdims[2]) * cdims[0] * cdims[1];
 
-                ASSERT_EQ(hs[sidx], hr[ridx] - hc[cidx]) << " at " << i << "," << k;
+                ASSERT_EQ(hs[sidx], hr[ridx] - hc[cidx])
+                    << " at " << i << "," << k;
             }
         }
     }
@@ -361,8 +337,7 @@ TEST(JIT, NonLinearLargeX)
     ASSERT_SUCCESS(af_release_array(s));
 }
 
-TEST(JIT, ISSUE_1894)
-{
+TEST(JIT, ISSUE_1894) {
     array a = randu(1);
     array b = tile(a, 2 * (1 << 20));
     eval(b);
@@ -372,13 +347,10 @@ TEST(JIT, ISSUE_1894)
     a.host(&ha);
     b.host(hb.data());
 
-    for (size_t i = 0; i < hb.size(); i++) {
-        ASSERT_EQ(ha, hb[i]);
-    }
+    for (size_t i = 0; i < hb.size(); i++) { ASSERT_EQ(ha, hb[i]); }
 }
 
-TEST(JIT, LinearLarge)
-{
+TEST(JIT, LinearLarge) {
     // Needs to be larger than 65535 * 256 (or 1 << 24)
     float v1 = std::rand() % 100;
     float v2 = std::rand() % 100;
@@ -393,14 +365,11 @@ TEST(JIT, LinearLarge)
     vector<float> hc(c.elements());
     c.host(hc.data());
 
-    for (size_t i = 0; i < hc.size(); i++) {
-        ASSERT_EQ(hc[i], v3);
-    }
+    for (size_t i = 0; i < hc.size(); i++) { ASSERT_EQ(hc[i], v3); }
 }
 
-TEST(JIT, NonLinearBuffers1)
-{
-    array a = randu(5, 5);
+TEST(JIT, NonLinearBuffers1) {
+    array a  = randu(5, 5);
     array a0 = a;
     for (int i = 0; i < 1000; i++) {
         array b = randu(1, 5);
@@ -409,23 +378,21 @@ TEST(JIT, NonLinearBuffers1)
     a.eval();
 }
 
-TEST(JIT, NonLinearBuffers2)
-{
+TEST(JIT, NonLinearBuffers2) {
     array a = randu(100, 310);
     array b = randu(10, 10);
     for (int i = 0; i < 300; i++) {
-        b += a(seq(10), seq(i, i+9)) * randu(10, 10);
+        b += a(seq(10), seq(i, i + 9)) * randu(10, 10);
     }
     b.eval();
 }
 
-TEST(JIT, TransposeBuffers)
-{
+TEST(JIT, TransposeBuffers) {
     const int num = 10;
-    array a = randu(1, num);
-    array b = randu(1, num);
-    array c =  a + b;
-    array d = a.T() + b.T();
+    array a       = randu(1, num);
+    array b       = randu(1, num);
+    array c       = a + b;
+    array d       = a.T() + b.T();
 
     vector<float> ha(a.elements());
     a.host(ha.data());
diff --git a/test/join.cpp b/test/join.cpp
index 200d4d576d..ab3760d1e9 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -7,81 +7,92 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/index.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
+#include <af/index.h>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::join;
 using af::randu;
 using af::sum;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Join : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Join : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         intl, uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Join, TestTypes);
 
 template<typename T>
-void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const unsigned in1, const unsigned resultIdx,
-        bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void joinTest(string pTestFile, const unsigned dim, const unsigned in0,
+              const unsigned in1, const unsigned resultIdx,
+              bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, int>(pTestFile,numDims,in,tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 i0dims = numDims[in0];
     dim4 i1dims = numDims[in1];
 
-    af_array in0Array = 0;
-    af_array in1Array = 0;
-    af_array outArray = 0;
+    af_array in0Array  = 0;
+    af_array in1Array  = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[in0].front()),
+                                       i0dims.ndims(), i0dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&in0Array, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&in0Array, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&in0Array, &(in[in0].front()), i0dims.ndims(), i0dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in0Array, &(in[in0].front()),
+                                       i0dims.ndims(), i0dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[in1].front()),
+                                       i1dims.ndims(), i1dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&in1Array, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&in1Array, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&in1Array, &(in[in1].front()), i1dims.ndims(), i1dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in1Array, &(in[in1].front()),
+                                       i1dims.ndims(), i1dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_join(&outArray, dim, in0Array, in1Array));
@@ -91,68 +102,67 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0, const un
 
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, outArray);
 
-    if(in0Array  != 0) af_release_array(in0Array);
-    if(in1Array  != 0) af_release_array(in1Array);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (in0Array != 0) af_release_array(in0Array);
+    if (in1Array != 0) af_release_array(in1Array);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define JOIN_INIT(desc, file, dim, in0, in1, resultIdx)                                     \
-    TYPED_TEST(Join, desc)                                                                  \
-    {                                                                                       \
-        joinTest<TypeParam>(string(TEST_DIR"/join/"#file".test"), dim, in0, in1, resultIdx);\
+#define JOIN_INIT(desc, file, dim, in0, in1, resultIdx)                        \
+    TYPED_TEST(Join, desc) {                                                   \
+        joinTest<TypeParam>(string(TEST_DIR "/join/" #file ".test"), dim, in0, \
+                            in1, resultIdx);                                   \
     }
 
-    JOIN_INIT(JoinBig0, join_big, 0, 0, 1, 0);
-    JOIN_INIT(JoinBig1, join_big, 1, 0, 2, 1);
-    JOIN_INIT(JoinBig2, join_big, 2, 0, 3, 2);
+JOIN_INIT(JoinBig0, join_big, 0, 0, 1, 0);
+JOIN_INIT(JoinBig1, join_big, 1, 0, 2, 1);
+JOIN_INIT(JoinBig2, join_big, 2, 0, 3, 2);
 
-    JOIN_INIT(JoinSmall0, join_small, 0, 0, 1, 0);
-    JOIN_INIT(JoinSmall1, join_small, 1, 0, 2, 1);
-    JOIN_INIT(JoinSmall2, join_small, 2, 0, 3, 2);
+JOIN_INIT(JoinSmall0, join_small, 0, 0, 1, 0);
+JOIN_INIT(JoinSmall1, join_small, 1, 0, 2, 1);
+JOIN_INIT(JoinSmall2, join_small, 2, 0, 3, 2);
 
-TEST(Join, JoinLargeDim)
-{
+TEST(Join, JoinLargeDim) {
     using af::constant;
     using af::deviceGC;
     using af::span;
 
-    //const int nx = 32;
+    // const int nx = 32;
     const int nx = 1;
     const int ny = 4 * 1024 * 1024;
     const int nw = 4 * 1024 * 1024;
 
     deviceGC();
     {
-        array in = randu(nx, ny, u8);
-        array joined = join(0, in, in);
-        dim4 in_dims = in.dims();
+        array in         = randu(nx, ny, u8);
+        array joined     = join(0, in, in);
+        dim4 in_dims     = in.dims();
         dim4 joined_dims = joined.dims();
 
-        ASSERT_EQ(2*in_dims[0], joined_dims[0]);
+        ASSERT_EQ(2 * in_dims[0], joined_dims[0]);
         ASSERT_EQ(0.f, sum<float>((joined(0, span) - joined(1, span)).as(f32)));
 
         array in2 = constant(1, (dim_t)nx, (dim_t)ny, (dim_t)2, (dim_t)nw, u8);
-        joined = join(3, in, in);
-        in_dims = in.dims();
+        joined    = join(3, in, in);
+        in_dims   = in.dims();
         joined_dims = joined.dims();
-        ASSERT_EQ(2*in_dims[3], joined_dims[3]);
+        ASSERT_EQ(2 * in_dims[3], joined_dims[3]);
     }
 }
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
-TEST(Join, CPP)
-{
+TEST(Join, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx = 2;
-    const unsigned dim = 2;
+    const unsigned dim       = 2;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/join/join_big.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/join/join_big.test"),
+                                 numDims, in, tests);
 
     dim4 i0dims = numDims[0];
     dim4 i1dims = numDims[3];
@@ -168,8 +178,7 @@ TEST(Join, CPP)
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, output);
 }
 
-TEST(JoinMany0, CPP)
-{
+TEST(JoinMany0, CPP) {
     if (noDoubleTests<float>()) return;
 
     array a0 = randu(10, 5);
@@ -177,13 +186,12 @@ TEST(JoinMany0, CPP)
     array a2 = randu(5, 5);
 
     array output = join(0, a0, a1, a2);
-    array gold = join(0, a0, join(0, a1, a2));
+    array gold   = join(0, a0, join(0, a1, a2));
 
     ASSERT_EQ(sum<float>(output - gold), 0);
 }
 
-TEST(JoinMany1, CPP)
-{
+TEST(JoinMany1, CPP) {
     if (noDoubleTests<float>()) return;
 
     array a0 = randu(20, 200);
@@ -191,8 +199,8 @@ TEST(JoinMany1, CPP)
     array a2 = randu(20, 10);
     array a3 = randu(20, 100);
 
-    int dim = 1;
+    int dim      = 1;
     array output = join(dim, a0, a1, a2, a3);
-    array gold = join(dim, a0, join(dim, a1, join(dim, a2, a3)));
+    array gold   = join(dim, a0, join(dim, a1, join(dim, a2, a3)));
     ASSERT_EQ(sum<float>(output - gold), 0);
 }
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index f952cc5998..0346a5120a 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -11,33 +11,32 @@
 // backends for sizes larger than 128x128 or more. You can read more about it on
 // issue https://github.com/arrayfire/arrayfire/issues/1617
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::count;
 using af::dim4;
 using af::dtype_traits;
 using af::max;
 using af::seq;
 using af::span;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
-TEST(LU, InPlaceSmall)
-{
+TEST(LU, InPlaceSmall) {
     if (noDoubleTests<float>()) return;
     if (noLAPACKTests()) return;
 
@@ -46,7 +45,8 @@ TEST(LU, InPlaceSmall)
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, float>(string(TEST_DIR"/lapack/lu.test"),numDims,in,tests);
+    readTests<float, float, float>(string(TEST_DIR "/lapack/lu.test"), numDims,
+                                   in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -63,9 +63,10 @@ TEST(LU, InPlaceSmall)
     for (int y = 0; y < (int)odims[1]; ++y) {
         for (int x = 0; x < (int)odims[0]; ++x) {
             // Check only upper triangle
-            if(x <= y) {
-            int elIter = y * odims[0] + x;
-            ASSERT_NEAR(tests[resultIdx][elIter], outData[elIter], 0.001) << "at: " << elIter << endl;
+            if (x <= y) {
+                int elIter = y * odims[0] + x;
+                ASSERT_NEAR(tests[resultIdx][elIter], outData[elIter], 0.001)
+                    << "at: " << elIter << endl;
             }
         }
     }
@@ -74,8 +75,7 @@ TEST(LU, InPlaceSmall)
     delete[] outData;
 }
 
-TEST(LU, SplitSmall)
-{
+TEST(LU, SplitSmall) {
     if (noDoubleTests<float>()) return;
     if (noLAPACKTests()) return;
 
@@ -84,7 +84,8 @@ TEST(LU, SplitSmall)
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, float>(string(TEST_DIR"/lapack/lufactorized.test"),numDims,in,tests);
+    readTests<float, float, float>(string(TEST_DIR "/lapack/lufactorized.test"),
+                                   numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -103,9 +104,10 @@ TEST(LU, SplitSmall)
     // Compare result
     for (int y = 0; y < (int)ldims[1]; ++y) {
         for (int x = 0; x < (int)ldims[0]; ++x) {
-            if(x < y) {
+            if (x < y) {
                 int elIter = y * ldims[0] + x;
-                ASSERT_NEAR(tests[resultIdx][elIter], lData[elIter], 0.001) << "at: " << elIter << endl;
+                ASSERT_NEAR(tests[resultIdx][elIter], lData[elIter], 0.001)
+                    << "at: " << elIter << endl;
             }
         }
     }
@@ -115,7 +117,8 @@ TEST(LU, SplitSmall)
     for (int y = 0; y < (int)udims[1]; ++y) {
         for (int x = 0; x < (int)udims[0]; ++x) {
             int elIter = y * (int)udims[0] + x;
-            ASSERT_NEAR(tests[resultIdx][elIter], uData[elIter], 0.001) << "at: " << elIter << endl;
+            ASSERT_NEAR(tests[resultIdx][elIter], uData[elIter], 0.001)
+                << "at: " << elIter << endl;
         }
     }
 
@@ -125,8 +128,7 @@ TEST(LU, SplitSmall)
 }
 
 template<typename T>
-void luTester(const int m, const int n, double eps)
-{
+void luTester(const int m, const int n, double eps) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -136,7 +138,6 @@ void luTester(const int m, const int n, double eps)
     array a_orig = randu(m, n, (dtype)dtype_traits<T>::af_type);
 #endif
 
-
     //! [ex_lu_unpacked]
     array l, u, pivot;
     lu(l, u, pivot, a_orig);
@@ -144,11 +145,17 @@ void luTester(const int m, const int n, double eps)
 
     //! [ex_lu_recon]
     array a_recon = matmul(l, u);
-    array a_perm = a_orig(pivot, span);
+    array a_perm  = a_orig(pivot, span);
     //! [ex_lu_recon]
 
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(a_recon - a_perm))), eps);
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(a_recon - a_perm))), eps);
+    ASSERT_NEAR(
+        0,
+        max<typename dtype_traits<T>::base_type>(abs(real(a_recon - a_perm))),
+        eps);
+    ASSERT_NEAR(
+        0,
+        max<typename dtype_traits<T>::base_type>(abs(imag(a_recon - a_perm))),
+        eps);
 
     //! [ex_lu_packed]
     array out = a_orig.copy();
@@ -157,22 +164,27 @@ void luTester(const int m, const int n, double eps)
     //! [ex_lu_packed]
 
     //! [ex_lu_extract]
-    array l2 = lower(out,  true);
+    array l2 = lower(out, true);
     array u2 = upper(out, false);
     //! [ex_lu_extract]
 
     ASSERT_EQ(count<uint>(pivot == pivot2), pivot.elements());
 
     int mn = std::min(m, n);
-    l2 = l2(span, seq(mn));
-    u2 = u2(seq(mn), span);
+    l2     = l2(span, seq(mn));
+    u2     = u2(seq(mn), span);
 
     array a_recon2 = matmul(l2, u2);
-    array a_perm2 = a_orig(pivot2, span);
-
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(real(a_recon2 - a_perm2))), eps);
-    ASSERT_NEAR(0, max<typename dtype_traits<T>::base_type>(abs(imag(a_recon2 - a_perm2))), eps);
-
+    array a_perm2  = a_orig(pivot2, span);
+
+    ASSERT_NEAR(
+        0,
+        max<typename dtype_traits<T>::base_type>(abs(real(a_recon2 - a_perm2))),
+        eps);
+    ASSERT_NEAR(
+        0,
+        max<typename dtype_traits<T>::base_type>(abs(imag(a_recon2 - a_perm2))),
+        eps);
 }
 
 template<typename T>
@@ -199,17 +211,12 @@ double eps<cdouble>() {
 }
 
 template<typename T>
-class LU : public ::testing::Test
-{
-
-};
+class LU : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(LU, TestTypes);
 
-TYPED_TEST(LU, SquareLarge) {
-    luTester<TypeParam>(500, 500, eps<TypeParam>());
-}
+TYPED_TEST(LU, SquareLarge) { luTester<TypeParam>(500, 500, eps<TypeParam>()); }
 
 TYPED_TEST(LU, SquareMultipleOfTwoLarge) {
     luTester<TypeParam>(512, 512, eps<TypeParam>());
diff --git a/test/manual_memory_test.cpp b/test/manual_memory_test.cpp
index c40ebc9c0d..408f3af19d 100644
--- a/test/manual_memory_test.cpp
+++ b/test/manual_memory_test.cpp
@@ -7,16 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <iostream>
-#include <testHelpers.hpp>
 
-TEST(Memory, recover)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Memory, recover) {
+    cleanSlate();  // Clean up everything done so far
 
     try {
         array vec[100];
@@ -24,23 +23,19 @@ TEST(Memory, recover)
         // Trying to allocate 1 Terrabyte of memory and trash the memory manager
         // should crash memory manager
         for (int i = 0; i < 1000; i++) {
-            vec[i] = randu(1024, 1024, 256); //Allocating 1GB
+            vec[i] = randu(1024, 1024, 256);  // Allocating 1GB
         }
 
-        ASSERT_EQ(true, false); //Is there a simple assert statement?
+        ASSERT_EQ(true, false);  // Is there a simple assert statement?
     } catch (exception &ae) {
-
         ASSERT_EQ(ae.err(), AF_ERR_NO_MEM);
 
-        const int num = 1000 * 1000;
+        const int num   = 1000 * 1000;
         const float val = 1.0;
 
-        array a = constant(val, num); // This should work as expected
+        array a    = constant(val, num);  // This should work as expected
         float *h_a = a.host<float>();
-        for (int i = 0; i < 1000 * 1000; i++) {
-            ASSERT_EQ(h_a[i], val);
-        }
+        for (int i = 0; i < 1000 * 1000; i++) { ASSERT_EQ(h_a[i], val); }
         freeHost(h_a);
     }
-
 }
diff --git a/test/match_template.cpp b/test/match_template.cpp
index f9174b4a26..2d64d2b934 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -7,70 +7,74 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::cout;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::dim4;
 using af::dtype_traits;
 using af::exception;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class MatchTemplate : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class MatchTemplate : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(MatchTemplate, TestTypes);
 
 template<typename T>
-void matchTemplateTest(string pTestFile, af_match_type pMatchType)
-{
-    typedef typename cond_type<is_same_type<T, double>::value, double, float>::type outType;
+void matchTemplateTest(string pTestFile, af_match_type pMatchType) {
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            outType;
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<T> >      in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<outType> > tests;
 
     readTests<T, outType, float>(pTestFile, numDims, in, tests);
 
-    dim4 sDims    = numDims[0];
-    dim4 tDims    = numDims[1];
+    dim4 sDims        = numDims[0];
+    dim4 tDims        = numDims[1];
     af_array outArray = 0;
     af_array sArray   = 0;
     af_array tArray   = 0;
 
-    ASSERT_SUCCESS(af_create_array(&sArray, &(in[0].front()),
-                sDims.ndims(), sDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&sArray, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&tArray, &(in[1].front()),
-                tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&tArray, &(in[1].front()), tDims.ndims(),
+                                   tDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_match_template(&outArray, sArray, tArray, pMatchType));
 
     vector<outType> outData(sDims.elements());
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
 
     vector<outType> currGoldBar = tests[0];
-    size_t nElems        = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
+    size_t nElems               = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)
+            << "at: " << elIter << endl;
     }
 
     // cleanup
@@ -79,39 +83,41 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(MatchTemplate, Matrix_SAD)
-{
-    matchTemplateTest<TypeParam>(string(TEST_DIR"/MatchTemplate/matrix_sad.test"), AF_SAD);
+TYPED_TEST(MatchTemplate, Matrix_SAD) {
+    matchTemplateTest<TypeParam>(
+        string(TEST_DIR "/MatchTemplate/matrix_sad.test"), AF_SAD);
 }
 
-TYPED_TEST(MatchTemplate, Matrix_SSD)
-{
-    matchTemplateTest<TypeParam>(string(TEST_DIR"/MatchTemplate/matrix_ssd.test"), AF_SSD);
+TYPED_TEST(MatchTemplate, Matrix_SSD) {
+    matchTemplateTest<TypeParam>(
+        string(TEST_DIR "/MatchTemplate/matrix_ssd.test"), AF_SSD);
 }
 
-TYPED_TEST(MatchTemplate, MatrixBatch_SAD)
-{
-    matchTemplateTest<TypeParam>(string(TEST_DIR"/MatchTemplate/matrix_sad_batch.test"), AF_SAD);
+TYPED_TEST(MatchTemplate, MatrixBatch_SAD) {
+    matchTemplateTest<TypeParam>(
+        string(TEST_DIR "/MatchTemplate/matrix_sad_batch.test"), AF_SAD);
 }
 
-TEST(MatchTemplate, InvalidMatchType)
-{
-    af_array inArray   = 0;
+TEST(MatchTemplate, InvalidMatchType) {
+    af_array inArray  = 0;
     af_array tArray   = 0;
-    af_array outArray  = 0;
+    af_array outArray = 0;
 
-    vector<float>   in(100, 1);
+    vector<float> in(100, 1);
 
     dim4 sDims(10, 10, 1, 1);
     dim4 tDims(4, 4, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                sDims.ndims(), sDims.get(), (af_dtype) dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&tArray, &in.front(),
-                tDims.ndims(), tDims.get(), (af_dtype) dtype_traits<float>::af_type));
+    ASSERT_SUCCESS(af_create_array(&tArray, &in.front(), tDims.ndims(),
+                                   tDims.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_match_template(&outArray, inArray, tArray, (af_match_type)-1));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_match_template(&outArray, inArray, tArray, (af_match_type)-1));
 
     ASSERT_SUCCESS(af_release_array(inArray));
     ASSERT_SUCCESS(af_release_array(tArray));
@@ -119,9 +125,8 @@ TEST(MatchTemplate, InvalidMatchType)
 
 ///////////////////////////////// CPP TESTS /////////////////////////////
 //
-TEST(MatchTemplate, CPP)
-{
-    vector<float>   in(100, 1);
+TEST(MatchTemplate, CPP) {
+    vector<float> in(100, 1);
 
     dim4 sDims(10, 10, 1, 1);
     dim4 tDims(4, 4, 1, 1);
@@ -131,7 +136,7 @@ TEST(MatchTemplate, CPP)
         array tmplt(tDims, &in.front());
 
         array out = matchTemplate(input, tmplt, (af_match_type)-1);
-    } catch(exception &e) {
+    } catch (exception &e) {
         cout << "Invalid Match test: " << e.what() << endl;
     }
 }
diff --git a/test/math.cpp b/test/math.cpp
index 3b4d4feee3..fc09c298eb 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -6,108 +6,103 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <complex>
 #include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
+#include <complex>
 
 // This makes the macros cleaner
-using std::abs;
-using std::endl;
-using std::vector;
 using af::array;
 using af::dtype_traits;
 using af::exception;
 using af::randu;
+using std::abs;
+using std::endl;
+using std::vector;
 
-const int num = 10000;
-const float flt_err = 1e-3;
+const int num        = 10000;
+const float flt_err  = 1e-3;
 const double dbl_err = 1e-10;
 
 typedef std::complex<float> complex_float;
 typedef std::complex<double> complex_double;
 
 template<typename T>
-T sigmoid(T in)
-{
+T sigmoid(T in) {
     return 1.0 / (1.0 + std::exp(-in));
 }
 
-#define TEST_REAL(T, func, err, lo, hi)                             \
-    TEST(MathTests, Test_##func##_##T)                              \
-    {                                                               \
-        try {                                                       \
-            if (noDoubleTests<T>()) return;                         \
-            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;       \
-            array a = (hi - lo) * randu(num, ty) + lo + err;        \
-            eval(a);                                                \
-            array b = func(a);                                      \
-            vector<T> h_a(a.elements());                            \
-            vector<T> h_b(b.elements());                            \
-            a.host(&h_a[0]);                                        \
-            b.host(&h_b[0]);                                        \
-                                                                    \
-            for (int i = 0; i < num; i++) {                         \
-                ASSERT_NEAR(h_b[i], func(h_a[i]), err) <<           \
-                    "for value: " << h_a[i] << endl;                \
-            }                                                       \
-        } catch (exception &ex) {                                   \
-            FAIL() << ex.what();                                    \
-        }                                                           \
-    }                                                               \
-
-#define TEST_CPLX(T, func, err, lo, hi)                             \
-    TEST(MathTests, Test_##func##_##T)                              \
-    {                                                               \
-        try {                                                       \
-            if (noDoubleTests<T>()) return;                         \
-            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;       \
-            array a = (hi - lo) * randu(num, ty) + lo + err;        \
-            eval(a);                                                \
-            array b = func(a);                                      \
-            vector<T> h_a(a.elements());                            \
-            vector<T> h_b(b.elements());                            \
-            a.host(&h_a[0]);                                        \
-            b.host(&h_b[0]);                                        \
-                                                                    \
-            for (int i = 0; i < num; i++) {                         \
-                T res = func(h_a[i]);                               \
-                ASSERT_NEAR(real(h_b[i]), real(res), err) <<        \
-                    "for real value: " << h_a[i] << endl;           \
-                ASSERT_NEAR(imag(h_b[i]), imag(res), err) <<        \
-                    "for imag value: " << h_a[i] << endl;           \
-            }                                                       \
-        } catch (exception &ex) {                                   \
-            FAIL() << ex.what();                                    \
-        }                                                           \
-    }                                                               \
+#define TEST_REAL(T, func, err, lo, hi)                          \
+    TEST(MathTests, Test_##func##_##T) {                         \
+        try {                                                    \
+            if (noDoubleTests<T>()) return;                      \
+            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;    \
+            array a     = (hi - lo) * randu(num, ty) + lo + err; \
+            eval(a);                                             \
+            array b = func(a);                                   \
+            vector<T> h_a(a.elements());                         \
+            vector<T> h_b(b.elements());                         \
+            a.host(&h_a[0]);                                     \
+            b.host(&h_b[0]);                                     \
+                                                                 \
+            for (int i = 0; i < num; i++) {                      \
+                ASSERT_NEAR(h_b[i], func(h_a[i]), err)           \
+                    << "for value: " << h_a[i] << endl;          \
+            }                                                    \
+        } catch (exception & ex) { FAIL() << ex.what(); }        \
+    }
+
+#define TEST_CPLX(T, func, err, lo, hi)                          \
+    TEST(MathTests, Test_##func##_##T) {                         \
+        try {                                                    \
+            if (noDoubleTests<T>()) return;                      \
+            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;    \
+            array a     = (hi - lo) * randu(num, ty) + lo + err; \
+            eval(a);                                             \
+            array b = func(a);                                   \
+            vector<T> h_a(a.elements());                         \
+            vector<T> h_b(b.elements());                         \
+            a.host(&h_a[0]);                                     \
+            b.host(&h_b[0]);                                     \
+                                                                 \
+            for (int i = 0; i < num; i++) {                      \
+                T res = func(h_a[i]);                            \
+                ASSERT_NEAR(real(h_b[i]), real(res), err)        \
+                    << "for real value: " << h_a[i] << endl;     \
+                ASSERT_NEAR(imag(h_b[i]), imag(res), err)        \
+                    << "for imag value: " << h_a[i] << endl;     \
+            }                                                    \
+        } catch (exception & ex) { FAIL() << ex.what(); }        \
+    }
 
 #define MATH_TESTS_FLOAT(func) TEST_REAL(float, func, flt_err, 0.05f, 0.95f)
 #define MATH_TESTS_DOUBLE(func) TEST_REAL(double, func, dbl_err, 0.05, 0.95)
 
-#define MATH_TESTS_CFLOAT(func) TEST_CPLX(complex_float, func, flt_err, 0.05f, 0.95f)
-#define MATH_TESTS_CDOUBLE(func) TEST_CPLX(complex_double, func, dbl_err, 0.05, 0.95)
+#define MATH_TESTS_CFLOAT(func) \
+    TEST_CPLX(complex_float, func, flt_err, 0.05f, 0.95f)
+#define MATH_TESTS_CDOUBLE(func) \
+    TEST_CPLX(complex_double, func, dbl_err, 0.05, 0.95)
 
-#define MATH_TESTS_REAL(func)                   \
-    MATH_TESTS_FLOAT(func)                      \
-    MATH_TESTS_DOUBLE(func)                     \
+#define MATH_TESTS_REAL(func) \
+    MATH_TESTS_FLOAT(func)    \
+    MATH_TESTS_DOUBLE(func)
 
-#define MATH_TESTS_CPLX(func)                   \
-    MATH_TESTS_CFLOAT(func)                     \
-    MATH_TESTS_CDOUBLE(func)                    \
+#define MATH_TESTS_CPLX(func) \
+    MATH_TESTS_CFLOAT(func)   \
+    MATH_TESTS_CDOUBLE(func)
 
-#define MATH_TESTS_ALL(func)                    \
-    MATH_TESTS_REAL(func)                       \
-    MATH_TESTS_CPLX(func)                       \
+#define MATH_TESTS_ALL(func) \
+    MATH_TESTS_REAL(func)    \
+    MATH_TESTS_CPLX(func)
 
-#define MATH_TESTS_LIMITS_REAL(func, lo, hi)    \
-    TEST_REAL(float, func, flt_err, lo, hi)     \
-    TEST_REAL(double, func, dbl_err, lo, hi)    \
+#define MATH_TESTS_LIMITS_REAL(func, lo, hi) \
+    TEST_REAL(float, func, flt_err, lo, hi)  \
+    TEST_REAL(double, func, dbl_err, lo, hi)
 
-#define MATH_TESTS_LIMITS_CPLX(func, lo, hi)            \
-    TEST_CPLX(complex_float, func, flt_err, lo, hi)     \
-    TEST_CPLX(complex_double, func, dbl_err, lo, hi)    \
+#define MATH_TESTS_LIMITS_CPLX(func, lo, hi)        \
+    TEST_CPLX(complex_float, func, flt_err, lo, hi) \
+    TEST_CPLX(complex_double, func, dbl_err, lo, hi)
 
 MATH_TESTS_ALL(sin)
 MATH_TESTS_ALL(cos)
@@ -151,16 +146,13 @@ MATH_TESTS_REAL(erf)
 MATH_TESTS_REAL(erfc)
 #endif
 
-TEST(MathTests, Not)
-{
-    array a = randu(5, 5, b8);
-    array b = !a;
+TEST(MathTests, Not) {
+    array a  = randu(5, 5, b8);
+    array b  = !a;
     char *ha = a.host<char>();
     char *hb = b.host<char>();
 
-    for(int i = 0; i < a.elements(); i++) {
-        ASSERT_EQ(ha[i] ^ hb[i], true);
-    }
+    for (int i = 0; i < a.elements(); i++) { ASSERT_EQ(ha[i] ^ hb[i], true); }
 
     af_free_host(ha);
     af_free_host(hb);
diff --git a/test/matrix_manipulation.cpp b/test/matrix_manipulation.cpp
index ff3d57c0aa..d9c6d554bb 100644
--- a/test/matrix_manipulation.cpp
+++ b/test/matrix_manipulation.cpp
@@ -8,23 +8,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
 #include <vector>
 
-using std::vector;
 using af::array;
 using af::join;
 using af::randu;
 using af::tile;
+using std::vector;
 
-TEST(MatrixManipulation, SNIPPET_matrix_manipulation_tile)
-{
+TEST(MatrixManipulation, SNIPPET_matrix_manipulation_tile) {
     //! [ex_matrix_manipulation_tile]
-    float h[] = {1, 2, 3, 4};
-    array small_arr = array(2, 2, h); // 2x2 matrix
+    float h[]       = {1, 2, 3, 4};
+    array small_arr = array(2, 2, h);  // 2x2 matrix
     af_print(small_arr);
-    array large_arr = tile(small_arr, 2, 3);  // produces 4x6 matrix: (2*2)x(2*3)
+    array large_arr =
+        tile(small_arr, 2, 3);  // produces 4x6 matrix: (2*2)x(2*3)
     af_print(large_arr);
     //! [ex_matrix_manipulation_tile]
 
@@ -36,23 +36,22 @@ TEST(MatrixManipulation, SNIPPET_matrix_manipulation_tile)
 
     unsigned fdim = large_arr.dims(0);
     unsigned sdim = large_arr.dims(1);
-    for(unsigned i = 0; i < sdim; i++) {
-        for(unsigned j = 0; j < fdim; j++) {
-            ASSERT_FLOAT_EQ(h[(i%2) * 2 + (j%2)], h_large_arr[i * fdim + j] );
+    for (unsigned i = 0; i < sdim; i++) {
+        for (unsigned j = 0; j < fdim; j++) {
+            ASSERT_FLOAT_EQ(h[(i % 2) * 2 + (j % 2)],
+                            h_large_arr[i * fdim + j]);
         }
     }
 }
 
-TEST(MatrixManipulation, SNIPPET_matrix_manipulation_join)
-{
-
+TEST(MatrixManipulation, SNIPPET_matrix_manipulation_join) {
     //! [ex_matrix_manipulation_join]
-    float hA[] = { 1, 2, 3, 4, 5, 6 };
-    float hB[] = { 10, 20, 30, 40, 50, 60, 70, 80, 90 };
-    array A = array(3, 2, hA);
-    array B = array(3, 3, hB);
+    float hA[] = {1, 2, 3, 4, 5, 6};
+    float hB[] = {10, 20, 30, 40, 50, 60, 70, 80, 90};
+    array A    = array(3, 2, hA);
+    array B    = array(3, 3, hB);
 
-    af_print(join(1, A, B)); // 3x5 matrix
+    af_print(join(1, A, B));  // 3x5 matrix
     // array result = join(0, A, B); // fail: dimension mismatch
     //! [ex_matrix_manipulation_join]
 
@@ -66,21 +65,20 @@ TEST(MatrixManipulation, SNIPPET_matrix_manipulation_join)
 
     unsigned fdim = out.dims(0);
     unsigned sdim = out.dims(1);
-    for(unsigned i = 0; i < sdim; i++) {
-        for(unsigned j = 0; j < fdim; j++) {
-            if( i < 2 ) {
-                ASSERT_FLOAT_EQ(hA[i * fdim + j], h_out[i * fdim + j]) << "At [" << i << ", " << j << "]";
-            }
-            else {
-                ASSERT_FLOAT_EQ(hB[(i - 2) * fdim + j], h_out[i * fdim + j]) << "At [" << i << ", " << j << "]";
+    for (unsigned i = 0; i < sdim; i++) {
+        for (unsigned j = 0; j < fdim; j++) {
+            if (i < 2) {
+                ASSERT_FLOAT_EQ(hA[i * fdim + j], h_out[i * fdim + j])
+                    << "At [" << i << ", " << j << "]";
+            } else {
+                ASSERT_FLOAT_EQ(hB[(i - 2) * fdim + j], h_out[i * fdim + j])
+                    << "At [" << i << ", " << j << "]";
             }
         }
     }
-
 }
 
-TEST(MatrixManipulation, SNIPPET_matrix_manipulation_mesh)
-{
+TEST(MatrixManipulation, SNIPPET_matrix_manipulation_mesh) {
     //! [ex_matrix_manipulation_mesh]
     float hx[] = {1, 2, 3, 4};
     float hy[] = {5, 6};
@@ -105,27 +103,27 @@ TEST(MatrixManipulation, SNIPPET_matrix_manipulation_mesh)
     vector<float> houty(outy.elements());
     outy.host(&houty.front());
 
-    for(unsigned i = 0; i < houtx.size(); i++) ASSERT_EQ(hx[i%4], houtx[i]) << "At [" << i << "]";
-    for(unsigned i = 0; i < houty.size(); i++) ASSERT_EQ(hy[i>3], houty[i]) << "At [" << i << "]";
+    for (unsigned i = 0; i < houtx.size(); i++)
+        ASSERT_EQ(hx[i % 4], houtx[i]) << "At [" << i << "]";
+    for (unsigned i = 0; i < houty.size(); i++)
+        ASSERT_EQ(hy[i > 3], houty[i]) << "At [" << i << "]";
 }
 
-TEST(MatrixManipulation, SNIPPET_matrix_manipulation_moddims)
-{
+TEST(MatrixManipulation, SNIPPET_matrix_manipulation_moddims) {
     //! [ex_matrix_manipulation_moddims]
     int hA[] = {1, 2, 3, 4, 5, 6};
-    array A = array(3, 2, hA);
+    array A  = array(3, 2, hA);
 
-    af_print(A); // 2x3 matrix
-    af_print(moddims(A, 2, 3)); // 2x3 matrix
-    af_print(moddims(A, 6, 1)); // 6x1 column vector
+    af_print(A);                 // 2x3 matrix
+    af_print(moddims(A, 2, 3));  // 2x3 matrix
+    af_print(moddims(A, 6, 1));  // 6x1 column vector
 
     // moddims(A, 2, 2); // fail: wrong number of elements
     // moddims(A, 8, 8); // fail: wrong number of elements
     //! [ex_matrix_manipulation_moddims]
 }
 
-TEST(MatrixManipulation, SNIPPET_matrix_manipulation_transpose)
-{
+TEST(MatrixManipulation, SNIPPET_matrix_manipulation_transpose) {
     //! [ex_matrix_manipulation_transpose]
     array x = randu(2, 2, f32);
     af_print(x.T());  // transpose (real)
diff --git a/test/matrixmarket.cpp b/test/matrixmarket.cpp
index 6ae6a8acef..71b8d5c86c 100644
--- a/test/matrixmarket.cpp
+++ b/test/matrixmarket.cpp
@@ -10,22 +10,19 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 
-TEST(Sparse, ReadRealMTXFile)
-{
+TEST(Sparse, ReadRealMTXFile) {
     af::array out;
     std::string file(TEST_DIR "/matrixmarket/HB/bcsstm02/bcsstm02.mtx");
     ASSERT_TRUE(mtxReadSparseMatrix(out, file.c_str()));
 }
 
-TEST(Sparse, ReadComplexMTXFile)
-{
+TEST(Sparse, ReadComplexMTXFile) {
     af::array out;
     std::string file(TEST_DIR "/matrixmarket/HB/young4c/young4c.mtx");
     ASSERT_TRUE(mtxReadSparseMatrix(out, file.c_str()));
 }
 
-TEST(Sparse, FailIntegerMTXRead)
-{
+TEST(Sparse, FailIntegerMTXRead) {
     af::array out;
     std::string file(TEST_DIR "/matrixmarket/JGD_Kocay/Trec4/Trec4.mtx");
     ASSERT_FALSE(mtxReadSparseMatrix(out, file.c_str()));
diff --git a/test/mean.cpp b/test/mean.cpp
index 2621f036f0..b73c69a1bb 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -7,87 +7,82 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <string>
-#include <vector>
+#include <algorithm>
 #include <ctime>
 #include <iostream>
-#include <algorithm>
-#include <testHelpers.hpp>
+#include <string>
+#include <vector>
 
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
 using af::constant;
 using af::dim4;
 using af::randu;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Mean : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Mean : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, intl, uintl, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, intl, uintl,
+                         char, uchar, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Mean, TestTypes);
 
 template<typename T>
 struct f32HelperType {
-   typedef typename cond_type<is_same_type<T, double>::value,
-                                             double,
-                                             float>::type type;
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            type;
 };
 
 template<typename T>
 struct c32HelperType {
-   typedef typename cond_type<is_same_type<T, cfloat>::value,
-                                             cfloat,
-                                             typename f32HelperType<T>::type >::type type;
+    typedef typename cond_type<is_same_type<T, cfloat>::value, cfloat,
+                               typename f32HelperType<T>::type>::type type;
 };
 
 template<typename T>
 struct elseType {
-   typedef typename cond_type< is_same_type<T, uintl>::value ||
-                               is_same_type<T, intl> ::value,
-                                              double,
-                                              T>::type type;
+    typedef typename cond_type<is_same_type<T, uintl>::value ||
+                                   is_same_type<T, intl>::value,
+                               double, T>::type type;
 };
 
 template<typename T>
 struct meanOutType {
-   typedef typename cond_type< is_same_type<T, float>   ::value ||
-                               is_same_type<T, int>     ::value ||
-                               is_same_type<T, uint>    ::value ||
-                               is_same_type<T, uchar>   ::value ||
-                               is_same_type<T, short>   ::value ||
-                               is_same_type<T, ushort>  ::value ||
-                               is_same_type<T, char>    ::value,
-                                              float,
-                              typename elseType<T>::type>::type type;
+    typedef typename cond_type<
+        is_same_type<T, float>::value || is_same_type<T, int>::value ||
+            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
+            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
 template<typename T>
-void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
-{
+void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
     typedef typename meanOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<int> >        in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<int> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(pFileName, numDims, in, tests);
+    readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
     if (!isWeighted) {
         dim4 dims = numDims[0];
@@ -103,9 +98,13 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
-            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
+                        1.0e-3)
+                << "at: " << elIter << endl;
+            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
+                        1.0e-3)
+                << "at: " << elIter << endl;
         }
     } else {
         dim4 dims  = numDims[0];
@@ -124,56 +123,55 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted=false)
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
-            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
+                        1.0e-3)
+                << "at: " << elIter << endl;
+            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
+                        1.0e-3)
+                << "at: " << elIter << endl;
         }
     }
 }
 
-TYPED_TEST(Mean, Dim0Matrix)
-{
+TYPED_TEST(Mean, Dim0Matrix) {
     meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim0_matrix.test"), 0);
 }
 
-TYPED_TEST(Mean, Dim1Cube)
-{
+TYPED_TEST(Mean, Dim1Cube) {
     meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim1_cube.test"), 1);
 }
 
-TYPED_TEST(Mean, Dim0HyperCube)
-{
-    meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim0_hypercube.test"), 0);
+TYPED_TEST(Mean, Dim0HyperCube) {
+    meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim0_hypercube.test"),
+                           0);
 }
 
-TYPED_TEST(Mean, Dim2Matrix)
-{
+TYPED_TEST(Mean, Dim2Matrix) {
     meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim2_matrix.test"), 2);
 }
 
-TYPED_TEST(Mean, Dim2Cube)
-{
+TYPED_TEST(Mean, Dim2Cube) {
     meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim2_cube.test"), 2);
 }
 
-TYPED_TEST(Mean, Dim2HyperCube)
-{
-    meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim2_hypercube.test"), 2);
+TYPED_TEST(Mean, Dim2HyperCube) {
+    meanDimTest<TypeParam>(string(TEST_DIR "/mean/mean_dim2_hypercube.test"),
+                           2);
 }
 
-TYPED_TEST(Mean, Wtd_Dim0Matrix)
-{
-    meanDimTest<TypeParam>(string(TEST_DIR "/mean/wtd_mean_dim0_mat.test"), 0, true);
+TYPED_TEST(Mean, Wtd_Dim0Matrix) {
+    meanDimTest<TypeParam>(string(TEST_DIR "/mean/wtd_mean_dim0_mat.test"), 0,
+                           true);
 }
 
-TYPED_TEST(Mean, Wtd_Dim1Matrix)
-{
-    meanDimTest<TypeParam>(string(TEST_DIR "/mean/wtd_mean_dim1_mat.test"), 1, true);
+TYPED_TEST(Mean, Wtd_Dim1Matrix) {
+    meanDimTest<TypeParam>(string(TEST_DIR "/mean/wtd_mean_dim1_mat.test"), 1,
+                           true);
 }
 
 template<typename T>
-void meanAllTest(T const_value, dim4 dims)
-{
+void meanAllTest(T const_value, dim4 dims) {
     typedef typename meanOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
@@ -184,10 +182,8 @@ void meanAllTest(T const_value, dim4 dims)
     vector<T> hundred(dims.elements(), const_value);
 
     outType gold = outType(0);
-    //for(auto i:hundred) gold += i;
-    for(int i = 0; i < (int)hundred.size(); i++) {
-        gold = gold + hundred[i];
-    }
+    // for(auto i:hundred) gold += i;
+    for (int i = 0; i < (int)hundred.size(); i++) { gold = gold + hundred[i]; }
     gold = gold / dims.elements();
 
     array a(dims, &(hundred.front()));
@@ -197,77 +193,52 @@ void meanAllTest(T const_value, dim4 dims)
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 }
 
-TEST(MeanAll, f64)
-{
-    meanAllTest<double>(2.1, dim4(10, 10, 1, 1));
-}
+TEST(MeanAll, f64) { meanAllTest<double>(2.1, dim4(10, 10, 1, 1)); }
 
-TEST(MeanAll, f32)
-{
-    meanAllTest<float>(2.1f, dim4(10, 5, 2, 1));
-}
+TEST(MeanAll, f32) { meanAllTest<float>(2.1f, dim4(10, 5, 2, 1)); }
 
-TEST(MeanAll, s32)
-{
-    meanAllTest<int>(2, dim4(5, 5, 2, 2));
-}
+TEST(MeanAll, s32) { meanAllTest<int>(2, dim4(5, 5, 2, 2)); }
 
-TEST(MeanAll, u32)
-{
-    meanAllTest<unsigned>(2, dim4(100, 1, 1, 1));
-}
+TEST(MeanAll, u32) { meanAllTest<unsigned>(2, dim4(100, 1, 1, 1)); }
 
-TEST(MeanAll, s8)
-{
-    meanAllTest<char>(2, dim4(5, 5, 2, 2));
-}
+TEST(MeanAll, s8) { meanAllTest<char>(2, dim4(5, 5, 2, 2)); }
 
-TEST(MeanAll, u8)
-{
-    meanAllTest<uchar>(2, dim4(100, 1, 1, 1));
-}
+TEST(MeanAll, u8) { meanAllTest<uchar>(2, dim4(100, 1, 1, 1)); }
 
-TEST(MeanAll, c32)
-{
-    meanAllTest<cfloat>(cfloat(2.1f), dim4(10, 5, 2, 1));
-}
+TEST(MeanAll, c32) { meanAllTest<cfloat>(cfloat(2.1f), dim4(10, 5, 2, 1)); }
 
-TEST(MeanAll, s16)
-{
-    meanAllTest<short>(2, dim4(5, 5, 2, 2));
-}
-
-TEST(MeanAll, u16)
-{
-    meanAllTest<ushort>(2, dim4(100, 1, 1, 1));
-}
+TEST(MeanAll, s16) { meanAllTest<short>(2, dim4(5, 5, 2, 2)); }
 
-TEST(MeanAll, c64)
-{
-    meanAllTest<cdouble>(cdouble(2.1), dim4(10, 10, 1, 1));
-}
+TEST(MeanAll, u16) { meanAllTest<ushort>(2, dim4(100, 1, 1, 1)); }
 
+TEST(MeanAll, c64) { meanAllTest<cdouble>(cdouble(2.1), dim4(10, 10, 1, 1)); }
 
 template<typename T>
-T random() { return T(std::rand()%10); }
+T random() {
+    return T(std::rand() % 10);
+}
 
-template<> cfloat random<cfloat>() { return cfloat(float(std::rand()%10), float(std::rand()%10)); }
+template<>
+cfloat random<cfloat>() {
+    return cfloat(float(std::rand() % 10), float(std::rand() % 10));
+}
 
-template<> cdouble random<cdouble>() { return cdouble(double(std::rand()%10), double(std::rand()%10)); }
+template<>
+cdouble random<cdouble>() {
+    return cdouble(double(std::rand() % 10), double(std::rand() % 10));
+}
 
 template<typename T>
-class WeightedMean : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class WeightedMean : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // register the type list
 TYPED_TEST_CASE(WeightedMean, TestTypes);
 
 template<typename T, typename wtsType>
-void weightedMeanAllTest(dim4 dims)
-{
+void weightedMeanAllTest(dim4 dims) {
     typedef typename meanOutType<T>::type outType;
 
     if (noDoubleTests<T>()) return;
@@ -287,8 +258,8 @@ void weightedMeanAllTest(dim4 dims)
     outType wtdSum = outType(0);
     wtsType wtsSum = wtsType(0);
 
-    for(int i = 0; i < (int)data.size(); i++) {
-        wtdSum = wtdSum + data[i]*wts[i];
+    for (int i = 0; i < (int)data.size(); i++) {
+        wtdSum = wtdSum + data[i] * wts[i];
         wtsSum = wtsSum + wts[i];
     }
 
@@ -302,18 +273,16 @@ void weightedMeanAllTest(dim4 dims)
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-2);
 }
 
-TYPED_TEST(WeightedMean, Basic)
-{
+TYPED_TEST(WeightedMean, Basic) {
     weightedMeanAllTest<TypeParam, float>(dim4(32, 30, 33, 17));
 }
 
-TEST(WeightedMean, Broadacst)
-{
+TEST(WeightedMean, Broadacst) {
     float val = 0.5f;
-    array a = randu(4096, 32);
-    array w = constant(val, a.dims());
-    array c = mean(a);
-    array d = mean(a, w);
+    array a   = randu(4096, 32);
+    array w   = constant(val, a.dims());
+    array c   = mean(a);
+    array d   = mean(a, w);
 
     vector<float> hc(c.elements());
     vector<float> hd(d.elements());
@@ -321,29 +290,28 @@ TEST(WeightedMean, Broadacst)
     c.host(hc.data());
     d.host(hd.data());
 
-    for(size_t i = 0; i < hc.size(); i++) {
-        //C and D are the same because they are normalized by the sum of the weights.
+    for (size_t i = 0; i < hc.size(); i++) {
+        // C and D are the same because they are normalized by the sum of the
+        // weights.
         ASSERT_NEAR(hc[i], hd[i], 1E-5);
     }
 }
 
-TEST(Mean, Issue2093)
-{
-  const int NELEMS = 512;
+TEST(Mean, Issue2093) {
+    const int NELEMS = 512;
 
-  array data = randu(1, NELEMS);
-  array wts  = constant(1.0f, 1, NELEMS);
-  vector<float> hdata(NELEMS);
-  data.host(hdata.data());
+    array data = randu(1, NELEMS);
+    array wts  = constant(1.0f, 1, NELEMS);
+    vector<float> hdata(NELEMS);
+    data.host(hdata.data());
 
-  array out = mean(data, wts, 1);
-  float outVal;
-  out.host(&outVal);
+    array out = mean(data, wts, 1);
+    float outVal;
+    out.host(&outVal);
 
-  float expected = 0.0;
-  for (size_t i=0; i<NELEMS; ++i)
-      expected += hdata[i];
-  expected /= NELEMS;
+    float expected = 0.0;
+    for (size_t i = 0; i < NELEMS; ++i) expected += hdata[i];
+    expected /= NELEMS;
 
-  ASSERT_NEAR(outVal, expected, 0.001);
+    ASSERT_NEAR(outVal, expected, 0.001);
 }
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 0222eb52be..61928e396f 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -7,80 +7,84 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <cmath>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
-#include <cmath>
 
-using std::string;
-using std::vector;
-using std::abs;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Meanshift : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Meanshift : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort, intl, uintl> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort,
+                         intl, uintl>
+    TestTypes;
 
 TYPED_TEST_CASE(Meanshift, TestTypes);
 
-TYPED_TEST(Meanshift, InvalidArgs)
-{
+TYPED_TEST(Meanshift, InvalidArgs) {
     if (noDoubleTests<TypeParam>()) return;
 
-    vector<TypeParam>   in(100,1);
+    vector<TypeParam> in(100, 1);
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    dim4 dims = dim4(100,1,1,1);
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<TypeParam>::af_type));
-    ASSERT_EQ(AF_ERR_SIZE, af_mean_shift(&outArray, inArray, 0.12f, 0.34f, 5, true));
+    dim4 dims = dim4(100, 1, 1, 1);
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<TypeParam>::af_type));
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_mean_shift(&outArray, inArray, 0.12f, 0.34f, 5, true));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename T, bool isColor>
-void meanshiftTest(string pTestFile, const float ss)
-{
+void meanshiftTest(string pTestFile, const float ss) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-
-        af_array inArray        = 0;
-        af_array inArray_f32    = 0;
-        af_array outArray       = 0;
-        af_array goldArray      = 0;
-        af_array goldArray_f32  = 0;
-        dim_t nElems            = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray       = 0;
+        af_array inArray_f32   = 0;
+        af_array outArray      = 0;
+        af_array goldArray     = 0;
+        af_array goldArray_f32 = 0;
+        dim_t nElems           = 0;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
-        outFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/meanshift/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/meanshift/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray_f32, inFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_load_image(&goldArray_f32, outFiles[testId].c_str(), isColor));
-        ASSERT_SUCCESS(conv_image<T>(&goldArray, goldArray_f32)); // af_load_image always returns float array
+        ASSERT_SUCCESS(
+            af_load_image(&goldArray_f32, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(conv_image<T>(
+            &goldArray,
+            goldArray_f32));  // af_load_image always returns float array
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         ASSERT_SUCCESS(af_mean_shift(&outArray, inArray, ss, 30.f, 5, isColor));
@@ -91,7 +95,8 @@ void meanshiftTest(string pTestFile, const float ss)
         vector<T> goldData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.02f));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
@@ -107,53 +112,53 @@ void meanshiftTest(string pTestFile, const float ss)
 //       Note: compareArraysRMSD is handling upcasting while working
 //       with two different type of types
 //
-#define IMAGE_TESTS(T)                                                              \
-    TEST(Meanshift, Grayscale_##T)                                                  \
-    {                                                                               \
-        meanshiftTest<T, false>(string(TEST_DIR"/meanshift/gray.test"), 6.67f);     \
-    }                                                                               \
-    TEST(Meanshift, Color_##T)                                                      \
-    {                                                                               \
-        meanshiftTest<T, true>(string(TEST_DIR"/meanshift/color.test"), 3.5f);      \
+#define IMAGE_TESTS(T)                                                   \
+    TEST(Meanshift, Grayscale_##T) {                                     \
+        meanshiftTest<T, false>(string(TEST_DIR "/meanshift/gray.test"), \
+                                6.67f);                                  \
+    }                                                                    \
+    TEST(Meanshift, Color_##T) {                                         \
+        meanshiftTest<T, true>(string(TEST_DIR "/meanshift/color.test"), \
+                               3.5f);                                    \
     }
 
-IMAGE_TESTS(float )
+IMAGE_TESTS(float)
 IMAGE_TESTS(double)
 
 //////////////////////////////////////// CPP ///////////////////////////////
 //
 
 using af::array;
-using af::iota;
 using af::constant;
+using af::iota;
 using af::loadImage;
 using af::max;
 using af::meanShift;
-using af::span;
 using af::seq;
+using af::span;
 
-TEST(Meanshift, Color_CPP)
-{
+TEST(Meanshift, Color_CPP) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
-    readImageTests(string(TEST_DIR"/meanshift/color.test"), inDims, inFiles, outSizes, outFiles);
+    readImageTests(string(TEST_DIR "/meanshift/color.test"), inDims, inFiles,
+                   outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        inFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
-        outFiles[testId].insert(0,string(TEST_DIR"/meanshift/"));
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        inFiles[testId].insert(0, string(TEST_DIR "/meanshift/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/meanshift/"));
 
-        array img   = loadImage(inFiles[testId].c_str(), true);
-        array gold  = loadImage(outFiles[testId].c_str(), true);
+        array img    = loadImage(inFiles[testId].c_str(), true);
+        array gold   = loadImage(outFiles[testId].c_str(), true);
         dim_t nElems = gold.elements();
-        array output= meanShift(img, 3.5f, 30.f, 5, true);
+        array output = meanShift(img, 3.5f, 30.f, 5, true);
 
         vector<float> outData(nElems);
         output.host((void*)outData.data());
@@ -161,21 +166,21 @@ TEST(Meanshift, Color_CPP)
         vector<float> goldData(nElems);
         gold.host((void*)goldData.data());
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.02f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.02f));
     }
 }
 
-TEST(Meanshift, GFOR)
-{
+TEST(Meanshift, GFOR) {
     dim4 dims = dim4(10, 10, 3);
-    array A = iota(dims);
-    array B = constant(0, dims);
+    array A   = iota(dims);
+    array B   = constant(0, dims);
 
     gfor(seq ii, 3) {
         B(span, span, ii) = meanShift(A(span, span, ii), 3, 5, 3);
     }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = meanShift(A(span, span, ii), 3, 5, 3);
         array b_ii = B(span, span, ii);
 
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 243cd73ec1..ce5ab824bf 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
 
 #include <testHelpers.hpp>
 
@@ -29,26 +29,21 @@ using std::vector;
 
 template<typename T>
 struct elseType {
-  typedef typename cond_type< is_same_type<T, uintl>::value ||
-                              is_same_type<T, intl> ::value,
-                              double,
-                              T>::type type;
+    typedef typename cond_type<is_same_type<T, uintl>::value ||
+                                   is_same_type<T, intl>::value,
+                               double, T>::type type;
 };
 
 template<typename T>
 struct varOutType {
-  typedef typename cond_type< is_same_type<T, float >::value ||
-                              is_same_type<T, int   >::value ||
-                              is_same_type<T, uint  >::value ||
-                              is_same_type<T, short >::value ||
-                              is_same_type<T, ushort>::value ||
-                              is_same_type<T, uchar >::value ||
-                              is_same_type<T, char  >::value,
-                              float,
-                              typename elseType<T>::type>::type type;
+    typedef typename cond_type<
+        is_same_type<T, float>::value || is_same_type<T, int>::value ||
+            is_same_type<T, uint>::value || is_same_type<T, short>::value ||
+            is_same_type<T, ushort>::value || is_same_type<T, uchar>::value ||
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
-
 template<typename T>
 using outType = typename varOutType<T>::type;
 
@@ -60,21 +55,19 @@ struct meanvar_test {
     af_array weights_;
     af_var_bias bias_;
     int dim_;
-    vector<outType<T>> mean_;
-    vector<outType<T>> variance_;
+    vector<outType<T> > mean_;
+    vector<outType<T> > variance_;
 
     meanvar_test(string description, af_array in, af_array weights,
-                af_var_bias bias, int dim,
-                vector<double> &&mean, vector<double> &&variance)
-      : test_description_(description)
-      , in_(0)
-      , weights_(0)
-      , bias_(bias)
-      , dim_(dim) {
+                 af_var_bias bias, int dim, vector<double> &&mean,
+                 vector<double> &&variance)
+        : test_description_(description)
+        , in_(0)
+        , weights_(0)
+        , bias_(bias)
+        , dim_(dim) {
         af_retain_array(&in_, in);
-        if (weights) {
-            af_retain_array(&weights_, weights);
-        }
+        if (weights) { af_retain_array(&weights_, weights); }
         mean_.reserve(mean.size());
         variance_.reserve(variance.size());
         std::copy(begin(mean), end(mean), back_inserter(mean_));
@@ -82,17 +75,15 @@ struct meanvar_test {
     }
 
     meanvar_test(const meanvar_test<T> &other)
-      : test_description_(other.test_description_)
-      , in_(0)
-      , weights_(0)
-      , bias_(other.bias_)
-      , dim_(other.dim_)
-      , mean_(other.mean_)
-      , variance_(other.variance_) {
+        : test_description_(other.test_description_)
+        , in_(0)
+        , weights_(0)
+        , bias_(other.bias_)
+        , dim_(other.dim_)
+        , mean_(other.mean_)
+        , variance_(other.variance_) {
         af_retain_array(&in_, other.in_);
-        if (other.weights_) {
-            af_retain_array(&weights_, other.weights_);
-        }
+        if (other.weights_) { af_retain_array(&weights_, other.weights_); }
     }
 
     ~meanvar_test() {
@@ -103,9 +94,9 @@ struct meanvar_test {
         }
     }
 
-    meanvar_test() = default;
+    meanvar_test()                        = default;
     meanvar_test(meanvar_test<T> &&other) = default;
-    meanvar_test& operator=(meanvar_test<T> &&other) = default;
+    meanvar_test &operator=(meanvar_test<T> &&other) = default;
 };
 
 template<typename T>
@@ -113,160 +104,165 @@ af_dtype meanvar_test<T>::af_type = dtype_traits<T>::af_type;
 
 template<typename T>
 class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
-public:
-  void meanvar_test_function(meanvar_test<T>& test) {
-      af_array mean, var;
-
-      // Cast to the expected type
-      af_array in = 0;
-      af_cast(&in, test.in_, (af_dtype) dtype_traits<T>::af_type);
-
-      EXPECT_EQ(AF_SUCCESS, af_meanvar(&mean, &var, in, test.weights_, test.bias_, test.dim_));
-
-      vector<outType<T>> h_mean(test.mean_.size()), h_var(test.variance_.size());
-
-      dim4 outDim(1);
-      af_get_dims(&outDim[0], &outDim[1], &outDim[2], &outDim[3], in);
-      outDim[test.dim_] = 1;
-
-      if (is_same_type<float, outType<T>>::value ||
-          is_same_type<cfloat, outType<T>>::value) {
-          ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
-          ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
-      } else {
-          ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.00001f);
-          ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.0001f);
-      }
-
-      ASSERT_SUCCESS(af_release_array(in));
-      ASSERT_SUCCESS(af_release_array(mean));
-      ASSERT_SUCCESS(af_release_array(var));
-  }
+   public:
+    void meanvar_test_function(meanvar_test<T> &test) {
+        af_array mean, var;
+
+        // Cast to the expected type
+        af_array in = 0;
+        af_cast(&in, test.in_, (af_dtype)dtype_traits<T>::af_type);
+
+        EXPECT_EQ(AF_SUCCESS, af_meanvar(&mean, &var, in, test.weights_,
+                                         test.bias_, test.dim_));
+
+        vector<outType<T> > h_mean(test.mean_.size()),
+            h_var(test.variance_.size());
+
+        dim4 outDim(1);
+        af_get_dims(&outDim[0], &outDim[1], &outDim[2], &outDim[3], in);
+        outDim[test.dim_] = 1;
+
+        if (is_same_type<float, outType<T> >::value ||
+            is_same_type<cfloat, outType<T> >::value) {
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
+            ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
+        } else {
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.00001f);
+            ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.0001f);
+        }
+
+        ASSERT_SUCCESS(af_release_array(in));
+        ASSERT_SUCCESS(af_release_array(mean));
+        ASSERT_SUCCESS(af_release_array(var));
+    }
 };
 
 af_array empty = 0;
 
-enum test_size {
-  MEANVAR_SMALL,
-  MEANVAR_LARGE
-};
+enum test_size { MEANVAR_SMALL, MEANVAR_LARGE };
 
 template<typename T>
-meanvar_test<T>
-meanvar_test_gen(string name, int in_index, int weight_index, af_var_bias bias, int dim, int mean_index, int var_index, test_size size) {
-
+meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
+                                 af_var_bias bias, int dim, int mean_index,
+                                 int var_index, test_size size) {
     vector<af_array> inputs;
-    vector<vector<double>> outputs;
-    if(size == MEANVAR_SMALL) {
+    vector<vector<double> > outputs;
+    if (size == MEANVAR_SMALL) {
         vector<af::dim4> numDims_;
         vector<vector<double> > in_;
         vector<vector<double> > tests_;
-        readTests<double, typename varOutType<double>::type, double> (TEST_DIR"/meanvar/meanvar.data", numDims_, in_, tests_);
+        readTests<double, typename varOutType<double>::type, double>(
+            TEST_DIR "/meanvar/meanvar.data", numDims_, in_, tests_);
 
         inputs.resize(in_.size());
-        for(size_t i = 0; i < in_.size(); i++) {
-            af_create_array(&inputs[i], &in_[i].front(),
-                            numDims_[i].ndims(), numDims_[i].get(), f64);
+        for (size_t i = 0; i < in_.size(); i++) {
+            af_create_array(&inputs[i], &in_[i].front(), numDims_[i].ndims(),
+                            numDims_[i].get(), f64);
         }
 
         outputs.resize(tests_.size());
-        for(size_t i = 0; i < tests_.size(); i++) {
+        for (size_t i = 0; i < tests_.size(); i++) {
             copy(tests_[i].begin(), tests_[i].end(), back_inserter(outputs[i]));
         }
     } else {
+        dim_t full_array_size             = 2000;
+        vector<vector<dim_t> > dimensions = {
+            {2000, 1, 1, 1},  // 0
+            {1, 2000, 1, 1},  // 1
+            {1, 1, 2000, 1},  // 2
+
+            {500, 4, 1, 1},  // 3
+            {4, 500, 1, 1},  // 4
+            {50, 40, 1, 1}   // 5
+        };
+
+        vector<double> large_(full_array_size);
+        for (size_t i = 0; i < large_.size(); i++) {
+            large_[i] = static_cast<double>(i);
+        }
 
-      dim_t full_array_size = 2000;
-      vector<vector<dim_t> > dimensions = {
-                                          {2000, 1, 1, 1},  // 0
-                                          {1, 2000, 1, 1},  // 1
-                                          {1, 1, 2000, 1},  // 2
-
-                                          {500, 4, 1, 1},  // 3
-                                          {4, 500, 1, 1},  // 4
-                                          {50, 40, 1, 1}   // 5
-      };
-
-      vector<double> large_(full_array_size);
-      for(size_t i = 0; i < large_.size(); i++) {
-          large_[i] = static_cast<double>(i);
-      }
-
-      inputs.resize(dimensions.size());
-      for(size_t i = 0; i < dimensions.size(); i++) {
-          af_create_array(&inputs[i], &large_.front(), 4, dimensions[i].data(), f64);
-      }
-
-      outputs.push_back(vector<double>(1, 999.5));
-      outputs.push_back(vector<double>(1, 333500));
-      outputs.push_back({249.50, 749.50, 1249.50, 1749.50});
-      outputs.push_back(vector<double>(4, 20875));
-    }
-    meanvar_test<T> out = meanvar_test<T> (name,
-                                           inputs[in_index],
-                                           (weight_index == -1) ? empty : inputs[weight_index],
-                                           bias,
-                                           dim,
-                                           move(outputs[mean_index]),
-                                           move(outputs[var_index]));
-
-    for(auto input : inputs) {
-      af_release_array(input);
+        inputs.resize(dimensions.size());
+        for (size_t i = 0; i < dimensions.size(); i++) {
+            af_create_array(&inputs[i], &large_.front(), 4,
+                            dimensions[i].data(), f64);
+        }
+
+        outputs.push_back(vector<double>(1, 999.5));
+        outputs.push_back(vector<double>(1, 333500));
+        outputs.push_back({249.50, 749.50, 1249.50, 1749.50});
+        outputs.push_back(vector<double>(4, 20875));
     }
+    meanvar_test<T> out = meanvar_test<T>(
+        name, inputs[in_index],
+        (weight_index == -1) ? empty : inputs[weight_index], bias, dim,
+        move(outputs[mean_index]), move(outputs[var_index]));
+
+    for (auto input : inputs) { af_release_array(input); }
     return out;
 }
 
-
 template<typename T>
-vector<meanvar_test<T> >
-small_test_values() {
-  return {
-                    // |           Name |   in_index | weight_index |                  bias |  dim | mean_index | var_index |
-    meanvar_test_gen<T>(    "Sample1Ddim0",           0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_SMALL),
-    meanvar_test_gen<T>(    "Sample1Ddim1",           1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_SMALL),
-    meanvar_test_gen<T>(    "Sample2Ddim0",           2,            -1,     AF_VARIANCE_SAMPLE,     0,           3,          4, MEANVAR_SMALL),
-    meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,     AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_SMALL),
-
-    meanvar_test_gen<T>("Population1Ddim0",           0,            -1, AF_VARIANCE_POPULATION,     0,           0,          2, MEANVAR_SMALL),
-    meanvar_test_gen<T>("Population1Ddim1",           1,            -1, AF_VARIANCE_POPULATION,     1,           0,          2, MEANVAR_SMALL),
-    meanvar_test_gen<T>("Population2Ddim0",           2,            -1, AF_VARIANCE_POPULATION,     0,           3,          5, MEANVAR_SMALL),
-    meanvar_test_gen<T>("Population2Ddim1",           2,            -1, AF_VARIANCE_POPULATION,     1,           6,          8, MEANVAR_SMALL)
-    };
+vector<meanvar_test<T> > small_test_values() {
+    return {
+        // |           Name |   in_index | weight_index |                  bias
+        // |  dim | mean_index | var_index |
+        meanvar_test_gen<T>("Sample1Ddim0", 0, -1, AF_VARIANCE_SAMPLE, 0, 0, 1,
+                            MEANVAR_SMALL),
+        meanvar_test_gen<T>("Sample1Ddim1", 1, -1, AF_VARIANCE_SAMPLE, 1, 0, 1,
+                            MEANVAR_SMALL),
+        meanvar_test_gen<T>("Sample2Ddim0", 2, -1, AF_VARIANCE_SAMPLE, 0, 3, 4,
+                            MEANVAR_SMALL),
+        meanvar_test_gen<T>("Sample2Ddim1", 2, -1, AF_VARIANCE_SAMPLE, 1, 6, 7,
+                            MEANVAR_SMALL),
+
+        meanvar_test_gen<T>("Population1Ddim0", 0, -1, AF_VARIANCE_POPULATION,
+                            0, 0, 2, MEANVAR_SMALL),
+        meanvar_test_gen<T>("Population1Ddim1", 1, -1, AF_VARIANCE_POPULATION,
+                            1, 0, 2, MEANVAR_SMALL),
+        meanvar_test_gen<T>("Population2Ddim0", 2, -1, AF_VARIANCE_POPULATION,
+                            0, 3, 5, MEANVAR_SMALL),
+        meanvar_test_gen<T>("Population2Ddim1", 2, -1, AF_VARIANCE_POPULATION,
+                            1, 6, 8, MEANVAR_SMALL)};
 }
 
 template<typename T>
-vector<meanvar_test<T> >
-large_test_values() {
-  return {
-                      // |           Name |   in_index | weight_index |                  bias |  dim | mean_index | var_index |
-    meanvar_test_gen<T>(    "Sample1Ddim0",           0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_LARGE),
-    meanvar_test_gen<T>(    "Sample1Ddim1",           1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_LARGE),
-    meanvar_test_gen<T>(    "Sample1Ddim2",           2,            -1,     AF_VARIANCE_SAMPLE,     2,           0,          1, MEANVAR_LARGE),
-    meanvar_test_gen<T>(    "Sample2Ddim0",           3,            -1,     AF_VARIANCE_SAMPLE,     0,           2,          3, MEANVAR_LARGE),
-    // TODO(umar) Add additional large tests
-    //meanvar_test_gen<T>(    "Sample2Ddim1",           3,            -1,     AF_VARIANCE_SAMPLE,     1,           2,          3, MEANVAR_LARGE),
-    //meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,     AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_LARGE),
-      };
+vector<meanvar_test<T> > large_test_values() {
+    return {
+        // |           Name |   in_index | weight_index |                  bias
+        // |  dim | mean_index | var_index |
+        meanvar_test_gen<T>("Sample1Ddim0", 0, -1, AF_VARIANCE_SAMPLE, 0, 0, 1,
+                            MEANVAR_LARGE),
+        meanvar_test_gen<T>("Sample1Ddim1", 1, -1, AF_VARIANCE_SAMPLE, 1, 0, 1,
+                            MEANVAR_LARGE),
+        meanvar_test_gen<T>("Sample1Ddim2", 2, -1, AF_VARIANCE_SAMPLE, 2, 0, 1,
+                            MEANVAR_LARGE),
+        meanvar_test_gen<T>("Sample2Ddim0", 3, -1, AF_VARIANCE_SAMPLE, 0, 2, 3,
+                            MEANVAR_LARGE),
+        // TODO(umar) Add additional large tests
+        // meanvar_test_gen<T>(    "Sample2Ddim1",           3,            -1,
+        // AF_VARIANCE_SAMPLE,     1,           2,          3, MEANVAR_LARGE),
+        // meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,
+        // AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_LARGE),
+    };
 }
 
-#define MEANVAR_TEST(NAME, TYPE)                                        \
-  using MeanVar##NAME = MeanVarTyped<TYPE>;                             \
-  INSTANTIATE_TEST_CASE_P(Small,                                        \
-                          MeanVar##NAME,                                \
-                          ::testing::ValuesIn(small_test_values<TYPE>()), \
-                          []( const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) { \
-                            return info.param.test_description_;        \
-                          });                                           \
-  INSTANTIATE_TEST_CASE_P(Large,                                        \
-                          MeanVar##NAME,                                \
-                          ::testing::ValuesIn(large_test_values<TYPE>()), \
-                          []( const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) { \
-                            return info.param.test_description_;        \
-                          });                                           \
-                                                                        \
-  TEST_P(MeanVar##NAME, Testing) {                                      \
-    meanvar_test<TYPE> test = GetParam();                               \
-    meanvar_test_function(test);                                        \
-  }                                                                     \
+#define MEANVAR_TEST(NAME, TYPE)                                              \
+    using MeanVar##NAME = MeanVarTyped<TYPE>;                                 \
+    INSTANTIATE_TEST_CASE_P(                                                  \
+        Small, MeanVar##NAME, ::testing::ValuesIn(small_test_values<TYPE>()), \
+        [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) {   \
+            return info.param.test_description_;                              \
+        });                                                                   \
+    INSTANTIATE_TEST_CASE_P(                                                  \
+        Large, MeanVar##NAME, ::testing::ValuesIn(large_test_values<TYPE>()), \
+        [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) {   \
+            return info.param.test_description_;                              \
+        });                                                                   \
+                                                                              \
+    TEST_P(MeanVar##NAME, Testing) {                                          \
+        meanvar_test<TYPE> test = GetParam();                                 \
+        meanvar_test_function(test);                                          \
+    }
 
 MEANVAR_TEST(Float, float)
 MEANVAR_TEST(Double, double)
@@ -281,20 +277,19 @@ MEANVAR_TEST(ComplexDouble, af::af_cdouble)
 
 #undef MEANVAR_TEST
 
-#define MEANVAR_TEST(NAME, TYPE)                                        \
-  using MeanVar##NAME = MeanVarTyped<TYPE>;                             \
-  INSTANTIATE_TEST_CASE_P(Small,                                        \
-                          MeanVar##NAME,                                \
-                          ::testing::ValuesIn(small_test_values<TYPE>()), \
-                          []( const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) { \
-                            return info.param.test_description_;        \
-                          });                                           \
-                                                                        \
-  TEST_P(MeanVar##NAME, Testing) {                                      \
-    meanvar_test<TYPE> test = GetParam();                               \
-    meanvar_test_function(test);                                        \
-  }                                                                     \
+#define MEANVAR_TEST(NAME, TYPE)                                              \
+    using MeanVar##NAME = MeanVarTyped<TYPE>;                                 \
+    INSTANTIATE_TEST_CASE_P(                                                  \
+        Small, MeanVar##NAME, ::testing::ValuesIn(small_test_values<TYPE>()), \
+        [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) {   \
+            return info.param.test_description_;                              \
+        });                                                                   \
+                                                                              \
+    TEST_P(MeanVar##NAME, Testing) {                                          \
+        meanvar_test<TYPE> test = GetParam();                                 \
+        meanvar_test_function(test);                                          \
+    }
 
 // Only test small sizes because the range of the large arrays go out of bounds
 MEANVAR_TEST(UnsignedChar, unsigned char)
-//MEANVAR_TEST(Bool, unsigned char) // TODO(umar): test this type
+// MEANVAR_TEST(Bool, unsigned char) // TODO(umar): test this type
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 169f673003..85c75a8437 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -7,59 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using af::dim4;
+using af::dtype_traits;
 using std::abs;
 using std::endl;
 using std::string;
 using std::vector;
-using af::dim4;
-using af::dtype_traits;
 
 template<typename T>
-class MedianFilter : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class MedianFilter : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class MedianFilter1d : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class MedianFilter1d : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(MedianFilter, TestTypes);
 TYPED_TEST_CASE(MedianFilter1d, TestTypes);
 
 template<typename T>
-void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
-{
+void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid,
+                 af_border_type pad) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<T> >      in;
-    vector<vector<T> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
-    readTests<T,T,int>(pTestFile, numDims, in, tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-    dim4 dims      = numDims[0];
-    af_array outArray  = 0;
-    af_array inArray   = 0;
+    dim4 dims         = numDims[0];
+    af_array outArray = 0;
+    af_array inArray  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_medfilt2(&outArray, inArray, w_len, w_wid, pad));
 
@@ -68,9 +68,10 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
-    size_t nElems        = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    size_t nElems         = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // cleanup
@@ -78,44 +79,47 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid, af_border_type pad)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(MedianFilter, ZERO_PAD_3x3)
-{
-    medfiltTest<TypeParam>(string(TEST_DIR"/medianfilter/zero_pad_3x3_window.test"), 3, 3, AF_PAD_ZERO);
+TYPED_TEST(MedianFilter, ZERO_PAD_3x3) {
+    medfiltTest<TypeParam>(
+        string(TEST_DIR "/medianfilter/zero_pad_3x3_window.test"), 3, 3,
+        AF_PAD_ZERO);
 }
 
-TYPED_TEST(MedianFilter, SYMMETRIC_PAD_3x3)
-{
-    medfiltTest<TypeParam>(string(TEST_DIR"/medianfilter/symmetric_pad_3x3_window.test"), 3, 3, AF_PAD_SYM);
+TYPED_TEST(MedianFilter, SYMMETRIC_PAD_3x3) {
+    medfiltTest<TypeParam>(
+        string(TEST_DIR "/medianfilter/symmetric_pad_3x3_window.test"), 3, 3,
+        AF_PAD_SYM);
 }
 
-TYPED_TEST(MedianFilter, BATCH_ZERO_PAD_3x3)
-{
-    medfiltTest<TypeParam>(string(TEST_DIR"/medianfilter/batch_zero_pad_3x3_window.test"), 3, 3, AF_PAD_ZERO);
+TYPED_TEST(MedianFilter, BATCH_ZERO_PAD_3x3) {
+    medfiltTest<TypeParam>(
+        string(TEST_DIR "/medianfilter/batch_zero_pad_3x3_window.test"), 3, 3,
+        AF_PAD_ZERO);
 }
 
-TYPED_TEST(MedianFilter, BATCH_SYMMETRIC_PAD_3x3)
-{
-    medfiltTest<TypeParam>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x3_window.test"), 3, 3, AF_PAD_SYM);
+TYPED_TEST(MedianFilter, BATCH_SYMMETRIC_PAD_3x3) {
+    medfiltTest<TypeParam>(
+        string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x3_window.test"), 3,
+        3, AF_PAD_SYM);
 }
 
-
 template<typename T>
-void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
-{
+void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<T> >      in;
-    vector<vector<T> >   tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
 
-    readTests<T,T,int>(pTestFile, numDims, in, tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
-    dim4 dims      = numDims[0];
-    af_array outArray  = 0;
-    af_array inArray   = 0;
+    dim4 dims         = numDims[0];
+    af_array outArray = 0;
+    af_array inArray  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_medfilt1(&outArray, inArray, w_wid, pad));
 
@@ -124,9 +128,10 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
 
     vector<T> currGoldBar = tests[0];
-    size_t nElems        = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    size_t nElems         = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // cleanup
@@ -134,56 +139,61 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(MedianFilter1d, ZERO_PAD_3)
-{
-    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
+TYPED_TEST(MedianFilter1d, ZERO_PAD_3) {
+    medfilt1_Test<TypeParam>(
+        string(TEST_DIR "/medianfilter/zero_pad_3x1_window.test"), 3,
+        AF_PAD_ZERO);
 }
 
-TYPED_TEST(MedianFilter1d, SYMMETRIC_PAD_3)
-{
-    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
+TYPED_TEST(MedianFilter1d, SYMMETRIC_PAD_3) {
+    medfilt1_Test<TypeParam>(
+        string(TEST_DIR "/medianfilter/symmetric_pad_3x1_window.test"), 3,
+        AF_PAD_SYM);
 }
 
-TYPED_TEST(MedianFilter1d, BATCH_ZERO_PAD_3)
-{
-    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_zero_pad_3x1_window.test"), 3, AF_PAD_ZERO);
+TYPED_TEST(MedianFilter1d, BATCH_ZERO_PAD_3) {
+    medfilt1_Test<TypeParam>(
+        string(TEST_DIR "/medianfilter/batch_zero_pad_3x1_window.test"), 3,
+        AF_PAD_ZERO);
 }
 
-TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3)
-{
-    medfilt1_Test<TypeParam>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"), 3, AF_PAD_SYM);
+TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3) {
+    medfilt1_Test<TypeParam>(
+        string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x1_window.test"), 3,
+        AF_PAD_SYM);
 }
 
-template<typename T,bool isColor>
-void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
-{
+template<typename T, bool isColor>
+void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray   = 0;
+        af_array outArray  = 0;
+        af_array goldArray = 0;
+        dim_t nElems       = 0;
 
-        af_array inArray  = 0;
-        af_array outArray = 0;
-        af_array goldArray= 0;
-        dim_t nElems   = 0;
+        inFiles[testId].insert(0, string(TEST_DIR "/medianfilter/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/medianfilter/"));
 
-        inFiles[testId].insert(0,string(TEST_DIR"/medianfilter/"));
-        outFiles[testId].insert(0,string(TEST_DIR"/medianfilter/"));
-
-        ASSERT_SUCCESS(af_load_image(&inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_SUCCESS(af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        ASSERT_SUCCESS(af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
+        ASSERT_SUCCESS(
+            af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
 
         vector<T> outData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
@@ -191,7 +201,8 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
         vector<T> goldData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.018f));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(outArray));
@@ -200,20 +211,20 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid)
 }
 
 template<typename T>
-void medfiltInputTest(void)
-{
+void medfiltInputTest(void) {
     if (noDoubleTests<T>()) return;
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<T>   in(100, 1);
+    vector<T> in(100, 1);
 
     // Check for 1D inputs -> medfilt1
     dim4 dims = dim4(100, 1, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_medfilt2(&outArray, inArray, 1, 1, AF_PAD_ZERO));
 
@@ -226,140 +237,128 @@ void medfiltInputTest(void)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(MedianFilter, InvalidArray)
-{
-    medfiltInputTest<TypeParam>();
-}
+TYPED_TEST(MedianFilter, InvalidArray) { medfiltInputTest<TypeParam>(); }
 
 template<typename T>
-void medfiltWindowTest(void)
-{
+void medfiltWindowTest(void) {
     if (noDoubleTests<T>()) return;
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<T>   in(100, 1);
+    vector<T> in(100, 1);
 
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 5, AF_PAD_ZERO));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(MedianFilter, InvalidWindow)
-{
-    medfiltWindowTest<TypeParam>();
-}
-
+TYPED_TEST(MedianFilter, InvalidWindow) { medfiltWindowTest<TypeParam>(); }
 
 template<typename T>
-void medfilt1d_WindowTest(void)
-{
+void medfilt1d_WindowTest(void) {
     if (noDoubleTests<T>()) return;
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<T>   in(100, 1);
+    vector<T> in(100, 1);
 
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, -1, AF_PAD_ZERO));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(MedianFilter1d, InvalidWindow)
-{
-    medfilt1d_WindowTest<TypeParam>();
-}
+TYPED_TEST(MedianFilter1d, InvalidWindow) { medfilt1d_WindowTest<TypeParam>(); }
 
 template<typename T>
-void medfiltPadTest(void)
-{
+void medfiltPadTest(void) {
     if (noDoubleTests<T>()) return;
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<T>   in(100, 1);
+    vector<T> in(100, 1);
 
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(3)));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_medfilt2(&outArray, inArray, 3, 3, af_border_type(3)));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt2(&outArray, inArray, 3, 3, af_border_type(-1)));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_medfilt2(&outArray, inArray, 3, 3, af_border_type(-1)));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(MedianFilter, InvalidPadType)
-{
-    medfiltPadTest<TypeParam>();
-}
+TYPED_TEST(MedianFilter, InvalidPadType) { medfiltPadTest<TypeParam>(); }
 
 template<typename T>
-void medfilt1d_PadTest(void)
-{
+void medfilt1d_PadTest(void) {
     if (noDoubleTests<T>()) return;
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
+    af_array inArray  = 0;
+    af_array outArray = 0;
 
-    vector<T>   in(100, 1);
+    vector<T> in(100, 1);
 
     // Check for 4D inputs
     dim4 dims(10, 10, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(3)));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_medfilt1(&outArray, inArray, 3, af_border_type(3)));
 
-    ASSERT_EQ(AF_ERR_ARG, af_medfilt1(&outArray, inArray, 3, af_border_type(-1)));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_medfilt1(&outArray, inArray, 3, af_border_type(-1)));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(MedianFilter1d, InvalidPadType)
-{
-    medfilt1d_PadTest<TypeParam>();
-}
+TYPED_TEST(MedianFilter1d, InvalidPadType) { medfilt1d_PadTest<TypeParam>(); }
 
 //////////////////////////////////// CPP ////////////////////////////////////
 //
 
 using af::array;
 
-TEST(MedianFilter, CPP)
-{
+TEST(MedianFilter, CPP) {
     if (noDoubleTests<float>()) return;
 
     const dim_t w_len = 3;
     const dim_t w_wid = 3;
 
-    vector<dim4>  numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float,float,int>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x3_window.test"),
-                               numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x3_window.test"),
+        numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = medfilt(input, w_len, w_wid, AF_PAD_SYM);
 
@@ -367,26 +366,27 @@ TEST(MedianFilter, CPP)
     output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    size_t nElems             = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(MedianFilter1d, CPP)
-{
+TEST(MedianFilter1d, CPP) {
     if (noDoubleTests<float>()) return;
 
     const dim_t w_wid = 3;
 
-    vector<dim4>  numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTests<float,float,int>(string(TEST_DIR"/medianfilter/batch_symmetric_pad_3x1_window.test"),
-                               numDims, in, tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x1_window.test"),
+        numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = medfilt1(input, w_wid, AF_PAD_SYM);
 
@@ -394,39 +394,32 @@ TEST(MedianFilter1d, CPP)
     output.host((void*)outData.data());
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter<< endl;
+    size_t nElems             = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 }
 
+TEST(MedianFilter, Docs) {
+    float input[] = {1.0000,  2.0000,  3.0000,  4.0000,  5.0000,  6.0000,
+                     7.0000,  8.0000,  9.0000,  10.0000, 11.0000, 12.0000,
+                     13.0000, 14.0000, 15.0000, 16.0000};
 
-TEST(MedianFilter, Docs)
-{
-    float input[] = {
-        1.0000,  2.0000,  3.0000,  4.0000,
-        5.0000,  6.0000,  7.0000,  8.0000,
-        9.0000, 10.0000, 11.0000, 12.0000,
-       13.0000, 14.0000, 15.0000, 16.0000
-    };
-
-    float gold[] = {
-        0.0000,  2.0000,  3.0000, 0.0000,
-        2.0000,  6.0000,  7.0000, 4.0000,
-        6.0000, 10.0000, 11.0000, 8.0000,
-        0.0000, 10.0000, 11.0000, 0.0000
-    };
+    float gold[] = {0.0000, 2.0000,  3.0000,  0.0000,  2.0000,  6.0000,
+                    7.0000, 4.0000,  6.0000,  10.0000, 11.0000, 8.0000,
+                    0.0000, 10.0000, 11.0000, 0.0000};
 
     //![ex_image_medfilt]
     array a = array(4, 4, input);
-    //af_print(a);
-    //a = 1.0000        5.0000        9.0000       13.0000
+    // af_print(a);
+    // a = 1.0000        5.0000        9.0000       13.0000
     //    2.0000        6.0000       10.0000       14.0000
     //    3.0000        7.0000       11.0000       15.0000
     //    4.0000        8.0000       12.0000       16.0000
     array b = medfilt(a, 3, 3, AF_PAD_ZERO);
-    //af_print(b);
-    //b=  0.0000        2.0000        6.0000        0.0000
+    // af_print(b);
+    // b=  0.0000        2.0000        6.0000        0.0000
     //    2.0000        6.0000       10.0000       10.0000
     //    3.0000        7.0000       11.0000       11.0000
     //    0.0000        4.0000        8.0000        0.0000
@@ -435,47 +428,41 @@ TEST(MedianFilter, Docs)
     float output[16];
     b.host((void*)output);
 
-    for (int i=0; i<16; ++i) {
+    for (int i = 0; i < 16; ++i) {
         ASSERT_EQ(output[i], gold[i]) << "output mismatch at i = " << i << endl;
     }
 }
 
-using af::iota;
 using af::constant;
-using af::span;
-using af::seq;
+using af::iota;
+using af::max;
 using af::medfilt;
 using af::medfilt1;
-using af::max;
+using af::seq;
+using af::span;
 
-TEST(MedianFilter, GFOR)
-{
+TEST(MedianFilter, GFOR) {
     dim4 dims = dim4(10, 10, 3);
-    array A = iota(dims);
-    array B = constant(0, dims);
+    array A   = iota(dims);
+    array B   = constant(0, dims);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = medfilt(A(span, span, ii));
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = medfilt(A(span, span, ii)); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = medfilt(A(span, span, ii));
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
 
-TEST(MedianFilter1d, GFOR)
-{
+TEST(MedianFilter1d, GFOR) {
     dim4 dims = dim4(10, 10, 3);
-    array A = iota(dims);
-    array B = constant(0, dims);
+    array A   = iota(dims);
+    array B   = constant(0, dims);
 
-    gfor(seq ii, 3) {
-        B(span, ii) = medfilt1(A(span, ii));
-    }
+    gfor(seq ii, 3) { B(span, ii) = medfilt1(A(span, ii)); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = medfilt1(A(span, ii));
         array b_ii = B(span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
diff --git a/test/median.cpp b/test/median.cpp
index 6668a39ca3..0103b9a72a 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
-using std::vector;
 using af::array;
 using af::dtype;
 using af::dtype_traits;
@@ -22,42 +21,41 @@ using af::randu;
 using af::seq;
 using af::span;
 using af::sum;
+using std::vector;
 
 template<typename Ti>
-array generateArray(int nx, int ny, int nz, int nw)
-{
+array generateArray(int nx, int ny, int nz, int nw) {
     array a = randu(nx, ny, nz, nw, (dtype)dtype_traits<Ti>::af_type);
     return a;
 }
 
 template<>
-array generateArray<int>(int nx, int ny, int nz, int nw)
-{
-    array a = (randu(nx, ny, nz, nw, (dtype)dtype_traits<float>::af_type) * 1e6).as(s32);
+array generateArray<int>(int nx, int ny, int nz, int nw) {
+    array a = (randu(nx, ny, nz, nw, (dtype)dtype_traits<float>::af_type) * 1e6)
+                  .as(s32);
     return a;
 }
 
 template<>
-array generateArray<unsigned int>(int nx, int ny, int nz, int nw)
-{
-    array a = (randu(nx, ny, nz, nw, (dtype)dtype_traits<float>::af_type) * 1e6).as(u32);
+array generateArray<unsigned int>(int nx, int ny, int nz, int nw) {
+    array a = (randu(nx, ny, nz, nw, (dtype)dtype_traits<float>::af_type) * 1e6)
+                  .as(u32);
     return a;
 }
 
 template<typename To, typename Ti>
-void median_flat(int nx, int ny=1, int nz=1, int nw=1)
-{
+void median_flat(int nx, int ny = 1, int nz = 1, int nw = 1) {
     if (noDoubleTests<Ti>()) return;
     array a = generateArray<Ti>(nx, ny, nz, nw);
 
     // Verification
-    array sa = sort(flat(a));
+    array sa  = sort(flat(a));
     dim_t mid = (sa.dims(0) + 1) / 2;
 
     To verify;
 
     To *h_sa = sa.as((af_dtype)dtype_traits<To>::af_type).host<To>();
-    if(sa.dims(0) % 2 == 1) {
+    if (sa.dims(0) % 2 == 1) {
         verify = h_sa[mid - 1];
     } else {
         verify = (h_sa[mid - 1] + h_sa[mid]) / (To)2;
@@ -72,34 +70,32 @@ void median_flat(int nx, int ny=1, int nz=1, int nw=1)
 }
 
 template<typename To, typename Ti, int dim>
-void median_test(int nx, int ny=1, int nz=1, int nw=1)
-{
+void median_test(int nx, int ny = 1, int nz = 1, int nw = 1) {
     if (noDoubleTests<Ti>()) return;
 
     array a = generateArray<Ti>(nx, ny, nz, nw);
 
     // If selected dim is higher than input ndims, then return
-    if(dim >= a.dims().ndims())
-        return;
+    if (dim >= a.dims().ndims()) return;
 
     array verify;
 
     // Verification
     array sa = sort(a, dim);
 
-    double mid = (a.dims(dim) + 1) / 2;
+    double mid  = (a.dims(dim) + 1) / 2;
     seq mSeq[4] = {span, span, span, span};
-    mSeq[dim] = seq(mid, mid, 1.0);
+    mSeq[dim]   = seq(mid, mid, 1.0);
 
-    if(sa.dims(dim) % 2 == 1) {
+    if (sa.dims(dim) % 2 == 1) {
         mSeq[dim] = mSeq[dim] - 1.0;
-        verify = sa(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
+        verify    = sa(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     } else {
         dim_t sdim[4] = {0};
-        sdim[dim] = 1;
-        sa = sa.as((af_dtype)dtype_traits<To>::af_type);
-        array sas = shift(sa, sdim[0], sdim[1], sdim[2], sdim[3]);
-        verify = ((sa + sas) / 2)(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
+        sdim[dim]     = 1;
+        sa            = sa.as((af_dtype)dtype_traits<To>::af_type);
+        array sas     = shift(sa, sdim[0], sdim[1], sdim[2], sdim[3]);
+        verify        = ((sa + sas) / 2)(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     }
 
     // Test Part
@@ -109,23 +105,11 @@ void median_test(int nx, int ny=1, int nz=1, int nw=1)
     ASSERT_NEAR(0, sum<double>(abs(out - verify)), 1e-5);
 }
 
-#define MEDIAN_FLAT(To, Ti)                     \
-    TEST(MedianFlat, Ti##_flat_even)            \
-    {                                           \
-        median_flat<To, Ti>(1000);              \
-    }                                           \
-    TEST(MedianFlat, Ti##_flat_odd)             \
-    {                                           \
-        median_flat<To, Ti>(783);               \
-    }                                           \
-    TEST(MedianFlat, Ti##_flat_multi_even)      \
-    {                                           \
-        median_flat<To, Ti>(24, 11, 3);         \
-    }                                           \
-    TEST(MedianFlat, Ti##_flat_multi_odd)       \
-    {                                           \
-        median_flat<To, Ti>(15, 21, 7);         \
-    }                                           \
+#define MEDIAN_FLAT(To, Ti)                                                    \
+    TEST(MedianFlat, Ti##_flat_even) { median_flat<To, Ti>(1000); }            \
+    TEST(MedianFlat, Ti##_flat_odd) { median_flat<To, Ti>(783); }              \
+    TEST(MedianFlat, Ti##_flat_multi_even) { median_flat<To, Ti>(24, 11, 3); } \
+    TEST(MedianFlat, Ti##_flat_multi_odd) { median_flat<To, Ti>(15, 21, 7); }
 
 MEDIAN_FLAT(float, float)
 MEDIAN_FLAT(float, int)
@@ -135,46 +119,29 @@ MEDIAN_FLAT(float, short)
 MEDIAN_FLAT(float, ushort)
 MEDIAN_FLAT(double, double)
 
-#define MEDIAN_TEST(To, Ti, dim)                \
-    TEST(Median, Ti##_1D_##dim##_even)          \
-    {                                           \
-        median_test<To, Ti, dim>(1000);         \
-    }                                           \
-    TEST(Median, Ti##_2D_##dim##_even)          \
-    {                                           \
-        median_test<To, Ti, dim>(1000, 25);     \
-    }                                           \
-    TEST(Median, Ti##_3D_##dim##_even)          \
-    {                                           \
-        median_test<To, Ti, dim>(100, 25, 4);   \
-    }                                           \
-    TEST(Median, Ti##_4D_##dim##_even)          \
-    {                                           \
-        median_test<To, Ti, dim>(100, 25, 2, 2);\
-    }                                           \
-    TEST(Median, Ti##_1D_##dim##_odd)           \
-    {                                           \
-        median_test<To, Ti, dim>(783);          \
-    }                                           \
-    TEST(Median, Ti##_2D_##dim##_odd)           \
-    {                                           \
-        median_test<To, Ti, dim>(783, 25);      \
-    }                                           \
-    TEST(Median, Ti##_3D_##dim##_odd)           \
-    {                                           \
-        median_test<To, Ti, dim>(123, 25, 3);   \
-    }                                           \
-    TEST(Median, Ti##_4D_##dim##_odd)           \
-    {                                           \
-        median_test<To, Ti, dim>(123, 25, 3, 3);\
+#define MEDIAN_TEST(To, Ti, dim)                                               \
+    TEST(Median, Ti##_1D_##dim##_even) { median_test<To, Ti, dim>(1000); }     \
+    TEST(Median, Ti##_2D_##dim##_even) { median_test<To, Ti, dim>(1000, 25); } \
+    TEST(Median, Ti##_3D_##dim##_even) {                                       \
+        median_test<To, Ti, dim>(100, 25, 4);                                  \
+    }                                                                          \
+    TEST(Median, Ti##_4D_##dim##_even) {                                       \
+        median_test<To, Ti, dim>(100, 25, 2, 2);                               \
+    }                                                                          \
+    TEST(Median, Ti##_1D_##dim##_odd) { median_test<To, Ti, dim>(783); }       \
+    TEST(Median, Ti##_2D_##dim##_odd) { median_test<To, Ti, dim>(783, 25); }   \
+    TEST(Median, Ti##_3D_##dim##_odd) {                                        \
+        median_test<To, Ti, dim>(123, 25, 3);                                  \
+    }                                                                          \
+    TEST(Median, Ti##_4D_##dim##_odd) {                                        \
+        median_test<To, Ti, dim>(123, 25, 3, 3);                               \
     }
 
-
-#define MEDIAN(To, Ti)      \
-    MEDIAN_TEST(To, Ti, 0)  \
-    MEDIAN_TEST(To, Ti, 1)  \
-    MEDIAN_TEST(To, Ti, 2)  \
-    MEDIAN_TEST(To, Ti, 3)  \
+#define MEDIAN(To, Ti)     \
+    MEDIAN_TEST(To, Ti, 0) \
+    MEDIAN_TEST(To, Ti, 1) \
+    MEDIAN_TEST(To, Ti, 2) \
+    MEDIAN_TEST(To, Ti, 3)
 
 MEDIAN(float, float)
 MEDIAN(float, int)
diff --git a/test/memory.cpp b/test/memory.cpp
index 91fd41e2f6..2d0af23d07 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -7,41 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
+#include <af/internal.h>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
-#include <testHelpers.hpp>
-#include <af/internal.h>
+#include <vector>
 
-using std::vector;
 using af::alloc;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::deviceGC;
 using af::deviceMemInfo;
 using af::dtype_traits;
 using af::randu;
 using af::seq;
 using af::span;
+using std::vector;
 
 const size_t step_bytes = 1024;
 
-TEST(Memory, Scope)
-{
+TEST(Memory, Scope) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     {
         array a = randu(5, 5);
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -50,60 +48,51 @@ TEST(Memory, Scope)
         ASSERT_EQ(lock_bytes, 1 * step_bytes);
     }
 
-
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
-    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+    ASSERT_EQ(lock_buffers, 0u);  // 0 because a is out of scope
 
     ASSERT_EQ(alloc_bytes, 1 * step_bytes);
     ASSERT_EQ(lock_bytes, 0u);
 }
 
 template<typename T>
-class MemAlloc: public ::testing::Test
-{
-    public:
-        virtual void SetUp() { }
+class MemAlloc : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble,
-                         int, unsigned int, intl, uintl,
-                         char, unsigned char, short, ushort
-                        > TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         intl, uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(MemAlloc, TestTypes);
 
-size_t roundUpToStep(size_t bytes)
-{
-    if (step_bytes == 0)
-        return bytes;
+size_t roundUpToStep(size_t bytes) {
+    if (step_bytes == 0) return bytes;
 
     size_t remainder = bytes % step_bytes;
-    if (remainder == 0)
-        return bytes;
+    if (remainder == 0) return bytes;
 
     return bytes + step_bytes - remainder;
 }
 
 template<typename T>
-void memAllocArrayScopeTest(int elements)
-{
+void memAllocArrayScopeTest(int elements) {
     if (noDoubleTests<T>()) return;
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     {
         array a = randu(elements, (af_dtype)dtype_traits<T>::af_type);
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -112,31 +101,28 @@ void memAllocArrayScopeTest(int elements)
         ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
     }
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
-    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+    ASSERT_EQ(lock_buffers, 0u);  // 0 because a is out of scope
 
     ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
     ASSERT_EQ(lock_bytes, 0u);
 }
 
 template<typename T>
-void memAllocPtrScopeTest(int elements)
-{
+void memAllocPtrScopeTest(int elements) {
     if (noDoubleTests<T>()) return;
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     {
         T *ptr = alloc<T>(elements);
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -147,23 +133,21 @@ void memAllocPtrScopeTest(int elements)
         af::free(ptr);
     }
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
-    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+    ASSERT_EQ(lock_buffers, 0u);  // 0 because a is out of scope
 
     ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
     ASSERT_EQ(lock_bytes, 0u);
 
     // Do without using templated alloc
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     {
         void *ptr = alloc(elements, (af_dtype)dtype_traits<T>::af_type);
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -174,58 +158,41 @@ void memAllocPtrScopeTest(int elements)
         af::free(ptr);
     }
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
-    ASSERT_EQ(lock_buffers, 0u); // 0 because a is out of scope
+    ASSERT_EQ(lock_buffers, 0u);  // 0 because a is out of scope
 
     ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
     ASSERT_EQ(lock_bytes, 0u);
 }
 
-TYPED_TEST(MemAlloc, ArrayScope25)
-{
-    memAllocArrayScopeTest<TypeParam>(25);
-}
+TYPED_TEST(MemAlloc, ArrayScope25) { memAllocArrayScopeTest<TypeParam>(25); }
 
-TYPED_TEST(MemAlloc, ArrayScope2048)
-{
+TYPED_TEST(MemAlloc, ArrayScope2048) {
     memAllocArrayScopeTest<TypeParam>(2048);
 }
 
-TYPED_TEST(MemAlloc, ArrayScope2293)
-{
+TYPED_TEST(MemAlloc, ArrayScope2293) {
     memAllocArrayScopeTest<TypeParam>(2293);
 }
 
-TYPED_TEST(MemAlloc, PtrScope25)
-{
-    memAllocPtrScopeTest<TypeParam>(25);
-}
+TYPED_TEST(MemAlloc, PtrScope25) { memAllocPtrScopeTest<TypeParam>(25); }
 
-TYPED_TEST(MemAlloc, PtrScope2048)
-{
-    memAllocPtrScopeTest<TypeParam>(2048);
-}
+TYPED_TEST(MemAlloc, PtrScope2048) { memAllocPtrScopeTest<TypeParam>(2048); }
 
-TYPED_TEST(MemAlloc, PtrScope2293)
-{
-    memAllocPtrScopeTest<TypeParam>(2293);
-}
+TYPED_TEST(MemAlloc, PtrScope2293) { memAllocPtrScopeTest<TypeParam>(2293); }
 
-TEST(Memory, SingleSizeLoop)
-{
+TEST(Memory, SingleSizeLoop) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     {
         array a = randu(5, 5);
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -233,13 +200,14 @@ TEST(Memory, SingleSizeLoop)
         ASSERT_EQ(lock_bytes, 1 * step_bytes);
 
         for (int i = 0; i < 100; i++) {
+            a = randu(5, 5);
 
-            a = randu(5,5);
+            deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
+                          &lock_buffers);
 
-            deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
-
-            ASSERT_EQ(alloc_buffers, 2u); //2 because a new one is created before a is destroyed
+            ASSERT_EQ(
+                alloc_buffers,
+                2u);  // 2 because a new one is created before a is destroyed
             ASSERT_EQ(lock_buffers, 1u);
             ASSERT_EQ(alloc_bytes, 2 * step_bytes);
             ASSERT_EQ(lock_bytes, 1 * step_bytes);
@@ -247,14 +215,13 @@ TEST(Memory, SingleSizeLoop)
     }
 }
 
-TEST(Memory, LargeLoop)
-{
+TEST(Memory, LargeLoop) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
-    const int num = step_bytes / sizeof(float);
+    const int num    = step_bytes / sizeof(float);
     size_t allocated = step_bytes;
 
     array a = randu(num);
@@ -265,17 +232,16 @@ TEST(Memory, LargeLoop)
 
     // Run a large loop that allocates more and more memory at each step
     for (int i = 0; i < 250; i++) {
-        array b = randu(num * (i + 1));
+        array b        = randu(num * (i + 1));
         size_t current = (i + 1) * step_bytes;
         allocated += current;
 
         // Verify that new buffers are being allocated
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         // Limit to 10 to check before garbage collection
         if (i < 10) {
-            ASSERT_EQ(alloc_buffers, (size_t)(i + 2)); //i is zero based
+            ASSERT_EQ(alloc_buffers, (size_t)(i + 2));  // i is zero based
             ASSERT_EQ(lock_buffers, 2u);
 
             ASSERT_EQ(alloc_bytes, allocated);
@@ -283,11 +249,10 @@ TEST(Memory, LargeLoop)
         }
     }
 
-    size_t old_alloc_bytes = alloc_bytes;
+    size_t old_alloc_bytes   = alloc_bytes;
     size_t old_alloc_buffers = alloc_buffers;
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(old_alloc_bytes, alloc_bytes);
     ASSERT_EQ(old_alloc_buffers, alloc_buffers);
@@ -296,19 +261,17 @@ TEST(Memory, LargeLoop)
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
 }
 
-TEST(Memory, IndexingOffset)
-{
+TEST(Memory, IndexingOffset) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     const int num = step_bytes / sizeof(float);
 
     array a = randu(num);
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -316,10 +279,9 @@ TEST(Memory, IndexingOffset)
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
 
     {
-        array b = a(seq(1, num/2)); // Should just be an offset
+        array b = a(seq(1, num / 2));  // Should just be an offset
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -327,31 +289,26 @@ TEST(Memory, IndexingOffset)
         ASSERT_EQ(lock_bytes, 1 * step_bytes);
     }
 
-
     // b should not have deleted a
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
     ASSERT_EQ(alloc_bytes, 1 * step_bytes);
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
-
 }
 
-TEST(Memory, IndexingCopy)
-{
+TEST(Memory, IndexingCopy) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     const int num = step_bytes / sizeof(float);
 
     array a = randu(num);
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -360,10 +317,9 @@ TEST(Memory, IndexingCopy)
 
     {
         // Should just a copy
-        array b = a(seq(0, num/2-1, 2));
+        array b = a(seq(0, num / 2 - 1, 2));
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 2u);
         ASSERT_EQ(lock_buffers, 2u);
@@ -371,31 +327,26 @@ TEST(Memory, IndexingCopy)
         ASSERT_EQ(lock_bytes, 2 * step_bytes);
     }
 
-
     // b should not have deleted a
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 2u);
     ASSERT_EQ(lock_buffers, 1u);
     ASSERT_EQ(alloc_bytes, 2 * step_bytes);
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
-
 }
 
-TEST(Memory, Assign)
-{
+TEST(Memory, Assign) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     const int num = step_bytes / sizeof(float);
 
     array a = randu(num);
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -403,11 +354,10 @@ TEST(Memory, Assign)
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
 
     {
-        array b = randu(num / 2);
+        array b         = randu(num / 2);
         a(seq(num / 2)) = b;
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 2u);
         ASSERT_EQ(lock_buffers, 2u);
@@ -415,32 +365,27 @@ TEST(Memory, Assign)
         ASSERT_EQ(lock_bytes, 2 * step_bytes);
     }
 
-
     // b should not have deleted a
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 2u);
     ASSERT_EQ(lock_buffers, 1u);
     ASSERT_EQ(alloc_bytes, 2 * step_bytes);
     ASSERT_EQ(lock_bytes, 1 * step_bytes);
-
 }
 
-TEST(Memory, AssignLoop)
-{
+TEST(Memory, AssignLoop) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
-    const int num = step_bytes / sizeof(float);
+    const int num  = step_bytes / sizeof(float);
     const int cols = 100;
 
     array a = randu(num, cols);
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -448,34 +393,31 @@ TEST(Memory, AssignLoop)
     ASSERT_EQ(lock_bytes, cols * step_bytes);
 
     for (int i = 0; i < cols; i++) {
-
-        array b = randu(num);
+        array b    = randu(num);
         a(span, i) = b;
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
-        ASSERT_EQ(alloc_buffers, 2u); // 3 because you need another scratch space for b
+        ASSERT_EQ(alloc_buffers,
+                  2u);  // 3 because you need another scratch space for b
         ASSERT_EQ(lock_buffers, 2u);
         ASSERT_EQ(alloc_bytes, (cols + 1) * step_bytes);
         ASSERT_EQ(lock_bytes, (cols + 1) * step_bytes);
     }
 }
 
-TEST(Memory, AssignRef)
-{
+TEST(Memory, AssignRef) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     const int num = step_bytes / sizeof(float);
 
-    array a = randu(num);
+    array a     = randu(num);
     array a_ref = a;
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -487,8 +429,7 @@ TEST(Memory, AssignRef)
         // This should do a full copy of a
         a(seq(num / 2)) = b;
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 3u);
         ASSERT_EQ(lock_buffers, 3u);
@@ -496,33 +437,28 @@ TEST(Memory, AssignRef)
         ASSERT_EQ(lock_bytes, 3 * step_bytes);
     }
 
-
     // b should not have deleted a
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 3u);
-    ASSERT_EQ(lock_buffers, 2u); // a_ref
+    ASSERT_EQ(lock_buffers, 2u);  // a_ref
     ASSERT_EQ(alloc_bytes, 3 * step_bytes);
-    ASSERT_EQ(lock_bytes, 2 * step_bytes); // a_ref
-
+    ASSERT_EQ(lock_bytes, 2 * step_bytes);  // a_ref
 }
 
-TEST(Memory, AssignRefLoop)
-{
+TEST(Memory, AssignRefLoop) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
-    const int num = step_bytes / sizeof(float);
+    const int num  = step_bytes / sizeof(float);
     const int cols = 100;
 
-    array a = randu(num, cols);
+    array a     = randu(num, cols);
     array a_ref = a;
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -530,12 +466,10 @@ TEST(Memory, AssignRefLoop)
     ASSERT_EQ(lock_bytes, cols * step_bytes);
 
     for (int i = 0; i < cols; i++) {
-
-        array b = randu(num);
+        array b    = randu(num);
         a(span, i) = b;
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 3u);
         ASSERT_EQ(lock_buffers, 3u);
@@ -543,30 +477,25 @@ TEST(Memory, AssignRefLoop)
         ASSERT_EQ(lock_bytes, (2 * cols + 1) * step_bytes);
     }
 
-
     // b should not have deleted a
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 3u);
-    ASSERT_EQ(lock_buffers, 2u); // a_ref
+    ASSERT_EQ(lock_buffers, 2u);  // a_ref
     ASSERT_EQ(alloc_bytes, (2 * cols + 1) * step_bytes);
-    ASSERT_EQ(lock_bytes, 2 * cols * step_bytes); // a_ref
-
+    ASSERT_EQ(lock_bytes, 2 * cols * step_bytes);  // a_ref
 }
 
-TEST(Memory, device)
-{
+TEST(Memory, device) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     {
         array a = randu(5, 5);
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -575,19 +504,17 @@ TEST(Memory, device)
 
         a.device<float>();
 
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
         ASSERT_EQ(alloc_bytes, 1 * step_bytes);
         ASSERT_EQ(lock_bytes, 1 * lock_bytes);
 
-        a.unlock(); //to reset the lock flag
+        a.unlock();  // to reset the lock flag
     }
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 0u);
@@ -595,13 +522,11 @@ TEST(Memory, device)
     ASSERT_EQ(lock_bytes, 0u);
 }
 
-TEST(Memory, unlock)
-{
-
+TEST(Memory, unlock) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     const dim_t num = step_bytes / sizeof(float);
 
@@ -610,8 +535,7 @@ TEST(Memory, unlock)
     af_array arr = 0;
     ASSERT_SUCCESS(af_create_array(&arr, &in[0], 1, &num, f32));
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -624,8 +548,7 @@ TEST(Memory, unlock)
         a.lock();
 
         // No new memory should be allocated
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -638,8 +561,7 @@ TEST(Memory, unlock)
     // Making sure all unlocked buffers are freed
     deviceGC();
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                  &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 0u);
     ASSERT_EQ(lock_buffers, 0u);
@@ -647,9 +569,9 @@ TEST(Memory, unlock)
     ASSERT_EQ(lock_bytes, 0u);
 }
 
-TEST(Memory, IndexedDevice)
-{
-    // This test is checking to see if calling .device() will force copy to a new buffer
+TEST(Memory, IndexedDevice) {
+    // This test is checking to see if calling .device() will force copy to a
+    // new buffer
     const int nx = 8;
     const int ny = 8;
 
@@ -661,8 +583,7 @@ TEST(Memory, IndexedDevice)
     int offx = nx / 4;
     int offy = ny / 4;
 
-    in = in(seq(offx, offx + nx/2 - 1),
-            seq(offy, offy + ny/2- 1));
+    in = in(seq(offx, offx + nx / 2 - 1), seq(offy, offy + ny / 2 - 1));
 
     int nxo = (int)in.dims(0);
     int nyo = (int)in.dims(1);
diff --git a/test/memory_lock.cpp b/test/memory_lock.cpp
index dd6bb3d5f2..5f1ca8034e 100644
--- a/test/memory_lock.cpp
+++ b/test/memory_lock.cpp
@@ -7,33 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
 using af::deviceGC;
 using af::deviceMemInfo;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 const size_t step_bytes = 1024;
 
 // This test should be by itself as it leaks memory intentionally
-TEST(Memory, lock)
-{
-
+TEST(Memory, lock) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    cleanSlate(); // Clean up everything done so far
+    cleanSlate();  // Clean up everything done so far
 
     const dim_t num = step_bytes / sizeof(float);
 
@@ -42,8 +40,7 @@ TEST(Memory, lock)
     af_array arr = 0;
     ASSERT_SUCCESS(af_create_array(&arr, &in[0], 1, &num, f32));
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
@@ -56,8 +53,7 @@ TEST(Memory, lock)
         a.lock();
 
         // No new memory should be allocated
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         ASSERT_EQ(alloc_buffers, 1u);
         ASSERT_EQ(lock_buffers, 1u);
@@ -68,8 +64,7 @@ TEST(Memory, lock)
     // Making sure all unlocked buffers are freed
     deviceGC();
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
     ASSERT_EQ(alloc_buffers, 1u);
     ASSERT_EQ(lock_buffers, 1u);
diff --git a/test/missing.cpp b/test/missing.cpp
index c06fdf8220..92eda5de4c 100644
--- a/test/missing.cpp
+++ b/test/missing.cpp
@@ -8,16 +8,15 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <af/array.h>
+#include <testHelpers.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
-#include <testHelpers.hpp>
 
 using namespace af;
 
-TEST(MissingFunctionTests, Dummy)
-{
-    array A = randu(10,10, f32);
+TEST(MissingFunctionTests, Dummy) {
+    array A = randu(10, 10, f32);
     af_print(A);
     af_print(arg(A));
     af_print(arg(complex(A, A)));
@@ -27,7 +26,7 @@ TEST(MissingFunctionTests, Dummy)
     af_print(root(2, A));
     af_print(A - 0.5);
     af_print(sign(A - 0.5));
-    af_print(minfilt(A, 3, 3) - erode(A, constant(1, 3,3)));
-    af_print(maxfilt(A, 3, 3) - dilate(A, constant(1, 3,3)));
+    af_print(minfilt(A, 3, 3) - erode(A, constant(1, 3, 3)));
+    af_print(maxfilt(A, 3, 3) - dilate(A, constant(1, 3, 3)));
     printf("%lf\n", norm(A));
 }
diff --git a/test/mmio/mmio.c b/test/mmio/mmio.c
index 4a1640791b..5777bf8fdb 100644
--- a/test/mmio/mmio.c
+++ b/test/mmio/mmio.c
@@ -1,21 +1,20 @@
 /*
-*   Matrix Market I/O library for ANSI C
-*
-*   See http://math.nist.gov/MatrixMarket for details.
-*
-*
-*/
+ *   Matrix Market I/O library for ANSI C
+ *
+ *   See http://math.nist.gov/MatrixMarket for details.
+ *
+ *
+ */
 
+#include <ctype.h>
 #include <stdio.h>
-#include <string.h>
 #include <stdlib.h>
-#include <ctype.h>
+#include <string.h>
 
 #include "mmio.h"
 
 int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
-                double **val_, int **I_, int **J_)
-{
+                               double **val_, int **I_, int **J_) {
     FILE *f;
     MM_typecode matcode;
     int M, N, nz;
@@ -23,22 +22,16 @@ int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
     double *val;
     int *I, *J;
 
-    if ((f = fopen(fname, "r")) == NULL)
-            return -1;
-
+    if ((f = fopen(fname, "r")) == NULL) return -1;
 
-    if (mm_read_banner(f, &matcode) != 0)
-    {
+    if (mm_read_banner(f, &matcode) != 0) {
         printf("mm_read_unsymetric: Could not process Matrix Market banner ");
         printf(" in file [%s]\n", fname);
         return -1;
     }
 
-
-
-    if ( !(mm_is_real(matcode) && mm_is_matrix(matcode) &&
-            mm_is_sparse(matcode)))
-    {
+    if (!(mm_is_real(matcode) && mm_is_matrix(matcode) &&
+          mm_is_sparse(matcode))) {
         fprintf(stderr, "Sorry, this application does not support ");
         fprintf(stderr, "Market Market type: [%s]\n",
                 mm_typecode_to_str(matcode));
@@ -47,34 +40,33 @@ int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
 
     /* find out size of sparse matrix: M, N, nz .... */
 
-    if (mm_read_mtx_crd_size(f, &M, &N, &nz) !=0)
-    {
-        fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n");
+    if (mm_read_mtx_crd_size(f, &M, &N, &nz) != 0) {
+        fprintf(stderr,
+                "read_unsymmetric_sparse(): could not parse matrix size.\n");
         return -1;
     }
 
-    *M_ = M;
-    *N_ = N;
+    *M_  = M;
+    *N_  = N;
     *nz_ = nz;
 
     /* reseve memory for matrices */
 
-    I = (int *) malloc(nz * sizeof(int));
-    J = (int *) malloc(nz * sizeof(int));
-    val = (double *) malloc(nz * sizeof(double));
+    I   = (int *)malloc(nz * sizeof(int));
+    J   = (int *)malloc(nz * sizeof(int));
+    val = (double *)malloc(nz * sizeof(double));
 
     *val_ = val;
-    *I_ = I;
-    *J_ = J;
+    *I_   = I;
+    *J_   = J;
 
     /* NOTE: when reading in doubles, ANSI C requires the use of the "l"  */
     /*   specifier as in "%lg", "%lf", "%le", otherwise errors will occur */
     /*  (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15)            */
 
-    for (i=0; i<nz; i++)
-    {
+    for (i = 0; i < nz; i++) {
         fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]);
-        I[i]--;  /* adjust from 1-based to 0-based */
+        I[i]--; /* adjust from 1-based to 0-based */
         J[i]--;
     }
     fclose(f);
@@ -82,18 +74,17 @@ int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
     return 0;
 }
 
-int mm_is_valid(MM_typecode matcode)
-{
+int mm_is_valid(MM_typecode matcode) {
     if (!mm_is_matrix(matcode)) return 0;
     if (mm_is_dense(matcode) && mm_is_pattern(matcode)) return 0;
     if (mm_is_real(matcode) && mm_is_hermitian(matcode)) return 0;
-    if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) ||
-                mm_is_skew(matcode))) return 0;
+    if (mm_is_pattern(matcode) &&
+        (mm_is_hermitian(matcode) || mm_is_skew(matcode)))
+        return 0;
     return 1;
 }
 
-int mm_read_banner(FILE *f, MM_typecode *matcode)
-{
+int mm_read_banner(FILE *f, MM_typecode *matcode) {
     char line[MM_MAX_LINE_LENGTH];
     char banner[MM_MAX_TOKEN_LENGTH];
     char mtx[MM_MAX_TOKEN_LENGTH];
@@ -102,91 +93,78 @@ int mm_read_banner(FILE *f, MM_typecode *matcode)
     char storage_scheme[MM_MAX_TOKEN_LENGTH];
     char *p;
 
-
     mm_clear_typecode(matcode);
 
-    if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL)
-        return MM_PREMATURE_EOF;
+    if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) return MM_PREMATURE_EOF;
 
     if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type,
-        storage_scheme) != 5)
+               storage_scheme) != 5)
         return MM_PREMATURE_EOF;
 
-    for (p=mtx; *p!='\0'; *p=tolower(*p),p++);  /* convert to lower case */
-    for (p=crd; *p!='\0'; *p=tolower(*p),p++);
-    for (p=data_type; *p!='\0'; *p=tolower(*p),p++);
-    for (p=storage_scheme; *p!='\0'; *p=tolower(*p),p++);
+    for (p = mtx; *p != '\0'; *p = tolower(*p), p++)
+        ; /* convert to lower case */
+    for (p = crd; *p != '\0'; *p = tolower(*p), p++)
+        ;
+    for (p = data_type; *p != '\0'; *p = tolower(*p), p++)
+        ;
+    for (p = storage_scheme; *p != '\0'; *p = tolower(*p), p++)
+        ;
 
     /* check for banner */
     if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0)
         return MM_NO_HEADER;
 
     /* first field should be "mtx" */
-    if (strcmp(mtx, MM_MTX_STR) != 0)
-        return  MM_UNSUPPORTED_TYPE;
+    if (strcmp(mtx, MM_MTX_STR) != 0) return MM_UNSUPPORTED_TYPE;
     mm_set_matrix(matcode);
 
-
     /* second field describes whether this is a sparse matrix (in coordinate
             storgae) or a dense array */
 
-
     if (strcmp(crd, MM_SPARSE_STR) == 0)
         mm_set_sparse(matcode);
-    else
-    if (strcmp(crd, MM_DENSE_STR) == 0)
-            mm_set_dense(matcode);
+    else if (strcmp(crd, MM_DENSE_STR) == 0)
+        mm_set_dense(matcode);
     else
         return MM_UNSUPPORTED_TYPE;
 
-
     /* third field */
 
     if (strcmp(data_type, MM_REAL_STR) == 0)
         mm_set_real(matcode);
-    else
-    if (strcmp(data_type, MM_COMPLEX_STR) == 0)
+    else if (strcmp(data_type, MM_COMPLEX_STR) == 0)
         mm_set_complex(matcode);
-    else
-    if (strcmp(data_type, MM_PATTERN_STR) == 0)
+    else if (strcmp(data_type, MM_PATTERN_STR) == 0)
         mm_set_pattern(matcode);
-    else
-    if (strcmp(data_type, MM_INT_STR) == 0)
+    else if (strcmp(data_type, MM_INT_STR) == 0)
         mm_set_integer(matcode);
     else
         return MM_UNSUPPORTED_TYPE;
 
-
     /* fourth field */
 
     if (strcmp(storage_scheme, MM_GENERAL_STR) == 0)
         mm_set_general(matcode);
-    else
-    if (strcmp(storage_scheme, MM_SYMM_STR) == 0)
+    else if (strcmp(storage_scheme, MM_SYMM_STR) == 0)
         mm_set_symmetric(matcode);
-    else
-    if (strcmp(storage_scheme, MM_HERM_STR) == 0)
+    else if (strcmp(storage_scheme, MM_HERM_STR) == 0)
         mm_set_hermitian(matcode);
-    else
-    if (strcmp(storage_scheme, MM_SKEW_STR) == 0)
+    else if (strcmp(storage_scheme, MM_SKEW_STR) == 0)
         mm_set_skew(matcode);
     else
         return MM_UNSUPPORTED_TYPE;
 
-
     return 0;
 }
 
-int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz)
-{
+int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz) {
     if (fprintf(f, "%d %d %d\n", M, N, nz) != 3)
         return MM_COULD_NOT_WRITE_FILE;
     else
         return 0;
 }
 
-int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz )
-{
+int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz) {
     char line[MM_MAX_LINE_LENGTH];
     int num_items_read;
 
@@ -194,67 +172,54 @@ int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz )
     *M = *N = *nz = 0;
 
     /* now continue scanning until you reach the end-of-comments */
-    do
-    {
-        if (fgets(line,MM_MAX_LINE_LENGTH,f) == NULL)
-            return MM_PREMATURE_EOF;
-    }while (line[0] == '%');
+    do {
+        if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) return MM_PREMATURE_EOF;
+    } while (line[0] == '%');
 
     /* line[] is either blank or has M,N, nz */
     if (sscanf(line, "%d %d %d", M, N, nz) == 3)
         return 0;
 
     else
-    do
-    {
-        num_items_read = fscanf(f, "%d %d %d", M, N, nz);
-        if (num_items_read == EOF) return MM_PREMATURE_EOF;
-    }
-    while (num_items_read != 3);
+        do {
+            num_items_read = fscanf(f, "%d %d %d", M, N, nz);
+            if (num_items_read == EOF) return MM_PREMATURE_EOF;
+        } while (num_items_read != 3);
 
     return 0;
 }
 
-
-int mm_read_mtx_array_size(FILE *f, int *M, int *N)
-{
+int mm_read_mtx_array_size(FILE *f, int *M, int *N) {
     char line[MM_MAX_LINE_LENGTH];
     int num_items_read;
     /* set return null parameter values, in case we exit with errors */
     *M = *N = 0;
 
     /* now continue scanning until you reach the end-of-comments */
-    do
-    {
-        if (fgets(line,MM_MAX_LINE_LENGTH,f) == NULL)
-            return MM_PREMATURE_EOF;
-    }while (line[0] == '%');
+    do {
+        if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) return MM_PREMATURE_EOF;
+    } while (line[0] == '%');
 
     /* line[] is either blank or has M,N, nz */
     if (sscanf(line, "%d %d", M, N) == 2)
         return 0;
 
     else /* we have a blank line */
-    do
-    {
-        num_items_read = fscanf(f, "%d %d", M, N);
-        if (num_items_read == EOF) return MM_PREMATURE_EOF;
-    }
-    while (num_items_read != 2);
+        do {
+            num_items_read = fscanf(f, "%d %d", M, N);
+            if (num_items_read == EOF) return MM_PREMATURE_EOF;
+        } while (num_items_read != 2);
 
     return 0;
 }
 
-int mm_write_mtx_array_size(FILE *f, int M, int N)
-{
+int mm_write_mtx_array_size(FILE *f, int M, int N) {
     if (fprintf(f, "%d %d\n", M, N) != 2)
         return MM_COULD_NOT_WRITE_FILE;
     else
         return 0;
 }
 
-
-
 /*-------------------------------------------------------------------------*/
 
 /******************************************************************/
@@ -262,65 +227,47 @@ int mm_write_mtx_array_size(FILE *f, int M, int N)
 /******************************************************************/
 
 int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[],
-        double val[], MM_typecode matcode)
-{
+                         double val[], MM_typecode matcode) {
     int i;
-    if (mm_is_complex(matcode))
-    {
-        for (i=0; i<nz; i++)
-            if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2*i], &val[2*i+1])
-                != 4) return MM_PREMATURE_EOF;
-    }
-    else if (mm_is_real(matcode))
-    {
-        for (i=0; i<nz; i++)
-        {
-            if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i])
-                != 3) return MM_PREMATURE_EOF;
-
+    if (mm_is_complex(matcode)) {
+        for (i = 0; i < nz; i++)
+            if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2 * i],
+                       &val[2 * i + 1]) != 4)
+                return MM_PREMATURE_EOF;
+    } else if (mm_is_real(matcode)) {
+        for (i = 0; i < nz; i++) {
+            if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3)
+                return MM_PREMATURE_EOF;
         }
     }
 
-    else if (mm_is_pattern(matcode))
-    {
-        for (i=0; i<nz; i++)
-            if (fscanf(f, "%d %d", &I[i], &J[i])
-                != 2) return MM_PREMATURE_EOF;
-    }
-    else
+    else if (mm_is_pattern(matcode)) {
+        for (i = 0; i < nz; i++)
+            if (fscanf(f, "%d %d", &I[i], &J[i]) != 2) return MM_PREMATURE_EOF;
+    } else
         return MM_UNSUPPORTED_TYPE;
 
     return 0;
-
 }
 
-int mm_read_mtx_crd_entry(FILE *f, int *I, int *J,
-        double *real, double *imag, MM_typecode matcode)
-{
-    if (mm_is_complex(matcode))
-    {
-            if (fscanf(f, "%d %d %lg %lg", I, J, real, imag)
-                != 4) return MM_PREMATURE_EOF;
-    }
-    else if (mm_is_real(matcode))
-    {
-            if (fscanf(f, "%d %d %lg\n", I, J, real)
-                != 3) return MM_PREMATURE_EOF;
+int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *imag,
+                          MM_typecode matcode) {
+    if (mm_is_complex(matcode)) {
+        if (fscanf(f, "%d %d %lg %lg", I, J, real, imag) != 4)
+            return MM_PREMATURE_EOF;
+    } else if (mm_is_real(matcode)) {
+        if (fscanf(f, "%d %d %lg\n", I, J, real) != 3) return MM_PREMATURE_EOF;
 
     }
 
-    else if (mm_is_pattern(matcode))
-    {
-            if (fscanf(f, "%d %d", I, J) != 2) return MM_PREMATURE_EOF;
-    }
-    else
+    else if (mm_is_pattern(matcode)) {
+        if (fscanf(f, "%d %d", I, J) != 2) return MM_PREMATURE_EOF;
+    } else
         return MM_UNSUPPORTED_TYPE;
 
     return 0;
-
 }
 
-
 /************************************************************************
     mm_read_mtx_crd()  fills M, N, nz, array of values, and return
                         type code, e.g. 'MCRS'
@@ -330,51 +277,39 @@ int mm_read_mtx_crd_entry(FILE *f, int *I, int *J,
 ************************************************************************/
 
 int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J,
-        double **val, MM_typecode *matcode)
-{
+                    double **val, MM_typecode *matcode) {
     int ret_code;
     FILE *f;
 
-    if (strcmp(fname, "stdin") == 0) f=stdin;
-    else
-    if ((f = fopen(fname, "r")) == NULL)
+    if (strcmp(fname, "stdin") == 0)
+        f = stdin;
+    else if ((f = fopen(fname, "r")) == NULL)
         return MM_COULD_NOT_READ_FILE;
 
-
-    if ((ret_code = mm_read_banner(f, matcode)) != 0)
-        return ret_code;
+    if ((ret_code = mm_read_banner(f, matcode)) != 0) return ret_code;
 
     if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) &&
-            mm_is_matrix(*matcode)))
+          mm_is_matrix(*matcode)))
         return MM_UNSUPPORTED_TYPE;
 
-    if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0)
-        return ret_code;
+    if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0) return ret_code;
 
-
-    *I = (int *)  malloc(*nz * sizeof(int));
-    *J = (int *)  malloc(*nz * sizeof(int));
+    *I   = (int *)malloc(*nz * sizeof(int));
+    *J   = (int *)malloc(*nz * sizeof(int));
     *val = NULL;
 
-    if (mm_is_complex(*matcode))
-    {
-        *val = (double *) malloc(*nz * 2 * sizeof(double));
-        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
-                *matcode);
+    if (mm_is_complex(*matcode)) {
+        *val     = (double *)malloc(*nz * 2 * sizeof(double));
+        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode);
         if (ret_code != 0) return ret_code;
-    }
-    else if (mm_is_real(*matcode))
-    {
-        *val = (double *) malloc(*nz * sizeof(double));
-        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
-                *matcode);
+    } else if (mm_is_real(*matcode)) {
+        *val     = (double *)malloc(*nz * sizeof(double));
+        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode);
         if (ret_code != 0) return ret_code;
     }
 
-    else if (mm_is_pattern(*matcode))
-    {
-        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val,
-                *matcode);
+    else if (mm_is_pattern(*matcode)) {
+        ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode);
         if (ret_code != 0) return ret_code;
     }
 
@@ -382,29 +317,26 @@ int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J,
     return 0;
 }
 
-int mm_write_banner(FILE *f, MM_typecode matcode)
-{
+int mm_write_banner(FILE *f, MM_typecode matcode) {
     char *str = mm_typecode_to_str(matcode);
     int ret_code;
 
     ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str);
     free(str);
-    if (ret_code !=2 )
+    if (ret_code != 2)
         return MM_COULD_NOT_WRITE_FILE;
     else
         return 0;
 }
 
 int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[],
-        double val[], MM_typecode matcode)
-{
+                     double val[], MM_typecode matcode) {
     FILE *f;
     int i;
 
     if (strcmp(fname, "stdout") == 0)
         f = stdout;
-    else
-    if ((f = fopen(fname, "w")) == NULL)
+    else if ((f = fopen(fname, "w")) == NULL)
         return MM_COULD_NOT_WRITE_FILE;
 
     /* print banner followed by typecode */
@@ -416,59 +348,51 @@ int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[],
 
     /* print values */
     if (mm_is_pattern(matcode))
-        for (i=0; i<nz; i++)
-            fprintf(f, "%d %d\n", I[i], J[i]);
-    else
-    if (mm_is_real(matcode))
-        for (i=0; i<nz; i++)
+        for (i = 0; i < nz; i++) fprintf(f, "%d %d\n", I[i], J[i]);
+    else if (mm_is_real(matcode))
+        for (i = 0; i < nz; i++)
             fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]);
-    else
-    if (mm_is_complex(matcode))
-        for (i=0; i<nz; i++)
-            fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2*i],
-                        val[2*i+1]);
-    else
-    {
+    else if (mm_is_complex(matcode))
+        for (i = 0; i < nz; i++)
+            fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2 * i],
+                    val[2 * i + 1]);
+    else {
         if (f != stdout) fclose(f);
         return MM_UNSUPPORTED_TYPE;
     }
 
-    if (f !=stdout) fclose(f);
+    if (f != stdout) fclose(f);
 
     return 0;
 }
 
-
 /**
-*  Create a new copy of a string s.  mm_strdup() is a common routine, but
-*  not part of ANSI C, so it is included here.  Used by mm_typecode_to_str().
-*
-*/
-char *mm_strdup(const char *s)
-{
-	int len = strlen(s);
-	char *s2 = (char *) malloc((len+1)*sizeof(char));
-	return strcpy(s2, s);
+ *  Create a new copy of a string s.  mm_strdup() is a common routine, but
+ *  not part of ANSI C, so it is included here.  Used by mm_typecode_to_str().
+ *
+ */
+char *mm_strdup(const char *s) {
+    int len  = strlen(s);
+    char *s2 = (char *)malloc((len + 1) * sizeof(char));
+    return strcpy(s2, s);
 }
 
-char  *mm_typecode_to_str(MM_typecode matcode)
-{
+char *mm_typecode_to_str(MM_typecode matcode) {
     char buffer[MM_MAX_LINE_LENGTH];
     char *types[4];
-	char *mm_strdup(const char *);
-    int error =0;
+    char *mm_strdup(const char *);
+    int error = 0;
 
     /* check for MTX type */
     if (mm_is_matrix(matcode))
         types[0] = MM_MTX_STR;
     else
-        error=1;
+        error = 1;
 
     /* check for CRD or ARR matrix */
     if (mm_is_sparse(matcode))
         types[1] = MM_SPARSE_STR;
-    else
-    if (mm_is_dense(matcode))
+    else if (mm_is_dense(matcode))
         types[1] = MM_DENSE_STR;
     else
         return NULL;
@@ -476,35 +400,27 @@ char  *mm_typecode_to_str(MM_typecode matcode)
     /* check for element data type */
     if (mm_is_real(matcode))
         types[2] = MM_REAL_STR;
-    else
-    if (mm_is_complex(matcode))
+    else if (mm_is_complex(matcode))
         types[2] = MM_COMPLEX_STR;
-    else
-    if (mm_is_pattern(matcode))
+    else if (mm_is_pattern(matcode))
         types[2] = MM_PATTERN_STR;
-    else
-    if (mm_is_integer(matcode))
+    else if (mm_is_integer(matcode))
         types[2] = MM_INT_STR;
     else
         return NULL;
 
-
     /* check for symmetry type */
     if (mm_is_general(matcode))
         types[3] = MM_GENERAL_STR;
-    else
-    if (mm_is_symmetric(matcode))
+    else if (mm_is_symmetric(matcode))
         types[3] = MM_SYMM_STR;
-    else
-    if (mm_is_hermitian(matcode))
+    else if (mm_is_hermitian(matcode))
         types[3] = MM_HERM_STR;
-    else
-    if (mm_is_skew(matcode))
+    else if (mm_is_skew(matcode))
         types[3] = MM_SKEW_STR;
     else
         return NULL;
 
-    sprintf(buffer,"%s %s %s %s", types[0], types[1], types[2], types[3]);
+    sprintf(buffer, "%s %s %s %s", types[0], types[1], types[2], types[3]);
     return mm_strdup(buffer);
-
 }
diff --git a/test/mmio/mmio.h b/test/mmio/mmio.h
index 51016caa30..160b8e6bc6 100644
--- a/test/mmio/mmio.h
+++ b/test/mmio/mmio.h
@@ -1,10 +1,10 @@
 /*
-*   Matrix Market I/O library for ANSI C
-*
-*   See http://math.nist.gov/MatrixMarket for details.
-*
-*
-*/
+ *   Matrix Market I/O library for ANSI C
+ *
+ *   See http://math.nist.gov/MatrixMarket for details.
+ *
+ *
+ */
 
 #ifndef MM_IO_H
 #define MM_IO_H
@@ -29,108 +29,102 @@ int mm_write_banner(FILE *f, MM_typecode matcode);
 int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz);
 int mm_write_mtx_array_size(FILE *f, int M, int N);
 
-
 /********************* MM_typecode query fucntions ***************************/
 
-#define mm_is_matrix(typecode)	((typecode)[0]=='M')
-
-#define mm_is_sparse(typecode)	((typecode)[1]=='C')
-#define mm_is_coordinate(typecode)((typecode)[1]=='C')
-#define mm_is_dense(typecode)	((typecode)[1]=='A')
-#define mm_is_array(typecode)	((typecode)[1]=='A')
+#define mm_is_matrix(typecode) ((typecode)[0] == 'M')
 
-#define mm_is_complex(typecode)	((typecode)[2]=='C')
-#define mm_is_real(typecode)		((typecode)[2]=='R')
-#define mm_is_pattern(typecode)	((typecode)[2]=='P')
-#define mm_is_integer(typecode) ((typecode)[2]=='I')
+#define mm_is_sparse(typecode) ((typecode)[1] == 'C')
+#define mm_is_coordinate(typecode) ((typecode)[1] == 'C')
+#define mm_is_dense(typecode) ((typecode)[1] == 'A')
+#define mm_is_array(typecode) ((typecode)[1] == 'A')
 
-#define mm_is_symmetric(typecode)((typecode)[3]=='S')
-#define mm_is_general(typecode)	((typecode)[3]=='G')
-#define mm_is_skew(typecode)	((typecode)[3]=='K')
-#define mm_is_hermitian(typecode)((typecode)[3]=='H')
+#define mm_is_complex(typecode) ((typecode)[2] == 'C')
+#define mm_is_real(typecode) ((typecode)[2] == 'R')
+#define mm_is_pattern(typecode) ((typecode)[2] == 'P')
+#define mm_is_integer(typecode) ((typecode)[2] == 'I')
 
-int mm_is_valid(MM_typecode matcode);		/* too complex for a macro */
+#define mm_is_symmetric(typecode) ((typecode)[3] == 'S')
+#define mm_is_general(typecode) ((typecode)[3] == 'G')
+#define mm_is_skew(typecode) ((typecode)[3] == 'K')
+#define mm_is_hermitian(typecode) ((typecode)[3] == 'H')
 
+int mm_is_valid(MM_typecode matcode); /* too complex for a macro */
 
 /********************* MM_typecode modify fucntions ***************************/
 
-#define mm_set_matrix(typecode)	((*typecode)[0]='M')
-#define mm_set_coordinate(typecode)	((*typecode)[1]='C')
-#define mm_set_array(typecode)	((*typecode)[1]='A')
-#define mm_set_dense(typecode)	mm_set_array(typecode)
-#define mm_set_sparse(typecode)	mm_set_coordinate(typecode)
+#define mm_set_matrix(typecode) ((*typecode)[0] = 'M')
+#define mm_set_coordinate(typecode) ((*typecode)[1] = 'C')
+#define mm_set_array(typecode) ((*typecode)[1] = 'A')
+#define mm_set_dense(typecode) mm_set_array(typecode)
+#define mm_set_sparse(typecode) mm_set_coordinate(typecode)
 
-#define mm_set_complex(typecode)((*typecode)[2]='C')
-#define mm_set_real(typecode)	((*typecode)[2]='R')
-#define mm_set_pattern(typecode)((*typecode)[2]='P')
-#define mm_set_integer(typecode)((*typecode)[2]='I')
+#define mm_set_complex(typecode) ((*typecode)[2] = 'C')
+#define mm_set_real(typecode) ((*typecode)[2] = 'R')
+#define mm_set_pattern(typecode) ((*typecode)[2] = 'P')
+#define mm_set_integer(typecode) ((*typecode)[2] = 'I')
 
+#define mm_set_symmetric(typecode) ((*typecode)[3] = 'S')
+#define mm_set_general(typecode) ((*typecode)[3] = 'G')
+#define mm_set_skew(typecode) ((*typecode)[3] = 'K')
+#define mm_set_hermitian(typecode) ((*typecode)[3] = 'H')
 
-#define mm_set_symmetric(typecode)((*typecode)[3]='S')
-#define mm_set_general(typecode)((*typecode)[3]='G')
-#define mm_set_skew(typecode)	((*typecode)[3]='K')
-#define mm_set_hermitian(typecode)((*typecode)[3]='H')
-
-#define mm_clear_typecode(typecode) ((*typecode)[0]=(*typecode)[1]= \
-									(*typecode)[2]=' ',(*typecode)[3]='G')
+#define mm_clear_typecode(typecode)                          \
+    ((*typecode)[0] = (*typecode)[1] = (*typecode)[2] = ' ', \
+     (*typecode)[3]                                   = 'G')
 
 #define mm_initialize_typecode(typecode) mm_clear_typecode(typecode)
 
-
 /********************* Matrix Market error codes ***************************/
 
-
-#define MM_COULD_NOT_READ_FILE	11
-#define MM_PREMATURE_EOF		12
-#define MM_NOT_MTX				13
-#define MM_NO_HEADER			14
-#define MM_UNSUPPORTED_TYPE		15
-#define MM_LINE_TOO_LONG		16
-#define MM_COULD_NOT_WRITE_FILE	17
-
+#define MM_COULD_NOT_READ_FILE 11
+#define MM_PREMATURE_EOF 12
+#define MM_NOT_MTX 13
+#define MM_NO_HEADER 14
+#define MM_UNSUPPORTED_TYPE 15
+#define MM_LINE_TOO_LONG 16
+#define MM_COULD_NOT_WRITE_FILE 17
 
 /******************** Matrix Market internal definitions ********************
 
    MM_matrix_typecode: 4-character sequence
 
-				    ojbect 		sparse/   	data        storage
-						  		dense     	type        scheme
+                    ojbect 		sparse/   	data        storage
+                                dense     	type        scheme
 
    string position:	 [0]        [1]			[2]         [3]
 
    Matrix typecode:  M(atrix)  C(oord)		R(eal)   	G(eneral)
-						        A(array)	C(omplex)   H(ermitian)
-											P(attern)   S(ymmetric)
-								    		I(nteger)	K(kew)
+                                A(array)	C(omplex)   H(ermitian)
+                                            P(attern)   S(ymmetric)
+                                            I(nteger)	K(kew)
 
  ***********************************************************************/
 
-#define MM_MTX_STR		"matrix"
-#define MM_ARRAY_STR	"array"
-#define MM_DENSE_STR	"array"
+#define MM_MTX_STR "matrix"
+#define MM_ARRAY_STR "array"
+#define MM_DENSE_STR "array"
 #define MM_COORDINATE_STR "coordinate"
-#define MM_SPARSE_STR	"coordinate"
-#define MM_COMPLEX_STR	"complex"
-#define MM_REAL_STR		"real"
-#define MM_INT_STR		"integer"
-#define MM_GENERAL_STR  "general"
-#define MM_SYMM_STR		"symmetric"
-#define MM_HERM_STR		"hermitian"
-#define MM_SKEW_STR		"skew-symmetric"
-#define MM_PATTERN_STR  "pattern"
-
+#define MM_SPARSE_STR "coordinate"
+#define MM_COMPLEX_STR "complex"
+#define MM_REAL_STR "real"
+#define MM_INT_STR "integer"
+#define MM_GENERAL_STR "general"
+#define MM_SYMM_STR "symmetric"
+#define MM_HERM_STR "hermitian"
+#define MM_SKEW_STR "skew-symmetric"
+#define MM_PATTERN_STR "pattern"
 
 /*  high level routines */
 
 int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[],
-		 double val[], MM_typecode matcode);
+                     double val[], MM_typecode matcode);
 int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[],
-		double val[], MM_typecode matcode);
+                         double val[], MM_typecode matcode);
 int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img,
-			MM_typecode matcode);
+                          MM_typecode matcode);
 
 int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_,
-                double **val_, int **I_, int **J_);
+                               double **val_, int **I_, int **J_);
 
 #ifdef __cplusplus
 }
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 9fe79bf3a9..263a557cfd 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -7,195 +7,203 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Moddims : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat.push_back(af_make_seq(1,2,1));
-            subMat.push_back(af_make_seq(1,3,1));
-        }
-        vector<af_seq> subMat;
+class Moddims : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat.push_back(af_make_seq(1, 2, 1));
+        subMat.push_back(af_make_seq(1, 3, 1));
+    }
+    vector<af_seq> subMat;
 };
 
 // create a list of types to be tested
 // TODO: complex types tests have to be added
-typedef ::testing::Types<float, double, int, unsigned, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, unsigned, char, unsigned char,
+                         short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Moddims, TestTypes);
 
 template<typename T>
-void moddimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=NULL)
-{
+void moddimsTest(string pTestFile, bool isSubRef = false,
+                 const vector<af_seq> *seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     T *outData;
 
     if (isSubRef) {
-        af_array inArray   = 0;
-        af_array subArray  = 0;
-        af_array outArray  = 0;
+        af_array inArray  = 0;
+        af_array subArray = 0;
+        af_array outArray = 0;
 
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&subArray,inArray,seqv->size(),&seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&subArray, inArray, seqv->size(), &seqv->front()));
 
         dim4 newDims(1);
         newDims[0] = 2;
         newDims[1] = 3;
-        ASSERT_SUCCESS(af_moddims(&outArray,subArray,newDims.ndims(),newDims.get()));
+        ASSERT_SUCCESS(
+            af_moddims(&outArray, subArray, newDims.ndims(), newDims.get()));
 
         dim_t nElems;
-        ASSERT_SUCCESS(af_get_elements(&nElems,outArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems, outArray));
 
-        outData          = new T[nElems];
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+        outData = new T[nElems];
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(outArray));
         ASSERT_SUCCESS(af_release_array(subArray));
     } else {
-        af_array inArray   = 0;
-        af_array outArray  = 0;
+        af_array inArray  = 0;
+        af_array outArray = 0;
 
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
         dim4 newDims(1);
         newDims[0] = dims[1];
-        newDims[1] = dims[0]*dims[2];
-        ASSERT_SUCCESS(af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
+        newDims[1] = dims[0] * dims[2];
+        ASSERT_SUCCESS(
+            af_moddims(&outArray, inArray, newDims.ndims(), newDims.get()));
 
-        outData          = new T[dims.elements()];
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+        outData = new T[dims.elements()];
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(outArray));
     }
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
-        vector<T> currGoldBar   = tests[testIter];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
+        vector<T> currGoldBar = tests[testIter];
+        size_t nElems         = currGoldBar.size();
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
     }
     delete[] outData;
 }
 
-TYPED_TEST(Moddims,Basic)
-{
-    moddimsTest<TypeParam>(string(TEST_DIR"/moddims/basic.test"));
+TYPED_TEST(Moddims, Basic) {
+    moddimsTest<TypeParam>(string(TEST_DIR "/moddims/basic.test"));
 }
 
-TYPED_TEST(Moddims,Subref)
-{
-    moddimsTest<TypeParam>(string(TEST_DIR"/moddims/subref.test"),true,&(this->subMat));
+TYPED_TEST(Moddims, Subref) {
+    moddimsTest<TypeParam>(string(TEST_DIR "/moddims/subref.test"), true,
+                           &(this->subMat));
 }
 
-
 template<typename T>
-void moddimsArgsTest(string pTestFile)
-{
+void moddimsArgsTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     af_array inArray   = 0;
     af_array outArray  = 0;
-    af_array outArray2  = 0;
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    af_array outArray2 = 0;
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     dim4 newDims(1);
     newDims[0] = dims[1];
-    newDims[1] = dims[0]*dims[2];
-    ASSERT_SUCCESS(af_moddims(&outArray,inArray,0,newDims.get()));
-    ASSERT_EQ(AF_ERR_ARG, af_moddims(&outArray2,inArray,newDims.ndims(),NULL));
+    newDims[1] = dims[0] * dims[2];
+    ASSERT_SUCCESS(af_moddims(&outArray, inArray, 0, newDims.get()));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_moddims(&outArray2, inArray, newDims.ndims(), NULL));
 
     ASSERT_SUCCESS(af_release_array(inArray));
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(Moddims,InvalidArgs)
-{
-    moddimsArgsTest<TypeParam>(string(TEST_DIR"/moddims/basic.test"));
+TYPED_TEST(Moddims, InvalidArgs) {
+    moddimsArgsTest<TypeParam>(string(TEST_DIR "/moddims/basic.test"));
 }
 
 template<typename T>
-void moddimsMismatchTest(string pTestFile)
-{
+void moddimsMismatchTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
-    af_array inArray   = 0;
-    af_array outArray  = 0;
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    af_array inArray  = 0;
+    af_array outArray = 0;
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     dim4 newDims(1);
-    newDims[0] = dims[1]-1;
-    newDims[1] = (dims[0]-1)*dims[2];
-    ASSERT_EQ(AF_ERR_SIZE, af_moddims(&outArray,inArray,newDims.ndims(),newDims.get()));
+    newDims[0] = dims[1] - 1;
+    newDims[1] = (dims[0] - 1) * dims[2];
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_moddims(&outArray, inArray, newDims.ndims(), newDims.get()));
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(Moddims,Mismatch)
-{
-    moddimsMismatchTest<TypeParam>(string(TEST_DIR"/moddims/basic.test"));
+TYPED_TEST(Moddims, Mismatch) {
+    moddimsMismatchTest<TypeParam>(string(TEST_DIR "/moddims/basic.test"));
 }
 
-
 /////////////////////////////////// CPP ///////////////////////////////////
 //
 
 using af::array;
 
 template<typename T>
-void cppModdimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=NULL)
-{
+void cppModdimsTest(string pTestFile, bool isSubRef = false,
+                    const vector<af_seq> *seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
     T *outData;
 
@@ -205,45 +213,45 @@ void cppModdimsTest(string pTestFile, bool isSubRef=false, const vector<af_seq>
         array subArray = input(seqv->at(0), seqv->at(1));
 
         dim4 newDims(1);
-        newDims[0] = 2;
-        newDims[1] = 3;
+        newDims[0]   = 2;
+        newDims[1]   = 3;
         array output = moddims(subArray, newDims.ndims(), newDims.get());
 
         dim_t nElems = output.elements();
-        outData = new T[nElems];
-        output.host((void*)outData);
+        outData      = new T[nElems];
+        output.host((void *)outData);
     } else {
         array input(dims, &(in[0].front()));
 
         dim4 newDims(1);
         newDims[0] = dims[1];
-        newDims[1] = dims[0]*dims[2];
+        newDims[1] = dims[0] * dims[2];
 
         array output = moddims(input, newDims.ndims(), newDims.get());
 
         outData = new T[dims.elements()];
-        output.host((void*)outData);
+        output.host((void *)outData);
     }
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
-        vector<T> currGoldBar   = tests[testIter];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
+        vector<T> currGoldBar = tests[testIter];
+        size_t nElems         = currGoldBar.size();
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
     }
     delete[] outData;
 }
 
-TEST(Moddims,Basic_CPP)
-{
-    cppModdimsTest<float>(string(TEST_DIR"/moddims/basic.test"));
+TEST(Moddims, Basic_CPP) {
+    cppModdimsTest<float>(string(TEST_DIR "/moddims/basic.test"));
 }
 
-TEST(Moddims,Subref_CPP)
-{
+TEST(Moddims, Subref_CPP) {
     vector<af_seq> subMat;
-    subMat.push_back(af_make_seq(1,2,1));
-    subMat.push_back(af_make_seq(1,3,1));
-    cppModdimsTest<float>(string(TEST_DIR"/moddims/subref.test"),true,&subMat);
+    subMat.push_back(af_make_seq(1, 2, 1));
+    subMat.push_back(af_make_seq(1, 3, 1));
+    cppModdimsTest<float>(string(TEST_DIR "/moddims/subref.test"), true,
+                          &subMat);
 }
diff --git a/test/moments.cpp b/test/moments.cpp
index 331996673e..a88550d1c1 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -7,34 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::identity;
 using af::loadImage;
 using af::max;
 using af::min;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Image : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Image : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
@@ -44,14 +42,13 @@ typedef ::testing::Types<float, double, int> TestTypes;
 TYPED_TEST_CASE(Image, TestTypes);
 
 template<typename T>
-void momentsTest(string pTestFile)
-{
+void momentsTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<float> >   tests;
+    vector<vector<T> > in;
+    vector<vector<float> > tests;
     readTests<T, float, float>(pTestFile, numDims, in, tests);
 
     array imgArray(numDims.front(), &in.front()[0]);
@@ -59,46 +56,53 @@ void momentsTest(string pTestFile)
     array momentsArray = moments(imgArray, AF_MOMENT_M00);
     vector<float> mData(momentsArray.elements());
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[0][i], mData[i], 4e-3 * tests[0][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_M01);
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[1][i], mData[i], 8e-3 * tests[1][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_M10);
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[2][i], mData[i], 3e-3 * tests[2][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_M11);
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[3][i], mData[i], 7e-3 * tests[3][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_FIRST_ORDER);
     mData.resize(momentsArray.elements());
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements()/4;i+=4) {
-        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-3 * tests[0][i] ) << "at: " << i   << endl;
-        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-3 * tests[1][i] ) << "at: " << i+1 << endl;
-        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-3 * tests[2][i] ) << "at: " << i+2 << endl;
-        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-3 * tests[3][i] ) << "at: " << i+3 << endl;
+    for (int i = 0; i < momentsArray.elements() / 4; i += 4) {
+        ASSERT_NEAR(tests[0][i], mData[i], 1e-3 * tests[0][i])
+            << "at: " << i << endl;
+        ASSERT_NEAR(tests[1][i], mData[i + 1], 1e-3 * tests[1][i])
+            << "at: " << i + 1 << endl;
+        ASSERT_NEAR(tests[2][i], mData[i + 2], 1e-3 * tests[2][i])
+            << "at: " << i + 2 << endl;
+        ASSERT_NEAR(tests[3][i], mData[i + 3], 1e-3 * tests[3][i])
+            << "at: " << i + 3 << endl;
     }
 }
 
-void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
-{
+void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor) {
     if (noImageIOTests()) return;
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
     readTests<float, float, float>(pTestFile, numDims, in, tests);
 
     array imgArray = loadImage(pImageFile.c_str(), isColor);
@@ -112,61 +116,67 @@ void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor)
 
     vector<float> mData(momentsArray.elements());
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[0][i], mData[i], 1e-2 * tests[0][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[0][i], mData[i], 1e-2 * tests[0][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_M01);
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[1][i], mData[i], 1e-2 * tests[1][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[1][i], mData[i], 1e-2 * tests[1][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_M10);
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[2][i], mData[i], 1e-2 * tests[2][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[2][i], mData[i], 1e-2 * tests[2][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_M11);
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements();++i) {
-        ASSERT_NEAR(tests[3][i], mData[i], 1e-2 * tests[3][i] ) << "at: " << i << endl;
+    for (int i = 0; i < momentsArray.elements(); ++i) {
+        ASSERT_NEAR(tests[3][i], mData[i], 1e-2 * tests[3][i])
+            << "at: " << i << endl;
     }
 
     momentsArray = moments(imgArray, AF_MOMENT_FIRST_ORDER);
     mData.resize(momentsArray.elements());
     momentsArray.host(&mData[0]);
-    for(int i=0; i<momentsArray.elements()/4;i+=4) {
-        ASSERT_NEAR(tests[0][i], mData[i]  , 1e-2 * tests[0][i] ) << "at: " << i   << endl;
-        ASSERT_NEAR(tests[1][i], mData[i+1], 1e-2 * tests[1][i] ) << "at: " << i+1 << endl;
-        ASSERT_NEAR(tests[2][i], mData[i+2], 1e-2 * tests[2][i] ) << "at: " << i+2 << endl;
-        ASSERT_NEAR(tests[3][i], mData[i+3], 1e-2 * tests[3][i] ) << "at: " << i+3 << endl;
+    for (int i = 0; i < momentsArray.elements() / 4; i += 4) {
+        ASSERT_NEAR(tests[0][i], mData[i], 1e-2 * tests[0][i])
+            << "at: " << i << endl;
+        ASSERT_NEAR(tests[1][i], mData[i + 1], 1e-2 * tests[1][i])
+            << "at: " << i + 1 << endl;
+        ASSERT_NEAR(tests[2][i], mData[i + 2], 1e-2 * tests[2][i])
+            << "at: " << i + 2 << endl;
+        ASSERT_NEAR(tests[3][i], mData[i + 3], 1e-2 * tests[3][i])
+            << "at: " << i + 3 << endl;
     }
 }
 
-TEST(IMAGE, MomentsImage)
-{
-    momentsOnImageTest(string(TEST_DIR"/moments/gray_seq_16_moments.test"), string(TEST_DIR"/imageio/gray_seq_16.png"), false);
+TEST(IMAGE, MomentsImage) {
+    momentsOnImageTest(string(TEST_DIR "/moments/gray_seq_16_moments.test"),
+                       string(TEST_DIR "/imageio/gray_seq_16.png"), false);
 }
 
-TEST(Image, MomentsImageBatch)
-{
-    momentsTest<float>(string(TEST_DIR"/moments/simple_mat_batch_moments.test"));
+TEST(Image, MomentsImageBatch) {
+    momentsTest<float>(
+        string(TEST_DIR "/moments/simple_mat_batch_moments.test"));
 }
 
-TEST(Image, MomentsBatch2D)
-{
-    momentsOnImageTest(string(TEST_DIR"/moments/color_seq_16_moments.test"), string(TEST_DIR"/imageio/color_seq_16.png"), true);
+TEST(Image, MomentsBatch2D) {
+    momentsOnImageTest(string(TEST_DIR "/moments/color_seq_16_moments.test"),
+                       string(TEST_DIR "/imageio/color_seq_16.png"), true);
 }
 
-TYPED_TEST(Image, MomentsSynthTypes)
-{
-    momentsTest<TypeParam>(string(TEST_DIR"/moments/simple_mat_moments.test"));
+TYPED_TEST(Image, MomentsSynthTypes) {
+    momentsTest<TypeParam>(string(TEST_DIR "/moments/simple_mat_moments.test"));
 }
 
-TEST(Image, Moment_Issue1957)
-{
+TEST(Image, Moment_Issue1957) {
     array A = identity(3, 3, b8);
 
     double m00;
diff --git a/test/morph.cpp b/test/morph.cpp
index c55eaec60e..28dcd4b1bf 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -7,71 +7,71 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using af::dim4;
+using af::dtype_traits;
 using std::abs;
 using std::endl;
 using std::string;
 using std::vector;
-using af::dim4;
-using af::dtype_traits;
 
 template<typename T>
-class Morph : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Morph : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Morph, TestTypes);
 
 template<typename inType, bool isDilation, bool isVolume>
-void morphTest(string pTestFile)
-{
+void morphTest(string pTestFile) {
     if (noDoubleTests<inType>()) return;
 
-    vector<dim4>       numDims;
-    vector<vector<inType> >      in;
-    vector<vector<inType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<inType> > tests;
 
-    readTests<inType,inType,int>(pTestFile, numDims, in, tests);
+    readTests<inType, inType, int>(pTestFile, numDims, in, tests);
 
-    dim4 dims      = numDims[0];
-    dim4 maskDims  = numDims[1];
+    dim4 dims          = numDims[0];
+    dim4 maskDims      = numDims[1];
     af_array outArray  = 0;
     af_array inArray   = 0;
     af_array maskArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
     ASSERT_SUCCESS(af_create_array(&maskArray, &(in[1].front()),
-                maskDims.ndims(), maskDims.get(), (af_dtype)dtype_traits<inType>::af_type));
+                                   maskDims.ndims(), maskDims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
 
     if (isDilation) {
         if (isVolume)
             ASSERT_SUCCESS(af_dilate3(&outArray, inArray, maskArray));
         else
             ASSERT_SUCCESS(af_dilate(&outArray, inArray, maskArray));
-    }
-    else {
+    } else {
         if (isVolume)
             ASSERT_SUCCESS(af_erode3(&outArray, inArray, maskArray));
         else
             ASSERT_SUCCESS(af_erode(&outArray, inArray, maskArray));
     }
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<inType> currGoldBar = tests[testIter];
         ASSERT_VEC_ARRAY_EQ(currGoldBar, dims, outArray);
     }
@@ -82,93 +82,89 @@ void morphTest(string pTestFile)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(Morph, Dilate3x3)
-{
-    morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate3x3.test"));
+TYPED_TEST(Morph, Dilate3x3) {
+    morphTest<TypeParam, true, false>(string(TEST_DIR "/morph/dilate3x3.test"));
 }
 
-TYPED_TEST(Morph, Erode3x3)
-{
-    morphTest<TypeParam, false, false>(string(TEST_DIR"/morph/erode3x3.test"));
+TYPED_TEST(Morph, Erode3x3) {
+    morphTest<TypeParam, false, false>(string(TEST_DIR "/morph/erode3x3.test"));
 }
 
-TYPED_TEST(Morph, Dilate4x4)
-{
-    morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate4x4.test"));
+TYPED_TEST(Morph, Dilate4x4) {
+    morphTest<TypeParam, true, false>(string(TEST_DIR "/morph/dilate4x4.test"));
 }
 
-TYPED_TEST(Morph, Dilate12x12)
-{
-    morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate12x12.test"));
+TYPED_TEST(Morph, Dilate12x12) {
+    morphTest<TypeParam, true, false>(
+        string(TEST_DIR "/morph/dilate12x12.test"));
 }
 
-TYPED_TEST(Morph, Erode4x4)
-{
-    morphTest<TypeParam, false, false>(string(TEST_DIR"/morph/erode4x4.test"));
+TYPED_TEST(Morph, Erode4x4) {
+    morphTest<TypeParam, false, false>(string(TEST_DIR "/morph/erode4x4.test"));
 }
 
-TYPED_TEST(Morph, Dilate3x3_Batch)
-{
-    morphTest<TypeParam, true, false>(string(TEST_DIR"/morph/dilate3x3_batch.test"));
+TYPED_TEST(Morph, Dilate3x3_Batch) {
+    morphTest<TypeParam, true, false>(
+        string(TEST_DIR "/morph/dilate3x3_batch.test"));
 }
 
-TYPED_TEST(Morph, Erode3x3_Batch)
-{
-    morphTest<TypeParam, false, false>(string(TEST_DIR"/morph/erode3x3_batch.test"));
+TYPED_TEST(Morph, Erode3x3_Batch) {
+    morphTest<TypeParam, false, false>(
+        string(TEST_DIR "/morph/erode3x3_batch.test"));
 }
 
-TYPED_TEST(Morph, Dilate3x3x3)
-{
-    morphTest<TypeParam, true, true>(string(TEST_DIR"/morph/dilate3x3x3.test"));
+TYPED_TEST(Morph, Dilate3x3x3) {
+    morphTest<TypeParam, true, true>(
+        string(TEST_DIR "/morph/dilate3x3x3.test"));
 }
 
-TYPED_TEST(Morph, Erode3x3x3)
-{
-    morphTest<TypeParam, false, true>(string(TEST_DIR"/morph/erode3x3x3.test"));
+TYPED_TEST(Morph, Erode3x3x3) {
+    morphTest<TypeParam, false, true>(
+        string(TEST_DIR "/morph/erode3x3x3.test"));
 }
 
-TYPED_TEST(Morph, Dilate4x4x4)
-{
-    morphTest<TypeParam, true, true>(string(TEST_DIR"/morph/dilate4x4x4.test"));
+TYPED_TEST(Morph, Dilate4x4x4) {
+    morphTest<TypeParam, true, true>(
+        string(TEST_DIR "/morph/dilate4x4x4.test"));
 }
 
-TYPED_TEST(Morph, Erode4x4x4)
-{
-    morphTest<TypeParam, false, true>(string(TEST_DIR"/morph/erode4x4x4.test"));
+TYPED_TEST(Morph, Erode4x4x4) {
+    morphTest<TypeParam, false, true>(
+        string(TEST_DIR "/morph/erode4x4x4.test"));
 }
 
 template<typename T, bool isDilation, bool isColor>
-void morphImageTest(string pTestFile)
-{
+void morphImageTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray   = 0;
+        af_array maskArray = 0;
+        af_array outArray  = 0;
+        af_array goldArray = 0;
+        dim_t nElems       = 0;
 
-        af_array inArray  = 0;
-        af_array maskArray= 0;
-        af_array outArray = 0;
-        af_array goldArray= 0;
-        dim_t nElems   = 0;
+        inFiles[testId].insert(0, string(TEST_DIR "/morph/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/morph/"));
 
-        inFiles[testId].insert(0,string(TEST_DIR"/morph/"));
-        outFiles[testId].insert(0,string(TEST_DIR"/morph/"));
+        dim4 mdims(3, 3, 1, 1);
+        ASSERT_SUCCESS(af_constant(&maskArray, 1.0, mdims.ndims(), mdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-        dim4 mdims(3,3,1,1);
-        ASSERT_SUCCESS(af_constant(&maskArray, 1.0,
-                    mdims.ndims(), mdims.get(), (af_dtype)dtype_traits<T>::af_type));
-
-        ASSERT_SUCCESS(af_load_image(&inArray, inFiles[testId].c_str(), isColor));
-        ASSERT_SUCCESS(af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(
+            af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
         if (isDilation)
@@ -182,7 +178,8 @@ void morphImageTest(string pTestFile)
         vector<T> goldData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.018f));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(maskArray));
@@ -191,37 +188,36 @@ void morphImageTest(string pTestFile)
     }
 }
 
-TEST(Morph, Grayscale)
-{
-    morphImageTest<float, true, false>(string(TEST_DIR"/morph/gray.test"));
+TEST(Morph, Grayscale) {
+    morphImageTest<float, true, false>(string(TEST_DIR "/morph/gray.test"));
 }
 
-TEST(Morph, ColorImage)
-{
-    morphImageTest<float, false, true>(string(TEST_DIR"/morph/color.test"));
+TEST(Morph, ColorImage) {
+    morphImageTest<float, false, true>(string(TEST_DIR "/morph/color.test"));
 }
 
 template<typename T, bool isDilation>
-void morphInputTest(void)
-{
+void morphInputTest(void) {
     if (noDoubleTests<T>()) return;
 
     af_array inArray   = 0;
     af_array maskArray = 0;
     af_array outArray  = 0;
 
-    vector<T>   in(100,1);
-    vector<T>   mask(9,1);
+    vector<T> in(100, 1);
+    vector<T> mask(9, 1);
 
     // Check for 1D inputs
-    dim4 dims = dim4(100,1,1,1);
-    dim4 mdims(3,3,1,1);
+    dim4 dims = dim4(100, 1, 1, 1);
+    dim4 mdims(3, 3, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(), mdims.ndims(),
+                                   mdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate(&outArray, inArray, maskArray));
@@ -233,37 +229,32 @@ void morphInputTest(void)
     ASSERT_SUCCESS(af_release_array(maskArray));
 }
 
-TYPED_TEST(Morph, DilateInvalidInput)
-{
-    morphInputTest<TypeParam,true>();
-}
+TYPED_TEST(Morph, DilateInvalidInput) { morphInputTest<TypeParam, true>(); }
 
-TYPED_TEST(Morph, ErodeInvalidInput)
-{
-    morphInputTest<TypeParam,false>();
-}
+TYPED_TEST(Morph, ErodeInvalidInput) { morphInputTest<TypeParam, false>(); }
 
 template<typename T, bool isDilation>
-void morphMaskTest(void)
-{
+void morphMaskTest(void) {
     if (noDoubleTests<T>()) return;
 
     af_array inArray   = 0;
     af_array maskArray = 0;
     af_array outArray  = 0;
 
-    vector<T>   in(100,1);
-    vector<T>   mask(16,1);
+    vector<T> in(100, 1);
+    vector<T> mask(16, 1);
 
     // Check for 4D mask
-    dim4 dims(10,10,1,1);
-    dim4 mdims(2,2,2,2);
+    dim4 dims(10, 10, 1, 1);
+    dim4 mdims(2, 2, 2, 2);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(), mdims.ndims(),
+                                   mdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate(&outArray, inArray, maskArray));
@@ -273,10 +264,11 @@ void morphMaskTest(void)
     ASSERT_SUCCESS(af_release_array(maskArray));
 
     // Check for 1D mask
-    mdims = dim4(16,1,1,1);
+    mdims = dim4(16, 1, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(), mdims.ndims(),
+                                   mdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate(&outArray, inArray, maskArray));
@@ -288,37 +280,32 @@ void morphMaskTest(void)
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(Morph, DilateInvalidMask)
-{
-    morphMaskTest<TypeParam,true>();
-}
+TYPED_TEST(Morph, DilateInvalidMask) { morphMaskTest<TypeParam, true>(); }
 
-TYPED_TEST(Morph, ErodeInvalidMask)
-{
-    morphMaskTest<TypeParam,false>();
-}
+TYPED_TEST(Morph, ErodeInvalidMask) { morphMaskTest<TypeParam, false>(); }
 
 template<typename T, bool isDilation>
-void morph3DMaskTest(void)
-{
+void morph3DMaskTest(void) {
     if (noDoubleTests<T>()) return;
 
     af_array inArray   = 0;
     af_array maskArray = 0;
     af_array outArray  = 0;
 
-    vector<T>   in(1000,1);
-    vector<T>   mask(81,1);
+    vector<T> in(1000, 1);
+    vector<T> mask(81, 1);
 
     // Check for 2D mask
-    dim4 dims(10,10,10,1);
-    dim4 mdims(9,9,1,1);
+    dim4 dims(10, 10, 10, 1);
+    dim4 mdims(9, 9, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(),
-                dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(), mdims.ndims(),
+                                   mdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate3(&outArray, inArray, maskArray));
@@ -328,10 +315,11 @@ void morph3DMaskTest(void)
     ASSERT_SUCCESS(af_release_array(maskArray));
 
     // Check for 4D mask
-    mdims = dim4(3,3,3,3);
+    mdims = dim4(3, 3, 3, 3);
 
-    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(),
-                mdims.ndims(), mdims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&maskArray, &mask.front(), mdims.ndims(),
+                                   mdims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     if (isDilation)
         ASSERT_EQ(AF_ERR_SIZE, af_dilate3(&outArray, inArray, maskArray));
@@ -343,53 +331,49 @@ void morph3DMaskTest(void)
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-TYPED_TEST(Morph, DilateVolumeInvalidMask)
-{
-    morph3DMaskTest<TypeParam,true>();
+TYPED_TEST(Morph, DilateVolumeInvalidMask) {
+    morph3DMaskTest<TypeParam, true>();
 }
 
-TYPED_TEST(Morph, ErodeVolumeInvalidMask)
-{
-    morph3DMaskTest<TypeParam,false>();
+TYPED_TEST(Morph, ErodeVolumeInvalidMask) {
+    morph3DMaskTest<TypeParam, false>();
 }
 
-
 ////////////////////////////////////// CPP //////////////////////////////////
 //
 
 using af::array;
 using af::constant;
-using af::loadImage;
 using af::erode;
 using af::iota;
+using af::loadImage;
 using af::max;
 using af::randu;
 using af::seq;
 using af::span;
 
 template<typename T, bool isDilation, bool isColor>
-void cppMorphImageTest(string pTestFile)
-{
+void cppMorphImageTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>       inDims;
-    vector<string>    inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<dim_t> outSizes;
-    vector<string>   outFiles;
+    vector<string> outFiles;
 
     readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        inFiles[testId].insert(0,string(TEST_DIR"/morph/"));
-        outFiles[testId].insert(0,string(TEST_DIR"/morph/"));
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        inFiles[testId].insert(0, string(TEST_DIR "/morph/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/morph/"));
 
-        array mask = constant(1.0, 3, 3);
-        array img = loadImage(inFiles[testId].c_str(), isColor);
-        array gold = loadImage(outFiles[testId].c_str(), isColor);
-        dim_t nElems   = gold.elements();
+        array mask   = constant(1.0, 3, 3);
+        array img    = loadImage(inFiles[testId].c_str(), isColor);
+        array gold   = loadImage(outFiles[testId].c_str(), isColor);
+        dim_t nElems = gold.elements();
         array output;
 
         if (isDilation)
@@ -403,73 +387,49 @@ void cppMorphImageTest(string pTestFile)
         vector<T> goldData(nElems);
         gold.host((void*)goldData.data());
 
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(), outData.data(), 0.018f));
+        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
+                                          outData.data(), 0.018f));
     }
 }
 
-TEST(Morph, Grayscale_CPP)
-{
-    cppMorphImageTest<float, true, false>(string(TEST_DIR"/morph/gray.test"));
+TEST(Morph, Grayscale_CPP) {
+    cppMorphImageTest<float, true, false>(string(TEST_DIR "/morph/gray.test"));
 }
 
-TEST(Morph, ColorImage_CPP)
-{
-    cppMorphImageTest<float, false, true>(string(TEST_DIR"/morph/color.test"));
+TEST(Morph, ColorImage_CPP) {
+    cppMorphImageTest<float, false, true>(string(TEST_DIR "/morph/color.test"));
 }
 
-TEST(Morph, GFOR)
-{
-    dim4 dims = dim4(10, 10, 3);
-    array A = iota(dims);
-    array B = constant(0, dims);
-    array mask = randu(3,3) > 0.3;
+TEST(Morph, GFOR) {
+    dim4 dims  = dim4(10, 10, 3);
+    array A    = iota(dims);
+    array B    = constant(0, dims);
+    array mask = randu(3, 3) > 0.3;
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = erode(A(span, span, ii), mask);
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = erode(A(span, span, ii), mask); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = erode(A(span, span, ii), mask);
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
 
-TEST(Morph, EdgeIssue1564)
-{
-    int inputData[10 * 10] =
-    {
-        0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
-        0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
-        0, 0, 0, 0, 0, 1, 1, 1, 1, 1
-    };
-    int goldData[10 * 10] =
-    {
-        0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
-        0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-        0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
-        0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
-        0, 0, 1, 1, 1, 0, 0, 1, 1, 1,
-        0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
-        0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
-        0, 0, 0, 0, 1, 1, 1, 1, 1, 1
-    };
+TEST(Morph, EdgeIssue1564) {
+    int inputData[10 * 10] = {0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                              0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                              0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                              0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                              0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
+                              0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+    int goldData[10 * 10]  = {0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,
+                             0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                             0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
+                             0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
+                             1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1,
+                             1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
     array input(10, 10, inputData);
-    int maskData[3 * 3] =
-    {
-        1, 1, 1,
-        1, 0, 1,
-        1, 1, 1
-    };
+    int maskData[3 * 3] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
     array mask(3, 3, maskData);
     array dilated = dilate(input.as(b8), mask.as(b8));
 
@@ -477,15 +437,14 @@ TEST(Morph, EdgeIssue1564)
     vector<char> outData(nElems);
     dilated.host((void*)outData.data());
 
-    for (size_t i=0; i<nElems; ++i) {
+    for (size_t i = 0; i < nElems; ++i) {
         ASSERT_EQ((int)outData[i], goldData[i]);
     }
 }
 
-TEST(Morph, UnsupportedKernel2D)
-{
+TEST(Morph, UnsupportedKernel2D) {
     const unsigned ndims = 2;
-    const dim_t dims[2] = {10, 10};
+    const dim_t dims[2]  = {10, 10};
     const dim_t kdims[2] = {32, 32};
 
     af_array in, mask, out;
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index c0f8cbf146..11bf9e78fa 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -8,13 +8,13 @@
  ********************************************************/
 
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
 using af::array;
 using af::cdouble;
@@ -29,52 +29,49 @@ using std::string;
 using std::vector;
 
 template<typename T>
-class NearestNeighbour : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class NearestNeighbour : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create lists of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short,
+                         ushort>
+    TestTypes;
 
 template<typename T>
-struct otype_t
-{
+struct otype_t {
     typedef T otype;
 };
 
 template<>
-struct otype_t<short>
-{
+struct otype_t<short> {
     typedef int otype;
 };
 
 template<>
-struct otype_t<ushort>
-{
+struct otype_t<ushort> {
     typedef uint otype;
 };
 
 template<>
-struct otype_t<uchar>
-{
+struct otype_t<uchar> {
     typedef uint otype;
 };
 
 // register the type list
-TYPED_TEST_CASE(NearestNeighbour,  TestTypes);
+TYPED_TEST_CASE(NearestNeighbour, TestTypes);
 
 template<typename T>
-void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type type)
-{
+void nearestNeighbourTest(string pTestFile, int feat_dim,
+                          const af_match_type type) {
     if (noDoubleTests<T>()) return;
 
     typedef typename otype_t<T>::otype To;
 
-    vector<dim4>         numDims;
-    vector<vector<T> >   in;
-    vector<vector<uint> >  tests;
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<uint> > tests;
 
     readTests<T, uint, uint>(pTestFile, numDims, in, tests);
 
@@ -85,12 +82,15 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
     af_array idx   = 0;
     af_array dist  = 0;
 
-    ASSERT_SUCCESS(af_create_array(&query, &(in[0].front()),
-                qDims.ndims(), qDims.get(), (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&train, &(in[1].front()),
-                tDims.ndims(), tDims.get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&query, &(in[0].front()), qDims.ndims(),
+                                   qDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&train, &(in[1].front()), tDims.ndims(),
+                                   tDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_nearest_neighbour(&idx, &dist, query, train, feat_dim, 1, type));
+    ASSERT_SUCCESS(
+        af_nearest_neighbour(&idx, &dist, query, train, feat_dim, 1, type));
 
     vector<uint> goldIdx  = tests[0];
     vector<uint> goldDist = tests[1];
@@ -98,11 +98,12 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
     uint *outIdx          = new uint[nElems];
     To *outDist           = new To[nElems];
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outIdx,  idx));
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outDist, dist));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outIdx, idx));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outDist, dist));
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ((To)goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ((To)goldDist[elIter], outDist[elIter])
+            << "at: " << elIter << endl;
     }
 
     delete[] outIdx;
@@ -116,61 +117,70 @@ void nearestNeighbourTest(string pTestFile, int feat_dim, const af_match_type ty
 /////////////////////////////////////////////////
 // SSD
 /////////////////////////////////////////////////
-TYPED_TEST(NearestNeighbour, NN_SSD_100_1000_Dim0)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/ssd_100_1000_dim0.test"), 0, AF_SSD);
+TYPED_TEST(NearestNeighbour, NN_SSD_100_1000_Dim0) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/ssd_100_1000_dim0.test"), 0,
+        AF_SSD);
 }
 
-TYPED_TEST(NearestNeighbour, NN_SSD_100_1000_Dim1)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/ssd_100_1000_dim1.test"), 1, AF_SSD);
+TYPED_TEST(NearestNeighbour, NN_SSD_100_1000_Dim1) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/ssd_100_1000_dim1.test"), 1,
+        AF_SSD);
 }
 
-TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim0)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/ssd_500_5000_dim0.test"), 0, AF_SSD);
+TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim0) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/ssd_500_5000_dim0.test"), 0,
+        AF_SSD);
 }
 
-TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim1)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/ssd_500_5000_dim1.test"), 1, AF_SSD);
+TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim1) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/ssd_500_5000_dim1.test"), 1,
+        AF_SSD);
 }
 
 /////////////////////////////////////////////////
 // SAD
 /////////////////////////////////////////////////
-TYPED_TEST(NearestNeighbour, NN_SAD_100_1000_Dim0)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/sad_100_1000_dim0.test"), 0, AF_SAD);
+TYPED_TEST(NearestNeighbour, NN_SAD_100_1000_Dim0) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/sad_100_1000_dim0.test"), 0,
+        AF_SAD);
 }
 
-TYPED_TEST(NearestNeighbour, NN_SAD_100_1000_Dim1)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/sad_100_1000_dim1.test"), 1, AF_SAD);
+TYPED_TEST(NearestNeighbour, NN_SAD_100_1000_Dim1) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/sad_100_1000_dim1.test"), 1,
+        AF_SAD);
 }
 
-TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim0)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/sad_500_5000_dim0.test"), 0, AF_SAD);
+TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim0) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/sad_500_5000_dim0.test"), 0,
+        AF_SAD);
 }
 
-TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim1)
-{
-    nearestNeighbourTest<TypeParam>(string(TEST_DIR"/nearest_neighbour/sad_500_5000_dim1.test"), 1, AF_SAD);
+TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim1) {
+    nearestNeighbourTest<TypeParam>(
+        string(TEST_DIR "/nearest_neighbour/sad_500_5000_dim1.test"), 1,
+        AF_SAD);
 }
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(NearestNeighbourSSD, CPP)
-{
-    vector<dim4>         numDims;
-    vector<vector<uint> >     in;
-    vector<vector<uint> >  tests;
+TEST(NearestNeighbourSSD, CPP) {
+    vector<dim4> numDims;
+    vector<vector<uint> > in;
+    vector<vector<uint> > tests;
 
-    readTests<uint, uint, uint>(TEST_DIR"/nearest_neighbour/ssd_500_5000_dim0.test", numDims, in, tests);
+    readTests<uint, uint, uint>(TEST_DIR
+                                "/nearest_neighbour/ssd_500_5000_dim0.test",
+                                numDims, in, tests);
 
-    dim4 qDims     = numDims[0];
-    dim4 tDims     = numDims[1];
+    dim4 qDims = numDims[0];
+    dim4 tDims = numDims[1];
 
     array query(qDims, &(in[0].front()));
     array train(tDims, &(in[1].front()));
@@ -187,24 +197,26 @@ TEST(NearestNeighbourSSD, CPP)
     idx.host(outIdx);
     dist.host(outDist);
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])
+            << "at: " << elIter << endl;
     }
 
     delete[] outIdx;
     delete[] outDist;
 }
 
-TEST(NearestNeighbourSAD, CPP)
-{
-    vector<dim4>         numDims;
-    vector<vector<uint> >     in;
-    vector<vector<uint> >  tests;
+TEST(NearestNeighbourSAD, CPP) {
+    vector<dim4> numDims;
+    vector<vector<uint> > in;
+    vector<vector<uint> > tests;
 
-    readTests<uint, uint, uint>(TEST_DIR"/nearest_neighbour/sad_100_1000_dim1.test", numDims, in, tests);
+    readTests<uint, uint, uint>(TEST_DIR
+                                "/nearest_neighbour/sad_100_1000_dim1.test",
+                                numDims, in, tests);
 
-    dim4 qDims     = numDims[0];
-    dim4 tDims     = numDims[1];
+    dim4 qDims = numDims[0];
+    dim4 tDims = numDims[1];
 
     array query(qDims, &(in[0].front()));
     array train(tDims, &(in[1].front()));
@@ -221,30 +233,25 @@ TEST(NearestNeighbourSAD, CPP)
     idx.host(outIdx);
     dist.host(outDist);
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_EQ(goldDist[elIter], outDist[elIter])<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])
+            << "at: " << elIter << endl;
     }
 
     delete[] outIdx;
     delete[] outDist;
 }
 
-TEST(NearestNeighbourSSD, small)
-{
-    const int ntrain = 1;
-    const int nquery = 5;
-    const int nfeat  = 2;
+TEST(NearestNeighbourSSD, small) {
+    const int ntrain            = 1;
+    const int nquery            = 5;
+    const int nfeat             = 2;
     float train[ntrain * nfeat] = {
-        5, 5,
+        5,
+        5,
     };
 
-    float query[5 * nfeat] = {
-        0, 0,
-        3.5, 4,
-        5, 5,
-        6, 5,
-        8, 6.5
-    };
+    float query[5 * nfeat] = {0, 0, 3.5, 4, 5, 5, 6, 5, 8, 6.5};
     array t(nfeat, ntrain, train);
     array q(nfeat, nquery, query);
     array indices;
@@ -256,36 +263,25 @@ TEST(NearestNeighbourSSD, small)
         (5 - 3.5) * (5 - 3.5) + (5 - 4) * (5 - 4),
         (5 - 5) * (5 - 5) + (5 - 5) * (5 - 5),
         (5 - 6) * (5 - 6) + (5 - 5) * (5 - 5),
-        (5 - 8) * (5 - 8) + (5 - 6.5) * (5 - 6.5)
-    };
+        (5 - 8) * (5 - 8) + (5 - 6.5) * (5 - 6.5)};
 
     vector<float> actualDistances(nquery);
     distances.host(&actualDistances[0]);
-    for (int i = 0; i < nquery; i++)
-    {
+    for (int i = 0; i < nquery; i++) {
         EXPECT_NEAR(expectedDistances[i], actualDistances[i], 1E-8);
     }
 }
 
-TEST(KNearestNeighbourSSD, small)
-{
+TEST(KNearestNeighbourSSD, small) {
     const int ntrain = 5;
     const int nquery = 3;
     const int nfeat  = 2;
 
     float query[nquery * nfeat] = {
-        5,  5,
-        0,  0,
-       10, 10,
+        5, 5, 0, 0, 10, 10,
     };
 
-    float train[ntrain * nfeat] = {
-        0,   0,
-        3.5, 4,
-        5,   5,
-        6,   5,
-        8,   6.5
-    };
+    float train[ntrain * nfeat] = {0, 0, 3.5, 4, 5, 5, 6, 5, 8, 6.5};
 
     array t(nfeat, ntrain, train);
     array q(nfeat, nquery, query);
@@ -298,12 +294,11 @@ TEST(KNearestNeighbourSSD, small)
         (5 - 5) * (5 - 5) + (5 - 5) * (5 - 5),
         (5 - 6) * (5 - 6) + (5 - 5) * (5 - 5),
 
-        (0 - 0)   * (0 - 0) + (0 - 0)   * (0 - 0),
+        (0 - 0) * (0 - 0) + (0 - 0) * (0 - 0),
         (0 - 3.5) * (0 - 4) + (0 - 3.5) * (0 - 4),
 
-        (10 - 8)   * (10 - 8)   + (10 - 6.5) * (10 - 6.5),
-        (10 - 6)   * (10 - 5)   + (10 - 6)   * (10 - 5)
-    };
+        (10 - 8) * (10 - 8) + (10 - 6.5) * (10 - 6.5),
+        (10 - 6) * (10 - 5) + (10 - 6) * (10 - 5)};
 
     vector<float> actualDistances(nquery);
     distances.host(&actualDistances[0]);
@@ -314,17 +309,23 @@ TEST(KNearestNeighbourSSD, small)
 
 struct nearest_neighbors_params {
     string testname_;
-    int    k_, nfeat_, ntrain_, nquery_;
-    int    feat_dim_;
+    int k_, nfeat_, ntrain_, nquery_;
+    int feat_dim_;
     dim4 qdims_, tdims_, idims_, ddims_;
     vector<float> query_;
     vector<float> train_;
     vector<unsigned int> indices_;
     vector<float> dists_;
 
-    nearest_neighbors_params(string testname, int k, int feat_dim, array query, array train, array indices, array dists)
-    : testname_(testname), k_(k), feat_dim_(feat_dim), query_(query.elements()), train_(train.elements()), indices_(indices.elements()), dists_(dists.elements())
-    {
+    nearest_neighbors_params(string testname, int k, int feat_dim, array query,
+                             array train, array indices, array dists)
+        : testname_(testname)
+        , k_(k)
+        , feat_dim_(feat_dim)
+        , query_(query.elements())
+        , train_(train.elements())
+        , indices_(indices.elements())
+        , dists_(dists.elements()) {
         qdims_ = query.dims();
         tdims_ = train.dims();
         idims_ = indices.dims();
@@ -338,18 +339,23 @@ struct nearest_neighbors_params {
 };
 
 template<typename TestClass>
-string testNameGenerator(const ::testing::TestParamInfo<typename TestClass::ParamType> info) {
+string testNameGenerator(
+    const ::testing::TestParamInfo<typename TestClass::ParamType> info) {
     return info.param.testname_;
 }
 
-class NearestNeighborsTest  : public ::testing::TestWithParam<nearest_neighbors_params> { };
-class KNearestNeighborsTest : public ::testing::TestWithParam<nearest_neighbors_params> { };
+class NearestNeighborsTest
+    : public ::testing::TestWithParam<nearest_neighbors_params> {};
+class KNearestNeighborsTest
+    : public ::testing::TestWithParam<nearest_neighbors_params> {};
 
-nearest_neighbors_params
-single_knn_data(const string testname, const int nquery, const int ntrain, const int nfeat, const int k, const int feat_dim) {
+nearest_neighbors_params single_knn_data(const string testname,
+                                         const int nquery, const int ntrain,
+                                         const int nfeat, const int k,
+                                         const int feat_dim) {
     array indices, dists;
     array query, train;
-    if(feat_dim == 0) {
+    if (feat_dim == 0) {
         query = constant(0, nfeat, nquery);
         train = constant(1, nfeat, ntrain);
     } else {
@@ -360,14 +366,16 @@ single_knn_data(const string testname, const int nquery, const int ntrain, const
     indices = constant(0, k, nquery, u32);
     dists   = constant(nfeat, k, nquery);
 
-    return nearest_neighbors_params(testname, k, feat_dim, query, train, indices, dists);
+    return nearest_neighbors_params(testname, k, feat_dim, query, train,
+                                    indices, dists);
 }
 
-nearest_neighbors_params
-knn_data(const string testname, const int nquery, const int ntrain, const int nfeat, const int k, const int feat_dim) {
+nearest_neighbors_params knn_data(const string testname, const int nquery,
+                                  const int ntrain, const int nfeat,
+                                  const int k, const int feat_dim) {
     array indices, dists;
     array query, train;
-    if(feat_dim == 0) {
+    if (feat_dim == 0) {
         query = constant(0, nfeat, nquery);
         train = range(dim4(nfeat, ntrain), 1);
     } else {
@@ -377,71 +385,68 @@ knn_data(const string testname, const int nquery, const int ntrain, const int nf
 
     indices = range(dim4(k, nquery), 0, u32);
     dists   = range(dim4(k, nquery));
-    dists  *= dists;
+    dists *= dists;
 
-    return nearest_neighbors_params(testname, k, feat_dim, query, train, indices, dists);
+    return nearest_neighbors_params(testname, k, feat_dim, query, train,
+                                    indices, dists);
 }
 
 vector<nearest_neighbors_params> genNNTests() {
-    return {single_knn_data("1q1t",      1,     1, 10, 1, 0),
-            single_knn_data("1q10t",     1,    10, 10, 1, 0),
-            single_knn_data("1q100t",    1,   100, 10, 1, 0),
-            single_knn_data("1q1000t",   1,  1000, 10, 1, 0),
-            single_knn_data("1q100000t", 1, 10000, 10, 1, 0),
-            single_knn_data("10q1t",        10, 1, 10, 1, 0),
-            single_knn_data("100q1t",      100, 1, 10, 1, 0),
-            single_knn_data("1000q1t",    1000, 1, 10, 1, 0),
-            single_knn_data("10000q1t",  10000, 1, 10, 1, 0),
-            single_knn_data("100000q1t", 10000, 1, 10, 1, 0),
-            single_knn_data("1q1tfl1",      10, 1,  1, 1, 0),
-            single_knn_data("1q1tfl2",      10, 1,  2, 1, 0),
-            single_knn_data("1q1tfl4",      10, 1,  4, 1, 0),
-            single_knn_data("1q1tfl8",      10, 1,  8, 1, 0),
-            single_knn_data("1q1tfl16",     10, 1, 16, 1, 0),
-            single_knn_data("1q1tfl32",     10, 1, 32, 1, 0),
-            single_knn_data("1q1tfl64",     10, 1, 64, 1, 0),
-            single_knn_data("1q1tfl128",    10, 1,128, 1, 0),
-            single_knn_data("1q1tfl256",    10, 1,256, 1, 0),
-            single_knn_data("1q1tfl10000",  10, 1,10000, 1, 0),
-            single_knn_data("10q1t1d",        10, 1, 10, 1, 1),
-            single_knn_data("100q1t1d",      100, 1, 10, 1, 1),
-            single_knn_data("1000q1t1d",    1000, 1, 10, 1, 1),
-            single_knn_data("10000q1t1d",  10000, 1, 10, 1, 1),
-            single_knn_data("100000q1t1d", 10000, 1, 10, 1, 1),
-           };
+    return {
+        single_knn_data("1q1t", 1, 1, 10, 1, 0),
+        single_knn_data("1q10t", 1, 10, 10, 1, 0),
+        single_knn_data("1q100t", 1, 100, 10, 1, 0),
+        single_knn_data("1q1000t", 1, 1000, 10, 1, 0),
+        single_knn_data("1q100000t", 1, 10000, 10, 1, 0),
+        single_knn_data("10q1t", 10, 1, 10, 1, 0),
+        single_knn_data("100q1t", 100, 1, 10, 1, 0),
+        single_knn_data("1000q1t", 1000, 1, 10, 1, 0),
+        single_knn_data("10000q1t", 10000, 1, 10, 1, 0),
+        single_knn_data("100000q1t", 10000, 1, 10, 1, 0),
+        single_knn_data("1q1tfl1", 10, 1, 1, 1, 0),
+        single_knn_data("1q1tfl2", 10, 1, 2, 1, 0),
+        single_knn_data("1q1tfl4", 10, 1, 4, 1, 0),
+        single_knn_data("1q1tfl8", 10, 1, 8, 1, 0),
+        single_knn_data("1q1tfl16", 10, 1, 16, 1, 0),
+        single_knn_data("1q1tfl32", 10, 1, 32, 1, 0),
+        single_knn_data("1q1tfl64", 10, 1, 64, 1, 0),
+        single_knn_data("1q1tfl128", 10, 1, 128, 1, 0),
+        single_knn_data("1q1tfl256", 10, 1, 256, 1, 0),
+        single_knn_data("1q1tfl10000", 10, 1, 10000, 1, 0),
+        single_knn_data("10q1t1d", 10, 1, 10, 1, 1),
+        single_knn_data("100q1t1d", 100, 1, 10, 1, 1),
+        single_knn_data("1000q1t1d", 1000, 1, 10, 1, 1),
+        single_knn_data("10000q1t1d", 10000, 1, 10, 1, 1),
+        single_knn_data("100000q1t1d", 10000, 1, 10, 1, 1),
+    };
 }
 
 vector<nearest_neighbors_params> genKNNTests() {
-    return { knn_data("1q1000t1k",   1,  1000, 1,   1, 0),
-             knn_data("1q1000t2k",   1,  1000, 1,   2, 0),
-             knn_data("1q1000t4k",   1,  1000, 1,   4, 0),
-             knn_data("1q1000t8k",   1,  1000, 1,   8, 0),
-             knn_data("1q1000t16k",  1,  1000, 1,  16, 0),
-             knn_data("1q1000t32k",  1,  1000, 1,  32, 0),
-             knn_data("1q1000t64k",  1,  1000, 1,  64, 0),
-             knn_data("1q1000t128k", 1,  1000, 1, 128, 0),
-             knn_data("1q1000t256k", 1,  1000, 1, 256, 0)
-            };
+    return {knn_data("1q1000t1k", 1, 1000, 1, 1, 0),
+            knn_data("1q1000t2k", 1, 1000, 1, 2, 0),
+            knn_data("1q1000t4k", 1, 1000, 1, 4, 0),
+            knn_data("1q1000t8k", 1, 1000, 1, 8, 0),
+            knn_data("1q1000t16k", 1, 1000, 1, 16, 0),
+            knn_data("1q1000t32k", 1, 1000, 1, 32, 0),
+            knn_data("1q1000t64k", 1, 1000, 1, 64, 0),
+            knn_data("1q1000t128k", 1, 1000, 1, 128, 0),
+            knn_data("1q1000t256k", 1, 1000, 1, 256, 0)};
 }
 
-INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD,
-                        NearestNeighborsTest,
+INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD, NearestNeighborsTest,
                         ::testing::ValuesIn(genNNTests()),
-                        testNameGenerator<NearestNeighborsTest>
-                        );
+                        testNameGenerator<NearestNeighborsTest>);
 
-INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD,
-                        KNearestNeighborsTest,
+INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD, KNearestNeighborsTest,
                         ::testing::ValuesIn(genKNNTests()),
-                        testNameGenerator<KNearestNeighborsTest>
-                        );
+                        testNameGenerator<KNearestNeighborsTest>);
 
 TEST_P(NearestNeighborsTest, SingleQTests) {
     nearest_neighbors_params params = GetParam();
     array query = array(params.qdims_, params.query_.data());
     array train = array(params.tdims_, params.train_.data());
 
-    const int k = params.k_;
+    const int k        = params.k_;
     const int feat_dim = params.feat_dim_;
 
     array indices, distances;
@@ -461,7 +466,7 @@ TEST_P(KNearestNeighborsTest, SingleQTests) {
     array query = array(params.qdims_, params.query_.data());
     array train = array(params.tdims_, params.train_.data());
 
-    const int k = params.k_;
+    const int k        = params.k_;
     const int feat_dim = params.feat_dim_;
 
     array indices, distances;
@@ -475,8 +480,7 @@ TEST_P(KNearestNeighborsTest, SingleQTests) {
     ASSERT_ARRAYS_NEAR(distances_gold, distances, 1e-5);
 }
 
-TEST(KNearestNeighbours, InvalidNegativeK)
-{
+TEST(KNearestNeighbours, InvalidNegativeK) {
     const int ntrain = 500;
     const int nquery = 1;
     const int nfeat  = 2;
@@ -487,11 +491,11 @@ TEST(KNearestNeighbours, InvalidNegativeK)
     array indices;
     array distances;
     int k = -1;
-    ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD), af::exception);
+    ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD),
+                 af::exception);
 }
 
-TEST(KNearestNeighbours, InvalidLargeK)
-{
+TEST(KNearestNeighbours, InvalidLargeK) {
     const int ntrain = 500;
     const int nquery = 1;
     const int nfeat  = 2;
@@ -502,6 +506,6 @@ TEST(KNearestNeighbours, InvalidLargeK)
     array indices;
     array distances;
     int k = 257;
-    ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD), af::exception);
+    ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD),
+                 af::exception);
 }
-
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index 3b747a924c..f64f417092 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -7,34 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
 #if defined(AF_OPENCL)
 #include <af/opencl.h>
 #include <iostream>
 
-using std::endl;
-using std::vector;
 using af::array;
 using af::constant;
 using af::getDeviceCount;
 using af::info;
 using af::randu;
 using af::setDevice;
+using std::endl;
+using std::vector;
 
-inline void checkErr(cl_int err, const char * name) {
+inline void checkErr(cl_int err, const char *name) {
     if (err != CL_SUCCESS) {
-        std::cerr << "ERROR: " << name  << " (" << err << ")" << endl;
+        std::cerr << "ERROR: " << name << " (" << err << ")" << endl;
         exit(EXIT_FAILURE);
     }
 }
 
-void getExternals(cl_device_id &deviceId, cl_context &context, cl_command_queue &queue)
-{
-    static cl_device_id dId = NULL;
-    static cl_context cId = NULL;
+void getExternals(cl_device_id &deviceId, cl_context &context,
+                  cl_command_queue &queue) {
+    static cl_device_id dId     = NULL;
+    static cl_context cId       = NULL;
     static cl_command_queue qId = NULL;
-    static bool call_once = true;
+    static bool call_once       = true;
 
     if (call_once) {
         cl_platform_id platformId = NULL;
@@ -43,20 +43,20 @@ void getExternals(cl_device_id &deviceId, cl_context &context, cl_command_queue
         cl_int errorCode = 0;
 
         checkErr(clGetPlatformIDs(1, &platformId, &numPlatforms),
-                "Get Platforms failed");
+                 "Get Platforms failed");
 
-        checkErr(clGetDeviceIDs(platformId, CL_DEVICE_TYPE_DEFAULT, 1, &dId, &numDevices),
-                "Get cl_device_id failed");
+        checkErr(clGetDeviceIDs(platformId, CL_DEVICE_TYPE_DEFAULT, 1, &dId,
+                                &numDevices),
+                 "Get cl_device_id failed");
 
         cId = clCreateContext(NULL, 1, &dId, NULL, NULL, &errorCode);
         checkErr(errorCode, "Context creation failed");
 
-        #ifdef CL_VERSION_2_0
+#ifdef CL_VERSION_2_0
         qId = clCreateCommandQueueWithProperties(cId, dId, 0, &errorCode);
-        #else
+#else
         qId = clCreateCommandQueue(cId, dId, 0, &errorCode);
-        #endif
-
+#endif
 
         checkErr(errorCode, "Command queue creation failed");
         call_once = false;
@@ -66,10 +66,9 @@ void getExternals(cl_device_id &deviceId, cl_context &context, cl_command_queue
     queue    = qId;
 }
 
-TEST(OCLExtContext, PushAndPop)
-{
-    cl_device_id deviceId = NULL;
-    cl_context context = NULL;
+TEST(OCLExtContext, PushAndPop) {
+    cl_device_id deviceId  = NULL;
+    cl_context context     = NULL;
     cl_command_queue queue = NULL;
 
     getExternals(deviceId, context, queue);
@@ -78,25 +77,24 @@ TEST(OCLExtContext, PushAndPop)
     info();
 
     afcl::addDevice(deviceId, context, queue);
-    ASSERT_EQ(true, dCount+1==getDeviceCount());
+    ASSERT_EQ(true, dCount + 1 == getDeviceCount());
     printf("\n%d devices after afcl::addDevice\n", getDeviceCount());
 
     afcl::deleteDevice(deviceId, context);
-    ASSERT_EQ(true, dCount==getDeviceCount());
+    ASSERT_EQ(true, dCount == getDeviceCount());
     printf("\n%d devices after afcl::deleteDevice\n\n", getDeviceCount());
     info();
 }
 
-TEST(OCLExtContext, set)
-{
-    cl_device_id deviceId = NULL;
-    cl_context context = NULL;
+TEST(OCLExtContext, set) {
+    cl_device_id deviceId  = NULL;
+    cl_context context     = NULL;
     cl_command_queue queue = NULL;
 
-    int dCount = getDeviceCount(); //Before user device addition
+    int dCount = getDeviceCount();  // Before user device addition
     setDevice(0);
     info();
-    array t = randu(5,5);
+    array t = randu(5, 5);
     af_print(t);
 
     getExternals(deviceId, context, queue);
@@ -105,17 +103,17 @@ TEST(OCLExtContext, set)
     info();
 
     printf("\n\nBefore setting device to newly added one\n\n");
-    setDevice(dCount); //In 0-based index, dCount is index of newly added device
+    setDevice(
+        dCount);  // In 0-based index, dCount is index of newly added device
     info();
 
     const int x = 5;
     const int y = 5;
     const int s = x * y;
-    array a = constant(1, x, y);
+    array a     = constant(1, x, y);
     vector<float> host(s);
-    a.host((void*)host.data());
-    for (int i=0; i<s; ++i)
-        ASSERT_EQ(host[i], 1.0f);
+    a.host((void *)host.data());
+    for (int i = 0; i < s; ++i) ASSERT_EQ(host[i], 1.0f);
 
     printf("\n\nAfter reset to default set of devices\n\n");
     setDevice(0);
@@ -123,25 +121,18 @@ TEST(OCLExtContext, set)
     af_print(t);
 }
 
-TEST(OCLCheck, DeviceType)
-{
+TEST(OCLCheck, DeviceType) {
     afcl::deviceType devType = afcl::getDeviceType();
-    cl_device_type type = -100;
-    clGetDeviceInfo(afcl::getDeviceId(),
-                    CL_DEVICE_TYPE,
-                    sizeof(cl_device_type),
-                    &type,
-                    NULL);
+    cl_device_type type      = -100;
+    clGetDeviceInfo(afcl::getDeviceId(), CL_DEVICE_TYPE, sizeof(cl_device_type),
+                    &type, NULL);
     ASSERT_EQ(type, (cl_device_type)devType);
 }
 
-TEST(OCLCheck, DevicePlatform)
-{
+TEST(OCLCheck, DevicePlatform) {
     afcl::platform platform = afcl::getPlatform();
     ASSERT_NE(platform, AFCL_PLATFORM_UNKNOWN);
 }
 #else
-TEST(OCLExtContext, NoopCPU)
-{
-}
+TEST(OCLExtContext, NoopCPU) {}
 #endif
diff --git a/test/orb.cpp b/test/orb.cpp
index 521e6d7510..637417c2bf 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -7,54 +7,50 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
+using af::array;
+using af::dim4;
+using af::features;
+using af::loadImage;
 using std::abs;
 using std::cout;
 using std::endl;
 using std::string;
 using std::vector;
-using af::array;
-using af::dim4;
-using af::features;
-using af::loadImage;
 
-typedef struct
-{
+typedef struct {
     float f[5];
     unsigned d[8];
 } feat_desc_t;
 
-typedef struct
-{
+typedef struct {
     float f[5];
 } feat_t;
 
-typedef struct
-{
+typedef struct {
     unsigned d[8];
 } desc_t;
 
-static bool feat_cmp(feat_desc_t i, feat_desc_t j)
-{
+static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
     for (int k = 0; k < 5; k++)
-        if (i.f[k] != j.f[k])
-            return (i.f[k] < j.f[k]);
+        if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
 
     return true;
 }
 
-static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, unsigned* desc, unsigned nfeat)
-{
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
+                               float* score, float* ori, float* size,
+                               unsigned* desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -62,13 +58,14 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
         feat[i].f[2] = score[i];
         feat[i].f[3] = ori[i];
         feat[i].f[4] = size[i];
-        for (unsigned j = 0; j < 8; j++)
-            feat[i].d[j] = desc[i * 8 + j];
+        for (unsigned j = 0; j < 8; j++) feat[i].d[j] = desc[i * 8 + j];
     }
 }
 
-static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<unsigned> >& desc, unsigned nfeat)
-{
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
+                               float* score, float* ori, float* size,
+                               vector<vector<unsigned> >& desc,
+                               unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -76,13 +73,12 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
         feat[i].f[2] = score[i];
         feat[i].f[3] = ori[i];
         feat[i].f[4] = size[i];
-        for (unsigned j = 0; j < 8; j++)
-            feat[i].d[j] = desc[i][j];
+        for (unsigned j = 0; j < 8; j++) feat[i].d[j] = desc[i][j];
     }
 }
 
-static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
-{
+static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f,
+                            vector<desc_t>& d) {
     f.resize(fd.size());
     d.resize(fd.size());
     for (size_t i = 0; i < fd.size(); i++) {
@@ -91,13 +87,11 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
         f[i].f[2] = fd[i].f[2];
         f[i].f[3] = fd[i].f[3];
         f[i].f[4] = fd[i].f[4];
-        for (unsigned j = 0; j < 8; j++)
-            d[i].d[j] = fd[i].d[j];
+        for (unsigned j = 0; j < 8; j++) d[i].d[j] = fd[i].d[j];
     }
 }
 
-static unsigned popcount(unsigned x)
-{
+static unsigned popcount(unsigned x) {
     x = x - ((x >> 1) & 0x55555555);
     x = (x & 0x33333333) + ((x >> 2) & 0x33333333);
     x = (x + (x >> 4)) & 0x0F0F0F0F;
@@ -106,17 +100,17 @@ static unsigned popcount(unsigned x)
     return x & 0x0000003F;
 }
 
-bool compareHamming(int data_size, unsigned *cpu, unsigned *gpu, unsigned thr = 1)
-{
+bool compareHamming(int data_size, unsigned* cpu, unsigned* gpu,
+                    unsigned thr = 1) {
     bool ret = true;
-    for(int i=0;i<data_size;i++)
-    {
+    for (int i = 0; i < data_size; i++) {
         unsigned x = (cpu[i] ^ gpu[i]);
-        if(popcount(x) > thr) {
+        if (popcount(x) > thr) {
             ret = false;
-            cout<<endl<<"@compareHamming: first mismatch."<<endl;
-            cout<<"(cpu,gpu,cpu-gpu)["<<i<<"] : {"<<cpu[i]<<","<<gpu[i]<<","<<cpu[i]-gpu[i]<<"}"<<endl;
-            cout<<endl;
+            cout << endl << "@compareHamming: first mismatch." << endl;
+            cout << "(cpu,gpu,cpu-gpu)[" << i << "] : {" << cpu[i] << ","
+                 << gpu[i] << "," << cpu[i] - gpu[i] << "}" << endl;
+            cout << endl;
             break;
         }
     }
@@ -124,10 +118,9 @@ bool compareHamming(int data_size, unsigned *cpu, unsigned *gpu, unsigned thr =
 }
 
 template<typename T>
-class ORB : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class ORB : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
@@ -135,32 +128,34 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(ORB, TestTypes);
 
 template<typename T>
-void orbTest(string pTestFile)
-{
+void orbTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>             inDims;
-    vector<string>           inFiles;
-    vector<vector<float> >    goldFeat;
+    vector<dim4> inDims;
+    vector<string> inFiles;
+    vector<vector<float> > goldFeat;
     vector<vector<unsigned> > goldDesc;
 
-    readImageFeaturesDescriptors<unsigned>(pTestFile, inDims, inFiles, goldFeat, goldDesc);
+    readImageFeaturesDescriptors<unsigned>(pTestFile, inDims, inFiles, goldFeat,
+                                           goldDesc);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        af_array inArray_f32  = 0;
-        af_array inArray      = 0;
-        af_array desc         = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray_f32 = 0;
+        af_array inArray     = 0;
+        af_array desc        = 0;
         af_features feat;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/orb/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/orb/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_orb(&feat, &desc, inArray, 20.0f, 400, 1.2f, 8, true));
+        ASSERT_SUCCESS(
+            af_orb(&feat, &desc, inArray, 20.0f, 400, 1.2f, 8, true));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
@@ -172,14 +167,14 @@ void orbTest(string pTestFile)
         ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
         ASSERT_SUCCESS(af_get_features_size(&size, feat));
 
-        float * outX           = new float[n];
-        float * outY           = new float[n];
-        float * outScore       = new float[n];
-        float * outOrientation = new float[n];
-        float * outSize        = new float[n];
+        float* outX           = new float[n];
+        float* outY           = new float[n];
+        float* outScore       = new float[n];
+        float* outOrientation = new float[n];
+        float* outSize        = new float[n];
         dim_t descSize;
         ASSERT_SUCCESS(af_get_elements(&descSize, desc));
-        unsigned * outDesc     = new unsigned[descSize];
+        unsigned* outDesc = new unsigned[descSize];
         ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
         ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
         ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
@@ -188,10 +183,14 @@ void orbTest(string pTestFile)
         ASSERT_SUCCESS(af_get_data_ptr((void*)outDesc, desc));
 
         vector<feat_desc_t> out_feat_desc;
-        array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, n);
+        array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation,
+                           outSize, outDesc, n);
 
         vector<feat_desc_t> gold_feat_desc;
-        array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
+        array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(),
+                           &goldFeat[1].front(), &goldFeat[2].front(),
+                           &goldFeat[3].front(), &goldFeat[4].front(), goldDesc,
+                           goldFeat[0].size());
 
         std::sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
         std::sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
@@ -205,15 +204,24 @@ void orbTest(string pTestFile)
         split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
         for (int elIter = 0; elIter < (int)n; elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]),
+                      1e-3)
+                << "at: " << elIter << endl;
         }
 
         // TODO: improve distance for single/double-precision interchangeability
-        EXPECT_TRUE(compareHamming(descSize, (unsigned*)&v_out_desc[0], (unsigned*)&v_gold_desc[0], 3));
+        EXPECT_TRUE(compareHamming(descSize, (unsigned*)&v_out_desc[0],
+                                   (unsigned*)&v_gold_desc[0], 3));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
@@ -230,30 +238,26 @@ void orbTest(string pTestFile)
     }
 }
 
-TYPED_TEST(ORB, Square)
-{
-  orbTest<TypeParam>(string(TEST_DIR"/orb/square.test"));
+TYPED_TEST(ORB, Square) {
+    orbTest<TypeParam>(string(TEST_DIR "/orb/square.test"));
 }
 
-TYPED_TEST(ORB, Lena)
-{
-  orbTest<TypeParam>(string(TEST_DIR"/orb/lena.test"));
-}
+TYPED_TEST(ORB, Lena) { orbTest<TypeParam>(string(TEST_DIR "/orb/lena.test")); }
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(ORB, CPP)
-{
+TEST(ORB, CPP) {
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>             inDims;
-    vector<string>           inFiles;
-    vector<vector<float> >    goldFeat;
+    vector<dim4> inDims;
+    vector<string> inFiles;
+    vector<vector<float> > goldFeat;
     vector<vector<unsigned> > goldDesc;
 
-    readImageFeaturesDescriptors<unsigned>(string(TEST_DIR"/orb/square.test"), inDims, inFiles, goldFeat, goldDesc);
-    inFiles[0].insert(0,string(TEST_DIR"/orb/"));
+    readImageFeaturesDescriptors<unsigned>(string(TEST_DIR "/orb/square.test"),
+                                           inDims, inFiles, goldFeat, goldDesc);
+    inFiles[0].insert(0, string(TEST_DIR "/orb/"));
 
     array in = loadImage(inFiles[0].c_str(), false);
 
@@ -261,12 +265,12 @@ TEST(ORB, CPP)
     array desc;
     orb(feat, desc, in, 20.0f, 400, 1.2f, 8, true);
 
-    float * outX           = new float[feat.getNumFeatures()];
-    float * outY           = new float[feat.getNumFeatures()];
-    float * outScore       = new float[feat.getNumFeatures()];
-    float * outOrientation = new float[feat.getNumFeatures()];
-    float * outSize        = new float[feat.getNumFeatures()];
-    unsigned * outDesc     = new unsigned[desc.elements()];
+    float* outX           = new float[feat.getNumFeatures()];
+    float* outY           = new float[feat.getNumFeatures()];
+    float* outScore       = new float[feat.getNumFeatures()];
+    float* outOrientation = new float[feat.getNumFeatures()];
+    float* outSize        = new float[feat.getNumFeatures()];
+    unsigned* outDesc     = new unsigned[desc.elements()];
     feat.getX().host(outX);
     feat.getY().host(outY);
     feat.getScore().host(outScore);
@@ -275,10 +279,14 @@ TEST(ORB, CPP)
     desc.host(outDesc);
 
     vector<feat_desc_t> out_feat_desc;
-    array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, feat.getNumFeatures());
+    array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation,
+                       outSize, outDesc, feat.getNumFeatures());
 
     vector<feat_desc_t> gold_feat_desc;
-    array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
+    array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(),
+                       &goldFeat[1].front(), &goldFeat[2].front(),
+                       &goldFeat[3].front(), &goldFeat[4].front(), goldDesc,
+                       goldFeat[0].size());
 
     std::sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
     std::sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
@@ -292,15 +300,21 @@ TEST(ORB, CPP)
     split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
     for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
-        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+            << "at: " << elIter << endl;
+        ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3)
+            << "at: " << elIter << endl;
     }
 
     // TODO: improve distance for single/double-precision interchangeability
-    EXPECT_TRUE(compareHamming(desc.elements(), (unsigned*)&v_out_desc[0], (unsigned*)&v_gold_desc[0], 3));
+    EXPECT_TRUE(compareHamming(desc.elements(), (unsigned*)&v_out_desc[0],
+                               (unsigned*)&v_gold_desc[0], 3));
 
     delete[] outX;
     delete[] outY;
diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 0059508c5f..85979f4aba 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -7,14 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <iostream>
 #include <complex>
-#include <testHelpers.hpp>
+#include <iostream>
 #include <limits>
 
 using af::array;
@@ -45,18 +45,18 @@ array makeComplex(dim4 dims, const vector<T>& real, const vector<T>& imag) {
 template<typename T>
 array readTestInput(string testFilePath) {
     typedef typename dtype_traits<T>::base_type InBaseType;
-    dtype outAfType = (dtype) dtype_traits<T>::af_type;
+    dtype outAfType = (dtype)dtype_traits<T>::af_type;
 
     vector<dim4> dimsVec;
     vector<vector<InBaseType> > inVec;
     vector<vector<InBaseType> > goldVec;
-    readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec, goldVec);
+    readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec,
+                                              goldVec);
     dim4 inDims = dimsVec[0];
 
     if (outAfType == c32 || outAfType == c64) {
         return makeComplex(inDims, inVec[1], inVec[2]);
-    }
-    else {
+    } else {
         return array(inDims, &inVec[0].front());
     }
 }
@@ -64,27 +64,24 @@ array readTestInput(string testFilePath) {
 template<typename T>
 array readTestGold(string testFilePath) {
     typedef typename dtype_traits<T>::base_type InBaseType;
-    dtype outAfType = (dtype) dtype_traits<T>::af_type;
+    dtype outAfType = (dtype)dtype_traits<T>::af_type;
 
     vector<dim4> dimsVec;
     vector<vector<InBaseType> > inVec;
     vector<vector<InBaseType> > goldVec;
-    readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec, goldVec);
+    readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec,
+                                              goldVec);
     dim4 goldDims(dimsVec[0][1], dimsVec[0][0]);
 
     if (outAfType == c32 || outAfType == c64) {
         return makeComplex(goldDims, goldVec[1], goldVec[2]);
-    }
-    else {
+    } else {
         return array(goldDims, &goldVec[0].front());
     }
 }
 
 template<typename T>
-class Pinverse : public ::testing::Test
-{
-
-};
+class Pinverse : public ::testing::Test {};
 
 // Epsilons taken from test/inverse.cpp
 template<typename T>
@@ -113,8 +110,8 @@ double eps<cdouble>() {
 template<typename T>
 double relEps(array in) {
     typedef typename af::dtype_traits<T>::base_type InBaseType;
-    return std::numeric_limits<InBaseType>::epsilon()
-        * std::max(in.dims(0), in.dims(1)) * af::max<double>(in);
+    return std::numeric_limits<InBaseType>::epsilon() *
+           std::max(in.dims(0), in.dims(1)) * af::max<double>(in);
 }
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
@@ -123,72 +120,84 @@ TYPED_TEST_CASE(Pinverse, TestTypes);
 // Test Moore-Penrose conditions in the following first 4 tests
 // See https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Definition
 TYPED_TEST(Pinverse, AApinvA_A) {
-    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in = readTestInput<TypeParam>(
+        string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, eps<TypeParam>());
 }
 
 TYPED_TEST(Pinverse, ApinvAApinv_Apinv) {
-    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in = readTestInput<TypeParam>(
+        string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
-    array out = matmul(inpinv, in, inpinv);
+    array out    = matmul(inpinv, in, inpinv);
     ASSERT_ARRAYS_NEAR(inpinv, out, eps<TypeParam>());
 }
 
 TYPED_TEST(Pinverse, AApinv_IsHermitian) {
-    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in = readTestInput<TypeParam>(
+        string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
     array aapinv = matmul(in, inpinv);
-    array out = matmul(in, inpinv).H();
+    array out    = matmul(in, inpinv).H();
     ASSERT_ARRAYS_NEAR(aapinv, out, eps<TypeParam>());
 }
 
 TYPED_TEST(Pinverse, ApinvA_IsHermitian) {
-    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in = readTestInput<TypeParam>(
+        string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
     array apinva = af::matmul(inpinv, in);
-    array out = af::matmul(inpinv, in).H();
+    array out    = af::matmul(inpinv, in).H();
     ASSERT_ARRAYS_NEAR(apinva, out, eps<TypeParam>());
 }
 
 TYPED_TEST(Pinverse, Large) {
-    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse640x480.test"));
+    array in = readTestInput<TypeParam>(
+        string(TEST_DIR "/pinverse/pinverse640x480.test"));
     array inpinv = pinverse(in);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, relEps<TypeParam>(in));
 }
 
 TYPED_TEST(Pinverse, LargeTall) {
-    array in = readTestInput<TypeParam>(string(TEST_DIR"/pinverse/pinverse640x480.test")).T();
+    array in = readTestInput<TypeParam>(
+                   string(TEST_DIR "/pinverse/pinverse640x480.test"))
+                   .T();
     array inpinv = pinverse(in);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, relEps<TypeParam>(in));
 }
 
 TEST(Pinverse, Square) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x10.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x10.test"));
     array inpinv = pinverse(in);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, eps<float>());
 }
 
 TEST(Pinverse, Dim1GtDim0) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse8x10.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse8x10.test"));
     array inpinv = pinverse(in);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, eps<float>());
 }
 
 TEST(Pinverse, CompareWithNumpy) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
-    array gold = readTestGold<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
+    array gold =
+        readTestGold<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array out = pinverse(in);
     ASSERT_ARRAYS_NEAR(gold, out, relEps<float>(gold));
 }
 
 TEST(Pinverse, SmallSigValExistsFloat) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     const dim_t dim0 = in.dims(0);
     const dim_t dim1 = in.dims(1);
 
@@ -199,23 +208,23 @@ TEST(Pinverse, SmallSigValExistsFloat) {
     af::svd(u, sVec, vT, in);
     dim_t sSize = sVec.elements();
 
-    sVec(2) = 1e-12;
-    af::array s = af::diag(sVec, 0, false);
-    af::array zeros = af::constant(0,
-                                   dim0 > sSize ? dim0 - sSize : sSize,
+    sVec(2)         = 1e-12;
+    af::array s     = af::diag(sVec, 0, false);
+    af::array zeros = af::constant(0, dim0 > sSize ? dim0 - sSize : sSize,
                                    dim1 > sSize ? dim1 - sSize : sSize);
-    s = af::join(dim0 > dim1 ? 0 : 1, s, zeros);
+    s               = af::join(dim0 > dim1 ? 0 : 1, s, zeros);
 
     // Make new input array that has a small non-zero value in its SVD sigma
-    in = af::matmul(u, s, vT);
+    in           = af::matmul(u, s, vT);
     array inpinv = pinverse(in);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
 
     ASSERT_ARRAYS_NEAR(in, out, eps<float>());
 }
 
 TEST(Pinverse, SmallSigValExistsDouble) {
-    array in = readTestInput<double>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<double>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     const dim_t dim0 = in.dims(0);
     const dim_t dim1 = in.dims(1);
 
@@ -226,28 +235,27 @@ TEST(Pinverse, SmallSigValExistsDouble) {
     svd(u, sVec, vT, in);
     dim_t sSize = sVec.elements();
 
-    sVec(2) = (double) 1e-16;
-    array s = diag(sVec, 0, false);
-    array zeros = constant(0,
-                           dim0 > sSize ? dim0 - sSize : sSize,
-                           dim1 > sSize ? dim1 - sSize : sSize,
-                           f64);
-    s = join(dim0 > dim1 ? 0 : 1, s, zeros);
+    sVec(2)     = (double)1e-16;
+    array s     = diag(sVec, 0, false);
+    array zeros = constant(0, dim0 > sSize ? dim0 - sSize : sSize,
+                           dim1 > sSize ? dim1 - sSize : sSize, f64);
+    s           = join(dim0 > dim1 ? 0 : 1, s, zeros);
 
     // Make new input array that has a small non-zero value in its SVD sigma
-    in = matmul(u, s, vT);
+    in           = matmul(u, s, vT);
     array inpinv = pinverse(in, 1e-15);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
 
     ASSERT_ARRAYS_NEAR(in, out, eps<double>());
 }
 
 TEST(Pinverse, Batching3D) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8x2.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8x2.test"));
     array inpinv0 = pinverse(in(span, span, 0));
     array inpinv1 = pinverse(in(span, span, 1));
 
-    array out = pinverse(in);
+    array out  = pinverse(in);
     array out0 = out(span, span, 0);
     array out1 = out(span, span, 1);
 
@@ -256,13 +264,14 @@ TEST(Pinverse, Batching3D) {
 }
 
 TEST(Pinverse, Batching4D) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8x2x2.test"));
+    array in = readTestInput<float>(
+        string(TEST_DIR "/pinverse/pinverse10x8x2x2.test"));
     array inpinv00 = pinverse(in(span, span, 0, 0));
     array inpinv01 = pinverse(in(span, span, 0, 1));
     array inpinv10 = pinverse(in(span, span, 1, 0));
     array inpinv11 = pinverse(in(span, span, 1, 1));
 
-    array out = pinverse(in);
+    array out   = pinverse(in);
     array out00 = out(span, span, 0, 0);
     array out01 = out(span, span, 0, 1);
     array out10 = out(span, span, 1, 0);
@@ -275,18 +284,22 @@ TEST(Pinverse, Batching4D) {
 }
 
 TEST(Pinverse, CustomTol) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in, 1e-12);
-    array out = matmul(in, inpinv, in);
+    array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, eps<float>());
 }
 
 TEST(Pinverse, C) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     af_array inpinv = 0, identity = 0, out = 0;
     ASSERT_SUCCESS(af_pinverse(&inpinv, in.get(), 1e-6, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&identity, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&out, identity, in.get(), AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(
+        af_matmul(&identity, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(
+        af_matmul(&out, identity, in.get(), AF_MAT_NONE, AF_MAT_NONE));
 
     ASSERT_ARRAYS_NEAR(in.get(), out, eps<float>());
 
@@ -296,11 +309,14 @@ TEST(Pinverse, C) {
 }
 
 TEST(Pinverse, C_CustomTol) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     af_array inpinv = 0, identity = 0, out = 0;
     ASSERT_SUCCESS(af_pinverse(&inpinv, in.get(), 1e-12, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&identity, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
-    ASSERT_SUCCESS(af_matmul(&out, identity, in.get(), AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(
+        af_matmul(&identity, in.get(), inpinv, AF_MAT_NONE, AF_MAT_NONE));
+    ASSERT_SUCCESS(
+        af_matmul(&out, identity, in.get(), AF_MAT_NONE, AF_MAT_NONE));
 
     ASSERT_ARRAYS_NEAR(in.get(), out, eps<float>());
 
@@ -310,7 +326,8 @@ TEST(Pinverse, C_CustomTol) {
 }
 
 TEST(Pinverse, NegativeTol) {
-    array in = readTestInput<float>(string(TEST_DIR"/pinverse/pinverse10x8.test"));
+    array in =
+        readTestInput<float>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array out;
     ASSERT_THROW(out = pinverse(in, -1.f), exception);
 }
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 16d5b20585..3ab6c72473 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -7,35 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::exception;
 using af::identity;
 using af::matmul;
 using af::max;
-
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 ///////////////////////////////// CPP ////////////////////////////////////
-TEST(QRFactorized, CPP)
-{
+TEST(QRFactorized, CPP) {
     if (noDoubleTests<float>()) return;
     if (noLAPACKTests()) return;
 
@@ -44,7 +42,8 @@ TEST(QRFactorized, CPP)
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, float>(string(TEST_DIR"/lapack/qrfactorized.test"),numDims,in,tests);
+    readTests<float, float, float>(string(TEST_DIR "/lapack/qrfactorized.test"),
+                                   numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -65,7 +64,8 @@ TEST(QRFactorized, CPP)
     for (int y = 0; y < (int)qdims[1]; ++y) {
         for (int x = 0; x < (int)qdims[0]; ++x) {
             int elIter = y * qdims[0] + x;
-            ASSERT_NEAR(tests[resultIdx][elIter], qData[elIter], 0.001) << "at: " << elIter << endl;
+            ASSERT_NEAR(tests[resultIdx][elIter], qData[elIter], 0.001)
+                << "at: " << elIter << endl;
         }
     }
 
@@ -74,9 +74,10 @@ TEST(QRFactorized, CPP)
     for (int y = 0; y < (int)rdims[1]; ++y) {
         for (int x = 0; x < (int)rdims[0]; ++x) {
             // Test only upper half
-            if(x <= y) {
+            if (x <= y) {
                 int elIter = y * rdims[0] + x;
-                ASSERT_NEAR(tests[resultIdx][elIter], rData[elIter], 0.001) << "at: " << elIter << endl;
+                ASSERT_NEAR(tests[resultIdx][elIter], rData[elIter], 0.001)
+                    << "at: " << elIter << endl;
             }
         }
     }
@@ -87,8 +88,7 @@ TEST(QRFactorized, CPP)
 }
 
 template<typename T>
-void qrTester(const int m, const int n, double eps)
-{
+void qrTester(const int m, const int n, double eps) {
     try {
         if (noDoubleTests<T>()) return;
         if (noLAPACKTests()) return;
@@ -131,8 +131,7 @@ void qrTester(const int m, const int n, double eps)
         ASSERT_NEAR(0, max<double>(abs(real(r2 - r))), eps);
         ASSERT_NEAR(0, max<double>(abs(imag(r2 - r))), eps);
 
-
-    } catch(exception &ex) {
+    } catch (exception& ex) {
         cout << ex.what() << endl;
         throw;
     }
@@ -161,10 +160,7 @@ double eps<cdouble>() {
     return 1e-5;
 }
 template<typename T>
-class QR : public ::testing::Test
-{
-
-};
+class QR : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(QR, TestTypes);
diff --git a/test/random.cpp b/test/random.cpp
index 9f7f66dba0..a4318c2d3c 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -7,71 +7,63 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
 using af::cdouble;
 using af::cfloat;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Random : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Random : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl, uintl, unsigned char> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
+                         uintl, unsigned char>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Random, TestTypes);
 
 template<typename T>
-class Random_norm : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Random_norm : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class RandomEngine : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class RandomEngine : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class RandomEngineSeed : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class RandomEngineSeed : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class RandomSeed : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class RandomSeed : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
@@ -94,110 +86,101 @@ typedef ::testing::Types<unsigned> TestTypesSeed;
 TYPED_TEST_CASE(RandomSeed, TestTypesSeed);
 
 template<typename T>
-void randuTest(dim4 & dims)
-{
+void randuTest(dim4 &dims) {
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
-    ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(),
+                            (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
-    if(outArray != 0) af_release_array(outArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
 template<typename T>
-void randnTest(dim4 &dims)
-{
+void randnTest(dim4 &dims) {
     if (noDoubleTests<T>()) return;
 
     af_array outArray = 0;
-    ASSERT_SUCCESS(af_randn(&outArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randn(&outArray, dims.ndims(), dims.get(),
+                            (af_dtype)dtype_traits<T>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
-    if(outArray != 0) af_release_array(outArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-#define RAND(d0, d1, d2, d3)                                \
-    TYPED_TEST(Random,randu_##d0##_##d1##_##d2##_##d3)      \
-    {                                                       \
-        dim4 dims(d0, d1, d2, d3);                          \
-        randuTest<TypeParam>(dims);                         \
-    }                                                       \
-    TYPED_TEST(Random_norm,randn_##d0##_##d1##_##d2##_##d3) \
-    {                                                       \
-        dim4 dims(d0, d1, d2, d3);                          \
-        randnTest<TypeParam>(dims);                         \
-    }                                                       \
-
-RAND(1024, 1024,    1,    1);
-RAND( 512,  512,    1,    1);
-RAND( 256,  256,    1,    1);
-RAND( 128,  128,    1,    1);
-RAND(  64,   64,    1,    1);
-RAND(  32,   32,    1,    1);
-RAND(  16,   16,    1,    1);
-RAND(   8,    8,    1,    1);
-RAND(   4,    4,    1,    1);
-RAND(   2,    2,    2,    2);
-RAND(   1,    1,    1,    1);
-RAND( 256,   16,    4,    2);
-RAND(  32,   16,    8,    4);
-RAND(   2,    4,   16,  256);
-RAND(   4,    8,   16,   32);
-
-RAND(  10,   10,   10,   10);
-
-RAND(1920, 1080,    1,    1);
-RAND(1280,  720,    1,    1);
-RAND( 640,  480,    1,    1);
-
-RAND( 215,   24,    6,    5);
-RAND( 132,   64,   23,    2);
-RAND(  15,   35,   50,    3);
-RAND(  77,   43,    8,    1);
-RAND( 123,   45,    6,    7);
-RAND( 345,   28,    9,    1);
-RAND(  79,   68,   12,    6);
-RAND(  45,    1,    1,    1);
+#define RAND(d0, d1, d2, d3)                                   \
+    TYPED_TEST(Random, randu_##d0##_##d1##_##d2##_##d3) {      \
+        dim4 dims(d0, d1, d2, d3);                             \
+        randuTest<TypeParam>(dims);                            \
+    }                                                          \
+    TYPED_TEST(Random_norm, randn_##d0##_##d1##_##d2##_##d3) { \
+        dim4 dims(d0, d1, d2, d3);                             \
+        randnTest<TypeParam>(dims);                            \
+    }
+
+RAND(1024, 1024, 1, 1);
+RAND(512, 512, 1, 1);
+RAND(256, 256, 1, 1);
+RAND(128, 128, 1, 1);
+RAND(64, 64, 1, 1);
+RAND(32, 32, 1, 1);
+RAND(16, 16, 1, 1);
+RAND(8, 8, 1, 1);
+RAND(4, 4, 1, 1);
+RAND(2, 2, 2, 2);
+RAND(1, 1, 1, 1);
+RAND(256, 16, 4, 2);
+RAND(32, 16, 8, 4);
+RAND(2, 4, 16, 256);
+RAND(4, 8, 16, 32);
+
+RAND(10, 10, 10, 10);
+
+RAND(1920, 1080, 1, 1);
+RAND(1280, 720, 1, 1);
+RAND(640, 480, 1, 1);
+
+RAND(215, 24, 6, 5);
+RAND(132, 64, 23, 2);
+RAND(15, 35, 50, 3);
+RAND(77, 43, 8, 1);
+RAND(123, 45, 6, 7);
+RAND(345, 28, 9, 1);
+RAND(79, 68, 12, 6);
+RAND(45, 1, 1, 1);
 
 template<typename T>
-void randuArgsTest()
-{
+void randuArgsTest() {
     if (noDoubleTests<T>()) return;
 
-    dim_t ndims = 4;
-    dim_t dims[] = {1, 2, 3, 0};
+    dim_t ndims       = 4;
+    dim_t dims[]      = {1, 2, 3, 0};
     af_array outArray = 0;
-    ASSERT_EQ(AF_ERR_SIZE, af_randu(&outArray, ndims, dims, (af_dtype) dtype_traits<char>::af_type));
+    ASSERT_EQ(AF_ERR_SIZE, af_randu(&outArray, ndims, dims,
+                                    (af_dtype)dtype_traits<char>::af_type));
     ASSERT_EQ(af_sync(-1), AF_SUCCESS);
-    if(outArray != 0) af_release_array(outArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Random,InvalidArgs)
-{
-    randuArgsTest<TypeParam>();
-}
+TYPED_TEST(Random, InvalidArgs) { randuArgsTest<TypeParam>(); }
 
 template<typename T>
-void randuDimsTest()
-{
+void randuDimsTest() {
     if (noDoubleTests<T>()) return;
 
-    dim4 dims(1, 65535*32, 1, 1);
-    array large_rand = randu(dims, (af_dtype) dtype_traits<T>::af_type);
-    ASSERT_EQ(large_rand.dims()[1], 65535*32);
+    dim4 dims(1, 65535 * 32, 1, 1);
+    array large_rand = randu(dims, (af_dtype)dtype_traits<T>::af_type);
+    ASSERT_EQ(large_rand.dims()[1], 65535 * 32);
 
-    dims = dim4(1, 1, 65535*32, 1);
-    large_rand = randu(dims, (af_dtype) dtype_traits<T>::af_type);
-    ASSERT_EQ(large_rand.dims()[2], 65535*32);
+    dims       = dim4(1, 1, 65535 * 32, 1);
+    large_rand = randu(dims, (af_dtype)dtype_traits<T>::af_type);
+    ASSERT_EQ(large_rand.dims()[2], 65535 * 32);
 
-    dims = dim4(1, 1, 1, 65535*32);
-    large_rand = randu(dims, (af_dtype) dtype_traits<T>::af_type);
-    ASSERT_EQ(large_rand.dims()[3], 65535*32);
+    dims       = dim4(1, 1, 1, 65535 * 32);
+    large_rand = randu(dims, (af_dtype)dtype_traits<T>::af_type);
+    ASSERT_EQ(large_rand.dims()[3], 65535 * 32);
 }
 
-TYPED_TEST(Random,InvalidDims)
-{
-    randuDimsTest<TypeParam>();
-}
+TYPED_TEST(Random, InvalidDims) { randuDimsTest<TypeParam>(); }
 
 ////////////////////////////////////// CPP /////////////////////////////////////
 //
@@ -215,8 +198,7 @@ using af::setSeed;
 using af::stdev;
 using af::sum;
 
-TEST(RandomEngine, Default)
-{
+TEST(RandomEngine, Default) {
     // Using default Random engine will cause segfaults
     // without setting one. This test should be before
     // setting it to test if default engine setup is working
@@ -224,8 +206,7 @@ TEST(RandomEngine, Default)
     randomEngine engine = getDefaultRandomEngine();
 }
 
-TEST(Random, CPP)
-{
+TEST(Random, CPP) {
     if (noDoubleTests<float>()) return;
 
     // TEST will fail if exception is thrown, which are thrown
@@ -246,15 +227,13 @@ TEST(Random, CPP)
 }
 
 template<typename T>
-void testSetSeed(const uintl seed0, const uintl seed1)
-{
-
+void testSetSeed(const uintl seed0, const uintl seed1) {
     if (noDoubleTests<T>()) return;
 
     uintl orig_seed = getSeed();
 
     const int num = 1024 * 1024;
-    dtype ty = (dtype)dtype_traits<T>::af_type;
+    dtype ty      = (dtype)dtype_traits<T>::af_type;
 
     setSeed(seed0);
     array in0 = randu(num, ty);
@@ -286,30 +265,27 @@ void testSetSeed(const uintl seed0, const uintl seed1)
             ASSERT_NE(h_in0[i], h_in1[i]) << "at : " << i;
         }
 
-        // Verify different arrays created one after the other with same seed differ
-        // b8 and u9 can clash because they generate a small set of values
+        // Verify different arrays created one after the other with same seed
+        // differ b8 and u9 can clash because they generate a small set of
+        // values
         if (ty != b8 && ty != u8) {
             ASSERT_NE(h_in2[i], h_in3[i]) << "at : " << i;
         }
     }
 
-    setSeed(orig_seed); // Reset the seed
+    setSeed(orig_seed);  // Reset the seed
 }
 
-TYPED_TEST(RandomSeed, setSeed)
-{
-    testSetSeed<TypeParam>(10101, 23232);
-}
+TYPED_TEST(RandomSeed, setSeed) { testSetSeed<TypeParam>(10101, 23232); }
 
 template<typename T>
-void testGetSeed(const uintl seed0, const uintl seed1)
-{
+void testGetSeed(const uintl seed0, const uintl seed1) {
     if (noDoubleTests<T>()) return;
 
     uintl orig_seed = getSeed();
 
     const int num = 1024;
-    dtype ty = (dtype)dtype_traits<T>::af_type;
+    dtype ty      = (dtype)dtype_traits<T>::af_type;
 
     setSeed(seed0);
     array in0 = randu(num, ty);
@@ -323,80 +299,68 @@ void testGetSeed(const uintl seed0, const uintl seed1)
     array in2 = randu(num, ty);
     ASSERT_EQ(getSeed(), seed0);
 
-    setSeed(orig_seed); // Reset the seed
+    setSeed(orig_seed);  // Reset the seed
 }
 
-TYPED_TEST(Random, getSeed)
-{
-    testGetSeed<TypeParam>(1234, 9876);
-}
+TYPED_TEST(Random, getSeed) { testGetSeed<TypeParam>(1234, 9876); }
 
-template <typename T>
-void testRandomEngineUniform(randomEngineType type)
-{
+template<typename T>
+void testRandomEngineUniform(randomEngineType type) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    int elem = 16*1024*1024;
+    int elem = 16 * 1024 * 1024;
     randomEngine r(type, 0);
     array A = randu(elem, ty, r);
-    T m = mean<T>(A);
-    T s = stdev<T>(A);
+    T m     = mean<T>(A);
+    T s     = stdev<T>(A);
     ASSERT_NEAR(m, 0.5, 1e-3);
     ASSERT_NEAR(s, 0.2887, 1e-2);
 }
 
-template <typename T>
-void testRandomEngineNormal(randomEngineType type)
-{
+template<typename T>
+void testRandomEngineNormal(randomEngineType type) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    int elem = 16*1024*1024;
+    int elem = 16 * 1024 * 1024;
     randomEngine r(type, 0);
     array A = randn(elem, ty, r);
-    T m = mean<T>(A);
-    T s = stdev<T>(A);
+    T m     = mean<T>(A);
+    T s     = stdev<T>(A);
     ASSERT_NEAR(m, 0, 1e-1);
     ASSERT_NEAR(s, 1, 1e-1);
 }
 
-TYPED_TEST(RandomEngine, philoxRandomEngineUniform)
-{
+TYPED_TEST(RandomEngine, philoxRandomEngineUniform) {
     testRandomEngineUniform<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, philoxRandomEngineNormal)
-{
+TYPED_TEST(RandomEngine, philoxRandomEngineNormal) {
     testRandomEngineNormal<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, threefryRandomEngineUniform)
-{
+TYPED_TEST(RandomEngine, threefryRandomEngineUniform) {
     testRandomEngineUniform<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
-TYPED_TEST(RandomEngine, threefryRandomEngineNormal)
-{
+TYPED_TEST(RandomEngine, threefryRandomEngineNormal) {
     testRandomEngineNormal<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
-TYPED_TEST(RandomEngine, mersenneRandomEngineUniform)
-{
+TYPED_TEST(RandomEngine, mersenneRandomEngineUniform) {
     testRandomEngineUniform<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
 
-TYPED_TEST(RandomEngine, mersenneRandomEngineNormal)
-{
+TYPED_TEST(RandomEngine, mersenneRandomEngineNormal) {
     testRandomEngineNormal<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
 
-template <typename T>
-void testRandomEngineSeed(randomEngineType type)
-{
-    int elem = 4*32*1024;
+template<typename T>
+void testRandomEngineSeed(randomEngineType type) {
+    int elem        = 4 * 32 * 1024;
     uintl orig_seed = 0;
-    uintl new_seed = 1;
+    uintl new_seed  = 1;
     randomEngine e(type, orig_seed);
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
@@ -412,10 +376,10 @@ void testRandomEngineSeed(randomEngineType type)
     vector<T> h3(elem);
     vector<T> h4(elem);
 
-    d1.host((void*)h1.data());
-    d2.host((void*)h2.data());
-    d3.host((void*)h3.data());
-    d4.host((void*)h4.data());
+    d1.host((void *)h1.data());
+    d2.host((void *)h2.data());
+    d3.host((void *)h3.data());
+    d4.host((void *)h4.data());
 
     for (int i = 0; i < elem; i++) {
         ASSERT_EQ(h1[i], h3[i]) << "at : " << i;
@@ -426,74 +390,66 @@ void testRandomEngineSeed(randomEngineType type)
     }
 }
 
-TYPED_TEST(RandomEngineSeed, philoxSeedUniform)
-{
+TYPED_TEST(RandomEngineSeed, philoxSeedUniform) {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngineSeed, threefrySeedUniform)
-{
+TYPED_TEST(RandomEngineSeed, threefrySeedUniform) {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
-TYPED_TEST(RandomEngineSeed, mersenneSeedUniform)
-{
+TYPED_TEST(RandomEngineSeed, mersenneSeedUniform) {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
 
-template <typename T>
-void testRandomEnginePeriod(randomEngineType type)
-{
+template<typename T>
+void testRandomEnginePeriod(randomEngineType type) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    int elem = 1024*1024;
-    int steps = 4*1024;
+    int elem  = 1024 * 1024;
+    int steps = 4 * 1024;
     randomEngine r(type, 0);
 
     array first = randu(elem, ty, r);
 
     for (int i = 0; i < steps; ++i) {
-        array step = randu(elem, ty, r);
+        array step     = randu(elem, ty, r);
         bool different = !allTrue<bool>(first == step);
         ASSERT_TRUE(different);
     }
 }
 
-TYPED_TEST(RandomEngine, DISABLED_philoxRandomEnginePeriod)
-{
+TYPED_TEST(RandomEngine, DISABLED_philoxRandomEnginePeriod) {
     testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, DISABLED_threefryRandomEnginePeriod)
-{
+TYPED_TEST(RandomEngine, DISABLED_threefryRandomEnginePeriod) {
     testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
-TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEnginePeriod)
-{
+TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEnginePeriod) {
     testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
 
-template <typename T>
+template<typename T>
 T chi2_statistic(array input, array expected) {
     expected *= sum<T>(input) / sum<T>(expected);
     array diff = input - expected;
     return sum<T>((diff * diff) / expected);
 }
 
-template <typename T>
-void testRandomEngineUniformChi2(randomEngineType type)
-{
+template<typename T>
+void testRandomEngineUniformChi2(randomEngineType type) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    int elem = 256*1024*1024;
+    int elem  = 256 * 1024 * 1024;
     int steps = 32;
-    int bins = 100;
+    int bins  = 100;
 
     array total_hist = constant(0.0, bins, ty);
-    array expected = constant(1.0/bins, bins, ty);
+    array expected   = constant(1.0 / bins, bins, ty);
 
     randomEngine r(type, 0);
 
@@ -502,38 +458,35 @@ void testRandomEngineUniformChi2(randomEngineType type)
     T lower = 48.68125;
     T upper = 173.87456;
 
-    bool prev_step = true;
+    bool prev_step  = true;
     bool prev_total = true;
     for (int i = 0; i < steps; ++i) {
         array step_hist = histogram(randu(elem, ty, r), bins, 0.0, 1.0);
-        T step_chi2 = chi2_statistic<T>(step_hist, expected);
+        T step_chi2     = chi2_statistic<T>(step_hist, expected);
         if (!prev_step) {
-          EXPECT_GT(step_chi2, lower) << "at step: " << i;
-          EXPECT_LT(step_chi2, upper) << "at step: " << i;
+            EXPECT_GT(step_chi2, lower) << "at step: " << i;
+            EXPECT_LT(step_chi2, upper) << "at step: " << i;
         }
         prev_step = step_chi2 > lower && step_chi2 < upper;
 
         total_hist += step_hist;
         T total_chi2 = chi2_statistic<T>(total_hist, expected);
         if (!prev_total) {
-          EXPECT_GT(total_chi2, lower) << "at step: " << i;
-          EXPECT_LT(total_chi2, upper) << "at step: " << i;
+            EXPECT_GT(total_chi2, lower) << "at step: " << i;
+            EXPECT_LT(total_chi2, upper) << "at step: " << i;
         }
         prev_total = total_chi2 > lower && total_chi2 < upper;
     }
 }
 
-TYPED_TEST(RandomEngine, DISABLED_philoxRandomEngineUniformChi2)
-{
+TYPED_TEST(RandomEngine, DISABLED_philoxRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, DISABLED_threefryRandomEngineUniformChi2)
-{
+TYPED_TEST(RandomEngine, DISABLED_threefryRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
-TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEngineUniformChi2)
-{
+TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
diff --git a/test/random_practrand.cpp b/test/random_practrand.cpp
index fb3ecf62bf..10c9da85e1 100644
--- a/test/random_practrand.cpp
+++ b/test/random_practrand.cpp
@@ -4,24 +4,24 @@
 // Commandline arguments: backend, device, rng_type
 // Example:
 // random_practrand 0 0 200 | RNG_test stdin32
-#include <cstdio>
-#include <cstdint>
 #include <arrayfire.h>
+#include <cstdint>
+#include <cstdio>
 
-int main(int argc, char ** argv) {
-  int backend = argc > 1 ? atoi(argv[1]) : 0;
-  setBackend(static_cast<Backend>(backend));
-  int device = argc > 2 ? atoi(argv[2]) : 0;
-  setDevice(device);
-  int rng = argc > 3 ? atoi(argv[3]) : 100;
-  setDefaultRandomEngineType(static_cast<randomEngineType>(rng));
+int main(int argc, char **argv) {
+    int backend = argc > 1 ? atoi(argv[1]) : 0;
+    setBackend(static_cast<Backend>(backend));
+    int device = argc > 2 ? atoi(argv[2]) : 0;
+    setDevice(device);
+    int rng = argc > 3 ? atoi(argv[3]) : 100;
+    setDefaultRandomEngineType(static_cast<randomEngineType>(rng));
 
-  setSeed(0xfe47fe0cc078ec30ULL);
-  int samples = 1024 * 1024;
-  while (1) {
-    array values = randu(samples, u32);
-    uint32_t *pvalues = values.host<uint32_t>();
-    fwrite((void*) pvalues, samples * sizeof(*pvalues), 1, stdout);
-    freeHost(pvalues);
-  }
+    setSeed(0xfe47fe0cc078ec30ULL);
+    int samples = 1024 * 1024;
+    while (1) {
+        array values      = randu(samples, u32);
+        uint32_t *pvalues = values.host<uint32_t>();
+        fwrite((void *)pvalues, samples * sizeof(*pvalues), 1, stdout);
+        freeHost(pvalues);
+    }
 }
diff --git a/test/range.cpp b/test/range.cpp
index 9246084ffd..3b9953eef0 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -7,79 +7,80 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::range;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Range : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Range : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, unsigned int, intl, uintl, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
+                         unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Range, TestTypes);
 
 template<typename T>
-void rangeTest(const uint x, const uint y, const uint z, const uint w, const uint dim)
-{
+void rangeTest(const uint x, const uint y, const uint z, const uint w,
+               const uint dim) {
     if (noDoubleTests<T>()) return;
 
     dim4 idims(x, y, z, w);
 
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_range(&outArray, idims.ndims(), idims.get(), dim, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_range(&outArray, idims.ndims(), idims.get(), dim,
+                            (af_dtype)dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     // Compare result
-    for(int w = 0; w < (int)idims[3]; w++) {
-        for(int z = 0; z < (int)idims[2]; z++) {
-            for(int y = 0; y < (int)idims[1]; y++) {
-                for(int x = 0; x < (int)idims[0]; x++) {
+    for (int w = 0; w < (int)idims[3]; w++) {
+        for (int z = 0; z < (int)idims[2]; z++) {
+            for (int y = 0; y < (int)idims[1]; y++) {
+                for (int x = 0; x < (int)idims[0]; x++) {
                     T val = 0;
-                    if(dim == 0) {
+                    if (dim == 0) {
                         val = x;
-                    } else if(dim == 1) {
+                    } else if (dim == 1) {
                         val = y;
-                    } else if(dim == 2) {
+                    } else if (dim == 2) {
                         val = z;
-                    } else if(dim == 3) {
+                    } else if (dim == 3) {
                         val = w;
                     }
-                    dim_t idx = w * idims[0] * idims[1] * idims[2]
-                                 + z * idims[0] * idims[1]
-                                 + y * idims[0] + x;
+                    dim_t idx = w * idims[0] * idims[1] * idims[2] +
+                                z * idims[0] * idims[1] + y * idims[0] + x;
 
                     ASSERT_EQ(val, outData[idx]) << "at: " << idx << endl;
                 }
@@ -90,44 +91,40 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w, const uin
     // Delete
     delete[] outData;
 
-    if(outArray  != 0) af_release_array(outArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-#define RANGE_INIT(desc, x, y, z, w, rep)                               \
-    TYPED_TEST(Range, desc)                                             \
-    {                                                                   \
-        rangeTest<TypeParam>(x, y, z, w, rep);                          \
-    }
+#define RANGE_INIT(desc, x, y, z, w, rep) \
+    TYPED_TEST(Range, desc) { rangeTest<TypeParam>(x, y, z, w, rep); }
 
-    RANGE_INIT(Range1D0, 100,  1, 1, 1, 0);
+RANGE_INIT(Range1D0, 100, 1, 1, 1, 0);
 
-    RANGE_INIT(Range2D0,  10, 20, 1, 1, 0);
-    RANGE_INIT(Range2D1, 100,  5, 1, 1, 1);
+RANGE_INIT(Range2D0, 10, 20, 1, 1, 0);
+RANGE_INIT(Range2D1, 100, 5, 1, 1, 1);
 
-    RANGE_INIT(Range3D0,  20,  6, 3, 1, 0);
-    RANGE_INIT(Range3D1,  10, 12, 5, 1, 1);
-    RANGE_INIT(Range3D2,  25, 30, 2, 1, 2);
+RANGE_INIT(Range3D0, 20, 6, 3, 1, 0);
+RANGE_INIT(Range3D1, 10, 12, 5, 1, 1);
+RANGE_INIT(Range3D2, 25, 30, 2, 1, 2);
 
-    RANGE_INIT(Range4D0,  20,  6, 3, 2, 0);
-    RANGE_INIT(Range4D1,  10, 12, 5, 2, 1);
-    RANGE_INIT(Range4D2,  25, 30, 2, 2, 2);
-    RANGE_INIT(Range4D3,  25, 30, 2, 2, 3);
+RANGE_INIT(Range4D0, 20, 6, 3, 2, 0);
+RANGE_INIT(Range4D1, 10, 12, 5, 2, 1);
+RANGE_INIT(Range4D2, 25, 30, 2, 2, 2);
+RANGE_INIT(Range4D3, 25, 30, 2, 2, 3);
 
-    RANGE_INIT(Range1DMaxDim0, 65535 * 32 + 1, 1, 1, 1, 0);
-    RANGE_INIT(Range1DMaxDim1, 1, 65535 * 32 + 1, 1, 1, 0);
-    RANGE_INIT(Range1DMaxDim2, 1, 1, 65535 * 32 + 1, 1, 0);
-    RANGE_INIT(Range1DMaxDim3, 1, 1, 1, 65535 * 32 + 1, 0);
+RANGE_INIT(Range1DMaxDim0, 65535 * 32 + 1, 1, 1, 1, 0);
+RANGE_INIT(Range1DMaxDim1, 1, 65535 * 32 + 1, 1, 1, 0);
+RANGE_INIT(Range1DMaxDim2, 1, 1, 65535 * 32 + 1, 1, 0);
+RANGE_INIT(Range1DMaxDim3, 1, 1, 1, 65535 * 32 + 1, 0);
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
-TEST(Range, CPP)
-{
+TEST(Range, CPP) {
     if (noDoubleTests<float>()) return;
 
-    const unsigned x = 23;
-    const unsigned y = 15;
-    const unsigned z = 4;
-    const unsigned w = 2;
+    const unsigned x   = 23;
+    const unsigned y   = 15;
+    const unsigned z   = 4;
+    const unsigned w   = 2;
     const unsigned dim = 2;
 
     dim4 idims(x, y, z, w);
@@ -138,23 +135,22 @@ TEST(Range, CPP)
     output.host((void*)outData);
 
     // Compare result
-    for(int w = 0; w < (int)idims[3]; w++) {
-        for(int z = 0; z < (int)idims[2]; z++) {
-            for(int y = 0; y < (int)idims[1]; y++) {
-                for(int x = 0; x < (int)idims[0]; x++) {
+    for (int w = 0; w < (int)idims[3]; w++) {
+        for (int z = 0; z < (int)idims[2]; z++) {
+            for (int y = 0; y < (int)idims[1]; y++) {
+                for (int x = 0; x < (int)idims[0]; x++) {
                     float val = 0;
-                    if(dim == 0) {
+                    if (dim == 0) {
                         val = x;
-                    } else if(dim == 1) {
+                    } else if (dim == 1) {
                         val = y;
-                    } else if(dim == 2) {
+                    } else if (dim == 2) {
                         val = z;
-                    } else if(dim == 3) {
+                    } else if (dim == 3) {
                         val = w;
                     }
                     dim_t idx = (w * idims[0] * idims[1] * idims[2]) +
-                                   (z * idims[0] * idims[1]) +
-                                   (y * idims[0]) + x;
+                                (z * idims[0] * idims[1]) + (y * idims[0]) + x;
                     ASSERT_EQ(val, outData[idx]) << "at: " << idx << endl;
                 }
             }
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index f859745d3b..3d06f83bed 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -7,49 +7,44 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::det;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::join;
 using af::randu;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Rank : public ::testing::Test
-{
-};
+class Rank : public ::testing::Test {};
 
 template<typename T>
-class Det : public ::testing::Test
-{
-};
+class Det : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(Rank, TestTypes);
 TYPED_TEST_CASE(Det, TestTypes);
 
 template<typename T>
-void rankSmall()
-{
+void rankSmall() {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -60,55 +55,43 @@ void rankSmall()
 }
 
 template<typename T>
-void rankBig(const int num)
-{
+void rankBig(const int num) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
-    array a = randu(num, num, dt);
+    array a  = randu(num, num, dt);
     ASSERT_EQ(num, (int)rank(a));
 
-    array b = randu(num, num/2, dt);
-    ASSERT_EQ(num/2, (int)rank(b));
-    ASSERT_EQ(num/2, (int)rank(transpose(b)));
+    array b = randu(num, num / 2, dt);
+    ASSERT_EQ(num / 2, (int)rank(b));
+    ASSERT_EQ(num / 2, (int)rank(transpose(b)));
 }
 
 template<typename T>
-void rankLow(const int num)
-{
+void rankLow(const int num) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
 
-    array a = randu(3 * num, num, dt);
-    array b = randu(3 * num, num, dt);
-    array c = a + 0.2 * b;
+    array a  = randu(3 * num, num, dt);
+    array b  = randu(3 * num, num, dt);
+    array c  = a + 0.2 * b;
     array in = join(1, a, b, c);
 
     // The last third is just a linear combination of first and second thirds
     ASSERT_EQ(2 * num, (int)rank(in));
 }
 
-TYPED_TEST(Rank, small)
-{
-    rankSmall<TypeParam>();
-}
+TYPED_TEST(Rank, small) { rankSmall<TypeParam>(); }
 
-TYPED_TEST(Rank, big)
-{
-    rankBig<TypeParam>(1024);
-}
+TYPED_TEST(Rank, big) { rankBig<TypeParam>(1024); }
 
-TYPED_TEST(Rank, low)
-{
-    rankBig<TypeParam>(512);
-}
+TYPED_TEST(Rank, low) { rankBig<TypeParam>(512); }
 
 template<typename T>
-void detTest()
-{
+void detTest() {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -116,18 +99,16 @@ void detTest()
 
     vector<dim4> numDims;
 
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float,float,float>(string(TEST_DIR"/lapack/detSmall.test"),numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(string(TEST_DIR "/lapack/detSmall.test"),
+                                   numDims, in, tests);
+    dim4 dims = numDims[0];
 
     array input = array(dims, &(in[0].front())).as(dt);
-    T output = det<T>(input);
+    T output    = det<T>(input);
 
     ASSERT_NEAR(abs((T)tests[0][0]), abs(output), 1e-6);
 }
 
-TYPED_TEST(Det, Small)
-{
-    detTest<TypeParam>();
-}
+TYPED_TEST(Det, Small) { detTest<TypeParam>(); }
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 1debe89f92..076dd49eb9 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -7,41 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
+#include <algorithm>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
-#include <algorithm>
+#include <vector>
 
-using std::vector;
-using std::complex;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::freeHost;
-
+using std::complex;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Reduce : public ::testing::Test
-{
-};
+class Reduce : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
+                         uchar, short, ushort>
+    TestTypes;
 TYPED_TEST_CASE(Reduce, TestTypes);
 
 typedef af_err (*reduceFunc)(af_array *, const af_array, const int);
 
 template<typename Ti, typename To, reduceFunc af_reduce>
-void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<af_seq> seqv=vector<af_seq>())
-{
+void reduceTest(string pTestFile, int off = 0, bool isSubRef = false,
+                const vector<af_seq> seqv = vector<af_seq>()) {
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
@@ -49,8 +48,8 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (pTestFile,numDims,data,tests);
-    dim4 dims       = numDims[0];
+    readTests<int, int, int>(pTestFile, numDims, data, tests);
+    dim4 dims = numDims[0];
 
     vector<Ti> in(data[0].begin(), data[0].end());
 
@@ -60,16 +59,20 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
 
     // Get input array
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
+        ASSERT_SUCCESS(
+            af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(),
+                            (af_dtype)af::dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
         ASSERT_SUCCESS(af_release_array(tempArray));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) af::dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(
+            af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(),
+                            (af_dtype)af::dtype_traits<Ti>::af_type));
     }
 
     // Compare result
     for (int d = 0; d < (int)tests.size(); ++d) {
-
         vector<To> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
@@ -80,23 +83,22 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
 
         // Get result
         vector<To> outData(dims.elements());
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
-        if(std::equal(currGoldBar.begin(), currGoldBar.end(), outData.begin()) == false)
-        {
+        if (std::equal(currGoldBar.begin(), currGoldBar.end(),
+                       outData.begin()) == false) {
             for (size_t elIter = 0; elIter < nElems; ++elIter) {
-
-                EXPECT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                                << " for dim " << d + off << endl;
+                EXPECT_EQ(currGoldBar[elIter], outData[elIter])
+                    << "at: " << elIter << " for dim " << d + off << endl;
             }
             af_print_array(outArray);
-            for(int i = 0; i < (int)nElems; i++) {
+            for (int i = 0; i < (int)nElems; i++) {
                 cout << currGoldBar[i] << ", ";
             }
 
             cout << endl;
-            for(int i = 0; i < (int)nElems; i++) {
+            for (int i = 0; i < (int)nElems; i++) {
                 cout << outData[i] << ", ";
             }
             FAIL();
@@ -108,41 +110,61 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef=false, const vector
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-template<typename T,reduceFunc OP>
+template<typename T, reduceFunc OP>
 struct promote_type {
     typedef T type;
 };
 
 // char and uchar are promoted to int for sum and product
-template<> struct promote_type<uchar , af_sum>       { typedef uint type; };
-template<> struct promote_type<char  , af_sum>       { typedef uint type; };
-template<> struct promote_type<short , af_sum>       { typedef int  type; };
-template<> struct promote_type<ushort, af_sum>       { typedef uint type; };
-template<> struct promote_type<uchar , af_product>   { typedef uint type; };
-template<> struct promote_type<char  , af_product>   { typedef uint type; };
-template<> struct promote_type<short , af_product>   { typedef int  type; };
-template<> struct promote_type<ushort, af_product>   { typedef uint type; };
-
-#define REDUCE_TESTS(FN)                                                                    \
-    TYPED_TEST(Reduce,Test_##FN)                                                            \
-    {                                                                                       \
-        reduceTest<TypeParam, typename promote_type<TypeParam, af_##FN>::type, af_##FN>(    \
-            string(TEST_DIR"/reduce/"#FN".test")                                            \
-            );                                                                              \
-    }                                                                                       \
+template<>
+struct promote_type<uchar, af_sum> {
+    typedef uint type;
+};
+template<>
+struct promote_type<char, af_sum> {
+    typedef uint type;
+};
+template<>
+struct promote_type<short, af_sum> {
+    typedef int type;
+};
+template<>
+struct promote_type<ushort, af_sum> {
+    typedef uint type;
+};
+template<>
+struct promote_type<uchar, af_product> {
+    typedef uint type;
+};
+template<>
+struct promote_type<char, af_product> {
+    typedef uint type;
+};
+template<>
+struct promote_type<short, af_product> {
+    typedef int type;
+};
+template<>
+struct promote_type<ushort, af_product> {
+    typedef uint type;
+};
+
+#define REDUCE_TESTS(FN)                                                       \
+    TYPED_TEST(Reduce, Test_##FN) {                                            \
+        reduceTest<TypeParam, typename promote_type<TypeParam, af_##FN>::type, \
+                   af_##FN>(string(TEST_DIR "/reduce/" #FN ".test"));          \
+    }
 
 REDUCE_TESTS(sum);
 REDUCE_TESTS(min);
 REDUCE_TESTS(max);
 
 #undef REDUCE_TESTS
-#define REDUCE_TESTS(FN, OT)                        \
-    TYPED_TEST(Reduce,Test_##FN)                    \
-    {                                               \
-        reduceTest<TypeParam, OT, af_##FN>(         \
-            string(TEST_DIR"/reduce/"#FN".test")    \
-            );                                      \
-    }                                               \
+#define REDUCE_TESTS(FN, OT)                          \
+    TYPED_TEST(Reduce, Test_##FN) {                   \
+        reduceTest<TypeParam, OT, af_##FN>(           \
+            string(TEST_DIR "/reduce/" #FN ".test")); \
+    }
 
 REDUCE_TESTS(any_true, unsigned char);
 REDUCE_TESTS(all_true, unsigned char);
@@ -150,14 +172,10 @@ REDUCE_TESTS(count, unsigned);
 
 #undef REDUCE_TESTS
 
-TEST(Reduce,Test_Reduce_Big0)
-{
+TEST(Reduce, Test_Reduce_Big0) {
     if (noDoubleTests<int>()) return;
 
-    reduceTest<int, int, af_sum>(
-        string(TEST_DIR"/reduce/big0.test"),
-        0
-        );
+    reduceTest<int, int, af_sum>(string(TEST_DIR "/reduce/big0.test"), 0);
 }
 
 /*
@@ -174,9 +192,8 @@ TEST(Reduce,Test_Reduce_Big1)
 
 /////////////////////////////////// CPP //////////////////////////////////
 //
-typedef af::array (*ReductionOp)(const af::array&, const int);
+typedef af::array (*ReductionOp)(const af::array &, const int);
 
-using af::NaN;
 using af::allTrue;
 using af::anyTrue;
 using af::constant;
@@ -184,6 +201,7 @@ using af::count;
 using af::iota;
 using af::max;
 using af::min;
+using af::NaN;
 using af::product;
 using af::randu;
 using af::round;
@@ -192,8 +210,7 @@ using af::span;
 using af::sum;
 
 template<typename Ti, typename To, ReductionOp reduce>
-void cppReduceTest(string pTestFile)
-{
+void cppReduceTest(string pTestFile) {
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
@@ -201,8 +218,8 @@ void cppReduceTest(string pTestFile)
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (pTestFile,numDims,data,tests);
-    dim4 dims       = numDims[0];
+    readTests<int, int, int>(pTestFile, numDims, data, tests);
+    dim4 dims = numDims[0];
 
     vector<Ti> in(data[0].begin(), data[0].end());
 
@@ -210,7 +227,6 @@ void cppReduceTest(string pTestFile)
 
     // Compare result
     for (int d = 0; d < (int)tests.size(); ++d) {
-
         vector<To> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
@@ -218,20 +234,19 @@ void cppReduceTest(string pTestFile)
 
         // Get result
         vector<To> outData(dims.elements());
-        output.host((void*)&outData.front());
+        output.host((void *)&outData.front());
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                            << " for dim " << d << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << " for dim " << d << endl;
         }
     }
 }
 
-TEST(Reduce, Test_Sum_Scalar_MaxDim)
-{
+TEST(Reduce, Test_Sum_Scalar_MaxDim) {
     const size_t largeDim = 65535 * 32 * 8 + 1;
-    array A = constant(1, dim4(1, largeDim, 1, 1));
+    array A               = constant(1, dim4(1, largeDim, 1, 1));
     ASSERT_EQ(sum<float>(A, 1), largeDim);
     A = constant(1, dim4(1, 1, largeDim, 1));
     ASSERT_EQ(sum<float>(A, 2), largeDim);
@@ -239,10 +254,9 @@ TEST(Reduce, Test_Sum_Scalar_MaxDim)
     ASSERT_EQ(sum<float>(A, 3), largeDim);
 }
 
-TEST(Reduce, Test_Min_Scalar_MaxDim)
-{
+TEST(Reduce, Test_Min_Scalar_MaxDim) {
     const size_t largeDim = 65535 * 32 * 8 + 1;
-    array A = iota(dim4(1, largeDim, 1, 1));
+    array A               = iota(dim4(1, largeDim, 1, 1));
     ASSERT_EQ(min(A, 1).scalar<float>(), 0.f);
     A = iota(dim4(1, 1, largeDim, 1));
     ASSERT_EQ(min(A, 2).scalar<float>(), 0.f);
@@ -250,10 +264,9 @@ TEST(Reduce, Test_Min_Scalar_MaxDim)
     ASSERT_EQ(min(A, 3).scalar<float>(), 0.f);
 }
 
-TEST(Reduce, Test_Max_Scalar_MaxDim)
-{
+TEST(Reduce, Test_Max_Scalar_MaxDim) {
     const size_t largeDim = 65535 * 32 * 8 + 1;
-    array A = iota(dim4(1, largeDim, 1, 1));
+    array A               = iota(dim4(1, largeDim, 1, 1));
     ASSERT_EQ(max(A, 1).scalar<float>(), largeDim - 1);
     A = iota(dim4(1, 1, largeDim, 1));
     ASSERT_EQ(max(A, 2).scalar<float>(), largeDim - 1);
@@ -261,10 +274,9 @@ TEST(Reduce, Test_Max_Scalar_MaxDim)
     ASSERT_EQ(max(A, 3).scalar<float>(), largeDim - 1);
 }
 
-TEST(Reduce, Test_anyTrue_Scalar_MaxDim)
-{
+TEST(Reduce, Test_anyTrue_Scalar_MaxDim) {
     const size_t largeDim = 65535 * 32 * 8 + 1;
-    array A = constant(1, dim4(1, largeDim, 1, 1));
+    array A               = constant(1, dim4(1, largeDim, 1, 1));
     ASSERT_EQ(anyTrue(A, 1).scalar<char>(), 1);
     A = constant(1, dim4(1, 1, largeDim, 1));
     ASSERT_EQ(anyTrue(A, 2).scalar<char>(), 1);
@@ -272,10 +284,9 @@ TEST(Reduce, Test_anyTrue_Scalar_MaxDim)
     ASSERT_EQ(anyTrue(A, 3).scalar<char>(), 1);
 }
 
-TEST(Reduce, Test_allTrue_Scalar_MaxDim)
-{
+TEST(Reduce, Test_allTrue_Scalar_MaxDim) {
     const size_t largeDim = 65535 * 32 * 8 + 1;
-    array A = constant(1, dim4(1, largeDim, 1, 1));
+    array A               = constant(1, dim4(1, largeDim, 1, 1));
     ASSERT_EQ(allTrue(A, 1).scalar<char>(), 1);
     A = constant(1, dim4(1, 1, largeDim, 1));
     ASSERT_EQ(allTrue(A, 2).scalar<char>(), 1);
@@ -283,10 +294,9 @@ TEST(Reduce, Test_allTrue_Scalar_MaxDim)
     ASSERT_EQ(allTrue(A, 3).scalar<char>(), 1);
 }
 
-TEST(Reduce, Test_count_Scalar_MaxDim)
-{
+TEST(Reduce, Test_count_Scalar_MaxDim) {
     const size_t largeDim = 65535 * 32 * 8 + 1;
-    array A = constant(1, dim4(1, largeDim, 1, 1));
+    array A               = constant(1, dim4(1, largeDim, 1, 1));
     ASSERT_EQ(count(A, 1).scalar<unsigned int>(), largeDim);
     A = constant(1, dim4(1, 1, largeDim, 1));
     ASSERT_EQ(count(A, 2).scalar<unsigned int>(), largeDim);
@@ -294,12 +304,9 @@ TEST(Reduce, Test_count_Scalar_MaxDim)
     ASSERT_EQ(count(A, 3).scalar<unsigned int>(), largeDim);
 }
 
-#define CPP_REDUCE_TESTS(FN, FNAME, Ti, To)         \
-    TEST(Reduce, Test_##FN##_CPP)                   \
-    {                                               \
-        cppReduceTest<Ti, To, FN>(                  \
-            string(TEST_DIR"/reduce/"#FNAME".test") \
-            );                                      \
+#define CPP_REDUCE_TESTS(FN, FNAME, Ti, To)                                    \
+    TEST(Reduce, Test_##FN##_CPP) {                                            \
+        cppReduceTest<Ti, To, FN>(string(TEST_DIR "/reduce/" #FNAME ".test")); \
     }
 
 CPP_REDUCE_TESTS(sum, sum, float, float);
@@ -309,206 +316,178 @@ CPP_REDUCE_TESTS(anyTrue, any_true, float, unsigned char);
 CPP_REDUCE_TESTS(allTrue, all_true, float, unsigned char);
 CPP_REDUCE_TESTS(count, count, float, unsigned);
 
-TEST(Reduce, Test_Product_Global)
-{
+TEST(Reduce, Test_Product_Global) {
     const int num = 100;
-    array a = 1 + round(5 * randu(num, 1)) / 100;
+    array a       = 1 + round(5 * randu(num, 1)) / 100;
 
-    float res = product<float>(a);
+    float res  = product<float>(a);
     float *h_a = a.host<float>();
     float gold = 1;
 
-    for (int i = 0; i < num; i++) {
-        gold *= h_a[i];
-    }
+    for (int i = 0; i < num; i++) { gold *= h_a[i]; }
 
     ASSERT_NEAR(gold, res, 1e-3);
     freeHost(h_a);
 }
 
-TEST(Reduce, Test_Sum_Global)
-{
+TEST(Reduce, Test_Sum_Global) {
     const int num = 10000;
-    array a = round(2 * randu(num, 1));
+    array a       = round(2 * randu(num, 1));
 
-    float res = sum<float>(a);
+    float res  = sum<float>(a);
     float *h_a = a.host<float>();
     float gold = 0;
 
-    for (int i = 0; i < num; i++) {
-        gold += h_a[i];
-    }
+    for (int i = 0; i < num; i++) { gold += h_a[i]; }
 
     ASSERT_EQ(gold, res);
     freeHost(h_a);
 }
 
-TEST(Reduce, Test_Count_Global)
-{
+TEST(Reduce, Test_Count_Global) {
     const int num = 10000;
-    array a = round(2 * randu(num, 1));
-    array b = a.as(b8);
+    array a       = round(2 * randu(num, 1));
+    array b       = a.as(b8);
 
-    int res = count<int>(b);
+    int res   = count<int>(b);
     char *h_b = b.host<char>();
-    int gold = 0;
+    int gold  = 0;
 
-    for (int i = 0; i < num; i++) {
-        gold += h_b[i];
-    }
+    for (int i = 0; i < num; i++) { gold += h_b[i]; }
 
     ASSERT_EQ(gold, res);
     freeHost(h_b);
 }
 
-TEST(Reduce, Test_min_Global)
-{
+TEST(Reduce, Test_min_Global) {
     if (noDoubleTests<double>()) return;
 
     const int num = 10000;
-    array a = randu(num, 1, f64);
-    double res = min<double>(a);
-    double *h_a = a.host<double>();
-    double gold = std::numeric_limits<double>::max();
+    array a       = randu(num, 1, f64);
+    double res    = min<double>(a);
+    double *h_a   = a.host<double>();
+    double gold   = std::numeric_limits<double>::max();
 
     if (noDoubleTests<double>()) return;
 
-    for (int i = 0; i < num; i++) {
-        gold = std::min(gold, h_a[i]);
-    }
+    for (int i = 0; i < num; i++) { gold = std::min(gold, h_a[i]); }
 
     ASSERT_EQ(gold, res);
     freeHost(h_a);
 }
 
-TEST(Reduce, Test_max_Global)
-{
+TEST(Reduce, Test_max_Global) {
     const int num = 10000;
-    array a = randu(num, 1);
-    float res = max<float>(a);
-    float *h_a = a.host<float>();
-    float gold = -std::numeric_limits<float>::max();
+    array a       = randu(num, 1);
+    float res     = max<float>(a);
+    float *h_a    = a.host<float>();
+    float gold    = -std::numeric_limits<float>::max();
 
-    for (int i = 0; i < num; i++) {
-        gold = std::max(gold, h_a[i]);
-    }
+    for (int i = 0; i < num; i++) { gold = std::max(gold, h_a[i]); }
 
     ASSERT_EQ(gold, res);
     freeHost(h_a);
 }
 
-
 template<typename T>
-void typed_assert_eq(T lhs, T rhs, bool both = true)
-{
+void typed_assert_eq(T lhs, T rhs, bool both = true) {
     UNUSED(both);
     ASSERT_EQ(lhs, rhs);
 }
 
 template<>
-void typed_assert_eq<float>(float lhs, float rhs, bool both)
-{
+void typed_assert_eq<float>(float lhs, float rhs, bool both) {
     UNUSED(both);
     ASSERT_FLOAT_EQ(lhs, rhs);
 }
 
 template<>
-void typed_assert_eq<double>(double lhs, double rhs, bool both)
-{
+void typed_assert_eq<double>(double lhs, double rhs, bool both) {
     UNUSED(both);
     ASSERT_DOUBLE_EQ(lhs, rhs);
 }
 
 template<>
-void typed_assert_eq<cfloat>(cfloat lhs, cfloat rhs, bool both)
-{
+void typed_assert_eq<cfloat>(cfloat lhs, cfloat rhs, bool both) {
     ASSERT_FLOAT_EQ(real(lhs), real(rhs));
-    if(both) {
-        ASSERT_FLOAT_EQ(imag(lhs), imag(rhs));
-    }
+    if (both) { ASSERT_FLOAT_EQ(imag(lhs), imag(rhs)); }
 }
 
 template<>
-void typed_assert_eq<cdouble>(cdouble lhs, cdouble rhs, bool both)
-{
+void typed_assert_eq<cdouble>(cdouble lhs, cdouble rhs, bool both) {
     ASSERT_DOUBLE_EQ(real(lhs), real(rhs));
-    if(both) {
-        ASSERT_DOUBLE_EQ(imag(lhs), imag(rhs));
-    }
+    if (both) { ASSERT_DOUBLE_EQ(imag(lhs), imag(rhs)); }
 }
 
-TYPED_TEST(Reduce, Test_All_Global)
-{
+TYPED_TEST(Reduce, Test_All_Global) {
     if (noDoubleTests<TypeParam>()) return;
 
     // Input size test
-    for(int i = 1; i < 1000; i+=100) {
+    for (int i = 1; i < 1000; i += 100) {
         int num = 10 * i;
-        vector<TypeParam> h_vals(num, (TypeParam)true);
-        array a(2, num/2, &h_vals.front());
+        vector<TypeParam> h_vals(num, (TypeParam) true);
+        array a(2, num / 2, &h_vals.front());
 
         TypeParam res = allTrue<TypeParam>(a);
-        typed_assert_eq((TypeParam)true, res, false);
+        typed_assert_eq((TypeParam) true, res, false);
 
         h_vals[3] = false;
-        a = array(2, num/2, &h_vals.front());
+        a         = array(2, num / 2, &h_vals.front());
 
         res = allTrue<TypeParam>(a);
-        typed_assert_eq((TypeParam)false, res, false);
+        typed_assert_eq((TypeParam) false, res, false);
     }
 
     // false value location test
     const int num = 10000;
-    vector<TypeParam> h_vals(num, (TypeParam)true);
-    for(int i = 1; i < 10000; i+=100) {
+    vector<TypeParam> h_vals(num, (TypeParam) true);
+    for (int i = 1; i < 10000; i += 100) {
         h_vals[i] = false;
-        array a(2, num/2, &h_vals.front());
+        array a(2, num / 2, &h_vals.front());
 
         TypeParam res = allTrue<TypeParam>(a);
-        typed_assert_eq((TypeParam)false, res, false);
+        typed_assert_eq((TypeParam) false, res, false);
 
         h_vals[i] = true;
     }
 }
 
-TYPED_TEST(Reduce, Test_Any_Global)
-{
+TYPED_TEST(Reduce, Test_Any_Global) {
     if (noDoubleTests<TypeParam>()) return;
 
     // Input size test
-    for(int i = 1; i < 1000; i+=100) {
+    for (int i = 1; i < 1000; i += 100) {
         int num = 10 * i;
-        vector<TypeParam> h_vals(num, (TypeParam)false);
-        array a(2, num/2, &h_vals.front());
+        vector<TypeParam> h_vals(num, (TypeParam) false);
+        array a(2, num / 2, &h_vals.front());
 
         TypeParam res = anyTrue<TypeParam>(a);
-        typed_assert_eq((TypeParam)false, res, false);
+        typed_assert_eq((TypeParam) false, res, false);
 
         h_vals[3] = true;
-        a = array(2, num/2, &h_vals.front());
+        a         = array(2, num / 2, &h_vals.front());
 
         res = anyTrue<TypeParam>(a);
-        typed_assert_eq((TypeParam)true, res, false);
+        typed_assert_eq((TypeParam) true, res, false);
     }
 
     // true value location test
     const int num = 10000;
-    vector<TypeParam> h_vals(num, (TypeParam)false);
-    for(int i = 1; i < 10000; i+=100) {
+    vector<TypeParam> h_vals(num, (TypeParam) false);
+    for (int i = 1; i < 10000; i += 100) {
         h_vals[i] = true;
-        array a(2, num/2, &h_vals.front());
+        array a(2, num / 2, &h_vals.front());
 
         TypeParam res = anyTrue<TypeParam>(a);
-        typed_assert_eq((TypeParam)true, res, false);
+        typed_assert_eq((TypeParam) true, res, false);
 
         h_vals[i] = false;
     }
 }
 
-TEST(MinMax, MinMaxNaN)
-{
-    const int num = 10000;
-    array A = randu(num);
+TEST(MinMax, MinMaxNaN) {
+    const int num      = 10000;
+    array A            = randu(num);
     A(where(A < 0.25)) = NaN;
 
     float minval = min<float>(A);
@@ -529,17 +508,12 @@ TEST(MinMax, MinMaxNaN)
     freeHost(h_A);
 }
 
-TEST(MinMax, MinCplxNaN)
-{
-    float real_wnan_data[] = {
-        0.005f, NAN, -6.3f, NAN, -0.5f,
-        NAN, NAN, 0.2f, -1205.4f, 8.9f
-    };
+TEST(MinMax, MinCplxNaN) {
+    float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
+                              NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
-    float imag_wnan_data[] = {
-        NAN, NAN, -9.0f, -0.005f, -0.3f,
-        0.007f, NAN, 0.1f, NAN, 4.5f
-    };
+    float imag_wnan_data[] = {NAN,    NAN, -9.0f, -0.005f, -0.3f,
+                              0.007f, NAN, 0.1f,  NAN,     4.5f};
 
     int rows = 5;
     int cols = 2;
@@ -547,12 +521,12 @@ TEST(MinMax, MinCplxNaN)
     array imag_wnan(rows, cols, imag_wnan_data);
     array a = af::complex(real_wnan, imag_wnan);
 
-    float gold_min_real[] = { -0.5f, 0.2f };
-    float gold_min_imag[] = { -0.3f, 0.1f };
+    float gold_min_real[] = {-0.5f, 0.2f};
+    float gold_min_imag[] = {-0.3f, 0.1f};
 
     array min_val = af::min(a);
 
-    vector< complex<float> > h_min_val(cols);
+    vector<complex<float> > h_min_val(cols);
     min_val.host(&h_min_val[0]);
 
     for (int i = 0; i < cols; i++) {
@@ -561,8 +535,7 @@ TEST(MinMax, MinCplxNaN)
     }
 }
 
-TEST(MinMax, MaxCplxNaN)
-{
+TEST(MinMax, MaxCplxNaN) {
     // 4th element is unusually large to cover the case where
     //  one part holds the largest value among the array,
     //  and the other part is NaN.
@@ -572,15 +545,11 @@ TEST(MinMax, MaxCplxNaN)
     //  magnitude will determine that that element is the max,
     //  whereas it should have been ignored since its other
     //  part is NaN
-    float real_wnan_data[] = {
-        0.005f, NAN, -6.3f, NAN, -0.5f,
-        NAN, NAN, 0.2f, -1205.4f, 8.9f
-    };
+    float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
+                              NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
-    float imag_wnan_data[] = {
-        NAN, NAN, -9.0f, -0.005f, -0.3f,
-        0.007f, NAN, 0.1f, NAN, 4.5f
-    };
+    float imag_wnan_data[] = {NAN,    NAN, -9.0f, -0.005f, -0.3f,
+                              0.007f, NAN, 0.1f,  NAN,     4.5f};
 
     int rows = 5;
     int cols = 2;
@@ -588,12 +557,12 @@ TEST(MinMax, MaxCplxNaN)
     array imag_wnan(rows, cols, imag_wnan_data);
     array a = af::complex(real_wnan, imag_wnan);
 
-    float gold_max_real[] = { -6.3f, 8.9f };
-    float gold_max_imag[] = { -9.0f, 4.5f };
+    float gold_max_real[] = {-6.3f, 8.9f};
+    float gold_max_imag[] = {-9.0f, 4.5f};
 
     array max_val = af::max(a);
 
-    vector< complex<float> > h_max_val(cols);
+    vector<complex<float> > h_max_val(cols);
     max_val.host(&h_max_val[0]);
 
     for (int i = 0; i < cols; i++) {
@@ -602,66 +571,58 @@ TEST(MinMax, MaxCplxNaN)
     }
 }
 
-TEST(Count, NaN)
-{
+TEST(Count, NaN) {
     const int num = 10000;
-    array A = round(5 * randu(num));
-    array B = A;
+    array A       = round(5 * randu(num));
+    array B       = A;
 
     A(where(A == 2)) = NaN;
 
     ASSERT_EQ(count<uint>(A), count<uint>(B));
 }
 
-TEST(Sum, NaN)
-{
-    const int num = 10000;
-    array A = randu(num);
+TEST(Sum, NaN) {
+    const int num      = 10000;
+    array A            = randu(num);
     A(where(A < 0.25)) = NaN;
 
     float res = sum<float>(A);
 
     ASSERT_EQ(std::isnan(res), true);
 
-    res = sum<float>(A, 0);
+    res        = sum<float>(A, 0);
     float *h_A = A.host<float>();
 
     float tmp = 0;
-    for (int i = 0; i < num; i++) {
-        tmp += std::isnan(h_A[i]) ? 0 : h_A[i];
-    }
+    for (int i = 0; i < num; i++) { tmp += std::isnan(h_A[i]) ? 0 : h_A[i]; }
 
-    ASSERT_NEAR(res/num, tmp/num, 1E-5);
+    ASSERT_NEAR(res / num, tmp / num, 1E-5);
     freeHost(h_A);
 }
 
-TEST(Product, NaN)
-{
+TEST(Product, NaN) {
     const int num = 5;
-    array A = randu(num);
-    A(2) = NaN;
+    array A       = randu(num);
+    A(2)          = NaN;
 
     float res = product<float>(A);
 
     ASSERT_EQ(std::isnan(res), true);
 
-    res = product<float>(A, 1);
+    res        = product<float>(A, 1);
     float *h_A = A.host<float>();
 
     float tmp = 1;
-    for (int i = 0; i < num; i++) {
-        tmp *= std::isnan(h_A[i]) ? 1 : h_A[i];
-    }
+    for (int i = 0; i < num; i++) { tmp *= std::isnan(h_A[i]) ? 1 : h_A[i]; }
 
-    ASSERT_NEAR(res/num, tmp/num, 1E-5);
+    ASSERT_NEAR(res / num, tmp / num, 1E-5);
     freeHost(h_A);
 }
 
-TEST(AnyAll, NaN)
-{
+TEST(AnyAll, NaN) {
     const int num = 10000;
-    array A = (randu(num) > 0.5).as(f32);
-    array B = A;
+    array A       = (randu(num) > 0.5).as(f32);
+    array B       = A;
 
     B(where(B == 0)) = NaN;
 
@@ -671,63 +632,54 @@ TEST(AnyAll, NaN)
     ASSERT_EQ(allTrue<bool>(A), false);
 }
 
-TEST(MaxAll, IndexedSmall)
-{
+TEST(MaxAll, IndexedSmall) {
     const int num = 1000;
-    const int st = 10;
-    const int en = num - 100;
-    array a = randu(num);
-    float b = max<float>(a(seq(st, en)));
+    const int st  = 10;
+    const int en  = num - 100;
+    array a       = randu(num);
+    float b       = max<float>(a(seq(st, en)));
 
     vector<float> ha(num);
     a.host(&ha[0]);
 
     float res = ha[st];
-    for (int i = st; i <= en; i++) {
-        res = std::max(res, ha[i]);
-    }
+    for (int i = st; i <= en; i++) { res = std::max(res, ha[i]); }
 
     ASSERT_EQ(b, res);
 }
 
-TEST(MaxAll, IndexedBig)
-{
+TEST(MaxAll, IndexedBig) {
     const int num = 100000;
-    const int st = 1000;
-    const int en = num - 1000;
-    array a = randu(num);
-    float b = max<float>(a(seq(st, en)));
+    const int st  = 1000;
+    const int en  = num - 1000;
+    array a       = randu(num);
+    float b       = max<float>(a(seq(st, en)));
 
     vector<float> ha(num);
     a.host(&ha[0]);
 
     float res = ha[st];
-    for (int i = st; i <= en; i++) {
-        res = std::max(res, ha[i]);
-    }
+    for (int i = st; i <= en; i++) { res = std::max(res, ha[i]); }
 
     ASSERT_EQ(b, res);
 }
 
-TEST(Reduce, KernelName)
-{
+TEST(Reduce, KernelName) {
     const int m = 64;
     const int n = 100;
     const int b = 5;
 
     array in = constant(0, m, n, b);
     for (int i = 0; i < b; i++) {
-        array tmp = randu(m, n);
+        array tmp         = randu(m, n);
         in(span, span, i) = tmp;
-        ASSERT_EQ(min<float>(in(span, span, i)),
-                  min<float>(tmp));
+        ASSERT_EQ(min<float>(in(span, span, i)), min<float>(tmp));
     }
 }
 
-TEST(Reduce, AllSmallIndexed)
-{
+TEST(Reduce, AllSmallIndexed) {
     const int len = 1000;
-    array a = af::range(dim4(len, 2));
-    array b = a(seq(len/2), span);
-    ASSERT_EQ(max<float>(b), len/2-1);
+    array a       = af::range(dim4(len, 2));
+    array b       = a(seq(len / 2), span);
+    ASSERT_EQ(max<float>(b), len / 2 - 1);
 }
diff --git a/test/regions.cpp b/test/regions.cpp
index b451b5224f..48172f0576 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -7,33 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
-#include <af/traits.hpp>
+#include <af/dim4.hpp>
 #include <af/image.h>
-#include <vector>
+#include <af/traits.hpp>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::regions;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Regions : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Regions : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
@@ -43,30 +42,36 @@ typedef ::testing::Types<float, double, int, unsigned, short, ushort> TestTypes;
 TYPED_TEST_CASE(Regions, TestTypes);
 
 template<typename T>
-void regionsTest(string pTestFile, af_connectivity connectivity, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void regionsTest(string pTestFile, af_connectivity connectivity,
+                 bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<uchar> > in;
     vector<vector<T> > tests;
-    readTests<uchar, T, unsigned>(pTestFile,numDims,in,tests);
+    readTests<uchar, T, unsigned>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array tempArray = 0;
-    af_array outArray = 0;
+    af_array outArray  = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<char>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<char>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<char>::af_type));
     }
 
-    ASSERT_SUCCESS(af_regions(&outArray, inArray, connectivity, (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_regions(&outArray, inArray, connectivity,
+                              (af_dtype)dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
@@ -75,42 +80,42 @@ void regionsTest(string pTestFile, af_connectivity connectivity, bool isSubRef =
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<T> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
+        size_t nElems         = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
     }
 
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define REGIONS_INIT(desc, file, conn, conn_type)                                           \
-    TYPED_TEST(Regions, desc)                                                               \
-    {                                                                                       \
-        regionsTest<TypeParam>(string(TEST_DIR"/regions/"#file"_"#conn".test"), conn_type); \
+#define REGIONS_INIT(desc, file, conn, conn_type)                             \
+    TYPED_TEST(Regions, desc) {                                               \
+        regionsTest<TypeParam>(                                               \
+            string(TEST_DIR "/regions/" #file "_" #conn ".test"), conn_type); \
     }
 
-    REGIONS_INIT(Regions0, regions_8x8, 4, AF_CONNECTIVITY_4);
-    REGIONS_INIT(Regions1, regions_8x8, 8, AF_CONNECTIVITY_8);
-    REGIONS_INIT(Regions2, regions_128x128, 4, AF_CONNECTIVITY_4);
-    REGIONS_INIT(Regions3, regions_128x128, 8, AF_CONNECTIVITY_8);
-
+REGIONS_INIT(Regions0, regions_8x8, 4, AF_CONNECTIVITY_4);
+REGIONS_INIT(Regions1, regions_8x8, 8, AF_CONNECTIVITY_8);
+REGIONS_INIT(Regions2, regions_128x128, 4, AF_CONNECTIVITY_4);
+REGIONS_INIT(Regions3, regions_128x128, 8, AF_CONNECTIVITY_8);
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(Regions, CPP)
-{
+TEST(Regions, CPP) {
     if (noDoubleTests<float>()) return;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, unsigned>(string(TEST_DIR"/regions/regions_8x8_4.test"),numDims,in,tests);
+    readTests<float, float, unsigned>(
+        string(TEST_DIR "/regions/regions_8x8_4.test"), numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, (float*)&(in[0].front()));
@@ -123,9 +128,10 @@ TEST(Regions, CPP)
     // Compare result
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<float> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
+        size_t nElems             = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
     }
 
@@ -134,34 +140,21 @@ TEST(Regions, CPP)
 }
 
 ///////////////////////////////// Documentation Examples ///////////////////
-TEST(Regions, Docs_8)
-{
+TEST(Regions, Docs_8) {
     // input data
-    uchar input[64] =  {
-        0, 0, 0, 0, 1, 0, 0, 0,
-        0, 0, 1, 0, 1, 0, 0, 1,
-        0, 0, 0, 1, 0, 0, 0, 0,
-        0, 0, 1, 0, 0, 1, 0, 0,
-        1, 0, 0, 1, 0, 0, 1, 0,
-        0, 0, 0, 1, 1, 0, 0, 1,
-        1, 1, 0, 0, 0, 0, 0, 0,
-        0, 1, 0, 1, 1, 1, 1, 0
-    };
+    uchar input[64] = {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
+                       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
+                       1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,
+                       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0};
     // gold output
-    float gold[64] =  {
-        0, 0, 0, 0, 1, 0, 0, 0,
-        0, 0, 1, 0, 1, 0, 0, 2,
-        0, 0, 0, 1, 0, 0, 0, 0,
-        0, 0, 1, 0, 0, 3, 0, 0,
-        4, 0, 0, 1, 0, 0, 3, 0,
-        0, 0, 0, 1, 1, 0, 0, 3,
-        5, 5, 0, 0, 0, 0, 0, 0,
-        0, 5, 0, 6, 6, 6, 6, 0
-    };
+    float gold[64] = {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 2,
+                      0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0,
+                      4, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 1, 1, 0, 0, 3,
+                      5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 6, 6, 6, 6, 0};
 
     //![ex_image_regions]
     array in(8, 8, input);
-    //af_print(in);
+    // af_print(in);
     // in =
     // 0   0   0   0   1   0   1   0
     // 0   0   0   0   0   0   1   1
@@ -174,7 +167,7 @@ TEST(Regions, Docs_8)
 
     // Compute the label matrix using 8-way connectivity
     array out = regions(in.as(b8), AF_CONNECTIVITY_8);
-    //af_print(out);
+    // af_print(out);
     // 0   0   0   0   4   0   5   0
     // 0   0   0   0   0   0   5   5
     // 0   1   0   1   0   0   0   0
@@ -185,80 +178,68 @@ TEST(Regions, Docs_8)
     // 0   2   0   0   0   3   0   0
     //![ex_image_regions]
 
-
     float output[64];
     out.host((void*)output);
 
-    for (int i=0; i<64; ++i) {
-        ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
+    for (int i = 0; i < 64; ++i) {
+        ASSERT_EQ(gold[i], output[i]) << " mismatch at i=" << i << endl;
     }
 }
 
-TEST(Regions, Docs_4)
-{
+TEST(Regions, Docs_4) {
     // input data
-    uchar input[64] =  {
-        0, 0, 0, 0, 1, 0, 0, 0,
-        0, 0, 1, 0, 1, 0, 0, 1,
-        0, 0, 0, 1, 0, 0, 0, 0,
-        0, 0, 1, 0, 0, 1, 0, 0,
-        1, 0, 0, 1, 0, 0, 1, 0,
-        0, 0, 0, 1, 1, 0, 0, 1,
-        1, 1, 0, 0, 0, 0, 0, 0,
-        0, 1, 0, 1, 1, 1, 1, 0
-    };
+    uchar input[64] = {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
+                       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
+                       1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,
+                       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0};
     // gold output
-    float gold[64] =  {
-        0.0000,  0.0000,  0.0000,  0.0000,  1.0000,  0.0000,  0.0000,  0.0000,
-        0.0000,  0.0000,  2.0000,  0.0000,  1.0000,  0.0000,  0.0000,  3.0000,
-        0.0000,  0.0000,  0.0000,  4.0000,  0.0000,  0.0000,  0.0000,  0.0000,
-        0.0000,  0.0000,  5.0000,  0.0000,  0.0000,  6.0000,  0.0000,  0.0000,
-        7.0000,  0.0000,  0.0000,  8.0000,  0.0000,  0.0000,  9.0000,  0.0000,
-        0.0000,  0.0000,  0.0000,  8.0000,  8.0000,  0.0000,  0.0000, 10.0000,
-        11.000, 11.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
-        0.0000, 11.0000,  0.0000, 12.0000, 12.0000, 12.0000, 12.0000,  0.0000
-    };
-
+    float gold[64] = {
+        0.0000, 0.0000,  0.0000, 0.0000,  1.0000,  0.0000,  0.0000,  0.0000,
+        0.0000, 0.0000,  2.0000, 0.0000,  1.0000,  0.0000,  0.0000,  3.0000,
+        0.0000, 0.0000,  0.0000, 4.0000,  0.0000,  0.0000,  0.0000,  0.0000,
+        0.0000, 0.0000,  5.0000, 0.0000,  0.0000,  6.0000,  0.0000,  0.0000,
+        7.0000, 0.0000,  0.0000, 8.0000,  0.0000,  0.0000,  9.0000,  0.0000,
+        0.0000, 0.0000,  0.0000, 8.0000,  8.0000,  0.0000,  0.0000,  10.0000,
+        11.000, 11.0000, 0.0000, 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
+        0.0000, 11.0000, 0.0000, 12.0000, 12.0000, 12.0000, 12.0000, 0.0000};
 
     //![ex_image_regions_4conn]
     array in(8, 8, input);
-    //af_print(in.T());
-    //in
-    //0  0  0  0  1  0  1  0
-    //0  0  0  0  0  0  1  1
-    //0  1  0  1  0  0  0  0
-    //0  0  1  0  1  1  0  1
-    //1  1  0  0  0  1  0  1
-    //0  0  0  1  0  0  0  1
-    //0  0  0  0  1  0  0  1
-    //0  1  0  0  0  1  0  0
+    // af_print(in.T());
+    // in
+    // 0  0  0  0  1  0  1  0
+    // 0  0  0  0  0  0  1  1
+    // 0  1  0  1  0  0  0  0
+    // 0  0  1  0  1  1  0  1
+    // 1  1  0  0  0  1  0  1
+    // 0  0  0  1  0  0  0  1
+    // 0  0  0  0  1  0  0  1
+    // 0  1  0  0  0  1  0  0
     // Compute the label matrix using 4-way connectivity
     array out = regions(in.as(b8), AF_CONNECTIVITY_4);
-    //af_print(out.T());
-    //out
-    //0  0  0  0  7  0 11  0
-    //0  0  0  0  0  0 11 11
-    //0  2  0  5  0  0  0  0
-    //0  0  4  0  8  8  0 12
-    //1  1  0  0  0  8  0 12
-    //0  0  0  6  0  0  0 12
-    //0  0  0  0  9  0  0 12
-    //0  3  0  0  0 10  0  0
+    // af_print(out.T());
+    // out
+    // 0  0  0  0  7  0 11  0
+    // 0  0  0  0  0  0 11 11
+    // 0  2  0  5  0  0  0  0
+    // 0  0  4  0  8  8  0 12
+    // 1  1  0  0  0  8  0 12
+    // 0  0  0  6  0  0  0 12
+    // 0  0  0  0  9  0  0 12
+    // 0  3  0  0  0 10  0  0
     //![ex_image_regions_4conn]
 
-
     float output[64];
     out.host((void*)output);
 
-    for (int i=0; i<64; ++i) {
-        ASSERT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
+    for (int i = 0; i < 64; ++i) {
+        ASSERT_EQ(gold[i], output[i]) << " mismatch at i=" << i << endl;
     }
 }
 
-TEST(Regions, WholeImageComponent)
-{
+TEST(Regions, WholeImageComponent) {
     const int dim = 101;
-    const int sz  = dim*dim;
+    const int sz  = dim * dim;
     vector<char> input(sz, 1);
     vector<float> gold(sz, 1.0f);
 
@@ -268,14 +249,13 @@ TEST(Regions, WholeImageComponent)
     vector<float> output(sz);
     out.host((void*)output.data());
 
-    for (int i=0; i<sz; ++i)
-        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
+    for (int i = 0; i < sz; ++i)
+        ASSERT_FLOAT_EQ(gold[i], output[i]) << " mismatch at i=" << i << endl;
 }
 
-TEST(Regions, NoComponentImage)
-{
+TEST(Regions, NoComponentImage) {
     const int dim = 101;
-    const int sz  = dim*dim;
+    const int sz  = dim * dim;
     vector<char> input(sz, 0);
     vector<float> gold(sz, 0.0f);
 
@@ -285,6 +265,6 @@ TEST(Regions, NoComponentImage)
     vector<float> output(sz);
     out.host((void*)output.data());
 
-    for (int i=0; i<sz; ++i)
-        ASSERT_FLOAT_EQ(gold[i], output[i])<<" mismatch at i="<<i<<endl;
+    for (int i = 0; i < sz; ++i)
+        ASSERT_FLOAT_EQ(gold[i], output[i]) << " mismatch at i=" << i << endl;
 }
diff --git a/test/reorder.cpp b/test/reorder.cpp
index 464589f56d..9156370f14 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
 using af::allTrue;
 using af::array;
@@ -31,49 +31,53 @@ using af::tile;
 using std::string;
 using std::vector;
 
-
 template<typename T>
-class Reorder : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Reorder : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Reorder, TestTypes);
 
 template<typename T>
-void reorderTest(string pTestFile, const unsigned resultIdx,
-                 const uint x, const uint y, const uint z, const uint w,
-                 bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void reorderTest(string pTestFile, const unsigned resultIdx, const uint x,
+                 const uint y, const uint z, const uint w,
+                 bool isSubRef = false, const vector<af_seq> *seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, int>(pTestFile,numDims,in,tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_reorder(&outArray, inArray, x, y, z, w));
@@ -81,66 +85,66 @@ void reorderTest(string pTestFile, const unsigned resultIdx,
     dim4 goldDims(idims[x], idims[y], idims[z], idims[w]);
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, outArray);
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define REORDER_INIT(desc, file, resultIdx, x, y, z, w)                                        \
-    TYPED_TEST(Reorder, desc)                                                                  \
-    {                                                                                       \
-        reorderTest<TypeParam>(string(TEST_DIR"/reorder/"#file".test"), resultIdx, x, y, z, w);   \
+#define REORDER_INIT(desc, file, resultIdx, x, y, z, w)                    \
+    TYPED_TEST(Reorder, desc) {                                            \
+        reorderTest<TypeParam>(string(TEST_DIR "/reorder/" #file ".test"), \
+                               resultIdx, x, y, z, w);                     \
     }
 
-    REORDER_INIT(Reorder012, reorder, 0, 0, 1, 2, 3);
-    REORDER_INIT(Reorder021, reorder, 1, 0, 2, 1, 3);
-    REORDER_INIT(Reorder102, reorder, 2, 1, 0, 2, 3);
-    REORDER_INIT(Reorder120, reorder, 3, 1, 2, 0, 3);
-    REORDER_INIT(Reorder201, reorder, 4, 2, 0, 1, 3);
-    REORDER_INIT(Reorder210, reorder, 5, 2, 1, 0, 3);
-
-    REORDER_INIT(Reorder0123, reorder4d, 0, 0, 1, 2, 3);
-    REORDER_INIT(Reorder0132, reorder4d, 1, 0, 1, 3, 2);
-    REORDER_INIT(Reorder0213, reorder4d, 2, 0, 2, 1, 3);
-    REORDER_INIT(Reorder0231, reorder4d, 3, 0, 2, 3, 1);
-    REORDER_INIT(Reorder0312, reorder4d, 4, 0, 3, 1, 2);
-    REORDER_INIT(Reorder0321, reorder4d, 5, 0, 3, 2, 1);
-
-    REORDER_INIT(Reorder1023, reorder4d, 6, 1, 0, 2, 3);
-    REORDER_INIT(Reorder1032, reorder4d, 7, 1, 0, 3, 2);
-    REORDER_INIT(Reorder1203, reorder4d, 8, 1, 2, 0, 3);
-    REORDER_INIT(Reorder1230, reorder4d, 9, 1, 2, 3, 0);
-    REORDER_INIT(Reorder1302, reorder4d,10, 1, 3, 0, 2);
-    REORDER_INIT(Reorder1320, reorder4d,11, 1, 3, 2, 0);
-
-    REORDER_INIT(Reorder2103, reorder4d,12, 2, 1, 0, 3);
-    REORDER_INIT(Reorder2130, reorder4d,13, 2, 1, 3, 0);
-    REORDER_INIT(Reorder2013, reorder4d,14, 2, 0, 1, 3);
-    REORDER_INIT(Reorder2031, reorder4d,15, 2, 0, 3, 1);
-    REORDER_INIT(Reorder2310, reorder4d,16, 2, 3, 1, 0);
-    REORDER_INIT(Reorder2301, reorder4d,17, 2, 3, 0, 1);
-
-    REORDER_INIT(Reorder3120, reorder4d,18, 3, 1, 2, 0);
-    REORDER_INIT(Reorder3102, reorder4d,19, 3, 1, 0, 2);
-    REORDER_INIT(Reorder3210, reorder4d,20, 3, 2, 1, 0);
-    REORDER_INIT(Reorder3201, reorder4d,21, 3, 2, 0, 1);
-    REORDER_INIT(Reorder3012, reorder4d,22, 3, 0, 1, 2);
-    REORDER_INIT(Reorder3021, reorder4d,23, 3, 0, 2, 1);
+REORDER_INIT(Reorder012, reorder, 0, 0, 1, 2, 3);
+REORDER_INIT(Reorder021, reorder, 1, 0, 2, 1, 3);
+REORDER_INIT(Reorder102, reorder, 2, 1, 0, 2, 3);
+REORDER_INIT(Reorder120, reorder, 3, 1, 2, 0, 3);
+REORDER_INIT(Reorder201, reorder, 4, 2, 0, 1, 3);
+REORDER_INIT(Reorder210, reorder, 5, 2, 1, 0, 3);
+
+REORDER_INIT(Reorder0123, reorder4d, 0, 0, 1, 2, 3);
+REORDER_INIT(Reorder0132, reorder4d, 1, 0, 1, 3, 2);
+REORDER_INIT(Reorder0213, reorder4d, 2, 0, 2, 1, 3);
+REORDER_INIT(Reorder0231, reorder4d, 3, 0, 2, 3, 1);
+REORDER_INIT(Reorder0312, reorder4d, 4, 0, 3, 1, 2);
+REORDER_INIT(Reorder0321, reorder4d, 5, 0, 3, 2, 1);
+
+REORDER_INIT(Reorder1023, reorder4d, 6, 1, 0, 2, 3);
+REORDER_INIT(Reorder1032, reorder4d, 7, 1, 0, 3, 2);
+REORDER_INIT(Reorder1203, reorder4d, 8, 1, 2, 0, 3);
+REORDER_INIT(Reorder1230, reorder4d, 9, 1, 2, 3, 0);
+REORDER_INIT(Reorder1302, reorder4d, 10, 1, 3, 0, 2);
+REORDER_INIT(Reorder1320, reorder4d, 11, 1, 3, 2, 0);
+
+REORDER_INIT(Reorder2103, reorder4d, 12, 2, 1, 0, 3);
+REORDER_INIT(Reorder2130, reorder4d, 13, 2, 1, 3, 0);
+REORDER_INIT(Reorder2013, reorder4d, 14, 2, 0, 1, 3);
+REORDER_INIT(Reorder2031, reorder4d, 15, 2, 0, 3, 1);
+REORDER_INIT(Reorder2310, reorder4d, 16, 2, 3, 1, 0);
+REORDER_INIT(Reorder2301, reorder4d, 17, 2, 3, 0, 1);
+
+REORDER_INIT(Reorder3120, reorder4d, 18, 3, 1, 2, 0);
+REORDER_INIT(Reorder3102, reorder4d, 19, 3, 1, 0, 2);
+REORDER_INIT(Reorder3210, reorder4d, 20, 3, 2, 1, 0);
+REORDER_INIT(Reorder3201, reorder4d, 21, 3, 2, 0, 1);
+REORDER_INIT(Reorder3012, reorder4d, 22, 3, 0, 1, 2);
+REORDER_INIT(Reorder3021, reorder4d, 23, 3, 0, 2, 1);
 
 ////////////////////////////////// CPP ///////////////////////////////////
 //
-TEST(Reorder, CPP)
-{
+TEST(Reorder, CPP) {
     const unsigned resultIdx = 0;
-    const unsigned x = 0;
-    const unsigned y = 1;
-    const unsigned z = 2;
-    const unsigned w = 3;
+    const unsigned x         = 0;
+    const unsigned y         = 1;
+    const unsigned z         = 2;
+    const unsigned w         = 3;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/reorder/reorder4d.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/reorder/reorder4d.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
 
@@ -151,16 +155,13 @@ TEST(Reorder, CPP)
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, output);
 }
 
-TEST(Reorder, ISSUE_1777)
-{
+TEST(Reorder, ISSUE_1777) {
     const int m = 5;
     const int n = 4;
     const int k = 3;
     vector<float> h_input(m * n);
 
-    for (int i = 0; i < m * n; i++) {
-        h_input[i] = (float)(i);
-    }
+    for (int i = 0; i < m * n; i++) { h_input[i] = (float)(i); }
 
     array a(m, n, &h_input[0]);
     array a_t = tile(a, 1, 1, 3);
@@ -177,11 +178,10 @@ TEST(Reorder, ISSUE_1777)
     }
 }
 
-TEST(Reorder, MaxDim)
-{
+TEST(Reorder, MaxDim) {
     if (noDoubleTests<float>()) return;
 
-    const size_t largeDim = 65535 * 32 + 1 ;
+    const size_t largeDim = 65535 * 32 + 1;
 
     array input  = range(dim4(2, largeDim, 2), 2);
     array output = reorder(input, 2, 1, 0);
@@ -192,7 +192,7 @@ TEST(Reorder, MaxDim)
 }
 
 TEST(Reorder, InputArrayUnchanged) {
-    float h_input[12] = {0.f, 1.f, 2.f, 3.f, 4.f, 5.f,
+    float h_input[12] = {0.f, 1.f, 2.f, 3.f, 4.f,  5.f,
                          6.f, 7.f, 8.f, 9.f, 10.f, 11.f};
     array input(2, 3, 2, h_input);
     array input_reord = reorder(input, 0, 2, 1);
diff --git a/test/replace.cpp b/test/replace.cpp
index cb6779f0de..6aa939ace5 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -7,39 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using af::NaN;
 using af::array;
 using af::cdouble;
 using af::cfloat;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
+using af::NaN;
 using af::randu;
 using af::seq;
 using af::span;
+using std::vector;
 
 template<typename T>
-class Replace : public ::testing::Test
-{
-};
+class Replace : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl, uchar, char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
+                         uchar, char, short, ushort>
+    TestTypes;
 
 TYPED_TEST_CASE(Replace, TestTypes);
 
 template<typename T>
-void replaceTest(const dim4 &dims)
-{
+void replaceTest(const dim4 &dims) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
@@ -75,20 +74,17 @@ void replaceTest(const dim4 &dims)
 }
 
 template<typename T>
-void replaceScalarTest(const dim4 &dims)
-{
+void replaceScalarTest(const dim4 &dims) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
 
-    if (a.isinteger()) {
-        a = (a % (1 << 30)).as(ty);
-    }
+    if (a.isinteger()) { a = (a % (1 << 30)).as(ty); }
 
-    array c = a.copy();
+    array c    = a.copy();
     array cond = randu(dims, ty) > a;
-    double b = 3;
+    double b   = 3;
 
     replace(c, cond, b);
     int num = (int)a.elements();
@@ -101,30 +97,21 @@ void replaceScalarTest(const dim4 &dims)
     c.host(&hc[0]);
     cond.host(&hcond[0]);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hcond[i] ? ha[i] : T(b));
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hc[i], hcond[i] ? ha[i] : T(b)); }
 }
 
-TYPED_TEST(Replace, Simple)
-{
-    replaceTest<TypeParam>(dim4(1024, 1024));
-}
+TYPED_TEST(Replace, Simple) { replaceTest<TypeParam>(dim4(1024, 1024)); }
 
-TYPED_TEST(Replace, Scalar)
-{
-    replaceScalarTest<TypeParam>(dim4(5, 5));
-}
+TYPED_TEST(Replace, Scalar) { replaceScalarTest<TypeParam>(dim4(5, 5)); }
 
-TEST(Replace, NaN)
-{
+TEST(Replace, NaN) {
     dim4 dims(1000, 1250);
     dtype ty = f32;
 
-    array a = randu(dims, ty);
+    array a                                 = randu(dims, ty);
     a(seq(a.dims(0) / 2), span, span, span) = NaN;
-    array c = a.copy();
-    float b = 0;
+    array c                                 = a.copy();
+    float b                                 = 0;
     replace(c, !isNaN(c), b);
 
     int num = (int)a.elements();
@@ -136,16 +123,15 @@ TEST(Replace, NaN)
     c.host(&hc[0]);
 
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], ( std::isnan(ha[i]) ? b : ha[i]) );
+        ASSERT_EQ(hc[i], (std::isnan(ha[i]) ? b : ha[i]));
     }
 }
 
-TEST(Replace, ISSUE_1249)
-{
+TEST(Replace, ISSUE_1249) {
     dim4 dims(2, 3, 4);
     array cond = randu(dims) > 0.5;
-    array a = randu(dims);
-    array b = a.copy();
+    array a    = randu(dims);
+    array b    = a.copy();
     replace(b, !cond, a - a * 0.9);
     array c = a - a * cond * 0.9;
 
@@ -156,18 +142,14 @@ TEST(Replace, ISSUE_1249)
     b.host(&hb[0]);
     c.host(&hc[0]);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hc[i], hb[i]) << "at " << i; }
 }
 
-
-TEST(Replace, 4D)
-{
+TEST(Replace, 4D) {
     dim4 dims(2, 3, 4, 2);
     array cond = randu(dims) > 0.5;
-    array a = randu(dims);
-    array b = a.copy();
+    array a    = randu(dims);
+    array b    = a.copy();
     replace(b, !cond, a - a * 0.9);
     array c = a - a * cond * 0.9;
 
@@ -178,13 +160,10 @@ TEST(Replace, 4D)
     b.host(&hb[0]);
     c.host(&hc[0]);
 
-    for (int i = 0; i < num; i++) {
-        ASSERT_EQ(hc[i], hb[i]) << "at " << i;
-    }
+    for (int i = 0; i < num; i++) { ASSERT_EQ(hc[i], hb[i]) << "at " << i; }
 }
 
-TEST(Replace, ISSUE_1683)
-{
+TEST(Replace, ISSUE_1683) {
     array A = randu(10, 20, f32);
     vector<float> ha1(A.elements());
     A.host(ha1.data());
@@ -199,14 +178,12 @@ TEST(Replace, ISSUE_1683)
     B.host(hb.data());
 
     // Ensures A is not modified by replace
-    for (int i = 0; i < (int)A.elements(); i++) {
-        ASSERT_EQ(ha1[i], ha2[i]);
-    }
+    for (int i = 0; i < (int)A.elements(); i++) { ASSERT_EQ(ha1[i], ha2[i]); }
 
     // Ensures replace on B works as expected
     for (int i = 0; i < (int)B.elements(); i++) {
         float val = ha1[i * A.dims(0)];
-        val = val < 0.5 ? 0 : val;
+        val       = val < 0.5 ? 0 : val;
         ASSERT_EQ(val, hb[i]);
     }
 }
diff --git a/test/resize.cpp b/test/resize.cpp
index 9990df2290..c994be1ed3 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -7,133 +7,138 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Resize : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Resize : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 template<typename T>
-class ResizeI : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-
-            subMat1.push_back(af_make_seq(0, 5, 1));
-            subMat1.push_back(af_make_seq(0, 5, 1));
-            subMat1.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
-        vector<af_seq> subMat1;
+class ResizeI : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+
+        subMat1.push_back(af_make_seq(0, 5, 1));
+        subMat1.push_back(af_make_seq(0, 5, 1));
+        subMat1.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
+    vector<af_seq> subMat1;
 };
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypesF;
-typedef ::testing::Types<int, unsigned, intl, uintl, unsigned char, char, short, ushort> TestTypesI;
+typedef ::testing::Types<int, unsigned, intl, uintl, unsigned char, char, short,
+                         ushort>
+    TestTypesI;
 
 // register the type list
 TYPED_TEST_CASE(Resize, TestTypesF);
 TYPED_TEST_CASE(ResizeI, TestTypesI);
 
-TYPED_TEST(Resize, InvalidDims)
-{
+TYPED_TEST(Resize, InvalidDims) {
     if (noDoubleTests<TypeParam>()) return;
 
-    vector<TypeParam> in(8*8);
+    vector<TypeParam> in(8 * 8);
 
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    dim4 dims = dim4(8,8,1,1);
+    dim4 dims = dim4(8, 8, 1, 1);
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(),
-                                          (af_dtype) dtype_traits<TypeParam>::af_type));
-    ASSERT_EQ(AF_ERR_SIZE, af_resize(&outArray, inArray, 0, 0, AF_INTERP_NEAREST));
+    ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<TypeParam>::af_type));
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_resize(&outArray, inArray, 0, 0, AF_INTERP_NEAREST));
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
 template<typename T>
-void compare(T test, T out, double err, size_t i)
-{
+void compare(T test, T out, double err, size_t i) {
     ASSERT_EQ(abs(test - out) < err, true) << "at: " << i << endl
-             << "for test = : " << test << endl
-             << "out data = : " << out << endl;
+                                           << "for test = : " << test << endl
+                                           << "out data = : " << out << endl;
 }
 
 template<>
-void compare<uintl>(uintl test, uintl out, double err, size_t i)
-{
-    ASSERT_EQ(((intl)test - (intl)out) < err, true) << "at: " << i << endl
-             << "for test = : " << test << endl
-             << "out data = : " << out << endl;
+void compare<uintl>(uintl test, uintl out, double err, size_t i) {
+    ASSERT_EQ(((intl)test - (intl)out) < err, true)
+        << "at: " << i << endl
+        << "for test = : " << test << endl
+        << "out data = : " << out << endl;
 }
 
 template<>
-void compare<uint>(uint test, uint out, double err, size_t i)
-{
-    ASSERT_EQ(((int)test - (int)out) < err, true) << "at: " << i << endl
-             << "for test = : " << test << endl
-             << "out data = : " << out << endl;
+void compare<uint>(uint test, uint out, double err, size_t i) {
+    ASSERT_EQ(((int)test - (int)out) < err, true)
+        << "at: " << i << endl
+        << "for test = : " << test << endl
+        << "out data = : " << out << endl;
 }
 
 template<>
-void compare<uchar>(uchar test, uchar out, double err, size_t i)
-{
-    ASSERT_EQ(((int)test - (int)out) < err, true) << "at: " << i << endl
-             << "for test = : " << test << endl
-             << "out data = : " << out << endl;
+void compare<uchar>(uchar test, uchar out, double err, size_t i) {
+    ASSERT_EQ(((int)test - (int)out) < err, true)
+        << "at: " << i << endl
+        << "for test = : " << test << endl
+        << "out data = : " << out << endl;
 }
 
 template<typename T>
-void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0, const dim_t odim1, const af_interp_type method, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0,
+                const dim_t odim1, const af_interp_type method,
+                bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
     if (isSubRef) {
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       dims.ndims(), dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
-
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_resize(&outArray, inArray, odim0, odim1, method));
@@ -152,203 +157,199 @@ void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0, c
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Float Types
 ///////////////////////////////////////////////////////////////////////////////
-TYPED_TEST(Resize, Resize3CSquareUpNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 0, 16, 16, AF_INTERP_NEAREST);
+TYPED_TEST(Resize, Resize3CSquareUpNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 0, 16, 16,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Resize, Resize3CSquareUpLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 1, 16, 16, AF_INTERP_BILINEAR);
+TYPED_TEST(Resize, Resize3CSquareUpLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 1, 16, 16,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Resize, Resize3CSquareDownNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 2, 4, 4, AF_INTERP_NEAREST);
+TYPED_TEST(Resize, Resize3CSquareDownNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 2, 4, 4,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Resize, Resize3CSquareDownLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 3, 4, 4, AF_INTERP_BILINEAR);
+TYPED_TEST(Resize, Resize3CSquareDownLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 3, 4, 4,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Resize, Resize3CSquareUpNearestSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 4, 10, 10, AF_INTERP_NEAREST,
-                          true, &(this->subMat0));
+TYPED_TEST(Resize, Resize3CSquareUpNearestSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 4, 10, 10,
+                          AF_INTERP_NEAREST, true, &(this->subMat0));
 }
 
-TYPED_TEST(Resize, Resize3CSquareUpLinearSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 5, 10, 10, AF_INTERP_BILINEAR,
-                          true, &(this->subMat0));
+TYPED_TEST(Resize, Resize3CSquareUpLinearSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 5, 10, 10,
+                          AF_INTERP_BILINEAR, true, &(this->subMat0));
 }
 
-TYPED_TEST(Resize, Resize3CSquareDownNearestSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 6, 3, 3, AF_INTERP_NEAREST,
-                          true, &(this->subMat0));
+TYPED_TEST(Resize, Resize3CSquareDownNearestSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 6, 3, 3,
+                          AF_INTERP_NEAREST, true, &(this->subMat0));
 }
 
-TYPED_TEST(Resize, Resize3CSquareDownLinearSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 7, 3, 3, AF_INTERP_BILINEAR,
-                          true, &(this->subMat0));
+TYPED_TEST(Resize, Resize3CSquareDownLinearSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 7, 3, 3,
+                          AF_INTERP_BILINEAR, true, &(this->subMat0));
 }
 
-TYPED_TEST(Resize, Resize1CRectangleUpNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/rectangle.test"), 0, 12, 16, AF_INTERP_NEAREST);
+TYPED_TEST(Resize, Resize1CRectangleUpNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/rectangle.test"), 0, 12, 16,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Resize, Resize1CRectangleUpLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/rectangle.test"), 1, 12, 16, AF_INTERP_BILINEAR);
+TYPED_TEST(Resize, Resize1CRectangleUpLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/rectangle.test"), 1, 12, 16,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Resize, Resize1CRectangleDownNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/rectangle.test"), 2, 6, 2, AF_INTERP_NEAREST);
+TYPED_TEST(Resize, Resize1CRectangleDownNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/rectangle.test"), 2, 6, 2,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Resize, Resize1CRectangleDownLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/rectangle.test"), 3, 6, 2, AF_INTERP_BILINEAR);
+TYPED_TEST(Resize, Resize1CRectangleDownLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/rectangle.test"), 3, 6, 2,
+                          AF_INTERP_BILINEAR);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Interger Types
 ///////////////////////////////////////////////////////////////////////////////
-TYPED_TEST(ResizeI, Resize3CSquareUpNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 0, 16, 16, AF_INTERP_NEAREST);
+TYPED_TEST(ResizeI, Resize3CSquareUpNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 0, 16, 16,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareUpLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 1, 16, 16, AF_INTERP_BILINEAR);
+TYPED_TEST(ResizeI, Resize3CSquareUpLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 1, 16, 16,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareDownNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 2, 4, 4, AF_INTERP_NEAREST);
+TYPED_TEST(ResizeI, Resize3CSquareDownNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 2, 4, 4,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareDownLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 3, 4, 4, AF_INTERP_BILINEAR);
+TYPED_TEST(ResizeI, Resize3CSquareDownLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 3, 4, 4,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareUpNearestSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 4, 10, 10, AF_INTERP_NEAREST,
-                          true, &(this->subMat0));
+TYPED_TEST(ResizeI, Resize3CSquareUpNearestSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 4, 10, 10,
+                          AF_INTERP_NEAREST, true, &(this->subMat0));
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareUpLinearSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 5, 10, 10, AF_INTERP_BILINEAR,
-                          true, &(this->subMat0));
+TYPED_TEST(ResizeI, Resize3CSquareUpLinearSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 5, 10, 10,
+                          AF_INTERP_BILINEAR, true, &(this->subMat0));
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareDownNearestSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 6, 3, 3, AF_INTERP_NEAREST,
-                          true, &(this->subMat0));
+TYPED_TEST(ResizeI, Resize3CSquareDownNearestSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 6, 3, 3,
+                          AF_INTERP_NEAREST, true, &(this->subMat0));
 }
 
-TYPED_TEST(ResizeI, Resize3CSquareDownLinearSubref)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/square.test"), 8, 3, 3, AF_INTERP_BILINEAR,
-                          true, &(this->subMat1));
+TYPED_TEST(ResizeI, Resize3CSquareDownLinearSubref) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/square.test"), 8, 3, 3,
+                          AF_INTERP_BILINEAR, true, &(this->subMat1));
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Float Types
 ///////////////////////////////////////////////////////////////////////////////
-TYPED_TEST(Resize, Resize1CLargeUpNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 0, 256, 256, AF_INTERP_NEAREST);
+TYPED_TEST(Resize, Resize1CLargeUpNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 0, 256, 256,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Resize, Resize1CLargeUpLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 1, 256, 256, AF_INTERP_BILINEAR);
+TYPED_TEST(Resize, Resize1CLargeUpLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 1, 256, 256,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Resize, Resize1CLargeDownNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 2, 32, 32, AF_INTERP_NEAREST);
+TYPED_TEST(Resize, Resize1CLargeDownNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 2, 32, 32,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Resize, Resize1CLargeDownLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 3, 32, 32, AF_INTERP_BILINEAR);
+TYPED_TEST(Resize, Resize1CLargeDownLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 3, 32, 32,
+                          AF_INTERP_BILINEAR);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Integer Types
 ///////////////////////////////////////////////////////////////////////////////
-TYPED_TEST(ResizeI, Resize1CLargeUpNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 0, 256, 256, AF_INTERP_NEAREST);
+TYPED_TEST(ResizeI, Resize1CLargeUpNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 0, 256, 256,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(ResizeI, Resize1CLargeUpLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 1, 256, 256, AF_INTERP_BILINEAR);
+TYPED_TEST(ResizeI, Resize1CLargeUpLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 1, 256, 256,
+                          AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(ResizeI, Resize1CLargeDownNearest)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 2, 32, 32, AF_INTERP_NEAREST);
+TYPED_TEST(ResizeI, Resize1CLargeDownNearest) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 2, 32, 32,
+                          AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(ResizeI, Resize1CLargeDownLinear)
-{
-    resizeTest<TypeParam>(string(TEST_DIR"/resize/large.test"), 3, 32, 32, AF_INTERP_BILINEAR);
+TYPED_TEST(ResizeI, Resize1CLargeDownLinear) {
+    resizeTest<TypeParam>(string(TEST_DIR "/resize/large.test"), 3, 32, 32,
+                          AF_INTERP_BILINEAR);
 }
 
 template<typename T>
-void resizeArgsTest(af_err err, string pTestFile, const dim4 odims, const af_interp_type method)
-{
+void resizeArgsTest(af_err err, string pTestFile, const dim4 odims,
+                    const af_interp_type method) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
 
-    af_array inArray = 0;
+    af_array inArray  = 0;
     af_array outArray = 0;
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_EQ(err, af_resize(&outArray, inArray, odims[0], odims[1], method));
 
-    if(inArray != 0) af_release_array(inArray);
-    if(outArray != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Resize,InvalidArgsDims0)
-{
+TYPED_TEST(Resize, InvalidArgsDims0) {
     dim4 dims(0, 5, 2, 1);
-    resizeArgsTest<TypeParam>(AF_ERR_SIZE, string(TEST_DIR"/resize/square.test"), dims, AF_INTERP_BILINEAR);
+    resizeArgsTest<TypeParam>(AF_ERR_SIZE,
+                              string(TEST_DIR "/resize/square.test"), dims,
+                              AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Resize,InvalidArgsMethod)
-{
+TYPED_TEST(Resize, InvalidArgsMethod) {
     dim4 dims(10, 10, 1, 1);
-    resizeArgsTest<TypeParam>(AF_ERR_ARG, string(TEST_DIR"/resize/square.test"), dims, AF_INTERP_CUBIC);
+    resizeArgsTest<TypeParam>(AF_ERR_ARG,
+                              string(TEST_DIR "/resize/square.test"), dims,
+                              AF_INTERP_CUBIC);
 }
 
 ///////////////////////////////// CPP ////////////////////////////////////
@@ -360,14 +361,14 @@ using af::max;
 using af::seq;
 using af::span;
 
-TEST(Resize, CPP)
-{
+TEST(Resize, CPP) {
     if (noDoubleTests<float>()) return;
 
     vector<dim4> numDims;
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/resize/square.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(string(TEST_DIR "/resize/square.test"),
+                                   numDims, in, tests);
 
     dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
@@ -377,14 +378,14 @@ TEST(Resize, CPP)
     ASSERT_VEC_ARRAY_NEAR(tests[0], goldDims, output, 0.0001);
 }
 
-TEST(ResizeScale1, CPP)
-{
+TEST(ResizeScale1, CPP) {
     if (noDoubleTests<float>()) return;
 
     vector<dim4> numDims;
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/resize/square.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(string(TEST_DIR "/resize/square.test"),
+                                   numDims, in, tests);
 
     dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
@@ -394,14 +395,14 @@ TEST(ResizeScale1, CPP)
     ASSERT_VEC_ARRAY_NEAR(tests[0], goldDims, output, 0.0001);
 }
 
-TEST(ResizeScale2, CPP)
-{
+TEST(ResizeScale2, CPP) {
     if (noDoubleTests<float>()) return;
 
     vector<dim4> numDims;
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/resize/square.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(string(TEST_DIR "/resize/square.test"),
+                                   numDims, in, tests);
 
     dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
@@ -411,17 +412,14 @@ TEST(ResizeScale2, CPP)
     ASSERT_VEC_ARRAY_NEAR(tests[0], goldDims, output, 0.0001);
 }
 
-TEST(Resize, ExtractGFOR)
-{
+TEST(Resize, ExtractGFOR) {
     dim4 dims = dim4(100, 100, 3);
-    array A = round(100 * randu(dims));
-    array B = constant(0, 200, 200, 3);
+    array A   = round(100 * randu(dims));
+    array B   = constant(0, 200, 200, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = resize(A(span, span, ii), 200, 200);
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = resize(A(span, span, ii), 200, 200); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = resize(A(span, span, ii), 200, 200);
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 2625e9c523..39fada8b1f 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -7,36 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Rotate : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Rotate : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Rotate, TestTypes);
@@ -44,26 +43,30 @@ TYPED_TEST_CASE(Rotate, TestTypes);
 #define PI 3.1415926535897931f
 
 template<typename T>
-void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, const bool crop, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
+                const bool crop, bool isSubRef = false,
+                const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     float theta = angle * PI / 180.0f;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_rotate(&outArray, inArray, theta, crop, AF_INTERP_NEAREST));
+    ASSERT_SUCCESS(
+        af_rotate(&outArray, inArray, theta, crop, AF_INTERP_NEAREST));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
@@ -80,97 +83,96 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
     // We expect 99.99% values to be same between the CPU/GPU versions and
     // ASSERT_EQ (in comments below) to pass for CUDA & OpenCL backends
     size_t fail_count = 0;
-    for(size_t i = 0; i < nElems; i++) {
-        if(abs((tests[resultIdx][i] - (T)outData[i])) > 0.001)
-            fail_count++;
+    for (size_t i = 0; i < nElems; i++) {
+        if (abs((tests[resultIdx][i] - (T)outData[i])) > 0.001) fail_count++;
     }
     ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.005));
 
-    //for (size_t elIter = 0; elIter < nElems; ++elIter) {
-    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
+    // for (size_t elIter = 0; elIter < nElems; ++elIter) {
+    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " <<
+    //    elIter << endl;
     //}
 
-
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define ROTATE_INIT(desc, file, resultIdx, angle, crop)                                         \
-    TYPED_TEST(Rotate, desc)                                                                    \
-    {                                                                                           \
-        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop);  \
+#define ROTATE_INIT(desc, file, resultIdx, angle, crop)                  \
+    TYPED_TEST(Rotate, desc) {                                           \
+        rotateTest<TypeParam>(string(TEST_DIR "/rotate/" #file ".test"), \
+                              resultIdx, angle, crop);                   \
     }
 
-    ROTATE_INIT(Square180NoCropRecenter     , rotate1,  0, 180, false);
-    ROTATE_INIT(Square180CropRecenter       , rotate1,  1, 180, true );
-    ROTATE_INIT(Square90NoCropRecenter      , rotate1,  2, 90 , false);
-    ROTATE_INIT(Square90CropRecenter        , rotate1,  3, 90 , true );
-    ROTATE_INIT(Square45NoCropRecenter      , rotate1,  4, 45 , false);
-    ROTATE_INIT(Square45CropRecenter        , rotate1,  5, 45 , true );
-    ROTATE_INIT(Squarem45NoCropRecenter     , rotate1,  6,-45 , false);
-    ROTATE_INIT(Squarem45CropRecenter       , rotate1,  7,-45 , true );
-    ROTATE_INIT(Square60NoCropRecenter      , rotate1,  8, 60 , false);
-    ROTATE_INIT(Square60CropRecenter        , rotate1,  9, 60 , true );
-    ROTATE_INIT(Square30NoCropRecenter      , rotate1, 10, 30 , false);
-    ROTATE_INIT(Square30CropRecenter        , rotate1, 11, 30 , true );
-    ROTATE_INIT(Square15NoCropRecenter      , rotate1, 12, 15 , false);
-    ROTATE_INIT(Square15CropRecenter        , rotate1, 13, 15 , true );
-    ROTATE_INIT(Square10NoCropRecenter      , rotate1, 14, 10 , false);
-    ROTATE_INIT(Square10CropRecenter        , rotate1, 15, 10 , true );
-    ROTATE_INIT(Square01NoCropRecenter      , rotate1, 16,  1 , false);
-    ROTATE_INIT(Square01CropRecenter        , rotate1, 17,  1 , true );
-    ROTATE_INIT(Square360NoCropRecenter     , rotate1, 18, 360, false);
-    ROTATE_INIT(Square360CropRecenter       , rotate1, 19, 360, true );
-    ROTATE_INIT(Squarem180NoCropRecenter    , rotate1, 20,-180, false);
-    ROTATE_INIT(Squarem180CropRecenter      , rotate1, 21,-180, false);
-    ROTATE_INIT(Square00NoCropRecenter      , rotate1, 22,  0 , false);
-    ROTATE_INIT(Square00CropRecenter        , rotate1, 23,  0 , true );
-
-    ROTATE_INIT(Rectangle180NoCropRecenter     , rotate2,  0, 180, false);
-    ROTATE_INIT(Rectangle180CropRecenter       , rotate2,  1, 180, true );
-    ROTATE_INIT(Rectangle90NoCropRecenter      , rotate2,  2, 90 , false);
-    ROTATE_INIT(Rectangle90CropRecenter        , rotate2,  3, 90 , true );
-    ROTATE_INIT(Rectangle45NoCropRecenter      , rotate2,  4, 45 , false);
-    ROTATE_INIT(Rectangle45CropRecenter        , rotate2,  5, 45 , true );
-    ROTATE_INIT(Rectanglem45NoCropRecenter     , rotate2,  6,-45 , false);
-    ROTATE_INIT(Rectanglem45CropRecenter       , rotate2,  7,-45 , true );
-    ROTATE_INIT(Rectangle60NoCropRecenter      , rotate2,  8, 60 , false);
-    ROTATE_INIT(Rectangle60CropRecenter        , rotate2,  9, 60 , true );
-    ROTATE_INIT(Rectangle30NoCropRecenter      , rotate2, 10, 30 , false);
-    ROTATE_INIT(Rectangle30CropRecenter        , rotate2, 11, 30 , true );
-    ROTATE_INIT(Rectangle15NoCropRecenter      , rotate2, 12, 15 , false);
-    ROTATE_INIT(Rectangle15CropRecenter        , rotate2, 13, 15 , true );
-    ROTATE_INIT(Rectangle10NoCropRecenter      , rotate2, 14, 10 , false);
-    ROTATE_INIT(Rectangle10CropRecenter        , rotate2, 15, 10 , true );
-    ROTATE_INIT(Rectangle01NoCropRecenter      , rotate2, 16,  1 , false);
-    ROTATE_INIT(Rectangle01CropRecenter        , rotate2, 17,  1 , true );
-    ROTATE_INIT(Rectangle360NoCropRecenter     , rotate2, 18, 360, false);
-    ROTATE_INIT(Rectangle360CropRecenter       , rotate2, 19, 360, true );
-    ROTATE_INIT(Rectanglem180NoCropRecenter    , rotate2, 20,-180, false);
-    ROTATE_INIT(Rectanglem180CropRecenter      , rotate2, 21,-180, false);
-    ROTATE_INIT(Rectangle00NoCropRecenter      , rotate2, 22,  0 , false);
-    ROTATE_INIT(Rectangle00CropRecenter        , rotate2, 23,  0 , true );
+ROTATE_INIT(Square180NoCropRecenter, rotate1, 0, 180, false);
+ROTATE_INIT(Square180CropRecenter, rotate1, 1, 180, true);
+ROTATE_INIT(Square90NoCropRecenter, rotate1, 2, 90, false);
+ROTATE_INIT(Square90CropRecenter, rotate1, 3, 90, true);
+ROTATE_INIT(Square45NoCropRecenter, rotate1, 4, 45, false);
+ROTATE_INIT(Square45CropRecenter, rotate1, 5, 45, true);
+ROTATE_INIT(Squarem45NoCropRecenter, rotate1, 6, -45, false);
+ROTATE_INIT(Squarem45CropRecenter, rotate1, 7, -45, true);
+ROTATE_INIT(Square60NoCropRecenter, rotate1, 8, 60, false);
+ROTATE_INIT(Square60CropRecenter, rotate1, 9, 60, true);
+ROTATE_INIT(Square30NoCropRecenter, rotate1, 10, 30, false);
+ROTATE_INIT(Square30CropRecenter, rotate1, 11, 30, true);
+ROTATE_INIT(Square15NoCropRecenter, rotate1, 12, 15, false);
+ROTATE_INIT(Square15CropRecenter, rotate1, 13, 15, true);
+ROTATE_INIT(Square10NoCropRecenter, rotate1, 14, 10, false);
+ROTATE_INIT(Square10CropRecenter, rotate1, 15, 10, true);
+ROTATE_INIT(Square01NoCropRecenter, rotate1, 16, 1, false);
+ROTATE_INIT(Square01CropRecenter, rotate1, 17, 1, true);
+ROTATE_INIT(Square360NoCropRecenter, rotate1, 18, 360, false);
+ROTATE_INIT(Square360CropRecenter, rotate1, 19, 360, true);
+ROTATE_INIT(Squarem180NoCropRecenter, rotate1, 20, -180, false);
+ROTATE_INIT(Squarem180CropRecenter, rotate1, 21, -180, false);
+ROTATE_INIT(Square00NoCropRecenter, rotate1, 22, 0, false);
+ROTATE_INIT(Square00CropRecenter, rotate1, 23, 0, true);
+
+ROTATE_INIT(Rectangle180NoCropRecenter, rotate2, 0, 180, false);
+ROTATE_INIT(Rectangle180CropRecenter, rotate2, 1, 180, true);
+ROTATE_INIT(Rectangle90NoCropRecenter, rotate2, 2, 90, false);
+ROTATE_INIT(Rectangle90CropRecenter, rotate2, 3, 90, true);
+ROTATE_INIT(Rectangle45NoCropRecenter, rotate2, 4, 45, false);
+ROTATE_INIT(Rectangle45CropRecenter, rotate2, 5, 45, true);
+ROTATE_INIT(Rectanglem45NoCropRecenter, rotate2, 6, -45, false);
+ROTATE_INIT(Rectanglem45CropRecenter, rotate2, 7, -45, true);
+ROTATE_INIT(Rectangle60NoCropRecenter, rotate2, 8, 60, false);
+ROTATE_INIT(Rectangle60CropRecenter, rotate2, 9, 60, true);
+ROTATE_INIT(Rectangle30NoCropRecenter, rotate2, 10, 30, false);
+ROTATE_INIT(Rectangle30CropRecenter, rotate2, 11, 30, true);
+ROTATE_INIT(Rectangle15NoCropRecenter, rotate2, 12, 15, false);
+ROTATE_INIT(Rectangle15CropRecenter, rotate2, 13, 15, true);
+ROTATE_INIT(Rectangle10NoCropRecenter, rotate2, 14, 10, false);
+ROTATE_INIT(Rectangle10CropRecenter, rotate2, 15, 10, true);
+ROTATE_INIT(Rectangle01NoCropRecenter, rotate2, 16, 1, false);
+ROTATE_INIT(Rectangle01CropRecenter, rotate2, 17, 1, true);
+ROTATE_INIT(Rectangle360NoCropRecenter, rotate2, 18, 360, false);
+ROTATE_INIT(Rectangle360CropRecenter, rotate2, 19, 360, true);
+ROTATE_INIT(Rectanglem180NoCropRecenter, rotate2, 20, -180, false);
+ROTATE_INIT(Rectanglem180CropRecenter, rotate2, 21, -180, false);
+ROTATE_INIT(Rectangle00NoCropRecenter, rotate2, 22, 0, false);
+ROTATE_INIT(Rectangle00CropRecenter, rotate2, 23, 0, true);
 
 ////////////////////////////////// CPP //////////////////////////////////////
 //
-TEST(Rotate, CPP)
-{
+TEST(Rotate, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx = 0;
-    const float angle = 180;
-    const bool crop = false;
+    const float angle        = 180;
+    const bool crop          = false;
 
     vector<dim4> numDims;
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/rotate/rotate1.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(string(TEST_DIR "/rotate/rotate1.test"),
+                                   numDims, in, tests);
 
-    dim4 dims = numDims[0];
+    dim4 dims   = numDims[0];
     float theta = angle * PI / 180.0f;
 
     array input(dims, &(in[0].front()));
@@ -191,9 +193,8 @@ TEST(Rotate, CPP)
     // We expect 99.99% values to be same between the CPU/GPU versions and
     // ASSERT_EQ (in comments below) to pass for CUDA & OpenCL backends
     size_t fail_count = 0;
-    for(size_t i = 0; i < nElems; i++) {
-        if(fabs(tests[resultIdx][i] - outData[i]) > 0.0001)
-            fail_count++;
+    for (size_t i = 0; i < nElems; i++) {
+        if (fabs(tests[resultIdx][i] - outData[i]) > 0.0001) fail_count++;
     }
     ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.01));
 
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 0300ef72b3..ee5d879737 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -7,40 +7,40 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class RotateLinear : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class RotateLinear : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(RotateLinear, TestTypes);
@@ -48,33 +48,39 @@ TYPED_TEST_CASE(RotateLinear, TestTypes);
 #define PI 3.1415926535897931f
 
 template<typename T>
-void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, const bool crop, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
+                const bool crop, bool isSubRef = false,
+                const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T, T, float>(pTestFile,numDims,in,tests);
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     float theta = angle * PI / 180.0f;
 
     if (isSubRef) {
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       dims.ndims(), dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
-
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
-    ASSERT_SUCCESS(af_rotate(&outArray, inArray, theta, crop, AF_INTERP_BILINEAR));
+    ASSERT_SUCCESS(
+        af_rotate(&outArray, inArray, theta, crop, AF_INTERP_BILINEAR));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
@@ -91,98 +97,99 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle, c
     // We expect 99.99% values to be same between the CPU/GPU versions and
     // ASSERT_EQ (in comments below) to pass for CUDA & OpenCL backends
     size_t fail_count = 0;
-    for(size_t i = 0; i < nElems; i++) {
-        if(abs((tests[resultIdx][i] - (T)outData[i])) > 0.001) {
+    for (size_t i = 0; i < nElems; i++) {
+        if (abs((tests[resultIdx][i] - (T)outData[i])) > 0.001) {
             fail_count++;
         }
     }
-    ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.02)) << "where count = " << fail_count << endl;
+    ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.02))
+        << "where count = " << fail_count << endl;
 
-    //for (size_t elIter = 0; elIter < nElems; ++elIter) {
-    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
+    // for (size_t elIter = 0; elIter < nElems; ++elIter) {
+    //    ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " <<
+    //    elIter << endl;
     //}
 
-
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define ROTATE_INIT(desc, file, resultIdx, angle, crop)                 \
-    TYPED_TEST(RotateLinear, desc)                                      \
-    {                                                                   \
-        rotateTest<TypeParam>(string(TEST_DIR"/rotate/"#file".test"), resultIdx, angle, crop); \
+#define ROTATE_INIT(desc, file, resultIdx, angle, crop)                  \
+    TYPED_TEST(RotateLinear, desc) {                                     \
+        rotateTest<TypeParam>(string(TEST_DIR "/rotate/" #file ".test"), \
+                              resultIdx, angle, crop);                   \
     }
 
-    ROTATE_INIT(Square180NoCropRecenter     , rotatelinear1,  0, 180, false);
-    ROTATE_INIT(Square180CropRecenter       , rotatelinear1,  1, 180, true );
-    ROTATE_INIT(Square90NoCropRecenter      , rotatelinear1,  2, 90 , false);
-    ROTATE_INIT(Square90CropRecenter        , rotatelinear1,  3, 90 , true );
-    ROTATE_INIT(Square45NoCropRecenter      , rotatelinear1,  4, 45 , false);
-    ROTATE_INIT(Square45CropRecenter        , rotatelinear1,  5, 45 , true );
-    ROTATE_INIT(Squarem45NoCropRecenter     , rotatelinear1,  6,-45 , false);
-    ROTATE_INIT(Squarem45CropRecenter       , rotatelinear1,  7,-45 , true );
-    ROTATE_INIT(Square60NoCropRecenter      , rotatelinear1,  8, 60 , false);
-    ROTATE_INIT(Square60CropRecenter        , rotatelinear1,  9, 60 , true );
-    ROTATE_INIT(Square30NoCropRecenter      , rotatelinear1, 10, 30 , false);
-    ROTATE_INIT(Square30CropRecenter        , rotatelinear1, 11, 30 , true );
-    ROTATE_INIT(Square15NoCropRecenter      , rotatelinear1, 12, 15 , false);
-    ROTATE_INIT(Square15CropRecenter        , rotatelinear1, 13, 15 , true );
-    ROTATE_INIT(Square10NoCropRecenter      , rotatelinear1, 14, 10 , false);
-    ROTATE_INIT(Square10CropRecenter        , rotatelinear1, 15, 10 , true );
-    ROTATE_INIT(Square01NoCropRecenter      , rotatelinear1, 16,  1 , false);
-    ROTATE_INIT(Square01CropRecenter        , rotatelinear1, 17,  1 , true );
-    ROTATE_INIT(Square360NoCropRecenter     , rotatelinear1, 18, 360, false);
-    ROTATE_INIT(Square360CropRecenter       , rotatelinear1, 19, 360, true );
-    ROTATE_INIT(Squarem180NoCropRecenter    , rotatelinear1, 20,-180, false);
-    ROTATE_INIT(Squarem180CropRecenter      , rotatelinear1, 21,-180, false);
-    ROTATE_INIT(Square00NoCropRecenter      , rotatelinear1, 22,  0 , false);
-    ROTATE_INIT(Square00CropRecenter        , rotatelinear1, 23,  0 , true );
-
-    ROTATE_INIT(Rectangle180NoCropRecenter     , rotatelinear2,  0, 180, false);
-    ROTATE_INIT(Rectangle180CropRecenter       , rotatelinear2,  1, 180, true );
-    ROTATE_INIT(Rectangle90NoCropRecenter      , rotatelinear2,  2, 90 , false);
-    ROTATE_INIT(Rectangle90CropRecenter        , rotatelinear2,  3, 90 , true );
-    ROTATE_INIT(Rectangle45NoCropRecenter      , rotatelinear2,  4, 45 , false);
-    ROTATE_INIT(Rectangle45CropRecenter        , rotatelinear2,  5, 45 , true );
-    ROTATE_INIT(Rectanglem45NoCropRecenter     , rotatelinear2,  6,-45 , false);
-    ROTATE_INIT(Rectanglem45CropRecenter       , rotatelinear2,  7,-45 , true );
-    ROTATE_INIT(Rectangle60NoCropRecenter      , rotatelinear2,  8, 60 , false);
-    ROTATE_INIT(Rectangle60CropRecenter        , rotatelinear2,  9, 60 , true );
-    ROTATE_INIT(Rectangle30NoCropRecenter      , rotatelinear2, 10, 30 , false);
-    ROTATE_INIT(Rectangle30CropRecenter        , rotatelinear2, 11, 30 , true );
-    ROTATE_INIT(Rectangle15NoCropRecenter      , rotatelinear2, 12, 15 , false);
-    ROTATE_INIT(Rectangle15CropRecenter        , rotatelinear2, 13, 15 , true );
-    ROTATE_INIT(Rectangle10NoCropRecenter      , rotatelinear2, 14, 10 , false);
-    ROTATE_INIT(Rectangle10CropRecenter        , rotatelinear2, 15, 10 , true );
-    ROTATE_INIT(Rectangle01NoCropRecenter      , rotatelinear2, 16,  1 , false);
-    ROTATE_INIT(Rectangle01CropRecenter        , rotatelinear2, 17,  1 , true );
-    ROTATE_INIT(Rectangle360NoCropRecenter     , rotatelinear2, 18, 360, false);
-    ROTATE_INIT(Rectangle360CropRecenter       , rotatelinear2, 19, 360, true );
-    ROTATE_INIT(Rectanglem180NoCropRecenter    , rotatelinear2, 20,-180, false);
-    ROTATE_INIT(Rectanglem180CropRecenter      , rotatelinear2, 21,-180, false);
-    ROTATE_INIT(Rectangle00NoCropRecenter      , rotatelinear2, 22,  0 , false);
-    ROTATE_INIT(Rectangle00CropRecenter        , rotatelinear2, 23,  0 , true );
+ROTATE_INIT(Square180NoCropRecenter, rotatelinear1, 0, 180, false);
+ROTATE_INIT(Square180CropRecenter, rotatelinear1, 1, 180, true);
+ROTATE_INIT(Square90NoCropRecenter, rotatelinear1, 2, 90, false);
+ROTATE_INIT(Square90CropRecenter, rotatelinear1, 3, 90, true);
+ROTATE_INIT(Square45NoCropRecenter, rotatelinear1, 4, 45, false);
+ROTATE_INIT(Square45CropRecenter, rotatelinear1, 5, 45, true);
+ROTATE_INIT(Squarem45NoCropRecenter, rotatelinear1, 6, -45, false);
+ROTATE_INIT(Squarem45CropRecenter, rotatelinear1, 7, -45, true);
+ROTATE_INIT(Square60NoCropRecenter, rotatelinear1, 8, 60, false);
+ROTATE_INIT(Square60CropRecenter, rotatelinear1, 9, 60, true);
+ROTATE_INIT(Square30NoCropRecenter, rotatelinear1, 10, 30, false);
+ROTATE_INIT(Square30CropRecenter, rotatelinear1, 11, 30, true);
+ROTATE_INIT(Square15NoCropRecenter, rotatelinear1, 12, 15, false);
+ROTATE_INIT(Square15CropRecenter, rotatelinear1, 13, 15, true);
+ROTATE_INIT(Square10NoCropRecenter, rotatelinear1, 14, 10, false);
+ROTATE_INIT(Square10CropRecenter, rotatelinear1, 15, 10, true);
+ROTATE_INIT(Square01NoCropRecenter, rotatelinear1, 16, 1, false);
+ROTATE_INIT(Square01CropRecenter, rotatelinear1, 17, 1, true);
+ROTATE_INIT(Square360NoCropRecenter, rotatelinear1, 18, 360, false);
+ROTATE_INIT(Square360CropRecenter, rotatelinear1, 19, 360, true);
+ROTATE_INIT(Squarem180NoCropRecenter, rotatelinear1, 20, -180, false);
+ROTATE_INIT(Squarem180CropRecenter, rotatelinear1, 21, -180, false);
+ROTATE_INIT(Square00NoCropRecenter, rotatelinear1, 22, 0, false);
+ROTATE_INIT(Square00CropRecenter, rotatelinear1, 23, 0, true);
+
+ROTATE_INIT(Rectangle180NoCropRecenter, rotatelinear2, 0, 180, false);
+ROTATE_INIT(Rectangle180CropRecenter, rotatelinear2, 1, 180, true);
+ROTATE_INIT(Rectangle90NoCropRecenter, rotatelinear2, 2, 90, false);
+ROTATE_INIT(Rectangle90CropRecenter, rotatelinear2, 3, 90, true);
+ROTATE_INIT(Rectangle45NoCropRecenter, rotatelinear2, 4, 45, false);
+ROTATE_INIT(Rectangle45CropRecenter, rotatelinear2, 5, 45, true);
+ROTATE_INIT(Rectanglem45NoCropRecenter, rotatelinear2, 6, -45, false);
+ROTATE_INIT(Rectanglem45CropRecenter, rotatelinear2, 7, -45, true);
+ROTATE_INIT(Rectangle60NoCropRecenter, rotatelinear2, 8, 60, false);
+ROTATE_INIT(Rectangle60CropRecenter, rotatelinear2, 9, 60, true);
+ROTATE_INIT(Rectangle30NoCropRecenter, rotatelinear2, 10, 30, false);
+ROTATE_INIT(Rectangle30CropRecenter, rotatelinear2, 11, 30, true);
+ROTATE_INIT(Rectangle15NoCropRecenter, rotatelinear2, 12, 15, false);
+ROTATE_INIT(Rectangle15CropRecenter, rotatelinear2, 13, 15, true);
+ROTATE_INIT(Rectangle10NoCropRecenter, rotatelinear2, 14, 10, false);
+ROTATE_INIT(Rectangle10CropRecenter, rotatelinear2, 15, 10, true);
+ROTATE_INIT(Rectangle01NoCropRecenter, rotatelinear2, 16, 1, false);
+ROTATE_INIT(Rectangle01CropRecenter, rotatelinear2, 17, 1, true);
+ROTATE_INIT(Rectangle360NoCropRecenter, rotatelinear2, 18, 360, false);
+ROTATE_INIT(Rectangle360CropRecenter, rotatelinear2, 19, 360, true);
+ROTATE_INIT(Rectanglem180NoCropRecenter, rotatelinear2, 20, -180, false);
+ROTATE_INIT(Rectanglem180CropRecenter, rotatelinear2, 21, -180, false);
+ROTATE_INIT(Rectangle00NoCropRecenter, rotatelinear2, 22, 0, false);
+ROTATE_INIT(Rectangle00CropRecenter, rotatelinear2, 23, 0, true);
 
 ////////////////////////////////// CPP //////////////////////////////////////
 
-TEST(RotateLinear, CPP)
-{
+TEST(RotateLinear, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx = 0;
-    const float angle = 180;
-    const bool crop = false;
+    const float angle        = 180;
+    const bool crop          = false;
 
     vector<dim4> numDims;
-    vector<vector<float> >   in;
-    vector<vector<float> >   tests;
-    readTests<float, float, float>(string(TEST_DIR"/rotate/rotatelinear1.test"),numDims,in,tests);
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(
+        string(TEST_DIR "/rotate/rotatelinear1.test"), numDims, in, tests);
 
-    dim4 dims = numDims[0];
+    dim4 dims   = numDims[0];
     float theta = angle * PI / 180.0f;
 
     array input(dims, &(in[0].front()));
@@ -203,9 +210,8 @@ TEST(RotateLinear, CPP)
     // We expect 99.99% values to be same between the CPU/GPU versions and
     // ASSERT_EQ (in comments below) to pass for CUDA & OpenCL backends
     size_t fail_count = 0;
-    for(size_t i = 0; i < nElems; i++) {
-        if(fabs(tests[resultIdx][i] - outData[i]) > 0.0001)
-            fail_count++;
+    for (size_t i = 0; i < nElems; i++) {
+        if (fabs(tests[resultIdx][i] - outData[i]) > 0.0001) fail_count++;
     }
     ASSERT_EQ(true, ((fail_count / (float)nElems) < 0.01));
 
diff --git a/test/sat.cpp b/test/sat.cpp
index 239ab63b59..89c09cd819 100644
--- a/test/sat.cpp
+++ b/test/sat.cpp
@@ -7,39 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::string;
-using std::vector;
 using af::accum;
 using af::allTrue;
 using af::array;
 using af::dtype_traits;
 using af::randu;
 using af::sat;
+using std::string;
+using std::vector;
 
 template<typename T>
-class SAT : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class SAT : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, uintl, intl, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, uintl, intl,
+                         short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(SAT, TestTypes);
 
-TYPED_TEST(SAT, IntegralImage)
-{
-    if(noDoubleTests<TypeParam>()) return;
+TYPED_TEST(SAT, IntegralImage) {
+    if (noDoubleTests<TypeParam>()) return;
 
     array a = randu(530, 671, (af_dtype)dtype_traits<TypeParam>::af_type);
     array b = accum(a, 0);
@@ -47,5 +47,5 @@ TYPED_TEST(SAT, IntegralImage)
 
     array s = sat(a);
 
-    EXPECT_EQ(true, allTrue<float>(c==s));
+    EXPECT_EQ(true, allTrue<float>(c == s));
 }
diff --git a/test/scan.cpp b/test/scan.cpp
index 9c2a1bf7da..5b3db22f1c 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -7,17 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/array.h>
 #include <af/device.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <algorithm>
-#include <arrayfire.h>
-#include <gtest/gtest.h>
 #include <iostream>
 #include <iterator>
 #include <string>
-#include <testHelpers.hpp>
 #include <utility>
 #include <vector>
 
@@ -33,8 +33,8 @@ using af::scan;
 using af::seq;
 using af::span;
 using af::sum;
-using std::cout;
 using std::copy;
+using std::cout;
 using std::endl;
 using std::string;
 using std::vector;
@@ -42,16 +42,16 @@ using std::vector;
 typedef af_err (*scanFunc)(af_array *, const af_array, const int);
 
 template<typename Ti, typename To, scanFunc af_scan>
-void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<af_seq> seqv=vector<af_seq>())
-{
+void scanTest(string pTestFile, int off = 0, bool isSubRef = false,
+              const vector<af_seq> seqv = vector<af_seq>()) {
     if (noDoubleTests<Ti>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (pTestFile,numDims,data,tests);
-    dim4 dims       = numDims[0];
+    readTests<int, int, int>(pTestFile, numDims, data, tests);
+    dim4 dims = numDims[0];
 
     vector<Ti> in(data[0].begin(), data[0].end());
 
@@ -61,12 +61,16 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
 
     // Get input array
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
         ASSERT_SUCCESS(af_release_array(tempArray));
     } else {
-
-        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<Ti>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<Ti>::af_type));
     }
 
     // Compare result
@@ -79,13 +83,12 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
         // Get result
         To *outData;
         outData = new To[dims.elements()];
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                << " for dim " << d +off
-                << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << " for dim " << d + off << endl;
         }
 
         // Delete
@@ -96,51 +99,40 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef=false, const vector<a
     ASSERT_SUCCESS(af_release_array(inArray));
 }
 
-#define SCAN_TESTS(FN, TAG, Ti, To)             \
-    TEST(Scan,Test_##FN##_##TAG)                \
-    {                                           \
-        scanTest<Ti, To, af_##FN>(              \
-            string(TEST_DIR"/scan/"#FN".test")  \
-            );                                  \
-    }                                           \
-
-SCAN_TESTS(accum, float   , float     , float     );
-SCAN_TESTS(accum, double  , double    , double    );
-SCAN_TESTS(accum, int     , int       , int       );
-SCAN_TESTS(accum, cfloat  , cfloat    , cfloat    );
-SCAN_TESTS(accum, cdouble , cdouble   , cdouble   );
-SCAN_TESTS(accum, unsigned, unsigned  , unsigned  );
-SCAN_TESTS(accum, intl    , intl      , intl      );
-SCAN_TESTS(accum, uintl   , uintl     , uintl     );
-SCAN_TESTS(accum, uchar   , uchar     , unsigned  );
-SCAN_TESTS(accum, short   , short     , int       );
-SCAN_TESTS(accum, ushort  , ushort    , uint      );
-
-TEST(Scan,Test_Scan_Big0)
-{
-    scanTest<int, int, af_accum>(
-        string(TEST_DIR"/scan/big0.test"),
-        0
-        );
+#define SCAN_TESTS(FN, TAG, Ti, To)                                       \
+    TEST(Scan, Test_##FN##_##TAG) {                                       \
+        scanTest<Ti, To, af_##FN>(string(TEST_DIR "/scan/" #FN ".test")); \
+    }
+
+SCAN_TESTS(accum, float, float, float);
+SCAN_TESTS(accum, double, double, double);
+SCAN_TESTS(accum, int, int, int);
+SCAN_TESTS(accum, cfloat, cfloat, cfloat);
+SCAN_TESTS(accum, cdouble, cdouble, cdouble);
+SCAN_TESTS(accum, unsigned, unsigned, unsigned);
+SCAN_TESTS(accum, intl, intl, intl);
+SCAN_TESTS(accum, uintl, uintl, uintl);
+SCAN_TESTS(accum, uchar, uchar, unsigned);
+SCAN_TESTS(accum, short, short, int);
+SCAN_TESTS(accum, ushort, ushort, uint);
+
+TEST(Scan, Test_Scan_Big0) {
+    scanTest<int, int, af_accum>(string(TEST_DIR "/scan/big0.test"), 0);
 }
 
-TEST(Scan,Test_Scan_Big1)
-{
-    scanTest<int, int, af_accum>(
-        string(TEST_DIR"/scan/big1.test"),
-        1
-        );
+TEST(Scan, Test_Scan_Big1) {
+    scanTest<int, int, af_accum>(string(TEST_DIR "/scan/big1.test"), 1);
 }
 
 ///////////////////////////////// CPP ////////////////////////////////////
-TEST(Accum, CPP)
-{
+TEST(Accum, CPP) {
     vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (string(TEST_DIR"/scan/accum.test"),numDims,data,tests);
-    dim4 dims       = numDims[0];
+    readTests<int, int, int>(string(TEST_DIR "/scan/accum.test"), numDims, data,
+                             tests);
+    dim4 dims = numDims[0];
 
     vector<float> in(data[0].begin(), data[0].end());
 
@@ -158,13 +150,12 @@ TEST(Accum, CPP)
         // Get result
         float *outData;
         outData = new float[dims.elements()];
-        output.host((void*)outData);
+        output.host((void *)outData);
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                << " for dim " << d
-                << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << " for dim " << d << endl;
         }
 
         // Delete
@@ -172,51 +163,48 @@ TEST(Accum, CPP)
     }
 }
 
-TEST(Accum, MaxDim)
-{
+TEST(Accum, MaxDim) {
     const size_t largeDim = 65535 * 32 + 1;
 
-    //first dimension kernel tests
-    array input = constant(0, 2, largeDim, 2, 2);
+    // first dimension kernel tests
+    array input                           = constant(0, 2, largeDim, 2, 2);
     input(span, seq(0, 9999), span, span) = 1;
 
-    array gold_first = constant(0, 2, largeDim, 2, 2);
+    array gold_first                           = constant(0, 2, largeDim, 2, 2);
     gold_first(span, seq(0, 9999), span, span) = range(2, 10000, 2, 2) + 1;
 
     array output_first = accum(input, 0);
     ASSERT_ARRAYS_EQ(gold_first, output_first);
 
-
-    input = constant(0, 2, 2, 2, largeDim);
+    input                                 = constant(0, 2, 2, 2, largeDim);
     input(span, span, span, seq(0, 9999)) = 1;
 
-    gold_first = constant(0, 2, 2, 2, largeDim);
+    gold_first                                 = constant(0, 2, 2, 2, largeDim);
     gold_first(span, span, span, seq(0, 9999)) = range(2, 2, 2, 10000) + 1;
 
     output_first = accum(input, 0);
     ASSERT_ARRAYS_EQ(gold_first, output_first);
 
-
-    //other dimension kernel tests
-    input = constant(0, 2, largeDim, 2, 2);
+    // other dimension kernel tests
+    input                                 = constant(0, 2, largeDim, 2, 2);
     input(span, seq(0, 9999), span, span) = 1;
 
     array gold_dim = constant(10000, 2, largeDim, 2, 2);
-    gold_dim(span, seq(0, 9999), span, span) = range(dim4(2, 10000, 2, 2), 1) + 1;
+    gold_dim(span, seq(0, 9999), span, span) =
+        range(dim4(2, 10000, 2, 2), 1) + 1;
 
     array output_dim = accum(input, 1);
     ASSERT_ARRAYS_EQ(gold_dim, output_dim);
 
-
-    input = constant(0, 2, 2, 2, largeDim);
+    input                                 = constant(0, 2, 2, 2, largeDim);
     input(span, span, span, seq(0, 9999)) = 1;
 
     gold_dim = constant(0, 2, 2, 2, largeDim);
-    gold_dim(span, span, span, seq(0, 9999)) = range(dim4(2, 2, 2, 10000), 1) + 1;
+    gold_dim(span, span, span, seq(0, 9999)) =
+        range(dim4(2, 2, 2, 10000), 1) + 1;
 
     output_dim = accum(input, 1);
     ASSERT_ARRAYS_EQ(gold_dim, output_dim);
-
 }
 
 TEST(Accum, DocSnippet) {
@@ -273,7 +261,7 @@ TEST(Scan, ExclusiveSum1D) {
     vector<int> h_in(in_size, 1);
     vector<int> h_gold(in_size, 0);
     for (int i = 1; i < h_gold.size(); ++i) {
-        h_gold[i] = h_in[i] + h_gold[i-1];
+        h_gold[i] = h_in[i] + h_gold[i - 1];
     }
 
     array in(in_size, &h_in.front());
@@ -339,20 +327,20 @@ TEST(Scan, ExclusiveSum2D_Dim2) {
 }
 
 TEST(Scan, ExclusiveSum2D_Dim3) {
-  const int in_size = 80000 * 2;
-  vector<int> h_in(in_size, 1);
-  vector<int> h_gold(in_size, 0);
-  for (int i = 1; i < h_gold.size() / 2; ++i) {
-    h_gold[i] = h_in[i] + h_gold[i - 1];
-  }
-  for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
-    h_gold[i] = h_in[i] + h_gold[i - 1];
-  }
-
-  array in(1, 1, 2, in_size / 2, &h_in.front());
-  array out = scan(in, 3, AF_BINARY_ADD, false);
-  array gold(in_size / 2, 2, &h_gold.front());
-  gold = af::reorder(gold, 2, 3, 1, 0);
-
-  ASSERT_ARRAYS_EQ(gold, out);
+    const int in_size = 80000 * 2;
+    vector<int> h_in(in_size, 1);
+    vector<int> h_gold(in_size, 0);
+    for (int i = 1; i < h_gold.size() / 2; ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+        h_gold[i] = h_in[i] + h_gold[i - 1];
+    }
+
+    array in(1, 1, 2, in_size / 2, &h_in.front());
+    array out = scan(in, 3, AF_BINARY_ADD, false);
+    array gold(in_size / 2, 2, &h_gold.front());
+    gold = af::reorder(gold, 2, 3, 1, 0);
+
+    ASSERT_ARRAYS_EQ(gold, out);
 }
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
index f3ef75edd6..4aeba5d00e 100644
--- a/test/scan_by_key.cpp
+++ b/test/scan_by_key.cpp
@@ -7,43 +7,39 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/array.h>
+#include <af/device.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/array.h>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
-#include <af/device.h>
-#include "binary_ops.hpp"
 #include <utility>
+#include <vector>
+#include "binary_ops.hpp"
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
-float randomInterval(float start, float end)
-{
-    return start + (end - start)*(std::rand()/float(RAND_MAX));
+float randomInterval(float start, float end) {
+    return start + (end - start) * (std::rand() / float(RAND_MAX));
 }
 
-int randomInterval(int start, int end)
-{
-    return start + std::rand()%(end - start);
+int randomInterval(int start, int end) {
+    return start + std::rand() % (end - start);
 }
 
-template <typename T>
-vector<T> createScanKey(dim4 dims, int scanDim,
-        const vector<int> &nodeLengths,
-        T keyStart, T keyEnd)
-{
+template<typename T>
+vector<T> createScanKey(dim4 dims, int scanDim, const vector<int> &nodeLengths,
+                        T keyStart, T keyEnd) {
     std::srand(0);
     int elemCount = dims.elements();
     vector<T> key(elemCount);
@@ -53,16 +49,13 @@ vector<T> createScanKey(dim4 dims, int scanDim,
 
     for (int start = 0; start < stride; ++start) {
         T keyval = (T)(0);
-        for (int index = start, i = 0;
-                index < elemCount;
-                index += stride, i = (i+1)%dims[scanDim]) {
+        for (int index = start, i = 0; index < elemCount;
+             index += stride, i   = (i + 1) % dims[scanDim]) {
             bool isNode = false;
             for (unsigned n = 0; n < nodeLengths.size(); ++n) {
-                if (i % nodeLengths[n] == 0) {
-                    isNode = true;
-                }
+                if (i % nodeLengths[n] == 0) { isNode = true; }
             }
-            if (isNode && (std::rand()%2)) {
+            if (isNode && (std::rand() % 2)) {
                 keyval = randomInterval(keyStart, keyEnd);
             }
             key[index] = keyval;
@@ -71,9 +64,8 @@ vector<T> createScanKey(dim4 dims, int scanDim,
     return key;
 }
 
-template <typename T>
-vector<T> createScanData(dim4 dims, T dataStart, T dataEnd)
-{
+template<typename T>
+vector<T> createScanData(dim4 dims, T dataStart, T dataEnd) {
     int elemCount = dims.elements();
     vector<T> in(elemCount);
     for (int i = 0; i < elemCount; ++i) {
@@ -82,13 +74,10 @@ vector<T> createScanData(dim4 dims, T dataStart, T dataEnd)
     return in;
 }
 
-template <typename Ti, typename Tk, typename To, af_binary_op op, bool inclusive_scan>
-void verify(dim4 dims,
-        const vector<Ti> &in,
-        const vector<Tk> &key,
-        const vector<To> &out,
-        int scanDim, double eps)
-{
+template<typename Ti, typename Tk, typename To, af_binary_op op,
+         bool inclusive_scan>
+void verify(dim4 dims, const vector<Ti> &in, const vector<Tk> &key,
+            const vector<To> &out, int scanDim, double eps) {
     std::srand(1);
     Binary<To, op> binOp;
     int elemCount = dims.elements();
@@ -98,10 +87,10 @@ void verify(dim4 dims,
 
     for (int start = 0; start < stride; ++start) {
         Tk keyval = key[start];
-        To gold = binOp.init();
-        for (int index = start + (!inclusive_scan)*stride, i = (!inclusive_scan);
-                index < elemCount;
-                index += stride, i = (i+1)%dims[scanDim]) {
+        To gold   = binOp.init();
+        for (int index = start + (!inclusive_scan) * stride,
+                 i     = (!inclusive_scan);
+             index < elemCount; index += stride, i = (i + 1) % dims[scanDim]) {
             if ((key[index] != keyval) || (i == 0)) {
                 keyval = key[index];
                 if (inclusive_scan) {
@@ -111,8 +100,8 @@ void verify(dim4 dims,
                     gold = binOp.init();
                 }
             } else {
-                To dataval = (To)in[index - (!inclusive_scan)*stride];
-                gold = binOp(gold, dataval);
+                To dataval = (To)in[index - (!inclusive_scan) * stride];
+                gold       = binOp(gold, dataval);
                 ASSERT_NEAR(gold, out[index], eps);
             }
         }
@@ -121,9 +110,10 @@ void verify(dim4 dims,
 
 template<typename Ti, typename To, af_binary_op op, bool inclusive_scan>
 void scanByKeyTest(dim4 dims, int scanDim, vector<int> nodeLengths,
-        int keyStart, int keyEnd, Ti dataStart, Ti dataEnd, double eps)
-{
-    vector<int> key = createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
+                   int keyStart, int keyEnd, Ti dataStart, Ti dataEnd,
+                   double eps) {
+    vector<int> key =
+        createScanKey<int>(dims, scanDim, nodeLengths, keyStart, keyEnd);
     vector<Ti> in = createScanData<Ti>(dims, dataStart, dataEnd);
 
     array afkey(dims, key.data());
@@ -135,75 +125,96 @@ void scanByKeyTest(dim4 dims, int scanDim, vector<int> nodeLengths,
     verify<Ti, int, To, op, inclusive_scan>(dims, in, key, out, scanDim, eps);
 }
 
-#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS)   \
-TEST(ScanByKey,Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM)                    \
-{                                                                               \
-    dim4 dims(X, Y, Z, W);                                                      \
-    int scanDim = DIM;                                                          \
-    int nodel[] = {37, 256};                                                    \
-    vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));            \
-    int keyStart = 0;                                                           \
-    int keyEnd = 15;                                                            \
-    int dataStart = DSTART;                                                     \
-    int dataEnd = DEND;                                                         \
-    scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths,                  \
-            keyStart, keyEnd, dataStart, dataEnd, EPS);                         \
-}
+#define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS) \
+    TEST(ScanByKey, Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM) {           \
+        dim4 dims(X, Y, Z, W);                                                \
+        int scanDim = DIM;                                                    \
+        int nodel[] = {37, 256};                                              \
+        vector<int> nodeLengths(nodel, nodel + sizeof(nodel) / sizeof(int));  \
+        int keyStart  = 0;                                                    \
+        int keyEnd    = 15;                                                   \
+        int dataStart = DSTART;                                               \
+        int dataEnd   = DEND;                                                 \
+        scanByKeyTest<Ti, To, FN, INC>(dims, scanDim, nodeLengths, keyStart,  \
+                                       keyEnd, dataStart, dataEnd, EPS);      \
+    }
 
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int,  true, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1,   int,   int, false, 0,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float,  true, 0, -5.0, 5.0, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16*1024, 1024, 1, 1, float, float, false, 0, -5.0, 5.0, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_ADD,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MIN,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
-
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int,  true, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1,   int,   int, false, 1,  -15,  15, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float,  true, 1,   -5,   5, 1e-3);
-SCAN_BY_KEY_TEST(AF_BINARY_MAX,  4*1024,  512, 1, 1, float, float, false, 1,   -5,   5, 1e-3);
-
-TEST(ScanByKey,Test_Scan_By_key_Simple_0)
-{
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16 * 1024, 1024, 1, 1, int, int, true, 0, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16 * 1024, 1024, 1, 1, int, int, false, 0, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16 * 1024, 1024, 1, 1, float, float, true, 0,
+                 -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 16 * 1024, 1024, 1, 1, float, float, false, 0,
+                 -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16 * 1024, 1024, 1, 1, int, int, true, 0, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16 * 1024, 1024, 1, 1, int, int, false, 0, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16 * 1024, 1024, 1, 1, float, float, true, 0,
+                 -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 16 * 1024, 1024, 1, 1, float, float, false, 0,
+                 -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16 * 1024, 1024, 1, 1, int, int, true, 0, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16 * 1024, 1024, 1, 1, int, int, false, 0, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16 * 1024, 1024, 1, 1, float, float, true, 0,
+                 -5.0, 5.0, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 16 * 1024, 1024, 1, 1, float, float, false, 0,
+                 -5.0, 5.0, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4 * 1024, 512, 1, 1, int, int, true, 1, -15, 15,
+                 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4 * 1024, 512, 1, 1, int, int, false, 1, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4 * 1024, 512, 1, 1, float, float, true, 1, -5,
+                 5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_ADD, 4 * 1024, 512, 1, 1, float, float, false, 1, -5,
+                 5, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 4 * 1024, 512, 1, 1, int, int, true, 1, -15, 15,
+                 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 4 * 1024, 512, 1, 1, int, int, false, 1, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 4 * 1024, 512, 1, 1, float, float, true, 1, -5,
+                 5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MIN, 4 * 1024, 512, 1, 1, float, float, false, 1, -5,
+                 5, 1e-3);
+
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 4 * 1024, 512, 1, 1, int, int, true, 1, -15, 15,
+                 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 4 * 1024, 512, 1, 1, int, int, false, 1, -15,
+                 15, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 4 * 1024, 512, 1, 1, float, float, true, 1, -5,
+                 5, 1e-3);
+SCAN_BY_KEY_TEST(AF_BINARY_MAX, 4 * 1024, 512, 1, 1, float, float, false, 1, -5,
+                 5, 1e-3);
+
+TEST(ScanByKey, Test_Scan_By_key_Simple_0) {
     dim4 dims(16, 8, 2, 1);
     int scanDim = 0;
     int nodel[] = {4, 8};
-    vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
-    int keyStart = 0;
-    int keyEnd = 15;
+    vector<int> nodeLengths(nodel, nodel + sizeof(nodel) / sizeof(int));
+    int keyStart  = 0;
+    int keyEnd    = 15;
     int dataStart = 2;
-    int dataEnd = 4;
-    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
-            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
+    int dataEnd   = 4;
+    scanByKeyTest<int, int, AF_BINARY_ADD, false>(
+        dims, scanDim, nodeLengths, keyStart, keyEnd, dataStart, dataEnd, 1e-5);
 }
 
-TEST(ScanByKey,Test_Scan_By_key_Simple_1)
-{
-    dim4 dims(8, 256+128, 1, 1);
+TEST(ScanByKey, Test_Scan_By_key_Simple_1) {
+    dim4 dims(8, 256 + 128, 1, 1);
     int scanDim = 1;
     int nodel[] = {4, 8};
-    vector<int> nodeLengths(nodel, nodel+sizeof(nodel)/sizeof(int));
-    int keyStart = 0;
-    int keyEnd = 15;
+    vector<int> nodeLengths(nodel, nodel + sizeof(nodel) / sizeof(int));
+    int keyStart  = 0;
+    int keyEnd    = 15;
     int dataStart = 2;
-    int dataEnd = 4;
-    scanByKeyTest<int, int, AF_BINARY_ADD, false>(dims, scanDim, nodeLengths,
-            keyStart, keyEnd, dataStart, dataEnd, 1e-5);
+    int dataEnd   = 4;
+    scanByKeyTest<int, int, AF_BINARY_ADD, false>(
+        dims, scanDim, nodeLengths, keyStart, keyEnd, dataStart, dataEnd, 1e-5);
 }
diff --git a/test/select.cpp b/test/select.cpp
index 9c85a18432..3f8ff2f664 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -20,7 +20,6 @@
 #include <string>
 #include <vector>
 
-using af::NaN;
 using af::array;
 using af::cdouble;
 using af::cfloat;
@@ -29,6 +28,7 @@ using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::eval;
+using af::NaN;
 using af::randu;
 using af::select;
 using af::seq;
@@ -38,18 +38,16 @@ using std::string;
 using std::stringstream;
 using std::vector;
 
-
 template<typename T>
-class Select : public ::testing::Test
-{
-};
+class Select : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl, uchar, char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
+                         uchar, char, short, ushort>
+    TestTypes;
 TYPED_TEST_CASE(Select, TestTypes);
 
 template<typename T>
-void selectTest(const dim4 &dims)
-{
+void selectTest(const dim4& dims) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
@@ -83,18 +81,15 @@ void selectTest(const dim4 &dims)
 }
 
 template<typename T, bool is_right>
-void selectScalarTest(const dim4 &dims)
-{
+void selectScalarTest(const dim4& dims) {
     if (noDoubleTests<T>()) return;
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    array a = randu(dims, ty);
+    array a    = randu(dims, ty);
     array cond = randu(dims, ty) > a;
-    double b = 3;
+    double b   = 3;
 
-    if (a.isinteger()) {
-        a = (a % (1 << 30)).as(ty);
-    }
+    if (a.isinteger()) { a = (a % (1 << 30)).as(ty); }
 
     array c = is_right ? select(cond, a, b) : select(cond, b, a);
 
@@ -119,30 +114,24 @@ void selectScalarTest(const dim4 &dims)
     }
 }
 
-TYPED_TEST(Select, Simple)
-{
-    selectTest<TypeParam>(dim4(1024, 1024));
-}
+TYPED_TEST(Select, Simple) { selectTest<TypeParam>(dim4(1024, 1024)); }
 
-TYPED_TEST(Select, RightScalar)
-{
+TYPED_TEST(Select, RightScalar) {
     selectScalarTest<TypeParam, true>(dim4(1000, 1000));
 }
 
-TYPED_TEST(Select, LeftScalar)
-{
+TYPED_TEST(Select, LeftScalar) {
     selectScalarTest<TypeParam, true>(dim4(1000, 1000));
 }
 
-TEST(Select, NaN)
-{
+TEST(Select, NaN) {
     dim4 dims(1000, 1250);
     dtype ty = f32;
 
-    array a = randu(dims, ty);
+    array a                                 = randu(dims, ty);
     a(seq(a.dims(0) / 2), span, span, span) = NaN;
-    float b = 0;
-    array c = select(isNaN(a), b, a);
+    float b                                 = 0;
+    array c                                 = select(isNaN(a), b, a);
 
     int num = (int)a.elements();
 
@@ -157,13 +146,12 @@ TEST(Select, NaN)
     }
 }
 
-TEST(Select, ISSUE_1249)
-{
+TEST(Select, ISSUE_1249) {
     dim4 dims(2, 3, 4);
     array cond = randu(dims) > 0.5;
-    array a = randu(dims);
-    array b = select(cond, a - a * 0.9, a);
-    array c = a - a * cond * 0.9;
+    array a    = randu(dims);
+    array b    = select(cond, a - a * 0.9, a);
+    array c    = a - a * cond * 0.9;
 
     int num = (int)dims.elements();
     vector<float> hb(num);
@@ -177,13 +165,12 @@ TEST(Select, ISSUE_1249)
     }
 }
 
-TEST(Select, 4D)
-{
+TEST(Select, 4D) {
     dim4 dims(2, 3, 4, 2);
     array cond = randu(dims) > 0.5;
-    array a = randu(dims);
-    array b = select(cond, a - a * 0.9, a);
-    array c = a - a * cond * 0.9;
+    array a    = randu(dims);
+    array b    = select(cond, a - a * 0.9, a);
+    array c    = a - a * cond * 0.9;
 
     int num = (int)dims.elements();
     vector<float> hb(num);
@@ -197,11 +184,10 @@ TEST(Select, 4D)
     }
 }
 
-TEST(Select, Issue_1730)
-{
+TEST(Select, Issue_1730) {
     const int n = 1000;
     const int m = 200;
-    array a = randu(n, m) - 0.5;
+    array a     = randu(n, m) - 0.5;
     eval(a);
 
     vector<float> ha1(a.elements());
@@ -211,9 +197,8 @@ TEST(Select, Issue_1730)
     const int n2 = n1 + n / 4;
 
     a(seq(n1, n2), span) =
-        select(a(seq(n1, n2), span) >= 0,
-                   a(seq(n1, n2), span),
-                   a(seq(n1, n2), span) * -1);
+        select(a(seq(n1, n2), span) >= 0, a(seq(n1, n2), span),
+               a(seq(n1, n2), span) * -1);
 
     vector<float> ha2(a.elements());
     a.host(&ha2[0]);
@@ -221,19 +206,20 @@ TEST(Select, Issue_1730)
     for (int j = 0; j < m; j++) {
         for (int i = 0; i < n; i++) {
             if (i < n1 || i > n2) {
-                ASSERT_FLOAT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha1[i], ha2[i])
+                    << "at (" << i << ", " << j << ")";
             } else {
-                ASSERT_FLOAT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : -ha1[i]))  << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : -ha1[i]))
+                    << "at (" << i << ", " << j << ")";
             }
         }
     }
 }
 
-TEST(Select, Issue_1730_scalar)
-{
+TEST(Select, Issue_1730_scalar) {
     const int n = 1000;
     const int m = 200;
-    array a = randu(n, m) - 0.5;
+    array a     = randu(n, m) - 0.5;
     eval(a);
 
     vector<float> ha1(a.elements());
@@ -244,9 +230,7 @@ TEST(Select, Issue_1730_scalar)
 
     float val = 0;
     a(seq(n1, n2), span) =
-        select(a(seq(n1, n2), span) >= 0,
-                   a(seq(n1, n2), span),
-                   val);
+        select(a(seq(n1, n2), span) >= 0, a(seq(n1, n2), span), val);
 
     vector<float> ha2(a.elements());
     a.host(&ha2[0]);
@@ -254,23 +238,24 @@ TEST(Select, Issue_1730_scalar)
     for (int j = 0; j < m; j++) {
         for (int i = 0; i < n; i++) {
             if (i < n1 || i > n2) {
-                ASSERT_FLOAT_EQ(ha1[i], ha2[i]) << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha1[i], ha2[i])
+                    << "at (" << i << ", " << j << ")";
             } else {
-                ASSERT_FLOAT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : val))  << "at (" << i << ", " << j << ")";
+                ASSERT_FLOAT_EQ(ha2[i], (ha1[i] >= 0 ? ha1[i] : val))
+                    << "at (" << i << ", " << j << ")";
             }
         }
     }
 }
 
-TEST(Select, MaxDim)
-{
+TEST(Select, MaxDim) {
     const size_t largeDim = 65535 * 32 + 1;
 
     array a    = constant(1, largeDim);
     array b    = constant(0, largeDim);
     array cond = constant(0, largeDim, b8);
 
-    array sel  = select(cond, a, b);
+    array sel = select(cond, a, b);
     float sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
@@ -279,7 +264,7 @@ TEST(Select, MaxDim)
     b    = constant(0, 1, largeDim);
     cond = constant(0, 1, largeDim, b8);
 
-    sel  = select(cond, a, b);
+    sel = select(cond, a, b);
     sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
@@ -288,7 +273,7 @@ TEST(Select, MaxDim)
     b    = constant(0, 1, 1, largeDim);
     cond = constant(0, 1, 1, largeDim, b8);
 
-    sel  = select(cond, a, b);
+    sel = select(cond, a, b);
     sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
@@ -297,7 +282,7 @@ TEST(Select, MaxDim)
     b    = constant(0, 1, 1, 1, largeDim);
     cond = constant(0, 1, 1, 1, largeDim, b8);
 
-    sel  = select(cond, a, b);
+    sel = select(cond, a, b);
     sum = af::sum<float>(sel);
 
     ASSERT_FLOAT_EQ(sum, 0.f);
@@ -308,151 +293,143 @@ struct select_params {
     dim4 cond;
     dim4 a;
     dim4 b;
-  select_params(dim4 out_, dim4 cond_, dim4 a_, dim4 b_)
-    : out(out_), cond(cond_), a(a_), b(b_)
-  {}
+    select_params(dim4 out_, dim4 cond_, dim4 a_, dim4 b_)
+        : out(out_), cond(cond_), a(a_), b(b_) {}
 };
 
 class Select_ : public ::testing::TestWithParam<select_params> {};
 
 string pd4(dim4 dims) {
     string out(32, '\0');
-    int len = snprintf(const_cast<char*>(out.data()), 32,
-                       "%lld_%lld_%lld_%lld", dims[0], dims[1], dims[2], dims[3]);
+    int len = snprintf(const_cast<char*>(out.data()), 32, "%lld_%lld_%lld_%lld",
+                       dims[0], dims[1], dims[2], dims[3]);
     out.resize(len);
     return out;
 }
 
-string testNameGenerator(const ::testing::TestParamInfo<Select_::ParamType> info) {
+string testNameGenerator(
+    const ::testing::TestParamInfo<Select_::ParamType> info) {
     stringstream ss;
-    ss << "out_" << pd4(info.param.out)
-       << "_cond_" << pd4(info.param.cond)
-       << "_a_" << pd4(info.param.a)
-       << "_b_" << pd4(info.param.b);
+    ss << "out_" << pd4(info.param.out) << "_cond_" << pd4(info.param.cond)
+       << "_a_" << pd4(info.param.a) << "_b_" << pd4(info.param.b);
     return ss.str();
 }
 
 vector<select_params> getSelectTestParams(int M, int N) {
-  const select_params _[] = {select_params(dim4(M), dim4(M), dim4(M), dim4(M)),
-          select_params(dim4(M, N), dim4(M, N), dim4(M, N), dim4(M, N)),
-          select_params(dim4(M, N, N), dim4(M, N, N), dim4(M, N, N), dim4(M, N, N)),
-          select_params(dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N)),
-          select_params(dim4(M, N), dim4(M, 1), dim4(M, 1), dim4(M, N)),
-          select_params(dim4(M, N), dim4(M, 1), dim4(M, N), dim4(M, 1)),
-          select_params(dim4(M, N), dim4(M, 1), dim4(M, N), dim4(M, N)),
-          select_params(dim4(M, N), dim4(M, N), dim4(M, 1), dim4(M, N)),
-          select_params(dim4(M, N), dim4(M, N), dim4(M, N), dim4(M, 1)),
-          select_params(dim4(M, N), dim4(M, N), dim4(M, 1), dim4(M, 1))};
-  return vector<select_params>(_, _ + sizeof(_) / sizeof(_[0]));
+    const select_params _[] = {
+        select_params(dim4(M), dim4(M), dim4(M), dim4(M)),
+        select_params(dim4(M, N), dim4(M, N), dim4(M, N), dim4(M, N)),
+        select_params(dim4(M, N, N), dim4(M, N, N), dim4(M, N, N),
+                      dim4(M, N, N)),
+        select_params(dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N),
+                      dim4(M, N, N, N)),
+        select_params(dim4(M, N), dim4(M, 1), dim4(M, 1), dim4(M, N)),
+        select_params(dim4(M, N), dim4(M, 1), dim4(M, N), dim4(M, 1)),
+        select_params(dim4(M, N), dim4(M, 1), dim4(M, N), dim4(M, N)),
+        select_params(dim4(M, N), dim4(M, N), dim4(M, 1), dim4(M, N)),
+        select_params(dim4(M, N), dim4(M, N), dim4(M, N), dim4(M, 1)),
+        select_params(dim4(M, N), dim4(M, N), dim4(M, 1), dim4(M, 1))};
+    return vector<select_params>(_, _ + sizeof(_) / sizeof(_[0]));
 }
 
-INSTANTIATE_TEST_CASE_P(
-  SmallDims,
-  Select_,
-  ::testing::ValuesIn(getSelectTestParams(10, 5)),
-  testNameGenerator);
+INSTANTIATE_TEST_CASE_P(SmallDims, Select_,
+                        ::testing::ValuesIn(getSelectTestParams(10, 5)),
+                        testNameGenerator);
 
-INSTANTIATE_TEST_CASE_P(
-  Dims33_9,
-  Select_,
-  ::testing::ValuesIn(getSelectTestParams(33, 9)),
-  testNameGenerator);
+INSTANTIATE_TEST_CASE_P(Dims33_9, Select_,
+                        ::testing::ValuesIn(getSelectTestParams(33, 9)),
+                        testNameGenerator);
 
-INSTANTIATE_TEST_CASE_P(
-  DimsLg,
-  Select_,
-  ::testing::ValuesIn(getSelectTestParams(512, 32)),
-  testNameGenerator);
+INSTANTIATE_TEST_CASE_P(DimsLg, Select_,
+                        ::testing::ValuesIn(getSelectTestParams(512, 32)),
+                        testNameGenerator);
 
 TEST_P(Select_, Batch) {
     select_params params = GetParam();
 
     float aval = 5.0f;
     float bval = 10.0f;
-    array a = constant(aval, params.a);
-    array b = constant(bval, params.b);
+    array a    = constant(aval, params.a);
+    array b    = constant(bval, params.b);
     array cond = (iota(params.cond) % 2).as(b8);
 
     array out = select(cond, a, b);
 
     EXPECT_EQ(out.dims(), params.out);
 
-    vector<float> h_out(out.elements()); out.host(h_out.data());
-    vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
+    vector<float> h_out(out.elements());
+    out.host(h_out.data());
+    vector<unsigned char> h_cond(cond.elements());
+    cond.host(h_cond.data());
 
     vector<float> gold(params.out.elements());
-    for(size_t i = 0; i < gold.size(); i++) {
+    for (size_t i = 0; i < gold.size(); i++) {
         gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
         ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
     }
 }
 
 struct selectlr_params {
-  dim4 out;
-  dim4 cond;
-  dim4 ab;
-  selectlr_params(dim4 out_, dim4 cond_, dim4 ab_)
-    : out(out_), cond(cond_), ab(ab_) {}
+    dim4 out;
+    dim4 cond;
+    dim4 ab;
+    selectlr_params(dim4 out_, dim4 cond_, dim4 ab_)
+        : out(out_), cond(cond_), ab(ab_) {}
 };
 
 class SelectLR_ : public ::testing::TestWithParam<selectlr_params> {};
 
 vector<selectlr_params> getSelectLRTestParams(int M, int N) {
-  const selectlr_params _[] = {
-              selectlr_params(dim4(M), dim4(M), dim4(M)),
-              selectlr_params(dim4(M, N), dim4(M, N), dim4(M, N)),
-              selectlr_params(dim4(M, N, N), dim4(M, N, N), dim4(M, N, N)),
-              selectlr_params(dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N)),
-              selectlr_params(dim4(M, N), dim4(M, 1), dim4(M, N)),
-              selectlr_params(dim4(M, N), dim4(M, N), dim4(M, 1))};
-
-  return vector<selectlr_params> (_, _+sizeof(_)/sizeof(_[0]));
+    const selectlr_params _[] = {
+        selectlr_params(dim4(M), dim4(M), dim4(M)),
+        selectlr_params(dim4(M, N), dim4(M, N), dim4(M, N)),
+        selectlr_params(dim4(M, N, N), dim4(M, N, N), dim4(M, N, N)),
+        selectlr_params(dim4(M, N, N, N), dim4(M, N, N, N), dim4(M, N, N, N)),
+        selectlr_params(dim4(M, N), dim4(M, 1), dim4(M, N)),
+        selectlr_params(dim4(M, N), dim4(M, N), dim4(M, 1))};
+
+    return vector<selectlr_params>(_, _ + sizeof(_) / sizeof(_[0]));
 }
 
-string testNameGeneratorLR(const ::testing::TestParamInfo<SelectLR_::ParamType> info) {
-  stringstream ss;
-  ss << "out_" << pd4(info.param.out)
-     << "_cond_" << pd4(info.param.cond)
-     << "_ab_" << pd4(info.param.ab);
-  return ss.str();
+string testNameGeneratorLR(
+    const ::testing::TestParamInfo<SelectLR_::ParamType> info) {
+    stringstream ss;
+    ss << "out_" << pd4(info.param.out) << "_cond_" << pd4(info.param.cond)
+       << "_ab_" << pd4(info.param.ab);
+    return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(
-                        SmallDims,
-                        SelectLR_,
+INSTANTIATE_TEST_CASE_P(SmallDims, SelectLR_,
                         ::testing::ValuesIn(getSelectLRTestParams(10, 5)),
                         testNameGeneratorLR);
 
-INSTANTIATE_TEST_CASE_P(
-                        Dims33_9,
-                        SelectLR_,
+INSTANTIATE_TEST_CASE_P(Dims33_9, SelectLR_,
                         ::testing::ValuesIn(getSelectLRTestParams(33, 9)),
                         testNameGeneratorLR);
 
-INSTANTIATE_TEST_CASE_P(
-                        DimsLg,
-                        SelectLR_,
+INSTANTIATE_TEST_CASE_P(DimsLg, SelectLR_,
                         ::testing::ValuesIn(getSelectLRTestParams(512, 32)),
                         testNameGeneratorLR);
 
-
 TEST_P(SelectLR_, BatchL) {
     selectlr_params params = GetParam();
 
     float aval = 5.0f;
     float bval = 10.0f;
-    array b = constant(bval, params.ab);
+    array b    = constant(bval, params.ab);
     array cond = (iota(params.cond) % 2).as(b8);
 
     array out = select(cond, static_cast<double>(aval), b);
 
     EXPECT_EQ(out.dims(), params.out);
 
-    vector<float> h_out(out.elements()); out.host(h_out.data());
-    vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
+    vector<float> h_out(out.elements());
+    out.host(h_out.data());
+    vector<unsigned char> h_cond(cond.elements());
+    cond.host(h_cond.data());
 
     vector<float> gold(params.out.elements());
-    for(size_t i = 0; i < gold.size(); i++) {
+    for (size_t i = 0; i < gold.size(); i++) {
         gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
         ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
     }
@@ -463,28 +440,30 @@ TEST_P(SelectLR_, BatchR) {
 
     float aval = 5.0f;
     float bval = 10.0f;
-    array a = constant(aval, params.ab);
+    array a    = constant(aval, params.ab);
     array cond = (iota(params.cond) % 2).as(b8);
 
     array out = select(cond, a, static_cast<double>(bval));
 
     EXPECT_EQ(out.dims(), params.out);
 
-    vector<float> h_out(out.elements()); out.host(h_out.data());
-    vector<unsigned char> h_cond(cond.elements()); cond.host(h_cond.data());
+    vector<float> h_out(out.elements());
+    out.host(h_out.data());
+    vector<unsigned char> h_cond(cond.elements());
+    cond.host(h_cond.data());
 
     vector<float> gold(params.out.elements());
-    for(size_t i = 0; i < gold.size(); i++) {
+    for (size_t i = 0; i < gold.size(); i++) {
         gold[i] = h_cond[i % h_cond.size()] ? aval : bval;
         ASSERT_FLOAT_EQ(gold[i], h_out[i]) << "at: " << i;
     }
 }
 
 TEST(Select, InvalidSizeOfAB) {
-    af_array a = 0;
-    af_array b = 0;
+    af_array a    = 0;
+    af_array b    = 0;
     af_array cond = 0;
-    af_array out = 0;
+    af_array out  = 0;
 
     double val = 0;
     dim_t dims = 10;
@@ -508,10 +487,10 @@ TEST(Select, InvalidSizeOfAB) {
 }
 
 TEST(Select, InvalidSizeOfCond) {
-    af_array a = 0;
-    af_array b = 0;
+    af_array a    = 0;
+    af_array b    = 0;
     af_array cond = 0;
-    af_array out = 0;
+    af_array out  = 0;
 
     double val = 0;
     dim_t dims = 10;
@@ -534,27 +513,29 @@ TEST(Select, InvalidSizeOfCond) {
     af_release_array(cond);
 }
 
-
 TEST(Select, SNIPPET_select) {
     //! [ex_data_select]
     int elements = 9;
     char hCond[] = {1, 0, 1, 0, 1, 0, 1, 0, 1};
-    float hA[]  = {2, 2, 2, 2, 2, 2, 2, 2, 2};
-    float hB[]  = {3, 3, 3, 3, 3, 3, 3, 3, 3};
+    float hA[]   = {2, 2, 2, 2, 2, 2, 2, 2, 2};
+    float hB[]   = {3, 3, 3, 3, 3, 3, 3, 3, 3};
 
     array cond(elements, hCond);
     array a(elements, hA);
     array b(elements, hB);
 
     array out = select(cond, a, b);
-    //out = {2, 3, 2, 3, 2, 3, 2, 3, 2};
+    // out = {2, 3, 2, 3, 2, 3, 2, 3, 2};
     //! [ex_data_select]
 
     //! [ex_data_select_c]
     vector<float> hOut(elements);
-    for(size_t i = 0; i < hOut.size(); i++) {
-        if(hCond[i]) { hOut[i] = hA[i]; }
-        else         { hOut[i] = hB[i]; }
+    for (size_t i = 0; i < hOut.size(); i++) {
+        if (hCond[i]) {
+            hOut[i] = hA[i];
+        } else {
+            hOut[i] = hB[i];
+        }
     }
     //! [ex_data_select_c]
 
diff --git a/test/set.cpp b/test/set.cpp
index 23a994a5db..ec695b38f2 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -7,76 +7,71 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/algorithm.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/algorithm.h>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-void uniqueTest(string pTestFile)
-{
+void uniqueTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (pTestFile,numDims,data,tests);
-
+    readTests<int, int, int>(pTestFile, numDims, data, tests);
 
     // Compare result
     for (int d = 0; d < (int)tests.size(); ++d) {
-
-        dim4 dims       = numDims[d];
+        dim4 dims = numDims[d];
         vector<T> in(data[d].begin(), data[d].end());
 
-        af_array inArray   = 0;
-        af_array outArray  = 0;
+        af_array inArray  = 0;
+        af_array outArray = 0;
 
         // Get input array
         ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
-                                              dims.get(), (af_dtype) dtype_traits<T>::af_type));
-
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        ASSERT_SUCCESS(af_set_unique(&outArray, inArray, d == 0 ? false : true));
+        ASSERT_SUCCESS(
+            af_set_unique(&outArray, inArray, d == 0 ? false : true));
 
         // Get result
-        vector<T >outData (currGoldBar.size());
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+        vector<T> outData(currGoldBar.size());
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                            << " for test: " << d << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << " for test: " << d << endl;
         }
 
-        if(inArray   != 0) af_release_array(inArray);
-        if(outArray  != 0) af_release_array(outArray);
+        if (inArray != 0) af_release_array(inArray);
+        if (outArray != 0) af_release_array(outArray);
     }
 }
 
-#define UNIQUE_TESTS(T)                             \
-    TEST(Set, Test_Unique_##T)                      \
-    {                                               \
-        uniqueTest<T>(TEST_DIR"/set/unique.test");  \
-    }                                               \
+#define UNIQUE_TESTS(T) \
+    TEST(Set, Test_Unique_##T) { uniqueTest<T>(TEST_DIR "/set/unique.test"); }
 
 UNIQUE_TESTS(float)
 UNIQUE_TESTS(double)
@@ -88,69 +83,67 @@ UNIQUE_TESTS(ushort)
 UNIQUE_TESTS(intl)
 UNIQUE_TESTS(uintl)
 
-typedef af_err (*setFunc)(af_array *, const af_array, const af_array, const bool);
+typedef af_err (*setFunc)(af_array *, const af_array, const af_array,
+                          const bool);
 
 template<typename T, setFunc af_set_func>
-void setTest(string pTestFile)
-{
+void setTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (pTestFile,numDims,data,tests);
-
+    readTests<int, int, int>(pTestFile, numDims, data, tests);
 
     // Compare result
     for (int d = 0; d < (int)tests.size(); d += 2) {
-
-        dim4 dims0       = numDims[d + 0];
+        dim4 dims0 = numDims[d + 0];
         vector<T> in0(data[d + 0].begin(), data[d + 0].end());
 
-        dim4 dims1       = numDims[d + 1];
+        dim4 dims1 = numDims[d + 1];
         vector<T> in1(data[d + 1].begin(), data[d + 1].end());
 
-        af_array inArray0   = 0;
-        af_array inArray1   = 0;
-        af_array outArray  = 0;
+        af_array inArray0 = 0;
+        af_array inArray1 = 0;
+        af_array outArray = 0;
 
         ASSERT_SUCCESS(af_create_array(&inArray0, &in0.front(), dims0.ndims(),
-                                              dims0.get(), (af_dtype) dtype_traits<T>::af_type));
-
+                                       dims0.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
         ASSERT_SUCCESS(af_create_array(&inArray1, &in1.front(), dims1.ndims(),
-                                              dims1.get(), (af_dtype) dtype_traits<T>::af_type));
+                                       dims1.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
         vector<T> currGoldBar(tests[d].begin(), tests[d].end());
 
         // Run sum
-        ASSERT_SUCCESS(af_set_func(&outArray, inArray0, inArray1, d == 0 ? false : true));
+        ASSERT_SUCCESS(
+            af_set_func(&outArray, inArray0, inArray1, d == 0 ? false : true));
 
         // Get result
         vector<T> outData(currGoldBar.size());
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
         size_t nElems = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter]) << "at: " << elIter
-                                                            << " for test: " << d << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << " for test: " << d << endl;
         }
 
-        if(inArray0   != 0) af_release_array(inArray0);
-        if(inArray1   != 0) af_release_array(inArray1);
-        if(outArray  != 0) af_release_array(outArray);
+        if (inArray0 != 0) af_release_array(inArray0);
+        if (inArray1 != 0) af_release_array(inArray1);
+        if (outArray != 0) af_release_array(outArray);
     }
 }
 
-#define SET_TESTS(T)                                                    \
-    TEST(Set, Test_Union_##T)                                           \
-    {                                                                   \
-        setTest<T, af_set_union>(TEST_DIR"/set/union.test");            \
-    }                                                                   \
-    TEST(Set, Test_Intersect_##T)                                       \
-    {                                                                   \
-        setTest<T, af_set_intersect>(TEST_DIR"/set/intersect.test");    \
-    }                                                                   \
+#define SET_TESTS(T)                                                  \
+    TEST(Set, Test_Union_##T) {                                       \
+        setTest<T, af_set_union>(TEST_DIR "/set/union.test");         \
+    }                                                                 \
+    TEST(Set, Test_Intersect_##T) {                                   \
+        setTest<T, af_set_intersect>(TEST_DIR "/set/intersect.test"); \
+    }
 
 SET_TESTS(float)
 SET_TESTS(double)
@@ -164,7 +157,6 @@ SET_TESTS(uintl)
 
 // Documentation examples for setUnique
 TEST(Set, SNIPPET_setUniqueSorted) {
-
     //! [ex_set_unique_sorted]
 
     // input data
@@ -174,18 +166,17 @@ TEST(Set, SNIPPET_setUniqueSorted) {
     // is_sorted flag specifies if input is sorted,
     // allows algorithm to skip internal sorting step
     const bool is_sorted = true;
-    af::array unique = setUnique(set, is_sorted);
+    af::array unique     = setUnique(set, is_sorted);
     // unique == { 1, 2, 3 };
 
     //! [ex_set_unique_sorted]
 
-    vector<int> unique_gold = { 1, 2, 3 };
+    vector<int> unique_gold = {1, 2, 3};
     dim4 gold_dim(3, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(unique_gold, gold_dim, unique);
 }
 
 TEST(Set, SNIPPET_setUniqueSortedDesc) {
-
     //! [ex_set_unique_desc]
 
     // input data
@@ -196,18 +187,17 @@ TEST(Set, SNIPPET_setUniqueSortedDesc) {
     // allows algorithm to skip internal sorting step
     // input can be sorted in ascending or descending order
     const bool is_sorted = true;
-    af::array unique = setUnique(set, is_sorted);
+    af::array unique     = setUnique(set, is_sorted);
     // unique == { 3, 2, 1 };
 
     //! [ex_set_unique_desc]
 
-    vector<int> unique_gold = { 3, 2, 1 };
+    vector<int> unique_gold = {3, 2, 1};
     dim4 gold_dim(3, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(unique_gold, gold_dim, unique);
 }
 
 TEST(Set, SNIPPET_setUniqueSimple) {
-
     //! [ex_set_unique_simple]
 
     // input data
@@ -219,14 +209,13 @@ TEST(Set, SNIPPET_setUniqueSimple) {
 
     //! [ex_set_unique_simple]
 
-    vector<int> unique_gold = { 1, 2, 3 };
+    vector<int> unique_gold = {1, 2, 3};
     dim4 gold_dim(3, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(unique_gold, gold_dim, unique);
 }
 
 // Documentation examples for setUnion
 TEST(Set, SNIPPET_setUnion) {
-
     //! [ex_set_union]
 
     // input data
@@ -244,13 +233,12 @@ TEST(Set, SNIPPET_setUnion) {
 
     //! [ex_set_union]
 
-    vector<int> union_gold = { 1, 2, 3, 4, 5 };
+    vector<int> union_gold = {1, 2, 3, 4, 5};
     dim4 gold_dim(5, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(union_gold, gold_dim, setAB);
 }
 
 TEST(Set, SNIPPET_setUnionSimple) {
-
     //! [ex_set_union_simple]
 
     // input data
@@ -264,14 +252,13 @@ TEST(Set, SNIPPET_setUnionSimple) {
 
     //! [ex_set_union_simple]
 
-    vector<int> union_gold = { 1, 2, 3, 4, 5 };
+    vector<int> union_gold = {1, 2, 3, 4, 5};
     dim4 gold_dim(5, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(union_gold, gold_dim, setAB);
 }
 
 // Documentation examples for setIntersect()
 TEST(Set, SNIPPET_setIntersect) {
-
     //! [ex_set_intersect]
 
     // input data
@@ -289,13 +276,12 @@ TEST(Set, SNIPPET_setIntersect) {
 
     //! [ex_set_intersect]
 
-    vector<int> intersect_gold = { 2, 3, 4 };
+    vector<int> intersect_gold = {2, 3, 4};
     dim4 gold_dim(3, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(intersect_gold, gold_dim, setA_B);
 }
 
 TEST(Set, SNIPPET_setIntersectSimple) {
-
     //! [ex_set_intersect_simple]
 
     // input data
@@ -309,7 +295,7 @@ TEST(Set, SNIPPET_setIntersectSimple) {
 
     //! [ex_set_intersect_simple]
 
-    vector<int> intersect_gold = { 3 };
+    vector<int> intersect_gold = {3};
     dim4 gold_dim(1, 1, 1, 1);
     ASSERT_VEC_ARRAY_EQ(intersect_gold, gold_dim, setA_B);
 }
diff --git a/test/shift.cpp b/test/shift.cpp
index 2cc4b1c4ef..3120c8674e 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -7,119 +7,123 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::product;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Shift : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Shift : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         intl, uintl, char, unsigned char, short, ushort>
+    TestTypes;
 // register the type list
 TYPED_TEST_CASE(Shift, TestTypes);
 
 template<typename T>
-void shiftTest(string pTestFile, const unsigned resultIdx,
-                 const int x, const int y, const int z, const int w,
-                 bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void shiftTest(string pTestFile, const unsigned resultIdx, const int x,
+               const int y, const int z, const int w, bool isSubRef = false,
+               const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, int>(pTestFile,numDims,in,tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_shift(&outArray, inArray, x, y, z, w));
 
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], idims, outArray);
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define SHIFT_INIT(desc, file, resultIdx, x, y, z, w)                                       \
-    TYPED_TEST(Shift, desc)                                                                 \
-    {                                                                                       \
-        shiftTest<TypeParam>(string(TEST_DIR"/shift/"#file".test"), resultIdx, x, y, z, w); \
+#define SHIFT_INIT(desc, file, resultIdx, x, y, z, w)                  \
+    TYPED_TEST(Shift, desc) {                                          \
+        shiftTest<TypeParam>(string(TEST_DIR "/shift/" #file ".test"), \
+                             resultIdx, x, y, z, w);                   \
     }
 
-SHIFT_INIT(Shift0,  shift4d, 0,    2,  0,  0,  0);
-    SHIFT_INIT(Shift1,  shift4d, 1,   -1,  0,  0,  0);
-    SHIFT_INIT(Shift2,  shift4d, 2,    3,  2,  0,  0);
-    SHIFT_INIT(Shift3,  shift4d, 3,   11, 22,  0,  0);
-    SHIFT_INIT(Shift4,  shift4d, 4,    0,  1,  0,  0);
-    SHIFT_INIT(Shift5,  shift4d, 5,    0, -6,  0,  0);
-    SHIFT_INIT(Shift6,  shift4d, 6,    0,  3,  1,  0);
-    SHIFT_INIT(Shift7,  shift4d, 7,    0,  0,  2,  0);
-    SHIFT_INIT(Shift8,  shift4d, 8,    0,  0, -2,  0);
-    SHIFT_INIT(Shift9,  shift4d, 9,    0,  0,  0,  1);
-    SHIFT_INIT(Shift10, shift4d, 10,   0,  0,  0, -1);
-    SHIFT_INIT(Shift11, shift4d, 11,   1,  1,  1,  1);
-    SHIFT_INIT(Shift12, shift4d, 12,  -1, -1, -1, -1);
-    SHIFT_INIT(Shift13, shift4d, 13,  21, 21, 21, 21);
-    SHIFT_INIT(Shift14, shift4d, 14, -21,-21,-21,-21);
-
+SHIFT_INIT(Shift0, shift4d, 0, 2, 0, 0, 0);
+SHIFT_INIT(Shift1, shift4d, 1, -1, 0, 0, 0);
+SHIFT_INIT(Shift2, shift4d, 2, 3, 2, 0, 0);
+SHIFT_INIT(Shift3, shift4d, 3, 11, 22, 0, 0);
+SHIFT_INIT(Shift4, shift4d, 4, 0, 1, 0, 0);
+SHIFT_INIT(Shift5, shift4d, 5, 0, -6, 0, 0);
+SHIFT_INIT(Shift6, shift4d, 6, 0, 3, 1, 0);
+SHIFT_INIT(Shift7, shift4d, 7, 0, 0, 2, 0);
+SHIFT_INIT(Shift8, shift4d, 8, 0, 0, -2, 0);
+SHIFT_INIT(Shift9, shift4d, 9, 0, 0, 0, 1);
+SHIFT_INIT(Shift10, shift4d, 10, 0, 0, 0, -1);
+SHIFT_INIT(Shift11, shift4d, 11, 1, 1, 1, 1);
+SHIFT_INIT(Shift12, shift4d, 12, -1, -1, -1, -1);
+SHIFT_INIT(Shift13, shift4d, 13, 21, 21, 21, 21);
+SHIFT_INIT(Shift14, shift4d, 14, -21, -21, -21, -21);
 
 ////////////////////////////////// CPP ///////////////////////////////////
 //
-TEST(Shift, CPP)
-{
+TEST(Shift, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx = 0;
-    const unsigned x = 2;
-    const unsigned y = 0;
-    const unsigned z = 0;
-    const unsigned w = 0;
+    const unsigned x         = 2;
+    const unsigned y         = 0;
+    const unsigned z         = 0;
+    const unsigned w         = 0;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/shift/shift4d.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/shift/shift4d.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -128,20 +132,19 @@ TEST(Shift, CPP)
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], idims, output);
 }
 
-TEST(Shift, MaxDim)
-{
+TEST(Shift, MaxDim) {
     if (noDoubleTests<float>()) return;
 
-    const size_t largeDim = 65535 * 32 + 1 ;
+    const size_t largeDim  = 65535 * 32 + 1;
     const unsigned shift_x = 1;
 
-    array input = range(dim4(2, largeDim));
+    array input  = range(dim4(2, largeDim));
     array output = shift(input, shift_x);
 
     output = abs(input - output);
     ASSERT_EQ(1.f, product<float>(output));
 
-    input = range(dim4(2, 1, 1, largeDim));
+    input  = range(dim4(2, 1, 1, largeDim));
     output = shift(input, shift_x);
 
     output = abs(input - output);
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index c5a403d90a..d880b0c9fa 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -7,54 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
+using af::array;
+using af::dim4;
+using af::features;
+using af::loadImage;
 using std::abs;
 using std::cout;
 using std::endl;
 using std::string;
 using std::vector;
-using af::array;
-using af::dim4;
-using af::features;
-using af::loadImage;
 
-typedef struct
-{
+typedef struct {
     float f[5];
     unsigned d[128];
 } feat_desc_t;
 
-typedef struct
-{
+typedef struct {
     float f[5];
 } feat_t;
 
-typedef struct
-{
+typedef struct {
     float d[128];
 } desc_t;
 #ifdef AF_WITH_NONFREE_SIFT
-static bool feat_cmp(feat_desc_t i, feat_desc_t j)
-{
+static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
     for (int k = 0; k < 5; k++)
-        if (round(i.f[k]*1e1f) != round(j.f[k]*1e1f))
-            return (round(i.f[k]*1e1f) < round(j.f[k]*1e1f));
+        if (round(i.f[k] * 1e1f) != round(j.f[k] * 1e1f))
+            return (round(i.f[k] * 1e1f) < round(j.f[k] * 1e1f));
 
     return true;
 }
 
-static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, float* desc, unsigned nfeat)
-{
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
+                               float* score, float* ori, float* size,
+                               float* desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -62,13 +59,13 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
         feat[i].f[2] = score[i];
         feat[i].f[3] = ori[i];
         feat[i].f[4] = size[i];
-        for (unsigned j = 0; j < 128; j++)
-            feat[i].d[j] = desc[i * 128 + j];
+        for (unsigned j = 0; j < 128; j++) feat[i].d[j] = desc[i * 128 + j];
     }
 }
 
-static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, float* score, float* ori, float* size, vector<vector<float> >& desc, unsigned nfeat)
-{
+static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
+                               float* score, float* ori, float* size,
+                               vector<vector<float> >& desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -76,13 +73,12 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y, fl
         feat[i].f[2] = score[i];
         feat[i].f[3] = ori[i];
         feat[i].f[4] = size[i];
-        for (unsigned j = 0; j < 128; j++)
-            feat[i].d[j] = desc[i][j];
+        for (unsigned j = 0; j < 128; j++) feat[i].d[j] = desc[i][j];
     }
 }
 
-static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<desc_t>& d)
-{
+static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f,
+                            vector<desc_t>& d) {
     f.resize(fd.size());
     d.resize(fd.size());
     for (size_t i = 0; i < fd.size(); i++) {
@@ -91,37 +87,38 @@ static void split_feat_desc(vector<feat_desc_t>& fd, vector<feat_t>& f, vector<d
         f[i].f[2] = fd[i].f[2];
         f[i].f[3] = fd[i].f[3];
         f[i].f[4] = fd[i].f[4];
-        for (unsigned j = 0; j < 128; j++)
-            d[i].d[j] = fd[i].d[j];
+        for (unsigned j = 0; j < 128; j++) d[i].d[j] = fd[i].d[j];
     }
 }
 
-static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu, float unit_thr = 1.f, float euc_thr = 1.f)
-{
-    bool ret = true;
+static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float* cpu,
+                             float* gpu, float unit_thr = 1.f,
+                             float euc_thr = 1.f) {
+    bool ret  = true;
     float sum = 0.0f;
 
     for (dim_t i = 0; i < ndesc; i++) {
         sum = 0.0f;
         for (dim_t l = 0; l < desc_len; l++) {
             dim_t idx = i * desc_len + l;
-            float x = (cpu[idx] - gpu[idx]);
-            sum += x*x;
+            float x   = (cpu[idx] - gpu[idx]);
+            sum += x * x;
             if (abs(x) > (float)unit_thr) {
                 ret = false;
-                cout<<endl<<"@compareEuclidean: unit mismatch."<<endl;
-                cout<<"(cpu,gpu,cpu-gpu)["<<i<<","<<l<<"] : {"<<cpu[idx]<<","<<gpu[idx]<<","<<cpu[idx]-gpu[idx]<<"}"<<endl;
-                cout<<endl;
+                cout << endl << "@compareEuclidean: unit mismatch." << endl;
+                cout << "(cpu,gpu,cpu-gpu)[" << i << "," << l << "] : {"
+                     << cpu[idx] << "," << gpu[idx] << ","
+                     << cpu[idx] - gpu[idx] << "}" << endl;
+                cout << endl;
                 break;
             }
         }
         if (sqrt(sum) > euc_thr) {
             ret = false;
-            cout<<endl<<"@compareEuclidean: distance mismatch."<<endl;
-            cout<<"Euclidean distance: "<<sqrt(sum)<<endl;
+            cout << endl << "@compareEuclidean: distance mismatch." << endl;
+            cout << "Euclidean distance: " << sqrt(sum) << endl;
         }
-        if (ret == false)
-            return ret;
+        if (ret == false) return ret;
     }
 
     return ret;
@@ -129,10 +126,9 @@ static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float *cpu, float *gpu
 #endif
 
 template<typename T>
-class SIFT : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class SIFT : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
@@ -140,33 +136,37 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(SIFT, TestTypes);
 
 template<typename T>
-void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeThr, float initSigma, bool doubleInput)
-{
+void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
+              float edgeThr, float initSigma, bool doubleInput) {
 #ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>           inDims;
-    vector<string>         inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > goldFeat;
     vector<vector<float> > goldDesc;
 
-    readImageFeaturesDescriptors<float>(pTestFile, inDims, inFiles, goldFeat, goldDesc);
+    readImageFeaturesDescriptors<float>(pTestFile, inDims, inFiles, goldFeat,
+                                        goldDesc);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
-        af_array inArray_f32  = 0;
-        af_array inArray      = 0;
-        af_array desc         = 0;
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array inArray_f32 = 0;
+        af_array inArray     = 0;
+        af_array desc        = 0;
         af_features feat;
 
-        inFiles[testId].insert(0,string(TEST_DIR"/sift/"));
+        inFiles[testId].insert(0, string(TEST_DIR "/sift/"));
 
-        ASSERT_SUCCESS(af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(
+            af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_sift(&feat, &desc, inArray, nLayers, contrastThr, edgeThr, initSigma, doubleInput, 1.f/256.f, 0.05f));
+        ASSERT_SUCCESS(af_sift(&feat, &desc, inArray, nLayers, contrastThr,
+                               edgeThr, initSigma, doubleInput, 1.f / 256.f,
+                               0.05f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
@@ -178,16 +178,17 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
         ASSERT_SUCCESS(af_get_features_orientation(&orientation, feat));
         ASSERT_SUCCESS(af_get_features_size(&size, feat));
 
-        float * outX           = new float[n];
-        float * outY           = new float[n];
-        float * outScore       = new float[n];
-        float * outOrientation = new float[n];
-        float * outSize        = new float[n];
+        float* outX           = new float[n];
+        float* outY           = new float[n];
+        float* outScore       = new float[n];
+        float* outOrientation = new float[n];
+        float* outSize        = new float[n];
         dim_t descSize;
         dim_t descDims[4];
         ASSERT_SUCCESS(af_get_elements(&descSize, desc));
-        ASSERT_SUCCESS(af_get_dims(&descDims[0], &descDims[1], &descDims[2], &descDims[3], desc));
-        float * outDesc     = new float[descSize];
+        ASSERT_SUCCESS(af_get_dims(&descDims[0], &descDims[1], &descDims[2],
+                                   &descDims[3], desc));
+        float* outDesc = new float[descSize];
         ASSERT_SUCCESS(af_get_data_ptr((void*)outX, x));
         ASSERT_SUCCESS(af_get_data_ptr((void*)outY, y));
         ASSERT_SUCCESS(af_get_data_ptr((void*)outScore, score));
@@ -196,13 +197,18 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
         ASSERT_SUCCESS(af_get_data_ptr((void*)outDesc, desc));
 
         vector<feat_desc_t> out_feat_desc;
-        array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, n);
+        array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation,
+                           outSize, outDesc, n);
 
         vector<feat_desc_t> gold_feat_desc;
-        array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
+        array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(),
+                           &goldFeat[1].front(), &goldFeat[2].front(),
+                           &goldFeat[3].front(), &goldFeat[4].front(), goldDesc,
+                           goldFeat[0].size());
 
         std::stable_sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
-        std::stable_sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
+        std::stable_sort(gold_feat_desc.begin(), gold_feat_desc.end(),
+                         feat_cmp);
 
         vector<feat_t> out_feat;
         vector<desc_t> v_out_desc;
@@ -213,14 +219,26 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
         split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
         for (int elIter = 0; elIter < (int)n; elIter++) {
-            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]),
+                      1e-3)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]),
+                      0.5f)
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]),
+                      1e-3)
+                << "at: " << elIter << endl;
         }
 
-        EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 4.5f));
+        EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1],
+                                     (float*)&v_out_desc[0],
+                                     (float*)&v_gold_desc[0], 2.f, 4.5f));
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
@@ -242,49 +260,51 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr, float edgeT
 #endif
 }
 
-#define SIFT_INIT(desc, image, nLayers, contrastThr, edgeThr, initSigma, doubleInput) \
-    TYPED_TEST(SIFT, desc)                                              \
-    {                                                                   \
-        for (int i = 0; i < 1; i++)                                     \
-            siftTest<TypeParam>(string(TEST_DIR"/sift/"#image".test"), nLayers, contrastThr, edgeThr, initSigma, doubleInput); \
+#define SIFT_INIT(desc, image, nLayers, contrastThr, edgeThr, initSigma,  \
+                  doubleInput)                                            \
+    TYPED_TEST(SIFT, desc) {                                              \
+        for (int i = 0; i < 1; i++)                                       \
+            siftTest<TypeParam>(string(TEST_DIR "/sift/" #image ".test"), \
+                                nLayers, contrastThr, edgeThr, initSigma, \
+                                doubleInput);                             \
     }
 
-    SIFT_INIT(Man_Default, man, 3, 0.04f, 10.0f, 1.6f, true);
-    SIFT_INIT(Man_2Layers, man_2layers, 2, 0.04f, 10.0f, 1.6f, true);
-    SIFT_INIT(Man_ContrastThr005, man_contrast005, 3, 0.05f, 10.0f, 1.6f, true);
-    SIFT_INIT(Man_EdgeThr5, man_edge5, 3, 0.04f, 5.0f, 1.6f, true);
-    SIFT_INIT(Man_InitSigma18, man_initsigma18, 3, 0.04f, 10.0f, 1.8f, true);
-    SIFT_INIT(Man_NoDoubleInput, man_nodoubleinput, 3, 0.04f, 10.0f, 1.6f, false);
+SIFT_INIT(Man_Default, man, 3, 0.04f, 10.0f, 1.6f, true);
+SIFT_INIT(Man_2Layers, man_2layers, 2, 0.04f, 10.0f, 1.6f, true);
+SIFT_INIT(Man_ContrastThr005, man_contrast005, 3, 0.05f, 10.0f, 1.6f, true);
+SIFT_INIT(Man_EdgeThr5, man_edge5, 3, 0.04f, 5.0f, 1.6f, true);
+SIFT_INIT(Man_InitSigma18, man_initsigma18, 3, 0.04f, 10.0f, 1.8f, true);
+SIFT_INIT(Man_NoDoubleInput, man_nodoubleinput, 3, 0.04f, 10.0f, 1.6f, false);
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(SIFT, CPP)
-{
+TEST(SIFT, CPP) {
 #ifdef AF_WITH_NONFREE_SIFT
     if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>           inDims;
-    vector<string>         inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > goldFeat;
     vector<vector<float> > goldDesc;
 
-    readImageFeaturesDescriptors<float>(string(TEST_DIR"/sift/man.test"), inDims, inFiles, goldFeat, goldDesc);
-    inFiles[0].insert(0,string(TEST_DIR"/sift/"));
+    readImageFeaturesDescriptors<float>(string(TEST_DIR "/sift/man.test"),
+                                        inDims, inFiles, goldFeat, goldDesc);
+    inFiles[0].insert(0, string(TEST_DIR "/sift/"));
 
     array in = loadImage(inFiles[0].c_str(), false);
 
     features feat;
     array desc;
-    sift(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f/256.f, 0.05f);
-
-    float * outX           = new float[feat.getNumFeatures()];
-    float * outY           = new float[feat.getNumFeatures()];
-    float * outScore       = new float[feat.getNumFeatures()];
-    float * outOrientation = new float[feat.getNumFeatures()];
-    float * outSize        = new float[feat.getNumFeatures()];
-    float * outDesc        = new float[desc.elements()];
-    dim4 descDims = desc.dims();
+    sift(feat, desc, in, 3, 0.04f, 10.0f, 1.6f, true, 1.f / 256.f, 0.05f);
+
+    float* outX           = new float[feat.getNumFeatures()];
+    float* outY           = new float[feat.getNumFeatures()];
+    float* outScore       = new float[feat.getNumFeatures()];
+    float* outOrientation = new float[feat.getNumFeatures()];
+    float* outSize        = new float[feat.getNumFeatures()];
+    float* outDesc        = new float[desc.elements()];
+    dim4 descDims         = desc.dims();
     feat.getX().host(outX);
     feat.getY().host(outY);
     feat.getScore().host(outScore);
@@ -293,10 +313,14 @@ TEST(SIFT, CPP)
     desc.host(outDesc);
 
     vector<feat_desc_t> out_feat_desc;
-    array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation, outSize, outDesc, feat.getNumFeatures());
+    array_to_feat_desc(out_feat_desc, outX, outY, outScore, outOrientation,
+                       outSize, outDesc, feat.getNumFeatures());
 
     vector<feat_desc_t> gold_feat_desc;
-    array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(), &goldFeat[1].front(), &goldFeat[2].front(), &goldFeat[3].front(), &goldFeat[4].front(), goldDesc, goldFeat[0].size());
+    array_to_feat_desc(gold_feat_desc, &goldFeat[0].front(),
+                       &goldFeat[1].front(), &goldFeat[2].front(),
+                       &goldFeat[3].front(), &goldFeat[4].front(), goldDesc,
+                       goldFeat[0].size());
 
     std::stable_sort(out_feat_desc.begin(), out_feat_desc.end(), feat_cmp);
     std::stable_sort(gold_feat_desc.begin(), gold_feat_desc.end(), feat_cmp);
@@ -310,14 +334,21 @@ TEST(SIFT, CPP)
     split_feat_desc(gold_feat_desc, gold_feat, v_gold_desc);
 
     for (int elIter = 0; elIter < (int)feat.getNumFeatures(); elIter++) {
-        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f) << "at: " << elIter << endl;
-        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[0] - gold_feat[elIter].f[0]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[1] - gold_feat[elIter].f[1]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e-3)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[3] - gold_feat[elIter].f[3]), 0.5f)
+            << "at: " << elIter << endl;
+        ASSERT_LE(fabs(out_feat[elIter].f[4] - gold_feat[elIter].f[4]), 1e-3)
+            << "at: " << elIter << endl;
     }
 
-    EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1], (float*)&v_out_desc[0], (float*)&v_gold_desc[0], 2.f, 4.5f));
+    EXPECT_TRUE(compareEuclidean(descDims[0], descDims[1],
+                                 (float*)&v_out_desc[0],
+                                 (float*)&v_gold_desc[0], 2.f, 4.5f));
 
     delete[] outX;
     delete[] outY;
diff --git a/test/sobel.cpp b/test/sobel.cpp
index b144b464f6..22f6f8f14b 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -7,60 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using af::dim4;
+using af::dtype_traits;
 using std::endl;
 using std::string;
 using std::vector;
-using af::dim4;
-using af::dtype_traits;
 
 template<typename T>
-class Sobel : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Sobel : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class Sobel_Integer : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Sobel_Integer : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double> TestTypes;
-typedef ::testing::Types<int, unsigned, char, unsigned char, short, ushort> TestTypesInt;
+typedef ::testing::Types<int, unsigned, char, unsigned char, short, ushort>
+    TestTypesInt;
 
 // register the type list
 TYPED_TEST_CASE(Sobel, TestTypes);
 TYPED_TEST_CASE(Sobel_Integer, TestTypesInt);
 
 template<typename Ti, typename To>
-void testSobelDerivatives(string pTestFile)
-{
+void testSobelDerivatives(string pTestFile) {
     if (noDoubleTests<Ti>()) return;
 
-    vector<dim4>  numDims;
-    vector<vector<Ti> >      in;
-    vector<vector<To> >   tests;
+    vector<dim4> numDims;
+    vector<vector<Ti> > in;
+    vector<vector<To> > tests;
 
-    readTests<Ti,To,int>(pTestFile, numDims, in, tests);
+    readTests<Ti, To, int>(pTestFile, numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims        = numDims[0];
     af_array dxArray = 0;
     af_array dyArray = 0;
     af_array inArray = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<Ti>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<Ti>::af_type));
 
     ASSERT_SUCCESS(af_sobel_operator(&dxArray, &dyArray, inArray, 3));
 
@@ -76,12 +75,12 @@ void testSobelDerivatives(string pTestFile)
     ASSERT_SUCCESS(af_release_array(dyArray));
 }
 
-TYPED_TEST(Sobel, Rectangle)
-{
-    testSobelDerivatives<TypeParam, TypeParam>(string(TEST_DIR"/sobel/rectangle.test"));
+TYPED_TEST(Sobel, Rectangle) {
+    testSobelDerivatives<TypeParam, TypeParam>(
+        string(TEST_DIR "/sobel/rectangle.test"));
 }
 
-TYPED_TEST(Sobel_Integer, Rectangle)
-{
-    testSobelDerivatives<TypeParam, int>(string(TEST_DIR"/sobel/rectangle.test"));
+TYPED_TEST(Sobel_Integer, Rectangle) {
+    testSobelDerivatives<TypeParam, int>(
+        string(TEST_DIR "/sobel/rectangle.test"));
 }
diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index 6ca7e3a621..e1860c41c0 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -9,30 +9,29 @@
 
 #pragma once
 #include <arrayfire.h>
-#include <af/dim4.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
+#include <vector>
 
-using std::vector;
-using std::string;
+using af::cdouble;
+using af::cfloat;
+using std::abs;
 using std::cout;
 using std::endl;
-using std::abs;
-using af::cfloat;
-using af::cdouble;
+using std::string;
+using std::vector;
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 
 template<typename T>
-void solveTester(const int m, const int n, const int k, double eps, int targetDevice=-1)
-{
-    if (targetDevice>=0)
-        af::setDevice(targetDevice);
+void solveTester(const int m, const int n, const int k, double eps,
+                 int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
 
     af::deviceGC();
 
@@ -56,15 +55,22 @@ void solveTester(const int m, const int n, const int k, double eps, int targetDe
     af::array B1 = af::matmul(A, X1);
     //! [ex_solve_recon]
 
-    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) / (m * k), eps);
-    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) / (m * k), eps);
+    ASSERT_NEAR(0,
+                af::sum<typename af::dtype_traits<T>::base_type>(
+                    af::abs(real(B0 - B1))) /
+                    (m * k),
+                eps);
+    ASSERT_NEAR(0,
+                af::sum<typename af::dtype_traits<T>::base_type>(
+                    af::abs(imag(B0 - B1))) /
+                    (m * k),
+                eps);
 }
 
 template<typename T>
-void solveLUTester(const int n, const int k, double eps, int targetDevice=-1)
-{
-    if (targetDevice>=0)
-        af::setDevice(targetDevice);
+void solveLUTester(const int n, const int k, double eps,
+                   int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
 
     af::deviceGC();
 
@@ -88,15 +94,22 @@ void solveLUTester(const int n, const int k, double eps, int targetDevice=-1)
 
     af::array B1 = af::matmul(A, X1);
 
-    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    ASSERT_NEAR(0,
+                af::sum<typename af::dtype_traits<T>::base_type>(
+                    af::abs(real(B0 - B1))) /
+                    (n * k),
+                eps);
+    ASSERT_NEAR(0,
+                af::sum<typename af::dtype_traits<T>::base_type>(
+                    af::abs(imag(B0 - B1))) /
+                    (n * k),
+                eps);
 }
 
 template<typename T>
-void solveTriangleTester(const int n, const int k, bool is_upper, double eps, int targetDevice=-1)
-{
-    if (targetDevice>=0)
-        af::setDevice(targetDevice);
+void solveTriangleTester(const int n, const int k, bool is_upper, double eps,
+                         int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
 
     af::deviceGC();
 
@@ -134,6 +147,14 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps, in
 
     af::array B1 = af::matmul(AT, X1);
 
-    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) / (n * k), eps);
-    ASSERT_NEAR(0, af::sum<typename af::dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) / (n * k), eps);
+    ASSERT_NEAR(0,
+                af::sum<typename af::dtype_traits<T>::base_type>(
+                    af::abs(real(B0 - B1))) /
+                    (n * k),
+                eps);
+    ASSERT_NEAR(0,
+                af::sum<typename af::dtype_traits<T>::base_type>(
+                    af::abs(imag(B0 - B1))) /
+                    (n * k),
+                eps);
 }
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 0581544088..e0919e5123 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -13,14 +13,11 @@
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
-#include "solve_common.hpp"
 #include <thread>
+#include "solve_common.hpp"
 
 template<typename T>
-class Solve : public ::testing::Test
-{
-
-};
+class Solve : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
 TYPED_TEST_CASE(Solve, TestTypes);
@@ -96,9 +93,7 @@ TYPED_TEST(Solve, LeastSquaresOverDeterminedMultipleOfTwoLarge) {
     solveTester<TypeParam>(1536, 1024, 1, eps<TypeParam>());
 }
 
-TYPED_TEST(Solve, LU) {
-    solveLUTester<TypeParam>(100, 10, eps<TypeParam>());
-}
+TYPED_TEST(Solve, LU) { solveLUTester<TypeParam>(100, 10, eps<TypeParam>()); }
 
 TYPED_TEST(Solve, LUMultipleOfTwo) {
     solveLUTester<TypeParam>(96, 64, eps<TypeParam>());
@@ -145,29 +140,33 @@ TYPED_TEST(Solve, TriangleLowerMultipleOfTwoLarge) {
 }
 
 #if !defined(AF_OPENCL)
-int nextTargetDeviceId()
-{
-  static int nextId = 0;
-  return nextId++;
-}
-
-#define SOLVE_LU_TESTS_THREADING(T, eps)                                                                          \
-    tests.emplace_back(solveLUTester<T>, 1000, 100, eps, nextTargetDeviceId()%numDevices);              \
-    tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps, nextTargetDeviceId()%numDevices);  \
-    tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, nextTargetDeviceId()%numDevices); \
-    tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps, nextTargetDeviceId()%numDevices);          \
-    tests.emplace_back(solveTester<T>, 800, 1000, 200, eps, nextTargetDeviceId()%numDevices);           \
-    tests.emplace_back(solveTester<T>, 800, 600, 64, eps, nextTargetDeviceId()%numDevices);             \
-
-TEST(Solve, Threading)
-{
-    cleanSlate(); // Clean up everything done so far
+int nextTargetDeviceId() {
+    static int nextId = 0;
+    return nextId++;
+}
+
+#define SOLVE_LU_TESTS_THREADING(T, eps)                              \
+    tests.emplace_back(solveLUTester<T>, 1000, 100, eps,              \
+                       nextTargetDeviceId() % numDevices);            \
+    tests.emplace_back(solveTriangleTester<T>, 1000, 100, true, eps,  \
+                       nextTargetDeviceId() % numDevices);            \
+    tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, \
+                       nextTargetDeviceId() % numDevices);            \
+    tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps,          \
+                       nextTargetDeviceId() % numDevices);            \
+    tests.emplace_back(solveTester<T>, 800, 1000, 200, eps,           \
+                       nextTargetDeviceId() % numDevices);            \
+    tests.emplace_back(solveTester<T>, 800, 600, 64, eps,             \
+                       nextTargetDeviceId() % numDevices);
+
+TEST(Solve, Threading) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     int numDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&numDevices));
-    ASSERT_EQ(true, numDevices>0);
+    ASSERT_EQ(true, numDevices > 0);
 
     SOLVE_LU_TESTS_THREADING(float, 0.01);
     SOLVE_LU_TESTS_THREADING(cfloat, 0.01);
@@ -176,9 +175,8 @@ TEST(Solve, Threading)
         SOLVE_LU_TESTS_THREADING(cdouble, 1E-5);
     }
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
 #undef SOLVE_LU_TESTS_THREADING
diff --git a/test/sort.cpp b/test/sort.cpp
index 12f93b7ea0..deffb366b8 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -7,67 +7,73 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Sort : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Sort : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl, uintl> TestTypes;
+typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
+                         uintl>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Sort, TestTypes);
 
 template<typename T>
-void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
+              bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<float> > tests;
-    readTests<T, float, int>(pTestFile,numDims,in,tests);
+    readTests<T, float, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array tempArray = 0;
-    af_array sxArray = 0;
+    af_array sxArray   = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_sort(&sxArray, inArray, 0, dir));
@@ -80,55 +86,55 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, bool
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] sxData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(sxArray   != 0) af_release_array(sxArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (sxArray != 0) af_release_array(sxArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define SORT_INIT(desc, file, dir, resultIdx0)                                      \
-    TYPED_TEST(Sort, desc)                                                          \
-    {                                                                               \
-        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0); \
+#define SORT_INIT(desc, file, dir, resultIdx0)                            \
+    TYPED_TEST(Sort, desc) {                                              \
+        sortTest<TypeParam>(string(TEST_DIR "/sort/" #file ".test"), dir, \
+                            resultIdx0);                                  \
     }
 
-    // Using same inputs as sort_index. So just skipping the index results
-    SORT_INIT(Sort0True,  sort, true, 0);
-    SORT_INIT(Sort0False, sort,false, 2);
-
-    SORT_INIT(Sort2d0False, basic_2d, true, 0);
+// Using same inputs as sort_index. So just skipping the index results
+SORT_INIT(Sort0True, sort, true, 0);
+SORT_INIT(Sort0False, sort, false, 2);
 
-    SORT_INIT(Sort10x10True,  sort_10x10, true,  0);
-    SORT_INIT(Sort10x10False, sort_10x10, false, 2);
-    SORT_INIT(Sort1000True,   sort_1000,  true,  0);
-    SORT_INIT(Sort1000False,  sort_1000,  false, 2);
-    SORT_INIT(SortMedTrue,    sort_med1,  true,  0);
-    SORT_INIT(SortMedFalse,   sort_med1,  false, 2);
+SORT_INIT(Sort2d0False, basic_2d, true, 0);
 
-    SORT_INIT(SortMed5True,   sort_med,   true,  0);
-    SORT_INIT(SortMed5False,  sort_med,   false, 2);
-    SORT_INIT(SortLargeTrue,  sort_large, true,  0);
-    SORT_INIT(SortLargeFalse, sort_large, false, 2);
+SORT_INIT(Sort10x10True, sort_10x10, true, 0);
+SORT_INIT(Sort10x10False, sort_10x10, false, 2);
+SORT_INIT(Sort1000True, sort_1000, true, 0);
+SORT_INIT(Sort1000False, sort_1000, false, 2);
+SORT_INIT(SortMedTrue, sort_med1, true, 0);
+SORT_INIT(SortMedFalse, sort_med1, false, 2);
 
+SORT_INIT(SortMed5True, sort_med, true, 0);
+SORT_INIT(SortMed5False, sort_med, false, 2);
+SORT_INIT(SortLargeTrue, sort_large, true, 0);
+SORT_INIT(SortLargeFalse, sort_large, false, 2);
 
 ////////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(Sort, CPPDim0)
-{
+TEST(Sort, CPPDim0) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = true;
+    const bool dir            = true;
     const unsigned resultIdx0 = 0;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -143,24 +149,25 @@ TEST(Sort, CPPDim0)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] sxData;
 }
 
-TEST(Sort, CPPDim1)
-{
+TEST(Sort, CPPDim1) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = true;
+    const bool dir            = true;
     const unsigned resultIdx0 = 0;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -169,7 +176,8 @@ TEST(Sort, CPPDim1)
 
     array output = sort(input_, 1, dir);
 
-    output = reorder(output, 1, 0, 2, 3); // Required for checking with test data
+    output =
+        reorder(output, 1, 0, 2, 3);  // Required for checking with test data
 
     size_t nElems = tests[resultIdx0].size();
 
@@ -179,24 +187,25 @@ TEST(Sort, CPPDim1)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] sxData;
 }
 
-TEST(Sort, CPPDim2)
-{
+TEST(Sort, CPPDim2) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = false;
+    const bool dir            = false;
     const unsigned resultIdx0 = 2;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_med.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -205,7 +214,8 @@ TEST(Sort, CPPDim2)
 
     array output = sort(input_, 2, dir);
 
-    output = reorder(output, 2, 0, 1, 3); // Required for checking with test data
+    output =
+        reorder(output, 2, 0, 1, 3);  // Required for checking with test data
 
     size_t nElems = tests[resultIdx0].size();
 
@@ -215,7 +225,8 @@ TEST(Sort, CPPDim2)
 
     // Compare result
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx0][elIter], sxData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Delete
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index a8102095a3..9db3b064a6 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -7,54 +7,56 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class SortByKey : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class SortByKey : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl, uintl> TestTypes;
+typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
+                         uintl>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(SortByKey, TestTypes);
 
 template<typename T>
-void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const unsigned resultIdx1, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
+              const unsigned resultIdx1, bool isSubRef = false,
+              const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, int>(pTestFile,numDims,in,tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
@@ -65,15 +67,22 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     af_array ovalArray = 0;
 
     if (isSubRef) {
-        //ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        // ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+        // idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
 
-        //ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        // ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(),
+        // &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&ikeyArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
-        ASSERT_SUCCESS(af_create_array(&ivalArray, &(in[1].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&ikeyArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&ivalArray, &(in[1].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
-    ASSERT_SUCCESS(af_sort_by_key(&okeyArray, &ovalArray, ikeyArray, ivalArray, 0, dir));
+    ASSERT_SUCCESS(
+        af_sort_by_key(&okeyArray, &ovalArray, ikeyArray, ivalArray, 0, dir));
 
     // Compare result
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, okeyArray);
@@ -85,48 +94,45 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, ovalArray);
 #endif
 
-    if(ikeyArray != 0) af_release_array(ikeyArray);
-    if(ivalArray != 0) af_release_array(ivalArray);
-    if(okeyArray != 0) af_release_array(okeyArray);
-    if(ovalArray != 0) af_release_array(ovalArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (ikeyArray != 0) af_release_array(ikeyArray);
+    if (ivalArray != 0) af_release_array(ivalArray);
+    if (okeyArray != 0) af_release_array(okeyArray);
+    if (ovalArray != 0) af_release_array(ovalArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)              \
-    TYPED_TEST(SortByKey, desc)                                         \
-    {                                                                   \
-        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0, resultIdx1); \
+#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)                \
+    TYPED_TEST(SortByKey, desc) {                                         \
+        sortTest<TypeParam>(string(TEST_DIR "/sort/" #file ".test"), dir, \
+                            resultIdx0, resultIdx1);                      \
     }
 
-    SORT_INIT(Sort0True,      sort_by_key_tiny,  true,  0, 1);
-    SORT_INIT(Sort0False,     sort_by_key_tiny,  false, 2, 3);
-    SORT_INIT(Sort10x10True,  sort_by_key_2D,    true,  0, 1);
-    SORT_INIT(Sort10x10False, sort_by_key_2D,    false, 2, 3);
-    SORT_INIT(Sort1000True,   sort_by_key_1000,  true,  0, 1);
-    SORT_INIT(SortMedTrue,    sort_by_key_med,   true,  0, 1);
-    SORT_INIT(Sort1000False,  sort_by_key_1000,  false, 2, 3);
-    SORT_INIT(SortMedFalse,   sort_by_key_med,   false, 2, 3);
-
-    SORT_INIT(SortLargeTrue,  sort_by_key_large, true,  0, 1);
-    SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
-
-
+SORT_INIT(Sort0True, sort_by_key_tiny, true, 0, 1);
+SORT_INIT(Sort0False, sort_by_key_tiny, false, 2, 3);
+SORT_INIT(Sort10x10True, sort_by_key_2D, true, 0, 1);
+SORT_INIT(Sort10x10False, sort_by_key_2D, false, 2, 3);
+SORT_INIT(Sort1000True, sort_by_key_1000, true, 0, 1);
+SORT_INIT(SortMedTrue, sort_by_key_med, true, 0, 1);
+SORT_INIT(Sort1000False, sort_by_key_1000, false, 2, 3);
+SORT_INIT(SortMedFalse, sort_by_key_med, false, 2, 3);
 
+SORT_INIT(SortLargeTrue, sort_by_key_large, true, 0, 1);
+SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
 
 ////////////////////////////////////// CPP ///////////////////////////////
 //
-TEST(SortByKey, CPPDim0)
-{
+TEST(SortByKey, CPPDim0) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = true;
+    const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_tiny.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_by_key_tiny.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array keys(idims, &(in[0].front()));
@@ -138,18 +144,18 @@ TEST(SortByKey, CPPDim0)
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, out_vals);
 }
 
-TEST(SortByKey, CPPDim1)
-{
+TEST(SortByKey, CPPDim1) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = true;
+    const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/sort/sort_by_key_large.test"), numDims, in, tests);
 
     dim4 idims = numDims[0];
     array keys(idims, &(in[0].front()));
@@ -168,18 +174,18 @@ TEST(SortByKey, CPPDim1)
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx1], idims, out_vals);
 }
 
-TEST(SortByKey, CPPDim2)
-{
+TEST(SortByKey, CPPDim2) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = false;
+    const bool dir            = false;
     const unsigned resultIdx0 = 2;
     const unsigned resultIdx1 = 3;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_by_key_large.test"),numDims,in,tests);
+    readTests<float, float, int>(
+        string(TEST_DIR "/sort/sort_by_key_large.test"), numDims, in, tests);
 
     dim4 idims = numDims[0];
     array keys(idims, &(in[0].front()));
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 8811bcf53b..4c8459752e 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -7,68 +7,75 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::cout;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class SortIndex : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class SortIndex : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl, uintl> TestTypes;
+typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
+                         uintl>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(SortIndex, TestTypes);
 
 template<typename T>
-void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const unsigned resultIdx1, bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
+              const unsigned resultIdx1, bool isSubRef = false,
+              const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<float> > tests;
-    readTests<T, float, int>(pTestFile,numDims,in,tests);
+    readTests<T, float, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
+    af_array inArray   = 0;
     af_array tempArray = 0;
-    af_array sxArray = 0;
-    af_array ixArray = 0;
+    af_array sxArray   = 0;
+    af_array ixArray   = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_sort_index(&sxArray, &ixArray, inArray, 0, dir));
@@ -83,50 +90,49 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0, const
     ASSERT_VEC_ARRAY_EQ(ixTest, idims, ixArray);
 #endif
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(sxArray   != 0) af_release_array(sxArray);
-    if(ixArray   != 0) af_release_array(ixArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (sxArray != 0) af_release_array(sxArray);
+    if (ixArray != 0) af_release_array(ixArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)              \
-    TYPED_TEST(SortIndex, desc)                                         \
-    {                                                                   \
-        sortTest<TypeParam>(string(TEST_DIR"/sort/"#file".test"), dir, resultIdx0, resultIdx1); \
+#define SORT_INIT(desc, file, dir, resultIdx0, resultIdx1)                \
+    TYPED_TEST(SortIndex, desc) {                                         \
+        sortTest<TypeParam>(string(TEST_DIR "/sort/" #file ".test"), dir, \
+                            resultIdx0, resultIdx1);                      \
     }
 
-    SORT_INIT(Sort0True,  sort, true, 0, 1);
-    SORT_INIT(Sort0False, sort,false, 2, 3);
-
-    SORT_INIT(Sort2d0False, basic_2d, true, 0, 1);
+SORT_INIT(Sort0True, sort, true, 0, 1);
+SORT_INIT(Sort0False, sort, false, 2, 3);
 
-    SORT_INIT(Sort10x10True,  sort_10x10, true,  0, 1);
-    SORT_INIT(Sort10x10False, sort_10x10, false, 2, 3);
-    SORT_INIT(Sort1000True,   sort_1000,  true,  0, 1);
-    SORT_INIT(SortMedTrue,    sort_med1,  true,  0, 1);
-    SORT_INIT(Sort1000False,  sort_1000,  false, 2, 3);
-    SORT_INIT(SortMedFalse,   sort_med1,  false, 2, 3);
+SORT_INIT(Sort2d0False, basic_2d, true, 0, 1);
 
-    SORT_INIT(SortMed5True,   sort_med,   true,  0, 1);
-    SORT_INIT(SortMed5False,  sort_med,   false, 2, 3);
-    SORT_INIT(SortLargeTrue,  sort_large, true,  0, 1);
-    SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
+SORT_INIT(Sort10x10True, sort_10x10, true, 0, 1);
+SORT_INIT(Sort10x10False, sort_10x10, false, 2, 3);
+SORT_INIT(Sort1000True, sort_1000, true, 0, 1);
+SORT_INIT(SortMedTrue, sort_med1, true, 0, 1);
+SORT_INIT(Sort1000False, sort_1000, false, 2, 3);
+SORT_INIT(SortMedFalse, sort_med1, false, 2, 3);
 
+SORT_INIT(SortMed5True, sort_med, true, 0, 1);
+SORT_INIT(SortMed5False, sort_med, false, 2, 3);
+SORT_INIT(SortLargeTrue, sort_large, true, 0, 1);
+SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
 
 //////////////////////////////////// CPP /////////////////////////////////
 //
-TEST(SortIndex, CPPDim0)
-{
+TEST(SortIndex, CPPDim0) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = true;
+    const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -139,18 +145,18 @@ TEST(SortIndex, CPPDim0)
     ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
 
-TEST(SortIndex, CPPDim1)
-{
+TEST(SortIndex, CPPDim1) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = true;
+    const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_10x10.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input_(idims, &(in[0].front()));
@@ -159,7 +165,7 @@ TEST(SortIndex, CPPDim1)
     array outValues, outIndices;
     sort(outValues, outIndices, input, 1, dir);
 
-    outValues  = reorder(outValues,  1, 0, 2, 3);
+    outValues  = reorder(outValues, 1, 0, 2, 3);
     outIndices = reorder(outIndices, 1, 0, 2, 3);
 
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
@@ -168,18 +174,18 @@ TEST(SortIndex, CPPDim1)
     ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
 
-TEST(SortIndex, CPPDim2)
-{
+TEST(SortIndex, CPPDim2) {
     if (noDoubleTests<float>()) return;
 
-    const bool dir = false;
+    const bool dir            = false;
     const unsigned resultIdx0 = 2;
     const unsigned resultIdx1 = 3;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/sort/sort_med.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/sort/sort_med.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input_(idims, &(in[0].front()));
@@ -188,7 +194,7 @@ TEST(SortIndex, CPPDim2)
     array outValues, outIndices;
     sort(outValues, outIndices, input, 2, dir);
 
-    outValues  = reorder(outValues,  2, 0, 1, 3);
+    outValues  = reorder(outValues, 2, 0, 1, 3);
     outIndices = reorder(outIndices, 2, 0, 1, 3);
 
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 6086a3a5e4..3ae9135944 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <testHelpers.hpp>
 #include <sparse_common.hpp>
+#include <testHelpers.hpp>
 
 using af::allTrue;
 using af::array;
@@ -19,43 +19,26 @@ using af::identity;
 using af::randu;
 using af::span;
 
-#define SPARSE_TESTS(T, eps)                                \
-    TEST(Sparse, T##Square)                                 \
-    {                                                       \
-        sparseTester<T>(1000, 1000, 100, 5, eps);           \
-    }                                                       \
-    TEST(Sparse, T##RectMultiple)                           \
-    {                                                       \
-        sparseTester<T>(2048, 1024, 512, 3, eps);           \
-    }                                                       \
-    TEST(Sparse, T##RectDense)                              \
-    {                                                       \
-        sparseTester<T>(500, 1000, 250, 1, eps);            \
-    }                                                       \
-    TEST(Sparse, T##MatVec)                                 \
-    {                                                       \
-        sparseTester<T>(625, 1331, 1, 2, eps);              \
-    }                                                       \
-    TEST(Sparse, Transpose_##T##MatVec)                     \
-    {                                                       \
-        sparseTransposeTester<T>(625, 1331, 1, 2, eps);     \
-    }                                                       \
-    TEST(Sparse, Transpose_##T##Square)                     \
-    {                                                       \
-        sparseTransposeTester<T>(1000, 1000, 100, 5, eps);  \
-    }                                                       \
-    TEST(Sparse, Transpose_##T##RectMultiple)               \
-    {                                                       \
-        sparseTransposeTester<T>(2048, 1024, 512, 3, eps);  \
-    }                                                       \
-    TEST(Sparse, Transpose_##T##RectDense)                  \
-    {                                                       \
-        sparseTransposeTester<T>(453, 751, 397, 1, eps);    \
-    }                                                       \
-    TEST(Sparse, T##ConvertCSR)                             \
-    {                                                       \
-        convertCSR<T>(2345, 5678, 0.5);                     \
-    }                                                       \
+#define SPARSE_TESTS(T, eps)                                                \
+    TEST(Sparse, T##Square) { sparseTester<T>(1000, 1000, 100, 5, eps); }   \
+    TEST(Sparse, T##RectMultiple) {                                         \
+        sparseTester<T>(2048, 1024, 512, 3, eps);                           \
+    }                                                                       \
+    TEST(Sparse, T##RectDense) { sparseTester<T>(500, 1000, 250, 1, eps); } \
+    TEST(Sparse, T##MatVec) { sparseTester<T>(625, 1331, 1, 2, eps); }      \
+    TEST(Sparse, Transpose_##T##MatVec) {                                   \
+        sparseTransposeTester<T>(625, 1331, 1, 2, eps);                     \
+    }                                                                       \
+    TEST(Sparse, Transpose_##T##Square) {                                   \
+        sparseTransposeTester<T>(1000, 1000, 100, 5, eps);                  \
+    }                                                                       \
+    TEST(Sparse, Transpose_##T##RectMultiple) {                             \
+        sparseTransposeTester<T>(2048, 1024, 512, 3, eps);                  \
+    }                                                                       \
+    TEST(Sparse, Transpose_##T##RectDense) {                                \
+        sparseTransposeTester<T>(453, 751, 397, 1, eps);                    \
+    }                                                                       \
+    TEST(Sparse, T##ConvertCSR) { convertCSR<T>(2345, 5678, 0.5); }
 
 SPARSE_TESTS(float, 1E-3)
 SPARSE_TESTS(double, 1E-5)
@@ -64,127 +47,137 @@ SPARSE_TESTS(cdouble, 1E-5)
 
 #undef SPARSE_TESTS
 
-#define CREATE_TESTS(STYPE)                                         \
-    TEST(Sparse, Create_##STYPE)                                    \
-    {                                                               \
-        createFunction<STYPE>();                                    \
-    }
+#define CREATE_TESTS(STYPE) \
+    TEST(Sparse, Create_##STYPE) { createFunction<STYPE>(); }
 
 CREATE_TESTS(AF_STORAGE_CSR)
 CREATE_TESTS(AF_STORAGE_COO)
 
 #undef CREATE_TESTS
 
-TEST(Sparse, Create_AF_STORAGE_CSC)
-{
+TEST(Sparse, Create_AF_STORAGE_CSC) {
     array d = identity(3, 3);
 
     af_array out = 0;
-    ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array_from_dense(&out, d.get(), AF_STORAGE_CSC));
+    ASSERT_EQ(AF_ERR_ARG,
+              af_create_sparse_array_from_dense(&out, d.get(), AF_STORAGE_CSC));
 
-    if(out != 0) af_release_array(out);
+    if (out != 0) af_release_array(out);
 }
 
-#define CAST_TESTS_TYPES(Ti, To, SUFFIX, M, N, F)                               \
-    TEST(Sparse, Cast_##Ti##_##To##_##SUFFIX)                                   \
-    {                                                                           \
-        sparseCastTester<Ti, To>(M, N, F);                                      \
-    }                                                                           \
-
-#define CAST_TESTS(Ti, To)                                                      \
-    CAST_TESTS_TYPES(Ti, To, 1, 1000, 1000,  5)                                 \
-    CAST_TESTS_TYPES(Ti, To, 2,  512, 1024,  2)                                 \
-
-CAST_TESTS(float  , float   )
-CAST_TESTS(float  , double  )
-CAST_TESTS(float  , cfloat  )
-CAST_TESTS(float  , cdouble )
+#define CAST_TESTS_TYPES(Ti, To, SUFFIX, M, N, F) \
+    TEST(Sparse, Cast_##Ti##_##To##_##SUFFIX) {   \
+        sparseCastTester<Ti, To>(M, N, F);        \
+    }
 
-CAST_TESTS(double , float   )
-CAST_TESTS(double , double  )
-CAST_TESTS(double , cfloat  )
-CAST_TESTS(double , cdouble )
+#define CAST_TESTS(Ti, To)                     \
+    CAST_TESTS_TYPES(Ti, To, 1, 1000, 1000, 5) \
+    CAST_TESTS_TYPES(Ti, To, 2, 512, 1024, 2)
 
-CAST_TESTS(cfloat , cfloat  )
-CAST_TESTS(cfloat , cdouble )
+CAST_TESTS(float, float)
+CAST_TESTS(float, double)
+CAST_TESTS(float, cfloat)
+CAST_TESTS(float, cdouble)
 
-CAST_TESTS(cdouble, cfloat  )
-CAST_TESTS(cdouble, cdouble )
+CAST_TESTS(double, float)
+CAST_TESTS(double, double)
+CAST_TESTS(double, cfloat)
+CAST_TESTS(double, cdouble)
 
+CAST_TESTS(cfloat, cfloat)
+CAST_TESTS(cfloat, cdouble)
 
+CAST_TESTS(cdouble, cfloat)
+CAST_TESTS(cdouble, cdouble)
 
-TEST(Sparse, ISSUE_1745)
-{
-  using af::where;
+TEST(Sparse, ISSUE_1745) {
+    using af::where;
 
-  array A = randu(4, 4);
-  A(1, span) = 0;
-  A(2, span) = 0;
+    array A    = randu(4, 4);
+    A(1, span) = 0;
+    A(2, span) = 0;
 
-  array idx = where(A);
-  array data = A(idx);
-  array row_idx = (idx / A.dims()[0]).as(s64);
-  array col_idx = (idx % A.dims()[0]).as(s64);
+    array idx     = where(A);
+    array data    = A(idx);
+    array row_idx = (idx / A.dims()[0]).as(s64);
+    array col_idx = (idx % A.dims()[0]).as(s64);
 
-  af_array A_sparse;
-  ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array(&A_sparse, A.dims(0), A.dims(1), data.get(), row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
+    af_array A_sparse;
+    ASSERT_EQ(AF_ERR_ARG, af_create_sparse_array(
+                              &A_sparse, A.dims(0), A.dims(1), data.get(),
+                              row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
 }
 
-TEST(Sparse, ISSUE_2134_COO)
-{
-  int rows[] = {0,0,0,1,1,2,2};
-  int cols[] = {0,1,2,0,1,0,2};
-  float values[] = {3,3,4,3,10,4,3};
-  array row(7, rows);
-  array col(7, cols);
-  array value(7, values);
-  af_array A = 0;
-  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
-  if(A != 0) af_release_array(A);
-  A = 0;
-  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSC));
-  if(A != 0) af_release_array(A);
-  A = 0;
-  EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_COO));
-  if(A != 0) af_release_array(A);
+TEST(Sparse, ISSUE_2134_COO) {
+    int rows[]     = {0, 0, 0, 1, 1, 2, 2};
+    int cols[]     = {0, 1, 2, 0, 1, 0, 2};
+    float values[] = {3, 3, 4, 3, 10, 4, 3};
+    array row(7, rows);
+    array col(7, cols);
+    array value(7, values);
+    af_array A = 0;
+    EXPECT_EQ(AF_ERR_SIZE,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_CSR));
+    if (A != 0) af_release_array(A);
+    A = 0;
+    EXPECT_EQ(AF_ERR_SIZE,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_CSC));
+    if (A != 0) af_release_array(A);
+    A = 0;
+    EXPECT_EQ(AF_SUCCESS,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_COO));
+    if (A != 0) af_release_array(A);
 }
 
-TEST(Sparse, ISSUE_2134_CSR)
-{
-  int rows[] = {0,3,5,7};
-  int cols[] = {0,1,2,0,1,0,2};
-  float values[] = {3,3,4,3,10,4,3};
-  array row(4, rows);
-  array col(7, cols);
-  array value(7, values);
-  af_array A = 0;
-  EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
-  if(A != 0) af_release_array(A);
-  A = 0;
-  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSC));
-  if(A != 0) af_release_array(A);
-  A = 0;
-  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_COO));
-  if(A != 0) af_release_array(A);
+TEST(Sparse, ISSUE_2134_CSR) {
+    int rows[]     = {0, 3, 5, 7};
+    int cols[]     = {0, 1, 2, 0, 1, 0, 2};
+    float values[] = {3, 3, 4, 3, 10, 4, 3};
+    array row(4, rows);
+    array col(7, cols);
+    array value(7, values);
+    af_array A = 0;
+    EXPECT_EQ(AF_SUCCESS,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_CSR));
+    if (A != 0) af_release_array(A);
+    A = 0;
+    EXPECT_EQ(AF_ERR_SIZE,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_CSC));
+    if (A != 0) af_release_array(A);
+    A = 0;
+    EXPECT_EQ(AF_ERR_SIZE,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_COO));
+    if (A != 0) af_release_array(A);
 }
 
-TEST(Sparse, ISSUE_2134_CSC)
-{
-  int rows[] = {0,0,0,1,1,2,2};
-  int cols[] = {0,3,5,7};
-  float values[] = {3,3,4,3,10,4,3};
-  array row(7, rows);
-  array col(4, cols);
-  array value(7, values);
-  af_array A = 0;
-  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSR));
-  if(A != 0) af_release_array(A);
-  A = 0;
-  EXPECT_EQ(AF_SUCCESS, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_CSC));
-  if(A != 0) af_release_array(A);
-  A = 0;
-  EXPECT_EQ(AF_ERR_SIZE, af_create_sparse_array(&A, 3, 3, value.get(), row.get(), col.get(), AF_STORAGE_COO));
-  if(A != 0) af_release_array(A);
+TEST(Sparse, ISSUE_2134_CSC) {
+    int rows[]     = {0, 0, 0, 1, 1, 2, 2};
+    int cols[]     = {0, 3, 5, 7};
+    float values[] = {3, 3, 4, 3, 10, 4, 3};
+    array row(7, rows);
+    array col(4, cols);
+    array value(7, values);
+    af_array A = 0;
+    EXPECT_EQ(AF_ERR_SIZE,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_CSR));
+    if (A != 0) af_release_array(A);
+    A = 0;
+    EXPECT_EQ(AF_SUCCESS,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_CSC));
+    if (A != 0) af_release_array(A);
+    A = 0;
+    EXPECT_EQ(AF_ERR_SIZE,
+              af_create_sparse_array(&A, 3, 3, value.get(), row.get(),
+                                     col.get(), AF_STORAGE_COO));
+    if (A != 0) af_release_array(A);
 }
 
 template<typename T>
@@ -200,21 +193,20 @@ TYPED_TEST(Sparse, DeepCopy) {
 
     array s;
     {
-        // Create a sparse array from a dense array. Make sure that the dense arrays
-        // are removed
+        // Create a sparse array from a dense array. Make sure that the dense
+        // arrays are removed
         array dense = randu(10, 10);
-        array d = makeSparse<TypeParam>(dense, 5);
-        s = sparse(d);
+        array d     = makeSparse<TypeParam>(dense, 5);
+        s           = sparse(d);
     }
 
-    // At this point only the sparse array will be allocated in memory. Determine
-    // how much memory is allocated by one sparse array
+    // At this point only the sparse array will be allocated in memory.
+    // Determine how much memory is allocated by one sparse array
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                      &lock_bytes, &lock_buffers);
-    size_t size_of_alloc = lock_bytes;
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    size_t size_of_alloc      = lock_bytes;
     size_t buffers_per_sparse = lock_buffers;
 
     {
@@ -222,19 +214,18 @@ TYPED_TEST(Sparse, DeepCopy) {
         s2.eval();
 
         // Make sure that the deep copy allocated additional memory
-        deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                          &lock_bytes, &lock_buffers);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
         EXPECT_NE(s.get(), s2.get()) << "The sparse arrays point to the same "
                                         "af_array object.";
-        EXPECT_EQ(size_of_alloc * 2,
-                  lock_bytes) << "The number of bytes allocated by the deep copy do "
-                                "not match the original array";
-
-        EXPECT_EQ(buffers_per_sparse * 2,
-                  lock_buffers) << "The number of buffers allocated by the deep "
-                                  "copy do not match the original array";
-        array d = dense(s);
+        EXPECT_EQ(size_of_alloc * 2, lock_bytes)
+            << "The number of bytes allocated by the deep copy do "
+               "not match the original array";
+
+        EXPECT_EQ(buffers_per_sparse * 2, lock_buffers)
+            << "The number of buffers allocated by the deep "
+               "copy do not match the original array";
+        array d  = dense(s);
         array d2 = dense(s2);
         ASSERT_ARRAYS_EQ(d, d2);
     }
@@ -245,13 +236,10 @@ TYPED_TEST(Sparse, Empty) {
 
     af_array ret = 0;
     dim_t rows = 0, cols = 0, nnz = 0;
-    EXPECT_EQ(AF_SUCCESS,
-              af_create_sparse_array_from_ptr(
-                  &ret,
-                  rows, cols,
-                  nnz, NULL, NULL, NULL,
-                  (af_dtype)dtype_traits<TypeParam>::af_type,
-                  AF_STORAGE_CSR, afHost));
+    EXPECT_EQ(AF_SUCCESS, af_create_sparse_array_from_ptr(
+                              &ret, rows, cols, nnz, NULL, NULL, NULL,
+                              (af_dtype)dtype_traits<TypeParam>::af_type,
+                              AF_STORAGE_CSR, afHost));
     bool sparse = false;
     EXPECT_EQ(AF_SUCCESS, af_is_sparse(&sparse, ret));
     EXPECT_EQ(true, sparse);
@@ -261,8 +249,7 @@ TYPED_TEST(Sparse, Empty) {
 TYPED_TEST(Sparse, EmptyDeepCopy) {
     if (noDoubleTests<TypeParam>()) return;
 
-    array a = sparse(0, 0,
-                     array(0, (af_dtype)dtype_traits<TypeParam>::af_type),
+    array a = sparse(0, 0, array(0, (af_dtype)dtype_traits<TypeParam>::af_type),
                      array(1, s32), array(0, s32));
     EXPECT_TRUE(a.issparse());
     EXPECT_EQ(0, sparseGetNNZ(a));
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index a29a644a8b..3d69cc81ff 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -7,43 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::deviceGC;
 using af::dim4;
 using af::freeHost;
 using af::max;
 using af::sum;
+using std::abs;
+using std::string;
+using std::vector;
 
 template<typename T>
-array makeSparse(array A, int factor)
-{
+array makeSparse(array A, int factor) {
     A = floor(A * 1000);
     A = A * ((A % factor) == 0) / 1000;
     return A;
 }
 
 template<>
-array makeSparse<cfloat>(array A, int factor)
-{
+array makeSparse<cfloat>(array A, int factor) {
     array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
+    r       = floor(r * 1000);
+    r       = r * ((r % factor) == 0) / 1000;
 
     array i = r / 2;
 
@@ -52,11 +50,10 @@ array makeSparse<cfloat>(array A, int factor)
 }
 
 template<>
-array makeSparse<cdouble>(array A, int factor)
-{
+array makeSparse<cdouble>(array A, int factor) {
     array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
+    r       = floor(r * 1000);
+    r       = r * ((r % factor) == 0) / 1000;
 
     array i = r / 2;
 
@@ -72,59 +69,39 @@ typedef enum {
 } af_op_t;
 
 template<af_op_t op>
-struct arith_op
-{
-    array operator()(array v1, array v2)
-    {
-        return v1;
-    }
+struct arith_op {
+    array operator()(array v1, array v2) { return v1; }
 };
 
 template<>
-struct arith_op<af_add_t>
-{
-    array operator()(array v1, array v2)
-    {
-        return v1 + v2;
-    }
+struct arith_op<af_add_t> {
+    array operator()(array v1, array v2) { return v1 + v2; }
 };
 
 template<>
-struct arith_op<af_sub_t>
-{
-    array operator()(array v1, array v2)
-    {
-        return v1 - v2;
-    }
+struct arith_op<af_sub_t> {
+    array operator()(array v1, array v2) { return v1 - v2; }
 };
 
 template<>
-struct arith_op<af_mul_t>
-{
-    array operator()(array v1, array v2)
-    {
-        return v1 * v2;
-    }
+struct arith_op<af_mul_t> {
+    array operator()(array v1, array v2) { return v1 * v2; }
 };
 
 template<>
-struct arith_op<af_div_t>
-{
-    array operator()(array v1, array v2)
-    {
-        return v1 / v2;
-    }
+struct arith_op<af_div_t> {
+    array operator()(array v1, array v2) { return v1 / v2; }
 };
 
 template<typename T>
-void sparseCompare(array A, array B, const double eps)
-{
+void sparseCompare(array A, array B, const double eps) {
 // This macro is used to check if either value is finite and then call assert
-// If neither value is finite, then they can be assumed to be equal to either inf or nan
-#define ASSERT_FINITE_EQ(V1, V2)                    \
-    if(std::isfinite(V1) || std::isfinite(V2)) {    \
-        ASSERT_NEAR(V1, V2, eps) << "at : " << i;   \
-    }                                               \
+// If neither value is finite, then they can be assumed to be equal to either
+// inf or nan
+#define ASSERT_FINITE_EQ(V1, V2)                  \
+    if (std::isfinite(V1) || std::isfinite(V2)) { \
+        ASSERT_NEAR(V1, V2, eps) << "at : " << i; \
+    }
 
     array AValues = sparseGetValues(A);
     array ARowIdx = sparseGetRowIdx(A);
@@ -138,14 +115,12 @@ void sparseCompare(array A, array B, const double eps)
     ASSERT_EQ(0, max<int>(ARowIdx - BRowIdx));
     ASSERT_EQ(0, max<int>(AColIdx - BColIdx));
 
-    T *ptrA = AValues.host<T>();
-    T *ptrB = BValues.host<T>();
-    for(int i = 0; i < AValues.elements(); i++) {
+    T* ptrA = AValues.host<T>();
+    T* ptrB = BValues.host<T>();
+    for (int i = 0; i < AValues.elements(); i++) {
         ASSERT_FINITE_EQ(real(ptrA[i]), real(ptrB[i]));
 
-        if(A.iscomplex()) {
-            ASSERT_FINITE_EQ(imag(ptrA[i]), imag(ptrB[i]));
-        }
+        if (A.iscomplex()) { ASSERT_FINITE_EQ(imag(ptrA[i]), imag(ptrB[i])); }
     }
     freeHost(ptrA);
     freeHost(ptrB);
@@ -154,8 +129,7 @@ void sparseCompare(array A, array B, const double eps)
 }
 
 template<typename T, af_op_t op>
-void sparseArithTester(const int m, const int n, int factor, const double eps)
-{
+void sparseArithTester(const int m, const int n, int factor, const double eps) {
     deviceGC();
 
     if (noDoubleTests<T>()) return;
@@ -176,11 +150,11 @@ void sparseArithTester(const int m, const int n, int factor, const double eps)
     // Arith Op
     array resR = arith_op<op>()(RA, B);
     array resO = arith_op<op>()(OA, B);
-    array resD = arith_op<op>()( A, B);
+    array resD = arith_op<op>()(A, B);
 
     array revR = arith_op<op>()(B, RA);
     array revO = arith_op<op>()(B, OA);
-    array revD = arith_op<op>()(B,  A);
+    array revD = arith_op<op>()(B, A);
 
     ASSERT_NEAR(0, sum<double>(abs(real(resR - resD))) / (m * n), eps);
     ASSERT_NEAR(0, sum<double>(abs(imag(resR - resD))) / (m * n), eps);
@@ -197,8 +171,8 @@ void sparseArithTester(const int m, const int n, int factor, const double eps)
 
 // Mul
 template<typename T>
-void sparseArithTesterMul(const int m, const int n, int factor, const double eps)
-{
+void sparseArithTesterMul(const int m, const int n, int factor,
+                          const double eps) {
     deviceGC();
 
     if (noDoubleTests<T>()) return;
@@ -257,8 +231,8 @@ void sparseArithTesterMul(const int m, const int n, int factor, const double eps
 
 // Div
 template<typename T>
-void sparseArithTesterDiv(const int m, const int n, int factor, const double eps)
-{
+void sparseArithTesterDiv(const int m, const int n, int factor,
+                          const double eps) {
     deviceGC();
 
     if (noDoubleTests<T>()) return;
@@ -282,9 +256,11 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
 
     // Assert division by sparse is not allowed
     af_array out_temp = 0;
-    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), RA.get(), false));
-    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_div(&out_temp, B.get(), OA.get(), false));
-    if(out_temp != 0) af_release_array(out_temp);
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED,
+              af_div(&out_temp, B.get(), RA.get(), false));
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED,
+              af_div(&out_temp, B.get(), OA.get(), false));
+    if (out_temp != 0) af_release_array(out_temp);
 
     // We will test this by converting the COO to CSR and CSR to COO and
     // comparing them. In essense, we are comparing the resR and resO
@@ -299,40 +275,35 @@ void sparseArithTesterDiv(const int m, const int n, int factor, const double eps
     sparseCompare<T>(resO, conO, eps);
 }
 
-#define ARITH_TESTS_OPS(T, M, N, F, EPS)                                    \
-    TEST(SPARSE_ARITH, T##_ADD_##M##_##N)                                   \
-    {                                                                       \
-        sparseArithTester<T, af_add_t>(M, N, F, EPS);                       \
-    }                                                                       \
-    TEST(SPARSE_ARITH, T##_SUB_##M##_##N)                                   \
-    {                                                                       \
-        sparseArithTester<T, af_sub_t>(M, N, F, EPS);                       \
-    }                                                                       \
-    TEST(SPARSE_ARITH, T##_MUL_##M##_##N)                                   \
-    {                                                                       \
-        sparseArithTesterMul<T>(M, N, F, EPS);                              \
-    }                                                                       \
-    TEST(SPARSE_ARITH, T##_DIV_##M##_##N)                                   \
-    {                                                                       \
-        sparseArithTesterDiv<T>(M, N, F, EPS);                              \
-    }                                                                       \
-
-#define ARITH_TESTS(T, eps)                                                 \
-    ARITH_TESTS_OPS(T, 10  , 10  , 5, eps)                                  \
-    ARITH_TESTS_OPS(T, 1024, 1024, 5, eps)                                  \
-    ARITH_TESTS_OPS(T, 100 , 100 , 1, eps)                                  \
-    ARITH_TESTS_OPS(T, 2048, 1000, 6, eps)                                  \
-    ARITH_TESTS_OPS(T, 123 , 278 , 5, eps)                                  \
-
-ARITH_TESTS(float  , 1e-6)
-ARITH_TESTS(double , 1e-6)
-ARITH_TESTS(cfloat , 1e-4) // This is mostly for complex division in OpenCL
+#define ARITH_TESTS_OPS(T, M, N, F, EPS)              \
+    TEST(SPARSE_ARITH, T##_ADD_##M##_##N) {           \
+        sparseArithTester<T, af_add_t>(M, N, F, EPS); \
+    }                                                 \
+    TEST(SPARSE_ARITH, T##_SUB_##M##_##N) {           \
+        sparseArithTester<T, af_sub_t>(M, N, F, EPS); \
+    }                                                 \
+    TEST(SPARSE_ARITH, T##_MUL_##M##_##N) {           \
+        sparseArithTesterMul<T>(M, N, F, EPS);        \
+    }                                                 \
+    TEST(SPARSE_ARITH, T##_DIV_##M##_##N) {           \
+        sparseArithTesterDiv<T>(M, N, F, EPS);        \
+    }
+
+#define ARITH_TESTS(T, eps)                \
+    ARITH_TESTS_OPS(T, 10, 10, 5, eps)     \
+    ARITH_TESTS_OPS(T, 1024, 1024, 5, eps) \
+    ARITH_TESTS_OPS(T, 100, 100, 1, eps)   \
+    ARITH_TESTS_OPS(T, 2048, 1000, 6, eps) \
+    ARITH_TESTS_OPS(T, 123, 278, 5, eps)
+
+ARITH_TESTS(float, 1e-6)
+ARITH_TESTS(double, 1e-6)
+ARITH_TESTS(cfloat, 1e-4)  // This is mostly for complex division in OpenCL
 ARITH_TESTS(cdouble, 1e-6)
 
 // Sparse-Sparse Arithmetic testing function
 template<typename T, af_op_t op>
-void ssArithmetic(const int m, const int n, int factor, const double eps)
-{
+void ssArithmetic(const int m, const int n, int factor, const double eps) {
     deviceGC();
 
     if (noDoubleTests<T>()) return;
@@ -357,43 +328,41 @@ void ssArithmetic(const int m, const int n, int factor, const double eps)
     array resS = binOp(spA, spB);
     array resD = binOp(A, B);
     array revS = binOp(spB, spA);
-    array revD = binOp(B,  A);
+    array revD = binOp(B, A);
 
     ASSERT_ARRAYS_NEAR(resD, dense(resS), eps);
     ASSERT_ARRAYS_NEAR(revD, dense(revS), eps);
 }
 
-#define SP_SP_ARITH_TEST(type, m, n, factor, eps)       \
-TEST(SparseSparseArith, type##_Addition_##m##_##n)      \
-{                                                       \
-    ssArithmetic<type, af_add_t>(m, n, factor, eps);    \
-}                                                       \
-TEST(SparseSparseArith, type##_Subtraction_##m##_##n)   \
-{                                                       \
-    ssArithmetic<type, af_sub_t>(m, n, factor, eps);    \
-}
-
-#define SP_SP_ARITH_TESTS(T, eps)                       \
-    SP_SP_ARITH_TEST(T, 10  , 10  , 5, eps)             \
-    SP_SP_ARITH_TEST(T, 1024, 1024, 5, eps)             \
-    SP_SP_ARITH_TEST(T, 100 , 100 , 1, eps)             \
-    SP_SP_ARITH_TEST(T, 2048, 1000, 6, eps)             \
-    SP_SP_ARITH_TEST(T, 123 , 278 , 5, eps)             \
+#define SP_SP_ARITH_TEST(type, m, n, factor, eps)           \
+    TEST(SparseSparseArith, type##_Addition_##m##_##n) {    \
+        ssArithmetic<type, af_add_t>(m, n, factor, eps);    \
+    }                                                       \
+    TEST(SparseSparseArith, type##_Subtraction_##m##_##n) { \
+        ssArithmetic<type, af_sub_t>(m, n, factor, eps);    \
+    }
 
-SP_SP_ARITH_TESTS(float  , 1e-6)
-SP_SP_ARITH_TESTS(double , 1e-6)
-SP_SP_ARITH_TESTS(cfloat , 1e-4) // This is mostly for complex division in OpenCL
+#define SP_SP_ARITH_TESTS(T, eps)           \
+    SP_SP_ARITH_TEST(T, 10, 10, 5, eps)     \
+    SP_SP_ARITH_TEST(T, 1024, 1024, 5, eps) \
+    SP_SP_ARITH_TEST(T, 100, 100, 1, eps)   \
+    SP_SP_ARITH_TEST(T, 2048, 1000, 6, eps) \
+    SP_SP_ARITH_TEST(T, 123, 278, 5, eps)
+
+SP_SP_ARITH_TESTS(float, 1e-6)
+SP_SP_ARITH_TESTS(double, 1e-6)
+SP_SP_ARITH_TESTS(cfloat,
+                  1e-4)  // This is mostly for complex division in OpenCL
 SP_SP_ARITH_TESTS(cdouble, 1e-6)
 
 #if defined(USE_MTX)
 
 // Sparse-Sparse Arithmetic testing function using mtx files
 template<af_op_t op>
-void ssArithmeticMTX(const char* op1, const char* op2)
-{
+void ssArithmeticMTX(const char* op1, const char* op2) {
     deviceGC();
 
-    //Re-enable when double is enabled if (noDoubleTests<T>()) return;
+    // Re-enable when double is enabled if (noDoubleTests<T>()) return;
 
     array cooA, cooB;
     ASSERT_TRUE(mtxReadSparseMatrix(cooA, op1));
@@ -411,30 +380,31 @@ void ssArithmeticMTX(const char* op1, const char* op2)
     array resS = binOp(spA, spB);
     array resD = binOp(A, B);
     array revS = binOp(spB, spA);
-    array revD = binOp(B,  A);
+    array revD = binOp(B, A);
 
     ASSERT_ARRAYS_NEAR(resD, dense(resS), 1e-4);
     ASSERT_ARRAYS_NEAR(revD, dense(revS), 1e-4);
 }
 
-TEST(SparseSparseArith, LinearProgrammingData)
-{
+TEST(SparseSparseArith, LinearProgrammingData) {
     std::string file1(TEST_DIR "/matrixmarket/LPnetlib/lpi_vol1/lpi_vol1.mtx");
     std::string file2(TEST_DIR "/matrixmarket/LPnetlib/lpi_qual/lpi_qual.mtx");
     ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
 }
 
-TEST(SparseSparseArith, SubsequentCircuitSimData)
-{
-    std::string file1(TEST_DIR "/matrixmarket/Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
-    std::string file2(TEST_DIR "/matrixmarket/Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
+TEST(SparseSparseArith, SubsequentCircuitSimData) {
+    std::string file1(TEST_DIR
+                      "/matrixmarket/Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
+    std::string file2(TEST_DIR
+                      "/matrixmarket/Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
     ssArithmeticMTX<af_sub_t>(file1.c_str(), file2.c_str());
 }
 
-TEST(SparseSparseArith, QuantumChemistryData)
-{
-    std::string file1(TEST_DIR "/matrixmarket/QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
-    std::string file2(TEST_DIR "/matrixmarket/QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
+TEST(SparseSparseArith, QuantumChemistryData) {
+    std::string file1(TEST_DIR
+                      "/matrixmarket/QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
+    std::string file2(TEST_DIR
+                      "/matrixmarket/QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
     ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
 }
 #endif
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index 34cfd12ae3..0e4f48ecfd 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -9,39 +9,38 @@
 
 #pragma once
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
+#include <vector>
 
-using std::vector;
-using std::string;
+using af::cdouble;
+using af::cfloat;
+using std::abs;
 using std::cout;
 using std::endl;
-using std::abs;
-using af::cfloat;
-using af::cdouble;
+using std::string;
+using std::vector;
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 
-template<typename T> static
-af::array makeSparse(af::array A, int factor)
-{
+template<typename T>
+static af::array makeSparse(af::array A, int factor) {
     A = floor(A * 1000);
     A = A * ((A % factor) == 0) / 1000;
     return A;
 }
 
 template<>
-af::array makeSparse<cfloat>(af::array A, int factor)
-{
+af::array makeSparse<cfloat>(af::array A, int factor) {
     af::array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
+    r           = floor(r * 1000);
+    r           = r * ((r % factor) == 0) / 1000;
 
     af::array i = r / 2;
 
@@ -50,11 +49,10 @@ af::array makeSparse<cfloat>(af::array A, int factor)
 }
 
 template<>
-af::array makeSparse<cdouble>(af::array A, int factor)
-{
+af::array makeSparse<cdouble>(af::array A, int factor) {
     af::array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
+    r           = floor(r * 1000);
+    r           = r * ((r % factor) == 0) / 1000;
 
     af::array i = r / 2;
 
@@ -62,17 +60,15 @@ af::array makeSparse<cdouble>(af::array A, int factor)
     return A;
 }
 
-static double calc_norm(af::array lhs, af::array rhs)
-{
-    return af::max<double>(af::abs(lhs - rhs) / (af::abs(lhs) + af::abs(rhs) + 1E-5));
+static double calc_norm(af::array lhs, af::array rhs) {
+    return af::max<double>(af::abs(lhs - rhs) /
+                           (af::abs(lhs) + af::abs(rhs) + 1E-5));
 }
 
-template<typename T> static
-void sparseTester(const int m, const int n, const int k, int factor, double eps,
-                  int targetDevice=-1)
-{
-    if (targetDevice>=0)
-        af::setDevice(targetDevice);
+template<typename T>
+static void sparseTester(const int m, const int n, const int k, int factor,
+                         double eps, int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
 
     af::deviceGC();
 
@@ -102,12 +98,11 @@ void sparseTester(const int m, const int n, const int k, int factor, double eps,
     ASSERT_NEAR(0, calc_norm(imag(dRes1), imag(sRes1)), eps);
 }
 
-template<typename T> static
-void sparseTransposeTester(const int m, const int n, const int k, int factor, double eps,
-                           int targetDevice=-1)
-{
-    if (targetDevice>=0)
-        af::setDevice(targetDevice);
+template<typename T>
+static void sparseTransposeTester(const int m, const int n, const int k,
+                                  int factor, double eps,
+                                  int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
 
     af::deviceGC();
 
@@ -142,11 +137,10 @@ void sparseTransposeTester(const int m, const int n, const int k, int factor, do
     ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
 }
 
-template<typename T> static
-void convertCSR(const int M, const int N, const float ratio, int targetDevice=-1)
-{
-    if (targetDevice>=0)
-        af::setDevice(targetDevice);
+template<typename T>
+static void convertCSR(const int M, const int N, const float ratio,
+                       int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
 
     if (noDoubleTests<T>()) return;
 #if 1
@@ -156,7 +150,7 @@ void convertCSR(const int M, const int N, const float ratio, int targetDevice=-1
 #endif
     a = a * (a > ratio);
 
-    af::array s = af::sparse(a, AF_STORAGE_CSR);
+    af::array s  = af::sparse(a, AF_STORAGE_CSR);
     af::array aa = af::dense(s);
 
     ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));
@@ -164,26 +158,26 @@ void convertCSR(const int M, const int N, const float ratio, int targetDevice=-1
 
 // This test essentially verifies that the sparse structures have the correct
 // dimensions and indices using a very basic test
-template<af_storage stype> static
-void createFunction()
-{
+template<af_storage stype>
+static void createFunction() {
     af::array in = af::sparse(af::identity(3, 3), stype);
 
     af::array values = sparseGetValues(in);
     af::array rowIdx = sparseGetRowIdx(in);
     af::array colIdx = sparseGetColIdx(in);
-    dim_t     nNZ    = sparseGetNNZ(in);
+    dim_t nNZ        = sparseGetNNZ(in);
 
     ASSERT_EQ(nNZ, values.elements());
 
     ASSERT_EQ(0, af::max<double>(values - af::constant(1, nNZ)));
-    ASSERT_EQ(0, af::max<int   >(rowIdx - af::range(af::dim4(rowIdx.elements()), 0, s32)));
-    ASSERT_EQ(0, af::max<int   >(colIdx - af::range(af::dim4(colIdx.elements()), 0, s32)));
+    ASSERT_EQ(0, af::max<int>(rowIdx -
+                              af::range(af::dim4(rowIdx.elements()), 0, s32)));
+    ASSERT_EQ(0, af::max<int>(colIdx -
+                              af::range(af::dim4(colIdx.elements()), 0, s32)));
 }
 
-template<typename Ti, typename To> static
-void sparseCastTester(const int m, const int n, int factor)
-{
+template<typename Ti, typename To>
+static void sparseCastTester(const int m, const int n, int factor) {
     if (noDoubleTests<Ti>()) return;
     if (noDoubleTests<To>()) return;
 
@@ -222,10 +216,12 @@ void sparseCastTester(const int m, const int n, int factor)
     ASSERT_EQ(0, af::max<int>(af::abs(iColIdx - oColIdx)));
 
     static const double eps = 1e-6;
-    if(iValues.iscomplex() && !oValues.iscomplex()) {
-        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)), eps);
-    } else if(!iValues.iscomplex() && oValues.iscomplex()) {
-        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))), eps);
+    if (iValues.iscomplex() && !oValues.iscomplex()) {
+        ASSERT_NEAR(0, af::max<double>(af::abs(af::abs(iValues) - oValues)),
+                    eps);
+    } else if (!iValues.iscomplex() && oValues.iscomplex()) {
+        ASSERT_NEAR(0, af::max<double>(af::abs(iValues - af::abs(oValues))),
+                    eps);
     } else {
         ASSERT_NEAR(0, af::max<double>(af::abs(iValues - oValues)), eps);
     }
diff --git a/test/sparse_convert.cpp b/test/sparse_convert.cpp
index c2ad68296f..c25db15d82 100644
--- a/test/sparse_convert.cpp
+++ b/test/sparse_convert.cpp
@@ -7,43 +7,41 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::max;
+using std::abs;
+using std::string;
+using std::vector;
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 
 template<typename T>
-array makeSparse(array A, int factor)
-{
+array makeSparse(array A, int factor) {
     A = floor(A * 1000);
     A = A * ((A % factor) == 0) / 1000;
     return A;
 }
 
 template<>
-array makeSparse<cfloat>(array A, int factor)
-{
+array makeSparse<cfloat>(array A, int factor) {
     array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
+    r       = floor(r * 1000);
+    r       = r * ((r % factor) == 0) / 1000;
 
     array i = r / 2;
 
@@ -52,11 +50,10 @@ array makeSparse<cfloat>(array A, int factor)
 }
 
 template<>
-array makeSparse<cdouble>(array A, int factor)
-{
+array makeSparse<cdouble>(array A, int factor) {
     array r = real(A);
-    r = floor(r * 1000);
-    r = r * ((r % factor) == 0) / 1000;
+    r       = floor(r * 1000);
+    r       = r * ((r % factor) == 0) / 1000;
 
     array i = r / 2;
 
@@ -65,8 +62,7 @@ array makeSparse<cdouble>(array A, int factor)
 }
 
 template<typename T, af_storage src, af_storage dest>
-void sparseConvertTester(const int m, const int n, int factor)
-{
+void sparseConvertTester(const int m, const int n, int factor) {
     if (noDoubleTests<T>()) return;
 
     array A = cpu_randu<T>(dim4(m, n));
@@ -108,38 +104,35 @@ void sparseConvertTester(const int m, const int n, int factor)
     ASSERT_EQ(0, max<double>(imag(dValues - s2dValues)));
 
     // Verify row and col indices
-    ASSERT_EQ(0, max<int   >(dRowIdx - s2dRowIdx));
-    ASSERT_EQ(0, max<int   >(dColIdx - s2dColIdx));
+    ASSERT_EQ(0, max<int>(dRowIdx - s2dRowIdx));
+    ASSERT_EQ(0, max<int>(dColIdx - s2dColIdx));
 }
 
-#define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F)                   \
-    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_##SUFFIX)                      \
-    {                                                                           \
-        sparseConvertTester<T, STYPE, DTYPE>(M, N, F);                          \
-    }                                                                           \
-    TEST(SPARSE_CONVERT, T##_##DTYPE##_##STYPE##_##SUFFIX)                      \
-    {                                                                           \
-        sparseConvertTester<T, DTYPE, STYPE>(M, N, F);                          \
-    }                                                                           \
-
-#define CONVERT_TESTS(T, STYPE, DTYPE)                                          \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 1, 1000, 1000,  5)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 2,  512,  512,  1)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 3,  512, 1024,  2)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 4, 2048, 1024, 10)                     \
-    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 5,  237, 411,   5)                     \
-
-CONVERT_TESTS(float  , AF_STORAGE_CSR, AF_STORAGE_COO)
-CONVERT_TESTS(double , AF_STORAGE_CSR, AF_STORAGE_COO)
-CONVERT_TESTS(cfloat , AF_STORAGE_CSR, AF_STORAGE_COO)
+#define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F) \
+    TEST(SPARSE_CONVERT, T##_##STYPE##_##DTYPE##_##SUFFIX) {  \
+        sparseConvertTester<T, STYPE, DTYPE>(M, N, F);        \
+    }                                                         \
+    TEST(SPARSE_CONVERT, T##_##DTYPE##_##STYPE##_##SUFFIX) {  \
+        sparseConvertTester<T, DTYPE, STYPE>(M, N, F);        \
+    }
+
+#define CONVERT_TESTS(T, STYPE, DTYPE)                      \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 1, 1000, 1000, 5)  \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 2, 512, 512, 1)    \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 3, 512, 1024, 2)   \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 4, 2048, 1024, 10) \
+    CONVERT_TESTS_TYPES(T, STYPE, DTYPE, 5, 237, 411, 5)
+
+CONVERT_TESTS(float, AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(double, AF_STORAGE_CSR, AF_STORAGE_COO)
+CONVERT_TESTS(cfloat, AF_STORAGE_CSR, AF_STORAGE_COO)
 CONVERT_TESTS(cdouble, AF_STORAGE_CSR, AF_STORAGE_COO)
 
 #undef CONVERT_TESTS
 #undef CONVERT_TESTS_TYPES
 
 // Test to check failure with CSC
-TEST(SPARSE_CONVERT, CSC_ARG_ERROR)
-{
+TEST(SPARSE_CONVERT, CSC_ARG_ERROR) {
     const int m = 100, n = 28, factor = 5;
 
     array A = cpu_randu<float>(dim4(m, n));
@@ -154,5 +147,5 @@ TEST(SPARSE_CONVERT, CSC_ARG_ERROR)
     af_array out = 0;
     ASSERT_EQ(AF_ERR_ARG, af_sparse_convert_to(&out, sA.get(), AF_STORAGE_CSC));
 
-    if(out != 0) af_release_array(out);
+    if (out != 0) af_release_array(out);
 }
diff --git a/test/stdev.cpp b/test/stdev.cpp
index 714f64c6e7..28f52f9662 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -7,21 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <string>
-#include <vector>
+#include <algorithm>
 #include <ctime>
 #include <iostream>
-#include <algorithm>
-#include <testHelpers.hpp>
+#include <string>
+#include <vector>
 
-using std::cout;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::cdouble;
 using af::cfloat;
@@ -29,67 +25,65 @@ using af::dim4;
 using af::exception;
 using af::seq;
 using af::stdev;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class StandardDev : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class StandardDev : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar> TestTypes;
+typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(StandardDev, TestTypes);
 
 template<typename T>
 struct f32HelperType {
-   typedef typename cond_type<is_same_type<T, double>::value,
-                                             double,
-                                             float>::type type;
+    typedef
+        typename cond_type<is_same_type<T, double>::value, double, float>::type
+            type;
 };
 
 template<typename T>
 struct c32HelperType {
-   typedef typename cond_type<is_same_type<T, cfloat>::value,
-                                             cfloat,
-                                             typename f32HelperType<T>::type >::type type;
+    typedef typename cond_type<is_same_type<T, cfloat>::value, cfloat,
+                               typename f32HelperType<T>::type>::type type;
 };
 
 template<typename T>
 struct elseType {
-   typedef typename cond_type< is_same_type<T, uintl>::value ||
-                               is_same_type<T, intl> ::value,
-                                              double,
-                                              T>::type type;
+    typedef typename cond_type<is_same_type<T, uintl>::value ||
+                                   is_same_type<T, intl>::value,
+                               double, T>::type type;
 };
 
 template<typename T>
 struct sdOutType {
-   typedef typename cond_type< is_same_type<T, float>   ::value ||
-                               is_same_type<T, int>     ::value ||
-                               is_same_type<T, uint>    ::value ||
-                               is_same_type<T, uchar>   ::value ||
-                               is_same_type<T, short>   ::value ||
-                               is_same_type<T, ushort>  ::value ||
-                               is_same_type<T, char>    ::value,
-                                              float,
-                              typename elseType<T>::type>::type type;
+    typedef typename cond_type<
+        is_same_type<T, float>::value || is_same_type<T, int>::value ||
+            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
+            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
 template<typename T>
-void stdevDimTest(string pFileName, dim_t dim=-1)
-{
+void stdevDimTest(string pFileName, dim_t dim = -1) {
     typedef typename sdOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<int> >       in;
-    vector<vector<float> >  tests;
+    vector<dim4> numDims;
+    vector<vector<int> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(pFileName, numDims, in, tests);
+    readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
     dim4 dims = numDims[0];
     vector<T> input(in[0].begin(), in[0].end());
@@ -100,103 +94,103 @@ void stdevDimTest(string pFileName, dim_t dim=-1)
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
-    size_t nElems    = currGoldBar.size();
+    size_t nElems = currGoldBar.size();
     vector<outType> outData(nElems);
 
     b.host((void*)outData.data());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
-        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
+                    1.0e-3)
+            << "at: " << elIter << endl;
+        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
+                    1.0e-3)
+            << "at: " << elIter << endl;
     }
 }
 
-TYPED_TEST(StandardDev, Dim0)
-{
+TYPED_TEST(StandardDev, Dim0) {
     stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_dim0.test"), 0);
 }
 
-TYPED_TEST(StandardDev, Dim1)
-{
+TYPED_TEST(StandardDev, Dim1) {
     stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_dim1.test"), 1);
 }
 
-TYPED_TEST(StandardDev, Dim2)
-{
-    stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim2.test"), 2);
+TYPED_TEST(StandardDev, Dim2) {
+    stdevDimTest<TypeParam>(
+        string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim2.test"), 2);
 }
 
-TYPED_TEST(StandardDev, Dim3)
-{
-    stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim3.test"), 3);
+TYPED_TEST(StandardDev, Dim3) {
+    stdevDimTest<TypeParam>(
+        string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim3.test"), 3);
 }
 
-TEST(StandardDev, InvalidDim)
-{
-    ASSERT_THROW(stdev(array(), 5), exception);
-}
+TEST(StandardDev, InvalidDim) { ASSERT_THROW(stdev(array(), 5), exception); }
 
-TEST(StandardDev, InvalidType)
-{
+TEST(StandardDev, InvalidType) {
     ASSERT_THROW(stdev(constant(cdouble(1.0, -1.0), 10)), exception);
 }
 
 template<typename T>
-void stdevDimIndexTest(string pFileName, dim_t dim=-1)
-{
+void stdevDimIndexTest(string pFileName, dim_t dim = -1) {
     typedef typename sdOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<int> >       in;
-    vector<vector<float> >  tests;
+    vector<dim4> numDims;
+    vector<vector<int> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(pFileName, numDims, in, tests);
+    readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
     dim4 dims = numDims[0];
     vector<T> input(in[0].begin(), in[0].end());
 
     array a(dims, &(input.front()));
-    array b = a(seq(2,6), seq(1,7));
+    array b = a(seq(2, 6), seq(1, 7));
 
     array c = stdev(b, dim);
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
-    size_t nElems    = currGoldBar.size();
+    size_t nElems = currGoldBar.size();
     vector<outType> outData(nElems);
 
     c.host((void*)outData.data());
 
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
-        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]), 1.0e-3)<< "at: " << elIter<< endl;
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
+                    1.0e-3)
+            << "at: " << elIter << endl;
+        ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
+                    1.0e-3)
+            << "at: " << elIter << endl;
     }
 }
 
-TYPED_TEST(StandardDev, IndexedArrayDim0)
-{
-    stdevDimIndexTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim0.test"), 0);
+TYPED_TEST(StandardDev, IndexedArrayDim0) {
+    stdevDimIndexTest<TypeParam>(
+        string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim0.test"), 0);
 }
 
-TYPED_TEST(StandardDev, IndexedArrayDim1)
-{
-    stdevDimIndexTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim1.test"), 1);
+TYPED_TEST(StandardDev, IndexedArrayDim1) {
+    stdevDimIndexTest<TypeParam>(
+        string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim1.test"), 1);
 }
 
-TYPED_TEST(StandardDev, All)
-{
+TYPED_TEST(StandardDev, All) {
     typedef typename sdOutType<TypeParam>::type outType;
     if (noDoubleTests<TypeParam>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>      numDims;
-    vector<vector<int> >       in;
-    vector<vector<float> >  tests;
+    vector<dim4> numDims;
+    vector<vector<int> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<int,float>(string(TEST_DIR "/stdev/mat_10x10_scalar.test"),
-                                 numDims, in, tests);
+    readTestsFromFile<int, float>(
+        string(TEST_DIR "/stdev/mat_10x10_scalar.test"), numDims, in, tests);
 
     dim4 dims = numDims[0];
     vector<TypeParam> input(in[0].begin(), in[0].end());
diff --git a/test/susan.cpp b/test/susan.cpp
index 258a3303b0..a8b276cc1a 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -7,44 +7,42 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/compatible.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/compatible.h>
-#include <string>
-#include <vector>
 #include <cmath>
-#include <testHelpers.hpp>
+#include <string>
 #include <typeinfo>
+#include <vector>
 
-using std::abs;
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
 using af::dim4;
 using af::exception;
 using af::features;
 using af::loadImage;
 using af::randu;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
-typedef struct
-{
+typedef struct {
     float f[5];
 } feat_t;
 
-static bool feat_cmp(feat_t i, feat_t j)
-{
+static bool feat_cmp(feat_t i, feat_t j) {
     for (int k = 0; k < 5; k++)
-        if (i.f[k] != j.f[k])
-            return (i.f[k] < j.f[k]);
+        if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
 
     return false;
 }
 
-static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score, float *orientation, float *size, unsigned nfeat)
-{
+static void array_to_feat(vector<feat_t> &feat, float *x, float *y,
+                          float *score, float *orientation, float *size,
+                          unsigned nfeat) {
     feat.resize(nfeat);
     for (unsigned i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -56,42 +54,41 @@ static void array_to_feat(vector<feat_t>& feat, float *x, float *y, float *score
 }
 
 template<typename T>
-class Susan : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Susan : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+    TestTypes;
 
 TYPED_TEST_CASE(Susan, TestTypes);
 
 template<typename T>
-void susanTest(string pTestFile, float t, float g)
-{
+void susanTest(string pTestFile, float t, float g) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
-    vector<dim4>        inDims;
-    vector<string>     inFiles;
+    vector<dim4> inDims;
+    vector<string> inFiles;
     vector<vector<float> > gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
     size_t testCount = inDims.size();
 
-    for (size_t testId=0; testId<testCount; ++testId) {
+    for (size_t testId = 0; testId < testCount; ++testId) {
         inFiles[testId].insert(0, string(TEST_DIR "/susan/"));
 
         array in = loadImage(inFiles[testId].c_str(), false);
 
         features out = susan(in, 3, t, g, 0.05f, 3);
 
-        vector<float> outX          (gold[0].size());
-        vector<float> outY          (gold[1].size());
-        vector<float> outScore      (gold[2].size());
+        vector<float> outX(gold[0].size());
+        vector<float> outY(gold[1].size());
+        vector<float> outScore(gold[2].size());
         vector<float> outOrientation(gold[3].size());
-        vector<float> outSize       (gold[4].size());
+        vector<float> outSize(gold[4].size());
         out.getX().host(outX.data());
         out.getY().host(outY.data());
         out.getScore().host(outScore.data());
@@ -100,85 +97,78 @@ void susanTest(string pTestFile, float t, float g)
 
         vector<feat_t> out_feat;
         array_to_feat(out_feat, outX.data(), outY.data(), outScore.data(),
-                      outOrientation.data(), outSize.data(), out.getNumFeatures());
+                      outOrientation.data(), outSize.data(),
+                      out.getNumFeatures());
 
         vector<feat_t> gold_feat;
-        array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(), &gold[2].front(), &gold[3].front(), &gold[4].front(), gold[0].size());
+        array_to_feat(gold_feat, &gold[0].front(), &gold[1].front(),
+                      &gold[2].front(), &gold[3].front(), &gold[4].front(),
+                      gold[0].size());
 
         std::sort(out_feat.begin(), out_feat.end(), feat_cmp);
         std::sort(gold_feat.begin(), gold_feat.end(), feat_cmp);
 
         for (int elIter = 0; elIter < (int)out.getNumFeatures(); elIter++) {
-            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1]) << "at: " << elIter << endl;
-            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3]) << "at: " << elIter << endl;
-            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4]) << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[0], gold_feat[elIter].f[0])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[1], gold_feat[elIter].f[1])
+                << "at: " << elIter << endl;
+            ASSERT_LE(fabs(out_feat[elIter].f[2] - gold_feat[elIter].f[2]), 1e2)
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[3], gold_feat[elIter].f[3])
+                << "at: " << elIter << endl;
+            ASSERT_EQ(out_feat[elIter].f[4], gold_feat[elIter].f[4])
+                << "at: " << elIter << endl;
         }
     }
 }
 
-#define SUSAN_TEST(image, tval, gval)                                   \
-    TYPED_TEST(Susan, image)                                            \
-    {                                                                   \
-        susanTest<TypeParam>(string(TEST_DIR "/susan/"#image".test"), tval, gval); \
+#define SUSAN_TEST(image, tval, gval)                                         \
+    TYPED_TEST(Susan, image) {                                                \
+        susanTest<TypeParam>(string(TEST_DIR "/susan/" #image ".test"), tval, \
+                             gval);                                           \
     }
 
 SUSAN_TEST(man_t32_g10, 32, 10);
 SUSAN_TEST(square_t32_g10, 32, 10);
 SUSAN_TEST(square_t32_g20, 32, 20);
 
-TEST(Susan, InvalidDims)
-{
+TEST(Susan, InvalidDims) {
     try {
-        array a = randu(256);
+        array a      = randu(256);
         features out = susan(a);
         EXPECT_TRUE(false);
-    } catch (exception &e) {
-        EXPECT_TRUE(true);
-    }
+    } catch (exception &e) { EXPECT_TRUE(true); }
 }
 
-TEST(Susan, InvalidRadius)
-{
+TEST(Susan, InvalidRadius) {
     try {
-        array a = randu(256);
+        array a      = randu(256);
         features out = susan(a, 10);
         EXPECT_TRUE(false);
-    } catch (exception &e) {
-        EXPECT_TRUE(true);
-    }
+    } catch (exception &e) { EXPECT_TRUE(true); }
 }
 
-TEST(Susan, InvalidThreshold)
-{
+TEST(Susan, InvalidThreshold) {
     try {
-        array a = randu(256);
+        array a      = randu(256);
         features out = susan(a, 3, -32, 10, 0.05f, 3);
         EXPECT_TRUE(false);
-    } catch (exception &e) {
-        EXPECT_TRUE(true);
-    }
+    } catch (exception &e) { EXPECT_TRUE(true); }
 }
 
-TEST(Susan, InvalidFeatureRatio)
-{
+TEST(Susan, InvalidFeatureRatio) {
     try {
-        array a = randu(256);
+        array a      = randu(256);
         features out = susan(a, 3, 32, 10, 1.3f, 3);
         EXPECT_TRUE(false);
-    } catch (exception &e) {
-        EXPECT_TRUE(true);
-    }
+    } catch (exception &e) { EXPECT_TRUE(true); }
 }
 
-TEST(Susan, InvalidEdge)
-{
+TEST(Susan, InvalidEdge) {
     try {
-        array a = randu(128, 128);
+        array a      = randu(128, 128);
         features out = susan(a, 3, 32, 10, 1.3f, 129);
         EXPECT_TRUE(false);
-    } catch (exception &e) {
-        EXPECT_TRUE(true);
-    }
+    } catch (exception &e) { EXPECT_TRUE(true); }
 }
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index cb6368b8d8..05749b1924 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
 using af::array;
 using af::cdouble;
@@ -35,32 +35,28 @@ using std::string;
 using std::vector;
 
 template<typename T>
-class svd : public ::testing::Test
-{
-};
+class svd : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 TYPED_TEST_CASE(svd, TestTypes);
 
 template<typename T>
-inline double get_val(T val)
-{
+inline double get_val(T val) {
     return val;
 }
 
-template<> inline double get_val<cfloat>(cfloat val)
-{
+template<>
+inline double get_val<cfloat>(cfloat val) {
     return abs(val);
 }
 
-template<> double get_val<cdouble>(cdouble val)
-{
+template<>
+double get_val<cdouble>(cdouble val) {
     return abs(val);
 }
 
 template<typename T>
-void svdTest(const int M, const int N)
-{
+void svdTest(const int M, const int N) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -89,14 +85,13 @@ void svdTest(const int M, const int N)
 }
 
 template<typename T>
-void svdInPlaceTest(const int M, const int N)
-{
+void svdInPlaceTest(const int M, const int N) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
-    array A = randu(M, N, ty);
+    array A      = randu(M, N, ty);
     array A_copy = A.copy();
 
     array U, S, Vt;
@@ -118,8 +113,7 @@ void svdInPlaceTest(const int M, const int N)
 }
 
 template<typename T>
-void checkInPlaceSameResults(const int M, const int N)
-{
+void checkInPlaceSameResults(const int M, const int N) {
     if (noDoubleTests<T>()) return;
     if (noLAPACKTests()) return;
 
@@ -137,30 +131,15 @@ void checkInPlaceSameResults(const int M, const int N)
     ASSERT_ARRAYS_EQ(v, vv);
 }
 
-TYPED_TEST(svd, Square)
-{
-    svdTest<TypeParam>(500, 500);
-}
+TYPED_TEST(svd, Square) { svdTest<TypeParam>(500, 500); }
 
-TYPED_TEST(svd, Rect0)
-{
-    svdTest<TypeParam>(500, 300);
-}
+TYPED_TEST(svd, Rect0) { svdTest<TypeParam>(500, 300); }
 
-TYPED_TEST(svd, Rect1)
-{
-    svdTest<TypeParam>(300, 500);
-}
+TYPED_TEST(svd, Rect1) { svdTest<TypeParam>(300, 500); }
 
-TYPED_TEST(svd, InPlaceSquare)
-{
-    svdInPlaceTest<TypeParam>(500, 500);
-}
+TYPED_TEST(svd, InPlaceSquare) { svdInPlaceTest<TypeParam>(500, 500); }
 
-TYPED_TEST(svd, InPlaceRect0)
-{
-    svdInPlaceTest<TypeParam>(500, 300);
-}
+TYPED_TEST(svd, InPlaceRect0) { svdInPlaceTest<TypeParam>(500, 300); }
 
 // dim0 < dim1 case not supported for now
 // TYPED_TEST(svd, InPlaceRect1)
@@ -168,13 +147,11 @@ TYPED_TEST(svd, InPlaceRect0)
 //     svdInPlaceTest<TypeParam>(300, 500);
 // }
 
-TYPED_TEST(svd, InPlaceSameResultsSquare)
-{
+TYPED_TEST(svd, InPlaceSameResultsSquare) {
     checkInPlaceSameResults<TypeParam>(10, 10);
 }
 
-TYPED_TEST(svd, InPlaceSameResultsRect0)
-{
+TYPED_TEST(svd, InPlaceSameResultsRect0) {
     checkInPlaceSameResults<TypeParam>(10, 8);
 }
 
@@ -189,4 +166,3 @@ TEST(svd, InPlaceRect0_Exception) {
     array u, s, v;
     EXPECT_THROW(svdInPlace(u, s, v, in), af::exception);
 }
-
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index d0c5892ad6..b2723cf537 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -10,12 +10,12 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 
+#include <arrayfire.h>
+#include <gtest/gtest.h>
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/internal.h>
 #include <af/traits.hpp>
-#include <arrayfire.h>
-#include <gtest/gtest.h>
 
 #include <algorithm>
 #include <cfloat>
@@ -34,7 +34,8 @@
 #include <mmio.h>
 #endif
 
-#define UNUSED(expr) do { (void)(expr); } while (0)
+#define UNUSED(expr) \
+    do { (void)(expr); } while (0)
 
 namespace aft {
 #pragma GCC diagnostic push
@@ -42,10 +43,10 @@ namespace aft {
 typedef intl intl;
 typedef uintl uintl;
 #pragma GCC diagnostic pop
-}
+}  // namespace aft
 
-using aft::uintl;
 using aft::intl;
+using aft::uintl;
 
 std::ostream &operator<<(std::ostream &os, af_err e) {
     return os << af_err_to_string(e);
@@ -54,31 +55,30 @@ std::ostream &operator<<(std::ostream &os, af_err e) {
 std::ostream &operator<<(std::ostream &os, af::dtype type) {
     std::string name;
     switch (type) {
-    case f32: name = "f32"; break;
-    case c32: name = "c32"; break;
-    case f64: name = "f64"; break;
-    case c64: name = "c64"; break;
-    case b8 : name = "b8" ; break;
-    case s32: name = "s32"; break;
-    case u32: name = "u32"; break;
-    case u8 : name = "u8" ; break;
-    case s64: name = "s64"; break;
-    case u64: name = "u64"; break;
-    case s16: name = "s16"; break;
-    case u16: name = "u16"; break;
-    default: assert(false && "Invalid type");
+        case f32: name = "f32"; break;
+        case c32: name = "c32"; break;
+        case f64: name = "f64"; break;
+        case c64: name = "c64"; break;
+        case b8: name = "b8"; break;
+        case s32: name = "s32"; break;
+        case u32: name = "u32"; break;
+        case u8: name = "u8"; break;
+        case s64: name = "s64"; break;
+        case u64: name = "u64"; break;
+        case s16: name = "s16"; break;
+        case u16: name = "u16"; break;
+        default: assert(false && "Invalid type");
     }
     return os << name;
 }
 
 namespace {
 
-typedef unsigned char  uchar;
-typedef unsigned int   uint;
+typedef unsigned char uchar;
+typedef unsigned int uint;
 typedef unsigned short ushort;
 
-std::string readNextNonEmptyLine(std::ifstream &file)
-{
+std::string readNextNonEmptyLine(std::ifstream &file) {
     std::string result = "";
     // Using a for loop to read the next non empty line
     for (std::string line; std::getline(file, line);) {
@@ -94,67 +94,61 @@ std::string readNextNonEmptyLine(std::ifstream &file)
 
 template<typename inType, typename outType, typename FileElementType>
 void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
-                std::vector<std::vector<inType> >   &testInputs,
-                std::vector<std::vector<outType> > &testOutputs)
-{
+               std::vector<std::vector<inType> > &testInputs,
+               std::vector<std::vector<outType> > &testOutputs) {
     using std::vector;
 
     std::ifstream testFile(FileName.c_str());
-    if(testFile.good()) {
+    if (testFile.good()) {
         unsigned inputCount;
         testFile >> inputCount;
         inputDims.resize(inputCount);
-        for(unsigned i=0; i<inputCount; i++) {
-            testFile >> inputDims[i];
-        }
+        for (unsigned i = 0; i < inputCount; i++) { testFile >> inputDims[i]; }
 
         unsigned testCount;
         testFile >> testCount;
         testOutputs.resize(testCount);
 
         vector<unsigned> testSizes(testCount);
-        for(unsigned i = 0; i < testCount; i++) {
-            testFile >> testSizes[i];
-        }
+        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
 
-        testInputs.resize(inputCount,vector<inType>(0));
-        for(unsigned k=0; k<inputCount; k++) {
+        testInputs.resize(inputCount, vector<inType>(0));
+        for (unsigned k = 0; k < inputCount; k++) {
             dim_t nElems = inputDims[k].elements();
             testInputs[k].resize(nElems);
             FileElementType tmp;
-            for(unsigned i = 0; i < nElems; i++) {
+            for (unsigned i = 0; i < nElems; i++) {
                 testFile >> tmp;
                 testInputs[k][i] = static_cast<inType>(tmp);
             }
         }
 
         testOutputs.resize(testCount, vector<outType>(0));
-        for(unsigned i = 0; i < testCount; i++) {
+        for (unsigned i = 0; i < testCount; i++) {
             testOutputs[i].resize(testSizes[i]);
             FileElementType tmp;
-            for(unsigned j = 0; j < testSizes[i]; j++) {
+            for (unsigned j = 0; j < testSizes[i]; j++) {
                 testFile >> tmp;
                 testOutputs[i][j] = static_cast<outType>(tmp);
             }
         }
-    }
-    else {
+    } else {
         FAIL() << "TEST FILE NOT FOUND";
     }
 }
 
 template<typename inType, typename outType>
-void readTestsFromFile(const std::string &FileName, std::vector<af::dim4> &inputDims,
-                std::vector<std::vector<inType> >  &testInputs,
-                std::vector<std::vector<outType> > &testOutputs)
-{
+void readTestsFromFile(const std::string &FileName,
+                       std::vector<af::dim4> &inputDims,
+                       std::vector<std::vector<inType> > &testInputs,
+                       std::vector<std::vector<outType> > &testOutputs) {
     using std::vector;
 
     std::ifstream testFile(FileName.c_str());
-    if(testFile.good()) {
+    if (testFile.good()) {
         unsigned inputCount;
         testFile >> inputCount;
-        for(unsigned i=0; i<inputCount; i++) {
+        for (unsigned i = 0; i < inputCount; i++) {
             af::dim4 temp(1);
             testFile >> temp;
             inputDims.push_back(temp);
@@ -165,49 +159,45 @@ void readTestsFromFile(const std::string &FileName, std::vector<af::dim4> &input
         testOutputs.resize(testCount);
 
         vector<unsigned> testSizes(testCount);
-        for(unsigned i = 0; i < testCount; i++) {
-            testFile >> testSizes[i];
-        }
+        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
 
-        testInputs.resize(inputCount,vector<inType>(0));
-        for(unsigned k=0; k<inputCount; k++) {
+        testInputs.resize(inputCount, vector<inType>(0));
+        for (unsigned k = 0; k < inputCount; k++) {
             dim_t nElems = inputDims[k].elements();
             testInputs[k].resize(nElems);
             inType tmp;
-            for(unsigned i = 0; i < nElems; i++) {
+            for (unsigned i = 0; i < nElems; i++) {
                 testFile >> tmp;
                 testInputs[k][i] = tmp;
             }
         }
 
         testOutputs.resize(testCount, vector<outType>(0));
-        for(unsigned i = 0; i < testCount; i++) {
+        for (unsigned i = 0; i < testCount; i++) {
             testOutputs[i].resize(testSizes[i]);
             outType tmp;
-            for(unsigned j = 0; j < testSizes[i]; j++) {
+            for (unsigned j = 0; j < testSizes[i]; j++) {
                 testFile >> tmp;
                 testOutputs[i][j] = tmp;
             }
         }
-    }
-    else {
+    } else {
         FAIL() << "TEST FILE NOT FOUND";
     }
 }
 
-inline void readImageTests(const std::string        &pFileName,
-                           std::vector<af::dim4>    &pInputDims,
+inline void readImageTests(const std::string &pFileName,
+                           std::vector<af::dim4> &pInputDims,
                            std::vector<std::string> &pTestInputs,
-                           std::vector<dim_t>    &pTestOutSizes,
-                           std::vector<std::string> &pTestOutputs)
-{
+                           std::vector<dim_t> &pTestOutSizes,
+                           std::vector<std::string> &pTestOutputs) {
     using std::vector;
 
     std::ifstream testFile(pFileName.c_str());
-    if(testFile.good()) {
+    if (testFile.good()) {
         unsigned inputCount;
         testFile >> inputCount;
-        for(unsigned i=0; i<inputCount; i++) {
+        for (unsigned i = 0; i < inputCount; i++) {
             af::dim4 temp(1);
             testFile >> temp;
             pInputDims.push_back(temp);
@@ -218,38 +208,36 @@ inline void readImageTests(const std::string        &pFileName,
         pTestOutputs.resize(testCount);
 
         pTestOutSizes.resize(testCount);
-        for(unsigned i = 0; i < testCount; i++) {
+        for (unsigned i = 0; i < testCount; i++) {
             testFile >> pTestOutSizes[i];
         }
 
         pTestInputs.resize(inputCount, "");
-        for(unsigned k=0; k<inputCount; k++) {
+        for (unsigned k = 0; k < inputCount; k++) {
             pTestInputs[k] = readNextNonEmptyLine(testFile);
         }
 
         pTestOutputs.resize(testCount, "");
-        for(unsigned i = 0; i < testCount; i++) {
+        for (unsigned i = 0; i < testCount; i++) {
             pTestOutputs[i] = readNextNonEmptyLine(testFile);
         }
-    }
-    else {
+    } else {
         FAIL() << "TEST FILE NOT FOUND";
     }
 }
 
 template<typename outType>
-void readImageTests(const std::string                 &pFileName,
-                    std::vector<af::dim4>             &pInputDims,
-                    std::vector<std::string>          &pTestInputs,
-                    std::vector<std::vector<outType> > &pTestOutputs)
-{
+void readImageTests(const std::string &pFileName,
+                    std::vector<af::dim4> &pInputDims,
+                    std::vector<std::string> &pTestInputs,
+                    std::vector<std::vector<outType> > &pTestOutputs) {
     using std::vector;
 
     std::ifstream testFile(pFileName.c_str());
-    if(testFile.good()) {
+    if (testFile.good()) {
         unsigned inputCount;
         testFile >> inputCount;
-        for(unsigned i=0; i<inputCount; i++) {
+        for (unsigned i = 0; i < inputCount; i++) {
             af::dim4 temp(1);
             testFile >> temp;
             pInputDims.push_back(temp);
@@ -260,44 +248,40 @@ void readImageTests(const std::string                 &pFileName,
         pTestOutputs.resize(testCount);
 
         vector<unsigned> testSizes(testCount);
-        for(unsigned i = 0; i < testCount; i++) {
-            testFile >> testSizes[i];
-        }
+        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
 
         pTestInputs.resize(inputCount, "");
-        for(unsigned k=0; k<inputCount; k++) {
+        for (unsigned k = 0; k < inputCount; k++) {
             pTestInputs[k] = readNextNonEmptyLine(testFile);
         }
 
         pTestOutputs.resize(testCount, vector<outType>(0));
-        for(unsigned i = 0; i < testCount; i++) {
+        for (unsigned i = 0; i < testCount; i++) {
             pTestOutputs[i].resize(testSizes[i]);
             outType tmp;
-            for(unsigned j = 0; j < testSizes[i]; j++) {
+            for (unsigned j = 0; j < testSizes[i]; j++) {
                 testFile >> tmp;
                 pTestOutputs[i][j] = tmp;
             }
         }
-    }
-    else {
+    } else {
         FAIL() << "TEST FILE NOT FOUND";
     }
 }
 
 template<typename descType>
-void readImageFeaturesDescriptors(const std::string                  &pFileName,
-                                  std::vector<af::dim4>              &pInputDims,
-                                  std::vector<std::string>           &pTestInputs,
-                                  std::vector<std::vector<float> >    &pTestFeats,
-                                  std::vector<std::vector<descType> > &pTestDescs)
-{
+void readImageFeaturesDescriptors(
+    const std::string &pFileName, std::vector<af::dim4> &pInputDims,
+    std::vector<std::string> &pTestInputs,
+    std::vector<std::vector<float> > &pTestFeats,
+    std::vector<std::vector<descType> > &pTestDescs) {
     using std::vector;
 
     std::ifstream testFile(pFileName.c_str());
-    if(testFile.good()) {
+    if (testFile.good()) {
         unsigned inputCount;
         testFile >> inputCount;
-        for(unsigned i=0; i<inputCount; i++) {
+        for (unsigned i = 0; i < inputCount; i++) {
             af::dim4 temp(1);
             testFile >> temp;
             pInputDims.push_back(temp);
@@ -310,31 +294,30 @@ void readImageFeaturesDescriptors(const std::string                  &pFileName,
         pTestFeats.resize(attrCount);
 
         pTestInputs.resize(inputCount, "");
-        for(unsigned k=0; k<inputCount; k++) {
+        for (unsigned k = 0; k < inputCount; k++) {
             pTestInputs[k] = readNextNonEmptyLine(testFile);
         }
 
         pTestFeats.resize(attrCount, vector<float>(0));
-        for(unsigned i = 0; i < attrCount; i++) {
+        for (unsigned i = 0; i < attrCount; i++) {
             pTestFeats[i].resize(featCount);
             float tmp;
-            for(unsigned j = 0; j < featCount; j++) {
+            for (unsigned j = 0; j < featCount; j++) {
                 testFile >> tmp;
                 pTestFeats[i][j] = tmp;
             }
         }
 
         pTestDescs.resize(featCount, vector<descType>(0));
-        for(unsigned i = 0; i < featCount; i++) {
+        for (unsigned i = 0; i < featCount; i++) {
             pTestDescs[i].resize(descLen);
             descType tmp;
-            for(unsigned j = 0; j < descLen; j++) {
+            for (unsigned j = 0; j < descLen; j++) {
                 testFile >> tmp;
                 pTestDescs[i][j] = tmp;
             }
         }
-    }
-    else {
+    } else {
         FAIL() << "TEST FILE NOT FOUND";
     }
 }
@@ -354,24 +337,23 @@ void readImageFeaturesDescriptors(const std::string                  &pFileName,
  * value of NRMSD. Hence, the range of RMSD is [0,255] for image inputs.
  */
 template<typename T>
-bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance)
-{
+bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance) {
     double accum  = 0.0;
-    double maxion = -FLT_MAX;//(double)std::numeric_limits<T>::lowest();
-    double minion = FLT_MAX;//(double)std::numeric_limits<T>::max();
+    double maxion = -FLT_MAX;  //(double)std::numeric_limits<T>::lowest();
+    double minion = FLT_MAX;   //(double)std::numeric_limits<T>::max();
 
-    for(dim_t i=0;i<data_size;i++)
-    {
+    for (dim_t i = 0; i < data_size; i++) {
         double dTemp = (double)data[i];
         double gTemp = (double)gold[i];
-        double diff  = gTemp-dTemp;
-        double err   = (std::isfinite(diff) && (std::abs(diff) > 1.0e-4)) ? diff : 0.0f;
-        accum  += std::pow(err, 2.0);
-        maxion  = std::max(maxion, dTemp);
-        minion  = std::min(minion, dTemp);
+        double diff  = gTemp - dTemp;
+        double err =
+            (std::isfinite(diff) && (std::abs(diff) > 1.0e-4)) ? diff : 0.0f;
+        accum += std::pow(err, 2.0);
+        maxion = std::max(maxion, dTemp);
+        minion = std::min(minion, dTemp);
     }
     accum /= data_size;
-    double NRMSD = std::sqrt(accum)/(maxion-minion);
+    double NRMSD = std::sqrt(accum) / (maxion - minion);
 
     if (std::isnan(NRMSD) || NRMSD > tolerance) {
 #ifndef NDEBUG
@@ -410,59 +392,68 @@ template<bool B, class T = void>
 struct enable_if {};
 
 template<class T>
-struct enable_if<true, T> { typedef T type; };
+struct enable_if<true, T> {
+    typedef T type;
+};
 
 template<typename T>
-inline double real(T val) { return (double)val; }
+inline double real(T val) {
+    return (double)val;
+}
 template<>
-inline double real<af::cdouble>(af::cdouble val) { return real(val); }
+inline double real<af::cdouble>(af::cdouble val) {
+    return real(val);
+}
 template<>
-inline double real<af::cfloat> (af::cfloat val) { return real(val); }
+inline double real<af::cfloat>(af::cfloat val) {
+    return real(val);
+}
 
 template<typename T>
-inline double imag(T val) { return (double)val; }
+inline double imag(T val) {
+    return (double)val;
+}
 template<>
-inline double imag<af::cdouble>(af::cdouble val) { return imag(val); }
+inline double imag<af::cdouble>(af::cdouble val) {
+    return imag(val);
+}
 template<>
-inline double imag<af::cfloat> (af::cfloat val) { return imag(val); }
-
+inline double imag<af::cfloat>(af::cfloat val) {
+    return imag(val);
+}
 
 template<class T>
 struct IsFloatingPoint {
-    static const bool value = is_same_type<float, T>::value  ||
-        is_same_type<double, T>::value  ||
-        is_same_type<long double, T>::value;
+    static const bool value = is_same_type<float, T>::value ||
+                              is_same_type<double, T>::value ||
+                              is_same_type<long double, T>::value;
 };
 
 template<typename T>
-bool noDoubleTests()
-{
-    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
-    bool isTypeDouble = (ty == f64) || (ty == c64);
-    int dev = af::getDevice();
+bool noDoubleTests() {
+    af::dtype ty           = (af::dtype)af::dtype_traits<T>::af_type;
+    bool isTypeDouble      = (ty == f64) || (ty == c64);
+    int dev                = af::getDevice();
     bool isDoubleSupported = af::isDoubleAvailable(dev);
 
     return ((isTypeDouble && !isDoubleSupported) ? true : false);
 }
 
-inline bool noImageIOTests()
-{
+inline bool noImageIOTests() {
     bool ret = !af::isImageIOAvailable();
-    if(ret) printf("Image IO Not Configured. Test will exit\n");
+    if (ret) printf("Image IO Not Configured. Test will exit\n");
     return ret;
 }
 
-inline bool noLAPACKTests()
-{
+inline bool noLAPACKTests() {
     bool ret = !af::isLAPACKAvailable();
-    if(ret) printf("LAPACK Not Configured. Test will exit\n");
+    if (ret) printf("LAPACK Not Configured. Test will exit\n");
     return ret;
 }
 
 // TODO: perform conversion on device for CUDA and OpenCL
 template<typename T>
-af_err conv_image(af_array *out, af_array in)
-{
+af_err conv_image(af_array *out, af_array in) {
     af_array outArray;
 
     dim_t d0, d1, d2, d3;
@@ -477,57 +468,56 @@ af_err conv_image(af_array *out, af_array in)
 
     T *out_data = new T[nElems];
 
-    for (int i = 0; i < (int)nElems; i++)
-        out_data[i] = (T)in_data[i];
+    for (int i = 0; i < (int)nElems; i++) out_data[i] = (T)in_data[i];
 
-    af_create_array(&outArray, out_data, idims.ndims(), idims.get(), (af_dtype) af::dtype_traits<T>::af_type);
+    af_create_array(&outArray, out_data, idims.ndims(), idims.get(),
+                    (af_dtype)af::dtype_traits<T>::af_type);
 
     std::swap(*out, outArray);
 
-    delete [] in_data;
-    delete [] out_data;
+    delete[] in_data;
+    delete[] out_data;
 
     return AF_SUCCESS;
 }
 
 template<typename T>
-af::array cpu_randu(const af::dim4 dims)
-{
+af::array cpu_randu(const af::dim4 dims) {
     typedef typename af::dtype_traits<T>::base_type BT;
 
-    bool isTypeCplx = is_same_type<T, af::cfloat>::value || is_same_type<T, af::cdouble>::value;
-    bool isTypeFloat = is_same_type<BT, float>::value || is_same_type<BT, double>::value;
+    bool isTypeCplx = is_same_type<T, af::cfloat>::value ||
+                      is_same_type<T, af::cdouble>::value;
+    bool isTypeFloat =
+        is_same_type<BT, float>::value || is_same_type<BT, double>::value;
 
     size_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
 
     std::vector<BT> out(elements);
-    for(size_t i = 0; i < elements; i++) {
-        out[i] = isTypeFloat ? (BT)(rand())/RAND_MAX : rand() % 100;
+    for (size_t i = 0; i < elements; i++) {
+        out[i] = isTypeFloat ? (BT)(rand()) / RAND_MAX : rand() % 100;
     }
 
     return af::array(dims, (T *)&out[0]);
 }
 
-void cleanSlate()
-{
-  const size_t step_bytes = 1024;
+void cleanSlate() {
+    const size_t step_bytes = 1024;
 
-  size_t alloc_bytes, alloc_buffers;
-  size_t lock_bytes, lock_buffers;
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
 
-  af::deviceGC();
+    af::deviceGC();
 
-  af::deviceMemInfo(&alloc_bytes, &alloc_buffers,
-                    &lock_bytes, &lock_buffers);
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
-  ASSERT_EQ(0u, alloc_buffers);
-  ASSERT_EQ(0u, lock_buffers);
-  ASSERT_EQ(0u, alloc_bytes);
-  ASSERT_EQ(0u, lock_bytes);
+    ASSERT_EQ(0u, alloc_buffers);
+    ASSERT_EQ(0u, lock_buffers);
+    ASSERT_EQ(0u, alloc_bytes);
+    ASSERT_EQ(0u, lock_bytes);
 
-  af::setMemStepSize(step_bytes);
+    af::setMemStepSize(step_bytes);
 
-  ASSERT_EQ(af::getMemStepSize(), step_bytes);
+    ASSERT_EQ(af::getMemStepSize(), step_bytes);
 }
 
 //********** arrayfire custom test asserts ***********
@@ -535,17 +525,14 @@ void cleanSlate()
 // Overloading unary + op is needed to make unsigned char values printable
 //  as numbers
 
-const af::cfloat& operator+(const af::cfloat& val) {
-    return val;
-}
+const af::cfloat &operator+(const af::cfloat &val) { return val; }
 
-const af::cdouble& operator+(const af::cdouble& val) {
-    return val;
-}
+const af::cdouble &operator+(const af::cdouble &val) { return val; }
 
 // Calculate a multi-dimensional coordinates' linearized index
 dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
-    return std::inner_product(coords.get(), coords.get()+4, strides.get(), 0LL);
+    return std::inner_product(coords.get(), coords.get() + 4, strides.get(),
+                              0LL);
 }
 
 // Calculate a linearized index's multi-dimensonal coordinates in an af::array,
@@ -562,18 +549,17 @@ af::dim4 unravelIdx(dim_t idx, af::dim4 dims, af::dim4 strides) {
 
 af::dim4 unravelIdx(dim_t idx, af::array arr) {
     af::dim4 dims = arr.dims();
-    af::dim4 st = af::getStrides(arr);
+    af::dim4 st   = af::getStrides(arr);
     return unravelIdx(idx, dims, st);
 }
 
-af::dim4 calcStrides(const af::dim4 &parentDim)
-{
+af::dim4 calcStrides(const af::dim4 &parentDim) {
     af::dim4 out(1, 1, 1, 1);
-    dim_t *out_dims = out.get();
-    const dim_t *parent_dims =  parentDim.get();
+    dim_t *out_dims          = out.get();
+    const dim_t *parent_dims = parentDim.get();
 
-    for (dim_t i=1; i < 4; i++) {
-        out_dims[i] = out_dims[i - 1] * parent_dims[i-1];
+    for (dim_t i = 1; i < 4; i++) {
+        out_dims[i] = out_dims[i - 1] * parent_dims[i - 1];
     }
 
     return out;
@@ -582,54 +568,44 @@ af::dim4 calcStrides(const af::dim4 &parentDim)
 std::string minimalDim4(af::dim4 coords, af::dim4 dims) {
     std::ostringstream os;
     os << "(" << coords[0];
-    if (dims[1] > 1 || dims[2] > 1 || dims[3] > 1) {
-        os << ", " << coords[1];
-    }
-    if (dims[2] > 1 || dims[3] > 1) {
-        os << ", " << coords[2];
-    }
-    if (dims[3] > 1) {
-        os << ", " << coords[3];
-    }
+    if (dims[1] > 1 || dims[2] > 1 || dims[3] > 1) { os << ", " << coords[1]; }
+    if (dims[2] > 1 || dims[3] > 1) { os << ", " << coords[2]; }
+    if (dims[3] > 1) { os << ", " << coords[3]; }
     os << ")";
 
     return os.str();
 }
 
 template<typename T>
-std::string printContext(const std::vector<T>& hGold, std::string goldName,
-                         const std::vector<T>& hOut, std::string outName,
-                         af::dim4 arrDims,
-                         af::dim4 arrStrides,
-                         dim_t idx) {
+std::string printContext(const std::vector<T> &hGold, std::string goldName,
+                         const std::vector<T> &hOut, std::string outName,
+                         af::dim4 arrDims, af::dim4 arrStrides, dim_t idx) {
     std::ostringstream os;
 
     af::dim4 coords = unravelIdx(idx, arrDims, arrStrides);
-    dim_t ctxWidth = 5;
+    dim_t ctxWidth  = 5;
 
     // Coordinates that span dim0
     af::dim4 coordsMinBound = coords;
-    coordsMinBound[0] = 0;
+    coordsMinBound[0]       = 0;
     af::dim4 coordsMaxBound = coords;
-    coordsMaxBound[0] = arrDims[0] - 1;
+    coordsMaxBound[0]       = arrDims[0] - 1;
 
     // dim0 positions that can be displayed
     dim_t dim0Start = std::max<dim_t>(0LL, coords[0] - ctxWidth);
-    dim_t dim0End = std::min<dim_t>(coords[0] + ctxWidth + 1LL, arrDims[0]);
+    dim_t dim0End   = std::min<dim_t>(coords[0] + ctxWidth + 1LL, arrDims[0]);
 
     // Linearized indices of values in vectors that can be displayed
-    dim_t vecStartIdx = std::max<dim_t>(ravelIdx(coordsMinBound, arrStrides),
-                                        idx - ctxWidth);
+    dim_t vecStartIdx =
+        std::max<dim_t>(ravelIdx(coordsMinBound, arrStrides), idx - ctxWidth);
 
     // Display as minimal coordinates as needed
     // First value is the range of dim0 positions that will be displayed
     os << "Viewing slice (" << dim0Start << ":" << dim0End - 1;
     if (arrDims[1] > 1 || arrDims[2] > 1 || arrDims[3] > 1)
         os << ", " << coords[1];
-    if (arrDims[2] > 1 || arrDims[3] > 1)
-        os << ", " << coords[2];
-    if (arrDims[3] > 1)
-        os << ", " << coords[3];
+    if (arrDims[2] > 1 || arrDims[3] > 1) os << ", " << coords[2];
+    if (arrDims[3] > 1) os << ", " << coords[3];
     os << "), dims are (" << arrDims << ") strides: (" << arrStrides << ")\n";
 
     dim_t ctxElems = dim0End - dim0Start;
@@ -651,7 +627,7 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
             tmpOs << "[" << dim0 << "]";
         else
             tmpOs << dim0;
-        ctxDim0[i] = tmpOs.str();
+        ctxDim0[i]     = tmpOs.str();
         size_t dim0Len = tmpOs.str().length();
         tmpOs.str(std::string());
 
@@ -659,8 +635,7 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
 
         if (valIdx == idx) {
             tmpOs << "[" << +hOut[valIdx] << "]";
-        }
-        else {
+        } else {
             tmpOs << +hOut[valIdx];
         }
         ctxOutVals[i] = tmpOs.str();
@@ -669,23 +644,23 @@ std::string printContext(const std::vector<T>& hGold, std::string goldName,
 
         if (valIdx == idx) {
             tmpOs << "[" << +hGold[valIdx] << "]";
-        }
-        else {
+        } else {
             tmpOs << +hGold[valIdx];
         }
         ctxGoldVals[i] = tmpOs.str();
         size_t goldLen = tmpOs.str().length();
         tmpOs.str(std::string());
 
-        int maxWidth = std::max<int>(dim0Len, outLen);
-        maxWidth = std::max<int>(maxWidth, goldLen);
+        int maxWidth      = std::max<int>(dim0Len, outLen);
+        maxWidth          = std::max<int>(maxWidth, goldLen);
         valFieldWidths[i] = maxWidth;
     }
 
     size_t varNameWidth = std::max<size_t>(goldName.length(), outName.length());
 
     // Display dim0 positions, output values, and reference values
-    os << std::right << std::setw(varNameWidth) << "" << "   ";
+    os << std::right << std::setw(varNameWidth) << ""
+       << "   ";
     for (uint i = 0; i < (dim0End - dim0Start); ++i) {
         os << std::setw(valFieldWidths[i] + 1) << std::right << ctxDim0[i];
     }
@@ -711,51 +686,50 @@ struct IntegerTag {};
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      const std::vector<T>& a, af::dim4 aDims,
-                                      const std::vector<T>& b, af::dim4 bDims,
+                                      const std::vector<T> &a, af::dim4 aDims,
+                                      const std::vector<T> &b, af::dim4 bDims,
                                       float maxAbsDiff, IntegerTag) {
     UNUSED(maxAbsDiff);
     typedef typename std::vector<T>::const_iterator iter;
-    std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(), b.begin());
+    std::pair<iter, iter> mismatches =
+        std::mismatch(a.begin(), a.end(), b.begin());
     iter bItr = mismatches.second;
 
     if (bItr == b.end()) {
         return ::testing::AssertionSuccess();
     } else {
-        dim_t idx = std::distance(b.begin(), bItr);
+        dim_t idx         = std::distance(b.begin(), bItr);
         af::dim4 aStrides = calcStrides(aDims);
         af::dim4 bStrides = calcStrides(bDims);
-        af::dim4 coords = unravelIdx(idx, bDims, bStrides);
+        af::dim4 coords   = unravelIdx(idx, bDims, bStrides);
 
         return ::testing::AssertionFailure()
-            << "VALUE DIFFERS at "
-            << minimalDim4(coords, aDims) << ":\n"
-            << printContext(a, aName, b, bName, aDims, aStrides, idx);
+               << "VALUE DIFFERS at " << minimalDim4(coords, aDims) << ":\n"
+               << printContext(a, aName, b, bName, aDims, aStrides, idx);
     }
 }
 
-struct absMatch{
+struct absMatch {
     float diff_;
     absMatch(float diff) : diff_(diff) {}
 
     template<typename T>
-    bool operator() (T lhs, T rhs) {
-        using std::abs;
+    bool operator()(T lhs, T rhs) {
         using af::abs;
+        using std::abs;
         return abs(rhs - lhs) <= diff_;
     }
 };
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      const std::vector<T>& a, af::dim4 aDims,
-                                      const std::vector<T>& b, af::dim4 bDims,
+                                      const std::vector<T> &a, af::dim4 aDims,
+                                      const std::vector<T> &b, af::dim4 bDims,
                                       float maxAbsDiff, FloatTag) {
     typedef typename std::vector<T>::const_iterator iter;
     // TODO(mark): Modify equality for float
-    std::pair<iter, iter> mismatches = std::mismatch(a.begin(), a.end(),
-                                                     b.begin(),
-                                                     absMatch(maxAbsDiff));
+    std::pair<iter, iter> mismatches =
+        std::mismatch(a.begin(), a.end(), b.begin(), absMatch(maxAbsDiff));
 
     iter aItr = mismatches.first;
     iter bItr = mismatches.second;
@@ -763,20 +737,19 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     if (aItr == a.end()) {
         return ::testing::AssertionSuccess();
     } else {
-        dim_t idx = std::distance(b.begin(), bItr);
+        dim_t idx       = std::distance(b.begin(), bItr);
         af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
 
         af::dim4 aStrides = calcStrides(aDims);
 
         ::testing::AssertionResult result =
-        ::testing::AssertionFailure()
-              << "VALUE DIFFERS at "
-              << minimalDim4(coords, aDims) << ":\n"
-              << printContext(a, aName, b, bName, aDims, aStrides, idx);
+            ::testing::AssertionFailure()
+            << "VALUE DIFFERS at " << minimalDim4(coords, aDims) << ":\n"
+            << printContext(a, aName, b, bName, aDims, aStrides, idx);
 
-        if(maxAbsDiff > 0) {
-            using std::abs;
+        if (maxAbsDiff > 0) {
             using af::abs;
+            using std::abs;
             double absdiff = abs(*aItr - *bItr);
             result << "\n  Actual diff: " << absdiff << "\n"
                    << "Expected diff: " << maxAbsDiff;
@@ -788,7 +761,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
-                                      const af::array& a, const af::array& b,
+                                      const af::array &a, const af::array &b,
                                       float maxAbsDiff) {
     typedef typename cond_type<
         IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
@@ -800,45 +773,67 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
 
     std::vector<T> hB(static_cast<size_t>(b.elements()));
     b.host(hB.data());
-    return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff, tag);
+    return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff,
+                         tag);
 }
 
 // Called by ASSERT_ARRAYS_EQ
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         const af::array& a, const af::array& b,
+                                         const af::array &a, const af::array &b,
                                          float maxAbsDiff = 0.f) {
     af::dtype aType = a.type();
     af::dtype bType = b.type();
     if (aType != bType)
         return ::testing::AssertionFailure()
-            << "TYPE MISMATCH: \n"
-            << "  Actual: " << bName << "(" << b.type() << ")\n"
-            << "Expected: " << aName << "(" << a.type() << ")";
+               << "TYPE MISMATCH: \n"
+               << "  Actual: " << bName << "(" << b.type() << ")\n"
+               << "Expected: " << aName << "(" << a.type() << ")";
 
     af::dtype arrDtype = aType;
     if (a.dims() != b.dims())
         return ::testing::AssertionFailure()
-            << "SIZE MISMATCH: \n"
-            << "  Actual: " << bName << "([" << b.dims() << "])\n"
-            << "Expected: " << aName << "([" << a.dims() << "])";
+               << "SIZE MISMATCH: \n"
+               << "  Actual: " << bName << "([" << b.dims() << "])\n"
+               << "Expected: " << aName << "([" << a.dims() << "])";
 
     switch (arrDtype) {
-    case f32: return elemWiseEq<float>              (aName, bName, a, b, maxAbsDiff); break;
-    case c32: return elemWiseEq<af::cfloat>         (aName, bName, a, b, maxAbsDiff); break;
-    case f64: return elemWiseEq<double>             (aName, bName, a, b, maxAbsDiff); break;
-    case c64: return elemWiseEq<af::cdouble>        (aName, bName, a, b, maxAbsDiff); break;
-    case b8:  return elemWiseEq<char>               (aName, bName, a, b, maxAbsDiff); break;
-    case s32: return elemWiseEq<int>                (aName, bName, a, b, maxAbsDiff); break;
-    case u32: return elemWiseEq<uint>               (aName, bName, a, b, maxAbsDiff); break;
-    case u8:  return elemWiseEq<uchar>              (aName, bName, a, b, maxAbsDiff); break;
-    case s64: return elemWiseEq<long long>          (aName, bName, a, b, maxAbsDiff); break;
-    case u64: return elemWiseEq<unsigned long long> (aName, bName, a, b, maxAbsDiff); break;
-    case s16: return elemWiseEq<short>              (aName, bName, a, b, maxAbsDiff); break;
-    case u16: return elemWiseEq<unsigned short>     (aName, bName, a, b, maxAbsDiff); break;
-    default:  return ::testing::AssertionFailure()
-            << "INVALID TYPE, see enum numbers: "
-            << bName << "(" << b.type() << ") and "
-            << aName << "(" << a.type() << ")";
+        case f32:
+            return elemWiseEq<float>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case c32:
+            return elemWiseEq<af::cfloat>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case f64:
+            return elemWiseEq<double>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case c64:
+            return elemWiseEq<af::cdouble>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case b8: return elemWiseEq<char>(aName, bName, a, b, maxAbsDiff); break;
+        case s32: return elemWiseEq<int>(aName, bName, a, b, maxAbsDiff); break;
+        case u32:
+            return elemWiseEq<uint>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case u8:
+            return elemWiseEq<uchar>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case s64:
+            return elemWiseEq<long long>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case u64:
+            return elemWiseEq<unsigned long long>(aName, bName, a, b,
+                                                  maxAbsDiff);
+            break;
+        case s16:
+            return elemWiseEq<short>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case u16:
+            return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);
+            break;
+        default:
+            return ::testing::AssertionFailure()
+                   << "INVALID TYPE, see enum numbers: " << bName << "("
+                   << b.type() << ") and " << aName << "(" << a.type() << ")";
     }
 
     return ::testing::AssertionSuccess();
@@ -846,33 +841,34 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
 
 // Called by ASSERT_VEC_ARRAY_EQ
 template<typename T>
-::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsName,
+::testing::AssertionResult assertArrayEq(std::string aName,
+                                         std::string aDimsName,
                                          std::string bName,
-                                         const std::vector<T>& hA, af::dim4 aDims,
-                                         const af::array& b,
+                                         const std::vector<T> &hA,
+                                         af::dim4 aDims, const af::array &b,
                                          float maxAbsDiff = 0.0f) {
-    af::dtype aDtype = (af::dtype) af::dtype_traits<T>::af_type;
+    af::dtype aDtype = (af::dtype)af::dtype_traits<T>::af_type;
     if (aDtype != b.type()) {
         return ::testing::AssertionFailure()
-            << "TYPE MISMATCH:\n"
-            << "  Actual: " << bName << "(" << b.type() << ")\n"
-            << "Expected: " << aName << "(" << aDtype << ")";
+               << "TYPE MISMATCH:\n"
+               << "  Actual: " << bName << "(" << b.type() << ")\n"
+               << "Expected: " << aName << "(" << aDtype << ")";
     }
 
-    if(aDims != b.dims()) {
+    if (aDims != b.dims()) {
         return ::testing::AssertionFailure()
-            << "SIZE MISMATCH:\n"
-            << "  Actual: " << bName << "([" << b.dims() << "])\n"
-            << "Expected: " << aDimsName << "([" << aDims << "])";
+               << "SIZE MISMATCH:\n"
+               << "  Actual: " << bName << "([" << b.dims() << "])\n"
+               << "Expected: " << aDimsName << "([" << aDims << "])";
     }
 
     // In case vector<T> a.size() != aDims.elements()
     if (hA.size() != static_cast<size_t>(aDims.elements()))
         return ::testing::AssertionFailure()
-            << "SIZE MISMATCH:\n"
-            << "  Actual: " << aDimsName << "([" << aDims << "] => "
-            << aDims.elements() << ")\n"
-            << "Expected: " << aName << ".size()(" << hA.size() << ")";
+               << "SIZE MISMATCH:\n"
+               << "  Actual: " << aDimsName << "([" << aDims << "] => "
+               << aDims.elements() << ")\n"
+               << "Expected: " << aName << ".size()(" << hA.size() << ")";
 
     typedef typename cond_type<
         IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
@@ -881,7 +877,8 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string aDimsNam
 
     std::vector<T> hB(b.elements());
     b.host(&hB.front());
-    return elemWiseEq<T>(aName, bName, hA, aDims, hB, b.dims(), maxAbsDiff, tag);
+    return elemWiseEq<T>(aName, bName, hA, aDims, hB, b.dims(), maxAbsDiff,
+                         tag);
 }
 
 // To support C API
@@ -897,10 +894,11 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
 
 // To support C API
 template<typename T>
-::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsName,
+::testing::AssertionResult assertArrayEq(std::string hA_name,
+                                         std::string aDimsName,
                                          std::string bName,
-                                         const std::vector<T>& hA, af::dim4 aDims,
-                                         const af_array b) {
+                                         const std::vector<T> &hA,
+                                         af::dim4 aDims, const af_array b) {
     af_array bb = 0;
     af_retain_array(&bb, b);
     af::array bbb(bb);
@@ -910,7 +908,8 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name, std::string aDimsN
 // Called by ASSERT_ARRAYS_NEAR
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
-                                           const af::array& a, const af::array& b,
+                                           const af::array &a,
+                                           const af::array &b,
                                            float maxAbsDiff) {
     UNUSED(maxAbsDiffName);
     return assertArrayEq(aName, bName, a, b, maxAbsDiff);
@@ -918,12 +917,10 @@ ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
 
 // Called by ASSERT_VEC_ARRAY_NEAR
 template<typename T>
-::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
-                                           std::string bName,
-                                           std::string maxAbsDiffName,
-                                           const std::vector<T>& hA, af::dim4 aDims,
-                                           const af::array& b,
-                                           float maxAbsDiff) {
+::testing::AssertionResult assertArrayNear(
+    std::string hA_name, std::string aDimsName, std::string bName,
+    std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
+    const af::array &b, float maxAbsDiff) {
     UNUSED(maxAbsDiffName);
     return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
 }
@@ -943,26 +940,24 @@ ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
 
 // To support C API
 template<typename T>
-::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDimsName,
-                                           std::string bName,
-                                           std::string maxAbsDiffName,
-                                           const std::vector<T>& hA, af::dim4 aDims,
-                                           const af_array b,
-                                           float maxAbsDiff) {
+::testing::AssertionResult assertArrayNear(
+    std::string hA_name, std::string aDimsName, std::string bName,
+    std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
+    const af_array b, float maxAbsDiff) {
     af_array bb = 0;
     af_retain_array(&bb, b);
     af::array bbb(bb);
     return assertArrayNear(hA_name, aDimsName, maxAbsDiffName, bName, hA, aDims,
-                         bbb, maxAbsDiff);
+                           bbb, maxAbsDiff);
 }
 
 /// Checks if the C-API arrayfire function returns successfully
 ///
 /// \param[in] CALL This is the arrayfire C function
-#define ASSERT_SUCCESS(CALL)                    \
-    ASSERT_EQ(AF_SUCCESS, CALL)
+#define ASSERT_SUCCESS(CALL) ASSERT_EQ(AF_SUCCESS, CALL)
 
-/// Compares two af::array or af_arrays for their types, dims, and values (strict equality).
+/// Compares two af::array or af_arrays for their types, dims, and values
+/// (strict equality).
 ///
 /// \param[in] EXPECTED The expected array of the assertion
 /// \param[in] ACTUAL The actual resulting array from the calculation
@@ -976,18 +971,21 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
 ///
 /// \param[in] EXPECTED The expected array of the assertion
 /// \param[in] ACTUAL The actual resulting array from the calculation
-#define ASSERT_SPECIAL_ARRAYS_EQ(EXPECTED, ACTUAL, META)               \
+#define ASSERT_SPECIAL_ARRAYS_EQ(EXPECTED, ACTUAL, META) \
     EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED, ACTUAL, META)
 
-/// Compares a std::vector with an af::/af_array for their types, dims, and values (strict equality).
+/// Compares a std::vector with an af::/af_array for their types, dims, and
+/// values (strict equality).
 ///
 /// \param[in] EXPECTED_VEC The vector that represents the expected array
 /// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
 /// \param[in] ACTUAL_ARR The actual resulting array from the calculation
 #define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
-    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR)
+    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
+                        ACTUAL_ARR)
 
-/// Compares two af::array or af_arrays for their type, dims, and values (with a given tolerance).
+/// Compares two af::array or af_arrays for their type, dims, and values (with a
+/// given tolerance).
 ///
 /// \param[in] EXPECTED Expected value of the assertion
 /// \param[in] ACTUAL Actual value of the calculation
@@ -998,49 +996,49 @@ ::testing::AssertionResult assertArrayNear(std::string hA_name, std::string aDim
 #define ASSERT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
     EXPECT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
 
-/// Compares a std::vector with an af::array for their dims and values (with a given tolerance).
+/// Compares a std::vector with an af::array for their dims and values (with a
+/// given tolerance).
 ///
 /// \param[in] EXPECTED_VEC The vector that represents the expected array
 /// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
 /// \param[in] ACTUAL_ARR The actual array from the calculation
 /// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
 ///            elements of EXPECTED and ACTUAL
-#define ASSERT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, MAX_ABSDIFF) \
-    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
-                            MAX_ABSDIFF)
+#define ASSERT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
+                              MAX_ABSDIFF)                                 \
+    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
+                        ACTUAL_ARR, MAX_ABSDIFF)
 
 #if defined(USE_MTX)
-::testing::AssertionResult
-mtxReadSparseMatrix(af::array &out, const char* fileName)
-{
+::testing::AssertionResult mtxReadSparseMatrix(af::array &out,
+                                               const char *fileName) {
     FILE *fileHandle;
 
     if ((fileHandle = fopen(fileName, "r")) == NULL) {
         return ::testing::AssertionFailure()
-            << "Failed to open mtx file: " << fileName <<"\n";
+               << "Failed to open mtx file: " << fileName << "\n";
     }
 
     MM_typecode matcode;
     if (mm_read_banner(fileHandle, &matcode)) {
         return ::testing::AssertionFailure()
-            << "Could not process Matrix Market banner.\n";
+               << "Could not process Matrix Market banner.\n";
     }
 
     if (!(mm_is_matrix(matcode) && mm_is_sparse(matcode))) {
         return ::testing::AssertionFailure()
-            << "Input mtx doesn't have a sparse matrix.\n";
+               << "Input mtx doesn't have a sparse matrix.\n";
     }
 
-    if(mm_is_integer(matcode)) {
-        return ::testing::AssertionFailure()
-            << "MTX file has integer data. \
+    if (mm_is_integer(matcode)) {
+        return ::testing::AssertionFailure() << "MTX file has integer data. \
                 Integer sparse matrices are not supported in ArrayFire yet.\n";
     }
 
-    int M=0, N=0, nz=0;
+    int M = 0, N = 0, nz = 0;
     if (mm_read_mtx_crd_size(fileHandle, &M, &N, &nz)) {
         return ::testing::AssertionFailure()
-            << "Failed to read matrix dimensions.\n";
+               << "Failed to read matrix dimensions.\n";
     }
 
     if (mm_is_real(matcode)) {
@@ -1048,59 +1046,63 @@ mtxReadSparseMatrix(af::array &out, const char* fileName)
         std::vector<int> J(nz);
         std::vector<float> V(nz);
 
-        for (int i=0; i < nz; ++i) {
+        for (int i = 0; i < nz; ++i) {
             int c, r;
             double v;
             int readCount = fscanf(fileHandle, "%d %d %lg\n", &r, &c, &v);
             if (readCount != 3) {
                 fclose(fileHandle);
                 return ::testing::AssertionFailure()
-                    << "\nEnd of file reached, expected more data, "
-                    << "following are some reasons this happens.\n"
-                    << "\t - use of template type that doesn't match data type\n"
-                    << "\t - the mtx file itself doesn't have enough data\n";
+                       << "\nEnd of file reached, expected more data, "
+                       << "following are some reasons this happens.\n"
+                       << "\t - use of template type that doesn't match data "
+                          "type\n"
+                       << "\t - the mtx file itself doesn't have enough data\n";
             }
-            I[i] = r-1;
-            J[i] = c-1;
+            I[i] = r - 1;
+            J[i] = c - 1;
             V[i] = (float)v;
         }
 
-        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), f32, AF_STORAGE_COO);
+        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), f32,
+                         AF_STORAGE_COO);
     } else if (mm_is_complex(matcode)) {
         std::vector<int> I(nz);
         std::vector<int> J(nz);
         std::vector<af::cfloat> V(nz);
 
-        for (int i=0; i < nz; ++i) {
+        for (int i = 0; i < nz; ++i) {
             int c, r;
             double real, imag;
-            int readCount = fscanf(fileHandle, "%d %d %lg %lg\n", &r, &c, &real, &imag);
+            int readCount =
+                fscanf(fileHandle, "%d %d %lg %lg\n", &r, &c, &real, &imag);
             if (readCount != 4) {
                 fclose(fileHandle);
                 return ::testing::AssertionFailure()
-                    << "\nEnd of file reached, expected more data, "
-                    << "following are some reasons this happens.\n"
-                    << "\t - use of template type that doesn't match data type\n"
-                    << "\t - the mtx file itself doesn't have enough data\n";
+                       << "\nEnd of file reached, expected more data, "
+                       << "following are some reasons this happens.\n"
+                       << "\t - use of template type that doesn't match data "
+                          "type\n"
+                       << "\t - the mtx file itself doesn't have enough data\n";
             }
-            I[i] = r-1;
-            J[i] = c-1;
+            I[i] = r - 1;
+            J[i] = c - 1;
             V[i] = af::cfloat(float(real), float(imag));
         }
 
-        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), c32, AF_STORAGE_COO);
+        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), c32,
+                         AF_STORAGE_COO);
     } else {
         return ::testing::AssertionFailure()
-            << "Unknown matcode from MTX FILE\n";
+               << "Unknown matcode from MTX FILE\n";
     }
 
-
     fclose(fileHandle);
     return ::testing::AssertionSuccess();
 }
-#endif //USE_MTX
+#endif  // USE_MTX
 
-} // namespace
+}  // namespace
 
 enum TestOutputArrayType {
     // Test af_* function when given a null array as its output
@@ -1116,9 +1118,11 @@ enum TestOutputArrayType {
     SUB_ARRAY,
 
     // Test af_* function when given an output array that was previously
-    // reordered (but after the reorder, has still the same shape as the expected
+    // reordered (but after the reorder, has still the same shape as the
+    // expected
     // output). This specifically uses the reorder behavior when dim0 is kept,
-    // and thus no data movement is done - only the dims and strides are modified
+    // and thus no data movement is done - only the dims and strides are
+    // modified
     REORDERED_ARRAY
 };
 
@@ -1130,34 +1134,29 @@ class TestOutputArrayInfo {
     af_seq out_subarr_idxs[4];
     TestOutputArrayType out_arr_type;
 
-public:
-
+   public:
     TestOutputArrayInfo(TestOutputArrayType arr_type)
-        :out_arr(0),
-         out_arr_cpy(0),
-         out_subarr(0),
-         out_subarr_ndims(0),
-         out_arr_type(arr_type)
-    {
-        for (uint i = 0; i < 4; ++i) {
-            out_subarr_idxs[i] = af_span;
-        }
+        : out_arr(0)
+        , out_arr_cpy(0)
+        , out_subarr(0)
+        , out_subarr_ndims(0)
+        , out_arr_type(arr_type) {
+        for (uint i = 0; i < 4; ++i) { out_subarr_idxs[i] = af_span; }
     }
 
     ~TestOutputArrayInfo() {
-        if (out_subarr)  af_release_array(out_subarr);
+        if (out_subarr) af_release_array(out_subarr);
         if (out_arr_cpy) af_release_array(out_arr_cpy);
-        if (out_arr)     af_release_array(out_arr);
+        if (out_arr) af_release_array(out_arr);
     }
 
     void init(const unsigned ndims, const dim_t *const dims,
-                    const af_dtype ty) {
+              const af_dtype ty) {
         ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
     }
 
-    void init(const unsigned ndims, const dim_t *const dims,
-                    const af_dtype ty,
-                    const af_seq *const subarr_idxs) {
+    void init(const unsigned ndims, const dim_t *const dims, const af_dtype ty,
+              const af_seq *const subarr_idxs) {
         ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
         ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
         for (uint i = 0; i < ndims; ++i) {
@@ -1171,45 +1170,43 @@ class TestOutputArrayInfo {
     af_array getOutput() {
         if (out_arr_type == SUB_ARRAY) {
             return out_subarr;
-        }
-        else {
+        } else {
             return out_arr;
         }
     }
 
     void setOutput(af_array array) {
-        if (out_arr != 0) {
-            ASSERT_SUCCESS(af_release_array(out_arr));
-        }
+        if (out_arr != 0) { ASSERT_SUCCESS(af_release_array(out_arr)); }
         out_arr = array;
     }
 
-    af_array getFullOutput()                 { return out_arr; }
-    af_array getFullOutputCopy()             { return out_arr_cpy; }
-    af_seq   *getSubArrayIdxs()              { return &out_subarr_idxs[0]; }
-    dim_t    getSubArrayNumDims()            { return out_subarr_ndims; }
+    af_array getFullOutput() { return out_arr; }
+    af_array getFullOutputCopy() { return out_arr_cpy; }
+    af_seq *getSubArrayIdxs() { return &out_subarr_idxs[0]; }
+    dim_t getSubArrayNumDims() { return out_subarr_ndims; }
     TestOutputArrayType getOutputArrayType() { return out_arr_type; }
 };
 
 // Generates a random array. testWriteToOutputArray expects that it will receive
 // the same af_array that this generates after the af_* function is called
-void genRegularArray(TestOutputArrayInfo *metadata,
-                     const unsigned ndims, const dim_t *const dims, const af_dtype ty) {
+void genRegularArray(TestOutputArrayInfo *metadata, const unsigned ndims,
+                     const dim_t *const dims, const af_dtype ty) {
     metadata->init(ndims, dims, ty);
 }
 
-// Generates a large, random array, and extracts a subarray for the af_* function
-// to use. testWriteToOutputArray expects that the large array that it receives is
-// equal to the same large array with the gold array injected on the same subarray location
-void genSubArray(TestOutputArrayInfo *metadata,
-                 const unsigned ndims, const dim_t *const dims, const af_dtype ty) {
+// Generates a large, random array, and extracts a subarray for the af_*
+// function to use. testWriteToOutputArray expects that the large array that it
+// receives is equal to the same large array with the gold array injected on the
+// same subarray location
+void genSubArray(TestOutputArrayInfo *metadata, const unsigned ndims,
+                 const dim_t *const dims, const af_dtype ty) {
     const dim_t pad_size = 2;
 
     // The large array is padded on both sides of each dimension
     // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
     dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
     for (uint i = 0; i < ndims; ++i) {
-        full_arr_dims[i] = dims[i] + 2*pad_size;
+        full_arr_dims[i] = dims[i] + 2 * pad_size;
     }
 
     // Calculate index of sub-array. These will be used also by
@@ -1217,7 +1214,7 @@ void genSubArray(TestOutputArrayInfo *metadata,
     // same location. Currently, this location is the center of the large array
     af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
     for (uint i = 0; i < ndims; ++i) {
-        af_seq idx = {pad_size, pad_size + dims[i] - 1.0, 1.0};
+        af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
         subarr_idxs[i] = idx;
     }
 
@@ -1227,14 +1224,12 @@ void genSubArray(TestOutputArrayInfo *metadata,
 // Generates a reordered array. testWriteToOutputArray expects that this array
 // will still have the correct output values from the af_* function, even though
 // the array was initially reordered.
-void genReorderedArray(TestOutputArrayInfo *metadata,
-                       const unsigned ndims, const dim_t *const dims, const af_dtype ty) {
+void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
+                       const dim_t *const dims, const af_dtype ty) {
     // The rest of this function assumes that dims has 4 elements. Just in case
     // dims has < 4 elements, use another dims array that is filled with 1s
     dim_t all_dims[4] = {1, 1, 1, 1};
-    for (uint i = 0; i < ndims; ++i) {
-        all_dims[i] = dims[i];
-    }
+    for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
 
     // This reorder combination will not move data around, but will simply
     // call modDims and modStrides (see src/api/c/reorder.cpp).
@@ -1245,37 +1240,29 @@ void genReorderedArray(TestOutputArrayInfo *metadata,
     // Shape the output array such that the reordered output array will have
     // the correct dimensions that the test asks for (i.e. must match dims arg)
     dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
-    for (uint i = 0; i < 4; ++i) {
-        init_dims[i] = all_dims[reorder_idxs[i]];
-    }
+    for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
     metadata->init(4, init_dims, ty);
 
     af_array reordered = 0;
     ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
-                              reorder_idxs[0], reorder_idxs[1],
-                              reorder_idxs[2], reorder_idxs[3]));
+                              reorder_idxs[0], reorder_idxs[1], reorder_idxs[2],
+                              reorder_idxs[3]));
     metadata->setOutput(reordered);
 }
 
 // Partner function of testWriteToOutputArray. This generates the "special"
 // array that testWriteToOutputArray will use to check if the af_* function
 // correctly uses an existing array as its output
-void genTestOutputArray(af_array *out_ptr,
-                        const unsigned ndims, const dim_t *const dims,
-                        const af_dtype ty,
-                        TestOutputArrayInfo* metadata) {
+void genTestOutputArray(af_array *out_ptr, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype ty,
+                        TestOutputArrayInfo *metadata) {
     switch (metadata->getOutputArrayType()) {
-    case FULL_ARRAY:
-        genRegularArray(metadata, ndims, dims, ty);
-        break;
-    case SUB_ARRAY:
-        genSubArray(metadata, ndims, dims, ty);
-        break;
-    case REORDERED_ARRAY:
-        genReorderedArray(metadata, ndims, dims, ty);
-        break;
-    default:
-        break;
+        case FULL_ARRAY: genRegularArray(metadata, ndims, dims, ty); break;
+        case SUB_ARRAY: genSubArray(metadata, ndims, dims, ty); break;
+        case REORDERED_ARRAY:
+            genReorderedArray(metadata, ndims, dims, ty);
+            break;
+        default: break;
     }
     *out_ptr = metadata->getOutput();
 }
@@ -1283,16 +1270,15 @@ void genTestOutputArray(af_array *out_ptr,
 // Partner function of genTestOutputArray. This uses the same "special"
 // array that genTestOutputArray generates, and checks whether the
 // af_* function wrote to that array correctly
-::testing::AssertionResult
-testWriteToOutputArray(std::string gold_name, std::string result_name,
-                       const af_array gold, const af_array out,
-                       TestOutputArrayInfo *metadata) {
+::testing::AssertionResult testWriteToOutputArray(
+    std::string gold_name, std::string result_name, const af_array gold,
+    const af_array out, TestOutputArrayInfo *metadata) {
     // In the case of NULL_ARRAY, the output array starts out as null.
     // After the af_* function is called, it shouldn't be null anymore
     if (metadata->getOutputArrayType() == NULL_ARRAY) {
         if (out == 0) {
             return ::testing::AssertionFailure()
-                << "Output af_array " << result_name << " is null";
+                   << "Output af_array " << result_name << " is null";
         }
         metadata->setOutput(out);
     }
@@ -1301,9 +1287,9 @@ testWriteToOutputArray(std::string gold_name, std::string result_name,
     else {
         if (metadata->getOutput() != out) {
             return ::testing::AssertionFailure()
-                << "af_array POINTER MISMATCH:\n"
-                << "  Actual: " << out << "\n"
-                << "Expected: " << metadata->getOutput();
+                   << "af_array POINTER MISMATCH:\n"
+                   << "  Actual: " << out << "\n"
+                   << "Expected: " << metadata->getOutput();
         }
     }
 
@@ -1312,17 +1298,14 @@ testWriteToOutputArray(std::string gold_name, std::string result_name,
         // subarray, the other should have already been injected with the af_*
         // function's output. Then we compare the two full arrays
         af_array gold_full_array = metadata->getFullOutputCopy();
-        af_assign_seq(&gold_full_array,
-                      gold_full_array,
+        af_assign_seq(&gold_full_array, gold_full_array,
                       metadata->getSubArrayNumDims(),
-                      metadata->getSubArrayIdxs(),
-                      gold);
+                      metadata->getSubArrayIdxs(), gold);
 
         return assertArrayEq(gold_name, result_name,
                              metadata->getFullOutputCopy(),
                              metadata->getFullOutput());
-    }
-    else {
+    } else {
         return assertArrayEq(gold_name, result_name, gold, out);
     }
 }
diff --git a/test/threading.cpp b/test/threading.cpp
index b8cfa51a96..200ce91cb2 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -7,26 +7,25 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cstddef>
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <sparse_common.hpp>
+#include <testHelpers.hpp>
 #include <af/traits.hpp>
-#include <thread>
-#include <complex>
 #include <chrono>
+#include <complex>
 #include <condition_variable>
-#include <thread>
+#include <cstddef>
 #include <iterator>
+#include <thread>
 #include <vector>
-#include <testHelpers.hpp>
-#include <sparse_common.hpp>
 
 using namespace af;
 
 using std::cout;
 using std::endl;
-using std::vector;
 using std::string;
+using std::vector;
 
 static const int THREAD_COUNT = 32;
 
@@ -36,15 +35,13 @@ static const unsigned ITERATION_COUNT = 10;
 static const unsigned ITERATION_COUNT = 1000;
 #endif
 
-int nextTargetDeviceId()
-{
+int nextTargetDeviceId() {
     static int nextId = 0;
     return nextId++;
 }
 
 void morphTest(const array input, const array mask, const bool isDilation,
-               const array gold, int targetDevice)
-{
+               const array gold, int targetDevice) {
     setDevice(targetDevice);
 
     vector<float> goldData(gold.elements());
@@ -54,88 +51,81 @@ void morphTest(const array input, const array mask, const bool isDilation,
 
     array out;
 
-    for (unsigned i=0; i<ITERATION_COUNT; ++i)
+    for (unsigned i = 0; i < ITERATION_COUNT; ++i)
         out = isDilation ? dilate(input, mask) : erode(input, mask);
 
     out.host((void*)outData.data());
 
-    ASSERT_EQ(true, compareArraysRMSD(gold.elements(), goldData.data(), outData.data(), 0.018f));
+    ASSERT_EQ(true, compareArraysRMSD(gold.elements(), goldData.data(),
+                                      outData.data(), 0.018f));
 }
 
-TEST(Threading, SetPerThreadActiveDevice)
-{
+TEST(Threading, SetPerThreadActiveDevice) {
     if (noImageIOTests()) return;
 
     vector<bool> isDilationFlags;
     vector<bool> isColorFlags;
     vector<string> files;
 
-    files.push_back( string(TEST_DIR "/morph/gray.test") );
+    files.push_back(string(TEST_DIR "/morph/gray.test"));
     isDilationFlags.push_back(true);
     isColorFlags.push_back(false);
 
-    files.push_back( string(TEST_DIR "/morph/color.test") );
+    files.push_back(string(TEST_DIR "/morph/color.test"));
     isDilationFlags.push_back(false);
     isColorFlags.push_back(true);
 
     vector<std::thread> tests;
     unsigned totalTestCount = 0;
 
-    for(size_t pos = 0; pos<files.size(); ++pos)
-    {
+    for (size_t pos = 0; pos < files.size(); ++pos) {
         const bool isDilation = isDilationFlags[pos];
         const bool isColor    = isColorFlags[pos];
 
-        vector<dim4>    inDims;
-        vector<string>  inFiles;
-        vector<dim_t>   outSizes;
-        vector<string>  outFiles;
+        vector<dim4> inDims;
+        vector<string> inFiles;
+        vector<dim_t> outSizes;
+        vector<string> outFiles;
 
         readImageTests(files[pos], inDims, inFiles, outSizes, outFiles);
 
         const unsigned testCount = inDims.size();
 
-        const dim4 maskdims(3,3,1,1);
+        const dim4 maskdims(3, 3, 1, 1);
 
-        for (size_t testId=0; testId<testCount; ++testId)
-        {
+        for (size_t testId = 0; testId < testCount; ++testId) {
             int trgtDeviceId = totalTestCount % getDeviceCount();
 
-            //prefix full path to image file names
-            inFiles[testId].insert(0,string(TEST_DIR "/morph/"));
-            outFiles[testId].insert(0,string(TEST_DIR "/morph/"));
+            // prefix full path to image file names
+            inFiles[testId].insert(0, string(TEST_DIR "/morph/"));
+            outFiles[testId].insert(0, string(TEST_DIR "/morph/"));
 
             setDevice(trgtDeviceId);
 
             const array mask = constant(1.0, maskdims);
 
-            array input= loadImage(inFiles[testId].c_str(), isColor);
-            array gold = loadImage(outFiles[testId].c_str(), isColor);
+            array input = loadImage(inFiles[testId].c_str(), isColor);
+            array gold  = loadImage(outFiles[testId].c_str(), isColor);
 
-            //Push the new test as a new thread of execution
-            tests.emplace_back(morphTest, input, mask, isDilation, gold, trgtDeviceId);
+            // Push the new test as a new thread of execution
+            tests.emplace_back(morphTest, input, mask, isDilation, gold,
+                               trgtDeviceId);
 
             totalTestCount++;
         }
     }
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
-enum ArithOp
-{
-    ADD, SUB, DIV, MUL
-};
+enum ArithOp { ADD, SUB, DIV, MUL };
 
-void calc(ArithOp opcode, array op1, array op2, float outValue)
-{
+void calc(ArithOp opcode, array op1, array op2, float outValue) {
     setDevice(0);
     array res;
-    for (unsigned i=0; i<ITERATION_COUNT; ++i)
-    {
-        switch(opcode) {
+    for (unsigned i = 0; i < ITERATION_COUNT; ++i) {
+        switch (opcode) {
             case ADD: res = op1 + op2; break;
             case SUB: res = op1 - op2; break;
             case DIV: res = op1 / op2; break;
@@ -146,56 +136,63 @@ void calc(ArithOp opcode, array op1, array op2, float outValue)
     vector<float> out(res.elements());
     res.host((void*)out.data());
 
-    for (unsigned i=0; i <out.size(); ++i)
-        ASSERT_EQ(out[i], outValue);
+    for (unsigned i = 0; i < out.size(); ++i) ASSERT_EQ(out[i], outValue);
 }
 
-TEST(Threading, SimultaneousRead)
-{
+TEST(Threading, SimultaneousRead) {
     setDevice(0);
     array A = constant(1.0, 100, 100);
     array B = constant(1.0, 100, 100);
 
     vector<std::thread> tests;
 
-    for (int t=0; t<THREAD_COUNT; ++t)
-    {
+    for (int t = 0; t < THREAD_COUNT; ++t) {
         ArithOp op;
         float outValue;
 
-        switch(t%4) {
-            case 0: op = ADD; outValue = 2.0f; break;
-            case 1: op = SUB; outValue = 0.0f; break;
-            case 2: op = DIV; outValue = 1.0f; break;
-            case 3: op = MUL; outValue = 1.0f; break;
+        switch (t % 4) {
+            case 0:
+                op       = ADD;
+                outValue = 2.0f;
+                break;
+            case 1:
+                op       = SUB;
+                outValue = 0.0f;
+                break;
+            case 2:
+                op       = DIV;
+                outValue = 1.0f;
+                break;
+            case 3:
+                op       = MUL;
+                outValue = 1.0f;
+                break;
         }
 
         tests.emplace_back(calc, op, A, B, outValue);
     }
 
-    for (int t=0; t<THREAD_COUNT; ++t)
-        if (tests[t].joinable())
-            tests[t].join();
+    for (int t = 0; t < THREAD_COUNT; ++t)
+        if (tests[t].joinable()) tests[t].join();
 }
 
 std::condition_variable cv;
 std::mutex cvMutex;
 size_t counter = THREAD_COUNT;
 
-void doubleAllocationTest()
-{
+void doubleAllocationTest() {
     setDevice(0);
 
-    //Block until all threads are launched and the
-    //counter variable hits zero
+    // Block until all threads are launched and the
+    // counter variable hits zero
     std::unique_lock<std::mutex> lock(cvMutex);
-    //Check for current thread launch counter value
-    //if reached zero, notify others to continue
-    //otherwise block current thread
-    if (--counter==0)
+    // Check for current thread launch counter value
+    // if reached zero, notify others to continue
+    // otherwise block current thread
+    if (--counter == 0)
         cv.notify_all();
     else
-        cv.wait(lock, [] {return counter==0;});
+        cv.wait(lock, [] { return counter == 0; });
     lock.unlock();
 
     array a = randu(5, 5);
@@ -205,121 +202,133 @@ void doubleAllocationTest()
     std::this_thread::sleep_for(std::chrono::seconds(2));
 }
 
-TEST(Threading, MemoryManagementScope)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Threading, MemoryManagementScope) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
-    for (int t=0; t<THREAD_COUNT; ++t)
+    for (int t = 0; t < THREAD_COUNT; ++t)
         tests.emplace_back(doubleAllocationTest);
 
-    for (int t=0; t<THREAD_COUNT; ++t)
-        if (tests[t].joinable())
-            tests[t].join();
+    for (int t = 0; t < THREAD_COUNT; ++t)
+        if (tests[t].joinable()) tests[t].join();
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-            &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
-    ASSERT_EQ( lock_buffers,     0u);
-    ASSERT_EQ(  lock_bytes,      0u);
-    ASSERT_EQ(alloc_buffers,    32u);
-    ASSERT_EQ(  alloc_bytes, 32768u);
+    ASSERT_EQ(lock_buffers, 0u);
+    ASSERT_EQ(lock_bytes, 0u);
+    ASSERT_EQ(alloc_buffers, 32u);
+    ASSERT_EQ(alloc_bytes, 32768u);
 }
 
-void jitAllocationTest()
-{
+void jitAllocationTest() {
     setDevice(0);
 
-    for (int i = 0; i < 100; ++i)
-        array a = constant(1, 5, 5);
+    for (int i = 0; i < 100; ++i) array a = constant(1, 5, 5);
 }
 
-TEST(Threading, MemoryManagement_JIT_Node)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Threading, MemoryManagement_JIT_Node) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
-    for (int t=0; t<THREAD_COUNT; ++t)
+    for (int t = 0; t < THREAD_COUNT; ++t)
         tests.emplace_back(jitAllocationTest);
 
-    for (int t=0; t<THREAD_COUNT; ++t)
-        if (tests[t].joinable())
-            tests[t].join();
+    for (int t = 0; t < THREAD_COUNT; ++t)
+        if (tests[t].joinable()) tests[t].join();
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
 
-    deviceMemInfo(&alloc_bytes, &alloc_buffers,
-            &lock_bytes, &lock_buffers);
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
-    ASSERT_EQ(alloc_buffers,     0u);
-    ASSERT_EQ( lock_buffers,     0u);
-    ASSERT_EQ(  alloc_bytes,     0u);
-    ASSERT_EQ(  lock_bytes,      0u);
+    ASSERT_EQ(alloc_buffers, 0u);
+    ASSERT_EQ(lock_buffers, 0u);
+    ASSERT_EQ(alloc_bytes, 0u);
+    ASSERT_EQ(lock_bytes, 0u);
 }
 
 template<typename inType, typename outType, bool isInverse>
-void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim_t pad2=0)
-{
+void fftTest(int targetDevice, string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0,
+             dim_t pad2 = 0) {
     if (noDoubleTests<inType>()) return;
     if (noDoubleTests<outType>()) return;
 
-    vector<dim4>        numDims;
-    vector<vector<inType> >       in;
-    vector<vector<outType> >   tests;
+    vector<dim4> numDims;
+    vector<vector<inType> > in;
+    vector<vector<outType> > tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
-    dim4 dims       = numDims[0];
-    af_array outArray   = 0;
-    af_array inArray    = 0;
+    dim4 dims         = numDims[0];
+    af_array outArray = 0;
+    af_array inArray  = 0;
 
     ASSERT_SUCCESS(af_set_device(targetDevice));
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
-                dims.ndims(), dims.get(), (af_dtype)dtype_traits<inType>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<inType>::af_type));
 
-    if (isInverse){
+    if (isInverse) {
         switch (dims.ndims()) {
-            case 1 : ASSERT_SUCCESS(af_ifft (&outArray, inArray, 1.0, pad0));              break;
-            case 2 : ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));        break;
-            case 3 : ASSERT_SUCCESS(af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));  break;
-            default: throw std::runtime_error("This error shouldn't happen, pls check");
+            case 1:
+                ASSERT_SUCCESS(af_ifft(&outArray, inArray, 1.0, pad0));
+                break;
+            case 2:
+                ASSERT_SUCCESS(af_ifft2(&outArray, inArray, 1.0, pad0, pad1));
+                break;
+            case 3:
+                ASSERT_SUCCESS(
+                    af_ifft3(&outArray, inArray, 1.0, pad0, pad1, pad2));
+                break;
+            default:
+                throw std::runtime_error(
+                    "This error shouldn't happen, pls check");
         }
     } else {
-        switch(dims.ndims()) {
-            case 1 : ASSERT_SUCCESS(af_fft (&outArray, inArray, 1.0, pad0));               break;
-            case 2 : ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));         break;
-            case 3 : ASSERT_SUCCESS(af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));   break;
-            default: throw std::runtime_error("This error shouldn't happen, pls check");
+        switch (dims.ndims()) {
+            case 1:
+                ASSERT_SUCCESS(af_fft(&outArray, inArray, 1.0, pad0));
+                break;
+            case 2:
+                ASSERT_SUCCESS(af_fft2(&outArray, inArray, 1.0, pad0, pad1));
+                break;
+            case 3:
+                ASSERT_SUCCESS(
+                    af_fft3(&outArray, inArray, 1.0, pad0, pad1, pad2));
+                break;
+            default:
+                throw std::runtime_error(
+                    "This error shouldn't happen, pls check");
         }
     }
 
-    size_t out_size = tests[0].size();
-    outType *outData= new outType[out_size];
+    size_t out_size  = tests[0].size();
+    outType* outData = new outType[out_size];
     ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
 
     vector<outType> goldBar(tests[0].begin(), tests[0].end());
 
     size_t test_size = 0;
-    switch(dims.ndims()) {
-        case 1  : test_size = dims[0]/2+1;                       break;
-        case 2  : test_size = dims[1] * (dims[0]/2+1);           break;
-        case 3  : test_size = dims[2] * dims[1] * (dims[0]/2+1); break;
-        default : test_size = dims[0]/2+1;                       break;
+    switch (dims.ndims()) {
+        case 1: test_size = dims[0] / 2 + 1; break;
+        case 2: test_size = dims[1] * (dims[0] / 2 + 1); break;
+        case 3: test_size = dims[2] * dims[1] * (dims[0] / 2 + 1); break;
+        default: test_size = dims[0] / 2 + 1; break;
     }
     outType output_scale = (outType)(isInverse ? test_size : 1);
-    for (size_t elIter=0; elIter<test_size; ++elIter) {
-        bool isUnderTolerance = abs(goldBar[elIter]-outData[elIter])<0.001;
-        ASSERT_EQ(true, isUnderTolerance)<<
-            "Expected value="<<goldBar[elIter] <<"\t Actual Value="<<
-            (output_scale*outData[elIter]) << " at: " << elIter <<
-            " from thread: "<< std::this_thread::get_id() << endl;
+    for (size_t elIter = 0; elIter < test_size; ++elIter) {
+        bool isUnderTolerance = abs(goldBar[elIter] - outData[elIter]) < 0.001;
+        ASSERT_EQ(true, isUnderTolerance)
+            << "Expected value=" << goldBar[elIter]
+            << "\t Actual Value=" << (output_scale * outData[elIter])
+            << " at: " << elIter
+            << " from thread: " << std::this_thread::get_id() << endl;
     }
 
     // cleanup
@@ -328,182 +337,246 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0=0, dim_t pad1=0, dim
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-#define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, file)                         \
-    {                                                                                       \
-        int targetDevice = nextTargetDeviceId() % numDevices;                               \
-        tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, file, 0, 0, 0);  \
+#define INSTANTIATE_TEST(func, name, is_inverse, in_t, out_t, file)        \
+    {                                                                      \
+        int targetDevice = nextTargetDeviceId() % numDevices;              \
+        tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, \
+                           file, 0, 0, 0);                                 \
     }
 
-#define INSTANTIATE_TEST_TP(func, name, is_inverse, in_t, out_t, file, p0, p1)              \
-    {                                                                                       \
-        int targetDevice = nextTargetDeviceId() % numDevices;                               \
-        tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice, file, p0, p1, 0);\
+#define INSTANTIATE_TEST_TP(func, name, is_inverse, in_t, out_t, file, p0, p1) \
+    {                                                                          \
+        int targetDevice = nextTargetDeviceId() % numDevices;                  \
+        tests.emplace_back(fftTest<in_t, out_t, is_inverse>, targetDevice,     \
+                           file, p0, p1, 0);                                   \
     }
 
-TEST(Threading, FFT_R2C)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Threading, FFT_R2C) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     int numDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&numDevices));
-    ASSERT_EQ(true, numDevices>0);
+    ASSERT_EQ(true, numDevices > 0);
 
     // Real to complex transforms
-    INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
-    INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
-    INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
+    INSTANTIATE_TEST(fft, R2C_Float, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft_r2c.test"));
+    INSTANTIATE_TEST(fft2, R2C_Float, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft2_r2c.test"));
+    INSTANTIATE_TEST(fft3, R2C_Float, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft3_r2c.test"));
 
     // Factors 7, 11, 13
-    INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft, R2C_Float_7_11_13, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft_r2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft2, R2C_Float_7_11_13, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft2_r2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft3, R2C_Float_7_11_13, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft3_r2c_7_11_13.test"));
 
     // transforms on padded and truncated arrays
-    INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-
+    INSTANTIATE_TEST_TP(fft2, R2C_Float_Trunc, false, float, cfloat,
+                        string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16, 16);
 
     if (noDoubleTests<double>()) {
         // Real to complex transforms
-        INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
-        INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
-        INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
+        INSTANTIATE_TEST(fft, R2C_Double, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft_r2c.test"));
+        INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft2_r2c.test"));
+        INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft3_r2c.test"));
 
         // Factors 7, 11, 13
-        INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+        INSTANTIATE_TEST(fft, R2C_Double_7_11_13, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft_r2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft2_r2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft3_r2c_7_11_13.test"));
 
         // transforms on padded and truncated arrays
-        INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+        INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble,
+                            string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16,
+                            16);
     }
 
-
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
-TEST(Threading, FFT_C2C)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Threading, FFT_C2C) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     int numDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&numDevices));
-    ASSERT_EQ(true, numDevices>0);
+    ASSERT_EQ(true, numDevices > 0);
 
     // complex to complex transforms
-    INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
-    INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
-    INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
-
-    INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft, C2C_Float, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft_c2c.test"));
+    INSTANTIATE_TEST(fft2, C2C_Float, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft3, C2C_Float, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft3_c2c.test"));
+
+    INSTANTIATE_TEST(fft, C2C_Float_7_11_13, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft_c2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft2, C2C_Float_7_11_13, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft2_c2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft3, C2C_Float_7_11_13, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft3_c2c_7_11_13.test"));
 
     // transforms on padded and truncated arrays
-    INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, C2C_Float_Pad, false, cfloat, cfloat,
+                        string(TEST_DIR "/signal/fft2_c2c_pad.test"), 16, 16);
 
     // inverse transforms
     // complex to complex transforms
-    INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
-    INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
-    INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
-
+    INSTANTIATE_TEST(ifft, C2C_Float, true, cfloat, cfloat,
+                     string(TEST_DIR "/signal/ifft_c2c.test"));
+    INSTANTIATE_TEST(ifft2, C2C_Float, true, cfloat, cfloat,
+                     string(TEST_DIR "/signal/ifft2_c2c.test"));
+    INSTANTIATE_TEST(ifft3, C2C_Float, true, cfloat, cfloat,
+                     string(TEST_DIR "/signal/ifft3_c2c.test"));
 
     if (noDoubleTests<double>()) {
-        INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
-        INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
-        INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
-
-        INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
-
-        INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
-
-        INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
-        INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
-        INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+        INSTANTIATE_TEST(fft, C2C_Double, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft_c2c.test"));
+        INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft2_c2c.test"));
+        INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft3_c2c.test"));
+
+        INSTANTIATE_TEST(fft, C2C_Double_7_11_13, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft_c2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft2_c2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft3_c2c_7_11_13.test"));
+
+        INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble,
+                            string(TEST_DIR "/signal/fft2_c2c_pad.test"), 16,
+                            16);
+
+        INSTANTIATE_TEST(ifft, C2C_Double, true, cdouble, cdouble,
+                         string(TEST_DIR "/signal/ifft_c2c.test"));
+        INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble,
+                         string(TEST_DIR "/signal/ifft2_c2c.test"));
+        INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble,
+                         string(TEST_DIR "/signal/ifft3_c2c.test"));
     }
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
-TEST(Threading, FFT_ALL)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Threading, FFT_ALL) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     int numDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&numDevices));
-    ASSERT_EQ(true, numDevices>0);
+    ASSERT_EQ(true, numDevices > 0);
 
     // Real to complex transforms
-    INSTANTIATE_TEST(fft ,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft_r2c.test") );
-    INSTANTIATE_TEST(fft2,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c.test"));
-    INSTANTIATE_TEST(fft3,  R2C_Float, false,  float,  cfloat, string(TEST_DIR"/signal/fft3_r2c.test"));
+    INSTANTIATE_TEST(fft, R2C_Float, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft_r2c.test"));
+    INSTANTIATE_TEST(fft2, R2C_Float, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft2_r2c.test"));
+    INSTANTIATE_TEST(fft3, R2C_Float, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft3_r2c.test"));
 
     // Factors 7, 11, 13
-    INSTANTIATE_TEST(fft , R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, R2C_Float_7_11_13 , false, float  , cfloat , string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft, R2C_Float_7_11_13, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft_r2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft2, R2C_Float_7_11_13, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft2_r2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft3, R2C_Float_7_11_13, false, float, cfloat,
+                     string(TEST_DIR "/signal/fft3_r2c_7_11_13.test"));
 
     // transforms on padded and truncated arrays
-    INSTANTIATE_TEST_TP(fft2,  R2C_Float_Trunc, false,  float,  cfloat, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, R2C_Float_Trunc, false, float, cfloat,
+                        string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16, 16);
 
     // complex to complex transforms
-    INSTANTIATE_TEST(fft ,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft_c2c.test") );
-    INSTANTIATE_TEST(fft2,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c.test"));
-    INSTANTIATE_TEST(fft3,  C2C_Float, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft3_c2c.test"));
-
-    INSTANTIATE_TEST(fft , C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft2, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-    INSTANTIATE_TEST(fft3, C2C_Float_7_11_13 , false, cfloat  , cfloat , string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
+    INSTANTIATE_TEST(fft, C2C_Float, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft_c2c.test"));
+    INSTANTIATE_TEST(fft2, C2C_Float, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft2_c2c.test"));
+    INSTANTIATE_TEST(fft3, C2C_Float, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft3_c2c.test"));
+
+    INSTANTIATE_TEST(fft, C2C_Float_7_11_13, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft_c2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft2, C2C_Float_7_11_13, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft2_c2c_7_11_13.test"));
+    INSTANTIATE_TEST(fft3, C2C_Float_7_11_13, false, cfloat, cfloat,
+                     string(TEST_DIR "/signal/fft3_c2c_7_11_13.test"));
 
     // transforms on padded and truncated arrays
-    INSTANTIATE_TEST_TP(fft2,  C2C_Float_Pad, false,  cfloat,  cfloat, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
+    INSTANTIATE_TEST_TP(fft2, C2C_Float_Pad, false, cfloat, cfloat,
+                        string(TEST_DIR "/signal/fft2_c2c_pad.test"), 16, 16);
 
     // inverse transforms
     // complex to complex transforms
-    INSTANTIATE_TEST(ifft ,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft_c2c.test") );
-    INSTANTIATE_TEST(ifft2,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft2_c2c.test"));
-    INSTANTIATE_TEST(ifft3,  C2C_Float, true,  cfloat,  cfloat, string(TEST_DIR"/signal/ifft3_c2c.test"));
+    INSTANTIATE_TEST(ifft, C2C_Float, true, cfloat, cfloat,
+                     string(TEST_DIR "/signal/ifft_c2c.test"));
+    INSTANTIATE_TEST(ifft2, C2C_Float, true, cfloat, cfloat,
+                     string(TEST_DIR "/signal/ifft2_c2c.test"));
+    INSTANTIATE_TEST(ifft3, C2C_Float, true, cfloat, cfloat,
+                     string(TEST_DIR "/signal/ifft3_c2c.test"));
 
     if (noDoubleTests<double>()) {
-        INSTANTIATE_TEST(fft , R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft_r2c.test") );
-        INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c.test"));
-        INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble, string(TEST_DIR"/signal/fft3_r2c.test"));
-        INSTANTIATE_TEST(fft , R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft_r2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft2_r2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double , cdouble, string(TEST_DIR"/signal/fft3_r2c_7_11_13.test") );
-        INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble, string(TEST_DIR"/signal/fft2_r2c_trunc.test"), 16, 16);
-        INSTANTIATE_TEST(fft , C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft_c2c.test") );
-        INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c.test"));
-        INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble, string(TEST_DIR"/signal/fft3_c2c.test"));
-        INSTANTIATE_TEST(fft , C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft_c2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft2_c2c_7_11_13.test") );
-        INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble , cdouble, string(TEST_DIR"/signal/fft3_c2c_7_11_13.test") );
-        INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble, string(TEST_DIR"/signal/fft2_c2c_pad.test"), 16, 16);
-        INSTANTIATE_TEST(ifft , C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft_c2c.test") );
-        INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft2_c2c.test"));
-        INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble, string(TEST_DIR"/signal/ifft3_c2c.test"));
+        INSTANTIATE_TEST(fft, R2C_Double, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft_r2c.test"));
+        INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft2_r2c.test"));
+        INSTANTIATE_TEST(fft3, R2C_Double, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft3_r2c.test"));
+        INSTANTIATE_TEST(fft, R2C_Double_7_11_13, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft_r2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft2, R2C_Double_7_11_13, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft2_r2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft3, R2C_Double_7_11_13, false, double, cdouble,
+                         string(TEST_DIR "/signal/fft3_r2c_7_11_13.test"));
+        INSTANTIATE_TEST_TP(fft2, R2C_Double_Trunc, false, double, cdouble,
+                            string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16,
+                            16);
+        INSTANTIATE_TEST(fft, C2C_Double, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft_c2c.test"));
+        INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft2_c2c.test"));
+        INSTANTIATE_TEST(fft3, C2C_Double, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft3_c2c.test"));
+        INSTANTIATE_TEST(fft, C2C_Double_7_11_13, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft_c2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft2, C2C_Double_7_11_13, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft2_c2c_7_11_13.test"));
+        INSTANTIATE_TEST(fft3, C2C_Double_7_11_13, false, cdouble, cdouble,
+                         string(TEST_DIR "/signal/fft3_c2c_7_11_13.test"));
+        INSTANTIATE_TEST_TP(fft2, C2C_Double_Pad, false, cdouble, cdouble,
+                            string(TEST_DIR "/signal/fft2_c2c_pad.test"), 16,
+                            16);
+        INSTANTIATE_TEST(ifft, C2C_Double, true, cdouble, cdouble,
+                         string(TEST_DIR "/signal/ifft_c2c.test"));
+        INSTANTIATE_TEST(ifft2, C2C_Double, true, cdouble, cdouble,
+                         string(TEST_DIR "/signal/ifft2_c2c.test"));
+        INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble,
+                         string(TEST_DIR "/signal/ifft3_c2c.test"));
     }
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
 template<typename T, bool isBVector>
-void cppMatMulCheck(int targetDevice, string TestFile)
-{
+void cppMatMulCheck(int targetDevice, string TestFile) {
     if (noDoubleTests<T>()) return;
 
     using std::vector;
@@ -511,7 +584,7 @@ void cppMatMulCheck(int targetDevice, string TestFile)
 
     vector<vector<T> > hData;
     vector<vector<T> > tests;
-    readTests<T,T,int>(TestFile, numDims, hData, tests);
+    readTests<T, T, int>(TestFile, numDims, hData, tests);
 
     setDevice(targetDevice);
 
@@ -520,13 +593,13 @@ void cppMatMulCheck(int targetDevice, string TestFile)
 
     dim4 atdims = numDims[0];
     {
-        dim_t f  =    atdims[0];
-        atdims[0]   =    atdims[1];
-        atdims[1]   =    f;
+        dim_t f   = atdims[0];
+        atdims[0] = atdims[1];
+        atdims[1] = f;
     }
     dim4 btdims = numDims[1];
     {
-        dim_t f = btdims[0];
+        dim_t f   = btdims[0];
         btdims[0] = btdims[1];
         btdims[1] = f;
     }
@@ -535,148 +608,147 @@ void cppMatMulCheck(int targetDevice, string TestFile)
     array bT = moddims(b, btdims.ndims(), btdims.get());
 
     vector<array> out(tests.size());
-    if(isBVector) {
-        out[0] = matmul(aT, b,    AF_MAT_NONE,    AF_MAT_NONE);
-        out[1] = matmul(bT, a,   AF_MAT_NONE,    AF_MAT_NONE);
-        out[2] = matmul(b, a,    AF_MAT_TRANS,       AF_MAT_NONE);
-        out[3] = matmul(bT, aT,   AF_MAT_NONE,    AF_MAT_TRANS);
-        out[4] = matmul(b, aT,    AF_MAT_TRANS,       AF_MAT_TRANS);
-    }
-    else {
-        out[0] = matmul(a, b, AF_MAT_NONE,   AF_MAT_NONE);
-        out[1] = matmul(a, bT, AF_MAT_NONE,   AF_MAT_TRANS);
-        out[2] = matmul(a, bT, AF_MAT_TRANS,      AF_MAT_NONE);
-        out[3] = matmul(aT, bT, AF_MAT_TRANS,      AF_MAT_TRANS);
+    if (isBVector) {
+        out[0] = matmul(aT, b, AF_MAT_NONE, AF_MAT_NONE);
+        out[1] = matmul(bT, a, AF_MAT_NONE, AF_MAT_NONE);
+        out[2] = matmul(b, a, AF_MAT_TRANS, AF_MAT_NONE);
+        out[3] = matmul(bT, aT, AF_MAT_NONE, AF_MAT_TRANS);
+        out[4] = matmul(b, aT, AF_MAT_TRANS, AF_MAT_TRANS);
+    } else {
+        out[0] = matmul(a, b, AF_MAT_NONE, AF_MAT_NONE);
+        out[1] = matmul(a, bT, AF_MAT_NONE, AF_MAT_TRANS);
+        out[2] = matmul(a, bT, AF_MAT_TRANS, AF_MAT_NONE);
+        out[3] = matmul(aT, bT, AF_MAT_TRANS, AF_MAT_TRANS);
     }
 
-    for(size_t i = 0; i < tests.size(); i++) {
+    for (size_t i = 0; i < tests.size(); i++) {
         dim_t elems = out[i].elements();
         vector<T> h_out(elems);
         out[i].host((void*)&h_out.front());
 
         if (false == equal(h_out.begin(), h_out.end(), tests[i].begin())) {
-
             cout << "Failed test " << i << "\nCalculated: " << endl;
-            std::copy(h_out.begin(), h_out.end(), std::ostream_iterator<T>(cout, ", "));
+            std::copy(h_out.begin(), h_out.end(),
+                      std::ostream_iterator<T>(cout, ", "));
             cout << "Expected: " << endl;
-            std::copy(tests[i].begin(), tests[i].end(), std::ostream_iterator<T>(cout, ", "));
+            std::copy(tests[i].begin(), tests[i].end(),
+                      std::ostream_iterator<T>(cout, ", "));
             FAIL();
         }
     }
 }
 
-#define TEST_BLAS_FOR_TYPE(TypeName)                                                \
-    tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
-            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/Basic.test");          \
-    tests.emplace_back(cppMatMulCheck<TypeName, false>,                             \
-            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/NonSquare.test");      \
-    tests.emplace_back(cppMatMulCheck<TypeName, true>,                              \
-            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/SquareVector.test");   \
-    tests.emplace_back(cppMatMulCheck<TypeName, true>,                              \
-            nextTargetDeviceId()%numDevices, TEST_DIR "/blas/RectangleVector.test");
-
-TEST(Threading, BLAS)
-{
-    cleanSlate(); // Clean up everything done so far
+#define TEST_BLAS_FOR_TYPE(TypeName)                        \
+    tests.emplace_back(cppMatMulCheck<TypeName, false>,     \
+                       nextTargetDeviceId() % numDevices,   \
+                       TEST_DIR "/blas/Basic.test");        \
+    tests.emplace_back(cppMatMulCheck<TypeName, false>,     \
+                       nextTargetDeviceId() % numDevices,   \
+                       TEST_DIR "/blas/NonSquare.test");    \
+    tests.emplace_back(cppMatMulCheck<TypeName, true>,      \
+                       nextTargetDeviceId() % numDevices,   \
+                       TEST_DIR "/blas/SquareVector.test"); \
+    tests.emplace_back(cppMatMulCheck<TypeName, true>,      \
+                       nextTargetDeviceId() % numDevices,   \
+                       TEST_DIR "/blas/RectangleVector.test");
+
+TEST(Threading, BLAS) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     int numDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&numDevices));
-    ASSERT_EQ(true, numDevices>0);
+    ASSERT_EQ(true, numDevices > 0);
 
-    TEST_BLAS_FOR_TYPE(      float);
-    TEST_BLAS_FOR_TYPE( cfloat);
+    TEST_BLAS_FOR_TYPE(float);
+    TEST_BLAS_FOR_TYPE(cfloat);
 
     if (noDoubleTests<double>()) {
-        TEST_BLAS_FOR_TYPE(     double);
+        TEST_BLAS_FOR_TYPE(double);
         TEST_BLAS_FOR_TYPE(cdouble);
     }
 
-    for (size_t testId=0; testId<tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
-#define SPARSE_TESTS(T, eps)                                                    \
-  tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps, nextTargetDeviceId()%numDevices);  \
-  tests.emplace_back(sparseTester<T>, 500, 1000, 250, 1, eps, nextTargetDeviceId()%numDevices);   \
-  tests.emplace_back(sparseTester<T>, 625, 1331, 1, 2, eps, nextTargetDeviceId()%numDevices);     \
-  tests.emplace_back(sparseTransposeTester<T>, 625, 1331, 1, 2, eps, nextTargetDeviceId()%numDevices); \
-  tests.emplace_back(sparseTransposeTester<T>, 453, 751, 397, 1, eps, nextTargetDeviceId()%numDevices);\
-  tests.emplace_back(convertCSR<T>, 2345, 5678, 0.5, nextTargetDeviceId()%numDevices);
-
-TEST(Threading, Sparse)
-{
-    cleanSlate(); // Clean up everything done so far
+#define SPARSE_TESTS(T, eps)                                            \
+    tests.emplace_back(sparseTester<T>, 1000, 1000, 100, 5, eps,        \
+                       nextTargetDeviceId() % numDevices);              \
+    tests.emplace_back(sparseTester<T>, 500, 1000, 250, 1, eps,         \
+                       nextTargetDeviceId() % numDevices);              \
+    tests.emplace_back(sparseTester<T>, 625, 1331, 1, 2, eps,           \
+                       nextTargetDeviceId() % numDevices);              \
+    tests.emplace_back(sparseTransposeTester<T>, 625, 1331, 1, 2, eps,  \
+                       nextTargetDeviceId() % numDevices);              \
+    tests.emplace_back(sparseTransposeTester<T>, 453, 751, 397, 1, eps, \
+                       nextTargetDeviceId() % numDevices);              \
+    tests.emplace_back(convertCSR<T>, 2345, 5678, 0.5,                  \
+                       nextTargetDeviceId() % numDevices);
+
+TEST(Threading, Sparse) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     int numDevices = 0;
     ASSERT_SUCCESS(af_get_device_count(&numDevices));
-    ASSERT_EQ(true, numDevices>0);
+    ASSERT_EQ(true, numDevices > 0);
 
-    SPARSE_TESTS(  float, 1E-3);
-    SPARSE_TESTS( cfloat, 1E-3);
+    SPARSE_TESTS(float, 1E-3);
+    SPARSE_TESTS(cfloat, 1E-3);
     if (noDoubleTests<double>()) {
-        SPARSE_TESTS( double, 1E-5);
+        SPARSE_TESTS(double, 1E-5);
         SPARSE_TESTS(cdouble, 1E-5);
     }
 
-    for (size_t testId=0; testId < tests.size(); ++testId)
-        if (tests[testId].joinable())
-            tests[testId].join();
+    for (size_t testId = 0; testId < tests.size(); ++testId)
+        if (tests[testId].joinable()) tests[testId].join();
 }
 
-TEST(Threading, DISABLED_MemoryManagerStressTest)
-{
-  vector<std::thread> threads;
-  for (int i = 0; i < THREAD_COUNT; i++) {
-    threads.emplace_back([] {
-        vector<array> arrg;
-        int size = 100;
-        int ex_count = 0;
-
-        // Continue until the memory runs out multiple times
-        while (true) {
-          try {
-            // constantly change size of the array allocated
-            size+=10;
-            arrg.push_back(randu(size));
-
-            // delete some values intermittently
-            if (!(size%200)) {
-              arrg.erase(std::begin(arrg), std::begin(arrg)+5);
+TEST(Threading, DISABLED_MemoryManagerStressTest) {
+    vector<std::thread> threads;
+    for (int i = 0; i < THREAD_COUNT; i++) {
+        threads.emplace_back([] {
+            vector<array> arrg;
+            int size     = 100;
+            int ex_count = 0;
+
+            // Continue until the memory runs out multiple times
+            while (true) {
+                try {
+                    // constantly change size of the array allocated
+                    size += 10;
+                    arrg.push_back(randu(size));
+
+                    // delete some values intermittently
+                    if (!(size % 200)) {
+                        arrg.erase(std::begin(arrg), std::begin(arrg) + 5);
+                    }
+                } catch (const exception& ex) {
+                    if (ex_count++ > 3) { break; }
+                }
             }
-          } catch( const exception &ex ) {
-            if (ex_count++ > 3) {
-              break;
-            }
-          }
-        }
-      });
-  }
-  for (auto& t : threads) {
-    t.join();
-  }
+        });
+    }
+    for (auto& t : threads) { t.join(); }
 }
 
-TEST(Threading, DISABLED_Sort)
-{
-    cleanSlate(); // Clean up everything done so far
+TEST(Threading, DISABLED_Sort) {
+    cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;
 
     ASSERT_SUCCESS(af_set_device(0));
 
-    for (int i=0; i<THREAD_COUNT; ++i) {
+    for (int i = 0; i < THREAD_COUNT; ++i) {
         tests.emplace_back([] {
             array a = randu(100, 100);
-            for (int k=0; k<100; ++k)
-                array b = sort(a);
+            for (int k = 0; k < 100; ++k) array b = sort(a);
         });
     }
 
-    for (auto& t: tests)
+    for (auto& t : tests)
         if (t.joinable()) t.join();
 }
diff --git a/test/tile.cpp b/test/tile.cpp
index d1855b397f..5326b9160b 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -7,150 +7,148 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::constant;
 using af::dim4;
 using af::dtype_traits;
 using af::product;
 using af::seq;
 using af::span;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Tile : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat0.push_back(af_make_seq(0, 4, 1));
-            subMat0.push_back(af_make_seq(2, 6, 1));
-            subMat0.push_back(af_make_seq(0, 2, 1));
-        }
-        vector<af_seq> subMat0;
+class Tile : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat0.push_back(af_make_seq(0, 4, 1));
+        subMat0.push_back(af_make_seq(2, 6, 1));
+        subMat0.push_back(af_make_seq(0, 2, 1));
+    }
+    vector<af_seq> subMat0;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         intl, uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Tile, TestTypes);
 
 template<typename T>
-void tileTest(string pTestFile, const unsigned resultIdx, const uint x, const uint y, const uint z, const uint w,
-              bool isSubRef = false, const vector<af_seq> * seqv = NULL)
-{
+void tileTest(string pTestFile, const unsigned resultIdx, const uint x,
+              const uint y, const uint z, const uint w, bool isSubRef = false,
+              const vector<af_seq>* seqv = NULL) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, int>(pTestFile,numDims,in,tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array tempArray = 0;
 
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
 
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv->size(), &seqv->front()));
     } else {
-        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()),
+                                       idims.ndims(), idims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     ASSERT_SUCCESS(af_tile(&outArray, inArray, x, y, z, w));
 
-    dim4 goldDims(idims[0] * x,
-                  idims[1] * y,
-                  idims[2] * z,
-                  idims[3] * w);
+    dim4 goldDims(idims[0] * x, idims[1] * y, idims[2] * z, idims[3] * w);
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, outArray);
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define TILE_INIT(desc, file, resultIdx, x, y, z, w)                                        \
-    TYPED_TEST(Tile, desc)                                                                  \
-    {                                                                                       \
-        tileTest<TypeParam>(string(TEST_DIR"/tile/"#file".test"), resultIdx, x, y, z, w);   \
+#define TILE_INIT(desc, file, resultIdx, x, y, z, w)                 \
+    TYPED_TEST(Tile, desc) {                                         \
+        tileTest<TypeParam>(string(TEST_DIR "/tile/" #file ".test"), \
+                            resultIdx, x, y, z, w);                  \
     }
 
-    TILE_INIT(Tile432, tile, 0, 4, 3, 2, 1);
-    TILE_INIT(Tile111, tile, 1, 1, 1, 1, 1);
-    TILE_INIT(Tile123, tile, 2, 1, 2, 3, 1);
-    TILE_INIT(Tile312, tile, 3, 3, 1, 2, 1);
-    TILE_INIT(Tile231, tile, 4, 2, 3, 1, 1);
+TILE_INIT(Tile432, tile, 0, 4, 3, 2, 1);
+TILE_INIT(Tile111, tile, 1, 1, 1, 1, 1);
+TILE_INIT(Tile123, tile, 2, 1, 2, 3, 1);
+TILE_INIT(Tile312, tile, 3, 3, 1, 2, 1);
+TILE_INIT(Tile231, tile, 4, 2, 3, 1, 1);
 
-    TILE_INIT(Tile3D432, tile_large3D, 0, 2, 2, 2, 1);
-    TILE_INIT(Tile3D111, tile_large3D, 1, 1, 1, 1, 1);
-    TILE_INIT(Tile3D123, tile_large3D, 2, 1, 2, 3, 1);
-    TILE_INIT(Tile3D312, tile_large3D, 3, 3, 1, 2, 1);
-    TILE_INIT(Tile3D231, tile_large3D, 4, 2, 3, 1, 1);
-
-    TILE_INIT(Tile2D432, tile_large2D, 0, 2, 2, 2, 1);
-    TILE_INIT(Tile2D111, tile_large2D, 1, 1, 1, 1, 1);
-    TILE_INIT(Tile2D123, tile_large2D, 2, 1, 2, 3, 1);
-    TILE_INIT(Tile2D312, tile_large2D, 3, 3, 1, 2, 1);
-    TILE_INIT(Tile2D231, tile_large2D, 4, 2, 3, 1, 1);
+TILE_INIT(Tile3D432, tile_large3D, 0, 2, 2, 2, 1);
+TILE_INIT(Tile3D111, tile_large3D, 1, 1, 1, 1, 1);
+TILE_INIT(Tile3D123, tile_large3D, 2, 1, 2, 3, 1);
+TILE_INIT(Tile3D312, tile_large3D, 3, 3, 1, 2, 1);
+TILE_INIT(Tile3D231, tile_large3D, 4, 2, 3, 1, 1);
 
+TILE_INIT(Tile2D432, tile_large2D, 0, 2, 2, 2, 1);
+TILE_INIT(Tile2D111, tile_large2D, 1, 1, 1, 1, 1);
+TILE_INIT(Tile2D123, tile_large2D, 2, 1, 2, 3, 1);
+TILE_INIT(Tile2D312, tile_large2D, 3, 3, 1, 2, 1);
+TILE_INIT(Tile2D231, tile_large2D, 4, 2, 3, 1, 1);
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
-TEST(Tile, CPP)
-{
+TEST(Tile, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx = 0;
-    const unsigned x = 2;
-    const unsigned y = 2;
-    const unsigned z = 2;
-    const unsigned w = 1;
+    const unsigned x         = 2;
+    const unsigned y         = 2;
+    const unsigned z         = 2;
+    const unsigned w         = 1;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/tile/tile_large3D.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/tile/tile_large3D.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
     array output = tile(input, x, y, z, w);
 
-    dim4 goldDims(idims[0] * x,
-                  idims[1] * y,
-                  idims[2] * z,
-                  idims[3] * w);
+    dim4 goldDims(idims[0] * x, idims[1] * y, idims[2] * z, idims[3] * w);
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx], goldDims, output);
 }
 
-TEST(Tile, MaxDim)
-{
+TEST(Tile, MaxDim) {
     if (noDoubleTests<float>()) return;
 
     const size_t largeDim = 65535 * 32 + 1;
-    const unsigned x = 1;
-    const unsigned z = 1;
-    unsigned y = 2;
-    unsigned w = 1;
+    const unsigned x      = 1;
+    const unsigned z      = 1;
+    unsigned y            = 2;
+    unsigned w            = 1;
 
-    array input = constant(1, 1, largeDim);
+    array input  = constant(1, 1, largeDim);
     array output = tile(input, x, y, z, w);
 
     ASSERT_EQ(1, output.dims(0));
@@ -172,7 +170,6 @@ TEST(Tile, MaxDim)
     ASSERT_EQ(2 * largeDim, output.dims(3));
 
     ASSERT_EQ(1.f, product<float>(output));
-
 }
 
 TEST(Tile, DocSnippet) {
@@ -226,20 +223,18 @@ TEST(Tile, DocSnippet) {
 // handle repeated x blocks. The kernels were exiting early which caused the
 // next iteration to fail
 TEST(Tile, LargeRepeatDim) {
-    long long dim0 = 33;
+    long long dim0     = 33;
     long long largeDim = 40001;
-    array temp_ones = af::iota(largeDim, dim4(1), u8);
-    temp_ones = af::moddims(temp_ones, 1, 1, largeDim);
+    array temp_ones    = af::iota(largeDim, dim4(1), u8);
+    temp_ones          = af::moddims(temp_ones, 1, 1, largeDim);
     temp_ones.eval();
 
-    array temp = tile(temp_ones,  dim0, 1, 1);
+    array temp = tile(temp_ones, dim0, 1, 1);
     temp.eval();
     vector<unsigned char> empty(dim0 * largeDim);
-    for(long long ii = 0; ii < largeDim; ii++) {
+    for (long long ii = 0; ii < largeDim; ii++) {
         int offset = ii * dim0;
-        for(int i = 0; i < dim0; i++) {
-            empty[offset + i] = ii;
-        }
+        for (int i = 0; i < dim0; i++) { empty[offset + i] = ii; }
     }
 
     ASSERT_VEC_ARRAY_EQ(empty, dim4(dim0, 1, largeDim), temp);
diff --git a/test/topk.cpp b/test/topk.cpp
index 9088bbcb37..a86150bb76 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
 #include <testHelpers.hpp>
 
 #include <algorithm>
@@ -42,17 +42,16 @@ using std::string;
 using std::stringstream;
 using std::vector;
 
-template<typename T> class TopK : public ::testing::Test {};
+template<typename T>
+class TopK : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, int, uint> TestTypes;
 
 TYPED_TEST_CASE(TopK, TestTypes);
 
 template<typename T>
-void topkTest(const int ndims, const dim_t* dims,
-              const unsigned k, const int dim,
-              const af_topk_function order)
-{
+void topkTest(const int ndims, const dim_t* dims, const unsigned k,
+              const int dim, const af_topk_function order) {
     af_dtype dtype = (af_dtype)dtype_traits<T>::af_type;
 
     af_array input, output, outindex;
@@ -60,13 +59,12 @@ void topkTest(const int ndims, const dim_t* dims,
     size_t ielems = 1;
     size_t oelems = 1;
 
-    for (int i=0; i<ndims; i++)
-    {
+    for (int i = 0; i < ndims; i++) {
         ielems *= dims[i];
         oelems *= (i == dim ? k : dims[i]);
     }
 
-    size_t bCount = ielems/dims[dim];
+    size_t bCount = ielems / dims[dim];
     size_t bSize  = dims[dim];
 
     vector<T> inData(ielems);
@@ -79,34 +77,31 @@ void topkTest(const int ndims, const dim_t* dims,
     vector<T> outData(oelems);
     vector<uint> outIdxs(oelems);
 
-
-    for (size_t b=0; b<bCount; b++)
-    {
+    for (size_t b = 0; b < bCount; b++) {
         using KeyValuePair = pair<T, uint>;
 
-        vector< KeyValuePair > kvPairs;
-        kvPairs.reserve(((b+1)*bSize));
+        vector<KeyValuePair> kvPairs;
+        kvPairs.reserve(((b + 1) * bSize));
 
-        for (size_t i = b*bSize; i<((b+1)*bSize); ++i)
-            kvPairs.push_back(make_pair(inData[i], (i-b*bSize)));
+        for (size_t i = b * bSize; i < ((b + 1) * bSize); ++i)
+            kvPairs.push_back(make_pair(inData[i], (i - b * bSize)));
 
-        if(order == AF_TOPK_MIN) {
+        if (order == AF_TOPK_MIN) {
             stable_sort(kvPairs.begin(), kvPairs.end(),
-                    [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
-                        return lhs.first < rhs.first;
-                    });
+                        [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
+                            return lhs.first < rhs.first;
+                        });
         } else {
             stable_sort(kvPairs.begin(), kvPairs.end(),
                         [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
-                          return lhs.first >= rhs.first;
+                            return lhs.first >= rhs.first;
                         });
         }
 
         auto it = kvPairs.begin();
-        for (size_t i=0; i<k; ++it, ++i)
-        {
-            outData[i+b*k] = it->first;
-            outIdxs[i+b*k] = it->second;
+        for (size_t i = 0; i < k; ++it, ++i) {
+            outData[i + b * k] = it->first;
+            outIdxs[i + b * k] = it->second;
         }
     }
 
@@ -119,81 +114,74 @@ void topkTest(const int ndims, const dim_t* dims,
     ASSERT_SUCCESS(af_get_data_ptr((void*)hovals.data(), output));
     ASSERT_SUCCESS(af_get_data_ptr((void*)hoidxs.data(), outindex));
 
-    for (size_t i=0; i<oelems; ++i)
-    {
-        switch(dtype)
-        {
-            case f64: EXPECT_DOUBLE_EQ(outData[i], hovals[i]) << "at: " << i; break;
-            case f32: EXPECT_FLOAT_EQ (outData[i], hovals[i]) << "at: " << i; break;
-            default : EXPECT_EQ(outData[i], hovals[i]); break;
+    for (size_t i = 0; i < oelems; ++i) {
+        switch (dtype) {
+            case f64:
+                EXPECT_DOUBLE_EQ(outData[i], hovals[i]) << "at: " << i;
+                break;
+            case f32:
+                EXPECT_FLOAT_EQ(outData[i], hovals[i]) << "at: " << i;
+                break;
+            default: EXPECT_EQ(outData[i], hovals[i]); break;
         }
         ASSERT_EQ(outIdxs[i], hoidxs[i]) << "at: " << i;
     }
 
-    ASSERT_SUCCESS(af_release_array(input    ));
-    ASSERT_SUCCESS(af_release_array(output   ));
-    ASSERT_SUCCESS(af_release_array(outindex ));
+    ASSERT_SUCCESS(af_release_array(input));
+    ASSERT_SUCCESS(af_release_array(output));
+    ASSERT_SUCCESS(af_release_array(outindex));
 }
 
-TYPED_TEST(TopK, Max1D0)
-{
+TYPED_TEST(TopK, Max1D0) {
     dim_t dims[4] = {100000, 1, 1, 1};
     topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MAX);
 }
 
-TYPED_TEST(TopK, Max2D0)
-{
+TYPED_TEST(TopK, Max2D0) {
     dim_t dims[4] = {10000, 10, 1, 1};
     topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MAX);
 }
 
-TYPED_TEST(TopK, Max3D0)
-{
-  dim_t dims[4] = {10000, 10, 10, 1};
-  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
+TYPED_TEST(TopK, Max3D0) {
+    dim_t dims[4] = {10000, 10, 10, 1};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
 }
 
-TYPED_TEST(TopK, Max4D0)
-{
-  dim_t dims[4] = {10000, 10, 10, 10};
-  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
+TYPED_TEST(TopK, Max4D0) {
+    dim_t dims[4] = {10000, 10, 10, 10};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
 }
 
-TYPED_TEST(TopK, MIN1D0)
-{
-  dim_t dims[4] = {100000, 1, 1, 1};
-  topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MIN);
+TYPED_TEST(TopK, MIN1D0) {
+    dim_t dims[4] = {100000, 1, 1, 1};
+    topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MIN);
 }
 
-TYPED_TEST(TopK, MIN2D0)
-{
-  dim_t dims[4] = {10000, 10, 1, 1};
-  topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MIN);
+TYPED_TEST(TopK, MIN2D0) {
+    dim_t dims[4] = {10000, 10, 1, 1};
+    topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MIN);
 }
 
-TYPED_TEST(TopK, MIN3D0)
-{
-  dim_t dims[4] = {10000, 10, 10, 1};
-  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
+TYPED_TEST(TopK, MIN3D0) {
+    dim_t dims[4] = {10000, 10, 10, 1};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
 }
 
-TYPED_TEST(TopK, MIN4D0)
-{
-  dim_t dims[4] = {10000, 10, 10, 10};
-  topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
+TYPED_TEST(TopK, MIN4D0) {
+    dim_t dims[4] = {10000, 10, 10, 10};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
 }
 
-TEST(TopK, ValidationCheck_DimN)
-{
+TEST(TopK, ValidationCheck_DimN) {
     dim_t dims[4] = {10, 10, 1, 1};
     af_array out, idx, in;
     ASSERT_SUCCESS(af_randu(&in, 2, dims, f32));
-    ASSERT_EQ(AF_ERR_NOT_SUPPORTED, af_topk(&out, &idx, in, 10, 1, AF_TOPK_MAX));
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED,
+              af_topk(&out, &idx, in, 10, 1, AF_TOPK_MAX));
     ASSERT_SUCCESS(af_release_array(in));
 }
 
-TEST(TopK, ValidationCheck_DefaultDim)
-{
+TEST(TopK, ValidationCheck_DefaultDim) {
     dim_t dims[4] = {10, 10, 1, 1};
     af_array out, idx, in;
     ASSERT_SUCCESS(af_randu(&in, 4, dims, f32));
@@ -203,110 +191,108 @@ TEST(TopK, ValidationCheck_DefaultDim)
     ASSERT_SUCCESS(af_release_array(idx));
 }
 
-
 struct topk_params {
-  int d0;
-  int d1;
-  int k;
-  int dim;
-  topkFunction order;
+    int d0;
+    int d1;
+    int k;
+    int dim;
+    topkFunction order;
 };
 
-ostream& operator<<(ostream& os, const topk_params &param) {
-  os << "d0: " << param.d0 << " d1: " << param.d1
-     << " k:  " << param.k  << " dim: " << param.dim
-     << " order: " << ((param.order == AF_TOPK_MAX) ? "MAX" : "MIN");
-  return os;
+ostream& operator<<(ostream& os, const topk_params& param) {
+    os << "d0: " << param.d0 << " d1: " << param.d1 << " k:  " << param.k
+       << " dim: " << param.dim
+       << " order: " << ((param.order == AF_TOPK_MAX) ? "MAX" : "MIN");
+    return os;
 }
 
 class TopKParams : public ::testing::TestWithParam<topk_params> {};
 
-INSTANTIATE_TEST_CASE_P(InstantiationName,
-                        TopKParams,
-                        ::testing::Values(
-                                          topk_params{100,   10,  32, 0, AF_TOPK_MIN},
-                                          topk_params{100,   10,  64, 0, AF_TOPK_MIN},
-                                          topk_params{100,   10,  32, 0, AF_TOPK_MAX},
-                                          topk_params{100,   10,  64, 0, AF_TOPK_MAX},
-                                          topk_params{100,   10,  5, 0,  AF_TOPK_MIN},
-                                          topk_params{1000,  10,  5, 0,  AF_TOPK_MIN},
-                                          topk_params{10000, 10,  5, 0,  AF_TOPK_MIN},
-                                          topk_params{100,   10,  5, 0,  AF_TOPK_MAX},
-                                          topk_params{1000,  10,  5, 0,  AF_TOPK_MAX},
-                                          topk_params{10000, 10,  5, 0,  AF_TOPK_MAX},
-                                          topk_params{10, 10,     5, 0,  AF_TOPK_MIN},
-                                          topk_params{10, 100,    5, 0,  AF_TOPK_MIN},
-                                          topk_params{10, 1000,   5, 0,  AF_TOPK_MIN},
-                                          topk_params{10, 10000,  5, 0,  AF_TOPK_MIN},
-                                          topk_params{10, 10,     5, 0,  AF_TOPK_MAX},
-                                          topk_params{10, 100,    5, 0,  AF_TOPK_MAX},
-                                          topk_params{10, 1000,   5, 0,  AF_TOPK_MAX},
-                                          topk_params{10, 10000,  5, 0,  AF_TOPK_MAX},
-                                          topk_params{1000, 10,   256, 0,  AF_TOPK_MAX}
-                                          ),
-                        []( const ::testing::TestParamInfo<TopKParams::ParamType> info) {
-                          stringstream ss;
-                          ss << "d0_" << info.param.d0
-                             << "_d1_" << info.param.d1
-                             << "_k_" << info.param.k
-                             << "_dim_" << info.param.dim
-                             << "_order_" << ((info.param.order == AF_TOPK_MAX) ? string("MAX")
-                                                              : string("MIN"));
-                          return ss.str();
-                        });
-
-string print_context(int idx0, int idx1, const vector<float> &val, const vector<unsigned> &idx) {
-  stringstream ss;
-  if(idx0 > 3 && idx1 > 3) {
-    for(int i = idx0 - 3; i < idx0 + 3; i++) {
-      ss << i << ": " << val[i] << " " << idx[i] << "\n";
-    }
-  } else {
-    int end = min(6, idx0+3);
-    for(int i = 0; i < end; i++) {
-      ss << i << ": " << val[i] << " " << idx[i] << "\n";
+INSTANTIATE_TEST_CASE_P(
+    InstantiationName, TopKParams,
+    ::testing::Values(topk_params{100, 10, 32, 0, AF_TOPK_MIN},
+                      topk_params{100, 10, 64, 0, AF_TOPK_MIN},
+                      topk_params{100, 10, 32, 0, AF_TOPK_MAX},
+                      topk_params{100, 10, 64, 0, AF_TOPK_MAX},
+                      topk_params{100, 10, 5, 0, AF_TOPK_MIN},
+                      topk_params{1000, 10, 5, 0, AF_TOPK_MIN},
+                      topk_params{10000, 10, 5, 0, AF_TOPK_MIN},
+                      topk_params{100, 10, 5, 0, AF_TOPK_MAX},
+                      topk_params{1000, 10, 5, 0, AF_TOPK_MAX},
+                      topk_params{10000, 10, 5, 0, AF_TOPK_MAX},
+                      topk_params{10, 10, 5, 0, AF_TOPK_MIN},
+                      topk_params{10, 100, 5, 0, AF_TOPK_MIN},
+                      topk_params{10, 1000, 5, 0, AF_TOPK_MIN},
+                      topk_params{10, 10000, 5, 0, AF_TOPK_MIN},
+                      topk_params{10, 10, 5, 0, AF_TOPK_MAX},
+                      topk_params{10, 100, 5, 0, AF_TOPK_MAX},
+                      topk_params{10, 1000, 5, 0, AF_TOPK_MAX},
+                      topk_params{10, 10000, 5, 0, AF_TOPK_MAX},
+                      topk_params{1000, 10, 256, 0, AF_TOPK_MAX}),
+    [](const ::testing::TestParamInfo<TopKParams::ParamType> info) {
+        stringstream ss;
+        ss << "d0_" << info.param.d0 << "_d1_" << info.param.d1 << "_k_"
+           << info.param.k << "_dim_" << info.param.dim << "_order_"
+           << ((info.param.order == AF_TOPK_MAX) ? string("MAX")
+                                                 : string("MIN"));
+        return ss.str();
+    });
+
+string print_context(int idx0, int idx1, const vector<float>& val,
+                     const vector<unsigned>& idx) {
+    stringstream ss;
+    if (idx0 > 3 && idx1 > 3) {
+        for (int i = idx0 - 3; i < idx0 + 3; i++) {
+            ss << i << ": " << val[i] << " " << idx[i] << "\n";
+        }
+    } else {
+        int end = min(6, idx0 + 3);
+        for (int i = 0; i < end; i++) {
+            ss << i << ": " << val[i] << " " << idx[i] << "\n";
+        }
     }
-  }
-  return ss.str();
+    return ss.str();
 }
 
 TEST_P(TopKParams, CPP) {
     topk_params params = GetParam();
-    int d0 = params.d0;
-    int d1 = params.d1;
-    int k = params.k;
-    int dim = params.dim;
+    int d0             = params.d0;
+    int d1             = params.d1;
+    int k              = params.k;
+    int dim            = params.dim;
     topkFunction order = params.order;
 
     array in = iota(dim4(d0, d1));
 
     // reverse the array if the order is ascending
-    if(order == AF_TOPK_MIN) {
-      in = -in + (d0 * d1-1);
-    }
+    if (order == AF_TOPK_MIN) { in = -in + (d0 * d1 - 1); }
     array val, idx;
     topk(val, idx, in, k, dim, order);
 
-    vector<float>    hval(k * d1);
+    vector<float> hval(k * d1);
     vector<unsigned> hidx(k * d1);
     val.host(&hval[0]);
     idx.host(&hidx[0]);
 
-    if(order == AF_TOPK_MIN) {
-        for(int j = d1 - 1, i = 0; j > 0; j--) {
-            for(int kidx = 0, goldidx = d0-1; kidx < k; i++, kidx++, goldidx--) {
+    if (order == AF_TOPK_MIN) {
+        for (int j = d1 - 1, i = 0; j > 0; j--) {
+            for (int kidx = 0, goldidx = d0 - 1; kidx < k;
+                 i++, kidx++, goldidx--) {
                 float gold = static_cast<float>(j * d0 + kidx);
-                ASSERT_FLOAT_EQ(gold, hval[i]) << print_context(i, kidx, hval, hidx);
-                ASSERT_EQ(goldidx, hidx[i])    << print_context(i, kidx, hval, hidx);
+                ASSERT_FLOAT_EQ(gold, hval[i])
+                    << print_context(i, kidx, hval, hidx);
+                ASSERT_EQ(goldidx, hidx[i])
+                    << print_context(i, kidx, hval, hidx);
             }
         }
     } else {
         for (int ii = 0, i = 0; ii < d1; ii++) {
-            for (int j = d0-1; j >= d0-k; --j, i++) {
-                float gold = static_cast<float>(ii * d0 + j);
+            for (int j = d0 - 1; j >= d0 - k; --j, i++) {
+                float gold  = static_cast<float>(ii * d0 + j);
                 int goldidx = j;
-                ASSERT_FLOAT_EQ(gold, hval[i]) << print_context(i, 0, hval, hidx);
-                ASSERT_EQ(goldidx, hidx[i])    << print_context(i, 0, hval, hidx);
+                ASSERT_FLOAT_EQ(gold, hval[i])
+                    << print_context(i, 0, hval, hidx);
+                ASSERT_EQ(goldidx, hidx[i]) << print_context(i, 0, hval, hidx);
             }
         }
     }
diff --git a/test/transform.cpp b/test/transform.cpp
index b1068b1baf..a6131135dd 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -7,64 +7,62 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::abs;
-using std::endl;
 using af::array;
 using af::dim4;
 using af::loadImage;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Transform : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class Transform : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class TransformInt : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class TransformInt : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
-typedef ::testing::Types<int, intl, uint, uintl, short, ushort, uchar> TestTypesInt;
+typedef ::testing::Types<int, intl, uint, uintl, short, ushort, uchar>
+    TestTypesInt;
 
 TYPED_TEST_CASE(Transform, TestTypes);
 TYPED_TEST_CASE(TransformInt, TestTypesInt);
 
 template<typename T>
-void transformTest(string pTestFile, string pHomographyFile, const af_interp_type method, const bool invert)
-{
+void transformTest(string pTestFile, string pHomographyFile,
+                   const af_interp_type method, const bool invert) {
     if (noDoubleTests<T>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inNumDims;
-    vector<string>   inFiles;
-    vector<dim_t>    goldNumDims;
-    vector<string>   goldFiles;
+    vector<string> inFiles;
+    vector<dim_t> goldNumDims;
+    vector<string> goldFiles;
 
     readImageTests(pTestFile, inNumDims, inFiles, goldNumDims, goldFiles);
 
-    inFiles[0].insert(0,string(TEST_DIR"/transform/"));
-    inFiles[1].insert(0,string(TEST_DIR"/transform/"));
-    goldFiles[0].insert(0,string(TEST_DIR"/transform/"));
+    inFiles[0].insert(0, string(TEST_DIR "/transform/"));
+    inFiles[1].insert(0, string(TEST_DIR "/transform/"));
+    goldFiles[0].insert(0, string(TEST_DIR "/transform/"));
 
     dim4 objDims = inNumDims[0];
 
-    vector<dim4>       HNumDims;
+    vector<dim4> HNumDims;
     vector<vector<float> > HIn;
     vector<vector<float> > HTests;
     readTests<float, float, float>(pHomographyFile, HNumDims, HIn, HTests);
@@ -72,12 +70,12 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     dim4 HDims = HNumDims[0];
 
     af_array sceneArray_f32 = 0;
-    af_array goldArray_f32 = 0;
-    af_array outArray_f32 = 0;
-    af_array sceneArray = 0;
-    af_array goldArray = 0;
-    af_array outArray = 0;
-    af_array HArray = 0;
+    af_array goldArray_f32  = 0;
+    af_array outArray_f32   = 0;
+    af_array sceneArray     = 0;
+    af_array goldArray      = 0;
+    af_array outArray       = 0;
+    af_array HArray         = 0;
 
     ASSERT_SUCCESS(af_load_image(&sceneArray_f32, inFiles[1].c_str(), false));
     ASSERT_SUCCESS(af_load_image(&goldArray_f32, goldFiles[0].c_str(), false));
@@ -85,9 +83,11 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     ASSERT_SUCCESS(conv_image<T>(&sceneArray, sceneArray_f32));
     ASSERT_SUCCESS(conv_image<T>(&goldArray, goldArray_f32));
 
-    ASSERT_SUCCESS(af_create_array(&HArray, &(HIn[0].front()), HDims.ndims(), HDims.get(), f32));
+    ASSERT_SUCCESS(af_create_array(&HArray, &(HIn[0].front()), HDims.ndims(),
+                                   HDims.get(), f32));
 
-    ASSERT_SUCCESS(af_transform(&outArray, sceneArray, HArray, objDims[0], objDims[1], method, invert));
+    ASSERT_SUCCESS(af_transform(&outArray, sceneArray, HArray, objDims[0],
+                                objDims[1], method, invert));
 
     // Get gold data
     dim_t goldEl = 0;
@@ -107,142 +107,138 @@ void transformTest(string pTestFile, string pHomographyFile, const af_interp_typ
     // this metric is necessary due to rounding errors between different
     // backends for AF_INTERP_NEAREST and AF_INTERP_LOWER
     const size_t maxErr = goldEl * 0.0001f;
-    size_t err = 0;
+    size_t err          = 0;
 
     for (dim_t elIter = 0; elIter < goldEl; elIter++) {
-        err += fabs((float)floor(outData[elIter]) - (float)floor(goldData[elIter])) > thr;
+        err += fabs((float)floor(outData[elIter]) -
+                    (float)floor(goldData[elIter])) > thr;
         if (err > maxErr) {
             ASSERT_LE(err, maxErr) << "at: " << elIter << endl;
         }
     }
 
-    if(sceneArray_f32 != 0) af_release_array(sceneArray_f32);
-    if(goldArray_f32  != 0) af_release_array(goldArray_f32);
-    if(outArray_f32   != 0) af_release_array(outArray_f32);
-    if(sceneArray     != 0) af_release_array(sceneArray);
-    if(goldArray      != 0) af_release_array(goldArray);
-    if(outArray       != 0) af_release_array(outArray);
-    if(HArray         != 0) af_release_array(HArray);
+    if (sceneArray_f32 != 0) af_release_array(sceneArray_f32);
+    if (goldArray_f32 != 0) af_release_array(goldArray_f32);
+    if (outArray_f32 != 0) af_release_array(outArray_f32);
+    if (sceneArray != 0) af_release_array(sceneArray);
+    if (goldArray != 0) af_release_array(goldArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (HArray != 0) af_release_array(HArray);
 }
 
-TYPED_TEST(Transform, PerspectiveNearest)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
-                             string(TEST_DIR"/transform/tux_tmat.test"),
+TYPED_TEST(Transform, PerspectiveNearest) {
+    transformTest<TypeParam>(string(TEST_DIR "/transform/tux_nearest.test"),
+                             string(TEST_DIR "/transform/tux_tmat.test"),
                              AF_INTERP_NEAREST, false);
 }
 
-TYPED_TEST(Transform, PerspectiveBilinear)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
-                             string(TEST_DIR"/transform/tux_tmat.test"),
+TYPED_TEST(Transform, PerspectiveBilinear) {
+    transformTest<TypeParam>(string(TEST_DIR "/transform/tux_bilinear.test"),
+                             string(TEST_DIR "/transform/tux_tmat.test"),
                              AF_INTERP_BILINEAR, false);
 }
 
-TYPED_TEST(Transform, PerspectiveLower)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
-                             string(TEST_DIR"/transform/tux_tmat.test"),
+TYPED_TEST(Transform, PerspectiveLower) {
+    transformTest<TypeParam>(string(TEST_DIR "/transform/tux_lower.test"),
+                             string(TEST_DIR "/transform/tux_tmat.test"),
                              AF_INTERP_LOWER, false);
 }
 
-TYPED_TEST(Transform, PerspectiveNearestInvert)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
-                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
-                             AF_INTERP_NEAREST, true);
+TYPED_TEST(Transform, PerspectiveNearestInvert) {
+    transformTest<TypeParam>(
+        string(TEST_DIR "/transform/tux_nearest.test"),
+        string(TEST_DIR "/transform/tux_tmat_inverse.test"), AF_INTERP_NEAREST,
+        true);
 }
 
-TYPED_TEST(Transform, PerspectiveBilinearInvert)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
-                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
-                             AF_INTERP_BILINEAR, true);
+TYPED_TEST(Transform, PerspectiveBilinearInvert) {
+    transformTest<TypeParam>(
+        string(TEST_DIR "/transform/tux_bilinear.test"),
+        string(TEST_DIR "/transform/tux_tmat_inverse.test"), AF_INTERP_BILINEAR,
+        true);
 }
 
-TYPED_TEST(Transform, PerspectiveLowerInvert)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
-                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
-                             AF_INTERP_LOWER, true);
+TYPED_TEST(Transform, PerspectiveLowerInvert) {
+    transformTest<TypeParam>(
+        string(TEST_DIR "/transform/tux_lower.test"),
+        string(TEST_DIR "/transform/tux_tmat_inverse.test"), AF_INTERP_LOWER,
+        true);
 }
 
-TYPED_TEST(TransformInt, PerspectiveNearest)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
-                             string(TEST_DIR"/transform/tux_tmat.test"),
+TYPED_TEST(TransformInt, PerspectiveNearest) {
+    transformTest<TypeParam>(string(TEST_DIR "/transform/tux_nearest.test"),
+                             string(TEST_DIR "/transform/tux_tmat.test"),
                              AF_INTERP_NEAREST, false);
 }
 
-TYPED_TEST(TransformInt, PerspectiveBilinear)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
-                             string(TEST_DIR"/transform/tux_tmat.test"),
+TYPED_TEST(TransformInt, PerspectiveBilinear) {
+    transformTest<TypeParam>(string(TEST_DIR "/transform/tux_bilinear.test"),
+                             string(TEST_DIR "/transform/tux_tmat.test"),
                              AF_INTERP_BILINEAR, false);
 }
 
-TYPED_TEST(TransformInt, PerspectiveLower)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
-                             string(TEST_DIR"/transform/tux_tmat.test"),
+TYPED_TEST(TransformInt, PerspectiveLower) {
+    transformTest<TypeParam>(string(TEST_DIR "/transform/tux_lower.test"),
+                             string(TEST_DIR "/transform/tux_tmat.test"),
                              AF_INTERP_LOWER, false);
 }
 
-TYPED_TEST(TransformInt, PerspectiveNearestInvert)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_nearest.test"),
-                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
-                             AF_INTERP_NEAREST, true);
+TYPED_TEST(TransformInt, PerspectiveNearestInvert) {
+    transformTest<TypeParam>(
+        string(TEST_DIR "/transform/tux_nearest.test"),
+        string(TEST_DIR "/transform/tux_tmat_inverse.test"), AF_INTERP_NEAREST,
+        true);
 }
 
-TYPED_TEST(TransformInt, PerspectiveBilinearInvert)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_bilinear.test"),
-                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
-                             AF_INTERP_BILINEAR, true);
+TYPED_TEST(TransformInt, PerspectiveBilinearInvert) {
+    transformTest<TypeParam>(
+        string(TEST_DIR "/transform/tux_bilinear.test"),
+        string(TEST_DIR "/transform/tux_tmat_inverse.test"), AF_INTERP_BILINEAR,
+        true);
 }
 
-TYPED_TEST(TransformInt, PerspectiveLowerInvert)
-{
-    transformTest<TypeParam>(string(TEST_DIR"/transform/tux_lower.test"),
-                             string(TEST_DIR"/transform/tux_tmat_inverse.test"),
-                             AF_INTERP_LOWER, true);
+TYPED_TEST(TransformInt, PerspectiveLowerInvert) {
+    transformTest<TypeParam>(
+        string(TEST_DIR "/transform/tux_lower.test"),
+        string(TEST_DIR "/transform/tux_tmat_inverse.test"), AF_INTERP_LOWER,
+        true);
 }
 
-
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(Transform, CPP)
-{
+TEST(Transform, CPP) {
     if (noImageIOTests()) return;
 
-    vector<dim4>   inDims;
+    vector<dim4> inDims;
     vector<string> inFiles;
-    vector<dim_t>  goldDim;
+    vector<dim_t> goldDim;
     vector<string> goldFiles;
 
     vector<dim4> HDims;
-    vector<vector<float> >   HIn;
-    vector<vector<float> >   HTests;
-    readTests<float, float, float>(TEST_DIR"/transform/tux_tmat.test",HDims,HIn,HTests);
+    vector<vector<float> > HIn;
+    vector<vector<float> > HTests;
+    readTests<float, float, float>(TEST_DIR "/transform/tux_tmat.test", HDims,
+                                   HIn, HTests);
 
-    readImageTests(string(TEST_DIR"/transform/tux_nearest.test"), inDims, inFiles, goldDim, goldFiles);
+    readImageTests(string(TEST_DIR "/transform/tux_nearest.test"), inDims,
+                   inFiles, goldDim, goldFiles);
 
-    inFiles[0].insert(0,string(TEST_DIR"/transform/"));
-    inFiles[1].insert(0,string(TEST_DIR"/transform/"));
+    inFiles[0].insert(0, string(TEST_DIR "/transform/"));
+    inFiles[1].insert(0, string(TEST_DIR "/transform/"));
 
-    goldFiles[0].insert(0,string(TEST_DIR"/transform/"));
+    goldFiles[0].insert(0, string(TEST_DIR "/transform/"));
 
-    array H = array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
+    array H  = array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
     array IH = array(HDims[0][0], HDims[0][1], &(HIn[0].front()));
 
     array scene_img = loadImage(inFiles[1].c_str(), false);
 
     array gold_img = loadImage(goldFiles[0].c_str(), false);
 
-    array out_img = transform(scene_img, IH, inDims[0][0], inDims[0][1], AF_INTERP_NEAREST, false);
+    array out_img = transform(scene_img, IH, inDims[0][0], inDims[0][1],
+                              AF_INTERP_NEAREST, false);
 
-    dim4 outDims = out_img.dims();
+    dim4 outDims  = out_img.dims();
     dim4 goldDims = gold_img.dims();
 
     vector<float> h_out_img(outDims[0] * outDims[1]);
@@ -250,14 +246,14 @@ TEST(Transform, CPP)
     vector<float> h_gold_img(goldDims[0] * goldDims[1]);
     gold_img.host(&h_gold_img.front());
 
-    const dim_t n = gold_img.elements();
+    const dim_t n   = gold_img.elements();
     const float thr = 1.0f;
 
     // Maximum number of wrong pixels must be <= 0.01% of number of elements,
     // this metric is necessary due to rounding errors between different
     // backends for AF_INTERP_NEAREST and AF_INTERP_LOWER
     const size_t maxErr = n * 0.0001f;
-    size_t err = 0;
+    size_t err          = 0;
 
     for (dim_t elIter = 0; elIter < n; elIter++) {
         err += fabs((int)h_out_img[elIter] - h_gold_img[elIter]) > thr;
@@ -271,28 +267,28 @@ TEST(Transform, CPP)
 // tf0 rotates by 90 clockwise
 // tf1 rotates by 90 counter clockwise
 // This test simply makes sure the batching is working correctly
-TEST(TransformBatching, CPP)
-{
-    vector<dim4>        vDims;
-    vector<vector<float> >  in;
-    vector<vector<float> >  gold;
-
-    readTests<float, float, int>(string(TEST_DIR"/transform/transform_batching.test"), vDims, in, gold);
-
-    array img0     (vDims[0], &(in[0].front()));
-    array img1     (vDims[1], &(in[1].front()));
-    array ip_tile  (vDims[2], &(in[2].front()));
-    array ip_quad  (vDims[3], &(in[3].front()));
-    array ip_mult  (vDims[4], &(in[4].front()));
-    array ip_tile3 (vDims[5], &(in[5].front()));
-    array ip_quad3 (vDims[6], &(in[6].front()));
-
-    array tf0      (vDims[7 + 0], &(in[7 + 0].front()));
-    array tf1      (vDims[7 + 1], &(in[7 + 1].front()));
-    array tf_tile  (vDims[7 + 2], &(in[7 + 2].front()));
-    array tf_quad  (vDims[7 + 3], &(in[7 + 3].front()));
-    array tf_mult  (vDims[7 + 4], &(in[7 + 4].front()));
-    array tf_mult3 (vDims[7 + 5], &(in[7 + 5].front()));
+TEST(TransformBatching, CPP) {
+    vector<dim4> vDims;
+    vector<vector<float> > in;
+    vector<vector<float> > gold;
+
+    readTests<float, float, int>(
+        string(TEST_DIR "/transform/transform_batching.test"), vDims, in, gold);
+
+    array img0(vDims[0], &(in[0].front()));
+    array img1(vDims[1], &(in[1].front()));
+    array ip_tile(vDims[2], &(in[2].front()));
+    array ip_quad(vDims[3], &(in[3].front()));
+    array ip_mult(vDims[4], &(in[4].front()));
+    array ip_tile3(vDims[5], &(in[5].front()));
+    array ip_quad3(vDims[6], &(in[6].front()));
+
+    array tf0(vDims[7 + 0], &(in[7 + 0].front()));
+    array tf1(vDims[7 + 1], &(in[7 + 1].front()));
+    array tf_tile(vDims[7 + 2], &(in[7 + 2].front()));
+    array tf_quad(vDims[7 + 3], &(in[7 + 3].front()));
+    array tf_mult(vDims[7 + 4], &(in[7 + 4].front()));
+    array tf_mult3(vDims[7 + 5], &(in[7 + 5].front()));
     array tf_mult3x(vDims[7 + 6], &(in[7 + 6].front()));
 
     const int X = img0.dims(0);
@@ -300,43 +296,53 @@ TEST(TransformBatching, CPP)
 
     ASSERT_EQ(gold.size(), 21u);
     vector<array> out(gold.size());
-    out[0 ] = transform(img0    , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
-    out[1 ] = transform(img0    , tf1      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
-    out[2 ] = transform(img1    , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
-    out[3 ] = transform(img1    , tf1      , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
-
-    out[4 ] = transform(img0    , tf_tile  , Y, X, AF_INTERP_NEAREST);  // 1,1 x N,1
-    out[5 ] = transform(img0    , tf_mult  , Y, X, AF_INTERP_NEAREST);  // 1,1 x N,N
-    out[6 ] = transform(img0    , tf_quad  , Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,N
-
-    out[7 ] = transform(ip_tile , tf0      , Y, X, AF_INTERP_NEAREST);  // N,1 x 1,1
-    out[8 ] = transform(ip_tile , tf_tile  , Y, X, AF_INTERP_NEAREST);  // N,1 x N,1
-    out[9 ] = transform(ip_tile , tf_mult  , Y, X, AF_INTERP_NEAREST);  // N,N x N,N
-    out[10] = transform(ip_tile , tf_quad  , Y, X, AF_INTERP_NEAREST);  // N,1 x 1,N
-
-    out[11] = transform(ip_quad , tf0      , Y, X, AF_INTERP_NEAREST);  // 1,N x 1,1
-    out[12] = transform(ip_quad , tf_quad  , Y, X, AF_INTERP_NEAREST);  // 1,N x 1,N
-    out[13] = transform(ip_quad , tf_mult  , Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
-    out[14] = transform(ip_quad , tf_tile  , Y, X, AF_INTERP_NEAREST);  // 1,N x N,1
-
-    out[15] = transform(ip_mult , tf0      , Y, X, AF_INTERP_NEAREST);  // N,N x 1,1
-    out[16] = transform(ip_mult , tf_tile  , Y, X, AF_INTERP_NEAREST);  // N,N x N,1
-    out[17] = transform(ip_mult , tf_mult  , Y, X, AF_INTERP_NEAREST);  // N,N x N,N
-    out[18] = transform(ip_mult , tf_quad  , Y, X, AF_INTERP_NEAREST);  // N,N x 1,N
-
-    out[19] = transform(ip_tile3, tf_mult3 , Y, X, AF_INTERP_NEAREST);  // N,1 x N,N
-    out[20] = transform(ip_quad3, tf_mult3x, Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
+    out[0] = transform(img0, tf0, Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+    out[1] = transform(img0, tf1, Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+    out[2] = transform(img1, tf0, Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+    out[3] = transform(img1, tf1, Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,1
+
+    out[4] = transform(img0, tf_tile, Y, X, AF_INTERP_NEAREST);  // 1,1 x N,1
+    out[5] = transform(img0, tf_mult, Y, X, AF_INTERP_NEAREST);  // 1,1 x N,N
+    out[6] = transform(img0, tf_quad, Y, X, AF_INTERP_NEAREST);  // 1,1 x 1,N
+
+    out[7] = transform(ip_tile, tf0, Y, X, AF_INTERP_NEAREST);      // N,1 x 1,1
+    out[8] = transform(ip_tile, tf_tile, Y, X, AF_INTERP_NEAREST);  // N,1 x N,1
+    out[9] = transform(ip_tile, tf_mult, Y, X, AF_INTERP_NEAREST);  // N,N x N,N
+    out[10] =
+        transform(ip_tile, tf_quad, Y, X, AF_INTERP_NEAREST);  // N,1 x 1,N
+
+    out[11] = transform(ip_quad, tf0, Y, X, AF_INTERP_NEAREST);  // 1,N x 1,1
+    out[12] =
+        transform(ip_quad, tf_quad, Y, X, AF_INTERP_NEAREST);  // 1,N x 1,N
+    out[13] =
+        transform(ip_quad, tf_mult, Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
+    out[14] =
+        transform(ip_quad, tf_tile, Y, X, AF_INTERP_NEAREST);  // 1,N x N,1
+
+    out[15] = transform(ip_mult, tf0, Y, X, AF_INTERP_NEAREST);  // N,N x 1,1
+    out[16] =
+        transform(ip_mult, tf_tile, Y, X, AF_INTERP_NEAREST);  // N,N x N,1
+    out[17] =
+        transform(ip_mult, tf_mult, Y, X, AF_INTERP_NEAREST);  // N,N x N,N
+    out[18] =
+        transform(ip_mult, tf_quad, Y, X, AF_INTERP_NEAREST);  // N,N x 1,N
+
+    out[19] =
+        transform(ip_tile3, tf_mult3, Y, X, AF_INTERP_NEAREST);  // N,1 x N,N
+    out[20] =
+        transform(ip_quad3, tf_mult3x, Y, X, AF_INTERP_NEAREST);  // 1,N x N,N
 
     array x_(dim4(35, 40, 1, 1), &(gold[1].front()));
 
-    for(int i = 0; i < (int)gold.size(); i++) {
+    for (int i = 0; i < (int)gold.size(); i++) {
         // Get result
         vector<float> outData(out[i].elements());
         out[i].host((void*)&outData.front());
 
-        for(int iter = 0; iter < (int)gold[i].size(); iter++) {
-            ASSERT_EQ(gold[i][iter], outData[iter]) << "at: " << iter << endl
-                    << "for " << i << "-th operation"<< endl;
+        for (int iter = 0; iter < (int)gold[i].size(); iter++) {
+            ASSERT_EQ(gold[i][iter], outData[iter])
+                << "at: " << iter << endl
+                << "for " << i << "-th operation" << endl;
         }
     }
 }
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index f959455f25..3c0858d0bb 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -7,27 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::array;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class TransformCoordinates : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {}
+class TransformCoordinates : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 typedef ::testing::Types<float, double> TestTypes;
@@ -35,19 +34,20 @@ typedef ::testing::Types<float, double> TestTypes;
 TYPED_TEST_CASE(TransformCoordinates, TestTypes);
 
 template<typename T>
-void transformCoordinatesTest(string pTestFile)
-{
+void transformCoordinatesTest(string pTestFile) {
     if (noDoubleTests<T>()) return;
 
-    vector<dim4>       inDims;
-    vector<vector<T> >     in;
+    vector<dim4> inDims;
+    vector<vector<T> > in;
     vector<vector<float> > gold;
 
     readTests<T, float, float>(pTestFile, inDims, in, gold);
 
-    af_array tfArray = 0;
+    af_array tfArray  = 0;
     af_array outArray = 0;
-    ASSERT_SUCCESS(af_create_array(&tfArray, &(in[0].front()), inDims[0].ndims(), inDims[0].get(), (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&tfArray, &(in[0].front()),
+                                   inDims[0].ndims(), inDims[0].get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     int nTests = in.size();
 
@@ -67,48 +67,50 @@ void transformCoordinatesTest(string pTestFile)
         const float thr = 1.f;
 
         for (dim_t elIter = 0; elIter < outEl; elIter++) {
-            ASSERT_LE(fabs(outData[elIter] - gold[test-1][elIter]), thr) << "at: " << elIter << endl;
+            ASSERT_LE(fabs(outData[elIter] - gold[test - 1][elIter]), thr)
+                << "at: " << elIter << endl;
         }
     }
 
-    if(tfArray  != 0) af_release_array(tfArray);
+    if (tfArray != 0) af_release_array(tfArray);
 }
 
-TYPED_TEST(TransformCoordinates, RotateMatrix)
-{
-    transformCoordinatesTest<TypeParam>(string(TEST_DIR"/transformCoordinates/rotate_matrix.test"));
+TYPED_TEST(TransformCoordinates, RotateMatrix) {
+    transformCoordinatesTest<TypeParam>(
+        string(TEST_DIR "/transformCoordinates/rotate_matrix.test"));
 }
 
-TYPED_TEST(TransformCoordinates, 3DMatrix)
-{
-    transformCoordinatesTest<TypeParam>(string(TEST_DIR"/transformCoordinates/3d_matrix.test"));
+TYPED_TEST(TransformCoordinates, 3DMatrix) {
+    transformCoordinatesTest<TypeParam>(
+        string(TEST_DIR "/transformCoordinates/3d_matrix.test"));
 }
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
-TEST(TransformCoordinates, CPP)
-{
-    vector<dim4>       inDims;
+TEST(TransformCoordinates, CPP) {
+    vector<dim4> inDims;
     vector<vector<float> > in;
     vector<vector<float> > gold;
 
-    readTests<float, float, float>(TEST_DIR"/transformCoordinates/3d_matrix.test",inDims,in,gold);
+    readTests<float, float, float>(
+        TEST_DIR "/transformCoordinates/3d_matrix.test", inDims, in, gold);
 
     array tf = array(inDims[0][0], inDims[0][1], &(in[0].front()));
 
     float d0 = in[1][0];
     float d1 = in[1][1];
 
-    array out = transformCoordinates(tf, d0, d1);
+    array out    = transformCoordinates(tf, d0, d1);
     dim4 outDims = out.dims();
 
     vector<float> h_out(outDims[0] * outDims[1]);
     out.host(&h_out.front());
 
-    const size_t n = gold[0].size();
+    const size_t n  = gold[0].size();
     const float thr = 1.f;
 
     for (size_t elIter = 0; elIter < n; elIter++) {
-        ASSERT_LE(fabs(h_out[elIter] - gold[0][elIter]), thr) << "at: " << elIter << endl;
+        ASSERT_LE(fabs(h_out[elIter] - gold[0][elIter]), thr)
+            << "at: " << elIter << endl;
     }
 }
diff --git a/test/translate.cpp b/test/translate.cpp
index 9cf8991ae2..9c724cfef4 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -7,38 +7,34 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using std::abs;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Translate : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Translate : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 template<typename T>
-class TranslateInt : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class TranslateInt : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
@@ -50,23 +46,27 @@ TYPED_TEST_CASE(Translate, TestTypes);
 TYPED_TEST_CASE(TranslateInt, TestTypesInt);
 
 template<typename T>
-void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims, const float tx, const float ty, const af_interp_type method, const float max_fail_count = 0.0001)
-{
+void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims,
+                   const float tx, const float ty, const af_interp_type method,
+                   const float max_fail_count = 0.0001) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
-    vector<vector<T> >   in;
-    vector<vector<float> >   tests;
-    readTests<T, float, float>(pTestFile,numDims,in,tests);
+    vector<vector<T> > in;
+    vector<vector<float> > tests;
+    readTests<T, float, float>(pTestFile, numDims, in, tests);
 
-    af_array inArray = 0;
+    af_array inArray  = 0;
     af_array outArray = 0;
 
     dim4 dims = numDims[0];
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_translate(&outArray, inArray, tx, ty, odims[0], odims[1], method));
+    ASSERT_SUCCESS(
+        af_translate(&outArray, inArray, tx, ty, odims[0], odims[1], method));
 
     // Get result
     T* outData = new T[tests[resultIdx].size()];
@@ -77,112 +77,112 @@ void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims, const
 
     size_t fail_count = 0;
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        if(abs((T)tests[resultIdx][elIter] - outData[elIter]) > 0.0001) {
+        if (abs((T)tests[resultIdx][elIter] - outData[elIter]) > 0.0001) {
             fail_count++;
         }
     }
     ASSERT_EQ(true, (((float)fail_count / (float)(nElems)) <= max_fail_count))
-             << "Fail Count  = " << fail_count << endl;
+        << "Fail Count  = " << fail_count << endl;
 
     // Delete
     delete[] outData;
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
 }
 
-TYPED_TEST(Translate, Small1)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 0,
-                             dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
+TYPED_TEST(Translate, Small1) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 0,
+        dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Translate, Small2)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 1,
-                             dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
+TYPED_TEST(Translate, Small2) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 1,
+        dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Translate, Small3)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 2,
-                             dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
+TYPED_TEST(Translate, Small3) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 2,
+        dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Translate, Small4)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 3,
-                             dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
+TYPED_TEST(Translate, Small4) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 3,
+        dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Translate, Large1)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 0,
-                             dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
+TYPED_TEST(Translate, Large1) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 0,
+        dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Translate, Large2)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 1,
-                             dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
+TYPED_TEST(Translate, Large2) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 1,
+        dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(Translate, Large3)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 2,
-                             dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR);
+TYPED_TEST(Translate, Large3) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 2,
+        dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(Translate, Large4)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 3,
-                             dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR);
+TYPED_TEST(Translate, Large4) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 3,
+        dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(TranslateInt, Small1)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 0,
-                             dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
+TYPED_TEST(TranslateInt, Small1) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 0,
+        dim4(10, 10, 1, 1), 3, 2, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(TranslateInt, Small2)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 1,
-                             dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
+TYPED_TEST(TranslateInt, Small2) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 1,
+        dim4(10, 10, 1, 1), -3, -2, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(TranslateInt, Small3)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 2,
-                             dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
+TYPED_TEST(TranslateInt, Small3) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 2,
+        dim4(15, 15, 1, 1), 1.5, 2.5, AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(TranslateInt, Small4)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_small_1.test"), 3,
-                             dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
+TYPED_TEST(TranslateInt, Small4) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_small_1.test"), 3,
+        dim4(15, 15, 1, 1), -1.5, -2.5, AF_INTERP_BILINEAR);
 }
 
-TYPED_TEST(TranslateInt, Large1)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 0,
-                             dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
+TYPED_TEST(TranslateInt, Large1) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 0,
+        dim4(250, 320, 1, 1), 10, 18, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(TranslateInt, Large2)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 1,
-                             dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
+TYPED_TEST(TranslateInt, Large2) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 1,
+        dim4(250, 320, 1, 1), -20, 24, AF_INTERP_NEAREST);
 }
 
-TYPED_TEST(TranslateInt, Large3)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 2,
-                             dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR, 0.001);
+TYPED_TEST(TranslateInt, Large3) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 2,
+        dim4(300, 400, 1, 1), 10.23, 12.72, AF_INTERP_BILINEAR, 0.001);
 }
 
-TYPED_TEST(TranslateInt, Large4)
-{
-    translateTest<TypeParam>(string(TEST_DIR"/translate/translate_large_1.test"), 3,
-                             dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR, 0.001);
+TYPED_TEST(TranslateInt, Large4) {
+    translateTest<TypeParam>(
+        string(TEST_DIR "/translate/translate_large_1.test"), 3,
+        dim4(300, 400, 1, 1), -15.69, -10.13, AF_INTERP_BILINEAR, 0.001);
 }
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 89a002e00e..5f071aef5a 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -7,89 +7,93 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::abs;
-using std::endl;
-using std::string;
-using std::vector;
 using af::allTrue;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Transpose : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-            subMat2D.push_back(af_make_seq(2,7,1));
-            subMat2D.push_back(af_make_seq(2,7,1));
-
-            subMat3D.push_back(af_make_seq(2,7,1));
-            subMat3D.push_back(af_make_seq(2,7,1));
-            subMat3D.push_back(af_span);
-        }
-        vector<af_seq> subMat2D;
-        vector<af_seq> subMat3D;
+class Transpose : public ::testing::Test {
+   public:
+    virtual void SetUp() {
+        subMat2D.push_back(af_make_seq(2, 7, 1));
+        subMat2D.push_back(af_make_seq(2, 7, 1));
+
+        subMat3D.push_back(af_make_seq(2, 7, 1));
+        subMat3D.push_back(af_make_seq(2, 7, 1));
+        subMat3D.push_back(af_span);
+    }
+    vector<af_seq> subMat2D;
+    vector<af_seq> subMat3D;
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
+                         short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Transpose, TestTypes);
 
 template<typename T>
-void trsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=NULL)
-{
-    if (noDoubleTests<T>())
-        return;
+void trsTest(string pTestFile, bool isSubRef = false,
+             const vector<af_seq> *seqv = NULL) {
+    if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
-    readTests<T,T,int>(pTestFile,numDims,in,tests);
-    dim4 dims       = numDims[0];
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
+    dim4 dims = numDims[0];
 
-    af_array outArray   = 0;
-    af_array inArray    = 0;
+    af_array outArray = 0;
+    af_array inArray  = 0;
     T *outData;
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                                   dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     // check if the test is for indexed Array
     if (isSubRef) {
-        dim4 newDims(dims[1]-4,dims[0]-4,dims[2],dims[3]);
+        dim4 newDims(dims[1] - 4, dims[0] - 4, dims[2], dims[3]);
         af_array subArray = 0;
-        ASSERT_SUCCESS(af_index(&subArray,inArray,seqv->size(),&seqv->front()));
-        ASSERT_SUCCESS(af_transpose(&outArray,subArray, false));
+        ASSERT_SUCCESS(
+            af_index(&subArray, inArray, seqv->size(), &seqv->front()));
+        ASSERT_SUCCESS(af_transpose(&outArray, subArray, false));
         // destroy the temporary indexed Array
         ASSERT_SUCCESS(af_release_array(subArray));
 
         dim_t nElems;
-        ASSERT_SUCCESS(af_get_elements(&nElems,outArray));
+        ASSERT_SUCCESS(af_get_elements(&nElems, outArray));
         outData = new T[nElems];
     } else {
-        ASSERT_SUCCESS(af_transpose(&outArray,inArray, false));
+        ASSERT_SUCCESS(af_transpose(&outArray, inArray, false));
         outData = new T[dims.elements()];
     }
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)outData, outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData, outArray));
 
-    for (size_t testIter=0; testIter<tests.size(); ++testIter) {
-        vector<T> currGoldBar   = tests[testIter];
-        size_t nElems        = currGoldBar.size();
-        for (size_t elIter=0; elIter<nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter],outData[elIter])<< "at: " << elIter<< endl;
+    for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
+        vector<T> currGoldBar = tests[testIter];
+        size_t nElems         = currGoldBar.size();
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
     }
 
@@ -99,71 +103,60 @@ void trsTest(string pTestFile, bool isSubRef=false, const vector<af_seq> *seqv=N
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TYPED_TEST(Transpose,Vector)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/vector.test"));
+TYPED_TEST(Transpose, Vector) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/vector.test"));
 }
 
-TYPED_TEST(Transpose,VectorBatch)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/vector_batch.test"));
+TYPED_TEST(Transpose, VectorBatch) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/vector_batch.test"));
 }
 
- TYPED_TEST(Transpose,Square)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/square.test"));
+TYPED_TEST(Transpose, Square) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/square.test"));
 }
 
-TYPED_TEST(Transpose,Rectangle)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/rectangle.test"));
+TYPED_TEST(Transpose, Rectangle) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/rectangle.test"));
 }
 
-TYPED_TEST(Transpose,Rectangle2)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/rectangle2.test"));
+TYPED_TEST(Transpose, Rectangle2) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/rectangle2.test"));
 }
 
-TYPED_TEST(Transpose,SquareBatch)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/square_batch.test"));
+TYPED_TEST(Transpose, SquareBatch) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/square_batch.test"));
 }
 
-TYPED_TEST(Transpose,RectangleBatch)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/rectangle_batch.test"));
+TYPED_TEST(Transpose, RectangleBatch) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/rectangle_batch.test"));
 }
 
-TYPED_TEST(Transpose,RectangleBatch2)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/rectangle_batch2.test"));
+TYPED_TEST(Transpose, RectangleBatch2) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/rectangle_batch2.test"));
 }
 
-TYPED_TEST(Transpose,Square512x512)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/square2.test"));
+TYPED_TEST(Transpose, Square512x512) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/square2.test"));
 }
 
-TYPED_TEST(Transpose,SubRef)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/offset.test"),true,&(this->subMat2D));
+TYPED_TEST(Transpose, SubRef) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/offset.test"), true,
+                       &(this->subMat2D));
 }
 
-TYPED_TEST(Transpose,SubRefBatch)
-{
-    trsTest<TypeParam>(string(TEST_DIR"/transpose/offset_batch.test"),true,&(this->subMat3D));
+TYPED_TEST(Transpose, SubRefBatch) {
+    trsTest<TypeParam>(string(TEST_DIR "/transpose/offset_batch.test"), true,
+                       &(this->subMat3D));
 }
 
-
 ////////////////////////////////////// CPP //////////////////////////////////
 //
 template<typename T>
-void trsCPPTest(string pFileName)
-{
+void trsCPPTest(string pFileName) {
     vector<dim4> numDims;
 
-    vector<vector<T> >   in;
-    vector<vector<T> >   tests;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
     readTests<T, T, int>(pFileName, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -173,13 +166,14 @@ void trsCPPTest(string pFileName)
     array output = transpose(input);
 
     T *outData = new T[dims.elements()];
-    output.host((void*)outData);
+    output.host((void *)outData);
 
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
         vector<T> currGoldBar = tests[testIter];
-        size_t nElems = currGoldBar.size();
+        size_t nElems         = currGoldBar.size();
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_EQ(currGoldBar[elIter], outData[elIter])<< "at: " << elIter << endl;
+            ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+                << "at: " << elIter << endl;
         }
     }
 
@@ -187,38 +181,37 @@ void trsCPPTest(string pFileName)
     delete[] outData;
 }
 
-TEST(Transpose, CPP_f64)
-{
-    trsCPPTest<double>(string(TEST_DIR"/transpose/rectangle_batch2.test"));
+TEST(Transpose, CPP_f64) {
+    trsCPPTest<double>(string(TEST_DIR "/transpose/rectangle_batch2.test"));
 }
 
-TEST(Transpose, CPP_f32)
-{
-    trsCPPTest<float>(string(TEST_DIR"/transpose/rectangle_batch2.test"));
+TEST(Transpose, CPP_f32) {
+    trsCPPTest<float>(string(TEST_DIR "/transpose/rectangle_batch2.test"));
 }
 
 template<typename T>
-void trsCPPConjTest(dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1)
-{
+void trsCPPConjTest(dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
     vector<dim4> numDims;
 
     dim4 dims(d0, d1, d2, d3);
 
     if (noDoubleTests<T>()) return;
 
-    array input = randu(dims, (af_dtype) dtype_traits<T>::af_type);
+    array input    = randu(dims, (af_dtype)dtype_traits<T>::af_type);
     array output_t = transpose(input, false);
     array output_c = transpose(input, true);
 
-    T *tData  = new T[dims.elements()];
+    T *tData = new T[dims.elements()];
     T *cData = new T[dims.elements()];
-    output_t.host((void*)tData);
-    output_c.host((void*)cData);
+    output_t.host((void *)tData);
+    output_c.host((void *)cData);
 
     size_t nElems = dims.elements();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(real(tData[elIter]), real(cData[elIter]), 1e-6)<< "at: " << elIter << endl;
-        ASSERT_NEAR(-imag(tData[elIter]), imag(cData[elIter]), 1e-6)<< "at: " << elIter << endl;
+        ASSERT_NEAR(real(tData[elIter]), real(cData[elIter]), 1e-6)
+            << "at: " << elIter << endl;
+        ASSERT_NEAR(-imag(tData[elIter]), imag(cData[elIter]), 1e-6)
+            << "at: " << elIter << endl;
     }
 
     // cleanup
@@ -226,23 +219,13 @@ void trsCPPConjTest(dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1)
     delete[] cData;
 }
 
-TEST(Transpose, CPP_c32_CONJ40x40)
-{
-    trsCPPConjTest<cfloat>(40, 40);
-}
+TEST(Transpose, CPP_c32_CONJ40x40) { trsCPPConjTest<cfloat>(40, 40); }
 
-TEST(Transpose, CPP_c32_CONJ2000x1)
-{
-    trsCPPConjTest<cfloat>(2000);
-}
+TEST(Transpose, CPP_c32_CONJ2000x1) { trsCPPConjTest<cfloat>(2000); }
 
-TEST(Transpose, CPP_c32_CONJ20x20x5)
-{
-    trsCPPConjTest<cfloat>(20, 20, 5);
-}
+TEST(Transpose, CPP_c32_CONJ20x20x5) { trsCPPConjTest<cfloat>(20, 20, 5); }
 
-TEST(Transpose, MaxDim)
-{
+TEST(Transpose, MaxDim) {
     const size_t largeDim = 65535 * 33 + 1;
 
     array input  = range(dim4(2, largeDim, 1, 1));
@@ -260,23 +243,19 @@ TEST(Transpose, MaxDim)
     ASSERT_ARRAYS_EQ(gold, output);
 }
 
-
-TEST(Transpose, GFOR)
-{
+TEST(Transpose, GFOR) {
     using af::constant;
     using af::max;
     using af::seq;
     using af::span;
 
     dim4 dims = dim4(100, 100, 3);
-    array A = round(100 * randu(dims));
-    array B = constant(0, 100, 100, 3);
+    array A   = round(100 * randu(dims));
+    array B   = constant(0, 100, 100, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = A(span, span, ii).T();
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = A(span, span, ii).T(); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = A(span, span, ii).T();
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index 5b01a7682a..7f3fee9b89 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -7,46 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::endl;
-using std::vector;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using std::endl;
+using std::vector;
 
 template<typename T>
-class Transpose : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Transpose : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
+                         short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Transpose, TestTypes);
 
 template<typename T>
-void transposeip_test(dim4 dims)
-{
-    if (noDoubleTests<T>())
-        return;
+void transposeip_test(dim4 dims) {
+    if (noDoubleTests<T>()) return;
 
     af_array inArray  = 0;
     af_array outArray = 0;
 
-    ASSERT_SUCCESS(af_randu(&inArray, dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_randu(&inArray, dims.ndims(), dims.get(),
+                            (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_transpose(&outArray, inArray, false));
     ASSERT_SUCCESS(af_transpose_inplace(inArray, false));
@@ -58,10 +57,9 @@ void transposeip_test(dim4 dims)
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-#define INIT_TEST(Side, D3, D4)                                                     \
-    TYPED_TEST(Transpose, TranposeIP_##Side)                                        \
-    {                                                                               \
-        transposeip_test<TypeParam>(dim4(Side, Side, D3, D4));                      \
+#define INIT_TEST(Side, D3, D4)                                \
+    TYPED_TEST(Transpose, TranposeIP_##Side) {                 \
+        transposeip_test<TypeParam>(dim4(Side, Side, D3, D4)); \
     }
 
 INIT_TEST(10, 1, 1);
@@ -73,13 +71,12 @@ INIT_TEST(25, 2, 2);
 
 ////////////////////////////////////// CPP //////////////////////////////////
 //
-void transposeInPlaceCPPTest()
-{
+void transposeInPlaceCPPTest() {
     if (noDoubleTests<float>()) return;
 
-    dim4 dims(64, 64, 1,1);
+    dim4 dims(64, 64, 1, 1);
 
-    array input = randu(dims);
+    array input  = randu(dims);
     array output = transpose(input);
     transposeInPlace(input);
 
diff --git a/test/triangle.cpp b/test/triangle.cpp
index 349d4110d9..56c1559e58 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -7,37 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/data.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/data.h>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
-using std::abs;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::freeHost;
+using std::abs;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Triangle : public ::testing::Test { };
+class Triangle : public ::testing::Test {};
 
-typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char, uchar, uintl, intl, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
+                         uchar, uintl, intl, short, ushort>
+    TestTypes;
 TYPED_TEST_CASE(Triangle, TestTypes);
 
 template<typename T>
-void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag=false)
-{
+void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag = false) {
     if (noDoubleTests<T>()) return;
 #if 1
     array in = cpu_randu<T>(dims);
@@ -45,9 +46,9 @@ void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag=false)
     array in = randu(dims, (dtype)dtype_traits<T>::af_type);
 #endif
 
-    T *h_in = in.host<T>();
-    array out = is_upper ?  upper(in, is_unit_diag) : lower(in, is_unit_diag);
-    T *h_out = out.host<T>();
+    T *h_in   = in.host<T>();
+    array out = is_upper ? upper(in, is_unit_diag) : lower(in, is_unit_diag);
+    T *h_out  = out.host<T>();
 
     int m = dims[0];
     int n = dims[1];
@@ -60,12 +61,12 @@ void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag=false)
 
             for (int x = 0; x < m; x++) {
                 T val = T(0);
-                if (((y <= x) && !is_upper) ||
-                    ((y >= x) &&  is_upper)) {
+                if (((y <= x) && !is_upper) || ((y >= x) && is_upper)) {
                     val = (is_unit_diag && y == x) ? (T)(1) : h_in[y_off + x];
                 }
 
-                ASSERT_EQ(h_out[y_off + x], val) << "at (" << x << ", " << y << ")";
+                ASSERT_EQ(h_out[y_off + x], val)
+                    << "at (" << x << ", " << y << ")";
             }
         }
     }
@@ -74,94 +75,76 @@ void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag=false)
     freeHost(h_out);
 }
 
-TYPED_TEST(Triangle, Lower2DRect0)
-{
+TYPED_TEST(Triangle, Lower2DRect0) {
     triangleTester<TypeParam>(dim4(500, 600), false);
 }
 
-TYPED_TEST(Triangle, Lower2DRect1)
-{
+TYPED_TEST(Triangle, Lower2DRect1) {
     triangleTester<TypeParam>(dim4(2003, 1775), false);
 }
 
-TYPED_TEST(Triangle, Lower2DSquare)
-{
+TYPED_TEST(Triangle, Lower2DSquare) {
     triangleTester<TypeParam>(dim4(2048, 2048), false);
 }
 
-TYPED_TEST(Triangle, Lower3D)
-{
+TYPED_TEST(Triangle, Lower3D) {
     triangleTester<TypeParam>(dim4(1000, 1000, 5), false);
 }
 
-TYPED_TEST(Triangle, Lower4D)
-{
+TYPED_TEST(Triangle, Lower4D) {
     triangleTester<TypeParam>(dim4(600, 900, 3, 2), false);
 }
 
-TYPED_TEST(Triangle, Upper2DRect0)
-{
+TYPED_TEST(Triangle, Upper2DRect0) {
     triangleTester<TypeParam>(dim4(500, 600), true);
 }
 
-TYPED_TEST(Triangle, Upper2DRect1)
-{
+TYPED_TEST(Triangle, Upper2DRect1) {
     triangleTester<TypeParam>(dim4(2003, 1775), true);
 }
 
-TYPED_TEST(Triangle, Upper2DSquare)
-{
+TYPED_TEST(Triangle, Upper2DSquare) {
     triangleTester<TypeParam>(dim4(2048, 2048), true);
 }
 
-TYPED_TEST(Triangle, Upper3D)
-{
+TYPED_TEST(Triangle, Upper3D) {
     triangleTester<TypeParam>(dim4(1000, 1000, 5), true);
 }
 
-TYPED_TEST(Triangle, Upper4D)
-{
+TYPED_TEST(Triangle, Upper4D) {
     triangleTester<TypeParam>(dim4(600, 900, 3, 2), true);
 }
 
-TYPED_TEST(Triangle, Lower2DRect0Unit)
-{
+TYPED_TEST(Triangle, Lower2DRect0Unit) {
     triangleTester<TypeParam>(dim4(500, 600), false, true);
 }
 
-TYPED_TEST(Triangle, Lower2DRect1Unit)
-{
+TYPED_TEST(Triangle, Lower2DRect1Unit) {
     triangleTester<TypeParam>(dim4(2003, 1775), false, true);
 }
 
-TYPED_TEST(Triangle, Lower2DSquareUnit)
-{
+TYPED_TEST(Triangle, Lower2DSquareUnit) {
     triangleTester<TypeParam>(dim4(2048, 2048), false, true);
 }
 
-TYPED_TEST(Triangle, Upper2DRect0Unit)
-{
+TYPED_TEST(Triangle, Upper2DRect0Unit) {
     triangleTester<TypeParam>(dim4(500, 600), true, true);
 }
 
-TYPED_TEST(Triangle, Upper2DRect1Unit)
-{
+TYPED_TEST(Triangle, Upper2DRect1Unit) {
     triangleTester<TypeParam>(dim4(2003, 1775), true, true);
 }
 
-TYPED_TEST(Triangle, Upper2DSquareUnit)
-{
+TYPED_TEST(Triangle, Upper2DSquareUnit) {
     triangleTester<TypeParam>(dim4(2048, 2048), true, true);
 }
 
-TYPED_TEST(Triangle, MaxDim)
-{
+TYPED_TEST(Triangle, MaxDim) {
     const size_t largeDim = 65535 * 32 + 1;
     triangleTester<TypeParam>(dim4(2, largeDim), true, true);
 }
 
-TEST(Lower, ExtractGFOR)
-{
+TEST(Lower, ExtractGFOR) {
     using af::constant;
     using af::lower;
     using af::max;
@@ -170,14 +153,12 @@ TEST(Lower, ExtractGFOR)
     using af::span;
 
     dim4 dims = dim4(100, 100, 3);
-    array A = round(100 * randu(dims));
-    array B = constant(0, 100, 100, 3);
+    array A   = round(100 * randu(dims));
+    array B   = constant(0, 100, 100, 3);
 
-    gfor(seq ii, 3) {
-        B(span, span, ii) = lower(A(span, span, ii));
-    }
+    gfor(seq ii, 3) { B(span, span, ii) = lower(A(span, span, ii)); }
 
-    for(int ii = 0; ii < 3; ii++) {
+    for (int ii = 0; ii < 3; ii++) {
         array c_ii = lower(A(span, span, ii));
         array b_ii = B(span, span, ii);
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 9510dd7112..7f2f53ca4a 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -7,64 +7,67 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::allTrue;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
 using af::range;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Unwrap : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Unwrap : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         intl, uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Unwrap, TestTypes);
 
 template<typename T>
-void unwrapTest(string pTestFile, const unsigned resultIdx,
-        const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px, const dim_t py)
-{
+void unwrapTest(string pTestFile, const unsigned resultIdx, const dim_t wx,
+                const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
+                const dim_t py) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
-    readTests<T, T, int>(pTestFile,numDims,in,tests);
+    readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
-    af_array inArray = 0;
-    af_array outArray = 0;
+    af_array inArray   = 0;
+    af_array outArray  = 0;
     af_array outArrayT = 0;
     af_array outArray2 = 0;
 
-    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(), idims.get(), (af_dtype) dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), idims.ndims(),
+                                   idims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
-    ASSERT_SUCCESS(af_unwrap(&outArray , inArray, wx, wy, sx, sy, px, py, true ));
-    ASSERT_SUCCESS(af_unwrap(&outArrayT, inArray, wx, wy, sx, sy, px, py, false));
+    ASSERT_SUCCESS(af_unwrap(&outArray, inArray, wx, wy, sx, sy, px, py, true));
+    ASSERT_SUCCESS(
+        af_unwrap(&outArrayT, inArray, wx, wy, sx, sy, px, py, false));
     ASSERT_SUCCESS(af_transpose(&outArray2, outArrayT, false));
 
     size_t nElems = tests[resultIdx].size();
@@ -74,95 +77,96 @@ void unwrapTest(string pTestFile, const unsigned resultIdx,
     // Compare is_column == true results
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData[0], outArray));
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Compare is_column == false results
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData[0], outArray2));
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(outArrayT != 0) af_release_array(outArrayT);
-    if(outArray2 != 0) af_release_array(outArray2);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (outArrayT != 0) af_release_array(outArrayT);
+    if (outArray2 != 0) af_release_array(outArray2);
 }
 
-#define UNWRAP_INIT(desc, file, resultIdx, wx, wy, sx, sy, px,py)       \
-    TYPED_TEST(Unwrap, desc)                                            \
-    {                                                                   \
-        unwrapTest<TypeParam>(string(TEST_DIR"/unwrap/"#file".test"),   \
-                              resultIdx, wx, wy, sx, sy, px, py);       \
+#define UNWRAP_INIT(desc, file, resultIdx, wx, wy, sx, sy, px, py)       \
+    TYPED_TEST(Unwrap, desc) {                                           \
+        unwrapTest<TypeParam>(string(TEST_DIR "/unwrap/" #file ".test"), \
+                              resultIdx, wx, wy, sx, sy, px, py);        \
     }
 
-    UNWRAP_INIT(UnwrapSmall00, unwrap_small,  0,  3,  3,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall01, unwrap_small,  1,  3,  3,  1,  1,  1,  1);
-    UNWRAP_INIT(UnwrapSmall02, unwrap_small,  2,  3,  3,  1,  1,  2,  2);
-    UNWRAP_INIT(UnwrapSmall03, unwrap_small,  3,  3,  3,  2,  2,  0,  0);
-    UNWRAP_INIT(UnwrapSmall04, unwrap_small,  4,  3,  3,  2,  2,  1,  1);
-    UNWRAP_INIT(UnwrapSmall05, unwrap_small,  5,  3,  3,  2,  2,  2,  2);
-    UNWRAP_INIT(UnwrapSmall06, unwrap_small,  6,  3,  3,  3,  3,  0,  0);
-    UNWRAP_INIT(UnwrapSmall07, unwrap_small,  7,  3,  3,  3,  3,  1,  1);
-    UNWRAP_INIT(UnwrapSmall08, unwrap_small,  8,  3,  3,  3,  3,  2,  2);
-    UNWRAP_INIT(UnwrapSmall09, unwrap_small,  9,  4,  4,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall10, unwrap_small, 10,  4,  4,  1,  1,  1,  1);
-    UNWRAP_INIT(UnwrapSmall11, unwrap_small, 11,  4,  4,  1,  1,  2,  2);
-    UNWRAP_INIT(UnwrapSmall12, unwrap_small, 12,  4,  4,  1,  1,  3,  3);
-    UNWRAP_INIT(UnwrapSmall13, unwrap_small, 13,  4,  4,  2,  2,  0,  0);
-    UNWRAP_INIT(UnwrapSmall14, unwrap_small, 14,  4,  4,  2,  2,  1,  1);
-    UNWRAP_INIT(UnwrapSmall15, unwrap_small, 15,  4,  4,  2,  2,  2,  2);
-    UNWRAP_INIT(UnwrapSmall16, unwrap_small, 16,  4,  4,  2,  2,  3,  3);
-    UNWRAP_INIT(UnwrapSmall17, unwrap_small, 17,  4,  4,  4,  4,  0,  0);
-    UNWRAP_INIT(UnwrapSmall18, unwrap_small, 18,  4,  4,  4,  4,  1,  1);
-    UNWRAP_INIT(UnwrapSmall19, unwrap_small, 19,  4,  4,  4,  4,  2,  2);
-    UNWRAP_INIT(UnwrapSmall20, unwrap_small, 20,  4,  4,  4,  4,  3,  3);
-    UNWRAP_INIT(UnwrapSmall21, unwrap_small, 21,  5,  5,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall22, unwrap_small, 22,  5,  5,  1,  1,  1,  1);
-    UNWRAP_INIT(UnwrapSmall23, unwrap_small, 23,  5,  5,  5,  5,  0,  0);
-    UNWRAP_INIT(UnwrapSmall24, unwrap_small, 24,  5,  5,  5,  5,  1,  1);
-    UNWRAP_INIT(UnwrapSmall25, unwrap_small, 25,  8,  8,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall26, unwrap_small, 26,  8,  8,  1,  1,  7,  7);
-    UNWRAP_INIT(UnwrapSmall27, unwrap_small, 27,  8,  8,  8,  8,  0,  0);
-    UNWRAP_INIT(UnwrapSmall28, unwrap_small, 28,  8,  8,  8,  8,  7,  7);
-    UNWRAP_INIT(UnwrapSmall29, unwrap_small, 29, 12, 12,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall30, unwrap_small, 30, 12, 12,  1,  1,  2,  2);
-    UNWRAP_INIT(UnwrapSmall31, unwrap_small, 31, 12, 12, 12, 12,  0,  0);
-    UNWRAP_INIT(UnwrapSmall32, unwrap_small, 32, 12, 12, 12, 12,  2,  2);
-    UNWRAP_INIT(UnwrapSmall33, unwrap_small, 33, 16, 16,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall34, unwrap_small, 34, 16, 16, 16, 16,  0,  0);
-    UNWRAP_INIT(UnwrapSmall35, unwrap_small, 35, 16, 16, 16, 16, 15, 15);
-    UNWRAP_INIT(UnwrapSmall36, unwrap_small, 36, 31, 31,  8,  8, 15, 15);
-    UNWRAP_INIT(UnwrapSmall37, unwrap_small, 37,  8, 12,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall38, unwrap_small, 38,  8, 12,  1,  1,  7, 11);
-    UNWRAP_INIT(UnwrapSmall39, unwrap_small, 39,  8, 12,  8, 12,  0,  0);
-    UNWRAP_INIT(UnwrapSmall40, unwrap_small, 40,  8, 12,  8, 12,  7, 11);
-    UNWRAP_INIT(UnwrapSmall41, unwrap_small, 41, 15, 10,  1,  1,  0,  0);
-    UNWRAP_INIT(UnwrapSmall42, unwrap_small, 42, 15, 10,  1,  1, 14,  9);
-    UNWRAP_INIT(UnwrapSmall43, unwrap_small, 43, 15, 10, 15, 10,  0,  0);
-
-   // FIXME: This test is faulty after fixing the copy paste errors in unwrap
-   // UNWRAP_INIT(UnwrapSmall44, unwrap_small, 44, 15, 10, 15, 10, 14,  9);
-    UNWRAP_INIT(UnwrapSmall45, unwrap_small, 45, 18, 16, 18, 16,  1,  0);
-    UNWRAP_INIT(UnwrapSmall46, unwrap_small, 46, 16, 18, 16, 18,  0,  1);
+UNWRAP_INIT(UnwrapSmall00, unwrap_small, 0, 3, 3, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall01, unwrap_small, 1, 3, 3, 1, 1, 1, 1);
+UNWRAP_INIT(UnwrapSmall02, unwrap_small, 2, 3, 3, 1, 1, 2, 2);
+UNWRAP_INIT(UnwrapSmall03, unwrap_small, 3, 3, 3, 2, 2, 0, 0);
+UNWRAP_INIT(UnwrapSmall04, unwrap_small, 4, 3, 3, 2, 2, 1, 1);
+UNWRAP_INIT(UnwrapSmall05, unwrap_small, 5, 3, 3, 2, 2, 2, 2);
+UNWRAP_INIT(UnwrapSmall06, unwrap_small, 6, 3, 3, 3, 3, 0, 0);
+UNWRAP_INIT(UnwrapSmall07, unwrap_small, 7, 3, 3, 3, 3, 1, 1);
+UNWRAP_INIT(UnwrapSmall08, unwrap_small, 8, 3, 3, 3, 3, 2, 2);
+UNWRAP_INIT(UnwrapSmall09, unwrap_small, 9, 4, 4, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall10, unwrap_small, 10, 4, 4, 1, 1, 1, 1);
+UNWRAP_INIT(UnwrapSmall11, unwrap_small, 11, 4, 4, 1, 1, 2, 2);
+UNWRAP_INIT(UnwrapSmall12, unwrap_small, 12, 4, 4, 1, 1, 3, 3);
+UNWRAP_INIT(UnwrapSmall13, unwrap_small, 13, 4, 4, 2, 2, 0, 0);
+UNWRAP_INIT(UnwrapSmall14, unwrap_small, 14, 4, 4, 2, 2, 1, 1);
+UNWRAP_INIT(UnwrapSmall15, unwrap_small, 15, 4, 4, 2, 2, 2, 2);
+UNWRAP_INIT(UnwrapSmall16, unwrap_small, 16, 4, 4, 2, 2, 3, 3);
+UNWRAP_INIT(UnwrapSmall17, unwrap_small, 17, 4, 4, 4, 4, 0, 0);
+UNWRAP_INIT(UnwrapSmall18, unwrap_small, 18, 4, 4, 4, 4, 1, 1);
+UNWRAP_INIT(UnwrapSmall19, unwrap_small, 19, 4, 4, 4, 4, 2, 2);
+UNWRAP_INIT(UnwrapSmall20, unwrap_small, 20, 4, 4, 4, 4, 3, 3);
+UNWRAP_INIT(UnwrapSmall21, unwrap_small, 21, 5, 5, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall22, unwrap_small, 22, 5, 5, 1, 1, 1, 1);
+UNWRAP_INIT(UnwrapSmall23, unwrap_small, 23, 5, 5, 5, 5, 0, 0);
+UNWRAP_INIT(UnwrapSmall24, unwrap_small, 24, 5, 5, 5, 5, 1, 1);
+UNWRAP_INIT(UnwrapSmall25, unwrap_small, 25, 8, 8, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall26, unwrap_small, 26, 8, 8, 1, 1, 7, 7);
+UNWRAP_INIT(UnwrapSmall27, unwrap_small, 27, 8, 8, 8, 8, 0, 0);
+UNWRAP_INIT(UnwrapSmall28, unwrap_small, 28, 8, 8, 8, 8, 7, 7);
+UNWRAP_INIT(UnwrapSmall29, unwrap_small, 29, 12, 12, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall30, unwrap_small, 30, 12, 12, 1, 1, 2, 2);
+UNWRAP_INIT(UnwrapSmall31, unwrap_small, 31, 12, 12, 12, 12, 0, 0);
+UNWRAP_INIT(UnwrapSmall32, unwrap_small, 32, 12, 12, 12, 12, 2, 2);
+UNWRAP_INIT(UnwrapSmall33, unwrap_small, 33, 16, 16, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall34, unwrap_small, 34, 16, 16, 16, 16, 0, 0);
+UNWRAP_INIT(UnwrapSmall35, unwrap_small, 35, 16, 16, 16, 16, 15, 15);
+UNWRAP_INIT(UnwrapSmall36, unwrap_small, 36, 31, 31, 8, 8, 15, 15);
+UNWRAP_INIT(UnwrapSmall37, unwrap_small, 37, 8, 12, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall38, unwrap_small, 38, 8, 12, 1, 1, 7, 11);
+UNWRAP_INIT(UnwrapSmall39, unwrap_small, 39, 8, 12, 8, 12, 0, 0);
+UNWRAP_INIT(UnwrapSmall40, unwrap_small, 40, 8, 12, 8, 12, 7, 11);
+UNWRAP_INIT(UnwrapSmall41, unwrap_small, 41, 15, 10, 1, 1, 0, 0);
+UNWRAP_INIT(UnwrapSmall42, unwrap_small, 42, 15, 10, 1, 1, 14, 9);
+UNWRAP_INIT(UnwrapSmall43, unwrap_small, 43, 15, 10, 15, 10, 0, 0);
+
+// FIXME: This test is faulty after fixing the copy paste errors in unwrap
+// UNWRAP_INIT(UnwrapSmall44, unwrap_small, 44, 15, 10, 15, 10, 14,  9);
+UNWRAP_INIT(UnwrapSmall45, unwrap_small, 45, 18, 16, 18, 16, 1, 0);
+UNWRAP_INIT(UnwrapSmall46, unwrap_small, 46, 16, 18, 16, 18, 0, 1);
 ///////////////////////////////// CPP ////////////////////////////////////
 //
-TEST(Unwrap, CPP)
-{
+TEST(Unwrap, CPP) {
     if (noDoubleTests<float>()) return;
 
     const unsigned resultIdx = 20;
-    const unsigned wx = 4;
-    const unsigned wy = 4;
-    const unsigned sx = 4;
-    const unsigned sy = 4;
-    const unsigned px = 3;
-    const unsigned py = 3;
+    const unsigned wx        = 4;
+    const unsigned wy        = 4;
+    const unsigned sx        = 4;
+    const unsigned sy        = 4;
+    const unsigned px        = 3;
+    const unsigned py        = 3;
 
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, int>(string(TEST_DIR"/unwrap/unwrap_small.test"),numDims,in,tests);
+    readTests<float, float, int>(string(TEST_DIR "/unwrap/unwrap_small.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, &(in[0].front()));
@@ -175,17 +179,17 @@ TEST(Unwrap, CPP)
     // Compare result
     size_t nElems = tests[resultIdx].size();
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter]) << "at: " << elIter << endl;
+        ASSERT_EQ(tests[resultIdx][elIter], outData[elIter])
+            << "at: " << elIter << endl;
     }
 
     // Delete
     delete[] outData;
 }
 
-TEST(Unwrap, MaxDim)
-{
+TEST(Unwrap, MaxDim) {
     const size_t largeDim = 65535 + 1;
-    array input = range(5, 5, largeDim);
+    array input           = range(5, 5, largeDim);
 
     const unsigned wx = 5;
     const unsigned wy = 5;
@@ -197,7 +201,7 @@ TEST(Unwrap, MaxDim)
     array output = unwrap(input, wx, wy, sx, sy, px, py);
 
     array gold = range(dim4(5, 5, 1, largeDim));
-    gold = moddims(gold, dim4(25, 1, largeDim));
+    gold       = moddims(gold, dim4(25, 1, largeDim));
 
     ASSERT_ARRAYS_EQ(gold, output);
 }
@@ -210,39 +214,27 @@ TEST(Unwrap, DocSnippet) {
     //  2.     5.     8.
     //  3.     6.     9.
 
-    array A_simple = unwrap(A,
-                            2, 2,  // window size
-                            1, 1); // stride (sliding window)
+    array A_simple = unwrap(A, 2, 2,  // window size
+                            1, 1);    // stride (sliding window)
     //  1.     2.     4.     5.
     //  2.     3.     5.     6.
     //  4.     5.     7.     8.
     //  5.     6.     8.     9.
 
-    array A_padded = unwrap(A,
-                            2, 2,  // window size
-                            2, 2,  // stride (distinct)
-                            1, 1); // padding
+    array A_padded = unwrap(A, 2, 2,  // window size
+                            2, 2,     // stride (distinct)
+                            1, 1);    // padding
     //  0.     0.     0.     5.
     //  0.     0.     4.     6.
     //  0.     2.     0.     8.
     //  1.     3.     7.     9.
     //! [ex_unwrap]
 
-    float gold_hA_simple[] = {
-        1, 2, 4, 5,
-        2, 3, 5, 6,
-        4, 5, 7, 8,
-        5, 6, 8, 9
-    };
+    float gold_hA_simple[] = {1, 2, 4, 5, 2, 3, 5, 6, 4, 5, 7, 8, 5, 6, 8, 9};
     array gold_A_simple(dim4(4, 4), gold_hA_simple);
     ASSERT_ARRAYS_EQ(gold_A_simple, A_simple);
 
-    float gold_hA_padded[] = {
-        0, 0, 0, 1,
-        0, 0, 2, 3,
-        0, 4, 0, 7,
-        5, 6, 8, 9
-    };
+    float gold_hA_padded[] = {0, 0, 0, 1, 0, 0, 2, 3, 0, 4, 0, 7, 5, 6, 8, 9};
     array gold_A_padded(dim4(4, 4), gold_hA_padded);
     ASSERT_ARRAYS_EQ(gold_A_padded, A_padded);
 }
diff --git a/test/var.cpp b/test/var.cpp
index 6b6c38b547..e148ff6d3a 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -7,59 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
-using std::string;
-using std::vector;
+using af::array;
 using af::cdouble;
 using af::cfloat;
-using af::array;
 using af::dim4;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Var : public ::testing::Test
-{
-
-};
+class Var : public ::testing::Test {};
 
-typedef ::testing::Types< float, double, cfloat, cdouble, uint, int, uintl, intl, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, uintl, intl,
+                         char, uchar, short, ushort>
+    TestTypes;
 TYPED_TEST_CASE(Var, TestTypes);
 
 template<typename T>
 struct elseType {
-   typedef typename cond_type< is_same_type<T, uintl>::value ||
-                               is_same_type<T, intl> ::value,
-                                              double,
-                                              T>::type type;
+    typedef typename cond_type<is_same_type<T, uintl>::value ||
+                                   is_same_type<T, intl>::value,
+                               double, T>::type type;
 };
 
 template<typename T>
 struct varOutType {
-   typedef typename cond_type< is_same_type<T, float >::value ||
-                               is_same_type<T, int   >::value ||
-                               is_same_type<T, uint  >::value ||
-                               is_same_type<T, short >::value ||
-                               is_same_type<T, ushort>::value ||
-                               is_same_type<T, uchar >::value ||
-                               is_same_type<T, char  >::value,
-                                              float,
-                              typename elseType<T>::type>::type type;
+    typedef typename cond_type<
+        is_same_type<T, float>::value || is_same_type<T, int>::value ||
+            is_same_type<T, uint>::value || is_same_type<T, short>::value ||
+            is_same_type<T, ushort>::value || is_same_type<T, uchar>::value ||
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
-
-
 //////////////////////////////// CPP ////////////////////////////////////
 // test var_all interface using cpp api
 
 template<typename T>
-void testCPPVar(T const_value, dim4 dims)
-{
+void testCPPVar(T const_value, dim4 dims) {
     typedef typename varOutType<T>::type outType;
     if (noDoubleTests<T>()) return;
     if (noDoubleTests<outType>()) return;
@@ -82,39 +74,33 @@ void testCPPVar(T const_value, dim4 dims)
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 
-    gold = outType(2.5);
-    outType tmp[] = { outType(0), outType(1), outType(2), outType(3),
-        outType(4) };
+    gold          = outType(2.5);
+    outType tmp[] = {outType(0), outType(1), outType(2), outType(3),
+                     outType(4)};
     array b(5, tmp);
     output = var<outType>(b, false);
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 
-    gold = outType(2);
+    gold   = outType(2);
     output = var<outType>(b, true);
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 }
 
-TYPED_TEST(Var, AllCPPSmall)
-{
-    testCPPVar<TypeParam>(2, dim4(10, 10, 1, 1));
-}
+TYPED_TEST(Var, AllCPPSmall) { testCPPVar<TypeParam>(2, dim4(10, 10, 1, 1)); }
 
-TYPED_TEST(Var, AllCPPMedium)
-{
+TYPED_TEST(Var, AllCPPMedium) {
     testCPPVar<TypeParam>(2, dim4(100, 100, 1, 1));
 }
 
-TYPED_TEST(Var, AllCPPLarge)
-{
+TYPED_TEST(Var, AllCPPLarge) {
     testCPPVar<TypeParam>(2, dim4(1000, 1000, 1, 1));
 }
 
-TYPED_TEST(Var, DimCPPSmall)
-{
+TYPED_TEST(Var, DimCPPSmall) {
     typedef typename varOutType<TypeParam>::type outType;
 
     if (noDoubleTests<TypeParam>()) return;
@@ -124,17 +110,17 @@ TYPED_TEST(Var, DimCPPSmall)
     vector<vector<TypeParam> > in;
     vector<vector<outType> > tests;
 
-    readTests<TypeParam, outType, double> (TEST_DIR"/var/var.data",numDims,in,tests);
+    readTests<TypeParam, outType, double>(TEST_DIR "/var/var.data", numDims, in,
+                                          tests);
 
-    for(size_t i = 0; i < in.size(); i++)
-    {
+    for (size_t i = 0; i < in.size(); i++) {
         array input(numDims[i], &in[i].front(), afHost);
 
         array bout  = var(input, false);
         array nbout = var(input, true);
 
         array bout1  = var(input, false, 1);
-        array nbout1 = var(input, true,  1);
+        array nbout1 = var(input, true, 1);
 
         vector<vector<outType> > h_out(4);
 
@@ -143,13 +129,13 @@ TYPED_TEST(Var, DimCPPSmall)
         h_out[2].resize(bout1.elements());
         h_out[3].resize(nbout1.elements());
 
-        bout.host(  &h_out[0].front());
-        nbout.host( &h_out[1].front());
-        bout1.host( &h_out[2].front());
+        bout.host(&h_out[0].front());
+        nbout.host(&h_out[1].front());
+        bout1.host(&h_out[2].front());
         nbout1.host(&h_out[3].front());
 
-        for(size_t j = 0; j < tests.size(); j++) {
-            for(size_t jj = 0; jj < tests[j].size(); jj++) {
+        for (size_t j = 0; j < tests.size(); j++) {
+            for (size_t jj = 0; jj < tests[j].size(); jj++) {
                 // NOTE: will work for all types
                 if (is_same_type<float, outType>::value ||
                     is_same_type<cfloat, outType>::value) {
@@ -166,11 +152,11 @@ TYPED_TEST(Var, DimCPPSmall)
 
 TEST(Var, ISSUE2117) {
     using af::constant;
-    using af::var;
     using af::sum;
+    using af::var;
 
     array myArray = constant(1, 1000, 3000);
-    myArray = var(myArray, true, 1);
+    myArray       = var(myArray, true, 1);
 
     ASSERT_NEAR(0.0f, sum<float>(myArray), 0.000001);
 }
diff --git a/test/where.cpp b/test/where.cpp
index 875ce5f505..a415328748 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -7,46 +7,48 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <af/array.h>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::vector;
-using std::string;
-using std::endl;
 using af::allTrue;
 using af::array;
-using af::cfloat;
 using af::cdouble;
+using af::cfloat;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
 using af::randu;
 using af::range;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Where : public ::testing::Test { };
+class Where : public ::testing::Test {};
 
-typedef ::testing::Types< float, double, cfloat, cdouble, int, uint, intl, uintl, char, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, uint, intl, uintl,
+                         char, uchar, short, ushort>
+    TestTypes;
 TYPED_TEST_CASE(Where, TestTypes);
 
 template<typename T>
-void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=vector<af_seq>())
-{
+void whereTest(string pTestFile, bool isSubRef = false,
+               const vector<af_seq> seqv = vector<af_seq>()) {
     if (noDoubleTests<T>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (pTestFile,numDims,data,tests);
-    dim4 dims       = numDims[0];
+    readTests<int, int, int>(pTestFile, numDims, data, tests);
+    dim4 dims = numDims[0];
 
     vector<T> in(data[0].begin(), data[0].end());
 
@@ -56,11 +58,15 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
 
     // Get input array
     if (isSubRef) {
-        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
-        ASSERT_SUCCESS(af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
+        ASSERT_SUCCESS(af_create_array(&tempArray, &in.front(), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(
+            af_index(&inArray, tempArray, seqv.size(), &seqv.front()));
     } else {
-
-        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(), dims.get(), (af_dtype) dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&inArray, &in.front(), dims.ndims(),
+                                       dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
     }
 
     // Compare result
@@ -71,36 +77,32 @@ void whereTest(string pTestFile, bool isSubRef=false, const vector<af_seq> seqv=
 
     ASSERT_VEC_ARRAY_EQ(currGoldBar, dim4(tests[0].size()), outArray);
 
-    if(inArray   != 0) af_release_array(inArray);
-    if(outArray  != 0) af_release_array(outArray);
-    if(tempArray != 0) af_release_array(tempArray);
+    if (inArray != 0) af_release_array(inArray);
+    if (outArray != 0) af_release_array(outArray);
+    if (tempArray != 0) af_release_array(tempArray);
 }
 
-#define WHERE_TESTS(T)                          \
-    TEST(Where,Test_##T)                        \
-    {                                           \
-        whereTest<T>(                           \
-            string(TEST_DIR"/where/where.test") \
-            );                                  \
-    }                                           \
-
-TYPED_TEST(Where, BasicC)
-{
-    whereTest<TypeParam>(string(TEST_DIR"/where/where.test") );
+#define WHERE_TESTS(T)                                      \
+    TEST(Where, Test_##T) {                                 \
+        whereTest<T>(string(TEST_DIR "/where/where.test")); \
+    }
+
+TYPED_TEST(Where, BasicC) {
+    whereTest<TypeParam>(string(TEST_DIR "/where/where.test"));
 }
 
 //////////////////////////////////// CPP /////////////////////////////////
 //
-TYPED_TEST(Where, CPP)
-{
+TYPED_TEST(Where, CPP) {
     if (noDoubleTests<TypeParam>()) return;
 
     vector<dim4> numDims;
 
     vector<vector<int> > data;
     vector<vector<int> > tests;
-    readTests<int,int,int> (string(TEST_DIR"/where/where.test"),numDims,data,tests);
-    dim4 dims       = numDims[0];
+    readTests<int, int, int>(string(TEST_DIR "/where/where.test"), numDims,
+                             data, tests);
+    dim4 dims = numDims[0];
 
     vector<float> in(data[0].begin(), data[0].end());
     array input(dims, &in.front(), afHost);
@@ -112,23 +114,21 @@ TYPED_TEST(Where, CPP)
     ASSERT_VEC_ARRAY_EQ(currGoldBar, dim4(tests[0].size()), output);
 }
 
-TEST(Where, MaxDim)
-{
+TEST(Where, MaxDim) {
     const size_t largeDim = 65535 * 32 + 2;
 
-    array input = range(dim4(1, largeDim), 1);
+    array input  = range(dim4(1, largeDim), 1);
     array output = where(input % 2 == 0);
-    array gold = 2 * range(largeDim/2);
+    array gold   = 2 * range(largeDim / 2);
     ASSERT_ARRAYS_EQ(gold.as(u32), output);
 
-    input = range(dim4(1, 1, 1, largeDim), 3);
+    input  = range(dim4(1, 1, 1, largeDim), 3);
     output = where(input % 2 == 0);
     ASSERT_ARRAYS_EQ(gold.as(u32), output);
 }
 
-TEST(Where, ISSUE_1259)
-{
-    array a = randu(10, 10, 10);
+TEST(Where, ISSUE_1259) {
+    array a       = randu(10, 10, 10);
     array indices = where(a > 2);
     ASSERT_EQ(indices.elements(), 0);
 }
diff --git a/test/wrap.cpp b/test/wrap.cpp
index d8446c7db3..af4c7b6f93 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -7,17 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
-#include <af/dim4.hpp>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
-#include <iostream>
+#include <algorithm>
 #include <complex>
+#include <iostream>
 #include <string>
-#include <testHelpers.hpp>
-#include <algorithm>
+#include <vector>
 
 using af::allTrue;
 using af::array;
@@ -35,52 +35,48 @@ using std::string;
 using std::vector;
 
 template<typename T>
-class Wrap : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Wrap : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int, intl, uintl, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
+                         intl, uintl, char, unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Wrap, TestTypes);
 
 template<typename T>
-inline double get_val(T val)
-{
+inline double get_val(T val) {
     return val;
 }
 
-template<> inline double get_val<cfloat>(cfloat val)
-{
+template<>
+inline double get_val<cfloat>(cfloat val) {
     return abs(val);
 }
 
-template<> inline double get_val<cdouble>(cdouble val)
-{
+template<>
+inline double get_val<cdouble>(cdouble val) {
     return abs(val);
 }
 
-template<> inline double get_val<unsigned char>(unsigned char val)
-{
+template<>
+inline double get_val<unsigned char>(unsigned char val) {
     return ((int)(val)) % 256;
 }
 
-template<> inline double get_val<char>(char val)
-{
+template<>
+inline double get_val<char>(char val) {
     return (val != 0);
 }
 
 template<typename T>
-void wrapTest(const dim_t ix, const dim_t iy,
-              const dim_t wx, const dim_t wy,
-              const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py,
-              bool cond)
-{
+void wrapTest(const dim_t ix, const dim_t iy, const dim_t wx, const dim_t wy,
+              const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+              bool cond) {
     if (noDoubleTests<T>()) return;
 
     const int nc = 1;
@@ -119,7 +115,7 @@ void wrapTest(const dim_t ix, const dim_t iy,
 
     array factor(ix, iy, &h_factor[0]);
 
-    array in_dim = unwrap(in, wx, wy, sx, sy, px, py, cond);
+    array in_dim  = unwrap(in, wx, wy, sx, sy, px, py, cond);
     array res_dim = wrap(in_dim, ix, iy, wx, wy, sx, sy, px, py, cond);
 
     ASSERT_EQ(in.elements(), res_dim.elements());
@@ -133,63 +129,59 @@ void wrapTest(const dim_t ix, const dim_t iy,
 
         for (int y = 0; y < iy; y++) {
             for (int x = 0; x < ix; x++) {
-
                 // FIXME: Use a better test
-                T ival = iptr[y * ix + x];
-                T rval = rptr[y * ix + x];
+                T ival     = iptr[y * ix + x];
+                T rval     = rptr[y * ix + x];
                 int factor = h_factor[y * ix + x];
 
                 if (get_val(ival) == 0) continue;
 
                 ASSERT_NEAR(get_val<T>(ival * factor), get_val<T>(rval), 1E-5)
-                    << "at " << x << "," << y <<  " for cond  == " << cond << endl;
+                    << "at " << x << "," << y << " for cond  == " << cond
+                    << endl;
             }
         }
-
     }
 }
 
-#define WRAP_INIT(desc, ix, iy, wx, wy, sx, sy, px,py)              \
-    TYPED_TEST(Wrap, Col##desc)                                     \
-    {                                                               \
-        wrapTest<TypeParam>(ix, iy, wx, wy, sx, sy, px, py, true ); \
+#define WRAP_INIT(desc, ix, iy, wx, wy, sx, sy, px, py)             \
+    TYPED_TEST(Wrap, Col##desc) {                                   \
+        wrapTest<TypeParam>(ix, iy, wx, wy, sx, sy, px, py, true);  \
     }                                                               \
-    TYPED_TEST(Wrap, Row##desc)                                     \
-    {                                                               \
+    TYPED_TEST(Wrap, Row##desc) {                                   \
         wrapTest<TypeParam>(ix, iy, wx, wy, sx, sy, px, py, false); \
     }
 
-    WRAP_INIT(00, 300, 100,  3,  3,  1,  1,  0,  0);
-    WRAP_INIT(01, 300, 100,  3,  3,  1,  1,  1,  1);
-    WRAP_INIT(03, 300, 100,  3,  3,  2,  2,  0,  0);
-    WRAP_INIT(04, 300, 100,  3,  3,  2,  2,  1,  1);
-    WRAP_INIT(05, 300, 100,  3,  3,  2,  2,  2,  2);
-    WRAP_INIT(06, 300, 100,  3,  3,  3,  3,  0,  0);
-    WRAP_INIT(07, 300, 100,  3,  3,  3,  3,  1,  1);
-    WRAP_INIT(08, 300, 100,  3,  3,  3,  3,  2,  2);
-    WRAP_INIT(09, 300, 100,  4,  4,  1,  1,  0,  0);
-    WRAP_INIT(13, 300, 100,  4,  4,  2,  2,  0,  0);
-    WRAP_INIT(14, 300, 100,  4,  4,  2,  2,  1,  1);
-    WRAP_INIT(15, 300, 100,  4,  4,  2,  2,  2,  2);
-    WRAP_INIT(16, 300, 100,  4,  4,  2,  2,  3,  3);
-    WRAP_INIT(17, 300, 100,  4,  4,  4,  4,  0,  0);
-    WRAP_INIT(18, 300, 100,  4,  4,  4,  4,  1,  1);
-    WRAP_INIT(19, 300, 100,  4,  4,  4,  4,  2,  2);
-    WRAP_INIT(27, 300, 100,  8,  8,  8,  8,  0,  0);
-    WRAP_INIT(28, 300, 100,  8,  8,  8,  8,  7,  7);
-    WRAP_INIT(31, 300, 100, 12, 12, 12, 12,  0,  0);
-    WRAP_INIT(32, 300, 100, 12, 12, 12, 12,  2,  2);
-    WRAP_INIT(35, 300, 100, 16, 16, 16, 16, 15, 15);
-    WRAP_INIT(36, 300, 100, 31, 31,  8,  8, 15, 15);
-    WRAP_INIT(39, 300, 100,  8, 12,  8, 12,  0,  0);
-    WRAP_INIT(40, 300, 100,  8, 12,  8, 12,  7, 11);
-    WRAP_INIT(43, 300, 100, 15, 10, 15, 10,  0,  0);
-    WRAP_INIT(44, 300, 100, 15, 10, 15, 10, 14,  9);
-
-TEST(Wrap, MaxDim)
-{
+WRAP_INIT(00, 300, 100, 3, 3, 1, 1, 0, 0);
+WRAP_INIT(01, 300, 100, 3, 3, 1, 1, 1, 1);
+WRAP_INIT(03, 300, 100, 3, 3, 2, 2, 0, 0);
+WRAP_INIT(04, 300, 100, 3, 3, 2, 2, 1, 1);
+WRAP_INIT(05, 300, 100, 3, 3, 2, 2, 2, 2);
+WRAP_INIT(06, 300, 100, 3, 3, 3, 3, 0, 0);
+WRAP_INIT(07, 300, 100, 3, 3, 3, 3, 1, 1);
+WRAP_INIT(08, 300, 100, 3, 3, 3, 3, 2, 2);
+WRAP_INIT(09, 300, 100, 4, 4, 1, 1, 0, 0);
+WRAP_INIT(13, 300, 100, 4, 4, 2, 2, 0, 0);
+WRAP_INIT(14, 300, 100, 4, 4, 2, 2, 1, 1);
+WRAP_INIT(15, 300, 100, 4, 4, 2, 2, 2, 2);
+WRAP_INIT(16, 300, 100, 4, 4, 2, 2, 3, 3);
+WRAP_INIT(17, 300, 100, 4, 4, 4, 4, 0, 0);
+WRAP_INIT(18, 300, 100, 4, 4, 4, 4, 1, 1);
+WRAP_INIT(19, 300, 100, 4, 4, 4, 4, 2, 2);
+WRAP_INIT(27, 300, 100, 8, 8, 8, 8, 0, 0);
+WRAP_INIT(28, 300, 100, 8, 8, 8, 8, 7, 7);
+WRAP_INIT(31, 300, 100, 12, 12, 12, 12, 0, 0);
+WRAP_INIT(32, 300, 100, 12, 12, 12, 12, 2, 2);
+WRAP_INIT(35, 300, 100, 16, 16, 16, 16, 15, 15);
+WRAP_INIT(36, 300, 100, 31, 31, 8, 8, 15, 15);
+WRAP_INIT(39, 300, 100, 8, 12, 8, 12, 0, 0);
+WRAP_INIT(40, 300, 100, 8, 12, 8, 12, 7, 11);
+WRAP_INIT(43, 300, 100, 15, 10, 15, 10, 0, 0);
+WRAP_INIT(44, 300, 100, 15, 10, 15, 10, 14, 9);
+
+TEST(Wrap, MaxDim) {
     const size_t largeDim = 65535 + 1;
-    array input = range(5, 5, 1, largeDim);
+    array input           = range(5, 5, 1, largeDim);
 
     const unsigned wx = 5;
     const unsigned wy = 5;
@@ -199,7 +191,7 @@ TEST(Wrap, MaxDim)
     const unsigned py = 0;
 
     array unwrapped = unwrap(input, wx, wy, sx, sy, px, py);
-    array output = wrap(unwrapped, 5, 5, wx, wy, sx, sy, px, py);
+    array output    = wrap(unwrapped, 5, 5, wx, wy, sx, sy, px, py);
 
     ASSERT_ARRAYS_EQ(output, input);
 }
@@ -212,20 +204,18 @@ TEST(Wrap, DocSnippet) {
     //  2.     5.     8.
     //  3.     6.     9.
 
-    array A_unwrapped = unwrap(A,
-                               2, 2,  // window size
-                               2, 2,  // stride (distinct)
-                               1, 1); // padding
+    array A_unwrapped = unwrap(A, 2, 2,  // window size
+                               2, 2,     // stride (distinct)
+                               1, 1);    // padding
     //  0.     0.     0.     5.
     //  0.     0.     4.     6.
     //  0.     2.     0.     8.
     //  1.     3.     7.     9.
 
-    array A_wrapped = wrap(A_unwrapped,
-                           3, 3,  // A's size
-                           2, 2,  // window size
-                           2, 2,  // stride (distinct)
-                           1, 1); // padding
+    array A_wrapped = wrap(A_unwrapped, 3, 3,  // A's size
+                           2, 2,               // window size
+                           2, 2,               // stride (distinct)
+                           1, 1);              // padding
     //  1.     4.     7.
     //  2.     5.     8.
     //  3.     6.     9.
@@ -239,17 +229,15 @@ TEST(Wrap, DocSnippet) {
     //  1.     1.     1.
     //  1.     1.     1.
     //  1.     1.     1.
-    array B_unwrapped = unwrap(B,
-                               2, 2,  // window size
-                               1, 1); // stride (sliding)
+    array B_unwrapped = unwrap(B, 2, 2,  // window size
+                               1, 1);    // stride (sliding)
     //  1.     1.     1.     1.
     //  1.     1.     1.     1.
     //  1.     1.     1.     1.
     //  1.     1.     1.     1.
-    array B_wrapped = wrap(B_unwrapped,
-                           3, 3,  // B's size
-                           2, 2,  // window size
-                           1, 1); // stride (sliding)
+    array B_wrapped = wrap(B_unwrapped, 3, 3,  // B's size
+                           2, 2,               // window size
+                           1, 1);              // stride (sliding)
     //  1.     2.     1.
     //  2.     4.     2.
     //  1.     2.     1.
diff --git a/test/write.cpp b/test/write.cpp
index f04ddd113c..bc3345fec6 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -7,46 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <vector>
 #include <iostream>
 #include <string>
-#include <testHelpers.hpp>
+#include <vector>
 
-using std::endl;
-using std::string;
-using std::vector;
 using af::array;
-using af::cfloat;
 using af::cdouble;
-using af::freeHost;
+using af::cfloat;
 using af::dim4;
 using af::dtype_traits;
+using af::freeHost;
+using std::endl;
+using std::string;
+using std::vector;
 
 template<typename T>
-class Write : public ::testing::Test
-{
-    public:
-        virtual void SetUp() {
-        }
+class Write : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char, unsigned char, short, ushort> TestTypes;
+typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
+                         unsigned char, short, ushort>
+    TestTypes;
 
 // register the type list
 TYPED_TEST_CASE(Write, TestTypes);
 
 template<typename T>
-void writeTest(dim4 dims)
-{
+void writeTest(dim4 dims) {
     if (noDoubleTests<T>()) return;
 
-    array A = randu(dims, (af_dtype) dtype_traits<T>::af_type);
-    array B = randu(dims, (af_dtype) dtype_traits<T>::af_type);
+    array A = randu(dims, (af_dtype)dtype_traits<T>::af_type);
+    array B = randu(dims, (af_dtype)dtype_traits<T>::af_type);
 
     array A_copy = A.copy();
     array B_copy = B.copy();
@@ -63,43 +62,25 @@ void writeTest(dim4 dims)
     freeHost(a_host);
 }
 
-TYPED_TEST(Write, Vector0)
-{
-    writeTest<TypeParam>(dim4(10));
-}
+TYPED_TEST(Write, Vector0) { writeTest<TypeParam>(dim4(10)); }
 
-TYPED_TEST(Write, Vector1)
-{
-    writeTest<TypeParam>(dim4(1000));
-}
+TYPED_TEST(Write, Vector1) { writeTest<TypeParam>(dim4(1000)); }
 
-TYPED_TEST(Write, Matrix0)
-{
-    writeTest<TypeParam>(dim4(64, 8));
-}
+TYPED_TEST(Write, Matrix0) { writeTest<TypeParam>(dim4(64, 8)); }
 
-TYPED_TEST(Write, Matrix1)
-{
-    writeTest<TypeParam>(dim4(256, 256));
-}
+TYPED_TEST(Write, Matrix1) { writeTest<TypeParam>(dim4(256, 256)); }
 
-TYPED_TEST(Write, Volume0)
-{
-    writeTest<TypeParam>(dim4(10, 10, 10));
-}
+TYPED_TEST(Write, Volume0) { writeTest<TypeParam>(dim4(10, 10, 10)); }
 
-TYPED_TEST(Write, Volume1)
-{
-    writeTest<TypeParam>(dim4(32, 64, 16));
-}
+TYPED_TEST(Write, Volume1) { writeTest<TypeParam>(dim4(32, 64, 16)); }
 
 TEST(Write, VoidPointer) {
-  vector<float> gold(100, 5);
+    vector<float> gold(100, 5);
 
-  array a(100);
+    array a(100);
 
-  void* h_gold = (void*)&gold.front();
-  a.write(h_gold, 100 * sizeof(float), afHost);
+    void *h_gold = (void *)&gold.front();
+    a.write(h_gold, 100 * sizeof(float), afHost);
 
-  ASSERT_VEC_ARRAY_EQ(gold, dim4(100), a);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(100), a);
 }
diff --git a/test/ycbcr_rgb.cpp b/test/ycbcr_rgb.cpp
index b3e239d391..8f5ea83a08 100644
--- a/test/ycbcr_rgb.cpp
+++ b/test/ycbcr_rgb.cpp
@@ -7,21 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <gtest/gtest.h>
 #include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <string>
 #include <vector>
-#include <testHelpers.hpp>
 
+using af::array;
+using af::dim4;
 using std::endl;
 using std::string;
 using std::vector;
-using af::array;
-using af::dim4;
 
-TEST(ycbcr_rgb, InvalidArray)
-{
+TEST(ycbcr_rgb, InvalidArray) {
     vector<float> in(100, 1);
 
     dim4 dims(100);
@@ -30,21 +29,21 @@ TEST(ycbcr_rgb, InvalidArray)
     try {
         array output = hsv2rgb(input);
         ASSERT_EQ(true, false);
-    } catch(af::exception) {
+    } catch (af::exception) {
         ASSERT_EQ(true, true);
         return;
     }
 }
 
-TEST(ycbcr2rgb, CPP)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(ycbcr2rgb, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(
+        string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = ycbcr2rgb(input);
 
@@ -52,39 +51,43 @@ TEST(ycbcr2rgb, CPP)
     output.host(outData.data());
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
+    size_t nElems             = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)
+            << "at: " << elIter << endl;
     }
 }
 
-TEST(ycbcr2rgb, MaxDim)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(ycbcr2rgb, MaxDim) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(
+        string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
-    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = tile(input, 1, ntile);
-    array output = ycbcr2rgb(input);
-    dim4 outDims = output.dims();
+    unsigned int ntile    = (largeDim + dims[1] - 1) / dims[1];
+    input                 = tile(input, 1, ntile);
+    array output          = ycbcr2rgb(input);
+    dim4 outDims          = output.dims();
 
     float *outData = new float[outDims.elements()];
-    output.host((void*)outData);
+    output.host((void *)outData);
 
     vector<float> currGoldBar = tests[0];
-    for(int z=0; z<outDims[2]; ++z) {
-        for(int y=0; y<outDims[1]; ++y) {
-            for(int x=0; x<outDims[0]; ++x) {
-                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
-                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
+    for (int z = 0; z < outDims[2]; ++z) {
+        for (int y = 0; y < outDims[1]; ++y) {
+            for (int x = 0; x < outDims[0]; ++x) {
+                int outIter =
+                    (z * outDims[1] * outDims[0]) + (y * outDims[0]) + x;
+                int goldIter =
+                    (z * dims[1] * dims[0]) + ((y % dims[1]) * dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)
+                    << "at: " << outIter << endl;
             }
         }
     }
@@ -93,15 +96,15 @@ TEST(ycbcr2rgb, MaxDim)
     delete[] outData;
 }
 
-TEST(rgb2ycbcr, CPP)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(rgb2ycbcr, CPP) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(
+        string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
     array output = rgb2ycbcr(input);
 
@@ -109,40 +112,43 @@ TEST(rgb2ycbcr, CPP)
     output.host(outData.data());
 
     vector<float> currGoldBar = tests[0];
-    size_t nElems = currGoldBar.size();
-    for (size_t elIter=0; elIter<nElems; ++elIter) {
-        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)<< "at: " << elIter<< endl;
+    size_t nElems             = currGoldBar.size();
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_NEAR(currGoldBar[elIter], outData[elIter], 1.0e-3)
+            << "at: " << elIter << endl;
     }
-
 }
 
-TEST(rgb2ycbcr, MaxDim)
-{
-    vector<dim4>      numDims;
-    vector<vector<float> >      in;
-    vector<vector<float> >   tests;
+TEST(rgb2ycbcr, MaxDim) {
+    vector<dim4> numDims;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
-    readTestsFromFile<float,float>(string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
+    readTestsFromFile<float, float>(
+        string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
 
-    dim4 dims    = numDims[0];
+    dim4 dims = numDims[0];
     array input(dims, &(in[0].front()));
 
     const size_t largeDim = 65535 * 16 + 1;
-    unsigned int ntile = (largeDim + dims[1] - 1)/dims[1];
-    input = tile(input, 1, ntile);
-    array output = rgb2ycbcr(input);
-    dim4 outDims = output.dims();
+    unsigned int ntile    = (largeDim + dims[1] - 1) / dims[1];
+    input                 = tile(input, 1, ntile);
+    array output          = rgb2ycbcr(input);
+    dim4 outDims          = output.dims();
 
     float *outData = new float[outDims.elements()];
-    output.host((void*)outData);
+    output.host((void *)outData);
 
     vector<float> currGoldBar = tests[0];
-    for(int z=0; z<outDims[2]; ++z) {
-        for(int y=0; y<outDims[1]; ++y) {
-            for(int x=0; x<outDims[0]; ++x) {
-                int outIter  = (z*outDims[1]*outDims[0]) + (y*outDims[0]) + x;
-                int goldIter = (z*dims[1]*dims[0]) + ((y%dims[1])*dims[0]) + x;
-                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)<< "at: " << outIter << endl;
+    for (int z = 0; z < outDims[2]; ++z) {
+        for (int y = 0; y < outDims[1]; ++y) {
+            for (int x = 0; x < outDims[0]; ++x) {
+                int outIter =
+                    (z * outDims[1] * outDims[0]) + (y * outDims[0]) + x;
+                int goldIter =
+                    (z * dims[1] * dims[0]) + ((y % dims[1]) * dims[0]) + x;
+                ASSERT_NEAR(currGoldBar[goldIter], outData[outIter], 1.0e-3)
+                    << "at: " << outIter << endl;
             }
         }
     }

From bef327fe36ff616bef9b7a6a015b6df18ea8f832 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 31 Dec 2018 15:12:56 +0530
Subject: [PATCH 1592/2677] Update forge submodule to use glm submodule

forge refactored glm to be as submodule instead of a dependency.
---
 extern/forge | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extern/forge b/extern/forge
index 64f0a7409d..8dd37341e1 160000
--- a/extern/forge
+++ b/extern/forge
@@ -1 +1 @@
-Subproject commit 64f0a7409d407ee7ec405f5018a4feb158e6e9e8
+Subproject commit 8dd37341e128bdcbd8b837a3d23c6cdf5d5d48ec

From 92547cf8c58c9ed43a906bcc1198b5ded810b48f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 3 Jan 2019 00:56:06 -0500
Subject: [PATCH 1593/2677] Change the af_device_array pointer to a non-const
 pointer

The af_device_array pointer is currently const. This is not appropriate
because the pointer may be changed in other operations. This commit
removes the const decorator from the pointer
---
 include/af/device.h        | 2 +-
 src/api/c/memory.cpp       | 2 +-
 src/api/cpp/array.cpp      | 2 +-
 src/api/unified/device.cpp | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/af/device.h b/include/af/device.h
index 2c36fb4b9b..ddc43f4dd6 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -335,7 +335,7 @@ extern "C" {
        Create array from device memory
        \ingroup construct_mat
     */
-    AFAPI af_err af_device_array(af_array *arr, const void *data, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_device_array(af_array *arr, void *data, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
        Get memory information from the memory manager
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 6740a7c893..e9c0e655ad 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -21,7 +21,7 @@
 
 using namespace detail;
 
-af_err af_device_array(af_array *arr, const void *data, const unsigned ndims,
+af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
                        const dim_t *const dims, const af_dtype type) {
     try {
         AF_CHECK(af_init());
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 0469ba3e6a..5a36058fe9 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -129,7 +129,7 @@ static void initDataArray(af_array *arr, const T *ptr, af::source src, dim_t d0,
                                      my_dims, ty));
             break;
         case afDevice:
-            AF_THROW(af_device_array(arr, (const void *)ptr, AF_MAX_DIMS,
+            AF_THROW(af_device_array(arr, (void *)ptr, AF_MAX_DIMS,
                                      my_dims, ty));
             break;
         default:
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index b438770d51..57d74fd476 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -89,7 +89,7 @@ af_err af_free_host(void *ptr) {
     return AF_SUCCESS;
 }
 
-af_err af_device_array(af_array *arr, const void *data, const unsigned ndims,
+af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
                        const dim_t *const dims, const af_dtype type) {
     return CALL(arr, data, ndims, dims, type);
 }

From cde4ede8f98b8e8cb687a7e1c4421eb3cbc45d38 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Jan 2019 14:37:30 +0530
Subject: [PATCH 1594/2677] Fix non-mkl & non-batch blas upstream call
 arguments

---
 src/backend/cpu/blas.cpp | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index cfcbcdcb44..adeb95daed 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -241,12 +241,16 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
                 dim_t incr =
                     (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
                 gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
-                               left.get(), lStrides[1], right.get(), incr, beta,
-                               output.get(), 1);
+                               static_cast<CBT*>(left.get()), lStrides[1],
+                               static_cast<CBT*>(right.get()), incr, beta,
+                               static_cast<BT*>(output.get()), 1);
             } else {
                 gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
-                               left.get(), lStrides[1], right.get(),
-                               rStrides[1], beta, output.get(), output.dims(0));
+                               static_cast<CBT*>(left.get()), lStrides[1],
+                               static_cast<CBT*>(right.get()),
+                               rStrides[1], beta,
+                               static_cast<BT*>(output.get()),
+                               output.dims(0));
             }
         } else {
             int batchSize = oDims[2] * oDims[3];
@@ -269,9 +273,9 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
                 int roff = z * (is_r_d2_batched * rStrides[2]) +
                            w * (is_r_d3_batched * rStrides[3]);
 
-                lptrs[n] = reinterpret_cast<CBT *>(left.get() + loff);
-                rptrs[n] = reinterpret_cast<CBT *>(right.get() + roff);
-                optrs[n] = reinterpret_cast<BT *>(
+                lptrs[n] = static_cast<CBT *>(left.get() + loff);
+                rptrs[n] = static_cast<CBT *>(right.get() + roff);
+                optrs[n] = static_cast<BT *>(
                     output.get() + z * oStrides[2] + w * oStrides[3]);
             }
 

From 09773d0a589f540fb959ed6b9e680e2f097c04c5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Jan 2019 20:49:12 +0530
Subject: [PATCH 1595/2677] Fix pointer cast expressions in cpu blas
 implementation

---
 src/backend/cpu/blas.cpp | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index adeb95daed..300574cc66 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -241,15 +241,15 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
                 dim_t incr =
                     (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
                 gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
-                               static_cast<CBT*>(left.get()), lStrides[1],
-                               static_cast<CBT*>(right.get()), incr, beta,
-                               static_cast<BT*>(output.get()), 1);
+                               reinterpret_cast<CBT*>(left.get()), lStrides[1],
+                               reinterpret_cast<CBT*>(right.get()), incr, beta,
+                               reinterpret_cast<BT*>(output.get()), 1);
             } else {
                 gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
-                               static_cast<CBT*>(left.get()), lStrides[1],
-                               static_cast<CBT*>(right.get()),
+                               reinterpret_cast<CBT*>(left.get()), lStrides[1],
+                               reinterpret_cast<CBT*>(right.get()),
                                rStrides[1], beta,
-                               static_cast<BT*>(output.get()),
+                               reinterpret_cast<BT*>(output.get()),
                                output.dims(0));
             }
         } else {
@@ -273,9 +273,9 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
                 int roff = z * (is_r_d2_batched * rStrides[2]) +
                            w * (is_r_d3_batched * rStrides[3]);
 
-                lptrs[n] = static_cast<CBT *>(left.get() + loff);
-                rptrs[n] = static_cast<CBT *>(right.get() + roff);
-                optrs[n] = static_cast<BT *>(
+                lptrs[n] = reinterpret_cast<CBT *>(left.get() + loff);
+                rptrs[n] = reinterpret_cast<CBT *>(right.get() + roff);
+                optrs[n] = reinterpret_cast<BT *>(
                     output.get() + z * oStrides[2] + w * oStrides[3]);
             }
 

From 7d44b1bd99ee030059f9dbc5173677d9115b5b75 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 4 Jan 2019 21:46:28 +0530
Subject: [PATCH 1596/2677] Generate only version header if forge is not build

---
 .../AFconfigure_forge_submodule.cmake         | 58 +++++++++++--------
 1 file changed, 33 insertions(+), 25 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index bde7b18728..fe9e5b796e 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -5,35 +5,43 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
-set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
-set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
-set(CMAKE_BUILD_TYPE Release)
-set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
-set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
-set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
-if (AF_BUILD_FORGE)
+if(AF_BUILD_FORGE)
+  set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
+  set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
+  set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
+  set(CMAKE_BUILD_TYPE Release)
+  set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
+  set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
+  set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
+
   add_subdirectory(extern/forge)
-else (AF_BUILD_FORGE)
-  add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
-endif (AF_BUILD_FORGE)
-mark_as_advanced(
-    FG_BUILD_EXAMPLES
-    FG_BUILD_DOCS
-    FG_WITH_FREEIMAGE
-    FG_USE_WINDOW_TOOLKIT
-    FG_USE_SYSTEM_CL2HPP
-    FG_ENABLE_HUNTER
-    glfw3_DIR
-    glm_DIR
-    )
-set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
-set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
 
-if (AF_BUILD_FORGE)
+  mark_as_advanced(
+      FG_BUILD_EXAMPLES
+      FG_BUILD_DOCS
+      FG_WITH_FREEIMAGE
+      FG_USE_WINDOW_TOOLKIT
+      FG_USE_SYSTEM_CL2HPP
+      FG_ENABLE_HUNTER
+      glfw3_DIR
+      glm_DIR
+      )
+  set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
+  set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
+
   install(FILES
       $<TARGET_FILE:forge>
       $<TARGET_SONAME_FILE:forge>
       DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
-endif (AF_BUILD_FORGE)
+else(AF_BUILD_FORGE)
+  set(FG_VERSION "1.0.0")
+  set(FG_VERSION_MAJOR 1)
+  set(FG_VERSION_MINOR 0)
+  set(FG_VERSION_PATCH 0)
+  set(FG_API_VERSION_CURRENT 10)
+  configure_file(
+    ${PROJECT_SOURCE_DIR}/extern/forge/CMakeModules/version.h.in
+    ${PROJECT_BINARY_DIR}/extern/forge/include/fg/version.h
+    )
+endif(AF_BUILD_FORGE)

From 62bf57b2a81975c4d05019e74d63b71ce00b15f5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 3 Jan 2019 11:00:17 -0500
Subject: [PATCH 1597/2677] Apply suggestions from clang-tidy tool on array.cpp

Clang-tidy is a tool for static analysis and other quality checks on software.
These changes are some of the suggestions from clang-tidy on the array.cpp file.
This commit does not add the clang-tidy config file because that is still a
work in progress.
---
 src/api/cpp/array.cpp | 254 ++++++++++++++++++++++--------------------
 src/api/cpp/error.hpp |   2 +-
 2 files changed, 137 insertions(+), 119 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 5a36058fe9..cba9df3f58 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -21,27 +21,35 @@
 
 #include <memory>
 #include <stdexcept>
+#include <vector>
 
-namespace af {
-static int gforDim(af_index_t *indices) {
+using af::calcDim;
+using af::dim4;
+using std::copy;
+using std::logic_error;
+using std::vector;
+
+namespace {
+int gforDim(af_index_t *indices) {
     for (int i = 0; i < AF_MAX_DIMS; i++) {
-        if (indices[i].isBatch) return i;
+        if (indices[i].isBatch) { return i; }
     }
     return -1;
 }
 
-static af_array gforReorder(const af_array in, unsigned dim) {
+af_array gforReorder(const af_array in, unsigned dim) {
     // This is here to stop gcc from complaining
-    if (dim > 3) AF_THROW_ERR("GFor: Dimension is invalid", AF_ERR_SIZE);
+    if (dim > 3) { AF_THROW_ERR("GFor: Dimension is invalid", AF_ERR_SIZE); }
     unsigned order[AF_MAX_DIMS] = {0, 1, 2, dim};
-    order[dim]                  = 3;
+
+    order[dim] = 3;
     af_array out;
     AF_THROW(af_reorder(&out, in, order[0], order[1], order[2], order[3]));
     return out;
 }
 
-static af::dim4 seqToDims(af_index_t *indices, af::dim4 parentDims,
-                          bool reorder = true) {
+af::dim4 seqToDims(af_index_t *indices, af::dim4 parentDims,
+                   bool reorder = true) {
     try {
         af::dim4 odims(1);
         for (int i = 0; i < AF_MAX_DIMS; i++) {
@@ -66,21 +74,48 @@ static af::dim4 seqToDims(af_index_t *indices, af::dim4 parentDims,
             }
         }
         return odims;
-    } catch (std::logic_error &err) { AF_THROW_ERR(err.what(), AF_ERR_SIZE); }
+    } catch (logic_error &err) { AF_THROW_ERR(err.what(), AF_ERR_SIZE); }
 }
 
-static unsigned numDims(const af_array arr) {
+unsigned numDims(const af_array arr) {
     unsigned nd;
     AF_THROW(af_get_numdims(&nd, arr));
     return nd;
 }
 
-static dim4 getDims(const af_array arr) {
+dim4 getDims(const af_array arr) {
     dim_t d0, d1, d2, d3;
     AF_THROW(af_get_dims(&d0, &d1, &d2, &d3, arr));
     return dim4(d0, d1, d2, d3);
 }
 
+void initEmptyArray(af_array *arr, af::dtype ty, dim_t d0, dim_t d1 = 1,
+                    dim_t d2 = 1, dim_t d3 = 1) {
+    dim_t my_dims[] = {d0, d1, d2, d3};
+    AF_THROW(af_create_handle(arr, AF_MAX_DIMS, my_dims, ty));
+}
+
+void initDataArray(af_array *arr, const void *ptr, af::dtype ty, af::source src,
+                   dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
+    dim_t my_dims[] = {d0, d1, d2, d3};
+    switch (src) {
+        case afHost:
+            AF_THROW(af_create_array(arr, ptr, AF_MAX_DIMS, my_dims, ty));
+            break;
+        case afDevice:
+            AF_THROW(af_device_array(arr, const_cast<void *>(ptr), AF_MAX_DIMS,
+                                     my_dims, ty));
+            break;
+        default:
+            AF_THROW_ERR(
+                "Can not create array from the requested source pointer",
+                AF_ERR_ARG);
+    }
+}
+}  // namespace
+
+namespace af {
+
 struct array::array_proxy::array_proxy_impl {
     array *parent_;          //< The original array
     af_index_t indices_[4];  //< Indexing array or seq objects
@@ -100,93 +135,72 @@ struct array::array_proxy::array_proxy_impl {
     void delete_on_destruction(bool val) { delete_on_destruction_ = val; }
 
     ~array_proxy_impl() {
-        if (delete_on_destruction_) delete parent_;
+        if (delete_on_destruction_) { delete parent_; }
     }
 
-   private:
-    array_proxy_impl(const array_proxy_impl &);
-    array_proxy_impl(const array_proxy_impl &&);
-    array_proxy_impl operator=(const array_proxy_impl &);
-    array_proxy_impl operator=(const array_proxy_impl &&);
+    array_proxy_impl(const array_proxy_impl &)  = delete;
+    array_proxy_impl(const array_proxy_impl &&) = delete;
+    array_proxy_impl operator=(const array_proxy_impl &) = delete;
+    array_proxy_impl operator=(const array_proxy_impl &&) = delete;
 };
 
 array::array(const af_array handle) : arr(handle) {}
 
-static void initEmptyArray(af_array *arr, af::dtype ty, dim_t d0, dim_t d1 = 1,
-                           dim_t d2 = 1, dim_t d3 = 1) {
-    dim_t my_dims[] = {d0, d1, d2, d3};
-    AF_THROW(af_create_handle(arr, AF_MAX_DIMS, my_dims, ty));
-}
-
-template<typename T>
-static void initDataArray(af_array *arr, const T *ptr, af::source src, dim_t d0,
-                          dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
-    af::dtype ty    = (af::dtype)dtype_traits<T>::af_type;
-    dim_t my_dims[] = {d0, d1, d2, d3};
-    switch (src) {
-        case afHost:
-            AF_THROW(af_create_array(arr, (const void *const)ptr, AF_MAX_DIMS,
-                                     my_dims, ty));
-            break;
-        case afDevice:
-            AF_THROW(af_device_array(arr, (void *)ptr, AF_MAX_DIMS,
-                                     my_dims, ty));
-            break;
-        default:
-            AF_THROW_ERR(
-                "Can not create array from the requested source pointer",
-                AF_ERR_ARG);
-    }
-}
-
-array::array() : arr(0) { initEmptyArray(&arr, f32, 0, 1, 1, 1); }
+array::array() : arr(nullptr) { initEmptyArray(&arr, f32, 0, 1, 1, 1); }
 
-array::array(const dim4 &dims, af::dtype ty) : arr(0) {
+array::array(const dim4 &dims, af::dtype ty) : arr(nullptr) {
     initEmptyArray(&arr, ty, dims[0], dims[1], dims[2], dims[3]);
 }
 
-array::array(dim_t d0, af::dtype ty) : arr(0) { initEmptyArray(&arr, ty, d0); }
+array::array(dim_t dim0, af::dtype ty) : arr(nullptr) {
+    initEmptyArray(&arr, ty, dim0);
+}
 
-array::array(dim_t d0, dim_t d1, af::dtype ty) : arr(0) {
-    initEmptyArray(&arr, ty, d0, d1);
+array::array(dim_t dim0, dim_t dim1, af::dtype ty) : arr(nullptr) {
+    initEmptyArray(&arr, ty, dim0, dim1);
 }
 
-array::array(dim_t d0, dim_t d1, dim_t d2, af::dtype ty) : arr(0) {
-    initEmptyArray(&arr, ty, d0, d1, d2);
+array::array(dim_t dim0, dim_t dim1, dim_t dim2, af::dtype ty) : arr(nullptr) {
+    initEmptyArray(&arr, ty, dim0, dim1, dim2);
 }
 
-array::array(dim_t d0, dim_t d1, dim_t d2, dim_t d3, af::dtype ty) : arr(0) {
-    initEmptyArray(&arr, ty, d0, d1, d2, d3);
+array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, af::dtype ty)
+    : arr(nullptr) {
+    initEmptyArray(&arr, ty, dim0, dim1, dim2, dim3);
 }
 
-#define INSTANTIATE(T)                                                        \
-    template<>                                                                \
-    AFAPI array::array(const dim4 &dims, const T *ptr, af::source src)        \
-        : arr(0) {                                                            \
-        initDataArray<T>(&arr, ptr, src, dims[0], dims[1], dims[2], dims[3]); \
-    }                                                                         \
-    template<>                                                                \
-    AFAPI array::array(dim_t d0, const T *ptr, af::source src) : arr(0) {     \
-        initDataArray<T>(&arr, ptr, src, d0);                                 \
-    }                                                                         \
-    template<>                                                                \
-    AFAPI array::array(dim_t d0, dim_t d1, const T *ptr, af::source src)      \
-        : arr(0) {                                                            \
-        initDataArray<T>(&arr, ptr, src, d0, d1);                             \
-    }                                                                         \
-    template<>                                                                \
-    AFAPI array::array(dim_t d0, dim_t d1, dim_t d2, const T *ptr,            \
-                       af::source src)                                        \
-        : arr(0) {                                                            \
-        initDataArray<T>(&arr, ptr, src, d0, d1, d2);                         \
-    }                                                                         \
-    template<>                                                                \
-    AFAPI array::array(dim_t d0, dim_t d1, dim_t d2, dim_t d3, const T *ptr,  \
-                       af::source src)                                        \
-        : arr(0)                                                              \
-                                                                              \
-    {                                                                         \
-        initDataArray<T>(&arr, ptr, src, d0, d1, d2, d3);                     \
+#define INSTANTIATE(T)                                                         \
+    template<>                                                                 \
+    AFAPI array::array(const dim4 &dims, const T *ptr, af::source src)         \
+        : arr(nullptr) {                                                       \
+        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
+        initDataArray(&arr, ptr, ty, src, dims[0], dims[1], dims[2], dims[3]); \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI array::array(dim_t dim0, const T *ptr, af::source src)               \
+        : arr(nullptr) {                                                       \
+        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
+        initDataArray(&arr, ptr, ty, src, dim0);                               \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI array::array(dim_t dim0, dim_t dim1, const T *ptr, af::source src)   \
+        : arr(nullptr) {                                                       \
+        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
+        initDataArray(&arr, ptr, ty, src, dim0, dim1);                         \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI array::array(dim_t dim0, dim_t dim1, dim_t dim2, const T *ptr,       \
+                       af::source src)                                         \
+        : arr(nullptr) {                                                       \
+        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
+        initDataArray(&arr, ptr, ty, src, dim0, dim1, dim2);                   \
+    }                                                                          \
+    template<>                                                                 \
+    AFAPI array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,         \
+                       const T *ptr, af::source src)                           \
+        : arr(nullptr) {                                                       \
+        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
+        initDataArray(&arr, ptr, ty, src, dim0, dim1, dim2, dim3);             \
     }
 
 INSTANTIATE(cdouble)
@@ -222,11 +236,11 @@ dim_t array::elements() const {
     return elems;
 }
 
-void array::host(void *data) const { AF_THROW(af_get_data_ptr(data, get())); }
+void array::host(void *ptr) const { AF_THROW(af_get_data_ptr(ptr, get())); }
 
 af_array array::get() { return arr; }
 
-af_array array::get() const { return ((array *)(this))->get(); }
+af_array array::get() const { return const_cast<array *>(this)->get(); }
 
 // Helper functions
 dim4 array::dims() const { return getDims(get()); }
@@ -248,7 +262,7 @@ size_t array::allocated() const {
 }
 
 array array::copy() const {
-    af_array other = 0;
+    af_array other = nullptr;
     AF_THROW(af_copy_array(&other, get()));
     return array(other);
 }
@@ -377,17 +391,17 @@ const array array::as(af::dtype type) const {
     return array(out);
 }
 
-array::array(const array &in) : arr(0) {
+array::array(const array &in) : arr(nullptr) {
     AF_THROW(af_retain_array(&arr, in.get()));
 }
 
-array::array(const array &input, const dim4 &dims) : arr(0) {
+array::array(const array &input, const dim4 &dims) : arr(nullptr) {
     AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims.get()));
 }
 
 array::array(const array &input, const dim_t dim0, const dim_t dim1,
              const dim_t dim2, const dim_t dim3)
-    : arr(0) {
+    : arr(nullptr) {
     dim_t dims[] = {dim0, dim1, dim2, dim3};
     AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims));
 }
@@ -398,7 +412,7 @@ array array::T() const { return transpose(*this); }
 array array::H() const { return transpose(*this, true); }
 
 void array::set(af_array tmp) {
-    if (arr) AF_THROW(af_release_array(arr));
+    if (arr) { AF_THROW(af_release_array(arr)); }
     arr = tmp;
 }
 
@@ -418,10 +432,11 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
 
         batch_assign = true;
         for (int i = 0; i < AF_MAX_DIMS; i++) {
-            if (this->impl->indices_[i].isBatch)
+            if (this->impl->indices_[i].isBatch) {
                 batch_assign &= (other_dims[i] == 1);
-            else
+            } else {
                 batch_assign &= (other_dims[i] == out_dims[i]);
+            }
         }
 
         if (batch_assign) {
@@ -435,13 +450,14 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
         } else if (out_dims != other_dims) {
             // HACK: This is a quick check to see if other has been reordered
             // inside gfor
-            // TODO: Figure out if this breaks and implement a cleaner method
+            // TODO(umar): Figure out if this breaks and implement a cleaner
+            // method
             other_arr    = gforReorder(other_arr, dim);
             is_reordered = true;
         }
     }
 
-    af_array par_arr = 0;
+    af_array par_arr = nullptr;
 
     if (impl->is_linear_) {
         AF_THROW(af_flat(&par_arr, impl->parent_->get()));
@@ -450,10 +466,10 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
         par_arr = impl->parent_->get();
     }
 
-    af_array tmp = 0;
+    af_array tmp = nullptr;
     AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices_, other_arr));
 
-    af_array res = 0;
+    af_array res = nullptr;
     if (impl->is_linear_) {
         AF_THROW(af_moddims(&res, tmp, this_dims.ndims(), this_dims.get()));
         AF_THROW(af_release_array(par_arr));
@@ -465,15 +481,16 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
     impl->parent_->set(res);
 
     if (dim >= 0 && (is_reordered || batch_assign)) {
-        if (other_arr) AF_THROW(af_release_array(other_arr));
+        if (other_arr) { AF_THROW(af_release_array(other_arr)); }
     }
     return *this;
 }
 
 array::array_proxy &af::array::array_proxy::operator=(
     const array::array_proxy &other) {
-    array out    = other;
-    return *this = out;
+    array out = other;
+    *this     = out;
+    return *this;
 }
 
 af::array::array_proxy::array_proxy(array &par, af_index_t *ssss, bool linear)
@@ -487,14 +504,12 @@ af::array::array_proxy::array_proxy(const array_proxy &other)
 af::array::array_proxy::array_proxy(array_proxy &&other) { impl = other.impl; }
 
 array::array_proxy &af::array::array_proxy::operator=(array_proxy &&other) {
-    array out    = other;
-    return *this = out;
+    impl = other.impl;
+    return *this;
 }
 #endif
 
-af::array::array_proxy::~array_proxy() {
-    if (impl) delete impl;
-}
+af::array::array_proxy::~array_proxy() { delete impl; }
 
 array array::array_proxy::as(dtype type) const {
     array out = *this;
@@ -551,7 +566,8 @@ MEM_FUNC(af_array, get)
         dim4 dims    = seqToDims(impl->indices_, pdims);                   \
         af::dtype ty = impl->parent_->type();                              \
         array cst    = constant(value, dims, ty);                          \
-        return this->operator OP(cst);                                     \
+        this->operator OP(cst);                                            \
+        return *this;                                                      \
     }
 
 #define ASSIGN_OP(OP, op1)              \
@@ -598,8 +614,8 @@ SELF_OP(/=, /)
 #undef SELF_OP
 
 array::array_proxy::operator array() const {
-    af_array tmp = 0;
-    af_array arr = 0;
+    af_array tmp = nullptr;
+    af_array arr = nullptr;
 
     if (impl->is_linear_) {
         AF_THROW(af_flat(&arr, impl->parent_->get()));
@@ -614,8 +630,8 @@ array::array_proxy::operator array() const {
 }
 
 array::array_proxy::operator array() {
-    af_array tmp = 0;
-    af_array arr = 0;
+    af_array tmp = nullptr;
+    af_array arr = nullptr;
 
     if (impl->is_linear_) {
         AF_THROW(af_flat(&arr, impl->parent_->get()));
@@ -629,7 +645,7 @@ array::array_proxy::operator array() {
     int dim = gforDim(impl->indices_);
     if (tmp && dim >= 0) {
         arr = gforReorder(tmp, dim);
-        if (tmp) AF_THROW(af_release_array(tmp));
+        if (tmp) { AF_THROW(af_release_array(tmp)); }
     } else {
         arr = tmp;
     }
@@ -666,10 +682,10 @@ MEM_INDEX(slices(int first, int last), slices(first, last));
 ///////////////////////////////////////////////////////////////////////////
 array &array::operator=(const array &other) {
     if (this->get() == other.get()) { return *this; }
-    // TODO: Unsafe. loses data if af_weak_copy fails
-    if (this->arr != 0) { AF_THROW(af_release_array(this->arr)); }
+    // TODO(umar): Unsafe. loses data if af_weak_copy fails
+    if (this->arr != nullptr) { AF_THROW(af_release_array(this->arr)); }
 
-    af_array temp = 0;
+    af_array temp = nullptr;
     AF_THROW(af_retain_array(&temp, other.get()));
     this->arr = temp;
     return *this;
@@ -719,7 +735,8 @@ ASSIGN_OP(/=, af_div)
         af::dim4 dims = this->dims();              \
         af::dtype ty  = this->type();              \
         array cst     = constant(value, dims, ty); \
-        return operator OP(cst);                   \
+        operator OP(cst);                          \
+        return *this;                              \
     }
 
 #define ASSIGN_OP(OP)                   \
@@ -747,18 +764,19 @@ ASSIGN_OP(=)
 
 af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type) {
     // If same, do not do anything
-    if (scalar_type == array_type) return scalar_type;
+    if (scalar_type == array_type) { return scalar_type; }
 
     // If complex, return appropriate complex type
     if (scalar_type == c32 || scalar_type == c64) {
-        if (array_type == f64 || array_type == c64) return c64;
+        if (array_type == f64 || array_type == c64) { return c64; }
         return c32;
     }
 
     // If 64 bit precision, do not lose precision
     if (array_type == f64 || array_type == c64 || array_type == f32 ||
-        array_type == c32)
+        array_type == c32) {
         return array_type;
+    }
 
     // Default to single precision by default when multiplying with scalar
     if ((scalar_type == f64 || scalar_type == c64) &&
@@ -912,9 +930,9 @@ AFAPI void array::write(const void *ptr, const size_t bytes, af::source src) {
 
 template<>
 AFAPI void *array::device() const {
-    void *ptr = NULL;
+    void *ptr = nullptr;
     AF_THROW(af_get_device_ptr(&ptr, get()));
-    return (void *)ptr;
+    return ptr;
 }
 
 // array_proxy instanciations
@@ -964,7 +982,7 @@ bool array::isLocked() const {
 void array::unlock() const { AF_THROW(af_unlock_array(get())); }
 
 void eval(int num, array **arrays) {
-    std::vector<af_array> outputs(num);
+    vector<af_array> outputs(num);
     for (int i = 0; i < num; i++) { outputs[i] = arrays[i]->get(); }
     AF_THROW(af_eval_multiple(num, &outputs[0]));
 }
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index d2d8a8ee24..4e4a464cce 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -20,7 +20,7 @@
         af::exception ex(msg, __PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
                          __err);                                              \
         af_free_host(msg);                                                    \
-        throw ex;                                                             \
+        throw ex; /* NOLINT(misc-throw-by-value-catch-by-reference)*/         \
     } while (0)
 
 #define AF_THROW_ERR(__msg, __err)                                       \

From 3dfff69d7b92872e56aee115865d0498cb467c12 Mon Sep 17 00:00:00 2001
From: Alessandro Bessi <alessandro.bessi@mail.com>
Date: Tue, 8 Jan 2019 10:28:52 +0100
Subject: [PATCH 1598/2677] fix Sample using the C API example

---
 docs/pages/getting_started.md | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index 6a6dac6325..5db2f67150 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -214,15 +214,16 @@ simply include the `arrayfire.h` header file and start coding!
     int main(void)
     {
         // generate random values
-        int n = 10000;
         af_array a;
-        af_randu(&a, n);
+        int n_dims = 1;
+        dim_t dims[] = {10000};
+        af_randu(&a, n_dims, dims, f32);
 
         // sum all the values
-        float result;
-        af_sum_all(&result, a, 0);
-
-        printf("sum: %g\n", sum);
+        double result;
+        af_sum_all(&result, 0, a);
+        printf("sum: %g\n", result);
+        
         return 0;
     }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From 4bdaa9e79c5a91918857472680edb7fbf6b2a5c4 Mon Sep 17 00:00:00 2001
From: William Tambellini <william.tambellini@gmail.com>
Date: Sat, 12 Jan 2019 09:01:20 -0800
Subject: [PATCH 1599/2677] Add env var to save generated kernels src to tmp
 dir (#2404)

* Use AF_JIT_KERNEL_TRACE environment variable to save kernel to file or display on stdout
  or stderr
---
 .gitignore                     |  1 +
 src/backend/common/defines.hpp |  3 +++
 src/backend/common/util.cpp    | 29 ++++++++++++++++++++++++++++-
 src/backend/common/util.hpp    |  6 +++++-
 src/backend/cuda/jit.cpp       |  1 +
 src/backend/opencl/jit.cpp     |  2 +-
 6 files changed, 39 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index b7e83d2e9a..9118753a0a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 CMakeCache.txt
 CMakeFiles/
 build*/
+Release/
 Makefile
 cmake_install.cmake
 GTAGS
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 4317a632dd..03a1a04dd8 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -52,10 +52,13 @@ typedef enum {
 #ifdef OS_WIN
 #include <Windows.h>
 using LibHandle = HMODULE;
+#define AF_PATH_SEPARATOR "\\"
 #elif defined(OS_MAC)
 using LibHandle = void*;
+#define AF_PATH_SEPARATOR "/"
 #elif defined(OS_LNX)
 using LibHandle = void*;
+#define AF_PATH_SEPARATOR "/"
 #else
 #error "Unsupported platform"
 #endif
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index f3df973473..0c5fbc7861 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2016, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -10,12 +10,15 @@
 /// This file contains platform independent utility functions
 #include <cstdlib>
 #include <string>
+#include <cstring>
 
 #if defined(OS_WIN)
 #include <Windows.h>
 #endif
 
 #include <af/defines.h>
+#include <common/util.hpp>
+#include <common/defines.hpp>
 
 using std::string;
 
@@ -55,3 +58,27 @@ const char *getName(af_dtype type) {
         default: return "unknown type";
     }
 }
+
+void saveKernel(const std::string& funcName, const std::string& jit_ker, const std::string& ext) {
+  static const char* jitKernelsOutput = getenv(saveJitKernelsEnvVarName);
+  if (!jitKernelsOutput)
+    return;
+  if (std::strcmp(jitKernelsOutput, "stdout") == 0) {
+    fprintf(stdout, jit_ker.c_str());
+    return;
+  }
+  if (std::strcmp(jitKernelsOutput, "stderr") == 0) {
+    fprintf(stderr, jit_ker.c_str());
+    return;
+  }
+  // Path to a folder  
+  const std::string ffp = std::string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
+  FILE* f = fopen(ffp.c_str(), "w");
+  if (!f) {
+    fprintf(stderr, "Cannot open file %s\n", ffp.c_str());
+    return;
+  }
+  if (fputs(jit_ker.c_str(), f) == EOF)
+    fprintf(stderr, "Failed to write kernel to file %s\n", ffp.c_str());
+  fclose(f);
+}
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index cafa1aa4a6..a6ddefbb7c 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2016, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -15,3 +15,7 @@
 #pragma once
 
 std::string getEnvVar(const std::string &key);
+
+// Dump the kernel sources only if the environment variable is defined
+static const char* saveJitKernelsEnvVarName = "AF_JIT_KERNEL_TRACE";
+void saveKernel(const std::string& funcName, const std::string& jit_ker, const std::string& ext);
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index fc5270ea99..d4cc7f6c41 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -348,6 +348,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
     if (idx == kernelCaches[device].end()) {
         string jit_ker = getKernelString(funcName, full_nodes, full_ids,
                                          output_ids, is_linear);
+        saveKernel(funcName, jit_ker, ".cu");
         entry          = compileKernel(funcName.c_str(), jit_ker);
         kernelCaches[device][funcName] = entry;
     } else {
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 9dfc8cb8ad..277f53684a 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -176,7 +176,7 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
     if (entry.prog == 0 && entry.ker == 0) {
         string jit_ker = getKernelString(funcName, full_nodes, full_ids,
                                          output_ids, is_linear);
-
+        saveKernel(funcName, jit_ker, ".cl");
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
         const int ker_lens[]   = {jit_cl_len, (int)jit_ker.size()};
 

From 9763d2f0dbaeb91f5353aedb89c24d15ac88de1a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 20 Jan 2019 18:37:15 -0500
Subject: [PATCH 1600/2677] Use SUPPORTED_TYPE_CHECK macro to test for
 supported type

This is a more general macro which will check if the device surpports
the types being checked. This is done to support double types but
will be required for fp16 in the future
---
 test/anisotropic_diffusion.cpp |  2 +-
 test/approx1.cpp               | 14 ++++----------
 test/approx2.cpp               | 12 +++---------
 test/array.cpp                 | 22 +++++++++++-----------
 test/assign.cpp                | 14 +++++++-------
 test/basic.cpp                 | 24 +++++-------------------
 test/bilateral.cpp             |  8 +++-----
 test/binary.cpp                | 32 ++++++++++++++++----------------
 test/blas.cpp                  |  4 ++--
 test/canny.cpp                 |  4 ++--
 test/cast.cpp                  |  8 ++++----
 test/cholesky_dense.cpp        |  2 +-
 test/compare.cpp               |  2 +-
 test/complex.cpp               | 32 ++++++++++++++++----------------
 test/constant.cpp              | 12 ++++++------
 test/convolve.cpp              | 30 +++++-------------------------
 test/corrcoef.cpp              |  4 ++--
 test/covariance.cpp            |  6 +++---
 test/diagonal.cpp              | 10 +++++-----
 test/diff1.cpp                 |  6 ++----
 test/diff2.cpp                 |  6 ++----
 test/dog.cpp                   |  4 ++--
 test/dot.cpp                   |  4 ++--
 test/fast.cpp                  |  3 +--
 test/fft.cpp                   | 24 ++++++++----------------
 test/fft_real.cpp              |  2 +-
 test/fftconvolve.cpp           | 10 ++--------
 test/gaussiankernel.cpp        |  2 +-
 test/gloh_nonfree.cpp          |  3 +--
 test/gradient.cpp              |  6 +-----
 test/gtest                     |  2 +-
 test/harris.cpp                |  3 +--
 test/histogram.cpp             |  7 ++-----
 test/homography.cpp            |  2 +-
 test/iir.cpp                   |  6 +++---
 test/imageio.cpp               |  6 ------
 test/index.cpp                 | 12 +++++-------
 test/inverse_deconv.cpp        |  2 +-
 test/inverse_dense.cpp         |  2 +-
 test/iota.cpp                  |  4 +---
 test/ireduce.cpp               |  6 +++---
 test/iterative_deconv.cpp      |  2 +-
 test/join.cpp                  |  8 +-------
 test/lu_dense.cpp              |  4 +---
 test/match_template.cpp        |  2 +-
 test/math.cpp                  |  4 ++--
 test/mean.cpp                  | 14 +++++++-------
 test/meanshift.cpp             |  5 ++---
 test/medfilt.cpp               | 20 ++++++++------------
 test/median.cpp                |  4 ++--
 test/memory.cpp                |  4 ++--
 test/moddims.cpp               |  8 ++++----
 test/moments.cpp               |  2 +-
 test/morph.cpp                 | 12 ++++++------
 test/nearest_neighbour.cpp     |  2 +-
 test/orb.cpp                   |  3 +--
 test/qr_dense.cpp              |  3 +--
 test/random.cpp                | 22 ++++++++++------------
 test/range.cpp                 |  4 +---
 test/rank_dense.cpp            |  8 ++++----
 test/reduce.cpp                | 20 ++++++++------------
 test/regions.cpp               |  4 +---
 test/reorder.cpp               |  4 +---
 test/replace.cpp               |  4 ++--
 test/resize.cpp                | 12 +++---------
 test/rotate.cpp                |  4 +---
 test/rotate_linear.cpp         |  4 +---
 test/sat.cpp                   |  2 +-
 test/scan.cpp                  |  4 +---
 test/select.cpp                |  4 ++--
 test/set.cpp                   |  4 ++--
 test/shift.cpp                 |  6 +-----
 test/sift_nonfree.cpp          |  3 +--
 test/sobel.cpp                 |  2 +-
 test/solve_common.hpp          |  6 +++---
 test/sort.cpp                  |  8 +-------
 test/sort_by_key.cpp           |  8 +-------
 test/sort_index.cpp            |  8 +-------
 test/sparse.cpp                |  6 +++---
 test/sparse_arith.cpp          | 10 +++++-----
 test/sparse_common.hpp         | 10 +++++-----
 test/sparse_convert.cpp        |  2 +-
 test/stdev.cpp                 | 12 ++++++------
 test/susan.cpp                 |  2 +-
 test/svd_dense.cpp             |  6 +++---
 test/testHelpers.hpp           |  3 +++
 test/tile.cpp                  |  6 +-----
 test/transform.cpp             |  2 +-
 test/transform_coordinates.cpp |  2 +-
 test/translate.cpp             |  2 +-
 test/transpose.cpp             |  6 +++---
 test/transpose_inplace.cpp     |  4 +---
 test/triangle.cpp              |  2 +-
 test/unwrap.cpp                |  4 +---
 test/var.cpp                   |  8 ++++----
 test/where.cpp                 |  4 ++--
 test/wrap.cpp                  |  2 +-
 test/write.cpp                 |  2 +-
 98 files changed, 272 insertions(+), 425 deletions(-)

diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 79c52d590f..3957e6aa7c 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -49,7 +49,7 @@ void imageTest(string pTestFile, const float dt, const float K,
         typename cond_type<is_same_type<T, double>::value, double, float>::type
             OutType;
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     using af::dim4;
diff --git a/test/approx1.cpp b/test/approx1.cpp
index bad97bb3c9..5a97db36e2 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -60,7 +60,7 @@ template<typename T>
 void approx1Test(string pTestFile, const unsigned resultIdx,
                  const af_interp_type method, bool isSubRef = false,
                  const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
@@ -133,7 +133,7 @@ template<typename T>
 void approx1CubicTest(string pTestFile, const unsigned resultIdx,
                       const af_interp_type method, bool isSubRef = false,
                       const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
@@ -221,7 +221,7 @@ TYPED_TEST(Approx1, Approx1Cubic) {
 template<typename T>
 void approx1ArgsTest(string pTestFile, const af_interp_type method,
                      const af_err err) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
@@ -268,7 +268,7 @@ TYPED_TEST(Approx1, Approx1ArgsInterpBilinear) {
 template<typename T>
 void approx1ArgsTestPrecision(string pTestFile, const unsigned,
                               const af_interp_type method) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
@@ -425,8 +425,6 @@ TEST(Approx1, CPPCubicBatch) {
 }
 
 TEST(Approx1, CPPNearestMaxDims) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
     array input           = randu(1, largeDim);
     array pos             = input.dims(0) * randu(1, largeDim);
@@ -444,8 +442,6 @@ TEST(Approx1, CPPNearestMaxDims) {
 }
 
 TEST(Approx1, CPPLinearMaxDims) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
     array input           = iota(dim4(1, largeDim), c32);
     array pos             = input.dims(0) * randu(1, largeDim);
@@ -463,8 +459,6 @@ TEST(Approx1, CPPLinearMaxDims) {
 }
 
 TEST(Approx1, CPPCubicMaxDims) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
     array input           = iota(dim4(1, largeDim), c32);
     array pos             = input.dims(0) * randu(1, largeDim);
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 10df492679..ca8b7b36b7 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -56,7 +56,7 @@ template<typename T>
 void approx2Test(string pTestFile, const unsigned resultIdx,
                  const af_interp_type method, bool isSubRef = false,
                  const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
@@ -143,7 +143,7 @@ TYPED_TEST(Approx2, LinearBatch) {
 template<typename T>
 void approx2ArgsTest(string pTestFile, const af_interp_type method,
                      const af_err err) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
     vector<vector<BT> > in;
@@ -200,7 +200,7 @@ template<typename T>
 void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx,
                               const af_interp_type method) {
     UNUSED(resultIdx);
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     vector<dim4> numDims;
     vector<vector<T> > in;
     vector<vector<T> > tests;
@@ -392,8 +392,6 @@ TEST(Approx2, CPPLinearBatch) {
 }
 
 TEST(Approx2, CPPNearestMaxDims) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
 
     array input = randu(1, largeDim);
@@ -415,8 +413,6 @@ TEST(Approx2, CPPNearestMaxDims) {
 }
 
 TEST(Approx2, CPPLinearMaxDims) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
 
     array input = randu(1, largeDim);
@@ -438,8 +434,6 @@ TEST(Approx2, CPPLinearMaxDims) {
 }
 
 TEST(Approx2, CPPCubicMaxDims) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
 
     array input = randu(1, largeDim);
diff --git a/test/array.cpp b/test/array.cpp
index 3ddf666370..acfc4fae30 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -47,7 +47,7 @@ TEST(Array, ConstructorDefault) {
 }
 
 TYPED_TEST(Array, ConstructorEmptyDim4) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     dim4 dims(3, 3, 3, 3);
@@ -62,7 +62,7 @@ TYPED_TEST(Array, ConstructorEmptyDim4) {
 }
 
 TYPED_TEST(Array, ConstructorEmpty1D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, type);
@@ -76,7 +76,7 @@ TYPED_TEST(Array, ConstructorEmpty1D) {
 }
 
 TYPED_TEST(Array, ConstructorEmpty2D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, type);
@@ -90,7 +90,7 @@ TYPED_TEST(Array, ConstructorEmpty2D) {
 }
 
 TYPED_TEST(Array, ConstructorEmpty3D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, 2, type);
@@ -104,7 +104,7 @@ TYPED_TEST(Array, ConstructorEmpty3D) {
 }
 
 TYPED_TEST(Array, ConstructorEmpty4D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a(2, 2, 2, 2, type);
@@ -118,7 +118,7 @@ TYPED_TEST(Array, ConstructorEmpty4D) {
 }
 
 TYPED_TEST(Array, ConstructorHostPointer1D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type    = (dtype)dtype_traits<TypeParam>::af_type;
     size_t nelems = 10;
@@ -138,7 +138,7 @@ TYPED_TEST(Array, ConstructorHostPointer1D) {
 }
 
 TYPED_TEST(Array, ConstructorHostPointer2D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type      = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 2;
@@ -160,7 +160,7 @@ TYPED_TEST(Array, ConstructorHostPointer2D) {
 }
 
 TYPED_TEST(Array, ConstructorHostPointer3D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type      = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 3;
@@ -182,7 +182,7 @@ TYPED_TEST(Array, ConstructorHostPointer3D) {
 }
 
 TYPED_TEST(Array, ConstructorHostPointer4D) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type      = (dtype)dtype_traits<TypeParam>::af_type;
     size_t ndims    = 4;
@@ -204,7 +204,7 @@ TYPED_TEST(Array, ConstructorHostPointer4D) {
 }
 
 TYPED_TEST(Array, TypeAttributes) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array one(10, type);
@@ -484,7 +484,7 @@ TEST(Device, JIT) {
 }
 
 TYPED_TEST(Array, Scalar) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dtype type = (dtype)dtype_traits<TypeParam>::af_type;
     array a    = randu(dim4(1), type);
diff --git a/test/assign.cpp b/test/assign.cpp
index 3efaf3704d..0480538fc7 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -100,8 +100,8 @@ TYPED_TEST_CASE(ArrayAssign, TestTypes);
 
 template<typename inType, typename outType>
 void assignTest(string pTestFile, const vector<af_seq> *seqv) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
@@ -145,7 +145,7 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv) {
 
 template<typename T>
 void assignTestCPP(string pTestFile, const vector<af_seq> &seqv) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     try {
         vector<dim4> numDims;
         vector<vector<T> > in;
@@ -284,7 +284,7 @@ TYPED_TEST(ArrayAssign, Cube2HyperCubeCPP) {
 
 template<typename T>
 void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     try {
         vector<dim4> numDims;
         vector<vector<T> > in;
@@ -354,7 +354,7 @@ TYPED_TEST(ArrayAssign, Scalar4DCPP) {
 }
 
 TYPED_TEST(ArrayAssign, AssignRowCPP) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     const int dimsize = 10;
     vector<TypeParam> input(100, 1);
@@ -405,7 +405,7 @@ TYPED_TEST(ArrayAssign, AssignRowCPP) {
 }
 
 TYPED_TEST(ArrayAssign, AssignColumnCPP) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     const int dimsize = 10;
     vector<TypeParam> input(100, 1);
@@ -456,7 +456,7 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP) {
 }
 
 TYPED_TEST(ArrayAssign, AssignSliceCPP) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
     const int dimsize = 10;
     vector<TypeParam> input(1000, 1);
     vector<TypeParam> sq(dimsize * dimsize);
diff --git a/test/basic.cpp b/test/basic.cpp
index 7bbc3747d4..c39e800408 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -20,8 +20,6 @@ using af::dim4;
 using std::vector;
 
 TEST(BasicTests, constant1000x1000) {
-    if (noDoubleTests<float>()) return;
-
     static const int ndims    = 2;
     static const int dim_size = 1000;
     dim_t d[ndims]            = {dim_size, dim_size};
@@ -40,8 +38,6 @@ TEST(BasicTests, constant1000x1000) {
 }
 
 TEST(BasicTests, constant10x10) {
-    if (noDoubleTests<float>()) return;
-
     static const int ndims    = 2;
     static const int dim_size = 10;
     dim_t d[2]                = {dim_size, dim_size};
@@ -60,8 +56,6 @@ TEST(BasicTests, constant10x10) {
 }
 
 TEST(BasicTests, constant100x100) {
-    if (noDoubleTests<float>()) return;
-
     static const int ndims    = 2;
     static const int dim_size = 100;
     dim_t d[2]                = {dim_size, dim_size};
@@ -81,8 +75,7 @@ TEST(BasicTests, constant100x100) {
 
 // TODO: Test All The Types \o/
 TEST(BasicTests, AdditionSameType) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     static const int ndims    = 2;
     static const int dim_size = 100;
@@ -129,7 +122,7 @@ TEST(BasicTests, AdditionSameType) {
 }
 
 TEST(BasicTests, Additionf64f64) {
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     static const int ndims    = 2;
     static const int dim_size = 100;
@@ -164,8 +157,7 @@ TEST(BasicTests, Additionf64f64) {
 }
 
 TEST(BasicTests, Additionf32f64) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     static const int ndims    = 2;
     static const int dim_size = 100;
@@ -200,8 +192,6 @@ TEST(BasicTests, Additionf32f64) {
 }
 
 TEST(BasicArrayTests, constant10x10) {
-    if (noDoubleTests<float>()) return;
-
     dim_t dim_size = 10;
     double valA    = 3.14;
     array a        = constant(valA, dim_size, dim_size, f32);
@@ -218,8 +208,6 @@ TEST(BasicArrayTests, constant10x10) {
 using af::dim4;
 
 TEST(BasicTests, constant100x100_CPP) {
-    if (noDoubleTests<float>()) return;
-
     static const int dim_size = 100;
     dim_t d[2]                = {dim_size, dim_size};
 
@@ -236,8 +224,7 @@ TEST(BasicTests, constant100x100_CPP) {
 
 // TODO: Test All The Types \o/
 TEST(BasicTests, AdditionSameType_CPP) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     static const int dim_size = 100;
     dim_t d[2]                = {dim_size, dim_size};
@@ -274,8 +261,7 @@ TEST(BasicTests, AdditionSameType_CPP) {
 }
 
 TEST(BasicTests, Additionf32f64_CPP) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     static const int dim_size = 100;
     dim_t d[2]                = {dim_size, dim_size};
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 2c12d66aa8..3db5c2c12c 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -24,7 +24,7 @@ using std::vector;
 
 template<typename T, bool isColor>
 void bilateralTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -88,7 +88,7 @@ TYPED_TEST_CASE(BilateralOnData, DataTestTypes);
 
 template<typename inType>
 void bilateralDataTest(string pTestFile) {
-    if (noDoubleTests<inType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
 
     typedef typename cond_type<is_same_type<inType, double>::value, double,
                                float>::type outType;
@@ -135,7 +135,7 @@ TYPED_TEST(BilateralOnData, Rectangle_Batch) {
 }
 
 TYPED_TEST(BilateralOnData, InvalidArgs) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     vector<TypeParam> in(100, 1);
 
@@ -158,8 +158,6 @@ using af::array;
 using af::bilateral;
 
 TEST(Bilateral, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
diff --git a/test/binary.cpp b/test/binary.cpp
index 4d7fbd91cd..98aead1922 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -45,9 +45,9 @@ af::array randgen(const int num, dtype ty) {
 
 #define BINARY_TESTS(Ta, Tb, Tc, func)                                \
     TEST(BinaryTests, Test_##func##_##Ta##_##Tb) {                    \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
@@ -66,8 +66,8 @@ af::array randgen(const int num, dtype ty) {
     }                                                                 \
                                                                       \
     TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_left) {             \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af::array a = randgen(num, ta);                               \
@@ -83,8 +83,8 @@ af::array randgen(const int num, dtype ty) {
     }                                                                 \
                                                                       \
     TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_right) {            \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
                                                                       \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
         Ta h_a      = 5.0;                                            \
@@ -101,9 +101,9 @@ af::array randgen(const int num, dtype ty) {
 
 #define BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Td, Te, func, err)      \
     TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb) {            \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
@@ -122,8 +122,8 @@ af::array randgen(const int num, dtype ty) {
     }                                                                 \
                                                                       \
     TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_left) {     \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af::array a = randgen(num, ta);                               \
@@ -139,9 +139,9 @@ af::array randgen(const int num, dtype ty) {
     }                                                                 \
                                                                       \
     TEST(BinaryTestsFloating, Test_##func##_##Ta##_##Tb##_right) {    \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
         Ta h_a      = 0.3;                                            \
@@ -308,7 +308,7 @@ TEST(BinaryTests, Test_pow_cfloat_float) {
 }
 
 TEST(BinaryTests, Test_pow_cdouble_cdouble) {
-    if (noDoubleTests<cdouble>()) return;
+    SUPPORTED_TYPE_CHECK(cdouble);
     af::array a         = randgen(num, c64);
     af::array b         = randgen(num, c64);
     af::array c         = af::pow(a, b);
diff --git a/test/blas.cpp b/test/blas.cpp
index 740284409d..b05f1bf2e7 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -43,7 +43,7 @@ TYPED_TEST_CASE(MatrixMultiply, TestTypes);
 
 template<typename T, bool isBVector>
 void MatMulCheck(string TestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -122,7 +122,7 @@ TYPED_TEST(MatrixMultiply, RectangleVector) {
 
 template<typename T, bool isBVector>
 void cppMatMulCheck(string TestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
diff --git a/test/canny.cpp b/test/canny.cpp
index 1e15de68d6..54fd55763e 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -36,7 +36,7 @@ TYPED_TEST_CASE(CannyEdgeDetector, TestTypes);
 
 template<typename T>
 void cannyTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -81,7 +81,7 @@ TYPED_TEST(CannyEdgeDetector, ArraySizeEqualBlockSize16x16) {
 
 template<typename T>
 void cannyImageOtsuTest(string pTestFile, bool isColor) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     using af::dim4;
diff --git a/test/cast.cpp b/test/cast.cpp
index e32fbea6ff..39fd2155ca 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -22,8 +22,8 @@ const int num = 10;
 
 template<typename Ti, typename To>
 void cast_test() {
-    if (noDoubleTests<Ti>()) return;
-    if (noDoubleTests<To>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
+    SUPPORTED_TYPE_CHECK(To);
 
     af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
     af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
@@ -75,8 +75,8 @@ CPLX_TEST_INVOKE(cdouble)
 // conversion explicit.
 template<typename Ti, typename To>
 void cast_test_complex_real() {
-    if (noDoubleTests<Ti>()) return;
-    if (noDoubleTests<To>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
+    SUPPORTED_TYPE_CHECK(To);
 
     af_dtype ta = (af_dtype)dtype_traits<Ti>::af_type;
     af_dtype tb = (af_dtype)dtype_traits<To>::af_type;
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index 038c5ede95..3800d0c0e1 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -33,7 +33,7 @@ using std::vector;
 
 template<typename T>
 void choleskyTester(const int n, double eps, bool is_upper) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
diff --git a/test/compare.cpp b/test/compare.cpp
index 7533bf8430..23b1b65865 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -29,7 +29,7 @@ TYPED_TEST_CASE(Compare, TestTypes);
 #define COMPARE(OP, Name)                                   \
     TYPED_TEST(Compare, Test_##Name) {                      \
         typedef TypeParam T;                                \
-        if (noDoubleTests<T>()) return;                     \
+        SUPPORTED_TYPE_CHECK(T);                            \
         const int num = 1 << 20;                            \
         af_dtype ty   = (af_dtype)dtype_traits<T>::af_type; \
         array a       = randu(num, ty);                     \
diff --git a/test/complex.cpp b/test/complex.cpp
index fbbc27b6ab..498203ec44 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -22,9 +22,9 @@ const int num = 10;
 
 #define COMPLEX_TESTS(Ta, Tb, Tc)                                     \
     TEST(ComplexTests, Test_##Ta##_##Tb) {                            \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype ta   = (af_dtype)dtype_traits<Ta>::af_type;          \
         af_dtype tb   = (af_dtype)dtype_traits<Tb>::af_type;          \
@@ -42,8 +42,8 @@ const int num = 10;
         freeHost(h_c);                                                \
     }                                                                 \
     TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_left) {                \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
                                                                       \
         af_dtype ta   = (af_dtype)dtype_traits<Ta>::af_type;          \
         array a       = randu(num, ta);                               \
@@ -59,8 +59,8 @@ const int num = 10;
     }                                                                 \
                                                                       \
     TEST(ComplexTests, Test_cplx_##Ta##_##Tb##_right) {               \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
                                                                       \
         af_dtype tb   = (af_dtype)dtype_traits<Tb>::af_type;          \
         Ta h_a        = 0.3;                                          \
@@ -75,9 +75,9 @@ const int num = 10;
         freeHost(h_c);                                                \
     }                                                                 \
     TEST(ComplexTests, Test_##Ta##_##Tb##_Real) {                     \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
@@ -93,9 +93,9 @@ const int num = 10;
         freeHost(h_d);                                                \
     }                                                                 \
     TEST(ComplexTests, Test_##Ta##_##Tb##_Imag) {                     \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
@@ -111,9 +111,9 @@ const int num = 10;
         freeHost(h_d);                                                \
     }                                                                 \
     TEST(ComplexTests, Test_##Ta##_##Tb##_Conj) {                     \
-        if (noDoubleTests<Ta>()) return;                              \
-        if (noDoubleTests<Tb>()) return;                              \
-        if (noDoubleTests<Tc>()) return;                              \
+        SUPPORTED_TYPE_CHECK(Ta);                                     \
+        SUPPORTED_TYPE_CHECK(Tb);                                     \
+        SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype ta   = (af_dtype)dtype_traits<Ta>::af_type;          \
         af_dtype tb   = (af_dtype)dtype_traits<Tb>::af_type;          \
diff --git a/test/constant.cpp b/test/constant.cpp
index f2ccd1af8c..10c4f43193 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -34,7 +34,7 @@ TYPED_TEST_CASE(Constant, TestTypes);
 
 template<typename T>
 void ConstantCPPCheck(T value) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     const int num = 1000;
     T val         = value;
@@ -49,7 +49,7 @@ void ConstantCPPCheck(T value) {
 
 template<typename T>
 void ConstantCCheck(T value) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     const int num = 1000;
     typedef typename dtype_traits<T>::base_type BT;
@@ -68,7 +68,7 @@ void ConstantCCheck(T value) {
 
 template<typename T>
 void IdentityCPPCheck() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     int num   = 1000;
     dtype dty = (dtype)dtype_traits<T>::af_type;
@@ -106,7 +106,7 @@ void IdentityCPPCheck() {
 
 template<typename T>
 void IdentityLargeDimCheck() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     const size_t largeDim = 65535 * 8 + 1;
 
@@ -126,7 +126,7 @@ void IdentityLargeDimCheck() {
 
 template<typename T>
 void IdentityCCheck() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     static const int num = 1000;
     dtype dty            = (dtype)dtype_traits<T>::af_type;
@@ -150,7 +150,7 @@ void IdentityCCheck() {
 
 template<typename T>
 void IdentityCPPError() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     static const int num = 1000;
     dtype dty            = (dtype)dtype_traits<T>::af_type;
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 94edf3ec52..e7f4ba2338 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -42,7 +42,7 @@ TYPED_TEST_CASE(Convolve, TestTypes);
 
 template<typename T>
 void convolveTest(string pTestFile, int baseDim, bool expand) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -215,7 +215,7 @@ TYPED_TEST(Convolve, Same_Cuboid_One2Many) {
 
 template<typename T>
 void sepConvolveTest(string pTestFile, bool expand) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -307,9 +307,6 @@ TYPED_TEST(Convolve, Separable2D_Same_Rectangle_Batch) {
 }
 
 TEST(Convolve, Separable_TypeCheck) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<int>()) return;
-
     dim4 sDims(10, 1, 1, 1);
     dim4 fDims(4, 1, 1, 1);
 
@@ -340,9 +337,6 @@ TEST(Convolve, Separable_TypeCheck) {
 }
 
 TEST(Convolve, Separable_DimCheck) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<int>()) return;
-
     dim4 sDims(10, 1, 1, 1);
     dim4 fDims(4, 1, 1, 1);
 
@@ -383,8 +377,6 @@ using af::span;
 using af::sum;
 
 TEST(Convolve1, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -418,8 +410,6 @@ TEST(Convolve1, CPP) {
 }
 
 TEST(Convolve2, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -456,8 +446,6 @@ TEST(Convolve2, CPP) {
 }
 
 TEST(Convolve3, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -493,8 +481,6 @@ TEST(Convolve3, CPP) {
 }
 
 TEST(Convolve, separable_CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -717,7 +703,7 @@ TEST(Convolve, 3D_C32) {
 }
 
 TEST(Convolve, 1D_C64) {
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     array A = randu(10, c64);
     array B = randu(3, c64);
@@ -732,7 +718,7 @@ TEST(Convolve, 1D_C64) {
 }
 
 TEST(Convolve, 2D_C64) {
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     array A = randu(10, 10, c64);
     array B = randu(3, 3, c64);
@@ -747,7 +733,7 @@ TEST(Convolve, 2D_C64) {
 }
 
 TEST(Convolve, 3D_C64) {
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     array A = randu(10, 10, 3, c64);
     array B = randu(3, 3, 3, c64);
@@ -762,8 +748,6 @@ TEST(Convolve, 3D_C64) {
 }
 
 TEST(ConvolveLargeDim1D, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const size_t n        = 10;
     const size_t largeDim = 65535 + 1;
 
@@ -782,8 +766,6 @@ TEST(ConvolveLargeDim1D, CPP) {
 }
 
 TEST(ConvolveLargeDim2D, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const size_t n        = 10;
     const size_t largeDim = 65535 + 1;
 
@@ -801,8 +783,6 @@ TEST(ConvolveLargeDim2D, CPP) {
 }
 
 TEST(DISABLED_ConvolveLargeDim3D, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const size_t n        = 3;
     const size_t largeDim = 65535 * 16 + 1;
 
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 21fb9aead2..7fa6e57ffa 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -69,8 +69,8 @@ struct ccOutType {
 
 TYPED_TEST(CorrelationCoefficient, All) {
     typedef typename ccOutType<TypeParam>::type outType;
-    if (noDoubleTests<TypeParam>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
diff --git a/test/covariance.cpp b/test/covariance.cpp
index d1ec20a2a3..aadc1a0ebd 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -74,8 +74,8 @@ struct covOutType {
 template<typename T>
 void covTest(string pFileName, bool isbiased = false) {
     typedef typename covOutType<T>::type outType;
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
@@ -126,7 +126,7 @@ TEST(Covariance, c32) {
 }
 
 TEST(Covariance, c64) {
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
     array a = constant(cdouble(1.0, -1.0), 10, c64);
     array b = constant(cdouble(2.0, -1.0), 10, c64);
     ASSERT_THROW(cov(a, b), exception);
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index 7bd93f0b07..378078bc65 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -33,7 +33,7 @@ typedef ::testing::Types<float, double, int, uint, char, unsigned char>
 TYPED_TEST_CASE(Diagonal, TestTypes);
 
 TYPED_TEST(Diagonal, Create) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
     try {
         static const int size = 1000;
         vector<TypeParam> input(size * size);
@@ -58,7 +58,7 @@ TYPED_TEST(Diagonal, Create) {
 }
 
 TYPED_TEST(Diagonal, DISABLED_CreateLargeDim) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
     try {
         deviceGC();
         {
@@ -72,7 +72,7 @@ TYPED_TEST(Diagonal, DISABLED_CreateLargeDim) {
 }
 
 TYPED_TEST(Diagonal, Extract) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     try {
         static const int size = 1000;
@@ -93,7 +93,7 @@ TYPED_TEST(Diagonal, Extract) {
 }
 
 TYPED_TEST(Diagonal, ExtractLargeDim) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     try {
         static const size_t n        = 10;
@@ -113,7 +113,7 @@ TYPED_TEST(Diagonal, ExtractLargeDim) {
 }
 
 TYPED_TEST(Diagonal, ExtractRect) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     try {
         static const int size0 = 1000, size1 = 900;
diff --git a/test/diff1.cpp b/test/diff1.cpp
index c261d0bbcb..510d9ce61b 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -55,7 +55,7 @@ TYPED_TEST_CASE(Diff1, TestTypes);
 template<typename T>
 void diff1Test(string pTestFile, unsigned dim, bool isSubRef = false,
                const vector<af_seq> *seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -147,7 +147,7 @@ TYPED_TEST(Diff1, Subref2) {
 
 template<typename T>
 void diff1ArgsTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -211,8 +211,6 @@ TEST(Diff1, DiffLargeDim) {
 }
 
 TEST(Diff1, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned dim = 0;
     vector<dim4> numDims;
 
diff --git a/test/diff2.cpp b/test/diff2.cpp
index fd74c9efd2..c5ff4ce9f3 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -60,7 +60,7 @@ TYPED_TEST_CASE(Diff2, TestTypes);
 template<typename T>
 void diff2Test(string pTestFile, unsigned dim, bool isSubRef = false,
                const vector<af_seq> *seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -149,7 +149,7 @@ TYPED_TEST(Diff2, Subref2) {
 
 template<typename T>
 void diff2ArgsTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -206,8 +206,6 @@ TEST(Diff2, DiffLargeDim) {
 ////////////////////////////////// CPP ////////////////////////////////////////
 //
 TEST(Diff2, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned dim = 1;
     vector<dim4> numDims;
 
diff --git a/test/dog.cpp b/test/dog.cpp
index 183d521d53..9b8e952567 100644
--- a/test/dog.cpp
+++ b/test/dog.cpp
@@ -40,7 +40,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
 TYPED_TEST_CASE(DOG, TestTypes);
 
 TYPED_TEST(DOG, Basic) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dim4 iDims(512, 512, 1, 1);
     array in = constant(1, iDims, (af_dtype)dtype_traits<float>::af_type);
@@ -58,7 +58,7 @@ TYPED_TEST(DOG, Basic) {
 }
 
 TYPED_TEST(DOG, Batch) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     dim4 iDims(512, 512, 3, 1);
     array in = constant(1, iDims, (af_dtype)dtype_traits<float>::af_type);
diff --git a/test/dot.cpp b/test/dot.cpp
index f8ad25a4ac..6308e6a290 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -51,7 +51,7 @@ template<typename T>
 void dotTest(string pTestFile, const int resultIdx,
              const af_mat_prop optLhs = AF_MAT_NONE,
              const af_mat_prop optRhs = AF_MAT_NONE) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -112,7 +112,7 @@ template<typename T>
 void dotAllTest(string pTestFile, const int resultIdx,
                 const af_mat_prop optLhs = AF_MAT_NONE,
                 const af_mat_prop optRhs = AF_MAT_NONE) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
diff --git a/test/fast.cpp b/test/fast.cpp
index 8aedebdd62..4dc0c8896f 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -68,7 +68,7 @@ TYPED_TEST_CASE(FixedFAST, FixedTestTypes);
 
 template<typename T>
 void fastTest(string pTestFile, bool nonmax) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -180,7 +180,6 @@ using af::features;
 using af::loadImage;
 
 TEST(FloatFAST, CPP) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/fft.cpp b/test/fft.cpp
index 274d5fa467..f6d951ae5c 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -59,8 +59,6 @@ TEST(fft, Invalid_Type) {
 }
 
 TEST(fft2, Invalid_Array) {
-    if (noDoubleTests<float>()) return;
-
     vector<float> in(100, 1);
 
     af_array inArray  = 0;
@@ -76,8 +74,6 @@ TEST(fft2, Invalid_Array) {
 }
 
 TEST(fft3, Invalid_Array) {
-    if (noDoubleTests<float>()) return;
-
     vector<float> in(100, 1);
 
     af_array inArray  = 0;
@@ -93,8 +89,6 @@ TEST(fft3, Invalid_Array) {
 }
 
 TEST(ifft2, Invalid_Array) {
-    if (noDoubleTests<float>()) return;
-
     vector<float> in(100, 1);
 
     af_array inArray  = 0;
@@ -110,8 +104,6 @@ TEST(ifft2, Invalid_Array) {
 }
 
 TEST(ifft3, Invalid_Array) {
-    if (noDoubleTests<float>()) return;
-
     vector<float> in(100, 1);
 
     af_array inArray  = 0;
@@ -128,8 +120,8 @@ TEST(ifft3, Invalid_Array) {
 
 template<typename inType, typename outType, bool isInverse>
 void fftTest(string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0, dim_t pad2 = 0) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
@@ -294,8 +286,8 @@ INSTANTIATE_TEST(ifft3, C2C_Double, true, cdouble, cdouble,
 template<typename inType, typename outType, int rank, bool isInverse>
 void fftBatchTest(string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0,
                   dim_t pad2 = 0) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
@@ -431,8 +423,8 @@ INSTANTIATE_BATCH_TEST(fft2, C2C_Double_Pad, 2, false, cdouble, cdouble,
 //
 template<typename inType, typename outType, bool isInverse>
 void cppFFTTest(string pTestFile) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
@@ -477,8 +469,8 @@ void cppFFTTest(string pTestFile) {
 
 template<typename inType, typename outType, bool isInverse>
 void cppDFTTest(string pTestFile) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
diff --git a/test/fft_real.cpp b/test/fft_real.cpp
index 456d09ff40..d0816d976c 100644
--- a/test/fft_real.cpp
+++ b/test/fft_real.cpp
@@ -54,7 +54,7 @@ array fft(const array &in, double norm) {
 template<typename Tc, int rank>
 void fft_real(dim4 dims) {
     typedef typename dtype_traits<Tc>::base_type Tr;
-    if (noDoubleTests<Tr>()) return;
+    SUPPORTED_TYPE_CHECK(Tr);
 
     dtype ty = (dtype)dtype_traits<Tr>::af_type;
     array a  = randu(dims, ty);
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index 165d6db605..98fa9c315c 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -50,7 +50,7 @@ TYPED_TEST_CASE(FFTConvolveLarge, TestTypesLarge);
 
 template<typename T, int baseDim>
 void fftconvolveTest(string pTestFile, bool expand) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -107,7 +107,7 @@ void fftconvolveTest(string pTestFile, bool expand) {
 template<typename T, int baseDim>
 void fftconvolveTestLarge(int sDim, int fDim, int sBatch, int fBatch,
                           bool expand) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     using af::seq;
 
@@ -344,8 +344,6 @@ TYPED_TEST(FFTConvolve, Same_Cuboid_One2Many) {
 }
 
 TEST(FFTConvolve1, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -379,8 +377,6 @@ TEST(FFTConvolve1, CPP) {
 }
 
 TEST(FFTConvolve2, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -417,8 +413,6 @@ TEST(FFTConvolve2, CPP) {
 }
 
 TEST(FFTConvolve3, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
diff --git a/test/gaussiankernel.cpp b/test/gaussiankernel.cpp
index a16435de4a..a6675720ef 100644
--- a/test/gaussiankernel.cpp
+++ b/test/gaussiankernel.cpp
@@ -34,7 +34,7 @@ TYPED_TEST_CASE(GaussianKernel, TestTypes);
 
 template<typename T>
 void gaussianKernelTest(string pFileName, double sigma) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index b61040e2c5..5687f2e559 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -139,7 +139,7 @@ TYPED_TEST_CASE(GLOH, TestTypes);
 template<typename T>
 void glohTest(string pTestFile) {
 #ifdef AF_WITH_NONFREE_SIFT
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -270,7 +270,6 @@ GLOH_INIT(man, man);
 //
 TEST(GLOH, CPP) {
 #ifdef AF_WITH_NONFREE_SIFT
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/gradient.cpp b/test/gradient.cpp
index 3a02c5aa02..98df0830c5 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -47,7 +47,7 @@ template<typename T>
 void gradTest(string pTestFile, const unsigned resultIdx0,
               const unsigned resultIdx1, bool isSubRef = false,
               const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -124,8 +124,6 @@ GRAD_INIT(Grad2, grad3D, 0, 1);
 using af::array;
 
 TEST(Grad, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
 
@@ -171,8 +169,6 @@ TEST(Grad, MaxDim) {
     using af::constant;
     using af::sum;
 
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 8 + 1;
 
     array input = constant(1, 2, largeDim);
diff --git a/test/gtest b/test/gtest
index 278aba369c..2fe3bd994b 160000
--- a/test/gtest
+++ b/test/gtest
@@ -1 +1 @@
-Subproject commit 278aba369c41e90e9e77a6f51443beb3692919cf
+Subproject commit 2fe3bd994b3189899d93f1d5a881e725e046fdc2
diff --git a/test/harris.cpp b/test/harris.cpp
index 0014444085..e4e832fc05 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -60,7 +60,7 @@ TYPED_TEST_CASE(Harris, TestTypes);
 
 template<typename T>
 void harrisTest(string pTestFile, float sigma, unsigned block_size) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -167,7 +167,6 @@ using af::harris;
 using af::loadImage;
 
 TEST(FloatHarris, CPP) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/histogram.cpp b/test/histogram.cpp
index 1b4e52fea6..c13c329a43 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -41,8 +41,8 @@ TYPED_TEST_CASE(Histogram, TestTypes);
 
 template<typename inType, typename outType>
 void histTest(string pTestFile, unsigned nbins, double minval, double maxval) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
 
@@ -114,9 +114,6 @@ using af::seq;
 using af::span;
 
 TEST(Histogram, CPP) {
-    if (noDoubleTests<float>()) return;
-    if (noDoubleTests<int>()) return;
-
     const unsigned nbins = 100;
     const double minval  = 0.0;
     const double maxval  = 99.0;
diff --git a/test/homography.cpp b/test/homography.cpp
index 068e9fa6f5..f305933396 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -48,7 +48,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     using af::dtype_traits;
     using af::Pi;
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/iir.cpp b/test/iir.cpp
index f5fcc8c134..dba2369061 100644
--- a/test/iir.cpp
+++ b/test/iir.cpp
@@ -42,7 +42,7 @@ TYPED_TEST_CASE(filter, TestTypes);
 template<typename T>
 void firTest(const int xrows, const int xcols, const int brows,
              const int bcols) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     try {
         dtype ty = (dtype)dtype_traits<T>::af_type;
         array x  = randu(xrows, xcols, ty);
@@ -81,7 +81,7 @@ TYPED_TEST(filter, firMatMat) { firTest<TypeParam>(5000, 10, 50, 10); }
 template<typename T>
 void iirA0Test(const int xrows, const int xcols, const int brows,
                const int bcols) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     try {
         dtype ty    = (dtype)dtype_traits<T>::af_type;
         array x     = randu(xrows, xcols, ty);
@@ -121,7 +121,7 @@ TYPED_TEST(filter, iirA0MatMat) { iirA0Test<TypeParam>(5000, 10, 50, 10); }
 
 template<typename T>
 void iirTest(const char *testFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     vector<dim4> inDims;
 
     vector<vector<T> > inputs;
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 78142da722..cd66348b9f 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -36,7 +36,6 @@ typedef ::testing::Types<float> TestTypes;
 TYPED_TEST_CASE(ImageIO, TestTypes);
 
 void loadImageTest(string pTestFile, string pImageFile, const bool isColor) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> numDims;
@@ -123,7 +122,6 @@ using af::saveImageMem;
 using af::span;
 
 TEST(ImageIO, CPP) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> numDims;
@@ -186,7 +184,6 @@ TEST(ImageIO, SaveBMPCPP) {
 }
 
 TEST(ImageMem, SaveMemPNG) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     array img =
@@ -202,7 +199,6 @@ TEST(ImageMem, SaveMemPNG) {
 }
 
 TEST(ImageMem, SaveMemJPG1) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     array img =
@@ -220,7 +216,6 @@ TEST(ImageMem, SaveMemJPG1) {
 }
 
 TEST(ImageMem, SaveMemJPG3) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     array img =
@@ -238,7 +233,6 @@ TEST(ImageMem, SaveMemJPG3) {
 }
 
 TEST(ImageMem, SaveMemBMP) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     array img =
diff --git a/test/index.cpp b/test/index.cpp
index 3cbedfdea2..c3ac48ef4d 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -43,7 +43,7 @@ void checkValues(const af_seq &seq, const T *data, const T *indexed_data,
 
 template<typename T>
 void DimCheck(const vector<af_seq> &seqs) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     static const int ndims   = 1;
     static const size_t dims = 100;
@@ -325,7 +325,7 @@ class Indexing2D : public ::testing::Test {
 template<typename T>
 void DimCheck2D(const vector<vector<af_seq> > &seqs, string TestFile,
                 size_t NDims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -539,7 +539,7 @@ class Indexing : public ::testing::Test {
 template<typename T>
 void DimCheckND(const vector<vector<af_seq> > &seqs, string TestFile,
                 size_t NDims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     // DimCheck2D function is generalized enough
     // to check 3d and 4d indexing
@@ -656,8 +656,6 @@ using af::span;
 using af::where;
 
 TEST(Indexing2D, ColumnContiniousCPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<vector<af_seq> > seqs;
 
     seqs.push_back(make_vec(af_span, af_make_seq(0, 6, 1)));
@@ -714,7 +712,7 @@ TYPED_TEST_CASE(lookup, ArrIdxTestTypes);
 
 template<typename T>
 void arrayIndexTest(string pTestFile, int dim) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -1252,7 +1250,7 @@ class IndexedMembers : public ::testing::Test {
 TYPED_TEST_CASE(IndexedMembers, AllTypes);
 
 TYPED_TEST(IndexedMembers, MemFuncs) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     const dim_t dimsize = 100;
     vector<TypeParam> in(dimsize * dimsize);
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index 31c6373246..986cae421f 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -37,7 +37,7 @@ void invDeconvImageTest(string pTestFile, const float gamma,
         typename cond_type<is_same_type<T, double>::value, double, float>::type
             OutType;
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     using af::dim4;
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index 832e25fabc..21981061ec 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -33,7 +33,7 @@ using std::abs;
 
 template<typename T>
 void inverseTester(const int m, const int n, double eps) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 #if 1
     array A = cpu_randu<T>(dim4(m, n));
diff --git a/test/iota.cpp b/test/iota.cpp
index ffd88f05de..555c5b12e9 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -47,7 +47,7 @@ TYPED_TEST_CASE(Iota, TestTypes);
 
 template<typename T>
 void iotaTest(const dim4 idims, const dim4 tdims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array outArray = 0;
 
@@ -102,8 +102,6 @@ using af::array;
 using af::iota;
 
 TEST(Iota, CPP) {
-    if (noDoubleTests<float>()) return;
-
     dim4 idims(23, 15, 1, 1);
     dim4 tdims(2, 2, 1, 1);
     dim4 fulldims;
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 025bf6d69f..8908daf6ce 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -29,7 +29,7 @@ using std::vector;
 
 #define MINMAXOP(fn, ty)                                        \
     TEST(IndexedReduce, fn##_##ty##_0) {                        \
-        if (noDoubleTests<ty>()) return;                        \
+        SUPPORTED_TYPE_CHECK(ty);                               \
         dtype dty    = (dtype)dtype_traits<ty>::af_type;        \
         const int nx = 10000;                                   \
         const int ny = 100;                                     \
@@ -52,7 +52,7 @@ using std::vector;
         af_free_host(h_idx);                                    \
     }                                                           \
     TEST(IndexedReduce, fn##_##ty##_1) {                        \
-        if (noDoubleTests<ty>()) return;                        \
+        SUPPORTED_TYPE_CHECK(ty);                               \
         dtype dty    = (dtype)dtype_traits<ty>::af_type;        \
         const int nx = 100;                                     \
         const int ny = 100;                                     \
@@ -76,7 +76,7 @@ using std::vector;
         af_free_host(h_idx);                                    \
     }                                                           \
     TEST(IndexedReduce, fn##_##ty##_all) {                      \
-        if (noDoubleTests<ty>()) return;                        \
+        SUPPORTED_TYPE_CHECK(ty);                               \
         dtype dty     = (dtype)dtype_traits<ty>::af_type;       \
         const int num = 100000;                                 \
         array in      = randu(num, dty);                        \
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 29c9e39592..77f4eaaf2b 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -37,7 +37,7 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
         typename cond_type<is_same_type<T, double>::value, double, float>::type
             OutType;
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     using af::dim4;
diff --git a/test/join.cpp b/test/join.cpp
index ab3760d1e9..0a37a38dc4 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -54,7 +54,7 @@ template<typename T>
 void joinTest(string pTestFile, const unsigned dim, const unsigned in0,
               const unsigned in1, const unsigned resultIdx,
               bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -153,8 +153,6 @@ TEST(Join, JoinLargeDim) {
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Join, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 2;
     const unsigned dim       = 2;
 
@@ -179,8 +177,6 @@ TEST(Join, CPP) {
 }
 
 TEST(JoinMany0, CPP) {
-    if (noDoubleTests<float>()) return;
-
     array a0 = randu(10, 5);
     array a1 = randu(20, 5);
     array a2 = randu(5, 5);
@@ -192,8 +188,6 @@ TEST(JoinMany0, CPP) {
 }
 
 TEST(JoinMany1, CPP) {
-    if (noDoubleTests<float>()) return;
-
     array a0 = randu(20, 200);
     array a1 = randu(20, 400);
     array a2 = randu(20, 10);
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 0346a5120a..3bd091bd49 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -37,7 +37,6 @@ using std::string;
 using std::vector;
 
 TEST(LU, InPlaceSmall) {
-    if (noDoubleTests<float>()) return;
     if (noLAPACKTests()) return;
 
     int resultIdx = 0;
@@ -76,7 +75,6 @@ TEST(LU, InPlaceSmall) {
 }
 
 TEST(LU, SplitSmall) {
-    if (noDoubleTests<float>()) return;
     if (noLAPACKTests()) return;
 
     int resultIdx = 0;
@@ -129,7 +127,7 @@ TEST(LU, SplitSmall) {
 
 template<typename T>
 void luTester(const int m, const int n, double eps) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
 #if 1
diff --git a/test/match_template.cpp b/test/match_template.cpp
index 2d64d2b934..a94ab94f15 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -42,7 +42,7 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType) {
     typedef
         typename cond_type<is_same_type<T, double>::value, double, float>::type
             outType;
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
diff --git a/test/math.cpp b/test/math.cpp
index fc09c298eb..84e84dc537 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -36,7 +36,7 @@ T sigmoid(T in) {
 #define TEST_REAL(T, func, err, lo, hi)                          \
     TEST(MathTests, Test_##func##_##T) {                         \
         try {                                                    \
-            if (noDoubleTests<T>()) return;                      \
+            SUPPORTED_TYPE_CHECK(T);                             \
             af_dtype ty = (af_dtype)dtype_traits<T>::af_type;    \
             array a     = (hi - lo) * randu(num, ty) + lo + err; \
             eval(a);                                             \
@@ -56,7 +56,7 @@ T sigmoid(T in) {
 #define TEST_CPLX(T, func, err, lo, hi)                          \
     TEST(MathTests, Test_##func##_##T) {                         \
         try {                                                    \
-            if (noDoubleTests<T>()) return;                      \
+            SUPPORTED_TYPE_CHECK(T);                             \
             af_dtype ty = (af_dtype)dtype_traits<T>::af_type;    \
             array a     = (hi - lo) * randu(num, ty) + lo + err; \
             eval(a);                                             \
diff --git a/test/mean.cpp b/test/mean.cpp
index b73c69a1bb..5a9185b334 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -75,8 +75,8 @@ struct meanOutType {
 template<typename T>
 void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
     typedef typename meanOutType<T>::type outType;
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
@@ -173,8 +173,8 @@ TYPED_TEST(Mean, Wtd_Dim1Matrix) {
 template<typename T>
 void meanAllTest(T const_value, dim4 dims) {
     typedef typename meanOutType<T>::type outType;
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
 
     using af::array;
     using af::mean;
@@ -241,9 +241,9 @@ template<typename T, typename wtsType>
 void weightedMeanAllTest(dim4 dims) {
     typedef typename meanOutType<T>::type outType;
 
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
-    if (noDoubleTests<wtsType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
+    SUPPORTED_TYPE_CHECK(wtsType);
 
     using af::array;
     using af::mean;
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 61928e396f..d6585f5979 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -35,7 +35,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort,
 TYPED_TEST_CASE(Meanshift, TestTypes);
 
 TYPED_TEST(Meanshift, InvalidArgs) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     vector<TypeParam> in(100, 1);
 
@@ -53,7 +53,7 @@ TYPED_TEST(Meanshift, InvalidArgs) {
 
 template<typename T, bool isColor>
 void meanshiftTest(string pTestFile, const float ss) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -138,7 +138,6 @@ using af::seq;
 using af::span;
 
 TEST(Meanshift, Color_CPP) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 85c75a8437..1fadf73afb 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -45,7 +45,7 @@ TYPED_TEST_CASE(MedianFilter1d, TestTypes);
 template<typename T>
 void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid,
                  af_border_type pad) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -105,7 +105,7 @@ TYPED_TEST(MedianFilter, BATCH_SYMMETRIC_PAD_3x3) {
 
 template<typename T>
 void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -165,7 +165,7 @@ TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3) {
 
 template<typename T, bool isColor>
 void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -212,7 +212,7 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid) {
 
 template<typename T>
 void medfiltInputTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray  = 0;
     af_array outArray = 0;
@@ -241,7 +241,7 @@ TYPED_TEST(MedianFilter, InvalidArray) { medfiltInputTest<TypeParam>(); }
 
 template<typename T>
 void medfiltWindowTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray  = 0;
     af_array outArray = 0;
@@ -264,7 +264,7 @@ TYPED_TEST(MedianFilter, InvalidWindow) { medfiltWindowTest<TypeParam>(); }
 
 template<typename T>
 void medfilt1d_WindowTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray  = 0;
     af_array outArray = 0;
@@ -287,7 +287,7 @@ TYPED_TEST(MedianFilter1d, InvalidWindow) { medfilt1d_WindowTest<TypeParam>(); }
 
 template<typename T>
 void medfiltPadTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray  = 0;
     af_array outArray = 0;
@@ -314,7 +314,7 @@ TYPED_TEST(MedianFilter, InvalidPadType) { medfiltPadTest<TypeParam>(); }
 
 template<typename T>
 void medfilt1d_PadTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray  = 0;
     af_array outArray = 0;
@@ -345,8 +345,6 @@ TYPED_TEST(MedianFilter1d, InvalidPadType) { medfilt1d_PadTest<TypeParam>(); }
 using af::array;
 
 TEST(MedianFilter, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const dim_t w_len = 3;
     const dim_t w_wid = 3;
 
@@ -374,8 +372,6 @@ TEST(MedianFilter, CPP) {
 }
 
 TEST(MedianFilter1d, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const dim_t w_wid = 3;
 
     vector<dim4> numDims;
diff --git a/test/median.cpp b/test/median.cpp
index 0103b9a72a..3c7e711b7f 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -45,7 +45,7 @@ array generateArray<unsigned int>(int nx, int ny, int nz, int nw) {
 
 template<typename To, typename Ti>
 void median_flat(int nx, int ny = 1, int nz = 1, int nw = 1) {
-    if (noDoubleTests<Ti>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
     array a = generateArray<Ti>(nx, ny, nz, nw);
 
     // Verification
@@ -71,7 +71,7 @@ void median_flat(int nx, int ny = 1, int nz = 1, int nw = 1) {
 
 template<typename To, typename Ti, int dim>
 void median_test(int nx, int ny = 1, int nz = 1, int nw = 1) {
-    if (noDoubleTests<Ti>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
 
     array a = generateArray<Ti>(nx, ny, nz, nw);
 
diff --git a/test/memory.cpp b/test/memory.cpp
index 2d0af23d07..b33b35ca79 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -82,7 +82,7 @@ size_t roundUpToStep(size_t bytes) {
 
 template<typename T>
 void memAllocArrayScopeTest(int elements) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
@@ -112,7 +112,7 @@ void memAllocArrayScopeTest(int elements) {
 
 template<typename T>
 void memAllocPtrScopeTest(int elements) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 263a557cfd..4d3114ef80 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -46,7 +46,7 @@ TYPED_TEST_CASE(Moddims, TestTypes);
 template<typename T>
 void moddimsTest(string pTestFile, bool isSubRef = false,
                  const vector<af_seq> *seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -127,7 +127,7 @@ TYPED_TEST(Moddims, Subref) {
 
 template<typename T>
 void moddimsArgsTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -160,7 +160,7 @@ TYPED_TEST(Moddims, InvalidArgs) {
 
 template<typename T>
 void moddimsMismatchTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -196,7 +196,7 @@ using af::array;
 template<typename T>
 void cppModdimsTest(string pTestFile, bool isSubRef = false,
                     const vector<af_seq> *seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
diff --git a/test/moments.cpp b/test/moments.cpp
index a88550d1c1..f0ea3072de 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -43,7 +43,7 @@ TYPED_TEST_CASE(Image, TestTypes);
 
 template<typename T>
 void momentsTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
diff --git a/test/morph.cpp b/test/morph.cpp
index 28dcd4b1bf..e91d8fe425 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -38,7 +38,7 @@ TYPED_TEST_CASE(Morph, TestTypes);
 
 template<typename inType, bool isDilation, bool isVolume>
 void morphTest(string pTestFile) {
-    if (noDoubleTests<inType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
@@ -135,7 +135,7 @@ TYPED_TEST(Morph, Erode4x4x4) {
 
 template<typename T, bool isDilation, bool isColor>
 void morphImageTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -198,7 +198,7 @@ TEST(Morph, ColorImage) {
 
 template<typename T, bool isDilation>
 void morphInputTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray   = 0;
     af_array maskArray = 0;
@@ -235,7 +235,7 @@ TYPED_TEST(Morph, ErodeInvalidInput) { morphInputTest<TypeParam, false>(); }
 
 template<typename T, bool isDilation>
 void morphMaskTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray   = 0;
     af_array maskArray = 0;
@@ -286,7 +286,7 @@ TYPED_TEST(Morph, ErodeInvalidMask) { morphMaskTest<TypeParam, false>(); }
 
 template<typename T, bool isDilation>
 void morph3DMaskTest(void) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray   = 0;
     af_array maskArray = 0;
@@ -354,7 +354,7 @@ using af::span;
 
 template<typename T, bool isDilation, bool isColor>
 void cppMorphImageTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 11bf9e78fa..e19f35ec22 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -65,7 +65,7 @@ TYPED_TEST_CASE(NearestNeighbour, TestTypes);
 template<typename T>
 void nearestNeighbourTest(string pTestFile, int feat_dim,
                           const af_match_type type) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     typedef typename otype_t<T>::otype To;
 
diff --git a/test/orb.cpp b/test/orb.cpp
index 637417c2bf..862b942555 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -129,7 +129,7 @@ TYPED_TEST_CASE(ORB, TestTypes);
 
 template<typename T>
 void orbTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -247,7 +247,6 @@ TYPED_TEST(ORB, Lena) { orbTest<TypeParam>(string(TEST_DIR "/orb/lena.test")); }
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(ORB, CPP) {
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 3ab6c72473..17fdafa1a6 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -34,7 +34,6 @@ using std::vector;
 
 ///////////////////////////////// CPP ////////////////////////////////////
 TEST(QRFactorized, CPP) {
-    if (noDoubleTests<float>()) return;
     if (noLAPACKTests()) return;
 
     int resultIdx = 0;
@@ -90,7 +89,7 @@ TEST(QRFactorized, CPP) {
 template<typename T>
 void qrTester(const int m, const int n, double eps) {
     try {
-        if (noDoubleTests<T>()) return;
+        SUPPORTED_TYPE_CHECK(T);
         if (noLAPACKTests()) return;
 
 #if 1
diff --git a/test/random.cpp b/test/random.cpp
index a4318c2d3c..b270b8cac7 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -87,7 +87,7 @@ TYPED_TEST_CASE(RandomSeed, TestTypesSeed);
 
 template<typename T>
 void randuTest(dim4 &dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array outArray = 0;
     ASSERT_SUCCESS(af_randu(&outArray, dims.ndims(), dims.get(),
@@ -98,7 +98,7 @@ void randuTest(dim4 &dims) {
 
 template<typename T>
 void randnTest(dim4 &dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array outArray = 0;
     ASSERT_SUCCESS(af_randn(&outArray, dims.ndims(), dims.get(),
@@ -150,7 +150,7 @@ RAND(45, 1, 1, 1);
 
 template<typename T>
 void randuArgsTest() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     dim_t ndims       = 4;
     dim_t dims[]      = {1, 2, 3, 0};
@@ -165,7 +165,7 @@ TYPED_TEST(Random, InvalidArgs) { randuArgsTest<TypeParam>(); }
 
 template<typename T>
 void randuDimsTest() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     dim4 dims(1, 65535 * 32, 1, 1);
     array large_rand = randu(dims, (af_dtype)dtype_traits<T>::af_type);
@@ -207,8 +207,6 @@ TEST(RandomEngine, Default) {
 }
 
 TEST(Random, CPP) {
-    if (noDoubleTests<float>()) return;
-
     // TEST will fail if exception is thrown, which are thrown
     // when only wrong inputs are thrown on bad access happens
     dim4 dims(1, 2, 3, 1);
@@ -228,7 +226,7 @@ TEST(Random, CPP) {
 
 template<typename T>
 void testSetSeed(const uintl seed0, const uintl seed1) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     uintl orig_seed = getSeed();
 
@@ -280,7 +278,7 @@ TYPED_TEST(RandomSeed, setSeed) { testSetSeed<TypeParam>(10101, 23232); }
 
 template<typename T>
 void testGetSeed(const uintl seed0, const uintl seed1) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     uintl orig_seed = getSeed();
 
@@ -306,7 +304,7 @@ TYPED_TEST(Random, getSeed) { testGetSeed<TypeParam>(1234, 9876); }
 
 template<typename T>
 void testRandomEngineUniform(randomEngineType type) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem = 16 * 1024 * 1024;
@@ -320,7 +318,7 @@ void testRandomEngineUniform(randomEngineType type) {
 
 template<typename T>
 void testRandomEngineNormal(randomEngineType type) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem = 16 * 1024 * 1024;
@@ -404,7 +402,7 @@ TYPED_TEST(RandomEngineSeed, mersenneSeedUniform) {
 
 template<typename T>
 void testRandomEnginePeriod(randomEngineType type) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem  = 1024 * 1024;
@@ -441,7 +439,7 @@ T chi2_statistic(array input, array expected) {
 
 template<typename T>
 void testRandomEngineUniformChi2(randomEngineType type) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem  = 256 * 1024 * 1024;
diff --git a/test/range.cpp b/test/range.cpp
index 3b9953eef0..918f063431 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -51,7 +51,7 @@ TYPED_TEST_CASE(Range, TestTypes);
 template<typename T>
 void rangeTest(const uint x, const uint y, const uint z, const uint w,
                const uint dim) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     dim4 idims(x, y, z, w);
 
@@ -119,8 +119,6 @@ RANGE_INIT(Range1DMaxDim3, 1, 1, 1, 65535 * 32 + 1, 0);
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Range, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned x   = 23;
     const unsigned y   = 15;
     const unsigned z   = 4;
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index 3d06f83bed..6f9879df16 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -45,7 +45,7 @@ TYPED_TEST_CASE(Det, TestTypes);
 
 template<typename T>
 void rankSmall() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     T ha[] = {1, 4, 7, 2, 5, 8, 3, 6, 20};
@@ -56,7 +56,7 @@ void rankSmall() {
 
 template<typename T>
 void rankBig(const int num) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
@@ -70,7 +70,7 @@ void rankBig(const int num) {
 
 template<typename T>
 void rankLow(const int num) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
@@ -92,7 +92,7 @@ TYPED_TEST(Rank, low) { rankBig<TypeParam>(512); }
 
 template<typename T>
 void detTest() {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 076dd49eb9..610d96ff47 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -41,8 +41,8 @@ typedef af_err (*reduceFunc)(af_array *, const af_array, const int);
 template<typename Ti, typename To, reduceFunc af_reduce>
 void reduceTest(string pTestFile, int off = 0, bool isSubRef = false,
                 const vector<af_seq> seqv = vector<af_seq>()) {
-    if (noDoubleTests<Ti>()) return;
-    if (noDoubleTests<To>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
+    SUPPORTED_TYPE_CHECK(To);
 
     vector<dim4> numDims;
 
@@ -173,16 +173,12 @@ REDUCE_TESTS(count, unsigned);
 #undef REDUCE_TESTS
 
 TEST(Reduce, Test_Reduce_Big0) {
-    if (noDoubleTests<int>()) return;
-
     reduceTest<int, int, af_sum>(string(TEST_DIR "/reduce/big0.test"), 0);
 }
 
 /*
 TEST(Reduce,Test_Reduce_Big1)
 {
-    if (noDoubleTests<int>()) return;
-
     reduceTest<int, int, af_sum>(
         string(TEST_DIR"/reduce/big1.test"),
         1
@@ -211,8 +207,8 @@ using af::sum;
 
 template<typename Ti, typename To, ReductionOp reduce>
 void cppReduceTest(string pTestFile) {
-    if (noDoubleTests<Ti>()) return;
-    if (noDoubleTests<To>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
+    SUPPORTED_TYPE_CHECK(To);
 
     vector<dim4> numDims;
 
@@ -360,7 +356,7 @@ TEST(Reduce, Test_Count_Global) {
 }
 
 TEST(Reduce, Test_min_Global) {
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     const int num = 10000;
     array a       = randu(num, 1, f64);
@@ -368,7 +364,7 @@ TEST(Reduce, Test_min_Global) {
     double *h_a   = a.host<double>();
     double gold   = std::numeric_limits<double>::max();
 
-    if (noDoubleTests<double>()) return;
+    SUPPORTED_TYPE_CHECK(double);
 
     for (int i = 0; i < num; i++) { gold = std::min(gold, h_a[i]); }
 
@@ -420,7 +416,7 @@ void typed_assert_eq<cdouble>(cdouble lhs, cdouble rhs, bool both) {
 }
 
 TYPED_TEST(Reduce, Test_All_Global) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     // Input size test
     for (int i = 1; i < 1000; i += 100) {
@@ -453,7 +449,7 @@ TYPED_TEST(Reduce, Test_All_Global) {
 }
 
 TYPED_TEST(Reduce, Test_Any_Global) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     // Input size test
     for (int i = 1; i < 1000; i += 100) {
diff --git a/test/regions.cpp b/test/regions.cpp
index 48172f0576..255fe20c37 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -44,7 +44,7 @@ TYPED_TEST_CASE(Regions, TestTypes);
 template<typename T>
 void regionsTest(string pTestFile, af_connectivity connectivity,
                  bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<uchar> > in;
@@ -109,8 +109,6 @@ REGIONS_INIT(Regions3, regions_128x128, 8, AF_CONNECTIVITY_8);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(Regions, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
diff --git a/test/reorder.cpp b/test/reorder.cpp
index 9156370f14..f835de8fea 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -54,7 +54,7 @@ template<typename T>
 void reorderTest(string pTestFile, const unsigned resultIdx, const uint x,
                  const uint y, const uint z, const uint w,
                  bool isSubRef = false, const vector<af_seq> *seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -179,8 +179,6 @@ TEST(Reorder, ISSUE_1777) {
 }
 
 TEST(Reorder, MaxDim) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
 
     array input  = range(dim4(2, largeDim, 2), 2);
diff --git a/test/replace.cpp b/test/replace.cpp
index 6aa939ace5..060993cfa2 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -39,7 +39,7 @@ TYPED_TEST_CASE(Replace, TestTypes);
 
 template<typename T>
 void replaceTest(const dim4 &dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
@@ -75,7 +75,7 @@ void replaceTest(const dim4 &dims) {
 
 template<typename T>
 void replaceScalarTest(const dim4 &dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
diff --git a/test/resize.cpp b/test/resize.cpp
index c994be1ed3..ab53631fd4 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -64,7 +64,7 @@ TYPED_TEST_CASE(Resize, TestTypesF);
 TYPED_TEST_CASE(ResizeI, TestTypesI);
 
 TYPED_TEST(Resize, InvalidDims) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     vector<TypeParam> in(8 * 8);
 
@@ -116,7 +116,7 @@ template<typename T>
 void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0,
                 const dim_t odim1, const af_interp_type method,
                 bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -317,7 +317,7 @@ TYPED_TEST(ResizeI, Resize1CLargeDownLinear) {
 template<typename T>
 void resizeArgsTest(af_err err, string pTestFile, const dim4 odims,
                     const af_interp_type method) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -362,8 +362,6 @@ using af::seq;
 using af::span;
 
 TEST(Resize, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -379,8 +377,6 @@ TEST(Resize, CPP) {
 }
 
 TEST(ResizeScale1, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
@@ -396,8 +392,6 @@ TEST(ResizeScale1, CPP) {
 }
 
 TEST(ResizeScale2, CPP) {
-    if (noDoubleTests<float>()) return;
-
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 39fada8b1f..1559fea00a 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -46,7 +46,7 @@ template<typename T>
 void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
                 const bool crop, bool isSubRef = false,
                 const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -160,8 +160,6 @@ ROTATE_INIT(Rectangle00CropRecenter, rotate2, 23, 0, true);
 ////////////////////////////////// CPP //////////////////////////////////////
 //
 TEST(Rotate, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 0;
     const float angle        = 180;
     const bool crop          = false;
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index ee5d879737..807859e91d 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -51,7 +51,7 @@ template<typename T>
 void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
                 const bool crop, bool isSubRef = false,
                 const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -177,8 +177,6 @@ ROTATE_INIT(Rectangle00CropRecenter, rotatelinear2, 23, 0, true);
 ////////////////////////////////// CPP //////////////////////////////////////
 
 TEST(RotateLinear, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 0;
     const float angle        = 180;
     const bool crop          = false;
diff --git a/test/sat.cpp b/test/sat.cpp
index 89c09cd819..b4811bb8e5 100644
--- a/test/sat.cpp
+++ b/test/sat.cpp
@@ -39,7 +39,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, uintl, intl,
 TYPED_TEST_CASE(SAT, TestTypes);
 
 TYPED_TEST(SAT, IntegralImage) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     array a = randu(530, 671, (af_dtype)dtype_traits<TypeParam>::af_type);
     array b = accum(a, 0);
diff --git a/test/scan.cpp b/test/scan.cpp
index 5b3db22f1c..2ae4c25d91 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -44,7 +44,7 @@ typedef af_err (*scanFunc)(af_array *, const af_array, const int);
 template<typename Ti, typename To, scanFunc af_scan>
 void scanTest(string pTestFile, int off = 0, bool isSubRef = false,
               const vector<af_seq> seqv = vector<af_seq>()) {
-    if (noDoubleTests<Ti>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
 
     vector<dim4> numDims;
 
@@ -136,8 +136,6 @@ TEST(Accum, CPP) {
 
     vector<float> in(data[0].begin(), data[0].end());
 
-    if (noDoubleTests<float>()) return;
-
     array input(dims, &(in.front()));
 
     // Compare result
diff --git a/test/select.cpp b/test/select.cpp
index 3f8ff2f664..a4d50971b3 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -48,7 +48,7 @@ TYPED_TEST_CASE(Select, TestTypes);
 
 template<typename T>
 void selectTest(const dim4& dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
@@ -82,7 +82,7 @@ void selectTest(const dim4& dims) {
 
 template<typename T, bool is_right>
 void selectScalarTest(const dim4& dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a    = randu(dims, ty);
diff --git a/test/set.cpp b/test/set.cpp
index ec695b38f2..f085da33b3 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -28,7 +28,7 @@ using std::vector;
 
 template<typename T>
 void uniqueTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -88,7 +88,7 @@ typedef af_err (*setFunc)(af_array *, const af_array, const af_array,
 
 template<typename T, setFunc af_set_func>
 void setTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
diff --git a/test/shift.cpp b/test/shift.cpp
index 3120c8674e..394a9cd8c2 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -51,7 +51,7 @@ template<typename T>
 void shiftTest(string pTestFile, const unsigned resultIdx, const int x,
                const int y, const int z, const int w, bool isSubRef = false,
                const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -111,8 +111,6 @@ SHIFT_INIT(Shift14, shift4d, 14, -21, -21, -21, -21);
 ////////////////////////////////// CPP ///////////////////////////////////
 //
 TEST(Shift, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 0;
     const unsigned x         = 2;
     const unsigned y         = 0;
@@ -133,8 +131,6 @@ TEST(Shift, CPP) {
 }
 
 TEST(Shift, MaxDim) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim  = 65535 * 32 + 1;
     const unsigned shift_x = 1;
 
diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index d880b0c9fa..f44f4e4b0a 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -139,7 +139,7 @@ template<typename T>
 void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
               float edgeThr, float initSigma, bool doubleInput) {
 #ifdef AF_WITH_NONFREE_SIFT
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -280,7 +280,6 @@ SIFT_INIT(Man_NoDoubleInput, man_nodoubleinput, 3, 0.04f, 10.0f, 1.6f, false);
 //
 TEST(SIFT, CPP) {
 #ifdef AF_WITH_NONFREE_SIFT
-    if (noDoubleTests<float>()) return;
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 22f6f8f14b..9f92e402e6 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -44,7 +44,7 @@ TYPED_TEST_CASE(Sobel_Integer, TestTypesInt);
 
 template<typename Ti, typename To>
 void testSobelDerivatives(string pTestFile) {
-    if (noDoubleTests<Ti>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
 
     vector<dim4> numDims;
     vector<vector<Ti> > in;
diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index e1860c41c0..341d0afc49 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -35,7 +35,7 @@ void solveTester(const int m, const int n, const int k, double eps,
 
     af::deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
 #if 1
@@ -74,7 +74,7 @@ void solveLUTester(const int n, const int k, double eps,
 
     af::deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
 #if 1
@@ -113,7 +113,7 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps,
 
     af::deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
 #if 1
diff --git a/test/sort.cpp b/test/sort.cpp
index deffb366b8..86b03eb8b2 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -50,7 +50,7 @@ TYPED_TEST_CASE(Sort, TestTypes);
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
               bool isSubRef = false, const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -125,8 +125,6 @@ SORT_INIT(SortLargeFalse, sort_large, false, 2);
 ////////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(Sort, CPPDim0) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = true;
     const unsigned resultIdx0 = 0;
 
@@ -158,8 +156,6 @@ TEST(Sort, CPPDim0) {
 }
 
 TEST(Sort, CPPDim1) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = true;
     const unsigned resultIdx0 = 0;
 
@@ -196,8 +192,6 @@ TEST(Sort, CPPDim1) {
 }
 
 TEST(Sort, CPPDim2) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = false;
     const unsigned resultIdx0 = 2;
 
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index 9db3b064a6..97b925e3e1 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -51,7 +51,7 @@ template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
               const unsigned resultIdx1, bool isSubRef = false,
               const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -122,8 +122,6 @@ SORT_INIT(SortLargeFalse, sort_by_key_large, false, 2, 3);
 ////////////////////////////////////// CPP ///////////////////////////////
 //
 TEST(SortByKey, CPPDim0) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
@@ -145,8 +143,6 @@ TEST(SortByKey, CPPDim0) {
 }
 
 TEST(SortByKey, CPPDim1) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
@@ -175,8 +171,6 @@ TEST(SortByKey, CPPDim1) {
 }
 
 TEST(SortByKey, CPPDim2) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = false;
     const unsigned resultIdx0 = 2;
     const unsigned resultIdx1 = 3;
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 4c8459752e..1c3335ee8f 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -51,7 +51,7 @@ template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
               const unsigned resultIdx1, bool isSubRef = false,
               const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -122,8 +122,6 @@ SORT_INIT(SortLargeFalse, sort_large, false, 2, 3);
 //////////////////////////////////// CPP /////////////////////////////////
 //
 TEST(SortIndex, CPPDim0) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
@@ -146,8 +144,6 @@ TEST(SortIndex, CPPDim0) {
 }
 
 TEST(SortIndex, CPPDim1) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = true;
     const unsigned resultIdx0 = 0;
     const unsigned resultIdx1 = 1;
@@ -175,8 +171,6 @@ TEST(SortIndex, CPPDim1) {
 }
 
 TEST(SortIndex, CPPDim2) {
-    if (noDoubleTests<float>()) return;
-
     const bool dir            = false;
     const unsigned resultIdx0 = 2;
     const unsigned resultIdx1 = 3;
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 3ae9135944..6a14192f27 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -187,7 +187,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble> SparseTypes;
 TYPED_TEST_CASE(Sparse, SparseTypes);
 
 TYPED_TEST(Sparse, DeepCopy) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     cleanSlate();
 
@@ -232,7 +232,7 @@ TYPED_TEST(Sparse, DeepCopy) {
 }
 
 TYPED_TEST(Sparse, Empty) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     af_array ret = 0;
     dim_t rows = 0, cols = 0, nnz = 0;
@@ -247,7 +247,7 @@ TYPED_TEST(Sparse, Empty) {
 }
 
 TYPED_TEST(Sparse, EmptyDeepCopy) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     array a = sparse(0, 0, array(0, (af_dtype)dtype_traits<TypeParam>::af_type),
                      array(1, s32), array(0, s32));
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 3d69cc81ff..63e9be4a3c 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -132,7 +132,7 @@ template<typename T, af_op_t op>
 void sparseArithTester(const int m, const int n, int factor, const double eps) {
     deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
 #if 1
     array A = cpu_randu<T>(dim4(m, n));
@@ -175,7 +175,7 @@ void sparseArithTesterMul(const int m, const int n, int factor,
                           const double eps) {
     deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
 #if 1
     array A = cpu_randu<T>(dim4(m, n));
@@ -235,7 +235,7 @@ void sparseArithTesterDiv(const int m, const int n, int factor,
                           const double eps) {
     deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
 #if 1
     array A = cpu_randu<T>(dim4(m, n));
@@ -306,7 +306,7 @@ template<typename T, af_op_t op>
 void ssArithmetic(const int m, const int n, int factor, const double eps) {
     deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
 #if 1
     array A = cpu_randu<T>(dim4(m, n));
@@ -362,7 +362,7 @@ template<af_op_t op>
 void ssArithmeticMTX(const char* op1, const char* op2) {
     deviceGC();
 
-    // Re-enable when double is enabled if (noDoubleTests<T>()) return;
+    // Re-enable when double is enabled SUPPORTED_TYPE_CHECK(T);
 
     array cooA, cooB;
     ASSERT_TRUE(mtxReadSparseMatrix(cooA, op1));
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index 0e4f48ecfd..1b0a6a56f5 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -72,7 +72,7 @@ static void sparseTester(const int m, const int n, const int k, int factor,
 
     af::deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
 #if 1
     af::array A = cpu_randu<T>(af::dim4(m, n));
@@ -106,7 +106,7 @@ static void sparseTransposeTester(const int m, const int n, const int k,
 
     af::deviceGC();
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
 #if 1
     af::array A = cpu_randu<T>(af::dim4(m, n));
@@ -142,7 +142,7 @@ static void convertCSR(const int M, const int N, const float ratio,
                        int targetDevice = -1) {
     if (targetDevice >= 0) af::setDevice(targetDevice);
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 #if 1
     af::array a = cpu_randu<T>(af::dim4(M, N));
 #else
@@ -178,8 +178,8 @@ static void createFunction() {
 
 template<typename Ti, typename To>
 static void sparseCastTester(const int m, const int n, int factor) {
-    if (noDoubleTests<Ti>()) return;
-    if (noDoubleTests<To>()) return;
+    SUPPORTED_TYPE_CHECK(Ti);
+    SUPPORTED_TYPE_CHECK(To);
 
     af::array A = cpu_randu<Ti>(af::dim4(m, n));
 
diff --git a/test/sparse_convert.cpp b/test/sparse_convert.cpp
index c25db15d82..04599e03ca 100644
--- a/test/sparse_convert.cpp
+++ b/test/sparse_convert.cpp
@@ -63,7 +63,7 @@ array makeSparse<cdouble>(array A, int factor) {
 
 template<typename T, af_storage src, af_storage dest>
 void sparseConvertTester(const int m, const int n, int factor) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     array A = cpu_randu<T>(dim4(m, n));
 
diff --git a/test/stdev.cpp b/test/stdev.cpp
index 28f52f9662..ee79958bf6 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -76,8 +76,8 @@ struct sdOutType {
 template<typename T>
 void stdevDimTest(string pFileName, dim_t dim = -1) {
     typedef typename sdOutType<T>::type outType;
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
@@ -136,8 +136,8 @@ TEST(StandardDev, InvalidType) {
 template<typename T>
 void stdevDimIndexTest(string pFileName, dim_t dim = -1) {
     typedef typename sdOutType<T>::type outType;
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
@@ -182,8 +182,8 @@ TYPED_TEST(StandardDev, IndexedArrayDim1) {
 
 TYPED_TEST(StandardDev, All) {
     typedef typename sdOutType<TypeParam>::type outType;
-    if (noDoubleTests<TypeParam>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
diff --git a/test/susan.cpp b/test/susan.cpp
index a8b276cc1a..223704bb26 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -66,7 +66,7 @@ TYPED_TEST_CASE(Susan, TestTypes);
 
 template<typename T>
 void susanTest(string pTestFile, float t, float g) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index 05749b1924..18b0173957 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -57,7 +57,7 @@ double get_val<cdouble>(cdouble val) {
 
 template<typename T>
 void svdTest(const int M, const int N) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
@@ -86,7 +86,7 @@ void svdTest(const int M, const int N) {
 
 template<typename T>
 void svdInPlaceTest(const int M, const int N) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
@@ -114,7 +114,7 @@ void svdInPlaceTest(const int M, const int N) {
 
 template<typename T>
 void checkInPlaceSameResults(const int M, const int N) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noLAPACKTests()) return;
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index b2723cf537..d1ee3c2e45 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -439,6 +439,9 @@ bool noDoubleTests() {
     return ((isTypeDouble && !isDoubleSupported) ? true : false);
 }
 
+#define SUPPORTED_TYPE_CHECK(type) \
+    if (noDoubleTests<type>()) return;
+
 inline bool noImageIOTests() {
     bool ret = !af::isImageIOAvailable();
     if (ret) printf("Image IO Not Configured. Test will exit\n");
diff --git a/test/tile.cpp b/test/tile.cpp
index 5326b9160b..85e716b63a 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -54,7 +54,7 @@ template<typename T>
 void tileTest(string pTestFile, const unsigned resultIdx, const uint x,
               const uint y, const uint z, const uint w, bool isSubRef = false,
               const vector<af_seq>* seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -117,8 +117,6 @@ TILE_INIT(Tile2D231, tile_large2D, 4, 2, 3, 1, 1);
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Tile, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 0;
     const unsigned x         = 2;
     const unsigned y         = 2;
@@ -140,8 +138,6 @@ TEST(Tile, CPP) {
 }
 
 TEST(Tile, MaxDim) {
-    if (noDoubleTests<float>()) return;
-
     const size_t largeDim = 65535 * 32 + 1;
     const unsigned x      = 1;
     const unsigned z      = 1;
diff --git a/test/transform.cpp b/test/transform.cpp
index a6131135dd..254781b698 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -46,7 +46,7 @@ TYPED_TEST_CASE(TransformInt, TestTypesInt);
 template<typename T>
 void transformTest(string pTestFile, string pHomographyFile,
                    const af_interp_type method, const bool invert) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
     vector<dim4> inNumDims;
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 3c0858d0bb..7d8805d043 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -35,7 +35,7 @@ TYPED_TEST_CASE(TransformCoordinates, TestTypes);
 
 template<typename T>
 void transformCoordinatesTest(string pTestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> inDims;
     vector<vector<T> > in;
diff --git a/test/translate.cpp b/test/translate.cpp
index 9c724cfef4..dcdb06953a 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -49,7 +49,7 @@ template<typename T>
 void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims,
                    const float tx, const float ty, const af_interp_type method,
                    const float max_fail_count = 0.0001) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 5f071aef5a..32927da3e0 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -52,7 +52,7 @@ TYPED_TEST_CASE(Transpose, TestTypes);
 template<typename T>
 void trsTest(string pTestFile, bool isSubRef = false,
              const vector<af_seq> *seqv = NULL) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -160,7 +160,7 @@ void trsCPPTest(string pFileName) {
     readTests<T, T, int>(pFileName, numDims, in, tests);
     dim4 dims = numDims[0];
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     array input(dims, &(in[0].front()));
     array output = transpose(input);
@@ -195,7 +195,7 @@ void trsCPPConjTest(dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
 
     dim4 dims(d0, d1, d2, d3);
 
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     array input    = randu(dims, (af_dtype)dtype_traits<T>::af_type);
     array output_t = transpose(input, false);
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index 7f3fee9b89..88d61cad16 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -39,7 +39,7 @@ TYPED_TEST_CASE(Transpose, TestTypes);
 
 template<typename T>
 void transposeip_test(dim4 dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     af_array inArray  = 0;
     af_array outArray = 0;
@@ -72,8 +72,6 @@ INIT_TEST(25, 2, 2);
 ////////////////////////////////////// CPP //////////////////////////////////
 //
 void transposeInPlaceCPPTest() {
-    if (noDoubleTests<float>()) return;
-
     dim4 dims(64, 64, 1, 1);
 
     array input  = randu(dims);
diff --git a/test/triangle.cpp b/test/triangle.cpp
index 56c1559e58..d59c5b0e95 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -39,7 +39,7 @@ TYPED_TEST_CASE(Triangle, TestTypes);
 
 template<typename T>
 void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag = false) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 #if 1
     array in = cpu_randu<T>(dims);
 #else
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 7f2f53ca4a..9224e90d8f 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -47,7 +47,7 @@ template<typename T>
 void unwrapTest(string pTestFile, const unsigned resultIdx, const dim_t wx,
                 const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
                 const dim_t py) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
     vector<vector<T> > in;
@@ -152,8 +152,6 @@ UNWRAP_INIT(UnwrapSmall46, unwrap_small, 46, 16, 18, 16, 18, 0, 1);
 ///////////////////////////////// CPP ////////////////////////////////////
 //
 TEST(Unwrap, CPP) {
-    if (noDoubleTests<float>()) return;
-
     const unsigned resultIdx = 20;
     const unsigned wx        = 4;
     const unsigned wy        = 4;
diff --git a/test/var.cpp b/test/var.cpp
index e148ff6d3a..60f71c0998 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -53,8 +53,8 @@ struct varOutType {
 template<typename T>
 void testCPPVar(T const_value, dim4 dims) {
     typedef typename varOutType<T>::type outType;
-    if (noDoubleTests<T>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(T);
+    SUPPORTED_TYPE_CHECK(outType);
 
     using af::array;
     using af::var;
@@ -103,8 +103,8 @@ TYPED_TEST(Var, AllCPPLarge) {
 TYPED_TEST(Var, DimCPPSmall) {
     typedef typename varOutType<TypeParam>::type outType;
 
-    if (noDoubleTests<TypeParam>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<TypeParam> > in;
diff --git a/test/where.cpp b/test/where.cpp
index a415328748..28c8a902b5 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -41,7 +41,7 @@ TYPED_TEST_CASE(Where, TestTypes);
 template<typename T>
 void whereTest(string pTestFile, bool isSubRef = false,
                const vector<af_seq> seqv = vector<af_seq>()) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
@@ -94,7 +94,7 @@ TYPED_TEST(Where, BasicC) {
 //////////////////////////////////// CPP /////////////////////////////////
 //
 TYPED_TEST(Where, CPP) {
-    if (noDoubleTests<TypeParam>()) return;
+    SUPPORTED_TYPE_CHECK(TypeParam);
 
     vector<dim4> numDims;
 
diff --git a/test/wrap.cpp b/test/wrap.cpp
index af4c7b6f93..41d5d37af2 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -77,7 +77,7 @@ template<typename T>
 void wrapTest(const dim_t ix, const dim_t iy, const dim_t wx, const dim_t wy,
               const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
               bool cond) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     const int nc = 1;
 
diff --git a/test/write.cpp b/test/write.cpp
index bc3345fec6..5a6d14c021 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -42,7 +42,7 @@ TYPED_TEST_CASE(Write, TestTypes);
 
 template<typename T>
 void writeTest(dim4 dims) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     array A = randu(dims, (af_dtype)dtype_traits<T>::af_type);
     array B = randu(dims, (af_dtype)dtype_traits<T>::af_type);

From ef5183d7192c5cbc1e80c81ae8d1f2992980b6b6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 23 Jan 2019 16:39:08 +0530
Subject: [PATCH 1601/2677] Add AF_JIT_KERNEL_TRACE environment variable docs

---
 docs/pages/configuring_arrayfire_environment.md | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 30f1d2c011..1a3abd3c6c 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -222,3 +222,18 @@ is useful for specialized build configurations that use the unified backend and
 build shared libraries separately.
 
 By default, no additional path will be searched for an empty value.
+
+
+AF_JIT_KERNEL_TRACE {#af_jit_kernel_trace}
+-------------------------------------------------------------------------------
+
+When set, this environment variable has to be set to one of the following
+three values:
+
+- stdout : generated kernels will be printed to standard output
+- stderr : generated kernels will be printed to standard error stream
+- absolute path to a folder on the disk where generated kernels will be stored
+
+CUDA backend kernels are stored in files with cu file extension.
+
+OpenCL backend kernels are stored in files with cl file extension.

From e91c3466d3799a7d9d3532d9677125fca6d53391 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 3 Jan 2019 11:18:59 -0500
Subject: [PATCH 1602/2677] Correct constness on createDeviceArray and Array<T>
 constructor

The createDeviceArray pointer was const when it shouldn't be. This is
similiar to correcting the const in the api function but applies it
to the internal functions.
---
 src/api/c/sparse.cpp                  |  5 +-
 src/backend/common/SparseArray.cpp    | 40 +++++++-------
 src/backend/common/SparseArray.hpp    | 14 ++---
 src/backend/common/sparse_helpers.hpp | 10 ++--
 src/backend/cpu/Array.cpp             | 75 +++++++++++++--------------
 src/backend/cpu/Array.hpp             | 39 +++++++-------
 src/backend/cpu/harris.cpp            |  4 +-
 src/backend/cpu/kernel/sort.hpp       |  2 +-
 src/backend/cpu/sparse.cpp            |  4 ++
 src/backend/cuda/Array.cpp            | 23 ++++----
 src/backend/cuda/Array.hpp            | 11 ++--
 src/backend/opencl/Array.cpp          | 68 ++++++++++++------------
 src/backend/opencl/Array.hpp          | 20 ++++---
 13 files changed, 164 insertions(+), 151 deletions(-)

diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 620781a6f7..8af07099a4 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -140,8 +140,9 @@ af_array createSparseArrayFromPtr(const af::dim4 &dims, const dim_t nNZ,
             sparse = common::createHostDataSparseArray(dims, nNZ, values,
                                                        rowIdx, colIdx, stype);
         else if (source == afDevice)
-            sparse = common::createDeviceDataSparseArray(dims, nNZ, values,
-                                                         rowIdx, colIdx, stype);
+            sparse = common::createDeviceDataSparseArray(
+                dims, nNZ, const_cast<T *>(values), const_cast<int *>(rowIdx),
+                const_cast<int *>(colIdx), stype);
     }
 
     return getHandle(sparse);
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index a06d0a13b6..9821d5c84d 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -48,11 +48,10 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
 #endif
 }
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
-                                 const int *const _rowIdx,
-                                 const int *const _colIdx,
-                                 const af::storage _storage, af_dtype _type,
-                                 bool _is_device, bool _copy_device)
+SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
+                                 int *const _colIdx, const af::storage _storage,
+                                 af_dtype _type, bool _is_device,
+                                 bool _copy_device)
     : info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true)
     , stype(_storage)
     , rowIdx(_is_device
@@ -127,15 +126,18 @@ SparseArray<T> createHostDataSparseArray(const af::dim4 &_dims, const dim_t nNZ,
                                          const int *const _rowIdx,
                                          const int *const _colIdx,
                                          const af::storage _storage) {
-    return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage,
-                          false);
+    return SparseArray<T>(_dims, nNZ, const_cast<T *>(_values),
+                          const_cast<int *>(_rowIdx),
+                          const_cast<int *>(_colIdx), _storage, false);
 }
 
 template<typename T>
-SparseArray<T> createDeviceDataSparseArray(
-    const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
-    const int *const _rowIdx, const int *const _colIdx,
-    const af::storage _storage, const bool _copy) {
+SparseArray<T> createDeviceDataSparseArray(const af::dim4 &_dims,
+                                           const dim_t nNZ, T *const _values,
+                                           int *const _rowIdx,
+                                           int *const _colIdx,
+                                           const af::storage _storage,
+                                           const bool _copy) {
     return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, true,
                           _copy);
 }
@@ -179,8 +181,8 @@ SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage)
 }
 
 template<typename T>
-SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, const T *const _values,
-                            const int *const _rowIdx, const int *const _colIdx,
+SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, T *const _values,
+                            int *const _rowIdx, int *const _colIdx,
                             const af::storage _storage, bool _is_device,
                             bool _copy_device)
     : base(_dims, _nNZ, _rowIdx, _colIdx, _storage,
@@ -219,9 +221,9 @@ SparseArray<T>::~SparseArray() {}
         const int *const _rowIdx, const int *const _colIdx,                  \
         const af::storage _storage);                                         \
     template SparseArray<T> createDeviceDataSparseArray<T>(                  \
-        const af::dim4 &_dims, const dim_t _nNZ, const T *const _values,     \
-        const int *const _rowIdx, const int *const _colIdx,                  \
-        const af::storage _storage, const bool _copy);                       \
+        const af::dim4 &_dims, const dim_t _nNZ, T *const _values,           \
+        int *const _rowIdx, int *const _colIdx, const af::storage _storage,  \
+        const bool _copy);                                                   \
     template SparseArray<T> createArrayDataSparseArray<T>(                   \
         const af::dim4 &_dims, const Array<T> &_values,                      \
         const Array<int> &_rowIdx, const Array<int> &_colIdx,                \
@@ -233,9 +235,9 @@ SparseArray<T>::~SparseArray() {}
     template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,         \
                                          af::storage _storage);              \
     template SparseArray<T>::SparseArray(                                    \
-        af::dim4 _dims, dim_t _nNZ, const T *const _values,                  \
-        const int *const _rowIdx, const int *const _colIdx,                  \
-        const af::storage _storage, bool _is_device, bool _copy_device);     \
+        af::dim4 _dims, dim_t _nNZ, T *const _values, int *const _rowIdx,    \
+        int *const _colIdx, const af::storage _storage, bool _is_device,     \
+        bool _copy_device);                                                  \
     template SparseArray<T>::SparseArray(                                    \
         af::dim4 _dims, const Array<T> &_values, const Array<int> &_rowIdx,  \
         const Array<int> &_colIdx, const af::storage _storage, bool _copy);  \
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 4059b34792..14db5b3a96 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -43,8 +43,8 @@ class SparseArrayBase {
     SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage,
                     af_dtype _type);
 
-    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, const int *const _rowIdx,
-                    const int *const _colIdx, const af::storage _storage,
+    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
+                    int *const _colIdx, const af::storage _storage,
                     af_dtype _type, bool _is_device = false,
                     bool _copy_device = false);
 
@@ -133,8 +133,8 @@ class SparseArray {
 
     SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage stype);
 
-    explicit SparseArray(af::dim4 _dims, dim_t _nNZ, const T *const _values,
-                         const int *const _rowIdx, const int *const _colIdx,
+    explicit SparseArray(af::dim4 _dims, dim_t _nNZ, T *const _values,
+                         int *const _rowIdx, int *const _colIdx,
                          const af::storage _storage, bool _is_device = false,
                          bool _copy_device = false);
 
@@ -218,9 +218,9 @@ class SparseArray {
         const af::storage _storage);
 
     friend SparseArray<T> createDeviceDataSparseArray<T>(
-        const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
-        const int *const _rowIdx, const int *const _colIdx,
-        const af::storage _storage, const bool _copy);
+        const af::dim4 &_dims, const dim_t nNZ, T *const _values,
+        int *const _rowIdx, int *const _colIdx, const af::storage _storage,
+        const bool _copy);
 
     friend SparseArray<T> createArrayDataSparseArray<T>(
         const af::dim4 &_dims, const Array<T> &_values,
diff --git a/src/backend/common/sparse_helpers.hpp b/src/backend/common/sparse_helpers.hpp
index 60929efde4..3dda68b16e 100644
--- a/src/backend/common/sparse_helpers.hpp
+++ b/src/backend/common/sparse_helpers.hpp
@@ -33,10 +33,12 @@ SparseArray<T> createHostDataSparseArray(const af::dim4 &_dims, const dim_t nNZ,
                                          const af::storage _storage);
 
 template<typename T>
-SparseArray<T> createDeviceDataSparseArray(
-    const af::dim4 &_dims, const dim_t nNZ, const T *const _values,
-    const int *const _rowIdx, const int *const _colIdx,
-    const af::storage _storage, const bool _copy = false);
+SparseArray<T> createDeviceDataSparseArray(const af::dim4 &_dims,
+                                           const dim_t nNZ, T *const _values,
+                                           int *const _rowIdx,
+                                           int *const _colIdx,
+                                           const af::storage _storage,
+                                           const bool _copy = false);
 
 template<typename T>
 SparseArray<T> createArrayDataSparseArray(const af::dim4 &_dims,
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 877aaa199d..6b06e1384b 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -62,7 +62,7 @@ Array<T>::Array(dim4 dims)
     , owner(true) {}
 
 template<typename T>
-Array<T>::Array(dim4 dims, const T *const in_data, bool is_device,
+Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
                 bool copy_device)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            (af_dtype)dtype_traits<T>::af_type)
@@ -86,7 +86,7 @@ Array<T>::Array(dim4 dims, const T *const in_data, bool is_device,
 }
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, Node_ptr n)
+Array<T>::Array(const af::dim4 &dims, Node_ptr n)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            (af_dtype)dtype_traits<T>::af_type)
     , data()
@@ -107,11 +107,11 @@ Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
     , owner(false) {}
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
-                const T *const in_data, bool is_device)
+Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
+                T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
            (af_dtype)dtype_traits<T>::af_type)
-    , data(is_device ? (T *)in_data : memAlloc<T>(info.total()).release(),
+    , data(is_device ? in_data : memAlloc<T>(info.total()).release(),
            memFree<T>)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -196,24 +196,24 @@ Node_ptr Array<T>::getNode() const {
 }
 
 template<typename T>
-Array<T> createHostDataArray(const dim4 &size, const T *const data) {
-    return Array<T>(size, data, false);
+Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
+    return Array<T>(dims, const_cast<T *>(data), false);
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &size, const void *data) {
-    return Array<T>(size, (const T *const)data, true);
+Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+    return Array<T>(dims, static_cast<T *>(data), true);
 }
 
 template<typename T>
-Array<T> createValueArray(const dim4 &size, const T &value) {
-    jit::ScalarNode<T> *node = new jit::ScalarNode<T>(value);
-    return createNodeArray<T>(size, Node_ptr(node));
+Array<T> createValueArray(const dim4 &dims, const T &value) {
+    auto *node = new jit::ScalarNode<T>(value);
+    return createNodeArray<T>(dims, Node_ptr(node));
 }
 
 template<typename T>
-Array<T> createEmptyArray(const dim4 &size) {
-    return Array<T>(size);
+Array<T> createEmptyArray(const dim4 &dims) {
+    return Array<T>(dims);
 }
 
 template<typename T>
@@ -316,30 +316,29 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
-#define INSTANTIATE(T)                                                       \
-    template Array<T> createHostDataArray<T>(const dim4 &size,               \
-                                             const T *const data);           \
-    template Array<T> createDeviceDataArray<T>(const dim4 &size,             \
-                                               const void *data);            \
-    template Array<T> createValueArray<T>(const dim4 &size, const T &value); \
-    template Array<T> createEmptyArray<T>(const dim4 &size);                 \
-    template Array<T> createSubArray<T>(                                     \
-        const Array<T> &parent, const vector<af_seq> &index, bool copy);     \
-    template void destroyArray<T>(Array<T> * A);                             \
-    template Array<T> createNodeArray<T>(const dim4 &size, Node_ptr node);   \
-    template void Array<T>::eval();                                          \
-    template void Array<T>::eval() const;                                    \
-    template T *Array<T>::device();                                          \
-    template Array<T>::Array(af::dim4 dims, const T *const in_data,          \
-                             bool is_device, bool copy_device);              \
-    template Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset,  \
-                             const T *const in_data, bool is_device);        \
-    template Node_ptr Array<T>::getNode() const;                             \
-    template void writeHostDataArray<T>(Array<T> & arr, const T *const data, \
-                                        const size_t bytes);                 \
-    template void writeDeviceDataArray<T>(                                   \
-        Array<T> & arr, const void *const data, const size_t bytes);         \
-    template void evalMultiple<T>(vector<Array<T> *> arrays);                \
+#define INSTANTIATE(T)                                                        \
+    template Array<T> createHostDataArray<T>(const dim4 &dims,                \
+                                             const T *const data);            \
+    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
+    template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
+    template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
+    template Array<T> createSubArray<T>(                                      \
+        const Array<T> &parent, const vector<af_seq> &index, bool copy);      \
+    template void destroyArray<T>(Array<T> * A);                              \
+    template Array<T> createNodeArray<T>(const dim4 &dims, Node_ptr node);    \
+    template void Array<T>::eval();                                           \
+    template void Array<T>::eval() const;                                     \
+    template T *Array<T>::device();                                           \
+    template Array<T>::Array(const af::dim4 &dims, T *const in_data,          \
+                             bool is_device, bool copy_device);               \
+    template Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides,   \
+                             dim_t offset, T *const in_data, bool is_device); \
+    template Node_ptr Array<T>::getNode() const;                              \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
+                                        const size_t bytes);                  \
+    template void writeDeviceDataArray<T>(                                    \
+        Array<T> & arr, const void *const data, const size_t bytes);          \
+    template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index ba5b08c2e8..c6392ce8ed 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -46,22 +46,24 @@ void evalMultiple(std::vector<Array<T> *> arrays);
 
 // Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
-Array<T> createNodeArray(const af::dim4 &size, jit::Node_ptr node);
+Array<T> createNodeArray(const af::dim4 &dims, jit::Node_ptr node);
 
-// Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
-Array<T> createValueArray(const af::dim4 &size, const T &value);
+Array<T> createValueArray(const af::dim4 &dims, const T &value);
 
-// Creates a new Array object on the heap and returns a reference to it.
+// Creates an array and copies from the \p data pointer located in host memory
+//
+// \param[in] dims The dimension of the array
+// \param[in] data The data that will be copied to the array
 template<typename T>
-Array<T> createHostDataArray(const af::dim4 &size, const T *const data);
+Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
 
 template<typename T>
 Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
-                            const T *const in_data, bool is_device) {
+                            T *const in_data, bool is_device) {
     return Array<T>(dims, strides, offset, in_data, is_device);
 }
 
@@ -78,7 +80,7 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 ///
 /// \param[in] size The dimension of the output array
 template<typename T>
-Array<T> createEmptyArray(const af::dim4 &size);
+Array<T> createEmptyArray(const af::dim4 &dims);
 
 template<typename T>
 Array<T> createSubArray(const Array<T> &parent,
@@ -118,13 +120,13 @@ class Array {
     Array() = default;
     Array(dim4 dims);
 
-    explicit Array(dim4 dims, const T *const in_data, bool is_device,
+    explicit Array(const af::dim4 &dims, T *const in_data, bool is_device,
                    bool copy_device = false);
-    Array(const Array<T> &parnt, const dim4 &dims, const dim_t &offset,
+    Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset,
           const dim4 &stride);
-    explicit Array(af::dim4 dims, jit::Node_ptr n);
-    Array(af::dim4 dims, af::dim4 strides, dim_t offset, const T *const in_data,
-          bool is_device = false);
+    explicit Array(const af::dim4 &dims, jit::Node_ptr n);
+    Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
+          T *const in_data, bool is_device = false);
 
    public:
     void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
@@ -223,16 +225,15 @@ class Array {
 
     friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
-    friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
-    friend Array<T> createHostDataArray<T>(const af::dim4 &size,
+    friend Array<T> createValueArray<T>(const af::dim4 &dims, const T &value);
+    friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size,
-                                             const void *data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
     friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
-                                          dim_t offset, const T *const in_data,
+                                          dim_t offset, T *const in_data,
                                           bool is_device);
 
-    friend Array<T> createEmptyArray<T>(const af::dim4 &size);
+    friend Array<T> createEmptyArray<T>(const af::dim4 &dims);
     friend Array<T> createNodeArray<T>(const af::dim4 &dims,
                                        jit::Node_ptr node);
 
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 100045e8eb..298ce3dae0 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -42,8 +42,8 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
     } else {
         gaussian1D<convAccT>(h_filter.get(), (int)filter_len, sigma);
     }
-    Array<convAccT> filter = createDeviceDataArray<convAccT>(
-        dim4(filter_len), (const void *)h_filter.release());
+    Array<convAccT> filter =
+        createDeviceDataArray<convAccT>(dim4(filter_len), h_filter.release());
     unsigned border_len = filter_len / 2 + 1;
 
     Array<T> ix = createEmptyArray<T>(idims);
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index f0bbf07a71..5c0bf21a99 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -24,7 +24,7 @@ void sort0Iterative(Param<T> val, bool isAscending) {
     // initialize original index locations
     T *val_ptr = val.get();
 
-    function<bool(T, T)> op = std::greater<T>();
+    std::function<bool(T, T)> op = std::greater<T>();
     if (isAscending) { op = std::less<T>(); }
 
     T *comp_ptr = nullptr;
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index f34e99d318..aef16e3738 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -26,6 +26,10 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
+#include <functional>
+
+using std::function;
+
 namespace cpu {
 
 using common::createArrayDataSparseArray;
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 0348e8a3d7..4b8ccc8ee6 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -303,23 +303,27 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
 }
 
 template<typename T>
-Array<T> createHostDataArray(const dim4 &size, const T *const data) {
-    return Array<T>(size, data, false);
+Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
+    bool is_device   = false;
+    bool copy_device = false;
+    return Array<T>(dims, data, is_device, copy_device);
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &size, const void *data) {
-    return Array<T>(size, (const T *const)data, true);
+Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+    bool is_device   = true;
+    bool copy_device = false;
+    return Array<T>(dims, static_cast<T *>(data), is_device, copy_device);
 }
 
 template<typename T>
-Array<T> createValueArray(const dim4 &size, const T &value) {
-    return createScalarNode<T>(size, value);
+Array<T> createValueArray(const dim4 &dims, const T &value) {
+    return createScalarNode<T>(dims, value);
 }
 
 template<typename T>
-Array<T> createEmptyArray(const dim4 &size) {
-    return Array<T>(size);
+Array<T> createEmptyArray(const dim4 &dims) {
+    return Array<T>(dims);
 }
 
 template<typename T>
@@ -403,8 +407,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
 #define INSTANTIATE(T)                                                        \
     template Array<T> createHostDataArray<T>(const dim4 &size,                \
                                              const T *const data);            \
-    template Array<T> createDeviceDataArray<T>(const dim4 &size,              \
-                                               const void *data);             \
+    template Array<T> createDeviceDataArray<T>(const dim4 &size, void *data); \
     template Array<T> createValueArray<T>(const dim4 &size, const T &value);  \
     template Array<T> createEmptyArray<T>(const dim4 &size);                  \
     template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);        \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 5e1997f9b6..2fe7c7130b 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -42,11 +42,15 @@ Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
 template<typename T>
 Array<T> createValueArray(const af::dim4 &size, const T &value);
 
+// Creates an array and copies from the \p data pointer located in host memory
+//
+// \param[in] dims The dimension of the array
+// \param[in] data The data that will be copied to the array
 template<typename T>
-Array<T> createHostDataArray(const af::dim4 &size, const T *const data);
+Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &size, const void *data);
+Array<T> createDeviceDataArray(const af::dim4 &size, void *data);
 
 template<typename T>
 Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
@@ -222,8 +226,7 @@ class Array {
     friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
     friend Array<T> createHostDataArray<T>(const af::dim4 &size,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size,
-                                             const void *data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, void *data);
     friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
                                           dim_t offset, const T *const in_data,
                                           bool is_device);
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index b127baf768..39ca75f480 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -335,28 +335,29 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
 }
 
 template<typename T>
-Array<T> createHostDataArray(const dim4 &size, const T *const data) {
+Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
     verifyDoubleSupport<T>();
-    return Array<T>(size, data);
+    return Array<T>(dims, data);
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &size, const void *data, bool copy) {
+Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
     verifyDoubleSupport<T>();
 
-    return Array<T>(size, (cl_mem)(data), 0, copy);
+    bool copy_device = false;
+    return Array<T>(dims, static_cast<cl_mem>(data), 0, copy_device);
 }
 
 template<typename T>
-Array<T> createValueArray(const dim4 &size, const T &value) {
+Array<T> createValueArray(const dim4 &dims, const T &value) {
     verifyDoubleSupport<T>();
-    return createScalarNode<T>(size, value);
+    return createScalarNode<T>(dims, value);
 }
 
 template<typename T>
-Array<T> createEmptyArray(const dim4 &size) {
+Array<T> createEmptyArray(const dim4 &dims) {
     verifyDoubleSupport<T>();
-    return Array<T>(size);
+    return Array<T>(dims);
 }
 
 template<typename T>
@@ -404,32 +405,31 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
-#define INSTANTIATE(T)                                                       \
-    template Array<T> createHostDataArray<T>(const dim4 &size,               \
-                                             const T *const data);           \
-    template Array<T> createDeviceDataArray<T>(const dim4 &size,             \
-                                               const void *data, bool copy); \
-    template Array<T> createValueArray<T>(const dim4 &size, const T &value); \
-    template Array<T> createEmptyArray<T>(const dim4 &size);                 \
-    template Array<T> createParamArray<T>(Param & tmp, bool owner);          \
-    template Array<T> createSubArray<T>(                                     \
-        const Array<T> &parent, const vector<af_seq> &index, bool copy);     \
-    template void destroyArray<T>(Array<T> * A);                             \
-    template Array<T> createNodeArray<T>(const dim4 &size, Node_ptr node);   \
-    template Array<T>::Array(dim4 dims, dim4 strides, dim_t offset,          \
-                             const T *const in_data, bool is_device);        \
-    template Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset,       \
-                             bool copy);                                     \
-    template Array<T>::~Array();                                             \
-    template Node_ptr Array<T>::getNode() const;                             \
-    template void Array<T>::eval();                                          \
-    template void Array<T>::eval() const;                                    \
-    template Buffer *Array<T>::device();                                     \
-    template void writeHostDataArray<T>(Array<T> & arr, const T *const data, \
-                                        const size_t bytes);                 \
-    template void writeDeviceDataArray<T>(                                   \
-        Array<T> & arr, const void *const data, const size_t bytes);         \
-    template void evalMultiple<T>(vector<Array<T> *> arrays);                \
+#define INSTANTIATE(T)                                                        \
+    template Array<T> createHostDataArray<T>(const dim4 &dims,                \
+                                             const T *const data);            \
+    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
+    template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
+    template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
+    template Array<T> createParamArray<T>(Param & tmp, bool owner);           \
+    template Array<T> createSubArray<T>(                                      \
+        const Array<T> &parent, const vector<af_seq> &index, bool copy);      \
+    template void destroyArray<T>(Array<T> * A);                              \
+    template Array<T> createNodeArray<T>(const dim4 &dims, Node_ptr node);    \
+    template Array<T>::Array(dim4 dims, dim4 strides, dim_t offset,           \
+                             const T *const in_data, bool is_device);         \
+    template Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset,        \
+                             bool copy);                                      \
+    template Array<T>::~Array();                                              \
+    template Node_ptr Array<T>::getNode() const;                              \
+    template void Array<T>::eval();                                           \
+    template void Array<T>::eval() const;                                     \
+    template Buffer *Array<T>::device();                                      \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
+                                        const size_t bytes);                  \
+    template void writeDeviceDataArray<T>(                                    \
+        Array<T> & arr, const void *const data, const size_t bytes);          \
+    template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 04a58c8082..0669998b7b 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -34,19 +34,18 @@ void evalNodes(std::vector<Param> &outputs, std::vector<common::Node *> nodes);
 
 /// Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
-Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
+Array<T> createNodeArray(const af::dim4 &dims, common::Node_ptr node);
 
 /// Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
-Array<T> createValueArray(const af::dim4 &size, const T &value);
+Array<T> createValueArray(const af::dim4 &dims, const T &value);
 
 /// Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
-Array<T> createHostDataArray(const af::dim4 &size, const T *const data);
+Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &size, const void *data,
-                               bool copy = false);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
 
 template<typename T>
 Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
@@ -67,7 +66,7 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 ///
 /// \param[in] size The dimension of the output array
 template<typename T>
-Array<T> createEmptyArray(const af::dim4 &size);
+Array<T> createEmptyArray(const af::dim4 &dims);
 
 /// Create an Array object from Param object.
 ///
@@ -255,16 +254,15 @@ class Array {
 
     friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
-    friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
-    friend Array<T> createHostDataArray<T>(const af::dim4 &size,
+    friend Array<T> createValueArray<T>(const af::dim4 &dims, const T &value);
+    friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size,
-                                             const void *data, bool copy);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
     friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
                                           dim_t offset, const T *const in_data,
                                           bool is_device);
 
-    friend Array<T> createEmptyArray<T>(const af::dim4 &size);
+    friend Array<T> createEmptyArray<T>(const af::dim4 &dims);
     friend Array<T> createParamArray<T>(Param &tmp, bool owner);
     friend Array<T> createNodeArray<T>(const af::dim4 &dims,
                                        common::Node_ptr node);

From d1d3d2724c5ffacc0f4bc1a3b890ce89224bfd42 Mon Sep 17 00:00:00 2001
From: William Tambellini <william.tambellini@gmail.com>
Date: Fri, 15 Feb 2019 04:14:48 -0800
Subject: [PATCH 1603/2677] Assert the nvidia drivers are recent enough for
 cuda

Added tracing to CUDA backend platform class
---
 src/backend/cuda/platform.cpp | 161 ++++++++++++++++++++++++++--------
 src/backend/cuda/platform.hpp |   6 +-
 2 files changed, 130 insertions(+), 37 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 89131ea37b..80506c959a 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -11,12 +11,14 @@
 #include <windows.h>
 #endif
 
+#include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
 #include <driver.h>
 #include <err_cuda.hpp>
 #include <platform.hpp>
+#include <spdlog/spdlog.h>
 #include <version.hpp>
 #include <af/cuda.h>
 #include <af/version.h>
@@ -36,8 +38,10 @@
 #include <vector>
 
 using namespace std;
+using std::to_string;
 
 namespace cuda {
+
 ///////////////////////////////////////////////////////////////////////////
 // HELPERS
 ///////////////////////////////////////////////////////////////////////////
@@ -129,13 +133,6 @@ static const std::string get_system(void) {
 #endif
 }
 
-template<typename T>
-static inline string toString(T val) {
-    stringstream s;
-    s << val;
-    return s.str();
-}
-
 static inline int getMinSupportedCompute(int cudaMajorVer) {
     // Vector of minimum supported compute versions
     // for CUDA toolkit (i+1).* where i is the index
@@ -160,14 +157,14 @@ string getDeviceInfo(int device) {
 
     bool show_braces = getActiveDeviceId() == device;
 
-    string id = (show_braces ? string("[") : "-") + toString(device) +
+    string id = (show_braces ? string("[") : "-") + to_string(device) +
                 (show_braces ? string("]") : "-");
     string name(dev.name);
-    string memory = toString((mem_gpu_total / (1024 * 1024)) +
-                             !!(mem_gpu_total % (1024 * 1024))) +
+    string memory = to_string((mem_gpu_total / (1024 * 1024)) +
+                              !!(mem_gpu_total % (1024 * 1024))) +
                     string(" MB");
-    string compute = string("CUDA Compute ") + toString(dev.major) +
-                     string(".") + toString(dev.minor);
+    string compute = string("CUDA Compute ") + to_string(dev.major) +
+                     string(".") + to_string(dev.minor);
 
     string info = id + string(" ") + name + string(", ") + memory +
                   string(", ") + compute + string("\n");
@@ -202,7 +199,6 @@ bool isDoubleSupported(int device) {
 
 void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
     if (getDeviceCount() <= 0) {
-        printf("No CUDA-capable devices detected.\n");
         return;
     }
 
@@ -243,19 +239,21 @@ string getDriverVersion() {
 #endif
         int driver = 0;
         CUDA_CHECK(cudaDriverGetVersion(&driver));
-        return string("CUDA Driver Version: ") + toString(driver);
+        return string("CUDA Driver Version: ") + to_string(driver);
     } else {
         return string(driverVersion);
     }
 }
 
+string int_version_to_string(int version) {
+    return to_string(version / 1000) + "." +
+           to_string((int)((version % 1000) / 100.));
+}
+
 string getCUDARuntimeVersion() {
     int runtime = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
-    if (runtime / 100.f > 0)
-        return toString((runtime / 1000) + (runtime % 1000) / 100.);
-    else
-        return toString(runtime / 1000) + string(".0");
+    return int_version_to_string(runtime);
 }
 
 unsigned getMaxJitSize() {
@@ -340,17 +338,10 @@ bool DeviceManager::checkGraphicsInteropCapability() {
         cudaError_t err = cudaGLGetDevices(
             &pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds,
             getDeviceCount(), cudaGLDeviceListAll);
-        if (err ==
-            63) {  // OS Support Failure - Happens when devices are only Tesla
+        if (err == cudaErrorOperatingSystem) {
+            // OS Support Failure - Happens when devices are in TCC mode or
+            // do not have a display connected
             capable = false;
-            printf(
-                "Warning: No CUDA Device capable of CUDA-OpenGL. CUDA-OpenGL "
-                "Interop will use CPU fallback.\n");
-            printf("Corresponding CUDA Error (%d): %s.\n", err,
-                   cudaGetErrorString(err));
-            printf(
-                "This may happen if all CUDA Devices are in TCC Mode and/or "
-                "not connected to a display.\n");
         }
         cudaGetLastError();  // Reset Errors
     });
@@ -469,10 +460,99 @@ SparseHandle sparseHandle() {
     return cusparseHandles[id].get()->get();
 }
 
+/// Map giving the minimum device driver needed in order to run a given version
+/// of CUDA for both Linux/Mac and Windows from:
+/// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
+// clang-format off
+static const std::map<std::string, std::pair<float, float>>
+    CudaToKernelVersionMap = {
+        {"10.0", {410.48f, 411.31f}},
+        {"9.2", {396.37f, 398.26f}},
+        {"9.1", {390.46f, 391.29f}},
+        {"9.0", {384.81f, 385.54f}},
+        {"8.0", {375.26f, 376.51f}},
+        {"7.5", {352.31f, 353.66f}},
+        {"7.0", {346.46f, 347.62f}}};
+// clang-format on
+
+// Check if the device driver version is recent enough to run the cuda libs
+// linked with afcuda:
+void DeviceManager::checkCudaVsDriverVersion() {
+    const std::string driverVersionString = getDriverVersion();
+    if (driverVersionString.empty()) {
+        // Do not perform a check if no driver version was found
+        AF_TRACE("Failed to retrieve nvidia driver version.");
+        return;
+    }
+    AF_TRACE("GPU driver version: {}", driverVersionString);
+
+    // Nvidia driver versions are hopefully float based X.Y
+    const float driverVersion = std::stof(driverVersionString);
+    if (driverVersion == 0) {
+        AF_TRACE("Failed to parse driver version: {}", driverVersionString);
+        return;
+    }
+
+    const std::string cudaRuntimeVersionString = getCUDARuntimeVersion();
+    if (cudaRuntimeVersionString.empty()) {
+        AF_TRACE("Failed to get CUDA runtime version");
+        return;
+    }
+
+    if (CudaToKernelVersionMap.find(cudaRuntimeVersionString) ==
+        CudaToKernelVersionMap.end()) {
+        AF_TRACE(
+            "CUDA runtime version({}) not recognized. Please create an issue "
+            "or a pull request on the ArrayFire repository to update the "
+            "CudaToKernelVersionMap variable with this version of the CUDA "
+            "Toolkit.",
+            cudaRuntimeVersionString);
+        return;
+    }
+
+    float minimumDriverVersion = 0;
+#if defined(OS_WIN)
+    minimumDriverVersion =
+        CudaToKernelVersionMap.at(cudaRuntimeVersionString).second;
+#else
+    minimumDriverVersion =
+        CudaToKernelVersionMap.at(cudaRuntimeVersionString).first;
+#endif
+
+    AF_TRACE("CUDA runtime version: {} (Minimum GPU driver required: {})",
+             cudaRuntimeVersionString, minimumDriverVersion);
+    if (driverVersion < minimumDriverVersion) {
+        string msg =
+            "ArrayFire was built with CUDA %s which requires GPU driver "
+            "version %.2f or later. Please download the latest drivers from "
+            "https://www.nvidia.com/drivers. Alternatively, you could rebuild "
+            "ArrayFire with CUDA Toolkit version %s to use the current "
+            "drivers.";
+
+        char buf[1024];
+        int supported_cuda_version = 0;
+        cudaDriverGetVersion(&supported_cuda_version);
+
+        snprintf(buf, 1024, msg.c_str(), cudaRuntimeVersionString.c_str(),
+                 minimumDriverVersion,
+                 int_version_to_string(supported_cuda_version).c_str());
+
+        AF_ERROR(buf, AF_ERR_DRIVER);
+    }
+}
+
 DeviceManager::DeviceManager()
-    : cuDevices(0), nDevices(0), fgMngr(new graphics::ForgeManager()) {
+    : cuDevices(0)
+    , nDevices(0)
+    , fgMngr(new graphics::ForgeManager())
+    , logger(common::loggerFactory("platform")) {
+    checkCudaVsDriverVersion();
+
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
-    if (nDevices == 0) throw runtime_error("No CUDA-Capable devices found");
+    AF_TRACE("Found {} CUDA devices", nDevices);
+    if (nDevices == 0) {
+        AF_ERROR("No CUDA capable devices found", AF_ERR_DRIVER);
+    }
     cuDevices.reserve(nDevices);
 
     int cudaRtVer = 0;
@@ -481,14 +561,19 @@ DeviceManager::DeviceManager()
 
     for (int i = 0; i < nDevices; i++) {
         cudaDevice_t dev;
-        cudaGetDeviceProperties(&dev.prop, i);
+        CUDA_CHECK(cudaGetDeviceProperties(&dev.prop, i));
         if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
+            AF_TRACE("Unsuppored device: {}", dev.prop.name);
             continue;
         } else {
             dev.flops = static_cast<size_t>(dev.prop.multiProcessorCount) *
                         compute2cores(dev.prop.major, dev.prop.minor) *
                         dev.prop.clockRate;
             dev.nativeId = i;
+            AF_TRACE(
+                "Found device: {} ({:3.3} GB | ~{} GFLOPs | {} SMs)",
+                dev.prop.name, dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
+                dev.flops / 1024. / 1024. * 2, dev.prop.multiProcessorCount);
             cuDevices.push_back(dev);
         }
     }
@@ -501,6 +586,7 @@ DeviceManager::DeviceManager()
     for (int i = 0; i < (int)MAX_DEVICES; i++) streams[i] = (cudaStream_t)0;
 
     std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
+    AF_TRACE("AF_CUDA_DEFAULT_DEVICE: {}", deviceENV);
     if (deviceENV.empty()) {
         setActiveDevice(0, cuDevices[0].nativeId);
     } else {
@@ -508,15 +594,20 @@ DeviceManager::DeviceManager()
         int def_device = -1;
         s >> def_device;
         if (def_device < 0 || def_device >= nDevices) {
-            printf("WARNING: AF_CUDA_DEFAULT_DEVICE is out of range\n");
-            printf("Setting default device as 0\n");
+            getLogger()->warn(
+                "AF_CUDA_DEFAULT_DEVICE({}) out of range. Setting default "
+                "device to 0.",
+                def_device);
             setActiveDevice(0, cuDevices[0].nativeId);
         } else {
             setActiveDevice(def_device, cuDevices[def_device].nativeId);
         }
     }
+    AF_TRACE("Default device: {}", getActiveDeviceId());
 }
 
+spdlog::logger *DeviceManager::getLogger() { return logger.get(); }
+
 void DeviceManager::sortDevices(sort_mode mode) {
     switch (mode) {
         case memory:
@@ -579,8 +670,8 @@ int DeviceManager::setActiveDevice(int device, int nId) {
         }
         cudaGetLastError();  // Reset error stack
 #ifndef NDEBUG
-        printf(
-            "Warning: Device %d is unavailable. Incrementing to next device \n",
+        getLogger()->warn(
+            "Warning: Device {} is unavailable. Using next available device \n",
             device);
 #endif
         // Comes here is the device is in exclusive mode or
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 22d4a6f3c2..bb90581f56 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -62,8 +62,6 @@ size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
 
-spdlog::logger* getLogger();
-
 int setDevice(int device);
 
 void sync(int device);
@@ -109,6 +107,8 @@ class DeviceManager {
 
     static DeviceManager& getInstance();
 
+    spdlog::logger* getLogger();
+
     friend MemoryManager& memoryManager();
 
     friend MemoryManagerPinned& pinnedMemoryManager();
@@ -151,9 +151,11 @@ class DeviceManager {
 
     // Attributes
     std::vector<cudaDevice_t> cuDevices;
+    std::shared_ptr<spdlog::logger> logger;
 
     enum sort_mode { flops = 0, memory = 1, compute = 2, none = 3 };
 
+    void checkCudaVsDriverVersion();
     void sortDevices(sort_mode mode = flops);
 
     int setActiveDevice(int device, int native = -1);

From a2ab1ab65fd342a9a5cdf5578c76ed1c149ae87f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 15 Feb 2019 07:15:44 -0500
Subject: [PATCH 1604/2677] Add free eval functions for const arrays

Add AF_HAS_VARIADIC_TEMPLATE definition to af/defines.h

Fixes #2427
---
 include/af/array.h   | 53 ++++++++++++++++++++++++++++++++++++++++++++
 include/af/defines.h |  7 +++++-
 test/CMakeLists.txt  |  2 +-
 test/jit.cpp         | 16 +++++++++++++
 4 files changed, 76 insertions(+), 2 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index c7ff468c63..f25755e1b6 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1394,6 +1394,59 @@ namespace af
 #endif
     }
 
+#if AF_API_VERSION >= 37
+
+    /// Evaluate an expression (nonblocking).
+    /**
+       \ingroup method_mat
+       @{
+    */
+    inline const array &eval(const array &a) { a.eval(); return a; }
+
+#ifdef AF_HAS_VARIADIC_TEMPLATES
+    template <typename... ARRAYS>
+    inline void eval(ARRAYS... in) {
+        array *arrays[] = {const_cast<array *>(&in)...};
+        eval((int)sizeof...(in), arrays);
+    }
+
+#else
+
+    inline void eval(const array &a, const array &b)
+    {
+        const array *arrays[] = {&a, &b};
+        return eval(2, const_cast<array **>(arrays));
+    }
+
+    inline void eval(const array &a, const array &b, const array &c)
+    {
+        const array *arrays[] = {&a, &b, &c};
+        return eval(3, const_cast<array **>(arrays));
+    }
+
+    inline void eval(const array &a, const array &b, const array &c,
+                     const array &d)
+    {
+        const array *arrays[] = {&a, &b, &c, &d};
+        return eval(4, const_cast<array **>(arrays));
+    }
+
+    inline void eval(const array &a, const array &b, const array &c,
+                     const array &d, const array &e)
+    {
+        const array *arrays[] = {&a, &b, &c, &d, &e};
+        return eval(5, const_cast<array **>(arrays));
+    }
+
+    inline void eval(const array &a, const array &b, const array &c,
+                     const array &d, const array &e, const array &f)
+    {
+        const array *arrays[] = {&a, &b, &c, &d, &e, &f};
+        return eval(6, const_cast<array **>(arrays));
+    }
+#endif  // __cplusplus > 199711L
+#endif
+
 #if AF_API_VERSION >= 34
     ///
     /// Turn the manual eval flag on or off
diff --git a/include/af/defines.h b/include/af/defines.h
index c38a9390a7..b0b2f1bc5e 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -27,6 +27,9 @@
     #define __PRETTY_FUNCTION__ __FUNCSIG__
     #define SIZE_T_FRMT_SPECIFIER "%Iu"
     #define AF_DEPRECATED(msg) __declspec(deprecated( msg ))
+    #if _MSC_VER >= 1800
+        #define AF_HAS_VARIADIC_TEMPLATES
+    #endif
 #else
     #define AFAPI   __attribute__((visibility("default")))
     #include <stdbool.h>
@@ -36,7 +39,9 @@
 #else
     #define AF_DEPRECATED(msg) __attribute__((deprecated))
 #endif
-
+    #if __cpp_variadic_templates >= 200704
+        #define AF_HAS_VARIADIC_TEMPLATES
+    #endif
 #endif
 
 // Known 64-bit x86 and ARM architectures use long long
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index eb072133b6..98294d263f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -221,7 +221,7 @@ make_test(SRC inverse_dense.cpp)
 make_test(SRC iota.cpp)
 make_test(SRC ireduce.cpp)
 make_test(SRC iterative_deconv.cpp)
-make_test(SRC jit.cpp)
+make_test(SRC jit.cpp CXX11)
 make_test(SRC join.cpp)
 make_test(SRC lu_dense.cpp)
 make_test(SRC main.cpp)
diff --git a/test/jit.cpp b/test/jit.cpp
index 4cfde76ee4..ef7eafc63b 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -411,3 +411,19 @@ TEST(JIT, TransposeBuffers) {
         ASSERT_FLOAT_EQ(hc[i], hd[i]);
     }
 }
+
+TEST(JIT, ConstEval7) {
+    const array a = constant(1, 1);
+    const array b = constant(1, 1);
+    const array c = constant(1, 1);
+    const array d = constant(1, 1);
+    const array e = constant(1, 1);
+    const array f = constant(1, 1);
+    const array g = constant(1, 1);
+
+    // I can't think of a good test for this.
+    EXPECT_NO_THROW({
+        eval(a, b, c, d, e, f, g);
+        af::sync();
+      });
+}

From a96293ea760a57e68afc31e77928bae772c21a86 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 12 Feb 2019 21:17:08 +0530
Subject: [PATCH 1605/2677] Fix limitation note in hamming matcher docs

---
 include/af/vision.h | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/include/af/vision.h b/include/af/vision.h
index 8376912ad2..39189468fa 100644
--- a/include/af/vision.h
+++ b/include/af/vision.h
@@ -194,8 +194,8 @@ AFAPI void gloh(features& feat, array& desc, const array& in, const unsigned n_l
    \param[in]  train is the array containing the data used as training data
    \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
                indicated here must be of equal length for both query and train arrays)
-   \param[in]  n_dist is the number of smallest distances to return (currently, only 1
-               is supported)
+   \param[in]  n_dist is the number of smallest distances to return (currently, only
+               values <= 256 are supported)
 
    \note Note: This is a special case of the \ref nearestNeighbour function with AF_SHD
     as dist_type
@@ -223,7 +223,7 @@ AFAPI void hammingMatcher(array& idx, array& dist,
    \param[in]  train is the array containing the data used as training data
    \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
                indicated here must be of equal length for both query and train arrays)
-   \param[in]  n_dist is the number of smallest distances to return (currently only 
+   \param[in]  n_dist is the number of smallest distances to return (currently only
                values <= 256 are supported)
    \param[in]  dist_type is the distance computation type. Currently \ref AF_SAD (sum
                of absolute differences), \ref AF_SSD (sum of squared differences), and
@@ -509,8 +509,8 @@ extern "C" {
        \param[in]  train is the array containing the data used as training data
        \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
                    indicated here must be of equal length for both query and train arrays)
-       \param[in]  n_dist is the number of smallest distances to return (currently, only 1
-                   is supported)
+       \param[in]  n_dist is the number of smallest distances to return (currently, only
+                   values <= 256 are supported)
 
        \ingroup cv_func_hamming_matcher
     */

From 9aeec8882b84719b450b2c49ba7fead6a920d427 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 18 Feb 2019 10:31:10 +0530
Subject: [PATCH 1606/2677] Correctly turn off mtx tests in cmake

---
 test/CMakeLists.txt                           |  6 +-
 .../download_sparse_datasets.cmake            | 77 ++++++++++---------
 2 files changed, 43 insertions(+), 40 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 98294d263f..05c210047c 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -278,10 +278,10 @@ make_test(SRC solve_dense.cpp       CXX11 SERIAL)
 make_test(SRC sort.cpp)
 make_test(SRC sort_by_key.cpp)
 make_test(SRC sort_index.cpp)
-make_test(SRC sparse.cpp            SERIAL
-          LIBRARIES mmio)
+make_test(SRC sparse.cpp SERIAL
+    $<$<BOOL:${AF_TEST_WITH_MTX_FILES}>:LIBRARIES mmio>)
 make_test(SRC sparse_arith.cpp
-          LIBRARIES mmio)
+    $<$<BOOL:${AF_TEST_WITH_MTX_FILES}>:LIBRARIES mmio>)
 make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
 make_test(SRC susan.cpp)
diff --git a/test/CMakeModules/download_sparse_datasets.cmake b/test/CMakeModules/download_sparse_datasets.cmake
index fbf099a42e..2c58a7b1c7 100644
--- a/test/CMakeModules/download_sparse_datasets.cmake
+++ b/test/CMakeModules/download_sparse_datasets.cmake
@@ -8,45 +8,48 @@
 set(URL "https://sparse.tamu.edu")
 
 function(download_mtx name group)
-  set(file_name "${group}/${name}.tar.gz")
-  if (NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
-    file(DOWNLOAD
-        "${URL}/MM/${file_name}"
-        ${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}
-        INACTIVITY_TIMEOUT 600
-        SHOW_PROGRESS
-        STATUS out_status
-        TLS_VERIFY ON
-        )
-    list(GET out_status 0 error_code)
-    list(GET out_status 1 error_string)
-    if (${error_code} EQUAL 0)
-      message("Downloaded ${name} file from sparse.tamu.edu")
-      file(MAKE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}")
-      execute_process(
-        COMMAND ${CMAKE_COMMAND} -E tar xzf "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}"
-        WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}"
-        )
-      message("Extracted mtx files to test data directory")
-    else ()
-      if (${error_code} EQUAL 503)
-          message(FATAL_ERROR "${URL} service unavailable")
-      elseif (${error_code} EQUAL 504)
-          message(FATAL_ERROR "Request to ${URL} timedout")
-      elseif (${error_code} EQUAL 521)
-        # CLOUDFLARE error code
-        message(FATAL_ERROR "Request to ${URL} has been refused")
-      elseif (${error_code} EQUAL 523)
-        # CLOUDFLARE error code
-        message(FATAL_ERROR "${URL} is unreachable")
+  if(AF_TEST_WITH_MTX_FILES)
+    set(file_name "${group}/${name}.tar.gz")
+    if (NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
+      file(DOWNLOAD
+          "${URL}/MM/${file_name}"
+          ${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}
+          INACTIVITY_TIMEOUT 600
+          SHOW_PROGRESS
+          STATUS out_status
+          TLS_VERIFY ON
+          )
+      list(GET out_status 0 error_code)
+      list(GET out_status 1 error_string)
+      if (${error_code} EQUAL 0)
+        message("Downloaded ${name} file from sparse.tamu.edu")
+        file(MAKE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}")
+        execute_process(
+          COMMAND ${CMAKE_COMMAND} -E tar xzf "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}"
+          WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}"
+          )
+        message("Extracted mtx files to test data directory")
       else ()
-        message("Failed to download ${name} file from sparse.tamu.edu")
-        message("Failure message: ${error_string}")
-      endif ()
-      file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
+        if (${error_code} EQUAL 503)
+            message(WARNING "${URL} service unavailable")
+        elseif (${error_code} EQUAL 504)
+            message(WARNING "Request to ${URL} timedout")
+        elseif (${error_code} EQUAL 521)
+          # CLOUDFLARE error code
+          message(WARNING "Request to ${URL} has been refused")
+        elseif (${error_code} EQUAL 523)
+          # CLOUDFLARE error code
+          message(WARNING "${URL} is unreachable")
+        else ()
+          message(WARNING  "Failed to download ${name} file from sparse.tamu.edu")
+          message(WARNING  "Failure message: ${error_string}")
+        endif ()
+        file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
 
-      #Force test with mtx files to be turned since one of the downloads failed
-      set(AF_TEST_WITH_MTX_FILES OFF)
+        set(AF_TEST_WITH_MTX_FILES OFF CACHE BOOL
+            "Download and run tests on large matrices form sparse.tamu.edu"
+            FORCE)
+      endif ()
     endif ()
   endif ()
 endfunction()

From e9f855d01c1f20e533e5311c3956519c5da8455a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 16 Feb 2019 23:18:47 -0500
Subject: [PATCH 1607/2677] Update core counts for Pascal, Volta, and Turing

---
 src/backend/cuda/platform.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 80506c959a..7006a2d4fb 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -53,8 +53,8 @@ static inline int compute2cores(int major, int minor) {
     } gpus[] = {
         {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
         {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
-        {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 128}, {0x61, 64},
-        {0x62, 128}, {-1, -1},
+        {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
+        {0x62, 128}, {0x70, 64},  {0x75, 64},  {-1, -1},
     };
 
     for (int i = 0; gpus[i].compute != -1; ++i) {

From 2099193cbcaf01f489270cf98fa4dc775f9e0f42 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Mon, 25 Feb 2019 16:50:22 -0500
Subject: [PATCH 1608/2677] Fix buffer overflow and expected output of kNN SSD
 small test

---
 test/nearest_neighbour.cpp | 30 ++++++++++++------------------
 1 file changed, 12 insertions(+), 18 deletions(-)

diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index e19f35ec22..4ef7f05f69 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -277,34 +277,28 @@ TEST(KNearestNeighbourSSD, small) {
     const int nquery = 3;
     const int nfeat  = 2;
 
-    float query[nquery * nfeat] = {
-        5, 5, 0, 0, 10, 10,
-    };
-
+    float query[nquery * nfeat] = {5, 5, 0, 0, 10, 10};
     float train[ntrain * nfeat] = {0, 0, 3.5, 4, 5, 5, 6, 5, 8, 6.5};
 
     array t(nfeat, ntrain, train);
     array q(nfeat, nquery, query);
     array indices;
-    array distances;
+    array actualDistances;
     const int k = 2;
-    nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD);
+    nearestNeighbour(indices, actualDistances, q, t, 0, k, AF_SSD);
 
-    float expectedDistances[nquery * ntrain] = {
-        (5 - 5) * (5 - 5) + (5 - 5) * (5 - 5),
-        (5 - 6) * (5 - 6) + (5 - 5) * (5 - 5),
+    vector<float> expectedDistances{
+        (5.f - 5.f) * (5.f - 5.f) + (5.f - 5.f) * (5.f - 5.f),
+        (5.f - 6.f) * (5.f - 6.f) + (5.f - 5.f) * (5.f - 5.f),
 
-        (0 - 0) * (0 - 0) + (0 - 0) * (0 - 0),
-        (0 - 3.5) * (0 - 4) + (0 - 3.5) * (0 - 4),
+        (0.f - 0.f) * (0.f - 0.f) + (0.f - 0.f) * (0.f - 0.f),
+        (0.f - 3.5f) * (0.f - 3.5f) + (0.f - 4.f) * (0.f - 4.f),
 
-        (10 - 8) * (10 - 8) + (10 - 6.5) * (10 - 6.5),
-        (10 - 6) * (10 - 5) + (10 - 6) * (10 - 5)};
+        (10.f - 8.f) * (10.f - 8.f) + (10.f - 6.5f) * (10.f - 6.5f),
+        (10.f - 6.f) * (10.f - 6.f) + (10.f - 5.f) * (10.f - 5.f)};
 
-    vector<float> actualDistances(nquery);
-    distances.host(&actualDistances[0]);
-    for (int i = 0; i < nquery; i++) {
-        EXPECT_NEAR(expectedDistances[i], actualDistances[i], 1E-8);
-    }
+    ASSERT_VEC_ARRAY_NEAR(expectedDistances, dim4(nfeat, nquery),
+                          actualDistances, 1E-8);
 }
 
 struct nearest_neighbors_params {

From 3ba56d15e897833f1136e72d18f9d7b8043228a8 Mon Sep 17 00:00:00 2001
From: Zhihao Yuan <zy@simplerose.com>
Date: Sun, 24 Feb 2019 05:14:19 -0600
Subject: [PATCH 1609/2677] Fix MKL linking order to enable threaded BLAS

The DLL linking order given in FindMKL does not follow that in
Intel's Link Line Advisory.  More specifically, thread layering
is linked before the interface library.  This causes MKL to not
to schedule BLAS level 3 operations (and gemv) on the threads it
created.
---
 CMakeModules/FindMKL.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 5baec89995..1a1e5b8204 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -260,14 +260,14 @@ if(MKL_FOUND)
     set_target_properties(MKL::MKL
       PROPERTIES
         IMPORTED_LOCATION "${MKL_Core_LINK_LIBRARY}"
-        INTERFACE_LINK_LIBRARIES "MKL::ThreadLayer;MKL::Interface;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_LINK_LIBRARIES "MKL::Interface;MKL::ThreadLayer;${CMAKE_DL_LIBS};${M_LIB}"
         INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
         IMPORTED_NO_SONAME TRUE)
   else()
     set_target_properties(MKL::MKL
       PROPERTIES
         IMPORTED_LOCATION "${MKL_Core_LINK_LIBRARY}"
-        INTERFACE_LINK_LIBRARIES "MKL::ThreadLayer;MKL::Interface;MKL::ThreadingLibrary;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_LINK_LIBRARIES "MKL::Interface;MKL::ThreadLayer;MKL::ThreadingLibrary;${CMAKE_DL_LIBS};${M_LIB}"
         INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
         IMPORTED_NO_SONAME TRUE)
   endif()

From 75db8c6789eef9dcb8d259b37a8cf58153e9a040 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 23 Feb 2019 20:51:41 +0530
Subject: [PATCH 1610/2677] Temporary fix for nvrtc install command for CUDA
 10.0

---
 src/backend/cuda/CMakeLists.txt | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index c1d3022170..b7257af3e3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -530,7 +530,14 @@ if(AF_INSTALL_STANDALONE)
   afcu_collect_libs(cublas)
   afcu_collect_libs(cusolver)
   afcu_collect_libs(cusparse)
-  afcu_collect_libs(nvrtc)
+
+  if(WIN32 AND ${CUDA_VERSION_MAJOR} EQUAL 10 AND ${CUDA_VERSION_MINOR} EQUAL 0)
+    install(FILES       "${dlib_path_prefix}/${PX}nvrtc64_100_0${SX}"
+            DESTINATION ${AF_INSTALL_BIN_DIR}
+            COMPONENT   cuda_dependencies)
+  else()
+    afcu_collect_libs(nvrtc)
+  endif()
 
   if(APPLE)
     afcu_collect_libs(cudart)

From fd5f5cb7caafb2b1f1d36716a4fd5dc089e220cd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 26 Feb 2019 09:36:55 +0530
Subject: [PATCH 1611/2677] Validate forge module before using in ForgeManager
 destructor

---
 src/backend/common/graphics_common.cpp | 52 +++++++++++++-------------
 1 file changed, 27 insertions(+), 25 deletions(-)

diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index c27c5b2f88..3bc59ba836 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -233,34 +233,36 @@ ForgeModule& forgePlugin() { return detail::forgeManager().plugin(); }
 ForgeManager::ForgeManager() : mPlugin(new ForgeModule()) {}
 
 ForgeManager::~ForgeManager() {
-    /* clear all OpenGL resource objects (images, plots, histograms etc) first
-     * and then delete the windows */
-    for (ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
-        mPlugin->fg_release_image(iter->second);
-
-    for (PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
-        mPlugin->fg_release_plot(iter->second);
-
-    for (HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
-        mPlugin->fg_release_histogram(iter->second);
-
-    for (SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
-        mPlugin->fg_release_surface(iter->second);
-
-    for (VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
-        mPlugin->fg_release_vector_field(iter->second);
-
-    for (ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end();
-         iter++) {
-        for (int i = 0; i < (int)(iter->second).size(); i++) {
-            fg_chart chrt = (iter->second)[i];
-            if (chrt) {
-                mChartAxesOverrideMap.erase((chrt));
-                mPlugin->fg_release_chart(chrt);
+    if (mPlugin->isLoaded()) {
+        /* clear all OpenGL resource objects (images, plots, histograms etc) first
+         * and then delete the windows */
+        for (ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
+            mPlugin->fg_release_image(iter->second);
+
+        for (PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
+            mPlugin->fg_release_plot(iter->second);
+
+        for (HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
+            mPlugin->fg_release_histogram(iter->second);
+
+        for (SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
+            mPlugin->fg_release_surface(iter->second);
+
+        for (VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
+            mPlugin->fg_release_vector_field(iter->second);
+
+        for (ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end();
+            iter++) {
+            for (int i = 0; i < (int)(iter->second).size(); i++) {
+                fg_chart chrt = (iter->second)[i];
+                if (chrt) {
+                    mChartAxesOverrideMap.erase((chrt));
+                    mPlugin->fg_release_chart(chrt);
+                }
             }
         }
+        mPlugin->fg_release_window(wnd->handle);
     }
-    mPlugin->fg_release_window(wnd->handle);
 }
 
 ForgeModule& ForgeManager::plugin() { return *mPlugin; }

From d646034a76bbed86d4aae5397ecf7374f0abd41d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 26 Feb 2019 09:37:17 +0530
Subject: [PATCH 1612/2677] Avoid using make_shared for VS(_MSC_VER > 1914) in
 CUDA backend

std::make_shared creates a temporary object that utilizes alignment greater
than alignof(std::max_align_t) for the type cdouble. When extended
alignment is used without enabling it explicitly, type_traits header
from VC++ throws a static assertion preventing successful build. Note
that this happens on VS updates beyond 15.7 i.e. _MSC_VER > 1914.
---
 src/backend/cuda/scalar.hpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/backend/cuda/scalar.hpp b/src/backend/cuda/scalar.hpp
index 9b7f6b2b8b..eb2a0fbf3b 100644
--- a/src/backend/cuda/scalar.hpp
+++ b/src/backend/cuda/scalar.hpp
@@ -17,8 +17,19 @@ namespace cuda {
 
 template<typename T>
 Array<T> createScalarNode(const dim4 &size, const T val) {
+#if _MSC_VER > 1914
+    // FIXME(pradeep) - Needed only in CUDA backend, didn't notice any
+    // issues in other backends.
+    // Either this gaurd or we need to enable extended alignment
+    // by defining _ENABLE_EXTENDED_ALIGNED_STORAGE before <type_traits>
+    // header is included
+    using ScalarNode = common::ScalarNode<T>;
+    using ScalarNodePtr = std::shared_ptr<ScalarNode>;
+    return createNodeArray<T>(size, ScalarNodePtr(new ScalarNode(val)));
+#else
     return createNodeArray<T>(size,
                               std::make_shared<common::ScalarNode<T>>(val));
+#endif
 }
 
 }  // namespace cuda

From da1c65235afeceed99b34b49865203982fb11341 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 23 Feb 2019 15:18:00 +0530
Subject: [PATCH 1613/2677] Install forge SONAME file on non-windows platform
 only

---
 CMakeModules/AFconfigure_forge_submodule.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index fe9e5b796e..0e911401cb 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -31,7 +31,8 @@ if(AF_BUILD_FORGE)
 
   install(FILES
       $<TARGET_FILE:forge>
-      $<TARGET_SONAME_FILE:forge>
+      $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
+      $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
       DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
 else(AF_BUILD_FORGE)

From 10013a23e45bed70af9a4325cb30f352803d1205 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Tue, 26 Feb 2019 02:09:06 -0500
Subject: [PATCH 1614/2677] Generate same random numbers across all backends
 (#2435)

* Changed behavior of CPU philoxUniform to emulate CUDA backend
* Add more tests to rng_match.
---
 src/backend/cpu/kernel/random_engine.hpp | 113 +++++++++++++------
 test/CMakeLists.txt                      |   1 +
 test/rng_match.cpp                       | 133 +++++++++++++++++++++++
 3 files changed, 214 insertions(+), 33 deletions(-)
 create mode 100644 test/rng_match.cpp

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 256f0ed548..acdcfa673b 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -10,11 +10,18 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/dispatch.hpp>
 #include <err_cpu.hpp>
 #include <kernel/random_engine_mersenne.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
 
+#include <algorithm>
+#include <cstring>
+
+using std::array;
+using std::memcpy;
+
 namespace cpu {
 namespace kernel {
 // Utils
@@ -30,88 +37,128 @@ static const double PI_VAL =
 #define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
-template<typename T>
+template <typename T>
 T transform(uint *val, int index) {
     T *oval = (T *)val;
     return oval[index];
 }
 
-template<>
+template <>
 char transform<char>(uint *val, int index) {
     char v = val[index >> 2] >> (8 << (index & 3));
     v      = (v & 0x1) ? 1 : 0;
     return v;
 }
 
-template<>
+template <>
 uchar transform<uchar>(uint *val, int index) {
-    uchar v = val[index >> 2] >> (8 << (index & 3));
+    uchar v = val[index >> 2] >> (index << 3);
     return v;
 }
 
-template<>
+template <>
 ushort transform<ushort>(uint *val, int index) {
     ushort v = val[index >> 1] >> (16 << (index & 1));
     return v;
 }
 
-template<>
+template <>
 short transform<short>(uint *val, int index) {
     return transform<ushort>(val, index);
 }
 
-template<>
+template <>
 uint transform<uint>(uint *val, int index) {
     return val[index];
 }
 
-template<>
+template <>
 int transform<int>(uint *val, int index) {
     return transform<uint>(val, index);
 }
 
-template<>
+template <>
 uintl transform<uintl>(uint *val, int index) {
     uintl v = (((uintl)val[index << 1]) << 32) | ((uintl)val[(index << 1) + 1]);
     return v;
 }
 
-template<>
+template <>
 intl transform<intl>(uint *val, int index) {
     return transform<uintl>(val, index);
 }
 
 // Generates rationals in [0, 1)
-template<>
+template <>
 float transform<float>(uint *val, int index) {
     return 1.f - (val[index] * FLT_FACTOR + HALF_FLT_FACTOR);
 }
 
 // Generates rationals in [0, 1)
-template<>
+template <>
 double transform<double>(uint *val, int index) {
     uintl v = transform<uintl>(val, index);
     return 1.0 - (v * DBL_FACTOR + HALF_DBL_FACTOR);
 }
 
-template<typename T>
+#define MAX_RESET_CTR_VAL 64
+#define WRITE_STRIDE 256
+
+// This implementation aims to emulate the corresponding method in the CUDA
+// backend, in order to produce the exact same numbers as CUDA.
+// A stride of WRITE_STRIDE (256) is applied between each write
+// (emulating the CUDA thread writing to 4 locations with a stride of
+// blockDim.x, which is 256).
+// ELEMS_PER_ITER correspond to elementsPerBlock in the CUDA backend, so each
+// "iter" (iteration) here correspond to a CUDA thread block doing its work.
+// This change was prompted by issue #2429
+template <typename T>
 void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
-    uint hi     = seed >> 32;
-    uint lo     = seed;
-    uint hic    = counter >> 32;
-    uint loc    = counter;
-    uint key[2] = {lo, hi};
-    uint ctr[4] = {loc, hic, 0, 0};
-
-    int reset = (4 * sizeof(uint)) / sizeof(T);
-    for (int i = 0; i < (int)elements; i += reset) {
-        philox(key, ctr);
-        int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
-        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(ctr, j); }
+    uint hi  = seed >> 32;
+    uint lo  = seed;
+    uint hic = counter >> 32;
+    uint loc = counter;
+
+    constexpr int RESET_CTR = MAX_RESET_CTR_VAL / sizeof(T);
+    constexpr int ELEMS_PER_ITER =
+        WRITE_STRIDE * 4 * sizeof(uint) / sizeof(T);
+
+    int num_iters = divup(elements, ELEMS_PER_ITER);
+    int len       = num_iters * ELEMS_PER_ITER;
+
+    constexpr int NUM_WRITES = 16 / sizeof(T);
+    for (int iter = 0; iter < len; iter += ELEMS_PER_ITER) {
+        for (int i = 0; i < WRITE_STRIDE; i += RESET_CTR) {
+            for (int j = 0; j < RESET_CTR; ++j) {
+                // first_write_idx is the first of the 4 locations that will
+                // be written to
+                ptrdiff_t first_write_idx = iter + i + j;
+                if (first_write_idx >= elements) { break; }
+
+                // Recalculate key and ctr to emulate how the CUDA backend
+                // calculates these per thread
+                uint key[2] = {lo, hi};
+                uint ctr[4] = {loc + (uint)first_write_idx,
+                               hic + (ctr[0] < loc), (ctr[1] < hic), 0};
+                philox(key, ctr);
+
+                // Use the same ctr array for each of the 4 locations,
+                // but each of the location gets a different ctr value
+                for (int buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
+                    int out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
+                    if (out_idx < elements) {
+                        out[out_idx] = transform<T>(ctr, buf_idx);
+                    }
+                }
+            }
+        }
     }
 }
 
-template<typename T>
+#undef MAX_RESET_CTR_VAL
+#undef WRITE_STRIDE
+
+template <typename T>
 void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi     = seed >> 32;
     uint lo     = seed;
@@ -131,7 +178,7 @@ void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
     }
 }
 
-template<typename T>
+template <typename T>
 void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
@@ -154,7 +201,7 @@ void boxMullerTransform(uint val[4], float *temp) {
                        transform<float>(val, 3));
 }
 
-template<typename T>
+template <typename T>
 void philoxNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi     = seed >> 32;
     uint lo     = seed;
@@ -173,7 +220,7 @@ void philoxNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     }
 }
 
-template<typename T>
+template <typename T>
 void threefryNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi     = seed >> 32;
     uint lo     = seed;
@@ -198,7 +245,7 @@ void threefryNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     }
 }
 
-template<typename T>
+template <typename T>
 void uniformDistributionMT(T *out, size_t elements, uint *const state,
                            const uint *const pos, const uint *const sh1,
                            const uint *const sh2, uint mask,
@@ -223,7 +270,7 @@ void uniformDistributionMT(T *out, size_t elements, uint *const state,
     state_write(state, l_state);
 }
 
-template<typename T>
+template <typename T>
 void normalDistributionMT(T *out, size_t elements, uint *const state,
                           const uint *const pos, const uint *const sh1,
                           const uint *const sh2, uint mask,
@@ -250,7 +297,7 @@ void normalDistributionMT(T *out, size_t elements, uint *const state,
     state_write(state, l_state);
 }
 
-template<typename T>
+template <typename T>
 void uniformDistributionCBRNG(T *out, size_t elements,
                               af_random_engine_type type, const uintl seed,
                               uintl counter) {
@@ -266,7 +313,7 @@ void uniformDistributionCBRNG(T *out, size_t elements,
     }
 }
 
-template<typename T>
+template <typename T>
 void normalDistributionCBRNG(T *out, size_t elements,
                              af_random_engine_type type, const uintl seed,
                              uintl counter) {
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 05c210047c..af84eb8d67 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -259,6 +259,7 @@ make_test(SRC regions.cpp)
 make_test(SRC reorder.cpp)
 make_test(SRC replace.cpp)
 make_test(SRC resize.cpp)
+make_test(SRC rng_match.cpp CXX11 BACKENDS "unified")
 make_test(SRC rotate.cpp)
 make_test(SRC rotate_linear.cpp)
 make_test(SRC sat.cpp)
diff --git a/test/rng_match.cpp b/test/rng_match.cpp
new file mode 100644
index 0000000000..d61c712b51
--- /dev/null
+++ b/test/rng_match.cpp
@@ -0,0 +1,133 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
+#include <sstream>
+#include <vector>
+
+using af::array;
+using af::dim4;
+using af::getAvailableBackends;
+using af::randomEngine;
+using af::randu;
+using af::setBackend;
+using af::setSeed;
+using std::get;
+using std::make_pair;
+using std::stringstream;
+using std::vector;
+
+enum param { engine, backend, size, seed, type };
+
+using rng_params = std::tuple < af::randomEngineType,
+                                std::pair < af::Backend, af::Backend>,
+                                af::dim4,
+                                int,
+                                af_dtype>;
+
+class RNGMatch : public ::testing::TestWithParam<rng_params> {
+   protected:
+    void SetUp() {
+        backends_available =
+            getAvailableBackends() & get<backend>(GetParam()).first;
+        backends_available =
+            backends_available &&
+            (getAvailableBackends() & get<backend>(GetParam()).second);
+
+        if (backends_available) {
+            setBackend(get<backend>(GetParam()).first);
+            randomEngine(get<engine>(GetParam()));
+            setSeed(get<seed>(GetParam()));
+            array tmp  = randu(get<size>(GetParam()), get<type>(GetParam()));
+            void* data = malloc(tmp.bytes());
+            tmp.host(data);
+
+            setBackend(get<backend>(GetParam()).second);
+            values[0] = array(get<size>(GetParam()), get<type>(GetParam()));
+            values[0].write(data, values[0].bytes());
+            free(data);
+            randomEngine(get<engine>(GetParam()));
+            setSeed(get<seed>(GetParam()));
+            values[1] = randu(get<size>(GetParam()), get<type>(GetParam()));
+        }
+    }
+
+    array values[2];
+    bool backends_available;
+};
+
+std::string engine_name(af::randomEngineType engine) {
+    switch (engine) {
+        case AF_RANDOM_ENGINE_PHILOX  : return "PHILOX";
+        case AF_RANDOM_ENGINE_THREEFRY: return "THREEFRY";
+        case AF_RANDOM_ENGINE_MERSENNE: return "MERSENNE";
+    }
+}
+
+std::string backend_name(af::Backend backend) {
+    switch (backend) {
+        case AF_BACKEND_DEFAULT: return "DEFAULT";
+        case AF_BACKEND_CPU    : return "CPU";
+        case AF_BACKEND_CUDA   : return "CUDA";
+        case AF_BACKEND_OPENCL : return "OPENCL";
+    }
+}
+
+std::string rngmatch_info(
+    const ::testing::TestParamInfo<RNGMatch::ParamType> info) {
+    stringstream ss;
+    ss << "size_" << get<size>(info.param)[0] << "_"
+       << backend_name(get<backend>(info.param).first) << "_"
+       << backend_name(get<backend>(info.param).second) << "_"
+       << get<size>(info.param)[1] << "_" << get<size>(info.param)[2] << "_"
+       << get<size>(info.param)[3] << "_seed_" << get<seed>(info.param)
+       << "_type_" << get<type>(info.param);
+    return ss.str();
+}
+
+INSTANTIATE_TEST_CASE_P(
+    PhiloxCPU_CUDA, RNGMatch,
+    ::testing::Combine(
+        ::testing::Values(AF_RANDOM_ENGINE_PHILOX),
+        ::testing::Values(make_pair(AF_BACKEND_CPU, AF_BACKEND_CUDA),
+                          make_pair(AF_BACKEND_CPU, AF_BACKEND_OPENCL)),
+        ::testing::Values(dim4(10),
+                          dim4(100),
+                          dim4(1000),
+                          dim4(10000),
+                          dim4(1E5),
+                          dim4(10, 10),
+                          dim4(10, 100),
+                          dim4(100, 100),
+                          dim4(1000, 100),
+                          dim4(10, 10, 10),
+                          dim4(10, 100, 10),
+                          dim4(100, 100, 10),
+                          dim4(1000, 100, 10),
+                          dim4(10, 10, 10, 10),
+                          dim4(10, 100, 10, 10),
+                          dim4(100, 100, 10, 10),
+                          dim4(1000, 100, 10, 10)),
+        ::testing::Values(12),
+        ::testing::Values(f32, f64, c32, c64, u8)),
+    rngmatch_info);
+
+TEST_P(RNGMatch, BackendEquals) {
+    if (backends_available) {
+        array actual   = values[0];
+        array expected = values[1];
+        ASSERT_ARRAYS_EQ(actual, expected);
+    } else {
+        printf("SKIPPED\n");
+    }
+}

From b89fab7852fa909b99336f1f09ac12ff5d6a1f68 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 3 Mar 2019 23:41:31 +0500
Subject: [PATCH 1615/2677] Improve CUDA version checking code (#2448)

* Improve CUDA version checking code

* Use cudaRuntimeGetVersion and cudaDriverGetVersion to check the
  compatibility of the toolkit versions vs driver version
* Prints warning messages if the driver version is not included in the
  CudaToDriverVersion in Debug builds.
* Fix int_version_to_string function

* Fix warnings and format strings in platform code and tests
---
 src/backend/cpu/platform.cpp  |   3 +-
 src/backend/cpu/platform.hpp  |   4 +-
 src/backend/cuda/platform.cpp | 200 ++++++++++++++++++++++------------
 src/backend/cuda/platform.hpp |   7 +-
 test/scan.cpp                 |  18 +--
 5 files changed, 149 insertions(+), 83 deletions(-)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 0da0f20e83..9266ce8cf3 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -248,7 +248,8 @@ bool& evalFlag() {
 DeviceManager::DeviceManager()
     : queues(MAX_QUEUES)
     , memManager(new MemoryManager())
-    , fgMngr(new graphics::ForgeManager()) {}
+    , fgMngr(new graphics::ForgeManager())
+    {}
 
 MemoryManager& memoryManager() {
     DeviceManager& inst = DeviceManager::getInstance();
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index dc33f6032f..6aff51ec4b 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -143,9 +143,9 @@ class DeviceManager {
     void operator=(DeviceManager const&) = delete;
 
     // Attributes
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
-    std::unique_ptr<MemoryManager> memManager;
     std::vector<queue> queues;
+    std::unique_ptr<MemoryManager> memManager;
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
     const CPUInfo cinfo;
 };
 }  // namespace cpu
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 7006a2d4fb..2df69c2a3e 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -137,7 +137,7 @@ static inline int getMinSupportedCompute(int cudaMajorVer) {
     // Vector of minimum supported compute versions
     // for CUDA toolkit (i+1).* where i is the index
     // of the vector
-    static const std::array<int, 10> minSV{1, 1, 1, 1, 1, 1, 2, 2, 3, 3};
+    static const std::array<int, 10> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3}};
 
     int CVSize = static_cast<int>(minSV.size());
     return (cudaMajorVer > CVSize ? minSV[CVSize - 1]
@@ -198,9 +198,7 @@ bool isDoubleSupported(int device) {
 }
 
 void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
-    if (getDeviceCount() <= 0) {
-        return;
-    }
+    if (getDeviceCount() <= 0) { return; }
 
     cudaDeviceProp dev = getDeviceProp(getActiveDeviceId());
 
@@ -239,7 +237,7 @@ string getDriverVersion() {
 #endif
         int driver = 0;
         CUDA_CHECK(cudaDriverGetVersion(&driver));
-        return string("CUDA Driver Version: ") + to_string(driver);
+        return to_string(driver);
     } else {
         return string(driverVersion);
     }
@@ -247,7 +245,7 @@ string getDriverVersion() {
 
 string int_version_to_string(int version) {
     return to_string(version / 1000) + "." +
-           to_string((int)((version % 1000) / 100.));
+           to_string((int)((version % 1000) / 10.));
 }
 
 string getCUDARuntimeVersion() {
@@ -460,92 +458,157 @@ SparseHandle sparseHandle() {
     return cusparseHandles[id].get()->get();
 }
 
+/// Struct represents the cuda toolkit version and its associated minimum
+/// required driver versions.
+struct ToolkitDriverVersions {
+    /// The CUDA Toolkit version returned by cudaDriverGetVersion or
+    /// cudaRuntimeGetVersion
+    int version;
+
+    /// The minimum GPU driver version required for the \p version toolkit on
+    /// Linux or macOS
+    float unix_min_version;
+
+    /// The minimum GPU driver version required for the \p version toolkit on
+    /// Windows
+    float windows_min_version;
+};
+
 /// Map giving the minimum device driver needed in order to run a given version
 /// of CUDA for both Linux/Mac and Windows from:
 /// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
 // clang-format off
-static const std::map<std::string, std::pair<float, float>>
-    CudaToKernelVersionMap = {
-        {"10.0", {410.48f, 411.31f}},
-        {"9.2", {396.37f, 398.26f}},
-        {"9.1", {390.46f, 391.29f}},
-        {"9.0", {384.81f, 385.54f}},
-        {"8.0", {375.26f, 376.51f}},
-        {"7.5", {352.31f, 353.66f}},
-        {"7.0", {346.46f, 347.62f}}};
+static const ToolkitDriverVersions
+    CudaToDriverVersion[] = {
+        {10000, 410.48f, 411.31f},
+        {9020,  396.37f, 398.26f},
+        {9010,  390.46f, 391.29f},
+        {9000,  384.81f, 385.54f},
+        {8000,  375.26f, 376.51f},
+        {7050,  352.31f, 353.66f},
+        {7000,  346.46f, 347.62f}};
 // clang-format on
 
+/// A debug only function that checks to see if the driver or runtime
+/// function is part of the CudaToDriverVersion array. If the runtime
+/// version is not part of the array then an error is thrown in debug
+/// mode. If the driver version is not part of the array, then a message
+/// is displayed in the error stream.
+///
+/// \param[in] runtime_version  The version integer returned by
+///                             cudaRuntimeGetVersion
+/// \param[in] driver_version   The version integer returned by
+///                             cudaDriverGetVersion
+/// \note: only works in debug builds
+void debugRuntimeCheck(int runtime_version, int driver_version) {
+#ifndef NDEBUG
+    auto runtime_it =
+        find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
+                [runtime_version](ToolkitDriverVersions ver) {
+                    return runtime_version == ver.version;
+                });
+    auto driver_it =
+        find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
+                [driver_version](ToolkitDriverVersions ver) {
+                    return driver_version == ver.version;
+                });
+
+    // If the runtime version is not part of the CudaToDriverVersion array,
+    // display a message in the trace. Do not throw an error unless this is
+    // a debug build
+    if (runtime_it == end(CudaToDriverVersion)) {
+        char buf[1024];
+        char err_msg[] =
+            "WARNING: CUDA runtime version(%s) not recognized. Please "
+            "create an issue or a pull request on the ArrayFire repository to "
+            "update the CudaToDriverVersion variable with this version of "
+            "the CUDA Toolkit.\n";
+        snprintf(buf, 1024, err_msg,
+                 int_version_to_string(runtime_version).c_str());
+        fprintf(stderr, err_msg,
+                int_version_to_string(runtime_version).c_str());
+        AF_ERROR(buf, AF_ERR_RUNTIME);
+    }
+
+    if (driver_it == end(CudaToDriverVersion)) {
+        char err_msg[] =
+            "WARNING: CUDA driver version(%s) not part of the "
+            "CudaToDriverVersion array. Please create an issue or a pull "
+            "request on the ArrayFire repository to update the "
+            "CudaToDriverVersion variable with this version of the CUDA "
+            "Toolkit.\n";
+        fprintf(stderr, err_msg,
+                int_version_to_string(driver_version).c_str());
+    }
+#endif
+}
+
 // Check if the device driver version is recent enough to run the cuda libs
 // linked with afcuda:
 void DeviceManager::checkCudaVsDriverVersion() {
     const std::string driverVersionString = getDriverVersion();
-    if (driverVersionString.empty()) {
-        // Do not perform a check if no driver version was found
-        AF_TRACE("Failed to retrieve nvidia driver version.");
-        return;
-    }
-    AF_TRACE("GPU driver version: {}", driverVersionString);
 
-    // Nvidia driver versions are hopefully float based X.Y
-    const float driverVersion = std::stof(driverVersionString);
-    if (driverVersion == 0) {
-        AF_TRACE("Failed to parse driver version: {}", driverVersionString);
-        return;
-    }
+    int driver  = 0;
+    int runtime = 0;
+    CUDA_CHECK(cudaDriverGetVersion(&driver));
+    CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
 
-    const std::string cudaRuntimeVersionString = getCUDARuntimeVersion();
-    if (cudaRuntimeVersionString.empty()) {
-        AF_TRACE("Failed to get CUDA runtime version");
-        return;
-    }
+    AF_TRACE("CUDA supported by the GPU Driver {} ArrayFire CUDA Runtime {}",
+             int_version_to_string(driver), int_version_to_string(runtime));
 
-    if (CudaToKernelVersionMap.find(cudaRuntimeVersionString) ==
-        CudaToKernelVersionMap.end()) {
-        AF_TRACE(
-            "CUDA runtime version({}) not recognized. Please create an issue "
-            "or a pull request on the ArrayFire repository to update the "
-            "CudaToKernelVersionMap variable with this version of the CUDA "
-            "Toolkit.",
-            cudaRuntimeVersionString);
-        return;
-    }
+    debugRuntimeCheck(runtime, driver);
 
-    float minimumDriverVersion = 0;
-#if defined(OS_WIN)
-    minimumDriverVersion =
-        CudaToKernelVersionMap.at(cudaRuntimeVersionString).second;
-#else
-    minimumDriverVersion =
-        CudaToKernelVersionMap.at(cudaRuntimeVersionString).first;
-#endif
-
-    AF_TRACE("CUDA runtime version: {} (Minimum GPU driver required: {})",
-             cudaRuntimeVersionString, minimumDriverVersion);
-    if (driverVersion < minimumDriverVersion) {
+    if (runtime > driver) {
         string msg =
             "ArrayFire was built with CUDA %s which requires GPU driver "
-            "version %.2f or later. Please download the latest drivers from "
-            "https://www.nvidia.com/drivers. Alternatively, you could rebuild "
-            "ArrayFire with CUDA Toolkit version %s to use the current "
-            "drivers.";
+            "version %.2f or later. Please download and install the latest "
+            "drivers from https://www.nvidia.com/drivers for your GPU. "
+            "Alternatively, you could rebuild ArrayFire with CUDA Toolkit "
+            "version %s to use the current drivers.";
+
+        auto runtime_it =
+            find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
+                    [runtime](ToolkitDriverVersions ver) {
+                        return runtime == ver.version;
+                    });
+
+        // If the runtime version is not part of the CudaToDriverVersion
+        // array, display a message in the trace. Do not throw an error
+        // unless this is a debug build
+        if (runtime_it == end(CudaToDriverVersion)) {
+            char buf[1024];
+            char err_msg[] =
+                "CUDA runtime version(%s) not recognized. Please create an "
+                "issue or a pull request on the ArrayFire repository to "
+                "update the CudaToDriverVersion variable with this "
+                "version of the CUDA Toolkit.";
+            snprintf(buf, 1024, err_msg,
+                     int_version_to_string(runtime).c_str());
+            AF_TRACE("{}", buf);
+            return;
+        }
 
-        char buf[1024];
-        int supported_cuda_version = 0;
-        cudaDriverGetVersion(&supported_cuda_version);
+        float minimumDriverVersion =
+#ifdef OS_WIN
+            runtime_it->windows_min_version;
+#else
+            runtime_it->unix_min_version;
+#endif
 
-        snprintf(buf, 1024, msg.c_str(), cudaRuntimeVersionString.c_str(),
-                 minimumDriverVersion,
-                 int_version_to_string(supported_cuda_version).c_str());
+        char buf[1024];
+        snprintf(buf, 1024, msg.c_str(), int_version_to_string(runtime).c_str(),
+                 minimumDriverVersion, int_version_to_string(driver).c_str());
 
         AF_ERROR(buf, AF_ERR_DRIVER);
     }
 }
 
 DeviceManager::DeviceManager()
-    : cuDevices(0)
+    : logger(common::loggerFactory("platform"))
+    , cuDevices(0)
     , nDevices(0)
     , fgMngr(new graphics::ForgeManager())
-    , logger(common::loggerFactory("platform")) {
+    {
     checkCudaVsDriverVersion();
 
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
@@ -571,7 +634,7 @@ DeviceManager::DeviceManager()
                         dev.prop.clockRate;
             dev.nativeId = i;
             AF_TRACE(
-                "Found device: {} ({:3.3} GB | ~{} GFLOPs | {} SMs)",
+                "Found device: {} ({:0.3} GB | ~{} GFLOPs | {} SMs)",
                 dev.prop.name, dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
                 dev.flops / 1024. / 1024. * 2, dev.prop.multiProcessorCount);
             cuDevices.push_back(dev);
@@ -671,7 +734,8 @@ int DeviceManager::setActiveDevice(int device, int nId) {
         cudaGetLastError();  // Reset error stack
 #ifndef NDEBUG
         getLogger()->warn(
-            "Warning: Device {} is unavailable. Using next available device \n",
+            "Warning: Device {} is unavailable. Using next available "
+            "device \n",
             device);
 #endif
         // Comes here is the device is in exclusive mode or
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index bb90581f56..0bd7898643 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -150,9 +150,6 @@ class DeviceManager {
     void operator=(DeviceManager const&);
 
     // Attributes
-    std::vector<cudaDevice_t> cuDevices;
-    std::shared_ptr<spdlog::logger> logger;
-
     enum sort_mode { flops = 0, memory = 1, compute = 2, none = 3 };
 
     void checkCudaVsDriverVersion();
@@ -160,6 +157,10 @@ class DeviceManager {
 
     int setActiveDevice(int device, int native = -1);
 
+    std::shared_ptr<spdlog::logger> logger;
+
+    std::vector<cudaDevice_t> cuDevices;
+
     int nDevices;
     cudaStream_t streams[MAX_DEVICES];
 
diff --git a/test/scan.cpp b/test/scan.cpp
index 2ae4c25d91..3d96d0f789 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -258,7 +258,7 @@ TEST(Scan, ExclusiveSum1D) {
     const int in_size = 80000;
     vector<int> h_in(in_size, 1);
     vector<int> h_gold(in_size, 0);
-    for (int i = 1; i < h_gold.size(); ++i) {
+    for (size_t i = 1; i < h_gold.size(); ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
 
@@ -272,10 +272,10 @@ TEST(Scan, ExclusiveSum2D_Dim0) {
     const int in_size = 80000 * 2;
     vector<int> h_in(in_size, 1);
     vector<int> h_gold(in_size, 0);
-    for (int i = 1; i < h_gold.size() / 2; ++i) {
+    for (size_t i = 1; i < h_gold.size() / 2; ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
-    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+    for (size_t i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
 
@@ -290,10 +290,10 @@ TEST(Scan, ExclusiveSum2D_Dim1) {
     const int in_size = 80000 * 2;
     vector<int> h_in(in_size, 1);
     vector<int> h_gold(in_size, 0);
-    for (int i = 1; i < h_gold.size() / 2; ++i) {
+    for (size_t i = 1; i < h_gold.size() / 2; ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
-    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+    for (size_t i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
 
@@ -309,10 +309,10 @@ TEST(Scan, ExclusiveSum2D_Dim2) {
     const int in_size = 80000 * 2;
     vector<int> h_in(in_size, 1);
     vector<int> h_gold(in_size, 0);
-    for (int i = 1; i < h_gold.size() / 2; ++i) {
+    for (size_t i = 1; i < h_gold.size() / 2; ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
-    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+    for (size_t i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
 
@@ -328,10 +328,10 @@ TEST(Scan, ExclusiveSum2D_Dim3) {
     const int in_size = 80000 * 2;
     vector<int> h_in(in_size, 1);
     vector<int> h_gold(in_size, 0);
-    for (int i = 1; i < h_gold.size() / 2; ++i) {
+    for (size_t i = 1; i < h_gold.size() / 2; ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
-    for (int i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
+    for (size_t i = h_gold.size() / 2 + 1; i < h_gold.size(); ++i) {
         h_gold[i] = h_in[i] + h_gold[i - 1];
     }
 

From 242e92fd0c45aa91fc8b377a3b5581eefd068ac9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 3 Mar 2019 19:11:59 +0500
Subject: [PATCH 1616/2677] Fix segfault in ForgeModule destructor when wnd is
 not created

---
 src/backend/common/graphics_common.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 3bc59ba836..2d150d914d 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -261,7 +261,7 @@ ForgeManager::~ForgeManager() {
                 }
             }
         }
-        mPlugin->fg_release_window(wnd->handle);
+        if(wnd) mPlugin->fg_release_window(wnd->handle);
     }
 }
 

From f38feccccf04a594c066c6ae6f81225d06d716c2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Mar 2019 01:18:57 +0500
Subject: [PATCH 1617/2677] Suppress Windows warnings in mmio lib

---
 test/mmio/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/test/mmio/CMakeLists.txt b/test/mmio/CMakeLists.txt
index 3ce5d57e17..5ef52292ad 100644
--- a/test/mmio/CMakeLists.txt
+++ b/test/mmio/CMakeLists.txt
@@ -17,3 +17,7 @@ target_include_directories(mmio
     )
 
 target_compile_definitions(mmio PUBLIC USE_MTX)
+
+if(WIN32)
+  target_compile_definitions(mmio PRIVATE _CRT_SECURE_NO_WARNINGS)
+endif()

From 1bfc2840f5d3d31607424a67208cd0a2a362cea8 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mposcablo@gmail.com>
Date: Tue, 5 Mar 2019 13:38:15 -0500
Subject: [PATCH 1618/2677] Clarified and added illustration to rotate docs
 (#2453)

---
 assets                 |  2 +-
 docs/details/image.dox | 54 ++++++++++++++++++++++++++----------------
 2 files changed, 34 insertions(+), 22 deletions(-)

diff --git a/assets b/assets
index 6b13342b97..729c7b6403 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit 6b13342b97ab8d0157e75f3fa5ee4ef2fa1b1078
+Subproject commit 729c7b64039e6433ae5ee521658ba20147efcb02
diff --git a/docs/details/image.dox b/docs/details/image.dox
index ef1f56affc..fe58584b98 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -600,27 +600,39 @@ af_print(resize(2, in, AF_INTERP_BILINEAR));
 \defgroup transform_func_rotate rotate
 \ingroup transform_mat
 
-Rotate an input image
-
-The angle theta is in radians.
-
-Rotating an input image can be done using \ref AF_INTERP_NEAREST,
-\ref AF_INTERP_BILINEAR or \ref AF_INTERP_LOWER interpolations. Nearest
-interpolation will pick the nearest value to the location, whereas bilinear
-interpolation will do a weighted interpolation for calculate the new size.
-
-This function does not differentiate between images and data. As long as
-the array is defined, it will rotate any type or size of array.
-
-The crop option allows you to choose whether to resize the image.
-If crop is set to false, ie. the entire rotated image will be a part of the
-array and the new array size will be greater than or equal to the input array
-size.
-If crop is set to true, then the new array size is same as the input array
-size and the data that falls outside the boundaries of the array is discarded.
-
-Any location of the rotated array that does not map to a location of the input
-array is set to 0.
+\brief Rotate an input image or array
+
+The rotation is done counter-clockwise, with an angle \p theta (in radians),
+using a specified \p method of interpolation to determine the values of the
+output array. Six types of interpolation are currently supported:
+
+- \ref AF_INTERP_NEAREST - nearest value to the location
+- \ref AF_INTERP_BILINEAR - weighted interpolation
+- \ref AF_INTERP_BILINEAR_COSINE - bilinear interpolation with cosine smoothing
+- \ref AF_INTERP_BICUBIC - bicubic interpolation
+- \ref AF_INTERP_BICUBIC_SPLINE - bicubic interpolation with Catmull-Rom splines
+- \ref AF_INTERP_LOWER - floor indexed
+
+Since the output image still needs to be an upright box, \p crop determines how
+to bound the output image, given the now-rotated image. The figure below
+illustrates the effect of changing this parameter.
+
+\image html rotate_illus.png "Effect of \p crop parameter on the output"
+
+Here, the original image is represented by the innermost box with the solid
+black and dashed orange lines, and the (theoretical) rotated image is the box
+with the solid orange lines. If \p crop is true, then the output image's
+dimensions will stay the same as the original image's, but the rotated image's
+portions outside the dashed orange lines will be cropped, and the rest of the
+output image (the area between the solid black and solid orange lines) will be
+filled with zeros. However, if \p crop is false, then the output image's
+dimensions might get bigger (as shown in this illustration), as represented by
+the outermost box with dashed black lines. This change in dimensions is
+necessary to accommodate all of the rotated image's data. The remainder of the
+output image will be filled with zeros, as represented by the area between the
+solid orange lines and dashed black lines. Note that the new dimensions in
+general (beyond this illustration) will be greater than or equal the original
+image's dimensions when \p crop is false.
 
 
 \defgroup transform_func_translate translate

From bce559f881006932de7c2a6787217dacc4c52937 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Mar 2019 14:10:03 +0500
Subject: [PATCH 1619/2677] Update CudaToDriverVersion array with CUDA 10.1

---
 src/backend/cuda/platform.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 2df69c2a3e..14a04bd750 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -480,6 +480,7 @@ struct ToolkitDriverVersions {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {10010, 418.39f, 418.96f},
         {10000, 410.48f, 411.31f},
         {9020,  396.37f, 398.26f},
         {9010,  390.46f, 391.29f},

From 15ccdd548fb1ccfbeebfcfba14534290e3d8ea02 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 8 Mar 2019 10:37:54 +0530
Subject: [PATCH 1620/2677] Use smart pointer to clean up forge resources
 (#2452)

* Use smart pointer to clean up forge resources.
*  Removed some unnecessary typedefs and moved all type aliases into ForgeManager class scope.
* Clang format changes
---
 src/api/c/window.cpp                   |  15 +-
 src/backend/common/graphics_common.cpp | 281 +++++++++-------------
 src/backend/common/graphics_common.hpp | 310 +++++++++++++++++++------
 3 files changed, 355 insertions(+), 251 deletions(-)

diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index a1d2e1afb9..b576c43990 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -22,20 +22,7 @@ using namespace graphics;
 af_err af_create_window(af_window* out, const int width, const int height,
                         const char* const title) {
     try {
-        ForgeManager& fgMngr = forgeManager();
-        fg_window mainWnd    = fgMngr.getMainWindow();
-
-        if (mainWnd == 0) {
-            AF_ERROR("OpenGL context creation failed", AF_ERR_INTERNAL);
-        }
-
-        fg_window temp = nullptr;
-
-        FG_CHECK(forgePlugin().fg_create_window(&temp, width, height, title,
-                                                mainWnd, false));
-
-        fgMngr.setWindowChartGrid(temp, 1, 1);
-
+        fg_window temp = forgeManager().getWindow(width, height, title, false);
         std::swap(*out, temp);
     }
     CATCHALL;
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 2d150d914d..dace645788 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -232,39 +232,6 @@ ForgeModule& forgePlugin() { return detail::forgeManager().plugin(); }
 
 ForgeManager::ForgeManager() : mPlugin(new ForgeModule()) {}
 
-ForgeManager::~ForgeManager() {
-    if (mPlugin->isLoaded()) {
-        /* clear all OpenGL resource objects (images, plots, histograms etc) first
-         * and then delete the windows */
-        for (ImgMapIter iter = mImgMap.begin(); iter != mImgMap.end(); iter++)
-            mPlugin->fg_release_image(iter->second);
-
-        for (PltMapIter iter = mPltMap.begin(); iter != mPltMap.end(); iter++)
-            mPlugin->fg_release_plot(iter->second);
-
-        for (HstMapIter iter = mHstMap.begin(); iter != mHstMap.end(); iter++)
-            mPlugin->fg_release_histogram(iter->second);
-
-        for (SfcMapIter iter = mSfcMap.begin(); iter != mSfcMap.end(); iter++)
-            mPlugin->fg_release_surface(iter->second);
-
-        for (VcfMapIter iter = mVcfMap.begin(); iter != mVcfMap.end(); iter++)
-            mPlugin->fg_release_vector_field(iter->second);
-
-        for (ChartMapIter iter = mChartMap.begin(); iter != mChartMap.end();
-            iter++) {
-            for (int i = 0; i < (int)(iter->second).size(); i++) {
-                fg_chart chrt = (iter->second)[i];
-                if (chrt) {
-                    mChartAxesOverrideMap.erase((chrt));
-                    mPlugin->fg_release_chart(chrt);
-                }
-            }
-        }
-        if(wnd) mPlugin->fg_release_window(wnd->handle);
-    }
-}
-
 ForgeModule& ForgeManager::plugin() { return *mPlugin; }
 
 fg_window ForgeManager::getMainWindow() {
@@ -289,33 +256,44 @@ fg_window ForgeManager::getMainWindow() {
             if (e != FG_ERR_NONE) {
                 AF_ERROR("Graphics Window creation failed", AF_ERR_INTERNAL);
             }
-            this->mPlugin->fg_make_window_current(w);
             this->setWindowChartGrid(w, 1, 1);
-            this->wnd.reset(new Window({w}));
+            this->mPlugin->fg_make_window_current(w);
+            this->mMainWindow.reset(new Window({w}));
             if (!gladLoadGL()) { AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB); }
         });
     }
 
-    return wnd->handle;
+    return mMainWindow->handle;
 }
 
-void ForgeManager::setWindowChartGrid(const fg_window window, const int r,
-                                      const int c) {
-    ChartMapIter iter = mChartMap.find(window);
-    GridMapIter gIter = mWndGridMap.find(window);
+fg_window ForgeManager::getWindow(const int w, const int h,
+                                  const char* const title,
+                                  const bool invisible) {
+    fg_window retVal = 0;
+    FG_CHECK(mPlugin->fg_create_window(&retVal, w, h, title,
+                getMainWindow(), invisible));
+    if (retVal == 0) {
+        AF_ERROR("Window creation failed", AF_ERR_INTERNAL);
+    }
+    setWindowChartGrid(retVal, 1, 1);
+    return retVal;
+}
+
+void ForgeManager::setWindowChartGrid(const fg_window window,
+                                      const int r, const int c) {
+    ChartMapIterator iter = mChartMap.find(window);
+    WindGridMapIterator gIter = mWndGridMap.find(window);
 
     if (iter != mChartMap.end()) {
         // ChartVec found. Clear it.
         // This has to be cleared as there is no guarantee that existing
         // chart types(2D/3D) match the future grid requirements
-        for (int i = 0; i < (int)(iter->second).size(); i++) {
-            fg_chart chrt = (iter->second)[i];
-            if (chrt) {
-                mChartAxesOverrideMap.erase(chrt);
-                FG_CHECK(mPlugin->fg_release_chart(chrt));
+        for (const ChartPtr& c: iter->second) {
+            if (c) {
+                mChartAxesOverrideMap.erase(c->handle);
             }
         }
-        (iter->second).clear();
+        (iter->second).clear(); // Clear ChartList
         gIter->second = std::make_pair<int, int>(1, 1);
     }
 
@@ -323,131 +301,109 @@ void ForgeManager::setWindowChartGrid(const fg_window window, const int r,
         mChartMap.erase(window);
         mWndGridMap.erase(window);
     } else {
-        mChartMap[window]   = std::vector<fg_chart>(r * c);
+        mChartMap[window]   = ChartList(r * c);
         mWndGridMap[window] = std::make_pair(r, c);
     }
 }
 
-WindGridDims_t ForgeManager::getWindowGrid(const fg_window window) {
-    GridMapIter gIter = mWndGridMap.find(window);
-
+ForgeManager::WindowGridDims
+ForgeManager::getWindowGrid(const fg_window window) {
+    WindGridMapIterator gIter = mWndGridMap.find(window);
     if (gIter == mWndGridMap.end()) {
         mWndGridMap[window] = std::make_pair(1, 1);
     }
-
     return mWndGridMap[window];
 }
 
 fg_chart ForgeManager::getChart(const fg_window window, const int r,
                                 const int c, const fg_chart_type ctype) {
-    fg_chart chart    = NULL;
-    ChartMapIter iter = mChartMap.find(window);
-    GridMapIter gIter = mWndGridMap.find(window);
+    fg_chart retVal   = NULL;
+    ChartMapIterator iter = mChartMap.find(window);
+    WindGridMapIterator gIter = mWndGridMap.find(window);
 
-    if (iter != mChartMap.end()) {
-        int gRows = std::get<0>(gIter->second);
-        int gCols = std::get<1>(gIter->second);
+    int rows = std::get<0>(gIter->second);
+    int cols = std::get<1>(gIter->second);
 
-        if (c >= gCols || r >= gRows)
-            AF_ERROR("Grid points are out of bounds", AF_ERR_TYPE);
+    if (c >= cols || r >= rows)
+        AF_ERROR("Window Grid points are out of bounds", AF_ERR_TYPE);
 
-        // upgrade to exclusive access to make changes
-        chart = (iter->second)[c * gRows + r];
+    // upgrade to exclusive access to make changes
+    ChartPtr& chart = (iter->second)[c * rows + r];
 
-        if (chart == NULL) {
-            // Chart has not been created
-            FG_CHECK(mPlugin->fg_create_chart(&chart, ctype));
-            (iter->second)[c * gRows + r] = chart;
-            // Set Axes override to false
-            mChartAxesOverrideMap[chart] = false;
-        } else {
-            fg_chart_type chart_type;
-            FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-            if (chart_type != ctype) {
-                // Existing chart is of incompatible type
-                mChartAxesOverrideMap.erase(chart);
-                FG_CHECK(mPlugin->fg_release_chart(chart));
-                FG_CHECK(mPlugin->fg_create_chart(&chart, ctype));
-                (iter->second)[c * gRows + r] = chart;
-                // Set Axes override to false
-                mChartAxesOverrideMap[chart] = false;
-            }
-        }
+    if (!chart) {
+        fg_chart temp = NULL;
+        FG_CHECK(mPlugin->fg_create_chart(&temp, ctype));
+        chart.reset(new Chart({temp}));
+        mChartAxesOverrideMap[chart->handle] = false;
     } else {
-        // The chart map for this was never created
-        // Which should never happen
+        fg_chart_type chart_type;
+        FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart->handle));
+        if (chart_type != ctype) {
+            // Existing chart is of incompatible type
+            mChartAxesOverrideMap.erase(chart->handle);
+            fg_chart temp = 0;
+            FG_CHECK(mPlugin->fg_create_chart(&temp, ctype));
+            chart.reset(new Chart({temp}));
+            mChartAxesOverrideMap[chart->handle] = false;
+        }
     }
-
-    return chart;
+    return chart->handle;
 }
 
-fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode,
-                                fg_dtype type) {
-    /* w, h needs to fall in the range of [0, 2^16]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Image object. So, this implementation
-     * is a limitation on how big of an image can be rendered
-     * using arrayfire graphics funtionality */
+long long ForgeManager::genImageKey(int w, int h, fg_channel_format mode,
+                                    fg_dtype type) {
     assert(w <= 2ll << 16);
     assert(h <= 2ll << 16);
     long long key = ((w & _16BIT) << 16) | (h & _16BIT);
-    key           = (((key << 16) | mode) << 16) | type;
+    key           = ((((key << 16) | (mode & _16BIT)) << 16) | (type | _16BIT));
+    return key;
+}
 
-    ChartKey_t keypair = std::make_pair(key, nullptr);
+fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode,
+                                fg_dtype type) {
+    auto key = genImageKey(w, h, mode, type);
 
-    ImgMapIter iter = mImgMap.find(keypair);
+    ChartKey keypair      = std::make_pair(key, nullptr);
+    ImageMapIterator iter = mImgMap.find(keypair);
 
     if (iter == mImgMap.end()) {
         fg_image img = nullptr;
         FG_CHECK(mPlugin->fg_create_image(&img, w, h, mode, type));
-        mImgMap[keypair] = img;
+        mImgMap[keypair] = ImagePtr(new Image({img}));
     }
-
-    return mImgMap[keypair];
+    return mImgMap[keypair]->handle;
 }
 
 fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
                                 fg_channel_format mode, fg_dtype type) {
-    /* w, h needs to fall in the range of [0, 2^16]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Image object. So, this implementation
-     * is a limitation on how big of an image can be rendered
-     * using arrayfire graphics funtionality */
-    assert(w <= 2ll << 16);
-    assert(h <= 2ll << 16);
-    long long key = ((w & _16BIT) << 16) | (h & _16BIT);
-    key           = (((key << 16) | mode) << 16) | type;
+    auto key = genImageKey(w, h, mode, type);
 
-    ChartKey_t keypair = std::make_pair(key, chart);
-
-    ImgMapIter iter = mImgMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, chart);
+    ImageMapIterator iter = mImgMap.find(keypair);
 
     if (iter == mImgMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-        if (chart_type != FG_CHART_2D)
+        if (chart_type != FG_CHART_2D) {
             AF_ERROR("Image can only be added to chart of type FG_CHART_2D",
                      AF_ERR_TYPE);
-
+        }
         fg_image img = nullptr;
         FG_CHECK(mPlugin->fg_create_image(&img, w, h, mode, type));
-        mImgMap[keypair] = img;
-
         FG_CHECK(mPlugin->fg_append_image_to_chart(chart, img));
-    }
 
-    return mImgMap[keypair];
+        mImgMap[keypair] = ImagePtr(new Image({img}));
+    }
+    return mImgMap[keypair]->handle;
 }
 
 fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
                               fg_plot_type ptype, fg_marker_type mtype) {
-    long long key = ((nPoints & _48BIT) << 48);
-    key |= (((((dtype & 0x000F) << 12) | (ptype & 0x000F)) << 8) |
-            (mtype & 0x000F));
-
-    ChartKey_t keypair = std::make_pair(key, chart);
+    long long key = (((long long)(nPoints)&_48BIT) << 16);
+    key |= (((dtype & _4BIT) << 12) | ((ptype & _4BIT) << 8) | (mtype & _8BIT));
 
-    PltMapIter iter = mPltMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, chart);
+    PlotMapIterator iter = mPltMap.find(keypair);
 
     if (iter == mPltMap.end()) {
         fg_chart_type chart_type;
@@ -456,78 +412,66 @@ fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
         fg_plot plt = nullptr;
         FG_CHECK(mPlugin->fg_create_plot(&plt, nPoints, dtype, chart_type,
                                          ptype, mtype));
-        mPltMap[keypair] = plt;
-
         FG_CHECK(mPlugin->fg_append_plot_to_chart(chart, plt));
-    }
 
-    return mPltMap[keypair];
+        mPltMap[keypair] = PlotPtr(new Plot({plt}));
+    }
+    return mPltMap[keypair]->handle;
 }
 
 fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins,
                                         fg_dtype type) {
-    long long key = ((nBins & _48BIT) << 48) | (type & _16BIT);
-
-    ChartKey_t keypair = std::make_pair(key, chart);
+    long long key = (((long long)(nBins)&_48BIT) << 16) | (type & _16BIT);
 
-    HstMapIter iter = mHstMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, chart);
+    HistogramMapIterator iter = mHstMap.find(keypair);
 
     if (iter == mHstMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-        if (chart_type != FG_CHART_2D)
+        if (chart_type != FG_CHART_2D) {
             AF_ERROR("Histogram can only be added to chart of type FG_CHART_2D",
                      AF_ERR_TYPE);
-
+        }
         fg_histogram hst = nullptr;
         FG_CHECK(mPlugin->fg_create_histogram(&hst, nBins, type));
-        mHstMap[keypair] = hst;
-
         FG_CHECK(mPlugin->fg_append_histogram_to_chart(chart, hst));
+        mHstMap[keypair] = HistogramPtr(new Histogram({hst}));
     }
-
-    return mHstMap[keypair];
+    return mHstMap[keypair]->handle;
 }
 
-fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY,
-                                    fg_dtype type) {
-    /* nX * nY needs to fall in the range of [0, 2^48]
-     * for the ForgeManager to correctly retrieve
-     * the necessary Forge Plot object. So, this implementation
-     * is a limitation on how big of an plot graph can be rendered
-     * using arrayfire graphics funtionality */
-    assert((long long)nX * nY <= 2ll << 48);
-    long long key = (((nX * nY) & _48BIT) << 48) | (type & _16BIT);
-
-    ChartKey_t keypair = std::make_pair(key, chart);
+fg_surface ForgeManager::getSurface(fg_chart chart,
+                                    int nX, int nY, fg_dtype type) {
+    long long surfaceSize = nX * (long long)(nY);
+    assert(surfaceSize <= 2ll << 48);
+    long long key = ((surfaceSize & _48BIT) << 16) | (type & _16BIT);
 
-    SfcMapIter iter = mSfcMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, chart);
+    SurfaceMapIterator iter = mSfcMap.find(keypair);
 
     if (iter == mSfcMap.end()) {
         fg_chart_type chart_type;
         FG_CHECK(mPlugin->fg_get_chart_type(&chart_type, chart));
-        if (chart_type != FG_CHART_3D)
+        if (chart_type != FG_CHART_3D) {
             AF_ERROR("Surface can only be added to chart of type FG_CHART_3D",
                      AF_ERR_TYPE);
-
+        }
         fg_surface surf = nullptr;
         FG_CHECK(mPlugin->fg_create_surface(&surf, nX, nY, type,
                                             FG_PLOT_SURFACE, FG_MARKER_NONE));
-        mSfcMap[keypair] = surf;
-
         FG_CHECK(mPlugin->fg_append_surface_to_chart(chart, surf));
+        mSfcMap[keypair] = SurfacePtr(new Surface({surf}));
     }
-
-    return mSfcMap[keypair];
+    return mSfcMap[keypair]->handle;
 }
 
-fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints,
-                                             fg_dtype type) {
-    long long key = (((nPoints)&_48BIT) << 48) | (type & _16BIT);
+fg_vector_field ForgeManager::getVectorField(fg_chart chart,
+                                             int nPoints, fg_dtype type) {
+    long long key = (((long long)(nPoints)&_48BIT) << 16) | (type & _16BIT);
 
-    ChartKey_t keypair = std::make_pair(key, chart);
-
-    VcfMapIter iter = mVcfMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, chart);
+    VecFieldMapIterator iter = mVcfMap.find(keypair);
 
     if (iter == mVcfMap.end()) {
         fg_chart_type chart_type;
@@ -535,25 +479,24 @@ fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints,
 
         fg_vector_field vfield = nullptr;
         FG_CHECK(mPlugin->fg_create_vector_field(&vfield, nPoints, type,
-                                                 chart_type));
-        mVcfMap[keypair] = vfield;
-
-        FG_CHECK(mPlugin->fg_append_vector_field_to_chart(chart, vfield));
+                    chart_type));
+        FG_CHECK(mPlugin->fg_append_vector_field_to_chart(chart,
+                    vfield));
+        mVcfMap[keypair] = VectorFieldPtr(new VectorField({vfield}));
     }
-
-    return mVcfMap[keypair];
+    return mVcfMap[keypair]->handle;
 }
 
-bool ForgeManager::getChartAxesOverride(fg_chart chart) {
-    ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
+bool ForgeManager::getChartAxesOverride(const fg_chart chart) {
+    AxesOverrideIterator iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
     return mChartAxesOverrideMap[chart];
 }
 
-void ForgeManager::setChartAxesOverride(fg_chart chart, bool flag) {
-    ChartAxesOverrideIter iter = mChartAxesOverrideMap.find(chart);
+void ForgeManager::setChartAxesOverride(const fg_chart chart, bool flag) {
+    AxesOverrideIterator iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 61fb8019fc..432bd16f6c 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -34,101 +34,275 @@ void makeContextCurrent(fg_window window);
 double step_round(const double in, const bool dir);
 
 namespace graphics {
-enum Defaults { WIDTH = 1280, HEIGHT = 720 };
-
-static const long long _16BIT = 0x000000000000FFFF;
-static const long long _32BIT = 0x00000000FFFFFFFF;
-static const long long _48BIT = 0x0000FFFFFFFFFFFF;
-
-typedef std::pair<long long, fg_chart> ChartKey_t;
-
-typedef std::map<ChartKey_t, fg_image> ImageMap_t;
-typedef std::map<ChartKey_t, fg_plot> PlotMap_t;
-typedef std::map<ChartKey_t, fg_histogram> HistogramMap_t;
-typedef std::map<ChartKey_t, fg_surface> SurfaceMap_t;
-typedef std::map<ChartKey_t, fg_vector_field> VectorFieldMap_t;
-
-typedef ImageMap_t::iterator ImgMapIter;
-typedef PlotMap_t::iterator PltMapIter;
-typedef HistogramMap_t::iterator HstMapIter;
-typedef SurfaceMap_t::iterator SfcMapIter;
-typedef VectorFieldMap_t::iterator VcfMapIter;
-
-typedef std::vector<fg_chart> ChartVec_t;
-typedef std::map<const fg_window, ChartVec_t> ChartMap_t;
-typedef std::pair<int, int> WindGridDims_t;
-typedef std::map<const fg_window, WindGridDims_t> WindGridMap_t;
-typedef ChartVec_t::iterator ChartVecIter;
-typedef ChartMap_t::iterator ChartMapIter;
-typedef WindGridMap_t::iterator GridMapIter;
-
-// Keeps track of which charts have manually assigned axes limits
-typedef std::map<fg_chart, bool> ChartAxesOverride_t;
-typedef ChartAxesOverride_t::iterator ChartAxesOverrideIter;
-
-/**
- * Only device manager class can create objects of this class.
- * You have to call forgeManager() defined in platform.hpp to
- * access the object. It manages the windows, and other
- * renderables (given below) that are drawed onto chosen window.
- * Renderables:
- *      fg_image
- *      fg_plot
- *      fg_histogram
- *      fg_surface
- *      fg_vector_field
- * */
-class ForgeManager {
-    struct Window {
-        fg_window handle;
-    };
-
-   private:
-    ForgeModule* mPlugin;
-    std::unique_ptr<Window> wnd;
-
-    ImageMap_t mImgMap;
-    PlotMap_t mPltMap;
-    HistogramMap_t mHstMap;
-    SurfaceMap_t mSfcMap;
-    VectorFieldMap_t mVcfMap;
 
-    ChartMap_t mChartMap;
-    WindGridMap_t mWndGridMap;
-    ChartAxesOverride_t mChartAxesOverrideMap;
+/// \brief The singleton manager class for Forge resources
+///
+/// Only device manager class can create objects of this class.
+/// You have to call forgeManager() defined in platform.hpp to
+/// access the object. It manages the windows, and other
+/// renderables (given below) that are drawed onto chosen window.
+/// Renderables:
+///      fg_image
+///      fg_plot
+///      fg_histogram
+///      fg_surface
+///      fg_vector_field
+///
+class ForgeManager {
+  public:
+    using WindowGridDims = std::pair<int, int>;
 
-   public:
     ForgeManager();
     ForgeManager(ForgeManager const&) = delete;
     ForgeManager& operator=(ForgeManager const&) = delete;
     ForgeManager(ForgeManager&&)                 = delete;
     ForgeManager& operator=(ForgeManager&&) = delete;
-    ~ForgeManager();
+
+    /// \brief Module used to invoke forge API calls
     ForgeModule& plugin();
+
+    /// \brief The main window with which all other windows share GL context
     fg_window getMainWindow();
 
+    /// \brief Create a window
+    ///
+    /// \param[in] width of the window
+    /// \param[in] height of the window
+    /// \param[in] title is the window title
+    /// \param[in] invisible indicates that if an invisible window
+    ///            has to be ceated
+    ///
+    /// \note Any window created will always shared OpenGL context with
+    ///       with the primary(getMainWindow()) window.
+    fg_window getWindow(const int width, const int height,
+                        const char* const title, const bool invisible = false);
+
+    /// \brief Set grid layout for a given Window
+    ///
+    /// Grid layout dictates how many renderables can be shown in a
+    /// single window. For example, if r = 2, c = 2, the entire rendering
+    /// area of the window will be split into four sections into which
+    /// different renderables can be drawn.
+    ///
+    /// \param[in] window is the target rendering context
+    /// \param[in] r is the number of rows in the grid
+    /// \param[in] c is the number of cols in the grid
     void setWindowChartGrid(const fg_window window, const int r, const int c);
 
-    WindGridDims_t getWindowGrid(const fg_window window);
+    /// \brief Get grid layout of a window
+    ///
+    /// This function fetches the grid layout set for given window, probably
+    /// which was set by the function \ref ForgeManager::setWindowChartGrid
+    ///
+    /// \param[in] window is the target rendering context
+    WindowGridDims getWindowGrid(const fg_window window);
 
+    /// \brief Find/Create a Chart
+    ///
+    /// This function tries to find a chart fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching chart
+    /// resource handle is returned. If no match is found, a new chart
+    /// with given parameters is created, cached and returned.
+    ///
+    /// \param[in] window is the target rendering context
+    /// \param[in] r is indicates the row index in the grid layout
+    ///            of the given \p window. This is usually 0 for grids having
+    ///            single cell a.k.a capable of drawing one renderable.
+    /// \param[in] c is indicates the col index in the grid layout
+    ///            of the given \p window. This is usually 0 for grids having
+    ///            single cell a.k.a capable of drawing one renderable.
+    /// \param[in] ctype is type renderables to be rendered on chart, 2D or 3D
     fg_chart getChart(const fg_window window, const int r, const int c,
                       const fg_chart_type ctype);
 
+    /// \brief Find/Create an Image
+    ///
+    /// This function tries to find an image fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching image
+    /// resource handle is returned. If no match is found, a new image
+    /// with given parameters is created, cached and returned.
+    ///
+    /// Also do keep in mind this function has to be used only when you
+    /// are rendering just an image to the window. If you want to render
+    /// an image embedded into set of plots or anything else, use the getImage
+    /// member function that takes in \ref fg_chart as first parameter.
+    ///
+    /// \param[in] w is width of the image
+    /// \param[in] h is height of the image
+    /// \param[in] mode is the pixel packing format in the image
+    /// \param[in] type is type of data to be stored in image buffer
+    ///
+    /// \note The width and height of image needs to fall in the range of
+    /// [0, 2^16] for the ForgeManager to correctly retrieve the necessary
+    /// Forge Image object. This is an implementation limitation on how big
+    /// of an image can be rendered using arrayfire graphics funtionality
     fg_image getImage(int w, int h, fg_channel_format mode, fg_dtype type);
 
-    fg_image getImage(fg_chart chart, int w, int h, fg_channel_format mode,
-                      fg_dtype type);
+    /// \brief Find/Create an Image to render in a Chart
+    ///
+    /// This function tries to find an image fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching image
+    /// resource handle is returned. If no match is found, a new image
+    /// with given parameters is created, cached and returned.
+    ///
+    /// \param[in] chart is the chart to which image will be rendered
+    /// \param[in] w is width of the image
+    /// \param[in] h is height of the image
+    /// \param[in] mode is the pixel packing format in the image
+    /// \param[in] type is type of data to be stored in image buffer
+    ///
+    /// \note The width and height of image needs to fall in the range of
+    /// [0, 2^16] for the ForgeManager to correctly retrieve the necessary
+    /// Forge Image object. This is an implementation limitation on how big
+    /// of an image can be rendered using arrayfire graphics funtionality
+    fg_image getImage(fg_chart chart, int w, int h,
+                      fg_channel_format mode, fg_dtype type);
 
+    /// \brief Find/Create a Plot to render in a Chart
+    ///
+    /// This function tries to find a plot fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching plot
+    /// resource handle is returned. If no match is found, a new plot
+    /// with given parameters is created, cached and returned.
+    ///
+    /// \param[in] chart is the chart to which plot will be rendered
+    /// \param[in] nPoints is number of points in the plot
+    /// \param[in] dtype is type of data to be stored in plot buffer
+    /// \param[in] ptype indicates the type of plot \ref fg_plot_type
+    /// \param[in] mtype indicates the type of marker/sprite to render original
+    ///            points passed in the data buffer, \ref fg_marker_type
+    ///
+    /// \note \p nPoints needs to fall in the range of [0, 2^48]
+    /// for the ForgeManager to correctly retrieve the necessary Forge
+    /// plot object. This is an implementation limitation on how big of a
+    /// plot can be rendered using arrayfire graphics funtionality
     fg_plot getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
                     fg_plot_type ptype, fg_marker_type mtype);
 
+    /// \brief Find/Create a Histogram to render in a Chart
+    ///
+    /// This function tries to find a histogram fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching histogram
+    /// resource handle is returned. If no match is found, a new histogram
+    /// with given parameters is created, cached and returned.
+    ///
+    /// \param[in] chart is the chart to which histogram will be rendered
+    /// \param[in] nBins is the total number of bins in the histogram
+    /// \param[in] type is type of data to be stored in histogram buffer
+    ///
+    /// \note \p nBins needs to fall in the range of [0, 2^48]
+    /// for the ForgeManager to correctly retrieve the necessary Forge
+    /// histogram object. This is an implementation limitation on how big
+    /// of a histogram can be rendered using arrayfire graphics funtionality
     fg_histogram getHistogram(fg_chart chart, int nBins, fg_dtype type);
 
+    /// \brief Find/Create a Surface to render in a Chart
+    ///
+    /// This function tries to find a surface fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching surface
+    /// resource handle is returned. If no match is found, a new surface
+    /// with given parameters is created, cached and returned.
+    ///
+    /// \param[in] chart is the chart to which surface will be rendered
+    /// \param[in] nX is length of the surface grid
+    /// \param[in] nY is width of the surface grid
+    /// \param[in] type is type of data to be stored in image buffer
+    ///
+    /// \note \p nX * \p nY needs to fall in the range of [0, 2^48]
+    /// for the ForgeManager to correctly retrieve the necessary Forge Surface
+    /// object. This is an implementation limitation on how big of a surface
+    /// can be rendered using arrayfire graphics funtionality
     fg_surface getSurface(fg_chart chart, int nX, int nY, fg_dtype type);
 
+    /// \brief Find/Create a Vector Field to render in a Chart
+    ///
+    /// This function tries to find a vector field fitting the given attributes
+    /// from forge resource cache. If a match is found, the matching vector
+    /// field resource handle is returned. If no match is found, a new vector
+    /// field with given parameters is created, cached and returned.
+    ///
+    /// \param[in] chart is the chart to which plot will be rendered
+    /// \param[in] nPoints is number of points in the 2D vector field
+    /// \param[in] type is type of data to be stored in plot buffer
+    ///
+    /// \note \p nPoints needs to fall in the range of [0, 2^48]
+    /// for the ForgeManager to correctly retrieve the necessary Forge vector
+    /// field object. This is an implementation limitation on how big of a
+    /// vector field can be rendered using arrayfire graphics funtionality
     fg_vector_field getVectorField(fg_chart chart, int nPoints, fg_dtype type);
 
-    bool getChartAxesOverride(fg_chart chart);
-    void setChartAxesOverride(fg_chart chart, bool flag = true);
+    /// \brief Get chart axes limits override flag
+    ///
+    /// \param[in] chart is the target chart for which axes limits will be
+    /// overriden
+    bool getChartAxesOverride(const fg_chart chart);
+
+    /// \brief Set chart axes limits override flag
+    ///
+    /// \param[in] chart is the target chart for which axes limits will be
+    /// overriden \param[in] flag indicates if axes limits are overriden or not
+    void setChartAxesOverride(const fg_chart chart, bool flag = true);
+
+  private:
+    constexpr static unsigned int WIDTH = 1280;
+    constexpr static unsigned int HEIGHT = 720;
+    constexpr static long long _4BIT     = 0x000000000000000F;
+    constexpr static long long _8BIT     = 0x00000000000000FF;
+    constexpr static long long _16BIT = 0x000000000000FFFF;
+    constexpr static long long _32BIT = 0x00000000FFFFFFFF;
+    constexpr static long long _48BIT = 0x0000FFFFFFFFFFFF;
+
+    long long genImageKey(int w, int h, fg_channel_format mode, fg_dtype type);
+
+#define DEFINE_WRAPPER_OBJECT(OBJECT, RELEASE)                           \
+    struct OBJECT {                                                      \
+        void* handle;                                                    \
+        struct Deleter {                                                 \
+            void operator()(OBJECT* pHandle) const {                     \
+                if (pHandle) { forgePlugin().RELEASE(pHandle->handle); } \
+            }                                                            \
+        };                                                               \
+    }
+
+    DEFINE_WRAPPER_OBJECT(Window, fg_release_window);
+    DEFINE_WRAPPER_OBJECT(Image, fg_release_image);
+    DEFINE_WRAPPER_OBJECT(Chart, fg_release_chart);
+    DEFINE_WRAPPER_OBJECT(Plot, fg_release_plot);
+    DEFINE_WRAPPER_OBJECT(Histogram, fg_release_histogram);
+    DEFINE_WRAPPER_OBJECT(Surface, fg_release_surface);
+    DEFINE_WRAPPER_OBJECT(VectorField, fg_release_vector_field);
+
+#undef DEFINE_WRAPPER_OBJECT
+
+    using ImagePtr        = std::unique_ptr<Image      , Image::Deleter      >;
+    using ChartPtr        = std::unique_ptr<Chart      , Chart::Deleter      >;
+    using PlotPtr         = std::unique_ptr<Plot       , Plot::Deleter       >;
+    using SurfacePtr      = std::unique_ptr<Surface    , Surface::Deleter    >;
+    using HistogramPtr    = std::unique_ptr<Histogram  , Histogram::Deleter  >;
+    using VectorFieldPtr  = std::unique_ptr<VectorField, VectorField::Deleter>;
+    using ChartList       = std::vector<ChartPtr>;
+    using ChartKey        = std::pair<long long, fg_chart>;
+
+    using ChartMapIterator     = std::map<fg_window, ChartList>::iterator;
+    using WindGridMapIterator  = std::map<fg_window, WindowGridDims>::iterator;
+    using AxesOverrideIterator = std::map<fg_chart, bool>::iterator;
+    using ImageMapIterator     = std::map<ChartKey, ImagePtr>::iterator;
+    using PlotMapIterator      = std::map<ChartKey, PlotPtr>::iterator;
+    using HistogramMapIterator = std::map<ChartKey, HistogramPtr>::iterator;
+    using SurfaceMapIterator   = std::map<ChartKey, SurfacePtr>::iterator;
+    using VecFieldMapIterator  = std::map<ChartKey, VectorFieldPtr>::iterator;
+
+    std::unique_ptr<ForgeModule> mPlugin;
+    std::unique_ptr<Window, Window::Deleter> mMainWindow;
+
+    std::map<fg_window, ChartList     > mChartMap;
+    std::map< ChartKey, ImagePtr      > mImgMap;
+    std::map< ChartKey, PlotPtr       > mPltMap;
+    std::map< ChartKey, HistogramPtr  > mHstMap;
+    std::map< ChartKey, SurfacePtr    > mSfcMap;
+    std::map< ChartKey, VectorFieldPtr> mVcfMap;
+    std::map<fg_window, WindowGridDims> mWndGridMap;
+    std::map< fg_chart, bool          > mChartAxesOverrideMap;
 };
+
 }  // namespace graphics

From 7797d01e65e35ca1cdd6f91eed244b859bcfd2ec Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 13 Mar 2019 03:10:51 +0530
Subject: [PATCH 1621/2677] Add CUDA runtime compilation support using nvrtc
 (#2437)

* Add CUDA runtime compilation support using nvrtc

Moved the following functions in CUDA backend to use runtime compilation
* Transpose (In place transpose hasn't been ported yet)
* Convolutions
* Scan and Scan by Key

The eventual goal is to use math.hpp even inside jit kernels and remove
the code-path controlled by isJIT parameter of compileKernel function.
---
 .../{CLKernelToH.cmake => FileToString.cmake} |   4 +-
 src/backend/cuda/CMakeLists.txt               |  55 +-
 src/backend/cuda/Param.hpp                    |  11 +-
 src/backend/cuda/backend.hpp                  |   5 +
 src/backend/cuda/convolve.cpp                 |   6 +-
 src/backend/cuda/jit.cpp                      | 138 +---
 src/backend/cuda/kernel/convolve.cu           | 602 ------------------
 src/backend/cuda/kernel/convolve.hpp          | 374 ++++++++++-
 src/backend/cuda/kernel/convolve1.cuh         |  77 +++
 src/backend/cuda/kernel/convolve2.cuh         | 100 +++
 src/backend/cuda/kernel/convolve3.cuh         | 112 ++++
 .../cuda/kernel/convolve_separable.cpp        |  31 +
 src/backend/cuda/kernel/convolve_separable.cu | 302 ---------
 .../cuda/kernel/convolve_separable.cuh        |  99 +++
 src/backend/cuda/kernel/harris.hpp            |  14 +-
 src/backend/cuda/kernel/orb.hpp               |   6 +-
 .../kernel/scan_by_key/scan_by_key_impl.cu    |   2 -
 src/backend/cuda/kernel/scan_dim.cuh          | 169 +++++
 src/backend/cuda/kernel/scan_dim.hpp          | 264 ++------
 src/backend/cuda/kernel/scan_dim_by_key.cuh   | 370 +++++++++++
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 479 ++------------
 src/backend/cuda/kernel/scan_first.cuh        | 137 ++++
 src/backend/cuda/kernel/scan_first.hpp        | 207 ++----
 src/backend/cuda/kernel/scan_first_by_key.cuh | 309 +++++++++
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 394 ++----------
 src/backend/cuda/kernel/shared.hpp            |  15 +
 src/backend/cuda/kernel/sift_nonfree.hpp      |  14 +-
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |   4 +-
 src/backend/cuda/kernel/transpose.cuh         |  78 +++
 src/backend/cuda/kernel/transpose.hpp         | 113 +---
 src/backend/cuda/kernel/where.cuh             |  59 ++
 src/backend/cuda/kernel/where.hpp             |  61 +-
 src/backend/cuda/math.cpp                     |  26 -
 src/backend/cuda/math.hpp                     |  36 +-
 src/backend/cuda/nvrtc/EnqueueArgs.hpp        |  54 ++
 src/backend/cuda/nvrtc/cache.cpp              | 368 +++++++++++
 src/backend/cuda/nvrtc/cache.hpp              | 172 +++++
 src/backend/cuda/{scan.cu => scan.cpp}        |  16 +-
 .../cuda/{scan_by_key.cu => scan_by_key.cpp}  |   0
 .../cuda/{transpose.cu => transpose.cpp}      |   9 +-
 src/backend/cuda/types.hpp                    |  15 +
 src/backend/cuda/{where.cu => where.cpp}      |   0
 src/backend/opencl/CMakeLists.txt             |   4 +-
 43 files changed, 2918 insertions(+), 2393 deletions(-)
 rename CMakeModules/{CLKernelToH.cmake => FileToString.cmake} (98%)
 delete mode 100644 src/backend/cuda/kernel/convolve.cu
 create mode 100644 src/backend/cuda/kernel/convolve1.cuh
 create mode 100644 src/backend/cuda/kernel/convolve2.cuh
 create mode 100644 src/backend/cuda/kernel/convolve3.cuh
 create mode 100644 src/backend/cuda/kernel/convolve_separable.cpp
 delete mode 100644 src/backend/cuda/kernel/convolve_separable.cu
 create mode 100644 src/backend/cuda/kernel/convolve_separable.cuh
 create mode 100644 src/backend/cuda/kernel/scan_dim.cuh
 create mode 100644 src/backend/cuda/kernel/scan_dim_by_key.cuh
 create mode 100644 src/backend/cuda/kernel/scan_first.cuh
 create mode 100644 src/backend/cuda/kernel/scan_first_by_key.cuh
 create mode 100644 src/backend/cuda/kernel/transpose.cuh
 create mode 100644 src/backend/cuda/kernel/where.cuh
 delete mode 100644 src/backend/cuda/math.cpp
 create mode 100644 src/backend/cuda/nvrtc/EnqueueArgs.hpp
 create mode 100644 src/backend/cuda/nvrtc/cache.cpp
 create mode 100644 src/backend/cuda/nvrtc/cache.hpp
 rename src/backend/cuda/{scan.cu => scan.cpp} (72%)
 rename src/backend/cuda/{scan_by_key.cu => scan_by_key.cpp} (100%)
 rename src/backend/cuda/{transpose.cu => transpose.cpp} (86%)
 rename src/backend/cuda/{where.cu => where.cpp} (100%)

diff --git a/CMakeModules/CLKernelToH.cmake b/CMakeModules/FileToString.cmake
similarity index 98%
rename from CMakeModules/CLKernelToH.cmake
rename to CMakeModules/FileToString.cmake
index dc8f857320..7004ba360e 100644
--- a/CMakeModules/CLKernelToH.cmake
+++ b/CMakeModules/FileToString.cmake
@@ -27,7 +27,7 @@ include(CMakeParseArguments)
 
 set(BIN2CPP_PROGRAM "bin2cpp")
 
-function(CL_KERNEL_TO_H)
+function(FILE_TO_STRING)
     cmake_parse_arguments(RTCS "" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;BINARY;NULLTERM" "SOURCES" ${ARGN})
 
     set(_output_files "")
@@ -69,4 +69,4 @@ function(CL_KERNEL_TO_H)
 
     set("${RTCS_VARNAME}" ${_output_files} PARENT_SCOPE)
     set("${RTCS_TARGETS}" ${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target PARENT_SCOPE)
-endfunction(CL_KERNEL_TO_H)
+endfunction(FILE_TO_STRING)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b7257af3e3..8550860523 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -15,7 +15,7 @@ find_cuda_helper_libs(nvrtc-builtins)
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
-include(CLKernelToH)
+include(FileToString)
 
 if(NOT CUDA_architecture_build_targets)
   cuda_detect_installed_gpus(detected_gpus)
@@ -55,7 +55,7 @@ set(jit_kernel_headers
 
 file(GLOB jit_src "kernel/jit.cuh")
 
-cl_kernel_to_h(
+file_to_string(
     SOURCES ${jit_src}
     VARNAME jit_files
     EXTENSION "hpp"
@@ -64,6 +64,39 @@ cl_kernel_to_h(
     NAMESPACE "cuda"
     )
 
+set(nvrtc_src
+  ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
+
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve1.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve2.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve3.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve_separable.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/where.cuh
+
+  ${PROJECT_SOURCE_DIR}/src/api/c/ops.hpp
+  ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
+
+  ${CMAKE_CURRENT_SOURCE_DIR}/Param.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/math.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/types.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/backend.hpp
+  )
+
+file_to_string(
+    SOURCES ${nvrtc_src}
+    VARNAME nvrtc_files
+    EXTENSION "hpp"
+    OUTPUT_DIR "nvrtc_kernel_headers"
+    TARGETS nvrtc_kernel_targets
+    NAMESPACE "cuda"
+    )
+
 ## Copied from FindCUDA.cmake
 ## The target_link_library needs to link with the cuda libraries using
 ## PRIVATE
@@ -121,11 +154,7 @@ include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
 cuda_add_library(afcuda
-    scan.cu
     sort.hpp
-    scan_by_key.cu
-    kernel/convolve.cu
-    kernel/convolve_separable.cu
 
     all.cu
     anisotropic_diffusion.cu
@@ -195,11 +224,10 @@ cuda_add_library(afcuda
     tile.cu
     topk.cu
     transform.cu
-    transpose.cu
+    transpose.cpp
     transpose_inplace.cu
     triangle.cu
     unwrap.cu
-    where.cu
     wrap.cu
 
     kernel/anisotropic_diffusion.hpp
@@ -210,6 +238,7 @@ cuda_add_library(afcuda
     kernel/canny.hpp
     kernel/config.hpp
     kernel/convolve.hpp
+    kernel/convolve_separable.cpp
     kernel/diagonal.hpp
     kernel/diff.hpp
     kernel/exampleFunction.hpp
@@ -340,7 +369,6 @@ cuda_add_library(afcuda
     lookup.hpp
     lu.hpp
     match_template.hpp
-    math.cpp
     math.hpp
     mean.hpp
     meanshift.hpp
@@ -368,7 +396,9 @@ cuda_add_library(afcuda
     resize.hpp
     rotate.hpp
     scalar.hpp
+    scan.cpp
     scan.hpp
+    scan_by_key.cpp
     scan_by_key.hpp
     select.hpp
     set.hpp
@@ -399,12 +429,15 @@ cuda_add_library(afcuda
     utility.hpp
     vector_field.cpp
     vector_field.hpp
+    where.cpp
     where.hpp
     wrap.hpp
 
     jit/BufferNode.hpp
     jit/kernel_generators.hpp
 
+    nvrtc/cache.cpp
+
     OPTIONS "${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
   )
 
@@ -417,7 +450,8 @@ if(AF_WITH_NONFREE)
   target_compile_definitions(afcuda PRIVATE AF_WITH_NONFREE_SIFT)
 endif()
 
-add_dependencies(afcuda ${jit_kernel_targets})
+add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
+add_dependencies(cuda_scan_by_key ${nvrtc_kernel_targets})
 
 target_include_directories (afcuda
   PUBLIC
@@ -457,6 +491,7 @@ target_link_libraries(afcuda
     ${CUDA_CUFFT_LIBRARIES}
     ${CUDA_cusolver_LIBRARY}
     ${CUDA_cusparse_LIBRARY}
+    ${CMAKE_DL_LIBS}
   )
 
 # If the driver is not found the cuda driver api need to be linked against the
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 7f15f86026..e51e8e831e 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -8,8 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <backend.hpp>
+#include <types.hpp>
+
+#ifndef __CUDACC_RTC__
 #include <af/defines.h>
+#endif
 
 namespace cuda {
 
@@ -29,7 +34,7 @@ class Param {
             strides[i] = istrides[i];
         }
     }
-    size_t elements() const noexcept {
+    __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
 };
@@ -65,7 +70,9 @@ class CParam {
         }
     }
 
-    __DH__ ~CParam() {}
+    __DH__ size_t elements() const noexcept {
+        return dims[0] * dims[1] * dims[2] * dims[3];
+    }
 };
 
 }  // namespace cuda
diff --git a/src/backend/cuda/backend.hpp b/src/backend/cuda/backend.hpp
index d785844dfd..33ce38d384 100644
--- a/src/backend/cuda/backend.hpp
+++ b/src/backend/cuda/backend.hpp
@@ -8,16 +8,21 @@
  ********************************************************/
 
 #pragma once
+
 #ifdef __DH__
 #undef __DH__
 #endif
 
+#ifdef __CUDACC_RTC__
+#define __DH__ __device__
+#else
 #ifdef __CUDACC__
 #include <cuda_runtime.h>
 #define __DH__ __device__ __host__
 #else
 #define __DH__
 #endif
+#endif
 
 namespace cuda {}
 
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index e4e4015ef0..1b2484cf0a 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -41,7 +41,7 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    kernel::convolve_nd<T, accT, baseDim, expand>(out, signal, filter, kind);
+    kernel::convolve_nd<T, accT>(out, signal, filter, kind, baseDim, expand);
 
     return out;
 }
@@ -68,8 +68,8 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
     Array<T> temp = createEmptyArray<T>(tDims);
     Array<T> out  = createEmptyArray<T>(oDims);
 
-    kernel::convolve2<T, accT, 0, expand>(temp, signal, c_filter);
-    kernel::convolve2<T, accT, 1, expand>(out, temp, r_filter);
+    kernel::convolve2<T, accT>(temp, signal, c_filter, 0, expand);
+    kernel::convolve2<T, accT>(out, temp, r_filter, 1, expand);
 
     return out;
 }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index d4cc7f6c41..ff9a3b6d7e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -17,14 +17,12 @@
 #include <math.hpp>
 #include <platform.hpp>
 
-#include <nvrtc.h>
+#include <nvrtc/cache.hpp>
 #include <af/dim4.hpp>
 
-#include <array>
 #include <cstdio>
 #include <functional>
 #include <map>
-#include <memory>
 #include <stdexcept>
 #include <thread>
 #include <vector>
@@ -35,15 +33,10 @@ using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
 
-using std::array;
 using std::hash;
-using std::lock_guard;
 using std::map;
-using std::mutex;
-using std::recursive_mutex;
 using std::string;
 using std::stringstream;
-using std::unique_ptr;
 using std::vector;
 
 static string getFuncName(const vector<Node *> &output_nodes,
@@ -206,135 +199,12 @@ struct Param
     return kerStream.str();
 }
 
-typedef struct {
-    CUmodule prog;
-    CUfunction ker;
-} kc_entry_t;
-
-#define CU_CHECK(fn)                                                      \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[1024];                                            \
-        const char *cu_err_name;                                          \
-        const char *cu_err_string;                                        \
-        cuGetErrorName(res, &cu_err_name);                                \
-        cuGetErrorString(res, &cu_err_string);                            \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), cu_err_string);                 \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
-    } while (0)
-
-#ifndef NDEBUG
-#define CU_LINK_CHECK(fn)                                                 \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[1024];                                            \
-        const char *cu_err_name;                                          \
-        cuGetErrorName(res, &cu_err_name);                                \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), linkError);                     \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
-    } while (0)
-#else
-#define CU_LINK_CHECK(fn) CU_CHECK(fn)
-#endif
-
-#ifndef NDEBUG
-#define NVRTC_CHECK(fn)                                \
-    do {                                               \
-        nvrtcResult res = fn;                          \
-        if (res == NVRTC_SUCCESS) break;               \
-        size_t logSize;                                \
-        nvrtcGetProgramLogSize(prog, &logSize);        \
-        unique_ptr<char[]> log(new char[logSize + 1]); \
-        char *logptr = log.get();                      \
-        nvrtcGetProgramLog(prog, logptr);              \
-        logptr[logSize] = '\x0';                       \
-        printf("%s\n", logptr);                        \
-        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
-    } while (0)
-#else
-#define NVRTC_CHECK(fn)                                                   \
-    do {                                                                  \
-        nvrtcResult res = fn;                                             \
-        if (res == NVRTC_SUCCESS) break;                                  \
-        char nvrtc_err_msg[1024];                                         \
-        snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
-                 "NVRTC Error(%d): %s\n", res, nvrtcGetErrorString(res)); \
-        AF_ERROR(nvrtc_err_msg, AF_ERR_INTERNAL);                         \
-    } while (0)
-#endif
-
-std::vector<char> compileToPTX(const char *ker_name, string jit_ker) {
-    nvrtcProgram prog;
-    size_t ptx_size;
-    std::vector<char> ptx;
-    NVRTC_CHECK(
-        nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 0, NULL, NULL));
-
-    auto dev = getDeviceProp(getActiveDeviceId());
-    array<char, 32> arch;
-    snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
-             dev.major, dev.minor);
-    const char *compiler_options[] = {
-        arch.data(),
-#if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
-        "--device-debug",
-        "--generate-line-info"
-#endif
-    };
-    int num_options = std::extent<decltype(compiler_options)>::value;
-    NVRTC_CHECK(nvrtcCompileProgram(prog, num_options, compiler_options));
-
-    NVRTC_CHECK(nvrtcGetPTXSize(prog, &ptx_size));
-    ptx.resize(ptx_size);
-    NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));
-    NVRTC_CHECK(nvrtcDestroyProgram(&prog));
-    return ptx;
-}
-
-static kc_entry_t compileKernel(const char *ker_name, string jit_ker) {
-    const size_t linkLogSize    = 1024;
-    char linkInfo[linkLogSize]  = {0};
-    char linkError[linkLogSize] = {0};
-
-    auto ptx = compileToPTX(ker_name, jit_ker);
-
-    CUlinkState linkState;
-    CUjit_option linkOptions[] = {
-        CU_JIT_INFO_LOG_BUFFER, CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
-        CU_JIT_ERROR_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
-        CU_JIT_LOG_VERBOSE};
-
-    void *linkOptionValues[] = {
-        linkInfo, reinterpret_cast<void *>(linkLogSize), linkError,
-        reinterpret_cast<void *>(linkLogSize), reinterpret_cast<void *>(1)};
-
-    CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
-    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void *)ptx.data(),
-                                ptx.size(), ker_name, 0, NULL, NULL));
-
-    void *cubin = nullptr;
-    size_t cubinSize;
-
-    CUmodule module;
-    CUfunction kernel;
-    CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
-    CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
-    CU_CHECK(cuModuleGetFunction(&kernel, module, ker_name));
-    CU_LINK_CHECK(cuLinkDestroy(linkState));
-    kc_entry_t entry = {module, kernel};
-    return entry;
-}
-
 static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<int> &output_ids,
                             const vector<const Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
-    typedef map<string, kc_entry_t> kc_t;
+    typedef map<string, Kernel> kc_t;
 
     thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
@@ -343,13 +213,13 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
     int device = getActiveDeviceId();
 
     kc_t::iterator idx = kernelCaches[device].find(funcName);
-    kc_entry_t entry{nullptr, nullptr};
+    Kernel entry{nullptr, nullptr};
 
     if (idx == kernelCaches[device].end()) {
         string jit_ker = getKernelString(funcName, full_nodes, full_ids,
                                          output_ids, is_linear);
         saveKernel(funcName, jit_ker, ".cu");
-        entry          = compileKernel(funcName.c_str(), jit_ker);
+        entry = buildKernel(funcName, jit_ker, {}, true);
         kernelCaches[device][funcName] = entry;
     } else {
         entry = idx->second;
diff --git a/src/backend/cuda/kernel/convolve.cu b/src/backend/cuda/kernel/convolve.cu
deleted file mode 100644
index c0b605875f..0000000000
--- a/src/backend/cuda/kernel/convolve.cu
+++ /dev/null
@@ -1,602 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Param.hpp>
-#include <backend.hpp>
-#include <common/dispatch.hpp>
-#include <convolve.hpp>
-#include <debug_cuda.hpp>
-#include <math.hpp>
-#include "shared.hpp"
-
-namespace cuda {
-
-namespace kernel {
-
-static const int THREADS = 256;
-
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-static const int CUBE_X = 8;
-static const int CUBE_Y = 8;
-static const int CUBE_Z = 4;
-
-// below shared MAX_*_LEN's are calculated based on
-// a maximum shared memory configuration of 48KB per block
-// considering complex types as well
-static const int MAX_CONV1_FILTER_LEN = 129;
-static const int MAX_CONV2_FILTER_LEN = 17;
-static const int MAX_CONV3_FILTER_LEN = 5;
-
-// we shall declare the maximum size required of above all three cases
-// and re-use the same constant memory locations for every case
-__constant__ char
-    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + THREADS) * sizeof(double)];
-
-template<typename T, typename aT, bool expand>
-__global__ void convolve1(Param<T> out, CParam<T> signal, int fLen, int nBBS0,
-                          int nBBS1, int o1, int o2, int o3, int s1, int s2,
-                          int s3) {
-    SharedMemory<T> shared;
-    T *shrdMem = shared.getPointer();
-
-    const int padding = fLen - 1;
-    const int shrdLen = blockDim.x + 2 * padding;
-    const unsigned b1 = blockIdx.x / nBBS0; /* [0 {1} 2 3] */
-    const unsigned b3 =
-        (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1; /* [0 1 2 {3}] */
-    const unsigned b2 =
-        (blockIdx.y + blockIdx.z * gridDim.y) - nBBS1 * b3; /* [0 1 {2} 3] */
-    if (b2 >= out.dims[2] || b3 >= out.dims[3]) return;
-
-    T *dst = (T *)out.ptr +
-             (b1 * out.strides[1] + /* activated with batched input signal */
-              o1 * out.strides[1] + /* activated with batched input filter */
-              b2 * out.strides[2] + /* activated with batched input signal */
-              o2 * out.strides[2] + /* activated with batched input filter */
-              b3 * out.strides[3] + /* activated with batched input signal */
-              o3 * out.strides[3]); /* activated with batched input filter */
-
-    const T *src =
-        (const T *)signal.ptr +
-        (b1 * signal.strides[1] + /* activated with batched input signal */
-         s1 * signal.strides[1] + /* activated with batched input filter */
-         b2 * signal.strides[2] + /* activated with batched input signal */
-         s2 * signal.strides[2] + /* activated with batched input filter */
-         b3 * signal.strides[3] + /* activated with batched input signal */
-         s3 * signal.strides[3]); /* activated with batched input filter */
-
-    const aT *impulse = (const aT *)cFilter;
-
-    int gx = blockDim.x * (blockIdx.x - b1 * nBBS0);
-
-    int s0 = signal.strides[0];
-    int d0 = signal.dims[0];
-    for (int i = threadIdx.x; i < shrdLen; i += blockDim.x) {
-        int idx    = gx - padding + i;
-        shrdMem[i] = (idx >= 0 && idx < d0) ? src[idx * s0] : scalar<T>(0);
-    }
-    __syncthreads();
-    gx += threadIdx.x;
-
-    if (gx < out.dims[0]) {
-        int lx   = threadIdx.x + padding + (expand ? 0 : fLen >> 1);
-        aT accum = scalar<aT>(0);
-        for (int f = 0; f < fLen; ++f) {
-            accum = accum + (shrdMem[lx - f] * impulse[f]);
-        }
-        dst[gx] = (T)accum;
-    }
-}
-
-template<typename T, typename aT, bool expand, int fLen0, int fLen1>
-__global__ void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
-                          int o2, int o3, int s2, int s3) {
-    const size_t C_SIZE =
-        (THREADS_X + 2 * (fLen0 - 1)) * (THREADS_Y + 2 * (fLen1 - 1));
-    __shared__ T shrdMem[C_SIZE];
-
-    const int radius0  = fLen0 - 1;
-    const int radius1  = fLen1 - 1;
-    const int padding0 = 2 * radius0;
-    const int padding1 = 2 * radius1;
-    const int shrdLen0 = THREADS_X + padding0;
-    const int shrdLen1 = THREADS_Y + padding1;
-
-    unsigned b0 = blockIdx.x / nBBS0;
-    unsigned b1 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
-    T *dst      = (T *)out.ptr +
-             (b0 * out.strides[2] + /* activated with batched input signal */
-              o2 * out.strides[2] + /* activated with batched input filter */
-              b1 * out.strides[3] + /* activated with batched input signal */
-              o3 * out.strides[3]); /* activated with batched input filter */
-
-    const T *src =
-        (const T *)signal.ptr +
-        (b0 * signal.strides[2] + /* activated with batched input signal */
-         s2 * signal.strides[2] + /* activated with batched input filter */
-         b1 * signal.strides[3] + /* activated with batched input signal */
-         s3 * signal.strides[3]); /* activated with batched input filter */
-
-    const aT *impulse = (const aT *)cFilter;
-
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-    int gx = THREADS_X * (blockIdx.x - b0 * nBBS0) + lx;
-    int gy =
-        THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) - b1 * nBBS1) + ly;
-
-    if (b1 >= out.dims[3]) return;
-
-    int s0 = signal.strides[0];
-    int s1 = signal.strides[1];
-    int d0 = signal.dims[0];
-    int d1 = signal.dims[1];
-    // below loops are traditional loops, they only run multiple
-    // times filter length is more than launch size
-#pragma unroll
-    for (int b = ly, gy2 = gy; b < shrdLen1; b += THREADS_Y, gy2 += THREADS_Y) {
-        int j     = gy2 - radius1;
-        bool is_j = j >= 0 && j < d1;
-        // move row_set THREADS_Y along coloumns
-#pragma unroll
-        for (int a = lx, gx2 = gx; a < shrdLen0;
-             a += THREADS_X, gx2 += THREADS_X) {
-            int i     = gx2 - radius0;
-            bool is_i = i >= 0 && i < d0;
-            shrdMem[b * shrdLen0 + a] =
-                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
-        }
-    }
-    __syncthreads();
-
-    if (gx < out.dims[0] && gy < out.dims[1]) {
-        int ci = lx + radius0 + (expand ? 0 : fLen0 >> 1);
-        int cj = ly + radius1 + (expand ? 0 : fLen1 >> 1);
-
-        aT accum = scalar<aT>(0);
-#pragma unroll
-        for (int fj = 0; fj < fLen1; ++fj) {
-#pragma unroll
-            for (int fi = 0; fi < fLen0; ++fi) {
-                aT f_val = impulse[fj * fLen0 + fi];
-                T s_val  = shrdMem[(cj - fj) * shrdLen0 + (ci - fi)];
-                accum    = accum + s_val * f_val;
-            }
-        }
-        dst[gy * out.strides[1] + gx] = (T)accum;
-    }
-}
-
-__inline__ __device__ int index(int i, int j, int k, int jstride, int kstride) {
-    return i + j * jstride + k * kstride;
-}
-
-template<typename T, typename aT, bool expand>
-__global__ void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
-                          int fLen2, int nBBS, int o3, int s3) {
-    SharedMemory<T> shared;
-
-    T *shrdMem   = shared.getPointer();
-    int radius0  = fLen0 - 1;
-    int radius1  = fLen1 - 1;
-    int radius2  = fLen2 - 1;
-    int shrdLen0 = blockDim.x + 2 * radius0;
-    int shrdLen1 = blockDim.y + 2 * radius1;
-    int shrdLen2 = blockDim.z + 2 * radius2;
-    int skStride = shrdLen0 * shrdLen1;
-    int fStride  = fLen0 * fLen1;
-    unsigned b2  = blockIdx.x / nBBS;
-
-    T *dst = (T *)out.ptr +
-             (b2 * out.strides[3] + /* activated with batched input signal */
-              o3 * out.strides[3]); /* activated with batched input filter */
-
-    const T *src =
-        (const T *)signal.ptr +
-        (b2 * signal.strides[3] + /* activated with batched input signal */
-         s3 * signal.strides[3]); /* activated with batched input filter */
-
-    const aT *impulse = (const aT *)cFilter;
-
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-    int lz = threadIdx.z;
-    int gx = blockDim.x * (blockIdx.x - b2 * nBBS) + lx;
-    int gy = blockDim.y * blockIdx.y + ly;
-    int gz = blockDim.z * blockIdx.z + lz;
-
-    int s0 = signal.strides[0];
-    int s1 = signal.strides[1];
-    int s2 = signal.strides[2];
-    int d0 = signal.dims[0];
-    int d1 = signal.dims[1];
-    int d2 = signal.dims[2];
-#pragma unroll
-    for (int c = lz, gz2 = gz; c < shrdLen2; c += CUBE_Z, gz2 += CUBE_Z) {
-        int k     = gz2 - radius2;
-        bool is_k = k >= 0 && k < d2;
-#pragma unroll
-        for (int b = ly, gy2 = gy; b < shrdLen1; b += CUBE_Y, gy2 += CUBE_Y) {
-            int j     = gy2 - radius1;
-            bool is_j = j >= 0 && j < d1;
-#pragma unroll
-            for (int a = lx, gx2 = gx; a < shrdLen0;
-                 a += CUBE_X, gx2 += CUBE_X) {
-                int i     = gx2 - radius0;
-                bool is_i = i >= 0 && i < d0;
-                shrdMem[c * skStride + b * shrdLen0 + a] =
-                    (is_i && is_j && is_k ? src[i * s0 + j * s1 + k * s2]
-                                          : scalar<T>(0));
-            }
-        }
-    }
-    __syncthreads();
-
-    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2]) {
-        int ci = lx + radius0 + (expand ? 0 : fLen0 >> 1);
-        int cj = ly + radius1 + (expand ? 0 : fLen1 >> 1);
-        int ck = lz + radius2 + (expand ? 0 : fLen2 >> 1);
-
-        aT accum = scalar<aT>(0);
-#pragma unroll
-        for (int fk = 0; fk < fLen2; ++fk) {
-#pragma unroll
-            for (int fj = 0; fj < fLen1; ++fj) {
-#pragma unroll
-                for (int fi = 0; fi < fLen0; ++fi) {
-                    aT f_val = impulse[index(fi, fj, fk, fLen0, fStride)];
-                    T s_val = shrdMem[index(ci - fi, cj - fj, ck - fk, shrdLen0,
-                                            skStride)];
-                    accum   = accum + s_val * f_val;
-                }
-            }
-        }
-        dst[index(gx, gy, gz, out.strides[1], out.strides[2])] = (T)accum;
-    }
-}
-
-struct conv_kparam_t {
-    dim3 mBlocks;
-    dim3 mThreads;
-    size_t mSharedSize;
-    int mBlk_x;
-    int mBlk_y;
-    bool outHasNoOffset;
-    bool inHasNoOffset;
-    bool launchMoreBlocks;
-    int o[3];
-    int s[3];
-};
-
-template<typename T>
-void prepareKernelArgs(conv_kparam_t &params, dim_t oDims[], dim_t fDims[],
-                       int baseDim) {
-    int batchDims[4] = {1, 1, 1, 1};
-    for (int i = baseDim; i < 4; ++i) {
-        batchDims[i] = (params.launchMoreBlocks ? 1 : oDims[i]);
-    }
-
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    if (baseDim == 1) {
-        params.mThreads = dim3(THREADS, 1);
-        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
-        params.mBlk_y   = batchDims[2];
-        params.mBlocks =
-            dim3(params.mBlk_x * batchDims[1], params.mBlk_y * batchDims[3]);
-        params.mSharedSize =
-            (params.mThreads.x + 2 * (fDims[0] - 1)) * sizeof(T);
-        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
-        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
-    } else if (baseDim == 2) {
-        params.mThreads = dim3(THREADS_X, THREADS_Y);
-        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
-        params.mBlk_y   = divup(oDims[1], params.mThreads.y);
-        params.mBlocks =
-            dim3(params.mBlk_x * batchDims[2], params.mBlk_y * batchDims[3]);
-        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
-        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
-    } else if (baseDim == 3) {
-        params.mThreads = dim3(CUBE_X, CUBE_Y, CUBE_Z);
-        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
-        params.mBlk_y   = divup(oDims[1], params.mThreads.y);
-        int blk_z       = divup(oDims[2], params.mThreads.z);
-        params.mBlocks =
-            dim3(params.mBlk_x * batchDims[3], params.mBlk_y, blk_z);
-        params.mSharedSize = (params.mThreads.x + 2 * (fDims[0] - 1)) *
-                             (params.mThreads.y + 2 * (fDims[1] - 1)) *
-                             (params.mThreads.z + 2 * (fDims[2] - 1)) *
-                             sizeof(T);
-        // todo: fold into x dimension according to old style
-        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
-        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
-    }
-}
-
-template<typename T, typename aT, bool expand, int f0, int f1>
-void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig) {
-    CUDA_LAUNCH((convolve2<T, aT, expand, f0, f1>), p.mBlocks, p.mThreads, out,
-                sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
-
-    POST_LAUNCH_CHECK();
-}
-
-template<typename T, typename aT, bool expand, int f0>
-void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f1) {
-    switch (f1) {
-        case 1: conv2Helper<T, aT, expand, f0, 1>(p, out, sig); break;
-        case 2: conv2Helper<T, aT, expand, f0, 2>(p, out, sig); break;
-        case 3: conv2Helper<T, aT, expand, f0, 3>(p, out, sig); break;
-        case 4: conv2Helper<T, aT, expand, f0, 4>(p, out, sig); break;
-        case 5: conv2Helper<T, aT, expand, f0, 5>(p, out, sig); break;
-        default: {
-            char errMessage[256];
-            snprintf(errMessage, sizeof(errMessage),
-                     "\nCUDA Convolution doesn't support %dx%d kernel\n", f0,
-                     f1);
-            CUDA_NOT_SUPPORTED(errMessage);
-        };
-    }
-}
-
-template<typename T, typename aT, bool expand>
-void conv2Helper(const conv_kparam_t &p, Param<T> out, CParam<T> sig, int f0,
-                 int f1) {
-    switch (f0) {
-        case 1: conv2Helper<T, aT, expand, 1>(p, out, sig, f1); break;
-        case 2: conv2Helper<T, aT, expand, 2>(p, out, sig, f1); break;
-        case 3: conv2Helper<T, aT, expand, 3>(p, out, sig, f1); break;
-        case 4: conv2Helper<T, aT, expand, 4>(p, out, sig, f1); break;
-        case 5: conv2Helper<T, aT, expand, 5>(p, out, sig, f1); break;
-        default: {
-            if (f0 == f1) {
-                switch (f1) {
-                    case 6:
-                        conv2Helper<T, aT, expand, 6, 6>(p, out, sig);
-                        break;
-                    case 7:
-                        conv2Helper<T, aT, expand, 7, 7>(p, out, sig);
-                        break;
-                    case 8:
-                        conv2Helper<T, aT, expand, 8, 8>(p, out, sig);
-                        break;
-                    case 9:
-                        conv2Helper<T, aT, expand, 9, 9>(p, out, sig);
-                        break;
-                    case 10:
-                        conv2Helper<T, aT, expand, 10, 10>(p, out, sig);
-                        break;
-                    case 11:
-                        conv2Helper<T, aT, expand, 11, 11>(p, out, sig);
-                        break;
-                    case 12:
-                        conv2Helper<T, aT, expand, 12, 12>(p, out, sig);
-                        break;
-                    case 13:
-                        conv2Helper<T, aT, expand, 13, 13>(p, out, sig);
-                        break;
-                    case 14:
-                        conv2Helper<T, aT, expand, 14, 14>(p, out, sig);
-                        break;
-                    case 15:
-                        conv2Helper<T, aT, expand, 15, 15>(p, out, sig);
-                        break;
-                    case 16:
-                        conv2Helper<T, aT, expand, 16, 16>(p, out, sig);
-                        break;
-                    case 17:
-                        conv2Helper<T, aT, expand, 17, 17>(p, out, sig);
-                        break;
-                    default: {
-                        char errMessage[256];
-                        snprintf(errMessage, sizeof(errMessage),
-                                 "\nCUDA 2D convolution doesn't support %dx%d "
-                                 "kernel\n",
-                                 f0, f1);
-                        CUDA_NOT_SUPPORTED(errMessage);
-                    };
-                }
-            } else {
-                {
-                    char errMessage[256];
-                    snprintf(errMessage, sizeof(errMessage),
-                             "\nCUDA 2D convolution doesn't support "
-                             "rectangular kernels\n");
-                    CUDA_NOT_SUPPORTED(errMessage);
-                };
-            }
-        } break;
-    }
-}
-
-template<typename T, typename aT, bool expand>
-void convolve_1d(conv_kparam_t &p, Param<T> out, CParam<T> sig,
-                 CParam<aT> filt) {
-    prepareKernelArgs<T>(p, out.dims, filt.dims, 1);
-
-    int filterLen = filt.dims[0];
-
-    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
-        int f3Off = b3 * filt.strides[3];
-
-        for (int b2 = 0; b2 < filt.dims[2]; ++b2) {
-            int f2Off = b2 * filt.strides[2];
-
-            for (int b1 = 0; b1 < filt.dims[1]; ++b1) {
-                int f1Off = b1 * filt.strides[1];
-
-                // FIXME: if the filter array is strided, direct copy of symbols
-                // might cause issues
-                CUDA_CHECK(cudaMemcpyToSymbolAsync(
-                    kernel::cFilter, filt.ptr + (f1Off + f2Off + f3Off),
-                    filterLen * sizeof(aT), 0, cudaMemcpyDeviceToDevice,
-                    cuda::getActiveStream()));
-
-                p.o[0] = (p.outHasNoOffset ? 0 : b1);
-                p.o[1] = (p.outHasNoOffset ? 0 : b2);
-                p.o[2] = (p.outHasNoOffset ? 0 : b3);
-                p.s[0] = (p.inHasNoOffset ? 0 : b1);
-                p.s[1] = (p.inHasNoOffset ? 0 : b2);
-                p.s[2] = (p.inHasNoOffset ? 0 : b3);
-
-                CUDA_LAUNCH_SMEM((convolve1<T, aT, expand>), p.mBlocks,
-                                 p.mThreads, p.mSharedSize, out, sig,
-                                 filt.dims[0], p.mBlk_x, p.mBlk_y, p.o[0],
-                                 p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
-
-                POST_LAUNCH_CHECK();
-            }
-        }
-    }
-}
-
-template<typename T, typename aT, bool expand>
-void convolve_2d(conv_kparam_t &p, Param<T> out, CParam<T> sig,
-                 CParam<aT> filt) {
-    prepareKernelArgs<T>(p, out.dims, filt.dims, 2);
-
-    int filterLen = filt.dims[0] * filt.dims[1];
-
-    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
-        int f3Off = b3 * filt.strides[3];
-
-        for (int b2 = 0; b2 < filt.dims[2]; ++b2) {
-            int f2Off = b2 * filt.strides[2];
-
-            // FIXME: if the filter array is strided, direct copy of symbols
-            // might cause issues
-            CUDA_CHECK(cudaMemcpyToSymbolAsync(
-                kernel::cFilter, filt.ptr + (f2Off + f3Off),
-                filterLen * sizeof(aT), 0, cudaMemcpyDeviceToDevice,
-                cuda::getActiveStream()));
-
-            p.o[1] = (p.outHasNoOffset ? 0 : b2);
-            p.o[2] = (p.outHasNoOffset ? 0 : b3);
-            p.s[1] = (p.inHasNoOffset ? 0 : b2);
-            p.s[2] = (p.inHasNoOffset ? 0 : b3);
-
-            conv2Helper<T, aT, expand>(p, out, sig, filt.dims[0], filt.dims[1]);
-        }
-    }
-}
-
-template<typename T, typename aT, bool expand>
-void convolve_3d(conv_kparam_t &p, Param<T> out, CParam<T> sig,
-                 CParam<aT> filt) {
-    prepareKernelArgs<T>(p, out.dims, filt.dims, 3);
-
-    int filterLen = filt.dims[0] * filt.dims[1] * filt.dims[2];
-
-    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
-        int f3Off = b3 * filt.strides[3];
-
-        // FIXME: if the filter array is strided, direct copy of symbols
-        // might cause issues
-        CUDA_CHECK(cudaMemcpyToSymbolAsync(
-            kernel::cFilter, filt.ptr + f3Off, filterLen * sizeof(aT), 0,
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
-        p.o[2] = (p.outHasNoOffset ? 0 : b3);
-        p.s[2] = (p.inHasNoOffset ? 0 : b3);
-
-        CUDA_LAUNCH_SMEM((convolve3<T, aT, expand>), p.mBlocks, p.mThreads,
-                         p.mSharedSize, out, sig, filt.dims[0], filt.dims[1],
-                         filt.dims[2], p.mBlk_x, p.o[2], p.s[2]);
-
-        POST_LAUNCH_CHECK();
-    }
-}
-
-template<typename T, typename aT, int baseDim, bool expand>
-void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt,
-                 AF_BATCH_KIND kind) {
-    bool callKernel = true;
-
-    int MCFL2 = kernel::MAX_CONV2_FILTER_LEN;
-    int MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
-    switch (baseDim) {
-        case 1:
-            if (filt.dims[0] > kernel::MAX_CONV1_FILTER_LEN) callKernel = false;
-            break;
-        case 2:
-            if ((filt.dims[0] * filt.dims[1]) > (MCFL2 * MCFL2))
-                callKernel = false;
-            break;
-        case 3:
-            if ((filt.dims[0] * filt.dims[1] * filt.dims[2]) >
-                (MCFL3 * MCFL3 * MCFL3))
-                callKernel = false;
-            break;
-    }
-
-    if (!callKernel) {
-        char errMessage[256];
-        snprintf(errMessage, sizeof(errMessage),
-                 "\nCUDA N Dimensional Convolution doesn't support "
-                 "%lldx%lldx%lld kernel\n",
-                 filt.dims[0], filt.dims[1], filt.dims[2]);
-        CUDA_NOT_SUPPORTED(errMessage);
-    }
-
-    conv_kparam_t param;
-    for (int i = 0; i < 3; ++i) {
-        param.o[i] = 0;
-        param.s[i] = 0;
-    }
-    param.launchMoreBlocks = kind == AF_BATCH_SAME || kind == AF_BATCH_RHS;
-    param.outHasNoOffset   = kind == AF_BATCH_LHS || kind == AF_BATCH_NONE;
-    param.inHasNoOffset    = kind != AF_BATCH_SAME;
-
-    switch (baseDim) {
-        case 1: convolve_1d<T, aT, expand>(param, out, signal, filt); break;
-        case 2: convolve_2d<T, aT, expand>(param, out, signal, filt); break;
-        case 3: convolve_3d<T, aT, expand>(param, out, signal, filt); break;
-    }
-
-    POST_LAUNCH_CHECK();
-}
-
-#define INSTANTIATE(T, aT)                                                     \
-    template void convolve_nd<T, aT, 1, true>(Param<T> out, CParam<T> signal,  \
-                                              CParam<aT> filter,               \
-                                              AF_BATCH_KIND kind);             \
-    template void convolve_nd<T, aT, 1, false>(Param<T> out, CParam<T> signal, \
-                                               CParam<aT> filter,              \
-                                               AF_BATCH_KIND kind);            \
-    template void convolve_nd<T, aT, 2, true>(Param<T> out, CParam<T> signal,  \
-                                              CParam<aT> filter,               \
-                                              AF_BATCH_KIND kind);             \
-    template void convolve_nd<T, aT, 2, false>(Param<T> out, CParam<T> signal, \
-                                               CParam<aT> filter,              \
-                                               AF_BATCH_KIND kind);            \
-    template void convolve_nd<T, aT, 3, true>(Param<T> out, CParam<T> signal,  \
-                                              CParam<aT> filter,               \
-                                              AF_BATCH_KIND kind);             \
-    template void convolve_nd<T, aT, 3, false>(Param<T> out, CParam<T> signal, \
-                                               CParam<aT> filter,              \
-                                               AF_BATCH_KIND kind);
-
-INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat, cfloat)
-INSTANTIATE(double, double)
-INSTANTIATE(float, float)
-INSTANTIATE(uint, float)
-INSTANTIATE(int, float)
-INSTANTIATE(uchar, float)
-INSTANTIATE(char, float)
-INSTANTIATE(ushort, float)
-INSTANTIATE(short, float)
-INSTANTIATE(uintl, float)
-INSTANTIATE(intl, float)
-
-}  // namespace kernel
-
-}  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 9daf1af3ce..3534b760b8 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,24 +7,376 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
+#include <common/defines.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include "shared.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/convolve1.hpp>
+#include <nvrtc_kernel_headers/convolve2.hpp>
+#include <nvrtc_kernel_headers/convolve3.hpp>
+#include <nvrtc_kernel_headers/convolve_separable.hpp>
+#include <traits.hpp>
 
-namespace cuda {
+#include <string>
 
+using std::string;
+
+namespace cuda {
 namespace kernel {
 
-template<typename T, typename accType, int baseDim, bool expand>
-void convolve_nd(Param<T> out, CParam<T> signal, CParam<accType> filter,
-                 AF_BATCH_KIND kind);
+static const int CONV_THREADS = 256;
 
-template<typename T, typename accType, int conv_dim, bool expand>
-void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter);
+static const int CONV2_THREADS_X = 16;
+static const int CONV2_THREADS_Y = 16;
 
-}  // namespace kernel
+static const int CONV3_CUBE_X = 8;
+static const int CONV3_CUBE_Y = 8;
+static const int CONV3_CUBE_Z = 4;
+
+// below shared MAX_*_LEN's are calculated based on
+// a maximum shared memory configuration of 48KB per block
+// considering complex types as well
+static const int MAX_CONV1_FILTER_LEN = 129;
+static const int MAX_CONV2_FILTER_LEN = 17;
+static const int MAX_CONV3_FILTER_LEN = 5;
+
+constexpr static const char* conv_c_name  = "cFilter";
+constexpr static const char* sconv_c_name = "sFilter";
+
+struct conv_kparam_t {
+    dim3 mBlocks;
+    dim3 mThreads;
+    size_t mSharedSize;
+    int mBlk_x;
+    int mBlk_y;
+    bool outHasNoOffset;
+    bool inHasNoOffset;
+    bool launchMoreBlocks;
+    int o[3];
+    int s[3];
+};
+
+template<typename T>
+void prepareKernelArgs(conv_kparam_t& params, dim_t oDims[], dim_t fDims[],
+                       int baseDim) {
+    int batchDims[4] = {1, 1, 1, 1};
+    for (int i = baseDim; i < 4; ++i) {
+        batchDims[i] = (params.launchMoreBlocks ? 1 : oDims[i]);
+    }
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    if (baseDim == 1) {
+        params.mThreads = dim3(CONV_THREADS, 1);
+        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
+        params.mBlk_y   = batchDims[2];
+        params.mBlocks =
+            dim3(params.mBlk_x * batchDims[1], params.mBlk_y * batchDims[3]);
+        params.mSharedSize =
+            (params.mThreads.x + 2 * (fDims[0] - 1)) * sizeof(T);
+        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
+        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
+    } else if (baseDim == 2) {
+        params.mThreads = dim3(CONV2_THREADS_X, CONV2_THREADS_Y);
+        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
+        params.mBlk_y   = divup(oDims[1], params.mThreads.y);
+        params.mBlocks =
+            dim3(params.mBlk_x * batchDims[2], params.mBlk_y * batchDims[3]);
+        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
+        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
+    } else if (baseDim == 3) {
+        params.mThreads = dim3(CONV3_CUBE_X, CONV3_CUBE_Y, CONV3_CUBE_Z);
+        params.mBlk_x   = divup(oDims[0], params.mThreads.x);
+        params.mBlk_y   = divup(oDims[1], params.mThreads.y);
+        int blk_z       = divup(oDims[2], params.mThreads.z);
+        params.mBlocks =
+            dim3(params.mBlk_x * batchDims[3], params.mBlk_y, blk_z);
+        params.mSharedSize = (params.mThreads.x + 2 * (fDims[0] - 1)) *
+                             (params.mThreads.y + 2 * (fDims[1] - 1)) *
+                             (params.mThreads.z + 2 * (fDims[2] - 1)) *
+                             sizeof(T);
+        // todo: fold into x dimension according to old style
+        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
+        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
+    }
+}
+
+template<typename T, typename aT>
+void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
+                 const bool expand) {
+    static const std::string src(convolve1_cuh, convolve1_cuh_len);
+
+    // clang-format off
+    auto conv = getKernel("cuda::convolve1", src,
+            {
+              TemplateTypename<T>(),
+              TemplateTypename<aT>(),
+              TemplateArg(expand)
+            },
+            {
+              DefineValue(MAX_CONV1_FILTER_LEN),
+              DefineValue(CONV_THREADS)
+            }
+            );
+    // clang-format on
+
+    prepareKernelArgs<T>(p, out.dims, filt.dims, 1);
+
+    size_t filterSize = filt.dims[0] * sizeof(aT);
+
+    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
+        int f3Off = b3 * filt.strides[3];
+
+        for (int b2 = 0; b2 < filt.dims[2]; ++b2) {
+            int f2Off = b2 * filt.strides[2];
+
+            for (int b1 = 0; b1 < filt.dims[1]; ++b1) {
+                int f1Off      = b1 * filt.strides[1];
+                const aT* fptr = filt.ptr + (f1Off + f2Off + f3Off);
+
+                // FIXME: case where filter array is strided
+                conv.setConstant(conv_c_name,
+                                 reinterpret_cast<CUdeviceptr>(fptr),
+                                 filterSize);
+
+                p.o[0] = (p.outHasNoOffset ? 0 : b1);
+                p.o[1] = (p.outHasNoOffset ? 0 : b2);
+                p.o[2] = (p.outHasNoOffset ? 0 : b3);
+                p.s[0] = (p.inHasNoOffset ? 0 : b1);
+                p.s[1] = (p.inHasNoOffset ? 0 : b2);
+                p.s[2] = (p.inHasNoOffset ? 0 : b3);
+
+                EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream(),
+                                  p.mSharedSize);
+                conv(qArgs, out, sig, filt.dims[0], p.mBlk_x, p.mBlk_y, p.o[0],
+                     p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
+                POST_LAUNCH_CHECK();
+            }
+        }
+    }
+}
+
+template<typename T, typename aT>
+void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
+                 const aT* fptr, int f0, int f1, const bool expand) {
+    const bool isFilterSizeLt5  = (f0 <= 5 && f1 <= 5);
+    const bool isFilterGt5AndSq = (f0 == f1 && f0 > 5 && f0 < 18);
+
+    if (!(isFilterSizeLt5 || isFilterGt5AndSq)) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCUDA Convolution doesn't support %dx%d kernel\n", f0, f1);
+        CUDA_NOT_SUPPORTED(errMessage);
+    }
+
+    static const std::string src(convolve2_cuh, convolve2_cuh_len);
+
+    // clang-format off
+    auto conv = getKernel("cuda::convolve2", src,
+            {
+              TemplateTypename<T>(),
+              TemplateTypename<aT>(),
+              TemplateArg(expand),
+              TemplateArg(f0),
+              TemplateArg(f1)
+            },
+            {
+              DefineValue(MAX_CONV1_FILTER_LEN),
+              DefineValue(CONV_THREADS),
+              DefineValue(CONV2_THREADS_X),
+              DefineValue(CONV2_THREADS_Y)
+            }
+            );
+    // clang-format on
+
+    // FIXME: case where filter array is strided
+    conv.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
+                     f0 * f1 * sizeof(aT));
+
+    EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream());
+    conv(qArgs, out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
+    POST_LAUNCH_CHECK();
+}
+
+template<typename T, typename aT>
+void convolve_2d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
+                 const bool expand) {
+    prepareKernelArgs<T>(p, out.dims, filt.dims, 2);
+
+    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
+        int f3Off = b3 * filt.strides[3];
+
+        for (int b2 = 0; b2 < filt.dims[2]; ++b2) {
+            int f2Off = b2 * filt.strides[2];
 
+            const aT* fptr = filt.ptr + (f2Off + f3Off);
+
+            p.o[1] = (p.outHasNoOffset ? 0 : b2);
+            p.o[2] = (p.outHasNoOffset ? 0 : b3);
+            p.s[1] = (p.inHasNoOffset ? 0 : b2);
+            p.s[2] = (p.inHasNoOffset ? 0 : b3);
+
+            conv2Helper<T, aT>(p, out, sig, fptr, filt.dims[0], filt.dims[1],
+                               expand);
+        }
+    }
+}
+
+template<typename T, typename aT>
+void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
+                 const bool expand) {
+    static const std::string src(convolve3_cuh, convolve3_cuh_len);
+
+    // clang-format off
+    auto conv = getKernel("cuda::convolve3", src,
+            {
+              TemplateTypename<T>(),
+              TemplateTypename<aT>(),
+              TemplateArg(expand)
+            },
+            {
+              DefineValue(MAX_CONV1_FILTER_LEN),
+              DefineValue(CONV_THREADS),
+              DefineValue(CONV3_CUBE_X),
+              DefineValue(CONV3_CUBE_Y),
+              DefineValue(CONV3_CUBE_Z)
+            }
+            );
+    // clang-format on
+
+    prepareKernelArgs<T>(p, out.dims, filt.dims, 3);
+
+    size_t filterSize = filt.dims[0] * filt.dims[1] * filt.dims[2] * sizeof(aT);
+
+    for (int b3 = 0; b3 < filt.dims[3]; ++b3) {
+        int f3Off = b3 * filt.strides[3];
+
+        const aT* fptr = filt.ptr + f3Off;
+
+        // FIXME: case where filter array is strided
+        conv.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
+                         filterSize);
+
+        p.o[2] = (p.outHasNoOffset ? 0 : b3);
+        p.s[2] = (p.inHasNoOffset ? 0 : b3);
+
+        EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream(),
+                          p.mSharedSize);
+        conv(qArgs, out, sig, filt.dims[0], filt.dims[1], filt.dims[2],
+             p.mBlk_x, p.o[2], p.s[2]);
+        POST_LAUNCH_CHECK();
+    }
+}
+
+template<typename T, typename aT>
+void convolve_nd(Param<T> out, CParam<T> signal, CParam<aT> filt,
+                 AF_BATCH_KIND kind, int baseDim, bool expand) {
+    bool callKernel = true;
+
+    int MCFL2 = kernel::MAX_CONV2_FILTER_LEN;
+    int MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
+    switch (baseDim) {
+        case 1:
+            if (filt.dims[0] > kernel::MAX_CONV1_FILTER_LEN) callKernel = false;
+            break;
+        case 2:
+            if ((filt.dims[0] * filt.dims[1]) > (MCFL2 * MCFL2))
+                callKernel = false;
+            break;
+        case 3:
+            if ((filt.dims[0] * filt.dims[1] * filt.dims[2]) >
+                (MCFL3 * MCFL3 * MCFL3))
+                callKernel = false;
+            break;
+    }
+
+    if (!callKernel) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCUDA N Dimensional Convolution doesn't support "
+                 "%lldx%lldx%lld kernel\n",
+                 filt.dims[0], filt.dims[1], filt.dims[2]);
+        CUDA_NOT_SUPPORTED(errMessage);
+    }
+
+    conv_kparam_t param;
+    for (int i = 0; i < 3; ++i) {
+        param.o[i] = 0;
+        param.s[i] = 0;
+    }
+    param.launchMoreBlocks = kind == AF_BATCH_SAME || kind == AF_BATCH_RHS;
+    param.outHasNoOffset   = kind == AF_BATCH_LHS || kind == AF_BATCH_NONE;
+    param.inHasNoOffset    = kind != AF_BATCH_SAME;
+
+    switch (baseDim) {
+        case 1: convolve_1d<T, aT>(param, out, signal, filt, expand); break;
+        case 2: convolve_2d<T, aT>(param, out, signal, filt, expand); break;
+        case 3: convolve_3d<T, aT>(param, out, signal, filt, expand); break;
+    }
+
+    POST_LAUNCH_CHECK();
+}
+
+static const int SCONV_THREADS_X = 16;
+static const int SCONV_THREADS_Y = 16;
+
+// below shared MAX_*_LEN's are calculated based on
+// a maximum shared memory configuration of 48KB per block
+// considering complex types as well
+static const int MAX_SCONV_FILTER_LEN = 31;
+
+template<typename T, typename aT>
+void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
+               bool expand) {
+    int fLen =
+        filter.dims[0] * filter.dims[1] * filter.dims[2] * filter.dims[3];
+
+    if (fLen > kernel::MAX_SCONV_FILTER_LEN) {
+        // TODO call upon fft
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCUDA convolution supports max kernel size of %d\n",
+                 kernel::MAX_SCONV_FILTER_LEN);
+        CUDA_NOT_SUPPORTED(errMessage);
+    }
+
+    static const std::string src(convolve_separable_cuh,
+                                 convolve_separable_cuh_len);
+    // clang-format off
+    auto conv = getKernel("cuda::convolve2_separable", src,
+            {
+              TemplateTypename<T>(),
+              TemplateTypename<aT>(),
+              TemplateArg(conv_dim),
+              TemplateArg(expand),
+              TemplateArg(fLen)
+            },
+            {
+              DefineValue(MAX_SCONV_FILTER_LEN),
+              DefineValue(SCONV_THREADS_X),
+              DefineValue(SCONV_THREADS_Y)
+            }
+            );
+    // clang-format on
+
+    dim3 threads(SCONV_THREADS_X, SCONV_THREADS_Y);
+
+    int blk_x = divup(out.dims[0], threads.x);
+    int blk_y = divup(out.dims[1], threads.y);
+
+    dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
+
+    // FIXME: case where filter array is strided
+    conv.setConstant(sconv_c_name, reinterpret_cast<CUdeviceptr>(filter.ptr),
+                     fLen * sizeof(aT));
+
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    conv(qArgs, out, signal, blk_x, blk_y);
+    POST_LAUNCH_CHECK();
+}
+
+}  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve1.cuh b/src/backend/cuda/kernel/convolve1.cuh
new file mode 100644
index 0000000000..765703cf99
--- /dev/null
+++ b/src/backend/cuda/kernel/convolve1.cuh
@@ -0,0 +1,77 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <shared.hpp>
+
+__constant__ char
+    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
+            sizeof(double)];
+
+namespace cuda {
+
+template<typename T, typename aT, bool expand>
+__global__
+void convolve1(Param<T> out, CParam<T> signal,
+               int fLen, int nBBS0, int nBBS1,
+               int o1, int o2, int o3, int s1, int s2, int s3) {
+    SharedMemory<T> shared;
+    T *shrdMem = shared.getPointer();
+
+    const int padding = fLen - 1;
+    const int shrdLen = blockDim.x + 2 * padding;
+    const unsigned b1 = blockIdx.x / nBBS0; /* [0 {1} 2 3] */
+    const unsigned b3 =
+        (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1; /* [0 1 2 {3}] */
+    const unsigned b2 =
+        (blockIdx.y + blockIdx.z * gridDim.y) - nBBS1 * b3; /* [0 1 {2} 3] */
+    if (b2 >= out.dims[2] || b3 >= out.dims[3]) return;
+
+    T *dst = (T *)out.ptr +
+             (b1 * out.strides[1] + /* activated with batched input signal */
+              o1 * out.strides[1] + /* activated with batched input filter */
+              b2 * out.strides[2] + /* activated with batched input signal */
+              o2 * out.strides[2] + /* activated with batched input filter */
+              b3 * out.strides[3] + /* activated with batched input signal */
+              o3 * out.strides[3]); /* activated with batched input filter */
+
+    const T *src =
+        (const T *)signal.ptr +
+        (b1 * signal.strides[1] + /* activated with batched input signal */
+         s1 * signal.strides[1] + /* activated with batched input filter */
+         b2 * signal.strides[2] + /* activated with batched input signal */
+         s2 * signal.strides[2] + /* activated with batched input filter */
+         b3 * signal.strides[3] + /* activated with batched input signal */
+         s3 * signal.strides[3]); /* activated with batched input filter */
+
+    const aT *impulse = (const aT *)cFilter;
+
+    int gx = blockDim.x * (blockIdx.x - b1 * nBBS0);
+
+    int s0 = signal.strides[0];
+    int d0 = signal.dims[0];
+    for (int i = threadIdx.x; i < shrdLen; i += blockDim.x) {
+        int idx    = gx - padding + i;
+        shrdMem[i] = (idx >= 0 && idx < d0) ? src[idx * s0] : scalar<T>(0);
+    }
+    __syncthreads();
+    gx += threadIdx.x;
+
+    if (gx < out.dims[0]) {
+        int lx   = threadIdx.x + padding + (expand ? 0 : fLen >> 1);
+        aT accum = scalar<aT>(0);
+        for (int f = 0; f < fLen; ++f) {
+            accum = accum + (shrdMem[lx - f] * impulse[f]);
+        }
+        dst[gx] = (T)accum;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/convolve2.cuh b/src/backend/cuda/kernel/convolve2.cuh
new file mode 100644
index 0000000000..7bd8fa4375
--- /dev/null
+++ b/src/backend/cuda/kernel/convolve2.cuh
@@ -0,0 +1,100 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+__constant__ char
+    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
+            sizeof(double)];
+
+namespace cuda {
+
+template<typename T, typename aT, bool expand, int fLen0, int fLen1>
+__global__
+void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
+               int o2, int o3, int s2, int s3) {
+    const size_t C_SIZE = (CONV2_THREADS_X + 2 * (fLen0 - 1)) *
+                          (CONV2_THREADS_Y + 2 * (fLen1 - 1));
+    __shared__ T shrdMem[C_SIZE];
+
+    const int radius0  = fLen0 - 1;
+    const int radius1  = fLen1 - 1;
+    const int padding0 = 2 * radius0;
+    const int padding1 = 2 * radius1;
+    const int shrdLen0 = CONV2_THREADS_X + padding0;
+    const int shrdLen1 = CONV2_THREADS_Y + padding1;
+
+    unsigned b0 = blockIdx.x / nBBS0;
+    unsigned b1 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
+    T *dst      = (T *)out.ptr +
+             (b0 * out.strides[2] + /* activated with batched input signal */
+              o2 * out.strides[2] + /* activated with batched input filter */
+              b1 * out.strides[3] + /* activated with batched input signal */
+              o3 * out.strides[3]); /* activated with batched input filter */
+
+    const T *src =
+        (const T *)signal.ptr +
+        (b0 * signal.strides[2] + /* activated with batched input signal */
+         s2 * signal.strides[2] + /* activated with batched input filter */
+         b1 * signal.strides[3] + /* activated with batched input signal */
+         s3 * signal.strides[3]); /* activated with batched input filter */
+
+    const aT *impulse = (const aT *)cFilter;
+
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+    int gx = CONV2_THREADS_X * (blockIdx.x - b0 * nBBS0) + lx;
+    int gy = CONV2_THREADS_Y *
+                 ((blockIdx.y + blockIdx.z * gridDim.y) - b1 * nBBS1) + ly;
+
+    if (b1 >= out.dims[3]) return;
+
+    int s0 = signal.strides[0];
+    int s1 = signal.strides[1];
+    int d0 = signal.dims[0];
+    int d1 = signal.dims[1];
+    // below loops are traditional loops, they only run multiple
+    // times filter length is more than launch size
+#pragma unroll
+    for (int b = ly, gy2 = gy; b < shrdLen1;
+         b += CONV2_THREADS_Y, gy2 += CONV2_THREADS_Y) {
+        int j     = gy2 - radius1;
+        bool is_j = j >= 0 && j < d1;
+        // move row_set CONV2_THREADS_Y along coloumns
+#pragma unroll
+        for (int a = lx, gx2 = gx; a < shrdLen0;
+             a += CONV2_THREADS_X, gx2 += CONV2_THREADS_X) {
+            int i     = gx2 - radius0;
+            bool is_i = i >= 0 && i < d0;
+            shrdMem[b * shrdLen0 + a] =
+                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
+        }
+    }
+    __syncthreads();
+
+    if (gx < out.dims[0] && gy < out.dims[1]) {
+        int ci = lx + radius0 + (expand ? 0 : fLen0 >> 1);
+        int cj = ly + radius1 + (expand ? 0 : fLen1 >> 1);
+
+        aT accum = scalar<aT>(0);
+#pragma unroll
+        for (int fj = 0; fj < fLen1; ++fj) {
+#pragma unroll
+            for (int fi = 0; fi < fLen0; ++fi) {
+                aT f_val = impulse[fj * fLen0 + fi];
+                T s_val  = shrdMem[(cj - fj) * shrdLen0 + (ci - fi)];
+                accum    = accum + s_val * f_val;
+            }
+        }
+        dst[gy * out.strides[1] + gx] = (T)accum;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/convolve3.cuh b/src/backend/cuda/kernel/convolve3.cuh
new file mode 100644
index 0000000000..08e671692c
--- /dev/null
+++ b/src/backend/cuda/kernel/convolve3.cuh
@@ -0,0 +1,112 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <shared.hpp>
+
+__constant__ char
+    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
+            sizeof(double)];
+
+namespace cuda {
+
+__inline__
+int index(int i, int j, int k, int jstride, int kstride) {
+    return i + j * jstride + k * kstride;
+}
+
+template<typename T, typename aT, bool expand>
+__global__
+void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
+               int fLen2, int nBBS, int o3, int s3) {
+    SharedMemory<T> shared;
+
+    T *shrdMem   = shared.getPointer();
+    int radius0  = fLen0 - 1;
+    int radius1  = fLen1 - 1;
+    int radius2  = fLen2 - 1;
+    int shrdLen0 = blockDim.x + 2 * radius0;
+    int shrdLen1 = blockDim.y + 2 * radius1;
+    int shrdLen2 = blockDim.z + 2 * radius2;
+    int skStride = shrdLen0 * shrdLen1;
+    int fStride  = fLen0 * fLen1;
+    unsigned b2  = blockIdx.x / nBBS;
+
+    T *dst = (T *)out.ptr +
+             (b2 * out.strides[3] + /* activated with batched input signal */
+              o3 * out.strides[3]); /* activated with batched input filter */
+
+    const T *src =
+        (const T *)signal.ptr +
+        (b2 * signal.strides[3] + /* activated with batched input signal */
+         s3 * signal.strides[3]); /* activated with batched input filter */
+
+    const aT *impulse = (const aT *)cFilter;
+
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+    int lz = threadIdx.z;
+    int gx = blockDim.x * (blockIdx.x - b2 * nBBS) + lx;
+    int gy = blockDim.y * blockIdx.y + ly;
+    int gz = blockDim.z * blockIdx.z + lz;
+
+    int s0 = signal.strides[0];
+    int s1 = signal.strides[1];
+    int s2 = signal.strides[2];
+    int d0 = signal.dims[0];
+    int d1 = signal.dims[1];
+    int d2 = signal.dims[2];
+#pragma unroll
+    for (int c = lz, gz2 = gz; c < shrdLen2;
+         c += CONV3_CUBE_Z, gz2 += CONV3_CUBE_Z) {
+        int k     = gz2 - radius2;
+        bool is_k = k >= 0 && k < d2;
+#pragma unroll
+        for (int b = ly, gy2 = gy; b < shrdLen1;
+             b += CONV3_CUBE_Y, gy2 += CONV3_CUBE_Y) {
+            int j     = gy2 - radius1;
+            bool is_j = j >= 0 && j < d1;
+#pragma unroll
+            for (int a = lx, gx2 = gx; a < shrdLen0;
+                 a += CONV3_CUBE_X, gx2 += CONV3_CUBE_X) {
+                int i     = gx2 - radius0;
+                bool is_i = i >= 0 && i < d0;
+                shrdMem[c * skStride + b * shrdLen0 + a] =
+                    (is_i && is_j && is_k ? src[i * s0 + j * s1 + k * s2]
+                                          : scalar<T>(0));
+            }
+        }
+    }
+    __syncthreads();
+
+    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2]) {
+        int ci = lx + radius0 + (expand ? 0 : fLen0 >> 1);
+        int cj = ly + radius1 + (expand ? 0 : fLen1 >> 1);
+        int ck = lz + radius2 + (expand ? 0 : fLen2 >> 1);
+
+        aT accum = scalar<aT>(0);
+#pragma unroll
+        for (int fk = 0; fk < fLen2; ++fk) {
+#pragma unroll
+            for (int fj = 0; fj < fLen1; ++fj) {
+#pragma unroll
+                for (int fi = 0; fi < fLen0; ++fi) {
+                    aT f_val = impulse[index(fi, fj, fk, fLen0, fStride)];
+                    T s_val = shrdMem[index(ci - fi, cj - fj, ck - fk, shrdLen0,
+                                            skStride)];
+                    accum   = accum + s_val * f_val;
+                }
+            }
+        }
+        dst[index(gx, gy, gz, out.strides[1], out.strides[2])] = (T)accum;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/convolve_separable.cpp b/src/backend/cuda/kernel/convolve_separable.cpp
new file mode 100644
index 0000000000..c95f48afeb
--- /dev/null
+++ b/src/backend/cuda/kernel/convolve_separable.cpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <kernel/convolve.hpp>
+
+namespace cuda {
+namespace kernel {
+
+#define INSTANTIATE(T, aT) \
+    template void convolve2<T, aT>(Param<T>, CParam<T>, CParam<aT>, int, bool);
+
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve_separable.cu b/src/backend/cuda/kernel/convolve_separable.cu
deleted file mode 100644
index a929d8cf15..0000000000
--- a/src/backend/cuda/kernel/convolve_separable.cu
+++ /dev/null
@@ -1,302 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Param.hpp>
-#include <backend.hpp>
-#include <common/dispatch.hpp>
-#include <convolve.hpp>
-#include <debug_cuda.hpp>
-#include <math.hpp>
-
-namespace cuda {
-namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-// below shared MAX_*_LEN's are calculated based on
-// a maximum shared memory configuration of 48KB per block
-// considering complex types as well
-static const int MAX_SCONV_FILTER_LEN = 31;
-
-// we shall declare the maximum size required of above all three cases
-// and re-use the same constant memory locations for every case
-__constant__ char sFilter[2 * THREADS_Y *
-                          (2 * (MAX_SCONV_FILTER_LEN - 1) + THREADS_X) *
-                          sizeof(double)];
-
-template<typename T, typename accType, int conv_dim, bool expand, int fLen>
-__global__ void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0,
-                                    int nBBS1) {
-    const int smem_len =
-        (conv_dim == 0 ? (THREADS_X + 2 * (fLen - 1)) * THREADS_Y
-                       : (THREADS_Y + 2 * (fLen - 1)) * THREADS_X);
-    __shared__ T shrdMem[smem_len];
-
-    const int radius  = fLen - 1;
-    const int padding = 2 * radius;
-    const int s0      = signal.strides[0];
-    const int s1      = signal.strides[1];
-    const int d0      = signal.dims[0];
-    const int d1      = signal.dims[1];
-    const int shrdLen = THREADS_X + (conv_dim == 0 ? padding : 0);
-
-    unsigned b2  = blockIdx.x / nBBS0;
-    unsigned b3  = blockIdx.y / nBBS1;
-    T *dst       = (T *)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-    const T *src = (const T *)signal.ptr +
-                   (b2 * signal.strides[2] + b3 * signal.strides[3]);
-    const accType *impulse = (const accType *)sFilter;
-
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-    int ox = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
-    int oy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
-    int gx = ox;
-    int gy = oy;
-
-    // below if-else statement is based on template parameter
-    if (conv_dim == 0) {
-        gx += (expand ? 0 : fLen >> 1);
-        int endX = ((fLen - 1) << 1) + THREADS_X;
-
-#pragma unroll
-        for (int lx = threadIdx.x, glb_x = gx; lx < endX;
-             lx += THREADS_X, glb_x += THREADS_X) {
-            int i     = glb_x - radius;
-            int j     = gy;
-            bool is_i = i >= 0 && i < d0;
-            bool is_j = j >= 0 && j < d1;
-            shrdMem[ly * shrdLen + lx] =
-                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
-        }
-
-    } else if (conv_dim == 1) {
-        gy += (expand ? 0 : fLen >> 1);
-        int endY = ((fLen - 1) << 1) + THREADS_Y;
-
-#pragma unroll
-        for (int ly = threadIdx.y, glb_y = gy; ly < endY;
-             ly += THREADS_Y, glb_y += THREADS_Y) {
-            int i     = gx;
-            int j     = glb_y - radius;
-            bool is_i = i >= 0 && i < d0;
-            bool is_j = j >= 0 && j < d1;
-            shrdMem[ly * shrdLen + lx] =
-                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
-        }
-    }
-    __syncthreads();
-
-    if (ox < out.dims[0] && oy < out.dims[1]) {
-        // below conditional statement is based on template parameter
-        int i         = (conv_dim == 0 ? lx : ly) + radius;
-        accType accum = scalar<accType>(0);
-#pragma unroll
-        for (int f = 0; f < fLen; ++f) {
-            accType f_val = impulse[f];
-            // below conditional statement is based on template parameter
-            int s_idx = (conv_dim == 0 ? (ly * shrdLen + (i - f))
-                                       : ((i - f) * shrdLen + lx));
-            T s_val   = shrdMem[s_idx];
-            accum     = accum + s_val * f_val;
-        }
-        dst[oy * out.strides[1] + ox] = (T)accum;
-    }
-}
-
-template<typename T, typename aT, int cDim, bool expand, int f>
-void conv2Helper(dim3 blks, dim3 thrds, Param<T> out, CParam<T> sig, int nBBS0,
-                 int nBBS1) {
-    CUDA_LAUNCH((convolve2_separable<T, aT, cDim, expand, f>), blks, thrds, out,
-                sig, nBBS0, nBBS1);
-}
-
-template<typename T, typename accType, int conv_dim, bool expand>
-void convolve2(Param<T> out, CParam<T> signal, CParam<accType> filter) {
-    int fLen =
-        filter.dims[0] * filter.dims[1] * filter.dims[2] * filter.dims[3];
-    if (fLen > kernel::MAX_SCONV_FILTER_LEN) {
-        // TODO call upon fft
-        char errMessage[256];
-        snprintf(errMessage, sizeof(errMessage),
-                 "\nCUDA convolution supports max kernel size of %d\n",
-                 kernel::MAX_SCONV_FILTER_LEN);
-        CUDA_NOT_SUPPORTED(errMessage);
-    }
-
-    dim3 threads(THREADS_X, THREADS_Y);
-
-    int blk_x = divup(out.dims[0], threads.x);
-    int blk_y = divup(out.dims[1], threads.y);
-
-    dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
-
-    // FIX ME: if the filter array is strided, direct copy of symbols
-    // might cause issues
-    CUDA_CHECK(cudaMemcpyToSymbolAsync(
-        kernel::sFilter, filter.ptr, fLen * sizeof(accType), 0,
-        cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
-    switch (fLen) {
-        case 2:
-            conv2Helper<T, accType, conv_dim, expand, 2>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 3:
-            conv2Helper<T, accType, conv_dim, expand, 3>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 4:
-            conv2Helper<T, accType, conv_dim, expand, 4>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 5:
-            conv2Helper<T, accType, conv_dim, expand, 5>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 6:
-            conv2Helper<T, accType, conv_dim, expand, 6>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 7:
-            conv2Helper<T, accType, conv_dim, expand, 7>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 8:
-            conv2Helper<T, accType, conv_dim, expand, 8>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 9:
-            conv2Helper<T, accType, conv_dim, expand, 9>(blocks, threads, out,
-                                                         signal, blk_x, blk_y);
-            break;
-        case 10:
-            conv2Helper<T, accType, conv_dim, expand, 10>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 11:
-            conv2Helper<T, accType, conv_dim, expand, 11>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 12:
-            conv2Helper<T, accType, conv_dim, expand, 12>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 13:
-            conv2Helper<T, accType, conv_dim, expand, 13>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 14:
-            conv2Helper<T, accType, conv_dim, expand, 14>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 15:
-            conv2Helper<T, accType, conv_dim, expand, 15>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 16:
-            conv2Helper<T, accType, conv_dim, expand, 16>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 17:
-            conv2Helper<T, accType, conv_dim, expand, 17>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 18:
-            conv2Helper<T, accType, conv_dim, expand, 18>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 19:
-            conv2Helper<T, accType, conv_dim, expand, 19>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 20:
-            conv2Helper<T, accType, conv_dim, expand, 20>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 21:
-            conv2Helper<T, accType, conv_dim, expand, 21>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 22:
-            conv2Helper<T, accType, conv_dim, expand, 22>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 23:
-            conv2Helper<T, accType, conv_dim, expand, 23>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 24:
-            conv2Helper<T, accType, conv_dim, expand, 24>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 25:
-            conv2Helper<T, accType, conv_dim, expand, 25>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 26:
-            conv2Helper<T, accType, conv_dim, expand, 26>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 27:
-            conv2Helper<T, accType, conv_dim, expand, 27>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 28:
-            conv2Helper<T, accType, conv_dim, expand, 28>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 29:
-            conv2Helper<T, accType, conv_dim, expand, 29>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 30:
-            conv2Helper<T, accType, conv_dim, expand, 30>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        case 31:
-            conv2Helper<T, accType, conv_dim, expand, 31>(blocks, threads, out,
-                                                          signal, blk_x, blk_y);
-            break;
-        default: {
-            char errMessage[256];
-            snprintf(errMessage, sizeof(errMessage),
-                     "\nCUDA Separable convolution doesn't support %d kernel\n",
-                     fLen);
-            CUDA_NOT_SUPPORTED(errMessage);
-        };
-    }
-
-    POST_LAUNCH_CHECK();
-}
-
-#define INSTANTIATE(T, accType)                                  \
-    template void convolve2<T, accType, 0, true>(                \
-        Param<T> out, CParam<T> signal, CParam<accType> filter); \
-    template void convolve2<T, accType, 0, false>(               \
-        Param<T> out, CParam<T> signal, CParam<accType> filter); \
-    template void convolve2<T, accType, 1, true>(                \
-        Param<T> out, CParam<T> signal, CParam<accType> filter); \
-    template void convolve2<T, accType, 1, false>(               \
-        Param<T> out, CParam<T> signal, CParam<accType> filter);
-
-INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(cfloat, cfloat)
-INSTANTIATE(double, double)
-INSTANTIATE(float, float)
-INSTANTIATE(uint, float)
-INSTANTIATE(int, float)
-INSTANTIATE(uchar, float)
-INSTANTIATE(char, float)
-INSTANTIATE(ushort, float)
-INSTANTIATE(short, float)
-INSTANTIATE(uintl, float)
-INSTANTIATE(intl, float)
-}  // namespace kernel
-}  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve_separable.cuh b/src/backend/cuda/kernel/convolve_separable.cuh
new file mode 100644
index 0000000000..8a2e076dec
--- /dev/null
+++ b/src/backend/cuda/kernel/convolve_separable.cuh
@@ -0,0 +1,99 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+__constant__ char sFilter[2 * SCONV_THREADS_Y *
+                          (2 * (MAX_SCONV_FILTER_LEN - 1) + SCONV_THREADS_X) *
+                          sizeof(double)];
+
+namespace cuda {
+
+template<typename T, typename accType, int conv_dim, bool expand, int fLen>
+__global__
+void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1) {
+    const int smem_len =
+        (conv_dim == 0 ? (SCONV_THREADS_X + 2 * (fLen - 1)) * SCONV_THREADS_Y
+                       : (SCONV_THREADS_Y + 2 * (fLen - 1)) * SCONV_THREADS_X);
+    __shared__ T shrdMem[smem_len];
+
+    const int radius  = fLen - 1;
+    const int padding = 2 * radius;
+    const int s0      = signal.strides[0];
+    const int s1      = signal.strides[1];
+    const int d0      = signal.dims[0];
+    const int d1      = signal.dims[1];
+    const int shrdLen = SCONV_THREADS_X + (conv_dim == 0 ? padding : 0);
+
+    unsigned b2  = blockIdx.x / nBBS0;
+    unsigned b3  = blockIdx.y / nBBS1;
+    T *dst       = (T *)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const T *src = (const T *)signal.ptr +
+                   (b2 * signal.strides[2] + b3 * signal.strides[3]);
+    const accType *impulse = (const accType *)sFilter;
+
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+    int ox = SCONV_THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
+    int oy = SCONV_THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
+    int gx = ox;
+    int gy = oy;
+
+    // below if-else statement is based on template parameter
+    if (conv_dim == 0) {
+        gx += (expand ? 0 : fLen >> 1);
+        int endX = ((fLen - 1) << 1) + SCONV_THREADS_X;
+
+#pragma unroll
+        for (int lx = threadIdx.x, glb_x = gx; lx < endX;
+             lx += SCONV_THREADS_X, glb_x += SCONV_THREADS_X) {
+            int i     = glb_x - radius;
+            int j     = gy;
+            bool is_i = i >= 0 && i < d0;
+            bool is_j = j >= 0 && j < d1;
+            shrdMem[ly * shrdLen + lx] =
+                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
+        }
+
+    } else if (conv_dim == 1) {
+        gy += (expand ? 0 : fLen >> 1);
+        int endY = ((fLen - 1) << 1) + SCONV_THREADS_Y;
+
+#pragma unroll
+        for (int ly = threadIdx.y, glb_y = gy; ly < endY;
+             ly += SCONV_THREADS_Y, glb_y += SCONV_THREADS_Y) {
+            int i     = gx;
+            int j     = glb_y - radius;
+            bool is_i = i >= 0 && i < d0;
+            bool is_j = j >= 0 && j < d1;
+            shrdMem[ly * shrdLen + lx] =
+                (is_i && is_j ? src[i * s0 + j * s1] : scalar<T>(0));
+        }
+    }
+    __syncthreads();
+
+    if (ox < out.dims[0] && oy < out.dims[1]) {
+        // below conditional statement is based on template parameter
+        int i         = (conv_dim == 0 ? lx : ly) + radius;
+        accType accum = scalar<accType>(0);
+#pragma unroll
+        for (int f = 0; f < fLen; ++f) {
+            accType f_val = impulse[f];
+            // below conditional statement is based on template parameter
+            int s_idx = (conv_dim == 0 ? (ly * shrdLen + (i - f))
+                                       : ((i - f) * shrdLen + lx));
+            T s_val   = shrdMem[s_idx];
+            accum     = accum + s_val * f_val;
+        }
+        dst[oy * out.strides[1] + ox] = (T)accum;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 21badbb305..7db3a1fc57 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
@@ -223,12 +225,12 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
     iyy_tmp.ptr        = iyy_tmp_alloc.get();
 
     // Convolve second-order derivatives with proper window filter
-    convolve2<T, convAccT, 0, false>(ixx_tmp, CParam<T>(ixx), filter);
-    convolve2<T, convAccT, 1, false>(ixx, CParam<T>(ixx_tmp), filter);
-    convolve2<T, convAccT, 0, false>(ixy_tmp, CParam<T>(ixy), filter);
-    convolve2<T, convAccT, 1, false>(ixy, CParam<T>(ixy_tmp), filter);
-    convolve2<T, convAccT, 0, false>(iyy_tmp, CParam<T>(iyy), filter);
-    convolve2<T, convAccT, 1, false>(iyy, CParam<T>(iyy_tmp), filter);
+    convolve2<T, convAccT>(ixx_tmp, CParam<T>(ixx), filter, 0, false);
+    convolve2<T, convAccT>(ixx, CParam<T>(ixx_tmp), filter, 1, false);
+    convolve2<T, convAccT>(ixy_tmp, CParam<T>(ixy), filter, 0, false);
+    convolve2<T, convAccT>(ixy, CParam<T>(ixy_tmp), filter, 1, false);
+    convolve2<T, convAccT>(iyy_tmp, CParam<T>(iyy), filter, 0, false);
+    convolve2<T, convAccT>(iyy, CParam<T>(iyy_tmp), filter, 1, false);
 
     // Number of corners is not known a priori, limit maximum number of corners
     // according to image dimensions
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index d1246a928e..5765f8da18 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
@@ -366,8 +368,8 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
             Array<T> lvl_tmp = createEmptyArray<T>(img_pyr[i].dims());
 
             // Separable Gaussian filtering to reduce noise sensitivity
-            convolve2<T, convAccT, 0, false>(lvl_tmp, img_pyr[i], gauss_filter);
-            convolve2<T, convAccT, 1, false>(img_pyr[i], lvl_tmp, gauss_filter);
+            convolve2<T, convAccT>(lvl_tmp, img_pyr[i], gauss_filter, 0, false);
+            convolve2<T, convAccT>(img_pyr[i], lvl_tmp, gauss_filter, 1, false);
         }
 
         float* d_size_lvl = memAlloc<float>(feat_pyr[i]).release();
diff --git a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
index 19654bfe33..39b0ae3a6f 100644
--- a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
+++ b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
@@ -7,10 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <backend.hpp>
 #include <kernel/scan_dim_by_key_impl.hpp>
 #include <kernel/scan_first_by_key_impl.hpp>
-#include <ops.hpp>
 
 // This file instantiates scan_dim_by_key as separate object files from CMake
 // The line below is read by CMake to determenine the instantiations
diff --git a/src/backend/cuda/kernel/scan_dim.cuh b/src/backend/cuda/kernel/scan_dim.cuh
new file mode 100644
index 0000000000..aa71f1bba9
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_dim.cuh
@@ -0,0 +1,169 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+
+namespace cuda {
+
+template<typename Ti, typename To, af_op_t op, int dim,
+         bool isFinalPass, uint DIMY, bool inclusive_scan>
+__global__
+void scan_dim(Param<To> out, Param<To> tmp, CParam<Ti> in,
+              uint blocks_x, uint blocks_y, uint blocks_dim, uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+    const int tid  = tidy * THREADS_X + tidx;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const Ti *iptr = in.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    int id_dim        = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    const int ostride_dim = out.strides[dim];
+    const int istride_dim = in.strides[dim];
+
+    __shared__ To s_val[THREADS_X * DIMY * 2];
+    __shared__ To s_tmp[THREADS_X];
+    To *sptr = s_val + tid;
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const bool isLast = (tidy == (DIMY - 1));
+
+    for (int k = 0; k < lim; k++) {
+        if (isLast) s_tmp[tidx] = val;
+
+        bool cond = (is_valid) && (id_dim < out_dim);
+        val       = cond ? transform(*iptr) : init;
+        *sptr     = val;
+        __syncthreads();
+
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMY; off *= 2) {
+            if (tidy >= off) val = binop(val, sptr[(start - off) * THREADS_X]);
+            start                   = DIMY - start;
+            sptr[start * THREADS_X] = val;
+
+            __syncthreads();
+        }
+
+        val = binop(val, s_tmp[tidx]);
+        if (inclusive_scan) {
+            if (cond) { *optr = val; }
+        } else if (is_valid) {
+            if (id_dim == (out_dim - 1)) {
+                *(optr - (id_dim * ostride_dim)) = init;
+            } else if (id_dim < (out_dim - 1)) {
+                *(optr + ostride_dim) = val;
+            }
+        }
+        id_dim += blockDim.y;
+        iptr += blockDim.y * istride_dim;
+        optr += blockDim.y * ostride_dim;
+        __syncthreads();
+    }
+
+    if (!isFinalPass && is_valid && (blockIdx_dim < tmp.dims[dim]) && isLast) {
+        *tptr = val;
+    }
+}
+
+template<typename To, af_op_t op, int dim>
+__global__
+void scan_dim_bcast(Param<To> out, CParam<To> tmp, uint blocks_x, uint blocks_y,
+                    uint blocks_dim, uint lim, bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const To *tptr = tmp.ptr;
+    To *optr       = out.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    const int id_dim  = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    // Shift broadcast one step to the right for exclusive scan (#2366)
+    int offset = inclusive_scan ? 0 : out.strides[dim];
+    optr += offset;
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    if (!is_valid) return;
+    if (blockIdx_dim == 0) return;
+
+    To accum = *(tptr - tmp.strides[dim]);
+
+    Binary<To, op> binop;
+    const int ostride_dim = out.strides[dim];
+
+    for (int k = 0, id = id_dim; is_valid && k < lim && (id < out_dim);
+         k++, id += blockDim.y) {
+        *optr = binop(*optr, accum);
+        optr += blockDim.y * ostride_dim;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 9901054a4c..730f957886 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -12,172 +12,38 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
-#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/scan_dim.hpp>
 #include "config.hpp"
 
 namespace cuda {
 namespace kernel {
 
-template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass,
-         uint DIMY, bool inclusive_scan>
-__global__ static void scan_dim_kernel(Param<To> out, Param<To> tmp,
-                                       CParam<Ti> in, uint blocks_x,
-                                       uint blocks_y, uint blocks_dim,
-                                       uint lim) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-    const int tid  = tidy * THREADS_X + tidx;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const int xid = blockIdx_x * blockDim.x + tidx;
-    const int yid = blockIdx_y;  // yid  of output. updated for input later.
-
-    int ids[4] = {xid, yid, zid, wid};
-
-    const Ti *iptr = in.ptr;
-    To *optr       = out.ptr;
-    To *tptr       = tmp.ptr;
-
-    // There is only one element per block for out
-    // There are blockDim.y elements per block for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
-    // in
-    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
-            ids[1] * tmp.strides[1] + ids[0];
-    const int blockIdx_dim = ids[dim];
-
-    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-            ids[1] * out.strides[1] + ids[0];
-    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-            ids[1] * in.strides[1] + ids[0];
-    int id_dim        = ids[dim];
-    const int out_dim = out.dims[dim];
-
-    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
-                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
-
-    const int ostride_dim = out.strides[dim];
-    const int istride_dim = in.strides[dim];
-
-    __shared__ To s_val[THREADS_X * DIMY * 2];
-    __shared__ To s_tmp[THREADS_X];
-    To *sptr = s_val + tid;
-
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-
-    const To init = Binary<To, op>::init();
-    To val        = init;
-
-    const bool isLast = (tidy == (DIMY - 1));
-
-    for (int k = 0; k < lim; k++) {
-        if (isLast) s_tmp[tidx] = val;
-
-        bool cond = (is_valid) && (id_dim < out_dim);
-        val       = cond ? transform(*iptr) : init;
-        *sptr     = val;
-        __syncthreads();
-
-        int start = 0;
-#pragma unroll
-        for (int off = 1; off < DIMY; off *= 2) {
-            if (tidy >= off) val = binop(val, sptr[(start - off) * THREADS_X]);
-            start                   = DIMY - start;
-            sptr[start * THREADS_X] = val;
-
-            __syncthreads();
-        }
-
-        val = binop(val, s_tmp[tidx]);
-        if (inclusive_scan) {
-            if (cond) { *optr = val; }
-        } else if (is_valid) {
-            if (id_dim == (out_dim - 1)) {
-                *(optr - (id_dim * ostride_dim)) = init;
-            } else if (id_dim < (out_dim - 1)) {
-                *(optr + ostride_dim) = val;
+static const std::string ScanDimSource(scan_dim_cuh, scan_dim_cuh_len);
+
+template<typename Ti, typename To, af_op_t op>
+static
+void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                       const uint threads_y, const dim_t blocks_all[4],
+                       int dim, bool isFinalPass, bool inclusive_scan) {
+    // clang-format off
+    auto scanDim = getKernel("cuda::scan_dim", ScanDimSource,
+            {
+              TemplateTypename<Ti>(),
+              TemplateTypename<To>(),
+              TemplateArg(op),
+              TemplateArg(dim),
+              TemplateArg(isFinalPass),
+              TemplateArg(threads_y),
+              TemplateArg(inclusive_scan)
+            },
+            {
+              DefineValue(THREADS_X)
             }
-        }
-        id_dim += blockDim.y;
-        iptr += blockDim.y * istride_dim;
-        optr += blockDim.y * ostride_dim;
-        __syncthreads();
-    }
-
-    if (!isFinalPass && is_valid && (blockIdx_dim < tmp.dims[dim]) && isLast) {
-        *tptr = val;
-    }
-}
-
-template<typename To, af_op_t op, int dim>
-__global__ static void bcast_dim_kernel(Param<To> out, CParam<To> tmp,
-                                        uint blocks_x, uint blocks_y,
-                                        uint blocks_dim, uint lim,
-                                        bool inclusive_scan) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const int xid = blockIdx_x * blockDim.x + tidx;
-    const int yid = blockIdx_y;  // yid  of output. updated for input later.
-
-    int ids[4] = {xid, yid, zid, wid};
-
-    const To *tptr = tmp.ptr;
-    To *optr       = out.ptr;
-
-    // There is only one element per block for out
-    // There are blockDim.y elements per block for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
-    // in
-    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
-            ids[1] * tmp.strides[1] + ids[0];
-    const int blockIdx_dim = ids[dim];
-
-    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-            ids[1] * out.strides[1] + ids[0];
-    const int id_dim  = ids[dim];
-    const int out_dim = out.dims[dim];
-
-    // Shift broadcast one step to the right for exclusive scan (#2366)
-    int offset = inclusive_scan ? 0 : out.strides[dim];
-    optr += offset;
-
-    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
-                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
-
-    if (!is_valid) return;
-    if (blockIdx_dim == 0) return;
-
-    To accum = *(tptr - tmp.strides[dim]);
+            );
+    // clang-format on
 
-    Binary<To, op> binop;
-    const int ostride_dim = out.strides[dim];
-
-    for (int k = 0, id = id_dim; is_valid && k < lim && (id < out_dim);
-         k++, id += blockDim.y) {
-        *optr = binop(*optr, accum);
-        optr += blockDim.y * ostride_dim;
-    }
-}
-
-template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass,
-         bool inclusive_scan>
-static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
-                              const uint threads_y, const dim_t blocks_all[4]) {
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
@@ -189,40 +55,27 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-    switch (threads_y) {
-        case 8:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 8,
-                                         inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_all[0],
-                        blocks_all[1], blocks_all[dim], lim);
-            break;
-        case 4:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 4,
-                                         inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_all[0],
-                        blocks_all[1], blocks_all[dim], lim);
-            break;
-        case 2:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 2,
-                                         inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_all[0],
-                        blocks_all[1], blocks_all[dim], lim);
-            break;
-        case 1:
-            CUDA_LAUNCH((scan_dim_kernel<Ti, To, op, dim, isFinalPass, 1,
-                                         inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_all[0],
-                        blocks_all[1], blocks_all[dim], lim);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    scanDim(qArgs, out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim],
+            lim);
     POST_LAUNCH_CHECK();
 }
 
-template<typename To, af_op_t op, int dim>
-static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
-                               const uint threads_y, const dim_t blocks_all[4],
-                               bool inclusive_scan) {
+template<typename To, af_op_t op>
+static
+void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
+                        const uint threads_y, const dim_t blocks_all[4],
+                        int dim, bool inclusive_scan) {
+    // clang-format off
+    auto bcastDim = getKernel("cuda::scan_dim_bcast", ScanDimSource,
+            {
+              TemplateTypename<To>(),
+              TemplateArg(op),
+              TemplateArg(dim)
+            }
+            );
+    // clang-format on
+
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
@@ -234,15 +87,15 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-    CUDA_LAUNCH((bcast_dim_kernel<To, op, dim>), blocks, threads, out, tmp,
-                blocks_all[0], blocks_all[1], blocks_all[dim], lim,
-                inclusive_scan);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    bcastDim(qArgs, out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim],
+             lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
-template<typename Ti, typename To, af_op_t op, int dim, bool inclusive_scan>
-static void scan_dim(Param<To> out, CParam<Ti> in) {
+template<typename Ti, typename To, af_op_t op>
+static void scan_dim(Param<To> out, CParam<Ti> in, int dim,
+                     bool inclusive_scan) {
     uint threads_y = std::min(THREADS_Y, nextpow2(out.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -252,9 +105,8 @@ static void scan_dim(Param<To> out, CParam<Ti> in) {
     blocks_all[dim] = divup(out.dims[dim], threads_y * REPEAT);
 
     if (blocks_all[dim] == 1) {
-        scan_dim_launcher<Ti, To, op, dim, true, inclusive_scan>(
-            out, out, in, threads_y, blocks_all);
-
+        scan_dim_launcher<Ti, To, op>(out, out, in, threads_y, blocks_all, dim,
+                                      true, inclusive_scan);
     } else {
         Param<To> tmp = out;
 
@@ -267,24 +119,24 @@ static void scan_dim(Param<To> out, CParam<Ti> in) {
         auto tmp_alloc   = memAlloc<To>(tmp_elements);
         tmp.ptr          = tmp_alloc.get();
 
-        scan_dim_launcher<Ti, To, op, dim, false, inclusive_scan>(
-            out, tmp, in, threads_y, blocks_all);
+        scan_dim_launcher<Ti, To, op>(out, tmp, in, threads_y, blocks_all, dim,
+                                      false, inclusive_scan);
 
         int bdim        = blocks_all[dim];
         blocks_all[dim] = 1;
 
         // FIXME: Is there an alternative to the if condition ?
         if (op == af_notzero_t) {
-            scan_dim_launcher<To, To, af_add_t, dim, true, true>(
-                tmp, tmp, tmp, threads_y, blocks_all);
+            scan_dim_launcher<To, To, af_add_t>(tmp, tmp, tmp, threads_y,
+                                                blocks_all, dim, true, true);
         } else {
-            scan_dim_launcher<To, To, op, dim, true, true>(
-                tmp, tmp, tmp, threads_y, blocks_all);
+            scan_dim_launcher<To, To, op>(tmp, tmp, tmp, threads_y, blocks_all,
+                                          dim, true, true);
         }
 
         blocks_all[dim] = bdim;
-        bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all,
-                                        inclusive_scan);
+        bcast_dim_launcher<To, op>(out, tmp, threads_y, blocks_all, dim,
+                                   inclusive_scan);
     }
 }
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.cuh b/src/backend/cuda/kernel/scan_dim_by_key.cuh
new file mode 100644
index 0000000000..d1aac13cfe
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_dim_by_key.cuh
@@ -0,0 +1,370 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+
+namespace cuda {
+
+template<typename Tk>
+__device__ inline
+char calculate_head_flags_dim(const Tk *kptr, int id, int stride) {
+    return (id == 0) ? 1 : ((*kptr) != (*(kptr - stride)));
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op>
+__global__
+void scanbykey_dim_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
+                            Param<int> tlid, CParam<Ti> in, CParam<Tk> key,
+                            int dim, uint blocks_x, uint blocks_y, uint lim,
+                            bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+    const int tid  = tidy * THREADS_X + tidx;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+    char *tfptr    = tflg.ptr;
+    int *tiptr     = tlid.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    tfptr += ids[3] * tflg.strides[3] + ids[2] * tflg.strides[2] +
+             ids[1] * tflg.strides[1] + ids[0];
+    tiptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] +
+             ids[1] * tlid.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    kptr += ids[3] * key.strides[3] + ids[2] * key.strides[2] +
+            ids[1] * key.strides[1] + ids[0];
+    int id_dim        = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    const int ostride_dim = out.strides[dim];
+    const int istride_dim = in.strides[dim];
+
+    __shared__ char s_flg[THREADS_X * DIMY * 2];
+    __shared__ To s_val[THREADS_X * DIMY * 2];
+    __shared__ char s_ftmp[THREADS_X];
+    __shared__ To s_tmp[THREADS_X];
+    __shared__ int boundaryid[THREADS_X];
+    To *sptr    = s_val + tid;
+    char *sfptr = s_flg + tid;
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const bool isLast = (tidy == (DIMY - 1));
+    if (isLast) {
+        s_tmp[tidx]      = val;
+        s_ftmp[tidx]     = 0;
+        boundaryid[tidx] = -1;
+    }
+    __syncthreads();
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+        if (id_dim < out_dim) {
+            flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
+        } else {
+            flag = 0;
+        }
+
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id_dim >= out_dim) {
+                val = init;
+            } else {
+                val = transform(*iptr);
+            }
+        } else {
+            if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
+                val = init;
+            } else {
+                val = transform(*(iptr - istride_dim));
+            }
+        }
+
+        // Add partial result from last iteration before scan operation
+        if ((tidy == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidx]);
+            flag = s_ftmp[tidx];
+        }
+
+        // Write to shared memory
+        *sptr  = val;
+        *sfptr = flag;
+        __syncthreads();
+
+        // Segmented Scan
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMY; off *= 2) {
+            if (tidy >= off) {
+                val = sfptr[start * THREADS_X]
+                          ? val
+                          : binop(val, sptr[(start - off) * THREADS_X]);
+                flag =
+                    sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
+            }
+            start                    = DIMY - start;
+            sptr[start * THREADS_X]  = val;
+            sfptr[start * THREADS_X] = flag;
+
+            __syncthreads();
+        }
+
+        // Identify segment boundary
+        if (tidy == 0) {
+            if ((s_ftmp[tidx] == 0) && (sfptr[start * THREADS_X] == 1)) {
+                boundaryid[tidx] = id_dim;
+            }
+        } else {
+            if ((sfptr[(start - 1) * THREADS_X] == 0) &&
+                (sfptr[start * THREADS_X] == 1)) {
+                boundaryid[tidx] = id_dim;
+            }
+        }
+        __syncthreads();
+
+        if (is_valid && (id_dim < out_dim)) *optr = val;
+        if (isLast) {
+            s_tmp[tidx]  = val;
+            s_ftmp[tidx] = flag;
+        }
+        id_dim += blockDim.y;
+        kptr += blockDim.y * key.strides[dim];
+        iptr += blockDim.y * istride_dim;
+        optr += blockDim.y * ostride_dim;
+        __syncthreads();
+    }
+
+    if (is_valid && (blockIdx_dim < tmp.dims[dim]) && isLast) {
+        *tptr        = val;
+        *tfptr       = flag;
+        int boundary = boundaryid[tidx];
+        *tiptr       = (boundary == -1) ? id_dim : boundary;
+    }
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op>
+__global__
+void scanbykey_dim_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
+                         int dim, uint blocks_x, uint blocks_y, uint lim,
+                         bool calculateFlags, bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+    const int tid  = tidy * THREADS_X + tidx;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    kptr += ids[3] * key.strides[3] + ids[2] * key.strides[2] +
+            ids[1] * key.strides[1] + ids[0];
+    int id_dim        = ids[dim];
+    const int out_dim = out.dims[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    const int ostride_dim = out.strides[dim];
+    const int istride_dim = in.strides[dim];
+
+    __shared__ char s_flg[THREADS_X * DIMY * 2];
+    __shared__ To s_val[THREADS_X * DIMY * 2];
+    __shared__ char s_ftmp[THREADS_X];
+    __shared__ To s_tmp[THREADS_X];
+    To *sptr    = s_val + tid;
+    char *sfptr = s_flg + tid;
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const bool isLast = (tidy == (DIMY - 1));
+    if (isLast) {
+        s_tmp[tidx]  = val;
+        s_ftmp[tidx] = 0;
+    }
+    __syncthreads();
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+        if (calculateFlags) {
+            if (id_dim < out_dim) {
+                flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
+            } else {
+                flag = 0;
+            }
+        } else {
+            flag = *kptr;
+        }
+
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id_dim >= out_dim) {
+                val = init;
+            } else {
+                val = transform(*iptr);
+            }
+        } else {
+            if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
+                val = init;
+            } else {
+                val = transform(*(iptr - istride_dim));
+            }
+        }
+
+        // Add partial result from last iteration before scan operation
+        if ((tidy == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidx]);
+            flag = s_ftmp[tidx];
+        }
+
+        // Write to shared memory
+        *sptr  = val;
+        *sfptr = flag;
+        __syncthreads();
+
+        // Segmented Scan
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMY; off *= 2) {
+            if (tidy >= off) {
+                val = sfptr[start * THREADS_X]
+                          ? val
+                          : binop(val, sptr[(start - off) * THREADS_X]);
+                flag =
+                    sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
+            }
+            start                    = DIMY - start;
+            sptr[start * THREADS_X]  = val;
+            sfptr[start * THREADS_X] = flag;
+
+            __syncthreads();
+        }
+
+        if (is_valid && (id_dim < out_dim)) *optr = val;
+        if (isLast) {
+            s_tmp[tidx]  = val;
+            s_ftmp[tidx] = flag;
+        }
+        id_dim += blockDim.y;
+        kptr += blockDim.y * key.strides[dim];
+        iptr += blockDim.y * istride_dim;
+        optr += blockDim.y * ostride_dim;
+        __syncthreads();
+    }
+}
+
+template<typename To, af_op_t op>
+__global__
+void scanbykey_dim_bcast(Param<To> out, CParam<To> tmp, Param<int> tlid,
+                         int dim, uint blocks_x, uint blocks_y,
+                         uint blocks_dim, uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+    const int yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    int ids[4] = {xid, yid, zid, wid};
+
+    const To *tptr  = tmp.ptr;
+    To *optr        = out.ptr;
+    const int *iptr = tlid.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
+            ids[1] * tmp.strides[1] + ids[0];
+    iptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] +
+            ids[1] * tlid.strides[1] + ids[0];
+    const int blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    const int id_dim = ids[dim];
+
+    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
+                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
+
+    if (!is_valid) return;
+    if (blockIdx_dim == 0) return;
+
+    int boundary = *iptr;
+    To accum     = *(tptr - tmp.strides[dim]);
+
+    Binary<To, op> binop;
+    const int ostride_dim = out.strides[dim];
+
+    for (int k = 0, id = id_dim; is_valid && k < lim && (id < boundary);
+         k++, id += blockDim.y) {
+        *optr = binop(*optr, accum);
+        optr += blockDim.y * ostride_dim;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 72deb5c880..2e42e52702 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -13,368 +13,51 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
-#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/scan_dim_by_key.hpp>
+#include <optypes.hpp>
+#include <traits.hpp>
 #include "config.hpp"
 
 namespace cuda {
 namespace kernel {
 
-template<typename Tk>
-__device__ inline static char calculate_head_flags_dim(const Tk *kptr, int id,
-                                                       int stride) {
-    return (id == 0) ? 1 : ((*kptr) != (*(kptr - stride)));
-}
-
-template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
-__global__ static void scan_dim_nonfinal_kernel(Param<To> out, Param<To> tmp,
-                                                Param<char> tflg,
-                                                Param<int> tlid, CParam<Ti> in,
-                                                CParam<Tk> key, int dim,
-                                                uint blocks_x, uint blocks_y,
-                                                uint lim, bool inclusive_scan) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-    const int tid  = tidy * THREADS_X + tidx;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = blockIdx.y / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
-    const int xid        = blockIdx_x * blockDim.x + tidx;
-    const int yid = blockIdx_y;  // yid  of output. updated for input later.
-
-    int ids[4] = {xid, yid, zid, wid};
-
-    const Ti *iptr = in.ptr;
-    const Tk *kptr = key.ptr;
-    To *optr       = out.ptr;
-    To *tptr       = tmp.ptr;
-    char *tfptr    = tflg.ptr;
-    int *tiptr     = tlid.ptr;
-
-    // There is only one element per block for out
-    // There are blockDim.y elements per block for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
-    // in
-    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
-            ids[1] * tmp.strides[1] + ids[0];
-    tfptr += ids[3] * tflg.strides[3] + ids[2] * tflg.strides[2] +
-             ids[1] * tflg.strides[1] + ids[0];
-    tiptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] +
-             ids[1] * tlid.strides[1] + ids[0];
-    const int blockIdx_dim = ids[dim];
-
-    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-            ids[1] * out.strides[1] + ids[0];
-    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-            ids[1] * in.strides[1] + ids[0];
-    kptr += ids[3] * key.strides[3] + ids[2] * key.strides[2] +
-            ids[1] * key.strides[1] + ids[0];
-    int id_dim        = ids[dim];
-    const int out_dim = out.dims[dim];
-
-    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
-                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
-
-    const int ostride_dim = out.strides[dim];
-    const int istride_dim = in.strides[dim];
-
-    __shared__ char s_flg[THREADS_X * DIMY * 2];
-    __shared__ To s_val[THREADS_X * DIMY * 2];
-    __shared__ char s_ftmp[THREADS_X];
-    __shared__ To s_tmp[THREADS_X];
-    __shared__ int boundaryid[THREADS_X];
-    To *sptr    = s_val + tid;
-    char *sfptr = s_flg + tid;
-
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-
-    const To init = Binary<To, op>::init();
-    To val        = init;
-
-    const bool isLast = (tidy == (DIMY - 1));
-    if (isLast) {
-        s_tmp[tidx]      = val;
-        s_ftmp[tidx]     = 0;
-        boundaryid[tidx] = -1;
-    }
-    __syncthreads();
-
-    char flag = 0;
-    for (int k = 0; k < lim; k++) {
-        if (id_dim < out_dim) {
-            flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
-        } else {
-            flag = 0;
-        }
-
-        // Load val from global in
-        if (inclusive_scan) {
-            if (id_dim >= out_dim) {
-                val = init;
-            } else {
-                val = transform(*iptr);
-            }
-        } else {
-            if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
-                val = init;
-            } else {
-                val = transform(*(iptr - istride_dim));
-            }
-        }
-
-        // Add partial result from last iteration before scan operation
-        if ((tidy == 0) && (flag == 0)) {
-            val  = binop(val, s_tmp[tidx]);
-            flag = s_ftmp[tidx];
-        }
-
-        // Write to shared memory
-        *sptr  = val;
-        *sfptr = flag;
-        __syncthreads();
-
-        // Segmented Scan
-        int start = 0;
-#pragma unroll
-        for (int off = 1; off < DIMY; off *= 2) {
-            if (tidy >= off) {
-                val = sfptr[start * THREADS_X]
-                          ? val
-                          : binop(val, sptr[(start - off) * THREADS_X]);
-                flag =
-                    sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
-            }
-            start                    = DIMY - start;
-            sptr[start * THREADS_X]  = val;
-            sfptr[start * THREADS_X] = flag;
-
-            __syncthreads();
-        }
-
-        // Identify segment boundary
-        if (tidy == 0) {
-            if ((s_ftmp[tidx] == 0) && (sfptr[start * THREADS_X] == 1)) {
-                boundaryid[tidx] = id_dim;
-            }
-        } else {
-            if ((sfptr[(start - 1) * THREADS_X] == 0) &&
-                (sfptr[start * THREADS_X] == 1)) {
-                boundaryid[tidx] = id_dim;
-            }
-        }
-        __syncthreads();
-
-        if (is_valid && (id_dim < out_dim)) *optr = val;
-        if (isLast) {
-            s_tmp[tidx]  = val;
-            s_ftmp[tidx] = flag;
-        }
-        id_dim += blockDim.y;
-        kptr += blockDim.y * key.strides[dim];
-        iptr += blockDim.y * istride_dim;
-        optr += blockDim.y * ostride_dim;
-        __syncthreads();
-    }
-
-    if (is_valid && (blockIdx_dim < tmp.dims[dim]) && isLast) {
-        *tptr        = val;
-        *tfptr       = flag;
-        int boundary = boundaryid[tidx];
-        *tiptr       = (boundary == -1) ? id_dim : boundary;
-    }
-}
-
-template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMY>
-__global__ static void scan_dim_final_kernel(Param<To> out, CParam<Ti> in,
-                                             CParam<Tk> key, int dim,
-                                             uint blocks_x, uint blocks_y,
-                                             uint lim, bool calculateFlags,
-                                             bool inclusive_scan) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-    const int tid  = tidy * THREADS_X + tidx;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = blockIdx.y / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
-    const int xid        = blockIdx_x * blockDim.x + tidx;
-    const int yid = blockIdx_y;  // yid  of output. updated for input later.
-
-    int ids[4] = {xid, yid, zid, wid};
-
-    const Ti *iptr = in.ptr;
-    const Tk *kptr = key.ptr;
-    To *optr       = out.ptr;
-
-    // There is only one element per block for out
-    // There are blockDim.y elements per block for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
-    // in
-
-    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-            ids[1] * out.strides[1] + ids[0];
-    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-            ids[1] * in.strides[1] + ids[0];
-    kptr += ids[3] * key.strides[3] + ids[2] * key.strides[2] +
-            ids[1] * key.strides[1] + ids[0];
-    int id_dim        = ids[dim];
-    const int out_dim = out.dims[dim];
-
-    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
-                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
-
-    const int ostride_dim = out.strides[dim];
-    const int istride_dim = in.strides[dim];
-
-    __shared__ char s_flg[THREADS_X * DIMY * 2];
-    __shared__ To s_val[THREADS_X * DIMY * 2];
-    __shared__ char s_ftmp[THREADS_X];
-    __shared__ To s_tmp[THREADS_X];
-    To *sptr    = s_val + tid;
-    char *sfptr = s_flg + tid;
-
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-
-    const To init = Binary<To, op>::init();
-    To val        = init;
-
-    const bool isLast = (tidy == (DIMY - 1));
-    if (isLast) {
-        s_tmp[tidx]  = val;
-        s_ftmp[tidx] = 0;
-    }
-    __syncthreads();
-
-    char flag = 0;
-    for (int k = 0; k < lim; k++) {
-        if (calculateFlags) {
-            if (id_dim < out_dim) {
-                flag = calculate_head_flags_dim(kptr, id_dim, key.strides[dim]);
-            } else {
-                flag = 0;
-            }
-        } else {
-            flag = *kptr;
-        }
-
-        // Load val from global in
-        if (inclusive_scan) {
-            if (id_dim >= out_dim) {
-                val = init;
-            } else {
-                val = transform(*iptr);
-            }
-        } else {
-            if ((id_dim == 0) || (id_dim >= out_dim) || flag) {
-                val = init;
-            } else {
-                val = transform(*(iptr - istride_dim));
-            }
-        }
-
-        // Add partial result from last iteration before scan operation
-        if ((tidy == 0) && (flag == 0)) {
-            val  = binop(val, s_tmp[tidx]);
-            flag = s_ftmp[tidx];
-        }
+static const std::string ScanDimByKeySource(scan_dim_by_key_cuh,
+                                            scan_dim_by_key_cuh_len);
 
-        // Write to shared memory
-        *sptr  = val;
-        *sfptr = flag;
-        __syncthreads();
-
-        // Segmented Scan
-        int start = 0;
-#pragma unroll
-        for (int off = 1; off < DIMY; off *= 2) {
-            if (tidy >= off) {
-                val = sfptr[start * THREADS_X]
-                          ? val
-                          : binop(val, sptr[(start - off) * THREADS_X]);
-                flag =
-                    sfptr[start * THREADS_X] | sfptr[(start - off) * THREADS_X];
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
+                                       Param<char> tflg, Param<int> tlid,
+                                       CParam<Ti> in, CParam<Tk> key,
+                                       const int dim, const uint threads_y,
+                                       const dim_t blocks_all[4],
+                                       bool inclusive_scan) {
+    // clang-format off
+    auto scanDimNonFinal = getKernel("cuda::scanbykey_dim_nonfinal",
+            ScanDimByKeySource,
+            {
+              TemplateTypename<Ti>(),
+              TemplateTypename<Tk>(),
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            },
+            {
+              DefineValue(THREADS_X),
+              DefineKeyValue(DIMY, threads_y)
             }
-            start                    = DIMY - start;
-            sptr[start * THREADS_X]  = val;
-            sfptr[start * THREADS_X] = flag;
-
-            __syncthreads();
-        }
-
-        if (is_valid && (id_dim < out_dim)) *optr = val;
-        if (isLast) {
-            s_tmp[tidx]  = val;
-            s_ftmp[tidx] = flag;
-        }
-        id_dim += blockDim.y;
-        kptr += blockDim.y * key.strides[dim];
-        iptr += blockDim.y * istride_dim;
-        optr += blockDim.y * ostride_dim;
-        __syncthreads();
-    }
-}
-
-template<typename To, af_op_t op>
-__global__ static void bcast_dim_kernel(Param<To> out, CParam<To> tmp,
-                                        Param<int> tlid, int dim, uint blocks_x,
-                                        uint blocks_y, uint blocks_dim,
-                                        uint lim) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = blockIdx.y / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
-    const int xid        = blockIdx_x * blockDim.x + tidx;
-    const int yid = blockIdx_y;  // yid  of output. updated for input later.
-
-    int ids[4] = {xid, yid, zid, wid};
-
-    const To *tptr  = tmp.ptr;
-    To *optr        = out.ptr;
-    const int *iptr = tlid.ptr;
-
-    // There is only one element per block for out
-    // There are blockDim.y elements per block for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
-    // in
-    tptr += ids[3] * tmp.strides[3] + ids[2] * tmp.strides[2] +
-            ids[1] * tmp.strides[1] + ids[0];
-    iptr += ids[3] * tlid.strides[3] + ids[2] * tlid.strides[2] +
-            ids[1] * tlid.strides[1] + ids[0];
-    const int blockIdx_dim = ids[dim];
-
-    ids[dim] = ids[dim] * blockDim.y * lim + tidy;
-    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-            ids[1] * out.strides[1] + ids[0];
-    const int id_dim = ids[dim];
-
-    bool is_valid = (ids[0] < out.dims[0]) && (ids[1] < out.dims[1]) &&
-                    (ids[2] < out.dims[2]) && (ids[3] < out.dims[3]);
-
-    if (!is_valid) return;
-    if (blockIdx_dim == 0) return;
+            );
+    // clang-format on
+    dim3 threads(THREADS_X, threads_y);
 
-    int boundary = *iptr;
-    To accum     = *(tptr - tmp.strides[dim]);
+    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-    Binary<To, op> binop;
-    const int ostride_dim = out.strides[dim];
+    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-    for (int k = 0, id = id_dim; is_valid && k < lim && (id < boundary);
-         k++, id += blockDim.y) {
-        *optr = binop(*optr, accum);
-        optr += blockDim.y * ostride_dim;
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    scanDimNonFinal(qArgs, out, tmp, tflg, tlid, in, key, dim, blocks_all[0],
+                    blocks_all[1], lim, inclusive_scan);
+    POST_LAUNCH_CHECK();
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
@@ -383,74 +66,30 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const uint threads_y,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
+    // clang-format off
+    auto scanDimFinal = getKernel("cuda::scanbykey_dim_final",
+            ScanDimByKeySource,
+            {
+              TemplateTypename<Ti>(),
+              TemplateTypename<Tk>(),
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            },
+            {
+              DefineValue(THREADS_X),
+              DefineKeyValue(DIMY, threads_y)
+            }
+            );
+    // clang-format on
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-    switch (threads_y) {
-        case 8:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 8>), blocks,
-                        threads, out, in, key, dim, blocks_all[0],
-                        blocks_all[1], lim, calculateFlags, inclusive_scan);
-            break;
-        case 4:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 4>), blocks,
-                        threads, out, in, key, dim, blocks_all[0],
-                        blocks_all[1], lim, calculateFlags, inclusive_scan);
-            break;
-        case 2:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 2>), blocks,
-                        threads, out, in, key, dim, blocks_all[0],
-                        blocks_all[1], lim, calculateFlags, inclusive_scan);
-            break;
-        case 1:
-            CUDA_LAUNCH((scan_dim_final_kernel<Ti, Tk, To, op, 1>), blocks,
-                        threads, out, in, key, dim, blocks_all[0],
-                        blocks_all[1], lim, calculateFlags, inclusive_scan);
-            break;
-    }
-
-    POST_LAUNCH_CHECK();
-}
-
-template<typename Ti, typename Tk, typename To, af_op_t op>
-static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
-                                       Param<char> tflg, Param<int> tlid,
-                                       CParam<Ti> in, CParam<Tk> key,
-                                       const int dim, const uint threads_y,
-                                       const dim_t blocks_all[4],
-                                       bool inclusive_scan) {
-    dim3 threads(THREADS_X, threads_y);
-
-    dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
-
-    uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
-
-    switch (threads_y) {
-        case 8:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 8>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, dim,
-                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
-            break;
-        case 4:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 4>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, dim,
-                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
-            break;
-        case 2:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 2>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, dim,
-                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
-            break;
-        case 1:
-            CUDA_LAUNCH((scan_dim_nonfinal_kernel<Ti, Tk, To, op, 1>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, dim,
-                        blocks_all[0], blocks_all[1], lim, inclusive_scan);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    scanDimFinal(qArgs, out, in, key, dim, blocks_all[0], blocks_all[1], lim,
+                 calculateFlags, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
@@ -458,15 +97,23 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
+    // clang-format off
+    auto bcastDim = getKernel("cuda::scanbykey_dim_bcast", ScanDimByKeySource,
+            {
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            }
+            );
+    // clang-format on
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
-    CUDA_LAUNCH((bcast_dim_kernel<To, op>), blocks, threads, out, tmp, tlid,
-                dim, blocks_all[0], blocks_all[1], blocks_all[dim], lim);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    bcastDim(qArgs, out, tmp, tlid, dim, blocks_all[0], blocks_all[1],
+             blocks_all[dim], lim);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/scan_first.cuh b/src/backend/cuda/kernel/scan_first.cuh
new file mode 100644
index 0000000000..e12e126d5e
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_first.cuh
@@ -0,0 +1,137 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+
+namespace cuda {
+
+template<typename Ti, typename To, af_op_t op,
+         bool isFinalPass, uint DIMX, bool inclusive_scan>
+__global__
+void scan_first(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                uint blocks_x, uint blocks_y, uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid = blockIdx_y * blockDim.y + tidy;
+
+    bool cond_yzw =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+
+    if (!cond_yzw) return;  // retire warps early
+
+    const Ti *iptr = in.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+
+    const int DIMY            = THREADS_PER_BLOCK / DIMX;
+    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+
+    __shared__ To s_val[SHARED_MEM_SIZE];
+    __shared__ To s_tmp[DIMY];
+
+    To *sptr = s_val + tidy * (2 * DIMX + 1);
+
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+
+    const To init = Binary<To, op>::init();
+    int id        = xid;
+    To val        = init;
+
+    const bool isLast = (tidx == (DIMX - 1));
+
+    for (int k = 0; k < lim; k++) {
+        if (isLast) s_tmp[tidy] = val;
+
+        bool cond  = (id < out.dims[0]);
+        val        = cond ? transform(iptr[id]) : init;
+        sptr[tidx] = val;
+        __syncthreads();
+
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (tidx >= off) val = binop(val, sptr[(start - off) + tidx]);
+            start              = DIMX - start;
+            sptr[start + tidx] = val;
+
+            __syncthreads();
+        }
+
+        val = binop(val, s_tmp[tidy]);
+
+        if (inclusive_scan) {
+            if (cond) { optr[id] = val; }
+        } else {
+            if (id == (out.dims[0] - 1)) {
+                optr[0] = init;
+            } else if (id < (out.dims[0] - 1)) {
+                optr[id + 1] = val;
+            }
+        }
+        id += blockDim.x;
+        __syncthreads();
+    }
+
+    if (!isFinalPass && isLast) { tptr[blockIdx_x] = val; }
+}
+
+template<typename To, af_op_t op>
+__global__
+void scan_first_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
+        uint blocks_y, uint lim, bool inclusive_scan) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int xid = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid = blockIdx_y * blockDim.y + tidy;
+
+    if (blockIdx_x == 0) return;
+
+    bool cond =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond) return;
+
+    To *optr       = out.ptr;
+    const To *tptr = tmp.ptr;
+
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+
+    Binary<To, op> binop;
+    To accum = tptr[blockIdx_x - 1];
+
+    // Shift broadcast one step to the right for exclusive scan (#2366)
+    int offset = !inclusive_scan;
+    for (int k = 0, id = xid + offset; k < lim && id < out.dims[0];
+         k++, id += blockDim.x) {
+        optr[id] = binop(accum, optr[id]);
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index fac586d222..a65bb155e4 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -12,138 +12,38 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
-#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/scan_first.hpp>
 #include "config.hpp"
 
 namespace cuda {
 namespace kernel {
-template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX,
-         bool inclusive_scan>
-__global__ static void scan_first_kernel(Param<To> out, Param<To> tmp,
-                                         CParam<Ti> in, uint blocks_x,
-                                         uint blocks_y, uint lim) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
 
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const int xid = blockIdx_x * blockDim.x * lim + tidx;
-    const int yid = blockIdx_y * blockDim.y + tidy;
-
-    bool cond_yzw =
-        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-
-    if (!cond_yzw) return;  // retire warps early
-
-    const Ti *iptr = in.ptr;
-    To *optr       = out.ptr;
-    To *tptr       = tmp.ptr;
-
-    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-
-    const int DIMY            = THREADS_PER_BLOCK / DIMX;
-    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
-
-    __shared__ To s_val[SHARED_MEM_SIZE];
-    __shared__ To s_tmp[DIMY];
-
-    To *sptr = s_val + tidy * (2 * DIMX + 1);
-
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-
-    const To init = Binary<To, op>::init();
-    int id        = xid;
-    To val        = init;
-
-    const bool isLast = (tidx == (DIMX - 1));
-
-    for (int k = 0; k < lim; k++) {
-        if (isLast) s_tmp[tidy] = val;
-
-        bool cond  = (id < out.dims[0]);
-        val        = cond ? transform(iptr[id]) : init;
-        sptr[tidx] = val;
-        __syncthreads();
-
-        int start = 0;
-#pragma unroll
-        for (int off = 1; off < DIMX; off *= 2) {
-            if (tidx >= off) val = binop(val, sptr[(start - off) + tidx]);
-            start              = DIMX - start;
-            sptr[start + tidx] = val;
-
-            __syncthreads();
-        }
-
-        val = binop(val, s_tmp[tidy]);
-
-        if (inclusive_scan) {
-            if (cond) { optr[id] = val; }
-        } else {
-            if (id == (out.dims[0] - 1)) {
-                optr[0] = init;
-            } else if (id < (out.dims[0] - 1)) {
-                optr[id + 1] = val;
+static const std::string ScanFirstSource(scan_first_cuh, scan_first_cuh_len);
+
+template<typename Ti, typename To, af_op_t op>
+static
+void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                         const uint blocks_x, const uint blocks_y,
+                         const uint threads_x, bool isFinalPass,
+                         bool inclusive_scan) {
+    // clang-format off
+    auto scanFirst = getKernel("cuda::scan_first", ScanFirstSource,
+            {
+              TemplateTypename<Ti>(),
+              TemplateTypename<To>(),
+              TemplateArg(op),
+              TemplateArg(isFinalPass),
+              TemplateArg(threads_x),
+              TemplateArg(inclusive_scan)
+            },
+            {
+              DefineValue(THREADS_PER_BLOCK)
             }
-        }
-        id += blockDim.x;
-        __syncthreads();
-    }
+            );
+    // clang-format on
 
-    if (!isFinalPass && isLast) { tptr[blockIdx_x] = val; }
-}
-
-template<typename To, af_op_t op>
-__global__ static void bcast_first_kernel(Param<To> out, CParam<To> tmp,
-                                          uint blocks_x, uint blocks_y,
-                                          uint lim, bool inclusive_scan) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const int xid = blockIdx_x * blockDim.x * lim + tidx;
-    const int yid = blockIdx_y * blockDim.y + tidy;
-
-    if (blockIdx_x == 0) return;
-
-    bool cond =
-        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-    if (!cond) return;
-
-    To *optr       = out.ptr;
-    const To *tptr = tmp.ptr;
-
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-
-    Binary<To, op> binop;
-    To accum = tptr[blockIdx_x - 1];
-
-    // Shift broadcast one step to the right for exclusive scan (#2366)
-    int offset = !inclusive_scan;
-    for (int k = 0, id = xid + offset; k < lim && id < out.dims[0];
-         k++, id += blockDim.x) {
-        optr[id] = binop(accum, optr[id]);
-    }
-}
-
-template<typename Ti, typename To, af_op_t op, bool isFinalPass,
-         bool inclusive_scan>
-static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
-                                const uint blocks_x, const uint blocks_y,
-                                const uint threads_x) {
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
@@ -154,29 +54,8 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-    switch (threads_x) {
-        case 32:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 32,
-                                           inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
-            break;
-        case 64:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 64,
-                                           inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
-            break;
-        case 128:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 128,
-                                           inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
-            break;
-        case 256:
-            CUDA_LAUNCH((scan_first_kernel<Ti, To, op, isFinalPass, 256,
-                                           inclusive_scan>),
-                        blocks, threads, out, tmp, in, blocks_x, blocks_y, lim);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    scanFirst(qArgs, out, tmp, in, blocks_x, blocks_y, lim);
     POST_LAUNCH_CHECK();
 }
 
@@ -184,6 +63,15 @@ template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
+    // clang-format off
+    auto bcastFirst = getKernel("cuda::scan_first_bcast", ScanFirstSource,
+            {
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            }
+            );
+    // clang-format on
+
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
@@ -194,14 +82,13 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-    CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp,
-                blocks_x, blocks_y, lim, inclusive_scan);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    bcastFirst(qArgs, out, tmp, blocks_x, blocks_y, lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-static void scan_first(Param<To> out, CParam<Ti> in) {
+template<typename Ti, typename To, af_op_t op>
+static void scan_first(Param<To> out, CParam<Ti> in, bool inclusive_scan) {
     uint threads_x = nextpow2(std::max(32u, (uint)out.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
     uint threads_y = THREADS_PER_BLOCK / threads_x;
@@ -210,8 +97,8 @@ static void scan_first(Param<To> out, CParam<Ti> in) {
     uint blocks_y = divup(out.dims[1], threads_y);
 
     if (blocks_x == 1) {
-        scan_first_launcher<Ti, To, op, true, inclusive_scan>(
-            out, out, in, blocks_x, blocks_y, threads_x);
+        scan_first_launcher<Ti, To, op>(out, out, in, blocks_x, blocks_y,
+                                        threads_x, true, inclusive_scan);
 
     } else {
         Param<To> tmp = out;
@@ -225,16 +112,16 @@ static void scan_first(Param<To> out, CParam<Ti> in) {
         auto tmp_alloc   = memAlloc<To>(tmp_elements);
         tmp.ptr          = tmp_alloc.get();
 
-        scan_first_launcher<Ti, To, op, false, inclusive_scan>(
-            out, tmp, in, blocks_x, blocks_y, threads_x);
+        scan_first_launcher<Ti, To, op>(out, tmp, in, blocks_x, blocks_y,
+                                        threads_x, false, inclusive_scan);
 
         // FIXME: Is there an alternative to the if condition ?
         if (op == af_notzero_t) {
-            scan_first_launcher<To, To, af_add_t, true, true>(
-                tmp, tmp, tmp, 1, blocks_y, threads_x);
+            scan_first_launcher<To, To, af_add_t>(tmp, tmp, tmp, 1, blocks_y,
+                                                  threads_x, true, true);
         } else {
-            scan_first_launcher<To, To, op, true, true>(tmp, tmp, tmp, 1,
-                                                        blocks_y, threads_x);
+            scan_first_launcher<To, To, op>(tmp, tmp, tmp, 1, blocks_y,
+                                            threads_x, true, true);
         }
 
         bcast_first_launcher<To, op>(out, tmp, blocks_x, blocks_y, threads_x,
diff --git a/src/backend/cuda/kernel/scan_first_by_key.cuh b/src/backend/cuda/kernel/scan_first_by_key.cuh
new file mode 100644
index 0000000000..351f4b8bf2
--- /dev/null
+++ b/src/backend/cuda/kernel/scan_first_by_key.cuh
@@ -0,0 +1,309 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+
+namespace cuda {
+
+template<typename Tk>
+__device__ inline
+char calculate_head_flags(const Tk *kptr, int id, int previd) {
+    return (id == 0) ? 1 : (kptr[id] != kptr[previd]);
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op>
+__global__
+void scanbykey_first_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
+                              Param<int> tlid, CParam<Ti> in, CParam<Tk> key,
+                              uint blocks_x, uint blocks_y, uint lim,
+                              bool inclusive_scan) {
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const int istride         = in.strides[0];
+    const int DIMY            = THREADS_PER_BLOCK / DIMX;
+    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+    __shared__ char s_flg[SHARED_MEM_SIZE];
+    __shared__ To s_val[SHARED_MEM_SIZE];
+    __shared__ char s_ftmp[DIMY];
+    __shared__ To s_tmp[DIMY];
+    __shared__ int boundaryid[DIMY];
+
+    const int tidx       = threadIdx.x;
+    const int tidy       = threadIdx.y;
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid        = blockIdx_y * blockDim.y + tidy;
+    bool cond_yzw =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond_yzw) return;  // retire warps early
+
+    To *sptr    = s_val + tidy * (2 * DIMX + 1);
+    char *sfptr = s_flg + tidy * (2 * DIMX + 1);
+    int id      = xid;
+
+    const bool isLast = (tidx == (DIMX - 1));
+    if (isLast) {
+        s_tmp[tidy]      = init;
+        s_ftmp[tidy]     = 0;
+        boundaryid[tidy] = -1;
+    }
+    __syncthreads();
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+    To *tptr       = tmp.ptr;
+    char *tfptr    = tflg.ptr;
+    int *tiptr     = tlid.ptr;
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+    tfptr +=
+        wid * tflg.strides[3] + zid * tflg.strides[2] + yid * tflg.strides[1];
+    tiptr +=
+        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+    char flag = 0;
+    for (int k = 0; k < lim; k++) {
+        if (id < out.dims[0]) {
+            flag = calculate_head_flags(kptr, id, id - 1);
+        } else {
+            flag = 0;
+        }
+
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id >= out.dims[0]) {
+                val = init;
+            } else {
+                val = transform(iptr[id]);
+            }
+        } else {
+            if ((id == 0) || (id >= out.dims[0]) || flag) {
+                val = init;
+            } else {
+                val = transform(iptr[id - istride]);
+            }
+        }
+
+        // Add partial result from last iteration before scan operation
+        if ((tidx == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidy]);
+            flag = s_ftmp[tidy];
+        }
+
+        // Write to shared memory
+        sptr[tidx]  = val;
+        sfptr[tidx] = flag;
+        __syncthreads();
+
+        // Segmented Scan
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (tidx >= off) {
+                val = sfptr[start + tidx]
+                          ? val
+                          : binop(val, sptr[(start - off) + tidx]);
+                flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
+            }
+            start               = DIMX - start;
+            sptr[start + tidx]  = val;
+            sfptr[start + tidx] = flag;
+
+            __syncthreads();
+        }
+
+        // Identify segment boundary
+        if (tidx == 0) {
+            if ((s_ftmp[tidy] == 0) && (sfptr[tidx] == 1)) {
+                boundaryid[tidy] = id;
+            }
+        } else {
+            if ((sfptr[tidx - 1] == 0) && (sfptr[tidx] == 1)) {
+                boundaryid[tidy] = id;
+            }
+        }
+        __syncthreads();
+
+        if (id < out.dims[0]) optr[id] = val;
+        if (isLast) {
+            s_tmp[tidy]  = val;
+            s_ftmp[tidy] = flag;
+        }
+        id += blockDim.x;
+        __syncthreads();
+    }
+    if (isLast) {
+        tptr[blockIdx_x]  = val;
+        tfptr[blockIdx_x] = flag;
+        int boundary      = boundaryid[tidy];
+        tiptr[blockIdx_x] = (boundary == -1) ? id : boundary;
+    }
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op>
+__global__
+void scanbykey_first_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
+                           uint blocks_x, uint blocks_y, uint lim,
+                           bool calculateFlags, bool inclusive_scan) {
+    Transform<Ti, To, op> transform;
+    Binary<To, op> binop;
+    const To init = Binary<To, op>::init();
+    To val        = init;
+
+    const int istride         = in.strides[0];
+    const int DIMY            = THREADS_PER_BLOCK / DIMX;
+    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
+    __shared__ char s_flg[SHARED_MEM_SIZE];
+    __shared__ To s_val[SHARED_MEM_SIZE];
+    __shared__ char s_ftmp[DIMY];
+    __shared__ To s_tmp[DIMY];
+
+    const int tidx       = threadIdx.x;
+    const int tidy       = threadIdx.y;
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid        = blockIdx_y * blockDim.y + tidy;
+    bool cond_yzw =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond_yzw) return;  // retire warps early
+
+    To *sptr    = s_val + tidy * (2 * DIMX + 1);
+    char *sfptr = s_flg + tidy * (2 * DIMX + 1);
+    int id      = xid;
+
+    const bool isLast = (tidx == (DIMX - 1));
+    if (isLast) {
+        s_tmp[tidy]  = init;
+        s_ftmp[tidy] = 0;
+    }
+    __syncthreads();
+
+    const Ti *iptr = in.ptr;
+    const Tk *kptr = key.ptr;
+    To *optr       = out.ptr;
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+    for (int k = 0; k < lim; k++) {
+        char flag = 0;
+        if (calculateFlags) {
+            if (id < out.dims[0]) {
+                flag = calculate_head_flags(kptr, id, id - key.strides[0]);
+            }
+        } else {
+            flag = kptr[id];
+        }
+
+        // Load val from global in
+        if (inclusive_scan) {
+            if (id >= out.dims[0]) {
+                val = init;
+            } else {
+                val = transform(iptr[id]);
+            }
+        } else {
+            if ((id == 0) || (id >= out.dims[0]) || flag) {
+                val = init;
+            } else {
+                val = transform(iptr[id - istride]);
+            }
+        }
+
+        // Add partial result from last iteration before scan operation
+        if ((tidx == 0) && (flag == 0)) {
+            val  = binop(val, s_tmp[tidy]);
+            flag = flag | s_ftmp[tidy];
+        }
+
+        // Write to shared memory
+        sptr[tidx]  = val;
+        sfptr[tidx] = flag;
+        __syncthreads();
+
+        // Segmented Scan
+        int start = 0;
+#pragma unroll
+        for (int off = 1; off < DIMX; off *= 2) {
+            if (tidx >= off) {
+                val = sfptr[start + tidx]
+                          ? val
+                          : binop(val, sptr[(start - off) + tidx]);
+                flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
+            }
+            start               = DIMX - start;
+            sptr[start + tidx]  = val;
+            sfptr[start + tidx] = flag;
+
+            __syncthreads();
+        }
+
+        if (id < out.dims[0]) optr[id] = val;
+        if (isLast) {
+            s_tmp[tidy]  = val;
+            s_ftmp[tidy] = flag;
+        }
+        id += blockDim.x;
+        __syncthreads();
+    }
+}
+
+template<typename To, af_op_t op>
+__global__
+void scanbykey_first_bcast(Param<To> out, Param<To> tmp, Param<int> tlid,
+                           uint blocks_x, uint blocks_y, uint lim) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int wid        = blockIdx.y / blocks_y;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
+    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
+    const int yid        = blockIdx_y * blockDim.y + tidy;
+
+    if (blockIdx_x == 0) return;
+
+    bool cond =
+        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
+    if (!cond) return;
+
+    To *optr        = out.ptr;
+    const To *tptr  = tmp.ptr;
+    const int *iptr = tlid.ptr;
+
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+    iptr +=
+        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+    Binary<To, op> binop;
+    int boundary = iptr[blockIdx_x];
+    To accum     = tptr[blockIdx_x - 1];
+
+    for (int k = 0, id = xid; k < lim && id < boundary; k++, id += blockDim.x) {
+        optr[id] = binop(accum, optr[id]);
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index c3c23fb2c5..5fb8967745 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -13,268 +13,17 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
-#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/scan_first_by_key.hpp>
+#include <optypes.hpp>
 #include "config.hpp"
 
 namespace cuda {
 namespace kernel {
-template<typename Tk>
-__device__ inline static char calculate_head_flags(const Tk *kptr, int id,
-                                                   int previd) {
-    return (id == 0) ? 1 : (kptr[id] != kptr[previd]);
-}
-
-template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
-__global__ static void scan_nonfinal_kernel(Param<To> out, Param<To> tmp,
-                                            Param<char> tflg, Param<int> tlid,
-                                            CParam<Ti> in, CParam<Tk> key,
-                                            uint blocks_x, uint blocks_y,
-                                            uint lim, bool inclusive_scan) {
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-    const To init = Binary<To, op>::init();
-    To val        = init;
-
-    const int istride         = in.strides[0];
-    const int DIMY            = THREADS_PER_BLOCK / DIMX;
-    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
-    __shared__ char s_flg[SHARED_MEM_SIZE];
-    __shared__ To s_val[SHARED_MEM_SIZE];
-    __shared__ char s_ftmp[DIMY];
-    __shared__ To s_tmp[DIMY];
-    __shared__ int boundaryid[DIMY];
-
-    const int tidx       = threadIdx.x;
-    const int tidy       = threadIdx.y;
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = blockIdx.y / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
-    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
-    const int yid        = blockIdx_y * blockDim.y + tidy;
-    bool cond_yzw =
-        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-    if (!cond_yzw) return;  // retire warps early
-
-    To *sptr    = s_val + tidy * (2 * DIMX + 1);
-    char *sfptr = s_flg + tidy * (2 * DIMX + 1);
-    int id      = xid;
-
-    const bool isLast = (tidx == (DIMX - 1));
-    if (isLast) {
-        s_tmp[tidy]      = init;
-        s_ftmp[tidy]     = 0;
-        boundaryid[tidy] = -1;
-    }
-    __syncthreads();
-
-    const Ti *iptr = in.ptr;
-    const Tk *kptr = key.ptr;
-    To *optr       = out.ptr;
-    To *tptr       = tmp.ptr;
-    char *tfptr    = tflg.ptr;
-    int *tiptr     = tlid.ptr;
-    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
-    kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-    tfptr +=
-        wid * tflg.strides[3] + zid * tflg.strides[2] + yid * tflg.strides[1];
-    tiptr +=
-        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
-
-    char flag = 0;
-    for (int k = 0; k < lim; k++) {
-        if (id < out.dims[0]) {
-            flag = calculate_head_flags(kptr, id, id - 1);
-        } else {
-            flag = 0;
-        }
-
-        // Load val from global in
-        if (inclusive_scan) {
-            if (id >= out.dims[0]) {
-                val = init;
-            } else {
-                val = transform(iptr[id]);
-            }
-        } else {
-            if ((id == 0) || (id >= out.dims[0]) || flag) {
-                val = init;
-            } else {
-                val = transform(iptr[id - istride]);
-            }
-        }
-
-        // Add partial result from last iteration before scan operation
-        if ((tidx == 0) && (flag == 0)) {
-            val  = binop(val, s_tmp[tidy]);
-            flag = s_ftmp[tidy];
-        }
-
-        // Write to shared memory
-        sptr[tidx]  = val;
-        sfptr[tidx] = flag;
-        __syncthreads();
-
-        // Segmented Scan
-        int start = 0;
-#pragma unroll
-        for (int off = 1; off < DIMX; off *= 2) {
-            if (tidx >= off) {
-                val = sfptr[start + tidx]
-                          ? val
-                          : binop(val, sptr[(start - off) + tidx]);
-                flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
-            }
-            start               = DIMX - start;
-            sptr[start + tidx]  = val;
-            sfptr[start + tidx] = flag;
-
-            __syncthreads();
-        }
-
-        // Identify segment boundary
-        if (tidx == 0) {
-            if ((s_ftmp[tidy] == 0) && (sfptr[tidx] == 1)) {
-                boundaryid[tidy] = id;
-            }
-        } else {
-            if ((sfptr[tidx - 1] == 0) && (sfptr[tidx] == 1)) {
-                boundaryid[tidy] = id;
-            }
-        }
-        __syncthreads();
-
-        if (id < out.dims[0]) optr[id] = val;
-        if (isLast) {
-            s_tmp[tidy]  = val;
-            s_ftmp[tidy] = flag;
-        }
-        id += blockDim.x;
-        __syncthreads();
-    }
-    if (isLast) {
-        tptr[blockIdx_x]  = val;
-        tfptr[blockIdx_x] = flag;
-        int boundary      = boundaryid[tidy];
-        tiptr[blockIdx_x] = (boundary == -1) ? id : boundary;
-    }
-}
-
-template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
-__global__ static void scan_final_kernel(Param<To> out, CParam<Ti> in,
-                                         CParam<Tk> key, uint blocks_x,
-                                         uint blocks_y, uint lim,
-                                         bool calculateFlags,
-                                         bool inclusive_scan) {
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-    const To init = Binary<To, op>::init();
-    To val        = init;
-
-    const int istride         = in.strides[0];
-    const int DIMY            = THREADS_PER_BLOCK / DIMX;
-    const int SHARED_MEM_SIZE = (2 * DIMX + 1) * (DIMY);
-    __shared__ char s_flg[SHARED_MEM_SIZE];
-    __shared__ To s_val[SHARED_MEM_SIZE];
-    __shared__ char s_ftmp[DIMY];
-    __shared__ To s_tmp[DIMY];
-
-    const int tidx       = threadIdx.x;
-    const int tidy       = threadIdx.y;
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = blockIdx.y / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
-    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
-    const int yid        = blockIdx_y * blockDim.y + tidy;
-    bool cond_yzw =
-        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-    if (!cond_yzw) return;  // retire warps early
-
-    To *sptr    = s_val + tidy * (2 * DIMX + 1);
-    char *sfptr = s_flg + tidy * (2 * DIMX + 1);
-    int id      = xid;
-
-    const bool isLast = (tidx == (DIMX - 1));
-    if (isLast) {
-        s_tmp[tidy]  = init;
-        s_ftmp[tidy] = 0;
-    }
-    __syncthreads();
 
-    const Ti *iptr = in.ptr;
-    const Tk *kptr = key.ptr;
-    To *optr       = out.ptr;
-    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
-    kptr += wid * key.strides[3] + zid * key.strides[2] + yid * key.strides[1];
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-
-    for (int k = 0; k < lim; k++) {
-        char flag = 0;
-        if (calculateFlags) {
-            if (id < out.dims[0]) {
-                flag = calculate_head_flags(kptr, id, id - key.strides[0]);
-            }
-        } else {
-            flag = kptr[id];
-        }
-
-        // Load val from global in
-        if (inclusive_scan) {
-            if (id >= out.dims[0]) {
-                val = init;
-            } else {
-                val = transform(iptr[id]);
-            }
-        } else {
-            if ((id == 0) || (id >= out.dims[0]) || flag) {
-                val = init;
-            } else {
-                val = transform(iptr[id - istride]);
-            }
-        }
-
-        // Add partial result from last iteration before scan operation
-        if ((tidx == 0) && (flag == 0)) {
-            val  = binop(val, s_tmp[tidy]);
-            flag = flag | s_ftmp[tidy];
-        }
-
-        // Write to shared memory
-        sptr[tidx]  = val;
-        sfptr[tidx] = flag;
-        __syncthreads();
-
-        // Segmented Scan
-        int start = 0;
-#pragma unroll
-        for (int off = 1; off < DIMX; off *= 2) {
-            if (tidx >= off) {
-                val = sfptr[start + tidx]
-                          ? val
-                          : binop(val, sptr[(start - off) + tidx]);
-                flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
-            }
-            start               = DIMX - start;
-            sptr[start + tidx]  = val;
-            sfptr[start + tidx] = flag;
-
-            __syncthreads();
-        }
-
-        if (id < out.dims[0]) optr[id] = val;
-        if (isLast) {
-            s_tmp[tidy]  = val;
-            s_ftmp[tidy] = flag;
-        }
-        id += blockDim.x;
-        __syncthreads();
-    }
-}
+static const std::string ScanFirstByKeySource(scan_first_by_key_cuh,
+                                              scan_first_by_key_cuh_len);
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
 static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
@@ -282,34 +31,29 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    CParam<Ti> in, CParam<Tk> key,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
+    // clang-format off
+    auto scanNonFinal = getKernel("cuda::scanbykey_first_nonfinal",
+            ScanFirstByKeySource,
+            {
+              TemplateTypename<Ti>(),
+              TemplateTypename<Tk>(),
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            },
+            {
+              DefineValue(THREADS_PER_BLOCK),
+              DefineKeyValue(DIMX, threads_x)
+            }
+            );
+    // clang-format on
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-    switch (threads_x) {
-        case 32:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 32>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
-                        blocks_y, lim, inclusive_scan);
-            break;
-        case 64:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 64>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
-                        blocks_y, lim, inclusive_scan);
-            break;
-        case 128:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 128>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
-                        blocks_y, lim, inclusive_scan);
-            break;
-        case 256:
-            CUDA_LAUNCH((scan_nonfinal_kernel<Ti, Tk, To, op, 256>), blocks,
-                        threads, out, tmp, tflg, tlid, in, key, blocks_x,
-                        blocks_y, lim, inclusive_scan);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    scanNonFinal(qArgs, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim,
+                 inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
@@ -318,85 +62,51 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
+    // clang-format off
+    auto scanFinal = getKernel("cuda::scanbykey_first_final",
+            ScanFirstByKeySource,
+            {
+              TemplateTypename<Ti>(),
+              TemplateTypename<Tk>(),
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            },
+            {
+              DefineValue(THREADS_PER_BLOCK),
+              DefineKeyValue(DIMX, threads_x)
+            }
+            );
+    // clang-format on
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
-    switch (threads_x) {
-        case 32:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 32>), blocks,
-                        threads, out, in, key, blocks_x, blocks_y, lim,
-                        calculateFlags, inclusive_scan);
-            break;
-        case 64:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 64>), blocks,
-                        threads, out, in, key, blocks_x, blocks_y, lim,
-                        calculateFlags, inclusive_scan);
-            break;
-        case 128:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 128>), blocks,
-                        threads, out, in, key, blocks_x, blocks_y, lim,
-                        calculateFlags, inclusive_scan);
-            break;
-        case 256:
-            CUDA_LAUNCH((scan_final_kernel<Ti, Tk, To, op, 256>), blocks,
-                        threads, out, in, key, blocks_x, blocks_y, lim,
-                        calculateFlags, inclusive_scan);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    scanFinal(qArgs, out, in, key, blocks_x, blocks_y, lim, calculateFlags,
+              inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
-template<typename To, af_op_t op>
-__global__ static void bcast_first_kernel(Param<To> out, Param<To> tmp,
-                                          Param<int> tlid, uint blocks_x,
-                                          uint blocks_y, uint lim) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int wid        = blockIdx.y / blocks_y;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int blockIdx_y = blockIdx.y - (blocks_y)*wid;
-    const int xid        = blockIdx_x * blockDim.x * lim + tidx;
-    const int yid        = blockIdx_y * blockDim.y + tidy;
-
-    if (blockIdx_x == 0) return;
-
-    bool cond =
-        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-    if (!cond) return;
-
-    To *optr        = out.ptr;
-    const To *tptr  = tmp.ptr;
-    const int *iptr = tlid.ptr;
-
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-    iptr +=
-        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
-
-    Binary<To, op> binop;
-    int boundary = iptr[blockIdx_x];
-    To accum     = tptr[blockIdx_x - 1];
-
-    for (int k = 0, id = xid; k < lim && id < boundary; k++, id += blockDim.x) {
-        optr[id] = binop(accum, optr[id]);
-    }
-}
-
 template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
+    // clang-format off
+    auto bcastFirst = getKernel("cuda::scanbykey_first_bcast",
+            ScanFirstByKeySource,
+            {
+              TemplateTypename<To>(),
+              TemplateArg(op)
+            }
+            );
+    // clang-format on
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
-    CUDA_LAUNCH((bcast_first_kernel<To, op>), blocks, threads, out, tmp, tlid,
-                blocks_x, blocks_y, lim);
 
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    bcastFirst(qArgs, out, tmp, tlid, blocks_x, blocks_y, lim);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/shared.hpp b/src/backend/cuda/kernel/shared.hpp
index bb23ea14e5..b945301d3b 100644
--- a/src/backend/cuda/kernel/shared.hpp
+++ b/src/backend/cuda/kernel/shared.hpp
@@ -9,8 +9,21 @@
 
 #pragma once
 
+#ifdef __CUDACC_RTC__
+
 namespace cuda {
+template<typename T>
+struct SharedMemory {
+    __DH__ T* getPointer() {
+        extern __shared__ T ptr[];
+        return ptr;
+    }
+};
+}  // namespace cuda
+
+#else
 
+namespace cuda {
 namespace kernel {
 
 template<typename T>
@@ -51,3 +64,5 @@ SPECIALIZE(uintl)
 
 }  // namespace kernel
 }  // namespace cuda
+
+#endif
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 8763c0ac13..a3e3337685 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -70,6 +70,8 @@
 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+#pragma once
+
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
@@ -1059,11 +1061,11 @@ Array<T> createInitialImage(CParam<T> img, const float init_sigma,
 
     if (double_input) {
         resize<T, AF_INTERP_BILINEAR>(init_img, img);
-        convolve2<T, convAccT, 0, false>(init_tmp, init_img, filter);
+        convolve2<T, convAccT>(init_tmp, init_img, filter, 0, false);
     } else
-        convolve2<T, convAccT, 0, false>(init_tmp, img, filter);
+        convolve2<T, convAccT>(init_tmp, img, filter, 0, false);
 
-    convolve2<T, convAccT, 1, false>(init_img, CParam<T>(init_tmp), filter);
+    convolve2<T, convAccT>(init_img, CParam<T>(init_tmp), filter, 1, false);
 
     return init_img;
 }
@@ -1111,9 +1113,9 @@ std::vector<Array<T>> buildGaussPyr(Param<T> init_img, const unsigned n_octaves,
                 Array<T> tmp = createEmptyArray<T>(tmp_pyr[src_idx].dims());
                 Array<convAccT> filter = gauss_filter<convAccT>(sig_layers[l]);
 
-                convolve2<T, convAccT, 0, false>(tmp, tmp_pyr[src_idx], filter);
-                convolve2<T, convAccT, 1, false>(tmp_pyr[idx], CParam<T>(tmp),
-                                                 filter);
+                convolve2<T, convAccT>(tmp, tmp_pyr[src_idx], filter, 0, false);
+                convolve2<T, convAccT>(tmp_pyr[idx], CParam<T>(tmp), filter, 1,
+                                       false);
 
                 // memFree(tmp.ptr);
             }
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 2860c0db99..e8726a3d73 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -33,7 +33,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
       file(RENAME "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
           "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
 
-      cuda_compile(scan_by_key_gen_files
+      cuda_compile(sort_by_key_gen_files
           ${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp
           OPTIONS
@@ -42,7 +42,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
             "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
       )
 
-      list(APPEND SORT_OBJ ${scan_by_key_gen_files})
+      list(APPEND SORT_OBJ ${sort_by_key_gen_files})
     endforeach(SBK_INST ${SBK_INSTS})
 endforeach(SBK_TYPE ${SBK_TYPES})
 
diff --git a/src/backend/cuda/kernel/transpose.cuh b/src/backend/cuda/kernel/transpose.cuh
new file mode 100644
index 0000000000..1307a043b3
--- /dev/null
+++ b/src/backend/cuda/kernel/transpose.cuh
@@ -0,0 +1,78 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool conjugate>
+__device__ T doOp(T in) {
+    if (conjugate)
+        return conj(in);
+    else
+        return in;
+}
+
+template<typename T, bool conjugate, bool is32Multiple>
+__global__ void transpose(Param<T> out, CParam<T> in,
+                          const int blocksPerMatX,
+                          const int blocksPerMatY) {
+    __shared__ T shrdMem[TILE_DIM][TILE_DIM + 1];
+
+    const int oDim0 = out.dims[0];
+    const int oDim1 = out.dims[1];
+    const int iDim0 = in.dims[0];
+    const int iDim1 = in.dims[1];
+
+    const int oStride1 = out.strides[1];
+    const int iStride1 = in.strides[1];
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    const int batchId_x  = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
+
+    const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
+
+    if (batchId_x >= in.dims[2] || batchId_y >= in.dims[3]) return;
+
+    const int x0 = TILE_DIM * blockIdx_x;
+    const int y0 = TILE_DIM * blockIdx_y;
+
+    int gx = lx + x0;
+    int gy = ly + y0;
+
+    in.ptr += batchId_x * in.strides[2] + batchId_y * in.strides[3];
+    out.ptr += batchId_x * out.strides[2] + batchId_y * out.strides[3];
+
+#pragma unroll
+    for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+        int gy_ = gy + repeat;
+        if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+            shrdMem[ly + repeat][lx] = in.ptr[gy_ * iStride1 + gx];
+    }
+    __syncthreads();
+
+    gx = lx + y0;
+    gy = ly + x0;
+
+#pragma unroll
+    for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+        int gy_ = gy + repeat;
+        if (is32Multiple || (gx < oDim0 && gy_ < oDim1))
+            out.ptr[gy_ * oStride1 + gx] =
+                doOp<T, conjugate>(shrdMem[lx][ly + repeat]);
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 8481115b90..33076fbabf 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -7,113 +7,58 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/transpose.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int TILE_DIM  = 32;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
-template<typename T, bool conjugate>
-__device__ T doOp(T in) {
-    if (conjugate)
-        return conj(in);
-    else
-        return in;
-}
-
-// Kernel is going access original data in coaleasced format
-template<typename T, bool conjugate, bool is32Multiple>
-__global__ void transpose(Param<T> out, CParam<T> in, const int blocksPerMatX,
-                          const int blocksPerMatY) {
-    __shared__ T shrdMem[TILE_DIM][TILE_DIM + 1];
-    // create variables to hold output dimensions
-    const int oDim0 = out.dims[0];
-    const int oDim1 = out.dims[1];
-    const int iDim0 = in.dims[0];
-    const int iDim1 = in.dims[1];
-
-    // calculate strides
-    const int oStride1 = out.strides[1];
-    const int iStride1 = in.strides[1];
-
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    // batch based block Id
-    const int batchId_x  = blockIdx.x / blocksPerMatX;
-    const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
-
-    const int batchId_y = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (batchId_y * blocksPerMatY);
-
-    if (batchId_x >= in.dims[2] || batchId_y >= in.dims[3]) return;
+template<typename T>
+void transpose(Param<T> out, CParam<T> in, const bool conjugate,
+               const bool is32multiple) {
+    static const std::string source(transpose_cuh, transpose_cuh_len);
+
+    // clang-format off
+    auto transpose = getKernel("cuda::transpose", source,
+            {
+              TemplateTypename<T>(),
+              TemplateArg(conjugate),
+              TemplateArg(is32multiple)
+            },
+            {
+              DefineValue(TILE_DIM),
+              DefineValue(THREADS_Y)
+            }
+            );
+    // clang-format on
 
-    const int x0 = TILE_DIM * blockIdx_x;
-    const int y0 = TILE_DIM * blockIdx_y;
-
-    // calculate global indices
-    int gx = lx + x0;
-    int gy = ly + y0;
-
-    // offset in and out based on batch id
-    in.ptr += batchId_x * in.strides[2] + batchId_y * in.strides[3];
-    out.ptr += batchId_x * out.strides[2] + batchId_y * out.strides[3];
-
-#pragma unroll
-    for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-        int gy_ = gy + repeat;
-        if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-            shrdMem[ly + repeat][lx] = in.ptr[gy_ * iStride1 + gx];
-    }
-    __syncthreads();
-
-    gx = lx + y0;
-    gy = ly + x0;
-
-#pragma unroll
-    for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-        int gy_ = gy + repeat;
-        if (is32Multiple || (gx < oDim0 && gy_ < oDim1))
-            out.ptr[gy_ * oStride1 + gx] =
-                doOp<T, conjugate>(shrdMem[lx][ly + repeat]);
-    }
-}
-
-template<typename T, bool conjugate>
-void transpose(Param<T> out, CParam<T> in) {
-    // dimensions passed to this function should be input dimensions
-    // any necessary transformations and dimension related calculations are
-    // carried out here and inside the kernel
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], TILE_DIM);
     int blk_y = divup(in.dims[1], TILE_DIM);
-    // launch batch * blk_x blocks along x dimension
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
     const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+        cuda::getDeviceProp(getActiveDeviceId()).maxGridSize[1];
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0) {
-        CUDA_LAUNCH((transpose<T, conjugate, true>), blocks, threads, out, in,
-                    blk_x, blk_y);
-    } else {
-        CUDA_LAUNCH((transpose<T, conjugate, false>), blocks, threads, out, in,
-                    blk_x, blk_y);
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    transpose(qArgs, out, in, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
-}  // namespace kernel
 
+}  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/where.cuh b/src/backend/cuda/kernel/where.cuh
new file mode 100644
index 0000000000..ac1f81cfa9
--- /dev/null
+++ b/src/backend/cuda/kernel/where.cuh
@@ -0,0 +1,59 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__
+void where(uint *optr, CParam<uint> otmp, CParam<uint> rtmp, CParam<T> in,
+           uint blocks_x, uint blocks_y, uint lim) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint xid = blockIdx_x * blockDim.x * lim + tidx;
+    const uint yid = blockIdx_y * blockDim.y + tidy;
+
+    const uint *otptr = otmp.ptr;
+    const uint *rtptr = rtmp.ptr;
+    const T *iptr     = in.ptr;
+
+    const uint off =
+        wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
+    const uint bid = wid * rtmp.strides[3] + zid * rtmp.strides[2] +
+                     yid * rtmp.strides[1] + blockIdx_x;
+
+    otptr +=
+        wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+
+    bool cond =
+        (yid < otmp.dims[1]) && (zid < otmp.dims[2]) && (wid < otmp.dims[3]);
+    T zero = scalar<T>(0);
+
+    if (!cond) return;
+
+    uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
+
+    for (uint k = 0, id = xid; k < lim && id < otmp.dims[0];
+         k++, id += blockDim.x) {
+        uint idx = otptr[id] + accum;
+        if (iptr[id] != zero) optr[idx - 1] = (off + id);
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index f971c96ae0..639052bcb6 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -12,60 +12,20 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
-#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/where.hpp>
 #include "config.hpp"
 #include "scan_first.hpp"
 
 namespace cuda {
 namespace kernel {
 
-template<typename T>
-__global__ static void get_out_idx(uint *optr, CParam<uint> otmp,
-                                   CParam<uint> rtmp, CParam<T> in,
-                                   uint blocks_x, uint blocks_y, uint lim) {
-    const uint tidx = threadIdx.x;
-    const uint tidy = threadIdx.y;
-
-    const uint zid        = blockIdx.x / blocks_x;
-    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const uint blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const uint xid = blockIdx_x * blockDim.x * lim + tidx;
-    const uint yid = blockIdx_y * blockDim.y + tidy;
-
-    const uint *otptr = otmp.ptr;
-    const uint *rtptr = rtmp.ptr;
-    const T *iptr     = in.ptr;
-
-    const uint off =
-        wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
-    const uint bid = wid * rtmp.strides[3] + zid * rtmp.strides[2] +
-                     yid * rtmp.strides[1] + blockIdx_x;
-
-    otptr +=
-        wid * otmp.strides[3] + zid * otmp.strides[2] + yid * otmp.strides[1];
-    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
-
-    bool cond =
-        (yid < otmp.dims[1]) && (zid < otmp.dims[2]) && (wid < otmp.dims[3]);
-    T zero = scalar<T>(0);
-
-    if (!cond) return;
-
-    uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
-
-    for (uint k = 0, id = xid; k < lim && id < otmp.dims[0];
-         k++, id += blockDim.x) {
-        uint idx = otptr[id] + accum;
-        if (iptr[id] != zero) optr[idx - 1] = (off + id);
-    }
-}
-
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
+    static const std::string src(where_cuh, where_cuh_len);
+    auto whereOp = getKernel("cuda::where", src, {TemplateTypename<T>()});
+
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
     uint threads_y = THREADS_PER_BLOCK / threads_x;
@@ -95,8 +55,8 @@ static void where(Param<uint> &out, CParam<T> in) {
     rtmp.ptr          = rtmp_alloc.get();
     otmp.ptr          = otmp_alloc.get();
 
-    scan_first_launcher<T, uint, af_notzero_t, false, true>(
-        otmp, rtmp, in, blocks_x, blocks_y, threads_x);
+    scan_first_launcher<T, uint, af_notzero_t>(
+        otmp, rtmp, in, blocks_x, blocks_y, threads_x, false, true);
 
     // Linearize the dimensions and perform scan
     Param<uint> ltmp = rtmp;
@@ -106,7 +66,7 @@ static void where(Param<uint> &out, CParam<T> in) {
         ltmp.strides[k] = rtmp_elements;
     }
 
-    scan_first<uint, uint, af_add_t, true>(ltmp, ltmp);
+    scan_first<uint, uint, af_add_t>(ltmp, ltmp, true);
 
     // Get output size and allocate output
     uint total;
@@ -135,11 +95,12 @@ static void where(Param<uint> &out, CParam<T> in) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((get_out_idx<T>), blocks, threads, out.ptr, otmp, rtmp, in,
-                blocks_x, blocks_y, lim);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    whereOp(qArgs, out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
     POST_LAUNCH_CHECK();
 
     out_alloc.release();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/math.cpp b/src/backend/cuda/math.cpp
deleted file mode 100644
index e6d8c90d7d..0000000000
--- a/src/backend/cuda/math.cpp
+++ /dev/null
@@ -1,26 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <math.hpp>
-
-namespace cuda {
-cfloat division(cfloat lhs, double rhs) {
-    cfloat retVal;
-    retVal.x = real(lhs) / rhs;
-    retVal.y = imag(lhs) / rhs;
-    return retVal;
-}
-
-cdouble division(cdouble lhs, double rhs) {
-    cdouble retVal;
-    retVal.x = real(lhs) / rhs;
-    retVal.y = imag(lhs) / rhs;
-    return retVal;
-}
-}  // namespace cuda
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index bbf64726d3..7cac0cf6fc 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -8,20 +8,33 @@
  ********************************************************/
 
 #pragma once
+
+#ifdef __CUDACC_RTC__
+
+#define CUDART_INF_F __int_as_float(0x7f800000)
+#define CUDART_INF __longlong_as_double(0x7ff0000000000000ULL)
+#define STATIC_ inline
+
+#else  //__CUDACC_RTC__
+
 #include <common/defines.hpp>
 #include <af/defines.h>
-#include "backend.hpp"
-#include "types.hpp"
 
 #ifdef __CUDACC__
 #include <cuda_runtime_api.h>
 #include <math_constants.h>
-#endif
+#endif  //__CUDACC__
 
 #include <algorithm>
 #include <limits>
 
+#endif  //__CUDACC_RTC__
+
+#include "backend.hpp"
+#include "types.hpp"
+
 namespace cuda {
+
 template<typename T>
 static inline __DH__ T abs(T val) {
     return abs(val);
@@ -328,11 +341,24 @@ template<typename T>
 static inline T division(T lhs, double rhs) {
     return lhs / rhs;
 }
-cfloat division(cfloat lhs, double rhs);
-cdouble division(cdouble lhs, double rhs);
+
+static inline cfloat division(cfloat lhs, double rhs) {
+    cfloat retVal;
+    retVal.x = real(lhs) / rhs;
+    retVal.y = imag(lhs) / rhs;
+    return retVal;
+}
+
+static inline cdouble division(cdouble lhs, double rhs) {
+    cdouble retVal;
+    retVal.x = real(lhs) / rhs;
+    retVal.y = imag(lhs) / rhs;
+    return retVal;
+}
 
 template<typename T>
 static inline __DH__ T clamp(const T value, const T lo, const T hi) {
     return max(lo, min(value, hi));
 }
+
 }  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/EnqueueArgs.hpp b/src/backend/cuda/nvrtc/EnqueueArgs.hpp
new file mode 100644
index 0000000000..0fd51ebdc5
--- /dev/null
+++ b/src/backend/cuda/nvrtc/EnqueueArgs.hpp
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <nvrtc.h>
+
+#include <vector>
+
+namespace cuda {
+
+///
+/// EnqueueArgs is a kernel launch configuration composition object
+///
+/// This structure is an composition of various parameters that are
+/// required to successfully launch a CUDA kernel.
+///
+struct EnqueueArgs {
+    // TODO(pradeep): this can be easily templated
+    // template<typename Queue, typename Event>
+    dim3 mBlocks;                  ///< Number of blocks per grid/kernel-launch
+    dim3 mThreads;                 ///< Number of threads per block
+    CUstream mStream;              ///< CUDA stream to enqueue the kernel on
+    unsigned int mSharedMemSize;   ///< Size(in bytes) of shared memory used
+    std::vector<CUevent> mEvents;  ///< Events to wait for kernel execution
+
+    ///
+    /// \brief EnqueueArgs constructor
+    ///
+    /// \param[in] blks is number of blocks per grid
+    /// \param[in] thrds is number of threads per block
+    /// \param[in] stream is CUDA steam on which kernel has to be enqueued
+    /// \param[in] sharedMemSize is number of bytes of shared memory allocation
+    /// \param[in] events is list of events to wait for kernel execution
+    ///
+    EnqueueArgs(dim3 blks, dim3 thrds, CUstream stream = 0,
+                const unsigned int sharedMemSize   = 0,
+                const std::vector<CUevent> &events = {})
+        : mBlocks(blks)
+        , mThreads(thrds)
+        , mStream(stream)
+        , mSharedMemSize(sharedMemSize)
+        , mEvents(events) {}
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
new file mode 100644
index 0000000000..176c1dd29f
--- /dev/null
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -0,0 +1,368 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel_headers/jit.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/Param.hpp>
+#include <nvrtc_kernel_headers/backend.hpp>
+#include <nvrtc_kernel_headers/cuComplex.hpp>
+#include <nvrtc_kernel_headers/math.hpp>
+#include <nvrtc_kernel_headers/ops.hpp>
+#include <nvrtc_kernel_headers/optypes.hpp>
+#include <nvrtc_kernel_headers/shared.hpp>
+#include <nvrtc_kernel_headers/types.hpp>
+#include <optypes.hpp>
+#include <platform.hpp>
+
+#include <algorithm>
+#include <array>
+#include <map>
+#include <memory>
+#include <string>
+#include <type_traits>
+
+using std::array;
+using std::extent;
+using std::map;
+using std::pair;
+using std::string;
+using std::to_string;
+using std::transform;
+using std::unique_ptr;
+using std::vector;
+
+namespace cuda {
+
+using kc_t = map<string, Kernel>;
+
+#ifndef NDEBUG
+#define CU_LINK_CHECK(fn)                                                 \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[1024];                                            \
+        const char* cu_err_name;                                          \
+        cuGetErrorName(res, &cu_err_name);                                \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), linkError);                     \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+    } while (0)
+#else
+#define CU_LINK_CHECK(fn) CU_CHECK(fn)
+#endif
+
+#ifndef NDEBUG
+#define NVRTC_CHECK(fn)                                \
+    do {                                               \
+        nvrtcResult res = fn;                          \
+        if (res == NVRTC_SUCCESS) break;               \
+        size_t logSize;                                \
+        nvrtcGetProgramLogSize(prog, &logSize);        \
+        unique_ptr<char[]> log(new char[logSize + 1]); \
+        char* logptr = log.get();                      \
+        nvrtcGetProgramLog(prog, logptr);              \
+        logptr[logSize] = '\x0';                       \
+        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
+    } while (0)
+#else
+#define NVRTC_CHECK(fn)                                                   \
+    do {                                                                  \
+        nvrtcResult res = fn;                                             \
+        if (res == NVRTC_SUCCESS) break;                                  \
+        char nvrtc_err_msg[1024];                                         \
+        snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
+                 "NVRTC Error(%d): %s\n", res, nvrtcGetErrorString(res)); \
+        AF_ERROR(nvrtc_err_msg, AF_ERR_INTERNAL);                         \
+    } while (0)
+#endif
+
+void Kernel::setConstant(const char* name, CUdeviceptr src, size_t bytes) {
+    CUdeviceptr dst = 0;
+    size_t size     = 0;
+    CU_CHECK(cuModuleGetGlobal(&dst, &size, prog, name));
+    CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, getActiveStream()));
+}
+
+Kernel buildKernel(const string& nameExpr, const string& jit_ker,
+                   const vector<string>& opts, const bool isJIT) {
+    const char* ker_name = nameExpr.c_str();
+
+    nvrtcProgram prog;
+    if (isJIT) {
+        NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 0,
+                                       NULL, NULL));
+    } else {
+        constexpr static const char* includeNames[] = {
+            "math.h",          // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            "vector_types.h",  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            "backend.hpp",    "complex.hpp", "jit.cuh",
+            "math.hpp",       "ops.hpp",     "optypes.hpp",
+            "Param.hpp",      "shared.hpp",  "types.hpp"};
+        constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
+        static const std::array<string, NumHeaders> sourceStrings = {
+            string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            string(backend_hpp, backend_hpp_len),
+            string(cuComplex_h, cuComplex_h_len),
+            string(jit_cuh, jit_cuh_len),
+            string(math_hpp, math_hpp_len),
+            string(ops_hpp, ops_hpp_len),
+            string(optypes_hpp, optypes_hpp_len),
+            string(Param_hpp, Param_hpp_len),
+            string(shared_hpp, shared_hpp_len),
+            string(types_hpp, types_hpp_len),
+        };
+        static const char* headers[] = {
+            sourceStrings[0].c_str(), sourceStrings[1].c_str(),
+            sourceStrings[2].c_str(), sourceStrings[3].c_str(),
+            sourceStrings[4].c_str(), sourceStrings[5].c_str(),
+            sourceStrings[6].c_str(), sourceStrings[7].c_str(),
+            sourceStrings[8].c_str(), sourceStrings[9].c_str(),
+            sourceStrings[10].c_str()};
+        NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
+                                       NumHeaders, headers, includeNames));
+    }
+
+    auto dev = getDeviceProp(getActiveDeviceId());
+    array<char, 32> arch;
+    if (dev.major == 7 && dev.minor > 2) {
+        // FIXME: This conditional can be removed completely
+        // when nvrtc enables >72 as valid compute value for
+        // --gpu-architecture option
+        dev.minor = 2;
+    }
+    snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
+             dev.major, dev.minor);
+    vector<const char*> compiler_options = {
+        arch.data(),
+        "--std=c++11",
+#if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
+        "--device-debug",
+        "--generate-line-info"
+#endif
+    };
+    if (!isJIT) {
+        for (auto& s : opts) { compiler_options.push_back(&s[0]); }
+        compiler_options.push_back("--device-as-default-execution-space");
+        NVRTC_CHECK(nvrtcAddNameExpression(prog, ker_name));
+    }
+
+    NVRTC_CHECK(nvrtcCompileProgram(prog, compiler_options.size(),
+                                    compiler_options.data()));
+    size_t ptx_size;
+    vector<char> ptx;
+    NVRTC_CHECK(nvrtcGetPTXSize(prog, &ptx_size));
+    ptx.resize(ptx_size);
+    NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));
+
+    const size_t linkLogSize    = 1024;
+    char linkInfo[linkLogSize]  = {0};
+    char linkError[linkLogSize] = {0};
+
+    CUlinkState linkState;
+    CUjit_option linkOptions[] = {
+        CU_JIT_INFO_LOG_BUFFER, CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
+        CU_JIT_ERROR_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
+        CU_JIT_LOG_VERBOSE};
+
+    void* linkOptionValues[] = {linkInfo, reinterpret_cast<void*>(linkLogSize),
+                                linkError, reinterpret_cast<void*>(linkLogSize),
+                                reinterpret_cast<void*>(1)};
+
+    CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
+    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.data(),
+                                ptx.size(), ker_name, 0, NULL, NULL));
+
+    void* cubin = nullptr;
+    size_t cubinSize;
+
+    CUmodule module;
+    CUfunction kernel;
+    CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
+    CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
+
+    const char* name = ker_name;
+    if (!isJIT) { NVRTC_CHECK(nvrtcGetLoweredName(prog, ker_name, &name)); }
+
+    CU_CHECK(cuModuleGetFunction(&kernel, module, name));
+    Kernel entry = {module, kernel};
+
+    CU_LINK_CHECK(cuLinkDestroy(linkState));
+    NVRTC_CHECK(nvrtcDestroyProgram(&prog));
+
+    return entry;
+}
+
+kc_t& getCache(int device) {
+    thread_local kc_t caches[DeviceManager::MAX_DEVICES];
+    return caches[device];
+}
+
+Kernel findKernel(int device, const string nameExpr) {
+    kc_t& cache = getCache(device);
+
+    kc_t::iterator iter = cache.find(nameExpr);
+
+    return (iter == cache.end() ? Kernel{0, 0} : iter->second);
+}
+
+void addKernelToCache(int device, const string nameExpr, Kernel entry) {
+    getCache(device).emplace(nameExpr, entry);
+}
+
+string getOpEnumStr(af_op_t val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(af_add_t);
+        CASE_STMT(af_sub_t);
+        CASE_STMT(af_mul_t);
+        CASE_STMT(af_div_t);
+
+        CASE_STMT(af_and_t);
+        CASE_STMT(af_or_t);
+        CASE_STMT(af_eq_t);
+        CASE_STMT(af_neq_t);
+        CASE_STMT(af_lt_t);
+        CASE_STMT(af_le_t);
+        CASE_STMT(af_gt_t);
+        CASE_STMT(af_ge_t);
+
+        CASE_STMT(af_bitor_t);
+        CASE_STMT(af_bitand_t);
+        CASE_STMT(af_bitxor_t);
+        CASE_STMT(af_bitshiftl_t);
+        CASE_STMT(af_bitshiftr_t);
+
+        CASE_STMT(af_min_t);
+        CASE_STMT(af_max_t);
+        CASE_STMT(af_cplx2_t);
+        CASE_STMT(af_atan2_t);
+        CASE_STMT(af_pow_t);
+        CASE_STMT(af_hypot_t);
+
+        CASE_STMT(af_sin_t);
+        CASE_STMT(af_cos_t);
+        CASE_STMT(af_tan_t);
+        CASE_STMT(af_asin_t);
+        CASE_STMT(af_acos_t);
+        CASE_STMT(af_atan_t);
+
+        CASE_STMT(af_sinh_t);
+        CASE_STMT(af_cosh_t);
+        CASE_STMT(af_tanh_t);
+        CASE_STMT(af_asinh_t);
+        CASE_STMT(af_acosh_t);
+        CASE_STMT(af_atanh_t);
+
+        CASE_STMT(af_exp_t);
+        CASE_STMT(af_expm1_t);
+        CASE_STMT(af_erf_t);
+        CASE_STMT(af_erfc_t);
+
+        CASE_STMT(af_log_t);
+        CASE_STMT(af_log10_t);
+        CASE_STMT(af_log1p_t);
+        CASE_STMT(af_log2_t);
+
+        CASE_STMT(af_sqrt_t);
+        CASE_STMT(af_cbrt_t);
+
+        CASE_STMT(af_abs_t);
+        CASE_STMT(af_cast_t);
+        CASE_STMT(af_cplx_t);
+        CASE_STMT(af_real_t);
+        CASE_STMT(af_imag_t);
+        CASE_STMT(af_conj_t);
+
+        CASE_STMT(af_floor_t);
+        CASE_STMT(af_ceil_t);
+        CASE_STMT(af_round_t);
+        CASE_STMT(af_trunc_t);
+        CASE_STMT(af_signbit_t);
+
+        CASE_STMT(af_rem_t);
+        CASE_STMT(af_mod_t);
+
+        CASE_STMT(af_tgamma_t);
+        CASE_STMT(af_lgamma_t);
+
+        CASE_STMT(af_notzero_t);
+
+        CASE_STMT(af_iszero_t);
+        CASE_STMT(af_isinf_t);
+        CASE_STMT(af_isnan_t);
+
+        CASE_STMT(af_sigmoid_t);
+
+        CASE_STMT(af_noop_t);
+
+        CASE_STMT(af_select_t);
+        CASE_STMT(af_not_select_t);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<typename T>
+string toString(T value) {
+    return to_string(value);
+}
+
+template string toString<int>(int);
+template string toString<long>(long);
+template string toString<long long>(long long);
+template string toString<unsigned>(unsigned);
+template string toString<unsigned long>(unsigned long);
+template string toString<unsigned long long>(unsigned long long);
+template string toString<float>(float);
+template string toString<double>(double);
+template string toString<long double>(long double);
+
+template<>
+string toString(bool val) {
+    return string(val ? "true" : "false");
+}
+
+template<>
+string toString(af_op_t val) {
+    return getOpEnumStr(val);
+}
+
+template<>
+string toString(const char* str) {
+    return string(str);
+}
+
+Kernel getKernel(const string& nameExpr, const string& source,
+                 const vector<TemplateArg>& targs,
+                 const vector<string>& compileOpts) {
+    vector<string> args;
+    args.reserve(targs.size());
+
+    transform(targs.begin(), targs.end(), std::back_inserter(args),
+              [](const TemplateArg& arg) -> string { return arg._tparam; });
+
+    string tInstance = nameExpr + "<" + args[0];
+    for (int i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
+    tInstance += ">";
+
+    int device    = getActiveDeviceId();
+    Kernel kernel = findKernel(device, tInstance);
+
+    if (kernel.prog == 0 || kernel.ker == 0) {
+        kernel = buildKernel(tInstance, source, compileOpts);
+        addKernelToCache(device, tInstance, kernel);
+    }
+
+    return kernel;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
new file mode 100644
index 0000000000..b513015598
--- /dev/null
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -0,0 +1,172 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <err_cuda.hpp>
+#include <nvrtc/EnqueueArgs.hpp>
+#include <traits.hpp>
+
+#include <cstdio>
+#include <string>
+#include <vector>
+
+#define CU_CHECK(fn)                                                      \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[1024];                                            \
+        const char* cu_err_name;                                          \
+        const char* cu_err_string;                                        \
+        cuGetErrorName(res, &cu_err_name);                                \
+        cuGetErrorString(res, &cu_err_string);                            \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), cu_err_string);                 \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+    } while (0)
+
+namespace cuda {
+
+///
+/// \brief Kernel Functor that wraps CUDA nvrtc constructs
+///
+/// This struct encapsulates CUmodule and CUfunction pointers that are required
+/// to execution of CUDA C++ kernels compiled at runtime.
+///
+struct Kernel {
+    CUmodule prog;   ///< CUmodule helps acquire kernel attributes
+    CUfunction ker;  ///< CUfuntion is the actual kernel blob to run
+
+    ///
+    /// \brief Copy data to constant qualified global variable of kernel
+    ///
+    /// This function copies data of `bytes` size from the device pointer to a
+    /// global(__constant__) variable declared inside the kernel.
+    ///
+    /// \param[in] name is the name of the global variable inside kernel
+    /// \param[in] src is the device pointer from which data will be copied
+    /// \param[in] bytes are the number of bytes of data to be copied
+    ///
+    void setConstant(const char* name, CUdeviceptr src, size_t bytes);
+
+    ///
+    /// \brief Enqueue Kernel per queueing criteria forwarding other parameters
+    ///
+    /// This operator overload enables Kernel object to work as functor that
+    /// internally executes the CUDA kernel stored inside the Kernel object.
+    /// All parameters that are passed in after the EnqueueArgs object are
+    /// essentially forwarded to cuLaunchKernel driver API call.
+    ///
+    /// \param[in] qArgs is an object of struct \ref EnqueueArgs
+    /// \param[in] args is the placeholder for variadic arguments
+    ///
+    template<typename... Args>
+    void operator()(const EnqueueArgs& qArgs, Args... args) {
+        void* params[] = {reinterpret_cast<void*>(&args)...};
+        for (auto& event : qArgs.mEvents) {
+            CU_CHECK(cuStreamWaitEvent(qArgs.mStream, event, 0));
+        }
+        CU_CHECK(cuLaunchKernel(
+            ker, qArgs.mBlocks.x, qArgs.mBlocks.y, qArgs.mBlocks.z,
+            qArgs.mThreads.x, qArgs.mThreads.y, qArgs.mThreads.z,
+            qArgs.mSharedMemSize, qArgs.mStream, params, NULL));
+    }
+};
+
+// TODO(pradeep): remove this in API and merge JIT and nvrtc caches
+Kernel buildKernel(const std::string& nameExpr,
+                   const std::string& jitSourceString,
+                   const std::vector<std::string>& opts = {},
+                   const bool isJIT                     = false);
+
+template<typename T>
+std::string toString(T value);
+
+struct TemplateArg {
+    std::string _tparam;
+
+    TemplateArg(std::string str) : _tparam(str) {}
+
+    template<typename T>
+    constexpr TemplateArg(T value) noexcept : _tparam(toString(value)) {}
+};
+
+template<typename T>
+struct TemplateTypename {
+    operator TemplateArg() const noexcept {
+        return {std::string(dtype_traits<T>::getName())};
+    }
+};
+
+template<>
+struct TemplateTypename<long long> {
+    operator TemplateArg() const noexcept {
+        return TemplateArg(std::string("long long"));
+    }
+};
+
+#define DefineKey(arg) "-D " #arg
+#define DefineValue(arg) "-D " #arg "=" + toString(arg)
+#define DefineKeyValue(key, arg) "-D " #key "=" + toString(arg)
+
+///
+/// \brief Find/Create-Cache a Kernel that fits the given criteria
+///
+/// This function takes in two vectors of strings apart from the main Kernel
+/// name, match criteria, to find a suitable kernel in the Kernel cache. It
+/// builds and caches a new Kernel object if one isn't found in the cache.
+///
+/// The paramter \p key has to be the unique name for a given CUDA kernel.
+/// The key has to be present in one of the entries of KernelMap defined in
+/// the header EnqueueArgs.hpp.
+///
+/// The parameter \p templateArgs is a list of stringified template arguments of
+/// the CUDA kernel. These strings are used to generate the template
+/// instantiation expression of the CUDA kernel during compilation stage. It is
+/// critical that these strings are provided in correct format.
+///
+/// The paramter \p compileOpts is a list of strings that lets you add
+/// definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` to the compiler. To
+/// enable easy stringification of variables into their definition equation,
+/// three helper macros are provided: TemplateArg, DefineKey and DefineValue.
+///
+/// Example Usage: transpose
+///
+/// \code
+/// static const std::string src(transpose_cuh, transpose_cuh_len);
+/// auto transpose = getKernel("cuda::transpose", src,
+///         {
+///           TemplateTypename<T>(),
+///           TemplateArg(conjugate),
+///           TemplateArg(is32multiple)
+///         },
+///         {
+///           DefineValue(TILE_DIM), // Results in a definition
+///                                  // "-D TILE_DIME=<Value of TILE_DIM>"
+///           DefineValue(THREADS_Y) // Results in a definition
+///                                  // "-D THREADS_Y=<Value of THREADS_Y>"
+///           DefineKeyValue(DIMY, threads_y)  // Results in a definition
+///                                            // "-D DIMY=<Value of threads_y>"
+///         }
+///         );
+/// \endcode
+///
+/// \param[in] nameExpr is the of name expressions to be instantiated while
+///            compiling the kernel.
+/// \param[in] source is the kernel source code string
+/// \param[in] templateArgs is a vector of strings containing stringified names
+///            of the template arguments of CUDA kernel to be compiled.
+/// \param[in] compileOpts is a vector of strings that enables the user to
+///            add definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` for
+///            the kernel compilation.
+///
+Kernel getKernel(const std::string& nameExpr, const std::string& source,
+                 const std::vector<TemplateArg>& templateArgs,
+                 const std::vector<std::string>& compileOpts = {});
+}  // namespace cuda
diff --git a/src/backend/cuda/scan.cu b/src/backend/cuda/scan.cpp
similarity index 72%
rename from src/backend/cuda/scan.cu
rename to src/backend/cuda/scan.cpp
index d3aa13eb8c..c6f2da12d2 100644
--- a/src/backend/cuda/scan.cu
+++ b/src/backend/cuda/scan.cpp
@@ -22,20 +22,10 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
     Array<To> out = createEmptyArray<To>(in.dims());
 
-    if (inclusive_scan) {
-        switch (dim) {
-            case 0: kernel::scan_first<Ti, To, op, true>(out, in); break;
-            case 1: kernel::scan_dim<Ti, To, op, 1, true>(out, in); break;
-            case 2: kernel::scan_dim<Ti, To, op, 2, true>(out, in); break;
-            case 3: kernel::scan_dim<Ti, To, op, 3, true>(out, in); break;
-        }
+    if (dim == 0) {
+        kernel::scan_first<Ti, To, op>(out, in, inclusive_scan);
     } else {
-        switch (dim) {
-            case 0: kernel::scan_first<Ti, To, op, false>(out, in); break;
-            case 1: kernel::scan_dim<Ti, To, op, 1, false>(out, in); break;
-            case 2: kernel::scan_dim<Ti, To, op, 2, false>(out, in); break;
-            case 3: kernel::scan_dim<Ti, To, op, 3, false>(out, in); break;
-        }
+        kernel::scan_dim<Ti, To, op>(out, in, dim, inclusive_scan);
     }
 
     return out;
diff --git a/src/backend/cuda/scan_by_key.cu b/src/backend/cuda/scan_by_key.cpp
similarity index 100%
rename from src/backend/cuda/scan_by_key.cu
rename to src/backend/cuda/scan_by_key.cpp
diff --git a/src/backend/cuda/transpose.cu b/src/backend/cuda/transpose.cpp
similarity index 86%
rename from src/backend/cuda/transpose.cu
rename to src/backend/cuda/transpose.cpp
index e9e33ca957..fa20d5bccc 100644
--- a/src/backend/cuda/transpose.cu
+++ b/src/backend/cuda/transpose.cpp
@@ -24,11 +24,10 @@ Array<T> transpose(const Array<T> &in, const bool conjugate) {
 
     Array<T> out = createEmptyArray<T>(outDims);
 
-    if (conjugate) {
-        kernel::transpose<T, true>(out, in);
-    } else {
-        kernel::transpose<T, false>(out, in);
-    }
+    const bool is32multiple =
+        inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+
+    kernel::transpose<T>(out, in, conjugate, is32multiple);
 
     return out;
 }
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 91d2df224e..c8282c6c12 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -8,10 +8,20 @@
  ********************************************************/
 
 #pragma once
+
+#ifdef __CUDACC_RTC__
+
+#include <complex.hpp>
+using dim_t = long long;
+
+#else  //__CUDACC_RTC__
+
 #include <cuComplex.h>
 #include <af/traits.hpp>
 
 namespace cuda {
+#endif  //__CUDACC_RTC__
+
 using cdouble = cuDoubleComplex;
 using cfloat  = cuFloatComplex;
 using intl    = long long;
@@ -19,7 +29,9 @@ using uchar   = unsigned char;
 using uint    = unsigned int;
 using uintl   = unsigned long long;
 using ushort  = unsigned short;
+using ulong   = unsigned long long;
 
+#ifndef __CUDACC_RTC__
 namespace {
 template<typename T>
 const char *shortname(bool caps = false) {
@@ -98,5 +110,8 @@ SPECIALIZE(long long)
 
 #undef SPECIALIZE
 }  // namespace
+#endif  //__CUDACC_RTC__
 
+#ifndef __CUDACC_RTC__
 }  // namespace cuda
+#endif  //__CUDACC_RTC__
diff --git a/src/backend/cuda/where.cu b/src/backend/cuda/where.cpp
similarity index 100%
rename from src/backend/cuda/where.cu
rename to src/backend/cuda/where.cpp
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 8c33b85d1d..a896e4411c 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -19,9 +19,9 @@ file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
 set( kernel_headers_dir
     "kernel_headers")
 
-include(CLKernelToH)
+include(FileToString)
 
-cl_kernel_to_h(
+file_to_string(
     SOURCES ${kernel_src}
     VARNAME kernel_files
     EXTENSION "hpp"

From 496d11102fb38bac0494febd2996e01762dae640 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 16 Mar 2019 10:56:22 -0400
Subject: [PATCH 1622/2677] Remove void* memAlloc instantiation. Replace with
 uchar

---
 src/backend/cuda/blas.cpp   | 6 +++---
 src/backend/cuda/memory.cpp | 1 -
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index d7443a2a82..31e9fb1b6a 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -221,9 +221,9 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
             optrs[n] = optr + z * oStrides[2] + w * oStrides[3];
         }
 
-        auto d_lptrs = memAlloc<void *>(batchSize);
-        auto d_rptrs = memAlloc<void *>(batchSize);
-        auto d_optrs = memAlloc<void *>(batchSize);
+        auto d_lptrs = memAlloc<uchar>(batchSize);
+        auto d_rptrs = memAlloc<uchar>(batchSize);
+        auto d_optrs = memAlloc<uchar>(batchSize);
 
         size_t bytes = batchSize * sizeof(T **);
         CUDA_CHECK(cudaMemcpyAsync(d_lptrs.get(), lptrs.data(), bytes,
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 0f8bfe130d..d6e332c5fa 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -119,7 +119,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
-INSTANTIATE(void *)
 
 MemoryManager::MemoryManager()
     : common::MemoryManager<cuda::MemoryManager>(

From 6da99359cf9b032a866475b115a47085c0ea52d8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 27 Mar 2019 07:21:10 +0530
Subject: [PATCH 1623/2677] Update forge submodule to v1.0.4 fix release

---
 extern/forge | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extern/forge b/extern/forge
index 8dd37341e1..650bf611de 160000
--- a/extern/forge
+++ b/extern/forge
@@ -1 +1 @@
-Subproject commit 8dd37341e128bdcbd8b837a3d23c6cdf5d5d48ec
+Subproject commit 650bf611de102a2cc0c32dba7646f8128f0300c8

From bdef3da0af442a4c34ce4f06e1c7150bb569090f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 1 Apr 2019 01:02:17 -0700
Subject: [PATCH 1624/2677] Suppress warnings & formatting changes (#2473)

* Make external library headers as system-headers so that the compiler does
  not print the warnings during compilation
* Fix various warnings inside ArrayFire
* Fix formatting
---
 .../AFconfigure_forge_submodule.cmake         |  1 +
 CMakeModules/CPackConfig.cmake                |  2 +-
 CMakeModules/boost_package.cmake              |  4 +-
 src/api/unified/symbol_manager.cpp            |  2 +-
 src/backend/common/CMakeLists.txt             |  1 +
 src/backend/common/graphics_common.cpp        |  1 -
 src/backend/common/util.cpp                   | 63 ++++++++++---------
 src/backend/cpu/kernel/random_engine.hpp      | 60 +++++++++---------
 src/backend/cpu/kernel/sparse_arith.hpp       |  1 +
 src/backend/cuda/nvrtc/cache.cpp              |  7 ++-
 src/backend/opencl/jit/kernel_generators.hpp  |  8 +--
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  5 +-
 src/backend/opencl/kernel/sparse_arith.hpp    |  4 +-
 test/rotate.cpp                               |  3 +-
 test/sort_by_key.cpp                          |  3 +-
 test/sparse_arith.cpp                         |  4 +-
 16 files changed, 88 insertions(+), 81 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index 0e911401cb..748e1ba48d 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -35,6 +35,7 @@ if(AF_BUILD_FORGE)
       $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
       DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
+  set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
 else(AF_BUILD_FORGE)
   set(FG_VERSION "1.0.0")
   set(FG_VERSION_MAJOR 1)
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 8086314196..fa2ea76c73 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -12,7 +12,7 @@ set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PROJECT_SOURCE_DIR}/CMakeModules/n
 include(Version)
 include(CPackIFW)
 
-set(CPACK_GENERATOR "STGZ;TGZ" CACHE STRINGS "STGZ;TGZ;DEB;RPM;productbuild")
+set(CPACK_GENERATOR "STGZ;TGZ" CACHE STRING "STGZ;TGZ;DEB;RPM;productbuild")
 set_property(CACHE CPACK_GENERATOR PROPERTY STRINGS STGZ DEB RPM productbuild)
 mark_as_advanced(CPACK_GENERATOR)
 
diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 067be012a1..1756f0d15d 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -32,7 +32,9 @@ if("${Boost_VERSION}" VERSION_LESS 106100)
   add_dependencies(Boost::boost boost_compute)
 
   set_target_properties(Boost::boost PROPERTIES
-    INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include")
+    INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
+    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
+    )
 endif()
 
 # NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 125990793c..f6d14e6fc0 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -95,7 +95,7 @@ string join_path(string first, ARGS... args) {
 }
 
 /*flag parameter is not used on windows platform */
-LibHandle openDynLibrary(const af_backend bknd_idx, int flag = RTLD_LAZY) {
+LibHandle openDynLibrary(const af_backend bknd_idx) {
     // The default search path is the colon separated list of paths stored in
     // the environment variables:
     string bkndLibName  = getBkndLibName(bknd_idx);
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index e78b2aeee6..905892271c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -70,6 +70,7 @@ target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
     ${ArrayFire_BINARY_DIR}
+  SYSTEM INTERFACE
     ${OPENGL_INCLUDE_DIR}
     ${ArrayFire_SOURCE_DIR}/extern/forge/include
     ${ArrayFire_BINARY_DIR}/extern/forge/include
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index dace645788..a90f826817 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -317,7 +317,6 @@ ForgeManager::getWindowGrid(const fg_window window) {
 
 fg_chart ForgeManager::getChart(const fg_window window, const int r,
                                 const int c, const fg_chart_type ctype) {
-    fg_chart retVal   = NULL;
     ChartMapIterator iter = mChartMap.find(window);
     WindGridMapIterator gIter = mWndGridMap.find(window);
 
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index 0c5fbc7861..a9f2941ca5 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -8,21 +8,21 @@
  ********************************************************/
 
 /// This file contains platform independent utility functions
-#include <cstdlib>
-#include <string>
-#include <cstring>
-
 #if defined(OS_WIN)
 #include <Windows.h>
 #endif
 
-#include <af/defines.h>
-#include <common/util.hpp>
 #include <common/defines.hpp>
+#include <common/util.hpp>
+#include <af/defines.h>
+
+#include <cstdlib>
+#include <cstring>
+#include <string>
 
 using std::string;
 
-string getEnvVar(const std::string &key) {
+string getEnvVar(const std::string& key) {
 #if defined(OS_WIN)
     DWORD bufSize =
         32767;  // limit according to GetEnvironment Variable documentation
@@ -36,12 +36,12 @@ string getEnvVar(const std::string &key) {
         return retVal;
     }
 #else
-    char *str = getenv(key.c_str());
+    char* str = getenv(key.c_str());
     return str == NULL ? string("") : string(str);
 #endif
 }
 
-const char *getName(af_dtype type) {
+const char* getName(af_dtype type) {
     switch (type) {
         case f32: return "float";
         case f64: return "double";
@@ -59,26 +59,27 @@ const char *getName(af_dtype type) {
     }
 }
 
-void saveKernel(const std::string& funcName, const std::string& jit_ker, const std::string& ext) {
-  static const char* jitKernelsOutput = getenv(saveJitKernelsEnvVarName);
-  if (!jitKernelsOutput)
-    return;
-  if (std::strcmp(jitKernelsOutput, "stdout") == 0) {
-    fprintf(stdout, jit_ker.c_str());
-    return;
-  }
-  if (std::strcmp(jitKernelsOutput, "stderr") == 0) {
-    fprintf(stderr, jit_ker.c_str());
-    return;
-  }
-  // Path to a folder  
-  const std::string ffp = std::string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
-  FILE* f = fopen(ffp.c_str(), "w");
-  if (!f) {
-    fprintf(stderr, "Cannot open file %s\n", ffp.c_str());
-    return;
-  }
-  if (fputs(jit_ker.c_str(), f) == EOF)
-    fprintf(stderr, "Failed to write kernel to file %s\n", ffp.c_str());
-  fclose(f);
+void saveKernel(const std::string& funcName, const std::string& jit_ker,
+                const std::string& ext) {
+    static const char* jitKernelsOutput = getenv(saveJitKernelsEnvVarName);
+    if (!jitKernelsOutput) return;
+    if (std::strcmp(jitKernelsOutput, "stdout") == 0) {
+        fputs(jit_ker.c_str(), stdout);
+        return;
+    }
+    if (std::strcmp(jitKernelsOutput, "stderr") == 0) {
+        fputs(jit_ker.c_str(), stderr);
+        return;
+    }
+    // Path to a folder
+    const std::string ffp =
+        std::string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
+    FILE* f = fopen(ffp.c_str(), "w");
+    if (!f) {
+        fprintf(stderr, "Cannot open file %s\n", ffp.c_str());
+        return;
+    }
+    if (fputs(jit_ker.c_str(), f) == EOF)
+        fprintf(stderr, "Failed to write kernel to file %s\n", ffp.c_str());
+    fclose(f);
 }
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index acdcfa673b..67bd072359 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -37,65 +37,65 @@ static const double PI_VAL =
 #define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
-template <typename T>
+template<typename T>
 T transform(uint *val, int index) {
     T *oval = (T *)val;
     return oval[index];
 }
 
-template <>
+template<>
 char transform<char>(uint *val, int index) {
     char v = val[index >> 2] >> (8 << (index & 3));
     v      = (v & 0x1) ? 1 : 0;
     return v;
 }
 
-template <>
+template<>
 uchar transform<uchar>(uint *val, int index) {
     uchar v = val[index >> 2] >> (index << 3);
     return v;
 }
 
-template <>
+template<>
 ushort transform<ushort>(uint *val, int index) {
     ushort v = val[index >> 1] >> (16 << (index & 1));
     return v;
 }
 
-template <>
+template<>
 short transform<short>(uint *val, int index) {
     return transform<ushort>(val, index);
 }
 
-template <>
+template<>
 uint transform<uint>(uint *val, int index) {
     return val[index];
 }
 
-template <>
+template<>
 int transform<int>(uint *val, int index) {
     return transform<uint>(val, index);
 }
 
-template <>
+template<>
 uintl transform<uintl>(uint *val, int index) {
     uintl v = (((uintl)val[index << 1]) << 32) | ((uintl)val[(index << 1) + 1]);
     return v;
 }
 
-template <>
+template<>
 intl transform<intl>(uint *val, int index) {
     return transform<uintl>(val, index);
 }
 
 // Generates rationals in [0, 1)
-template <>
+template<>
 float transform<float>(uint *val, int index) {
     return 1.f - (val[index] * FLT_FACTOR + HALF_FLT_FACTOR);
 }
 
 // Generates rationals in [0, 1)
-template <>
+template<>
 double transform<double>(uint *val, int index) {
     uintl v = transform<uintl>(val, index);
     return 1.0 - (v * DBL_FACTOR + HALF_DBL_FACTOR);
@@ -112,27 +112,27 @@ double transform<double>(uint *val, int index) {
 // ELEMS_PER_ITER correspond to elementsPerBlock in the CUDA backend, so each
 // "iter" (iteration) here correspond to a CUDA thread block doing its work.
 // This change was prompted by issue #2429
-template <typename T>
+template<typename T>
 void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi  = seed >> 32;
     uint lo  = seed;
     uint hic = counter >> 32;
     uint loc = counter;
 
-    constexpr int RESET_CTR = MAX_RESET_CTR_VAL / sizeof(T);
-    constexpr int ELEMS_PER_ITER =
+    constexpr size_t RESET_CTR = MAX_RESET_CTR_VAL / sizeof(T);
+    constexpr size_t ELEMS_PER_ITER =
         WRITE_STRIDE * 4 * sizeof(uint) / sizeof(T);
 
     int num_iters = divup(elements, ELEMS_PER_ITER);
-    int len       = num_iters * ELEMS_PER_ITER;
+    size_t len    = num_iters * ELEMS_PER_ITER;
 
-    constexpr int NUM_WRITES = 16 / sizeof(T);
-    for (int iter = 0; iter < len; iter += ELEMS_PER_ITER) {
-        for (int i = 0; i < WRITE_STRIDE; i += RESET_CTR) {
-            for (int j = 0; j < RESET_CTR; ++j) {
+    constexpr size_t NUM_WRITES = 16 / sizeof(T);
+    for (size_t iter = 0; iter < len; iter += ELEMS_PER_ITER) {
+        for (size_t i = 0; i < WRITE_STRIDE; i += RESET_CTR) {
+            for (size_t j = 0; j < RESET_CTR; ++j) {
                 // first_write_idx is the first of the 4 locations that will
                 // be written to
-                ptrdiff_t first_write_idx = iter + i + j;
+                uintptr_t first_write_idx = iter + i + j;
                 if (first_write_idx >= elements) { break; }
 
                 // Recalculate key and ctr to emulate how the CUDA backend
@@ -144,8 +144,8 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
 
                 // Use the same ctr array for each of the 4 locations,
                 // but each of the location gets a different ctr value
-                for (int buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
-                    int out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
+                for (size_t buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
+                    size_t out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
                     if (out_idx < elements) {
                         out[out_idx] = transform<T>(ctr, buf_idx);
                     }
@@ -158,7 +158,7 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
 #undef MAX_RESET_CTR_VAL
 #undef WRITE_STRIDE
 
-template <typename T>
+template<typename T>
 void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi     = seed >> 32;
     uint lo     = seed;
@@ -178,7 +178,7 @@ void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
     }
 }
 
-template <typename T>
+template<typename T>
 void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
@@ -201,7 +201,7 @@ void boxMullerTransform(uint val[4], float *temp) {
                        transform<float>(val, 3));
 }
 
-template <typename T>
+template<typename T>
 void philoxNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi     = seed >> 32;
     uint lo     = seed;
@@ -220,7 +220,7 @@ void philoxNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     }
 }
 
-template <typename T>
+template<typename T>
 void threefryNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     uint hi     = seed >> 32;
     uint lo     = seed;
@@ -245,7 +245,7 @@ void threefryNormal(T *out, size_t elements, const uintl seed, uintl counter) {
     }
 }
 
-template <typename T>
+template<typename T>
 void uniformDistributionMT(T *out, size_t elements, uint *const state,
                            const uint *const pos, const uint *const sh1,
                            const uint *const sh2, uint mask,
@@ -270,7 +270,7 @@ void uniformDistributionMT(T *out, size_t elements, uint *const state,
     state_write(state, l_state);
 }
 
-template <typename T>
+template<typename T>
 void normalDistributionMT(T *out, size_t elements, uint *const state,
                           const uint *const pos, const uint *const sh1,
                           const uint *const sh2, uint mask,
@@ -297,7 +297,7 @@ void normalDistributionMT(T *out, size_t elements, uint *const state,
     state_write(state, l_state);
 }
 
-template <typename T>
+template<typename T>
 void uniformDistributionCBRNG(T *out, size_t elements,
                               af_random_engine_type type, const uintl seed,
                               uintl counter) {
@@ -313,7 +313,7 @@ void uniformDistributionCBRNG(T *out, size_t elements,
     }
 }
 
-template <typename T>
+template<typename T>
 void normalDistributionCBRNG(T *out, size_t elements,
                              af_random_engine_type type, const uintl seed,
                              uintl counter) {
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index f9492d5aaa..2c4afcfb8f 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -130,6 +130,7 @@ void sparseArithOpS(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 static void calcOutNNZ(Param<int> outRowIdx, const uint M, const uint N,
                        CParam<int> lRowIdx, CParam<int> lColIdx,
                        CParam<int> rRowIdx, CParam<int> rColIdx) {
+    UNUSED(N);
     int *orPtr       = outRowIdx.get();
     const int *lrPtr = lRowIdx.get();
     const int *lcPtr = lColIdx.get();
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 176c1dd29f..5b4b206af3 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -105,7 +105,7 @@ Kernel buildKernel(const string& nameExpr, const string& jit_ker,
             "math.hpp",       "ops.hpp",     "optypes.hpp",
             "Param.hpp",      "shared.hpp",  "types.hpp"};
         constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
-        static const std::array<string, NumHeaders> sourceStrings = {
+        static const std::array<string, NumHeaders> sourceStrings = {{
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             string(backend_hpp, backend_hpp_len),
@@ -117,7 +117,8 @@ Kernel buildKernel(const string& nameExpr, const string& jit_ker,
             string(Param_hpp, Param_hpp_len),
             string(shared_hpp, shared_hpp_len),
             string(types_hpp, types_hpp_len),
-        };
+        }};
+
         static const char* headers[] = {
             sourceStrings[0].c_str(), sourceStrings[1].c_str(),
             sourceStrings[2].c_str(), sourceStrings[3].c_str(),
@@ -351,7 +352,7 @@ Kernel getKernel(const string& nameExpr, const string& source,
               [](const TemplateArg& arg) -> string { return arg._tparam; });
 
     string tInstance = nameExpr + "<" + args[0];
-    for (int i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
+    for (size_t i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
     tInstance += ">";
 
     int device    = getActiveDeviceId();
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 473b3d2c80..56e2149f5b 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -44,8 +44,8 @@ int setKernelArguments(
 }
 
 /// Generates the code to calculate the offsets for a buffer
-void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
-                           const std::string& type_str) {
+inline void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
+                                  const std::string& type_str) {
     UNUSED(type_str);
     std::string idx_str  = std::string("int idx") + std::to_string(id);
     std::string info_str = std::string("iInfo") + std::to_string(id);
@@ -63,8 +63,8 @@ void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
 }
 
 /// Generates the code to read a buffer and store it in a local variable
-void generateBufferRead(std::stringstream& kerStream, int id,
-                        const std::string& type_str) {
+inline void generateBufferRead(std::stringstream& kerStream, int id,
+                               const std::string& type_str) {
     kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
               << "];\n";
 }
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index aad5b2b6f6..41f821f181 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -30,7 +30,10 @@ foreach(SBK_TYPE ${SBK_TYPES})
         ../../api/c
         ../common
         ../../../include
-        ${CMAKE_CURRENT_BINARY_DIR}
+        ${CMAKE_CURRENT_BINARY_DIR})
+
+    target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+      SYSTEM PRIVATE
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index b8593aae9b..a1b7445ddc 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -259,6 +259,9 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
                           const uint N, uint nnzA, const Param lrowIdx,
                           const Param lcolIdx, uint nnzB, const Param rrowIdx,
                           const Param rcolIdx) {
+    UNUSED(N);
+    UNUSED(nnzA);
+    UNUSED(nnzB);
     std::string refName = std::string("csr_calc_output_NNZ");
     int device          = getActiveDeviceId();
     kc_entry_t entry    = kernelCache(device, refName);
@@ -307,7 +310,6 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
     if (entry.prog == 0 && entry.ker == 0) {
         const T iden_val =
             (op == af_mul_t || op == af_div_t ? scalar<T>(1) : scalar<T>(0));
-        ToNumStr<T> toNumStr;
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D OP=" << getOpString<op>() << " -D IDENTITY_VALUE=(T)("
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 1559fea00a..7a576804ae 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -44,8 +44,7 @@ TYPED_TEST_CASE(Rotate, TestTypes);
 
 template<typename T>
 void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
-                const bool crop, bool isSubRef = false,
-                const vector<af_seq>* seqv = NULL) {
+                const bool crop) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index 97b925e3e1..dc7382e159 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -49,8 +49,7 @@ TYPED_TEST_CASE(SortByKey, TestTypes);
 
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
-              const unsigned resultIdx1, bool isSubRef = false,
-              const vector<af_seq>* seqv = NULL) {
+              const unsigned resultIdx1, bool isSubRef = false) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 63e9be4a3c..ecbd30ea46 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -69,9 +69,7 @@ typedef enum {
 } af_op_t;
 
 template<af_op_t op>
-struct arith_op {
-    array operator()(array v1, array v2) { return v1; }
-};
+struct arith_op;
 
 template<>
 struct arith_op<af_add_t> {

From b35eaaeb902cb407dbf76cac91e99174077067f4 Mon Sep 17 00:00:00 2001
From: jacobkahn <jacobkahn1@gmail.com>
Date: Wed, 20 Mar 2019 12:39:24 -0700
Subject: [PATCH 1625/2677] Fix BLAS gemm func generators with newest MSVC 19
 on VS 2017 - Build fails with Microsoft Visual Studio 2017/MSVC 19.15.26732
 for x64 - Reinterpret or explicit cast required to convert
 `gemm_batch_func_dev<void*>` to `gemm_vatch_func_dev<T>` (same with gemv) for
 types `cfloat` and `cdouble`

---
 src/backend/cpu/blas.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 300574cc66..cfde212cf6 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -151,10 +151,10 @@ using gemm_batch_func_def = void (*)(
     template<typename T>    \
     FUNC##_func_def<T> FUNC##_func();
 
-#define BLAS_FUNC(FUNC, TYPE, PREFIX)           \
-    template<>                                  \
-    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
-        return &cblas_##PREFIX##FUNC;           \
+#define BLAS_FUNC(FUNC, TYPE, PREFIX)                        \
+    template<>                                               \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() {              \
+        return (FUNC##_func_def<TYPE>)&cblas_##PREFIX##FUNC; \
     }
 
 BLAS_FUNC_DEF(gemm)

From a51a54f07757863360dc8a9056a5aef3d9c113d1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 Mar 2019 23:36:07 -0700
Subject: [PATCH 1626/2677] Add Apple platform to jit parameter size check

---
 src/backend/opencl/Array.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 39ca75f480..610a42daa6 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -247,8 +247,8 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
 
             bool isBufferLimit =
                 lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
-
-            bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA;
+            bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA ||
+                            getActivePlatform() == AFCL_PLATFORM_APPLE;
             // We eval in the following cases.
             // 1. Too many bytes are locked up by JIT causing memory pressure.
             // Too many bytes is assumed to be half of all bytes allocated so

From 3224099e0e613aee6bc754f31a2523e2aea2261e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 8 Apr 2019 06:34:38 -0400
Subject: [PATCH 1627/2677] Remove the use of addition and scalars in tile jit
 nodes (#2472)

* Remove the use of addition and scalars in tile jit nodes

Tiles used to be performed using the add jit node with a zero scalar. This
performed extra operations and created an extra parameter which was
unnecessary.

* Additional tests for JITed tile functions
---
 src/api/c/tile.cpp           |   7 +-
 src/backend/cpu/unary.hpp    |  10 ++-
 src/backend/cuda/unary.hpp   |  15 +++-
 src/backend/opencl/unary.hpp |  11 ++-
 test/jit.cpp                 | 163 ++++++++++++++++++++++++++++++++++-
 5 files changed, 187 insertions(+), 19 deletions(-)

diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 411db64d79..749d8eb8b0 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -13,6 +13,8 @@
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <tile.hpp>
+
+#include <unary.hpp>
 #include <af/arith.h>
 #include <af/data.h>
 
@@ -39,10 +41,7 @@ static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
     }
 
     if (take_jit_path) {
-        // FIXME: This Should ideally call a NOP function, but adding 0 should
-        // be OK This does not allocate any memory, just a JIT node
-        Array<T> tmpArray = createValueArray<T>(outDims, scalar<T>(0));
-        return getHandle(arithOp<T, af_add_t>(inArray, tmpArray, outDims));
+        return getHandle(unaryOp<T, af_noop_t>(inArray, outDims));
     } else {
         return getHandle(tile<T>(inArray, tileDims));
     }
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 2584cd30e0..bf78a0f10f 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -73,11 +73,12 @@ UNARY_OP(lgamma)
 #undef UNARY_OP_FN
 
 template<typename T, af_op_t op>
-Array<T> unaryOp(const Array<T> &in) {
+Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     jit::Node_ptr in_node          = in.getNode();
     jit::UnaryNode<T, T, op> *node = new jit::UnaryNode<T, T, op>(in_node);
 
-    return createNodeArray<T>(in.dims(), jit::Node_ptr(node));
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    return createNodeArray<T>(outDim, jit::Node_ptr(node));
 }
 
 #define iszero(a) ((a) == 0)
@@ -96,12 +97,13 @@ CHECK_FN(iszero, iszero)
 #undef iszero
 
 template<typename T, af_op_t op>
-Array<char> checkOp(const Array<T> &in) {
+Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     jit::Node_ptr in_node = in.getNode();
     jit::UnaryNode<char, T, op> *node =
         new jit::UnaryNode<char, T, op>(in_node);
 
-    return createNodeArray<char>(in.dims(), jit::Node_ptr(node));
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    return createNodeArray<char>(outDim, jit::Node_ptr(node));
 }
 
 }  // namespace cpu
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 5f30b99f0b..64c951596f 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -69,29 +69,36 @@ UNARY_FN(floor)
 UNARY_FN(isinf)
 UNARY_FN(isnan)
 UNARY_FN(iszero)
+UNARY_DECL(noop, "__noop")
 
 #undef UNARY_DECL
 #undef UNARY_FN
 
 template<typename T, af_op_t op>
-Array<T> unaryOp(const Array<T> &in) {
+Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     common::Node_ptr in_node = in.getNode();
 
     common::UnaryNode *node = new common::UnaryNode(
         getFullName<T>(), shortname<T>(true), unaryName<op>(), in_node, op);
 
-    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
+    if(outDim == dim4(-1, -1, -1, -1)) {
+        outDim = in.dims();
+    }
+    return createNodeArray<T>(outDim, common::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
-Array<char> checkOp(const Array<T> &in) {
+Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     common::Node_ptr in_node = in.getNode();
 
     common::UnaryNode *node =
         new common::UnaryNode(getFullName<char>(), shortname<char>(true),
                               unaryName<op>(), in_node, op);
 
-    return createNodeArray<char>(in.dims(), common::Node_ptr(node));
+    if(outDim == dim4(-1, -1, -1, -1)) {
+        outDim = in.dims();
+    }
+    return createNodeArray<char>(outDim, common::Node_ptr(node));
 }
 
 }  // namespace cuda
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 290f864bd7..8a385ea001 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -69,29 +69,32 @@ UNARY_FN(floor)
 UNARY_FN(isinf)
 UNARY_FN(isnan)
 UNARY_FN(iszero)
+UNARY_DECL(noop, "__noop")
 
 #undef UNARY_FN
 
 template<typename T, af_op_t op>
-Array<T> unaryOp(const Array<T> &in) {
+Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     common::Node_ptr in_node = in.getNode();
 
     common::UnaryNode *node =
         new common::UnaryNode(dtype_traits<T>::getName(), shortname<T>(true),
                               unaryName<op>(), in_node, op);
 
-    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    return createNodeArray<T>(outDim, common::Node_ptr(node));
 }
 
 template<typename T, af_op_t op>
-Array<char> checkOp(const Array<T> &in) {
+Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     common::Node_ptr in_node = in.getNode();
 
     common::UnaryNode *node = new common::UnaryNode(
         dtype_traits<char>::getName(), shortname<char>(true), unaryName<op>(),
         in_node, op);
 
-    return createNodeArray<char>(in.dims(), common::Node_ptr(node));
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    return createNodeArray<char>(outDim, common::Node_ptr(node));
 }
 
 }  // namespace opencl
diff --git a/test/jit.cpp b/test/jit.cpp
index ef7eafc63b..2e8a40693e 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/arith.h>
@@ -45,7 +46,7 @@ TEST(JIT, CPP_JIT_HASH) {
         array d    = a + b;
         array e    = a + c;
         array f1   = d * e - e;
-        float *hF1 = f1.host<float>();
+        float* hF1 = f1.host<float>();
 
         for (int i = 0; i < num; i++) { ASSERT_EQ(hF1[i], valF1); }
 
@@ -57,7 +58,7 @@ TEST(JIT, CPP_JIT_HASH) {
         array d    = a + b;
         array e    = a + c;
         array f2   = d * e - d;
-        float *hF2 = f2.host<float>();
+        float* hF2 = f2.host<float>();
 
         for (int i = 0; i < num; i++) { ASSERT_EQ(hF2[i], valF2); }
 
@@ -425,5 +426,161 @@ TEST(JIT, ConstEval7) {
     EXPECT_NO_THROW({
         eval(a, b, c, d, e, f, g);
         af::sync();
-      });
+    });
+}
+
+using af::dim4;
+
+struct tile_params {
+    dim4 in_dim;
+    dim4 tile;
+    dim4 out_dim;
+    tile_params(dim4 in, dim4 t, dim4 out)
+        : in_dim(in), tile(t), out_dim(out) {}
+};
+
+std::ostream& operator<<(std::ostream& os, const tile_params& tp) {
+    os << "in_dim: " << tp.in_dim << "; tile parameters: " << tp.tile
+       << "; out_dim " << tp.out_dim << ";";
+    return os;
+}
+
+class JIT : public ::testing::TestWithParam<tile_params> {
+   protected:
+    void SetUp() {
+        tile_params params = GetParam();
+        vector<float> vals(params.in_dim.elements());
+        iota(vals.begin(), vals.end(), 0);
+        in = array(params.in_dim, &vals.front());
+
+        // clang-format off
+        gold.resize(params.out_dim.elements());
+        dim_t tile_dim[4] = {params.tile[0], params.tile[1], params.tile[2],
+                             params.tile[3]};
+
+        dim_t istride[4] = {1,
+                            params.in_dim[0],
+                            params.in_dim[0] * params.in_dim[1],
+                            params.in_dim[0] * params.in_dim[1] * params.in_dim[2]};
+        dim_t ostride[4] = {1,
+                            params.out_dim[0],
+                            params.out_dim[0] * params.out_dim[1],
+                            params.out_dim[0] * params.out_dim[1] * params.out_dim[2]};
+
+        for (int i = 0; i < 4; i++) {
+            if (tile_dim[i] != 1) { istride[i] = 0; }
+        }
+
+        for (int l = 0; l < params.out_dim[3]; l++) {
+            for (int k = 0; k < params.out_dim[2]; k++) {
+                for (int j = 0; j < params.out_dim[1]; j++) {
+                    for (int i = 0; i < params.out_dim[0]; i++) {
+                        gold[l * ostride[3] +
+                            k * ostride[2] +
+                            j * ostride[1] +
+                            i * ostride[0]] = vals[l * istride[3] +
+                                                    k * istride[2] +
+                                                    j * istride[1] +
+                                                    i * istride[0]];
+                    }
+                }
+            }
+        }
+        // clang-format on
+    }
+
+   public:
+    array in;
+    vector<float> gold;
+};
+
+void replace_all(std::string& str, const std::string& oldStr,
+                 const std::string& newStr) {
+    std::string::size_type pos = 0u;
+    while ((pos = str.find(oldStr, pos)) != std::string::npos) {
+        str.replace(pos, oldStr.length(), newStr);
+        pos += newStr.length();
+    }
+}
+
+std::string concat_dim4(dim4 d) {
+    std::stringstream ss;
+    ss << d;
+    std::string s = ss.str();
+    replace_all(s, " ", "_");
+    return s;
+}
+std::string tile_info(const ::testing::TestParamInfo<JIT::ParamType> info) {
+    std::stringstream ss;
+    ss << "in_" << concat_dim4(info.param.in_dim) << "_tile_"
+       << concat_dim4(info.param.tile);
+    return ss.str();
+}
+
+// clang-format off
+INSTANTIATE_TEST_CASE_P(
+                        JitTile, JIT,
+                                                   //  input_dim            tile_dim             output_dim
+                        ::testing::Values(
+                                          tile_params( dim4(10),            dim4(1, 10),         dim4(10, 10)),
+                                          tile_params( dim4(10),            dim4(1, 1, 10),      dim4(10, 1, 10)),
+                                          tile_params( dim4(10),            dim4(1, 1, 1, 10),   dim4(10, 1, 1, 10)),
+                                          tile_params( dim4(1, 10),         dim4(10),            dim4(10, 10)),
+                                          tile_params( dim4(1, 10),         dim4(1, 1, 10),      dim4(1, 10, 10)),
+                                          tile_params( dim4(1, 10),         dim4(1, 1, 1, 10),   dim4(1, 10, 1, 10)),
+
+                                          tile_params( dim4(10, 10),        dim4(1, 1, 10),      dim4(10, 10, 10)),
+                                          tile_params( dim4(10, 10),        dim4(1, 1, 1, 10),   dim4(10, 10, 1, 10)),
+
+                                          tile_params( dim4(1, 1, 10),      dim4(10),            dim4(10, 1, 10)),
+                                          tile_params( dim4(1, 1, 10),      dim4(1, 10),         dim4(1, 10, 10)),
+                                          tile_params( dim4(1, 1, 10),      dim4(1, 1, 1, 10),   dim4(1, 1, 10, 10)),
+
+                                          tile_params( dim4(1, 10, 10),     dim4(10),            dim4(10, 10, 10)),
+                                          tile_params( dim4(10, 1, 10),     dim4(1, 10),         dim4(10, 10, 10)),
+                                          tile_params( dim4(10, 1, 10),     dim4(1, 1, 1, 10),   dim4(10, 1, 10, 10)),
+                                          tile_params( dim4(1, 10, 10),     dim4(1, 1, 1, 10),   dim4(1, 10, 10, 10)),
+                                          tile_params( dim4(10, 10, 10),    dim4(1, 1, 1, 10),   dim4(10, 10, 10, 10)),
+
+                                          tile_params( dim4(1, 1, 1, 10),   dim4(10),            dim4(10, 1, 1, 10)),
+                                          tile_params( dim4(1, 10, 1, 10),  dim4(10),            dim4(10, 10, 1, 10)),
+                                          tile_params( dim4(1, 1, 10, 10),  dim4(10),            dim4(10, 1, 10, 10)),
+                                          tile_params( dim4(1, 10, 10, 10), dim4(10),            dim4(10, 10, 10, 10)),
+
+                                          tile_params( dim4(1, 1, 1, 10),   dim4(1, 10),         dim4(1, 10, 1, 10)),
+                                          tile_params( dim4(10, 1, 1, 10),  dim4(1, 10),         dim4(10, 10, 1, 10)),
+                                          tile_params( dim4(1, 1, 10, 10),  dim4(1, 10),         dim4(1, 10, 10, 10)),
+
+                                          tile_params( dim4(1, 1, 1, 10),   dim4(1, 1, 10),      dim4(1, 1, 10, 10)),
+                                          tile_params( dim4(10, 1, 1, 10),  dim4(1, 1, 10),      dim4(10, 1, 10, 10)),
+                                          tile_params( dim4(1, 10, 1, 10),  dim4(1, 1, 10),      dim4(1, 10, 10, 10)),
+                                          tile_params( dim4(10, 10, 1, 10), dim4(1, 1, 10),      dim4(10, 10, 10, 10))
+                                          ),
+                        tile_info
+                        );
+// clang-format on
+
+TEST_P(JIT, Tile) {
+    tile_params params = GetParam();
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+    size_t alloc_bytes2, alloc_buffers2;
+    size_t lock_bytes2, lock_buffers2;
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    array out = tile(in, params.tile);
+    af::deviceMemInfo(&alloc_bytes2, &alloc_buffers2, &lock_bytes2,
+                      &lock_buffers2);
+
+    // Make sure that the dimensions we are testing here are JIT nodes
+    // by checking that no new buffers are created.
+    ASSERT_EQ(alloc_bytes, alloc_bytes2)
+        << "Tile operation created a buffer therefore not a JIT node";
+    ASSERT_EQ(alloc_buffers, alloc_buffers2)
+        << "Tile operation created a buffer therefore not a JIT node";
+    ASSERT_EQ(lock_bytes, lock_bytes2)
+        << "Tile operation created a buffer therefore not a JIT node";
+    ASSERT_EQ(alloc_buffers, alloc_buffers2)
+        << "Tile operation created a buffer therefore not a JIT node";
+
+    ASSERT_VEC_ARRAY_EQ(gold, params.out_dim, out);
 }

From 193693721f6dea5e9e95fdfdb9c959a49d1c6503 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 8 Apr 2019 22:06:34 +0530
Subject: [PATCH 1628/2677] Fix af::array::array_proxy move assignment operator
  (#2479)

* Fix af::array::array_proxy move assignment operator

move assignment operator of array_proxy wasn't doing couple of
things correctly.
* releasing existing
* nullifying the move-from objects pointer

This resulted in double-free

* Unit test for double-free issue of proxy &&= operator

* Minor changes to array death test & windows fixes
---
 src/api/cpp/array.cpp |  9 ++++++++-
 test/CMakeLists.txt   |  2 +-
 test/array.cpp        | 39 +++++++++++++++++++++++++++++++++++++++
 3 files changed, 48 insertions(+), 2 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index cba9df3f58..33f900b09c 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -501,10 +501,17 @@ af::array::array_proxy::array_proxy(const array_proxy &other)
                                 other.impl->is_linear_)) {}
 
 #if __cplusplus > 199711L
-af::array::array_proxy::array_proxy(array_proxy &&other) { impl = other.impl; }
+af::array::array_proxy::array_proxy(array_proxy &&other) {
+   impl = other.impl;
+   other.impl = nullptr;
+}
 
 array::array_proxy &af::array::array_proxy::operator=(array_proxy &&other) {
+    if (&other == this)
+        return *this;
+    delete this->impl;
     impl = other.impl;
+    other.impl = nullptr;
     return *this;
 }
 #endif
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index af84eb8d67..92e88cde8d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -167,7 +167,7 @@ endfunction(make_test)
 make_test(SRC anisotropic_diffusion.cpp)
 make_test(SRC approx1.cpp)
 make_test(SRC approx2.cpp)
-make_test(SRC array.cpp)
+make_test(SRC array.cpp CXX11)
 make_test(SRC arrayio.cpp)
 make_test(SRC assign.cpp)
 make_test(SRC backend.cpp)
diff --git a/test/array.cpp b/test/array.cpp
index acfc4fae30..7b8256a451 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -7,10 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <cstddef>
+#include <cstdlib>
 
 using namespace af;
 using std::vector;
@@ -18,6 +20,9 @@ using std::vector;
 template<typename T>
 class Array : public ::testing::Test {};
 
+template<typename T>
+using ArrayDeathTest = Array<T>;
+
 typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
                          int, uint, intl, uintl, short, ushort>
     TestTypes;
@@ -501,3 +506,37 @@ TEST(Array, ScalarTypeMismatch) {
 
     EXPECT_THROW(a.scalar<int>(), exception);
 }
+
+void deathTest() {
+    info();
+    setDevice(0);
+
+    array A = randu(5, 3, f32);
+
+    array B = sin(A) + 1.5;
+
+    B(seq(0, 2), 1) = B(seq(0, 2), 1) * -1;
+
+    array C = fft(B);
+
+    array c = C.row(end);
+
+    dim4 dims(16, 4, 1, 1);
+    array r = constant(2, dims);
+
+    array S = scan(r, 0, AF_BINARY_MUL);
+
+    float d[] = {1, 2, 3, 4, 5, 6};
+    array D(2, 3, d, afHost);
+
+    D.col(0) = D.col(end);
+
+    array vals, inds;
+    sort(vals, inds, A);
+
+    _exit(0);
+}
+
+TEST(ArrayDeathTest, ProxyMoveAssignmentOperator) {
+    EXPECT_EXIT(deathTest(), ::testing::ExitedWithCode(0), "");
+}

From 9007742719f6decfb705b49781d6d2aaf23402de Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 7 Apr 2019 00:32:53 -0400
Subject: [PATCH 1629/2677] Treat all JIT buffer parameters as non-linear
 parameters

This commit addresses very large kernels that were generated by the
JIT for smaller buffers. This was occuring because of how we were
estimating the JIT parameter sizes.

When targeting linear kernels we only pass the pointer to the kernel
instead of the entire dims and stride arrays. This generates smaller
parameter sets so we calculated kernels based on the pointer size
instead of the Param<T> size.

The issue occured when this kernel came in below the threshold for
evaluation but when it was used as a node to another operation, the
generated kernel became a non-linear kernel requiring us to pass
the Param<T> object for each buffer. This causes the size to go past
the limit of the parameters supported by CUDA and compilation fails.

This commit treats all kernels as non-linear kernel when calculating
the parameter sizes.

Fixes #2436 #2389
---
 src/backend/common/jit/Node.hpp       |  2 +-
 src/backend/common/jit/ScalarNode.hpp |  4 +--
 src/backend/cuda/Array.cpp            | 31 +++++++-----------
 src/backend/opencl/Array.cpp          | 47 +++++++++++++++++++--------
 test/jit.cpp                          | 22 ++++++++++++-
 test/sparse_common.hpp                |  2 +-
 6 files changed, 69 insertions(+), 39 deletions(-)

diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 83c2b90ebd..8eb72b88bc 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -99,7 +99,7 @@ class Node {
 
     // Return the size of the parameter in bytes that will be passed to the
     // kernel
-    virtual short getParamBytes() const { return 0; }
+    virtual size_t getParamBytes() const { return 0; }
 
     // Return the size of the size of the buffer node in bytes. Zero otherwise
     virtual size_t getBytes() const { return 0; }
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index b381728adc..643804d218 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -54,8 +54,8 @@ class ScalarNode : public common::Node {
     }
 
     // Return the info for the params and the size of the buffers
-    virtual short getParamBytes() const final {
-        return static_cast<short>(sizeof(T));
+    virtual size_t getParamBytes() const final {
+        return sizeof(T);
     }
 };
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 4b8ccc8ee6..b9c3137669 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -241,7 +241,7 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
             // TODO: Find better solution than the following emperical solution.
             if (node->getHeight() > 25 || isBufferLimit) {
                 // This is the size of the params that are passed by default
-                constexpr int param_base_size =
+                constexpr size_t param_base_size =
                     sizeof(Param<T>) + (4 * sizeof(uint));
 
                 // This is the maximum size of the params that can be allowed by
@@ -249,30 +249,25 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
                 // some_buffer_size) BUT kernels who's kernel sizes come close
                 // to this value are not passing and cuModuleLoadDataEx is
                 // failing with CUDA_ERROR_INVALID_IMAGE(200). 35*sizeof(int)
-                // seems to be the magic number that passes all tests. I have no
-                // idea why this is the case.
-                constexpr int max_param_size =
+                // seems to be the magic number that passes all tests.
+                constexpr size_t max_param_size =
                     (4096 - (sizeof(Param<T>) + 35 * sizeof(uint)));
                 Node *n = node.get();
 
                 struct tree_info {
-                    size_t buffer_size;
-                    int num_buffers;
-                    int param_scalar_size;
-                    bool is_linear;
+                    size_t total_buffer_size;
+                    size_t num_buffers;
+                    size_t param_scalar_size;
                 };
                 NodeIterator<> end_node;
-                dim4 outdim    = out.dims();
                 tree_info info = accumulate(
-                    NodeIterator<>(n), end_node, tree_info{0, 0, 0, true},
+                    NodeIterator<>(n), end_node, tree_info{0, 0, 0},
                     [=](tree_info &prev, const Node &node) {
                         if (node.isBuffer()) {
                             const auto &buf_node =
                                 static_cast<const BufferNode<T> &>(node);
-                            prev.buffer_size += buf_node.getBytes();
+                            prev.total_buffer_size += buf_node.getBytes();
                             prev.num_buffers++;
-                            prev.is_linear &=
-                                buf_node.isLinear((dim_t *)outdim.get());
                         } else {
                             prev.param_scalar_size += node.getParamBytes();
                         }
@@ -280,19 +275,15 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
                         // arrays will be represented by their parent size.
                         return prev;
                     });
-                int param_size = param_base_size + info.param_scalar_size;
-                if (info.is_linear) {
-                    param_size += info.num_buffers * sizeof(T *);
-                } else {
-                    param_size += info.num_buffers * sizeof(Param<T>);
-                }
+                size_t param_size = param_base_size + info.param_scalar_size;
+                param_size += info.num_buffers * sizeof(Param<T>);
 
                 // TODO: the buffer_size check here is very conservative. It
                 // will trigger an evaluation of the node in most cases. We
                 // should be checking the amount of memory available to guard
                 // this eval
                 if (param_size >= max_param_size ||
-                    info.buffer_size * 2 > lock_bytes) {
+                    info.total_buffer_size * 2 > lock_bytes) {
                     out.eval();
                 }
             }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 610a42daa6..3733c7a0c6 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -260,36 +260,55 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
             // TODO: Find better solution than the following emperical solution.
             bool isParamLimit = (isNvidia && node->getHeight() > 24);
             if (isParamLimit || isBufferLimit) {
-                // This is the maximum non-linear buffers that are allowed in
-                // the parameter list
-                constexpr int max_nonlinear_buffer_count = 48;
+                // This was added to the base size to make kernels pass. I
+                // picked this number by creating very large kernels and then
+                // increased this number until the test passed. Then I added a
+                // small number and made this nice and even.
+                constexpr size_t nvidia_parameter_magic = 768;
+
+                // This is the base parameter size if the kernel had no
+                // arguments
+                constexpr size_t param_base_size =
+                    sizeof(T *) + sizeof(KParam) + (3 * sizeof(uint)) +
+                    nvidia_parameter_magic;
+
+                // This is the maximum size of the params that can be allowed by
+                // CUDA NOTE: This number should have been (4096 -
+                // some_buffer_size) BUT kernels who's kernel sizes come close
+                // to this value are not passing and cuModuleLoadDataEx is
+                // failing with CUDA_ERROR_INVALID_IMAGE(200). 35*sizeof(int)
+                // seems to be the magic number that passes all tests.
+                constexpr size_t max_param_size =
+                    (4096 - (sizeof(KParam) + 35 * sizeof(uint)));
 
                 Node *n = node.get();
 
                 struct tree_info {
-                    size_t buffer_size;
-                    int num_buffers;
-                    bool is_linear;
+                    size_t total_buffer_size;
+                    size_t num_buffers;
+                    size_t param_scalar_size;
                 };
                 NodeIterator<> it(n);
-                dim4 outdim    = out.dims();
                 tree_info info = accumulate(
-                    it, NodeIterator<>(), tree_info{0, 0, true},
+                    it, NodeIterator<>(), tree_info{0, 0, 0},
                     [=](tree_info &prev, Node &n) {
                         if (n.isBuffer()) {
                             auto &buf_node = static_cast<BufferNode &>(n);
-                            prev.buffer_size += buf_node.getBytes();
+                            prev.total_buffer_size += buf_node.getBytes();
                             prev.num_buffers++;
-                            prev.is_linear &=
-                                buf_node.isLinear((dim_t *)outdim.get());
+                        } else {
+                            prev.param_scalar_size += node->getParamBytes();
                         }
                         // getBytes returns the size of the data Array. Sub
                         // arrays will be represented by their parent size.
                         return prev;
                     });
-                isBufferLimit = 2 * info.buffer_size > lock_bytes;
-                isParamLimit  = isNvidia && !info.is_linear &&
-                               info.num_buffers >= max_nonlinear_buffer_count;
+                isBufferLimit = 2 * info.total_buffer_size > lock_bytes;
+                size_t param_size =
+                    (info.num_buffers * (sizeof(KParam) + sizeof(T *)) +
+                     info.param_scalar_size + param_base_size);
+
+                isParamLimit = isNvidia && param_size >= max_param_size;
 
                 if (isBufferLimit || isParamLimit) { out.eval(); }
             }
diff --git a/test/jit.cpp b/test/jit.cpp
index 2e8a40693e..2ed23977a9 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -422,7 +422,6 @@ TEST(JIT, ConstEval7) {
     const array f = constant(1, 1);
     const array g = constant(1, 1);
 
-    // I can't think of a good test for this.
     EXPECT_NO_THROW({
         eval(a, b, c, d, e, f, g);
         af::sync();
@@ -584,3 +583,24 @@ TEST_P(JIT, Tile) {
 
     ASSERT_VEC_ARRAY_EQ(gold, params.out_dim, out);
 }
+
+/// This test creates a large jit tree with very small buffers. I am
+/// performing random JIT operations on the arrays. In each iteration
+/// I am also creating a new buffer nodes. This test was generated
+/// to address with large parameter sizes in CUDA. See issues #2436
+/// and #2389
+TEST(JIT, LargeJitTree) {
+    dim_t d0 = 30;
+    array a  = randu(d0, 5);
+    array b  = randu(d0, 1);
+    array c  = randu(d0, 1);
+    EXPECT_NO_THROW({
+        for (int i = 0; i < 500; i++) {
+            b += cos(pow(sin(c * 0.3f), 2) + pow(randu(d0, 1) - 3, 2) * 1.1f +
+                     3);
+            a = floor(a + tile(b, 1, 5));
+        }
+        eval(a);
+        af::sync();
+    });
+}
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index 1b0a6a56f5..70fb055859 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -138,7 +138,7 @@ static void sparseTransposeTester(const int m, const int n, const int k,
 }
 
 template<typename T>
-static void convertCSR(const int M, const int N, const float ratio,
+static void convertCSR(const int M, const int N, const double ratio,
                        int targetDevice = -1) {
     if (targetDevice >= 0) af::setDevice(targetDevice);
 

From 899b712a8ffb5350f2d7ead20aecab593f06acca Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 9 Apr 2019 01:53:03 +0530
Subject: [PATCH 1630/2677] Use compute for nvrtc based on CUDA toolkit version
 (#2477)

---
 src/backend/cuda/jit.cpp         |  2 +-
 src/backend/cuda/nvrtc/cache.cpp | 23 +++++++-------
 src/backend/cuda/nvrtc/cache.hpp |  4 +--
 src/backend/cuda/platform.cpp    | 53 +++++++++++++++++++++++++++-----
 src/backend/cuda/platform.hpp    |  8 ++++-
 5 files changed, 68 insertions(+), 22 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index ff9a3b6d7e..83bc402e9e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -219,7 +219,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
         string jit_ker = getKernelString(funcName, full_nodes, full_ids,
                                          output_ids, is_linear);
         saveKernel(funcName, jit_ker, ".cu");
-        entry = buildKernel(funcName, jit_ker, {}, true);
+        entry = buildKernel(device, funcName, jit_ker, {}, true);
         kernelCaches[device][funcName] = entry;
     } else {
         entry = idx->second;
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 5b4b206af3..3b26803acc 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -22,13 +22,19 @@
 
 #include <algorithm>
 #include <array>
+#include <iterator>
 #include <map>
 #include <memory>
 #include <string>
 #include <type_traits>
+#include <utility>
 
 using std::array;
+using std::begin;
+using std::end;
 using std::extent;
+using std::find_if;
+using std::make_pair;
 using std::map;
 using std::pair;
 using std::string;
@@ -89,8 +95,9 @@ void Kernel::setConstant(const char* name, CUdeviceptr src, size_t bytes) {
     CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, getActiveStream()));
 }
 
-Kernel buildKernel(const string& nameExpr, const string& jit_ker,
-                   const vector<string>& opts, const bool isJIT) {
+Kernel buildKernel(const int device, const string& nameExpr,
+                   const string& jit_ker, const vector<string>& opts,
+                   const bool isJIT) {
     const char* ker_name = nameExpr.c_str();
 
     nvrtcProgram prog;
@@ -130,16 +137,10 @@ Kernel buildKernel(const string& nameExpr, const string& jit_ker,
                                        NumHeaders, headers, includeNames));
     }
 
-    auto dev = getDeviceProp(getActiveDeviceId());
+    auto computeFlag = getComputeCapability(device);
     array<char, 32> arch;
-    if (dev.major == 7 && dev.minor > 2) {
-        // FIXME: This conditional can be removed completely
-        // when nvrtc enables >72 as valid compute value for
-        // --gpu-architecture option
-        dev.minor = 2;
-    }
     snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
-             dev.major, dev.minor);
+             computeFlag.first, computeFlag.second);
     vector<const char*> compiler_options = {
         arch.data(),
         "--std=c++11",
@@ -359,7 +360,7 @@ Kernel getKernel(const string& nameExpr, const string& source,
     Kernel kernel = findKernel(device, tInstance);
 
     if (kernel.prog == 0 || kernel.ker == 0) {
-        kernel = buildKernel(tInstance, source, compileOpts);
+        kernel = buildKernel(device, tInstance, source, compileOpts);
         addKernelToCache(device, tInstance, kernel);
     }
 
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index b513015598..d4444a9b1d 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -80,10 +80,10 @@ struct Kernel {
 };
 
 // TODO(pradeep): remove this in API and merge JIT and nvrtc caches
-Kernel buildKernel(const std::string& nameExpr,
+Kernel buildKernel(const int device, const std::string& nameExpr,
                    const std::string& jitSourceString,
                    const std::vector<std::string>& opts = {},
-                   const bool isJIT                     = false);
+                   const bool isJIT = false);
 
 template<typename T>
 std::string toString(T value);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 14a04bd750..b72c1d74e1 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -42,9 +42,43 @@ using std::to_string;
 
 namespace cuda {
 
-///////////////////////////////////////////////////////////////////////////
-// HELPERS
-///////////////////////////////////////////////////////////////////////////
+void findJitDevCompute(pair<int, int>& prop) {
+    struct cuNVRTCcompute {
+        /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
+        int cuda_version;
+        /// Maximum major compute flag supported by cuda_version
+        int major;
+        /// Maximum minor compute flag supported by cuda_version
+        int minor;
+    };
+    static const cuNVRTCcompute Toolkit2Compute[] = {
+        {10010, 7, 5},
+        {10000, 7, 2},
+        {9020,  7, 2},
+        {9010,  7, 2},
+        {9000,  7, 2},
+        {8000,  5, 3},
+        {7050,  5, 3},
+        {7000,  5, 3}
+    };
+    int runtime_cuda_ver = 0;
+    CUDA_CHECK(cudaRuntimeGetVersion(&runtime_cuda_ver));
+    auto tkit_max_compute =
+        find_if(begin(Toolkit2Compute), end(Toolkit2Compute),
+                [runtime_cuda_ver](cuNVRTCcompute v) {
+                    return runtime_cuda_ver == v.cuda_version;
+                });
+    if ((tkit_max_compute == end(Toolkit2Compute)) ||
+        (prop.first  > tkit_max_compute->major &&
+         prop.second > tkit_max_compute->minor)) {
+        prop = make_pair(tkit_max_compute->major, tkit_max_compute->minor);
+    }
+}
+
+pair<int, int> getComputeCapability(const int device) {
+    return DeviceManager::getInstance().devJitComputes[device];
+}
+
 // pulled from CUTIL from CUDA SDK
 static inline int compute2cores(int major, int minor) {
     struct {
@@ -144,9 +178,6 @@ static inline int getMinSupportedCompute(int cudaMajorVer) {
                                   : minSV[cudaMajorVer - 1]);
 }
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper Functions
-///////////////////////////////////////////////////////////////////////////
 int getBackend() { return AF_BACKEND_CUDA; }
 
 string getDeviceInfo(int device) {
@@ -647,7 +678,15 @@ DeviceManager::DeviceManager()
 
     // Initialize all streams to 0.
     // Streams will be created in setActiveDevice()
-    for (int i = 0; i < (int)MAX_DEVICES; i++) streams[i] = (cudaStream_t)0;
+    for (size_t i = 0; i < MAX_DEVICES; i++) {
+        streams[i] = (cudaStream_t)0;
+        if (i < nDevices) {
+            auto prop = make_pair(cuDevices[i].prop.major,
+                    cuDevices[i].prop.minor);
+            findJitDevCompute(prop);
+            devJitComputes.emplace_back(prop);
+        }
+    }
 
     std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
     AF_TRACE("AF_CUDA_DEFAULT_DEVICE: {}", deviceENV);
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 0bd7898643..39a26e68a5 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -20,6 +20,7 @@
 
 #include <memory>
 #include <string>
+#include <utility>
 #include <vector>
 
 namespace spdlog {
@@ -71,6 +72,8 @@ bool synchronize_calls();
 
 cudaDeviceProp getDeviceProp(int device);
 
+std::pair<int, int> getComputeCapability(const int device);
+
 struct cudaDevice_t {
     cudaDeviceProp prop;
     size_t flops;
@@ -101,7 +104,7 @@ SparseHandle sparseHandle();
 
 class DeviceManager {
    public:
-    static const unsigned MAX_DEVICES = 16;
+    static const size_t MAX_DEVICES = 16;
 
     static bool checkGraphicsInteropCapability();
 
@@ -139,6 +142,8 @@ class DeviceManager {
 
     friend cudaDeviceProp getDeviceProp(int device);
 
+    friend std::pair<int, int> getComputeCapability(const int device);
+
    private:
     DeviceManager();
 
@@ -160,6 +165,7 @@ class DeviceManager {
     std::shared_ptr<spdlog::logger> logger;
 
     std::vector<cudaDevice_t> cuDevices;
+    std::vector<std::pair<int, int>> devJitComputes;
 
     int nDevices;
     cudaStream_t streams[MAX_DEVICES];

From 6c28d07218045f8ab997a6b455db24a8786a3f5b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 8 Apr 2019 20:09:25 -0400
Subject: [PATCH 1631/2677] Fix error in batch matmul because of invalid buffer
 size

Allocated incorrect amount of memory in the batched case. This worked
for small sizes because we allocate more than necessary but failed
for larger batch sizes
---
 src/backend/cuda/blas.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 31e9fb1b6a..4ad984cabe 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -221,11 +221,10 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
             optrs[n] = optr + z * oStrides[2] + w * oStrides[3];
         }
 
-        auto d_lptrs = memAlloc<uchar>(batchSize);
-        auto d_rptrs = memAlloc<uchar>(batchSize);
-        auto d_optrs = memAlloc<uchar>(batchSize);
-
         size_t bytes = batchSize * sizeof(T **);
+        auto d_lptrs = memAlloc<uchar>(bytes);
+        auto d_rptrs = memAlloc<uchar>(bytes);
+        auto d_optrs = memAlloc<uchar>(bytes);
         CUDA_CHECK(cudaMemcpyAsync(d_lptrs.get(), lptrs.data(), bytes,
                                    cudaMemcpyHostToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(d_rptrs.get(), rptrs.data(), bytes,
@@ -233,6 +232,9 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
         CUDA_CHECK(cudaMemcpyAsync(d_optrs.get(), optrs.data(), bytes,
                                    cudaMemcpyHostToDevice, getActiveStream()));
 
+        // Call this before the gemm call so that you don't have to wait for the
+        // computation. Even though it would make more sense to put it
+        // afterwards
         CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         CUBLAS_CHECK(gemmBatched_func<T>()(

From 7d1530130d8585166bb53ebfbd8d5d317ec46013 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 16 Apr 2019 12:28:04 -0400
Subject: [PATCH 1632/2677] Move parameters to shared memory (#2487)

* Use shared memory instead of args for pass Params to JIT kernels

* Used shared memory for params in OpenCL

* Move exit condition lower in the kernel

* Addressed feedback.
---
 src/backend/common/jit/BufferNodeBase.hpp    |  17 +-
 src/backend/common/jit/Node.hpp              |  53 +++++-
 src/backend/common/jit/ShiftNodeBase.hpp     |  17 +-
 src/backend/cuda/Array.cpp                   |  10 +-
 src/backend/cuda/Param.hpp                   |  48 +++---
 src/backend/cuda/jit.cpp                     | 165 ++++++++++++++-----
 src/backend/cuda/jit/BufferNode.hpp          |   2 +-
 src/backend/cuda/jit/ShiftNode.hpp           |  20 +++
 src/backend/cuda/jit/kernel_generators.hpp   |  58 ++++---
 src/backend/cuda/nvrtc/cache.cpp             |   1 +
 src/backend/cuda/shift.cpp                   |   6 +-
 src/backend/opencl/CMakeLists.txt            |   1 +
 src/backend/opencl/jit.cpp                   |  97 +++++++++--
 src/backend/opencl/jit/ShiftNode.hpp         |  17 ++
 src/backend/opencl/jit/kernel_generators.hpp |  78 ++++-----
 src/backend/opencl/memory.cpp                |   5 +-
 src/backend/opencl/memory.hpp                |   6 +-
 src/backend/opencl/shift.cpp                 |   5 +-
 test/jit.cpp                                 |  90 +++++++++-
 19 files changed, 522 insertions(+), 174 deletions(-)
 create mode 100644 src/backend/cuda/jit/ShiftNode.hpp
 create mode 100644 src/backend/opencl/jit/ShiftNode.hpp

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 525555341e..30bf461a7d 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -9,8 +9,11 @@
 
 #pragma once
 #include <backend.hpp>
+#include <common/jit/Node.hpp>
 #include <jit/kernel_generators.hpp>
 
+#include <iomanip>
+#include <mutex>
 #include <sstream>
 
 namespace common {
@@ -22,14 +25,18 @@ class BufferNodeBase : public common::Node {
     ParamType m_param;
     unsigned m_bytes;
     std::once_flag m_set_data_flag;
+    int param_index;
     bool m_linear_buffer;
 
    public:
+    using param_type = ParamType;
     BufferNodeBase(const char *type_str, const char *name_str)
         : Node(type_str, name_str, 0, {}) {}
 
     bool isBuffer() const final { return true; }
 
+    bool requiresGlobalMemoryAccess() const final { return true; }
+
     void setData(ParamType param, DataType data, const unsigned bytes,
                  bool is_linear) {
         std::call_once(m_set_data_flag,
@@ -63,14 +70,17 @@ class BufferNodeBase : public common::Node {
 
     int setArgs(int start_id, bool is_linear,
                 std::function<void(int id, const void *ptr, size_t arg_size)>
-                    setArg) const override {
+                    setArg) const final {
         return detail::setKernelArguments(start_id, is_linear, setArg, m_data,
-                                          m_param);
+                                          m_param, param_index);
     }
 
+    void setParamIndex(int index) final { param_index = index; }
+    int getParamIndex() const final { return param_index; }
+
     void genOffsets(std::stringstream &kerStream, int id,
                     bool is_linear) const final {
-        detail::generateBufferOffsets(kerStream, id, is_linear, m_type_str);
+        detail::generateBufferOffsets(kerStream, id, is_linear);
     }
 
     void genFuncs(std::stringstream &kerStream,
@@ -86,6 +96,7 @@ class BufferNodeBase : public common::Node {
     }
 
     size_t getBytes() const final { return m_bytes; }
+    ParamType &getParam() { return m_param; }
 };
 
 }  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 8eb72b88bc..f50b86d96b 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -49,28 +49,46 @@ class Node {
     int getNodesMap(Node_map_t &node_map, std::vector<const Node *> &full_nodes,
                     std::vector<Node_ids> &full_ids) const;
 
+    /// Generates the string that will be used to hash the kernel
     virtual void genKerName(std::stringstream &kerStream,
-                            const Node_ids &ids) const {
-        UNUSED(kerStream);
-        UNUSED(ids);
-    }
+                            const Node_ids &ids) const = 0;
+
+    /// Generates the function parameters for the node.
+    ///
+    /// \param[in/out] kerStream  The string will be written to this stream
+    /// \param[in]     ids        The integer id of the node and its children
+    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genParams(std::stringstream &kerStream, int id,
                            bool is_linear) const {
         UNUSED(kerStream);
         UNUSED(id);
         UNUSED(is_linear);
     }
+
+    /// Generates the variable that stores the thread's/work-item's offset into
+    /// the memory.
+    ///
+    /// \param[in/out] kerStream  The string will be written to this stream
+    /// \param[in]     ids        The integer id of the node and its children
+    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genOffsets(std::stringstream &kerStream, int id,
                             bool is_linear) const {
         UNUSED(kerStream);
         UNUSED(id);
         UNUSED(is_linear);
     }
+
+    /// Generates the code for the operation of the node.
+    ///
+    /// Generates the soruce code of the operation that the node needs to
+    /// perform. For example this function will create the string
+    /// "val2 = __add(val1, val2);" for the addition node.
+    ///
+    /// \param[in/out] kerStream  The string will be written to this stream
+    /// \param[in]     ids        The integer id of the node and its children
+    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genFuncs(std::stringstream &kerStream,
-                          const Node_ids &ids) const {
-        UNUSED(kerStream);
-        UNUSED(ids);
-    }
+                          const Node_ids &ids) const = 0;
 
     /// Calls the setArg function on each of the arguments passed into the
     /// kernel
@@ -90,6 +108,11 @@ class Node {
         return start_id;
     }
 
+    // Sets the index of the Param object stored in global memory.
+    virtual void setParamIndex(int index) { UNUSED(index); }
+    // Gets the index of the Param object stored in global memory.
+    virtual int getParamIndex() const { return -1; }
+
     virtual void getInfo(unsigned &len, unsigned &buf_count,
                          unsigned &bytes) const {
         UNUSED(buf_count);
@@ -103,6 +126,13 @@ class Node {
 
     // Return the size of the size of the buffer node in bytes. Zero otherwise
     virtual size_t getBytes() const { return 0; }
+
+    // Returns true if the node requires global memory access. This is true
+    // for buffer nodes and shift nodes. This implies that the Node needs
+    // access to the shape of the object to perform indexing operations
+    virtual bool requiresGlobalMemoryAccess() const { return false; }
+
+    // Returns true if this node is a Buffer
     virtual bool isBuffer() const { return false; }
     virtual bool isLinear(dim_t dims[4]) const {
         UNUSED(dims);
@@ -115,6 +145,13 @@ class Node {
     virtual ~Node() {}
 };
 
+// Returns true if the node requires global memory access. This is true
+// for buffer nodes and shift nodes. This implies that the Node needs
+// access to the shape of the object to perform indexing operations
+static inline bool requiresGlobalMemoryAccess(Node &node) {
+    return node.requiresGlobalMemoryAccess();
+}
+
 struct Node_ids {
     std::array<int, Node::kMaxChildren> child_ids;
     int id;
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index d02ebab0e2..afca388de8 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -9,13 +9,16 @@
 
 #pragma once
 
+#include <common/jit/Node.hpp>
 #include <jit/BufferNode.hpp>
 #include <jit/kernel_generators.hpp>
 
+#include <Param.hpp>
 #include <backend.hpp>
-#include <iomanip>
 
 #include <array>
+#include <functional>
+#include <iomanip>
 #include <memory>
 #include <sstream>
 #include <string>
@@ -41,6 +44,8 @@ class ShiftNodeBase : public Node {
         return false;
     }
 
+    bool requiresGlobalMemoryAccess() const final { return true; }
+
     void genKerName(std::stringstream &kerStream,
                     const common::Node_ids &ids) const final {
         kerStream << "_" << m_name_str;
@@ -67,9 +72,17 @@ class ShiftNodeBase : public Node {
         return curr_id + 4;
     }
 
+    void setParamIndex(int index) final { m_buffer_node->setParamIndex(index); }
+    int getParamIndex() const final { return m_buffer_node->getParamIndex(); }
+
+    typename BufferNode::param_type getParam() {
+        return m_buffer_node->getParam();
+    }
+
     void genOffsets(std::stringstream &kerStream, int id,
                     bool is_linear) const final {
-        detail::generateShiftNodeOffsets(kerStream, id, is_linear, m_type_str);
+        UNUSED(is_linear);
+        detail::generateShiftNodeOffsets(kerStream, id);
     }
 
     void genFuncs(std::stringstream &kerStream,
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index b9c3137669..145d54dd2c 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -28,6 +28,7 @@ using common::NodeIterator;
 using cuda::jit::BufferNode;
 
 using std::accumulate;
+using std::find_if;
 using std::shared_ptr;
 using std::vector;
 
@@ -233,16 +234,11 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
             //    pressure. Too many bytes is assumed to be half of all bytes
             //    allocated so far.
             //
-            // 2. Too many buffers in a nonlinear kernel cause param space
-            //    overflow. This happens when the number of nodes reaches 50
-            //    (51 including output). Too many buffers can occur in a tree
-            //    of size 25 in the worst case.
-            //
             // TODO: Find better solution than the following emperical solution.
             if (node->getHeight() > 25 || isBufferLimit) {
                 // This is the size of the params that are passed by default
                 constexpr size_t param_base_size =
-                    sizeof(Param<T>) + (4 * sizeof(uint));
+                    sizeof(Param<T>) + sizeof(Param<T>*)+ (5 * sizeof(uint));
 
                 // This is the maximum size of the params that can be allowed by
                 // CUDA NOTE: This number should have been (4096 -
@@ -276,7 +272,7 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
                         return prev;
                     });
                 size_t param_size = param_base_size + info.param_scalar_size;
-                param_size += info.num_buffers * sizeof(Param<T>);
+                param_size += info.num_buffers * (sizeof(T*) + sizeof(int));
 
                 // TODO: the buffer_size check here is very conservative. It
                 // will trigger an evaluation of the node in most cases. We
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index e51e8e831e..6a8889b41a 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -21,22 +21,25 @@ namespace cuda {
 template<typename T>
 class Param {
    public:
-    T *ptr;
     dim_t dims[4];
     dim_t strides[4];
+    T *ptr;
 
-    __DH__ Param() : ptr(nullptr) {}
+    __DH__ Param() noexcept : ptr(nullptr) {}
+
+    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides) noexcept
+        : dims{idims[0], idims[1], idims[2], idims[3]}
+        , strides{istrides[0], istrides[1], istrides[2], istrides[3]}
+        , ptr(iptr) {}
 
-    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides)
-        : ptr(iptr) {
-        for (int i = 0; i < 4; i++) {
-            dims[i]    = idims[i];
-            strides[i] = istrides[i];
-        }
-    }
     __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
+
+    Param(const Param<T> &other) noexcept = default;
+    Param(Param<T> &&other) noexcept = default;
+    Param<T> &operator=(const Param<T> &other) noexcept = default;
+    Param<T> &operator=(Param<T> &&other) noexcept = default;
 };
 
 template<typename T>
@@ -51,28 +54,27 @@ Param<T> flat(Param<T> in) {
 template<typename T>
 class CParam {
    public:
-    const T *ptr;
     dim_t dims[4];
     dim_t strides[4];
+    const T *ptr;
 
     __DH__ CParam(const T *iptr, const dim_t *idims, const dim_t *istrides)
-        : ptr(iptr) {
-        for (int i = 0; i < 4; i++) {
-            dims[i]    = idims[i];
-            strides[i] = istrides[i];
-        }
-    }
+        : dims{idims[0], idims[1], idims[2], idims[3]}
+        , strides{istrides[0], istrides[1], istrides[2], istrides[3]}
+        , ptr(iptr) {}
 
-    __DH__ CParam(Param<T> &in) : ptr(in.ptr) {
-        for (int i = 0; i < 4; i++) {
-            dims[i]    = in.dims[i];
-            strides[i] = in.strides[i];
-        }
-    }
+    __DH__ CParam(Param<T> &in)
+        : dims{in.dims[0], in.dims[1], in.dims[2], in.dims[3]}
+        , strides{in.strides[0], in.strides[1], in.strides[2], in.strides[3]}
+        , ptr(in.ptr) {}
 
     __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
-};
 
+    CParam(const CParam<T> &other) noexcept = default;
+    CParam(CParam<T> &&other) noexcept      = default;
+    CParam<T> &operator=(const CParam<T> &other) noexcept = default;
+    CParam<T> &operator=(CParam<T> &&other) noexcept = default;
+};
 }  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 83bc402e9e..273183518d 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -13,7 +13,11 @@
 
 #include <Array.hpp>
 #include <common/jit/Node.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
+#include <debug_cuda.hpp>
+#include <jit/BufferNode.hpp>
+#include <jit/ShiftNode.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 
@@ -32,6 +36,10 @@ namespace cuda {
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
+using common::NodeIterator;
+using common::requiresGlobalMemoryAccess;
+using cuda::jit::BufferNode;
+using cuda::jit::ShiftNode;
 
 using std::hash;
 using std::map;
@@ -39,6 +47,12 @@ using std::string;
 using std::stringstream;
 using std::vector;
 
+template<typename TL, typename TR>
+bool equal_shape(const Param<TL> &lhs, const Param<TR> &rhs) {
+    return std::equal(lhs.dims, lhs.dims + 4, rhs.dims) &&
+           std::equal(lhs.strides, lhs.strides + 4, rhs.strides);
+}
+
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids, bool is_linear) {
@@ -72,12 +86,11 @@ static string getKernelString(const string funcName,
     const std::string includeFileStr(jit_cuh, jit_cuh_len);
 
     const std::string paramTStr = R"JIT(
-template<typename T>
 struct Param
 {
-  T *ptr;
   dim_t dims[4];
   dim_t strides[4];
+  void *ptr;
 };
 )JIT";
 
@@ -91,7 +104,9 @@ struct Param
 
     static const char *kernelVoid = "extern \"C\" __global__ void\n";
     static const char *dimParams =
-        "uint blocks_x, uint blocks_y, uint blocks_x_total, uint num_odims";
+        "uint blocks_x, uint blocks_y, uint "
+        "blocks_x_total, uint num_odims";
+    static const char *globalParams = "Param* dims, int num_params, ";
 
     static const char *loopStart = R"JIT(
     for (int blockIdx_x = blockIdx.x; blockIdx_x < blocks_x_total; blockIdx_x += gridDim.x) {
@@ -99,46 +114,61 @@ struct Param
     static const char *loopEnd   = "}\n\n";
 
     static const char *blockStart = "{\n\n";
-    static const char *blockEnd   = "\n\n}";
+    static const char *blockEnd   = "\n}\n";
 
     static const char *linearIndex = R"JIT(
-        uint threadId = threadIdx.x;
-        long long idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
-        if (idx >= outref.dims[3] * outref.strides[3]) return;
-        )JIT";
+    uint threadId = threadIdx.x;
+    dim_t idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
+    if (idx >= outref.dims[3] * outref.strides[3]) continue;
+    )JIT";
 
     static const char *generalIndex = R"JIT(
-        long long id0 = 0, id1 = 0, id2 = 0, id3 = 0;
-        long blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
-        if (num_odims > 2) {
-            id2 = blockIdx_x / blocks_x;
-            id0 = blockIdx_x - id2 * blocks_x;
-            id0 = threadIdx.x + id0 * blockDim.x;
-            if (num_odims > 3) {
-                id3 = blockIdx_y / blocks_y;
-                id1 = blockIdx_y - id3 * blocks_y;
-                id1 = threadIdx.y + id1 * blockDim.y;
-            } else {
-                id1 = threadIdx.y + blockDim.y * blockIdx_y;
-            }
+    dim_t id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+    dim_t blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
+    if (num_odims > 2) {
+        id2 = blockIdx_x / blocks_x;
+        id0 = blockIdx_x - id2 * blocks_x;
+        id0 = threadIdx.x + id0 * blockDim.x;
+        if (num_odims > 3) {
+            id3 = blockIdx_y / blocks_y;
+            id1 = blockIdx_y - id3 * blocks_y;
+            id1 = threadIdx.y + id1 * blockDim.y;
         } else {
-            id3 = 0;
-            id2 = 0;
             id1 = threadIdx.y + blockDim.y * blockIdx_y;
-            id0 = threadIdx.x + blockDim.x * blockIdx_x;
         }
+    } else {
+        id3 = 0;
+        id2 = 0;
+        id1 = threadIdx.y + blockDim.y * blockIdx_y;
+        id0 = threadIdx.x + blockDim.x * blockIdx_x;
+    }
 
-        bool cond = id0 < outref.dims[0] &&
-                    id1 < outref.dims[1] &&
-                    id2 < outref.dims[2] &&
-                    id3 < outref.dims[3];
+    bool cond = id0 < outref.dims[0] &&
+                id1 < outref.dims[1] &&
+                id2 < outref.dims[2] &&
+                id3 < outref.dims[3];
 
-        if (!cond) { continue; }
+    dim_t idx = outref.strides[3] * id3 +
+                outref.strides[2] * id2 +
+                outref.strides[1] * id1 + id0;
 
-        long long idx = outref.strides[3] * id3 +
-                        outref.strides[2] * id2 +
-                        outref.strides[1] * id1 + id0;
-        )JIT";
+    if (threadIdx.x < num_params && threadIdx.y == 0) {
+        int tidx = threadIdx.x;
+        block_offsets[tidx] = (id3 < params[tidx].dims[3]) * params[tidx].strides[3] * id3 +
+                              (id2 < params[tidx].dims[2]) * params[tidx].strides[2] * id2;
+    }
+    __syncthreads();
+    if (cond) {
+    )JIT";
+
+    string paramreads = R"JIT(
+        extern __shared__ char smem[];
+        Param *params = reinterpret_cast<Param*>(smem);
+        dim_t *block_offsets = reinterpret_cast<dim_t*>(smem+(num_params * sizeof(Param)));
+
+        if (threadIdx.x < num_params) { params[threadIdx.x] = dims[threadIdx.x]; }
+        __syncthreads();
+    )JIT";
 
     stringstream inParamStream;
     stringstream outParamStream;
@@ -146,6 +176,7 @@ struct Param
     stringstream offsetsStream;
     stringstream opsStream;
     stringstream outrefstream;
+    outrefstream << "const Param &outref = out" << output_ids[0] << ";\n";
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
         const auto &node     = full_nodes[i];
@@ -158,16 +189,15 @@ struct Param
         node->genFuncs(opsStream, ids_curr);
     }
 
-    outrefstream << "const Param<" << full_nodes[output_ids[0]]->getTypeStr()
-                 << "> &outref = out" << output_ids[0] << ";\n";
-
     for (int i = 0; i < (int)output_ids.size(); i++) {
         int id = output_ids[i];
         // Generate output parameters
-        outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out"
-                       << id << ", \n";
+        outParamStream << "Param out" << id << ", \n";
+
         // Generate code to write the output
-        outWriteStream << "out" << id << ".ptr[idx] = val" << id << ";\n";
+        outWriteStream << "((" << full_nodes[id]->getTypeStr() << "*)(out" << id
+                       << ".ptr))"
+                       << "[idx] = val" << id << ";\n";
     }
 
     // Put various blocks into a single stream
@@ -180,10 +210,12 @@ struct Param
     kerStream << "(\n";
     kerStream << inParamStream.str();
     kerStream << outParamStream.str();
+    if (!is_linear) { kerStream << globalParams; }
     kerStream << dimParams;
     kerStream << ")\n";
     kerStream << blockStart;
     kerStream << outrefstream.str();
+    if (!is_linear) { kerStream << paramreads; }
     kerStream << loopStart;
     if (is_linear) {
         kerStream << linearIndex;
@@ -194,6 +226,7 @@ struct Param
     kerStream << opsStream.str();
     kerStream << outWriteStream.str();
     kerStream << loopEnd;
+    if (!is_linear) kerStream << "}";
     kerStream << blockEnd;
 
     return kerStream.str();
@@ -235,7 +268,6 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
 
     if (num_outputs == 0) return;
 
-    // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
     thread_local vector<const Node *> full_nodes;
     thread_local vector<Node_ids> full_ids;
@@ -249,9 +281,39 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
         full_ids.reserve(1024);
     }
 
+    vector<Param<T>> params;
     for (auto &node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
         output_ids.push_back(id);
+
+        NodeIterator<> end_node;
+        auto bufit = NodeIterator<>(node);
+        while (bufit != end_node) {
+            bufit = find_if(bufit, end_node, requiresGlobalMemoryAccess);
+            if (bufit != end_node) {
+                Param<T> param;
+
+                // TODO(umar): This is a hack. We need to clean up this API
+                // so that the if statement is not necessary
+                if (bufit->isBuffer()) {
+                    param = static_cast<BufferNode<T> &>(*bufit).getParam();
+                } else {
+                    param = static_cast<ShiftNode<T> &>(*bufit).getParam();
+                }
+
+                auto it = find_if(begin(params), end(params),
+                                  [&param](const Param<T> &p) {
+                                      return equal_shape<T, T>(param, p);
+                                  });
+                if (it == end(params)) {
+                    params.push_back(param);
+                    bufit->setParamIndex(params.size() - 1);
+                } else {
+                    bufit->setParamIndex(distance(begin(params), it));
+                }
+                bufit++;
+            }
+        }
     }
 
     bool is_linear = true;
@@ -307,7 +369,6 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
     }
 
     vector<void *> args;
-
     for (const auto &node : full_nodes) {
         node->setArgs(0, is_linear,
                       [&](int /*id*/, const void *ptr, size_t /*size*/) {
@@ -319,14 +380,32 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
         args.push_back((void *)&outputs[i]);
     }
 
+    uptr<uchar> dparam;
+    void *ptr         = nullptr;
+    int param_count   = 0;
+    size_t smem_bytes = 0;
+    if (!is_linear) {
+        smem_bytes  = (sizeof(Param<T>) + sizeof(dim_t)) * params.size();
+        param_count = params.size();
+        dparam      = memAlloc<uchar>(params.size() * sizeof(Param<T>));
+        CUDA_CHECK(cudaMemcpyAsync(dparam.get(), params.data(),
+                                   params.size() * sizeof(Param<T>),
+                                   cudaMemcpyHostToDevice, getActiveStream()));
+        ptr = dparam.get();
+        args.push_back(&ptr);
+        args.push_back((void *)&param_count);
+    }
+
     args.push_back((void *)&blocks_x_);
     args.push_back((void *)&blocks_y_);
     args.push_back((void *)&blocks_x_total);
     args.push_back((void *)&num_odims);
 
     CU_CHECK(cuLaunchKernel(ker, blocks_x, blocks_y, blocks_z, threads_x,
-                            threads_y, 1, 0, getActiveStream(), args.data(),
-                            NULL));
+                            threads_y, 1, smem_bytes, getActiveStream(),
+                            args.data(), NULL));
+
+    POST_LAUNCH_CHECK();
 
     // Reset the thread local vectors
     nodes.clear();
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
index 371a263245..4c4d36ffe8 100644
--- a/src/backend/cuda/jit/BufferNode.hpp
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -15,5 +15,5 @@ namespace cuda {
 namespace jit {
 template<typename T>
 using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
-}
+}  // namespace jit
 }  // namespace cuda
diff --git a/src/backend/cuda/jit/ShiftNode.hpp b/src/backend/cuda/jit/ShiftNode.hpp
new file mode 100644
index 0000000000..c2b9e7a44c
--- /dev/null
+++ b/src/backend/cuda/jit/ShiftNode.hpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/jit/BufferNodeBase.hpp>
+#include <common/jit/ShiftNodeBase.hpp>
+
+namespace cuda {
+namespace jit {
+
+template<typename T>
+using ShiftNode = common::ShiftNodeBase<BufferNode<T>>;
+
+}  // namespace jit
+}  // namespace cuda
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index 3414e439b9..ee03062484 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
 #include <common/jit/Node.hpp>
 
 #include <functional>
@@ -21,64 +22,70 @@ namespace cuda {
 namespace {
 
 /// Creates a string that will be used to declare the parameter of kernel
-void generateParamDeclaration(std::stringstream& kerStream, int id,
-                              bool is_linear, const std::string& m_type_str) {
+inline void generateParamDeclaration(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& m_type_str) {
     if (is_linear) {
         kerStream << m_type_str << " *in" << id << "_ptr,\n";
     } else {
-        kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
+        kerStream << m_type_str << " *in" << id << "_ptr, int in_index" << id
+                  << ",\n";
     }
 }
 
 /// Calls the setArg function to set the arguments for a kernel call
 template<typename T>
-int setKernelArguments(
+inline int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-    const std::shared_ptr<T>& ptr, const Param<T>& info) {
+    const std::shared_ptr<T>& ptr, const Param<T>& info,
+    const int& param_index) {
     UNUSED(ptr);
     if (is_linear) {
         setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
     } else {
-        setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
+        // setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
+        setArg(start_id++, static_cast<const void*>(&info.ptr), sizeof(T*));
+        setArg(start_id, &param_index, sizeof(int));
     }
     return start_id + 1;
 }
 
 /// Generates the code to calculate the offsets for a buffer
-void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
-                           const std::string& type_str) {
-    std::string idx_str = std::string("int idx") + std::to_string(id);
+inline void generateBufferOffsets(std::stringstream& kerStream, int id,
+                                  bool is_linear) {
+    std::string idx_str = std::string("\n\t\tdim_t idx") + std::to_string(id);
 
     if (is_linear) {
-        kerStream << idx_str << " = idx;\n";
+        kerStream << idx_str << " = idx;";
     } else {
-        std::string info_str = std::string("in") + std::to_string(id);
-        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
-                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
-                  << info_str << ".dims[1]) * " << info_str
-                  << ".strides[1] * id1 + (id0 < " << info_str
-                  << ".dims[0]) * id0;\n";
-        kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
+        // clang-format off
+        std::string in_index = "in_index" + std::to_string(id);
+        std::string block_offset = "block_offsets[" + in_index + "]";
+        std::string in_param = "params[" + in_index + "]";
+
+        kerStream << idx_str << " = " << block_offset
+                  << "\n + ((id1 < " << in_param << ".dims[1]) * " << in_param << ".strides[1] * id1)"
+                  << "\n + ((id0 < " << in_param << ".dims[0]) * id0);";
+        // clang-format on
     }
 }
 
 /// Generates the code to read a buffer and store it in a local variable
-void generateBufferRead(std::stringstream& kerStream, int id,
-                        const std::string& type_str) {
+inline void generateBufferRead(std::stringstream& kerStream, int id,
+                               const std::string& type_str) {
     kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
 
-void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
-                              bool is_linear, const std::string& type_str) {
-    UNUSED(is_linear);
+inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id) {
     std::string idx_str   = std::string("idx") + std::to_string(id);
     std::string info_str  = std::string("in") + std::to_string(id);
     std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
     std::string shift_str = std::string("shift") + std::to_string(id) + "_";
 
+    kerStream << "Param& " << info_str << " = "
+              << "params[in_index" << id << "];\n";
     for (int i = 0; i < 4; i++) {
         kerStream << "int " << id_str << i << " = __circular_mod(id" << i
                   << " + " << shift_str << i << ", " << info_str << ".dims["
@@ -96,11 +103,10 @@ void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
               << "1;\n";
     kerStream << idx_str << " += (" << id_str << "0 < " << info_str
               << ".dims[0]) * " << id_str << "0;\n";
-    kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
 }
 
-void generateShiftNodeRead(std::stringstream& kerStream, int id,
-                           const std::string& type_str) {
+inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
+                                  const std::string& type_str) {
     kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 3b26803acc..e09e57f147 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -74,6 +74,7 @@ using kc_t = map<string, Kernel>;
         char* logptr = log.get();                      \
         nvrtcGetProgramLog(prog, logptr);              \
         logptr[logSize] = '\x0';                       \
+        puts(logptr);                                  \
         AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
     } while (0)
 #else
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index c5ab83248e..e6e232239b 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -11,6 +11,7 @@
 #include <common/jit/ShiftNodeBase.hpp>
 #include <err_cuda.hpp>
 #include <jit/BufferNode.hpp>
+#include <jit/ShiftNode.hpp>
 #include <shift.hpp>
 
 #include <memory>
@@ -21,6 +22,7 @@ using common::Node_ptr;
 using common::ShiftNodeBase;
 
 using cuda::jit::BufferNode;
+using cuda::jit::ShiftNode;
 
 using std::array;
 using std::make_shared;
@@ -28,12 +30,10 @@ using std::static_pointer_cast;
 using std::string;
 
 namespace cuda {
-template<typename T>
-using ShiftNode = ShiftNodeBase<BufferNode<T>>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
-    // Shift should only be the first node in the JIT tree.
+    // Shift should only be the leaf node in the JIT tree.
     // Force input to be evaluated so that in is always a buffer.
     in.eval();
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a896e4411c..f3a67c09db 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -342,6 +342,7 @@ target_sources(afopencl
 target_sources(afopencl
   PRIVATE
     jit/BufferNode.hpp
+    jit/ShiftNode.hpp
     jit/kernel_generators.hpp
   )
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 277f53684a..72ecbe6efe 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -10,8 +10,11 @@
 #include <Array.hpp>
 #include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
+#include <jit/BufferNode.hpp>
+#include <jit/ShiftNode.hpp>
 #include <kernel_headers/jit.hpp>
 #include <program.hpp>
 #include <af/dim4.hpp>
@@ -24,11 +27,16 @@
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
+using common::NodeIterator;
+using common::requiresGlobalMemoryAccess;
+using opencl::jit::BufferNode;
+using opencl::jit::ShiftNode;
 
 using cl::Buffer;
 using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
+using cl::Local;
 using cl::NDRange;
 using cl::NullRange;
 using cl::Program;
@@ -40,6 +48,11 @@ using std::vector;
 
 namespace opencl {
 
+bool equal_shape(const KParam &lhs, const KParam &rhs) {
+    return std::equal(lhs.dims, lhs.dims + 4, rhs.dims) &&
+           std::equal(lhs.strides, lhs.strides + 4, rhs.strides);
+}
+
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids, bool is_linear) {
@@ -69,22 +82,27 @@ static string getKernelString(const string funcName,
                               const vector<int> &output_ids, bool is_linear) {
     // Common OpenCL code
     // This part of the code does not change with the kernel.
-
     static const char *kernelVoid = "__kernel void\n";
+    static const char *nonLinearParams =
+        "__global KParam* dims, int num_params,\n"
+        "__local KParam* params, __local dim_t* block_offsets,\n";
     static const char *dimParams =
         "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
     static const char *blockStart = "{\n\n";
-    static const char *blockEnd   = "\n\n}";
+    static const char *blockEnd   = "\n}\n";
 
     static const char *linearIndex = R"JIT(
-        uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
-        uint threadId = get_local_id(0);
-        int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;
+        size_t groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
+        size_t threadId = get_local_id(0);
+        size_t idx = groupId * get_local_size(0) * get_local_size(1) + threadId;
         if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;
         )JIT";
 
     static const char *generalIndex = R"JIT(
-        uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        if (get_local_id(0) < num_params) {
+            params[get_local_id(0)] = dims[get_local_id(0)];
+        }
+        dim_t id0 = 0, id1 = 0, id2 = 0, id3 = 0;
         if (num_odims > 2) {
             id2 = get_group_id(0) / groups_0;
             id0 = get_group_id(0) - id2 * groups_0;
@@ -106,11 +124,18 @@ static string getKernelString(const string funcName,
                     id1 < oInfo.dims[1] &&
                     id2 < oInfo.dims[2] &&
                     id3 < oInfo.dims[3];
+        size_t idx = oInfo.strides[3] * id3 +
+                     oInfo.strides[2] * id2 +
+                     oInfo.strides[1] * id1 +
+                     id0 + oInfo.offset;
+
+        if (get_local_id(0) < num_params && get_local_id(1) == 0) {
+            dim_t tidx = get_local_id(0);
+            block_offsets[tidx] = (id3 < params[tidx].dims[3]) * params[tidx].strides[3] * id3 +
+                                  (id2 < params[tidx].dims[2]) * params[tidx].strides[2] * id2;
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
         if (!cond) return;
-        int idx = oInfo.strides[3] * id3 +
-                  oInfo.strides[2] * id2 +
-                  oInfo.strides[1] * id1 +
-                  id0 + oInfo.offset;
         )JIT";
 
     stringstream inParamStream;
@@ -118,6 +143,8 @@ static string getKernelString(const string funcName,
     stringstream outWriteStream;
     stringstream offsetsStream;
     stringstream opsStream;
+    stringstream outrefstream;
+    outrefstream << "const Param outref = out" << output_ids[0] << ";\n";
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
         const auto &node     = full_nodes[i];
@@ -146,6 +173,7 @@ static string getKernelString(const string funcName,
     kerStream << "(\n";
     kerStream << inParamStream.str();
     kerStream << outParamStream.str();
+    if (!is_linear) { kerStream << nonLinearParams; }
     kerStream << dimParams;
     kerStream << ")\n";
     kerStream << blockStart;
@@ -215,13 +243,43 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
         full_ids.reserve(1024);
     }
 
+    vector<KParam> params;
     for (auto &node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
         output_ids.push_back(id);
+
+        NodeIterator<> end_node;
+        auto bufit = NodeIterator<>(node);
+        while (bufit != end_node) {
+            bufit = find_if(bufit, end_node, requiresGlobalMemoryAccess);
+            if (bufit != end_node) {
+                KParam param;
+
+                // TODO(umar): This is a hack. We need to clean up this API
+                // so that the if statement is not necessary
+                if (bufit->isBuffer()) {
+                    param = static_cast<BufferNode &>(*bufit).getParam();
+                } else {
+                    param = static_cast<ShiftNode &>(*bufit).getParam();
+                }
+
+                auto it = find_if(begin(params), end(params),
+                                  [&param](const KParam &p) {
+                                      return equal_shape(param, p);
+                                  });
+                if (it == end(params)) {
+                    params.push_back(param);
+                    bufit->setParamIndex(params.size() - 1);
+                } else {
+                    bufit->setParamIndex(distance(begin(params), it));
+                }
+                ++bufit;
+            }
+        }
     }
 
     bool is_linear = true;
-    for (auto node : full_nodes) {
+    for (const auto &node : full_nodes) {
         is_linear &= node->isLinear(outputs[0].info.dims);
     }
 
@@ -283,6 +341,23 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
         ++nargs;
     }
 
+    size_t smem_bytes = 0;
+    int param_count   = 0;
+    uptr dparam;
+    if (!is_linear) {
+        param_count = params.size();
+        dparam      = memAlloc<uchar>(params.size() * sizeof(KParam));
+
+        getQueue().enqueueWriteBuffer(*(dparam.get()), CL_FALSE, 0,
+                                      params.size() * sizeof(KParam),
+                                      params.data());
+
+        ker.setArg(nargs++, *(dparam.get()));
+        ker.setArg(nargs++, param_count);
+        ker.setArg(nargs++, Local(sizeof(KParam) * params.size()));
+        ker.setArg(nargs++, Local(sizeof(dim_t) * params.size()));
+    }
+
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
diff --git a/src/backend/opencl/jit/ShiftNode.hpp b/src/backend/opencl/jit/ShiftNode.hpp
new file mode 100644
index 0000000000..4eb48776ed
--- /dev/null
+++ b/src/backend/opencl/jit/ShiftNode.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/jit/ShiftNodeBase.hpp>
+#include <jit/BufferNode.hpp>
+
+namespace opencl {
+namespace jit {
+  using ShiftNode = common::ShiftNodeBase<BufferNode>;
+} //  namespace jit
+} //  namespace opencl
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 56e2149f5b..990a5e1f8c 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -11,74 +11,78 @@
 #include <sstream>
 #include <string>
 
+#include <functional>
+#include <memory>
+#include <sstream>
+#include <string>
+
 namespace opencl {
 
 namespace {
 
 /// Creates a string that will be used to declare the parameter of kernel
-void generateParamDeclaration(std::stringstream& kerStream, int id,
-                              bool is_linear, const std::string& m_type_str) {
+inline void generateParamDeclaration(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& m_type_str) {
+    kerStream << "__global " << m_type_str << " *in" << id << "_ptr, dim_t in"
+              << id << "_offset, ";
     if (is_linear) {
-        kerStream << "__global " << m_type_str << " *in" << id
-                  << ", dim_t iInfo" << id << "_offset, \n";
+        kerStream << "\n";
     } else {
-        kerStream << "__global " << m_type_str << " *in" << id
-                  << ", KParam iInfo" << id << ", \n";
+        kerStream << "int in_index" << id << ", \n";
     }
 }
 
 /// Calls the setArg function to set the arguments for a kernel call
-int setKernelArguments(
+inline int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-    const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
-    setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
-           sizeof(cl_mem));
-    if (is_linear) {
-        setArg(start_id + 1, static_cast<const void*>(&info.offset),
-               sizeof(dim_t));
-    } else {
-        setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam));
+    const std::shared_ptr<cl::Buffer>& ptr, const KParam& info,
+    const int& param_index) {
+    cl_mem& buf = (*ptr)();
+    setArg(start_id++, static_cast<const void*>(&buf), sizeof(cl_mem));
+    setArg(start_id++, static_cast<const void*>(&info.offset), sizeof(dim_t));
+    if (is_linear == false) {
+        setArg(start_id++, static_cast<const void*>(&param_index), sizeof(int));
     }
-    return start_id + 2;
+    return start_id;
 }
 
 /// Generates the code to calculate the offsets for a buffer
-inline void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
-                                  const std::string& type_str) {
-    UNUSED(type_str);
-    std::string idx_str  = std::string("int idx") + std::to_string(id);
-    std::string info_str = std::string("iInfo") + std::to_string(id);
+inline void generateBufferOffsets(std::stringstream& kerStream, int id,
+                                  bool is_linear) {
+    std::string idx_str    = std::string("dim_t idx") + std::to_string(id);
+    std::string offset_str = std::string("in") + std::to_string(id) + "_offset";
 
     if (is_linear) {
-        kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
+        kerStream << idx_str << " = idx + " << offset_str << ";\n";
     } else {
-        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
-                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
-                  << info_str << ".dims[1]) * " << info_str
-                  << ".strides[1] * id1 + (id0 < " << info_str
-                  << ".dims[0]) * id0 + " << info_str << ".offset;\n";
+        std::string in_index     = "in_index" + std::to_string(id);
+        std::string block_offset = "block_offsets[" + in_index + "]";
+        std::string in_param     = "params[" + in_index + "]";
+
+        kerStream << idx_str << " = " << block_offset << "\n + ((id1 < "
+                  << in_param << ".dims[1]) * " << in_param
+                  << ".strides[1] * id1)"
+                  << "\n + ((id0 < " << in_param << ".dims[0]) * id0) + "
+                  << offset_str << ";\n";
     }
 }
 
 /// Generates the code to read a buffer and store it in a local variable
 inline void generateBufferRead(std::stringstream& kerStream, int id,
                                const std::string& type_str) {
-    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
+    kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
 
-inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
-                                     bool is_linear,
-                                     const std::string& type_str) {
-    UNUSED(is_linear);
-    UNUSED(type_str);
+inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id) {
     std::string idx_str   = std::string("idx") + std::to_string(id);
-    std::string info_str  = std::string("iInfo") + std::to_string(id);
+    std::string info_str  = std::string("in") + std::to_string(id);
     std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
     std::string shift_str = std::string("shift") + std::to_string(id) + "_";
 
+    kerStream << "KParam " << info_str << " = params[in_index" << id << "];\n";
     for (int i = 0; i < 4; i++) {
         kerStream << "int " << id_str << i << " = __circular_mod(id" << i
                   << " + " << shift_str << i << ", " << info_str << ".dims["
@@ -95,12 +99,12 @@ inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
               << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
               << "1;\n";
     kerStream << idx_str << " += (" << id_str << "0 < " << info_str
-              << ".dims[0]) * " << id_str << "0 + " << info_str << ".offset;\n";
+              << ".dims[0]) * " << id_str << "0 + " << info_str << "_offset;\n";
 }
 
 inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
                                   const std::string& type_str) {
-    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
+    kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
 }  // namespace
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 4accb8fb16..47af4acb7c 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -50,12 +50,11 @@ void printMemInfo(const char *msg, const int device) {
 }
 
 template<typename T>
-unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
+uptr memAlloc(
     const size_t &elements) {
     cl::Buffer *ptr = static_cast<cl::Buffer *>(
         memoryManager().alloc(elements * sizeof(T), false));
-    return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(ptr,
-                                                                bufferFree);
+    return uptr(ptr, bufferFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index c4298b1404..fab566c81b 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -24,9 +24,11 @@ namespace opencl {
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
 
+using uptr = std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>>;
+
 template<typename T>
-std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>> memAlloc(
-    const size_t &elements);
+uptr memAlloc(const size_t &elements);
+
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index da86c46cdf..af3ab0c046 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <common/jit/ShiftNodeBase.hpp>
+#include <opencl/jit/ShiftNode.hpp>
 #include <err_opencl.hpp>
 #include <shift.hpp>
 
@@ -18,7 +18,7 @@
 using af::dim4;
 
 using common::Node_ptr;
-using common::ShiftNodeBase;
+using opencl::jit::ShiftNode;
 using opencl::jit::BufferNode;
 
 using std::array;
@@ -27,7 +27,6 @@ using std::static_pointer_cast;
 using std::string;
 
 namespace opencl {
-using ShiftNode = ShiftNodeBase<BufferNode>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
diff --git a/test/jit.cpp b/test/jit.cpp
index 2ed23977a9..8391237565 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -16,6 +16,7 @@
 
 using af::array;
 using af::constant;
+using af::dim4;
 using af::eval;
 using af::freeHost;
 using af::gforSet;
@@ -124,10 +125,16 @@ TEST(JIT, CPP_Multi_linear) {
     x.host(&hx[0]);
     y.host(&hy[0]);
 
+    vector<int> goldx(num);
+    vector<int> goldy(num);
+
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ((ha[i] + hb[i]), hx[i]);
-        ASSERT_EQ((ha[i] - hb[i]), hy[i]);
+        goldx[i] = ha[i] + hb[i];
+        goldy[i] = ha[i] - hb[i];
     }
+
+    ASSERT_VEC_ARRAY_EQ(goldx, dim4(num), x);
+    ASSERT_VEC_ARRAY_EQ(goldy, dim4(num), y);
 }
 
 TEST(JIT, CPP_strided) {
@@ -604,3 +611,82 @@ TEST(JIT, LargeJitTree) {
         af::sync();
     });
 }
+
+TEST(JIT, TwoLargeNonLinear) {
+    int dimsize = 10;
+    array a     = constant(0, dimsize, dimsize);
+    array aa    = constant(0, dimsize, dimsize);
+    array b     = constant(0, dimsize, dimsize);
+    array bb    = constant(0, dimsize, dimsize);
+
+    int val = 0;
+    for (int i = 0; i < 24; i++) {
+        array ones = constant(1, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+
+        a += tile(twos, 1, dimsize) + ones;
+        aa += tile(twos, 1, dimsize) + ones;
+        val += 3;
+    }
+
+    for (int i = 0; i < 24; i++) {
+        array ones = constant(1, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+        b += tile(twos, 1, dimsize) + ones;
+        bb += tile(twos, 1, dimsize) + ones;
+    }
+    array c  = a + b;
+    array cc = aa + bb;
+    eval(c, cc);
+
+    vector<float> gold(a.elements(), val * 2);
+    ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c);
+}
+
+TEST(JIT, IndexingColumn) {
+    array a = constant(1, 512, 32);
+    array b = constant(2, 512);
+    a.eval();
+    b.eval();
+
+    array c = a(af::span, 31) + b;
+
+    vector<float> gold(512, 3.0f);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(512), c);
+}
+
+TEST(JIT, IndexingRow) {
+    array a = constant(1, 32, 512);
+    array b = constant(2, 1, 512);
+    a.eval();
+    b.eval();
+
+    array c = a(31, af::span) + b;
+
+    vector<float> gold(512, 3.0f);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(1, 512), c);
+}
+
+TEST(JIT, DISABLED_ManyConstants) {
+    array res  = constant(1, 1);
+    array res2 = tile(res, 1, 10);
+    array res3 = randu(1);
+    array res4 = tile(res3, 1, 10);
+    array res5 = randu(1);
+    array res6 = tile(res5, 1, 10);
+    array res7 = randu(1);
+    array res8 = tile(res7, 1, 10);
+
+    for (int i = 0; i < 80; i++) { res2 = res2 + randu(1, 10); }
+    for (int i = 0; i < 80; i++) { res4 = res4 + tile(randu(1), 1, 10); }
+    for (int i = 0; i < 80; i++) { res6 = res6 + tile(randu(1), 1, 10); }
+    for (int i = 0; i < 80; i++) { res8 = res8 + 1.0f; }
+
+    // This still fails in the current implementation
+    eval(res2, res4, res6);//, res8);
+    af::sync();
+}

From ef3bb7441fa6d498a4eac4ef5ce38a31731e0a48 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 17 Apr 2019 10:21:31 +0530
Subject: [PATCH 1633/2677] Cleanup cuda libraries in DeviceManager destructor

The following are the resource handles whose cleanup
has been moved to destructor of DeviceManager in CUDA backend.
- cuBLAS
- cuSparse
- cuSolver

fft plancache cleanup has also been moved to DeviceManager destructor
---
 src/backend/cuda/platform.cpp | 132 ++++++++++++++++++++--------------
 src/backend/cuda/platform.hpp |   1 +
 2 files changed, 78 insertions(+), 55 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index b72c1d74e1..7845f3951f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -30,6 +30,7 @@
 #include <algorithm>
 #include <array>
 #include <cstdio>
+#include <memory>
 #include <mutex>
 #include <sstream>
 #include <stdexcept>
@@ -38,7 +39,6 @@
 #include <vector>
 
 using namespace std;
-using std::to_string;
 
 namespace cuda {
 
@@ -178,6 +178,66 @@ static inline int getMinSupportedCompute(int cudaMajorVer) {
                                   : minSV[cudaMajorVer - 1]);
 }
 
+unique_ptr<cublasHandle> &cublasManager(const int deviceId) {
+    thread_local unique_ptr<cublasHandle> handles[DeviceManager::MAX_DEVICES];
+    thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
+
+    call_once(initFlags[deviceId], [&] {
+        handles[deviceId].reset(new cublasHandle());
+        // TODO(pradeep) When multiple streams per device
+        // is added to CUDA backend, move the cublasSetStream
+        // call outside of call_once scope.
+        CUBLAS_CHECK(cublasSetStream(handles[deviceId]->get(),
+                                     cuda::getStream(deviceId)));
+    });
+
+    return handles[deviceId];
+}
+
+unique_ptr<PlanCache> &cufftManager(const int deviceId) {
+    thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
+    thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
+    call_once(initFlags[deviceId],
+              [&] { caches[deviceId].reset(new PlanCache()); });
+    return caches[deviceId];
+}
+
+unique_ptr<cusolverDnHandle> &cusolverManager(const int deviceId) {
+    thread_local unique_ptr<cusolverDnHandle>
+        handles[DeviceManager::MAX_DEVICES];
+    thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
+    call_once(initFlags[deviceId], [&] {
+        handles[deviceId].reset(new cusolverDnHandle());
+        // TODO(pradeep) When multiple streams per device
+        // is added to CUDA backend, move the cublasSetStream
+        // call outside of call_once scope.
+        CUSOLVER_CHECK(cusolverDnSetStream(handles[deviceId]->get(),
+                                           cuda::getStream(deviceId)));
+    });
+    // TODO(pradeep) prior to this change, stream was being synced in get solver
+    // handle because of some cusolver bug. Re-enable that if this change
+    // doesn't work and sovler tests fail.
+    // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
+    // cuSolver Streams patch:
+    // CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(deviceId)));
+
+    return handles[deviceId];
+}
+
+unique_ptr<cusparseHandle> &cusparseManager(const int deviceId) {
+    thread_local unique_ptr<cusparseHandle> handles[DeviceManager::MAX_DEVICES];
+    thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
+    call_once(initFlags[deviceId], [&] {
+        handles[deviceId].reset(new cusparseHandle());
+        // TODO(pradeep) When multiple streams per device
+        // is added to CUDA backend, move the cublasSetStream
+        // call outside of call_once scope.
+        CUSPARSE_CHECK(cusparseSetStream(handles[deviceId]->get(),
+                                         cuda::getStream(deviceId)));
+    });
+    return handles[deviceId];
+}
+
 int getBackend() { return AF_BACKEND_CUDA; }
 
 string getDeviceInfo(int device) {
@@ -424,69 +484,19 @@ GraphicsResourceManager &interopManager() {
 }
 
 PlanCache &fftManager() {
-    thread_local PlanCache cufftManagers[DeviceManager::MAX_DEVICES];
-
-    return cufftManagers[getActiveDeviceId()];
+    return *(cufftManager(cuda::getActiveDeviceId()).get());
 }
 
 BlasHandle blasHandle() {
-    thread_local std::unique_ptr<cublasHandle>
-        cublasHandles[DeviceManager::MAX_DEVICES];
-    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
-
-    int id = cuda::getActiveDeviceId();
-
-    std::call_once(initFlags[id],
-                   [&] { cublasHandles[id].reset(new cublasHandle()); });
-
-    CUBLAS_CHECK(
-        cublasSetStream(cublasHandles[id].get()->get(), cuda::getStream(id)));
-
-    return cublasHandles[id].get()->get();
+    return cublasManager(cuda::getActiveDeviceId())->get();
 }
 
 SolveHandle solverDnHandle() {
-    thread_local std::unique_ptr<cusolverDnHandle>
-        cusolverHandles[DeviceManager::MAX_DEVICES];
-    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
-
-    int id = cuda::getActiveDeviceId();
-
-    std::call_once(initFlags[id],
-                   [&] { cusolverHandles[id].reset(new cusolverDnHandle()); });
-
-    // FIXME
-    // This is not an ideal case. It's just a hack.
-    // The correct way to do is to use
-    // CUSOLVER_CHECK(cusolverDnSetStream(cuda::getStream(cuda::getActiveDeviceId())))
-    // in the class constructor.
-    // However, this is causing a lot of the cusolver functions to fail.
-    // The only way to fix them is to use cudaDeviceSynchronize() and
-    //     cudaStreamSynchronize()
-    // all over the place, but even then some calls like getrs in solve_lu
-    // continue to fail on any stream other than 0.
-    //
-    // cuSolver Streams patch:
-    // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(id)));
-
-    return cusolverHandles[id].get()->get();
+    return cusolverManager(cuda::getActiveDeviceId())->get();
 }
 
 SparseHandle sparseHandle() {
-    thread_local std::unique_ptr<cusparseHandle>
-        cusparseHandles[DeviceManager::MAX_DEVICES];
-    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
-
-    int id = cuda::getActiveDeviceId();
-
-    std::call_once(initFlags[id],
-                   [&] { cusparseHandles[id].reset(new cusparseHandle()); });
-
-    CUSPARSE_CHECK(cusparseSetStream(cusparseHandles[id].get()->get(),
-                                     cuda::getStream(id)));
-
-    return cusparseHandles[id].get()->get();
+    return cusparseManager(cuda::getActiveDeviceId())->get();
 }
 
 /// Struct represents the cuda toolkit version and its associated minimum
@@ -709,6 +719,18 @@ DeviceManager::DeviceManager()
     AF_TRACE("Default device: {}", getActiveDeviceId());
 }
 
+DeviceManager::~DeviceManager() {
+    // Reset unique_ptrs for all cu[BLAS | Sparse | Solver]
+    // handles of all devices
+    for (int i = 0; i < nDevices; ++i) {
+        setDevice(i);
+        cublasManager(i).reset();
+        cufftManager(i).reset();
+        cusolverManager(i).reset();
+        cusparseManager(i).reset();
+    }
+}
+
 spdlog::logger *DeviceManager::getLogger() { return logger.get(); }
 
 void DeviceManager::sortDevices(sort_mode mode) {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 39a26e68a5..a693136669 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -109,6 +109,7 @@ class DeviceManager {
     static bool checkGraphicsInteropCapability();
 
     static DeviceManager& getInstance();
+    ~DeviceManager();
 
     spdlog::logger* getLogger();
 

From 51015390a70a32d390ce7f62d4e8bfd54b420abf Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 18 Apr 2019 15:30:34 +0530
Subject: [PATCH 1634/2677] Correct default value of z-title in setAxesTitles
 fn

Based on this default value, either a 2D or 3D chart is chosen.

Added a note to the C-API documentation to notify the user of
behavior of af_set_axes_titles.
---
 include/af/graphics.h | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index a1dbf5d3da..e67b93a64c 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -483,7 +483,7 @@ class AFAPI Window {
         */
         void setAxesTitles(const char * const xtitle = "X-Axis",
                            const char * const ytitle = "Y-Axis",
-                           const char * const ztitle = "Z-Axis");
+                           const char * const ztitle = NULL);
 #endif
         /**
            Setup grid layout for multiview mode in a window
@@ -1062,7 +1062,15 @@ AFAPI af_err af_set_axes_limits_3d(const af_window wind,
 
 #if AF_API_VERSION >= 34
 /**
-   C Interface wrapper for setting axes titles for histogram/plot/surface/vector field
+   C Interface wrapper for setting axes titles for histogram/plot/surface/vector
+   field
+
+   Passing correct value to \p ztitle dictates the right behavior when it comes
+   to setting the axes titles appropriately.  If the user is targeting a two
+   dimensional chart on the window \p wind, then the user needs to pass NULL to
+   \p ztitle so that internal caching mechanism understands this window requires
+   a 2D chart. Any non NULL value passed to \p ztitle will result in ArrayFire
+   thinking the \p wind intends to use a 3D chart.
 
    \param[in] wind is the window handle
    \param[in] xtitle is the name of the x-axis

From 4f50e5cd9f8fba8b9f202fc4e05ca7c5a189490a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 1 May 2019 19:48:34 +0530
Subject: [PATCH 1635/2677] Cleanup platform headers in all backends (#2499)

* Cleanup platform headers in all backends

* Address feedback
---
 src/backend/common/jit/Node.cpp               |   1 +
 src/backend/common/jit/Node.hpp               |   1 +
 src/backend/cpu/CMakeLists.txt                |   2 +
 src/backend/cpu/device_manager.cpp            | 131 +++++
 src/backend/cpu/device_manager.hpp            | 117 ++++
 src/backend/cpu/iota.cpp                      |   2 -
 src/backend/cpu/platform.cpp                  | 151 +----
 src/backend/cpu/platform.hpp                  | 104 +---
 src/backend/cpu/set.cpp                       |  24 +-
 src/backend/cuda/CMakeLists.txt               |   2 +
 src/backend/cuda/blas.cpp                     |   3 +-
 src/backend/cuda/cholesky.cu                  |   1 +
 src/backend/cuda/cublas.hpp                   |   3 +-
 src/backend/cuda/cusolverDn.hpp               |   4 +-
 src/backend/cuda/cusparse.hpp                 |   3 +-
 src/backend/cuda/device_manager.cpp           | 512 +++++++++++++++++
 src/backend/cuda/device_manager.hpp           | 107 ++++
 src/backend/cuda/hist_graphics.cpp            |   1 +
 src/backend/cuda/image.cpp                    |   1 +
 src/backend/cuda/jit.cpp                      |   1 +
 src/backend/cuda/lu.cu                        |   1 +
 src/backend/cuda/nvrtc/cache.cpp              |   1 +
 src/backend/cuda/platform.cpp                 | 498 ++---------------
 src/backend/cuda/platform.hpp                 | 117 +---
 src/backend/cuda/plot.cpp                     |   1 +
 src/backend/cuda/qr.cu                        |   1 +
 src/backend/cuda/solve.cu                     |   1 +
 src/backend/cuda/sparse.cu                    |   2 +-
 src/backend/cuda/sparse_arith.cu              |   5 +-
 src/backend/cuda/sparse_blas.cpp              |   3 +-
 src/backend/cuda/surface.cpp                  |   1 +
 src/backend/cuda/vector_field.cpp             |   1 +
 src/backend/opencl/CMakeLists.txt             |   2 +
 src/backend/opencl/device_manager.cpp         | 446 +++++++++++++++
 src/backend/opencl/device_manager.hpp         | 117 ++++
 src/backend/opencl/kernel/approx.hpp          |   1 +
 src/backend/opencl/kernel/join.hpp            |   1 +
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   1 +
 src/backend/opencl/platform.cpp               | 521 ++----------------
 src/backend/opencl/platform.hpp               | 137 ++---
 src/backend/opencl/sparse_arith.cpp           |   4 +-
 41 files changed, 1645 insertions(+), 1388 deletions(-)
 create mode 100644 src/backend/cpu/device_manager.cpp
 create mode 100644 src/backend/cpu/device_manager.hpp
 create mode 100644 src/backend/cuda/device_manager.cpp
 create mode 100644 src/backend/cuda/device_manager.hpp
 create mode 100644 src/backend/opencl/device_manager.cpp
 create mode 100644 src/backend/opencl/device_manager.hpp

diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index d6d8400af6..9fdcfd72d2 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/defines.hpp>
 #include <common/jit/Node.hpp>
 
 #include <string>
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index f50b86d96b..cc02b97693 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <af/defines.h>
 #include <optypes.hpp>
 #include <platform.hpp>
 
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index abbfe47b77..a70ee738f7 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -37,6 +37,8 @@ target_sources(afcpu
     convolve.hpp
     copy.cpp
     copy.hpp
+    device_manager.cpp
+    device_manager.hpp
     diagonal.cpp
     diagonal.hpp
     diff.cpp
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
new file mode 100644
index 0000000000..afb6258b54
--- /dev/null
+++ b/src/backend/cpu/device_manager.cpp
@@ -0,0 +1,131 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/graphics_common.hpp>
+#include <device_manager.hpp>
+#include <af/version.h>
+
+#include <cctype>
+#include <sstream>
+
+using std::string;
+
+#ifdef CPUID_CAPABLE
+
+CPUInfo::CPUInfo()
+    : mVendorId("")
+    , mModelName("")
+    , mNumSMT(0)
+    , mNumCores(0)
+    , mNumLogCpus(0)
+    , mIsHTT(false) {
+    // Get vendor name EAX=0
+    CPUID cpuID1(1, 0);
+    mIsHTT = cpuID1.EDX() & HTT_POS;
+
+    CPUID cpuID0(0, 0);
+    uint32_t HFS = cpuID0.EAX();
+    mVendorId += string((const char*)&cpuID0.EBX(), 4);
+    mVendorId += string((const char*)&cpuID0.EDX(), 4);
+    mVendorId += string((const char*)&cpuID0.ECX(), 4);
+
+    string upVId = mVendorId;
+
+    for_each(upVId.begin(), upVId.end(), [](char& in) { in = ::toupper(in); });
+
+    // Get num of cores
+    if (upVId.find("INTEL") != std::string::npos) {
+        mVendorId = "Intel";
+        if (HFS >= 11) {
+            for (int lvl = 0; lvl < MAX_INTEL_TOP_LVL; ++lvl) {
+                CPUID cpuID4(0x0B, lvl);
+                uint32_t currLevel = (LVL_TYPE & cpuID4.ECX()) >> 8;
+                switch (currLevel) {
+                    case 0x01: mNumSMT = LVL_CORES & cpuID4.EBX(); break;
+                    case 0x02: mNumLogCpus = LVL_CORES & cpuID4.EBX(); break;
+                    default: break;
+                }
+            }
+            // Fixes Possible divide by zero error
+            // TODO: Fix properly
+            mNumCores = mNumLogCpus / (mNumSMT == 0 ? 1 : mNumSMT);
+        } else {
+            if (HFS >= 1) {
+                mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
+                if (HFS >= 4) {
+                    mNumCores = 1 + ((CPUID(4, 0).EAX() >> 26) & 0x3F);
+                }
+            }
+            if (mIsHTT) {
+                if (!(mNumCores > 1)) {
+                    mNumCores   = 1;
+                    mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
+                }
+            } else {
+                mNumCores = mNumLogCpus = 1;
+            }
+        }
+    } else if (upVId.find("AMD") != std::string::npos) {
+        mVendorId = "AMD";
+        if (HFS >= 1) {
+            mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
+            if (CPUID(0x80000000, 0).EAX() >= 8) {
+                mNumCores = 1 + ((CPUID(0x80000008, 0).ECX() & 0xFF));
+            }
+        }
+        if (mIsHTT) {
+            if (!(mNumCores > 1)) {
+                mNumCores   = 1;
+                mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
+            }
+        } else {
+            mNumCores = mNumLogCpus = 1;
+        }
+    } else {
+        mVendorId = "Unknown";
+    }
+    // Get processor brand string
+    // This seems to be working for both Intel & AMD vendors
+    for (unsigned i = 0x80000002; i < 0x80000005; ++i) {
+        CPUID cpuID(i, 0);
+        mModelName += string((const char*)&cpuID.EAX(), 4);
+        mModelName += string((const char*)&cpuID.EBX(), 4);
+        mModelName += string((const char*)&cpuID.ECX(), 4);
+        mModelName += string((const char*)&cpuID.EDX(), 4);
+    }
+    mModelName = string(mModelName.c_str());
+}
+
+#else
+
+CPUInfo::CPUInfo()
+    : mVendorId("Unknown")
+    , mModelName("Unknown")
+    , mNumSMT(1)
+    , mNumCores(1)
+    , mNumLogCpus(1)
+    , mIsHTT(false) {}
+
+#endif
+
+namespace cpu {
+
+DeviceManager::DeviceManager()
+    : queues(MAX_QUEUES)
+    , memManager(new MemoryManager())
+    , fgMngr(new graphics::ForgeManager()) {}
+
+DeviceManager& DeviceManager::getInstance() {
+    static DeviceManager* my_instance = new DeviceManager();
+    return *my_instance;
+}
+
+CPUInfo DeviceManager::getCPUInfo() const { return cinfo; }
+
+}  // namespace cpu
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
new file mode 100644
index 0000000000..e0c43c00c5
--- /dev/null
+++ b/src/backend/cpu/device_manager.hpp
@@ -0,0 +1,117 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <platform.hpp>
+#include <queue.hpp>
+#include <memory>
+#include <string>
+
+#if defined(AF_WITH_CPUID) &&                                       \
+    (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || \
+     defined(_M_IX86) || defined(_WIN64))
+#define CPUID_CAPABLE
+#endif
+
+#ifdef _WIN32
+#include <intrin.h>
+#include <limits.h>
+typedef unsigned __int32 uint32_t;
+#else
+#include <stdint.h>
+#endif
+
+#ifdef CPUID_CAPABLE
+
+#define MAX_INTEL_TOP_LVL 4
+
+class CPUID {
+    uint32_t regs[4];
+
+   public:
+    explicit CPUID(unsigned funcId, unsigned subFuncId) {
+#ifdef _WIN32
+        __cpuidex((int*)regs, (int)funcId, (int)subFuncId);
+
+#else
+        asm volatile("cpuid"
+                     : "=a"(regs[0]), "=b"(regs[1]), "=c"(regs[2]),
+                       "=d"(regs[3])
+                     : "a"(funcId), "c"(subFuncId));
+#endif
+    }
+
+    inline const uint32_t& EAX() const { return regs[0]; }
+    inline const uint32_t& EBX() const { return regs[1]; }
+    inline const uint32_t& ECX() const { return regs[2]; }
+    inline const uint32_t& EDX() const { return regs[3]; }
+};
+
+#endif
+
+class CPUInfo {
+   public:
+    CPUInfo();
+    std::string vendor() const { return mVendorId; }
+    std::string model() const { return mModelName; }
+    int threads() const { return mNumLogCpus; }
+
+   private:
+    // Bit positions for data extractions
+    static const uint32_t LVL_NUM   = 0x000000FF;
+    static const uint32_t LVL_TYPE  = 0x0000FF00;
+    static const uint32_t LVL_CORES = 0x0000FFFF;
+    static const uint32_t HTT_POS   = 0x10000000;
+
+    // Attributes
+    std::string mVendorId;
+    std::string mModelName;
+    int mNumSMT;
+    int mNumCores;
+    int mNumLogCpus;
+    bool mIsHTT;
+};
+
+namespace cpu {
+
+class DeviceManager {
+   public:
+    static const int MAX_QUEUES           = 1;
+    static const int NUM_DEVICES          = 1;
+    static const int ACTIVE_DEVICE_ID     = 0;
+    static const bool IS_DOUBLE_SUPPORTED = true;
+
+    static DeviceManager& getInstance();
+
+    friend queue& getQueue(int device);
+
+    friend MemoryManager& memoryManager();
+
+    friend graphics::ForgeManager& forgeManager();
+
+    CPUInfo getCPUInfo() const;
+
+   private:
+    DeviceManager();
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&) = delete;
+    void operator=(DeviceManager const&) = delete;
+
+    // Attributes
+    std::vector<queue> queues;
+    std::unique_ptr<MemoryManager> memManager;
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    const CPUInfo cinfo;
+};
+
+}  // namespace cpu
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 8ae2a8c00f..1ca65d5332 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -14,8 +14,6 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-using namespace std;
-
 namespace cpu {
 
 template<typename T>
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 9266ce8cf3..fd63aa5cd6 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -7,126 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/defines.hpp>
-#include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
+#include <device_manager.hpp>
 #include <platform.hpp>
 #include <version.hpp>
 #include <af/version.h>
 
+#include <algorithm>
 #include <cctype>
 #include <sstream>
 
-using namespace std;
-
-#ifdef CPUID_CAPABLE
-
-CPUInfo::CPUInfo()
-    : mVendorId("")
-    , mModelName("")
-    , mNumSMT(0)
-    , mNumCores(0)
-    , mNumLogCpus(0)
-    , mIsHTT(false) {
-    // Get vendor name EAX=0
-    CPUID cpuID1(1, 0);
-    mIsHTT = cpuID1.EDX() & HTT_POS;
-
-    CPUID cpuID0(0, 0);
-    uint32_t HFS = cpuID0.EAX();
-    mVendorId += string((const char*)&cpuID0.EBX(), 4);
-    mVendorId += string((const char*)&cpuID0.EDX(), 4);
-    mVendorId += string((const char*)&cpuID0.ECX(), 4);
-
-    string upVId = mVendorId;
-
-    for_each(upVId.begin(), upVId.end(), [](char& in) { in = ::toupper(in); });
-
-    // Get num of cores
-    if (upVId.find("INTEL") != std::string::npos) {
-        mVendorId = "Intel";
-        if (HFS >= 11) {
-            for (int lvl = 0; lvl < MAX_INTEL_TOP_LVL; ++lvl) {
-                CPUID cpuID4(0x0B, lvl);
-                uint32_t currLevel = (LVL_TYPE & cpuID4.ECX()) >> 8;
-                switch (currLevel) {
-                    case 0x01: mNumSMT = LVL_CORES & cpuID4.EBX(); break;
-                    case 0x02: mNumLogCpus = LVL_CORES & cpuID4.EBX(); break;
-                    default: break;
-                }
-            }
-            // Fixes Possible divide by zero error
-            // TODO: Fix properly
-            mNumCores = mNumLogCpus / (mNumSMT == 0 ? 1 : mNumSMT);
-        } else {
-            if (HFS >= 1) {
-                mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
-                if (HFS >= 4) {
-                    mNumCores = 1 + ((CPUID(4, 0).EAX() >> 26) & 0x3F);
-                }
-            }
-            if (mIsHTT) {
-                if (!(mNumCores > 1)) {
-                    mNumCores   = 1;
-                    mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
-                }
-            } else {
-                mNumCores = mNumLogCpus = 1;
-            }
-        }
-    } else if (upVId.find("AMD") != std::string::npos) {
-        mVendorId = "AMD";
-        if (HFS >= 1) {
-            mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
-            if (CPUID(0x80000000, 0).EAX() >= 8) {
-                mNumCores = 1 + ((CPUID(0x80000008, 0).ECX() & 0xFF));
-            }
-        }
-        if (mIsHTT) {
-            if (!(mNumCores > 1)) {
-                mNumCores   = 1;
-                mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
-            }
-        } else {
-            mNumCores = mNumLogCpus = 1;
-        }
-    } else {
-        mVendorId = "Unknown";
-    }
-    // Get processor brand string
-    // This seems to be working for both Intel & AMD vendors
-    for (unsigned i = 0x80000002; i < 0x80000005; ++i) {
-        CPUID cpuID(i, 0);
-        mModelName += string((const char*)&cpuID.EAX(), 4);
-        mModelName += string((const char*)&cpuID.EBX(), 4);
-        mModelName += string((const char*)&cpuID.ECX(), 4);
-        mModelName += string((const char*)&cpuID.EDX(), 4);
-    }
-    mModelName = string(mModelName.c_str());
-}
-
-#else
-
-CPUInfo::CPUInfo()
-    : mVendorId("")
-    , mModelName("")
-    , mNumSMT(0)
-    , mNumCores(0)
-    , mNumLogCpus(0)
-    , mIsHTT(false) {
-    mVendorId   = "Unknown";
-    mModelName  = "Unknown";
-    mNumSMT     = 1;
-    mNumCores   = 1;
-    mNumLogCpus = 1;
-}
-
-#endif
+using std::endl;
+using std::not1;
+using std::ostringstream;
+using std::ptr_fun;
+using std::stoi;
+using std::string;
 
 namespace cpu {
 
-static const std::string get_system(void) {
-    std::string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
+static const string get_system(void) {
+    string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
 #if defined(OS_LNX)
@@ -140,24 +41,23 @@ static const std::string get_system(void) {
 
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
-static inline std::string& ltrim(std::string& s) {
+static inline string& ltrim(string& s) {
     s.erase(s.begin(),
-            std::find_if(s.begin(), s.end(),
-                         std::not1(std::ptr_fun<int, int>(std::isspace))));
+            find_if(s.begin(), s.end(), not1(ptr_fun<int, int>(isspace))));
     return s;
 }
 
 int getBackend() { return AF_BACKEND_CPU; }
 
-std::string getDeviceInfo() {
+string getDeviceInfo() {
     const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
 
-    std::ostringstream info;
+    ostringstream info;
 
     info << "ArrayFire v" << AF_VERSION << " (CPU, " << get_system()
-         << ", build " << AF_REVISION << ")" << std::endl;
+         << ", build " << AF_REVISION << ")" << endl;
 
-    std::string model = cinfo.model();
+    string model = cinfo.model();
 
     size_t memMB = getDeviceMemorySize(getActiveDeviceId()) / 1048576;
 
@@ -172,7 +72,7 @@ std::string getDeviceInfo() {
 #ifndef NDEBUG
     info << AF_COMPILER_STR;
 #endif
-    info << std::endl;
+    info << endl;
 
     return info.str();
 }
@@ -197,9 +97,9 @@ unsigned getMaxJitSize() {
 
     thread_local int length = 0;
     if (length == 0) {
-        std::string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
+        string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            length = std::stoi(env_var);
+            length = stoi(env_var);
         } else {
             length = MAX_JIT_LEN;
         }
@@ -236,8 +136,6 @@ queue& getQueue(int device) {
     return DeviceManager::getInstance().queues[device];
 }
 
-CPUInfo DeviceManager::getCPUInfo() const { return cinfo; }
-
 void sync(int device) { getQueue(device).sync(); }
 
 bool& evalFlag() {
@@ -245,12 +143,6 @@ bool& evalFlag() {
     return flag;
 }
 
-DeviceManager::DeviceManager()
-    : queues(MAX_QUEUES)
-    , memManager(new MemoryManager())
-    , fgMngr(new graphics::ForgeManager())
-    {}
-
 MemoryManager& memoryManager() {
     DeviceManager& inst = DeviceManager::getInstance();
     return *(inst.memManager);
@@ -260,9 +152,4 @@ graphics::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
-DeviceManager& DeviceManager::getInstance() {
-    static DeviceManager* my_instance = new DeviceManager();
-    return *my_instance;
-}
-
 }  // namespace cpu
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 6aff51ec4b..78271e5009 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -9,83 +9,17 @@
 
 #pragma once
 
-#include <memory.hpp>
 #include <queue.hpp>
-#include <algorithm>
-#include <array>
-#include <memory>
 #include <string>
 
-#if defined(AF_WITH_CPUID) &&                                       \
-    (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || \
-     defined(_M_IX86) || defined(_WIN64))
-#define CPUID_CAPABLE
-#endif
-
-#ifdef _WIN32
-#include <intrin.h>
-#include <limits.h>
-typedef unsigned __int32 uint32_t;
-#else
-#include <stdint.h>
-#endif
-
-#ifdef CPUID_CAPABLE
-
-#define MAX_INTEL_TOP_LVL 4
-
-class CPUID {
-    uint32_t regs[4];
-
-   public:
-    explicit CPUID(unsigned funcId, unsigned subFuncId) {
-#ifdef _WIN32
-        __cpuidex((int*)regs, (int)funcId, (int)subFuncId);
-
-#else
-        asm volatile("cpuid"
-                     : "=a"(regs[0]), "=b"(regs[1]), "=c"(regs[2]),
-                       "=d"(regs[3])
-                     : "a"(funcId), "c"(subFuncId));
-#endif
-    }
-
-    inline const uint32_t& EAX() const { return regs[0]; }
-    inline const uint32_t& EBX() const { return regs[1]; }
-    inline const uint32_t& ECX() const { return regs[2]; }
-    inline const uint32_t& EDX() const { return regs[3]; }
-};
-
-#endif
-
-class CPUInfo {
-   public:
-    CPUInfo();
-    std::string vendor() const { return mVendorId; }
-    std::string model() const { return mModelName; }
-    int threads() const { return mNumLogCpus; }
-
-   private:
-    // Bit positions for data extractions
-    static const uint32_t LVL_NUM   = 0x000000FF;
-    static const uint32_t LVL_TYPE  = 0x0000FF00;
-    static const uint32_t LVL_CORES = 0x0000FFFF;
-    static const uint32_t HTT_POS   = 0x10000000;
-
-    // Attributes
-    std::string mVendorId;
-    std::string mModelName;
-    int mNumSMT;
-    int mNumCores;
-    int mNumLogCpus;
-    bool mIsHTT;
-};
-
 namespace graphics {
 class ForgeManager;
 }
 
 namespace cpu {
+
+class MemoryManager;
+
 int getBackend();
 
 std::string getDeviceInfo();
@@ -116,36 +50,4 @@ MemoryManager& memoryManager();
 
 graphics::ForgeManager& forgeManager();
 
-class DeviceManager {
-   public:
-    static const int MAX_QUEUES           = 1;
-    static const int NUM_DEVICES          = 1;
-    static const int ACTIVE_DEVICE_ID     = 0;
-    static const bool IS_DOUBLE_SUPPORTED = true;
-
-    static DeviceManager& getInstance();
-
-    friend queue& getQueue(int device);
-
-    friend MemoryManager& memoryManager();
-
-    friend graphics::ForgeManager& forgeManager();
-
-    CPUInfo getCPUInfo() const;
-
-   private:
-    DeviceManager();
-    // Following two declarations are required to
-    // avoid copying accidental copy/assignment
-    // of instance returned by getInstance to other
-    // variables
-    DeviceManager(DeviceManager const&) = delete;
-    void operator=(DeviceManager const&) = delete;
-
-    // Attributes
-    std::vector<queue> queues;
-    std::unique_ptr<MemoryManager> memManager;
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
-    const CPUInfo cinfo;
-};
 }  // namespace cpu
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index b588de332f..4b9960a92a 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -21,8 +21,11 @@
 
 namespace cpu {
 
-using namespace std;
 using af::dim4;
+using std::distance;
+using std::set_intersection;
+using std::set_union;
+using std::unique;
 
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
@@ -39,8 +42,8 @@ Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
     getQueue().sync();
 
     T *ptr     = out.get();
-    T *last    = std::unique(ptr, ptr + in.elements());
-    dim_t dist = (dim_t)std::distance(ptr, last);
+    T *last    = unique(ptr, ptr + in.elements());
+    dim_t dist = (dim_t)distance(ptr, last);
 
     dim4 dims(dist, 1, 1, 1);
     out.resetDims(dims);
@@ -70,11 +73,10 @@ Array<T> setUnion(const Array<T> &first, const Array<T> &second,
     Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
     T *ptr = out.get();
-    T *last =
-        std::set_union(uFirst.get(), uFirst.get() + first_elements,
-                       uSecond.get(), uSecond.get() + second_elements, ptr);
+    T *last = set_union(uFirst.get(), uFirst.get() + first_elements,
+                        uSecond.get(), uSecond.get() + second_elements, ptr);
 
-    dim_t dist = (dim_t)std::distance(ptr, last);
+    dim_t dist = (dim_t)distance(ptr, last);
     dim4 dims(dist, 1, 1, 1);
     out.resetDims(dims);
 
@@ -103,11 +105,11 @@ Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
     Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
     T *ptr  = out.get();
-    T *last = std::set_intersection(uFirst.get(), uFirst.get() + first_elements,
-                                    uSecond.get(),
-                                    uSecond.get() + second_elements, ptr);
+    T *last =
+        set_intersection(uFirst.get(), uFirst.get() + first_elements,
+                         uSecond.get(), uSecond.get() + second_elements, ptr);
 
-    dim_t dist = (dim_t)std::distance(ptr, last);
+    dim_t dist = (dim_t)distance(ptr, last);
     dim4 dims(dist, 1, 1, 1);
     out.resetDims(dims);
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8550860523..30088878b7 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -335,6 +335,8 @@ cuda_add_library(afcuda
     cusolverDn.hpp
     cusparse.cpp
     cusparse.hpp
+    device_manager.cpp
+    device_manager.hpp
     debug_cuda.hpp
     diagonal.hpp
     diff.hpp
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 4ad984cabe..adf1c1bc2e 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -150,7 +150,8 @@ BLAS_FUNC(dot, cdouble, false, Z, u)
 #undef BLAS_FUNC
 #undef BLAS_FUNC_DEF
 
-using namespace std;
+using std::max;
+using std::vector;
 
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cu
index 4128ecc9e9..9d824e1a10 100644
--- a/src/backend/cuda/cholesky.cu
+++ b/src/backend/cuda/cholesky.cu
@@ -12,6 +12,7 @@
 
 #include <copy.hpp>
 #include <cublas_v2.h>
+#include <cusolverDn.hpp>
 #include <identity.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index cf767dc30e..26f401ce3a 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -13,7 +13,8 @@
 #include <cublas_v2.h>
 
 namespace cuda {
-typedef cublasHandle_t BlasHandle;
+
+using BlasHandle = cublasHandle_t;
 
 const char* errorString(cublasStatus_t err);
 
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index 4d46ec9439..31283c27cc 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -15,7 +15,8 @@
 #include <cusolverDn.h>
 
 namespace cuda {
-typedef cusolverDnHandle_t SolveHandle;
+
+using SolveHandle = cusolverDnHandle_t;
 
 const char* errorString(cusolverStatus_t err);
 
@@ -40,4 +41,5 @@ class cusolverDnHandle
 
     void destroyHandle(SolveHandle handle) { cusolverDnDestroy(handle); }
 };
+
 }  // namespace cuda
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index bbac77d5df..0916908779 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -13,7 +13,8 @@
 #include <cusparse_v2.h>
 
 namespace cuda {
-typedef cusparseHandle_t SparseHandle;
+
+using SparseHandle = cusparseHandle_t;
 
 const char* errorString(cusparseStatus_t err);
 
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
new file mode 100644
index 0000000000..944be4ee87
--- /dev/null
+++ b/src/backend/cuda/device_manager.cpp
@@ -0,0 +1,512 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if defined(OS_WIN)
+#include <windows.h>
+#endif
+
+#include <GraphicsResourceManager.hpp>
+#include <common/Logger.hpp>
+#include <common/defines.hpp>
+#include <common/host_memory.hpp>
+#include <common/util.hpp>
+#include <device_manager.hpp>
+#include <driver.h>
+#include <err_cuda.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include <spdlog/spdlog.h>
+#include <version.hpp>
+#include <af/cuda.h>
+#include <af/version.h>
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+#include <common/graphics_common.hpp>
+#define __gl_h_  // FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+#include <cuda_gl_interop.h>
+
+#include <algorithm>
+#include <array>
+#include <cstdio>
+#include <memory>
+#include <mutex>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <thread>
+#include <vector>
+
+using std::begin;
+using std::end;
+using std::find_if;
+using std::make_pair;
+using std::pair;
+using std::string;
+using std::stringstream;
+
+namespace cuda {
+
+void findJitDevCompute(pair<int, int>& prop) {
+    struct cuNVRTCcompute {
+        /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
+        int cuda_version;
+        /// Maximum major compute flag supported by cuda_version
+        int major;
+        /// Maximum minor compute flag supported by cuda_version
+        int minor;
+    };
+    static const cuNVRTCcompute Toolkit2Compute[] = {
+        {10010, 7, 5},
+        {10000, 7, 2},
+        {9020,  7, 2},
+        {9010,  7, 2},
+        {9000,  7, 2},
+        {8000,  5, 3},
+        {7050,  5, 3},
+        {7000,  5, 3}
+    };
+    int runtime_cuda_ver = 0;
+    CUDA_CHECK(cudaRuntimeGetVersion(&runtime_cuda_ver));
+    auto tkit_max_compute =
+        find_if(begin(Toolkit2Compute), end(Toolkit2Compute),
+                [runtime_cuda_ver](cuNVRTCcompute v) {
+                    return runtime_cuda_ver == v.cuda_version;
+                });
+    if ((tkit_max_compute == end(Toolkit2Compute)) ||
+        (prop.first  > tkit_max_compute->major &&
+         prop.second > tkit_max_compute->minor)) {
+        prop = make_pair(tkit_max_compute->major, tkit_max_compute->minor);
+    }
+}
+
+pair<int, int> getComputeCapability(const int device) {
+    return DeviceManager::getInstance().devJitComputes[device];
+}
+
+// pulled from CUTIL from CUDA SDK
+static inline int compute2cores(int major, int minor) {
+    struct {
+        int compute;  // 0xMm (hex), M = major version, m = minor version
+        int cores;
+    } gpus[] = {
+        {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
+        {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
+        {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
+        {0x62, 128}, {0x70, 64},  {0x75, 64},  {-1, -1},
+    };
+
+    for (int i = 0; gpus[i].compute != -1; ++i) {
+        if (gpus[i].compute == (major << 4) + minor) return gpus[i].cores;
+    }
+    return 0;
+}
+
+// Return true if greater, false if lesser.
+// if equal, it continues to next comparison
+#define COMPARE(a, b, f)                   \
+    do {                                   \
+        if ((a)->f > (b)->f) return true;  \
+        if ((a)->f < (b)->f) return false; \
+        break;                             \
+    } while (0)
+
+static inline bool card_compare_compute(const cudaDevice_t &l,
+                                        const cudaDevice_t &r) {
+    const cudaDevice_t *lc = &l;
+    const cudaDevice_t *rc = &r;
+
+    COMPARE(lc, rc, prop.major);
+    COMPARE(lc, rc, prop.minor);
+    COMPARE(lc, rc, flops);
+    COMPARE(lc, rc, prop.totalGlobalMem);
+    COMPARE(lc, rc, nativeId);
+    return false;
+}
+
+static inline bool card_compare_flops(const cudaDevice_t &l,
+                                      const cudaDevice_t &r) {
+    const cudaDevice_t *lc = &l;
+    const cudaDevice_t *rc = &r;
+
+    COMPARE(lc, rc, flops);
+    COMPARE(lc, rc, prop.totalGlobalMem);
+    COMPARE(lc, rc, prop.major);
+    COMPARE(lc, rc, prop.minor);
+    COMPARE(lc, rc, nativeId);
+    return false;
+}
+
+static inline bool card_compare_mem(const cudaDevice_t &l,
+                                    const cudaDevice_t &r) {
+    const cudaDevice_t *lc = &l;
+    const cudaDevice_t *rc = &r;
+
+    COMPARE(lc, rc, prop.totalGlobalMem);
+    COMPARE(lc, rc, flops);
+    COMPARE(lc, rc, prop.major);
+    COMPARE(lc, rc, prop.minor);
+    COMPARE(lc, rc, nativeId);
+    return false;
+}
+
+static inline bool card_compare_num(const cudaDevice_t &l,
+                                    const cudaDevice_t &r) {
+    const cudaDevice_t *lc = &l;
+    const cudaDevice_t *rc = &r;
+
+    COMPARE(lc, rc, nativeId);
+    return false;
+}
+
+static inline int getMinSupportedCompute(int cudaMajorVer) {
+    // Vector of minimum supported compute versions
+    // for CUDA toolkit (i+1).* where i is the index
+    // of the vector
+    static const std::array<int, 10> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3}};
+
+    int CVSize = static_cast<int>(minSV.size());
+    return (cudaMajorVer > CVSize ? minSV[CVSize - 1]
+                                  : minSV[cudaMajorVer - 1]);
+}
+
+bool DeviceManager::checkGraphicsInteropCapability() {
+    static std::once_flag checkInteropFlag;
+    thread_local bool capable = true;
+
+    std::call_once(checkInteropFlag, []() {
+        unsigned int pCudaEnabledDeviceCount = 0;
+        int pCudaGraphicsEnabledDeviceIds    = 0;
+        cudaGetLastError();  // Reset Errors
+        cudaError_t err = cudaGLGetDevices(
+            &pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds,
+            getDeviceCount(), cudaGLDeviceListAll);
+        if (err == cudaErrorOperatingSystem) {
+            // OS Support Failure - Happens when devices are in TCC mode or
+            // do not have a display connected
+            capable = false;
+        }
+        cudaGetLastError();  // Reset Errors
+    });
+
+    return capable;
+}
+
+DeviceManager &DeviceManager::getInstance() {
+    static DeviceManager *my_instance = new DeviceManager();
+    return *my_instance;
+}
+
+/// Struct represents the cuda toolkit version and its associated minimum
+/// required driver versions.
+struct ToolkitDriverVersions {
+    /// The CUDA Toolkit version returned by cudaDriverGetVersion or
+    /// cudaRuntimeGetVersion
+    int version;
+
+    /// The minimum GPU driver version required for the \p version toolkit on
+    /// Linux or macOS
+    float unix_min_version;
+
+    /// The minimum GPU driver version required for the \p version toolkit on
+    /// Windows
+    float windows_min_version;
+};
+
+/// Map giving the minimum device driver needed in order to run a given version
+/// of CUDA for both Linux/Mac and Windows from:
+/// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
+// clang-format off
+static const ToolkitDriverVersions
+    CudaToDriverVersion[] = {
+        {10010, 418.39f, 418.96f},
+        {10000, 410.48f, 411.31f},
+        {9020,  396.37f, 398.26f},
+        {9010,  390.46f, 391.29f},
+        {9000,  384.81f, 385.54f},
+        {8000,  375.26f, 376.51f},
+        {7050,  352.31f, 353.66f},
+        {7000,  346.46f, 347.62f}};
+// clang-format on
+
+/// A debug only function that checks to see if the driver or runtime
+/// function is part of the CudaToDriverVersion array. If the runtime
+/// version is not part of the array then an error is thrown in debug
+/// mode. If the driver version is not part of the array, then a message
+/// is displayed in the error stream.
+///
+/// \param[in] runtime_version  The version integer returned by
+///                             cudaRuntimeGetVersion
+/// \param[in] driver_version   The version integer returned by
+///                             cudaDriverGetVersion
+/// \note: only works in debug builds
+void debugRuntimeCheck(int runtime_version, int driver_version) {
+#ifndef NDEBUG
+    auto runtime_it =
+        find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
+                [runtime_version](ToolkitDriverVersions ver) {
+                    return runtime_version == ver.version;
+                });
+    auto driver_it =
+        find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
+                [driver_version](ToolkitDriverVersions ver) {
+                    return driver_version == ver.version;
+                });
+
+    // If the runtime version is not part of the CudaToDriverVersion array,
+    // display a message in the trace. Do not throw an error unless this is
+    // a debug build
+    if (runtime_it == end(CudaToDriverVersion)) {
+        char buf[1024];
+        char err_msg[] =
+            "WARNING: CUDA runtime version(%s) not recognized. Please "
+            "create an issue or a pull request on the ArrayFire repository to "
+            "update the CudaToDriverVersion variable with this version of "
+            "the CUDA Toolkit.\n";
+        snprintf(buf, 1024, err_msg,
+                 int_version_to_string(runtime_version).c_str());
+        fprintf(stderr, err_msg,
+                int_version_to_string(runtime_version).c_str());
+        AF_ERROR(buf, AF_ERR_RUNTIME);
+    }
+
+    if (driver_it == end(CudaToDriverVersion)) {
+        char err_msg[] =
+            "WARNING: CUDA driver version(%s) not part of the "
+            "CudaToDriverVersion array. Please create an issue or a pull "
+            "request on the ArrayFire repository to update the "
+            "CudaToDriverVersion variable with this version of the CUDA "
+            "Toolkit.\n";
+        fprintf(stderr, err_msg,
+                int_version_to_string(driver_version).c_str());
+    }
+#endif
+}
+
+// Check if the device driver version is recent enough to run the cuda libs
+// linked with afcuda:
+void DeviceManager::checkCudaVsDriverVersion() {
+    const std::string driverVersionString = getDriverVersion();
+
+    int driver  = 0;
+    int runtime = 0;
+    CUDA_CHECK(cudaDriverGetVersion(&driver));
+    CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
+
+    AF_TRACE("CUDA supported by the GPU Driver {} ArrayFire CUDA Runtime {}",
+             int_version_to_string(driver), int_version_to_string(runtime));
+
+    debugRuntimeCheck(runtime, driver);
+
+    if (runtime > driver) {
+        string msg =
+            "ArrayFire was built with CUDA %s which requires GPU driver "
+            "version %.2f or later. Please download and install the latest "
+            "drivers from https://www.nvidia.com/drivers for your GPU. "
+            "Alternatively, you could rebuild ArrayFire with CUDA Toolkit "
+            "version %s to use the current drivers.";
+
+        auto runtime_it =
+            find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
+                    [runtime](ToolkitDriverVersions ver) {
+                        return runtime == ver.version;
+                    });
+
+        // If the runtime version is not part of the CudaToDriverVersion
+        // array, display a message in the trace. Do not throw an error
+        // unless this is a debug build
+        if (runtime_it == end(CudaToDriverVersion)) {
+            char buf[1024];
+            char err_msg[] =
+                "CUDA runtime version(%s) not recognized. Please create an "
+                "issue or a pull request on the ArrayFire repository to "
+                "update the CudaToDriverVersion variable with this "
+                "version of the CUDA Toolkit.";
+            snprintf(buf, 1024, err_msg,
+                     int_version_to_string(runtime).c_str());
+            AF_TRACE("{}", buf);
+            return;
+        }
+
+        float minimumDriverVersion =
+#ifdef OS_WIN
+            runtime_it->windows_min_version;
+#else
+            runtime_it->unix_min_version;
+#endif
+
+        char buf[1024];
+        snprintf(buf, 1024, msg.c_str(), int_version_to_string(runtime).c_str(),
+                 minimumDriverVersion, int_version_to_string(driver).c_str());
+
+        AF_ERROR(buf, AF_ERR_DRIVER);
+    }
+}
+
+DeviceManager::DeviceManager()
+    : logger(common::loggerFactory("platform"))
+    , cuDevices(0)
+    , nDevices(0)
+    , fgMngr(new graphics::ForgeManager())
+    {
+    checkCudaVsDriverVersion();
+
+    CUDA_CHECK(cudaGetDeviceCount(&nDevices));
+    AF_TRACE("Found {} CUDA devices", nDevices);
+    if (nDevices == 0) {
+        AF_ERROR("No CUDA capable devices found", AF_ERR_DRIVER);
+    }
+    cuDevices.reserve(nDevices);
+
+    int cudaRtVer = 0;
+    CUDA_CHECK(cudaRuntimeGetVersion(&cudaRtVer));
+    int cudaMajorVer = cudaRtVer / 1000;
+
+    for (int i = 0; i < nDevices; i++) {
+        cudaDevice_t dev;
+        CUDA_CHECK(cudaGetDeviceProperties(&dev.prop, i));
+        if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
+            AF_TRACE("Unsuppored device: {}", dev.prop.name);
+            continue;
+        } else {
+            dev.flops = static_cast<size_t>(dev.prop.multiProcessorCount) *
+                        compute2cores(dev.prop.major, dev.prop.minor) *
+                        dev.prop.clockRate;
+            dev.nativeId = i;
+            AF_TRACE(
+                "Found device: {} ({:0.3} GB | ~{} GFLOPs | {} SMs)",
+                dev.prop.name, dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
+                dev.flops / 1024. / 1024. * 2, dev.prop.multiProcessorCount);
+            cuDevices.push_back(dev);
+        }
+    }
+    nDevices = cuDevices.size();
+
+    sortDevices();
+
+    // Initialize all streams to 0.
+    // Streams will be created in setActiveDevice()
+    for (size_t i = 0; i < MAX_DEVICES; i++) {
+        streams[i] = (cudaStream_t)0;
+        if (i < nDevices) {
+            auto prop = make_pair(cuDevices[i].prop.major,
+                    cuDevices[i].prop.minor);
+            findJitDevCompute(prop);
+            devJitComputes.emplace_back(prop);
+        }
+    }
+
+    std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
+    AF_TRACE("AF_CUDA_DEFAULT_DEVICE: {}", deviceENV);
+    if (deviceENV.empty()) {
+        setActiveDevice(0, cuDevices[0].nativeId);
+    } else {
+        stringstream s(deviceENV);
+        int def_device = -1;
+        s >> def_device;
+        if (def_device < 0 || def_device >= nDevices) {
+            getLogger()->warn(
+                "AF_CUDA_DEFAULT_DEVICE({}) out of range. Setting default "
+                "device to 0.",
+                def_device);
+            setActiveDevice(0, cuDevices[0].nativeId);
+        } else {
+            setActiveDevice(def_device, cuDevices[def_device].nativeId);
+        }
+    }
+    AF_TRACE("Default device: {}", getActiveDeviceId());
+}
+
+spdlog::logger *DeviceManager::getLogger() { return logger.get(); }
+
+void DeviceManager::sortDevices(sort_mode mode) {
+    switch (mode) {
+        case memory:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_mem);
+            break;
+        case flops:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_flops);
+            break;
+        case compute:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_compute);
+            break;
+        case none:
+        default:
+            std::stable_sort(cuDevices.begin(), cuDevices.end(),
+                             card_compare_num);
+            break;
+    }
+}
+
+int DeviceManager::setActiveDevice(int device, int nId) {
+    thread_local bool retryFlag = true;
+
+    int numDevices = cuDevices.size();
+
+    if (device >= numDevices) return -1;
+
+    int old = getActiveDeviceId();
+
+    if (nId == -1) nId = getDeviceNativeId(device);
+
+    cudaError_t err = cudaSetDevice(nId);
+
+    if (err == cudaSuccess) {
+        tlocalActiveDeviceId() = device;
+        return old;
+    }
+
+    // For the first time a thread calls setDevice,
+    // if the requested device is unavailable, try checking
+    // for other available devices - while loop below
+    if (!retryFlag) {
+        CUDA_CHECK(err);
+        return old;
+    }
+
+    // Comes only when retryFlag is true. Set it to false
+    retryFlag = false;
+
+    while (true) {
+        // Check for errors other than DevicesUnavailable
+        // If success, return. Else throw error
+        // If DevicesUnavailable, try other devices (while loop below)
+        if (err != cudaErrorDeviceAlreadyInUse) {
+            CUDA_CHECK(err);
+            tlocalActiveDeviceId() = device;
+            return old;
+        }
+        cudaGetLastError();  // Reset error stack
+#ifndef NDEBUG
+        getLogger()->warn(
+            "Warning: Device {} is unavailable. Using next available "
+            "device \n",
+            device);
+#endif
+        // Comes here is the device is in exclusive mode or
+        // otherwise fails streamCreate with this error.
+        // All other errors will error out
+        device++;
+        if (device >= numDevices) break;
+
+        // Can't call getNativeId here as it will cause an infinite loop with
+        // the constructor
+        nId = cuDevices[device].nativeId;
+
+        err = cudaSetDevice(nId);
+    }
+
+    // If all devices fail with DeviceAlreadyInUse, then throw this error
+    CUDA_CHECK(err);
+
+    return old;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
new file mode 100644
index 0000000000..c2c73c89b1
--- /dev/null
+++ b/src/backend/cuda/device_manager.hpp
@@ -0,0 +1,107 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <platform.hpp>
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace cuda {
+
+struct cudaDevice_t {
+    cudaDeviceProp prop;
+    size_t flops;
+    int nativeId;
+};
+
+int& tlocalActiveDeviceId();
+
+class DeviceManager {
+   public:
+    static const size_t MAX_DEVICES = 16;
+
+    static bool checkGraphicsInteropCapability();
+
+    static DeviceManager& getInstance();
+    ~DeviceManager();
+
+    spdlog::logger* getLogger();
+
+    friend MemoryManager& memoryManager();
+
+    friend MemoryManagerPinned& pinnedMemoryManager();
+
+    friend graphics::ForgeManager& forgeManager();
+
+    friend GraphicsResourceManager& interopManager();
+
+    friend std::string getDeviceInfo(int device);
+
+    friend std::string getPlatformInfo();
+
+    friend std::string getDriverVersion();
+
+    friend std::string getCUDARuntimeVersion();
+
+    friend std::string getDeviceInfo();
+
+    friend int getDeviceCount();
+
+    friend int getDeviceNativeId(int device);
+
+    friend int getDeviceIdFromNativeId(int nativeId);
+
+    friend cudaStream_t getStream(int device);
+
+    friend int setDevice(int device);
+
+    friend cudaDeviceProp getDeviceProp(int device);
+
+    friend std::pair<int, int> getComputeCapability(const int device);
+
+   private:
+    DeviceManager();
+
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&);
+    void operator=(DeviceManager const&);
+
+    // Attributes
+    enum sort_mode { flops = 0, memory = 1, compute = 2, none = 3 };
+
+    void checkCudaVsDriverVersion();
+    void sortDevices(sort_mode mode = flops);
+
+    int setActiveDevice(int device, int native = -1);
+
+    std::shared_ptr<spdlog::logger> logger;
+
+    std::vector<cudaDevice_t> cuDevices;
+    std::vector<std::pair<int, int>> devJitComputes;
+
+    int nDevices;
+    cudaStream_t streams[MAX_DEVICES];
+
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
+
+    std::unique_ptr<MemoryManager> memManager;
+
+    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+
+    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 2dcda99e89..88feeed330 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <debug_cuda.hpp>
+#include <device_manager.hpp>
 #include <err_cuda.hpp>
 #include <hist_graphics.hpp>
 
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index de253d1dd6..996606888c 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -13,6 +13,7 @@
 #include <Array.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <debug_cuda.hpp>
+#include <device_manager.hpp>
 #include <err_cuda.hpp>
 #include <image.hpp>
 
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 273183518d..cd3af45fa9 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -16,6 +16,7 @@
 #include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
+#include <device_manager.hpp>
 #include <jit/BufferNode.hpp>
 #include <jit/ShiftNode.hpp>
 #include <math.hpp>
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cu
index 2fdf9bf45c..bc89874e10 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cu
@@ -12,6 +12,7 @@
 
 #include <common/err_common.hpp>
 #include <copy.hpp>
+#include <cusolverDn.hpp>
 #include <math.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index e09e57f147..82b6e7e293 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <device_manager.hpp>
 #include <kernel_headers/jit.hpp>
 #include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/Param.hpp>
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 7845f3951f..9c01ba02d8 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -11,13 +11,19 @@
 #include <windows.h>
 #endif
 
+#include <GraphicsResourceManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
+#include <cublas.hpp>
+#include <cufft.hpp>
+#include <cusolverDn.hpp>
+#include <cusparse.hpp>
 #include <driver.h>
+#include <device_manager.hpp>
 #include <err_cuda.hpp>
-#include <platform.hpp>
+#include <memory.hpp>
 #include <spdlog/spdlog.h>
 #include <version.hpp>
 #include <af/cuda.h>
@@ -38,122 +44,16 @@
 #include <thread>
 #include <vector>
 
-using namespace std;
+using std::call_once;
+using std::once_flag;
+using std::ostringstream;
+using std::runtime_error;
+using std::string;
+using std::to_string;
+using std::unique_ptr;
 
 namespace cuda {
 
-void findJitDevCompute(pair<int, int>& prop) {
-    struct cuNVRTCcompute {
-        /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
-        int cuda_version;
-        /// Maximum major compute flag supported by cuda_version
-        int major;
-        /// Maximum minor compute flag supported by cuda_version
-        int minor;
-    };
-    static const cuNVRTCcompute Toolkit2Compute[] = {
-        {10010, 7, 5},
-        {10000, 7, 2},
-        {9020,  7, 2},
-        {9010,  7, 2},
-        {9000,  7, 2},
-        {8000,  5, 3},
-        {7050,  5, 3},
-        {7000,  5, 3}
-    };
-    int runtime_cuda_ver = 0;
-    CUDA_CHECK(cudaRuntimeGetVersion(&runtime_cuda_ver));
-    auto tkit_max_compute =
-        find_if(begin(Toolkit2Compute), end(Toolkit2Compute),
-                [runtime_cuda_ver](cuNVRTCcompute v) {
-                    return runtime_cuda_ver == v.cuda_version;
-                });
-    if ((tkit_max_compute == end(Toolkit2Compute)) ||
-        (prop.first  > tkit_max_compute->major &&
-         prop.second > tkit_max_compute->minor)) {
-        prop = make_pair(tkit_max_compute->major, tkit_max_compute->minor);
-    }
-}
-
-pair<int, int> getComputeCapability(const int device) {
-    return DeviceManager::getInstance().devJitComputes[device];
-}
-
-// pulled from CUTIL from CUDA SDK
-static inline int compute2cores(int major, int minor) {
-    struct {
-        int compute;  // 0xMm (hex), M = major version, m = minor version
-        int cores;
-    } gpus[] = {
-        {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
-        {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
-        {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
-        {0x62, 128}, {0x70, 64},  {0x75, 64},  {-1, -1},
-    };
-
-    for (int i = 0; gpus[i].compute != -1; ++i) {
-        if (gpus[i].compute == (major << 4) + minor) return gpus[i].cores;
-    }
-    return 0;
-}
-
-// Return true if greater, false if lesser.
-// if equal, it continues to next comparison
-#define COMPARE(a, b, f)                   \
-    do {                                   \
-        if ((a)->f > (b)->f) return true;  \
-        if ((a)->f < (b)->f) return false; \
-        break;                             \
-    } while (0)
-
-static inline bool card_compare_compute(const cudaDevice_t &l,
-                                        const cudaDevice_t &r) {
-    const cudaDevice_t *lc = &l;
-    const cudaDevice_t *rc = &r;
-
-    COMPARE(lc, rc, prop.major);
-    COMPARE(lc, rc, prop.minor);
-    COMPARE(lc, rc, flops);
-    COMPARE(lc, rc, prop.totalGlobalMem);
-    COMPARE(lc, rc, nativeId);
-    return false;
-}
-
-static inline bool card_compare_flops(const cudaDevice_t &l,
-                                      const cudaDevice_t &r) {
-    const cudaDevice_t *lc = &l;
-    const cudaDevice_t *rc = &r;
-
-    COMPARE(lc, rc, flops);
-    COMPARE(lc, rc, prop.totalGlobalMem);
-    COMPARE(lc, rc, prop.major);
-    COMPARE(lc, rc, prop.minor);
-    COMPARE(lc, rc, nativeId);
-    return false;
-}
-
-static inline bool card_compare_mem(const cudaDevice_t &l,
-                                    const cudaDevice_t &r) {
-    const cudaDevice_t *lc = &l;
-    const cudaDevice_t *rc = &r;
-
-    COMPARE(lc, rc, prop.totalGlobalMem);
-    COMPARE(lc, rc, flops);
-    COMPARE(lc, rc, prop.major);
-    COMPARE(lc, rc, prop.minor);
-    COMPARE(lc, rc, nativeId);
-    return false;
-}
-
-static inline bool card_compare_num(const cudaDevice_t &l,
-                                    const cudaDevice_t &r) {
-    const cudaDevice_t *lc = &l;
-    const cudaDevice_t *rc = &r;
-
-    COMPARE(lc, rc, nativeId);
-    return false;
-}
-
 static const std::string get_system(void) {
     std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
 
@@ -178,7 +78,7 @@ static inline int getMinSupportedCompute(int cudaMajorVer) {
                                   : minSV[cudaMajorVer - 1]);
 }
 
-unique_ptr<cublasHandle> &cublasManager(const int deviceId) {
+unique_ptr<cublasHandle>& cublasManager(const int deviceId) {
     thread_local unique_ptr<cublasHandle> handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
 
@@ -194,7 +94,7 @@ unique_ptr<cublasHandle> &cublasManager(const int deviceId) {
     return handles[deviceId];
 }
 
-unique_ptr<PlanCache> &cufftManager(const int deviceId) {
+unique_ptr<PlanCache>& cufftManager(const int deviceId) {
     thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId],
@@ -202,7 +102,7 @@ unique_ptr<PlanCache> &cufftManager(const int deviceId) {
     return caches[deviceId];
 }
 
-unique_ptr<cusolverDnHandle> &cusolverManager(const int deviceId) {
+unique_ptr<cusolverDnHandle>& cusolverManager(const int deviceId) {
     thread_local unique_ptr<cusolverDnHandle>
         handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -224,7 +124,7 @@ unique_ptr<cusolverDnHandle> &cusolverManager(const int deviceId) {
     return handles[deviceId];
 }
 
-unique_ptr<cusparseHandle> &cusparseManager(const int deviceId) {
+unique_ptr<cusparseHandle>& cusparseManager(const int deviceId) {
     thread_local unique_ptr<cusparseHandle> handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId], [&] {
@@ -238,6 +138,18 @@ unique_ptr<cusparseHandle> &cusparseManager(const int deviceId) {
     return handles[deviceId];
 }
 
+DeviceManager::~DeviceManager() {
+    // Reset unique_ptrs for all cu[BLAS | Sparse | Solver]
+    // handles of all devices
+    for (int i = 0; i < nDevices; ++i) {
+        setDevice(i);
+        cublasManager(i).reset();
+        cufftManager(i).reset();
+        cusolverManager(i).reset();
+        cusparseManager(i).reset();
+    }
+}
+
 int getBackend() { return AF_BACKEND_CUDA; }
 
 string getDeviceInfo(int device) {
@@ -361,7 +273,7 @@ unsigned getMaxJitSize() {
     return length;
 }
 
-int &tlocalActiveDeviceId() {
+int& tlocalActiveDeviceId() {
     thread_local int activeDeviceId = 0;
 
     return activeDeviceId;
@@ -416,33 +328,6 @@ cudaDeviceProp getDeviceProp(int device) {
     return DeviceManager::getInstance().cuDevices[0].prop;
 }
 
-bool DeviceManager::checkGraphicsInteropCapability() {
-    static std::once_flag checkInteropFlag;
-    thread_local bool capable = true;
-
-    std::call_once(checkInteropFlag, []() {
-        unsigned int pCudaEnabledDeviceCount = 0;
-        int pCudaGraphicsEnabledDeviceIds    = 0;
-        cudaGetLastError();  // Reset Errors
-        cudaError_t err = cudaGLGetDevices(
-            &pCudaEnabledDeviceCount, &pCudaGraphicsEnabledDeviceIds,
-            getDeviceCount(), cudaGLDeviceListAll);
-        if (err == cudaErrorOperatingSystem) {
-            // OS Support Failure - Happens when devices are in TCC mode or
-            // do not have a display connected
-            capable = false;
-        }
-        cudaGetLastError();  // Reset Errors
-    });
-
-    return capable;
-}
-
-DeviceManager &DeviceManager::getInstance() {
-    static DeviceManager *my_instance = new DeviceManager();
-    return *my_instance;
-}
-
 MemoryManager &memoryManager() {
     static std::once_flag flag;
 
@@ -499,326 +384,6 @@ SparseHandle sparseHandle() {
     return cusparseManager(cuda::getActiveDeviceId())->get();
 }
 
-/// Struct represents the cuda toolkit version and its associated minimum
-/// required driver versions.
-struct ToolkitDriverVersions {
-    /// The CUDA Toolkit version returned by cudaDriverGetVersion or
-    /// cudaRuntimeGetVersion
-    int version;
-
-    /// The minimum GPU driver version required for the \p version toolkit on
-    /// Linux or macOS
-    float unix_min_version;
-
-    /// The minimum GPU driver version required for the \p version toolkit on
-    /// Windows
-    float windows_min_version;
-};
-
-/// Map giving the minimum device driver needed in order to run a given version
-/// of CUDA for both Linux/Mac and Windows from:
-/// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
-// clang-format off
-static const ToolkitDriverVersions
-    CudaToDriverVersion[] = {
-        {10010, 418.39f, 418.96f},
-        {10000, 410.48f, 411.31f},
-        {9020,  396.37f, 398.26f},
-        {9010,  390.46f, 391.29f},
-        {9000,  384.81f, 385.54f},
-        {8000,  375.26f, 376.51f},
-        {7050,  352.31f, 353.66f},
-        {7000,  346.46f, 347.62f}};
-// clang-format on
-
-/// A debug only function that checks to see if the driver or runtime
-/// function is part of the CudaToDriverVersion array. If the runtime
-/// version is not part of the array then an error is thrown in debug
-/// mode. If the driver version is not part of the array, then a message
-/// is displayed in the error stream.
-///
-/// \param[in] runtime_version  The version integer returned by
-///                             cudaRuntimeGetVersion
-/// \param[in] driver_version   The version integer returned by
-///                             cudaDriverGetVersion
-/// \note: only works in debug builds
-void debugRuntimeCheck(int runtime_version, int driver_version) {
-#ifndef NDEBUG
-    auto runtime_it =
-        find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
-                [runtime_version](ToolkitDriverVersions ver) {
-                    return runtime_version == ver.version;
-                });
-    auto driver_it =
-        find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
-                [driver_version](ToolkitDriverVersions ver) {
-                    return driver_version == ver.version;
-                });
-
-    // If the runtime version is not part of the CudaToDriverVersion array,
-    // display a message in the trace. Do not throw an error unless this is
-    // a debug build
-    if (runtime_it == end(CudaToDriverVersion)) {
-        char buf[1024];
-        char err_msg[] =
-            "WARNING: CUDA runtime version(%s) not recognized. Please "
-            "create an issue or a pull request on the ArrayFire repository to "
-            "update the CudaToDriverVersion variable with this version of "
-            "the CUDA Toolkit.\n";
-        snprintf(buf, 1024, err_msg,
-                 int_version_to_string(runtime_version).c_str());
-        fprintf(stderr, err_msg,
-                int_version_to_string(runtime_version).c_str());
-        AF_ERROR(buf, AF_ERR_RUNTIME);
-    }
-
-    if (driver_it == end(CudaToDriverVersion)) {
-        char err_msg[] =
-            "WARNING: CUDA driver version(%s) not part of the "
-            "CudaToDriverVersion array. Please create an issue or a pull "
-            "request on the ArrayFire repository to update the "
-            "CudaToDriverVersion variable with this version of the CUDA "
-            "Toolkit.\n";
-        fprintf(stderr, err_msg,
-                int_version_to_string(driver_version).c_str());
-    }
-#endif
-}
-
-// Check if the device driver version is recent enough to run the cuda libs
-// linked with afcuda:
-void DeviceManager::checkCudaVsDriverVersion() {
-    const std::string driverVersionString = getDriverVersion();
-
-    int driver  = 0;
-    int runtime = 0;
-    CUDA_CHECK(cudaDriverGetVersion(&driver));
-    CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
-
-    AF_TRACE("CUDA supported by the GPU Driver {} ArrayFire CUDA Runtime {}",
-             int_version_to_string(driver), int_version_to_string(runtime));
-
-    debugRuntimeCheck(runtime, driver);
-
-    if (runtime > driver) {
-        string msg =
-            "ArrayFire was built with CUDA %s which requires GPU driver "
-            "version %.2f or later. Please download and install the latest "
-            "drivers from https://www.nvidia.com/drivers for your GPU. "
-            "Alternatively, you could rebuild ArrayFire with CUDA Toolkit "
-            "version %s to use the current drivers.";
-
-        auto runtime_it =
-            find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
-                    [runtime](ToolkitDriverVersions ver) {
-                        return runtime == ver.version;
-                    });
-
-        // If the runtime version is not part of the CudaToDriverVersion
-        // array, display a message in the trace. Do not throw an error
-        // unless this is a debug build
-        if (runtime_it == end(CudaToDriverVersion)) {
-            char buf[1024];
-            char err_msg[] =
-                "CUDA runtime version(%s) not recognized. Please create an "
-                "issue or a pull request on the ArrayFire repository to "
-                "update the CudaToDriverVersion variable with this "
-                "version of the CUDA Toolkit.";
-            snprintf(buf, 1024, err_msg,
-                     int_version_to_string(runtime).c_str());
-            AF_TRACE("{}", buf);
-            return;
-        }
-
-        float minimumDriverVersion =
-#ifdef OS_WIN
-            runtime_it->windows_min_version;
-#else
-            runtime_it->unix_min_version;
-#endif
-
-        char buf[1024];
-        snprintf(buf, 1024, msg.c_str(), int_version_to_string(runtime).c_str(),
-                 minimumDriverVersion, int_version_to_string(driver).c_str());
-
-        AF_ERROR(buf, AF_ERR_DRIVER);
-    }
-}
-
-DeviceManager::DeviceManager()
-    : logger(common::loggerFactory("platform"))
-    , cuDevices(0)
-    , nDevices(0)
-    , fgMngr(new graphics::ForgeManager())
-    {
-    checkCudaVsDriverVersion();
-
-    CUDA_CHECK(cudaGetDeviceCount(&nDevices));
-    AF_TRACE("Found {} CUDA devices", nDevices);
-    if (nDevices == 0) {
-        AF_ERROR("No CUDA capable devices found", AF_ERR_DRIVER);
-    }
-    cuDevices.reserve(nDevices);
-
-    int cudaRtVer = 0;
-    CUDA_CHECK(cudaRuntimeGetVersion(&cudaRtVer));
-    int cudaMajorVer = cudaRtVer / 1000;
-
-    for (int i = 0; i < nDevices; i++) {
-        cudaDevice_t dev;
-        CUDA_CHECK(cudaGetDeviceProperties(&dev.prop, i));
-        if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
-            AF_TRACE("Unsuppored device: {}", dev.prop.name);
-            continue;
-        } else {
-            dev.flops = static_cast<size_t>(dev.prop.multiProcessorCount) *
-                        compute2cores(dev.prop.major, dev.prop.minor) *
-                        dev.prop.clockRate;
-            dev.nativeId = i;
-            AF_TRACE(
-                "Found device: {} ({:0.3} GB | ~{} GFLOPs | {} SMs)",
-                dev.prop.name, dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
-                dev.flops / 1024. / 1024. * 2, dev.prop.multiProcessorCount);
-            cuDevices.push_back(dev);
-        }
-    }
-    nDevices = cuDevices.size();
-
-    sortDevices();
-
-    // Initialize all streams to 0.
-    // Streams will be created in setActiveDevice()
-    for (size_t i = 0; i < MAX_DEVICES; i++) {
-        streams[i] = (cudaStream_t)0;
-        if (i < nDevices) {
-            auto prop = make_pair(cuDevices[i].prop.major,
-                    cuDevices[i].prop.minor);
-            findJitDevCompute(prop);
-            devJitComputes.emplace_back(prop);
-        }
-    }
-
-    std::string deviceENV = getEnvVar("AF_CUDA_DEFAULT_DEVICE");
-    AF_TRACE("AF_CUDA_DEFAULT_DEVICE: {}", deviceENV);
-    if (deviceENV.empty()) {
-        setActiveDevice(0, cuDevices[0].nativeId);
-    } else {
-        stringstream s(deviceENV);
-        int def_device = -1;
-        s >> def_device;
-        if (def_device < 0 || def_device >= nDevices) {
-            getLogger()->warn(
-                "AF_CUDA_DEFAULT_DEVICE({}) out of range. Setting default "
-                "device to 0.",
-                def_device);
-            setActiveDevice(0, cuDevices[0].nativeId);
-        } else {
-            setActiveDevice(def_device, cuDevices[def_device].nativeId);
-        }
-    }
-    AF_TRACE("Default device: {}", getActiveDeviceId());
-}
-
-DeviceManager::~DeviceManager() {
-    // Reset unique_ptrs for all cu[BLAS | Sparse | Solver]
-    // handles of all devices
-    for (int i = 0; i < nDevices; ++i) {
-        setDevice(i);
-        cublasManager(i).reset();
-        cufftManager(i).reset();
-        cusolverManager(i).reset();
-        cusparseManager(i).reset();
-    }
-}
-
-spdlog::logger *DeviceManager::getLogger() { return logger.get(); }
-
-void DeviceManager::sortDevices(sort_mode mode) {
-    switch (mode) {
-        case memory:
-            std::stable_sort(cuDevices.begin(), cuDevices.end(),
-                             card_compare_mem);
-            break;
-        case flops:
-            std::stable_sort(cuDevices.begin(), cuDevices.end(),
-                             card_compare_flops);
-            break;
-        case compute:
-            std::stable_sort(cuDevices.begin(), cuDevices.end(),
-                             card_compare_compute);
-            break;
-        case none:
-        default:
-            std::stable_sort(cuDevices.begin(), cuDevices.end(),
-                             card_compare_num);
-            break;
-    }
-}
-
-int DeviceManager::setActiveDevice(int device, int nId) {
-    thread_local bool retryFlag = true;
-
-    int numDevices = cuDevices.size();
-
-    if (device >= numDevices) return -1;
-
-    int old = getActiveDeviceId();
-
-    if (nId == -1) nId = getDeviceNativeId(device);
-
-    cudaError_t err = cudaSetDevice(nId);
-
-    if (err == cudaSuccess) {
-        tlocalActiveDeviceId() = device;
-        return old;
-    }
-
-    // For the first time a thread calls setDevice,
-    // if the requested device is unavailable, try checking
-    // for other available devices - while loop below
-    if (!retryFlag) {
-        CUDA_CHECK(err);
-        return old;
-    }
-
-    // Comes only when retryFlag is true. Set it to false
-    retryFlag = false;
-
-    while (true) {
-        // Check for errors other than DevicesUnavailable
-        // If success, return. Else throw error
-        // If DevicesUnavailable, try other devices (while loop below)
-        if (err != cudaErrorDeviceAlreadyInUse) {
-            CUDA_CHECK(err);
-            tlocalActiveDeviceId() = device;
-            return old;
-        }
-        cudaGetLastError();  // Reset error stack
-#ifndef NDEBUG
-        getLogger()->warn(
-            "Warning: Device {} is unavailable. Using next available "
-            "device \n",
-            device);
-#endif
-        // Comes here is the device is in exclusive mode or
-        // otherwise fails streamCreate with this error.
-        // All other errors will error out
-        device++;
-        if (device >= numDevices) break;
-
-        // Can't call getNativeId here as it will cause an infinite loop with
-        // the constructor
-        nId = cuDevices[device].nativeId;
-
-        err = cudaSetDevice(nId);
-    }
-
-    // If all devices fail with DeviceAlreadyInUse, then throw this error
-    CUDA_CHECK(err);
-
-    return old;
-}
-
 void sync(int device) {
     int currDevice = getActiveDeviceId();
     setDevice(device);
@@ -835,6 +400,7 @@ bool &evalFlag() {
     thread_local bool flag = true;
     return flag;
 }
+
 }  // namespace cuda
 
 af_err afcu_get_stream(cudaStream_t *stream, int id) {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index a693136669..a1f485c324 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -9,19 +9,22 @@
 
 #pragma once
 
-#include <GraphicsResourceManager.hpp>
-#include <cublas.hpp>
 #include <cuda.h>
 #include <cuda_runtime.h>
-#include <cufft.hpp>
-#include <cusolverDn.hpp>
-#include <cusparse.hpp>
-#include <memory.hpp>
-
-#include <memory>
 #include <string>
-#include <utility>
-#include <vector>
+
+/* Forward declarations of Opaque structure holding
+ * the following library contexts
+ *  * cuBLAS
+ *  * cuSparse
+ *  * cuSolver
+ */
+struct cublasContext;
+typedef struct cublasContext* BlasHandle;
+struct cusparseContext;
+typedef struct cusparseContext* SparseHandle;
+struct cusolverDnContext;
+typedef struct cusolverDnContext* SolveHandle;
 
 namespace spdlog {
 class logger;
@@ -32,6 +35,12 @@ class ForgeManager;
 }
 
 namespace cuda {
+
+class GraphicsResourceManager;
+class MemoryManager;
+class MemoryManagerPinned;
+class PlanCache;
+
 int getBackend();
 
 std::string getDeviceInfo();
@@ -39,6 +48,7 @@ std::string getDeviceInfo(int device);
 
 std::string getPlatformInfo();
 
+std::string int_version_to_string(int version);
 std::string getDriverVersion();
 
 std::string getCUDARuntimeVersion();
@@ -74,16 +84,8 @@ cudaDeviceProp getDeviceProp(int device);
 
 std::pair<int, int> getComputeCapability(const int device);
 
-struct cudaDevice_t {
-    cudaDeviceProp prop;
-    size_t flops;
-    int nativeId;
-};
-
 bool& evalFlag();
 
-///////////////////////// BEGIN Sub-Managers ///////////////////
-//
 MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
@@ -99,84 +101,5 @@ BlasHandle blasHandle();
 SolveHandle solverDnHandle();
 
 SparseHandle sparseHandle();
-//
-///////////////////////// END Sub-Managers /////////////////////
-
-class DeviceManager {
-   public:
-    static const size_t MAX_DEVICES = 16;
-
-    static bool checkGraphicsInteropCapability();
-
-    static DeviceManager& getInstance();
-    ~DeviceManager();
-
-    spdlog::logger* getLogger();
-
-    friend MemoryManager& memoryManager();
-
-    friend MemoryManagerPinned& pinnedMemoryManager();
-
-    friend graphics::ForgeManager& forgeManager();
-
-    friend GraphicsResourceManager& interopManager();
-
-    friend std::string getDeviceInfo(int device);
-
-    friend std::string getPlatformInfo();
-
-    friend std::string getDriverVersion();
-
-    friend std::string getCUDARuntimeVersion();
-
-    friend std::string getDeviceInfo();
-
-    friend int getDeviceCount();
-
-    friend int getDeviceNativeId(int device);
-
-    friend int getDeviceIdFromNativeId(int nativeId);
-
-    friend cudaStream_t getStream(int device);
-
-    friend int setDevice(int device);
-
-    friend cudaDeviceProp getDeviceProp(int device);
-
-    friend std::pair<int, int> getComputeCapability(const int device);
-
-   private:
-    DeviceManager();
-
-    // Following two declarations are required to
-    // avoid copying accidental copy/assignment
-    // of instance returned by getInstance to other
-    // variables
-    DeviceManager(DeviceManager const&);
-    void operator=(DeviceManager const&);
-
-    // Attributes
-    enum sort_mode { flops = 0, memory = 1, compute = 2, none = 3 };
-
-    void checkCudaVsDriverVersion();
-    void sortDevices(sort_mode mode = flops);
-
-    int setActiveDevice(int device, int native = -1);
-
-    std::shared_ptr<spdlog::logger> logger;
-
-    std::vector<cudaDevice_t> cuDevices;
-    std::vector<std::pair<int, int>> devJitComputes;
-
-    int nDevices;
-    cudaStream_t streams[MAX_DEVICES];
-
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
-
-    std::unique_ptr<MemoryManager> memManager;
-
-    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
 
-    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
-};
 }  // namespace cuda
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index ea7dce05ae..9d4128f98d 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <debug_cuda.hpp>
+#include <device_manager.hpp>
 #include <err_cuda.hpp>
 #include <plot.hpp>
 
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cu
index 336e1350a2..48bee4f150 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cu
@@ -12,6 +12,7 @@
 
 #include <copy.hpp>
 #include <cublas_v2.h>
+#include <cusolverDn.hpp>
 #include <identity.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 901367eaa1..4019170d2d 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -12,6 +12,7 @@
 
 #include <copy.hpp>
 #include <cublas_v2.h>
+#include <cusolverDn.hpp>
 #include <identity.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 7c82e02a6e..cc0bf85224 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -18,6 +18,7 @@
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
+#include <cusparse.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -26,7 +27,6 @@
 namespace cuda {
 
 using namespace common;
-using namespace std;
 
 // cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
 //                                  int m, int n, int nnz,
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 7003a8f836..a3c1364fb9 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -18,6 +18,7 @@
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
+#include <cusparse.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -26,11 +27,11 @@
 namespace cuda {
 
 using namespace common;
-using namespace std;
+using std::numeric_limits;
 
 template<typename T>
 T getInf() {
-    return scalar<T>(std::numeric_limits<T>::infinity());
+    return scalar<T>(numeric_limits<T>::infinity());
 }
 
 template<>
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index 725a742e0e..d563ff52a6 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <cuda_runtime.h>
+#include <cusparse.hpp>
 #include <platform.hpp>
 #include <sparse_blas.hpp>
 
@@ -19,8 +20,6 @@
 
 namespace cuda {
 
-using namespace std;
-
 cusparseOperation_t toCusparseTranspose(af_mat_prop opt) {
     cusparseOperation_t out = CUSPARSE_OPERATION_NON_TRANSPOSE;
     switch (opt) {
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index ed6cab0e63..6644d22eb5 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <debug_cuda.hpp>
+#include <device_manager.hpp>
 #include <err_cuda.hpp>
 #include <surface.hpp>
 
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 9f0ecd5783..60506c4597 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <debug_cuda.hpp>
+#include <device_manager.hpp>
 #include <err_cuda.hpp>
 #include <vector_field.hpp>
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f3a67c09db..0bc7a47d93 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -74,6 +74,8 @@ target_sources(afopencl
     copy.hpp
     count.cpp
     debug_opencl.hpp
+    device_manager.cpp
+    device_manager.hpp
     diagonal.cpp
     diagonal.hpp
     diff.cpp
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
new file mode 100644
index 0000000000..eee068dc3c
--- /dev/null
+++ b/src/backend/opencl/device_manager.cpp
@@ -0,0 +1,446 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+// Include this before af/opencl.h
+// Causes conflict between system cl.hpp and opencl/cl.hpp
+#include <common/graphics_common.hpp>
+
+#include <GraphicsResourceManager.hpp>
+#include <blas.hpp>
+#include <clfft.hpp>
+#include <common/defines.hpp>
+#include <common/host_memory.hpp>
+#include <common/util.hpp>
+#include <device_manager.hpp>
+#include <err_opencl.hpp>
+#include <errorcodes.hpp>
+#include <version.hpp>
+#include <af/opencl.h>
+#include <af/version.h>
+
+#ifdef OS_MAC
+#include <OpenGL/CGLCurrent.h>
+#endif
+
+#include <boost/compute/context.hpp>
+#include <boost/compute/utility/program_cache.hpp>
+
+#include <algorithm>
+#include <iterator>
+#include <sstream>
+#include <string>
+#include <vector>
+
+using cl::CommandQueue;
+using cl::Context;
+using cl::Device;
+using cl::Platform;
+using std::begin;
+using std::end;
+using std::find;
+using std::string;
+using std::stringstream;
+using std::vector;
+
+namespace opencl {
+
+#if defined(OS_MAC)
+static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
+#else
+static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
+#endif
+
+bool checkExtnAvailability(const Device& pDevice, string pName) {
+    bool ret_val = false;
+    // find the extension required
+    string exts = pDevice.getInfo<CL_DEVICE_EXTENSIONS>();
+    stringstream ss(exts);
+    string item;
+    while (getline(ss, item, ' ')) {
+        if (item == pName) {
+            ret_val = true;
+            break;
+        }
+    }
+    return ret_val;
+}
+
+static afcl::deviceType getDeviceTypeEnum(Device dev) {
+    return (afcl::deviceType)dev.getInfo<CL_DEVICE_TYPE>();
+}
+
+static inline bool compare_default(const Device* ldev, const Device* rdev) {
+    const cl_device_type device_types[] = {CL_DEVICE_TYPE_GPU,
+                                           CL_DEVICE_TYPE_ACCELERATOR};
+
+    auto l_dev_type = ldev->getInfo<CL_DEVICE_TYPE>();
+    auto r_dev_type = rdev->getInfo<CL_DEVICE_TYPE>();
+
+    // This ensures GPU > ACCELERATOR > CPU
+    for (auto current_type : device_types) {
+        auto is_l_curr_type = l_dev_type == current_type;
+        auto is_r_curr_type = r_dev_type == current_type;
+
+        if (is_l_curr_type && !is_r_curr_type) return true;
+        if (!is_l_curr_type && is_r_curr_type) return false;
+    }
+
+    // For GPUs, this ensures discrete > integrated
+    auto is_l_integrated = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+    auto is_r_integrated = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+
+    if (!is_l_integrated && is_r_integrated) return true;
+    if (is_l_integrated && !is_r_integrated) return false;
+
+    // At this point, the devices are of same type.
+    // Sort based on emperical evidence of preferred platforms
+
+    // Prefer AMD first
+    string lPlatName = getPlatformName(*ldev);
+    string rPlatName = getPlatformName(*rdev);
+
+    if (l_dev_type == CL_DEVICE_TYPE_GPU && r_dev_type == CL_DEVICE_TYPE_GPU) {
+        // If GPU, prefer AMD > NVIDIA > Beignet / Intel > APPLE
+        const char* platforms[] = {"AMD", "NVIDIA", "APPLE", "INTEL",
+                                   "BEIGNET"};
+
+        for (auto ref_name : platforms) {
+            if (verify_present(lPlatName, ref_name) &&
+                !verify_present(rPlatName, ref_name))
+                return true;
+
+            if (!verify_present(lPlatName, ref_name) &&
+                verify_present(rPlatName, ref_name))
+                return false;
+        }
+
+        // Intel falls back to compare based on memory
+    } else {
+        // If CPU, prefer Intel > AMD > POCL > APPLE
+        const char* platforms[] = {"INTEL", "AMD", "POCL", "APPLE"};
+
+        for (auto ref_name : platforms) {
+            if (verify_present(lPlatName, ref_name) &&
+                !verify_present(rPlatName, ref_name))
+                return true;
+
+            if (!verify_present(lPlatName, ref_name) &&
+                verify_present(rPlatName, ref_name))
+                return false;
+        }
+    }
+
+    // Compare device compute versions
+
+    {
+        // Check Device OpenCL Version
+        auto lversion = ldev->getInfo<CL_DEVICE_VERSION>();
+        auto rversion = rdev->getInfo<CL_DEVICE_VERSION>();
+
+        bool lres =
+            (lversion[7] > rversion[7]) ||
+            ((lversion[7] == rversion[7]) && (lversion[9] > rversion[9]));
+
+        bool rres =
+            (lversion[7] < rversion[7]) ||
+            ((lversion[7] == rversion[7]) && (lversion[9] < rversion[9]));
+
+        if (lres) return true;
+        if (rres) return false;
+    }
+
+    // Default criteria, sort based on memory
+    // Sort based on memory
+    auto l_mem = ldev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+    auto r_mem = rdev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+    return l_mem >= r_mem;
+}
+
+DeviceManager::DeviceManager()
+    : mUserDeviceOffset(0)
+    , fgMngr(new graphics::ForgeManager())
+    , mFFTSetup(new clfftSetupData) {
+    vector<Platform> platforms;
+    Platform::get(&platforms);
+
+    // This is all we need because the sort takes care of the order of devices
+#ifdef OS_MAC
+    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+#else
+    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
+#endif
+
+    string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
+
+    if (deviceENV.compare("GPU") == 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
+    } else if (deviceENV.compare("CPU") == 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
+    } else if (deviceENV.compare("ACC") >= 0) {
+        DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
+    }
+
+    // Iterate through platforms, get all available devices and store them
+    for (auto& platform : platforms) {
+        vector<Device> current_devices;
+
+        try {
+            platform.getDevices(DEVICE_TYPES, &current_devices);
+        } catch (const cl::Error& err) {
+            if (err.err() != CL_DEVICE_NOT_FOUND) { throw; }
+        }
+        for (auto dev : current_devices) {
+            mDevices.push_back(new Device(dev));
+        }
+    }
+
+    int nDevices = mDevices.size();
+
+    if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
+
+    // Sort OpenCL devices based on default criteria
+    stable_sort(mDevices.begin(), mDevices.end(), compare_default);
+
+    // Create contexts and queues once the sort is done
+    for (int i = 0; i < nDevices; i++) {
+        cl_platform_id device_platform =
+            mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
+        cl_context_properties cps[3] = {
+            CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
+
+        Context* ctx     = new Context(*mDevices[i], cps);
+        CommandQueue* cq = new CommandQueue(*ctx, *mDevices[i]);
+        mContexts.push_back(ctx);
+        mQueues.push_back(cq);
+        mIsGLSharingOn.push_back(false);
+        mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
+        mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
+    }
+
+    bool default_device_set = false;
+    deviceENV               = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
+    if (!deviceENV.empty()) {
+        stringstream s(deviceENV);
+        int def_device = -1;
+        s >> def_device;
+        if (def_device < 0 || def_device >= (int)nDevices) {
+            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
+            printf("Setting default device as 0\n");
+        } else {
+            setActiveContext(def_device);
+            default_device_set = true;
+        }
+    }
+
+    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
+    if (!default_device_set && !deviceENV.empty()) {
+        cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
+        if (deviceENV.compare("CPU") == 0) {
+            default_device_type = CL_DEVICE_TYPE_CPU;
+        } else if (deviceENV.compare("ACC") >= 0) {
+            default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
+        }
+
+        bool default_device_set = false;
+        for (int i = 0; i < nDevices; i++) {
+            if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
+                default_device_set = true;
+                setActiveContext(i);
+                break;
+            }
+        }
+        if (!default_device_set) {
+            printf(
+                "WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
+                deviceENV.c_str());
+            printf("Using default device as 0\n");
+        }
+    }
+
+    // Define AF_DISABLE_GRAPHICS with any value to disable initialization
+    string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
+    if (fgMngr->plugin().isLoaded() && noGraphicsENV.empty()) {
+        // If forge library was successfully loaded and
+        // AF_DISABLE_GRAPHICS is not defined
+        try {
+            /* loop over devices and replace contexts with
+             * OpenGL shared contexts whereever applicable */
+            int devCount      = mDevices.size();
+            fg_window wHandle = fgMngr->getMainWindow();
+            for (int i = 0; i < devCount; ++i) markDeviceForInterop(i, wHandle);
+        } catch (...) {}
+    }
+
+    mUserDeviceOffset = mDevices.size();
+    // Initialize FFT setup data structure
+    CLFFT_CHECK(clfftInitSetupData(mFFTSetup.get()));
+    CLFFT_CHECK(clfftSetup(mFFTSetup.get()));
+
+    // Initialize clBlas library
+    initBlas();
+
+    // Cache Boost program_cache
+    namespace compute = boost::compute;
+    for (auto ctx : mContexts) {
+        compute::context c(ctx->get());
+        BoostProgCache currCache = compute::program_cache::get_global_cache(c);
+        mBoostProgCacheVector.emplace_back(new BoostProgCache(currCache));
+    }
+}
+
+DeviceManager& DeviceManager::getInstance() {
+    static DeviceManager* my_instance = new DeviceManager();
+    return *my_instance;
+}
+
+DeviceManager::~DeviceManager() {
+    for (int i = 0; i < getDeviceCount(); ++i) {
+        delete gfxManagers[i].release();
+    }
+#ifndef OS_WIN
+    // TODO: FIXME:
+    // clfftTeardown() causes a "Pure Virtual Function Called" crash on
+    // Windows for Intel devices. This causes tests to fail.
+    clfftTeardown();
+#endif
+
+    deInitBlas();
+
+    // deCache Boost program_cache
+#ifndef OS_WIN
+    namespace compute = boost::compute;
+    for (auto bCache : mBoostProgCacheVector) delete bCache;
+#endif
+
+    delete memManager.release();
+    delete pinnedMemManager.release();
+
+    // TODO: FIXME:
+    // OpenCL libs on Windows platforms
+    // are crashing the application at program exit
+    // most probably a reference counting issue based
+    // on the investigation done so far. This problem
+    // doesn't seem to happen on Linux or MacOSX.
+    // So, clean up OpenCL resources on non-Windows platforms
+#ifndef OS_WIN
+    for (auto q : mQueues) delete q;
+    for (auto c : mContexts) delete c;
+    for (auto d : mDevices) delete d;
+#endif
+}
+
+void DeviceManager::markDeviceForInterop(const int device,
+                                         const void* wHandle) {
+    try {
+        if (device >= (int)mQueues.size() ||
+            device >= (int)DeviceManager::MAX_DEVICES) {
+            throw cl::Error(CL_INVALID_DEVICE,
+                            "Invalid device passed for CL-GL Interop");
+        } else {
+            mQueues[device]->finish();
+
+            // check if the device has CL_GL sharing extension enabled
+            bool temp =
+                checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
+            if (!temp) {
+                /* return silently if given device has not OpenGL sharing
+                 * extension enabled so that regular queue is used for it */
+                return;
+            }
+
+            // call forge to get OpenGL sharing context and details
+            Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
+
+            long long wnd_ctx, wnd_dsp;
+            fgMngr->plugin().fg_get_window_context_handle(
+                &wnd_ctx, const_cast<fg_window>(wHandle));
+            fgMngr->plugin().fg_get_window_display_handle(
+                &wnd_dsp, const_cast<fg_window>(wHandle));
+#ifdef OS_MAC
+            CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
+            CGLShareGroupObj cgl_share_group =
+                CGLGetShareGroup(cgl_current_ctx);
+
+            cl_context_properties cps[] = {
+                CL_CONTEXT_PROPERTY_USE_CGL_SHAREGROUP_APPLE,
+                (cl_context_properties)cgl_share_group, 0};
+#else
+            cl_context_properties cps[] = {
+                CL_GL_CONTEXT_KHR,
+                (cl_context_properties)wnd_ctx,
+#if defined(_WIN32) || defined(_MSC_VER)
+                CL_WGL_HDC_KHR,
+                (cl_context_properties)wnd_dsp,
+#else
+                CL_GLX_DISPLAY_KHR,
+                (cl_context_properties)wnd_dsp,
+#endif
+                CL_CONTEXT_PLATFORM,
+                (cl_context_properties)plat(),
+                0
+            };
+
+            // Check if current OpenCL device is belongs to the OpenGL context
+            {
+                cl_context_properties test_cps[] = {
+                    CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
+                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(), 0};
+
+                // Load the extension
+                // If cl_khr_gl_sharing is available, this function should be
+                // present This has been checked earlier, it comes to this point
+                // only if it is found
+                auto func = (clGetGLContextInfoKHR_fn)
+                    clGetExtensionFunctionAddressForPlatform(
+                        plat(), "clGetGLContextInfoKHR");
+
+                // If the function doesn't load, bail early
+                if (!func) return;
+
+                // Get all devices associated with opengl context
+                vector<cl_device_id> devices(16);
+                size_t ret = 0;
+                cl_int err = func(test_cps, CL_DEVICES_FOR_GL_CONTEXT_KHR,
+                                  devices.size() * sizeof(cl_device_id),
+                                  &devices[0], &ret);
+                if (err != CL_SUCCESS) return;
+                int num = ret / sizeof(cl_device_id);
+                devices.resize(num);
+
+                // Check if current device is present in the associated devices
+                cl_device_id current_device = (*mDevices[device])();
+                auto res = find(begin(devices), end(devices), current_device);
+
+                if (res == end(devices)) return;
+            }
+#endif
+
+            // Change current device to use GL sharing
+            Context* ctx     = new Context(*mDevices[device], cps);
+            CommandQueue* cq = new CommandQueue(*ctx, *mDevices[device]);
+
+            // May be fixes the AMD GL issues we see on windows?
+#if !defined(_WIN32) && !defined(_MSC_VER)
+            delete mContexts[device];
+            delete mQueues[device];
+#endif
+
+            mContexts[device]      = ctx;
+            mQueues[device]        = cq;
+            mIsGLSharingOn[device] = true;
+        }
+    } catch (const cl::Error& ex) {
+        /* If replacing the original context with GL shared context
+         * failes, don't throw an error and instead fall back to
+         * original context and use copy via host to support graphics
+         * on that particular OpenCL device. So mark it as no GL sharing */
+    }
+}
+
+}  // namespace opencl
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
new file mode 100644
index 0000000000..35da2a0ed2
--- /dev/null
+++ b/src/backend/opencl/device_manager.hpp
@@ -0,0 +1,117 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include <common/MemoryManager.hpp>
+#include <platform.hpp>
+
+// Forward declaration from clFFT.h
+struct clfftSetupData_;
+
+namespace opencl {
+
+class DeviceManager {
+    friend MemoryManager& memoryManager();
+
+    friend MemoryManagerPinned& pinnedMemoryManager();
+
+    friend graphics::ForgeManager& forgeManager();
+
+    friend GraphicsResourceManager& interopManager();
+
+    friend PlanCache& fftManager();
+
+    friend void addKernelToCache(int device, const std::string& key,
+                                 const kc_entry_t entry);
+
+    friend void removeKernelFromCache(int device, const std::string& key);
+
+    friend kc_entry_t kernelCache(int device, const std::string& key);
+
+    friend std::string getDeviceInfo();
+
+    friend int getDeviceCount();
+
+    friend int getDeviceIdFromNativeId(cl_device_id id);
+
+    friend const cl::Context& getContext();
+
+    friend cl::CommandQueue& getQueue();
+
+    friend const cl::Device& getDevice(int id);
+
+    friend size_t getDeviceMemorySize(int device);
+
+    friend bool isGLSharingSupported();
+
+    friend bool isDoubleSupported(int device);
+
+    friend void devprop(char* d_name, char* d_platform, char* d_toolkit,
+                        char* d_compute);
+
+    friend int setDevice(int device);
+
+    friend void addDeviceContext(cl_device_id dev, cl_context cxt,
+                                 cl_command_queue que);
+
+    friend void setDeviceContext(cl_device_id dev, cl_context cxt);
+
+    friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
+
+    friend int getActiveDeviceType();
+
+    friend int getActivePlatform();
+
+   public:
+    static const unsigned MAX_DEVICES = 32;
+
+    static DeviceManager& getInstance();
+
+    ~DeviceManager();
+
+   protected:
+    using clfftSetupData = clfftSetupData_;
+
+    DeviceManager();
+
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&);
+    void operator=(DeviceManager const&);
+    void markDeviceForInterop(const int device, const void* wHandle);
+
+   private:
+    // Attributes
+    common::mutex_t deviceMutex;
+    std::vector<cl::Device*> mDevices;
+    std::vector<cl::Context*> mContexts;
+    std::vector<cl::CommandQueue*> mQueues;
+    std::vector<bool> mIsGLSharingOn;
+    std::vector<int> mDeviceTypes;
+    std::vector<int> mPlatforms;
+    unsigned mUserDeviceOffset;
+
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<MemoryManager> memManager;
+    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+    std::unique_ptr<clfftSetupData> mFFTSetup;
+
+    using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
+    std::vector<BoostProgCache*> mBoostProgCacheVector;
+};
+
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 9d4731d610..9f1f8583a8 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/approx1.hpp>
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 7a4837a73e..c33a7c4e51 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/join.hpp>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 42486369c9..65ba414afa 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -14,6 +14,7 @@
 #include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_dim_by_key.hpp>
+#include <memory.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <type_util.hpp>
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 76a2aae494..aaaaf2d1cd 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -11,16 +11,16 @@
 // Causes conflict between system cl.hpp and opencl/cl.hpp
 #include <common/graphics_common.hpp>
 
+#include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
+#include <cache.hpp>
 #include <clfft.hpp>
-#include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
+#include <device_manager.hpp>
 #include <err_opencl.hpp>
 #include <errorcodes.hpp>
-#include <platform.hpp>
 #include <version.hpp>
-#include <af/opencl.h>
 #include <af/version.h>
 
 #ifdef OS_MAC
@@ -37,32 +37,35 @@
 #include <map>
 #include <mutex>
 #include <sstream>
-#include <stdexcept>
 #include <string>
 #include <thread>
 #include <utility>
 #include <vector>
 
-using std::ostringstream;
-using std::runtime_error;
-using std::string;
-using std::vector;
-
 using cl::CommandQueue;
 using cl::Context;
 using cl::Device;
 using cl::Platform;
+using std::begin;
+using std::call_once;
+using std::end;
+using std::endl;
+using std::find_if;
+using std::get;
+using std::make_pair;
+using std::map;
+using std::once_flag;
+using std::ostringstream;
+using std::pair;
+using std::ptr_fun;
+using std::string;
+using std::to_string;
+using std::vector;
 
 namespace opencl {
 
-#if defined(OS_MAC)
-static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
-#else
-static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
-#endif
-
-static const std::string get_system(void) {
-    std::string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
+static const string get_system(void) {
+    string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
 #if defined(OS_LNX)
@@ -76,130 +79,23 @@ static const std::string get_system(void) {
 
 int getBackend() { return AF_BACKEND_OPENCL; }
 
-static inline bool verify_present(std::string pname, const char* ref) {
-    return pname.find(ref) != std::string::npos;
-}
-
-static inline bool compare_default(const Device* ldev, const Device* rdev) {
-    const cl_device_type device_types[] = {CL_DEVICE_TYPE_GPU,
-                                           CL_DEVICE_TYPE_ACCELERATOR};
-
-    auto l_dev_type = ldev->getInfo<CL_DEVICE_TYPE>();
-    auto r_dev_type = rdev->getInfo<CL_DEVICE_TYPE>();
-
-    // This ensures GPU > ACCELERATOR > CPU
-    for (auto current_type : device_types) {
-        auto is_l_curr_type = l_dev_type == current_type;
-        auto is_r_curr_type = r_dev_type == current_type;
-
-        if (is_l_curr_type && !is_r_curr_type) return true;
-        if (!is_l_curr_type && is_r_curr_type) return false;
-    }
-
-    // For GPUs, this ensures discrete > integrated
-    auto is_l_integrated = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-    auto is_r_integrated = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-
-    if (!is_l_integrated && is_r_integrated) return true;
-    if (is_l_integrated && !is_r_integrated) return false;
-
-    // At this point, the devices are of same type.
-    // Sort based on emperical evidence of preferred platforms
-
-    // Prefer AMD first
-    std::string lPlatName = getPlatformName(*ldev);
-    std::string rPlatName = getPlatformName(*rdev);
-
-    if (l_dev_type == CL_DEVICE_TYPE_GPU && r_dev_type == CL_DEVICE_TYPE_GPU) {
-        // If GPU, prefer AMD > NVIDIA > Beignet / Intel > APPLE
-        const char* platforms[] = {"AMD", "NVIDIA", "APPLE", "INTEL",
-                                   "BEIGNET"};
-
-        for (auto ref_name : platforms) {
-            if (verify_present(lPlatName, ref_name) &&
-                !verify_present(rPlatName, ref_name))
-                return true;
-
-            if (!verify_present(lPlatName, ref_name) &&
-                verify_present(rPlatName, ref_name))
-                return false;
-        }
-
-        // Intel falls back to compare based on memory
-    } else {
-        // If CPU, prefer Intel > AMD > POCL > APPLE
-        const char* platforms[] = {"INTEL", "AMD", "POCL", "APPLE"};
-
-        for (auto ref_name : platforms) {
-            if (verify_present(lPlatName, ref_name) &&
-                !verify_present(rPlatName, ref_name))
-                return true;
-
-            if (!verify_present(lPlatName, ref_name) &&
-                verify_present(rPlatName, ref_name))
-                return false;
-        }
-    }
-
-    // Compare device compute versions
-
-    {
-        // Check Device OpenCL Version
-        auto lversion = ldev->getInfo<CL_DEVICE_VERSION>();
-        auto rversion = rdev->getInfo<CL_DEVICE_VERSION>();
-
-        bool lres =
-            (lversion[7] > rversion[7]) ||
-            ((lversion[7] == rversion[7]) && (lversion[9] > rversion[9]));
-
-        bool rres =
-            (lversion[7] < rversion[7]) ||
-            ((lversion[7] == rversion[7]) && (lversion[9] < rversion[9]));
-
-        if (lres) return true;
-        if (rres) return false;
-    }
-
-    // Default criteria, sort based on memory
-    // Sort based on memory
-    auto l_mem = ldev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
-    auto r_mem = rdev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
-    return l_mem >= r_mem;
-}
-
-static afcl::deviceType getDeviceTypeEnum(cl::Device dev) {
-    return (afcl::deviceType)dev.getInfo<CL_DEVICE_TYPE>();
-}
-
-static afcl::platform getPlatformEnum(cl::Device dev) {
-    std::string pname = getPlatformName(dev);
-    if (verify_present(pname, "AMD")) return AFCL_PLATFORM_AMD;
-    if (verify_present(pname, "NVIDIA")) return AFCL_PLATFORM_NVIDIA;
-    if (verify_present(pname, "INTEL")) return AFCL_PLATFORM_INTEL;
-    if (verify_present(pname, "APPLE")) return AFCL_PLATFORM_APPLE;
-    if (verify_present(pname, "BEIGNET")) return AFCL_PLATFORM_BEIGNET;
-    if (verify_present(pname, "POCL")) return AFCL_PLATFORM_POCL;
-    return AFCL_PLATFORM_UNKNOWN;
-}
-
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
-static inline std::string& ltrim(std::string& s) {
+static inline string& ltrim(string& s) {
     s.erase(s.begin(),
-            std::find_if(s.begin(), s.end(),
-                         std::not1(std::ptr_fun<int, int>(std::isspace))));
+            find_if(s.begin(), s.end(), not1(ptr_fun<int, int>(isspace))));
     return s;
 }
 
-static std::string platformMap(std::string& platStr) {
-    typedef std::map<std::string, std::string> strmap_t;
+static string platformMap(string& platStr) {
+    typedef map<string, string> strmap_t;
     static const strmap_t platMap = {
-        std::make_pair("NVIDIA CUDA", "NVIDIA"),
-        std::make_pair("Intel(R) OpenCL", "INTEL"),
-        std::make_pair("AMD Accelerated Parallel Processing", "AMD"),
-        std::make_pair("Intel Gen OCL Driver", "BEIGNET"),
-        std::make_pair("Apple", "APPLE"),
-        std::make_pair("Portable Computing Language", "POCL"),
+        make_pair("NVIDIA CUDA", "NVIDIA"),
+        make_pair("Intel(R) OpenCL", "INTEL"),
+        make_pair("AMD Accelerated Parallel Processing", "AMD"),
+        make_pair("Intel Gen OCL Driver", "BEIGNET"),
+        make_pair("Apple", "APPLE"),
+        make_pair("Portable Computing Language", "POCL"),
     };
 
     auto idx = platMap.find(platStr);
@@ -211,7 +107,7 @@ static std::string platformMap(std::string& platStr) {
     }
 }
 
-std::string getDeviceInfo() {
+string getDeviceInfo() {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     vector<cl::Device*> devices;
@@ -231,8 +127,7 @@ std::string getDeviceInfo() {
         string dstr      = device->getInfo<CL_DEVICE_NAME>();
         bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
 
-        string id = (show_braces ? string("[") : "-") +
-                    std::to_string(nDevices) +
+        string id = (show_braces ? string("[") : "-") + to_string(nDevices) +
                     (show_braces ? string("]") : "-");
 
         size_t msize = device->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
@@ -251,22 +146,22 @@ std::string getDeviceInfo() {
         info << " -- Unified Memory ("
              << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif
-        info << std::endl;
+        info << endl;
 
         nDevices++;
     }
     return info.str();
 }
 
-std::string getPlatformName(const cl::Device& device) {
+string getPlatformName(const cl::Device& device) {
     const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
-    std::string platStr = platform.getInfo<CL_PLATFORM_NAME>();
+    string platStr = platform.getInfo<CL_PLATFORM_NAME>();
     return platformMap(platStr);
 }
 
-typedef std::pair<unsigned, unsigned> device_id_t;
+typedef pair<unsigned, unsigned> device_id_t;
 
-std::pair<unsigned, unsigned>& tlocalActiveDeviceId() {
+pair<unsigned, unsigned>& tlocalActiveDeviceId() {
     // First element is active context id
     // Second element is active queue id
     thread_local device_id_t activeDeviceId(0, 0);
@@ -275,7 +170,7 @@ std::pair<unsigned, unsigned>& tlocalActiveDeviceId() {
 }
 
 void setActiveContext(int device) {
-    tlocalActiveDeviceId() = std::make_pair(device, device);
+    tlocalActiveDeviceId() = make_pair(device, device);
 }
 
 int getDeviceCount() {
@@ -289,7 +184,7 @@ int getDeviceCount() {
 int getActiveDeviceId() {
     // Second element is the queue id, which is
     // what we mean by active device id in opencl backend
-    return std::get<1>(tlocalActiveDeviceId());
+    return get<1>(tlocalActiveDeviceId());
 }
 
 int getDeviceIdFromNativeId(cl_device_id id) {
@@ -313,7 +208,7 @@ int getActiveDeviceType() {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    return devMngr.mDeviceTypes[std::get<1>(devId)];
+    return devMngr.mDeviceTypes[get<1>(devId)];
 }
 
 int getActivePlatform() {
@@ -323,7 +218,7 @@ int getActivePlatform() {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    return devMngr.mPlatforms[std::get<1>(devId)];
+    return devMngr.mPlatforms[get<1>(devId)];
 }
 const Context& getContext() {
     device_id_t& devId = tlocalActiveDeviceId();
@@ -332,7 +227,7 @@ const Context& getContext() {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    return *(devMngr.mContexts[std::get<0>(devId)]);
+    return *(devMngr.mContexts[get<0>(devId)]);
 }
 
 CommandQueue& getQueue() {
@@ -342,13 +237,13 @@ CommandQueue& getQueue() {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    return *(devMngr.mQueues[std::get<1>(devId)]);
+    return *(devMngr.mQueues[get<1>(devId)]);
 }
 
 const cl::Device& getDevice(int id) {
     device_id_t& devId = tlocalActiveDeviceId();
 
-    if (id == -1) id = std::get<1>(devId);
+    if (id == -1) id = get<1>(devId);
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -412,7 +307,7 @@ bool isGLSharingSupported() {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    return devMngr.mIsGLSharingOn[std::get<1>(devId)];
+    return devMngr.mIsGLSharingOn[get<1>(devId)];
 }
 
 bool isDoubleSupported(int device) {
@@ -454,7 +349,7 @@ void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
                 com_str        = com_str.substr(7, 3);
 
                 // strip out whitespace from the device string:
-                const std::string& whitespace = " \t";
+                const string& whitespace = " \t";
                 const auto strBegin = dev_str.find_first_not_of(whitespace);
                 const auto strEnd   = dev_str.find_last_not_of(whitespace);
                 const auto strRange = strEnd - strBegin + 1;
@@ -506,21 +401,6 @@ void sync(int device) {
     setDevice(currDevice);
 }
 
-bool checkExtnAvailability(const Device& pDevice, std::string pName) {
-    bool ret_val = false;
-    // find the extension required
-    std::string exts = pDevice.getInfo<CL_DEVICE_EXTENSIONS>();
-    std::stringstream ss(exts);
-    std::string item;
-    while (std::getline(ss, item, ' ')) {
-        if (item == pName) {
-            ret_val = true;
-            break;
-        }
-    }
-    return ret_val;
-}
-
 void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
     clRetainDevice(dev);
     clRetainContext(ctx);
@@ -629,8 +509,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
         device_id_t& devId = tlocalActiveDeviceId();
 
         if (deleteIdx < (int)devId.first) {
-            device_id_t newVals =
-                std::make_pair(devId.first - 1, devId.second - 1);
+            device_id_t newVals = make_pair(devId.first - 1, devId.second - 1);
             devId = newVals;
         }
     }
@@ -650,9 +529,9 @@ unsigned getMaxJitSize() {
 
     thread_local int length = 0;
     if (length == 0) {
-        std::string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
+        string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            length = std::stoi(env_var);
+            length = stoi(env_var);
         } else {
             length = MAX_JIT_LEN;
         }
@@ -666,22 +545,22 @@ bool& evalFlag() {
 }
 
 MemoryManager& memoryManager() {
-    static std::once_flag flag;
+    static once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&] { inst.memManager.reset(new MemoryManager()); });
+    call_once(flag, [&] { inst.memManager.reset(new MemoryManager()); });
 
     return *(inst.memManager.get());
 }
 
 MemoryManagerPinned& pinnedMemoryManager() {
-    static std::once_flag flag;
+    static once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(
-        flag, [&] { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
+    call_once(flag,
+              [&] { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
 
     return *(inst.pinnedMemManager.get());
 }
@@ -691,13 +570,13 @@ graphics::ForgeManager& forgeManager() {
 }
 
 GraphicsResourceManager& interopManager() {
-    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    static once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = getActiveDeviceId();
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(initFlags[id], [&] {
+    call_once(initFlags[id], [&] {
         inst.gfxManagers[id].reset(new GraphicsResourceManager());
     });
 
@@ -716,16 +595,15 @@ kc_t& getKernelCache(int device) {
     return kernelCaches[device];
 }
 
-void addKernelToCache(int device, const std::string& key,
-                      const kc_entry_t entry) {
+void addKernelToCache(int device, const string& key, const kc_entry_t entry) {
     getKernelCache(device).emplace(key, entry);
 }
 
-void removeKernelFromCache(int device, const std::string& key) {
+void removeKernelFromCache(int device, const string& key) {
     getKernelCache(device).erase(key);
 }
 
-kc_entry_t kernelCache(int device, const std::string& key) {
+kc_entry_t kernelCache(int device, const string& key) {
     kc_t& cache = getKernelCache(device);
 
     kc_t::iterator iter = cache.find(key);
@@ -733,287 +611,6 @@ kc_entry_t kernelCache(int device, const std::string& key) {
     return (iter == cache.end() ? kc_entry_t{0, 0} : iter->second);
 }
 
-DeviceManager& DeviceManager::getInstance() {
-    static DeviceManager* my_instance = new DeviceManager();
-    return *my_instance;
-}
-
-DeviceManager::~DeviceManager() {
-    for (int i = 0; i < getDeviceCount(); ++i) {
-        delete gfxManagers[i].release();
-    }
-#ifndef OS_WIN
-    // TODO: FIXME:
-    // clfftTeardown() causes a "Pure Virtual Function Called" crash on
-    // Windows for Intel devices. This causes tests to fail.
-    clfftTeardown();
-#endif
-
-    deInitBlas();
-
-    // deCache Boost program_cache
-#ifndef OS_WIN
-    namespace compute = boost::compute;
-    for (auto bCache : mBoostProgCacheVector) delete bCache;
-#endif
-
-    delete memManager.release();
-    delete pinnedMemManager.release();
-
-    // TODO: FIXME:
-    // OpenCL libs on Windows platforms
-    // are crashing the application at program exit
-    // most probably a reference counting issue based
-    // on the investigation done so far. This problem
-    // doesn't seem to happen on Linux or MacOSX.
-    // So, clean up OpenCL resources on non-Windows platforms
-#ifndef OS_WIN
-    for (auto q : mQueues) delete q;
-    for (auto c : mContexts) delete c;
-    for (auto d : mDevices) delete d;
-#endif
-}
-
-DeviceManager::DeviceManager()
-    : mUserDeviceOffset(0)
-    , fgMngr(new graphics::ForgeManager())
-    , mFFTSetup(new clfftSetupData) {
-    std::vector<cl::Platform> platforms;
-    Platform::get(&platforms);
-
-    // This is all we need because the sort takes care of the order of devices
-#ifdef OS_MAC
-    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
-#else
-    cl_device_type DEVICE_TYPES = CL_DEVICE_TYPE_ALL;
-#endif
-
-    std::string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
-
-    if (deviceENV.compare("GPU") == 0) {
-        DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
-    } else if (deviceENV.compare("CPU") == 0) {
-        DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
-    } else if (deviceENV.compare("ACC") >= 0) {
-        DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
-    }
-
-    // Iterate through platforms, get all available devices and store them
-    for (auto& platform : platforms) {
-        std::vector<Device> current_devices;
-
-        try {
-            platform.getDevices(DEVICE_TYPES, &current_devices);
-        } catch (const cl::Error& err) {
-            if (err.err() != CL_DEVICE_NOT_FOUND) { throw; }
-        }
-        for (auto dev : current_devices) {
-            mDevices.push_back(new Device(dev));
-        }
-    }
-
-    int nDevices = mDevices.size();
-
-    if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
-
-    // Sort OpenCL devices based on default criteria
-    std::stable_sort(mDevices.begin(), mDevices.end(), compare_default);
-
-    // Create contexts and queues once the sort is done
-    for (int i = 0; i < nDevices; i++) {
-        cl_platform_id device_platform =
-            mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
-        cl_context_properties cps[3] = {
-            CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
-
-        Context* ctx     = new Context(*mDevices[i], cps);
-        CommandQueue* cq = new CommandQueue(*ctx, *mDevices[i]);
-        mContexts.push_back(ctx);
-        mQueues.push_back(cq);
-        mIsGLSharingOn.push_back(false);
-        mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
-        mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
-    }
-
-    bool default_device_set = false;
-    deviceENV               = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");
-    if (!deviceENV.empty()) {
-        std::stringstream s(deviceENV);
-        int def_device = -1;
-        s >> def_device;
-        if (def_device < 0 || def_device >= (int)nDevices) {
-            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
-            printf("Setting default device as 0\n");
-        } else {
-            setActiveContext(def_device);
-            default_device_set = true;
-        }
-    }
-
-    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
-    if (!default_device_set && !deviceENV.empty()) {
-        cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
-        if (deviceENV.compare("CPU") == 0) {
-            default_device_type = CL_DEVICE_TYPE_CPU;
-        } else if (deviceENV.compare("ACC") >= 0) {
-            default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
-        }
-
-        bool default_device_set = false;
-        for (int i = 0; i < nDevices; i++) {
-            if (mDevices[i]->getInfo<CL_DEVICE_TYPE>() == default_device_type) {
-                default_device_set = true;
-                setActiveContext(i);
-                break;
-            }
-        }
-        if (!default_device_set) {
-            printf(
-                "WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
-                deviceENV.c_str());
-            printf("Using default device as 0\n");
-        }
-    }
-
-    // Define AF_DISABLE_GRAPHICS with any value to disable initialization
-    std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
-    if (fgMngr->plugin().isLoaded() && noGraphicsENV.empty()) {
-        // If forge library was successfully loaded and
-        // AF_DISABLE_GRAPHICS is not defined
-        try {
-            /* loop over devices and replace contexts with
-             * OpenGL shared contexts whereever applicable */
-            int devCount      = mDevices.size();
-            fg_window wHandle = fgMngr->getMainWindow();
-            for (int i = 0; i < devCount; ++i) markDeviceForInterop(i, wHandle);
-        } catch (...) {}
-    }
-
-    mUserDeviceOffset = mDevices.size();
-    // Initialize FFT setup data structure
-    CLFFT_CHECK(clfftInitSetupData(mFFTSetup.get()));
-    CLFFT_CHECK(clfftSetup(mFFTSetup.get()));
-
-    // Initialize clBlas library
-    initBlas();
-
-    // Cache Boost program_cache
-    namespace compute = boost::compute;
-    for (auto ctx : mContexts) {
-        compute::context c(ctx->get());
-        BoostProgCache currCache = compute::program_cache::get_global_cache(c);
-        mBoostProgCacheVector.emplace_back(new BoostProgCache(currCache));
-    }
-}
-
-void DeviceManager::markDeviceForInterop(const int device,
-                                         const void* wHandle) {
-    try {
-        if (device >= (int)mQueues.size() ||
-            device >= (int)DeviceManager::MAX_DEVICES) {
-            throw cl::Error(CL_INVALID_DEVICE,
-                            "Invalid device passed for CL-GL Interop");
-        } else {
-            mQueues[device]->finish();
-
-            // check if the device has CL_GL sharing extension enabled
-            bool temp =
-                checkExtnAvailability(*mDevices[device], CL_GL_SHARING_EXT);
-            if (!temp) {
-                /* return silently if given device has not OpenGL sharing
-                 * extension enabled so that regular queue is used for it */
-                return;
-            }
-
-            // call forge to get OpenGL sharing context and details
-            cl::Platform plat(mDevices[device]->getInfo<CL_DEVICE_PLATFORM>());
-
-            long long wnd_ctx, wnd_dsp;
-            fgMngr->plugin().fg_get_window_context_handle(
-                &wnd_ctx, const_cast<fg_window>(wHandle));
-            fgMngr->plugin().fg_get_window_display_handle(
-                &wnd_dsp, const_cast<fg_window>(wHandle));
-#ifdef OS_MAC
-            CGLContextObj cgl_current_ctx = CGLGetCurrentContext();
-            CGLShareGroupObj cgl_share_group =
-                CGLGetShareGroup(cgl_current_ctx);
-
-            cl_context_properties cps[] = {
-                CL_CONTEXT_PROPERTY_USE_CGL_SHAREGROUP_APPLE,
-                (cl_context_properties)cgl_share_group, 0};
-#else
-            cl_context_properties cps[] = {
-                CL_GL_CONTEXT_KHR,
-                (cl_context_properties)wnd_ctx,
-#if defined(_WIN32) || defined(_MSC_VER)
-                CL_WGL_HDC_KHR,
-                (cl_context_properties)wnd_dsp,
-#else
-                CL_GLX_DISPLAY_KHR,
-                (cl_context_properties)wnd_dsp,
-#endif
-                CL_CONTEXT_PLATFORM,
-                (cl_context_properties)plat(),
-                0
-            };
-
-            // Check if current OpenCL device is belongs to the OpenGL context
-            {
-                cl_context_properties test_cps[] = {
-                    CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
-                    CL_CONTEXT_PLATFORM, (cl_context_properties)plat(), 0};
-
-                // Load the extension
-                // If cl_khr_gl_sharing is available, this function should be
-                // present This has been checked earlier, it comes to this point
-                // only if it is found
-                auto func = (clGetGLContextInfoKHR_fn)
-                    clGetExtensionFunctionAddressForPlatform(
-                        plat(), "clGetGLContextInfoKHR");
-
-                // If the function doesn't load, bail early
-                if (!func) return;
-
-                // Get all devices associated with opengl context
-                std::vector<cl_device_id> devices(16);
-                size_t ret = 0;
-                cl_int err = func(test_cps, CL_DEVICES_FOR_GL_CONTEXT_KHR,
-                                  devices.size() * sizeof(cl_device_id),
-                                  &devices[0], &ret);
-                if (err != CL_SUCCESS) return;
-                int num = ret / sizeof(cl_device_id);
-                devices.resize(num);
-
-                // Check if current device is present in the associated devices
-                cl_device_id current_device = (*mDevices[device])();
-                auto res = std::find(std::begin(devices), std::end(devices),
-                                     current_device);
-
-                if (res == std::end(devices)) return;
-            }
-#endif
-
-            // Change current device to use GL sharing
-            Context* ctx     = new Context(*mDevices[device], cps);
-            CommandQueue* cq = new CommandQueue(*ctx, *mDevices[device]);
-
-            // May be fixes the AMD GL issues we see on windows?
-#if !defined(_WIN32) && !defined(_MSC_VER)
-            delete mContexts[device];
-            delete mQueues[device];
-#endif
-
-            mContexts[device]      = ctx;
-            mQueues[device]        = cq;
-            mIsGLSharingOn[device] = true;
-        }
-    } catch (const cl::Error& ex) {
-        /* If replacing the original context with GL shared context
-         * failes, don't throw an error and instead fall back to
-         * original context and use copy via host to support graphics
-         * on that particular OpenCL device. So mark it as no GL sharing */
-    }
-}
 }  // namespace opencl
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index cde9c04b13..da2d3825ab 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #define CL_HPP_ENABLE_EXCEPTIONS
 #define CL_HPP_MINIMUM_OPENCL_VERSION 120
 #define CL_HPP_TARGET_OPENCL_VERSION 120
@@ -20,13 +21,8 @@
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
 
-#include <memory>
+#include <af/opencl.h>
 #include <string>
-#include <vector>
-
-#include <GraphicsResourceManager.hpp>
-#include <cache.hpp>
-#include <memory.hpp>
 
 namespace boost {
 template<typename T>
@@ -41,15 +37,19 @@ namespace graphics {
 class ForgeManager;
 }
 
-// Forward declaration from clFFT.h
-struct clfftSetupData_;
-typedef clfftSetupData_ clfftSetupData;
-
 namespace opencl {
-// Forward declaration from clfft.hpp
-class PlanCache;
 
-struct kc_entry_t;
+// Forward declarations
+class GraphicsResourceManager;
+struct kc_entry_t;  // kernel cache entry
+class MemoryManager;
+class MemoryManagerPinned;
+class PlanCache;  // clfft
+
+static inline bool verify_present(std::string pname, const char* ref) {
+    return pname.find(ref) != std::string::npos;
+}
+
 int getBackend();
 
 std::string getDeviceInfo();
@@ -97,12 +97,11 @@ void sync(int device);
 bool synchronize_calls();
 
 int getActiveDeviceType();
+
 int getActivePlatform();
 
 bool& evalFlag();
 
-///////////////////////// BEGIN Sub-Managers ///////////////////
-//
 MemoryManager& memoryManager();
 
 MemoryManagerPinned& pinnedMemoryManager();
@@ -114,102 +113,28 @@ GraphicsResourceManager& interopManager();
 PlanCache& fftManager();
 
 void addKernelToCache(int device, const std::string& key,
-                      const opencl::kc_entry_t entry);
+                      const kc_entry_t entry);
 
 void removeKernelFromCache(int device, const std::string& key);
 
 kc_entry_t kernelCache(int device, const std::string& key);
-//
-///////////////////////// END Sub-Managers /////////////////////
-
-class DeviceManager {
-    friend MemoryManager& memoryManager();
-
-    friend MemoryManagerPinned& pinnedMemoryManager();
-
-    friend graphics::ForgeManager& forgeManager();
-
-    friend GraphicsResourceManager& interopManager();
-
-    friend PlanCache& fftManager();
-
-    friend void addKernelToCache(int device, const std::string& key,
-                                 const kc_entry_t entry);
-
-    friend void removeKernelFromCache(int device, const std::string& key);
-
-    friend kc_entry_t kernelCache(int device, const std::string& key);
-
-    friend std::string getDeviceInfo();
-
-    friend int getDeviceCount();
-
-    friend int getDeviceIdFromNativeId(cl_device_id id);
-
-    friend const cl::Context& getContext();
 
-    friend cl::CommandQueue& getQueue();
-
-    friend const cl::Device& getDevice(int id);
-
-    friend size_t getDeviceMemorySize(int device);
-
-    friend bool isGLSharingSupported();
-
-    friend bool isDoubleSupported(int device);
-
-    friend void devprop(char* d_name, char* d_platform, char* d_toolkit,
-                        char* d_compute);
-
-    friend int setDevice(int device);
-
-    friend void addDeviceContext(cl_device_id dev, cl_context cxt,
-                                 cl_command_queue que);
-
-    friend void setDeviceContext(cl_device_id dev, cl_context cxt);
-
-    friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
-
-    friend int getActiveDeviceType();
-
-    friend int getActivePlatform();
-
-   public:
-    static const unsigned MAX_DEVICES = 32;
-
-    static DeviceManager& getInstance();
-
-    ~DeviceManager();
-
-   protected:
-    DeviceManager();
-
-    // Following two declarations are required to
-    // avoid copying accidental copy/assignment
-    // of instance returned by getInstance to other
-    // variables
-    DeviceManager(DeviceManager const&);
-    void operator=(DeviceManager const&);
-    void markDeviceForInterop(const int device, const void* wHandle);
-
-   private:
-    // Attributes
-    common::mutex_t deviceMutex;
-    std::vector<cl::Device*> mDevices;
-    std::vector<cl::Context*> mContexts;
-    std::vector<cl::CommandQueue*> mQueues;
-    std::vector<bool> mIsGLSharingOn;
-    std::vector<int> mDeviceTypes;
-    std::vector<int> mPlatforms;
-    unsigned mUserDeviceOffset;
+static afcl::platform getPlatformEnum(cl::Device dev) {
+    std::string pname = getPlatformName(dev);
+    if (verify_present(pname, "AMD")) return AFCL_PLATFORM_AMD;
+    else if (verify_present(pname, "NVIDIA"))
+        return AFCL_PLATFORM_NVIDIA;
+    else if (verify_present(pname, "INTEL"))
+        return AFCL_PLATFORM_INTEL;
+    else if (verify_present(pname, "APPLE"))
+        return AFCL_PLATFORM_APPLE;
+    else if (verify_present(pname, "BEIGNET"))
+        return AFCL_PLATFORM_BEIGNET;
+    else if (verify_present(pname, "POCL"))
+        return AFCL_PLATFORM_POCL;
+    return AFCL_PLATFORM_UNKNOWN;
+}
 
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
-    std::unique_ptr<MemoryManager> memManager;
-    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
-    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
-    std::unique_ptr<clfftSetupData> mFFTSetup;
+void setActiveContext(int device);
 
-    using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
-    std::vector<BoostProgCache*> mBoostProgCacheVector;
-};
 }  // namespace opencl
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index 5759608979..da376b3ee5 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -27,11 +27,11 @@
 namespace opencl {
 
 using namespace common;
-using namespace std;
+using std::numeric_limits;
 
 template<typename T>
 T getInf() {
-    return scalar<T>(std::numeric_limits<T>::infinity());
+    return scalar<T>(numeric_limits<T>::infinity());
 }
 
 template<>

From 302dfcdbcc8a4edc53ed908cf18f168af69b092b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 26 Apr 2019 10:45:22 +0530
Subject: [PATCH 1636/2677] Exclude forge from ALL cmake target

This resolves the problem of forge install files being included
along with ArrayFire installables.

To enable forge packagining when building binaries, forge is added
as a dependency to af* targets when it is built.
---
 .../AFconfigure_forge_submodule.cmake         | 58 ++++++++-----------
 src/backend/cpu/CMakeLists.txt                |  4 ++
 src/backend/cuda/CMakeLists.txt               |  4 ++
 src/backend/opencl/CMakeLists.txt             |  4 ++
 4 files changed, 36 insertions(+), 34 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index 748e1ba48d..945e524954 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -5,45 +5,35 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-if(AF_BUILD_FORGE)
-  set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
-  set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
-  set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
-  set(CMAKE_BUILD_TYPE Release)
-  set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
-  set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
-  set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
+set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
+set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
+set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
+set(CMAKE_BUILD_TYPE Release)
+set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
+set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
+set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
-  add_subdirectory(extern/forge)
-
-  mark_as_advanced(
-      FG_BUILD_EXAMPLES
-      FG_BUILD_DOCS
-      FG_WITH_FREEIMAGE
-      FG_USE_WINDOW_TOOLKIT
-      FG_USE_SYSTEM_CL2HPP
-      FG_ENABLE_HUNTER
-      glfw3_DIR
-      glm_DIR
-      )
-  set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
-  set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
+add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
+mark_as_advanced(
+    FG_BUILD_EXAMPLES
+    FG_BUILD_DOCS
+    FG_WITH_FREEIMAGE
+    FG_USE_WINDOW_TOOLKIT
+    FG_USE_SYSTEM_CL2HPP
+    FG_ENABLE_HUNTER
+    glfw3_DIR
+    glm_DIR
+    )
+set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
+set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
 
+if (AF_BUILD_FORGE AND AF_INSTALL_STANDALONE)
   install(FILES
       $<TARGET_FILE:forge>
       $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
       $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
       DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
-  set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
-else(AF_BUILD_FORGE)
-  set(FG_VERSION "1.0.0")
-  set(FG_VERSION_MAJOR 1)
-  set(FG_VERSION_MINOR 0)
-  set(FG_VERSION_PATCH 0)
-  set(FG_API_VERSION_CURRENT 10)
-  configure_file(
-    ${PROJECT_SOURCE_DIR}/extern/forge/CMakeModules/version.h.in
-    ${PROJECT_BINARY_DIR}/extern/forge/include/fg/version.h
-    )
-endif(AF_BUILD_FORGE)
+endif ()
+
+set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index a70ee738f7..31ee92aaa9 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -349,6 +349,10 @@ if(LAPACK_FOUND OR MKL_FOUND)
       WITH_LINEAR_ALGEBRA)
 endif()
 
+if (AF_BUILD_FORGE)
+  add_dependencies(afcpu forge)
+endif ()
+
 install(TARGETS afcpu
   EXPORT ArrayFireCPUTargets
   COMPONENT cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 30088878b7..20beacedc3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -523,6 +523,10 @@ if(APPLE)
   target_link_libraries(afcuda PUBLIC -Wl,-rpath,${CUDA_LIBRARIES_PATH})
 endif()
 
+if (AF_BUILD_FORGE)
+  add_dependencies(afcuda forge)
+endif ()
+
 install(TARGETS afcuda
   EXPORT ArrayFireCUDATargets
   COMPONENT cuda
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 0bc7a47d93..407107e665 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -508,6 +508,10 @@ if(LAPACK_FOUND OR MKL_FOUND)
       WITH_LINEAR_ALGEBRA)
 endif(LAPACK_FOUND OR MKL_FOUND)
 
+if (AF_BUILD_FORGE)
+  add_dependencies(afopencl forge)
+endif ()
+
 install(TARGETS afopencl
   EXPORT ArrayFireOpenCLTargets
   COMPONENT opencl

From 2804a72f071d677f36d47f548fe72fe814c315c3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 26 Apr 2019 17:59:28 -0400
Subject: [PATCH 1637/2677] Remove default implementation of unaryName struct

---
 src/backend/cpu/unary.hpp    | 1 +
 src/backend/cuda/unary.hpp   | 4 +---
 src/backend/opencl/unary.hpp | 4 +---
 3 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index bf78a0f10f..af608165f1 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <jit/UnaryNode.hpp>
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 64c951596f..0e713b69ae 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -15,9 +15,7 @@
 namespace cuda {
 
 template<af_op_t op>
-static const char *unaryName() {
-    return "__noop";
-}
+static const char *unaryName();
 
 #define UNARY_DECL(OP, FNAME)                      \
     template<>                                     \
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 8a385ea001..cc25a81758 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -15,9 +15,7 @@
 namespace opencl {
 
 template<af_op_t op>
-static const char *unaryName() {
-    return "__noop";
-}
+static const char *unaryName();
 
 #define UNARY_DECL(OP, FNAME)                      \
     template<>                                     \

From d14ea2a80cbe75feee4f593c2f74288bbefe4ac3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 26 Apr 2019 18:18:15 -0400
Subject: [PATCH 1638/2677] Add the reciprocal square root function

---
 docs/details/arith.dox           |  9 +++++++++
 include/af/arith.h               | 22 ++++++++++++++++++++++
 src/api/c/optypes.hpp            |  1 +
 src/api/c/unary.cpp              |  1 +
 src/api/cpp/unary.cpp            |  1 +
 src/api/unified/arith.cpp        |  1 +
 src/backend/cpu/unary.hpp        |  6 ++++++
 src/backend/cuda/nvrtc/cache.cpp |  1 +
 src/backend/cuda/unary.hpp       |  1 +
 src/backend/opencl/unary.hpp     |  1 +
 test/math.cpp                    |  6 ++++++
 11 files changed, 50 insertions(+)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 056c126d53..eb5e8f404d 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -528,6 +528,15 @@ Square root of input arrays
 
 \copydoc arith_real_only
 
+\defgroup arith_func_rsqrt rsqrt
+
+\ingroup explog_mat
+
+The reciprocal or inverse square root of input arrays
+
+\f[ \frac{1}{\sqrt{x}} \f]
+
+\copydoc arith_real_only
 
 \defgroup arith_func_cbrt cbrt
 
diff --git a/include/af/arith.h b/include/af/arith.h
index 8e8a282c66..6089b3619d 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -502,6 +502,16 @@ namespace af
     /// \ingroup arith_func_sqrt
     AFAPI array sqrt   (const array &in);
 
+#if AF_API_VERSION >= 37
+    /// C++ Interface for reciprocal square root of input
+    ///
+    /// \param[in] in is input
+    /// \return the reciprocal square root of input
+    ///
+    /// \ingroup arith_func_rsqrt
+    AFAPI array rsqrt   (const array &in);
+#endif
+
     /// C++ Interface for cube root of input
     ///
     /// \param[in] in is input
@@ -1324,6 +1334,18 @@ extern "C" {
     */
     AFAPI af_err af_sqrt    (af_array *out, const af_array in);
 
+#if AF_API_VERSION >= 37
+    /**
+      C Interface for reciprocal  square root
+
+      \param[out] out will contain the reciprocal square root of \p in
+      \param[in] in is input
+      \return \ref AF_SUCCESS if the execution completes properly
+
+      \ingroup arith_func_rsqrt
+    */
+    AFAPI af_err af_rsqrt    (af_array *out, const af_array in);
+#endif
     /**
        C Interface for cube root
 
diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index cecf3bd8a0..a20e52048a 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -95,4 +95,5 @@ typedef enum {
 
     af_select_t,
     af_not_select_t,
+    af_rsqrt_t
 } af_op_t;
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index a2edd31f3f..7ad9e91542 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -127,6 +127,7 @@ UNARY(log1p)
 UNARY(log2)
 
 UNARY(cbrt)
+UNARY(rsqrt)
 
 UNARY(tgamma)
 UNARY(lgamma)
diff --git a/src/api/cpp/unary.cpp b/src/api/cpp/unary.cpp
index eea4b4d83e..65e3cfd4c3 100644
--- a/src/api/cpp/unary.cpp
+++ b/src/api/cpp/unary.cpp
@@ -65,6 +65,7 @@ INSTANTIATE(log10)
 INSTANTIATE(log2)
 
 INSTANTIATE(sqrt)
+INSTANTIATE(rsqrt)
 INSTANTIATE(cbrt)
 
 INSTANTIATE(iszero)
diff --git a/src/api/unified/arith.cpp b/src/api/unified/arith.cpp
index 7158af2e33..9330373036 100644
--- a/src/api/unified/arith.cpp
+++ b/src/api/unified/arith.cpp
@@ -90,6 +90,7 @@ UNARY_HAPI_DEF(af_log1p)
 UNARY_HAPI_DEF(af_log10)
 UNARY_HAPI_DEF(af_log2)
 UNARY_HAPI_DEF(af_sqrt)
+UNARY_HAPI_DEF(af_rsqrt)
 UNARY_HAPI_DEF(af_cbrt)
 UNARY_HAPI_DEF(af_factorial)
 UNARY_HAPI_DEF(af_tgamma)
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index af608165f1..a511f98918 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -21,6 +21,11 @@ T sigmoid(T in) {
     return (1.0) / (1 + std::exp(-in));
 }
 
+template<typename T>
+T rsqrt(T in) {
+    return  pow(in, -0.5);
+}
+
 #define UNARY_OP_FN(op, fn)                                               \
     template<typename T>                                                  \
     struct UnOp<T, T, af_##op##_t> {                                      \
@@ -65,6 +70,7 @@ UNARY_OP(log1p)
 UNARY_OP(log2)
 
 UNARY_OP(sqrt)
+UNARY_OP_FN(rsqrt, rsqrt)
 UNARY_OP(cbrt)
 
 UNARY_OP(tgamma)
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 82b6e7e293..6ea2e3f4fa 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -310,6 +310,7 @@ string getOpEnumStr(af_op_t val) {
 
         CASE_STMT(af_select_t);
         CASE_STMT(af_not_select_t);
+        CASE_STMT(af_rsqrt_t);
     }
 #undef CASE_STMT
     return retVal;
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 0e713b69ae..c4a20e8174 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -56,6 +56,7 @@ UNARY_FN(log10)
 UNARY_FN(log2)
 
 UNARY_FN(sqrt)
+UNARY_FN(rsqrt)
 UNARY_FN(cbrt)
 
 UNARY_FN(trunc)
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index cc25a81758..e7ef82c6d1 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -56,6 +56,7 @@ UNARY_FN(log10)
 UNARY_FN(log2)
 
 UNARY_FN(sqrt)
+UNARY_FN(rsqrt)
 UNARY_FN(cbrt)
 
 UNARY_FN(trunc)
diff --git a/test/math.cpp b/test/math.cpp
index 84e84dc537..fd195b800d 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -33,6 +33,11 @@ T sigmoid(T in) {
     return 1.0 / (1.0 + std::exp(-in));
 }
 
+template<typename T>
+T rsqrt(T in) {
+    return 1.0/sqrt(in);
+}
+
 #define TEST_REAL(T, func, err, lo, hi)                          \
     TEST(MathTests, Test_##func##_##T) {                         \
         try {                                                    \
@@ -121,6 +126,7 @@ MATH_TESTS_ALL(exp)
 MATH_TESTS_ALL(log)
 MATH_TESTS_REAL(log10)
 MATH_TESTS_REAL(log2)
+MATH_TESTS_REAL(rsqrt)
 
 MATH_TESTS_REAL(sigmoid)
 

From e9de40dca90ed53a1efc9e64cd91168769cfdc59 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 May 2019 19:23:31 +0530
Subject: [PATCH 1639/2677] New Window method: setAxesLabelFormat (#2495)

* New Window method: setAxesLabelFormat

This member function of af::Window class enables the user
to set the format in which chart axes labels are generated.

The formats for each axis are specified as a printf-style
format specifier.
---
 include/af/graphics.h                  | 45 ++++++++++++++++++++++++++
 src/api/c/window.cpp                   | 35 ++++++++++++++++++++
 src/api/cpp/graphics.cpp               |  7 ++++
 src/api/unified/graphics.cpp           |  7 ++++
 src/backend/common/forge_loader.hpp    |  1 +
 src/backend/common/graphics_common.cpp |  1 +
 6 files changed, 96 insertions(+)

diff --git a/include/af/graphics.h b/include/af/graphics.h
index e67b93a64c..df06c4b395 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -485,6 +485,22 @@ class AFAPI Window {
                            const char * const ytitle = "Y-Axis",
                            const char * const ztitle = NULL);
 #endif
+
+#if AF_API_VERSION >= 37
+        /**
+           Setup the axes label formats for charts
+
+           \param[in] xformat is a printf-style format specifier for x-axis
+           \param[in] yformat is a printf-style format specifier for y-axis
+           \param[in] zformat is a printf-style format specifier for z-axis
+
+           \ingroup gfx_func_window
+        */
+        void setAxesLabelFormat(const char *const xformat = "4.1%f",
+                                const char *const yformat = "4.1%f",
+                                const char *const zformat = NULL);
+#endif
+
         /**
            Setup grid layout for multiview mode in a window
 
@@ -1088,6 +1104,35 @@ AFAPI af_err af_set_axes_titles(const af_window wind,
                                 const af_cell* const props);
 #endif
 
+#if AF_API_VERSION >= 37
+/**
+   C Interface wrapper for setting axes labels formats for charts
+
+   Axes labels use printf style format specifiers. Default specifier for the
+   data displayed as labels is `%4.1f`. This function lets the user change this
+   label formatting to whichever format that fits their data range and precision.
+
+   \param[in] wind is the window handle
+   \param[in] xformat is a printf-style format specifier for x-axis
+   \param[in] yformat is a printf-style format specifier for y-axis
+   \param[in] zformat is a printf-style format specifier for z-axis
+   \param[in] props is structure \ref af_cell that has the properties that
+              are used for the current rendering.
+
+   \note \p zformat can be NULL in which case ArrayFire understands that the
+   label formats are meant for a 2D chart corresponding to this \p wind
+   or a specific cell in multi-viewport mode (provided via \p props argument).
+   A non NULL value to \p zformat means the label formats belong to a 3D chart.
+
+   \ingroup gfx_func_window
+*/
+AFAPI af_err af_set_axes_label_format(const af_window wind,
+                                      const char *const xformat,
+                                      const char *const yformat,
+                                      const char *const zformat,
+                                      const af_cell *const props);
+#endif
+
 /**
    C Interface wrapper for showing a window
 
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index b576c43990..92da1b35fe 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -221,6 +221,41 @@ af_err af_set_axes_titles(const af_window window, const char* const xtitle,
     return AF_SUCCESS;
 }
 
+af_err af_set_axes_label_format(const af_window window,
+                                const char* const xformat,
+                                const char* const yformat,
+                                const char* const zformat,
+                                const af_cell* const props) {
+    try {
+        if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
+
+        ARG_ASSERT(2, xformat != nullptr);
+        ARG_ASSERT(3, yformat != nullptr);
+
+        ForgeManager& fgMngr = forgeManager();
+
+        fg_chart chart = nullptr;
+
+        fg_chart_type ctype = (zformat ? FG_CHART_3D : FG_CHART_2D);
+
+        if (props->col > -1 && props->row > -1)
+            chart = fgMngr.getChart(window, props->row, props->col, ctype);
+        else
+            chart = fgMngr.getChart(window, 0, 0, ctype);
+
+        if (ctype == FG_CHART_2D) {
+            FG_CHECK(forgePlugin().fg_set_chart_label_format(chart, xformat,
+                                                             yformat, "3.2%f"));
+        } else {
+            ARG_ASSERT(4, zformat != nullptr);
+            FG_CHECK(forgePlugin().fg_set_chart_label_format(chart, xformat,
+                                                             yformat, zformat));
+        }
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_show(const af_window wind) {
     try {
         if (wind == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index 85faa917bc..dff95979c8 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -189,6 +189,13 @@ void Window::setAxesTitles(const char* const xtitle, const char* const ytitle,
     AF_THROW(af_set_axes_titles(get(), xtitle, ytitle, ztitle, &temp));
 }
 
+void Window::setAxesLabelFormat(const char* const xformat,
+                                const char* const yformat,
+                                const char* const zformat) {
+    af_cell temp{_r, _c, NULL, AF_COLORMAP_DEFAULT};
+    AF_THROW(af_set_axes_label_format(get(), xformat, yformat, zformat, &temp));
+}
+
 void Window::show() {
     AF_THROW(af_show(get()));
     _r = -1;
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index bb7afe7b6d..30181c4221 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -172,6 +172,13 @@ af_err af_set_axes_titles(const af_window wind, const char* const xtitle,
     return CALL(wind, xtitle, ytitle, ztitle, props);
 }
 
+af_err af_set_axes_label_format(const af_window wind, const char* const xformat,
+                                const char* const yformat,
+                                const char* const zformat,
+                                const af_cell* const props) {
+    return CALL(wind, xformat, yformat, zformat, props);
+}
+
 af_err af_show(const af_window wind) { return CALL(wind); }
 
 af_err af_is_window_closed(bool* out, const af_window wind) {
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index 39e3e2d23e..15b8c81447 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -78,6 +78,7 @@ class ForgeModule : public common::DependencyModule {
     MODULE_MEMBER(fg_get_chart_axes_limits);
     MODULE_MEMBER(fg_set_chart_axes_limits);
     MODULE_MEMBER(fg_set_chart_axes_titles);
+    MODULE_MEMBER(fg_set_chart_label_format);
     MODULE_MEMBER(fg_append_image_to_chart);
     MODULE_MEMBER(fg_append_plot_to_chart);
     MODULE_MEMBER(fg_append_histogram_to_chart);
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index a90f826817..a154041a46 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -82,6 +82,7 @@ ForgeModule::ForgeModule() : DependencyModule("forge", nullptr) {
         FG_MODULE_FUNCTION_INIT(fg_get_chart_axes_limits);
         FG_MODULE_FUNCTION_INIT(fg_set_chart_axes_limits);
         FG_MODULE_FUNCTION_INIT(fg_set_chart_axes_titles);
+        FG_MODULE_FUNCTION_INIT(fg_set_chart_label_format);
         FG_MODULE_FUNCTION_INIT(fg_append_image_to_chart);
         FG_MODULE_FUNCTION_INIT(fg_append_plot_to_chart);
         FG_MODULE_FUNCTION_INIT(fg_append_histogram_to_chart);

From 6f8c4bbf96128c1f9c251d6b8af1b358a3992ce5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 26 Apr 2019 21:10:19 -0400
Subject: [PATCH 1640/2677] Revert "Move parameters to shared memory (#2487)"

This reverts commit 7d1530130d8585166bb53ebfbd8d5d317ec46013.
---
 src/backend/common/jit/BufferNodeBase.hpp    |  17 +-
 src/backend/common/jit/Node.hpp              |  53 +-----
 src/backend/common/jit/ShiftNodeBase.hpp     |  17 +-
 src/backend/cuda/Array.cpp                   |  10 +-
 src/backend/cuda/Param.hpp                   |  48 +++---
 src/backend/cuda/jit.cpp                     | 167 +++++--------------
 src/backend/cuda/jit/BufferNode.hpp          |   2 +-
 src/backend/cuda/jit/ShiftNode.hpp           |  20 ---
 src/backend/cuda/jit/kernel_generators.hpp   |  58 +++----
 src/backend/cuda/nvrtc/cache.cpp             |   1 -
 src/backend/cuda/shift.cpp                   |   6 +-
 src/backend/opencl/CMakeLists.txt            |   1 -
 src/backend/opencl/jit.cpp                   |  97 ++---------
 src/backend/opencl/jit/ShiftNode.hpp         |  17 --
 src/backend/opencl/jit/kernel_generators.hpp |  78 ++++-----
 src/backend/opencl/memory.cpp                |   5 +-
 src/backend/opencl/memory.hpp                |   6 +-
 src/backend/opencl/shift.cpp                 |   5 +-
 test/jit.cpp                                 |  90 +---------
 19 files changed, 175 insertions(+), 523 deletions(-)
 delete mode 100644 src/backend/cuda/jit/ShiftNode.hpp
 delete mode 100644 src/backend/opencl/jit/ShiftNode.hpp

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 30bf461a7d..525555341e 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -9,11 +9,8 @@
 
 #pragma once
 #include <backend.hpp>
-#include <common/jit/Node.hpp>
 #include <jit/kernel_generators.hpp>
 
-#include <iomanip>
-#include <mutex>
 #include <sstream>
 
 namespace common {
@@ -25,18 +22,14 @@ class BufferNodeBase : public common::Node {
     ParamType m_param;
     unsigned m_bytes;
     std::once_flag m_set_data_flag;
-    int param_index;
     bool m_linear_buffer;
 
    public:
-    using param_type = ParamType;
     BufferNodeBase(const char *type_str, const char *name_str)
         : Node(type_str, name_str, 0, {}) {}
 
     bool isBuffer() const final { return true; }
 
-    bool requiresGlobalMemoryAccess() const final { return true; }
-
     void setData(ParamType param, DataType data, const unsigned bytes,
                  bool is_linear) {
         std::call_once(m_set_data_flag,
@@ -70,17 +63,14 @@ class BufferNodeBase : public common::Node {
 
     int setArgs(int start_id, bool is_linear,
                 std::function<void(int id, const void *ptr, size_t arg_size)>
-                    setArg) const final {
+                    setArg) const override {
         return detail::setKernelArguments(start_id, is_linear, setArg, m_data,
-                                          m_param, param_index);
+                                          m_param);
     }
 
-    void setParamIndex(int index) final { param_index = index; }
-    int getParamIndex() const final { return param_index; }
-
     void genOffsets(std::stringstream &kerStream, int id,
                     bool is_linear) const final {
-        detail::generateBufferOffsets(kerStream, id, is_linear);
+        detail::generateBufferOffsets(kerStream, id, is_linear, m_type_str);
     }
 
     void genFuncs(std::stringstream &kerStream,
@@ -96,7 +86,6 @@ class BufferNodeBase : public common::Node {
     }
 
     size_t getBytes() const final { return m_bytes; }
-    ParamType &getParam() { return m_param; }
 };
 
 }  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index cc02b97693..9cd2fe51ef 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -50,46 +50,28 @@ class Node {
     int getNodesMap(Node_map_t &node_map, std::vector<const Node *> &full_nodes,
                     std::vector<Node_ids> &full_ids) const;
 
-    /// Generates the string that will be used to hash the kernel
     virtual void genKerName(std::stringstream &kerStream,
-                            const Node_ids &ids) const = 0;
-
-    /// Generates the function parameters for the node.
-    ///
-    /// \param[in/out] kerStream  The string will be written to this stream
-    /// \param[in]     ids        The integer id of the node and its children
-    /// \param[in]     is_linear  True if the kernel is a linear kernel
+                            const Node_ids &ids) const {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
     virtual void genParams(std::stringstream &kerStream, int id,
                            bool is_linear) const {
         UNUSED(kerStream);
         UNUSED(id);
         UNUSED(is_linear);
     }
-
-    /// Generates the variable that stores the thread's/work-item's offset into
-    /// the memory.
-    ///
-    /// \param[in/out] kerStream  The string will be written to this stream
-    /// \param[in]     ids        The integer id of the node and its children
-    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genOffsets(std::stringstream &kerStream, int id,
                             bool is_linear) const {
         UNUSED(kerStream);
         UNUSED(id);
         UNUSED(is_linear);
     }
-
-    /// Generates the code for the operation of the node.
-    ///
-    /// Generates the soruce code of the operation that the node needs to
-    /// perform. For example this function will create the string
-    /// "val2 = __add(val1, val2);" for the addition node.
-    ///
-    /// \param[in/out] kerStream  The string will be written to this stream
-    /// \param[in]     ids        The integer id of the node and its children
-    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genFuncs(std::stringstream &kerStream,
-                          const Node_ids &ids) const = 0;
+                          const Node_ids &ids) const {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
 
     /// Calls the setArg function on each of the arguments passed into the
     /// kernel
@@ -109,11 +91,6 @@ class Node {
         return start_id;
     }
 
-    // Sets the index of the Param object stored in global memory.
-    virtual void setParamIndex(int index) { UNUSED(index); }
-    // Gets the index of the Param object stored in global memory.
-    virtual int getParamIndex() const { return -1; }
-
     virtual void getInfo(unsigned &len, unsigned &buf_count,
                          unsigned &bytes) const {
         UNUSED(buf_count);
@@ -127,13 +104,6 @@ class Node {
 
     // Return the size of the size of the buffer node in bytes. Zero otherwise
     virtual size_t getBytes() const { return 0; }
-
-    // Returns true if the node requires global memory access. This is true
-    // for buffer nodes and shift nodes. This implies that the Node needs
-    // access to the shape of the object to perform indexing operations
-    virtual bool requiresGlobalMemoryAccess() const { return false; }
-
-    // Returns true if this node is a Buffer
     virtual bool isBuffer() const { return false; }
     virtual bool isLinear(dim_t dims[4]) const {
         UNUSED(dims);
@@ -146,13 +116,6 @@ class Node {
     virtual ~Node() {}
 };
 
-// Returns true if the node requires global memory access. This is true
-// for buffer nodes and shift nodes. This implies that the Node needs
-// access to the shape of the object to perform indexing operations
-static inline bool requiresGlobalMemoryAccess(Node &node) {
-    return node.requiresGlobalMemoryAccess();
-}
-
 struct Node_ids {
     std::array<int, Node::kMaxChildren> child_ids;
     int id;
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index afca388de8..d02ebab0e2 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -9,16 +9,13 @@
 
 #pragma once
 
-#include <common/jit/Node.hpp>
 #include <jit/BufferNode.hpp>
 #include <jit/kernel_generators.hpp>
 
-#include <Param.hpp>
 #include <backend.hpp>
+#include <iomanip>
 
 #include <array>
-#include <functional>
-#include <iomanip>
 #include <memory>
 #include <sstream>
 #include <string>
@@ -44,8 +41,6 @@ class ShiftNodeBase : public Node {
         return false;
     }
 
-    bool requiresGlobalMemoryAccess() const final { return true; }
-
     void genKerName(std::stringstream &kerStream,
                     const common::Node_ids &ids) const final {
         kerStream << "_" << m_name_str;
@@ -72,17 +67,9 @@ class ShiftNodeBase : public Node {
         return curr_id + 4;
     }
 
-    void setParamIndex(int index) final { m_buffer_node->setParamIndex(index); }
-    int getParamIndex() const final { return m_buffer_node->getParamIndex(); }
-
-    typename BufferNode::param_type getParam() {
-        return m_buffer_node->getParam();
-    }
-
     void genOffsets(std::stringstream &kerStream, int id,
                     bool is_linear) const final {
-        UNUSED(is_linear);
-        detail::generateShiftNodeOffsets(kerStream, id);
+        detail::generateShiftNodeOffsets(kerStream, id, is_linear, m_type_str);
     }
 
     void genFuncs(std::stringstream &kerStream,
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 145d54dd2c..b9c3137669 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -28,7 +28,6 @@ using common::NodeIterator;
 using cuda::jit::BufferNode;
 
 using std::accumulate;
-using std::find_if;
 using std::shared_ptr;
 using std::vector;
 
@@ -234,11 +233,16 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
             //    pressure. Too many bytes is assumed to be half of all bytes
             //    allocated so far.
             //
+            // 2. Too many buffers in a nonlinear kernel cause param space
+            //    overflow. This happens when the number of nodes reaches 50
+            //    (51 including output). Too many buffers can occur in a tree
+            //    of size 25 in the worst case.
+            //
             // TODO: Find better solution than the following emperical solution.
             if (node->getHeight() > 25 || isBufferLimit) {
                 // This is the size of the params that are passed by default
                 constexpr size_t param_base_size =
-                    sizeof(Param<T>) + sizeof(Param<T>*)+ (5 * sizeof(uint));
+                    sizeof(Param<T>) + (4 * sizeof(uint));
 
                 // This is the maximum size of the params that can be allowed by
                 // CUDA NOTE: This number should have been (4096 -
@@ -272,7 +276,7 @@ Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
                         return prev;
                     });
                 size_t param_size = param_base_size + info.param_scalar_size;
-                param_size += info.num_buffers * (sizeof(T*) + sizeof(int));
+                param_size += info.num_buffers * sizeof(Param<T>);
 
                 // TODO: the buffer_size check here is very conservative. It
                 // will trigger an evaluation of the node in most cases. We
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 6a8889b41a..e51e8e831e 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -21,25 +21,22 @@ namespace cuda {
 template<typename T>
 class Param {
    public:
+    T *ptr;
     dim_t dims[4];
     dim_t strides[4];
-    T *ptr;
-
-    __DH__ Param() noexcept : ptr(nullptr) {}
 
-    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides) noexcept
-        : dims{idims[0], idims[1], idims[2], idims[3]}
-        , strides{istrides[0], istrides[1], istrides[2], istrides[3]}
-        , ptr(iptr) {}
+    __DH__ Param() : ptr(nullptr) {}
 
+    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides)
+        : ptr(iptr) {
+        for (int i = 0; i < 4; i++) {
+            dims[i]    = idims[i];
+            strides[i] = istrides[i];
+        }
+    }
     __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
-
-    Param(const Param<T> &other) noexcept = default;
-    Param(Param<T> &&other) noexcept = default;
-    Param<T> &operator=(const Param<T> &other) noexcept = default;
-    Param<T> &operator=(Param<T> &&other) noexcept = default;
 };
 
 template<typename T>
@@ -54,27 +51,28 @@ Param<T> flat(Param<T> in) {
 template<typename T>
 class CParam {
    public:
+    const T *ptr;
     dim_t dims[4];
     dim_t strides[4];
-    const T *ptr;
 
     __DH__ CParam(const T *iptr, const dim_t *idims, const dim_t *istrides)
-        : dims{idims[0], idims[1], idims[2], idims[3]}
-        , strides{istrides[0], istrides[1], istrides[2], istrides[3]}
-        , ptr(iptr) {}
+        : ptr(iptr) {
+        for (int i = 0; i < 4; i++) {
+            dims[i]    = idims[i];
+            strides[i] = istrides[i];
+        }
+    }
 
-    __DH__ CParam(Param<T> &in)
-        : dims{in.dims[0], in.dims[1], in.dims[2], in.dims[3]}
-        , strides{in.strides[0], in.strides[1], in.strides[2], in.strides[3]}
-        , ptr(in.ptr) {}
+    __DH__ CParam(Param<T> &in) : ptr(in.ptr) {
+        for (int i = 0; i < 4; i++) {
+            dims[i]    = in.dims[i];
+            strides[i] = in.strides[i];
+        }
+    }
 
     __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
-
-    CParam(const CParam<T> &other) noexcept = default;
-    CParam(CParam<T> &&other) noexcept      = default;
-    CParam<T> &operator=(const CParam<T> &other) noexcept = default;
-    CParam<T> &operator=(CParam<T> &&other) noexcept = default;
 };
+
 }  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index cd3af45fa9..6b0921b67f 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -13,16 +13,12 @@
 
 #include <Array.hpp>
 #include <common/jit/Node.hpp>
-#include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
 #include <device_manager.hpp>
-#include <jit/BufferNode.hpp>
-#include <jit/ShiftNode.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-
 #include <nvrtc/cache.hpp>
+#include <platform.hpp>
 #include <af/dim4.hpp>
 
 #include <cstdio>
@@ -37,10 +33,6 @@ namespace cuda {
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
-using common::NodeIterator;
-using common::requiresGlobalMemoryAccess;
-using cuda::jit::BufferNode;
-using cuda::jit::ShiftNode;
 
 using std::hash;
 using std::map;
@@ -48,12 +40,6 @@ using std::string;
 using std::stringstream;
 using std::vector;
 
-template<typename TL, typename TR>
-bool equal_shape(const Param<TL> &lhs, const Param<TR> &rhs) {
-    return std::equal(lhs.dims, lhs.dims + 4, rhs.dims) &&
-           std::equal(lhs.strides, lhs.strides + 4, rhs.strides);
-}
-
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids, bool is_linear) {
@@ -87,11 +73,12 @@ static string getKernelString(const string funcName,
     const std::string includeFileStr(jit_cuh, jit_cuh_len);
 
     const std::string paramTStr = R"JIT(
+template<typename T>
 struct Param
 {
+  T *ptr;
   dim_t dims[4];
   dim_t strides[4];
-  void *ptr;
 };
 )JIT";
 
@@ -105,9 +92,7 @@ struct Param
 
     static const char *kernelVoid = "extern \"C\" __global__ void\n";
     static const char *dimParams =
-        "uint blocks_x, uint blocks_y, uint "
-        "blocks_x_total, uint num_odims";
-    static const char *globalParams = "Param* dims, int num_params, ";
+        "uint blocks_x, uint blocks_y, uint blocks_x_total, uint num_odims";
 
     static const char *loopStart = R"JIT(
     for (int blockIdx_x = blockIdx.x; blockIdx_x < blocks_x_total; blockIdx_x += gridDim.x) {
@@ -115,61 +100,46 @@ struct Param
     static const char *loopEnd   = "}\n\n";
 
     static const char *blockStart = "{\n\n";
-    static const char *blockEnd   = "\n}\n";
+    static const char *blockEnd   = "\n\n}";
 
     static const char *linearIndex = R"JIT(
-    uint threadId = threadIdx.x;
-    dim_t idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
-    if (idx >= outref.dims[3] * outref.strides[3]) continue;
-    )JIT";
+        uint threadId = threadIdx.x;
+        long long idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
+        if (idx >= outref.dims[3] * outref.strides[3]) return;
+        )JIT";
 
     static const char *generalIndex = R"JIT(
-    dim_t id0 = 0, id1 = 0, id2 = 0, id3 = 0;
-    dim_t blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
-    if (num_odims > 2) {
-        id2 = blockIdx_x / blocks_x;
-        id0 = blockIdx_x - id2 * blocks_x;
-        id0 = threadIdx.x + id0 * blockDim.x;
-        if (num_odims > 3) {
-            id3 = blockIdx_y / blocks_y;
-            id1 = blockIdx_y - id3 * blocks_y;
-            id1 = threadIdx.y + id1 * blockDim.y;
+        long long id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        long blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
+        if (num_odims > 2) {
+            id2 = blockIdx_x / blocks_x;
+            id0 = blockIdx_x - id2 * blocks_x;
+            id0 = threadIdx.x + id0 * blockDim.x;
+            if (num_odims > 3) {
+                id3 = blockIdx_y / blocks_y;
+                id1 = blockIdx_y - id3 * blocks_y;
+                id1 = threadIdx.y + id1 * blockDim.y;
+            } else {
+                id1 = threadIdx.y + blockDim.y * blockIdx_y;
+            }
         } else {
+            id3 = 0;
+            id2 = 0;
             id1 = threadIdx.y + blockDim.y * blockIdx_y;
+            id0 = threadIdx.x + blockDim.x * blockIdx_x;
         }
-    } else {
-        id3 = 0;
-        id2 = 0;
-        id1 = threadIdx.y + blockDim.y * blockIdx_y;
-        id0 = threadIdx.x + blockDim.x * blockIdx_x;
-    }
-
-    bool cond = id0 < outref.dims[0] &&
-                id1 < outref.dims[1] &&
-                id2 < outref.dims[2] &&
-                id3 < outref.dims[3];
-
-    dim_t idx = outref.strides[3] * id3 +
-                outref.strides[2] * id2 +
-                outref.strides[1] * id1 + id0;
 
-    if (threadIdx.x < num_params && threadIdx.y == 0) {
-        int tidx = threadIdx.x;
-        block_offsets[tidx] = (id3 < params[tidx].dims[3]) * params[tidx].strides[3] * id3 +
-                              (id2 < params[tidx].dims[2]) * params[tidx].strides[2] * id2;
-    }
-    __syncthreads();
-    if (cond) {
-    )JIT";
+        bool cond = id0 < outref.dims[0] &&
+                    id1 < outref.dims[1] &&
+                    id2 < outref.dims[2] &&
+                    id3 < outref.dims[3];
 
-    string paramreads = R"JIT(
-        extern __shared__ char smem[];
-        Param *params = reinterpret_cast<Param*>(smem);
-        dim_t *block_offsets = reinterpret_cast<dim_t*>(smem+(num_params * sizeof(Param)));
+        if (!cond) { continue; }
 
-        if (threadIdx.x < num_params) { params[threadIdx.x] = dims[threadIdx.x]; }
-        __syncthreads();
-    )JIT";
+        long long idx = outref.strides[3] * id3 +
+                        outref.strides[2] * id2 +
+                        outref.strides[1] * id1 + id0;
+        )JIT";
 
     stringstream inParamStream;
     stringstream outParamStream;
@@ -177,7 +147,6 @@ struct Param
     stringstream offsetsStream;
     stringstream opsStream;
     stringstream outrefstream;
-    outrefstream << "const Param &outref = out" << output_ids[0] << ";\n";
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
         const auto &node     = full_nodes[i];
@@ -190,15 +159,16 @@ struct Param
         node->genFuncs(opsStream, ids_curr);
     }
 
+    outrefstream << "const Param<" << full_nodes[output_ids[0]]->getTypeStr()
+                 << "> &outref = out" << output_ids[0] << ";\n";
+
     for (int i = 0; i < (int)output_ids.size(); i++) {
         int id = output_ids[i];
         // Generate output parameters
-        outParamStream << "Param out" << id << ", \n";
-
+        outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out"
+                       << id << ", \n";
         // Generate code to write the output
-        outWriteStream << "((" << full_nodes[id]->getTypeStr() << "*)(out" << id
-                       << ".ptr))"
-                       << "[idx] = val" << id << ";\n";
+        outWriteStream << "out" << id << ".ptr[idx] = val" << id << ";\n";
     }
 
     // Put various blocks into a single stream
@@ -211,12 +181,10 @@ struct Param
     kerStream << "(\n";
     kerStream << inParamStream.str();
     kerStream << outParamStream.str();
-    if (!is_linear) { kerStream << globalParams; }
     kerStream << dimParams;
     kerStream << ")\n";
     kerStream << blockStart;
     kerStream << outrefstream.str();
-    if (!is_linear) { kerStream << paramreads; }
     kerStream << loopStart;
     if (is_linear) {
         kerStream << linearIndex;
@@ -227,7 +195,6 @@ struct Param
     kerStream << opsStream.str();
     kerStream << outWriteStream.str();
     kerStream << loopEnd;
-    if (!is_linear) kerStream << "}";
     kerStream << blockEnd;
 
     return kerStream.str();
@@ -269,6 +236,7 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
 
     if (num_outputs == 0) return;
 
+    // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
     thread_local vector<const Node *> full_nodes;
     thread_local vector<Node_ids> full_ids;
@@ -282,39 +250,9 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
         full_ids.reserve(1024);
     }
 
-    vector<Param<T>> params;
     for (auto &node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
         output_ids.push_back(id);
-
-        NodeIterator<> end_node;
-        auto bufit = NodeIterator<>(node);
-        while (bufit != end_node) {
-            bufit = find_if(bufit, end_node, requiresGlobalMemoryAccess);
-            if (bufit != end_node) {
-                Param<T> param;
-
-                // TODO(umar): This is a hack. We need to clean up this API
-                // so that the if statement is not necessary
-                if (bufit->isBuffer()) {
-                    param = static_cast<BufferNode<T> &>(*bufit).getParam();
-                } else {
-                    param = static_cast<ShiftNode<T> &>(*bufit).getParam();
-                }
-
-                auto it = find_if(begin(params), end(params),
-                                  [&param](const Param<T> &p) {
-                                      return equal_shape<T, T>(param, p);
-                                  });
-                if (it == end(params)) {
-                    params.push_back(param);
-                    bufit->setParamIndex(params.size() - 1);
-                } else {
-                    bufit->setParamIndex(distance(begin(params), it));
-                }
-                bufit++;
-            }
-        }
     }
 
     bool is_linear = true;
@@ -370,6 +308,7 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
     }
 
     vector<void *> args;
+
     for (const auto &node : full_nodes) {
         node->setArgs(0, is_linear,
                       [&](int /*id*/, const void *ptr, size_t /*size*/) {
@@ -381,32 +320,14 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
         args.push_back((void *)&outputs[i]);
     }
 
-    uptr<uchar> dparam;
-    void *ptr         = nullptr;
-    int param_count   = 0;
-    size_t smem_bytes = 0;
-    if (!is_linear) {
-        smem_bytes  = (sizeof(Param<T>) + sizeof(dim_t)) * params.size();
-        param_count = params.size();
-        dparam      = memAlloc<uchar>(params.size() * sizeof(Param<T>));
-        CUDA_CHECK(cudaMemcpyAsync(dparam.get(), params.data(),
-                                   params.size() * sizeof(Param<T>),
-                                   cudaMemcpyHostToDevice, getActiveStream()));
-        ptr = dparam.get();
-        args.push_back(&ptr);
-        args.push_back((void *)&param_count);
-    }
-
     args.push_back((void *)&blocks_x_);
     args.push_back((void *)&blocks_y_);
     args.push_back((void *)&blocks_x_total);
     args.push_back((void *)&num_odims);
 
     CU_CHECK(cuLaunchKernel(ker, blocks_x, blocks_y, blocks_z, threads_x,
-                            threads_y, 1, smem_bytes, getActiveStream(),
-                            args.data(), NULL));
-
-    POST_LAUNCH_CHECK();
+                            threads_y, 1, 0, getActiveStream(), args.data(),
+                            NULL));
 
     // Reset the thread local vectors
     nodes.clear();
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
index 4c4d36ffe8..371a263245 100644
--- a/src/backend/cuda/jit/BufferNode.hpp
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -15,5 +15,5 @@ namespace cuda {
 namespace jit {
 template<typename T>
 using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
-}  // namespace jit
+}
 }  // namespace cuda
diff --git a/src/backend/cuda/jit/ShiftNode.hpp b/src/backend/cuda/jit/ShiftNode.hpp
deleted file mode 100644
index c2b9e7a44c..0000000000
--- a/src/backend/cuda/jit/ShiftNode.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-/*******************************************************
- * Copyright (c) 2019, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <common/jit/BufferNodeBase.hpp>
-#include <common/jit/ShiftNodeBase.hpp>
-
-namespace cuda {
-namespace jit {
-
-template<typename T>
-using ShiftNode = common::ShiftNodeBase<BufferNode<T>>;
-
-}  // namespace jit
-}  // namespace cuda
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index ee03062484..3414e439b9 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <Param.hpp>
 #include <common/jit/Node.hpp>
 
 #include <functional>
@@ -22,70 +21,64 @@ namespace cuda {
 namespace {
 
 /// Creates a string that will be used to declare the parameter of kernel
-inline void generateParamDeclaration(std::stringstream& kerStream, int id,
-                                     bool is_linear,
-                                     const std::string& m_type_str) {
+void generateParamDeclaration(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& m_type_str) {
     if (is_linear) {
         kerStream << m_type_str << " *in" << id << "_ptr,\n";
     } else {
-        kerStream << m_type_str << " *in" << id << "_ptr, int in_index" << id
-                  << ",\n";
+        kerStream << "Param<" << m_type_str << "> in" << id << ",\n";
     }
 }
 
 /// Calls the setArg function to set the arguments for a kernel call
 template<typename T>
-inline int setKernelArguments(
+int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-    const std::shared_ptr<T>& ptr, const Param<T>& info,
-    const int& param_index) {
+    const std::shared_ptr<T>& ptr, const Param<T>& info) {
     UNUSED(ptr);
     if (is_linear) {
         setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
     } else {
-        // setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
-        setArg(start_id++, static_cast<const void*>(&info.ptr), sizeof(T*));
-        setArg(start_id, &param_index, sizeof(int));
+        setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
     }
     return start_id + 1;
 }
 
 /// Generates the code to calculate the offsets for a buffer
-inline void generateBufferOffsets(std::stringstream& kerStream, int id,
-                                  bool is_linear) {
-    std::string idx_str = std::string("\n\t\tdim_t idx") + std::to_string(id);
+void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
+                           const std::string& type_str) {
+    std::string idx_str = std::string("int idx") + std::to_string(id);
 
     if (is_linear) {
-        kerStream << idx_str << " = idx;";
+        kerStream << idx_str << " = idx;\n";
     } else {
-        // clang-format off
-        std::string in_index = "in_index" + std::to_string(id);
-        std::string block_offset = "block_offsets[" + in_index + "]";
-        std::string in_param = "params[" + in_index + "]";
-
-        kerStream << idx_str << " = " << block_offset
-                  << "\n + ((id1 < " << in_param << ".dims[1]) * " << in_param << ".strides[1] * id1)"
-                  << "\n + ((id0 < " << in_param << ".dims[0]) * id0);";
-        // clang-format on
+        std::string info_str = std::string("in") + std::to_string(id);
+        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
+                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
+                  << info_str << ".dims[1]) * " << info_str
+                  << ".strides[1] * id1 + (id0 < " << info_str
+                  << ".dims[0]) * id0;\n";
+        kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
     }
 }
 
 /// Generates the code to read a buffer and store it in a local variable
-inline void generateBufferRead(std::stringstream& kerStream, int id,
-                               const std::string& type_str) {
+void generateBufferRead(std::stringstream& kerStream, int id,
+                        const std::string& type_str) {
     kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
 
-inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id) {
+void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& type_str) {
+    UNUSED(is_linear);
     std::string idx_str   = std::string("idx") + std::to_string(id);
     std::string info_str  = std::string("in") + std::to_string(id);
     std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
     std::string shift_str = std::string("shift") + std::to_string(id) + "_";
 
-    kerStream << "Param& " << info_str << " = "
-              << "params[in_index" << id << "];\n";
     for (int i = 0; i < 4; i++) {
         kerStream << "int " << id_str << i << " = __circular_mod(id" << i
                   << " + " << shift_str << i << ", " << info_str << ".dims["
@@ -103,10 +96,11 @@ inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id) {
               << "1;\n";
     kerStream << idx_str << " += (" << id_str << "0 < " << info_str
               << ".dims[0]) * " << id_str << "0;\n";
+    kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
 }
 
-inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
-                                  const std::string& type_str) {
+void generateShiftNodeRead(std::stringstream& kerStream, int id,
+                           const std::string& type_str) {
     kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 6ea2e3f4fa..67f667892e 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -75,7 +75,6 @@ using kc_t = map<string, Kernel>;
         char* logptr = log.get();                      \
         nvrtcGetProgramLog(prog, logptr);              \
         logptr[logSize] = '\x0';                       \
-        puts(logptr);                                  \
         AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
     } while (0)
 #else
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index e6e232239b..c5ab83248e 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -11,7 +11,6 @@
 #include <common/jit/ShiftNodeBase.hpp>
 #include <err_cuda.hpp>
 #include <jit/BufferNode.hpp>
-#include <jit/ShiftNode.hpp>
 #include <shift.hpp>
 
 #include <memory>
@@ -22,7 +21,6 @@ using common::Node_ptr;
 using common::ShiftNodeBase;
 
 using cuda::jit::BufferNode;
-using cuda::jit::ShiftNode;
 
 using std::array;
 using std::make_shared;
@@ -30,10 +28,12 @@ using std::static_pointer_cast;
 using std::string;
 
 namespace cuda {
+template<typename T>
+using ShiftNode = ShiftNodeBase<BufferNode<T>>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
-    // Shift should only be the leaf node in the JIT tree.
+    // Shift should only be the first node in the JIT tree.
     // Force input to be evaluated so that in is always a buffer.
     in.eval();
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 407107e665..47ae3365ae 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -344,7 +344,6 @@ target_sources(afopencl
 target_sources(afopencl
   PRIVATE
     jit/BufferNode.hpp
-    jit/ShiftNode.hpp
     jit/kernel_generators.hpp
   )
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 72ecbe6efe..277f53684a 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -10,11 +10,8 @@
 #include <Array.hpp>
 #include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
-#include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
-#include <jit/BufferNode.hpp>
-#include <jit/ShiftNode.hpp>
 #include <kernel_headers/jit.hpp>
 #include <program.hpp>
 #include <af/dim4.hpp>
@@ -27,16 +24,11 @@
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
-using common::NodeIterator;
-using common::requiresGlobalMemoryAccess;
-using opencl::jit::BufferNode;
-using opencl::jit::ShiftNode;
 
 using cl::Buffer;
 using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::Local;
 using cl::NDRange;
 using cl::NullRange;
 using cl::Program;
@@ -48,11 +40,6 @@ using std::vector;
 
 namespace opencl {
 
-bool equal_shape(const KParam &lhs, const KParam &rhs) {
-    return std::equal(lhs.dims, lhs.dims + 4, rhs.dims) &&
-           std::equal(lhs.strides, lhs.strides + 4, rhs.strides);
-}
-
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids, bool is_linear) {
@@ -82,27 +69,22 @@ static string getKernelString(const string funcName,
                               const vector<int> &output_ids, bool is_linear) {
     // Common OpenCL code
     // This part of the code does not change with the kernel.
+
     static const char *kernelVoid = "__kernel void\n";
-    static const char *nonLinearParams =
-        "__global KParam* dims, int num_params,\n"
-        "__local KParam* params, __local dim_t* block_offsets,\n";
     static const char *dimParams =
         "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
     static const char *blockStart = "{\n\n";
-    static const char *blockEnd   = "\n}\n";
+    static const char *blockEnd   = "\n\n}";
 
     static const char *linearIndex = R"JIT(
-        size_t groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
-        size_t threadId = get_local_id(0);
-        size_t idx = groupId * get_local_size(0) * get_local_size(1) + threadId;
+        uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
+        uint threadId = get_local_id(0);
+        int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;
         if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;
         )JIT";
 
     static const char *generalIndex = R"JIT(
-        if (get_local_id(0) < num_params) {
-            params[get_local_id(0)] = dims[get_local_id(0)];
-        }
-        dim_t id0 = 0, id1 = 0, id2 = 0, id3 = 0;
+        uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
         if (num_odims > 2) {
             id2 = get_group_id(0) / groups_0;
             id0 = get_group_id(0) - id2 * groups_0;
@@ -124,18 +106,11 @@ static string getKernelString(const string funcName,
                     id1 < oInfo.dims[1] &&
                     id2 < oInfo.dims[2] &&
                     id3 < oInfo.dims[3];
-        size_t idx = oInfo.strides[3] * id3 +
-                     oInfo.strides[2] * id2 +
-                     oInfo.strides[1] * id1 +
-                     id0 + oInfo.offset;
-
-        if (get_local_id(0) < num_params && get_local_id(1) == 0) {
-            dim_t tidx = get_local_id(0);
-            block_offsets[tidx] = (id3 < params[tidx].dims[3]) * params[tidx].strides[3] * id3 +
-                                  (id2 < params[tidx].dims[2]) * params[tidx].strides[2] * id2;
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
         if (!cond) return;
+        int idx = oInfo.strides[3] * id3 +
+                  oInfo.strides[2] * id2 +
+                  oInfo.strides[1] * id1 +
+                  id0 + oInfo.offset;
         )JIT";
 
     stringstream inParamStream;
@@ -143,8 +118,6 @@ static string getKernelString(const string funcName,
     stringstream outWriteStream;
     stringstream offsetsStream;
     stringstream opsStream;
-    stringstream outrefstream;
-    outrefstream << "const Param outref = out" << output_ids[0] << ";\n";
 
     for (int i = 0; i < (int)full_nodes.size(); i++) {
         const auto &node     = full_nodes[i];
@@ -173,7 +146,6 @@ static string getKernelString(const string funcName,
     kerStream << "(\n";
     kerStream << inParamStream.str();
     kerStream << outParamStream.str();
-    if (!is_linear) { kerStream << nonLinearParams; }
     kerStream << dimParams;
     kerStream << ")\n";
     kerStream << blockStart;
@@ -243,43 +215,13 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
         full_ids.reserve(1024);
     }
 
-    vector<KParam> params;
     for (auto &node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
         output_ids.push_back(id);
-
-        NodeIterator<> end_node;
-        auto bufit = NodeIterator<>(node);
-        while (bufit != end_node) {
-            bufit = find_if(bufit, end_node, requiresGlobalMemoryAccess);
-            if (bufit != end_node) {
-                KParam param;
-
-                // TODO(umar): This is a hack. We need to clean up this API
-                // so that the if statement is not necessary
-                if (bufit->isBuffer()) {
-                    param = static_cast<BufferNode &>(*bufit).getParam();
-                } else {
-                    param = static_cast<ShiftNode &>(*bufit).getParam();
-                }
-
-                auto it = find_if(begin(params), end(params),
-                                  [&param](const KParam &p) {
-                                      return equal_shape(param, p);
-                                  });
-                if (it == end(params)) {
-                    params.push_back(param);
-                    bufit->setParamIndex(params.size() - 1);
-                } else {
-                    bufit->setParamIndex(distance(begin(params), it));
-                }
-                ++bufit;
-            }
-        }
     }
 
     bool is_linear = true;
-    for (const auto &node : full_nodes) {
+    for (auto node : full_nodes) {
         is_linear &= node->isLinear(outputs[0].info.dims);
     }
 
@@ -341,23 +283,6 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
         ++nargs;
     }
 
-    size_t smem_bytes = 0;
-    int param_count   = 0;
-    uptr dparam;
-    if (!is_linear) {
-        param_count = params.size();
-        dparam      = memAlloc<uchar>(params.size() * sizeof(KParam));
-
-        getQueue().enqueueWriteBuffer(*(dparam.get()), CL_FALSE, 0,
-                                      params.size() * sizeof(KParam),
-                                      params.data());
-
-        ker.setArg(nargs++, *(dparam.get()));
-        ker.setArg(nargs++, param_count);
-        ker.setArg(nargs++, Local(sizeof(KParam) * params.size()));
-        ker.setArg(nargs++, Local(sizeof(dim_t) * params.size()));
-    }
-
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
diff --git a/src/backend/opencl/jit/ShiftNode.hpp b/src/backend/opencl/jit/ShiftNode.hpp
deleted file mode 100644
index 4eb48776ed..0000000000
--- a/src/backend/opencl/jit/ShiftNode.hpp
+++ /dev/null
@@ -1,17 +0,0 @@
-/*******************************************************
- * Copyright (c) 2019, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <common/jit/ShiftNodeBase.hpp>
-#include <jit/BufferNode.hpp>
-
-namespace opencl {
-namespace jit {
-  using ShiftNode = common::ShiftNodeBase<BufferNode>;
-} //  namespace jit
-} //  namespace opencl
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 990a5e1f8c..56e2149f5b 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -11,78 +11,74 @@
 #include <sstream>
 #include <string>
 
-#include <functional>
-#include <memory>
-#include <sstream>
-#include <string>
-
 namespace opencl {
 
 namespace {
 
 /// Creates a string that will be used to declare the parameter of kernel
-inline void generateParamDeclaration(std::stringstream& kerStream, int id,
-                                     bool is_linear,
-                                     const std::string& m_type_str) {
-    kerStream << "__global " << m_type_str << " *in" << id << "_ptr, dim_t in"
-              << id << "_offset, ";
+void generateParamDeclaration(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& m_type_str) {
     if (is_linear) {
-        kerStream << "\n";
+        kerStream << "__global " << m_type_str << " *in" << id
+                  << ", dim_t iInfo" << id << "_offset, \n";
     } else {
-        kerStream << "int in_index" << id << ", \n";
+        kerStream << "__global " << m_type_str << " *in" << id
+                  << ", KParam iInfo" << id << ", \n";
     }
 }
 
 /// Calls the setArg function to set the arguments for a kernel call
-inline int setKernelArguments(
+int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-    const std::shared_ptr<cl::Buffer>& ptr, const KParam& info,
-    const int& param_index) {
-    cl_mem& buf = (*ptr)();
-    setArg(start_id++, static_cast<const void*>(&buf), sizeof(cl_mem));
-    setArg(start_id++, static_cast<const void*>(&info.offset), sizeof(dim_t));
-    if (is_linear == false) {
-        setArg(start_id++, static_cast<const void*>(&param_index), sizeof(int));
+    const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
+    setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
+           sizeof(cl_mem));
+    if (is_linear) {
+        setArg(start_id + 1, static_cast<const void*>(&info.offset),
+               sizeof(dim_t));
+    } else {
+        setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam));
     }
-    return start_id;
+    return start_id + 2;
 }
 
 /// Generates the code to calculate the offsets for a buffer
-inline void generateBufferOffsets(std::stringstream& kerStream, int id,
-                                  bool is_linear) {
-    std::string idx_str    = std::string("dim_t idx") + std::to_string(id);
-    std::string offset_str = std::string("in") + std::to_string(id) + "_offset";
+inline void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
+                                  const std::string& type_str) {
+    UNUSED(type_str);
+    std::string idx_str  = std::string("int idx") + std::to_string(id);
+    std::string info_str = std::string("iInfo") + std::to_string(id);
 
     if (is_linear) {
-        kerStream << idx_str << " = idx + " << offset_str << ";\n";
+        kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
     } else {
-        std::string in_index     = "in_index" + std::to_string(id);
-        std::string block_offset = "block_offsets[" + in_index + "]";
-        std::string in_param     = "params[" + in_index + "]";
-
-        kerStream << idx_str << " = " << block_offset << "\n + ((id1 < "
-                  << in_param << ".dims[1]) * " << in_param
-                  << ".strides[1] * id1)"
-                  << "\n + ((id0 < " << in_param << ".dims[0]) * id0) + "
-                  << offset_str << ";\n";
+        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
+                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
+                  << info_str << ".dims[1]) * " << info_str
+                  << ".strides[1] * id1 + (id0 < " << info_str
+                  << ".dims[0]) * id0 + " << info_str << ".offset;\n";
     }
 }
 
 /// Generates the code to read a buffer and store it in a local variable
 inline void generateBufferRead(std::stringstream& kerStream, int id,
                                const std::string& type_str) {
-    kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
+    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
               << "];\n";
 }
 
-inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id) {
+inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& type_str) {
+    UNUSED(is_linear);
+    UNUSED(type_str);
     std::string idx_str   = std::string("idx") + std::to_string(id);
-    std::string info_str  = std::string("in") + std::to_string(id);
+    std::string info_str  = std::string("iInfo") + std::to_string(id);
     std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
     std::string shift_str = std::string("shift") + std::to_string(id) + "_";
 
-    kerStream << "KParam " << info_str << " = params[in_index" << id << "];\n";
     for (int i = 0; i < 4; i++) {
         kerStream << "int " << id_str << i << " = __circular_mod(id" << i
                   << " + " << shift_str << i << ", " << info_str << ".dims["
@@ -99,12 +95,12 @@ inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id) {
               << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
               << "1;\n";
     kerStream << idx_str << " += (" << id_str << "0 < " << info_str
-              << ".dims[0]) * " << id_str << "0 + " << info_str << "_offset;\n";
+              << ".dims[0]) * " << id_str << "0 + " << info_str << ".offset;\n";
 }
 
 inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
                                   const std::string& type_str) {
-    kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
+    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
               << "];\n";
 }
 }  // namespace
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 47af4acb7c..4accb8fb16 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -50,11 +50,12 @@ void printMemInfo(const char *msg, const int device) {
 }
 
 template<typename T>
-uptr memAlloc(
+unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
     const size_t &elements) {
     cl::Buffer *ptr = static_cast<cl::Buffer *>(
         memoryManager().alloc(elements * sizeof(T), false));
-    return uptr(ptr, bufferFree);
+    return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(ptr,
+                                                                bufferFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index fab566c81b..c4298b1404 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -24,11 +24,9 @@ namespace opencl {
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
 
-using uptr = std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>>;
-
 template<typename T>
-uptr memAlloc(const size_t &elements);
-
+std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>> memAlloc(
+    const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index af3ab0c046..da86c46cdf 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <opencl/jit/ShiftNode.hpp>
+#include <common/jit/ShiftNodeBase.hpp>
 #include <err_opencl.hpp>
 #include <shift.hpp>
 
@@ -18,7 +18,7 @@
 using af::dim4;
 
 using common::Node_ptr;
-using opencl::jit::ShiftNode;
+using common::ShiftNodeBase;
 using opencl::jit::BufferNode;
 
 using std::array;
@@ -27,6 +27,7 @@ using std::static_pointer_cast;
 using std::string;
 
 namespace opencl {
+using ShiftNode = ShiftNodeBase<BufferNode>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
diff --git a/test/jit.cpp b/test/jit.cpp
index 8391237565..2ed23977a9 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -16,7 +16,6 @@
 
 using af::array;
 using af::constant;
-using af::dim4;
 using af::eval;
 using af::freeHost;
 using af::gforSet;
@@ -125,16 +124,10 @@ TEST(JIT, CPP_Multi_linear) {
     x.host(&hx[0]);
     y.host(&hy[0]);
 
-    vector<int> goldx(num);
-    vector<int> goldy(num);
-
     for (int i = 0; i < num; i++) {
-        goldx[i] = ha[i] + hb[i];
-        goldy[i] = ha[i] - hb[i];
+        ASSERT_EQ((ha[i] + hb[i]), hx[i]);
+        ASSERT_EQ((ha[i] - hb[i]), hy[i]);
     }
-
-    ASSERT_VEC_ARRAY_EQ(goldx, dim4(num), x);
-    ASSERT_VEC_ARRAY_EQ(goldy, dim4(num), y);
 }
 
 TEST(JIT, CPP_strided) {
@@ -611,82 +604,3 @@ TEST(JIT, LargeJitTree) {
         af::sync();
     });
 }
-
-TEST(JIT, TwoLargeNonLinear) {
-    int dimsize = 10;
-    array a     = constant(0, dimsize, dimsize);
-    array aa    = constant(0, dimsize, dimsize);
-    array b     = constant(0, dimsize, dimsize);
-    array bb    = constant(0, dimsize, dimsize);
-
-    int val = 0;
-    for (int i = 0; i < 24; i++) {
-        array ones = constant(1, dimsize, dimsize);
-        ones.eval();
-        array twos = constant(2, dimsize);
-        twos.eval();
-
-        a += tile(twos, 1, dimsize) + ones;
-        aa += tile(twos, 1, dimsize) + ones;
-        val += 3;
-    }
-
-    for (int i = 0; i < 24; i++) {
-        array ones = constant(1, dimsize, dimsize);
-        ones.eval();
-        array twos = constant(2, dimsize);
-        twos.eval();
-        b += tile(twos, 1, dimsize) + ones;
-        bb += tile(twos, 1, dimsize) + ones;
-    }
-    array c  = a + b;
-    array cc = aa + bb;
-    eval(c, cc);
-
-    vector<float> gold(a.elements(), val * 2);
-    ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c);
-}
-
-TEST(JIT, IndexingColumn) {
-    array a = constant(1, 512, 32);
-    array b = constant(2, 512);
-    a.eval();
-    b.eval();
-
-    array c = a(af::span, 31) + b;
-
-    vector<float> gold(512, 3.0f);
-    ASSERT_VEC_ARRAY_EQ(gold, dim4(512), c);
-}
-
-TEST(JIT, IndexingRow) {
-    array a = constant(1, 32, 512);
-    array b = constant(2, 1, 512);
-    a.eval();
-    b.eval();
-
-    array c = a(31, af::span) + b;
-
-    vector<float> gold(512, 3.0f);
-    ASSERT_VEC_ARRAY_EQ(gold, dim4(1, 512), c);
-}
-
-TEST(JIT, DISABLED_ManyConstants) {
-    array res  = constant(1, 1);
-    array res2 = tile(res, 1, 10);
-    array res3 = randu(1);
-    array res4 = tile(res3, 1, 10);
-    array res5 = randu(1);
-    array res6 = tile(res5, 1, 10);
-    array res7 = randu(1);
-    array res8 = tile(res7, 1, 10);
-
-    for (int i = 0; i < 80; i++) { res2 = res2 + randu(1, 10); }
-    for (int i = 0; i < 80; i++) { res4 = res4 + tile(randu(1), 1, 10); }
-    for (int i = 0; i < 80; i++) { res6 = res6 + tile(randu(1), 1, 10); }
-    for (int i = 0; i < 80; i++) { res8 = res8 + 1.0f; }
-
-    // This still fails in the current implementation
-    eval(res2, res4, res6);//, res8);
-    af::sync();
-}

From cdfcca91623e213ac0faefd790508c66a70e667f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 27 Apr 2019 02:25:05 -0400
Subject: [PATCH 1641/2677] Add back minor changes and tests from revert commit

---
 src/backend/common/jit/Node.hpp | 38 ++++++++++----
 src/backend/cuda/Param.hpp      | 48 ++++++++++--------
 src/backend/cuda/jit.cpp        |  9 ++--
 test/jit.cpp                    | 90 ++++++++++++++++++++++++++++++++-
 4 files changed, 147 insertions(+), 38 deletions(-)

diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 9cd2fe51ef..e31da4f7cd 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #pragma once
-#include <af/defines.h>
 #include <optypes.hpp>
 #include <platform.hpp>
+#include <af/defines.h>
 
 #include <array>
 #include <functional>
@@ -50,28 +50,46 @@ class Node {
     int getNodesMap(Node_map_t &node_map, std::vector<const Node *> &full_nodes,
                     std::vector<Node_ids> &full_ids) const;
 
+    /// Generates the string that will be used to hash the kernel
     virtual void genKerName(std::stringstream &kerStream,
-                            const Node_ids &ids) const {
-        UNUSED(kerStream);
-        UNUSED(ids);
-    }
+                            const Node_ids &ids) const = 0;
+
+    /// Generates the function parameters for the node.
+    ///
+    /// \param[in/out] kerStream  The string will be written to this stream
+    /// \param[in]     ids        The integer id of the node and its children
+    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genParams(std::stringstream &kerStream, int id,
                            bool is_linear) const {
         UNUSED(kerStream);
         UNUSED(id);
         UNUSED(is_linear);
     }
+
+    /// Generates the variable that stores the thread's/work-item's offset into
+    /// the memory.
+    ///
+    /// \param[in/out] kerStream  The string will be written to this stream
+    /// \param[in]     ids        The integer id of the node and its children
+    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genOffsets(std::stringstream &kerStream, int id,
                             bool is_linear) const {
         UNUSED(kerStream);
         UNUSED(id);
         UNUSED(is_linear);
     }
+
+    /// Generates the code for the operation of the node.
+    ///
+    /// Generates the soruce code of the operation that the node needs to
+    /// perform. For example this function will create the string
+    /// "val2 = __add(val1, val2);" for the addition node.
+    ///
+    /// \param[in/out] kerStream  The string will be written to this stream
+    /// \param[in]     ids        The integer id of the node and its children
+    /// \param[in]     is_linear  True if the kernel is a linear kernel
     virtual void genFuncs(std::stringstream &kerStream,
-                          const Node_ids &ids) const {
-        UNUSED(kerStream);
-        UNUSED(ids);
-    }
+                          const Node_ids &ids) const = 0;
 
     /// Calls the setArg function on each of the arguments passed into the
     /// kernel
@@ -104,6 +122,8 @@ class Node {
 
     // Return the size of the size of the buffer node in bytes. Zero otherwise
     virtual size_t getBytes() const { return 0; }
+
+    // Returns true if this node is a Buffer
     virtual bool isBuffer() const { return false; }
     virtual bool isLinear(dim_t dims[4]) const {
         UNUSED(dims);
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index e51e8e831e..9ac4c71d3c 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -21,22 +21,26 @@ namespace cuda {
 template<typename T>
 class Param {
    public:
-    T *ptr;
     dim_t dims[4];
     dim_t strides[4];
+    T *ptr;
 
-    __DH__ Param() : ptr(nullptr) {}
+    __DH__ Param() noexcept : ptr(nullptr) {}
+
+    __DH__
+    Param(T *iptr, const dim_t *idims, const dim_t *istrides) noexcept
+        : dims{idims[0], idims[1], idims[2], idims[3]}
+        , strides{istrides[0], istrides[1], istrides[2], istrides[3]}
+        , ptr(iptr) {}
 
-    __DH__ Param(T *iptr, const dim_t *idims, const dim_t *istrides)
-        : ptr(iptr) {
-        for (int i = 0; i < 4; i++) {
-            dims[i]    = idims[i];
-            strides[i] = istrides[i];
-        }
-    }
     __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
+
+    Param(const Param<T> &other) noexcept = default;
+    Param(Param<T> &&other) noexcept      = default;
+    Param<T> &operator=(const Param<T> &other) noexcept = default;
+    Param<T> &operator=(Param<T> &&other) noexcept = default;
 };
 
 template<typename T>
@@ -51,28 +55,28 @@ Param<T> flat(Param<T> in) {
 template<typename T>
 class CParam {
    public:
-    const T *ptr;
     dim_t dims[4];
     dim_t strides[4];
+    const T *ptr;
 
     __DH__ CParam(const T *iptr, const dim_t *idims, const dim_t *istrides)
-        : ptr(iptr) {
-        for (int i = 0; i < 4; i++) {
-            dims[i]    = idims[i];
-            strides[i] = istrides[i];
-        }
-    }
+        : dims{idims[0], idims[1], idims[2], idims[3]}
+        , strides{istrides[0], istrides[1], istrides[2], istrides[3]}
+        , ptr(iptr) {}
 
-    __DH__ CParam(Param<T> &in) : ptr(in.ptr) {
-        for (int i = 0; i < 4; i++) {
-            dims[i]    = in.dims[i];
-            strides[i] = in.strides[i];
-        }
-    }
+    __DH__ CParam(Param<T> &in)
+        : dims{in.dims[0], in.dims[1], in.dims[2], in.dims[3]}
+        , strides{in.strides[0], in.strides[1], in.strides[2], in.strides[3]}
+        , ptr(in.ptr) {}
 
     __DH__ size_t elements() const noexcept {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
+
+    CParam(const CParam<T> &other) noexcept = default;
+    CParam(CParam<T> &&other) noexcept      = default;
+    CParam<T> &operator=(const CParam<T> &other) noexcept = default;
+    CParam<T> &operator=(CParam<T> &&other) noexcept = default;
 };
 
 }  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 6b0921b67f..a20aa462ee 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -74,11 +74,10 @@ static string getKernelString(const string funcName,
 
     const std::string paramTStr = R"JIT(
 template<typename T>
-struct Param
-{
-  T *ptr;
-  dim_t dims[4];
-  dim_t strides[4];
+struct Param {
+    dim_t dims[4];
+    dim_t strides[4];
+    T *ptr;
 };
 )JIT";
 
diff --git a/test/jit.cpp b/test/jit.cpp
index 2ed23977a9..9a4054f79b 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -16,6 +16,7 @@
 
 using af::array;
 using af::constant;
+using af::dim4;
 using af::eval;
 using af::freeHost;
 using af::gforSet;
@@ -124,10 +125,15 @@ TEST(JIT, CPP_Multi_linear) {
     x.host(&hx[0]);
     y.host(&hy[0]);
 
+    vector<int> goldx(num);
+    vector<int> goldy(num);
     for (int i = 0; i < num; i++) {
-        ASSERT_EQ((ha[i] + hb[i]), hx[i]);
-        ASSERT_EQ((ha[i] - hb[i]), hy[i]);
+        goldx[i] = ha[i] + hb[i];
+        goldy[i] = ha[i] - hb[i];
     }
+
+    ASSERT_VEC_ARRAY_EQ(goldx, dim4(num), x);
+    ASSERT_VEC_ARRAY_EQ(goldy, dim4(num), y);
 }
 
 TEST(JIT, CPP_strided) {
@@ -604,3 +610,83 @@ TEST(JIT, LargeJitTree) {
         af::sync();
     });
 }
+
+
+TEST(JIT, DISABLED_TwoLargeNonLinear) {
+    int dimsize = 10;
+    array a     = constant(0, dimsize, dimsize);
+    array aa    = constant(0, dimsize, dimsize);
+    array b     = constant(0, dimsize, dimsize);
+    array bb    = constant(0, dimsize, dimsize);
+
+    int val = 0;
+    for (int i = 0; i < 24; i++) {
+        array ones = constant(1, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+
+        a += tile(twos, 1, dimsize) + ones;
+        aa += tile(twos, 1, dimsize) + ones;
+        val += 3;
+    }
+
+    for (int i = 0; i < 24; i++) {
+        array ones = constant(1, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+        b += tile(twos, 1, dimsize) + ones;
+        bb += tile(twos, 1, dimsize) + ones;
+    }
+    array c  = a + b;
+    array cc = aa + bb;
+    eval(c, cc);
+
+    vector<float> gold(a.elements(), val * 2);
+    ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c);
+}
+
+TEST(JIT, IndexingColumn) {
+    array a = constant(1, 512, 32);
+    array b = constant(2, 512);
+    a.eval();
+    b.eval();
+
+    array c = a(af::span, 31) + b;
+
+    vector<float> gold(512, 3.0f);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(512), c);
+}
+
+TEST(JIT, IndexingRow) {
+    array a = constant(1, 32, 512);
+    array b = constant(2, 1, 512);
+    a.eval();
+    b.eval();
+
+    array c = a(31, af::span) + b;
+
+    vector<float> gold(512, 3.0f);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(1, 512), c);
+}
+
+TEST(JIT, DISABLED_ManyConstants) {
+    array res  = constant(1, 1);
+    array res2 = tile(res, 1, 10);
+    array res3 = randu(1);
+    array res4 = tile(res3, 1, 10);
+    array res5 = randu(1);
+    array res6 = tile(res5, 1, 10);
+    array res7 = randu(1);
+    array res8 = tile(res7, 1, 10);
+
+    for (int i = 0; i < 80; i++) { res2 = res2 + randu(1, 10); }
+    for (int i = 0; i < 80; i++) { res4 = res4 + tile(randu(1), 1, 10); }
+    for (int i = 0; i < 80; i++) { res6 = res6 + tile(randu(1), 1, 10); }
+    for (int i = 0; i < 80; i++) { res8 = res8 + 1.0f; }
+
+    // This still fails in the current implementation
+    eval(res2, res4, res6);//, res8);
+    af::sync();
+}

From f4f92de19029079bddfdfb4f85cb8edcaa99cb94 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 27 Apr 2019 02:11:37 -0400
Subject: [PATCH 1642/2677] Check the JIT tree size when creating binary and
 unary nodes

Checks the JIT tree size when creating binary and unary nodes. We
were performing this check at createNodeArray and at that point
it was not possible to call eval on the child nodes in case
the size of the JIT kernel became too large.
---
 src/backend/common/jit/BufferNodeBase.hpp |   3 +
 src/backend/common/jit/NaryNode.hpp       |  28 ++++
 src/backend/cpu/Array.cpp                 |  58 ++++----
 src/backend/cpu/Array.hpp                 |   3 +
 src/backend/cuda/Array.cpp                | 152 ++++++++++-----------
 src/backend/cuda/Array.hpp                |  10 ++
 src/backend/cuda/binary.hpp               |  20 +--
 src/backend/cuda/select.cu                |  52 ++++---
 src/backend/cuda/unary.hpp                |  39 +++---
 src/backend/opencl/Array.cpp              | 157 +++++++++++-----------
 src/backend/opencl/Array.hpp              |  10 ++
 src/backend/opencl/binary.hpp             |  20 +--
 src/backend/opencl/scalar.hpp             |   2 +-
 src/backend/opencl/select.cpp             |  28 +++-
 src/backend/opencl/unary.hpp              |  28 ++--
 test/jit.cpp                              | 101 ++++++++++++--
 16 files changed, 452 insertions(+), 259 deletions(-)

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 525555341e..1d8bf60361 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -10,7 +10,10 @@
 #pragma once
 #include <backend.hpp>
 #include <jit/kernel_generators.hpp>
+#include <common/jit/Node.hpp>
 
+#include <iomanip>
+#include <mutex>
 #include <sstream>
 
 namespace common {
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 95b2c1cca4..4e29428bb8 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -10,6 +10,9 @@
 #pragma once
 #include <common/jit/Node.hpp>
 
+#include <Array.hpp>
+#include <backend.hpp>
+
 #include <array>
 #include <iomanip>
 #include <sstream>
@@ -61,4 +64,29 @@ class NaryNode : public Node {
         kerStream << ");\n";
     }
 };
+
+template<typename Ti, int N, typename FUNC>
+common::Node_ptr createNaryNode(
+    const af::dim4 &odims, FUNC createNode,
+    std::array<const detail::Array<Ti>*, N> &&children) {
+    std::array<common::Node_ptr, N> childNodes;
+    for (int i = 0; i < N; i++) { childNodes[i] = children[i]->getNode(); }
+
+    common::Node_ptr ptr = createNode(childNodes);
+
+    if (detail::passesJitHeuristics<Ti>(ptr.get())) {
+        return ptr;
+    } else {
+        int max_height_index = 0;
+        int max_height       = 0;
+        for (int i = 0; i < N; i++) {
+            if (max_height < childNodes[i]->getHeight()) {
+                max_height_index = i;
+                max_height       = childNodes[i]->getHeight();
+            }
+        }
+        children[max_height_index]->eval();
+        return createNaryNode<Ti, N>(odims, createNode, move(children));
+    }
+}
 }  // namespace common
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 6b06e1384b..39035f96f2 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -217,39 +217,35 @@ Array<T> createEmptyArray(const dim4 &dims) {
 }
 
 template<typename T>
-Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
-    Array<T> out = Array<T>(dims, node);
-
-    if (evalFlag()) {
-        if (node->getHeight() >= (int)getMaxJitSize()) {
-            out.eval();
-        } else {
-            size_t alloc_bytes, alloc_buffers;
-            size_t lock_bytes, lock_buffers;
-
-            deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
-                             &lock_buffers);
-
-            // Check if approaching the memory limit
-            if (lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers()) {
-                Node *n = node.get();
-
-                NodeIterator<jit::Node> it(n);
-                NodeIterator<jit::Node> end_node;
-                size_t bytes =
-                    accumulate(it, end_node, size_t(0),
-                               [=](const size_t prev, const Node &n) {
-                                   // getBytes returns the size of the data
-                                   // Array. Sub arrays will be represented by
-                                   // their parent size.
-                                   return prev + n.getBytes();
-                               });
-
-                if (2 * bytes > lock_bytes) { out.eval(); }
-            }
-        }
+bool passesJitHeuristics(Node *root_node) {
+    if (!evalFlag()) return true;
+    if (root_node->getHeight() >= (int)getMaxJitSize()) { return false; }
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+
+    // Check if approaching the memory limit
+    if (lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers()) {
+        NodeIterator<jit::Node> it(root_node);
+        NodeIterator<jit::Node> end_node;
+        size_t bytes = accumulate(it, end_node, size_t(0),
+                                  [=](const size_t prev, const Node &n) {
+                                      // getBytes returns the size of the data
+                                      // Array. Sub arrays will be represented
+                                      // by their parent size.
+                                      return prev + n.getBytes();
+                                  });
+
+        if (2 * bytes > lock_bytes) { return false; }
     }
+    return true;
+}
 
+template<typename T>
+Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    Array<T> out = Array<T>(dims, node);
     return out;
 }
 
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index c6392ce8ed..1612395550 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -90,6 +90,9 @@ Array<T> createSubArray(const Array<T> &parent,
 template<typename T>
 void destroyArray(Array<T> *A);
 
+template<typename T>
+bool passesJitHeuristics(jit::Node *node);
+
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     T *ptr = arr.device();
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index b9c3137669..a8a9988418 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -210,86 +210,85 @@ Node_ptr Array<T>::getNode() const {
     return node;
 }
 
+/// This function should be called after a new JIT node is created. It will
+/// return true if the newly created node will generate a valid kernel. If
+/// false the node will fail to compile or the node and its referenced buffers
+/// are consuming too many resources. If false, the node's child nodes should
+/// be evaluated before continuing.
+///
+/// We eval in the following cases:
+///
+/// 1. Too many bytes are locked up by JIT causing memory
+///    pressure. Too many bytes is assumed to be half of all bytes
+///    allocated so far.
+///
+/// 2. The number of parameters we are passing into the kernel exceeds the
+///    limitation on the platform. For NVIDIA this is 4096 bytes. The
 template<typename T>
-Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
-    Array<T> out = Array<T>(dims, node);
-
-    if (evalFlag()) {
-        if (node->getHeight() >= (int)getMaxJitSize()) {
-            out.eval();
-        } else {
-            size_t alloc_bytes, alloc_buffers;
-            size_t lock_bytes, lock_buffers;
-
-            deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
-                             &lock_buffers);
-
-            bool isBufferLimit =
-                lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
-
-            // We eval in the following cases.
-            //
-            // 1. Too many bytes are locked up by JIT causing memory
-            //    pressure. Too many bytes is assumed to be half of all bytes
-            //    allocated so far.
-            //
-            // 2. Too many buffers in a nonlinear kernel cause param space
-            //    overflow. This happens when the number of nodes reaches 50
-            //    (51 including output). Too many buffers can occur in a tree
-            //    of size 25 in the worst case.
-            //
-            // TODO: Find better solution than the following emperical solution.
-            if (node->getHeight() > 25 || isBufferLimit) {
-                // This is the size of the params that are passed by default
-                constexpr size_t param_base_size =
-                    sizeof(Param<T>) + (4 * sizeof(uint));
-
-                // This is the maximum size of the params that can be allowed by
-                // CUDA NOTE: This number should have been (4096 -
-                // some_buffer_size) BUT kernels who's kernel sizes come close
-                // to this value are not passing and cuModuleLoadDataEx is
-                // failing with CUDA_ERROR_INVALID_IMAGE(200). 35*sizeof(int)
-                // seems to be the magic number that passes all tests.
-                constexpr size_t max_param_size =
-                    (4096 - (sizeof(Param<T>) + 35 * sizeof(uint)));
-                Node *n = node.get();
-
-                struct tree_info {
-                    size_t total_buffer_size;
-                    size_t num_buffers;
-                    size_t param_scalar_size;
-                };
-                NodeIterator<> end_node;
-                tree_info info = accumulate(
-                    NodeIterator<>(n), end_node, tree_info{0, 0, 0},
-                    [=](tree_info &prev, const Node &node) {
-                        if (node.isBuffer()) {
-                            const auto &buf_node =
-                                static_cast<const BufferNode<T> &>(node);
-                            prev.total_buffer_size += buf_node.getBytes();
-                            prev.num_buffers++;
-                        } else {
-                            prev.param_scalar_size += node.getParamBytes();
-                        }
-                        // getBytes returns the size of the data Array. Sub
-                        // arrays will be represented by their parent size.
-                        return prev;
-                    });
-                size_t param_size = param_base_size + info.param_scalar_size;
-                param_size += info.num_buffers * sizeof(Param<T>);
-
-                // TODO: the buffer_size check here is very conservative. It
-                // will trigger an evaluation of the node in most cases. We
-                // should be checking the amount of memory available to guard
-                // this eval
-                if (param_size >= max_param_size ||
-                    info.total_buffer_size * 2 > lock_bytes) {
-                    out.eval();
-                }
-            }
+bool passesJitHeuristics(Node *root_node) {
+    if (!evalFlag()) return true;
+    if (root_node->getHeight() >= (int)getMaxJitSize()) { return false; }
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+
+    bool isBufferLimit =
+        lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
+
+    // A lightweight check based on the height of the node. This is an
+    // inexpensive operation and does not traverse the JIT tree.
+    if (root_node->getHeight() > 6 || isBufferLimit) {
+        // The size of the parameters without any extra arguments from the
+        // JIT tree. This includes one output Param object and 4 integers.
+        constexpr size_t base_param_size =
+            sizeof(Param<T>) + (4 * sizeof(uint));
+
+        // This is the maximum size of the params that can be allowed by the
+        // CUDA platform.
+        constexpr size_t max_param_size = 4096 - base_param_size;
+
+        struct tree_info {
+            size_t total_buffer_size;
+            size_t num_buffers;
+            size_t param_scalar_size;
+        };
+        NodeIterator<> end_node;
+        tree_info info =
+            accumulate(NodeIterator<>(root_node), end_node, tree_info{0, 0, 0},
+                       [](tree_info &prev, const Node &node) {
+                           if (node.isBuffer()) {
+                               const auto &buf_node =
+                                   static_cast<const BufferNode<T> &>(node);
+                               // getBytes returns the size of the data Array.
+                               // Sub arrays will be represented by their parent
+                               // size.
+                               prev.total_buffer_size += buf_node.getBytes();
+                               prev.num_buffers++;
+                           } else {
+                               prev.param_scalar_size += node.getParamBytes();
+                           }
+                           return prev;
+                       });
+        size_t param_size =
+            info.num_buffers * sizeof(Param<T>) + info.param_scalar_size;
+
+        // TODO: the buffer_size check here is very conservative. It
+        // will trigger an evaluation of the node in most cases. We
+        // should be checking the amount of memory available to guard
+        // this eval
+        if (param_size >= max_param_size ||
+            info.total_buffer_size * 2 > lock_bytes) {
+            return false;
         }
     }
+    return true;
+}
 
+template<typename T>
+Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    Array<T> out = Array<T>(dims, node);
     return out;
 }
 
@@ -421,6 +420,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(std::vector<Array<T> *> arrays);            \
+    template bool passesJitHeuristics<T>(Node * n);                           \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 2fe7c7130b..6414ae7e2c 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -90,6 +90,16 @@ Array<T> createSubArray(const Array<T> &parent,
 template<typename T>
 void destroyArray(Array<T> *A);
 
+/// \brief Checks if the Node can be compiled successfully and the buffers
+///        references are not consuming most of the allocated memory
+///
+/// \param [in] node The root node which needs to be checked
+///
+/// \returns false if the kernel generated by this node will fail to compile
+///          or its nodes are consuming too much memory.
+template<typename T>
+bool passesJitHeuristics(common::Node *node);
+
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     T *ptr = arr.device();
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index 11803d2752..c6272ee545 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 #include <common/jit/BinaryNode.hpp>
+#include <common/jit/NaryNode.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
 #include <af/dim4.hpp>
@@ -132,15 +133,18 @@ struct BinOp<To, Ti, af_hypot_t> {
 template<typename To, typename Ti, af_op_t op>
 Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
                            const af::dim4 &odims) {
-    BinOp<To, Ti, op> bop;
-
-    common::Node_ptr lhs_node = lhs.getNode();
-    common::Node_ptr rhs_node = rhs.getNode();
-    common::BinaryNode *node =
-        new common::BinaryNode(getFullName<To>(), shortname<To>(true),
-                               bop.name(), lhs_node, rhs_node, (int)(op));
+    using common::Node;
+    using common::Node_ptr;
+
+    auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
+        BinOp<To, Ti, op> bop;
+        return Node_ptr(new common::BinaryNode(
+            getFullName<To>(), shortname<To>(true), bop.name(), operands[0],
+            operands[1], (int)(op)));
+    };
 
-    return createNodeArray<To>(odims, common::Node_ptr(node));
+    Node_ptr out = common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
+    return createNodeArray<To>(odims, out);
 }
 
 }  // namespace cuda
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index e3d15eed48..4a1e7c8edd 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -13,8 +13,12 @@
 #include <scalar.hpp>
 #include <select.hpp>
 
+#include <memory>
+
 using common::NaryNode;
 using common::Node_ptr;
+using std::make_shared;
+using std::max;
 
 namespace cuda {
 template<typename T>
@@ -35,15 +39,25 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto cond_node = cond.getNode();
     auto a_node    = a.getNode();
     auto b_node    = b.getNode();
-    int height     = std::max(a_node->getHeight(), b_node->getHeight());
-    height         = std::max(height, cond_node->getHeight()) + 1;
-
-    NaryNode *node =
-        new NaryNode(getFullName<T>(), shortname<T>(true), "__select", 3,
-                     {{cond_node, a_node, b_node}}, (int)af_select_t, height);
+    int height     = max(a_node->getHeight(), b_node->getHeight());
+    height         = max(height, cond_node->getHeight()) + 1;
+    auto node      = make_shared<NaryNode>(
+        NaryNode(getFullName<T>(), shortname<T>(true), "__select", 3,
+                 {{cond_node, a_node, b_node}}, (int)af_select_t, height));
 
-    Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
-    return out;
+    if (detail::passesJitHeuristics<T>(node.get())) {
+        return createNodeArray<T>(odims, node);
+    } else {
+        if (a_node->getHeight() >
+            max(b_node->getHeight(), cond_node->getHeight())) {
+            a.eval();
+        } else if (b_node->getHeight() > cond_node->getHeight()) {
+            b.eval();
+        } else {
+            cond.eval();
+        }
+        return createSelectNode<T>(cond, a, b, odims);
+    }
 }
 
 template<typename T, bool flip>
@@ -53,16 +67,24 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto a_node    = a.getNode();
     Array<T> b     = createScalarNode<T>(odims, scalar<T>(b_val));
     auto b_node    = b.getNode();
-    int height     = std::max(a_node->getHeight(), b_node->getHeight());
-    height         = std::max(height, cond_node->getHeight()) + 1;
+    int height     = max(a_node->getHeight(), b_node->getHeight());
+    height         = max(height, cond_node->getHeight()) + 1;
 
-    NaryNode *node = new NaryNode(
+    auto node = make_shared<NaryNode>(NaryNode(
         getFullName<T>(), shortname<T>(true),
-        flip ? "__not_select" : "__select", 3, {{cond_node, a_node, b_node}},
-        (int)(flip ? af_not_select_t : af_select_t), height);
+        (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
+        (int)(flip ? af_not_select_t : af_select_t), height));
 
-    Array<T> out = createNodeArray<T>(odims, Node_ptr(node));
-    return out;
+    if (detail::passesJitHeuristics<T>(node.get())) {
+        return createNodeArray<T>(odims, node);
+    } else {
+        if(a_node->getHeight() > max(b_node->getHeight(), cond_node->getHeight())) {
+            a.eval();
+        } else {
+            cond.eval();
+        }
+        return createSelectNode<T, flip>(cond, a, b_val, odims);
+    }
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index c4a20e8174..f133140ab1 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/jit/NaryNode.hpp>
 #include <common/jit/UnaryNode.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
@@ -75,29 +76,33 @@ UNARY_DECL(noop, "__noop")
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    common::Node_ptr in_node = in.getNode();
-
-    common::UnaryNode *node = new common::UnaryNode(
-        getFullName<T>(), shortname<T>(true), unaryName<op>(), in_node, op);
-
-    if(outDim == dim4(-1, -1, -1, -1)) {
-        outDim = in.dims();
-    }
-    return createNodeArray<T>(outDim, common::Node_ptr(node));
+    using common::Node;
+    using common::Node_ptr;
+    using std::array;
+
+    auto createUnary = [](array<Node_ptr, 1> &operands) {
+        return common::Node_ptr(new common::UnaryNode(
+            getFullName<T>(), shortname<T>(true), unaryName<op>(), operands[0], op));
+    };
+
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    Node_ptr out = common::createNaryNode<T, 1>(outDim, createUnary, {&in});
+    return createNodeArray<T>(outDim, out);
 }
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    common::Node_ptr in_node = in.getNode();
+    using common::Node_ptr;
 
-    common::UnaryNode *node =
-        new common::UnaryNode(getFullName<char>(), shortname<char>(true),
-                              unaryName<op>(), in_node, op);
+    auto createUnary = [](std::array<Node_ptr, 1> &operands) {
+        return Node_ptr(
+            new common::UnaryNode(getFullName<char>(), shortname<char>(true),
+                                  unaryName<op>(), operands[0], op));
+    };
 
-    if(outDim == dim4(-1, -1, -1, -1)) {
-        outDim = in.dims();
-    }
-    return createNodeArray<char>(outDim, common::Node_ptr(node));
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    Node_ptr out = common::createNaryNode<T, 1>(outDim, createUnary, {&in});
+    return createNodeArray<char>(outDim, out);
 }
 
 }  // namespace cuda
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 3733c7a0c6..998782e74a 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -230,91 +230,85 @@ Node_ptr Array<T>::getNode() const {
     return node;
 }
 
+/// This function should be called after a new JIT node is created. It will
+/// return true if the newly created node will generate a valid kernel. If
+/// false the node will fail to compile or the node and its referenced buffers
+/// are consuming too many resources. If false, the node's child nodes should
+/// be evaluated before continuing.
+///
+/// We eval in the following cases:
+///
+/// 1. Too many bytes are locked up by JIT causing memory
+///    pressure. Too many bytes is assumed to be half of all bytes
+///    allocated so far.
+///
+/// 2. The number of parameters we are passing into the kernel exceeds the
+///    limitation on the platform. For NVIDIA this is 4096 bytes. The
+template<typename T>
+bool passesJitHeuristics(Node *root_node) {
+    if (!evalFlag()) return true;
+    if (root_node->getHeight() >= (int)getMaxJitSize()) { return false; }
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+
+    bool isBufferLimit =
+        lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
+    bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA ||
+                    getActivePlatform() == AFCL_PLATFORM_APPLE;
+
+    // A lightweight check based on the height of the node. This is an
+    // inexpensive operation and does not traverse the JIT tree.
+    bool isParamLimit = (isNvidia && root_node->getHeight() > 6);
+    if (isParamLimit || isBufferLimit) {
+        // This is the base parameter size if the kernel had no
+        // arguments
+        constexpr size_t base_param_size =
+            sizeof(T *) + sizeof(KParam) + (3 * sizeof(uint));
+
+        // This is the maximum size of the params that can be allowed by the
+        // CUDA platform.
+        constexpr size_t max_param_size = (4096 - base_param_size);
+
+        struct tree_info {
+            size_t total_buffer_size;
+            size_t num_buffers;
+            size_t param_scalar_size;
+        };
+        NodeIterator<> it(root_node);
+        tree_info info =
+            accumulate(it, NodeIterator<>(), tree_info{0, 0, 0},
+                       [](tree_info &prev, Node &n) {
+                           if (n.isBuffer()) {
+                               auto &buf_node = static_cast<BufferNode &>(n);
+                               // getBytes returns the size of the data Array.
+                               // Sub arrays will be represented by their parent
+                               // size.
+                               prev.total_buffer_size += buf_node.getBytes();
+                               prev.num_buffers++;
+                           } else {
+                               prev.param_scalar_size += n.getParamBytes();
+                           }
+                           return prev;
+                       });
+        isBufferLimit = 2 * info.total_buffer_size > lock_bytes;
+
+        size_t param_size = (info.num_buffers * (sizeof(KParam) + sizeof(T *)) +
+                             info.param_scalar_size);
+
+        isParamLimit = isNvidia && param_size >= max_param_size;
+
+        if (isBufferLimit || isParamLimit) { return false; }
+    }
+    return true;
+}
+
 template<typename T>
 Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
     verifyDoubleSupport<T>();
     Array<T> out = Array<T>(dims, node);
-
-    if (evalFlag()) {
-        if (node->getHeight() >= (int)getMaxJitSize()) {
-            out.eval();
-        } else {
-            size_t alloc_bytes, alloc_buffers;
-            size_t lock_bytes, lock_buffers;
-
-            deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes,
-                             &lock_buffers);
-
-            bool isBufferLimit =
-                lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
-            bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA ||
-                            getActivePlatform() == AFCL_PLATFORM_APPLE;
-            // We eval in the following cases.
-            // 1. Too many bytes are locked up by JIT causing memory pressure.
-            // Too many bytes is assumed to be half of all bytes allocated so
-            // far.
-            // 2. Too many buffers in a nonlinear kernel cause param space
-            // overflow. Too many buffers comes out to be about 48 (49 including
-            // output). Too many buffers can occur in a tree of size 24 in the
-            // worst case scenario. This error only happens on nvidia devices.
-            // TODO: Find better solution than the following emperical solution.
-            bool isParamLimit = (isNvidia && node->getHeight() > 24);
-            if (isParamLimit || isBufferLimit) {
-                // This was added to the base size to make kernels pass. I
-                // picked this number by creating very large kernels and then
-                // increased this number until the test passed. Then I added a
-                // small number and made this nice and even.
-                constexpr size_t nvidia_parameter_magic = 768;
-
-                // This is the base parameter size if the kernel had no
-                // arguments
-                constexpr size_t param_base_size =
-                    sizeof(T *) + sizeof(KParam) + (3 * sizeof(uint)) +
-                    nvidia_parameter_magic;
-
-                // This is the maximum size of the params that can be allowed by
-                // CUDA NOTE: This number should have been (4096 -
-                // some_buffer_size) BUT kernels who's kernel sizes come close
-                // to this value are not passing and cuModuleLoadDataEx is
-                // failing with CUDA_ERROR_INVALID_IMAGE(200). 35*sizeof(int)
-                // seems to be the magic number that passes all tests.
-                constexpr size_t max_param_size =
-                    (4096 - (sizeof(KParam) + 35 * sizeof(uint)));
-
-                Node *n = node.get();
-
-                struct tree_info {
-                    size_t total_buffer_size;
-                    size_t num_buffers;
-                    size_t param_scalar_size;
-                };
-                NodeIterator<> it(n);
-                tree_info info = accumulate(
-                    it, NodeIterator<>(), tree_info{0, 0, 0},
-                    [=](tree_info &prev, Node &n) {
-                        if (n.isBuffer()) {
-                            auto &buf_node = static_cast<BufferNode &>(n);
-                            prev.total_buffer_size += buf_node.getBytes();
-                            prev.num_buffers++;
-                        } else {
-                            prev.param_scalar_size += node->getParamBytes();
-                        }
-                        // getBytes returns the size of the data Array. Sub
-                        // arrays will be represented by their parent size.
-                        return prev;
-                    });
-                isBufferLimit = 2 * info.total_buffer_size > lock_bytes;
-                size_t param_size =
-                    (info.num_buffers * (sizeof(KParam) + sizeof(T *)) +
-                     info.param_scalar_size + param_base_size);
-
-                isParamLimit = isNvidia && param_size >= max_param_size;
-
-                if (isBufferLimit || isParamLimit) { out.eval(); }
-            }
-        }
-    }
-
     return out;
 }
 
@@ -449,6 +443,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
+    template bool passesJitHeuristics<T>(Node * node);                        \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 0669998b7b..f098dd289c 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -85,6 +85,16 @@ Array<T> createSubArray(const Array<T> &parent,
 template<typename T>
 void destroyArray(Array<T> *A);
 
+/// \brief Checks if the Node can be compiled successfully and the buffers
+///        references are not consuming most of the allocated memory
+///
+/// \param [in] node The root node which needs to be checked
+///
+/// \returns false if the kernel generated by this node will fail to compile
+///          or its nodes are consuming too much memory.
+template<typename T>
+bool passesJitHeuristics(common::Node *node);
+
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const cl::Buffer *buf = arr.device();
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 2e910a0432..6b6c9496b0 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -133,15 +133,19 @@ struct BinOp<To, Ti, af_hypot_t> {
 template<typename To, typename Ti, af_op_t op>
 Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
                            const af::dim4 &odims) {
-    BinOp<To, Ti, op> bop;
-
-    common::Node_ptr lhs_node = lhs.getNode();
-    common::Node_ptr rhs_node = rhs.getNode();
-    common::BinaryNode *node =
-        new common::BinaryNode(dtype_traits<To>::getName(), shortname<To>(true),
-                               bop.name(), lhs_node, rhs_node, (int)(op));
+    using common::Node;
+    using common::Node_ptr;
+
+    auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
+        BinOp<To, Ti, op> bop;
+        return Node_ptr(new common::BinaryNode(
+            getFullName<To>(), shortname<To>(true), bop.name(), operands[0],
+            operands[1], (int)(op)));
+    };
 
-    return createNodeArray<To>(odims, common::Node_ptr(node));
+    Node_ptr out =
+        common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
+    return createNodeArray<To>(odims, out);
 }
 
 }  // namespace opencl
diff --git a/src/backend/opencl/scalar.hpp b/src/backend/opencl/scalar.hpp
index f52b22cf7a..420b38144d 100644
--- a/src/backend/opencl/scalar.hpp
+++ b/src/backend/opencl/scalar.hpp
@@ -17,7 +17,7 @@ namespace opencl {
 template<typename T>
 Array<T> createScalarNode(const dim4 &size, const T val) {
     return createNodeArray<T>(size,
-                              common::Node_ptr(new common::ScalarNode<T>(val)));
+                              std::make_shared<common::ScalarNode<T>>(val));
 }
 
 }  // namespace opencl
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index b6e512b975..7aebb0026b 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -36,8 +36,19 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         NaryNode(dtype_traits<T>::getName(), shortname<T>(true), "__select", 3,
                  {{cond_node, a_node, b_node}}, (int)af_select_t, height));
 
-    Array<T> out = createNodeArray<T>(odims, node);
-    return out;
+    if (detail::passesJitHeuristics<T>(node.get())) {
+        return createNodeArray<T>(odims, node);
+    } else {
+        if (a_node->getHeight() >
+            max(b_node->getHeight(), cond_node->getHeight())) {
+            a.eval();
+        } else if (b_node->getHeight() > cond_node->getHeight()) {
+            b.eval();
+        } else {
+            cond.eval();
+        }
+        return createSelectNode<T>(cond, a, b, odims);
+    }
 }
 
 template<typename T, bool flip>
@@ -55,8 +66,17 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         (int)(flip ? af_not_select_t : af_select_t), height));
 
-    Array<T> out = createNodeArray<T>(odims, node);
-    return out;
+    if (detail::passesJitHeuristics<T>(node.get())) {
+        return createNodeArray<T>(odims, node);
+    } else {
+        if (a_node->getHeight() >
+            max(b_node->getHeight(), cond_node->getHeight())) {
+            a.eval();
+        } else {
+            cond.eval();
+        }
+        return createSelectNode<T, flip>(cond, a, b_val, odims);
+    }
 }
 
 template<typename T>
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index e7ef82c6d1..66a2cf41a5 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -74,26 +74,34 @@ UNARY_DECL(noop, "__noop")
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    common::Node_ptr in_node = in.getNode();
+    using common::Node;
+    using common::Node_ptr;
+    using std::array;
 
-    common::UnaryNode *node =
-        new common::UnaryNode(dtype_traits<T>::getName(), shortname<T>(true),
-                              unaryName<op>(), in_node, op);
+    auto createUnary = [](array<Node_ptr, 1> &operands) {
+        return common::Node_ptr(
+            new common::UnaryNode(getFullName<T>(), shortname<T>(true),
+                                  unaryName<op>(), operands[0], op));
+    };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
-    return createNodeArray<T>(outDim, common::Node_ptr(node));
+    Node_ptr out = common::createNaryNode<T, 1>(outDim, createUnary, {&in});
+    return createNodeArray<T>(outDim, out);
 }
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    common::Node_ptr in_node = in.getNode();
+    using common::Node_ptr;
 
-    common::UnaryNode *node = new common::UnaryNode(
-        dtype_traits<char>::getName(), shortname<char>(true), unaryName<op>(),
-        in_node, op);
+    auto createUnary = [](std::array<Node_ptr, 1> &operands) {
+        return Node_ptr(
+            new common::UnaryNode(getFullName<char>(), shortname<char>(true),
+                                  unaryName<op>(), operands[0], op));
+    };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
-    return createNodeArray<char>(outDim, common::Node_ptr(node));
+    Node_ptr out = common::createNaryNode<T, 1>(outDim, createUnary, {&in});
+    return createNodeArray<char>(outDim, out);
 }
 
 }  // namespace opencl
diff --git a/test/jit.cpp b/test/jit.cpp
index 9a4054f79b..99c7f37849 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -14,6 +14,8 @@
 #include <af/array.h>
 #include <af/data.h>
 
+#include <tuple>
+
 using af::array;
 using af::constant;
 using af::dim4;
@@ -23,6 +25,9 @@ using af::gforSet;
 using af::randn;
 using af::randu;
 using af::seq;
+using std::get;
+using std::to_string;
+using std::tuple;
 using std::vector;
 
 TEST(JIT, CPP_JIT_HASH) {
@@ -117,13 +122,9 @@ TEST(JIT, CPP_Multi_linear) {
 
     vector<int> ha(num);
     vector<int> hb(num);
-    vector<int> hx(num);
-    vector<int> hy(num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
-    x.host(&hx[0]);
-    y.host(&hy[0]);
 
     vector<int> goldx(num);
     vector<int> goldy(num);
@@ -611,8 +612,7 @@ TEST(JIT, LargeJitTree) {
     });
 }
 
-
-TEST(JIT, DISABLED_TwoLargeNonLinear) {
+TEST(JIT, TwoLargeNonLinear) {
     int dimsize = 10;
     array a     = constant(0, dimsize, dimsize);
     array aa    = constant(0, dimsize, dimsize);
@@ -620,7 +620,7 @@ TEST(JIT, DISABLED_TwoLargeNonLinear) {
     array bb    = constant(0, dimsize, dimsize);
 
     int val = 0;
-    for (int i = 0; i < 24; i++) {
+    for (int i = 0; i < 23; i++) {
         array ones = constant(1, dimsize, dimsize);
         ones.eval();
         array twos = constant(2, dimsize);
@@ -631,7 +631,7 @@ TEST(JIT, DISABLED_TwoLargeNonLinear) {
         val += 3;
     }
 
-    for (int i = 0; i < 24; i++) {
+    for (int i = 0; i < 23; i++) {
         array ones = constant(1, dimsize, dimsize);
         ones.eval();
         array twos = constant(2, dimsize);
@@ -647,6 +647,91 @@ TEST(JIT, DISABLED_TwoLargeNonLinear) {
     ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c);
 }
 
+std::string select_info(
+    const ::testing::TestParamInfo<std::tuple<int, int, int> > info) {
+    return "a_" + to_string(get<0>(info.param)) + "_b_" +
+           to_string(get<1>(info.param)) + "_cond_" +
+           to_string(get<2>(info.param));
+}
+
+class JITSelect : public ::testing::TestWithParam<std::tuple<int, int, int> > {
+   protected:
+    void SetUp() {}
+};
+
+// clang-format off
+INSTANTIATE_TEST_CASE_P(
+                        JitSelect, JITSelect,
+                        testing::Combine(
+                                         testing::Range(10, 22),
+                                         testing::Range(10, 22),
+                                         testing::Range(10, 22)),
+                        select_info);
+TEST_P(JITSelect, SelectLargeNonLinear) {
+    int dimsize = 10;
+    array a     = constant(0, dimsize, dimsize);
+    array b     = constant(0, dimsize, dimsize);
+    array cond  = constant(0, dimsize, dimsize);
+
+    int val = 0;
+    for (int i = 0; i < std::get<0>(GetParam()); i++) {
+        array ones = constant(1, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+
+        a += tile(twos, 1, dimsize) + ones;
+        val += 3;
+    }
+
+    for (int i = 0; i < std::get<1>(GetParam()); i++) {
+        array ones = constant(2, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+        b += tile(twos, 1, dimsize) + ones;
+    }
+
+
+    for (int i = 0; i < std::get<2>(GetParam()); i++) {
+        array ones = constant(1, dimsize, dimsize);
+        ones.eval();
+        array twos = constant(2, dimsize);
+        twos.eval();
+        array fives = constant(5, dimsize, dimsize);
+        fives.eval();
+        cond += tile(twos, 1, dimsize) + ones;
+        cond = cond < fives;
+    }
+
+    array c  = select(cond, a, b);
+    c.eval();
+
+    vector<float> gold(a.elements(), val);
+    ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c);
+}
+
+TEST(JIT, AllBuffers) {
+  int buffers = 128;
+  vector<array> arrs(buffers);
+
+  for(int i = 0; i < buffers; i++) {
+    arrs[i] = constant(1, 5);
+    arrs[i].eval();
+  }
+
+  int inc = 2;
+  for(int ii = buffers/2; ii > 2; ii/=2) {
+      for(int i = 0; i < arrs.size(); i += inc) {
+          arrs[i] = arrs[i] + arrs[i + inc/2];
+      }
+      inc *= 2;
+  }
+  arrs[0] = tile(arrs[0], 1, 5) + tile(arrs[64],1, 5);
+  arrs[0].eval();
+  af::sync();
+}
+
 TEST(JIT, IndexingColumn) {
     array a = constant(1, 512, 32);
     array b = constant(2, 512);

From 31b8252ff1a0fc885abe75ea543b20994724db08 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 May 2019 16:24:02 -0400
Subject: [PATCH 1643/2677] Revert "Exclude forge from ALL cmake target"

This reverts commit 302dfcdbcc8a4edc53ed908cf18f168af69b092b.
---
 .../AFconfigure_forge_submodule.cmake         | 58 +++++++++++--------
 src/backend/cpu/CMakeLists.txt                |  4 --
 src/backend/cuda/CMakeLists.txt               |  4 --
 src/backend/opencl/CMakeLists.txt             |  4 --
 4 files changed, 34 insertions(+), 36 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index 945e524954..748e1ba48d 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -5,35 +5,45 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
-set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
-set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
-set(CMAKE_BUILD_TYPE Release)
-set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
-set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
-set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
+if(AF_BUILD_FORGE)
+  set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
+  set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
+  set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
+  set(CMAKE_BUILD_TYPE Release)
+  set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
+  set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
+  set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
-add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
-mark_as_advanced(
-    FG_BUILD_EXAMPLES
-    FG_BUILD_DOCS
-    FG_WITH_FREEIMAGE
-    FG_USE_WINDOW_TOOLKIT
-    FG_USE_SYSTEM_CL2HPP
-    FG_ENABLE_HUNTER
-    glfw3_DIR
-    glm_DIR
-    )
-set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
-set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
+  add_subdirectory(extern/forge)
+
+  mark_as_advanced(
+      FG_BUILD_EXAMPLES
+      FG_BUILD_DOCS
+      FG_WITH_FREEIMAGE
+      FG_USE_WINDOW_TOOLKIT
+      FG_USE_SYSTEM_CL2HPP
+      FG_ENABLE_HUNTER
+      glfw3_DIR
+      glm_DIR
+      )
+  set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
+  set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
 
-if (AF_BUILD_FORGE AND AF_INSTALL_STANDALONE)
   install(FILES
       $<TARGET_FILE:forge>
       $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
       $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
       DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
-endif ()
-
-set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
+  set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
+else(AF_BUILD_FORGE)
+  set(FG_VERSION "1.0.0")
+  set(FG_VERSION_MAJOR 1)
+  set(FG_VERSION_MINOR 0)
+  set(FG_VERSION_PATCH 0)
+  set(FG_API_VERSION_CURRENT 10)
+  configure_file(
+    ${PROJECT_SOURCE_DIR}/extern/forge/CMakeModules/version.h.in
+    ${PROJECT_BINARY_DIR}/extern/forge/include/fg/version.h
+    )
+endif(AF_BUILD_FORGE)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 31ee92aaa9..a70ee738f7 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -349,10 +349,6 @@ if(LAPACK_FOUND OR MKL_FOUND)
       WITH_LINEAR_ALGEBRA)
 endif()
 
-if (AF_BUILD_FORGE)
-  add_dependencies(afcpu forge)
-endif ()
-
 install(TARGETS afcpu
   EXPORT ArrayFireCPUTargets
   COMPONENT cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 20beacedc3..30088878b7 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -523,10 +523,6 @@ if(APPLE)
   target_link_libraries(afcuda PUBLIC -Wl,-rpath,${CUDA_LIBRARIES_PATH})
 endif()
 
-if (AF_BUILD_FORGE)
-  add_dependencies(afcuda forge)
-endif ()
-
 install(TARGETS afcuda
   EXPORT ArrayFireCUDATargets
   COMPONENT cuda
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 47ae3365ae..136f4bccaa 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -507,10 +507,6 @@ if(LAPACK_FOUND OR MKL_FOUND)
       WITH_LINEAR_ALGEBRA)
 endif(LAPACK_FOUND OR MKL_FOUND)
 
-if (AF_BUILD_FORGE)
-  add_dependencies(afopencl forge)
-endif ()
-
 install(TARGETS afopencl
   EXPORT ArrayFireOpenCLTargets
   COMPONENT opencl

From 75aca96be5b6c411811190016b7e287d1bac5b54 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 May 2019 18:31:08 -0400
Subject: [PATCH 1644/2677] Update boost compute version to 1.70

Boost Compute has a bug in the older versions. This version is
required for the OpenCL version of scan
---
 CMakeModules/boost_package.cmake | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 1756f0d15d..bbd0fef57d 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -7,14 +7,14 @@
 
 find_package(Boost)
 
-if("${Boost_VERSION}" VERSION_LESS 106100)
-  set(VER boost-1.61.0)
-  set(MD5 7e1c433b48825d8cb2effa963823aec8)
+if("${Boost_VERSION}" VERSION_LESS 107000)
+  set(VER 1.70.0)
+  set(MD5 e160ec0ff825fc2850ea4614323b1fb5)
   include(ExternalProject)
 
   ExternalProject_Add(
     boost_compute
-    URL       https://github.com/boostorg/compute/archive/${VER}.tar.gz
+    URL       https://github.com/boostorg/compute/archive/boost-${VER}.tar.gz
     URL_MD5   ${MD5}
     INSTALL_COMMAND ""
     CONFIGURE_COMMAND ""
@@ -22,7 +22,11 @@ if("${Boost_VERSION}" VERSION_LESS 106100)
     )
 
   ExternalProject_Get_Property(boost_compute source_dir)
-  message(STATUS "BOOST_COMPUTE: ${source_dir}")
+
+  if(NOT EXISTS ${source_dir}/include)
+      message(WARNING "WARN: Found Boost v${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION}."
+                      " Required ${VER}. Build will download Boost Compute.")
+  endif()
   make_directory(${source_dir}/include)
 
   if(NOT TARGET Boost::boost)
@@ -37,7 +41,9 @@ if("${Boost_VERSION}" VERSION_LESS 106100)
     )
 endif()
 
-# NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
-# will try to link with libboost-chrono.
-set_target_properties(Boost::boost PROPERTIES INTERFACE_COMPILE_DEFINITIONS
-  "BOOST_CHRONO_HEADER_ONLY;BOOST_COMPUTE_THREAD_SAFE;BOOST_COMPUTE_HAVE_THREAD_LOCAL")
+if(TARGET Boost::boost)
+  # NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
+  # will try to link with libboost-chrono.
+  set_target_properties(Boost::boost PROPERTIES INTERFACE_COMPILE_DEFINITIONS
+    "BOOST_CHRONO_HEADER_ONLY;BOOST_COMPUTE_THREAD_SAFE;BOOST_COMPUTE_HAVE_THREAD_LOCAL")
+endif()

From e2fd6047d1c7befca4a062850cc124b162a0e6e4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 May 2019 18:31:48 -0400
Subject: [PATCH 1645/2677] Fix warning in the configuration step for gtest

---
 test/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 92e88cde8d..20e8060e77 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -23,7 +23,7 @@ if(NOT TARGET gtest)
         CACHE INTERNAL "Required so that the libs Runtime is not set to MT DLL")
   endif()
 
-  add_subdirectory(gtest/googletest EXCLUDE_FROM_ALL)
+  add_subdirectory(gtest EXCLUDE_FROM_ALL)
   set_target_properties(gtest gtest_main
     PROPERTIES
       FOLDER "ExternalProjectTargets/gtest")

From 49aca6e52f85a52350e72e0c48b4af9c1339e267 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 May 2019 19:53:42 -0400
Subject: [PATCH 1646/2677] Update OpenCL compile definitionsl to avoid
 warnings

---
 src/backend/opencl/CMakeLists.txt                    | 10 ++++++++--
 src/backend/opencl/kernel/scan_by_key/CMakeLists.txt |  1 +
 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt |  1 +
 src/backend/opencl/platform.hpp                      |  4 ----
 4 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 136f4bccaa..f1e680b903 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -30,6 +30,13 @@ file_to_string(
     NAMESPACE "opencl"
     )
 
+set(opencl_compile_definitions
+        CL_TARGET_OPENCL_VERSION=120
+        CL_HPP_TARGET_OPENCL_VERSION=120
+        CL_HPP_MINIMUM_OPENCL_VERSION=120
+        CL_HPP_ENABLE_EXCEPTIONS
+        CL_USE_DEPRECATED_OPENCL_1_2_APIS)
+
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/sort_by_key/CMakeLists.txt)
 
@@ -397,8 +404,7 @@ set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
 target_compile_definitions(afopencl
   PRIVATE
-    CL_USE_DEPRECATED_OPENCL_1_2_APIS
-    __CL_ENABLE_EXCEPTIONS
+    ${opencl_compile_definitions}
     AF_OPENCL
   )
 
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index f717a31ec5..9a796c9e77 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -52,6 +52,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     arrayfire_set_default_cxx_flags(opencl_scan_by_key_${SBK_BINARY_OP})
     target_compile_definitions(opencl_scan_by_key_${SBK_BINARY_OP}
       PRIVATE
+        ${opencl_compile_definitions}
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
         TYPE=${SBK_BINARY_OP} AFDLL)
     target_sources(opencl_scan_by_key
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 41f821f181..d618ff2f47 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -51,6 +51,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
 
     target_compile_definitions(opencl_sort_by_key_${SBK_TYPE}
       PRIVATE
+        ${opencl_compile_definitions}
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
         TYPE=${SBK_TYPE} AFDLL)
     target_sources(opencl_sort_by_key
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index da2d3825ab..1ceac97fb0 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -9,10 +9,6 @@
 
 #pragma once
 
-#define CL_HPP_ENABLE_EXCEPTIONS
-#define CL_HPP_MINIMUM_OPENCL_VERSION 120
-#define CL_HPP_TARGET_OPENCL_VERSION 120
-
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 #pragma GCC diagnostic ignored "-Wunused-parameter"

From 344f840195d2c3963775cf6cd16b940c8d76efd2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 May 2019 22:33:47 -0400
Subject: [PATCH 1647/2677] Fix several conversion warnings in tests

---
 examples/graphics/gravity_sim_init.h | 14000 ++++++++++++-------------
 test/jit.cpp                         |     2 +-
 test/mean.cpp                        |     5 +-
 test/reduce.cpp                      |    10 +-
 test/rng_match.cpp                   |    43 +-
 test/scan.cpp                        |    10 +-
 test/sort_index.cpp                  |    12 +-
 test/stdev.cpp                       |    11 +-
 test/testHelpers.hpp                 |     5 +
 test/where.cpp                       |    11 +-
 10 files changed, 7070 insertions(+), 7039 deletions(-)

diff --git a/examples/graphics/gravity_sim_init.h b/examples/graphics/gravity_sim_init.h
index afa6abd538..0c98115f0d 100644
--- a/examples/graphics/gravity_sim_init.h
+++ b/examples/graphics/gravity_sim_init.h
@@ -1,7004 +1,7004 @@
 const int HBD_NUM_ELEMENTS = 4000 * 7;
 // halo, bulge, and disk particles
-float hbd[] = {4.9161855e-03,  -1.5334119e+00, -8.3381424e+00, 4.4288845e+00,
-               -2.3778248e-01, 4.2592272e-02,  -4.4895774e-01, 4.9161855e-03,
-               1.9886702e-02,  6.0085773e+00,  3.1188631e-01,  8.1422836e-01,
-               -1.4591325e-02, 7.5382882e-01,  4.9161855e-03,  1.1676190e+00,
-               -4.6193779e-01, -5.0477743e-01, -1.4803666e+00, 5.6056118e-01,
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-               -1.2225604e+00, 3.8227065e+00,  -4.0077796e+00, 3.7918901e-01,
-               -3.4038458e-02, -2.2999659e-01, 4.9161855e-03,  -1.6463979e+00,
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-               2.9407086e-02,  4.9161855e-03,  5.4664793e+00,  -3.7369993e-01,
-               1.8591646e+00,  6.9752198e-01,  5.2111161e-01,  -5.1446843e-01,
-               4.9161855e-03,  -2.0373304e+00, 2.6609144e+00,  -1.8289629e+00,
-               5.7756305e-01,  -3.7016757e-03, -1.2520009e-01, 4.9161855e-03,
-               -4.3900475e-01, 1.6747446e+00,  4.9002385e+00,  2.5009772e-01,
-               -1.8630438e-01, 3.6023688e-01,  4.9161855e-03,  -6.4800224e+00,
-               1.0171971e+00,  2.6008205e+00,  7.6939821e-02,  3.9370355e-01,
-               1.5263109e-02,  4.9161855e-03,  7.7535975e-01,  -6.5957302e-01,
-               -1.4328420e-01, 1.3423905e-01,  -1.1076678e+00, 2.9757038e-01,
+float hbd[] = {4.9161855e-03f,  -1.5334119e+00f, -8.3381424e+00f, 4.4288845e+00f,
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+               2.5225203e+00f,  -6.5798134e-02f, -3.5946497e-01f, 1.7471495e-01f,
+               4.9161855e-03f,  -3.7135857e-01f, 4.8796633e-01f,  -3.7898597e-01f,
+               8.5347527e-01f,  2.2493289e-01f,  -2.7678892e-01f, 4.9161855e-03f,
+               2.2072470e+00f,  -2.5046587e+00f, 2.6029270e+00f,  3.0826443e-01f,
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+               4.9161855e-03f,  -1.3268198e+00f, -2.3238692e+00f, 6.7897618e-01f,
+               3.0518329e-01f,  6.8463421e-01f,  -7.1791840e-01f, 4.9161855e-03f,
+               -5.2054877e+00f, 2.0948052e+00f,  1.9656231e+00f,  7.4416548e-01f,
+               4.4825464e-01f,  -3.2727838e-01f, 4.9161855e-03f,  -8.2616639e-01f,
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+               8.7982547e-01f,  3.6643305e-01f,  -3.8197124e-01f, -1.1440479e-01f,
+               4.9161855e-03f,  3.5198438e-01f,  4.9096385e-01f,  -6.6494130e-02f,
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+               -5.3155913e+00f, -4.5045247e+00f, 1.8077201e-01f,  -4.4904891e-01f,
+               4.7391072e-01f,  4.9161855e-03f,  -1.0766581e-01f, 6.7338924e+00f,
+               6.1174130e+00f,  -2.3362583e-01f, 7.6430768e-02f,  -2.4832390e-01f,
+               4.9161855e-03f,  -4.9775305e-01f, 1.6378751e+00f,  -2.6263945e+00f,
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+               -3.8946674e+00f, -1.4725525e+00f, 2.4148097e+00f,  -1.7075756e-01f,
+               5.3592271e-01f,  7.2393781e-01f,  4.9161855e-03f,  6.8583161e-02f,
+               -1.5991354e+00f, -3.0150402e-01f, 1.5219669e-01f,  -5.6440836e-01f,
+               1.5284424e+00f,  4.9161855e-03f,  -4.2822695e+00f, 4.0367408e+00f,
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+               4.9161855e-03f,  5.3348875e-01f,  -3.6950427e-01f, 1.0364149e+00f,
+               7.8612208e-02f,  -2.7073494e-01f, 1.9663854e-01f,  4.9161855e-03f,
+               -3.3353384e+00f, 4.3220544e+00f,  -1.5343003e+00f, 6.7457032e-01f,
+               -1.8098858e-01f, 7.6241505e-01f,  4.9161855e-03f,  -8.8430309e+00f,
+               6.6101489e+00f,  2.2365890e+00f,  -2.9622875e-03f, -5.7892501e-01f,
+               2.3848678e-01f,  4.9161855e-03f,  -2.7121809e+00f, -3.7584829e+00f,
+               2.4702384e+00f,  3.9350358e-01f,  -6.7748266e-01f, -5.7142133e-01f,
+               4.9161855e-03f,  1.7517463e+00f,  -5.2237463e-01f, 1.2052536e+00f,
+               2.6133826e-01f,  -4.3084338e-01f, -2.8758329e-01f, 4.9161855e-03f,
+               -4.4221100e-01f, 2.4987850e-01f,  -9.0834004e-01f, -1.6435069e+00f,
+               -3.5537782e-01f, -5.6679737e-02f, 4.9161855e-03f,  9.5630264e+00f,
+               7.2472978e-01f,  -2.7188256e+00f, 4.1388586e-01f,  -2.7986884e-01f,
+               9.9171564e-02f,  4.9161855e-03f,  -2.5304942e+00f, -1.9891304e-01f,
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+               4.2524221e-04f,  -1.9546813e-01f, 1.3906172e-01f,  -5.5975009e-02f,
+               -1.0969467e-01f, -1.2633232e+00f, -4.3421894e-02f, 4.2524221e-04f,
+               -1.4044075e-01f, -2.6630515e-01f, 6.1962787e-02f,  4.6771467e-01f,
+               -6.9051319e-01f, 2.6465434e-01f,  4.2524221e-04f,  1.7195286e-01f,
+               -5.2851868e-01f, -1.6422449e-01f, 1.1703679e-01f,  7.2824037e-01f,
+               -3.6378372e-01f, 4.2524221e-04f,  1.0194746e-01f,  -9.7751893e-02f,
+               1.6529745e-01f,  2.4984296e-01f,  3.8181201e-02f,  2.7078211e-01f,
+               4.2524221e-04f,  2.0533490e-01f,  1.9480339e-01f,  -6.6993818e-02f,
+               3.9745870e-01f,  -7.9133675e-02f, -1.1942380e-01f, 4.2524221e-04f,
+               -3.9208923e-02f, 9.8150961e-02f,  1.0030308e-01f,  -5.7831265e-02f,
+               -6.4350224e-01f, 8.4775603e-01f,  4.2524221e-04f,  1.3816082e-01f,
+               -1.4092979e-02f, -1.0894109e-01f, 2.8519067e-01f,  5.8030725e-01f,
+               6.5652287e-01f,  4.2524221e-04f,  3.1362314e-02f,  -6.5740333e-03f,
+               6.7480214e-02f,  4.2265895e-01f,  -5.1995921e-01f, -2.8980300e-02f,
+               4.2524221e-04f,  -1.1953717e-01f, 1.5453845e-01f,  1.3720915e-01f,
+               -1.5399654e-01f, -1.2724885e-01f, 6.4902240e-01f,  4.2524221e-04f,
+               -2.4549389e-01f, -7.9987049e-02f, 8.9279823e-02f,  -9.2930816e-02f,
+               -6.1336237e-01f, 4.7973198e-01f,  4.2524221e-04f,  2.5360553e-02f,
+               -2.6513871e-02f, 5.4526389e-02f,  -9.8100655e-02f, 6.5327984e-01f,
+               -5.2721924e-01f, 4.2524221e-04f,  -1.0606319e-01f, -6.9447577e-02f,
+               4.3061398e-02f,  -1.0653659e+00f, 6.2340677e-01f,  4.6419606e-02f};
diff --git a/test/jit.cpp b/test/jit.cpp
index 99c7f37849..4a1ff6e4cb 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -456,7 +456,7 @@ class JIT : public ::testing::TestWithParam<tile_params> {
     void SetUp() {
         tile_params params = GetParam();
         vector<float> vals(params.in_dim.elements());
-        iota(vals.begin(), vals.end(), 0);
+        iota(vals.begin(), vals.end(), 0.f);
         in = array(params.in_dim, &vals.front());
 
         // clang-format off
diff --git a/test/mean.cpp b/test/mean.cpp
index 5a9185b334..49d01d17a3 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -110,7 +110,10 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
         dim4 dims  = numDims[0];
         dim4 wdims = numDims[1];
         vector<T> input(in[0].begin(), in[0].end());
-        vector<float> weights(in[1].begin(), in[1].end());
+        vector<float> weights(in[1].size());
+        transform(in[1].begin(), in[1].end(),
+                  weights.begin(),
+                  convert_to<float, int>);
 
         array inArray(dims, &(input.front()));
         array wtsArray(wdims, &(weights.front()));
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 610d96ff47..035361e38d 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -51,7 +51,10 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef = false,
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
-    vector<Ti> in(data[0].begin(), data[0].end());
+    vector<Ti> in(data[0].size());
+    transform(data[0].begin(), data[0].end(),
+              in.begin(),
+              convert_to<Ti, int>);
 
     af_array inArray   = 0;
     af_array outArray  = 0;
@@ -217,7 +220,10 @@ void cppReduceTest(string pTestFile) {
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
-    vector<Ti> in(data[0].begin(), data[0].end());
+    vector<Ti> in(data[0].size());
+    transform(data[0].begin(), data[0].end(),
+              in.begin(),
+              convert_to<Ti, int>);
 
     array input(dims, &in.front());
 
diff --git a/test/rng_match.cpp b/test/rng_match.cpp
index d61c712b51..0d10c0d0fc 100644
--- a/test/rng_match.cpp
+++ b/test/rng_match.cpp
@@ -29,11 +29,9 @@ using std::vector;
 
 enum param { engine, backend, size, seed, type };
 
-using rng_params = std::tuple < af::randomEngineType,
-                                std::pair < af::Backend, af::Backend>,
-                                af::dim4,
-                                int,
-                                af_dtype>;
+using rng_params =
+    std::tuple<af::randomEngineType, std::pair<af::Backend, af::Backend>,
+               af::dim4, int, af_dtype>;
 
 class RNGMatch : public ::testing::TestWithParam<rng_params> {
    protected:
@@ -68,18 +66,20 @@ class RNGMatch : public ::testing::TestWithParam<rng_params> {
 
 std::string engine_name(af::randomEngineType engine) {
     switch (engine) {
-        case AF_RANDOM_ENGINE_PHILOX  : return "PHILOX";
+        case AF_RANDOM_ENGINE_PHILOX: return "PHILOX";
         case AF_RANDOM_ENGINE_THREEFRY: return "THREEFRY";
         case AF_RANDOM_ENGINE_MERSENNE: return "MERSENNE";
+        default: return "UNKNOWN ENGINE";
     }
 }
 
 std::string backend_name(af::Backend backend) {
     switch (backend) {
         case AF_BACKEND_DEFAULT: return "DEFAULT";
-        case AF_BACKEND_CPU    : return "CPU";
-        case AF_BACKEND_CUDA   : return "CUDA";
-        case AF_BACKEND_OPENCL : return "OPENCL";
+        case AF_BACKEND_CPU: return "CPU";
+        case AF_BACKEND_CUDA: return "CUDA";
+        case AF_BACKEND_OPENCL: return "OPENCL";
+        default: return "UNKNOWN BACKEND";
     }
 }
 
@@ -101,25 +101,14 @@ INSTANTIATE_TEST_CASE_P(
         ::testing::Values(AF_RANDOM_ENGINE_PHILOX),
         ::testing::Values(make_pair(AF_BACKEND_CPU, AF_BACKEND_CUDA),
                           make_pair(AF_BACKEND_CPU, AF_BACKEND_OPENCL)),
-        ::testing::Values(dim4(10),
-                          dim4(100),
-                          dim4(1000),
-                          dim4(10000),
-                          dim4(1E5),
-                          dim4(10, 10),
-                          dim4(10, 100),
-                          dim4(100, 100),
-                          dim4(1000, 100),
-                          dim4(10, 10, 10),
-                          dim4(10, 100, 10),
-                          dim4(100, 100, 10),
-                          dim4(1000, 100, 10),
-                          dim4(10, 10, 10, 10),
-                          dim4(10, 100, 10, 10),
-                          dim4(100, 100, 10, 10),
+        ::testing::Values(dim4(10), dim4(100), dim4(1000), dim4(10000),
+                          dim4(1E5), dim4(10, 10), dim4(10, 100),
+                          dim4(100, 100), dim4(1000, 100), dim4(10, 10, 10),
+                          dim4(10, 100, 10), dim4(100, 100, 10),
+                          dim4(1000, 100, 10), dim4(10, 10, 10, 10),
+                          dim4(10, 100, 10, 10), dim4(100, 100, 10, 10),
                           dim4(1000, 100, 10, 10)),
-        ::testing::Values(12),
-        ::testing::Values(f32, f64, c32, c64, u8)),
+        ::testing::Values(12), ::testing::Values(f32, f64, c32, c64, u8)),
     rngmatch_info);
 
 TEST_P(RNGMatch, BackendEquals) {
diff --git a/test/scan.cpp b/test/scan.cpp
index 3d96d0f789..580a4acd9e 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -53,7 +53,10 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef = false,
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
-    vector<Ti> in(data[0].begin(), data[0].end());
+    vector<Ti> in(data[0].size());
+    transform(data[0].begin(), data[0].end(),
+              in.begin(),
+              convert_to<Ti, int>);
 
     af_array inArray   = 0;
     af_array outArray  = 0;
@@ -134,7 +137,10 @@ TEST(Accum, CPP) {
                              tests);
     dim4 dims = numDims[0];
 
-    vector<float> in(data[0].begin(), data[0].end());
+    vector<float> in(data[0].size());
+    transform(data[0].begin(), data[0].end(),
+              in.begin(),
+              convert_to<float, int>);
 
     array input(dims, &(in.front()));
 
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 1c3335ee8f..f10623ba67 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -80,7 +80,11 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
 
     ASSERT_SUCCESS(af_sort_index(&sxArray, &ixArray, inArray, 0, dir));
 
-    vector<T> sxTest(tests[resultIdx0].begin(), tests[resultIdx0].end());
+    vector<T> sxTest(tests[resultIdx0].size());
+    transform(tests[resultIdx0].begin(), tests[resultIdx0].end(),
+              sxTest.begin(),
+              convert_to<T, float>);
+
     ASSERT_VEC_ARRAY_EQ(sxTest, idims, sxArray);
 
 #ifdef AF_OPENCL
@@ -139,7 +143,11 @@ TEST(SortIndex, CPPDim0) {
 
     ASSERT_VEC_ARRAY_EQ(tests[resultIdx0], idims, outValues);
 
-    vector<unsigned> ixTest(tests[resultIdx1].begin(), tests[resultIdx1].end());
+    vector<unsigned> ixTest(tests[resultIdx1].size());
+    transform(tests[resultIdx1].begin(), tests[resultIdx1].end(),
+              ixTest.begin(),
+              convert_to<unsigned, float>);
+
     ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
 
diff --git a/test/stdev.cpp b/test/stdev.cpp
index ee79958bf6..51879c6dff 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -193,12 +193,19 @@ TYPED_TEST(StandardDev, All) {
         string(TEST_DIR "/stdev/mat_10x10_scalar.test"), numDims, in, tests);
 
     dim4 dims = numDims[0];
-    vector<TypeParam> input(in[0].begin(), in[0].end());
+    vector<TypeParam> input(in[0].size());
+    transform(in[0].begin(), in[0].end(),
+              input.begin(),
+              convert_to<TypeParam, int>);
 
     array a(dims, &(input.front()));
     outType b = stdev<outType>(a);
 
-    vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
+    vector<outType> currGoldBar(tests[0].size());
+    transform(tests[0].begin(), tests[0].end(),
+              currGoldBar.begin(),
+              convert_to<outType, float>);
+
     ASSERT_NEAR(::real(currGoldBar[0]), ::real(b), 1.0e-3);
     ASSERT_NEAR(::imag(currGoldBar[0]), ::imag(b), 1.0e-3);
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index d1ee3c2e45..87a1af5291 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -454,6 +454,11 @@ inline bool noLAPACKTests() {
     return ret;
 }
 
+template<typename TO, typename FROM>
+TO convert_to(FROM in) {
+    return TO(in);
+}
+
 // TODO: perform conversion on device for CUDA and OpenCL
 template<typename T>
 af_err conv_image(af_array *out, af_array in) {
diff --git a/test/where.cpp b/test/where.cpp
index 28c8a902b5..caf9e80c7a 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -50,7 +50,10 @@ void whereTest(string pTestFile, bool isSubRef = false,
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
-    vector<T> in(data[0].begin(), data[0].end());
+    vector<T> in(data[0].size());
+    transform(data[0].begin(), data[0].end(),
+              in.begin(),
+              convert_to<T, int>);
 
     af_array inArray   = 0;
     af_array outArray  = 0;
@@ -104,7 +107,11 @@ TYPED_TEST(Where, CPP) {
                              data, tests);
     dim4 dims = numDims[0];
 
-    vector<float> in(data[0].begin(), data[0].end());
+    vector<float> in(data[0].size());
+    transform(data[0].begin(), data[0].end(),
+              in.begin(),
+              convert_to<float, int>);
+
     array input(dims, &in.front(), afHost);
     array output = where(input);
 

From b52ea7f3ec438bf892a21a5e22df9a7b1ca37887 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 4 May 2019 00:23:35 -0400
Subject: [PATCH 1648/2677] Update clBlast to 1.5.0

---
 CMakeModules/build_CLBlast.cmake | 30 +++++++++++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 4fa20ddd85..be2cfce794 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -7,16 +7,44 @@
 
 include(ExternalProject)
 
+find_program(GIT git)
+
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
 set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
+if(APPLE)
+  # We need this patch on macOS until #PR 356 is merged in the CLBlast repo
+  write_file(clblast.patch
+"diff --git a/src/clpp11.hpp b/src/clpp11.hpp
+index 9446499..786f7db 100644
+--- a/src/clpp11.hpp
++++ b/src/clpp11.hpp
+@@ -358,8 +358,10 @@ class Device {
+
+   // Returns if the Nvidia chip is a Volta or later archicture (sm_70 or higher)
+   bool IsPostNVIDIAVolta() const {
+-    assert(HasExtension(\"cl_nv_device_attribute_query\"));
+-    return GetInfo<cl_uint>(CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV) >= 7;
++    if(HasExtension(\"cl_nv_device_attribute_query\")) {
++      return GetInfo<cl_uint>(CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV) >= 7;
++    }
++    return false;
+   }
+
+   // Retrieves the above extra information (if present)
+")
+
+  set(CLBLAST_PATCH_COMMAND ${GIT} apply ${ArrayFire_BINARY_DIR}/clblast.patch)
+endif()
+
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 43e3f27254c4f7e4a0b332f5b88965c53c20bdd1 # v1.4.0 plus CLBlast #295
+    GIT_TAG 1.5.0
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
+    PATCH_COMMAND ${CLBLAST_PATCH_COMMAND}
     BUILD_BYPRODUCTS ${CLBlast_location}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/
       -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}

From e9ee48d99c41e664f32b54dcf2680d4e57e70b92 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 9 May 2019 13:15:53 -0400
Subject: [PATCH 1649/2677] Check for variadic templates in jit test to support
 older compilers

---
 test/jit.cpp | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/test/jit.cpp b/test/jit.cpp
index 4a1ff6e4cb..847b948ef5 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -427,12 +427,19 @@ TEST(JIT, ConstEval7) {
     const array d = constant(1, 1);
     const array e = constant(1, 1);
     const array f = constant(1, 1);
-    const array g = constant(1, 1);
 
+#if (__cpp_variadic_templates >= 200704)
     EXPECT_NO_THROW({
+        const array g = constant(1, 1);
         eval(a, b, c, d, e, f, g);
         af::sync();
     });
+#else
+    EXPECT_NO_THROW({
+        eval(a, b, c, d, e, f);
+        af::sync();
+    });
+#endif
 }
 
 using af::dim4;

From 9b452c1b38a64e395c79bc9506da690f929fa629 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 May 2019 22:58:43 -0400
Subject: [PATCH 1650/2677] Adjust the max jit kernel param size for AMD GPUs

The AMD GPUs seem to have an undocumented max size for the parameter
kernels.
---
 src/backend/opencl/Array.cpp | 29 ++++++++++++++++++++++-------
 1 file changed, 22 insertions(+), 7 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 998782e74a..8c28ad52ff 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -256,12 +256,17 @@ bool passesJitHeuristics(Node *root_node) {
 
     bool isBufferLimit =
         lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
-    bool isNvidia = getActivePlatform() == AFCL_PLATFORM_NVIDIA ||
-                    getActivePlatform() == AFCL_PLATFORM_APPLE;
+    auto platform = getActivePlatform();
 
-    // A lightweight check based on the height of the node. This is an
-    // inexpensive operation and does not traverse the JIT tree.
-    bool isParamLimit = (isNvidia && root_node->getHeight() > 6);
+    // The Apple platform can have the nvidia card or the AMD card
+    bool isNvidia =
+        platform == AFCL_PLATFORM_NVIDIA || platform == AFCL_PLATFORM_APPLE;
+    bool isAmd =
+        platform == AFCL_PLATFORM_AMD || platform == AFCL_PLATFORM_APPLE;
+
+    // A lightweight check based on the height of the node. This is
+    // an inexpensive operation and does not traverse the JIT tree.
+    bool isParamLimit = (root_node->getHeight() > 6);
     if (isParamLimit || isBufferLimit) {
         // This is the base parameter size if the kernel had no
         // arguments
@@ -270,7 +275,17 @@ bool passesJitHeuristics(Node *root_node) {
 
         // This is the maximum size of the params that can be allowed by the
         // CUDA platform.
-        constexpr size_t max_param_size = (4096 - base_param_size);
+        constexpr size_t max_nvidia_param_size = (4096 - base_param_size);
+        constexpr size_t max_amd_param_size    = (3670 - base_param_size);
+
+        size_t max_param_size = 0;
+        if (isNvidia) {
+            max_param_size = max_nvidia_param_size;
+        } else if (isAmd) {
+            max_param_size = max_amd_param_size;
+        } else {
+            max_param_size = 8192;
+        }
 
         struct tree_info {
             size_t total_buffer_size;
@@ -298,7 +313,7 @@ bool passesJitHeuristics(Node *root_node) {
         size_t param_size = (info.num_buffers * (sizeof(KParam) + sizeof(T *)) +
                              info.param_scalar_size);
 
-        isParamLimit = isNvidia && param_size >= max_param_size;
+        isParamLimit = param_size >= max_param_size;
 
         if (isBufferLimit || isParamLimit) { return false; }
     }

From e4e14eb392aae09aa981060fcfab8aa232c219d8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 10 May 2019 02:04:02 -0400
Subject: [PATCH 1651/2677] Refactor MatrixAlgebraHandle to HandleBase and make
 it generic

---
 src/backend/common/CMakeLists.txt          |  2 +-
 src/backend/common/HandleBase.hpp          | 39 ++++++++++++++++++++++
 src/backend/common/MatrixAlgebraHandle.hpp | 31 -----------------
 src/backend/cuda/cublas.cpp                |  3 --
 src/backend/cuda/cublas.hpp                | 11 +++---
 src/backend/cuda/cusolverDn.hpp            | 14 +++-----
 src/backend/cuda/cusparse.cpp              |  3 --
 src/backend/cuda/cusparse.hpp              | 13 +++-----
 src/backend/cuda/platform.cpp              | 12 +++----
 src/backend/cuda/sparse.cu                 |  6 ++--
 10 files changed, 62 insertions(+), 72 deletions(-)
 create mode 100644 src/backend/common/HandleBase.hpp
 delete mode 100644 src/backend/common/MatrixAlgebraHandle.hpp

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 905892271c..b668fbd303 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -27,7 +27,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/MatrixAlgebraHandle.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/HandleBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
diff --git a/src/backend/common/HandleBase.hpp b/src/backend/common/HandleBase.hpp
new file mode 100644
index 0000000000..bcc2813c5c
--- /dev/null
+++ b/src/backend/common/HandleBase.hpp
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace common {
+template<typename T, typename H>
+class HandleBase {
+    H handle_;
+
+   public:
+    HandleBase() : handle_(0) { static_cast<T*>(this)->createHandle(&handle_); }
+    ~HandleBase() { static_cast<T*>(this)->destroyHandle(handle_); }
+
+    operator H() { return handle_; }
+    H* get() { return &handle_; }
+
+    HandleBase(HandleBase const&) = delete;
+    void operator=(HandleBase const&) = delete;
+
+    HandleBase(HandleBase &&h) = default;
+    HandleBase& operator=(HandleBase &&h) = default;
+};
+}  // namespace common
+
+#define CREATE_HANDLE(NAME, TYPE, CREATE_FUNCTION, DESTROY_FUNCTION, CHECK_FUNCTION) \
+    class NAME : public common::HandleBase<NAME, TYPE> {              \
+       public:                                                        \
+        void createHandle(TYPE* handle) {                             \
+            CHECK_FUNCTION(CREATE_FUNCTION(handle));                  \
+        }                                                             \
+        void destroyHandle(TYPE handle) { DESTROY_FUNCTION(handle); } \
+    };
diff --git a/src/backend/common/MatrixAlgebraHandle.hpp b/src/backend/common/MatrixAlgebraHandle.hpp
deleted file mode 100644
index c90ea613b0..0000000000
--- a/src/backend/common/MatrixAlgebraHandle.hpp
+++ /dev/null
@@ -1,31 +0,0 @@
-/*******************************************************
- * Copyright (c) 2016, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <common/err_common.hpp>
-#include <cstdio>
-
-namespace common {
-template<typename T, typename H>
-class MatrixAlgebraHandle {
-   public:
-    MatrixAlgebraHandle() { static_cast<T*>(this)->createHandle(&handle); }
-
-    ~MatrixAlgebraHandle() { static_cast<T*>(this)->destroyHandle(handle); }
-
-    H get() const { return handle; }
-
-   private:
-    MatrixAlgebraHandle(MatrixAlgebraHandle const&);
-    void operator=(MatrixAlgebraHandle const&);
-
-    H handle;
-};
-}  // namespace common
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index aeabf961c2..29a0023a18 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -30,7 +30,4 @@ const char* errorString(cublasStatus_t err) {
     }
 }
 
-void cublasHandle::createHandle(BlasHandle* handle) {
-    CUBLAS_CHECK(cublasCreate(handle));
-}
 }  // namespace cuda
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index 26f401ce3a..bca9504c68 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <common/MatrixAlgebraHandle.hpp>
+#include <common/HandleBase.hpp>
 #include <common/defines.hpp>
 #include <cublas_v2.h>
 
@@ -30,10 +30,7 @@ const char* errorString(cublasStatus_t err);
         }                                                                   \
     } while (0)
 
-class cublasHandle
-    : public common::MatrixAlgebraHandle<cublasHandle, BlasHandle> {
-   public:
-    void createHandle(BlasHandle* handle);
-    void destroyHandle(BlasHandle handle) { cublasDestroy(handle); }
-};
+CREATE_HANDLE(cublasHandle, cublasHandle_t, cublasCreate, cublasDestroy,
+              CUBLAS_CHECK);
+
 }  // namespace cuda
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index 31283c27cc..5ffa8deb84 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -9,8 +9,9 @@
 
 #pragma once
 
-#include <common/MatrixAlgebraHandle.hpp>
+#include <common/HandleBase.hpp>
 #include <common/defines.hpp>
+#include <common/err_common.hpp>
 #include <cuda_runtime.h>
 #include <cusolverDn.h>
 
@@ -32,14 +33,7 @@ const char* errorString(cusolverStatus_t err);
         }                                                                   \
     } while (0)
 
-class cusolverDnHandle
-    : public common::MatrixAlgebraHandle<cusolverDnHandle, SolveHandle> {
-   public:
-    void createHandle(SolveHandle* handle) {
-        CUSOLVER_CHECK(cusolverDnCreate(handle));
-    }
-
-    void destroyHandle(SolveHandle handle) { cusolverDnDestroy(handle); }
-};
+CREATE_HANDLE(cusolverDnHandle, cusolverDnHandle_t, cusolverDnCreate,
+              cusolverDnDestroy, CUSOLVER_CHECK);
 
 }  // namespace cuda
diff --git a/src/backend/cuda/cusparse.cpp b/src/backend/cuda/cusparse.cpp
index 79323f27b4..a2471d6267 100644
--- a/src/backend/cuda/cusparse.cpp
+++ b/src/backend/cuda/cusparse.cpp
@@ -37,7 +37,4 @@ const char* errorString(cusparseStatus_t err) {
     }
 }
 
-void cusparseHandle::createHandle(SparseHandle* handle) {
-    CUSPARSE_CHECK(cusparseCreate(handle));
-}
 }  // namespace cuda
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 0916908779..65fad6dac5 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -8,8 +8,9 @@
  ********************************************************/
 
 #pragma once
-#include <common/MatrixAlgebraHandle.hpp>
+#include <common/HandleBase.hpp>
 #include <common/defines.hpp>
+#include <common/err_common.hpp>
 #include <cusparse_v2.h>
 
 namespace cuda {
@@ -24,16 +25,12 @@ const char* errorString(cusparseStatus_t err);
         if (_error != CUSPARSE_STATUS_SUCCESS) {                              \
             char _err_msg[1024];                                              \
             snprintf(_err_msg, sizeof(_err_msg), "CUSPARSE Error (%d): %s\n", \
-                     (int)(_error), errorString(_error));                     \
+                     (int)(_error), cuda::errorString(_error));               \
                                                                               \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
         }                                                                     \
     } while (0)
 
-class cusparseHandle
-    : public common::MatrixAlgebraHandle<cusparseHandle, SparseHandle> {
-   public:
-    void createHandle(SparseHandle* handle);
-    void destroyHandle(SparseHandle handle) { cusparseDestroy(handle); }
-};
+CREATE_HANDLE(cusparseHandle, cusparseHandle_t, cusparseCreate, cusparseDestroy,
+              CUSPARSE_CHECK);
 }  // namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 9c01ba02d8..683e02f598 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -87,7 +87,7 @@ unique_ptr<cublasHandle>& cublasManager(const int deviceId) {
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUBLAS_CHECK(cublasSetStream(handles[deviceId]->get(),
+        CUBLAS_CHECK(cublasSetStream(*handles[deviceId],
                                      cuda::getStream(deviceId)));
     });
 
@@ -111,7 +111,7 @@ unique_ptr<cusolverDnHandle>& cusolverManager(const int deviceId) {
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUSOLVER_CHECK(cusolverDnSetStream(handles[deviceId]->get(),
+        CUSOLVER_CHECK(cusolverDnSetStream(*handles[deviceId],
                                            cuda::getStream(deviceId)));
     });
     // TODO(pradeep) prior to this change, stream was being synced in get solver
@@ -132,7 +132,7 @@ unique_ptr<cusparseHandle>& cusparseManager(const int deviceId) {
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUSPARSE_CHECK(cusparseSetStream(handles[deviceId]->get(),
+        CUSPARSE_CHECK(cusparseSetStream(*handles[deviceId],
                                          cuda::getStream(deviceId)));
     });
     return handles[deviceId];
@@ -373,15 +373,15 @@ PlanCache &fftManager() {
 }
 
 BlasHandle blasHandle() {
-    return cublasManager(cuda::getActiveDeviceId())->get();
+    return *cublasManager(cuda::getActiveDeviceId());
 }
 
 SolveHandle solverDnHandle() {
-    return cusolverManager(cuda::getActiveDeviceId())->get();
+    return *cusolverManager(cuda::getActiveDeviceId());
 }
 
 SparseHandle sparseHandle() {
-    return cusparseManager(cuda::getActiveDeviceId())->get();
+    return *cusparseManager(cuda::getActiveDeviceId());
 }
 
 void sync(int device) {
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index cc0bf85224..f34458f8fe 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -10,9 +10,6 @@
 #include <kernel/sparse.hpp>
 #include <sparse.hpp>
 
-#include <stdexcept>
-#include <string>
-
 #include <arith.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
@@ -24,6 +21,9 @@
 #include <platform.hpp>
 #include <where.hpp>
 
+#include <stdexcept>
+#include <string>
+
 namespace cuda {
 
 using namespace common;

From de13008a759fd36b1b9e0b53fdb48d11d7b5d28a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 10 May 2019 02:11:53 -0400
Subject: [PATCH 1652/2677] Fix memory leak in sparse arith

---
 src/backend/cuda/cusparse.hpp    |  3 +++
 src/backend/cuda/sparse_arith.cu | 20 ++++++++++----------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 65fad6dac5..006c865b87 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -33,4 +33,7 @@ const char* errorString(cusparseStatus_t err);
 
 CREATE_HANDLE(cusparseHandle, cusparseHandle_t, cusparseCreate, cusparseDestroy,
               CUSPARSE_CHECK);
+
+CREATE_HANDLE(cusparseMatDescrHandle, cusparseMatDescr_t,
+              cusparseCreateMatDescr, cusparseDestroyMatDescr, CUSPARSE_CHECK);
 }  // namespace cuda
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index a3c1364fb9..a3ea691c7b 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -141,9 +141,7 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
 
-    cusparseMatDescr_t desc;
-    cusparseCreateMatDescr(&desc);
-
+    cusparseMatDescrHandle desc;
     const dim4 ldims = lhs.dims();
 
     const int M = ldims[0];
@@ -163,16 +161,18 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     int baseC, nnzC;
     int *nnzcDevHostPtr = &nnzC;
 
-    cusparseXcsrgeamNnz(sparseHandle(), M, N, desc, nnzA, csrRowPtrA,
-                        csrColPtrA, desc, nnzB, csrRowPtrB, csrColPtrB, desc,
-                        csrRowPtrC, nnzcDevHostPtr);
+    CUSPARSE_CHECK(cusparseXcsrgeamNnz(
+        sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
+        csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr));
     if (NULL != nnzcDevHostPtr) {
         nnzC = *nnzcDevHostPtr;
     } else {
-        cudaMemcpyAsync(&nnzC, csrRowPtrC + M, sizeof(int),
-                        cudaMemcpyDeviceToHost, cuda::getActiveStream());
-        cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int), cudaMemcpyDeviceToHost,
-                        cuda::getActiveStream());
+        CUDA_CHECK(cudaMemcpyAsync(&nnzC, csrRowPtrC + M, sizeof(int),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         nnzC -= baseC;
     }

From c69ec15483cc85f9c7d42dfbabea35c2c8c308b9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 10 May 2019 14:09:55 -0400
Subject: [PATCH 1653/2677] Create a unique_handle class to manage C library
 handles

---
 src/backend/common/CMakeLists.txt    |   1 +
 src/backend/common/unique_handle.hpp | 110 +++++++++++++++++++++++++++
 2 files changed, 111 insertions(+)
 create mode 100644 src/backend/common/unique_handle.hpp

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index b668fbd303..9829d56761 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -49,6 +49,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/unique_handle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.hpp
     ${ArrayFire_BINARY_DIR}/version.hpp
diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
new file mode 100644
index 0000000000..266717df8a
--- /dev/null
+++ b/src/backend/common/unique_handle.hpp
@@ -0,0 +1,110 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+namespace common {
+
+/// Deletes a handle.
+///
+/// This function deletes a handle. Handle are usually typedefed pointers
+/// which are created by a C API of a library.
+///
+/// \param[in] handle the handle that will deleted by the destroy function
+/// \note This function will need to be specialized for each type of handle
+template<typename T>
+void handle_deleter(T handle) noexcept;
+
+/// Creates a handle
+/// This function creates a handle. Handle are usually typedefed pointers
+/// which are created by a C API of a library.
+///
+/// \param[in] handle the handle that will be initialzed by the create function
+/// \note This function will need to be specialized for each type of handle
+template<typename T>
+void handle_creator(T *handle) noexcept;
+
+/// \brief A generic class to manage basic RAII lifetimes for C handles
+///
+/// This class manages the lifetimes of C handles found in many types of
+/// libraries. This class is non-copiable but can be moved.
+///
+/// You can use this class with a new handle by using the CREATE_HANDLE macro in
+/// the src/backend/*/handle.cpp file. This macro instantiates the
+/// handle_createor and handle_deleter functions used by this class.
+///
+/// \code{.cpp}
+/// CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
+/// \code{.cpp}
+template<typename T>
+class unique_handle {
+    T handle_;
+
+   public:
+    /// Default constructor. Initializes the handle to zero. Does not call the
+    /// create function
+    constexpr unique_handle() noexcept : handle_(0) {}
+    void create() {
+        if (!handle_) handle_creator(&handle_);
+    }
+
+    /// \brief Takes ownership of a previously created handle
+    ///
+    /// \param[in] handle The handle to manage by this object
+    explicit constexpr unique_handle(T handle) : handle_(handle){};
+
+    /// \brief Deletes the handle if created.
+    ~unique_handle() noexcept {
+        if (handle_) handle_deleter(handle_);
+    };
+
+    /// \brief Implicit converter for the handle
+    constexpr operator const T &() const noexcept { return handle_; }
+
+    explicit unique_handle(const unique_handle &other) noexcept      = delete;
+    constexpr explicit unique_handle(unique_handle &&other) noexcept = default;
+
+    unique_handle &operator=(unique_handle &other) noexcept = delete;
+    unique_handle &operator=(unique_handle &&other) noexcept = default;
+
+    // Returns true if the \p other unique_handle is the same as this handle
+    constexpr bool operator==(unique_handle &other) const noexcept {
+        return handle_ == other.handle_;
+    }
+
+    // Returns true if the \p other handle is the same as this handle
+    constexpr bool operator==(T &other) const noexcept {
+        return handle_ == other;
+    }
+
+    // Returns true if the \p other handle is the same as this handle
+    constexpr bool operator==(T other) const noexcept {
+        return handle_ == other;
+    }
+};
+}  // namespace common
+
+/// specializes the handle_creater and handle_deleter functions for a specific
+/// handle
+///
+/// \param[in] HANDLE The type of the handle
+/// \param[in] CREATE The create function for the handle
+/// \param[in] DESTROY The destroy function for the handle
+/// \note Do not add this macro to another namespace, The macro provides a
+///       namespace for the functions.
+#define CREATE_HANDLE(HANDLE, CREATE, DESTROY)              \
+    namespace common {                                      \
+    template<>                                              \
+    void handle_deleter<HANDLE>(HANDLE handle) noexcept {   \
+        DESTROY(handle);                                    \
+    }                                                       \
+    template<>                                              \
+    void handle_creator<HANDLE>(HANDLE * handle) noexcept { \
+        CREATE(handle);                                     \
+    }                                                       \
+    }  // namespace common

From fc2e8002655fbb560944017f6c71c98de997d7b0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 10 May 2019 15:17:52 -0400
Subject: [PATCH 1654/2677] Refactor cublas,cusolver,cusparse handles to
 unique_handle

---
 src/backend/cuda/CMakeLists.txt  |  1 +
 src/backend/cuda/cublas.hpp      |  9 +-------
 src/backend/cuda/cusolverDn.hpp  | 12 +---------
 src/backend/cuda/cusparse.hpp    |  9 --------
 src/backend/cuda/handle.cpp      | 23 +++++++++++++++++++
 src/backend/cuda/platform.cpp    | 39 +++++++++++++++++---------------
 src/backend/cuda/sparse_arith.cu |  4 +++-
 7 files changed, 50 insertions(+), 47 deletions(-)
 create mode 100644 src/backend/cuda/handle.cpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 30088878b7..b37767b67c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -351,6 +351,7 @@ cuda_add_library(afcuda
     GraphicsResourceManager.cpp
     GraphicsResourceManager.hpp
     gradient.hpp
+    handle.cpp
     harris.hpp
     hist_graphics.cpp
     hist_graphics.hpp
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index bca9504c68..e51454ec32 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -8,14 +8,11 @@
  ********************************************************/
 
 #pragma once
-#include <common/HandleBase.hpp>
 #include <common/defines.hpp>
 #include <cublas_v2.h>
 
 namespace cuda {
 
-using BlasHandle = cublasHandle_t;
-
 const char* errorString(cublasStatus_t err);
 
 #define CUBLAS_CHECK(fn)                                                    \
@@ -24,13 +21,9 @@ const char* errorString(cublasStatus_t err);
         if (_error != CUBLAS_STATUS_SUCCESS) {                              \
             char _err_msg[1024];                                            \
             snprintf(_err_msg, sizeof(_err_msg), "CUBLAS Error (%d): %s\n", \
-                     (int)(_error), errorString(_error));                   \
-                                                                            \
+                     (int)(_error), cuda::errorString(_error));             \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
         }                                                                   \
     } while (0)
 
-CREATE_HANDLE(cublasHandle, cublasHandle_t, cublasCreate, cublasDestroy,
-              CUBLAS_CHECK);
-
 }  // namespace cuda
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index 5ffa8deb84..241c89035f 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -8,17 +8,10 @@
  ********************************************************/
 
 #pragma once
-
-#include <common/HandleBase.hpp>
-#include <common/defines.hpp>
-#include <common/err_common.hpp>
-#include <cuda_runtime.h>
 #include <cusolverDn.h>
 
 namespace cuda {
 
-using SolveHandle = cusolverDnHandle_t;
-
 const char* errorString(cusolverStatus_t err);
 
 #define CUSOLVER_CHECK(fn)                                                  \
@@ -27,13 +20,10 @@ const char* errorString(cusolverStatus_t err);
         if (_error != CUSOLVER_STATUS_SUCCESS) {                            \
             char _err_msg[1024];                                            \
             snprintf(_err_msg, sizeof(_err_msg), "CUBLAS Error (%d): %s\n", \
-                     (int)(_error), errorString(_error));                   \
+                     (int)(_error), cuda::errorString(_error));             \
                                                                             \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
         }                                                                   \
     } while (0)
 
-CREATE_HANDLE(cusolverDnHandle, cusolverDnHandle_t, cusolverDnCreate,
-              cusolverDnDestroy, CUSOLVER_CHECK);
-
 }  // namespace cuda
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 006c865b87..7a00da9eb6 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -8,15 +8,12 @@
  ********************************************************/
 
 #pragma once
-#include <common/HandleBase.hpp>
 #include <common/defines.hpp>
 #include <common/err_common.hpp>
 #include <cusparse_v2.h>
 
 namespace cuda {
 
-using SparseHandle = cusparseHandle_t;
-
 const char* errorString(cusparseStatus_t err);
 
 #define CUSPARSE_CHECK(fn)                                                    \
@@ -30,10 +27,4 @@ const char* errorString(cusparseStatus_t err);
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
         }                                                                     \
     } while (0)
-
-CREATE_HANDLE(cusparseHandle, cusparseHandle_t, cusparseCreate, cusparseDestroy,
-              CUSPARSE_CHECK);
-
-CREATE_HANDLE(cusparseMatDescrHandle, cusparseMatDescr_t,
-              cusparseCreateMatDescr, cusparseDestroyMatDescr, CUSPARSE_CHECK);
 }  // namespace cuda
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
new file mode 100644
index 0000000000..bef747f7ae
--- /dev/null
+++ b/src/backend/cuda/handle.cpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/unique_handle.hpp>
+#include <cublas.hpp>
+#include <cusolverDn.hpp>
+#include <cusparse.hpp>
+#include <cufft.hpp>
+
+// clang-format off
+CREATE_HANDLE(cusparseMatDescr_t, cusparseCreateMatDescr, cusparseDestroyMatDescr);
+CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
+CREATE_HANDLE(cublasHandle_t, cublasCreate, cublasDestroy);
+CREATE_HANDLE(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
+CREATE_HANDLE(cufftHandle, cufftCreate, cufftDestroy);
+
+// clang-format on
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 683e02f598..305dd6178f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -15,6 +15,7 @@
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
+#include <common/unique_handle.hpp>
 #include <common/util.hpp>
 #include <cublas.hpp>
 #include <cufft.hpp>
@@ -52,6 +53,8 @@ using std::string;
 using std::to_string;
 using std::unique_ptr;
 
+using common::unique_handle;
+
 namespace cuda {
 
 static const std::string get_system(void) {
@@ -78,20 +81,20 @@ static inline int getMinSupportedCompute(int cudaMajorVer) {
                                   : minSV[cudaMajorVer - 1]);
 }
 
-unique_ptr<cublasHandle>& cublasManager(const int deviceId) {
-    thread_local unique_ptr<cublasHandle> handles[DeviceManager::MAX_DEVICES];
+unique_handle<cublasHandle_t>* cublasManager(const int deviceId) {
+    thread_local unique_handle<cublasHandle_t> handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     call_once(initFlags[deviceId], [&] {
-        handles[deviceId].reset(new cublasHandle());
+        handles[deviceId].create();
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUBLAS_CHECK(cublasSetStream(*handles[deviceId],
+        CUBLAS_CHECK(cublasSetStream(handles[deviceId],
                                      cuda::getStream(deviceId)));
     });
 
-    return handles[deviceId];
+    return &handles[deviceId];
 }
 
 unique_ptr<PlanCache>& cufftManager(const int deviceId) {
@@ -102,16 +105,16 @@ unique_ptr<PlanCache>& cufftManager(const int deviceId) {
     return caches[deviceId];
 }
 
-unique_ptr<cusolverDnHandle>& cusolverManager(const int deviceId) {
-    thread_local unique_ptr<cusolverDnHandle>
+unique_handle<cusolverDnHandle_t>* cusolverManager(const int deviceId) {
+    thread_local unique_handle<cusolverDnHandle_t>
         handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId], [&] {
-        handles[deviceId].reset(new cusolverDnHandle());
+        handles[deviceId].create();
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUSOLVER_CHECK(cusolverDnSetStream(*handles[deviceId],
+        CUSOLVER_CHECK(cusolverDnSetStream(handles[deviceId],
                                            cuda::getStream(deviceId)));
     });
     // TODO(pradeep) prior to this change, stream was being synced in get solver
@@ -121,21 +124,21 @@ unique_ptr<cusolverDnHandle>& cusolverManager(const int deviceId) {
     // cuSolver Streams patch:
     // CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(deviceId)));
 
-    return handles[deviceId];
+    return &handles[deviceId];
 }
 
-unique_ptr<cusparseHandle>& cusparseManager(const int deviceId) {
-    thread_local unique_ptr<cusparseHandle> handles[DeviceManager::MAX_DEVICES];
+unique_handle<cusparseHandle_t>* cusparseManager(const int deviceId) {
+    thread_local unique_handle<cusparseHandle_t> handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId], [&] {
-        handles[deviceId].reset(new cusparseHandle());
+        handles[deviceId].create();
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUSPARSE_CHECK(cusparseSetStream(*handles[deviceId],
+        CUSPARSE_CHECK(cusparseSetStream(handles[deviceId],
                                          cuda::getStream(deviceId)));
     });
-    return handles[deviceId];
+    return &handles[deviceId];
 }
 
 DeviceManager::~DeviceManager() {
@@ -143,10 +146,10 @@ DeviceManager::~DeviceManager() {
     // handles of all devices
     for (int i = 0; i < nDevices; ++i) {
         setDevice(i);
-        cublasManager(i).reset();
+        delete cusolverManager(i);
+        delete cusparseManager(i);
         cufftManager(i).reset();
-        cusolverManager(i).reset();
-        cusparseManager(i).reset();
+        delete cublasManager(i);
     }
 }
 
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index a3ea691c7b..fe753d68b9 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -16,6 +16,7 @@
 #include <arith.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/unique_handle.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
 #include <cusparse.hpp>
@@ -141,7 +142,8 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
 
-    cusparseMatDescrHandle desc;
+    common::unique_handle<cusparseMatDescr_t> desc;
+    desc.create();
     const dim4 ldims = lhs.dims();
 
     const int M = ldims[0];

From 8d0cb9ef4d4582d577dc060019ce8d48db848ec7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 16 May 2019 17:15:14 -0400
Subject: [PATCH 1655/2677] Fix ASSERT_VEC_ARRAY_NEAR's argument order when
 displaying errors

---
 test/testHelpers.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 87a1af5291..402b77b8bb 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -955,7 +955,7 @@ ::testing::AssertionResult assertArrayNear(
     af_array bb = 0;
     af_retain_array(&bb, b);
     af::array bbb(bb);
-    return assertArrayNear(hA_name, aDimsName, maxAbsDiffName, bName, hA, aDims,
+    return assertArrayNear(hA_name, aDimsName, bName, maxAbsDiffName, hA, aDims,
                            bbb, maxAbsDiff);
 }
 

From f50155d36e1dce384506927451bc2f25f6ca41ab Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 13 May 2019 16:12:56 -0400
Subject: [PATCH 1656/2677] Create a make_handle factory for unique_handle

---
 src/backend/common/unique_handle.hpp | 16 +++++++++++++---
 src/backend/cuda/sparse_arith.cu     |  3 +--
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index 266717df8a..95bc7e110d 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -66,10 +66,10 @@ class unique_handle {
     /// \brief Implicit converter for the handle
     constexpr operator const T &() const noexcept { return handle_; }
 
-    explicit unique_handle(const unique_handle &other) noexcept      = delete;
-    constexpr explicit unique_handle(unique_handle &&other) noexcept = default;
+    unique_handle(const unique_handle &other)      noexcept = delete;
+    constexpr unique_handle(unique_handle &&other) noexcept = default;
 
-    unique_handle &operator=(unique_handle &other) noexcept = delete;
+    unique_handle &operator=(unique_handle &other)  noexcept = delete;
     unique_handle &operator=(unique_handle &&other) noexcept = default;
 
     // Returns true if the \p other unique_handle is the same as this handle
@@ -87,6 +87,16 @@ class unique_handle {
         return handle_ == other;
     }
 };
+
+/// \brief Returns an initialized handle object. The create function on this
+///        object is already called
+template<typename T>
+unique_handle<T> make_handle() {
+    unique_handle<T> h;
+    h.create();
+    return h;
+}
+
 }  // namespace common
 
 /// specializes the handle_creater and handle_deleter functions for a specific
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index fe753d68b9..64f395173a 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -142,8 +142,7 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
 
-    common::unique_handle<cusparseMatDescr_t> desc;
-    desc.create();
+    auto desc = make_handle<cusparseMatDescr_t>();
     const dim4 ldims = lhs.dims();
 
     const int M = ldims[0];

From b1f3955c09eabb861fabb1489f2bf16ad24e3e02 Mon Sep 17 00:00:00 2001
From: Shady Boukhary <sb199898.sb@gmail.com>
Date: Mon, 20 May 2019 14:20:28 -0400
Subject: [PATCH 1657/2677] Fix typo in stdev documentation (#2515)

---
 docs/details/statistics.dox | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/details/statistics.dox b/docs/details/statistics.dox
index 29507a5ef8..dab5bf25d4 100644
--- a/docs/details/statistics.dox
+++ b/docs/details/statistics.dox
@@ -31,7 +31,7 @@ Find the variance of values in the input
 
 \ingroup basicstats_mat
 
-Find the standar deviation of values in the input
+Find the standard deviation of values in the input
 
 \copydoc batch_detail_stat
 

From 6d6053c84c1cca5ca31322a8df6e117dd9d0d550 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 May 2019 23:19:39 +0530
Subject: [PATCH 1658/2677] Add platform flags for nvcc options

---
 src/backend/cuda/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b37767b67c..992b79a75b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -141,6 +141,7 @@ function(cuda_add_library cuda_target)
 endfunction()
 
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
+arrayfire_get_platform_definitions(platform_flags)
 
 if(AF_WITH_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")
   # This definition is required in addition to the definition below because in
@@ -441,7 +442,7 @@ cuda_add_library(afcuda
 
     nvrtc/cache.cpp
 
-    OPTIONS "${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
+    OPTIONS "${platform_flags} ${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
   )
 
 arrayfire_set_default_cxx_flags(afcuda)

From dfc112dc5578e8842569bfbd026d32f4ab0f14f6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 May 2019 23:40:02 +0530
Subject: [PATCH 1659/2677] Use get_target_propterty to set INCLUDE_DIRS for
 nvcc

Using generator expressions doesn't work until CMake version 3.6.0. To
retain minimum CMake version of 3.5.1, generator expression based join
command has been replaced with this change.
---
 src/backend/cuda/CMakeLists.txt | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 992b79a75b..719867e551 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -35,6 +35,8 @@ mark_as_advanced(
     CUDA_LIBRARIES_PATH
     CUDA_architecture_build_targets)
 
+get_target_property(COMMON_INTERFACE_DIRS afcommon_interface INTERFACE_INCLUDE_DIRECTORIES)
+
 cuda_include_directories(
   ${CMAKE_CURRENT_BINARY_DIR}
   ${CMAKE_CURRENT_SOURCE_DIR}
@@ -46,8 +48,7 @@ cuda_include_directories(
   ${ArrayFire_SOURCE_DIR}/src/api/c
   ${ArrayFire_SOURCE_DIR}/src/backend
 
-  # NOTE: Space after comma is necessary
-  $<JOIN:$<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>, >
+  ${COMMON_INTERFACE_DIRS}
   )
 
 set(jit_kernel_headers

From 9e8061f509fb7da0be2dc2478ce2297318c63da6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 22 May 2019 14:39:52 -0400
Subject: [PATCH 1660/2677] Address issues with modified inputs for fftC2R
 functions.

The fftC2R function was modifying the input array when the fftw library
was used. This was happening because the fftw library does not have
and algorithm that can compute the fft without modifying the input array.

This problem does not seem to be happening on the MKL based fft.
---
 src/backend/cpu/fft.cpp        |  20 ++-
 src/backend/cpu/kernel/fft.hpp |   2 +-
 test/fft.cpp                   | 252 ++++++++++++++++++++++++++-------
 3 files changed, 220 insertions(+), 54 deletions(-)

diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index b0f0fc97ae..3e037a5e7a 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Array.hpp>
+#include <copy.hpp>
 #include <fft.hpp>
 #include <kernel/fft.hpp>
-
-#include <Array.hpp>
 #include <platform.hpp>
 
 #include <af/dim4.hpp>
@@ -44,11 +44,23 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
 
 template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
-    in.eval();
-
     Array<Tr> out = createEmptyArray<Tr>(odims);
+
+#ifdef USE_MKL
     getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
                        in, in.getDataDims(), odims);
+#else
+    if (rank > 1 || odims.ndims() > 1) {
+        // FFTW does not have a input preserving algorithm for multidimensional
+        // c2r FFTs
+        Array<Tc> in_ = copyArray<Tc>(in);
+        getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
+                          in_, in.getDataDims(), odims);
+    } else {
+        getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
+                          in, in.getDataDims(), odims);
+    }
+#endif
 
     return out;
 }
diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
index 94df2374ef..207b6c0bf0 100644
--- a/src/backend/cpu/kernel/fft.hpp
+++ b/src/backend/cpu/kernel/fft.hpp
@@ -121,7 +121,7 @@ void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in,
     plan = transform.create(rank, t_dims, (int)batch, (Tr *)in.get(), in_embed,
                             (int)istrides[0], (int)istrides[rank],
                             (ctype_t *)out.get(), out_embed, (int)ostrides[0],
-                            (int)ostrides[rank], FFTW_ESTIMATE);
+                            (int)ostrides[rank], FFTW_ESTIMATE | FFTW_PRESERVE_INPUT);
 
     transform.execute(plan);
     transform.destroy(plan);
diff --git a/test/fft.cpp b/test/fft.cpp
index f6d951ae5c..204c1637a5 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -6,12 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
+#include <sstream>
 #include <stdexcept>
 #include <string>
 #include <vector>
@@ -41,6 +44,7 @@ using af::span;
 using std::abs;
 using std::endl;
 using std::string;
+using std::stringstream;
 using std::vector;
 
 TEST(fft, Invalid_Type) {
@@ -682,82 +686,232 @@ TEST(fft3, GFOR) {
     freeHost(h_c);
 }
 
-TEST(fft, InPlace) {
+void fft2InPlaceFunc() {
     array a = randu(1024, 1024, c32);
-    array b = fft(a);
-    fftInPlace(a);
+    array b = fft2(a);
+    fft2InPlace(a);
 
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(ifft, InPlace) {
-    array a = randu(1024, 1024, c32);
-    array b = ifft(a);
-    ifftInPlace(a);
+using af::getDevice;
+using af::getDeviceCount;
+using af::setDevice;
 
-    vector<cfloat> ha(a.elements());
-    vector<cfloat> hb(b.elements());
+#define DEVICE_ITERATE(func)                             \
+    do {                                                 \
+        const char *ENV = getenv("AF_MULTI_GPU_TESTS");  \
+        if (ENV && ENV[0] == '0') {                      \
+            func;                                        \
+        } else {                                         \
+            int oldDevice = getDevice();                 \
+            for (int i = 0; i < getDeviceCount(); i++) { \
+                setDevice(i);                            \
+                func;                                    \
+            }                                            \
+            setDevice(oldDevice);                        \
+        }                                                \
+    } while (0);
 
-    ASSERT_ARRAYS_EQ(a, b);
+TEST(FFT2, MultiGPUInPlaceSquare_CPP) { DEVICE_ITERATE((fft2InPlaceFunc())); }
+
+struct fft_params {
+    dim4 input_dims_;
+    bool is_odd_;
+    double norm_factor_;
+    fft_params(dim4 dim, bool is_odd, double norm_factor)
+        : input_dims_(dim), is_odd_(is_odd), norm_factor_(norm_factor) {}
+};
+
+class FFTBase : public ::testing::TestWithParam<fft_params> {};
+
+class FFTC2R2D : public FFTBase {};
+class FFT2D : public FFTBase {};
+class FFTC2R3D : public FFTBase {};
+class FFT3D : public FFTBase {};
+class FFTC2R : public FFTBase {};
+class FFTND : public FFTBase {};
+
+string to_test_params(const ::testing::TestParamInfo<FFTBase::ParamType> info) {
+    stringstream ss;
+    ss << "d0_" << info.param.input_dims_[0] << "_d1_"
+       << info.param.input_dims_[1] << "_d2_" << info.param.input_dims_[2]
+       << "_d3_" << info.param.input_dims_[3] << "_"
+       << ((info.param.is_odd_) ? string("odd") : string("even")) << "_norm_"
+       << info.param.norm_factor_;
+    string out = ss.str();
+    return out.replace(out.find("."), 1, "_");
 }
 
-TEST(fft2, InPlace) {
-    array a = randu(1024, 1024, c32);
-    array b = fft2(a);
-    fft2InPlace(a);
+INSTANTIATE_TEST_CASE_P(Inputs2D, FFTC2R2D,
+                        ::testing::Values(
+                            fft_params(dim4(513, 512), false, 0.5),
+                            fft_params(dim4(1025, 1024), false, 0.5),
+                            fft_params(dim4(2049, 2048), false, 0.5)
+                            ),
+                        to_test_params);
+
+INSTANTIATE_TEST_CASE_P(
+    Inputs2D, FFT2D,
+    ::testing::Values(fft_params(dim4(512, 512), false, 0.5),
+                      fft_params(dim4(1024, 1024), false, 0.5),
+                      fft_params(dim4(2048, 2048), false, 0.5)),
+    to_test_params);
+
+INSTANTIATE_TEST_CASE_P(
+    Inputs3D, FFTC2R3D,
+    ::testing::Values(fft_params(dim4(512, 512, 3), false, 0.5),
+                      fft_params(dim4(1024, 1024, 3), false, 0.5),
+                      fft_params(dim4(2048, 2048, 3), false, 0.5)),
+    to_test_params);
+
+INSTANTIATE_TEST_CASE_P(Inputs3D, FFT3D,
+                        ::testing::Values(
+                            fft_params(dim4(1024, 1024, 3), true, 0.5),
+                            fft_params(dim4(1024, 1024, 3), false, 0.5)),
+                        to_test_params);
+
+
+INSTANTIATE_TEST_CASE_P(InputsND, FFTND,
+                        ::testing::Values(
+                            fft_params(dim4(512), false, 0.5),
+                            fft_params(dim4(1024), false, 0.5),
+                            fft_params(dim4(1024, 1024), false, 0.5),
+                            fft_params(dim4(1024, 1024, 3), false, 0.5)),
+                        to_test_params);
+
+
+INSTANTIATE_TEST_CASE_P(InputsND, FFTC2R,
+                        ::testing::Values(
+                            fft_params(dim4(513), false, 0.5),
+                            fft_params(dim4(1025), false, 0.5),
+                            fft_params(dim4(1025, 1024), false, 0.5),
+                            fft_params(dim4(1025, 1024, 3), false, 0.5)),
+                        to_test_params);
+
+// Does not work well with CUDA 10.1
+// TEST_P(FFTC2R2D, Complex32ToRealInputsPreserved) {
+//     fft_params params = GetParam();
+//     af::array a       = af::randu(params.input_dims_, c32);
+//     af::array a_copy  = a.copy();
+//     af::array out     = af::fftC2R<2>(a, params.is_odd_, params.norm_factor_);
+//
+//     ASSERT_ARRAYS_EQ(a_copy, a);
+// }
+//
+// TEST_P(FFTC2R2D, Complex64ToRealInputsPreserved) {
+//     fft_params params = GetParam();
+//     af::array a       = af::randu(params.input_dims_, c64);
+//     af::array a_copy  = a.copy();
+//     af::array out     = af::fftC2R<2>(a, params.is_odd_, params.norm_factor_);
+//
+//     ASSERT_ARRAYS_EQ(a_copy, a);
+// }
 
-    ASSERT_ARRAYS_EQ(a, b);
+TEST_P(FFT2D, Real32ToComplexInputsPreserved) {
+    fft_params params = GetParam();
+    af::array a       = af::randu(params.input_dims_, f32);
+    af::array a_copy  = a.copy();
+    af::array out     = af::fftR2C<2>(a, a.dims(), params.norm_factor_);
+
+    ASSERT_ARRAYS_EQ(a_copy, a);
 }
 
-TEST(ifft2, InPlace) {
-    array a = randu(1024, 1024, c32);
-    array b = ifft2(a);
-    ifft2InPlace(a);
+TEST_P(FFT2D, Real64ToComplexInputsPreserved) {
+    fft_params params = GetParam();
+    af::array a       = af::randu(params.input_dims_, f64);
+    af::array a_copy  = a.copy();
+    af::array out     = af::fftR2C<2>(a, a.dims(), params.norm_factor_);
 
-    ASSERT_ARRAYS_EQ(a, b);
+    ASSERT_ARRAYS_EQ(a_copy, a);
 }
 
-TEST(fft3, InPlace) {
-    array a = randu(32, 32, 32, c32);
-    array b = fft3(a);
-    fft3InPlace(a);
+TEST_P(FFTC2R, Complex32ToRInputsPreserved) {
+    fft_params params = GetParam();
+    af::array a       = af::randu(params.input_dims_, c32);
+    af::array a_copy  = a.copy();
+    af::array out     = af::fftC2R<1>(a, params.is_odd_, params.norm_factor_);
+
+    ASSERT_ARRAYS_EQ(a_copy, a);
+}
+
+TEST_P(FFTC2R, Complex64ToRInputsPreserved) {
+    fft_params params = GetParam();
+    af::array a       = af::randu(params.input_dims_, c64);
+    af::array a_copy  = a.copy();
+    af::array out     = af::fftC2R<1>(a, params.is_odd_, params.norm_factor_);
+
+    ASSERT_ARRAYS_EQ(a_copy, a);
+}
+
+TEST_P(FFTND, Real32ToComplexInputsPreserved) {
+    fft_params params = GetParam();
+    af::array a       = af::randu(params.input_dims_, f32);
+    af::array a_copy  = a.copy();
+    af::array out     = af::fftR2C<1>(a, a.dims(), params.norm_factor_);
+
+    ASSERT_ARRAYS_EQ(a_copy, a);
+}
+
+TEST_P(FFTND, Real64ToComplexInputsPreserved) {
+    fft_params params = GetParam();
+    af::array a       = af::randu(params.input_dims_, f64);
+    af::array a_copy  = a.copy();
+    af::array out     = af::fftR2C<1>(a, a.dims(), params.norm_factor_);
+
+    ASSERT_ARRAYS_EQ(a_copy, a);
+}
+
+TEST_P(FFTND, InPlaceFFTMatchesOutOfPlace) {
+    fft_params params = GetParam();
+    array a           = randu(params.input_dims_, c32);
+    array b           = fft(a);
+    fftInPlace(a);
 
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-TEST(ifft3, InPlace) {
-    array a = randu(32, 32, 32, c32);
-    array b = ifft3(a);
-    ifft3InPlace(a);
+TEST_P(FFTND, InPlaceIFFTMatchesOutOfPlace) {
+    fft_params params = GetParam();
+    array a           = randu(params.input_dims_, c32);
+    array b           = ifft(a);
+    ifftInPlace(a);
 
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-void fft2InPlaceFunc() {
-    array a = randu(1024, 1024, c32);
-    array b = fft2(a);
+TEST_P(FFT2D, InPlaceFFT2MatchesOutOfPlace) {
+    fft_params params = GetParam();
+    array a           = randu(params.input_dims_, c32);
+    array b           = fft2(a);
     fft2InPlace(a);
 
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-using af::getDevice;
-using af::getDeviceCount;
-using af::setDevice;
+TEST_P(FFT2D, InPlaceIFFT2MatchesOutOfPlace) {
+    fft_params params = GetParam();
+    array a           = randu(params.input_dims_, c32);
+    array b           = ifft2(a);
+    ifft2InPlace(a);
 
-#define DEVICE_ITERATE(func)                             \
-    do {                                                 \
-        const char *ENV = getenv("AF_MULTI_GPU_TESTS");  \
-        if (ENV && ENV[0] == '0') {                      \
-            func;                                        \
-        } else {                                         \
-            int oldDevice = getDevice();                 \
-            for (int i = 0; i < getDeviceCount(); i++) { \
-                setDevice(i);                            \
-                func;                                    \
-            }                                            \
-            setDevice(oldDevice);                        \
-        }                                                \
-    } while (0);
+    ASSERT_ARRAYS_EQ(a, b);
+}
 
-TEST(FFT2, MultiGPUInPlaceSquare_CPP) { DEVICE_ITERATE((fft2InPlaceFunc())); }
+TEST_P(FFT3D, InPlaceFFT3MatchesOutOfPlace) {
+    fft_params params = GetParam();
+    array a           = randu(params.input_dims_, c32);
+    array b           = fft3(a);
+    fft3InPlace(a);
+
+    ASSERT_ARRAYS_EQ(a, b);
+}
+
+TEST_P(FFTC2R3D, InPlaceIFFT3MatchesOutOfPlace) {
+    fft_params params = GetParam();
+    array a           = randu(params.input_dims_, c32);
+    array b           = ifft3(a);
+    ifft3InPlace(a);
+
+    ASSERT_ARRAYS_EQ(a, b);
+}

From b811f87f260a44604edfee7c12a829929b8b4946 Mon Sep 17 00:00:00 2001
From: jacobkahn <jacobkahn1@gmail.com>
Date: Tue, 28 May 2019 11:20:33 -0700
Subject: [PATCH 1661/2677] Properly include spdlog for AF_TRACE macro usage

Sometimes, the build fails on Ubuntu 18.04 with gcc 7.3.0. It's tough to repro, but it happens when spdlog::logger isn't a complete type and AF_TRACE macros are compiled/included in a particular order (https://github.com/arrayfire/arrayfire/issues/2522).
---
 src/backend/common/Logger.hpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index b00f9ac303..85c79a25be 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -11,10 +11,8 @@
 
 #include <memory>
 #include <string>
+#include <spdlog/spdlog.h>
 
-namespace spdlog {
-class logger;
-}
 namespace common {
 std::shared_ptr<spdlog::logger> loggerFactory(std::string name);
 std::string bytesToString(size_t bytes);

From b5286ef8257993c3edc91317b12d215213ec679a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 22 May 2019 14:15:31 +0530
Subject: [PATCH 1662/2677] Fix convolve3 launch configuration in CUDA backend

Batched input of Convolve3 was incorrectly folding batch size
into CUDA launch grid's y & z dimensions. Since the batch for 3d
inputs can happen along 4th dimension only, folding it onto x dimesion
of CUDA grid is sufficient.
---
 src/backend/cuda/kernel/convolve.hpp |  3 ---
 test/convolve.cpp                    | 20 ++++++++++++++++++++
 test/data                            |  2 +-
 3 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 3534b760b8..ad58e75251 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -98,9 +98,6 @@ void prepareKernelArgs(conv_kparam_t& params, dim_t oDims[], dim_t fDims[],
                              (params.mThreads.y + 2 * (fDims[1] - 1)) *
                              (params.mThreads.z + 2 * (fDims[2] - 1)) *
                              sizeof(T);
-        // todo: fold into x dimension according to old style
-        params.mBlocks.z = divup(params.mBlocks.y, maxBlocksY);
-        params.mBlocks.y = divup(params.mBlocks.y, params.mBlocks.z);
     }
 }
 
diff --git a/test/convolve.cpp b/test/convolve.cpp
index e7f4ba2338..c82c7b42b5 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -804,3 +804,23 @@ TEST(DISABLED_ConvolveLargeDim3D, CPP) {
     // TODO: fix product by indexing
     // ASSERT_EQ(1.f, product<float>(output));
 }
+
+TEST(Convolve, CuboidBatchLaunchBugFix) {
+    std::string testFile(TEST_DIR "/convolve/conv3d_launch_bug.test");
+
+    vector<dim4> numDims;
+    vector< vector<float> > in;
+    vector< vector<float> > tests;
+
+    readTests<float, float, float>(testFile, numDims, in, tests);
+
+    dim4 sDims        = numDims[0];
+    dim4 fDims        = numDims[1];
+
+    af::array signal(sDims, in[0].data());
+    af::array filter(fDims, in[1].data());
+
+    af::array output = convolve3(signal, filter);
+
+    ASSERT_VEC_ARRAY_NEAR(tests[0], sDims, output, 1.0e-3);
+}
diff --git a/test/data b/test/data
index 2ef3476e57..1074f2cbac 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 2ef3476e5798ed2396219b9189e4dde90e37f531
+Subproject commit 1074f2cbac403575bb0adbc9df6bdf1b09fd3ea9

From c889f36dc896dd570ba863ce6b869ff331f3fad3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 30 May 2019 20:33:24 +0530
Subject: [PATCH 1663/2677] Fix documentation of convolution functions

---
 docs/details/signal.dox | 150 +++++++++++++++++++++++-----------------
 include/af/signal.h     |  30 ++++----
 2 files changed, 102 insertions(+), 78 deletions(-)

diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index 34482aa9da..4f206f2867 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -19,21 +19,8 @@ batch mode convolutions take place.
 - **Identical Batches** - A set of filters applied onto to a set of inputs in one-to-one correspondence.
 - **Non overlapping Batches** - All batched filters are applied to all batched signals. The batch dimension of Signal and Filter **should not** be the same.
 
-
-
-\page signal_func_conv2_batch_desc convolve2
-
-For example, if the signal is two dimensional with m & n as sizes along the 0th & 1st dimensions
-respectively, then the possible batch operations are as follows.
-
-| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode | Explanation |
-|:-----------------------:|:-----------------:|:-----------------:|:----------:|:------------|
-| [m n 1 1] | [m n 1 1] | [m n 1 1] | No Batch  | Output will be a single convolve array |
-| [m n 1 1] | [m n p 1] | [m n p 1] | Filter is Batched | p filters applied to same input |
-| [m n p 1] | [m n 1 1] | [m n p 1] | Signal is Batched | 1 filter applied to p inputs |
-| [m n p 1] | [m n p 1] | [m n p 1] | Identical Batches | p filters applied to p inputs in one-to-one correspondence |
-| [m n p 1] | [m n 1 q] | [m n p q] | Non-overlapping batches | q filters applied to p inputs in to produce p x q results |
-| [m n 1 p] | [m n q 1] | [m n q p] | Non-overlapping batches | q filters applied to p inputs in to produce q x p results |
+Non overlapping batch mode is not supported in spatial mode i.e. if the user passes \ref
+AF_CONV_SPATIAL explicitly to the functions.
 
 
 \page signal_func_fft_desc fft
@@ -58,95 +45,132 @@ factor is calculated internally based on the input data provided.
 \addtogroup arrayfire_func
 @{
 
-\defgroup signal_func_convolve convolve
+\defgroup signal_func_convolve N-Dimensional Convolutions
 \ingroup convolve_mat
 
-\brief Convolution Integral for any dimensional data
+\brief Convolution Integral for any(one through three) dimensional data
 
 \copydoc signal_func_conv_desc
 
-\copydoc signal_func_conv2_batch_desc
+This version of convolution function delegates the call to respective
+1D, 2D or 3D convolution functions internally.
+
+Convolution dimensionality is \f$ \min (sd, fd) \f$ where sd & fd are dimensionality of
+signal and filter respectively. This formulation only decides the dimensionality
+of convolution. Please check the respective convolve (hyperlinked below) function
+documentation to find out the kind of batch operations possible.
+- \ref signal_func_convolve1
+- \ref signal_func_convolve2
+- \ref signal_func_convolve3
 
+Given below are some examples.
 
+| Input Dimensions | Filter Dimensions | Convolve Dimension |
+|:----------------:|:-----------------:|:------------------:|
+| [m n 1 1]        | [m 1 1 1]         |   1D               |
+| [m 1 1 1]        | [m n 1 1]         |   1D               |
+| [m n 1 1]        | [m n 1 1]         |   2D               |
+| [m n 1 1]        | [m n p 1]         |   2D               |
+| [m n 1 p]        | [m n 1 q]         |   3D               |
+| [m n p 1]        | [m n q 1]         |   3D               |
 
 
-\defgroup signal_func_convolve1 convolve1
+\defgroup signal_func_convolve_sep Separable 2D Convolution
 \ingroup convolve_mat
 
-\brief Convolution Integral for one dimensional data
+\brief Separable Convolution
 
-\copydoc signal_func_conv_desc
+Separable Convolution is faster equivalent of the canonical 2D convolution with
+an additional prerequisite that the filter/kernel can be decomposed into two
+separate spatial vectors. A classic example of such separable kernels
+is sobel operator. Given below is decomposition of vertical gradient of sobel operator.
+
+\f$
+\begin{bmatrix}
+-1 & 0 & +1 \\
+-2 & 0 & +2 \\
+-1 & 0 & +1 \\
+\end{bmatrix}
+\f$
+
+can be decomposed into two vectors shown below.
 
-For example, if the input size is m along 0th dimension, then the possible batch operations are as follows.
+\f$
+\begin{bmatrix}
+1 \\
+2 \\
+1 \\
+\end{bmatrix}
+\f$
 
-| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode | Explanation |
-|:-----------------------:|:-----------------:|:-----------------:|:----------:|:------------|
-| [m n 1 1] | [m n 1 1] | [m n 1 1] | No Batch  | Output will be a single convolve array |
-| [m n 1 1] | [m n p 1] | [m n p 1] | Filter is Batched | p filters applied to same input |
-| [m n p 1] | [m n 1 1] | [m n p 1] | Signal is Batched | 1 filter applied to p inputs |
-| [m n p 1] | [m n p 1] | [m n p 1] | Identical Batches | p filters applied to p inputs in one-to-one correspondence |
-| [m n p 1] | [m n 1 q] | [m n p q] | Non-overlapping batches | q filters applied to p inputs in to produce p x q results |
-| [m n 1 p] | [m n q 1] | [m n q p] | Non-overlapping batches | q filters applied to p inputs in to produce q x p results |
+\f$
+\begin{bmatrix}
+-1 & 0 & +1 \\
+\end{bmatrix}
+\f$
 
 
-\defgroup signal_func_convolve2 convolve2
+\defgroup signal_func_convolve1 1D Convolutions
 \ingroup convolve_mat
 
-\brief Convolution Integral for two dimensional data
+\brief Convolution Integral for one dimensional data
 
 \copydoc signal_func_conv_desc
 
-\copydoc signal_func_conv2_batch_desc
-
+For one dimensional signals(lets say m is size of 0th dimension), below batch operations are possible.
 
+| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode              | Description |
+|:-----------------------:|:-----------------:|:-----------------:|:-----------------------:|:------------|
+| [m 1 1 1]               | [m 1 1 1]         | [m 1 1 1]         | No Batch                | Output will be a single convolved array |
+| [m 1 1 1]               | [m n 1 1]         | [m n 1 1]         | Filter is Batched       | n filters applied to same input |
+| [m n 1 1]               | [m 1 1 1]         | [m n 1 1]         | Signal is Batched       | 1 filter applied to n inputs |
+| [m n p q]               | [m n p q]         | [m n p q]         | Identical Batches       | n*p*q filters applied to n*p*q inputs in one-to-one correspondence |
+| [m n 1 1]               | [m 1 p q]         | [m n p q]         | Non-overlapping batches | p*q filters applied to n inputs to produce n x p x q results |
 
-\defgroup signal_func_convolve3 convolve3
-\ingroup convolve_mat
+The last entry in the table has more permutations than shown here.
 
-\brief Convolution Integral for three dimensional data
 
-\copydoc signal_func_conv_desc
 
-For example, if the signal is three dimensional with m, n & p sizes along the 0th, 1st & 2nd dimensions
-respectively, then the possible batch operations are as follows.
+\defgroup signal_func_convolve2 2D Convolutions
+\ingroup convolve_mat
 
-| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode | Explanation |
-|:-----------------------:|:-----------------:|:-----------------:|:----------:|:------------|
-| [m n 1 1] | [m n 1 1] | [m n 1 1] | No Batch  | Output will be a single convolve array |
-| [m n 1 1] | [m n p 1] | [m n p 1] | Filter is Batched | p filters applied to same input |
-| [m n p 1] | [m n 1 1] | [m n p 1] | Signal is Batched | 1 filter applied to p inputs |
-| [m n p 1] | [m n p 1] | [m n p 1] | Identical Batches | p filters applied to p inputs in one-to-one correspondence |
-| [m n p 1] | [m n 1 q] | [m n p q] | Non-overlapping batches | q filters applied to p inputs in to produce p x q results |
-| [m n 1 p] | [m n q 1] | [m n q p] | Non-overlapping batches | q filters applied to p inputs in to produce q x p results |
+\brief Convolution Integral for two dimensional data
 
-===============================================================================
+\copydoc signal_func_conv_desc
 
-\defgroup signal_func_fft_convolve fftConvolve
-\ingroup convolve_mat
+For two dimensional signals, the following are the possible batch operations possible.
+Lets say m & n as sizes along the 0th & 1st dimensions respectively and p & q are the
+some integral numbers greater than one.
 
-\brief Convolution using Fast Fourier Transform
+| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode              | Description |
+|:-----------------------:|:-----------------:|:-----------------:|:-----------------------:|:------------|
+| [m n 1 1]               | [m n 1 1]         | [m n 1 1]         | No Batch                | Output will be a single convolved array |
+| [m n 1 1]               | [m n p 1]         | [m n p 1]         | Filter is Batched       | p filters applied to same input |
+| [m n p 1]               | [m n 1 1]         | [m n p 1]         | Signal is Batched       | 1 filter applied to p inputs |
+| [m n p q]               | [m n p q]         | [m n p q]         | Identical Batches       | p*q filters applied to p*q inputs in one-to-one correspondence |
+| [m n p 1]               | [m n 1 q]         | [m n p q]         | Non-overlapping batches | q filters applied to p inputs in to produce p x q results |
+| [m n 1 p]               | [m n q 1]         | [m n q p]         | Non-overlapping batches | q filters applied to p inputs in to produce q x p results |
 
-\copydoc signal_func_conv_desc
 
-===============================================================================
 
-\defgroup signal_func_fft_convolve2 fftConvolve2
+\defgroup signal_func_convolve3 3D Convolutions
 \ingroup convolve_mat
 
-\brief 2D Convolution using Fast Fourier Transform
+\brief Convolution Integral for three dimensional data
 
 \copydoc signal_func_conv_desc
 
-===============================================================================
+For three dimensional inputs with m, n & p sizes along the 0th, 1st & 2nd dimensions
+respectively, given below are the possible batch operations.
 
-\defgroup signal_func_fft_convolve3 fftConvolve3
-\ingroup convolve_mat
-
-\brief 3D Convolution using Fast Fourier Transform
+| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode              | Description |
+|:-----------------------:|:-----------------:|:-----------------:|:-----------------------:|:------------|
+| [m n p 1]               | [a b c 1]         | [m n p 1]         | No Batch                | Output will be a single convolve array |
+| [m n p 1]               | [a b c d]         | [m n p d]         | Filter is Batched       | d filters applied to same input |
+| [m n p q]               | [a b c 1]         | [m n p q]         | Signal is Batched       | 1 filter applied to q inputs |
+| [m n p k]               | [a b c k]         | [m n p k]         | Identical Batches       | k filters applied to k inputs in one-to-one correspondence |
 
-\copydoc signal_func_conv_desc
 
-===============================================================================
 
 \defgroup signal_func_fft fft
 \ingroup fft_mat
diff --git a/include/af/signal.h b/include/af/signal.h
index 75408c3deb..b4f739d772 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -552,7 +552,7 @@ AFAPI array convolve(const array& signal, const array& filter, const convMode mo
 
    \note Separable convolution only supports two(ONE-to-ONE and MANY-to-ONE) batch modes from the ones described in the detailed description section.
 
-   \ingroup signal_func_convolve
+   \ingroup signal_func_convolve_sep
  */
 AFAPI array convolve(const array& col_filter, const array& row_filter, const array& signal, const convMode mode=AF_CONV_DEFAULT);
 
@@ -615,43 +615,43 @@ AFAPI array convolve3(const array& signal, const array& filter, const convMode m
    \param[in]  mode indicates if the convolution should be expanded or not(where output size equals input)
    \return     the convolved array
 
-   \ingroup signal_func_fft_convolve
+   \ingroup signal_func_convolve
  */
 AFAPI array fftConvolve(const array& signal, const array& filter, const convMode mode=AF_CONV_DEFAULT);
 
 /**
-   C++ Interface for convolution on one dimensional signals
+   C++ Interface for convolution on 1D signals using FFT
 
    \param[in]  signal is the input signal
    \param[in]  filter is the signal that shall be used for the convolution operation
    \param[in]  mode indicates if the convolution should be expanded or not(where output size equals input)
    \return     the convolved array
 
-   \ingroup signal_func_fft_convolve1
+   \ingroup signal_func_convolve1
  */
 AFAPI array fftConvolve1(const array& signal, const array& filter, const convMode mode=AF_CONV_DEFAULT);
 
 /**
-   C++ Interface for convolution on two dimensional signals
+   C++ Interface for convolution on 2D signals using FFT
 
    \param[in]  signal is the input signal
    \param[in]  filter is the signal that shall be used for the convolution operation
    \param[in]  mode indicates if the convolution should be expanded or not(where output size equals input)
    \return     the convolved array
 
-   \ingroup signal_func_fft_convolve2
+   \ingroup signal_func_convolve2
  */
 AFAPI array fftConvolve2(const array& signal, const array& filter, const convMode mode=AF_CONV_DEFAULT);
 
 /**
-   C++ Interface for convolution on three dimensional signals
+   C++ Interface for convolution on 3D signals using FFT
 
    \param[in]  signal is the input signal
    \param[in]  filter is the signal that shall be used for the convolution operation
    \param[in]  mode indicates if the convolution should be expanded or not(where output size equals input)
    \return     the convolved array
 
-   \ingroup signal_func_fftconvolve3
+   \ingroup signal_func_convolve3
  */
 AFAPI array fftConvolve3(const array& signal, const array& filter, const convMode mode=AF_CONV_DEFAULT);
 
@@ -1233,12 +1233,12 @@ AFAPI af_err af_convolve3(af_array *out, const af_array signal, const af_array f
    \note Separable convolution only supports two(ONE-to-ONE and MANY-to-ONE) batch modes from the ones described
          in the detailed description section.
 
-   \ingroup signal_func_convolve
+   \ingroup signal_func_convolve_sep
  */
 AFAPI af_err af_convolve2_sep(af_array *out, const af_array col_filter, const af_array row_filter, const af_array signal, const af_conv_mode mode);
 
 /**
-   C Interface for FFT-based convolution on one dimensional signals
+   C Interface for convolution on 1D signals using FFT
 
    \param[out] out is convolved array
    \param[in]  signal is the input signal
@@ -1247,12 +1247,12 @@ AFAPI af_err af_convolve2_sep(af_array *out, const af_array col_filter, const af
    \return     \ref AF_SUCCESS if the convolution is successful,
                otherwise an appropriate error code is returned.
 
-   \ingroup signal_func_fft_convolve1
+   \ingroup signal_func_convolve1
  */
 AFAPI af_err af_fft_convolve1(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode);
 
 /**
-   C Interface for FFT-based convolution on two dimensional signals
+   C Interface for convolution on 2D signals using FFT
 
    \param[out] out is convolved array
    \param[in]  signal is the input signal
@@ -1261,12 +1261,12 @@ AFAPI af_err af_fft_convolve1(af_array *out, const af_array signal, const af_arr
    \return     \ref AF_SUCCESS if the convolution is successful,
                otherwise an appropriate error code is returned.
 
-   \ingroup signal_func_fft_convolve2
+   \ingroup signal_func_convolve2
  */
 AFAPI af_err af_fft_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode);
 
 /**
-   C Interface for FFT-based convolution on three dimensional signals
+   C Interface for convolution on 3D signals using FFT
 
    \param[out] out is convolved array
    \param[in]  signal is the input signal
@@ -1275,7 +1275,7 @@ AFAPI af_err af_fft_convolve2(af_array *out, const af_array signal, const af_arr
    \return     \ref AF_SUCCESS if the convolution is successful,
                otherwise an appropriate error code is returned.
 
-   \ingroup signal_func_fft_convolve3
+   \ingroup signal_func_convolve3
  */
 AFAPI af_err af_fft_convolve3(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode);
 

From 974a8a312296129c5d9bf90a408d66f6ba6c0923 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Fri, 31 May 2019 00:51:05 -0400
Subject: [PATCH 1664/2677] Update k-means example to show graphical result
 (#2521)

---
 examples/machine_learning/kmeans.cpp | 34 +++++++++++++---------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index 65369f671e..43d2111bad 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -12,6 +12,7 @@
 #include <af/util.h>
 #include <cstdlib>
 #include <iostream>
+#include <string>
 
 using namespace af;
 
@@ -112,7 +113,7 @@ int kmeans_demo(int k, bool console) {
     printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);
 
     array img =
-        loadImage(ASSETS_DIR "/examples/images/vegetable-woman.jpg", true) /
+        loadImage(ASSETS_DIR "/examples/images/spider.jpg") /
         255;  // [0-255]
 
     int w = img.dims(0), h = img.dims(1), c = img.dims(2);
@@ -128,26 +129,23 @@ int kmeans_demo(int k, bool console) {
     kmeans(means_dbl, clusters_dbl, vec, k * 2);
 
     if (!console) {
-#if 0
         array out_full = moddims(means_full(span, clusters_full, span), img.dims());
         array out_half = moddims(means_half(span, clusters_half, span), img.dims());
         array out_dbl  = moddims(means_dbl (span, clusters_dbl , span), img.dims());
 
-        char str_full[32], str_half[32], str_dbl[32];
-        sprintf(str_full, "%2d clusters", k);
-        sprintf(str_half, "%2d clusters", k/2);
-        sprintf(str_dbl , "%2d clusters", k*2);
-
-        fig("color","default");
-        fig("sub",2,2,1); image(img); fig("title","input");
-        fig("sub",2,2,2); image(out_full); fig("title", str_full);
-        fig("sub",2,2,3); image(out_half); fig("title", str_half);
-        fig("sub",2,2,4); image(out_dbl ); fig("title", str_dbl );
-        printf("Hit enter to finish\n");
-        getchar();
-#else
-        printf("Graphics not implemented yet\n");
-#endif
+        af::Window wnd(800, 800, "ArrayFire K-Means Demo");
+        wnd.grid(2, 2);
+        std::string out_full_caption = "k = " + std::to_string(k);
+        std::string out_half_caption = "k = " + std::to_string(k / 2);
+        std::string out_dbl_caption = "k = " + std::to_string(k * 2);
+        while (!wnd.close()) {
+            wnd(0, 0).image(img, "Input Image");
+            wnd(0, 1).image(out_full, out_full_caption.c_str());
+            wnd(1, 0).image(out_half, out_half_caption.c_str());
+            wnd(1, 1).image(out_dbl, out_dbl_caption.c_str());
+            wnd.show();
+        }
+
     } else {
         means_full =
             moddims(means_full, means_full.dims(1), means_full.dims(2));
@@ -166,7 +164,7 @@ int kmeans_demo(int k, bool console) {
 int main(int argc, char **argv) {
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
-    int k        = argc > 3 ? atoi(argv[3]) : 16;
+    int k        = argc > 3 ? atoi(argv[3]) : 8;
 
     try {
         af::setDevice(device);

From 093efe5878b6b50fdd253a36c6a17ffd72be4790 Mon Sep 17 00:00:00 2001
From: ShalokShalom <shalokshalom@protonmail.ch>
Date: Fri, 26 Apr 2019 20:53:14 +0200
Subject: [PATCH 1665/2677] Include native  API and link its documentation

---
 README.md | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6552ace4db..efa2ace799 100644
--- a/README.md
+++ b/README.md
@@ -103,9 +103,13 @@ Quick links:
 * [Examples](http://www.arrayfire.org/docs/examples.htm)
 * [Blog](http://arrayfire.com/blog/)
 
-## Language wrappers
+## Language support
 
-ArrayFire has several official and third-party language wrappers.
+ArrayFire has several official and third-party language API`s:
+
+__Native__
+
+* [C++](http://arrayfire.org/docs/gettingstarted.htm#gettingstarted_api_usage)
 
 __Official wrappers__
 

From 0982f49ae78fa7ede1272f8175ffc2e0fb9941ab Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Mon, 3 Jun 2019 01:12:32 -0500
Subject: [PATCH 1666/2677] Added samples to sparse documentation (#2326)

* Added usage samples for docs.

Currently, some of the tests are failing across all backends. It seems
some of the dense arrays are being read in row-major order.

* Minor sparse improvements.

* Condensed sparse samples.

Added checks for NNZ and sparse representation type.
---
 include/af/sparse.h  |   8 +++
 src/api/c/sparse.cpp |  20 +++---
 test/sparse.cpp      | 163 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 181 insertions(+), 10 deletions(-)

diff --git a/include/af/sparse.h b/include/af/sparse.h
index 82a14952ef..1bda32d8fb 100644
--- a/include/af/sparse.h
+++ b/include/af/sparse.h
@@ -32,6 +32,8 @@ namespace af
        \param[in] stype is the storage format of the sparse array
        \return \ref af::array for the sparse array
 
+       \snippet test/sparse.cpp ex_sparse_af_arrays
+
        \ingroup sparse_func_create
      */
     AFAPI array sparse(const dim_t nRows, const dim_t nCols,
@@ -60,6 +62,8 @@ namespace af
                   if the arrays are device arrays.
        \return \ref af::array for the sparse array
 
+       \snippet test/sparse.cpp ex_sparse_host_arrays
+
        \ingroup sparse_func_create
      */
     AFAPI array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
@@ -77,6 +81,8 @@ namespace af
        \param[in] stype is the storage format of the sparse array
        \return \ref af::array for the sparse array with the given storage type
 
+       \snippet test/sparse.cpp ex_sparse_from_dense
+
        \ingroup sparse_func_create
      */
     AFAPI array sparse(const array dense, const af::storage stype = AF_STORAGE_CSR);
@@ -98,6 +104,8 @@ namespace af
        \param[in] sparse is the source sparse matrix
        \return dense \ref af::array from sparse
 
+       \snippet test/sparse.cpp ex_dense_from_sparse
+
        \ingroup sparse_func_dense
      */
     AFAPI array dense(const array sparse);
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 8af07099a4..c093504db5 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -45,7 +45,7 @@ const SparseArrayBase &getSparseArrayBase(const af_array in,
 // Sparse Creation
 ////////////////////////////////////////////////////////////////////////////////
 template<typename T>
-af_array createSparseArrayFromData(const af::dim4 &dims, const af_array values,
+af_array createSparseArrayFromData(const dim4 &dims, const af_array values,
                                    const af_array rowIdx, const af_array colIdx,
                                    const af::storage stype) {
     SparseArray<T> sparse = common::createArrayDataSparseArray(
@@ -96,9 +96,9 @@ af_err af_create_sparse_array(af_array *out, const dim_t nRows,
             DIM_ASSERT(5, (dim_t)cInfo.elements() == nCols + 1);
         }
 
-        af_array output = 0;
+        af_array output = nullptr;
 
-        af::dim4 dims(nRows, nCols);
+        dim4 dims(nRows, nCols);
 
         switch (vInfo.getType()) {
             case f32:
@@ -168,9 +168,9 @@ af_err af_create_sparse_array_from_ptr(
 
         TYPE_ASSERT(type == f32 || type == f64 || type == c32 || type == c64);
 
-        af_array output = 0;
+        af_array output = nullptr;
 
-        af::dim4 dims(nRows, nCols);
+        dim4 dims(nRows, nCols);
 
         switch (type) {
             case f32:
@@ -319,9 +319,9 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
             return af_create_sparse_array_from_dense(out, in, destStorage);
         }
 
-        af_array output = 0;
+        af_array output = nullptr;
 
-        const SparseArrayBase base = getSparseArrayBase(in);
+        const SparseArrayBase &base = getSparseArrayBase(in);
 
         // Dense not allowed as input -> Should never happen with
         // SparseArrayBase CSC is currently not supported
@@ -360,9 +360,9 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
 
 af_err af_sparse_to_dense(af_array *out, const af_array in) {
     try {
-        af_array output = 0;
+        af_array output = nullptr;
 
-        const SparseArrayBase base = getSparseArrayBase(in);
+        const SparseArrayBase &base = getSparseArrayBase(in);
 
         // Dense not allowed as input -> Should never happen
         // To convert from dense to type, use the create* functions
@@ -414,7 +414,7 @@ af_err af_sparse_get_values(af_array *out, const af_array in) {
     try {
         const SparseArrayBase base = getSparseArrayBase(in);
 
-        af_array output = 0;
+        af_array output = nullptr;
 
         switch (base.getType()) {
             case f32: output = getSparseValues<float>(in); break;
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 6a14192f27..1e92385536 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -14,6 +14,7 @@
 using af::allTrue;
 using af::array;
 using af::deviceMemInfo;
+using af::dim4;
 using af::dtype_traits;
 using af::identity;
 using af::randu;
@@ -258,3 +259,165 @@ TYPED_TEST(Sparse, EmptyDeepCopy) {
     EXPECT_TRUE(b.issparse());
     EXPECT_EQ(0, sparseGetNNZ(b));
 }
+
+TEST(Sparse, CPPSparseFromHostArrays)
+{
+    //! [ex_sparse_host_arrays]
+
+    float  vals[] = { 5, 8, 3, 6 };
+    int row_ptr[] = { 0, 0, 2, 3, 4 };
+    int col_idx[] = { 0, 1, 2, 1 };
+    const int M = 4, N = 4, nnz = 4;
+
+    // Create sparse array (CSR) from host pointers to values, row
+    // pointers, and column indices.
+    array sparse = af::sparse(M, N, nnz, vals, row_ptr, col_idx, f32, AF_STORAGE_CSR, afHost);
+
+    // sparse
+    //     values:  [ 5.0, 8.0, 3.0, 6.0 ]
+    //     row_ptr: [ 0, 0, 2, 3, 4 ]
+    //     col_idx: [ 0, 1, 2, 1 ]
+
+    //! [ex_sparse_host_arrays]
+
+    array sparse_vals, sparse_row_ptr, sparse_col_idx;
+    af::storage sparse_storage;
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+
+    ASSERT_ARRAYS_EQ(sparse_vals   , array(dim4(nnz,1), vals));
+    ASSERT_ARRAYS_EQ(sparse_row_ptr, array(dim4(M+1,1), row_ptr));
+    ASSERT_ARRAYS_EQ(sparse_col_idx, array(dim4(nnz,1), col_idx));
+    ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
+    ASSERT_EQ(sparseGetNNZ(sparse), nnz);
+}
+
+TEST(Sparse, CPPSparseFromAFArrays)
+{
+    //! [ex_sparse_af_arrays]
+
+    float v[] = { 5, 8, 3, 6 };
+    int   r[] = { 0, 0, 2, 3, 4 };
+    int   c[] = { 0, 1, 2, 1 };
+    const int M = 4, N = 4, nnz = 4;
+    array    vals = array(dim4(nnz), v);
+    array row_ptr = array(dim4(M+1), r);
+    array col_idx = array(dim4(nnz), c);
+
+    // Create sparse array (CSR) from af::arrays containing values,
+    // row pointers, and column indices.
+    array sparse = af::sparse(M, N, vals, row_ptr, col_idx, AF_STORAGE_CSR);
+
+    // sparse
+    //     values:  [ 5.0, 8.0, 3.0, 6.0 ]
+    //     row_ptr: [ 0, 0, 2, 3, 4 ]
+    //     col_idx: [ 0, 1, 2, 1 ]
+
+    //! [ex_sparse_af_arrays]
+
+    array sparse_vals, sparse_row_ptr, sparse_col_idx;
+    af::storage sparse_storage;
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+
+    ASSERT_ARRAYS_EQ(sparse_vals   , vals);
+    ASSERT_ARRAYS_EQ(sparse_row_ptr, row_ptr);
+    ASSERT_ARRAYS_EQ(sparse_col_idx, col_idx);
+    ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
+    ASSERT_EQ(sparseGetNNZ(sparse), nnz);
+}
+
+TEST(Sparse, CPPSparseFromDenseUsage)
+{
+    float dns[] = { 0, 5, 0, 0,
+                    0, 8, 0, 6,
+                    0, 0, 3, 0,
+                    0, 0, 0, 0 };
+    const int M = 4, N = 4, nnz = 4;
+    array dense(dim4(M,N), dns);
+
+    //! [ex_sparse_from_dense]
+
+    // dense
+    //     0     0     0     0
+    //     5     8     0     0
+    //     0     0     3     0
+    //     0     6     0     0
+
+    // Convert dense af::array to its sparse (CSR) representation.
+    array sparse = af::sparse(dense, AF_STORAGE_CSR);
+
+    // sparse
+    //     values:  [ 5.0, 8.0, 3.0, 6.0 ]
+    //     row_ptr: [ 0, 0, 2, 3, 4 ]
+    //     col_idx: [ 0, 1, 2, 1 ]
+
+    //! [ex_sparse_from_dense]
+
+    float v[] = { 5, 8, 3, 6 };
+    int   r[] = { 0, 0, 2, 3, 4 };
+    int   c[] = { 0, 1, 2, 1 };
+    array gold_vals(   dim4(nnz), v);
+    array gold_row_ptr(dim4(M+1), r);
+    array gold_col_idx(dim4(nnz), c);
+
+    array sparse_vals, sparse_row_ptr, sparse_col_idx;
+    af::storage sparse_storage;
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+
+    ASSERT_ARRAYS_EQ(sparse_vals   , gold_vals);
+    ASSERT_ARRAYS_EQ(sparse_row_ptr, gold_row_ptr);
+    ASSERT_ARRAYS_EQ(sparse_col_idx, gold_col_idx);
+    ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
+    ASSERT_EQ(sparseGetNNZ(sparse), nnz);
+}
+
+TEST(Sparse, CPPDenseToSparseToDenseUsage)
+{
+    float g[] = { 0, 5, 0, 0,
+                  0, 8, 0, 6,
+                  0, 0, 3, 0,
+                  0, 0, 0, 0 };
+    const int M = 4, N = 4;
+    array in(dim4(M,N), g);
+    array sparse = af::sparse(in, AF_STORAGE_CSR);
+
+    //! [ex_dense_from_sparse]
+
+    // sparse
+    //     values:  [ 5.0, 8.0, 3.0, 6.0 ]
+    //     row_ptr: [ 0, 0, 2, 3, 4 ]
+    //     col_idx: [ 0, 1, 2, 1 ]
+
+    // Get dense representation of given sparse af::array.
+    array dense = af::dense(sparse);
+
+    // dense
+    //     0     0     0     0
+    //     5     8     0     0
+    //     0     0     3     0
+    //     0     6     0     0
+
+    //! [ex_dense_from_sparse]
+
+    float v[] = { 5, 8, 3, 6 };
+    int   r[] = { 0, 0, 2, 3, 4 };
+    int   c[] = { 0, 1, 2, 1 };
+    const int nnz = 4;
+    array gold_vals(   dim4(nnz), v);
+    array gold_row_ptr(dim4(M+1), r);
+    array gold_col_idx(dim4(nnz), c);
+
+    array sparse_vals, sparse_row_ptr, sparse_col_idx;
+    af::storage sparse_storage;
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+
+    ASSERT_ARRAYS_EQ(sparse_vals   , gold_vals);
+    ASSERT_ARRAYS_EQ(sparse_row_ptr, gold_row_ptr);
+    ASSERT_ARRAYS_EQ(sparse_col_idx, gold_col_idx);
+    ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
+    ASSERT_EQ(sparseGetNNZ(sparse), nnz);
+
+    // Check dense array
+    array gold(dim4(M,N), g);
+    ASSERT_ARRAYS_EQ(in, gold);
+    ASSERT_ARRAYS_EQ(dense, gold);
+}

From b72dbe4a52e1ff9285069e918bd2f8df9b240f19 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 3 Jun 2019 16:45:18 -0400
Subject: [PATCH 1667/2677] Add iomp allocation to lsan suppression. Rename
 suppression file

---
 CMakeModules/ASANSuppression.txt | 5 -----
 CMakeModules/LSANSuppression.txt | 9 +++++++++
 2 files changed, 9 insertions(+), 5 deletions(-)
 delete mode 100644 CMakeModules/ASANSuppression.txt
 create mode 100644 CMakeModules/LSANSuppression.txt

diff --git a/CMakeModules/ASANSuppression.txt b/CMakeModules/ASANSuppression.txt
deleted file mode 100644
index f5f58ad789..0000000000
--- a/CMakeModules/ASANSuppression.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-# This is a known leak.
-leak:getKernel
-#leak:libOpenCL
-leak:libnvidia-ptxjitcompile
-leak:tbb::internal::task_stream
diff --git a/CMakeModules/LSANSuppression.txt b/CMakeModules/LSANSuppression.txt
new file mode 100644
index 0000000000..6dcc15556c
--- /dev/null
+++ b/CMakeModules/LSANSuppression.txt
@@ -0,0 +1,9 @@
+# This is a known leak.
+leak:getKernel
+#leak:libOpenCL
+leak:libnvidia-ptxjitcompile
+leak:tbb::internal::task_stream
+
+# Allocated by Intel's OpenMP implementation during inverse_dense_cpu
+# This is not something we can control in ArrayFire
+leak:kmp_alloc_cpp*::bget

From f22a937ae56aea8f9bff7f4a2ebbcc42e5fef79a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 3 Jun 2019 18:41:47 -0400
Subject: [PATCH 1668/2677] Update coverage exclude files to remove glad

---
 CMakeModules/CTestCustom.cmake | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index d92a809721..8ae083e646 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -1,17 +1,18 @@
 
 
-set(CTEST_CUSTOM_ERROR_POST_CONTEXT 20)
-set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 20)
+set(CTEST_CUSTOM_ERROR_POST_CONTEXT 30)
+set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 30)
 set(CTEST_CUSTOM_POST_TEST ./test/print_info)
 
 list(APPEND CTEST_CUSTOM_COVERAGE_EXCLUDE
-	"test/gtest/*"
+  "test/gtest/*"
 
   # All external and third_party libraries
-	"extern/spdlog/*"
-	"src/backend/cpu/threads/*"
-	"src/backend/cuda/cub/*"
-	"cl2.hpp"
+  "extern/*"
+  "test/mmio/*"
+  "src/backend/cpu/threads/*"
+  "src/backend/cuda/cub/*"
+  "cl2.hpp"
 
   # Remove bin2cpp from coverage
-	"CMakeModules/*")
+  "CMakeModules/*")

From d910b1c218e5b081fdb23605c516efcbbb16a68e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 3 Jun 2019 16:28:41 +0530
Subject: [PATCH 1669/2677] Move fftw preserve flag to correct transform call

Earlier, the FFTW_PRESERVE_INPUT flag was present for real-to-complex
transform instead of complex-to-real which is where it is needed to
avoid modification of input data in out-of-place transformations using
FFTW.
---
 src/backend/cpu/kernel/fft.hpp | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
index 207b6c0bf0..e11f295d07 100644
--- a/src/backend/cpu/kernel/fft.hpp
+++ b/src/backend/cpu/kernel/fft.hpp
@@ -121,7 +121,7 @@ void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in,
     plan = transform.create(rank, t_dims, (int)batch, (Tr *)in.get(), in_embed,
                             (int)istrides[0], (int)istrides[rank],
                             (ctype_t *)out.get(), out_embed, (int)ostrides[0],
-                            (int)ostrides[rank], FFTW_ESTIMATE | FFTW_PRESERVE_INPUT);
+                            (int)ostrides[rank], FFTW_ESTIMATE);
 
     transform.execute(plan);
     transform.destroy(plan);
@@ -149,10 +149,21 @@ void fft_c2r(Param<Tr> out, const af::dim4 oDataDims, CParam<Tc> in,
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= odims[i]; }
 
+    // By default, fftw estimate flag is sufficient for most transforms.
+    // However, complex to real transforms modify the input data memory
+    // while performing the transformation. To avoid that, we need to pass
+    // FFTW_PRESERVE_INPUT also. This flag however only works for 1D
+    // transforms and for higher level transformations, a copy of input
+    // data is passed onto the upstream FFTW calls.
+    unsigned int flags = FFTW_ESTIMATE;
+    if (rank == 1) {
+        flags |= FFTW_PRESERVE_INPUT;
+    }
+
     plan = transform.create(rank, t_dims, (int)batch, (ctype_t *)in.get(),
                             in_embed, (int)istrides[0], (int)istrides[rank],
                             (Tr *)out.get(), out_embed, (int)ostrides[0],
-                            (int)ostrides[rank], FFTW_ESTIMATE);
+                            (int)ostrides[rank], flags);
 
     transform.execute(plan);
     transform.destroy(plan);

From ac9119d895c6fdb21640a0cd8e7cba4bf8fac923 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 4 Jun 2019 18:52:54 -0400
Subject: [PATCH 1670/2677] Pass Array object by reference instead of by value
 in sparse matmul

---
 src/api/c/canny.cpp                |  2 +-
 src/backend/cpu/sparse_blas.cpp    | 10 +++++-----
 src/backend/cpu/sparse_blas.hpp    |  2 +-
 src/backend/cuda/sparse_blas.cpp   |  8 ++++----
 src/backend/cuda/sparse_blas.hpp   |  2 +-
 src/backend/opencl/sparse_blas.cpp |  8 ++++----
 src/backend/opencl/sparse_blas.hpp |  2 +-
 7 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 06d7a7c090..6c1341ff61 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -177,7 +177,7 @@ std::pair<Array<char>, Array<char>> computeCandidates(
 }
 
 template<typename T>
-af_array cannyHelper(const Array<T> in, const float t1,
+af_array cannyHelper(const Array<T>& in, const float t1,
                      const af_canny_threshold ct, const float t2,
                      const unsigned sw, const bool isf) {
     static const vector<float> v{-0.11021f, -0.23691f, -0.30576f, -0.23691f,
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index d7b14c6d7d..19b3dc9649 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -200,7 +200,7 @@ SPARSE_FUNC(mm, cfloat, c)
 SPARSE_FUNC(mm, cdouble, z)
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
     // MKL: CSRMM Does not support optRhs
     UNUSED(optRhs);
@@ -398,7 +398,7 @@ void mtm(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 }
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
     UNUSED(optRhs);
     lhs.eval();
@@ -457,9 +457,9 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
 
 #endif  // #if USE_MKL
 
-#define INSTANTIATE_SPARSE(T)                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
-                                const Array<T> rhs, af_mat_prop optLhs, \
+#define INSTANTIATE_SPARSE(T)                                            \
+    template Array<T> matmul<T>(const common::SparseArray<T> &lhs,       \
+                                const Array<T> &rhs, af_mat_prop optLhs, \
                                 af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/cpu/sparse_blas.hpp b/src/backend/cpu/sparse_blas.hpp
index 8d8d3d531c..54da96c282 100644
--- a/src/backend/cpu/sparse_blas.hpp
+++ b/src/backend/cpu/sparse_blas.hpp
@@ -14,7 +14,7 @@
 namespace cpu {
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cpp
index d563ff52a6..59d462780f 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cpp
@@ -104,7 +104,7 @@ SPARSE_FUNC(csrmv, cdouble, Z)
 #undef SPARSE_FUNC_DEF
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
     UNUSED(optRhs);
     // Similar Operations to GEMM
@@ -157,9 +157,9 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     return out;
 }
 
-#define INSTANTIATE_SPARSE(T)                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
-                                const Array<T> rhs, af_mat_prop optLhs, \
+#define INSTANTIATE_SPARSE(T)                                            \
+    template Array<T> matmul<T>(const common::SparseArray<T> &lhs,       \
+                                const Array<T> &rhs, af_mat_prop optLhs, \
                                 af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/cuda/sparse_blas.hpp b/src/backend/cuda/sparse_blas.hpp
index 9b012400d7..3ff5e38520 100644
--- a/src/backend/cuda/sparse_blas.hpp
+++ b/src/backend/cuda/sparse_blas.hpp
@@ -13,7 +13,7 @@
 namespace cuda {
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index b666d4bdcb..5aaf396291 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -35,7 +35,7 @@ namespace opencl {
 using namespace common;
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
+Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
 #if defined(WITH_LINEAR_ALGEBRA)
     if (OpenCLCPUOffload(
@@ -85,9 +85,9 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhsIn,
     return out;
 }
 
-#define INSTANTIATE_SPARSE(T)                                           \
-    template Array<T> matmul<T>(const common::SparseArray<T> lhs,       \
-                                const Array<T> rhs, af_mat_prop optLhs, \
+#define INSTANTIATE_SPARSE(T)                                            \
+    template Array<T> matmul<T>(const common::SparseArray<T>& lhs,       \
+                                const Array<T>& rhs, af_mat_prop optLhs, \
                                 af_mat_prop optRhs);
 
 INSTANTIATE_SPARSE(float)
diff --git a/src/backend/opencl/sparse_blas.hpp b/src/backend/opencl/sparse_blas.hpp
index 9849beb54a..788fe3fd3c 100644
--- a/src/backend/opencl/sparse_blas.hpp
+++ b/src/backend/opencl/sparse_blas.hpp
@@ -14,7 +14,7 @@
 namespace opencl {
 
 template<typename T>
-Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
+Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
 }

From 5e973d016cccec00c9cfd5b8a1675bb7fb27afcd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 14 Jun 2019 21:54:33 +0530
Subject: [PATCH 1671/2677] Fix BLAS gemm generators in opencl cpu fallback

---
 src/backend/opencl/cpu/cpu_blas.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 11b2451d4e..00a0477085 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -113,7 +113,7 @@ using gemv_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
 #define BLAS_FUNC(FUNC, TYPE, PREFIX)           \
     template<>                                  \
     FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
-        return &cblas_##PREFIX##FUNC;           \
+        return (FUNC##_func_def<TYPE>)&cblas_##PREFIX##FUNC; \
     }
 
 BLAS_FUNC_DEF(gemm)

From d1180ec76696152d292df427b81fcb76b4dfae0e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Jun 2019 11:17:36 -0400
Subject: [PATCH 1672/2677] Fix array_proxy move constructor.

---
 src/api/cpp/array.cpp |  9 ++-------
 test/CMakeLists.txt   |  8 ++++----
 test/assign.cpp       | 15 +++++++++++++++
 3 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 33f900b09c..03387d1f2a 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -500,21 +500,16 @@ af::array::array_proxy::array_proxy(const array_proxy &other)
     : impl(new array_proxy_impl(*other.impl->parent_, other.impl->indices_,
                                 other.impl->is_linear_)) {}
 
-#if __cplusplus > 199711L
 af::array::array_proxy::array_proxy(array_proxy &&other) {
    impl = other.impl;
    other.impl = nullptr;
 }
 
 array::array_proxy &af::array::array_proxy::operator=(array_proxy &&other) {
-    if (&other == this)
-        return *this;
-    delete this->impl;
-    impl = other.impl;
-    other.impl = nullptr;
+    array out = other;
+    *this = out;
     return *this;
 }
-#endif
 
 af::array::array_proxy::~array_proxy() { delete impl; }
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 20e8060e77..2a880298eb 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -169,7 +169,7 @@ make_test(SRC approx1.cpp)
 make_test(SRC approx2.cpp)
 make_test(SRC array.cpp CXX11)
 make_test(SRC arrayio.cpp)
-make_test(SRC assign.cpp)
+make_test(SRC assign.cpp CXX11)
 make_test(SRC backend.cpp)
 make_test(SRC basic.cpp)
 make_test(SRC basic_c.c)
@@ -213,17 +213,17 @@ make_test(SRC homography.cpp)
 make_test(SRC hsv_rgb.cpp)
 make_test(SRC iir.cpp)
 make_test(SRC imageio.cpp)
-make_test(SRC index.cpp)
+make_test(SRC index.cpp CXX11)
 make_test(SRC info.cpp)
 make_test(SRC internal.cpp)
 make_test(SRC inverse_deconv.cpp)
-make_test(SRC inverse_dense.cpp)
+make_test(SRC inverse_dense.cpp SERIAL)
 make_test(SRC iota.cpp)
 make_test(SRC ireduce.cpp)
 make_test(SRC iterative_deconv.cpp)
 make_test(SRC jit.cpp CXX11)
 make_test(SRC join.cpp)
-make_test(SRC lu_dense.cpp)
+make_test(SRC lu_dense.cpp SERIAL)
 make_test(SRC main.cpp)
 #make_test(manual_memory_test.cpp)
 make_test(SRC match_template.cpp)
diff --git a/test/assign.cpp b/test/assign.cpp
index 0480538fc7..9550a20618 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -1019,3 +1019,18 @@ TEST(Assign, ISSUE_1677) {
         FAIL() << "ArrayFire exception: " << ex.what();
     } catch (...) { FAIL() << "Unknown exception thrown"; }
 }
+
+TEST(Index, ISSUE_2533) {
+    int elements = 5 * 10;
+    std::vector<float> gold(elements, 0);
+
+    int assigned_elements = 5 * 6;
+    for (int i = 0; i < assigned_elements; i++) { gold[i] = 1; }
+
+    af::array a = constant(0, 5, 10);
+    af::array b = constant(1, 5, 10);
+
+    a(af::span, af::seq(0, 5)) = b(af::span, af::seq(0, 5));
+
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(5, 10), a);
+}

From d2c94cb7b07fe9c8f0783375251b8ffcda8ca901 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 15 Jun 2019 18:08:40 -0400
Subject: [PATCH 1673/2677] Sets flags to enforce c++ compliance and correct
 cplusplus definition

* Sets the "permissive-" flag which instructs the compiler to enforce
  strict compliance to the standard
* Sets the Zc:__cplusplus flag which corrects the __cplusplus defintion
  so that move constructors are enabled in MSVC
---
 CMakeModules/platform.cmake | 15 +++++++++++++++
 test/CMakeLists.txt         |  2 +-
 test/assign.cpp             |  4 ++--
 3 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
index 68c66d9b2d..cfaf92dd5d 100644
--- a/CMakeModules/platform.cmake
+++ b/CMakeModules/platform.cmake
@@ -24,4 +24,19 @@ if(WIN32)
   # C4275: Warnings about using non-exported classes as base class of an
   #        exported class
   add_compile_options(/wd4068 /wd4275)
+
+  # MSVC incorrectly sets the cplusplus to 199711L even if the compiler supports
+  # c++11 features. This flag sets it to the correct standard supported by the
+  # compiler
+  check_cxx_compiler_flag(/Zc:__cplusplus cplusplus_define)
+  if(cplusplus_define)
+    add_compile_options(/Zc:__cplusplus)
+  endif()
+
+  # The "permissive-" option enforces strict(er?) standards compliance by
+  # MSVC
+  check_cxx_compiler_flag(/permissive- cxx_compliance)
+  if(cxx_compliance)
+    add_compile_options(/permissive-)
+  endif()
 endif()
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 2a880298eb..45c1a8c153 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -201,7 +201,7 @@ make_test(SRC flat.cpp)
 make_test(SRC flip.cpp)
 make_test(SRC gaussiankernel.cpp)
 make_test(SRC gen_assign.cpp)
-make_test(SRC gen_index.cpp)
+make_test(SRC gen_index.cpp CXX11)
 make_test(SRC getting_started.cpp)
 make_test(SRC gfor.cpp)
 make_test(SRC gradient.cpp)
diff --git a/test/assign.cpp b/test/assign.cpp
index 9550a20618..77f085a290 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -553,8 +553,8 @@ TEST(ArrayAssign, CPP_ASSIGN_TO_INDEXED) {
 
     array input(10, 2, &in.front(), afHost);
 
-    input(span, 0) =
-        input(span, 1);  // <-- Tests array_proxy to array_proxy assignment
+    // Tests array_proxy to array_proxy assignment
+    input(span, 0) = input(span, 1);
 
     vector<int> out(20);
     input.host(&out.front());

From e6428c9487226a24c21ecba8380850eb9fb89ab5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 17 Jun 2019 16:18:31 +0530
Subject: [PATCH 1674/2677] Remove OpenMP compile flag in CUDA backend

This flag isn't needed based on recent tests. If it is causing any
performance regression, it will be reverted and the following
flag to disable two-phase lookup for cuda backend on windows will
be added back.

/permissive flag does not work with two-phase-lookup enabled
for projects with openmp support enabled.
---
 CMakeLists.txt                  | 1 -
 src/backend/cuda/CMakeLists.txt | 9 ---------
 2 files changed, 10 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f62299b836..caed962582 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -28,7 +28,6 @@ set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for
 find_package(CUDA 7.0)
 find_package(OpenCL 1.2)
 find_package(OpenGL)
-find_package(OpenMP)
 find_package(FreeImage)
 find_package(Threads)
 find_package(FFTW)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 719867e551..a68d0dc5e2 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -472,15 +472,6 @@ target_include_directories (afcuda
     ${CMAKE_CURRENT_BINARY_DIR}
 )
 
-if(OpenMP_CXX_FOUND)
-  target_link_libraries(afcuda
-    PRIVATE
-      OpenMP::OpenMP_CXX
-    )
-elseif(NOT APPLE)
-  message(FATAL_ERROR "OpenMP is required to compile CUDA Backend")
-endif()
-
 set_target_properties(afcuda PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
 target_link_libraries(afcuda

From a546bb67be4481e53c11ec4a5ff15ff87158dfcf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Jun 2019 10:48:25 -0400
Subject: [PATCH 1675/2677] Fix the definition of sparse_mv and sparse_mm calls
 for MKL

---
 src/backend/cpu/sparse_blas.cpp            | 8 ++++----
 src/backend/opencl/cpu/cpu_sparse_blas.cpp | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 19b3dc9649..f95cbb4501 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -165,15 +165,15 @@ SPARSE_FUNC(create_csr, cdouble, z)
 //                 MKL_INT ldy);
 
 template<typename T>
-using mv_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
+using mv_func_def = sparse_status_t (*)(const sparse_operation_t, scale_type<T>,
                                         const sparse_matrix_t,
-                                        struct matrix_descr, cptr_type<T>,
+                                        matrix_descr, cptr_type<T>,
                                         scale_type<T>, ptr_type<T>);
 
 template<typename T>
-using mm_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
+using mm_func_def = sparse_status_t (*)(const sparse_operation_t, scale_type<T>,
                                         const sparse_matrix_t,
-                                        struct matrix_descr, sparse_layout_t,
+                                        matrix_descr, sparse_layout_t,
                                         cptr_type<T>, int, int, scale_type<T>,
                                         ptr_type<T>, int);
 
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index dd5031bc24..35c0a1a2dd 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -102,13 +102,13 @@ using create_csr_func_def = sparse_status_t (*)(sparse_matrix_t *,
 template<typename T>
 using mv_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
                                         const sparse_matrix_t,
-                                        struct matrix_descr, cptr_type<T>,
+                                        matrix_descr, cptr_type<T>,
                                         scale_type<T>, ptr_type<T>);
 
 template<typename T>
 using mm_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
                                         const sparse_matrix_t,
-                                        struct matrix_descr, sparse_layout_t,
+                                        matrix_descr, sparse_layout_t,
                                         cptr_type<T>, int, int, scale_type<T>,
                                         ptr_type<T>, int);
 

From 2a8c97ad662cce760ca37dbbb485d899e0cf170f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Jun 2019 20:19:47 -0400
Subject: [PATCH 1676/2677] Move thrust checks out of cuda_debug.hpp

---
 src/backend/cuda/CMakeLists.txt               |  1 +
 src/backend/cuda/debug_cuda.hpp               | 31 --------------
 src/backend/cuda/debug_thrust.hpp             | 40 +++++++++++++++++++
 src/backend/cuda/kernel/regions.hpp           |  1 +
 src/backend/cuda/kernel/sift_nonfree.hpp      |  1 +
 src/backend/cuda/kernel/sort.hpp              |  1 +
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |  1 +
 src/backend/cuda/memory.hpp                   |  3 +-
 src/backend/cuda/set.cu                       |  1 +
 9 files changed, 48 insertions(+), 32 deletions(-)
 create mode 100644 src/backend/cuda/debug_thrust.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a68d0dc5e2..6eb6fd07db 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -340,6 +340,7 @@ cuda_add_library(afcuda
     device_manager.cpp
     device_manager.hpp
     debug_cuda.hpp
+    debug_thrust.hpp
     diagonal.hpp
     diff.hpp
     driver.cpp
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index 56170c7088..f9482b9521 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -10,37 +10,6 @@
 #pragma once
 #include <err_cuda.hpp>
 #include <platform.hpp>
-#include <thrust/system/cuda/detail/par.h>
-#include <thrust/version.h>
-#include <ThrustAllocator.cuh>
-
-namespace cuda {
-template<typename T>
-using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T>>;
-}
-
-#define THRUST_STREAM thrust::cuda::par.on(cuda::getActiveStream())
-
-#if THRUST_MAJOR_VERSION >= 1 && THRUST_MINOR_VERSION >= 8
-
-#define THRUST_SELECT(fn, ...) fn(THRUST_STREAM, __VA_ARGS__)
-#define THRUST_SELECT_OUT(res, fn, ...) res = fn(THRUST_STREAM, __VA_ARGS__)
-
-#else
-
-#define THRUST_SELECT(fn, ...)                                      \
-    do {                                                            \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        fn(__VA_ARGS__);                                            \
-    } while (0)
-
-#define THRUST_SELECT_OUT(res, fn, ...)                             \
-    do {                                                            \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        res = fn(__VA_ARGS__);                                      \
-    } while (0)
-
-#endif
 
 #define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...) \
     fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__)
diff --git a/src/backend/cuda/debug_thrust.hpp b/src/backend/cuda/debug_thrust.hpp
new file mode 100644
index 0000000000..02eb9b7ea8
--- /dev/null
+++ b/src/backend/cuda/debug_thrust.hpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <thrust/system/cuda/detail/par.h>
+#include <thrust/version.h>
+#include <ThrustAllocator.cuh>
+
+namespace cuda {
+template<typename T>
+using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T>>;
+}
+
+#define THRUST_STREAM thrust::cuda::par.on(cuda::getActiveStream())
+
+#if THRUST_MAJOR_VERSION >= 1 && THRUST_MINOR_VERSION >= 8
+
+#define THRUST_SELECT(fn, ...) fn(THRUST_STREAM, __VA_ARGS__)
+#define THRUST_SELECT_OUT(res, fn, ...) res = fn(THRUST_STREAM, __VA_ARGS__)
+
+#else
+
+#define THRUST_SELECT(fn, ...)                                      \
+    do {                                                            \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
+        fn(__VA_ARGS__);                                            \
+    } while (0)
+
+#define THRUST_SELECT_OUT(res, fn, ...)                             \
+    do {                                                            \
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
+        res = fn(__VA_ARGS__);                                      \
+    } while (0)
+
+#endif
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index cb3ffa4d67..85a4556bde 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -9,6 +9,7 @@
 
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <debug_thrust.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index a3e3337685..3c8b4d92a4 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -75,6 +75,7 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
+#include <debug_thrust.hpp>
 #include <memory.hpp>
 #include "shared.hpp"
 
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index b03af555f9..14b2b57ed2 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <debug_thrust.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
 #include <iota.hpp>
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 3a5c22b926..4a824e0a89 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <debug_thrust.hpp>
 #include <debug_cuda.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
 #include <thrust/sort.h>
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 4fd6afeb9d..4fa6e1562f 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -9,10 +9,11 @@
 #pragma once
 
 #include <common/MemoryManager.hpp>
-#include <cstdlib>
 
+#include <cstdlib>
 #include <functional>
 #include <memory>
+
 namespace cuda {
 template<typename T>
 void memFree(T *ptr);
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index 5e9446b27a..8e52eaec8d 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
+#include <debug_thrust.hpp>
 #include <set.hpp>
 #include <sort.hpp>
 #include <af/dim4.hpp>

From 2e496c460fcb70b0f9e0824efba8789d401e1d69 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Jun 2019 23:05:09 -0400
Subject: [PATCH 1677/2677] Fix leak in SIFT test

---
 test/sift_nonfree.cpp | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/test/sift_nonfree.cpp b/test/sift_nonfree.cpp
index f44f4e4b0a..db61436bca 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift_nonfree.cpp
@@ -243,12 +243,8 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_SUCCESS(af_release_array(x));
-        ASSERT_SUCCESS(af_release_array(y));
-        ASSERT_SUCCESS(af_release_array(score));
-        ASSERT_SUCCESS(af_release_array(orientation));
-        ASSERT_SUCCESS(af_release_array(size));
         ASSERT_SUCCESS(af_release_array(desc));
+        ASSERT_SUCCESS(af_release_features(feat));
 
         delete[] outX;
         delete[] outY;

From 005350212d5b120d54dc71b89b110ea65b15c31a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Jun 2019 23:09:12 -0400
Subject: [PATCH 1678/2677] Pass parameters to enqueue by reference instead of
 by value.

When passing arguments to Queue. We were currently passing all arguments
by value but this was causing issues because the operator Param<T> function
would eval a function as it should. But since we were calling these functions
on a copied Array<T> object, this eval was not propagating to the original
Array<T>.
---
 src/backend/cpu/queue.hpp | 6 +++---
 src/backend/cpu/solve.cpp | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 26f96159e8..3c76e76d26 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -56,12 +56,12 @@ class queue {
                      getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
 
     template<typename F, typename... Args>
-    void enqueue(const F func, Args... args) {
+    void enqueue(const F func, Args&&... args) {
         count++;
         if (sync_calls) {
-            func(toParam(args)...);
+            func(toParam(std::forward<Args>(args))...);
         } else {
-            aQueue.enqueue(func, toParam(args)...);
+            aQueue.enqueue(func, toParam(std::forward<Args>(args))...);
         }
 #ifndef NDEBUG
         sync();
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 431eeacf83..b10bd22d3d 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -83,7 +83,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
     int NRHS   = b.dims()[1];
     Array<T> B = copyArray<T>(b);
 
-    auto func = [=](Param<T> A, Param<T> B, Param<int> pivot, int N, int NRHS) {
+    auto func = [=](CParam<T> A, Param<T> B, CParam<int> pivot, int N, int NRHS) {
         getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N', N, NRHS, A.get(),
                         A.strides(1), pivot.get(), B.get(), B.strides(1));
     };
@@ -102,7 +102,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     int N      = B.dims()[0];
     int NRHS   = B.dims()[1];
 
-    auto func = [=](Param<T> A, Param<T> B, int N, int NRHS,
+    auto func = [=](const CParam<T> A, Param<T> B, int N, int NRHS,
                     const af_mat_prop options) {
         trtrs_func<T>()(AF_LAPACK_COL_MAJOR, options & AF_MAT_UPPER ? 'U' : 'L',
                         'N',  // transpose flag

From a362e4939b0aefaa520ee9867c519fcbf2c9e9a2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Jun 2019 23:20:22 -0400
Subject: [PATCH 1679/2677] Document and improve cpu/Param.hpp

---
 src/backend/cpu/Param.hpp | 100 +++++++++++++++++++++++++++++---------
 1 file changed, 78 insertions(+), 22 deletions(-)

diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
index 55006f1c62..ec3613e21f 100644
--- a/src/backend/cpu/Param.hpp
+++ b/src/backend/cpu/Param.hpp
@@ -14,6 +14,9 @@
 
 namespace cpu {
 
+/// \brief Constant parameter object who's memory cannot be modified. Params
+///        represent the view of the memory in the kernel object. They do not
+///        own the memory.
 template<typename T>
 class CParam {
    private:
@@ -22,7 +25,8 @@ class CParam {
     af::dim4 m_strides;
 
    public:
-    CParam(const T *iptr, const af::dim4 &idims, const af::dim4 &istrides)
+    CParam(const T *iptr, const af::dim4 &idims,
+           const af::dim4 &istrides) noexcept
         : m_ptr(iptr) {
         for (int i = 0; i < 4; i++) {
             m_dims[i]    = idims[i];
@@ -30,17 +34,36 @@ class CParam {
         }
     }
 
-    const T *get() const { return m_ptr; }
+    /// \brief returns the pointer to the memory
+    constexpr const T *get() const noexcept { return m_ptr; }
 
-    af::dim4 dims() const { return m_dims; }
+    /// Gets the shape/dimension of the memory
+    af::dim4 dims() const noexcept { return m_dims; }
 
-    af::dim4 strides() const { return m_strides; }
+    /// Gets the stride of the memory
+    af::dim4 strides() const noexcept { return m_strides; }
 
-    dim_t dims(int i) const { return m_dims[i]; }
+    /// Returns the size of a particular dimension
+    ///
+    /// \param[in] i The dimension
+    constexpr dim_t dims(int i) const noexcept { return m_dims[i]; }
 
-    dim_t strides(int i) const { return m_strides[i]; }
+    /// Returns the stride of a particular dimension
+    ///
+    /// \param[in] i The dimension
+    constexpr dim_t strides(int i) const noexcept { return m_strides[i]; }
+
+    constexpr CParam()                    = delete;
+    constexpr CParam(const CParam &other) = default;
+    constexpr CParam(CParam &&other)      = default;
+    CParam<T> &operator=(CParam &&other) noexcept = default;
+    CParam<T> &operator=(const CParam &other) noexcept = default;
+    ~CParam()                                          = default;
 };
 
+/// \brief Parameter object usually passed into kernels. Params
+///        represent the view of the memory in the kernel object. They do not
+///        own the memory.
 template<typename T>
 class Param {
    private:
@@ -49,9 +72,11 @@ class Param {
     af::dim4 m_strides;
 
    public:
-    Param() : m_ptr(nullptr) {}
+    /// Creates an empty Param object pointing to null
+    Param() noexcept : m_ptr(nullptr) {}
 
-    Param(T *iptr, const af::dim4 &idims, const af::dim4 &istrides)
+    /// Creates an new Param object given a pointer, dimension and strides
+    Param(T *iptr, const af::dim4 &idims, const af::dim4 &istrides) noexcept
         : m_ptr(iptr) {
         for (int i = 0; i < 4; i++) {
             m_dims[i]    = idims[i];
@@ -59,41 +84,72 @@ class Param {
         }
     }
 
-    T *get() { return m_ptr; }
+    /// returns the pointer to the object
+    T *get() noexcept { return m_ptr; }
 
-    operator CParam<T>() const {
+    /// Param to CParam implicit conversion operator
+    constexpr operator CParam<T>() const noexcept {
         return CParam<T>(const_cast<T *>(m_ptr), m_dims, m_strides);
     }
 
-    af::dim4 dims() const { return m_dims; }
+    /// Gets the shape/dimension of the memory
+    af::dim4 dims() const noexcept { return m_dims; }
+
+    /// Gets the stride of the memory
+    af::dim4 strides() const noexcept { return m_strides; }
 
-    af::dim4 strides() const { return m_strides; }
+    /// Returns the size of a particular dimension
+    ///
+    /// \param[in] i The dimension
+    constexpr dim_t dims(int i) const noexcept { return m_dims[i]; }
 
-    dim_t dims(int i) const { return m_dims[i]; }
+    /// Returns the stride of a particular dimension
+    ///
+    /// \param[in] i The dimension
+    constexpr dim_t strides(int i) const noexcept { return m_strides[i]; }
 
-    dim_t strides(int i) const { return m_strides[i]; }
+    ~Param()                            = default;
+    constexpr Param(const Param &other) = default;
+    constexpr Param(Param &&other)      = default;
+    Param<T> &operator=(Param &&other) noexcept = default;
+    Param<T> &operator=(const Param &other) noexcept = default;
 };
 
 template<typename T>
 class Array;
 
 // These functions are needed to convert Array<T> to Param<T> when queueing up
-// functions. This is necessary because the memory used by Array<T> can be put
-// back into the queue faster. This is fine becacuse we only have 1 compute
-// queue. This ensures there's no race conditions.
+// functions. This is fine becacuse we only have 1 compute queue. This ensures
+// there's no race conditions.
+
+/// \brief Converts Array<T> to Param<T> or CParam<T> based on the constness
+///        of the Array<T> object. If called on anything else, the object is
+///        returned unchanged.
+///
+/// \param[in] val The value to convert to Param<T>
 template<typename T>
-T toParam(const T &val) {
+const T &toParam(const T &val) noexcept {
     return val;
 }
 
+/// \brief Converts Array<T> to Param<T> or CParam<T> based on the constness
+///        of the Array<T> object. If called on anything else, the object is
+///        returned unchanged.
+///
+/// \param[in] val The value to convert to Param<T>
 template<typename T>
-Param<T> toParam(Array<T> &val) {
-    return (Param<T>)(val);
+Param<T> toParam(Array<T> &val) noexcept {
+    return Param<T>(val.get(), val.dims(), val.strides());
 }
 
+/// \brief Converts Array<T> to Param<T> or CParam<T> based on the constness
+///        of the Array<T> object. If called on anything else, the object is
+///        returned unchanged.
+///
+/// \param[in] val The value to convert to Param<T>
 template<typename T>
-CParam<T> toParam(const Array<T> &val) {
-    return (CParam<T>)(val);
+CParam<T> toParam(const Array<T> &val) noexcept {
+    return CParam<T>(val.get(), val.dims(), val.strides());
 }
 
 }  // namespace cpu

From cec6b0e0eee25a59f7a4466bf3b9a3348aae4971 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Jun 2019 01:26:34 -0400
Subject: [PATCH 1680/2677] Remove eval calls in the CPU backend

---
 src/backend/cpu/approx.cpp              |  7 -------
 src/backend/cpu/assign.cpp              |  3 ---
 src/backend/cpu/bilateral.cpp           |  1 -
 src/backend/cpu/blas.cpp                |  6 ------
 src/backend/cpu/canny.cpp               |  9 ---------
 src/backend/cpu/cholesky.cpp            |  4 ----
 src/backend/cpu/convolve.cpp            |  7 -------
 src/backend/cpu/copy.cpp                |  3 ---
 src/backend/cpu/copy.hpp                |  1 -
 src/backend/cpu/diagonal.cpp            |  4 ----
 src/backend/cpu/diff.cpp                |  4 ----
 src/backend/cpu/exampleFunction.cpp     |  8 --------
 src/backend/cpu/fft.cpp                 |  3 ---
 src/backend/cpu/fftconvolve.cpp         |  3 ---
 src/backend/cpu/gradient.cpp            |  4 ----
 src/backend/cpu/harris.cpp              |  2 --
 src/backend/cpu/histogram.cpp           |  3 ---
 src/backend/cpu/homography.cpp          |  8 +-------
 src/backend/cpu/hsv_rgb.cpp             |  4 ----
 src/backend/cpu/iir.cpp                 |  4 ----
 src/backend/cpu/image.cpp               |  4 ++--
 src/backend/cpu/index.cpp               |  3 ---
 src/backend/cpu/inverse.cpp             |  2 --
 src/backend/cpu/ireduce.cpp             |  5 -----
 src/backend/cpu/join.cpp                | 11 +++++++----
 src/backend/cpu/kernel/sift_nonfree.hpp |  2 --
 src/backend/cpu/lookup.cpp              |  4 ----
 src/backend/cpu/lu.cpp                  |  7 -------
 src/backend/cpu/match_template.cpp      |  3 ---
 src/backend/cpu/mean.cpp                |  5 -----
 src/backend/cpu/meanshift.cpp           |  2 --
 src/backend/cpu/medfilt.cpp             |  4 ----
 src/backend/cpu/moments.cpp             |  3 ---
 src/backend/cpu/morph.cpp               |  7 -------
 src/backend/cpu/nearest_neighbour.cpp   |  5 -----
 src/backend/cpu/padarray.cpp            |  4 ----
 src/backend/cpu/qr.cpp                  |  7 -------
 src/backend/cpu/reduce.cpp              |  1 -
 src/backend/cpu/regions.cpp             |  4 ----
 src/backend/cpu/reorder.cpp             |  2 --
 src/backend/cpu/resize.cpp              |  2 --
 src/backend/cpu/rotate.cpp              |  2 --
 src/backend/cpu/scan.cpp                |  1 -
 src/backend/cpu/scan_by_key.cpp         |  3 ---
 src/backend/cpu/select.cpp              |  7 -------
 src/backend/cpu/set.cpp                 | 10 ----------
 src/backend/cpu/shift.cpp               |  2 --
 src/backend/cpu/sobel.cpp               |  1 -
 src/backend/cpu/solve.cpp               | 10 ----------
 src/backend/cpu/sort.cpp                |  4 ----
 src/backend/cpu/sort_by_key.cpp         |  3 ---
 src/backend/cpu/sort_index.cpp          |  3 ---
 src/backend/cpu/sparse.cpp              |  6 ------
 src/backend/cpu/sparse_arith.cpp        | 12 ------------
 src/backend/cpu/sparse_blas.cpp         |  7 -------
 src/backend/cpu/susan.cpp               |  2 --
 src/backend/cpu/svd.cpp                 |  5 -----
 src/backend/cpu/tile.cpp                |  2 --
 src/backend/cpu/transform.cpp           |  3 ---
 src/backend/cpu/transpose.cpp           |  3 ---
 src/backend/cpu/triangle.cpp            |  2 --
 src/backend/cpu/unwrap.cpp              |  2 --
 src/backend/cpu/where.cpp               |  4 +---
 src/backend/cpu/wrap.cpp                |  2 --
 src/backend/cuda/sort_index.cu          |  1 -
 65 files changed, 11 insertions(+), 261 deletions(-)

diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 8ca9f0c656..789a9c0817 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -18,9 +18,6 @@ template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const int xdim, const Tp &xi_beg, const Tp &xi_step,
              const af_interp_type method, const float offGrid) {
-    yi.eval();
-    xo.eval();
-
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -46,10 +43,6 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
                   const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
                   const int ydim, const Tp &yi_beg, const Tp &yi_step,
                   const af_interp_type method, const float offGrid) {
-    zi.eval();
-    xo.eval();
-    yo.eval();
-
     dim4 odims  = zi.dims();
     odims[xdim] = xo.dims()[xdim];
     odims[ydim] = xo.dims()[ydim];
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index ccee91957c..cedbf9fa00 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -30,9 +30,6 @@ using std::vector;
 namespace cpu {
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
-    out.eval();
-    rhs.eval();
-
     vector<bool> isSeq(4);
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output dimensions, offsets & offsets
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 53629e9b9e..8198689a62 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -22,7 +22,6 @@ namespace cpu {
 template<typename inType, typename outType, bool isColor>
 Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
                          const float &c_sigma) {
-    in.eval();
     const dim4 dims    = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
     getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in,
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index cfde212cf6..af7e93c14c 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -204,9 +204,6 @@ toCblasTranspose(af_mat_prop opt) {
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
                 af_mat_prop optRhs) {
-    lhs.eval();
-    rhs.eval();
-
     CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
     CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
 
@@ -314,9 +311,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
-    lhs.eval();
-    rhs.eval();
-
     Array<T> out = createEmptyArray<T>(af::dim4(1));
     if (optLhs == AF_MAT_CONJ && optRhs == AF_MAT_CONJ) {
         getQueue().enqueue(kernel::dot<T, false, true>, out, lhs, rhs, optLhs,
diff --git a/src/backend/cpu/canny.cpp b/src/backend/cpu/canny.cpp
index 830cda2601..55ac39049a 100644
--- a/src/backend/cpu/canny.cpp
+++ b/src/backend/cpu/canny.cpp
@@ -19,12 +19,7 @@ namespace cpu {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
                                    const Array<float>& gy) {
-    mag.eval();
-    gx.eval();
-    gy.eval();
-
     Array<float> out = createValueArray<float>(mag.dims(), 0);
-    out.eval();
 
     getQueue().enqueue(kernel::nonMaxSuppression<float>, out, mag, gx, gy);
 
@@ -33,11 +28,7 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
 
 Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
                                      const Array<char>& weak) {
-    strong.eval();
-    weak.eval();
-
     Array<char> out = createValueArray<char>(strong.dims(), 0);
-    out.eval();
 
     getQueue().enqueue(kernel::edgeTrackingHysteresis<char>, out, strong, weak);
 
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index a19a4afc37..34d73a5205 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -45,8 +45,6 @@ CH_FUNC(potrf, cdouble, z)
 
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
-    in.eval();
-
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
@@ -60,8 +58,6 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
 
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper) {
-    in.eval();
-
     dim4 iDims = in.dims();
     int N      = iDims[0];
 
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index fdc6830931..ba6594df30 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -23,9 +23,6 @@ namespace cpu {
 template<typename T, typename accT, int baseDim, bool expand>
 Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
                   AF_BATCH_KIND kind) {
-    signal.eval();
-    filter.eval();
-
     auto sDims = signal.dims();
     auto fDims = filter.dims();
 
@@ -56,10 +53,6 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
                    Array<accT> const& r_filter) {
-    signal.eval();
-    c_filter.eval();
-    r_filter.eval();
-
     auto sDims = signal.dims();
     dim4 tDims = sDims;
     dim4 oDims = sDims;
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index eae7901047..00e70082ad 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -43,7 +43,6 @@ void copyData(T *to, const Array<T> &from) {
 
 template<typename T>
 Array<T> copyArray(const Array<T> &A) {
-    A.eval();
     Array<T> out = createEmptyArray<T>(A.dims());
     getQueue().enqueue(kernel::copy<T, T>, out, A);
     return out;
@@ -54,8 +53,6 @@ void copyArray(Array<outType> &out, Array<inType> const &in) {
     static_assert(
         !(is_complex<inType>::value && !is_complex<outType>::value),
         "Cannot copy from complex Array<T> to a non complex Array<T>");
-    out.eval();
-    in.eval();
     getQueue().enqueue(kernel::copy<outType, inType>, out, in);
 }
 
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 8dd45d281f..5b02711b63 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -46,7 +46,6 @@ Array<T> padArrayBorders(const Array<T> &in, const dim4 &lowerBoundPadding,
 
     auto ret = (btype == AF_PAD_ZERO ? createValueArray<T>(oDims, scalar<T>(0))
                                      : createEmptyArray<T>(oDims));
-    ret.eval();
 
     getQueue().enqueue(kernel::padBorders<T>, ret, in, lowerBoundPadding,
                        upperBoundPadding, btype);
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 2b2c0c6a16..68f67f926a 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -22,8 +22,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
-    in.eval();
-
     int size     = in.dims()[0] + std::abs(num);
     int batch    = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
@@ -35,8 +33,6 @@ Array<T> diagCreate(const Array<T> &in, const int num) {
 
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num) {
-    in.eval();
-
     const dim4 idims = in.dims();
     dim_t size       = std::min(idims[0], idims[1]) - std::abs(num);
     Array<T> out     = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 411d207f89..a64b7dbe3c 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -19,8 +19,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim) {
-    in.eval();
-
     // Decrement dimension of select dimension
     af::dim4 dims = in.dims();
     dims[dim]--;
@@ -34,8 +32,6 @@ Array<T> diff1(const Array<T> &in, const int dim) {
 
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim) {
-    in.eval();
-
     // Decrement dimension of select dimension
     af::dim4 dims = in.dims();
     dims[dim] -= 2;
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index 1dc9b4a935..f912cf7d66 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -26,14 +26,6 @@ namespace cpu {
 template<typename T>
 Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
                          const af_someenum_t method) {
-    a.eval();  // All input Arrays should call eval mandatorily
-               // in CPU backend function implementations. Since
-               // the cpu fns are asynchronous launches, any Arrays
-               // that are either views/JIT nodes needs to evaluated
-               // before they are passed onto functions that are
-               // enqueued onto the queues.
-    b.eval();
-
     dim4 outputDims;  // this should be '= in.dims();' in most cases
                       // but would definitely depend on the type of
                       // algorithm you are implementing.
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 3e037a5e7a..af9c0f3248 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -23,15 +23,12 @@ void setFFTPlanCacheSize(size_t numPlans) { UNUSED(numPlans); }
 
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in) {
-    in.eval();
     getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in,
                        in.getDataDims());
 }
 
 template<typename Tc, typename Tr, int rank>
 Array<Tc> fft_r2c(const Array<Tr> &in) {
-    in.eval();
-
     dim4 odims    = in.dims();
     odims[0]      = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 95e950c3cf..93cc27227f 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -24,9 +24,6 @@ template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
          dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
-    signal.eval();
-    filter.eval();
-
     const af::dim4 sd = signal.dims();
     const af::dim4 fd = filter.dims();
 
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index 341ef94fde..711cd72c49 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -20,10 +20,6 @@ namespace cpu {
 
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
-    grad0.eval();
-    grad1.eval();
-    in.eval();
-
     getQueue().enqueue(kernel::gradient<T>, grad0, grad1, in);
 }
 
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 298ce3dae0..180a556943 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -29,8 +29,6 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
                 const unsigned max_corners, const float min_response,
                 const float sigma, const unsigned filter_len,
                 const float k_thr) {
-    in.eval();
-
     dim4 idims = in.dims();
 
     // Window filter
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 0ed3c3f198..4e05216ccd 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -21,12 +21,9 @@ namespace cpu {
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
                          const double &minval, const double &maxval) {
-    in.eval();
-
     const dim4 inDims  = in.dims();
     dim4 outDims       = dim4(nbins, 1, inDims[2], inDims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
-    out.eval();
 
     getQueue().enqueue(kernel::histogram<outType, inType, isLinear>, out, in,
                        nbins, minval, maxval);
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index aba40175ac..6dea1f25a3 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -179,10 +179,9 @@ int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
     float dst_scale = sqrt(2.0f) / sqrt(dst_var);
 
     Array<T> A = createValueArray<T>(af::dim4(9, 9), (T)0);
-    A.eval();
-    getQueue().sync();
     af::dim4 Adims = A.dims();
     T* A_ptr       = A.get();
+    getQueue().sync();
 
     for (unsigned j = 0; j < 4; j++) {
         float srcx = (src_pt_x[j] - x_src_mean) * src_scale;
@@ -359,11 +358,6 @@ int homography(Array<T>& bestH, const Array<float>& x_src,
                const Array<float>& y_dst, const Array<float>& initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations) {
-    x_src.eval();
-    y_src.eval();
-    x_dst.eval();
-    y_dst.eval();
-
     const af::dim4 idims    = x_src.dims();
     const unsigned nsamples = idims[0];
 
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index 1d2758f1a9..eb37f3a118 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -20,8 +20,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
 
     getQueue().enqueue(kernel::hsv2rgb<T>, out, in);
@@ -31,8 +29,6 @@ Array<T> hsv2rgb(const Array<T>& in) {
 
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
 
     getQueue().enqueue(kernel::rgb2hsv<T>, out, in);
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 1f0ce4ed7c..801e02a67f 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -21,10 +21,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
-    b.eval();
-    a.eval();
-    x.eval();
-
     AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
     if (x.ndims() != b.ndims()) {
         type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 95836264ed..0336e9de1e 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -24,11 +24,11 @@ namespace cpu {
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image) {
     ForgeModule &_ = graphics::forgePlugin();
-    in.eval();
-    getQueue().sync();
 
     CheckGL("Before CopyArrayToImage");
     const T *d_X       = in.get();
+    getQueue().sync();
+
     unsigned data_size = 0, buffer = 0;
     FG_CHECK(_.fg_get_pixel_buffer(&buffer, image));
     FG_CHECK(_.fg_get_image_size(&data_size, image));
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index aacbc784f2..953e5fcdc5 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -24,8 +24,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
-    in.eval();
-
     vector<bool> isSeq(4);
     vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
@@ -43,7 +41,6 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     for (unsigned x = 0; x < isSeq.size(); ++x) {
         if (!isSeq[x]) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
-            idxArrs[x].eval();
             // set output array ith dimension value
             oDims[x] = idxArrs[x].elements();
         }
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index abfd63031d..47230f21d3 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -48,8 +48,6 @@ INV_FUNC(getri, cdouble, z)
 
 template<typename T>
 Array<T> inverse(const Array<T> &in) {
-    in.eval();
-
     int M = in.dims()[0];
     int N = in.dims()[1];
 
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 06137be7d3..e31ee40ffd 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -26,10 +26,6 @@ using ireduce_dim_func = std::function<void(Param<T>, Param<uint>, const dim_t,
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim) {
-    out.eval();
-    loc.eval();
-    in.eval();
-
     dim4 odims                                           = in.dims();
     odims[dim]                                           = 1;
     static const ireduce_dim_func<op, T> ireduce_funcs[] = {
@@ -41,7 +37,6 @@ void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
 
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in) {
-    in.eval();
     getQueue().sync();
 
     af::dim4 dims    = in.dims();
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 0cb2c93315..8667da9ad2 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -13,13 +13,12 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+#include <algorithm>
+
 namespace cpu {
 
 template<typename Tx, typename Ty>
 Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
-    first.eval();
-    second.eval();
-
     // All dimensions except join dimension must be equal
     // Compute output dims
     af::dim4 odims;
@@ -43,7 +42,6 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
 
 template<typename T>
 Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
-    for (unsigned i = 0; i < inputs.size(); ++i) inputs[i].eval();
     // All dimensions except join dimension must be equal
     // Compute output dims
     af::dim4 odims;
@@ -64,6 +62,11 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
         }
     }
 
+    std::vector<Array<T> *> input_ptrs(inputs.size());
+    std::transform(
+        begin(inputs), end(inputs), begin(input_ptrs),
+        [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
+    evalMultiple(input_ptrs);
     std::vector<CParam<T>> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index a5cc4741dc..2382ae2e7b 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -904,8 +904,6 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
     using std::function;
     using std::unique_ptr;
     using std::vector;
-    in.eval();
-    getQueue().sync();
     af::dim4 idims = in.dims();
 
     unsigned min_dim = min(idims[0], idims[1]);
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index a0bf4bbac2..33300d0675 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -17,9 +17,6 @@ namespace cpu {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim) {
-    input.eval();
-    indices.eval();
-
     const dim4 iDims = input.dims();
 
     dim4 oDims(1);
@@ -27,7 +24,6 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
         oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
-
     getQueue().enqueue(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
 
     return out;
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index efedf867a8..22a3a25d57 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -47,11 +47,6 @@ LU_FUNC(getrf, cdouble, z)
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
         const Array<T> &in) {
-    lower.eval();
-    upper.eval();
-    pivot.eval();
-    in.eval();
-
     dim4 iDims = in.dims();
     int M      = iDims[0];
     int N      = iDims[1];
@@ -70,8 +65,6 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
 
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
-    in.eval();
-
     dim4 iDims = in.dims();
     Array<int> pivot =
         createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index c429dae52e..9e6dda9431 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -20,9 +20,6 @@ namespace cpu {
 
 template<typename InT, typename OutT, af_match_type MatchT>
 Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg) {
-    sImg.eval();
-    tImg.eval();
-
     Array<OutT> out = createEmptyArray<OutT>(sImg.dims());
 
     getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg,
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index 9710819a3e..6298a76d4c 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -25,8 +25,6 @@ using mean_dim_func = std::function<void(
 
 template<typename Ti, typename Tw, typename To>
 Array<To> mean(const Array<Ti> &in, const int dim) {
-    in.eval();
-
     dim4 odims    = in.dims();
     odims[dim]    = 1;
     Array<To> out = createEmptyArray<To>(odims);
@@ -45,9 +43,6 @@ using mean_weighted_dim_func =
 
 template<typename T, typename Tw>
 Array<T> mean(const Array<T> &in, const Array<Tw> &wt, const int dim) {
-    in.eval();
-    wt.eval();
-
     dim4 odims   = in.dims();
     odims[dim]   = 1;
     Array<T> out = createEmptyArray<T>(odims);
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index 81b40236dd..df326dd86c 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -26,8 +26,6 @@ template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numInterations,
                    const bool &isColor) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
 
     if (isColor)
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index deff345b6f..44f611536d 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -20,8 +20,6 @@ namespace cpu {
 
 template<typename T, af_border_type pad>
 Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
 
     getQueue().enqueue(kernel::medfilt1<T, pad>, out, in, w_wid);
@@ -31,8 +29,6 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
 
 template<typename T, af_border_type pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
 
     getQueue().enqueue(kernel::medfilt2<T, pad>, out, in, w_len, w_wid);
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index 04eeac5dc6..a1ddf7d333 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -26,7 +26,6 @@ using af::dim4;
 
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment) {
-    in.eval();
     dim4 odims, idims = in.dims();
     dim_t moments_dim = bitCount(moment);
 
@@ -36,8 +35,6 @@ Array<float> moments(const Array<T> &in, const af_moment_type moment) {
     odims[3] = idims[3];
 
     Array<float> out = createValueArray<float>(odims, 0.f);
-    out.eval();
-
     getQueue().enqueue(kernel::moments<T>, out, in, moment);
     getQueue().sync();
     return out;
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index 7b4a5e2786..ca0268917b 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -22,10 +22,6 @@ namespace cpu {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     af::borderType padType = isDilation ? AF_PAD_ZERO : AF_PAD_CLAMP_TO_EDGE;
-
-    in.eval();
-    mask.eval();
-
     const af::dim4 idims = in.dims();
     const af::dim4 mdims = mask.dims();
 
@@ -48,9 +44,6 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask) {
 
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
-    in.eval();
-    mask.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
 
     getQueue().enqueue(kernel::morph3d<T, isDilation>, out, in, mask);
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index e033e1ef1b..4df5cd37f9 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -24,11 +24,6 @@ template<typename T, typename To>
 void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
                        const Array<T>& train, const uint dist_dim,
                        const uint n_dist, const af_match_type dist_type) {
-    idx.eval();
-    dist.eval();
-    query.eval();
-    train.eval();
-
     uint sample_dim  = (dist_dim == 0) ? 1 : 0;
     const dim4 qDims = query.dims();
     const dim4 tDims = train.dims();
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
index 2e23f2ff97..a83d287448 100644
--- a/src/backend/cpu/padarray.cpp
+++ b/src/backend/cpu/padarray.cpp
@@ -24,7 +24,6 @@
 namespace cpu {
 template<typename T>
 void multiply_inplace(Array<T>& in, double val) {
-    in.eval();
     getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, static_cast<T>(0),
                        val);
 }
@@ -33,11 +32,8 @@ template<typename inType, typename outType>
 Array<outType> padArray(const Array<inType>& in, const dim4& dims,
                         outType default_value, double factor) {
     Array<outType> ret = createValueArray<outType>(dims, default_value);
-    ret.eval();
-    in.eval();
     getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in,
                        static_cast<outType>(default_value), factor);
-
     return ret;
 }
 
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index e0cfb94334..5cdafa0481 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -63,11 +63,6 @@ GQR_FUNC(gqr, cdouble, zungqr)
 
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
-    q.eval();
-    r.eval();
-    t.eval();
-    in.eval();
-
     dim4 iDims = in.dims();
     int M      = iDims[0];
     int N      = iDims[1];
@@ -92,8 +87,6 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
 
 template<typename T>
 Array<T> qr_inplace(Array<T> &in) {
-    in.eval();
-
     dim4 iDims = in.dims();
     int M      = iDims[0];
     int N      = iDims[1];
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 6e735e289b..9acb6351af 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -39,7 +39,6 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
                  double nanval) {
     dim4 odims = in.dims();
     odims[dim] = 1;
-    in.eval();
 
     Array<To> out = createEmptyArray<To>(odims);
     static const reduce_dim_func<op, Ti, To> reduce_funcs[4] = {
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index e6895b7983..061358a4ec 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -25,11 +25,7 @@ namespace cpu {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
-    in.eval();
-
     Array<T> out = createValueArray(in.dims(), (T)0);
-    out.eval();
-
     getQueue().enqueue(kernel::regions<T>, out, in, connectivity);
 
     return out;
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 57d63584d4..69a77a9ca6 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -17,8 +17,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
-    in.eval();
-
     const af::dim4 iDims = in.dims();
     af::dim4 oDims(0);
     for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 17bd317818..6049d0753c 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -23,8 +23,6 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
     af::dim4 odims(odim0, odim1, idims[2], idims[3]);
     // Create output placeholder
     Array<T> out = createValueArray(odims, (T)0);
-    out.eval();
-    in.eval();
 
     switch (method) {
         case AF_INTERP_NEAREST:
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 074e9d6bf5..7a0fada05f 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -18,8 +18,6 @@ namespace cpu {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(odims);
 
     switch (method) {
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 9893cbb282..4522c60799 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -24,7 +24,6 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
     dim4 dims     = in.dims();
     Array<To> out = createEmptyArray<To>(dims);
-    in.eval();
 
     if (inclusive_scan) {
         switch (in.ndims()) {
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index 63b592703e..d9a0e44bbe 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -29,9 +29,6 @@ Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
     kernel::scan_dim_by_key<op, Ti, Tk, To, 3> func3(inclusive_scan);
     kernel::scan_dim_by_key<op, Ti, Tk, To, 4> func4(inclusive_scan);
 
-    in.eval();
-    key.eval();
-
     switch (in.ndims()) {
         case 1: getQueue().enqueue(func1, out, 0, key, 0, in, 0, dim); break;
         case 2: getQueue().enqueue(func2, out, 0, key, 0, in, 0, dim); break;
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 62bebb1dd4..7fdd5f7711 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -20,19 +20,12 @@ namespace cpu {
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
             const Array<T> &b) {
-    out.eval();
-    cond.eval();
-    a.eval();
-    b.eval();
     getQueue().enqueue(kernel::select<T>, out, cond, a, b);
 }
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
                    const double &b) {
-    out.eval();
-    cond.eval();
-    a.eval();
     getQueue().enqueue(kernel::select_scalar<T, flip>, out, cond, a, b);
 }
 
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 4b9960a92a..b409634298 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -29,8 +29,6 @@ using std::unique;
 
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(af::dim4());
     if (is_sorted)
         out = copyArray<T>(in);
@@ -53,10 +51,6 @@ Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
 template<typename T>
 Array<T> setUnion(const Array<T> &first, const Array<T> &second,
                   const bool is_unique) {
-    first.eval();
-    second.eval();
-    getQueue().sync();
-
     Array<T> uFirst  = first;
     Array<T> uSecond = second;
 
@@ -86,10 +80,6 @@ Array<T> setUnion(const Array<T> &first, const Array<T> &second,
 template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique) {
-    first.eval();
-    second.eval();
-    getQueue().sync();
-
     Array<T> uFirst  = first;
     Array<T> uSecond = second;
 
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index e2a3d3060b..5126cda592 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -17,8 +17,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
-    in.eval();
-
     Array<T> out = createEmptyArray<T>(in.dims());
     const af::dim4 temp(sdims[0], sdims[1], sdims[2], sdims[3]);
 
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index f1b00d46e7..76ecf17dc6 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -23,7 +23,6 @@ template<typename Ti, typename To>
 std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
                                                  const unsigned &ker_size) {
     UNUSED(ker_size);
-    img.eval();
     // ket_size is for future proofing, this argument is not used
     // currently
     Array<To> dx = createEmptyArray<To>(img.dims());
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index b10bd22d3d..75553ca5b5 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -75,10 +75,6 @@ template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options) {
     UNUSED(options);
-    A.eval();
-    pivot.eval();
-    b.eval();
-
     int N      = A.dims()[0];
     int NRHS   = b.dims()[1];
     Array<T> B = copyArray<T>(b);
@@ -95,9 +91,6 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
 template<typename T>
 Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
                        const af_mat_prop options) {
-    A.eval();
-    b.eval();
-
     Array<T> B = copyArray<T>(b);
     int N      = B.dims()[0];
     int NRHS   = B.dims()[1];
@@ -117,9 +110,6 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options) {
-    a.eval();
-    b.eval();
-
     if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
     }
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 413d684434..01c8e266da 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -45,8 +45,6 @@ void sortBatched(Array<T>& val, bool isAscending) {
 
     // Needs to be ascending (true) in order to maintain the indices properly
     sort_by_key<uint, T>(key, val, resKey, resVal, 0, true);
-    val.eval();
-
     val.setDataDims(inDims);  // This is correct only for dim0
 }
 
@@ -62,8 +60,6 @@ void sort0(Array<T>& val, bool isAscending) {
 
 template<typename T>
 Array<T> sort(const Array<T>& in, const unsigned dim, bool isAscending) {
-    in.eval();
-
     Array<T> out = copyArray<T>(in);
     switch (dim) {
         case 0: sort0<T>(out, isAscending); break;
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 9f7dd825d1..f888758a12 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -21,9 +21,6 @@ namespace cpu {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
                  const Array<Tv> &ival, const uint dim, bool isAscending) {
-    ikey.eval();
-    ival.eval();
-
     okey = copyArray<Tk>(ikey);
     oval = copyArray<Tv>(ival);
 
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index c123d65ff1..4e1d07b559 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -24,12 +24,9 @@ namespace cpu {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
                 const uint dim, bool isAscending) {
-    in.eval();
-
     // okey is values, oval is indices
     okey = copyArray<T>(in);
     oval = range<uint>(in.dims(), dim);
-    oval.eval();
 
     switch (dim) {
         case 0:
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index aef16e3738..6409c0789b 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -38,8 +38,6 @@ using common::SparseArray;
 
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
-    in.eval();
-
     if (stype == AF_STORAGE_CSR) {
         uint nNZ = reduce_all<af_notzero_t, T, uint>(in);
 
@@ -59,7 +57,6 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
         dim_t nNZ = nonZeroIdx.elements();
 
         auto cnst = createValueArray<int>(dim4(nNZ), in.dims()[0]);
-        cnst.eval();
 
         auto rowIdx =
             arithOp<int, af_mod_t>(nonZeroIdx, cnst, nonZeroIdx.dims());
@@ -80,10 +77,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
 
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
-    in.eval();
-
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
-    dense.eval();
 
     Array<T> values   = in.getValues();
     Array<int> rowIdx = in.getRowIdx();
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index 8772680985..ec2383b244 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -51,9 +51,6 @@ cdouble getInf() {
 template<typename T, af_op_t op>
 Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
                   const bool reverse) {
-    lhs.eval();
-    rhs.eval();
-
     Array<T> out  = createEmptyArray<T>(dim4(0));
     Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
     switch (op) {
@@ -64,7 +61,6 @@ Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
             break;
         default: out = copyArray<T>(rhs);
     }
-    out.eval();
     switch (lhs.getStorage()) {
         case AF_STORAGE_CSR:
             getQueue().enqueue(kernel::sparseArithOpD<T, op, AF_STORAGE_CSR>,
@@ -87,13 +83,9 @@ Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
                        const bool reverse) {
-    lhs.eval();
-    rhs.eval();
-
     SparseArray<T> out = createArrayDataSparseArray<T>(
         lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
         lhs.getStorage(), true);
-    out.eval();
     switch (out.getStorage()) {
         case AF_STORAGE_CSR:
             getQueue().enqueue(kernel::sparseArithOpS<T, op, AF_STORAGE_CSR>,
@@ -117,9 +109,6 @@ template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     af::storage sfmt = lhs.getStorage();
 
-    lhs.eval();
-    rhs.eval();
-
     const dim4 dims = lhs.dims();
     const uint M    = dims[0];
     const uint N    = dims[1];
@@ -132,7 +121,6 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
 
     uint nnz = rowArr.get()[M];
     auto out = createEmptySparseArray<T>(dims, nnz, sfmt);
-    out.eval();
 
     copyArray(out.getRowIdx(), rowArr);
 
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index f95cbb4501..285805f636 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -205,9 +205,6 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
     // MKL: CSRMM Does not support optRhs
     UNUSED(optRhs);
 
-    lhs.eval();
-    rhs.eval();
-
     // Similar Operations to GEMM
     sparse_operation_t lOpts = toSparseTranspose(optLhs);
 
@@ -225,7 +222,6 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
     // int K = lDims[lColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
-    out.eval();
 
     auto func = [=](Param<T> output, CParam<T> values, CParam<int> rowIdx,
                     CParam<int> colIdx, const dim_t sdim0, const dim_t sdim1,
@@ -401,8 +397,6 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
     UNUSED(optRhs);
-    lhs.eval();
-    rhs.eval();
 
     // Similar Operations to GEMM
     sparse_operation_t lOpts = toSparseTranspose(optLhs);
@@ -417,7 +411,6 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
     int N             = rDims[rColDim];
 
     Array<T> out = createValueArray<T>(af::dim4(M, N, 1, 1), scalar<T>(0));
-    out.eval();
 
     auto func = [=](Param<T> output, CParam<T> values, CParam<int> rowIdx,
                     CParam<int> colIdx, CParam<T> right) {
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index ccdfbcd040..7f69925b16 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -26,8 +26,6 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                const Array<T> &in, const unsigned radius, const float diff_thr,
                const float geom_thr, const float feature_ratio,
                const unsigned edge) {
-    in.eval();
-
     dim4 idims                = in.dims();
     const unsigned corner_lim = in.elements() * feature_ratio;
 
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index d484ac42a5..7093689812 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -59,11 +59,6 @@ SVD_FUNC(gesvd, cdouble, double, z)
 
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
-    s.eval();
-    u.eval();
-    vt.eval();
-    in.eval();
-
     auto func = [=](Param<Tr> s, Param<T> u, Param<T> vt, Param<T> in) {
         dim4 iDims = in.dims();
         int M      = iDims[0];
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 2c21396fd5..a733eb30de 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -16,8 +16,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
-    in.eval();
-
     const af::dim4 iDims = in.dims();
     af::dim4 oDims       = iDims;
     oDims *= tileDims;
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 9dc5a5cae3..e91301f85f 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -19,9 +19,6 @@ template<typename T>
 Array<T> transform(const Array<T> &in, const Array<float> &tf,
                    const af::dim4 &odims, const af_interp_type method,
                    const bool inverse, const bool perspective) {
-    in.eval();
-    tf.eval();
-
     Array<T> out = createEmptyArray<T>(odims);
 
     switch (method) {
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index f55ed82a15..d05baed95b 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -21,8 +21,6 @@ namespace cpu {
 
 template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate) {
-    in.eval();
-
     const dim4 inDims  = in.dims();
     const dim4 outDims = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
     // create an array with first two dimensions swapped
@@ -35,7 +33,6 @@ Array<T> transpose(const Array<T> &in, const bool conjugate) {
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate) {
-    in.eval();
     getQueue().enqueue(kernel::transpose_inplace<T>, in, conjugate);
 }
 
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index db2baaf559..655c31a0be 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -18,13 +18,11 @@ namespace cpu {
 
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in) {
-    in.eval();
     getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>
 Array<T> triangle(const Array<T> &in) {
-    in.eval();
     Array<T> out = createEmptyArray<T>(in.dims());
     triangle<T, is_upper, is_unit_diag>(out, in);
     return out;
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index a003205bec..a80b7d9b5e 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -20,8 +20,6 @@ template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
                 const bool is_column) {
-    in.eval();
-
     af::dim4 idims = in.dims();
     dim_t nx       = (idims[0] + 2 * px - wx) / sx + 1;
     dim_t ny       = (idims[1] + 2 * py - wy) / sy + 1;
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index acd735ef6f..7d76a98aa5 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -22,15 +22,13 @@ namespace cpu {
 
 template<typename T>
 Array<uint> where(const Array<T> &in) {
-    in.eval();
-    getQueue().sync();
-
     const dim_t *dims    = in.dims().get();
     const dim_t *strides = in.strides().get();
     static const T zero  = scalar<T>(0);
 
     const T *iptr = in.get();
     auto out_vec  = memAlloc<uint>(in.elements());
+    getQueue().sync();
 
     dim_t count = 0;
     dim_t idx   = 0;
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index d55baeb19c..9b58453069 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -24,8 +24,6 @@ Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
     af::dim4 odims(ox, oy, idims[2], idims[3]);
 
     Array<T> out = createValueArray<T>(odims, scalar<T>(0));
-    out.eval();
-    in.eval();
 
     if (is_column) {
         getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px,
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index ea176789d7..9d1a88822e 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -23,7 +23,6 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
                 const uint dim, bool isAscending) {
     okey = copyArray<T>(in);
     oval = range<uint>(in.dims(), dim);
-    oval.eval();
 
     switch (dim) {
         case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;

From e62570abcbdd7f3fca1552368665be0cbd4fe4a0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Jun 2019 16:30:10 +0530
Subject: [PATCH 1681/2677] Use correct reduce op type for boolean product

---
 src/api/c/reduce.cpp | 28 +++++++++++++++++-----------
 test/reduce.cpp      | 19 +++++++++++++++++++
 2 files changed, 36 insertions(+), 11 deletions(-)

diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 9506ca87cd..3898748882 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -155,12 +155,15 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
             case u8:
                 res = reduce<op, uchar, uint>(in, dim, change_nan, nanval);
                 break;
-                // Make sure you are adding only "1" for every non zero value,
-                // even if op == af_add_t
-            case b8:
-                res = reduce<af_notzero_t, char, uint>(in, dim, change_nan,
+            case b8: {
+                if (op == af_mul_t) {
+                    res = reduce<af_and_t, char, char>(in, dim, change_nan,
                                                        nanval);
-                break;
+                } else {
+                    res = reduce<af_notzero_t, char, uint>(in, dim, change_nan,
+                                                           nanval);
+                }
+            } break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, res);
@@ -358,12 +361,15 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
                 *real_val =
                     (double)reduce_all<op, uchar, uint>(in, change_nan, nanval);
                 break;
-                // Make sure you are adding only "1" for every non zero value,
-                // even if op == af_add_t
-            case b8:
-                *real_val = (double)reduce_all<af_notzero_t, char, uint>(
-                    in, change_nan, nanval);
-                break;
+            case b8: {
+                if (op == af_mul_t) {
+                    *real_val = (double)reduce_all<af_and_t, char, char>(
+                        in, change_nan, nanval);
+                } else {
+                    *real_val = (double)reduce_all<af_notzero_t, char, uint>(
+                        in, change_nan, nanval);
+                }
+            } break;
 
             case c32:
                 cfval = reduce_all<op, cfloat, cfloat>(in);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 035361e38d..b08894abac 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -685,3 +685,22 @@ TEST(Reduce, AllSmallIndexed) {
     array b       = a(seq(len / 2), span);
     ASSERT_EQ(max<float>(b), len / 2 - 1);
 }
+
+TEST(ProductAll, BoolIn_ISSUE2543_All_Ones) {
+    ASSERT_EQ(true, product<int>(constant(1, 5, 5, b8)) > 0);
+}
+
+TEST(ProductAll, BoolIn_ISSUE2543_Random_Values) {
+    array in = randu(5, 5, b8);
+    vector<char> hostData(25);
+    in.host(hostData.data());
+    unsigned int gold = 1;
+    for (size_t i = 0; i < hostData.size(); ++i) { gold *= hostData[i]; }
+    const unsigned int out = product<unsigned int>(in);
+    ASSERT_EQ(gold, out);
+}
+
+TEST(Product, BoolIn_ISSUE2543) {
+    array A = randu(5, 5, b8);
+    ASSERT_ARRAYS_EQ(allTrue(A), product(A));
+}

From 86057956bcdeb3ea51773dd9e14af3fc283ab3ef Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 31 May 2019 01:33:04 -0400
Subject: [PATCH 1682/2677] Enforce dependencies between allocs and frees using
 events

* Creates an event class which is based on CUevent and OpenCL Events
* Use these events to enforce dependencies between frees and allocs
  to make sure that the data is not overwritten in later operations.
  This is not really an issue now because we are only running ops on
  one stream, but this will be an issue when we move to multiple streams
* Track MemoryEvent pairs in the memory manager.
---
 src/backend/common/EventBase.hpp         | 81 ++++++++++++++++++++++++
 src/backend/common/MemoryManager.hpp     | 56 ++++++++++------
 src/backend/common/MemoryManagerImpl.hpp | 41 +++++++-----
 src/backend/cpu/CMakeLists.txt           |  3 +
 src/backend/cpu/Event.cpp                | 20 ++++++
 src/backend/cpu/Event.hpp                | 49 ++++++++++++++
 src/backend/cpu/device_manager.cpp       |  1 +
 src/backend/cpu/memory.cpp               | 27 ++++++--
 src/backend/cpu/platform.cpp             |  1 +
 src/backend/cpu/queue.hpp                | 27 ++++++--
 src/backend/cpu/threads                  |  2 +-
 src/backend/cuda/CMakeLists.txt          |  2 +
 src/backend/cuda/Event.cpp               | 20 ++++++
 src/backend/cuda/Event.hpp               | 59 +++++++++++++++++
 src/backend/cuda/memory.cpp              | 38 +++++++----
 src/backend/opencl/CMakeLists.txt        |  2 +
 src/backend/opencl/Event.cpp             | 19 ++++++
 src/backend/opencl/Event.hpp             | 48 ++++++++++++++
 src/backend/opencl/memory.cpp            | 34 +++++++---
 src/backend/opencl/topk.cpp              |  4 +-
 20 files changed, 461 insertions(+), 73 deletions(-)
 create mode 100644 src/backend/common/EventBase.hpp
 create mode 100644 src/backend/cpu/Event.cpp
 create mode 100644 src/backend/cpu/Event.hpp
 create mode 100644 src/backend/cuda/Event.cpp
 create mode 100644 src/backend/cuda/Event.hpp
 create mode 100644 src/backend/opencl/Event.cpp
 create mode 100644 src/backend/opencl/Event.hpp

diff --git a/src/backend/common/EventBase.hpp b/src/backend/common/EventBase.hpp
new file mode 100644
index 0000000000..55dfd706f4
--- /dev/null
+++ b/src/backend/common/EventBase.hpp
@@ -0,0 +1,81 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+#include <utility>
+
+namespace common {
+template<typename NativeEventPolicy>
+class EventBase {
+    using QueueType = typename NativeEventPolicy::QueueType;
+    using EventType = typename NativeEventPolicy::EventType;
+    using ErrorType = typename NativeEventPolicy::ErrorType;
+    EventType e_;
+
+   public:
+    /// Default constructor of the Event object. Does not create the event.
+    constexpr EventBase() noexcept : e_() {}
+
+    /// Deleted copy constructor
+    ///
+    /// The event object can only be moved.
+    EventBase(EventBase &other) = delete;
+
+    /// \brief Move constructor of the Event object. Resets the moved object to
+    ///        an invalid event.
+    EventBase(EventBase &&other) noexcept
+        : e_(std::forward<EventType>(other.e_)) {
+        other.e_ = 0;
+    }
+
+    /// \brief Event destructor. Calls the destroy event call on the native API
+    ~EventBase() noexcept {
+        if (e_) NativeEventPolicy::destroyEvent(&e_);
+    }
+
+    /// \brief Creates the event object by calling the native create API
+    ErrorType create() noexcept { return NativeEventPolicy::createEvent(&e_); }
+
+    /// \brief Adds the event on the queue. Once this point on the program
+    ///        is executed, the event is marked complete.
+    ///
+    /// \returns the error code for the mark call
+    ErrorType mark(QueueType &queue) noexcept {
+        return NativeEventPolicy::markEvent(&e_, queue);
+    }
+
+    /// \brief This is an asynchronous function which will block the
+    ///        queue/stream from progressing before continuing forward. It will
+    ///        not block the calling thread.
+    ///
+    /// \param queue The queue that will wait for the previous tasks to complete
+    ///
+    /// \returns the error code for the wait call
+    ErrorType enqueueWait(QueueType &queue) noexcept {
+        return NativeEventPolicy::waitForEvent(&e_, queue);
+    }
+
+    /// \brief This function will block the calling thread until the event has
+    ///        completed
+    ErrorType block() const noexcept {
+        return NativeEventPolicy::syncForEvent();
+    }
+
+    /// \brief Returns true if the event is a valid event.
+    constexpr operator bool() const { return e_; }
+
+    EventBase &operator=(EventBase &other) = delete;
+
+    EventBase &operator=(EventBase &&other) noexcept {
+        e_       = std::move(other.e_);
+        other.e_ = 0;
+        return *this;
+    }
+};
+
+}  // namespace common
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index 2159819ecd..c236b5c87f 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -9,6 +9,8 @@
 
 #pragma once
 
+#include <Event.hpp>
+#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <common/err_common.hpp>
 #include <common/util.hpp>
@@ -29,26 +31,35 @@ namespace common {
 using mutex_t      = std::mutex;
 using lock_guard_t = std::lock_guard<mutex_t>;
 
-const unsigned MAX_BUFFERS = 1000;
-const size_t ONE_GB        = 1 << 30;
+constexpr unsigned MAX_BUFFERS = 1000;
+constexpr size_t ONE_GB        = 1 << 30;
+
+struct MemoryEventPair {
+    void *ptr;
+    detail::Event e;
+    MemoryEventPair(MemoryEventPair &other)  = delete;
+    MemoryEventPair(MemoryEventPair &&other) = default;
+    MemoryEventPair &operator=(MemoryEventPair &&other) = default;
+    MemoryEventPair &operator=(MemoryEventPair &other) = delete;
+};
 
 template<typename T>
 class MemoryManager {
-    typedef struct {
+    struct locked_info {
         bool manager_lock;
         bool user_lock;
         size_t bytes;
-    } locked_info;
+    };
 
     using locked_t    = typename std::unordered_map<void *, locked_info>;
     using locked_iter = typename locked_t::iterator;
 
-    using free_t    = std::unordered_map<size_t, std::vector<void *>>;
-    using free_iter = free_t::iterator;
+    using free_t    = std::unordered_map<size_t, std::vector<MemoryEventPair>>;
+    using free_iter = typename free_t::iterator;
 
     using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
 
-    typedef struct memory_info {
+    struct memory_info {
         locked_t locked_map;
         free_t free_map;
 
@@ -58,16 +69,20 @@ class MemoryManager {
         size_t total_buffers;
         size_t max_bytes;
 
-        memory_info() {
-            // Calling getMaxMemorySize() here calls the virtual function that
-            // returns 0 Call it from outside the constructor.
-            max_bytes     = ONE_GB;
-            total_bytes   = 0;
-            total_buffers = 0;
-            lock_bytes    = 0;
-            lock_buffers  = 0;
-        }
-    } memory_info;
+        memory_info()
+            // Calling getMaxMemorySize() here calls the virtual function
+            // that returns 0 Call it from outside the constructor.
+            : max_bytes(ONE_GB)
+            , total_bytes(0)
+            , total_buffers(0)
+            , lock_bytes(0)
+            , lock_buffers(0) {}
+
+        memory_info(memory_info &other)  = delete;
+        memory_info(memory_info &&other) = default;
+        memory_info &operator=(memory_info &other) = delete;
+        memory_info &operator=(memory_info &&other) = default;
+    };
 
     size_t mem_step_size;
     unsigned max_buffers;
@@ -102,7 +117,7 @@ class MemoryManager {
     /// bytes. If there is already a free buffer available, it will use
     /// that buffer. Otherwise, it will allocate a new buffer using the
     /// nativeAlloc function.
-    void *alloc(const size_t size, bool user_lock);
+    MemoryEventPair alloc(const size_t size, bool user_lock);
 
     /// returns the size of the buffer at the pointer allocated by the memory
     /// manager.
@@ -110,9 +125,10 @@ class MemoryManager {
 
     /// Frees or marks the pointer for deletion during the nex garbage
     /// collection event
-    void unlock(void *ptr, bool user_unlock);
+    void unlock(void *ptr, detail::Event &&e, bool user_unlock);
 
-    /// Frees all buffers which are not locked by the user or not being used.
+    /// Frees all buffers which are not locked by the user or not being
+    /// used.
     void garbageCollect();
 
     void printInfo(const char *msg, const int device);
diff --git a/src/backend/common/MemoryManagerImpl.hpp b/src/backend/common/MemoryManagerImpl.hpp
index cf45ff348d..d274c7d6cb 100644
--- a/src/backend/common/MemoryManagerImpl.hpp
+++ b/src/backend/common/MemoryManagerImpl.hpp
@@ -43,8 +43,8 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
 
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
-    // the lock is being held becasue the CPU backend calls sync.
-    vector<void *> free_ptrs;
+    // the lock is being held because the CPU backend calls sync.
+    vector<MemoryEventPair> free_ptrs;
     size_t bytes_freed   = 0;
     memory_info &current = memory[device];
     {
@@ -57,7 +57,9 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
             size_t num_ptrs = kv.second.size();
             // Free memory by pushing the last element into the free_ptrs
             // vector which will be freed once outside of the lock
-            for (auto p : kv.second) { free_ptrs.push_back(p); }
+            for (auto &p : kv.second) {
+                free_ptrs.emplace_back(MemoryEventPair{p.ptr, std::move(p.e)});
+            }
             current.total_bytes -= num_ptrs * kv.first;
             bytes_freed += num_ptrs * kv.first;
             current.total_buffers -= num_ptrs;
@@ -68,7 +70,9 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
     AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(),
              bytesToString(bytes_freed));
     // Free memory outside of the lock
-    for (auto ptr : free_ptrs) { this->nativeFree(ptr); }
+    for (auto &ptr : free_ptrs) {
+        this->nativeFree(ptr.ptr);
+    }
 }
 
 template<typename T>
@@ -127,8 +131,8 @@ void MemoryManager<T>::setMaxMemorySize() {
 }
 
 template<typename T>
-void *MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
-    void *ptr          = nullptr;
+MemoryEventPair MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
+    MemoryEventPair ptr = {nullptr, detail::Event()};
     size_t alloc_bytes = this->debug_mode
                              ? bytes
                              : (divup(bytes, mem_step_size) * mem_step_size);
@@ -147,31 +151,31 @@ void *MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
             free_iter iter = current.free_map.find(alloc_bytes);
 
             if (iter != current.free_map.end() && !iter->second.empty()) {
-                ptr = iter->second.back();
+                ptr = std::move(iter->second.back());
                 iter->second.pop_back();
-                current.locked_map[ptr] = info;
+                current.locked_map[ptr.ptr] = info;
                 current.lock_bytes += alloc_bytes;
                 current.lock_buffers++;
             }
         }
 
         // Only comes here if buffer size not found or in debug mode
-        if (ptr == nullptr) {
+        if (ptr.ptr == nullptr) {
             // Perform garbage collection if memory can not be allocated
             try {
-                ptr = this->nativeAlloc(alloc_bytes);
+                ptr.ptr = this->nativeAlloc(alloc_bytes);
             } catch (const AfError &ex) {
                 // If out of memory, run garbage collect and try again
                 if (ex.getError() != AF_ERR_NO_MEM) throw;
                 this->garbageCollect();
-                ptr = this->nativeAlloc(alloc_bytes);
+                ptr.ptr = this->nativeAlloc(alloc_bytes);
             }
 
             lock_guard_t lock(this->memory_mutex);
             // Increment these two only when it succeeds to come here.
             current.total_bytes += alloc_bytes;
             current.total_buffers += 1;
-            current.locked_map[ptr] = info;
+            current.locked_map[ptr.ptr] = info;
             current.lock_bytes += alloc_bytes;
             current.lock_buffers++;
         }
@@ -189,7 +193,7 @@ size_t MemoryManager<T>::allocated(void *ptr) {
 }
 
 template<typename T>
-void MemoryManager<T>::unlock(void *ptr, bool user_unlock) {
+void MemoryManager<T>::unlock(void *ptr, detail::Event &&e, bool user_unlock) {
     // Shortcut for empty arrays
     if (!ptr) return;
 
@@ -215,7 +219,9 @@ void MemoryManager<T>::unlock(void *ptr, bool user_unlock) {
         }
 
         // Return early if either one is locked
-        if ((iter->second).user_lock || (iter->second).manager_lock) return;
+        if ((iter->second).user_lock || (iter->second).manager_lock) {
+            return;
+        }
 
         size_t bytes = iter->second.bytes;
         current.lock_bytes -= iter->second.bytes;
@@ -229,7 +235,7 @@ void MemoryManager<T>::unlock(void *ptr, bool user_unlock) {
                 current.total_bytes -= iter->second.bytes;
             }
         } else {
-            current.free_map[bytes].push_back(ptr);
+            current.free_map[bytes].emplace_back(MemoryEventPair{ptr, std::move(e)});
         }
         current.locked_map.erase(iter);
     }
@@ -282,7 +288,7 @@ void MemoryManager<T>::printInfo(const char *msg, const int device) {
         }
 
         for (auto &ptr : kv.second) {
-            printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr, size, unit,
+            printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr.ptr, size, unit,
                    status_mngr, status_user);
         }
     }
@@ -319,7 +325,8 @@ void MemoryManager<T>::userLock(const void *ptr) {
 
 template<typename T>
 void MemoryManager<T>::userUnlock(const void *ptr) {
-    this->unlock(const_cast<void *>(ptr), true);
+    detail::Event e;
+    this->unlock(const_cast<void *>(ptr), std::move(e), true);
 }
 
 template<typename T>
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index a70ee738f7..415e1e8710 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -44,6 +44,8 @@ target_sources(afcpu
     diff.cpp
     diff.hpp
     err_cpu.hpp
+    Event.cpp
+    Event.hpp
     exampleFunction.cpp
     exampleFunction.hpp
     fast.cpp
@@ -263,6 +265,7 @@ endif(AF_WITH_CPUID)
 target_sources(afcpu
   PRIVATE
     ${CMAKE_CURRENT_SOURCE_DIR}/threads/async_queue.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/threads/event.hpp
   )
 
 arrayfire_set_default_cxx_flags(afcpu)
diff --git a/src/backend/cpu/Event.cpp b/src/backend/cpu/Event.cpp
new file mode 100644
index 0000000000..f462444ec8
--- /dev/null
+++ b/src/backend/cpu/Event.cpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Event.hpp>
+#include <queue.hpp>
+
+namespace cpu {
+/// \brief Creates a new event and marks it in the queue
+Event make_event(cpu::queue &queue) {
+    Event e;
+    if (0 == e.create()) { e.mark(queue); }
+    return e;
+}
+}  // namespace cpu
diff --git a/src/backend/cpu/Event.hpp b/src/backend/cpu/Event.hpp
new file mode 100644
index 0000000000..1ff0f0e678
--- /dev/null
+++ b/src/backend/cpu/Event.hpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <queue.hpp>
+
+#include <common/EventBase.hpp>
+
+#include <atomic>
+#include <thread>
+
+namespace cpu {
+
+class CPUEventPolicy {
+   public:
+    using EventType = queue_event;
+    using QueueType = queue;
+    using ErrorType = int;
+
+    static int createEvent(queue_event *e) noexcept { return e->create(); }
+
+    static int markEvent(queue_event *e, cpu::queue &stream) noexcept {
+        return e->mark(stream);
+    }
+
+    static int waitForEvent(queue_event *e, cpu::queue &stream) noexcept {
+        return e->wait(stream);
+    }
+
+    static int syncForEvent(queue_event *e) noexcept {
+        e->sync();
+        return 0;
+    }
+
+    static int destroyEvent(queue_event *e) noexcept { return 0; }
+};
+
+using Event = common::EventBase<CPUEventPolicy>;
+
+/// \brief Creates a new event and marks it in the queue
+Event make_event(cpu::queue &queue);
+
+}  // namespace cpu
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
index afb6258b54..5d48fbf03a 100644
--- a/src/backend/cpu/device_manager.cpp
+++ b/src/backend/cpu/device_manager.cpp
@@ -10,6 +10,7 @@
 #include <common/graphics_common.hpp>
 #include <device_manager.hpp>
 #include <af/version.h>
+#include <memory.hpp>
 
 #include <cctype>
 #include <sstream>
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 60858b1551..df2a20012a 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -17,6 +17,8 @@
 #include <spdlog/spdlog.h>
 #include <types.hpp>
 
+#include <utility>
+
 template class common::MemoryManager<cpu::MemoryManager>;
 
 #ifndef AF_MEM_DEBUG
@@ -30,6 +32,7 @@ template class common::MemoryManager<cpu::MemoryManager>;
 using common::bytesToString;
 
 using std::function;
+using std::move;
 using std::unique_ptr;
 
 namespace cpu {
@@ -53,22 +56,29 @@ template<typename T>
 unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements) {
     T *ptr = nullptr;
 
-    ptr = (T *)memoryManager().alloc(elements * sizeof(T), false);
+    common::MemoryEventPair me = memoryManager().alloc(elements * sizeof(T), false);
+    if(me.e) me.e.enqueueWait(getQueue());
+    ptr = (T *)me.ptr;
     return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
     void *ptr = nullptr;
-    ptr       = memoryManager().alloc(bytes, true);
-    return ptr;
+    common::MemoryEventPair me = memoryManager().alloc(bytes, true);
+    if (me.e) me.e.enqueueWait(getQueue());
+    return me.ptr;
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, false);
+    Event e = make_event(getQueue());
+    return memoryManager().unlock((void *)ptr, move(e), false);
 }
 
-void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
+void memFreeUser(void *ptr) {
+    Event e = make_event(getQueue());
+    memoryManager().unlock((void *)ptr, move(e), true);
+}
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
@@ -86,12 +96,15 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    return (T *)memoryManager().alloc(elements * sizeof(T), false);
+    common::MemoryEventPair me = memoryManager().alloc(elements * sizeof(T), false);
+    if (me.e) me.e.enqueueWait(getQueue());
+    return (T*)me.ptr;
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, false);
+    Event e = make_event(getQueue());
+    return memoryManager().unlock((void *)ptr, move(e), false);
 }
 
 bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index fd63aa5cd6..990f31ae9a 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <device_manager.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 3c76e76d26..55ee77e429 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -6,10 +6,12 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <Param.hpp>
 #include <common/util.hpp>
-#include <memory.hpp>
+
+#include <algorithm>
 
 // FIXME: Is there a better way to check for std::future not being supported ?
 #if defined(AF_DISABLE_CPU_ASYNC) || \
@@ -37,15 +39,16 @@ class queue_impl {
 
 #else
 
-#include <async_queue.hpp>
+#include <threads/async_queue.hpp>
+#include <threads/event.hpp>
 #define __SYNCHRONOUS_ARCH 0
-typedef async_queue queue_impl;
+using queue_impl = threads::async_queue;
+using event_impl = threads::event;
 
 #endif
 
-#pragma once
-
 namespace cpu {
+  bool checkMemoryLimit();
 
 /// Wraps the async_queue class
 class queue {
@@ -79,10 +82,24 @@ class queue {
         return (!sync_calls) ? aQueue.is_worker() : false;
     }
 
+    friend class queue_event;
    private:
     int count;
     const bool sync_calls;
     queue_impl aQueue;
 };
 
+  class queue_event {
+    event_impl event_;
+  public:
+    queue_event() = default;
+    queue_event(int val) : event_(val) {}
+
+    int create() { return event_.create(); }
+
+    int mark(queue &q) { return event_.mark(q.aQueue); }
+    int wait(queue &q) { return event_.wait(q.aQueue); }
+    int sync() noexcept { return event_.sync(); }
+    operator bool() const noexcept { return event_; }
+  };
 }  // namespace cpu
diff --git a/src/backend/cpu/threads b/src/backend/cpu/threads
index 5e778ce0a7..bebd15282a 160000
--- a/src/backend/cpu/threads
+++ b/src/backend/cpu/threads
@@ -1 +1 @@
-Subproject commit 5e778ce0a7f0f80af9d32ea3569df3dbec834f59
+Subproject commit bebd15282a5f4388689a7db3a1f2d95eff79b3e3
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6eb6fd07db..018193c0ce 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -174,6 +174,8 @@ cuda_add_library(afcuda
     dilate3d.cu
     erode.cu
     erode3d.cu
+    Event.cpp
+    Event.hpp
     exampleFunction.cu
     fast.cu
     fast_pyramid.cu
diff --git a/src/backend/cuda/Event.cpp b/src/backend/cuda/Event.cpp
new file mode 100644
index 0000000000..8ae1c6fab1
--- /dev/null
+++ b/src/backend/cuda/Event.cpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cuda_runtime_api.h>
+#include <Event.hpp>
+
+namespace cuda {
+/// \brief Creates a new event and marks it in the queue
+Event make_event(cudaStream_t queue) {
+    Event e;
+    if (e.create() == CUDA_SUCCESS) { e.mark(queue); }
+    return e;
+}
+}  // namespace cuda
diff --git a/src/backend/cuda/Event.hpp b/src/backend/cuda/Event.hpp
new file mode 100644
index 0000000000..d3fd9ab1d2
--- /dev/null
+++ b/src/backend/cuda/Event.hpp
@@ -0,0 +1,59 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <common/EventBase.hpp>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+namespace cuda {
+
+class CUDARuntimeEventPolicy {
+   public:
+    using EventType = CUevent;
+    using QueueType = CUstream;
+    using ErrorType = CUresult;
+
+    static ErrorType createEvent(CUevent *e) noexcept {
+      auto err = cuEventCreate(e, CU_EVENT_DISABLE_TIMING | CU_EVENT_BLOCKING_SYNC);
+        // printf("create %p: error: %s\n", *e, cudaGetErrorName(err));
+        return err;
+    }
+
+    static ErrorType markEvent(CUevent *e,
+                               QueueType &stream) noexcept {
+        auto err = cuEventRecord(*e, stream);
+        // printf("mark   %p: error: %s\n", *e, cudaGetErrorName(err));
+        return err;
+    }
+
+    static ErrorType waitForEvent(CUevent *e,
+                                  QueueType &stream) noexcept {
+      auto err = cuStreamWaitEvent(stream, *e, 0);
+        // printf("wait   %p: error: %s\n", *e, cudaGetErrorName(err));
+        return err;
+    }
+
+    static ErrorType syncForEvent(CUevent *e) noexcept {
+        return cuEventSynchronize(*e);
+    }
+
+    static ErrorType destroyEvent(CUevent *e) noexcept {
+        auto err = cuEventDestroy(*e);
+        // printf("destroy %p: error: %s\n", *e, cudaGetErrorName(err));
+        return err;
+    }
+};
+
+using Event = common::EventBase<CUDARuntimeEventPolicy>;
+
+/// \brief Creates a new event and marks it in the stream
+Event make_event(cudaStream_t stream);
+
+}  // namespace cuda
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index d6e332c5fa..bc87809490 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -9,6 +9,7 @@
 
 #include <memory.hpp>
 
+#include <Event.hpp>
 #include <common/Logger.hpp>
 #include <common/MemoryManagerImpl.hpp>
 #include <common/dispatch.hpp>
@@ -35,11 +36,9 @@ template class common::MemoryManager<cuda::MemoryManagerPinned>;
 #endif
 
 using common::bytesToString;
+using common::MemoryEventPair;
 
-using std::function;
-using std::lock_guard;
-using std::recursive_mutex;
-using std::unique_ptr;
+using std::move;
 
 namespace cuda {
 void setMemStepSize(size_t step_bytes) {
@@ -60,20 +59,30 @@ void printMemInfo(const char *msg, const int device) {
 
 template<typename T>
 uptr<T> memAlloc(const size_t &elements) {
-    size_t size = elements * sizeof(T);
-    return uptr<T>(static_cast<T *>(memoryManager().alloc(size, false)),
-                   memFree<T>);
+    size_t size                = elements * sizeof(T);
+    MemoryEventPair me = memoryManager().alloc(size, false);
+    cudaStream_t stream        = getActiveStream();
+    if (me.e) me.e.enqueueWait(stream);
+    return uptr<T>(static_cast<T *>(me.ptr), memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
-    return memoryManager().alloc(bytes, true);
+    MemoryEventPair me = memoryManager().alloc(bytes, true);
+    cudaStream_t stream        = getActiveStream();
+    if (me.e) me.e.enqueueWait(stream);
+    return me.ptr;
 }
+
 template<typename T>
 void memFree(T *ptr) {
-    memoryManager().unlock((void *)ptr, false);
+    Event e = make_event(getActiveStream());
+    memoryManager().unlock((void *)ptr, move(e), false);
 }
 
-void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
+void memFreeUser(void *ptr) {
+    Event e = make_event(getActiveStream());
+    memoryManager().unlock((void *)ptr, move(e), true);
+}
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
@@ -91,12 +100,17 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    return (T *)pinnedMemoryManager().alloc(elements * sizeof(T), false);
+    MemoryEventPair me =
+        pinnedMemoryManager().alloc(elements * sizeof(T), false);
+    cudaStream_t stream = getActiveStream();
+    if (me.e) me.e.enqueueWait(stream);
+    return (T *)me.ptr;
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    return pinnedMemoryManager().unlock((void *)ptr, false);
+    Event e = make_event(getActiveStream());
+    return pinnedMemoryManager().unlock((void *)ptr, move(e), false);
 }
 
 bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f1e680b903..ec7a5f7664 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -96,6 +96,8 @@ target_sources(afopencl
     err_opencl.hpp
     errorcodes.cpp
     errorcodes.hpp
+    Event.hpp
+    Event.cpp
     exampleFunction.cpp
     exampleFunction.hpp
     fast.cpp
diff --git a/src/backend/opencl/Event.cpp b/src/backend/opencl/Event.cpp
new file mode 100644
index 0000000000..fa7257e28f
--- /dev/null
+++ b/src/backend/opencl/Event.cpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Event.hpp>
+
+namespace opencl {
+/// \brief Creates a new event and marks it in the queue
+Event make_event(cl::CommandQueue &queue) {
+    Event e;
+    if (e.create() == CL_SUCCESS) { e.mark(queue()); }
+    return e;
+}
+}  // namespace opencl
diff --git a/src/backend/opencl/Event.hpp b/src/backend/opencl/Event.hpp
new file mode 100644
index 0000000000..bc3b4cf7ff
--- /dev/null
+++ b/src/backend/opencl/Event.hpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <common/EventBase.hpp>
+#include <platform.hpp>
+
+namespace opencl {
+class OpenCLEventPolicy {
+   public:
+    using EventType = cl_event;
+    using QueueType = cl_command_queue;
+    using ErrorType = cl_int;
+
+    static cl_int createEvent(cl_event *e) noexcept {
+        // Events are created when you mark them
+        return CL_SUCCESS;
+    }
+
+    static cl_int markEvent(cl_event *e, cl_command_queue stream) noexcept {
+        return clEnqueueMarkerWithWaitList(stream, 0, nullptr, e);
+    }
+
+    static cl_int waitForEvent(cl_event *e, cl_command_queue stream) noexcept {
+        return clEnqueueMarkerWithWaitList(stream, 1, e, nullptr);
+    }
+
+    static cl_int syncForEvent(cl_event *e) noexcept {
+        return clWaitForEvents(1, e);
+    }
+
+    static cl_int destroyEvent(cl_event *e) noexcept {
+        return clReleaseEvent(*e);
+    }
+};
+
+using Event = common::EventBase<OpenCLEventPolicy>;
+
+/// \brief Creates a new event and marks it in the queue
+Event make_event(cl::CommandQueue &queue);
+
+}  // namespace opencl
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 4accb8fb16..e8a5f01b8c 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -28,8 +28,10 @@ template class common::MemoryManager<opencl::MemoryManagerPinned>;
 #endif
 
 using common::bytesToString;
+using common::MemoryEventPair;
 
 using std::function;
+using std::move;
 using std::unique_ptr;
 
 namespace opencl {
@@ -52,28 +54,38 @@ void printMemInfo(const char *msg, const int device) {
 template<typename T>
 unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
     const size_t &elements) {
-    cl::Buffer *ptr = static_cast<cl::Buffer *>(
-        memoryManager().alloc(elements * sizeof(T), false));
+    MemoryEventPair me = memoryManager().alloc(elements * sizeof(T), false);
+    if (me.e) me.e.enqueueWait(getQueue()());
+    cl::Buffer *ptr = static_cast<cl::Buffer *>(me.ptr);
     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(ptr,
                                                                 bufferFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
-    return memoryManager().alloc(bytes, true);
+    MemoryEventPair me = memoryManager().alloc(bytes, true);
+    if (me.e) me.e.enqueueWait(getQueue()());
+    return me.ptr;
 }
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, false);
+    Event e = make_event(getQueue());
+    return memoryManager().unlock((void *)ptr, move(e), false);
 }
 
-void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
+void memFreeUser(void *ptr) {
+    Event e = make_event(getQueue());
+    memoryManager().unlock((void *)ptr, move(e), true);
+}
 
 cl::Buffer *bufferAlloc(const size_t &bytes) {
-    return (cl::Buffer *)memoryManager().alloc(bytes, false);
+    MemoryEventPair me = memoryManager().alloc(bytes, false);
+    me.e.enqueueWait(getQueue()());
+    return static_cast<cl::Buffer *>(me.ptr);
 }
 
 void bufferFree(cl::Buffer *buf) {
-    return memoryManager().unlock((void *)buf, false);
+    Event e = make_event(getQueue());
+    return memoryManager().unlock((void *)buf, move(e), false);
 }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
@@ -92,12 +104,16 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    return (T *)pinnedMemoryManager().alloc(elements * sizeof(T), false);
+    MemoryEventPair me =
+        pinnedMemoryManager().alloc(elements * sizeof(T), false);
+    me.e.enqueueWait(getQueue()());
+    return static_cast<T *>(me.ptr);
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    return pinnedMemoryManager().unlock((void *)ptr, false);
+    Event e = make_event(getQueue());
+    return pinnedMemoryManager().unlock((void *)ptr, move(e), false);
 }
 
 bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 4f71d5260c..bdef1369f1 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -69,7 +69,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         Buffer* ibuf         = indices.get();
         Buffer* vbuf         = values.get();
 
-        Event ev_in, ev_val, ev_ind;
+        cl::Event ev_in, ev_val, ev_ind;
 
         T* ptr     = static_cast<T*>(getQueue().enqueueMapBuffer(
             *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
@@ -84,7 +84,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
 
         // Create a linear index
         iota(begin(idx), end(idx), 0);
-        Event::waitForEvents({ev_in, ev_ind});
+        cl::Event::waitForEvents({ev_in, ev_ind});
 
         int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
         for (int i = 0; i < iter; i++) {

From e316d438fbc39b8cdeb213412e9ff10e5511fb43 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 5 Jun 2019 18:41:32 -0400
Subject: [PATCH 1683/2677] Use pointer to store default random engine

---
 src/api/c/random.cpp | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 71a026dfab..c288e92d0e 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -27,9 +27,7 @@ using namespace common;
 using af::dim4;
 
 Array<uint> emptyArray() {
-    static const Array<uint> EMPTY_ARRAY = createEmptyArray<uint>(af::dim4(0));
-
-    return EMPTY_ARRAY;
+    return createEmptyArray<uint>(af::dim4(0));
 }
 
 struct RandomEngine {
@@ -46,18 +44,15 @@ struct RandomEngine {
 
     RandomEngine(void)
         : type(AF_RANDOM_ENGINE_DEFAULT)
-        , seed(new uintl)
-        , counter(new uintl)
+        , seed(new uintl())
+        , counter(new uintl())
         , pos(emptyArray())
         , sh1(emptyArray())
         , sh2(emptyArray())
         , mask(0)
         , recursion_table(emptyArray())
         , temper_table(emptyArray())
-        , state(emptyArray()) {
-        *seed    = 0;
-        *counter = 0;
-    }
+        , state(emptyArray()) {}
 };
 
 af_random_engine getRandomEngineHandle(const RandomEngine engine) {
@@ -73,8 +68,9 @@ RandomEngine *getRandomEngine(const af_random_engine engineHandle) {
     return (RandomEngine *)engineHandle;
 }
 
+namespace {
 template<typename T>
-static inline af_array uniformDistribution_(const af::dim4 &dims,
+inline af_array uniformDistribution_(const af::dim4 &dims,
                                             RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(uniformDistribution<T>(dims, e->pos, e->sh1, e->sh2,
@@ -87,7 +83,7 @@ static inline af_array uniformDistribution_(const af::dim4 &dims,
 }
 
 template<typename T>
-static inline af_array normalDistribution_(const af::dim4 &dims,
+inline af_array normalDistribution_(const af::dim4 &dims,
                                            RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(normalDistribution<T>(dims, e->pos, e->sh1, e->sh2,
@@ -99,7 +95,7 @@ static inline af_array normalDistribution_(const af::dim4 &dims,
     }
 }
 
-static void validateRandomType(const af_random_engine_type type) {
+void validateRandomType(const af_random_engine_type type) {
     if ((type != AF_RANDOM_ENGINE_PHILOX_4X32_10) &&
         (type != AF_RANDOM_ENGINE_THREEFRY_2X32_16) &&
         (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) &&
@@ -110,13 +106,14 @@ static void validateRandomType(const af_random_engine_type type) {
         AF_ERROR("Invalid random type", AF_ERR_ARG);
     }
 }
+}
 
 af_err af_get_default_random_engine(af_random_engine *r) {
     try {
         AF_CHECK(af_init());
 
-        thread_local RandomEngine re;
-        *r = static_cast<af_random_engine>(&re);
+        thread_local RandomEngine *re = new RandomEngine;
+        *r = static_cast<af_random_engine>(re);
         return AF_SUCCESS;
     }
     CATCHALL;

From a9120f6cee86692101c0bd32e25720494769b3f1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 17 Jun 2019 20:31:25 -0400
Subject: [PATCH 1684/2677] Avoid deleting in the meanvar test's destructor on
 Windows

---
 test/meanvar.cpp | 80 +++++++++++++++++++++---------------------------
 1 file changed, 35 insertions(+), 45 deletions(-)

diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index ce5ab824bf..2f26c09d65 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -73,6 +73,10 @@ struct meanvar_test {
         std::copy(begin(mean), end(mean), back_inserter(mean_));
         std::copy(begin(variance), end(variance), back_inserter(variance_));
     }
+    meanvar_test()                        = default;
+    meanvar_test(meanvar_test<T> &&other) = default;
+    meanvar_test &operator=(meanvar_test<T> &&other) = default;
+    meanvar_test &operator=(meanvar_test<T> &other) = delete;
 
     meanvar_test(const meanvar_test<T> &other)
         : test_description_(other.test_description_)
@@ -87,16 +91,14 @@ struct meanvar_test {
     }
 
     ~meanvar_test() {
+#ifndef _WIN32
         af_release_array(in_);
         if (weights_) {
             af_release_array(weights_);
             weights_ = 0;
         }
+#endif
     }
-
-    meanvar_test()                        = default;
-    meanvar_test(meanvar_test<T> &&other) = default;
-    meanvar_test &operator=(meanvar_test<T> &&other) = default;
 };
 
 template<typename T>
@@ -105,7 +107,7 @@ af_dtype meanvar_test<T>::af_type = dtype_traits<T>::af_type;
 template<typename T>
 class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
    public:
-    void meanvar_test_function(meanvar_test<T> &test) {
+    void meanvar_test_function(const meanvar_test<T> &test) {
         af_array mean, var;
 
         // Cast to the expected type
@@ -192,10 +194,10 @@ meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
         outputs.push_back({249.50, 749.50, 1249.50, 1749.50});
         outputs.push_back(vector<double>(4, 20875));
     }
-    meanvar_test<T> out = meanvar_test<T>(
-        name, inputs[in_index],
-        (weight_index == -1) ? empty : inputs[weight_index], bias, dim,
-        move(outputs[mean_index]), move(outputs[var_index]));
+    meanvar_test<T> out(name, inputs[in_index],
+                        (weight_index == -1) ? empty : inputs[weight_index],
+                        bias, dim, move(outputs[mean_index]),
+                        move(outputs[var_index]));
 
     for (auto input : inputs) { af_release_array(input); }
     return out;
@@ -203,46 +205,34 @@ meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
 
 template<typename T>
 vector<meanvar_test<T> > small_test_values() {
+    // clang-format off
     return {
-        // |           Name |   in_index | weight_index |                  bias
-        // |  dim | mean_index | var_index |
-        meanvar_test_gen<T>("Sample1Ddim0", 0, -1, AF_VARIANCE_SAMPLE, 0, 0, 1,
-                            MEANVAR_SMALL),
-        meanvar_test_gen<T>("Sample1Ddim1", 1, -1, AF_VARIANCE_SAMPLE, 1, 0, 1,
-                            MEANVAR_SMALL),
-        meanvar_test_gen<T>("Sample2Ddim0", 2, -1, AF_VARIANCE_SAMPLE, 0, 3, 4,
-                            MEANVAR_SMALL),
-        meanvar_test_gen<T>("Sample2Ddim1", 2, -1, AF_VARIANCE_SAMPLE, 1, 6, 7,
-                            MEANVAR_SMALL),
-
-        meanvar_test_gen<T>("Population1Ddim0", 0, -1, AF_VARIANCE_POPULATION,
-                            0, 0, 2, MEANVAR_SMALL),
-        meanvar_test_gen<T>("Population1Ddim1", 1, -1, AF_VARIANCE_POPULATION,
-                            1, 0, 2, MEANVAR_SMALL),
-        meanvar_test_gen<T>("Population2Ddim0", 2, -1, AF_VARIANCE_POPULATION,
-                            0, 3, 5, MEANVAR_SMALL),
-        meanvar_test_gen<T>("Population2Ddim1", 2, -1, AF_VARIANCE_POPULATION,
-                            1, 6, 8, MEANVAR_SMALL)};
+        //                  |           Name |   in_index | weight_index |                  bias |  dim | mean_index | var_index |
+        meanvar_test_gen<T>(   "Sample1Ddim0",           0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_SMALL),
+        meanvar_test_gen<T>(   "Sample1Ddim1",           1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_SMALL),
+        meanvar_test_gen<T>(   "Sample2Ddim0",           2,            -1,     AF_VARIANCE_SAMPLE,     0,           3,          4, MEANVAR_SMALL),
+        meanvar_test_gen<T>(   "Sample2Ddim1",           2,            -1,     AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_SMALL),
+
+        meanvar_test_gen<T>("Population1Ddim0",          0,            -1, AF_VARIANCE_POPULATION,     0,           0,          2, MEANVAR_SMALL),
+        meanvar_test_gen<T>("Population1Ddim1",          1,            -1, AF_VARIANCE_POPULATION,     1,           0,          2, MEANVAR_SMALL),
+        meanvar_test_gen<T>("Population2Ddim0",          2,            -1, AF_VARIANCE_POPULATION,     0,           3,          5, MEANVAR_SMALL),
+        meanvar_test_gen<T>("Population2Ddim1",          2,            -1, AF_VARIANCE_POPULATION,     1,           6,          8, MEANVAR_SMALL)};
+    // clang-format on
 }
 
 template<typename T>
 vector<meanvar_test<T> > large_test_values() {
     return {
-        // |           Name |   in_index | weight_index |                  bias
-        // |  dim | mean_index | var_index |
-        meanvar_test_gen<T>("Sample1Ddim0", 0, -1, AF_VARIANCE_SAMPLE, 0, 0, 1,
-                            MEANVAR_LARGE),
-        meanvar_test_gen<T>("Sample1Ddim1", 1, -1, AF_VARIANCE_SAMPLE, 1, 0, 1,
-                            MEANVAR_LARGE),
-        meanvar_test_gen<T>("Sample1Ddim2", 2, -1, AF_VARIANCE_SAMPLE, 2, 0, 1,
-                            MEANVAR_LARGE),
-        meanvar_test_gen<T>("Sample2Ddim0", 3, -1, AF_VARIANCE_SAMPLE, 0, 2, 3,
-                            MEANVAR_LARGE),
+        // clang-format off
+        //               |           Name |     in_index | weight_index |                  bias |  dim | mean_index | var_index |
+        meanvar_test_gen<T>("Sample1Ddim0",             0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_LARGE),
+        meanvar_test_gen<T>("Sample1Ddim1",             1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_LARGE),
+        meanvar_test_gen<T>("Sample1Ddim2",             2,            -1,     AF_VARIANCE_SAMPLE,     2,           0,          1, MEANVAR_LARGE),
+        meanvar_test_gen<T>("Sample2Ddim0",             3,            -1,     AF_VARIANCE_SAMPLE,     0,           2,          3, MEANVAR_LARGE),
         // TODO(umar) Add additional large tests
-        // meanvar_test_gen<T>(    "Sample2Ddim1",           3,            -1,
-        // AF_VARIANCE_SAMPLE,     1,           2,          3, MEANVAR_LARGE),
-        // meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1,
-        // AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_LARGE),
+        // meanvar_test_gen<T>(    "Sample2Ddim1",           3,            -1, AF_VARIANCE_SAMPLE,     1,           2,          3, MEANVAR_LARGE),
+        // meanvar_test_gen<T>(    "Sample2Ddim1",           2,            -1, AF_VARIANCE_SAMPLE,     1,           6,          7, MEANVAR_LARGE),
+        // clang-format on
     };
 }
 
@@ -260,7 +250,7 @@ vector<meanvar_test<T> > large_test_values() {
         });                                                                   \
                                                                               \
     TEST_P(MeanVar##NAME, Testing) {                                          \
-        meanvar_test<TYPE> test = GetParam();                                 \
+        const meanvar_test<TYPE> &test = GetParam();                          \
         meanvar_test_function(test);                                          \
     }
 
@@ -281,12 +271,12 @@ MEANVAR_TEST(ComplexDouble, af::af_cdouble)
     using MeanVar##NAME = MeanVarTyped<TYPE>;                                 \
     INSTANTIATE_TEST_CASE_P(                                                  \
         Small, MeanVar##NAME, ::testing::ValuesIn(small_test_values<TYPE>()), \
-        [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) {   \
+        [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> &info) {  \
             return info.param.test_description_;                              \
         });                                                                   \
                                                                               \
     TEST_P(MeanVar##NAME, Testing) {                                          \
-        meanvar_test<TYPE> test = GetParam();                                 \
+        const meanvar_test<TYPE> &test = GetParam();                          \
         meanvar_test_function(test);                                          \
     }
 

From d1c3b6635767519c14242e40bebaa0564a6197a5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Jun 2019 21:31:13 -0400
Subject: [PATCH 1685/2677] Update the threads submodule

---
 src/backend/cpu/threads | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cpu/threads b/src/backend/cpu/threads
index bebd15282a..c483ad32b6 160000
--- a/src/backend/cpu/threads
+++ b/src/backend/cpu/threads
@@ -1 +1 @@
-Subproject commit bebd15282a5f4388689a7db3a1f2d95eff79b3e3
+Subproject commit c483ad32b68c0301d91ff5d2bfc88d02589e9a43

From 4159b1ab80592234a4003f9d88f92f809f9e65e0 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Wed, 27 Mar 2019 22:57:17 -0500
Subject: [PATCH 1686/2677] Add af_gemm

* Make af_gemm accept preallocated arrays
* Update the docs and add test for doc snippet
---
 include/af/blas.h                   |  70 ++++++
 src/api/c/blas.cpp                  | 140 ++++++++++--
 src/api/c/pinverse.cpp              |  10 +-
 src/api/c/transform_coordinates.cpp |   7 +-
 src/api/unified/blas.cpp            |   8 +
 src/backend/cpu/blas.cpp            | 191 +++++++++-------
 src/backend/cpu/blas.hpp            |   8 +-
 src/backend/cuda/blas.cpp           |  74 +++----
 src/backend/cuda/blas.hpp           |   5 +-
 src/backend/opencl/blas.cpp         |  50 ++---
 src/backend/opencl/blas.hpp         |   6 +-
 src/backend/opencl/cpu/cpu_blas.cpp |  93 ++++----
 src/backend/opencl/cpu/cpu_blas.hpp |   6 +-
 src/backend/opencl/solve.cpp        |   6 +-
 test/blas.cpp                       | 323 +++++++++++++++++++++++++++-
 test/testHelpers.hpp                |   9 +
 16 files changed, 780 insertions(+), 226 deletions(-)

diff --git a/include/af/blas.h b/include/af/blas.h
index a0ac2f81b3..495697c635 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -229,6 +229,76 @@ namespace af
 extern "C" {
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+        \brief BLAS general matrix multiply (GEMM) of two \ref af_array objects
+
+        \details
+        This provides a general interface to the BLAS level 3 general matrix
+        multiply (GEMM), which is generally defined as:
+
+        \f[
+        C = \alpha * opA(A)opB(B) + \beta * C
+        \f]
+
+        where \f$\alpha\f$ (\p alpha) and \f$\beta\f$ (\p beta) are both scalars;
+        \f$A\f$ and \f$B\f$ are the matrix multiply operands; and \f$opA\f$ and
+        \f$opB\f$ are noop (if \p AF_MAT_NONE) or transpose (if \p AF_MAT_TRANS)
+        operations on \f$A\f$ or \f$B\f$ before the actual GEMM operation. Batched
+        GEMM is supported if at least either \f$A\f$ or \f$B\f$ have more than
+        two dimensions (see \ref af::matmul for more details on broadcasting).
+        However, only one \p alpha and one \p beta can be used for all of the
+        batched matrix operands.
+
+        The \ref af_array that \p out points to can be used both as an input and
+        output. An allocation will be performed if you pass a null \ref af_array
+        handle (i.e. `af_array c = 0;`). If a valid \ref af_array is passed as
+        \f$C\f$, the operation will be performed on that \ref af_array itself. The C
+        \ref af_array must be the correct type and shape; otherwise, an error will
+        be thrown.
+
+        \note Passing an af_array that has not been initialized to the C array
+        is will cause undefined behavior.
+
+        This example demonstrates the usage of the af_gemm function on two
+        matrices. The \f$C\f$ \ref af_array handle is initialized to zero here,
+        so \ref af_gemm will perform an allocation.
+
+        \snippet test/blas.cpp ex_af_gemm_alloc
+
+        The following example shows how you can write to a previously allocated \ref
+        af_array using the \ref af_gemm call. Here we are going to use the \ref
+        af_array s from the previous example and index into the first slice. Only
+        the first slice of the original \f$C\f$ af_array will be modified by this
+        operation.
+
+        \snippet test/blas.cpp ex_af_gemm_overwrite
+
+        \param[in,out] C     Pointer to the output \ref af_array
+
+        \param[in]     opA   Operation to perform on A before the multiplication
+
+        \param[in]     opB   Operation to perform on B before the multiplication
+
+        \param[in]     alpha The alpha value; must be the same type as \p lhs
+                            and \p rhs
+
+        \param[in]     A     Left-hand side operand
+
+        \param[in]     B     Right-hand side operand
+
+        \param[in]     beta  The beta value; must be the same type as \p lhs
+                            and \p rhs
+
+        \return AF_SUCCESS if the operation is successful.
+
+        \ingroup blas_func_matmul
+    */
+    AFAPI af_err af_gemm(af_array *C, const af_mat_prop opA, const af_mat_prop opB,
+                         const void *alpha, const af_array A, const af_array B,
+                         const void *beta);
+#endif
+
     /**
         \brief Matrix multiply of two \ref af_array
 
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 1bde6589c6..b7453178a8 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -17,7 +17,10 @@
 #include <sparse_handle.hpp>
 #include <af/array.h>
 #include <af/blas.h>
+#include <af/data.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
+#include <type_util.hpp>
 
 template<typename T>
 static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
@@ -27,10 +30,14 @@ static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
 }
 
 template<typename T>
-static inline af_array matmul(const af_array lhs, const af_array rhs,
-                              af_mat_prop optLhs, af_mat_prop optRhs) {
-    return getHandle(
-        detail::matmul<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
+static inline void gemm(af_array *out, af_mat_prop optLhs, af_mat_prop optRhs,
+                        const T* alpha,
+                        const af_array lhs, const af_array rhs,
+                        const T* betas) {
+    detail::gemm<T>(getArray<T>(*out), optLhs, optRhs,
+                    alpha,
+                    getArray<T>(lhs), getArray<T>(rhs),
+                    betas);
 }
 
 template<typename T>
@@ -105,16 +112,15 @@ af_err af_sparse_matmul(af_array *out, const af_array lhs, const af_array rhs,
     return AF_SUCCESS;
 }
 
-af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
-                 const af_mat_prop optLhs, const af_mat_prop optRhs) {
-    using namespace detail;
+af_err af_gemm(af_array *out,
+               const af_mat_prop optLhs, const af_mat_prop optRhs,
+               const void* alpha, const af_array lhs, const af_array rhs,
+               const void* beta) {
+    using namespace detail; // needed for cfloat and cdouble
 
     try {
-        const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
-        const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
-
-        if (lhsInfo.isSparse())
-            return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
+        const ArrayInfo &lhsInfo    = getInfo(lhs, false, true);
+        const ArrayInfo &rhsInfo    = getInfo(rhs, true, true);
 
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
@@ -131,11 +137,11 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        dim4 lDims = lhsInfo.dims();
-        dim4 rDims = rhsInfo.dims();
+        af::dim4 lDims = lhsInfo.dims();
+        af::dim4 rDims = rhsInfo.dims();
 
         if (lDims.ndims() > 2 && rDims.ndims() > 2) {
-            DIM_ASSERT(1, lDims.ndims() == rDims.ndims());
+            DIM_ASSERT(3, lDims.ndims() == rDims.ndims());
             if (lDims[2] != rDims[2] && lDims[2] != 1 && rDims[2] != 1) {
                 AF_ERROR("Batch size mismatch along dimension 2", AF_ERR_BATCH);
             }
@@ -145,26 +151,116 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
         }
 
         TYPE_ASSERT(lhs_type == rhs_type);
-        af_array output = 0;
 
         int aColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
         int bRowDim = (optRhs == AF_MAT_NONE) ? 0 : 1;
 
         DIM_ASSERT(1, lhsInfo.dims()[aColDim] == rhsInfo.dims()[bRowDim]);
 
+        // Assume that *out is either initialized to null or an actual af_array
+        // Otherwise, this function has undefined behavior
+        af_array output = 0;
+        if (*out) {
+            output = *out;
+        }
+        else {
+            const int aRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+            const int bColDim = (optRhs == AF_MAT_NONE) ? 1 : 0;
+            const int M = lDims[aRowDim];
+            const int N = rDims[bColDim];
+            const dim_t d2 = std::max(lDims[2], rDims[2]);
+            const dim_t d3 = std::max(lDims[3], rDims[3]);
+            const af::dim4 oDims = af::dim4(M, N, d2, d3);
+            AF_CHECK(af_create_handle(&output, lhsInfo.ndims(),
+                                      oDims.get(), lhs_type));
+        }
+
         switch (lhs_type) {
-            case f32: output = matmul<float>(lhs, rhs, optLhs, optRhs); break;
-            case c32: output = matmul<cfloat>(lhs, rhs, optLhs, optRhs); break;
-            case f64: output = matmul<double>(lhs, rhs, optLhs, optRhs); break;
-            case c64: output = matmul<cdouble>(lhs, rhs, optLhs, optRhs); break;
-            default: TYPE_ERROR(1, lhs_type);
+            case f32: gemm<float>  (&output, optLhs, optRhs,
+                                    static_cast<const float*  >(alpha), lhs, rhs,
+                                    static_cast<const float*  >(beta)); break;
+            case c32: gemm<cfloat> (&output, optLhs, optRhs,
+                                    static_cast<const cfloat* >(alpha), lhs, rhs,
+                                    static_cast<const cfloat* >(beta)); break;
+            case f64: gemm<double> (&output, optLhs, optRhs,
+                                    static_cast<const double* >(alpha), lhs, rhs,
+                                    static_cast<const double* >(beta)); break;
+            case c64: gemm<cdouble>(&output, optLhs, optRhs,
+                                    static_cast<const cdouble*>(alpha), lhs, rhs,
+                                    static_cast<const cdouble*>(beta)); break;
+            default: TYPE_ERROR(3, lhs_type);
         }
+
         std::swap(*out, output);
     }
     CATCHALL
     return AF_SUCCESS;
 }
 
+af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
+                 const af_mat_prop optLhs, const af_mat_prop optRhs) {
+    using namespace detail; // needed for cfloat and cdouble
+
+    try {
+
+        const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
+        const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
+
+        if (lhsInfo.isSparse())
+            return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
+
+        const int aRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+        const int bColDim = (optRhs == AF_MAT_NONE) ? 1 : 0;
+
+        const af::dim4 lDims = lhsInfo.dims();
+        const af::dim4 rDims = rhsInfo.dims();
+        const int M = lDims[aRowDim];
+        const int N = rDims[bColDim];
+
+        const dim_t d2 = std::max(lDims[2], rDims[2]);
+        const dim_t d3 = std::max(lDims[3], rDims[3]);
+        const af::dim4 oDims = af::dim4(M, N, d2, d3);
+        const int num_batch = oDims[2] * oDims[3];
+
+        af_array gemm_out = 0;
+        AF_CHECK(af_create_handle(&gemm_out, oDims.ndims(), oDims.get(), lhsInfo.getType()));
+
+        af_dtype lhs_type = lhsInfo.getType();
+        switch (lhs_type) {
+            case f32: {
+                    float alpha = 1.f;
+                    float beta  = 0.f;
+                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
+                    break;
+            }
+            case c32: {
+                    cfloat alpha = {1.f, 0.f};
+                    cfloat beta  = {0.f, 0.f};
+
+                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
+                    break;
+            }
+            case f64: {
+                    double alpha = 1.0;
+                    double beta  = 0.0;
+                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
+                    break;
+            }
+            case c64: {
+                    cdouble alpha = {1.0, 0.0};
+                    cdouble beta  = {0.0, 0.0};
+                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
+                    break;
+            }
+            default: TYPE_ERROR(1, lhs_type);
+        }
+
+        std::swap(*out, gemm_out);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
               const af_mat_prop optLhs, const af_mat_prop optRhs) {
     using namespace detail;
@@ -205,7 +301,7 @@ af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
         }
         std::swap(*out, output);
     }
-    CATCHALL
+    CATCHALL;
     return AF_SUCCESS;
 }
 
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 86d5c677ad..418be4e6f5 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -129,8 +129,14 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol) {
                          0, uT.dims()[2] - 1, 0, uT.dims()[3] - 1);
     }
 
-    Array<T> out = matmul<T>(matmul<T>(v, sPinv, AF_MAT_NONE, AF_MAT_NONE), uT,
-                             AF_MAT_NONE, AF_MAT_NONE);
+    Array<T> vsPinv = createEmptyArray<T>(dim4(v.dims()[0], sPinv.dims()[1], P, Q));
+    Array<T> out    = createEmptyArray<T>(dim4(vsPinv.dims()[0], uT.dims()[1], P, Q));
+
+    T alpha = scalar<T>(1.0);
+    T beta = scalar<T>(0.0);
+
+    gemm<T>(vsPinv, AF_MAT_NONE, AF_MAT_NONE, &alpha, v, sPinv, &beta);
+    gemm<T>(out, AF_MAT_NONE, AF_MAT_NONE, &alpha, vsPinv, uT, &beta);
 
     return out;
 }
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index 8ef7ded16d..f1666b5b4e 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -25,7 +25,12 @@ using namespace detail;
 template<typename T>
 Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs,
                          std::vector<af_seq> idx) {
-    return matmul(lhs, createSubArray(rhs, idx), AF_MAT_NONE, AF_MAT_NONE);
+    Array<T> rhs_sub = createSubArray(rhs, idx);
+    Array<T> out     = createEmptyArray<T>(dim4(lhs.dims()[0], rhs_sub.dims()[1], lhs.dims()[2], lhs.dims()[3]));
+    T alpha = scalar<T>(1.0);
+    T beta = scalar<T>(0.0);
+    gemm(out, AF_MAT_NONE, AF_MAT_NONE, &alpha, lhs, rhs_sub, &beta);
+    return out;
 }
 
 template<typename T>
diff --git a/src/api/unified/blas.cpp b/src/api/unified/blas.cpp
index 4c8aa61e3f..a4f1f5788a 100644
--- a/src/api/unified/blas.cpp
+++ b/src/api/unified/blas.cpp
@@ -10,6 +10,14 @@
 #include <af/blas.h>
 #include "symbol_manager.hpp"
 
+AFAPI af_err af_gemm(af_array *out,
+                     const af_mat_prop optLhs, const af_mat_prop optRhs,
+                     const void* alpha, const af_array lhs, const af_array rhs,
+                     const void* beta) {
+    CHECK_ARRAYS(*out, lhs, rhs);
+    return CALL(out, optLhs, optRhs, alpha, lhs, rhs, beta);
+}
+
 af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                  const af_mat_prop optLhs, const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index af7e93c14c..8829499b9d 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -112,38 +112,68 @@ using cptr_type =
 template<typename T>
 using ptr_type = typename conditional<is_complex<T>::value,
                                       typename blas_base<T>::type *, T *>::type;
-template<typename T>
-using scale_type =
-    typename conditional<is_complex<T>::value,
-                         const typename blas_base<T>::type *, const T>::type;
 
-template<typename T>
-using batch_scale_type =
-    typename conditional<is_complex<T>::value,
-                         const typename blas_base<T>::type *, const T *>::type;
+template<typename T, bool batched = false>
+struct scale_type {
+    const T val;
+    scale_type(const T* val_ptr)
+        : val(*val_ptr){}
+    using api_type = const typename conditional<is_complex<T>::value,
+                                                const typename blas_base<T>::type *,
+                                                const typename conditional<batched,  const T*, const T>::type>::type;
+
+    api_type getScale() const;
+};
+
+template<typename T, bool batched>
+typename scale_type<T, batched>::api_type scale_type<T, batched>::getScale() const {
+    return val;
+}
+
+#define INSTANTIATE_BATCHED(TYPE)                                                    \
+template<>                                                                           \
+typename scale_type<TYPE, true>::api_type scale_type<TYPE, true>::getScale() const { \
+    return &val;                                                                     \
+}
+
+INSTANTIATE_BATCHED(float);
+INSTANTIATE_BATCHED(double);
+#undef INSTANTIATE_BATCHED
+
+#define INSTANTIATE_COMPLEX(TYPE, BATCHED)                                        \
+template<>                                                                        \
+scale_type<TYPE, BATCHED>::api_type scale_type<TYPE, BATCHED>::getScale() const { \
+    return reinterpret_cast<const blas_base<TYPE>::type * const>(&val);           \
+}
+
+INSTANTIATE_COMPLEX(cfloat, true);
+INSTANTIATE_COMPLEX(cfloat, false);
+INSTANTIATE_COMPLEX(cdouble, true);
+INSTANTIATE_COMPLEX(cdouble, false);
+#undef INSTANTIATE_COMPLEX
 
 template<typename T>
 using gemm_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
                                const CBLAS_TRANSPOSE, const blasint,
-                               const blasint, const blasint, scale_type<T>,
+                               const blasint, const blasint, typename scale_type<T>::api_type,
                                cptr_type<T>, const blasint, cptr_type<T>,
-                               const blasint, scale_type<T>, ptr_type<T>,
+                               const blasint, typename scale_type<T>::api_type, ptr_type<T>,
                                const blasint);
 
 template<typename T>
 using gemv_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
-                               const blasint, const blasint, scale_type<T>,
+                               const blasint, const blasint, typename scale_type<T>::api_type,
                                cptr_type<T>, const blasint, cptr_type<T>,
-                               const blasint, scale_type<T>, ptr_type<T>,
+                               const blasint, typename scale_type<T>::api_type, ptr_type<T>,
                                const blasint);
 
 #ifdef USE_MKL
 template<typename T>
 using gemm_batch_func_def = void (*)(
     const CBLAS_LAYOUT, const CBLAS_TRANSPOSE *, const CBLAS_TRANSPOSE *,
-    const MKL_INT *, const MKL_INT *, const MKL_INT *, batch_scale_type<T>,
+    const MKL_INT *, const MKL_INT *, const MKL_INT *, typename scale_type<T, true>::api_type,
     cptr_type<T> *, const MKL_INT *, cptr_type<T> *, const MKL_INT *,
-    batch_scale_type<T>, ptr_type<T> *, const MKL_INT *, const MKL_INT,
+    typename scale_type<T, true>::api_type, ptr_type<T> *, const MKL_INT *, const MKL_INT,
     const MKL_INT *);
 #endif
 
@@ -177,18 +207,6 @@ BLAS_FUNC(gemm_batch, cfloat, c)
 BLAS_FUNC(gemm_batch, cdouble, z)
 #endif
 
-template<typename T, int value>
-typename enable_if<is_floating_point<T>::value, scale_type<T>>::type
-getScale() {
-    return T(value);
-}
-
-template<typename T, int value>
-typename enable_if<is_complex<T>::value, scale_type<T>>::type getScale() {
-    static T val(value);
-    return (const typename blas_base<T>::type *)&val;
-}
-
 CBLAS_TRANSPOSE
 toCblasTranspose(af_mat_prop opt) {
     CBLAS_TRANSPOSE out = CblasNoTrans;
@@ -202,52 +220,54 @@ toCblasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs) {
-    CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
-    CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
-
-    int aRowDim = (lOpts == CblasNoTrans) ? 0 : 1;
-    int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
-    int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
-
-    auto lDims = lhs.dims();
-    auto rDims = rhs.dims();
-    int M      = lDims[aRowDim];
-    int N      = rDims[bColDim];
-    int K      = lDims[aColDim];
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta) {
+    const CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
+    const CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
+
+    const int aRowDim = (lOpts == CblasNoTrans) ? 0 : 1;
+    const int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
+    const int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
+
+    const dim4 lDims = lhs.dims();
+    const dim4 rDims = rhs.dims();
+    const int M      = lDims[aRowDim];
+    const int N      = rDims[bColDim];
+    const int K      = lDims[aColDim];
+    const dim4 oDims = out.dims();
 
     using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
-    dim_t d2 = std::max(lDims[2], rDims[2]);
-    dim_t d3 = std::max(lDims[3], rDims[3]);
-    const dim4 oDims(M, N, d2, d3);
-    Array<T> out = createEmptyArray<T>(oDims);
+    auto alpha_ = scale_type<T, false>(alpha);
+    auto beta_ = scale_type<T, false>(beta);
+    auto alpha_batched = scale_type<T, true>(alpha);
+    auto beta_batched = scale_type<T, true>(beta);
 
     auto func = [=](Param<T> output, CParam<T> left, CParam<T> right) {
-        auto alpha = getScale<T, 1>();
-        auto beta  = getScale<T, 0>();
-
         dim4 lStrides = left.strides();
         dim4 rStrides = right.strides();
         dim4 oStrides = output.strides();
 
-        if (oDims.ndims() <= 2) {
-            if (rDims[bColDim] == 1) {
+        if (output.dims().ndims() <= 2) {
+            if (right.dims()[bColDim] == 1) {
                 dim_t incr =
                     (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
+                gemv_func<T>()(CblasColMajor, lOpts,
+                               lDims[0], lDims[1], alpha_.getScale(),
                                reinterpret_cast<CBT*>(left.get()), lStrides[1],
-                               reinterpret_cast<CBT*>(right.get()), incr, beta,
-                               reinterpret_cast<BT*>(output.get()), 1);
+                               reinterpret_cast<CBT*>(right.get()), incr,
+                               beta_.getScale(),
+                               reinterpret_cast<BT*>(output.get()), oStrides[0]);
             } else {
-                gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
-                               reinterpret_cast<CBT*>(left.get()), lStrides[1],
-                               reinterpret_cast<CBT*>(right.get()),
-                               rStrides[1], beta,
-                               reinterpret_cast<BT*>(output.get()),
-                               output.dims(0));
+                gemm_func<T>()(CblasColMajor, lOpts, rOpts,
+                               M, N, K, alpha_.getScale(),
+                               reinterpret_cast<CBT *>(left.get()), lStrides[1],
+                               reinterpret_cast<CBT *>(right.get()), rStrides[1],
+                               beta_.getScale(),
+                               reinterpret_cast<BT *>(output.get()), oStrides[1]);
             }
         } else {
             int batchSize = oDims[2] * oDims[3];
@@ -283,29 +303,36 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
             const MKL_INT ldb = rStrides[1];
             const MKL_INT ldc = oStrides[1];
 
-            gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts, &M, &N, &K,
-                                 &alpha, lptrs.data(), &lda, rptrs.data(), &ldb,
-                                 &beta, optrs.data(), &ldc, 1, &batchSize);
+            gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts,
+                                 &M, &N, &K,
+                                 alpha_batched.getScale(),
+                                 lptrs.data(), &lda, rptrs.data(), &ldb,
+                                 beta_batched.getScale(),
+                                 optrs.data(), &ldc, 1, &batchSize);
 #else
             for (int n = 0; n < batchSize; n++) {
                 if (rDims[bColDim] == 1) {
                     dim_t incr =
                         (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                    gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1],
-                                   alpha, lptrs[n], lStrides[1], rptrs[n], incr,
-                                   beta, optrs[n], 1);
+                    gemv_func<T>()(CblasColMajor, lOpts,
+                                   lDims[0], lDims[1],
+                                   alpha_.getScale(),
+                                   lptrs[n], lStrides[1], rptrs[n], incr,
+                                   beta_.getScale(),
+                                   optrs[n], oStrides[0]);
                 } else {
-                    gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha,
+                    gemm_func<T>()(CblasColMajor, lOpts, rOpts,
+                                   M, N, K,
+                                   alpha_.getScale(),
                                    lptrs[n], lStrides[1], rptrs[n], rStrides[1],
-                                   beta, optrs[n], output.dims(0));
+                                   beta_.getScale(),
+                                   optrs[n], oStrides[1]);
                 }
             }
 #endif
         }
     };
     getQueue().enqueue(func, out, lhs, rhs);
-
-    return out;
 }
 
 template<typename T>
@@ -331,24 +358,26 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
 #undef BT
 #undef REINTEPRET_CAST
 
-#define INSTANTIATE_BLAS(TYPE)                                \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
-                                      const Array<TYPE> &rhs, \
-                                      af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_GEMM(TYPE)                                          \
+    template void gemm<TYPE>(Array<TYPE> &out,                          \
+                             af_mat_prop optLhs, af_mat_prop optRhs,    \
+                             const TYPE *alphas, const Array<TYPE> &lhs,\
+                             const Array<TYPE> &rhs,                    \
+                             const TYPE *beta)
 
-INSTANTIATE_BLAS(float)
-INSTANTIATE_BLAS(cfloat)
-INSTANTIATE_BLAS(double)
-INSTANTIATE_BLAS(cdouble)
+INSTANTIATE_GEMM(float);
+INSTANTIATE_GEMM(cfloat);
+INSTANTIATE_GEMM(double);
+INSTANTIATE_GEMM(cdouble);
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
                                    const Array<TYPE> &rhs, af_mat_prop optLhs, \
-                                   af_mat_prop optRhs);
+                                   af_mat_prop optRhs)
 
-INSTANTIATE_DOT(float)
-INSTANTIATE_DOT(double)
-INSTANTIATE_DOT(cfloat)
-INSTANTIATE_DOT(cdouble)
+INSTANTIATE_DOT(float);
+INSTANTIATE_DOT(double);
+INSTANTIATE_DOT(cfloat);
+INSTANTIATE_DOT(cdouble);
 
 }  // namespace cpu
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 5a85e3cbc2..f39cb64f59 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -8,14 +8,16 @@
  ********************************************************/
 
 #include <Array.hpp>
-
 #include <af/defines.h>
 
 namespace cpu {
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta);
+
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs);
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index adf1c1bc2e..1934235c9e 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -154,28 +154,23 @@ using std::max;
 using std::vector;
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs) {
-    cublasOperation_t lOpts = toCblasTranspose(optLhs);
-    cublasOperation_t rOpts = toCblasTranspose(optRhs);
-
-    int aRowDim = (lOpts == CUBLAS_OP_N) ? 0 : 1;
-    int aColDim = (lOpts == CUBLAS_OP_N) ? 1 : 0;
-    int bColDim = (rOpts == CUBLAS_OP_N) ? 1 : 0;
-
-    dim4 lDims = lhs.dims();
-    dim4 rDims = rhs.dims();
-    int M      = lDims[aRowDim];
-    int N      = rDims[bColDim];
-    int K      = lDims[aColDim];
-
-    dim_t d2     = std::max(lDims[2], rDims[2]);
-    dim_t d3     = std::max(lDims[3], rDims[3]);
-    dim4 oDims   = dim4(M, N, d2, d3);
-    Array<T> out = createEmptyArray<T>(oDims);
-
-    T alpha = scalar<T>(1);
-    T beta  = scalar<T>(0);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta) {
+    const cublasOperation_t lOpts = toCblasTranspose(optLhs);
+    const cublasOperation_t rOpts = toCblasTranspose(optRhs);
+
+    const int aRowDim = (lOpts == CUBLAS_OP_N) ? 0 : 1;
+    const int aColDim = (lOpts == CUBLAS_OP_N) ? 1 : 0;
+    const int bColDim = (rOpts == CUBLAS_OP_N) ? 1 : 0;
+
+    const dim4 lDims = lhs.dims();
+    const dim4 rDims = rhs.dims();
+    const int M      = lDims[aRowDim];
+    const int N      = rDims[bColDim];
+    const int K      = lDims[aColDim];
+    const dim4 oDims = out.dims();
 
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
@@ -184,18 +179,18 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     if (oDims.ndims() <= 2) {
         if (rDims[bColDim] == 1) {
             dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-            N          = lDims[aColDim];
             CUBLAS_CHECK(gemv_func<T>()(blasHandle(), lOpts, lDims[0], lDims[1],
-                                        &alpha, lhs.get(), lStrides[1],
-                                        rhs.get(), incr, &beta, out.get(), 1));
+                                        alpha, lhs.get(), lStrides[1],
+                                        rhs.get(), incr, beta, out.get(), 1));
         } else {
             CUBLAS_CHECK(gemm_func<T>()(blasHandle(), lOpts, rOpts, M, N, K,
-                                        &alpha, lhs.get(), lStrides[1],
-                                        rhs.get(), rStrides[1], &beta,
-                                        out.get(), oDims[0]));
+                                        alpha, lhs.get(), lStrides[1],
+                                        rhs.get(), rStrides[1], beta,
+                                        out.get(), oStrides[1]));
         }
     } else {
         int batchSize = oDims[2] * oDims[3];
+
         std::vector<const T *> lptrs(batchSize);
         std::vector<const T *> rptrs(batchSize);
         std::vector<T *> optrs(batchSize);
@@ -239,12 +234,10 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
         CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         CUBLAS_CHECK(gemmBatched_func<T>()(
-            blasHandle(), lOpts, rOpts, M, N, K, &alpha,
+            blasHandle(), lOpts, rOpts, M, N, K, alpha,
             (const T **)d_lptrs.get(), lStrides[1], (const T **)d_rptrs.get(),
-            rStrides[1], &beta, (T **)d_optrs.get(), oStrides[1], batchSize));
+            rStrides[1], beta, (T **)d_optrs.get(), oStrides[1], batchSize));
     }
-
-    return out;
 }
 
 template<typename T>
@@ -278,15 +271,16 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans, bool is_upper,
         lhs.get(), lStrides[1], rhs.get(), rStrides[1]));
 }
 
-#define INSTANTIATE_BLAS(TYPE)                                \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
-                                      const Array<TYPE> &rhs, \
-                                      af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_GEMM(TYPE)                                                         \
+    template void gemm<TYPE>(Array<TYPE> &out, af_mat_prop optLhs, af_mat_prop optRhs, \
+                             const TYPE *alpha,                       \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs,           \
+                             const TYPE *beta);
 
-INSTANTIATE_BLAS(float)
-INSTANTIATE_BLAS(cfloat)
-INSTANTIATE_BLAS(double)
-INSTANTIATE_BLAS(cdouble)
+INSTANTIATE_GEMM(float)
+INSTANTIATE_GEMM(cfloat)
+INSTANTIATE_GEMM(double)
+INSTANTIATE_GEMM(cdouble)
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index c7199e257f..7325688116 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -12,8 +12,9 @@
 namespace cuda {
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta);
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 436cbb95ef..e65ff1c58f 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <complex>
+#include <vector>
 
 #include <Array.hpp>
 #include <arith.hpp>
@@ -44,12 +45,15 @@ toBlasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs) {
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta) {
 #if defined(WITH_LINEAR_ALGEBRA)
     if (OpenCLCPUOffload(
             false)) {  // Do not force offload gemm on OSX Intel devices
-        return cpu::matmul(lhs, rhs, optLhs, optRhs);
+        cpu::gemm(out, optLhs, optRhs, alpha,
+                  lhs, rhs, beta);
     }
 #endif
     const auto lOpts = toBlasTranspose(optLhs);
@@ -64,14 +68,7 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     const int M      = lDims[aRowDim];
     const int N      = rDims[bColDim];
     const int K      = lDims[aColDim];
-
-    dim_t d2     = std::max(lDims[2], rDims[2]);
-    dim_t d3     = std::max(lDims[3], rDims[3]);
-    dim4 oDims   = af::dim4(M, N, d2, d3);
-    Array<T> out = createEmptyArray<T>(oDims);
-
-    const auto alpha = scalar<T>(1);
-    const auto beta  = scalar<T>(0);
+    const dim4 oDims = out.dims();
 
     const dim4 lStrides = lhs.strides();
     const dim4 rStrides = rhs.strides();
@@ -101,22 +98,20 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
         if (rDims[bColDim] == 1) {
             dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
             gpu_blas_gemv_func<T> gemv;
-            OPENCL_BLAS_CHECK(gemv(lOpts, lDims[0], lDims[1], alpha,
+            OPENCL_BLAS_CHECK(gemv(lOpts, lDims[0], lDims[1], *alpha,
                                    (*lhs.get())(), lOffset, lStrides[1],
-                                   (*rhs.get())(), rOffset, incr, beta,
-                                   (*out.get())(), oOffset, 1, 1, &getQueue()(),
+                                   (*rhs.get())(), rOffset, incr, *beta,
+                                   (*out.get())(), oOffset, oStrides[0], 1, &getQueue()(),
                                    0, nullptr, &event()));
         } else {
             gpu_blas_gemm_func<T> gemm;
-            OPENCL_BLAS_CHECK(gemm(lOpts, rOpts, M, N, K, alpha, (*lhs.get())(),
+            OPENCL_BLAS_CHECK(gemm(lOpts, rOpts, M, N, K, *alpha, (*lhs.get())(),
                                    lOffset, lStrides[1], (*rhs.get())(),
-                                   rOffset, rStrides[1], beta, (*out.get())(),
-                                   oOffset, out.dims()[0], 1, &getQueue()(), 0,
+                                   rOffset, rStrides[1], *beta, (*out.get())(),
+                                   oOffset, oStrides[1], 1, &getQueue()(), 0,
                                    nullptr, &event()));
         }
     }
-
-    return out;
 }
 
 template<typename T>
@@ -129,15 +124,16 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
-#define INSTANTIATE_BLAS(TYPE)                                \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
-                                      const Array<TYPE> &rhs, \
-                                      af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_GEMM(TYPE)                                                         \
+    template void gemm<TYPE>(Array<TYPE> &out, af_mat_prop optLhs, af_mat_prop optRhs, \
+                             const TYPE *alpha,                    \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs,           \
+                             const TYPE *beta);
 
-INSTANTIATE_BLAS(float)
-INSTANTIATE_BLAS(cfloat)
-INSTANTIATE_BLAS(double)
-INSTANTIATE_BLAS(cdouble)
+INSTANTIATE_GEMM(float)
+INSTANTIATE_GEMM(cfloat)
+INSTANTIATE_GEMM(double)
+INSTANTIATE_GEMM(cdouble)
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index c034607d29..39c07c2954 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -15,12 +15,14 @@
 // such as CLBlast or clBLAS.
 
 namespace opencl {
+
 void initBlas();
 void deInitBlas();
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta);
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 00a0477085..7739ba7502 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -91,6 +91,21 @@ using scale_type =
     typename conditional<is_complex<T>::value,
                          const typename blas_base<T>::type *, const T>::type;
 
+template<typename T>
+scale_type<T> getOneScalar(const T* const vals) {
+    return vals[0];
+}
+
+template<>
+scale_type<cfloat> getOneScalar(const cfloat* const vals) {
+    return reinterpret_cast<scale_type<cfloat>>(vals);
+}
+
+template<>
+scale_type<cdouble> getOneScalar(const cdouble* const vals) {
+    return reinterpret_cast<scale_type<cdouble>>(vals);
+}
+
 template<typename T>
 using gemm_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
                                const CBLAS_TRANSPOSE, const blasint,
@@ -153,36 +168,30 @@ toCblasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs) {
-    CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
-    CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
-
-    int aRowDim = (lOpts == CblasNoTrans) ? 0 : 1;
-    int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
-    int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
-
-    dim4 lDims = lhs.dims();
-    dim4 rDims = rhs.dims();
-    int M      = lDims[aRowDim];
-    int N      = rDims[bColDim];
-    int K      = lDims[aColDim];
-    dim_t d2   = std::max(lDims[2], rDims[2]);
-    dim_t d3   = std::max(lDims[3], rDims[3]);
-    dim4 oDims = af::dim4(M, N, d2, d3);
-
-    // FIXME: Leaks on errors.
-    Array<T> out = createValueArray<T>(oDims, scalar<T>(0));
-    auto alpha   = getScale<T, 1>();
-    auto beta    = getScale<T, 0>();
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta) {
+    using BT  = typename blas_base<T>::type;
+    using CBT = const typename blas_base<T>::type;
+
+    const CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
+    const CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
+
+    const int aRowDim = (lOpts == CblasNoTrans) ? 0 : 1;
+    const int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
+    const int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
+
+    const dim4 lDims = lhs.dims();
+    const dim4 rDims = rhs.dims();
+    const int M      = lDims[aRowDim];
+    const int N      = rDims[bColDim];
+    const int K      = lDims[aColDim];
+    const dim4 oDims = out.dims();
 
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
     dim4 oStrides = out.strides();
 
-    using BT  = typename blas_base<T>::type;
-    using CBT = const typename blas_base<T>::type;
-
     int batchSize = oDims[2] * oDims[3];
 
     bool is_l_d2_batched = (oDims[2] == lDims[2]);
@@ -210,28 +219,30 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
 
         if (rDims[bColDim] == 1) {
             dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
-            N          = lDims[aColDim];
-            gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1], alpha,
-                           lptr, lStrides[1], rptr, incr, beta, optr, 1);
+            gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1],
+                           getOneScalar<T>(alpha),
+                           lptr, lStrides[1], rptr, incr,
+                           getOneScalar<T>(beta), optr, 1);
         } else {
-            gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K, alpha, lptr,
-                           lStrides[1], rptr, rStrides[1], beta, optr,
-                           out.dims()[0]);
+            gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K,
+                           getOneScalar<T>(alpha), lptr,
+                           lStrides[1], rptr, rStrides[1],
+                           getOneScalar<T>(beta),
+                           optr, oStrides[1]);
         }
     }
-
-    return out;
 }
 
-#define INSTANTIATE_BLAS(TYPE)                                \
-    template Array<TYPE> matmul<TYPE>(const Array<TYPE> &lhs, \
-                                      const Array<TYPE> &rhs, \
-                                      af_mat_prop optLhs, af_mat_prop optRhs);
+#define INSTANTIATE_GEMM(TYPE)                                                         \
+    template void gemm<TYPE>(Array<TYPE> &out, af_mat_prop optLhs, af_mat_prop optRhs, \
+                             const TYPE *alpha,                       \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs,           \
+                             const TYPE *beta);
 
-INSTANTIATE_BLAS(float)
-INSTANTIATE_BLAS(cfloat)
-INSTANTIATE_BLAS(double)
-INSTANTIATE_BLAS(cdouble)
+INSTANTIATE_GEMM(float)
+INSTANTIATE_GEMM(cfloat)
+INSTANTIATE_GEMM(double)
+INSTANTIATE_GEMM(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
index 2aafe0dc90..179ee8d633 100644
--- a/src/backend/opencl/cpu/cpu_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -11,8 +11,10 @@
 
 namespace opencl {
 namespace cpu {
+
 template<typename T>
-Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
-                af_mat_prop optRhs);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
+          const T *beta);
 }
 }  // namespace opencl
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 1ba3ec56e8..ad04d2cc1c 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -160,7 +160,11 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
                            A.getOffset(), A.strides()[1], &h_tau[0], (*dT)(),
                            tmp.getOffset(), NB, queue, &info);
 
-        B = matmul(A, B, AF_MAT_NONE, AF_MAT_NONE);
+        Array<T> B_new = createEmptyArray<T>(dim4(A.dims()[0], B.dims()[1]));
+        T alpha = scalar<T>(1.0);
+        T beta = scalar<T>(0.0);
+        gemm<T>(B_new, AF_MAT_NONE, AF_MAT_NONE, &alpha, A, B, &beta);
+        B = B_new;
 #endif
     } else if (M > N) {
         // Least squres for this case is solved using the following
diff --git a/test/blas.cpp b/test/blas.cpp
index b05f1bf2e7..48c71c5c28 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
@@ -15,6 +16,7 @@
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
+#include <algorithm>
 
 using af::array;
 using af::cdouble;
@@ -33,12 +35,13 @@ using std::cout;
 using std::endl;
 using std::ostream_iterator;
 using std::string;
+using std::stringstream;
 using std::vector;
 
 template<typename T>
 class MatrixMultiply : public ::testing::Test {};
 
-typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
+typedef ::testing::Types<float, double, cdouble, cfloat> TestTypes;
 TYPED_TEST_CASE(MatrixMultiply, TestTypes);
 
 template<typename T, bool isBVector>
@@ -91,7 +94,7 @@ void MatMulCheck(string TestFile) {
         dim4 dd;
         dim_t* d = dd.get();
         af_get_dims(&d[0], &d[1], &d[2], &d[3], out[i]);
-        ASSERT_VEC_ARRAY_EQ(tests[i], dd, out[i]);
+        ASSERT_VEC_ARRAY_NEAR(tests[i], dd, out[i], 1e-3);
     }
 
     ASSERT_SUCCESS(af_release_array(a));
@@ -319,3 +322,319 @@ TEST(MatrixMultiply, RhsBroadcastBatched) {
         }
     }
 }
+
+float alpha = 1.f;
+float beta = 0.f;
+
+float h_lhs[9] =         {1.f, 4.f, 7.f,
+                          2.f, 5.f, 8.f,
+                          3.f, 6.f, 9.f};
+
+float h_lhs_tall[6] =    {1.f, 3.f, 5.f,
+                          2.f, 4.f, 6.f};
+
+float h_lhs_wide[6] =    {1.f, 4.f,
+                          2.f, 5.f,
+                          3.f, 6.f};
+
+float h_lhs_batch[18] =  {1.f, 4.f, 7.f,
+                          2.f, 5.f, 8.f,
+                          3.f, 6.f, 9.f,
+
+                          8.f, 2.f, 5.f,
+                          3.f, 4.f, 7.f,
+                          1.f, 0.f, 6.f};
+
+float h_rhs[9] =         {9.f, 6.f, 3.f,
+                          8.f, 5.f, 2.f,
+                          7.f, 4.f, 1.f};
+
+float h_rhs_tall[6] =    {9.f, 7.f, 5.f,
+                          8.f, 6.f, 4.f};
+
+float h_rhs_wide[6] =    {9.f, 6.f,
+                          8.f, 5.f,
+                          7.f, 4.f};
+
+float h_gold[9] =        {30.f, 84.f, 138.f,
+                          24.f, 69.f, 114.f,
+                          18.f, 54.f, 90.f};
+
+float h_gold_NN[9] =     {21.f, 51.f, 81.f,
+                          18.f, 44.f, 70.f,
+                          15.f, 37.f, 59.f};
+
+float h_gold_NT[9] =     {25.f, 59.f, 93.f,
+                          19.f, 45.f, 71.f,
+                          13.f, 31.f, 49.f};
+
+float h_gold_TN[4] =     {55.f, 76.f,
+                          46.f, 64.f};
+
+float h_gold_TT[4] =     {68.f, 92.f,
+                          41.f, 56.f};
+
+float h_gold_batch[18] = {30.f, 84.f, 138.f,
+                          24.f, 69.f, 114.f,
+                          18.f, 54.f, 90.f,
+
+                          93.f, 42.f, 105.f,
+                          81.f, 36.f, 87.f,
+                          69.f, 30.f, 69.f};
+
+struct test_params {
+    af_mat_prop opt_lhs;
+    af_mat_prop opt_rhs;
+    float *alpha;
+    float *h_lhs;
+    float *h_rhs;
+    float *h_gold;
+    dim4 lhs_dims;
+    dim4 rhs_dims;
+    dim4 out_dims;
+    float *beta;
+    TestOutputArrayType out_array_type;
+
+    test_params(af_mat_prop optl, af_mat_prop optr,
+                float *a,
+                float *l, float *r, float *g,
+                dim4 ldims, dim4 rdims, dim4 odims,
+                float *b,
+                TestOutputArrayType t)
+        :opt_lhs(optl), opt_rhs(optr),
+         alpha(a),
+         h_lhs(l), h_rhs(r), h_gold(g),
+         lhs_dims(ldims), rhs_dims(rdims), out_dims(odims),
+         beta(b),
+         out_array_type(t) {}
+};
+
+class Gemm : public ::testing::TestWithParam<test_params> {
+   protected:
+    af_array lhs;
+    af_array rhs;
+    af_array gold;
+    af_array out;
+    TestOutputArrayInfo metadata;
+
+    void SetUp() {
+        test_params params = GetParam();
+
+        lhs  = 0;
+        rhs = 0;
+        out = 0;
+        gold = 0;
+
+        ASSERT_SUCCESS(
+            af_create_array(&lhs, params.h_lhs, params.lhs_dims.ndims(), params.lhs_dims.get(), f32));
+        ASSERT_SUCCESS(
+            af_create_array(&rhs, params.h_rhs, params.rhs_dims.ndims(), params.rhs_dims.get(), f32));
+
+        dim_t gold_dim0 = params.opt_lhs == AF_MAT_TRANS ? params.lhs_dims[1] : params.lhs_dims[0];
+        dim_t gold_dim1 = params.opt_rhs == AF_MAT_TRANS ? params.rhs_dims[0] : params.rhs_dims[1];
+        dim_t gold_dim2 = std::max(params.lhs_dims[2], params.rhs_dims[2]);
+        dim_t gold_dim3 = std::max(params.lhs_dims[3], params.rhs_dims[3]);
+        dim4 gold_dims(gold_dim0, gold_dim1, gold_dim2, gold_dim3);
+
+        metadata = TestOutputArrayInfo(params.out_array_type);
+        genTestOutputArray(&out, params.out_dims.ndims(), params.out_dims.get(), f32,
+                           &metadata);
+
+        ASSERT_SUCCESS(af_create_array(&gold, params.h_gold, gold_dims.ndims(),
+                                       gold_dims.get(), f32));
+    }
+
+    void TearDown() {
+        if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
+        if (rhs != 0) { ASSERT_SUCCESS(af_release_array(rhs)); }
+        if (lhs != 0) { ASSERT_SUCCESS(af_release_array(lhs)); }
+    }
+};
+
+void replace_all(std::string& str, const std::string& oldStr,
+                 const std::string& newStr) {
+    std::string::size_type pos = 0u;
+    while ((pos = str.find(oldStr, pos)) != std::string::npos) {
+        str.replace(pos, oldStr.length(), newStr);
+        pos += newStr.length();
+    }
+}
+
+std::string concat_dim4(dim4 d) {
+    std::stringstream ss;
+    ss << d;
+    std::string s = ss.str();
+    replace_all(s, " ", "x");
+    return s;
+}
+
+string out_info(const ::testing::TestParamInfo<Gemm::ParamType> info) {
+    test_params params = info.param;
+
+    stringstream ss;
+    switch (params.out_array_type) {
+    case NULL_ARRAY:
+        ss << "NullOut";
+        break;
+    case FULL_ARRAY:
+        ss << "FullOut";
+        break;
+    case SUB_ARRAY:
+        ss << "SubarrayOut";
+        break;
+    case REORDERED_ARRAY:
+        ss << "ReorderedOut";
+        break;
+    default:
+        ss << "UnknownOutArrayType";
+        break;
+    }
+
+    ss << "_" << concat_dim4(params.lhs_dims) << "_" << concat_dim4(params.rhs_dims);
+
+    ss << "_";
+    ss << (params.opt_lhs == AF_MAT_TRANS ? "T" : "N");
+    ss << (params.opt_rhs == AF_MAT_TRANS ? "T" : "N");
+
+    if (params.lhs_dims[2] > 1 || params.rhs_dims[2] > 1) {
+        ss << "_Batched";
+    }
+
+    return ss.str();
+}
+
+// clang-format off
+INSTANTIATE_TEST_CASE_P(
+    Square, Gemm,
+    ::testing::Values(
+        //          lhs_opts     rhs_opts     alpha  lhs    rhs    gold    lhs_dims    rhs_dims    out_dims    beta  out_array_type
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs, h_rhs, h_gold, dim4(3, 3), dim4(3, 3), dim4(3, 3), &beta, NULL_ARRAY     ),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs, h_rhs, h_gold, dim4(3, 3), dim4(3, 3), dim4(3, 3), &beta, FULL_ARRAY     ),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs, h_rhs, h_gold, dim4(3, 3), dim4(3, 3), dim4(3, 3), &beta, SUB_ARRAY      ),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs, h_rhs, h_gold, dim4(3, 3), dim4(3, 3), dim4(3, 3), &beta, REORDERED_ARRAY)
+        ),
+    out_info
+    );
+// clang-format on
+
+// clang-format off
+INSTANTIATE_TEST_CASE_P(
+    Batched, Gemm,
+    ::testing::Values(
+        //          lhs_opts     rhs_opts     alpha  lhs          rhs    gold          lhs_dims       rhs_dims    out_dims       beta  out_array_type
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs_batch, h_rhs, h_gold_batch, dim4(3, 3, 2), dim4(3, 3), dim4(3, 3, 2), &beta, NULL_ARRAY     ),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs_batch, h_rhs, h_gold_batch, dim4(3, 3, 2), dim4(3, 3), dim4(3, 3, 2), &beta, FULL_ARRAY     ),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs_batch, h_rhs, h_gold_batch, dim4(3, 3, 2), dim4(3, 3), dim4(3, 3, 2), &beta, SUB_ARRAY      ),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_lhs_batch, h_rhs, h_gold_batch, dim4(3, 3, 2), dim4(3, 3), dim4(3, 3, 2), &beta, REORDERED_ARRAY)
+        ),
+    out_info
+    );
+// clang-format on
+
+// clang-format off
+INSTANTIATE_TEST_CASE_P(
+    NonSquare, Gemm,
+    ::testing::Values(
+        //          lhs_opts      rhs_opts      alpha  lhs         rhs         gold       lhs_dims    rhs_dims    out_dims    beta  out_array_type
+        test_params(AF_MAT_NONE,  AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_wide, h_gold_NN, dim4(3, 2), dim4(2, 3), dim4(3, 3), &beta, NULL_ARRAY),
+        test_params(AF_MAT_NONE,  AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_tall, h_gold_NT, dim4(3, 2), dim4(3, 2), dim4(3, 3), &beta, NULL_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_tall, h_gold_TN, dim4(3, 2), dim4(3, 2), dim4(2, 2), &beta, NULL_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_wide, h_gold_TT, dim4(3, 2), dim4(2, 3), dim4(2, 2), &beta, NULL_ARRAY),
+
+        test_params(AF_MAT_NONE,  AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_wide, h_gold_NN, dim4(3, 2), dim4(2, 3), dim4(3, 3), &beta, FULL_ARRAY),
+        test_params(AF_MAT_NONE,  AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_tall, h_gold_NT, dim4(3, 2), dim4(3, 2), dim4(3, 3), &beta, FULL_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_tall, h_gold_TN, dim4(3, 2), dim4(3, 2), dim4(2, 2), &beta, FULL_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_wide, h_gold_TT, dim4(3, 2), dim4(2, 3), dim4(2, 2), &beta, FULL_ARRAY),
+
+        test_params(AF_MAT_NONE,  AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_wide, h_gold_NN, dim4(3, 2), dim4(2, 3), dim4(3, 3), &beta, SUB_ARRAY),
+        test_params(AF_MAT_NONE,  AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_tall, h_gold_NT, dim4(3, 2), dim4(3, 2), dim4(3, 3), &beta, SUB_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_tall, h_gold_TN, dim4(3, 2), dim4(3, 2), dim4(2, 2), &beta, SUB_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_wide, h_gold_TT, dim4(3, 2), dim4(2, 3), dim4(2, 2), &beta, SUB_ARRAY),
+
+        test_params(AF_MAT_NONE,  AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_wide, h_gold_NN, dim4(3, 2), dim4(2, 3), dim4(3, 3), &beta, REORDERED_ARRAY),
+        test_params(AF_MAT_NONE,  AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_tall, h_gold_NT, dim4(3, 2), dim4(3, 2), dim4(3, 3), &beta, REORDERED_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_tall, h_gold_TN, dim4(3, 2), dim4(3, 2), dim4(2, 2), &beta, REORDERED_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_wide, h_gold_TT, dim4(3, 2), dim4(2, 3), dim4(2, 2), &beta, REORDERED_ARRAY)
+        ),
+    out_info
+    );
+// clang-format on
+
+TEST_P(Gemm, UsePreallocatedOutArray) {
+    test_params params = GetParam();
+    ASSERT_SUCCESS(af_gemm(&out, params.opt_lhs, params.opt_rhs,
+                           params.alpha, lhs, rhs, params.beta));
+
+    ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+}
+
+TEST(Gemm, DocSnippet) {
+    //! [ex_af_gemm_alloc]
+    af_array A, B;
+
+    dim_t adims[] = {5, 3, 2};
+    dim_t bdims[] = {3, 5, 2};
+    af_constant(&A, 1, 3, adims, f32);
+    af_constant(&B, 1, 3, bdims, f32);
+
+    float alpha = 1.f;
+    float beta  = 0.f;
+
+    // Undefined behavior!
+    // af_array undef;
+    // af_gemm(&undef, AF_MAT_NONE, AF_MAT_NONE, &alpha, a.get(), b.get(), &beta);
+
+    af_array C = 0;
+    af_gemm(&C, AF_MAT_NONE, AF_MAT_NONE, &alpha, A, B, &beta);
+    // C =
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+
+    //! [ex_af_gemm_alloc]
+
+    af_array c1_copy = 0;
+    ASSERT_SUCCESS(af_retain_array(&c1_copy, C));
+    af::array c1(c1_copy);
+    af::array gold1 = af::constant(3, 5, 5, 2, f32);
+    ASSERT_ARRAYS_EQ(gold1, c1);
+
+    //! [ex_af_gemm_overwrite]
+    alpha = 1.f;
+    beta  = 1.f;
+    af_seq first_slice[] = {af_span, af_span, {0., 0., 1.}};
+    af_array Asub, Bsub, Csub;
+    af_index(&Asub, A, 3, first_slice);
+    af_index(&Bsub, B, 3, first_slice);
+    af_index(&Csub, C, 3, first_slice);
+    af_gemm(&Csub, AF_MAT_NONE, AF_MAT_NONE, &alpha, Asub, Bsub, &beta);
+    // C =
+    //  6.   6.   6.   6.   6.
+    //  6.   6.   6.   6.   6.
+    //  6.   6.   6.   6.   6.
+    //  6.   6.   6.   6.   6.
+    //  6.   6.   6.   6.   6.
+    //
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //  3.   3.   3.   3.   3.
+    //! [ex_af_gemm_overwrite]
+
+    af_array c2_copy = 0;
+    ASSERT_SUCCESS(af_retain_array(&c2_copy, C));
+    af::array c2(c2_copy);
+    vector<float> gold2(5*5*2, 3);
+    fill(gold2.begin(), gold2.begin() + (5 * 5), 6);
+
+    ASSERT_VEC_ARRAY_EQ(gold2, dim4(5, 5, 2), c2);
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 402b77b8bb..19e2760017 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -1143,6 +1143,15 @@ class TestOutputArrayInfo {
     TestOutputArrayType out_arr_type;
 
    public:
+    TestOutputArrayInfo()
+        : out_arr(0)
+        , out_arr_cpy(0)
+        , out_subarr(0)
+        , out_subarr_ndims(0)
+        , out_arr_type(NULL_ARRAY) {
+        for (uint i = 0; i < 4; ++i) { out_subarr_idxs[i] = af_span; }
+    }
+
     TestOutputArrayInfo(TestOutputArrayType arr_type)
         : out_arr(0)
         , out_arr_cpy(0)

From 2d25439b33bf8258907345962ef91656ebe117d7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Jun 2019 22:29:38 -0400
Subject: [PATCH 1687/2677] Fix CMake warning about the CMP0073 policy

---
 CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index caed962582..947645e3b9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,6 +10,10 @@ project(ArrayFire
   VERSION 3.7.0
   LANGUAGES C CXX )
 
+if(POLICY CMP0073)
+  cmake_policy(SET CMP0073 NEW)
+endif()
+
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 include(AFInstallDirs)
 include(CMakeDependentOption)

From 5b0322c420e52ec66415328bcb62b19f4a16e94c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Jun 2019 18:11:00 -0400
Subject: [PATCH 1688/2677] Return after cpu offloading in OpenCL

---
 src/backend/opencl/blas.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index e65ff1c58f..c0cb7f974e 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -50,10 +50,10 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
           const Array<T> &lhs, const Array<T> &rhs,
           const T *beta) {
 #if defined(WITH_LINEAR_ALGEBRA)
-    if (OpenCLCPUOffload(
-            false)) {  // Do not force offload gemm on OSX Intel devices
-        cpu::gemm(out, optLhs, optRhs, alpha,
-                  lhs, rhs, beta);
+    // Do not force offload gemm on OSX Intel devices
+    if (OpenCLCPUOffload(false)) {
+        cpu::gemm(out, optLhs, optRhs, alpha, lhs, rhs, beta);
+        return;
     }
 #endif
     const auto lOpts = toBlasTranspose(optLhs);

From cc36ce055f2063e22dea756da9ac8837e2de2c81 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 24 Jun 2019 13:57:42 +0530
Subject: [PATCH 1689/2677] Remove glbinding references from documentation

---
 CMakeModules/CPackConfig.cmake | 3 +--
 docs/pages/using_on_linux.md   | 1 -
 docs/pages/using_on_osx.md     | 1 -
 3 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index fa2ea76c73..9753c0f6b5 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -260,7 +260,6 @@ endif ()
 
 get_native_path(zlib_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/zlib-libpng License.txt")
 get_native_path(boost_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/Boost Software License.txt")
-get_native_path(mit_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/MIT License.txt")
 get_native_path(fimg_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/FreeImage Public License.txt")
 get_native_path(apache_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/Apache-2.0.txt")
 get_native_path(sift_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/OpenSIFT License.txt")
@@ -288,7 +287,7 @@ cpack_ifw_configure_component(cmake)
 cpack_ifw_configure_component(documentation)
 cpack_ifw_configure_component(examples)
 cpack_ifw_configure_component(licenses FORCED_INSTALLATION
-  LICENSES "GLFW" ${zlib_lic_path} "glbinding" ${mit_lic_path} "FreeImage" ${fimg_lic_path}
+  LICENSES "GLFW" ${zlib_lic_path} "FreeImage" ${fimg_lic_path}
   "Boost" ${boost_lic_path} "clBLAS, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
   "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
 )
diff --git a/docs/pages/using_on_linux.md b/docs/pages/using_on_linux.md
index 9dbb347d41..87cab953bc 100644
--- a/docs/pages/using_on_linux.md
+++ b/docs/pages/using_on_linux.md
@@ -19,7 +19,6 @@ installer will populate files in the following sub-directories:
     lib/libforge*               - Visualization library
     lib/libcu*                  - CUDA backend dependencies
     lib/libOpenCL.so            - OpenCL ICD Loader library
-    lib/libglbinding*           - OpenGL graphics dependencies
     share/ArrayFire/cmake/*     - CMake config (find) scripts
     share/ArrayFire/examples/*  - All ArrayFire examples
 
diff --git a/docs/pages/using_on_osx.md b/docs/pages/using_on_osx.md
index 6fd8ad9cb3..f5643e3f93 100644
--- a/docs/pages/using_on_osx.md
+++ b/docs/pages/using_on_osx.md
@@ -18,7 +18,6 @@ directory with files in the following sub-directories:
     lib/libaf*                  - CPU, CUDA, and OpenCL libraries (.a, .so)
     lib/libforge*               - Visualization library
     lib/libcu*                  - CUDA backend dependencies
-    lib/libglbinding*           - OpenGL graphics dependencies
     share/ArrayFire/cmake/*     - CMake config scripts
     share/ArrayFire/examples/*  - ArrayFire examples
 

From 0551c44189cd67fe801bdcaaa57dcb46cf40da26 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 26 Jun 2019 21:42:34 -0400
Subject: [PATCH 1690/2677] Update to the C++ 14 standard

---
 CMakeModules/InternalUtils.cmake | 4 ++--
 src/backend/cuda/nvrtc/cache.cpp | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index fc4a1beb6e..4f165719d3 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -30,7 +30,7 @@ endfunction()
 
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
   if(NOT MSVC)
-    set(flags "-std=c++11 -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
+    set(flags "-std=c++14 -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
   else()
     set(flags "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275 -Xcompiler /bigobj -Xcompiler /EHsc")
     if(CMAKE_GENERATOR MATCHES "Ninja")
@@ -104,7 +104,7 @@ macro(arrayfire_set_cmake_default_variables)
   set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR};${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
 
-  set(CMAKE_CXX_STANDARD 11)
+  set(CMAKE_CXX_STANDARD 14)
   set(CMAKE_CXX_EXTENSIONS OFF)
   set(CMAKE_CXX_VISIBILITY_PRESET hidden)
 
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 67f667892e..9cfbc0b9b7 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -144,7 +144,7 @@ Kernel buildKernel(const int device, const string& nameExpr,
              computeFlag.first, computeFlag.second);
     vector<const char*> compiler_options = {
         arch.data(),
-        "--std=c++11",
+        "--std=c++14",
 #if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
         "--device-debug",
         "--generate-line-info"

From 95d32c3bf9eeeedd4c04000941da16b472f7b235 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 27 Jun 2019 10:26:01 +0530
Subject: [PATCH 1691/2677] Remove glbinding license file

---
 LICENSES/MIT License.txt | 7 -------
 1 file changed, 7 deletions(-)
 delete mode 100644 LICENSES/MIT License.txt

diff --git a/LICENSES/MIT License.txt b/LICENSES/MIT License.txt
deleted file mode 100644
index 900e2c71b5..0000000000
--- a/LICENSES/MIT License.txt	
+++ /dev/null
@@ -1,7 +0,0 @@
-Copyright (c) 2014-2015 Computer Graphics Systems Group at the Hasso-Plattner-Institute and CG Internals GmbH, Germany. 
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file

From 8146d9195ff46f3b439d2a4ca6408251b7d32305 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 27 Jun 2019 20:17:39 +0530
Subject: [PATCH 1692/2677] Move fftw calls from kernel namespace to cpu

---
 src/backend/cpu/CMakeLists.txt |   1 -
 src/backend/cpu/fft.cpp        | 173 ++++++++++++++++++++++++++++++---
 src/backend/cpu/kernel/fft.hpp | 173 ---------------------------------
 3 files changed, 160 insertions(+), 187 deletions(-)
 delete mode 100644 src/backend/cpu/kernel/fft.hpp

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 415e1e8710..53a56e6b75 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -206,7 +206,6 @@ target_sources(afcpu
     kernel/dot.hpp
     kernel/exampleFunction.hpp
     kernel/fast.hpp
-    kernel/fft.hpp
     kernel/fftconvolve.hpp
     kernel/gradient.hpp
     kernel/harris.hpp
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index af9c0f3248..2b7f3158f5 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -7,24 +7,101 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <fft.hpp>
+
 #include <Array.hpp>
 #include <copy.hpp>
-#include <fft.hpp>
-#include <kernel/fft.hpp>
+#include <fftw3.h>
 #include <platform.hpp>
-
+#include <types.hpp>
 #include <af/dim4.hpp>
 
+#include <type_traits>
+
 using af::dim4;
 
 namespace cpu {
 
+template<typename T>
+struct fftw_transform;
+
+#define TRANSFORM(PRE, TY)                                             \
+    template<>                                                         \
+    struct fftw_transform<TY> {                                        \
+        typedef PRE##_plan plan_t;                                     \
+        typedef PRE##_complex ctype_t;                                 \
+                                                                       \
+        template<typename... Args>                                     \
+        plan_t create(Args... args) {                                  \
+            return PRE##_plan_many_dft(args...);                       \
+        }                                                              \
+        void execute(plan_t plan) { return PRE##_execute(plan); }      \
+        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); } \
+    };
+
+TRANSFORM(fftwf, cfloat)
+TRANSFORM(fftw, cdouble)
+
+template<typename To, typename Ti>
+struct fftw_real_transform;
+
+#define TRANSFORM_REAL(PRE, To, Ti, POST)                              \
+    template<>                                                         \
+    struct fftw_real_transform<To, Ti> {                               \
+        typedef PRE##_plan plan_t;                                     \
+        typedef PRE##_complex ctype_t;                                 \
+                                                                       \
+        template<typename... Args>                                     \
+        plan_t create(Args... args) {                                  \
+            return PRE##_plan_many_dft_##POST(args...);                \
+        }                                                              \
+        void execute(plan_t plan) { return PRE##_execute(plan); }      \
+        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); } \
+    };
+
+TRANSFORM_REAL(fftwf, cfloat, float, r2c)
+TRANSFORM_REAL(fftw, cdouble, double, r2c)
+TRANSFORM_REAL(fftwf, float, cfloat, c2r)
+TRANSFORM_REAL(fftw, double, cdouble, c2r)
+
+template<int rank>
+void computeDims(int rdims[rank], const af::dim4 &idims) {
+    for (int i = 0; i < rank; i++) { rdims[i] = idims[(rank - 1) - i]; }
+}
+
 void setFFTPlanCacheSize(size_t numPlans) { UNUSED(numPlans); }
 
 template<typename T, int rank, bool direction>
 void fft_inplace(Array<T> &in) {
-    getQueue().enqueue(kernel::fft_inplace<T, rank, direction>, in,
-                       in.getDataDims());
+    auto func = [=](Param<T> in, const af::dim4 iDataDims) {
+        int t_dims[rank];
+        int in_embed[rank];
+
+        const af::dim4 idims = in.dims();
+
+        computeDims<rank>(t_dims, idims);
+        computeDims<rank>(in_embed, iDataDims);
+
+        const af::dim4 istrides = in.strides();
+
+        typedef typename fftw_transform<T>::ctype_t ctype_t;
+        typename fftw_transform<T>::plan_t plan;
+
+        fftw_transform<T> transform;
+
+        int batch = 1;
+        for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+
+        plan = transform.create(
+            rank, t_dims, (int)batch, (ctype_t *)in.get(), in_embed,
+            (int)istrides[0], (int)istrides[rank], (ctype_t *)in.get(),
+            in_embed, (int)istrides[0], (int)istrides[rank],
+            direction ? FFTW_FORWARD : FFTW_BACKWARD, FFTW_ESTIMATE);
+
+        transform.execute(plan);
+        transform.destroy(plan);
+    };
+    getQueue().enqueue(func, in, in.getDataDims());
 }
 
 template<typename Tc, typename Tr, int rank>
@@ -33,8 +110,39 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
     odims[0]      = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    getQueue().enqueue(kernel::fft_r2c<Tc, Tr, rank>, out, out.getDataDims(),
-                       in, in.getDataDims());
+    auto func = [=](Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in,
+                    const af::dim4 iDataDims) {
+        af::dim4 idims = in.dims();
+
+        int t_dims[rank];
+        int in_embed[rank];
+        int out_embed[rank];
+
+        computeDims<rank>(t_dims, idims);
+        computeDims<rank>(in_embed, iDataDims);
+        computeDims<rank>(out_embed, oDataDims);
+
+        const af::dim4 istrides = in.strides();
+        const af::dim4 ostrides = out.strides();
+
+        typedef typename fftw_real_transform<Tc, Tr>::ctype_t ctype_t;
+        typename fftw_real_transform<Tc, Tr>::plan_t plan;
+
+        fftw_real_transform<Tc, Tr> transform;
+
+        int batch = 1;
+        for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+
+        plan = transform.create(
+            rank, t_dims, (int)batch, (Tr *)in.get(), in_embed,
+            (int)istrides[0], (int)istrides[rank], (ctype_t *)out.get(),
+            out_embed, (int)ostrides[0], (int)ostrides[rank], FFTW_ESTIMATE);
+
+        transform.execute(plan);
+        transform.destroy(plan);
+    };
+
+    getQueue().enqueue(func, out, out.getDataDims(), in, in.getDataDims());
 
     return out;
 }
@@ -43,19 +151,58 @@ template<typename Tr, typename Tc, int rank>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     Array<Tr> out = createEmptyArray<Tr>(odims);
 
+    auto func = [=](Param<Tr> out, const af::dim4 oDataDims, CParam<Tc> in,
+                    const af::dim4 iDataDims, const af::dim4 odims) {
+        int t_dims[rank];
+        int in_embed[rank];
+        int out_embed[rank];
+
+        computeDims<rank>(t_dims, odims);
+        computeDims<rank>(in_embed, iDataDims);
+        computeDims<rank>(out_embed, oDataDims);
+
+        const af::dim4 istrides = in.strides();
+        const af::dim4 ostrides = out.strides();
+
+        typedef typename fftw_real_transform<Tr, Tc>::ctype_t ctype_t;
+        typename fftw_real_transform<Tr, Tc>::plan_t plan;
+
+        fftw_real_transform<Tr, Tc> transform;
+
+        int batch = 1;
+        for (int i = rank; i < 4; i++) { batch *= odims[i]; }
+
+        // By default, fftw estimate flag is sufficient for most transforms.
+        // However, complex to real transforms modify the input data memory
+        // while performing the transformation. To avoid that, we need to pass
+        // FFTW_PRESERVE_INPUT also. This flag however only works for 1D
+        // transforms and for higher level transformations, a copy of input
+        // data is passed onto the upstream FFTW calls.
+        unsigned int flags = FFTW_ESTIMATE;
+        if (rank == 1) { flags |= FFTW_PRESERVE_INPUT; }
+
+        plan = transform.create(rank, t_dims, (int)batch, (ctype_t *)in.get(),
+                                in_embed, (int)istrides[0], (int)istrides[rank],
+                                (Tr *)out.get(), out_embed, (int)ostrides[0],
+                                (int)ostrides[rank], flags);
+
+        transform.execute(plan);
+        transform.destroy(plan);
+    };
+
 #ifdef USE_MKL
-    getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
-                       in, in.getDataDims(), odims);
+    getQueue().enqueue(func, out, out.getDataDims(), in, in.getDataDims(),
+                       odims);
 #else
     if (rank > 1 || odims.ndims() > 1) {
         // FFTW does not have a input preserving algorithm for multidimensional
         // c2r FFTs
         Array<Tc> in_ = copyArray<Tc>(in);
-        getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
-                          in_, in.getDataDims(), odims);
+        getQueue().enqueue(func, out, out.getDataDims(), in_, in.getDataDims(),
+                           odims);
     } else {
-        getQueue().enqueue(kernel::fft_c2r<Tr, Tc, rank>, out, out.getDataDims(),
-                          in, in.getDataDims(), odims);
+        getQueue().enqueue(func, out, out.getDataDims(), in, in.getDataDims(),
+                           odims);
     }
 #endif
 
diff --git a/src/backend/cpu/kernel/fft.hpp b/src/backend/cpu/kernel/fft.hpp
deleted file mode 100644
index e11f295d07..0000000000
--- a/src/backend/cpu/kernel/fft.hpp
+++ /dev/null
@@ -1,173 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <Param.hpp>
-#include <fftw3.h>
-#include <types.hpp>
-
-#include <af/dim4.hpp>
-
-namespace cpu {
-namespace kernel {
-
-template<int rank>
-void computeDims(int rdims[rank], const af::dim4 &idims) {
-    for (int i = 0; i < rank; i++) { rdims[i] = idims[(rank - 1) - i]; }
-}
-
-template<typename T>
-struct fftw_transform;
-
-#define TRANSFORM(PRE, TY)                                             \
-    template<>                                                         \
-    struct fftw_transform<TY> {                                        \
-        typedef PRE##_plan plan_t;                                     \
-        typedef PRE##_complex ctype_t;                                 \
-                                                                       \
-        template<typename... Args>                                     \
-        plan_t create(Args... args) {                                  \
-            return PRE##_plan_many_dft(args...);                       \
-        }                                                              \
-        void execute(plan_t plan) { return PRE##_execute(plan); }      \
-        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); } \
-    };
-
-TRANSFORM(fftwf, cfloat)
-TRANSFORM(fftw, cdouble)
-
-template<typename To, typename Ti>
-struct fftw_real_transform;
-
-#define TRANSFORM_REAL(PRE, To, Ti, POST)                              \
-    template<>                                                         \
-    struct fftw_real_transform<To, Ti> {                               \
-        typedef PRE##_plan plan_t;                                     \
-        typedef PRE##_complex ctype_t;                                 \
-                                                                       \
-        template<typename... Args>                                     \
-        plan_t create(Args... args) {                                  \
-            return PRE##_plan_many_dft_##POST(args...);                \
-        }                                                              \
-        void execute(plan_t plan) { return PRE##_execute(plan); }      \
-        void destroy(plan_t plan) { return PRE##_destroy_plan(plan); } \
-    };
-
-TRANSFORM_REAL(fftwf, cfloat, float, r2c)
-TRANSFORM_REAL(fftw, cdouble, double, r2c)
-TRANSFORM_REAL(fftwf, float, cfloat, c2r)
-TRANSFORM_REAL(fftw, double, cdouble, c2r)
-
-template<typename T, int rank, bool direction>
-void fft_inplace(Param<T> in, const af::dim4 iDataDims) {
-    int t_dims[rank];
-    int in_embed[rank];
-
-    const af::dim4 idims = in.dims();
-
-    computeDims<rank>(t_dims, idims);
-    computeDims<rank>(in_embed, iDataDims);
-
-    const af::dim4 istrides = in.strides();
-
-    typedef typename fftw_transform<T>::ctype_t ctype_t;
-    typename fftw_transform<T>::plan_t plan;
-
-    fftw_transform<T> transform;
-
-    int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
-
-    plan = transform.create(
-        rank, t_dims, (int)batch, (ctype_t *)in.get(), in_embed,
-        (int)istrides[0], (int)istrides[rank], (ctype_t *)in.get(), in_embed,
-        (int)istrides[0], (int)istrides[rank],
-        direction ? FFTW_FORWARD : FFTW_BACKWARD, FFTW_ESTIMATE);
-
-    transform.execute(plan);
-    transform.destroy(plan);
-}
-
-template<typename Tc, typename Tr, int rank>
-void fft_r2c(Param<Tc> out, const af::dim4 oDataDims, CParam<Tr> in,
-             const af::dim4 iDataDims) {
-    af::dim4 idims = in.dims();
-
-    int t_dims[rank];
-    int in_embed[rank];
-    int out_embed[rank];
-
-    computeDims<rank>(t_dims, idims);
-    computeDims<rank>(in_embed, iDataDims);
-    computeDims<rank>(out_embed, oDataDims);
-
-    const af::dim4 istrides = in.strides();
-    const af::dim4 ostrides = out.strides();
-
-    typedef typename fftw_real_transform<Tc, Tr>::ctype_t ctype_t;
-    typename fftw_real_transform<Tc, Tr>::plan_t plan;
-
-    fftw_real_transform<Tc, Tr> transform;
-
-    int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
-
-    plan = transform.create(rank, t_dims, (int)batch, (Tr *)in.get(), in_embed,
-                            (int)istrides[0], (int)istrides[rank],
-                            (ctype_t *)out.get(), out_embed, (int)ostrides[0],
-                            (int)ostrides[rank], FFTW_ESTIMATE);
-
-    transform.execute(plan);
-    transform.destroy(plan);
-}
-
-template<typename Tr, typename Tc, int rank>
-void fft_c2r(Param<Tr> out, const af::dim4 oDataDims, CParam<Tc> in,
-             const af::dim4 iDataDims, const af::dim4 odims) {
-    int t_dims[rank];
-    int in_embed[rank];
-    int out_embed[rank];
-
-    computeDims<rank>(t_dims, odims);
-    computeDims<rank>(in_embed, iDataDims);
-    computeDims<rank>(out_embed, oDataDims);
-
-    const af::dim4 istrides = in.strides();
-    const af::dim4 ostrides = out.strides();
-
-    typedef typename fftw_real_transform<Tr, Tc>::ctype_t ctype_t;
-    typename fftw_real_transform<Tr, Tc>::plan_t plan;
-
-    fftw_real_transform<Tr, Tc> transform;
-
-    int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= odims[i]; }
-
-    // By default, fftw estimate flag is sufficient for most transforms.
-    // However, complex to real transforms modify the input data memory
-    // while performing the transformation. To avoid that, we need to pass
-    // FFTW_PRESERVE_INPUT also. This flag however only works for 1D
-    // transforms and for higher level transformations, a copy of input
-    // data is passed onto the upstream FFTW calls.
-    unsigned int flags = FFTW_ESTIMATE;
-    if (rank == 1) {
-        flags |= FFTW_PRESERVE_INPUT;
-    }
-
-    plan = transform.create(rank, t_dims, (int)batch, (ctype_t *)in.get(),
-                            in_embed, (int)istrides[0], (int)istrides[rank],
-                            (Tr *)out.get(), out_embed, (int)ostrides[0],
-                            (int)ostrides[rank], flags);
-
-    transform.execute(plan);
-    transform.destroy(plan);
-}
-
-}  // namespace kernel
-}  // namespace cpu

From a23c00b9e376775446b4d3a01a85e90aa840ef9e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 28 Jun 2019 16:54:59 -0400
Subject: [PATCH 1693/2677] Initial f16 support (#2413)

* Implement f16 support

* Add half support for gemm
* Add half support for JIT
* Add support functions for half data type
* Add half support to reductions.
* Add support for join
* Add support for transpose
---
 CMakeModules/FileToString.cmake               |   12 +-
 CMakeModules/InternalUtils.cmake              |   11 +
 LICENSES/Half(MIT) License.txt                |   21 +
 docs/details/device.dox                       |   11 +
 examples/benchmarks/blas.cpp                  |    1 -
 extern/half/include/half.hpp                  | 3067 +++++++++++++++++
 include/af/array.h                            |   33 +-
 include/af/defines.h                          |   10 +
 include/af/device.h                           |   24 +-
 include/af/half.h                             |   29 +
 include/af/traits.hpp                         |   12 +
 include/arrayfire.h                           |    1 +
 src/api/c/array.cpp                           |   22 +
 src/api/c/binary.cpp                          |    5 +
 src/api/c/blas.cpp                            |   14 +-
 src/api/c/cast.cpp                            |    8 +-
 src/api/c/data.cpp                            |    4 +-
 src/api/c/device.cpp                          |   14 +
 src/api/c/handle.hpp                          |   19 +
 src/api/c/join.cpp                            |    4 +
 src/api/c/moddims.cpp                         |    3 +
 src/api/c/ops.hpp                             |  128 +-
 src/api/c/print.cpp                           |   10 +-
 src/api/c/random.cpp                          |    5 +
 src/api/c/reduce.cpp                          |    8 +
 src/api/c/reorder.cpp                         |    7 +-
 src/api/c/select.cpp                          |    3 +
 src/api/c/type_util.cpp                       |    2 +
 src/api/cpp/array.cpp                         |    5 +
 src/api/cpp/data.cpp                          |   11 +-
 src/api/cpp/device.cpp                        |    6 +
 src/api/cpp/seq.cpp                           |    8 +-
 src/api/unified/array.cpp                     |    1 +
 src/api/unified/device.cpp                    |    4 +
 src/backend/common/ArrayInfo.cpp              |    4 +-
 src/backend/common/ArrayInfo.hpp              |    2 +
 src/backend/common/CMakeLists.txt             |   10 +-
 src/backend/common/SparseArray.hpp            |    2 +
 src/backend/common/err_common.cpp             |    2 +
 src/backend/common/half.cpp                   |    9 +
 src/backend/common/half.hpp                   |  801 +++++
 src/backend/common/kernel_type.hpp            |   33 +
 src/backend/common/traits.hpp                 |   29 +
 src/backend/cpu/Array.cpp                     |   19 +-
 src/backend/cpu/Array.hpp                     |    1 +
 src/backend/cpu/arith.hpp                     |   10 +-
 src/backend/cpu/cast.hpp                      |   80 +-
 src/backend/cpu/copy.cpp                      |    5 +
 src/backend/cpu/device_manager.hpp            |    3 +
 src/backend/cpu/jit/BinaryNode.hpp            |    2 +-
 src/backend/cpu/jit/BufferNode.hpp            |   15 +-
 src/backend/cpu/jit/Node.hpp                  |    5 +-
 src/backend/cpu/jit/UnaryNode.hpp             |    5 +-
 src/backend/cpu/join.cpp                      |    5 +
 src/backend/cpu/kernel/random_engine.hpp      |   65 +-
 src/backend/cpu/kernel/reduce.hpp             |   11 +-
 src/backend/cpu/kernel/transpose.hpp          |   10 +-
 src/backend/cpu/math.hpp                      |    2 +-
 src/backend/cpu/memory.cpp                    |    4 +-
 src/backend/cpu/platform.cpp                  |    5 +
 src/backend/cpu/platform.hpp                  |    2 +
 src/backend/cpu/random_engine.cpp             |    6 +-
 src/backend/cpu/reduce.cpp                    |   23 +-
 src/backend/cpu/reorder.cpp                   |    8 +-
 src/backend/cpu/select.cpp                    |    7 +-
 src/backend/cpu/transpose.cpp                 |    8 +-
 src/backend/cpu/types.hpp                     |   25 +
 src/backend/cuda/Array.cpp                    |    3 +
 src/backend/cuda/Array.hpp                    |   12 +-
 src/backend/cuda/CMakeLists.txt               |   26 +-
 src/backend/cuda/all.cu                       |    4 +
 src/backend/cuda/any.cu                       |    4 +
 src/backend/cuda/blas.cpp                     |  217 +-
 src/backend/cuda/cast.hpp                     |    6 +
 src/backend/cuda/copy.cu                      |   17 +-
 src/backend/cuda/count.cu                     |    4 +
 src/backend/cuda/jit.cpp                      |   15 +-
 src/backend/cuda/join.cu                      |    5 +
 src/backend/cuda/kernel/convolve.hpp          |    8 +-
 src/backend/cuda/kernel/jit.cuh               |    2 +
 src/backend/cuda/kernel/memcopy.hpp           |   37 +-
 src/backend/cuda/kernel/random_engine.hpp     |  151 +-
 src/backend/cuda/kernel/reduce.hpp            |   56 +-
 src/backend/cuda/kernel/scan_dim.hpp          |    2 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |    2 +-
 src/backend/cuda/kernel/scan_first.hpp        |    2 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |    3 +-
 src/backend/cuda/kernel/shared.hpp            |    8 +-
 src/backend/cuda/kernel/transpose.hpp         |    2 +-
 src/backend/cuda/kernel/where.hpp             |    2 +-
 src/backend/cuda/math.hpp                     |   37 +-
 src/backend/cuda/max.cu                       |    4 +
 src/backend/cuda/memory.cpp                   |    3 +
 src/backend/cuda/min.cu                       |    4 +
 src/backend/cuda/nvrtc/cache.cpp              |  125 +-
 src/backend/cuda/nvrtc/cache.hpp              |   19 +-
 src/backend/cuda/platform.cpp                 |    6 +
 src/backend/cuda/platform.hpp                 |    4 +
 src/backend/cuda/print.hpp                    |    1 +
 src/backend/cuda/product.cu                   |    4 +
 src/backend/cuda/random_engine.cu             |    5 +
 src/backend/cuda/reorder.cu                   |    4 +
 src/backend/cuda/select.cu                    |    4 +
 src/backend/cuda/sum.cu                       |    6 +
 src/backend/cuda/traits.hpp                   |    2 +-
 src/backend/cuda/transpose.cpp                |    3 +
 src/backend/cuda/types.hpp                    |   65 +-
 src/backend/opencl/Array.cpp                  |   33 +-
 src/backend/opencl/Array.hpp                  |    1 +
 src/backend/opencl/CMakeLists.txt             |    1 +
 src/backend/opencl/all.cpp                    |    4 +
 src/backend/opencl/any.cpp                    |    4 +
 src/backend/opencl/blas.cpp                   |   32 +-
 src/backend/opencl/copy.cpp                   |   10 +-
 src/backend/opencl/count.cpp                  |    4 +
 src/backend/opencl/device_manager.hpp         |    2 +
 src/backend/opencl/err_opencl.hpp             |    6 +-
 src/backend/opencl/join.cpp                   |    6 +
 src/backend/opencl/kernel/jit.cl              |    6 +
 src/backend/opencl/kernel/memcopy.hpp         |    1 +
 src/backend/opencl/kernel/reduce.hpp          |   12 +-
 src/backend/opencl/kernel/transpose.cl        |    6 +
 src/backend/opencl/magma/magma_blas_clblast.h |    3 +
 src/backend/opencl/max.cpp                    |    4 +
 src/backend/opencl/min.cpp                    |    4 +
 src/backend/opencl/platform.cpp               |   11 +
 src/backend/opencl/platform.hpp               |    3 +
 src/backend/opencl/product.cpp                |    4 +
 src/backend/opencl/random_engine.cpp          |    6 +-
 src/backend/opencl/reorder.cpp                |    4 +
 src/backend/opencl/select.cpp                 |    3 +
 src/backend/opencl/sum.cpp                    |    4 +
 src/backend/opencl/traits.hpp                 |    2 +-
 src/backend/opencl/transpose.cpp              |    7 +-
 src/backend/opencl/types.cpp                  |  115 +
 src/backend/opencl/types.hpp                  |   69 +-
 test/CMakeLists.txt                           |    2 +
 test/array.cpp                                |   11 +
 test/blas.cpp                                 |    7 +-
 test/half.cpp                                 |   88 +
 test/join.cpp                                 |    2 +-
 test/random.cpp                               |    5 +-
 test/reduce.cpp                               |  175 +-
 test/testHelpers.hpp                          |   52 +-
 144 files changed, 5914 insertions(+), 420 deletions(-)
 create mode 100644 LICENSES/Half(MIT) License.txt
 create mode 100644 extern/half/include/half.hpp
 create mode 100644 include/af/half.h
 create mode 100644 src/backend/common/half.cpp
 create mode 100644 src/backend/common/half.hpp
 create mode 100644 src/backend/common/kernel_type.hpp
 create mode 100644 src/backend/common/traits.hpp
 create mode 100644 src/backend/opencl/types.cpp
 create mode 100644 test/half.cpp

diff --git a/CMakeModules/FileToString.cmake b/CMakeModules/FileToString.cmake
index 7004ba360e..061ddcced9 100644
--- a/CMakeModules/FileToString.cmake
+++ b/CMakeModules/FileToString.cmake
@@ -28,7 +28,7 @@ include(CMakeParseArguments)
 set(BIN2CPP_PROGRAM "bin2cpp")
 
 function(FILE_TO_STRING)
-    cmake_parse_arguments(RTCS "" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;BINARY;NULLTERM" "SOURCES" ${ARGN})
+    cmake_parse_arguments(RTCS "WITH_EXTENSION;NULLTERM" "VARNAME;EXTENSION;OUTPUT_DIR;TARGETS;NAMESPACE;BINARY" "SOURCES" ${ARGN})
 
     set(_output_files "")
     foreach(_input_file ${RTCS_SOURCES})
@@ -42,14 +42,18 @@ function(FILE_TO_STRING)
         if(${RTCS_BINARY})
             set(_binary "--binary")
         endif(${RTCS_BINARY})
-        if(${RTCS_NULLTERM})
+        if(RTCS_NULLTERM)
             set(_nullterm "--nullterm")
-        endif(${RTCS_NULLTERM})
+        endif(RTCS_NULLTERM)
 
         string(REPLACE "." "_" var_name ${var_name})
 
         set(_output_path "${CMAKE_CURRENT_BINARY_DIR}/${RTCS_OUTPUT_DIR}")
-        set(_output_file "${_output_path}/${_name_we}.${RTCS_EXTENSION}")
+        if(RTCS_WITH_EXTENSION)
+          set(_output_file "${_output_path}/${var_name}.${RTCS_EXTENSION}")
+        else()
+          set(_output_file "${_output_path}/${_name_we}.${RTCS_EXTENSION}")
+        endif()
 
         add_custom_command(
             OUTPUT ${_output_file}
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 4f165719d3..0a35491c24 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -172,6 +172,17 @@ macro(arrayfire_set_cmake_default_variables)
   if(APPLE)
     set(CMAKE_INSTALL_RPATH "/opt/arrayfire/lib")
   endif()
+
+  include(WriteCompilerDetectionHeader)
+  write_compiler_detection_header(
+          FILE ${ArrayFire_BINARY_DIR}/include/compiler_header.h
+          PREFIX AF
+          COMPILERS MSVC GNU Clang AppleClang Intel
+          FEATURES cxx_constexpr cxx_relaxed_constexpr cxx_alignas cxx_thread_local
+          #[VERSION <version>]
+          #[PROLOG <prolog>]
+          #[EPILOG <epilog>]
+          )
 endmacro()
 
 mark_as_advanced(
diff --git a/LICENSES/Half(MIT) License.txt b/LICENSES/Half(MIT) License.txt
new file mode 100644
index 0000000000..abee50b132
--- /dev/null
+++ b/LICENSES/Half(MIT) License.txt	
@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) 2012-2017 Christian Rau
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/docs/details/device.dox b/docs/details/device.dox
index 1aa43e7465..11f02eabef 100644
--- a/docs/details/device.dox
+++ b/docs/details/device.dox
@@ -46,6 +46,17 @@ floating point operations
 
 ===============================================================================
 
+\defgroup device_func_half isHalfAvailable
+\ingroup device_mat
+
+\brief Check if half(16-bit) precision floating point support is available for
+       specified device
+
+These functions check if a device has support to perform half precision
+floating point operations
+
+===============================================================================
+
 \defgroup device_func_set setDevice
 \ingroup device_mat
 
diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index fac3368e49..afed05fc48 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -18,7 +18,6 @@ using namespace af;
 static array A;  // populated before each timing
 static void fn() {
     array B = matmul(A, A);  // matrix multiply
-    B.eval();                // ensure evaluated
 }
 
 int main(int argc, char** argv) {
diff --git a/extern/half/include/half.hpp b/extern/half/include/half.hpp
new file mode 100644
index 0000000000..ab70791db9
--- /dev/null
+++ b/extern/half/include/half.hpp
@@ -0,0 +1,3067 @@
+// half - IEEE 754-based half-precision floating point library.
+//
+// Copyright (c) 2012-2017 Christian Rau <rauy@users.sourceforge.net>
+//
+// Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy,
+// modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following conditions:
+//
+// The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+// WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+// COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+// ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+// Version 1.12.0
+
+/// \file
+/// Main header file for half precision functionality.
+
+#ifndef HALF_HALF_HPP
+#define HALF_HALF_HPP
+
+/// Combined gcc version number.
+#define HALF_GNUC_VERSION (__GNUC__*100+__GNUC_MINOR__)
+
+//check C++11 language features
+#if defined(__clang__)										//clang
+	#if __has_feature(cxx_static_assert) && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT)
+		#define HALF_ENABLE_CPP11_STATIC_ASSERT 1
+	#endif
+	#if __has_feature(cxx_constexpr) && !defined(HALF_ENABLE_CPP11_CONSTEXPR)
+		#define HALF_ENABLE_CPP11_CONSTEXPR 1
+	#endif
+	#if __has_feature(cxx_noexcept) && !defined(HALF_ENABLE_CPP11_NOEXCEPT)
+		#define HALF_ENABLE_CPP11_NOEXCEPT 1
+	#endif
+	#if __has_feature(cxx_user_literals) && !defined(HALF_ENABLE_CPP11_USER_LITERALS)
+		#define HALF_ENABLE_CPP11_USER_LITERALS 1
+	#endif
+	#if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) && !defined(HALF_ENABLE_CPP11_LONG_LONG)
+		#define HALF_ENABLE_CPP11_LONG_LONG 1
+	#endif
+/*#elif defined(__INTEL_COMPILER)								//Intel C++
+	#if __INTEL_COMPILER >= 1100 && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT)		????????
+		#define HALF_ENABLE_CPP11_STATIC_ASSERT 1
+	#endif
+	#if __INTEL_COMPILER >= 1300 && !defined(HALF_ENABLE_CPP11_CONSTEXPR)			????????
+		#define HALF_ENABLE_CPP11_CONSTEXPR 1
+	#endif
+	#if __INTEL_COMPILER >= 1300 && !defined(HALF_ENABLE_CPP11_NOEXCEPT)			????????
+		#define HALF_ENABLE_CPP11_NOEXCEPT 1
+	#endif
+	#if __INTEL_COMPILER >= 1100 && !defined(HALF_ENABLE_CPP11_LONG_LONG)			????????
+		#define HALF_ENABLE_CPP11_LONG_LONG 1
+	#endif*/
+#elif defined(__GNUC__)										//gcc
+	#if defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L
+		#if HALF_GNUC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT)
+			#define HALF_ENABLE_CPP11_STATIC_ASSERT 1
+		#endif
+		#if HALF_GNUC_VERSION >= 406 && !defined(HALF_ENABLE_CPP11_CONSTEXPR)
+			#define HALF_ENABLE_CPP11_CONSTEXPR 1
+		#endif
+		#if HALF_GNUC_VERSION >= 406 && !defined(HALF_ENABLE_CPP11_NOEXCEPT)
+			#define HALF_ENABLE_CPP11_NOEXCEPT 1
+		#endif
+		#if HALF_GNUC_VERSION >= 407 && !defined(HALF_ENABLE_CPP11_USER_LITERALS)
+			#define HALF_ENABLE_CPP11_USER_LITERALS 1
+		#endif
+		#if !defined(HALF_ENABLE_CPP11_LONG_LONG)
+			#define HALF_ENABLE_CPP11_LONG_LONG 1
+		#endif
+	#endif
+#elif defined(_MSC_VER)										//Visual C++
+	#if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_CONSTEXPR)
+		#define HALF_ENABLE_CPP11_CONSTEXPR 1
+	#endif
+	#if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_NOEXCEPT)
+		#define HALF_ENABLE_CPP11_NOEXCEPT 1
+	#endif
+	#if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_USER_LITERALS)
+		#define HALF_ENABLE_CPP11_USER_LITERALS 1
+	#endif
+	#if _MSC_VER >= 1600 && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT)
+		#define HALF_ENABLE_CPP11_STATIC_ASSERT 1
+	#endif
+	#if _MSC_VER >= 1310 && !defined(HALF_ENABLE_CPP11_LONG_LONG)
+		#define HALF_ENABLE_CPP11_LONG_LONG 1
+	#endif
+	#define HALF_POP_WARNINGS 1
+	#pragma warning(push)
+	#pragma warning(disable : 4099 4127 4146)	//struct vs class, constant in if, negative unsigned
+#endif
+
+//check C++11 library features
+#include <utility>
+#if defined(_LIBCPP_VERSION)								//libc++
+	#if defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103
+		#ifndef HALF_ENABLE_CPP11_TYPE_TRAITS
+			#define HALF_ENABLE_CPP11_TYPE_TRAITS 1
+		#endif
+		#ifndef HALF_ENABLE_CPP11_CSTDINT
+			#define HALF_ENABLE_CPP11_CSTDINT 1
+		#endif
+		#ifndef HALF_ENABLE_CPP11_CMATH
+			#define HALF_ENABLE_CPP11_CMATH 1
+		#endif
+		#ifndef HALF_ENABLE_CPP11_HASH
+			#define HALF_ENABLE_CPP11_HASH 1
+		#endif
+	#endif
+#elif defined(__GLIBCXX__)									//libstdc++
+	#if defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103
+		#ifdef __clang__
+			#if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_TYPE_TRAITS)
+				#define HALF_ENABLE_CPP11_TYPE_TRAITS 1
+			#endif
+			#if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_CSTDINT)
+				#define HALF_ENABLE_CPP11_CSTDINT 1
+			#endif
+			#if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_CMATH)
+				#define HALF_ENABLE_CPP11_CMATH 1
+			#endif
+			#if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_HASH)
+				#define HALF_ENABLE_CPP11_HASH 1
+			#endif
+		#else
+			#if HALF_GNUC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_CSTDINT)
+				#define HALF_ENABLE_CPP11_CSTDINT 1
+			#endif
+			#if HALF_GNUC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_CMATH)
+				#define HALF_ENABLE_CPP11_CMATH 1
+			#endif
+			#if HALF_GNUC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_HASH)
+				#define HALF_ENABLE_CPP11_HASH 1
+			#endif
+		#endif
+	#endif
+#elif defined(_CPPLIB_VER)									//Dinkumware/Visual C++
+	#if _CPPLIB_VER >= 520
+		#ifndef HALF_ENABLE_CPP11_TYPE_TRAITS
+			#define HALF_ENABLE_CPP11_TYPE_TRAITS 1
+		#endif
+		#ifndef HALF_ENABLE_CPP11_CSTDINT
+			#define HALF_ENABLE_CPP11_CSTDINT 1
+		#endif
+		#ifndef HALF_ENABLE_CPP11_HASH
+			#define HALF_ENABLE_CPP11_HASH 1
+		#endif
+	#endif
+	#if _CPPLIB_VER >= 610
+		#ifndef HALF_ENABLE_CPP11_CMATH
+			#define HALF_ENABLE_CPP11_CMATH 1
+		#endif
+	#endif
+#endif
+#undef HALF_GNUC_VERSION
+
+//support constexpr
+#if HALF_ENABLE_CPP11_CONSTEXPR
+	#define HALF_CONSTEXPR			constexpr
+	#define HALF_CONSTEXPR_CONST	constexpr
+#else
+	#define HALF_CONSTEXPR
+	#define HALF_CONSTEXPR_CONST	const
+#endif
+
+//support noexcept
+#if HALF_ENABLE_CPP11_NOEXCEPT
+	#define HALF_NOEXCEPT	noexcept
+	#define HALF_NOTHROW	noexcept
+#else
+	#define HALF_NOEXCEPT
+	#define HALF_NOTHROW	throw()
+#endif
+
+#include <algorithm>
+#include <iostream>
+#include <limits>
+#include <climits>
+#include <cmath>
+#include <cstring>
+#if HALF_ENABLE_CPP11_TYPE_TRAITS
+	#include <type_traits>
+#endif
+#if HALF_ENABLE_CPP11_CSTDINT
+	#include <cstdint>
+#endif
+#if HALF_ENABLE_CPP11_HASH
+	#include <functional>
+#endif
+
+
+/// Default rounding mode.
+/// This specifies the rounding mode used for all conversions between [half](\ref half_float::half)s and `float`s as well as
+/// for the half_cast() if not specifying a rounding mode explicitly. It can be redefined (before including half.hpp) to one
+/// of the standard rounding modes using their respective constants or the equivalent values of `std::float_round_style`:
+///
+/// `std::float_round_style`         | value | rounding
+/// ---------------------------------|-------|-------------------------
+/// `std::round_indeterminate`       | -1    | fastest (default)
+/// `std::round_toward_zero`         | 0     | toward zero
+/// `std::round_to_nearest`          | 1     | to nearest
+/// `std::round_toward_infinity`     | 2     | toward positive infinity
+/// `std::round_toward_neg_infinity` | 3     | toward negative infinity
+///
+/// By default this is set to `-1` (`std::round_indeterminate`), which uses truncation (round toward zero, but with overflows
+/// set to infinity) and is the fastest rounding mode possible. It can even be set to `std::numeric_limits<float>::round_style`
+/// to synchronize the rounding mode with that of the underlying single-precision implementation.
+#ifndef HALF_ROUND_STYLE
+	#define HALF_ROUND_STYLE	-1			// = std::round_indeterminate
+#endif
+
+/// Tie-breaking behaviour for round to nearest.
+/// This specifies if ties in round to nearest should be resolved by rounding to the nearest even value. By default this is
+/// defined to `0` resulting in the faster but slightly more biased behaviour of rounding away from zero in half-way cases (and
+/// thus equal to the round() function), but can be redefined to `1` (before including half.hpp) if more IEEE-conformant
+/// behaviour is needed.
+#ifndef HALF_ROUND_TIES_TO_EVEN
+	#define HALF_ROUND_TIES_TO_EVEN	0		// ties away from zero
+#endif
+
+/// Value signaling overflow.
+/// In correspondence with `HUGE_VAL[F|L]` from `<cmath>` this symbol expands to a positive value signaling the overflow of an
+/// operation, in particular it just evaluates to positive infinity.
+#define HUGE_VALH	std::numeric_limits<half_float::half>::infinity()
+
+/// Fast half-precision fma function.
+/// This symbol is only defined if the fma() function generally executes as fast as, or faster than, a separate
+/// half-precision multiplication followed by an addition. Due to the internal single-precision implementation of all
+/// arithmetic operations, this is in fact always the case.
+#define FP_FAST_FMAH	1
+
+#ifndef FP_ILOGB0
+	#define FP_ILOGB0		INT_MIN
+#endif
+#ifndef FP_ILOGBNAN
+	#define FP_ILOGBNAN		INT_MAX
+#endif
+#ifndef FP_SUBNORMAL
+	#define FP_SUBNORMAL	0
+#endif
+#ifndef FP_ZERO
+	#define FP_ZERO			1
+#endif
+#ifndef FP_NAN
+	#define FP_NAN			2
+#endif
+#ifndef FP_INFINITE
+	#define FP_INFINITE		3
+#endif
+#ifndef FP_NORMAL
+	#define FP_NORMAL		4
+#endif
+
+
+/// Main namespace for half precision functionality.
+/// This namespace contains all the functionality provided by the library.
+namespace half_float
+{
+	class half;
+
+#if HALF_ENABLE_CPP11_USER_LITERALS
+	/// Library-defined half-precision literals.
+	/// Import this namespace to enable half-precision floating point literals:
+	/// ~~~~{.cpp}
+	/// using namespace half_float::literal;
+	/// half_float::half = 4.2_h;
+	/// ~~~~
+	namespace literal
+	{
+		half operator""_h(long double);
+	}
+#endif
+
+	/// \internal
+	/// \brief Implementation details.
+	namespace detail
+	{
+	#if HALF_ENABLE_CPP11_TYPE_TRAITS
+		/// Conditional type.
+		template<bool B,typename T,typename F> struct conditional : std::conditional<B,T,F> {};
+
+		/// Helper for tag dispatching.
+		template<bool B> struct bool_type : std::integral_constant<bool,B> {};
+		using std::true_type;
+		using std::false_type;
+
+		/// Type traits for floating point types.
+		template<typename T> struct is_float : std::is_floating_point<T> {};
+	#else
+		/// Conditional type.
+		template<bool,typename T,typename> struct conditional { typedef T type; };
+		template<typename T,typename F> struct conditional<false,T,F> { typedef F type; };
+
+		/// Helper for tag dispatching.
+		template<bool> struct bool_type {};
+		typedef bool_type<true> true_type;
+		typedef bool_type<false> false_type;
+
+		/// Type traits for floating point types.
+		template<typename> struct is_float : false_type {};
+		template<typename T> struct is_float<const T> : is_float<T> {};
+		template<typename T> struct is_float<volatile T> : is_float<T> {};
+		template<typename T> struct is_float<const volatile T> : is_float<T> {};
+		template<> struct is_float<float> : true_type {};
+		template<> struct is_float<double> : true_type {};
+		template<> struct is_float<long double> : true_type {};
+	#endif
+
+		/// Type traits for floating point bits.
+		template<typename T> struct bits { typedef unsigned char type; };
+		template<typename T> struct bits<const T> : bits<T> {};
+		template<typename T> struct bits<volatile T> : bits<T> {};
+		template<typename T> struct bits<const volatile T> : bits<T> {};
+
+	#if HALF_ENABLE_CPP11_CSTDINT
+		/// Unsigned integer of (at least) 16 bits width.
+		typedef std::uint_least16_t uint16;
+
+		/// Unsigned integer of (at least) 32 bits width.
+		template<> struct bits<float> { typedef std::uint_least32_t type; };
+
+		/// Unsigned integer of (at least) 64 bits width.
+		template<> struct bits<double> { typedef std::uint_least64_t type; };
+	#else
+		/// Unsigned integer of (at least) 16 bits width.
+		typedef unsigned short uint16;
+
+		/// Unsigned integer of (at least) 32 bits width.
+		template<> struct bits<float> : conditional<std::numeric_limits<unsigned int>::digits>=32,unsigned int,unsigned long> {};
+
+		#if HALF_ENABLE_CPP11_LONG_LONG
+			/// Unsigned integer of (at least) 64 bits width.
+			template<> struct bits<double> : conditional<std::numeric_limits<unsigned long>::digits>=64,unsigned long,unsigned long long> {};
+		#else
+			/// Unsigned integer of (at least) 64 bits width.
+			template<> struct bits<double> { typedef unsigned long type; };
+		#endif
+	#endif
+
+		/// Tag type for binary construction.
+		struct binary_t {};
+
+		/// Tag for binary construction.
+		HALF_CONSTEXPR_CONST binary_t binary = binary_t();
+
+		/// Temporary half-precision expression.
+		/// This class represents a half-precision expression which just stores a single-precision value internally.
+		struct expr
+		{
+			/// Conversion constructor.
+			/// \param f single-precision value to convert
+			explicit HALF_CONSTEXPR expr(float f) HALF_NOEXCEPT : value_(f) {}
+
+			/// Conversion to single-precision.
+			/// \return single precision value representing expression value
+			HALF_CONSTEXPR operator float() const HALF_NOEXCEPT { return value_; }
+
+		private:
+			/// Internal expression value stored in single-precision.
+			float value_;
+		};
+
+		/// SFINAE helper for generic half-precision functions.
+		/// This class template has to be specialized for each valid combination of argument types to provide a corresponding
+		/// `type` member equivalent to \a T.
+		/// \tparam T type to return
+		template<typename T,typename,typename=void,typename=void> struct enable {};
+		template<typename T> struct enable<T,half,void,void> { typedef T type; };
+		template<typename T> struct enable<T,expr,void,void> { typedef T type; };
+		template<typename T> struct enable<T,half,half,void> { typedef T type; };
+		template<typename T> struct enable<T,half,expr,void> { typedef T type; };
+		template<typename T> struct enable<T,expr,half,void> { typedef T type; };
+		template<typename T> struct enable<T,expr,expr,void> { typedef T type; };
+		template<typename T> struct enable<T,half,half,half> { typedef T type; };
+		template<typename T> struct enable<T,half,half,expr> { typedef T type; };
+		template<typename T> struct enable<T,half,expr,half> { typedef T type; };
+		template<typename T> struct enable<T,half,expr,expr> { typedef T type; };
+		template<typename T> struct enable<T,expr,half,half> { typedef T type; };
+		template<typename T> struct enable<T,expr,half,expr> { typedef T type; };
+		template<typename T> struct enable<T,expr,expr,half> { typedef T type; };
+		template<typename T> struct enable<T,expr,expr,expr> { typedef T type; };
+
+		/// Return type for specialized generic 2-argument half-precision functions.
+		/// This class template has to be specialized for each valid combination of argument types to provide a corresponding
+		/// `type` member denoting the appropriate return type.
+		/// \tparam T first argument type
+		/// \tparam U first argument type
+		template<typename T,typename U> struct result : enable<expr,T,U> {};
+		template<> struct result<half,half> { typedef half type; };
+
+		/// \name Classification helpers
+		/// \{
+
+		/// Check for infinity.
+		/// \tparam T argument type (builtin floating point type)
+		/// \param arg value to query
+		/// \retval true if infinity
+		/// \retval false else
+		template<typename T> bool builtin_isinf(T arg)
+		{
+		#if HALF_ENABLE_CPP11_CMATH
+			return std::isinf(arg);
+		#elif defined(_MSC_VER)
+			return !::_finite(static_cast<double>(arg)) && !::_isnan(static_cast<double>(arg));
+		#else
+			return arg == std::numeric_limits<T>::infinity() || arg == -std::numeric_limits<T>::infinity();
+		#endif
+		}
+
+		/// Check for NaN.
+		/// \tparam T argument type (builtin floating point type)
+		/// \param arg value to query
+		/// \retval true if not a number
+		/// \retval false else
+		template<typename T> bool builtin_isnan(T arg)
+		{
+		#if HALF_ENABLE_CPP11_CMATH
+			return std::isnan(arg);
+		#elif defined(_MSC_VER)
+			return ::_isnan(static_cast<double>(arg)) != 0;
+		#else
+			return arg != arg;
+		#endif
+		}
+
+		/// Check sign.
+		/// \tparam T argument type (builtin floating point type)
+		/// \param arg value to query
+		/// \retval true if signbit set
+		/// \retval false else
+		template<typename T> bool builtin_signbit(T arg)
+		{
+		#if HALF_ENABLE_CPP11_CMATH
+			return std::signbit(arg);
+		#else
+			return arg < T() || (arg == T() && T(1)/arg < T());
+		#endif
+		}
+
+		/// \}
+		/// \name Conversion
+		/// \{
+
+		/// Convert IEEE single-precision to half-precision.
+		/// Credit for this goes to [Jeroen van der Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf).
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \param value single-precision value
+		/// \return binary representation of half-precision value
+		template<std::float_round_style R> uint16 float2half_impl(float value, true_type)
+		{
+			typedef bits<float>::type uint32;
+			uint32 bits;// = *reinterpret_cast<uint32*>(&value);		//violating strict aliasing!
+			std::memcpy(&bits, &value, sizeof(float));
+/*			uint16 hbits = (bits>>16) & 0x8000;
+			bits &= 0x7FFFFFFF;
+			int exp = bits >> 23;
+			if(exp == 255)
+				return hbits | 0x7C00 | (0x3FF&-static_cast<unsigned>((bits&0x7FFFFF)!=0));
+			if(exp > 142)
+			{
+				if(R == std::round_toward_infinity)
+					return hbits | 0x7C00 - (hbits>>15);
+				if(R == std::round_toward_neg_infinity)
+					return hbits | 0x7BFF + (hbits>>15);
+				return hbits | 0x7BFF + (R!=std::round_toward_zero);
+			}
+			int g, s;
+			if(exp > 112)
+			{
+				g = (bits>>12) & 1;
+				s = (bits&0xFFF) != 0;
+				hbits |= ((exp-112)<<10) | ((bits>>13)&0x3FF);
+			}
+			else if(exp > 101)
+			{
+				int i = 125 - exp;
+				bits = (bits&0x7FFFFF) | 0x800000;
+				g = (bits>>i) & 1;
+				s = (bits&((1L<<i)-1)) != 0;
+				hbits |= bits >> (i+1);
+			}
+			else
+			{
+				g = 0;
+				s = bits != 0;
+			}
+			if(R == std::round_to_nearest)
+				#if HALF_ROUND_TIES_TO_EVEN
+					hbits += g & (s|hbits);
+				#else
+					hbits += g;
+				#endif
+			else if(R == std::round_toward_infinity)
+				hbits += ~(hbits>>15) & (s|g);
+			else if(R == std::round_toward_neg_infinity)
+				hbits += (hbits>>15) & (g|s);
+*/			static const uint16 base_table[512] = {
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+				0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100,
+				0x0200, 0x0400, 0x0800, 0x0C00, 0x1000, 0x1400, 0x1800, 0x1C00, 0x2000, 0x2400, 0x2800, 0x2C00, 0x3000, 0x3400, 0x3800, 0x3C00,
+				0x4000, 0x4400, 0x4800, 0x4C00, 0x5000, 0x5400, 0x5800, 0x5C00, 0x6000, 0x6400, 0x6800, 0x6C00, 0x7000, 0x7400, 0x7800, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+				0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8001, 0x8002, 0x8004, 0x8008, 0x8010, 0x8020, 0x8040, 0x8080, 0x8100,
+				0x8200, 0x8400, 0x8800, 0x8C00, 0x9000, 0x9400, 0x9800, 0x9C00, 0xA000, 0xA400, 0xA800, 0xAC00, 0xB000, 0xB400, 0xB800, 0xBC00,
+				0xC000, 0xC400, 0xC800, 0xCC00, 0xD000, 0xD400, 0xD800, 0xDC00, 0xE000, 0xE400, 0xE800, 0xEC00, 0xF000, 0xF400, 0xF800, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+				0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00 };
+			static const unsigned char shift_table[512] = {
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
+				13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 13,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
+				13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+				24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 13 };
+			uint16 hbits = base_table[bits>>23] + static_cast<uint16>((bits&0x7FFFFF)>>shift_table[bits>>23]);
+			if(R == std::round_to_nearest)
+				hbits += (((bits&0x7FFFFF)>>(shift_table[bits>>23]-1))|(((bits>>23)&0xFF)==102)) & ((hbits&0x7C00)!=0x7C00)
+				#if HALF_ROUND_TIES_TO_EVEN
+					& (((((static_cast<uint32>(1)<<(shift_table[bits>>23]-1))-1)&bits)!=0)|hbits)
+				#endif
+				;
+			else if(R == std::round_toward_zero)
+				hbits -= ((hbits&0x7FFF)==0x7C00) & ~shift_table[bits>>23];
+			else if(R == std::round_toward_infinity)
+				hbits += ((((bits&0x7FFFFF&((static_cast<uint32>(1)<<(shift_table[bits>>23]))-1))!=0)|(((bits>>23)<=102)&
+					((bits>>23)!=0)))&(hbits<0x7C00)) - ((hbits==0xFC00)&((bits>>23)!=511));
+			else if(R == std::round_toward_neg_infinity)
+				hbits += ((((bits&0x7FFFFF&((static_cast<uint32>(1)<<(shift_table[bits>>23]))-1))!=0)|(((bits>>23)<=358)&
+					((bits>>23)!=256)))&(hbits<0xFC00)&(hbits>>15)) - ((hbits==0x7C00)&((bits>>23)!=255));
+			return hbits;
+		}
+
+		/// Convert IEEE double-precision to half-precision.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \param value double-precision value
+		/// \return binary representation of half-precision value
+		template<std::float_round_style R> uint16 float2half_impl(double value, true_type)
+		{
+			typedef bits<float>::type uint32;
+			typedef bits<double>::type uint64;
+			uint64 bits;// = *reinterpret_cast<uint64*>(&value);		//violating strict aliasing!
+			std::memcpy(&bits, &value, sizeof(double));
+			uint32 hi = bits >> 32, lo = bits & 0xFFFFFFFF;
+			uint16 hbits = (hi>>16) & 0x8000;
+			hi &= 0x7FFFFFFF;
+			int exp = hi >> 20;
+			if(exp == 2047)
+				return hbits | 0x7C00 | (0x3FF&-static_cast<unsigned>((bits&0xFFFFFFFFFFFFF)!=0));
+			if(exp > 1038)
+			{
+				if(R == std::round_toward_infinity)
+					return hbits | 0x7C00 - (hbits>>15);
+				if(R == std::round_toward_neg_infinity)
+					return hbits | 0x7BFF + (hbits>>15);
+				return hbits | 0x7BFF + (R!=std::round_toward_zero);
+			}
+			int g, s = lo != 0;
+			if(exp > 1008)
+			{
+				g = (hi>>9) & 1;
+				s |= (hi&0x1FF) != 0;
+				hbits |= ((exp-1008)<<10) | ((hi>>10)&0x3FF);
+			}
+			else if(exp > 997)
+			{
+				int i = 1018 - exp;
+				hi = (hi&0xFFFFF) | 0x100000;
+				g = (hi>>i) & 1;
+				s |= (hi&((1L<<i)-1)) != 0;
+				hbits |= hi >> (i+1);
+			}
+			else
+			{
+				g = 0;
+				s |= hi != 0;
+			}
+			if(R == std::round_to_nearest)
+				#if HALF_ROUND_TIES_TO_EVEN
+					hbits += g & (s|hbits);
+				#else
+					hbits += g;
+				#endif
+			else if(R == std::round_toward_infinity)
+				hbits += ~(hbits>>15) & (s|g);
+			else if(R == std::round_toward_neg_infinity)
+				hbits += (hbits>>15) & (g|s);
+			return hbits;
+		}
+
+		/// Convert non-IEEE floating point to half-precision.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam T source type (builtin floating point type)
+		/// \param value floating point value
+		/// \return binary representation of half-precision value
+		template<std::float_round_style R,typename T> uint16 float2half_impl(T value, ...)
+		{
+			uint16 hbits = static_cast<unsigned>(builtin_signbit(value)) << 15;
+			if(value == T())
+				return hbits;
+			if(builtin_isnan(value))
+				return hbits | 0x7FFF;
+			if(builtin_isinf(value))
+				return hbits | 0x7C00;
+			int exp;
+			std::frexp(value, &exp);
+			if(exp > 16)
+			{
+				if(R == std::round_toward_infinity)
+					return hbits | 0x7C00 - (hbits>>15);
+				else if(R == std::round_toward_neg_infinity)
+					return hbits | 0x7BFF + (hbits>>15);
+				return hbits | 0x7BFF + (R!=std::round_toward_zero);
+			}
+			if(exp < -13)
+				value = std::ldexp(value, 24);
+			else
+			{
+				value = std::ldexp(value, 11-exp);
+				hbits |= ((exp+13)<<10);
+			}
+			T ival, frac = std::modf(value, &ival);
+			hbits += static_cast<uint16>(std::abs(static_cast<int>(ival)));
+			if(R == std::round_to_nearest)
+			{
+				frac = std::abs(frac);
+				#if HALF_ROUND_TIES_TO_EVEN
+					hbits += (frac>T(0.5)) | ((frac==T(0.5))&hbits);
+				#else
+					hbits += frac >= T(0.5);
+				#endif
+			}
+			else if(R == std::round_toward_infinity)
+				hbits += frac > T();
+			else if(R == std::round_toward_neg_infinity)
+				hbits += frac < T();
+			return hbits;
+		}
+
+		/// Convert floating point to half-precision.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam T source type (builtin floating point type)
+		/// \param value floating point value
+		/// \return binary representation of half-precision value
+		template<std::float_round_style R,typename T> uint16 float2half(T value)
+		{
+			return float2half_impl<R>(value, bool_type<std::numeric_limits<T>::is_iec559&&sizeof(typename bits<T>::type)==sizeof(T)>());
+		}
+
+		/// Convert integer to half-precision floating point.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam S `true` if value negative, `false` else
+		/// \tparam T type to convert (builtin integer type)
+		/// \param value non-negative integral value
+		/// \return binary representation of half-precision value
+		template<std::float_round_style R,bool S,typename T> uint16 int2half_impl(T value)
+		{
+		#if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS
+			static_assert(std::is_integral<T>::value, "int to half conversion only supports builtin integer types");
+		#endif
+			if(S)
+				value = -value;
+			uint16 bits = S << 15;
+			if(value > 0xFFFF)
+			{
+				if(R == std::round_toward_infinity)
+					bits |= 0x7C00 - S;
+				else if(R == std::round_toward_neg_infinity)
+					bits |= 0x7BFF + S;
+				else
+					bits |= 0x7BFF + (R!=std::round_toward_zero);
+			}
+			else if(value)
+			{
+				unsigned int m = value, exp = 24;
+				for(; m<0x400; m<<=1,--exp) ;
+				for(; m>0x7FF; m>>=1,++exp) ;
+				bits |= (exp<<10) + m;
+				if(exp > 24)
+				{
+					if(R == std::round_to_nearest)
+						bits += (value>>(exp-25)) & 1
+						#if HALF_ROUND_TIES_TO_EVEN
+							& (((((1<<(exp-25))-1)&value)!=0)|bits)
+						#endif
+						;
+					else if(R == std::round_toward_infinity)
+						bits += ((value&((1<<(exp-24))-1))!=0) & !S;
+					else if(R == std::round_toward_neg_infinity)
+						bits += ((value&((1<<(exp-24))-1))!=0) & S;
+				}
+			}
+			return bits;
+		}
+
+		/// Convert integer to half-precision floating point.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam T type to convert (builtin integer type)
+		/// \param value integral value
+		/// \return binary representation of half-precision value
+		template<std::float_round_style R,typename T> uint16 int2half(T value)
+		{
+			return (value<0) ? int2half_impl<R,true>(value) : int2half_impl<R,false>(value);
+		}
+
+		/// Convert half-precision to IEEE single-precision.
+		/// Credit for this goes to [Jeroen van der Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf).
+		/// \param value binary representation of half-precision value
+		/// \return single-precision value
+		inline float half2float_impl(uint16 value, float, true_type)
+		{
+			typedef bits<float>::type uint32;
+/*			uint32 bits = static_cast<uint32>(value&0x8000) << 16;
+			int abs = value & 0x7FFF;
+			if(abs)
+			{
+				bits |= 0x38000000 << static_cast<unsigned>(abs>=0x7C00);
+				for(; abs<0x400; abs<<=1,bits-=0x800000) ;
+				bits += static_cast<uint32>(abs) << 13;
+			}
+*/			static const uint32 mantissa_table[2048] = {
+				0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34A00000, 0x34C00000, 0x34E00000, 0x35000000, 0x35100000, 0x35200000, 0x35300000, 0x35400000, 0x35500000, 0x35600000, 0x35700000,
+				0x35800000, 0x35880000, 0x35900000, 0x35980000, 0x35A00000, 0x35A80000, 0x35B00000, 0x35B80000, 0x35C00000, 0x35C80000, 0x35D00000, 0x35D80000, 0x35E00000, 0x35E80000, 0x35F00000, 0x35F80000,
+				0x36000000, 0x36040000, 0x36080000, 0x360C0000, 0x36100000, 0x36140000, 0x36180000, 0x361C0000, 0x36200000, 0x36240000, 0x36280000, 0x362C0000, 0x36300000, 0x36340000, 0x36380000, 0x363C0000,
+				0x36400000, 0x36440000, 0x36480000, 0x364C0000, 0x36500000, 0x36540000, 0x36580000, 0x365C0000, 0x36600000, 0x36640000, 0x36680000, 0x366C0000, 0x36700000, 0x36740000, 0x36780000, 0x367C0000,
+				0x36800000, 0x36820000, 0x36840000, 0x36860000, 0x36880000, 0x368A0000, 0x368C0000, 0x368E0000, 0x36900000, 0x36920000, 0x36940000, 0x36960000, 0x36980000, 0x369A0000, 0x369C0000, 0x369E0000,
+				0x36A00000, 0x36A20000, 0x36A40000, 0x36A60000, 0x36A80000, 0x36AA0000, 0x36AC0000, 0x36AE0000, 0x36B00000, 0x36B20000, 0x36B40000, 0x36B60000, 0x36B80000, 0x36BA0000, 0x36BC0000, 0x36BE0000,
+				0x36C00000, 0x36C20000, 0x36C40000, 0x36C60000, 0x36C80000, 0x36CA0000, 0x36CC0000, 0x36CE0000, 0x36D00000, 0x36D20000, 0x36D40000, 0x36D60000, 0x36D80000, 0x36DA0000, 0x36DC0000, 0x36DE0000,
+				0x36E00000, 0x36E20000, 0x36E40000, 0x36E60000, 0x36E80000, 0x36EA0000, 0x36EC0000, 0x36EE0000, 0x36F00000, 0x36F20000, 0x36F40000, 0x36F60000, 0x36F80000, 0x36FA0000, 0x36FC0000, 0x36FE0000,
+				0x37000000, 0x37010000, 0x37020000, 0x37030000, 0x37040000, 0x37050000, 0x37060000, 0x37070000, 0x37080000, 0x37090000, 0x370A0000, 0x370B0000, 0x370C0000, 0x370D0000, 0x370E0000, 0x370F0000,
+				0x37100000, 0x37110000, 0x37120000, 0x37130000, 0x37140000, 0x37150000, 0x37160000, 0x37170000, 0x37180000, 0x37190000, 0x371A0000, 0x371B0000, 0x371C0000, 0x371D0000, 0x371E0000, 0x371F0000,
+				0x37200000, 0x37210000, 0x37220000, 0x37230000, 0x37240000, 0x37250000, 0x37260000, 0x37270000, 0x37280000, 0x37290000, 0x372A0000, 0x372B0000, 0x372C0000, 0x372D0000, 0x372E0000, 0x372F0000,
+				0x37300000, 0x37310000, 0x37320000, 0x37330000, 0x37340000, 0x37350000, 0x37360000, 0x37370000, 0x37380000, 0x37390000, 0x373A0000, 0x373B0000, 0x373C0000, 0x373D0000, 0x373E0000, 0x373F0000,
+				0x37400000, 0x37410000, 0x37420000, 0x37430000, 0x37440000, 0x37450000, 0x37460000, 0x37470000, 0x37480000, 0x37490000, 0x374A0000, 0x374B0000, 0x374C0000, 0x374D0000, 0x374E0000, 0x374F0000,
+				0x37500000, 0x37510000, 0x37520000, 0x37530000, 0x37540000, 0x37550000, 0x37560000, 0x37570000, 0x37580000, 0x37590000, 0x375A0000, 0x375B0000, 0x375C0000, 0x375D0000, 0x375E0000, 0x375F0000,
+				0x37600000, 0x37610000, 0x37620000, 0x37630000, 0x37640000, 0x37650000, 0x37660000, 0x37670000, 0x37680000, 0x37690000, 0x376A0000, 0x376B0000, 0x376C0000, 0x376D0000, 0x376E0000, 0x376F0000,
+				0x37700000, 0x37710000, 0x37720000, 0x37730000, 0x37740000, 0x37750000, 0x37760000, 0x37770000, 0x37780000, 0x37790000, 0x377A0000, 0x377B0000, 0x377C0000, 0x377D0000, 0x377E0000, 0x377F0000,
+				0x37800000, 0x37808000, 0x37810000, 0x37818000, 0x37820000, 0x37828000, 0x37830000, 0x37838000, 0x37840000, 0x37848000, 0x37850000, 0x37858000, 0x37860000, 0x37868000, 0x37870000, 0x37878000,
+				0x37880000, 0x37888000, 0x37890000, 0x37898000, 0x378A0000, 0x378A8000, 0x378B0000, 0x378B8000, 0x378C0000, 0x378C8000, 0x378D0000, 0x378D8000, 0x378E0000, 0x378E8000, 0x378F0000, 0x378F8000,
+				0x37900000, 0x37908000, 0x37910000, 0x37918000, 0x37920000, 0x37928000, 0x37930000, 0x37938000, 0x37940000, 0x37948000, 0x37950000, 0x37958000, 0x37960000, 0x37968000, 0x37970000, 0x37978000,
+				0x37980000, 0x37988000, 0x37990000, 0x37998000, 0x379A0000, 0x379A8000, 0x379B0000, 0x379B8000, 0x379C0000, 0x379C8000, 0x379D0000, 0x379D8000, 0x379E0000, 0x379E8000, 0x379F0000, 0x379F8000,
+				0x37A00000, 0x37A08000, 0x37A10000, 0x37A18000, 0x37A20000, 0x37A28000, 0x37A30000, 0x37A38000, 0x37A40000, 0x37A48000, 0x37A50000, 0x37A58000, 0x37A60000, 0x37A68000, 0x37A70000, 0x37A78000,
+				0x37A80000, 0x37A88000, 0x37A90000, 0x37A98000, 0x37AA0000, 0x37AA8000, 0x37AB0000, 0x37AB8000, 0x37AC0000, 0x37AC8000, 0x37AD0000, 0x37AD8000, 0x37AE0000, 0x37AE8000, 0x37AF0000, 0x37AF8000,
+				0x37B00000, 0x37B08000, 0x37B10000, 0x37B18000, 0x37B20000, 0x37B28000, 0x37B30000, 0x37B38000, 0x37B40000, 0x37B48000, 0x37B50000, 0x37B58000, 0x37B60000, 0x37B68000, 0x37B70000, 0x37B78000,
+				0x37B80000, 0x37B88000, 0x37B90000, 0x37B98000, 0x37BA0000, 0x37BA8000, 0x37BB0000, 0x37BB8000, 0x37BC0000, 0x37BC8000, 0x37BD0000, 0x37BD8000, 0x37BE0000, 0x37BE8000, 0x37BF0000, 0x37BF8000,
+				0x37C00000, 0x37C08000, 0x37C10000, 0x37C18000, 0x37C20000, 0x37C28000, 0x37C30000, 0x37C38000, 0x37C40000, 0x37C48000, 0x37C50000, 0x37C58000, 0x37C60000, 0x37C68000, 0x37C70000, 0x37C78000,
+				0x37C80000, 0x37C88000, 0x37C90000, 0x37C98000, 0x37CA0000, 0x37CA8000, 0x37CB0000, 0x37CB8000, 0x37CC0000, 0x37CC8000, 0x37CD0000, 0x37CD8000, 0x37CE0000, 0x37CE8000, 0x37CF0000, 0x37CF8000,
+				0x37D00000, 0x37D08000, 0x37D10000, 0x37D18000, 0x37D20000, 0x37D28000, 0x37D30000, 0x37D38000, 0x37D40000, 0x37D48000, 0x37D50000, 0x37D58000, 0x37D60000, 0x37D68000, 0x37D70000, 0x37D78000,
+				0x37D80000, 0x37D88000, 0x37D90000, 0x37D98000, 0x37DA0000, 0x37DA8000, 0x37DB0000, 0x37DB8000, 0x37DC0000, 0x37DC8000, 0x37DD0000, 0x37DD8000, 0x37DE0000, 0x37DE8000, 0x37DF0000, 0x37DF8000,
+				0x37E00000, 0x37E08000, 0x37E10000, 0x37E18000, 0x37E20000, 0x37E28000, 0x37E30000, 0x37E38000, 0x37E40000, 0x37E48000, 0x37E50000, 0x37E58000, 0x37E60000, 0x37E68000, 0x37E70000, 0x37E78000,
+				0x37E80000, 0x37E88000, 0x37E90000, 0x37E98000, 0x37EA0000, 0x37EA8000, 0x37EB0000, 0x37EB8000, 0x37EC0000, 0x37EC8000, 0x37ED0000, 0x37ED8000, 0x37EE0000, 0x37EE8000, 0x37EF0000, 0x37EF8000,
+				0x37F00000, 0x37F08000, 0x37F10000, 0x37F18000, 0x37F20000, 0x37F28000, 0x37F30000, 0x37F38000, 0x37F40000, 0x37F48000, 0x37F50000, 0x37F58000, 0x37F60000, 0x37F68000, 0x37F70000, 0x37F78000,
+				0x37F80000, 0x37F88000, 0x37F90000, 0x37F98000, 0x37FA0000, 0x37FA8000, 0x37FB0000, 0x37FB8000, 0x37FC0000, 0x37FC8000, 0x37FD0000, 0x37FD8000, 0x37FE0000, 0x37FE8000, 0x37FF0000, 0x37FF8000,
+				0x38000000, 0x38004000, 0x38008000, 0x3800C000, 0x38010000, 0x38014000, 0x38018000, 0x3801C000, 0x38020000, 0x38024000, 0x38028000, 0x3802C000, 0x38030000, 0x38034000, 0x38038000, 0x3803C000,
+				0x38040000, 0x38044000, 0x38048000, 0x3804C000, 0x38050000, 0x38054000, 0x38058000, 0x3805C000, 0x38060000, 0x38064000, 0x38068000, 0x3806C000, 0x38070000, 0x38074000, 0x38078000, 0x3807C000,
+				0x38080000, 0x38084000, 0x38088000, 0x3808C000, 0x38090000, 0x38094000, 0x38098000, 0x3809C000, 0x380A0000, 0x380A4000, 0x380A8000, 0x380AC000, 0x380B0000, 0x380B4000, 0x380B8000, 0x380BC000,
+				0x380C0000, 0x380C4000, 0x380C8000, 0x380CC000, 0x380D0000, 0x380D4000, 0x380D8000, 0x380DC000, 0x380E0000, 0x380E4000, 0x380E8000, 0x380EC000, 0x380F0000, 0x380F4000, 0x380F8000, 0x380FC000,
+				0x38100000, 0x38104000, 0x38108000, 0x3810C000, 0x38110000, 0x38114000, 0x38118000, 0x3811C000, 0x38120000, 0x38124000, 0x38128000, 0x3812C000, 0x38130000, 0x38134000, 0x38138000, 0x3813C000,
+				0x38140000, 0x38144000, 0x38148000, 0x3814C000, 0x38150000, 0x38154000, 0x38158000, 0x3815C000, 0x38160000, 0x38164000, 0x38168000, 0x3816C000, 0x38170000, 0x38174000, 0x38178000, 0x3817C000,
+				0x38180000, 0x38184000, 0x38188000, 0x3818C000, 0x38190000, 0x38194000, 0x38198000, 0x3819C000, 0x381A0000, 0x381A4000, 0x381A8000, 0x381AC000, 0x381B0000, 0x381B4000, 0x381B8000, 0x381BC000,
+				0x381C0000, 0x381C4000, 0x381C8000, 0x381CC000, 0x381D0000, 0x381D4000, 0x381D8000, 0x381DC000, 0x381E0000, 0x381E4000, 0x381E8000, 0x381EC000, 0x381F0000, 0x381F4000, 0x381F8000, 0x381FC000,
+				0x38200000, 0x38204000, 0x38208000, 0x3820C000, 0x38210000, 0x38214000, 0x38218000, 0x3821C000, 0x38220000, 0x38224000, 0x38228000, 0x3822C000, 0x38230000, 0x38234000, 0x38238000, 0x3823C000,
+				0x38240000, 0x38244000, 0x38248000, 0x3824C000, 0x38250000, 0x38254000, 0x38258000, 0x3825C000, 0x38260000, 0x38264000, 0x38268000, 0x3826C000, 0x38270000, 0x38274000, 0x38278000, 0x3827C000,
+				0x38280000, 0x38284000, 0x38288000, 0x3828C000, 0x38290000, 0x38294000, 0x38298000, 0x3829C000, 0x382A0000, 0x382A4000, 0x382A8000, 0x382AC000, 0x382B0000, 0x382B4000, 0x382B8000, 0x382BC000,
+				0x382C0000, 0x382C4000, 0x382C8000, 0x382CC000, 0x382D0000, 0x382D4000, 0x382D8000, 0x382DC000, 0x382E0000, 0x382E4000, 0x382E8000, 0x382EC000, 0x382F0000, 0x382F4000, 0x382F8000, 0x382FC000,
+				0x38300000, 0x38304000, 0x38308000, 0x3830C000, 0x38310000, 0x38314000, 0x38318000, 0x3831C000, 0x38320000, 0x38324000, 0x38328000, 0x3832C000, 0x38330000, 0x38334000, 0x38338000, 0x3833C000,
+				0x38340000, 0x38344000, 0x38348000, 0x3834C000, 0x38350000, 0x38354000, 0x38358000, 0x3835C000, 0x38360000, 0x38364000, 0x38368000, 0x3836C000, 0x38370000, 0x38374000, 0x38378000, 0x3837C000,
+				0x38380000, 0x38384000, 0x38388000, 0x3838C000, 0x38390000, 0x38394000, 0x38398000, 0x3839C000, 0x383A0000, 0x383A4000, 0x383A8000, 0x383AC000, 0x383B0000, 0x383B4000, 0x383B8000, 0x383BC000,
+				0x383C0000, 0x383C4000, 0x383C8000, 0x383CC000, 0x383D0000, 0x383D4000, 0x383D8000, 0x383DC000, 0x383E0000, 0x383E4000, 0x383E8000, 0x383EC000, 0x383F0000, 0x383F4000, 0x383F8000, 0x383FC000,
+				0x38400000, 0x38404000, 0x38408000, 0x3840C000, 0x38410000, 0x38414000, 0x38418000, 0x3841C000, 0x38420000, 0x38424000, 0x38428000, 0x3842C000, 0x38430000, 0x38434000, 0x38438000, 0x3843C000,
+				0x38440000, 0x38444000, 0x38448000, 0x3844C000, 0x38450000, 0x38454000, 0x38458000, 0x3845C000, 0x38460000, 0x38464000, 0x38468000, 0x3846C000, 0x38470000, 0x38474000, 0x38478000, 0x3847C000,
+				0x38480000, 0x38484000, 0x38488000, 0x3848C000, 0x38490000, 0x38494000, 0x38498000, 0x3849C000, 0x384A0000, 0x384A4000, 0x384A8000, 0x384AC000, 0x384B0000, 0x384B4000, 0x384B8000, 0x384BC000,
+				0x384C0000, 0x384C4000, 0x384C8000, 0x384CC000, 0x384D0000, 0x384D4000, 0x384D8000, 0x384DC000, 0x384E0000, 0x384E4000, 0x384E8000, 0x384EC000, 0x384F0000, 0x384F4000, 0x384F8000, 0x384FC000,
+				0x38500000, 0x38504000, 0x38508000, 0x3850C000, 0x38510000, 0x38514000, 0x38518000, 0x3851C000, 0x38520000, 0x38524000, 0x38528000, 0x3852C000, 0x38530000, 0x38534000, 0x38538000, 0x3853C000,
+				0x38540000, 0x38544000, 0x38548000, 0x3854C000, 0x38550000, 0x38554000, 0x38558000, 0x3855C000, 0x38560000, 0x38564000, 0x38568000, 0x3856C000, 0x38570000, 0x38574000, 0x38578000, 0x3857C000,
+				0x38580000, 0x38584000, 0x38588000, 0x3858C000, 0x38590000, 0x38594000, 0x38598000, 0x3859C000, 0x385A0000, 0x385A4000, 0x385A8000, 0x385AC000, 0x385B0000, 0x385B4000, 0x385B8000, 0x385BC000,
+				0x385C0000, 0x385C4000, 0x385C8000, 0x385CC000, 0x385D0000, 0x385D4000, 0x385D8000, 0x385DC000, 0x385E0000, 0x385E4000, 0x385E8000, 0x385EC000, 0x385F0000, 0x385F4000, 0x385F8000, 0x385FC000,
+				0x38600000, 0x38604000, 0x38608000, 0x3860C000, 0x38610000, 0x38614000, 0x38618000, 0x3861C000, 0x38620000, 0x38624000, 0x38628000, 0x3862C000, 0x38630000, 0x38634000, 0x38638000, 0x3863C000,
+				0x38640000, 0x38644000, 0x38648000, 0x3864C000, 0x38650000, 0x38654000, 0x38658000, 0x3865C000, 0x38660000, 0x38664000, 0x38668000, 0x3866C000, 0x38670000, 0x38674000, 0x38678000, 0x3867C000,
+				0x38680000, 0x38684000, 0x38688000, 0x3868C000, 0x38690000, 0x38694000, 0x38698000, 0x3869C000, 0x386A0000, 0x386A4000, 0x386A8000, 0x386AC000, 0x386B0000, 0x386B4000, 0x386B8000, 0x386BC000,
+				0x386C0000, 0x386C4000, 0x386C8000, 0x386CC000, 0x386D0000, 0x386D4000, 0x386D8000, 0x386DC000, 0x386E0000, 0x386E4000, 0x386E8000, 0x386EC000, 0x386F0000, 0x386F4000, 0x386F8000, 0x386FC000,
+				0x38700000, 0x38704000, 0x38708000, 0x3870C000, 0x38710000, 0x38714000, 0x38718000, 0x3871C000, 0x38720000, 0x38724000, 0x38728000, 0x3872C000, 0x38730000, 0x38734000, 0x38738000, 0x3873C000,
+				0x38740000, 0x38744000, 0x38748000, 0x3874C000, 0x38750000, 0x38754000, 0x38758000, 0x3875C000, 0x38760000, 0x38764000, 0x38768000, 0x3876C000, 0x38770000, 0x38774000, 0x38778000, 0x3877C000,
+				0x38780000, 0x38784000, 0x38788000, 0x3878C000, 0x38790000, 0x38794000, 0x38798000, 0x3879C000, 0x387A0000, 0x387A4000, 0x387A8000, 0x387AC000, 0x387B0000, 0x387B4000, 0x387B8000, 0x387BC000,
+				0x387C0000, 0x387C4000, 0x387C8000, 0x387CC000, 0x387D0000, 0x387D4000, 0x387D8000, 0x387DC000, 0x387E0000, 0x387E4000, 0x387E8000, 0x387EC000, 0x387F0000, 0x387F4000, 0x387F8000, 0x387FC000,
+				0x38000000, 0x38002000, 0x38004000, 0x38006000, 0x38008000, 0x3800A000, 0x3800C000, 0x3800E000, 0x38010000, 0x38012000, 0x38014000, 0x38016000, 0x38018000, 0x3801A000, 0x3801C000, 0x3801E000,
+				0x38020000, 0x38022000, 0x38024000, 0x38026000, 0x38028000, 0x3802A000, 0x3802C000, 0x3802E000, 0x38030000, 0x38032000, 0x38034000, 0x38036000, 0x38038000, 0x3803A000, 0x3803C000, 0x3803E000,
+				0x38040000, 0x38042000, 0x38044000, 0x38046000, 0x38048000, 0x3804A000, 0x3804C000, 0x3804E000, 0x38050000, 0x38052000, 0x38054000, 0x38056000, 0x38058000, 0x3805A000, 0x3805C000, 0x3805E000,
+				0x38060000, 0x38062000, 0x38064000, 0x38066000, 0x38068000, 0x3806A000, 0x3806C000, 0x3806E000, 0x38070000, 0x38072000, 0x38074000, 0x38076000, 0x38078000, 0x3807A000, 0x3807C000, 0x3807E000,
+				0x38080000, 0x38082000, 0x38084000, 0x38086000, 0x38088000, 0x3808A000, 0x3808C000, 0x3808E000, 0x38090000, 0x38092000, 0x38094000, 0x38096000, 0x38098000, 0x3809A000, 0x3809C000, 0x3809E000,
+				0x380A0000, 0x380A2000, 0x380A4000, 0x380A6000, 0x380A8000, 0x380AA000, 0x380AC000, 0x380AE000, 0x380B0000, 0x380B2000, 0x380B4000, 0x380B6000, 0x380B8000, 0x380BA000, 0x380BC000, 0x380BE000,
+				0x380C0000, 0x380C2000, 0x380C4000, 0x380C6000, 0x380C8000, 0x380CA000, 0x380CC000, 0x380CE000, 0x380D0000, 0x380D2000, 0x380D4000, 0x380D6000, 0x380D8000, 0x380DA000, 0x380DC000, 0x380DE000,
+				0x380E0000, 0x380E2000, 0x380E4000, 0x380E6000, 0x380E8000, 0x380EA000, 0x380EC000, 0x380EE000, 0x380F0000, 0x380F2000, 0x380F4000, 0x380F6000, 0x380F8000, 0x380FA000, 0x380FC000, 0x380FE000,
+				0x38100000, 0x38102000, 0x38104000, 0x38106000, 0x38108000, 0x3810A000, 0x3810C000, 0x3810E000, 0x38110000, 0x38112000, 0x38114000, 0x38116000, 0x38118000, 0x3811A000, 0x3811C000, 0x3811E000,
+				0x38120000, 0x38122000, 0x38124000, 0x38126000, 0x38128000, 0x3812A000, 0x3812C000, 0x3812E000, 0x38130000, 0x38132000, 0x38134000, 0x38136000, 0x38138000, 0x3813A000, 0x3813C000, 0x3813E000,
+				0x38140000, 0x38142000, 0x38144000, 0x38146000, 0x38148000, 0x3814A000, 0x3814C000, 0x3814E000, 0x38150000, 0x38152000, 0x38154000, 0x38156000, 0x38158000, 0x3815A000, 0x3815C000, 0x3815E000,
+				0x38160000, 0x38162000, 0x38164000, 0x38166000, 0x38168000, 0x3816A000, 0x3816C000, 0x3816E000, 0x38170000, 0x38172000, 0x38174000, 0x38176000, 0x38178000, 0x3817A000, 0x3817C000, 0x3817E000,
+				0x38180000, 0x38182000, 0x38184000, 0x38186000, 0x38188000, 0x3818A000, 0x3818C000, 0x3818E000, 0x38190000, 0x38192000, 0x38194000, 0x38196000, 0x38198000, 0x3819A000, 0x3819C000, 0x3819E000,
+				0x381A0000, 0x381A2000, 0x381A4000, 0x381A6000, 0x381A8000, 0x381AA000, 0x381AC000, 0x381AE000, 0x381B0000, 0x381B2000, 0x381B4000, 0x381B6000, 0x381B8000, 0x381BA000, 0x381BC000, 0x381BE000,
+				0x381C0000, 0x381C2000, 0x381C4000, 0x381C6000, 0x381C8000, 0x381CA000, 0x381CC000, 0x381CE000, 0x381D0000, 0x381D2000, 0x381D4000, 0x381D6000, 0x381D8000, 0x381DA000, 0x381DC000, 0x381DE000,
+				0x381E0000, 0x381E2000, 0x381E4000, 0x381E6000, 0x381E8000, 0x381EA000, 0x381EC000, 0x381EE000, 0x381F0000, 0x381F2000, 0x381F4000, 0x381F6000, 0x381F8000, 0x381FA000, 0x381FC000, 0x381FE000,
+				0x38200000, 0x38202000, 0x38204000, 0x38206000, 0x38208000, 0x3820A000, 0x3820C000, 0x3820E000, 0x38210000, 0x38212000, 0x38214000, 0x38216000, 0x38218000, 0x3821A000, 0x3821C000, 0x3821E000,
+				0x38220000, 0x38222000, 0x38224000, 0x38226000, 0x38228000, 0x3822A000, 0x3822C000, 0x3822E000, 0x38230000, 0x38232000, 0x38234000, 0x38236000, 0x38238000, 0x3823A000, 0x3823C000, 0x3823E000,
+				0x38240000, 0x38242000, 0x38244000, 0x38246000, 0x38248000, 0x3824A000, 0x3824C000, 0x3824E000, 0x38250000, 0x38252000, 0x38254000, 0x38256000, 0x38258000, 0x3825A000, 0x3825C000, 0x3825E000,
+				0x38260000, 0x38262000, 0x38264000, 0x38266000, 0x38268000, 0x3826A000, 0x3826C000, 0x3826E000, 0x38270000, 0x38272000, 0x38274000, 0x38276000, 0x38278000, 0x3827A000, 0x3827C000, 0x3827E000,
+				0x38280000, 0x38282000, 0x38284000, 0x38286000, 0x38288000, 0x3828A000, 0x3828C000, 0x3828E000, 0x38290000, 0x38292000, 0x38294000, 0x38296000, 0x38298000, 0x3829A000, 0x3829C000, 0x3829E000,
+				0x382A0000, 0x382A2000, 0x382A4000, 0x382A6000, 0x382A8000, 0x382AA000, 0x382AC000, 0x382AE000, 0x382B0000, 0x382B2000, 0x382B4000, 0x382B6000, 0x382B8000, 0x382BA000, 0x382BC000, 0x382BE000,
+				0x382C0000, 0x382C2000, 0x382C4000, 0x382C6000, 0x382C8000, 0x382CA000, 0x382CC000, 0x382CE000, 0x382D0000, 0x382D2000, 0x382D4000, 0x382D6000, 0x382D8000, 0x382DA000, 0x382DC000, 0x382DE000,
+				0x382E0000, 0x382E2000, 0x382E4000, 0x382E6000, 0x382E8000, 0x382EA000, 0x382EC000, 0x382EE000, 0x382F0000, 0x382F2000, 0x382F4000, 0x382F6000, 0x382F8000, 0x382FA000, 0x382FC000, 0x382FE000,
+				0x38300000, 0x38302000, 0x38304000, 0x38306000, 0x38308000, 0x3830A000, 0x3830C000, 0x3830E000, 0x38310000, 0x38312000, 0x38314000, 0x38316000, 0x38318000, 0x3831A000, 0x3831C000, 0x3831E000,
+				0x38320000, 0x38322000, 0x38324000, 0x38326000, 0x38328000, 0x3832A000, 0x3832C000, 0x3832E000, 0x38330000, 0x38332000, 0x38334000, 0x38336000, 0x38338000, 0x3833A000, 0x3833C000, 0x3833E000,
+				0x38340000, 0x38342000, 0x38344000, 0x38346000, 0x38348000, 0x3834A000, 0x3834C000, 0x3834E000, 0x38350000, 0x38352000, 0x38354000, 0x38356000, 0x38358000, 0x3835A000, 0x3835C000, 0x3835E000,
+				0x38360000, 0x38362000, 0x38364000, 0x38366000, 0x38368000, 0x3836A000, 0x3836C000, 0x3836E000, 0x38370000, 0x38372000, 0x38374000, 0x38376000, 0x38378000, 0x3837A000, 0x3837C000, 0x3837E000,
+				0x38380000, 0x38382000, 0x38384000, 0x38386000, 0x38388000, 0x3838A000, 0x3838C000, 0x3838E000, 0x38390000, 0x38392000, 0x38394000, 0x38396000, 0x38398000, 0x3839A000, 0x3839C000, 0x3839E000,
+				0x383A0000, 0x383A2000, 0x383A4000, 0x383A6000, 0x383A8000, 0x383AA000, 0x383AC000, 0x383AE000, 0x383B0000, 0x383B2000, 0x383B4000, 0x383B6000, 0x383B8000, 0x383BA000, 0x383BC000, 0x383BE000,
+				0x383C0000, 0x383C2000, 0x383C4000, 0x383C6000, 0x383C8000, 0x383CA000, 0x383CC000, 0x383CE000, 0x383D0000, 0x383D2000, 0x383D4000, 0x383D6000, 0x383D8000, 0x383DA000, 0x383DC000, 0x383DE000,
+				0x383E0000, 0x383E2000, 0x383E4000, 0x383E6000, 0x383E8000, 0x383EA000, 0x383EC000, 0x383EE000, 0x383F0000, 0x383F2000, 0x383F4000, 0x383F6000, 0x383F8000, 0x383FA000, 0x383FC000, 0x383FE000,
+				0x38400000, 0x38402000, 0x38404000, 0x38406000, 0x38408000, 0x3840A000, 0x3840C000, 0x3840E000, 0x38410000, 0x38412000, 0x38414000, 0x38416000, 0x38418000, 0x3841A000, 0x3841C000, 0x3841E000,
+				0x38420000, 0x38422000, 0x38424000, 0x38426000, 0x38428000, 0x3842A000, 0x3842C000, 0x3842E000, 0x38430000, 0x38432000, 0x38434000, 0x38436000, 0x38438000, 0x3843A000, 0x3843C000, 0x3843E000,
+				0x38440000, 0x38442000, 0x38444000, 0x38446000, 0x38448000, 0x3844A000, 0x3844C000, 0x3844E000, 0x38450000, 0x38452000, 0x38454000, 0x38456000, 0x38458000, 0x3845A000, 0x3845C000, 0x3845E000,
+				0x38460000, 0x38462000, 0x38464000, 0x38466000, 0x38468000, 0x3846A000, 0x3846C000, 0x3846E000, 0x38470000, 0x38472000, 0x38474000, 0x38476000, 0x38478000, 0x3847A000, 0x3847C000, 0x3847E000,
+				0x38480000, 0x38482000, 0x38484000, 0x38486000, 0x38488000, 0x3848A000, 0x3848C000, 0x3848E000, 0x38490000, 0x38492000, 0x38494000, 0x38496000, 0x38498000, 0x3849A000, 0x3849C000, 0x3849E000,
+				0x384A0000, 0x384A2000, 0x384A4000, 0x384A6000, 0x384A8000, 0x384AA000, 0x384AC000, 0x384AE000, 0x384B0000, 0x384B2000, 0x384B4000, 0x384B6000, 0x384B8000, 0x384BA000, 0x384BC000, 0x384BE000,
+				0x384C0000, 0x384C2000, 0x384C4000, 0x384C6000, 0x384C8000, 0x384CA000, 0x384CC000, 0x384CE000, 0x384D0000, 0x384D2000, 0x384D4000, 0x384D6000, 0x384D8000, 0x384DA000, 0x384DC000, 0x384DE000,
+				0x384E0000, 0x384E2000, 0x384E4000, 0x384E6000, 0x384E8000, 0x384EA000, 0x384EC000, 0x384EE000, 0x384F0000, 0x384F2000, 0x384F4000, 0x384F6000, 0x384F8000, 0x384FA000, 0x384FC000, 0x384FE000,
+				0x38500000, 0x38502000, 0x38504000, 0x38506000, 0x38508000, 0x3850A000, 0x3850C000, 0x3850E000, 0x38510000, 0x38512000, 0x38514000, 0x38516000, 0x38518000, 0x3851A000, 0x3851C000, 0x3851E000,
+				0x38520000, 0x38522000, 0x38524000, 0x38526000, 0x38528000, 0x3852A000, 0x3852C000, 0x3852E000, 0x38530000, 0x38532000, 0x38534000, 0x38536000, 0x38538000, 0x3853A000, 0x3853C000, 0x3853E000,
+				0x38540000, 0x38542000, 0x38544000, 0x38546000, 0x38548000, 0x3854A000, 0x3854C000, 0x3854E000, 0x38550000, 0x38552000, 0x38554000, 0x38556000, 0x38558000, 0x3855A000, 0x3855C000, 0x3855E000,
+				0x38560000, 0x38562000, 0x38564000, 0x38566000, 0x38568000, 0x3856A000, 0x3856C000, 0x3856E000, 0x38570000, 0x38572000, 0x38574000, 0x38576000, 0x38578000, 0x3857A000, 0x3857C000, 0x3857E000,
+				0x38580000, 0x38582000, 0x38584000, 0x38586000, 0x38588000, 0x3858A000, 0x3858C000, 0x3858E000, 0x38590000, 0x38592000, 0x38594000, 0x38596000, 0x38598000, 0x3859A000, 0x3859C000, 0x3859E000,
+				0x385A0000, 0x385A2000, 0x385A4000, 0x385A6000, 0x385A8000, 0x385AA000, 0x385AC000, 0x385AE000, 0x385B0000, 0x385B2000, 0x385B4000, 0x385B6000, 0x385B8000, 0x385BA000, 0x385BC000, 0x385BE000,
+				0x385C0000, 0x385C2000, 0x385C4000, 0x385C6000, 0x385C8000, 0x385CA000, 0x385CC000, 0x385CE000, 0x385D0000, 0x385D2000, 0x385D4000, 0x385D6000, 0x385D8000, 0x385DA000, 0x385DC000, 0x385DE000,
+				0x385E0000, 0x385E2000, 0x385E4000, 0x385E6000, 0x385E8000, 0x385EA000, 0x385EC000, 0x385EE000, 0x385F0000, 0x385F2000, 0x385F4000, 0x385F6000, 0x385F8000, 0x385FA000, 0x385FC000, 0x385FE000,
+				0x38600000, 0x38602000, 0x38604000, 0x38606000, 0x38608000, 0x3860A000, 0x3860C000, 0x3860E000, 0x38610000, 0x38612000, 0x38614000, 0x38616000, 0x38618000, 0x3861A000, 0x3861C000, 0x3861E000,
+				0x38620000, 0x38622000, 0x38624000, 0x38626000, 0x38628000, 0x3862A000, 0x3862C000, 0x3862E000, 0x38630000, 0x38632000, 0x38634000, 0x38636000, 0x38638000, 0x3863A000, 0x3863C000, 0x3863E000,
+				0x38640000, 0x38642000, 0x38644000, 0x38646000, 0x38648000, 0x3864A000, 0x3864C000, 0x3864E000, 0x38650000, 0x38652000, 0x38654000, 0x38656000, 0x38658000, 0x3865A000, 0x3865C000, 0x3865E000,
+				0x38660000, 0x38662000, 0x38664000, 0x38666000, 0x38668000, 0x3866A000, 0x3866C000, 0x3866E000, 0x38670000, 0x38672000, 0x38674000, 0x38676000, 0x38678000, 0x3867A000, 0x3867C000, 0x3867E000,
+				0x38680000, 0x38682000, 0x38684000, 0x38686000, 0x38688000, 0x3868A000, 0x3868C000, 0x3868E000, 0x38690000, 0x38692000, 0x38694000, 0x38696000, 0x38698000, 0x3869A000, 0x3869C000, 0x3869E000,
+				0x386A0000, 0x386A2000, 0x386A4000, 0x386A6000, 0x386A8000, 0x386AA000, 0x386AC000, 0x386AE000, 0x386B0000, 0x386B2000, 0x386B4000, 0x386B6000, 0x386B8000, 0x386BA000, 0x386BC000, 0x386BE000,
+				0x386C0000, 0x386C2000, 0x386C4000, 0x386C6000, 0x386C8000, 0x386CA000, 0x386CC000, 0x386CE000, 0x386D0000, 0x386D2000, 0x386D4000, 0x386D6000, 0x386D8000, 0x386DA000, 0x386DC000, 0x386DE000,
+				0x386E0000, 0x386E2000, 0x386E4000, 0x386E6000, 0x386E8000, 0x386EA000, 0x386EC000, 0x386EE000, 0x386F0000, 0x386F2000, 0x386F4000, 0x386F6000, 0x386F8000, 0x386FA000, 0x386FC000, 0x386FE000,
+				0x38700000, 0x38702000, 0x38704000, 0x38706000, 0x38708000, 0x3870A000, 0x3870C000, 0x3870E000, 0x38710000, 0x38712000, 0x38714000, 0x38716000, 0x38718000, 0x3871A000, 0x3871C000, 0x3871E000,
+				0x38720000, 0x38722000, 0x38724000, 0x38726000, 0x38728000, 0x3872A000, 0x3872C000, 0x3872E000, 0x38730000, 0x38732000, 0x38734000, 0x38736000, 0x38738000, 0x3873A000, 0x3873C000, 0x3873E000,
+				0x38740000, 0x38742000, 0x38744000, 0x38746000, 0x38748000, 0x3874A000, 0x3874C000, 0x3874E000, 0x38750000, 0x38752000, 0x38754000, 0x38756000, 0x38758000, 0x3875A000, 0x3875C000, 0x3875E000,
+				0x38760000, 0x38762000, 0x38764000, 0x38766000, 0x38768000, 0x3876A000, 0x3876C000, 0x3876E000, 0x38770000, 0x38772000, 0x38774000, 0x38776000, 0x38778000, 0x3877A000, 0x3877C000, 0x3877E000,
+				0x38780000, 0x38782000, 0x38784000, 0x38786000, 0x38788000, 0x3878A000, 0x3878C000, 0x3878E000, 0x38790000, 0x38792000, 0x38794000, 0x38796000, 0x38798000, 0x3879A000, 0x3879C000, 0x3879E000,
+				0x387A0000, 0x387A2000, 0x387A4000, 0x387A6000, 0x387A8000, 0x387AA000, 0x387AC000, 0x387AE000, 0x387B0000, 0x387B2000, 0x387B4000, 0x387B6000, 0x387B8000, 0x387BA000, 0x387BC000, 0x387BE000,
+				0x387C0000, 0x387C2000, 0x387C4000, 0x387C6000, 0x387C8000, 0x387CA000, 0x387CC000, 0x387CE000, 0x387D0000, 0x387D2000, 0x387D4000, 0x387D6000, 0x387D8000, 0x387DA000, 0x387DC000, 0x387DE000,
+				0x387E0000, 0x387E2000, 0x387E4000, 0x387E6000, 0x387E8000, 0x387EA000, 0x387EC000, 0x387EE000, 0x387F0000, 0x387F2000, 0x387F4000, 0x387F6000, 0x387F8000, 0x387FA000, 0x387FC000, 0x387FE000 };
+			static const uint32 exponent_table[64] = {
+				0x00000000, 0x00800000, 0x01000000, 0x01800000, 0x02000000, 0x02800000, 0x03000000, 0x03800000, 0x04000000, 0x04800000, 0x05000000, 0x05800000, 0x06000000, 0x06800000, 0x07000000, 0x07800000,
+				0x08000000, 0x08800000, 0x09000000, 0x09800000, 0x0A000000, 0x0A800000, 0x0B000000, 0x0B800000, 0x0C000000, 0x0C800000, 0x0D000000, 0x0D800000, 0x0E000000, 0x0E800000, 0x0F000000, 0x47800000,
+				0x80000000, 0x80800000, 0x81000000, 0x81800000, 0x82000000, 0x82800000, 0x83000000, 0x83800000, 0x84000000, 0x84800000, 0x85000000, 0x85800000, 0x86000000, 0x86800000, 0x87000000, 0x87800000,
+				0x88000000, 0x88800000, 0x89000000, 0x89800000, 0x8A000000, 0x8A800000, 0x8B000000, 0x8B800000, 0x8C000000, 0x8C800000, 0x8D000000, 0x8D800000, 0x8E000000, 0x8E800000, 0x8F000000, 0xC7800000 };
+			static const unsigned short offset_table[64] = {
+				   0, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
+				   0, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024 };
+			uint32 bits = mantissa_table[offset_table[value>>10]+(value&0x3FF)] + exponent_table[value>>10];
+//			return *reinterpret_cast<float*>(&bits);			//violating strict aliasing!
+			float out;
+			std::memcpy(&out, &bits, sizeof(float));
+			return out;
+		}
+
+		/// Convert half-precision to IEEE double-precision.
+		/// \param value binary representation of half-precision value
+		/// \return double-precision value
+		inline double half2float_impl(uint16 value, double, true_type)
+		{
+			typedef bits<float>::type uint32;
+			typedef bits<double>::type uint64;
+			uint32 hi = static_cast<uint32>(value&0x8000) << 16;
+			int abs = value & 0x7FFF;
+			if(abs)
+			{
+				hi |= 0x3F000000 << static_cast<unsigned>(abs>=0x7C00);
+				for(; abs<0x400; abs<<=1,hi-=0x100000) ;
+				hi += static_cast<uint32>(abs) << 10;
+			}
+			uint64 bits = static_cast<uint64>(hi) << 32;
+//			return *reinterpret_cast<double*>(&bits);			//violating strict aliasing!
+			double out;
+			std::memcpy(&out, &bits, sizeof(double));
+			return out;
+		}
+
+		/// Convert half-precision to non-IEEE floating point.
+		/// \tparam T type to convert to (builtin integer type)
+		/// \param value binary representation of half-precision value
+		/// \return floating point value
+		template<typename T> T half2float_impl(uint16 value, T, ...)
+		{
+			T out;
+			int abs = value & 0x7FFF;
+			if(abs > 0x7C00)
+				out = std::numeric_limits<T>::has_quiet_NaN ? std::numeric_limits<T>::quiet_NaN() : T();
+			else if(abs == 0x7C00)
+				out = std::numeric_limits<T>::has_infinity ? std::numeric_limits<T>::infinity() : std::numeric_limits<T>::max();
+			else if(abs > 0x3FF)
+				out = std::ldexp(static_cast<T>((abs&0x3FF)|0x400), (abs>>10)-25);
+			else
+				out = std::ldexp(static_cast<T>(abs), -24);
+			return (value&0x8000) ? -out : out;
+		}
+
+		/// Convert half-precision to floating point.
+		/// \tparam T type to convert to (builtin integer type)
+		/// \param value binary representation of half-precision value
+		/// \return floating point value
+		template<typename T> T half2float(uint16 value)
+		{
+			return half2float_impl(value, T(), bool_type<std::numeric_limits<T>::is_iec559&&sizeof(typename bits<T>::type)==sizeof(T)>());
+		}
+
+		/// Convert half-precision floating point to integer.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam E `true` for round to even, `false` for round away from zero
+		/// \tparam T type to convert to (buitlin integer type with at least 16 bits precision, excluding any implicit sign bits)
+		/// \param value binary representation of half-precision value
+		/// \return integral value
+		template<std::float_round_style R,bool E,typename T> T half2int_impl(uint16 value)
+		{
+		#if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS
+			static_assert(std::is_integral<T>::value, "half to int conversion only supports builtin integer types");
+		#endif
+			unsigned int e = value & 0x7FFF;
+			if(e >= 0x7C00)
+				return (value&0x8000) ? std::numeric_limits<T>::min() : std::numeric_limits<T>::max();
+			if(e < 0x3800)
+			{
+				if(R == std::round_toward_infinity)
+					return T(~(value>>15)&(e!=0));
+				else if(R == std::round_toward_neg_infinity)
+					return -T(value>0x8000);
+				return T();
+			}
+			unsigned int m = (value&0x3FF) | 0x400;
+			e >>= 10;
+			if(e < 25)
+			{
+				if(R == std::round_to_nearest)
+					m += (1<<(24-e)) - (~(m>>(25-e))&E);
+				else if(R == std::round_toward_infinity)
+					m += ((value>>15)-1) & ((1<<(25-e))-1U);
+				else if(R == std::round_toward_neg_infinity)
+					m += -(value>>15) & ((1<<(25-e))-1U);
+				m >>= 25 - e;
+			}
+			else
+				m <<= e - 25;
+			return (value&0x8000) ? -static_cast<T>(m) : static_cast<T>(m);
+		}
+
+		/// Convert half-precision floating point to integer.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam T type to convert to (buitlin integer type with at least 16 bits precision, excluding any implicit sign bits)
+		/// \param value binary representation of half-precision value
+		/// \return integral value
+		template<std::float_round_style R,typename T> T half2int(uint16 value) { return half2int_impl<R,HALF_ROUND_TIES_TO_EVEN,T>(value); }
+
+		/// Convert half-precision floating point to integer using round-to-nearest-away-from-zero.
+		/// \tparam T type to convert to (buitlin integer type with at least 16 bits precision, excluding any implicit sign bits)
+		/// \param value binary representation of half-precision value
+		/// \return integral value
+		template<typename T> T half2int_up(uint16 value) { return half2int_impl<std::round_to_nearest,0,T>(value); }
+
+		/// Round half-precision number to nearest integer value.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \tparam E `true` for round to even, `false` for round away from zero
+		/// \param value binary representation of half-precision value
+		/// \return half-precision bits for nearest integral value
+		template<std::float_round_style R,bool E> uint16 round_half_impl(uint16 value)
+		{
+			unsigned int e = value & 0x7FFF;
+			uint16 result = value;
+			if(e < 0x3C00)
+			{
+				result &= 0x8000;
+				if(R == std::round_to_nearest)
+					result |= 0x3C00U & -(e>=(0x3800+E));
+				else if(R == std::round_toward_infinity)
+					result |= 0x3C00U & -(~(value>>15)&(e!=0));
+				else if(R == std::round_toward_neg_infinity)
+					result |= 0x3C00U & -(value>0x8000);
+			}
+			else if(e < 0x6400)
+			{
+				e = 25 - (e>>10);
+				unsigned int mask = (1<<e) - 1;
+				if(R == std::round_to_nearest)
+					result += (1<<(e-1)) - (~(result>>e)&E);
+				else if(R == std::round_toward_infinity)
+					result += mask & ((value>>15)-1);
+				else if(R == std::round_toward_neg_infinity)
+					result += mask & -(value>>15);
+				result &= ~mask;
+			}
+			return result;
+		}
+
+		/// Round half-precision number to nearest integer value.
+		/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest rounding
+		/// \param value binary representation of half-precision value
+		/// \return half-precision bits for nearest integral value
+		template<std::float_round_style R> uint16 round_half(uint16 value) { return round_half_impl<R,HALF_ROUND_TIES_TO_EVEN>(value); }
+
+		/// Round half-precision number to nearest integer value using round-to-nearest-away-from-zero.
+		/// \param value binary representation of half-precision value
+		/// \return half-precision bits for nearest integral value
+		inline uint16 round_half_up(uint16 value) { return round_half_impl<std::round_to_nearest,0>(value); }
+		/// \}
+
+		struct functions;
+		template<typename> struct unary_specialized;
+		template<typename,typename> struct binary_specialized;
+		template<typename,typename,std::float_round_style> struct half_caster;
+	}
+
+	/// Half-precision floating point type.
+	/// This class implements an IEEE-conformant half-precision floating point type with the usual arithmetic operators and
+	/// conversions. It is implicitly convertible to single-precision floating point, which makes artihmetic expressions and
+	/// functions with mixed-type operands to be of the most precise operand type. Additionally all arithmetic operations
+	/// (and many mathematical functions) are carried out in single-precision internally. All conversions from single- to
+	/// half-precision are done using the library's default rounding mode, but temporary results inside chained arithmetic
+	/// expressions are kept in single-precision as long as possible (while of course still maintaining a strong half-precision type).
+	///
+	/// According to the C++98/03 definition, the half type is not a POD type. But according to C++11's less strict and
+	/// extended definitions it is both a standard layout type and a trivially copyable type (even if not a POD type), which
+	/// means it can be standard-conformantly copied using raw binary copies. But in this context some more words about the
+	/// actual size of the type. Although the half is representing an IEEE 16-bit type, it does not neccessarily have to be of
+	/// exactly 16-bits size. But on any reasonable implementation the actual binary representation of this type will most
+	/// probably not ivolve any additional "magic" or padding beyond the simple binary representation of the underlying 16-bit
+	/// IEEE number, even if not strictly guaranteed by the standard. But even then it only has an actual size of 16 bits if
+	/// your C++ implementation supports an unsigned integer type of exactly 16 bits width. But this should be the case on
+	/// nearly any reasonable platform.
+	///
+	/// So if your C++ implementation is not totally exotic or imposes special alignment requirements, it is a reasonable
+	/// assumption that the data of a half is just comprised of the 2 bytes of the underlying IEEE representation.
+	class half
+	{
+		friend struct detail::functions;
+		friend struct detail::unary_specialized<half>;
+		friend struct detail::binary_specialized<half,half>;
+		template<typename,typename,std::float_round_style> friend struct detail::half_caster;
+		friend class std::numeric_limits<half>;
+	#if HALF_ENABLE_CPP11_HASH
+		friend struct std::hash<half>;
+	#endif
+	#if HALF_ENABLE_CPP11_USER_LITERALS
+		friend half literal::operator""_h(long double);
+	#endif
+
+	public:
+		/// Default constructor.
+		/// This initializes the half to 0. Although this does not match the builtin types' default-initialization semantics
+		/// and may be less efficient than no initialization, it is needed to provide proper value-initialization semantics.
+		HALF_CONSTEXPR half() HALF_NOEXCEPT : data_() {}
+
+		/// Copy constructor.
+		/// \tparam T type of concrete half expression
+		/// \param rhs half expression to copy from
+		half(detail::expr rhs) : data_(detail::float2half<round_style>(static_cast<float>(rhs))) {}
+
+		/// Conversion constructor.
+		/// \param rhs float to convert
+		explicit half(float rhs) : data_(detail::float2half<round_style>(rhs)) {}
+	
+		/// Conversion to single-precision.
+		/// \return single precision value representing expression value
+		operator float() const { return detail::half2float<float>(data_); }
+
+		/// Assignment operator.
+		/// \tparam T type of concrete half expression
+		/// \param rhs half expression to copy from
+		/// \return reference to this half
+		half& operator=(detail::expr rhs) { return *this = static_cast<float>(rhs); }
+
+		/// Arithmetic assignment.
+		/// \tparam T type of concrete half expression
+		/// \param rhs half expression to add
+		/// \return reference to this half
+		template<typename T> typename detail::enable<half&,T>::type operator+=(T rhs) { return *this += static_cast<float>(rhs); }
+
+		/// Arithmetic assignment.
+		/// \tparam T type of concrete half expression
+		/// \param rhs half expression to subtract
+		/// \return reference to this half
+		template<typename T> typename detail::enable<half&,T>::type operator-=(T rhs) { return *this -= static_cast<float>(rhs); }
+
+		/// Arithmetic assignment.
+		/// \tparam T type of concrete half expression
+		/// \param rhs half expression to multiply with
+		/// \return reference to this half
+		template<typename T> typename detail::enable<half&,T>::type operator*=(T rhs) { return *this *= static_cast<float>(rhs); }
+
+		/// Arithmetic assignment.
+		/// \tparam T type of concrete half expression
+		/// \param rhs half expression to divide by
+		/// \return reference to this half
+		template<typename T> typename detail::enable<half&,T>::type operator/=(T rhs) { return *this /= static_cast<float>(rhs); }
+
+		/// Assignment operator.
+		/// \param rhs single-precision value to copy from
+		/// \return reference to this half
+		half& operator=(float rhs) { data_ = detail::float2half<round_style>(rhs); return *this; }
+
+		/// Arithmetic assignment.
+		/// \param rhs single-precision value to add
+		/// \return reference to this half
+		half& operator+=(float rhs) { data_ = detail::float2half<round_style>(detail::half2float<float>(data_)+rhs); return *this; }
+
+		/// Arithmetic assignment.
+		/// \param rhs single-precision value to subtract
+		/// \return reference to this half
+		half& operator-=(float rhs) { data_ = detail::float2half<round_style>(detail::half2float<float>(data_)-rhs); return *this; }
+
+		/// Arithmetic assignment.
+		/// \param rhs single-precision value to multiply with
+		/// \return reference to this half
+		half& operator*=(float rhs) { data_ = detail::float2half<round_style>(detail::half2float<float>(data_)*rhs); return *this; }
+
+		/// Arithmetic assignment.
+		/// \param rhs single-precision value to divide by
+		/// \return reference to this half
+		half& operator/=(float rhs) { data_ = detail::float2half<round_style>(detail::half2float<float>(data_)/rhs); return *this; }
+
+		/// Prefix increment.
+		/// \return incremented half value
+		half& operator++() { return *this += 1.0f; }
+
+		/// Prefix decrement.
+		/// \return decremented half value
+		half& operator--() { return *this -= 1.0f; }
+
+		/// Postfix increment.
+		/// \return non-incremented half value
+		half operator++(int) { half out(*this); ++*this; return out; }
+
+		/// Postfix decrement.
+		/// \return non-decremented half value
+		half operator--(int) { half out(*this); --*this; return out; }
+	
+	private:
+		/// Rounding mode to use
+		static const std::float_round_style round_style = (std::float_round_style)(HALF_ROUND_STYLE);
+
+		/// Constructor.
+		/// \param bits binary representation to set half to
+		HALF_CONSTEXPR half(detail::binary_t, detail::uint16 bits) HALF_NOEXCEPT : data_(bits) {}
+
+		/// Internal binary representation
+		detail::uint16 data_;
+	};
+
+#if HALF_ENABLE_CPP11_USER_LITERALS
+	namespace literal
+	{
+		/// Half literal.
+		/// While this returns an actual half-precision value, half literals can unfortunately not be constant expressions due
+		/// to rather involved conversions.
+		/// \param value literal value
+		/// \return half with given value (if representable)
+		inline half operator""_h(long double value) { return half(detail::binary, detail::float2half<half::round_style>(value)); }
+	}
+#endif
+
+	namespace detail
+	{
+		/// Wrapper implementing unspecialized half-precision functions.
+		struct functions
+		{
+			/// Addition implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Half-precision sum stored in single-precision
+			static expr plus(float x, float y) { return expr(x+y); }
+
+			/// Subtraction implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Half-precision difference stored in single-precision
+			static expr minus(float x, float y) { return expr(x-y); }
+
+			/// Multiplication implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Half-precision product stored in single-precision
+			static expr multiplies(float x, float y) { return expr(x*y); }
+
+			/// Division implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Half-precision quotient stored in single-precision
+			static expr divides(float x, float y) { return expr(x/y); }
+
+			/// Output implementation.
+			/// \param out stream to write to
+			/// \param arg value to write
+			/// \return reference to stream
+			template<typename charT,typename traits> static std::basic_ostream<charT,traits>& write(std::basic_ostream<charT,traits> &out, float arg) { return out << arg; }
+
+			/// Input implementation.
+			/// \param in stream to read from
+			/// \param arg half to read into
+			/// \return reference to stream
+			template<typename charT,typename traits> static std::basic_istream<charT,traits>& read(std::basic_istream<charT,traits> &in, half &arg)
+			{
+				float f;
+				if(in >> f)
+					arg = f;
+				return in;
+			}
+
+			/// Modulo implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Half-precision division remainder stored in single-precision
+			static expr fmod(float x, float y) { return expr(std::fmod(x, y)); }
+
+			/// Remainder implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Half-precision division remainder stored in single-precision
+			static expr remainder(float x, float y)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::remainder(x, y));
+			#else
+				if(builtin_isnan(x) || builtin_isnan(y))
+					return expr(std::numeric_limits<float>::quiet_NaN());
+				float ax = std::fabs(x), ay = std::fabs(y);
+				if(ax >= 65536.0f || ay < std::ldexp(1.0f, -24))
+					return expr(std::numeric_limits<float>::quiet_NaN());
+				if(ay >= 65536.0f)
+					return expr(x);
+				if(ax == ay)
+					return expr(builtin_signbit(x) ? -0.0f : 0.0f);
+				ax = std::fmod(ax, ay+ay);
+				float y2 = 0.5f * ay;
+				if(ax > y2)
+				{
+					ax -= ay;
+					if(ax >= y2)
+						ax -= ay;
+				}
+				return expr(builtin_signbit(x) ? -ax : ax);
+			#endif
+			}
+
+			/// Remainder implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \param quo address to store quotient bits at
+			/// \return Half-precision division remainder stored in single-precision
+			static expr remquo(float x, float y, int *quo)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::remquo(x, y, quo));
+			#else
+				if(builtin_isnan(x) || builtin_isnan(y))
+					return expr(std::numeric_limits<float>::quiet_NaN());
+				bool sign = builtin_signbit(x), qsign = static_cast<bool>(sign^builtin_signbit(y));
+				float ax = std::fabs(x), ay = std::fabs(y);
+				if(ax >= 65536.0f || ay < std::ldexp(1.0f, -24))
+					return expr(std::numeric_limits<float>::quiet_NaN());
+				if(ay >= 65536.0f)
+					return expr(x);
+				if(ax == ay)
+					return *quo = qsign ? -1 : 1, expr(sign ? -0.0f : 0.0f);
+				ax = std::fmod(ax, 8.0f*ay);
+				int cquo = 0;
+				if(ax >= 4.0f * ay)
+				{
+					ax -= 4.0f * ay;
+					cquo += 4;
+				}
+				if(ax >= 2.0f * ay)
+				{
+					ax -= 2.0f * ay;
+					cquo += 2;
+				}
+				float y2 = 0.5f * ay;
+				if(ax > y2)
+				{
+					ax -= ay;
+					++cquo;
+					if(ax >= y2)
+					{
+						ax -= ay;
+						++cquo;
+					}
+				}
+				return *quo = qsign ? -cquo : cquo, expr(sign ? -ax : ax);
+			#endif
+			}
+
+			/// Positive difference implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return Positive difference stored in single-precision
+			static expr fdim(float x, float y)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::fdim(x, y));
+			#else
+				return expr((x<=y) ? 0.0f : (x-y));
+			#endif
+			}
+
+			/// Fused multiply-add implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \param z third operand
+			/// \return \a x * \a y + \a z stored in single-precision
+			static expr fma(float x, float y, float z)
+			{
+			#if HALF_ENABLE_CPP11_CMATH && defined(FP_FAST_FMAF)
+				return expr(std::fma(x, y, z));
+			#else
+				return expr(x*y+z);
+			#endif
+			}
+
+			/// Get NaN.
+			/// \return Half-precision quiet NaN
+			static half nanh() { return half(binary, 0x7FFF); }
+
+			/// Exponential implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr exp(float arg) { return expr(std::exp(arg)); }
+
+			/// Exponential implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr expm1(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::expm1(arg));
+			#else
+				return expr(static_cast<float>(std::exp(static_cast<double>(arg))-1.0));
+			#endif
+			}
+
+			/// Binary exponential implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr exp2(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::exp2(arg));
+			#else
+				return expr(static_cast<float>(std::exp(arg*0.69314718055994530941723212145818)));
+			#endif
+			}
+
+			/// Logarithm implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr log(float arg) { return expr(std::log(arg)); }
+
+			/// Common logarithm implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr log10(float arg) { return expr(std::log10(arg)); }
+
+			/// Logarithm implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr log1p(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::log1p(arg));
+			#else
+				return expr(static_cast<float>(std::log(1.0+arg)));
+			#endif
+			}
+
+			/// Binary logarithm implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr log2(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::log2(arg));
+			#else
+				return expr(static_cast<float>(std::log(static_cast<double>(arg))*1.4426950408889634073599246810019));
+			#endif
+			}
+
+			/// Square root implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr sqrt(float arg) { return expr(std::sqrt(arg)); }
+
+			/// Cubic root implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr cbrt(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::cbrt(arg));
+			#else
+				if(builtin_isnan(arg) || builtin_isinf(arg))
+					return expr(arg);
+				return expr(builtin_signbit(arg) ? -static_cast<float>(std::pow(-static_cast<double>(arg), 1.0/3.0)) :
+					static_cast<float>(std::pow(static_cast<double>(arg), 1.0/3.0)));
+			#endif
+			}
+
+			/// Hypotenuse implementation.
+			/// \param x first argument
+			/// \param y second argument
+			/// \return function value stored in single-preicision
+			static expr hypot(float x, float y)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::hypot(x, y));
+			#else
+				return expr((builtin_isinf(x) || builtin_isinf(y)) ? std::numeric_limits<float>::infinity() :
+					static_cast<float>(std::sqrt(static_cast<double>(x)*x+static_cast<double>(y)*y)));
+			#endif
+			}
+
+			/// Power implementation.
+			/// \param base value to exponentiate
+			/// \param exp power to expontiate to
+			/// \return function value stored in single-preicision
+			static expr pow(float base, float exp) { return expr(std::pow(base, exp)); }
+
+			/// Sine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr sin(float arg) { return expr(std::sin(arg)); }
+
+			/// Cosine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr cos(float arg) { return expr(std::cos(arg)); }
+
+			/// Tan implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr tan(float arg) { return expr(std::tan(arg)); }
+
+			/// Arc sine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr asin(float arg) { return expr(std::asin(arg)); }
+
+			/// Arc cosine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr acos(float arg) { return expr(std::acos(arg)); }
+
+			/// Arc tangent implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr atan(float arg) { return expr(std::atan(arg)); }
+
+			/// Arc tangent implementation.
+			/// \param x first argument
+			/// \param y second argument
+			/// \return function value stored in single-preicision
+			static expr atan2(float x, float y) { return expr(std::atan2(x, y)); }
+
+			/// Hyperbolic sine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr sinh(float arg) { return expr(std::sinh(arg)); }
+
+			/// Hyperbolic cosine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr cosh(float arg) { return expr(std::cosh(arg)); }
+
+			/// Hyperbolic tangent implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr tanh(float arg) { return expr(std::tanh(arg)); }
+
+			/// Hyperbolic area sine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr asinh(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::asinh(arg));
+			#else
+				return expr((arg==-std::numeric_limits<float>::infinity()) ? arg : static_cast<float>(std::log(arg+std::sqrt(arg*arg+1.0))));
+			#endif
+			}
+
+			/// Hyperbolic area cosine implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr acosh(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::acosh(arg));
+			#else
+				return expr((arg<-1.0f) ? std::numeric_limits<float>::quiet_NaN() : static_cast<float>(std::log(arg+std::sqrt(arg*arg-1.0))));
+			#endif
+			}
+
+			/// Hyperbolic area tangent implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr atanh(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::atanh(arg));
+			#else
+				return expr(static_cast<float>(0.5*std::log((1.0+arg)/(1.0-arg))));
+			#endif
+			}
+
+			/// Error function implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr erf(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::erf(arg));
+			#else
+				return expr(static_cast<float>(erf(static_cast<double>(arg))));
+			#endif
+			}
+
+			/// Complementary implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr erfc(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::erfc(arg));
+			#else
+				return expr(static_cast<float>(1.0-erf(static_cast<double>(arg))));
+			#endif
+			}
+
+			/// Gamma logarithm implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr lgamma(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::lgamma(arg));
+			#else
+				if(builtin_isinf(arg))
+					return expr(std::numeric_limits<float>::infinity());
+				if(arg < 0.0f)
+				{
+					float i, f = std::modf(-arg, &i);
+					if(f == 0.0f)
+						return expr(std::numeric_limits<float>::infinity());
+					return expr(static_cast<float>(1.1447298858494001741434273513531-
+						std::log(std::abs(std::sin(3.1415926535897932384626433832795*f)))-lgamma(1.0-arg)));
+				}
+				return expr(static_cast<float>(lgamma(static_cast<double>(arg))));
+			#endif
+			}
+
+			/// Gamma implementation.
+			/// \param arg function argument
+			/// \return function value stored in single-preicision
+			static expr tgamma(float arg)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::tgamma(arg));
+			#else
+				if(arg == 0.0f)
+					return builtin_signbit(arg) ? expr(-std::numeric_limits<float>::infinity()) : expr(std::numeric_limits<float>::infinity());
+				if(arg < 0.0f)
+				{
+					float i, f = std::modf(-arg, &i);
+					if(f == 0.0f)
+						return expr(std::numeric_limits<float>::quiet_NaN());
+					double value = 3.1415926535897932384626433832795 / (std::sin(3.1415926535897932384626433832795*f)*std::exp(lgamma(1.0-arg)));
+					return expr(static_cast<float>((std::fmod(i, 2.0f)==0.0f) ? -value : value));
+				}
+				if(builtin_isinf(arg))
+					return expr(arg);
+				return expr(static_cast<float>(std::exp(lgamma(static_cast<double>(arg)))));
+			#endif
+			}
+
+			/// Floor implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static half floor(half arg) { return half(binary, round_half<std::round_toward_neg_infinity>(arg.data_)); }
+
+			/// Ceiling implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static half ceil(half arg) { return half(binary, round_half<std::round_toward_infinity>(arg.data_)); }
+
+			/// Truncation implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static half trunc(half arg) { return half(binary, round_half<std::round_toward_zero>(arg.data_)); }
+
+			/// Nearest integer implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static half round(half arg) { return half(binary, round_half_up(arg.data_)); }
+
+			/// Nearest integer implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static long lround(half arg) { return detail::half2int_up<long>(arg.data_); }
+
+			/// Nearest integer implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static half rint(half arg) { return half(binary, round_half<half::round_style>(arg.data_)); }
+
+			/// Nearest integer implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static long lrint(half arg) { return detail::half2int<half::round_style,long>(arg.data_); }
+
+		#if HALF_ENABLE_CPP11_LONG_LONG
+			/// Nearest integer implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static long long llround(half arg) { return detail::half2int_up<long long>(arg.data_); }
+
+			/// Nearest integer implementation.
+			/// \param arg value to round
+			/// \return rounded value
+			static long long llrint(half arg) { return detail::half2int<half::round_style,long long>(arg.data_); }
+		#endif
+
+			/// Decompression implementation.
+			/// \param arg number to decompress
+			/// \param exp address to store exponent at
+			/// \return normalized significant
+			static half frexp(half arg, int *exp)
+			{
+				int m = arg.data_ & 0x7FFF, e = -14;
+				if(m >= 0x7C00 || !m)
+					return *exp = 0, arg;
+				for(; m<0x400; m<<=1,--e) ;
+				return *exp = e+(m>>10), half(binary, (arg.data_&0x8000)|0x3800|(m&0x3FF));
+			}
+
+			/// Decompression implementation.
+			/// \param arg number to decompress
+			/// \param iptr address to store integer part at
+			/// \return fractional part
+			static half modf(half arg, half *iptr)
+			{
+				unsigned int e = arg.data_ & 0x7FFF;
+				if(e >= 0x6400)
+					return *iptr = arg, half(binary, arg.data_&(0x8000U|-(e>0x7C00)));
+				if(e < 0x3C00)
+					return iptr->data_ = arg.data_ & 0x8000, arg;
+				e >>= 10;
+				unsigned int mask = (1<<(25-e)) - 1, m = arg.data_ & mask;
+				iptr->data_ = arg.data_ & ~mask;
+				if(!m)
+					return half(binary, arg.data_&0x8000);
+				for(; m<0x400; m<<=1,--e) ;
+				return half(binary, static_cast<uint16>((arg.data_&0x8000)|(e<<10)|(m&0x3FF)));
+			}
+
+			/// Scaling implementation.
+			/// \param arg number to scale
+			/// \param exp power of two to scale by
+			/// \return scaled number
+			static half scalbln(half arg, long exp)
+			{
+				unsigned int m = arg.data_ & 0x7FFF;
+				if(m >= 0x7C00 || !m)
+					return arg;
+				for(; m<0x400; m<<=1,--exp) ;
+				exp += m >> 10;
+				uint16 value = arg.data_ & 0x8000;
+				if(exp > 30)
+				{
+					if(half::round_style == std::round_toward_zero)
+						value |= 0x7BFF;
+					else if(half::round_style == std::round_toward_infinity)
+						value |= 0x7C00 - (value>>15);
+					else if(half::round_style == std::round_toward_neg_infinity)
+						value |= 0x7BFF + (value>>15);
+					else
+						value |= 0x7C00;
+				}
+				else if(exp > 0)
+					value |= (exp<<10) | (m&0x3FF);
+				else if(exp > -11)
+				{
+					m = (m&0x3FF) | 0x400;
+					if(half::round_style == std::round_to_nearest)
+					{
+						m += 1 << -exp;
+					#if HALF_ROUND_TIES_TO_EVEN
+						m -= (m>>(1-exp)) & 1;
+					#endif
+					}
+					else if(half::round_style == std::round_toward_infinity)
+						m += ((value>>15)-1) & ((1<<(1-exp))-1U);
+					else if(half::round_style == std::round_toward_neg_infinity)
+						m += -(value>>15) & ((1<<(1-exp))-1U);
+					value |= m >> (1-exp);
+				}
+				else if(half::round_style == std::round_toward_infinity)
+					value -= (value>>15) - 1;
+				else if(half::round_style == std::round_toward_neg_infinity)
+					value += value >> 15;
+				return half(binary, value);
+			}
+
+			/// Exponent implementation.
+			/// \param arg number to query
+			/// \return floating point exponent
+			static int ilogb(half arg)
+			{
+				int abs = arg.data_ & 0x7FFF;
+				if(!abs)
+					return FP_ILOGB0;
+				if(abs < 0x7C00)
+				{
+					int exp = (abs>>10) - 15;
+					if(abs < 0x400)
+						for(; abs<0x200; abs<<=1,--exp) ;
+					return exp;
+				}
+				if(abs > 0x7C00)
+					return FP_ILOGBNAN;
+				return INT_MAX;
+			}
+
+			/// Exponent implementation.
+			/// \param arg number to query
+			/// \return floating point exponent
+			static half logb(half arg)
+			{
+				int abs = arg.data_ & 0x7FFF;
+				if(!abs)
+					return half(binary, 0xFC00);
+				if(abs < 0x7C00)
+				{
+					int exp = (abs>>10) - 15;
+					if(abs < 0x400)
+						for(; abs<0x200; abs<<=1,--exp) ;
+					uint16 bits = (exp<0) << 15;
+					if(exp)
+					{
+						unsigned int m = std::abs(exp) << 6, e = 18;
+						for(; m<0x400; m<<=1,--e) ;
+						bits |= (e<<10) + m;
+					}
+					return half(binary, bits);
+				}
+				if(abs > 0x7C00)
+					return arg;
+				return half(binary, 0x7C00);
+			}
+
+			/// Enumeration implementation.
+			/// \param from number to increase/decrease
+			/// \param to direction to enumerate into
+			/// \return next representable number
+			static half nextafter(half from, half to)
+			{
+				uint16 fabs = from.data_ & 0x7FFF, tabs = to.data_ & 0x7FFF;
+				if(fabs > 0x7C00)
+					return from;
+				if(tabs > 0x7C00 || from.data_ == to.data_ || !(fabs|tabs))
+					return to;
+				if(!fabs)
+					return half(binary, (to.data_&0x8000)+1);
+				bool lt = ((fabs==from.data_) ? static_cast<int>(fabs) : -static_cast<int>(fabs)) <
+					((tabs==to.data_) ? static_cast<int>(tabs) : -static_cast<int>(tabs));
+				return half(binary, from.data_+(((from.data_>>15)^static_cast<unsigned>(lt))<<1)-1);
+			}
+
+			/// Enumeration implementation.
+			/// \param from number to increase/decrease
+			/// \param to direction to enumerate into
+			/// \return next representable number
+			static half nexttoward(half from, long double to)
+			{
+				if(isnan(from))
+					return from;
+				long double lfrom = static_cast<long double>(from);
+				if(builtin_isnan(to) || lfrom == to)
+					return half(static_cast<float>(to));
+				if(!(from.data_&0x7FFF))
+					return half(binary, (static_cast<detail::uint16>(builtin_signbit(to))<<15)+1);
+				return half(binary, from.data_+(((from.data_>>15)^static_cast<unsigned>(lfrom<to))<<1)-1);
+			}
+
+			/// Sign implementation
+			/// \param x first operand
+			/// \param y second operand
+			/// \return composed value
+			static half copysign(half x, half y) { return half(binary, x.data_^((x.data_^y.data_)&0x8000)); }
+
+			/// Classification implementation.
+			/// \param arg value to classify
+			/// \retval true if infinite number
+			/// \retval false else
+			static int fpclassify(half arg)
+			{
+				unsigned int abs = arg.data_ & 0x7FFF;
+				return abs ? ((abs>0x3FF) ? ((abs>=0x7C00) ? ((abs>0x7C00) ? FP_NAN : FP_INFINITE) : FP_NORMAL) :FP_SUBNORMAL) : FP_ZERO;
+			}
+
+			/// Classification implementation.
+			/// \param arg value to classify
+			/// \retval true if finite number
+			/// \retval false else
+			static bool isfinite(half arg) { return (arg.data_&0x7C00) != 0x7C00; }
+
+			/// Classification implementation.
+			/// \param arg value to classify
+			/// \retval true if infinite number
+			/// \retval false else
+			static bool isinf(half arg) { return (arg.data_&0x7FFF) == 0x7C00; }
+
+			/// Classification implementation.
+			/// \param arg value to classify
+			/// \retval true if not a number
+			/// \retval false else
+			static bool isnan(half arg) { return (arg.data_&0x7FFF) > 0x7C00; }
+
+			/// Classification implementation.
+			/// \param arg value to classify
+			/// \retval true if normal number
+			/// \retval false else
+			static bool isnormal(half arg) { return ((arg.data_&0x7C00)!=0) & ((arg.data_&0x7C00)!=0x7C00); }
+
+			/// Sign bit implementation.
+			/// \param arg value to check
+			/// \retval true if signed
+			/// \retval false if unsigned
+			static bool signbit(half arg) { return (arg.data_&0x8000) != 0; }
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if operands equal
+			/// \retval false else
+			static bool isequal(half x, half y) { return (x.data_==y.data_ || !((x.data_|y.data_)&0x7FFF)) && !isnan(x); }
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if operands not equal
+			/// \retval false else
+			static bool isnotequal(half x, half y) { return (x.data_!=y.data_ && ((x.data_|y.data_)&0x7FFF)) || isnan(x); }
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if \a x > \a y
+			/// \retval false else
+			static bool isgreater(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				return xabs<=0x7C00 && yabs<=0x7C00 && (((xabs==x.data_) ? xabs : -xabs) > ((yabs==y.data_) ? yabs : -yabs));
+			}
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if \a x >= \a y
+			/// \retval false else
+			static bool isgreaterequal(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				return xabs<=0x7C00 && yabs<=0x7C00 && (((xabs==x.data_) ? xabs : -xabs) >= ((yabs==y.data_) ? yabs : -yabs));
+			}
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if \a x < \a y
+			/// \retval false else
+			static bool isless(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				return xabs<=0x7C00 && yabs<=0x7C00 && (((xabs==x.data_) ? xabs : -xabs) < ((yabs==y.data_) ? yabs : -yabs));
+			}
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if \a x <= \a y
+			/// \retval false else
+			static bool islessequal(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				return xabs<=0x7C00 && yabs<=0x7C00 && (((xabs==x.data_) ? xabs : -xabs) <= ((yabs==y.data_) ? yabs : -yabs));
+			}
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if either \a x > \a y nor \a x < \a y
+			/// \retval false else
+			static bool islessgreater(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				if(xabs > 0x7C00 || yabs > 0x7C00)
+					return false;
+				int a = (xabs==x.data_) ? xabs : -xabs, b = (yabs==y.data_) ? yabs : -yabs;
+				return a < b || a > b;
+			}
+
+			/// Comparison implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \retval true if operand unordered
+			/// \retval false else
+			static bool isunordered(half x, half y) { return isnan(x) || isnan(y); }
+
+		private:
+			static double erf(double arg)
+			{
+				if(builtin_isinf(arg))
+					return (arg<0.0) ? -1.0 : 1.0;
+				double x2 = arg * arg, ax2 = 0.147 * x2, value = std::sqrt(1.0-std::exp(-x2*(1.2732395447351626861510701069801+ax2)/(1.0+ax2)));
+				return builtin_signbit(arg) ? -value : value;
+			}
+
+			static double lgamma(double arg)
+			{
+				double v = 1.0;
+				for(; arg<8.0; ++arg) v *= arg;
+				double w = 1.0 / (arg*arg);
+				return (((((((-0.02955065359477124183006535947712*w+0.00641025641025641025641025641026)*w+
+					-0.00191752691752691752691752691753)*w+8.4175084175084175084175084175084e-4)*w+
+					-5.952380952380952380952380952381e-4)*w+7.9365079365079365079365079365079e-4)*w+
+					-0.00277777777777777777777777777778)*w+0.08333333333333333333333333333333)/arg +
+					0.91893853320467274178032973640562 - std::log(v) - arg + (arg-0.5) * std::log(arg);
+			}
+		};
+
+		/// Wrapper for unary half-precision functions needing specialization for individual argument types.
+		/// \tparam T argument type
+		template<typename T> struct unary_specialized
+		{
+			/// Negation implementation.
+			/// \param arg value to negate
+			/// \return negated value
+			static HALF_CONSTEXPR half negate(half arg) { return half(binary, arg.data_^0x8000); }
+
+			/// Absolute value implementation.
+			/// \param arg function argument
+			/// \return absolute value
+			static half fabs(half arg) { return half(binary, arg.data_&0x7FFF); }
+		};
+		template<> struct unary_specialized<expr>
+		{
+			static HALF_CONSTEXPR expr negate(float arg) { return expr(-arg); }
+			static expr fabs(float arg) { return expr(std::fabs(arg)); }
+		};
+
+		/// Wrapper for binary half-precision functions needing specialization for individual argument types.
+		/// \tparam T first argument type
+		/// \tparam U first argument type
+		template<typename T,typename U> struct binary_specialized
+		{
+			/// Minimum implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return minimum value
+			static expr fmin(float x, float y)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::fmin(x, y));
+			#else
+				if(builtin_isnan(x))
+					return expr(y);
+				if(builtin_isnan(y))
+					return expr(x);
+				return expr(std::min(x, y));
+			#endif
+			}
+
+			/// Maximum implementation.
+			/// \param x first operand
+			/// \param y second operand
+			/// \return maximum value
+			static expr fmax(float x, float y)
+			{
+			#if HALF_ENABLE_CPP11_CMATH
+				return expr(std::fmax(x, y));
+			#else
+				if(builtin_isnan(x))
+					return expr(y);
+				if(builtin_isnan(y))
+					return expr(x);
+				return expr(std::max(x, y));
+			#endif
+			}
+		};
+		template<> struct binary_specialized<half,half>
+		{
+			static half fmin(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				if(xabs > 0x7C00)
+					return y;
+				if(yabs > 0x7C00)
+					return x;
+				return (((xabs==x.data_) ? xabs : -xabs) > ((yabs==y.data_) ? yabs : -yabs)) ? y : x;
+			}
+			static half fmax(half x, half y)
+			{
+				int xabs = x.data_ & 0x7FFF, yabs = y.data_ & 0x7FFF;
+				if(xabs > 0x7C00)
+					return y;
+				if(yabs > 0x7C00)
+					return x;
+				return (((xabs==x.data_) ? xabs : -xabs) < ((yabs==y.data_) ? yabs : -yabs)) ? y : x;
+			}
+		};
+
+		/// Helper class for half casts.
+		/// This class template has to be specialized for all valid cast argument to define an appropriate static `cast` member
+		/// function and a corresponding `type` member denoting its return type.
+		/// \tparam T destination type
+		/// \tparam U source type
+		/// \tparam R rounding mode to use
+		template<typename T,typename U,std::float_round_style R=(std::float_round_style)(HALF_ROUND_STYLE)> struct half_caster {};
+		template<typename U,std::float_round_style R> struct half_caster<half,U,R>
+		{
+		#if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS
+			static_assert(std::is_arithmetic<U>::value, "half_cast from non-arithmetic type unsupported");
+		#endif
+
+			static half cast(U arg) { return cast_impl(arg, is_float<U>()); };
+
+		private:
+			static half cast_impl(U arg, true_type) { return half(binary, float2half<R>(arg)); }
+			static half cast_impl(U arg, false_type) { return half(binary, int2half<R>(arg)); }
+		};
+		template<typename T,std::float_round_style R> struct half_caster<T,half,R>
+		{
+		#if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS
+			static_assert(std::is_arithmetic<T>::value, "half_cast to non-arithmetic type unsupported");
+		#endif
+
+			static T cast(half arg) { return cast_impl(arg, is_float<T>()); }
+
+		private:
+			static T cast_impl(half arg, true_type) { return half2float<T>(arg.data_); }
+			static T cast_impl(half arg, false_type) { return half2int<R,T>(arg.data_); }
+		};
+		template<typename T,std::float_round_style R> struct half_caster<T,expr,R>
+		{
+		#if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS
+			static_assert(std::is_arithmetic<T>::value, "half_cast to non-arithmetic type unsupported");
+		#endif
+
+			static T cast(expr arg) { return cast_impl(arg, is_float<T>()); }
+
+		private:
+			static T cast_impl(float arg, true_type) { return static_cast<T>(arg); }
+			static T cast_impl(half arg, false_type) { return half2int<R,T>(arg.data_); }
+		};
+		template<std::float_round_style R> struct half_caster<half,half,R>
+		{
+			static half cast(half arg) { return arg; }
+		};
+		template<std::float_round_style R> struct half_caster<half,expr,R> : half_caster<half,half,R> {};
+
+		/// \name Comparison operators
+		/// \{
+
+		/// Comparison for equality.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if operands equal
+		/// \retval false else
+		template<typename T,typename U> typename enable<bool,T,U>::type operator==(T x, U y) { return functions::isequal(x, y); }
+
+		/// Comparison for inequality.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if operands not equal
+		/// \retval false else
+		template<typename T,typename U> typename enable<bool,T,U>::type operator!=(T x, U y) { return functions::isnotequal(x, y); }
+
+		/// Comparison for less than.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x less than \a y
+		/// \retval false else
+		template<typename T,typename U> typename enable<bool,T,U>::type operator<(T x, U y) { return functions::isless(x, y); }
+
+		/// Comparison for greater than.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x greater than \a y
+		/// \retval false else
+		template<typename T,typename U> typename enable<bool,T,U>::type operator>(T x, U y) { return functions::isgreater(x, y); }
+
+		/// Comparison for less equal.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x less equal \a y
+		/// \retval false else
+		template<typename T,typename U> typename enable<bool,T,U>::type operator<=(T x, U y) { return functions::islessequal(x, y); }
+
+		/// Comparison for greater equal.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x greater equal \a y
+		/// \retval false else
+		template<typename T,typename U> typename enable<bool,T,U>::type operator>=(T x, U y) { return functions::isgreaterequal(x, y); }
+
+		/// \}
+		/// \name Arithmetic operators
+		/// \{
+
+		/// Add halfs.
+		/// \param x left operand
+		/// \param y right operand
+		/// \return sum of half expressions
+		template<typename T,typename U> typename enable<expr,T,U>::type operator+(T x, U y) { return functions::plus(x, y); }
+
+		/// Subtract halfs.
+		/// \param x left operand
+		/// \param y right operand
+		/// \return difference of half expressions
+		template<typename T,typename U> typename enable<expr,T,U>::type operator-(T x, U y) { return functions::minus(x, y); }
+
+		/// Multiply halfs.
+		/// \param x left operand
+		/// \param y right operand
+		/// \return product of half expressions
+		template<typename T,typename U> typename enable<expr,T,U>::type operator*(T x, U y) { return functions::multiplies(x, y); }
+
+		/// Divide halfs.
+		/// \param x left operand
+		/// \param y right operand
+		/// \return quotient of half expressions
+		template<typename T,typename U> typename enable<expr,T,U>::type operator/(T x, U y) { return functions::divides(x, y); }
+
+		/// Identity.
+		/// \param arg operand
+		/// \return uncahnged operand
+		template<typename T> HALF_CONSTEXPR typename enable<T,T>::type operator+(T arg) { return arg; }
+
+		/// Negation.
+		/// \param arg operand
+		/// \return negated operand
+		template<typename T> HALF_CONSTEXPR typename enable<T,T>::type operator-(T arg) { return unary_specialized<T>::negate(arg); }
+
+		/// \}
+		/// \name Input and output
+		/// \{
+
+		/// Output operator.
+		/// \param out output stream to write into
+		/// \param arg half expression to write
+		/// \return reference to output stream
+		template<typename T,typename charT,typename traits> typename enable<std::basic_ostream<charT,traits>&,T>::type
+			operator<<(std::basic_ostream<charT,traits> &out, T arg) { return functions::write(out, arg); }
+
+		/// Input operator.
+		/// \param in input stream to read from
+		/// \param arg half to read into
+		/// \return reference to input stream
+		template<typename charT,typename traits> std::basic_istream<charT,traits>&
+			operator>>(std::basic_istream<charT,traits> &in, half &arg) { return functions::read(in, arg); }
+
+		/// \}
+		/// \name Basic mathematical operations
+		/// \{
+
+		/// Absolute value.
+		/// \param arg operand
+		/// \return absolute value of \a arg
+//		template<typename T> typename enable<T,T>::type abs(T arg) { return unary_specialized<T>::fabs(arg); }
+		inline half abs(half arg) { return unary_specialized<half>::fabs(arg); }
+		inline expr abs(expr arg) { return unary_specialized<expr>::fabs(arg); }
+
+		/// Absolute value.
+		/// \param arg operand
+		/// \return absolute value of \a arg
+//		template<typename T> typename enable<T,T>::type fabs(T arg) { return unary_specialized<T>::fabs(arg); }
+		inline half fabs(half arg) { return unary_specialized<half>::fabs(arg); }
+		inline expr fabs(expr arg) { return unary_specialized<expr>::fabs(arg); }
+
+		/// Remainder of division.
+		/// \param x first operand
+		/// \param y second operand
+		/// \return remainder of floating point division.
+//		template<typename T,typename U> typename enable<expr,T,U>::type fmod(T x, U y) { return functions::fmod(x, y); }
+		inline expr fmod(half x, half y) { return functions::fmod(x, y); }
+		inline expr fmod(half x, expr y) { return functions::fmod(x, y); }
+		inline expr fmod(expr x, half y) { return functions::fmod(x, y); }
+		inline expr fmod(expr x, expr y) { return functions::fmod(x, y); }
+
+		/// Remainder of division.
+		/// \param x first operand
+		/// \param y second operand
+		/// \return remainder of floating point division.
+//		template<typename T,typename U> typename enable<expr,T,U>::type remainder(T x, U y) { return functions::remainder(x, y); }
+		inline expr remainder(half x, half y) { return functions::remainder(x, y); }
+		inline expr remainder(half x, expr y) { return functions::remainder(x, y); }
+		inline expr remainder(expr x, half y) { return functions::remainder(x, y); }
+		inline expr remainder(expr x, expr y) { return functions::remainder(x, y); }
+
+		/// Remainder of division.
+		/// \param x first operand
+		/// \param y second operand
+		/// \param quo address to store some bits of quotient at
+		/// \return remainder of floating point division.
+//		template<typename T,typename U> typename enable<expr,T,U>::type remquo(T x, U y, int *quo) { return functions::remquo(x, y, quo); }
+		inline expr remquo(half x, half y, int *quo) { return functions::remquo(x, y, quo); }
+		inline expr remquo(half x, expr y, int *quo) { return functions::remquo(x, y, quo); }
+		inline expr remquo(expr x, half y, int *quo) { return functions::remquo(x, y, quo); }
+		inline expr remquo(expr x, expr y, int *quo) { return functions::remquo(x, y, quo); }
+
+		/// Fused multiply add.
+		/// \param x first operand
+		/// \param y second operand
+		/// \param z third operand
+		/// \return ( \a x * \a y ) + \a z rounded as one operation.
+//		template<typename T,typename U,typename V> typename enable<expr,T,U,V>::type fma(T x, U y, V z) { return functions::fma(x, y, z); }
+		inline expr fma(half x, half y, half z) { return functions::fma(x, y, z); }
+		inline expr fma(half x, half y, expr z) { return functions::fma(x, y, z); }
+		inline expr fma(half x, expr y, half z) { return functions::fma(x, y, z); }
+		inline expr fma(half x, expr y, expr z) { return functions::fma(x, y, z); }
+		inline expr fma(expr x, half y, half z) { return functions::fma(x, y, z); }
+		inline expr fma(expr x, half y, expr z) { return functions::fma(x, y, z); }
+		inline expr fma(expr x, expr y, half z) { return functions::fma(x, y, z); }
+		inline expr fma(expr x, expr y, expr z) { return functions::fma(x, y, z); }
+
+		/// Maximum of half expressions.
+		/// \param x first operand
+		/// \param y second operand
+		/// \return maximum of operands
+//		template<typename T,typename U> typename result<T,U>::type fmax(T x, U y) { return binary_specialized<T,U>::fmax(x, y); }
+		inline half fmax(half x, half y) { return binary_specialized<half,half>::fmax(x, y); }
+		inline expr fmax(half x, expr y) { return binary_specialized<half,expr>::fmax(x, y); }
+		inline expr fmax(expr x, half y) { return binary_specialized<expr,half>::fmax(x, y); }
+		inline expr fmax(expr x, expr y) { return binary_specialized<expr,expr>::fmax(x, y); }
+
+		/// Minimum of half expressions.
+		/// \param x first operand
+		/// \param y second operand
+		/// \return minimum of operands
+//		template<typename T,typename U> typename result<T,U>::type fmin(T x, U y) { return binary_specialized<T,U>::fmin(x, y); }
+		inline half fmin(half x, half y) { return binary_specialized<half,half>::fmin(x, y); }
+		inline expr fmin(half x, expr y) { return binary_specialized<half,expr>::fmin(x, y); }
+		inline expr fmin(expr x, half y) { return binary_specialized<expr,half>::fmin(x, y); }
+		inline expr fmin(expr x, expr y) { return binary_specialized<expr,expr>::fmin(x, y); }
+
+		/// Positive difference.
+		/// \param x first operand
+		/// \param y second operand
+		/// \return \a x - \a y or 0 if difference negative
+//		template<typename T,typename U> typename enable<expr,T,U>::type fdim(T x, U y) { return functions::fdim(x, y); }
+		inline expr fdim(half x, half y) { return functions::fdim(x, y); }
+		inline expr fdim(half x, expr y) { return functions::fdim(x, y); }
+		inline expr fdim(expr x, half y) { return functions::fdim(x, y); }
+		inline expr fdim(expr x, expr y) { return functions::fdim(x, y); }
+
+		/// Get NaN value.
+		/// \return quiet NaN
+		inline half nanh(const char*) { return functions::nanh(); }
+
+		/// \}
+		/// \name Exponential functions
+		/// \{
+
+		/// Exponential function.
+		/// \param arg function argument
+		/// \return e raised to \a arg
+//		template<typename T> typename enable<expr,T>::type exp(T arg) { return functions::exp(arg); }
+		inline expr exp(half arg) { return functions::exp(arg); }
+		inline expr exp(expr arg) { return functions::exp(arg); }
+
+		/// Exponential minus one.
+		/// \param arg function argument
+		/// \return e raised to \a arg subtracted by 1
+//		template<typename T> typename enable<expr,T>::type expm1(T arg) { return functions::expm1(arg); }
+		inline expr expm1(half arg) { return functions::expm1(arg); }
+		inline expr expm1(expr arg) { return functions::expm1(arg); }
+
+		/// Binary exponential.
+		/// \param arg function argument
+		/// \return 2 raised to \a arg
+//		template<typename T> typename enable<expr,T>::type exp2(T arg) { return functions::exp2(arg); }
+		inline expr exp2(half arg) { return functions::exp2(arg); }
+		inline expr exp2(expr arg) { return functions::exp2(arg); }
+
+		/// Natural logorithm.
+		/// \param arg function argument
+		/// \return logarithm of \a arg to base e
+//		template<typename T> typename enable<expr,T>::type log(T arg) { return functions::log(arg); }
+		inline expr log(half arg) { return functions::log(arg); }
+		inline expr log(expr arg) { return functions::log(arg); }
+
+		/// Common logorithm.
+		/// \param arg function argument
+		/// \return logarithm of \a arg to base 10
+//		template<typename T> typename enable<expr,T>::type log10(T arg) { return functions::log10(arg); }
+		inline expr log10(half arg) { return functions::log10(arg); }
+		inline expr log10(expr arg) { return functions::log10(arg); }
+
+		/// Natural logorithm.
+		/// \param arg function argument
+		/// \return logarithm of \a arg plus 1 to base e
+//		template<typename T> typename enable<expr,T>::type log1p(T arg) { return functions::log1p(arg); }
+		inline expr log1p(half arg) { return functions::log1p(arg); }
+		inline expr log1p(expr arg) { return functions::log1p(arg); }
+
+		/// Binary logorithm.
+		/// \param arg function argument
+		/// \return logarithm of \a arg to base 2
+//		template<typename T> typename enable<expr,T>::type log2(T arg) { return functions::log2(arg); }
+		inline expr log2(half arg) { return functions::log2(arg); }
+		inline expr log2(expr arg) { return functions::log2(arg); }
+
+		/// \}
+		/// \name Power functions
+		/// \{
+
+		/// Square root.
+		/// \param arg function argument
+		/// \return square root of \a arg
+//		template<typename T> typename enable<expr,T>::type sqrt(T arg) { return functions::sqrt(arg); }
+		inline expr sqrt(half arg) { return functions::sqrt(arg); }
+		inline expr sqrt(expr arg) { return functions::sqrt(arg); }
+
+		/// Cubic root.
+		/// \param arg function argument
+		/// \return cubic root of \a arg
+//		template<typename T> typename enable<expr,T>::type cbrt(T arg) { return functions::cbrt(arg); }
+		inline expr cbrt(half arg) { return functions::cbrt(arg); }
+		inline expr cbrt(expr arg) { return functions::cbrt(arg); }
+
+		/// Hypotenuse function.
+		/// \param x first argument
+		/// \param y second argument
+		/// \return square root of sum of squares without internal over- or underflows
+//		template<typename T,typename U> typename enable<expr,T,U>::type hypot(T x, U y) { return functions::hypot(x, y); }
+		inline expr hypot(half x, half y) { return functions::hypot(x, y); }
+		inline expr hypot(half x, expr y) { return functions::hypot(x, y); }
+		inline expr hypot(expr x, half y) { return functions::hypot(x, y); }
+		inline expr hypot(expr x, expr y) { return functions::hypot(x, y); }
+
+		/// Power function.
+		/// \param base first argument
+		/// \param exp second argument
+		/// \return \a base raised to \a exp
+//		template<typename T,typename U> typename enable<expr,T,U>::type pow(T base, U exp) { return functions::pow(base, exp); }
+		inline expr pow(half base, half exp) { return functions::pow(base, exp); }
+		inline expr pow(half base, expr exp) { return functions::pow(base, exp); }
+		inline expr pow(expr base, half exp) { return functions::pow(base, exp); }
+		inline expr pow(expr base, expr exp) { return functions::pow(base, exp); }
+
+		/// \}
+		/// \name Trigonometric functions
+		/// \{
+
+		/// Sine function.
+		/// \param arg function argument
+		/// \return sine value of \a arg
+//		template<typename T> typename enable<expr,T>::type sin(T arg) { return functions::sin(arg); }
+		inline expr sin(half arg) { return functions::sin(arg); }
+		inline expr sin(expr arg) { return functions::sin(arg); }
+
+		/// Cosine function.
+		/// \param arg function argument
+		/// \return cosine value of \a arg
+//		template<typename T> typename enable<expr,T>::type cos(T arg) { return functions::cos(arg); }
+		inline expr cos(half arg) { return functions::cos(arg); }
+		inline expr cos(expr arg) { return functions::cos(arg); }
+
+		/// Tangent function.
+		/// \param arg function argument
+		/// \return tangent value of \a arg
+//		template<typename T> typename enable<expr,T>::type tan(T arg) { return functions::tan(arg); }
+		inline expr tan(half arg) { return functions::tan(arg); }
+		inline expr tan(expr arg) { return functions::tan(arg); }
+
+		/// Arc sine.
+		/// \param arg function argument
+		/// \return arc sine value of \a arg
+//		template<typename T> typename enable<expr,T>::type asin(T arg) { return functions::asin(arg); }
+		inline expr asin(half arg) { return functions::asin(arg); }
+		inline expr asin(expr arg) { return functions::asin(arg); }
+
+		/// Arc cosine function.
+		/// \param arg function argument
+		/// \return arc cosine value of \a arg
+//		template<typename T> typename enable<expr,T>::type acos(T arg) { return functions::acos(arg); }
+		inline expr acos(half arg) { return functions::acos(arg); }
+		inline expr acos(expr arg) { return functions::acos(arg); }
+
+		/// Arc tangent function.
+		/// \param arg function argument
+		/// \return arc tangent value of \a arg
+//		template<typename T> typename enable<expr,T>::type atan(T arg) { return functions::atan(arg); }
+		inline expr atan(half arg) { return functions::atan(arg); }
+		inline expr atan(expr arg) { return functions::atan(arg); }
+
+		/// Arc tangent function.
+		/// \param x first argument
+		/// \param y second argument
+		/// \return arc tangent value
+//		template<typename T,typename U> typename enable<expr,T,U>::type atan2(T x, U y) { return functions::atan2(x, y); }
+		inline expr atan2(half x, half y) { return functions::atan2(x, y); }
+		inline expr atan2(half x, expr y) { return functions::atan2(x, y); }
+		inline expr atan2(expr x, half y) { return functions::atan2(x, y); }
+		inline expr atan2(expr x, expr y) { return functions::atan2(x, y); }
+
+		/// \}
+		/// \name Hyperbolic functions
+		/// \{
+
+		/// Hyperbolic sine.
+		/// \param arg function argument
+		/// \return hyperbolic sine value of \a arg
+//		template<typename T> typename enable<expr,T>::type sinh(T arg) { return functions::sinh(arg); }
+		inline expr sinh(half arg) { return functions::sinh(arg); }
+		inline expr sinh(expr arg) { return functions::sinh(arg); }
+
+		/// Hyperbolic cosine.
+		/// \param arg function argument
+		/// \return hyperbolic cosine value of \a arg
+//		template<typename T> typename enable<expr,T>::type cosh(T arg) { return functions::cosh(arg); }
+		inline expr cosh(half arg) { return functions::cosh(arg); }
+		inline expr cosh(expr arg) { return functions::cosh(arg); }
+
+		/// Hyperbolic tangent.
+		/// \param arg function argument
+		/// \return hyperbolic tangent value of \a arg
+//		template<typename T> typename enable<expr,T>::type tanh(T arg) { return functions::tanh(arg); }
+		inline expr tanh(half arg) { return functions::tanh(arg); }
+		inline expr tanh(expr arg) { return functions::tanh(arg); }
+
+		/// Hyperbolic area sine.
+		/// \param arg function argument
+		/// \return area sine value of \a arg
+//		template<typename T> typename enable<expr,T>::type asinh(T arg) { return functions::asinh(arg); }
+		inline expr asinh(half arg) { return functions::asinh(arg); }
+		inline expr asinh(expr arg) { return functions::asinh(arg); }
+
+		/// Hyperbolic area cosine.
+		/// \param arg function argument
+		/// \return area cosine value of \a arg
+//		template<typename T> typename enable<expr,T>::type acosh(T arg) { return functions::acosh(arg); }
+		inline expr acosh(half arg) { return functions::acosh(arg); }
+		inline expr acosh(expr arg) { return functions::acosh(arg); }
+
+		/// Hyperbolic area tangent.
+		/// \param arg function argument
+		/// \return area tangent value of \a arg
+//		template<typename T> typename enable<expr,T>::type atanh(T arg) { return functions::atanh(arg); }
+		inline expr atanh(half arg) { return functions::atanh(arg); }
+		inline expr atanh(expr arg) { return functions::atanh(arg); }
+
+		/// \}
+		/// \name Error and gamma functions
+		/// \{
+
+		/// Error function.
+		/// \param arg function argument
+		/// \return error function value of \a arg
+//		template<typename T> typename enable<expr,T>::type erf(T arg) { return functions::erf(arg); }
+		inline expr erf(half arg) { return functions::erf(arg); }
+		inline expr erf(expr arg) { return functions::erf(arg); }
+
+		/// Complementary error function.
+		/// \param arg function argument
+		/// \return 1 minus error function value of \a arg
+//		template<typename T> typename enable<expr,T>::type erfc(T arg) { return functions::erfc(arg); }
+		inline expr erfc(half arg) { return functions::erfc(arg); }
+		inline expr erfc(expr arg) { return functions::erfc(arg); }
+
+		/// Natural logarithm of gamma function.
+		/// \param arg function argument
+		/// \return natural logarith of gamma function for \a arg
+//		template<typename T> typename enable<expr,T>::type lgamma(T arg) { return functions::lgamma(arg); }
+		inline expr lgamma(half arg) { return functions::lgamma(arg); }
+		inline expr lgamma(expr arg) { return functions::lgamma(arg); }
+
+		/// Gamma function.
+		/// \param arg function argument
+		/// \return gamma function value of \a arg
+//		template<typename T> typename enable<expr,T>::type tgamma(T arg) { return functions::tgamma(arg); }
+		inline expr tgamma(half arg) { return functions::tgamma(arg); }
+		inline expr tgamma(expr arg) { return functions::tgamma(arg); }
+
+		/// \}
+		/// \name Rounding
+		/// \{
+
+		/// Nearest integer not less than half value.
+		/// \param arg half to round
+		/// \return nearest integer not less than \a arg
+//		template<typename T> typename enable<half,T>::type ceil(T arg) { return functions::ceil(arg); }
+		inline half ceil(half arg) { return functions::ceil(arg); }
+		inline half ceil(expr arg) { return functions::ceil(arg); }
+
+		/// Nearest integer not greater than half value.
+		/// \param arg half to round
+		/// \return nearest integer not greater than \a arg
+//		template<typename T> typename enable<half,T>::type floor(T arg) { return functions::floor(arg); }
+		inline half floor(half arg) { return functions::floor(arg); }
+		inline half floor(expr arg) { return functions::floor(arg); }
+
+		/// Nearest integer not greater in magnitude than half value.
+		/// \param arg half to round
+		/// \return nearest integer not greater in magnitude than \a arg
+//		template<typename T> typename enable<half,T>::type trunc(T arg) { return functions::trunc(arg); }
+		inline half trunc(half arg) { return functions::trunc(arg); }
+		inline half trunc(expr arg) { return functions::trunc(arg); }
+
+		/// Nearest integer.
+		/// \param arg half to round
+		/// \return nearest integer, rounded away from zero in half-way cases
+//		template<typename T> typename enable<half,T>::type round(T arg) { return functions::round(arg); }
+		inline half round(half arg) { return functions::round(arg); }
+		inline half round(expr arg) { return functions::round(arg); }
+
+		/// Nearest integer.
+		/// \param arg half to round
+		/// \return nearest integer, rounded away from zero in half-way cases
+//		template<typename T> typename enable<long,T>::type lround(T arg) { return functions::lround(arg); }
+		inline long lround(half arg) { return functions::lround(arg); }
+		inline long lround(expr arg) { return functions::lround(arg); }
+
+		/// Nearest integer using half's internal rounding mode.
+		/// \param arg half expression to round
+		/// \return nearest integer using default rounding mode
+//		template<typename T> typename enable<half,T>::type nearbyint(T arg) { return functions::nearbyint(arg); }
+		inline half nearbyint(half arg) { return functions::rint(arg); }
+		inline half nearbyint(expr arg) { return functions::rint(arg); }
+
+		/// Nearest integer using half's internal rounding mode.
+		/// \param arg half expression to round
+		/// \return nearest integer using default rounding mode
+//		template<typename T> typename enable<half,T>::type rint(T arg) { return functions::rint(arg); }
+		inline half rint(half arg) { return functions::rint(arg); }
+		inline half rint(expr arg) { return functions::rint(arg); }
+
+		/// Nearest integer using half's internal rounding mode.
+		/// \param arg half expression to round
+		/// \return nearest integer using default rounding mode
+//		template<typename T> typename enable<long,T>::type lrint(T arg) { return functions::lrint(arg); }
+		inline long lrint(half arg) { return functions::lrint(arg); }
+		inline long lrint(expr arg) { return functions::lrint(arg); }
+	#if HALF_ENABLE_CPP11_LONG_LONG
+		/// Nearest integer.
+		/// \param arg half to round
+		/// \return nearest integer, rounded away from zero in half-way cases
+//		template<typename T> typename enable<long long,T>::type llround(T arg) { return functions::llround(arg); }
+		inline long long llround(half arg) { return functions::llround(arg); }
+		inline long long llround(expr arg) { return functions::llround(arg); }
+
+		/// Nearest integer using half's internal rounding mode.
+		/// \param arg half expression to round
+		/// \return nearest integer using default rounding mode
+//		template<typename T> typename enable<long long,T>::type llrint(T arg) { return functions::llrint(arg); }
+		inline long long llrint(half arg) { return functions::llrint(arg); }
+		inline long long llrint(expr arg) { return functions::llrint(arg); }
+	#endif
+
+		/// \}
+		/// \name Floating point manipulation
+		/// \{
+
+		/// Decompress floating point number.
+		/// \param arg number to decompress
+		/// \param exp address to store exponent at
+		/// \return significant in range [0.5, 1)
+//		template<typename T> typename enable<half,T>::type frexp(T arg, int *exp) { return functions::frexp(arg, exp); }
+		inline half frexp(half arg, int *exp) { return functions::frexp(arg, exp); }
+		inline half frexp(expr arg, int *exp) { return functions::frexp(arg, exp); }
+
+		/// Multiply by power of two.
+		/// \param arg number to modify
+		/// \param exp power of two to multiply with
+		/// \return \a arg multplied by 2 raised to \a exp
+//		template<typename T> typename enable<half,T>::type ldexp(T arg, int exp) { return functions::scalbln(arg, exp); }
+		inline half ldexp(half arg, int exp) { return functions::scalbln(arg, exp); }
+		inline half ldexp(expr arg, int exp) { return functions::scalbln(arg, exp); }
+
+		/// Extract integer and fractional parts.
+		/// \param arg number to decompress
+		/// \param iptr address to store integer part at
+		/// \return fractional part
+//		template<typename T> typename enable<half,T>::type modf(T arg, half *iptr) { return functions::modf(arg, iptr); }
+		inline half modf(half arg, half *iptr) { return functions::modf(arg, iptr); }
+		inline half modf(expr arg, half *iptr) { return functions::modf(arg, iptr); }
+
+		/// Multiply by power of two.
+		/// \param arg number to modify
+		/// \param exp power of two to multiply with
+		/// \return \a arg multplied by 2 raised to \a exp
+//		template<typename T> typename enable<half,T>::type scalbn(T arg, int exp) { return functions::scalbln(arg, exp); }
+		inline half scalbn(half arg, int exp) { return functions::scalbln(arg, exp); }
+		inline half scalbn(expr arg, int exp) { return functions::scalbln(arg, exp); }
+
+		/// Multiply by power of two.
+		/// \param arg number to modify
+		/// \param exp power of two to multiply with
+		/// \return \a arg multplied by 2 raised to \a exp	
+//		template<typename T> typename enable<half,T>::type scalbln(T arg, long exp) { return functions::scalbln(arg, exp); }
+		inline half scalbln(half arg, long exp) { return functions::scalbln(arg, exp); }
+		inline half scalbln(expr arg, long exp) { return functions::scalbln(arg, exp); }
+
+		/// Extract exponent.
+		/// \param arg number to query
+		/// \return floating point exponent
+		/// \retval FP_ILOGB0 for zero
+		/// \retval FP_ILOGBNAN for NaN
+		/// \retval MAX_INT for infinity
+//		template<typename T> typename enable<int,T>::type ilogb(T arg) { return functions::ilogb(arg); }
+		inline int ilogb(half arg) { return functions::ilogb(arg); }
+		inline int ilogb(expr arg) { return functions::ilogb(arg); }
+
+		/// Extract exponent.
+		/// \param arg number to query
+		/// \return floating point exponent
+//		template<typename T> typename enable<half,T>::type logb(T arg) { return functions::logb(arg); }
+		inline half logb(half arg) { return functions::logb(arg); }
+		inline half logb(expr arg) { return functions::logb(arg); }
+
+		/// Next representable value.
+		/// \param from value to compute next representable value for
+		/// \param to direction towards which to compute next value
+		/// \return next representable value after \a from in direction towards \a to
+//		template<typename T,typename U> typename enable<half,T,U>::type nextafter(T from, U to) { return functions::nextafter(from, to); }
+		inline half nextafter(half from, half to) { return functions::nextafter(from, to); }
+		inline half nextafter(half from, expr to) { return functions::nextafter(from, to); }
+		inline half nextafter(expr from, half to) { return functions::nextafter(from, to); }
+		inline half nextafter(expr from, expr to) { return functions::nextafter(from, to); }
+
+		/// Next representable value.
+		/// \param from value to compute next representable value for
+		/// \param to direction towards which to compute next value
+		/// \return next representable value after \a from in direction towards \a to
+//		template<typename T> typename enable<half,T>::type nexttoward(T from, long double to) { return functions::nexttoward(from, to); }
+		inline half nexttoward(half from, long double to) { return functions::nexttoward(from, to); }
+		inline half nexttoward(expr from, long double to) { return functions::nexttoward(from, to); }
+
+		/// Take sign.
+		/// \param x value to change sign for
+		/// \param y value to take sign from
+		/// \return value equal to \a x in magnitude and to \a y in sign
+//		template<typename T,typename U> typename enable<half,T,U>::type copysign(T x, U y) { return functions::copysign(x, y); }
+		inline half copysign(half x, half y) { return functions::copysign(x, y); }
+		inline half copysign(half x, expr y) { return functions::copysign(x, y); }
+		inline half copysign(expr x, half y) { return functions::copysign(x, y); }
+		inline half copysign(expr x, expr y) { return functions::copysign(x, y); }
+
+		/// \}
+		/// \name Floating point classification
+		/// \{
+
+
+		/// Classify floating point value.
+		/// \param arg number to classify
+		/// \retval FP_ZERO for positive and negative zero
+		/// \retval FP_SUBNORMAL for subnormal numbers
+		/// \retval FP_INFINITY for positive and negative infinity
+		/// \retval FP_NAN for NaNs
+		/// \retval FP_NORMAL for all other (normal) values
+//		template<typename T> typename enable<int,T>::type fpclassify(T arg) { return functions::fpclassify(arg); }
+		inline int fpclassify(half arg) { return functions::fpclassify(arg); }
+		inline int fpclassify(expr arg) { return functions::fpclassify(arg); }
+
+		/// Check if finite number.
+		/// \param arg number to check
+		/// \retval true if neither infinity nor NaN
+		/// \retval false else
+//		template<typename T> typename enable<bool,T>::type isfinite(T arg) { return functions::isfinite(arg); }
+		inline bool isfinite(half arg) { return functions::isfinite(arg); }
+		inline bool isfinite(expr arg) { return functions::isfinite(arg); }
+
+		/// Check for infinity.
+		/// \param arg number to check
+		/// \retval true for positive or negative infinity
+		/// \retval false else
+//		template<typename T> typename enable<bool,T>::type isinf(T arg) { return functions::isinf(arg); }
+		inline bool isinf(half arg) { return functions::isinf(arg); }
+		inline bool isinf(expr arg) { return functions::isinf(arg); }
+
+		/// Check for NaN.
+		/// \param arg number to check
+		/// \retval true for NaNs
+		/// \retval false else
+//		template<typename T> typename enable<bool,T>::type isnan(T arg) { return functions::isnan(arg); }
+		inline bool isnan(half arg) { return functions::isnan(arg); }
+		inline bool isnan(expr arg) { return functions::isnan(arg); }
+
+		/// Check if normal number.
+		/// \param arg number to check
+		/// \retval true if normal number
+		/// \retval false if either subnormal, zero, infinity or NaN
+//		template<typename T> typename enable<bool,T>::type isnormal(T arg) { return functions::isnormal(arg); }
+		inline bool isnormal(half arg) { return functions::isnormal(arg); }
+		inline bool isnormal(expr arg) { return functions::isnormal(arg); }
+
+		/// Check sign.
+		/// \param arg number to check
+		/// \retval true for negative number
+		/// \retval false for positive number
+//		template<typename T> typename enable<bool,T>::type signbit(T arg) { return functions::signbit(arg); }
+		inline bool signbit(half arg) { return functions::signbit(arg); }
+		inline bool signbit(expr arg) { return functions::signbit(arg); }
+
+		/// \}
+		/// \name Comparison
+		/// \{
+
+		/// Comparison for greater than.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x greater than \a y
+		/// \retval false else
+//		template<typename T,typename U> typename enable<bool,T,U>::type isgreater(T x, U y) { return functions::isgreater(x, y); }
+		inline bool isgreater(half x, half y) { return functions::isgreater(x, y); }
+		inline bool isgreater(half x, expr y) { return functions::isgreater(x, y); }
+		inline bool isgreater(expr x, half y) { return functions::isgreater(x, y); }
+		inline bool isgreater(expr x, expr y) { return functions::isgreater(x, y); }
+
+		/// Comparison for greater equal.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x greater equal \a y
+		/// \retval false else
+//		template<typename T,typename U> typename enable<bool,T,U>::type isgreaterequal(T x, U y) { return functions::isgreaterequal(x, y); }
+		inline bool isgreaterequal(half x, half y) { return functions::isgreaterequal(x, y); }
+		inline bool isgreaterequal(half x, expr y) { return functions::isgreaterequal(x, y); }
+		inline bool isgreaterequal(expr x, half y) { return functions::isgreaterequal(x, y); }
+		inline bool isgreaterequal(expr x, expr y) { return functions::isgreaterequal(x, y); }
+
+		/// Comparison for less than.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x less than \a y
+		/// \retval false else
+//		template<typename T,typename U> typename enable<bool,T,U>::type isless(T x, U y) { return functions::isless(x, y); }
+		inline bool isless(half x, half y) { return functions::isless(x, y); }
+		inline bool isless(half x, expr y) { return functions::isless(x, y); }
+		inline bool isless(expr x, half y) { return functions::isless(x, y); }
+		inline bool isless(expr x, expr y) { return functions::isless(x, y); }
+
+		/// Comparison for less equal.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if \a x less equal \a y
+		/// \retval false else
+//		template<typename T,typename U> typename enable<bool,T,U>::type islessequal(T x, U y) { return functions::islessequal(x, y); }
+		inline bool islessequal(half x, half y) { return functions::islessequal(x, y); }
+		inline bool islessequal(half x, expr y) { return functions::islessequal(x, y); }
+		inline bool islessequal(expr x, half y) { return functions::islessequal(x, y); }
+		inline bool islessequal(expr x, expr y) { return functions::islessequal(x, y); }
+
+		/// Comarison for less or greater.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if either less or greater
+		/// \retval false else
+//		template<typename T,typename U> typename enable<bool,T,U>::type islessgreater(T x, U y) { return functions::islessgreater(x, y); }
+		inline bool islessgreater(half x, half y) { return functions::islessgreater(x, y); }
+		inline bool islessgreater(half x, expr y) { return functions::islessgreater(x, y); }
+		inline bool islessgreater(expr x, half y) { return functions::islessgreater(x, y); }
+		inline bool islessgreater(expr x, expr y) { return functions::islessgreater(x, y); }
+
+		/// Check if unordered.
+		/// \param x first operand
+		/// \param y second operand
+		/// \retval true if unordered (one or two NaN operands)
+		/// \retval false else
+//		template<typename T,typename U> typename enable<bool,T,U>::type isunordered(T x, U y) { return functions::isunordered(x, y); }
+		inline bool isunordered(half x, half y) { return functions::isunordered(x, y); }
+		inline bool isunordered(half x, expr y) { return functions::isunordered(x, y); }
+		inline bool isunordered(expr x, half y) { return functions::isunordered(x, y); }
+		inline bool isunordered(expr x, expr y) { return functions::isunordered(x, y); }
+
+		/// \name Casting
+		/// \{
+
+		/// Cast to or from half-precision floating point number.
+		/// This casts between [half](\ref half_float::half) and any built-in arithmetic type. The values are converted
+		/// directly using the given rounding mode, without any roundtrip over `float` that a `static_cast` would otherwise do.
+		/// It uses the default rounding mode.
+		///
+		/// Using this cast with neither of the two types being a [half](\ref half_float::half) or with any of the two types
+		/// not being a built-in arithmetic type (apart from [half](\ref half_float::half), of course) results in a compiler
+		/// error and casting between [half](\ref half_float::half)s is just a no-op.
+		/// \tparam T destination type (half or built-in arithmetic type)
+		/// \tparam U source type (half or built-in arithmetic type)
+		/// \param arg value to cast
+		/// \return \a arg converted to destination type
+		template<typename T,typename U> T half_cast(U arg) { return half_caster<T,U>::cast(arg); }
+
+		/// Cast to or from half-precision floating point number.
+		/// This casts between [half](\ref half_float::half) and any built-in arithmetic type. The values are converted
+		/// directly using the given rounding mode, without any roundtrip over `float` that a `static_cast` would otherwise do.
+		///
+		/// Using this cast with neither of the two types being a [half](\ref half_float::half) or with any of the two types
+		/// not being a built-in arithmetic type (apart from [half](\ref half_float::half), of course) results in a compiler
+		/// error and casting between [half](\ref half_float::half)s is just a no-op.
+		/// \tparam T destination type (half or built-in arithmetic type)
+		/// \tparam R rounding mode to use.
+		/// \tparam U source type (half or built-in arithmetic type)
+		/// \param arg value to cast
+		/// \return \a arg converted to destination type
+		template<typename T,std::float_round_style R,typename U> T half_cast(U arg) { return half_caster<T,U,R>::cast(arg); }
+		/// \}
+	}
+
+	using detail::operator==;
+	using detail::operator!=;
+	using detail::operator<;
+	using detail::operator>;
+	using detail::operator<=;
+	using detail::operator>=;
+	using detail::operator+;
+	using detail::operator-;
+	using detail::operator*;
+	using detail::operator/;
+	using detail::operator<<;
+	using detail::operator>>;
+
+	using detail::abs;
+	using detail::fabs;
+	using detail::fmod;
+	using detail::remainder;
+	using detail::remquo;
+	using detail::fma;
+	using detail::fmax;
+	using detail::fmin;
+	using detail::fdim;
+	using detail::nanh;
+	using detail::exp;
+	using detail::expm1;
+	using detail::exp2;
+	using detail::log;
+	using detail::log10;
+	using detail::log1p;
+	using detail::log2;
+	using detail::sqrt;
+	using detail::cbrt;
+	using detail::hypot;
+	using detail::pow;
+	using detail::sin;
+	using detail::cos;
+	using detail::tan;
+	using detail::asin;
+	using detail::acos;
+	using detail::atan;
+	using detail::atan2;
+	using detail::sinh;
+	using detail::cosh;
+	using detail::tanh;
+	using detail::asinh;
+	using detail::acosh;
+	using detail::atanh;
+	using detail::erf;
+	using detail::erfc;
+	using detail::lgamma;
+	using detail::tgamma;
+	using detail::ceil;
+	using detail::floor;
+	using detail::trunc;
+	using detail::round;
+	using detail::lround;
+	using detail::nearbyint;
+	using detail::rint;
+	using detail::lrint;
+#if HALF_ENABLE_CPP11_LONG_LONG
+	using detail::llround;
+	using detail::llrint;
+#endif
+	using detail::frexp;
+	using detail::ldexp;
+	using detail::modf;
+	using detail::scalbn;
+	using detail::scalbln;
+	using detail::ilogb;
+	using detail::logb;
+	using detail::nextafter;
+	using detail::nexttoward;
+	using detail::copysign;
+	using detail::fpclassify;
+	using detail::isfinite;
+	using detail::isinf;
+	using detail::isnan;
+	using detail::isnormal;
+	using detail::signbit;
+	using detail::isgreater;
+	using detail::isgreaterequal;
+	using detail::isless;
+	using detail::islessequal;
+	using detail::islessgreater;
+	using detail::isunordered;
+
+	using detail::half_cast;
+}
+
+
+/// Extensions to the C++ standard library.
+namespace std
+{
+	/// Numeric limits for half-precision floats.
+	/// Because of the underlying single-precision implementation of many operations, it inherits some properties from
+	/// `std::numeric_limits<float>`.
+	template<> class numeric_limits<half_float::half> : public numeric_limits<float>
+	{
+	public:
+		/// Supports signed values.
+		static HALF_CONSTEXPR_CONST bool is_signed = true;
+
+		/// Is not exact.
+		static HALF_CONSTEXPR_CONST bool is_exact = false;
+
+		/// Doesn't provide modulo arithmetic.
+		static HALF_CONSTEXPR_CONST bool is_modulo = false;
+
+		/// IEEE conformant.
+		static HALF_CONSTEXPR_CONST bool is_iec559 = true;
+
+		/// Supports infinity.
+		static HALF_CONSTEXPR_CONST bool has_infinity = true;
+
+		/// Supports quiet NaNs.
+		static HALF_CONSTEXPR_CONST bool has_quiet_NaN = true;
+
+		/// Supports subnormal values.
+		static HALF_CONSTEXPR_CONST float_denorm_style has_denorm = denorm_present;
+
+		/// Rounding mode.
+		/// Due to the mix of internal single-precision computations (using the rounding mode of the underlying
+		/// single-precision implementation) with the rounding mode of the single-to-half conversions, the actual rounding
+		/// mode might be `std::round_indeterminate` if the default half-precision rounding mode doesn't match the
+		/// single-precision rounding mode.
+		static HALF_CONSTEXPR_CONST float_round_style round_style = (std::numeric_limits<float>::round_style==
+			half_float::half::round_style) ? half_float::half::round_style : round_indeterminate;
+
+		/// Significant digits.
+		static HALF_CONSTEXPR_CONST int digits = 11;
+
+		/// Significant decimal digits.
+		static HALF_CONSTEXPR_CONST int digits10 = 3;
+
+		/// Required decimal digits to represent all possible values.
+		static HALF_CONSTEXPR_CONST int max_digits10 = 5;
+
+		/// Number base.
+		static HALF_CONSTEXPR_CONST int radix = 2;
+
+		/// One more than smallest exponent.
+		static HALF_CONSTEXPR_CONST int min_exponent = -13;
+
+		/// Smallest normalized representable power of 10.
+		static HALF_CONSTEXPR_CONST int min_exponent10 = -4;
+
+		/// One more than largest exponent
+		static HALF_CONSTEXPR_CONST int max_exponent = 16;
+
+		/// Largest finitely representable power of 10.
+		static HALF_CONSTEXPR_CONST int max_exponent10 = 4;
+
+		/// Smallest positive normal value.
+		static HALF_CONSTEXPR half_float::half min() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x0400); }
+
+		/// Smallest finite value.
+		static HALF_CONSTEXPR half_float::half lowest() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0xFBFF); }
+
+		/// Largest finite value.
+		static HALF_CONSTEXPR half_float::half max() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7BFF); }
+
+		/// Difference between one and next representable value.
+		static HALF_CONSTEXPR half_float::half epsilon() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x1400); }
+
+		/// Maximum rounding error.
+		static HALF_CONSTEXPR half_float::half round_error() HALF_NOTHROW
+			{ return half_float::half(half_float::detail::binary, (round_style==std::round_to_nearest) ? 0x3800 : 0x3C00); }
+
+		/// Positive infinity.
+		static HALF_CONSTEXPR half_float::half infinity() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7C00); }
+
+		/// Quiet NaN.
+		static HALF_CONSTEXPR half_float::half quiet_NaN() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7FFF); }
+
+		/// Signalling NaN.
+		static HALF_CONSTEXPR half_float::half signaling_NaN() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7DFF); }
+
+		/// Smallest positive subnormal value.
+		static HALF_CONSTEXPR half_float::half denorm_min() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x0001); }
+	};
+
+#if HALF_ENABLE_CPP11_HASH
+	/// Hash function for half-precision floats.
+	/// This is only defined if C++11 `std::hash` is supported and enabled.
+	template<> struct hash<half_float::half> //: unary_function<half_float::half,size_t>
+	{
+		/// Type of function argument.
+		typedef half_float::half argument_type;
+
+		/// Function return type.
+		typedef size_t result_type;
+
+		/// Compute hash function.
+		/// \param arg half to hash
+		/// \return hash value
+		result_type operator()(argument_type arg) const
+			{ return hash<half_float::detail::uint16>()(static_cast<unsigned>(arg.data_)&-(arg.data_!=0x8000)); }
+	};
+#endif
+}
+
+
+#undef HALF_CONSTEXPR
+#undef HALF_CONSTEXPR_CONST
+#undef HALF_NOEXCEPT
+#undef HALF_NOTHROW
+#ifdef HALF_POP_WARNINGS
+	#pragma warning(pop)
+	#undef HALF_POP_WARNINGS
+#endif
+
+#endif
diff --git a/include/af/array.h b/include/af/array.h
index f25755e1b6..452cb3dacd 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -118,6 +118,9 @@ namespace af
             inline bool isreal() const { return !iscomplex(); }
             bool isdouble() const;
             bool issingle() const;
+#if AF_API_VERSION >= 37
+            bool ishalf() const;
+#endif
             bool isrealfloating() const;
             bool isfloating() const;
             bool isinteger() const;
@@ -638,17 +641,24 @@ namespace af
         bool isdouble() const;
 
         /**
-           \brief Returns true if the array type is neither \ref f64 nor \ref c64
+           \brief Returns true if the array type is either \ref f32 nor \ref c32
         */
         bool issingle() const;
 
+#if AF_API_VERSION >= 37
+        /**
+           \brief Returns true if the array type is \ref f16
+        */
+        bool ishalf() const;
+#endif
+
         /**
-           \brief Returns true if the array type is \ref f32 or \ref f64
+           \brief Returns true if the array type is \ref f16 \ref f32 or \ref f64
         */
         bool isrealfloating() const;
 
         /**
-           \brief Returns true if the array type is \ref f32, \ref f64, \ref c32 or \ref c64
+           \brief Returns true if the array type is \ref f16 \ref f32, \ref f64, \ref c32 or \ref c64
         */
         bool isfloating() const;
 
@@ -1702,7 +1712,7 @@ extern "C" {
 
         This is mutually exclusive to \ref af_is_complex
 
-        \param[out] result is true if arr is NOT of type \ref c32 or \ref c64, otherwise false
+        \param[out] result is true if arr is NOT \ref c32 or \ref c64, otherwise false
         \param[in] arr is the input array
 
         \returns error codes
@@ -1729,6 +1739,18 @@ extern "C" {
     */
     AFAPI af_err af_is_single       (bool *result, const af_array arr);
 
+#if AF_API_VERSION >= 37
+    /**
+        \brief Check if an array is 16 bit floating point type
+
+        \param[out] result is true if arr is of type \ref f16 otherwise false
+        \param[in] arr     is the input array
+
+        \returns error codes
+    */
+    AFAPI af_err af_is_half(bool *result, const af_array arr);
+#endif
+
     /**
         \brief Check if an array is real floating point type
 
@@ -1744,7 +1766,8 @@ extern "C" {
 
         This is a combination of \ref af_is_realfloating and \ref af_is_complex
 
-        \param[out] result is true if arr is of type \ref f32, \ref f64, \ref c32 or \ref c64, otherwise false
+        \param[out] result is true if arr is of type \ref f16 \ref f32, \ref
+                           f64, \ref c32 or \ref c64, otherwise false
         \param[in] arr is the input array
 
         \returns error codes
diff --git a/include/af/defines.h b/include/af/defines.h
index b0b2f1bc5e..bd26252ddc 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -162,6 +162,13 @@ typedef enum {
     ///
     , AF_ERR_NO_GFX         = 402
 
+#if AF_API_VERSION >= 37
+    ///
+    /// This device does not support half
+    ///
+    , AF_ERR_NO_HALF        = 403
+#endif
+
     // 500-599 Errors specific to heterogenous API
 
 #if AF_API_VERSION >= 32
@@ -216,6 +223,9 @@ typedef enum {
 #if AF_API_VERSION >= 32
     , u16    ///< 16-bit unsigned integral values
 #endif
+#if AF_API_VERSION >= 37
+    , f16    ///< 16-bit floating point value
+#endif
 } af_dtype;
 
 typedef enum {
diff --git a/include/af/device.h b/include/af/device.h
index ddc43f4dd6..45f6bf6a4c 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -75,10 +75,21 @@ namespace af
     ///
     /// \param[in] device the ID of the device to query
     ///
-    /// \returns true if the \p device supports double precision operations. false otherwise
+    /// \returns true if the \p device supports double precision operations.
+    ///          false otherwise
     /// \ingroup device_func_dbl
     AFAPI bool isDoubleAvailable(const int device);
 
+    /// \brief Queries the current device for half precision floating point
+    ///        support
+    ///
+    /// \param[in] device the ID of the device to query
+    ///
+    /// \returns true if the \p device supports half precision operations.
+    ///          false otherwise
+    /// \ingroup device_func_half
+    AFAPI bool isHalfAvailable(const int device);
+
     /// \brief Sets the current device
     ///
     /// \param[in] device The ID of the target device
@@ -278,6 +289,11 @@ extern "C" {
     */
     AFAPI af_err af_get_dbl_support(bool* available, const int device);
 
+    /**
+       \ingroup device_func_half
+    */
+    AFAPI af_err af_get_half_support(bool *available, const int device);
+
     /**
        \ingroup device_func_set
     */
@@ -296,14 +312,16 @@ extern "C" {
     /**
        \ingroup device_func_alloc
 
-       This device memory returned by this function can only be freed using af_free_device
+       This device memory returned by this function can only be freed using
+       af_free_device
     */
     AFAPI af_err af_alloc_device(void **ptr, const dim_t bytes);
 
     /**
        \ingroup device_func_free
 
-       This function will free a device pointer even if it has been previously locked.
+       This function will free a device pointer even if it has been previously
+       locked.
     */
     AFAPI af_err af_free_device(void *ptr);
 
diff --git a/include/af/half.h b/include/af/half.h
new file mode 100644
index 0000000000..961dd5f099
--- /dev/null
+++ b/include/af/half.h
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+typedef struct {
+    union {
+        unsigned short data_ : 16;
+        struct {
+            unsigned short fraction : 10;
+            unsigned short exponent : 5;
+            unsigned short sign : 1;
+        };
+    };
+} af_half;
+
+#ifdef __cplusplus
+namespace af {
+#endif
+typedef af_half half;
+#ifdef __cplusplus
+}
+#endif
diff --git a/include/af/traits.hpp b/include/af/traits.hpp
index 29a1a58ea4..6c7d1bf5fa 100644
--- a/include/af/traits.hpp
+++ b/include/af/traits.hpp
@@ -14,6 +14,7 @@
 #include <complex>
 #include <af/defines.h>
 #include <af/complex.h>
+#include <af/half.h>
 
 namespace af {
 
@@ -163,6 +164,17 @@ struct dtype_traits<unsigned short> {
 };
 #endif
 
+#if AF_API_VERSION >= 37
+template<>
+struct dtype_traits<half> {
+    enum {
+        af_type = f16 ,
+        ctype = f16
+    };
+    typedef half base_type;
+    static const char* getName() { return "half"; }
+};
+#endif
 }
 
 #endif
diff --git a/include/arrayfire.h b/include/arrayfire.h
index cfcd82221c..89f2f83722 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -309,6 +309,7 @@
 #include "af/features.h"
 #include "af/gfor.h"
 #include "af/graphics.h"
+#include "af/half.h"
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index c5d402004b..1a7620c289 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
@@ -16,6 +17,8 @@
 #include <af/sparse.h>
 
 using namespace detail;
+
+using common::half;
 using common::SparseArrayBase;
 
 af_array createHandle(af::dim4 d, af_dtype dtype) {
@@ -34,6 +37,7 @@ af_array createHandle(af::dim4 d, af_dtype dtype) {
         case u64: return createHandle<uintl>(d);
         case s16: return createHandle<short>(d);
         case u16: return createHandle<ushort>(d);
+        case f16: return createHandle<half>(d);
         default: TYPE_ERROR(3, dtype);
     }
 }
@@ -54,6 +58,7 @@ af_err af_get_data_ptr(void *data, const af_array arr) {
             case u64: copyData(static_cast<uintl *>(data), arr); break;
             case s16: copyData(static_cast<short *>(data), arr); break;
             case u16: copyData(static_cast<ushort *>(data), arr); break;
+            case f16: copyData(static_cast<half *>(data), arr); break;
             default: TYPE_ERROR(1, type);
         }
     }
@@ -112,6 +117,10 @@ af_err af_create_array(af_array *result, const void *const data,
                 out =
                     createHandleFromData(d, static_cast<const ushort *>(data));
                 break;
+            case f16:
+                out = createHandleFromData(
+                    d, static_cast<const half *>(data));
+                break;
             default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
@@ -177,6 +186,7 @@ af_err af_copy_array(af_array *out, const af_array in) {
                     case u64: res = copyArray<uintl>(in); break;
                     case s16: res = copyArray<short>(in); break;
                     case u16: res = copyArray<ushort>(in); break;
+                    case f16: res = copyArray<half>(in); break;
                     default: TYPE_ERROR(1, type);
                 }
             }
@@ -207,6 +217,7 @@ af_err af_get_data_ref_count(int *use_count, const af_array in) {
             case u64: res = getArray<uintl>(in).useCount(); break;
             case s16: res = getArray<short>(in).useCount(); break;
             case u16: res = getArray<ushort>(in).useCount(); break;
+            case f16: res = getArray<half>(in).useCount(); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*use_count, res);
@@ -242,6 +253,7 @@ af_err af_release_array(af_array arr) {
                 case u64: releaseHandle<uintl>(arr); break;
                 case s16: releaseHandle<short>(arr); break;
                 case u16: releaseHandle<ushort>(arr); break;
+                case f16: releaseHandle<half>(arr); break;
                 default: TYPE_ERROR(0, type);
             }
         }
@@ -277,6 +289,7 @@ af_array retain(const af_array in) {
             case u64: return retainHandle<uintl>(in);
             case s16: return retainHandle<short>(in);
             case u16: return retainHandle<ushort>(in);
+            case f16: return retainHandle<half>(in);
             default: TYPE_ERROR(1, ty);
         }
     }
@@ -345,6 +358,10 @@ af_err af_write_array(af_array arr, const void *data, const size_t bytes,
             case u16:
                 write_array(arr, static_cast<const ushort *>(data), bytes, src);
                 break;
+            case f16:
+                write_array(arr, static_cast<const half *>(data), bytes,
+                            src);
+                break;
             default: TYPE_ERROR(4, type);
         }
     }
@@ -414,6 +431,7 @@ INSTANTIATE(af_is_complex, isComplex)
 INSTANTIATE(af_is_real, isReal)
 INSTANTIATE(af_is_double, isDouble)
 INSTANTIATE(af_is_single, isSingle)
+INSTANTIATE(af_is_half, isHalf)
 INSTANTIATE(af_is_realfloating, isRealFloating)
 INSTANTIATE(af_is_floating, isFloating)
 INSTANTIATE(af_is_integer, isInteger)
@@ -475,6 +493,10 @@ af_err af_get_scalar(void *output_value, const af_array arr) {
                 getScalar<cdouble>(reinterpret_cast<cdouble *>(output_value),
                                    arr);
                 break;
+            case f16:
+                getScalar<half>(
+                    static_cast<half *>(output_value), arr);
+                break;
             default: TYPE_ERROR(4, type);
         }
     }
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index ce06041910..6c49edd740 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -24,8 +24,11 @@
 #include <logic.hpp>
 #include <sparse_arith.hpp>
 
+#include <common/half.hpp>
+
 using namespace detail;
 using af::dim4;
+using common::half;
 
 template<typename T, af_op_t op>
 static inline af_array arithOp(const af_array lhs, const af_array rhs,
@@ -77,6 +80,7 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
             case u64: res = arithOp<uintl, op>(lhs, rhs, odims); break;
             case s16: res = arithOp<short, op>(lhs, rhs, odims); break;
             case u16: res = arithOp<ushort, op>(lhs, rhs, odims); break;
+            case f16: res = arithOp<half, op>(lhs, rhs, odims); break;
             default: TYPE_ERROR(0, otype);
         }
 
@@ -108,6 +112,7 @@ static af_err af_arith_real(af_array *out, const af_array lhs,
             case u64: res = arithOp<uintl, op>(lhs, rhs, odims); break;
             case s16: res = arithOp<short, op>(lhs, rhs, odims); break;
             case u16: res = arithOp<ushort, op>(lhs, rhs, odims); break;
+            case f16: res = arithOp<half, op>(lhs, rhs, odims); break;
             default: TYPE_ERROR(0, otype);
         }
 
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index b7453178a8..34f100b6aa 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -7,21 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/blas.h>
+
 #include <Array.hpp>
 #include <backend.hpp>
 #include <blas.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <sparse_blas.hpp>
 #include <sparse_handle.hpp>
+
 #include <af/array.h>
-#include <af/blas.h>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <type_util.hpp>
 
+using common::half;
+
 template<typename T>
 static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
                                     af_mat_prop optLhs, af_mat_prop optRhs) {
@@ -188,6 +193,13 @@ af_err af_gemm(af_array *out,
             case c64: gemm<cdouble>(&output, optLhs, optRhs,
                                     static_cast<const cdouble*>(alpha), lhs, rhs,
                                     static_cast<const cdouble*>(beta)); break;
+#ifndef AF_CPU
+            case f16:
+              gemm<half>(&output, optLhs, optRhs,
+                                 static_cast<const half *>(alpha), lhs, rhs,
+                                 static_cast<const half *>(beta)); break;
+          break;
+#endif
             default: TYPE_ERROR(3, lhs_type);
         }
 
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 8309b4a834..32ecf959f5 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -18,9 +18,11 @@
 #include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 
 using namespace detail;
+using common::half;
 
 static af_array cast(const af_array in, const af_dtype type) {
     const ArrayInfo& info = getInfo(in, false, true);
@@ -49,6 +51,7 @@ static af_array cast(const af_array in, const af_dtype type) {
             case u64: return getHandle(castArray<uintl>(in));
             case s16: return getHandle(castArray<short>(in));
             case u16: return getHandle(castArray<ushort>(in));
+            case f16: return getHandle(castArray<half>(in));
             default: TYPE_ERROR(2, type);
         }
     }
@@ -59,10 +62,11 @@ af_err af_cast(af_array* out, const af_array in, const af_dtype type) {
         const ArrayInfo& info = getInfo(in, false, true);
 
         af_dtype inType = info.getType();
-        if ((inType == c32 || inType == c64) && (type == f32 || type == f64)) {
+        if ((inType == c32 || inType == c64) &&
+            (type == f32 || type == f64 || type == f16)) {
             AF_ERROR(
                 "Casting is not allowed from complex (c32/c64) to real "
-                "(f32/f64) types.\n"
+                "(f16/f32/f64) types.\n"
                 "Use abs, real, imag etc to convert complex to floating type.",
                 AF_ERR_TYPE);
         }
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 8aad509cbe..de949f9b41 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -9,6 +9,7 @@
 
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <diagonal.hpp>
 #include <handle.hpp>
@@ -23,9 +24,9 @@
 #include <af/device.h>
 #include <af/dim4.hpp>
 #include <af/util.h>
-#include <complex>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
@@ -68,6 +69,7 @@ af_err af_constant(af_array *result, const double value, const unsigned ndims,
             case u64: out = createHandleFromValue<uintl>(d, value); break;
             case s16: out = createHandleFromValue<short>(d, value); break;
             case u16: out = createHandleFromValue<ushort>(d, value); break;
+            case f16: out = createHandleFromValue<half>(d, value); break;
             default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 47a3ba69fa..629150d901 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -10,16 +10,20 @@
 #include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
 #include <sparse_handle.hpp>
+
 #include <af/backend.h>
 #include <af/device.h>
 #include <af/dim4.hpp>
 #include <af/version.h>
+
 #include <cstring>
 
 using namespace detail;
+using common::half;
 
 af_err af_set_backend(const af_backend bknd) {
     try {
@@ -118,6 +122,14 @@ af_err af_get_dbl_support(bool* available, const int device) {
     return AF_SUCCESS;
 }
 
+af_err af_get_half_support(bool* available, const int device) {
+    try {
+        *available = isHalfSupported(device);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_get_device_count(int* nDevices) {
     try {
         *nDevices = getDeviceCount();
@@ -193,6 +205,7 @@ af_err af_eval(af_array arr) {
                 case u64: eval<uintl>(arr); break;
                 case s16: eval<short>(arr); break;
                 case u16: eval<ushort>(arr); break;
+                case f16: eval<half>(arr); break;
                 default: TYPE_ERROR(0, type);
             }
         }
@@ -247,6 +260,7 @@ af_err af_eval_multiple(int num, af_array* arrays) {
             case u64: evalMultiple<uintl>(num, arrays); break;
             case s16: evalMultiple<short>(num, arrays); break;
             case u16: evalMultiple<ushort>(num, arrays); break;
+            case f16: evalMultiple<half>(num, arrays); break;
             default: TYPE_ERROR(0, type);
         }
     }
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 66eb435b21..a97c2c422d 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -12,9 +12,11 @@
 #include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <math.hpp>
 #include <types.hpp>
+
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -55,6 +57,13 @@ const detail::Array<T> &getArray(const af_array &arr) {
     return *A;
 }
 
+template<>
+const detail::Array<common::half> &getArray<common::half>(const af_array &arr) {
+  const detail::Array<common::half> *A = static_cast<const detail::Array<common::half> *>(arr);
+    if (f16 != A->getType()) AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+    return *A;
+}
+
 template<typename T>
 detail::Array<T> &getArray(af_array &arr) {
     detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
@@ -63,6 +72,14 @@ detail::Array<T> &getArray(af_array &arr) {
     return *A;
 }
 
+template<>
+detail::Array<common::half> &getArray<common::half>(af_array &arr) {
+    detail::Array<common::half> *A = static_cast<detail::Array<common::half> *>(arr);
+    if (f16 != A->getType())
+        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+    return *A;
+}
+
 template<typename To>
 detail::Array<To> castArray(const af_array &in) {
     using detail::cdouble;
@@ -87,6 +104,8 @@ detail::Array<To> castArray(const af_array &in) {
         case u64: return detail::cast<To, uintl>(getArray<uintl>(in));
         case s16: return detail::cast<To, short>(getArray<short>(in));
         case u16: return detail::cast<To, ushort>(getArray<ushort>(in));
+        case f16:
+            return detail::cast<To, common::half>(getArray<common::half>(in));
         default: TYPE_ERROR(1, info.getType());
     }
 }
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 4eaebb56a4..34d6f7a12d 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -10,12 +10,14 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <af/data.h>
 #include <vector>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 template<typename Tx, typename Ty>
@@ -77,6 +79,7 @@ af_err af_join(af_array *out, const int dim, const af_array first,
             case s16: output = join<short, short>(dim, first, second); break;
             case u16: output = join<ushort, ushort>(dim, first, second); break;
             case u8: output = join<uchar, uchar>(dim, first, second); break;
+            case f16: output = join<half, half>(dim, first, second); break;
             default: TYPE_ERROR(1, finfo.getType());
         }
         std::swap(*out, output);
@@ -131,6 +134,7 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
             case s16: output = join_many<short>(dim, n_arrays, inputs); break;
             case u16: output = join_many<ushort>(dim, n_arrays, inputs); break;
             case u8: output = join_many<uchar>(dim, n_arrays, inputs); break;
+            case f16: output = join_many<half>(dim, n_arrays, inputs); break;
             default: TYPE_ERROR(1, info[0].getType());
         }
         std::swap(*out, output);
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 9975371e69..794c54e902 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -9,6 +9,7 @@
 
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <af/data.h>
@@ -16,6 +17,7 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 namespace {
@@ -62,6 +64,7 @@ af_err af_moddims(af_array* out, const af_array in, const unsigned ndims,
             case u64: output = modDims<uintl>(in, newDims); break;
             case s16: output = modDims<short>(in, newDims); break;
             case u16: output = modDims<ushort>(in, newDims); break;
+            case f16: output = modDims<half>(in, newDims); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index 9987b21c77..a2c4a4ffa3 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <backend.hpp>
+#include <common/half.hpp>
 #include <math.hpp>
 
 #ifndef __DH__
@@ -24,51 +25,82 @@ using namespace detail;
 
 template<typename T, af_op_t op>
 struct Binary {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ detail::compute_t<T> init();
 
-    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs);
 };
 
 template<typename T>
 struct Binary<T, af_add_t> {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::scalar<detail::compute_t<T>>(0);
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return lhs + rhs;
+    }
 };
 
 template<typename T>
 struct Binary<T, af_mul_t> {
-    static __DH__ T init() { return detail::scalar<T>(1); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::scalar<detail::compute_t<T>>(1);
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return lhs * rhs; }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return lhs * rhs;
+    }
 };
 
 template<typename T>
 struct Binary<T, af_or_t> {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::scalar<detail::compute_t<T>>(0);
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return lhs || rhs; }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return lhs || rhs;
+    }
 };
 
 template<typename T>
 struct Binary<T, af_and_t> {
-    static __DH__ T init() { return detail::scalar<T>(1); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::scalar<detail::compute_t<T>>(1);
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return lhs && rhs; }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return lhs && rhs;
+    }
 };
 
 template<typename T>
 struct Binary<T, af_notzero_t> {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::scalar<detail::compute_t<T>>(0);
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return lhs + rhs;
+    }
 };
 
 template<typename T>
 struct Binary<T, af_min_t> {
-    static __DH__ T init() { return detail::maxval<T>(); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::maxval<detail::compute_t<T>>();
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return detail::min(lhs, rhs);
+    }
 };
 
 template<>
@@ -80,14 +112,17 @@ struct Binary<char, af_min_t> {
     }
 };
 
-#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                       \
-    template<>                                                              \
-    struct Binary<T, af_min_t> {                                            \
-        static __DH__ T init() {                                            \
-            return detail::scalar<T>(detail::maxval<Tr>());                 \
-        }                                                                   \
-                                                                            \
-        __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); } \
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                          \
+    template<>                                                                 \
+    struct Binary<detail::compute_t<T>, af_min_t> {                            \
+        static __DH__ detail::compute_t<T> init() {                            \
+            return detail::scalar<detail::compute_t<T>>(detail::maxval<Tr>()); \
+        }                                                                      \
+                                                                               \
+        __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,       \
+                                               detail::compute_t<T> rhs) {     \
+            return detail::min(lhs, rhs);                                      \
+        }                                                                      \
     };
 
 SPECIALIZE_COMPLEX_MIN(cfloat, float)
@@ -97,9 +132,14 @@ SPECIALIZE_COMPLEX_MIN(cdouble, double)
 
 template<typename T>
 struct Binary<T, af_max_t> {
-    static __DH__ T init() { return detail::minval<T>(); }
+    static __DH__ detail::compute_t<T> init() {
+        return detail::minval<detail::compute_t<T>>();
+    }
 
-    __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); }
+    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
+                                           detail::compute_t<T> rhs) {
+        return detail::max(lhs, rhs);
+    }
 };
 
 template<>
@@ -111,14 +151,18 @@ struct Binary<char, af_max_t> {
     }
 };
 
-#define SPECIALIZE_COMPLEX_MAX(T, Tr)                                       \
-    template<>                                                              \
-    struct Binary<T, af_max_t> {                                            \
-        static __DH__ T init() {                                            \
-            return detail::scalar<T>(detail::scalar<Tr>(0));                \
-        }                                                                   \
-                                                                            \
-        __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); } \
+#define SPECIALIZE_COMPLEX_MAX(T, Tr)                                      \
+    template<>                                                             \
+    struct Binary<T, af_max_t> {                                           \
+        static __DH__ detail::compute_t<T> init() {                        \
+            return detail::scalar<detail::compute_t<T>>(                   \
+                detail::scalar<Tr>(0));                                    \
+        }                                                                  \
+                                                                           \
+        __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,   \
+                                               detail::compute_t<T> rhs) { \
+            return detail::max(lhs, rhs);                                  \
+        }                                                                  \
     };
 
 SPECIALIZE_COMPLEX_MAX(cfloat, float)
@@ -128,34 +172,42 @@ SPECIALIZE_COMPLEX_MAX(cdouble, double)
 
 template<typename Ti, typename To, af_op_t op>
 struct Transform {
-    __DH__ To operator()(Ti in) { return (To)(in); }
+    __DH__ To operator()(detail::compute_t<Ti> in) {
+        return static_cast<To>(in);
+    }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_min_t> {
-    __DH__ To operator()(Ti in) {
+    __DH__ To operator()(detail::compute_t<Ti> in) {
         return (To)(IS_NAN(in) ? Binary<To, af_min_t>::init() : in);
     }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_max_t> {
-    __DH__ To operator()(Ti in) {
+    __DH__ To operator()(detail::compute_t<Ti> in) {
         return (To)(IS_NAN(in) ? Binary<To, af_max_t>::init() : in);
     }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_or_t> {
-    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0)); }
+    __DH__ To operator()(detail::compute_t<Ti> in) {
+        return (in != detail::scalar<detail::compute_t<Ti>>(0));
+    }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_and_t> {
-    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0)); }
+    __DH__ To operator()(detail::compute_t<Ti> in) {
+        return (in != detail::scalar<detail::compute_t<Ti>>(0));
+    }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_notzero_t> {
-    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0)); }
+    __DH__ To operator()(detail::compute_t<Ti> in) {
+        return (in != detail::scalar<detail::compute_t<Ti>>(0));
+    }
 };
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 09f508f5dc..642046c35a 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -7,17 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <print.hpp>
+
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
-#include <print.hpp>
 #include <sparse_handle.hpp>
 #include <type_util.hpp>
+
 #include <af/array.h>
 #include <af/data.h>
 #include <af/internal.h>
+
 #include <iomanip>
 #include <iostream>
 #include <sstream>
@@ -27,6 +31,8 @@
 #include <af/index.h>
 
 using namespace detail;
+
+using common::half;
 using std::cout;
 using std::endl;
 using std::ostream;
@@ -153,6 +159,7 @@ af_err af_print_array(af_array arr) {
                 case u64: print<uintl>(NULL, arr, 4); break;
                 case s16: print<short>(NULL, arr, 4); break;
                 case u16: print<ushort>(NULL, arr, 4); break;
+                case f16: print<half>(NULL, arr, 4); break;
                 default: TYPE_ERROR(1, type);
             }
         }
@@ -191,6 +198,7 @@ af_err af_print_array_gen(const char *exp, const af_array arr,
                 case u64: print<uintl>(exp, arr, precision); break;
                 case s16: print<short>(exp, arr, precision); break;
                 case u16: print<ushort>(exp, arr, precision); break;
+                case f16: print<half>(exp, arr, precision); break;
                 default: TYPE_ERROR(1, type);
             }
         }
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index c288e92d0e..862a0a0241 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -12,6 +12,7 @@
 #include <backend.hpp>
 #include <common/MersenneTwister.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <random_engine.hpp>
 #include <types.hpp>
@@ -268,6 +269,7 @@ af_err af_random_uniform(af_array *out, const unsigned ndims,
             case u16: result = uniformDistribution_<ushort>(d, e); break;
             case u8: result = uniformDistribution_<uchar>(d, e); break;
             case b8: result = uniformDistribution_<char>(d, e); break;
+            case f16: result = uniformDistribution_<half>(d, e); break;
             default: TYPE_ERROR(4, type);
         }
         std::swap(*out, result);
@@ -291,6 +293,7 @@ af_err af_random_normal(af_array *out, const unsigned ndims,
             case c32: result = normalDistribution_<cfloat>(d, e); break;
             case f64: result = normalDistribution_<double>(d, e); break;
             case c64: result = normalDistribution_<cdouble>(d, e); break;
+            case f16: result = normalDistribution_<half>(d, e); break;
             default: TYPE_ERROR(4, type);
         }
         std::swap(*out, result);
@@ -332,6 +335,7 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t *const dims,
             case u16: result = uniformDistribution_<ushort>(d, e); break;
             case u8: result = uniformDistribution_<uchar>(d, e); break;
             case b8: result = uniformDistribution_<char>(d, e); break;
+            case f16: result = uniformDistribution_<half>(d, e); break;
             default: TYPE_ERROR(3, type);
         }
         std::swap(*out, result);
@@ -356,6 +360,7 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t *const dims,
             case c32: result = normalDistribution_<cfloat>(d, e); break;
             case f64: result = normalDistribution_<double>(d, e); break;
             case c64: result = normalDistribution_<cdouble>(d, e); break;
+            case f16: result = normalDistribution_<half>(d, e); break;
             default: TYPE_ERROR(3, type);
         }
         std::swap(*out, result);
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 3898748882..ccc72de17b 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -9,6 +9,7 @@
 
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <ireduce.hpp>
 #include <math.hpp>
@@ -20,6 +21,7 @@
 #include <complex>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 template<af_op_t op, typename Ti, typename To>
@@ -58,6 +60,7 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim) {
             case s16: res = reduce<op, short, To>(in, dim); break;
             case b8: res = reduce<op, char, To>(in, dim); break;
             case u8: res = reduce<op, uchar, To>(in, dim); break;
+            case f16: res = reduce<op, half, To>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
 
@@ -94,6 +97,7 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim) {
             case s16: res = reduce<op, short, short>(in, dim); break;
             case b8: res = reduce<op, char, char>(in, dim); break;
             case u8: res = reduce<op, uchar, uchar>(in, dim); break;
+            case f16: res = reduce<op, half, half>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
 
@@ -164,6 +168,9 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
                                                            nanval);
                 }
             } break;
+            case f16:
+                res = reduce<op, half, float>(in, dim, change_nan, nanval);
+                break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, res);
@@ -468,6 +475,7 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in,
             case s16: ireduce<op, short>(&res, &loc, in, dim); break;
             case b8: ireduce<op, char>(&res, &loc, in, dim); break;
             case u8: ireduce<op, uchar>(&res, &loc, in, dim); break;
+              //case f16: ireduce<op, half>(&res, &loc, in, dim); break;
             default: TYPE_ERROR(1, type);
         }
 
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index e6be05846c..418d1180cf 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -7,16 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <reorder.hpp>
+
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
-#include <reorder.hpp>
 #include <transpose.hpp>
+
 #include <af/blas.h>
 #include <af/data.h>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 template<typename T>
@@ -100,6 +104,7 @@ af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims) {
             case u64: output = reorder<uintl>(in, rdims); break;
             case s16: output = reorder<short>(in, rdims); break;
             case u16: output = reorder<ushort>(in, rdims); break;
+            case f16: output = reorder<half>(in, rdims); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index e330e9a958..a2d636d245 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -9,6 +9,7 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
 #include <optypes.hpp>
@@ -20,6 +21,7 @@
 
 using namespace detail;
 using af::dim4;
+using common::half;
 
 template<typename T>
 af_array select(const af_array cond, const af_array a, const af_array b,
@@ -68,6 +70,7 @@ af_err af_select(af_array* out, const af_array cond, const af_array a,
             case u16: res = select<ushort>(cond, a, b, odims); break;
             case u8: res = select<uchar>(cond, a, b, odims); break;
             case b8: res = select<char>(cond, a, b, odims); break;
+            case f16: res = select<half>(cond, a, b, odims); break;
             default: TYPE_ERROR(2, ainfo.getType());
         }
 
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index 636a451cdb..4b70df3295 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -10,6 +10,7 @@
 #include <type_util.hpp>
 
 #include <common/err_common.hpp>
+#include <af/half.h>
 #include <af/util.h>
 
 size_t size_of(af_dtype type) {
@@ -27,6 +28,7 @@ size_t size_of(af_dtype type) {
             case u16: return sizeof(unsigned short);
             case s64: return sizeof(long long);
             case u64: return sizeof(unsigned long long);
+            case f16: return sizeof(af_half);
             default: TYPE_ERROR(1, type);
         }
     }
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 03387d1f2a..212765740d 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -19,6 +19,8 @@
 #include <af/util.h>
 #include "error.hpp"
 
+#include <af/half.h>
+
 #include <memory>
 #include <stdexcept>
 #include <vector>
@@ -215,6 +217,7 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(short)
 INSTANTIATE(unsigned short)
+INSTANTIATE(af_half)
 
 #undef INSTANTIATE
 
@@ -283,6 +286,7 @@ INSTANTIATE(column)
 INSTANTIATE(complex)
 INSTANTIATE(double)
 INSTANTIATE(single)
+INSTANTIATE(half)
 INSTANTIATE(realfloating)
 INSTANTIATE(floating)
 INSTANTIATE(integer)
@@ -551,6 +555,7 @@ MEM_FUNC(bool, iscolumn)
 MEM_FUNC(bool, iscomplex)
 MEM_FUNC(bool, isdouble)
 MEM_FUNC(bool, issingle)
+MEM_FUNC(bool, ishalf)
 MEM_FUNC(bool, isrealfloating)
 MEM_FUNC(bool, isfloating)
 MEM_FUNC(bool, isinteger)
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 163c0731fb..dfe51c8986 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -6,13 +6,14 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <af/data.h>
 
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/complex.h>
-#include <af/data.h>
 #include <af/defines.h>
 #include <af/gfor.h>
+#include <af/half.h>
 #include <af/traits.hpp>
 #include "error.hpp"
 
@@ -40,6 +41,13 @@ struct is_complex<af::cdouble> {
     static const bool value = true;
 };
 
+array constant(af_half val, const dim4 &dims, const dtype type) {
+    af_array res;
+    AF_THROW(af_constant(&res, 0,  //(double)val,
+                         dims.ndims(), dims.get(), type));
+    return array(res);
+}
+
 template<typename T,
          typename = typename enable_if<is_complex<T>::value == false, T>::type>
 array constant(T val, const dim4 &dims, const dtype type) {
@@ -126,6 +134,7 @@ CONSTANT(unsigned long long);
 CONSTANT(bool);
 CONSTANT(short);
 CONSTANT(unsigned short);
+CONSTANT(half);
 
 #undef CONSTANT
 
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index f639451507..45076aa863 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -90,6 +90,12 @@ bool isDoubleAvailable(const int device) {
     return temp;
 }
 
+bool isHalfAvailable(const int device) {
+    bool temp;
+    AF_THROW(af_get_half_support(&temp, device));
+    return temp;
+}
+
 int deviceget() { return getDevice(); }
 
 void sync(int device) { AF_THROW(af_sync(device)); }
diff --git a/src/api/cpp/seq.cpp b/src/api/cpp/seq.cpp
index 8a17759ef4..5f849a5acd 100644
--- a/src/api/cpp/seq.cpp
+++ b/src/api/cpp/seq.cpp
@@ -10,8 +10,11 @@
 #include <af/array.h>
 #include <af/data.h>
 #include <af/seq.h>
+
 #include "error.hpp"
 
+#include <cmath>
+
 namespace af {
 int end = -1;
 seq span(af_span);
@@ -21,7 +24,7 @@ void seq::init(double begin, double end, double step) {
     this->s.end   = end;
     this->s.step  = step;
     if (step != 0) {  // Not Span
-        size = fabs((end - begin) / step) + 1;
+        size = std::fabs((end - begin) / step) + 1;
     } else {
         size = 0;
     }
@@ -69,7 +72,8 @@ seq::seq(seq other, bool is_gfor)
 seq::operator array() const {
     double diff = s.end - s.begin;
     dim_t len =
-        (int)((diff + fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) / s.step);
+        (int)((diff + std::fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) /
+              s.step);
 
     array tmp = (m_gfor) ? range(1, 1, 1, len, 3) : range(len);
 
diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index 388b9319a2..d90ae0d4ea 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -103,6 +103,7 @@ ARRAY_HAPI_DEF(af_is_complex)
 ARRAY_HAPI_DEF(af_is_real)
 ARRAY_HAPI_DEF(af_is_double)
 ARRAY_HAPI_DEF(af_is_single)
+ARRAY_HAPI_DEF(af_is_half)
 ARRAY_HAPI_DEF(af_is_realfloating)
 ARRAY_HAPI_DEF(af_is_floating)
 ARRAY_HAPI_DEF(af_is_integer)
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 57d74fd476..c7629d59d1 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -61,6 +61,10 @@ af_err af_get_dbl_support(bool *available, const int device) {
     return CALL(available, device);
 }
 
+af_err af_get_half_support(bool *available, const int device) {
+    return CALL(available, device);
+}
+
 af_err af_set_device(const int device) { return CALL(device); }
 
 af_err af_get_device(int *device) { return CALL(device); }
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index 6e29345e44..bdade9d76e 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -101,7 +101,9 @@ bool ArrayInfo::isDouble() const { return (type == f64 || type == c64); }
 
 bool ArrayInfo::isSingle() const { return (type == f32 || type == c32); }
 
-bool ArrayInfo::isRealFloating() const { return (type == f64 || type == f32); }
+bool ArrayInfo::isHalf() const { return (type == f16); }
+
+bool ArrayInfo::isRealFloating() const { return (type == f64 || type == f32 || type == f16); }
 
 bool ArrayInfo::isFloating() const { return (!isInteger() && !isBool()); }
 
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index 99313ed4c4..868ae12c90 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -139,6 +139,8 @@ class ArrayInfo {
 
     bool isSingle() const;
 
+    bool isHalf() const;
+
     bool isRealFloating() const;
 
     bool isFloating() const;
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 9829d56761..2c96ab097c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -25,9 +25,10 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/HandleBase.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/HandleBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
@@ -42,13 +43,16 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/dispatch.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/err_common.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/graphics_common.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/half.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/half.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/kernel_type.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/traits.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/unique_handle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.hpp
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 14db5b3a96..474e1e539d 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -85,6 +85,7 @@ class SparseArrayBase {
     INSTANTIATE_INFO(bool, isReal)
     INSTANTIATE_INFO(bool, isDouble)
     INSTANTIATE_INFO(bool, isSingle)
+    INSTANTIATE_INFO(bool, isHalf)
     INSTANTIATE_INFO(bool, isRealFloating)
     INSTANTIATE_INFO(bool, isFloating)
     INSTANTIATE_INFO(bool, isInteger)
@@ -174,6 +175,7 @@ class SparseArray {
     INSTANTIATE_INFO(bool, isReal)
     INSTANTIATE_INFO(bool, isDouble)
     INSTANTIATE_INFO(bool, isSingle)
+    INSTANTIATE_INFO(bool, isHalf)
     INSTANTIATE_INFO(bool, isRealFloating)
     INSTANTIATE_INFO(bool, isFloating)
     INSTANTIATE_INFO(bool, isInteger)
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 5be423e1d0..7d51e842cb 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -191,6 +191,8 @@ const char *af_err_to_string(const af_err err) {
         case AF_ERR_NO_GFX:
             return "Graphics functionality unavailable. "
                    "ArrayFire compiled without Graphics support";
+        case AF_ERR_NO_HALF:
+            return "Half precision floats not supported for this device";
         case AF_ERR_LOAD_LIB: return "Failed to load dynamic library. ";
         case AF_ERR_LOAD_SYM: return "Failed to load symbol";
         case AF_ERR_ARR_BKND_MISMATCH:
diff --git a/src/backend/common/half.cpp b/src/backend/common/half.cpp
new file mode 100644
index 0000000000..96c5ef4ff9
--- /dev/null
+++ b/src/backend/common/half.cpp
@@ -0,0 +1,9 @@
+
+#include <common/half.hpp>
+
+namespace common {
+std::ostream &operator<<(std::ostream &os, const half &val) {
+    os << float(val);
+    return os;
+}
+}  // namespace common
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
new file mode 100644
index 0000000000..d445417bb9
--- /dev/null
+++ b/src/backend/common/half.hpp
@@ -0,0 +1,801 @@
+#pragma once
+
+#if defined(NVCC) || defined(__CUDACC_RTC__)
+#include <cuda_fp16.h>
+#endif
+
+#include <backend.hpp>
+
+#ifndef __CUDACC_RTC__
+#include <cstring>
+#include <ostream>
+#include <type_traits>
+
+#include <limits>
+#endif
+
+#if AF_COMPILER_CXX_RELAXED_CONSTEXPR
+#define CONSTEXPR_DH AF_CONSTEXPR __DH__
+#else
+#define CONSTEXPR_DH __DH__
+#endif
+
+namespace common {
+
+#ifndef __CUDACC_RTC__
+/// Convert integer to half-precision floating point.
+///
+/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest
+///           rounding
+/// \tparam S `true` if value negative, `false` else
+/// \tparam T type to convert (builtin integer type)
+///
+/// \param value non-negative integral value
+///
+/// \return binary representation of half-precision value
+template<std::float_round_style R, bool S, typename T>
+CONSTEXPR_DH uint16_t int2half(T value) noexcept {
+    static_assert(std::is_integral<T>::value,
+                  "int to half conversion only supports builtin integer types");
+    if (S) value = -value;
+    uint16_t bits = S << 15;
+    if (value > 0xFFFF) {
+        if (R == std::round_toward_infinity)
+            bits |= 0x7C00 - S;
+        else if (R == std::round_toward_neg_infinity)
+            bits |= 0x7BFF + S;
+        else
+            bits |= 0x7BFF + (R != std::round_toward_zero);
+    } else if (value) {
+        uint32_t m = value, exp = 24;
+        for (; m < 0x400; m <<= 1, --exp)
+            ;
+        for (; m > 0x7FF; m >>= 1, ++exp)
+            ;
+        bits |= (exp << 10) + m;
+        if (exp > 24) {
+            if (R == std::round_to_nearest)
+                bits += (value >> (exp - 25)) & 1
+#if HALF_ROUND_TIES_TO_EVEN
+                        & (((((1 << (exp - 25)) - 1) & value) != 0) | bits)
+#endif
+                    ;
+            else if (R == std::round_toward_infinity)
+                bits += ((value & ((1 << (exp - 24)) - 1)) != 0) & !S;
+            else if (R == std::round_toward_neg_infinity)
+                bits += ((value & ((1 << (exp - 24)) - 1)) != 0) & S;
+        }
+    }
+    return bits;
+}
+
+/// Convert IEEE single-precision to half-precision.
+/// Credit for this goes to [Jeroen van der
+/// Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf).
+/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest
+/// rounding
+///
+/// \param value single-precision value
+/// \return binary representation of half-precision value
+template<std::float_round_style R = std::round_to_nearest>
+CONSTEXPR_DH uint16_t float2half(float value) noexcept {
+    uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
+                        // //violating strict aliasing!
+    std::memcpy(&bits, &value, sizeof(float));
+    uint16_t base_table[512] = {
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
+        0x0000, 0x0000, 0x0000, 0x0000, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010,
+        0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x0C00, 0x1000,
+        0x1400, 0x1800, 0x1C00, 0x2000, 0x2400, 0x2800, 0x2C00, 0x3000, 0x3400,
+        0x3800, 0x3C00, 0x4000, 0x4400, 0x4800, 0x4C00, 0x5000, 0x5400, 0x5800,
+        0x5C00, 0x6000, 0x6400, 0x6800, 0x6C00, 0x7000, 0x7400, 0x7800, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x7C00,
+        0x7C00, 0x7C00, 0x7C00, 0x7C00, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+        0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8001,
+        0x8002, 0x8004, 0x8008, 0x8010, 0x8020, 0x8040, 0x8080, 0x8100, 0x8200,
+        0x8400, 0x8800, 0x8C00, 0x9000, 0x9400, 0x9800, 0x9C00, 0xA000, 0xA400,
+        0xA800, 0xAC00, 0xB000, 0xB400, 0xB800, 0xBC00, 0xC000, 0xC400, 0xC800,
+        0xCC00, 0xD000, 0xD400, 0xD800, 0xDC00, 0xE000, 0xE400, 0xE800, 0xEC00,
+        0xF000, 0xF400, 0xF800, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
+        0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00};
+
+    uint8_t shift_table[512] = {
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 23, 22, 21, 20, 19,
+        18, 17, 16, 15, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
+        13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 13, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 23,
+        22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13,
+        13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
+        13, 13, 13, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
+        24, 24, 24, 24, 24, 24, 24, 13};
+    uint16_t hbits =
+        base_table[bits >> 23] +
+        static_cast<uint16_t>((bits & 0x7FFFFF) >> shift_table[bits >> 23]);
+    if (R == std::round_to_nearest)
+        hbits +=
+            (((bits & 0x7FFFFF) >> (shift_table[bits >> 23] - 1)) |
+             (((bits >> 23) & 0xFF) == 102)) &
+            ((hbits & 0x7C00) != 0x7C00)
+#if HALF_ROUND_TIES_TO_EVEN
+            &
+            (((((static_cast<uint32>(1) << (shift_table[bits >> 23] - 1)) - 1) &
+               bits) != 0) |
+             hbits)
+#endif
+            ;
+    else if (R == std::round_toward_zero)
+        hbits -= ((hbits & 0x7FFF) == 0x7C00) & ~shift_table[bits >> 23];
+    else if (R == std::round_toward_infinity)
+        hbits += ((((bits & 0x7FFFFF &
+                     ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) -
+                      1)) != 0) |
+                   (((bits >> 23) <= 102) & ((bits >> 23) != 0))) &
+                  (hbits < 0x7C00)) -
+                 ((hbits == 0xFC00) & ((bits >> 23) != 511));
+    else if (R == std::round_toward_neg_infinity)
+        hbits += ((((bits & 0x7FFFFF &
+                     ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) -
+                      1)) != 0) |
+                   (((bits >> 23) <= 358) & ((bits >> 23) != 256))) &
+                  (hbits < 0xFC00) & (hbits >> 15)) -
+                 ((hbits == 0x7C00) & ((bits >> 23) != 255));
+    return hbits;
+}
+
+__DH__ inline float half2float(uint16_t value) noexcept {
+    // return _cvtsh_ss(data.data_);
+    uint32_t mantissa_table[2048] = {
+        0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34A00000,
+        0x34C00000, 0x34E00000, 0x35000000, 0x35100000, 0x35200000, 0x35300000,
+        0x35400000, 0x35500000, 0x35600000, 0x35700000, 0x35800000, 0x35880000,
+        0x35900000, 0x35980000, 0x35A00000, 0x35A80000, 0x35B00000, 0x35B80000,
+        0x35C00000, 0x35C80000, 0x35D00000, 0x35D80000, 0x35E00000, 0x35E80000,
+        0x35F00000, 0x35F80000, 0x36000000, 0x36040000, 0x36080000, 0x360C0000,
+        0x36100000, 0x36140000, 0x36180000, 0x361C0000, 0x36200000, 0x36240000,
+        0x36280000, 0x362C0000, 0x36300000, 0x36340000, 0x36380000, 0x363C0000,
+        0x36400000, 0x36440000, 0x36480000, 0x364C0000, 0x36500000, 0x36540000,
+        0x36580000, 0x365C0000, 0x36600000, 0x36640000, 0x36680000, 0x366C0000,
+        0x36700000, 0x36740000, 0x36780000, 0x367C0000, 0x36800000, 0x36820000,
+        0x36840000, 0x36860000, 0x36880000, 0x368A0000, 0x368C0000, 0x368E0000,
+        0x36900000, 0x36920000, 0x36940000, 0x36960000, 0x36980000, 0x369A0000,
+        0x369C0000, 0x369E0000, 0x36A00000, 0x36A20000, 0x36A40000, 0x36A60000,
+        0x36A80000, 0x36AA0000, 0x36AC0000, 0x36AE0000, 0x36B00000, 0x36B20000,
+        0x36B40000, 0x36B60000, 0x36B80000, 0x36BA0000, 0x36BC0000, 0x36BE0000,
+        0x36C00000, 0x36C20000, 0x36C40000, 0x36C60000, 0x36C80000, 0x36CA0000,
+        0x36CC0000, 0x36CE0000, 0x36D00000, 0x36D20000, 0x36D40000, 0x36D60000,
+        0x36D80000, 0x36DA0000, 0x36DC0000, 0x36DE0000, 0x36E00000, 0x36E20000,
+        0x36E40000, 0x36E60000, 0x36E80000, 0x36EA0000, 0x36EC0000, 0x36EE0000,
+        0x36F00000, 0x36F20000, 0x36F40000, 0x36F60000, 0x36F80000, 0x36FA0000,
+        0x36FC0000, 0x36FE0000, 0x37000000, 0x37010000, 0x37020000, 0x37030000,
+        0x37040000, 0x37050000, 0x37060000, 0x37070000, 0x37080000, 0x37090000,
+        0x370A0000, 0x370B0000, 0x370C0000, 0x370D0000, 0x370E0000, 0x370F0000,
+        0x37100000, 0x37110000, 0x37120000, 0x37130000, 0x37140000, 0x37150000,
+        0x37160000, 0x37170000, 0x37180000, 0x37190000, 0x371A0000, 0x371B0000,
+        0x371C0000, 0x371D0000, 0x371E0000, 0x371F0000, 0x37200000, 0x37210000,
+        0x37220000, 0x37230000, 0x37240000, 0x37250000, 0x37260000, 0x37270000,
+        0x37280000, 0x37290000, 0x372A0000, 0x372B0000, 0x372C0000, 0x372D0000,
+        0x372E0000, 0x372F0000, 0x37300000, 0x37310000, 0x37320000, 0x37330000,
+        0x37340000, 0x37350000, 0x37360000, 0x37370000, 0x37380000, 0x37390000,
+        0x373A0000, 0x373B0000, 0x373C0000, 0x373D0000, 0x373E0000, 0x373F0000,
+        0x37400000, 0x37410000, 0x37420000, 0x37430000, 0x37440000, 0x37450000,
+        0x37460000, 0x37470000, 0x37480000, 0x37490000, 0x374A0000, 0x374B0000,
+        0x374C0000, 0x374D0000, 0x374E0000, 0x374F0000, 0x37500000, 0x37510000,
+        0x37520000, 0x37530000, 0x37540000, 0x37550000, 0x37560000, 0x37570000,
+        0x37580000, 0x37590000, 0x375A0000, 0x375B0000, 0x375C0000, 0x375D0000,
+        0x375E0000, 0x375F0000, 0x37600000, 0x37610000, 0x37620000, 0x37630000,
+        0x37640000, 0x37650000, 0x37660000, 0x37670000, 0x37680000, 0x37690000,
+        0x376A0000, 0x376B0000, 0x376C0000, 0x376D0000, 0x376E0000, 0x376F0000,
+        0x37700000, 0x37710000, 0x37720000, 0x37730000, 0x37740000, 0x37750000,
+        0x37760000, 0x37770000, 0x37780000, 0x37790000, 0x377A0000, 0x377B0000,
+        0x377C0000, 0x377D0000, 0x377E0000, 0x377F0000, 0x37800000, 0x37808000,
+        0x37810000, 0x37818000, 0x37820000, 0x37828000, 0x37830000, 0x37838000,
+        0x37840000, 0x37848000, 0x37850000, 0x37858000, 0x37860000, 0x37868000,
+        0x37870000, 0x37878000, 0x37880000, 0x37888000, 0x37890000, 0x37898000,
+        0x378A0000, 0x378A8000, 0x378B0000, 0x378B8000, 0x378C0000, 0x378C8000,
+        0x378D0000, 0x378D8000, 0x378E0000, 0x378E8000, 0x378F0000, 0x378F8000,
+        0x37900000, 0x37908000, 0x37910000, 0x37918000, 0x37920000, 0x37928000,
+        0x37930000, 0x37938000, 0x37940000, 0x37948000, 0x37950000, 0x37958000,
+        0x37960000, 0x37968000, 0x37970000, 0x37978000, 0x37980000, 0x37988000,
+        0x37990000, 0x37998000, 0x379A0000, 0x379A8000, 0x379B0000, 0x379B8000,
+        0x379C0000, 0x379C8000, 0x379D0000, 0x379D8000, 0x379E0000, 0x379E8000,
+        0x379F0000, 0x379F8000, 0x37A00000, 0x37A08000, 0x37A10000, 0x37A18000,
+        0x37A20000, 0x37A28000, 0x37A30000, 0x37A38000, 0x37A40000, 0x37A48000,
+        0x37A50000, 0x37A58000, 0x37A60000, 0x37A68000, 0x37A70000, 0x37A78000,
+        0x37A80000, 0x37A88000, 0x37A90000, 0x37A98000, 0x37AA0000, 0x37AA8000,
+        0x37AB0000, 0x37AB8000, 0x37AC0000, 0x37AC8000, 0x37AD0000, 0x37AD8000,
+        0x37AE0000, 0x37AE8000, 0x37AF0000, 0x37AF8000, 0x37B00000, 0x37B08000,
+        0x37B10000, 0x37B18000, 0x37B20000, 0x37B28000, 0x37B30000, 0x37B38000,
+        0x37B40000, 0x37B48000, 0x37B50000, 0x37B58000, 0x37B60000, 0x37B68000,
+        0x37B70000, 0x37B78000, 0x37B80000, 0x37B88000, 0x37B90000, 0x37B98000,
+        0x37BA0000, 0x37BA8000, 0x37BB0000, 0x37BB8000, 0x37BC0000, 0x37BC8000,
+        0x37BD0000, 0x37BD8000, 0x37BE0000, 0x37BE8000, 0x37BF0000, 0x37BF8000,
+        0x37C00000, 0x37C08000, 0x37C10000, 0x37C18000, 0x37C20000, 0x37C28000,
+        0x37C30000, 0x37C38000, 0x37C40000, 0x37C48000, 0x37C50000, 0x37C58000,
+        0x37C60000, 0x37C68000, 0x37C70000, 0x37C78000, 0x37C80000, 0x37C88000,
+        0x37C90000, 0x37C98000, 0x37CA0000, 0x37CA8000, 0x37CB0000, 0x37CB8000,
+        0x37CC0000, 0x37CC8000, 0x37CD0000, 0x37CD8000, 0x37CE0000, 0x37CE8000,
+        0x37CF0000, 0x37CF8000, 0x37D00000, 0x37D08000, 0x37D10000, 0x37D18000,
+        0x37D20000, 0x37D28000, 0x37D30000, 0x37D38000, 0x37D40000, 0x37D48000,
+        0x37D50000, 0x37D58000, 0x37D60000, 0x37D68000, 0x37D70000, 0x37D78000,
+        0x37D80000, 0x37D88000, 0x37D90000, 0x37D98000, 0x37DA0000, 0x37DA8000,
+        0x37DB0000, 0x37DB8000, 0x37DC0000, 0x37DC8000, 0x37DD0000, 0x37DD8000,
+        0x37DE0000, 0x37DE8000, 0x37DF0000, 0x37DF8000, 0x37E00000, 0x37E08000,
+        0x37E10000, 0x37E18000, 0x37E20000, 0x37E28000, 0x37E30000, 0x37E38000,
+        0x37E40000, 0x37E48000, 0x37E50000, 0x37E58000, 0x37E60000, 0x37E68000,
+        0x37E70000, 0x37E78000, 0x37E80000, 0x37E88000, 0x37E90000, 0x37E98000,
+        0x37EA0000, 0x37EA8000, 0x37EB0000, 0x37EB8000, 0x37EC0000, 0x37EC8000,
+        0x37ED0000, 0x37ED8000, 0x37EE0000, 0x37EE8000, 0x37EF0000, 0x37EF8000,
+        0x37F00000, 0x37F08000, 0x37F10000, 0x37F18000, 0x37F20000, 0x37F28000,
+        0x37F30000, 0x37F38000, 0x37F40000, 0x37F48000, 0x37F50000, 0x37F58000,
+        0x37F60000, 0x37F68000, 0x37F70000, 0x37F78000, 0x37F80000, 0x37F88000,
+        0x37F90000, 0x37F98000, 0x37FA0000, 0x37FA8000, 0x37FB0000, 0x37FB8000,
+        0x37FC0000, 0x37FC8000, 0x37FD0000, 0x37FD8000, 0x37FE0000, 0x37FE8000,
+        0x37FF0000, 0x37FF8000, 0x38000000, 0x38004000, 0x38008000, 0x3800C000,
+        0x38010000, 0x38014000, 0x38018000, 0x3801C000, 0x38020000, 0x38024000,
+        0x38028000, 0x3802C000, 0x38030000, 0x38034000, 0x38038000, 0x3803C000,
+        0x38040000, 0x38044000, 0x38048000, 0x3804C000, 0x38050000, 0x38054000,
+        0x38058000, 0x3805C000, 0x38060000, 0x38064000, 0x38068000, 0x3806C000,
+        0x38070000, 0x38074000, 0x38078000, 0x3807C000, 0x38080000, 0x38084000,
+        0x38088000, 0x3808C000, 0x38090000, 0x38094000, 0x38098000, 0x3809C000,
+        0x380A0000, 0x380A4000, 0x380A8000, 0x380AC000, 0x380B0000, 0x380B4000,
+        0x380B8000, 0x380BC000, 0x380C0000, 0x380C4000, 0x380C8000, 0x380CC000,
+        0x380D0000, 0x380D4000, 0x380D8000, 0x380DC000, 0x380E0000, 0x380E4000,
+        0x380E8000, 0x380EC000, 0x380F0000, 0x380F4000, 0x380F8000, 0x380FC000,
+        0x38100000, 0x38104000, 0x38108000, 0x3810C000, 0x38110000, 0x38114000,
+        0x38118000, 0x3811C000, 0x38120000, 0x38124000, 0x38128000, 0x3812C000,
+        0x38130000, 0x38134000, 0x38138000, 0x3813C000, 0x38140000, 0x38144000,
+        0x38148000, 0x3814C000, 0x38150000, 0x38154000, 0x38158000, 0x3815C000,
+        0x38160000, 0x38164000, 0x38168000, 0x3816C000, 0x38170000, 0x38174000,
+        0x38178000, 0x3817C000, 0x38180000, 0x38184000, 0x38188000, 0x3818C000,
+        0x38190000, 0x38194000, 0x38198000, 0x3819C000, 0x381A0000, 0x381A4000,
+        0x381A8000, 0x381AC000, 0x381B0000, 0x381B4000, 0x381B8000, 0x381BC000,
+        0x381C0000, 0x381C4000, 0x381C8000, 0x381CC000, 0x381D0000, 0x381D4000,
+        0x381D8000, 0x381DC000, 0x381E0000, 0x381E4000, 0x381E8000, 0x381EC000,
+        0x381F0000, 0x381F4000, 0x381F8000, 0x381FC000, 0x38200000, 0x38204000,
+        0x38208000, 0x3820C000, 0x38210000, 0x38214000, 0x38218000, 0x3821C000,
+        0x38220000, 0x38224000, 0x38228000, 0x3822C000, 0x38230000, 0x38234000,
+        0x38238000, 0x3823C000, 0x38240000, 0x38244000, 0x38248000, 0x3824C000,
+        0x38250000, 0x38254000, 0x38258000, 0x3825C000, 0x38260000, 0x38264000,
+        0x38268000, 0x3826C000, 0x38270000, 0x38274000, 0x38278000, 0x3827C000,
+        0x38280000, 0x38284000, 0x38288000, 0x3828C000, 0x38290000, 0x38294000,
+        0x38298000, 0x3829C000, 0x382A0000, 0x382A4000, 0x382A8000, 0x382AC000,
+        0x382B0000, 0x382B4000, 0x382B8000, 0x382BC000, 0x382C0000, 0x382C4000,
+        0x382C8000, 0x382CC000, 0x382D0000, 0x382D4000, 0x382D8000, 0x382DC000,
+        0x382E0000, 0x382E4000, 0x382E8000, 0x382EC000, 0x382F0000, 0x382F4000,
+        0x382F8000, 0x382FC000, 0x38300000, 0x38304000, 0x38308000, 0x3830C000,
+        0x38310000, 0x38314000, 0x38318000, 0x3831C000, 0x38320000, 0x38324000,
+        0x38328000, 0x3832C000, 0x38330000, 0x38334000, 0x38338000, 0x3833C000,
+        0x38340000, 0x38344000, 0x38348000, 0x3834C000, 0x38350000, 0x38354000,
+        0x38358000, 0x3835C000, 0x38360000, 0x38364000, 0x38368000, 0x3836C000,
+        0x38370000, 0x38374000, 0x38378000, 0x3837C000, 0x38380000, 0x38384000,
+        0x38388000, 0x3838C000, 0x38390000, 0x38394000, 0x38398000, 0x3839C000,
+        0x383A0000, 0x383A4000, 0x383A8000, 0x383AC000, 0x383B0000, 0x383B4000,
+        0x383B8000, 0x383BC000, 0x383C0000, 0x383C4000, 0x383C8000, 0x383CC000,
+        0x383D0000, 0x383D4000, 0x383D8000, 0x383DC000, 0x383E0000, 0x383E4000,
+        0x383E8000, 0x383EC000, 0x383F0000, 0x383F4000, 0x383F8000, 0x383FC000,
+        0x38400000, 0x38404000, 0x38408000, 0x3840C000, 0x38410000, 0x38414000,
+        0x38418000, 0x3841C000, 0x38420000, 0x38424000, 0x38428000, 0x3842C000,
+        0x38430000, 0x38434000, 0x38438000, 0x3843C000, 0x38440000, 0x38444000,
+        0x38448000, 0x3844C000, 0x38450000, 0x38454000, 0x38458000, 0x3845C000,
+        0x38460000, 0x38464000, 0x38468000, 0x3846C000, 0x38470000, 0x38474000,
+        0x38478000, 0x3847C000, 0x38480000, 0x38484000, 0x38488000, 0x3848C000,
+        0x38490000, 0x38494000, 0x38498000, 0x3849C000, 0x384A0000, 0x384A4000,
+        0x384A8000, 0x384AC000, 0x384B0000, 0x384B4000, 0x384B8000, 0x384BC000,
+        0x384C0000, 0x384C4000, 0x384C8000, 0x384CC000, 0x384D0000, 0x384D4000,
+        0x384D8000, 0x384DC000, 0x384E0000, 0x384E4000, 0x384E8000, 0x384EC000,
+        0x384F0000, 0x384F4000, 0x384F8000, 0x384FC000, 0x38500000, 0x38504000,
+        0x38508000, 0x3850C000, 0x38510000, 0x38514000, 0x38518000, 0x3851C000,
+        0x38520000, 0x38524000, 0x38528000, 0x3852C000, 0x38530000, 0x38534000,
+        0x38538000, 0x3853C000, 0x38540000, 0x38544000, 0x38548000, 0x3854C000,
+        0x38550000, 0x38554000, 0x38558000, 0x3855C000, 0x38560000, 0x38564000,
+        0x38568000, 0x3856C000, 0x38570000, 0x38574000, 0x38578000, 0x3857C000,
+        0x38580000, 0x38584000, 0x38588000, 0x3858C000, 0x38590000, 0x38594000,
+        0x38598000, 0x3859C000, 0x385A0000, 0x385A4000, 0x385A8000, 0x385AC000,
+        0x385B0000, 0x385B4000, 0x385B8000, 0x385BC000, 0x385C0000, 0x385C4000,
+        0x385C8000, 0x385CC000, 0x385D0000, 0x385D4000, 0x385D8000, 0x385DC000,
+        0x385E0000, 0x385E4000, 0x385E8000, 0x385EC000, 0x385F0000, 0x385F4000,
+        0x385F8000, 0x385FC000, 0x38600000, 0x38604000, 0x38608000, 0x3860C000,
+        0x38610000, 0x38614000, 0x38618000, 0x3861C000, 0x38620000, 0x38624000,
+        0x38628000, 0x3862C000, 0x38630000, 0x38634000, 0x38638000, 0x3863C000,
+        0x38640000, 0x38644000, 0x38648000, 0x3864C000, 0x38650000, 0x38654000,
+        0x38658000, 0x3865C000, 0x38660000, 0x38664000, 0x38668000, 0x3866C000,
+        0x38670000, 0x38674000, 0x38678000, 0x3867C000, 0x38680000, 0x38684000,
+        0x38688000, 0x3868C000, 0x38690000, 0x38694000, 0x38698000, 0x3869C000,
+        0x386A0000, 0x386A4000, 0x386A8000, 0x386AC000, 0x386B0000, 0x386B4000,
+        0x386B8000, 0x386BC000, 0x386C0000, 0x386C4000, 0x386C8000, 0x386CC000,
+        0x386D0000, 0x386D4000, 0x386D8000, 0x386DC000, 0x386E0000, 0x386E4000,
+        0x386E8000, 0x386EC000, 0x386F0000, 0x386F4000, 0x386F8000, 0x386FC000,
+        0x38700000, 0x38704000, 0x38708000, 0x3870C000, 0x38710000, 0x38714000,
+        0x38718000, 0x3871C000, 0x38720000, 0x38724000, 0x38728000, 0x3872C000,
+        0x38730000, 0x38734000, 0x38738000, 0x3873C000, 0x38740000, 0x38744000,
+        0x38748000, 0x3874C000, 0x38750000, 0x38754000, 0x38758000, 0x3875C000,
+        0x38760000, 0x38764000, 0x38768000, 0x3876C000, 0x38770000, 0x38774000,
+        0x38778000, 0x3877C000, 0x38780000, 0x38784000, 0x38788000, 0x3878C000,
+        0x38790000, 0x38794000, 0x38798000, 0x3879C000, 0x387A0000, 0x387A4000,
+        0x387A8000, 0x387AC000, 0x387B0000, 0x387B4000, 0x387B8000, 0x387BC000,
+        0x387C0000, 0x387C4000, 0x387C8000, 0x387CC000, 0x387D0000, 0x387D4000,
+        0x387D8000, 0x387DC000, 0x387E0000, 0x387E4000, 0x387E8000, 0x387EC000,
+        0x387F0000, 0x387F4000, 0x387F8000, 0x387FC000, 0x38000000, 0x38002000,
+        0x38004000, 0x38006000, 0x38008000, 0x3800A000, 0x3800C000, 0x3800E000,
+        0x38010000, 0x38012000, 0x38014000, 0x38016000, 0x38018000, 0x3801A000,
+        0x3801C000, 0x3801E000, 0x38020000, 0x38022000, 0x38024000, 0x38026000,
+        0x38028000, 0x3802A000, 0x3802C000, 0x3802E000, 0x38030000, 0x38032000,
+        0x38034000, 0x38036000, 0x38038000, 0x3803A000, 0x3803C000, 0x3803E000,
+        0x38040000, 0x38042000, 0x38044000, 0x38046000, 0x38048000, 0x3804A000,
+        0x3804C000, 0x3804E000, 0x38050000, 0x38052000, 0x38054000, 0x38056000,
+        0x38058000, 0x3805A000, 0x3805C000, 0x3805E000, 0x38060000, 0x38062000,
+        0x38064000, 0x38066000, 0x38068000, 0x3806A000, 0x3806C000, 0x3806E000,
+        0x38070000, 0x38072000, 0x38074000, 0x38076000, 0x38078000, 0x3807A000,
+        0x3807C000, 0x3807E000, 0x38080000, 0x38082000, 0x38084000, 0x38086000,
+        0x38088000, 0x3808A000, 0x3808C000, 0x3808E000, 0x38090000, 0x38092000,
+        0x38094000, 0x38096000, 0x38098000, 0x3809A000, 0x3809C000, 0x3809E000,
+        0x380A0000, 0x380A2000, 0x380A4000, 0x380A6000, 0x380A8000, 0x380AA000,
+        0x380AC000, 0x380AE000, 0x380B0000, 0x380B2000, 0x380B4000, 0x380B6000,
+        0x380B8000, 0x380BA000, 0x380BC000, 0x380BE000, 0x380C0000, 0x380C2000,
+        0x380C4000, 0x380C6000, 0x380C8000, 0x380CA000, 0x380CC000, 0x380CE000,
+        0x380D0000, 0x380D2000, 0x380D4000, 0x380D6000, 0x380D8000, 0x380DA000,
+        0x380DC000, 0x380DE000, 0x380E0000, 0x380E2000, 0x380E4000, 0x380E6000,
+        0x380E8000, 0x380EA000, 0x380EC000, 0x380EE000, 0x380F0000, 0x380F2000,
+        0x380F4000, 0x380F6000, 0x380F8000, 0x380FA000, 0x380FC000, 0x380FE000,
+        0x38100000, 0x38102000, 0x38104000, 0x38106000, 0x38108000, 0x3810A000,
+        0x3810C000, 0x3810E000, 0x38110000, 0x38112000, 0x38114000, 0x38116000,
+        0x38118000, 0x3811A000, 0x3811C000, 0x3811E000, 0x38120000, 0x38122000,
+        0x38124000, 0x38126000, 0x38128000, 0x3812A000, 0x3812C000, 0x3812E000,
+        0x38130000, 0x38132000, 0x38134000, 0x38136000, 0x38138000, 0x3813A000,
+        0x3813C000, 0x3813E000, 0x38140000, 0x38142000, 0x38144000, 0x38146000,
+        0x38148000, 0x3814A000, 0x3814C000, 0x3814E000, 0x38150000, 0x38152000,
+        0x38154000, 0x38156000, 0x38158000, 0x3815A000, 0x3815C000, 0x3815E000,
+        0x38160000, 0x38162000, 0x38164000, 0x38166000, 0x38168000, 0x3816A000,
+        0x3816C000, 0x3816E000, 0x38170000, 0x38172000, 0x38174000, 0x38176000,
+        0x38178000, 0x3817A000, 0x3817C000, 0x3817E000, 0x38180000, 0x38182000,
+        0x38184000, 0x38186000, 0x38188000, 0x3818A000, 0x3818C000, 0x3818E000,
+        0x38190000, 0x38192000, 0x38194000, 0x38196000, 0x38198000, 0x3819A000,
+        0x3819C000, 0x3819E000, 0x381A0000, 0x381A2000, 0x381A4000, 0x381A6000,
+        0x381A8000, 0x381AA000, 0x381AC000, 0x381AE000, 0x381B0000, 0x381B2000,
+        0x381B4000, 0x381B6000, 0x381B8000, 0x381BA000, 0x381BC000, 0x381BE000,
+        0x381C0000, 0x381C2000, 0x381C4000, 0x381C6000, 0x381C8000, 0x381CA000,
+        0x381CC000, 0x381CE000, 0x381D0000, 0x381D2000, 0x381D4000, 0x381D6000,
+        0x381D8000, 0x381DA000, 0x381DC000, 0x381DE000, 0x381E0000, 0x381E2000,
+        0x381E4000, 0x381E6000, 0x381E8000, 0x381EA000, 0x381EC000, 0x381EE000,
+        0x381F0000, 0x381F2000, 0x381F4000, 0x381F6000, 0x381F8000, 0x381FA000,
+        0x381FC000, 0x381FE000, 0x38200000, 0x38202000, 0x38204000, 0x38206000,
+        0x38208000, 0x3820A000, 0x3820C000, 0x3820E000, 0x38210000, 0x38212000,
+        0x38214000, 0x38216000, 0x38218000, 0x3821A000, 0x3821C000, 0x3821E000,
+        0x38220000, 0x38222000, 0x38224000, 0x38226000, 0x38228000, 0x3822A000,
+        0x3822C000, 0x3822E000, 0x38230000, 0x38232000, 0x38234000, 0x38236000,
+        0x38238000, 0x3823A000, 0x3823C000, 0x3823E000, 0x38240000, 0x38242000,
+        0x38244000, 0x38246000, 0x38248000, 0x3824A000, 0x3824C000, 0x3824E000,
+        0x38250000, 0x38252000, 0x38254000, 0x38256000, 0x38258000, 0x3825A000,
+        0x3825C000, 0x3825E000, 0x38260000, 0x38262000, 0x38264000, 0x38266000,
+        0x38268000, 0x3826A000, 0x3826C000, 0x3826E000, 0x38270000, 0x38272000,
+        0x38274000, 0x38276000, 0x38278000, 0x3827A000, 0x3827C000, 0x3827E000,
+        0x38280000, 0x38282000, 0x38284000, 0x38286000, 0x38288000, 0x3828A000,
+        0x3828C000, 0x3828E000, 0x38290000, 0x38292000, 0x38294000, 0x38296000,
+        0x38298000, 0x3829A000, 0x3829C000, 0x3829E000, 0x382A0000, 0x382A2000,
+        0x382A4000, 0x382A6000, 0x382A8000, 0x382AA000, 0x382AC000, 0x382AE000,
+        0x382B0000, 0x382B2000, 0x382B4000, 0x382B6000, 0x382B8000, 0x382BA000,
+        0x382BC000, 0x382BE000, 0x382C0000, 0x382C2000, 0x382C4000, 0x382C6000,
+        0x382C8000, 0x382CA000, 0x382CC000, 0x382CE000, 0x382D0000, 0x382D2000,
+        0x382D4000, 0x382D6000, 0x382D8000, 0x382DA000, 0x382DC000, 0x382DE000,
+        0x382E0000, 0x382E2000, 0x382E4000, 0x382E6000, 0x382E8000, 0x382EA000,
+        0x382EC000, 0x382EE000, 0x382F0000, 0x382F2000, 0x382F4000, 0x382F6000,
+        0x382F8000, 0x382FA000, 0x382FC000, 0x382FE000, 0x38300000, 0x38302000,
+        0x38304000, 0x38306000, 0x38308000, 0x3830A000, 0x3830C000, 0x3830E000,
+        0x38310000, 0x38312000, 0x38314000, 0x38316000, 0x38318000, 0x3831A000,
+        0x3831C000, 0x3831E000, 0x38320000, 0x38322000, 0x38324000, 0x38326000,
+        0x38328000, 0x3832A000, 0x3832C000, 0x3832E000, 0x38330000, 0x38332000,
+        0x38334000, 0x38336000, 0x38338000, 0x3833A000, 0x3833C000, 0x3833E000,
+        0x38340000, 0x38342000, 0x38344000, 0x38346000, 0x38348000, 0x3834A000,
+        0x3834C000, 0x3834E000, 0x38350000, 0x38352000, 0x38354000, 0x38356000,
+        0x38358000, 0x3835A000, 0x3835C000, 0x3835E000, 0x38360000, 0x38362000,
+        0x38364000, 0x38366000, 0x38368000, 0x3836A000, 0x3836C000, 0x3836E000,
+        0x38370000, 0x38372000, 0x38374000, 0x38376000, 0x38378000, 0x3837A000,
+        0x3837C000, 0x3837E000, 0x38380000, 0x38382000, 0x38384000, 0x38386000,
+        0x38388000, 0x3838A000, 0x3838C000, 0x3838E000, 0x38390000, 0x38392000,
+        0x38394000, 0x38396000, 0x38398000, 0x3839A000, 0x3839C000, 0x3839E000,
+        0x383A0000, 0x383A2000, 0x383A4000, 0x383A6000, 0x383A8000, 0x383AA000,
+        0x383AC000, 0x383AE000, 0x383B0000, 0x383B2000, 0x383B4000, 0x383B6000,
+        0x383B8000, 0x383BA000, 0x383BC000, 0x383BE000, 0x383C0000, 0x383C2000,
+        0x383C4000, 0x383C6000, 0x383C8000, 0x383CA000, 0x383CC000, 0x383CE000,
+        0x383D0000, 0x383D2000, 0x383D4000, 0x383D6000, 0x383D8000, 0x383DA000,
+        0x383DC000, 0x383DE000, 0x383E0000, 0x383E2000, 0x383E4000, 0x383E6000,
+        0x383E8000, 0x383EA000, 0x383EC000, 0x383EE000, 0x383F0000, 0x383F2000,
+        0x383F4000, 0x383F6000, 0x383F8000, 0x383FA000, 0x383FC000, 0x383FE000,
+        0x38400000, 0x38402000, 0x38404000, 0x38406000, 0x38408000, 0x3840A000,
+        0x3840C000, 0x3840E000, 0x38410000, 0x38412000, 0x38414000, 0x38416000,
+        0x38418000, 0x3841A000, 0x3841C000, 0x3841E000, 0x38420000, 0x38422000,
+        0x38424000, 0x38426000, 0x38428000, 0x3842A000, 0x3842C000, 0x3842E000,
+        0x38430000, 0x38432000, 0x38434000, 0x38436000, 0x38438000, 0x3843A000,
+        0x3843C000, 0x3843E000, 0x38440000, 0x38442000, 0x38444000, 0x38446000,
+        0x38448000, 0x3844A000, 0x3844C000, 0x3844E000, 0x38450000, 0x38452000,
+        0x38454000, 0x38456000, 0x38458000, 0x3845A000, 0x3845C000, 0x3845E000,
+        0x38460000, 0x38462000, 0x38464000, 0x38466000, 0x38468000, 0x3846A000,
+        0x3846C000, 0x3846E000, 0x38470000, 0x38472000, 0x38474000, 0x38476000,
+        0x38478000, 0x3847A000, 0x3847C000, 0x3847E000, 0x38480000, 0x38482000,
+        0x38484000, 0x38486000, 0x38488000, 0x3848A000, 0x3848C000, 0x3848E000,
+        0x38490000, 0x38492000, 0x38494000, 0x38496000, 0x38498000, 0x3849A000,
+        0x3849C000, 0x3849E000, 0x384A0000, 0x384A2000, 0x384A4000, 0x384A6000,
+        0x384A8000, 0x384AA000, 0x384AC000, 0x384AE000, 0x384B0000, 0x384B2000,
+        0x384B4000, 0x384B6000, 0x384B8000, 0x384BA000, 0x384BC000, 0x384BE000,
+        0x384C0000, 0x384C2000, 0x384C4000, 0x384C6000, 0x384C8000, 0x384CA000,
+        0x384CC000, 0x384CE000, 0x384D0000, 0x384D2000, 0x384D4000, 0x384D6000,
+        0x384D8000, 0x384DA000, 0x384DC000, 0x384DE000, 0x384E0000, 0x384E2000,
+        0x384E4000, 0x384E6000, 0x384E8000, 0x384EA000, 0x384EC000, 0x384EE000,
+        0x384F0000, 0x384F2000, 0x384F4000, 0x384F6000, 0x384F8000, 0x384FA000,
+        0x384FC000, 0x384FE000, 0x38500000, 0x38502000, 0x38504000, 0x38506000,
+        0x38508000, 0x3850A000, 0x3850C000, 0x3850E000, 0x38510000, 0x38512000,
+        0x38514000, 0x38516000, 0x38518000, 0x3851A000, 0x3851C000, 0x3851E000,
+        0x38520000, 0x38522000, 0x38524000, 0x38526000, 0x38528000, 0x3852A000,
+        0x3852C000, 0x3852E000, 0x38530000, 0x38532000, 0x38534000, 0x38536000,
+        0x38538000, 0x3853A000, 0x3853C000, 0x3853E000, 0x38540000, 0x38542000,
+        0x38544000, 0x38546000, 0x38548000, 0x3854A000, 0x3854C000, 0x3854E000,
+        0x38550000, 0x38552000, 0x38554000, 0x38556000, 0x38558000, 0x3855A000,
+        0x3855C000, 0x3855E000, 0x38560000, 0x38562000, 0x38564000, 0x38566000,
+        0x38568000, 0x3856A000, 0x3856C000, 0x3856E000, 0x38570000, 0x38572000,
+        0x38574000, 0x38576000, 0x38578000, 0x3857A000, 0x3857C000, 0x3857E000,
+        0x38580000, 0x38582000, 0x38584000, 0x38586000, 0x38588000, 0x3858A000,
+        0x3858C000, 0x3858E000, 0x38590000, 0x38592000, 0x38594000, 0x38596000,
+        0x38598000, 0x3859A000, 0x3859C000, 0x3859E000, 0x385A0000, 0x385A2000,
+        0x385A4000, 0x385A6000, 0x385A8000, 0x385AA000, 0x385AC000, 0x385AE000,
+        0x385B0000, 0x385B2000, 0x385B4000, 0x385B6000, 0x385B8000, 0x385BA000,
+        0x385BC000, 0x385BE000, 0x385C0000, 0x385C2000, 0x385C4000, 0x385C6000,
+        0x385C8000, 0x385CA000, 0x385CC000, 0x385CE000, 0x385D0000, 0x385D2000,
+        0x385D4000, 0x385D6000, 0x385D8000, 0x385DA000, 0x385DC000, 0x385DE000,
+        0x385E0000, 0x385E2000, 0x385E4000, 0x385E6000, 0x385E8000, 0x385EA000,
+        0x385EC000, 0x385EE000, 0x385F0000, 0x385F2000, 0x385F4000, 0x385F6000,
+        0x385F8000, 0x385FA000, 0x385FC000, 0x385FE000, 0x38600000, 0x38602000,
+        0x38604000, 0x38606000, 0x38608000, 0x3860A000, 0x3860C000, 0x3860E000,
+        0x38610000, 0x38612000, 0x38614000, 0x38616000, 0x38618000, 0x3861A000,
+        0x3861C000, 0x3861E000, 0x38620000, 0x38622000, 0x38624000, 0x38626000,
+        0x38628000, 0x3862A000, 0x3862C000, 0x3862E000, 0x38630000, 0x38632000,
+        0x38634000, 0x38636000, 0x38638000, 0x3863A000, 0x3863C000, 0x3863E000,
+        0x38640000, 0x38642000, 0x38644000, 0x38646000, 0x38648000, 0x3864A000,
+        0x3864C000, 0x3864E000, 0x38650000, 0x38652000, 0x38654000, 0x38656000,
+        0x38658000, 0x3865A000, 0x3865C000, 0x3865E000, 0x38660000, 0x38662000,
+        0x38664000, 0x38666000, 0x38668000, 0x3866A000, 0x3866C000, 0x3866E000,
+        0x38670000, 0x38672000, 0x38674000, 0x38676000, 0x38678000, 0x3867A000,
+        0x3867C000, 0x3867E000, 0x38680000, 0x38682000, 0x38684000, 0x38686000,
+        0x38688000, 0x3868A000, 0x3868C000, 0x3868E000, 0x38690000, 0x38692000,
+        0x38694000, 0x38696000, 0x38698000, 0x3869A000, 0x3869C000, 0x3869E000,
+        0x386A0000, 0x386A2000, 0x386A4000, 0x386A6000, 0x386A8000, 0x386AA000,
+        0x386AC000, 0x386AE000, 0x386B0000, 0x386B2000, 0x386B4000, 0x386B6000,
+        0x386B8000, 0x386BA000, 0x386BC000, 0x386BE000,
+
+        0x386E0000, 0x386E2000, 0x386E4000, 0x386E6000, 0x386E8000, 0x386EA000,
+        0x386EC000, 0x386EE000, 0x386F0000, 0x386F2000, 0x386F4000, 0x386F6000,
+        0x386F8000, 0x386FA000, 0x386FC000, 0x386FE000, 0x38700000, 0x38702000,
+        0x38704000, 0x38706000, 0x38708000, 0x3870A000, 0x3870C000, 0x3870E000,
+        0x38710000, 0x38712000, 0x38714000, 0x38716000, 0x38718000, 0x3871A000,
+        0x3871C000, 0x3871E000, 0x38720000, 0x38722000, 0x38724000, 0x38726000,
+        0x38728000, 0x3872A000, 0x3872C000, 0x3872E000, 0x38730000, 0x38732000,
+        0x38734000, 0x38736000, 0x38738000, 0x3873A000, 0x3873C000, 0x3873E000,
+        0x38740000, 0x38742000, 0x38744000, 0x38746000, 0x38748000, 0x3874A000,
+        0x3874C000, 0x3874E000, 0x38750000, 0x38752000, 0x38754000, 0x38756000,
+        0x38758000, 0x3875A000, 0x3875C000, 0x3875E000, 0x38760000, 0x38762000,
+        0x38764000, 0x38766000, 0x38768000, 0x3876A000, 0x3876C000, 0x3876E000,
+        0x38770000, 0x38772000, 0x38774000, 0x38776000, 0x38778000, 0x3877A000,
+        0x3877C000, 0x3877E000, 0x38780000, 0x38782000, 0x38784000, 0x38786000,
+        0x38788000, 0x3878A000, 0x3878C000, 0x3878E000, 0x38790000, 0x38792000,
+        0x38794000, 0x38796000, 0x38798000, 0x3879A000, 0x3879C000, 0x3879E000,
+        0x387A0000, 0x387A2000, 0x387A4000, 0x387A6000, 0x387A8000, 0x387AA000,
+        0x387AC000, 0x387AE000, 0x387B0000, 0x387B2000, 0x387B4000, 0x387B6000,
+        0x387B8000, 0x387BA000, 0x387BC000, 0x387BE000, 0x387C0000, 0x387C2000,
+        0x387C4000, 0x387C6000, 0x387C8000, 0x387CA000, 0x387CC000, 0x387CE000,
+        0x387D0000, 0x387D2000, 0x387D4000, 0x387D6000, 0x387D8000, 0x387DA000,
+        0x387DC000, 0x387DE000, 0x387E0000, 0x387E2000, 0x387E4000, 0x387E6000,
+        0x387E8000, 0x387EA000, 0x387EC000, 0x387EE000, 0x387F0000, 0x387F2000,
+        0x387F4000, 0x387F6000, 0x387F8000, 0x387FA000, 0x387FC000, 0x387FE000};
+    uint32_t exponent_table[64] = {
+        0x00000000, 0x00800000, 0x01000000, 0x01800000, 0x02000000, 0x02800000,
+        0x03000000, 0x03800000, 0x04000000, 0x04800000, 0x05000000, 0x05800000,
+        0x06000000, 0x06800000, 0x07000000, 0x07800000, 0x08000000, 0x08800000,
+        0x09000000, 0x09800000, 0x0A000000, 0x0A800000, 0x0B000000, 0x0B800000,
+        0x0C000000, 0x0C800000, 0x0D000000, 0x0D800000, 0x0E000000, 0x0E800000,
+        0x0F000000, 0x47800000, 0x80000000, 0x80800000, 0x81000000, 0x81800000,
+        0x82000000, 0x82800000, 0x83000000, 0x83800000, 0x84000000, 0x84800000,
+        0x85000000, 0x85800000, 0x86000000, 0x86800000, 0x87000000, 0x87800000,
+        0x88000000, 0x88800000, 0x89000000, 0x89800000, 0x8A000000, 0x8A800000,
+        0x8B000000, 0x8B800000, 0x8C000000, 0x8C800000, 0x8D000000, 0x8D800000,
+        0x8E000000, 0x8E800000, 0x8F000000, 0xC7800000};
+    uint16_t offset_table[64] = {
+        0,    1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
+        1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
+        1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 0,
+        1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
+        1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
+        1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024};
+    uint32_t bits =
+        mantissa_table[offset_table[value >> 10] + (value & 0x3FF)] +
+        exponent_table[value >> 10];
+    float out = 0.0f;
+    std::memcpy(&out, &bits, sizeof(float));
+    return out;
+}
+#endif  // __CUDACC_RTC__
+
+namespace internal {
+/// Tag type for binary construction.
+struct binary_t {};
+
+/// Tag for binary construction.
+static constexpr binary_t binary;
+}  // namespace internal
+
+class alignas(2) half {
+#if defined(__CUDA_ARCH__)
+    __half data_;
+#else
+    uint16_t data_;
+    /// Constructor.
+    /// \param bits binary representation to set half to
+    CONSTEXPR_DH half(internal::binary_t, uint16_t bits) noexcept
+        : data_(bits) {}
+#endif
+
+#if !defined(NVCC) && !defined(__CUDACC_RTC__)
+    // NVCC on OSX performs a weird transformation where it removes the std::
+    // namespace and complains that the std:: namespace is not there
+    friend class std::numeric_limits<half>;
+    friend struct std::hash<half>;
+#endif
+
+   public:
+    half() = default;
+    CONSTEXPR_DH explicit half(double value) noexcept
+#ifdef __CUDA_ARCH__
+        : data_(__float2half(value)) {
+#else
+        : data_(float2half<std::round_to_nearest>(value)) {
+#endif
+    }
+
+    CONSTEXPR_DH explicit half(float value) noexcept
+#ifdef __CUDA_ARCH__
+        : data_(__float2half(value)) {
+#else
+        : data_(float2half<std::round_to_nearest>(value)) {
+#endif
+    }
+
+#ifndef __CUDACC_RTC__
+    template<typename T>
+    CONSTEXPR_DH explicit half(T value) noexcept
+        : data_((value < 0) ? int2half<std::round_to_nearest, true>(value)
+                            : int2half<std::round_to_nearest, false>(value)) {}
+
+    CONSTEXPR_DH half& operator=(const double& value) noexcept {
+        data_ = float2half<std::round_to_nearest>(value);
+        return *this;
+    }
+#endif
+
+#if defined(__CUDA_ARCH__)
+    CONSTEXPR_DH explicit half(const __half& value) noexcept : data_(value) {}
+    CONSTEXPR_DH half& operator=(__half&& value) noexcept {
+        data_ = value;
+        return *this;
+    }
+#endif
+
+    __DH__ operator float() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2float(data_);
+#else
+        return half2float(data_);
+#endif
+    };
+
+#if defined(__CUDA_ARCH__)
+    CONSTEXPR_DH operator __half() const noexcept { return data_; };
+#endif
+};
+
+#ifndef __CUDA_ARCH__
+std::ostream& operator<<(std::ostream& os, const half& val);
+#endif
+
+}  // namespace common
+
+#if !defined(NVCC) && !defined(__CUDACC_RTC__)
+/// Extensions to the C++ standard library.
+namespace std {
+/// Numeric limits for half-precision floats.
+/// Because of the underlying single-precision implementation of many
+/// operations, it inherits some properties from `std::numeric_limits<float>`.
+template<>
+class numeric_limits<common::half> : public numeric_limits<float> {
+   public:
+    /// Supports signed values.
+    static constexpr bool is_signed = true;
+
+    /// Is not exact.
+    static constexpr bool is_exact = false;
+
+    /// Doesn't provide modulo arithmetic.
+    static constexpr bool is_modulo = false;
+
+    /// IEEE conformant.
+    static constexpr bool is_iec559 = true;
+
+    /// Supports infinity.
+    static constexpr bool has_infinity = true;
+
+    /// Supports quiet NaNs.
+    static constexpr bool has_quiet_NaN = true;
+
+    /// Supports subnormal values.
+    static constexpr float_denorm_style has_denorm = denorm_present;
+
+    /// Rounding mode.
+    /// Due to the mix of internal single-precision computations (using the
+    /// rounding mode of the underlying single-precision implementation) with
+    /// the rounding mode of the single-to-half conversions, the actual rounding
+    /// mode might be `std::round_indeterminate` if the default half-precision
+    /// rounding mode doesn't match the single-precision rounding mode.
+    static constexpr float_round_style round_style =
+        std::numeric_limits<float>::round_style;
+
+    /// Significant digits.
+    static constexpr int digits = 11;
+
+    /// Significant decimal digits.
+    static constexpr int digits10 = 3;
+
+    /// Required decimal digits to represent all possible values.
+    static constexpr int max_digits10 = 5;
+
+    /// Number base.
+    static constexpr int radix = 2;
+
+    /// One more than smallest exponent.
+    static constexpr int min_exponent = -13;
+
+    /// Smallest normalized representable power of 10.
+    static constexpr int min_exponent10 = -4;
+
+    /// One more than largest exponent
+    static constexpr int max_exponent = 16;
+
+    /// Largest finitely representable power of 10.
+    static constexpr int max_exponent10 = 4;
+
+    /// Smallest positive normal value.
+    static CONSTEXPR_DH common::half min() noexcept {
+        return common::half(common::internal::binary, 0x0400);
+    }
+
+    /// Smallest finite value.
+    static CONSTEXPR_DH common::half lowest() noexcept {
+        return common::half(common::internal::binary, 0xFBFF);
+    }
+
+    /// Largest finite value.
+    static CONSTEXPR_DH common::half max() noexcept {
+        return common::half(common::internal::binary, 0x7BFF);
+    }
+
+    /// Difference between one and next representable value.
+    static CONSTEXPR_DH common::half epsilon() noexcept {
+        return common::half(common::internal::binary, 0x1400);
+    }
+
+    /// Maximum rounding error.
+    static CONSTEXPR_DH common::half round_error() noexcept {
+        return common::half(
+            common::internal::binary,
+            (round_style == std::round_to_nearest) ? 0x3800 : 0x3C00);
+    }
+
+    /// Positive infinity.
+    static CONSTEXPR_DH common::half infinity() noexcept {
+        return common::half(common::internal::binary, 0x7C00);
+    }
+
+    /// Quiet NaN.
+    static CONSTEXPR_DH common::half quiet_NaN() noexcept {
+        return common::half(common::internal::binary, 0x7FFF);
+    }
+
+    /// Signalling NaN.
+    static CONSTEXPR_DH common::half signaling_NaN() noexcept {
+        return common::half(common::internal::binary, 0x7DFF);
+    }
+
+    /// Smallest positive subnormal value.
+    static CONSTEXPR_DH common::half denorm_min() noexcept {
+        return common::half(common::internal::binary, 0x0001);
+    }
+};
+
+/// Hash function for half-precision floats.
+/// This is only defined if C++11 `std::hash` is supported and enabled.
+template<>
+struct hash<common::half>  //: unary_function<common::half,size_t>
+{
+    /// Type of function argument.
+    typedef common::half argument_type;
+
+    /// Function return type.
+    typedef size_t result_type;
+
+    /// Compute hash function.
+    /// \param arg half to hash
+    /// \return hash value
+    result_type operator()(argument_type arg) const {
+        return std::hash<uint16_t>()(
+            static_cast<unsigned>(arg.data_) &
+            -(*reinterpret_cast<uint16_t*>(&arg.data_) != 0x8000));
+    }
+};
+}  // namespace std
+
+namespace common {
+static bool isinf(::common::half val) noexcept {
+    return val == std::numeric_limits<::common::half>::infinity() ||
+           val == -std::numeric_limits<::common::half>::infinity();
+}
+}  // namespace common
+#endif
diff --git a/src/backend/common/kernel_type.hpp b/src/backend/common/kernel_type.hpp
new file mode 100644
index 0000000000..90cabb8c42
--- /dev/null
+++ b/src/backend/common/kernel_type.hpp
@@ -0,0 +1,33 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace common {
+
+/// \brief Maps a type between its data representation and the type used
+///        during compute operations
+///
+/// This struct defines two types. The data type is used to reference the
+/// data of an array. The compute type will be used during the computation.
+/// The kernel is responsible for converting from the data type to the
+/// computation type.
+/// For most types these types will be the same. For fp16 type the compute
+/// type will be float on platforms that don't support 16 bit floating point
+/// operations.
+template<typename T>
+struct kernel_type {
+    /// The type used to represent the data values
+    using data = T;
+
+    /// The type used when performing a computation
+    using compute = T;
+
+    /// The type defined by the compute framework for this type
+    using native = compute;
+};
+}
diff --git a/src/backend/common/traits.hpp b/src/backend/common/traits.hpp
new file mode 100644
index 0000000000..8f27ce952f
--- /dev/null
+++ b/src/backend/common/traits.hpp
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <af/defines.h>
+
+namespace af {
+template<typename T>
+struct dtype_traits;
+}
+
+namespace common {
+class half;
+}
+
+namespace af {
+template<>
+struct dtype_traits<common::half> {
+    enum { af_type = f16, ctype = f16 };
+    typedef common::half base_type;
+    static const char* getName() { return "half"; }
+};
+}  // namespace af
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 39035f96f2..7fe3f9a376 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -14,6 +14,7 @@
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <common/jit/NodeIterator.hpp>
+#include <common/traits.hpp>
 #include <copy.hpp>
 #include <jit/BufferNode.hpp>
 #include <jit/Node.hpp>
@@ -23,6 +24,7 @@
 #include <queue.hpp>
 #include <traits.hpp>
 
+#include <common/half.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/seq.h>
@@ -33,19 +35,19 @@
 #include <cstring>
 #include <type_traits>
 
-namespace cpu {
-
-using common::NodeIterator;
-using jit::BufferNode;
-using jit::Node;
-using jit::Node_map_t;
-using jit::Node_ptr;
-
 using af::dim4;
+using common::half;
+using common::NodeIterator;
+using cpu::jit::BufferNode;
+using cpu::jit::Node;
+using cpu::jit::Node_map_t;
+using cpu::jit::Node_ptr;
 using std::copy;
 using std::is_standard_layout;
 using std::vector;
 
+namespace cpu {
+
 template<typename T>
 Node_ptr bufferNodePtr() {
     return Node_ptr(reinterpret_cast<Node *>(new BufferNode<T>()));
@@ -349,5 +351,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 1612395550..8c94d9acb0 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -162,6 +162,7 @@ class Array {
     INFO_IS_FUNC(isReal)
     INFO_IS_FUNC(isDouble)
     INFO_IS_FUNC(isSingle)
+    INFO_IS_FUNC(isHalf);
     INFO_IS_FUNC(isRealFloating)
     INFO_IS_FUNC(isFloating)
     INFO_IS_FUNC(isInteger)
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index dc40eeb228..16c33c9100 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -19,8 +19,9 @@ namespace cpu {
 #define ARITH_FN(OP, op)                                                 \
     template<typename T>                                                 \
     struct BinOp<T, T, OP> {                                             \
-        void eval(jit::array<T> &out, const jit::array<T> &lhs,          \
-                  const jit::array<T> &rhs, int lim) const {             \
+        void eval(jit::array<compute_t<T>> &out,                         \
+                  const jit::array<compute_t<T>> &lhs,                   \
+                  const jit::array<compute_t<T>> &rhs, int lim) const {  \
             for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
         }                                                                \
     };
@@ -63,8 +64,9 @@ STATIC_ double __rem<double>(double lhs, double rhs) {
 #define NUMERIC_FN(OP, FN)                                                 \
     template<typename T>                                                   \
     struct BinOp<T, T, OP> {                                               \
-        void eval(jit::array<T> &out, const jit::array<T> &lhs,            \
-                  const jit::array<T> &rhs, int lim) {                     \
+        void eval(jit::array<compute_t<T>> &out,                           \
+                  const jit::array<compute_t<T>> &lhs,                     \
+                  const jit::array<compute_t<T>> &rhs, int lim) {          \
             for (int i = 0; i < lim; i++) { out[i] = FN(lhs[i], rhs[i]); } \
         }                                                                  \
     };
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 6b5fa0fd0a..5c7723402e 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -26,6 +26,78 @@ struct UnOp<To, Ti, af_cast_t> {
     }
 };
 
+/// NOTE(umar): The next specializations have multiple eval functions because
+/// the f16 data type needs to be converted to and from the compute type.
+/// Here, we have specializations for real numbers as well as the complex
+/// numbers
+/// TODO(umar): make a macro to reduce repeat code
+
+template<typename To>
+struct UnOp<To, common::half, af_cast_t> {
+    typedef common::half Ti;
+
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) {
+            float val = in[i];
+            out[i]    = To(val);
+        }
+    }
+
+    void eval(jit::array<To> &out, const jit::array<float> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = To(in[i]); }
+    }
+};
+
+template<typename Ti>
+struct UnOp<common::half, Ti, af_cast_t> {
+    typedef common::half To;
+
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) {
+            float val = in[i];
+            out[i]    = To(val);
+        }
+    }
+
+    void eval(jit::array<float> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = float(in[i]); }
+    }
+};
+
+template<>
+struct UnOp<common::half, std::complex<float>, af_cast_t> {
+    typedef common::half To;
+    typedef std::complex<float> Ti;
+
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) {
+            float val = std::abs(in[i]);
+            out[i]    = To(val);
+        }
+    }
+
+    void eval(jit::array<float> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = std::abs(in[i]); }
+    }
+};
+
+template<>
+struct UnOp<common::half, std::complex<double>, af_cast_t> {
+    typedef common::half To;
+    typedef std::complex<double> Ti;
+
+    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) {
+            float val = std::abs(in[i]);
+            out[i]    = To(val);
+        }
+    }
+
+    void eval(jit::array<float> &out, const jit::array<Ti> &in, int lim) {
+        for (int i = 0; i < lim; i++) { out[i] = std::abs(in[i]); }
+    }
+};
+
 template<typename To>
 struct UnOp<To, std::complex<float>, af_cast_t> {
     typedef std::complex<float> Ti;
@@ -43,10 +115,10 @@ struct UnOp<To, std::complex<double>, af_cast_t> {
 };
 
 // DO NOT REMOVE THE TWO SPECIALIZATIONS BELOW
-// These specializations are required because we partially specialize when Ti =
-// std::complex<T> The partial specializations above expect output to be real.
-// so they To(std::abs(v)) instead of To(v) which results in incorrect values
-// when To is complex.
+// These specializations are required because we partially specialize when
+// Ti = std::complex<T> The partial specializations above expect output to
+// be real. so they To(std::abs(v)) instead of To(v) which results in
+// incorrect values when To is complex.
 
 template<>
 struct UnOp<std::complex<float>, std::complex<double>, af_cast_t> {
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 00e70082ad..6eb2836956 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/complex.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <err_cpu.hpp>
 #include <kernel/copy.hpp>
@@ -22,6 +23,7 @@
 #include <cstdio>
 #include <cstring>
 
+using common::half;
 using common::is_complex;
 
 namespace cpu {
@@ -72,6 +74,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 #define INSTANTIATE_COPY_ARRAY(SRC_T)                                 \
     template void copyArray<SRC_T, float>(Array<float> & dst,         \
@@ -109,6 +112,7 @@ INSTANTIATE_COPY_ARRAY(uchar)
 INSTANTIATE_COPY_ARRAY(char)
 INSTANTIATE_COPY_ARRAY(ushort)
 INSTANTIATE_COPY_ARRAY(short)
+INSTANTIATE_COPY_ARRAY(half)
 
 #define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                        \
     template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,      \
@@ -140,4 +144,5 @@ INSTANTIATE_GETSCALAR(intl)
 INSTANTIATE_GETSCALAR(uintl)
 INSTANTIATE_GETSCALAR(short)
 INSTANTIATE_GETSCALAR(ushort)
+INSTANTIATE_GETSCALAR(half)
 }  // namespace cpu
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index e0c43c00c5..0a7d9d7828 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -88,6 +88,9 @@ class DeviceManager {
     static const int ACTIVE_DEVICE_ID     = 0;
     static const bool IS_DOUBLE_SUPPORTED = true;
 
+    // TODO(umar): Half is not supported for BLAS and FFT on x86_64
+    static const bool IS_HALF_SUPPORTED   = true;
+
     static DeviceManager& getInstance();
 
     friend queue& getQueue(int device);
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 4e69165717..f9442ad049 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -36,7 +36,7 @@ class BinaryNode : public TNode<To> {
 
    public:
     BinaryNode(Node_ptr lhs, Node_ptr rhs)
-        : TNode<To>(0, std::max(lhs->getHeight(), rhs->getHeight()) + 1,
+        : TNode<To>(To(0), std::max(lhs->getHeight(), rhs->getHeight()) + 1,
                     {{lhs, rhs}})
         , m_lhs(reinterpret_cast<TNode<Ti> *>(lhs.get()))
         , m_rhs(reinterpret_cast<TNode<Ti> *>(rhs.get())) {}
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index f3729c6198..3df49b054b 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -29,7 +29,7 @@ class BufferNode : public TNode<T> {
     bool m_linear_buffer;
 
    public:
-    BufferNode() : TNode<T>(0, 0, {}) {}
+    BufferNode() : TNode<T>(T(0), 0, {}) {}
 
     void setData(shared_ptr<T> data, unsigned bytes, dim_t data_off,
                  const dim_t *dims, const dim_t *strides,
@@ -48,20 +48,24 @@ class BufferNode : public TNode<T> {
     }
 
     void calc(int x, int y, int z, int w, int lim) final {
+        using Tc = compute_t<T>;
+
         dim_t l_off = 0;
         l_off += (w < (int)m_dims[3]) * w * m_strides[3];
         l_off += (z < (int)m_dims[2]) * z * m_strides[2];
         l_off += (y < (int)m_dims[1]) * y * m_strides[1];
-        T *in_ptr  = m_ptr + l_off;
-        T *out_ptr = this->m_val.data();
+        T *in_ptr   = m_ptr + l_off;
+        Tc *out_ptr = this->m_val.data();
         for (int i = 0; i < lim; i++) {
             out_ptr[i] = in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0];
         }
     }
 
     void calc(int idx, int lim) final {
-        T *in_ptr  = m_ptr + idx;
-        T *out_ptr = this->m_val.data();
+        using Tc = compute_t<T>;
+
+        T *in_ptr   = m_ptr + idx;
+        Tc *out_ptr = this->m_val.data();
         for (int i = 0; i < lim; i++) { out_ptr[i] = in_ptr[i]; }
     }
 
@@ -85,5 +89,4 @@ class BufferNode : public TNode<T> {
 };
 
 }  // namespace jit
-
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index 952f015072..a2b5721527 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <common/defines.hpp>
+#include <common/half.hpp>
 #include <optypes.hpp>
 
 #include <array>
@@ -98,7 +99,7 @@ class Node {
 template<typename T>
 class TNode : public Node {
    public:
-    alignas(16) jit::array<T> m_val;
+    alignas(16) jit::array<compute_t<T>> m_val;
 
    public:
     TNode(T val, const int height,
@@ -110,6 +111,6 @@ class TNode : public Node {
 
 template<typename T>
 using TNode_ptr = std::shared_ptr<TNode<T>>;
-}  // namespace jit
 
+}  // namespace jit
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 31d87ebce0..9dce5e57b0 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -10,9 +10,10 @@
 #pragma once
 #include <math.hpp>
 #include <optypes.hpp>
-#include <vector>
 #include "Node.hpp"
 
+#include <vector>
+
 namespace cpu {
 template<typename To, typename Ti, af_op_t op>
 struct UnOp {
@@ -31,7 +32,7 @@ class UnaryNode : public TNode<To> {
 
    public:
     UnaryNode(Node_ptr child)
-        : TNode<To>(0, child->getHeight() + 1, {{child}})
+        : TNode<To>(To(0), child->getHeight() + 1, {{child}})
         , m_child(reinterpret_cast<TNode<Ti> *>(child.get())) {}
 
     void calc(int x, int y, int z, int w, int lim) final {
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 8667da9ad2..94234101e1 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -10,11 +10,14 @@
 #include <Array.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
 #include <algorithm>
 
+using common::half;
+
 namespace cpu {
 
 template<typename Tx, typename Ty>
@@ -122,6 +125,7 @@ INSTANTIATE(uchar, uchar)
 INSTANTIATE(char, char)
 INSTANTIATE(ushort, ushort)
 INSTANTIATE(short, short)
+INSTANTIATE(half, half)
 
 #undef INSTANTIATE
 
@@ -141,6 +145,7 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace cpu
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 67bd072359..656e64c504 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -11,10 +11,12 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <err_cpu.hpp>
 #include <kernel/random_engine_mersenne.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
+#include <types.hpp>
 
 #include <algorithm>
 #include <cstring>
@@ -28,6 +30,11 @@ namespace kernel {
 static const double PI_VAL =
     3.1415926535897932384626433832795028841971693993751058209749445923078164;
 
+// Conversion to half adapted from Random123
+#define USHORTMAX 0xffff
+#define HALF_FACTOR ((1.0f) / (USHORTMAX + (1.0f)))
+#define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+
 // Conversion to floats adapted from Random123
 #define UINTMAX 0xffffffff
 #define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
@@ -37,8 +44,8 @@ static const double PI_VAL =
 #define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
-template<typename T>
-T transform(uint *val, int index) {
+template<typename T, typename To = T>
+To transform(uint *val, int index) {
     T *oval = (T *)val;
     return oval[index];
 }
@@ -94,6 +101,13 @@ float transform<float>(uint *val, int index) {
     return 1.f - (val[index] * FLT_FACTOR + HALF_FLT_FACTOR);
 }
 
+// Generates rationals in [0, 1)
+template<>
+float transform<common::half, float>(uint *val, int index) {
+    ushort v = val[index >> 1] >> (16 << (index & 1));
+    return 1.f - (v * HALF_FACTOR + HALF_HALF_FACTOR);
+}
+
 // Generates rationals in [0, 1)
 template<>
 double transform<double>(uint *val, int index) {
@@ -147,7 +161,8 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
                 for (size_t buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
                     size_t out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
                     if (out_idx < elements) {
-                        out[out_idx] = transform<T>(ctr, buf_idx);
+                        out[out_idx] =
+                            transform<data_t<T>, compute_t<T>>(ctr, buf_idx);
                     }
                 }
             }
@@ -174,31 +189,47 @@ void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
         ++ctr[0];
         ctr[1] += (ctr[0] == 0);
         int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
-        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(val, j); }
+        for (int j = 0; j < lim; ++j) {
+            out[i + j] = transform<data_t<T>, compute_t<T>>(val, j);
+        }
     }
 }
 
 template<typename T>
-void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
+void boxMullerTransform(data_t<T> *const out1, data_t<T> *const out2,
+                        const compute_t<T> r1, const compute_t<T> r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
-    T r     = sqrt((T)(-2.0) * log((T)(1.0) - r1));
-    T theta = 2 * (T)PI_VAL * ((T)(1.0) - r2);
-    *out1   = r * sin(theta);
-    *out2   = r * cos(theta);
+    using Tc = compute_t<T>;
+    Tc r     = sqrt((Tc)(-2.0) * log((Tc)(1.0) - r1));
+    Tc theta = 2 * (Tc)PI_VAL * ((Tc)(1.0) - r2);
+    *out1    = r * sin(theta);
+    *out2    = r * cos(theta);
 }
 
 void boxMullerTransform(uint val[4], double *temp) {
-    boxMullerTransform(&temp[0], &temp[1], transform<double>(val, 0),
-                       transform<double>(val, 1));
+    boxMullerTransform<double>(&temp[0], &temp[1], transform<double>(val, 0),
+                               transform<double>(val, 1));
 }
 
 void boxMullerTransform(uint val[4], float *temp) {
-    boxMullerTransform(&temp[0], &temp[1], transform<float>(val, 0),
-                       transform<float>(val, 1));
-    boxMullerTransform(&temp[2], &temp[3], transform<float>(val, 2),
-                       transform<float>(val, 3));
+    boxMullerTransform<float>(&temp[0], &temp[1], transform<float>(val, 0),
+                              transform<float>(val, 1));
+    boxMullerTransform<float>(&temp[2], &temp[3], transform<float>(val, 2),
+                              transform<float>(val, 3));
+}
+
+void boxMullerTransform(uint val[4], common::half *temp) {
+    using common::half;
+    boxMullerTransform<half>(&temp[0], &temp[1], transform<half, float>(val, 0),
+                             transform<half, float>(val, 1));
+    boxMullerTransform<half>(&temp[2], &temp[3], transform<half, float>(val, 2),
+                             transform<half, float>(val, 3));
+    boxMullerTransform<half>(&temp[4], &temp[5], transform<half, float>(val, 4),
+                             transform<half, float>(val, 5));
+    boxMullerTransform<half>(&temp[6], &temp[7], transform<half, float>(val, 6),
+                             transform<half, float>(val, 7));
 }
 
 template<typename T>
@@ -264,7 +295,9 @@ void uniformDistributionMT(T *out, size_t elements, uint *const state,
         mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table,
                  temper_table);
         int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
-        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(o, j); }
+        for (int j = 0; j < lim; ++j) {
+            out[i + j] = transform<data_t<T>, compute_t<T>>(o, j);
+        }
     }
 
     state_write(state, l_state);
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index c036152216..1361cbc162 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 #include <ops.hpp>
+#include <common/half.hpp>
 
 namespace cpu {
 namespace kernel {
@@ -36,8 +37,8 @@ struct reduce_dim {
 
 template<af_op_t op, typename Ti, typename To>
 struct reduce_dim<op, Ti, To, 0> {
-    Transform<Ti, To, op> transform;
-    Binary<To, op> reduce;
+    Transform<Ti, compute_t<To>, op> transform;
+    Binary<compute_t<To>, op> reduce;
     void operator()(Param<To> out, const dim_t outOffset, CParam<Ti> in,
                     const dim_t inOffset, const int dim, bool change_nan,
                     double nanval) {
@@ -48,14 +49,14 @@ struct reduce_dim<op, Ti, To, 0> {
         Ti const* const inPtr = in.get() + inOffset;
         dim_t stride          = istrides[dim];
 
-        To out_val = Binary<To, op>::init();
+        compute_t<To> out_val = Binary<compute_t<To>, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
-            To in_val = transform(inPtr[i * stride]);
+            compute_t<To> in_val = transform(inPtr[i * stride]);
             if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
             out_val = reduce(in_val, out_val);
         }
 
-        *outPtr = out_val;
+        *outPtr = data_t<To>(out_val);
     }
 };
 
diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
index cfe9c4001f..0851b4cd69 100644
--- a/src/backend/cpu/kernel/transpose.hpp
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -33,9 +33,9 @@ cdouble getConjugate(const cdouble &in) {
 
 template<typename T, bool conjugate>
 void transpose(Param<T> output, CParam<T> input) {
-    const dim4 odims    = output.dims();
-    const dim4 ostrides = output.strides();
-    const dim4 istrides = input.strides();
+    const af::dim4 odims    = output.dims();
+    const af::dim4 ostrides = output.strides();
+    const af::dim4 istrides = input.strides();
 
     T *out            = output.get();
     T const *const in = input.get();
@@ -72,8 +72,8 @@ void transpose(Param<T> out, CParam<T> in, const bool conjugate) {
 
 template<typename T, bool conjugate>
 void transpose_inplace(Param<T> input) {
-    const dim4 idims    = input.dims();
-    const dim4 istrides = input.strides();
+    const af::dim4 idims    = input.dims();
+    const af::dim4 istrides = input.strides();
 
     T *in = input.get();
 
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 1d83c72ccf..1ef0239dba 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -85,7 +85,7 @@ STATIC_ double minval() {
 
 template<typename T>
 static T scalar(double val) {
-    return (T)(val);
+    return T(val);
 }
 
 template<typename To, typename Ti>
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index df2a20012a..ac3010ecd7 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -11,6 +11,7 @@
 
 #include <common/Logger.hpp>
 #include <common/MemoryManagerImpl.hpp>
+#include <common/half.hpp>
 #include <err_cpu.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
@@ -30,7 +31,7 @@ template class common::MemoryManager<cpu::MemoryManager>;
 #endif
 
 using common::bytesToString;
-
+using common::half;
 using std::function;
 using std::move;
 using std::unique_ptr;
@@ -128,6 +129,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+INSTANTIATE(half)
 
 MemoryManager::MemoryManager()
     : common::MemoryManager<cpu::MemoryManager>(
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 990f31ae9a..264fe2d7ab 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -83,6 +83,11 @@ bool isDoubleSupported(int device) {
     return DeviceManager::IS_DOUBLE_SUPPORTED;
 }
 
+bool isHalfSupported(int device) {
+    UNUSED(device);
+    return DeviceManager::IS_HALF_SUPPORTED;
+}
+
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
     const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
 
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 78271e5009..0a3f8b9403 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -26,6 +26,8 @@ std::string getDeviceInfo();
 
 bool isDoubleSupported(int device);
 
+bool isHalfSupported(int device);
+
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 477d4a7d51..81aa060ac8 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -8,9 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/random_engine.hpp>
 #include <af/dim4.hpp>
-#include <cassert>
+
+using common::half;
 
 namespace cpu {
 void initMersenneState(Array<uint> &state, const uintl seed,
@@ -149,9 +151,11 @@ INSTANTIATE_UNIFORM(char)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(half)
 
 INSTANTIATE_NORMAL(float)
 INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(half)
 
 COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
 COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 9acb6351af..27f53faa56 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -8,16 +8,19 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/reduce.hpp>
 #include <ops.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <reduce.hpp>
 #include <af/dim4.hpp>
+
 #include <complex>
 #include <functional>
 
 using af::dim4;
+using common::half;
 
 template<>
 struct Binary<cdouble, af_add_t> {
@@ -61,12 +64,12 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     Transform<Ti, To, op> transform;
     Binary<To, op> reduce;
 
-    To out = Binary<To, op>::init();
+    compute_t<To> out = Binary<To, op>::init();
 
     // Decrement dimension of select dimension
-    af::dim4 dims    = in.dims();
-    af::dim4 strides = in.strides();
-    const Ti *inPtr  = in.get();
+    af::dim4 dims           = in.dims();
+    af::dim4 strides        = in.strides();
+    const data_t<Ti> *inPtr = in.get();
 
     for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
@@ -80,7 +83,7 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
                 for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
 
-                    To in_val = transform(inPtr[idx]);
+                    compute_t<To> in_val = transform(inPtr[idx]);
                     if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
                     out = reduce(in_val, out);
                 }
@@ -88,7 +91,7 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
         }
     }
 
-    return out;
+    return data_t<To>(out);
 }
 
 #define INSTANTIATE(ROp, Ti, To)                                               \
@@ -110,6 +113,7 @@ INSTANTIATE(af_min_t, char, char)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
+INSTANTIATE(af_min_t, half, half)
 
 // max
 INSTANTIATE(af_max_t, float, float)
@@ -124,6 +128,7 @@ INSTANTIATE(af_max_t, char, char)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
+INSTANTIATE(af_max_t, half, half)
 
 // sum
 INSTANTIATE(af_add_t, float, float)
@@ -146,6 +151,8 @@ INSTANTIATE(af_add_t, short, int)
 INSTANTIATE(af_add_t, short, float)
 INSTANTIATE(af_add_t, ushort, uint)
 INSTANTIATE(af_add_t, ushort, float)
+INSTANTIATE(af_add_t, half, half)
+INSTANTIATE(af_add_t, half, float)
 
 // mul
 INSTANTIATE(af_mul_t, float, float)
@@ -160,6 +167,7 @@ INSTANTIATE(af_mul_t, char, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
+INSTANTIATE(af_mul_t, half, float)
 
 // count
 INSTANTIATE(af_notzero_t, float, uint)
@@ -174,6 +182,7 @@ INSTANTIATE(af_notzero_t, char, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
+INSTANTIATE(af_notzero_t, half, uint)
 
 // anytrue
 INSTANTIATE(af_or_t, float, char)
@@ -188,6 +197,7 @@ INSTANTIATE(af_or_t, char, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
+INSTANTIATE(af_or_t, half, char)
 
 // alltrue
 INSTANTIATE(af_and_t, float, char)
@@ -202,5 +212,6 @@ INSTANTIATE(af_and_t, char, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
+INSTANTIATE(af_and_t, half, char)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 69a77a9ca6..4bc4646e01 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -6,12 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <kernel/reorder.hpp>
+#include <reorder.hpp>
 
 #include <Array.hpp>
-#include <kernel/reorder.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <reorder.hpp>
+
+using common::half;
 
 namespace cpu {
 
@@ -41,5 +44,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 7fdd5f7711..31812949de 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -6,14 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <kernel/select.hpp>
+#include <select.hpp>
 
 #include <Array.hpp>
-#include <kernel/select.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <select.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cpu {
 
@@ -51,5 +53,6 @@ INSTANTIATE(char)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index d05baed95b..cd5a6b5c8e 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -6,16 +6,19 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <kernel/transpose.hpp>
+#include <transpose.hpp>
 
 #include <Array.hpp>
-#include <kernel/transpose.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
-#include <transpose.hpp>
 #include <af/dim4.hpp>
+
 #include <cassert>
 #include <utility>
 
 using af::dim4;
+using common::half;
 
 namespace cpu {
 
@@ -52,5 +55,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 5025cbb543..e88d46c208 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <complex>
+#include <common/kernel_type.hpp>
 
 namespace cpu {
 using cdouble = std::complex<double>;
@@ -18,4 +19,28 @@ using uint    = unsigned int;
 using uchar   = unsigned char;
 using uintl   = unsigned long long;
 using ushort  = unsigned short;
+
+template<typename T>
+using compute_t = typename common::kernel_type<T>::compute;
+
+template<typename T>
+using data_t = typename common::kernel_type<T>::data;
+
 }  // namespace cpu
+
+namespace common {
+template<typename T>
+class kernel_type;
+
+class half;
+
+template<>
+struct kernel_type<common::half> {
+    using data = common::half;
+
+    // These are the types within a kernel
+    using native = float;
+
+    using compute = float;
+};
+}  // namespace common
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index a8a9988418..953616f35f 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
 #include <err_cuda.hpp>
@@ -22,6 +23,7 @@
 #include <numeric>
 
 using af::dim4;
+using common::half;
 using common::Node;
 using common::Node_ptr;
 using common::NodeIterator;
@@ -435,5 +437,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 6414ae7e2c..edc6503636 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -165,6 +165,7 @@ class Array {
     INFO_IS_FUNC(isReal);
     INFO_IS_FUNC(isDouble);
     INFO_IS_FUNC(isSingle);
+    INFO_IS_FUNC(isHalf);
     INFO_IS_FUNC(isRealFloating);
     INFO_IS_FUNC(isFloating);
     INFO_IS_FUNC(isInteger);
@@ -220,13 +221,14 @@ class Array {
         return data.use_count();
     }
 
-    operator Param<T>() {
-        return Param<T>(this->get(), this->dims().get(), this->strides().get());
+    operator Param<data_t<T>>() {
+        return Param<data_t<T>>(this->get(), this->dims().get(),
+                                        this->strides().get());
     }
 
-    operator CParam<T>() const {
-        return CParam<T>(this->get(), this->dims().get(),
-                         this->strides().get());
+    operator CParam<data_t<T>>() const {
+        return CParam<data_t<T>>(this->get(), this->dims().get(),
+                                         this->strides().get());
     }
 
     common::Node_ptr getNode();
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 018193c0ce..4372f5ece3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -51,37 +51,41 @@ cuda_include_directories(
   ${COMMON_INTERFACE_DIRS}
   )
 
-set(jit_kernel_headers
-    "kernel_headers")
-
 file(GLOB jit_src "kernel/jit.cuh")
 
 file_to_string(
     SOURCES ${jit_src}
     VARNAME jit_files
     EXTENSION "hpp"
-    OUTPUT_DIR ${jit_kernel_headers}
+    OUTPUT_DIR "kernel_headers"
     TARGETS jit_kernel_targets
     NAMESPACE "cuda"
+    WITH_EXTENSION
     )
 
 set(nvrtc_src
   ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
+  ${CUDA_INCLUDE_DIRS}/cuda_fp16.h
+  ${CUDA_INCLUDE_DIRS}/cuda_fp16.hpp
 
-  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/../common/half.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/../common/kernel_type.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve1.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve2.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve3.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve_separable.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim.cuh
-  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/where.cuh
 
   ${PROJECT_SOURCE_DIR}/src/api/c/ops.hpp
   ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
+  ${PROJECT_SOURCE_DIR}/include/af/defines.h
+  ${PROJECT_SOURCE_DIR}/include/af/traits.hpp
 
   ${CMAKE_CURRENT_SOURCE_DIR}/Param.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/math.hpp
@@ -96,6 +100,8 @@ file_to_string(
     OUTPUT_DIR "nvrtc_kernel_headers"
     TARGETS nvrtc_kernel_targets
     NAMESPACE "cuda"
+    WITH_EXTENSION
+    NULLTERM
     )
 
 ## Copied from FindCUDA.cmake
@@ -450,7 +456,11 @@ cuda_add_library(afcuda
   )
 
 arrayfire_set_default_cxx_flags(afcuda)
-target_compile_definitions(afcuda PRIVATE AF_CUDA)
+
+# CUDA_NO_HALF prevents the inclusion of the half class in the global namespace
+# which conflicts with the half class in ArrayFire's common namespace. prefer
+# using __half class instead for CUDA
+target_compile_definitions(afcuda PRIVATE AF_CUDA CUDA_NO_HALF)
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
diff --git a/src/backend/cuda/all.cu b/src/backend/cuda/all.cu
index 07cc308329..b681a87384 100644
--- a/src/backend/cuda/all.cu
+++ b/src/backend/cuda/all.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // alltrue
@@ -23,4 +26,5 @@ INSTANTIATE(af_and_t, char, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
+INSTANTIATE(af_and_t, half, char)
 }  // namespace cuda
diff --git a/src/backend/cuda/any.cu b/src/backend/cuda/any.cu
index eb8004dd92..2da5d3349f 100644
--- a/src/backend/cuda/any.cu
+++ b/src/backend/cuda/any.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // anytrue
@@ -23,4 +26,5 @@ INSTANTIATE(af_or_t, char, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
+INSTANTIATE(af_or_t, half, char)
 }  // namespace cuda
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 1934235c9e..8df0dc8f39 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define NVCC
 #include <blas.hpp>
 #include <cublas_v2.h>
 #include <cuda_runtime.h>
@@ -14,14 +15,23 @@
 
 #include <arith.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <complex.hpp>
 #include <cublas.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <reduce.hpp>
+
 #include <cassert>
+#include <functional>
 #include <stdexcept>
 #include <string>
+#include <vector>
+
+using common::half;
+using common::kernel_type;
+using std::is_same;
+using std::vector;
 
 namespace cuda {
 
@@ -37,47 +47,33 @@ cublasOperation_t toCblasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-struct gemm_func_def_t {
-    typedef cublasStatus_t (*gemm_func_def)(cublasHandle_t, cublasOperation_t,
-                                            cublasOperation_t, int, int, int,
-                                            const T *, const T *, int,
-                                            const T *, int, const T *, T *,
-                                            int);
-};
+using gemm_func_def = std::function<cublasStatus_t(
+    cublasHandle_t, cublasOperation_t, cublasOperation_t, int, int, int,
+    const T *, const T *, int, const T *, int, const T *, T *, int)>;
 
 template<typename T>
-struct gemmBatched_func_def_t {
-    typedef cublasStatus_t (*gemmBatched_func_def)(
-        cublasHandle_t, cublasOperation_t, cublasOperation_t, int, int, int,
-        const T *, const T **, int, const T **, int, const T *, T **, int, int);
-};
+using gemmBatched_func_def = std::function<cublasStatus_t(
+    cublasHandle_t, cublasOperation_t, cublasOperation_t, int, int, int,
+    const T *, const T **, int, const T **, int, const T *, T **, int, int)>;
 
 template<typename T>
-struct gemv_func_def_t {
-    typedef cublasStatus_t (*gemv_func_def)(cublasHandle_t, cublasOperation_t,
-                                            int, int, const T *, const T *, int,
-                                            const T *, int, const T *, T *,
-                                            int);
-};
+using gemv_func_def = std::function<cublasStatus_t(
+    cublasHandle_t, cublasOperation_t, int, int, const T *, const T *, int,
+    const T *, int, const T *, T *, int)>;
 
 template<typename T>
-struct trsm_func_def_t {
-    typedef cublasStatus_t (*trsm_func_def)(cublasHandle_t, cublasSideMode_t,
-                                            cublasFillMode_t, cublasOperation_t,
-                                            cublasDiagType_t, int, int,
-                                            const T *, const T *, int, T *,
-                                            int);
-};
+using trsm_func_def = std::function<cublasStatus_t(
+    cublasHandle_t, cublasSideMode_t, cublasFillMode_t, cublasOperation_t,
+    cublasDiagType_t, int, int, const T *, const T *, int, T *, int)>;
 
 #define BLAS_FUNC_DEF(FUNC) \
     template<typename T>    \
-    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
+    FUNC##_func_def<T> FUNC##_func();
 
-#define BLAS_FUNC(FUNC, TYPE, PREFIX)                                       \
-    template<>                                                              \
-    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
-        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
-               cublas##PREFIX##FUNC;                                        \
+#define BLAS_FUNC(FUNC, TYPE, PREFIX)           \
+    template<>                                  \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+        return &cublas##PREFIX##FUNC;           \
     }
 
 BLAS_FUNC_DEF(gemm)
@@ -85,12 +81,14 @@ BLAS_FUNC(gemm, float, S)
 BLAS_FUNC(gemm, cfloat, C)
 BLAS_FUNC(gemm, double, D)
 BLAS_FUNC(gemm, cdouble, Z)
+BLAS_FUNC(gemm, __half, H)
 
 BLAS_FUNC_DEF(gemmBatched)
 BLAS_FUNC(gemmBatched, float, S)
 BLAS_FUNC(gemmBatched, cfloat, C)
 BLAS_FUNC(gemmBatched, double, D)
 BLAS_FUNC(gemmBatched, cdouble, Z)
+BLAS_FUNC(gemmBatched, __half, H)
 
 BLAS_FUNC_DEF(gemv)
 BLAS_FUNC(gemv, float, S)
@@ -98,6 +96,14 @@ BLAS_FUNC(gemv, cfloat, C)
 BLAS_FUNC(gemv, double, D)
 BLAS_FUNC(gemv, cdouble, Z)
 
+template<>
+gemv_func_def<half> gemv_func<half>() {
+    assert(1 != 1 && "GEMV for half is not available.");
+    return gemv_func_def<half>();
+}
+
+// BLAS_FUNC(gemv, __half, S)  // TODO(umar): Not implemented in CUDA
+
 BLAS_FUNC_DEF(trsm)
 BLAS_FUNC(trsm, float, S)
 BLAS_FUNC(trsm, cfloat, C)
@@ -150,14 +156,116 @@ BLAS_FUNC(dot, cdouble, false, Z, u)
 #undef BLAS_FUNC
 #undef BLAS_FUNC_DEF
 
-using std::max;
-using std::vector;
+template<typename T>
+cudaDataType_t getType();
+
+template<>
+cudaDataType_t getType<float>() {
+    return CUDA_R_32F;
+}
+
+template<>
+cudaDataType_t getType<cfloat>() {
+    return CUDA_C_32F;
+}
+
+template<>
+cudaDataType_t getType<double>() {
+    return CUDA_R_64F;
+}
+
+template<>
+cudaDataType_t getType<cdouble>() {
+    return CUDA_C_64F;
+}
+
+template<>
+cudaDataType_t getType<half>() {
+    return CUDA_R_16F;
+}
+
+template<typename T>
+cublasGemmAlgo_t selectGEMMAlgorithm() {
+    auto dev              = getDeviceProp(getActiveDeviceId());
+    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
+    return algo;
+}
+
+template<>
+cublasGemmAlgo_t selectGEMMAlgorithm<__half>() {
+    auto dev              = getDeviceProp(getActiveDeviceId());
+    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
+    if (dev.major >= 7) { algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP; }
+    return algo;
+}
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta) {
+cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
+                             cublasOperation_t rOpts, int M, int N, int K,
+                             const T *alpha, const Array<T> &lhs, dim_t lStride,
+                             const Array<T> &rhs, dim_t rStride, const T *beta,
+                             Array<T> &out, dim_t oleading) {
+    auto prop = getDeviceProp(getActiveDeviceId());
+    if (prop.major > 3) {
+        return cublasGemmEx(
+            blasHandle(), lOpts, rOpts, M, N, K, alpha, lhs.get(), getType<T>(),
+            lStride, rhs.get(), getType<T>(), rStride, beta, out.get(),
+            getType<T>(), out.strides()[1],
+            getType<T>(),  // Compute type
+
+            // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
+            // for the cublasGemm*Ex functions, the performance of the
+            // fp32 numbers seem to increase dramatically. Their numerical
+            // accuracy is also different compared to regular gemm fuctions.
+            // The CUBLAS_GEMM_DEFAULT algorithm selection does not experience
+            // this change. Does this imply that the TENSOR_OP function
+            // performs the computation in fp16 bit even when the compute
+            // type is CUDA_R_32F?
+            selectGEMMAlgorithm<T>());
+    } else {
+        using Nt = typename common::kernel_type<T>::native;
+        return gemm_func<Nt>()(blasHandle(), lOpts, rOpts, M, N, K, (Nt *)alpha,
+                               (Nt *)lhs.get(), lStride, (Nt *)rhs.get(),
+                               rStride, (Nt *)beta, (Nt *)out.get(), oleading);
+    }
+}
+
+template<typename T>
+cublasStatus_t gemmBatchedDispatch(BlasHandle handle,
+                                    cublasOperation_t lOpts,
+                                    cublasOperation_t rOpts, int M, int N,
+                                    int K, const T *alpha, const T **lptrs,
+                                    int lStrides, const T **rptrs, int rStrides,
+                                    const T *beta, T **optrs, int oStrides,
+                                    int batchSize) {
+    auto prop = getDeviceProp(getActiveDeviceId());
+    if (prop.major > 3) {
+        return cublasGemmBatchedEx(
+            blasHandle(), lOpts, rOpts, M, N, K, alpha, (const void **)lptrs,
+            getType<T>(), lStrides, (const void **)rptrs, getType<T>(),
+            rStrides, beta, (void **)optrs, getType<T>(), oStrides, batchSize,
+            getType<T>(),  // Compute type
+            // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
+            // for the cublasGemm*Ex functions, the performance of the
+            // fp32 numbers seem to increase dramatically. Their numerical
+            // accuracy is also different compared to regular gemm fuctions.
+            // The CUBLAS_GEMM_DEFAULT algorithm selection does not experience
+            // this change. Does this imply that the TENSOR_OP function
+            // performs the computation in fp16 bit even when the compute
+            // type is CUDA_R_32F?
+            selectGEMMAlgorithm<T>());
+    } else {
+        using Nt = typename common::kernel_type<T>::native;
+        return gemmBatched_func<Nt>()(
+            blasHandle(), lOpts, rOpts, M, N, K, (const Nt *)alpha,
+            (const Nt **)lptrs, lStrides, (const Nt **)rptrs, rStrides,
+            (const Nt *)beta, (Nt **)optrs, oStrides, batchSize);
+    }
+}
+
+template<typename T>
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
     const cublasOperation_t lOpts = toCblasTranspose(optLhs);
     const cublasOperation_t rOpts = toCblasTranspose(optRhs);
 
@@ -179,21 +287,25 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
     if (oDims.ndims() <= 2) {
         if (rDims[bColDim] == 1) {
             dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+            if (is_same<T, half>::value) {
+                AF_ERROR(
+                    "GEMV does not support half, Please create an issue on "
+                    "the GitHub repo.",
+                    AF_ERR_NOT_SUPPORTED);
+            }
             CUBLAS_CHECK(gemv_func<T>()(blasHandle(), lOpts, lDims[0], lDims[1],
                                         alpha, lhs.get(), lStrides[1],
                                         rhs.get(), incr, beta, out.get(), 1));
         } else {
-            CUBLAS_CHECK(gemm_func<T>()(blasHandle(), lOpts, rOpts, M, N, K,
-                                        alpha, lhs.get(), lStrides[1],
-                                        rhs.get(), rStrides[1], beta,
-                                        out.get(), oStrides[1]));
+            CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K,
+                                          alpha, lhs, lStrides[1], rhs,
+                                          rStrides[1], beta, out, oStrides[1]));
         }
     } else {
         int batchSize = oDims[2] * oDims[3];
-
-        std::vector<const T *> lptrs(batchSize);
-        std::vector<const T *> rptrs(batchSize);
-        std::vector<T *> optrs(batchSize);
+        vector<const T *> lptrs(batchSize);
+        vector<const T *> rptrs(batchSize);
+        vector<T *> optrs(batchSize);
 
         bool is_l_d2_batched = oDims[2] == lDims[2];
         bool is_l_d3_batched = oDims[3] == lDims[3];
@@ -233,10 +345,12 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
         // afterwards
         CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
-        CUBLAS_CHECK(gemmBatched_func<T>()(
+        using Nt = typename common::kernel_type<T>::native;
+        CUBLAS_CHECK(gemmBatchedDispatch(
             blasHandle(), lOpts, rOpts, M, N, K, alpha,
             (const T **)d_lptrs.get(), lStrides[1], (const T **)d_rptrs.get(),
-            rStrides[1], beta, (T **)d_optrs.get(), oStrides[1], batchSize));
+            rStrides[1], beta, (T **)d_optrs.get(), oStrides[1],
+            batchSize));
     }
 }
 
@@ -271,16 +385,17 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans, bool is_upper,
         lhs.get(), lStrides[1], rhs.get(), rStrides[1]));
 }
 
-#define INSTANTIATE_GEMM(TYPE)                                                         \
-    template void gemm<TYPE>(Array<TYPE> &out, af_mat_prop optLhs, af_mat_prop optRhs, \
-                             const TYPE *alpha,                       \
-                             const Array<TYPE> &lhs, const Array<TYPE> &rhs,           \
+#define INSTANTIATE_GEMM(TYPE)                                               \
+    template void gemm<TYPE>(Array<TYPE> & out, af_mat_prop optLhs,          \
+                             af_mat_prop optRhs, const TYPE *alpha,          \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs, \
                              const TYPE *beta);
 
 INSTANTIATE_GEMM(float)
 INSTANTIATE_GEMM(cfloat)
 INSTANTIATE_GEMM(double)
 INSTANTIATE_GEMM(cdouble)
+INSTANTIATE_GEMM(half)
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 0297efee61..dec6778293 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 #include <common/jit/UnaryNode.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
@@ -38,6 +39,11 @@ CAST_FN(short)
 CAST_FN(float)
 CAST_FN(double)
 
+template<typename Ti>
+struct CastOp<common::half, Ti> {
+    const char *name() { return "(__half)"; }
+};
+
 #define CAST_CFN(TYPE)                                    \
     template<typename Ti>                                 \
     struct CastOp<TYPE, Ti> {                             \
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 9b4a624d9a..5f7993f85a 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -14,8 +14,14 @@
 #include <kernel/memcopy.hpp>
 #include <math.hpp>
 
+#include <Array.hpp>
 #include <common/complex.hpp>
+#include <common/half.hpp>
+#include <cuda_runtime_api.h>
+#include <err_cuda.hpp>
+#include <math.hpp>
 
+using common::half;
 using common::is_complex;
 
 namespace cuda {
@@ -125,6 +131,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 #define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
     template Array<float> padArray<SRC_T, float>(                         \
@@ -163,6 +170,9 @@ INSTANTIATE(ushort)
     template Array<char> padArray<SRC_T, char>(                           \
         Array<SRC_T> const &src, dim4 const &dims, char default_value,    \
         double factor);                                                   \
+    template Array<half> padArray<SRC_T, half>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, half default_value,    \
+        double factor);                                                   \
     template void copyArray<SRC_T, float>(Array<float> & dst,             \
                                           Array<SRC_T> const &src);       \
     template void copyArray<SRC_T, double>(Array<double> & dst,           \
@@ -186,8 +196,10 @@ INSTANTIATE(ushort)
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,             \
                                           Array<SRC_T> const &src);       \
     template void copyArray<SRC_T, char>(Array<char> & dst,               \
+                                         Array<SRC_T> const &src);        \
+    template void copyArray<SRC_T, half>(Array<half> & dst,               \
                                          Array<SRC_T> const &src);
-
+    
 INSTANTIATE_PAD_ARRAY(float)
 INSTANTIATE_PAD_ARRAY(double)
 INSTANTIATE_PAD_ARRAY(int)
@@ -198,6 +210,7 @@ INSTANTIATE_PAD_ARRAY(short)
 INSTANTIATE_PAD_ARRAY(ushort)
 INSTANTIATE_PAD_ARRAY(uchar)
 INSTANTIATE_PAD_ARRAY(char)
+INSTANTIATE_PAD_ARRAY(half)
 
 #define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
     template Array<cfloat> padArray<SRC_T, cfloat>(                       \
@@ -238,4 +251,6 @@ INSTANTIATE_GETSCALAR(intl)
 INSTANTIATE_GETSCALAR(uintl)
 INSTANTIATE_GETSCALAR(short)
 INSTANTIATE_GETSCALAR(ushort)
+INSTANTIATE_GETSCALAR(half)
+
 }  // namespace cuda
diff --git a/src/backend/cuda/count.cu b/src/backend/cuda/count.cu
index 25590f4704..c15c543cdb 100644
--- a/src/backend/cuda/count.cu
+++ b/src/backend/cuda/count.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // count
@@ -23,4 +26,5 @@ INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
 INSTANTIATE(af_notzero_t, char, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
+INSTANTIATE(af_notzero_t, half, uint)
 }  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index a20aa462ee..c8b1e5ba76 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
-#include <err_cuda.hpp>
-#include <kernel_headers/jit.hpp>
-
 #include <Array.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <common/jit/Node.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
 #include <device_manager.hpp>
+#include <err_cuda.hpp>
+#include <kernel_headers/jit_cuh.hpp>
 #include <math.hpp>
 #include <nvrtc/cache.hpp>
 #include <platform.hpp>
@@ -28,8 +28,7 @@
 #include <thread>
 #include <vector>
 
-namespace cuda {
-
+using common::half;
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
@@ -40,6 +39,8 @@ using std::string;
 using std::stringstream;
 using std::vector;
 
+namespace cuda {
+
 static string getFuncName(const vector<Node *> &output_nodes,
                           const vector<const Node *> &full_nodes,
                           const vector<Node_ids> &full_ids, bool is_linear) {
@@ -358,6 +359,7 @@ template void evalNodes<intl>(Param<intl> out, Node *node);
 template void evalNodes<uintl>(Param<uintl> out, Node *node);
 template void evalNodes<short>(Param<short> out, Node *node);
 template void evalNodes<ushort>(Param<ushort> out, Node *node);
+template void evalNodes<half>(Param<half> out, Node *node);
 
 template void evalNodes<float>(vector<Param<float>> &out, vector<Node *> node);
 template void evalNodes<double>(vector<Param<double>> &out,
@@ -375,4 +377,5 @@ template void evalNodes<uintl>(vector<Param<uintl>> &out, vector<Node *> node);
 template void evalNodes<short>(vector<Param<short>> &out, vector<Node *> node);
 template void evalNodes<ushort>(vector<Param<ushort>> &out,
                                 vector<Node *> node);
+template void evalNodes<half>(vector<Param<half>> &out, vector<Node *> node);
 }  // namespace cuda
diff --git a/src/backend/cuda/join.cu b/src/backend/cuda/join.cu
index 3ab17e55d4..9096ed9434 100644
--- a/src/backend/cuda/join.cu
+++ b/src/backend/cuda/join.cu
@@ -9,10 +9,13 @@
 
 #include <Array.hpp>
 #include <err_cuda.hpp>
+#include <common/half.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 template<int dim>
 af::dim4 calcOffset(const af::dim4 dims) {
@@ -159,6 +162,7 @@ INSTANTIATE(short, short)
 INSTANTIATE(ushort, ushort)
 INSTANTIATE(uchar, uchar)
 INSTANTIATE(char, char)
+INSTANTIATE(half, half)
 
 #undef INSTANTIATE
 
@@ -178,6 +182,7 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
+INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index ad58e75251..d44b42ded1 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -14,10 +14,10 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/convolve1.hpp>
-#include <nvrtc_kernel_headers/convolve2.hpp>
-#include <nvrtc_kernel_headers/convolve3.hpp>
-#include <nvrtc_kernel_headers/convolve_separable.hpp>
+#include <nvrtc_kernel_headers/convolve1_cuh.hpp>
+#include <nvrtc_kernel_headers/convolve2_cuh.hpp>
+#include <nvrtc_kernel_headers/convolve3_cuh.hpp>
+#include <nvrtc_kernel_headers/convolve_separable_cuh.hpp>
 #include <traits.hpp>
 
 #include <string>
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 635041af8e..6909dc10a8 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -13,6 +13,8 @@ typedef cuFloatComplex cfloat;
 typedef double2 cuDoubleComplex;
 typedef cuDoubleComplex cdouble;
 
+#include <cuda_fp16.h>
+
 // ----------------------------------------------
 // COMMON OPERATIONS
 // ----------------------------------------------
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index a2b6dd39c9..3a1cca433b 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -10,7 +10,9 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_cuda.hpp>
+#include <types.hpp>
 
 #include <algorithm>
 
@@ -86,11 +88,10 @@ void memcopy(T *out, const dim_t *ostrides, const T *in, const dim_t *idims,
     POST_LAUNCH_CHECK();
 }
 
-////////////////////////////// BEGIN - templated help functions for copy_kernel
-///////////////////////////////////
+///////////// BEGIN - templated help functions for copy_kernel /////////////////
 template<typename T>
 __inline__ __device__ static T scale(T value, double factor) {
-    return (T)(value * factor);
+    return (T)(double(value) * factor);
 }
 
 template<>
@@ -108,6 +109,31 @@ __inline__ __device__ outType convertType(inType value) {
     return (outType)value;
 }
 
+template<>
+__inline__ __device__ char convertType<compute_t<common::half>, char>(
+    compute_t<common::half> value) {
+    return (char)((short)value);
+}
+
+template<>
+__inline__ __device__ compute_t<common::half>
+convertType<char, compute_t<common::half>>(char value) {
+    return compute_t<common::half>(value);
+}
+
+template<>
+__inline__ __device__ cuda::uchar
+convertType<compute_t<common::half>, cuda::uchar>(
+    compute_t<common::half> value) {
+    return (cuda::uchar)((short)value);
+}
+
+template<>
+__inline__ __device__ compute_t<common::half>
+convertType<cuda::uchar, compute_t<common::half>>(cuda::uchar value) {
+    return compute_t<common::half>(value);
+}
+
 template<>
 __inline__ __device__ cdouble convertType<cfloat, cdouble>(cfloat value) {
     return cuComplexFloatToDouble(value);
@@ -139,8 +165,9 @@ OTHER_SPECIALIZATIONS(short)
 OTHER_SPECIALIZATIONS(ushort)
 OTHER_SPECIALIZATIONS(uchar)
 OTHER_SPECIALIZATIONS(char)
-////////////////////////////// END - templated help functions for copy_kernel
-/////////////////////////////////////
+OTHER_SPECIALIZATIONS(common::half)
+
+//////////// END - templated help functions for copy_kernel ////////////////////
 
 template<typename inType, typename outType, bool same_dims>
 __global__ static void copy_kernel(Param<outType> dst, CParam<inType> src,
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 6140b9efec..8e06bb56e6 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -26,6 +26,11 @@ static const int THREADS = 256;
 #define PI_VAL \
     3.1415926535897932384626433832795028841971693993751058209749445923078164
 
+// Conversion to half adapted from Random123
+#define USHORTMAX 0xffff
+#define HALF_FACTOR ((1.0f) / (USHORTMAX + (1.0f)))
+#define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+
 // Conversion to floats adapted from Random123
 #define UINTMAX 0xffffffff
 #define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
@@ -35,6 +40,13 @@ static const int THREADS = 256;
 #define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
+// Generates rationals in (0, 1]
+__device__ static compute_t<common::half> getHalf(const uint &num) {
+    ushort v = num;
+    return (compute_t<common::half>)(v * HALF_FACTOR +
+                                     HALF_HALF_FACTOR);
+}
+
 // Generates rationals in (0, 1]
 __device__ static float getFloat(const uint &num) {
     return (num * FLT_FACTOR + HALF_FLT_FACTOR);
@@ -46,20 +58,54 @@ __device__ static double getDouble(const uint &num1, const uint &num2) {
     return (num * DBL_FACTOR + HALF_DBL_FACTOR);
 }
 
+namespace {
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
+__device__ __half hlog(const __half a) { return 0; }
+__device__ __half hsqrt(const __half a) { return 0; }
+__device__ __half hsin(const __half a) { return 0; }
+__device__ __half hcos(const __half a) { return 0; }
+#endif
+
+#define MATH_FUNC(OP, HALF_OP)         \
+    template<typename T>               \
+    __device__ T OP(T val) {           \
+        return ::OP(val);              \
+    }                                  \
+    template<>                         \
+    __device__ __half OP(__half val) { \
+        return HALF_OP(val);           \
+    }
+
+MATH_FUNC(log, hlog)
+MATH_FUNC(sqrt, hsqrt)
+MATH_FUNC(sin, hsin)
+MATH_FUNC(cos, hcos)
+}  // namespace
+
+template<typename T>
+constexpr __device__ T neg_two() {
+    return -2.0;
+}
+
 template<typename T>
-__device__ static void boxMullerTransform(T *const out1, T *const out2,
-                                          const T &r1, const T &r2) {
+constexpr __device__ T two_pi() {
+    return 2.0 * PI_VAL;
+};
+
+template<typename Td, typename Tc>
+__device__ static void boxMullerTransform(Td *const out1, Td *const out2,
+                                          const Tc &r1, const Tc &r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
-    T r     = sqrt((T)(-2.0) * log(r1));
-    T theta = 2 * (T)PI_VAL * r2;
-    *out1   = r * sin(theta);
-    *out2   = r * cos(theta);
+    Tc r     = sqrt(neg_two<Tc>() * log(r1));
+    Tc theta = two_pi<Tc>() * r2;
+    *out1    = Td(r * sin(theta));
+    *out2    = Td(r * cos(theta));
 }
 
 // Writes without boundary checking
-
 __device__ static void writeOut128Bytes(uchar *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
@@ -185,6 +231,19 @@ __device__ static void writeOut128Bytes(cdouble *out, const uint &index,
     out[index].y = 1.0 - getDouble(r3, r4);
 }
 
+__device__ static void writeOut128Bytes(common::half *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]                  = getHalf(r1);
+    out[index + blockDim.x]     = getHalf(r1 >> 16);
+    out[index + 2 * blockDim.x] = getHalf(r2);
+    out[index + 3 * blockDim.x] = getHalf(r2 >> 16);
+    out[index + 4 * blockDim.x] = getHalf(r3);
+    out[index + 5 * blockDim.x] = getHalf(r3 >> 16);
+    out[index + 6 * blockDim.x] = getHalf(r4);
+    out[index + 7 * blockDim.x] = getHalf(r4 >> 16);
+}
+
 // Normalized writes without boundary checking
 
 __device__ static void boxMullerWriteOut128Bytes(float *out, const uint &index,
@@ -225,6 +284,24 @@ __device__ static void boxMullerWriteOut128Bytes(cdouble *out,
                        getDouble(r3, r4));
 }
 
+__device__ static void boxMullerWriteOut128Bytes(common::half *out,
+                                                 const uint &index,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], &out[index + blockDim.x], getHalf(r1),
+                       getHalf(r1 >> 16));
+    boxMullerTransform(&out[index + 2 * blockDim.x],
+                       &out[index + 3 * blockDim.x], getHalf(r2),
+                       getHalf(r2 >> 16));
+    boxMullerTransform(&out[index + 4 * blockDim.x],
+                       &out[index + 5 * blockDim.x], getHalf(r3),
+                       getHalf(r3 >> 16));
+    boxMullerTransform(&out[index + 6 * blockDim.x],
+                       &out[index + 7 * blockDim.x], getHalf(r4),
+                       getHalf(r4 >> 16));
+}
+
 // Writes with boundary checking
 
 __device__ static void partialWriteOut128Bytes(uchar *out, const uint &index,
@@ -488,6 +565,66 @@ __device__ static void partialBoxMullerWriteOut128Bytes(
     }
 }
 
+__device__ static void partialWriteOut128Bytes(common::half *out,
+                                               const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = getHalf(r1); }
+    if (index + blockDim.x < elements) {
+        out[index + blockDim.x] = getHalf(r1 >> 16);
+    }
+    if (index + 2 * blockDim.x < elements) {
+        out[index + 2 * blockDim.x] = getHalf(r2);
+    }
+    if (index + 3 * blockDim.x < elements) {
+        out[index + 3 * blockDim.x] = getHalf(r2 >> 16);
+    }
+    if (index + 4 * blockDim.x < elements) {
+        out[index + 4 * blockDim.x] = getHalf(r3);
+    }
+    if (index + 5 * blockDim.x < elements) {
+        out[index + 5 * blockDim.x] = getHalf(r3 >> 16);
+    }
+    if (index + 6 * blockDim.x < elements) {
+        out[index + 6 * blockDim.x] = getHalf(r4);
+    }
+    if (index + 7 * blockDim.x < elements) {
+        out[index + 7 * blockDim.x] = getHalf(r4 >> 16);
+    }
+}
+
+// Normalized writes with boundary checking
+__device__ static void partialBoxMullerWriteOut128Bytes(
+    common::half *out, const uint &index, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    common::half n[8];
+    boxMullerTransform(n + 0, n + 1, getHalf(r1), getHalf(r1 >> 16));
+    boxMullerTransform(n + 2, n + 3, getHalf(r2), getHalf(r2 >> 16));
+    boxMullerTransform(n + 4, n + 5, getHalf(r3), getHalf(r3 >> 16));
+    boxMullerTransform(n + 6, n + 7, getHalf(r4), getHalf(r4 >> 16));
+    if (index < elements) { out[index] = n[0]; }
+    if (index + blockDim.x < elements) { out[index + blockDim.x] = n[1]; }
+    if (index + 2 * blockDim.x < elements) {
+        out[index + 2 * blockDim.x] = n[2];
+    }
+    if (index + 3 * blockDim.x < elements) {
+        out[index + 3 * blockDim.x] = n[3];
+    }
+    if (index + 4 * blockDim.x < elements) {
+        out[index + 4 * blockDim.x] = n[4];
+    }
+    if (index + 5 * blockDim.x < elements) {
+        out[index + 5 * blockDim.x] = n[5];
+    }
+    if (index + 6 * blockDim.x < elements) {
+        out[index + 6 * blockDim.x] = n[6];
+    }
+    if (index + 7 * blockDim.x < elements) {
+        out[index + 7 * blockDim.x] = n[7];
+    }
+}
+
 template<typename T>
 __global__ void uniformPhilox(T *out, uint hi, uint lo, uint hic, uint loc,
                               uint elementsPerBlock, uint elements) {
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 712925b501..16808d7a54 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -38,7 +38,7 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
     const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
     const uint xid        = blockIdx_x * blockDim.x + tidx;
 
-    __shared__ To s_val[THREADS_X * DIMY];
+    __shared__ compute_t<To> s_val[THREADS_X * DIMY];
 
     const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
     const uint blockIdx_y =
@@ -51,14 +51,16 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
     // There are blockDim.y elements per block for in
     // Hence increment ids[dim] just after offseting out and before offsetting
     // in
-    To *const optr = out.ptr + ids[3] * out.strides[3] +
-                     ids[2] * out.strides[2] + ids[1] * out.strides[1] + ids[0];
+    data_t<To> *const optr = out.ptr + ids[3] * out.strides[3] +
+                             ids[2] * out.strides[2] + ids[1] * out.strides[1] +
+                             ids[0];
 
     const uint blockIdx_dim = ids[dim];
     ids[dim]                = ids[dim] * blockDim.y + tidy;
 
-    const Ti *iptr = in.ptr + ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-                     ids[1] * in.strides[1] + ids[0];
+    const data_t<Ti> *iptr = in.ptr + ids[3] * in.strides[3] +
+                             ids[2] * in.strides[2] + ids[1] * in.strides[1] +
+                             ids[0];
 
     const uint id_dim_in   = ids[dim];
     const uint istride_dim = in.strides[dim];
@@ -68,10 +70,10 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
 
     Transform<Ti, To, op> transform;
     Binary<To, op> reduce;
-    To out_val = Binary<To, op>::init();
+    compute_t<To> out_val = Binary<To, op>::init();
     for (int id = id_dim_in; is_valid && (id < in.dims[dim]);
          id += offset_dim * blockDim.y) {
-        To in_val = transform(*iptr);
+        compute_t<To> in_val = transform(*iptr);
         if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
         out_val = reduce(in_val, out_val);
         iptr    = iptr + offset_dim * blockDim.y * istride_dim;
@@ -79,7 +81,7 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
 
     s_val[tid] = out_val;
 
-    To *s_ptr = s_val + tid;
+    compute_t<To> *s_ptr = s_val + tid;
     __syncthreads();
 
     if (DIMY == 8) {
@@ -196,23 +198,24 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
     Binary<To, op> reduce;
     Transform<Ti, To, op> transform;
 
-    __shared__ To s_val[THREADS_PER_BLOCK];
+    __shared__ compute_t<To> s_val[THREADS_PER_BLOCK];
 
     const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
     const uint blockIdx_y =
         (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
     const uint yid = blockIdx_y * blockDim.y + tidy;
 
-    const Ti *const iptr = in.ptr + (wid * in.strides[3] + zid * in.strides[2] +
-                                     yid * in.strides[1]);
+    const data_t<Ti> *const iptr =
+        in.ptr +
+        (wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1]);
 
     if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
 
     int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-    To out_val = Binary<To, op>::init();
+    compute_t<To> out_val = Binary<To, op>::init();
     for (int id = xid; id < lim; id += DIMX) {
-        To in_val = transform(iptr[id]);
+        compute_t<To> in_val = transform(iptr[id]);
         if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
         out_val = reduce(in_val, out_val);
     }
@@ -220,7 +223,7 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
     s_val[tid] = out_val;
 
     __syncthreads();
-    To *s_ptr = s_val + tidy * DIMX;
+    compute_t<To> *s_ptr = s_val + tidy * DIMX;
 
     if (DIMX == 256) {
         if (tidx < 128) s_ptr[tidx] = reduce(s_ptr[tidx], s_ptr[tidx + 128]);
@@ -237,15 +240,16 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
         __syncthreads();
     }
 
-    typedef cub::WarpReduce<To> WarpReduce;
+    typedef cub::WarpReduce<compute_t<To>> WarpReduce;
     __shared__ typename WarpReduce::TempStorage temp_storage;
 
-    To warp_val = s_ptr[tidx];
-    out_val     = WarpReduce(temp_storage).Reduce(warp_val, reduce);
+    compute_t<To> warp_val = s_ptr[tidx];
+    out_val                = WarpReduce(temp_storage).Reduce(warp_val, reduce);
 
-    To *const optr = out.ptr + (wid * out.strides[3] + zid * out.strides[2] +
-                                yid * out.strides[1]);
-    if (tidx == 0) optr[blockIdx_x] = out_val;
+    data_t<To> *const optr =
+        out.ptr +
+        (wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1]);
+    if (tidx == 0) optr[blockIdx_x] = data_t<To>(out_val);
 }
 
 template<typename Ti, typename To, af_op_t op>
@@ -384,10 +388,10 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
         Binary<To, op> reduce;
-        To out = Binary<To, op>::init();
+        compute_t<To> out = Binary<To, op>::init();
         for (int i = 0; i < tmp_elements; i++) { out = reduce(out, h_data[i]); }
 
-        return out;
+        return data_t<To>(out);
     } else {
         std::vector<Ti> h_data(in_elements);
         CUDA_CHECK(
@@ -397,16 +401,16 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
 
         Transform<Ti, To, op> transform;
         Binary<To, op> reduce;
-        To out       = Binary<To, op>::init();
-        To nanval_to = scalar<To>(nanval);
+        compute_t<To> out       = Binary<To, op>::init();
+        compute_t<To> nanval_to = scalar<To>(nanval);
 
         for (int i = 0; i < in_elements; i++) {
-            To in_val = transform(h_data[i]);
+            compute_t<To> in_val = transform(h_data[i]);
             if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval_to;
             out = reduce(out, in_val);
         }
 
-        return out;
+        return data_t<To>(out);
     }
 }
 
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 730f957886..0b8685c8a9 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -14,7 +14,7 @@
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/scan_dim.hpp>
+#include <nvrtc_kernel_headers/scan_dim_cuh.hpp>
 #include "config.hpp"
 
 namespace cuda {
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 2e42e52702..c8ab453658 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -15,7 +15,7 @@
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/scan_dim_by_key.hpp>
+#include <nvrtc_kernel_headers/scan_dim_by_key_cuh.hpp>
 #include <optypes.hpp>
 #include <traits.hpp>
 #include "config.hpp"
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index a65bb155e4..15e0a01682 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -14,7 +14,7 @@
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/scan_first.hpp>
+#include <nvrtc_kernel_headers/scan_first_cuh.hpp>
 #include "config.hpp"
 
 namespace cuda {
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 5fb8967745..cdbdb29893 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -15,10 +15,11 @@
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/scan_first_by_key.hpp>
+#include <nvrtc_kernel_headers/scan_first_by_key_cuh.hpp>
 #include <optypes.hpp>
 #include "config.hpp"
 
+
 namespace cuda {
 namespace kernel {
 
diff --git a/src/backend/cuda/kernel/shared.hpp b/src/backend/cuda/kernel/shared.hpp
index b945301d3b..5ad92be9da 100644
--- a/src/backend/cuda/kernel/shared.hpp
+++ b/src/backend/cuda/kernel/shared.hpp
@@ -29,13 +29,7 @@ namespace kernel {
 template<typename T>
 struct SharedMemory {
     // return a pointer to the runtime-sized shared memory array.
-    __device__ T* getPointer() {
-        extern __device__ void
-        Error_UnsupportedType();  // Ensure that we won't compile any
-                                  // un-specialized types
-        Error_UnsupportedType();
-        return (T*)0;
-    }
+    __device__ T* getPointer();
 };
 
 #define SPECIALIZE(T)                         \
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 33076fbabf..6e002fb3bf 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -13,7 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/transpose.hpp>
+#include <nvrtc_kernel_headers/transpose_cuh.hpp>
 
 #include <string>
 
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 639052bcb6..32159691ca 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -14,7 +14,7 @@
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/where.hpp>
+#include <nvrtc_kernel_headers/where_cuh.hpp>
 #include "config.hpp"
 #include "scan_first.hpp"
 
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 7cac0cf6fc..42b4c1df27 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -18,6 +18,7 @@
 #else  //__CUDACC_RTC__
 
 #include <common/defines.hpp>
+#include <common/half.hpp>
 #include <af/defines.h>
 
 #ifdef __CUDACC__
@@ -30,6 +31,8 @@
 
 #endif  //__CUDACC_RTC__
 
+#include <cuda_fp16.h>
+
 #include "backend.hpp"
 #include "types.hpp"
 
@@ -53,23 +56,43 @@ static inline __DH__ size_t max(size_t lhs, size_t rhs) {
     return lhs > rhs ? lhs : rhs;
 }
 
-#ifndef __CUDA_ARCH__
+#ifdef __CUDA_ARCH__
 template<typename T>
-static inline __DH__ T min(T lhs, T rhs) {
-    return std::min(lhs, rhs);
+inline __DH__ T min(T lhs, T rhs) {
+    return ::min(lhs, rhs);
 }
+
 template<typename T>
-static inline __DH__ T max(T lhs, T rhs) {
-    return std::max(lhs, rhs);
+inline __DH__ T max(T lhs, T rhs) {
+    return ::max(lhs, rhs);
+}
+
+template<>
+inline __DH__ __half min<__half>(__half lhs, __half rhs) {
+#if __CUDA_ARCH__ >= 530
+    return __hlt(lhs, rhs) ? lhs : rhs;
+#else
+    return (float)lhs < (float)rhs ? lhs : rhs;
+#endif
+}
+
+template<>
+inline __DH__ __half max<__half>(__half lhs, __half rhs) {
+#if __CUDA_ARCH__ >= 530
+    return __hgt(lhs, rhs) ? lhs : rhs;
+#else
+    return (float)lhs > (float)rhs ? lhs : rhs;
+#endif
 }
+
 #else
 template<typename T>
 static inline __DH__ T min(T lhs, T rhs) {
-    return ::min(lhs, rhs);
+    return std::min(lhs, rhs);
 }
 template<typename T>
 static inline __DH__ T max(T lhs, T rhs) {
-    return ::max(lhs, rhs);
+    return std::max(lhs, rhs);
 }
 #endif
 
diff --git a/src/backend/cuda/max.cu b/src/backend/cuda/max.cu
index c74fc46cf5..337262dc15 100644
--- a/src/backend/cuda/max.cu
+++ b/src/backend/cuda/max.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // max
@@ -23,4 +26,5 @@ INSTANTIATE(af_max_t, char, char)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
+INSTANTIATE(af_max_t, half, half)
 }  // namespace cuda
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index bc87809490..6da993bca8 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -13,6 +13,7 @@
 #include <common/Logger.hpp>
 #include <common/MemoryManagerImpl.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <common/util.hpp>
 #include <cuda.h>
 #include <cuda_runtime.h>
@@ -37,6 +38,7 @@ template class common::MemoryManager<cuda::MemoryManagerPinned>;
 
 using common::bytesToString;
 using common::MemoryEventPair;
+using common::half;
 
 using std::move;
 
@@ -133,6 +135,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 MemoryManager::MemoryManager()
     : common::MemoryManager<cuda::MemoryManager>(
diff --git a/src/backend/cuda/min.cu b/src/backend/cuda/min.cu
index 14721080a5..30ad8bc186 100644
--- a/src/backend/cuda/min.cu
+++ b/src/backend/cuda/min.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // min
@@ -23,4 +26,5 @@ INSTANTIATE(af_min_t, char, char)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
+INSTANTIATE(af_min_t, half, half)
 }  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 9cfbc0b9b7..89424519c3 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -8,16 +8,22 @@
  ********************************************************/
 
 #include <device_manager.hpp>
-#include <kernel_headers/jit.hpp>
+#include <kernel_headers/jit_cuh.hpp>
 #include <nvrtc/cache.hpp>
-#include <nvrtc_kernel_headers/Param.hpp>
-#include <nvrtc_kernel_headers/backend.hpp>
-#include <nvrtc_kernel_headers/cuComplex.hpp>
-#include <nvrtc_kernel_headers/math.hpp>
-#include <nvrtc_kernel_headers/ops.hpp>
-#include <nvrtc_kernel_headers/optypes.hpp>
-#include <nvrtc_kernel_headers/shared.hpp>
-#include <nvrtc_kernel_headers/types.hpp>
+#include <nvrtc_kernel_headers/Param_hpp.hpp>
+#include <nvrtc_kernel_headers/backend_hpp.hpp>
+#include <nvrtc_kernel_headers/cuComplex_h.hpp>
+#include <nvrtc_kernel_headers/cuda_fp16_h.hpp>
+#include <nvrtc_kernel_headers/cuda_fp16_hpp.hpp>
+#include <nvrtc_kernel_headers/defines_h.hpp>
+#include <nvrtc_kernel_headers/half_hpp.hpp>
+#include <nvrtc_kernel_headers/kernel_type_hpp.hpp>
+#include <nvrtc_kernel_headers/math_hpp.hpp>
+#include <nvrtc_kernel_headers/ops_hpp.hpp>
+#include <nvrtc_kernel_headers/optypes_hpp.hpp>
+#include <nvrtc_kernel_headers/shared_hpp.hpp>
+#include <nvrtc_kernel_headers/traits_hpp.hpp>
+#include <nvrtc_kernel_headers/types_hpp.hpp>
 #include <optypes.hpp>
 #include <platform.hpp>
 
@@ -48,13 +54,13 @@ namespace cuda {
 
 using kc_t = map<string, Kernel>;
 
-#ifndef NDEBUG
+#ifdef NDEBUG
 #define CU_LINK_CHECK(fn)                                                 \
     do {                                                                  \
         CUresult res = fn;                                                \
         if (res == CUDA_SUCCESS) break;                                   \
         char cu_err_msg[1024];                                            \
-        const char* cu_err_name;                                          \
+        const char *cu_err_name;                                          \
         cuGetErrorName(res, &cu_err_name);                                \
         snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
                  cu_err_name, (int)(res), linkError);                     \
@@ -72,15 +78,16 @@ using kc_t = map<string, Kernel>;
         size_t logSize;                                \
         nvrtcGetProgramLogSize(prog, &logSize);        \
         unique_ptr<char[]> log(new char[logSize + 1]); \
-        char* logptr = log.get();                      \
+        char *logptr = log.get();                      \
         nvrtcGetProgramLog(prog, logptr);              \
         logptr[logSize] = '\x0';                       \
+        puts(logptr);                                  \
         AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
     } while (0)
 #else
 #define NVRTC_CHECK(fn)                                                   \
     do {                                                                  \
-        nvrtcResult res = fn;                                             \
+        nvrtcResult res = (fn);                                           \
         if (res == NVRTC_SUCCESS) break;                                  \
         char nvrtc_err_msg[1024];                                         \
         snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
@@ -89,29 +96,47 @@ using kc_t = map<string, Kernel>;
     } while (0)
 #endif
 
-void Kernel::setConstant(const char* name, CUdeviceptr src, size_t bytes) {
+void Kernel::setConstant(const char *name, CUdeviceptr src, size_t bytes) {
     CUdeviceptr dst = 0;
     size_t size     = 0;
     CU_CHECK(cuModuleGetGlobal(&dst, &size, prog, name));
     CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, getActiveStream()));
 }
 
-Kernel buildKernel(const int device, const string& nameExpr,
-                   const string& jit_ker, const vector<string>& opts,
+Kernel buildKernel(const int device, const string &nameExpr,
+                   const string &jit_ker, const vector<string> &opts,
                    const bool isJIT) {
-    const char* ker_name = nameExpr.c_str();
+    const char *ker_name = nameExpr.c_str();
 
     nvrtcProgram prog;
     if (isJIT) {
-        NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 0,
-                                       NULL, NULL));
+        array<const char *, 2> headers = {
+            cuda_fp16_hpp,
+            cuda_fp16_h,
+        };
+        array<const char *, 2> header_names = {"cuda_fp16.hpp", "cuda_fp16.h"};
+        NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 2,
+                                       headers.data(), header_names.data()));
     } else {
-        constexpr static const char* includeNames[] = {
+        constexpr static const char *includeNames[] = {
             "math.h",          // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             "vector_types.h",  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
-            "backend.hpp",    "complex.hpp", "jit.cuh",
-            "math.hpp",       "ops.hpp",     "optypes.hpp",
-            "Param.hpp",      "shared.hpp",  "types.hpp"};
+            "backend.hpp",
+            "cuComplex.h",
+            "jit.cuh",
+            "math.hpp",
+            "ops.hpp",
+            "optypes.hpp",
+            "Param.hpp",
+            "shared.hpp",
+            "types.hpp",
+            "cuda_fp16.hpp",
+            "cuda_fp16.h",
+            "common/half.hpp",
+            "common/kernel_type.hpp",
+            "af/traits.hpp",
+        };
+
         constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
         static const std::array<string, NumHeaders> sourceStrings = {{
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
@@ -125,15 +150,23 @@ Kernel buildKernel(const int device, const string& nameExpr,
             string(Param_hpp, Param_hpp_len),
             string(shared_hpp, shared_hpp_len),
             string(types_hpp, types_hpp_len),
+            string(cuda_fp16_hpp, cuda_fp16_hpp_len),
+            string(cuda_fp16_h, cuda_fp16_h_len),
+            string(half_hpp, half_hpp_len),
+            string(kernel_type_hpp, kernel_type_hpp_len),
+            string(traits_hpp, traits_hpp_len),
         }};
 
-        static const char* headers[] = {
-            sourceStrings[0].c_str(), sourceStrings[1].c_str(),
-            sourceStrings[2].c_str(), sourceStrings[3].c_str(),
-            sourceStrings[4].c_str(), sourceStrings[5].c_str(),
-            sourceStrings[6].c_str(), sourceStrings[7].c_str(),
-            sourceStrings[8].c_str(), sourceStrings[9].c_str(),
-            sourceStrings[10].c_str()};
+        static const char *headers[] = {
+            sourceStrings[0].c_str(),  sourceStrings[1].c_str(),
+            sourceStrings[2].c_str(),  sourceStrings[3].c_str(),
+            sourceStrings[4].c_str(),  sourceStrings[5].c_str(),
+            sourceStrings[6].c_str(),  sourceStrings[7].c_str(),
+            sourceStrings[8].c_str(),  sourceStrings[9].c_str(),
+            sourceStrings[10].c_str(), sourceStrings[11].c_str(),
+            sourceStrings[12].c_str(), sourceStrings[13].c_str(),
+            sourceStrings[14].c_str(), sourceStrings[15].c_str(),
+        };
         NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
                                        NumHeaders, headers, includeNames));
     }
@@ -142,7 +175,7 @@ Kernel buildKernel(const int device, const string& nameExpr,
     array<char, 32> arch;
     snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
              computeFlag.first, computeFlag.second);
-    vector<const char*> compiler_options = {
+    vector<const char *> compiler_options = {
         arch.data(),
         "--std=c++14",
 #if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
@@ -151,7 +184,7 @@ Kernel buildKernel(const int device, const string& nameExpr,
 #endif
     };
     if (!isJIT) {
-        for (auto& s : opts) { compiler_options.push_back(&s[0]); }
+        for (auto &s : opts) { compiler_options.push_back(&s[0]); }
         compiler_options.push_back("--device-as-default-execution-space");
         NVRTC_CHECK(nvrtcAddNameExpression(prog, ker_name));
     }
@@ -174,15 +207,15 @@ Kernel buildKernel(const int device, const string& nameExpr,
         CU_JIT_ERROR_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
         CU_JIT_LOG_VERBOSE};
 
-    void* linkOptionValues[] = {linkInfo, reinterpret_cast<void*>(linkLogSize),
-                                linkError, reinterpret_cast<void*>(linkLogSize),
-                                reinterpret_cast<void*>(1)};
+    void *linkOptionValues[] = {
+        linkInfo, reinterpret_cast<void *>(linkLogSize), linkError,
+        reinterpret_cast<void *>(linkLogSize), reinterpret_cast<void *>(1)};
 
     CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
-    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void*)ptx.data(),
+    CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void *)ptx.data(),
                                 ptx.size(), ker_name, 0, NULL, NULL));
 
-    void* cubin = nullptr;
+    void *cubin = nullptr;
     size_t cubinSize;
 
     CUmodule module;
@@ -190,7 +223,7 @@ Kernel buildKernel(const int device, const string& nameExpr,
     CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
     CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
 
-    const char* name = ker_name;
+    const char *name = ker_name;
     if (!isJIT) { NVRTC_CHECK(nvrtcGetLoweredName(prog, ker_name, &name)); }
 
     CU_CHECK(cuModuleGetFunction(&kernel, module, name));
@@ -202,13 +235,13 @@ Kernel buildKernel(const int device, const string& nameExpr,
     return entry;
 }
 
-kc_t& getCache(int device) {
+kc_t &getCache(int device) {
     thread_local kc_t caches[DeviceManager::MAX_DEVICES];
     return caches[device];
 }
 
 Kernel findKernel(int device, const string nameExpr) {
-    kc_t& cache = getCache(device);
+    kc_t &cache = getCache(device);
 
     kc_t::iterator iter = cache.find(nameExpr);
 
@@ -220,7 +253,7 @@ void addKernelToCache(int device, const string nameExpr, Kernel entry) {
 }
 
 string getOpEnumStr(af_op_t val) {
-    const char* retVal = NULL;
+    const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
     switch (val) {
@@ -341,18 +374,18 @@ string toString(af_op_t val) {
 }
 
 template<>
-string toString(const char* str) {
+string toString(const char *str) {
     return string(str);
 }
 
-Kernel getKernel(const string& nameExpr, const string& source,
-                 const vector<TemplateArg>& targs,
-                 const vector<string>& compileOpts) {
+Kernel getKernel(const string &nameExpr, const string &source,
+                 const vector<TemplateArg> &targs,
+                 const vector<string> &compileOpts) {
     vector<string> args;
     args.reserve(targs.size());
 
     transform(targs.begin(), targs.end(), std::back_inserter(args),
-              [](const TemplateArg& arg) -> string { return arg._tparam; });
+              [](const TemplateArg &arg) -> string { return arg._tparam; });
 
     string tInstance = nameExpr + "<" + args[0];
     for (size_t i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index d4444a9b1d..3f26bb3d2c 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -104,12 +104,21 @@ struct TemplateTypename {
     }
 };
 
-template<>
-struct TemplateTypename<long long> {
-    operator TemplateArg() const noexcept {
-        return TemplateArg(std::string("long long"));
+#define SPECIALIZE(TYPE, NAME)                      \
+    template<>                                      \
+    struct TemplateTypename<TYPE> {                 \
+        operator TemplateArg() const noexcept {     \
+            return TemplateArg(std::string(#NAME)); \
+        }                                           \
     }
-};
+
+SPECIALIZE(unsigned char, cuda::uchar);
+SPECIALIZE(unsigned int, cuda::uint);
+SPECIALIZE(unsigned short, cuda::ushort);
+SPECIALIZE(long long, long long);
+SPECIALIZE(unsigned long long, unsigned long long);
+
+#undef SPECIALIZE
 
 #define DefineKey(arg) "-D " #arg
 #define DefineValue(arg) "-D " #arg "=" + toString(arg)
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 305dd6178f..aa5d2ed373 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -203,6 +203,12 @@ bool isDoubleSupported(int device) {
     return true;
 }
 
+bool isHalfSupported(int device) {
+    auto prop = getDeviceProp(device);
+    float compute = prop.major * 1000 + prop.minor * 10;
+    return compute >= 5030;
+}
+
 void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
     if (getDeviceCount() <= 0) { return; }
 
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index a1f485c324..c8205512eb 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -53,8 +53,12 @@ std::string getDriverVersion();
 
 std::string getCUDARuntimeVersion();
 
+// Returns true if double is supported by the device
 bool isDoubleSupported(int device);
 
+// Returns true if half is supported by the device
+bool isHalfSupported(int device);
+
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
 unsigned getMaxJitSize();
diff --git a/src/backend/cuda/print.hpp b/src/backend/cuda/print.hpp
index a61811a478..97fe7a22ff 100644
--- a/src/backend/cuda/print.hpp
+++ b/src/backend/cuda/print.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <backend.hpp>
+#include <types.hpp>
 #include <ostream>
 
 namespace cuda {
diff --git a/src/backend/cuda/product.cu b/src/backend/cuda/product.cu
index 532f983ce2..42a38dae3a 100644
--- a/src/backend/cuda/product.cu
+++ b/src/backend/cuda/product.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // mul
@@ -23,4 +26,5 @@ INSTANTIATE(af_mul_t, char, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
+INSTANTIATE(af_mul_t, half, float)
 }  // namespace cuda
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 8cbb61d4ed..46714825d3 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -8,10 +8,13 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/random_engine.hpp>
 #include <af/dim4.hpp>
 #include <cassert>
 
+using common::half;
+
 namespace cuda {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> tbl) {
@@ -142,9 +145,11 @@ INSTANTIATE_UNIFORM(char)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(half)
 
 INSTANTIATE_NORMAL(float)
 INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(half)
 
 COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
 COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
diff --git a/src/backend/cuda/reorder.cu b/src/backend/cuda/reorder.cu
index 1bb7e5a932..2d449d8a54 100644
--- a/src/backend/cuda/reorder.cu
+++ b/src/backend/cuda/reorder.cu
@@ -8,11 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/reorder.hpp>
 #include <reorder.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
@@ -42,5 +45,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index 4a1e7c8edd..29c27acda8 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <common/jit/NaryNode.hpp>
 #include <err_cuda.hpp>
 #include <kernel/select.hpp>
@@ -15,6 +16,7 @@
 
 #include <memory>
 
+using common::half;
 using common::NaryNode;
 using common::Node_ptr;
 using std::make_shared;
@@ -118,4 +120,6 @@ INSTANTIATE(char);
 INSTANTIATE(uchar);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
+INSTANTIATE(half);
+
 }  // namespace cuda
diff --git a/src/backend/cuda/sum.cu b/src/backend/cuda/sum.cu
index adc93c9b79..3dcd357700 100644
--- a/src/backend/cuda/sum.cu
+++ b/src/backend/cuda/sum.cu
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace cuda {
 // sum
@@ -31,4 +34,7 @@ INSTANTIATE(af_add_t, short, int)
 INSTANTIATE(af_add_t, short, float)
 INSTANTIATE(af_add_t, ushort, uint)
 INSTANTIATE(af_add_t, ushort, float)
+INSTANTIATE(af_add_t, half, half)
+INSTANTIATE(af_add_t, half, float)
+
 }  // namespace cuda
diff --git a/src/backend/cuda/traits.hpp b/src/backend/cuda/traits.hpp
index ffabcf0a66..7edd6f40a0 100644
--- a/src/backend/cuda/traits.hpp
+++ b/src/backend/cuda/traits.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
+#include <common/traits.hpp>
 #include <cuComplex.h>
-#include <af/traits.hpp>
 
 namespace af {
 
diff --git a/src/backend/cuda/transpose.cpp b/src/backend/cuda/transpose.cpp
index fa20d5bccc..e48fb8f735 100644
--- a/src/backend/cuda/transpose.cpp
+++ b/src/backend/cuda/transpose.cpp
@@ -11,8 +11,10 @@
 #include <kernel/transpose.hpp>
 #include <transpose.hpp>
 #include <af/dim4.hpp>
+#include <common/half.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -47,5 +49,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index c8282c6c12..7067768c0a 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -9,19 +9,26 @@
 
 #pragma once
 
+#include <common/kernel_type.hpp>
+#include <cuComplex.h>
+#include <cuda_fp16.h>
+
+namespace common {
+    class half;
+}
+
 #ifdef __CUDACC_RTC__
 
-#include <complex.hpp>
 using dim_t = long long;
 
 #else  //__CUDACC_RTC__
 
-#include <cuComplex.h>
 #include <af/traits.hpp>
 
-namespace cuda {
 #endif  //__CUDACC_RTC__
 
+namespace cuda {
+
 using cdouble = cuDoubleComplex;
 using cfloat  = cuFloatComplex;
 using intl    = long long;
@@ -31,6 +38,12 @@ using uintl   = unsigned long long;
 using ushort  = unsigned short;
 using ulong   = unsigned long long;
 
+template<typename T>
+using compute_t = typename common::kernel_type<T>::compute;
+
+template<typename T>
+using data_t = typename common::kernel_type<T>::data;
+
 #ifndef __CUDACC_RTC__
 namespace {
 template<typename T>
@@ -85,6 +98,10 @@ template<>
 const char *shortname<ushort>(bool caps) {
     return caps ? "Q" : "q";
 }
+template<>
+const char *shortname<common::half>(bool caps) {
+    return caps ? "H" : "h";
+}
 
 template<typename T>
 const char *getFullName();
@@ -108,10 +125,48 @@ SPECIALIZE(unsigned int)
 SPECIALIZE(unsigned long long)
 SPECIALIZE(long long)
 
+template<>
+const char *getFullName<common::half>() {
+    return "half";
+}
 #undef SPECIALIZE
 }  // namespace
 #endif  //__CUDACC_RTC__
 
-#ifndef __CUDACC_RTC__
+  //#ifndef __CUDACC_RTC__
 }  // namespace cuda
-#endif  //__CUDACC_RTC__
+//#endif  //__CUDACC_RTC__
+
+
+namespace common {
+  template<typename T>
+  class kernel_type;
+}
+
+namespace common {
+template<>
+struct kernel_type<common::half> {
+    using data = common::half;
+
+#ifdef __CUDA_ARCH__
+
+    // These are the types within a kernel
+
+#if __CUDA_ARCH__ > 530 && __CUDA_ARCH__ != 610
+    using compute = __half;
+#else
+    using compute = float;
+#endif
+#else
+
+    // outside of a cuda kernel use float
+    using compute = float;
+
+#if defined(NVCC) || defined(__CUDACC_RTC__)
+    using native  = __half;
+#else
+    using native = common::half;
+#endif
+#endif
+};
+}
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 8c28ad52ff..2132c96963 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -8,8 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
+
+#include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <common/util.hpp>
+#include <common/traits.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
 #include <jit/BufferNode.hpp>
@@ -26,6 +29,7 @@ using af::dim4;
 
 using cl::Buffer;
 
+using common::half;
 using common::Node;
 using common::Node_ptr;
 using common::NodeIterator;
@@ -322,7 +326,7 @@ bool passesJitHeuristics(Node *root_node) {
 
 template<typename T>
 Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
-    verifyDoubleSupport<T>();
+    verifyTypeSupport<T>();
     Array<T> out = Array<T>(dims, node);
     return out;
 }
@@ -363,34 +367,34 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
 }
 
 template<typename T>
-Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
-    verifyDoubleSupport<T>();
-    return Array<T>(dims, data);
+Array<T> createHostDataArray(const dim4 &size, const T *const data) {
+    verifyTypeSupport<T>();
+    return Array<T>(size, data);
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
-    verifyDoubleSupport<T>();
+Array<T> createDeviceDataArray(const dim4 &size, void *data) {
+    verifyTypeSupport<T>();
 
     bool copy_device = false;
-    return Array<T>(dims, static_cast<cl_mem>(data), 0, copy_device);
+    return Array<T>(size, static_cast<cl_mem>(data), 0, copy_device);
 }
 
 template<typename T>
-Array<T> createValueArray(const dim4 &dims, const T &value) {
-    verifyDoubleSupport<T>();
-    return createScalarNode<T>(dims, value);
+Array<T> createValueArray(const dim4 &size, const T &value) {
+    verifyTypeSupport<T>();
+    return createScalarNode<T>(size, value);
 }
 
 template<typename T>
-Array<T> createEmptyArray(const dim4 &dims) {
-    verifyDoubleSupport<T>();
-    return Array<T>(dims);
+Array<T> createEmptyArray(const dim4 &size) {
+    verifyTypeSupport<T>();
+    return Array<T>(size);
 }
 
 template<typename T>
 Array<T> createParamArray(Param &tmp, bool owner) {
-    verifyDoubleSupport<T>();
+    verifyTypeSupport<T>();
     return Array<T>(tmp, owner);
 }
 
@@ -473,5 +477,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index f098dd289c..5c95e8c430 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -165,6 +165,7 @@ class Array {
     INFO_IS_FUNC(isReal);
     INFO_IS_FUNC(isDouble);
     INFO_IS_FUNC(isSingle);
+    INFO_IS_FUNC(isHalf);
     INFO_IS_FUNC(isRealFloating);
     INFO_IS_FUNC(isFloating);
     INFO_IS_FUNC(isInteger);
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index ec7a5f7664..f11d9ab60a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -238,6 +238,7 @@ target_sources(afopencl
     triangle.cpp
     triangle.hpp
     types.hpp
+    types.cpp
     unary.hpp
     unwrap.cpp
     unwrap.hpp
diff --git a/src/backend/opencl/all.cpp b/src/backend/opencl/all.cpp
index 271ca86499..5825b3af4a 100644
--- a/src/backend/opencl/all.cpp
+++ b/src/backend/opencl/all.cpp
@@ -7,8 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/half.hpp>
 #include "reduce_impl.hpp"
 
+using common::half;
+
 namespace opencl {
 // alltrue
 INSTANTIATE(af_and_t, float, char)
@@ -23,4 +26,5 @@ INSTANTIATE(af_and_t, char, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
+INSTANTIATE(af_and_t, half, char)
 }  // namespace opencl
diff --git a/src/backend/opencl/any.cpp b/src/backend/opencl/any.cpp
index 2636a8c26f..21ae5e6970 100644
--- a/src/backend/opencl/any.cpp
+++ b/src/backend/opencl/any.cpp
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace opencl {
 // anytrue
@@ -23,4 +26,5 @@ INSTANTIATE(af_or_t, char, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
+INSTANTIATE(af_or_t, half, char)
 }  // namespace opencl
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index c0cb7f974e..0af6d33e23 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -7,18 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <complex>
-#include <vector>
+#include <blas.hpp>
 
 #include <Array.hpp>
 #include <arith.hpp>
-#include <blas.hpp>
+#include <common/half.hpp>
+#include <common/traits.hpp>
 #include <complex.hpp>
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <reduce.hpp>
 #include <transpose.hpp>
 
+#include <complex>
+#include <vector>
+
 // Includes one of the supported OpenCL BLAS back-ends (e.g. clBLAS, CLBlast)
 #include <magma/magma_blas.h>
 
@@ -26,6 +29,8 @@
 #include <cpu/cpu_blas.hpp>
 #endif
 
+using common::half;
+
 namespace opencl {
 
 void initBlas() { gpu_blas_init(); }
@@ -44,6 +49,22 @@ toBlasTranspose(af_mat_prop opt) {
     }
 }
 
+template<typename T>
+void gemm_fallback(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+                   const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
+                   const T *beta) {
+    cpu::gemm(out, optLhs, optRhs, alpha, lhs, rhs, beta);
+}
+
+template<>
+void gemm_fallback<half>(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+                          const half *alpha,
+                          const Array<half> &lhs, const Array<half> &rhs,
+                          const half *beta) {
+    assert(false && "CPU fallback not implemented for f16");
+}
+
+
 template<typename T>
 void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
           const T *alpha,
@@ -51,8 +72,8 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
           const T *beta) {
 #if defined(WITH_LINEAR_ALGEBRA)
     // Do not force offload gemm on OSX Intel devices
-    if (OpenCLCPUOffload(false)) {
-        cpu::gemm(out, optLhs, optRhs, alpha, lhs, rhs, beta);
+    if (OpenCLCPUOffload(false) && (af_dtype)dtype_traits<T>::af_type != f16) {
+        gemm_fallback(out, optLhs, optRhs, alpha, lhs, rhs, beta);
         return;
     }
 #endif
@@ -134,6 +155,7 @@ INSTANTIATE_GEMM(float)
 INSTANTIATE_GEMM(cfloat)
 INSTANTIATE_GEMM(double)
 INSTANTIATE_GEMM(cdouble)
+INSTANTIATE_GEMM(half)
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index aa9a1da287..4e61e347db 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -6,14 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <copy.hpp>
+#include <kernel/memcopy.hpp>
 
 #include <Array.hpp>
 #include <common/complex.hpp>
-#include <copy.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
-#include <kernel/memcopy.hpp>
 #include <math.hpp>
 
+using common::half;
 using common::is_complex;
 
 namespace opencl {
@@ -137,6 +139,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 #define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
     template Array<float> padArray<SRC_T, float>(                         \
@@ -210,6 +213,7 @@ INSTANTIATE_PAD_ARRAY(uchar)
 INSTANTIATE_PAD_ARRAY(char)
 INSTANTIATE_PAD_ARRAY(short)
 INSTANTIATE_PAD_ARRAY(ushort)
+INSTANTIATE_PAD_ARRAY(half)
 
 #define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
     template Array<cfloat> padArray<SRC_T, cfloat>(                       \
@@ -248,4 +252,6 @@ INSTANTIATE_GETSCALAR(intl)
 INSTANTIATE_GETSCALAR(uintl)
 INSTANTIATE_GETSCALAR(short)
 INSTANTIATE_GETSCALAR(ushort)
+INSTANTIATE_GETSCALAR(half)
+
 }  // namespace opencl
diff --git a/src/backend/opencl/count.cpp b/src/backend/opencl/count.cpp
index c8ae0bf692..fd1f6b3381 100644
--- a/src/backend/opencl/count.cpp
+++ b/src/backend/opencl/count.cpp
@@ -7,8 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/half.hpp>
 #include "reduce_impl.hpp"
 
+using common::half;
+
 namespace opencl {
 // count
 INSTANTIATE(af_notzero_t, float, uint)
@@ -23,4 +26,5 @@ INSTANTIATE(af_notzero_t, char, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
+INSTANTIATE(af_notzero_t, half, uint)
 }  // namespace opencl
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 35da2a0ed2..6ce0d7cca6 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -57,6 +57,8 @@ class DeviceManager {
 
     friend bool isDoubleSupported(int device);
 
+    friend bool isHalfSupported(int device);
+
     friend void devprop(char* d_name, char* d_platform, char* d_toolkit,
                         char* d_compute);
 
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 2d11178056..a060ccd1b0 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <errorcodes.hpp>
 #include <platform.hpp>
 #include <types.hpp>
@@ -22,10 +23,13 @@
 
 namespace opencl {
 template<typename T>
-void verifyDoubleSupport() {
+void verifyTypeSupport() {
     if ((std::is_same<T, double>::value || std::is_same<T, cdouble>::value) &&
         !isDoubleSupported(getActiveDeviceId())) {
         AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
+    } else if (std::is_same<T, common::half>::value &&
+               !isHalfSupported(getActiveDeviceId())) {
+        AF_ERROR("Half precision not supported", AF_ERR_NO_HALF);
     }
 }
 }  // namespace opencl
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 8dcf24048f..2936f7b228 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -8,11 +8,15 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
+
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 template<int dim>
 af::dim4 calcOffset(const af::dim4 dims) {
@@ -159,6 +163,7 @@ INSTANTIATE(short, short)
 INSTANTIATE(ushort, ushort)
 INSTANTIATE(uchar, uchar)
 INSTANTIATE(char, char)
+INSTANTIATE(half, half)
 
 #undef INSTANTIATE
 
@@ -178,6 +183,7 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
+INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index ec6da04b6c..01906fefce 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -7,6 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma OPENCL EXTENSION all : enable
+#ifdef cl_khr_fp16
+#else
+#define half short
+#endif
+
 #define __select(cond, a, b) (cond) ? (a) : (b)
 #define __not_select(cond, a, b) (cond) ? (b) : (a)
 #define __circular_mod(a, b) ((a) < (b)) ? (a) : (a - b)
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index fed2f17b4d..4c82a17bf7 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/traits.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/copy.hpp>
 #include <kernel_headers/memcopy.hpp>
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index be7ecf0d98..31416eec86 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/reduce_dim.hpp>
@@ -33,6 +34,7 @@ using cl::Kernel;
 using cl::KernelFunctor;
 using cl::NDRange;
 using cl::Program;
+using common::half;
 using std::string;
 using std::unique_ptr;
 
@@ -275,11 +277,11 @@ To reduce_all(Param in, int change_nan, double nanval) {
                                      sizeof(To) * tmp_elements, h_ptr.data());
 
         Binary<To, op> reduce;
-        To out = Binary<To, op>::init();
+        compute_t<To> out = Binary<To, op>::init();
         for (int i = 0; i < (int)tmp_elements; i++) {
             out = reduce(out, h_ptr[i]);
         }
-        return out;
+        return data_t<To>(out);
     } else {
         std::vector<Ti> h_ptr(in_elements);
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
@@ -288,8 +290,8 @@ To reduce_all(Param in, int change_nan, double nanval) {
 
         Transform<Ti, To, op> transform;
         Binary<To, op> reduce;
-        To out       = Binary<To, op>::init();
-        To nanval_to = scalar<To>(nanval);
+        compute_t<To> out       = Binary<To, op>::init();
+        compute_t<To> nanval_to = scalar<To>(nanval);
 
         for (int i = 0; i < (int)in_elements; i++) {
             To in_val = transform(h_ptr[i]);
@@ -297,7 +299,7 @@ To reduce_all(Param in, int change_nan, double nanval) {
             out = reduce(out, in_val);
         }
 
-        return out;
+        return data_t<To>(out);
     }
 }
 
diff --git a/src/backend/opencl/kernel/transpose.cl b/src/backend/opencl/kernel/transpose.cl
index 7b486f49fc..5fce019be8 100644
--- a/src/backend/opencl/kernel/transpose.cl
+++ b/src/backend/opencl/kernel/transpose.cl
@@ -15,6 +15,12 @@ T doOp(T in) {
 #define doOp(in) in
 #endif
 
+#pragma OPENCL EXTENSION all : enable
+#ifdef cl_khr_fp16
+#else
+#define half short
+#endif
+
 __kernel void transpose(__global T *oData, const KParam out,
                         const __global T *iData, const KParam in,
                         const int blocksPerMatX, const int blocksPerMatY) {
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 4ba8dac927..463dd41092 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -12,6 +12,7 @@
 #include <complex>
 
 #include <common/defines.hpp>
+#include <common/half.hpp>
 
 #include <clblast.h>
 #include <err_clblast.hpp>
@@ -49,6 +50,7 @@ clblast::Side           clblast_side_const ( magma_side_t  side  );
 template <typename T> struct CLBlastType { using Type = T; };
 template <> struct CLBlastType<cfloat> { using Type = std::complex<float>; };
 template <> struct CLBlastType<cdouble> { using Type = std::complex<double>; };
+template <> struct CLBlastType<common::half> { using Type = cl_half; };
 
 // Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
 template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(const T val);
@@ -56,6 +58,7 @@ template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(con
 // Specializations of the above function
 template <> float inline toCLBlastConstant(const float val) { return val; }
 template <> double inline toCLBlastConstant(const double val) { return val; }
+template <> cl_half inline toCLBlastConstant(const common::half val) { return val; }
 template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
 template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
 
diff --git a/src/backend/opencl/max.cpp b/src/backend/opencl/max.cpp
index eaaba7ee11..de8621427a 100644
--- a/src/backend/opencl/max.cpp
+++ b/src/backend/opencl/max.cpp
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace opencl {
 // max
@@ -23,4 +26,5 @@ INSTANTIATE(af_max_t, char, char)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
+INSTANTIATE(af_max_t, half, half)
 }  // namespace opencl
diff --git a/src/backend/opencl/min.cpp b/src/backend/opencl/min.cpp
index b1eb210175..69aa38efae 100644
--- a/src/backend/opencl/min.cpp
+++ b/src/backend/opencl/min.cpp
@@ -7,8 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/half.hpp>
 #include "reduce_impl.hpp"
 
+using common::half;
+
 namespace opencl {
 // min
 INSTANTIATE(af_min_t, float, float)
@@ -23,4 +26,5 @@ INSTANTIATE(af_min_t, char, char)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
+INSTANTIATE(af_min_t, half, half)
 }  // namespace opencl
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index aaaaf2d1cd..da1924baa0 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -322,6 +322,17 @@ bool isDoubleSupported(int device) {
     return (dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0);
 }
 
+bool isHalfSupported(int device) {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    cl::Device dev;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        dev = *devMngr.mDevices[device];
+    }
+    return (dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_HALF>() > 0);
+}
+
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
     unsigned nDevices        = 0;
     unsigned currActiveDevId = (unsigned)getActiveDeviceId();
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 1ceac97fb0..5ed0810135 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -76,6 +76,9 @@ bool isGLSharingSupported();
 
 bool isDoubleSupported(int device);
 
+// Returns true if 16-bit precision floats are supported by the device
+bool isHalfSupported(int device);
+
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
 std::string getPlatformName(const cl::Device& device);
diff --git a/src/backend/opencl/product.cpp b/src/backend/opencl/product.cpp
index 01e131c092..3bcd9fee9d 100644
--- a/src/backend/opencl/product.cpp
+++ b/src/backend/opencl/product.cpp
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #include "reduce_impl.hpp"
+#include <common/half.hpp>
+
+using common::half;
 
 namespace opencl {
 // sum
@@ -23,4 +26,5 @@ INSTANTIATE(af_mul_t, char, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
+INSTANTIATE(af_mul_t, half, float)
 }  // namespace opencl
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 0208c8d2a1..976b8a7cc2 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -8,9 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/random_engine.hpp>
 #include <af/dim4.hpp>
-#include <cassert>
+
+using common::half;
 
 namespace opencl {
 void initMersenneState(Array<uint> &state, const uintl seed,
@@ -138,9 +140,11 @@ INSTANTIATE_UNIFORM(char)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(half)
 
 INSTANTIATE_NORMAL(float)
 INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(half)
 
 COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
 COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
diff --git a/src/backend/opencl/reorder.cpp b/src/backend/opencl/reorder.cpp
index 6786e6e82a..637654d49d 100644
--- a/src/backend/opencl/reorder.cpp
+++ b/src/backend/opencl/reorder.cpp
@@ -8,11 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <kernel/reorder.hpp>
 #include <reorder.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
@@ -42,4 +45,5 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace opencl
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 7aebb0026b..1612214d30 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -10,6 +10,7 @@
 #include <select.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <common/jit/NaryNode.hpp>
 #include <err_opencl.hpp>
 #include <scalar.hpp>
@@ -18,6 +19,7 @@
 
 using af::dim4;
 
+using common::half;
 using common::NaryNode;
 
 using std::make_shared;
@@ -122,6 +124,7 @@ INSTANTIATE(char);
 INSTANTIATE(uchar);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
+INSTANTIATE(half);
 
 #undef INSTANTIATE
 }  // namespace opencl
diff --git a/src/backend/opencl/sum.cpp b/src/backend/opencl/sum.cpp
index 69bc820219..781a6c8eee 100644
--- a/src/backend/opencl/sum.cpp
+++ b/src/backend/opencl/sum.cpp
@@ -7,8 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/half.hpp>
 #include "reduce_impl.hpp"
 
+using common::half;
+
 namespace opencl {
 // sum
 INSTANTIATE(af_add_t, float, float)
@@ -31,4 +34,5 @@ INSTANTIATE(af_add_t, short, int)
 INSTANTIATE(af_add_t, short, float)
 INSTANTIATE(af_add_t, ushort, uint)
 INSTANTIATE(af_add_t, ushort, float)
+INSTANTIATE(af_add_t, half, float)
 }  // namespace opencl
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index be65c32a4c..60dbedad15 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -10,8 +10,8 @@
 #pragma once
 
 #include <common/defines.hpp>
+#include <common/traits.hpp>
 #include <types.hpp>
-#include <af/traits.hpp>
 #include <sstream>
 #include <string>
 
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index fc7b8b439d..ce1760b26e 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -6,13 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <kernel/transpose.hpp>
 #include <transpose.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace opencl {
 
@@ -53,5 +55,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
new file mode 100644
index 0000000000..7d46f9fc91
--- /dev/null
+++ b/src/backend/opencl/types.cpp
@@ -0,0 +1,115 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <types.hpp>
+
+#include <common/half.hpp>
+#include <type_util.hpp>
+
+#include <cmath>
+#include <string>
+#include <sstream>
+
+using common::half;
+
+namespace opencl {
+
+template<typename T>
+inline std::string ToNumStr<T>::operator()(T val) {
+    ToNum<T> toNum;
+    return std::to_string(toNum(val));
+}
+
+template<>
+std::string ToNumStr<float>::operator()(float val) {
+    static const char *PINF = "+INFINITY";
+    static const char *NINF = "-INFINITY";
+    if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
+    return std::to_string(val);
+}
+
+template<>
+std::string ToNumStr<double>::operator()(double val) {
+    static const char *PINF = "+INFINITY";
+    static const char *NINF = "-INFINITY";
+    if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
+    return std::to_string(val);
+}
+
+template<>
+std::string ToNumStr<cfloat>::operator()(cfloat val) {
+    ToNumStr<float> realStr;
+    std::stringstream s;
+    s << "{" << realStr(val.s[0]) << "," << realStr(val.s[1]) << "}";
+    return s.str();
+}
+
+template<>
+std::string ToNumStr<cdouble>::operator()(cdouble val) {
+    ToNumStr<double> realStr;
+    std::stringstream s;
+    s << "{" << realStr(val.s[0]) << "," << realStr(val.s[1]) << "}";
+    return s.str();
+}
+
+template<>
+std::string ToNumStr<half>::operator()(half val) {
+    static const char *PINF = "+INFINITY";
+    static const char *NINF = "-INFINITY";
+    if (common::isinf(val)) { return val < 0 ? NINF : PINF; }
+    return std::to_string(val);
+}
+
+template<>
+template<>
+std::string ToNumStr<half>::operator()<float>(float val) {
+    static const char *PINF = "+INFINITY";
+    static const char *NINF = "-INFINITY";
+    if (common::isinf(half(val))) { return val < 0 ? NINF : PINF; }
+    return std::to_string(val);
+}
+
+
+#define INSTANTIATE(TYPE)                       \
+  template struct ToNumStr<TYPE>
+
+  INSTANTIATE(float);
+  INSTANTIATE(double);
+  INSTANTIATE(cfloat);
+  INSTANTIATE(cdouble);
+  INSTANTIATE(short);
+  INSTANTIATE(ushort);
+  INSTANTIATE(int);
+  INSTANTIATE(uint);
+  INSTANTIATE(intl);
+  INSTANTIATE(uintl);
+  INSTANTIATE(uchar);
+  INSTANTIATE(char);
+  INSTANTIATE(half);
+
+#undef INSTANTIATE
+
+}  // namespace opencl
+
+namespace common {
+template<typename T>
+class kernel_type;
+}
+
+namespace common {
+template<>
+struct kernel_type<common::half> {
+    using data = common::half;
+
+    using compute = cl_half;
+
+    // These are the types within a kernel
+    using native = cl_half;
+};
+}
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 6b94af1501..f2d40b096a 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -16,11 +16,10 @@
 #include <CL/cl.h>
 #endif
 #pragma GCC diagnostic pop
-#include <type_util.hpp>
-#include <af/traits.hpp>
 
-#include <cmath>
-#include <sstream>
+#include <common/traits.hpp>
+#include <common/kernel_type.hpp>
+
 #include <string>
 
 namespace opencl {
@@ -33,59 +32,16 @@ using uintl   = unsigned long long;
 using ushort  = cl_ushort;
 
 template<typename T>
-struct ToNumStr {
-    inline std::string operator()(T val) {
-        ToNum<T> toNum;
-        return std::to_string(toNum(val));
-    }
-};
-
-template<>
-struct ToNumStr<float> {
-    inline std::string operator()(float val) {
-        static const char *PINF = "+INFINITY";
-        static const char *NINF = "-INFINITY";
-        if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
-        return std::to_string(val);
-    }
-};
-
-template<>
-struct ToNumStr<double> {
-    inline std::string operator()(double val) {
-        static const char *PINF = "+INFINITY";
-        static const char *NINF = "-INFINITY";
-        if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
-        return std::to_string(val);
-    }
-};
+using compute_t = typename common::kernel_type<T>::compute;
 
-template<>
-struct ToNumStr<cfloat> {
-    inline std::string operator()(cfloat val) {
-        ToNumStr<float> realStr;
-        std::stringstream s;
-        s << "{";
-        s << realStr(val.s[0]);
-        s << ",";
-        s << realStr(val.s[1]);
-        s << "}";
-        return s.str();
-    }
-};
+template<typename T>
+using data_t = typename common::kernel_type<T>::data;
 
-template<>
-struct ToNumStr<cdouble> {
-    inline std::string operator()(cdouble val) {
-        ToNumStr<double> realStr;
-        std::stringstream s;
-        s << "{";
-        s << realStr(val.s[0]);
-        s << ",";
-        s << realStr(val.s[1]);
-        s << "}";
-        return s.str();
-    }
+template<typename T>
+struct ToNumStr {
+    std::string operator()(T val);
+    template<typename CONVERSION_TYPE>
+    std::string operator()(CONVERSION_TYPE val);
 };
 
 namespace {
@@ -142,12 +98,11 @@ template<>
 inline const char *shortname<ushort>(bool caps) {
     return caps ? "Q" : "q";
 }
+}  // namespace
 
 template<typename T>
 const char *getFullName() {
     return af::dtype_traits<T>::getName();
 }
 
-}  // namespace
-
 }  // namespace opencl
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 45c1a8c153..3763dbd753 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -102,6 +102,7 @@ function(make_test)
     add_executable(${target} ${mt_args_SRC})
     target_include_directories(${target}
       PRIVATE
+        ${ArrayFire_SOURCE_DIR}/extern/half/include
         ${CMAKE_SOURCE_DIR}
         ${CMAKE_CURRENT_SOURCE_DIR}
       )
@@ -206,6 +207,7 @@ make_test(SRC getting_started.cpp)
 make_test(SRC gfor.cpp)
 make_test(SRC gradient.cpp)
 make_test(SRC gray_rgb.cpp)
+make_test(SRC half.cpp)
 make_test(SRC hamming.cpp)
 make_test(SRC harris.cpp)
 make_test(SRC histogram.cpp)
diff --git a/test/array.cpp b/test/array.cpp
index 7b8256a451..3e1b9a2d63 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -335,6 +335,17 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
             break;
+        case f16:
+            EXPECT_TRUE(one.isfloating());
+            EXPECT_FALSE(one.isdouble());
+            EXPECT_FALSE(one.issingle());
+            EXPECT_TRUE(one.isrealfloating());
+            EXPECT_FALSE(one.isinteger());
+            EXPECT_TRUE(one.isreal());
+            EXPECT_FALSE(one.iscomplex());
+            EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
+            break;
     }
 }
 
diff --git a/test/blas.cpp b/test/blas.cpp
index 48c71c5c28..35489f0762 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -233,6 +233,7 @@ TYPED_TEST(MatrixMultiply, MultiGPURectangleVector_CPP) {
         TEST_DIR "/blas/RectangleVector.test")));
 }
 
+float batch_tol = 1E-2;
 TEST(MatrixMultiply, Batched) {
     const int M  = 512;
     const int K  = 512;
@@ -251,7 +252,7 @@ TEST(MatrixMultiply, Batched) {
                     array b_ij = b(span, span, i, j);
                     array c_ij = c(span, span, i, j);
                     array res  = matmul(a_ij, b_ij);
-                    ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
+                    ASSERT_ARRAYS_NEAR(c_ij, res, batch_tol);
                 }
             }
         }
@@ -291,7 +292,7 @@ TEST(MatrixMultiply, LhsBroadcastBatched) {
                     array b_ij = b(span, span, i, j);
                     array c_ij = c(span, span, i, j);
                     array res  = matmul(a, b_ij);
-                    ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
+                    ASSERT_ARRAYS_NEAR(c_ij, res, batch_tol);
                 }
             }
         }
@@ -316,7 +317,7 @@ TEST(MatrixMultiply, RhsBroadcastBatched) {
                     array a_ij = a(span, span, i, j);
                     array c_ij = c(span, span, i, j);
                     array res  = matmul(a_ij, b);
-                    ASSERT_ARRAYS_NEAR(c_ij, res, 2E-4);
+                    ASSERT_ARRAYS_NEAR(c_ij, res, batch_tol);
                 }
             }
         }
diff --git a/test/half.cpp b/test/half.cpp
new file mode 100644
index 0000000000..6ce640b83b
--- /dev/null
+++ b/test/half.cpp
@@ -0,0 +1,88 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+
+#include <../extern/half/include/half.hpp>
+#include <testHelpers.hpp>
+
+using af::array;
+using af::constant;
+using af::half;
+using std::vector;
+
+TEST(Half, print) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    array aa = af::constant(3.14, 3, 3, f16);
+    array bb = af::constant(2, 3, 3, f16);
+    af_print(aa);
+}
+
+struct convert_params {
+    af_dtype from, to;
+    double value;
+    convert_params(af_dtype f, af_dtype t, double v)
+        : from(f), to(t), value(v) {}
+};
+
+class HalfConvert : public ::testing::TestWithParam<convert_params> {};
+
+INSTANTIATE_TEST_CASE_P(ToF16, HalfConvert,
+                        ::testing::Values(convert_params(f32, f16, 10),
+                                          convert_params(f64, f16, 10),
+                                          convert_params(s32, f16, 10),
+                                          convert_params(u32, f16, 10),
+                                          convert_params(u8, f16, 10),
+                                          convert_params(s64, f16, 10),
+                                          convert_params(u64, f16, 10),
+                                          convert_params(s16, f16, 10),
+                                          convert_params(u16, f16, 10),
+                                          convert_params(f16, f16, 10)));
+
+INSTANTIATE_TEST_CASE_P(FromF16, HalfConvert,
+                        ::testing::Values(convert_params(f16, f32, 10),
+                                          convert_params(f16, f64, 10),
+                                          convert_params(f16, s32, 10),
+                                          convert_params(f16, u32, 10),
+                                          // causes compilation failures with
+                                          // nvrtc
+                                          // convert_params(f16, u8, 10),
+                                          convert_params(f16, s64, 10),
+                                          convert_params(f16, u64, 10),
+                                          convert_params(f16, s16, 10),
+                                          convert_params(f16, u16, 10),
+                                          convert_params(f16, f16, 10)));
+
+TEST_P(HalfConvert, convert) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    convert_params params = GetParam();
+
+    array from = af::constant(params.value, 3, 3, params.from);
+    array to   = from.as(params.to);
+
+    ASSERT_EQ(from.type(), params.from);
+    ASSERT_EQ(to.type(), params.to);
+
+    array gold = af::constant(params.value, 3, 3, params.to);
+    ASSERT_ARRAYS_EQ(gold, to);
+}
+
+TEST(Half, arith) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    array aa = af::constant(3.14, 3, 3, f16);
+    array bb = af::constant(1, 3, 3, f16);
+
+    array gold   = constant(4.14, 3, 3, f16);
+    array result = bb + aa;
+
+    ASSERT_ARRAYS_EQ(gold, result);
+}
diff --git a/test/join.cpp b/test/join.cpp
index 0a37a38dc4..f747d1a3c3 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -44,7 +44,7 @@ class Join : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort>
+                         intl, uintl, char, unsigned char, short, ushort, af_half>
     TestTypes;
 
 // register the type list
diff --git a/test/random.cpp b/test/random.cpp
index b270b8cac7..0a2dbf2a71 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -36,7 +36,7 @@ class Random : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
-                         uintl, unsigned char>
+                         uintl, unsigned char, af_half>
     TestTypes;
 
 // register the type list
@@ -67,7 +67,7 @@ class RandomSeed : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble> TestTypesNorm;
+typedef ::testing::Types<float, cfloat, double, cdouble, af_half> TestTypesNorm;
 // register the type list
 TYPED_TEST_CASE(Random_norm, TestTypesNorm);
 
@@ -356,6 +356,7 @@ TYPED_TEST(RandomEngine, mersenneRandomEngineNormal) {
 
 template<typename T>
 void testRandomEngineSeed(randomEngineType type) {
+    SUPPORTED_TYPE_CHECK(T);
     int elem        = 4 * 32 * 1024;
     uintl orig_seed = 0;
     uintl new_seed  = 1;
diff --git a/test/reduce.cpp b/test/reduce.cpp
index b08894abac..0e04197efb 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
@@ -704,3 +704,176 @@ TEST(Product, BoolIn_ISSUE2543) {
     array A = randu(5, 5, b8);
     ASSERT_ARRAYS_EQ(allTrue(A), product(A));
 }
+
+struct reduce_params {
+    double element_value;
+    dim4 arr_dim;
+    dim4 result_dim;
+    int reduce_dim;
+  reduce_params(double ev, dim4 ad, dim4 result_d, int red_dim)
+    : element_value(ev)
+    , arr_dim(ad)
+    , result_dim(result_d)
+    , reduce_dim(red_dim) {}
+};
+
+class ReduceHalf : public ::testing::TestWithParam<reduce_params> {};
+
+INSTANTIATE_TEST_CASE_P(
+    SumFirstNonZeroDim, ReduceHalf,
+    ::testing::Values(
+        reduce_params(1, dim4(10), dim4(1), -1),
+        reduce_params(1, dim4(10, 10), dim4(1, 10), -1),
+        reduce_params(1, dim4(10, 10, 10), dim4(1, 10, 10), -1),
+        reduce_params(1, dim4(10, 10, 10, 10), dim4(1, 10, 10, 10), -1),
+
+        reduce_params(1, dim4(2048), dim4(1), -1),
+        reduce_params(1, dim4(2048, 10), dim4(1, 10), -1),
+        reduce_params(1, dim4(2048, 10, 10), dim4(1, 10, 10), -1),
+        reduce_params(1, dim4(2048, 10, 10, 10), dim4(1, 10, 10, 10), -1),
+
+        reduce_params(1, dim4(2049), dim4(1), -1),
+        reduce_params(1, dim4(2049, 10), dim4(1, 10), -1),
+        reduce_params(1, dim4(2049, 10, 10), dim4(1, 10, 10), -1),
+        reduce_params(1, dim4(2049, 10, 10, 10), dim4(1, 10, 10, 10), -1),
+
+        reduce_params(1, dim4(8192), dim4(1), -1),
+        reduce_params(1, dim4(8192, 10), dim4(1, 10), -1),
+        reduce_params(1, dim4(8192, 10, 10), dim4(1, 10, 10), -1),
+        reduce_params(1, dim4(8192, 10, 10, 10), dim4(1, 10, 10, 10), -1)));
+
+INSTANTIATE_TEST_CASE_P(
+    SumNonZeroDim, ReduceHalf,
+    ::testing::Values(
+        reduce_params(1.25, dim4(10, 10), dim4(10), 1),
+        reduce_params(1.25, dim4(10, 10, 10), dim4(10, 1, 10), 1),
+        reduce_params(1.25, dim4(10, 10, 10, 10), dim4(10, 1, 10, 10), 1),
+
+        reduce_params(1.25, dim4(10, 2048), dim4(10), 1),
+        reduce_params(1.25, dim4(10, 2048, 10), dim4(10, 1, 10), 1),
+        reduce_params(1.25, dim4(10, 2048, 10, 10), dim4(10, 1, 10, 10), 1),
+
+        reduce_params(1.25, dim4(10, 2049), dim4(10), 1),
+        reduce_params(1.25, dim4(10, 2049, 10), dim4(10, 1, 10), 1),
+        reduce_params(1.25, dim4(10, 2049, 10, 10), dim4(10, 1, 10, 10), 1),
+
+        reduce_params(1.25, dim4(10, 8192), dim4(10), 1),
+        reduce_params(1.25, dim4(10, 8192, 10), dim4(10, 1, 10), 1),
+        reduce_params(1.25, dim4(10, 8192, 10, 10), dim4(10, 1, 10, 10), 1),
+
+        reduce_params(1.25, dim4(10, 10, 10), dim4(10, 10, 1), 2),
+        reduce_params(1.25, dim4(10, 10, 10, 10), dim4(10, 10, 1, 10), 2),
+
+        reduce_params(1.25, dim4(10, 10, 2048), dim4(10, 10, 1), 2),
+        reduce_params(1.25, dim4(10, 10, 2048, 10), dim4(10, 10, 1, 10), 2),
+
+        reduce_params(1.25, dim4(10, 10, 2049), dim4(10, 10, 1), 2),
+        reduce_params(1.25, dim4(10, 10, 2049, 10), dim4(10, 10, 1, 10), 2),
+
+        reduce_params(1.25, dim4(10, 10, 8192), dim4(10, 10, 1), 2),
+        reduce_params(1.25, dim4(10, 10, 8192, 10), dim4(10, 10, 1, 10), 2)));
+
+TEST_P(ReduceHalf, Sum) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    reduce_params param = GetParam();
+
+    array arr = constant(param.element_value, param.arr_dim, f16);
+
+    size_t elements = 0;
+    if (param.reduce_dim == -1) {
+        elements = param.arr_dim[0];
+    } else {
+        elements = param.arr_dim[param.reduce_dim];
+    }
+
+    double result_value = param.element_value * elements;
+    array gold = constant(result_value, param.result_dim, f32);
+
+    array result = sum(arr, param.reduce_dim);
+    ASSERT_ARRAYS_EQ(gold, result);
+}
+
+TEST_P(ReduceHalf, Product) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    reduce_params param = GetParam();
+
+    array arr  = constant(param.element_value, param.arr_dim, f16);
+
+    size_t elements = 0;
+    if (param.reduce_dim == -1) {
+        elements = param.arr_dim[0];
+    } else {
+        elements = param.arr_dim[param.reduce_dim];
+    }
+
+    double result_value = pow(param.element_value, elements);
+
+    if(isinf((float)result_value)) {
+        SUCCEED();
+        return;
+    }
+    array gold = constant(result_value, param.result_dim, f32);
+
+    array result = product(arr, param.reduce_dim);
+    ASSERT_ARRAYS_EQ(gold, result);
+}
+
+// TODO(umar): HalfMin
+TEST(ReduceHalf, Min) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    float harr[] = { 1, 2, 3, 4, 5, 6, 7 };
+    array arr(7, harr);
+    arr = arr.as(f16);
+    array out = min(arr);
+
+    array gold = constant(1, 1, f16);
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+// TODO(umar): HalfMax
+TEST(ReduceHalf, Max) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    float harr[] = {1, 2, 3, 4, 5, 6, 7};
+    array arr(7, harr);
+    arr       = arr.as(f16);
+    array out = max(arr);
+
+    array gold = constant(7, 1, f16);
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+// TODO(umar): HalfCount
+TEST(ReduceHalf, Count) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    float harr[] = {1, 2, 3, 4, 5, 6, 7};
+    array arr(7, harr);
+    arr       = arr.as(f16);
+    array out = count(arr);
+
+    array gold = constant(7, 1, u32);
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+// TODO(umar): HalfAnyTrue
+TEST(ReduceHalf, AnyTrue) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    float harr[] = {1, 2, 3, 4, 5, 6, 7};
+    array arr(7, harr);
+    arr       = arr.as(f16);
+    array out = anyTrue(arr);
+
+    array gold = constant(1, 1, b8);
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
+// TODO(umar): HalfAllTrue
+TEST(ReduceHalf, AllTrue) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    float harr[] = {1, 2, 3, 4, 5, 6, 7};
+    array arr(7, harr);
+    arr       = arr.as(f16);
+    array out = allTrue(arr);
+
+    array gold = constant(1, 1, b8);
+    ASSERT_ARRAYS_EQ(gold, out);
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 19e2760017..6668be508a 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -12,6 +12,7 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <af/array.h>
 #include <af/dim4.hpp>
 #include <af/internal.h>
@@ -34,6 +35,16 @@
 #include <mmio.h>
 #endif
 
+bool operator==(const af_half &lhs, const af_half &rhs) {
+    return lhs.data_ == rhs.data_;
+}
+
+std::ostream &operator<<(std::ostream &os, const af_half &val) {
+    float out = *reinterpret_cast<const half_float::half *>(&val);
+    os << out;
+    return os;
+}
+
 #define UNUSED(expr) \
     do { (void)(expr); } while (0)
 
@@ -67,6 +78,7 @@ std::ostream &operator<<(std::ostream &os, af::dtype type) {
         case u64: name = "u64"; break;
         case s16: name = "s16"; break;
         case u16: name = "u16"; break;
+        case f16: name = "f16"; break;
         default: assert(false && "Invalid type");
     }
     return os << name;
@@ -92,6 +104,22 @@ std::string readNextNonEmptyLine(std::ifstream &file) {
     return result;
 }
 
+template<typename To, typename Ti>
+To convert(Ti in) {
+    return static_cast<To>(in);
+}
+
+template<>
+float convert(af::half in) {
+    return static_cast<float>(half_float::half(in.data_));
+}
+
+template<>
+af_half convert(int in) {
+    half_float::half h = half_float::half(in);
+    return *reinterpret_cast<af_half*>(&h);
+}
+
 template<typename inType, typename outType, typename FileElementType>
 void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
                std::vector<std::vector<inType> > &testInputs,
@@ -119,7 +147,7 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
             FileElementType tmp;
             for (unsigned i = 0; i < nElems; i++) {
                 testFile >> tmp;
-                testInputs[k][i] = static_cast<inType>(tmp);
+                testInputs[k][i] = convert<inType, FileElementType>(tmp);
             }
         }
 
@@ -129,7 +157,7 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
             FileElementType tmp;
             for (unsigned j = 0; j < testSizes[i]; j++) {
                 testFile >> tmp;
-                testOutputs[i][j] = static_cast<outType>(tmp);
+                testOutputs[i][j] = convert<outType, FileElementType>(tmp);
             }
         }
     } else {
@@ -439,8 +467,19 @@ bool noDoubleTests() {
     return ((isTypeDouble && !isDoubleSupported) ? true : false);
 }
 
+template<typename T>
+bool noHalfTests() {
+    af::dtype ty           = (af::dtype)af::dtype_traits<T>::af_type;
+    bool isTypeHalf        = (ty == f16);
+    int dev                = af::getDevice();
+    bool isHalfSupported   = af::isHalfAvailable(dev);
+
+    return ((isTypeHalf && !isHalfSupported) ? true : false);
+}
+
 #define SUPPORTED_TYPE_CHECK(type) \
-    if (noDoubleTests<type>()) return;
+  if (noDoubleTests<type>()) return; \
+  if (noHalfTests<type>()) return
 
 inline bool noImageIOTests() {
     bool ret = !af::isImageIOAvailable();
@@ -537,6 +576,10 @@ const af::cfloat &operator+(const af::cfloat &val) { return val; }
 
 const af::cdouble &operator+(const af::cdouble &val) { return val; }
 
+const af_half& operator+(const af_half& val) {
+    return val;
+}
+
 // Calculate a multi-dimensional coordinates' linearized index
 dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
     return std::inner_product(coords.get(), coords.get() + 4, strides.get(),
@@ -838,6 +881,9 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
         case u16:
             return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);
             break;
+        case f16:
+            return elemWiseEq<af::half>(aName, bName, a, b, maxAbsDiff);
+            break;
         default:
             return ::testing::AssertionFailure()
                    << "INVALID TYPE, see enum numbers: " << bName << "("

From 4621bd6a83f0e971f16e1c353ccd7e9c6cd2a757 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Mon, 1 Jul 2019 17:02:28 -0700
Subject: [PATCH 1694/2677] af_matmul for f16

---
 examples/benchmarks/blas.cpp | 12 ++++++++++--
 src/api/c/blas.cpp           | 16 +++++++++++-----
 test/blas.cpp                | 36 ++++++++++++++++++++++++++++++++++++
 3 files changed, 57 insertions(+), 7 deletions(-)

diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index afed05fc48..54a062436d 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -11,6 +11,7 @@
 #include <math.h>
 #include <stdio.h>
 #include <cstdlib>
+#include <string>
 
 using namespace af;
 
@@ -25,11 +26,18 @@ int main(int argc, char** argv) {
     try {
         int device = argc > 1 ? atoi(argv[1]) : 0;
         setDevice(device);
+
+        const std::string dtype(argc > 2 ? argv[2] : "f32");
+        const af_dtype dt = (dtype == "f16" ? f16 : f32);
+
+        if (dt == f16)
+          printf("Device %d isHalfAvailable ? %s\n", device, isHalfAvailable(device) ? "yes" : "no");
+
         info();
 
-        printf("Benchmark N-by-N matrix multiply\n");
+        printf("Benchmark N-by-N matrix multiply at %s \n", dtype.c_str());
         for (int n = 128; n <= 2048; n += 128) {
-            printf("%4d x %4d: ", n, n);
+            printf("%4d x %4d: ", n, n, dt);
             A             = constant(1, n, n);
             double time   = timeit(fn);  // time in seconds
             double gflops = 2.0 * powf(n, 3) / (time * 1e9);
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 34f100b6aa..0cfbc75919 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -194,11 +194,9 @@ af_err af_gemm(af_array *out,
                                     static_cast<const cdouble*>(alpha), lhs, rhs,
                                     static_cast<const cdouble*>(beta)); break;
 #ifndef AF_CPU
-            case f16:
-              gemm<half>(&output, optLhs, optRhs,
-                                 static_cast<const half *>(alpha), lhs, rhs,
-                                 static_cast<const half *>(beta)); break;
-          break;
+            case f16: gemm<half>(&output, optLhs, optRhs,
+                                    static_cast<const half *>(alpha), lhs, rhs,
+                                    static_cast<const half *>(beta)); break;
 #endif
             default: TYPE_ERROR(3, lhs_type);
         }
@@ -239,6 +237,14 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
 
         af_dtype lhs_type = lhsInfo.getType();
         switch (lhs_type) {
+#ifndef AF_CPU
+            case f16: {
+                    static const half alpha(1.0f);
+                    static const half beta(0.0f);
+                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
+                    break;
+            }
+#endif
             case f32: {
                     float alpha = 1.f;
                     float beta  = 0.f;
diff --git a/test/blas.cpp b/test/blas.cpp
index 35489f0762..35be8cd17c 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -13,6 +13,7 @@
 #include <testHelpers.hpp>
 #include <af/blas.h>
 #include <af/defines.h>
+#include <af/half.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 #include <string>
@@ -383,6 +384,41 @@ float h_gold_batch[18] = {30.f, 84.f, 138.f,
                           81.f, 36.f, 87.f,
                           69.f, 30.f, 69.f};
 
+
+TEST(MatrixMultiply, float) {
+    array A32 = array(3, 3, h_lhs);
+    array B32 = array(3, 3, h_rhs);
+    af_array C32 = 0;
+    const float alpha32 = 1.0f;
+    const float beta32 = 0.0f;
+    af_gemm(&C32, AF_MAT_NONE, AF_MAT_NONE, &alpha32, A32.get(), B32.get(), &beta32);
+    array expected32 = array(3, 3, h_gold);
+    ASSERT_ARRAYS_NEAR(expected32, af::array(C32), 0.0001);
+}
+
+#ifndef AF_CPU
+TEST(MatrixMultiply, half) {
+    SUPPORTED_TYPE_CHECK(af_half);
+
+    array A16 = array(3, 3, h_lhs).as(f16);
+    array B16 = array(3, 3, h_rhs).as(f16);
+    array expected16 = array(3, 3, h_gold).as(f16);
+
+    {
+        af_array C16 = 0;
+        const af_half alpha16 = {0x03c00}; // 1.0 : 0 01111 0000000000
+        const af_half beta16 = {0x00000}; //  0.0 : 0 00000 0000000000
+        af_gemm(&C16, AF_MAT_NONE, AF_MAT_NONE, &alpha16, A16.get(), B16.get(), &beta16);
+        af::array C(C16);
+        ASSERT_ARRAYS_NEAR(expected16, C, 0.00001);
+    }
+    {
+        array C16 = matmul(A16, B16);
+        ASSERT_ARRAYS_NEAR(expected16, C16, 0.000001);
+    }
+}
+#endif
+
 struct test_params {
     af_mat_prop opt_lhs;
     af_mat_prop opt_rhs;

From c8f0e93df8bed3183710ecf855fcfa96f7c9d103 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 5 Jul 2019 18:47:33 +0530
Subject: [PATCH 1695/2677] Refactor image proc fns to use nvrtc API (#2560)

* Refactor image proc fns to use nvrtc API

* Style changes in nvrtc updated kernel wrappers

* Remove obsolete file

* Address feedback
---
 src/backend/cuda/CMakeLists.txt               |  89 ++--
 src/backend/cuda/Param.hpp                    |   3 -
 ...diffusion.cu => anisotropic_diffusion.cpp} |   6 +-
 src/backend/cuda/{approx.cu => approx.cpp}    |  46 +--
 .../cuda/{bilateral.cu => bilateral.cpp}      |   3 +-
 src/backend/cuda/{canny.cu => canny.cpp}      |   4 -
 src/backend/cuda/{dilate.cu => dilate.cpp}    |   0
 .../cuda/{dilate3d.cu => dilate3d.cpp}        |   0
 src/backend/cuda/{erode.cu => erode.cpp}      |   0
 src/backend/cuda/{erode3d.cu => erode3d.cpp}  |   0
 ...exampleFunction.cu => exampleFunction.cpp} |  21 +-
 .../cuda/{histogram.cu => histogram.cpp}      |   5 +-
 src/backend/cuda/{hsv_rgb.cu => hsv_rgb.cpp}  |   8 +-
 .../cuda/kernel/anisotropic_diffusion.cuh     | 194 +++++++++
 .../cuda/kernel/anisotropic_diffusion.hpp     | 207 +---------
 src/backend/cuda/kernel/approx.hpp            | 153 ++-----
 src/backend/cuda/kernel/approx1.cuh           |  67 +++
 src/backend/cuda/kernel/approx2.cuh           |  74 ++++
 src/backend/cuda/kernel/bilateral.cuh         | 113 ++++++
 src/backend/cuda/kernel/bilateral.hpp         | 116 +-----
 src/backend/cuda/kernel/canny.cuh             | 323 +++++++++++++++
 src/backend/cuda/kernel/canny.hpp             | 370 ++---------------
 src/backend/cuda/kernel/convolve.hpp          | 100 ++---
 src/backend/cuda/kernel/exampleFunction.cuh   |  37 ++
 src/backend/cuda/kernel/exampleFunction.hpp   |  75 ++--
 src/backend/cuda/kernel/fast_pyramid.hpp      |   2 +-
 src/backend/cuda/kernel/histogram.cuh         |  68 ++++
 src/backend/cuda/kernel/histogram.hpp         |  79 +---
 src/backend/cuda/kernel/hsv_rgb.cuh           |  84 ++++
 src/backend/cuda/kernel/hsv_rgb.hpp           |  85 +---
 src/backend/cuda/kernel/interp.hpp            |  16 +-
 src/backend/cuda/kernel/match_template.cuh    | 121 ++++++
 src/backend/cuda/kernel/match_template.hpp    | 130 +-----
 src/backend/cuda/kernel/meanshift.cuh         | 129 ++++++
 src/backend/cuda/kernel/meanshift.hpp         | 150 +------
 src/backend/cuda/kernel/medfilt.cuh           | 288 +++++++++++++
 src/backend/cuda/kernel/medfilt.hpp           | 382 ++----------------
 src/backend/cuda/kernel/moments.cuh           |  59 +++
 src/backend/cuda/kernel/moments.hpp           |  63 +--
 src/backend/cuda/kernel/morph.cuh             | 231 +++++++++++
 src/backend/cuda/kernel/morph.hpp             | 343 +++-------------
 src/backend/cuda/kernel/pad_array_borders.cuh |  89 ++++
 src/backend/cuda/kernel/pad_array_borders.hpp | 110 +----
 src/backend/cuda/kernel/resize.cuh            | 122 ++++++
 src/backend/cuda/kernel/resize.hpp            | 157 +------
 src/backend/cuda/kernel/rotate.cuh            |  72 ++++
 src/backend/cuda/kernel/rotate.hpp            |  84 +---
 src/backend/cuda/kernel/scan_dim.hpp          |  52 +--
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |  59 +--
 src/backend/cuda/kernel/scan_first.hpp        |  44 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  59 +--
 src/backend/cuda/kernel/sift_nonfree.hpp      |   5 +-
 src/backend/cuda/kernel/sobel.cuh             |  86 ++++
 src/backend/cuda/kernel/sobel.hpp             | 102 +----
 src/backend/cuda/kernel/transform.cuh         | 174 ++++++++
 src/backend/cuda/kernel/transform.hpp         | 218 ++--------
 src/backend/cuda/kernel/transpose.hpp         |  15 +-
 src/backend/cuda/kernel/transpose_inplace.cuh | 120 ++++++
 src/backend/cuda/kernel/transpose_inplace.hpp | 137 +------
 src/backend/cuda/kernel/where.hpp             |   4 +-
 .../{match_template.cu => match_template.cpp} |   8 +-
 src/backend/cuda/math.hpp                     |  13 +-
 .../cuda/{meanshift.cu => meanshift.cpp}      |  13 +-
 src/backend/cuda/{medfilt.cu => medfilt.cpp}  |  13 +-
 src/backend/cuda/{moments.cu => moments.cpp}  |   2 +
 src/backend/cuda/morph3d_impl.hpp             |  19 +-
 src/backend/cuda/morph_impl.hpp               |  21 +-
 src/backend/cuda/nvrtc/cache.cpp              | 112 ++++-
 src/backend/cuda/nvrtc/cache.hpp              |  24 ++
 ...array_borders.cu => pad_array_borders.cpp} |   4 +-
 src/backend/cuda/{resize.cu => resize.cpp}    |  17 +-
 src/backend/cuda/{rotate.cu => rotate.cpp}    |  25 +-
 src/backend/cuda/{sobel.cu => sobel.cpp}      |   0
 .../cuda/{transform.cu => transform.cpp}      |  27 +-
 ...spose_inplace.cu => transpose_inplace.cpp} |   9 +-
 src/backend/cuda/utility.cpp                  |  33 ++
 src/backend/cuda/utility.hpp                  |   5 +-
 77 files changed, 3261 insertions(+), 3037 deletions(-)
 rename src/backend/cuda/{anisotropic_diffusion.cu => anisotropic_diffusion.cpp} (84%)
 rename src/backend/cuda/{approx.cu => approx.cpp} (55%)
 rename src/backend/cuda/{bilateral.cu => bilateral.cpp} (93%)
 rename src/backend/cuda/{canny.cu => canny.cpp} (99%)
 rename src/backend/cuda/{dilate.cu => dilate.cpp} (100%)
 rename src/backend/cuda/{dilate3d.cu => dilate3d.cpp} (100%)
 rename src/backend/cuda/{erode.cu => erode.cpp} (100%)
 rename src/backend/cuda/{erode3d.cu => erode3d.cpp} (100%)
 rename src/backend/cuda/{exampleFunction.cu => exampleFunction.cpp} (76%)
 rename src/backend/cuda/{histogram.cu => histogram.cpp} (92%)
 rename src/backend/cuda/{hsv_rgb.cu => hsv_rgb.cpp} (90%)
 create mode 100644 src/backend/cuda/kernel/anisotropic_diffusion.cuh
 create mode 100644 src/backend/cuda/kernel/approx1.cuh
 create mode 100644 src/backend/cuda/kernel/approx2.cuh
 create mode 100644 src/backend/cuda/kernel/bilateral.cuh
 create mode 100644 src/backend/cuda/kernel/canny.cuh
 create mode 100644 src/backend/cuda/kernel/exampleFunction.cuh
 create mode 100644 src/backend/cuda/kernel/histogram.cuh
 create mode 100644 src/backend/cuda/kernel/hsv_rgb.cuh
 create mode 100644 src/backend/cuda/kernel/match_template.cuh
 create mode 100644 src/backend/cuda/kernel/meanshift.cuh
 create mode 100644 src/backend/cuda/kernel/medfilt.cuh
 create mode 100644 src/backend/cuda/kernel/moments.cuh
 create mode 100644 src/backend/cuda/kernel/morph.cuh
 create mode 100644 src/backend/cuda/kernel/pad_array_borders.cuh
 create mode 100644 src/backend/cuda/kernel/resize.cuh
 create mode 100644 src/backend/cuda/kernel/rotate.cuh
 create mode 100644 src/backend/cuda/kernel/sobel.cuh
 create mode 100644 src/backend/cuda/kernel/transform.cuh
 create mode 100644 src/backend/cuda/kernel/transpose_inplace.cuh
 rename src/backend/cuda/{match_template.cu => match_template.cpp} (92%)
 rename src/backend/cuda/{meanshift.cu => meanshift.cpp} (78%)
 rename src/backend/cuda/{medfilt.cu => medfilt.cpp} (90%)
 rename src/backend/cuda/{moments.cu => moments.cpp} (98%)
 rename src/backend/cuda/{pad_array_borders.cu => pad_array_borders.cpp} (98%)
 rename src/backend/cuda/{resize.cu => resize.cpp} (76%)
 rename src/backend/cuda/{rotate.cu => rotate.cpp} (66%)
 rename src/backend/cuda/{sobel.cu => sobel.cpp} (100%)
 rename src/backend/cuda/{transform.cu => transform.cpp} (64%)
 rename src/backend/cuda/{transpose_inplace.cu => transpose_inplace.cpp} (81%)
 create mode 100644 src/backend/cuda/utility.cpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4372f5ece3..ad79c9e616 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -64,33 +64,55 @@ file_to_string(
     )
 
 set(nvrtc_src
-  ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
   ${CUDA_INCLUDE_DIRS}/cuda_fp16.h
   ${CUDA_INCLUDE_DIRS}/cuda_fp16.hpp
+  ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
+  ${CUDA_TOOLKIT_ROOT_DIR}/include/math_constants.h
+
+  ${PROJECT_SOURCE_DIR}/src/api/c/ops.hpp
+  ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
+  ${PROJECT_SOURCE_DIR}/include/af/defines.h
+  ${PROJECT_SOURCE_DIR}/include/af/traits.hpp
+  ${PROJECT_BINARY_DIR}/include/af/version.h
 
+  ${CMAKE_CURRENT_SOURCE_DIR}/Param.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/backend.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/interp.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/math.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/types.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/half.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/kernel_type.hpp
+
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/anisotropic_diffusion.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/approx1.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/approx2.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/bilateral.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/canny.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve1.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve2.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve3.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve_separable.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/exampleFunction.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/histogram.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/hsv_rgb.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/match_template.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/meanshift.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/medfilt.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/moments.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/morph.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/pad_array_borders.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/resize.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/rotate.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key.cuh
-  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sobel.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transform.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose_inplace.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/where.cuh
-
-  ${PROJECT_SOURCE_DIR}/src/api/c/ops.hpp
-  ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
-  ${PROJECT_SOURCE_DIR}/include/af/defines.h
-  ${PROJECT_SOURCE_DIR}/include/af/traits.hpp
-
-  ${CMAKE_CURRENT_SOURCE_DIR}/Param.hpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/math.hpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/types.hpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/backend.hpp
   )
 
 file_to_string(
@@ -165,32 +187,32 @@ cuda_add_library(afcuda
     sort.hpp
 
     all.cu
-    anisotropic_diffusion.cu
+    anisotropic_diffusion.cpp
     any.cu
-    approx.cu
+    approx.cpp
     assign.cu
-    bilateral.cu
-    canny.cu
+    bilateral.cpp
+    canny.cpp
     cholesky.cu
     copy.cu
     count.cu
     diagonal.cu
     diff.cu
-    dilate.cu
-    dilate3d.cu
-    erode.cu
-    erode3d.cu
+    dilate.cpp
+    dilate3d.cpp
+    erode.cpp
+    erode3d.cpp
     Event.cpp
     Event.hpp
-    exampleFunction.cu
+    exampleFunction.cpp
     fast.cu
     fast_pyramid.cu
     fftconvolve.cu
     gradient.cu
     harris.cu
-    histogram.cu
+    histogram.cpp
     homography.cu
-    hsv_rgb.cu
+    hsv_rgb.cpp
     identity.cu
     iir.cu
     index.cu
@@ -200,28 +222,28 @@ cuda_add_library(afcuda
     join.cu
     lookup.cu
     lu.cu
-    match_template.cu
+    match_template.cpp
     max.cu
     mean.cu
-    meanshift.cu
-    medfilt.cu
+    meanshift.cpp
+    medfilt.cpp
     min.cu
-    moments.cu
+    moments.cpp
     nearest_neighbour.cu
     orb.cu
-    pad_array_borders.cu
+    pad_array_borders.cpp
     product.cu
     qr.cu
     random_engine.cu
     range.cu
     regions.cu
     reorder.cu
-    resize.cu
-    rotate.cu
+    resize.cpp
+    rotate.cpp
     select.cu
     set.cu
     sift.cu
-    sobel.cu
+    sobel.cpp
     solve.cu
     sort.cu
     sort_by_key.cu
@@ -233,9 +255,9 @@ cuda_add_library(afcuda
     svd.cu
     tile.cu
     topk.cu
-    transform.cu
+    transform.cpp
     transpose.cpp
-    transpose_inplace.cu
+    transpose_inplace.cpp
     triangle.cu
     unwrap.cu
     wrap.cu
@@ -440,6 +462,7 @@ cuda_add_library(afcuda
     types.hpp
     unary.hpp
     unwrap.hpp
+    utility.cpp
     utility.hpp
     vector_field.cpp
     vector_field.hpp
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 9ac4c71d3c..07f5376164 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -11,10 +11,7 @@
 
 #include <backend.hpp>
 #include <types.hpp>
-
-#ifndef __CUDACC_RTC__
 #include <af/defines.h>
-#endif
 
 namespace cuda {
 
diff --git a/src/backend/cuda/anisotropic_diffusion.cu b/src/backend/cuda/anisotropic_diffusion.cpp
similarity index 84%
rename from src/backend/cuda/anisotropic_diffusion.cu
rename to src/backend/cuda/anisotropic_diffusion.cpp
index 100485fcbf..3d6294ed46 100644
--- a/src/backend/cuda/anisotropic_diffusion.cu
+++ b/src/backend/cuda/anisotropic_diffusion.cpp
@@ -17,10 +17,8 @@ template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq) {
-    if (eq == AF_DIFFUSION_MCDE)
-        kernel::anisotropicDiffusion<T, true>(inout, dt, mct, fftype);
-    else
-        kernel::anisotropicDiffusion<T, false>(inout, dt, mct, fftype);
+    kernel::anisotropicDiffusion<T>(inout, dt, mct, fftype,
+                                    eq == AF_DIFFUSION_MCDE);
 }
 
 #define INSTANTIATE(T)                                     \
diff --git a/src/backend/cuda/approx.cu b/src/backend/cuda/approx.cpp
similarity index 55%
rename from src/backend/cuda/approx.cu
rename to src/backend/cuda/approx.cpp
index faf8cde44d..1fd861828a 100644
--- a/src/backend/cuda/approx.cu
+++ b/src/backend/cuda/approx.cpp
@@ -11,31 +11,15 @@
 #include <approx.hpp>
 #include <err_cuda.hpp>
 #include <kernel/approx.hpp>
-#include <stdexcept>
+#include <utility.hpp>
 
 namespace cuda {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const int xdim, const Tp &xi_beg, const Tp &xi_step,
              const af_interp_type method, const float offGrid) {
-    switch (method) {
-        case AF_INTERP_NEAREST:
-        case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp, 1>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       offGrid, method);
-            break;
-        case AF_INTERP_LINEAR:
-        case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp, 2>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       offGrid, method);
-            break;
-        case AF_INTERP_CUBIC:
-        case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp, 3>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       offGrid, method);
-            break;
-        default: break;
-    }
+    kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid, method,
+                            interpOrder(method));
 }
 
 template<typename Ty, typename Tp>
@@ -50,28 +34,8 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
     // Create output placeholder
     Array<Ty> zo = createEmptyArray<Ty>(odims);
 
-    switch (method) {
-        case AF_INTERP_NEAREST:
-        case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp, 1>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                       ydim, yi_beg, yi_step, offGrid, method);
-            break;
-        case AF_INTERP_LINEAR:
-        case AF_INTERP_BILINEAR:
-        case AF_INTERP_LINEAR_COSINE:
-        case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp, 2>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                       ydim, yi_beg, yi_step, offGrid, method);
-            break;
-        case AF_INTERP_CUBIC:
-        case AF_INTERP_BICUBIC:
-        case AF_INTERP_CUBIC_SPLINE:
-        case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp, 3>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                       ydim, yi_beg, yi_step, offGrid, method);
-            break;
-        default: break;
-    }
+    kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg,
+                            yi_step, offGrid, method, interpOrder(method));
     return zo;
 }
 
diff --git a/src/backend/cuda/bilateral.cu b/src/backend/cuda/bilateral.cpp
similarity index 93%
rename from src/backend/cuda/bilateral.cu
rename to src/backend/cuda/bilateral.cpp
index ade1977757..090ca8b65c 100644
--- a/src/backend/cuda/bilateral.cu
+++ b/src/backend/cuda/bilateral.cpp
@@ -19,8 +19,9 @@ namespace cuda {
 template<typename inType, typename outType, bool isColor>
 Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
                          const float &c_sigma) {
+    UNUSED(isColor);
     Array<outType> out = createEmptyArray<outType>(in.dims());
-    kernel::bilateral<inType, outType, isColor>(out, in, s_sigma, c_sigma);
+    kernel::bilateral<inType, outType>(out, in, s_sigma, c_sigma);
     return out;
 }
 
diff --git a/src/backend/cuda/canny.cu b/src/backend/cuda/canny.cpp
similarity index 99%
rename from src/backend/cuda/canny.cu
rename to src/backend/cuda/canny.cpp
index a3aa187cc6..a967aaf3ee 100644
--- a/src/backend/cuda/canny.cu
+++ b/src/backend/cuda/canny.cpp
@@ -19,18 +19,14 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
                                    const Array<float>& gy) {
     Array<float> out = createValueArray<float>(mag.dims(), 0);
-
     kernel::nonMaxSuppression<float>(out, mag, gx, gy);
-
     return out;
 }
 
 Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
                                      const Array<char>& weak) {
     Array<char> out = createValueArray<char>(strong.dims(), 0);
-
     kernel::edgeTrackingHysteresis<char>(out, strong, weak);
-
     return out;
 }
 }  // namespace cuda
diff --git a/src/backend/cuda/dilate.cu b/src/backend/cuda/dilate.cpp
similarity index 100%
rename from src/backend/cuda/dilate.cu
rename to src/backend/cuda/dilate.cpp
diff --git a/src/backend/cuda/dilate3d.cu b/src/backend/cuda/dilate3d.cpp
similarity index 100%
rename from src/backend/cuda/dilate3d.cu
rename to src/backend/cuda/dilate3d.cpp
diff --git a/src/backend/cuda/erode.cu b/src/backend/cuda/erode.cpp
similarity index 100%
rename from src/backend/cuda/erode.cu
rename to src/backend/cuda/erode.cpp
diff --git a/src/backend/cuda/erode3d.cu b/src/backend/cuda/erode3d.cpp
similarity index 100%
rename from src/backend/cuda/erode3d.cu
rename to src/backend/cuda/erode3d.cpp
diff --git a/src/backend/cuda/exampleFunction.cu b/src/backend/cuda/exampleFunction.cpp
similarity index 76%
rename from src/backend/cuda/exampleFunction.cu
rename to src/backend/cuda/exampleFunction.cpp
index 15bf8cdc6f..f4b7a7fc8f 100644
--- a/src/backend/cuda/exampleFunction.cu
+++ b/src/backend/cuda/exampleFunction.cpp
@@ -7,19 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>  // header with cuda backend specific
-                      // Array class implementation that inherits
-                      // ArrayInfo base class
+// header with cuda backend specific
+// Array class implementation that inherits
+// ArrayInfo base class
+#include <Array.hpp>
 
 #include <exampleFunction.hpp>  // cuda backend function header
 
-#include <err_cuda.hpp>  // error check functions and Macros
-                         // specific to cuda backend
+// error check functions and Macros
+// specific to cuda backend
+#include <err_cuda.hpp>
 
-#include <kernel/exampleFunction.hpp>  // this header under the folder src/cuda/kernel
-          // defines the CUDA kernel and its wrapper
-          // function to which the main computation of your
-          // algorithm should be relayed to
+// this header is under the folder src/cuda/kernel
+// defines the CUDA kernel and its wrapper
+// function to which the main computation of your
+// algorithm should be relayed to
+#include <kernel/exampleFunction.hpp>
 
 using af::dim4;
 
diff --git a/src/backend/cuda/histogram.cu b/src/backend/cuda/histogram.cpp
similarity index 92%
rename from src/backend/cuda/histogram.cu
rename to src/backend/cuda/histogram.cpp
index ecf5211289..8e2b879d7a 100644
--- a/src/backend/cuda/histogram.cu
+++ b/src/backend/cuda/histogram.cpp
@@ -26,9 +26,8 @@ Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
     dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
-    kernel::histogram<inType, outType, isLinear>(out, in, nbins, minval,
-                                                 maxval);
-
+    kernel::histogram<inType, outType>(out, in, nbins, minval, maxval,
+                                       isLinear);
     return out;
 }
 
diff --git a/src/backend/cuda/hsv_rgb.cu b/src/backend/cuda/hsv_rgb.cpp
similarity index 90%
rename from src/backend/cuda/hsv_rgb.cu
rename to src/backend/cuda/hsv_rgb.cpp
index c985853e73..13d1a95187 100644
--- a/src/backend/cuda/hsv_rgb.cu
+++ b/src/backend/cuda/hsv_rgb.cpp
@@ -20,18 +20,14 @@ namespace cuda {
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    kernel::hsv2rgb_convert<T, true>(out, in);
-
+    kernel::hsv2rgb_convert<T>(out, in, true);
     return out;
 }
 
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    kernel::hsv2rgb_convert<T, false>(out, in);
-
+    kernel::hsv2rgb_convert<T>(out, in, false);
     return out;
 }
 
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.cuh b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
new file mode 100644
index 0000000000..29e635870d
--- /dev/null
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
@@ -0,0 +1,194 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+__forceinline__ __device__
+int index(const int x, const int y, const int dim0,
+          const int dim1, const int stride0, const int stride1) {
+    return clamp(x, 0, dim0 - 1) * stride0 + clamp(y, 0, dim1 - 1) * stride1;
+}
+
+__device__
+float quadratic(const float value) { return 1.0 / (1.0 + value); }
+
+__device__
+float computeGradientBasedUpdate(const float mct, const float C,
+                                 const float S, const float N,
+                                 const float W, const float E,
+                                 const float SE, const float SW,
+                                 const float NE, const float NW,
+                                 const af::fluxFunction fftype) {
+    float delta = 0;
+
+    float dx, dy, df, db, cx, cxd;
+
+    // centralized derivatives
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
+
+    // half-d's and conductance along first dimension
+    df = E - C;
+    db = C - W;
+
+    if (fftype == AF_FLUX_EXPONENTIAL) {
+        cx  = expf((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd = expf((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
+    } else {
+        cx =
+            quadratic((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd =
+            quadratic((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
+    }
+    delta += (cx * df - cxd * db);
+
+    // half-d's and conductance along second dimension
+    df = S - C;
+    db = C - N;
+
+    if (fftype == AF_FLUX_EXPONENTIAL) {
+        cx  = expf((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd = expf((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
+    } else {
+        cx =
+            quadratic((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd =
+            quadratic((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
+    }
+    delta += (cx * df - cxd * db);
+
+    return delta;
+}
+
+__device__
+float computeCurvatureBasedUpdate(const float mct, const float C,
+                                  const float S, const float N,
+                                  const float W, const float E,
+                                  const float SE, const float SW,
+                                  const float NE, const float NW,
+                                  const af::fluxFunction fftype) {
+    float delta     = 0;
+    float prop_grad = 0;
+
+    float df0, db0;
+    float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
+
+    // centralized derivatives
+    dx = (E - W) * 0.5f;
+    dy = (S - N) * 0.5f;
+
+    // half-d's and conductance along first dimension
+    df  = E - C;
+    db  = C - W;
+    df0 = df;
+    db0 = db;
+
+    gmsqf = (df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2));
+    gmsqb = (db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2));
+
+    gmf = sqrtf(1.0e-10 + gmsqf);
+    gmb = sqrtf(1.0e-10 + gmsqb);
+
+    cx  = expf(gmsqf * mct);
+    cxd = expf(gmsqb * mct);
+
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
+
+    // half-d's and conductance along second dimension
+    df = S - C;
+    db = C - N;
+
+    gmsqf = (df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2));
+    gmsqb = (db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2));
+    gmf   = sqrtf(1.0e-10 + gmsqf);
+    gmb   = sqrtf(1.0e-10 + gmsqb);
+
+    cx  = expf(gmsqf * mct);
+    cxd = expf(gmsqb * mct);
+
+    delta += ((df / gmf) * cx - (db / gmb) * cxd);
+
+    if (delta > 0) {
+        prop_grad +=
+            (powf(fminf(db0, 0.0f), 2.0f) + powf(fmaxf(df0, 0.0f), 2.0f));
+        prop_grad +=
+            (powf(fminf(db, 0.0f), 2.0f) + powf(fmaxf(df, 0.0f), 2.0f));
+    } else {
+        prop_grad +=
+            (powf(fmaxf(db0, 0.0f), 2.0f) + powf(fminf(df0, 0.0f), 2.0f));
+        prop_grad +=
+            (powf(fmaxf(db, 0.0f), 2.0f) + powf(fminf(df, 0.0f), 2.0f));
+    }
+
+    return sqrtf(prop_grad) * delta;
+}
+
+template<typename T, bool isMCDE>
+__global__
+void diffUpdate(Param<T> inout, const float dt, const float mct,
+                const af::fluxFunction fftype, const unsigned blkX,
+                const unsigned blkY) {
+    const unsigned RADIUS         = 1;
+    const unsigned SHRD_MEM_WIDTH = THREADS_X + 2 * RADIUS;   // Coloumns
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2 * RADIUS;  // Rows
+
+    __shared__ float shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    const int b2 = blockIdx.x / blkX;
+    const int b3 = blockIdx.y / blkY;
+
+    const int gx = blockDim.x * (blockIdx.x - b2 * blkX) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
+
+    T* img = (T*)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
+
+#pragma unroll
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y) {
+#pragma unroll
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x) {
+            int idx       = index(gx2 - RADIUS, gy2 - RADIUS, inout.dims[0],
+                            inout.dims[1], inout.strides[0], inout.strides[1]);
+            shrdMem[b][a] = img[idx];
+        }
+    }
+
+    __syncthreads();
+
+    if (gx < inout.dims[0] && gy < inout.dims[1]) {
+        int i       = lx + RADIUS;
+        int j       = ly + RADIUS;
+        float C     = shrdMem[j][i];
+        float delta = 0;
+
+        if (isMCDE) {
+            delta = computeCurvatureBasedUpdate(
+                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
+        } else {
+            delta = computeGradientBasedUpdate(
+                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
+        }
+
+        img[gx * inout.strides[0] + gy * inout.strides[1]] =
+            (T)(C + delta * dt);
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index 31cadece1e..bcff2c4989 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -7,201 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/anisotropic_diffusion_cuh.hpp>
+#include <af/defines.h>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 static const int THREADS_X = 32;
 static const int THREADS_Y = 8;
 
-inline __device__ int clamp(const int value, const int low, const int high) {
-    return max(low, min(value, high));
-}
-
-__forceinline__ __device__ int index(const int x, const int y, const int dim0,
-                                     const int dim1, const int stride0,
-                                     const int stride1) {
-    return clamp(x, 0, dim0 - 1) * stride0 + clamp(y, 0, dim1 - 1) * stride1;
-}
-
-__device__ float quadratic(const float value) { return 1.0 / (1.0 + value); }
-
-__device__ float computeGradientBasedUpdate(const float mct, const float C,
-                                            const float S, const float N,
-                                            const float W, const float E,
-                                            const float SE, const float SW,
-                                            const float NE, const float NW,
-                                            const af_flux_function fftype) {
-    float delta = 0;
-
-    float dx, dy, df, db, cx, cxd;
-
-    // centralized derivatives
-    dx = (E - W) * 0.5f;
-    dy = (S - N) * 0.5f;
-
-    // half-d's and conductance along first dimension
-    df = E - C;
-    db = C - W;
-
-    if (fftype == AF_FLUX_EXPONENTIAL) {
-        cx  = expf((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
-        cxd = expf((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
-    } else {
-        cx =
-            quadratic((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
-        cxd =
-            quadratic((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
-    }
-    delta += (cx * df - cxd * db);
-
-    // half-d's and conductance along second dimension
-    df = S - C;
-    db = C - N;
-
-    if (fftype == AF_FLUX_EXPONENTIAL) {
-        cx  = expf((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
-        cxd = expf((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
-    } else {
-        cx =
-            quadratic((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
-        cxd =
-            quadratic((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
-    }
-    delta += (cx * df - cxd * db);
-
-    return delta;
-}
-
-__device__ float computeCurvatureBasedUpdate(const float mct, const float C,
-                                             const float S, const float N,
-                                             const float W, const float E,
-                                             const float SE, const float SW,
-                                             const float NE, const float NW,
-                                             const af_flux_function fftype) {
-    float delta     = 0;
-    float prop_grad = 0;
-
-    float df0, db0;
-    float dx, dy, df, db, cx, cxd, gmf, gmb, gmsqf, gmsqb;
-
-    // centralized derivatives
-    dx = (E - W) * 0.5f;
-    dy = (S - N) * 0.5f;
-
-    // half-d's and conductance along first dimension
-    df  = E - C;
-    db  = C - W;
-    df0 = df;
-    db0 = db;
-
-    gmsqf = (df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2));
-    gmsqb = (db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2));
-
-    gmf = sqrtf(1.0e-10 + gmsqf);
-    gmb = sqrtf(1.0e-10 + gmsqb);
-
-    cx  = expf(gmsqf * mct);
-    cxd = expf(gmsqb * mct);
-
-    delta += ((df / gmf) * cx - (db / gmb) * cxd);
-
-    // half-d's and conductance along second dimension
-    df = S - C;
-    db = C - N;
-
-    gmsqf = (df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2));
-    gmsqb = (db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2));
-    gmf   = sqrtf(1.0e-10 + gmsqf);
-    gmb   = sqrtf(1.0e-10 + gmsqb);
-
-    cx  = expf(gmsqf * mct);
-    cxd = expf(gmsqb * mct);
-
-    delta += ((df / gmf) * cx - (db / gmb) * cxd);
-
-    if (delta > 0) {
-        prop_grad +=
-            (powf(fminf(db0, 0.0f), 2.0f) + powf(fmaxf(df0, 0.0f), 2.0f));
-        prop_grad +=
-            (powf(fminf(db, 0.0f), 2.0f) + powf(fmaxf(df, 0.0f), 2.0f));
-    } else {
-        prop_grad +=
-            (powf(fmaxf(db0, 0.0f), 2.0f) + powf(fminf(df0, 0.0f), 2.0f));
-        prop_grad +=
-            (powf(fmaxf(db, 0.0f), 2.0f) + powf(fminf(df, 0.0f), 2.0f));
-    }
-
-    return sqrtf(prop_grad) * delta;
-}
-
-template<typename T, bool isMCDE>
-static __global__ void diffUpdate(Param<T> inout, const float dt,
-                                  const float mct,
-                                  const af_flux_function fftype,
-                                  const unsigned blkX, const unsigned blkY) {
-    const unsigned RADIUS         = 1;
-    const unsigned SHRD_MEM_WIDTH = THREADS_X + 2 * RADIUS;   // Coloumns
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2 * RADIUS;  // Rows
-
-    __shared__ float shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
-
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    const int b2 = blockIdx.x / blkX;
-    const int b3 = blockIdx.y / blkY;
-
-    const int gx = blockDim.x * (blockIdx.x - b2 * blkX) + lx;
-    const int gy = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
-
-    T* img = (T*)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
-
-#pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
-         b += blockDim.y, gy2 += blockDim.y) {
-#pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
-             a += blockDim.x, gx2 += blockDim.x) {
-            int idx       = index(gx2 - RADIUS, gy2 - RADIUS, inout.dims[0],
-                            inout.dims[1], inout.strides[0], inout.strides[1]);
-            shrdMem[b][a] = img[idx];
-        }
-    }
-
-    __syncthreads();
-
-    if (gx < inout.dims[0] && gy < inout.dims[1]) {
-        int i       = lx + RADIUS;
-        int j       = ly + RADIUS;
-        float C     = shrdMem[j][i];
-        float delta = 0;
-
-        if (isMCDE) {
-            delta = computeCurvatureBasedUpdate(
-                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
-                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
-                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
-        } else {
-            delta = computeGradientBasedUpdate(
-                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
-                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
-                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
-        }
-
-        img[gx * inout.strides[0] + gy * inout.strides[1]] =
-            (T)(C + delta * dt);
-    }
-}
-
-template<typename T, bool isMCDE>
+template<typename T>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
-                          const af_flux_function fftype) {
+                          const af::fluxFunction fftype, bool isMCDE) {
+    static const std::string source(anisotropic_diffusion_cuh,
+                                    anisotropic_diffusion_cuh_len);
+    auto diffUpdate = getKernel("cuda::diffUpdate", source,
+                             {TemplateTypename<T>(), TemplateArg(isMCDE)},
+                             {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+
     dim3 threads(THREADS_X, THREADS_Y, 1);
 
     int blkX = divup(inout.dims[0], threads.x);
@@ -218,10 +49,12 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
         blocks.z = blkZ;
     }
 
-    CUDA_LAUNCH((diffUpdate<T, isMCDE>), blocks, threads, inout, dt, mct,
-                fftype, blkX, blkY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    diffUpdate(qArgs, inout, dt, mct, fftype, blkX, blkY);
 
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index c3878a0a54..d7716e90a8 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -10,129 +10,31 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include "interp.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/approx1_cuh.hpp>
+#include <nvrtc_kernel_headers/approx2_cuh.hpp>
+#include <af/defines.h>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 // Kernel Launch Config Values
 static const int TX      = 16;
 static const int TY      = 16;
 static const int THREADS = 256;
 
-template<typename Ty, typename Tp, int order>
-__global__ void approx1_kernel(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
-                               const int xdim, const Tp xi_beg,
-                               const Tp xi_step, const float offGrid,
-                               const int blocksMatX, const bool batch,
-                               af_interp_type method) {
-    const int idy        = blockIdx.x / blocksMatX;
-    const int blockIdx_x = blockIdx.x - idy * blocksMatX;
-    const int idx        = blockIdx_x * blockDim.x + threadIdx.x;
-
-    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / yo.dims[2];
-    const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * yo.dims[2];
-
-    if (idx >= yo.dims[0] || idy >= yo.dims[1] || idz >= yo.dims[2] ||
-        idw >= yo.dims[3])
-        return;
-
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
-                        xo.dims[3] > 1};
-    bool is_yi_off[] = {true, true, true, true};
-    is_yi_off[xdim]  = false;
-
-    const int yo_idx =
-        idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx;
-    int xo_idx = idx * is_xo_off[0];
-    xo_idx += idw * xo.strides[3] * is_xo_off[3];
-    xo_idx += idz * xo.strides[2] * is_xo_off[2];
-    xo_idx += idy * xo.strides[1] * is_xo_off[1];
-
-    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
-    if (x < 0 || yi.dims[xdim] < x + 1) {
-        yo.ptr[yo_idx] = scalar<Ty>(offGrid);
-        return;
-    }
-
-    int yi_idx = idx * is_yi_off[0];
-    yi_idx += idw * yi.strides[3] * is_yi_off[3];
-    yi_idx += idz * yi.strides[2] * is_yi_off[2];
-    yi_idx += idy * yi.strides[1] * is_yi_off[1];
-
-    // FIXME: Only cubic interpolation is doing clamping
-    // We need to make it consistent across all methods
-    // Not changing the behavior because tests will fail
-    bool clamp = order == 3;
-
-    Interp1<Ty, Tp, order> interp;
-    interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp, xdim);
-}
-
-template<typename Ty, typename Tp, int order>
-__global__ void approx2_kernel(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
-                               const int xdim, const Tp xi_beg,
-                               const Tp xi_step, CParam<Tp> yo, const int ydim,
-                               const Tp yi_beg, const Tp yi_step,
-                               const float offGrid, const int blocksMatX,
-                               const int blocksMatY, const bool batch,
-                               af_interp_type method) {
-    const int idz        = blockIdx.x / blocksMatX;
-    const int blockIdx_x = blockIdx.x - idz * blocksMatX;
-    const int idx        = threadIdx.x + blockIdx_x * blockDim.x;
-
-    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksMatY;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksMatY;
-    const int idy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (idx >= zo.dims[0] || idy >= zo.dims[1] || idz >= zo.dims[2] ||
-        idw >= zo.dims[3])
-        return;
-
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
-                        xo.dims[3] > 1};
-    bool is_zi_off[] = {true, true, true, true};
-    is_zi_off[xdim]  = false;
-    is_zi_off[ydim]  = false;
-
-    const int zo_idx =
-        idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx;
-    int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
-    int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
-    xo_idx +=
-        idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2] * is_xo_off[2];
-    yo_idx +=
-        idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2] * is_xo_off[2];
-
-    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
-    const Tp y = (yo.ptr[yo_idx] - yi_beg) / yi_step;
-    if (x < 0 || y < 0 || zi.dims[xdim] < x + 1 || zi.dims[ydim] < y + 1) {
-        zo.ptr[zo_idx] = scalar<Ty>(offGrid);
-        return;
-    }
-
-    int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0];
-    zi_idx +=
-        idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
-
-    // FIXME: Only cubic interpolation is doing clamping
-    // We need to make it consistent across all methods
-    // Not changing the behavior because tests will fail
-    bool clamp = order == 3;
-
-    Interp2<Ty, Tp, order> interp;
-    interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp, xdim, ydim);
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp>
 void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, const float offGrid,
-             af_interp_type method) {
+             const af::interpType method, const int order) {
+    static const std::string source(approx1_cuh, approx1_cuh_len);
+
+    auto approx1 = getKernel(
+        "cuda::approx1", source,
+        {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
+
     dim3 threads(THREADS, 1, 1);
     int blocksPerMat = divup(yo.dims[0], threads.x);
     dim3 blocks(blocksPerMat * yo.dims[1], yo.dims[2] * yo.dims[3]);
@@ -144,16 +46,25 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((approx1_kernel<Ty, Tp, order>), blocks, threads, yo, yi, xo,
-                xdim, xi_beg, xi_step, offGrid, blocksPerMat, batch, method);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    approx1(qArgs, yo, yi, xo, xdim, xi_beg, xi_step, offGrid, blocksPerMat,
+            batch, method);
+
     POST_LAUNCH_CHECK();
 }
 
-template<typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp>
 void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, CParam<Tp> yo, const int ydim,
              const Tp &yi_beg, const Tp &yi_step, const float offGrid,
-             af_interp_type method) {
+             const af::interpType method, const int order) {
+    static const std::string source(approx2_cuh, approx2_cuh_len);
+
+    auto approx2 = getKernel(
+        "cuda::approx2", source,
+        {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
+
     dim3 threads(TX, TY, 1);
     int blocksPerMatX = divup(zo.dims[0], threads.x);
     int blocksPerMatY = divup(zo.dims[1], threads.y);
@@ -166,9 +77,11 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((approx2_kernel<Ty, Tp, order>), blocks, threads, zo, zi, xo,
-                xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step, offGrid,
-                blocksPerMatX, blocksPerMatY, batch, method);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    approx2(qArgs, zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+            offGrid, blocksPerMatX, blocksPerMatY, batch, method);
+
     POST_LAUNCH_CHECK();
 }
 }  // namespace kernel
diff --git a/src/backend/cuda/kernel/approx1.cuh b/src/backend/cuda/kernel/approx1.cuh
new file mode 100644
index 0000000000..e009a990cc
--- /dev/null
+++ b/src/backend/cuda/kernel/approx1.cuh
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <interp.hpp>
+
+namespace cuda {
+
+template<typename Ty, typename Tp, int order>
+__global__
+void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
+             const int xdim, const Tp xi_beg,
+             const Tp xi_step, const float offGrid,
+             const int blocksMatX, const bool batch,
+             af::interpType method) {
+    const int idy        = blockIdx.x / blocksMatX;
+    const int blockIdx_x = blockIdx.x - idy * blocksMatX;
+    const int idx        = blockIdx_x * blockDim.x + threadIdx.x;
+
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / yo.dims[2];
+    const int idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * yo.dims[2];
+
+    if (idx >= yo.dims[0] || idy >= yo.dims[1] || idz >= yo.dims[2] ||
+        idw >= yo.dims[3])
+        return;
+
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                        xo.dims[3] > 1};
+    bool is_yi_off[] = {true, true, true, true};
+    is_yi_off[xdim]  = false;
+
+    const int yo_idx =
+        idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx;
+    int xo_idx = idx * is_xo_off[0];
+    xo_idx += idw * xo.strides[3] * is_xo_off[3];
+    xo_idx += idz * xo.strides[2] * is_xo_off[2];
+    xo_idx += idy * xo.strides[1] * is_xo_off[1];
+
+    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+    if (x < 0 || yi.dims[xdim] < x + 1) {
+        yo.ptr[yo_idx] = scalar<Ty>(offGrid);
+        return;
+    }
+
+    int yi_idx = idx * is_yi_off[0];
+    yi_idx += idw * yi.strides[3] * is_yi_off[3];
+    yi_idx += idz * yi.strides[2] * is_yi_off[2];
+    yi_idx += idy * yi.strides[1] * is_yi_off[1];
+
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    bool clamp = order == 3;
+
+    Interp1<Ty, Tp, order> interp;
+    interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp, xdim);
+}
+
+}
diff --git a/src/backend/cuda/kernel/approx2.cuh b/src/backend/cuda/kernel/approx2.cuh
new file mode 100644
index 0000000000..aa182e9b60
--- /dev/null
+++ b/src/backend/cuda/kernel/approx2.cuh
@@ -0,0 +1,74 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <interp.hpp>
+
+namespace cuda {
+
+template<typename Ty, typename Tp, int order>
+__global__
+void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
+             const int xdim, const Tp xi_beg,
+             const Tp xi_step, CParam<Tp> yo, const int ydim,
+             const Tp yi_beg, const Tp yi_step,
+             const float offGrid, const int blocksMatX,
+             const int blocksMatY, const bool batch,
+             af::interpType method) {
+    const int idz        = blockIdx.x / blocksMatX;
+    const int blockIdx_x = blockIdx.x - idz * blocksMatX;
+    const int idx        = threadIdx.x + blockIdx_x * blockDim.x;
+
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksMatY;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksMatY;
+    const int idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (idx >= zo.dims[0] || idy >= zo.dims[1] || idz >= zo.dims[2] ||
+        idw >= zo.dims[3])
+        return;
+
+    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                        xo.dims[3] > 1};
+    bool is_zi_off[] = {true, true, true, true};
+    is_zi_off[xdim]  = false;
+    is_zi_off[ydim]  = false;
+
+    const int zo_idx =
+        idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx;
+    int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
+    int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
+    xo_idx +=
+        idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2] * is_xo_off[2];
+    yo_idx +=
+        idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2] * is_xo_off[2];
+
+    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+    const Tp y = (yo.ptr[yo_idx] - yi_beg) / yi_step;
+    if (x < 0 || y < 0 || zi.dims[xdim] < x + 1 || zi.dims[ydim] < y + 1) {
+        zo.ptr[zo_idx] = scalar<Ty>(offGrid);
+        return;
+    }
+
+    int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0];
+    zi_idx +=
+        idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
+
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    bool clamp = order == 3;
+
+    Interp2<Ty, Tp, order> interp;
+    interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp, xdim, ydim);
+}
+
+}
diff --git a/src/backend/cuda/kernel/bilateral.cuh b/src/backend/cuda/kernel/bilateral.cuh
new file mode 100644
index 0000000000..fb618005ac
--- /dev/null
+++ b/src/backend/cuda/kernel/bilateral.cuh
@@ -0,0 +1,113 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <shared.hpp>
+
+namespace cuda {
+
+inline __device__
+int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
+}
+
+template<typename inType, typename outType>
+inline __device__
+void load2ShrdMem(outType *shrd, const inType *const in,
+                  int lx, int ly, int shrdStride, int dim0,
+                  int dim1, int gx, int gy, int inStride1,
+                  int inStride0) {
+    shrd[ly * shrdStride + lx] = in[lIdx(
+        clamp(gx, 0, dim0 - 1), clamp(gy, 0, dim1 - 1), inStride1, inStride0)];
+}
+
+template<typename inType, typename outType>
+__global__
+void bilateral(Param<outType> out, CParam<inType> in,
+               float sigma_space, float sigma_color,
+               int gaussOff, int nBBS0, int nBBS1) {
+    SharedMemory<outType> shared;
+    outType *localMem = shared.getPointer();
+    outType *gauss2d  = localMem + gaussOff;
+
+    const int radius                    = max((int)(sigma_space * 1.5f), 1);
+    const int padding                   = 2 * radius;
+    const int window_size               = padding + 1;
+    const int shrdLen                   = THREADS_X + padding;
+    const float variance_range          = sigma_color * sigma_color;
+    const float variance_space          = sigma_space * sigma_space;
+    const float variance_space_neg2     = -2.0 * variance_space;
+    const float inv_variance_range_neg2 = -0.5 / variance_range;
+
+    // gfor batch offsets
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+    const inType *iptr =
+        (const inType *)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    outType *optr =
+        (outType *)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+
+    const int gx = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    // generate gauss2d spatial variance values for block
+    if (lx < window_size && ly < window_size) {
+        int x = lx - radius;
+        int y = ly - radius;
+        gauss2d[ly * window_size + lx] =
+            __expf(((x * x) + (y * y)) / variance_space_neg2);
+    }
+
+    // pull image to local memory
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += blockDim.y, gy2 += blockDim.y) {
+        // move row_set get_local_size(1) along coloumns
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            load2ShrdMem<inType, outType>(
+                localMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
+                gx2 - radius, gy2 - radius, in.strides[1], in.strides[0]);
+        }
+    }
+
+    __syncthreads();
+
+    if (gx < in.dims[0] && gy < in.dims[1]) {
+        lx += radius;
+        ly += radius;
+        const outType center_color = localMem[ly * shrdLen + lx];
+        outType res                = 0;
+        outType norm               = 0;
+        int joff                   = (ly - radius) * shrdLen + (lx - radius);
+        int goff                   = 0;
+
+#pragma unroll
+        for (int wj = 0; wj < window_size; ++wj) {
+#pragma unroll
+            for (int wi = 0; wi < window_size; ++wi) {
+                const outType tmp_color = localMem[joff + wi];
+                const outType c         = center_color - tmp_color;
+                const outType gauss_range =
+                    __expf(c * c * inv_variance_range_neg2);
+                const outType weight = gauss2d[goff + wi] * gauss_range;
+                norm += weight;
+                res += tmp_color * weight;
+            }
+            joff += shrdLen;
+            goff += window_size;
+        }
+        optr[gy * out.strides[1] + gx] = res / norm;
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 045897b89a..7271e56757 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -8,116 +8,29 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include "shared.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/bilateral_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-inline __device__ int lIdx(int x, int y, int stride1, int stride0) {
-    return (y * stride1 + x * stride0);
-}
-
-template<typename inType, typename outType>
-inline __device__ void load2ShrdMem(outType *shrd, const inType *const in,
-                                    int lx, int ly, int shrdStride, int dim0,
-                                    int dim1, int gx, int gy, int inStride1,
-                                    int inStride0) {
-    shrd[ly * shrdStride + lx] = in[lIdx(
-        clamp(gx, 0, dim0 - 1), clamp(gy, 0, dim1 - 1), inStride1, inStride0)];
-}
-
 template<typename inType, typename outType>
-static __global__ void bilateralKernel(Param<outType> out, CParam<inType> in,
-                                       float sigma_space, float sigma_color,
-                                       int gaussOff, int nBBS0, int nBBS1) {
-    SharedMemory<outType> shared;
-    outType *localMem = shared.getPointer();
-    outType *gauss2d  = localMem + gaussOff;
-
-    const int radius                    = max((int)(sigma_space * 1.5f), 1);
-    const int padding                   = 2 * radius;
-    const int window_size               = padding + 1;
-    const int shrdLen                   = THREADS_X + padding;
-    const float variance_range          = sigma_color * sigma_color;
-    const float variance_space          = sigma_space * sigma_space;
-    const float variance_space_neg2     = -2.0 * variance_space;
-    const float inv_variance_range_neg2 = -0.5 / variance_range;
-
-    // gfor batch offsets
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
-    const inType *iptr =
-        (const inType *)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
-    outType *optr =
-        (outType *)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-
-    const int gx = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
-    const int gy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
-
-    // generate gauss2d spatial variance values for block
-    if (lx < window_size && ly < window_size) {
-        int x = lx - radius;
-        int y = ly - radius;
-        gauss2d[ly * window_size + lx] =
-            __expf(((x * x) + (y * y)) / variance_space_neg2);
-    }
-
-    // pull image to local memory
-    for (int b = ly, gy2 = gy; b < shrdLen;
-         b += blockDim.y, gy2 += blockDim.y) {
-        // move row_set get_local_size(1) along coloumns
-        for (int a = lx, gx2 = gx; a < shrdLen;
-             a += blockDim.x, gx2 += blockDim.x) {
-            load2ShrdMem<inType, outType>(
-                localMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
-                gx2 - radius, gy2 - radius, in.strides[1], in.strides[0]);
-        }
-    }
-
-    __syncthreads();
-
-    if (gx < in.dims[0] && gy < in.dims[1]) {
-        lx += radius;
-        ly += radius;
-        const outType center_color = localMem[ly * shrdLen + lx];
-        outType res                = 0;
-        outType norm               = 0;
-        int joff                   = (ly - radius) * shrdLen + (lx - radius);
-        int goff                   = 0;
-
-#pragma unroll
-        for (int wj = 0; wj < window_size; ++wj) {
-#pragma unroll
-            for (int wi = 0; wi < window_size; ++wi) {
-                const outType tmp_color = localMem[joff + wi];
-                const outType c         = center_color - tmp_color;
-                const outType gauss_range =
-                    __expf(c * c * inv_variance_range_neg2);
-                const outType weight = gauss2d[goff + wi] * gauss_range;
-                norm += weight;
-                res += tmp_color * weight;
-            }
-            joff += shrdLen;
-            goff += window_size;
-        }
-        optr[gy * out.strides[1] + gx] = res / norm;
-    }
-}
-
-template<typename inType, typename outType, bool isColor>
 void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
+    static const std::string source(bilateral_cuh, bilateral_cuh_len);
+
+    auto bilateral =
+        getKernel("cuda::bilateral", source,
+                  {TemplateTypename<inType>(), TemplateTypename<outType>()},
+                  {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], THREADS_X);
@@ -142,13 +55,12 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    CUDA_LAUNCH_SMEM((bilateralKernel<inType, outType>), blocks, threads,
-                     total_shrd_size, out, in, s_sigma, c_sigma, num_shrd_elems,
-                     blk_x, blk_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(), total_shrd_size);
+
+    bilateral(qArgs, out, in, s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/canny.cuh b/src/backend/cuda/kernel/canny.cuh
new file mode 100644
index 0000000000..7ff2d5b172
--- /dev/null
+++ b/src/backend/cuda/kernel/canny.cuh
@@ -0,0 +1,323 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+// hasChanged is a variable in kernel space
+// used to track the convergence of
+// the breath first search algorithm
+__device__ int hasChanged = 0;
+
+namespace cuda {
+
+__forceinline__ __device__
+int lIdx(int x, int y, int stride0, int stride1) {
+    return (x * stride0 + y * stride1);
+}
+
+template<typename T>
+__global__
+void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
+                       CParam<T> dy, unsigned nBBS0, unsigned nBBS1) {
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Coloumns
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
+
+    // Declared shared memory with 1 pixel border
+    __shared__ T shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    // local thread indices
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    const T* mag = (const T*)in.ptr +
+                   (b2 * in.strides[2] + b3 * in.strides[3]) + in.strides[1] +
+                   1;
+    const T* dX = (const T*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]) +
+                  dx.strides[1] + 1;
+    const T* dY = (const T*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]) +
+                  dy.strides[1] + 1;
+    T* out = (float*)output.ptr +
+             (b2 * output.strides[2] + b3 * output.strides[3]) +
+             output.strides[1] + 1;
+
+    // pull image to shared memory
+#pragma unroll
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y)
+#pragma unroll
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x)
+            shrdMem[b][a] =
+                mag[lIdx(gx2 - 1, gy2 - 1, in.strides[0], in.strides[1])];
+
+    int i = lx + 1;
+    int j = ly + 1;
+
+    __syncthreads();
+
+    if (gx < in.dims[0] - 2 && gy < in.dims[1] - 2) {
+        int idx = lIdx(gx, gy, in.strides[0], in.strides[1]);
+
+        const float cmag = shrdMem[j][i];
+
+        if (cmag == 0.0f)
+            out[idx] = (T)0;
+        else {
+            const float dx = dX[idx];
+            const float dy = dY[idx];
+            const float se = shrdMem[j + 1][i + 1];
+            const float nw = shrdMem[j - 1][i - 1];
+            const float ea = shrdMem[j][i + 1];
+            const float we = shrdMem[j][i - 1];
+            const float ne = shrdMem[j - 1][i + 1];
+            const float sw = shrdMem[j + 1][i - 1];
+            const float no = shrdMem[j - 1][i];
+            const float so = shrdMem[j + 1][i];
+
+            float a1, a2, b1, b2, alpha;
+
+            if (dx >= 0) {
+                if (dy >= 0) {
+                    const bool isTrue = (dx - dy) >= 0;
+
+                    a1    = isTrue ? ea : so;
+                    a2    = isTrue ? we : no;
+                    b1    = se;
+                    b2    = nw;
+                    alpha = isTrue ? dy / dx : dx / dy;
+                } else {
+                    const bool isTrue = (dx + dy) >= 0;
+
+                    a1    = isTrue ? ea : no;
+                    a2    = isTrue ? we : so;
+                    b1    = ne;
+                    b2    = sw;
+                    alpha = isTrue ? -dy / dx : dx / -dy;
+                }
+            } else {
+                if (dy >= 0) {
+                    const bool isTrue = (dx + dy) >= 0;
+
+                    a1    = isTrue ? so : we;
+                    a2    = isTrue ? no : ea;
+                    b1    = sw;
+                    b2    = ne;
+                    alpha = isTrue ? -dx / dy : dy / -dx;
+                } else {
+                    const bool isTrue = (-dx + dy) >= 0;
+
+                    a1    = isTrue ? we : no;
+                    a2    = isTrue ? ea : so;
+                    b1    = nw;
+                    b2    = se;
+                    alpha = isTrue ? -dy / dx : dx / -dy;
+                }
+            }
+
+            float mag1 = (1 - alpha) * a1 + alpha * b1;
+            float mag2 = (1 - alpha) * a2 + alpha * b2;
+
+            if (cmag > mag1 && cmag > mag2) {
+                out[idx] = cmag;
+            } else {
+                out[idx] = (T)0;
+            }
+        }
+    }
+}
+
+template<typename T>
+__global__
+void initEdgeOut(Param<T> output, CParam<T> strong, CParam<T> weak,
+                 unsigned nBBS0, unsigned nBBS1) {
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    const T* wPtr = weak.ptr + (b2 * weak.strides[2] + b3 * weak.strides[3]) +
+                    weak.strides[1] + 1;
+    const T* sPtr = strong.ptr +
+                    (b2 * strong.strides[2] + b3 * strong.strides[3]) +
+                    strong.strides[1] + 1;
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
+              output.strides[1] + 1;
+
+    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2)) {
+        int idx   = lIdx(gx, gy, output.strides[0], output.strides[1]);
+        oPtr[idx] = (sPtr[idx] > 0 ? STRONG : (wPtr[idx] > 0 ? WEAK : NOEDGE));
+    }
+}
+
+#define VALID_BLOCK_IDX(j, i)                             \
+    ((j) > 0 && (j) < (SHRD_MEM_HEIGHT - 1) && (i) > 0 && \
+     (i) < (SHRD_MEM_WIDTH - 1))
+
+template<typename T>
+__global__
+void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Cols
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
+
+    // shared memory with 1 pixel border
+    // strong and weak images are binary(char) images thus,
+    // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
+    __shared__ int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+
+    // local thread indices
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
+              output.strides[1] + 1;
+
+    // pull image to shared memory
+#pragma unroll
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y) {
+#pragma unroll
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x) {
+            int x = gx2 - 1;
+            int y = gy2 - 1;
+            if (x >= 0 && x < output.dims[0] && y >= 0 && y < output.dims[1])
+                outMem[b][a] =
+                    oPtr[lIdx(x, y, output.strides[0], output.strides[1])];
+            else
+                outMem[b][a] = NOEDGE;
+        }
+    }
+
+    int i = lx + 1;
+    int j = ly + 1;
+
+    __syncthreads();
+
+    int continueIter = 1;
+
+    while (continueIter) {
+        int cu = outMem[j][i];
+        int nw = outMem[j - 1][i - 1];
+        int no = outMem[j - 1][i];
+        int ne = outMem[j - 1][i + 1];
+        int ea = outMem[j][i + 1];
+        int se = outMem[j + 1][i + 1];
+        int so = outMem[j + 1][i];
+        int sw = outMem[j + 1][i - 1];
+        int we = outMem[j][i - 1];
+
+        bool hasStrongNeighbour =
+            nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
+            se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
+
+        if (cu == WEAK && hasStrongNeighbour) outMem[j][i] = STRONG;
+
+        __syncthreads();
+
+        // Check if there are any STRONG pixels with weak neighbours.
+        // This search however ignores 1-pixel border encompassing the
+        // shared memory tile region.
+
+        cu = outMem[j][i];
+
+        bool _nw =
+            outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
+        bool _no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+        bool _ne =
+            outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
+        bool _ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+        bool _se =
+            outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
+        bool _so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+        bool _sw =
+            outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
+        bool _we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+
+        bool hasWeakNeighbour =
+            _nw || _no || _ne || _ea || _se || _so || _sw || _we;
+
+        continueIter = __syncthreads_or(cu == STRONG && hasWeakNeighbour);
+    };
+
+    // Check if any 1-pixel border ring
+    // has weak pixels with strong candidates
+    // within the main region, then increment hasChanged.
+    int cu = outMem[j][i];
+    int nw = outMem[j - 1][i - 1];
+    int no = outMem[j - 1][i];
+    int ne = outMem[j - 1][i + 1];
+    int ea = outMem[j][i + 1];
+    int se = outMem[j + 1][i + 1];
+    int so = outMem[j + 1][i];
+    int sw = outMem[j + 1][i - 1];
+    int we = outMem[j][i - 1];
+
+    bool hasWeakNeighbour = nw == WEAK || no == WEAK || ne == WEAK ||
+                            ea == WEAK || se == WEAK || so == WEAK ||
+                            sw == WEAK || we == WEAK;
+
+    if (__syncthreads_or(cu == STRONG && hasWeakNeighbour) && lx == 0 &&
+        ly == 0)
+        atomicAdd(&hasChanged, 1);
+
+    // Update output with shared memory result
+    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2))
+        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1])] = outMem[j][i];
+}
+
+template<typename T>
+__global__
+void suppressLeftOver(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
+    // batch offsets for 3rd and 4th dimension
+    const unsigned b2 = blockIdx.x / nBBS0;
+    const unsigned b3 = blockIdx.y / nBBS1;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
+
+    // Offset input and output pointers to second pixel of second coloumn/row
+    // to skip the border
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
+              output.strides[1] + 1;
+
+    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2)) {
+        int idx = lIdx(gx, gy, output.strides[0], output.strides[1]);
+        T val   = oPtr[idx];
+        if (val == WEAK) oPtr[idx] = NOEDGE;
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index ed683bca24..85affc325b 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -8,14 +8,16 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/canny_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 static const int STRONG = 1;
 static const int WEAK   = 2;
 static const int NOEDGE = 0;
@@ -23,135 +25,16 @@ static const int NOEDGE = 0;
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-__forceinline__ __device__ int lIdx(int x, int y, int stride0, int stride1) {
-    return (x * stride0 + y * stride1);
-}
-
-template<typename T>
-static __global__ void nonMaxSuppressionKernel(Param<float> output,
-                                               CParam<T> in, CParam<T> dx,
-                                               CParam<T> dy, unsigned nBBS0,
-                                               unsigned nBBS1) {
-    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Coloumns
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
-
-    // Declared shared memory with 1 pixel border
-    __shared__ T shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
-
-    // local thread indices
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    // batch offsets for 3rd and 4th dimension
-    const unsigned b2 = blockIdx.x / nBBS0;
-    const unsigned b3 = blockIdx.y / nBBS1;
-
-    // global indices
-    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
-
-    // Offset input and output pointers to second pixel of second coloumn/row
-    // to skip the border
-    const T* mag = (const T*)in.ptr +
-                   (b2 * in.strides[2] + b3 * in.strides[3]) + in.strides[1] +
-                   1;
-    const T* dX = (const T*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]) +
-                  dx.strides[1] + 1;
-    const T* dY = (const T*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]) +
-                  dy.strides[1] + 1;
-    T* out = (float*)output.ptr +
-             (b2 * output.strides[2] + b3 * output.strides[3]) +
-             output.strides[1] + 1;
-
-    // pull image to shared memory
-#pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
-         b += blockDim.y, gy2 += blockDim.y)
-#pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
-             a += blockDim.x, gx2 += blockDim.x)
-            shrdMem[b][a] =
-                mag[lIdx(gx2 - 1, gy2 - 1, in.strides[0], in.strides[1])];
-
-    int i = lx + 1;
-    int j = ly + 1;
-
-    __syncthreads();
-
-    if (gx < in.dims[0] - 2 && gy < in.dims[1] - 2) {
-        int idx = lIdx(gx, gy, in.strides[0], in.strides[1]);
-
-        const float cmag = shrdMem[j][i];
-
-        if (cmag == 0.0f)
-            out[idx] = (T)0;
-        else {
-            const float dx = dX[idx];
-            const float dy = dY[idx];
-            const float se = shrdMem[j + 1][i + 1];
-            const float nw = shrdMem[j - 1][i - 1];
-            const float ea = shrdMem[j][i + 1];
-            const float we = shrdMem[j][i - 1];
-            const float ne = shrdMem[j - 1][i + 1];
-            const float sw = shrdMem[j + 1][i - 1];
-            const float no = shrdMem[j - 1][i];
-            const float so = shrdMem[j + 1][i];
-
-            float a1, a2, b1, b2, alpha;
-
-            if (dx >= 0) {
-                if (dy >= 0) {
-                    const bool isTrue = (dx - dy) >= 0;
-
-                    a1    = isTrue ? ea : so;
-                    a2    = isTrue ? we : no;
-                    b1    = se;
-                    b2    = nw;
-                    alpha = isTrue ? dy / dx : dx / dy;
-                } else {
-                    const bool isTrue = (dx + dy) >= 0;
-
-                    a1    = isTrue ? ea : no;
-                    a2    = isTrue ? we : so;
-                    b1    = ne;
-                    b2    = sw;
-                    alpha = isTrue ? -dy / dx : dx / -dy;
-                }
-            } else {
-                if (dy >= 0) {
-                    const bool isTrue = (dx + dy) >= 0;
-
-                    a1    = isTrue ? so : we;
-                    a2    = isTrue ? no : ea;
-                    b1    = sw;
-                    b2    = ne;
-                    alpha = isTrue ? -dx / dy : dy / -dx;
-                } else {
-                    const bool isTrue = (-dx + dy) >= 0;
-
-                    a1    = isTrue ? we : no;
-                    a2    = isTrue ? ea : so;
-                    b1    = nw;
-                    b2    = se;
-                    alpha = isTrue ? -dy / dx : dx / -dy;
-                }
-            }
-
-            float mag1 = (1 - alpha) * a1 + alpha * b1;
-            float mag2 = (1 - alpha) * a2 + alpha * b2;
-
-            if (cmag > mag1 && cmag > mag2) {
-                out[idx] = cmag;
-            } else {
-                out[idx] = (T)0;
-            }
-        }
-    }
-}
-
 template<typename T>
 void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
+    static const std::string source(canny_cuh, canny_cuh_len);
+
+    auto nonMaxSuppress =
+        getKernel("cuda::nonMaxSuppression", source, {TemplateTypename<T>()},
+                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     // Launch only threads to process non-border pixels
@@ -161,196 +44,28 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
     // launch batch * blk_x blocks along x dimension
     dim3 blocks(blk_x * magnitude.dims[2], blk_y * magnitude.dims[3]);
 
-    CUDA_LAUNCH(nonMaxSuppressionKernel<T>, blocks, threads, output, magnitude,
-                dx, dy, blk_x, blk_y);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    nonMaxSuppress(qArgs, output, magnitude, dx, dy, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
 
-template<typename T>
-static __global__ void initEdgeOutKernel(Param<T> output, CParam<T> strong,
-                                         CParam<T> weak, unsigned nBBS0,
-                                         unsigned nBBS1) {
-    // batch offsets for 3rd and 4th dimension
-    const unsigned b2 = blockIdx.x / nBBS0;
-    const unsigned b3 = blockIdx.y / nBBS1;
-
-    // global indices
-    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
-
-    // Offset input and output pointers to second pixel of second coloumn/row
-    // to skip the border
-    const T* wPtr = weak.ptr + (b2 * weak.strides[2] + b3 * weak.strides[3]) +
-                    weak.strides[1] + 1;
-    const T* sPtr = strong.ptr +
-                    (b2 * strong.strides[2] + b3 * strong.strides[3]) +
-                    strong.strides[1] + 1;
-    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
-              output.strides[1] + 1;
-
-    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2)) {
-        int idx   = lIdx(gx, gy, output.strides[0], output.strides[1]);
-        oPtr[idx] = (sPtr[idx] > 0 ? STRONG : (wPtr[idx] > 0 ? WEAK : NOEDGE));
-    }
-}
-
-// hasChanged is a variable in kernel space
-// used to track the convergence of
-// the breath first search algorithm
-__device__ int hasChanged = 0;
-
-#define VALID_BLOCK_IDX(j, i)                             \
-    ((j) > 0 && (j) < (SHRD_MEM_HEIGHT - 1) && (i) > 0 && \
-     (i) < (SHRD_MEM_WIDTH - 1))
-
-template<typename T>
-static __global__ void edgeTrackKernel(Param<T> output, unsigned nBBS0,
-                                       unsigned nBBS1) {
-    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Cols
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
-
-    // shared memory with 1 pixel border
-    // strong and weak images are binary(char) images thus,
-    // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
-    __shared__ int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
-
-    // local thread indices
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    // batch offsets for 3rd and 4th dimension
-    const unsigned b2 = blockIdx.x / nBBS0;
-    const unsigned b3 = blockIdx.y / nBBS1;
-
-    // global indices
-    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
-
-    // Offset input and output pointers to second pixel of second coloumn/row
-    // to skip the border
-    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
-              output.strides[1] + 1;
-
-    // pull image to shared memory
-#pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
-         b += blockDim.y, gy2 += blockDim.y) {
-#pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
-             a += blockDim.x, gx2 += blockDim.x) {
-            int x = gx2 - 1;
-            int y = gy2 - 1;
-            if (x >= 0 && x < output.dims[0] && y >= 0 && y < output.dims[1])
-                outMem[b][a] =
-                    oPtr[lIdx(x, y, output.strides[0], output.strides[1])];
-            else
-                outMem[b][a] = NOEDGE;
-        }
-    }
-
-    int i = lx + 1;
-    int j = ly + 1;
-
-    __syncthreads();
-
-    int continueIter = 1;
-
-    while (continueIter) {
-        int cu = outMem[j][i];
-        int nw = outMem[j - 1][i - 1];
-        int no = outMem[j - 1][i];
-        int ne = outMem[j - 1][i + 1];
-        int ea = outMem[j][i + 1];
-        int se = outMem[j + 1][i + 1];
-        int so = outMem[j + 1][i];
-        int sw = outMem[j + 1][i - 1];
-        int we = outMem[j][i - 1];
-
-        bool hasStrongNeighbour =
-            nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
-            se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
-
-        if (cu == WEAK && hasStrongNeighbour) outMem[j][i] = STRONG;
-
-        __syncthreads();
-
-        // Check if there are any STRONG pixels with weak neighbours.
-        // This search however ignores 1-pixel border encompassing the
-        // shared memory tile region.
-
-        cu = outMem[j][i];
-
-        bool _nw =
-            outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
-        bool _no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
-        bool _ne =
-            outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
-        bool _ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
-        bool _se =
-            outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
-        bool _so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
-        bool _sw =
-            outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
-        bool _we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
-
-        bool hasWeakNeighbour =
-            _nw || _no || _ne || _ea || _se || _so || _sw || _we;
-
-        continueIter = __syncthreads_or(cu == STRONG && hasWeakNeighbour);
-    };
-
-    // Check if any 1-pixel border ring
-    // has weak pixels with strong candidates
-    // within the main region, then increment hasChanged.
-    int cu = outMem[j][i];
-    int nw = outMem[j - 1][i - 1];
-    int no = outMem[j - 1][i];
-    int ne = outMem[j - 1][i + 1];
-    int ea = outMem[j][i + 1];
-    int se = outMem[j + 1][i + 1];
-    int so = outMem[j + 1][i];
-    int sw = outMem[j + 1][i - 1];
-    int we = outMem[j][i - 1];
-
-    bool hasWeakNeighbour = nw == WEAK || no == WEAK || ne == WEAK ||
-                            ea == WEAK || se == WEAK || so == WEAK ||
-                            sw == WEAK || we == WEAK;
-
-    if (__syncthreads_or(cu == STRONG && hasWeakNeighbour) && lx == 0 &&
-        ly == 0)
-        atomicAdd(&hasChanged, 1);
-
-    // Update output with shared memory result
-    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2))
-        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1])] = outMem[j][i];
-}
-
-template<typename T>
-static __global__ void suppressLeftOverKernel(Param<T> output, unsigned nBBS0,
-                                              unsigned nBBS1) {
-    // batch offsets for 3rd and 4th dimension
-    const unsigned b2 = blockIdx.x / nBBS0;
-    const unsigned b3 = blockIdx.y / nBBS1;
-
-    // global indices
-    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
-
-    // Offset input and output pointers to second pixel of second coloumn/row
-    // to skip the border
-    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
-              output.strides[1] + 1;
-
-    if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2)) {
-        int idx = lIdx(gx, gy, output.strides[0], output.strides[1]);
-        T val   = oPtr[idx];
-        if (val == WEAK) oPtr[idx] = NOEDGE;
-    }
-}
-
 template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
+    static const std::string source(canny_cuh, canny_cuh_len);
+
+    auto initEdgeOut =
+        getKernel("cuda::initEdgeOut", source, {TemplateTypename<T>()},
+                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto edgeTrack =
+        getKernel("cuda::edgeTrack", source, {TemplateTypename<T>()},
+                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto suppressLeftOver =
+        getKernel("cuda::suppressLeftOver", source, {TemplateTypename<T>()},
+                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     // Launch only threads to process non-border pixels
@@ -360,34 +75,19 @@ void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     // launch batch * blk_x blocks along x dimension
     dim3 blocks(blk_x * weak.dims[2], blk_y * weak.dims[3]);
 
-    CUDA_LAUNCH(initEdgeOutKernel<T>, blocks, threads, output, strong, weak,
-                blk_x, blk_y);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    initEdgeOut(qArgs, output, strong, weak, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 
     int notFinished = 1;
-
     while (notFinished) {
         notFinished = 0;
-        CUDA_CHECK(cudaMemcpyToSymbolAsync(
-            hasChanged, &notFinished, sizeof(int), 0, cudaMemcpyHostToDevice,
-            cuda::getStream(cuda::getActiveDeviceId())));
-
-        CUDA_LAUNCH(edgeTrackKernel<T>, blocks, threads, output, blk_x, blk_y);
-
+        edgeTrack.setScalar("hasChanged", notFinished);
+        edgeTrack(qArgs, output, blk_x, blk_y);
         POST_LAUNCH_CHECK();
-
-        CUDA_CHECK(cudaMemcpyFromSymbolAsync(
-            &notFinished, hasChanged, sizeof(int), 0, cudaMemcpyDeviceToHost,
-            cuda::getStream(cuda::getActiveDeviceId())));
-
-        CUDA_CHECK(
-            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        edgeTrack.getScalar(notFinished, "hasChanged");
     }
-
-    CUDA_LAUNCH(suppressLeftOverKernel<T>, blocks, threads, output, blk_x,
-                blk_y);
-
+    suppressLeftOver(qArgs, output, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
 }  // namespace kernel
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index d44b42ded1..5589416f2a 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -106,19 +106,10 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     static const std::string src(convolve1_cuh, convolve1_cuh_len);
 
-    // clang-format off
-    auto conv = getKernel("cuda::convolve1", src,
-            {
-              TemplateTypename<T>(),
-              TemplateTypename<aT>(),
-              TemplateArg(expand)
-            },
-            {
-              DefineValue(MAX_CONV1_FILTER_LEN),
-              DefineValue(CONV_THREADS)
-            }
-            );
-    // clang-format on
+    auto convolve1 = getKernel(
+        "cuda::convolve1", src,
+        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
+        {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS)});
 
     prepareKernelArgs<T>(p, out.dims, filt.dims, 1);
 
@@ -135,7 +126,7 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                 const aT* fptr = filt.ptr + (f1Off + f2Off + f3Off);
 
                 // FIXME: case where filter array is strided
-                conv.setConstant(conv_c_name,
+                convolve1.setConstant(conv_c_name,
                                  reinterpret_cast<CUdeviceptr>(fptr),
                                  filterSize);
 
@@ -148,7 +139,7 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
 
                 EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream(),
                                   p.mSharedSize);
-                conv(qArgs, out, sig, filt.dims[0], p.mBlk_x, p.mBlk_y, p.o[0],
+                convolve1(qArgs, out, sig, filt.dims[0], p.mBlk_x, p.mBlk_y, p.o[0],
                      p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
                 POST_LAUNCH_CHECK();
             }
@@ -171,30 +162,19 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
 
     static const std::string src(convolve2_cuh, convolve2_cuh_len);
 
-    // clang-format off
-    auto conv = getKernel("cuda::convolve2", src,
-            {
-              TemplateTypename<T>(),
-              TemplateTypename<aT>(),
-              TemplateArg(expand),
-              TemplateArg(f0),
-              TemplateArg(f1)
-            },
-            {
-              DefineValue(MAX_CONV1_FILTER_LEN),
-              DefineValue(CONV_THREADS),
-              DefineValue(CONV2_THREADS_X),
-              DefineValue(CONV2_THREADS_Y)
-            }
-            );
-    // clang-format on
+    auto convolve2 =
+        getKernel("cuda::convolve2", src,
+                  {TemplateTypename<T>(), TemplateTypename<aT>(),
+                   TemplateArg(expand), TemplateArg(f0), TemplateArg(f1)},
+                  {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+                   DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
 
     // FIXME: case where filter array is strided
-    conv.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
+    convolve2.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
                      f0 * f1 * sizeof(aT));
 
     EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream());
-    conv(qArgs, out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
+    convolve2(qArgs, out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
     POST_LAUNCH_CHECK();
 }
 
@@ -227,22 +207,12 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     static const std::string src(convolve3_cuh, convolve3_cuh_len);
 
-    // clang-format off
-    auto conv = getKernel("cuda::convolve3", src,
-            {
-              TemplateTypename<T>(),
-              TemplateTypename<aT>(),
-              TemplateArg(expand)
-            },
-            {
-              DefineValue(MAX_CONV1_FILTER_LEN),
-              DefineValue(CONV_THREADS),
-              DefineValue(CONV3_CUBE_X),
-              DefineValue(CONV3_CUBE_Y),
-              DefineValue(CONV3_CUBE_Z)
-            }
-            );
-    // clang-format on
+    auto convolve3 = getKernel(
+        "cuda::convolve3", src,
+        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
+        {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+         DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
+         DefineValue(CONV3_CUBE_Z)});
 
     prepareKernelArgs<T>(p, out.dims, filt.dims, 3);
 
@@ -254,7 +224,7 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
         const aT* fptr = filt.ptr + f3Off;
 
         // FIXME: case where filter array is strided
-        conv.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
+        convolve3.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
                          filterSize);
 
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
@@ -262,7 +232,7 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
 
         EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream(),
                           p.mSharedSize);
-        conv(qArgs, out, sig, filt.dims[0], filt.dims[1], filt.dims[2],
+        convolve3(qArgs, out, sig, filt.dims[0], filt.dims[1], filt.dims[2],
              p.mBlk_x, p.o[2], p.s[2]);
         POST_LAUNCH_CHECK();
     }
@@ -342,22 +312,12 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
 
     static const std::string src(convolve_separable_cuh,
                                  convolve_separable_cuh_len);
-    // clang-format off
-    auto conv = getKernel("cuda::convolve2_separable", src,
-            {
-              TemplateTypename<T>(),
-              TemplateTypename<aT>(),
-              TemplateArg(conv_dim),
-              TemplateArg(expand),
-              TemplateArg(fLen)
-            },
-            {
-              DefineValue(MAX_SCONV_FILTER_LEN),
-              DefineValue(SCONV_THREADS_X),
-              DefineValue(SCONV_THREADS_Y)
-            }
-            );
-    // clang-format on
+    auto convolve2_separable = getKernel(
+        "cuda::convolve2_separable", src,
+        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(conv_dim),
+         TemplateArg(expand), TemplateArg(fLen)},
+        {DefineValue(MAX_SCONV_FILTER_LEN), DefineValue(SCONV_THREADS_X),
+         DefineValue(SCONV_THREADS_Y)});
 
     dim3 threads(SCONV_THREADS_X, SCONV_THREADS_Y);
 
@@ -367,11 +327,11 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
 
     // FIXME: case where filter array is strided
-    conv.setConstant(sconv_c_name, reinterpret_cast<CUdeviceptr>(filter.ptr),
+    convolve2_separable.setConstant(sconv_c_name, reinterpret_cast<CUdeviceptr>(filter.ptr),
                      fLen * sizeof(aT));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    conv(qArgs, out, signal, blk_x, blk_y);
+    convolve2_separable(qArgs, out, signal, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/exampleFunction.cuh b/src/backend/cuda/kernel/exampleFunction.cuh
new file mode 100644
index 0000000000..9670d89ef6
--- /dev/null
+++ b/src/backend/cuda/kernel/exampleFunction.cuh
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b,
+                            const af_someenum_t p) {
+    // get current thread global identifiers along required dimensions
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+
+    if (i < a.dims[0] && j < a.dims[1]) {
+        // if needed use strides array to compute linear index of arrays
+        int src1Idx = i + j * a.strides[1];
+        int src2Idx = i + j * b.strides[1];
+        int dstIdx  = i + j * c.strides[1];
+
+        T* dst        = c.ptr;
+        const T* src1 = a.ptr;
+        const T* src2 = b.ptr;
+
+        // kernel algorithm goes here
+        dst[dstIdx] = src1[src1Idx] + src2[src2Idx];
+    }
+}
+
+} //namespace cuda
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 5386dd8fb4..be14157987 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,23 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.hpp>  // CUDA specific math functions
-
-#include <Param.hpp>  // This header has the declaration of structures
-                      // that are passed onto kernel. Operator overloads
-                      // for creating Param objects from cuda::Array<T>
-                      // objects is automatic, no special work is needed.
-                      // Hence, the CUDA kernel wrapper function takes in
-                      // Param and CParam(constant version of Param) instead
-                      // of cuda::Array<T>
+#include <Param.hpp>
 
 #include <common/dispatch.hpp>  // common utility header for CUDA & OpenCL backends
                                 // has the divup macro
 
-#include <err_cuda.hpp>  // CUDA specific error check functions and macros
-
 #include <debug_cuda.hpp>  // For Debug only related CUDA validations
 
+#include <nvrtc/cache.hpp>  // nvrtc cache mechanims API
+
+#include <nvrtc_kernel_headers/exampleFunction_cuh.hpp>  //kernel generated by nvrtc
+
+#include <string>
+
 namespace cuda {
 
 namespace kernel {
@@ -31,54 +27,33 @@ namespace kernel {
 static const unsigned TX = 16;  // Kernel Launch Config Values
 static const unsigned TY = 16;  // Kernel Launch Config Values
 
-template<typename T>
-__global__ void exampleFuncKernel(Param<T> c, CParam<T> a, CParam<T> b,
-                                  const af_someenum_t p) {
-    // get current thread global identifiers along required dimensions
-    int i = blockDim.x * blockIdx.x + threadIdx.x;
-    int j = blockDim.y * blockIdx.y + threadIdx.y;
-
-    if (i < a.dims[0] && j < a.dims[1]) {
-        // if needed use strides array to compute linear index of arrays
-        int src1Idx = i + j * a.strides[1];
-        int src2Idx = i + j * b.strides[1];
-        int dstIdx  = i + j * c.strides[1];
-
-        T* dst        = c.ptr;
-        const T* src1 = a.ptr;
-        const T* src2 = b.ptr;
-
-        // kernel algorithm goes here
-        dst[dstIdx] = src1[src1Idx] + src2[src2Idx];
-    }
-}
-
 template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
+    static const std::string source(exampleFunction_cuh,
+                                    exampleFunction_cuh_len);
+    auto exampleFunc = getKernel("cuda::exampleFunc", source,
+                             {
+                                 TemplateTypename<T>(),
+                             });
+
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
     int blk_x = divup(c.dims[0], threads.x);
     int blk_y = divup(c.dims[1], threads.y);
-    dim3 blocks(blk_x, blk_y);  // set your opencl launch config for grid
-
-    // launch your kernel
-    // One must use CUDA_LAUNCH macro to launch their kernels to ensure
-    // that the kernel is launched on an appropriate stream
-    //
-    // Use CUDA_LAUNCH macro for launching kernels that don't use dynamic shared
-    // memory
-    //
-    // Use CUDA_LAUNCH_SMEM macro for launching kernsl that use dynamic shared
-    // memory
-    //
-    // CUDA_LAUNCH_SMEM takes in an additional parameter, size of shared memory,
-    // after threads paramters, which are then followed by kernel parameters
-    CUDA_LAUNCH((exampleFuncKernel<T>), blocks, threads, c, a, b, p);
+    dim3 blocks(blk_x, blk_y);  // set your cuda launch config for grid
+
+    // EnqueueArgs encapsulates CUDA kernel launch
+    // configuration paramters. There are various versions
+    // of EnqueueArgs constructors that you can use depending
+    // on your CUDA kernels needs such as shared memory etc.
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    // Call the kernel functor retrieved using getKernel
+    exampleFunc(qArgs, c, a, b, p);
 
     POST_LAUNCH_CHECK();  // Macro for post kernel launch checks
                           // these checks are carried  ONLY IN DEBUG mode
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/kernel/fast_pyramid.hpp
index 9ee4008e73..dbd33ec953 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/kernel/fast_pyramid.hpp
@@ -72,7 +72,7 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
                       round(indims[1] / lvl_scl[i]));
 
             img_pyr.push_back(createEmptyArray<T>(dims));
-            resize<T, AF_INTERP_BILINEAR>(img_pyr[i], img_pyr[i - 1]);
+            resize<T>(img_pyr[i], img_pyr[i - 1], AF_INTERP_BILINEAR);
         }
     }
 
diff --git a/src/backend/cuda/kernel/histogram.cuh b/src/backend/cuda/kernel/histogram.cuh
new file mode 100644
index 0000000000..34666eeb09
--- /dev/null
+++ b/src/backend/cuda/kernel/histogram.cuh
@@ -0,0 +1,68 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <shared.hpp>
+
+namespace cuda {
+
+template<typename inType, typename outType, bool isLinear>
+__global__
+void histogram(Param<outType> out, CParam<inType> in, int len, int nbins,
+               float minval, float maxval, int nBBS) {
+    SharedMemory<outType> shared;
+    outType *shrdMem = shared.getPointer();
+
+    // offset input and output to account for batch ops
+    unsigned b2 = blockIdx.x / nBBS;
+    const inType *iptr =
+        in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
+    outType *optr = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
+
+    int start = (blockIdx.x - b2 * nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
+    int end   = min((start + THRD_LOAD * blockDim.x), len);
+    float step = (maxval - minval) / (float)nbins;
+
+    // If nbins > max shared memory allocated, then just use atomicAdd on global
+    // memory
+    bool use_global = nbins > MAX_BINS;
+
+    // Skip initializing shared memory
+    if (!use_global) {
+        for (int i = threadIdx.x; i < nbins; i += blockDim.x) shrdMem[i] = 0;
+        __syncthreads();
+    }
+
+    for (int row = start; row < end; row += blockDim.x) {
+        int idx =
+            isLinear
+                ? row
+                : ((row % in.dims[0]) + (row / in.dims[0]) * in.strides[1]);
+        int bin = (int)((iptr[idx] - minval) / step);
+        bin     = (bin < 0) ? 0 : bin;
+        bin     = (bin >= nbins) ? (nbins - 1) : bin;
+
+        if (use_global) {
+            atomicAdd((optr + bin), 1);
+        } else {
+            atomicAdd((shrdMem + bin), 1);
+        }
+    }
+
+    // No need to write to global if use_global is true
+    if (!use_global) {
+        __syncthreads();
+        for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
+            atomicAdd((optr + i), shrdMem[i]);
+        }
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 40d91cfc21..580fa7c52a 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -7,76 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <backend.hpp>
+#include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include "shared.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/histogram_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 constexpr int MAX_BINS  = 4000;
 constexpr int THREADS_X = 256;
 constexpr int THRD_LOAD = 16;
 
-__forceinline__ __device__ int minimum(int a, int b) { return (a < b ? a : b); }
-
-template<typename inType, typename outType, bool isLinear>
-static __global__ void histogramKernel(Param<outType> out, CParam<inType> in,
-                                       int len, int nbins, float minval,
-                                       float maxval, int nBBS) {
-    SharedMemory<outType> shared;
-    outType *shrdMem = shared.getPointer();
-
-    // offset input and output to account for batch ops
-    unsigned b2 = blockIdx.x / nBBS;
-    const inType *iptr =
-        in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
-    outType *optr = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
-
-    int start = (blockIdx.x - b2 * nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
-    int end   = minimum((start + THRD_LOAD * blockDim.x), len);
-    float step = (maxval - minval) / (float)nbins;
-
-    // If nbins > max shared memory allocated, then just use atomicAdd on global
-    // memory
-    bool use_global = nbins > MAX_BINS;
-
-    // Skip initializing shared memory
-    if (!use_global) {
-        for (int i = threadIdx.x; i < nbins; i += blockDim.x) shrdMem[i] = 0;
-        __syncthreads();
-    }
-
-    for (int row = start; row < end; row += blockDim.x) {
-        int idx =
-            isLinear
-                ? row
-                : ((row % in.dims[0]) + (row / in.dims[0]) * in.strides[1]);
-        int bin = (int)((iptr[idx] - minval) / step);
-        bin     = (bin < 0) ? 0 : bin;
-        bin     = (bin >= nbins) ? (nbins - 1) : bin;
-
-        if (use_global) {
-            atomicAdd((optr + bin), 1);
-        } else {
-            atomicAdd((shrdMem + bin), 1);
-        }
-    }
+template<typename inType, typename outType>
+void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
+               float maxval, bool isLinear) {
+    static const std::string source(histogram_cuh, histogram_cuh_len);
 
-    // No need to write to global if use_global is true
-    if (!use_global) {
-        __syncthreads();
-        for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
-            atomicAdd((optr + i), shrdMem[i]);
-        }
-    }
-}
+    auto histogram =
+        getKernel("cuda::histogram", source,
+                  {TemplateTypename<inType>(), TemplateTypename<outType>(),
+                   TemplateArg(isLinear)},
+                  {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
-template<typename inType, typename outType, bool isLinear>
-void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
-               float maxval) {
     dim3 threads(kernel::THREADS_X, 1);
 
     int nElems = in.dims[0] * in.dims[1];
@@ -87,13 +43,10 @@ void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
     // If nbins > MAX_BINS, we are using global memory so smem_size can be 0;
     int smem_size = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 0;
 
-    CUDA_LAUNCH_SMEM((histogramKernel<inType, outType, isLinear>), blocks,
-                     threads, smem_size, out, in, nElems, nbins, minval, maxval,
-                     blk_x);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(), smem_size);
+    histogram(qArgs, out, in, nElems, nbins, minval, maxval, blk_x);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/hsv_rgb.cuh b/src/backend/cuda/kernel/hsv_rgb.cuh
new file mode 100644
index 0000000000..ca7322777c
--- /dev/null
+++ b/src/backend/cuda/kernel/hsv_rgb.cuh
@@ -0,0 +1,84 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename T, bool isHSV2RGB>
+__global__
+void hsvrgbConverter(Param<T> out, CParam<T> in, int nBBS) {
+    // batch offsets
+    unsigned batchId = blockIdx.x / nBBS;
+    const T* src     = (const T*)in.ptr + (batchId * in.strides[3]);
+    T* dst           = (T*)out.ptr + (batchId * out.strides[3]);
+    // global indices
+    int gx = blockDim.x * (blockIdx.x - batchId * nBBS) + threadIdx.x;
+    int gy = blockDim.y * (blockIdx.y + blockIdx.z * gridDim.y) + threadIdx.y;
+
+    if (gx < out.dims[0] && gy < out.dims[1] && batchId < out.dims[3]) {
+        int oIdx0 = gx + gy * out.strides[1];
+        int oIdx1 = oIdx0 + out.strides[2];
+        int oIdx2 = oIdx1 + out.strides[2];
+
+        int iIdx0 = gx * in.strides[0] + gy * in.strides[1];
+        int iIdx1 = iIdx0 + in.strides[2];
+        int iIdx2 = iIdx1 + in.strides[2];
+
+        if (isHSV2RGB) {
+            T H = src[iIdx0];
+            T S = src[iIdx1];
+            T V = src[iIdx2];
+
+            T R, G, B;
+            R = G = B = 0;
+
+            int i = (int)(H * 6);
+            T f   = H * 6 - i;
+            T p   = V * (1 - S);
+            T q   = V * (1 - f * S);
+            T t   = V * (1 - (1 - f) * S);
+
+            switch (i % 6) {
+                case 0: R = V, G = t, B = p; break;
+                case 1: R = q, G = V, B = p; break;
+                case 2: R = p, G = V, B = t; break;
+                case 3: R = p, G = q, B = V; break;
+                case 4: R = t, G = p, B = V; break;
+                case 5: R = V, G = p, B = q; break;
+            }
+
+            dst[oIdx0] = R;
+            dst[oIdx1] = G;
+            dst[oIdx2] = B;
+        } else {
+            T R     = src[iIdx0];
+            T G     = src[iIdx1];
+            T B     = src[iIdx2];
+            T Cmax  = fmax(fmax(R, G), B);
+            T Cmin  = fmin(fmin(R, G), B);
+            T delta = Cmax - Cmin;
+
+            T H = 0;
+
+            if (Cmax != Cmin) {
+                if (Cmax == R) H = (G - B) / delta + (G < B ? 6 : 0);
+                if (Cmax == G) H = (B - R) / delta + 2;
+                if (Cmax == B) H = (R - G) / delta + 4;
+                H = H / 6.0f;
+            }
+
+            dst[oIdx0] = H;
+            dst[oIdx1] = Cmax == 0.0f ? 0 : delta / Cmax;
+            dst[oIdx2] = Cmax;
+        }
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 5712f848e5..ff143676d3 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -8,88 +8,26 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/hsv_rgb_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-template<typename T, bool isHSV2RGB>
-__global__ void convert(Param<T> out, CParam<T> in, int nBBS) {
-    // batch offsets
-    unsigned batchId = blockIdx.x / nBBS;
-    const T* src     = (const T*)in.ptr + (batchId * in.strides[3]);
-    T* dst           = (T*)out.ptr + (batchId * out.strides[3]);
-    // global indices
-    int gx = blockDim.x * (blockIdx.x - batchId * nBBS) + threadIdx.x;
-    int gy = blockDim.y * (blockIdx.y + blockIdx.z * gridDim.y) + threadIdx.y;
-
-    if (gx < out.dims[0] && gy < out.dims[1] && batchId < out.dims[3]) {
-        int oIdx0 = gx + gy * out.strides[1];
-        int oIdx1 = oIdx0 + out.strides[2];
-        int oIdx2 = oIdx1 + out.strides[2];
-
-        int iIdx0 = gx * in.strides[0] + gy * in.strides[1];
-        int iIdx1 = iIdx0 + in.strides[2];
-        int iIdx2 = iIdx1 + in.strides[2];
-
-        if (isHSV2RGB) {
-            T H = src[iIdx0];
-            T S = src[iIdx1];
-            T V = src[iIdx2];
-
-            T R, G, B;
-            R = G = B = 0;
-
-            int i = (int)(H * 6);
-            T f   = H * 6 - i;
-            T p   = V * (1 - S);
-            T q   = V * (1 - f * S);
-            T t   = V * (1 - (1 - f) * S);
+template<typename T>
+void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
+    static const std::string source(hsv_rgb_cuh, hsv_rgb_cuh_len);
 
-            switch (i % 6) {
-                case 0: R = V, G = t, B = p; break;
-                case 1: R = q, G = V, B = p; break;
-                case 2: R = p, G = V, B = t; break;
-                case 3: R = p, G = q, B = V; break;
-                case 4: R = t, G = p, B = V; break;
-                case 5: R = V, G = p, B = q; break;
-            }
+    auto hsvrgbConverter = getKernel("cuda::hsvrgbConverter", source,
+                             {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
 
-            dst[oIdx0] = R;
-            dst[oIdx1] = G;
-            dst[oIdx2] = B;
-        } else {
-            T R     = src[iIdx0];
-            T G     = src[iIdx1];
-            T B     = src[iIdx2];
-            T Cmax  = fmax(fmax(R, G), B);
-            T Cmin  = fmin(fmin(R, G), B);
-            T delta = Cmax - Cmin;
-
-            T H = 0;
-
-            if (Cmax != Cmin) {
-                if (Cmax == R) H = (G - B) / delta + (G < B ? 6 : 0);
-                if (Cmax == G) H = (B - R) / delta + 2;
-                if (Cmax == B) H = (R - G) / delta + 4;
-                H = H / 6.0f;
-            }
-
-            dst[oIdx0] = H;
-            dst[oIdx1] = Cmax == 0.0f ? 0 : delta / Cmax;
-            dst[oIdx2] = Cmax;
-        }
-    }
-}
-
-template<typename T, bool isHSV2RGB>
-void hsv2rgb_convert(Param<T> out, CParam<T> in) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
@@ -104,11 +42,10 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((convert<T, isHSV2RGB>), blocks, threads, out, in, blk_x);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    hsvrgbConverter(qArgs, out, in, blk_x);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index a899f69156..ee2fa727aa 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -7,10 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math_constants.h>
+#include <math.hpp>
 
 namespace cuda {
-namespace kernel {
 
 template<typename T>
 struct itype_t {
@@ -92,7 +91,7 @@ struct Interp1 {};
 template<typename Ty, typename Tp>
 struct Interp1<Ty, Tp, 1> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
-                               Tp x, af_interp_type method, int batch,
+                               Tp x, af::interpType method, int batch,
                                bool clamp, int xdim = 0, int batch_dim = 1) {
         Ty zero = scalar<Ty>(0);
 
@@ -117,7 +116,7 @@ struct Interp1<Ty, Tp, 1> {
 template<typename Ty, typename Tp>
 struct Interp1<Ty, Tp, 2> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
-                               Tp x, af_interp_type method, int batch,
+                               Tp x, af::interpType method, int batch,
                                bool clamp, int xdim = 0, int batch_dim = 1) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -153,7 +152,7 @@ struct Interp1<Ty, Tp, 2> {
 template<typename Ty, typename Tp>
 struct Interp1<Ty, Tp, 3> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
-                               Tp x, af_interp_type method, int batch,
+                               Tp x, af::interpType method, int batch,
                                bool clamp, int xdim = 0, int batch_dim = 1) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
@@ -191,7 +190,7 @@ struct Interp2 {};
 template<typename Ty, typename Tp>
 struct Interp2<Ty, Tp, 1> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
-                               Tp x, Tp y, af_interp_type method, int batch,
+                               Tp x, Tp y, af::interpType method, int batch,
                                bool clamp, int xdim = 0, int ydim = 1,
                                int batch_dim = 2) {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
@@ -226,7 +225,7 @@ struct Interp2<Ty, Tp, 1> {
 template<typename Ty, typename Tp>
 struct Interp2<Ty, Tp, 2> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
-                               Tp x, Tp y, af_interp_type method, int batch,
+                               Tp x, Tp y, af::interpType method, int batch,
                                bool clamp, int xdim = 0, int ydim = 1,
                                int batch_dim = 2) {
         typedef typename itype_t<Tp>::wtype WT;
@@ -279,7 +278,7 @@ struct Interp2<Ty, Tp, 2> {
 template<typename Ty, typename Tp>
 struct Interp2<Ty, Tp, 3> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
-                               Tp x, Tp y, af_interp_type method, int batch,
+                               Tp x, Tp y, af::interpType method, int batch,
                                bool clamp, int xdim = 0, int ydim = 1,
                                int batch_dim = 2) {
         typedef typename itype_t<Tp>::wtype WT;
@@ -331,5 +330,4 @@ struct Interp2<Ty, Tp, 3> {
     }
 };
 
-}  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/match_template.cuh b/src/backend/cuda/kernel/match_template.cuh
new file mode 100644
index 0000000000..daffdb9ceb
--- /dev/null
+++ b/src/backend/cuda/kernel/match_template.cuh
@@ -0,0 +1,121 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename inType, typename outType, af::matchType mType, bool needMean>
+__global__
+void matchTemplate(Param<outType> out, CParam<inType> srch,
+                   CParam<inType> tmplt, int nBBS0, int nBBS1) {
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+
+    int gx = threadIdx.x + (blockIdx.x - b2 * nBBS0) * blockDim.x;
+    int gy = threadIdx.y + (blockIdx.y - b3 * nBBS1) * blockDim.y;
+
+    if (gx < srch.dims[0] && gy < srch.dims[1]) {
+        const int tDim0    = tmplt.dims[0];
+        const int tDim1    = tmplt.dims[1];
+        const int sDim0    = srch.dims[0];
+        const int sDim1    = srch.dims[1];
+        const inType* tptr = (const inType*)tmplt.ptr;
+        int winNumElems    = tDim0 * tDim1;
+
+        outType tImgMean = outType(0);
+        if (needMean) {
+            for (int tj = 0; tj < tDim1; tj++) {
+                int tjStride = tj * tmplt.strides[1];
+
+                for (int ti = 0; ti < tDim0; ti++) {
+                    tImgMean += (outType)tptr[tjStride + ti * tmplt.strides[0]];
+                }
+            }
+            tImgMean /= winNumElems;
+        }
+
+        const inType* sptr = (const inType*)srch.ptr +
+                             (b2 * srch.strides[2] + b3 * srch.strides[3]);
+        outType* optr =
+            (outType*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+
+        // mean for window
+        // this variable will be used based on mType value
+        outType wImgMean = outType(0);
+        if (needMean) {
+            for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
+                int jStride = j * srch.strides[1];
+
+                for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
+                    inType sVal = ((j < sDim1 && i < sDim0)
+                                       ? sptr[jStride + i * srch.strides[0]]
+                                       : inType(0));
+                    wImgMean += (outType)sVal;
+                }
+            }
+            wImgMean /= winNumElems;
+        }
+
+        // run the window match metric
+        outType disparity = outType(0);
+
+        for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
+            int jStride  = j * srch.strides[1];
+            int tjStride = tj * tmplt.strides[1];
+
+            for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
+                inType sVal = ((j < sDim1 && i < sDim0)
+                                   ? sptr[jStride + i * srch.strides[0]]
+                                   : inType(0));
+                inType tVal = tptr[tjStride + ti * tmplt.strides[0]];
+
+                outType temp;
+                switch (mType) {
+                    case AF_SAD:
+                        disparity += fabs((outType)sVal - (outType)tVal);
+                        break;
+                    case AF_ZSAD:
+                        disparity += fabs((outType)sVal - wImgMean -
+                                          (outType)tVal + tImgMean);
+                        break;
+                    case AF_LSAD:
+                        disparity +=
+                            fabs((outType)sVal - (wImgMean / tImgMean) * tVal);
+                        break;
+                    case AF_SSD:
+                        disparity += ((outType)sVal - (outType)tVal) *
+                                     ((outType)sVal - (outType)tVal);
+                        break;
+                    case AF_ZSSD:
+                        temp = ((outType)sVal - wImgMean - (outType)tVal +
+                                tImgMean);
+                        disparity += temp * temp;
+                        break;
+                    case AF_LSSD:
+                        temp = ((outType)sVal - (wImgMean / tImgMean) * tVal);
+                        disparity += temp * temp;
+                        break;
+                    case AF_NCC:
+                        // TODO: furture implementation
+                        break;
+                    case AF_ZNCC:
+                        // TODO: furture implementation
+                        break;
+                    case AF_SHD:
+                        // TODO: furture implementation
+                        break;
+                }
+            }
+        }
+        optr[gy * out.strides[1] + gx] = disparity;
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 454054c276..9fc9554866 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -8,127 +8,31 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/match_template_cuh.hpp>
+#include <af/defines.h>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-template<typename inType, typename outType, af_match_type mType, bool needMean>
-__global__ void matchTemplate(Param<outType> out, CParam<inType> srch,
-                              CParam<inType> tmplt, int nBBS0, int nBBS1) {
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
-
-    int gx = threadIdx.x + (blockIdx.x - b2 * nBBS0) * blockDim.x;
-    int gy = threadIdx.y + (blockIdx.y - b3 * nBBS1) * blockDim.y;
-
-    if (gx < srch.dims[0] && gy < srch.dims[1]) {
-        const int tDim0    = tmplt.dims[0];
-        const int tDim1    = tmplt.dims[1];
-        const int sDim0    = srch.dims[0];
-        const int sDim1    = srch.dims[1];
-        const inType* tptr = (const inType*)tmplt.ptr;
-        int winNumElems    = tDim0 * tDim1;
-
-        outType tImgMean = outType(0);
-        if (needMean) {
-            for (int tj = 0; tj < tDim1; tj++) {
-                int tjStride = tj * tmplt.strides[1];
-
-                for (int ti = 0; ti < tDim0; ti++) {
-                    tImgMean += (outType)tptr[tjStride + ti * tmplt.strides[0]];
-                }
-            }
-            tImgMean /= winNumElems;
-        }
-
-        const inType* sptr = (const inType*)srch.ptr +
-                             (b2 * srch.strides[2] + b3 * srch.strides[3]);
-        outType* optr =
-            (outType*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-
-        // mean for window
-        // this variable will be used based on mType value
-        outType wImgMean = outType(0);
-        if (needMean) {
-            for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
-                int jStride = j * srch.strides[1];
-
-                for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
-                    inType sVal = ((j < sDim1 && i < sDim0)
-                                       ? sptr[jStride + i * srch.strides[0]]
-                                       : inType(0));
-                    wImgMean += (outType)sVal;
-                }
-            }
-            wImgMean /= winNumElems;
-        }
-
-        // run the window match metric
-        outType disparity = outType(0);
-
-        for (int tj = 0, j = gy; tj < tDim1; tj++, j++) {
-            int jStride  = j * srch.strides[1];
-            int tjStride = tj * tmplt.strides[1];
-
-            for (int ti = 0, i = gx; ti < tDim0; ti++, i++) {
-                inType sVal = ((j < sDim1 && i < sDim0)
-                                   ? sptr[jStride + i * srch.strides[0]]
-                                   : inType(0));
-                inType tVal = tptr[tjStride + ti * tmplt.strides[0]];
+template<typename inType, typename outType>
+void matchTemplate(Param<outType> out, CParam<inType> srch,
+                   CParam<inType> tmplt, const af::matchType mType,
+                   bool needMean) {
+    static const std::string source(match_template_cuh, match_template_cuh_len);
 
-                outType temp;
-                switch (mType) {
-                    case AF_SAD:
-                        disparity += fabs((outType)sVal - (outType)tVal);
-                        break;
-                    case AF_ZSAD:
-                        disparity += fabs((outType)sVal - wImgMean -
-                                          (outType)tVal + tImgMean);
-                        break;
-                    case AF_LSAD:
-                        disparity +=
-                            fabs((outType)sVal - (wImgMean / tImgMean) * tVal);
-                        break;
-                    case AF_SSD:
-                        disparity += ((outType)sVal - (outType)tVal) *
-                                     ((outType)sVal - (outType)tVal);
-                        break;
-                    case AF_ZSSD:
-                        temp = ((outType)sVal - wImgMean - (outType)tVal +
-                                tImgMean);
-                        disparity += temp * temp;
-                        break;
-                    case AF_LSSD:
-                        temp = ((outType)sVal - (wImgMean / tImgMean) * tVal);
-                        disparity += temp * temp;
-                        break;
-                    case AF_NCC:
-                        // TODO: furture implementation
-                        break;
-                    case AF_ZNCC:
-                        // TODO: furture implementation
-                        break;
-                    case AF_SHD:
-                        // TODO: furture implementation
-                        break;
-                }
-            }
-        }
+    auto matchTemplate =
+        getKernel("cuda::matchTemplate", source,
+                  {TemplateTypename<inType>(), TemplateTypename<outType>(),
+                   TemplateArg(mType), TemplateArg(needMean)});
 
-        optr[gy * out.strides[1] + gx] = disparity;
-    }
-}
-
-template<typename inType, typename outType, af_match_type mType, bool needMean>
-void matchTemplate(Param<outType> out, CParam<inType> srch,
-                   CParam<inType> tmplt) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(srch.dims[0], threads.x);
@@ -136,12 +40,10 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
 
     dim3 blocks(blk_x * srch.dims[2], blk_y * srch.dims[3]);
 
-    CUDA_LAUNCH((matchTemplate<inType, outType, mType, needMean>), blocks,
-                threads, out, srch, tmplt, blk_x, blk_y);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    matchTemplate(qArgs, out, srch, tmplt, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/meanshift.cuh b/src/backend/cuda/kernel/meanshift.cuh
new file mode 100644
index 0000000000..4e599385e3
--- /dev/null
+++ b/src/backend/cuda/kernel/meanshift.cuh
@@ -0,0 +1,129 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename AccType, typename T, int channels>
+__global__
+void meanshift(Param<T> out, CParam<T> in, int radius, float cvar,
+               uint numIters, int nBBS0, int nBBS1) {
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+    const T* iptr =
+        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr      = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
+
+    if (gx >= in.dims[0] || gy >= in.dims[1]) return;
+
+    int meanPosI = gx;
+    int meanPosJ = gy;
+
+    T currentCenterColors[channels];
+    T tempColors[channels];
+
+    AccType currentMeanColors[channels];
+
+#pragma unroll
+    for (int ch = 0; ch < channels; ++ch)
+        currentCenterColors[ch] = iptr[(
+            gx * in.strides[0] + gy * in.strides[1] + ch * in.strides[2])];
+
+    const int dim0LenLmt = in.dims[0] - 1;
+    const int dim1LenLmt = in.dims[1] - 1;
+
+    // scope of meanshift iterations begin
+    for (uint it = 0; it < numIters; ++it) {
+        int oldMeanPosJ = meanPosJ;
+        int oldMeanPosI = meanPosI;
+        unsigned count  = 0;
+
+        int shift_x = 0;
+        int shift_y = 0;
+
+#pragma unroll
+        for (int ch = 0; ch < channels; ++ch) currentMeanColors[ch] = 0;
+
+        for (int wj = -radius; wj <= radius; ++wj) {
+            int hit_count = 0;
+            int tj        = meanPosJ + wj;
+
+            if (tj < 0 || tj > dim1LenLmt) continue;
+
+            for (int wi = -radius; wi <= radius; ++wi) {
+                int ti = meanPosI + wi;
+
+                if (ti < 0 || ti > dim0LenLmt) continue;
+
+                AccType norm = 0;
+#pragma unroll
+                for (int ch = 0; ch < channels; ++ch) {
+                    tempColors[ch] =
+                        iptr[(ti * in.strides[0] + tj * in.strides[1] +
+                              ch * in.strides[2])];
+                    AccType diff = (AccType)currentCenterColors[ch] -
+                                   (AccType)tempColors[ch];
+                    norm += (diff * diff);
+                }
+
+                if (norm <= cvar) {
+#pragma unroll
+                    for (int ch = 0; ch < channels; ++ch)
+                        currentMeanColors[ch] += (AccType)tempColors[ch];
+
+                    shift_x += ti;
+                    ++hit_count;
+                }
+            }
+            count += hit_count;
+            shift_y += tj * hit_count;
+        }
+
+        if (count == 0) break;
+
+        const AccType fcount = 1 / (AccType)count;
+
+        meanPosI = __float2int_rz(shift_x * fcount);
+        meanPosJ = __float2int_rz(shift_y * fcount);
+
+#pragma unroll
+        for (int ch = 0; ch < channels; ++ch)
+            currentMeanColors[ch] =
+                __float2int_rz(currentMeanColors[ch] * fcount);
+
+        AccType norm = 0;
+#pragma unroll
+        for (int ch = 0; ch < channels; ++ch) {
+            AccType diff =
+                (AccType)currentCenterColors[ch] - currentMeanColors[ch];
+            norm += (diff * diff);
+        }
+
+        bool stop = (meanPosJ == oldMeanPosJ && meanPosI == oldMeanPosI) ||
+                    ((abs(oldMeanPosJ - meanPosJ) +
+                      abs(oldMeanPosI - meanPosI) + norm) <= 1);
+
+#pragma unroll
+        for (int ch = 0; ch < channels; ++ch)
+            currentCenterColors[ch] = (T)(currentMeanColors[ch]);
+
+        if (stop) break;
+    }  // scope of meanshift iterations end
+
+#pragma unroll
+    for (int ch = 0; ch < channels; ++ch)
+        optr[(gx * out.strides[0] + gy * out.strides[1] +
+              ch * out.strides[2])] = currentCenterColors[ch];
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index 4d8304e964..9f5988172a 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -8,160 +8,50 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/meanshift_cuh.hpp>
 
+#include <string>
 #include <type_traits>
 
 namespace cuda {
 namespace kernel {
+
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-template<typename AccType, typename T, int channels>
-static __global__ void meanshiftKernel(Param<T> out, CParam<T> in, int radius,
-                                       float cvar, uint numIters, int nBBS0,
-                                       int nBBS1) {
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
-    const T* iptr =
-        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
-    T* optr      = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + threadIdx.x;
-    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + threadIdx.y;
-
-    if (gx >= in.dims[0] || gy >= in.dims[1]) return;
-
-    int meanPosI = gx;
-    int meanPosJ = gy;
-
-    T currentCenterColors[channels];
-    T tempColors[channels];
-
-    AccType currentMeanColors[channels];
-
-#pragma unroll
-    for (int ch = 0; ch < channels; ++ch)
-        currentCenterColors[ch] = iptr[(
-            gx * in.strides[0] + gy * in.strides[1] + ch * in.strides[2])];
-
-    const int dim0LenLmt = in.dims[0] - 1;
-    const int dim1LenLmt = in.dims[1] - 1;
-
-    // scope of meanshift iterations begin
-    for (uint it = 0; it < numIters; ++it) {
-        int oldMeanPosJ = meanPosJ;
-        int oldMeanPosI = meanPosI;
-        unsigned count  = 0;
-
-        int shift_x = 0;
-        int shift_y = 0;
-
-#pragma unroll
-        for (int ch = 0; ch < channels; ++ch) currentMeanColors[ch] = 0;
-
-        for (int wj = -radius; wj <= radius; ++wj) {
-            int hit_count = 0;
-            int tj        = meanPosJ + wj;
-
-            if (tj < 0 || tj > dim1LenLmt) continue;
-
-            for (int wi = -radius; wi <= radius; ++wi) {
-                int ti = meanPosI + wi;
-
-                if (ti < 0 || ti > dim0LenLmt) continue;
-
-                AccType norm = 0;
-#pragma unroll
-                for (int ch = 0; ch < channels; ++ch) {
-                    tempColors[ch] =
-                        iptr[(ti * in.strides[0] + tj * in.strides[1] +
-                              ch * in.strides[2])];
-                    AccType diff = (AccType)currentCenterColors[ch] -
-                                   (AccType)tempColors[ch];
-                    norm += (diff * diff);
-                }
-
-                if (norm <= cvar) {
-#pragma unroll
-                    for (int ch = 0; ch < channels; ++ch)
-                        currentMeanColors[ch] += (AccType)tempColors[ch];
-
-                    shift_x += ti;
-                    ++hit_count;
-                }
-            }
-            count += hit_count;
-            shift_y += tj * hit_count;
-        }
-
-        if (count == 0) break;
-
-        const AccType fcount = 1 / (AccType)count;
-
-        meanPosI = __float2int_rz(shift_x * fcount);
-        meanPosJ = __float2int_rz(shift_y * fcount);
-
-#pragma unroll
-        for (int ch = 0; ch < channels; ++ch)
-            currentMeanColors[ch] =
-                __float2int_rz(currentMeanColors[ch] * fcount);
-
-        AccType norm = 0;
-#pragma unroll
-        for (int ch = 0; ch < channels; ++ch) {
-            AccType diff =
-                (AccType)currentCenterColors[ch] - currentMeanColors[ch];
-            norm += (diff * diff);
-        }
-
-        bool stop = (meanPosJ == oldMeanPosJ && meanPosI == oldMeanPosI) ||
-                    ((abs(oldMeanPosJ - meanPosJ) +
-                      abs(oldMeanPosI - meanPosI) + norm) <= 1);
-
-#pragma unroll
-        for (int ch = 0; ch < channels; ++ch)
-            currentCenterColors[ch] = (T)(currentMeanColors[ch]);
-
-        if (stop) break;
-    }  // scope of meanshift iterations end
-
-#pragma unroll
-    for (int ch = 0; ch < channels; ++ch)
-        optr[(gx * out.strides[0] + gy * out.strides[1] +
-              ch * out.strides[2])] = currentCenterColors[ch];
-}
-
-template<typename T, bool IsColor>
+template<typename T>
 void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
-               const float chromaticSigma, const uint numIters) {
+               const float chromaticSigma, const uint numIters, bool IsColor) {
     typedef typename std::conditional<std::is_same<T, double>::value, double,
                                       float>::type AccType;
+    static const std::string source(meanshift_cuh, meanshift_cuh_len);
 
-    static dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
+    auto meanshift =
+        getKernel("cuda::meanshift", source,
+                  {
+                      TemplateTypename<AccType>(), TemplateTypename<T>(),
+                      TemplateArg((IsColor ? 3 : 1))  // channels
+                  });
 
-    int blk_x = divup(in.dims[0], THREADS_X);
-    int blk_y = divup(in.dims[1], THREADS_Y);
+    static dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
+    int blk_x        = divup(in.dims[0], THREADS_X);
+    int blk_y        = divup(in.dims[1], THREADS_Y);
     const int bCount = (IsColor ? 1 : in.dims[2]);
 
     dim3 blocks(blk_x * bCount, blk_y * in.dims[3]);
 
     // clamp spatical and chromatic sigma's
-    int radius = std::max((int)(spatialSigma * 1.5f), 1);
-
+    int radius       = std::max((int)(spatialSigma * 1.5f), 1);
     const float cvar = chromaticSigma * chromaticSigma;
 
-    if (IsColor)
-        CUDA_LAUNCH((meanshiftKernel<AccType, T, 3>), blocks, threads, out, in,
-                    radius, cvar, numIters, blk_x, blk_y);
-    else
-        CUDA_LAUNCH((meanshiftKernel<AccType, T, 1>), blocks, threads, out, in,
-                    radius, cvar, numIters, blk_x, blk_y);
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    meanshift(qArgs, out, in, radius, cvar, numIters, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/medfilt.cuh b/src/backend/cuda/kernel/medfilt.cuh
new file mode 100644
index 0000000000..d04c9ec1db
--- /dev/null
+++ b/src/backend/cuda/kernel/medfilt.cuh
@@ -0,0 +1,288 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <shared.hpp>
+
+namespace cuda {
+
+// Exchange trick: Morgan McGuire, ShaderX 2008
+#define swap(a, b)           \
+    {                        \
+        T tmp = a;           \
+        a     = min(a, b);   \
+        b     = max(tmp, b); \
+    }
+
+__forceinline__ __device__
+int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
+}
+
+template<typename T, af::borderType pad>
+__device__
+void load2ShrdMem(T* shrd, const T* in, int lx, int ly,
+                  int shrdStride, int dim0, int dim1, int gx, int gy,
+                  int inStride1, int inStride0) {
+    switch (pad) {
+        case AF_PAD_ZERO: {
+            if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
+                shrd[lIdx(lx, ly, shrdStride, 1)] = T(0);
+            else
+                shrd[lIdx(lx, ly, shrdStride, 1)] =
+                    in[lIdx(gx, gy, inStride1, inStride0)];
+        } break;
+        case AF_PAD_SYM: {
+            if (gx < 0) gx *= -1;
+            if (gy < 0) gy *= -1;
+            if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
+            if (gy >= dim1) gy = 2 * (dim1 - 1) - gy;
+
+            shrd[lIdx(lx, ly, shrdStride, 1)] =
+                in[lIdx(gx, gy, inStride1, inStride0)];
+        } break;
+    }
+}
+
+template<typename T, af::borderType pad>
+__device__
+void load2ShrdMem_1d(T* shrd, const T* in, int lx, int dim0, int gx,
+                     int inStride0) {
+    switch (pad) {
+        case AF_PAD_ZERO: {
+            if (gx < 0 || gx >= dim0)
+                shrd[lx] = T(0);
+            else
+                shrd[lx] = in[gx];
+        } break;
+        case AF_PAD_SYM: {
+            if (gx < 0) gx *= -1;
+            if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
+
+            shrd[lx] = in[gx];
+        } break;
+    }
+}
+
+template<typename T, af::borderType pad, unsigned w_len, unsigned w_wid>
+__global__
+void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1) {
+    __shared__ T shrdMem[(THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1)];
+
+    // calculate necessary offset and window parameters
+    const int padding = w_len - 1;
+    const int halo    = padding / 2;
+    const int shrdLen = blockDim.x + padding;
+
+    // batch offsets
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+    const T* iptr =
+        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+
+    // local neighborhood indices
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+
+    // global indices
+    int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    // pull image to local memory
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += blockDim.y, gy2 += blockDim.y) {
+        // move row_set get_local_size(1) along coloumns
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            load2ShrdMem<T, pad>(shrdMem, iptr, a, b, shrdLen, in.dims[0],
+                                 in.dims[1], gx2 - halo, gy2 - halo,
+                                 in.strides[1], in.strides[0]);
+        }
+    }
+
+    __syncthreads();
+
+    // Only continue if we're at a valid location
+    if (gx < in.dims[0] && gy < in.dims[1]) {
+        const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
+        // pull top half from shared memory into local memory
+        T v[ARR_SIZE];
+#pragma unroll
+        for (int k = 0; k <= w_wid / 2; k++) {
+#pragma unroll
+            for (int i = 0; i < w_len; i++) {
+                v[w_len * k + i] = shrdMem[lIdx(lx + i, ly + k, shrdLen, 1)];
+            }
+        }
+
+        // with each pass, remove min and max values and add new value
+        // initial sort
+        // ensure min in first half, max in second half
+#pragma unroll
+        for (int i = 0; i < ARR_SIZE / 2; i++) {
+            swap(v[i], v[ARR_SIZE - 1 - i]);
+        }
+        // move min in first half to first pos
+#pragma unroll
+        for (int i = 1; i < (ARR_SIZE + 1) / 2; i++) { swap(v[0], v[i]); }
+        // move max in second half to last pos
+#pragma unroll
+        for (int i = ARR_SIZE - 2; i >= ARR_SIZE / 2; i--) {
+            swap(v[i], v[ARR_SIZE - 1]);
+        }
+
+        int last = ARR_SIZE - 1;
+
+        for (int k = 1 + w_wid / 2; k < w_wid; k++) {
+            for (int j = 0; j < w_len; j++) {
+                // add new contestant to first position in array
+                v[0] = shrdMem[lIdx(lx + j, ly + k, shrdLen, 1)];
+
+                last--;
+
+                // place max in last half, min in first half
+                for (int i = 0; i < (last + 1) / 2; i++) {
+                    swap(v[i], v[last - i]);
+                }
+                // now perform swaps on each half such that
+                // max is in last pos, min is in first pos
+                for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
+                for (int i = last - 1; i >= (last + 1) / 2; i--) {
+                    swap(v[i], v[last]);
+                }
+            }
+        }
+
+        // no more new contestants
+        // may still have to sort the last row
+        // each outer loop drops the min and max
+        for (int k = 1; k < w_len / 2; k++) {
+            // move max/min into respective halves
+            for (int i = k; i < w_len / 2; i++) {
+                swap(v[i], v[w_len - 1 - i]);
+            }
+            // move min into first pos
+            for (int i = k + 1; i <= w_len / 2; i++) { swap(v[k], v[i]); }
+            // move max into last pos
+            for (int i = w_len - k - 2; i >= w_len / 2; i--) {
+                swap(v[i], v[w_len - 1 - k]);
+            }
+        }
+
+        // pick the middle element of the first row
+        optr[gy * out.strides[1] + gx * out.strides[0]] = v[w_len / 2];
+    }
+}
+
+template<typename T, af::borderType pad, unsigned ARR_SIZE>
+__global__
+void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0) {
+    SharedMemory<T> shared;
+    T* shrdMem = shared.getPointer();
+
+    // calculate necessary offset and window parameters
+    const int padding = w_wid - 1;
+    const int halo    = padding / 2;
+    const int shrdLen = blockDim.x + padding;
+
+    // batch offsets
+    unsigned b1 = blockIdx.x / nBBS0;
+    unsigned b2 = blockIdx.y;
+    unsigned b3 = blockIdx.z;
+
+    const T* iptr =
+        (const T*)in.ptr +
+        (b1 * in.strides[1] + b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr = (T*)out.ptr +
+              (b1 * in.strides[1] + b2 * out.strides[2] + b3 * out.strides[3]);
+
+    // local neighborhood indices
+    int lx = threadIdx.x;
+
+    // global indices
+    int gx = blockDim.x * (blockIdx.x - b1 * nBBS0) + lx;
+
+    // pull signal to local memory
+    for (int a = lx, gx2 = gx; a < shrdLen;
+         a += blockDim.x, gx2 += blockDim.x) {
+        load2ShrdMem_1d<T, pad>(shrdMem, iptr, a, in.dims[0], gx2 - halo,
+                                in.strides[0]);
+    }
+
+    __syncthreads();
+
+    // Only continue if we're at a valid location
+    if (gx < in.dims[0]) {
+        const int ARR_BOUNDARY = (w_wid - w_wid / 2) + 1;
+        // pull top half from shared memory into local memory
+        T v[ARR_SIZE];
+
+#pragma unroll
+        for (int k = 0; k <= w_wid / 2 + 1; k++) { v[k] = shrdMem[lx + k]; }
+        // with each pass, remove min and max values and add new value
+        // initial sort
+        // ensure min in first half, max in second half
+#pragma unroll
+        for (int i = 0; i < ARR_BOUNDARY / 2; i++) {
+            swap(v[i], v[ARR_BOUNDARY - 1 - i]);
+        }
+        // move min in first half to first pos
+#pragma unroll
+        for (int i = 1; i < (ARR_BOUNDARY + 1) / 2; i++) { swap(v[0], v[i]); }
+        // move max in second half to last pos
+#pragma unroll
+        for (int i = ARR_BOUNDARY - 2; i >= ARR_BOUNDARY / 2; i--) {
+            swap(v[i], v[ARR_BOUNDARY - 1]);
+        }
+
+        int last = ARR_BOUNDARY - 1;
+
+        for (int k = w_wid / 2 + 2; k < w_wid; k++) {
+            // add new contestant to first position in array
+            v[0] = shrdMem[lx + k];
+
+            last--;
+
+            // place max in last half, min in first half
+            for (int i = 0; i < (last + 1) / 2; i++) {
+                swap(v[i], v[last - i]);
+            }
+            // now perform swaps on each half such that
+            // max is in last pos, min is in first pos
+            for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
+            for (int i = last - 1; i >= (last + 1) / 2; i--) {
+                swap(v[i], v[last]);
+            }
+        }
+
+        // no more new contestants
+        // may still have to sort the last row
+        // each outer loop drops the min and max
+        for (int k = 0; k < last; k++) {
+            // move max/min into respective halves
+            for (int i = k; i < ARR_BOUNDARY / 2; i++) {
+                swap(v[i], v[ARR_BOUNDARY - 1 - i]);
+            }
+            // move min into first pos
+            for (int i = k + 1; i <= ARR_BOUNDARY / 2; i++) {
+                swap(v[k], v[i]);
+            }
+            // move max into last pos
+            for (int i = ARR_BOUNDARY - k - 2; i >= ARR_BOUNDARY / 2; i--) {
+                swap(v[i], v[ARR_BOUNDARY - 1 - k]);
+            }
+        }
+
+        // pick the middle element of the first row
+        optr[gx * out.strides[0]] = v[last / 2];
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 8816098b85..8fa8c1ff79 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -8,294 +8,33 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <platform.hpp>
-#include "shared.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/medfilt_cuh.hpp>
+#include <af/defines.h>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int MAX_MEDFILTER1_LEN = 121;
 static const int MAX_MEDFILTER2_LEN = 15;
+static const int THREADS_X          = 16;
+static const int THREADS_Y          = 16;
 
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-// Exchange trick: Morgan McGuire, ShaderX 2008
-#define swap(a, b)           \
-    {                        \
-        T tmp = a;           \
-        a     = min(a, b);   \
-        b     = max(tmp, b); \
-    }
-
-__forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
-    return (y * stride1 + x * stride0);
-}
-
-template<typename T, af_border_type pad>
-__device__ void load2ShrdMem(T* shrd, const T* in, int lx, int ly,
-                             int shrdStride, int dim0, int dim1, int gx, int gy,
-                             int inStride1, int inStride0) {
-    switch (pad) {
-        case AF_PAD_ZERO: {
-            if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
-                shrd[lIdx(lx, ly, shrdStride, 1)] = T(0);
-            else
-                shrd[lIdx(lx, ly, shrdStride, 1)] =
-                    in[lIdx(gx, gy, inStride1, inStride0)];
-        } break;
-        case AF_PAD_SYM: {
-            if (gx < 0) gx *= -1;
-            if (gy < 0) gy *= -1;
-            if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
-            if (gy >= dim1) gy = 2 * (dim1 - 1) - gy;
-
-            shrd[lIdx(lx, ly, shrdStride, 1)] =
-                in[lIdx(gx, gy, inStride1, inStride0)];
-        } break;
-    }
-}
-
-template<typename T, af_border_type pad>
-__device__ void load2ShrdMem_1d(T* shrd, const T* in, int lx, int dim0, int gx,
-                                int inStride0) {
-    switch (pad) {
-        case AF_PAD_ZERO: {
-            if (gx < 0 || gx >= dim0)
-                shrd[lx] = T(0);
-            else
-                shrd[lx] = in[gx];
-        } break;
-        case AF_PAD_SYM: {
-            if (gx < 0) gx *= -1;
-            if (gx >= dim0) gx = 2 * (dim0 - 1) - gx;
-
-            shrd[lx] = in[gx];
-        } break;
-    }
-}
-
-template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
-__global__ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1) {
-    __shared__ T shrdMem[(THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1)];
-
-    // calculate necessary offset and window parameters
-    const int padding = w_len - 1;
-    const int halo    = padding / 2;
-    const int shrdLen = blockDim.x + padding;
-
-    // batch offsets
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
-    const T* iptr =
-        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
-    T* optr = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-
-    // local neighborhood indices
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-
-    // global indices
-    int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
-    int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
-
-    // pull image to local memory
-    for (int b = ly, gy2 = gy; b < shrdLen;
-         b += blockDim.y, gy2 += blockDim.y) {
-        // move row_set get_local_size(1) along coloumns
-        for (int a = lx, gx2 = gx; a < shrdLen;
-             a += blockDim.x, gx2 += blockDim.x) {
-            load2ShrdMem<T, pad>(shrdMem, iptr, a, b, shrdLen, in.dims[0],
-                                 in.dims[1], gx2 - halo, gy2 - halo,
-                                 in.strides[1], in.strides[0]);
-        }
-    }
-
-    __syncthreads();
-
-    // Only continue if we're at a valid location
-    if (gx < in.dims[0] && gy < in.dims[1]) {
-        const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
-        // pull top half from shared memory into local memory
-        T v[ARR_SIZE];
-#pragma unroll
-        for (int k = 0; k <= w_wid / 2; k++) {
-#pragma unroll
-            for (int i = 0; i < w_len; i++) {
-                v[w_len * k + i] = shrdMem[lIdx(lx + i, ly + k, shrdLen, 1)];
-            }
-        }
-
-        // with each pass, remove min and max values and add new value
-        // initial sort
-        // ensure min in first half, max in second half
-#pragma unroll
-        for (int i = 0; i < ARR_SIZE / 2; i++) {
-            swap(v[i], v[ARR_SIZE - 1 - i]);
-        }
-        // move min in first half to first pos
-#pragma unroll
-        for (int i = 1; i < (ARR_SIZE + 1) / 2; i++) { swap(v[0], v[i]); }
-        // move max in second half to last pos
-#pragma unroll
-        for (int i = ARR_SIZE - 2; i >= ARR_SIZE / 2; i--) {
-            swap(v[i], v[ARR_SIZE - 1]);
-        }
-
-        int last = ARR_SIZE - 1;
-
-        for (int k = 1 + w_wid / 2; k < w_wid; k++) {
-            for (int j = 0; j < w_len; j++) {
-                // add new contestant to first position in array
-                v[0] = shrdMem[lIdx(lx + j, ly + k, shrdLen, 1)];
-
-                last--;
-
-                // place max in last half, min in first half
-                for (int i = 0; i < (last + 1) / 2; i++) {
-                    swap(v[i], v[last - i]);
-                }
-                // now perform swaps on each half such that
-                // max is in last pos, min is in first pos
-                for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
-                for (int i = last - 1; i >= (last + 1) / 2; i--) {
-                    swap(v[i], v[last]);
-                }
-            }
-        }
-
-        // no more new contestants
-        // may still have to sort the last row
-        // each outer loop drops the min and max
-        for (int k = 1; k < w_len / 2; k++) {
-            // move max/min into respective halves
-            for (int i = k; i < w_len / 2; i++) {
-                swap(v[i], v[w_len - 1 - i]);
-            }
-            // move min into first pos
-            for (int i = k + 1; i <= w_len / 2; i++) { swap(v[k], v[i]); }
-            // move max into last pos
-            for (int i = w_len - k - 2; i >= w_len / 2; i--) {
-                swap(v[i], v[w_len - 1 - k]);
-            }
-        }
-
-        // pick the middle element of the first row
-        optr[gy * out.strides[1] + gx * out.strides[0]] = v[w_len / 2];
-    }
-}
-
-template<typename T, af_border_type pad, unsigned ARR_SIZE>
-__global__ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid,
-                         int nBBS0) {
-    SharedMemory<T> shared;
-    T* shrdMem = shared.getPointer();
-
-    // calculate necessary offset and window parameters
-    const int padding = w_wid - 1;
-    const int halo    = padding / 2;
-    const int shrdLen = blockDim.x + padding;
-
-    // batch offsets
-    unsigned b1 = blockIdx.x / nBBS0;
-    unsigned b2 = blockIdx.y;
-    unsigned b3 = blockIdx.z;
-
-    const T* iptr =
-        (const T*)in.ptr +
-        (b1 * in.strides[1] + b2 * in.strides[2] + b3 * in.strides[3]);
-    T* optr = (T*)out.ptr +
-              (b1 * in.strides[1] + b2 * out.strides[2] + b3 * out.strides[3]);
-
-    // local neighborhood indices
-    int lx = threadIdx.x;
-
-    // global indices
-    int gx = blockDim.x * (blockIdx.x - b1 * nBBS0) + lx;
-
-    // pull signal to local memory
-    for (int a = lx, gx2 = gx; a < shrdLen;
-         a += blockDim.x, gx2 += blockDim.x) {
-        load2ShrdMem_1d<T, pad>(shrdMem, iptr, a, in.dims[0], gx2 - halo,
-                                in.strides[0]);
-    }
-
-    __syncthreads();
-
-    // Only continue if we're at a valid location
-    if (gx < in.dims[0]) {
-        const int ARR_BOUNDARY = (w_wid - w_wid / 2) + 1;
-        // pull top half from shared memory into local memory
-        T v[ARR_SIZE];
-
-#pragma unroll
-        for (int k = 0; k <= w_wid / 2 + 1; k++) { v[k] = shrdMem[lx + k]; }
-        // with each pass, remove min and max values and add new value
-        // initial sort
-        // ensure min in first half, max in second half
-#pragma unroll
-        for (int i = 0; i < ARR_BOUNDARY / 2; i++) {
-            swap(v[i], v[ARR_BOUNDARY - 1 - i]);
-        }
-        // move min in first half to first pos
-#pragma unroll
-        for (int i = 1; i < (ARR_BOUNDARY + 1) / 2; i++) { swap(v[0], v[i]); }
-        // move max in second half to last pos
-#pragma unroll
-        for (int i = ARR_BOUNDARY - 2; i >= ARR_BOUNDARY / 2; i--) {
-            swap(v[i], v[ARR_BOUNDARY - 1]);
-        }
-
-        int last = ARR_BOUNDARY - 1;
-
-        for (int k = w_wid / 2 + 2; k < w_wid; k++) {
-            // add new contestant to first position in array
-            v[0] = shrdMem[lx + k];
-
-            last--;
-
-            // place max in last half, min in first half
-            for (int i = 0; i < (last + 1) / 2; i++) {
-                swap(v[i], v[last - i]);
-            }
-            // now perform swaps on each half such that
-            // max is in last pos, min is in first pos
-            for (int i = 1; i <= last / 2; i++) { swap(v[0], v[i]); }
-            for (int i = last - 1; i >= (last + 1) / 2; i--) {
-                swap(v[i], v[last]);
-            }
-        }
-
-        // no more new contestants
-        // may still have to sort the last row
-        // each outer loop drops the min and max
-        for (int k = 0; k < last; k++) {
-            // move max/min into respective halves
-            for (int i = k; i < ARR_BOUNDARY / 2; i++) {
-                swap(v[i], v[ARR_BOUNDARY - 1 - i]);
-            }
-            // move min into first pos
-            for (int i = k + 1; i <= ARR_BOUNDARY / 2; i++) {
-                swap(v[k], v[i]);
-            }
-            // move max into last pos
-            for (int i = ARR_BOUNDARY - k - 2; i >= ARR_BOUNDARY / 2; i--) {
-                swap(v[i], v[ARR_BOUNDARY - 1 - k]);
-            }
-        }
+template<typename T>
+void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
+              int w_wid) {
+    UNUSED(w_wid);
+    static const std::string source(medfilt_cuh, medfilt_cuh_len);
 
-        // pick the middle element of the first row
-        optr[gx * out.strides[0]] = v[last / 2];
-    }
-}
+    auto medfilt2 = getKernel("cuda::medfilt2", source,
+                            {TemplateTypename<T>(), TemplateArg(pad),
+                             TemplateArg(w_len), TemplateArg(w_wid)},
+                            {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
-template<typename T, af_border_type pad>
-void medfilt2(Param<T> out, CParam<T> in, int w_len, int w_wid) {
-    UNUSED(w_wid);
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
@@ -303,42 +42,19 @@ void medfilt2(Param<T> out, CParam<T> in, int w_len, int w_wid) {
 
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
-    switch (w_len) {
-        case 3:
-            CUDA_LAUNCH((medfilt2<T, pad, 3, 3>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-        case 5:
-            CUDA_LAUNCH((medfilt2<T, pad, 5, 5>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-        case 7:
-            CUDA_LAUNCH((medfilt2<T, pad, 7, 7>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-        case 9:
-            CUDA_LAUNCH((medfilt2<T, pad, 9, 9>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-        case 11:
-            CUDA_LAUNCH((medfilt2<T, pad, 11, 11>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-        case 13:
-            CUDA_LAUNCH((medfilt2<T, pad, 13, 13>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-        case 15:
-            CUDA_LAUNCH((medfilt2<T, pad, 15, 15>), blocks, threads, out, in,
-                        blk_x, blk_y);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    medfilt2(qArgs, out, in, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
 
-template<typename T, af_border_type pad>
-void medfilt1(Param<T> out, CParam<T> in, int w_wid) {
+template<typename T>
+void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
+    static const std::string source(medfilt_cuh, medfilt_cuh_len);
+
+    auto medfilt1 = getKernel(
+        "cuda::medfilt1", source,
+        {TemplateTypename<T>(), TemplateArg(pad), TemplateArg(w_wid)});
+
     const dim3 threads(THREADS_X);
 
     int blk_x = divup(in.dims[0], threads.x);
@@ -347,52 +63,10 @@ void medfilt1(Param<T> out, CParam<T> in, int w_wid) {
 
     const size_t shrdMemBytes = sizeof(T) * (THREADS_X + w_wid - 1);
 
-    switch (w_wid) {
-        case 3:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 3>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 5:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 4>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 7:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 5>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 9:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 6>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 11:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 7>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 13:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 8>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 15:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 9>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 17:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 10>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        case 19:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 11>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-        default:
-            CUDA_LAUNCH_SMEM((medfilt1<T, pad, 62>), blocks, threads,
-                             shrdMemBytes, out, in, w_wid, blk_x);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(), shrdMemBytes);
+    medfilt1(qArgs, out, in, w_wid, blk_x);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/moments.cuh b/src/backend/cuda/kernel/moments.cuh
new file mode 100644
index 0000000000..765b15d2a8
--- /dev/null
+++ b/src/backend/cuda/kernel/moments.cuh
@@ -0,0 +1,59 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__
+void moments(Param<float> out, CParam<T> in, af::momentType moment, const bool pBatch) {
+    const dim_t idw = blockIdx.y / in.dims[2];
+    const dim_t idz = blockIdx.y - idw * in.dims[2];
+
+    const dim_t idy = blockIdx.x;
+    dim_t idx       = threadIdx.x;
+
+    if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3]) return;
+
+    extern __shared__ float blk_moment_sum[];
+    if (threadIdx.x < out.dims[0]) { blk_moment_sum[threadIdx.x] = 0.f; }
+    __syncthreads();
+
+    dim_t mId = idy * in.strides[1] + idx;
+    if (pBatch) { mId += idw * in.strides[3] + idz * in.strides[2]; }
+
+    for (; idx < in.dims[0]; idx += blockDim.x) {
+        dim_t m_off = 0;
+        float val   = (float)in.ptr[mId];
+        mId += blockDim.x;
+
+        if ((moment & AF_MOMENT_M00) > 0) {
+            atomicAdd(blk_moment_sum + m_off++, val);
+        }
+        if ((moment & AF_MOMENT_M01) > 0) {
+            atomicAdd(blk_moment_sum + m_off++, idx * val);
+        }
+        if ((moment & AF_MOMENT_M10) > 0) {
+            atomicAdd(blk_moment_sum + m_off++, idy * val);
+        }
+        if ((moment & AF_MOMENT_M11) > 0) {
+            atomicAdd(blk_moment_sum + m_off, idx * idy * val);
+        }
+    }
+
+    __syncthreads();
+
+    float *offset = const_cast<float *>(
+        out.ptr + (idw * out.strides[3] + idz * out.strides[2]) + threadIdx.x);
+    if (threadIdx.x < out.dims[0])
+        atomicAdd(offset, blk_moment_sum[threadIdx.x]);
+}
+
+}
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index a263f77839..511ec9b3ea 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -8,74 +8,35 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/moments_cuh.hpp>
 #include <af/defines.h>
 
+#include <string>
+
 namespace cuda {
 namespace kernel {
 
-// Kernel Launch Config Values
 static const int THREADS = 128;
 
 template<typename T>
-__global__ void moments_kernel(Param<float> out, CParam<T> in,
-                               af_moment_type moment, const bool pBatch) {
-    const dim_t idw = blockIdx.y / in.dims[2];
-    const dim_t idz = blockIdx.y - idw * in.dims[2];
-
-    const dim_t idy = blockIdx.x;
-    dim_t idx       = threadIdx.x;
-
-    if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3]) return;
-
-    extern __shared__ float blk_moment_sum[];
-    if (threadIdx.x < out.dims[0]) { blk_moment_sum[threadIdx.x] = 0.f; }
-    __syncthreads();
-
-    dim_t mId = idy * in.strides[1] + idx;
-    if (pBatch) { mId += idw * in.strides[3] + idz * in.strides[2]; }
-
-    for (; idx < in.dims[0]; idx += blockDim.x) {
-        dim_t m_off = 0;
-        float val   = (float)in.ptr[mId];
-        mId += blockDim.x;
+void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
+    static const std::string source(moments_cuh, moments_cuh_len);
 
-        if ((moment & AF_MOMENT_M00) > 0) {
-            atomicAdd(blk_moment_sum + m_off++, val);
-        }
-        if ((moment & AF_MOMENT_M01) > 0) {
-            atomicAdd(blk_moment_sum + m_off++, idx * val);
-        }
-        if ((moment & AF_MOMENT_M10) > 0) {
-            atomicAdd(blk_moment_sum + m_off++, idy * val);
-        }
-        if ((moment & AF_MOMENT_M11) > 0) {
-            atomicAdd(blk_moment_sum + m_off, idx * idy * val);
-        }
-    }
+    auto moments = getKernel("cuda::moments", source, {TemplateTypename<T>()});
 
-    __syncthreads();
-
-    float *offset = const_cast<float *>(
-        out.ptr + (idw * out.strides[3] + idz * out.strides[2]) + threadIdx.x);
-    if (threadIdx.x < out.dims[0])
-        atomicAdd(offset, blk_moment_sum[threadIdx.x]);
-}
-
-// Wrapper functions
-template<typename T>
-void moments(Param<float> out, CParam<T> in, const af_moment_type moment) {
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
 
     bool pBatch = !(in.dims[2] == 1 && in.dims[3] == 1);
 
-    CUDA_LAUNCH_SMEM((moments_kernel<T>), blocks, threads,
-                     sizeof(float) * out.dims[0], out, in, moment, pBatch);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(),
+                      sizeof(float) * out.dims[0]);
+
+    moments(qArgs, out, in, moment, pBatch);
+
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/morph.cuh b/src/backend/cuda/kernel/morph.cuh
new file mode 100644
index 0000000000..fbe62487d4
--- /dev/null
+++ b/src/backend/cuda/kernel/morph.cuh
@@ -0,0 +1,231 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <ops.hpp>
+#include <shared.hpp>
+
+// cFilter is used by both 2d morph and 3d morph
+// Maximum kernel size supported for 2d morph is 19x19*8 = 2888
+// Maximum kernel size supported for 3d morph is 7x7x7*8 = 2744
+// We will declare a char array as __constant__ array and allocate
+// size necessary to hold doubles of FILTER_LEN*FILTER_LEN
+__constant__ char
+    cFilter[MAX_MORPH_FILTER_LEN * MAX_MORPH_FILTER_LEN * sizeof(double)];
+
+namespace cuda {
+
+__forceinline__ __device__
+int lIdx(int x, int y, int stride1, int stride0) {
+    return (y * stride1 + x * stride0);
+}
+
+template<typename T, bool isDilation>
+inline __device__
+void load2ShrdMem(T* shrd, const T* const in, int lx, int ly, int shrdStride,
+                  int dim0, int dim1, int gx, int gy,
+                  int inStride1, int inStride0) {
+    T val =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1) {
+        val = in[lIdx(gx, gy, inStride1, inStride0)];
+    }
+    shrd[lIdx(lx, ly, shrdStride, 1)] = val;
+}
+
+// kernel assumes mask/filter is square and hence does the
+// necessary operations accordingly.
+//
+// Notes on template arguments for morphKernel:
+//   * T is the data type of the image & kernel
+//   * isDilation indicates if the current kernel invocation is an erosion
+//   operation or dilation operation
+//   * SeLength is the structuring element length a.k.a the kernel window
+//   length. This template parameter takes precedence over the kernel argument
+//   `windLen`.
+//
+// Please make sure at least one of the following variables is not 0.
+//  * SeLength (structuring element a.k.a window/kernel)
+//  * windLen
+// If SeLength is > 0, then that will override the kernel argument.
+template<typename T, bool isDilation, int SeLength = 0>
+__global__
+void morph(Param<T> out, CParam<T> in, int nBBS0, int nBBS1, int windLen = 0) {
+    windLen = (SeLength > 0 ? SeLength : windLen);
+
+    SharedMemory<T> shared;
+    T* shrdMem = shared.getPointer();
+
+    // calculate necessary offset and window parameters
+    const int halo = windLen / 2;
+    const int padding =
+        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
+    const int shrdLen  = blockDim.x + padding + 1;
+    const int shrdLen1 = blockDim.y + padding;
+
+    // gfor batch offsets
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+    const T* iptr =
+        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    T* optr = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    // global indices
+    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
+    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    // pull image to local memory
+    for (int b = ly, gy2 = gy; b < shrdLen1;
+         b += blockDim.y, gy2 += blockDim.y) {
+        // move row_set get_local_size(1) along coloumns
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            load2ShrdMem<T, isDilation>(
+                shrdMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
+                gx2 - halo, gy2 - halo, in.strides[1], in.strides[0]);
+        }
+    }
+
+    int i = lx + halo;
+    int j = ly + halo;
+
+    __syncthreads();
+
+    const T* d_filt = (const T*)cFilter;
+    T acc =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+#pragma unroll
+    for (int wj = 0; wj < windLen; ++wj) {
+        int joff   = wj * windLen;
+        int w_joff = (j + wj - halo) * shrdLen;
+#pragma unroll
+        for (int wi = 0; wi < windLen; ++wi) {
+            if (d_filt[joff + wi] > (T)0) {
+                T cur = shrdMem[w_joff + (i + wi - halo)];
+                if (isDilation)
+                    acc = max(acc, cur);
+                else
+                    acc = min(acc, cur);
+            }
+        }
+    }
+
+    if (gx < in.dims[0] && gy < in.dims[1]) {
+        int outIdx   = lIdx(gx, gy, out.strides[1], out.strides[0]);
+        optr[outIdx] = acc;
+    }
+}
+
+__forceinline__ __device__
+int lIdx3D(int x, int y, int z, int stride2, int stride1, int stride0) {
+    return (z * stride2 + y * stride1 + x * stride0);
+}
+
+template<typename T, bool isDilation>
+inline __device__
+void load2ShrdVolume(T* shrd, const T* const in, int lx, int ly, int lz,
+                     int shrdStride1, int shrdStride2, int dim0, int dim1,
+                     int dim2, int gx, int gy, int gz,
+                     int inStride2, int inStride1, int inStride0) {
+    T val =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1 && gz >= 0 && gz < dim2) {
+        val = in[gx * inStride0 + gy * inStride1 + gz * inStride2];
+    }
+    shrd[lx + ly * shrdStride1 + lz * shrdStride2] = val;
+}
+
+// kernel assumes mask/filter is square and hence does the
+// necessary operations accordingly.
+template<typename T, bool isDilation, int windLen>
+__global__
+void morph3D(Param<T> out, CParam<T> in, int nBBS) {
+    SharedMemory<T> shared;
+    T* shrdMem = shared.getPointer();
+
+    const int halo = windLen / 2;
+    const int padding =
+        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
+
+    const int se_area  = windLen * windLen;
+    const int shrdLen  = blockDim.x + padding + 1;
+    const int shrdLen1 = blockDim.y + padding;
+    const int shrdLen2 = blockDim.z + padding;
+    const int shrdArea = shrdLen * shrdLen1;
+
+    // gfor batch offsets
+    unsigned batchId = blockIdx.x / nBBS;
+
+    const T* iptr = (const T*)in.ptr + (batchId * in.strides[3]);
+    T* optr       = (T*)out.ptr + (batchId * out.strides[3]);
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+    const int lz = threadIdx.z;
+
+    const int gx = blockDim.x * (blockIdx.x - batchId * nBBS) + lx;
+    const int gy = blockDim.y * blockIdx.y + ly;
+    const int gz = blockDim.z * blockIdx.z + lz;
+
+    for (int c = lz, gz2 = gz; c < shrdLen2;
+         c += blockDim.z, gz2 += blockDim.z) {
+        for (int b = ly, gy2 = gy; b < shrdLen1;
+             b += blockDim.y, gy2 += blockDim.y) {
+            for (int a = lx, gx2 = gx; a < shrdLen;
+                 a += blockDim.x, gx2 += blockDim.x) {
+                load2ShrdVolume<T, isDilation>(
+                    shrdMem, iptr, a, b, c, shrdLen, shrdArea, in.dims[0],
+                    in.dims[1], in.dims[2], gx2 - halo, gy2 - halo, gz2 - halo,
+                    in.strides[2], in.strides[1], in.strides[0]);
+            }
+        }
+    }
+
+    __syncthreads();
+    // indices of voxel owned by current thread
+    int i = lx + halo;
+    int j = ly + halo;
+    int k = lz + halo;
+
+    const T* d_filt = (const T*)cFilter;
+    T acc =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+#pragma unroll
+    for (int wk = 0; wk < windLen; ++wk) {
+        int koff   = wk * se_area;
+        int w_koff = (k + wk - halo) * shrdArea;
+#pragma unroll
+        for (int wj = 0; wj < windLen; ++wj) {
+            int joff   = wj * windLen;
+            int w_joff = (j + wj - halo) * shrdLen;
+#pragma unroll
+            for (int wi = 0; wi < windLen; ++wi) {
+                if (d_filt[koff + joff + wi]) {
+                    T cur = shrdMem[w_koff + w_joff + i + wi - halo];
+                    if (isDilation)
+                        acc = max(acc, cur);
+                    else
+                        acc = min(acc, cur);
+                }
+            }
+        }
+    }
+
+    if (gx < in.dims[0] && gy < in.dims[1] && gz < in.dims[2]) {
+        int outIdx =
+            gz * out.strides[2] + gy * out.strides[1] + gx * out.strides[0];
+        optr[outIdx] = acc;
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index b533b91af0..60207f1cfd 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -8,240 +8,41 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <ops.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/morph_cuh.hpp>
+
 #include <limits>
-#include "shared.hpp"
+#include <string>
 
 namespace cuda {
 namespace kernel {
-static const int MAX_MORPH_FILTER_LEN = 17;
-// cFilter is used by both 2d morph and 3d morph
-// Maximum kernel size supported for 2d morph is 19x19*8 = 2888
-// Maximum kernel size supported for 3d morph is 7x7x7*8 = 2744
-// We will declare a char array as __constant__ array and allocate
-// size necessary to hold doubles of FILTER_LEN*FILTER_LEN
-__constant__ char
-    cFilter[MAX_MORPH_FILTER_LEN * MAX_MORPH_FILTER_LEN * sizeof(double)];
-
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-static const int CUBE_X = 8;
-static const int CUBE_Y = 8;
-static const int CUBE_Z = 8;
-
-__forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
-    return (y * stride1 + x * stride0);
-}
-
-template<typename T, bool isDilation>
-inline __device__ void load2ShrdMem(T* shrd, const T* const in, int lx, int ly,
-                                    int shrdStride, int dim0, int dim1, int gx,
-                                    int gy, int inStride1, int inStride0) {
-    T val =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1) {
-        val = in[lIdx(gx, gy, inStride1, inStride0)];
-    }
-    shrd[lIdx(lx, ly, shrdStride, 1)] = val;
-}
-
-// kernel assumes mask/filter is square and hence does the
-// necessary operations accordingly.
-//
-// Notes on template arguments for morphKernel:
-//   * T is the data type of the image & kernel
-//   * isDilation indicates if the current kernel invocation is an erosion
-//   operation or dilation operation
-//   * SeLength is the structuring element length a.k.a the kernel window
-//   length. This template parameter takes precedence over the kernel argument
-//   `windLen`.
-//
-// Please make sure at least one of the following variables is not 0.
-//  * SeLength (structuring element a.k.a window/kernel)
-//  * windLen
-// If SeLength is > 0, then that will override the kernel argument.
-template<typename T, bool isDilation, int SeLength = 0>
-static __global__ void morphKernel(Param<T> out, CParam<T> in, int nBBS0,
-                                   int nBBS1, int windLen = 0) {
-    windLen = (SeLength > 0 ? SeLength : windLen);
-
-    // get shared memory pointer
-    SharedMemory<T> shared;
-    T* shrdMem = shared.getPointer();
-
-    // calculate necessary offset and window parameters
-    const int halo = windLen / 2;
-    const int padding =
-        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
-    const int shrdLen  = blockDim.x + padding + 1;
-    const int shrdLen1 = blockDim.y + padding;
-
-    // gfor batch offsets
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
-    const T* iptr =
-        (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
-    T* optr = (T*)out.ptr + (b2 * out.strides[2] + b3 * out.strides[3]);
-
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    // global indices
-    const int gx = blockDim.x * (blockIdx.x - b2 * nBBS0) + lx;
-    const int gy = blockDim.y * (blockIdx.y - b3 * nBBS1) + ly;
-
-    // pull image to local memory
-    for (int b = ly, gy2 = gy; b < shrdLen1;
-         b += blockDim.y, gy2 += blockDim.y) {
-        // move row_set get_local_size(1) along coloumns
-        for (int a = lx, gx2 = gx; a < shrdLen;
-             a += blockDim.x, gx2 += blockDim.x) {
-            load2ShrdMem<T, isDilation>(
-                shrdMem, iptr, a, b, shrdLen, in.dims[0], in.dims[1],
-                gx2 - halo, gy2 - halo, in.strides[1], in.strides[0]);
-        }
-    }
-
-    int i = lx + halo;
-    int j = ly + halo;
-
-    __syncthreads();
-
-    const T* d_filt = (const T*)cFilter;
-    T acc =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-#pragma unroll
-    for (int wj = 0; wj < windLen; ++wj) {
-        int joff   = wj * windLen;
-        int w_joff = (j + wj - halo) * shrdLen;
-#pragma unroll
-        for (int wi = 0; wi < windLen; ++wi) {
-            if (d_filt[joff + wi] > (T)0) {
-                T cur = shrdMem[w_joff + (i + wi - halo)];
-                if (isDilation)
-                    acc = max(acc, cur);
-                else
-                    acc = min(acc, cur);
-            }
-        }
-    }
-
-    if (gx < in.dims[0] && gy < in.dims[1]) {
-        int outIdx   = lIdx(gx, gy, out.strides[1], out.strides[0]);
-        optr[outIdx] = acc;
-    }
-}
 
-__forceinline__ __device__ int lIdx3D(int x, int y, int z, int stride2,
-                                      int stride1, int stride0) {
-    return (z * stride2 + y * stride1 + x * stride0);
-}
-
-template<typename T, bool isDilation>
-inline __device__ void load2ShrdVolume(T* shrd, const T* const in, int lx,
-                                       int ly, int lz, int shrdStride1,
-                                       int shrdStride2, int dim0, int dim1,
-                                       int dim2, int gx, int gy, int gz,
-                                       int inStride2, int inStride1,
-                                       int inStride0) {
-    T val =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-    if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1 && gz >= 0 && gz < dim2) {
-        val = in[gx * inStride0 + gy * inStride1 + gz * inStride2];
-    }
-    shrd[lx + ly * shrdStride1 + lz * shrdStride2] = val;
-}
-
-// kernel assumes mask/filter is square and hence does the
-// necessary operations accordingly.
-template<typename T, bool isDilation, int windLen>
-static __global__ void morph3DKernel(Param<T> out, CParam<T> in, int nBBS) {
-    // get shared memory pointer
-    SharedMemory<T> shared;
-    T* shrdMem = shared.getPointer();
-
-    const int halo = windLen / 2;
-    const int padding =
-        (windLen % 2 == 0 ? (windLen - 1) : (2 * (windLen / 2)));
-
-    const int se_area  = windLen * windLen;
-    const int shrdLen  = blockDim.x + padding + 1;
-    const int shrdLen1 = blockDim.y + padding;
-    const int shrdLen2 = blockDim.z + padding;
-    const int shrdArea = shrdLen * shrdLen1;
-
-    // gfor batch offsets
-    unsigned batchId = blockIdx.x / nBBS;
-
-    const T* iptr = (const T*)in.ptr + (batchId * in.strides[3]);
-    T* optr       = (T*)out.ptr + (batchId * out.strides[3]);
-
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-    const int lz = threadIdx.z;
-
-    const int gx = blockDim.x * (blockIdx.x - batchId * nBBS) + lx;
-    const int gy = blockDim.y * blockIdx.y + ly;
-    const int gz = blockDim.z * blockIdx.z + lz;
+static const int MAX_MORPH_FILTER_LEN = 17;
+static const int THREADS_X            = 16;
+static const int THREADS_Y            = 16;
+static const int CUBE_X               = 8;
+static const int CUBE_Y               = 8;
+static const int CUBE_Z               = 8;
 
-    for (int c = lz, gz2 = gz; c < shrdLen2;
-         c += blockDim.z, gz2 += blockDim.z) {
-        for (int b = ly, gy2 = gy; b < shrdLen1;
-             b += blockDim.y, gy2 += blockDim.y) {
-            for (int a = lx, gx2 = gx; a < shrdLen;
-                 a += blockDim.x, gx2 += blockDim.x) {
-                load2ShrdVolume<T, isDilation>(
-                    shrdMem, iptr, a, b, c, shrdLen, shrdArea, in.dims[0],
-                    in.dims[1], in.dims[2], gx2 - halo, gy2 - halo, gz2 - halo,
-                    in.strides[2], in.strides[1], in.strides[0]);
-            }
-        }
-    }
+template<typename T>
+void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
+    static const std::string source(morph_cuh, morph_cuh_len);
 
-    __syncthreads();
-    // indices of voxel owned by current thread
-    int i = lx + halo;
-    int j = ly + halo;
-    int k = lz + halo;
+    const int windLen  = mask.dims[0];
+    const int SeLength = (windLen <= 10 ? windLen : 0);
 
-    const T* d_filt = (const T*)cFilter;
-    T acc =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-#pragma unroll
-    for (int wk = 0; wk < windLen; ++wk) {
-        int koff   = wk * se_area;
-        int w_koff = (k + wk - halo) * shrdArea;
-#pragma unroll
-        for (int wj = 0; wj < windLen; ++wj) {
-            int joff   = wj * windLen;
-            int w_joff = (j + wj - halo) * shrdLen;
-#pragma unroll
-            for (int wi = 0; wi < windLen; ++wi) {
-                if (d_filt[koff + joff + wi]) {
-                    T cur = shrdMem[w_koff + w_joff + i + wi - halo];
-                    if (isDilation)
-                        acc = max(acc, cur);
-                    else
-                        acc = min(acc, cur);
-                }
-            }
-        }
-    }
+    auto morph = getKernel(
+        "cuda::morph", source,
+        {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(SeLength)},
+        {
+            DefineValue(MAX_MORPH_FILTER_LEN),
+        });
 
-    if (gx < in.dims[0] && gy < in.dims[1] && gz < in.dims[2]) {
-        int outIdx =
-            gz * out.strides[2] + gy * out.strides[1] + gx * out.strides[0];
-        optr[outIdx] = acc;
-    }
-}
+    morph.setConstant("cFilter", reinterpret_cast<CUdeviceptr>(mask.ptr),
+                      mask.dims[0] * mask.dims[1] * sizeof(T));
 
-template<typename T, bool isDilation>
-void morph(Param<T> out, CParam<T> in, int windLen) {
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
     int blk_x = divup(in.dims[0], THREADS_X);
@@ -255,54 +56,27 @@ void morph(Param<T> out, CParam<T> in, int windLen) {
         kernel::THREADS_X + padding + 1;  // +1 for to avoid bank conflicts
     int shrdSize = shrdLen * (kernel::THREADS_Y + padding) * sizeof(T);
 
-    switch (windLen) {
-        case 2:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 2>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 3:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 3>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 4:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 4>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 5:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 5>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 6:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 6>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 7:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 7>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 8:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 8>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 9:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 9>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        case 10:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation, 10>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y);
-            break;
-        default:
-            CUDA_LAUNCH_SMEM((morphKernel<T, isDilation>), blocks, threads,
-                             shrdSize, out, in, blk_x, blk_y, windLen);
-            break;
-    }
-
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(), shrdSize);
+    morph(qArgs, out, in, blk_x, blk_y, windLen);
     POST_LAUNCH_CHECK();
 }
 
-template<typename T, bool isDilation>
-void morph3d(Param<T> out, CParam<T> in, int windLen) {
+template<typename T>
+void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
+    static const std::string source(morph_cuh, morph_cuh_len);
+
+    const int windLen = mask.dims[0];
+
+    auto morph3D = getKernel(
+        "cuda::morph3D", source,
+        {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(windLen)},
+        {
+            DefineValue(MAX_MORPH_FILTER_LEN),
+        });
+
+    morph3D.setConstant("cFilter", reinterpret_cast<CUdeviceptr>(mask.ptr),
+                      mask.dims[0] * mask.dims[1] * mask.dims[2] * sizeof(T));
+
     dim3 threads(kernel::CUBE_X, kernel::CUBE_Y, kernel::CUBE_Z);
 
     int blk_x = divup(in.dims[0], CUBE_X);
@@ -317,37 +91,14 @@ void morph3d(Param<T> out, CParam<T> in, int windLen) {
     int shrdSize = shrdLen * (kernel::CUBE_Y + padding) *
                    (kernel::CUBE_Z + padding) * sizeof(T);
 
-    switch (windLen) {
-        case 2:
-            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 2>), blocks, threads,
-                             shrdSize, out, in, blk_x);
-            break;
-        case 3:
-            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 3>), blocks, threads,
-                             shrdSize, out, in, blk_x);
-            break;
-        case 4:
-            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 4>), blocks, threads,
-                             shrdSize, out, in, blk_x);
-            break;
-        case 5:
-            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 5>), blocks, threads,
-                             shrdSize, out, in, blk_x);
-            break;
-        case 6:
-            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 6>), blocks, threads,
-                             shrdSize, out, in, blk_x);
-            break;
-        case 7:
-            CUDA_LAUNCH_SMEM((morph3DKernel<T, isDilation, 7>), blocks, threads,
-                             shrdSize, out, in, blk_x);
-            break;
-        default:
-            CUDA_NOT_SUPPORTED(
-                "Morph 3D does not support kernels larger than 7.");
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(), shrdSize);
+    if (windLen <= 7) {
+        morph3D(qArgs, out, in, blk_x);
+    } else {
+        CUDA_NOT_SUPPORTED("Morph 3D does not support kernels larger than 7.");
     }
-
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/pad_array_borders.cuh b/src/backend/cuda/kernel/pad_array_borders.cuh
new file mode 100644
index 0000000000..bff5f86af8
--- /dev/null
+++ b/src/backend/cuda/kernel/pad_array_borders.cuh
@@ -0,0 +1,89 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace  cuda {
+
+template<af::borderType BType>
+__device__
+int idxByndEdge(const int i, const int lb, const int len) {
+    uint retVal;
+    switch (BType) {
+        case AF_PAD_SYM:
+            retVal =
+                ((i < lb || i >= (lb + len)) ? ((len - 1) - ((i - lb) % len))
+                                             : i - lb);
+            break;
+        case AF_PAD_CLAMP_TO_EDGE: retVal = clamp(i - lb, 0, len - 1); break;
+        default:  // AF_PAD_ZERO
+            retVal = 0;
+            break;
+    }
+    return retVal;
+}
+
+template<typename T, af::borderType BType>
+__global__
+void padBorders(Param<T> out, CParam<T> in, const int l0,
+               const int l1, const int l2, const int l3,
+               unsigned blk_x, unsigned blk_y) {
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+    const int k  = blockIdx.x / blk_x;
+    const int l  = blockIdx.y / blk_y;
+
+    const int blockIdx_x = blockIdx.x - (blk_x)*k;
+    const int blockIdx_y = blockIdx.y - (blk_y)*l;
+    const int i          = blockIdx_x * blockDim.x + lx;
+    const int j          = blockIdx_y * blockDim.y + ly;
+
+    const int d0 = in.dims[0];
+    const int d1 = in.dims[1];
+    const int d2 = in.dims[2];
+    const int d3 = in.dims[3];
+    const int s0 = in.strides[0];
+    const int s1 = in.strides[1];
+    const int s2 = in.strides[2];
+    const int s3 = in.strides[3];
+
+    const T* src = in.ptr;
+    T* dst       = out.ptr;
+
+    bool isNotPadding =
+        (l >= l3 && l < (d3 + l3)) && (k >= l2 && k < (d2 + l2)) &&
+        (j >= l1 && j < (d1 + l1)) && (i >= l0 && i < (d0 + l0));
+    T value = scalar<T>(0);
+
+    if (isNotPadding) {
+        unsigned iLOff = (l - l3) * s3;
+        unsigned iKOff = (k - l2) * s2;
+        unsigned iJOff = (j - l1) * s1;
+        unsigned iIOff = (i - l0) * s0;
+
+        value = src[iLOff + iKOff + iJOff + iIOff];
+    } else if (BType != AF_PAD_ZERO) {
+        unsigned iLOff = idxByndEdge<BType>(l, l3, d3) * s3;
+        unsigned iKOff = idxByndEdge<BType>(k, l2, d2) * s2;
+        unsigned iJOff = idxByndEdge<BType>(j, l1, d1) * s1;
+        unsigned iIOff = idxByndEdge<BType>(i, l0, d0) * s0;
+
+        value = src[iLOff + iKOff + iJOff + iIOff];
+    }
+
+    if (i < out.dims[0] && j < out.dims[1] && k < out.dims[2] &&
+        l < out.dims[3]) {
+        unsigned off = (l * out.strides[3] + k * out.strides[2] +
+                        j * out.strides[1] + i * out.strides[0]);
+        dst[off]     = value;
+    }
+}
+
+}
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index ecc4135d65..e3aff9b25d 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -8,93 +8,30 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <platform.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/pad_array_borders_cuh.hpp>
+#include <af/defines.h>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 static const int PADB_THREADS_X = 32;
 static const int PADB_THREADS_Y = 8;
 
-template<af_border_type BType>
-__device__ int idxByndEdge(const int i, const int lb, const int len) {
-    uint retVal;
-    switch (BType) {
-        case AF_PAD_SYM:
-            retVal =
-                ((i < lb || i >= (lb + len)) ? ((len - 1) - ((i - lb) % len))
-                                             : i - lb);
-            break;
-        case AF_PAD_CLAMP_TO_EDGE: retVal = clamp(i - lb, 0, len - 1); break;
-        default:  // AF_PAD_ZERO
-            retVal = 0;
-            break;
-    }
-    return retVal;
-}
-
-template<typename T, af_border_type BType>
-__global__ void padBordersKernel(Param<T> out, CParam<T> in, const int l0,
-                                 const int l1, const int l2, const int l3,
-                                 unsigned blk_x, unsigned blk_y) {
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-    const int k  = blockIdx.x / blk_x;
-    const int l  = blockIdx.y / blk_y;
-
-    const int blockIdx_x = blockIdx.x - (blk_x)*k;
-    const int blockIdx_y = blockIdx.y - (blk_y)*l;
-    const int i          = blockIdx_x * blockDim.x + lx;
-    const int j          = blockIdx_y * blockDim.y + ly;
-
-    const int d0 = in.dims[0];
-    const int d1 = in.dims[1];
-    const int d2 = in.dims[2];
-    const int d3 = in.dims[3];
-    const int s0 = in.strides[0];
-    const int s1 = in.strides[1];
-    const int s2 = in.strides[2];
-    const int s3 = in.strides[3];
-
-    const T* src = in.ptr;
-    T* dst       = out.ptr;
-
-    bool isNotPadding =
-        (l >= l3 && l < (d3 + l3)) && (k >= l2 && k < (d2 + l2)) &&
-        (j >= l1 && j < (d1 + l1)) && (i >= l0 && i < (d0 + l0));
-    T value = scalar<T>(0);
-
-    if (isNotPadding) {
-        unsigned iLOff = (l - l3) * s3;
-        unsigned iKOff = (k - l2) * s2;
-        unsigned iJOff = (j - l1) * s1;
-        unsigned iIOff = (i - l0) * s0;
-
-        value = src[iLOff + iKOff + iJOff + iIOff];
-    } else if (BType != AF_PAD_ZERO) {
-        unsigned iLOff = idxByndEdge<BType>(l, l3, d3) * s3;
-        unsigned iKOff = idxByndEdge<BType>(k, l2, d2) * s2;
-        unsigned iJOff = idxByndEdge<BType>(j, l1, d1) * s1;
-        unsigned iIOff = idxByndEdge<BType>(i, l0, d0) * s0;
-
-        value = src[iLOff + iKOff + iJOff + iIOff];
-    }
-
-    if (i < out.dims[0] && j < out.dims[1] && k < out.dims[2] &&
-        l < out.dims[3]) {
-        unsigned off = (l * out.strides[3] + k * out.strides[2] +
-                        j * out.strides[1] + i * out.strides[0]);
-        dst[off]     = value;
-    }
-}
-
 template<typename T>
 void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
                 const af::borderType btype) {
+    static const std::string source(pad_array_borders_cuh,
+                                    pad_array_borders_cuh_len);
+    auto padBorders = getKernel("cuda::padBorders", source,
+                                {TemplateTypename<T>(), TemplateArg(btype)});
+
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
 
     int blk_x = divup(out.dims[0], PADB_THREADS_X);
@@ -102,24 +39,13 @@ void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
 
     dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
 
-    switch (btype) {
-        case AF_PAD_SYM:
-            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_SYM>), blocks, threads, out,
-                        in, lBoundPadding[0], lBoundPadding[1],
-                        lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
-            break;
-        case AF_PAD_CLAMP_TO_EDGE:
-            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_CLAMP_TO_EDGE>), blocks,
-                        threads, out, in, lBoundPadding[0], lBoundPadding[1],
-                        lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
-            break;
-        default:
-            CUDA_LAUNCH((padBordersKernel<T, AF_PAD_ZERO>), blocks, threads,
-                        out, in, lBoundPadding[0], lBoundPadding[1],
-                        lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
-            break;
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    padBorders(qArgs, out, in, lBoundPadding[0], lBoundPadding[1],
+               lBoundPadding[2], lBoundPadding[3], blk_x, blk_y);
+
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/resize.cuh b/src/backend/cuda/kernel/resize.cuh
new file mode 100644
index 0000000000..22a0d1d159
--- /dev/null
+++ b/src/backend/cuda/kernel/resize.cuh
@@ -0,0 +1,122 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <interp.hpp>
+
+namespace cuda {
+
+// nearest-neighbor resampling
+template<typename T>
+__host__ __device__
+void resize_n(Param<T> out, CParam<T> in, const int o_off,
+              const int i_off, const int blockIdx_x,
+              const int blockIdx_y, const float xf,
+              const float yf) {
+    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
+    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    int ix = round(ox * xf);
+    int iy = round(oy * yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    out.ptr[o_off + ox + oy * out.strides[1]] =
+        in.ptr[i_off + ix + iy * in.strides[1]];
+}
+
+// bilinear resampling
+template<typename T>
+__host__ __device__
+void resize_b(Param<T> out, CParam<T> in, const int o_off,
+              const int i_off, const int blockIdx_x,
+              const int blockIdx_y, const float xf_,
+              const float yf_) {
+    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
+    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    float xf = ox * xf_;
+    float yf = oy * yf_;
+
+    int ix = floorf(xf);
+    int iy = floorf(yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    float b = xf - ix;
+    float a = yf - iy;
+
+    const int ix2 = ix + 1 < in.dims[0] ? ix + 1 : ix;
+    const int iy2 = iy + 1 < in.dims[1] ? iy + 1 : iy;
+
+    typedef typename itype_t<T>::wtype WT;
+    typedef typename itype_t<T>::vtype VT;
+
+    const T *iptr = in.ptr + i_off;
+
+    const VT p1 = iptr[ix + in.strides[1] * iy];
+    const VT p2 = iptr[ix + in.strides[1] * iy2];
+    const VT p3 = iptr[ix2 + in.strides[1] * iy];
+    const VT p4 = iptr[ix2 + in.strides[1] * iy2];
+
+    VT val = scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
+             scalar<WT>((a) * (1.0f - b)) * p2 +
+             scalar<WT>((1.0f - a) * (b)) * p3 + scalar<WT>((a) * (b)) * p4;
+
+    out.ptr[o_off + ox + oy * out.strides[1]] = val;
+}
+
+// lower resampling
+template<typename T>
+__host__ __device__
+void resize_l(Param<T> out, CParam<T> in, const int o_off,
+              const int i_off, const int blockIdx_x,
+              const int blockIdx_y, const float xf,
+              const float yf) {
+    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
+    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    int ix = (ox * xf);
+    int iy = (oy * yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    out.ptr[o_off + ox + oy * out.strides[1]] =
+        in.ptr[i_off + ix + iy * in.strides[1]];
+}
+
+template<typename T, af::interpType method>
+__global__
+void resize(Param<T> out, CParam<T> in, const int b0,
+            const int b1, const float xf, const float yf) {
+    const int bIdx = blockIdx.x / b0;
+    const int bIdy = blockIdx.y / b1;
+    // channel adjustment
+    const int i_off      = bIdx * in.strides[2] + bIdy * in.strides[3];
+    const int o_off      = bIdx * out.strides[2] + bIdy * out.strides[3];
+    const int blockIdx_x = blockIdx.x - bIdx * b0;
+    const int blockIdx_y = blockIdx.y - bIdy * b1;
+
+    // core
+    if (method == AF_INTERP_NEAREST) {
+        resize_n(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
+    } else if (method == AF_INTERP_BILINEAR) {
+        resize_b(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
+    } else if (method == AF_INTERP_LOWER) {
+        resize_l(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index b45c6c85e3..b3e96760cc 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -10,156 +10,26 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/resize_cuh.hpp>
+#include <af/defines.h>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 // Kernel Launch Config Values
 static const unsigned TX = 16;
 static const unsigned TY = 16;
 
 template<typename T>
-struct itype_t {
-    typedef float wtype;
-    typedef float vtype;
-};
-
-template<>
-struct itype_t<double> {
-    typedef double wtype;
-    typedef double vtype;
-};
-
-template<>
-struct itype_t<cfloat> {
-    typedef float wtype;
-    typedef cfloat vtype;
-};
-
-template<>
-struct itype_t<cdouble> {
-    typedef double wtype;
-    typedef cdouble vtype;
-};
-
-///////////////////////////////////////////////////////////////////////////
-// nearest-neighbor resampling
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-__host__ __device__ void resize_n(Param<T> out, CParam<T> in, const int o_off,
-                                  const int i_off, const int blockIdx_x,
-                                  const int blockIdx_y, const float xf,
-                                  const float yf) {
-    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
-    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    int ix = round(ox * xf);
-    int iy = round(oy * yf);
-
-    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
-    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
-
-    out.ptr[o_off + ox + oy * out.strides[1]] =
-        in.ptr[i_off + ix + iy * in.strides[1]];
-}
-
-///////////////////////////////////////////////////////////////////////////
-// bilinear resampling
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-__host__ __device__ void resize_b(Param<T> out, CParam<T> in, const int o_off,
-                                  const int i_off, const int blockIdx_x,
-                                  const int blockIdx_y, const float xf_,
-                                  const float yf_) {
-    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
-    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    float xf = ox * xf_;
-    float yf = oy * yf_;
-
-    int ix = floorf(xf);
-    int iy = floorf(yf);
-
-    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
-    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
-
-    float b = xf - ix;
-    float a = yf - iy;
-
-    const int ix2 = ix + 1 < in.dims[0] ? ix + 1 : ix;
-    const int iy2 = iy + 1 < in.dims[1] ? iy + 1 : iy;
+void resize(Param<T> out, CParam<T> in, af_interp_type method) {
+    static const std::string source(resize_cuh, resize_cuh_len);
 
-    typedef typename itype_t<T>::wtype WT;
-    typedef typename itype_t<T>::vtype VT;
+    auto resize = getKernel("cuda::resize", source,
+                            {TemplateTypename<T>(), TemplateArg(method)});
 
-    const T *iptr = in.ptr + i_off;
-
-    const VT p1 = iptr[ix + in.strides[1] * iy];
-    const VT p2 = iptr[ix + in.strides[1] * iy2];
-    const VT p3 = iptr[ix2 + in.strides[1] * iy];
-    const VT p4 = iptr[ix2 + in.strides[1] * iy2];
-
-    VT val = scalar<WT>((1.0f - a) * (1.0f - b)) * p1 +
-             scalar<WT>((a) * (1.0f - b)) * p2 +
-             scalar<WT>((1.0f - a) * (b)) * p3 + scalar<WT>((a) * (b)) * p4;
-
-    out.ptr[o_off + ox + oy * out.strides[1]] = val;
-}
-
-///////////////////////////////////////////////////////////////////////////
-// lower resampling
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-__host__ __device__ void resize_l(Param<T> out, CParam<T> in, const int o_off,
-                                  const int i_off, const int blockIdx_x,
-                                  const int blockIdx_y, const float xf,
-                                  const float yf) {
-    const int ox = threadIdx.x + blockIdx_x * blockDim.x;
-    const int oy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    int ix = (ox * xf);
-    int iy = (oy * yf);
-
-    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
-    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
-    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
-
-    out.ptr[o_off + ox + oy * out.strides[1]] =
-        in.ptr[i_off + ix + iy * in.strides[1]];
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Resize Kernel
-///////////////////////////////////////////////////////////////////////////
-template<typename T, af_interp_type method>
-__global__ void resize_kernel(Param<T> out, CParam<T> in, const int b0,
-                              const int b1, const float xf, const float yf) {
-    const int bIdx = blockIdx.x / b0;
-    const int bIdy = blockIdx.y / b1;
-    // channel adjustment
-    const int i_off      = bIdx * in.strides[2] + bIdy * in.strides[3];
-    const int o_off      = bIdx * out.strides[2] + bIdy * out.strides[3];
-    const int blockIdx_x = blockIdx.x - bIdx * b0;
-    const int blockIdx_y = blockIdx.y - bIdy * b1;
-
-    // core
-    if (method == AF_INTERP_NEAREST) {
-        resize_n(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
-    } else if (method == AF_INTERP_BILINEAR) {
-        resize_b(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
-    } else if (method == AF_INTERP_LOWER) {
-        resize_l(out, in, o_off, i_off, blockIdx_x, blockIdx_y, xf, yf);
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, af_interp_type method>
-void resize(Param<T> out, CParam<T> in) {
     dim3 threads(TX, TY, 1);
     dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
     int blocksPerMatX = blocks.x;
@@ -170,9 +40,12 @@ void resize(Param<T> out, CParam<T> in) {
     float xf = (float)in.dims[0] / (float)out.dims[0];
     float yf = (float)in.dims[1] / (float)out.dims[1];
 
-    CUDA_LAUNCH((resize_kernel<T, method>), blocks, threads, out, in,
-                blocksPerMatX, blocksPerMatY, xf, yf);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    resize(qArgs, out, in, blocksPerMatX, blocksPerMatY, xf, yf);
+
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/rotate.cuh b/src/backend/cuda/kernel/rotate.cuh
new file mode 100644
index 0000000000..ab4b2ba79f
--- /dev/null
+++ b/src/backend/cuda/kernel/rotate.cuh
@@ -0,0 +1,72 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <interp.hpp>
+
+namespace cuda {
+
+typedef struct {
+    float tmat[6];
+} tmat_t;
+
+template<typename T, int order>
+__global__ void rotate(Param<T> out, CParam<T> in, const tmat_t t,
+                       const int nimages, const int nbatches,
+                       const int blocksXPerImage,
+                       const int blocksYPerImage,
+                       af::interpType method) {
+    // Compute which image set
+    const int setId      = blockIdx.x / blocksXPerImage;
+    const int blockIdx_x = blockIdx.x - setId * blocksXPerImage;
+
+    const int batch      = blockIdx.y / blocksYPerImage;
+    const int blockIdx_y = blockIdx.y - batch * blocksYPerImage;
+
+    // Get thread indices
+    const int xido = blockIdx_x * blockDim.x + threadIdx.x;
+    const int yido = blockIdx_y * blockDim.y + threadIdx.y;
+
+    const int limages = min(out.dims[2] - setId * nimages, nimages);
+
+    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
+
+    // Compute input index
+    typedef typename itype_t<T>::wtype WT;
+    WT xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
+    WT yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
+
+    // Global offset
+    //          Offset for transform channel + Offset for image channel.
+    int outoff     = setId * nimages * out.strides[2] + batch * out.strides[3];
+    int inoff      = setId * nimages * in.strides[2] + batch * in.strides[3];
+    const int loco = outoff + (yido * out.strides[1] + xido);
+
+    if (order > 1) {
+        // Special conditions to deal with boundaries for bilinear and bicubic
+        // FIXME: Ideally this condition should be removed or be present for all
+        // methods But tests are expecting a different behavior for bilinear and
+        // nearest
+        if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi ||
+            in.dims[1] < yidi) {
+            for (int i = 0; i < nimages; i++) {
+                out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
+            }
+            return;
+        }
+    }
+
+    Interp2<T, WT, order> interp;
+    // FIXME: Nearest and lower do not do clamping, but other methods do
+    // Make it consistent
+    bool clamp = order != 1;
+    interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 708a221f86..0fd2273c32 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -7,15 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include "interp.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/rotate_cuh.hpp>
+#include <af/defines.h>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 // Kernel Launch Config Values
 constexpr unsigned TX = 16;
 constexpr unsigned TY = 16;
@@ -26,68 +31,14 @@ typedef struct {
     float tmat[6];
 } tmat_t;
 
-///////////////////////////////////////////////////////////////////////////
-// Rotate Kernel
-///////////////////////////////////////////////////////////////////////////
-template<typename T, int order>
-__global__ static void rotate_kernel(Param<T> out, CParam<T> in, const tmat_t t,
-                                     const int nimages, const int nbatches,
-                                     const int blocksXPerImage,
-                                     const int blocksYPerImage,
-                                     af_interp_type method) {
-    // Compute which image set
-    const int setId      = blockIdx.x / blocksXPerImage;
-    const int blockIdx_x = blockIdx.x - setId * blocksXPerImage;
-
-    const int batch      = blockIdx.y / blocksYPerImage;
-    const int blockIdx_y = blockIdx.y - batch * blocksYPerImage;
-
-    // Get thread indices
-    const int xido = blockIdx_x * blockDim.x + threadIdx.x;
-    const int yido = blockIdx_y * blockDim.y + threadIdx.y;
-
-    const int limages = min(out.dims[2] - setId * nimages, nimages);
-
-    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
-
-    // Compute input index
-    typedef typename itype_t<T>::wtype WT;
-    WT xidi = xido * t.tmat[0] + yido * t.tmat[1] + t.tmat[2];
-    WT yidi = xido * t.tmat[3] + yido * t.tmat[4] + t.tmat[5];
-
-    // Global offset
-    //          Offset for transform channel + Offset for image channel.
-    int outoff     = setId * nimages * out.strides[2] + batch * out.strides[3];
-    int inoff      = setId * nimages * in.strides[2] + batch * in.strides[3];
-    const int loco = outoff + (yido * out.strides[1] + xido);
-
-    if (order > 1) {
-        // Special conditions to deal with boundaries for bilinear and bicubic
-        // FIXME: Ideally this condition should be removed or be present for all
-        // methods But tests are expecting a different behavior for bilinear and
-        // nearest
-        if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] < xidi ||
-            in.dims[1] < yidi) {
-            for (int i = 0; i < nimages; i++) {
-                out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
-            }
-            return;
-        }
-    }
+template<typename T>
+void rotate(Param<T> out, CParam<T> in, const float theta,
+            const af::interpType method, const int order) {
+    static const std::string source(rotate_cuh, rotate_cuh_len);
 
-    Interp2<T, WT, order> interp;
-    // FIXME: Nearest and lower do not do clamping, but other methods do
-    // Make it consistent
-    bool clamp = order != 1;
-    interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
-}
+    auto rotate = getKernel("cuda::rotate", source,
+                            {TemplateTypename<T>(), TemplateArg(order)});
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, int order>
-void rotate(Param<T> out, CParam<T> in, const float theta,
-            af_interp_type method) {
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
     {
@@ -127,10 +78,13 @@ void rotate(Param<T> out, CParam<T> in, const float theta,
 
     blocks.y = blocks.y * nbatches;
 
-    CUDA_LAUNCH((rotate_kernel<T, order>), blocks, threads, out, in, t, nimages,
-                nbatches, blocksXPerImage, blocksYPerImage, method);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    rotate(qArgs, out, in, t, nimages, nbatches, blocksXPerImage,
+           blocksYPerImage, method);
 
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 0b8685c8a9..5a9815ae7b 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -23,26 +23,15 @@ namespace kernel {
 static const std::string ScanDimSource(scan_dim_cuh, scan_dim_cuh_len);
 
 template<typename Ti, typename To, af_op_t op>
-static
-void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
-                       const uint threads_y, const dim_t blocks_all[4],
-                       int dim, bool isFinalPass, bool inclusive_scan) {
-    // clang-format off
-    auto scanDim = getKernel("cuda::scan_dim", ScanDimSource,
-            {
-              TemplateTypename<Ti>(),
-              TemplateTypename<To>(),
-              TemplateArg(op),
-              TemplateArg(dim),
-              TemplateArg(isFinalPass),
-              TemplateArg(threads_y),
-              TemplateArg(inclusive_scan)
-            },
-            {
-              DefineValue(THREADS_X)
-            }
-            );
-    // clang-format on
+static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                              const uint threads_y, const dim_t blocks_all[4],
+                              int dim, bool isFinalPass, bool inclusive_scan) {
+    auto scan_dim =
+        getKernel("cuda::scan_dim", ScanDimSource,
+                  {TemplateTypename<Ti>(), TemplateTypename<To>(),
+                   TemplateArg(op), TemplateArg(dim), TemplateArg(isFinalPass),
+                   TemplateArg(threads_y), TemplateArg(inclusive_scan)},
+                  {DefineValue(THREADS_X)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -56,25 +45,18 @@ void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanDim(qArgs, out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim],
+    scan_dim(qArgs, out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim],
             lim);
     POST_LAUNCH_CHECK();
 }
 
 template<typename To, af_op_t op>
-static
-void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
-                        const uint threads_y, const dim_t blocks_all[4],
-                        int dim, bool inclusive_scan) {
-    // clang-format off
-    auto bcastDim = getKernel("cuda::scan_dim_bcast", ScanDimSource,
-            {
-              TemplateTypename<To>(),
-              TemplateArg(op),
-              TemplateArg(dim)
-            }
-            );
-    // clang-format on
+static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
+                               const uint threads_y, const dim_t blocks_all[4],
+                               int dim, bool inclusive_scan) {
+    auto scan_dim_bcast =
+        getKernel("cuda::scan_dim_bcast", ScanDimSource,
+                  {TemplateTypename<To>(), TemplateArg(op), TemplateArg(dim)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -88,7 +70,7 @@ void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    bcastDim(qArgs, out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim],
+    scan_dim_bcast(qArgs, out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim],
              lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index c8ab453658..91fcb1d0a9 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -33,21 +33,12 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const int dim, const uint threads_y,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
-    // clang-format off
-    auto scanDimNonFinal = getKernel("cuda::scanbykey_dim_nonfinal",
-            ScanDimByKeySource,
-            {
-              TemplateTypename<Ti>(),
-              TemplateTypename<Tk>(),
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            },
-            {
-              DefineValue(THREADS_X),
-              DefineKeyValue(DIMY, threads_y)
-            }
-            );
-    // clang-format on
+    auto scanbykey_dim_nonfinal =
+        getKernel("cuda::scanbykey_dim_nonfinal", ScanDimByKeySource,
+                  {TemplateTypename<Ti>(), TemplateTypename<Tk>(),
+                   TemplateTypename<To>(), TemplateArg(op)},
+                  {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
@@ -55,7 +46,7 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanDimNonFinal(qArgs, out, tmp, tflg, tlid, in, key, dim, blocks_all[0],
+    scanbykey_dim_nonfinal(qArgs, out, tmp, tflg, tlid, in, key, dim, blocks_all[0],
                     blocks_all[1], lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
@@ -66,21 +57,12 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const uint threads_y,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
-    // clang-format off
-    auto scanDimFinal = getKernel("cuda::scanbykey_dim_final",
-            ScanDimByKeySource,
-            {
-              TemplateTypename<Ti>(),
-              TemplateTypename<Tk>(),
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            },
-            {
-              DefineValue(THREADS_X),
-              DefineKeyValue(DIMY, threads_y)
-            }
-            );
-    // clang-format on
+    auto scanbykey_dim_final =
+        getKernel("cuda::scanbykey_dim_final", ScanDimByKeySource,
+                  {TemplateTypename<Ti>(), TemplateTypename<Tk>(),
+                   TemplateTypename<To>(), TemplateArg(op)},
+                  {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
@@ -88,7 +70,7 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanDimFinal(qArgs, out, in, key, dim, blocks_all[0], blocks_all[1], lim,
+    scanbykey_dim_final(qArgs, out, in, key, dim, blocks_all[0], blocks_all[1], lim,
                  calculateFlags, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
@@ -97,22 +79,15 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
-    // clang-format off
-    auto bcastDim = getKernel("cuda::scanbykey_dim_bcast", ScanDimByKeySource,
-            {
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            }
-            );
-    // clang-format on
+    auto scanbykey_dim_bcast = getKernel("cuda::scanbykey_dim_bcast", ScanDimByKeySource,
+                              {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(THREADS_X, threads_y);
-
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    bcastDim(qArgs, out, tmp, tlid, dim, blocks_all[0], blocks_all[1],
+    scanbykey_dim_bcast(qArgs, out, tmp, tlid, dim, blocks_all[0], blocks_all[1],
              blocks_all[dim], lim);
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 15e0a01682..a339452caf 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -23,26 +23,16 @@ namespace kernel {
 static const std::string ScanFirstSource(scan_first_cuh, scan_first_cuh_len);
 
 template<typename Ti, typename To, af_op_t op>
-static
-void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
-                         const uint blocks_x, const uint blocks_y,
-                         const uint threads_x, bool isFinalPass,
-                         bool inclusive_scan) {
-    // clang-format off
-    auto scanFirst = getKernel("cuda::scan_first", ScanFirstSource,
-            {
-              TemplateTypename<Ti>(),
-              TemplateTypename<To>(),
-              TemplateArg(op),
-              TemplateArg(isFinalPass),
-              TemplateArg(threads_x),
-              TemplateArg(inclusive_scan)
-            },
-            {
-              DefineValue(THREADS_PER_BLOCK)
-            }
-            );
-    // clang-format on
+static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                                const uint blocks_x, const uint blocks_y,
+                                const uint threads_x, bool isFinalPass,
+                                bool inclusive_scan) {
+    auto scan_first =
+        getKernel("cuda::scan_first", ScanFirstSource,
+                  {TemplateTypename<Ti>(), TemplateTypename<To>(),
+                   TemplateArg(op), TemplateArg(isFinalPass),
+                   TemplateArg(threads_x), TemplateArg(inclusive_scan)},
+                  {DefineValue(THREADS_PER_BLOCK)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -55,7 +45,7 @@ void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanFirst(qArgs, out, tmp, in, blocks_x, blocks_y, lim);
+    scan_first(qArgs, out, tmp, in, blocks_x, blocks_y, lim);
     POST_LAUNCH_CHECK();
 }
 
@@ -63,14 +53,8 @@ template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
-    // clang-format off
-    auto bcastFirst = getKernel("cuda::scan_first_bcast", ScanFirstSource,
-            {
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            }
-            );
-    // clang-format on
+    auto scan_first_bcast = getKernel("cuda::scan_first_bcast", ScanFirstSource,
+                                {TemplateTypename<To>(), TemplateArg(op)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -83,7 +67,7 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    bcastFirst(qArgs, out, tmp, blocks_x, blocks_y, lim, inclusive_scan);
+    scan_first_bcast(qArgs, out, tmp, blocks_x, blocks_y, lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index cdbdb29893..8672a4e978 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -19,7 +19,6 @@
 #include <optypes.hpp>
 #include "config.hpp"
 
-
 namespace cuda {
 namespace kernel {
 
@@ -32,28 +31,18 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    CParam<Ti> in, CParam<Tk> key,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
-    // clang-format off
-    auto scanNonFinal = getKernel("cuda::scanbykey_first_nonfinal",
-            ScanFirstByKeySource,
-            {
-              TemplateTypename<Ti>(),
-              TemplateTypename<Tk>(),
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            },
-            {
-              DefineValue(THREADS_PER_BLOCK),
-              DefineKeyValue(DIMX, threads_x)
-            }
-            );
-    // clang-format on
+    auto scanbykey_first_nonfinal = getKernel(
+        "cuda::scanbykey_first_nonfinal", ScanFirstByKeySource,
+        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
+         TemplateArg(op)},
+        {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanNonFinal(qArgs, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim,
+    scanbykey_first_nonfinal(qArgs, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim,
                  inclusive_scan);
     POST_LAUNCH_CHECK();
 }
@@ -63,28 +52,18 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
-    // clang-format off
-    auto scanFinal = getKernel("cuda::scanbykey_first_final",
-            ScanFirstByKeySource,
-            {
-              TemplateTypename<Ti>(),
-              TemplateTypename<Tk>(),
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            },
-            {
-              DefineValue(THREADS_PER_BLOCK),
-              DefineKeyValue(DIMX, threads_x)
-            }
-            );
-    // clang-format on
+    auto scanbykey_first_final = getKernel(
+        "cuda::scanbykey_first_final", ScanFirstByKeySource,
+        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
+         TemplateArg(op)},
+        {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanFinal(qArgs, out, in, key, blocks_x, blocks_y, lim, calculateFlags,
+    scanbykey_first_final(qArgs, out, in, key, blocks_x, blocks_y, lim, calculateFlags,
               inclusive_scan);
     POST_LAUNCH_CHECK();
 }
@@ -93,21 +72,15 @@ template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
-    // clang-format off
-    auto bcastFirst = getKernel("cuda::scanbykey_first_bcast",
-            ScanFirstByKeySource,
-            {
-              TemplateTypename<To>(),
-              TemplateArg(op)
-            }
-            );
-    // clang-format on
+    auto scanbykey_first_bcast =
+        getKernel("cuda::scanbykey_first_bcast", ScanFirstByKeySource,
+                  {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    bcastFirst(qArgs, out, tmp, tlid, blocks_x, blocks_y, lim);
+    scanbykey_first_bcast(qArgs, out, tmp, tlid, blocks_x, blocks_y, lim);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 3c8b4d92a4..66cd0147bb 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -1061,7 +1061,7 @@ Array<T> createInitialImage(CParam<T> img, const float init_sigma,
     Array<convAccT> filter = gauss_filter<convAccT>(s);
 
     if (double_input) {
-        resize<T, AF_INTERP_BILINEAR>(init_img, img);
+        resize<T, InterpolationType::Bilinear>(init_img, img);
         convolve2<T, convAccT>(init_tmp, init_img, filter, 0, false);
     } else
         convolve2<T, convAccT>(init_tmp, img, filter, 0, false);
@@ -1108,7 +1108,8 @@ std::vector<Array<T>> buildGaussPyr(Param<T> init_img, const unsigned n_octaves,
                 tmp_pyr.push_back(
                     createEmptyArray<T>({tmp_pyr[src_idx].dims()[0] / 2,
                                          tmp_pyr[src_idx].dims()[1] / 2}));
-                resize<T, AF_INTERP_BILINEAR>(tmp_pyr[idx], tmp_pyr[src_idx]);
+                resize<T, InterpolationType::Bilinear>(tmp_pyr[idx],
+                                                       tmp_pyr[src_idx]);
             } else {
                 tmp_pyr.push_back(createEmptyArray<T>(tmp_pyr[src_idx].dims()));
                 Array<T> tmp = createEmptyArray<T>(tmp_pyr[src_idx].dims());
diff --git a/src/backend/cuda/kernel/sobel.cuh b/src/backend/cuda/kernel/sobel.cuh
new file mode 100644
index 0000000000..418b14d3bf
--- /dev/null
+++ b/src/backend/cuda/kernel/sobel.cuh
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename Ti>
+__device__ Ti load2ShrdMem(const Ti* in, int dim0, int dim1, int gx, int gy,
+                           int inStride1, int inStride0) {
+    if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
+        return Ti(0);
+    else
+        return in[gx * inStride0 + gy * inStride1];
+}
+
+template<typename Ti, typename To>
+__global__ void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0,
+                         int nBBS1) {
+    __shared__ Ti shrdMem[THREADS_X + 2][THREADS_Y + 2];
+
+    // calculate necessary offset and window parameters
+    const int radius  = 1;
+    const int padding = 2 * radius;
+    const int shrdLen = blockDim.x + padding;
+
+    // batch offsets
+    unsigned b2 = blockIdx.x / nBBS0;
+    unsigned b3 = blockIdx.y / nBBS1;
+    const Ti* iptr =
+        (const Ti*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
+    To* dxptr = (To*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]);
+    To* dyptr = (To*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]);
+
+    // local neighborhood indices
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+
+    // global indices
+    int gx = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
+    int gy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
+
+    for (int b = ly, gy2 = gy; b < shrdLen;
+         b += blockDim.y, gy2 += blockDim.y) {
+        for (int a = lx, gx2 = gx; a < shrdLen;
+             a += blockDim.x, gx2 += blockDim.x) {
+            shrdMem[a][b] =
+                load2ShrdMem<Ti>(iptr, in.dims[0], in.dims[1], gx2 - radius,
+                                 gy2 - radius, in.strides[1], in.strides[0]);
+        }
+    }
+
+    __syncthreads();
+
+    // Only continue if we're at a valid location
+    if (gx < in.dims[0] && gy < in.dims[1]) {
+        int i  = lx + radius;
+        int j  = ly + radius;
+        int _i = i - 1;
+        int i_ = i + 1;
+        int _j = j - 1;
+        int j_ = j + 1;
+
+        float NW = shrdMem[_i][_j];
+        float SW = shrdMem[i_][_j];
+        float NE = shrdMem[_i][j_];
+        float SE = shrdMem[i_][j_];
+
+        float t1                       = shrdMem[i][_j];
+        float t2                       = shrdMem[i][j_];
+        dxptr[gy * dx.strides[1] + gx] = (NW + SW - (NE + SE) + 2 * (t1 - t2));
+
+        t1                             = shrdMem[_i][j];
+        t2                             = shrdMem[i_][j];
+        dyptr[gy * dy.strides[1] + gx] = (NW + NE - (SW + SE) + 2 * (t1 - t2));
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index 2b1649f382..b3a1cb6065 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -7,92 +7,35 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/sobel_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int THREADS_X = 16;
 static const int THREADS_Y = 16;
 
-template<typename Ti>
-__device__ Ti load2ShrdMem(const Ti* in, int dim0, int dim1, int gx, int gy,
-                           int inStride1, int inStride0) {
-    if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
-        return Ti(0);
-    else
-        return in[gx * inStride0 + gy * inStride1];
-}
-
-template<typename Ti, typename To>
-__global__ void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0,
-                         int nBBS1) {
-    __shared__ Ti shrdMem[THREADS_X + 2][THREADS_Y + 2];
-
-    // calculate necessary offset and window parameters
-    const int radius  = 1;
-    const int padding = 2 * radius;
-    const int shrdLen = blockDim.x + padding;
-
-    // batch offsets
-    unsigned b2 = blockIdx.x / nBBS0;
-    unsigned b3 = blockIdx.y / nBBS1;
-    const Ti* iptr =
-        (const Ti*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
-    To* dxptr = (To*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]);
-    To* dyptr = (To*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]);
-
-    // local neighborhood indices
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-
-    // global indices
-    int gx = THREADS_X * (blockIdx.x - b2 * nBBS0) + lx;
-    int gy = THREADS_Y * (blockIdx.y - b3 * nBBS1) + ly;
-
-    for (int b = ly, gy2 = gy; b < shrdLen;
-         b += blockDim.y, gy2 += blockDim.y) {
-        for (int a = lx, gx2 = gx; a < shrdLen;
-             a += blockDim.x, gx2 += blockDim.x) {
-            shrdMem[a][b] =
-                load2ShrdMem<Ti>(iptr, in.dims[0], in.dims[1], gx2 - radius,
-                                 gy2 - radius, in.strides[1], in.strides[0]);
-        }
-    }
-
-    __syncthreads();
-
-    // Only continue if we're at a valid location
-    if (gx < in.dims[0] && gy < in.dims[1]) {
-        int i  = lx + radius;
-        int j  = ly + radius;
-        int _i = i - 1;
-        int i_ = i + 1;
-        int _j = j - 1;
-        int j_ = j + 1;
-
-        float NW = shrdMem[_i][_j];
-        float SW = shrdMem[i_][_j];
-        float NE = shrdMem[_i][j_];
-        float SE = shrdMem[i_][j_];
-
-        float t1                       = shrdMem[i][_j];
-        float t2                       = shrdMem[i][j_];
-        dxptr[gy * dx.strides[1] + gx] = (NW + SW - (NE + SE) + 2 * (t1 - t2));
-
-        t1                             = shrdMem[_i][j];
-        t2                             = shrdMem[i_][j];
-        dyptr[gy * dy.strides[1] + gx] = (NW + NE - (SW + SE) + 2 * (t1 - t2));
-    }
-}
-
 template<typename Ti, typename To>
 void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
            const unsigned& ker_size) {
+    UNUSED(ker_size);
+    static const std::string source(sobel_cuh, sobel_cuh_len);
+
+    auto sobel3x3 = getKernel("cuda::sobel3x3", source,
+                              {
+                                  TemplateTypename<Ti>(),
+                                  TemplateTypename<To>(),
+                              },
+                              {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.dims[0], threads.x);
@@ -100,17 +43,14 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
 
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
-    // TODO: add more cases when 5x5 and 7x7 kernels are done
-    switch (ker_size) {
-        case 3:
-            CUDA_LAUNCH((sobel3x3<Ti, To>), blocks, threads, dx, dy, in, blk_x,
-                        blk_y);
-            break;
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    // TODO: call other cases when support for 5x5 & 7x7 is added
+    // Note: This is checked at sobel API entry point
+    sobel3x3(qArgs, dx, dy, in, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/transform.cuh b/src/backend/cuda/kernel/transform.cuh
new file mode 100644
index 0000000000..fbb870f8a7
--- /dev/null
+++ b/src/backend/cuda/kernel/transform.cuh
@@ -0,0 +1,174 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <interp.hpp>
+
+__constant__ float
+    c_tmat[3072];  // Allows 512 Affine Transforms and 340 Persp. Transforms
+
+namespace cuda {
+
+template<typename T>
+__device__
+void calc_transf_inverse(T *txo, const T *txi, const bool perspective) {
+    if (perspective) {
+        txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
+        txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
+        txo[2] = txi[1] * txi[5] - txi[2] * txi[4];
+
+        txo[3] = -(txi[3] * txi[8] - txi[5] * txi[6]);
+        txo[4] = txi[0] * txi[8] - txi[2] * txi[6];
+        txo[5] = -(txi[0] * txi[5] - txi[2] * txi[3]);
+
+        txo[6] = txi[3] * txi[7] - txi[4] * txi[6];
+        txo[7] = -(txi[0] * txi[7] - txi[1] * txi[6]);
+        txo[8] = txi[0] * txi[4] - txi[1] * txi[3];
+
+        T det = txi[0] * txo[0] + txi[1] * txo[3] + txi[2] * txo[6];
+
+        txo[0] /= det;
+        txo[1] /= det;
+        txo[2] /= det;
+        txo[3] /= det;
+        txo[4] /= det;
+        txo[5] /= det;
+        txo[6] /= det;
+        txo[7] /= det;
+        txo[8] /= det;
+    } else {
+        T det = txi[0] * txi[4] - txi[1] * txi[3];
+
+        txo[0] = txi[4] / det;
+        txo[1] = txi[3] / det;
+        txo[3] = txi[1] / det;
+        txo[4] = txi[0] / det;
+
+        txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+        txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+    }
+}
+
+template<typename T, bool inverse, int order>
+__global__
+void transform(Param<T> out, CParam<T> in,
+               const int nImg2, const int nImg3,
+               const int nTfs2, const int nTfs3,
+               const int batchImg2,
+               const int blocksXPerImage, const int blocksYPerImage,
+               const bool perspective, af::interpType method) {
+    // Image Ids
+    const int imgId2 = blockIdx.x / blocksXPerImage;
+    const int imgId3 = blockIdx.y / blocksYPerImage;
+
+    // Block in local image
+    const int blockIdx_x = blockIdx.x - imgId2 * blocksXPerImage;
+    const int blockIdx_y = blockIdx.y - imgId3 * blocksYPerImage;
+
+    // Get thread indices in local image
+    const int xido = blockIdx_x * blockDim.x + threadIdx.x;
+    const int yido = blockIdx_y * blockDim.y + threadIdx.y;
+
+    // Image iteration loop count for image batching
+    int limages = min(max(out.dims[2] - imgId2 * nImg2, 1), batchImg2);
+
+    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
+
+    // Index of transform
+    const int eTfs2 = max((nTfs2 / nImg2), 1);
+    const int eTfs3 = max((nTfs3 / nImg3), 1);
+
+    int t_idx3        = -1;  // init
+    int t_idx2        = -1;  // init
+    int t_idx2_offset = 0;
+
+    if (nTfs3 == 1) {
+        t_idx3 = 0;  // Always 0 as only 1 transform defined
+    } else {
+        if (nTfs3 == nImg3) {
+            t_idx3 = imgId3;  // One to one batch with all transforms defined
+        } else {
+            t_idx3        = blockIdx.z / eTfs2;  // Transform batched, calculate
+            t_idx2_offset = t_idx3 * nTfs2;
+        }
+    }
+
+    if (nTfs2 == 1) {
+        t_idx2 = 0;  // Always 0 as only 1 transform defined
+    } else {
+        if (nTfs2 == nImg2) {
+            t_idx2 = imgId2;  // One to one batch with all transforms defined
+        } else {
+            t_idx2 =
+                blockIdx.z - t_idx2_offset;  // Transform batched, calculate
+        }
+    }
+
+    // Linear transform index
+    const int t_idx = t_idx2 + t_idx3 * nTfs2;
+    int outoff      = 0;
+
+    // Global offsets
+    const int inoff =
+        imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
+    if (nImg2 == nTfs2 || nImg2 > 1) {  // One-to-One or Image on dim2
+        outoff += imgId2 * batchImg2 * out.strides[2];
+    } else {  // Transform batched on dim2
+        outoff += t_idx2 * out.strides[2];
+    }
+
+    if (nImg3 == nTfs3 || nImg3 > 1) {  // One-to-One or Image on dim3
+        outoff += imgId3 * out.strides[3];
+    } else {  // Transform batched on dim2
+        outoff += t_idx3 * out.strides[3];
+    }
+
+    // Transform is in constant memory.
+    const int transf_len  = (perspective ? 9 : 6);
+    const float *tmat_ptr = c_tmat + t_idx * transf_len;
+    float tmat[9];
+
+    // We expect a inverse transform matrix by default
+    // If it is an forward transform, then we need its inverse
+    if (inverse) {
+#pragma unroll 3
+        for (int i = 0; i < transf_len; i++) tmat[i] = tmat_ptr[i];
+    } else {
+        calc_transf_inverse(tmat, tmat_ptr, perspective);
+    }
+
+    const int loco = outoff + (yido * out.strides[1] + xido);
+
+    // Compute input index
+    typedef typename itype_t<T>::wtype WT;
+    WT xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
+    WT yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
+
+    if (perspective) {
+        const WT W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+        xidi /= W;
+        yidi /= W;
+    }
+
+    if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi ||
+        in.dims[1] <= yidi) {
+        for (int i = 0; i < limages; i++) {
+            out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
+        }
+        return;
+    }
+
+    Interp2<T, WT, order> interp;
+    // FIXME: Nearest and lower do not do clamping, but other methods do
+    // Make it consistent
+    bool clamp = order != 1;
+    interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
+}
+
+}
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 291db28d1b..a749104f90 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -7,198 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include "interp.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/transform_cuh.hpp>
+#include <af/defines.h>
+
+#include <algorithm>
+#include <string>
 
 namespace cuda {
 namespace kernel {
+
 // Kernel Launch Config Values
 static const unsigned TX = 16;
 static const unsigned TY = 16;
 // Used for batching images
 static const unsigned TI = 4;
 
-__constant__ float
-    c_tmat[3072];  // Allows 512 Affine Transforms and 340 Persp. Transforms
-
 template<typename T>
-__host__ __device__ void calc_transf_inverse(T *txo, const T *txi,
-                                             const bool perspective) {
-    if (perspective) {
-        txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
-        txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
-        txo[2] = txi[1] * txi[5] - txi[2] * txi[4];
-
-        txo[3] = -(txi[3] * txi[8] - txi[5] * txi[6]);
-        txo[4] = txi[0] * txi[8] - txi[2] * txi[6];
-        txo[5] = -(txi[0] * txi[5] - txi[2] * txi[3]);
-
-        txo[6] = txi[3] * txi[7] - txi[4] * txi[6];
-        txo[7] = -(txi[0] * txi[7] - txi[1] * txi[6]);
-        txo[8] = txi[0] * txi[4] - txi[1] * txi[3];
-
-        T det = txi[0] * txo[0] + txi[1] * txo[3] + txi[2] * txo[6];
-
-        txo[0] /= det;
-        txo[1] /= det;
-        txo[2] /= det;
-        txo[3] /= det;
-        txo[4] /= det;
-        txo[5] /= det;
-        txo[6] /= det;
-        txo[7] /= det;
-        txo[8] /= det;
-    } else {
-        T det = txi[0] * txi[4] - txi[1] * txi[3];
-
-        txo[0] = txi[4] / det;
-        txo[1] = txi[3] / det;
-        txo[3] = txi[1] / det;
-        txo[4] = txi[0] / det;
-
-        txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
-        txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Transform Kernel
-///////////////////////////////////////////////////////////////////////////
-template<typename T, bool inverse, int order>
-__global__ static void transform_kernel(
-    Param<T> out, CParam<T> in, const int nImg2, const int nImg3,
-    const int nTfs2, const int nTfs3, const int batchImg2,
-    const int blocksXPerImage, const int blocksYPerImage,
-    const bool perspective, af_interp_type method) {
-    // Image Ids
-    const int imgId2 = blockIdx.x / blocksXPerImage;
-    const int imgId3 = blockIdx.y / blocksYPerImage;
-
-    // Block in local image
-    const int blockIdx_x = blockIdx.x - imgId2 * blocksXPerImage;
-    const int blockIdx_y = blockIdx.y - imgId3 * blocksYPerImage;
-
-    // Get thread indices in local image
-    const int xido = blockIdx_x * blockDim.x + threadIdx.x;
-    const int yido = blockIdx_y * blockDim.y + threadIdx.y;
-
-    // Image iteration loop count for image batching
-    int limages = min(max(out.dims[2] - imgId2 * nImg2, 1), batchImg2);
-
-    if (xido >= out.dims[0] || yido >= out.dims[1]) return;
-
-    // Index of transform
-    const int eTfs2 = max((nTfs2 / nImg2), 1);
-    const int eTfs3 = max((nTfs3 / nImg3), 1);
-
-    int t_idx3        = -1;  // init
-    int t_idx2        = -1;  // init
-    int t_idx2_offset = 0;
-
-    if (nTfs3 == 1) {
-        t_idx3 = 0;  // Always 0 as only 1 transform defined
-    } else {
-        if (nTfs3 == nImg3) {
-            t_idx3 = imgId3;  // One to one batch with all transforms defined
-        } else {
-            t_idx3        = blockIdx.z / eTfs2;  // Transform batched, calculate
-            t_idx2_offset = t_idx3 * nTfs2;
-        }
-    }
-
-    if (nTfs2 == 1) {
-        t_idx2 = 0;  // Always 0 as only 1 transform defined
-    } else {
-        if (nTfs2 == nImg2) {
-            t_idx2 = imgId2;  // One to one batch with all transforms defined
-        } else {
-            t_idx2 =
-                blockIdx.z - t_idx2_offset;  // Transform batched, calculate
-        }
-    }
-
-    // Linear transform index
-    const int t_idx = t_idx2 + t_idx3 * nTfs2;
-    int outoff      = 0;
-
-    // Global offsets
-    const int inoff =
-        imgId2 * batchImg2 * in.strides[2] + imgId3 * in.strides[3];
-    if (nImg2 == nTfs2 || nImg2 > 1) {  // One-to-One or Image on dim2
-        outoff += imgId2 * batchImg2 * out.strides[2];
-    } else {  // Transform batched on dim2
-        outoff += t_idx2 * out.strides[2];
-    }
-
-    if (nImg3 == nTfs3 || nImg3 > 1) {  // One-to-One or Image on dim3
-        outoff += imgId3 * out.strides[3];
-    } else {  // Transform batched on dim2
-        outoff += t_idx3 * out.strides[3];
-    }
-
-    // Transform is in constant memory.
-    const int transf_len  = (perspective ? 9 : 6);
-    const float *tmat_ptr = c_tmat + t_idx * transf_len;
-    float tmat[9];
-
-    // We expect a inverse transform matrix by default
-    // If it is an forward transform, then we need its inverse
-    if (inverse) {
-#pragma unroll 3
-        for (int i = 0; i < transf_len; i++) tmat[i] = tmat_ptr[i];
-    } else {
-        calc_transf_inverse(tmat, tmat_ptr, perspective);
-    }
-
-    const int loco = outoff + (yido * out.strides[1] + xido);
-
-    // Compute input index
-    typedef typename itype_t<T>::wtype WT;
-    WT xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
-    WT yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
-
-    if (perspective) {
-        const WT W = xido * tmat[6] + yido * tmat[7] + tmat[8];
-        xidi /= W;
-        yidi /= W;
-    }
-
-    if (xidi < -0.0001 || yidi < -0.0001 || in.dims[0] <= xidi ||
-        in.dims[1] <= yidi) {
-        for (int i = 0; i < limages; i++) {
-            out.ptr[loco + i * out.strides[2]] = scalar<T>(0.0f);
-        }
-        return;
-    }
-
-    Interp2<T, WT, order> interp;
-    // FIXME: Nearest and lower do not do clamping, but other methods do
-    // Make it consistent
-    bool clamp = order != 1;
-    interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, int order>
 void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
-               const bool perspective, af_interp_type method) {
-    const int nImg2 = in.dims[2];
-    const int nImg3 = in.dims[3];
-    const int nTfs2 = tf.dims[2];
-    const int nTfs3 = tf.dims[3];
+               const bool perspective, const af::interpType method, int order) {
+    static const std::string src(transform_cuh, transform_cuh_len);
+
+    auto transform = getKernel(
+        "cuda::transform", src,
+        {TemplateTypename<T>(), TemplateArg(inverse), TemplateArg(order)});
 
-    const int tf_len = (perspective) ? 9 : 6;
+    const unsigned int nImg2  = in.dims[2];
+    const unsigned int nImg3  = in.dims[3];
+    const unsigned int nTfs2  = tf.dims[2];
+    const unsigned int nTfs3  = tf.dims[3];
+    const unsigned int tf_len = (perspective) ? 9 : 6;
 
     // Copy transform to constant memory.
-    CUDA_CHECK(cudaMemcpyToSymbolAsync(
-        c_tmat, tf.ptr, nTfs2 * nTfs3 * tf_len * sizeof(float), 0,
-        cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+    transform.setConstant("c_tmat", reinterpret_cast<CUdeviceptr>(tf.ptr),
+                          nTfs2 * nTfs3 * tf_len * sizeof(float));
 
     dim3 threads(TX, TY, 1);
     dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
@@ -210,24 +57,21 @@ void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
     // One-to-one batching is only done on blocks.x
     // TODO If dim2 is not one-to-one batched, then divide blocks.x by factor
     int batchImg2 = 1;
-    if (nImg2 != nTfs2) batchImg2 = min(nImg2, TI);
+    if (nImg2 != nTfs2) batchImg2 = std::min(nImg2, TI);
 
     blocks.x *= (nImg2 / batchImg2);
     blocks.y *= nImg3;
 
     // Use blocks.z for transforms
-    blocks.z *= max((nTfs2 / nImg2), 1) * max((nTfs3 / nImg3), 1);
-
-    if (inverse) {
-        CUDA_LAUNCH((transform_kernel<T, true, order>), blocks, threads, out,
-                    in, nImg2, nImg3, nTfs2, nTfs3, batchImg2, blocksXPerImage,
-                    blocksYPerImage, perspective, method);
-    } else {
-        CUDA_LAUNCH((transform_kernel<T, false, order>), blocks, threads, out,
-                    in, nImg2, nImg3, nTfs2, nTfs3, batchImg2, blocksXPerImage,
-                    blocksYPerImage, perspective, method);
-    }
+    blocks.z *= std::max((nTfs2 / nImg2), 1u) * std::max((nTfs3 / nImg3), 1u);
+
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    transform(qArgs, out, in, nImg2, nImg3, nTfs2, nTfs3, batchImg2,
+              blocksXPerImage, blocksYPerImage, perspective, method);
+
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 6e002fb3bf..5473ba128a 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -29,19 +29,10 @@ void transpose(Param<T> out, CParam<T> in, const bool conjugate,
                const bool is32multiple) {
     static const std::string source(transpose_cuh, transpose_cuh_len);
 
-    // clang-format off
     auto transpose = getKernel("cuda::transpose", source,
-            {
-              TemplateTypename<T>(),
-              TemplateArg(conjugate),
-              TemplateArg(is32multiple)
-            },
-            {
-              DefineValue(TILE_DIM),
-              DefineValue(THREADS_Y)
-            }
-            );
-    // clang-format on
+                               {TemplateTypename<T>(), TemplateArg(conjugate),
+                                TemplateArg(is32multiple)},
+                               {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/transpose_inplace.cuh b/src/backend/cuda/kernel/transpose_inplace.cuh
new file mode 100644
index 0000000000..733db729c0
--- /dev/null
+++ b/src/backend/cuda/kernel/transpose_inplace.cuh
@@ -0,0 +1,120 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool conjugate>
+__device__ T doOp(T in) {
+    if (conjugate)
+        return conj(in);
+    else
+        return in;
+}
+
+// Hint from txbob
+// https://devtalk.nvidia.com/default/topic/765696/efficient-in-place-transpose-of-multiple-square-float-matrices
+//
+// Kernel is going access original data in colleased format
+template<typename T, bool conjugate, bool is32Multiple>
+__global__ void transposeIP(Param<T> in, const int blocksPerMatX,
+                            const int blocksPerMatY) {
+    __shared__ T shrdMem_s[TILE_DIM][TILE_DIM + 1];
+    __shared__ T shrdMem_d[TILE_DIM][TILE_DIM + 1];
+
+    // create variables to hold output dimensions
+    const int iDim0 = in.dims[0];
+    const int iDim1 = in.dims[1];
+
+    // calculate strides
+    const int iStride1 = in.strides[1];
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+
+    // batch based block Id
+    const int batchId_x  = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
+
+    const int batchId_y  = blockIdx.y / blocksPerMatY;
+    const int blockIdx_y = (blockIdx.y - batchId_y * blocksPerMatY);
+
+    const int x0 = TILE_DIM * blockIdx_x;
+    const int y0 = TILE_DIM * blockIdx_y;
+
+    // offset in and out based on batch id
+    T *iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
+
+    if (blockIdx_y > blockIdx_x) {  // Off diagonal blocks
+        // calculate global indices
+        int gx = lx + x0;
+        int gy = ly + y0;
+        int dx = lx + y0;
+        int dy = ly + x0;
+
+        // Copy to shared memory
+#pragma unroll
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
+
+            int dy_ = dy + repeat;
+            if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
+                shrdMem_d[ly + repeat][lx] = iptr[dy_ * iStride1 + dx];
+        }
+
+        __syncthreads();
+
+        // Copy from shared to global memory
+#pragma unroll
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int dy_ = dy + repeat;
+            if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
+                iptr[dy_ * iStride1 + dx] =
+                    doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
+
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                iptr[gy_ * iStride1 + gx] =
+                    doOp<T, conjugate>(shrdMem_d[lx][ly + repeat]);
+        }
+
+    } else if (blockIdx_y == blockIdx_x) {  // Diagonal blocks
+        // calculate global indices
+        int gx = lx + x0;
+        int gy = ly + y0;
+
+        // offset in and out based on batch id
+        iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
+
+        // Copy to shared memory
+#pragma unroll
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
+        }
+
+        __syncthreads();
+
+        // Copy from shared to global memory
+#pragma unroll
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
+                iptr[gy_ * iStride1 + gx] =
+                    doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
+        }
+    }
+}
+
+} //namespace cuda
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index f192d1c8d1..303c5abbd6 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -7,127 +7,33 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/transpose_inplace_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int TILE_DIM  = 32;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
-template<typename T, bool conjugate>
-__device__ T doOp(T in) {
-    if (conjugate)
-        return conj(in);
-    else
-        return in;
-}
-
-// Hint from txbob
-// https://devtalk.nvidia.com/default/topic/765696/efficient-in-place-transpose-of-multiple-square-float-matrices
-//
-// Kernel is going access original data in colleased format
-template<typename T, bool conjugate, bool is32Multiple>
-__global__ void transposeIP(Param<T> in, const int blocksPerMatX,
-                            const int blocksPerMatY) {
-    __shared__ T shrdMem_s[TILE_DIM][TILE_DIM + 1];
-    __shared__ T shrdMem_d[TILE_DIM][TILE_DIM + 1];
-
-    // create variables to hold output dimensions
-    const int iDim0 = in.dims[0];
-    const int iDim1 = in.dims[1];
-
-    // calculate strides
-    const int iStride1 = in.strides[1];
-
-    const int lx = threadIdx.x;
-    const int ly = threadIdx.y;
-
-    // batch based block Id
-    const int batchId_x  = blockIdx.x / blocksPerMatX;
-    const int blockIdx_x = (blockIdx.x - batchId_x * blocksPerMatX);
-
-    const int batchId_y  = blockIdx.y / blocksPerMatY;
-    const int blockIdx_y = (blockIdx.y - batchId_y * blocksPerMatY);
-
-    const int x0 = TILE_DIM * blockIdx_x;
-    const int y0 = TILE_DIM * blockIdx_y;
-
-    // offset in and out based on batch id
-    T *iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
-
-    if (blockIdx_y > blockIdx_x) {  // Off diagonal blocks
-        // calculate global indices
-        int gx = lx + x0;
-        int gy = ly + y0;
-        int dx = lx + y0;
-        int dy = ly + x0;
-
-        // Copy to shared memory
-#pragma unroll
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy + repeat;
-            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
+template<typename T>
+void transpose_inplace(Param<T> in, const bool conjugate,
+                       const bool is32multiple) {
+    static const std::string source(transpose_inplace_cuh,
+                                    transpose_inplace_cuh_len);
+    auto transposeIP = getKernel("cuda::transposeIP", source,
+                               {TemplateTypename<T>(), TemplateArg(conjugate),
+                                TemplateArg(is32multiple)},
+                               {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
-            int dy_ = dy + repeat;
-            if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
-                shrdMem_d[ly + repeat][lx] = iptr[dy_ * iStride1 + dx];
-        }
-
-        __syncthreads();
-
-        // Copy from shared to global memory
-#pragma unroll
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int dy_ = dy + repeat;
-            if (is32Multiple || (dx < iDim0 && dy_ < iDim1))
-                iptr[dy_ * iStride1 + dx] =
-                    doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
-
-            int gy_ = gy + repeat;
-            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                iptr[gy_ * iStride1 + gx] =
-                    doOp<T, conjugate>(shrdMem_d[lx][ly + repeat]);
-        }
-
-    } else if (blockIdx_y == blockIdx_x) {  // Diagonal blocks
-        // calculate global indices
-        int gx = lx + x0;
-        int gy = ly + y0;
-
-        // offset in and out based on batch id
-        iptr = in.ptr + batchId_x * in.strides[2] + batchId_y * in.strides[3];
-
-        // Copy to shared memory
-#pragma unroll
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy + repeat;
-            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                shrdMem_s[ly + repeat][lx] = iptr[gy_ * iStride1 + gx];
-        }
-
-        __syncthreads();
-
-        // Copy from shared to global memory
-#pragma unroll
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy + repeat;
-            if (is32Multiple || (gx < iDim0 && gy_ < iDim1))
-                iptr[gy_ * iStride1 + gx] =
-                    doOp<T, conjugate>(shrdMem_s[lx][ly + repeat]);
-        }
-    }
-}
-
-template<typename T, bool conjugate>
-void transpose_inplace(Param<T> in) {
     // dimensions passed to this function should be input dimensions
     // any necessary transformations and dimension related calculations are
     // carried out here and inside the kernel
@@ -135,19 +41,14 @@ void transpose_inplace(Param<T> in) {
 
     int blk_x = divup(in.dims[0], TILE_DIM);
     int blk_y = divup(in.dims[1], TILE_DIM);
-
-    // launch batch * blk_x blocks along x dimension
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
 
-    if (in.dims[0] % TILE_DIM == 0 && in.dims[1] % TILE_DIM == 0)
-        CUDA_LAUNCH((transposeIP<T, conjugate, true>), blocks, threads, in,
-                    blk_x, blk_y);
-    else
-        CUDA_LAUNCH((transposeIP<T, conjugate, false>), blocks, threads, in,
-                    blk_x, blk_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    transposeIP(qArgs, in, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
-}  // namespace kernel
 
+}  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 32159691ca..383c434870 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -24,7 +24,7 @@ namespace kernel {
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
     static const std::string src(where_cuh, where_cuh_len);
-    auto whereOp = getKernel("cuda::where", src, {TemplateTypename<T>()});
+    auto where = getKernel("cuda::where", src, {TemplateTypename<T>()});
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
@@ -96,7 +96,7 @@ static void where(Param<uint> &out, CParam<T> in) {
     blocks.y = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    whereOp(qArgs, out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
+    where(qArgs, out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
     POST_LAUNCH_CHECK();
 
     out_alloc.release();
diff --git a/src/backend/cuda/match_template.cu b/src/backend/cuda/match_template.cpp
similarity index 92%
rename from src/backend/cuda/match_template.cu
rename to src/backend/cuda/match_template.cpp
index d13cd5d6b9..61c2528aca 100644
--- a/src/backend/cuda/match_template.cu
+++ b/src/backend/cuda/match_template.cpp
@@ -21,15 +21,9 @@ template<typename inType, typename outType, af_match_type mType>
 Array<outType> match_template(const Array<inType> &sImg,
                               const Array<inType> &tImg) {
     Array<outType> out = createEmptyArray<outType>(sImg.dims());
-
     bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
                     mType == AF_LSSD || mType == AF_ZNCC;
-
-    if (needMean)
-        kernel::matchTemplate<inType, outType, mType, true>(out, sImg, tImg);
-    else
-        kernel::matchTemplate<inType, outType, mType, false>(out, sImg, tImg);
-
+    kernel::matchTemplate<inType, outType>(out, sImg, tImg, mType, needMean);
     return out;
 }
 
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 42b4c1df27..f77208958f 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -11,19 +11,14 @@
 
 #ifdef __CUDACC_RTC__
 
-#define CUDART_INF_F __int_as_float(0x7f800000)
-#define CUDART_INF __longlong_as_double(0x7ff0000000000000ULL)
 #define STATIC_ inline
 
 #else  //__CUDACC_RTC__
 
 #include <common/defines.hpp>
-#include <common/half.hpp>
-#include <af/defines.h>
 
 #ifdef __CUDACC__
 #include <cuda_runtime_api.h>
-#include <math_constants.h>
 #endif  //__CUDACC__
 
 #include <algorithm>
@@ -31,10 +26,12 @@
 
 #endif  //__CUDACC_RTC__
 
-#include <cuda_fp16.h>
+#include <backend.hpp>
+#include <types.hpp>
+#include <af/defines.h>
 
-#include "backend.hpp"
-#include "types.hpp"
+#include <cuda_fp16.h>
+#include <math_constants.h>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/meanshift.cu b/src/backend/cuda/meanshift.cpp
similarity index 78%
rename from src/backend/cuda/meanshift.cu
rename to src/backend/cuda/meanshift.cpp
index fcc9075bdc..3f22ab53dd 100644
--- a/src/backend/cuda/meanshift.cu
+++ b/src/backend/cuda/meanshift.cpp
@@ -21,16 +21,9 @@ Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor) {
     const dim4 dims = in.dims();
-
-    Array<T> out = createEmptyArray<T>(dims);
-
-    if (isColor)
-        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma,
-                                   numIterations);
-    else
-        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma,
-                                    numIterations);
-
+    Array<T> out    = createEmptyArray<T>(dims);
+    kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma, numIterations,
+                         isColor);
     return out;
 }
 
diff --git a/src/backend/cuda/medfilt.cu b/src/backend/cuda/medfilt.cpp
similarity index 90%
rename from src/backend/cuda/medfilt.cu
rename to src/backend/cuda/medfilt.cpp
index ed0b8a75d3..41386203cc 100644
--- a/src/backend/cuda/medfilt.cu
+++ b/src/backend/cuda/medfilt.cpp
@@ -7,10 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <medfilt.hpp>
+
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/medfilt.hpp>
-#include <medfilt.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
@@ -23,10 +24,9 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
     const dim4 dims = in.dims();
+    Array<T> out    = createEmptyArray<T>(dims);
 
-    Array<T> out = createEmptyArray<T>(dims);
-
-    kernel::medfilt1<T, pad>(out, in, w_wid);
+    kernel::medfilt1<T>(out, in, pad, w_wid);
 
     return out;
 }
@@ -37,10 +37,9 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     ARG_ASSERT(2, (w_len % 2 != 0));
 
     const dim4 dims = in.dims();
+    Array<T> out    = createEmptyArray<T>(dims);
 
-    Array<T> out = createEmptyArray<T>(dims);
-
-    kernel::medfilt2<T, pad>(out, in, w_len, w_wid);
+    kernel::medfilt2<T>(out, in, pad, w_len, w_wid);
 
     return out;
 }
diff --git a/src/backend/cuda/moments.cu b/src/backend/cuda/moments.cpp
similarity index 98%
rename from src/backend/cuda/moments.cu
rename to src/backend/cuda/moments.cpp
index 0f88a53c5f..f963650148 100644
--- a/src/backend/cuda/moments.cu
+++ b/src/backend/cuda/moments.cpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <moments.hpp>
+
 #include <Array.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
index 667114dc60..094bd815e8 100644
--- a/src/backend/cuda/morph3d_impl.hpp
+++ b/src/backend/cuda/morph3d_impl.hpp
@@ -19,23 +19,12 @@ namespace cuda {
 template<typename T, bool isDilation>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
     const dim4 mdims = mask.dims();
-
-    if (mdims[0] != mdims[1] || mdims[0] != mdims[2])
+    if (mdims[0] != mdims[1] || mdims[0] != mdims[2]) {
         CUDA_NOT_SUPPORTED("Only cubic masks are supported");
-
-    if (mdims[0] > 7) CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported");
-
+    }
+    if (mdims[0] > 7) { CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported"); }
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    CUDA_CHECK(cudaMemcpyToSymbolAsync(
-        kernel::cFilter, mask.get(), mdims[0] * mdims[1] * mdims[2] * sizeof(T),
-        0, cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
-    if (isDilation)
-        kernel::morph3d<T, true>(out, in, mdims[0]);
-    else
-        kernel::morph3d<T, false>(out, in, mdims[0]);
-
+    kernel::morph3d<T>(out, in, mask, isDilation);
     return out;
 }
 
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index e811a1d4a6..a998fe7a6e 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -19,23 +19,14 @@ namespace cuda {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     const dim4 mdims = mask.dims();
-
-    if (mdims[0] != mdims[1])
+    if (mdims[0] != mdims[1]) {
         CUDA_NOT_SUPPORTED("Rectangular masks are not supported");
-
-    if (mdims[0] > 19) CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
-
+    }
+    if (mdims[0] > 19) {
+        CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
+    }
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    CUDA_CHECK(cudaMemcpyToSymbolAsync(
-        kernel::cFilter, mask.get(), mdims[0] * mdims[1] * sizeof(T), 0,
-        cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-
-    if (isDilation)
-        kernel::morph<T, true>(out, in, mdims[0]);
-    else
-        kernel::morph<T, false>(out, in, mdims[0]);
-
+    kernel::morph<T>(out, in, mask, isDilation);
     return out;
 }
 
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 89424519c3..30449d059b 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -7,9 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <nvrtc/cache.hpp>
+
 #include <device_manager.hpp>
 #include <kernel_headers/jit_cuh.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/Param_hpp.hpp>
 #include <nvrtc_kernel_headers/backend_hpp.hpp>
 #include <nvrtc_kernel_headers/cuComplex_h.hpp>
@@ -17,15 +18,20 @@
 #include <nvrtc_kernel_headers/cuda_fp16_hpp.hpp>
 #include <nvrtc_kernel_headers/defines_h.hpp>
 #include <nvrtc_kernel_headers/half_hpp.hpp>
+#include <nvrtc_kernel_headers/interp_hpp.hpp>
 #include <nvrtc_kernel_headers/kernel_type_hpp.hpp>
+#include <nvrtc_kernel_headers/math_constants_h.hpp>
 #include <nvrtc_kernel_headers/math_hpp.hpp>
 #include <nvrtc_kernel_headers/ops_hpp.hpp>
 #include <nvrtc_kernel_headers/optypes_hpp.hpp>
 #include <nvrtc_kernel_headers/shared_hpp.hpp>
 #include <nvrtc_kernel_headers/traits_hpp.hpp>
 #include <nvrtc_kernel_headers/types_hpp.hpp>
+#include <nvrtc_kernel_headers/version_h.hpp>
 #include <optypes.hpp>
 #include <platform.hpp>
+#include <af/defines.h>
+#include <af/version.h>
 
 #include <algorithm>
 #include <array>
@@ -103,6 +109,25 @@ void Kernel::setConstant(const char *name, CUdeviceptr src, size_t bytes) {
     CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, getActiveStream()));
 }
 
+template<typename T>
+void Kernel::setScalar(const char *name, T value) {
+    CUdeviceptr dst = 0;
+    CU_CHECK(cuModuleGetGlobal(&dst, NULL, prog, name));
+    CU_CHECK(cuMemcpyHtoDAsync(dst, &value, sizeof(T), getActiveStream()));
+    CU_CHECK(cuStreamSynchronize(getActiveStream()));
+}
+
+template<typename T>
+void Kernel::getScalar(T &out, const char *name) {
+    CUdeviceptr src = 0;
+    CU_CHECK(cuModuleGetGlobal(&src, NULL, prog, name));
+    CU_CHECK(cuMemcpyDtoHAsync(&out, src, sizeof(T), getActiveStream()));
+    CU_CHECK(cuStreamSynchronize(getActiveStream()));
+}
+
+template void Kernel::setScalar<int>(const char *, int);
+template void Kernel::getScalar<int>(int &, const char *);
+
 Kernel buildKernel(const int device, const string &nameExpr,
                    const string &jit_ker, const vector<string> &opts,
                    const bool isJIT) {
@@ -120,6 +145,8 @@ Kernel buildKernel(const int device, const string &nameExpr,
     } else {
         constexpr static const char *includeNames[] = {
             "math.h",          // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            "stdbool.h",       // DUMMY ENTRY TO SATISFY af/defines.h inclusion
+            "stdlib.h",        // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             "vector_types.h",  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             "backend.hpp",
             "cuComplex.h",
@@ -135,11 +162,17 @@ Kernel buildKernel(const int device, const string &nameExpr,
             "common/half.hpp",
             "common/kernel_type.hpp",
             "af/traits.hpp",
+            "interp.hpp",
+            "math_constants.h",
+            "af/defines.h",
+            "af/version.h",
         };
 
         constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
         static const std::array<string, NumHeaders> sourceStrings = {{
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            string(""),  // DUMMY ENTRY TO SATISFY af/defines.h inclusion
+            string(""),  // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             string(backend_hpp, backend_hpp_len),
             string(cuComplex_h, cuComplex_h_len),
@@ -155,6 +188,10 @@ Kernel buildKernel(const int device, const string &nameExpr,
             string(half_hpp, half_hpp_len),
             string(kernel_type_hpp, kernel_type_hpp_len),
             string(traits_hpp, traits_hpp_len),
+            string(interp_hpp, interp_hpp_len),
+            string(math_constants_h, math_constants_h_len),
+            string(defines_h, defines_h_len),
+            string(version_h, version_h_len),
         }};
 
         static const char *headers[] = {
@@ -166,6 +203,9 @@ Kernel buildKernel(const int device, const string &nameExpr,
             sourceStrings[10].c_str(), sourceStrings[11].c_str(),
             sourceStrings[12].c_str(), sourceStrings[13].c_str(),
             sourceStrings[14].c_str(), sourceStrings[15].c_str(),
+            sourceStrings[16].c_str(), sourceStrings[17].c_str(),
+            sourceStrings[18].c_str(), sourceStrings[19].c_str(),
+            sourceStrings[20].c_str(), sourceStrings[21].c_str(),
         };
         NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
                                        NumHeaders, headers, includeNames));
@@ -378,6 +418,76 @@ string toString(const char *str) {
     return string(str);
 }
 
+template<>
+string toString(af_interp_type p) {
+    const char *retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_INTERP_NEAREST);
+        CASE_STMT(AF_INTERP_LINEAR);
+        CASE_STMT(AF_INTERP_BILINEAR);
+        CASE_STMT(AF_INTERP_CUBIC);
+        CASE_STMT(AF_INTERP_LOWER);
+        CASE_STMT(AF_INTERP_LINEAR_COSINE);
+        CASE_STMT(AF_INTERP_BILINEAR_COSINE);
+        CASE_STMT(AF_INTERP_BICUBIC);
+        CASE_STMT(AF_INTERP_CUBIC_SPLINE);
+        CASE_STMT(AF_INTERP_BICUBIC_SPLINE);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_border_type p) {
+    const char *retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_PAD_ZERO);
+        CASE_STMT(AF_PAD_SYM);
+        CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_moment_type p) {
+    const char *retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_MOMENT_M00);
+        CASE_STMT(AF_MOMENT_M01);
+        CASE_STMT(AF_MOMENT_M10);
+        CASE_STMT(AF_MOMENT_M11);
+        CASE_STMT(AF_MOMENT_FIRST_ORDER);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_match_type p) {
+    const char *retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_SAD);
+        CASE_STMT(AF_ZSAD);
+        CASE_STMT(AF_LSAD);
+        CASE_STMT(AF_SSD);
+        CASE_STMT(AF_ZSSD);
+        CASE_STMT(AF_LSSD);
+        CASE_STMT(AF_NCC);
+        CASE_STMT(AF_ZNCC);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
 Kernel getKernel(const string &nameExpr, const string &source,
                  const vector<TemplateArg> &targs,
                  const vector<string> &compileOpts) {
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index 3f26bb3d2c..00d11834a5 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -55,6 +55,30 @@ struct Kernel {
     ///
     void setConstant(const char* name, CUdeviceptr src, size_t bytes);
 
+    ///
+    /// \brief Copy scalar to device qualified global variable of kernel
+    ///
+    /// This function copies a single value of type T from host variable
+    /// to a global(__device__) variable declared inside the kernel.
+    ///
+    /// \param[in] name is the name of the global variable inside kernel
+    /// \param[in] value is the value of type T
+    ///
+    template<typename T>
+    void setScalar(const char* name, T value);
+
+    ///
+    /// \brief Fetch scalar from device qualified global variable of kernel
+    ///
+    /// This function copies a single value of type T from a global(__device__)
+    /// variable declared inside the kernel to host.
+    ///
+    /// \param[in] name is the name of the global variable inside kernel
+    /// \param[in] value is the value of type T
+    ///
+    template<typename T>
+    void getScalar(T& out, const char* name);
+
     ///
     /// \brief Enqueue Kernel per queueing criteria forwarding other parameters
     ///
diff --git a/src/backend/cuda/pad_array_borders.cu b/src/backend/cuda/pad_array_borders.cpp
similarity index 98%
rename from src/backend/cuda/pad_array_borders.cu
rename to src/backend/cuda/pad_array_borders.cpp
index 0986731f59..369237d5d6 100644
--- a/src/backend/cuda/pad_array_borders.cu
+++ b/src/backend/cuda/pad_array_borders.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <copy.hpp>
+
+#include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/pad_array_borders.hpp>
-#include <af/dim4.hpp>
 
 namespace cuda {
 template<typename T>
diff --git a/src/backend/cuda/resize.cu b/src/backend/cuda/resize.cpp
similarity index 76%
rename from src/backend/cuda/resize.cu
rename to src/backend/cuda/resize.cpp
index 901a617ee1..b7e882d31c 100644
--- a/src/backend/cuda/resize.cu
+++ b/src/backend/cuda/resize.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <resize.hpp>
+
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/resize.hpp>
-#include <resize.hpp>
-#include <stdexcept>
 
 namespace cuda {
 template<typename T>
@@ -22,18 +22,7 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    switch (method) {
-        case AF_INTERP_NEAREST:
-            kernel::resize<T, AF_INTERP_NEAREST>(out, in);
-            break;
-        case AF_INTERP_BILINEAR:
-            kernel::resize<T, AF_INTERP_BILINEAR>(out, in);
-            break;
-        case AF_INTERP_LOWER:
-            kernel::resize<T, AF_INTERP_LOWER>(out, in);
-            break;
-        default: break;
-    }
+    kernel::resize<T>(out, in, method);
 
     return out;
 }
diff --git a/src/backend/cuda/rotate.cu b/src/backend/cuda/rotate.cpp
similarity index 66%
rename from src/backend/cuda/rotate.cu
rename to src/backend/cuda/rotate.cpp
index 828a189d89..7c26164a8c 100644
--- a/src/backend/cuda/rotate.cu
+++ b/src/backend/cuda/rotate.cpp
@@ -7,33 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <kernel/rotate.hpp>
 #include <rotate.hpp>
-#include <stdexcept>
+
+#include <kernel/rotate.hpp>
+#include <utility.hpp>
 
 namespace cuda {
+
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method) {
     Array<T> out = createEmptyArray<T>(odims);
-
-    switch (method) {
-        case AF_INTERP_NEAREST:
-        case AF_INTERP_LOWER:
-            kernel::rotate<T, 1>(out, in, theta, method);
-            break;
-        case AF_INTERP_BILINEAR:
-        case AF_INTERP_BILINEAR_COSINE:
-            kernel::rotate<T, 2>(out, in, theta, method);
-            break;
-        case AF_INTERP_BICUBIC:
-        case AF_INTERP_BICUBIC_SPLINE:
-            kernel::rotate<T, 3>(out, in, theta, method);
-            break;
-        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-    }
-
+    kernel::rotate<T>(out, in, theta, method, interpOrder(method));
     return out;
 }
 
diff --git a/src/backend/cuda/sobel.cu b/src/backend/cuda/sobel.cpp
similarity index 100%
rename from src/backend/cuda/sobel.cu
rename to src/backend/cuda/sobel.cpp
diff --git a/src/backend/cuda/transform.cu b/src/backend/cuda/transform.cpp
similarity index 64%
rename from src/backend/cuda/transform.cu
rename to src/backend/cuda/transform.cpp
index afea4a3856..513a378410 100644
--- a/src/backend/cuda/transform.cu
+++ b/src/backend/cuda/transform.cpp
@@ -7,34 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <kernel/transform.hpp>
 #include <transform.hpp>
-#include <stdexcept>
+
+#include <kernel/transform.hpp>
+#include <utility.hpp>
 
 namespace cuda {
 template<typename T>
 Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af_interp_type method,
+                   const af::dim4 &odims, const af::interpType method,
                    const bool inverse, const bool perspective) {
     Array<T> out = createEmptyArray<T>(odims);
-
-    switch (method) {
-        case AF_INTERP_NEAREST:
-        case AF_INTERP_LOWER:
-            kernel::transform<T, 1>(out, in, tf, inverse, perspective, method);
-            break;
-        case AF_INTERP_BILINEAR:
-        case AF_INTERP_BILINEAR_COSINE:
-            kernel::transform<T, 2>(out, in, tf, inverse, perspective, method);
-            break;
-        case AF_INTERP_BICUBIC:
-        case AF_INTERP_BICUBIC_SPLINE:
-            kernel::transform<T, 3>(out, in, tf, inverse, perspective, method);
-            break;
-        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-    }
-
+    kernel::transform<T>(out, in, tf, inverse, perspective, method,
+                         interpOrder(method));
     return out;
 }
 
diff --git a/src/backend/cuda/transpose_inplace.cu b/src/backend/cuda/transpose_inplace.cpp
similarity index 81%
rename from src/backend/cuda/transpose_inplace.cu
rename to src/backend/cuda/transpose_inplace.cpp
index fc2c723d02..e70415c163 100644
--- a/src/backend/cuda/transpose_inplace.cu
+++ b/src/backend/cuda/transpose_inplace.cpp
@@ -18,11 +18,10 @@ namespace cuda {
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate) {
-    if (conjugate) {
-        kernel::transpose_inplace<T, true>(in);
-    } else {
-        kernel::transpose_inplace<T, false>(in);
-    }
+    const dim4 inDims = in.dims();
+    const bool is32multiple =
+        inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+    kernel::transpose_inplace<T>(in, conjugate, is32multiple);
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/cuda/utility.cpp b/src/backend/cuda/utility.cpp
new file mode 100644
index 0000000000..a315f4d28d
--- /dev/null
+++ b/src/backend/cuda/utility.cpp
@@ -0,0 +1,33 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <utility.hpp>
+
+#include <err_cuda.hpp>
+
+namespace cuda {
+
+int interpOrder(const af_interp_type p) noexcept {
+    int order = 1;
+    switch (p) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER: order = 1; break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+        case AF_INTERP_BILINEAR_COSINE: order = 2; break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+        case AF_INTERP_BICUBIC_SPLINE: order = 3; break;
+    }
+    return order;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/utility.hpp b/src/backend/cuda/utility.hpp
index 7133da542a..f54435f484 100644
--- a/src/backend/cuda/utility.hpp
+++ b/src/backend/cuda/utility.hpp
@@ -8,8 +8,9 @@
  ********************************************************/
 
 #pragma once
+
+#include <backend.hpp>
 #include <af/defines.h>
-#include "backend.hpp"
 
 namespace cuda {
 
@@ -25,4 +26,6 @@ static __DH__ dim_t trimIndex(const int &idx, const dim_t &len) {
     return ret_val;
 }
 
+int interpOrder(const af_interp_type p) noexcept;
+
 }  // namespace cuda

From 5f0b487db249ffc41f17d56550af152d5c148d8b Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Fri, 5 Jul 2019 12:27:13 -0700
Subject: [PATCH 1696/2677] Fix selectGEMMAlgorithm for half and computetype

---
 src/backend/cuda/blas.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 8df0dc8f39..64e6d58a54 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -192,7 +192,7 @@ cublasGemmAlgo_t selectGEMMAlgorithm() {
 }
 
 template<>
-cublasGemmAlgo_t selectGEMMAlgorithm<__half>() {
+cublasGemmAlgo_t selectGEMMAlgorithm<half>() {
     auto dev              = getDeviceProp(getActiveDeviceId());
     cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
     if (dev.major >= 7) { algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP; }
@@ -211,7 +211,7 @@ cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
             blasHandle(), lOpts, rOpts, M, N, K, alpha, lhs.get(), getType<T>(),
             lStride, rhs.get(), getType<T>(), rStride, beta, out.get(),
             getType<T>(), out.strides()[1],
-            getType<T>(),  // Compute type
+            getType<compute_t<T>>(), // Compute type
 
             // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
             // for the cublasGemm*Ex functions, the performance of the
@@ -244,7 +244,7 @@ cublasStatus_t gemmBatchedDispatch(BlasHandle handle,
             blasHandle(), lOpts, rOpts, M, N, K, alpha, (const void **)lptrs,
             getType<T>(), lStrides, (const void **)rptrs, getType<T>(),
             rStrides, beta, (void **)optrs, getType<T>(), oStrides, batchSize,
-            getType<T>(),  // Compute type
+            getType<compute_t<T>>(), // compute type
             // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
             // for the cublasGemm*Ex functions, the performance of the
             // fp32 numbers seem to increase dramatically. Their numerical

From da7760503679707b793bfe43d2ec3b671e45bfea Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Fri, 5 Jul 2019 13:44:14 -0700
Subject: [PATCH 1697/2677] Fix benchmark blas example datatype

---
 examples/benchmarks/blas.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index 54a062436d..ca41f8e220 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -37,8 +37,8 @@ int main(int argc, char** argv) {
 
         printf("Benchmark N-by-N matrix multiply at %s \n", dtype.c_str());
         for (int n = 128; n <= 2048; n += 128) {
-            printf("%4d x %4d: ", n, n, dt);
-            A             = constant(1, n, n);
+            printf("%4d x %4d: ", n, n);
+            A             = constant(1, n, n, dt);
             double time   = timeit(fn);  // time in seconds
             double gflops = 2.0 * powf(n, 3) / (time * 1e9);
             if (gflops > peak) peak = gflops;

From dc32e97e284a175a52071b55e5bce717a441351e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 4 Jul 2019 11:04:07 -0400
Subject: [PATCH 1698/2677] Change no<Type>Tests to accept dtype instead of
 template parameters

---
 test/getting_started.cpp |  2 +-
 test/solve_dense.cpp     |  2 +-
 test/testHelpers.hpp     | 14 +++++---------
 test/threading.cpp       | 16 ++++++++--------
 4 files changed, 15 insertions(+), 19 deletions(-)

diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index ca148c3380..ac77f58cf5 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -58,7 +58,7 @@ TEST(GettingStarted, SNIPPET_getting_started_gen) {
             ASSERT_FLOAT_EQ(0, output[i]);
     }
 
-    if (!noDoubleTests<double>()) {
+    if (!noDoubleTests(f64)) {
         array ones = constant(1, 3, 2, f64);
         vector<double> output(ones.elements());
         ones.host(&output.front());
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index e0919e5123..5014357566 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -170,7 +170,7 @@ TEST(Solve, Threading) {
 
     SOLVE_LU_TESTS_THREADING(float, 0.01);
     SOLVE_LU_TESTS_THREADING(cfloat, 0.01);
-    if (noDoubleTests<double>()) {
+    if (noDoubleTests(f64)) {
         SOLVE_LU_TESTS_THREADING(double, 1E-5);
         SOLVE_LU_TESTS_THREADING(cdouble, 1E-5);
     }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 6668be508a..dd5f7c659e 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -457,9 +457,7 @@ struct IsFloatingPoint {
                               is_same_type<long double, T>::value;
 };
 
-template<typename T>
-bool noDoubleTests() {
-    af::dtype ty           = (af::dtype)af::dtype_traits<T>::af_type;
+bool noDoubleTests(af::dtype ty) {
     bool isTypeDouble      = (ty == f64) || (ty == c64);
     int dev                = af::getDevice();
     bool isDoubleSupported = af::isDoubleAvailable(dev);
@@ -467,9 +465,7 @@ bool noDoubleTests() {
     return ((isTypeDouble && !isDoubleSupported) ? true : false);
 }
 
-template<typename T>
-bool noHalfTests() {
-    af::dtype ty           = (af::dtype)af::dtype_traits<T>::af_type;
+bool noHalfTests(af::dtype ty) {
     bool isTypeHalf        = (ty == f16);
     int dev                = af::getDevice();
     bool isHalfSupported   = af::isHalfAvailable(dev);
@@ -477,9 +473,9 @@ bool noHalfTests() {
     return ((isTypeHalf && !isHalfSupported) ? true : false);
 }
 
-#define SUPPORTED_TYPE_CHECK(type) \
-  if (noDoubleTests<type>()) return; \
-  if (noHalfTests<type>()) return
+#define SUPPORTED_TYPE_CHECK(type)                                        \
+    if (noDoubleTests((af_dtype)af::dtype_traits<type>::af_type)) return; \
+    if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type)) return
 
 inline bool noImageIOTests() {
     bool ret = !af::isImageIOAvailable();
diff --git a/test/threading.cpp b/test/threading.cpp
index 200ce91cb2..e0a4cd7cd6 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -255,8 +255,8 @@ TEST(Threading, MemoryManagement_JIT_Node) {
 template<typename inType, typename outType, bool isInverse>
 void fftTest(int targetDevice, string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0,
              dim_t pad2 = 0) {
-    if (noDoubleTests<inType>()) return;
-    if (noDoubleTests<outType>()) return;
+    SUPPORTED_TYPE_CHECK(inType);
+    SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<inType> > in;
@@ -380,7 +380,7 @@ TEST(Threading, FFT_R2C) {
     INSTANTIATE_TEST_TP(fft2, R2C_Float_Trunc, false, float, cfloat,
                         string(TEST_DIR "/signal/fft2_r2c_trunc.test"), 16, 16);
 
-    if (noDoubleTests<double>()) {
+    if (noDoubleTests(f64)) {
         // Real to complex transforms
         INSTANTIATE_TEST(fft, R2C_Double, false, double, cdouble,
                          string(TEST_DIR "/signal/fft_r2c.test"));
@@ -444,7 +444,7 @@ TEST(Threading, FFT_C2C) {
     INSTANTIATE_TEST(ifft3, C2C_Float, true, cfloat, cfloat,
                      string(TEST_DIR "/signal/ifft3_c2c.test"));
 
-    if (noDoubleTests<double>()) {
+    if (noDoubleTests(f64)) {
         INSTANTIATE_TEST(fft, C2C_Double, false, cdouble, cdouble,
                          string(TEST_DIR "/signal/fft_c2c.test"));
         INSTANTIATE_TEST(fft2, C2C_Double, false, cdouble, cdouble,
@@ -532,7 +532,7 @@ TEST(Threading, FFT_ALL) {
     INSTANTIATE_TEST(ifft3, C2C_Float, true, cfloat, cfloat,
                      string(TEST_DIR "/signal/ifft3_c2c.test"));
 
-    if (noDoubleTests<double>()) {
+    if (noDoubleTests(f64)) {
         INSTANTIATE_TEST(fft, R2C_Double, false, double, cdouble,
                          string(TEST_DIR "/signal/fft_r2c.test"));
         INSTANTIATE_TEST(fft2, R2C_Double, false, double, cdouble,
@@ -577,7 +577,7 @@ TEST(Threading, FFT_ALL) {
 
 template<typename T, bool isBVector>
 void cppMatMulCheck(int targetDevice, string TestFile) {
-    if (noDoubleTests<T>()) return;
+    SUPPORTED_TYPE_CHECK(T);
 
     using std::vector;
     vector<dim4> numDims;
@@ -664,7 +664,7 @@ TEST(Threading, BLAS) {
     TEST_BLAS_FOR_TYPE(float);
     TEST_BLAS_FOR_TYPE(cfloat);
 
-    if (noDoubleTests<double>()) {
+    if (noDoubleTests(f64)) {
         TEST_BLAS_FOR_TYPE(double);
         TEST_BLAS_FOR_TYPE(cdouble);
     }
@@ -698,7 +698,7 @@ TEST(Threading, Sparse) {
 
     SPARSE_TESTS(float, 1E-3);
     SPARSE_TESTS(cfloat, 1E-3);
-    if (noDoubleTests<double>()) {
+    if (noDoubleTests(f64)) {
         SPARSE_TESTS(double, 1E-5);
         SPARSE_TESTS(cdouble, 1E-5);
     }

From 958959f388e44dd080ec1c1742fb46fad2cdce84 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Jul 2019 01:33:48 -0400
Subject: [PATCH 1699/2677] Fix bug in half2float conversion

---
 src/backend/common/half.hpp | 67 ++++++++++++++++++++-----------------
 1 file changed, 36 insertions(+), 31 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index d445417bb9..184bfbd6f4 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -77,12 +77,12 @@ CONSTEXPR_DH uint16_t int2half(T value) noexcept {
 ///
 /// \param value single-precision value
 /// \return binary representation of half-precision value
-template<std::float_round_style R = std::round_to_nearest>
+template<std::float_round_style R = std::numeric_limits<float>::round_style>
 CONSTEXPR_DH uint16_t float2half(float value) noexcept {
     uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
                         // //violating strict aliasing!
     std::memcpy(&bits, &value, sizeof(float));
-    uint16_t base_table[512] = {
+    static const uint16_t base_table[512] = {
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
@@ -141,7 +141,7 @@ CONSTEXPR_DH uint16_t float2half(float value) noexcept {
         0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
         0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00};
 
-    uint8_t shift_table[512] = {
+    static const uint8_t shift_table[512] = {
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
@@ -522,33 +522,36 @@ __DH__ inline float half2float(uint16_t value) noexcept {
         0x38694000, 0x38696000, 0x38698000, 0x3869A000, 0x3869C000, 0x3869E000,
         0x386A0000, 0x386A2000, 0x386A4000, 0x386A6000, 0x386A8000, 0x386AA000,
         0x386AC000, 0x386AE000, 0x386B0000, 0x386B2000, 0x386B4000, 0x386B6000,
-        0x386B8000, 0x386BA000, 0x386BC000, 0x386BE000,
-
-        0x386E0000, 0x386E2000, 0x386E4000, 0x386E6000, 0x386E8000, 0x386EA000,
-        0x386EC000, 0x386EE000, 0x386F0000, 0x386F2000, 0x386F4000, 0x386F6000,
-        0x386F8000, 0x386FA000, 0x386FC000, 0x386FE000, 0x38700000, 0x38702000,
-        0x38704000, 0x38706000, 0x38708000, 0x3870A000, 0x3870C000, 0x3870E000,
-        0x38710000, 0x38712000, 0x38714000, 0x38716000, 0x38718000, 0x3871A000,
-        0x3871C000, 0x3871E000, 0x38720000, 0x38722000, 0x38724000, 0x38726000,
-        0x38728000, 0x3872A000, 0x3872C000, 0x3872E000, 0x38730000, 0x38732000,
-        0x38734000, 0x38736000, 0x38738000, 0x3873A000, 0x3873C000, 0x3873E000,
-        0x38740000, 0x38742000, 0x38744000, 0x38746000, 0x38748000, 0x3874A000,
-        0x3874C000, 0x3874E000, 0x38750000, 0x38752000, 0x38754000, 0x38756000,
-        0x38758000, 0x3875A000, 0x3875C000, 0x3875E000, 0x38760000, 0x38762000,
-        0x38764000, 0x38766000, 0x38768000, 0x3876A000, 0x3876C000, 0x3876E000,
-        0x38770000, 0x38772000, 0x38774000, 0x38776000, 0x38778000, 0x3877A000,
-        0x3877C000, 0x3877E000, 0x38780000, 0x38782000, 0x38784000, 0x38786000,
-        0x38788000, 0x3878A000, 0x3878C000, 0x3878E000, 0x38790000, 0x38792000,
-        0x38794000, 0x38796000, 0x38798000, 0x3879A000, 0x3879C000, 0x3879E000,
-        0x387A0000, 0x387A2000, 0x387A4000, 0x387A6000, 0x387A8000, 0x387AA000,
-        0x387AC000, 0x387AE000, 0x387B0000, 0x387B2000, 0x387B4000, 0x387B6000,
-        0x387B8000, 0x387BA000, 0x387BC000, 0x387BE000, 0x387C0000, 0x387C2000,
-        0x387C4000, 0x387C6000, 0x387C8000, 0x387CA000, 0x387CC000, 0x387CE000,
-        0x387D0000, 0x387D2000, 0x387D4000, 0x387D6000, 0x387D8000, 0x387DA000,
-        0x387DC000, 0x387DE000, 0x387E0000, 0x387E2000, 0x387E4000, 0x387E6000,
-        0x387E8000, 0x387EA000, 0x387EC000, 0x387EE000, 0x387F0000, 0x387F2000,
-        0x387F4000, 0x387F6000, 0x387F8000, 0x387FA000, 0x387FC000, 0x387FE000};
-    uint32_t exponent_table[64] = {
+        0x386B8000, 0x386BA000, 0x386BC000, 0x386BE000, 0x386C0000, 0x386C2000,
+        0x386C4000, 0x386C6000, 0x386C8000, 0x386CA000, 0x386CC000, 0x386CE000,
+        0x386D0000, 0x386D2000, 0x386D4000, 0x386D6000, 0x386D8000, 0x386DA000,
+        0x386DC000, 0x386DE000, 0x386E0000, 0x386E2000, 0x386E4000, 0x386E6000,
+        0x386E8000, 0x386EA000, 0x386EC000, 0x386EE000, 0x386F0000, 0x386F2000,
+        0x386F4000, 0x386F6000, 0x386F8000, 0x386FA000, 0x386FC000, 0x386FE000,
+        0x38700000, 0x38702000, 0x38704000, 0x38706000, 0x38708000, 0x3870A000,
+        0x3870C000, 0x3870E000, 0x38710000, 0x38712000, 0x38714000, 0x38716000,
+        0x38718000, 0x3871A000, 0x3871C000, 0x3871E000, 0x38720000, 0x38722000,
+        0x38724000, 0x38726000, 0x38728000, 0x3872A000, 0x3872C000, 0x3872E000,
+        0x38730000, 0x38732000, 0x38734000, 0x38736000, 0x38738000, 0x3873A000,
+        0x3873C000, 0x3873E000, 0x38740000, 0x38742000, 0x38744000, 0x38746000,
+        0x38748000, 0x3874A000, 0x3874C000, 0x3874E000, 0x38750000, 0x38752000,
+        0x38754000, 0x38756000, 0x38758000, 0x3875A000, 0x3875C000, 0x3875E000,
+        0x38760000, 0x38762000, 0x38764000, 0x38766000, 0x38768000, 0x3876A000,
+        0x3876C000, 0x3876E000, 0x38770000, 0x38772000, 0x38774000, 0x38776000,
+        0x38778000, 0x3877A000, 0x3877C000, 0x3877E000, 0x38780000, 0x38782000,
+        0x38784000, 0x38786000, 0x38788000, 0x3878A000, 0x3878C000, 0x3878E000,
+        0x38790000, 0x38792000, 0x38794000, 0x38796000, 0x38798000, 0x3879A000,
+        0x3879C000, 0x3879E000, 0x387A0000, 0x387A2000, 0x387A4000, 0x387A6000,
+        0x387A8000, 0x387AA000, 0x387AC000, 0x387AE000, 0x387B0000, 0x387B2000,
+        0x387B4000, 0x387B6000, 0x387B8000, 0x387BA000, 0x387BC000, 0x387BE000,
+        0x387C0000, 0x387C2000, 0x387C4000, 0x387C6000, 0x387C8000, 0x387CA000,
+        0x387CC000, 0x387CE000, 0x387D0000, 0x387D2000, 0x387D4000, 0x387D6000,
+        0x387D8000, 0x387DA000, 0x387DC000, 0x387DE000, 0x387E0000, 0x387E2000,
+        0x387E4000, 0x387E6000, 0x387E8000, 0x387EA000, 0x387EC000, 0x387EE000,
+        0x387F0000, 0x387F2000, 0x387F4000, 0x387F6000, 0x387F8000, 0x387FA000,
+        0x387FC000, 0x387FE000};
+
+    static const uint32_t exponent_table[64] = {
         0x00000000, 0x00800000, 0x01000000, 0x01800000, 0x02000000, 0x02800000,
         0x03000000, 0x03800000, 0x04000000, 0x04800000, 0x05000000, 0x05800000,
         0x06000000, 0x06800000, 0x07000000, 0x07800000, 0x08000000, 0x08800000,
@@ -560,13 +563,15 @@ __DH__ inline float half2float(uint16_t value) noexcept {
         0x88000000, 0x88800000, 0x89000000, 0x89800000, 0x8A000000, 0x8A800000,
         0x8B000000, 0x8B800000, 0x8C000000, 0x8C800000, 0x8D000000, 0x8D800000,
         0x8E000000, 0x8E800000, 0x8F000000, 0xC7800000};
-    uint16_t offset_table[64] = {
+
+    static const uint16_t offset_table[64] = {
         0,    1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 0,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024};
+
     uint32_t bits =
         mantissa_table[offset_table[value >> 10] + (value & 0x3FF)] +
         exponent_table[value >> 10];

From 546791e58f6032c2ef57f3be4973750efada7839 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Jul 2019 02:14:02 -0400
Subject: [PATCH 1700/2677] Add half support for assign, index,  tile, and
 lookup

---
 src/api/c/array.cpp           |   9 +--
 src/api/c/assign.cpp          |   7 +-
 src/api/c/index.cpp           |   5 ++
 src/api/c/tile.cpp            |   7 +-
 src/api/cpp/CMakeLists.txt    |   2 +-
 src/api/cpp/array.cpp         |  22 +++++--
 src/backend/cpu/assign.cpp    |   3 +
 src/backend/cpu/copy.cpp      |   2 +
 src/backend/cpu/index.cpp     |   7 +-
 src/backend/cpu/lookup.cpp    |  10 ++-
 src/backend/cpu/tile.cpp      |   9 ++-
 src/backend/cpu/unary.hpp     |   8 ++-
 src/backend/cuda/assign.cu    |   8 ++-
 src/backend/cuda/index.cu     |   7 +-
 src/backend/cuda/lookup.cu    |  12 +++-
 src/backend/cuda/tile.cu      |   4 ++
 src/backend/opencl/assign.cpp |   8 ++-
 src/backend/opencl/copy.cpp   |   5 ++
 src/backend/opencl/index.cpp  |   8 ++-
 src/backend/opencl/lookup.cpp |  13 +++-
 src/backend/opencl/tile.cpp   |   8 ++-
 test/assign.cpp               |  18 ++++--
 test/gen_index.cpp            | 117 +++++++++++++++++++++++++++++-----
 test/index.cpp                |   8 ++-
 test/testHelpers.hpp          |  11 ++++
 test/tile.cpp                 |   8 ++-
 26 files changed, 262 insertions(+), 64 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 1a7620c289..9cbf2c9929 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -118,8 +118,7 @@ af_err af_create_array(af_array *result, const void *const data,
                     createHandleFromData(d, static_cast<const ushort *>(data));
                 break;
             case f16:
-                out = createHandleFromData(
-                    d, static_cast<const half *>(data));
+                out = createHandleFromData(d, static_cast<const half *>(data));
                 break;
             default: TYPE_ERROR(4, type);
         }
@@ -359,8 +358,7 @@ af_err af_write_array(af_array arr, const void *data, const size_t bytes,
                 write_array(arr, static_cast<const ushort *>(data), bytes, src);
                 break;
             case f16:
-                write_array(arr, static_cast<const half *>(data), bytes,
-                            src);
+                write_array(arr, static_cast<const half *>(data), bytes, src);
                 break;
             default: TYPE_ERROR(4, type);
         }
@@ -494,8 +492,7 @@ af_err af_get_scalar(void *output_value, const af_array arr) {
                                    arr);
                 break;
             case f16:
-                getScalar<half>(
-                    static_cast<half *>(output_value), arr);
+                getScalar<half>(static_cast<half *>(output_value), arr);
                 break;
             default: TYPE_ERROR(4, type);
         }
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index c844ed52ec..54a2c85698 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -6,13 +6,14 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <assign.hpp>
 
 #include <Array.hpp>
-#include <assign.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/complex.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <indexing_common.hpp>
@@ -32,6 +33,7 @@ using std::vector;
 
 using common::convert2Canonical;
 using common::createSpanIndex;
+using common::half;
 using common::if_complex;
 using common::if_real;
 
@@ -109,6 +111,7 @@ static if_real<T> assign(Array<T>& out, const vector<af_seq> iv,
         case u16: assign<T, ushort>(out, iv, getArray<ushort>(in)); break;
         case u8: assign<T, uchar>(out, iv, getArray<uchar>(in)); break;
         case b8: assign<T, char>(out, iv, getArray<char>(in)); break;
+        case f16: assign<T, half>(out, iv, getArray<half>(in)); break;
         default: TYPE_ERROR(1, iType); break;
     }
 }
@@ -185,6 +188,7 @@ af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
                     case u16: assign(getArray<ushort>(res), inSeqs, rhs); break;
                     case u8: assign(getArray<uchar>(res), inSeqs, rhs); break;
                     case b8: assign(getArray<char>(res), inSeqs, rhs); break;
+                    case f16: assign(getArray<half>(res), inSeqs, rhs); break;
                     default: TYPE_ERROR(1, oType); break;
                 }
             }
@@ -360,6 +364,7 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
                 case u16: genAssign<ushort>(output, ptr, rhs); break;
                 case u8: genAssign<uchar>(output, ptr, rhs); break;
                 case b8: genAssign<char>(output, ptr, rhs); break;
+                case f16: genAssign<half>(output, ptr, rhs); break;
                 default: TYPE_ERROR(1, rhsType);
             }
         } catch (...) {
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 45de2c14a0..3ecdb64874 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -33,6 +33,7 @@ using std::vector;
 
 using common::convert2Canonical;
 using common::createSpanIndex;
+using common::half;
 
 namespace common {
 af_index_t createSpanIndex() {
@@ -100,6 +101,7 @@ af_err af_index(af_array* result, const af_array in, const unsigned ndims,
             case s64: out = indexBySeqs<intl>(in, indices_); break;
             case u64: out = indexBySeqs<uintl>(in, indices_); break;
             case u8: out = indexBySeqs<uchar>(in, indices_); break;
+            case f16: out = indexBySeqs<half>(in, indices_); break;
             default: TYPE_ERROR(1, type);
         }
         swap(*result, out);
@@ -133,6 +135,7 @@ static af_array lookup(const af_array& in, const af_array& idx,
         case u16: return lookup<ushort, idx_t>(in, idx, dim);
         case u8: return lookup<uchar, idx_t>(in, idx, dim);
         case b8: return lookup<char, idx_t>(in, idx, dim);
+        case f16: return lookup<half, idx_t>(in, idx, dim);
         default: TYPE_ERROR(1, inType);
     }
 }
@@ -168,6 +171,7 @@ af_err af_lookup(af_array* out, const af_array in, const af_array indices,
             case s64: output = lookup<intl>(in, indices, dim); break;
             case u64: output = lookup<uintl>(in, indices, dim); break;
             case u8: output = lookup<uchar>(in, indices, dim); break;
+            case f16: output = lookup<half>(in, indices, dim); break;
             default: TYPE_ERROR(1, idxType);
         }
         std::swap(*out, output);
@@ -270,6 +274,7 @@ af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
             case s16: output = genIndex<short>(in, ptr); break;
             case u8: output = genIndex<uchar>(in, ptr); break;
             case b8: output = genIndex<char>(in, ptr); break;
+            case f16: output = genIndex<half>(in, ptr); break;
             default: TYPE_ERROR(1, inType);
         }
         std::swap(*out, output);
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 749d8eb8b0..e59592c541 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -7,18 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <tile.hpp>
+
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
-#include <tile.hpp>
-
 #include <unary.hpp>
 #include <af/arith.h>
 #include <af/data.h>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 template<typename T>
@@ -71,6 +73,7 @@ af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims) {
             case s16: output = tile<short>(in, tileDims); break;
             case u16: output = tile<ushort>(in, tileDims); break;
             case u8: output = tile<uchar>(in, tileDims); break;
+            case f16: output = tile<half>(in, tileDims); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 53d3aa97c6..27b43dd1b5 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -85,8 +85,8 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/ycbcr_rgb.cpp
 )
 
-
 target_include_directories(cpp_api_interface
   INTERFACE
     ${CMAKE_SOURCE_DIR}/src/api/c
+    ${ArrayFire_SOURCE_DIR}/extern/half/include
 )
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 212765740d..b6d3845237 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -6,20 +6,22 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
+#include <af/array.h>
+
 #include <af/algorithm.h>
 #include <af/arith.h>
-#include <af/array.h>
 #include <af/blas.h>
 #include <af/data.h>
 #include <af/device.h>
 #include <af/gfor.h>
+#include <af/half.h>
 #include <af/index.h>
 #include <af/internal.h>
 #include <af/traits.hpp>
 #include <af/util.h>
 #include "error.hpp"
-
-#include <af/half.h>
+#include "half.hpp"  //note: NOT common. From extern/half/include/half.hpp
 
 #include <memory>
 #include <stdexcept>
@@ -171,6 +173,13 @@ array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, af::dtype ty)
     initEmptyArray(&arr, ty, dim0, dim1, dim2, dim3);
 }
 
+template<>
+struct dtype_traits<half_float::half> {
+    enum { af_type = f16, ctype = f16 };
+    typedef half base_type;
+    static const char *getName() { return "half"; }
+};
+
 #define INSTANTIATE(T)                                                         \
     template<>                                                                 \
     AFAPI array::array(const dim4 &dims, const T *ptr, af::source src)         \
@@ -218,6 +227,7 @@ INSTANTIATE(unsigned long long)
 INSTANTIATE(short)
 INSTANTIATE(unsigned short)
 INSTANTIATE(af_half)
+INSTANTIATE(half_float::half)
 
 #undef INSTANTIATE
 
@@ -505,13 +515,13 @@ af::array::array_proxy::array_proxy(const array_proxy &other)
                                 other.impl->is_linear_)) {}
 
 af::array::array_proxy::array_proxy(array_proxy &&other) {
-   impl = other.impl;
-   other.impl = nullptr;
+    impl       = other.impl;
+    other.impl = nullptr;
 }
 
 array::array_proxy &af::array::array_proxy::operator=(array_proxy &&other) {
     array out = other;
-    *this = out;
+    *this     = out;
     return *this;
 }
 
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index cedbf9fa00..d6f60c72db 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -12,6 +12,7 @@
 
 #include <Array.hpp>
 #include <Param.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
 #include <types.hpp>
@@ -25,6 +26,7 @@
 #include <vector>
 
 using af::dim4;
+using common::half;
 using std::vector;
 
 namespace cpu {
@@ -68,5 +70,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 6eb2836956..f68713790d 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -100,6 +100,8 @@ INSTANTIATE(half)
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, char>(Array<char> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, half>(Array<half> & dst,           \
                                          Array<SRC_T> const &src);
 
 INSTANTIATE_COPY_ARRAY(float)
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 953e5fcdc5..f9aa108ae6 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -7,17 +7,21 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <index.hpp>
+
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
-#include <index.hpp>
 #include <kernel/index.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
+
 #include <utility>
 #include <vector>
 
 using af::dim4;
+using common::half;
 using std::vector;
 
 namespace cpu {
@@ -70,5 +74,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 33300d0675..10eb97b36a 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -6,13 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
 #include <kernel/lookup.hpp>
 #include <lookup.hpp>
+
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <cstdlib>
 
+using common::half;
+
 namespace cpu {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
@@ -47,7 +50,9 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
-                                       const unsigned);
+                                       const unsigned);                        \
+    template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
+                                      const unsigned);
 
 INSTANTIATE(float);
 INSTANTIATE(cfloat);
@@ -61,4 +66,5 @@ INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(ushort);
 INSTANTIATE(short);
+INSTANTIATE(half);
 }  // namespace cpu
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index a733eb30de..ac9197f11b 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -7,11 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <kernel/tile.hpp>
-#include <platform.hpp>
 #include <tile.hpp>
 
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <platform.hpp>
+
+using common::half;
+
 namespace cpu {
 
 template<typename T>
@@ -46,5 +50,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index a511f98918..c2e7a441dc 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -23,7 +23,7 @@ T sigmoid(T in) {
 
 template<typename T>
 T rsqrt(T in) {
-    return  pow(in, -0.5);
+    return pow(in, -0.5);
 }
 
 #define UNARY_OP_FN(op, fn)                                               \
@@ -81,8 +81,10 @@ UNARY_OP(lgamma)
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    jit::Node_ptr in_node          = in.getNode();
-    jit::UnaryNode<T, T, op> *node = new jit::UnaryNode<T, T, op>(in_node);
+    using UnaryNode = jit::UnaryNode<compute_t<T>, compute_t<T>, op>;
+
+    jit::Node_ptr in_node = in.getNode();
+    UnaryNode *node       = new UnaryNode(in_node);
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
     return createNodeArray<T>(outDim, jit::Node_ptr(node));
diff --git a/src/backend/cuda/assign.cu b/src/backend/cuda/assign.cu
index 092b6cc1f2..06265efe32 100644
--- a/src/backend/cuda/assign.cu
+++ b/src/backend/cuda/assign.cu
@@ -7,14 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <assign.hpp>
+#include <kernel/assign.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
-#include <kernel/assign.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -72,5 +75,6 @@ INSTANTIATE(char)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cu
index 583e4ff3af..07743cf956 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cu
@@ -6,15 +6,17 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <index.hpp>
+#include <kernel/index.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
-#include <index.hpp>
-#include <kernel/index.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -78,5 +80,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/lookup.cu b/src/backend/cuda/lookup.cu
index 725f238f50..e8ca726bca 100644
--- a/src/backend/cuda/lookup.cu
+++ b/src/backend/cuda/lookup.cu
@@ -7,9 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <lookup.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/lookup.hpp>
-#include <lookup.hpp>
+
+using common::half;
 
 namespace cuda {
 template<typename in_t, typename idx_t>
@@ -61,7 +66,9 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
-                                       const unsigned);
+                                       const unsigned);                        \
+    template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
+                                      const unsigned);
 
 INSTANTIATE(float);
 INSTANTIATE(cfloat);
@@ -75,4 +82,5 @@ INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
+INSTANTIATE(half);
 }  // namespace cuda
diff --git a/src/backend/cuda/tile.cu b/src/backend/cuda/tile.cu
index 541601e8a0..174b609864 100644
--- a/src/backend/cuda/tile.cu
+++ b/src/backend/cuda/tile.cu
@@ -8,11 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/tile.hpp>
 #include <tile.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
@@ -46,5 +49,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 11fd915e30..b695ca08c5 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -7,15 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <assign.hpp>
+#include <kernel/assign.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
-#include <kernel/assign.hpp>
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace opencl {
 
@@ -80,5 +83,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 4e61e347db..7e43a19dd1 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -178,6 +178,9 @@ INSTANTIATE(half)
     template Array<char> padArray<SRC_T, char>(                           \
         Array<SRC_T> const &src, dim4 const &dims, char default_value,    \
         double factor);                                                   \
+    template Array<half> padArray<SRC_T, half>(                           \
+        Array<SRC_T> const &src, dim4 const &dims, half default_value,    \
+        double factor);                                                   \
     template void copyArray<SRC_T, float>(Array<float> & dst,             \
                                           Array<SRC_T> const &src);       \
     template void copyArray<SRC_T, double>(Array<double> & dst,           \
@@ -201,6 +204,8 @@ INSTANTIATE(half)
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,             \
                                           Array<SRC_T> const &src);       \
     template void copyArray<SRC_T, char>(Array<char> & dst,               \
+                                         Array<SRC_T> const &src);        \
+    template void copyArray<SRC_T, half>(Array<half> & dst,               \
                                          Array<SRC_T> const &src);
 
 INSTANTIATE_PAD_ARRAY(float)
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index b153abc9e2..4189d3ab4d 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -7,14 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <index.hpp>
+#include <kernel/index.hpp>
+
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <handle.hpp>
-#include <index.hpp>
-#include <kernel/index.hpp>
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace opencl {
 
 template<typename T>
@@ -84,5 +87,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 0e5d756bc1..692b26b768 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -7,12 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_opencl.hpp>
 #include <kernel/lookup.hpp>
 #include <lookup.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_opencl.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace opencl {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
@@ -53,7 +57,9 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
-                                       const unsigned);
+                                       const unsigned);                        \
+    template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
+                                      const unsigned)
 
 INSTANTIATE(float);
 INSTANTIATE(cfloat);
@@ -67,4 +73,5 @@ INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(ushort);
 INSTANTIATE(short);
+INSTANTIATE(half);
 }  // namespace opencl
diff --git a/src/backend/opencl/tile.cpp b/src/backend/opencl/tile.cpp
index 4524f4fd68..5c32c4582c 100644
--- a/src/backend/opencl/tile.cpp
+++ b/src/backend/opencl/tile.cpp
@@ -6,12 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <kernel/tile.hpp>
 #include <tile.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
@@ -41,5 +44,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/test/assign.cpp b/test/assign.cpp
index 77f085a290..a9985042cf 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -12,6 +12,9 @@
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
+#include <half.hpp>
+
 #include <string>
 #include <vector>
 
@@ -31,6 +34,13 @@ using std::endl;
 using std::string;
 using std::vector;
 
+namespace half_float {
+std::ostream &operator<<(std::ostream &os, half_float::half val) {
+    os << (float)val;
+    return os;
+}
+}  // namespace half_float
+
 template<typename T>
 class ArrayAssign : public ::testing::Test {
    public:
@@ -92,7 +102,7 @@ class ArrayAssign : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, uchar,
-                         intl, uintl, short, ushort>
+                         intl, uintl, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
@@ -357,7 +367,7 @@ TYPED_TEST(ArrayAssign, AssignRowCPP) {
     SUPPORTED_TYPE_CHECK(TypeParam);
 
     const int dimsize = 10;
-    vector<TypeParam> input(100, 1);
+    vector<TypeParam> input(100, TypeParam(1.0));
     vector<TypeParam> sq(dimsize);
     vector<int> arIdx(2);
     for (int i = 0; i < (int)sq.size(); i++) sq[i] = i;
@@ -408,7 +418,7 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP) {
     SUPPORTED_TYPE_CHECK(TypeParam);
 
     const int dimsize = 10;
-    vector<TypeParam> input(100, 1);
+    vector<TypeParam> input(100, TypeParam(1.0));
     vector<TypeParam> sq(dimsize);
     vector<int> arIdx(2);
     for (int i = 0; i < (int)sq.size(); i++) sq[i] = i;
@@ -458,7 +468,7 @@ TYPED_TEST(ArrayAssign, AssignColumnCPP) {
 TYPED_TEST(ArrayAssign, AssignSliceCPP) {
     SUPPORTED_TYPE_CHECK(TypeParam);
     const int dimsize = 10;
-    vector<TypeParam> input(1000, 1);
+    vector<TypeParam> input(1000, TypeParam(1.0));
     vector<TypeParam> sq(dimsize * dimsize);
     vector<int> arIdx(2);
     for (int i = 0; i < (int)sq.size(); i++) sq[i] = i;
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index 14033ac4c2..f0b9dc5b09 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -6,28 +6,127 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 
-#include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 
-#include <testHelpers.hpp>
 #include <algorithm>
 #include <functional>
 #include <iostream>
+#include <sstream>
 #include <string>
+#include <tuple>
 #include <vector>
 
 using af::dim4;
 using af::dtype_traits;
 using std::endl;
+using std::get;
 using std::ostream_iterator;
 using std::string;
+using std::stringstream;
 using std::vector;
 
+struct index_test {
+    string filename_;
+    dim4 dims_;
+    index_test(string filename, dim4 dims) : filename_(filename), dims_(dims) {}
+};
+
+using index_params = std::tuple<index_test, af_dtype, af_dtype>;
+
+class IndexGeneralizedLegacy : public ::testing::TestWithParam<index_params> {
+    void SetUp() {
+        index_params params = GetParam();
+        vector<dim4> numDims;
+        vector<vector<float> > in;
+        vector<vector<float> > tests;
+
+        if (noDoubleTests(get<1>(params))) return;
+        if (noHalfTests(get<1>(params))) return;
+
+        if (noDoubleTests(get<2>(params))) return;
+        if (noHalfTests(get<2>(params))) return;
+        readTestsFromFile<float, float>(get<0>(params).filename_, numDims, in,
+                                        tests);
+
+        dim4 dims0 = numDims[0];
+        dim4 dims1 = numDims[1];
+
+        af_array inTmp = 0;
+        ASSERT_SUCCESS(af_create_array(&inTmp, &(in[0].front()), dims0.ndims(),
+                                       dims0.get(), f32));
+
+        ASSERT_SUCCESS(af_cast(&inArray_, inTmp, get<1>(params)));
+
+        af_array idxTmp = 0;
+        ASSERT_SUCCESS(af_create_array(&idxTmp, &(in[1].front()), dims1.ndims(),
+                                       dims1.get(), f32));
+        ASSERT_SUCCESS(af_cast(&idxArray_, idxTmp, get<2>(params)));
+
+        vector<float> hgold = tests[0];
+        af_array goldTmp;
+        af_create_array(&goldTmp, &hgold.front(), get<0>(params).dims_.ndims(),
+                        get<0>(params).dims_.get(), f32);
+        ASSERT_SUCCESS(af_cast(&gold_, goldTmp, get<1>(params)));
+    }
+
+    void TearDown() {
+        if (inArray_) ASSERT_SUCCESS(af_release_array(inArray_));
+        if (idxArray_) ASSERT_SUCCESS(af_release_array(idxArray_));
+    }
+
+   public:
+    IndexGeneralizedLegacy() : gold_(0), inArray_(0), idxArray_(0) {}
+
+    af_array gold_;
+    af_array inArray_;
+    af_array idxArray_;
+};
+
+string testNameGenerator(
+    const ::testing::TestParamInfo<IndexGeneralizedLegacy::ParamType> info) {
+    stringstream ss;
+    ss << "type_" << get<1>(info.param) << "_idx_type_" << get<2>(info.param);
+    return ss.str();
+}
+
+INSTANTIATE_TEST_CASE_P(
+    Legacy, IndexGeneralizedLegacy,
+    ::testing::Combine(
+        ::testing::Values(index_test(
+            string(TEST_DIR "/gen_index/s0_3s0_1s1_2a.test"), dim4(4, 2, 2))),
+        ::testing::Values(f32, f64, c32, c64, u64, s64, u16, s16, u8, b8, f16),
+        ::testing::Values(f32, f64, u64, s64, u16, s16, u8, f16)),
+    testNameGenerator);
+
+TEST_P(IndexGeneralizedLegacy, SSSA) {
+    index_params params = GetParam();
+    if (noDoubleTests(get<1>(params))) return;
+    if (noHalfTests(get<1>(params))) return;
+
+    if (noDoubleTests(get<2>(params))) return;
+    if (noHalfTests(get<2>(params))) return;
+
+    af_array outArray = 0;
+    af_index_t indexes[4];
+    indexes[0].idx.seq = af_make_seq(0, 3, 1);
+    indexes[1].idx.seq = af_make_seq(0, 1, 1);
+    indexes[2].idx.seq = af_make_seq(1, 2, 1);
+    indexes[3].idx.arr = idxArray_;
+    indexes[0].isSeq   = true;
+    indexes[1].isSeq   = true;
+    indexes[2].isSeq   = true;
+    indexes[3].isSeq   = false;
+    ASSERT_SUCCESS(af_index_gen(&outArray, inArray_, 4, indexes));
+    ASSERT_ARRAYS_EQ(gold_, outArray);
+}
+
 void testGeneralIndexOneArray(string pTestFile, const dim_t ndims,
                               af_index_t *indexs, int arrayDim) {
     vector<dim4> numDims;
@@ -69,20 +168,6 @@ void testGeneralIndexOneArray(string pTestFile, const dim_t ndims,
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
-TEST(GeneralIndex, SSSA) {
-    af_index_t indexs[4];
-    indexs[0].idx.seq = af_make_seq(0, 3, 1);
-    indexs[1].idx.seq = af_make_seq(0, 1, 1);
-    indexs[2].idx.seq = af_make_seq(1, 2, 1);
-    indexs[0].isSeq   = true;
-    indexs[1].isSeq   = true;
-    indexs[2].isSeq   = true;
-    indexs[3].isSeq   = false;
-
-    testGeneralIndexOneArray(string(TEST_DIR "/gen_index/s0_3s0_1s1_2a.test"),
-                             4, indexs, 3);
-}
-
 TEST(GeneralIndex, ASSS) {
     af_index_t indexs[4];
     indexs[1].idx.seq = af_make_seq(0, 9, 1);
diff --git a/test/index.cpp b/test/index.cpp
index c3ac48ef4d..4e6fb88d25 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -9,12 +9,13 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 
-#include <testHelpers.hpp>
 #include <algorithm>
 #include <functional>
 #include <iostream>
@@ -137,7 +138,8 @@ class Indexing1D : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned,
-                         unsigned char, intl, uintl, short, ushort>
+                         unsigned char, intl, uintl, short, ushort,
+                         half_float::half>
     AllTypes;
 TYPED_TEST_CASE(Indexing1D, AllTypes);
 
@@ -706,7 +708,7 @@ class lookup : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double, int, unsigned, unsigned char, short,
-                         ushort, intl, uintl>
+                         ushort, intl, uintl, half_float::half>
     ArrIdxTestTypes;
 TYPED_TEST_CASE(lookup, ArrIdxTestTypes);
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index dd5f7c659e..5c862607f0 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -14,6 +14,7 @@
 #include <gtest/gtest.h>
 #include <half.hpp>
 #include <af/array.h>
+#include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/internal.h>
 #include <af/traits.hpp>
@@ -84,6 +85,16 @@ std::ostream &operator<<(std::ostream &os, af::dtype type) {
     return os << name;
 }
 
+namespace af {
+template<>
+struct dtype_traits<half_float::half> {
+    enum { af_type = f16, ctype = f16 };
+    typedef half base_type;
+    static const char *getName() { return "half"; }
+};
+
+}  // namespace af
+
 namespace {
 
 typedef unsigned char uchar;
diff --git a/test/tile.cpp b/test/tile.cpp
index 85e716b63a..d7bcefbeef 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -7,12 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <arrayfire.h>
+#include <af/data.h>
+
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
 #include <complex>
 #include <iostream>
 #include <string>
@@ -44,7 +47,8 @@ class Tile : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort>
+                         intl, uintl, char, unsigned char, short, ushort,
+                         half_float::half>
     TestTypes;
 
 // register the type list

From e48f64c1793b654469a57fbd36b8366a714725b2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 7 Jul 2019 22:54:50 -0400
Subject: [PATCH 1701/2677] Fix the compute type for architectures other than
 6.1

The compute type is always float on the host side code. This was incorrectly
setting the compute type to float on architectures other than 6.1. This
commit creates a separate function for selecting the type.
---
 src/backend/cuda/blas.cpp  | 50 +++++++++++++++++++++++++-------------
 src/backend/cuda/types.hpp |  5 +---
 2 files changed, 34 insertions(+), 21 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 64e6d58a54..52bc8534eb 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -184,6 +184,19 @@ cudaDataType_t getType<half>() {
     return CUDA_R_16F;
 }
 
+template<typename T>
+cudaDataType_t getComputeType() {
+    return getType<T>();
+}
+
+template<>
+cudaDataType_t getComputeType<half>() {
+    auto dev            = getDeviceProp(getActiveDeviceId());
+    cudaDataType_t algo = getType<half>();
+    if (dev.major == 6 && dev.minor == 1) { algo = CUDA_R_32F; }
+    return algo;
+}
+
 template<typename T>
 cublasGemmAlgo_t selectGEMMAlgorithm() {
     auto dev              = getDeviceProp(getActiveDeviceId());
@@ -199,19 +212,24 @@ cublasGemmAlgo_t selectGEMMAlgorithm<half>() {
     return algo;
 }
 
+template<>
+cublasGemmAlgo_t selectGEMMAlgorithm<__half>() {
+    return selectGEMMAlgorithm<common::half>();
+}
+
 template<typename T>
 cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
-                             cublasOperation_t rOpts, int M, int N, int K,
-                             const T *alpha, const Array<T> &lhs, dim_t lStride,
-                             const Array<T> &rhs, dim_t rStride, const T *beta,
-                             Array<T> &out, dim_t oleading) {
+                            cublasOperation_t rOpts, int M, int N, int K,
+                            const T *alpha, const Array<T> &lhs, dim_t lStride,
+                            const Array<T> &rhs, dim_t rStride, const T *beta,
+                            Array<T> &out, dim_t oleading) {
     auto prop = getDeviceProp(getActiveDeviceId());
     if (prop.major > 3) {
         return cublasGemmEx(
             blasHandle(), lOpts, rOpts, M, N, K, alpha, lhs.get(), getType<T>(),
             lStride, rhs.get(), getType<T>(), rStride, beta, out.get(),
             getType<T>(), out.strides()[1],
-            getType<compute_t<T>>(), // Compute type
+            getComputeType<T>(),  // Compute type
 
             // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
             // for the cublasGemm*Ex functions, the performance of the
@@ -231,20 +249,19 @@ cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
 }
 
 template<typename T>
-cublasStatus_t gemmBatchedDispatch(BlasHandle handle,
-                                    cublasOperation_t lOpts,
-                                    cublasOperation_t rOpts, int M, int N,
-                                    int K, const T *alpha, const T **lptrs,
-                                    int lStrides, const T **rptrs, int rStrides,
-                                    const T *beta, T **optrs, int oStrides,
-                                    int batchSize) {
+cublasStatus_t gemmBatchedDispatch(BlasHandle handle, cublasOperation_t lOpts,
+                                   cublasOperation_t rOpts, int M, int N, int K,
+                                   const T *alpha, const T **lptrs,
+                                   int lStrides, const T **rptrs, int rStrides,
+                                   const T *beta, T **optrs, int oStrides,
+                                   int batchSize) {
     auto prop = getDeviceProp(getActiveDeviceId());
     if (prop.major > 3) {
         return cublasGemmBatchedEx(
             blasHandle(), lOpts, rOpts, M, N, K, alpha, (const void **)lptrs,
             getType<T>(), lStrides, (const void **)rptrs, getType<T>(),
             rStrides, beta, (void **)optrs, getType<T>(), oStrides, batchSize,
-            getType<compute_t<T>>(), // compute type
+            getComputeType<T>(),  // compute type
             // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
             // for the cublasGemm*Ex functions, the performance of the
             // fp32 numbers seem to increase dramatically. Their numerical
@@ -298,8 +315,8 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
                                         rhs.get(), incr, beta, out.get(), 1));
         } else {
             CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K,
-                                          alpha, lhs, lStrides[1], rhs,
-                                          rStrides[1], beta, out, oStrides[1]));
+                                         alpha, lhs, lStrides[1], rhs,
+                                         rStrides[1], beta, out, oStrides[1]));
         }
     } else {
         int batchSize = oDims[2] * oDims[3];
@@ -349,8 +366,7 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         CUBLAS_CHECK(gemmBatchedDispatch(
             blasHandle(), lOpts, rOpts, M, N, K, alpha,
             (const T **)d_lptrs.get(), lStrides[1], (const T **)d_rptrs.get(),
-            rStrides[1], beta, (T **)d_optrs.get(), oStrides[1],
-            batchSize));
+            rStrides[1], beta, (T **)d_optrs.get(), oStrides[1], batchSize));
     }
 }
 
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 7067768c0a..b0fbe9c935 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -149,16 +149,13 @@ struct kernel_type<common::half> {
     using data = common::half;
 
 #ifdef __CUDA_ARCH__
-
     // These are the types within a kernel
-
-#if __CUDA_ARCH__ > 530 && __CUDA_ARCH__ != 610
+#if __CUDA_ARCH__ >= 530 && __CUDA_ARCH__ != 610
     using compute = __half;
 #else
     using compute = float;
 #endif
 #else
-
     // outside of a cuda kernel use float
     using compute = float;
 

From 2644dfd6d2802b3129d3d5b1167ad4a37199b22d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 7 Jul 2019 22:57:48 -0400
Subject: [PATCH 1702/2677] Fix bug in SIFT caused by the change in the resize
 API

The resize funtion now takes the interpolation argument as a function
argument instead of a template argument.
---
 src/backend/cuda/kernel/sift_nonfree.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 66cd0147bb..45723c6483 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -78,6 +78,7 @@
 #include <debug_thrust.hpp>
 #include <memory.hpp>
 #include "shared.hpp"
+#include <af/defines.h>
 
 #include "convolve.hpp"
 #include "resize.hpp"
@@ -1061,7 +1062,7 @@ Array<T> createInitialImage(CParam<T> img, const float init_sigma,
     Array<convAccT> filter = gauss_filter<convAccT>(s);
 
     if (double_input) {
-        resize<T, InterpolationType::Bilinear>(init_img, img);
+        resize<T>(init_img, img, AF_INTERP_BILINEAR);
         convolve2<T, convAccT>(init_tmp, init_img, filter, 0, false);
     } else
         convolve2<T, convAccT>(init_tmp, img, filter, 0, false);
@@ -1108,8 +1109,7 @@ std::vector<Array<T>> buildGaussPyr(Param<T> init_img, const unsigned n_octaves,
                 tmp_pyr.push_back(
                     createEmptyArray<T>({tmp_pyr[src_idx].dims()[0] / 2,
                                          tmp_pyr[src_idx].dims()[1] / 2}));
-                resize<T, InterpolationType::Bilinear>(tmp_pyr[idx],
-                                                       tmp_pyr[src_idx]);
+                resize<T>(tmp_pyr[idx], tmp_pyr[src_idx], AF_INTERP_BILINEAR);
             } else {
                 tmp_pyr.push_back(createEmptyArray<T>(tmp_pyr[src_idx].dims()));
                 Array<T> tmp = createEmptyArray<T>(tmp_pyr[src_idx].dims());

From 73093cd4439079bc26d86f686d69b44caa38212e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 7 Jul 2019 22:59:12 -0400
Subject: [PATCH 1703/2677] Fix several leaks in tests

---
 test/blas.cpp         | 11 +++++++++--
 test/gen_index.cpp    |  5 +++++
 test/gloh_nonfree.cpp |  6 +-----
 3 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/test/blas.cpp b/test/blas.cpp
index 35be8cd17c..71418ce781 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -406,8 +406,8 @@ TEST(MatrixMultiply, half) {
 
     {
         af_array C16 = 0;
-        const af_half alpha16 = {0x03c00}; // 1.0 : 0 01111 0000000000
-        const af_half beta16 = {0x00000}; //  0.0 : 0 00000 0000000000
+        const half_float::half alpha16(1.0f);
+        const half_float::half beta16(0.0f);
         af_gemm(&C16, AF_MAT_NONE, AF_MAT_NONE, &alpha16, A16.get(), B16.get(), &beta16);
         af::array C(C16);
         ASSERT_ARRAYS_NEAR(expected16, C, 0.00001);
@@ -673,5 +673,12 @@ TEST(Gemm, DocSnippet) {
     vector<float> gold2(5*5*2, 3);
     fill(gold2.begin(), gold2.begin() + (5 * 5), 6);
 
+    af_release_array(A);
+    af_release_array(B);
+    af_release_array(C);
+    af_release_array(Asub);
+    af_release_array(Bsub);
+    af_release_array(Csub);
+
     ASSERT_VEC_ARRAY_EQ(gold2, dim4(5, 5, 2), c2);
 }
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index f0b9dc5b09..5b8ea27765 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -63,22 +63,26 @@ class IndexGeneralizedLegacy : public ::testing::TestWithParam<index_params> {
                                        dims0.get(), f32));
 
         ASSERT_SUCCESS(af_cast(&inArray_, inTmp, get<1>(params)));
+        af_release_array(inTmp);
 
         af_array idxTmp = 0;
         ASSERT_SUCCESS(af_create_array(&idxTmp, &(in[1].front()), dims1.ndims(),
                                        dims1.get(), f32));
         ASSERT_SUCCESS(af_cast(&idxArray_, idxTmp, get<2>(params)));
+        af_release_array(idxTmp);
 
         vector<float> hgold = tests[0];
         af_array goldTmp;
         af_create_array(&goldTmp, &hgold.front(), get<0>(params).dims_.ndims(),
                         get<0>(params).dims_.get(), f32);
         ASSERT_SUCCESS(af_cast(&gold_, goldTmp, get<1>(params)));
+        af_release_array(goldTmp);
     }
 
     void TearDown() {
         if (inArray_) ASSERT_SUCCESS(af_release_array(inArray_));
         if (idxArray_) ASSERT_SUCCESS(af_release_array(idxArray_));
+        if (gold_) ASSERT_SUCCESS(af_release_array(gold_));
     }
 
    public:
@@ -125,6 +129,7 @@ TEST_P(IndexGeneralizedLegacy, SSSA) {
     indexes[3].isSeq   = false;
     ASSERT_SUCCESS(af_index_gen(&outArray, inArray_, 4, indexes));
     ASSERT_ARRAYS_EQ(gold_, outArray);
+    af_release_array(outArray);
 }
 
 void testGeneralIndexOneArray(string pTestFile, const dim_t ndims,
diff --git a/test/gloh_nonfree.cpp b/test/gloh_nonfree.cpp
index 5687f2e559..f9f02cc679 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh_nonfree.cpp
@@ -242,12 +242,8 @@ void glohTest(string pTestFile) {
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
 
-        ASSERT_SUCCESS(af_release_array(x));
-        ASSERT_SUCCESS(af_release_array(y));
-        ASSERT_SUCCESS(af_release_array(score));
-        ASSERT_SUCCESS(af_release_array(orientation));
-        ASSERT_SUCCESS(af_release_array(size));
         ASSERT_SUCCESS(af_release_array(desc));
+        ASSERT_SUCCESS(af_release_features(feat));
 
         delete[] outX;
         delete[] outY;

From ca0a896c38a05e0dd03a0b4e59016dc4879291f7 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Mon, 8 Jul 2019 13:31:10 -0700
Subject: [PATCH 1704/2677] Remove Cuda CreateEvent SYNC flag

---
 src/backend/cuda/Event.hpp | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/src/backend/cuda/Event.hpp b/src/backend/cuda/Event.hpp
index d3fd9ab1d2..be4a0b9551 100644
--- a/src/backend/cuda/Event.hpp
+++ b/src/backend/cuda/Event.hpp
@@ -21,22 +21,21 @@ class CUDARuntimeEventPolicy {
     using ErrorType = CUresult;
 
     static ErrorType createEvent(CUevent *e) noexcept {
-      auto err = cuEventCreate(e, CU_EVENT_DISABLE_TIMING | CU_EVENT_BLOCKING_SYNC);
-        // printf("create %p: error: %s\n", *e, cudaGetErrorName(err));
+        // Creating events with the CU_EVENT_BLOCKING_SYNC flag 
+        // severly impacts the speed if/when creating many arrays
+        auto err = cuEventCreate(e, CU_EVENT_DISABLE_TIMING);
         return err;
     }
 
     static ErrorType markEvent(CUevent *e,
                                QueueType &stream) noexcept {
         auto err = cuEventRecord(*e, stream);
-        // printf("mark   %p: error: %s\n", *e, cudaGetErrorName(err));
         return err;
     }
 
     static ErrorType waitForEvent(CUevent *e,
                                   QueueType &stream) noexcept {
-      auto err = cuStreamWaitEvent(stream, *e, 0);
-        // printf("wait   %p: error: %s\n", *e, cudaGetErrorName(err));
+        auto err = cuStreamWaitEvent(stream, *e, 0);
         return err;
     }
 
@@ -46,7 +45,6 @@ class CUDARuntimeEventPolicy {
 
     static ErrorType destroyEvent(CUevent *e) noexcept {
         auto err = cuEventDestroy(*e);
-        // printf("destroy %p: error: %s\n", *e, cudaGetErrorName(err));
         return err;
     }
 };

From acfac26c38b3d52847ec1412de51b4d9be9cbe25 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 Jul 2019 16:35:24 -0400
Subject: [PATCH 1705/2677] Improve the indexing tutorial (#2558)

---
 docs/doxygen.mk        |   2 +-
 docs/pages/indexing.md | 334 ++++++++++++++++++++++++++++++++++++++--
 test/index.cpp         | 335 ++++++++++++++++++++++++++++++++++++++++-
 3 files changed, 656 insertions(+), 15 deletions(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 5d4e0237d9..05c4e12c33 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -769,7 +769,7 @@ WARN_NO_PARAMDOC       = YES
 # a warning is encountered.
 # The default value is: NO.
 
-WARN_AS_ERROR          = YES
+WARN_AS_ERROR          = NO
 
 # The WARN_FORMAT tag determines the format of the warning messages that doxygen
 # can produce. The string should contain the $file, $line, and $text tags, which
diff --git a/docs/pages/indexing.md b/docs/pages/indexing.md
index 3e46194c53..7ec779ff91 100644
--- a/docs/pages/indexing.md
+++ b/docs/pages/indexing.md
@@ -1,18 +1,332 @@
 Indexing {#indexing}
 ========
 
-There are several ways of referencing values.  ArrayFire uses
-parenthesis for subscripted referencing instead of the traditional
-square bracket notation.  Indexing is zero-based, i.e. the first
-element is at index zero (<tt>A(0)</tt>).  Indexing can be done
-with mixtures of:
-* integer scalars
-* [seq()](\ref af::seq) representing a linear sequence
-* [end](\ref af::end) representing the last element of a dimension
-* [span](\ref af::span) representing the entire dimension
+Indexing in ArrayFire is a powerful but easy to abuse feature of the af::array
+class. This feature allows you to reference or copy subsections of a larger array
+and perform operations on only a subset of elements.
+
+Indexing in ArrayFire can be performed using the parenthesis operator or one of
+the member functions of the af::array class. These functions allow you to
+reference one or a range of elements from the original array.
+
+Here we will demonstrate some of the ways you can use indexing in ArrayFire and
+discuss ways to minimize the memory and performance impact of these operations.
+
+Lets start by creating a new 4x4 matrix of floating point numbers:
+
+\snippet test/index.cpp index_tutorial_1
+
+ArrayFire is column-major so the resulting A array will look like this:
+
+\f[
+\begin{bmatrix}
+    0 & 4 & 8 & 12 \\
+    1 & 5 & 9 & 13 \\
+    2 & 6 & 10 & 14 \\
+    3 & 7 & 11 & 15
+\end{bmatrix}
+\f]
+
+This is a two dimensional array so we can access the first element of this
+matrix by passing `0,0` into the parenthesis operator of the af::array.
+
+\snippet test/index.cpp index_tutorial_first_element
+
+\f[ A(2, 3) = [ 14 ] \f]
+
+We can also access the array using linear indexing by passing in one value. Here
+we are accessing the fifth element of the array.
+
+\snippet test/index.cpp index_tutorial_fifth_element
+
+\f[ A(5) = [ 5 ] \f]
+
+\note Normally you want to avoid accessing individual elements of the array like this
+for performance reasons.
+
+Indexing with negative values will access from the end of the array. For example,
+the value negative one and negative two(-2) will return the last and second to
+last element of the array, respectively. ArrayFire provides the `end` alias for
+this which also allows you to index the last element of the array.
+
+\snippet test/index.cpp index_tutorial_negative_indexing
+
+## Indexing slices and subarrays
+
+You can access regions of the array via the af::seq and af::span objects. The
+span objects allows you to select the entire set of elements across a particular
+dimension/axis of an array. For example, we can select the third column of the
+array by passing span as the first argument and 2 as the second argument to the
+parenthesis operator.
+
+\snippet test/index.cpp index_tutorial_third_column
+
+\f[
+A(span, 2) =
+\begin{bmatrix}
+    8 \\
+    9 \\
+    10 \\
+    11
+\end{bmatrix}
+\f]
+
+You can read that as saying that you want all values across the first dimension,
+but only from index 2 of the second dimension.
+
+You can access the second row by passing (1, span) to the array
+
+\snippet test/index.cpp index_tutorial_second_row
+
+\f[ A(1, span) = [ 1, 5, 9, 13 ] \f]
+
+You can use the af::seq (short for sequence) object to define a range when
+indexing. For example, if you want to get the first two columns, you can access
+the array by passing af::span for the first argument and af::seq(2) as the
+second argument.
+
+\snippet test/index.cpp index_tutorial_first_two_columns
+
+\f[
+A(span, seq(2)) =
+\begin{bmatrix}
+     0 & 4 \\
+     1 & 5 \\
+     2 & 6 \\
+     3 & 7
+\end{bmatrix}
+\f]
+
+There are three constructors for af::seq.
+
+* af::seq(N): Defines a range between 0 and N-1
+* af::seq(begin, end) Defines a range between begin and end inclusive
+* af::seq(begin, end, step) defines a range between begin and end striding by step values
+
+The last constructor that can help create non-continuous ranges. For example,
+you can select the second and forth(last) rows by passing (seq(1, end, 2), span)
+to the indexing operator.
+
+\snippet test/index.cpp index_tutorial_second_and_fourth_rows
+
+\f[
+A(seq(1, end, 2), span) =
+\begin{bmatrix}
+     1 & 5 &  9 & 13 \\
+     3 & 7 & 11 & 15
+\end{bmatrix}
+\f]
+
+## Indexing using af::array
+
+You can also index using other af::array objects. ArrayFire performs a Cartesian
+product of the input arrays.
+
+\snippet test/index.cpp index_tutorial_array_indexing
+
+\f[
+A =
+\begin{bmatrix}
+    0 & 4 & 8 & 12 \\
+    1 & 5 & 9 & 13 \\
+    2 & 6 & 10 & 14 \\
+    3 & 7 & 11 & 15
+\end{bmatrix}
+\\
+A(
+\begin{bmatrix}
+2 \\ 1 \\ 3
+\end{bmatrix}
+,
+\begin{bmatrix}
+3 \\ 1 \\ 2
+\end{bmatrix}
+) =
+
+\begin{bmatrix}
+(2,3) & (2,1) & (2,2) \\
+(1,3) & (1,1) & (1,2) \\
+(3,3) & (3,1) & (3,2)
+\end{bmatrix}
+=
+\begin{bmatrix}
+14 & 6 & 10 \\
+13 & 5 &  9 \\
+15 & 7 & 11
+\end{bmatrix}
+\f]
+
+
+If you want to index an af::array using coordinate arrays, you can do that using the
+af::approx1 and af::approx2 functions.
+
+\snippet test/index.cpp index_tutorial_approx
+
+\f[
+approx2(A,
+\begin{bmatrix}
+2 \\ 1 \\ 3
+\end{bmatrix}
+,
+\begin{bmatrix}
+3 \\ 1 \\ 2
+\end{bmatrix}
+) =
+\begin{bmatrix}
+(2,3) \\
+(1,1) \\
+(3,2)
+\end{bmatrix}
+=
+\begin{bmatrix}
+14 \\
+ 5 \\
+11
+\end{bmatrix}
+\f]
+
+Boolean(b8) arrays can be used to index into another array. In this type of
+indexing the non-zero values will be selected by the boolean operation. If we
+want to select all values less than 5, we can pass a boolean expression into
+the parenthesis operator.
+
+\snippet test/index.cpp index_tutorial_boolean
+
+\f[
+out =
+\begin{bmatrix}
+0 \\ 1 \\ 2 \\ 3 \\ 4
+\end{bmatrix}
+\f]
+
+## References and copies
+
+All ArrayFire indexing functions return af::array(technically its an array_proxy
+class) objects. These objects may be new arrays or they may reference the
+original array depending on the type of indexing that was performed on them.
+
+- If an array was indexed using another af::array or it was indexed using the
+af::approx functions, then a new array is created. It does not reference the
+original data.
+- If an array was indexed using a scalar, af::seq or af::span, then
+the resulting array will reference the original data IF the first dimension is
+continuous. The following lines will not allocate additional memory.
+
+\note The new arrays wither references or newly allocated arrays, are
+independent of the original data. Meaning that any changes to the original array
+will not propagate to the references. Likewise, any changes to the reference
+arrays will not modify the original data.
+
+\snippet test/index.cpp index_tutorial_references
+
+The following code snippet shows some examples of indexing that will allocate
+new memory.
+
+\snippet test/index.cpp index_tutorial_copies
+
+Notice that even though the copy3 array is referencing continuous memory in the
+original array, a new array is created because we used an array to index into
+the af::array.
+
+## Assignment
+
+An assignment on an af::array will replace the array with the result of the
+expression on the right hand side of the equal(=) operator. This means that the
+type and shape of the result can be different from the array on the left had
+side of the equal operator. Assignments will not update the array that was
+previously referenced through an indexing operation. Here is an example:
+
+\snippet test/index.cpp index_tutorial_assignment
+
+The `ref` array is created by indexing into the data array. The initialized
+`ref` array points to the data array and does not allocate memory when it is
+created. After the matmul call, the `ref` array will not be pointing to the data
+array. The matmul call will not update the values of the data array.
+
+You can update the contents of an af::array by assigning with the operator
+parenthesis. For example, if you wanted to change the third column of the
+`A` array you can do that by assigning to `A(span, 2)`.
+
+\snippet test/index.cpp index_tutorial_assignment_third_column
+
+\f[
+ref =
+\begin{bmatrix}
+     8  \\
+     9  \\
+    10  \\
+    11
+\end{bmatrix}
+A =
+\begin{bmatrix}
+    0 & 4 & 3.14 & 12 \\
+    1 & 5 & 3.14 & 13 \\
+    2 & 6 & 3.14 & 14 \\
+    3 & 7 & 3.14 & 15
+\end{bmatrix}
+\f]
+
+This will update only the array being modified. If there are arrays that
+are referring to this array because of an indexing operation, those values
+will remain unchanged.
+
+Allocation will only be performed if there are other arrays referencing the data
+at the point of assignment. In the previous example, an allocation will be
+performed when assigning to the `A` array because the `ref` array is pointing
+to the original data. Here is another example demonstrating when an allocation
+will occur:
+
+\snippet test/index.cpp index_tutorial_assignment_alloc
+
+In this example, no allocation will take place because when the `ref` object
+is created, it is pointing to `A`'s data. Once it goes out of scope, no data
+points to `A`, therefore when the assignment takes place, the data is modified in
+place instead of being copied to a new address.
+
+You can also assign to arrays using another af::arrays as an indexing array.
+This works in a similar way to the other types of assignment but care must be
+taken to assure that the indexes are unique. Non-unique indexes will result in a
+race condition which will cause non-deterministic values.
+
+\snippet test/index.cpp index_tutorial_assignment_race_condition
+
+\f[
+idx =
+\begin{bmatrix}
+     4  \\
+     3  \\
+     4  \\
+     0
+\end{bmatrix}
+vals =
+\begin{bmatrix}
+     9  \\
+     8  \\
+     7  \\
+     6
+\end{bmatrix}
+\\
+A =
+\begin{bmatrix}
+    6 & 9\ or\ 7 &  8 & 12 \\
+    1 &   5    &  9 & 13 \\
+    2 &   6    & 10 & 14 \\
+    8 &   7    & 11 & 15
+\end{bmatrix}
+\f]
+
+## Member functions
+
+There are several member functions which allow you to index into an af::array. These
+functions have similar functionality but may be easier to parse for some.
+
 * [row(i)](\ref af::array::row) or [col(i)](\ref af::array::col) specifying a single row/column
 * [rows(first,last)](\ref af::array::rows) or [cols(first,last)](\ref af::array::cols)
- specifying a span of rows or columns
+  specifying multiple rows or columns
+* [slice(i)](\ref af::array::slice) or [slices(first, last)](\ref af::array::slices) to
+  select one or a range of slices
+
+# Additional examples
 
 See \ref  index_mat for the full listing.
 
diff --git a/test/index.cpp b/test/index.cpp
index 4e6fb88d25..ef5fd11b9b 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1315,24 +1315,64 @@ TEST(Indexing, SNIPPET_indexing_first) {
     //! [ex_indexing_first]
     array A = array(seq(1, 9), 3, 3);
     af_print(A);
+    // 1.0000 4.0000 7.0000
+    // 2.0000 5.0000 8.0000
+    // 3.0000 6.0000 9.0000
+
+    af_print(A(0));  // first element
+    // 1.0000
 
-    af_print(A(0));     // first element
     af_print(A(0, 1));  // first row, second column
+    // 4.0000
+
+    af_print(A(end));  // last element
+    // 9.0000
+
+    af_print(A(-1));   // also last element
+    // 9.0000
 
-    af_print(A(end));      // last element
-    af_print(A(-1));       // also last element
     af_print(A(end - 1));  // second-to-last element
+    // 8.0000
 
     af_print(A(1, span));      // second row
+    // 2.0000     5.0000     8.0000
+
     af_print(A.row(end));      // last row
+    // 3.0000     6.0000     9.0000
+
     af_print(A.cols(1, end));  // all but first column
+    // 4.0000     7.0000
+    // 5.0000     8.0000
+    // 6.0000     9.0000
 
     float b_host[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
     array b(10, 1, b_host);
     af_print(b(seq(3)));
+    // 0.0000
+    // 1.0000
+    // 2.0000
+
     af_print(b(seq(1, 7)));
+    // 1.0000
+    // 2.0000
+    // 3.0000
+    // 4.0000
+    // 5.0000
+    // 6.0000
+    // 7.0000
+
     af_print(b(seq(1, 7, 2)));
+    // 1.0000
+    // 3.0000
+    // 5.0000
+    // 7.0000
+
     af_print(b(seq(0, end, 2)));
+    // 0.0000
+    // 2.0000
+    // 4.0000
+    // 6.0000
+    // 8.0000
     //! [ex_indexing_first]
 
     array lin_first    = A(0);
@@ -1390,20 +1430,46 @@ TEST(Indexing, SNIPPET_indexing_set) {
     //! [ex_indexing_set]
     array A = constant(0, 3, 3);
     af_print(A);
+    // 0.0000     0.0000     0.0000
+    // 0.0000     0.0000     0.0000
+    // 0.0000     0.0000     0.0000
 
     // setting entries to a constant
     A(span) = 4;  // fill entire array
     af_print(A);
+    // 4.0000     4.0000     4.0000
+    // 4.0000     4.0000     4.0000
+    // 4.0000     4.0000     4.0000
 
     A.row(0) = -1;  // first row
     af_print(A);
+    // -1.0000    -1.0000    -1.0000
+    //  4.0000     4.0000     4.0000
+    //  4.0000     4.0000     4.0000
 
     A(seq(3)) = 3.1415;  // first three elements
     af_print(A);
+    // 3.1415    -1.0000    -1.0000
+    // 3.1415     4.0000     4.0000
+    // 3.1415     4.0000     4.0000
 
     // copy in another matrix
     array B  = constant(1, 4, 4, s32);
+    af_print(B);
+    //          1          1          1          1
+    //          1          1          1          1
+    //          1          1          1          1
+    //          1          1          1          1
+
     B.row(0) = randu(1, 4, f32);  // set a row to random values (also upcast)
+
+    // The first rows are zeros because randu returns values from 0.0 - 1.0
+    // and they were converted to the type of B which is s32
+    af_print(B);
+    //          0          0          0          0
+    //          1          1          1          1
+    //          1          1          1          1
+    //          1          1          1          1
     //! [ex_indexing_set]
     // TODO: Confirm the outputs are correct. see #697
 }
@@ -1413,21 +1479,25 @@ TEST(Indexing, SNIPPET_indexing_ref) {
     float h_inds[] = {0, 4, 2, 1};  // zero-based indexing
     array inds(1, 4, h_inds);
     af_print(inds);
+    // 0.0000     4.0000     2.0000     1.0000
 
     array B = randu(1, 4);
     af_print(B);
+    // 0.5471     0.3114     0.5535     0.3800
 
     array c = B(inds);  // get
     af_print(c);
+    // 0.5471     0.3800     0.5535     0.3114
 
     B(inds) = -1;              // set to scalar
     B(inds) = constant(0, 4);  // zero indices
     af_print(B);
+    // 0.0000     0.0000     0.0000     0.0000
     //! [ex_indexing_ref]
     // TODO: Confirm the outputs are correct. see #697
 }
 
-TEST(Indexing, SNIPPET_indexing_copy) {
+TEST(Indexing, IndexingCopy) {
     array A = constant(0, 1, s32);
     af::index s1;
     s1 = af::index(A);
@@ -1693,3 +1763,260 @@ TEST(Index, ISSUE_2273_Flipped) {
 
     ASSERT_ARRAYS_EQ(input_slice_gold, input_slice);
 }
+
+// clang-format off
+class IndexDocs : public ::testing::Test {
+public:
+  array A;
+
+  void SetUp() {
+    //![index_tutorial_1]
+    float data[] = {0,  1,  2,  3,
+                    4,  5,  6,  7,
+                    8,  9, 10, 11,
+                   12, 13, 14, 15};
+    af::array A(4, 4, data);
+    //![index_tutorial_1]
+    this->A = A;
+  }
+};
+
+TEST_F(IndexDocs, Precondition) {
+  vector<float> gold(4*4);
+  std::iota(gold.begin(), gold.end(), 0.f);
+  ASSERT_VEC_ARRAY_EQ(gold, dim4(4, 4), A);
+}
+
+TEST_F(IndexDocs, 2_3Element) {
+    array out =
+    //![index_tutorial_first_element]
+    // Returns an array pointing to the first element
+    A(2, 3); // WARN: avoid doing this. Demo only
+    //![index_tutorial_first_element]
+    vector<float> gold(1, 14.f);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(1), out);
+}
+
+TEST_F(IndexDocs, FifthElement) {
+    array out =
+    //![index_tutorial_fifth_element]
+    // Returns an array pointing to the fifth element
+    A(5);
+    //![index_tutorial_fifth_element]
+    vector<float> gold(1, 5.f);
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(1), out);
+}
+
+TEST_F(IndexDocs, NegativeIndexing) {
+    //![index_tutorial_negative_indexing]
+    array ref0 = A(2, -1);    // 14 second row last column
+    array ref1 = A(2, end);   // 14 Same as above
+    array ref2 = A(2, -2);    // 10 Second row, second to last(third) column
+    array ref3 = A(2, end-1); // 10 Same as above
+    //![index_tutorial_negative_indexing]
+    vector<float> gold1(1, 14.f);
+    vector<float> gold2(1, 10.f);
+    ASSERT_VEC_ARRAY_EQ(gold1, dim4(1), ref0);
+    ASSERT_VEC_ARRAY_EQ(gold1, dim4(1), ref1);
+    ASSERT_VEC_ARRAY_EQ(gold2, dim4(1), ref2);
+    ASSERT_VEC_ARRAY_EQ(gold2, dim4(1), ref3);
+}
+
+TEST_F(IndexDocs, ThirdColumn) {
+    array out =
+    //![index_tutorial_third_column]
+    // Returns an array pointing to the third column
+    A(span, 2);
+    //![index_tutorial_third_column]
+    vector<float> gold{8, 9, 10, 11};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(4), out);
+}
+
+TEST_F(IndexDocs, SecondRow) {
+    array out =
+    //![index_tutorial_second_row]
+    // Returns an array pointing to the second row
+    A(1, span);
+    //![index_tutorial_second_row]
+    vector<float> gold{1, 5, 9, 13};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(1, 4), out);
+}
+
+TEST_F(IndexDocs, FirstTwoColumns) {
+    array out =
+    //![index_tutorial_first_two_columns]
+    // Returns an array pointing to the first two columns
+    A(span, seq(2));
+    //![index_tutorial_first_two_columns]
+    vector<float> gold{0, 1, 2, 3, 4, 5, 6, 7};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(4, 2), out);
+}
+
+TEST_F(IndexDocs, SecondAndFourthRows) {
+    array out =
+    //![index_tutorial_second_and_fourth_rows]
+    // Returns an array pointing to the second and fourth rows
+    A(seq(1, end, 2), span);
+    //![index_tutorial_second_and_fourth_rows]
+    vector<float> gold{1, 3, 5, 7, 9, 11, 13, 15};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(2, 4), out);
+}
+
+
+TEST_F(IndexDocs, Arrays) {
+    //![index_tutorial_array_indexing]
+    vector<int> hidx = {2, 1, 3};
+    vector<int> hidy = {3, 1, 2};
+    array idx(3, hidx.data());
+    array idy(3, hidy.data());
+
+    array out = A(idx, idy);
+    //![index_tutorial_array_indexing]
+
+    vector<float> gold{
+   14.f,    13.f,    15.f,
+    6.f,     5.f,     7.f,
+   10.f,     9.f,    11.f};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(3, 3), out);
+}
+
+
+TEST_F(IndexDocs, Approx) {
+    //![index_tutorial_approx]
+    vector<float> hidx = {2, 1, 3};
+    vector<float> hidy = {3, 1, 2};
+    array idx(3, hidx.data());
+    array idy(3, hidy.data());
+
+    array out = approx2(A, idx, idy);
+    //![index_tutorial_approx]
+
+    vector<float> gold{14.f, 5.f, 11.f};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(3), out);
+}
+
+TEST_F(IndexDocs, Boolean) {
+    //![index_tutorial_boolean]
+    array out = A(A < 5);
+    //![index_tutorial_boolean]
+    vector<float> gold = {0, 1, 2, 3, 4};
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(5), out);
+}
+
+TEST_F(IndexDocs, References) {
+    deviceGC();
+    size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    //![index_tutorial_references]
+    array reference = A(span, 1);
+    array reference2 = A(seq(3), 1);
+    array reference3 = A(seq(2), span);
+    //![index_tutorial_references]
+
+    size_t alloc_bytes2, alloc_buffers2, lock_bytes2, lock_buffers2;
+    deviceMemInfo(&alloc_bytes2, &alloc_buffers2, &lock_bytes2, &lock_buffers2);
+
+    ASSERT_EQ(0, lock_buffers2 - lock_buffers);
+}
+
+TEST_F(IndexDocs, Copies) {
+    deviceGC();
+    size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    //![index_tutorial_copies]
+    array copy = A(2, span);
+    array copy2 = A(seq(1, 3, 2), span);
+
+
+    int hidx[] = {0, 1, 2};
+    array idx(3, hidx);
+    array copy3 = A(idx, span);
+    //![index_tutorial_copies]
+
+    size_t alloc_bytes2, alloc_buffers2, lock_bytes2, lock_buffers2;
+    deviceMemInfo(&alloc_bytes2, &alloc_buffers2, &lock_bytes2, &lock_buffers2);
+
+    ASSERT_EQ(3, lock_buffers2 - lock_buffers);
+}
+
+TEST_F(IndexDocs, Assignment) {
+    deviceGC();
+    size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+
+    //![index_tutorial_assignment]
+    array inputA = constant(3, 10, 10);
+    array inputB = constant(2, 10, 10);
+    array data   = constant(1, 10, 10);
+
+    // Points to the second column of data. Does not allocate memory
+    array ref = data(span, 1);
+
+    // This call does NOT update data. Memory allocated in matmul
+    ref = matmul(inputA, inputB);
+    // reference does not point to the same memory as the data array
+    //![index_tutorial_assignment]
+
+    size_t alloc_bytes2, alloc_buffers2, lock_bytes2, lock_buffers2;
+    deviceMemInfo(&alloc_bytes2, &alloc_buffers2, &lock_bytes2, &lock_buffers2);
+
+    vector<float> gold_reference(100, 60);
+    vector<float> gold_data(100, 1);
+    ASSERT_VEC_ARRAY_EQ(gold_reference, dim4(10, 10), ref);
+    ASSERT_VEC_ARRAY_EQ(gold_data, dim4(10, 10), data);
+    ASSERT_EQ(4, lock_buffers2 - lock_buffers);
+}
+
+TEST_F(IndexDocs, AssignmentThirdColumn) {
+    vector<float> gold(A.elements());
+    A.host(gold.data());
+
+    deviceGC();
+    size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    //![index_tutorial_assignment_third_column]
+    array reference = A(span, 2);
+    A(span, 2) = 3.14f;
+    assert(allTrue<bool>(reference != A(span, 2)));
+    //![index_tutorial_assignment_third_column]
+    vector<float> gold_reference(begin(gold) + 8, begin(gold)+12);
+    ASSERT_VEC_ARRAY_EQ(gold_reference, dim4(4), reference);
+    gold[8] = gold[9] = gold[10] = gold[11] = 3.14f;
+    ASSERT_VEC_ARRAY_EQ(gold, A.dims(), A);
+
+    size_t alloc_bytes2, alloc_buffers2, lock_bytes2, lock_buffers2;
+    deviceMemInfo(&alloc_bytes2, &alloc_buffers2, &lock_bytes2, &lock_buffers2);
+
+    ASSERT_EQ(1, lock_buffers2 - lock_buffers);
+}
+
+TEST_F(IndexDocs, AssignmentAlloc) {
+    deviceGC();
+    size_t alloc_bytes, alloc_buffers, lock_bytes, lock_buffers;
+    deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    //![index_tutorial_assignment_alloc]
+    {
+        // No allocation performed. ref points to A's memory
+        array ref = A(span, 2);
+    } // ref goes out of scope. No one point's to A's memory
+    A(span, 2) = 3.14f; // No allocation performed.
+    //![index_tutorial_assignment_alloc]
+
+    size_t alloc_bytes2, alloc_buffers2, lock_bytes2, lock_buffers2;
+    deviceMemInfo(&alloc_bytes2, &alloc_buffers2, &lock_bytes2, &lock_buffers2);
+
+    ASSERT_EQ(0, lock_buffers2 - lock_buffers);
+}
+
+TEST_F(IndexDocs, AssignmentRaceCondition) {
+    //![index_tutorial_assignment_race_condition]
+    vector<int> hidx = {4, 3, 4, 0};
+    vector<float> hvals = {9.f, 8.f, 7.f, 6.f};
+    array idx(4, hidx.data());
+    array vals(4, hvals.data());
+
+    A(idx) = vals; // nondeterministic. A(4) can be 9 or 7
+    //![index_tutorial_assignment_race_condition]
+}
+
+// clang-format on

From 0982007f8e8d80822395b0ec435135e33b5119a0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 10 Jul 2019 13:47:25 +0530
Subject: [PATCH 1706/2677] Fix pointless compare warning in half header

---
 src/backend/common/half.hpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 184bfbd6f4..c0948eaa9b 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -626,7 +626,13 @@ class alignas(2) half {
     }
 
 #ifndef __CUDACC_RTC__
-    template<typename T>
+    template<typename T,
+        typename std::enable_if_t<std::is_unsigned<T>::value>* = nullptr>
+    CONSTEXPR_DH explicit half(T value) noexcept
+        : data_(int2half<std::round_to_nearest, false>(value)) {}
+
+    template<typename T,
+        typename std::enable_if_t<std::is_signed<T>::value>* = nullptr>
     CONSTEXPR_DH explicit half(T value) noexcept
         : data_((value < 0) ? int2half<std::round_to_nearest, true>(value)
                             : int2half<std::round_to_nearest, false>(value)) {}

From 6205b9650366793a4b5c5163f0c10e88ebc14207 Mon Sep 17 00:00:00 2001
From: Shady Boukhary <sb199898.sb@gmail.com>
Date: Wed, 10 Jul 2019 16:27:34 -0400
Subject: [PATCH 1707/2677] OpenCL Assign: Create single allocation to avoid
 OSX errors

---
 src/backend/opencl/assign.cpp | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index b695ca08c5..839bc06097 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -55,16 +55,17 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
             idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
             bPtrs[x]   = idxArrs[x].get();
         } else {
-            // alloc an 1-element buffer to avoid OpenCL from failing
-            bPtrs[x] = bufferAlloc(sizeof(uint));
+            // alloc an 1-element buffer to avoid OpenCL from failing using
+            // direct buffer allocation as opposed to mem manager to avoid
+            // reference count desprepancies between different backends
+            static cl::Buffer *empty = new Buffer(getContext(),
+                                                   CL_MEM_READ_ONLY,
+                                                   sizeof(uint));
+            bPtrs[x] = empty;
         }
     }
 
     kernel::assign<T>(out, rhs, p, bPtrs);
-
-    for (dim_t x = 0; x < 4; ++x) {
-        if (p.isSeq[x]) bufferFree(bPtrs[x]);
-    }
 }
 
 #define INSTANTIATE(T)                                                \

From 04c755aaeda745437cb7412da9649457b708b30d Mon Sep 17 00:00:00 2001
From: Shady Boukhary <sb199898.sb@gmail.com>
Date: Mon, 8 Jul 2019 16:58:59 -0400
Subject: [PATCH 1708/2677] Fixed unnecessary device to device memcpy during
 array assignment

---
 src/api/c/assign.cpp  |  3 ++-
 src/api/cpp/array.cpp | 26 +++++++++++++++++++-------
 test/memory.cpp       | 25 +++++++++++++++++++++++++
 3 files changed, 46 insertions(+), 8 deletions(-)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 54a2c85698..0211b72df1 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -267,8 +267,9 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
         if (*out != lhs) {
             int count = 0;
             AF_CHECK(af_get_data_ref_count(&count, lhs));
-            if (count > 1)
+            if (count > 1) {
                 AF_CHECK(af_copy_array(&output, lhs));
+	    }
             else
                 output = retain(lhs);
         } else {
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index b6d3845237..ef69dc3b54 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -471,25 +471,37 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
         }
     }
 
-    af_array par_arr = nullptr;
+    af_array par_arr = 0;
 
+    dim4 parent_dims = impl->parent_->dims();
     if (impl->is_linear_) {
         AF_THROW(af_flat(&par_arr, impl->parent_->get()));
+        // The set call will dereference the impl->parent_ array. We are doing
+        // this because the af_flat call above increases the reference count of the
+        // parent array which triggers a copy operation. This triggers a copy operation
+        // inside the af_assign_gen function below. The parent array will be reverted
+        // to the original array and shape later in the code.
+        af_array empty = 0;
+        impl->parent_->set(empty);
         nd = 1;
     } else {
         par_arr = impl->parent_->get();
     }
 
-    af_array tmp = nullptr;
-    AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices_, other_arr));
+    af_array flat_res = 0;
+    AF_THROW(af_assign_gen(&flat_res, par_arr, nd, impl->indices_, other_arr));
 
-    af_array res = nullptr;
+    af_array res = 0;
+    af_array unflattened = 0;
     if (impl->is_linear_) {
-        AF_THROW(af_moddims(&res, tmp, this_dims.ndims(), this_dims.get()));
+        AF_THROW(af_moddims(&res, flat_res, this_dims.ndims(), this_dims.get()));
+        // Unflatten the af_array and reset the original reference
+        AF_THROW(af_moddims(&unflattened, par_arr, parent_dims.ndims(), parent_dims.get()));
+        impl->parent_->set(unflattened);
         AF_THROW(af_release_array(par_arr));
-        AF_THROW(af_release_array(tmp));
+        AF_THROW(af_release_array(flat_res));
     } else {
-        res = tmp;
+        res = flat_res;
     }
 
     impl->parent_->set(res);
diff --git a/test/memory.cpp b/test/memory.cpp
index b33b35ca79..abbe008199 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -522,6 +522,31 @@ TEST(Memory, device) {
     ASSERT_EQ(lock_bytes, 0u);
 }
 
+
+TEST(Memory, Assign2D) {
+    size_t alloc_bytes, alloc_buffers;
+    size_t alloc_bytes_after, alloc_buffers_after;
+    size_t lock_bytes, lock_buffers;
+    size_t lock_bytes_after, lock_buffers_after;
+
+    cleanSlate();  // Clean up everything done so far
+    {
+        array a    = af::randu(10, 10, f32);
+        unsigned hb[] = {3, 5, 6, 8, 9};
+        array b(5, hb);
+        array c   = af::randu(5, f32);
+        deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+        a(b) = c;
+    }
+
+    deviceMemInfo(&alloc_bytes_after, &alloc_buffers_after, &lock_bytes_after,
+                  &lock_buffers_after);
+
+    // Check if assigned allocated extra buffers
+    ASSERT_EQ(alloc_buffers, alloc_buffers_after);
+    ASSERT_EQ(alloc_bytes, alloc_bytes_after);
+}
+
 TEST(Memory, unlock) {
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;

From be306c1645f58c9380e4e062cb6f76d7c1996a86 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 11 Jul 2019 22:27:44 -0400
Subject: [PATCH 1709/2677] Guard enqueue wait in the opencl backend for all
 calls to wait

---
 src/backend/opencl/memory.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index e8a5f01b8c..c324a40331 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -79,7 +79,7 @@ void memFreeUser(void *ptr) {
 
 cl::Buffer *bufferAlloc(const size_t &bytes) {
     MemoryEventPair me = memoryManager().alloc(bytes, false);
-    me.e.enqueueWait(getQueue()());
+    if (me.e) me.e.enqueueWait(getQueue()());
     return static_cast<cl::Buffer *>(me.ptr);
 }
 
@@ -106,7 +106,7 @@ template<typename T>
 T *pinnedAlloc(const size_t &elements) {
     MemoryEventPair me =
         pinnedMemoryManager().alloc(elements * sizeof(T), false);
-    me.e.enqueueWait(getQueue()());
+    if (me.e) me.e.enqueueWait(getQueue()());
     return static_cast<T *>(me.ptr);
 }
 

From f596f8c0c3155a2e7e7fa51330dddb0b8d350418 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Thu, 11 Jul 2019 13:34:12 -0700
Subject: [PATCH 1710/2677] Fix half ComputeType for CudaCompute 6.1 GPUs

---
 src/backend/cuda/blas.cpp | 11 ++++++++++-
 test/blas.cpp             |  4 +++-
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 52bc8534eb..0203e7208f 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -193,7 +193,16 @@ template<>
 cudaDataType_t getComputeType<half>() {
     auto dev            = getDeviceProp(getActiveDeviceId());
     cudaDataType_t algo = getType<half>();
-    if (dev.major == 6 && dev.minor == 1) { algo = CUDA_R_32F; }
+    // There is probbaly a bug in nvidia cuda docs and/or drivers: According to
+    // https://docs.nvidia.com/cuda/cublas/index.html#cublas-GemmEx computeType
+    // could be 32F even if A/B inputs are 16F. But CudaCompute 6.1 GPUs (for
+    // example GTX10X0) dont seem to be capbale to compute at f32 when the
+    // inputs are f16: results are inf if trying to do so and cublasGemmEx even
+    // returns OK. At the moment let's comment out : the drawback is just that
+    // the speed of f16 computation on these GPUs is very slow:
+    //
+    // if (dev.major == // 6 && dev.minor == 1) { algo = CUDA_R_32F; }
+
     return algo;
 }
 
diff --git a/test/blas.cpp b/test/blas.cpp
index 71418ce781..521611bb54 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -397,6 +397,7 @@ TEST(MatrixMultiply, float) {
 }
 
 #ifndef AF_CPU
+
 TEST(MatrixMultiply, half) {
     SUPPORTED_TYPE_CHECK(af_half);
 
@@ -408,7 +409,7 @@ TEST(MatrixMultiply, half) {
         af_array C16 = 0;
         const half_float::half alpha16(1.0f);
         const half_float::half beta16(0.0f);
-        af_gemm(&C16, AF_MAT_NONE, AF_MAT_NONE, &alpha16, A16.get(), B16.get(), &beta16);
+        ASSERT_SUCCESS(af_gemm(&C16, AF_MAT_NONE, AF_MAT_NONE, &alpha16, A16.get(), B16.get(), &beta16));
         af::array C(C16);
         ASSERT_ARRAYS_NEAR(expected16, C, 0.00001);
     }
@@ -417,6 +418,7 @@ TEST(MatrixMultiply, half) {
         ASSERT_ARRAYS_NEAR(expected16, C16, 0.000001);
     }
 }
+
 #endif
 
 struct test_params {

From e73488d0d597c06173478f78e1c64ff0e0a47dfc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 15 Jul 2019 20:32:53 -0400
Subject: [PATCH 1711/2677] Update CDash drop site to use https

---
 CTestConfig.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CTestConfig.cmake b/CTestConfig.cmake
index ae3a6b355c..9bd3a5c9dc 100644
--- a/CTestConfig.cmake
+++ b/CTestConfig.cmake
@@ -7,7 +7,7 @@
 set(CTEST_PROJECT_NAME "ArrayFire")
 set(CTEST_NIGHTLY_START_TIME "01:00:00 UTC")
 
-set(CTEST_DROP_METHOD "http")
+set(CTEST_DROP_METHOD "https")
 set(CTEST_DROP_SITE "ci.arrayfire.org")
 set(CTEST_DROP_LOCATION "/submit.php?project=ArrayFire")
 set(CTEST_DROP_SITE_CDASH TRUE)

From 6b177a248701f2cf9ab5511266a370b2c6f6639a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 16 Jul 2019 03:57:46 -0400
Subject: [PATCH 1712/2677] Add a function to set the cuBLAS Math Mode (#2584)

* Add a function to set the cuBLAS Math Mode

* Include missing cuda.cpp file. Add AF_DEFINE_CUDA_TYPES definition
---
 include/af/cuda.h                   | 34 +++++++++++++++++++
 src/api/unified/CMakeLists.txt      |  1 +
 src/api/unified/cuda.cpp            | 52 +++++++++++++++++++++++++++++
 src/backend/cuda/device_manager.cpp |  1 +
 src/backend/cuda/platform.cpp       |  8 +++++
 5 files changed, 96 insertions(+)
 create mode 100644 src/api/unified/cuda.cpp

diff --git a/include/af/cuda.h b/include/af/cuda.h
index dbf1480a80..27908427e7 100644
--- a/include/af/cuda.h
+++ b/include/af/cuda.h
@@ -10,13 +10,33 @@
 #pragma once
 #include <af/defines.h>
 #include <af/exception.h>
+
+/// This file contain functions that apply only to the CUDA backend. It will
+/// include cuda headers when it is built with NVCC. Otherwise the you can
+/// define the AF_DEFINE_CUDA_TYPES before including this file and it will
+/// define the cuda types used in this header.
+
+#ifdef __NVCC__
 #include <cuda.h>
 #include <cuda_runtime.h>
+#include <cublas_v2.h>
+#else
+#ifdef AF_DEFINE_CUDA_TYPES
+typedef struct CUstream_st *cudaStream_t;
+
+/*Enum for default math mode/tensor operation*/
+typedef enum {
+  CUBLAS_DEFAULT_MATH = 0,
+  CUBLAS_TENSOR_OP_MATH = 1
+} cublasMath_t;
+#endif
+#endif
 
 #ifdef __cplusplus
 extern "C" {
 #endif
 
+
 #if AF_API_VERSION >= 31
 /**
    Get the stream for the CUDA device with \p id in ArrayFire context
@@ -55,6 +75,20 @@ AFAPI af_err afcu_get_native_id(int* nativeid, int id);
 AFAPI af_err afcu_set_native_id(int nativeid);
 #endif
 
+#if AF_API_VERSION >= 37
+/**
+    Sets the cuBLAS math mode for the internal handle
+
+    See the cuBLAS documentation for additional details
+
+    \param[in] mode The cublasMath_t type to set
+    \returns \ref af_err error code
+
+    \ingroup cuda_mat
+*/
+AFAPI af_err afcu_cublasSetMathMode(cublasMath_t mode);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index f6fb2404de..a1c588ce5c 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -9,6 +9,7 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/arith.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
diff --git a/src/api/unified/cuda.cpp b/src/api/unified/cuda.cpp
new file mode 100644
index 0000000000..451b0ebf78
--- /dev/null
+++ b/src/api/unified/cuda.cpp
@@ -0,0 +1,52 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include "symbol_manager.hpp"
+#include <af/backend.h>
+
+#define AF_DEFINE_CUDA_TYPES
+#include <af/cuda.h>
+
+af_err afcu_get_stream(cudaStream_t* stream, int id) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if(backend == AF_BACKEND_CUDA) {
+        return CALL(stream, id);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcu_get_native_id(int* nativeid, int id) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if(backend == AF_BACKEND_CUDA) {
+        return CALL(nativeid, id);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+
+af_err afcu_set_native_id(int nativeid) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if(backend == AF_BACKEND_CUDA) {
+        return CALL(nativeid);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+
+af_err afcu_cublasSetMathMode(cublasMath_t mode) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if(backend == AF_BACKEND_CUDA) {
+        return CALL(mode);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 944be4ee87..b4ccaf4736 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -23,6 +23,7 @@
 #include <platform.hpp>
 #include <spdlog/spdlog.h>
 #include <version.hpp>
+#include <cublas_v2.h> // needed for af/cuda.h
 #include <af/cuda.h>
 #include <af/version.h>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index aa5d2ed373..9228ffaacc 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -435,3 +435,11 @@ af_err afcu_set_native_id(int nativeid) {
     CATCHALL;
     return AF_SUCCESS;
 }
+
+af_err afcu_cublasSetMathMode(cublasMath_t mode) {
+    try {
+        CUBLAS_CHECK(cublasSetMathMode(cuda::blasHandle(), mode));
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}

From eb891603f01df5fd7da915258594c5e4ca58adf8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 Jul 2019 07:35:06 -0400
Subject: [PATCH 1713/2677] Fix random number generator for f16 and short on
 the CPU backend (#2587)

* Simplify the transform template in cpu random.hpp

* Fix undefined behavior in CPU RNG for f16 and short

The CPU random number generator was incorrectly calculating the shifts
for the transform function. This logic seemed to be copied from the
uchar transform function. The incorrect behavior only appeared on
Windows and for arrays larger than 256 elements. In this commit I
mask the shifted value before the float factor is calculated.
---
 src/backend/cpu/kernel/random_engine.hpp | 34 ++++++++++++------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 656e64c504..4500afc7db 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -44,8 +44,8 @@ static const double PI_VAL =
 #define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
-template<typename T, typename To = T>
-To transform(uint *val, int index) {
+template<typename T>
+T transform(uint *val, int index) {
     T *oval = (T *)val;
     return oval[index];
 }
@@ -65,7 +65,7 @@ uchar transform<uchar>(uint *val, int index) {
 
 template<>
 ushort transform<ushort>(uint *val, int index) {
-    ushort v = val[index >> 1] >> (16 << (index & 1));
+    ushort v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
     return v;
 }
 
@@ -103,9 +103,9 @@ float transform<float>(uint *val, int index) {
 
 // Generates rationals in [0, 1)
 template<>
-float transform<common::half, float>(uint *val, int index) {
-    ushort v = val[index >> 1] >> (16 << (index & 1));
-    return 1.f - (v * HALF_FACTOR + HALF_HALF_FACTOR);
+common::half transform<common::half>(uint *val, int index) {
+    float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
+    return static_cast<common::half>(1.f - (v * HALF_FACTOR + HALF_HALF_FACTOR));
 }
 
 // Generates rationals in [0, 1)
@@ -162,7 +162,7 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
                     size_t out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
                     if (out_idx < elements) {
                         out[out_idx] =
-                            transform<data_t<T>, compute_t<T>>(ctr, buf_idx);
+                            transform<T>(ctr, buf_idx);
                     }
                 }
             }
@@ -190,7 +190,7 @@ void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
         ctr[1] += (ctr[0] == 0);
         int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
         for (int j = 0; j < lim; ++j) {
-            out[i + j] = transform<data_t<T>, compute_t<T>>(val, j);
+            out[i + j] = transform<T>(val, j);
         }
     }
 }
@@ -222,14 +222,14 @@ void boxMullerTransform(uint val[4], float *temp) {
 
 void boxMullerTransform(uint val[4], common::half *temp) {
     using common::half;
-    boxMullerTransform<half>(&temp[0], &temp[1], transform<half, float>(val, 0),
-                             transform<half, float>(val, 1));
-    boxMullerTransform<half>(&temp[2], &temp[3], transform<half, float>(val, 2),
-                             transform<half, float>(val, 3));
-    boxMullerTransform<half>(&temp[4], &temp[5], transform<half, float>(val, 4),
-                             transform<half, float>(val, 5));
-    boxMullerTransform<half>(&temp[6], &temp[7], transform<half, float>(val, 6),
-                             transform<half, float>(val, 7));
+    boxMullerTransform<half>(&temp[0], &temp[1], transform<half>(val, 0),
+                             transform<half>(val, 1));
+    boxMullerTransform<half>(&temp[2], &temp[3], transform<half>(val, 2),
+                             transform<half>(val, 3));
+    boxMullerTransform<half>(&temp[4], &temp[5], transform<half>(val, 4),
+                             transform<half>(val, 5));
+    boxMullerTransform<half>(&temp[6], &temp[7], transform<half>(val, 6),
+                             transform<half>(val, 7));
 }
 
 template<typename T>
@@ -296,7 +296,7 @@ void uniformDistributionMT(T *out, size_t elements, uint *const state,
                  temper_table);
         int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
         for (int j = 0; j < lim; ++j) {
-            out[i + j] = transform<data_t<T>, compute_t<T>>(o, j);
+            out[i + j] = transform<T>(o, j);
         }
     }
 

From 42d3612c3d983a0b31694eb458552ee007a84553 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 Jul 2019 07:49:30 -0400
Subject: [PATCH 1714/2677] Add f16 support for unary types (#2585)

* Add f16 support for unary types

* Addressed feedback
---
 src/api/c/complex.cpp             |  6 +++
 src/api/c/implicit.cpp            |  8 +--
 src/api/c/unary.cpp               |  6 +++
 src/backend/cpu/complex.hpp       | 13 ++---
 src/backend/cpu/jit/UnaryNode.hpp |  6 ++-
 src/backend/cpu/unary.hpp         | 15 +++---
 src/backend/cuda/math.hpp         |  4 ++
 test/CMakeLists.txt               |  2 +-
 test/math.cpp                     | 86 ++++++++++++-------------------
 test/testHelpers.hpp              | 24 ++++++++-
 10 files changed, 92 insertions(+), 78 deletions(-)

diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index 969d3a4501..e34b6fa13f 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -10,6 +10,7 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
 #include <optypes.hpp>
@@ -22,6 +23,7 @@
 
 using namespace detail;
 using af::dim4;
+using common::half;
 
 template<typename To, typename Ti>
 static inline af_array cplx(const af_array lhs, const af_array rhs,
@@ -171,6 +173,7 @@ af_err af_abs(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
+        if(in_type == f16) { type = f16; }
 
         switch (type) {
             case f32:
@@ -185,6 +188,9 @@ af_err af_abs(af_array *out, const af_array in) {
             case c64:
                 res = getHandle(abs<double, cdouble>(castArray<cdouble>(in)));
                 break;
+            case f16:
+                res = getHandle(abs<half, half>(getArray<half>(in)));
+                break;
             default: TYPE_ERROR(1, in_type); break;
         }
 
diff --git a/src/api/c/implicit.cpp b/src/api/c/implicit.cpp
index b55834ced2..fbb6ba3262 100644
--- a/src/api/c/implicit.cpp
+++ b/src/api/c/implicit.cpp
@@ -29,21 +29,15 @@ af_dtype implicit(const af_dtype lty, const af_dtype rty) {
 
     if (lty == f64 || rty == f64) return f64;
     if (lty == f32 || rty == f32) return f32;
+    if ((lty == f16) || (rty == f16)) return f16;
 
     if ((lty == u64) || (rty == u64)) return u64;
-
     if ((lty == s64) || (rty == s64)) return s64;
-
     if ((lty == u32) || (rty == u32)) return u32;
-
     if ((lty == s32) || (rty == s32)) return s32;
-
     if ((lty == u16) || (rty == u16)) return u16;
-
     if ((lty == s16) || (rty == s16)) return s16;
-
     if ((lty == u8) || (rty == u8)) return u8;
-
     if ((lty == b8) && (rty == b8)) return b8;
 
     return f32;
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 7ad9e91542..a921c4f5d5 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -18,6 +18,7 @@
 #include <cast.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <complex.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
@@ -29,6 +30,7 @@
 #include <af/data.h>
 #include <af/defines.h>
 
+using common::half;
 using namespace detail;
 
 template<typename T, af_op_t op>
@@ -61,8 +63,10 @@ static af_err af_unary(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
+        if (in_type == f16) { type = f16; }
 
         switch (type) {
+            case f16: res = unaryOp<half, op>(in); break;
             case f32: res = unaryOp<float, op>(in); break;
             case f64: res = unaryOp<double, op>(in); break;
             default: TYPE_ERROR(1, in_type); break;
@@ -84,12 +88,14 @@ static af_err af_unary_complex(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
+        if (in_type == f16) { type = f16; }
 
         switch (type) {
             case f32: res = unaryOp<float, op>(in); break;
             case f64: res = unaryOp<double, op>(in); break;
             case c32: res = unaryOpCplx<cfloat, float, op>(in); break;
             case c64: res = unaryOpCplx<cdouble, double, op>(in); break;
+            case f16: res = unaryOp<half, op>(in); break;
             default: TYPE_ERROR(1, in_type); break;
         }
 
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index 65e7a2e343..2659c3c811 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -37,12 +37,13 @@ Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
     return createNodeArray<To>(odims, jit::Node_ptr(node));
 }
 
-#define CPLX_UNARY_FN(op)                                                   \
-    template<typename To, typename Ti>                                      \
-    struct UnOp<To, Ti, af_##op##_t> {                                      \
-        void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) { \
-            for (int i = 0; i < lim; i++) { out[i] = std::op(in[i]); }      \
-        }                                                                   \
+#define CPLX_UNARY_FN(op)                                              \
+    template<typename To, typename Ti>                                 \
+    struct UnOp<To, Ti, af_##op##_t> {                                 \
+        void eval(jit::array<compute_t<To>> &out,                      \
+                  const jit::array<compute_t<Ti>> &in, int lim) {      \
+            for (int i = 0; i < lim; i++) { out[i] = std::op(in[i]); } \
+        }                                                              \
     };
 
 CPLX_UNARY_FN(real)
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 9dce5e57b0..0cf6f2f83c 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <math.hpp>
 #include <optypes.hpp>
+#include <types.hpp>
 #include "Node.hpp"
 
 #include <vector>
@@ -17,8 +18,9 @@
 namespace cpu {
 template<typename To, typename Ti, af_op_t op>
 struct UnOp {
-    void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) const {
-        for (int i = 0; i < lim; i++) { out[i] = To(in[i]); }
+    void eval(jit::array<compute_t<To>> &out,
+              const jit::array<compute_t<Ti>> &in, int lim) const {
+        for (int i = 0; i < lim; i++) { out[i] = in[i]; }
     }
 };
 
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index c2e7a441dc..a8c1e6518c 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -26,12 +26,13 @@ T rsqrt(T in) {
     return pow(in, -0.5);
 }
 
-#define UNARY_OP_FN(op, fn)                                               \
-    template<typename T>                                                  \
-    struct UnOp<T, T, af_##op##_t> {                                      \
-        void eval(jit::array<T> &out, const jit::array<T> &in, int lim) { \
-            for (int i = 0; i < lim; i++) { out[i] = fn(in[i]); }         \
-        }                                                                 \
+#define UNARY_OP_FN(op, fn)                                       \
+    template<typename T>                                          \
+    struct UnOp<T, T, af_##op##_t> {                              \
+        void eval(jit::array<compute_t<T>> &out,                  \
+                  const jit::array<compute_t<T>> &in, int lim) {  \
+            for (int i = 0; i < lim; i++) { out[i] = fn(in[i]); } \
+        }                                                         \
     };
 
 #define UNARY_OP(op) UNARY_OP_FN(op, std::op)
@@ -81,7 +82,7 @@ UNARY_OP(lgamma)
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using UnaryNode = jit::UnaryNode<compute_t<T>, compute_t<T>, op>;
+    using UnaryNode = jit::UnaryNode<T, T, op>;
 
     jit::Node_ptr in_node = in.getNode();
     UnaryNode *node       = new UnaryNode(in_node);
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index f77208958f..f636d9c77b 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -54,6 +54,10 @@ static inline __DH__ size_t max(size_t lhs, size_t rhs) {
 }
 
 #ifdef __CUDA_ARCH__
+static inline __device__ __half abs(__half val) {
+    return __short_as_half(__half_as_short(val) & 0x7FFF);
+}
+
 template<typename T>
 inline __DH__ T min(T lhs, T rhs) {
     return ::min(lhs, rhs);
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 3763dbd753..8c98b8d9f8 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -229,7 +229,7 @@ make_test(SRC lu_dense.cpp SERIAL)
 make_test(SRC main.cpp)
 #make_test(manual_memory_test.cpp)
 make_test(SRC match_template.cpp)
-make_test(SRC math.cpp)
+make_test(SRC math.cpp CXX11)
 make_test(SRC matrix_manipulation.cpp)
 make_test(SRC mean.cpp)
 make_test(SRC meanshift.cpp)
diff --git a/test/math.cpp b/test/math.cpp
index fd195b800d..8776220a21 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/data.h>
@@ -14,14 +15,17 @@
 
 // This makes the macros cleaner
 using af::array;
+using af::dim4;
 using af::dtype_traits;
 using af::exception;
 using af::randu;
+using half_float::half;
 using std::abs;
 using std::endl;
 using std::vector;
 
 const int num        = 10000;
+const float hlf_err  = 1e-2;
 const float flt_err  = 1e-3;
 const double dbl_err = 1e-10;
 
@@ -30,66 +34,42 @@ typedef std::complex<double> complex_double;
 
 template<typename T>
 T sigmoid(T in) {
-    return 1.0 / (1.0 + std::exp(-in));
+    return T(1.0 / (1.0 + std::exp(-in)));
 }
 
 template<typename T>
 T rsqrt(T in) {
-    return 1.0/sqrt(in);
+    return T(1.0 / sqrt(in));
 }
 
-#define TEST_REAL(T, func, err, lo, hi)                          \
-    TEST(MathTests, Test_##func##_##T) {                         \
-        try {                                                    \
-            SUPPORTED_TYPE_CHECK(T);                             \
-            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;    \
-            array a     = (hi - lo) * randu(num, ty) + lo + err; \
-            eval(a);                                             \
-            array b = func(a);                                   \
-            vector<T> h_a(a.elements());                         \
-            vector<T> h_b(b.elements());                         \
-            a.host(&h_a[0]);                                     \
-            b.host(&h_b[0]);                                     \
-                                                                 \
-            for (int i = 0; i < num; i++) {                      \
-                ASSERT_NEAR(h_b[i], func(h_a[i]), err)           \
-                    << "for value: " << h_a[i] << endl;          \
-            }                                                    \
-        } catch (exception & ex) { FAIL() << ex.what(); }        \
+#define MATH_TEST(T, func, err, lo, hi)                                     \
+    TEST(MathTests, Test_##func##_##T) {                                    \
+        try {                                                               \
+            SUPPORTED_TYPE_CHECK(T);                                        \
+            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;               \
+            array a     = (hi - lo) * randu(num, ty) + lo + err;            \
+            a           = a.as(ty);                                         \
+            eval(a);                                                        \
+            array b = func(a);                                              \
+            vector<T> h_a(a.elements());                                    \
+            a.host(&h_a[0]);                                                \
+            for (int i = 0; i < h_a.size(); i++) { h_a[i] = func(h_a[i]); } \
+                                                                            \
+            ASSERT_VEC_ARRAY_NEAR(h_a, dim4(h_a.size()), b, err);           \
+        } catch (exception & ex) { FAIL() << ex.what(); }                   \
     }
 
-#define TEST_CPLX(T, func, err, lo, hi)                          \
-    TEST(MathTests, Test_##func##_##T) {                         \
-        try {                                                    \
-            SUPPORTED_TYPE_CHECK(T);                             \
-            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;    \
-            array a     = (hi - lo) * randu(num, ty) + lo + err; \
-            eval(a);                                             \
-            array b = func(a);                                   \
-            vector<T> h_a(a.elements());                         \
-            vector<T> h_b(b.elements());                         \
-            a.host(&h_a[0]);                                     \
-            b.host(&h_b[0]);                                     \
-                                                                 \
-            for (int i = 0; i < num; i++) {                      \
-                T res = func(h_a[i]);                            \
-                ASSERT_NEAR(real(h_b[i]), real(res), err)        \
-                    << "for real value: " << h_a[i] << endl;     \
-                ASSERT_NEAR(imag(h_b[i]), imag(res), err)        \
-                    << "for imag value: " << h_a[i] << endl;     \
-            }                                                    \
-        } catch (exception & ex) { FAIL() << ex.what(); }        \
-    }
-
-#define MATH_TESTS_FLOAT(func) TEST_REAL(float, func, flt_err, 0.05f, 0.95f)
-#define MATH_TESTS_DOUBLE(func) TEST_REAL(double, func, dbl_err, 0.05, 0.95)
+#define MATH_TESTS_HALF(func) MATH_TEST(half, func, hlf_err, 0.05f, 0.95f)
+#define MATH_TESTS_FLOAT(func) MATH_TEST(float, func, flt_err, 0.05f, 0.95f)
+#define MATH_TESTS_DOUBLE(func) MATH_TEST(double, func, dbl_err, 0.05, 0.95)
 
 #define MATH_TESTS_CFLOAT(func) \
-    TEST_CPLX(complex_float, func, flt_err, 0.05f, 0.95f)
+    MATH_TEST(complex_float, func, flt_err, 0.05f, 0.95f)
 #define MATH_TESTS_CDOUBLE(func) \
-    TEST_CPLX(complex_double, func, dbl_err, 0.05, 0.95)
+    MATH_TEST(complex_double, func, dbl_err, 0.05, 0.95)
 
 #define MATH_TESTS_REAL(func) \
+    MATH_TESTS_HALF(func)     \
     MATH_TESTS_FLOAT(func)    \
     MATH_TESTS_DOUBLE(func)
 
@@ -102,12 +82,13 @@ T rsqrt(T in) {
     MATH_TESTS_CPLX(func)
 
 #define MATH_TESTS_LIMITS_REAL(func, lo, hi) \
-    TEST_REAL(float, func, flt_err, lo, hi)  \
-    TEST_REAL(double, func, dbl_err, lo, hi)
+    MATH_TEST(half, func, hlf_err, lo, hi)   \
+    MATH_TEST(float, func, flt_err, lo, hi)  \
+    MATH_TEST(double, func, dbl_err, lo, hi)
 
 #define MATH_TESTS_LIMITS_CPLX(func, lo, hi)        \
-    TEST_CPLX(complex_float, func, flt_err, lo, hi) \
-    TEST_CPLX(complex_double, func, dbl_err, lo, hi)
+    MATH_TEST(complex_float, func, flt_err, lo, hi) \
+    MATH_TEST(complex_double, func, dbl_err, lo, hi)
 
 MATH_TESTS_ALL(sin)
 MATH_TESTS_ALL(cos)
@@ -134,8 +115,7 @@ MATH_TESTS_LIMITS_REAL(abs, -10, 10)
 MATH_TESTS_LIMITS_REAL(ceil, -10, 10)
 MATH_TESTS_LIMITS_REAL(floor, -10, 10)
 
-#if __cplusplus > 199711L
-
+#if __cplusplus > 199711L || _MSC_VER >= 1800
 MATH_TESTS_CPLX(asin)
 MATH_TESTS_CPLX(acos)
 MATH_TESTS_CPLX(atan)
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 5c862607f0..ccd8bcd85c 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -89,7 +89,7 @@ namespace af {
 template<>
 struct dtype_traits<half_float::half> {
     enum { af_type = f16, ctype = f16 };
-    typedef half base_type;
+    typedef half_float::half base_type;
     static const char *getName() { return "half"; }
 };
 
@@ -463,7 +463,8 @@ inline double imag<af::cfloat>(af::cfloat val) {
 
 template<class T>
 struct IsFloatingPoint {
-    static const bool value = is_same_type<float, T>::value ||
+    static const bool value = is_same_type<half_float::half, T>::value ||
+                              is_same_type<float, T>::value ||
                               is_same_type<double, T>::value ||
                               is_same_type<long double, T>::value;
 };
@@ -578,6 +579,25 @@ void cleanSlate() {
 
 // Overloading unary + op is needed to make unsigned char values printable
 //  as numbers
+af_half abs(af_half in) {
+    half_float::half in_;
+    memcpy(&in_, &in, sizeof(af_half));
+    half_float::half out_ = abs(in_);
+    af_half out;
+    memcpy(&out, &out_, sizeof(af_half));
+    return out;
+}
+
+af_half operator-(af_half lhs, af_half rhs) {
+    half_float::half lhs_;
+    half_float::half rhs_;
+    memcpy(&lhs_, &lhs, sizeof(af_half));
+    memcpy(&rhs_, &rhs, sizeof(af_half));
+    half_float::half out = lhs_ - rhs_;
+    af_half o;
+    memcpy(&o, &out, sizeof(af_half));
+    return o;
+}
 
 const af::cfloat &operator+(const af::cfloat &val) { return val; }
 

From 6456de19960bd91b6fc05cfcbcb4c1e4f8b07d10 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 Jul 2019 12:27:30 -0400
Subject: [PATCH 1715/2677] Add half support for several data functions (#2572)

* Add support for several data functions. Refactor half to be explicit

Added support for

- logical binary operations
- diagonal
- identity
- iota
- ireduce
- range
- triangle
- transpose
- transpose_inplace

The OpenCL backend compiles but it is probably broken for f16. I will
be testing that in another PR.

* Address compilation issues on older hardware

* rebased and fixed errors

* Fix minimum and maximum values for half in kernel code

* Fix compilation issue os osx

* Fix compilation issue in random
---
 CMakeModules/InternalUtils.cmake              |   2 +-
 src/api/c/binary.cpp                          |   1 +
 src/api/c/data.cpp                            |   7 +
 src/api/c/ops.hpp                             | 131 ++----
 src/api/c/reduce.cpp                          |  10 +-
 src/api/c/transpose.cpp                       |   4 +
 src/api/cpp/array.cpp                         |   4 +
 src/api/cpp/data.cpp                          |   2 +
 src/backend/common/half.hpp                   | 438 ++++++++++++++++--
 src/backend/cpu/cast.hpp                      |   4 +-
 src/backend/cpu/diagonal.cpp                  |   4 +
 src/backend/cpu/identity.cpp                  |   8 +-
 src/backend/cpu/iota.cpp                      |   8 +-
 src/backend/cpu/ireduce.cpp                   |   9 +-
 src/backend/cpu/jit/BinaryNode.hpp            |  12 +-
 src/backend/cpu/jit/BufferNode.hpp            |   6 +-
 src/backend/cpu/jit/Node.hpp                  |   3 +-
 src/backend/cpu/kernel/copy.hpp               |  10 +-
 src/backend/cpu/kernel/iota.hpp               |   8 +-
 src/backend/cpu/kernel/random_engine.hpp      |   6 +-
 src/backend/cpu/kernel/range.hpp              |   3 +
 src/backend/cpu/kernel/reduce.hpp             |   6 +-
 src/backend/cpu/kernel/select.hpp             |   9 +-
 src/backend/cpu/range.cpp                     |   8 +-
 src/backend/cpu/reduce.cpp                    |   9 +-
 src/backend/cpu/triangle.cpp                  |   8 +-
 src/backend/cuda/copy.cu                      |  11 +-
 src/backend/cuda/diagonal.cu                  |   4 +
 src/backend/cuda/identity.cu                  |   8 +-
 src/backend/cuda/iota.cu                      |   4 +
 src/backend/cuda/ireduce.cu                   |   8 +-
 src/backend/cuda/kernel/iota.hpp              |   4 +-
 src/backend/cuda/kernel/ireduce.hpp           |  24 +-
 src/backend/cuda/kernel/lookup.hpp            |   2 +-
 src/backend/cuda/kernel/memcopy.hpp           |   7 +-
 src/backend/cuda/kernel/range.hpp             |  12 +-
 src/backend/cuda/kernel/reduce.hpp            |  34 +-
 src/backend/cuda/math.hpp                     |  17 +
 src/backend/cuda/range.cu                     |   8 +-
 src/backend/cuda/transpose_inplace.cpp        |   3 +
 src/backend/cuda/triangle.cu                  |   7 +-
 src/backend/opencl/diagonal.cpp               |   4 +
 src/backend/opencl/identity.cpp               |   8 +-
 src/backend/opencl/iota.cpp                   |   9 +-
 src/backend/opencl/ireduce.cpp                |   8 +-
 src/backend/opencl/magma/magma_blas_clblast.h |   4 +-
 src/backend/opencl/range.cpp                  |   8 +-
 src/backend/opencl/traits.hpp                 |   4 +-
 src/backend/opencl/transpose_inplace.cpp      |   3 +
 src/backend/opencl/triangle.cpp               |   7 +-
 src/backend/opencl/types.cpp                  |  12 +-
 test/assign.cpp                               |   7 -
 test/compare.cpp                              |   3 +-
 test/constant.cpp                             |   9 +-
 test/diagonal.cpp                             |  18 +-
 test/iota.cpp                                 |   2 +-
 test/range.cpp                                |  29 +-
 test/testHelpers.hpp                          |  10 +-
 test/transpose.cpp                            |   4 +-
 test/triangle.cpp                             |   4 +-
 60 files changed, 743 insertions(+), 283 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 0a35491c24..82c9627886 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -30,7 +30,7 @@ endfunction()
 
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
   if(NOT MSVC)
-    set(flags "-std=c++14 -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
+    set(flags "-std=c++14 --expt-relaxed-constexpr -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
   else()
     set(flags "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275 -Xcompiler /bigobj -Xcompiler /EHsc")
     if(CMAKE_GENERATOR MATCHES "Ninja")
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 6c49edd740..d4ddf3a211 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -448,6 +448,7 @@ static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs,
             case u64: res = logicOp<uintl, op>(lhs, rhs, odims); break;
             case s16: res = logicOp<short, op>(lhs, rhs, odims); break;
             case u16: res = logicOp<ushort, op>(lhs, rhs, odims); break;
+            case f16: res = logicOp<half, op>(lhs, rhs, odims); break;
             default: TYPE_ERROR(0, type);
         }
 
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index de949f9b41..450ca73e29 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -186,6 +186,7 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t *const dims,
                 // Removed because of bool type. Functions implementations
                 // exist.
             case b8: result = identity_<char>(d); break;
+            case f16: result = identity_<half>(d); break;
             default: TYPE_ERROR(3, type);
         }
         std::swap(*out, result);
@@ -223,6 +224,7 @@ af_err af_range(af_array *result, const unsigned ndims, const dim_t *const dims,
             case s16: out = range_<short>(d, seq_dim); break;
             case u16: out = range_<ushort>(d, seq_dim); break;
             case u8: out = range_<uchar>(d, seq_dim); break;
+            case f16: out = range_<half>(d, seq_dim); break;
             default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
@@ -262,6 +264,7 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t *const dims,
             case s16: out = iota_<short>(d, t); break;
             case u16: out = iota_<ushort>(d, t); break;
             case u8: out = iota_<uchar>(d, t); break;
+            case f16: out = iota_<half>(d, t); break;
             default: TYPE_ERROR(4, type);
         }
         std::swap(*result, out);
@@ -309,6 +312,7 @@ af_err af_diag_create(af_array *out, const af_array in, const int num) {
                 // Removed because of bool type. Functions implementations
                 // exist.
             case b8: result = diagCreate<char>(in, num); break;
+            case f16: result = diagCreate<half>(in, num); break;
             default: TYPE_ERROR(1, type);
         }
 
@@ -347,6 +351,7 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num) {
                 // Removed because of bool type. Functions implementations
                 // exist.
             case b8: result = diagExtract<char>(in, num); break;
+            case f16: result = diagExtract<half>(in, num); break;
             default: TYPE_ERROR(1, type);
         }
 
@@ -386,6 +391,7 @@ af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
             case u16: res = triangle<ushort, false>(in, is_unit_diag); break;
             case u8: res = triangle<uchar, false>(in, is_unit_diag); break;
             case b8: res = triangle<char, false>(in, is_unit_diag); break;
+            case f16: res = triangle<half, false>(in, is_unit_diag); break;
         }
         std::swap(*out, res);
     }
@@ -414,6 +420,7 @@ af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
             case u16: res = triangle<ushort, true>(in, is_unit_diag); break;
             case u8: res = triangle<uchar, true>(in, is_unit_diag); break;
             case b8: res = triangle<char, true>(in, is_unit_diag); break;
+            case f16: res = triangle<half, true>(in, is_unit_diag); break;
         }
         std::swap(*out, res);
     }
diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index a2c4a4ffa3..eb8f67fd38 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -25,82 +25,51 @@ using namespace detail;
 
 template<typename T, af_op_t op>
 struct Binary {
-    static __DH__ detail::compute_t<T> init();
+    static __DH__ T init();
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs);
+    __DH__ T operator()(T lhs, T rhs);
 };
 
 template<typename T>
 struct Binary<T, af_add_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::scalar<detail::compute_t<T>>(0);
-    }
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return lhs + rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_mul_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::scalar<detail::compute_t<T>>(1);
-    }
+    static __DH__ T init() { return detail::scalar<T>(1); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return lhs * rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs * rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_or_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::scalar<detail::compute_t<T>>(0);
-    }
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return lhs || rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs || rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_and_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::scalar<detail::compute_t<T>>(1);
-    }
+    static __DH__ T init() { return detail::scalar<T>(1); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return lhs && rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs && rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_notzero_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::scalar<detail::compute_t<T>>(0);
-    }
+    static __DH__ T init() { return detail::scalar<T>(0); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return lhs + rhs;
-    }
+    __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_min_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::maxval<detail::compute_t<T>>();
-    }
+    static __DH__ T init() { return detail::maxval<T>(); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return detail::min(lhs, rhs);
-    }
+    __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); }
 };
 
 template<>
@@ -112,17 +81,14 @@ struct Binary<char, af_min_t> {
     }
 };
 
-#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                          \
-    template<>                                                                 \
-    struct Binary<detail::compute_t<T>, af_min_t> {                            \
-        static __DH__ detail::compute_t<T> init() {                            \
-            return detail::scalar<detail::compute_t<T>>(detail::maxval<Tr>()); \
-        }                                                                      \
-                                                                               \
-        __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,       \
-                                               detail::compute_t<T> rhs) {     \
-            return detail::min(lhs, rhs);                                      \
-        }                                                                      \
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                       \
+    template<>                                                              \
+    struct Binary<T, af_min_t> {                                            \
+        static __DH__ T init() {                                            \
+            return detail::scalar<T>(detail::maxval<Tr>());                 \
+        }                                                                   \
+                                                                            \
+        __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); } \
     };
 
 SPECIALIZE_COMPLEX_MIN(cfloat, float)
@@ -132,14 +98,9 @@ SPECIALIZE_COMPLEX_MIN(cdouble, double)
 
 template<typename T>
 struct Binary<T, af_max_t> {
-    static __DH__ detail::compute_t<T> init() {
-        return detail::minval<detail::compute_t<T>>();
-    }
+    static __DH__ T init() { return detail::minval<T>(); }
 
-    __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,
-                                           detail::compute_t<T> rhs) {
-        return detail::max(lhs, rhs);
-    }
+    __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); }
 };
 
 template<>
@@ -151,18 +112,14 @@ struct Binary<char, af_max_t> {
     }
 };
 
-#define SPECIALIZE_COMPLEX_MAX(T, Tr)                                      \
-    template<>                                                             \
-    struct Binary<T, af_max_t> {                                           \
-        static __DH__ detail::compute_t<T> init() {                        \
-            return detail::scalar<detail::compute_t<T>>(                   \
-                detail::scalar<Tr>(0));                                    \
-        }                                                                  \
-                                                                           \
-        __DH__ detail::compute_t<T> operator()(detail::compute_t<T> lhs,   \
-                                               detail::compute_t<T> rhs) { \
-            return detail::max(lhs, rhs);                                  \
-        }                                                                  \
+#define SPECIALIZE_COMPLEX_MAX(T, Tr)                                       \
+    template<>                                                              \
+    struct Binary<T, af_max_t> {                                            \
+        static __DH__ T init() {                                            \
+            return detail::scalar<T>(detail::scalar<Tr>(0));                \
+        }                                                                   \
+                                                                            \
+        __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); } \
     };
 
 SPECIALIZE_COMPLEX_MAX(cfloat, float)
@@ -172,42 +129,34 @@ SPECIALIZE_COMPLEX_MAX(cdouble, double)
 
 template<typename Ti, typename To, af_op_t op>
 struct Transform {
-    __DH__ To operator()(detail::compute_t<Ti> in) {
-        return static_cast<To>(in);
-    }
+    __DH__ To operator()(Ti in) { return static_cast<To>(in); }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_min_t> {
-    __DH__ To operator()(detail::compute_t<Ti> in) {
-        return (To)(IS_NAN(in) ? Binary<To, af_min_t>::init() : in);
+    __DH__ To operator()(Ti in) {
+        return IS_NAN(in) ? Binary<To, af_min_t>::init() : To(in);
     }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_max_t> {
-    __DH__ To operator()(detail::compute_t<Ti> in) {
-        return (To)(IS_NAN(in) ? Binary<To, af_max_t>::init() : in);
+    __DH__ To operator()(Ti in) {
+        return IS_NAN(in) ? Binary<To, af_max_t>::init() : To(in);
     }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_or_t> {
-    __DH__ To operator()(detail::compute_t<Ti> in) {
-        return (in != detail::scalar<detail::compute_t<Ti>>(0));
-    }
+    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0.)); }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_and_t> {
-    __DH__ To operator()(detail::compute_t<Ti> in) {
-        return (in != detail::scalar<detail::compute_t<Ti>>(0));
-    }
+    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0.)); }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_notzero_t> {
-    __DH__ To operator()(detail::compute_t<Ti> in) {
-        return (in != detail::scalar<detail::compute_t<Ti>>(0));
-    }
+    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0.)); }
 };
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index ccc72de17b..8fdb55996a 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -247,6 +247,7 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in) {
             case s16: *real = (double)reduce_all<op, short, To>(in); break;
             case b8: *real = (double)reduce_all<op, char, To>(in); break;
             case u8: *real = (double)reduce_all<op, uchar, To>(in); break;
+            case f16: *real = (double)reduce_all<op, half, To>(in); break;
             default: TYPE_ERROR(1, type);
         }
     }
@@ -293,6 +294,9 @@ static af_err reduce_all_common(double *real_val, double *imag_val,
             case u8:
                 *real_val = (double)reduce_all<op, uchar, uchar>(in);
                 break;
+            case f16:
+                *real_val = (double)reduce_all<op, half, half>(in);
+                break;
 
             case c32:
                 cfval = reduce_all<op, cfloat, cfloat>(in);
@@ -391,6 +395,10 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
                 *real_val = real(cdval);
                 *imag_val = imag(cdval);
                 break;
+            case f16:
+                *real_val = (double)reduce_all<op, half, float>(in, change_nan,
+                                                                nanval);
+                break;
 
             default: TYPE_ERROR(1, type);
         }
@@ -475,7 +483,7 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in,
             case s16: ireduce<op, short>(&res, &loc, in, dim); break;
             case b8: ireduce<op, char>(&res, &loc, in, dim); break;
             case u8: ireduce<op, uchar>(&res, &loc, in, dim); break;
-              //case f16: ireduce<op, half>(&res, &loc, in, dim); break;
+            case f16: ireduce<op, half>(&res, &loc, in, dim); break;
             default: TYPE_ERROR(1, type);
         }
 
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index 52875f79e4..33140b9978 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -9,6 +9,7 @@
 
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <transpose.hpp>
 #include <af/arith.h>
@@ -18,6 +19,7 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 template<typename T>
@@ -63,6 +65,7 @@ af_err af_transpose(af_array* out, af_array in, const bool conjugate) {
             case u64: output = trs<uintl>(in, conjugate); break;
             case s16: output = trs<short>(in, conjugate); break;
             case u16: output = trs<ushort>(in, conjugate); break;
+            case f16: output = trs<half>(in, conjugate); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -102,6 +105,7 @@ af_err af_transpose_inplace(af_array in, const bool conjugate) {
             case u64: transpose_inplace<uintl>(in, conjugate); break;
             case s16: transpose_inplace<short>(in, conjugate); break;
             case u16: transpose_inplace<ushort>(in, conjugate); break;
+            case f16: transpose_inplace<half>(in, conjugate); break;
             default: TYPE_ERROR(1, type);
         }
     }
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index ef69dc3b54..251181bdd3 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -949,6 +949,8 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(short)
 INSTANTIATE(unsigned short)
+INSTANTIATE(af_half)
+INSTANTIATE(half_float::half)
 
 template<>
 AFAPI void array::write(const void *ptr, const size_t bytes, af::source src) {
@@ -989,6 +991,8 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(short)
 INSTANTIATE(unsigned short)
+INSTANTIATE(af_half)
+INSTANTIATE(half_float::half)
 
 #undef INSTANTIATE
 #undef TEMPLATE_MEM_FUNC
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index dfe51c8986..4d28b9371d 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -16,6 +16,7 @@
 #include <af/half.h>
 #include <af/traits.hpp>
 #include "error.hpp"
+#include <half.hpp>
 
 #include <type_traits>
 
@@ -135,6 +136,7 @@ CONSTANT(bool);
 CONSTANT(short);
 CONSTANT(unsigned short);
 CONSTANT(half);
+CONSTANT(half_float::half);
 
 #undef CONSTANT
 
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index c0948eaa9b..630652629f 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -1,3 +1,12 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
 #pragma once
 
 #if defined(NVCC) || defined(__CUDACC_RTC__)
@@ -9,9 +18,12 @@
 #ifndef __CUDACC_RTC__
 #include <cstring>
 #include <ostream>
+#include <string>
 #include <type_traits>
 
 #include <limits>
+#else
+using uint16_t = unsigned short;
 #endif
 
 #if AF_COMPILER_CXX_RELAXED_CONSTEXPR
@@ -22,7 +34,14 @@
 
 namespace common {
 
+#if defined(__CUDA_ARCH__)
+using native_half_t = __half;
+#else
+using native_half_t = uint16_t;
+#endif
+
 #ifndef __CUDACC_RTC__
+
 /// Convert integer to half-precision floating point.
 ///
 /// \tparam R rounding mode to use, `std::round_indeterminate` for fastest
@@ -34,7 +53,7 @@ namespace common {
 ///
 /// \return binary representation of half-precision value
 template<std::float_round_style R, bool S, typename T>
-CONSTEXPR_DH uint16_t int2half(T value) noexcept {
+CONSTEXPR_DH native_half_t int2half_impl(T value) noexcept {
     static_assert(std::is_integral<T>::value,
                   "int to half conversion only supports builtin integer types");
     if (S) value = -value;
@@ -69,6 +88,23 @@ CONSTEXPR_DH uint16_t int2half(T value) noexcept {
     return bits;
 }
 
+template<typename T, std::float_round_style R = std::round_to_nearest,
+         typename std::enable_if_t<std::is_integral<T>::value &&
+                                   std::is_signed<T>::value>* = nullptr>
+CONSTEXPR_DH native_half_t int2half(T value) noexcept {
+    uint16_t out;
+    out = (value < 0) ? int2half_impl<R, true, T>(value)
+                      : int2half_impl<R, false, T>(value);
+    return out;
+}
+
+template<typename T, std::float_round_style R = std::round_to_nearest,
+         typename std::enable_if_t<std::is_integral<T>::value &&
+                                   std::is_unsigned<T>::value>* = nullptr>
+CONSTEXPR_DH native_half_t int2half(T value) noexcept {
+    return int2half_impl<R, false, T>(value);
+}
+
 /// Convert IEEE single-precision to half-precision.
 /// Credit for this goes to [Jeroen van der
 /// Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf).
@@ -77,8 +113,8 @@ CONSTEXPR_DH uint16_t int2half(T value) noexcept {
 ///
 /// \param value single-precision value
 /// \return binary representation of half-precision value
-template<std::float_round_style R = std::numeric_limits<float>::round_style>
-CONSTEXPR_DH uint16_t float2half(float value) noexcept {
+template<std::float_round_style R = std::round_indeterminate>
+CONSTEXPR_DH native_half_t float2half_impl(float value) noexcept {
     uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
                         // //violating strict aliasing!
     std::memcpy(&bits, &value, sizeof(float));
@@ -205,7 +241,73 @@ CONSTEXPR_DH uint16_t float2half(float value) noexcept {
     return hbits;
 }
 
-__DH__ inline float half2float(uint16_t value) noexcept {
+/// Convert IEEE double-precision to half-precision.
+///
+/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest
+///           rounding
+/// \param value double-precision value
+///
+/// \return binary representation of half-precision value
+template<std::float_round_style R>
+CONSTEXPR_DH native_half_t float2half_impl(double value) {
+    uint64_t bits;  // = *reinterpret_cast<uint64*>(&value);		//violating
+                    // strict aliasing!
+    std::memcpy(&bits, &value, sizeof(double));
+    uint32_t hi = bits >> 32, lo = bits & 0xFFFFFFFF;
+    uint16_t hbits = (hi >> 16) & 0x8000;
+    hi &= 0x7FFFFFFF;
+    int exp = hi >> 20;
+    if (exp == 2047)
+        return hbits | 0x7C00 |
+               (0x3FF & -static_cast<unsigned>((bits & 0xFFFFFFFFFFFFF) != 0));
+    if (exp > 1038) {
+        if (R == std::round_toward_infinity)
+            return hbits | 0x7C00 - (hbits >> 15);
+        if (R == std::round_toward_neg_infinity)
+            return hbits | 0x7BFF + (hbits >> 15);
+        return hbits | 0x7BFF + (R != std::round_toward_zero);
+    }
+    int g, s = lo != 0;
+    if (exp > 1008) {
+        g = (hi >> 9) & 1;
+        s |= (hi & 0x1FF) != 0;
+        hbits |= ((exp - 1008) << 10) | ((hi >> 10) & 0x3FF);
+    } else if (exp > 997) {
+        int i = 1018 - exp;
+        hi    = (hi & 0xFFFFF) | 0x100000;
+        g     = (hi >> i) & 1;
+        s |= (hi & ((1L << i) - 1)) != 0;
+        hbits |= hi >> (i + 1);
+    } else {
+        g = 0;
+        s |= hi != 0;
+    }
+    if (R == std::round_to_nearest)
+#if HALF_ROUND_TIES_TO_EVEN
+        hbits += g & (s | hbits);
+#else
+        hbits += g;
+#endif
+    else if (R == std::round_toward_infinity)
+        hbits += ~(hbits >> 15) & (s | g);
+    else if (R == std::round_toward_neg_infinity)
+        hbits += (hbits >> 15) & (g | s);
+    return hbits;
+}
+
+template<typename T, std::float_round_style R = std::round_to_nearest>
+CONSTEXPR_DH native_half_t float2half(T val) {
+#ifdef __CUDA_ARCH__
+    return __float2half(val);
+#else
+    return float2half_impl<R>(val);
+#endif
+}
+
+CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
+#ifdef __CUDA_ARCH__
+    return __half2float(value);
+#else
     // return _cvtsh_ss(data.data_);
     uint32_t mantissa_table[2048] = {
         0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34A00000,
@@ -578,7 +680,102 @@ __DH__ inline float half2float(uint16_t value) noexcept {
     float out = 0.0f;
     std::memcpy(&out, &bits, sizeof(float));
     return out;
+#endif
 }
+
+/// Convert half-precision floating point to integer.
+///
+/// \tparam R rounding mode to use, `std::round_indeterminate` for fastest
+///           rounding
+/// \tparam E `true` for round to even, `false` for round away from
+///           zero
+/// \tparam T type to convert to (buitlin integer type with at least 16
+///           bits precision, excluding any implicit sign bits) \param value
+///           binary representation of half-precision value \return integral
+///           value
+/// \param value The value to convert to integer
+template<std::float_round_style R, bool E, typename T>
+__DH__ T half2int(native_half_t value) {
+    static_assert(std::is_integral<T>::value,
+                  "half to int conversion only supports builtin integer types");
+    unsigned int e = value & 0x7FFF;
+    if (e >= 0x7C00)
+        return (value & 0x8000) ? std::numeric_limits<T>::min()
+                                : std::numeric_limits<T>::max();
+    if (e < 0x3800) {
+        if (R == std::round_toward_infinity)
+            return T(~(value >> 15) & (e != 0));
+        else if (R == std::round_toward_neg_infinity)
+            return -T(value > 0x8000);
+        return T();
+    }
+    unsigned int m = (value & 0x3FF) | 0x400;
+    e >>= 10;
+    if (e < 25) {
+        if (R == std::round_to_nearest)
+            m += (1 << (24 - e)) - (~(m >> (25 - e)) & E);
+        else if (R == std::round_toward_infinity)
+            m += ((value >> 15) - 1) & ((1 << (25 - e)) - 1U);
+        else if (R == std::round_toward_neg_infinity)
+            m += -(value >> 15) & ((1 << (25 - e)) - 1U);
+        m >>= 25 - e;
+    } else
+        m <<= e - 25;
+    return (value & 0x8000) ? -static_cast<T>(m) : static_cast<T>(m);
+}
+
+#else
+
+template<typename T>
+CONSTEXPR_DH native_half_t float2half(T value) {
+    return __float2half(value);
+}
+
+CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
+    return __half2float(value);
+}
+
+template<typename T>
+CONSTEXPR_DH native_half_t int2half(T value) noexcept;
+
+template<>
+CONSTEXPR_DH native_half_t int2half(int value) noexcept {
+    return __int2half_rn(value);
+}
+
+template<>
+CONSTEXPR_DH native_half_t int2half(unsigned value) noexcept {
+    return __uint2half_rn(value);
+}
+
+template<>
+CONSTEXPR_DH native_half_t int2half(long long value) noexcept {
+    return __ll2half_rn(value);
+}
+
+template<>
+CONSTEXPR_DH native_half_t int2half(unsigned long long value) noexcept {
+    return __ull2half_rn(value);
+}
+
+template<>
+CONSTEXPR_DH native_half_t int2half(short value) noexcept {
+    return __short2half_rn(value);
+}
+template<>
+CONSTEXPR_DH native_half_t int2half(unsigned short value) noexcept {
+    return __ushort2half_rn(value);
+}
+
+template<>
+CONSTEXPR_DH native_half_t int2half(char value) noexcept {
+    return __ull2half_rn(value);
+}
+template<>
+CONSTEXPR_DH native_half_t int2half(unsigned char value) noexcept {
+    return __ull2half_rn(value);
+}
+
 #endif  // __CUDACC_RTC__
 
 namespace internal {
@@ -589,16 +786,25 @@ struct binary_t {};
 static constexpr binary_t binary;
 }  // namespace internal
 
+class half;
+
+CONSTEXPR_DH static inline bool operator==(common::half lhs,
+                                           common::half rhs) noexcept;
+CONSTEXPR_DH static inline bool operator!=(common::half lhs,
+                                           common::half rhs) noexcept;
+CONSTEXPR_DH static inline bool operator<(common::half lhs,
+                                          common::half rhs) noexcept;
+CONSTEXPR_DH static inline bool operator<(common::half lhs, float rhs) noexcept;
+CONSTEXPR_DH static inline bool isinf(half val) noexcept;
+
+/// Classification implementation.
+/// \param arg value to classify
+/// \retval true if not a number
+/// \retval false else
+CONSTEXPR_DH static inline bool isnan(common::half val) noexcept;
+
 class alignas(2) half {
-#if defined(__CUDA_ARCH__)
-    __half data_;
-#else
-    uint16_t data_;
-    /// Constructor.
-    /// \param bits binary representation to set half to
-    CONSTEXPR_DH half(internal::binary_t, uint16_t bits) noexcept
-        : data_(bits) {}
-#endif
+    native_half_t data_;
 
 #if !defined(NVCC) && !defined(__CUDACC_RTC__)
     // NVCC on OSX performs a weird transformation where it removes the std::
@@ -609,36 +815,25 @@ class alignas(2) half {
 
    public:
     half() = default;
-    CONSTEXPR_DH explicit half(double value) noexcept
-#ifdef __CUDA_ARCH__
-        : data_(__float2half(value)) {
-#else
-        : data_(float2half<std::round_to_nearest>(value)) {
-#endif
-    }
 
-    CONSTEXPR_DH explicit half(float value) noexcept
-#ifdef __CUDA_ARCH__
-        : data_(__float2half(value)) {
-#else
-        : data_(float2half<std::round_to_nearest>(value)) {
-#endif
+    /// Constructor.
+    /// \param bits binary representation to set half to
+    CONSTEXPR_DH half(internal::binary_t, uint16_t bits) noexcept : data_() {
+        memcpy(&data_, &bits, sizeof(uint16_t));
     }
 
-#ifndef __CUDACC_RTC__
-    template<typename T,
-        typename std::enable_if_t<std::is_unsigned<T>::value>* = nullptr>
-    CONSTEXPR_DH explicit half(T value) noexcept
-        : data_(int2half<std::round_to_nearest, false>(value)) {}
+    CONSTEXPR_DH explicit half(double value) noexcept
+        : data_(float2half<double>(value)) {}
 
-    template<typename T,
-        typename std::enable_if_t<std::is_signed<T>::value>* = nullptr>
-    CONSTEXPR_DH explicit half(T value) noexcept
-        : data_((value < 0) ? int2half<std::round_to_nearest, true>(value)
-                            : int2half<std::round_to_nearest, false>(value)) {}
+    CONSTEXPR_DH explicit half(float value) noexcept
+        : data_(float2half<float>(value)) {}
+
+#ifndef __CUDA_RTC__
+    template<typename T>
+    CONSTEXPR_DH explicit half(T value) noexcept : data_(int2half<T>(value)) {}
 
     CONSTEXPR_DH half& operator=(const double& value) noexcept {
-        data_ = float2half<std::round_to_nearest>(value);
+        data_ = float2half<double>(value);
         return *this;
     }
 #endif
@@ -651,26 +846,160 @@ class alignas(2) half {
     }
 #endif
 
-    __DH__ operator float() const noexcept {
+    CONSTEXPR_DH explicit operator float() const noexcept {
+        return half2float(data_);
+    }
+
+    CONSTEXPR_DH explicit operator double() const noexcept {
+        // TODO(umar): convert directly to double
+        return half2float(data_);
+    }
+
+    CONSTEXPR_DH explicit operator short() const noexcept {
 #ifdef __CUDA_ARCH__
-        return __half2float(data_);
+        return __half2short_rn(data_);
 #else
-        return half2float(data_);
+        return half2int<std::round_indeterminate, true, short>(data_);
 #endif
-    };
+    }
+
+    CONSTEXPR_DH explicit operator long long() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2ll_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, long long int>(data_);
+#endif
+    }
+
+    CONSTEXPR_DH explicit operator int() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2int_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, int>(data_);
+#endif
+    }
+
+    CONSTEXPR_DH explicit operator unsigned() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2uint_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, unsigned>(data_);
+#endif
+    }
+
+    CONSTEXPR_DH explicit operator unsigned short() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2ushort_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, unsigned>(data_);
+#endif
+    }
+
+    CONSTEXPR_DH explicit operator unsigned long long() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2ull_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, unsigned>(data_);
+#endif
+    }
+
+    CONSTEXPR_DH explicit operator char() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2short_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, char>(data_);
+#endif
+    }
+
+    CONSTEXPR_DH explicit operator unsigned char() const noexcept {
+#ifdef __CUDA_ARCH__
+        return __half2short_rn(data_);
+#else
+        return half2int<std::round_indeterminate, true, unsigned char>(data_);
+#endif
+    }
 
 #if defined(__CUDA_ARCH__)
     CONSTEXPR_DH operator __half() const noexcept { return data_; };
 #endif
+
+    friend CONSTEXPR_DH bool operator==(half lhs, half rhs) noexcept;
+    friend CONSTEXPR_DH bool operator!=(half lhs, half rhs) noexcept;
+    friend CONSTEXPR_DH bool operator<(common::half lhs,
+                                       common::half rhs) noexcept;
+    friend CONSTEXPR_DH bool operator<(common::half lhs, float rhs) noexcept;
+    friend CONSTEXPR_DH bool isinf(half val) noexcept;
+    friend CONSTEXPR_DH inline bool isnan(half val) noexcept;
+
+    CONSTEXPR_DH common::half operator-() const {
+#if __CUDA_ARCH__ >= 530
+        return common::half(__hneg(data_));
+#elif defined(__CUDA_ARCH__)
+        return common::half(-(__half2float(data_)));
+#else
+        return common::half(internal::binary, data_ ^ 0x8000);
+#endif
+    }
+
+    CONSTEXPR_DH common::half operator+() const { return *this; }
 };
 
+CONSTEXPR_DH static inline bool operator==(common::half lhs,
+                                           common::half rhs) noexcept {
+#if __CUDA_ARCH__ >= 530
+    return __heq(lhs.data_, rhs.data_);
+#elif defined(__CUDA_ARCH__)
+    return __half2float(lhs.data_) == __half2float(rhs.data_);
+#else
+    return (lhs.data_ == rhs.data_ || !((lhs.data_ | rhs.data_) & 0x7FFF)) &&
+           !isnan(lhs);
+#endif
+}
+
+CONSTEXPR_DH static inline bool operator!=(common::half lhs,
+                                           common::half rhs) noexcept {
+#if __CUDA_ARCH__ >= 530
+    return __hne(lhs.data_, rhs.data_);
+#else
+    return !(lhs == rhs);
+#endif
+}
+
+CONSTEXPR_DH static inline bool operator<(common::half lhs,
+                                          common::half rhs) noexcept {
+#if __CUDA_ARCH__ >= 530
+    return __hlt(lhs.data_, rhs.data_);
+#elif defined(__CUDA_ARCH__)
+    return __half2float(lhs.data_) < __half2float(rhs.data_);
+#else
+    int xabs = lhs.data_ & 0x7FFF, yabs = rhs.data_ & 0x7FFF;
+    return xabs <= 0x7C00 && yabs <= 0x7C00 &&
+           (((xabs == lhs.data_) ? xabs : -xabs) <
+            ((yabs == rhs.data_) ? yabs : -yabs));
+#endif
+}
+
+CONSTEXPR_DH static inline bool operator<(common::half lhs,
+                                          float rhs) noexcept {
+#if defined(__CUDA_ARCH__)
+    return __half2float(lhs.data_) < rhs;
+#else
+    return static_cast<float>(lhs) < rhs;
+#endif
+}
+
 #ifndef __CUDA_ARCH__
 std::ostream& operator<<(std::ostream& os, const half& val);
+
+static inline std::string to_string(const half&& val) {
+    return std::to_string(static_cast<float>(val));
+}
 #endif
 
 }  // namespace common
 
 #if !defined(NVCC) && !defined(__CUDACC_RTC__)
+//#endif
 /// Extensions to the C++ standard library.
 namespace std {
 /// Numeric limits for half-precision floats.
@@ -801,12 +1130,31 @@ struct hash<common::half>  //: unary_function<common::half,size_t>
             -(*reinterpret_cast<uint16_t*>(&arg.data_) != 0x8000));
     }
 };
+
 }  // namespace std
+#endif
 
 namespace common {
-static bool isinf(::common::half val) noexcept {
-    return val == std::numeric_limits<::common::half>::infinity() ||
-           val == -std::numeric_limits<::common::half>::infinity();
+CONSTEXPR_DH
+static bool isinf(half val) noexcept {
+#if __CUDA_ARCH__ >= 530
+    return __hisinf(val.data_);
+#elif defined(__CUDA_ARCH__)
+    return ::isinf(__half2float(val));
+#else
+    return val == std::numeric_limits<half>::infinity() ||
+           val == -std::numeric_limits<half>::infinity();
+#endif
 }
-}  // namespace common
+
+CONSTEXPR_DH static inline bool isnan(half val) noexcept {
+#if __CUDA_ARCH__ >= 530
+    return __hisnan(val.data_);
+#elif defined(__CUDA_ARCH__)
+    return ::isnan(__half2float(val));
+#else
+    return (val.data_ & 0x7FFF) > 0x7C00;
 #endif
+}
+
+}  // namespace common
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 5c7723402e..ad919405d2 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -38,7 +38,7 @@ struct UnOp<To, common::half, af_cast_t> {
 
     void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
         for (int i = 0; i < lim; i++) {
-            float val = in[i];
+            float val = static_cast<float>(in[i]);
             out[i]    = To(val);
         }
     }
@@ -54,7 +54,7 @@ struct UnOp<common::half, Ti, af_cast_t> {
 
     void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
         for (int i = 0; i < lim; i++) {
-            float val = in[i];
+            float val = static_cast<float>(in[i]);
             out[i]    = To(val);
         }
     }
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 68f67f926a..e52b0d5c0c 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -11,6 +11,7 @@
 #include <kernel/diagonal.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -18,6 +19,8 @@
 #include <algorithm>
 #include <cstdlib>
 
+using common::half;
+
 namespace cpu {
 
 template<typename T>
@@ -58,5 +61,6 @@ INSTANTIATE_DIAGONAL(char)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
+INSTANTIATE_DIAGONAL(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index 7ae8f4a96c..c6a8af4dbb 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -6,14 +6,17 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <identity.hpp>
 #include <kernel/identity.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace cpu {
 
 template<typename T>
@@ -40,5 +43,6 @@ INSTANTIATE_IDENTITY(char)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
+INSTANTIATE_IDENTITY(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 1ca65d5332..cb7b88d83d 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -6,14 +6,17 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <iota.hpp>
 #include <kernel/iota.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
+using common::half;
+
 namespace cpu {
 
 template<typename T>
@@ -39,5 +42,6 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index e31ee40ffd..e700c4b708 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -6,16 +6,19 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <ireduce.hpp>
 #include <kernel/ireduce.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
+
 #include <complex>
 
 using af::dim4;
+using common::half;
 
 namespace cpu {
 
@@ -84,6 +87,7 @@ INSTANTIATE(af_min_t, char)
 INSTANTIATE(af_min_t, uchar)
 INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
+INSTANTIATE(af_min_t, half)
 
 // max
 INSTANTIATE(af_max_t, float)
@@ -98,5 +102,6 @@ INSTANTIATE(af_max_t, char)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
+INSTANTIATE(af_max_t, half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index f9442ad049..05f23952df 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -29,17 +29,17 @@ struct BinOp {
 namespace jit {
 
 template<typename To, typename Ti, af_op_t op>
-class BinaryNode : public TNode<To> {
+class BinaryNode : public TNode<compute_t<To>> {
    protected:
-    BinOp<To, Ti, op> m_op;
-    TNode<Ti> *m_lhs, *m_rhs;
+    BinOp<compute_t<To>, compute_t<Ti>, op> m_op;
+    TNode<compute_t<Ti>> *m_lhs, *m_rhs;
 
    public:
     BinaryNode(Node_ptr lhs, Node_ptr rhs)
-        : TNode<To>(To(0), std::max(lhs->getHeight(), rhs->getHeight()) + 1,
+        : TNode<compute_t<To>>(compute_t<To>(0), std::max(lhs->getHeight(), rhs->getHeight()) + 1,
                     {{lhs, rhs}})
-        , m_lhs(reinterpret_cast<TNode<Ti> *>(lhs.get()))
-        , m_rhs(reinterpret_cast<TNode<Ti> *>(rhs.get())) {}
+        , m_lhs(reinterpret_cast<TNode<compute_t<Ti>> *>(lhs.get()))
+        , m_rhs(reinterpret_cast<TNode<compute_t<Ti>> *>(rhs.get())) {}
 
     void calc(int x, int y, int z, int w, int lim) final {
         UNUSED(x);
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index 3df49b054b..4caaa967ef 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -57,7 +57,7 @@ class BufferNode : public TNode<T> {
         T *in_ptr   = m_ptr + l_off;
         Tc *out_ptr = this->m_val.data();
         for (int i = 0; i < lim; i++) {
-            out_ptr[i] = in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0];
+            out_ptr[i] = static_cast<Tc>(in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0]);
         }
     }
 
@@ -66,7 +66,9 @@ class BufferNode : public TNode<T> {
 
         T *in_ptr   = m_ptr + idx;
         Tc *out_ptr = this->m_val.data();
-        for (int i = 0; i < lim; i++) { out_ptr[i] = in_ptr[i]; }
+        for (int i = 0; i < lim; i++) {
+            out_ptr[i] = static_cast<Tc>(in_ptr[i]);
+        }
     }
 
     void getInfo(unsigned &len, unsigned &buf_count,
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index a2b5721527..5b309be338 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -105,7 +105,8 @@ class TNode : public Node {
     TNode(T val, const int height,
           const std::array<Node_ptr, kMaxChildren> children)
         : Node(height, children) {
-        m_val.fill(val);
+        using namespace common;
+        m_val.fill(static_cast<compute_t<T>>(val));
     }
 };
 
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index b81b6328f8..b0bde70e6a 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -47,8 +47,8 @@ void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value,
     af::dim4 src_strides = src.strides();
     af::dim4 dst_strides = dst.strides();
 
-    InT const* const src_ptr = src.get();
-    OutT* dst_ptr            = dst.get();
+    data_t<InT> const* const src_ptr = src.get();
+    data_t<OutT>* dst_ptr            = dst.get();
 
     dim_t trgt_l = std::min(dst_dims[3], src_dims[3]);
     dim_t trgt_k = std::min(dst_dims[2], src_dims[2]);
@@ -71,11 +71,13 @@ void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value,
                 bool isJvalid  = j < trgt_j;
 
                 for (dim_t i = 0; i < dst_dims[0]; ++i) {
-                    OutT temp = default_value;
+                    data_t<OutT> temp = default_value;
                     if (isLvalid && isKvalid && isJvalid && i < trgt_i) {
                         dim_t src_idx =
                             i * src_strides[0] + src_joff + src_koff + src_loff;
-                        temp = OutT(src_ptr[src_idx]) * OutT(factor);
+                        // The conversions here are necessary because the half type does not convert to
+                        // complex automatically
+                        temp = compute_t<OutT>(compute_t<InT>(src_ptr[src_idx])) * compute_t<OutT>(factor);
                     }
                     dim_t dst_idx =
                         i * dst_strides[0] + dst_joff + dst_koff + dst_loff;
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index 74be5ee6bc..2c0044fdeb 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -16,18 +16,18 @@ namespace kernel {
 template<typename T>
 void iota(Param<T> output, const af::dim4& sdims) {
     const af::dim4 dims    = output.dims();
-    T* out                 = output.get();
+    data_t<T>* out      = output.get();
     const af::dim4 strides = output.strides();
 
     for (dim_t w = 0; w < dims[3]; w++) {
         dim_t offW = w * strides[3];
-        T valW     = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
+        dim_t valW     = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
         for (dim_t z = 0; z < dims[2]; z++) {
             dim_t offWZ = offW + z * strides[2];
-            T valZ      = valW + (z % sdims[2]) * sdims[0] * sdims[1];
+            dim_t valZ      = valW + (z % sdims[2]) * sdims[0] * sdims[1];
             for (dim_t y = 0; y < dims[1]; y++) {
                 dim_t offWZY = offWZ + y * strides[1];
-                T valY       = valZ + (y % sdims[1]) * sdims[0];
+                dim_t valY       = valZ + (y % sdims[1]) * sdims[0];
                 for (dim_t x = 0; x < dims[0]; x++) {
                     dim_t id = offWZY + x;
                     out[id]  = valY + (x % sdims[0]);
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 4500afc7db..963d36db5d 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -197,13 +197,13 @@ void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
 
 template<typename T>
 void boxMullerTransform(data_t<T> *const out1, data_t<T> *const out2,
-                        const compute_t<T> r1, const compute_t<T> r2) {
+                        const T r1, const T r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
     using Tc = compute_t<T>;
-    Tc r     = sqrt((Tc)(-2.0) * log((Tc)(1.0) - r1));
-    Tc theta = 2 * (Tc)PI_VAL * ((Tc)(1.0) - r2);
+    Tc r     = sqrt((Tc)(-2.0) * log((Tc)(1.0) - static_cast<Tc>(r1)));
+    Tc theta = 2 * (Tc)PI_VAL * ((Tc)(1.0) - static_cast<Tc>(r2));
     *out1    = r * sin(theta);
     *out2    = r * cos(theta);
 }
diff --git a/src/backend/cpu/kernel/range.hpp b/src/backend/cpu/kernel/range.hpp
index 12ae94d5b7..dd6995386f 100644
--- a/src/backend/cpu/kernel/range.hpp
+++ b/src/backend/cpu/kernel/range.hpp
@@ -9,6 +9,9 @@
 
 #pragma once
 #include <Param.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index 1361cbc162..d1d8a71459 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -37,7 +37,7 @@ struct reduce_dim {
 
 template<af_op_t op, typename Ti, typename To>
 struct reduce_dim<op, Ti, To, 0> {
-    Transform<Ti, compute_t<To>, op> transform;
+    Transform<data_t<Ti>, compute_t<To>, op> transform;
     Binary<compute_t<To>, op> reduce;
     void operator()(Param<To> out, const dim_t outOffset, CParam<Ti> in,
                     const dim_t inOffset, const int dim, bool change_nan,
@@ -45,8 +45,8 @@ struct reduce_dim<op, Ti, To, 0> {
         const af::dim4 istrides = in.strides();
         const af::dim4 idims    = in.dims();
 
-        To* const outPtr      = out.get() + outOffset;
-        Ti const* const inPtr = in.get() + inOffset;
+        data_t<To> * const outPtr      = out.get() + outOffset;
+        data_t<Ti> const* const inPtr = in.get() + inOffset;
         dim_t stride          = istrides[dim];
 
         compute_t<To> out_val = Binary<compute_t<To>, op>::init();
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index 6b7534995e..6ab9e9ec5b 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -81,9 +81,9 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a,
     af::dim4 odims    = out.dims();
     af::dim4 ostrides = out.strides();
 
-    const T *aptr    = a.get();
-    T *optr          = out.get();
-    const char *cptr = cond.get();
+    const data_t<T> *aptr = a.get();
+    data_t<T> *optr       = out.get();
+    const char *cptr      = cond.get();
 
     bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
                         adims[2] == odims[2], adims[3] == odims[3]};
@@ -108,7 +108,8 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a,
 
                 for (int i = 0; i < odims[0]; i++) {
                     bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
-                    T aval    = is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1];
+                    compute_t<T> aval = static_cast<compute_t<T>>(
+                        is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1]);
                     optr[o_off1 + i] = (flip ^ cval) ? aval : b;
                 }
             }
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index 98455398b4..b2fc132547 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -6,18 +6,21 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <kernel/range.hpp>
+#include <range.hpp>
 
 #include <Array.hpp>
 #include <err_cpu.hpp>
-#include <kernel/range.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
-#include <range.hpp>
+
 #include <algorithm>
 #include <numeric>
 #include <stdexcept>
 
+using common::half;
+
 namespace cpu {
 
 template<typename T>
@@ -53,5 +56,6 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 27f53faa56..cae71e102f 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -61,10 +61,10 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     in.eval();
     getQueue().sync();
 
-    Transform<Ti, To, op> transform;
-    Binary<To, op> reduce;
+    Transform<Ti, compute_t<To>, op> transform;
+    Binary<compute_t<To>, op> reduce;
 
-    compute_t<To> out = Binary<To, op>::init();
+    compute_t<To> out = Binary<compute_t<To>, op>::init();
 
     // Decrement dimension of select dimension
     af::dim4 dims           = in.dims();
@@ -83,7 +83,8 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
                 for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
 
-                    compute_t<To> in_val = transform(inPtr[idx]);
+                    compute_t<To> in_val =
+                        transform(inPtr[idx]);
                     if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
                     out = reduce(in_val, out);
                 }
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 655c31a0be..7d0cbed448 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -6,14 +6,17 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <kernel/triangle.hpp>
+#include <triangle.hpp>
 
 #include <Array.hpp>
-#include <kernel/triangle.hpp>
+#include <common/half.hpp>
 #include <math.hpp>
 #include <platform.hpp>
-#include <triangle.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace cpu {
 
 template<typename T, bool is_upper, bool is_unit_diag>
@@ -53,5 +56,6 @@ INSTANTIATE(char)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cu
index 5f7993f85a..7ffd487a51 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cu
@@ -7,18 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <copy.hpp>
-#include <cuda_runtime_api.h>
-#include <err_cuda.hpp>
-#include <kernel/memcopy.hpp>
-#include <math.hpp>
-
 #include <Array.hpp>
 #include <common/complex.hpp>
 #include <common/half.hpp>
+#include <copy.hpp>
 #include <cuda_runtime_api.h>
 #include <err_cuda.hpp>
+#include <kernel/memcopy.hpp>
 #include <math.hpp>
 
 using common::half;
@@ -199,7 +194,7 @@ INSTANTIATE(half)
                                          Array<SRC_T> const &src);        \
     template void copyArray<SRC_T, half>(Array<half> & dst,               \
                                          Array<SRC_T> const &src);
-    
+
 INSTANTIATE_PAD_ARRAY(float)
 INSTANTIATE_PAD_ARRAY(double)
 INSTANTIATE_PAD_ARRAY(int)
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cu
index aa111bcc53..2a2f07b594 100644
--- a/src/backend/cuda/diagonal.cu
+++ b/src/backend/cuda/diagonal.cu
@@ -8,12 +8,15 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <diagonal.hpp>
 #include <err_cuda.hpp>
 #include <kernel/diagonal.hpp>
 #include <math.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
@@ -53,5 +56,6 @@ INSTANTIATE_DIAGONAL(char)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
+INSTANTIATE_DIAGONAL(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/identity.cu b/src/backend/cuda/identity.cu
index 3f781b9151..293489c216 100644
--- a/src/backend/cuda/identity.cu
+++ b/src/backend/cuda/identity.cu
@@ -6,13 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <identity.hpp>
+#include <kernel/identity.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <debug_cuda.hpp>
-#include <identity.hpp>
-#include <kernel/identity.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 Array<T> identity(const dim4& dims) {
@@ -36,5 +39,6 @@ INSTANTIATE_IDENTITY(char)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
+INSTANTIATE_IDENTITY(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/iota.cu b/src/backend/cuda/iota.cu
index 81e8fbca6b..f79cb6c492 100644
--- a/src/backend/cuda/iota.cu
+++ b/src/backend/cuda/iota.cu
@@ -8,12 +8,15 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <iota.hpp>
 #include <kernel/iota.hpp>
 #include <math.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
@@ -37,4 +40,5 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace cuda
diff --git a/src/backend/cuda/ireduce.cu b/src/backend/cuda/ireduce.cu
index 6dc0f72efd..400fdf522b 100644
--- a/src/backend/cuda/ireduce.cu
+++ b/src/backend/cuda/ireduce.cu
@@ -6,18 +6,20 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <ireduce.hpp>
 
 #include <Array.hpp>
-#include <ireduce.hpp>
+#include <common/half.hpp>
 #include <af/dim4.hpp>
-#include <complex>
 
 #undef _GLIBCXX_USE_INT128
 #include <err_cuda.hpp>
 #include <kernel/ireduce.hpp>
+
 #include <complex>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -50,6 +52,7 @@ INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
 INSTANTIATE(af_min_t, char)
 INSTANTIATE(af_min_t, uchar)
+INSTANTIATE(af_min_t, half)
 
 // max
 INSTANTIATE(af_max_t, float)
@@ -64,4 +67,5 @@ INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
 INSTANTIATE(af_max_t, char)
 INSTANTIATE(af_max_t, uchar)
+INSTANTIATE(af_max_t, half)
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 9744515a33..a28cc72b07 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -41,7 +41,7 @@ __global__ void iota_kernel(Param<T> out, const int s0, const int s1,
 
     const int ozw = ow * out.strides[3] + oz * out.strides[2];
 
-    T val = (ow % s3) * s2 * s1 * s0;
+    dim_t val = (ow % s3) * s2 * s1 * s0;
     val += (oz % s2) * s1 * s0;
 
     const int incy = blocksPerMatY * blockDim.y;
@@ -49,7 +49,7 @@ __global__ void iota_kernel(Param<T> out, const int s0, const int s1,
 
     for (int oy = yy; oy < out.dims[1]; oy += incy) {
         int oyzw = ozw + oy * out.strides[1];
-        T valY   = val + (oy % s1) * s0;
+        dim_t valY   = val + (oy % s1) * s0;
         for (int ox = xx; ox < out.dims[0]; ox += incx) {
             int oidx = oyzw + ox;
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 864b083a8a..8c16a7eb1f 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -60,7 +60,7 @@ struct MinMaxOp {
     T m_val;
     uint m_idx;
     __host__ __device__ MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<T, op>::init(); }
+        if (is_nan(val)) { m_val = Binary<compute_t<T>, op>::init(); }
     }
 
     __host__ __device__ void operator()(T val, uint idx) {
@@ -309,8 +309,8 @@ __global__ static void ireduce_first_kernel(Param<T> out, uint *olptr,
     const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
     const uint yid = blockIdx_y * blockDim.y + tidy;
 
-    const T *iptr = in.ptr;
-    T *optr       = out.ptr;
+    const data_t<T> *iptr = in.ptr;
+    data_t<T> *optr       = out.ptr;
 
     iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
     optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
@@ -324,29 +324,29 @@ __global__ static void ireduce_first_kernel(Param<T> out, uint *olptr,
 
     int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-    T val    = Binary<T, op>::init();
-    uint idx = xid;
+    compute_t<T> val = Binary<compute_t<T>, op>::init();
+    uint idx         = xid;
 
     if (xid < lim) {
-        val = iptr[xid];
+        val = static_cast<compute_t<T>>(iptr[xid]);
         if (!is_first) idx = ilptr[xid];
     }
 
-    MinMaxOp<op, T> Op(val, idx);
+    MinMaxOp<op, compute_t<T>> Op(val, idx);
 
-    __shared__ T s_val[THREADS_PER_BLOCK];
+    __shared__ compute_t<T> s_val[THREADS_PER_BLOCK];
     __shared__ uint s_idx[THREADS_PER_BLOCK];
 
     for (int id = xid + DIMX; id < lim; id += DIMX) {
-        Op(iptr[id], (!is_first) ? ilptr[id] : id);
+        Op(static_cast<compute_t<T>>(iptr[id]), (!is_first) ? ilptr[id] : id);
     }
 
     s_val[tid] = Op.m_val;
     s_idx[tid] = Op.m_idx;
     __syncthreads();
 
-    T *s_vptr    = s_val + tidy * DIMX;
-    uint *s_iptr = s_idx + tidy * DIMX;
+    compute_t<T> *s_vptr = s_val + tidy * DIMX;
+    uint *s_iptr         = s_idx + tidy * DIMX;
 
     if (DIMX == 256) {
         if (tidx < 128) {
@@ -375,7 +375,7 @@ __global__ static void ireduce_first_kernel(Param<T> out, uint *olptr,
         __syncthreads();
     }
 
-    warp_reduce<T, op>(s_vptr, s_iptr, tidx);
+    warp_reduce<compute_t<T>, op>(s_vptr, s_iptr, tidx);
 
     if (tidx == 0) {
         optr[blockIdx_x]  = s_vptr[0];
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 67eeec8891..e8dbe6a9d5 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -34,7 +34,7 @@ __global__ void lookup1D(Param<in_t> out, CParam<in_t> in,
     int en = min(out.dims[vDim], idx + THRD_LOAD * THREADS);
 
     for (int oIdx = idx; oIdx < en; oIdx += THREADS) {
-        int iIdx     = trimIndex(idxPtr[oIdx], in.dims[vDim]);
+        int iIdx     = trimIndex(static_cast<int>(idxPtr[oIdx]), in.dims[vDim]);
         outPtr[oIdx] = inPtr[iIdx];
     }
 }
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 3a1cca433b..724cf0b6bd 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <Param.hpp>
 #include <backend.hpp>
@@ -106,7 +107,7 @@ __inline__ __device__ cdouble scale<cdouble>(cdouble value, double factor) {
 
 template<typename inType, typename outType>
 __inline__ __device__ outType convertType(inType value) {
-    return (outType)value;
+    return static_cast<outType>(value);
 }
 
 template<>
@@ -147,12 +148,12 @@ __inline__ __device__ cfloat convertType<cdouble, cfloat>(cdouble value) {
 #define OTHER_SPECIALIZATIONS(IN_T)                                        \
     template<>                                                             \
     __inline__ __device__ cfloat convertType<IN_T, cfloat>(IN_T value) {   \
-        return make_cuFloatComplex(value, 0.0f);                           \
+        return make_cuFloatComplex(static_cast<float>(value), 0.0f);       \
     }                                                                      \
                                                                            \
     template<>                                                             \
     __inline__ __device__ cdouble convertType<IN_T, cdouble>(IN_T value) { \
-        return make_cuDoubleComplex(value, 0.0);                           \
+        return make_cuDoubleComplex(static_cast<double>(value), 0.0);      \
     }
 
 OTHER_SPECIALIZATIONS(float)
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 590079aae2..c5d8bf1c41 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -13,6 +13,8 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 
+#include <type_traits>
+
 namespace cuda {
 namespace kernel {
 // Kernel Launch Config Values
@@ -45,17 +47,17 @@ __global__ void range_kernel(Param<T> out, const int dim,
 
     const int ozw = ow * out.strides[3] + oz * out.strides[2];
 
-    T valZW = (mul3 * ow) + (mul2 * oz);
+    int valZW = (mul3 * ow) + (mul2 * oz);
 
     const int incy = blocksPerMatY * blockDim.y;
     const int incx = blocksPerMatX * blockDim.x;
 
     for (int oy = yy; oy < out.dims[1]; oy += incy) {
-        T valYZW = valZW + (mul1 * oy);
-        int oyzw = ozw + oy * out.strides[1];
+        compute_t<T> valYZW = valZW + (mul1 * oy);
+        int oyzw     = ozw + oy * out.strides[1];
         for (int ox = xx; ox < out.dims[0]; ox += incx) {
-            int oidx = oyzw + ox;
-            T val    = valYZW + (ox * mul0);
+            int oidx         = oyzw + ox;
+            compute_t<T> val = valYZW + static_cast<compute_t<T>>(ox * mul0);
 
             out.ptr[oidx] = val;
         }
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 16808d7a54..be5c59e975 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -68,13 +68,13 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
     bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
                     (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
 
-    Transform<Ti, To, op> transform;
-    Binary<To, op> reduce;
-    compute_t<To> out_val = Binary<To, op>::init();
+    Transform<Ti, compute_t<To>, op> transform;
+    Binary<compute_t<To>, op> reduce;
+    compute_t<To> out_val = Binary<compute_t<To>, op>::init();
     for (int id = id_dim_in; is_valid && (id < in.dims[dim]);
          id += offset_dim * blockDim.y) {
         compute_t<To> in_val = transform(*iptr);
-        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : compute_t<To>(nanval);
         out_val = reduce(in_val, out_val);
         iptr    = iptr + offset_dim * blockDim.y * istride_dim;
     }
@@ -195,8 +195,8 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
     const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
     const uint xid        = blockIdx_x * blockDim.x * repeat + tidx;
 
-    Binary<To, op> reduce;
-    Transform<Ti, To, op> transform;
+    Binary<compute_t<To>, op> reduce;
+    Transform<Ti, compute_t<To>, op> transform;
 
     __shared__ compute_t<To> s_val[THREADS_PER_BLOCK];
 
@@ -213,10 +213,12 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
 
     int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-    compute_t<To> out_val = Binary<To, op>::init();
+    compute_t<To> out_val = Binary<compute_t<To>, op>::init();
     for (int id = xid; id < lim; id += DIMX) {
         compute_t<To> in_val = transform(iptr[id]);
-        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+        if (change_nan)
+            in_val =
+                !IS_NAN(in_val) ? in_val : static_cast<compute_t<To>>(nanval);
         out_val = reduce(in_val, out_val);
     }
 
@@ -387,9 +389,11 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
                             cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-        Binary<To, op> reduce;
-        compute_t<To> out = Binary<To, op>::init();
-        for (int i = 0; i < tmp_elements; i++) { out = reduce(out, h_data[i]); }
+        Binary<compute_t<To>, op> reduce;
+        compute_t<To> out = Binary<compute_t<To>, op>::init();
+        for (int i = 0; i < tmp_elements; i++) {
+            out = reduce(out, compute_t<To>(h_data[i]));
+        }
 
         return data_t<To>(out);
     } else {
@@ -399,10 +403,10 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
                             cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
-        compute_t<To> out       = Binary<To, op>::init();
-        compute_t<To> nanval_to = scalar<To>(nanval);
+        Transform<Ti, compute_t<To>, op> transform;
+        Binary<compute_t<To>, op> reduce;
+        compute_t<To> out       = Binary<compute_t<To>, op>::init();
+        compute_t<To> nanval_to = scalar<compute_t<To>>(nanval);
 
         for (int i = 0; i < in_elements; i++) {
             compute_t<To> in_val = transform(h_data[i]);
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index f636d9c77b..6a4660d86e 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -29,6 +29,7 @@
 #include <backend.hpp>
 #include <types.hpp>
 #include <af/defines.h>
+#include <common/half.hpp>
 
 #include <cuda_fp16.h>
 #include <math_constants.h>
@@ -231,6 +232,22 @@ template<>
 __device__ ushort maxval<ushort>() {
     return ((ushort)1) << (8 * sizeof(ushort) - 1);
 }
+template<>
+__device__ common::half maxval<common::half>() {
+    return common::half(65537.f);
+}
+template<>
+__device__ common::half minval<common::half>() {
+    return common::half(-65537.f);
+}
+template<>
+__device__ __half maxval<__half>() {
+    return __float2half(65537.f);
+}
+template<>
+__device__ __half minval<__half>() {
+    return __float2half(-65537.f);
+}
 #endif
 
 #define upcast cuComplexFloatToDouble
diff --git a/src/backend/cuda/range.cu b/src/backend/cuda/range.cu
index 39b3bcb980..1a10e28ab4 100644
--- a/src/backend/cuda/range.cu
+++ b/src/backend/cuda/range.cu
@@ -6,14 +6,17 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <range.hpp>
+#include <kernel/range.hpp>
 
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <kernel/range.hpp>
 #include <math.hpp>
-#include <range.hpp>
+
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim) {
@@ -45,4 +48,5 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace cuda
diff --git a/src/backend/cuda/transpose_inplace.cpp b/src/backend/cuda/transpose_inplace.cpp
index e70415c163..d0c9163f89 100644
--- a/src/backend/cuda/transpose_inplace.cpp
+++ b/src/backend/cuda/transpose_inplace.cpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/transpose_inplace.hpp>
 #include <transpose.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -39,5 +41,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/triangle.cu b/src/backend/cuda/triangle.cu
index 25b2d22858..81e75337e5 100644
--- a/src/backend/cuda/triangle.cu
+++ b/src/backend/cuda/triangle.cu
@@ -6,13 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <kernel/triangle.hpp>
 #include <triangle.hpp>
+
+#include <Array.hpp>
 #include <af/dim4.hpp>
+#include <common/half.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -53,4 +55,5 @@ INSTANTIATE(char)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace cuda
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 198e22f349..96624f90b7 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -8,12 +8,15 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <diagonal.hpp>
 #include <err_opencl.hpp>
 #include <kernel/diagonal.hpp>
 #include <math.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
@@ -53,5 +56,6 @@ INSTANTIATE_DIAGONAL(char)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
+INSTANTIATE_DIAGONAL(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/identity.cpp b/src/backend/opencl/identity.cpp
index 16c144d12f..27a092448c 100644
--- a/src/backend/opencl/identity.cpp
+++ b/src/backend/opencl/identity.cpp
@@ -6,13 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <identity.hpp>
+#include <kernel/identity.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
-#include <identity.hpp>
-#include <kernel/identity.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 Array<T> identity(const dim4& dims) {
@@ -36,5 +39,6 @@ INSTANTIATE_IDENTITY(char)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
+INSTANTIATE_IDENTITY(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/iota.cpp b/src/backend/opencl/iota.cpp
index 6582a4b952..ebd0b5824d 100644
--- a/src/backend/opencl/iota.cpp
+++ b/src/backend/opencl/iota.cpp
@@ -6,14 +6,18 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <iota.hpp>
+#include <kernel/iota.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
-#include <iota.hpp>
-#include <kernel/iota.hpp>
 #include <math.hpp>
+
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
@@ -37,4 +41,5 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace opencl
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index 01077f7174..fc79e6ef06 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -6,16 +6,18 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <ireduce.hpp>
+#include <kernel/ireduce.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
-#include <ireduce.hpp>
-#include <kernel/ireduce.hpp>
 #include <ops.hpp>
 #include <af/dim4.hpp>
 #include <complex>
 
 using af::dim4;
+using common::half;
 
 namespace opencl {
 
@@ -48,6 +50,7 @@ INSTANTIATE(af_min_t, char)
 INSTANTIATE(af_min_t, uchar)
 INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
+INSTANTIATE(af_min_t, half)
 
 // max
 INSTANTIATE(af_max_t, float)
@@ -62,4 +65,5 @@ INSTANTIATE(af_max_t, char)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
+INSTANTIATE(af_max_t, half)
 }  // namespace opencl
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 463dd41092..898e1e498a 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -58,7 +58,9 @@ template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(con
 // Specializations of the above function
 template <> float inline toCLBlastConstant(const float val) { return val; }
 template <> double inline toCLBlastConstant(const double val) { return val; }
-template <> cl_half inline toCLBlastConstant(const common::half val) { return val; }
+template <> cl_half inline toCLBlastConstant(const common::half val) {
+    return static_cast<float>(val);
+}
 template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
 template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
 
diff --git a/src/backend/opencl/range.cpp b/src/backend/opencl/range.cpp
index 848f6d8ea0..e6b4c76eaf 100644
--- a/src/backend/opencl/range.cpp
+++ b/src/backend/opencl/range.cpp
@@ -6,14 +6,17 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <kernel/range.hpp>
+#include <range.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
-#include <kernel/range.hpp>
 #include <math.hpp>
-#include <range.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim) {
@@ -45,4 +48,5 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace opencl
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 60dbedad15..589ac4d625 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -46,7 +46,9 @@ STATIC_ bool iscplx<cl_double2>() {
 
 template<typename T>
 STATIC_ std::string scalar_to_option(const T &val) {
-    return std::to_string(+val);
+    using namespace common;
+    using namespace std;
+    return to_string(+val);
 }
 
 template<>
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index 441d154244..e36dedb0cb 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/transpose_inplace.hpp>
 #include <transpose.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace opencl {
 
@@ -50,5 +52,6 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index 13825bff6f..7c42555b91 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -6,13 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-
-#include <Array.hpp>
 #include <kernel/triangle.hpp>
 #include <triangle.hpp>
+
+#include <Array.hpp>
 #include <af/dim4.hpp>
+#include <common/half.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace opencl {
 
@@ -53,5 +55,6 @@ INSTANTIATE(char)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index 7d46f9fc91..52f3a5a330 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -30,7 +30,7 @@ template<>
 std::string ToNumStr<float>::operator()(float val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
+    if (std::isinf(val)) { return val < 0.f ? NINF : PINF; }
     return std::to_string(val);
 }
 
@@ -38,7 +38,7 @@ template<>
 std::string ToNumStr<double>::operator()(double val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (std::isinf(val)) { return val < 0 ? NINF : PINF; }
+    if (std::isinf(val)) { return val < 0. ? NINF : PINF; }
     return std::to_string(val);
 }
 
@@ -60,10 +60,12 @@ std::string ToNumStr<cdouble>::operator()(cdouble val) {
 
 template<>
 std::string ToNumStr<half>::operator()(half val) {
+    using namespace std;
+    using namespace common;
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (common::isinf(val)) { return val < 0 ? NINF : PINF; }
-    return std::to_string(val);
+    if (common::isinf(val)) { return val < 0.f ? NINF : PINF; }
+    return to_string(move(val));
 }
 
 template<>
@@ -71,7 +73,7 @@ template<>
 std::string ToNumStr<half>::operator()<float>(float val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (common::isinf(half(val))) { return val < 0 ? NINF : PINF; }
+    if (common::isinf(half(val))) { return val < 0.f ? NINF : PINF; }
     return std::to_string(val);
 }
 
diff --git a/test/assign.cpp b/test/assign.cpp
index a9985042cf..0e2aea05d7 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -34,13 +34,6 @@ using std::endl;
 using std::string;
 using std::vector;
 
-namespace half_float {
-std::ostream &operator<<(std::ostream &os, half_float::half val) {
-    os << (float)val;
-    return os;
-}
-}  // namespace half_float
-
 template<typename T>
 class ArrayAssign : public ::testing::Test {
    public:
diff --git a/test/compare.cpp b/test/compare.cpp
index 23b1b65865..2c1c4fa5a5 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -9,6 +9,7 @@
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <half.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
@@ -22,7 +23,7 @@ template<typename T>
 class Compare : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, uint, int, intl, uintl, uchar, short,
-                         ushort>
+                         ushort, half_float::half>
     TestTypes;
 TYPED_TEST_CASE(Compare, TestTypes);
 
diff --git a/test/constant.cpp b/test/constant.cpp
index 10c4f43193..ce9541ff3c 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
@@ -28,7 +29,7 @@ template<typename T>
 class Constant : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
-                         uchar, uintl, intl, short, ushort>
+                         uchar, uintl, intl, short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_CASE(Constant, TestTypes);
 
@@ -53,7 +54,7 @@ void ConstantCCheck(T value) {
 
     const int num = 1000;
     typedef typename dtype_traits<T>::base_type BT;
-    BT val    = ::real(value);
+    BT val(::real(value));
     dtype dty = (dtype)dtype_traits<T>::af_type;
     af_array out;
     dim_t dim[] = {(dim_t)num};
@@ -163,9 +164,9 @@ void IdentityCPPError() {
     SUCCEED();
 }
 
-TYPED_TEST(Constant, basicCPP) { ConstantCPPCheck<TypeParam>(5); }
+TYPED_TEST(Constant, basicCPP) { ConstantCPPCheck<TypeParam>(TypeParam(5)); }
 
-TYPED_TEST(Constant, basicC) { ConstantCCheck<TypeParam>(5); }
+TYPED_TEST(Constant, basicC) { ConstantCCheck<TypeParam>(TypeParam(5)); }
 
 TYPED_TEST(Constant, IdentityC) { IdentityCCheck<TypeParam>(); }
 
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index 378078bc65..a73a2096ff 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -9,8 +9,12 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 
+#include <cmath>
+#include <vector>
+
 using af::array;
 using af::constant;
 using af::deviceGC;
@@ -22,13 +26,13 @@ using af::seq;
 using af::span;
 using af::sum;
 using std::abs;
-using std::endl;
 using std::vector;
 
 template<typename T>
 class Diagonal : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint, char, unsigned char>
+typedef ::testing::Types<float, double, int, uint, char, unsigned char,
+                         half_float::half>
     TestTypes;
 TYPED_TEST_CASE(Diagonal, TestTypes);
 
@@ -54,7 +58,7 @@ TYPED_TEST(Diagonal, Create) {
                 }
             }
         }
-    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
+    } catch (const exception& ex) { FAIL() << ex.what(); }
 }
 
 TYPED_TEST(Diagonal, DISABLED_CreateLargeDim) {
@@ -68,7 +72,7 @@ TYPED_TEST(Diagonal, DISABLED_CreateLargeDim) {
 
             ASSERT_EQ(largeDim, sum<float>(out));
         }
-    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
+    } catch (const exception& ex) { FAIL() << ex.what(); }
 }
 
 TYPED_TEST(Diagonal, Extract) {
@@ -89,7 +93,7 @@ TYPED_TEST(Diagonal, Extract) {
                 ASSERT_EQ(input[i * data.dims(0) + i], h_out[i]);
             }
         }
-    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
+    } catch (const exception& ex) { FAIL() << ex.what(); }
 }
 
 TYPED_TEST(Diagonal, ExtractLargeDim) {
@@ -109,7 +113,7 @@ TYPED_TEST(Diagonal, ExtractLargeDim) {
 
         ASSERT_EQ(n * largeDim, sum<float>(out1));
 
-    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
+    } catch (const exception& ex) { FAIL() << ex.what(); }
 }
 
 TYPED_TEST(Diagonal, ExtractRect) {
@@ -135,7 +139,7 @@ TYPED_TEST(Diagonal, ExtractRect) {
                 }
             }
         }
-    } catch (const exception& ex) { FAIL() << ex.what() << endl; }
+    } catch (const exception& ex) { FAIL() << ex.what(); }
 }
 
 TEST(Diagonal, ExtractGFOR) {
diff --git a/test/iota.cpp b/test/iota.cpp
index 555c5b12e9..09cba79a94 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -39,7 +39,7 @@ class Iota : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
-                         unsigned char, short, ushort>
+                         unsigned char, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/range.cpp b/test/range.cpp
index 918f063431..f3c4b0d5a0 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -10,6 +10,7 @@
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <half.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
@@ -40,13 +41,22 @@ class Range : public ::testing::Test {
     vector<af_seq> subMat0;
 };
 
+template<typename T>
+class RangeMax : public Range<T> {};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
+                         unsigned char, short, ushort, half_float::half>
+    AllTypes;
+
 // create a list of types to be tested
 typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
                          unsigned char, short, ushort>
-    TestTypes;
+    RegularTypes;
 
 // register the type list
-TYPED_TEST_CASE(Range, TestTypes);
+TYPED_TEST_CASE(Range, AllTypes);
+TYPED_TEST_CASE(RangeMax, RegularTypes);
 
 template<typename T>
 void rangeTest(const uint x, const uint y, const uint z, const uint w,
@@ -69,7 +79,7 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w,
         for (int z = 0; z < (int)idims[2]; z++) {
             for (int y = 0; y < (int)idims[1]; y++) {
                 for (int x = 0; x < (int)idims[0]; x++) {
-                    T val = 0;
+                    T val(0);
                     if (dim == 0) {
                         val = x;
                     } else if (dim == 1) {
@@ -82,7 +92,7 @@ void rangeTest(const uint x, const uint y, const uint z, const uint w,
                     dim_t idx = w * idims[0] * idims[1] * idims[2] +
                                 z * idims[0] * idims[1] + y * idims[0] + x;
 
-                    ASSERT_EQ(val, outData[idx]) << "at: " << idx << endl;
+                    ASSERT_EQ(val, outData[idx]) << "at: " << idx;
                 }
             }
         }
@@ -111,10 +121,13 @@ RANGE_INIT(Range4D1, 10, 12, 5, 2, 1);
 RANGE_INIT(Range4D2, 25, 30, 2, 2, 2);
 RANGE_INIT(Range4D3, 25, 30, 2, 2, 3);
 
-RANGE_INIT(Range1DMaxDim0, 65535 * 32 + 1, 1, 1, 1, 0);
-RANGE_INIT(Range1DMaxDim1, 1, 65535 * 32 + 1, 1, 1, 0);
-RANGE_INIT(Range1DMaxDim2, 1, 1, 65535 * 32 + 1, 1, 0);
-RANGE_INIT(Range1DMaxDim3, 1, 1, 1, 65535 * 32 + 1, 0);
+#define RANGE_MAX_INIT(desc, x, y, z, w, rep) \
+    TYPED_TEST(RangeMax, desc) { rangeTest<TypeParam>(x, y, z, w, rep); }
+
+RANGE_MAX_INIT(Range1DMaxDim0, 65535 * 32 + 1, 1, 1, 1, 0);
+RANGE_MAX_INIT(Range1DMaxDim1, 1, 65535 * 32 + 1, 1, 1, 0);
+RANGE_MAX_INIT(Range1DMaxDim2, 1, 1, 65535 * 32 + 1, 1, 0);
+RANGE_MAX_INIT(Range1DMaxDim3, 1, 1, 1, 65535 * 32 + 1, 0);
 
 ///////////////////////////////// CPP ////////////////////////////////////
 //
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index ccd8bcd85c..011dcfd450 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -46,6 +46,13 @@ std::ostream &operator<<(std::ostream &os, const af_half &val) {
     return os;
 }
 
+namespace half_float {
+std::ostream &operator<<(std::ostream &os, half_float::half val) {
+    os << (float)val;
+    return os;
+}
+}  // namespace half_float
+
 #define UNUSED(expr) \
     do { (void)(expr); } while (0)
 
@@ -543,7 +550,8 @@ af::array cpu_randu(const af::dim4 dims) {
     bool isTypeCplx = is_same_type<T, af::cfloat>::value ||
                       is_same_type<T, af::cdouble>::value;
     bool isTypeFloat =
-        is_same_type<BT, float>::value || is_same_type<BT, double>::value;
+      is_same_type<BT, float>::value || is_same_type<BT, double>::value ||
+      is_same_type<BT, half_float::half>::value;
 
     size_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
 
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 32927da3e0..72543d2e7a 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -9,9 +9,11 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
 #include <string>
 #include <vector>
 
@@ -43,7 +45,7 @@ class Transpose : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
-                         short, ushort>
+                         short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/triangle.cpp b/test/triangle.cpp
index d59c5b0e95..ab25d5f0ca 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -10,10 +10,12 @@
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <half.hpp>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
 #include <complex>
 #include <iostream>
 #include <string>
@@ -33,7 +35,7 @@ template<typename T>
 class Triangle : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
-                         uchar, uintl, intl, short, ushort>
+                         uchar, uintl, intl, short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_CASE(Triangle, TestTypes);
 

From d78d4d8d31d8945e153bd1bc24340c334050e693 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 19 Jul 2019 14:56:00 -0400
Subject: [PATCH 1716/2677] Fix several OpenCL fp16 kernels. (#2589)

Fixed several fp16 opencl kernels

- rotate
- triangle
- reduce
- JIT
- identity
- lookup
- random
- range
- transpose
- iota
---
 src/backend/opencl/jit.cpp                    |  4 +-
 src/backend/opencl/kernel/identity.hpp        | 34 +++++---
 src/backend/opencl/kernel/iota.hpp            | 19 +++--
 src/backend/opencl/kernel/jit.cl              |  6 --
 src/backend/opencl/kernel/lookup.hpp          | 36 ++++----
 src/backend/opencl/kernel/ops.cl              |  2 +
 src/backend/opencl/kernel/random_engine.hpp   | 55 ++++++------
 .../opencl/kernel/random_engine_write.cl      | 83 ++++++++++++++++++-
 src/backend/opencl/kernel/range.hpp           | 20 +++--
 src/backend/opencl/kernel/reduce.hpp          | 15 +++-
 src/backend/opencl/kernel/transpose.cl        |  6 --
 src/backend/opencl/kernel/transpose.hpp       | 20 +++--
 src/backend/opencl/kernel/triangle.hpp        | 20 +++--
 src/backend/opencl/magma/magma_blas_clblast.h |  3 +-
 src/backend/opencl/program.cpp                |  5 ++
 15 files changed, 214 insertions(+), 114 deletions(-)

diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 277f53684a..2f511d7ed9 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -183,7 +183,9 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
         Program prog;
         buildProgram(
             prog, 2, ker_strs, ker_lens,
-            isDoubleSupported(device) ? string(" -D USE_DOUBLE") : string(""));
+            (isDoubleSupported(device) ? string(" -D USE_DOUBLE") : string("")) +
+            (isHalfSupported(device) ? string(" -D USE_HALF") : string(""))
+                     );
 
         entry.prog = new Program(prog);
         entry.ker  = new Kernel(*entry.prog, funcName.c_str());
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 72e3071d77..cb1ac8e0f6 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/identity.hpp>
 #include <math.hpp>
@@ -17,35 +18,42 @@
 #include <traits.hpp>
 #include "config.hpp"
 
-using af::scalar_to_option;
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::ostringstream;
-using std::string;
-
 namespace opencl {
 namespace kernel {
 template<typename T>
 static void identity(Param out) {
-    std::string refName = std::string("identity_kernel") +
+
+    using af::scalar_to_option;
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::NDRange;
+    using cl::Program;
+    using common::half;
+    using std::ostringstream;
+    using std::string;
+    using std::is_same;
+
+    string refName = std::string("identity_kernel") +
                           std::string(dtype_traits<T>::getName());
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
+        ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName() << " -D ONE=(T)("
                 << scalar_to_option(scalar<T>(1)) << ")"
                 << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+        if (is_same<T, double>::value || is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
+        if (is_same<T, half>::value) {
+          options << " -D USE_HALF";
+        }
+
         const char* ker_strs[] = {identity_cl};
         const int ker_lens[]   = {identity_cl_len};
         Program prog;
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index e214813fae..2ce8ee04f5 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/iota.hpp>
 #include <program.hpp>
@@ -18,14 +19,6 @@
 #include <af/dim4.hpp>
 #include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-
 namespace opencl {
 namespace kernel {
 // Kernel Launch Config Values
@@ -36,6 +29,14 @@ static const int TILEY   = 32;
 
 template<typename T>
 void iota(Param out, const af::dim4& sdims) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::NDRange;
+    using cl::Program;
+    using std::string;
+
     std::string refName =
         std::string("iota_kernel_") + std::string(dtype_traits<T>::getName());
 
@@ -49,6 +50,8 @@ void iota(Param out, const af::dim4& sdims) {
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
+        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+
         const char* ker_strs[] = {iota_cl};
         const int ker_lens[]   = {iota_cl_len};
         Program prog;
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index 01906fefce..ec6da04b6c 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -7,12 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#pragma OPENCL EXTENSION all : enable
-#ifdef cl_khr_fp16
-#else
-#define half short
-#endif
-
 #define __select(cond, a, b) (cond) ? (a) : (b)
 #define __not_select(cond, a, b) (cond) ? (b) : (a)
 #define __circular_mod(a, b) ((a) < (b)) ? (a) : (a - b)
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 05fec3bced..4748da3cf6 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -11,20 +11,13 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/lookup.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-
 namespace opencl {
 namespace kernel {
 static const int THREADS_X = 32;
@@ -32,25 +25,40 @@ static const int THREADS_Y = 8;
 
 template<typename in_t, typename idx_t, unsigned dim>
 void lookup(Param out, const Param in, const Param indices) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::NDRange;
+    using cl::Program;
+    using std::string;
+    using std::is_same;
+    using std::ostringstream;
+    using std::to_string;
+
     std::string refName =
-        std::string("lookupND_") + std::string(dtype_traits<in_t>::getName()) +
-        std::string(dtype_traits<idx_t>::getName()) + std::to_string(dim);
+        string("lookupND_") + string(dtype_traits<in_t>::getName()) +
+        string(dtype_traits<idx_t>::getName()) + to_string(dim);
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
+        ostringstream options;
         options << " -D in_t=" << dtype_traits<in_t>::getName()
                 << " -D idx_t=" << dtype_traits<idx_t>::getName()
                 << " -D DIM=" << dim;
 
-        if (std::is_same<in_t, double>::value ||
-            std::is_same<in_t, cdouble>::value ||
-            std::is_same<idx_t, double>::value) {
+        if (is_same<in_t, double>::value ||
+            is_same<in_t, cdouble>::value ||
+            is_same<idx_t, double>::value) {
             options << " -D USE_DOUBLE";
         }
 
+        if (is_same<in_t, common::half>::value) {
+          options << " -D USE_HALF";
+        }
+
         const char* ker_strs[] = {lookup_cl};
         const int ker_lens[]   = {lookup_cl_len};
         Program prog;
diff --git a/src/backend/opencl/kernel/ops.cl b/src/backend/opencl/kernel/ops.cl
index a15c934fc4..e383a871b2 100644
--- a/src/backend/opencl/kernel/ops.cl
+++ b/src/backend/opencl/kernel/ops.cl
@@ -63,6 +63,7 @@ uint transform(Ti in) { return (in != 0); }
 #ifdef MIN_OP
 
 #if CPLX
+#undef IS_NAN
 #define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
 #endif
 
@@ -83,6 +84,7 @@ T binOp(T lhs, T rhs) { return sabs(lhs) < sabs(rhs) ? lhs : rhs; }
 #ifdef MAX_OP
 
 #if CPLX
+#undef IS_NAN
 #define IS_NAN(in) !((in.x) == (in.x)) || !((in.y) == (in.y))
 #endif
 
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 29dc16891c..62f678dff4 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -29,14 +29,6 @@
 #include <kernel_headers/random_engine_mersenne.hpp>
 #include <kernel_headers/random_engine_mersenne_init.hpp>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-
 static const int N          = 351;
 static const int TABLE_SIZE = 16;
 static const int MAX_BLOCKS = 32;
@@ -47,9 +39,9 @@ namespace kernel {
 static const uint THREADS = 256;
 
 template<typename T>
-static Kernel get_random_engine_kernel(const af_random_engine_type type,
-                                       const int kerIdx,
-                                       const uint elementsPerBlock) {
+static cl::Kernel get_random_engine_kernel(const af_random_engine_type type,
+                                           const int kerIdx,
+                                           const uint elementsPerBlock) {
     using std::string;
     using std::to_string;
     string engineName;
@@ -92,14 +84,15 @@ static Kernel get_random_engine_kernel(const af_random_engine_type type,
             options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
         }
         if (std::is_same<T, double>::value) { options << " -D USE_DOUBLE"; }
+        if (std::is_same<T, common::half>::value) { options << " -D USE_HALF"; }
 #if defined(OS_MAC)  // Because apple is "special"
         options << " -D IS_APPLE"
                 << " -D log10_val=" << std::log(10.0);
 #endif
         cl::Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "generate");
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "generate");
 
         addKernelToCache(device, ref_name, entry);
     }
@@ -107,7 +100,7 @@ static Kernel get_random_engine_kernel(const af_random_engine_type type,
     return *entry.ker;
 }
 
-static Kernel get_mersenne_init_kernel(void) {
+static cl::Kernel get_mersenne_init_kernel(void) {
     using std::string;
     using std::to_string;
     string engineName;
@@ -122,8 +115,8 @@ static Kernel get_mersenne_init_kernel(void) {
         std::string emptyOptionString;
         cl::Program prog;
         buildProgram(prog, 1, &ker_str, &ker_len, emptyOptionString);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "initState");
+        entry.prog = new cl::Program(prog);
+        entry.ker  = new cl::Kernel(*entry.prog, "initState");
 
         addKernelToCache(device, ref_name, entry);
     }
@@ -143,16 +136,16 @@ static void randomDistribution(cl::Buffer out, const size_t elements,
     uint hic = counter >> 32;
     uint loc = counter;
 
-    NDRange local(THREADS, 1);
-    NDRange global(THREADS * groups, 1);
+    cl::NDRange local(THREADS, 1);
+    cl::NDRange global(THREADS * groups, 1);
 
     if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) ||
         (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
-        Kernel ker =
+        cl::Kernel ker =
             get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
         auto randomEngineOp =
-            KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
-        randomEngineOp(EnqueueArgs(getQueue(), global, local), out, elements,
+            cl::KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
+        randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out, elements,
                        hic, loc, hi, lo);
     }
 
@@ -171,15 +164,15 @@ void randomDistribution(cl::Buffer out, const size_t elements, cl::Buffer state,
     blocks                     = (blocks > MAX_BLOCKS) ? MAX_BLOCKS : blocks;
     int elementsPerBlock       = divup(elements, blocks);
 
-    NDRange local(threads, 1);
-    NDRange global(threads * blocks, 1);
-    Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213,
+    cl::NDRange local(threads, 1);
+    cl::NDRange global(threads * blocks, 1);
+    cl::Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213,
                                              kerIdx, elementsPerBlock);
     auto randomEngineOp =
-        KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
+        cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
                       cl::Buffer, uint, cl::Buffer, cl::Buffer, uint, uint>(
             ker);
-    randomEngineOp(EnqueueArgs(getQueue(), global, local), out, state, pos, sh1,
+    randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out, state, pos, sh1,
                    sh2, mask, recursion_table, temper_table, elementsPerBlock,
                    elements);
     CL_DEBUG_FINISH(getQueue());
@@ -219,12 +212,12 @@ void normalDistributionMT(cl::Buffer out, const size_t elements,
 }
 
 void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
-    NDRange local(THREADS_PER_GROUP, 1);
-    NDRange global(local[0] * MAX_BLOCKS, 1);
+    cl::NDRange local(THREADS_PER_GROUP, 1);
+    cl::NDRange global(local[0] * MAX_BLOCKS, 1);
 
-    Kernel ker  = get_mersenne_init_kernel();
-    auto initOp = KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
-    initOp(EnqueueArgs(getQueue(), global, local), state, table, seed);
+    cl::Kernel ker  = get_mersenne_init_kernel();
+    auto initOp = cl::KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
+    initOp(cl::EnqueueArgs(getQueue(), global, local), state, table, seed);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 22288cac1a..4aa2a9722f 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -363,8 +363,8 @@ void boxMullerWriteOut128Bytes_float(__global float *out,
                                      const uint *const r3,
                                      const uint *const r4) {
     float n1, n2, n3, n4;
-    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform(&n3, &n4, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T*)&n1, (T*)&n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T*)&n3, (T*)&n4, getFloat(r1), getFloat(r2));
     out[*index]               = n1;
     out[*index + THREADS]     = n2;
     out[*index + 2 * THREADS] = n3;
@@ -377,8 +377,8 @@ void partialBoxMullerWriteOut128Bytes_float(
     const uint *const r2, const uint *const r3, const uint *const r4,
     const uint *const elements) {
     float n1, n2, n3, n4;
-    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
+    boxMullerTransform((T*)&n1, (T*)&n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T*)&n3, (T*)&n4, getFloat(r3), getFloat(r4));
     if (*index < *elements) { out[*index] = n1; }
     if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
     if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = n3; }
@@ -439,6 +439,81 @@ void partialBoxMullerWriteOut128Bytes_double(
 #endif
 #endif
 
+#ifdef USE_HALF
+
+// Conversion to floats adapted from Random123
+#define USHORTMAX 0xffff
+#define HALF_FACTOR ((1.0f) / (USHORTMAX + (1.0f)))
+#define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+
+// Generates rationals in (0, 1]
+half getHalf(const uint *const num, int index) {
+    float v = num[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
+    return 1.0f - (v * HALF_FACTOR + HALF_HALF_FACTOR);
+}
+
+void writeOut128Bytes_half(__global half *out, const uint *const index,
+                             const uint *const r1, const uint *const r2,
+                             const uint *const r3, const uint *const r4) {
+    out[*index]               = getHalf(r1, 0);
+    out[*index + THREADS]     = getHalf(r1, 1);
+    out[*index + 2 * THREADS] = getHalf(r2, 0);
+    out[*index + 3 * THREADS] = getHalf(r2, 1);
+    out[*index + 4 * THREADS] = getHalf(r3, 0);
+    out[*index + 5 * THREADS] = getHalf(r3, 1);
+    out[*index + 6 * THREADS] = getHalf(r4, 0);
+    out[*index + 7 * THREADS] = getHalf(r4, 1);
+}
+
+void partialWriteOut128Bytes_half(__global half *out,
+                                    const uint *const index,
+                                    const uint *const r1, const uint *const r2,
+                                    const uint *const r3, const uint *const r4,
+                                    const uint *const elements) {
+    if (*index               < *elements) { out[*index              ] = getHalf(r1, 0); }
+    if (*index +     THREADS < *elements) { out[*index +     THREADS] = getHalf(r1, 1); }
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = getHalf(r2, 0); }
+    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = getHalf(r2, 1); }
+    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = getHalf(r3, 0); }
+    if (*index + 5 * THREADS < *elements) { out[*index + 5 * THREADS] = getHalf(r3, 1); }
+    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = getHalf(r4, 0); }
+    if (*index + 7 * THREADS < *elements) { out[*index + 7 * THREADS] = getHalf(r4, 1); }
+}
+
+#if RAND_DIST == 1
+void boxMullerWriteOut128Bytes_half(
+    __global half *out, const uint *const index, const uint *const r1,
+    const uint *const r2, const uint *const r3, const uint *const r4) {
+    boxMullerTransform(&out[*index], &out[*index + THREADS], getHalf(r1, 0), getHalf(r1, 1));
+    boxMullerTransform(&out[*index + 2 * THREADS], &out[*index + 3 * THREADS], getHalf(r2, 0), getHalf(r2, 1));
+    boxMullerTransform(&out[*index + 4 * THREADS], &out[*index + 5 * THREADS], getHalf(r3, 0), getHalf(r3, 1));
+    boxMullerTransform(&out[*index + 6 * THREADS], &out[*index + 7 * THREADS], getHalf(r4, 0), getHalf(r4, 1));
+}
+
+void partialBoxMullerWriteOut128Bytes_half(
+    __global half *out, const uint *const index, const uint *const r1,
+    const uint *const r2, const uint *const r3, const uint *const r4,
+    const uint *const elements) {
+    half n1, n2;
+    boxMullerTransform(&n1, &n2, getHalf(r1, 0), getHalf(r1, 1));
+    if (*index < *elements) { out[*index] = n1; }
+    if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
+
+    boxMullerTransform(&n1, &n2, getHalf(r2, 0), getHalf(r2, 1));
+    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = n1; }
+    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = n2; }
+
+    boxMullerTransform(&n1, &n2, getHalf(r3, 0), getHalf(r3, 1));
+    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = n1; }
+    if (*index + 5 * THREADS < *elements) { out[*index + 5 * THREADS] = n2; }
+
+    boxMullerTransform(&n1, &n2, getHalf(r4, 0), getHalf(r4, 1));
+    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = n1; }
+    if (*index + 7 * THREADS < *elements) { out[*index + 7 * THREADS] = n2; }
+}
+#endif
+#endif
+
 #define PASTER(x, y) x##_##y
 #define EVALUATOR(x, y) PASTER(x, y)
 #define EVALUATE_T(function) EVALUATOR(function, T)
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index b3f4af8527..cf90221347 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -11,20 +11,13 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/range.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-
 namespace opencl {
 namespace kernel {
 // Kernel Launch Config Values
@@ -35,6 +28,14 @@ static const int RANGE_TILEY = 32;
 
 template<typename T>
 void range(Param out, const int dim) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::NDRange;
+    using cl::Program;
+    using std::string;
+
     std::string refName =
         std::string("range_kernel_") + std::string(dtype_traits<T>::getName());
 
@@ -47,6 +48,9 @@ void range(Param out, const int dim) {
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
+        if (std::is_same<T, common::half>::value)
+          options << " -D USE_HALF";
+
         const char* ker_strs[] = {range_cl};
         const int ker_lens[]   = {range_cl_len};
         Program prog;
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 31416eec86..3affd06471 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -28,6 +28,9 @@
 #include "config.hpp"
 #include "names.hpp"
 
+namespace opencl {
+namespace kernel {
+
 using cl::Buffer;
 using cl::EnqueueArgs;
 using cl::Kernel;
@@ -38,10 +41,6 @@ using common::half;
 using std::string;
 using std::unique_ptr;
 
-namespace opencl {
-
-namespace kernel {
-
 template<typename Ti, typename To, af_op_t op>
 void reduce_dim_launcher(Param out, Param in, const int dim,
                          const uint threads_y, const uint groups_all[4],
@@ -70,6 +69,10 @@ void reduce_dim_launcher(Param out, Param in, const int dim,
             options << " -D USE_DOUBLE";
         }
 
+        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
+            options << " -D USE_HALF";
+        }
+
         const char *ker_strs[] = {ops_cl, reduce_dim_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_dim_cl_len};
         Program prog;
@@ -166,6 +169,10 @@ void reduce_first_launcher(Param out, Param in, const uint groups_x,
             options << " -D USE_DOUBLE";
         }
 
+        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
+            options << " -D USE_HALF";
+        }
+
         const char *ker_strs[] = {ops_cl, reduce_first_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_first_cl_len};
         Program prog;
diff --git a/src/backend/opencl/kernel/transpose.cl b/src/backend/opencl/kernel/transpose.cl
index 5fce019be8..7b486f49fc 100644
--- a/src/backend/opencl/kernel/transpose.cl
+++ b/src/backend/opencl/kernel/transpose.cl
@@ -15,12 +15,6 @@ T doOp(T in) {
 #define doOp(in) in
 #endif
 
-#pragma OPENCL EXTENSION all : enable
-#ifdef cl_khr_fp16
-#else
-#define half short
-#endif
-
 __kernel void transpose(__global T *oData, const KParam out,
                         const __global T *iData, const KParam in,
                         const int blocksPerMatX, const int blocksPerMatY) {
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 9f643db75a..d3263ebe8e 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -18,14 +18,6 @@
 #include <types.hpp>
 #include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-
 namespace opencl {
 namespace kernel {
 static const int TILE_DIM  = 32;
@@ -34,7 +26,15 @@ static const int THREADS_Y = 256 / TILE_DIM;
 
 template<typename T, bool conjugate, bool IS32MULTIPLE>
 void transpose(Param out, const Param in, cl::CommandQueue queue) {
-    std::string refName =
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::NDRange;
+    using cl::Program;
+    using std::string;
+
+    string refName =
         std::string("transpose_") + std::string(dtype_traits<T>::getName()) +
         std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
 
@@ -51,6 +51,8 @@ void transpose(Param out, const Param in, cl::CommandQueue queue) {
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
+        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+
         const char* ker_strs[] = {transpose_cl};
         const int ker_lens[]   = {transpose_cl_len};
         Program prog;
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 6bedf6e723..d0b05eb4b8 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/triangle.hpp>
 #include <math.hpp>
@@ -19,15 +20,6 @@
 #include <types.hpp>
 #include <string>
 
-using af::scalar_to_option;
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-
 namespace opencl {
 namespace kernel {
 // Kernel Launch Config Values
@@ -42,6 +34,14 @@ void triangle(Param out, const Param in) {
                           std::string(dtype_traits<T>::getName()) +
                           std::to_string(is_upper) +
                           std::to_string(is_unit_diag);
+    using af::scalar_to_option;
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Kernel;
+    using cl::KernelFunctor;
+    using cl::NDRange;
+    using cl::Program;
+    using std::string;
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
@@ -56,6 +56,8 @@ void triangle(Param out, const Param in) {
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
+        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+
         const char* ker_strs[] = {triangle_cl};
         const int ker_lens[]   = {triangle_cl_len};
         Program prog;
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 898e1e498a..da816919fd 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -59,13 +59,14 @@ template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(con
 template <> float inline toCLBlastConstant(const float val) { return val; }
 template <> double inline toCLBlastConstant(const double val) { return val; }
 template <> cl_half inline toCLBlastConstant(const common::half val) {
-    return static_cast<float>(val);
+    return static_cast<cl_half>(static_cast<float>(val));
 }
 template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
 template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
 
 // Conversions to CLBlast basic types
 template <typename T> struct CLBlastBasicType { using Type = T; };
+template <> struct CLBlastBasicType<common::half> { using Type = cl_half; };
 template <> struct CLBlastBasicType<cfloat> { using Type = float; };
 template <> struct CLBlastBasicType<cdouble> { using Type = double; };
 
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 2f19c4a8e1..586d2b3e33 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -25,6 +25,11 @@ const static std::string DEFAULT_MACROS_STR(
                                            #ifdef USE_DOUBLE\n\
                                            #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
                                            #endif\n                     \
+                                           #ifdef USE_HALF\n\
+                                           #pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
+                                           #else\n                     \
+                                           #define half short\n          \
+                                           #endif\n                      \
                                            #ifndef M_PI\n               \
                                            #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
                                            #endif\n                     \

From 119a8c40339ac24d2a3b8066bb84d3847ff7261b Mon Sep 17 00:00:00 2001
From: Gaika <hub@gnom.us>
Date: Fri, 19 Jul 2019 13:03:32 -0700
Subject: [PATCH 1717/2677] fp16 support: flat, clamp, select_scalar  (#2583)

* Support flat for f16

* support f16 for clamp

* add select_scalar for fp16

* Unit tests for clamp, flat, moddims, and select
---
 src/api/c/clamp.cpp   |   3 +
 src/api/c/moddims.cpp |   1 +
 src/api/c/select.cpp  |   6 ++
 test/clamp.cpp        | 124 ++++++++++++++++++++++++++++++++++++++++++
 test/flat.cpp         |  12 ++++
 test/moddims.cpp      |   2 +-
 test/select.cpp       |   3 +-
 7 files changed, 149 insertions(+), 2 deletions(-)

diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index 464383d05c..4312534903 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -17,12 +17,14 @@
 #include <af/array.h>
 #include <af/data.h>
 #include <af/defines.h>
+#include <common/half.hpp>
 
 #include <arith.hpp>
 #include <logic.hpp>
 
 using namespace detail;
 using af::dim4;
+using common::half;
 
 template<typename T>
 static inline af_array clampOp(const af_array in, const af_array lo,
@@ -61,6 +63,7 @@ af_err af_clamp(af_array* out, const af_array in, const af_array lo,
             case u64: res = clampOp<uintl>(in, lo, hi, odims); break;
             case s16: res = clampOp<short>(in, lo, hi, odims); break;
             case u16: res = clampOp<ushort>(in, lo, hi, odims); break;
+            case f16: res = clampOp<half>(in, lo, hi, odims); break;
             default: TYPE_ERROR(0, otype);
         }
 
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 794c54e902..d368fc2e5b 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -97,6 +97,7 @@ af_err af_flat(af_array* out, const af_array in) {
                 case u64: output = flat<uintl>(in); break;
                 case s16: output = flat<short>(in); break;
                 case u16: output = flat<ushort>(in); break;
+                case f16: output = flat<half>(in); break;
                 default: TYPE_ERROR(1, type);
             }
             std::swap(*out, output);
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index a2d636d245..2ee030c1b0 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -109,6 +109,9 @@ af_err af_select_scalar_r(af_array* out, const af_array cond, const af_array a,
         af_array res;
 
         switch (ainfo.getType()) {
+            case f16:
+                res = select_scalar<half, false>(cond, a, b, odims);
+                break;
             case f32:
                 res = select_scalar<float, false>(cond, a, b, odims);
                 break;
@@ -171,6 +174,9 @@ af_err af_select_scalar_l(af_array* out, const af_array cond, const double a,
         af_array res;
 
         switch (binfo.getType()) {
+            case f16:
+                res = select_scalar<half, true>(cond, b, a, odims);
+                break;
             case f32:
                 res = select_scalar<float, true>(cond, b, a, odims);
                 break;
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 747bd7ec2b..bd1227392c 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -7,19 +7,143 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/defines.h>
+#include <af/traits.hpp>
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+
+#include <iostream>
 
 using af::array;
+using af::dim4;
+using af::dtype;
 using af::randu;
 using std::abs;
+using std::string;
+using std::stringstream;
 using std::vector;
 
 const int num = 10000;
 
+struct clamp_params {
+    dim4 size_;
+    dtype in_type_;
+    dtype lo_type_;
+    dtype hi_type_;
+    dtype out_type_;
+
+    clamp_params(dim4 size, dtype itype, dtype ltype, dtype htype, dtype otype)
+        : size_(size)
+        , in_type_(itype)
+        , lo_type_(ltype)
+        , hi_type_(htype)
+        , out_type_(otype) {}
+};
+
+template<typename T>
+class Clamp : public ::testing::TestWithParam<clamp_params> {
+   public:
+    void SetUp() {
+        clamp_params params = GetParam();
+        if (noDoubleTests(params.in_type_)) return;
+        if (noHalfTests(params.in_type_)) return;
+
+        in_ = randu(params.size_, params.in_type_);
+        lo_ = randu(params.size_, params.lo_type_) / T(10);
+        hi_ = T(1) - randu(params.size_, params.hi_type_) / T(10);
+        lo_ = lo_.as(params.lo_type_);
+        hi_ = hi_.as(params.hi_type_);
+
+        size_t num = params.size_.elements();
+        vector<T> hgold(num), hin(num), hlo(num), hhi(num);
+        in_.as((dtype)af::dtype_traits<T>::af_type).host(&hin[0]);
+        lo_.as((dtype)af::dtype_traits<T>::af_type).host(&hlo[0]);
+        hi_.as((dtype)af::dtype_traits<T>::af_type).host(&hhi[0]);
+
+        for (int i = 0; i < num; i++) {
+            if (hin[i] < hlo[i])      hgold[i] = hlo[i];
+            else if (hin[i] > hhi[i]) hgold[i] = hhi[i];
+            else                      hgold[i] = hin[i];
+        }
+
+        gold_ = array(params.size_, &hgold[0]);
+        gold_ = gold_.as(params.out_type_);
+        gold_.eval();
+    }
+
+    af::array in_;
+    af::array lo_;
+    af::array hi_;
+    af::array gold_;
+};
+
+string pd4(dim4 dims) {
+    string out(32, '\0');
+    int len = snprintf(const_cast<char*>(out.data()), 32, "%lld_%lld_%lld_%lld",
+                       dims[0], dims[1], dims[2], dims[3]);
+    out.resize(len);
+    return out;
+}
+
+string testNameGenerator(const ::testing::TestParamInfo<clamp_params> info) {
+    stringstream ss;
+    ss << "size_" << pd4(info.param.size_) << "_in_" << info.param.in_type_
+       << "_lo_" << info.param.lo_type_ << "_hi_" << info.param.hi_type_;
+    return ss.str();
+}
+
+typedef Clamp<double> ClampFloatingPoint;
+
+// clang-format off
+INSTANTIATE_TEST_CASE_P(
+    SmallDims, ClampFloatingPoint,
+    ::testing::Values(
+                      clamp_params(dim4(10), f32, f32, f32, f32),
+                      clamp_params(dim4(10), f64, f32, f32, f64),
+                      clamp_params(dim4(10), f16, f32, f32, f32),
+                      clamp_params(dim4(10), f64, f64, f64, f64),
+                      clamp_params(dim4(10), f16, f16, f16, f16),
+                      clamp_params(dim4(10), s32, f32, f32, f32),
+                      clamp_params(dim4(10), u32, f32, f32, f32),
+                      clamp_params(dim4(10), u8,  f32, f32, f32),
+                      clamp_params(dim4(10), b8,  f32, f32, f32),
+                      clamp_params(dim4(10), s64, f32, f32, f32),
+                      clamp_params(dim4(10), u64, f32, f32, f32),
+                      clamp_params(dim4(10), s16, f32, f32, f32),
+                      clamp_params(dim4(10), u16, f32, f32, f32),
+
+                      clamp_params(dim4(10, 10), f32, f32, f32, f32),
+                      clamp_params(dim4(10, 10), f64, f32, f32, f64),
+                      clamp_params(dim4(10, 10), f16, f32, f32, f32),
+                      clamp_params(dim4(10, 10), f64, f64, f64, f64),
+                      clamp_params(dim4(10, 10), f16, f16, f16, f16),
+
+                      clamp_params(dim4(10, 10, 10), f32, f32, f32, f32),
+                      clamp_params(dim4(10, 10, 10), f64, f32, f32, f64),
+                      clamp_params(dim4(10, 10, 10), f16, f32, f32, f32),
+                      clamp_params(dim4(10, 10, 10), f64, f64, f64, f64),
+                      clamp_params(dim4(10, 10, 10), f16, f16, f16, f16)
+                      ),
+    testNameGenerator);
+// clang-format on
+
+TEST_P(ClampFloatingPoint, Basic) {
+    clamp_params params = GetParam();
+    if (noDoubleTests(params.in_type_)) return;
+    if (noHalfTests(params.in_type_)) return;
+    array out = clamp(in_, lo_, hi_);
+    ASSERT_ARRAYS_NEAR(gold_, out, 1e-5);
+}
+
 TEST(ClampTests, FloatArrayArray) {
     array in = randu(num, f32);
     array lo = randu(num, f32) / 10;        // Ensure lo <= 0.1
diff --git a/test/flat.cpp b/test/flat.cpp
index 2d3745c135..8df08f0346 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -33,6 +33,18 @@ TEST(FlatTests, Test_flat_1D) {
     ASSERT_ARRAYS_EQ(in, out);
 }
 
+TEST(FlatTests, Test_flat_2D_Half) {
+    if (noHalfTests(f16)) return;
+    const int num = 10;
+    array in      = randu(num, num, f16);
+    array out     = flat(in);
+
+    vector<half_float::half> gold(num*num);
+    in.host(&gold[0]);
+
+    ASSERT_VEC_ARRAY_EQ(gold, dim4(num * num), out);
+}
+
 TEST(FlatTests, Test_flat_2D) {
     const int nx = 200;
     const int ny = 200;
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 4d3114ef80..52c7596472 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -37,7 +37,7 @@ class Moddims : public ::testing::Test {
 // create a list of types to be tested
 // TODO: complex types tests have to be added
 typedef ::testing::Types<float, double, int, unsigned, char, unsigned char,
-                         short, ushort>
+                         short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/select.cpp b/test/select.cpp
index a4d50971b3..730f37f6ee 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -10,6 +10,7 @@
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 
 #include <af/dim4.hpp>
@@ -42,7 +43,7 @@ template<typename T>
 class Select : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
-                         uchar, char, short, ushort>
+                         uchar, char, short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_CASE(Select, TestTypes);
 

From dace44253b14c118974bf75aba3c4c746cbb912c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 19 Jul 2019 21:48:35 -0400
Subject: [PATCH 1718/2677] Adjust JIT param size for AMD ROCm/Vega GPUs

---
 src/backend/opencl/Array.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 2132c96963..39e0de210f 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -280,7 +280,7 @@ bool passesJitHeuristics(Node *root_node) {
         // This is the maximum size of the params that can be allowed by the
         // CUDA platform.
         constexpr size_t max_nvidia_param_size = (4096 - base_param_size);
-        constexpr size_t max_amd_param_size    = (3670 - base_param_size);
+        constexpr size_t max_amd_param_size    = (3520 - base_param_size);
 
         size_t max_param_size = 0;
         if (isNvidia) {

From 6fec6b86925ac398f931ad90429ca0237d9fad85 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 19 Jul 2019 22:01:18 -0400
Subject: [PATCH 1719/2677] Fix fp16 type conversion for OpenCL gemm

---
 src/backend/opencl/magma/magma_blas_clblast.h | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index da816919fd..573cb7b062 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -59,7 +59,9 @@ template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(con
 template <> float inline toCLBlastConstant(const float val) { return val; }
 template <> double inline toCLBlastConstant(const double val) { return val; }
 template <> cl_half inline toCLBlastConstant(const common::half val) {
-    return static_cast<cl_half>(static_cast<float>(val));
+    cl_half out;
+    memcpy(&out, &val, sizeof(cl_half));
+    return out;
 }
 template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
 template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }

From 70e9100be76705d1d1852761ea68afcdfcba4046 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 19 Jul 2019 22:13:02 -0400
Subject: [PATCH 1720/2677] Fix OpenCL compilation error for ROCm sparse

---
 src/backend/opencl/kernel/csrmv.cl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index 552912a7b3..c37482cc55 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -101,6 +101,9 @@ __kernel void csrmv_block(__global T *output, __global const T *values,
     int rowNext = get_group_id(0);
     __local int s_rowId;
 
+    // Each thread stores part of the output result
+    __local T s_outval[THREADS];
+
     // Each groups performs multiple "dot" operations
     while (true) {
 #if USE_GREEDY
@@ -120,9 +123,6 @@ __kernel void csrmv_block(__global T *output, __global const T *values,
 #endif
         if (rowId >= M) return;
 
-        // Each thread stores part of the output result
-        __local T s_outval[THREADS];
-
         int colStart = rowidx[rowId];
         int colEnd   = rowidx[rowId + 1];
         T outval     = 0;

From d302b22ec3bf25a5925b3efa2805df06b062eb45 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 20 Jul 2019 18:01:24 -0400
Subject: [PATCH 1721/2677] Use cmake generated compiler header instead of
 compiler detection

---
 CMakeLists.txt                     |  1 +
 CMakeModules/InternalUtils.cmake   |  8 +++++---
 include/af/array.h                 | 10 ++++++----
 include/af/defines.h               |  7 ++++---
 include/af/index.h                 |  2 +-
 src/api/cpp/index.cpp              |  2 --
 src/backend/common/ArrayInfo.hpp   |  8 --------
 src/backend/common/SparseArray.cpp |  8 --------
 src/backend/common/SparseArray.hpp |  2 --
 src/backend/common/dim4.cpp        |  2 +-
 src/backend/common/half.hpp        | 12 ++++++------
 11 files changed, 24 insertions(+), 38 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 947645e3b9..38113e2d30 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -209,6 +209,7 @@ install(DIRECTORY include/ DESTINATION ${AF_INSTALL_INC_DIR}
 ## The ArrayFire version file is generated and won't be included above, install
 ## it separately.
 install(FILES ${ArrayFire_BINARY_DIR}/include/af/version.h
+              ${ArrayFire_BINARY_DIR}/include/af/compilers.h
         DESTINATION "${AF_INSTALL_INC_DIR}/af/"
         COMPONENT headers)
 
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 82c9627886..d68676d801 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -175,10 +175,12 @@ macro(arrayfire_set_cmake_default_variables)
 
   include(WriteCompilerDetectionHeader)
   write_compiler_detection_header(
-          FILE ${ArrayFire_BINARY_DIR}/include/compiler_header.h
+          FILE ${ArrayFire_BINARY_DIR}/include/af/compilers.h
           PREFIX AF
-          COMPILERS MSVC GNU Clang AppleClang Intel
-          FEATURES cxx_constexpr cxx_relaxed_constexpr cxx_alignas cxx_thread_local
+          COMPILERS AppleClang Clang GNU Intel MSVC
+          # NOTE: cxx_attribute_deprecated does not work well with C
+          FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas cxx_static_assert
+          ALLOW_UNKNOWN_COMPILERS
           #[VERSION <version>]
           #[PROLOG <prolog>]
           #[EPILOG <epilog>]
diff --git a/include/af/array.h b/include/af/array.h
index 452cb3dacd..6bfd95e3b1 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -8,13 +8,15 @@
  ********************************************************/
 
 #pragma once
+#include <af/compilers.h>
 #include <af/defines.h>
+#include <af/index.h>
 #include <af/seq.h>
 #include <af/util.h>
-#include <af/index.h>
 
 #ifdef __cplusplus
 #include <af/traits.hpp>
+
 namespace af
 {
 
@@ -49,7 +51,7 @@ namespace af
         public:
             array_proxy(array& par, af_index_t *ssss, bool linear = false);
             array_proxy(const array_proxy &other);
-#if __cplusplus > 199711L
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
             array_proxy(array_proxy &&other);
             array_proxy & operator=(array_proxy &&other);
 #endif
@@ -1413,7 +1415,7 @@ namespace af
     */
     inline const array &eval(const array &a) { a.eval(); return a; }
 
-#ifdef AF_HAS_VARIADIC_TEMPLATES
+#if AF_COMPILER_CXX_VARIADIC_TEMPLATES
     template <typename... ARRAYS>
     inline void eval(ARRAYS... in) {
         array *arrays[] = {const_cast<array *>(&in)...};
@@ -1454,7 +1456,7 @@ namespace af
         const array *arrays[] = {&a, &b, &c, &d, &e, &f};
         return eval(6, const_cast<array **>(arrays));
     }
-#endif  // __cplusplus > 199711L
+#endif // AF_COMPILER_CXX_VARIADIC_TEMPLATES
 #endif
 
 #if AF_API_VERSION >= 34
diff --git a/include/af/defines.h b/include/af/defines.h
index bd26252ddc..6cf8ad63fe 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -9,6 +9,10 @@
 
 #pragma once
 
+#ifndef __CUDACC_RTC__
+#include <af/compilers.h>
+#endif
+
 #if defined(_WIN32) || defined(_MSC_VER)
     // http://msdn.microsoft.com/en-us/library/b0084kay(v=VS.80).aspx
     // http://msdn.microsoft.com/en-us/library/3y1sfaz2%28v=VS.80%29.aspx
@@ -39,9 +43,6 @@
 #else
     #define AF_DEPRECATED(msg) __attribute__((deprecated))
 #endif
-    #if __cpp_variadic_templates >= 200704
-        #define AF_HAS_VARIADIC_TEMPLATES
-    #endif
 #endif
 
 // Known 64-bit x86 and ARM architectures use long long
diff --git a/include/af/index.h b/include/af/index.h
index e91735da94..513422c510 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -138,7 +138,7 @@ class AFAPI index {
     ///
     index & operator=(const index& idx0);
 
-#if __cplusplus > 199711L
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
     ///
     /// \brief Move constructor
     ///
diff --git a/src/api/cpp/index.cpp b/src/api/cpp/index.cpp
index a585275c4a..bbc22bfdf0 100644
--- a/src/api/cpp/index.cpp
+++ b/src/api/cpp/index.cpp
@@ -82,7 +82,6 @@ index &index::operator=(const index &idx0) {
     return *this;
 }
 
-#if __cplusplus > 199711L
 index::index(index &&idx0) {
     impl              = idx0.impl;
     idx0.impl.idx.arr = nullptr;
@@ -93,7 +92,6 @@ index &index::operator=(index &&idx0) {
     idx0.impl.idx.arr = nullptr;
     return *this;
 }
-#endif
 
 static bool operator==(const af_seq &lhs, const af_seq &rhs) {
     return lhs.begin == rhs.begin && lhs.end == rhs.end && lhs.step == rhs.step;
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index 868ae12c90..334556d4fa 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -56,13 +56,11 @@ class ArrayInfo {
         , dim_strides(stride)
         , is_sparse(false) {
         setId(id);
-#if __cplusplus > 199711l
         static_assert(
             offsetof(ArrayInfo, devId) == 0,
             "ArrayInfo::devId must be the first member variable of ArrayInfo. \
                    devId is used to encode the backend into the integer. \
                    This is then used in the unified backend to check mismatched arrays.");
-#endif
     }
 
     ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride,
@@ -74,21 +72,17 @@ class ArrayInfo {
         , dim_strides(stride)
         , is_sparse(sparse) {
         setId(id);
-#if __cplusplus > 199711l
         static_assert(
             offsetof(ArrayInfo, devId) == 0,
             "ArrayInfo::devId must be the first member variable of ArrayInfo. \
                    devId is used to encode the backend into the integer. \
                    This is then used in the unified backend to check mismatched arrays.");
-#endif
     }
 
-#if __cplusplus > 199711L
     // Copy constructors are deprecated if there is a
     // user-defined destructor in c++11
     ArrayInfo()                       = default;
     ArrayInfo(const ArrayInfo& other) = default;
-#endif
 
     const af_dtype& getType() const { return type; }
 
@@ -153,10 +147,8 @@ class ArrayInfo {
 
     bool isSparse() const;
 };
-#if __cplusplus > 199711l
 static_assert(std::is_standard_layout<ArrayInfo>::value,
               "ArrayInfo must be a standard layout type");
-#endif
 
 af::dim4 toDims(const std::vector<af_seq>& seqs, const af::dim4& parentDims);
 
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index 9821d5c84d..8a56b4b851 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -41,11 +41,9 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
     , stype(_storage)
     , rowIdx(createValueArray<int>(dim4(ROW_LENGTH), 0))
     , colIdx(createValueArray<int>(dim4(COL_LENGTH), 0)) {
-#if __cplusplus > 199711l
     static_assert(offsetof(SparseArrayBase, info) == 0,
                   "SparseArrayBase::info must be the first member variable of "
                   "SparseArrayBase.");
-#endif
 }
 
 SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
@@ -64,11 +62,9 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
                         ? createDeviceDataArray<int>(dim4(COL_LENGTH), _colIdx)
                         : createValueArray<int>(dim4(COL_LENGTH), 0))
                  : createHostDataArray<int>(dim4(COL_LENGTH), _colIdx)) {
-#if __cplusplus > 199711L
     static_assert(offsetof(SparseArrayBase, info) == 0,
                   "SparseArrayBase::info must be the first member variable of "
                   "SparseArrayBase.");
-#endif
     if (_is_device && _copy_device) {
         writeDeviceDataArray<int>(rowIdx, _rowIdx, ROW_LENGTH * sizeof(int));
         writeDeviceDataArray<int>(colIdx, _colIdx, COL_LENGTH * sizeof(int));
@@ -83,11 +79,9 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, const Array<int> &_rowIdx,
     , stype(_storage)
     , rowIdx(_copy ? copyArray<int>(_rowIdx) : _rowIdx)
     , colIdx(_copy ? copyArray<int>(_colIdx) : _colIdx) {
-#if __cplusplus > 199711L
     static_assert(offsetof(SparseArrayBase, info) == 0,
                   "SparseArrayBase::info must be the first member variable of "
                   "SparseArrayBase.");
-#endif
 }
 
 SparseArrayBase::SparseArrayBase(const SparseArrayBase &base, bool copy)
@@ -171,13 +165,11 @@ template<typename T>
 SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage)
     : base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type)
     , values(createValueArray<T>(dim4(_nNZ), scalar<T>(0))) {
-#if __cplusplus > 199711L
     static_assert(std::is_standard_layout<SparseArray<T>>::value,
                   "SparseArray<T> must be a standard layout type");
     static_assert(offsetof(SparseArray<T>, base) == 0,
                   "SparseArray<T>::base must be the first member variable of "
                   "SparseArray<T>");
-#endif
 }
 
 template<typename T>
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 474e1e539d..0f02922865 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -117,10 +117,8 @@ class SparseArrayBase {
     /// Returns the storage format of the SparseArray
     af::storage getStorage() const { return stype; }
 };
-#if __cplusplus > 199711L
 static_assert(std::is_standard_layout<SparseArrayBase>::value,
               "SparseArrayBase must be a standard layout type");
-#endif
 
 ////////////////////////////////////////////////////////////////////////////
 // Sparse Array Class
diff --git a/src/backend/common/dim4.cpp b/src/backend/common/dim4.cpp
index 74d0a83e63..a17165451c 100644
--- a/src/backend/common/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -16,7 +16,7 @@
 
 namespace af {
 
-#if __cplusplus > 199711l
+#if AF_COMPILER_CXX_STATIC_ASSERT
 static_assert(std::is_standard_layout<dim4>::value,
               "af::dim4 must be a standard layout type");
 #endif
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 630652629f..5ce3afc2c7 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -27,7 +27,7 @@ using uint16_t = unsigned short;
 #endif
 
 #if AF_COMPILER_CXX_RELAXED_CONSTEXPR
-#define CONSTEXPR_DH AF_CONSTEXPR __DH__
+#define CONSTEXPR_DH constexpr __DH__
 #else
 #define CONSTEXPR_DH __DH__
 #endif
@@ -118,7 +118,7 @@ CONSTEXPR_DH native_half_t float2half_impl(float value) noexcept {
     uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
                         // //violating strict aliasing!
     std::memcpy(&bits, &value, sizeof(float));
-    static const uint16_t base_table[512] = {
+    constexpr uint16_t base_table[512] = {
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
@@ -177,7 +177,7 @@ CONSTEXPR_DH native_half_t float2half_impl(float value) noexcept {
         0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00,
         0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00, 0xFC00};
 
-    static const uint8_t shift_table[512] = {
+    constexpr uint8_t shift_table[512] = {
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
@@ -653,7 +653,7 @@ CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
         0x387F0000, 0x387F2000, 0x387F4000, 0x387F6000, 0x387F8000, 0x387FA000,
         0x387FC000, 0x387FE000};
 
-    static const uint32_t exponent_table[64] = {
+    constexpr uint32_t exponent_table[64] = {
         0x00000000, 0x00800000, 0x01000000, 0x01800000, 0x02000000, 0x02800000,
         0x03000000, 0x03800000, 0x04000000, 0x04800000, 0x05000000, 0x05800000,
         0x06000000, 0x06800000, 0x07000000, 0x07800000, 0x08000000, 0x08800000,
@@ -666,7 +666,7 @@ CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
         0x8B000000, 0x8B800000, 0x8C000000, 0x8C800000, 0x8D000000, 0x8D800000,
         0x8E000000, 0x8E800000, 0x8F000000, 0xC7800000};
 
-    static const uint16_t offset_table[64] = {
+    constexpr uint16_t offset_table[64] = {
         0,    1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 0,
@@ -695,7 +695,7 @@ CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
 ///           value
 /// \param value The value to convert to integer
 template<std::float_round_style R, bool E, typename T>
-__DH__ T half2int(native_half_t value) {
+constexpr T half2int(native_half_t value) {
     static_assert(std::is_integral<T>::value,
                   "half to int conversion only supports builtin integer types");
     unsigned int e = value & 0x7FFF;

From b0913ffe77b22adecb3ee94ac069547b04034dd1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 21 Jul 2019 00:29:56 -0400
Subject: [PATCH 1722/2677] Accept a zeroed af_array in af_release_array

---
 include/af/array.h  | 2 ++
 src/api/c/array.cpp | 1 +
 2 files changed, 3 insertions(+)

diff --git a/include/af/array.h b/include/af/array.h
index 6bfd95e3b1..055d26f32a 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1540,6 +1540,8 @@ extern "C" {
 
     /**
        \brief Reduce the reference count of the \ref af_array
+
+       \note Zero initialized af_arrays can be accepted after version 3.7
     */
     AFAPI af_err af_release_array(af_array arr);
 
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 9cbf2c9929..e4500c488c 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -227,6 +227,7 @@ af_err af_get_data_ref_count(int *use_count, const af_array in) {
 
 af_err af_release_array(af_array arr) {
     try {
+        if(arr == 0) return AF_SUCCESS;
         const ArrayInfo &info = getInfo(arr, false, false);
         af_dtype type         = info.getType();
 

From 16ee45e3eb9aad54a251eb0bfdf1a92500f7cc16 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 21 Jul 2019 00:30:59 -0400
Subject: [PATCH 1723/2677] Add move constructor and operator= to af::array

---
 include/af/array.h    | 27 ++++++++++++++++++++++++++-
 src/api/cpp/array.cpp | 26 +++++++++++++++++++-------
 2 files changed, 45 insertions(+), 8 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 055d26f32a..43a966a531 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -169,7 +169,7 @@ namespace af
             @{
         */
         /**
-            Create undimensioned array (no data, undefined size)
+            Create an uninitialized array (no data, undefined size)
 
             \code
             array A, B, C;   // creates three arrays called A, B and C
@@ -177,6 +177,31 @@ namespace af
         */
         array();
 
+#if AF_API_VERSION >= 37
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
+        /**
+            Move constructor
+
+            Moves the \p other af::array into the current af::array. After this
+            operation, the \p other array will not be left uninitialized.
+
+            \param[in] other The array to be moved
+        */
+        array(array &&other) AF_NOEXCEPT;
+
+        /**
+            Move assignment operator
+
+            Moves the array into the current array. After this operation the
+            \p other array is left uninitialized. The previously referenced
+            af_array of the current object is released.
+
+            \param[in] other The array to be moved
+            \returns the reference to the current array
+        */
+        array &operator=(array &&other) AF_NOEXCEPT;
+#endif
+#endif
         /**
             Creates an array from an \ref af_array handle
             \param handle the af_array object.
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 251181bdd3..c7ba461c0b 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -152,6 +152,15 @@ array::array(const af_array handle) : arr(handle) {}
 
 array::array() : arr(nullptr) { initEmptyArray(&arr, f32, 0, 1, 1, 1); }
 
+array::array(array &&other) noexcept : arr(other.arr) { other.arr = 0; }
+
+array &array::operator=(array &&other)  noexcept {
+    af_release_array(arr);
+    arr       = other.arr;
+    other.arr = 0;
+    return *this;
+}
+
 array::array(const dim4 &dims, af::dtype ty) : arr(nullptr) {
     initEmptyArray(&arr, ty, dims[0], dims[1], dims[2], dims[3]);
 }
@@ -477,10 +486,11 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
     if (impl->is_linear_) {
         AF_THROW(af_flat(&par_arr, impl->parent_->get()));
         // The set call will dereference the impl->parent_ array. We are doing
-        // this because the af_flat call above increases the reference count of the
-        // parent array which triggers a copy operation. This triggers a copy operation
-        // inside the af_assign_gen function below. The parent array will be reverted
-        // to the original array and shape later in the code.
+        // this because the af_flat call above increases the reference count of
+        // the parent array which triggers a copy operation. This triggers a
+        // copy operation inside the af_assign_gen function below. The parent
+        // array will be reverted to the original array and shape later in the
+        // code.
         af_array empty = 0;
         impl->parent_->set(empty);
         nd = 1;
@@ -491,12 +501,14 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
     af_array flat_res = 0;
     AF_THROW(af_assign_gen(&flat_res, par_arr, nd, impl->indices_, other_arr));
 
-    af_array res = 0;
+    af_array res         = 0;
     af_array unflattened = 0;
     if (impl->is_linear_) {
-        AF_THROW(af_moddims(&res, flat_res, this_dims.ndims(), this_dims.get()));
+        AF_THROW(
+            af_moddims(&res, flat_res, this_dims.ndims(), this_dims.get()));
         // Unflatten the af_array and reset the original reference
-        AF_THROW(af_moddims(&unflattened, par_arr, parent_dims.ndims(), parent_dims.get()));
+        AF_THROW(af_moddims(&unflattened, par_arr, parent_dims.ndims(),
+                            parent_dims.get()));
         impl->parent_->set(unflattened);
         AF_THROW(af_release_array(par_arr));
         AF_THROW(af_release_array(flat_res));

From 9f344b02aa02f26693f4196d941512ebf086912a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 21 Jul 2019 00:31:29 -0400
Subject: [PATCH 1724/2677] Update LSANSuppression file for leaks in
 af_default_random_engine

---
 CMakeModules/LSANSuppression.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CMakeModules/LSANSuppression.txt b/CMakeModules/LSANSuppression.txt
index 6dcc15556c..dca058df0f 100644
--- a/CMakeModules/LSANSuppression.txt
+++ b/CMakeModules/LSANSuppression.txt
@@ -7,3 +7,7 @@ leak:tbb::internal::task_stream
 # Allocated by Intel's OpenMP implementation during inverse_dense_cpu
 # This is not something we can control in ArrayFire
 leak:kmp_alloc_cpp*::bget
+
+# ArrayFire leaks the default random engine on each thread. This is to avoid
+# errors on exit on Windows.
+leak:af_get_default_random_engine

From d39948c503dafd61cbc5f76a82c6f4a2daec536a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 21 Jul 2019 01:58:22 -0400
Subject: [PATCH 1725/2677] Fix coverage exclude regular expressions

---
 CMakeLists.txt                 |  6 +++---
 CMakeModules/CTestCustom.cmake | 23 ++++++++++++++---------
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 38113e2d30..437f128a63 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -320,11 +320,11 @@ configure_package_config_file(
 # CMake for examples and tests.
 unset(CMAKE_CXX_VISIBILITY_PRESET)
 
-include(CTest)
-
 configure_file(
   ${CMAKE_MODULE_PATH}/CTestCustom.cmake
-  ${PROJECT_BINARY_DIR}/CTestCustom.cmake)
+  ${ArrayFire_BINARY_DIR}/CTestCustom.cmake)
+
+include(CTest)
 
 # Handle depricated BUILD_TEST variable if found.
 if(BUILD_TEST)
diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index 8ae083e646..69e4e72d04 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -1,18 +1,23 @@
+# Copyright (c) 2019, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
 
-
-set(CTEST_CUSTOM_ERROR_POST_CONTEXT 30)
-set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 30)
+set(CTEST_CUSTOM_ERROR_POST_CONTEXT 50)
+set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 50)
 set(CTEST_CUSTOM_POST_TEST ./test/print_info)
 
 list(APPEND CTEST_CUSTOM_COVERAGE_EXCLUDE
-  "test/gtest/*"
+  "test"
 
   # All external and third_party libraries
-  "extern/*"
-  "test/mmio/*"
-  "src/backend/cpu/threads/*"
-  "src/backend/cuda/cub/*"
+  "extern/.*"
+  "test/mmio/.*"
+  "src/backend/cpu/threads/.*"
+  "src/backend/cuda/cub/.*"
   "cl2.hpp"
 
   # Remove bin2cpp from coverage
-  "CMakeModules/*")
+  "CMakeModules/.*")

From e590fc0d2441addf140b6a96bd0d72666a2572b1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 22 Jul 2019 01:49:17 -0400
Subject: [PATCH 1726/2677] Fix CMP0074 policy warnings. add set_policies macro

---
 CMakeLists.txt                   |  8 ++++----
 CMakeModules/InternalUtils.cmake | 10 ++++++++++
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 437f128a63..312bd97058 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,10 +10,6 @@ project(ArrayFire
   VERSION 3.7.0
   LANGUAGES C CXX )
 
-if(POLICY CMP0073)
-  cmake_policy(SET CMP0073 NEW)
-endif()
-
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 include(AFInstallDirs)
 include(CMakeDependentOption)
@@ -24,6 +20,10 @@ include(platform)
 include(GetPrerequisites)
 include(CheckCXXCompilerFlag)
 
+set_policies(
+  TYPE NEW
+  POLICIES CMP0073
+           CMP0074)
 arrayfire_set_cmake_default_variables()
 
 #Set Intel OpenMP as default MKL thread layer
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index d68676d801..0cbb5cbc48 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -187,6 +187,16 @@ macro(arrayfire_set_cmake_default_variables)
           )
 endmacro()
 
+macro(set_policies)
+  cmake_parse_arguments(SP "" "TYPE" "POLICIES" ${ARGN})
+  foreach(_policy ${SP_POLICIES})
+    if(POLICY ${_policy})
+      message(STATUS ${_policy} ${SP_TYPE})
+      cmake_policy(SET ${_policy} ${SP_TYPE})
+    endif()
+  endforeach()
+endmacro()
+
 mark_as_advanced(
     pkgcfg_lib_PC_CBLAS_cblas
     pkgcfg_lib_PC_LAPACKE_lapacke

From 65aeb9a3ec4d4c77020a5fee32d9666d7bab957e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 Jul 2019 12:30:02 +0530
Subject: [PATCH 1727/2677] Fix sobel operator in all backends

The gradient computation equations where flipped earlier which is
fixed now.

Also, modified the sobel to use same border management as OpenCV.
Any pixel accessed outside the borders is reflected across the border
now. The test data has also been updated accordingly.
---
 src/backend/cpu/kernel/sobel.hpp   | 63 ++++++++++--------------------
 src/backend/cuda/kernel/sobel.cuh  | 26 ++++++------
 src/backend/opencl/kernel/sobel.cl | 25 ++++++------
 test/data                          |  2 +-
 test/sobel.cpp                     |  4 ++
 5 files changed, 55 insertions(+), 65 deletions(-)

diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index 255a6bb741..6a45f6e1c4 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -8,7 +8,10 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
+#include <math.hpp>
+
 #include <cassert>
 
 namespace cpu {
@@ -20,64 +23,40 @@ void derivative(Param<To> output, CParam<Ti> input) {
     const af::dim4 istrides = input.strides();
     const af::dim4 ostrides = output.strides();
 
+    auto reflect101 = [](int index, int endIndex) -> int {
+        return std::abs(endIndex - std::abs(endIndex - index));
+    };
+
     for (dim_t b3 = 0; b3 < dims[3]; ++b3) {
         To* optr       = output.get() + b3 * ostrides[3];
         const Ti* iptr = input.get() + b3 * istrides[3];
         for (dim_t b2 = 0; b2 < dims[2]; ++b2) {
             for (dim_t j = 0; j < dims[1]; ++j) {
                 int joff    = j;
-                int _joff   = j - 1;
-                int joff_   = j + 1;
+                int _joff   = reflect101(j - 1, static_cast<int>(dims[1]-1));
+                int joff_   = reflect101(j + 1, static_cast<int>(dims[1]-1));
                 int joffset = j * ostrides[1];
 
                 for (dim_t i = 0; i < dims[0]; ++i) {
                     To accum = To(0);
 
                     int ioff  = i;
-                    int _ioff = i - 1;
-                    int ioff_ = i + 1;
+                    int _ioff = reflect101(i - 1, static_cast<int>(dims[0]-1));
+                    int ioff_ = reflect101(i + 1, static_cast<int>(dims[0]-1));
 
-                    To NW =
-                        (_ioff >= 0 && _joff >= 0)
-                            ? iptr[_joff * istrides[1] + _ioff * istrides[0]]
-                            : 0;
-                    To SW =
-                        (ioff_ < (int)dims[0] && _joff >= 0)
-                            ? iptr[_joff * istrides[1] + ioff_ * istrides[0]]
-                            : 0;
-                    To NE =
-                        (_ioff >= 0 && joff_ < (int)dims[1])
-                            ? iptr[joff_ * istrides[1] + _ioff * istrides[0]]
-                            : 0;
-                    To SE =
-                        (ioff_ < (int)dims[0] && joff_ < (int)dims[1])
-                            ? iptr[joff_ * istrides[1] + ioff_ * istrides[0]]
-                            : 0;
+                    To NW = iptr[_joff * istrides[1] + _ioff * istrides[0]];
+                    To SW = iptr[_joff * istrides[1] + ioff_ * istrides[0]];
+                    To NE = iptr[joff_ * istrides[1] + _ioff * istrides[0]];
+                    To SE = iptr[joff_ * istrides[1] + ioff_ * istrides[0]];
 
                     if (isDX) {
-                        To W =
-                            _joff >= 0
-                                ? iptr[_joff * istrides[1] + ioff * istrides[0]]
-                                : 0;
-
-                        To E =
-                            joff_ < (int)dims[1]
-                                ? iptr[joff_ * istrides[1] + ioff * istrides[0]]
-                                : 0;
-
-                        accum = NW + SW - (NE + SE) + 2 * (W - E);
+                        To N  = iptr[joff * istrides[1] + _ioff * istrides[0]];
+                        To S  = iptr[joff * istrides[1] + ioff_ * istrides[0]];
+                        accum = SW + SE - (NW + NE) + 2 * (S - N);
                     } else {
-                        To N =
-                            _ioff >= 0
-                                ? iptr[joff * istrides[1] + _ioff * istrides[0]]
-                                : 0;
-
-                        To S =
-                            ioff_ < (int)dims[0]
-                                ? iptr[joff * istrides[1] + ioff_ * istrides[0]]
-                                : 0;
-
-                        accum = NW + NE - (SW + SE) + 2 * (N - S);
+                        To W  = iptr[_joff * istrides[1] + ioff * istrides[0]];
+                        To E  = iptr[joff_ * istrides[1] + ioff * istrides[0]];
+                        accum = NE + SE - (NW + SW) + 2 * (E - W);
                     }
 
                     optr[joffset + i * ostrides[0]] = accum;
diff --git a/src/backend/cuda/kernel/sobel.cuh b/src/backend/cuda/kernel/sobel.cuh
index 418b14d3bf..1ed9b7b0af 100644
--- a/src/backend/cuda/kernel/sobel.cuh
+++ b/src/backend/cuda/kernel/sobel.cuh
@@ -12,13 +12,17 @@
 
 namespace cuda {
 
+__device__
+int reflect101(int index, int endIndex) {
+    return abs(endIndex - abs(endIndex - index));
+}
+
 template<typename Ti>
-__device__ Ti load2ShrdMem(const Ti* in, int dim0, int dim1, int gx, int gy,
+__device__ Ti load2ShrdMem(const Ti* in, int d0, int d1, int gx, int gy,
                            int inStride1, int inStride0) {
-    if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
-        return Ti(0);
-    else
-        return in[gx * inStride0 + gy * inStride1];
+    int idx = reflect101(gx, d0-1) * inStride0 +
+              reflect101(gy, d1-1) * inStride1;
+    return in[idx];
 }
 
 template<typename Ti, typename To>
@@ -73,13 +77,13 @@ __global__ void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0,
         float NE = shrdMem[_i][j_];
         float SE = shrdMem[i_][j_];
 
-        float t1                       = shrdMem[i][_j];
-        float t2                       = shrdMem[i][j_];
-        dxptr[gy * dx.strides[1] + gx] = (NW + SW - (NE + SE) + 2 * (t1 - t2));
+        float t1  = shrdMem[_i][j];
+        float t2  = shrdMem[i_][j];
+        dxptr[gy * dx.strides[1] + gx] = (SW + SE - (NW + NE) + 2 * (t2 - t1));
 
-        t1                             = shrdMem[_i][j];
-        t2                             = shrdMem[i_][j];
-        dyptr[gy * dy.strides[1] + gx] = (NW + NE - (SW + SE) + 2 * (t1 - t2));
+        t1 = shrdMem[i][_j];
+        t2 = shrdMem[i][j_];
+        dyptr[gy * dy.strides[1] + gx] = (NE + SE - (NW + SW) + 2 * (t2 - t1));
     }
 }
 
diff --git a/src/backend/opencl/kernel/sobel.cl b/src/backend/opencl/kernel/sobel.cl
index 9a85a15b9f..9ef11d9e2f 100644
--- a/src/backend/opencl/kernel/sobel.cl
+++ b/src/backend/opencl/kernel/sobel.cl
@@ -7,12 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-Ti load2LocalMem(global const Ti* in, int dim0, int dim1, int gx, int gy,
+int reflect101(int index, int endIndex) {
+    return abs(endIndex - (int)abs(endIndex - index));
+}
+
+Ti load2LocalMem(global const Ti* in, int d0, int d1, int gx, int gy,
                  int inStride1, int inStride0) {
-    if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
-        return (Ti)0;
-    else
-        return in[gx * inStride0 + gy * inStride1];
+    int idx = reflect101(gx, d0-1) * inStride0 +
+              reflect101(gy, d1-1) * inStride1;
+    return in[idx];
 }
 
 kernel void sobel3x3(global To* dx, KParam dxInfo, global To* dy, KParam dyInfo,
@@ -63,14 +66,14 @@ kernel void sobel3x3(global To* dx, KParam dxInfo, global To* dy, KParam dyInfo,
         float NE = localMem[_i + shrdLen * j_];
         float SE = localMem[i_ + shrdLen * j_];
 
-        float t1 = localMem[i + shrdLen * _j];
-        float t2 = localMem[i + shrdLen * j_];
+        float t1 = localMem[_i + shrdLen * j];
+        float t2 = localMem[i_ + shrdLen * j];
         dxptr[gy * dxInfo.strides[1] + gx] =
-            (NW + SW - (NE + SE) + 2 * (t1 - t2));
+            (SW + SE - (NW + NE) + 2 * (t2 - t1));
 
-        t1 = localMem[_i + shrdLen * j];
-        t2 = localMem[i_ + shrdLen * j];
+        t1 = localMem[i + shrdLen * _j];
+        t2 = localMem[i + shrdLen * j_];
         dyptr[gy * dyInfo.strides[1] + gx] =
-            (NW + NE - (SW + SE) + 2 * (t1 - t2));
+            (NE + SE - (NW + SW) + 2 * (t2 - t1));
     }
 }
diff --git a/test/data b/test/data
index 1074f2cbac..c556bc9870 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 1074f2cbac403575bb0adbc9df6bdf1b09fd3ea9
+Subproject commit c556bc98706f3eee5c0e567d909b443182a6d1aa
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 9f92e402e6..8acd873108 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -75,6 +75,10 @@ void testSobelDerivatives(string pTestFile) {
     ASSERT_SUCCESS(af_release_array(dyArray));
 }
 
+
+// rectangle test data is generated using opencv
+// border type is set to cv.BORDER_REFLECT_101 in opencv
+
 TYPED_TEST(Sobel, Rectangle) {
     testSobelDerivatives<TypeParam, TypeParam>(
         string(TEST_DIR "/sobel/rectangle.test"));

From f4c4c4503fe8c06d3ca5588b86645445ef92e958 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 Jul 2019 12:30:28 +0530
Subject: [PATCH 1728/2677] Fix gradient factor equation in canny
 nonmaxsuppression

Fix gradient factor equation in canny nonmaxsuppression. In the case of negative gradients along both x and y directions, the interpolation factor was incorrectly being calculated.

Apart from this, sobel derivaties respective sobel masks were flipped along edge direction (not a transpose, but flip along axes). Terrible bug introduced by me and fixed now in f5e4e0e56ee00a893a11b3b190190aa8d0dd8c7b
---
 src/backend/cpu/kernel/canny.hpp              | 41 ++++++++++---------
 src/backend/cuda/kernel/canny.cuh             | 32 +++++++--------
 .../opencl/kernel/nonmax_suppression.cl       | 32 +++++++--------
 test/data                                     |  2 +-
 4 files changed, 54 insertions(+), 53 deletions(-)

diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
index 4c7c1a7246..412d209b6b 100644
--- a/src/backend/cpu/kernel/canny.hpp
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -34,9 +34,10 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dxParam,
 
             for (dim_t j = 1; j < dims[1] - 1; ++j, offset += 2) {
                 for (dim_t i = 1; i < dims[0] - 1; ++i, ++offset) {
-                    if (mag[offset] == 0)
+                    T curr = mag[offset];
+                    if (curr == 0) {
                         out[offset] = (T)0;
-                    else {
+                    } else {
                         const float se = mag[offset + dims[0] + 1];
                         const float nw = mag[offset - dims[0] - 1];
                         const float ea = mag[offset + 1];
@@ -52,47 +53,47 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dxParam,
 
                         if (dx >= 0) {
                             if (dy >= 0) {
-                                const bool isTrue = (dx - dy) >= 0;
+                                const bool isDxMagGreater = (dx - dy) >= 0;
 
-                                a1    = isTrue ? ea : so;
-                                a2    = isTrue ? we : no;
+                                a1    = isDxMagGreater ? ea : so;
+                                a2    = isDxMagGreater ? we : no;
                                 b1    = se;
                                 b2    = nw;
-                                alpha = isTrue ? dy / dx : dx / dy;
+                                alpha = isDxMagGreater ? dy / dx : dx / dy;
                             } else {
-                                const bool isTrue = (dx + dy) >= 0;
+                                const bool isDyMagGreater = (dx + dy) >= 0;
 
-                                a1    = isTrue ? ea : no;
-                                a2    = isTrue ? we : so;
+                                a1    = isDyMagGreater ? ea : no;
+                                a2    = isDyMagGreater ? we : so;
                                 b1    = ne;
                                 b2    = sw;
-                                alpha = isTrue ? -dy / dx : dx / -dy;
+                                alpha = isDyMagGreater ? -dy / dx : dx / -dy;
                             }
                         } else {
                             if (dy >= 0) {
-                                const bool isTrue = (dx + dy) >= 0;
+                                const bool isDyMagGreater = (dx + dy) >= 0;
 
-                                a1    = isTrue ? so : we;
-                                a2    = isTrue ? no : ea;
+                                a1    = isDyMagGreater ? so : we;
+                                a2    = isDyMagGreater ? no : ea;
                                 b1    = sw;
                                 b2    = ne;
-                                alpha = isTrue ? -dx / dy : dy / -dx;
+                                alpha = isDyMagGreater ? -dx / dy : dy / -dx;
                             } else {
-                                const bool isTrue = (-dx + dy) >= 0;
+                                const bool isDxMagGreater = (-dx + dy) >= 0;
 
-                                a1    = isTrue ? we : no;
-                                a2    = isTrue ? ea : so;
+                                a1    = isDxMagGreater ? we : no;
+                                a2    = isDxMagGreater ? ea : so;
                                 b1    = nw;
                                 b2    = se;
-                                alpha = isTrue ? -dy / dx : dx / -dy;
+                                alpha = isDxMagGreater ? dy / dx : dx / dy;
                             }
                         }
 
                         float mag1 = (1 - alpha) * a1 + alpha * b1;
                         float mag2 = (1 - alpha) * a2 + alpha * b2;
 
-                        if (mag[offset] > mag1 && mag[offset] > mag2) {
-                            out[offset] = mag[offset];
+                        if (curr > mag1 && curr > mag2) {
+                            out[offset] = curr;
                         } else {
                             out[offset] = (T)0;
                         }
diff --git a/src/backend/cuda/kernel/canny.cuh b/src/backend/cuda/kernel/canny.cuh
index 7ff2d5b172..d0cf25f582 100644
--- a/src/backend/cuda/kernel/canny.cuh
+++ b/src/backend/cuda/kernel/canny.cuh
@@ -95,39 +95,39 @@ void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
 
             if (dx >= 0) {
                 if (dy >= 0) {
-                    const bool isTrue = (dx - dy) >= 0;
+                    const bool isDxMagGreater = (dx - dy) >= 0;
 
-                    a1    = isTrue ? ea : so;
-                    a2    = isTrue ? we : no;
+                    a1    = isDxMagGreater ? ea : so;
+                    a2    = isDxMagGreater ? we : no;
                     b1    = se;
                     b2    = nw;
-                    alpha = isTrue ? dy / dx : dx / dy;
+                    alpha = isDxMagGreater ? dy / dx : dx / dy;
                 } else {
-                    const bool isTrue = (dx + dy) >= 0;
+                    const bool isDyMagGreater = (dx + dy) >= 0;
 
-                    a1    = isTrue ? ea : no;
-                    a2    = isTrue ? we : so;
+                    a1    = isDyMagGreater ? ea : no;
+                    a2    = isDyMagGreater ? we : so;
                     b1    = ne;
                     b2    = sw;
-                    alpha = isTrue ? -dy / dx : dx / -dy;
+                    alpha = isDyMagGreater ? -dy / dx : dx / -dy;
                 }
             } else {
                 if (dy >= 0) {
-                    const bool isTrue = (dx + dy) >= 0;
+                    const bool isDxMagGreater = (dx + dy) >= 0;
 
-                    a1    = isTrue ? so : we;
-                    a2    = isTrue ? no : ea;
+                    a1    = isDxMagGreater ? so : we;
+                    a2    = isDxMagGreater ? no : ea;
                     b1    = sw;
                     b2    = ne;
-                    alpha = isTrue ? -dx / dy : dy / -dx;
+                    alpha = isDxMagGreater ? -dx / dy : dy / -dx;
                 } else {
-                    const bool isTrue = (-dx + dy) >= 0;
+                    const bool isDyMagGreater = (-dx + dy) >= 0;
 
-                    a1    = isTrue ? we : no;
-                    a2    = isTrue ? ea : so;
+                    a1    = isDyMagGreater ? we : no;
+                    a2    = isDyMagGreater ? ea : so;
                     b1    = nw;
                     b2    = se;
-                    alpha = isTrue ? -dy / dx : dx / -dy;
+                    alpha = isDyMagGreater ? dy / dx : dx / dy;
                 }
             }
 
diff --git a/src/backend/opencl/kernel/nonmax_suppression.cl b/src/backend/opencl/kernel/nonmax_suppression.cl
index 1b5a627454..7b56cc42ab 100644
--- a/src/backend/opencl/kernel/nonmax_suppression.cl
+++ b/src/backend/opencl/kernel/nonmax_suppression.cl
@@ -75,39 +75,39 @@ __kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
 
             if (dx >= 0) {
                 if (dy >= 0) {
-                    const bool isTrue = (dx - dy) >= 0;
+                    const bool isDxMagGreater = (dx - dy) >= 0;
 
-                    a1    = isTrue ? ea : so;
-                    a2    = isTrue ? we : no;
+                    a1    = isDxMagGreater ? ea : so;
+                    a2    = isDxMagGreater ? we : no;
                     b1    = se;
                     b2    = nw;
-                    alpha = isTrue ? dy / dx : dx / dy;
+                    alpha = isDxMagGreater ? dy / dx : dx / dy;
                 } else {
-                    const bool isTrue = (dx + dy) >= 0;
+                    const bool isDyMagGreater = (dx + dy) >= 0;
 
-                    a1    = isTrue ? ea : no;
-                    a2    = isTrue ? we : so;
+                    a1    = isDyMagGreater ? ea : no;
+                    a2    = isDyMagGreater ? we : so;
                     b1    = ne;
                     b2    = sw;
-                    alpha = isTrue ? -dy / dx : dx / -dy;
+                    alpha = isDyMagGreater ? -dy / dx : dx / -dy;
                 }
             } else {
                 if (dy >= 0) {
-                    const bool isTrue = (dx + dy) >= 0;
+                    const bool isDxMagGreater = (dx + dy) >= 0;
 
-                    a1    = isTrue ? so : we;
-                    a2    = isTrue ? no : ea;
+                    a1    = isDxMagGreater ? so : we;
+                    a2    = isDxMagGreater ? no : ea;
                     b1    = sw;
                     b2    = ne;
-                    alpha = isTrue ? -dx / dy : dy / -dx;
+                    alpha = isDxMagGreater ? -dx / dy : dy / -dx;
                 } else {
-                    const bool isTrue = (-dx + dy) >= 0;
+                    const bool isDyMagGreater = (-dx + dy) >= 0;
 
-                    a1    = isTrue ? we : no;
-                    a2    = isTrue ? ea : so;
+                    a1    = isDyMagGreater ? we : no;
+                    a2    = isDyMagGreater ? ea : so;
                     b1    = nw;
                     b2    = se;
-                    alpha = isTrue ? -dy / dx : dx / -dy;
+                    alpha = isDyMagGreater ? dy / dx : dx / dy;
                 }
             }
 
diff --git a/test/data b/test/data
index c556bc9870..a7bbe5ee63 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit c556bc98706f3eee5c0e567d909b443182a6d1aa
+Subproject commit a7bbe5ee6376ba23ac50687866c28b94620104fd

From 2fea8ac5391fdfd0a18c19bea69e23de70bfae0f Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Fri, 26 Jul 2019 23:58:19 -0400
Subject: [PATCH 1729/2677] Restore behaviour of af_approx* functions. Rename
 new function to af_approx*_v2

* Restore af_approx1's old output array allocation, rename new functionality
as v2
* Consolidate the two af_approx1_uniforms into af_approx1_common
* Add docs for approx _v2 functions
* Add tests for af_approx_uniform_v2
* Make tests for special output arrays into typed tests
* Make tests for special output arrays for uniform approx typed
---
 include/af/signal.h  |  75 ++++++++++++--
 src/api/c/approx.cpp |  77 +++++++++-----
 test/approx1.cpp     | 237 +++++++++++++++++++++++++++++++++++++++----
 3 files changed, 337 insertions(+), 52 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index b4f739d772..0741350de0 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -753,7 +753,31 @@ extern "C" {
 /**
    C Interface for signals interpolation on one dimensional signals.
 
-   \param[in,out] out      is the interpolated array.
+   \param[out]    out      is the interpolated array.
+   \param[in]     in       is the multidimensional input array. Values assumed to
+                           lie uniformly spaced indices in the range of `[0, n)`,
+                           where `n` is the number of elements in the array.
+   \param[in]     pos      positions of the interpolation points along the first
+                           dimension.
+   \param[in]     method   is the interpolation method to be used. The following
+                           types (defined in enum \ref af_interp_type)
+                           are supported: nearest neighbor, linear, and cubic.
+   \param[in]     off_grid is the default value for any indices outside the
+                           valid range of indices.
+   \return        \ref AF_SUCCESS if the interpolation operation is successful,
+                  otherwise an appropriate error code is returned.
+
+   \ingroup signal_func_approx1
+ */
+AFAPI af_err af_approx1(af_array *out, const af_array in, const af_array pos,
+                        const af_interp_type method, const float off_grid);
+
+#if AF_API_VERSION >= 37
+/**
+   C Interface for the version of \ref af_approx1 that accepts a preallocated
+   output array
+
+   \param[in,out] out      is the interpolated array (can be preallocated).
    \param[in]     in       is the multidimensional input array. Values assumed to
                            lie uniformly spaced indices in the range of `[0, n)`,
                            where `n` is the number of elements in the array.
@@ -770,11 +794,14 @@ extern "C" {
    \note \p out can either be a null or existing `af_array` object. If it is a
          sub-array of an existing `af_array`, only the corresponding portion of
          the `af_array` will be overwritten
+   \note Passing an `af_array` that has not been initialized to \p out will cause
+         undefined behavior.
 
    \ingroup signal_func_approx1
  */
-AFAPI af_err af_approx1(af_array *out, const af_array in, const af_array pos,
-                        const af_interp_type method, const float off_grid);
+AFAPI af_err af_approx1_v2(af_array *out, const af_array in, const af_array pos,
+                           const af_interp_type method, const float off_grid);
+#endif
 
 /**
    C Interface for signals interpolation on two dimensional signals.
@@ -810,7 +837,7 @@ AFAPI af_err af_approx2(af_array *out, const af_array in, const af_array pos0, c
    The blue dots represent indices whose values are known. The red dots
    represent indices whose values are unknown.
 
-   \param[in,out] out        the interpolated array.
+   \param[out]    out        the interpolated array.
    \param[in]     in         is the multidimensional input array. Values lie on
                              uniformly spaced indices determined by `idx_start`
                              and `idx_step`.
@@ -829,10 +856,6 @@ AFAPI af_err af_approx2(af_array *out, const af_array in, const af_array pos0, c
    \return        \ref AF_SUCCESS if the interpolation operation is successful,
                   otherwise an appropriate error code is returned.
 
-   \note \p out can either be a null or existing `af_array` object. If it is a
-         sub-array of an existing `af_array`, only the corresponding portion of
-         the `af_array` will be overwritten
-
    \ingroup signal_func_approx1
  */
 AFAPI af_err af_approx1_uniform(af_array *out, const af_array in,
@@ -840,6 +863,42 @@ AFAPI af_err af_approx1_uniform(af_array *out, const af_array in,
                                 const double idx_start, const double idx_step,
                                 const af_interp_type method, const float off_grid);
 
+/**
+   C Interface for the version of \ref af_approx1_uniform that accepts a
+   preallocated output array
+
+   \param[in,out] out        the interpolated array (can be preallocated).
+   \param[in]     in         is the multidimensional input array. Values lie on
+                             uniformly spaced indices determined by `idx_start`
+                             and `idx_step`.
+   \param[in]     pos        positions of the interpolation points along
+                             `interp_dim`.
+   \param[in]     interp_dim is the dimension to perform interpolation across.
+   \param[in]     idx_start  is the first index value along `interp_dim`.
+   \param[in]     idx_step   is the uniform spacing value between subsequent
+                             indices along `interp_dim`.
+   \param[in]     method     is the interpolation method to be used. The
+                             following types (defined in enum
+                             \ref af_interp_type) are supported: nearest
+                             neighbor, linear, and cubic.
+   \param[in]     off_grid   is the default value for any indices outside the
+                             valid range of indices.
+   \return        \ref AF_SUCCESS if the interpolation operation is successful,
+                  otherwise an appropriate error code is returned.
+
+   \note \p out can either be a null or existing `af_array` object. If it is a
+         sub-array of an existing `af_array`, only the corresponding portion of
+         the `af_array` will be overwritten
+   \note Passing an `af_array` to \p out that has not been initialized will cause
+         undefined behavior.
+
+   \ingroup signal_func_approx1
+ */
+AFAPI af_err af_approx1_uniform_v2(af_array *out, const af_array in,
+                                   const af_array pos, const int interp_dim,
+                                   const double idx_start, const double idx_step,
+                                   const af_interp_type method, const float off_grid);
+
 /**
    C Interface for signals interpolation on two dimensional signals alog specified dimensions.
 
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 321048e5d8..565efa4418 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -43,27 +43,30 @@ static inline af_array approx2(const af_array zi, const af_array xo,
                                  yi_beg, yi_step, method, offGrid));
 }
 
-af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
-                          const int xdim, const double xi_beg,
-                          const double xi_step, const af_interp_type method,
-                          const float offGrid) {
+af_err af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
+                         const int xdim, const double xi_beg,
+                         const double xi_step, const af_interp_type method,
+                         const float offGrid, const bool allocate_yo) {
     try {
+        ARG_ASSERT(0, yo != 0);
+        ARG_ASSERT(1, yi != 0);
+        ARG_ASSERT(2, xo != 0);
+
         const ArrayInfo &yi_info = getInfo(yi);
         const ArrayInfo &xo_info = getInfo(xo);
 
         const dim4 yi_dims = yi_info.dims();
         const dim4 xo_dims = xo_info.dims();
+        dim4 yo_dims  = yi_dims;
+        yo_dims[xdim] = xo_dims[xdim];
 
-        ARG_ASSERT(1, yi_info.isFloating());  // Only floating and complex types
-        ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
-        ARG_ASSERT(1, yi_info.isSingle() ==
-                          xo_info.isSingle());  // Must have same precision
-        ARG_ASSERT(1, yi_info.isDouble() ==
-                          xo_info.isDouble());  // Must have same precision
+        ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
+        ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
+        ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle());    // Must have same precision
+        ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble());    // Must have same precision
         ARG_ASSERT(3, xdim >= 0 && xdim < 4);
 
-        // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2],
-        // yi_dims[3])
+        // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
         if (xo_dims[xdim] != xo_dims.elements()) {
             for (int i = 0; i < 4; i++) {
                 if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
@@ -71,20 +74,20 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
         }
 
         ARG_ASSERT(5, xi_step != 0);
-        ARG_ASSERT(
-            6,
-            (method == AF_INTERP_CUBIC || method == AF_INTERP_CUBIC_SPLINE ||
-             method == AF_INTERP_LINEAR || method == AF_INTERP_LINEAR_COSINE ||
-             method == AF_INTERP_LOWER || method == AF_INTERP_NEAREST));
+        ARG_ASSERT(6, (method == AF_INTERP_CUBIC         ||
+                       method == AF_INTERP_CUBIC_SPLINE  ||
+                       method == AF_INTERP_LINEAR        ||
+                       method == AF_INTERP_LINEAR_COSINE ||
+                       method == AF_INTERP_LOWER         ||
+                       method == AF_INTERP_NEAREST));
 
         if (yi_dims.ndims() == 0 || xo_dims.ndims() == 0) {
-            *yo = createHandle(dim4(0, 0, 0, 0), yi_info.getType());
-            return AF_SUCCESS;
+            return af_create_handle(yo, 0, nullptr, yi_info.getType());
         }
 
-        dim4 yo_dims  = yi_dims;
-        yo_dims[xdim] = xo_dims[xdim];
-        if (*yo == 0) { *yo = createHandle(yo_dims, yi_info.getType()); }
+        if (allocate_yo) {
+            *yo = createHandle(yo_dims, yi_info.getType());
+        }
 
         DIM_ASSERT(1, getInfo(*yo).dims() == yo_dims);
 
@@ -113,9 +116,37 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
     return AF_SUCCESS;
 }
 
+af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
+                          const int xdim, const double xi_beg,
+                          const double xi_step, const af_interp_type method,
+                          const float offGrid) {
+    return af_approx1_common(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid,
+                             true);
+}
+
+af_err af_approx1_uniform_v2(af_array *yo, const af_array yi, const af_array xo,
+                             const int xdim, const double xi_beg,
+                             const double xi_step, const af_interp_type method,
+                             const float offGrid) {
+    if (yo == 0) return AF_ERR_ARG;
+    // Since this v2, assume that the output has already been initialized
+    // either to null or an existing af_array
+    return af_approx1_common(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid,
+                             *yo == 0);
+}
+
 af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
                   const af_interp_type method, const float offGrid) {
-    return af_approx1_uniform(yo, yi, xo, 0, 0.0, 1.0, method, offGrid);
+    return af_approx1_common(yo, yi, xo, 0, 0.0, 1.0, method, offGrid, true);
+}
+
+af_err af_approx1_v2(af_array *yo, const af_array yi, const af_array xo,
+                     const af_interp_type method, const float offGrid) {
+    if (yo == 0) return AF_ERR_ARG;
+    // Since this is v2, assume that the output has already been initialized
+    // either to null or an existing af_array
+    return af_approx1_common(yo, yi, xo, 0, 0.0, 1.0, method, offGrid,
+                             *yo == 0);
 }
 
 af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 5a97db36e2..20c568c24e 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -829,25 +829,47 @@ TEST(Approx1, CPPEmptyPosAndInput) {
     ASSERT_TRUE(interp.isempty());
 }
 
+template<typename T>
 void testSpclOutArray(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
                       float* h_pos, dim4 pos_dims,
                       TestOutputArrayType out_array_type) {
+    SUPPORTED_TYPE_CHECK(T);
+    typedef typename dtype_traits<T>::base_type BT;
+
+    vector<T> h_gold_cast(gold_dims.elements());
+    vector<T> h_in_cast(in_dims.elements());
+    vector<BT> h_pos_cast(pos_dims.elements());
+
+    for (int i = 0; i < gold_dims.elements(); ++i) {
+        h_gold_cast[i] = static_cast<T>(h_gold[i]);
+    }
+    for (int i = 0; i < in_dims.elements(); ++i) {
+        h_in_cast[i] = static_cast<T>(h_in[i]);
+    }
+    for (int i = 0; i < pos_dims.elements(); ++i) {
+        h_pos_cast[i] = static_cast<BT>(h_pos[i]);
+    }
+
     af_array in  = 0;
     af_array pos = 0;
-    ASSERT_SUCCESS(
-        af_create_array(&in, h_in, in_dims.ndims(), in_dims.get(), f32));
-    ASSERT_SUCCESS(
-        af_create_array(&pos, h_pos, pos_dims.ndims(), pos_dims.get(), f32));
+
+    ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
+                                   in_dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos, &h_pos_cast.front(), pos_dims.ndims(),
+                                   pos_dims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
 
     af_array out = 0;
     TestOutputArrayInfo metadata(out_array_type);
-    genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(), f32,
-                       &metadata);
-    ASSERT_SUCCESS(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0));
+    genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                       (af_dtype)dtype_traits<T>::af_type, &metadata);
+    ASSERT_SUCCESS(af_approx1_v2(&out, in, pos, AF_INTERP_LINEAR, 0));
 
     af_array gold = 0;
-    ASSERT_SUCCESS(af_create_array(&gold, h_gold, gold_dims.ndims(),
-                                   gold_dims.get(), f32));
+    ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
+                                   gold_dims.ndims(), gold_dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
 
@@ -856,7 +878,7 @@ void testSpclOutArray(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
     if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
 }
 
-TEST(Approx1, UseNullOutputArray) {
+TYPED_TEST(Approx1, UseNullOutputArray) {
     float h_in[3] = {10.0f, 20.0f, 30.0f};
     dim4 in_dims(3);
 
@@ -867,11 +889,11 @@ TEST(Approx1, UseNullOutputArray) {
     dim4 gold_dims(5);
 
     SCOPED_TRACE("UseNullOutputArray");
-    testSpclOutArray(h_gold, gold_dims, h_in, in_dims, h_pos, pos_dims,
-                     NULL_ARRAY);
+    testSpclOutArray<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
+                                pos_dims, NULL_ARRAY);
 }
 
-TEST(Approx1, UseFullExistingOutputArray) {
+TYPED_TEST(Approx1, UseFullExistingOutputArray) {
     float h_in[3] = {10.0f, 20.0f, 30.0f};
     dim4 in_dims(3);
 
@@ -882,11 +904,11 @@ TEST(Approx1, UseFullExistingOutputArray) {
     dim4 gold_dims(5);
 
     SCOPED_TRACE("UseFullExistingOutputArray");
-    testSpclOutArray(h_gold, gold_dims, h_in, in_dims, h_pos, pos_dims,
-                     FULL_ARRAY);
+    testSpclOutArray<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
+                                pos_dims, FULL_ARRAY);
 }
 
-TEST(Approx1, UseExistingOutputSubArray) {
+TYPED_TEST(Approx1, UseExistingOutputSubArray) {
     float h_in[3] = {10.0f, 20.0f, 30.0f};
     dim4 in_dims(3);
 
@@ -897,11 +919,125 @@ TEST(Approx1, UseExistingOutputSubArray) {
     dim4 gold_subarr_dims(5);
 
     SCOPED_TRACE("UseExistingOutputSubArray");
-    testSpclOutArray(h_gold_subarr, gold_subarr_dims, h_in, in_dims, h_pos,
-                     pos_dims, SUB_ARRAY);
+    testSpclOutArray<TypeParam>(h_gold_subarr, gold_subarr_dims, h_in, in_dims,
+                                h_pos, pos_dims, SUB_ARRAY);
 }
 
-TEST(Approx1, UseReorderedOutputArray) {
+TYPED_TEST(Approx1, UseReorderedOutputArray) {
+    float h_in[9] = {10.0f, 20.0f, 30.0f,
+                     40.0f, 50.0f, 60.0f,
+                     70.0f, 80.0f, 90.0f};
+    dim4 in_dims(3, 3);
+
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims(5);
+
+    float h_gold[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
+                        40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
+                        70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
+    dim4 gold_dims(5, 3);
+
+    SCOPED_TRACE("UseReorderedOutputArray");
+    testSpclOutArray<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
+                                pos_dims, REORDERED_ARRAY);
+}
+
+template<typename T>
+void testSpclOutArrayUniform(float* h_gold, dim4 gold_dims, float* h_in,
+                             dim4 in_dims, float* h_pos, dim4 pos_dims,
+                             TestOutputArrayType out_array_type) {
+    SUPPORTED_TYPE_CHECK(T);
+    typedef typename dtype_traits<T>::base_type BT;
+
+    vector<T> h_gold_cast(gold_dims.elements());
+    vector<T> h_in_cast(in_dims.elements());
+    vector<BT> h_pos_cast(pos_dims.elements());
+
+    for (int i = 0; i < gold_dims.elements(); ++i) {
+        h_gold_cast[i] = static_cast<T>(h_gold[i]);
+    }
+    for (int i = 0; i < in_dims.elements(); ++i) {
+        h_in_cast[i] = static_cast<T>(h_in[i]);
+    }
+    for (int i = 0; i < pos_dims.elements(); ++i) {
+        h_pos_cast[i] = static_cast<BT>(h_pos[i]);
+    }
+
+    af_array in  = 0;
+    af_array pos = 0;
+
+    ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
+                                   in_dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&pos, &h_pos_cast.front(), pos_dims.ndims(),
+                                   pos_dims.get(),
+                                   (af_dtype)dtype_traits<BT>::af_type));
+
+    af_array out = 0;
+    TestOutputArrayInfo metadata(out_array_type);
+    genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                       (af_dtype)dtype_traits<T>::af_type, &metadata);
+    ASSERT_SUCCESS(af_approx1_uniform_v2(&out, in, pos, 0, 0.0, 1.0,
+                                         AF_INTERP_LINEAR, 0.f));
+
+    af_array gold = 0;
+    ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
+                                   gold_dims.ndims(), gold_dims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+
+    ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+
+    if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
+    if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
+    if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
+}
+
+TYPED_TEST(Approx1, UseNullOutputArrayUniform) {
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
+
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims(5);
+
+    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
+    dim4 gold_dims(5);
+
+    SCOPED_TRACE("UseNullOutputArray");
+    testSpclOutArrayUniform<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
+                                       pos_dims, NULL_ARRAY);
+}
+
+TYPED_TEST(Approx1, UseFullExistingOutputArrayUniform) {
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
+
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims(5);
+
+    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
+    dim4 gold_dims(5);
+
+    SCOPED_TRACE("UseFullExistingOutputArray");
+    testSpclOutArrayUniform<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
+                                       pos_dims, FULL_ARRAY);
+}
+
+TYPED_TEST(Approx1, UseExistingOutputSubArrayUniform) {
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
+
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims(5);
+
+    float h_gold_subarr[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
+    dim4 gold_subarr_dims(5);
+
+    SCOPED_TRACE("UseExistingOutputSubArray");
+    testSpclOutArrayUniform<TypeParam>(h_gold_subarr, gold_subarr_dims, h_in,
+                                       in_dims, h_pos, pos_dims, SUB_ARRAY);
+}
+
+TYPED_TEST(Approx1, UseReorderedOutputArrayUniform) {
     float h_in[9] = {10.0f, 20.0f, 30.0f, 40.0f, 50.0f,
                      60.0f, 70.0f, 80.0f, 90.0f};
     dim4 in_dims(3, 3);
@@ -914,6 +1050,65 @@ TEST(Approx1, UseReorderedOutputArray) {
     dim4 gold_dims(5, 3);
 
     SCOPED_TRACE("UseReorderedOutputArray");
-    testSpclOutArray(h_gold, gold_dims, h_in, in_dims, h_pos, pos_dims,
-                     REORDERED_ARRAY);
+    testSpclOutArrayUniform<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
+                                       pos_dims, REORDERED_ARRAY);
+}
+
+TEST(Approx1, NullOutputPtrApprox1) {
+    af_array* out_ptr = 0;
+    af_array in       = 0;
+    af_array pos      = 0;
+    ASSERT_EQ(af_approx1(out_ptr, in, pos, AF_INTERP_LINEAR, 0.f), AF_ERR_ARG);
+}
+
+TEST(Approx1, NullOutputPtrApprox1Uniform) {
+    af_array* out_ptr = 0;
+    af_array in       = 0;
+    af_array pos      = 0;
+    ASSERT_EQ(af_approx1_uniform(out_ptr, in, pos, 0, 0.0, 1.0,
+                                 AF_INTERP_LINEAR, 0.f),
+              AF_ERR_ARG);
+}
+
+TEST(Approx1, NullOutputPtrApprox1V2) {
+    af_array* out_ptr = 0;
+    af_array in       = 0;
+    af_array pos      = 0;
+    ASSERT_EQ(af_approx1_v2(out_ptr, in, pos, AF_INTERP_LINEAR, 0.f),
+              AF_ERR_ARG);
+}
+
+TEST(Approx1, NullOutputPtrApprox1UniformV2) {
+    af_array* out_ptr = 0;
+    af_array in       = 0;
+    af_array pos      = 0;
+    ASSERT_EQ(af_approx1_uniform_v2(out_ptr, in, pos, 0, 0.0, 1.0,
+                                    AF_INTERP_LINEAR, 0.f),
+              AF_ERR_ARG);
+}
+
+TEST(Approx1, NullInputArray) {
+    af_array out = 0;
+    af_array in  = 0;
+    af_array pos = 0;
+
+    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
+    dim4 pos_dims(5);
+    ASSERT_SUCCESS(
+        af_create_array(&pos, h_pos, pos_dims.ndims(), pos_dims.get(), f32));
+
+    ASSERT_EQ(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0.f), AF_ERR_ARG);
+}
+
+TEST(Approx1, NullPosArray) {
+    af_array out = 0;
+    af_array in  = 0;
+    af_array pos = 0;
+
+    float h_in[3] = {10.0f, 20.0f, 30.0f};
+    dim4 in_dims(3);
+    ASSERT_SUCCESS(
+        af_create_array(&in, h_in, in_dims.ndims(), in_dims.get(), f32));
+
+    ASSERT_EQ(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0.f), AF_ERR_ARG);
 }

From a099f813869811e1599fdc289b74bc43b63735ed Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 30 Jul 2019 15:17:31 -0400
Subject: [PATCH 1730/2677] Add approx1_*_v2 functions to the unified backend

---
 src/api/unified/signal.cpp | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 8d9c7ac4dd..15e6bdd44a 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -17,6 +17,12 @@ af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
     return CALL(yo, yi, xo, method, offGrid);
 }
 
+af_err af_approx1_v2(af_array *yo, const af_array yi, const af_array xo,
+                  const af_interp_type method, const float offGrid) {
+  CHECK_ARRAYS(yi, xo);
+  return CALL(yo, yi, xo, method, offGrid);
+}
+
 af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
                   const af_array yo, const af_interp_type method,
                   const float offGrid) {
@@ -32,6 +38,14 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
     return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
 }
 
+af_err af_approx1_uniform_v2(af_array *yo, const af_array yi, const af_array xo,
+                             const int xdim, const double xi_beg,
+                             const double xi_step, const af_interp_type method,
+                             const float offGrid) {
+    CHECK_ARRAYS(yi, xo);
+    return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
+}
+
 af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
                           const int xdim, const double xi_beg,
                           const double xi_step, const af_array yo,

From e9e90f00d538e3c58cf0f83113553b4d86a002d5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 30 Jul 2019 15:17:53 -0400
Subject: [PATCH 1731/2677] Remove print from set_policies

---
 CMakeModules/InternalUtils.cmake | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 0cbb5cbc48..bf45b750f2 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -191,7 +191,6 @@ macro(set_policies)
   cmake_parse_arguments(SP "" "TYPE" "POLICIES" ${ARGN})
   foreach(_policy ${SP_POLICIES})
     if(POLICY ${_policy})
-      message(STATUS ${_policy} ${SP_TYPE})
       cmake_policy(SET ${_policy} ${SP_TYPE})
     endif()
   endforeach()

From 9b307e73f852afeae092f71378ceb9afc1af27d8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 30 Jul 2019 16:27:35 -0400
Subject: [PATCH 1732/2677] Update the CHECK_ARRAY macro to accept pointer to
 af_array.

---
 src/api/unified/blas.cpp           |  2 +-
 src/api/unified/signal.cpp         | 14 +++++++-------
 src/api/unified/symbol_manager.cpp | 10 +++++++++-
 src/api/unified/symbol_manager.hpp | 12 +++++++++---
 4 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/src/api/unified/blas.cpp b/src/api/unified/blas.cpp
index a4f1f5788a..e82b9994ed 100644
--- a/src/api/unified/blas.cpp
+++ b/src/api/unified/blas.cpp
@@ -14,7 +14,7 @@ AFAPI af_err af_gemm(af_array *out,
                      const af_mat_prop optLhs, const af_mat_prop optRhs,
                      const void* alpha, const af_array lhs, const af_array rhs,
                      const void* beta) {
-    CHECK_ARRAYS(*out, lhs, rhs);
+    CHECK_ARRAYS(out, lhs, rhs);
     return CALL(out, optLhs, optRhs, alpha, lhs, rhs, beta);
 }
 
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 15e6bdd44a..8491cb234e 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -13,20 +13,20 @@
 
 af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
                   const af_interp_type method, const float offGrid) {
-    CHECK_ARRAYS(yi, xo);
+    CHECK_ARRAYS(yo, yi, xo);
     return CALL(yo, yi, xo, method, offGrid);
 }
 
 af_err af_approx1_v2(af_array *yo, const af_array yi, const af_array xo,
-                  const af_interp_type method, const float offGrid) {
-  CHECK_ARRAYS(yi, xo);
-  return CALL(yo, yi, xo, method, offGrid);
+                     const af_interp_type method, const float offGrid) {
+    CHECK_ARRAYS(yo, yi, xo);
+    return CALL(yo, yi, xo, method, offGrid);
 }
 
 af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
                   const af_array yo, const af_interp_type method,
                   const float offGrid) {
-    CHECK_ARRAYS(zi, xo, yo);
+    CHECK_ARRAYS(zo, zi, xo, yo);
     return CALL(zo, zi, xo, yo, method, offGrid);
 }
 
@@ -34,7 +34,7 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
                           const int xdim, const double xi_beg,
                           const double xi_step, const af_interp_type method,
                           const float offGrid) {
-    CHECK_ARRAYS(yi, xo);
+    CHECK_ARRAYS(yo, yi, xo);
     return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
 }
 
@@ -42,7 +42,7 @@ af_err af_approx1_uniform_v2(af_array *yo, const af_array yi, const af_array xo,
                              const int xdim, const double xi_beg,
                              const double xi_step, const af_interp_type method,
                              const float offGrid) {
-    CHECK_ARRAYS(yi, xo);
+    CHECK_ARRAYS(yo, yi, xo);
     return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
 }
 
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index f6d14e6fc0..696a2f5488 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -230,7 +230,7 @@ af_err AFSymbolManager::setBackend(af::Backend bknd) {
     }
 }
 
-bool checkArray(af_backend activeBackend, af_array a) {
+bool checkArray(af_backend activeBackend, const af_array a) {
     // Convert af_array into int to retrieve the backend info.
     // See ArrayInfo.hpp for more
     af_backend backend = (af_backend)0;
@@ -246,6 +246,14 @@ bool checkArray(af_backend activeBackend, af_array a) {
     return backend == activeBackend;
 }
 
+bool checkArray(af_backend activeBackend, const af_array* a) {
+    if (a) {
+        return checkArray(activeBackend, *a);
+    } else {
+        return true;
+    }
+}
+
 bool checkArrays(af_backend activeBackend) {
     UNUSED(activeBackend);
     // Dummy
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 48459e90c2..da8d4a7a87 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -106,7 +106,8 @@ class AFSymbolManager {
 };
 
 // Helper functions to ensure all the input arrays are on the active backend
-bool checkArray(af_backend activeBackend, af_array a);
+bool checkArray(af_backend activeBackend, const af_array a);
+bool checkArray(af_backend activeBackend, const af_array *a);
 bool checkArrays(af_backend activeBackend);
 
 template<typename T, typename... Args>
@@ -116,8 +117,13 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 
 }  // namespace unified
 
-// Macro to check af_array as inputs. The arguments to this macro should be
-// only input af_arrays. Not outputs or other types.
+/// Checks if the active backend and the af_arrays are the same.
+///
+/// Checks if the active backend and the af_array's backend match. If they do
+/// not match, an error is returned. This macro accepts pointer to af_arrays
+/// and af_arrays. Null pointers to af_arrays are considered acceptable.
+///
+/// \param[in] Any number of af_arrays or pointer to af_arrays
 #define CHECK_ARRAYS(...)                                                     \
     do {                                                                      \
         af_backend backendId =                                                \

From addd08981afd79245792c37960e872679a854784 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Mon, 5 Aug 2019 13:36:49 -0700
Subject: [PATCH 1733/2677] add a JIT half test

---
 test/jit.cpp | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/test/jit.cpp b/test/jit.cpp
index 847b948ef5..3e315400ea 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -619,18 +619,18 @@ TEST(JIT, LargeJitTree) {
     });
 }
 
-TEST(JIT, TwoLargeNonLinear) {
+void testTwoLargeNonLinear(const af_dtype dt) {
     int dimsize = 10;
-    array a     = constant(0, dimsize, dimsize);
-    array aa    = constant(0, dimsize, dimsize);
-    array b     = constant(0, dimsize, dimsize);
-    array bb    = constant(0, dimsize, dimsize);
+    array a     = constant(0, dimsize, dimsize, dt);
+    array aa    = constant(0, dimsize, dimsize, dt);
+    array b     = constant(0, dimsize, dimsize, dt);
+    array bb    = constant(0, dimsize, dimsize, dt);
 
     int val = 0;
     for (int i = 0; i < 23; i++) {
-        array ones = constant(1, dimsize, dimsize);
+        array ones = constant(1, dimsize, dimsize, dt);
         ones.eval();
-        array twos = constant(2, dimsize);
+        array twos = constant(2, dimsize, dt);
         twos.eval();
 
         a += tile(twos, 1, dimsize) + ones;
@@ -639,9 +639,9 @@ TEST(JIT, TwoLargeNonLinear) {
     }
 
     for (int i = 0; i < 23; i++) {
-        array ones = constant(1, dimsize, dimsize);
+        array ones = constant(1, dimsize, dimsize, dt);
         ones.eval();
-        array twos = constant(2, dimsize);
+        array twos = constant(2, dimsize, dt);
         twos.eval();
         b += tile(twos, 1, dimsize) + ones;
         bb += tile(twos, 1, dimsize) + ones;
@@ -651,7 +651,16 @@ TEST(JIT, TwoLargeNonLinear) {
     eval(c, cc);
 
     vector<float> gold(a.elements(), val * 2);
-    ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c);
+    ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c.as(f32));
+}
+
+TEST(JIT, TwoLargeNonLinear) {
+  testTwoLargeNonLinear(f32);
+}
+
+TEST(JIT, TwoLargeNonLinearHalf) {
+  if (noHalfTests(f16)) return;
+  testTwoLargeNonLinear(f16);
 }
 
 std::string select_info(

From bc1e32cbdcfc31dde80f1ff4394e70b66e3b6313 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Thu, 8 Aug 2019 13:22:17 -0400
Subject: [PATCH 1734/2677] Add af_approx2*_v2 functions (#2604)

* Add approx2*_v2 functions
* Make typed test fixtures for approx2 tests
---
 include/af/signal.h           | 278 ++++++++++++++++-------
 src/api/c/approx.cpp          | 352 ++++++++++++++++-------------
 src/api/unified/signal.cpp    |  20 +-
 src/backend/cpu/approx.cpp    |  26 +--
 src/backend/cpu/approx.hpp    |   9 +-
 src/backend/cuda/approx.cpp   |  27 +--
 src/backend/cuda/approx.hpp   |   9 +-
 src/backend/opencl/approx.cpp |  28 +--
 src/backend/opencl/approx.hpp |   9 +-
 test/approx1.cpp              | 403 ++++++++++++++++------------------
 test/approx2.cpp              | 306 ++++++++++++++++++++++++++
 11 files changed, 960 insertions(+), 507 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index 0741350de0..6d148dc7c6 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -753,19 +753,21 @@ extern "C" {
 /**
    C Interface for signals interpolation on one dimensional signals.
 
-   \param[out]    out      is the interpolated array.
-   \param[in]     in       is the multidimensional input array. Values assumed to
-                           lie uniformly spaced indices in the range of `[0, n)`,
-                           where `n` is the number of elements in the array.
-   \param[in]     pos      positions of the interpolation points along the first
-                           dimension.
-   \param[in]     method   is the interpolation method to be used. The following
-                           types (defined in enum \ref af_interp_type)
-                           are supported: nearest neighbor, linear, and cubic.
-   \param[in]     off_grid is the default value for any indices outside the
+   \param[out] out      is the interpolated array.
+   \param[in]  in       is the multidimensional input array. Values assumed
+                        to lie uniformly spaced indices in the range of
+                        `[0, n)`, where `n` is the number of elements in the
+                        array.
+   \param[in]  pos      positions of the interpolation points along the first
+                        dimension.
+   \param[in]  method   is the interpolation method to be used. The following
+                        types (defined in enum \ref af_interp_type)
+                        are supported: nearest neighbor, linear, and cubic.
+   \param[in]  off_grid is the default value for any indices outside the
                            valid range of indices.
-   \return        \ref AF_SUCCESS if the interpolation operation is successful,
-                  otherwise an appropriate error code is returned.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
 
    \ingroup signal_func_approx1
  */
@@ -778,9 +780,10 @@ AFAPI af_err af_approx1(af_array *out, const af_array in, const af_array pos,
    output array
 
    \param[in,out] out      is the interpolated array (can be preallocated).
-   \param[in]     in       is the multidimensional input array. Values assumed to
-                           lie uniformly spaced indices in the range of `[0, n)`,
-                           where `n` is the number of elements in the array.
+   \param[in]     in       is the multidimensional input array. Values assumed
+                           to lie uniformly spaced indices in the range of
+                           `[0, n)`, where `n` is the number of elements in the
+                           array.
    \param[in]     pos      positions of the interpolation points along the first
                            dimension.
    \param[in]     method   is the interpolation method to be used. The following
@@ -788,14 +791,15 @@ AFAPI af_err af_approx1(af_array *out, const af_array in, const af_array pos,
                            are supported: nearest neighbor, linear, and cubic.
    \param[in]     off_grid is the default value for any indices outside the
                            valid range of indices.
-   \return        \ref AF_SUCCESS if the interpolation operation is successful,
-                  otherwise an appropriate error code is returned.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
 
    \note \p out can either be a null or existing `af_array` object. If it is a
          sub-array of an existing `af_array`, only the corresponding portion of
          the `af_array` will be overwritten
-   \note Passing an `af_array` that has not been initialized to \p out will cause
-         undefined behavior.
+   \note Passing an `af_array` that has not been initialized to \p out will
+         cause undefined behavior.
 
    \ingroup signal_func_approx1
  */
@@ -806,62 +810,112 @@ AFAPI af_err af_approx1_v2(af_array *out, const af_array in, const af_array pos,
 /**
    C Interface for signals interpolation on two dimensional signals.
 
-   \param[out] out the interpolated array.
-   \param[in]  in is the multidimensional input array. Values assumed to lie uniformly spaced indices in the range of `[0, n)` along both interpolation dimensions. `n` is the number of elements in the array.
-   \param[in]  pos0 positions of the interpolation points along the first dimension.
-   \param[in]  pos1 positions of the interpolation points along the second dimension.
-   \param[in]  method is the interpolation method to be used. All interpolation types defined in \ref af_interp_type are supported.
-   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
-   \return     \ref AF_SUCCESS if the interpolation operation is successful,
-               otherwise an appropriate error code is returned.
+   \param[out] out      the interpolated array.
+   \param[in]  in       is the multidimensional input array. Values assumed to
+                        lie uniformly spaced indices in the range of `[0, n)`
+                        along both interpolation dimensions. `n` is the number
+                        of elements in the array.
+   \param[in]  pos0     positions of the interpolation points along the first
+                        dimension.
+   \param[in]  pos1     positions of the interpolation points along the second
+                        dimension.
+   \param[in]  method   is the interpolation method to be used. All
+                        interpolation types defined in \ref af_interp_type are
+                        supported.
+   \param[in]  off_grid is the default value for any indices outside the valid
+                        range of indices.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
 
    \ingroup signal_func_approx2
  */
-AFAPI af_err af_approx2(af_array *out, const af_array in, const af_array pos0, const af_array pos1,
+AFAPI af_err af_approx2(af_array *out, const af_array in,
+                        const af_array pos0, const af_array pos1,
                         const af_interp_type method, const float off_grid);
 
 #if AF_API_VERSION >= 37
 /**
-   C Interface for signals interpolation on one dimensional signals along specified dimension.
+   C Interface for the version of \ref af_approx2 that accepts a preallocated
+   output array
 
-   af_approx1_uniform() accepts the dimension to perform the
-   interpolation along the input. It also accepts start and step
-   values which define the uniform range of corresponding indices.
+   \param[in,out] out      the interpolated array (can be preallocated).
+   \param[in]     in       is the multidimensional input array. Values assumed
+                           to lie uniformly spaced indices in the range of
+                           `[0, n)` along both interpolation dimensions. `n` is
+                           the number of elements in the array.
+   \param[in]     pos0     positions of the interpolation points along the first
+                           dimension.
+   \param[in]     pos1     positions of the interpolation points along the
+                           second dimension.
+   \param[in]     method   is the interpolation method to be used. All
+                           interpolation types defined in \ref af_interp_type
+                           are supported.
+   \param[in]     off_grid is the default value for any indices outside the
+                           valid range of indices.
 
-   The following image illustrates what the range of indices
-   corresponding to the input values look like if `idx_start` and
-   `idx_step` are set to an arbitrary value of 10,
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
+
+   \note \p out can either be a null or existing `af_array` object. If it is a
+         sub-array of an existing `af_array`, only the corresponding portion of
+         the `af_array` will be overwritten
+   \note Passing an `af_array` to \p out that has not been initialized will
+         cause undefined behavior.
+
+   \ingroup signal_func_approx2
+ */
+AFAPI af_err af_approx2_v2(af_array *out, const af_array in,
+                           const af_array pos0, const af_array pos1,
+                           const af_interp_type method, const float off_grid);
+#endif
+
+
+#if AF_API_VERSION >= 37
+/**
+   C Interface for signals interpolation on one dimensional signals along
+   specified dimension.
+
+   af_approx1_uniform() accepts the dimension to perform the interpolation along
+   the input. It also accepts start and step values which define the uniform
+   range of corresponding indices.
+
+   The following image illustrates what the range of indices corresponding to
+   the input values look like if `idx_start` and `idx_step` are set to an
+   arbitrary value of 10,
 
    \image html approx1_arbitrary_idx.png "approx1() using idx_start=10.0, idx_step=10.0"
 
    The blue dots represent indices whose values are known. The red dots
    represent indices whose values are unknown.
 
-   \param[out]    out        the interpolated array.
-   \param[in]     in         is the multidimensional input array. Values lie on
-                             uniformly spaced indices determined by `idx_start`
-                             and `idx_step`.
-   \param[in]     pos        positions of the interpolation points along
-                             `interp_dim`.
-   \param[in]     interp_dim is the dimension to perform interpolation across.
-   \param[in]     idx_start  is the first index value along `interp_dim`.
-   \param[in]     idx_step   is the uniform spacing value between subsequent
-                             indices along `interp_dim`.
-   \param[in]     method     is the interpolation method to be used. The
-                             following types (defined in enum
-                             \ref af_interp_type) are supported: nearest
-                             neighbor, linear, and cubic.
-   \param[in]     off_grid   is the default value for any indices outside the
-                             valid range of indices.
-   \return        \ref AF_SUCCESS if the interpolation operation is successful,
-                  otherwise an appropriate error code is returned.
+   \param[out] out        the interpolated array.
+   \param[in]  in         is the multidimensional input array. Values lie on
+                          uniformly spaced indices determined by `idx_start`
+                          and `idx_step`.
+   \param[in]  pos        positions of the interpolation points along
+                          `interp_dim`.
+   \param[in]  interp_dim is the dimension to perform interpolation across.
+   \param[in]  idx_start  is the first index value along `interp_dim`.
+   \param[in]  idx_step   is the uniform spacing value between subsequent
+                          indices along `interp_dim`.
+   \param[in]  method     is the interpolation method to be used. The
+                          following types (defined in enum
+                          \ref af_interp_type) are supported: nearest
+                          neighbor, linear, and cubic.
+   \param[in]  off_grid   is the default value for any indices outside the
+                          valid range of indices.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
 
    \ingroup signal_func_approx1
  */
 AFAPI af_err af_approx1_uniform(af_array *out, const af_array in,
                                 const af_array pos, const int interp_dim,
                                 const double idx_start, const double idx_step,
-                                const af_interp_type method, const float off_grid);
+                                const af_interp_type method,
+                                const float off_grid);
 
 /**
    C Interface for the version of \ref af_approx1_uniform that accepts a
@@ -883,50 +937,116 @@ AFAPI af_err af_approx1_uniform(af_array *out, const af_array in,
                              neighbor, linear, and cubic.
    \param[in]     off_grid   is the default value for any indices outside the
                              valid range of indices.
-   \return        \ref AF_SUCCESS if the interpolation operation is successful,
-                  otherwise an appropriate error code is returned.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
 
    \note \p out can either be a null or existing `af_array` object. If it is a
          sub-array of an existing `af_array`, only the corresponding portion of
          the `af_array` will be overwritten
-   \note Passing an `af_array` to \p out that has not been initialized will cause
-         undefined behavior.
+   \note Passing an `af_array` to \p out that has not been initialized will
+         cause undefined behavior.
 
    \ingroup signal_func_approx1
  */
 AFAPI af_err af_approx1_uniform_v2(af_array *out, const af_array in,
                                    const af_array pos, const int interp_dim,
-                                   const double idx_start, const double idx_step,
-                                   const af_interp_type method, const float off_grid);
+                                   const double idx_start,
+                                   const double idx_step,
+                                   const af_interp_type method,
+                                   const float off_grid);
 
 /**
-   C Interface for signals interpolation on two dimensional signals alog specified dimensions.
+   C Interface for signals interpolation on two dimensional signals along
+   specified dimensions.
 
-   af_approx2_uniform() accepts two dimensions to perform the
-   interpolation along the input. It also accepts start and step
-   values which define the uniform range of corresponding indices.
+   af_approx2_uniform() accepts two dimensions to perform the interpolation
+   along the input. It also accepts start and step values which define the
+   uniform range of corresponding indices.
 
-   \param[out] out the interpolated array.
-   \param[in]  in is the multidimensional input array.
-   \param[in]  pos0 positions of the interpolation points along `interp_dim0`.
-   \param[in]  interp_dim0 is the first dimension to perform interpolation across.
+   \param[out] out            the interpolated array.
+   \param[in]  in             is the multidimensional input array.
+   \param[in]  pos0           positions of the interpolation points along
+                              `interp_dim0`.
+   \param[in]  interp_dim0    is the first dimension to perform interpolation
+                              across.
    \param[in]  idx_start_dim0 is the first index value along `interp_dim0`.
-   \param[in]  idx_step_dim0 is the uniform spacing value between subsequent indices along `interp_dim0`.
-   \param[in]  pos1 positions of the interpolation points along `interp_dim1`.
-   \param[in]  interp_dim1 is the second dimension to perform interpolation across.
+   \param[in]  idx_step_dim0  is the uniform spacing value between subsequent
+                              indices along `interp_dim0`.
+   \param[in]  pos1           positions of the interpolation points along
+                              `interp_dim1`.
+   \param[in]  interp_dim1    is the second dimension to perform interpolation
+                              across.
    \param[in]  idx_start_dim1 is the first index value along `interp_dim1`.
-   \param[in]  idx_step_dim1 is the uniform spacing value between subsequent indices along `interp_dim1`.
-   \param[in]  method is the interpolation method to be used. All interpolation types defined in \ref af_interp_type are supported.
-   \param[in]  off_grid is the default value for any indices outside the valid range of indices.
-   \return     \ref AF_SUCCESS if the interpolation operation is successful,
-               otherwise an appropriate error code is returned.
+   \param[in]  idx_step_dim1  is the uniform spacing value between subsequent
+                              indices along `interp_dim1`.
+   \param[in]  method         is the interpolation method to be used. All
+                              interpolation types defined in \ref af_interp_type
+                              are supported.
+   \param[in]  off_grid       is the default value for any indices outside the
+                              valid range of indices.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
 
    \ingroup signal_func_approx2
  */
 AFAPI af_err af_approx2_uniform(af_array *out, const af_array in,
-                                const af_array pos0, const int interp_dim0, const double idx_start_dim0, const double idx_step_dim0,
-                                const af_array pos1, const int interp_dim1, const double idx_start_dim1, const double idx_step_dim1,
-                                const af_interp_type method, const float off_grid);
+                                const af_array pos0, const int interp_dim0,
+                                const double idx_start_dim0,
+                                const double idx_step_dim0,
+                                const af_array pos1, const int interp_dim1,
+                                const double idx_start_dim1,
+                                const double idx_step_dim1,
+                                const af_interp_type method,
+                                const float off_grid);
+
+/**
+   C Interface for the version of \ref af_approx2_uniform that accepts a
+   preallocated output array
+
+   \param[in,out] out            the interpolated array.
+   \param[in]     in             is the multidimensional input array.
+   \param[in]     pos0           positions of the interpolation points along
+                                 `interp_dim0`.
+   \param[in]     interp_dim0    is the first dimension to perform interpolation
+                                 across.
+   \param[in]     idx_start_dim0 is the first index value along `interp_dim0`.
+   \param[in]     idx_step_dim0  is the uniform spacing value between subsequent
+                                 indices along `interp_dim0`.
+   \param[in]     pos1           positions of the interpolation points along
+                                 `interp_dim1`.
+   \param[in]     interp_dim1    is the second dimension to perform
+                                 interpolation across.
+   \param[in]     idx_start_dim1 is the first index value along `interp_dim1`.
+   \param[in]     idx_step_dim1  is the uniform spacing value between subsequent
+                                 indices along `interp_dim1`.
+   \param[in]     method         is the interpolation method to be used. All
+                                 interpolation types defined in
+                                 \ref af_interp_type are supported.
+   \param[in]     off_grid       is the default value for any indices outside
+                                 the valid range of indices.
+
+   \return \ref AF_SUCCESS if the interpolation operation is successful,
+           otherwise an appropriate error code is returned.
+
+   \note \p out can either be a null or existing `af_array` object. If it is a
+         sub-array of an existing `af_array`, only the corresponding portion of
+         the `af_array` will be overwritten
+   \note Passing an `af_array` to \p out that has not been initialized will
+         cause undefined behavior.
+
+   \ingroup signal_func_approx2
+ */
+AFAPI af_err af_approx2_uniform_v2(af_array *out, const af_array in,
+                                   const af_array pos0, const int interp_dim0,
+                                   const double idx_start_dim0,
+                                   const double idx_step_dim0,
+                                   const af_array pos1, const int interp_dim1,
+                                   const double idx_start_dim1,
+                                   const double idx_step_dim1,
+                                   const af_interp_type method,
+                                   const float off_grid);
 #endif
 
 /**
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index 565efa4418..d01e22a762 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -32,121 +32,202 @@ inline void approx1(af_array *yo, const af_array yi, const af_array xo,
 }  // namespace
 
 template<typename Ty, typename Tp>
-static inline af_array approx2(const af_array zi, const af_array xo,
-                               const int xdim, const Tp &xi_beg,
-                               const Tp &xi_step, const af_array yo,
-                               const int ydim, const Tp &yi_beg,
-                               const Tp &yi_step, const af_interp_type method,
-                               const float offGrid) {
-    return getHandle(approx2<Ty>(getArray<Ty>(zi), getArray<Tp>(xo), xdim,
-                                 xi_beg, xi_step, getArray<Tp>(yo), ydim,
-                                 yi_beg, yi_step, method, offGrid));
+inline void approx2(af_array *zo, const af_array zi, const af_array xo,
+                    const int xdim, const Tp &xi_beg, const Tp &xi_step,
+                    const af_array yo, const int ydim, const Tp &yi_beg,
+                    const Tp &yi_step, const af_interp_type method,
+                    const float offGrid) {
+    approx2<Ty>(getArray<Ty>(*zo), getArray<Ty>(zi), getArray<Tp>(xo), xdim,
+                xi_beg, xi_step, getArray<Tp>(yo), ydim, yi_beg, yi_step,
+                method, offGrid);
 }
 
-af_err af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
-                         const int xdim, const double xi_beg,
-                         const double xi_step, const af_interp_type method,
-                         const float offGrid, const bool allocate_yo) {
-    try {
-        ARG_ASSERT(0, yo != 0);
-        ARG_ASSERT(1, yi != 0);
-        ARG_ASSERT(2, xo != 0);
-
-        const ArrayInfo &yi_info = getInfo(yi);
-        const ArrayInfo &xo_info = getInfo(xo);
-
-        const dim4 yi_dims = yi_info.dims();
-        const dim4 xo_dims = xo_info.dims();
-        dim4 yo_dims  = yi_dims;
-        yo_dims[xdim] = xo_dims[xdim];
-
-        ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
-        ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
-        ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle());    // Must have same precision
-        ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble());    // Must have same precision
-        ARG_ASSERT(3, xdim >= 0 && xdim < 4);
-
-        // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
-        if (xo_dims[xdim] != xo_dims.elements()) {
-            for (int i = 0; i < 4; i++) {
-                if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
-            }
-        }
+void af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
+                       const int xdim, const double xi_beg,
+                       const double xi_step, const af_interp_type method,
+                       const float offGrid, const bool allocate_yo) {
+    ARG_ASSERT(0, yo != 0);  // *yo (the af_array) can be null, but not yo
+    ARG_ASSERT(1, yi != 0);
+    ARG_ASSERT(2, xo != 0);
 
-        ARG_ASSERT(5, xi_step != 0);
-        ARG_ASSERT(6, (method == AF_INTERP_CUBIC         ||
-                       method == AF_INTERP_CUBIC_SPLINE  ||
-                       method == AF_INTERP_LINEAR        ||
-                       method == AF_INTERP_LINEAR_COSINE ||
-                       method == AF_INTERP_LOWER         ||
-                       method == AF_INTERP_NEAREST));
+    const ArrayInfo &yi_info = getInfo(yi);
+    const ArrayInfo &xo_info = getInfo(xo);
 
-        if (yi_dims.ndims() == 0 || xo_dims.ndims() == 0) {
-            return af_create_handle(yo, 0, nullptr, yi_info.getType());
-        }
+    const dim4 yi_dims = yi_info.dims();
+    const dim4 xo_dims = xo_info.dims();
+    dim4 yo_dims       = yi_dims;
+    yo_dims[xdim]      = xo_dims[xdim];
 
-        if (allocate_yo) {
-            *yo = createHandle(yo_dims, yi_info.getType());
-        }
+    ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
+    ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
+    ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle());    // Must have same precision
+    ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble());    // Must have same precision
+    ARG_ASSERT(3, xdim >= 0 && xdim < 4);
 
-        DIM_ASSERT(1, getInfo(*yo).dims() == yo_dims);
-
-        switch (yi_info.getType()) {
-            case f32:
-                approx1<float, float>(yo, yi, xo, xdim, xi_beg, xi_step, method,
-                                      offGrid);
-                break;
-            case f64:
-                approx1<double, double>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                        method, offGrid);
-                break;
-            case c32:
-                approx1<cfloat, float>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       method, offGrid);
-                break;
-            case c64:
-                approx1<cdouble, double>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                         method, offGrid);
-                break;
-            default: TYPE_ERROR(1, yi_info.getType());
+    // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
+    if (xo_dims[xdim] != xo_dims.elements()) {
+        for (int i = 0; i < 4; i++) {
+            if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
         }
     }
-    CATCHALL;
 
-    return AF_SUCCESS;
+    ARG_ASSERT(5, xi_step != 0);
+    ARG_ASSERT(6, (method == AF_INTERP_CUBIC         ||
+                   method == AF_INTERP_CUBIC_SPLINE  ||
+                   method == AF_INTERP_LINEAR        ||
+                   method == AF_INTERP_LINEAR_COSINE ||
+                   method == AF_INTERP_LOWER         ||
+                   method == AF_INTERP_NEAREST));
+
+    if (yi_dims.ndims() == 0 || xo_dims.ndims() == 0) {
+        af_create_handle(yo, 0, nullptr, yi_info.getType());
+        return;
+    }
+
+    if (allocate_yo) { *yo = createHandle(yo_dims, yi_info.getType()); }
+
+    DIM_ASSERT(0, getInfo(*yo).dims() == yo_dims);
+
+    switch (yi_info.getType()) {
+        case f32:
+            approx1<float, float>(yo, yi, xo, xdim, xi_beg, xi_step, method,
+                                  offGrid);
+            break;
+        case f64:
+            approx1<double, double>(yo, yi, xo, xdim, xi_beg, xi_step, method,
+                                    offGrid);
+            break;
+        case c32:
+            approx1<cfloat, float>(yo, yi, xo, xdim, xi_beg, xi_step, method,
+                                   offGrid);
+            break;
+        case c64:
+            approx1<cdouble, double>(yo, yi, xo, xdim, xi_beg, xi_step, method,
+                                     offGrid);
+            break;
+        default: TYPE_ERROR(1, yi_info.getType());
+    }
 }
 
 af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
                           const int xdim, const double xi_beg,
                           const double xi_step, const af_interp_type method,
                           const float offGrid) {
-    return af_approx1_common(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid,
-                             true);
+    try {
+        af_approx1_common(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid,
+                          true);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
 }
 
 af_err af_approx1_uniform_v2(af_array *yo, const af_array yi, const af_array xo,
                              const int xdim, const double xi_beg,
                              const double xi_step, const af_interp_type method,
                              const float offGrid) {
-    if (yo == 0) return AF_ERR_ARG;
-    // Since this v2, assume that the output has already been initialized
-    // either to null or an existing af_array
-    return af_approx1_common(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid,
-                             *yo == 0);
+    try {
+        ARG_ASSERT(0, yo != 0);  // need to dereference yo in next call
+        af_approx1_common(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid,
+                          *yo == 0);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
 }
 
 af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
                   const af_interp_type method, const float offGrid) {
-    return af_approx1_common(yo, yi, xo, 0, 0.0, 1.0, method, offGrid, true);
+    try {
+        af_approx1_common(yo, yi, xo, 0, 0.0, 1.0, method, offGrid, true);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
 }
 
 af_err af_approx1_v2(af_array *yo, const af_array yi, const af_array xo,
                      const af_interp_type method, const float offGrid) {
-    if (yo == 0) return AF_ERR_ARG;
-    // Since this is v2, assume that the output has already been initialized
-    // either to null or an existing af_array
-    return af_approx1_common(yo, yi, xo, 0, 0.0, 1.0, method, offGrid,
-                             *yo == 0);
+    try {
+        ARG_ASSERT(0, yo != 0);  // need to dereference yo in next call
+        af_approx1_common(yo, yi, xo, 0, 0.0, 1.0, method, offGrid, *yo == 0);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+void af_approx2_common(af_array *zo, const af_array zi, const af_array xo,
+                       const int xdim, const double xi_beg,
+                       const double xi_step, const af_array yo, const int ydim,
+                       const double yi_beg, const double yi_step,
+                       const af_interp_type method, const float offGrid,
+                       bool allocate_zo) {
+    ARG_ASSERT(0, zo != 0);  // *zo (the af_array) can be null, but not zo
+    ARG_ASSERT(1, zi != 0);
+    ARG_ASSERT(2, xo != 0);
+    ARG_ASSERT(6, yo != 0);
+
+    const ArrayInfo &zi_info = getInfo(zi);
+    const ArrayInfo &xo_info = getInfo(xo);
+    const ArrayInfo &yo_info = getInfo(yo);
+
+    dim4 zi_dims = zi_info.dims();
+    dim4 xo_dims = xo_info.dims();
+    dim4 yo_dims = yo_info.dims();
+
+    ARG_ASSERT(1, zi_info.isFloating());                      // Only floating and complex types
+    ARG_ASSERT(2, xo_info.isRealFloating());                  // Only floating types
+    ARG_ASSERT(4, yo_info.isRealFloating());                  // Only floating types
+    ARG_ASSERT(2, xo_info.getType() == yo_info.getType());    // Must have same type
+    ARG_ASSERT(1, zi_info.isSingle() == xo_info.isSingle());  // Must have same precision
+    ARG_ASSERT(1, zi_info.isDouble() == xo_info.isDouble());  // Must have same precision
+    DIM_ASSERT(2, xo_dims == yo_dims);                        // POS0 and POS1 must have same dims
+
+    ARG_ASSERT(3, xdim >= 0 && xdim < 4);
+    ARG_ASSERT(5, ydim >= 0 && ydim < 4);
+    ARG_ASSERT(7, xi_step != 0);
+    ARG_ASSERT(9, yi_step != 0);
+
+    // POS should either be (x, y, 1, 1) or (x, y, zi_dims[2], zi_dims[3])
+    if (xo_dims[xdim] * xo_dims[ydim] != xo_dims.elements()) {
+        for (int i = 0; i < 4; i++) {
+            if (xdim != i && ydim != i) DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
+        }
+    }
+
+    if (zi_dims.ndims() == 0 || xo_dims.ndims() == 0 || yo_dims.ndims() == 0) {
+        af_create_handle(zo, 0, nullptr, zi_info.getType());
+        return;
+    }
+
+    dim4 zo_dims  = zi_info.dims();
+    zo_dims[xdim] = xo_info.dims()[xdim];
+    zo_dims[ydim] = xo_info.dims()[ydim];
+
+    if (allocate_zo) { *zo = createHandle(zo_dims, zi_info.getType()); }
+
+    DIM_ASSERT(0, getInfo(*zo).dims() == zo_dims);
+
+    switch (zi_info.getType()) {
+        case f32:
+            approx2<float, float>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                  yi_beg, yi_step, method, offGrid);
+            break;
+        case f64:
+            approx2<double, double>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, method, offGrid);
+            break;
+        case c32:
+            approx2<cfloat, float>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                   yi_beg, yi_step, method, offGrid);
+            break;
+        case c64:
+            approx2<cdouble, double>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                     ydim, yi_beg, yi_step, method, offGrid);
+            break;
+        default: TYPE_ERROR(1, zi_info.getType());
+    }
 }
 
 af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
@@ -156,69 +237,24 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
                           const double yi_step, const af_interp_type method,
                           const float offGrid) {
     try {
-        const ArrayInfo &zi_info = getInfo(zi);
-        const ArrayInfo &xo_info = getInfo(xo);
-        const ArrayInfo &yo_info = getInfo(yo);
-
-        dim4 zi_dims = zi_info.dims();
-        dim4 xo_dims = xo_info.dims();
-        dim4 yo_dims = yo_info.dims();
-
-        ARG_ASSERT(1, zi_info.isFloating());  // Only floating and complex types
-        ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
-        ARG_ASSERT(4, yo_info.isRealFloating());  // Only floating types
-        ARG_ASSERT(
-            2, xo_info.getType() == yo_info.getType());  // Must have same type
-        ARG_ASSERT(1, zi_info.isSingle() ==
-                          xo_info.isSingle());  // Must have same precision
-        ARG_ASSERT(1, zi_info.isDouble() ==
-                          xo_info.isDouble());  // Must have same precision
-        DIM_ASSERT(2, xo_dims == yo_dims);  // POS0 and POS1 must have same dims
-
-        ARG_ASSERT(3, xdim >= 0 && xdim < 4);
-        ARG_ASSERT(5, ydim >= 0 && ydim < 4);
-        ARG_ASSERT(7, xi_step != 0);
-        ARG_ASSERT(9, yi_step != 0);
-
-        // POS should either be (x, y, 1, 1) or (x, y, zi_dims[2], zi_dims[3])
-        if (xo_dims[xdim] * xo_dims[ydim] != xo_dims.elements()) {
-            for (int i = 0; i < 4; i++) {
-                if (xdim != i && ydim != i)
-                    DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
-            }
-        }
+        af_approx2_common(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg,
+                          yi_step, method, offGrid, true);
+    }
+    CATCHALL;
 
-        if (zi_dims.ndims() == 0 || xo_dims.ndims() == 0 ||
-            yo_dims.ndims() == 0) {
-            return af_create_handle(zo, 0, nullptr, zi_info.getType());
-        }
+    return AF_SUCCESS;
+}
 
-        af_array output;
-
-        switch (zi_info.getType()) {
-            case f32:
-                output = approx2<float, float>(zi, xo, xdim, xi_beg, xi_step,
-                                               yo, ydim, yi_beg, yi_step,
-                                               method, offGrid);
-                break;
-            case f64:
-                output = approx2<double, double>(zi, xo, xdim, xi_beg, xi_step,
-                                                 yo, ydim, yi_beg, yi_step,
-                                                 method, offGrid);
-                break;
-            case c32:
-                output = approx2<cfloat, float>(zi, xo, xdim, xi_beg, xi_step,
-                                                yo, ydim, yi_beg, yi_step,
-                                                method, offGrid);
-                break;
-            case c64:
-                output = approx2<cdouble, double>(zi, xo, xdim, xi_beg, xi_step,
-                                                  yo, ydim, yi_beg, yi_step,
-                                                  method, offGrid);
-                break;
-            default: TYPE_ERROR(1, zi_info.getType());
-        }
-        std::swap(*zo, output);
+af_err af_approx2_uniform_v2(af_array *zo, const af_array zi, const af_array xo,
+                             const int xdim, const double xi_beg,
+                             const double xi_step, const af_array yo,
+                             const int ydim, const double yi_beg,
+                             const double yi_step, const af_interp_type method,
+                             const float offGrid) {
+    try {
+        ARG_ASSERT(0, zo != 0);  // need to dereference zo in next call
+        af_approx2_common(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg,
+                          yi_step, method, offGrid, *zo == 0);
     }
     CATCHALL;
 
@@ -228,6 +264,24 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
 af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
                   const af_array yo, const af_interp_type method,
                   const float offGrid) {
-    return af_approx2_uniform(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method,
-                              offGrid);
+    try {
+        af_approx2_common(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method,
+                          offGrid, true);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_approx2_v2(af_array *zo, const af_array zi, const af_array xo,
+                     const af_array yo, const af_interp_type method,
+                     const float offGrid) {
+    try {
+        ARG_ASSERT(0, zo != 0);  // need to dereference zo in next call
+        af_approx2_common(zo, zi, xo, 0, 0.0, 1.0, yo, 1, 0.0, 1.0, method,
+                          offGrid, *zo == 0);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
 }
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 8491cb234e..e3ef1d76e2 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -30,6 +30,13 @@ af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
     return CALL(zo, zi, xo, yo, method, offGrid);
 }
 
+af_err af_approx2_v2(af_array *zo, const af_array zi, const af_array xo,
+                     const af_array yo, const af_interp_type method,
+                     const float offGrid) {
+    CHECK_ARRAYS(zo, zi, xo, yo);
+    return CALL(zo, zi, xo, yo, method, offGrid);
+}
+
 af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
                           const int xdim, const double xi_beg,
                           const double xi_step, const af_interp_type method,
@@ -52,7 +59,18 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
                           const int ydim, const double yi_beg,
                           const double yi_step, const af_interp_type method,
                           const float offGrid) {
-    CHECK_ARRAYS(zi, xo, yo);
+    CHECK_ARRAYS(zo, zi, xo, yo);
+    return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
+                method, offGrid);
+}
+
+af_err af_approx2_uniform_v2(af_array *zo, const af_array zi, const af_array xo,
+                             const int xdim, const double xi_beg,
+                             const double xi_step, const af_array yo,
+                             const int ydim, const double yi_beg,
+                             const double yi_step, const af_interp_type method,
+                             const float offGrid) {
+    CHECK_ARRAYS(zo, zi, xo, yo);
     return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
                 method, offGrid);
 }
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 789a9c0817..1d027eba2c 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -39,16 +39,11 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
 }
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
-                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid) {
-    dim4 odims  = zi.dims();
-    odims[xdim] = xo.dims()[xdim];
-    odims[ydim] = xo.dims()[ydim];
-
-    Array<Ty> zo = createEmptyArray<Ty>(odims);
-
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid) {
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -74,7 +69,6 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
             break;
         default: break;
     }
-    return zo;
 }
 
 #define INSTANTIATE(Ty, Tp)                                       \
@@ -82,11 +76,11 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
         Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
         const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
         const af_interp_type method, const float offGrid);        \
-    template Array<Ty> approx2<Ty, Tp>(                           \
-        const Array<Ty> &zi, const Array<Tp> &xo, const int xdim, \
-        const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo, \
-        const int ydim, const Tp &yi_beg, const Tp &yi_step,      \
-        const af_interp_type method, const float offGrid);
+    template void approx2<Ty, Tp>(                                \
+        Array<Ty> & zo, const Array<Ty> &zi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const Array<Tp> &yo, const int ydim, const Tp &yi_beg,    \
+        const Tp &yi_step, const af_interp_type method, const float offGrid);
 
 INSTANTIATE(float, float)
 INSTANTIATE(double, double)
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index 49a67e39d0..21a79bcb54 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -17,8 +17,9 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const af_interp_type method, const float offGrid);
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
-                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid);
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid);
 }  // namespace cpu
diff --git a/src/backend/cuda/approx.cpp b/src/backend/cuda/approx.cpp
index 1fd861828a..0c1bc0bb1f 100644
--- a/src/backend/cuda/approx.cpp
+++ b/src/backend/cuda/approx.cpp
@@ -23,20 +23,13 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
 }
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
-                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid) {
-    af::dim4 odims = zi.dims();
-    odims[xdim]    = xo.dims()[xdim];
-    odims[ydim]    = xo.dims()[ydim];
-
-    // Create output placeholder
-    Array<Ty> zo = createEmptyArray<Ty>(odims);
-
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid) {
     kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg,
                             yi_step, offGrid, method, interpOrder(method));
-    return zo;
 }
 
 #define INSTANTIATE(Ty, Tp)                                       \
@@ -44,11 +37,11 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
         Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
         const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
         const af_interp_type method, const float offGrid);        \
-    template Array<Ty> approx2<Ty, Tp>(                           \
-        const Array<Ty> &zi, const Array<Tp> &xo, const int xdim, \
-        const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo, \
-        const int ydim, const Tp &yi_beg, const Tp &yi_step,      \
-        const af_interp_type method, const float offGrid);
+    template void approx2<Ty, Tp>(                                \
+        Array<Ty> & zo, const Array<Ty> &zi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const Array<Tp> &yo, const int ydim, const Tp &yi_beg,    \
+        const Tp &yi_step, const af_interp_type method, const float offGrid);
 
 INSTANTIATE(float, float)
 INSTANTIATE(double, double)
diff --git a/src/backend/cuda/approx.hpp b/src/backend/cuda/approx.hpp
index c3f21afd38..0d459970f1 100644
--- a/src/backend/cuda/approx.hpp
+++ b/src/backend/cuda/approx.hpp
@@ -16,8 +16,9 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const af_interp_type method, const float offGrid);
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
-                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid);
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid);
 }  // namespace cuda
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index f425377e52..462cc95cd3 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -39,17 +39,11 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
 }
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
-                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid) {
-    af::dim4 odims = zi.dims();
-    odims[xdim]    = xo.dims()[xdim];
-    odims[ydim]    = xo.dims()[ydim];
-
-    // Create output placeholder
-    Array<Ty> zo = createEmptyArray<Ty>(odims);
-
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid) {
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -72,8 +66,6 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
             break;
         default: break;
     }
-
-    return zo;
 }
 
 #define INSTANTIATE(Ty, Tp)                                       \
@@ -81,11 +73,11 @@ Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
         Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
         const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
         const af_interp_type method, const float offGrid);        \
-    template Array<Ty> approx2<Ty, Tp>(                           \
-        const Array<Ty> &zi, const Array<Tp> &xo, const int xdim, \
-        const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo, \
-        const int ydim, const Tp &yi_beg, const Tp &yi_step,      \
-        const af_interp_type method, const float offGrid);
+    template void approx2<Ty, Tp>(                                \
+        Array<Ty> & zo, const Array<Ty> &zi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const Array<Tp> &yo, const int ydim, const Tp &yi_beg,    \
+        const Tp &yi_step, const af_interp_type method, const float offGrid);
 
 INSTANTIATE(float, float)
 INSTANTIATE(double, double)
diff --git a/src/backend/opencl/approx.hpp b/src/backend/opencl/approx.hpp
index 4ae6362d64..addb8fe73c 100644
--- a/src/backend/opencl/approx.hpp
+++ b/src/backend/opencl/approx.hpp
@@ -16,8 +16,9 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const af_interp_type method, const float offGrid);
 
 template<typename Ty, typename Tp>
-Array<Ty> approx2(const Array<Ty> &zi, const Array<Tp> &xo, const int xdim,
-                  const Tp &xi_beg, const Tp &xi_step, const Array<Tp> &yo,
-                  const int ydim, const Tp &yi_beg, const Tp &yi_step,
-                  const af_interp_type method, const float offGrid);
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid);
 }  // namespace opencl
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 20c568c24e..72542b773b 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -830,285 +830,258 @@ TEST(Approx1, CPPEmptyPosAndInput) {
 }
 
 template<typename T>
-void testSpclOutArray(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
-                      float* h_pos, dim4 pos_dims,
-                      TestOutputArrayType out_array_type) {
-    SUPPORTED_TYPE_CHECK(T);
+class Approx1V2 : public ::testing::Test {
+   protected:
     typedef typename dtype_traits<T>::base_type BT;
 
-    vector<T> h_gold_cast(gold_dims.elements());
-    vector<T> h_in_cast(in_dims.elements());
-    vector<BT> h_pos_cast(pos_dims.elements());
-
-    for (int i = 0; i < gold_dims.elements(); ++i) {
-        h_gold_cast[i] = static_cast<T>(h_gold[i]);
-    }
-    for (int i = 0; i < in_dims.elements(); ++i) {
-        h_in_cast[i] = static_cast<T>(h_in[i]);
-    }
-    for (int i = 0; i < pos_dims.elements(); ++i) {
-        h_pos_cast[i] = static_cast<BT>(h_pos[i]);
-    }
+    vector<T> h_gold_cast;
+    vector<T> h_in_cast;
+    vector<BT> h_pos_cast;
 
-    af_array in  = 0;
-    af_array pos = 0;
+    dim4 gold_dims;
+    dim4 in_dims;
+    dim4 pos_dims;
 
-    ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
-                                   in_dims.get(),
-                                   (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&pos, &h_pos_cast.front(), pos_dims.ndims(),
-                                   pos_dims.get(),
-                                   (af_dtype)dtype_traits<BT>::af_type));
+    af_array gold;
+    af_array in;
+    af_array pos;
 
-    af_array out = 0;
-    TestOutputArrayInfo metadata(out_array_type);
-    genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
-                       (af_dtype)dtype_traits<T>::af_type, &metadata);
-    ASSERT_SUCCESS(af_approx1_v2(&out, in, pos, AF_INTERP_LINEAR, 0));
+    Approx1V2() : gold(0), in(0), pos(0) {}
 
-    af_array gold = 0;
-    ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
-                                   gold_dims.ndims(), gold_dims.get(),
-                                   (af_dtype)dtype_traits<T>::af_type));
+    void SetUp() {}
 
-    ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+    void releaseArrays() {
+        if (pos != 0)  { ASSERT_SUCCESS(af_release_array(pos)); }
+        if (in != 0)   { ASSERT_SUCCESS(af_release_array(in)); }
+        if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
+    }
 
-    if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
-    if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
-    if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
-}
+    void TearDown() { releaseArrays(); }
 
-TYPED_TEST(Approx1, UseNullOutputArray) {
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
+    void setTestData(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
+                     float* h_pos, dim4 pos_dims) {
+        releaseArrays();
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+        gold = 0;
+        in   = 0;
+        pos  = 0;
 
-    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    dim4 gold_dims(5);
+        this->gold_dims = gold_dims;
+        this->in_dims   = in_dims;
+        this->pos_dims  = pos_dims;
 
-    SCOPED_TRACE("UseNullOutputArray");
-    testSpclOutArray<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
-                                pos_dims, NULL_ARRAY);
-}
+        for (int i = 0; i < gold_dims.elements(); ++i) {
+            h_gold_cast.push_back(static_cast<T>(h_gold[i]));
+        }
+        for (int i = 0; i < in_dims.elements(); ++i) {
+            h_in_cast.push_back(static_cast<T>(h_in[i]));
+        }
+        for (int i = 0; i < pos_dims.elements(); ++i) {
+            h_pos_cast.push_back(static_cast<BT>(h_pos[i]));
+        }
 
-TYPED_TEST(Approx1, UseFullExistingOutputArray) {
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
+        ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
+                                       gold_dims.ndims(), gold_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
+                                       in_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&pos, &h_pos_cast.front(),
+                                       pos_dims.ndims(), pos_dims.get(),
+                                       (af_dtype)dtype_traits<BT>::af_type));
+    }
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+    void testSpclOutArray(TestOutputArrayType out_array_type) {
+        SUPPORTED_TYPE_CHECK(T);
 
-    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    dim4 gold_dims(5);
+        af_array out = 0;
+        TestOutputArrayInfo metadata(out_array_type);
+        genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                           (af_dtype)dtype_traits<T>::af_type, &metadata);
 
-    SCOPED_TRACE("UseFullExistingOutputArray");
-    testSpclOutArray<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
-                                pos_dims, FULL_ARRAY);
-}
+        ASSERT_SUCCESS(af_approx1_v2(&out, in, pos, AF_INTERP_LINEAR, 0));
+        ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+    }
 
-TYPED_TEST(Approx1, UseExistingOutputSubArray) {
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
+    void testSpclOutArrayUniform(TestOutputArrayType out_array_type) {
+        SUPPORTED_TYPE_CHECK(T);
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+        af_array out = 0;
+        TestOutputArrayInfo metadata(out_array_type);
+        genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                           (af_dtype)dtype_traits<T>::af_type, &metadata);
 
-    float h_gold_subarr[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    dim4 gold_subarr_dims(5);
+        ASSERT_SUCCESS(af_approx1_uniform_v2(&out, in, pos, 0, 0.0, 1.0,
+                                             AF_INTERP_LINEAR, 0.f));
+        ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+    }
+};
 
-    SCOPED_TRACE("UseExistingOutputSubArray");
-    testSpclOutArray<TypeParam>(h_gold_subarr, gold_subarr_dims, h_in, in_dims,
-                                h_pos, pos_dims, SUB_ARRAY);
-}
+TYPED_TEST_CASE(Approx1V2, TestTypes);
 
-TYPED_TEST(Approx1, UseReorderedOutputArray) {
-    float h_in[9] = {10.0f, 20.0f, 30.0f,
-                     40.0f, 50.0f, 60.0f,
-                     70.0f, 80.0f, 90.0f};
-    dim4 in_dims(3, 3);
+class SimpleTestData {
+   public:
+    static const int h_gold_size = 15;
+    static const int h_in_size   = 9;
+    static const int h_pos_size  = 5;
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+    vector<float> h_gold;
+    vector<float> h_in;
+    vector<float> h_pos;
 
-    float h_gold[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
-                        40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
-                        70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
-    dim4 gold_dims(5, 3);
+    dim4 gold_dims;
+    dim4 in_dims;
+    dim4 pos_dims;
 
-    SCOPED_TRACE("UseReorderedOutputArray");
-    testSpclOutArray<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
-                                pos_dims, REORDERED_ARRAY);
-}
+    SimpleTestData() : gold_dims(5, 3), in_dims(3, 3), pos_dims(5) {
+        float gold_arr[h_gold_size] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f,
+                                       40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
+                                       70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
 
-template<typename T>
-void testSpclOutArrayUniform(float* h_gold, dim4 gold_dims, float* h_in,
-                             dim4 in_dims, float* h_pos, dim4 pos_dims,
-                             TestOutputArrayType out_array_type) {
-    SUPPORTED_TYPE_CHECK(T);
-    typedef typename dtype_traits<T>::base_type BT;
+        float in_arr[h_in_size] = {10.0f, 20.0f, 30.0f,
+                                   40.0f, 50.0f, 60.0f,
+                                   70.0f, 80.0f, 90.0f};
 
-    vector<T> h_gold_cast(gold_dims.elements());
-    vector<T> h_in_cast(in_dims.elements());
-    vector<BT> h_pos_cast(pos_dims.elements());
+        float pos_arr[h_pos_size] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
 
-    for (int i = 0; i < gold_dims.elements(); ++i) {
-        h_gold_cast[i] = static_cast<T>(h_gold[i]);
+        h_gold.assign(gold_arr, gold_arr + h_gold_size);
+        h_in.assign(in_arr, in_arr + h_in_size);
+        h_pos.assign(pos_arr, pos_arr + h_pos_size);
     }
-    for (int i = 0; i < in_dims.elements(); ++i) {
-        h_in_cast[i] = static_cast<T>(h_in[i]);
-    }
-    for (int i = 0; i < pos_dims.elements(); ++i) {
-        h_pos_cast[i] = static_cast<BT>(h_pos[i]);
-    }
-
-    af_array in  = 0;
-    af_array pos = 0;
-
-    ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
-                                   in_dims.get(),
-                                   (af_dtype)dtype_traits<T>::af_type));
-    ASSERT_SUCCESS(af_create_array(&pos, &h_pos_cast.front(), pos_dims.ndims(),
-                                   pos_dims.get(),
-                                   (af_dtype)dtype_traits<BT>::af_type));
-
-    af_array out = 0;
-    TestOutputArrayInfo metadata(out_array_type);
-    genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
-                       (af_dtype)dtype_traits<T>::af_type, &metadata);
-    ASSERT_SUCCESS(af_approx1_uniform_v2(&out, in, pos, 0, 0.0, 1.0,
-                                         AF_INTERP_LINEAR, 0.f));
+};
 
-    af_array gold = 0;
-    ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
-                                   gold_dims.ndims(), gold_dims.get(),
-                                   (af_dtype)dtype_traits<T>::af_type));
+template<typename T>
+class Approx1V2Simple : public Approx1V2<T> {
+   protected:
+    void SetUp() {
+        SimpleTestData data;
+        this->setTestData(&data.h_gold.front(), data.gold_dims,
+                          &data.h_in.front(), data.in_dims, &data.h_pos.front(),
+                          data.pos_dims);
+    }
+};
 
-    ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+TYPED_TEST_CASE(Approx1V2Simple, TestTypes);
 
-    if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
-    if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
-    if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
+TYPED_TEST(Approx1V2Simple, UseNullOutputArray) {
+    this->testSpclOutArray(NULL_ARRAY);
 }
 
-TYPED_TEST(Approx1, UseNullOutputArrayUniform) {
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
-
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+TYPED_TEST(Approx1V2Simple, UseFullExistingOutputArray) {
+    this->testSpclOutArray(FULL_ARRAY);
+}
 
-    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    dim4 gold_dims(5);
+TYPED_TEST(Approx1V2Simple, UseExistingOutputSubArray) {
+    this->testSpclOutArray(SUB_ARRAY);
+}
 
-    SCOPED_TRACE("UseNullOutputArray");
-    testSpclOutArrayUniform<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
-                                       pos_dims, NULL_ARRAY);
+TYPED_TEST(Approx1V2Simple, UseReorderedOutputArray) {
+    this->testSpclOutArray(REORDERED_ARRAY);
 }
 
-TYPED_TEST(Approx1, UseFullExistingOutputArrayUniform) {
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
+TYPED_TEST(Approx1V2Simple, UniformUseNullOutputArray) {
+    this->testSpclOutArrayUniform(NULL_ARRAY);
+}
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+TYPED_TEST(Approx1V2Simple, UniformUseFullExistingOutputArray) {
+    this->testSpclOutArrayUniform(FULL_ARRAY);
+}
 
-    float h_gold[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    dim4 gold_dims(5);
+TYPED_TEST(Approx1V2Simple, UniformUseExistingOutputSubArray) {
+    this->testSpclOutArrayUniform(SUB_ARRAY);
+}
 
-    SCOPED_TRACE("UseFullExistingOutputArray");
-    testSpclOutArrayUniform<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
-                                       pos_dims, FULL_ARRAY);
+TYPED_TEST(Approx1V2Simple, UniformUseReorderedOutputArray) {
+    this->testSpclOutArrayUniform(REORDERED_ARRAY);
 }
 
-TYPED_TEST(Approx1, UseExistingOutputSubArrayUniform) {
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
+class Approx1NullArgs : public ::testing::Test {
+   protected:
+    af_array out;
+    af_array in;
+    af_array pos;
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+    Approx1NullArgs() : out(0), in(0), pos(0) {}
 
-    float h_gold_subarr[5] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f};
-    dim4 gold_subarr_dims(5);
+    void SetUp() {
+        SimpleTestData data;
 
-    SCOPED_TRACE("UseExistingOutputSubArray");
-    testSpclOutArrayUniform<TypeParam>(h_gold_subarr, gold_subarr_dims, h_in,
-                                       in_dims, h_pos, pos_dims, SUB_ARRAY);
-}
+        ASSERT_SUCCESS(af_create_array(&in, &data.h_in.front(),
+                                       data.in_dims.ndims(), data.in_dims.get(),
+                                       f32));
+        ASSERT_SUCCESS(af_create_array(&pos, &data.h_pos.front(),
+                                       data.pos_dims.ndims(),
+                                       data.pos_dims.get(), f32));
+    }
 
-TYPED_TEST(Approx1, UseReorderedOutputArrayUniform) {
-    float h_in[9] = {10.0f, 20.0f, 30.0f, 40.0f, 50.0f,
-                     60.0f, 70.0f, 80.0f, 90.0f};
-    dim4 in_dims(3, 3);
+    void TearDown() {
+        if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
+        if (in != 0)  { ASSERT_SUCCESS(af_release_array(in)); }
+    }
+};
 
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
+TEST_F(Approx1NullArgs, NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1(out_ptr, this->in, this->pos, AF_INTERP_LINEAR, 0.f));
+}
 
-    float h_gold[15] = {10.0f, 15.0f, 20.0f, 25.0f, 30.0f, 40.0f, 45.0f, 50.0f,
-                        55.0f, 60.0f, 70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
-    dim4 gold_dims(5, 3);
+TEST_F(Approx1NullArgs, NullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1(&this->out, 0, this->pos, AF_INTERP_LINEAR, 0.f));
+}
 
-    SCOPED_TRACE("UseReorderedOutputArray");
-    testSpclOutArrayUniform<TypeParam>(h_gold, gold_dims, h_in, in_dims, h_pos,
-                                       pos_dims, REORDERED_ARRAY);
+TEST_F(Approx1NullArgs, NullPosArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1(&this->out, this->in, 0, AF_INTERP_LINEAR, 0.f));
 }
 
-TEST(Approx1, NullOutputPtrApprox1) {
+TEST_F(Approx1NullArgs, V2NullOutputPtr) {
     af_array* out_ptr = 0;
-    af_array in       = 0;
-    af_array pos      = 0;
-    ASSERT_EQ(af_approx1(out_ptr, in, pos, AF_INTERP_LINEAR, 0.f), AF_ERR_ARG);
+    ASSERT_EQ(AF_ERR_ARG, af_approx1_v2(out_ptr, this->in, this->pos,
+                                        AF_INTERP_LINEAR, 0.f));
 }
 
-TEST(Approx1, NullOutputPtrApprox1Uniform) {
-    af_array* out_ptr = 0;
-    af_array in       = 0;
-    af_array pos      = 0;
-    ASSERT_EQ(af_approx1_uniform(out_ptr, in, pos, 0, 0.0, 1.0,
-                                 AF_INTERP_LINEAR, 0.f),
-              AF_ERR_ARG);
+TEST_F(Approx1NullArgs, V2NullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1_v2(&this->out, 0, this->pos, AF_INTERP_LINEAR, 0.f));
 }
 
-TEST(Approx1, NullOutputPtrApprox1V2) {
-    af_array* out_ptr = 0;
-    af_array in       = 0;
-    af_array pos      = 0;
-    ASSERT_EQ(af_approx1_v2(out_ptr, in, pos, AF_INTERP_LINEAR, 0.f),
-              AF_ERR_ARG);
+TEST_F(Approx1NullArgs, V2NullPosArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1_v2(&this->out, this->in, 0, AF_INTERP_LINEAR, 0.f));
 }
 
-TEST(Approx1, NullOutputPtrApprox1UniformV2) {
+TEST_F(Approx1NullArgs, UniformNullOutputPtr) {
     af_array* out_ptr = 0;
-    af_array in       = 0;
-    af_array pos      = 0;
-    ASSERT_EQ(af_approx1_uniform_v2(out_ptr, in, pos, 0, 0.0, 1.0,
-                                    AF_INTERP_LINEAR, 0.f),
-              AF_ERR_ARG);
+    ASSERT_EQ(AF_ERR_ARG, af_approx1_uniform(out_ptr, this->in, this->pos, 0,
+                                             0.0, 1.0, AF_INTERP_LINEAR, 0.f));
 }
 
-TEST(Approx1, NullInputArray) {
-    af_array out = 0;
-    af_array in  = 0;
-    af_array pos = 0;
-
-    float h_pos[5] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
-    dim4 pos_dims(5);
-    ASSERT_SUCCESS(
-        af_create_array(&pos, h_pos, pos_dims.ndims(), pos_dims.get(), f32));
+TEST_F(Approx1NullArgs, UniformNullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx1_uniform(&this->out, 0, this->pos, 0, 0.0,
+                                             1.0, AF_INTERP_LINEAR, 0.f));
+}
 
-    ASSERT_EQ(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0.f), AF_ERR_ARG);
+TEST_F(Approx1NullArgs, UniformNullPosArray) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx1_uniform(&this->out, this->in, 0, 0, 0.0,
+                                             1.0, AF_INTERP_LINEAR, 0.f));
 }
 
-TEST(Approx1, NullPosArray) {
-    af_array out = 0;
-    af_array in  = 0;
-    af_array pos = 0;
+TEST_F(Approx1NullArgs, V2UniformNullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1_uniform_v2(out_ptr, this->in, this->pos, 0, 0.0, 1.0,
+                                    AF_INTERP_LINEAR, 0.f));
+}
 
-    float h_in[3] = {10.0f, 20.0f, 30.0f};
-    dim4 in_dims(3);
-    ASSERT_SUCCESS(
-        af_create_array(&in, h_in, in_dims.ndims(), in_dims.get(), f32));
+TEST_F(Approx1NullArgs, V2UniformNullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx1_uniform_v2(&this->out, 0, this->pos, 0, 0.0, 1.0,
+                                    AF_INTERP_LINEAR, 0.f));
+}
 
-    ASSERT_EQ(af_approx1(&out, in, pos, AF_INTERP_LINEAR, 0.f), AF_ERR_ARG);
+TEST_F(Approx1NullArgs, V2UniformNullPosArray) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx1_uniform_v2(&this->out, this->in, 0, 0, 0.0,
+                                                1.0, AF_INTERP_LINEAR, 0.f));
 }
diff --git a/test/approx2.cpp b/test/approx2.cpp
index ca8b7b36b7..7f840e3c5f 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -753,3 +753,309 @@ TEST(Approx2, CPPEmptyPosAndInput) {
     ASSERT_TRUE(pos.isempty());
     ASSERT_TRUE(interpolated.isempty());
 }
+
+template<typename T>
+class Approx2V2 : public ::testing::Test {
+   protected:
+    typedef typename dtype_traits<T>::base_type BT;
+
+    vector<T> h_gold_cast;
+    vector<T> h_in_cast;
+    vector<BT> h_pos1_cast;
+    vector<BT> h_pos2_cast;
+
+    dim4 gold_dims;
+    dim4 in_dims;
+    dim4 pos1_dims;
+    dim4 pos2_dims;
+
+    af_array gold;
+    af_array in;
+    af_array pos1;
+    af_array pos2;
+
+    Approx2V2() : gold(0), in(0), pos1(0), pos2(0) {}
+
+    void SetUp() {}
+
+    void releaseArrays() {
+        if (pos2 != 0) { ASSERT_SUCCESS(af_release_array(pos2)); }
+        if (pos1 != 0) { ASSERT_SUCCESS(af_release_array(pos1)); }
+        if (in != 0)   { ASSERT_SUCCESS(af_release_array(in)); }
+        if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
+    }
+
+    void TearDown() { releaseArrays(); }
+
+    void setTestData(float* h_gold, dim4 gold_dims, float* h_in, dim4 in_dims,
+                     float* h_pos1, dim4 pos1_dims, float* h_pos2,
+                     dim4 pos2_dims) {
+        releaseArrays();
+
+        gold = 0;
+        in   = 0;
+        pos1 = 0;
+        pos2 = 0;
+
+        this->gold_dims = gold_dims;
+        this->in_dims   = in_dims;
+        this->pos1_dims = pos1_dims;
+        this->pos2_dims = pos2_dims;
+
+        for (int i = 0; i < gold_dims.elements(); ++i) {
+            h_gold_cast.push_back(static_cast<T>(h_gold[i]));
+        }
+        for (int i = 0; i < in_dims.elements(); ++i) {
+            h_in_cast.push_back(static_cast<T>(h_in[i]));
+        }
+        for (int i = 0; i < pos1_dims.elements(); ++i) {
+            h_pos1_cast.push_back(static_cast<BT>(h_pos1[i]));
+        }
+        for (int i = 0; i < pos2_dims.elements(); ++i) {
+            h_pos2_cast.push_back(static_cast<BT>(h_pos2[i]));
+        }
+
+        ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
+                                       gold_dims.ndims(), gold_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
+                                       in_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&pos1, &h_pos1_cast.front(),
+                                       pos1_dims.ndims(), pos1_dims.get(),
+                                       (af_dtype)dtype_traits<BT>::af_type));
+        ASSERT_SUCCESS(af_create_array(&pos2, &h_pos2_cast.front(),
+                                       pos2_dims.ndims(), pos2_dims.get(),
+                                       (af_dtype)dtype_traits<BT>::af_type));
+    }
+
+    void testSpclOutArray(TestOutputArrayType out_array_type) {
+        SUPPORTED_TYPE_CHECK(T);
+
+        af_array out = 0;
+        TestOutputArrayInfo metadata(out_array_type);
+        genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                           (af_dtype)dtype_traits<T>::af_type, &metadata);
+
+        ASSERT_SUCCESS(
+            af_approx2_v2(&out, in, pos1, pos2, AF_INTERP_LINEAR, 0));
+        ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+    }
+
+    void testSpclOutArrayUniform(TestOutputArrayType out_array_type) {
+        SUPPORTED_TYPE_CHECK(T);
+
+        af_array out = 0;
+        TestOutputArrayInfo metadata(out_array_type);
+        genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                           (af_dtype)dtype_traits<T>::af_type, &metadata);
+
+        ASSERT_SUCCESS(af_approx2_uniform_v2(&out, in, pos1, 0, 0.0, 1.0, pos2,
+                                             1, 0.0, 1.0, AF_INTERP_LINEAR, 0));
+        ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
+    }
+};
+
+TYPED_TEST_CASE(Approx2V2, TestTypes);
+
+class SimpleTestData {
+   public:
+    static const int h_gold_size = 4;
+    static const int h_in_size   = 9;
+    static const int h_pos1_size = 4;
+    static const int h_pos2_size = 4;
+
+    vector<float> h_gold;
+    vector<float> h_in;
+    vector<float> h_pos1;
+    vector<float> h_pos2;
+
+    dim4 gold_dims;
+    dim4 in_dims;
+    dim4 pos1_dims;
+    dim4 pos2_dims;
+
+   public:
+    SimpleTestData()
+        : gold_dims(2, 2), in_dims(3, 3), pos1_dims(2, 2), pos2_dims(2, 2) {
+        float gold_arr[h_gold_size] = {1.5, 1.5, 2.5, 2.5};
+
+        float in_arr[h_in_size] = {1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0};
+
+        float pos1_arr[h_pos1_size] = {0.5, 1.5, 0.5, 1.5};
+
+        float pos2_arr[h_pos2_size] = {0.5, 0.5, 1.5, 1.5};
+
+        h_gold.assign(gold_arr, gold_arr + h_gold_size);
+        h_in.assign(in_arr, in_arr + h_in_size);
+        h_pos1.assign(pos1_arr, pos1_arr + h_pos1_size);
+        h_pos2.assign(pos2_arr, pos2_arr + h_pos2_size);
+    }
+};
+
+template<typename T>
+class Approx2V2Simple : public Approx2V2<T> {
+   protected:
+    void SetUp() {
+        SimpleTestData data;
+        this->setTestData(&data.h_gold.front(), data.gold_dims,
+                          &data.h_in.front(), data.in_dims,
+                          &data.h_pos1.front(), data.pos1_dims,
+                          &data.h_pos2.front(), data.pos2_dims);
+    }
+};
+
+TYPED_TEST_CASE(Approx2V2Simple, TestTypes);
+
+TYPED_TEST(Approx2V2Simple, UseNullOutputArray) {
+    this->testSpclOutArray(NULL_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UseFullExistingOutputArray) {
+    this->testSpclOutArray(FULL_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UseExistingOutputSubArray) {
+    this->testSpclOutArray(SUB_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UseReorderedOutputArray) {
+    this->testSpclOutArray(REORDERED_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UniformUseNullOutputArray) {
+    this->testSpclOutArrayUniform(NULL_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UniformUseFullExistingOutputArray) {
+    this->testSpclOutArrayUniform(FULL_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UniformUseExistingOutputSubArray) {
+    this->testSpclOutArrayUniform(SUB_ARRAY);
+}
+
+TYPED_TEST(Approx2V2Simple, UniformUseReorderedOutputArray) {
+    this->testSpclOutArrayUniform(REORDERED_ARRAY);
+}
+
+class Approx2NullArgs : public ::testing::Test {
+   protected:
+    af_array out;
+    af_array in;
+    af_array pos1;
+    af_array pos2;
+
+    Approx2NullArgs() : out(0), in(0), pos1(0), pos2(0) {}
+
+    void SetUp() {
+        SimpleTestData data;
+        ASSERT_SUCCESS(af_create_array(&in, &data.h_in.front(),
+                                       data.in_dims.ndims(), data.in_dims.get(),
+                                       f32));
+        ASSERT_SUCCESS(af_create_array(&pos1, &data.h_pos1.front(),
+                                       data.pos1_dims.ndims(),
+                                       data.pos1_dims.get(), f32));
+        ASSERT_SUCCESS(af_create_array(&pos2, &data.h_pos2.front(),
+                                       data.pos2_dims.ndims(),
+                                       data.pos2_dims.get(), f32));
+    }
+
+    void TearDown() {
+        if (pos2 != 0) { ASSERT_SUCCESS(af_release_array(pos2)); }
+        if (pos1 != 0) { ASSERT_SUCCESS(af_release_array(pos1)); }
+        if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
+    }
+};
+
+TEST_F(Approx2NullArgs, NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG, af_approx2(out_ptr, this->in, this->pos1, this->pos2,
+                                     AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, NullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2(&this->out, 0, this->pos1, this->pos2,
+                                     AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, NullPos1Array) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2(&this->out, this->in, 0, this->pos2,
+                                     AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, NullPos2Array) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2(&this->out, this->in, this->pos1, 0,
+                                     AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, V2NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_v2(out_ptr, this->in, this->pos1,
+                                        this->pos2, AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, V2NullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_v2(&this->out, 0, this->pos1, this->pos2,
+                                        AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, V2NullPos1Array) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_v2(&this->out, this->in, 0, this->pos2,
+                                        AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, V2NullPos2Array) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_v2(&this->out, this->in, this->pos1, 0,
+                                        AF_INTERP_LINEAR, 0.f));
+}
+
+TEST_F(Approx2NullArgs, UniformNullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx2_uniform(out_ptr, this->in, this->pos1, 0, 0.0, 1.0,
+                                 this->pos2, 1, 0.0, 1.0, AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, UniformNullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx2_uniform(&this->out, 0, this->pos1, 0, 0.0, 1.0,
+                                 this->pos2, 1, 0.0, 1.0, AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, UniformNullPos1Array) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx2_uniform(&this->out, this->in, 0, 0, 0.0, 1.0,
+                                 this->pos2, 1, 0.0, 1.0, AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, UniformNullPos2Array) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx2_uniform(&this->out, this->in, this->pos1, 0, 0.0, 1.0,
+                                 0, 1, 0.0, 1.0, AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, V2UniformNullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_uniform_v2(out_ptr, this->in, this->pos1,
+                                                0, 0.0, 1.0, this->pos2, 1, 0.0,
+                                                1.0, AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, V2UniformNullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_uniform_v2(&this->out, 0, this->pos1, 0,
+                                                0.0, 1.0, this->pos2, 1, 0.0,
+                                                1.0, AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, V2UniformNullPos1Array) {
+    ASSERT_EQ(AF_ERR_ARG, af_approx2_uniform_v2(&this->out, this->in, 0, 0, 0.0,
+                                                1.0, this->pos2, 1, 0.0, 1.0,
+                                                AF_INTERP_LINEAR, 0));
+}
+
+TEST_F(Approx2NullArgs, V2UniformNullPos2Array) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_approx2_uniform_v2(&this->out, this->in, this->pos1, 0, 0.0,
+                                    1.0, 0, 1, 0.0, 1.0, AF_INTERP_LINEAR, 0));
+}

From a998e1de8cab354086f8a8600e223b60e66a8e65 Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Sun, 18 Aug 2019 19:59:38 -0700
Subject: [PATCH 1735/2677] Fix minor grammar issues

---
 docs/pages/interop_cuda.md   | 4 ++--
 docs/pages/interop_opencl.md | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index 2ef88af3ff..dd6a8acfab 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -19,9 +19,9 @@ code. ArrayFire provides several functions to ease this process including:
 | Function              | Purpose                                             |
 |-----------------------|-----------------------------------------------------|
 | af::array(...)        | Construct an ArrayFire Array from device memory     |
-| af::array.device()    | Obtain a pointer to the device memory (implies lock() |
+| af::array.device()    | Obtain a pointer to the device memory (implies `lock()`) |
 | af::array.lock()      | Removes ArrayFire's control of a device memory pointer |
-| af::array.unlock()    | Restore's ArrayFire's control over a device memory pointer |
+| af::array.unlock()    | Restores ArrayFire's control over a device memory pointer |
 | af::getDevice()       | Gets the current ArrayFire device ID                |
 | af::setDevice()       | Switches ArrayFire to the specified device          |
 | afcu::getNativeId()   | Converts an ArrayFire device ID to a CUDA device ID |
diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 1f31076be8..9b65c8eadf 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -19,9 +19,9 @@ code. ArrayFire provides several functions to ease this process including:
 | Function              | Purpose                                             |
 |-----------------------|-----------------------------------------------------|
 | af::array(...)        | Construct an ArrayFire array from cl_mem references or cl::Buffer objects |
-| af::array.device()    | Obtain a pointer to the cl_mem reference (implies lock()) |
+| af::array.device()    | Obtain a pointer to the cl_mem reference (implies `lock()`) |
 | af::array.lock()      | Removes ArrayFire's control of a cl_mem buffer            |
-| af::array.unlock()    | Restore's ArrayFire's control over a cl_mem buffer        |
+| af::array.unlock()    | Restores ArrayFire's control over a cl_mem buffer         |
 | afcl::getPlatform()   | Get ArrayFire's current cl_platform                       |
 | af::getDevice()       | Get the current ArrayFire Device ID                       |
 | afcl::getDeviceId()   | Get ArrayFire's current cl_device_id                      |

From 80386a2c0cc34a1880462d94d2042557d97c4483 Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Sun, 18 Aug 2019 20:29:59 -0700
Subject: [PATCH 1736/2677] Fix minor issues in documentation

---
 docs/pages/interop_cuda.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index dd6a8acfab..c3cfed3b9c 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -43,7 +43,7 @@ If your kernels can share the ArrayFire CUDA stream, you should:
 1. Include the 'af/afcuda.h' header in your source code
 2. Use ArrayFire as normal
 3. Ensure any JIT kernels have executed using `af::eval()`
-4. Obtain device pointers from ArrayFire array objects using
+4. Obtain device pointers from ArrayFire array objects using `array::device()`
 5. Determine ArrayFire's CUDA stream
 6. Set arguments and run your kernel in ArrayFire's stream
 7. Return control of af::array memory to ArrayFire
@@ -94,7 +94,7 @@ int main() {
     // ... resume ArrayFire operations
     af_print(x);
 
-    // Because the device pointers, d_x and d_y, were returned to ArrayFire's
+    // Because the device pointer `d_x` was returned to ArrayFire's
     // control by the unlock function, there is no need to free them using
     // cudaFree()
 

From 773000fa41cc36cec7b9ce16cd107c19fbc5c859 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 20 Aug 2019 01:54:06 -0400
Subject: [PATCH 1737/2677] Fixes infinite loop when eval-ing under memory
 pressure (#2608)

* Fixes infinite loop when eval-ing under memory pressure

Adds different return codes to jit heuristic allowing
different behaviour depending on why eval is triggered

* rename enum vars to CamelCase
---
 src/backend/common/defines.hpp      |  7 ++++++
 src/backend/common/jit/NaryNode.hpp | 36 +++++++++++++++++++----------
 src/backend/cpu/Array.cpp           | 10 ++++----
 src/backend/cpu/Array.hpp           |  2 +-
 src/backend/cuda/Array.cpp          | 18 ++++++++-------
 src/backend/cuda/Array.hpp          |  2 +-
 src/backend/cuda/select.cu          |  4 ++--
 src/backend/opencl/Array.cpp        | 17 +++++++++-----
 src/backend/opencl/Array.hpp        |  2 +-
 src/backend/opencl/select.cpp       |  4 ++--
 10 files changed, 64 insertions(+), 38 deletions(-)

diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 03a1a04dd8..ec8bf97cec 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -49,6 +49,13 @@ typedef enum {
     AF_BATCH_DIFF,             /* signal and filter have different batch size */
 } AF_BATCH_KIND;
 
+enum class kJITHeuristics {
+    Pass                = 0, /* no eval necessary */
+    TreeHeight          = 1, /* eval due to jit tree height */
+    KernelParameterSize = 2, /* eval due to many kernel parameters */
+    MemoryPressure      = 3  /* eval due to memory pressure */
+};
+
 #ifdef OS_WIN
 #include <Windows.h>
 using LibHandle = HMODULE;
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 4e29428bb8..c8a6f084f3 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -8,10 +8,11 @@
  ********************************************************/
 
 #pragma once
-#include <common/jit/Node.hpp>
 
 #include <Array.hpp>
 #include <backend.hpp>
+#include <common/defines.hpp>
+#include <common/jit/Node.hpp>
 
 #include <array>
 #include <iomanip>
@@ -74,19 +75,30 @@ common::Node_ptr createNaryNode(
 
     common::Node_ptr ptr = createNode(childNodes);
 
-    if (detail::passesJitHeuristics<Ti>(ptr.get())) {
-        return ptr;
-    } else {
-        int max_height_index = 0;
-        int max_height       = 0;
-        for (int i = 0; i < N; i++) {
-            if (max_height < childNodes[i]->getHeight()) {
-                max_height_index = i;
-                max_height       = childNodes[i]->getHeight();
+    switch(static_cast<kJITHeuristics>(detail::passesJitHeuristics<Ti>(ptr.get()))) {
+        case kJITHeuristics::Pass: {
+            return ptr;
+        }
+        case kJITHeuristics::TreeHeight:
+        case kJITHeuristics::KernelParameterSize: {
+            int max_height_index = 0;
+            int max_height       = 0;
+            for (int i = 0; i < N; i++) {
+                if (max_height < childNodes[i]->getHeight()) {
+                    max_height_index = i;
+                    max_height       = childNodes[i]->getHeight();
+                }
             }
+
+            children[max_height_index]->eval();
+            return createNaryNode<Ti, N>(odims, createNode, move(children));
+        }
+        case kJITHeuristics::MemoryPressure: {
+            for (auto &c : children) { c->eval(); } //TODO: use evalMultiple()
+            return ptr;
         }
-        children[max_height_index]->eval();
-        return createNaryNode<Ti, N>(odims, createNode, move(children));
     }
+    assert("MISSING HEURISTIC EVALUATION" && 1 == 0);
+    return ptr;
 }
 }  // namespace common
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 7fe3f9a376..e22c44f6b8 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -219,9 +219,9 @@ Array<T> createEmptyArray(const dim4 &dims) {
 }
 
 template<typename T>
-bool passesJitHeuristics(Node *root_node) {
-    if (!evalFlag()) return true;
-    if (root_node->getHeight() >= (int)getMaxJitSize()) { return false; }
+kJITHeuristics passesJitHeuristics(Node *root_node) {
+    if (!evalFlag()) return kJITHeuristics::Pass;
+    if (root_node->getHeight() >= (int)getMaxJitSize()) { return kJITHeuristics::TreeHeight; }
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
@@ -240,9 +240,9 @@ bool passesJitHeuristics(Node *root_node) {
                                       return prev + n.getBytes();
                                   });
 
-        if (2 * bytes > lock_bytes) { return false; }
+        if (2 * bytes > lock_bytes) { return kJITHeuristics::MemoryPressure; }
     }
-    return true;
+    return kJITHeuristics::Pass;
 }
 
 template<typename T>
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 8c94d9acb0..d983964656 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -91,7 +91,7 @@ template<typename T>
 void destroyArray(Array<T> *A);
 
 template<typename T>
-bool passesJitHeuristics(jit::Node *node);
+kJITHeuristics passesJitHeuristics(jit::Node *node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 953616f35f..09e4f11ce6 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -227,9 +227,9 @@ Node_ptr Array<T>::getNode() const {
 /// 2. The number of parameters we are passing into the kernel exceeds the
 ///    limitation on the platform. For NVIDIA this is 4096 bytes. The
 template<typename T>
-bool passesJitHeuristics(Node *root_node) {
-    if (!evalFlag()) return true;
-    if (root_node->getHeight() >= (int)getMaxJitSize()) { return false; }
+kJITHeuristics passesJitHeuristics(Node *root_node) {
+    if (!evalFlag()) { return kJITHeuristics::Pass; }
+    if (root_node->getHeight() >= (int)getMaxJitSize()) { return kJITHeuristics::TreeHeight; }
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
@@ -280,12 +280,14 @@ bool passesJitHeuristics(Node *root_node) {
         // will trigger an evaluation of the node in most cases. We
         // should be checking the amount of memory available to guard
         // this eval
-        if (param_size >= max_param_size ||
-            info.total_buffer_size * 2 > lock_bytes) {
-            return false;
+        if (param_size >= max_param_size) {
+            return kJITHeuristics::KernelParameterSize;
+        }
+        if (info.total_buffer_size * 2 > lock_bytes) {
+            return kJITHeuristics::MemoryPressure;
         }
     }
-    return true;
+    return kJITHeuristics::Pass;
 }
 
 template<typename T>
@@ -422,7 +424,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(std::vector<Array<T> *> arrays);            \
-    template bool passesJitHeuristics<T>(Node * n);                           \
+    template kJITHeuristics passesJitHeuristics<T>(Node * n);                 \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index edc6503636..af4f08384b 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -98,7 +98,7 @@ void destroyArray(Array<T> *A);
 /// \returns false if the kernel generated by this node will fail to compile
 ///          or its nodes are consuming too much memory.
 template<typename T>
-bool passesJitHeuristics(common::Node *node);
+kJITHeuristics passesJitHeuristics(common::Node *node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cu
index 29c27acda8..764f1997cf 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cu
@@ -47,7 +47,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         NaryNode(getFullName<T>(), shortname<T>(true), "__select", 3,
                  {{cond_node, a_node, b_node}}, (int)af_select_t, height));
 
-    if (detail::passesJitHeuristics<T>(node.get())) {
+    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
     } else {
         if (a_node->getHeight() >
@@ -77,7 +77,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         (int)(flip ? af_not_select_t : af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get())) {
+    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
     } else {
         if(a_node->getHeight() > max(b_node->getHeight(), cond_node->getHeight())) {
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 39e0de210f..79c9f91a3a 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -249,9 +249,9 @@ Node_ptr Array<T>::getNode() const {
 /// 2. The number of parameters we are passing into the kernel exceeds the
 ///    limitation on the platform. For NVIDIA this is 4096 bytes. The
 template<typename T>
-bool passesJitHeuristics(Node *root_node) {
-    if (!evalFlag()) return true;
-    if (root_node->getHeight() >= (int)getMaxJitSize()) { return false; }
+kJITHeuristics passesJitHeuristics(Node *root_node) {
+    if (!evalFlag()) { return kJITHeuristics::Pass; }
+    if (root_node->getHeight() >= (int)getMaxJitSize()) { return kJITHeuristics::TreeHeight; }
 
     size_t alloc_bytes, alloc_buffers;
     size_t lock_bytes, lock_buffers;
@@ -319,9 +319,14 @@ bool passesJitHeuristics(Node *root_node) {
 
         isParamLimit = param_size >= max_param_size;
 
-        if (isBufferLimit || isParamLimit) { return false; }
+        if (isParamLimit) {
+            return kJITHeuristics::KernelParameterSize;
+        }
+        if (isBufferLimit) {
+            return kJITHeuristics::MemoryPressure;
+        }
     }
-    return true;
+    return kJITHeuristics::Pass;
 }
 
 template<typename T>
@@ -462,7 +467,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
-    template bool passesJitHeuristics<T>(Node * node);                        \
+    template kJITHeuristics passesJitHeuristics<T>(Node * node);              \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 5c95e8c430..a078df1195 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -93,7 +93,7 @@ void destroyArray(Array<T> *A);
 /// \returns false if the kernel generated by this node will fail to compile
 ///          or its nodes are consuming too much memory.
 template<typename T>
-bool passesJitHeuristics(common::Node *node);
+kJITHeuristics passesJitHeuristics(common::Node *node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 1612214d30..64006f6218 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -38,7 +38,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         NaryNode(dtype_traits<T>::getName(), shortname<T>(true), "__select", 3,
                  {{cond_node, a_node, b_node}}, (int)af_select_t, height));
 
-    if (detail::passesJitHeuristics<T>(node.get())) {
+    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
     } else {
         if (a_node->getHeight() >
@@ -68,7 +68,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         (int)(flip ? af_not_select_t : af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get())) {
+    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
     } else {
         if (a_node->getHeight() >

From 78b887c7806ea1257c886b46161bb0aea60254e7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 19 Aug 2019 15:16:30 -0400
Subject: [PATCH 1738/2677] Create an array from a __half device pointer. Lock
 and unlock f16 arrays

---
 src/api/c/memory.cpp          | 10 ++++++++++
 src/api/cpp/array.cpp         |  9 +++++++++
 src/backend/cuda/platform.cpp | 12 +++++++++++-
 src/backend/cuda/traits.hpp   |  8 ++++++++
 4 files changed, 38 insertions(+), 1 deletion(-)

diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index e9c0e655ad..04a94099d9 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
@@ -21,6 +22,8 @@
 
 using namespace detail;
 
+using common::half;
+
 af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
                        const dim_t *const dims, const af_dtype type) {
     try {
@@ -72,6 +75,9 @@ af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
             case b8:
                 res = getHandle(createDeviceDataArray<char>(d, data));
                 break;
+            case f16:
+                res = getHandle(createDeviceDataArray<half>(d, data));
+                break;
             default: TYPE_ERROR(4, type);
         }
 
@@ -100,6 +106,7 @@ af_err af_get_device_ptr(void **data, const af_array arr) {
             case u16: *data = getDevicePtr(getArray<ushort>(arr)); break;
             case u8: *data = getDevicePtr(getArray<uchar>(arr)); break;
             case b8: *data = getDevicePtr(getArray<char>(arr)); break;
+            case f16: *data = getDevicePtr(getArray<half>(arr)); break;
 
             default: TYPE_ERROR(4, type);
         }
@@ -136,6 +143,7 @@ af_err af_lock_array(const af_array arr) {
             case u16: lockArray<ushort>(arr); break;
             case u8: lockArray<uchar>(arr); break;
             case b8: lockArray<char>(arr); break;
+            case f16: lockArray<half>(arr); break;
             default: TYPE_ERROR(4, type);
         }
     }
@@ -169,6 +177,7 @@ af_err af_is_locked_array(bool *res, const af_array arr) {
             case u16: *res = checkUserLock<ushort>(arr); break;
             case u8: *res = checkUserLock<uchar>(arr); break;
             case b8: *res = checkUserLock<char>(arr); break;
+            case f16: *res = checkUserLock<half>(arr); break;
             default: TYPE_ERROR(4, type);
         }
     }
@@ -204,6 +213,7 @@ af_err af_unlock_array(const af_array arr) {
             case u16: unlockArray<ushort>(arr); break;
             case u8: unlockArray<uchar>(arr); break;
             case b8: unlockArray<char>(arr); break;
+            case f16: unlockArray<half>(arr); break;
             default: TYPE_ERROR(4, type);
         }
     }
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index c7ba461c0b..feed340c40 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -23,6 +23,12 @@
 #include "error.hpp"
 #include "half.hpp"  //note: NOT common. From extern/half/include/half.hpp
 
+#ifdef AF_CUDA
+// NOTE: Adding ifdef here to avoid copying code constructor in the cuda backend
+#include <traits.hpp>
+#include <cuda_fp16.h>
+#endif
+
 #include <memory>
 #include <stdexcept>
 #include <vector>
@@ -237,6 +243,9 @@ INSTANTIATE(short)
 INSTANTIATE(unsigned short)
 INSTANTIATE(af_half)
 INSTANTIATE(half_float::half)
+#ifdef AF_CUDA
+INSTANTIATE(__half);
+#endif
 
 #undef INSTANTIATE
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 9228ffaacc..26aa9d8679 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -21,13 +21,14 @@
 #include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
-#include <driver.h>
 #include <device_manager.hpp>
+#include <driver.h>
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <spdlog/spdlog.h>
 #include <version.hpp>
 #include <af/cuda.h>
+#include <af/device.h>
 #include <af/version.h>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <common/graphics_common.hpp>
@@ -443,3 +444,12 @@ af_err afcu_cublasSetMathMode(cublasMath_t mode) {
     CATCHALL;
     return AF_SUCCESS;
 }
+
+namespace af {
+  template<>
+  __half* array::device<__half>() const {
+    void *ptr = NULL;
+    af_get_device_ptr(&ptr, get());
+    return (__half *)ptr;
+  }
+}
diff --git a/src/backend/cuda/traits.hpp b/src/backend/cuda/traits.hpp
index 7edd6f40a0..3ca7a63324 100644
--- a/src/backend/cuda/traits.hpp
+++ b/src/backend/cuda/traits.hpp
@@ -11,6 +11,7 @@
 
 #include <common/traits.hpp>
 #include <cuComplex.h>
+#include <cuda_fp16.h>
 
 namespace af {
 
@@ -28,6 +29,13 @@ struct dtype_traits<cuDoubleComplex> {
     static const char* getName() { return "cuDoubleComplex"; }
 };
 
+template<>
+struct dtype_traits<__half> {
+    enum { af_type = f16 };
+    typedef __half base_type;
+    static const char* getName() { return "__half"; }
+};
+
 }  // namespace af
 
 using af::dtype_traits;

From 3a787833e78a53855b26dd44260910a04cb82f5e Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Wed, 21 Aug 2019 22:39:49 -0700
Subject: [PATCH 1739/2677] Clarify array reference counting (#2617)

* Clarify array reference counting

* Expound on reference counting

* Fixed issues for review
---
 include/af/array.h | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 43a966a531..db52db24cd 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -203,7 +203,10 @@ namespace af
 #endif
 #endif
         /**
-            Creates an array from an \ref af_array handle
+            Creates an array from an \ref af_array handle. Does not increment 
+            a reference counter: the array assumes ownership of the handle. To 
+            share the array between multiple objects, use this in conjunction 
+            with \ref af_retain_array.
             \param handle the af_array object.
          */
         explicit
@@ -1571,7 +1574,7 @@ extern "C" {
     AFAPI af_err af_release_array(af_array arr);
 
     /**
-       Increments an \ref af_array reference count
+       Increments an \ref af_array reference count.
     */
     AFAPI af_err af_retain_array(af_array *out, const af_array in);
 

From 6c518adbf41ff0781ae6bde8ba9707c7a99c3058 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 21 Aug 2019 00:36:46 -0400
Subject: [PATCH 1740/2677] Add f16 support for dot on the CUDA and OpenCL
 backends

---
 src/api/c/blas.cpp          |  6 ++++++
 src/api/c/ops.hpp           |  1 -
 src/backend/cuda/blas.cpp   |  1 +
 src/backend/opencl/blas.cpp |  1 +
 src/backend/opencl/math.cpp |  5 +++++
 src/backend/opencl/math.hpp |  1 +
 src/backend/opencl/sum.cpp  |  1 +
 test/dot.cpp                | 20 ++++++++++++++++----
 8 files changed, 31 insertions(+), 5 deletions(-)

diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 0cfbc75919..dace62653f 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -311,6 +311,9 @@ af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
         af_array output = 0;
 
         switch (lhs_type) {
+#ifndef AF_CPU
+            case f16: output = dot<half>(lhs, rhs, optLhs, optRhs); break;
+#endif
             case f32: output = dot<float>(lhs, rhs, optLhs, optRhs); break;
             case c32: output = dot<cfloat>(lhs, rhs, optLhs, optRhs); break;
             case f64: output = dot<double>(lhs, rhs, optLhs, optRhs); break;
@@ -347,6 +350,9 @@ af_err af_dot_all(double *rval, double *ival, const af_array lhs,
         af_dtype lhs_type = lhsInfo.getType();
 
         switch (lhs_type) {
+#ifndef AF_CPU
+            case f16: *rval = static_cast<double>(dotAll<half>(out)); break;
+#endif
             case f32: *rval = dotAll<float>(out); break;
             case f64: *rval = dotAll<double>(out); break;
             case c32: {
diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index eb8f67fd38..db9187e05a 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <backend.hpp>
-#include <common/half.hpp>
 #include <math.hpp>
 
 #ifndef __DH__
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 0203e7208f..b80975aefc 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -431,6 +431,7 @@ INSTANTIATE_DOT(float)
 INSTANTIATE_DOT(double)
 INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
+INSTANTIATE_DOT(half)
 
 #define INSTANTIATE_TRSM(TYPE)                                               \
     template void trsm<TYPE>(const Array<TYPE> &lhs, Array<TYPE> &rhs,       \
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 0af6d33e23..72b0be34f2 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -166,5 +166,6 @@ INSTANTIATE_DOT(float)
 INSTANTIATE_DOT(double)
 INSTANTIATE_DOT(cfloat)
 INSTANTIATE_DOT(cdouble)
+INSTANTIATE_DOT(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/math.cpp b/src/backend/opencl/math.cpp
index 80ffd3f66a..ff445a710a 100644
--- a/src/backend/opencl/math.cpp
+++ b/src/backend/opencl/math.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include "math.hpp"
+#include <common/half.hpp>
 
 namespace opencl {
 bool operator==(cfloat a, cfloat b) {
@@ -24,6 +25,10 @@ cfloat operator+(cfloat a, cfloat b) {
     return res;
 }
 
+common::half operator+(common::half a, common::half b) noexcept {
+    return common::half(static_cast<float>(a) + static_cast<float>(b));
+}
+
 cdouble operator+(cdouble a, cdouble b) {
     cdouble res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
     return res;
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 9b2cd80630..06a728fac4 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -145,6 +145,7 @@ cdouble operator+(cdouble a, cdouble b);
 cdouble operator+(cdouble a);
 cfloat operator*(cfloat a, cfloat b);
 cdouble operator*(cdouble a, cdouble b);
+common::half operator+(common::half lhs, common::half rhs) noexcept;
 }  // namespace opencl
 
 #if defined(__GNUC__) || defined(__GNUG__)
diff --git a/src/backend/opencl/sum.cpp b/src/backend/opencl/sum.cpp
index 781a6c8eee..fc02b072c9 100644
--- a/src/backend/opencl/sum.cpp
+++ b/src/backend/opencl/sum.cpp
@@ -34,5 +34,6 @@ INSTANTIATE(af_add_t, short, int)
 INSTANTIATE(af_add_t, short, float)
 INSTANTIATE(af_add_t, ushort, uint)
 INSTANTIATE(af_add_t, ushort, float)
+INSTANTIATE(af_add_t, half, half)
 INSTANTIATE(af_add_t, half, float)
 }  // namespace opencl
diff --git a/test/dot.cpp b/test/dot.cpp
index 6308e6a290..f3cd11f251 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -12,6 +12,7 @@
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <half.hpp>
 #include <complex>
 #include <string>
 #include <vector>
@@ -40,13 +41,20 @@ class DotC : public ::testing::Test {
 };
 
 // create lists of types to be tested
+#ifdef AF_CPU
 typedef ::testing::Types<float, double> TestTypesF;
+#else
+typedef ::testing::Types<half_float::half, float, double> TestTypesF;
+#endif
 typedef ::testing::Types<cfloat, cdouble> TestTypesC;
 
 // register the type list
 TYPED_TEST_CASE(DotF, TestTypesF);
 TYPED_TEST_CASE(DotC, TestTypesC);
 
+bool isinf(af::af_cfloat val) { return isinf(val.real) || isinf(val.imag); }
+bool isinf(af::af_cdouble val) { return isinf(val.real) || isinf(val.imag); }
+
 template<typename T>
 void dotTest(string pTestFile, const int resultIdx,
              const af_mat_prop optLhs = AF_MAT_NONE,
@@ -81,9 +89,11 @@ void dotTest(string pTestFile, const int resultIdx,
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), out));
 
-    for (size_t elIter = 0; elIter < nElems; ++elIter) {
-        ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)
-            << "at: " << elIter << endl;
+    if(false == (isinf(outData.front()) && isinf(goldData[0]))) {
+        for (size_t elIter = 0; elIter < nElems; ++elIter) {
+            ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)
+                << "at: " << elIter << endl;
+        }
     }
 
     ASSERT_SUCCESS(af_release_array(a));
@@ -138,7 +148,9 @@ void dotAllTest(string pTestFile, const int resultIdx,
 
     vector<T> goldData = tests[resultIdx];
 
-    compare<T>(rval, ival, goldData[0]);
+    if(false == (isinf(rval) && isinf(goldData[0]))) {
+        compare<T>(rval, ival, goldData[0]);
+    }
 
     ASSERT_SUCCESS(af_release_array(a));
     ASSERT_SUCCESS(af_release_array(b));

From c24e13c21ce4a27c3118a699609208c916951850 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 21 Aug 2019 15:37:22 -0400
Subject: [PATCH 1741/2677] Adds padding to max kernel parameters to avoid jit
 compilation errors

---
 src/backend/cuda/Array.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 09e4f11ce6..f0912b26f1 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -247,9 +247,11 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
         constexpr size_t base_param_size =
             sizeof(Param<T>) + (4 * sizeof(uint));
 
+        // extra padding for safety to avoid failure during compilation
+        constexpr size_t jit_padding_size = 256; //@umar dontfix!
         // This is the maximum size of the params that can be allowed by the
         // CUDA platform.
-        constexpr size_t max_param_size = 4096 - base_param_size;
+        constexpr size_t max_param_size = 4096 - base_param_size - jit_padding_size;
 
         struct tree_info {
             size_t total_buffer_size;

From 8fdfbfcee747f47e27e72331978cc9f82e10712b Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Wed, 21 Aug 2019 18:06:49 -0700
Subject: [PATCH 1742/2677] Use a comma instead of a colon to match
 documentation

---
 docs/pages/configuring_arrayfire_environment.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 1a3abd3c6c..0e7a6e1d7d 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -170,7 +170,7 @@ to stdout. Currently the following modules are supported:
 Tracing displays the information that could be useful when debugging or
 optimizing your application. Here is how you would use this variable:
 
-    AF_TRACE=mem:unified ./myprogram
+    AF_TRACE=mem,unified ./myprogram
 
 This will print information about memory operations such as allocations,
 deallocations, and garbage collection.

From 7f774a046fa2d6c4e5c5ea810d14f74484be6d8d Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Sat, 24 Aug 2019 02:17:07 -0700
Subject: [PATCH 1743/2677] Clarify array::elements() documentation (#2622)

* Clarify what elements() does

* Update function documentation for af::array::elements in response to review

* Fix documentation text
---
 include/af/array.h | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index db52db24cd..d4f25cb06f 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -575,7 +575,7 @@ namespace af
         af_array get() const;
 
         /**
-           get the number of elements in array
+           Get the total number of elements across all dimensions of the array
         */
         dim_t elements() const;
 
@@ -1634,7 +1634,7 @@ extern "C" {
         @{
     */
     /**
-        \brief Gets the number of elements in an array.
+        \brief Get the total number of elements across all dimensions of the array
 
         \param[out] elems is the output that contains number of elements of \p arr
         \param[in] arr is the input array

From 3dc3da1db254ded090a48427bd07f5d428b61c2f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Aug 2019 01:04:23 -0400
Subject: [PATCH 1744/2677] Add support for gemm/dot for CPU using conversion
 to float (#2624)

* Add support for gemm for CPU using conversion to float

* Add support for dot for half using conversion
---
 src/api/c/blas.cpp       |  8 --------
 src/backend/cpu/blas.cpp | 32 ++++++++++++++++++++++++++------
 test/blas.cpp            |  4 ----
 3 files changed, 26 insertions(+), 18 deletions(-)

diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index dace62653f..3aa4d0a4a6 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -193,11 +193,9 @@ af_err af_gemm(af_array *out,
             case c64: gemm<cdouble>(&output, optLhs, optRhs,
                                     static_cast<const cdouble*>(alpha), lhs, rhs,
                                     static_cast<const cdouble*>(beta)); break;
-#ifndef AF_CPU
             case f16: gemm<half>(&output, optLhs, optRhs,
                                     static_cast<const half *>(alpha), lhs, rhs,
                                     static_cast<const half *>(beta)); break;
-#endif
             default: TYPE_ERROR(3, lhs_type);
         }
 
@@ -237,14 +235,12 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
 
         af_dtype lhs_type = lhsInfo.getType();
         switch (lhs_type) {
-#ifndef AF_CPU
             case f16: {
                     static const half alpha(1.0f);
                     static const half beta(0.0f);
                     AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
                     break;
             }
-#endif
             case f32: {
                     float alpha = 1.f;
                     float beta  = 0.f;
@@ -311,9 +307,7 @@ af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
         af_array output = 0;
 
         switch (lhs_type) {
-#ifndef AF_CPU
             case f16: output = dot<half>(lhs, rhs, optLhs, optRhs); break;
-#endif
             case f32: output = dot<float>(lhs, rhs, optLhs, optRhs); break;
             case c32: output = dot<cfloat>(lhs, rhs, optLhs, optRhs); break;
             case f64: output = dot<double>(lhs, rhs, optLhs, optRhs); break;
@@ -350,9 +344,7 @@ af_err af_dot_all(double *rval, double *ival, const af_array lhs,
         af_dtype lhs_type = lhsInfo.getType();
 
         switch (lhs_type) {
-#ifndef AF_CPU
             case f16: *rval = static_cast<double>(dotAll<half>(out)); break;
-#endif
             case f32: *rval = dotAll<float>(out); break;
             case f64: *rval = dotAll<double>(out); break;
             case c32: {
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 8829499b9d..ae482e2744 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -15,9 +15,12 @@
 
 #include <Array.hpp>
 #include <Param.hpp>
+#include <cast.hpp>
 #include <common/blas_headers.hpp>
 #include <common/complex.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
+#include <copy.hpp>
 #include <kernel/dot.hpp>
 #include <platform.hpp>
 #include <types.hpp>
@@ -30,20 +33,18 @@
 #include <type_traits>
 #include <vector>
 
-using std::vector;
-
-namespace cpu {
-
 using af::dtype_traits;
-
+using common::half;
+using common::is_complex;
 using std::add_const;
 using std::add_pointer;
 using std::conditional;
 using std::enable_if;
 using std::is_floating_point;
 using std::remove_const;
+using std::vector;
 
-using common::is_complex;
+namespace cpu {
 
 // clang-format off
 // Some implementations of BLAS require void* for complex pointers while others
@@ -335,6 +336,18 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
     getQueue().enqueue(func, out, lhs, rhs);
 }
 
+template<>
+void gemm<half>(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+                const half *alpha, const Array<half> &lhs,
+                const Array<half> &rhs, const half *beta) {
+    Array<float> outArr     = createValueArray<float>(out.dims(), 0);
+    const float float_alpha = static_cast<float>(*alpha);
+    const float float_beta  = static_cast<float>(*beta);
+    gemm<float>(outArr, optLhs, optRhs, &float_alpha, cast<float>(lhs),
+                cast<float>(rhs), &float_beta);
+    copyArray(out, outArr);
+}
+
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
@@ -355,6 +368,13 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     return out;
 }
 
+template<>
+Array<half> dot<half>(const Array<half> &lhs, const Array<half> &rhs, af_mat_prop optLhs,
+                      af_mat_prop optRhs) {
+    Array<float> out = dot(cast<float>(lhs), cast<float>(rhs), optLhs, optRhs);
+    return cast<half>(out);
+}
+
 #undef BT
 #undef REINTEPRET_CAST
 
diff --git a/test/blas.cpp b/test/blas.cpp
index 521611bb54..81d8d659c5 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -396,8 +396,6 @@ TEST(MatrixMultiply, float) {
     ASSERT_ARRAYS_NEAR(expected32, af::array(C32), 0.0001);
 }
 
-#ifndef AF_CPU
-
 TEST(MatrixMultiply, half) {
     SUPPORTED_TYPE_CHECK(af_half);
 
@@ -419,8 +417,6 @@ TEST(MatrixMultiply, half) {
     }
 }
 
-#endif
-
 struct test_params {
     af_mat_prop opt_lhs;
     af_mat_prop opt_rhs;

From 710f949f1e0a49887ff2f98ebff90224acb58beb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Aug 2019 16:07:41 -0400
Subject: [PATCH 1745/2677] Remove CUDA_cublas_device_LIBRARY to avoid errors
 in older cmake

---
 src/backend/cuda/CMakeLists.txt | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ad79c9e616..a0cb01ab61 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -510,6 +510,12 @@ target_include_directories (afcuda
 
 set_target_properties(afcuda PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
+# Remove cublas_device library which is no longer included with the cuda
+# toolkit. Fixes issues with older CMake versions
+if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
+  list(REMOVE_ITEM CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_device_LIBRARY})
+endif()
+
 target_link_libraries(afcuda
   PRIVATE
     c_api_interface

From 3618aff0f1c043cb2776b335063b2066b4808ddf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Aug 2019 16:08:52 -0400
Subject: [PATCH 1746/2677] Add compiler.h to support older cmake versions

---
 CMakeModules/InternalUtils.cmake |  30 ++-
 CMakeModules/compilers.h         | 444 +++++++++++++++++++++++++++++++
 2 files changed, 462 insertions(+), 12 deletions(-)
 create mode 100644 CMakeModules/compilers.h

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index bf45b750f2..f8311ec9ed 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -173,18 +173,24 @@ macro(arrayfire_set_cmake_default_variables)
     set(CMAKE_INSTALL_RPATH "/opt/arrayfire/lib")
   endif()
 
-  include(WriteCompilerDetectionHeader)
-  write_compiler_detection_header(
-          FILE ${ArrayFire_BINARY_DIR}/include/af/compilers.h
-          PREFIX AF
-          COMPILERS AppleClang Clang GNU Intel MSVC
-          # NOTE: cxx_attribute_deprecated does not work well with C
-          FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas cxx_static_assert
-          ALLOW_UNKNOWN_COMPILERS
-          #[VERSION <version>]
-          #[PROLOG <prolog>]
-          #[EPILOG <epilog>]
-          )
+  # This code is used to generate the compilers.h file in CMakeModules. Not all
+  # features of this modules are supported in the versions of CMake we wish to
+  # support so we are directly including the files here
+  # include(WriteCompilerDetectionHeader)
+  # write_compiler_detection_header(
+  #         FILE ${ArrayFire_BINARY_DIR}/include/af/compilers.h
+  #         PREFIX AF
+  #         COMPILERS AppleClang Clang GNU Intel MSVC
+  #         # NOTE: cxx_attribute_deprecated does not work well with C
+  #         FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas cxx_static_assert
+  #         ALLOW_UNKNOWN_COMPILERS
+  #         #[VERSION <version>]
+  #         #[PROLOG <prolog>]
+  #         #[EPILOG <epilog>]
+  #         )
+  configure_file(
+    ${CMAKE_MODULE_PATH}/compilers.h
+    ${ArrayFire_BINARY_DIR}/include/af/compilers.h)
 endmacro()
 
 macro(set_policies)
diff --git a/CMakeModules/compilers.h b/CMakeModules/compilers.h
new file mode 100644
index 0000000000..02851d18fb
--- /dev/null
+++ b/CMakeModules/compilers.h
@@ -0,0 +1,444 @@
+
+// This is a generated file. Do not edit!
+
+#ifndef AF_COMPILER_DETECTION_H
+#define AF_COMPILER_DETECTION_H
+
+#ifdef __cplusplus
+# define AF_COMPILER_IS_Comeau 0
+# define AF_COMPILER_IS_Intel 0
+# define AF_COMPILER_IS_PathScale 0
+# define AF_COMPILER_IS_Embarcadero 0
+# define AF_COMPILER_IS_Borland 0
+# define AF_COMPILER_IS_Watcom 0
+# define AF_COMPILER_IS_OpenWatcom 0
+# define AF_COMPILER_IS_SunPro 0
+# define AF_COMPILER_IS_HP 0
+# define AF_COMPILER_IS_Compaq 0
+# define AF_COMPILER_IS_zOS 0
+# define AF_COMPILER_IS_XLClang 0
+# define AF_COMPILER_IS_XL 0
+# define AF_COMPILER_IS_VisualAge 0
+# define AF_COMPILER_IS_PGI 0
+# define AF_COMPILER_IS_Cray 0
+# define AF_COMPILER_IS_TI 0
+# define AF_COMPILER_IS_Fujitsu 0
+# define AF_COMPILER_IS_GHS 0
+# define AF_COMPILER_IS_SCO 0
+# define AF_COMPILER_IS_ARMCC 0
+# define AF_COMPILER_IS_AppleClang 0
+# define AF_COMPILER_IS_ARMClang 0
+# define AF_COMPILER_IS_Clang 0
+# define AF_COMPILER_IS_GNU 0
+# define AF_COMPILER_IS_MSVC 0
+# define AF_COMPILER_IS_ADSP 0
+# define AF_COMPILER_IS_IAR 0
+# define AF_COMPILER_IS_MIPSpro 0
+
+#if defined(__COMO__)
+# undef AF_COMPILER_IS_Comeau
+# define AF_COMPILER_IS_Comeau 1
+
+#elif defined(__INTEL_COMPILER) || defined(__ICC)
+# undef AF_COMPILER_IS_Intel
+# define AF_COMPILER_IS_Intel 1
+
+#elif defined(__PATHCC__)
+# undef AF_COMPILER_IS_PathScale
+# define AF_COMPILER_IS_PathScale 1
+
+#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__)
+# undef AF_COMPILER_IS_Embarcadero
+# define AF_COMPILER_IS_Embarcadero 1
+
+#elif defined(__BORLANDC__)
+# undef AF_COMPILER_IS_Borland
+# define AF_COMPILER_IS_Borland 1
+
+#elif defined(__WATCOMC__) && __WATCOMC__ < 1200
+# undef AF_COMPILER_IS_Watcom
+# define AF_COMPILER_IS_Watcom 1
+
+#elif defined(__WATCOMC__)
+# undef AF_COMPILER_IS_OpenWatcom
+# define AF_COMPILER_IS_OpenWatcom 1
+
+#elif defined(__SUNPRO_CC)
+# undef AF_COMPILER_IS_SunPro
+# define AF_COMPILER_IS_SunPro 1
+
+#elif defined(__HP_aCC)
+# undef AF_COMPILER_IS_HP
+# define AF_COMPILER_IS_HP 1
+
+#elif defined(__DECCXX)
+# undef AF_COMPILER_IS_Compaq
+# define AF_COMPILER_IS_Compaq 1
+
+#elif defined(__IBMCPP__) && defined(__COMPILER_VER__)
+# undef AF_COMPILER_IS_zOS
+# define AF_COMPILER_IS_zOS 1
+
+#elif defined(__ibmxl__) && defined(__clang__)
+# undef AF_COMPILER_IS_XLClang
+# define AF_COMPILER_IS_XLClang 1
+
+#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800
+# undef AF_COMPILER_IS_XL
+# define AF_COMPILER_IS_XL 1
+
+#elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800
+# undef AF_COMPILER_IS_VisualAge
+# define AF_COMPILER_IS_VisualAge 1
+
+#elif defined(__PGI)
+# undef AF_COMPILER_IS_PGI
+# define AF_COMPILER_IS_PGI 1
+
+#elif defined(_CRAYC)
+# undef AF_COMPILER_IS_Cray
+# define AF_COMPILER_IS_Cray 1
+
+#elif defined(__TI_COMPILER_VERSION__)
+# undef AF_COMPILER_IS_TI
+# define AF_COMPILER_IS_TI 1
+
+#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version)
+# undef AF_COMPILER_IS_Fujitsu
+# define AF_COMPILER_IS_Fujitsu 1
+
+#elif defined(__ghs__)
+# undef AF_COMPILER_IS_GHS
+# define AF_COMPILER_IS_GHS 1
+
+#elif defined(__SCO_VERSION__)
+# undef AF_COMPILER_IS_SCO
+# define AF_COMPILER_IS_SCO 1
+
+#elif defined(__ARMCC_VERSION) && !defined(__clang__)
+# undef AF_COMPILER_IS_ARMCC
+# define AF_COMPILER_IS_ARMCC 1
+
+#elif defined(__clang__) && defined(__apple_build_version__)
+# undef AF_COMPILER_IS_AppleClang
+# define AF_COMPILER_IS_AppleClang 1
+
+#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION)
+# undef AF_COMPILER_IS_ARMClang
+# define AF_COMPILER_IS_ARMClang 1
+
+#elif defined(__clang__)
+# undef AF_COMPILER_IS_Clang
+# define AF_COMPILER_IS_Clang 1
+
+#elif defined(__GNUC__) || defined(__GNUG__)
+# undef AF_COMPILER_IS_GNU
+# define AF_COMPILER_IS_GNU 1
+
+#elif defined(_MSC_VER)
+# undef AF_COMPILER_IS_MSVC
+# define AF_COMPILER_IS_MSVC 1
+
+#elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__)
+# undef AF_COMPILER_IS_ADSP
+# define AF_COMPILER_IS_ADSP 1
+
+#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC)
+# undef AF_COMPILER_IS_IAR
+# define AF_COMPILER_IS_IAR 1
+
+
+#endif
+
+#  if AF_COMPILER_IS_AppleClang
+
+#    if !(((__clang_major__ * 100) + __clang_minor__) >= 400)
+#      error Unsupported compiler version
+#    endif
+
+# define AF_COMPILER_VERSION_MAJOR (__clang_major__)
+# define AF_COMPILER_VERSION_MINOR (__clang_minor__)
+# define AF_COMPILER_VERSION_PATCH (__clang_patchlevel__)
+# if defined(_MSC_VER)
+   /* _MSC_VER = VVRR */
+#  define AF_SIMULATE_VERSION_MAJOR (_MSC_VER / 100)
+#  define AF_SIMULATE_VERSION_MINOR (_MSC_VER % 100)
+# endif
+# define AF_COMPILER_VERSION_TWEAK (__apple_build_version__)
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_rvalue_references)
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 1
+#    else
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_noexcept)
+#      define AF_COMPILER_CXX_NOEXCEPT 1
+#    else
+#      define AF_COMPILER_CXX_NOEXCEPT 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_variadic_templates)
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 1
+#    else
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_alignas)
+#      define AF_COMPILER_CXX_ALIGNAS 1
+#    else
+#      define AF_COMPILER_CXX_ALIGNAS 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_static_assert)
+#      define AF_COMPILER_CXX_STATIC_ASSERT 1
+#    else
+#      define AF_COMPILER_CXX_STATIC_ASSERT 0
+#    endif
+
+#  elif AF_COMPILER_IS_Clang
+
+#    if !(((__clang_major__ * 100) + __clang_minor__) >= 301)
+#      error Unsupported compiler version
+#    endif
+
+# define AF_COMPILER_VERSION_MAJOR (__clang_major__)
+# define AF_COMPILER_VERSION_MINOR (__clang_minor__)
+# define AF_COMPILER_VERSION_PATCH (__clang_patchlevel__)
+# if defined(_MSC_VER)
+   /* _MSC_VER = VVRR */
+#  define AF_SIMULATE_VERSION_MAJOR (_MSC_VER / 100)
+#  define AF_SIMULATE_VERSION_MINOR (_MSC_VER % 100)
+# endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_rvalue_references)
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 1
+#    else
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_noexcept)
+#      define AF_COMPILER_CXX_NOEXCEPT 1
+#    else
+#      define AF_COMPILER_CXX_NOEXCEPT 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_variadic_templates)
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 1
+#    else
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_alignas)
+#      define AF_COMPILER_CXX_ALIGNAS 1
+#    else
+#      define AF_COMPILER_CXX_ALIGNAS 0
+#    endif
+
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_static_assert)
+#      define AF_COMPILER_CXX_STATIC_ASSERT 1
+#    else
+#      define AF_COMPILER_CXX_STATIC_ASSERT 0
+#    endif
+
+#  elif AF_COMPILER_IS_GNU
+
+#    if !((__GNUC__ * 100 + __GNUC_MINOR__) >= 404)
+#      error Unsupported compiler version
+#    endif
+
+# if defined(__GNUC__)
+#  define AF_COMPILER_VERSION_MAJOR (__GNUC__)
+# else
+#  define AF_COMPILER_VERSION_MAJOR (__GNUG__)
+# endif
+# if defined(__GNUC_MINOR__)
+#  define AF_COMPILER_VERSION_MINOR (__GNUC_MINOR__)
+# endif
+# if defined(__GNUC_PATCHLEVEL__)
+#  define AF_COMPILER_VERSION_PATCH (__GNUC_PATCHLEVEL__)
+# endif
+
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 404 && (__cplusplus >= 201103L || (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 1
+#    else
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 0
+#    endif
+
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 406 && (__cplusplus >= 201103L || (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_NOEXCEPT 1
+#    else
+#      define AF_COMPILER_CXX_NOEXCEPT 0
+#    endif
+
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 404 && (__cplusplus >= 201103L || (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 1
+#    else
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 0
+#    endif
+
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 408 && __cplusplus >= 201103L
+#      define AF_COMPILER_CXX_ALIGNAS 1
+#    else
+#      define AF_COMPILER_CXX_ALIGNAS 0
+#    endif
+
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 404 && (__cplusplus >= 201103L || (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_STATIC_ASSERT 1
+#    else
+#      define AF_COMPILER_CXX_STATIC_ASSERT 0
+#    endif
+
+#  elif AF_COMPILER_IS_Intel
+
+#    if !(__INTEL_COMPILER >= 1210)
+#      error Unsupported compiler version
+#    endif
+
+  /* __INTEL_COMPILER = VRP */
+# define AF_COMPILER_VERSION_MAJOR (__INTEL_COMPILER/100)
+# define AF_COMPILER_VERSION_MINOR (__INTEL_COMPILER/10 % 10)
+# if defined(__INTEL_COMPILER_UPDATE)
+#  define AF_COMPILER_VERSION_PATCH (__INTEL_COMPILER_UPDATE)
+# else
+#  define AF_COMPILER_VERSION_PATCH (__INTEL_COMPILER   % 10)
+# endif
+# if defined(__INTEL_COMPILER_BUILD_DATE)
+  /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */
+#  define AF_COMPILER_VERSION_TWEAK (__INTEL_COMPILER_BUILD_DATE)
+# endif
+# if defined(_MSC_VER)
+   /* _MSC_VER = VVRR */
+#  define AF_SIMULATE_VERSION_MAJOR (_MSC_VER / 100)
+#  define AF_SIMULATE_VERSION_MINOR (_MSC_VER % 100)
+# endif
+# if defined(__GNUC__)
+#  define AF_SIMULATE_VERSION_MAJOR (__GNUC__)
+# elif defined(__GNUG__)
+#  define AF_SIMULATE_VERSION_MAJOR (__GNUG__)
+# endif
+# if defined(__GNUC_MINOR__)
+#  define AF_SIMULATE_VERSION_MINOR (__GNUC_MINOR__)
+# endif
+# if defined(__GNUC_PATCHLEVEL__)
+#  define AF_SIMULATE_VERSION_PATCH (__GNUC_PATCHLEVEL__)
+# endif
+
+#    if (__cpp_rvalue_references >= 200610 || __INTEL_COMPILER >= 1210) && ((__cplusplus >= 201103L) || defined(__INTEL_CXX11_MODE__) || defined(__GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 1
+#    else
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 0
+#    endif
+
+#    if __INTEL_COMPILER >= 1400 && ((__cplusplus >= 201103L) || defined(__INTEL_CXX11_MODE__) || defined(__GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_NOEXCEPT 1
+#    else
+#      define AF_COMPILER_CXX_NOEXCEPT 0
+#    endif
+
+#    if (__cpp_variadic_templates >= 200704 || __INTEL_COMPILER >= 1210) && ((__cplusplus >= 201103L) || defined(__INTEL_CXX11_MODE__) || defined(__GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 1
+#    else
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 0
+#    endif
+
+#    if __INTEL_COMPILER >= 1500 && ((__cplusplus >= 201103L) || defined(__INTEL_CXX11_MODE__) || defined(__GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_ALIGNAS 1
+#    else
+#      define AF_COMPILER_CXX_ALIGNAS 0
+#    endif
+
+#    if (__cpp_static_assert >= 200410 || __INTEL_COMPILER >= 1210) && ((__cplusplus >= 201103L) || defined(__INTEL_CXX11_MODE__) || defined(__GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_STATIC_ASSERT 1
+#    else
+#      define AF_COMPILER_CXX_STATIC_ASSERT 0
+#    endif
+
+#  elif AF_COMPILER_IS_MSVC
+
+#    if !(_MSC_VER >= 1600)
+#      error Unsupported compiler version
+#    endif
+
+  /* _MSC_VER = VVRR */
+# define AF_COMPILER_VERSION_MAJOR (_MSC_VER / 100)
+# define AF_COMPILER_VERSION_MINOR (_MSC_VER % 100)
+# if defined(_MSC_FULL_VER)
+#  if _MSC_VER >= 1400
+    /* _MSC_FULL_VER = VVRRPPPPP */
+#   define AF_COMPILER_VERSION_PATCH (_MSC_FULL_VER % 100000)
+#  else
+    /* _MSC_FULL_VER = VVRRPPPP */
+#   define AF_COMPILER_VERSION_PATCH (_MSC_FULL_VER % 10000)
+#  endif
+# endif
+# if defined(_MSC_BUILD)
+#  define AF_COMPILER_VERSION_TWEAK (_MSC_BUILD)
+# endif
+
+#    if _MSC_VER >= 1600
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 1
+#    else
+#      define AF_COMPILER_CXX_RVALUE_REFERENCES 0
+#    endif
+
+#    if _MSC_VER >= 1900
+#      define AF_COMPILER_CXX_NOEXCEPT 1
+#    else
+#      define AF_COMPILER_CXX_NOEXCEPT 0
+#    endif
+
+#    if _MSC_VER >= 1800
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 1
+#    else
+#      define AF_COMPILER_CXX_VARIADIC_TEMPLATES 0
+#    endif
+
+#    if _MSC_VER >= 1900
+#      define AF_COMPILER_CXX_ALIGNAS 1
+#    else
+#      define AF_COMPILER_CXX_ALIGNAS 0
+#    endif
+
+#    if _MSC_VER >= 1600
+#      define AF_COMPILER_CXX_STATIC_ASSERT 1
+#    else
+#      define AF_COMPILER_CXX_STATIC_ASSERT 0
+#    endif
+
+#  endif
+
+#  if defined(AF_COMPILER_CXX_NOEXCEPT) && AF_COMPILER_CXX_NOEXCEPT
+#    define AF_NOEXCEPT noexcept
+#    define AF_NOEXCEPT_EXPR(X) noexcept(X)
+#  else
+#    define AF_NOEXCEPT
+#    define AF_NOEXCEPT_EXPR(X)
+#  endif
+
+
+#  if defined(AF_COMPILER_CXX_ALIGNAS) && AF_COMPILER_CXX_ALIGNAS
+#    define AF_ALIGNAS(X) alignas(X)
+#  elif AF_COMPILER_IS_GNU || AF_COMPILER_IS_Clang || AF_COMPILER_IS_AppleClang
+#    define AF_ALIGNAS(X) __attribute__ ((__aligned__(X)))
+#  elif AF_COMPILER_IS_MSVC
+#    define AF_ALIGNAS(X) __declspec(align(X))
+#  else
+#    define AF_ALIGNAS(X)
+#  endif
+
+#  if defined(AF_COMPILER_CXX_STATIC_ASSERT) && AF_COMPILER_CXX_STATIC_ASSERT
+#    define AF_STATIC_ASSERT(X) static_assert(X, #X)
+#    define AF_STATIC_ASSERT_MSG(X, MSG) static_assert(X, MSG)
+#  else
+#    define AF_STATIC_ASSERT_JOIN(X, Y) AF_STATIC_ASSERT_JOIN_IMPL(X, Y)
+#    define AF_STATIC_ASSERT_JOIN_IMPL(X, Y) X##Y
+template<bool> struct AFStaticAssert;
+template<> struct AFStaticAssert<true>{};
+#    define AF_STATIC_ASSERT(X) enum { AF_STATIC_ASSERT_JOIN(AFStaticAssertEnum, __LINE__) = sizeof(AFStaticAssert<X>) }
+#    define AF_STATIC_ASSERT_MSG(X, MSG) enum { AF_STATIC_ASSERT_JOIN(AFStaticAssertEnum, __LINE__) = sizeof(AFStaticAssert<X>) }
+#  endif
+
+#endif
+
+#endif

From a9b0b67755c87c37bac0006f3ecfc49991547c45 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Aug 2019 16:31:58 -0400
Subject: [PATCH 1747/2677] Add workaround CUDA definitions for older cmake
 versions

---
 CMakeModules/InternalUtils.cmake              | 10 +++---
 src/backend/cuda/CMakeLists.txt               | 33 ++++++++++---------
 .../cuda/kernel/scan_by_key/CMakeLists.txt    |  3 --
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |  3 --
 4 files changed, 23 insertions(+), 26 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index f8311ec9ed..7944926130 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -30,20 +30,20 @@ endfunction()
 
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
   if(NOT MSVC)
-    set(flags "-std=c++14 --expt-relaxed-constexpr -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden")
+    set(flags -std=c++14 --expt-relaxed-constexpr -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden)
   else()
-    set(flags "-Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275 -Xcompiler /bigobj -Xcompiler /EHsc")
+    set(flags -Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275 -Xcompiler /bigobj -Xcompiler /EHsc)
     if(CMAKE_GENERATOR MATCHES "Ninja")
-      set(flags "${flags} -Xcompiler /FS")
+      set(flags ${flags} -Xcompiler /FS)
     endif()
   endif()
 
   if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
       CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0" AND
       ${CUDA_VERSION_MAJOR} LESS 8)
-    set(flags "${flags} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED")
+    set(flags ${flags} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED)
   endif()
-  set(${cuda_flags} "${flags}" PARENT_SCOPE)
+  set(${cuda_flags} ${flags} PARENT_SCOPE)
 endfunction()
 
 include(CheckCXXCompilerFlag)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a0cb01ab61..b44b23fdda 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -172,14 +172,20 @@ endfunction()
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
 arrayfire_get_platform_definitions(platform_flags)
 
-if(AF_WITH_NONFREE AND CMAKE_VERSION VERSION_LESS "3.7")
-  # This definition is required in addition to the definition below because in
-  # an older verion of cmake definitions added using target_compile_definitions
-  # were not added to the nvcc flags. This manually adds these definitions and
-  # pass them to the options parameter in cuda_add_library
-  string(APPEND cuda_cxx_flags " -DAF_WITH_NONFREE_SIFT")
+# This definition is required in addition to the definition below because in
+# an older verion of cmake definitions added using target_compile_definitions
+# were not added to the nvcc flags. This manually adds these definitions and
+# pass them to the options parameter in cuda_add_library
+if(AF_WITH_NONFREE)
+  set(cxx_definitions -DAF_WITH_NONFREE_SIFT)
 endif()
 
+# CUDA_NO_HALF prevents the inclusion of the half class in the global namespace
+# which conflicts with the half class in ArrayFire's common namespace. prefer
+# using __half class instead for CUDA
+list(APPEND cxx_definitions -DAF_CUDA;-DCUDA_NO_HALF)
+list(APPEND cuda_cxx_flags ${cxx_definitions})
+
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
@@ -475,22 +481,19 @@ cuda_add_library(afcuda
 
     nvrtc/cache.cpp
 
-    OPTIONS "${platform_flags} ${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\""
+    OPTIONS ${platform_flags} ${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\"
   )
 
 arrayfire_set_default_cxx_flags(afcuda)
 
-# CUDA_NO_HALF prevents the inclusion of the half class in the global namespace
-# which conflicts with the half class in ArrayFire's common namespace. prefer
-# using __half class instead for CUDA
-target_compile_definitions(afcuda PRIVATE AF_CUDA CUDA_NO_HALF)
+# NOTE: Do not add additional CUDA specific definitions here. Add it to the
+# cxx_definitions variable above. cxx_definitions is used to propigate
+# definitions to the scan_by_key and thrust_sort_by_key targets as well as the
+# cuda library above.
+target_compile_options(afcuda PRIVATE ${cxx_definitions})
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
-if(AF_WITH_NONFREE)
-  target_compile_definitions(afcuda PRIVATE AF_WITH_NONFREE_SIFT)
-endif()
-
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
 add_dependencies(cuda_scan_by_key ${nvrtc_kernel_targets})
 
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index 78dd2b6341..55e7e1f234 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -16,9 +16,6 @@ endforeach()
 
 cuda_add_cuda_include_once()
 
-arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
-arrayfire_get_platform_definitions(platform_flags)
-
 foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     # When using cuda_compile with older versions of FindCUDA. The generated targets
     # have the same names as the source file. Since we are using the same file for
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index e8726a3d73..3772040761 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -17,9 +17,6 @@ foreach(STR ${FILESTRINGS})
     endif()
 endforeach()
 
-arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
-arrayfire_get_platform_definitions(platform_flags)
-
 foreach(SBK_TYPE ${SBK_TYPES})
     foreach(SBK_INST ${SBK_INSTS})
 

From 3184bef7e220e1b962ff4432a052f7e4c0839492 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Aug 2019 16:54:05 -0400
Subject: [PATCH 1748/2677] Remove guards around cpu blas functions with the
 OpenCL backend

---
 src/backend/opencl/blas.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 72b0be34f2..a71a774e71 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -24,10 +24,7 @@
 
 // Includes one of the supported OpenCL BLAS back-ends (e.g. clBLAS, CLBlast)
 #include <magma/magma_blas.h>
-
-#if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_blas.hpp>
-#endif
 
 using common::half;
 

From 2b6d645ddd23f9861e910afd30e570a18cd933a3 Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Wed, 28 Aug 2019 07:46:40 -0700
Subject: [PATCH 1749/2677] Improve af_print_mem_info's documentation (#2615)

Document the table generated by the af_print_mem_info table.
---
 include/af/array.h                   |  2 +-
 include/af/device.h                  | 33 ++++++++++++++++++----------
 src/backend/common/MemoryManager.hpp |  2 +-
 3 files changed, 24 insertions(+), 13 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index d4f25cb06f..caffaded3a 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -1583,7 +1583,7 @@ extern "C" {
        \ingroup method_mat
        @{
 
-       Get the use count of `af_array`
+       Get the reference count of \ref af_array
     */
     AFAPI af_err af_get_data_ref_count(int *use_count, const af_array in);
 #endif
diff --git a/include/af/device.h b/include/af/device.h
index 45f6bf6a4c..6c7db03e0c 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -363,17 +363,28 @@ extern "C" {
                                     size_t *lock_bytes, size_t *lock_buffers);
 
 #if AF_API_VERSION >= 33
-    ///
-    /// Prints buffer details from the ArrayFire Device Manager
-    //
-    /// \param [in] msg A message to print before the table
-    /// \param [in] device_id print the memory info of the specified device.
-    ///  -1 signifies active device.
-    ///
-    /// return AF_SUCCESS if successful
-    ///
-    /// \ingroup device_func_mem
-    ///
+    /**
+       Prints buffer details from the ArrayFire Device Manager.
+
+       The result is a table with several columns:
+
+        * POINTER:   The hex address of the array's device or pinned-memory
+                     pointer
+        * SIZE:      Human-readable size of the array
+        * AF LOCK:   Indicates whether ArrayFire is using this chunk of memory.
+                     If not, the chunk is ready for reuse.
+        * USER LOCK: If set, ArrayFire is prevented from freeing this memory.
+                     The chunk is not ready for re-use even if all ArrayFire's
+                     references to it go out of scope.
+
+       \param [in] msg A message to print before the table
+       \param [in] device_id print the memory info of the specified device.
+       -1 signifies active device.
+      
+       \returns AF_SUCCESS if successful
+      
+       \ingroup device_func_mem
+    */
     AFAPI af_err af_print_mem_info(const char *msg, const int device_id);
 #endif
 
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
index c236b5c87f..1da2f91f3b 100644
--- a/src/backend/common/MemoryManager.hpp
+++ b/src/backend/common/MemoryManager.hpp
@@ -123,7 +123,7 @@ class MemoryManager {
     /// manager.
     size_t allocated(void *ptr);
 
-    /// Frees or marks the pointer for deletion during the nex garbage
+    /// Frees or marks the pointer for deletion during the next garbage
     /// collection event
     void unlock(void *ptr, detail::Event &&e, bool user_unlock);
 

From dae7c16f8857364b9f6da81f93bc37f3f926e75c Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 29 Aug 2019 19:34:15 -0400
Subject: [PATCH 1750/2677] Dilated convolve2 and backward gradients (#2359)

* Added af_convolve2_nn which can perform dilated convolutions and can support padding.
* Added af_convolve2_gradient_nn
* CUDA backend uses cuDNN which is now a hard dependency to build ArrayFire
* CPU and OpenCL backends use a combination of unwrap and matmul to perform the operation.
---
 docs/details/signal.dox                   |  59 ++--
 include/af/defines.h                      |  10 +
 include/af/ml.h                           |  95 ++++++
 include/af/signal.h                       |  62 ++++
 include/arrayfire.h                       |  13 +
 src/api/c/CMakeLists.txt                  |   1 +
 src/api/c/convolve.cpp                    | 141 +++++++++
 src/api/c/unwrap.cpp                      |   2 +-
 src/api/cpp/convolve.cpp                  |  40 ++-
 src/api/unified/CMakeLists.txt            |   1 +
 src/api/unified/ml.cpp                    |  27 ++
 src/api/unified/signal.cpp                |  25 ++
 src/backend/common/indexing_helpers.hpp   |  31 ++
 src/backend/common/unique_handle.hpp      |  12 +-
 src/backend/cpu/blas.hpp                  |  19 +-
 src/backend/cpu/convolve.cpp              | 185 ++++++++++--
 src/backend/cpu/convolve.hpp              |  29 +-
 src/backend/cpu/kernel/unwrap.hpp         |  26 +-
 src/backend/cpu/kernel/wrap.hpp           |  79 ++++-
 src/backend/cpu/unwrap.cpp                |  26 +-
 src/backend/cpu/unwrap.hpp                |   2 +-
 src/backend/cpu/wrap.cpp                  |  35 ++-
 src/backend/cpu/wrap.hpp                  |   8 +-
 src/backend/cuda/CMakeLists.txt           |   4 +
 src/backend/cuda/blas.hpp                 |  17 +-
 src/backend/cuda/convolve.cpp             | 332 ++++++++++++++++++--
 src/backend/cuda/convolve.hpp             |  29 +-
 src/backend/cuda/cudnn.cpp                |  40 +++
 src/backend/cuda/cudnn.hpp                |  31 ++
 src/backend/cuda/handle.cpp               |   6 +
 src/backend/cuda/kernel/unwrap.hpp        |  83 ++---
 src/backend/cuda/platform.cpp             |  58 ++--
 src/backend/cuda/platform.hpp             |  16 +-
 src/backend/cuda/unwrap.cu                |  25 +-
 src/backend/cuda/unwrap.hpp               |   2 +-
 src/backend/opencl/blas.hpp               |  18 +-
 src/backend/opencl/convolve.cpp           | 175 ++++++++++-
 src/backend/opencl/convolve.hpp           |  29 +-
 src/backend/opencl/kernel/unwrap.cl       |  43 +--
 src/backend/opencl/kernel/unwrap.hpp      |  14 +-
 src/backend/opencl/kernel/wrap.hpp        |  63 +++-
 src/backend/opencl/kernel/wrap_dilated.cl |  79 +++++
 src/backend/opencl/unwrap.cpp             |  20 +-
 src/backend/opencl/unwrap.hpp             |   2 +-
 src/backend/opencl/wrap.cpp               |  29 ++
 src/backend/opencl/wrap.hpp               |   7 +-
 test/CMakeLists.txt                       |   2 +-
 test/convolve.cpp                         | 351 +++++++++++++++++++++-
 48 files changed, 2115 insertions(+), 288 deletions(-)
 create mode 100644 include/af/ml.h
 create mode 100644 src/api/unified/ml.cpp
 create mode 100644 src/backend/common/indexing_helpers.hpp
 create mode 100644 src/backend/cuda/cudnn.cpp
 create mode 100644 src/backend/cuda/cudnn.hpp
 create mode 100644 src/backend/opencl/kernel/wrap_dilated.cl

diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index 4f206f2867..3bb937db3e 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -119,13 +119,13 @@ can be decomposed into two vectors shown below.
 
 For one dimensional signals(lets say m is size of 0th dimension), below batch operations are possible.
 
-| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode              | Description |
-|:-----------------------:|:-----------------:|:-----------------:|:-----------------------:|:------------|
-| [m 1 1 1]               | [m 1 1 1]         | [m 1 1 1]         | No Batch                | Output will be a single convolved array |
-| [m 1 1 1]               | [m n 1 1]         | [m n 1 1]         | Filter is Batched       | n filters applied to same input |
-| [m n 1 1]               | [m 1 1 1]         | [m n 1 1]         | Signal is Batched       | 1 filter applied to n inputs |
-| [m n p q]               | [m n p q]         | [m n p q]         | Identical Batches       | n*p*q filters applied to n*p*q inputs in one-to-one correspondence |
-| [m n 1 1]               | [m 1 p q]         | [m n p q]         | Non-overlapping batches | p*q filters applied to n inputs to produce n x p x q results |
+| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                                        |
+| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :----------------------------------------------------------------- |
+| [m 1 1 1]                 | [m 1 1 1]           | [m 1 1 1]           | No Batch                  | Output will be a single convolved array                            |
+| [m 1 1 1]                 | [m n 1 1]           | [m n 1 1]           | Filter is Batched         | n filters applied to same input                                    |
+| [m n 1 1]                 | [m 1 1 1]           | [m n 1 1]           | Signal is Batched         | 1 filter applied to n inputs                                       |
+| [m n p q]                 | [m n p q]           | [m n p q]           | Identical Batches         | n*p*q filters applied to n*p*q inputs in one-to-one correspondence |
+| [m n 1 1]                 | [m 1 p q]           | [m n p q]           | Non-overlapping batches   | p*q filters applied to n inputs to produce n x p x q results       |
 
 The last entry in the table has more permutations than shown here.
 
@@ -138,20 +138,31 @@ The last entry in the table has more permutations than shown here.
 
 \copydoc signal_func_conv_desc
 
-For two dimensional signals, the following are the possible batch operations possible.
-Lets say m & n as sizes along the 0th & 1st dimensions respectively and p & q are the
-some integral numbers greater than one.
+For two dimensional signals, the following are the possible batch operations
+possible. Lets say m & n as sizes along the 0th & 1st dimensions respectively
+and p & q are the some integral numbers greater than one.
 
-| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode              | Description |
-|:-----------------------:|:-----------------:|:-----------------:|:-----------------------:|:------------|
-| [m n 1 1]               | [m n 1 1]         | [m n 1 1]         | No Batch                | Output will be a single convolved array |
-| [m n 1 1]               | [m n p 1]         | [m n p 1]         | Filter is Batched       | p filters applied to same input |
-| [m n p 1]               | [m n 1 1]         | [m n p 1]         | Signal is Batched       | 1 filter applied to p inputs |
-| [m n p q]               | [m n p q]         | [m n p q]         | Identical Batches       | p*q filters applied to p*q inputs in one-to-one correspondence |
-| [m n p 1]               | [m n 1 q]         | [m n p q]         | Non-overlapping batches | q filters applied to p inputs in to produce p x q results |
-| [m n 1 p]               | [m n q 1]         | [m n q p]         | Non-overlapping batches | q filters applied to p inputs in to produce q x p results |
+| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                                    |
+| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :------------------------------------------------------------- |
+| [m n 1 1]                 | [m n 1 1]           | [m n 1 1]           | No Batch                  | Output will be a single convolved array                        |
+| [m n 1 1]                 | [m n p 1]           | [m n p 1]           | Filter is Batched         | p filters applied to same input                                |
+| [m n p 1]                 | [m n 1 1]           | [m n p 1]           | Signal is Batched         | 1 filter applied to p inputs                                   |
+| [m n p q]                 | [m n p q]           | [m n p q]           | Identical Batches         | p*q filters applied to p*q inputs in one-to-one correspondence |
+| [m n p 1]                 | [m n 1 q]           | [m n p q]           | Non-overlapping batches   | q filters applied to p inputs in to produce p x q results      |
+| [m n 1 p]                 | [m n q 1]           | [m n q p]           | Non-overlapping batches   | q filters applied to p inputs in to produce q x p results      |
 
+* Batching behavior of convolve2_nn functions
 
+Batching behavior in the new convolutions functions have changed. These
+functions can perform a 2D convolution on 3D signal and filters.
+
+| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                               |
+| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :-------------------------------------------------------- |
+| [m n 1 1]                 | [m n 1 1]           | [m n 1 1]           | No Batch                  | Output will be a single convolved array                   |
+| [m n 1 1]                 | [m n p 1]           | [m n p 1]           | *Invalid d2 must be same* | N/A                                                       |
+| [m n p 1]                 | [m n 1 1]           | [m n p 1]           | *Invalid Batch*           | N/A                                                       |
+| [m n p 1]                 | [m n p 1]           | [m n 1 1]           | No Batch                  | 3D Signal and 3D filter convoled to 2D result             |
+| [m n p qs]                | [m n p qf]          | [m n qf qs]         | Batch qs * qf             | qs signals and qf filsters to create qs * qf results      |
 
 \defgroup signal_func_convolve3 3D Convolutions
 \ingroup convolve_mat
@@ -163,12 +174,12 @@ some integral numbers greater than one.
 For three dimensional inputs with m, n & p sizes along the 0th, 1st & 2nd dimensions
 respectively, given below are the possible batch operations.
 
-| Input Signal Dimensions | Filter Dimensions | Output Dimensions | Batch Mode              | Description |
-|:-----------------------:|:-----------------:|:-----------------:|:-----------------------:|:------------|
-| [m n p 1]               | [a b c 1]         | [m n p 1]         | No Batch                | Output will be a single convolve array |
-| [m n p 1]               | [a b c d]         | [m n p d]         | Filter is Batched       | d filters applied to same input |
-| [m n p q]               | [a b c 1]         | [m n p q]         | Signal is Batched       | 1 filter applied to q inputs |
-| [m n p k]               | [a b c k]         | [m n p k]         | Identical Batches       | k filters applied to k inputs in one-to-one correspondence |
+| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                                |
+| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :------------                                              |
+| [m n p 1]                 | [a b c 1]           | [m n p 1]           | No Batch                  | Output will be a single convolve array                     |
+| [m n p 1]                 | [a b c d]           | [m n p d]           | Filter is Batched         | d filters applied to same input                            |
+| [m n p q]                 | [a b c 1]           | [m n p q]           | Signal is Batched         | 1 filter applied to q inputs                               |
+| [m n p k]                 | [a b c k]           | [m n p k]           | Identical Batches         | k filters applied to k inputs in one-to-one correspondence |
 
 
 
diff --git a/include/af/defines.h b/include/af/defines.h
index 6cf8ad63fe..d511b408ac 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -528,6 +528,15 @@ typedef enum {
 } af_inverse_deconv_algo;
 #endif
 
+#if AF_API_VERSION >= 37
+typedef enum {
+    AF_CONV_GRADIENT_DEFAULT = 0,
+    AF_CONV_GRADIENT_FILTER  = 1,
+    AF_CONV_GRADIENT_DATA    = 2,
+    AF_CONV_GRADIENT_BIAS    = 3
+} af_conv_gradient_type;
+#endif
+
 #ifdef __cplusplus
 namespace af
 {
@@ -581,6 +590,7 @@ namespace af
     typedef af_var_bias varBias;
     typedef af_iterative_deconv_algo iterativeDeconvAlgo;
     typedef af_inverse_deconv_algo inverseDeconvAlgo;
+    typedef af_conv_gradient_type convGradientType;
 #endif
 }
 
diff --git a/include/af/ml.h b/include/af/ml.h
new file mode 100644
index 0000000000..20ad02999e
--- /dev/null
+++ b/include/af/ml.h
@@ -0,0 +1,95 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <af/defines.h>
+
+#ifdef __cplusplus
+namespace af
+{
+class array;
+class dim4;
+
+#if AF_API_VERSION >= 37
+    /**
+        C++ interface for calculating backward pass gradient of 2D convolution
+        This function calculates the gradient with respect to the output
+        of the \ref convolve2_nn() function that uses the machine learning
+        formulation for the dimensions of the signals and filters
+
+        \param[in]  incoming_gradient gradients to be distributed in backwards pass
+        \param[in]  original_signal input signal to forward pass of convolution
+                    assumed structure of input is ( d0 x d1 x d2 x N )
+        \param[in]  original_filter input filter to forward pass of convolution
+                    assumed structure of input is ( d0 x d1 x d2 x N )
+        \param[in]  convolved_output output from forward pass of convolution
+        \param[in]  stride specifies strides along each dimension for original convolution
+        \param[in]  padding specifies padding width along each dimension for original convolution
+        \param[in]  dilation specifies filter dilation along each dimension for original convolution
+        \param[in]  grad_type specifies which gradient to return
+        \return     gradient wrt/grad_type
+
+        \ingroup ml_convolution
+    */
+    AFAPI array convolve2GradientNN(const array& incoming_gradient,
+                                    const array& original_signal,
+                                    const array& original_filter,
+                                    const array& convolved_output,
+                                    const dim4 stride, const dim4 padding, const dim4 dilation,
+                                    convGradientType grad_type);
+
+#endif
+
+}
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if AF_API_VERSION >= 37
+    /**
+        C interface for calculating backward pass gradient of 2D convolution
+        This function calculates the gradient with respect to the output
+        of the \ref convolve2_nn() function that uses the machine learning
+        formulation for the dimensions of the signals and filters
+
+        \param[out] out gradient wrt/gradType
+        \param[in]  incoming_gradient gradients to be distributed in backwards pass
+        \param[in]  original_signal input signal to forward pass of convolution
+                    assumed structure of input is ( d0 x d1 x d2 x N )
+        \param[in]  original_filter input filter to forward pass of convolution
+                    assumed structure of input is ( d0 x d1 x d2 x N )
+        \param[in]  convolved_output output from forward pass of convolution
+        \param[in]  stride_dims specifies number of stride dimensions
+        \param[in]  strides array of stride values
+        \param[in]  padding_dims number of padding dimensions
+        \param[in]  paddings array of padding values
+        \param[in]  dilation_dims number of dilation dimensions
+        \param[in]  dilations array of dilation values
+        \param[in]  grad_type specifies which gradient to return
+        \return     \ref AF_SUCCESS if the execution completes properly
+
+        \ingroup ml_convolution
+    */
+    AFAPI af_err af_convolve2_gradient_nn(af_array *out,
+                                          const af_array incoming_gradient,
+                                          const af_array original_signal,
+                                          const af_array original_filter,
+                                          const af_array convolved_output,
+                                          const unsigned stride_dims,   const dim_t *strides,
+                                          const unsigned padding_dims,  const dim_t *paddings,
+                                          const unsigned dilation_dims, const dim_t *dilations,
+                                          af_conv_gradient_type grad_type);
+#endif
+
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/include/af/signal.h b/include/af/signal.h
index 6d148dc7c6..902e85e5c0 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -590,6 +590,33 @@ AFAPI array convolve1(const array& signal, const array& filter, const convMode m
  */
 AFAPI array convolve2(const array& signal, const array& filter, const convMode mode=AF_CONV_DEFAULT, const convDomain domain=AF_CONV_AUTO);
 
+/**
+   C++ Interface for 2D convolution
+
+   This version of convolution is consistent with the machine learning
+   formulation that will spatially convolve a filter on 2-dimensions against a
+   signal. Multiple signals and filters can be batched against each other.
+   Furthermore, the signals and filters can be multi-dimensional however their
+   dimensions must match.
+
+   Example:
+   Signals with dimensions: d0 x d1 x d2 x Ns
+   Filters with dimensions: d0 x d1 x d2 x Nf
+
+   Resulting Convolution: d0 x d1 x Nf x Ns
+
+   \param[in]  signal   is the input signal
+   \param[in]  filter   is the filter that will be used for the convolution operation
+   \param[in]  stride   specifies the filter strides along each dimension
+   \param[in]  padding  specifies the padding along each dimension
+   \param[in]  dilation specifies the amount to dilate the filter before convolution
+   \return              the convolved array
+
+   \ingroup signal_func_convolve2
+ */
+AFAPI array convolve2NN(const array& signal, const array& filter,
+                        const dim4 stride, const dim4 padding, const dim4 dilation);
+
 /**
    C++ Interface for convolution on three dimensional signals
 
@@ -1381,6 +1408,41 @@ AFAPI af_err af_convolve1(af_array *out, const af_array signal, const af_array f
  */
 AFAPI af_err af_convolve2(af_array *out, const af_array signal, const af_array filter, const af_conv_mode mode, af_conv_domain domain);
 
+/**
+   C Interface for 2D convolution
+
+   This version of convolution is consistent with the machine learning
+   formulation that will spatially convolve a filter on 2-dimensions against a
+   signal. Multiple signals and filters can be batched against each other.
+   Furthermore, the signals and filters can be multi-dimensional however their
+   dimensions must match.
+
+   Example:
+   Signals with dimensions: d0 x d1 x d2 x Ns
+   Filters with dimensions: d0 x d1 x d2 x Nf
+
+   Resulting Convolution: d0 x d1 x Nf x Ns
+
+   \param[out] out is convolved array
+   \param[in]  signal is the input signal
+   \param[in]  filter is the filter that will be used for the convolution operation
+   \param[in]  stride_dims specifies the number of stride dimension parameters
+   \param[in]  strides array of values specifying the amounts the filter strides along each dimension
+   \param[in]  padding_dims specifies the number of padding dimension parameters
+   \param[in]  paddings array of values specifying the amounts to pad along each dimension
+   \param[in]  dilation_dims specifies the number of dilation dimension parameters
+   \param[in]  dilations array of values specifying the amounts to dilate the filter
+               before convolving along each dimension
+   \return     \ref AF_SUCCESS if the convolution is successful,
+               otherwise an appropriate error code is returned.
+
+   \ingroup signal_func_convolve2
+ */
+AFAPI af_err af_convolve2_nn(af_array *out, const af_array signal, const af_array filter,
+                             const unsigned stride_dims,   const dim_t *strides,
+                             const unsigned padding_dims,  const dim_t *paddings,
+                             const unsigned dilation_dims, const dim_t *dilations);
+
 /**
    C Interface for convolution on three dimensional signals
 
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 89f2f83722..4356f9fc70 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -290,6 +290,18 @@
         contained in the \p afcu namespace provide methods to get the stream and native
         device id that ArrayFire is using.
    @}
+
+   @defgroup ml Machine Learning
+   @{
+
+     Machine learning functions
+
+     @defgroup ml_pool Pooling operations
+     Pool 2D, ND, maxpooling, minpooling, meanpooling
+
+     @defgroup ml_convolution Convolutions
+     Forward and backward convolution passes
+   @}
 @}
 
 
@@ -313,6 +325,7 @@
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
+#include "af/ml.h"
 #include "af/random.h"
 #include "af/seq.h"
 #include "af/signal.h"
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 6f7bbd4c0f..d4738425f4 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -32,6 +32,7 @@ target_sources(c_api_interface
   ${ArrayFire_SOURCE_DIR}/include/af/internal.h
   ${ArrayFire_SOURCE_DIR}/include/af/lapack.h
   ${ArrayFire_SOURCE_DIR}/include/af/macros.h
+  ${ArrayFire_SOURCE_DIR}/include/af/ml.h
   ${ArrayFire_SOURCE_DIR}/include/af/opencl.h
   ${ArrayFire_SOURCE_DIR}/include/af/random.h
   ${ArrayFire_SOURCE_DIR}/include/af/seq.h
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 9303583944..1d557533e4 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -10,18 +10,22 @@
 #include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <convolve.hpp>
 #include <fftconvolve.hpp>
 #include <handle.hpp>
 #include <tile.hpp>
+
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
+#include <af/ml.h>
 #include <af/signal.h>
 
 #include <cstdio>
 
 using af::dim4;
+using common::half;
 using namespace detail;
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
@@ -303,6 +307,61 @@ af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
     CATCHALL;
 }
 
+template<typename T>
+inline static af_array convolve2Strided(const af_array &s, const af_array &f,
+                                        const dim4 stride, const dim4 padding,
+                                        const dim4 dilation) {
+    return getHandle(convolve2<T>(getArray<T>(s), getArray<T>(f), stride,
+                                  padding, dilation));
+}
+
+af_err af_convolve2_nn(af_array *out, const af_array signal,
+                       const af_array filter, const unsigned stride_dims,
+                       const dim_t *strides, const unsigned padding_dims,
+                       const dim_t *paddings, const unsigned dilation_dims,
+                       const dim_t *dilations) {
+    try {
+        const ArrayInfo &sInfo = getInfo(signal);
+        const ArrayInfo &fInfo = getInfo(filter);
+
+        af::dim4 sDims = sInfo.dims();
+        af::dim4 fDims = fInfo.dims();
+
+        const af_dtype signalType = sInfo.getType();
+
+        ARG_ASSERT(3, stride_dims > 0 && stride_dims <= 2);
+        ARG_ASSERT(5, padding_dims > 0 && padding_dims <= 2);
+        ARG_ASSERT(7, dilation_dims > 0 && dilation_dims <= 2);
+
+        dim4 stride(stride_dims, strides);
+        dim4 padding(padding_dims, paddings);
+        dim4 dilation(dilation_dims, dilations);
+
+        // assert number of features matches between signal and filter
+        DIM_ASSERT(1, sDims[2] == fDims[2]);
+
+        af_array output;
+        switch (signalType) {
+            case f32:
+                output = convolve2Strided<float>(signal, filter, stride,
+                                                 padding, dilation);
+                break;
+            case f64:
+                output = convolve2Strided<double>(signal, filter, stride,
+                                                  padding, dilation);
+                break;
+            case f16:
+                output = convolve2Strided<half>(signal, filter, stride, padding,
+                                                dilation);
+                break;
+            default: TYPE_ERROR(1, signalType);
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
                     const af_conv_mode mode, af_conv_domain domain) {
     try {
@@ -333,3 +392,85 @@ af_err af_convolve2_sep(af_array *out, const af_array signal,
     }
     CATCHALL;
 }
+
+template<typename T>
+af_array conv2GradCall(const af_array incoming_gradient,
+                       const af_array original_signal,
+                       const af_array original_filter,
+                       const af_array convolved_output, af::dim4 stride,
+                       af::dim4 padding, af::dim4 dilation,
+                       af_conv_gradient_type grad_type) {
+    if (grad_type == AF_CONV_GRADIENT_FILTER) {
+        return getHandle(detail::conv2FilterGradient<T>(
+            getArray<T>(incoming_gradient), getArray<T>(original_signal),
+            getArray<T>(original_filter), getArray<T>(convolved_output), stride,
+            padding, dilation));
+    } else {
+        return getHandle(detail::conv2DataGradient<T>(
+            getArray<T>(incoming_gradient), getArray<T>(original_signal),
+            getArray<T>(original_filter), getArray<T>(convolved_output), stride,
+            padding, dilation));
+    }
+}
+
+af_err af_convolve2_gradient_nn(
+    af_array *out, const af_array incoming_gradient,
+    const af_array original_signal, const af_array original_filter,
+    const af_array convolved_output, const unsigned stride_dims,
+    const dim_t *strides, const unsigned padding_dims, const dim_t *paddings,
+    const unsigned dilation_dims, const dim_t *dilations,
+    af_conv_gradient_type grad_type) {
+    try {
+        const ArrayInfo &iinfo = getInfo(incoming_gradient);
+        af::dim4 iDims         = iinfo.dims();
+
+        const ArrayInfo &sinfo = getInfo(original_signal);
+        af::dim4 sDims         = sinfo.dims();
+
+        const ArrayInfo &finfo = getInfo(original_filter);
+        af::dim4 fDims         = finfo.dims();
+
+        const ArrayInfo &oinfo = getInfo(convolved_output);
+        af::dim4 oDims         = oinfo.dims();
+
+        DIM_ASSERT(1, iDims == oDims);
+        DIM_ASSERT(3, oDims[2] == fDims[3]);
+        DIM_ASSERT(3, oDims[3] == sDims[3]);
+        DIM_ASSERT(2, sDims[2] == fDims[2]);
+
+        af_array output;
+
+        ARG_ASSERT(3, stride_dims > 0 && stride_dims <= 2);
+        ARG_ASSERT(5, padding_dims > 0 && padding_dims <= 2);
+        ARG_ASSERT(7, dilation_dims > 0 && dilation_dims <= 2);
+
+        af::dim4 stride(stride_dims, strides);
+        af::dim4 padding(padding_dims, paddings);
+        af::dim4 dilation(dilation_dims, dilations);
+
+        af_dtype type = oinfo.getType();
+        switch (type) {
+            case f32:
+                output = conv2GradCall<float>(
+                    incoming_gradient, original_signal, original_filter,
+                    convolved_output, stride, padding, dilation, grad_type);
+                break;
+            case f64:
+                output = conv2GradCall<double>(
+                    incoming_gradient, original_signal, original_filter,
+                    convolved_output, stride, padding, dilation, grad_type);
+                break;
+            case f16:
+                output = conv2GradCall<half>(
+                    incoming_gradient, original_signal, original_filter,
+                    convolved_output, stride, padding, dilation, grad_type);
+                break;
+            default: TYPE_ERROR(1, type);
+        }
+        // output array is pooled array
+        std::swap(output, *out);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index 8da2d81cd3..4636adb389 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -23,7 +23,7 @@ static inline af_array unwrap(const af_array in, const dim_t wx, const dim_t wy,
                               const dim_t sx, const dim_t sy, const dim_t px,
                               const dim_t py, const bool is_column) {
     return getHandle(
-        unwrap<T>(getArray<T>(in), wx, wy, sx, sy, px, py, is_column));
+        unwrap<T>(getArray<T>(in), wx, wy, sx, sy, px, py, 1, 1, is_column));
 }
 
 af_err af_unwrap(af_array* out, const af_array in, const dim_t wx,
diff --git a/src/api/cpp/convolve.cpp b/src/api/cpp/convolve.cpp
index 9245b5b298..4b5ce62177 100644
--- a/src/api/cpp/convolve.cpp
+++ b/src/api/cpp/convolve.cpp
@@ -9,13 +9,15 @@
 
 #include <af/array.h>
 #include <af/compatible.h>
+#include <af/dim4.hpp>
+#include <af/ml.h>
 #include <af/signal.h>
 #include <algorithm>
 #include "error.hpp"
 
 namespace af {
 
-array convolve(const array& signal, const array& filter, const convMode mode,
+array convolve(const array &signal, const array &filter, const convMode mode,
                convDomain domain) {
     unsigned sN = signal.numdims();
     unsigned fN = filter.numdims();
@@ -28,36 +30,60 @@ array convolve(const array& signal, const array& filter, const convMode mode,
     }
 }
 
-array convolve(const array& col_filter, const array& row_filter,
-               const array& signal, const convMode mode) {
+array convolve(const array &col_filter, const array &row_filter,
+               const array &signal, const convMode mode) {
     af_array out = 0;
     AF_THROW(af_convolve2_sep(&out, col_filter.get(), row_filter.get(),
                               signal.get(), mode));
     return array(out);
 }
 
-array convolve1(const array& signal, const array& filter, const convMode mode,
+array convolve1(const array &signal, const array &filter, const convMode mode,
                 convDomain domain) {
     af_array out = 0;
     AF_THROW(af_convolve1(&out, signal.get(), filter.get(), mode, domain));
     return array(out);
 }
 
-array convolve2(const array& signal, const array& filter, const convMode mode,
+array convolve2(const array &signal, const array &filter, const convMode mode,
                 convDomain domain) {
     af_array out = 0;
     AF_THROW(af_convolve2(&out, signal.get(), filter.get(), mode, domain));
     return array(out);
 }
 
-array convolve3(const array& signal, const array& filter, const convMode mode,
+array convolve2NN(const array &signal, const array &filter, const dim4 stride,
+                  const dim4 padding, const dim4 dilation) {
+    af_array out = 0;
+    AF_THROW(af_convolve2_nn(
+        &out, signal.get(), filter.get(), stride.ndims(), stride.get(),
+        padding.ndims(), padding.get(), dilation.ndims(), dilation.get()));
+    return array(out);
+}
+
+array convolve2GradientNN(const array &incoming_gradient,
+                          const array &original_signal,
+                          const array &original_filter,
+                          const array &convolved_output, const dim4 stride,
+                          const dim4 padding, const dim4 dilation,
+                          af_conv_gradient_type gradType) {
+    af_array out = 0;
+    AF_THROW(af_convolve2_gradient_nn(&out, incoming_gradient.get(),
+                                      original_signal.get(), original_filter.get(),
+                                      convolved_output.get(), stride.ndims(),
+                                      stride.get(), padding.ndims(), padding.get(),
+                                      dilation.ndims(), dilation.get(), gradType));
+    return array(out);
+}
+
+array convolve3(const array &signal, const array &filter, const convMode mode,
                 convDomain domain) {
     af_array out = 0;
     AF_THROW(af_convolve3(&out, signal.get(), filter.get(), mode, domain));
     return array(out);
 }
 
-array filter(const array& image, const array& kernel) {
+array filter(const array &image, const array &kernel) {
     return convolve(image, kernel, AF_CONV_DEFAULT, AF_CONV_AUTO);
 }
 
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index a1c588ce5c..ef133a7da8 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -19,6 +19,7 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ml.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/signal.cpp
diff --git a/src/api/unified/ml.cpp b/src/api/unified/ml.cpp
new file mode 100644
index 0000000000..1723cfc7a7
--- /dev/null
+++ b/src/api/unified/ml.cpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <af/ml.h>
+#include <af/array.h>
+#include "symbol_manager.hpp"
+
+af_err af_convolve2_gradient_nn(af_array *out, const af_array incoming_gradient,
+                                const af_array original_signal,
+                                const af_array original_filter,
+                                const af_array convolved_output,
+                                const unsigned stride_dims, const dim_t *strides,
+                                const unsigned padding_dims, const dim_t *paddings,
+                                const unsigned dilation_dims,
+                                const dim_t *dilations,
+                                af_conv_gradient_type gradType) {
+    CHECK_ARRAYS(incoming_gradient, original_signal, original_filter,
+                 convolved_output);
+    return CALL(out, incoming_gradient, original_signal, original_filter,
+                convolved_output, stride_dims, strides, padding_dims, paddings,
+                dilation_dims, dilations, gradType);
+}
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index e3ef1d76e2..10dd6a15a9 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <af/array.h>
+#include <af/dim4.hpp>
 #include <af/signal.h>
 #include "symbol_manager.hpp"
 
@@ -169,6 +170,30 @@ CONV_HAPI_DEF(af_convolve1)
 CONV_HAPI_DEF(af_convolve2)
 CONV_HAPI_DEF(af_convolve3)
 
+af_err af_convolve2_nn(af_array *out, const af_array signal,
+                       const af_array filter, const unsigned stride_dims,
+                       const dim_t *strides, const unsigned padding_dims,
+                       const dim_t *paddings, const unsigned dilation_dims,
+                       const dim_t *dilations) {
+    CHECK_ARRAYS(signal, filter);
+    return CALL(out, signal, filter, stride_dims, strides, padding_dims,
+                paddings, dilation_dims, dilations);
+}
+
+af_err af_convolve2_gradient_nn(
+    af_array *out, const af_array incoming_gradient,
+    const af_array original_signal, const af_array original_filter,
+    const af_array convolved_output, const unsigned stride_dims,
+    const dim_t *strides, const unsigned padding_dims, const dim_t *paddings,
+    const unsigned dilation_dims, const dim_t *dilations,
+    af_conv_gradient_type grad_type) {
+
+    CHECK_ARRAYS(incoming_gradient, original_signal, original_filter, convolved_output);
+    return CALL(out, incoming_gradient, original_signal, original_filter, convolved_output,
+                stride_dims, strides, padding_dims, paddings, dilation_dims, dilations, grad_type);
+
+}
+
 #define FFT_CONV_HAPI_DEF(af_func)                                   \
     af_err af_func(af_array *out, const af_array signal,             \
                    const af_array filter, const af_conv_mode mode) { \
diff --git a/src/backend/common/indexing_helpers.hpp b/src/backend/common/indexing_helpers.hpp
new file mode 100644
index 0000000000..1808fabe43
--- /dev/null
+++ b/src/backend/common/indexing_helpers.hpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+
+#include <array>
+
+namespace common {
+// will generate indexes to flip input array
+// of size original dims according to axes specified in flip
+template<typename T>
+detail::Array<T> flip(const detail::Array<T> &in,
+                      const std::array<bool, AF_MAX_DIMS> flip) {
+    std::vector<af_seq> index(4, af_span);
+    af::dim4 dims = in.dims();
+
+    for (int i = 0; i < AF_MAX_DIMS; ++i) {
+        if (flip[i]) { index[i] = {(double)(dims[i] - 1), 0, -1}; }
+    }
+    return createSubArray(in, index);
+}
+}  // namespace common
diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index 95bc7e110d..baf351597b 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -56,7 +56,7 @@ class unique_handle {
     /// \brief Takes ownership of a previously created handle
     ///
     /// \param[in] handle The handle to manage by this object
-    explicit constexpr unique_handle(T handle) : handle_(handle){};
+    explicit constexpr unique_handle(T handle) noexcept : handle_(handle){};
 
     /// \brief Deletes the handle if created.
     ~unique_handle() noexcept {
@@ -67,10 +67,16 @@ class unique_handle {
     constexpr operator const T &() const noexcept { return handle_; }
 
     unique_handle(const unique_handle &other)      noexcept = delete;
-    constexpr unique_handle(unique_handle &&other) noexcept = default;
+    constexpr unique_handle(unique_handle &&other) noexcept
+      : handle_(other.handle_) {
+      other.handle_ = 0;
+    }
 
     unique_handle &operator=(unique_handle &other)  noexcept = delete;
-    unique_handle &operator=(unique_handle &&other) noexcept = default;
+    unique_handle &operator=(unique_handle &&other) noexcept {
+        handle_ = other.handle_;
+        other.handle_ = 0;
+    }
 
     // Returns true if the \p other unique_handle is the same as this handle
     constexpr bool operator==(unique_handle &other) const noexcept {
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index f39cb64f59..956ba6a963 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -13,10 +13,21 @@
 namespace cpu {
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+
+template<typename T>
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
+    int Mdim     = optLhs == AF_MAT_NONE ? 0 : 1;
+    int Ndim     = optRhs == AF_MAT_NONE ? 1 : 0;
+    Array<T> res = createEmptyArray<T>(
+        dim4(lhs.dims()[Mdim], rhs.dims()[Ndim], lhs.dims()[2], lhs.dims()[3]));
+    static const T alpha = T(1.0);
+    static const T beta  = T(0.0);
+    gemm(res, optLhs, optRhs, &alpha, lhs, rhs, &beta);
+    return res;
+}
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index ba6594df30..3e3e8e730c 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -8,20 +8,33 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <arith.hpp>
+#include <blas.hpp>
 #include <common/defines.hpp>
+#include <common/indexing_helpers.hpp>
+#include <common/half.hpp>
 #include <convolve.hpp>
+#include <handle.hpp>
 #include <kernel/convolve.hpp>
 #include <platform.hpp>
+#include <reorder.hpp>
+#include <transpose.hpp>
+#include <unwrap.hpp>
+#include <wrap.hpp>
+#include <vector>
 
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::flip;
+using common::half;
+using std::vector;
 
 namespace cpu {
 
 template<typename T, typename accT, int baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind) {
     auto sDims = signal.dims();
     auto fDims = filter.dims();
@@ -51,8 +64,8 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
 }
 
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
-                   Array<accT> const& r_filter) {
+Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
+                   Array<accT> const &r_filter) {
     auto sDims = signal.dims();
     dim4 tDims = sDims;
     dim4 oDims = sDims;
@@ -80,30 +93,30 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
 }
 
 #define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve<T, accT, 1, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 1, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 1, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 1, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 2, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 2, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 2, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 2, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 3, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 3, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 3, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 3, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve2<T, accT, true>(Array<T> const& signal,       \
-                                               Array<accT> const& c_filter,  \
-                                               Array<accT> const& r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const& signal,      \
-                                                Array<accT> const& c_filter, \
-                                                Array<accT> const& r_filter);
+    template Array<T> convolve2<T, accT, true>(Array<T> const &signal,       \
+                                               Array<accT> const &c_filter,  \
+                                               Array<accT> const &r_filter); \
+    template Array<T> convolve2<T, accT, false>(Array<T> const &signal,      \
+                                                Array<accT> const &c_filter, \
+                                                Array<accT> const &r_filter);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
@@ -117,5 +130,135 @@ INSTANTIATE(ushort, float)
 INSTANTIATE(short, float)
 INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
+                          const dim4 stride, const dim4 padding,
+                          const dim4 dilation) {
+    dim4 sDims = signal.dims();
+    dim4 fDims = filter.dims();
+
+    dim_t outputWidth =
+        1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
+                stride[0];
+    dim_t outputHeight =
+        1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
+                stride[1];
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(signal, fDims[0], fDims[1], stride[0], stride[1], padding[0],
+               padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsedFilter = flip(filter, {1, 1, 0, 0});
+    collapsedFilter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> res =
+        matmul(unwrapped, collapsedFilter, AF_MAT_TRANS, AF_MAT_NONE);
+    res.modDims(dim4(outputWidth, outputHeight, signal.dims()[3],
+                     collapsedFilter.dims()[1]));
+    Array<T> out = reorder(res, dim4(0, 1, 3, 2));
+
+    return out;
+}
+
+template<typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation) {
+    Array<T> out = createEmptyArray<T>(dim4());
+    out = convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> convolve2<T>(Array<T> const &signal,                    \
+                                   Array<T> const &filter, const dim4 stride, \
+                                   const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation) {
+    const dim4 cDims = incoming_gradient.dims();
+    const dim4 sDims = original_signal.dims();
+    const dim4 fDims = original_filter.dims();
+
+    Array<T> collapsed_filter = flip(original_filter, {1, 1, 0, 0});
+    collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    Array<T> res =
+        matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
+    res.modDims(dim4(res.dims()[0] / sDims[3], sDims[3], fDims[0] * fDims[1],
+                     sDims[2]));
+    res = reorder(res, dim4(0, 2, 3, 1));
+
+    const bool retCols = false;
+    res = wrap_dilated(res, sDims[0], sDims[1], fDims[0], fDims[1], stride[0],
+                       stride[1], padding[0], padding[1], dilation[0],
+                       dilation[1], retCols);
+
+    return res;
+}
+
+template<typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation) {
+    const dim4 cDims = incoming_gradient.dims();
+    const dim4 fDims = original_filter.dims();
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(original_signal, fDims[0], fDims[1], stride[0], stride[1],
+               padding[0], padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    Array<T> res =
+        matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
+    res.modDims(dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+
+    return flip(res, {1, 1, 0, 0});
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> conv2DataGradient<T>(                                 \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);        \
+    template Array<T> conv2FilterGradient<T>(                               \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
 
 }  // namespace cpu
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index ba366c51e6..7f882e4ce8 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -12,12 +12,29 @@
 
 namespace cpu {
 
-template<typename T, typename accT, int baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+template <typename T, typename accT, int baseDim, bool expand>
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind);
 
-template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
-                   Array<accT> const& r_filter);
+template <typename T, typename accT, bool expand>
+Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
+                   Array<accT> const &r_filter);
 
-}  // namespace cpu
+template <typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation);
+
+template <typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation);
+
+template <typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation);
+}
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
index e928136abb..cade2cb0b7 100644
--- a/src/backend/cpu/kernel/unwrap.hpp
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -15,30 +15,30 @@
 namespace cpu {
 namespace kernel {
 
-template<typename T, int d>
+template<typename T>
 void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
-                const dim_t sx, const dim_t sy, const dim_t px,
-                const dim_t py) {
-    const T* inPtr = in.get();
-    T* outPtr      = out.get();
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const dim_t dx, const dim_t dy, const int d) {
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
 
     af::dim4 idims    = in.dims();
     af::dim4 odims    = out.dims();
     af::dim4 istrides = in.strides();
     af::dim4 ostrides = out.strides();
 
-    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
+    dim_t nx = 1 + (idims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
 
     for (dim_t w = 0; w < odims[3]; w++) {
         for (dim_t z = 0; z < odims[2]; z++) {
             dim_t cOut    = w * ostrides[3] + z * ostrides[2];
             dim_t cIn     = w * istrides[3] + z * istrides[2];
-            const T* iptr = inPtr + cIn;
-            T* optr_      = outPtr + cOut;
+            const T *iptr = inPtr + cIn;
+            T *optr_      = outPtr + cOut;
 
             for (dim_t col = 0; col < odims[d]; col++) {
                 // Offset output ptr
-                T* optr = optr_ + col * ostrides[d];
+                T *optr = optr_ + col * ostrides[d];
 
                 // Calculate input window index
                 dim_t winy = (col / nx);
@@ -52,13 +52,13 @@ void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
 
                 // Short cut condition ensuring all values within input
                 // dimensions
-                bool cond = (spx >= 0 && spx + wx < idims[0] && spy >= 0 &&
-                             spy + wy < idims[1]);
+                bool cond = (spx >= 0 && spx + (wx * dx) < idims[0] &&
+                             spy >= 0 && spy + (wy * dy) < idims[1]);
 
                 for (dim_t y = 0; y < wy; y++) {
+                    dim_t ypad = spy + y * dy;
                     for (dim_t x = 0; x < wx; x++) {
-                        dim_t xpad = spx + x;
-                        dim_t ypad = spy + y;
+                        dim_t xpad = spx + x * dx;
 
                         dim_t oloc = (y * wx + x);
                         if (d == 0) oloc *= ostrides[1];
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 43b2b995e1..5990fec6eb 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -14,11 +14,11 @@
 namespace cpu {
 namespace kernel {
 
-template<typename T, int d>
+template <typename T, int d>
 void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
               const dim_t sx, const dim_t sy, const dim_t px, const dim_t py) {
-    const T* inPtr = in.get();
-    T* outPtr      = out.get();
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
 
     af::dim4 idims    = in.dims();
     af::dim4 odims    = out.dims();
@@ -31,12 +31,12 @@ void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
         for (dim_t z = 0; z < idims[2]; z++) {
             dim_t cIn      = w * istrides[3] + z * istrides[2];
             dim_t cOut     = w * ostrides[3] + z * ostrides[2];
-            const T* iptr_ = inPtr + cIn;
-            T* optr        = outPtr + cOut;
+            const T *iptr_ = inPtr + cIn;
+            T *optr        = outPtr + cOut;
 
             for (dim_t col = 0; col < idims[d]; col++) {
                 // Offset output ptr
-                const T* iptr = iptr_ + col * istrides[d];
+                const T *iptr = iptr_ + col * istrides[d];
 
                 // Calculate input window index
                 dim_t winy = (col / nx);
@@ -75,5 +75,68 @@ void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
     }
 }
 
-}  // namespace kernel
-}  // namespace cpu
+template <typename T>
+void wrap_dim_dilated(Param<T> out, CParam<T> in, const dim_t wx,
+                      const dim_t wy, const dim_t sx, const dim_t sy,
+                      const dim_t px, const dim_t py, const dim_t dx,
+                      const dim_t dy, const int d) {
+    const T *inPtr = in.get();
+    T *outPtr      = out.get();
+
+    af::dim4 idims    = in.dims();
+    af::dim4 odims    = out.dims();
+    af::dim4 istrides = in.strides();
+    af::dim4 ostrides = out.strides();
+
+    dim_t nx = 1 + (odims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+
+    for (dim_t w = 0; w < idims[3]; w++) {
+        for (dim_t z = 0; z < idims[2]; z++) {
+            dim_t cIn      = w * istrides[3] + z * istrides[2];
+            dim_t cOut     = w * ostrides[3] + z * ostrides[2];
+            const data_t<T> *iptr_ = inPtr + cIn;
+            data_t<T> *optr        = outPtr + cOut;
+
+            for (dim_t col = 0; col < idims[d]; col++) {
+                // Offset output ptr
+                const data_t<T> *iptr = iptr_ + col * istrides[d];
+
+                // Calculate input window index
+                dim_t winy = (col / nx);
+                dim_t winx = (col % nx);
+
+                dim_t startx = winx * sx;
+                dim_t starty = winy * sy;
+
+                dim_t spx = startx - px;
+                dim_t spy = starty - py;
+
+                // Short cut condition ensuring all values within input
+                // dimensions
+                bool cond = (spx >= 0 && spx + (wx * dx) < odims[0] &&
+                             spy >= 0 && spy + (wy * dy) < odims[1]);
+
+                for (dim_t y = 0; y < wy; y++) {
+                    dim_t ypad = spy + y * dy;
+                    for (dim_t x = 0; x < wx; x++) {
+                        dim_t xpad = spx + x * dx;
+
+                        dim_t iloc = (y * wx + x);
+                        if (d == 0) iloc *= istrides[1];
+
+                        if (cond || (xpad >= 0 && xpad < odims[0] &&
+                                     ypad >= 0 && ypad < odims[1])) {
+                            dim_t oloc =
+                                (ypad * ostrides[1] + xpad * ostrides[0]);
+                            // FIXME: When using threads, atomize this
+                            optr[oloc] = static_cast<compute_t<T>>(optr[oloc]) + static_cast<compute_t<T>>(iptr[iloc]);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+} // kernel namespace
+} // cpu namespace
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index a80b7d9b5e..ce062b6b8a 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -9,20 +9,23 @@
 
 #include <Array.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <kernel/unwrap.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <unwrap.hpp>
 
-namespace cpu {
+using common::half;
 
+namespace cpu {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                const bool is_column) {
+                const dim_t dx, const dim_t dy, const bool is_column) {
     af::dim4 idims = in.dims();
-    dim_t nx       = (idims[0] + 2 * px - wx) / sx + 1;
-    dim_t ny       = (idims[1] + 2 * py - wy) / sy + 1;
+
+    dim_t nx = 1 + (idims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    dim_t ny = 1 + (idims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
 
     af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
 
@@ -30,13 +33,9 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
 
     Array<T> outArray = createEmptyArray<T>(odims);
 
-    if (is_column) {
-        getQueue().enqueue(kernel::unwrap_dim<T, 1>, outArray, in, wx, wy, sx,
-                           sy, px, py);
-    } else {
-        getQueue().enqueue(kernel::unwrap_dim<T, 0>, outArray, in, wx, wy, sx,
-                           sy, px, py);
-    }
+    const int d = (is_column) ? 1 : 0;
+    getQueue().enqueue(kernel::unwrap_dim<T>, outArray, in, wx, wy, sx, sy, px,
+                       py, dx, dy, d);
 
     return outArray;
 }
@@ -44,7 +43,8 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
 #define INSTANTIATE(T)                                                      \
     template Array<T> unwrap<T>(                                            \
         const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
-        const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+        const dim_t sy, const dim_t px, const dim_t py, const dim_t dx,     \
+        const dim_t dy, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -58,5 +58,7 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
+#undef INSTANTIATE
 
 }  // namespace cpu
diff --git a/src/backend/cpu/unwrap.hpp b/src/backend/cpu/unwrap.hpp
index b1d15490cf..260605734d 100644
--- a/src/backend/cpu/unwrap.hpp
+++ b/src/backend/cpu/unwrap.hpp
@@ -13,5 +13,5 @@ namespace cpu {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                const bool is_column);
+                const dim_t dx, const dim_t dy, const bool is_column);
 }
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 9b58453069..a92869ef0b 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -9,14 +9,17 @@
 
 #include <Array.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <kernel/wrap.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <wrap.hpp>
 
+using common::half;
+
 namespace cpu {
 
-template<typename T>
+template <typename T>
 Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
               const dim_t px, const dim_t py, const bool is_column) {
@@ -54,5 +57,35 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+#undef INSTANTIATE
+
+template <typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column) {
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
+    out.eval();
+    in.eval();
+
+    getQueue().enqueue(kernel::wrap_dim_dilated<T>, out, in, wx, wy, sx, sy, px,
+                       py, dx, dy, is_column);
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> wrap_dilated<T>(                                      \
+        const Array<T> &in, const dim_t ox, const dim_t oy, const dim_t wx, \
+        const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,     \
+        const dim_t py, const dim_t dx, const dim_t dy, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(half)
+#undef INSTANTIATE
 
 }  // namespace cpu
diff --git a/src/backend/cpu/wrap.hpp b/src/backend/cpu/wrap.hpp
index 2463e49f76..ced3a74a4a 100644
--- a/src/backend/cpu/wrap.hpp
+++ b/src/backend/cpu/wrap.hpp
@@ -10,8 +10,14 @@
 #include <Array.hpp>
 
 namespace cpu {
-template<typename T>
+template <typename T>
 Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
               const dim_t px, const dim_t py, const bool is_column);
+
+template <typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column);
 }
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b44b23fdda..6a0064644b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -12,6 +12,7 @@ dependency_check(CUDA_FOUND "CUDA not found.")
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
+find_cuda_helper_libs(cudnn)
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
@@ -367,6 +368,8 @@ cuda_add_library(afcuda
     copy.hpp
     cublas.cpp
     cublas.hpp
+    cudnn.cpp
+    cudnn.hpp
     cufft.cpp
     cufft.hpp
     cusolverDn.cpp
@@ -532,6 +535,7 @@ target_link_libraries(afcuda
     ${CUDA_CUFFT_LIBRARIES}
     ${CUDA_cusolver_LIBRARY}
     ${CUDA_cusparse_LIBRARY}
+    ${CUDA_cudnn_LIBRARY}
     ${CMAKE_DL_LIBS}
   )
 
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index 7325688116..ce1aac1f3a 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -10,11 +10,22 @@
 #include <Array.hpp>
 
 namespace cuda {
+template<typename T>
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta);
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
+    int Mdim     = optLhs == AF_MAT_NONE ? 0 : 1;
+    int Ndim     = optRhs == AF_MAT_NONE ? 1 : 0;
+    Array<T> res = createEmptyArray<T>(
+        dim4(lhs.dims()[Mdim], rhs.dims()[Ndim], lhs.dims()[2], lhs.dims()[3]));
+    constexpr T alpha = 1.0;
+    constexpr T beta  = 0.0;
+    gemm(res, optLhs, optRhs, &alpha, lhs, rhs, &beta);
+    return res;
+}
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 1b2484cf0a..3f9f0d4bd9 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -8,17 +8,52 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <cast.hpp>
+#include <common/half.hpp>
+#include <common/unique_handle.hpp>
 #include <convolve.hpp>
+#include <cudnn.hpp>
 #include <err_cuda.hpp>
 #include <kernel/convolve.hpp>
+#include <platform.hpp>
 #include <af/dim4.hpp>
+#include <type_traits>
 
 using af::dim4;
+using common::half;
+using common::make_handle;
+using common::unique_handle;
+using std::conditional;
+using std::is_same;
 
 namespace cuda {
 
+template<typename T>
+cudnnDataType_t getCudnnDataType();
+
+template<>
+cudnnDataType_t getCudnnDataType<float>() {
+    return CUDNN_DATA_FLOAT;
+}
+template<>
+cudnnDataType_t getCudnnDataType<double>() {
+    return CUDNN_DATA_DOUBLE;
+}
+template<>
+cudnnDataType_t getCudnnDataType<int>() {
+    return CUDNN_DATA_INT32;
+}
+template<>
+cudnnDataType_t getCudnnDataType<unsigned char>() {
+    return CUDNN_DATA_UINT8;
+}
+template<>
+cudnnDataType_t getCudnnDataType<half>() {
+    return CUDNN_DATA_HALF;
+}
+
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind) {
     const dim4 sDims = signal.dims();
     const dim4 fDims = filter.dims();
@@ -46,9 +81,35 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
     return out;
 }
 
+void cudnnSet(cudnnTensorDescriptor_t desc, cudnnDataType_t cudnn_dtype,
+              dim4 dims) {
+    CUDNN_CHECK(cudnnSetTensor4dDescriptor(desc, CUDNN_TENSOR_NCHW, cudnn_dtype,
+                                           dims[3], dims[2], dims[1], dims[0]));
+}
+
+void cudnnSet(cudnnFilterDescriptor_t desc, cudnnDataType_t cudnn_dtype,
+              dim4 dims) {
+    CUDNN_CHECK(cudnnSetFilter4dDescriptor(desc, cudnn_dtype, CUDNN_TENSOR_NCHW,
+                                           dims[3], dims[2], dims[1], dims[0]));
+}
+
+template<typename Desc, typename T>
+unique_handle<Desc> toCudnn(Array<T> arr) {
+    dim4 dims = arr.dims();
+
+    auto descriptor             = make_handle<Desc>();
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+    cudnnSet(descriptor, cudnn_dtype, dims);
+    return descriptor;
+}
+
+template<typename T>
+using scale_type =
+    typename conditional<is_same<T, double>::value, double, float>::type;
+
 template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
-                   Array<accT> const& r_filter) {
+Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
+                   Array<accT> const &r_filter) {
     const dim4 cfDims = c_filter.dims();
     const dim4 rfDims = r_filter.dims();
 
@@ -75,30 +136,30 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
 }
 
 #define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve<T, accT, 1, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 1, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 1, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 1, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 2, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 2, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 2, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 2, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 3, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 3, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 3, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 3, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve2<T, accT, true>(Array<T> const& signal,       \
-                                               Array<accT> const& c_filter,  \
-                                               Array<accT> const& r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const& signal,      \
-                                                Array<accT> const& c_filter, \
-                                                Array<accT> const& r_filter);
+    template Array<T> convolve2<T, accT, true>(Array<T> const &signal,       \
+                                               Array<accT> const &c_filter,  \
+                                               Array<accT> const &r_filter); \
+    template Array<T> convolve2<T, accT, false>(Array<T> const &signal,      \
+                                                Array<accT> const &c_filter, \
+                                                Array<accT> const &r_filter);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
@@ -112,5 +173,234 @@ INSTANTIATE(ushort, float)
 INSTANTIATE(short, float)
 INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
+                         const dim4 stride, const dim4 padding,
+                         const dim4 dilation) {
+    auto cudnn = nnHandle();
+
+    dim4 sDims = signal.dims();
+    dim4 fDims = filter.dims();
+
+    const int n = sDims[3];
+    const int c = sDims[2];
+    const int h = sDims[1];
+    const int w = sDims[0];
+
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+    auto input_descriptor       = toCudnn<cudnnTensorDescriptor_t>(signal);
+    auto filter_descriptor      = toCudnn<cudnnFilterDescriptor_t>(filter);
+
+    // create convolution descriptor
+    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
+        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
+        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
+
+    // get output dimensions
+    const int tensorDims = 4;
+    int convolved_output_dim[tensorDims];
+    CUDNN_CHECK(cudnnGetConvolutionNdForwardOutputDim(
+        convolution_descriptor, input_descriptor, filter_descriptor, tensorDims,
+        convolved_output_dim));
+
+    // create output descriptor
+    const int n_out = convolved_output_dim[0];
+    const int c_out = convolved_output_dim[1];
+    const int h_out = convolved_output_dim[2];
+    const int w_out = convolved_output_dim[3];
+
+    // prepare output array and scratch space
+    dim4 odims(w_out, h_out, c_out, n_out);
+    Array<T> out = createEmptyArray<T>(odims);
+
+    auto output_descriptor = toCudnn<cudnnTensorDescriptor_t>(out);
+
+    // get convolution algorithm
+    const int memory_limit =
+        0;  // TODO: set to remaining space in memory manager?
+    cudnnConvolutionFwdAlgo_t convolution_algorithm;
+    CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
+        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+        output_descriptor, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, memory_limit,
+        &convolution_algorithm));
+
+    // figure out scratch space memory requirements
+    size_t workspace_bytes;
+    CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
+        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+        output_descriptor, convolution_algorithm, &workspace_bytes));
+
+    auto workspace_buffer = memAlloc<char>(workspace_bytes);
+
+    // perform convolution
+    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
+    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    CUDNN_CHECK(cudnnConvolutionForward(
+        cudnn, &alpha, input_descriptor, signal.device(), filter_descriptor,
+        filter.device(), convolution_descriptor, convolution_algorithm,
+        (void *)workspace_buffer.get(), workspace_bytes, &beta,
+        output_descriptor, out.device()));
+
+    return out;
+}
+
+template<typename T>
+constexpr void checkTypeSupport() {
+    static_assert(std::is_same<float, T>::value ||
+                      std::is_same<double, T>::value ||
+                      std::is_same<half, T>::value,
+                  "Invalid CuDNN data type: only f64, f32, f16 are supported");
+}
+
+template<typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation) {
+    checkTypeSupport<T>();
+    return convolve2_cudnn<T>(signal, filter, stride, padding, dilation);
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> convolve2<T>(Array<T> const &signal,                    \
+                                   Array<T> const &filter, const dim4 stride, \
+                                   const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation) {
+    auto cudnn = nnHandle();
+
+    dim4 iDims = incoming_gradient.dims();
+    dim4 sDims = original_signal.dims();
+    dim4 fDims = original_filter.dims();
+
+    // create dx descriptor
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+    auto x_descriptor  = toCudnn<cudnnTensorDescriptor_t>(original_signal);
+    auto dy_descriptor = toCudnn<cudnnTensorDescriptor_t>(incoming_gradient);
+
+    // create convolution descriptor
+    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
+        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
+        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
+
+    // create output filter gradient descriptor
+    auto dw_descriptor = toCudnn<cudnnFilterDescriptor_t>(original_filter);
+
+    // determine algorithm to use
+    cudnnConvolutionBwdFilterAlgo_t bwd_filt_convolution_algorithm;
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(
+        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+        dw_descriptor, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0,
+        &bwd_filt_convolution_algorithm));
+
+    // figure out scratch space memory requirements
+    size_t workspace_bytes;
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
+        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+        dw_descriptor, bwd_filt_convolution_algorithm, &workspace_bytes));
+    // prepare output array and scratch space
+    Array<T> out = createEmptyArray<T>(fDims);
+
+    auto workspace_buffer = memAlloc<char>(workspace_bytes);
+
+    // perform convolution
+    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
+    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    CUDNN_CHECK(cudnnConvolutionBackwardFilter(
+        cudnn, &alpha, x_descriptor, original_signal.device(), dy_descriptor,
+        incoming_gradient.device(), convolution_descriptor,
+        bwd_filt_convolution_algorithm, (void *)workspace_buffer.get(),
+        workspace_bytes, &beta, dw_descriptor, out.device()));
+
+    return out;
+}
+
+template<typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation) {
+    auto cudnn = nnHandle();
+
+    dim4 iDims = incoming_gradient.dims();
+    dim4 sDims = original_signal.dims();
+    dim4 fDims = original_filter.dims();
+
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+
+    // create x descriptor
+    auto dx_descriptor = toCudnn<cudnnTensorDescriptor_t>(original_signal);
+    auto dy_descriptor = toCudnn<cudnnTensorDescriptor_t>(incoming_gradient);
+
+    // create output filter gradient descriptor
+    auto w_descriptor = make_handle<cudnnFilterDescriptor_t>();
+    CUDNN_CHECK(cudnnSetFilter4dDescriptor(w_descriptor, cudnn_dtype,
+                                           CUDNN_TENSOR_NCHW, fDims[3],
+                                           fDims[2], fDims[1], fDims[0]));
+
+    // create convolution descriptor
+    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
+        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
+        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
+
+    cudnnConvolutionBwdDataAlgo_t bwd_data_convolution_algorithm;
+    if ((dilation[0] == 1 && dilation[1] == 1) || is_same<T, half>::value) {
+        bwd_data_convolution_algorithm = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
+    } else {
+        bwd_data_convolution_algorithm = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
+    }
+
+    // figure out scratch space memory requirements
+    size_t workspace_bytes;
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
+        cudnn, w_descriptor, dy_descriptor, convolution_descriptor,
+        dx_descriptor, bwd_data_convolution_algorithm, &workspace_bytes));
+
+    dim4 odims(sDims[0], sDims[1], sDims[2], sDims[3]);
+    Array<T> out = createEmptyArray<T>(odims);
+
+    auto workspace_buffer = memAlloc<char>(workspace_bytes);
+
+    // perform convolution
+    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
+    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+
+    CUDNN_CHECK(cudnnConvolutionBackwardData(
+        cudnn, &alpha, w_descriptor, original_filter.get(), dy_descriptor,
+        incoming_gradient.get(), convolution_descriptor,
+        bwd_data_convolution_algorithm, (void *)workspace_buffer.get(),
+        workspace_bytes, &beta, dx_descriptor, out.device()));
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> conv2DataGradient<T>(                                 \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);        \
+    template Array<T> conv2FilterGradient<T>(                               \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
 
 }  // namespace cuda
diff --git a/src/backend/cuda/convolve.hpp b/src/backend/cuda/convolve.hpp
index 01b211dbc5..36b2c8b56d 100644
--- a/src/backend/cuda/convolve.hpp
+++ b/src/backend/cuda/convolve.hpp
@@ -11,12 +11,29 @@
 
 namespace cuda {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+template <typename T, typename accT, dim_t baseDim, bool expand>
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind);
 
-template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
-                   Array<accT> const& r_filter);
+template <typename T, typename accT, bool expand>
+Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
+                   Array<accT> const &r_filter);
 
-}  // namespace cuda
+template <typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation);
+
+template <typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation);
+
+template <typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation);
+}
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
new file mode 100644
index 0000000000..06fffbbd4b
--- /dev/null
+++ b/src/backend/cuda/cudnn.cpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/err_common.hpp>
+#include <cudnn.hpp>
+
+namespace cuda {
+
+const char *errorString(cudnnStatus_t err) {
+    switch (err) {
+        case CUDNN_STATUS_SUCCESS: return "CUDNN_STATUS_SUCCESS";
+        case CUDNN_STATUS_NOT_INITIALIZED:
+            return "CUDNN_STATUS_NOT_INITIALIZED";
+        case CUDNN_STATUS_ALLOC_FAILED: return "CUDNN_STATUS_ALLOC_FAILED";
+        case CUDNN_STATUS_BAD_PARAM: return "CUDNN_STATUS_BAD_PARAM";
+        case CUDNN_STATUS_INTERNAL_ERROR: return "CUDNN_STATUS_INTERNAL_ERROR";
+        case CUDNN_STATUS_INVALID_VALUE: return "CUDNN_STATUS_INVALID_VALUE";
+        case CUDNN_STATUS_ARCH_MISMATCH: return "CUDNN_STATUS_ARCH_MISMATCH";
+        case CUDNN_STATUS_MAPPING_ERROR: return "CUDNN_STATUS_MAPPING_ERROR";
+        case CUDNN_STATUS_EXECUTION_FAILED:
+            return "CUDNN_STATUS_EXECUTION_FAILED";
+        case CUDNN_STATUS_NOT_SUPPORTED: return "CUDNN_STATUS_NOT_SUPPORTED";
+        case CUDNN_STATUS_LICENSE_ERROR: return "CUDNN_STATUS_LICENSE_ERROR";
+        case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
+            return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
+        case CUDNN_STATUS_RUNTIME_IN_PROGRESS:
+            return "CUDNN_STATUS_RUNTIME_IN_PROGRESS";
+        case CUDNN_STATUS_RUNTIME_FP_OVERFLOW:
+            return "CUDNN_STATUS_RUNTIME_FP_OVERFLOW";
+        default: return "UNKNOWN";
+    }
+}
+
+}
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
new file mode 100644
index 0000000000..a7bc85499b
--- /dev/null
+++ b/src/backend/cuda/cudnn.hpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/defines.hpp>
+#include <cudnn.h>
+
+namespace cuda {
+
+const char *errorString(cudnnStatus_t err);
+
+#define CUDNN_CHECK(fn)                                                    \
+    do {                                                                   \
+        cudnnStatus_t _error = (fn);                                       \
+        if (_error != CUDNN_STATUS_SUCCESS) {                              \
+            char _err_msg[1024];                                           \
+            snprintf(_err_msg, sizeof(_err_msg), "CUDNN Error (%d): %s\n", \
+                     (int)(_error), errorString(_error));                  \
+                                                                           \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                           \
+        }                                                                  \
+    } while (0)
+
+}
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
index bef747f7ae..8dc6823a6b 100644
--- a/src/backend/cuda/handle.cpp
+++ b/src/backend/cuda/handle.cpp
@@ -9,6 +9,7 @@
 
 #include <common/unique_handle.hpp>
 #include <cublas.hpp>
+#include <cudnn.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
 #include <cufft.hpp>
@@ -19,5 +20,10 @@ CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
 CREATE_HANDLE(cublasHandle_t, cublasCreate, cublasDestroy);
 CREATE_HANDLE(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
 CREATE_HANDLE(cufftHandle, cufftCreate, cufftDestroy);
+CREATE_HANDLE(cudnnHandle_t, cudnnCreate, cudnnDestroy);
+CREATE_HANDLE(cudnnTensorDescriptor_t, cudnnCreateTensorDescriptor, cudnnDestroyTensorDescriptor);
+CREATE_HANDLE(cudnnFilterDescriptor_t, cudnnCreateFilterDescriptor, cudnnDestroyFilterDescriptor);
+CREATE_HANDLE(cudnnConvolutionDescriptor_t, cudnnCreateConvolutionDescriptor, cudnnDestroyConvolutionDescriptor);
+
 
 // clang-format on
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index e0bf4616cc..8b08ab0099 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -22,8 +22,8 @@ namespace kernel {
 template<typename T, bool is_column>
 __global__ void unwrap_kernel(Param<T> out, CParam<T> in, const int wx,
                               const int wy, const int sx, const int sy,
-                              const int px, const int py, const int nx,
-                              int reps) {
+                              const int px, const int py, const int dx,
+                              const int dy, const int nx, int reps) {
     // Compute channel and volume
     const int w = (blockIdx.y + blockIdx.z * gridDim.y) / in.dims[2];
     const int z = (blockIdx.y + blockIdx.z * gridDim.y) % in.dims[2];
@@ -51,28 +51,27 @@ __global__ void unwrap_kernel(Param<T> out, CParam<T> in, const int wx,
     T* optr       = out.ptr + cOut + id * (is_column ? out.strides[1] : 1);
     const T* iptr = in.ptr + cIn;
 
-    bool cond = (spx >= 0 && spx + wx < in.dims[0] && spy >= 0 &&
-                 spy + wy < in.dims[1]);
+    // Compute output index local to column
+    int outIdx        = is_column ? threadIdx.x : threadIdx.y;
+    const int oStride = is_column ? blockDim.x : blockDim.y;
+    bool cond         = (spx >= 0 && spx + (wx * dx) < in.dims[0] && spy >= 0 &&
+                 spy + (wy * dy) < in.dims[1]);
 
     for (int i = 0; i < reps; i++) {
-        // Compute output index local to column
-        const int outIdx = is_column ? (i * blockDim.x + threadIdx.x)
-                                     : (i * blockDim.y + threadIdx.y);
-
         if (outIdx >= (is_column ? out.dims[0] : out.dims[1])) return;
 
         // Compute input index local to window
         const int x = outIdx % wx;
         const int y = outIdx / wx;
 
-        const int xpad = spx + x;
-        const int ypad = spy + y;
+        const int xpad = spx + x * dx;
+        const int ypad = spy + y * dy;
 
         // Copy
         T val = scalar<T>(0.0);
         if (cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 &&
                      ypad < in.dims[1])) {
-            const int inIdx = ypad * in.strides[1] + xpad;
+            const int inIdx = ypad * in.strides[1] + xpad * in.strides[0];
             val             = iptr[inIdx];
         }
 
@@ -81,64 +80,44 @@ __global__ void unwrap_kernel(Param<T> out, CParam<T> in, const int wx,
         } else {
             optr[outIdx * out.strides[1]] = val;
         }
+        outIdx += oStride;
     }
 }
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
 template<typename T>
-void unwrap_col(Param<T> out, CParam<T> in, const int wx, const int wy,
-                const int sx, const int sy, const int px, const int py,
-                const int nx) {
-    int TX = std::min(THREADS_PER_BLOCK, nextpow2(out.dims[0]));
+void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
+            const int sx, const int sy, const int px, const int py,
+            const int dx, const int dy, const int nx, const bool is_column) {
+    dim3 threads, blocks;
+    int reps;
 
-    dim3 threads(TX, THREADS_PER_BLOCK / TX);
-    dim3 blocks(divup(out.dims[1], threads.y), out.dims[2] * out.dims[3]);
+    if (is_column) {
+        int TX = std::min(THREADS_PER_BLOCK, nextpow2(out.dims[0]));
 
-    int reps = divup((wx * wy),
+        threads = dim3(TX, THREADS_PER_BLOCK / TX);
+        blocks = dim3(divup(out.dims[1], threads.y), out.dims[2] * out.dims[3]);
+        reps   = divup((wx * wy),
                      threads.x);  // is > 1 only when TX == 256 && wx * wy > 256
+    } else {
+        threads = dim3(THREADS_X, THREADS_Y);
+        blocks = dim3(divup(out.dims[0], threads.x), out.dims[2] * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
-
-    CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads, out, in, wx, wy, sx,
-                sy, px, py, nx, reps);
-
-    POST_LAUNCH_CHECK();
-}
-
-template<typename T>
-void unwrap_row(Param<T> out, CParam<T> in, const int wx, const int wy,
-                const int sx, const int sy, const int px, const int py,
-                const int nx) {
-    dim3 threads(THREADS_X, THREADS_Y);
-    dim3 blocks(divup(out.dims[0], threads.x), out.dims[2] * out.dims[3]);
-
-    int reps = divup((wx * wy), threads.y);
+        reps = divup((wx * wy), threads.y);
+    }
 
     const int maxBlocksY =
         cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads, out, in, wx, wy, sx,
-                sy, px, py, nx, reps);
-
-    POST_LAUNCH_CHECK();
-}
-
-template<typename T>
-void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
-            const int sx, const int sy, const int px, const int py,
-            const int nx, const bool is_column) {
     if (is_column) {
-        unwrap_col<T>(out, in, wx, wy, sx, sy, px, py, nx);
+        CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads, out, in, wx, wy,
+                    sx, sy, px, py, dx, dy, nx, reps);
     } else {
-        unwrap_row<T>(out, in, wx, wy, sx, sy, px, py, nx);
+        CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads, out, in, wx, wy,
+                    sx, sy, px, py, dx, dy, nx, reps);
     }
+    POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 26aa9d8679..aa1d59aa77 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -18,6 +18,7 @@
 #include <common/unique_handle.hpp>
 #include <common/util.hpp>
 #include <cublas.hpp>
+#include <cudnn.hpp>
 #include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
@@ -82,8 +83,9 @@ static inline int getMinSupportedCompute(int cudaMajorVer) {
                                   : minSV[cudaMajorVer - 1]);
 }
 
-unique_handle<cublasHandle_t>* cublasManager(const int deviceId) {
-    thread_local unique_handle<cublasHandle_t> handles[DeviceManager::MAX_DEVICES];
+unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
+    thread_local unique_handle<cublasHandle_t>
+        handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     call_once(initFlags[deviceId], [&] {
@@ -91,14 +93,28 @@ unique_handle<cublasHandle_t>* cublasManager(const int deviceId) {
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUBLAS_CHECK(cublasSetStream(handles[deviceId],
-                                     cuda::getStream(deviceId)));
+        CUBLAS_CHECK(
+            cublasSetStream(handles[deviceId], cuda::getStream(deviceId)));
     });
 
     return &handles[deviceId];
 }
 
-unique_ptr<PlanCache>& cufftManager(const int deviceId) {
+unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
+    thread_local unique_handle<cudnnHandle_t>
+        cudnnHandles[DeviceManager::MAX_DEVICES];
+    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+
+    std::call_once(initFlags[deviceId],
+                   [&] { cudnnHandles[deviceId].create(); });
+
+    CUDNN_CHECK(
+        cudnnSetStream(cudnnHandles[deviceId], cuda::getStream(deviceId)));
+
+    return &cudnnHandles[deviceId];
+}
+
+unique_ptr<PlanCache> &cufftManager(const int deviceId) {
     thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId],
@@ -106,7 +122,7 @@ unique_ptr<PlanCache>& cufftManager(const int deviceId) {
     return caches[deviceId];
 }
 
-unique_handle<cusolverDnHandle_t>* cusolverManager(const int deviceId) {
+unique_handle<cusolverDnHandle_t> *cusolverManager(const int deviceId) {
     thread_local unique_handle<cusolverDnHandle_t>
         handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
@@ -115,8 +131,8 @@ unique_handle<cusolverDnHandle_t>* cusolverManager(const int deviceId) {
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUSOLVER_CHECK(cusolverDnSetStream(handles[deviceId],
-                                           cuda::getStream(deviceId)));
+        CUSOLVER_CHECK(
+            cusolverDnSetStream(handles[deviceId], cuda::getStream(deviceId)));
     });
     // TODO(pradeep) prior to this change, stream was being synced in get solver
     // handle because of some cusolver bug. Re-enable that if this change
@@ -128,16 +144,17 @@ unique_handle<cusolverDnHandle_t>* cusolverManager(const int deviceId) {
     return &handles[deviceId];
 }
 
-unique_handle<cusparseHandle_t>* cusparseManager(const int deviceId) {
-    thread_local unique_handle<cusparseHandle_t> handles[DeviceManager::MAX_DEVICES];
+unique_handle<cusparseHandle_t> *cusparseManager(const int deviceId) {
+    thread_local unique_handle<cusparseHandle_t>
+        handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId], [&] {
         handles[deviceId].create();
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUSPARSE_CHECK(cusparseSetStream(handles[deviceId],
-                                         cuda::getStream(deviceId)));
+        CUSPARSE_CHECK(
+            cusparseSetStream(handles[deviceId], cuda::getStream(deviceId)));
     });
     return &handles[deviceId];
 }
@@ -151,6 +168,7 @@ DeviceManager::~DeviceManager() {
         delete cusparseManager(i);
         cufftManager(i).reset();
         delete cublasManager(i);
+        delete nnManager(i);
     }
 }
 
@@ -205,7 +223,7 @@ bool isDoubleSupported(int device) {
 }
 
 bool isHalfSupported(int device) {
-    auto prop = getDeviceProp(device);
+    auto prop     = getDeviceProp(device);
     float compute = prop.major * 1000 + prop.minor * 10;
     return compute >= 5030;
 }
@@ -283,7 +301,7 @@ unsigned getMaxJitSize() {
     return length;
 }
 
-int& tlocalActiveDeviceId() {
+int &tlocalActiveDeviceId() {
     thread_local int activeDeviceId = 0;
 
     return activeDeviceId;
@@ -382,9 +400,9 @@ PlanCache &fftManager() {
     return *(cufftManager(cuda::getActiveDeviceId()).get());
 }
 
-BlasHandle blasHandle() {
-    return *cublasManager(cuda::getActiveDeviceId());
-}
+BlasHandle blasHandle() { return *cublasManager(cuda::getActiveDeviceId()); }
+
+cudnnHandle_t nnHandle() { return *nnManager(cuda::getActiveDeviceId()); }
 
 SolveHandle solverDnHandle() {
     return *cusolverManager(cuda::getActiveDeviceId());
@@ -446,10 +464,10 @@ af_err afcu_cublasSetMathMode(cublasMath_t mode) {
 }
 
 namespace af {
-  template<>
-  __half* array::device<__half>() const {
+template<>
+__half *array::device<__half>() const {
     void *ptr = NULL;
     af_get_device_ptr(&ptr, get());
     return (__half *)ptr;
-  }
 }
+}  // namespace af
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index c8205512eb..6c1360621b 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -11,7 +11,9 @@
 
 #include <cuda.h>
 #include <cuda_runtime.h>
+
 #include <string>
+#include <utility>
 
 /* Forward declarations of Opaque structure holding
  * the following library contexts
@@ -25,6 +27,8 @@ struct cusparseContext;
 typedef struct cusparseContext* SparseHandle;
 struct cusolverDnContext;
 typedef struct cusolverDnContext* SolveHandle;
+struct cudnnContext;
+typedef struct cudnnContext* cudnnHandle_t;
 
 namespace spdlog {
 class logger;
@@ -88,20 +92,22 @@ cudaDeviceProp getDeviceProp(int device);
 
 std::pair<int, int> getComputeCapability(const int device);
 
-bool& evalFlag();
+bool &evalFlag();
 
 MemoryManager& memoryManager();
 
-MemoryManagerPinned& pinnedMemoryManager();
+MemoryManagerPinned &pinnedMemoryManager();
 
-graphics::ForgeManager& forgeManager();
+graphics::ForgeManager &forgeManager();
 
-GraphicsResourceManager& interopManager();
+GraphicsResourceManager &interopManager();
 
-PlanCache& fftManager();
+PlanCache &fftManager();
 
 BlasHandle blasHandle();
 
+cudnnHandle_t nnHandle();
+
 SolveHandle solverDnHandle();
 
 SparseHandle sparseHandle();
diff --git a/src/backend/cuda/unwrap.cu b/src/backend/cuda/unwrap.cu
index 605bf09a67..6722c65bcd 100644
--- a/src/backend/cuda/unwrap.cu
+++ b/src/backend/cuda/unwrap.cu
@@ -14,33 +14,32 @@
 #include <stdexcept>
 
 namespace cuda {
+
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                const bool is_column) {
+                const dim_t dx, const dim_t dy, const bool is_column) {
     af::dim4 idims = in.dims();
 
-    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-    dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
+    dim_t nx = 1 + (idims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    dim_t ny = 1 + (idims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
 
-    af::dim4 odims;
+    af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
 
-    if (is_column) {
-        odims = dim4(wx * wy, nx * ny, idims[2], idims[3]);
-    } else {
-        odims = dim4(nx * ny, wx * wy, idims[2], idims[3]);
-    }
+    if (!is_column) { std::swap(odims[0], odims[1]); }
 
-    // Create output placeholder
     Array<T> outArray = createEmptyArray<T>(odims);
-    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, nx, is_column);
+    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, dx, dy, nx,
+                      is_column);
+
     return outArray;
 }
 
 #define INSTANTIATE(T)                                                      \
     template Array<T> unwrap<T>(                                            \
         const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
-        const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+        const dim_t sy, const dim_t px, const dim_t py, const dim_t dx,     \
+        const dim_t dy, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -54,4 +53,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+#undef INSTANTIATE
+
 }  // namespace cuda
diff --git a/src/backend/cuda/unwrap.hpp b/src/backend/cuda/unwrap.hpp
index a03b4a2e39..1a348d93e2 100644
--- a/src/backend/cuda/unwrap.hpp
+++ b/src/backend/cuda/unwrap.hpp
@@ -13,5 +13,5 @@ namespace cuda {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                const bool is_column);
+                const dim_t dx, const dim_t dy, const bool is_column);
 }
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index 39c07c2954..22c2e1ec02 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -20,9 +20,21 @@ void initBlas();
 void deInitBlas();
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+
+template<typename T>
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
+    int Mdim     = optLhs == AF_MAT_NONE ? 0 : 1;
+    int Ndim     = optRhs == AF_MAT_NONE ? 1 : 0;
+    Array<T> res = createEmptyArray<T>(
+        dim4(lhs.dims()[Mdim], rhs.dims()[Ndim], lhs.dims()[2], lhs.dims()[3]));
+    static const T alpha = T(1.0);
+    static const T beta  = T(0.0);
+    gemm(res, optLhs, optRhs, &alpha, lhs, rhs, &beta);
+    return res;
+}
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 2cfdb7c159..40a2895a95 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -8,17 +8,30 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <blas.hpp>
+#include <common/half.hpp>
+#include <common/indexing_helpers.hpp>
 #include <convolve.hpp>
 #include <err_opencl.hpp>
+#include <handle.hpp>
 #include <kernel/convolve.hpp>
+#include <reorder.hpp>
+#include <transpose.hpp>
+#include <unwrap.hpp>
+#include <wrap.hpp>
+#include <af/defines.h>
 #include <af/dim4.hpp>
+#include <vector>
 
 using af::dim4;
+using common::flip;
+using common::half;
+using std::vector;
 
 namespace opencl {
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind) {
     const dim4 sDims = signal.dims();
     const dim4 fDims = filter.dims();
@@ -72,23 +85,23 @@ Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
 }
 
 #define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve<T, accT, 1, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 1, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 1, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 1, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 2, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 2, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 2, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 2, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 3, true>(Array<T> const& signal,     \
-                                                 Array<accT> const& filter,  \
+    template Array<T> convolve<T, accT, 3, true>(Array<T> const &signal,     \
+                                                 Array<accT> const &filter,  \
                                                  AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 3, false>(Array<T> const& signal,    \
-                                                  Array<accT> const& filter, \
+    template Array<T> convolve<T, accT, 3, false>(Array<T> const &signal,    \
+                                                  Array<accT> const &filter, \
                                                   AF_BATCH_KIND kind);
 
 INSTANTIATE(cdouble, cdouble)
@@ -103,5 +116,141 @@ INSTANTIATE(ushort, float)
 INSTANTIATE(short, float)
 INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
+                          const dim4 stride, const dim4 padding,
+                          const dim4 dilation) {
+    dim4 sDims = signal.dims();
+    dim4 fDims = filter.dims();
+
+    dim_t outputWidth =
+        1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
+                stride[0];
+    dim_t outputHeight =
+        1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
+                stride[1];
+    dim4 oDims = dim4(outputWidth, outputHeight, fDims[3], sDims[3]);
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(signal, fDims[0], fDims[1], stride[0], stride[1], padding[0],
+               padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsedFilter = filter;
+
+    collapsedFilter = flip(collapsedFilter, {1, 1, 0, 0});
+    collapsedFilter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> res =
+        matmul(unwrapped, collapsedFilter, AF_MAT_TRANS, AF_MAT_NONE);
+    res.modDims(dim4(outputWidth, outputHeight, signal.dims()[3],
+                     collapsedFilter.dims()[1]));
+    Array<T> out = reorder(res, dim4(0, 1, 3, 2));
+
+    return out;
+}
+
+template<typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation) {
+    Array<T> out =
+        convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
+
+    return out;
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> convolve2<T>(Array<T> const &signal,                    \
+                                   Array<T> const &filter, const dim4 stride, \
+                                   const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation) {
+    const dim4 cDims = incoming_gradient.dims();
+    const dim4 sDims = original_signal.dims();
+    const dim4 fDims = original_filter.dims();
+
+    Array<T> collapsed_filter = original_filter;
+
+    collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
+    collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    Array<T> res =
+        matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
+    res.modDims(dim4(res.dims()[0] / sDims[3], sDims[3], fDims[0] * fDims[1],
+                     sDims[2]));
+    res = reorder(res, dim4(0, 2, 3, 1));
+
+    const bool retCols = false;
+    res = wrap_dilated(res, sDims[0], sDims[1], fDims[0], fDims[1], stride[0],
+                       stride[1], padding[0], padding[1], dilation[0],
+                       dilation[1], retCols);
+
+    return res;
+}
+
+template<typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation) {
+    const dim4 cDims = incoming_gradient.dims();
+    const dim4 sDims = original_signal.dims();
+    const dim4 fDims = original_filter.dims();
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(original_signal, fDims[0], fDims[1], stride[0], stride[1],
+               padding[0], padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    Array<T> res =
+        matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
+    res.modDims(dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+
+    return flip(res, {1, 1, 0, 0});
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> conv2DataGradient<T>(                                 \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);        \
+    template Array<T> conv2FilterGradient<T>(                               \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
 
 }  // namespace opencl
diff --git a/src/backend/opencl/convolve.hpp b/src/backend/opencl/convolve.hpp
index 7216ee1663..59aafe7322 100644
--- a/src/backend/opencl/convolve.hpp
+++ b/src/backend/opencl/convolve.hpp
@@ -11,12 +11,29 @@
 
 namespace opencl {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
-Array<T> convolve(Array<T> const& signal, Array<accT> const& filter,
+template <typename T, typename accT, dim_t baseDim, bool expand>
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind);
 
-template<typename T, typename accT, bool expand>
-Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
-                   Array<accT> const& r_filter);
+template <typename T, typename accT, bool expand>
+Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
+                   Array<accT> const &r_filter);
 
-}  // namespace opencl
+template <typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation);
+
+template <typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation);
+
+template <typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation);
+}
diff --git a/src/backend/opencl/kernel/unwrap.cl b/src/backend/opencl/kernel/unwrap.cl
index 09a4216329..92bddc6c5f 100644
--- a/src/backend/opencl/kernel/unwrap.cl
+++ b/src/backend/opencl/kernel/unwrap.cl
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2018, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void unwrap_kernel(__global T* d_out, const KParam out,
-                            __global const T* d_in, const KParam in,
+__kernel void unwrap_kernel(__global T *d_out, const KParam out,
+                            __global const T *d_in, const KParam in,
                             const int wx, const int wy, const int sx,
                             const int sy, const int px, const int py,
-                            const int nx, const int reps) {
+                            const int dx, const int dy, const int nx,
+                            const int reps) {
     // Compute channel and volume
     const int w = get_group_id(1) / in.dims[2];
-    const int z =
-        get_group_id(1) - w * in.dims[2];  // get_group_id(1) % in.dims[2];
+    const int z = get_group_id(1) - w * in.dims[2];  // get_group_id(1) % in.dims[2];
 
     if (w >= in.dims[3] || z >= in.dims[2]) return;
 
@@ -24,11 +24,11 @@ __kernel void unwrap_kernel(__global T* d_out, const KParam out,
     const int cIn  = w * in.strides[3] + z * in.strides[2];
 
     // Compute the output column index
-    const int id = is_column
+    const int id = IS_COLUMN
                        ? (get_group_id(0) * get_local_size(1) + get_local_id(1))
                        : get_global_id(0);
 
-    if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
+    if (id >= (IS_COLUMN ? out.dims[1] : out.dims[0])) return;
 
     // Compute the starting index of window in x and y of input
     const int startx = (id % nx) * sx;
@@ -38,26 +38,25 @@ __kernel void unwrap_kernel(__global T* d_out, const KParam out,
     const int spy = starty - py;
 
     // Offset the global pointers to the respective starting indices
-    __global T* optr = d_out + cOut + id * (is_column ? out.strides[1] : 1);
-    __global const T* iptr = d_in + cIn + in.offset;
+    __global T *optr = d_out + cOut + id * (IS_COLUMN ? out.strides[1] : 1);
+    __global const T *iptr = d_in + cIn + in.offset;
 
-    bool cond = (spx >= 0 && spx + wx < in.dims[0] && spy >= 0 &&
-                 spy + wy < in.dims[1]);
+    bool cond = (spx >= 0 && spx + (wx * dx) < in.dims[0] && spy >= 0 &&
+                 spy + (wy * dy) < in.dims[1]);
 
-    for (int i = 0; i < reps; i++) {
-        // Compute output index local to column
-        const int outIdx = is_column
-                               ? (i * get_local_size(0) + get_local_id(0))
-                               : (i * get_local_size(1) + get_local_id(1));
+    // Compute output index local to column
+    int outIdx = IS_COLUMN ? get_local_id(0) : get_local_id(1);
+    const int oStride = IS_COLUMN ? get_local_size(0) : get_local_size(1);
 
-        if (outIdx >= (is_column ? out.dims[0] : out.dims[1])) return;
+    for(int i = 0; i < reps; i++) {
+        if (outIdx >= (IS_COLUMN ? out.dims[0] : out.dims[1])) return;
 
         // Compute input index local to window
         const int y = outIdx / wx;
         const int x = outIdx % wx;
 
-        const int xpad = spx + x;
-        const int ypad = spy + y;
+        const int xpad = spx + x * dx;
+        const int ypad = spy + y * dy;
 
         // Copy
         T val = ZERO;
@@ -67,10 +66,12 @@ __kernel void unwrap_kernel(__global T* d_out, const KParam out,
             val             = iptr[inIdx];
         }
 
-        if (is_column) {
+        if (IS_COLUMN) {
             optr[outIdx] = val;
         } else {
             optr[outIdx * out.strides[1]] = val;
         }
+
+        outIdx += oStride;
     }
 }
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 89c6052b95..d4d0ea96e1 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <Array.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
@@ -16,7 +17,6 @@
 #include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 #include <map>
 #include <mutex>
 #include <string>
@@ -32,10 +32,12 @@ using std::string;
 
 namespace opencl {
 namespace kernel {
+
 template<typename T>
 void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
             const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-            const dim_t nx, const bool is_column) {
+            const dim_t dx, const dim_t dy, const dim_t nx,
+            const bool is_column) {
     std::string ref_name = std::string("unwrap_") +
                            std::string(dtype_traits<T>::getName()) +
                            std::string("_") + std::to_string(is_column);
@@ -47,7 +49,7 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     if (entry.prog == 0 && entry.ker == 0) {
         ToNumStr<T> toNumStr;
         std::ostringstream options;
-        options << " -D is_column=" << is_column
+        options << " -D IS_COLUMN=" << is_column
                 << " -D ZERO=" << toNumStr(scalar<T>(0))
                 << " -D T=" << dtype_traits<T>::getName();
 
@@ -87,12 +89,14 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     auto unwrapOp =
         KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
                       const int, const int, const int, const int, const int,
-                      const int, const int, const int>(*entry.ker);
+                      const int, const int, const int, const int, const int>(
+            *entry.ker);
 
     unwrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, wx, wy, sx, sy, px, py, nx, reps);
+             *in.data, in.info, wx, wy, sx, sy, px, py, dx, dy, nx, reps);
 
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index fd1787939f..3139a367a3 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #pragma once
+#include <Array.hpp>
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/wrap.hpp>
+#include <kernel_headers/wrap_dilated.hpp>
 #include <math.hpp>
 #include <program.hpp>
 #include <traits.hpp>
@@ -32,7 +34,8 @@ using std::string;
 
 namespace opencl {
 namespace kernel {
-template<typename T>
+
+template <typename T>
 void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
           const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
           const bool is_column) {
@@ -53,7 +56,6 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
-
         Program prog;
         buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
 
@@ -86,5 +88,62 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
 
     CL_DEBUG_FINISH(getQueue());
 }
+
+template <typename T>
+void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
+                  const dim_t sx, const dim_t sy, const dim_t px,
+                  const dim_t py, const dim_t dx, const dim_t dy,
+                  const bool is_column) {
+    std::string ref_name = std::string("wrap_dilated_") +
+                           std::string(dtype_traits<T>::getName()) +
+                           std::string("_") + std::to_string(is_column);
+
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<T> toNumStr;
+        std::ostringstream options;
+        options << " -D is_column=" << is_column
+                << " -D ZERO=" << toNumStr(scalar<T>(0))
+                << " -D T=" << dtype_traits<T>::getName();
+
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        Program prog;
+        buildProgram(prog, wrap_dilated_cl, wrap_dilated_cl_len, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "wrap_dilated_kernel");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
+
+    NDRange local(THREADS_X, THREADS_Y);
+
+    dim_t groups_x = divup(out.info.dims[0], local[0]);
+    dim_t groups_y = divup(out.info.dims[1], local[1]);
+
+    NDRange global(local[0] * groups_x * out.info.dims[2],
+                   local[1] * groups_y * out.info.dims[3]);
+
+    auto wrapOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const int, const int, const int, const int, const int,
+                      const int, const int, const int, const int, const int,
+                      const int, const int>(*entry.ker);
+
+    wrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, wx, wy, sx, sy, px, py, dx, dy, nx, ny, groups_x,
+           groups_y);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/wrap_dilated.cl b/src/backend/opencl/kernel/wrap_dilated.cl
new file mode 100644
index 0000000000..e3f81ac4dc
--- /dev/null
+++ b/src/backend/opencl/kernel/wrap_dilated.cl
@@ -0,0 +1,79 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel void wrap_dilated_kernel(__global T *optr, KParam out,
+                                  __global T *iptr, KParam in, const int wx,
+                                  const int wy, const int sx, const int sy,
+                                  const int px, const int py, const int dx,
+                                  const int dy, const int nx, const int ny,
+                                  int groups_x, int groups_y) {
+    int idx2 = get_group_id(0) / groups_x;
+    int idx3 = get_group_id(1) / groups_y;
+
+    int groupId_x = get_group_id(0) - idx2 * groups_x;
+    int groupId_y = get_group_id(1) - idx3 * groups_y;
+
+    int oidx0 = get_local_id(0) + get_local_size(0) * groupId_x;
+    int oidx1 = get_local_id(1) + get_local_size(1) * groupId_y;
+
+    optr += idx2 * out.strides[2] + idx3 * out.strides[3];
+    iptr += idx2 * in.strides[2] + idx3 * in.strides[3] + in.offset;
+
+    if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1]) return;
+
+    int eff_wx = wx + (wx - 1) * (dx - 1);
+    int eff_wy = wy + (wy - 1) * (dy - 1);
+
+    int pidx0 = oidx0 + px;
+    int pidx1 = oidx1 + py;
+
+    // The last time a value appears in the unwrapped index is padded_index /
+    // stride
+    // Each previous index has the value appear "stride" locations earlier
+    // We work our way back from the last index
+
+    const int y_start = (pidx1 < eff_wy) ? 0 : (pidx1 - eff_wy) / sy + 1;
+    const int y_end   = min(pidx1 / sy + 1, ny);
+
+    const int x_start = (pidx0 < eff_wx) ? 0 : (pidx0 - eff_wx) / sx + 1;
+    const int x_end   = min(pidx0 / sx + 1, nx);
+
+    T val   = ZERO;
+    int idx = 1;
+
+    for (int y = y_start; y < y_end; y++) {
+        int fy      = (pidx1 - y * sy);
+        bool yvalid = (fy % dy == 0) && (y < ny);
+        fy /= dy;
+
+        int win_end_y = fy * wx;
+        int dim_end_y = y * nx;
+
+        for (int x = x_start; x < x_end; x++) {
+            int fx      = (pidx0 - x * sx);
+            bool xvalid = (fx % dx == 0) && (x < nx);
+            fx /= dx;
+
+            int win_end = win_end_y + fx;
+            int dim_end = dim_end_y + x;
+
+            if (is_column) {
+                idx = dim_end * in.strides[1] + win_end;
+            } else {
+                idx = dim_end + win_end * in.strides[1];
+            }
+
+            T ival;
+            ival = (yvalid && xvalid) ? iptr[idx] : ZERO;
+            val  = val + ival;
+        }
+    }
+
+    optr[oidx1 * out.strides[1] + oidx0] = val;
+}
diff --git a/src/backend/opencl/unwrap.cpp b/src/backend/opencl/unwrap.cpp
index 01bd852513..08a7999788 100644
--- a/src/backend/opencl/unwrap.cpp
+++ b/src/backend/opencl/unwrap.cpp
@@ -9,27 +9,31 @@
 
 #include <Array.hpp>
 #include <err_opencl.hpp>
+#include <common/half.hpp>
 #include <kernel/unwrap.hpp>
 #include <unwrap.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
+
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                const bool is_column) {
+                const dim_t dx, const dim_t dy, const bool is_column) {
     af::dim4 idims = in.dims();
 
-    dim_t nx = (idims[0] + 2 * px - wx) / sx + 1;
-    dim_t ny = (idims[1] + 2 * py - wy) / sy + 1;
+    dim_t nx = 1 + (idims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    dim_t ny = 1 + (idims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
 
     af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
 
     if (!is_column) { std::swap(odims[0], odims[1]); }
 
-    // Create output placeholder
     Array<T> outArray = createEmptyArray<T>(odims);
-    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, nx, is_column);
+    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, dx, dy, nx,
+                      is_column);
 
     return outArray;
 }
@@ -37,7 +41,8 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
 #define INSTANTIATE(T)                                                      \
     template Array<T> unwrap<T>(                                            \
         const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
-        const dim_t sy, const dim_t px, const dim_t py, const bool is_column);
+        const dim_t sy, const dim_t px, const dim_t py, const dim_t dx,     \
+        const dim_t dy, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -51,4 +56,7 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
 }  // namespace opencl
diff --git a/src/backend/opencl/unwrap.hpp b/src/backend/opencl/unwrap.hpp
index 68a076b1d1..35b6b617f5 100644
--- a/src/backend/opencl/unwrap.hpp
+++ b/src/backend/opencl/unwrap.hpp
@@ -13,5 +13,5 @@ namespace opencl {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
-                const bool is_column);
+                const dim_t dx, const dim_t dy, const bool is_column);
 }
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index dd44904f29..73868e4fa4 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -9,12 +9,15 @@
 
 #include <Array.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <kernel/wrap.hpp>
 #include <math.hpp>
 #include <wrap.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 
 template<typename T>
@@ -47,4 +50,30 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column) {
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
+
+    kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy, is_column);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> wrap_dilated<T>(                                      \
+        const Array<T> &in, const dim_t ox, const dim_t oy, const dim_t wx, \
+        const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,     \
+        const dim_t py, const dim_t dx, const dim_t dy, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
 }  // namespace opencl
diff --git a/src/backend/opencl/wrap.hpp b/src/backend/opencl/wrap.hpp
index ee2f750a17..bae5447dc1 100644
--- a/src/backend/opencl/wrap.hpp
+++ b/src/backend/opencl/wrap.hpp
@@ -10,9 +10,14 @@
 #include <Array.hpp>
 
 namespace opencl {
-template<typename T>
+template <typename T>
 Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
               const dim_t px, const dim_t py, const bool is_column);
 
+template <typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column);
 }
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 8c98b8d9f8..9236045bff 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -184,7 +184,7 @@ make_test(SRC clamp.cpp)
 make_test(SRC compare.cpp)
 make_test(SRC complex.cpp)
 make_test(SRC constant.cpp)
-make_test(SRC convolve.cpp)
+make_test(SRC convolve.cpp CXX11)
 make_test(SRC corrcoef.cpp)
 make_test(SRC covariance.cpp)
 make_test(SRC diagonal.cpp)
diff --git a/test/convolve.cpp b/test/convolve.cpp
index c82c7b42b5..4b35cd2d4d 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
@@ -83,7 +84,7 @@ void convolveTest(string pTestFile, int baseDim, bool expand) {
     size_t nElems         = currGoldBar.size();
     vector<T> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])
@@ -249,7 +250,7 @@ void sepConvolveTest(string pTestFile, bool expand) {
     size_t nElems         = currGoldBar.size();
     vector<T> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])
@@ -511,7 +512,7 @@ TEST(Convolve, separable_CPP) {
     size_t nElems             = output.elements();
     vector<float> outData(nElems);
 
-    output.host((void*)&outData.front());
+    output.host((void *)&outData.front());
 
     for (size_t elIter = 0; elIter < nElems; ++elIter) {
         ASSERT_EQ(currGoldBar[elIter], outData[elIter])
@@ -809,13 +810,13 @@ TEST(Convolve, CuboidBatchLaunchBugFix) {
     std::string testFile(TEST_DIR "/convolve/conv3d_launch_bug.test");
 
     vector<dim4> numDims;
-    vector< vector<float> > in;
-    vector< vector<float> > tests;
+    vector<vector<float> > in;
+    vector<vector<float> > tests;
 
     readTests<float, float, float>(testFile, numDims, in, tests);
 
-    dim4 sDims        = numDims[0];
-    dim4 fDims        = numDims[1];
+    dim4 sDims = numDims[0];
+    dim4 fDims = numDims[1];
 
     af::array signal(sDims, in[0].data());
     af::array filter(fDims, in[1].data());
@@ -824,3 +825,339 @@ TEST(Convolve, CuboidBatchLaunchBugFix) {
 
     ASSERT_VEC_ARRAY_NEAR(tests[0], sDims, output, 1.0e-3);
 }
+
+struct conv2_strided_params {
+    string testname_;
+    dim4 signal_sz_, filt_sz_, stride_, padding_, dilation_;
+
+    conv2_strided_params(string testname, dim4 signal_sz, dim4 filt_sz,
+                         dim4 stride, dim4 padding, dim4 dilation)
+        : testname_(testname)
+        , signal_sz_(signal_sz)
+        , filt_sz_(filt_sz)
+        , stride_(stride)
+        , padding_(padding)
+        , dilation_(dilation) {}
+};
+
+template<typename TestClass>
+string testNameGenerator(
+    const ::testing::TestParamInfo<typename TestClass::ParamType> info) {
+    return info.param.testname_;
+}
+
+class Conv2ConsistencyTest
+    : public ::testing::TestWithParam<conv2_strided_params> {};
+
+conv2_strided_params conv2_consistency_data(dim4 signal_sz, dim4 filt_sz) {
+    dim4 stride(1, 1);
+    dim4 padding(filt_sz[0] / 2, filt_sz[1] / 2);
+    dim4 dilation(1, 1);
+    std::string testname =
+        "conv2_consistency_" + std::to_string(signal_sz[0]) +
+        std::to_string(signal_sz[1]) + std::to_string(signal_sz[2]) +
+        std::to_string(signal_sz[3]) + "__" + std::to_string(filt_sz[0]) +
+        std::to_string(filt_sz[1]) + std::to_string(filt_sz[2]) +
+        std::to_string(filt_sz[3]) + "__" + "s" + std::to_string(stride[0]) +
+        std::to_string(stride[1]) + "_" + "p" + std::to_string(padding[0]) +
+        std::to_string(padding[1]) + "_" + "d" + std::to_string(dilation[0]) +
+        std::to_string(dilation[1]);
+
+    return conv2_strided_params(testname, signal_sz, filt_sz, stride, padding,
+                                dilation);
+}
+vector<conv2_strided_params> genConsistencyTests() {
+    // TODO: test nfilters and nfeatures
+    return {conv2_consistency_data(dim4(10, 10), dim4(3, 3)),
+            conv2_consistency_data(dim4(11, 11), dim4(5, 5)),
+            conv2_consistency_data(dim4(12, 12), dim4(7, 7)),
+            conv2_consistency_data(dim4(19, 19), dim4(9, 9)),
+            conv2_consistency_data(dim4(33, 33), dim4(3, 3)),
+            conv2_consistency_data(dim4(255, 255), dim4(3, 3)),
+            conv2_consistency_data(dim4(256, 256), dim4(3, 3)),
+            conv2_consistency_data(dim4(257, 257), dim4(3, 3))};
+}
+
+INSTANTIATE_TEST_CASE_P(Conv2Consistency, Conv2ConsistencyTest,
+                        ::testing::ValuesIn(genConsistencyTests()),
+                        testNameGenerator<Conv2ConsistencyTest>);
+
+TEST_P(Conv2ConsistencyTest, RandomConvolutions) {
+    conv2_strided_params params = GetParam();
+    array signal                = randn(params.signal_sz_);
+    array filter                = randn(params.filt_sz_);
+
+    array out_native = convolve2(signal, filter);
+    array out = convolve2NN(signal, filter, params.stride_, params.padding_,
+                             params.dilation_);
+
+    ASSERT_ARRAYS_NEAR(out_native, out, 1e-5);
+}
+
+template<typename T>
+float tolerance();
+
+template<>
+float tolerance<float>() { return 1e-4; }
+
+template<>
+float tolerance<double>() { return 1e-4; }
+
+template<>
+float tolerance<half_float::half>() { return 3e-2; }
+
+template<typename T>
+void convolve2stridedTest(string pTestFile, dim4 stride, dim4 padding,
+                          dim4 dilation) {
+    SUPPORTED_TYPE_CHECK(T);
+
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
+
+    dim4 sDims         = numDims[0];
+    dim4 fDims         = numDims[1];
+    af_array signal    = 0;
+    af_array filter    = 0;
+    af_array convolved = 0;
+
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+
+    ASSERT_SUCCESS(af_convolve2_nn(&convolved, signal, filter, stride.ndims(),
+                                   stride.get(), padding.ndims(), padding.get(),
+                                   dilation.ndims(), dilation.get()));
+
+    vector<T> &currGoldBar = tests[0];
+
+    size_t nElems = currGoldBar.size();
+
+    dim_t expectedDim0 =
+        1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
+                stride[0];
+    dim_t expectedDim1 =
+        1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
+                stride[1];
+
+    auto gdim = dim4(expectedDim0, expectedDim1, fDims[3], sDims[3]);
+    ASSERT_VEC_ARRAY_NEAR(currGoldBar, gdim, convolved, tolerance<T>());
+
+    ASSERT_SUCCESS(af_release_array(convolved));
+    ASSERT_SUCCESS(af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(filter));
+}
+
+template<typename T>
+void convolve2GradientTest(string pTestFile, dim4 stride, dim4 padding,
+                           dim4 dilation) {
+    SUPPORTED_TYPE_CHECK(T);
+
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+
+    readTests<T, T, float>(pTestFile, numDims, in, tests);
+
+    dim4 sDims         = numDims[0];
+    dim4 fDims         = numDims[1];
+    af_array signal    = 0;
+    af_array filter    = 0;
+    af_array convolved = 0;
+
+    ASSERT_SUCCESS(af_create_array(&signal, &(in[0].front()), sDims.ndims(),
+                                   sDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+    ASSERT_SUCCESS(af_create_array(&filter, &(in[1].front()), fDims.ndims(),
+                                   fDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+
+    vector<T> &currGoldBar = tests[0];
+    size_t nElems          = currGoldBar.size();
+
+    dim_t expectedDim0 =
+        1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
+                stride[0];
+    dim_t expectedDim1 =
+        1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
+                stride[1];
+    dim4 cDims(expectedDim0, expectedDim1, fDims[3], sDims[3]);
+    ASSERT_EQ(nElems, cDims.elements());
+
+    ASSERT_SUCCESS(af_create_array(&convolved, &(currGoldBar.front()),
+                                   cDims.ndims(), cDims.get(),
+                                   (af_dtype)dtype_traits<T>::af_type));
+
+    af_array incoming_gradient = 0;
+    ASSERT_SUCCESS(af_constant(&incoming_gradient, 1, cDims.ndims(),
+                               cDims.get(),
+                               (af_dtype)dtype_traits<T>::af_type));
+
+    af_array filter_gradient = 0;
+    ASSERT_SUCCESS(af_convolve2_gradient_nn(
+        &filter_gradient, incoming_gradient, signal, filter, convolved,
+        stride.ndims(), stride.get(), padding.ndims(), padding.get(),
+        dilation.ndims(), dilation.get(), AF_CONV_GRADIENT_FILTER));
+
+    af_array data_gradient = 0;
+    ASSERT_SUCCESS(af_convolve2_gradient_nn(
+        &data_gradient, incoming_gradient, signal, filter, convolved,
+        stride.ndims(), stride.get(), padding.ndims(), padding.get(),
+        dilation.ndims(), dilation.get(), AF_CONV_GRADIENT_DATA));
+
+    vector<T> &dataGradientGold = tests[1];
+    ASSERT_VEC_ARRAY_NEAR(dataGradientGold, sDims, data_gradient, tolerance<T>());
+
+    vector<T> &filterGradientGold = tests[2];
+    ASSERT_VEC_ARRAY_NEAR(filterGradientGold, fDims, filter_gradient, tolerance<T>());
+
+    ASSERT_SUCCESS(af_release_array(incoming_gradient));
+    ASSERT_SUCCESS(af_release_array(convolved));
+    ASSERT_SUCCESS(af_release_array(signal));
+    ASSERT_SUCCESS(af_release_array(filter));
+    ASSERT_SUCCESS(af_release_array(filter_gradient));
+    ASSERT_SUCCESS(af_release_array(data_gradient));
+}
+
+template<typename T>
+class ConvolveStrided : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
+};
+// create a list of types to be tested
+typedef ::testing::Types<float, double, half_float::half>
+    TestTypesStrided;  // TODO: integral types??
+
+// register the type list
+TYPED_TEST_CASE(ConvolveStrided, TestTypesStrided);
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt33_s11_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig810_filt33_s11_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig81011_filt3311_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt33_s11_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt33_s33_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s33_p11_d11.test"),
+        dim4(3, 3), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt33_s33_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s33_p11_d11.test"),
+        dim4(3, 3), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt55_s55_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt5511_s55_p11_d11.test"),
+        dim4(5, 5), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt55_s55_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt5511_s55_p11_d11.test"),
+        dim4(5, 5), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt77_s77_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt7711_s77_p11_d11.test"),
+        dim4(7, 7), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt77_s77_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt7711_s77_p11_d11.test"),
+        dim4(7, 7), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt33_s11_p11_d22) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s11_p11_d22.test"),
+        dim4(1, 1), dim4(1, 1), dim4(2, 2));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt33_s11_p11_d22) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s11_p11_d22.test"),
+        dim4(1, 1), dim4(1, 1), dim4(2, 2));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt33_s11_p11_d33) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s11_p11_d33.test"),
+        dim4(1, 1), dim4(1, 1), dim4(3, 3));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt33_s11_p11_d33) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3311_s11_p11_d33.test"),
+        dim4(1, 1), dim4(1, 1), dim4(3, 3));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt35_s11_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3511_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt35_s11_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3511_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt53_s11_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt5311_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt53_s11_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt5311_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig1010_filt35_s31_p11_d21) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3511_s31_p11_d21.test"),
+        dim4(3, 1), dim4(1, 1), dim4(2, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig1010_filt35_s31_p11_d21) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig101011_filt3511_s31_p11_d21.test"),
+        dim4(3, 1), dim4(1, 1), dim4(2, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Strided_sig81032_filt3334_s11_p11_d11) {
+    convolve2stridedTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig81032_filt3334_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}
+
+TYPED_TEST(ConvolveStrided, Gradient_sig81032_filt3334_s11_p11_d11) {
+    convolve2GradientTest<TypeParam>(
+        string(TEST_DIR "/convolve/sig81032_filt3334_s11_p11_d11.test"),
+        dim4(1, 1), dim4(1, 1), dim4(1, 1));
+}

From c5894512e76a6106605755dbab59280a21bb3f49 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 1 Sep 2019 20:03:47 -0400
Subject: [PATCH 1751/2677] Fix mDeviceType pushback in addDeviceContext. Fix
 NodeIterator condition

* Fix the compare_default comparitor to confom to the strict weak ordering
  requirement for the less than operator of the sort
* Fix the order of the condition in the NodeIterator that allowed access
  to a staticly allocated array
* Fix a missing push_back to the mDeviceType vector in the
  addDeviceContext function
---
 src/backend/common/jit/NodeIterator.hpp | 2 +-
 src/backend/opencl/device_manager.cpp   | 2 +-
 src/backend/opencl/platform.cpp         | 1 +
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index b5dd3a1998..9b3671cee0 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -31,7 +31,7 @@ class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
 
     /// Copies the children of the \p n Node to the end of the tree vector
     void copy_children_to_end(Node* n) {
-        for (int i = 0; n->m_children[i] != nullptr && i < Node::kMaxChildren;
+        for (int i = 0; i < Node::kMaxChildren && n->m_children[i] != nullptr;
              i++) {
             auto ptr = n->m_children[i].get();
             if (find(begin(tree), end(tree), ptr) == end(tree)) {
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index eee068dc3c..42d58356a0 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -159,7 +159,7 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
     // Sort based on memory
     auto l_mem = ldev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
     auto r_mem = rdev->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
-    return l_mem >= r_mem;
+    return l_mem > r_mem;
 }
 
 DeviceManager::DeviceManager()
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index da1924baa0..a7f1e59394 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -434,6 +434,7 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
         devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
+        devMngr.mDeviceTypes.push_back(tDevice->getInfo<CL_DEVICE_TYPE>());
         nDevices = devMngr.mDevices.size() - 1;
 
         // cache the boost program_cache object, clean up done on program exit

From ba3cbddaceaa337f2c31704d81fe70e8db8b34f4 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Wed, 4 Sep 2019 16:59:58 -0400
Subject: [PATCH 1752/2677] Remove sub tabs in Tutorials docs page (#2633)

---
 docs/layout.xml         | 18 +-----------------
 docs/pages/tutorials.md | 17 +++++++++++++++++
 2 files changed, 18 insertions(+), 17 deletions(-)
 create mode 100644 docs/pages/tutorials.md

diff --git a/docs/layout.xml b/docs/layout.xml
index d2f18bc324..69e8ec8da3 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -2,23 +2,7 @@
   <!-- Navigation index tabs for HTML output -->
   <navindex>
     <tab type="mainpage" visible="yes" title="" />
-    <tab type="usergroup" visible="yes" title="Tutorials">
-      <tab type="user" url="\ref installing" visible="yes" title="Installation"/>
-      <tab type="user" url="\ref using_on_linux" visible="yes" title="Using on Linux"/>
-      <tab type="user" url="\ref using_on_windows" visible="yes" title="Using on Windows"/>
-      <tab type="user" url="\ref using_on_osx" visible="yes" title="Using on OSX"/>
-      <tab type="user" url="\ref gettingstarted" visible="yes" title="Getting Started"/>
-      <tab type="user" url="\ref vectorization" visible="yes" title="Introduction to Vectorization"/>
-      <tab type="user" url="\ref matrixmanipulation" visible="yes" title="Array and Matrix Manipulation"/>
-      <tab type="user" url="\ref interop_cuda" visible="yes" title="CUDA Interoperability"/>
-      <tab type="user" url="\ref interop_opencl" visible="yes" title="OpenCL Interoperability"/>
-      <tab type="user" url="\ref unifiedbackend" visible="yes" title="Unified Backend"/>
-      <tab type="user" url="\ref forge_visualization" visible="yes" title="Forge Visualization"/>
-      <tab type="user" url="\ref indexing" visible="yes" title="Indexing"/>
-      <tab type="user" url="\ref timing" visible="yes" title="Timing ArrayFire"/>
-      <tab type="user" url="\ref configuring_environment" visible="yes" title="Configuring ArrayFire Environment"/>
-      <tab type="usergroup" url="\ref page_gfor" visible="yes" title="GFOR Usage"/>
-    </tab>
+    <tab type="user" url="\ref tutorials" visible="yes" title="Tutorials"/>
     <tab type="modules" visible="yes" title="Functions" intro="Documentation grouped according to category:"/>
     <tab type="user" url="\ref releasenotes" visible="yes" title="Release Notes"/>
     <tab type="examples" visible="yes" title="" intro=""/>
diff --git a/docs/pages/tutorials.md b/docs/pages/tutorials.md
new file mode 100644
index 0000000000..32721dbd8c
--- /dev/null
+++ b/docs/pages/tutorials.md
@@ -0,0 +1,17 @@
+# Tutorials {#tutorials}
+
+* [Installation](\ref installing)
+* [Using on Linux](\ref using_on_linux)
+* [Using on Windows](\ref using_on_windows)
+* [Using on OSX](\ref using_on_osx)
+* [Getting Started](\ref gettingstarted)
+* [Introduction to Vectorization](\ref vectorization)
+* [Array and Matrix Manipulation](\ref matrixmanipulation)
+* [CUDA Interoperability](\ref interop_cuda)
+* [OpenCL Interoperability](\ref interop_opencl)
+* [Unified Backend](\ref unifiedbackend)
+* [Forge Visualization](\ref forge_visualization)
+* [Indexing](\ref indexing)
+* [Timing ArrayFire](\ref timing)
+* [Configuring ArrayFire Environment](\ref configuring_environment)
+* [GFOR Usage](\ref page_gfor)

From 0a6d9ca9bc44cc6d5178ecd423228cbd7836667e Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Thu, 5 Sep 2019 01:09:07 -0400
Subject: [PATCH 1753/2677] Clarify and correct nearest neighbour documentation
 (#2628)

---
 docs/details/vision.dox    |  73 +++++++++++++++++-----
 include/af/vision.h        | 122 ++++++++++++++++++++++---------------
 test/nearest_neighbour.cpp |  77 +++++++++++++++++++++++
 3 files changed, 210 insertions(+), 62 deletions(-)

diff --git a/docs/details/vision.dox b/docs/details/vision.dox
index 99582c3729..619030a58c 100644
--- a/docs/details/vision.dox
+++ b/docs/details/vision.dox
@@ -133,20 +133,65 @@ equal to the number of features contained in the query array.
 \defgroup cv_func_nearest_neighbour nearestNeighbour
 \ingroup featmatcher_mat
 
-\brief Nearest Neighbour
-
-Calculates nearest distances between two 2-dimensional arrays containing
-features based on the type of distance computation chosen. Currently, \ref
-AF_SAD (sum of absolute differences), \ref AF_SSD (sum of squared differences)
-and \ref AF_SHD (hamming distance) are supported.
-One of the arrays containing the training data and the other the
-query data. One of the dimensions of the both arrays must be equal among them,
-identifying the length of each feature. The other dimension indicates the
-total number of features in each of the training and query arrays. Two
-1-dimensional arrays are created as results, one containg the smallest N
-distances of the query array and another containing the indices of these
-distances in the training array. The resulting 1-dimensional arrays have length
-equal to the number of features contained in the query array.
+\brief Determine the nearest neighbouring points to a given set of points
+
+A "point" is simply a geometric point's coordinates in an n-dimensional space,
+which can be specified along the dimension specified by `dist_dim` (can be 0 or
+1). A list of such points can be enumerated along the dimension other than the
+one specified by `dist_dim` (excluding dim2 and dim3). By default, `dist_dim` is
+0, so a point's coordinates in this case must be specified along dim0, and the
+list of points must be enumerated along dim1. Consequently, if `dist_dim` is 1,
+then a point's coordinates must be specified along dim1, and the list must be
+enumerated along dim0.
+
+The arrays \p train and \p query are both a list of points, and one must have
+the same data layout as the other. This function calculates which points in the
+\p train are nearest to each point in \p query, based on the distance metric
+specified by \p dist_type: \ref AF_SAD (sum of absolute differences), \ref
+AF_SSD (sum of squared differences, the default option), or \ref AF_SHD (hamming
+distance). The resulting \p n_dist nearest neighboring points are described in
+two output arrays:
+- \p idx:  contains the index of each result that corresponds to the point in
+           \p train
+- \p dist: contains the distance from the query point to the result's
+           corresponding point in \p train
+
+In both the output arrays \p idx and \p dist, the nearest neighbor results for a
+single query are enumerated along dim0, in which the \f$ith\f$ result is the
+\f$ith\f$ nearest point to the query point. The result set for each query point
+is placed along dim1 (columns) of \p idx and \p dist, in the order that the
+queries appear in \p query. Therefore, the output arrays will have a shape of \p
+n_dist \f$\times\f$ the number of queries (regardless of the data layout of the
+input arrays, or the value of `dist_dim`).
+
+For illustration, a simple example is given below for 1 query in 1-dimensional
+space. There are 6 points in \p train, and 3 nearest neighbors are queried for
+(\p n_dist is 3), so there are 3 elements in the results for this single query,
+enumerated along dim0. The results \p idx and \p dist contain the 3 points
+closest to 1.25, ordered from nearest to farthest: point 0 in \p train (1.) with
+an SSD distance of 0.0625 from the query, point 1 (2.) with a distance of
+0.5625, and point 2 (3.) with a distance of 3.0625.
+
+\snippet test/nearest_neighbour.cpp ex_nearest_1
+
+A slightly more complicated example is given below. There are 2 \p query points
+and 6 \p train points, and they are in 3-dimensional space (each point's
+coordinates are specified along dim0, and the list of points is enumerated along
+dim1). Note that in the output arrays \p idx and \p dist, there are 2 sets of
+results now, one for each query. The result set located on the the first column
+of \p idx and \p dist correspond to the first query (the first column in \p
+query), and the result set on the second column of \p idx and \p dist correspond
+to the second query (second column in \p query). Thus, for example, the second
+query point is (7.5, 9., 1.), and the point closest to it in \p train is point
+3, which is (8., 9., 1.), which has a SSD distance of 0.25 from the query point.
+
+\snippet test/nearest_neighbour.cpp ex_nearest_2
+
+Note that it does not make sense for the \p train and \p query array shapes to
+have a third and fourth dimension, because a 2-dimensional array is sufficient
+to describe a list of points, no matter how long the list is or how many
+dimensions in space do the points span. Therefore, this function requires both
+input arrays to be at most 2-dimensional.
 
 =======================================================================
 
diff --git a/include/af/vision.h b/include/af/vision.h
index 39189468fa..5400112fe4 100644
--- a/include/af/vision.h
+++ b/include/af/vision.h
@@ -208,26 +208,39 @@ AFAPI void hammingMatcher(array& idx, array& dist,
 
 #if AF_API_VERSION >= 31
 /**
-   C++ Interface wrapper for Nearest Neighbour
-
-   \param[out] idx is an array of MxN size, where M is equal to the number of query
-               features and N is equal to n_dist. The value at position IxJ indicates
-               the index of the Jth smallest distance to the Ith query value in the
-               train data array.
-               the index of the Ith smallest distance of the Mth query.
-   \param[out] dist is an array of MxN size, where M is equal to the number of query
-               features and N is equal to n_dist. The value at position IxJ indicates
-               the distance of the Jth smallest distance to the Ith query value in the
-               train data array based on the dist_type chosen.
-   \param[in]  query is the array containing the data to be queried
-   \param[in]  train is the array containing the data used as training data
-   \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
-               indicated here must be of equal length for both query and train arrays)
-   \param[in]  n_dist is the number of smallest distances to return (currently only
-               values <= 256 are supported)
-   \param[in]  dist_type is the distance computation type. Currently \ref AF_SAD (sum
-               of absolute differences), \ref AF_SSD (sum of squared differences), and
-               \ref AF_SHD (hamming distances) are supported.
+   C++ interface wrapper for determining the nearest neighbouring points to a
+   given set of points
+
+   \param[out] idx       is an array of \f$M \times N\f$ size, where \f$M\f$ is
+                         \p n_dist and \f$N\f$ is the number of queries. The
+                         value at position \f$i,j\f$ is the index of the point
+                         in \p train along dim1 (if \p dist_dim is 0) or along
+                         dim 0 (if \p dist_dim is 1), with the \f$ith\f$
+                         smallest distance to the \f$jth\f$ \p query point.
+   \param[out] dist      is an array of \f$M \times N\f$ size, where \f$M\f$ is
+                         \p n_dist and \f$N\f$ is the number of queries. The
+                         value at position \f$i,j\f$ is the distance from the
+                         \f$jth\f$ query point to the point in \p train referred
+                         to by \p idx(\f$i,j\f$). This distance is computed
+                         according to the \p dist_type chosen.
+   \param[in]  query     is the array containing the points to be queried. The
+                         points must be described along dim0 and listed along
+                         dim1 if \p dist_dim is 0, or vice versa if \p dist_dim
+                         is 1.
+   \param[in]  train     is the array containing the points used as training
+                         data. The points must be described along dim0 and
+                         listed along dim1 if \p dist_dim is 0, or vice versa if
+                         \p dist_dim is 1.
+   \param[in]  dist_dim  indicates the dimension that the distance computation
+                         will use to determine a point's coordinates. The \p
+                         train and \p query arrays must both use this dimension
+                         for describing a point's coordinates
+   \param[in]  n_dist    is the number of nearest neighbour points to return
+                         (currently only values <= 256 are supported)
+   \param[in]  dist_type is the distance computation type. Currently \ref
+                         AF_SAD (sum of absolute differences), \ref AF_SSD (sum
+                         of squared differences), and \ref AF_SHD (hamming
+                         distances) are supported.
 
    \ingroup cv_func_nearest_neighbour
  */
@@ -519,34 +532,47 @@ extern "C" {
                                     const dim_t dist_dim, const unsigned n_dist);
 
 #if AF_API_VERSION >= 31
-    /**
-        C Interface wrapper for Nearest Neighbour
-
-        \param[out] idx is an array of MxN size, where M is equal to the number of query
-                    features and N is equal to n_dist. The value at position IxJ indicates
-                    the index of the Jth smallest distance to the Ith query value in the
-                    train data array.
-                    the index of the Ith smallest distance of the Mth query.
-        \param[out] dist is an array of MxN size, where M is equal to the number of query
-                    features and N is equal to n_dist. The value at position IxJ indicates
-                    the distance of the Jth smallest distance to the Ith query value in the
-                    train data array based on the dist_type chosen.
-        \param[in]  query is the array containing the data to be queried
-        \param[in]  train is the array containing the data used as training data
-        \param[in]  dist_dim indicates the dimension to analyze for distance (the dimension
-                    indicated here must be of equal length for both query and train arrays)
-        \param[in]  n_dist is the number of smallest distances to return (currently, only
-                    values <= 256 are supported)
-        \param[in]  dist_type is the distance computation type. Currently \ref AF_SAD (sum
-                    of absolute differences), \ref AF_SSD (sum of squared differences), and
-                    \ref AF_SHD (hamming distances) are supported.
-
-        \ingroup cv_func_nearest_neighbour
-    */
-    AFAPI af_err af_nearest_neighbour(af_array* idx, af_array* dist,
-                                      const af_array query, const af_array train,
-                                      const dim_t dist_dim, const unsigned n_dist,
-                                      const af_match_type dist_type);
+/**
+   C++ interface wrapper for determining the nearest neighbouring points to a
+   given set of points
+
+   \param[out] idx       is an array of \f$M \times N\f$ size, where \f$M\f$ is
+                         \p n_dist and \f$N\f$ is the number of queries. The
+                         value at position \f$i,j\f$ is the index of the point
+                         in \p train along dim1 (if \p dist_dim is 0) or along
+                         dim 0 (if \p dist_dim is 1), with the \f$ith\f$
+                         smallest distance to the \f$jth\f$ \p query point.
+   \param[out] dist      is an array of \f$M \times N\f$ size, where \f$M\f$ is
+                         \p n_dist and \f$N\f$ is the number of queries. The
+                         value at position \f$i,j\f$ is the distance from the
+                         \f$jth\f$ query point to the point in \p train referred
+                         to by \p idx(\f$i,j\f$). This distance is computed
+                         according to the \p dist_type chosen.
+   \param[in]  query     is the array containing the points to be queried. The
+                         points must be described along dim0 and listed along
+                         dim1 if \p dist_dim is 0, or vice versa if \p dist_dim
+                         is 1.
+   \param[in]  train     is the array containing the points used as training
+                         data. The points must be described along dim0 and
+                         listed along dim1 if \p dist_dim is 0, or vice versa if
+                         \p dist_dim is 1.
+   \param[in]  dist_dim  indicates the dimension that the distance computation
+                         will use to determine a point's coordinates. The \p
+                         train and \p query arrays must both use this dimension
+                         for describing a point's coordinates
+   \param[in]  n_dist    is the number of nearest neighbour points to return
+                         (currently only values <= 256 are supported)
+   \param[in]  dist_type is the distance computation type. Currently \ref
+                         AF_SAD (sum of absolute differences), \ref AF_SSD (sum
+                         of squared differences), and \ref AF_SHD (hamming
+                         distances) are supported.
+
+   \ingroup cv_func_nearest_neighbour
+ */
+AFAPI af_err af_nearest_neighbour(af_array* idx, af_array* dist,
+                                  const af_array query, const af_array train,
+                                  const dim_t dist_dim, const unsigned n_dist,
+                                  const af_match_type dist_type);
 #endif
 
     /**
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 4ef7f05f69..9c4815c25a 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -503,3 +503,80 @@ TEST(KNearestNeighbours, InvalidLargeK) {
     ASSERT_THROW(nearestNeighbour(indices, distances, q, t, 0, k, AF_SSD),
                  af::exception);
 }
+
+TEST(NearestNeighbour, DocSnippet1) {
+    //! [ex_nearest_1]
+    float h_pts[6] = {1.f, 2.f, 3.f, 8.f, 9.f, 10.f};
+    array pts(dim4(1, 6), h_pts);
+    //  1.   2.   3.   8.   9.   10.
+
+    float h_query = 1.25f;
+    array query(dim4(1), &h_query);
+    //  1.25
+
+    array idx;
+    array dist;
+    nearestNeighbour(idx, dist, query, pts, 0, 3);
+    // idx
+    //  0.
+    //  1.
+    //  2.
+    //
+    // dist
+    //  0.0625
+    //  0.5625
+    //  3.0625
+
+    //! [ex_nearest_1]
+
+    unsigned int h_gold_idx[3] = {0, 1, 2};
+    float h_gold_dist[3] = {0.0625f, 0.5625f, 3.0625f};
+    array gold_idx(dim4(3), h_gold_idx);
+    array gold_dist(dim4(3), h_gold_dist);
+    ASSERT_ARRAYS_EQ(gold_idx, idx);
+    ASSERT_ARRAYS_EQ(gold_dist, dist);
+}
+
+TEST(NearestNeighbour, DocSnippet2) {
+    //! [ex_nearest_2]
+    float h_pts[18] = {0.f, 0.f, 0.f,
+                       1.f, 0.f, 0.f,
+                       0.f, 1.f, 0.f,
+                       8.f, 9.f, 1.f,
+                       9.f, 8.f, 1.f,
+                       9.f, 9.f, 1.f};
+    array pts(dim4(3, 6), h_pts);
+    //  0.    1.    0.    8.    9.    9.
+    //  0.    0.    1.    9.    8.    9.
+    //  0.    0.    0.    1.    1.    1.
+
+    float h_query[6] = {1.5f, 0.f, 0.f,
+                        7.5f, 9.f, 1.f};
+    array query(dim4(3, 2), h_query);
+    //  1.5   7.5
+    //  0.    9.
+    //  0.    1.
+
+    array idx;
+    array dist;
+    nearestNeighbour(idx, dist, query, pts, 0, 3);
+    // idx
+    //  1     3
+    //  0     5
+    //  2     4
+    //
+    // dist
+    //  0.25  0.25
+    //  2.25  2.25
+    //  3.25  3.25
+    //! [ex_nearest_2]
+
+    unsigned int h_gold_idx[6] = {1, 0, 2,
+                                  3, 5, 4};
+    float h_gold_dist[6] = {0.25f, 2.25f, 3.25f,
+                            0.25f, 2.25f, 3.25f};
+    array gold_idx(dim4(3, 2), h_gold_idx);
+    array gold_dist(dim4(3, 2), h_gold_dist);
+    ASSERT_ARRAYS_EQ(gold_idx, idx);
+    ASSERT_ARRAYS_EQ(gold_dist, dist);
+}

From 4b1d52bfd994752509755e2b51de9387b0d2469a Mon Sep 17 00:00:00 2001
From: Richard Barnes <rbarnes@umn.edu>
Date: Thu, 5 Sep 2019 00:19:55 -0700
Subject: [PATCH 1754/2677] Add debugging.md to tutorials page (#2620)

* Add debugging.md

* Add AF_JIT_KERNEL_TRACE to debugging.md

* Improvements to debugging documentation

* Hyperlink env variables from debugging to configure page
---
 docs/pages/debugging.md | 29 +++++++++++++++++++++++++++++
 docs/pages/tutorials.md |  1 +
 2 files changed, 30 insertions(+)
 create mode 100644 docs/pages/debugging.md

diff --git a/docs/pages/debugging.md b/docs/pages/debugging.md
new file mode 100644
index 0000000000..bf02679796
--- /dev/null
+++ b/docs/pages/debugging.md
@@ -0,0 +1,29 @@
+Debugging ArrayFire Issues {#debugging}
+===============================================================================
+
+Using Environment Variables
+---------------------------
+
+ * [`AF_PRINT_ERRORS=1`](configuring_environment.htm#af_print_errors) : Makes exception's messages more helpful
+ * [`AF_TRACE=all`](configuring_environment.htm#af_trace): Print ArrayFire message stream to console
+ * [`AF_JIT_KERNEL_TRACE=stdout`](configuring_environment.htm#af_jit_kernel_trace): Writes out source code generated by ArrayFire's JIT to the specified target
+
+
+
+Tips in Language Bindings
+-------------------------
+
+### C++
+
+* `af_print_mem_info("message", -1);`: Print table of memory used by ArrayFire on the active GPU
+
+### Python
+
+* `arrayfire.device.print_mem_info("message")`: Print table of memory used by ArrayFire on the active GPU
+
+
+
+Further Reading
+---------------
+
+See the [ArrayFire README](https://github.com/arrayfire/arrayfire) for support information.
diff --git a/docs/pages/tutorials.md b/docs/pages/tutorials.md
index 32721dbd8c..f6056b8e19 100644
--- a/docs/pages/tutorials.md
+++ b/docs/pages/tutorials.md
@@ -14,4 +14,5 @@
 * [Indexing](\ref indexing)
 * [Timing ArrayFire](\ref timing)
 * [Configuring ArrayFire Environment](\ref configuring_environment)
+* [Debugging ArrayFire Code](\ref debugging)
 * [GFOR Usage](\ref page_gfor)

From b409e09b45eb2a43661021430c74acba9a6c7cd3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 10 Sep 2019 01:32:42 +0530
Subject: [PATCH 1755/2677] Fix meanAll for small sub-arrays in CUDA/OpenCL
 (#2638)

For small arrays ArrayFire will perform the mean calculation on the
CPU for both OpenCL and CUDA. The CPU offload implementation
cannot handle non-linear arrays so we have added guards for
that scenario.
---
 src/backend/cuda/kernel/mean.hpp   | 23 +++++++++++------------
 src/backend/opencl/kernel/mean.hpp | 21 +++++++++++----------
 test/mean.cpp                      | 15 +++++++++++++++
 3 files changed, 37 insertions(+), 22 deletions(-)

diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 2944b81cf8..393f09fa56 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -507,15 +507,14 @@ template<typename Ti, typename Tw, typename To>
 To mean_all(CParam<Ti> in) {
     using std::unique_ptr;
     int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
+    bool is_linear = (in.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &=
+            (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+    }
 
     // FIXME: Use better heuristics to get to the optimum number
-    if (in_elements > 4096) {
-        bool is_linear = (in.strides[0] == 1);
-        for (int k = 1; k < 4; k++) {
-            is_linear &=
-                (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
-        }
-
+    if (in_elements > 4096 || !is_linear) {
         if (is_linear) {
             in.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
@@ -531,12 +530,12 @@ To mean_all(CParam<Ti> in) {
         uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
         uint blocks_y = divup(in.dims[1], threads_y);
 
-        Param<Tw> iwt;
-        Array<To> tmpOut = createEmptyArray<To>(
-            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
-        Array<Tw> tmpCt = createEmptyArray<Tw>(
-            {blocks_x, in.dims[1], in.dims[2], in.dims[3]});
+        dim4 outDims(blocks_x, in.dims[1], in.dims[2], in.dims[3]);
 
+        Array<To> tmpOut = createEmptyArray<To>(outDims);
+        Array<Tw> tmpCt  = createEmptyArray<Tw>(outDims);
+
+        Param<Tw> iwt;
         mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x,
                                         blocks_y, threads_x);
 
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index dfde1e850e..b0ac5099fe 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -458,15 +458,14 @@ template<typename Ti, typename Tw, typename To>
 To mean_all(Param in) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+    bool is_linear = (in.info.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.info.strides[k] ==
+                (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    }
 
     // FIXME: Use better heuristics to get to the optimum number
-    if (in_elements > 4096) {
-        bool is_linear = (in.info.strides[0] == 1);
-        for (int k = 1; k < 4; k++) {
-            is_linear &= (in.info.strides[k] ==
-                          (in.info.strides[k - 1] * in.info.dims[k - 1]));
-        }
-
+    if (in_elements > 4096 || !is_linear) {
         if (is_linear) {
             in.info.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
@@ -482,10 +481,12 @@ To mean_all(Param in) {
         uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
         uint groups_y = divup(in.info.dims[1], threads_y);
 
-        Array<To> tmpOut = createEmptyArray<To>(groups_x);
-        Array<Tw> tmpCt  = createEmptyArray<Tw>(groups_x);
-        Param iWt;
+        dim4 outDims(groups_x, in.info.dims[1],
+                     in.info.dims[2], in.info.dims[3]);
+        Array<To> tmpOut = createEmptyArray<To>(outDims);
+        Array<Tw> tmpCt  = createEmptyArray<Tw>(outDims);
 
+        Param iWt;
         mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x,
                                         groups_x, groups_y);
 
diff --git a/test/mean.cpp b/test/mean.cpp
index 49d01d17a3..d8d90b194f 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -318,3 +318,18 @@ TEST(Mean, Issue2093) {
 
     ASSERT_NEAR(outVal, expected, 0.001);
 }
+
+TEST(MeanAll, SubArray) {
+    //Fixes Issue 2636
+    using af::span;
+    using af::mean;
+    using af::sum;
+
+    const dim4 inDims(10, 10, 10, 10);
+
+    array in  = randu(inDims);
+    array sub = in(0, span, span, span);
+
+    size_t nElems = sub.elements();
+    ASSERT_FLOAT_EQ(mean<float>(sub), sum<float>(sub)/nElems);
+}

From 39670fe8e563250eb51043e5e4e69a083cbe2248 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 7 Sep 2019 01:11:54 +0530
Subject: [PATCH 1756/2677] Refactor numerical constant to AF_MAX_DIMS in
 convolve

---
 src/api/c/convolve.cpp    |  9 +++++----
 src/api/c/fftconvolve.cpp | 13 +++++++------
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 1d557533e4..e2f95fdd09 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -67,14 +67,15 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
 
     if (sn == baseDim && fn == baseDim)
         return AF_BATCH_NONE;
-    else if (sn == baseDim && (fn > baseDim && fn <= 4))
+    else if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS))
         return AF_BATCH_RHS;
-    else if ((sn > baseDim && sn <= 4) && fn == baseDim)
+    else if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim)
         return AF_BATCH_LHS;
-    else if ((sn > baseDim && sn <= 4) && (fn > baseDim && fn <= 4)) {
+    else if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
+             (fn > baseDim && fn <= AF_MAX_DIMS)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved       = true;
-        for (dim_t i = baseDim; i < 4; i++) {
+        for (dim_t i = baseDim; i < AF_MAX_DIMS; i++) {
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
             isInterleaved &=
                 (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index a26d0e41d7..32694b11e7 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -33,7 +33,7 @@ static inline af_array fftconvolve_fallback(const af_array signal,
     dim4 psdims(1, 1, 1, 1);
     dim4 pfdims(1, 1, 1, 1);
 
-    std::vector<af_seq> index(4);
+    std::vector<af_seq> index(AF_MAX_DIMS);
 
     int count = 1;
     for (int i = 0; i < baseDim; i++) {
@@ -58,7 +58,7 @@ static inline af_array fftconvolve_fallback(const af_array signal,
         index[i].step = 1;
     }
 
-    for (int i = baseDim; i < 4; i++) {
+    for (int i = baseDim; i < AF_MAX_DIMS; i++) {
         odims[i]  = std::max(sdims[i], fdims[i]);
         psdims[i] = sdims[i];
         pfdims[i] = fdims[i];
@@ -106,14 +106,15 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
 
     if (sn == baseDim && fn == baseDim)
         return AF_BATCH_NONE;
-    else if (sn == baseDim && (fn > baseDim && fn <= 4))
+    else if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS))
         return AF_BATCH_RHS;
-    else if ((sn > baseDim && sn <= 4) && fn == baseDim)
+    else if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim)
         return AF_BATCH_LHS;
-    else if ((sn > baseDim && sn <= 4) && (fn > baseDim && fn <= 4)) {
+    else if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
+             (fn > baseDim && fn <= AF_MAX_DIMS)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved       = true;
-        for (dim_t i = baseDim; i < 4; i++) {
+        for (dim_t i = baseDim; i < AF_MAX_DIMS; i++) {
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
             isInterleaved &=
                 (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);

From 929a64124b89f5e12befc307341d1fabc4c37ef1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 20 Sep 2019 10:55:44 +0530
Subject: [PATCH 1757/2677] Improve convolution batch support documentation

Also includes the following changes
* Fix reference warnings in ml header
* Improve conv docs in general
---
 assets                  |   2 +-
 docs/arrayfire.css      |   3 +
 docs/details/signal.dox | 234 +++++++++++++++++++++++++++++++---------
 docs/doxygen.mk         |  12 ++-
 include/af/ml.h         |   4 +-
 5 files changed, 198 insertions(+), 57 deletions(-)

diff --git a/assets b/assets
index 729c7b6403..fcc798248b 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit 729c7b64039e6433ae5ee521658ba20147efcb02
+Subproject commit fcc798248b9985dd4ded5b3adf51bb1af63cea90
diff --git a/docs/arrayfire.css b/docs/arrayfire.css
index e4fe2860be..9785b2b7ea 100644
--- a/docs/arrayfire.css
+++ b/docs/arrayfire.css
@@ -70,6 +70,9 @@ div.scaled > img
 
 div.scaled > img:hover
 {
+    z-index             :   255; /* Hovered image to be shown on top of all */
+    background          :   #ffffff;
+    border              :   1px solid #000000;
     -ms-transform       :   scale(2, 2);
     -webkit-transform   :   scale(2, 2);
     -moz-transform      :   scale(2, 2);
diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index 3bb937db3e..ef50f296d4 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -10,17 +10,18 @@ if a and b are the coefficients.
 Another way to think about it is that the filter kernel is centered on each pixel in a,
 and the output for that pixel or data point is the sum of the products.
 
-Depending on the dimensions of the input signal and the filter signal, any one of the following
+Depending on the size of the signal and the filter, any one of the following
 batch mode convolutions take place.
 
 - **No Batch**   - Single filter applied to single input.
 - **Filter is Batched**  - Many filters applied on same input
 - **Signal is Batched**  - Single filter applied to a set of inputs.
 - **Identical Batches** - A set of filters applied onto to a set of inputs in one-to-one correspondence.
-- **Non overlapping Batches** - All batched filters are applied to all batched signals. The batch dimension of Signal and Filter **should not** be the same.
+- **Non overlapping Batches** - All batched filters are applied to all batched signals. The batch
+  axis of Signal and Filter **should not** be the same.
 
-Non overlapping batch mode is not supported in spatial mode i.e. if the user passes \ref
-AF_CONV_SPATIAL explicitly to the functions.
+\note All non-overlapping(interleaved) convolutions default to frequency domain
+      \ref AF_CONV_FREQ irrespective of the provided convolution mode argument.
 
 
 \page signal_func_fft_desc fft
@@ -56,23 +57,32 @@ This version of convolution function delegates the call to respective
 1D, 2D or 3D convolution functions internally.
 
 Convolution dimensionality is \f$ \min (sd, fd) \f$ where sd & fd are dimensionality of
-signal and filter respectively. This formulation only decides the dimensionality
-of convolution. Please check the respective convolve (hyperlinked below) function
-documentation to find out the kind of batch operations possible.
+signal and filter respectively.  This formulation only decides the dimensionality of convolution.
+
+Given below are some examples on how convolution dimensionality is computed.
+
+| Signal Size    | Filter Size    | Input Rank | Filter Rank | Convolve Dimensionality   |
+|:--------------:|:--------------:|:----------:|:-----------:|:-------------------------:|
+| \dims{m,n,1,1} | \dims{m,1,1,1} |     2      |      1      |   \f$ min(2, 1) => \f$ 1D |
+| \dims{m,1,1,1} | \dims{m,n,1,1} |     1      |      2      |   \f$ min(1, 2) => \f$ 1D |
+| \dims{m,n,1,1} | \dims{m,n,1,1} |     2      |      2      |   \f$ min(2, 2) => \f$ 2D |
+| \dims{m,n,1,1} | \dims{m,n,p,1} |     2      |      3      |   \f$ min(2, 3) => \f$ 2D |
+| \dims{m,n,1,p} | \dims{m,n,1,q} |     4      |      4      |   3D |
+| \dims{m,n,p,1} | \dims{m,n,q,1} |     3      |      3      |   \f$ min(3, 3) => \f$ 3D |
+
+\note In the cases similar to the fifth row of the above table,
+      signal and filter are of rank 4, the function delegates the
+      operation to three dimensional convolution \ref signal_func_convolve3
+
+If the operation you intend to perform doesn't align with what this
+function does, please check the rank specific convolve functions (hyperlinked below)
+documentation to find out more.
+
 - \ref signal_func_convolve1
 - \ref signal_func_convolve2
 - \ref signal_func_convolve3
 
-Given below are some examples.
 
-| Input Dimensions | Filter Dimensions | Convolve Dimension |
-|:----------------:|:-----------------:|:------------------:|
-| [m n 1 1]        | [m 1 1 1]         |   1D               |
-| [m 1 1 1]        | [m n 1 1]         |   1D               |
-| [m n 1 1]        | [m n 1 1]         |   2D               |
-| [m n 1 1]        | [m n p 1]         |   2D               |
-| [m n 1 p]        | [m n 1 q]         |   3D               |
-| [m n p 1]        | [m n q 1]         |   3D               |
 
 
 \defgroup signal_func_convolve_sep Separable 2D Convolution
@@ -117,17 +127,24 @@ can be decomposed into two vectors shown below.
 
 \copydoc signal_func_conv_desc
 
-For one dimensional signals(lets say m is size of 0th dimension), below batch operations are possible.
+For one dimensional signals (lets say m is size of 0th axis), below batch operations are possible.
+
+| Signal Size    | Filter Size    | Output Size    | Batch Mode               | Description                                                        |
+| :------------: | :------------: | :------------: | :----------------------: | :----------------------------------------------------------------- |
+| \dims{m,1,1,1} | \dims{m,1,1,1} | \dims{m,1,1,1} | No Batch                 | Output will be a single convolved array                            |
+| \dims{m,1,1,1} | \dims{m,n,1,1} | \dims{m,n,1,1} | Filter is Batched        | n filters applied to same input                                    |
+| \dims{m,n,1,1} | \dims{m,1,1,1} | \dims{m,n,1,1} | Signal is Batched        | 1 filter applied to n inputs                                       |
+| \dims{m,n,p,q} | \dims{m,n,p,q} | \dims{m,n,p,q} | Identical Batches        | n*p*q filters applied to n*p*q inputs in one-to-one correspondence |
+| \dims{m,n,1,1} | \dims{m,1,p,q} | \dims{m,n,p,q} | Non-overlapping batches  | p*q filters applied to n inputs to produce n x p x q results       |
 
-| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                                        |
-| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :----------------------------------------------------------------- |
-| [m 1 1 1]                 | [m 1 1 1]           | [m 1 1 1]           | No Batch                  | Output will be a single convolved array                            |
-| [m 1 1 1]                 | [m n 1 1]           | [m n 1 1]           | Filter is Batched         | n filters applied to same input                                    |
-| [m n 1 1]                 | [m 1 1 1]           | [m n 1 1]           | Signal is Batched         | 1 filter applied to n inputs                                       |
-| [m n p q]                 | [m n p q]           | [m n p q]           | Identical Batches         | n*p*q filters applied to n*p*q inputs in one-to-one correspondence |
-| [m n 1 1]                 | [m 1 p q]           | [m n p q]           | Non-overlapping batches   | p*q filters applied to n inputs to produce n x p x q results       |
+There are various other permutations of signal and filter sizes that fall under
+the category of non-overlapping batch mode that are not listed in the above
+table. For any signal and filter size combination to fall under the
+non-overlapping batch mode, they should satisfy one of the following conditions.
+- Signal and filter size along a given batch axis (\f$ > 1 \f$) should be same.
+- Either signal size or filter size along a given batch axis (\f$ > 1 \f$) should be equal to one.
 
-The last entry in the table has more permutations than shown here.
+\note For the above tabular illustrations, we assumed \ref af_conv_mode is \ref AF_CONV_DEFAULT.
 
 
 
@@ -138,31 +155,140 @@ The last entry in the table has more permutations than shown here.
 
 \copydoc signal_func_conv_desc
 
-For two dimensional signals, the following are the possible batch operations
-possible. Lets say m & n as sizes along the 0th & 1st dimensions respectively
-and p & q are the some integral numbers greater than one.
+A detailed explanation of each batch mode for 2D convolutions is provided below.
+Given below are definitions of variables and constants that are used to
+facilitate easy illustration of the operations.
+
+- \f$[M\quad N]\f$, \f$[A\quad B]\f$ are signal, filter sizes along
+  \f$0^{th}\f$ & \f$1^{st}\f$ axes respectively.
+- \f$P\f$ and \f$Q\f$ are two constants, integers greater than one.
+- \f$ p \f$ is an integer variable with range \f$ \ 0 \leq p < P \f$.
+- \f$ q \f$ is an integer variable with range \f$ \ 0 \leq q < Q \f$.
+- O, S and F are notations for Output, Signal and Filter respectively.
+
+We have also used images to showcase some examples which follow the
+below notation.
+
+- Each blue line is a two dimensional matrix.
+- Each orange line indicates a full 2d convolution operation.
+- Suffix of each letter indicates indices along \f$ 3^{rd}\f$ and \f$ 4^{th}\f$
+  axes in the order of appearance from left to right in the suffix.
+- O, S and F are notations for Output, Signal and Filter respectively.
+
+### No Batch
+
+Given below is an example of no batch mode.
+
+\image html "conv_docs_images/basic.png" "Single 2d convolution with 2d filter"
+
+For input size \dims{M,N,1,1} and filter size \dims{A,B,1,1}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,1,1} = \convolve_eq{\shape_t{S,M,N,1,1},\shape_t{F,A,B,1,1}}
+
+
+### Batched Filter
+
+Given below is an example of filter batch mode.
+
+\image html "conv_docs_images/filter.png" "Single signal convolved with many filters independently"
+
+For input size \dims{M,N,1,1} and filter size \dims{A,B,P,1}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,P,1} = \set_eq{\convolve_t{\shape_t{S,M,N,1,1},\shape_t{f,A,B,p,1}}, \forall \shape_t{f,A,B,p,1} \in \shape_t{F,A,B,P,1}}
+
+
+### Batched Signal
+
+Given below is an example of signal batch mode.
+
+\image html "conv_docs_images/signal.png" "Single filter convolved with many signals independently"
+
+For input size \dims{M,N,P,1} and filter size \dims{A,B,1,1}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,P,1} = \set_eq{\convolve_t{\shape_t{s,M,N,p,1},\shape_t{F,A,B,1,1}}, \forall \shape_t{s,M,N,p,1} \in \shape_t{S,M,N,P,1}}
+
+
+### Identical Batch Sizes
+
+Given below is an example of identical batch mode.
 
-| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                                    |
-| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :------------------------------------------------------------- |
-| [m n 1 1]                 | [m n 1 1]           | [m n 1 1]           | No Batch                  | Output will be a single convolved array                        |
-| [m n 1 1]                 | [m n p 1]           | [m n p 1]           | Filter is Batched         | p filters applied to same input                                |
-| [m n p 1]                 | [m n 1 1]           | [m n p 1]           | Signal is Batched         | 1 filter applied to p inputs                                   |
-| [m n p q]                 | [m n p q]           | [m n p q]           | Identical Batches         | p*q filters applied to p*q inputs in one-to-one correspondence |
-| [m n p 1]                 | [m n 1 q]           | [m n p q]           | Non-overlapping batches   | q filters applied to p inputs in to produce p x q results      |
-| [m n 1 p]                 | [m n q 1]           | [m n q p]           | Non-overlapping batches   | q filters applied to p inputs in to produce q x p results      |
+\image html "conv_docs_images/identical.png" "Many signals convolved with many filters in one-on-one manner"
 
-* Batching behavior of convolve2_nn functions
+For input size \dims{M,N,P,Q} and filter size \dims{A,B,P,Q}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,P,Q} = \set_eq{\convolve_t{\shape_t{s,M,N,p,q},\shape_t{f,A,B,p,q}}, \forall \shape_t{s,M,N,p,q} \in \shape_t{S,M,N,P,Q} \land \forall \shape_t{f,M,N,p,q} \in \shape_t{F,M,N,P,Q}}
+
+
+### Non-overlapping Batches
+
+Four different kinds of signal and filter size combinations are handled in this batch mode. Each one
+of them are explained in respective sections below.
+
+#### Combination 1
+
+For input size \dims{M,N,P,1} and filter size \dims{A,B,1,Q}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,P,Q} = \set_eq{\set_t{\convolve_t{\shape_t{s,M,N,p,1},\shape_t{f,A,B,1,q}}, \forall \shape_t{s,M,N,p,1} \in \shape_t{S,M,N,P,1}}, \forall \shape_t{f,A,B,1,q} \in \shape_t{F,A,B,1,Q}}
+
+Given below is an example of this batch mode.
+
+\image html "conv_docs_images/non-overlapping_1.png"
+
+#### Combination 2
+
+For input size \dims{M,N,P,1} and filter size \dims{A,B,P,Q}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,P,Q} = \set_eq{\set_t{\convolve_t{\shape_t{s,M,N,p,1},\shape_t{f,A,B,p,q}}, \forall \shape_t{f,A,B,p,q} \in \shape_t{F,A,B,P,Q}}, \forall \shape_t{s,M,N,p,1} \in \shape_t{S,M,N,P,1}}
+
+Given below is an example of this batch mode.
+
+\image html "conv_docs_images/non-overlapping_2.png"
+
+#### Combination 3
+
+For input size \dims{M,N,1,P} and filter size \dims{A,B,Q,1}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,Q,P} = \set_eq{\set_t{\convolve_t{\shape_t{s,M,N,1,p},\shape_t{f,A,B,q,1}}, \forall \shape_t{s,M,N,1,p} \in \shape_t{S,M,N,1,P}}, \forall \shape_t{f,A,B,q,1} \in \shape_t{F,A,B,Q,1}}
+
+Given below is an example of this batch mode.
+
+\image html "conv_docs_images/non-overlapping_3.png"
+
+#### Combination 4
+
+For input size \dims{M,N,P,Q} and filter size \dims{A,B,P,1}, the following set-builder
+notation gives a formal definition of all convolutions performed in this mode.
+
+\shape_eq{O,M,N,P,Q} = \set_eq{\set_t{\convolve_t{\shape_t{s,M,N,p,q},\shape_t{f,A,B,p,1}}, \forall \shape_t{s,M,N,p,q} \in \shape_t{S,M,N,P,Q}}, \forall \shape_t{f,A,B,p,1} \in \shape_t{F,A,B,P,1}}
+
+Given below is an example of this batch mode.
+
+\image html "conv_docs_images/non-overlapping_4.png"
+
+
+The batching behavior of convolve2NN functions(\ref af_convolve2_nn() and
+\ref convolve2NN() ) is different from convolve2. The new functions can perform 2D
+convolution on 3D signals and filters in a way that is more aligned with
+convolutional neural networks.
+
+| Signal Size         | Filter Size         | Output Size         | Batch Mode     | Description                                         |
+| :-----------------: | :-----------------: | :-----------------: | :------------: | :-------------------------------------------------- |
+| \dims{M, N, 1, 1}   | \dims{M, N, 1, 1}   | \dims{M, N, 1, 1}   | No Batch       | Output will be a single convolved array             |
+| \dims{M, N, 1, 1}   | \dims{M, N, P, 1}   | \dims{M, N, P, 1}   | *Invalid*      | Size along second axis should be same               |
+| \dims{M, N, P, 1}   | \dims{M, N, 1, 1}   | \dims{M, N, P, 1}   | *Invalid*      | Size along second axis should be same               |
+| \dims{M, N, P, 1}   | \dims{M, N, P, 1}   | \dims{M, N, 1, 1}   | No Batch       | 3D Signal and 3D filter convoled to 2D result       |
+| \dims{M, N, P, Qs}  | \dims{M, N, P, Qf}  | \dims{M, N, Qf, Qs} | Batch Qs * Qf  | Qs signals and Qf filters to create Qs * Qf results |
+
+\note For the above tabular illustrations, we will assume \ref af_conv_mode is \ref AF_CONV_DEFAULT.
 
-Batching behavior in the new convolutions functions have changed. These
-functions can perform a 2D convolution on 3D signal and filters.
 
-| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                               |
-| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :-------------------------------------------------------- |
-| [m n 1 1]                 | [m n 1 1]           | [m n 1 1]           | No Batch                  | Output will be a single convolved array                   |
-| [m n 1 1]                 | [m n p 1]           | [m n p 1]           | *Invalid d2 must be same* | N/A                                                       |
-| [m n p 1]                 | [m n 1 1]           | [m n p 1]           | *Invalid Batch*           | N/A                                                       |
-| [m n p 1]                 | [m n p 1]           | [m n 1 1]           | No Batch                  | 3D Signal and 3D filter convoled to 2D result             |
-| [m n p qs]                | [m n p qf]          | [m n qf qs]         | Batch qs * qf             | qs signals and qf filsters to create qs * qf results      |
 
 \defgroup signal_func_convolve3 3D Convolutions
 \ingroup convolve_mat
@@ -171,15 +297,17 @@ functions can perform a 2D convolution on 3D signal and filters.
 
 \copydoc signal_func_conv_desc
 
-For three dimensional inputs with m, n & p sizes along the 0th, 1st & 2nd dimensions
+For three dimensional inputs with m, n & p sizes along the 0th, 1st & 2nd axes
 respectively, given below are the possible batch operations.
 
-| Input Signal Dimensions   | Filter Dimensions   | Output Dimensions   | Batch Mode                | Description                                                |
-| :-----------------------: | :-----------------: | :-----------------: | :-----------------------: | :------------                                              |
-| [m n p 1]                 | [a b c 1]           | [m n p 1]           | No Batch                  | Output will be a single convolve array                     |
-| [m n p 1]                 | [a b c d]           | [m n p d]           | Filter is Batched         | d filters applied to same input                            |
-| [m n p q]                 | [a b c 1]           | [m n p q]           | Signal is Batched         | 1 filter applied to q inputs                               |
-| [m n p k]                 | [a b c k]           | [m n p k]           | Identical Batches         | k filters applied to k inputs in one-to-one correspondence |
+| Signal Size        | Filter Size        | Output Size        | Batch Mode         | Description                                                |
+| :----------------: | :----------------: | :----------------: | :----------------: |:-----------------------------------------------------------|
+| \dims{m, n, p, 1}  | \dims{a, b, c, 1}  | \dims{m, n, p, 1}  | No Batch           | Output will be a single convolve array                     |
+| \dims{m, n, p, 1}  | \dims{a, b, c, d}  | \dims{m, n, p, d}  | Filter is Batched  | d filters applied to same input                            |
+| \dims{m, n, p, q}  | \dims{a, b, c, 1}  | \dims{m, n, p, q}  | Signal is Batched  | 1 filter applied to q inputs                               |
+| \dims{m, n, p, k}  | \dims{a, b, c, k}  | \dims{m, n, p, k}  | Identical Batches  | k filters applied to k inputs in one-to-one correspondence |
+
+\note For the above tabular illustrations, we assumed \ref af_conv_mode is \ref AF_CONV_DEFAULT.
 
 
 
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 05c4e12c33..85d362c3de 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -240,13 +240,23 @@ ALIASES                = "support{1}=<DIV class=\"support\">\1</DIV>" \
                          "jit=<IMG src=\"jit.png\" alt=\"JIT Support\" />" \
                          "democode{1}=\htmlonly \n <pre class=\"cpp afw\"> <code class=\"cpp\"> \1 </code> </pre> \n \endhtmlonly" \
                          "imagegroup{1}=<DIV CLASS=\"image_group\"> \1 </DIV>" \
-                         "smallimage{2}=\htmlonly <div class=\"scaled\"><img src=\"\1\" alt=\"\2\"><div class=\"caption\">\2</div></div> \endhtmlonly" \
+                         "smallimage{2}=\htmlonly <div class=\"scaled\"><img src=\"\1\" alt=\"\2\"></div> \endhtmlonly" \
                          "funcgroups{3}=\ingroup \3 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "funcgroups{4}=\ingroup \3 \4 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "funcgroups{5}=\ingroup \3 \4 \5 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "funcgroups{6}=\ingroup \3 \4 \5 \6 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "endfuncgroups=@} \n @}"
 
+# Now add special commands for math equations. All of the following commands
+# are only expected to be used inside math mode
+ALIASES += "dims{4}=\f$ [\1 \ \2 \ \3 \ \4] \f$"
+ALIASES += "shape_eq{5}=\f$ \underset{[\2 \ \3 \ \4 \ \5]}{\1} \f$"
+ALIASES += "shape_t{5}=\underset{[\2 \ \3 \ \4 \ \5]}{\1}"
+ALIASES += "convolve_eq{2}=\f$ \1 \ast \2 \f$"
+ALIASES += "convolve_t{2}=\1 \ast \2"
+ALIASES += "set_eq{2}=\f$ \left\\{ \1 \ \Bigg\vert \ \2 \right\\} \f$"
+ALIASES += "set_t{2}=\left\\\{ \1 \ \Bigg\vert \ \2 \right\\\}"
+
 # This tag can be used to specify a number of word-keyword mappings (TCL only).
 # A mapping has the form "name=value". For example adding "class=itcl::class"
 # will allow you to use the command class in the itcl::class meaning.
diff --git a/include/af/ml.h b/include/af/ml.h
index 20ad02999e..c1581fe887 100644
--- a/include/af/ml.h
+++ b/include/af/ml.h
@@ -20,7 +20,7 @@ class dim4;
     /**
         C++ interface for calculating backward pass gradient of 2D convolution
         This function calculates the gradient with respect to the output
-        of the \ref convolve2_nn() function that uses the machine learning
+        of the \ref convolve2NN() function that uses the machine learning
         formulation for the dimensions of the signals and filters
 
         \param[in]  incoming_gradient gradients to be distributed in backwards pass
@@ -57,7 +57,7 @@ extern "C" {
     /**
         C interface for calculating backward pass gradient of 2D convolution
         This function calculates the gradient with respect to the output
-        of the \ref convolve2_nn() function that uses the machine learning
+        of the \ref convolve2NN() function that uses the machine learning
         formulation for the dimensions of the signals and filters
 
         \param[out] out gradient wrt/gradType

From c71d3f22b2950b8d2e50951fcb847c8526c99051 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 23 Oct 2019 20:40:02 +0530
Subject: [PATCH 1758/2677] Remove obsolete cmake config version template
 (#2654)

---
 CMakeModules/ArrayFireConfigVersion.cmake.in | 73 --------------------
 1 file changed, 73 deletions(-)
 delete mode 100644 CMakeModules/ArrayFireConfigVersion.cmake.in

diff --git a/CMakeModules/ArrayFireConfigVersion.cmake.in b/CMakeModules/ArrayFireConfigVersion.cmake.in
deleted file mode 100644
index cb32c868d2..0000000000
--- a/CMakeModules/ArrayFireConfigVersion.cmake.in
+++ /dev/null
@@ -1,73 +0,0 @@
-#=============================================================================
-# Copyright (c) 2015, ArrayFire
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without modification,
-# are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright notice, this
-#   list of conditions and the following disclaimer in the documentation and/or
-#   other materials provided with the distribution.
-#
-# * Neither the name of the ArrayFire nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#=============================================================================
-
-# This is a basic version file for the Config-mode of find_package().
-#
-# The created file sets PACKAGE_VERSION_EXACT if the current version string and
-# the requested version string are exactly the same and it sets
-# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version,
-# but only if the requested major version is the same as the current one.
-
-
-set(PACKAGE_VERSION "@ArrayFire_VERSION_MAJOR@@ArrayFire_VERSION_MINOR@")
-
-if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}" )
-  set(PACKAGE_VERSION_COMPATIBLE FALSE)
-else()
-
-  if("@ArrayFire_VERSION_MAJOR@@ArrayFire_VERSION_MINOR@" MATCHES "^([0-9]+)\\.")
-    set(ArrayFire_VERSION_MAJOR "${CMAKE_MATCH_1}")
-  else()
-    set(ArrayFire_VERSION_MAJOR "@ArrayFire_VERSION_MAJOR@@ArrayFire_VERSION_MINOR@")
-  endif()
-
-  if("${PACKAGE_FIND_VERSION_MAJOR}" STREQUAL "${ArrayFire_VERSION_MAJOR}")
-    set(PACKAGE_VERSION_COMPATIBLE TRUE)
-  else()
-    set(PACKAGE_VERSION_COMPATIBLE FALSE)
-  endif()
-
-  if( "${PACKAGE_FIND_VERSION}" STREQUAL "${PACKAGE_VERSION}")
-      set(PACKAGE_VERSION_EXACT TRUE)
-  endif()
-endif()
-
-
-# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
-if("${CMAKE_SIZEOF_VOID_P}"  STREQUAL ""  OR "@CMAKE_SIZEOF_VOID_P@" STREQUAL "")
-   return()
-endif()
-
-# check that the installed version has the same 32/64bit-ness as the one which is currently searching:
-if(NOT "${CMAKE_SIZEOF_VOID_P}" STREQUAL "@CMAKE_SIZEOF_VOID_P@")
-  math(EXPR installedBits "@CMAKE_SIZEOF_VOID_P@ * 8")
-  set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)")
-  set(PACKAGE_VERSION_UNSUITABLE TRUE)
-endif()

From 1bcdf6140915c241ef70f66a3a4538cfc9508679 Mon Sep 17 00:00:00 2001
From: Alexey Kuleshevich <lehins@yandex.ru>
Date: Mon, 4 Nov 2019 22:12:46 +0300
Subject: [PATCH 1759/2677] Fix the links in README

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index efa2ace799..e6103c8aeb 100644
--- a/README.md
+++ b/README.md
@@ -42,11 +42,11 @@ Build from source by following instructions on our
 ## Examples
 
 The following examples are simplified versions of
-[`helloworld.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/helloworld/helloworld.cpp)
+[`helloworld.cpp`](https://github.com/arrayfire/arrayfire/blob/master/examples/helloworld/helloworld.cpp)
 and
-[`conway_pretty.cpp`](https://github.com/arrayfire/arrayfire/tree/devel/examples/graphics/conway_pretty.cpp),
+[`conway_pretty.cpp`](https://github.com/arrayfire/arrayfire/blob/master/examples/graphics/conway_pretty.cpp),
 respectively. For more code examples, visit the
-[`examples/`](https://github.com/arrayfire/arrayfire/tree/devel/examples)
+[`examples/`](https://github.com/arrayfire/arrayfire/blob/master/examples/)
 directory.
 
 #### Hello, world!

From c30d5455f3d60582cb81968551b69fadcfc7c880 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 6 Nov 2019 21:23:16 +0530
Subject: [PATCH 1760/2677] Correct docs grouping for neg operator

---
 docs/details/arith.dox | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index eb5e8f404d..49069c9567 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -143,7 +143,7 @@ Logical not of an input
 
 \defgroup arith_func_neg neg
 
-\ingroup logic_mat
+\ingroup numeric_mat
 
 Negative of an input
 

From a4485443684cc9ddbcbcaeb35b9024317904c518 Mon Sep 17 00:00:00 2001
From: Jacob Kahn <jacobkahn1@gmail.com>
Date: Thu, 5 Dec 2019 19:41:47 -0800
Subject: [PATCH 1761/2677] Add framework for extensible ArrayFire memory
 managers (#2461)

Many different use cases require performance across many different memory
allocation patterns. Even different devices/backends have different costs
associated with memory allocations/manipulations. Having the flexibility to
implement different memory management schemes can help optimize performance for
the use case and backend.

This commit adds the ability to replace the default memory manager to a
specialized user defined memory manager. This commit also exposes the events API
to the user which allows you to synchronize tasks between two streams. The
events API will be disabled in a future commit but it can be used in the future
once we add support for streams.

ArrayFire will use the user defined memory manager whenever it allocates or
frees memory. The memory manager is exposed using the C API using function
pointers. The memory manager handle is created by the user during initialization
and the user sets several function pointers to define the behavior.

The default memory manager behavior has not changed with this commit.
---
 include/af/event.h                            | 111 +++
 include/af/memory.h                           | 736 ++++++++++++++++++
 include/arrayfire.h                           |  79 +-
 src/api/c/CMakeLists.txt                      |   7 +
 src/api/c/buffer_info.cpp                     | 119 +++
 src/api/c/events.cpp                          |  70 ++
 src/api/c/events.hpp                          |  18 +
 src/api/c/memory.cpp                          | 470 ++++++++++-
 src/api/c/memory_manager.hpp                  | 252 ++++++
 .../c/memory_manager_impl.hpp}                | 247 +++---
 src/api/c/memoryapi.hpp                       |  97 +++
 src/api/cpp/CMakeLists.txt                    |   2 +
 src/api/cpp/array.cpp                         |   4 +-
 src/api/cpp/buffer_info.cpp                   |  74 ++
 src/api/cpp/event.cpp                         |  41 +
 src/api/unified/CMakeLists.txt                |   2 +
 src/api/unified/event.cpp                     |  25 +
 src/api/unified/memory.cpp                    | 165 ++++
 src/backend/common/CMakeLists.txt             |   1 -
 src/backend/common/EventBase.hpp              |   9 +-
 src/backend/common/MemoryManager.hpp          | 159 ----
 src/backend/cpu/Array.cpp                     |  17 +-
 src/backend/cpu/Array.hpp                     |   1 -
 src/backend/cpu/Event.cpp                     |  54 +-
 src/backend/cpu/Event.hpp                     |  25 +-
 src/backend/cpu/device_manager.cpp            |  51 +-
 src/backend/cpu/device_manager.hpp            |  32 +-
 src/backend/cpu/memory.cpp                    | 108 +--
 src/backend/cpu/memory.hpp                    |  29 +-
 src/backend/cpu/platform.cpp                  |  19 +-
 src/backend/cpu/platform.hpp                  |  21 +-
 src/backend/cpu/queue.hpp                     |  16 +-
 src/backend/cuda/Array.cpp                    |  22 +-
 src/backend/cuda/CMakeLists.txt               |   7 +-
 src/backend/cuda/Event.cpp                    |  58 +-
 src/backend/cuda/Event.hpp                    |  25 +-
 src/backend/cuda/cufft.cpp                    |   2 +-
 src/backend/cuda/device_manager.cpp           |  79 +-
 src/backend/cuda/device_manager.hpp           |  34 +-
 src/backend/cuda/memory.cpp                   | 130 ++--
 src/backend/cuda/memory.hpp                   |  47 +-
 src/backend/cuda/platform.cpp                 |  43 +-
 src/backend/cuda/platform.hpp                 |  25 +-
 src/backend/opencl/Array.cpp                  |  29 +-
 src/backend/opencl/Event.cpp                  |  54 +-
 src/backend/opencl/Event.hpp                  |  17 +-
 src/backend/opencl/clfft.hpp                  |   2 +-
 src/backend/opencl/device_manager.cpp         |  53 ++
 src/backend/opencl/device_manager.hpp         |  34 +-
 src/backend/opencl/memory.cpp                 | 146 ++--
 src/backend/opencl/memory.hpp                 |  43 +-
 src/backend/opencl/platform.cpp               |  51 +-
 src/backend/opencl/platform.hpp               |  28 +-
 test/CMakeLists.txt                           |   3 +-
 test/event.cpp                                |  54 ++
 test/memory.cpp                               | 413 +++++++++-
 56 files changed, 3797 insertions(+), 663 deletions(-)
 create mode 100644 include/af/event.h
 create mode 100644 include/af/memory.h
 create mode 100644 src/api/c/buffer_info.cpp
 create mode 100644 src/api/c/events.cpp
 create mode 100644 src/api/c/events.hpp
 create mode 100644 src/api/c/memory_manager.hpp
 rename src/{backend/common/MemoryManagerImpl.hpp => api/c/memory_manager_impl.hpp} (58%)
 create mode 100644 src/api/c/memoryapi.hpp
 create mode 100644 src/api/cpp/buffer_info.cpp
 create mode 100644 src/api/cpp/event.cpp
 create mode 100644 src/api/unified/event.cpp
 create mode 100644 src/api/unified/memory.cpp
 delete mode 100644 src/backend/common/MemoryManager.hpp
 create mode 100644 test/event.cpp

diff --git a/include/af/event.h b/include/af/event.h
new file mode 100644
index 0000000000..5428cf3471
--- /dev/null
+++ b/include/af/event.h
@@ -0,0 +1,111 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <af/defines.h>
+
+#if AF_API_VERSION >= 37
+
+typedef void* af_event;
+
+#ifdef __cplusplus
+namespace af {
+
+/**
+    C++ RAII interface for manipulating events
+*/
+class AFAPI event {
+    af_event e_;
+
+   public:
+    event(af_event e);
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
+    event(event&& other);
+    event& operator=(event&& other);
+#endif
+    event();
+    ~event();
+
+    af_event get() const;
+
+    void mark();
+
+    void enqueue();
+
+    void block() const;
+
+   private:
+    event& operator=(const event& other);
+    event(const event& other);
+};
+
+}  // namespace af
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+   \brief Create a new \ref af_event handle
+
+   \param[in] eventHandle the input event handle
+
+   \ingroup event_api
+*/
+AFAPI af_err af_create_event(af_event* eventHandle);
+
+/**
+   \brief Release the \ref af_event handle
+
+   \param[in] eventHandle the input event handle
+
+   \ingroup event_api
+*/
+AFAPI af_err af_delete_event(af_event eventHandle);
+
+/**
+   marks the \ref af_event on the active computation stream. If the \ref
+   af_event is enqueued/waited on later, any operations that are currently
+   enqueued on the event stream will be completed before any events that are
+   enqueued after the call to enqueue
+
+   \param[in] eventHandle the input event handle
+
+   \ingroup event_api
+*/
+AFAPI af_err af_mark_event(const af_event eventHandle);
+
+/**
+   enqueues the \ref af_event and all enqueued events on the active stream.
+   All operations enqueued after a call to enqueue will not be executed
+   until operations on the stream when mark was called are complete
+
+   \param[in] eventHandle the input event handle
+
+   \ingroup event_api
+*/
+AFAPI af_err af_enqueue_wait_event(const af_event eventHandle);
+
+/**
+   blocks the calling thread on events until all events on the computation
+   stream before mark was called are complete
+
+   \param[in] eventHandle the input event handle
+
+   \ingroup event_api
+*/
+AFAPI af_err af_block_event(const af_event eventHandle);
+
+#ifdef __cplusplus
+}
+#endif  // __cplusplus
+
+#endif  // AF_API_VERSION >= 37
diff --git a/include/af/memory.h b/include/af/memory.h
new file mode 100644
index 0000000000..0a9d631b8f
--- /dev/null
+++ b/include/af/memory.h
@@ -0,0 +1,736 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <af/defines.h>
+#include <af/event.h>
+
+#include <stddef.h>
+
+#if AF_API_VERSION >= 37
+
+typedef void* af_buffer_info;
+
+typedef void* af_memory_manager;
+
+#ifdef __cplusplus
+namespace af {
+
+/// A simple RAII wrapper for af_buffer_info
+class AFAPI buffer_info {
+    af_buffer_info p_;
+
+   public:
+    buffer_info(af_buffer_info p);
+    buffer_info(void* ptr, af_event event);
+    ~buffer_info();
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
+    buffer_info(buffer_info&& other);
+    buffer_info& operator=(buffer_info&& other);
+#endif
+    void* getPtr() const;
+    void setPtr(void* ptr);
+    af_event getEvent() const;
+    void setEvent(af_event event);
+    af_buffer_info get() const;
+    af_event unlockEvent();
+    void* unlockPtr();
+
+   private:
+    buffer_info& operator=(const buffer_info& other);
+    buffer_info(const buffer_info& other);
+};
+
+}  // namespace af
+#endif  // __cplusplus
+
+#ifdef __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+/**
+   \brief Creates an \ref af_buffer_info handle from an \ref af_event event and
+   ptr
+
+   \param[in] buf The \ref af_buffer_info object to be created
+   \param[in] ptr A pointer
+   \param[in] event An \ref af_event
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_create_buffer_info(af_buffer_info* buf, void* ptr,
+                                   af_event event);
+
+/**
+   \brief deletes the \ref af_buffer_info and its resources
+
+   Deletes the \ref af_buffer_info object and its tracked resources. If buffer
+   still holds a pointer, that pointer is freed. Does NOT enqueue a wait on the
+   associated event
+
+   \param[in] buf The \ref af_buffer_info object that will be deleted
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_delete_buffer_info(af_buffer_info buf);
+
+/**
+   \brief Retrieves a pointer from an \ref af_buffer_info
+
+   \param[out] ptr The associated pointer
+   \param[in] buf The \ref af_buffer_info object
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_buffer_info_get_ptr(void** ptr, af_buffer_info buf);
+
+/**
+   \brief Retrieves an \ref af_event from an \ref af_buffer_info
+
+   \param[out] event The associated event
+   \param[in] buf The \ref af_buffer_info object
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_buffer_info_get_event(af_event* event, af_buffer_info buf);
+
+/**
+   \brief Sets a pointer on an \ref af_buffer_info
+
+   \param[in] buf The \ref af_buffer_info object
+   \param[in] ptr The pointer to set
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_buffer_info_set_ptr(af_buffer_info buf, void* ptr);
+
+/**
+   \brief Sets an \ref af_event on an \ref af_buffer_info
+
+   \param[in] buf The \ref af_buffer_info object
+   \param[in] event The \ref af_event to set
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_buffer_info_set_event(af_buffer_info buf, af_event event);
+
+/**
+   \brief Disassociates the \ref af_event from the \ref af_buffer_info object
+
+   Gets the \ref af_event and disassociated it from the af_buffer_info object.
+   Deleting the af_buffer_info object will not affect this event.
+
+   \param[out] event The \ref af_event that will be disassociated. If NULL no
+   event is returned and the event is NOT freed
+   \param[in] buf The target \ref af_buffer_info object
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_unlock_buffer_info_event(af_event* event, af_buffer_info buf);
+
+/**
+   \brief Disassociates the pointer from the \ref af_buffer_info object
+
+   Gets the pointer and disassociated it from the \ref af_buffer_info object.
+   Deleting the \ref af_buffer_info object will not affect this pointer.
+
+   \param[out] ptr The pointer that will be disassociated. If NULL no
+   pointer is returned and the data is NOT freed.
+   \param[in] buf The target \ref af_buffer_info object
+   \returns AF_SUCCESS
+
+   \ingroup buffer_info
+*/
+AFAPI af_err af_unlock_buffer_info_ptr(void** ptr, af_buffer_info buf);
+
+/**
+   \brief Called after a memory manager is set and becomes active.
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_initialize_fn)(af_memory_manager handle);
+
+/**
+   \brief Called after a memory manager is unset and becomes unused
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_shutdown_fn)(af_memory_manager handle);
+
+/**
+   \brief Function pointer that will be called by ArrayFire to allocate memory.
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] buffer_info a pointer to a \ref af_buffer_info containing the
+   pointer to the allocated buffer and an associated \ref af_event
+   \param[in] bytes number of bytes to allocate
+   \param[in] user_lock a truthy value corresponding to whether or not the
+   memory should have a user lock associated with it
+   \param[in] ndims the number of dimensions associated with the allocated
+   memory. This value is currently always 1
+   \param[in,out] dims a \ref dim_t containing the dimensions of the allocation
+   by number of elements. After the function returns, the pointer contains the
+   shape of the allocated tensor
+   \param[in] element_size the number of bytes per element of allocated memory
+
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_alloc_fn)(af_memory_manager handle,
+                                             af_buffer_info* buffer_info,
+                                             /* bool */ int user_lock,
+                                             const unsigned ndims, dim_t* dims,
+                                             const unsigned element_size);
+
+/**
+   \brief Checks the amount of allocated memory for a pointer
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] size the size of the allocated memory for the pointer
+   \param[in] ptr the pointer to query
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_allocated_fn)(af_memory_manager handle,
+                                                 size_t* size, void* ptr);
+
+/**
+   \brief Unlocks memory from use
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] ptr the pointer to query
+   \param[in] event a new \ref af_event which will be marked before the free is
+   executed such that enqueing a wait on this event
+   \param[in] user_unlock frees the memory from user lock
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_unlock_fn)(af_memory_manager handle,
+                                              void* ptr, af_event event,
+                                              /* bool */ int user_unlock);
+
+/**
+   \brief Called to signal the memory manager should free memory if possible
+
+   Called by some external functions that allocate their own memory if they
+   receive an out of memory in order to free up other memory on a device
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_signal_memory_cleanup_fn)(
+    af_memory_manager handle);
+
+/**
+   \brief Populates a character array with human readable information about the
+   current state of the memory manager.
+
+   Prints useful information about the memory manger and its state. No format is
+   enforced and can include any information that could be useful to the user.
+   This function is only called by \ref af_print_mem_info.
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] a buffer to which a message will be populated
+   \param[in] the device id for which to print memory
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_print_info_fn)(af_memory_manager handle,
+                                                  char* buffer, int id);
+
+/**
+   \brief Called to lock a buffer as user-owned memory
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[in] ptr pointer to the buffer to lock
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_user_lock_fn)(af_memory_manager handle,
+                                                 void* ptr);
+
+/**
+   \brief Called to unlock a buffer from user-owned memory
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[in] ptr pointer to the buffer to unlock
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_user_unlock_fn)(af_memory_manager handle,
+                                                   void* ptr);
+
+/**
+   \brief Queries if a buffer is user locked
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] out a truthy value corresponding to if the buffer is user locked
+   \param[in] ptr pointer to the buffer to query
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_is_user_locked_fn)(af_memory_manager handle,
+                                                      int* out, void* ptr);
+
+/**
+   \brief Gets memory pressure for a memory manager
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] pressure the memory pressure value
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_get_memory_pressure_fn)(af_memory_manager,
+                                                           float* pressure);
+
+/**
+   \brief Called to query if additions to the JIT tree would exert too much
+   memory pressure
+
+   The ArrayFire JIT compiler will call this function to determine if the number
+   of bytes referenced by the buffers in the JIT tree are causing too much
+   memory pressure on the system.
+
+   If the memory manager decides that the pressure is too great, the JIT tree
+   will be evaluated and this COULD result in some buffers being freed if they
+   are not referenced by other af_arrays. If the memory pressure is not too
+   great the JIT tree may not be evaluated and could continue to get bigger.
+
+   The default memory manager will trigger an evaluation if the buffers in the
+   JIT tree account for half of all buffers allocated.
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[out] out a truthy value if too much memory pressure is exerted
+   \param[in] size the total number of bytes allocated by all the buffer nodes
+   in the current JIT tree
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef af_err (*af_memory_manager_jit_tree_exceeds_memory_pressure_fn)(
+    af_memory_manager handle, int* out, size_t size);
+
+/**
+   \brief Adds a new device to the memory manager (OpenCL only)
+
+   \param[in] handle a pointer to the active \ref af_memory_manager handle
+   \param[in] id the id of the device to add
+   \returns AF_SUCCESS
+
+   \ingroup memory_manager_api
+*/
+typedef void (*af_memory_manager_add_memory_management_fn)(
+    af_memory_manager handle, int id);
+
+/**
+    \brief Removes a device from the memory manager (OpenCL only)
+
+    \param[in] handle a pointer to the active \ref af_memory_manager handle
+    \param[in] id the id of the device to remove
+    \returns AF_SUCCESS
+
+    \ingroup memory_manager_api
+*/
+typedef void (*af_memory_manager_remove_memory_management_fn)(af_memory_manager,
+                                                              int id);
+
+/**
+   \brief Creates an \ref af_memory_manager handle
+
+   Creates a blank af_memory_manager with no attached function pointers.
+
+   \param[out] out \ref af_memory_manager
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_create_memory_manager(af_memory_manager* out);
+
+/**
+   \brief Destroys an \ref af_memory_manager handle.
+
+   Destroys a memory manager handle, does NOT call the
+   \ref af_memory_manager_shutdown_fn associated with the af_memory_manager.
+
+   \param[in] handle the \ref af_memory_manager handle to be destroyed
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_release_memory_manager(af_memory_manager handle);
+
+/**
+   \brief Sets an af_memory_manager to be the default memory manager for
+   non-pinned memory allocations in ArrayFire.
+
+   Registers the given memory manager as the AF memory manager non-pinned
+   memory allocations - does NOT shut down or release the existing memory
+   manager or free any associated memory.
+
+   \param[in] handle the \ref af_memory_manager handle to use
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_set_memory_manager(af_memory_manager handle);
+
+/**
+   \brief Sets an af_memory_manager to be the default memory manager for
+   pinned memory allocations in ArrayFire.
+
+   Registers the given memory manager as the AF memory manager for pinned
+   memory allocations - does NOT shut down or release the existing memory
+   manager or free any associated memory.
+
+   \param[in] handle the \ref af_memory_manager handle to use
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_set_memory_manager_pinned(af_memory_manager handle);
+
+/**
+   \brief Reset the memory manager being used in ArrayFire to the default
+   memory manager, shutting down the existing memory manager.
+
+   Calls the associated af_memory_manager_shutdown_fn on
+   the existing memory manager. If the default memory manager is set,
+   ALL associated memory will be freed on shutdown. Custom behavior that
+   does not free all memory can be defined for a custom memory manager
+   as per the specific implementation of its associated
+   af_memory_manager_shutdown_fn.
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_unset_memory_manager();
+
+/**
+   \brief Reset the pinned memory manager being used in ArrayFire to the
+   default memory manager, shutting down the existing pinned memory manager.
+
+   Calls the associated af_memory_manager_shutdown_fn on
+   the existing pinned memory manager. If the default memory manager is set,
+   ALL associated pinned memory will be freed on shutdown. Custom behavior that
+   does not free all pinned memory can be defined for a custom memory manager
+   as per the specific implementation of its associated
+   af_memory_manager_shutdown_fn.
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_unset_memory_manager_pinned();
+
+/**
+   \brief Gets the payload ptr from an \ref af_memory_manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[out] payload pointer to the payload pointer
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_get_payload(af_memory_manager handle,
+                                           void** payload);
+
+/**
+   \brief Sets the payload ptr from an \ref af_memory_manager
+
+   A payload can be any user defined memory associated with the memory manager
+   and can be used to track state of the memory manager. It is not used directly
+   by ArrayFire.
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[out] payload pointer to the payload pointer
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_payload(af_memory_manager handle,
+                                           void* payload);
+
+/**
+   \brief Sets an \ref af_memory_manager_initialize_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_initialize_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_initialize_fn(
+    af_memory_manager handle, af_memory_manager_initialize_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_shutdown_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_shutdown_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_shutdown_fn(
+    af_memory_manager handle, af_memory_manager_shutdown_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_alloc_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_alloc_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_alloc_fn(af_memory_manager handle,
+                                            af_memory_manager_alloc_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_allocated_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_allocated_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_allocated_fn(
+    af_memory_manager handle, af_memory_manager_allocated_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_unlock_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_unlock_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_unlock_fn(af_memory_manager handle,
+                                             af_memory_manager_unlock_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_signal_memory_cleanup_fn for a memory
+   manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_signal_memory_cleanup_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_signal_memory_cleanup_fn(
+    af_memory_manager handle, af_memory_manager_signal_memory_cleanup_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_print_info_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_print_info_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_print_info_fn(
+    af_memory_manager handle, af_memory_manager_print_info_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_user_lock_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_user_lock_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_user_lock_fn(
+    af_memory_manager handle, af_memory_manager_user_lock_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_user_unlock_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_user_unlock_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_user_unlock_fn(
+    af_memory_manager handle, af_memory_manager_user_unlock_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_is_user_locked_fn for a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_is_user_locked_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_is_user_locked_fn(
+    af_memory_manager handle, af_memory_manager_is_user_locked_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_get_memory_pressure_fn for a memory
+   manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_get_memory_pressure_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_get_memory_pressure_fn(
+    af_memory_manager handle, af_memory_manager_get_memory_pressure_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_jit_tree_exceeds_memory_pressure_fn for
+   a memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_jit_tree_exceeds_memory_pressure_fn
+   to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn(
+    af_memory_manager handle,
+    af_memory_manager_jit_tree_exceeds_memory_pressure_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_add_memory_management_fn for a memory
+   manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_add_memory_management_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_add_memory_management_fn(
+    af_memory_manager handle, af_memory_manager_add_memory_management_fn fn);
+
+/**
+   \brief Sets an \ref af_memory_manager_remove_memory_management_fn for a
+   memory manager
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[in] fn the \ref af_memory_manager_remove_memory_management_fn to set
+
+   \returns AF_SUCCESS
+   \ingroup memory_manager_utils
+*/
+AFAPI af_err af_memory_manager_set_remove_memory_management_fn(
+    af_memory_manager handle, af_memory_manager_remove_memory_management_fn fn);
+
+////////////////// Native memory interface functions
+
+/**
+   \brief Gets the id of the currently-active device
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[out] id the id of the active device
+
+   \returns AF_SUCCESS
+   \ingroup native_memory_interface
+*/
+AFAPI af_err af_memory_manager_get_active_device_id(af_memory_manager handle,
+                                                    int* id);
+
+/**
+   \brief Allocates memory with a native memory function for the active backend
+
+   \param[in] handle the \ref af_memory_manager handle
+   \param[out] ptr the pointer to the allocated buffer (for the CUDA and CPU
+   backends). For the OpenCL backend, this is a pointer to a cl::Buffer, which
+   can be cast accordingly
+   \param[in] size the size of the pointer allocation
+
+   \returns AF_SUCCESS
+   \ingroup native_memory_interface
+*/
+AFAPI af_err af_memory_manager_native_alloc(af_memory_manager handle,
+                                            void** ptr, size_t size);
+
+/**
+    \brief Frees a pointer with a native memory function for the active backend
+
+    \param[in] handle the \ref af_memory_manager handle
+    \param[in] ptr the pointer to free
+
+    \returns AF_SUCCESS
+    \ingroup native_memory_interface
+*/
+AFAPI af_err af_memory_manager_native_free(af_memory_manager handle, void* ptr);
+
+/** \brief Gets the maximum memory size for a managed device.
+
+  \param[in] handle the \ref af_memory_manager handle
+  \param[out] size the max memory size for the device
+  \param[in] id the device id
+
+  \returns AF_SUCCESS
+  \ingroup native_memory_interface */
+AFAPI af_err af_memory_manager_get_max_memory_size(af_memory_manager handle,
+                                                   size_t* size, int id);
+
+/**
+\brief Gets the memory pressure threshold for a memory manager.
+
+  \param[in] handle the \ref af_memory_manager handle
+  \param[out] value the memory pressure threshold
+
+  \returns AF_SUCCESS
+  \ingroup native_memory_interface
+*/
+AFAPI af_err af_memory_manager_get_memory_pressure_threshold(
+    af_memory_manager handle, float* value);
+
+/**
+    \brief Sets the memory pressure threshold for a memory manager.
+
+    The memory pressure threshold determines when the JIT tree evaluates based
+    on how much memory usage there is. If the value returned by \ref
+    af_memory_manager_get_memory_pressure_fn exceeds the memory pressure
+    threshold, the JIT will evaluate a subtree if generated kernels are valid.
+
+    \param[in] handle the \ref af_memory_manager handle
+    \param[in] value the new threshold value
+
+    \returns AF_SUCCESS
+    \ingroup native_memory_interface
+*/
+AFAPI af_err af_memory_manager_set_memory_pressure_threshold(
+    af_memory_manager handle, float value);
+
+#ifdef __cplusplus
+}
+#endif  // __cplusplus
+#endif  // AF_API_VERSION >= 37
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 4356f9fc70..56369b3642 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -97,6 +97,81 @@
       diff, gradient, etc.
    @}
 
+   @defgroup memory_manager Memory Management
+   @{
+
+      \brief Interfaces for writing custom memory managers.
+
+      Create and set a custom memory manager by first defining the relevant
+closures for each required function, for example:
+
+      \code{.cpp}
+      af_err my_initialize(af_memory_manager manager) {
+          void* myPayload = malloc(sizeof(MyPayload_t));
+          af_memory_manager_set_payload(manager, myPayload);
+          // ...
+      }
+
+      af_err my_allocated(af_memory_manager handle, size_t* size, void* ptr) {
+          void* myPayload;
+          af_memory_manager_get_payload(manager, &myPayload);
+          // ...
+      }
+      \endcode
+
+      Create an \ref af_memory_manager and attach relevant closures:
+
+      \code{.cpp}
+      af_memory_manager manager;
+      af_create_memory_manager(&manager);
+
+      af_memory_manager_set_initialize_fn(manager, my_initialize);
+      af_memory_manager_set_allocated_fn(manager, my_allocated);
+
+      // ...
+       \endcode
+
+      Set the memory manager to be active, which shuts down the existing memory
+manager:
+
+      \code{.cpp}
+      af_set_memory_manager(manager);
+      \endcode
+
+      Unset to re-create and reset an instance of the default memory manager:
+
+      \code{.cpp}
+      af_unset_memory_manager();
+      \endcode
+
+      @defgroup buffer_info Buffer Info
+      \brief An interface for managing information about memory (pointers and
+\ref af_event)
+
+      @defgroup native_memory_interface Native Memory Interface
+      \brief Native alloc, native free, get device id, etc.
+
+      @defgroup memory_manager_utils Memory Manager Utils
+      \brief Set and unset memory managers, set and get manager payloads,
+function setters
+
+      @defgroup memory_manager_api Memory Manager API
+      \brief Functions for defining custom memory managers
+
+   @}
+
+   @defgroup event Events
+   @{
+
+      \brief Managing ArrayFire Events which allows manipulation of operations
+on computation queues.
+
+
+
+      @defgroup event_api Event API
+      af_create_event, af_mark_event, etc.
+   @}
+
    @defgroup linalg_mat Linear Algebra
    @{
 
@@ -313,10 +388,11 @@
 #include "af/array.h"
 #include "af/backend.h"
 #include "af/blas.h"
-#include "af/constants.h"
 #include "af/complex.h"
+#include "af/constants.h"
 #include "af/data.h"
 #include "af/device.h"
+#include "af/event.h"
 #include "af/exception.h"
 #include "af/features.h"
 #include "af/gfor.h"
@@ -325,6 +401,7 @@
 #include "af/image.h"
 #include "af/index.h"
 #include "af/lapack.h"
+#include "af/memory.h"
 #include "af/ml.h"
 #include "af/random.h"
 #include "af/seq.h"
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index d4738425f4..12dfe47862 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -23,6 +23,7 @@ target_sources(c_api_interface
   ${ArrayFire_SOURCE_DIR}/include/af/defines.h
   ${ArrayFire_SOURCE_DIR}/include/af/device.h
   ${ArrayFire_SOURCE_DIR}/include/af/dim4.hpp
+  ${ArrayFire_SOURCE_DIR}/include/af/event.h
   ${ArrayFire_SOURCE_DIR}/include/af/exception.h
   ${ArrayFire_SOURCE_DIR}/include/af/features.h
   ${ArrayFire_SOURCE_DIR}/include/af/gfor.h
@@ -33,6 +34,7 @@ target_sources(c_api_interface
   ${ArrayFire_SOURCE_DIR}/include/af/lapack.h
   ${ArrayFire_SOURCE_DIR}/include/af/macros.h
   ${ArrayFire_SOURCE_DIR}/include/af/ml.h
+  ${ArrayFire_SOURCE_DIR}/include/af/memory.h
   ${ArrayFire_SOURCE_DIR}/include/af/opencl.h
   ${ArrayFire_SOURCE_DIR}/include/af/random.h
   ${ArrayFire_SOURCE_DIR}/include/af/seq.h
@@ -55,6 +57,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/buffer_info.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/canny.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cast.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cholesky.cpp
@@ -70,6 +73,8 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/diff.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dog.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/events.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/events.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/exampleFunction.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
@@ -106,6 +111,8 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/meanshift.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/median.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/memoryapi.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/memory_manager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moddims.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/morph.cpp
diff --git a/src/api/c/buffer_info.cpp b/src/api/c/buffer_info.cpp
new file mode 100644
index 0000000000..07c40d735e
--- /dev/null
+++ b/src/api/c/buffer_info.cpp
@@ -0,0 +1,119 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <memoryapi.hpp>
+
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <af/device.h>
+#include <af/event.h>
+#include <af/memory.h>
+
+BufferInfo &getBufferInfo(const af_buffer_info handle) {
+    return *(BufferInfo *)handle;
+}
+
+af_buffer_info getHandle(BufferInfo &buf) {
+    BufferInfo *handle;
+    handle = &buf;
+    return (af_buffer_info)handle;
+}
+
+detail::Event &getEventFromBufferInfoHandle(const af_buffer_info handle) {
+    return getEvent(getBufferInfo(handle).event);
+}
+
+af_err af_create_buffer_info(af_buffer_info *handle, void *ptr,
+                             af_event event) {
+    try {
+        BufferInfo *buf = new BufferInfo({ptr, event});
+        *handle         = getHandle(*((BufferInfo *)buf));
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_delete_buffer_info(af_buffer_info handle) {
+    try {
+        /// NB: deleting a memory event buf does frees the associated memory
+        /// and deletes the associated event. Use unlock functions to free
+        /// resources individually
+        BufferInfo &buf = getBufferInfo(handle);
+        af_delete_event(buf.event);
+        if (buf.ptr) { af_free_device(buf.ptr); }
+
+        delete (BufferInfo *)handle;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_buffer_info_get_ptr(void **ptr, af_buffer_info handle) {
+    try {
+        BufferInfo &buf = getBufferInfo(handle);
+        *ptr            = buf.ptr;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_buffer_info_get_event(af_event *event, af_buffer_info handle) {
+    try {
+        BufferInfo &buf = getBufferInfo(handle);
+        *event          = buf.event;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_buffer_info_set_ptr(af_buffer_info handle, void *ptr) {
+    try {
+        BufferInfo &buf = getBufferInfo(handle);
+        buf.ptr         = ptr;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_buffer_info_set_event(af_buffer_info handle, af_event event) {
+    try {
+        BufferInfo &buf = getBufferInfo(handle);
+        buf.event       = event;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_unlock_buffer_info_event(af_event *event, af_buffer_info handle) {
+    try {
+        af_buffer_info_get_event(event, handle);
+        BufferInfo &buf = getBufferInfo(handle);
+        buf.event       = 0;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_unlock_buffer_info_ptr(void **ptr, af_buffer_info handle) {
+    try {
+        af_buffer_info_get_ptr(ptr, handle);
+        BufferInfo &buf = getBufferInfo(handle);
+        buf.ptr         = 0;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/events.cpp b/src/api/c/events.cpp
new file mode 100644
index 0000000000..25bd9cb285
--- /dev/null
+++ b/src/api/c/events.cpp
@@ -0,0 +1,70 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <events.hpp>
+
+#include <Event.hpp>
+#include <common/err_common.hpp>
+#include <af/device.h>
+#include <af/event.h>
+
+using namespace detail;
+
+Event &getEvent(const af_event handle) {
+    Event &event = *(Event *)handle;
+    return event;
+}
+
+af_event getHandle(const Event &event) { return (af_event)&event; }
+
+af_err af_create_event(af_event *handle) {
+    try {
+        AF_CHECK(af_init());
+        *handle = createEvent();
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_delete_event(af_event handle) {
+    try {
+        releaseEvent(handle);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_mark_event(const af_event handle) {
+    try {
+        markEventOnActiveQueue(handle);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_enqueue_wait_event(const af_event handle) {
+    try {
+        enqueueWaitOnActiveQueue(handle);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_block_event(const af_event handle) {
+    try {
+        block(handle);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/events.hpp b/src/api/c/events.hpp
new file mode 100644
index 0000000000..68cd0f5a96
--- /dev/null
+++ b/src/api/c/events.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Event.hpp>
+#include <backend.hpp>
+#include <af/event.h>
+
+af_event getHandle(const detail::Event& event);
+
+detail::Event& getEvent(const af_event eventHandle);
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 04a94099d9..53cda23f7d 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,18 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <memoryapi.hpp>
+
 #include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <handle.hpp>
-#include <memory.hpp>
+#include <memory_manager.hpp>
 #include <platform.hpp>
 #include <af/backend.h>
 #include <af/device.h>
 #include <af/dim4.hpp>
+#include <af/memory.h>
 #include <af/version.h>
-#include <cstring>
+
+#include <utility>
 
 using namespace detail;
 
@@ -93,7 +97,7 @@ af_err af_get_device_ptr(void **data, const af_array arr) {
         af_dtype type = getInfo(arr).getType();
 
         switch (type) {
-                // FIXME: Perform copy if memory not continuous
+            // FIXME: Perform copy if memory not continuous
             case f32: *data = getDevicePtr(getArray<float>(arr)); break;
             case f64: *data = getDevicePtr(getArray<double>(arr)); break;
             case c32: *data = getDevicePtr(getArray<cfloat>(arr)); break;
@@ -283,7 +287,7 @@ af_err af_print_mem_info(const char *msg, const int device_id) {
 
 af_err af_device_gc() {
     try {
-        garbageCollect();
+        signalMemoryCleanup();
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -313,3 +317,459 @@ af_err af_get_mem_step_size(size_t *step_bytes) {
     CATCHALL;
     return AF_SUCCESS;
 }
+
+////////////////////////////////////////////////////////////////////////////////
+// Memory Manager API
+////////////////////////////////////////////////////////////////////////////////
+
+MemoryManager &getMemoryManager(const af_memory_manager handle) {
+    return *(MemoryManager *)handle;
+}
+
+af_memory_manager getHandle(MemoryManager &manager) {
+    MemoryManager *handle;
+    handle = &manager;
+    return (af_memory_manager)handle;
+}
+
+af_err af_create_memory_manager(af_memory_manager *manager) {
+    try {
+        AF_CHECK(af_init());
+        std::unique_ptr<MemoryManager> m(new MemoryManager());
+        *manager = getHandle(*m);
+        m.release();
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_release_memory_manager(af_memory_manager handle) {
+    try {
+        // NB: does NOT reset the internal memory manager to be the default:
+        // af_unset_memory_manager_pinned must be used to fully-reset with a new
+        // AF default memory manager
+        delete (MemoryManager *)handle;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_set_memory_manager(af_memory_manager mgr) {
+    try {
+        std::unique_ptr<MemoryManagerFunctionWrapper> newManager(
+            new MemoryManagerFunctionWrapper(mgr));
+        // Calls shutdown() on the existing memory manager, but does not free
+        // the associated handle, if there is one
+        detail::setMemoryManager(std::move(newManager));
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_unset_memory_manager() {
+    try {
+        detail::resetMemoryManager();
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_set_memory_manager_pinned(af_memory_manager mgr) {
+    try {
+        // NB: does NOT free if a non-default implementation is set as the
+        // current memory manager - the user is responsible for freeing any
+        // controlled memory
+        std::unique_ptr<MemoryManagerFunctionWrapper> newManager(
+            new MemoryManagerFunctionWrapper(mgr));
+
+        // Calls shutdown() on the existing memory manager
+        detail::setMemoryManagerPinned(std::move(newManager));
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_unset_memory_manager_pinned() {
+    try {
+        detail::resetMemoryManagerPinned();
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_get_payload(af_memory_manager handle, void **payload) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        *payload               = manager.payload;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_payload(af_memory_manager handle, void *payload) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.payload        = payload;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Native memory interface wrapper implementations
+
+af_err af_memory_manager_get_active_device_id(af_memory_manager handle,
+                                              int *id) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        *id                    = manager.wrapper->getActiveDeviceId();
+    }
+
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_native_alloc(af_memory_manager handle, void **ptr,
+                                      size_t size) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        *ptr                   = manager.wrapper->nativeAlloc(size);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_native_free(af_memory_manager handle, void *ptr) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.wrapper->nativeFree(ptr);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_get_max_memory_size(af_memory_manager handle,
+                                             size_t *size, int id) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        *size                  = manager.wrapper->getMaxMemorySize(id);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_get_memory_pressure_threshold(af_memory_manager handle,
+                                                       float *value) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.wrapper->getMemoryPressureThreshold();
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_memory_pressure_threshold(af_memory_manager handle,
+                                                       float value) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.wrapper->setMemoryPressureThreshold(value);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Function setters
+
+af_err af_memory_manager_set_initialize_fn(af_memory_manager handle,
+                                           af_memory_manager_initialize_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.initialize_fn  = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_shutdown_fn(af_memory_manager handle,
+                                         af_memory_manager_shutdown_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.shutdown_fn    = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_alloc_fn(af_memory_manager handle,
+                                      af_memory_manager_alloc_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.alloc_fn       = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_allocated_fn(af_memory_manager handle,
+                                          af_memory_manager_allocated_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.allocated_fn   = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_unlock_fn(af_memory_manager handle,
+                                       af_memory_manager_unlock_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.unlock_fn      = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_signal_memory_cleanup_fn(
+    af_memory_manager handle, af_memory_manager_signal_memory_cleanup_fn fn) {
+    try {
+        MemoryManager &manager           = getMemoryManager(handle);
+        manager.signal_memory_cleanup_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_print_info_fn(af_memory_manager handle,
+                                           af_memory_manager_print_info_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.print_info_fn  = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_user_lock_fn(af_memory_manager handle,
+                                          af_memory_manager_user_lock_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.user_lock_fn   = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_user_unlock_fn(
+    af_memory_manager handle, af_memory_manager_user_unlock_fn fn) {
+    try {
+        MemoryManager &manager = getMemoryManager(handle);
+        manager.user_unlock_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_is_user_locked_fn(
+    af_memory_manager handle, af_memory_manager_is_user_locked_fn fn) {
+    try {
+        MemoryManager &manager    = getMemoryManager(handle);
+        manager.is_user_locked_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_get_memory_pressure_fn(
+    af_memory_manager handle, af_memory_manager_get_memory_pressure_fn fn) {
+    try {
+        MemoryManager &manager         = getMemoryManager(handle);
+        manager.get_memory_pressure_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn(
+    af_memory_manager handle,
+    af_memory_manager_jit_tree_exceeds_memory_pressure_fn fn) {
+    try {
+        MemoryManager &manager                      = getMemoryManager(handle);
+        manager.jit_tree_exceeds_memory_pressure_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_add_memory_management_fn(
+    af_memory_manager handle, af_memory_manager_add_memory_management_fn fn) {
+    try {
+        MemoryManager &manager           = getMemoryManager(handle);
+        manager.add_memory_management_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_memory_manager_set_remove_memory_management_fn(
+    af_memory_manager handle,
+    af_memory_manager_remove_memory_management_fn fn) {
+    try {
+        MemoryManager &manager              = getMemoryManager(handle);
+        manager.remove_memory_management_fn = fn;
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Memory Manager wrapper implementations
+
+MemoryManagerFunctionWrapper::MemoryManagerFunctionWrapper(
+    af_memory_manager handle)
+    : handle_(handle) {
+    MemoryManager &manager = getMemoryManager(handle_);
+    manager.wrapper        = this;
+}
+
+MemoryManagerFunctionWrapper::~MemoryManagerFunctionWrapper() {
+    MemoryManager &manager = getMemoryManager(handle_);
+    manager.wrapper        = 0;
+}
+
+void MemoryManagerFunctionWrapper::initialize() {
+    AF_CHECK(getMemoryManager(handle_).initialize_fn(handle_));
+}
+
+void MemoryManagerFunctionWrapper::shutdown() {
+    AF_CHECK(getMemoryManager(handle_).shutdown_fn(handle_));
+}
+
+af_buffer_info MemoryManagerFunctionWrapper::alloc(
+    bool user_lock, const unsigned ndims, dim_t *dims,
+    const unsigned element_size) {
+    af_buffer_info bufferInfo;
+    AF_CHECK(getMemoryManager(handle_).alloc_fn(
+        handle_, &bufferInfo, (int)user_lock, ndims, dims, element_size));
+    return bufferInfo;
+}
+
+size_t MemoryManagerFunctionWrapper::allocated(void *ptr) {
+    size_t out;
+    AF_CHECK(getMemoryManager(handle_).allocated_fn(handle_, &out, ptr));
+    return out;
+}
+
+void MemoryManagerFunctionWrapper::unlock(void *ptr, af_event e,
+                                          bool user_unlock) {
+    AF_CHECK(
+        getMemoryManager(handle_).unlock_fn(handle_, ptr, e, (int)user_unlock));
+}
+
+void MemoryManagerFunctionWrapper::signalMemoryCleanup() {
+    AF_CHECK(getMemoryManager(handle_).signal_memory_cleanup_fn(handle_));
+}
+
+void MemoryManagerFunctionWrapper::printInfo(const char *msg,
+                                             const int device) {
+    AF_CHECK(getMemoryManager(handle_).print_info_fn(
+        handle_, const_cast<char *>(msg), device));
+}
+
+void MemoryManagerFunctionWrapper::userLock(const void *ptr) {
+    AF_CHECK(getMemoryManager(handle_).user_lock_fn(handle_,
+                                                    const_cast<void *>(ptr)));
+}
+
+void MemoryManagerFunctionWrapper::userUnlock(const void *ptr) {
+    AF_CHECK(getMemoryManager(handle_).user_unlock_fn(handle_,
+                                                      const_cast<void *>(ptr)));
+}
+
+bool MemoryManagerFunctionWrapper::isUserLocked(const void *ptr) {
+    int out;
+    AF_CHECK(getMemoryManager(handle_).is_user_locked_fn(
+        handle_, &out, const_cast<void *>(ptr)));
+    return (bool)out;
+}
+
+void MemoryManagerFunctionWrapper::usageInfo(size_t *alloc_bytes,
+                                             size_t *alloc_buffers,
+                                             size_t *lock_bytes,
+                                             size_t *lock_buffers) {
+    // Not implemented in the public memory manager API, but for backward
+    // compatibility reasons, needs to be in the common memory manager interface
+    // so that it can be used with the default memory manager. Called from
+    // deviceMemoryInfo from a backend - throws so as to properly propagate
+    AF_ERROR(
+        "Device memory info/usage info not supported "
+        "for custom memory manager",
+        AF_ERR_NOT_SUPPORTED);
+}
+
+float MemoryManagerFunctionWrapper::getMemoryPressure() {
+    float out;
+    AF_CHECK(getMemoryManager(handle_).get_memory_pressure_fn(handle_, &out));
+    return out;
+}
+
+bool MemoryManagerFunctionWrapper::jitTreeExceedsMemoryPressure(size_t bytes) {
+    int out;
+    AF_CHECK(getMemoryManager(handle_).jit_tree_exceeds_memory_pressure_fn(
+        handle_, &out, bytes));
+    return (bool)out;
+}
+
+size_t MemoryManagerFunctionWrapper::getMemStepSize() {
+    // Not implemented in the public memory manager API, but for backward
+    // compatibility reasons, needs to be in the common memory manager interface
+    // so that it can be used with the default memory manager. Call into the
+    // backend implementation so the exception can be properly propagated
+    AF_ERROR("Memory step size API not implemented for custom memory manager",
+             AF_ERR_NOT_SUPPORTED);
+}
+
+void MemoryManagerFunctionWrapper::setMemStepSize(size_t new_step_size) {
+    // Not implemented in the public memory manager API, but for backward
+    // compatibility reasons, needs to be in the common memory manager interface
+    // so that it can be used with the default memory manager.
+    AF_ERROR("Memory step size API not implemented for custom memory manager ",
+             AF_ERR_NOT_SUPPORTED);
+}
+
+void MemoryManagerFunctionWrapper::addMemoryManagement(int device) {
+    getMemoryManager(handle_).add_memory_management_fn(handle_, device);
+}
+
+void MemoryManagerFunctionWrapper::removeMemoryManagement(int device) {
+    getMemoryManager(handle_).remove_memory_management_fn(handle_, device);
+}
diff --git a/src/api/c/memory_manager.hpp b/src/api/c/memory_manager.hpp
new file mode 100644
index 0000000000..4398f6f4fe
--- /dev/null
+++ b/src/api/c/memory_manager.hpp
@@ -0,0 +1,252 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <backend.hpp>
+#include <common/dispatch.hpp>
+#include <common/err_common.hpp>
+#include <common/util.hpp>
+#include <af/event.h>
+#include <af/memory.h>
+
+#include <algorithm>
+#include <functional>
+#include <iomanip>
+#include <memory>
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
+
+namespace spdlog {
+class logger;
+}
+namespace common {
+using mutex_t      = std::mutex;
+using lock_guard_t = std::lock_guard<mutex_t>;
+
+constexpr unsigned MAX_BUFFERS = 1000;
+constexpr size_t ONE_GB        = 1 << 30;
+
+namespace memory {
+
+/**
+ * An interface that provides backend-specific memory management functions,
+ * typically calling a dedicated backend-specific native API. Stored, wrapped,
+ * and called by a MemoryManagerBase, from which calls to its interface are
+ * delegated.
+ */
+class AllocatorInterface {
+   public:
+    AllocatorInterface() = default;
+    virtual ~AllocatorInterface() {}
+    virtual void shutdown()                       = 0;
+    virtual int getActiveDeviceId()               = 0;
+    virtual size_t getMaxMemorySize(int id)       = 0;
+    virtual void *nativeAlloc(const size_t bytes) = 0;
+    virtual void nativeFree(void *ptr)            = 0;
+    virtual spdlog::logger *getLogger() final { return this->logger.get(); }
+
+   protected:
+    std::shared_ptr<spdlog::logger> logger;
+};
+
+/**
+ * A internal base interface for a memory manager which is exposed to AF
+ * internals. Externally, both the default AF memory manager implementation and
+ * custom memory manager implementations are wrapped in a derived implementation
+ * of this interface.
+ */
+class MemoryManagerBase {
+   public:
+    MemoryManagerBase()        = default;
+    MemoryManagerBase &operator=(const MemoryManagerBase &) = delete;
+    MemoryManagerBase(const MemoryManagerBase &)            = delete;
+    virtual ~MemoryManagerBase() {}
+    // Shuts down the allocator interface which calls shutdown on the subclassed
+    // memory manager with device-specific context
+    virtual void shutdownAllocator() {
+        if (nmi_) nmi_->shutdown();
+    }
+    virtual void initialize()                                              = 0;
+    virtual void shutdown()                                                = 0;
+    virtual af_buffer_info alloc(bool user_lock, const unsigned ndims,
+                                 dim_t *dims, const unsigned element_size) = 0;
+    virtual size_t allocated(void *ptr)                                    = 0;
+    virtual void unlock(void *ptr, af_event e, bool user_unlock)           = 0;
+    virtual void signalMemoryCleanup()                                     = 0;
+    virtual void printInfo(const char *msg, const int device)              = 0;
+    virtual void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                           size_t *lock_bytes, size_t *lock_buffers)       = 0;
+    virtual void userLock(const void *ptr)                                 = 0;
+    virtual void userUnlock(const void *ptr)                               = 0;
+    virtual bool isUserLocked(const void *ptr)                             = 0;
+    virtual size_t getMemStepSize()                                        = 0;
+    virtual void setMemStepSize(size_t new_step_size)                      = 0;
+
+    /// Backend-specific functions
+    // OpenCL
+    virtual void addMemoryManagement(int device)    = 0;
+    virtual void removeMemoryManagement(int device) = 0;
+
+    int getActiveDeviceId() { return nmi_->getActiveDeviceId(); }
+    size_t getMaxMemorySize(int id) { return nmi_->getMaxMemorySize(id); }
+    void *nativeAlloc(const size_t bytes) { return nmi_->nativeAlloc(bytes); }
+    void nativeFree(void *ptr) { nmi_->nativeFree(ptr); }
+    virtual spdlog::logger *getLogger() final { return nmi_->getLogger(); }
+    virtual void setAllocator(std::unique_ptr<AllocatorInterface> nmi) {
+        nmi_ = std::move(nmi);
+    }
+
+    // Memory pressure functions
+    void setMemoryPressureThreshold(float pressure) {
+        memoryPressureThreshold_ = pressure;
+    }
+    float getMemoryPressureThreshold() const {
+        return memoryPressureThreshold_;
+    }
+    virtual float getMemoryPressure()                       = 0;
+    virtual bool jitTreeExceedsMemoryPressure(size_t bytes) = 0;
+
+   private:
+    // A threshold at or above which JIT evaluations will be triggered due to
+    // memory pressure. Settable via a call to setMemoryPressureThreshold
+    float memoryPressureThreshold_{1.0};
+    // A backend-specific memory manager, containing backend-specific
+    // methods that call native memory manipulation functions in a device
+    // API. We need to wrap these since they are opaquely called by the
+    // memory manager.
+    std::unique_ptr<AllocatorInterface> nmi_;
+};
+
+/******************** Default memory manager implementation *******************/
+
+struct locked_info {
+    bool manager_lock;
+    bool user_lock;
+    size_t bytes;
+};
+
+using locked_t    = typename std::unordered_map<void *, memory::locked_info>;
+using locked_iter = typename locked_t::iterator;
+
+using free_t    = std::unordered_map<size_t, std::vector<af_buffer_info>>;
+using free_iter = typename free_t::iterator;
+
+using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
+
+struct memory_info {
+    locked_t locked_map;
+    free_t free_map;
+
+    size_t lock_bytes;
+    size_t lock_buffers;
+    size_t total_bytes;
+    size_t total_buffers;
+    size_t max_bytes;
+
+    memory_info()
+        // Calling getMaxMemorySize() here calls the virtual function
+        // that returns 0 Call it from outside the constructor.
+        : max_bytes(ONE_GB)
+        , total_bytes(0)
+        , total_buffers(0)
+        , lock_bytes(0)
+        , lock_buffers(0) {}
+
+    memory_info(memory_info &other)  = delete;
+    memory_info(memory_info &&other) = default;
+    memory_info &operator=(memory_info &other) = delete;
+    memory_info &operator=(memory_info &&other) = default;
+};
+
+}  // namespace memory
+
+class DefaultMemoryManager final : public memory::MemoryManagerBase {
+    size_t mem_step_size;
+    unsigned max_buffers;
+
+    bool debug_mode;
+
+    memory::memory_info &getCurrentMemoryInfo();
+
+   public:
+    DefaultMemoryManager(int num_devices, unsigned max_buffers, bool debug);
+
+    // Initializes the memory manager
+    virtual void initialize() override;
+
+    // Shuts down the memory manager
+    virtual void shutdown() override;
+
+    // Intended to be used with OpenCL backend, where
+    // users are allowed to add external devices(context, device pair)
+    // to the list of devices automatically detected by the library
+    void addMemoryManagement(int device) override;
+
+    // Intended to be used with OpenCL backend, where
+    // users are allowed to add external devices(context, device pair)
+    // to the list of devices automatically detected by the library
+    void removeMemoryManagement(int device) override;
+
+    void setMaxMemorySize();
+
+    /// Returns a pointer of size at least long
+    ///
+    /// This funciton will return a memory location of at least \p size
+    /// bytes. If there is already a free buffer available, it will use
+    /// that buffer. Otherwise, it will allocate a new buffer using the
+    /// nativeAlloc function.
+    af_buffer_info alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+                         const unsigned element_size) override;
+
+    /// returns the size of the buffer at the pointer allocated by the memory
+    /// manager.
+    size_t allocated(void *ptr) override;
+
+    /// Frees or marks the pointer for deletion during the nex garbage
+    /// collection event
+    void unlock(void *ptr, af_event e, bool user_unlock) override;
+
+    /// Frees all buffers which are not locked by the user or not being
+    /// used.
+    void signalMemoryCleanup() override;
+
+    void printInfo(const char *msg, const int device) override;
+    void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                   size_t *lock_bytes, size_t *lock_buffers);
+    void userLock(const void *ptr) override;
+    void userUnlock(const void *ptr) override;
+    bool isUserLocked(const void *ptr) override;
+    size_t getMemStepSize() override;
+    void setMemStepSize(size_t new_step_size) override;
+    float getMemoryPressure() override;
+    bool jitTreeExceedsMemoryPressure(size_t bytes) override;
+
+   protected:
+    DefaultMemoryManager()                                  = delete;
+    ~DefaultMemoryManager()                                 = default;
+    DefaultMemoryManager(const DefaultMemoryManager &other) = delete;
+    // DefaultMemoryManager(const DefaultMemoryManager &&other) = default;
+    DefaultMemoryManager &operator=(const DefaultMemoryManager &other) = delete;
+    // DefaultMemoryManager &operator=(const DefaultMemoryManager &&other) =
+    //     default;
+    mutex_t memory_mutex;
+    // backend-specific
+    std::vector<common::memory::memory_info> memory;
+    // backend-agnostic
+    void cleanDeviceMemoryManager(int device);
+};
+
+}  // namespace common
diff --git a/src/backend/common/MemoryManagerImpl.hpp b/src/api/c/memory_manager_impl.hpp
similarity index 58%
rename from src/backend/common/MemoryManagerImpl.hpp
rename to src/api/c/memory_manager_impl.hpp
index d274c7d6cb..9ec283ba18 100644
--- a/src/backend/common/MemoryManagerImpl.hpp
+++ b/src/api/c/memory_manager_impl.hpp
@@ -7,12 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Event.hpp>
 #include <common/Logger.hpp>
-#include <common/MemoryManager.hpp>
-
+#include <memory_manager.hpp>
+#include <memoryapi.hpp>
+#include <af/event.h>
+#include <af/memory.h>
 #include <string>
 #include <vector>
 
+using af::buffer_info;
+using af::event;
 using std::max;
 using std::stoi;
 using std::string;
@@ -21,32 +26,20 @@ using std::vector;
 using spdlog::logger;
 
 namespace common {
-template<typename T>
-typename MemoryManager<T>::memory_info &
-MemoryManager<T>::getCurrentMemoryInfo() {
-    return memory[this->getActiveDeviceId()];
-}
-
-template<typename T>
-inline int MemoryManager<T>::getActiveDeviceId() {
-    return static_cast<T *>(this)->getActiveDeviceId();
-}
 
-template<typename T>
-inline size_t MemoryManager<T>::getMaxMemorySize(int id) {
-    return static_cast<T *>(this)->getMaxMemorySize(id);
+memory::memory_info &DefaultMemoryManager::getCurrentMemoryInfo() {
+    return memory[this->getActiveDeviceId()];
 }
 
-template<typename T>
-void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
+void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     if (this->debug_mode) return;
 
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
     // the lock is being held because the CPU backend calls sync.
-    vector<MemoryEventPair> free_ptrs;
-    size_t bytes_freed   = 0;
-    memory_info &current = memory[device];
+    vector<af_buffer_info> free_ptrs;
+    size_t bytes_freed           = 0;
+    memory::memory_info &current = memory[device];
     {
         lock_guard_t lock(this->memory_mutex);
         // Return if all buffers are locked
@@ -57,9 +50,7 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
             size_t num_ptrs = kv.second.size();
             // Free memory by pushing the last element into the free_ptrs
             // vector which will be freed once outside of the lock
-            for (auto &p : kv.second) {
-                free_ptrs.emplace_back(MemoryEventPair{p.ptr, std::move(p.e)});
-            }
+            for (auto &pair : kv.second) { free_ptrs.emplace_back(pair); }
             current.total_bytes -= num_ptrs * kv.first;
             bytes_freed += num_ptrs * kv.first;
             current.total_buffers -= num_ptrs;
@@ -70,18 +61,21 @@ void MemoryManager<T>::cleanDeviceMemoryManager(int device) {
     AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(),
              bytesToString(bytes_freed));
     // Free memory outside of the lock
-    for (auto &ptr : free_ptrs) {
-        this->nativeFree(ptr.ptr);
+    for (auto &pair : free_ptrs) {
+        auto *bufferInfo = (BufferInfo *)pair;
+        void *ptr        = bufferInfo->ptr;
+        this->nativeFree(ptr);
+        // Release resources
+        delete (detail::Event *)bufferInfo->event;
+        delete bufferInfo;
     }
 }
 
-template<typename T>
-MemoryManager<T>::MemoryManager(int num_devices, unsigned max_buffers,
-                                bool debug)
+DefaultMemoryManager::DefaultMemoryManager(int num_devices,
+                                           unsigned max_buffers, bool debug)
     : mem_step_size(1024)
     , max_buffers(max_buffers)
     , memory(num_devices)
-    , logger(loggerFactory("mem"))
     , debug_mode(debug) {
     // Check for environment variables
 
@@ -95,8 +89,11 @@ MemoryManager<T>::MemoryManager(int num_devices, unsigned max_buffers,
     if (!env_var.empty()) this->max_buffers = max(1, stoi(env_var));
 }
 
-template<typename T>
-void MemoryManager<T>::addMemoryManagement(int device) {
+void DefaultMemoryManager::initialize() { this->setMaxMemorySize(); }
+
+void DefaultMemoryManager::shutdown() { signalMemoryCleanup(); }
+
+void DefaultMemoryManager::addMemoryManagement(int device) {
     // If there is a memory manager allocated for this device id, we might
     // as well use it and the buffers allocated for it
     if (static_cast<size_t>(device) < memory.size()) return;
@@ -107,18 +104,16 @@ void MemoryManager<T>::addMemoryManagement(int device) {
     memory.resize(memory.size() + device + 1);
 }
 
-template<typename T>
-void MemoryManager<T>::removeMemoryManagement(int device) {
+void DefaultMemoryManager::removeMemoryManagement(int device) {
     if ((size_t)device >= memory.size())
         AF_ERROR("No matching device found", AF_ERR_ARG);
 
-    // Do garbage collection for the device and leave the memory_info struct
-    // from the memory vector intact
+    // Do garbage collection for the device and leave the memory::memory_info
+    // struct from the memory vector intact
     cleanDeviceMemoryManager(device);
 }
 
-template<typename T>
-void MemoryManager<T>::setMaxMemorySize() {
+void DefaultMemoryManager::setMaxMemorySize() {
     for (unsigned n = 0; n < memory.size(); n++) {
         // Calls garbage collection when: total_bytes > memsize * 0.75 when
         // memsize < 4GB total_bytes > memsize - 1 GB when memsize >= 4GB If
@@ -130,85 +125,122 @@ void MemoryManager<T>::setMaxMemorySize() {
     }
 }
 
-template<typename T>
-MemoryEventPair MemoryManager<T>::alloc(const size_t bytes, bool user_lock) {
-    MemoryEventPair ptr = {nullptr, detail::Event()};
+float DefaultMemoryManager::getMemoryPressure() {
+    lock_guard_t lock(this->memory_mutex);
+    memory::memory_info &current = this->getCurrentMemoryInfo();
+    if (current.lock_bytes > current.max_bytes ||
+        current.lock_buffers > max_buffers) {
+        return 1.0;
+    } else {
+        return 0.0;
+    }
+}
+
+bool DefaultMemoryManager::jitTreeExceedsMemoryPressure(size_t bytes) {
+    lock_guard_t lock(this->memory_mutex);
+    memory::memory_info &current = this->getCurrentMemoryInfo();
+    return 2 * bytes > current.lock_bytes;
+}
+
+af_buffer_info DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
+                                           dim_t *dims,
+                                           const unsigned element_size) {
+    size_t bytes = element_size;
+    for (unsigned i = 0; i < ndims; ++i) { bytes *= dims[i]; }
+
+    auto *event        = new detail::Event();
+    auto *bufferInfo   = new BufferInfo();
+    bufferInfo->ptr    = nullptr;
+    bufferInfo->event  = getHandle(*event);
     size_t alloc_bytes = this->debug_mode
                              ? bytes
                              : (divup(bytes, mem_step_size) * mem_step_size);
 
     if (bytes > 0) {
-        memory_info &current = this->getCurrentMemoryInfo();
-        locked_info info     = {!user_lock, user_lock, alloc_bytes};
+        memory::memory_info &current = this->getCurrentMemoryInfo();
+        memory::locked_info info     = {!user_lock, user_lock, alloc_bytes};
 
         // There is no memory cache in debug mode
         if (!this->debug_mode) {
             // FIXME: Add better checks for garbage collection
             // Perhaps look at total memory available as a metric
-            if (this->checkMemoryLimit()) { this->garbageCollect(); }
+            if (getMemoryPressure() > getMemoryPressureThreshold()) {
+                this->signalMemoryCleanup();
+            }
 
             lock_guard_t lock(this->memory_mutex);
-            free_iter iter = current.free_map.find(alloc_bytes);
+            memory::free_iter iter = current.free_map.find(alloc_bytes);
 
             if (iter != current.free_map.end() && !iter->second.empty()) {
-                ptr = std::move(iter->second.back());
+                // Delete existing buffer info and underlying event
+                delete event;
+                delete bufferInfo;
+                // Set to existing in from free map
+                bufferInfo = (BufferInfo *)iter->second.back();
+                event      = (detail::Event *)bufferInfo->event;
                 iter->second.pop_back();
-                current.locked_map[ptr.ptr] = info;
+                void *ptrM               = bufferInfo->ptr;
+                current.locked_map[ptrM] = info;
                 current.lock_bytes += alloc_bytes;
                 current.lock_buffers++;
             }
         }
 
+        void *ptr = bufferInfo->ptr;
         // Only comes here if buffer size not found or in debug mode
-        if (ptr.ptr == nullptr) {
+        if (ptr == nullptr) {
             // Perform garbage collection if memory can not be allocated
             try {
-                ptr.ptr = this->nativeAlloc(alloc_bytes);
+                ptr             = this->nativeAlloc(alloc_bytes);
+                bufferInfo->ptr = ptr;
             } catch (const AfError &ex) {
                 // If out of memory, run garbage collect and try again
                 if (ex.getError() != AF_ERR_NO_MEM) throw;
-                this->garbageCollect();
-                ptr.ptr = this->nativeAlloc(alloc_bytes);
+                this->signalMemoryCleanup();
+                ptr             = this->nativeAlloc(alloc_bytes);
+                bufferInfo->ptr = ptr;
             }
-
             lock_guard_t lock(this->memory_mutex);
             // Increment these two only when it succeeds to come here.
             current.total_bytes += alloc_bytes;
             current.total_buffers += 1;
-            current.locked_map[ptr.ptr] = info;
+            current.locked_map[ptr] = info;
             current.lock_bytes += alloc_bytes;
             current.lock_buffers++;
         }
     }
-    return ptr;
+    return (af_buffer_info)bufferInfo;
 }
 
-template<typename T>
-size_t MemoryManager<T>::allocated(void *ptr) {
+size_t DefaultMemoryManager::allocated(void *ptr) {
     if (!ptr) return 0;
-    memory_info &current = this->getCurrentMemoryInfo();
-    locked_iter iter     = current.locked_map.find((void *)ptr);
+    memory::memory_info &current = this->getCurrentMemoryInfo();
+    memory::locked_iter iter     = current.locked_map.find((void *)ptr);
     if (iter == current.locked_map.end()) return 0;
     return (iter->second).bytes;
 }
 
-template<typename T>
-void MemoryManager<T>::unlock(void *ptr, detail::Event &&e, bool user_unlock) {
+void DefaultMemoryManager::unlock(void *ptr, af_event eventHandle,
+                                  bool user_unlock) {
     // Shortcut for empty arrays
-    if (!ptr) return;
+    if (!ptr) {
+        delete (detail::Event *)eventHandle;
+        return;
+    }
 
     // Frees the pointer outside the lock.
-    uptr_t freed_ptr(nullptr, [this](void *p) { this->nativeFree(p); });
+    memory::uptr_t freed_ptr(nullptr, [this](void *p) { this->nativeFree(p); });
     {
         lock_guard_t lock(this->memory_mutex);
-        memory_info &current = this->getCurrentMemoryInfo();
+        memory::memory_info &current = this->getCurrentMemoryInfo();
 
-        locked_iter iter = current.locked_map.find((void *)ptr);
+        memory::locked_iter iter = current.locked_map.find((void *)ptr);
 
         // Pointer not found in locked map
         if (iter == current.locked_map.end()) {
             // Probably came from user, just free it
             freed_ptr.reset(ptr);
+            delete (detail::Event *)eventHandle;
             return;
         }
 
@@ -220,6 +252,7 @@ void MemoryManager<T>::unlock(void *ptr, detail::Event &&e, bool user_unlock) {
 
         // Return early if either one is locked
         if ((iter->second).user_lock || (iter->second).manager_lock) {
+            delete (detail::Event *)eventHandle;
             return;
         }
 
@@ -234,21 +267,23 @@ void MemoryManager<T>::unlock(void *ptr, detail::Event &&e, bool user_unlock) {
                 current.total_buffers--;
                 current.total_bytes -= iter->second.bytes;
             }
+            delete (detail::Event *)eventHandle;
         } else {
-            current.free_map[bytes].emplace_back(MemoryEventPair{ptr, std::move(e)});
+            auto *info  = new BufferInfo();
+            info->ptr   = ptr;
+            info->event = eventHandle;
+            current.free_map[bytes].emplace_back((af_buffer_info)info);
         }
         current.locked_map.erase(iter);
     }
 }
 
-template<typename T>
-void MemoryManager<T>::garbageCollect() {
+void DefaultMemoryManager::signalMemoryCleanup() {
     cleanDeviceMemoryManager(this->getActiveDeviceId());
 }
 
-template<typename T>
-void MemoryManager<T>::printInfo(const char *msg, const int device) {
-    const memory_info &current = memory[device];
+void DefaultMemoryManager::printInfo(const char *msg, const int device) {
+    const memory::memory_info &current = this->getCurrentMemoryInfo();
 
     printf("%s\n", msg);
     printf(
@@ -287,8 +322,9 @@ void MemoryManager<T>::printInfo(const char *msg, const int device) {
             unit = "MB";
         }
 
-        for (auto &ptr : kv.second) {
-            printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr.ptr, size, unit,
+        for (auto &pair : kv.second) {
+            void *ptr = ((BufferInfo *)pair)->ptr;
+            printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr, size, unit,
                    status_mngr, status_user);
         }
     }
@@ -296,10 +332,9 @@ void MemoryManager<T>::printInfo(const char *msg, const int device) {
     printf("---------------------------------------------------------\n");
 }
 
-template<typename T>
-void MemoryManager<T>::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                                  size_t *lock_bytes, size_t *lock_buffers) {
-    const memory_info &current = this->getCurrentMemoryInfo();
+void DefaultMemoryManager::usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                                     size_t *lock_bytes, size_t *lock_buffers) {
+    const memory::memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
     if (alloc_bytes) *alloc_bytes = current.total_bytes;
     if (alloc_buffers) *alloc_buffers = current.total_buffers;
@@ -307,33 +342,31 @@ void MemoryManager<T>::bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
     if (lock_buffers) *lock_buffers = current.lock_buffers;
 }
 
-template<typename T>
-void MemoryManager<T>::userLock(const void *ptr) {
-    memory_info &current = this->getCurrentMemoryInfo();
+void DefaultMemoryManager::userLock(const void *ptr) {
+    memory::memory_info &current = this->getCurrentMemoryInfo();
 
     lock_guard_t lock(this->memory_mutex);
 
-    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    memory::locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
     if (iter != current.locked_map.end()) {
         iter->second.user_lock = true;
     } else {
-        locked_info info = {false, true, 100};  // This number is not relevant
+        memory::locked_info info = {false, true,
+                                    100};  // This number is not relevant
 
         current.locked_map[(void *)ptr] = info;
     }
 }
 
-template<typename T>
-void MemoryManager<T>::userUnlock(const void *ptr) {
-    detail::Event e;
-    this->unlock(const_cast<void *>(ptr), std::move(e), true);
+void DefaultMemoryManager::userUnlock(const void *ptr) {
+    auto *e = new detail::Event();
+    this->unlock(const_cast<void *>(ptr), getHandle(*e), true);
 }
 
-template<typename T>
-bool MemoryManager<T>::isUserLocked(const void *ptr) {
-    memory_info &current = this->getCurrentMemoryInfo();
+bool DefaultMemoryManager::isUserLocked(const void *ptr) {
+    memory::memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
-    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    memory::locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
     if (iter != current.locked_map.end()) {
         return iter->second.user_lock;
     } else {
@@ -341,48 +374,14 @@ bool MemoryManager<T>::isUserLocked(const void *ptr) {
     }
 }
 
-template<typename T>
-size_t MemoryManager<T>::getMemStepSize() {
+size_t DefaultMemoryManager::getMemStepSize() {
     lock_guard_t lock(this->memory_mutex);
     return this->mem_step_size;
 }
 
-template<typename T>
-size_t MemoryManager<T>::getMaxBytes() {
-    lock_guard_t lock(this->memory_mutex);
-    return this->getCurrentMemoryInfo().max_bytes;
-}
-
-template<typename T>
-unsigned MemoryManager<T>::getMaxBuffers() {
-    return this->max_buffers;
-}
-
-template<typename T>
-logger *MemoryManager<T>::getLogger() {
-    return this->logger.get();
-}
-
-template<typename T>
-void MemoryManager<T>::setMemStepSize(size_t new_step_size) {
+void DefaultMemoryManager::setMemStepSize(size_t new_step_size) {
     lock_guard_t lock(this->memory_mutex);
     this->mem_step_size = new_step_size;
 }
 
-template<typename T>
-inline void *MemoryManager<T>::nativeAlloc(const size_t bytes) {
-    return static_cast<T *>(this)->nativeAlloc(bytes);
-}
-
-template<typename T>
-inline void MemoryManager<T>::nativeFree(void *ptr) {
-    static_cast<T *>(this)->nativeFree(ptr);
-}
-
-template<typename T>
-bool MemoryManager<T>::checkMemoryLimit() {
-    const memory_info &current = this->getCurrentMemoryInfo();
-    return current.lock_bytes >= current.max_bytes ||
-           current.total_buffers >= this->max_buffers;
-}
 }  // namespace common
diff --git a/src/api/c/memoryapi.hpp b/src/api/c/memoryapi.hpp
new file mode 100644
index 0000000000..1453e2e148
--- /dev/null
+++ b/src/api/c/memoryapi.hpp
@@ -0,0 +1,97 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <memory_manager.hpp>
+
+#include <events.hpp>
+#include <af/event.h>
+#include <af/memory.h>
+
+////////////////////////////////////////////////////////////////////////////////
+// Buffer Info
+////////////////////////////////////////////////////////////////////////////////
+
+struct BufferInfo {
+    void *ptr;
+    af_event event;
+};
+
+BufferInfo &getBufferInfo(const af_buffer_info pair);
+
+af_buffer_info getHandle(BufferInfo &pairHandle);
+
+detail::Event &getEventFromBufferInfoHandle(const af_buffer_info handle);
+
+////////////////////////////////////////////////////////////////////////////////
+// Memory Manager API
+////////////////////////////////////////////////////////////////////////////////
+
+/**
+ * An internal wrapper around an af_memory_manager which calls function pointers
+ * on a af_memory_manager via calls to a MemoryManagerBase
+ */
+class MemoryManagerFunctionWrapper final : public common::memory::MemoryManagerBase {
+    af_memory_manager handle_;
+
+   public:
+    MemoryManagerFunctionWrapper(af_memory_manager handle);
+    ~MemoryManagerFunctionWrapper();
+    void initialize() override;
+    void shutdown() override;
+    af_buffer_info alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+                         const unsigned element_size) override;
+    size_t allocated(void *ptr) override;
+    void unlock(void *ptr, af_event e, bool user_unlock) override;
+    void signalMemoryCleanup() override;
+    void printInfo(const char *msg, const int device) override;
+    void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                   size_t *lock_bytes, size_t *lock_buffers) override;
+    void userLock(const void *ptr) override;
+    void userUnlock(const void *ptr) override;
+    bool isUserLocked(const void *ptr) override;
+    size_t getMemStepSize() override;
+    void setMemStepSize(size_t new_step_size) override;
+    float getMemoryPressure() override;
+    bool jitTreeExceedsMemoryPressure(size_t bytes) override;
+
+    void addMemoryManagement(int device) override;
+    void removeMemoryManagement(int device) override;
+};
+
+struct MemoryManager {
+    // Callbacks from public API
+    af_memory_manager_initialize_fn initialize_fn;
+    af_memory_manager_shutdown_fn shutdown_fn;
+    af_memory_manager_alloc_fn alloc_fn;
+    af_memory_manager_allocated_fn allocated_fn;
+    af_memory_manager_unlock_fn unlock_fn;
+    af_memory_manager_print_info_fn print_info_fn;
+    af_memory_manager_user_lock_fn user_lock_fn;
+    af_memory_manager_user_unlock_fn user_unlock_fn;
+    af_memory_manager_is_user_locked_fn is_user_locked_fn;
+    af_memory_manager_get_memory_pressure_fn get_memory_pressure_fn;
+    af_memory_manager_signal_memory_cleanup_fn signal_memory_cleanup_fn;
+    af_memory_manager_add_memory_management_fn add_memory_management_fn;
+    af_memory_manager_remove_memory_management_fn remove_memory_management_fn;
+    af_memory_manager_jit_tree_exceeds_memory_pressure_fn
+        jit_tree_exceeds_memory_pressure_fn;
+    // A generic payload on which data can be stored on the af_memory_manager
+    // and is accessible from the handle
+    void *payload;
+    // A pointer to the MemoryManagerFunctionWrapper wrapping this struct that
+    // facilitates calling native memory functions directly from the handle. The
+    // lifetime of the wrapper is controlled by the relevant device manager
+    MemoryManagerFunctionWrapper *wrapper;
+};
+
+MemoryManager &getMemoryManager(const af_memory_manager manager);
+
+af_memory_manager getHandle(MemoryManager &manager);
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 27b43dd1b5..8aaa67295a 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -11,6 +11,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/buffer_info.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/canny.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/clamp.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/colorspace.cpp
@@ -23,6 +24,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/diff.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dog.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/event.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/exampleFunction.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/exception.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index feed340c40..b440a1d37e 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -25,8 +25,8 @@
 
 #ifdef AF_CUDA
 // NOTE: Adding ifdef here to avoid copying code constructor in the cuda backend
-#include <traits.hpp>
 #include <cuda_fp16.h>
+#include <traits.hpp>
 #endif
 
 #include <memory>
@@ -160,7 +160,7 @@ array::array() : arr(nullptr) { initEmptyArray(&arr, f32, 0, 1, 1, 1); }
 
 array::array(array &&other) noexcept : arr(other.arr) { other.arr = 0; }
 
-array &array::operator=(array &&other)  noexcept {
+array &array::operator=(array &&other) noexcept {
     af_release_array(arr);
     arr       = other.arr;
     other.arr = 0;
diff --git a/src/api/cpp/buffer_info.cpp b/src/api/cpp/buffer_info.cpp
new file mode 100644
index 0000000000..2d2e7f9f64
--- /dev/null
+++ b/src/api/cpp/buffer_info.cpp
@@ -0,0 +1,74 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/err_common.hpp>
+#include <af/event.h>
+#include <af/memory.h>
+
+namespace af {
+
+buffer_info::buffer_info(void* ptr, af_event event) {
+    AF_CHECK(af_create_buffer_info(&p_, ptr, event));
+}
+
+buffer_info::buffer_info(af_buffer_info p) : p_(p) {}
+
+buffer_info::buffer_info(buffer_info&& other) : p_(other.p_) { other.p_ = 0; }
+
+buffer_info& buffer_info::operator=(buffer_info&& other) {
+    af_delete_buffer_info(this->p_);
+    this->p_ = other.p_;
+    other.p_ = 0;
+    return *this;
+}
+
+buffer_info::~buffer_info() {
+    // No throw dtor
+    af_delete_buffer_info(p_);
+}
+
+void* buffer_info::getPtr() const {
+    void* ptr;
+    AF_CHECK(af_buffer_info_get_ptr(&ptr, p_));
+    return ptr;
+}
+
+af_event buffer_info::getEvent() const {
+    af_event e;
+    AF_CHECK(af_buffer_info_get_event(&e, p_));
+    return e;
+}
+
+void buffer_info::setPtr(void* ptr) {
+    AF_CHECK(af_buffer_info_set_ptr(p_, ptr));
+}
+
+void buffer_info::setEvent(af_event event) {
+    AF_CHECK(af_buffer_info_set_event(p_, event));
+}
+
+af_event buffer_info::unlockEvent() {
+    af_event event;
+    AF_CHECK(af_unlock_buffer_info_event(&event, p_));
+    // Zero out the event
+    AF_CHECK(af_buffer_info_set_event(p_, 0));
+    return event;
+}
+
+void* buffer_info::unlockPtr() {
+    void* ptr;
+    AF_CHECK(af_unlock_buffer_info_ptr(&ptr, p_));
+    // Zero out the ptr
+    AF_CHECK(af_buffer_info_set_ptr(p_, 0));
+    return ptr;
+}
+
+af_buffer_info buffer_info::get() const { return p_; }
+
+}  // namespace af
diff --git a/src/api/cpp/event.cpp b/src/api/cpp/event.cpp
new file mode 100644
index 0000000000..b032d324d5
--- /dev/null
+++ b/src/api/cpp/event.cpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/event.h>
+#include "error.hpp"
+
+namespace af {
+
+event::event() { AF_THROW(af_create_event(&e_)); }
+
+event::event(af_event e) : e_(e) {}
+
+event::~event() {
+    // No dtor throw
+    af_delete_event(e_);
+}
+
+event::event(event&& other) : e_(other.e_) { other.e_ = 0; }
+
+event& event::operator=(event&& other) {
+    af_delete_event(this->e_);
+    this->e_ = other.e_;
+    other.e_ = 0;
+    return *this;
+}
+
+af_event event::get() const { return e_; }
+
+void event::mark() { AF_THROW(af_mark_event(e_)); }
+
+void event::enqueue() { AF_THROW(af_enqueue_wait_event(e_)); }
+
+void event::block() const { AF_THROW(af_block_event(e_)); }
+
+}  // namespace af
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index ef133a7da8..a931558e81 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -13,12 +13,14 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/event.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/features.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/graphics.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/image.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ml.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
diff --git a/src/api/unified/event.cpp b/src/api/unified/event.cpp
new file mode 100644
index 0000000000..4439df362d
--- /dev/null
+++ b/src/api/unified/event.cpp
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/event.h>
+#include "symbol_manager.hpp"
+
+af_err af_create_event(af_event* eventHandle) { return CALL(eventHandle); }
+
+af_err af_delete_event(af_event eventHandle) {
+    return CALL(eventHandle);
+}
+
+af_err af_mark_event(const af_event eventHandle) { return CALL(eventHandle); }
+
+af_err af_enqueue_wait_event(const af_event eventHandle) {
+    return CALL(eventHandle);
+}
+
+af_err af_block_event(const af_event eventHandle) { return CALL(eventHandle); }
diff --git a/src/api/unified/memory.cpp b/src/api/unified/memory.cpp
new file mode 100644
index 0000000000..6f47a461d9
--- /dev/null
+++ b/src/api/unified/memory.cpp
@@ -0,0 +1,165 @@
+/*******************************************************
+ * Copyright (c) 2015, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/memory.h>
+#include "symbol_manager.hpp"
+
+af_err af_create_buffer_info(af_buffer_info* pair, void* ptr, af_event event) {
+    return CALL(pair, ptr, event);
+}
+
+af_err af_delete_buffer_info(af_buffer_info pair) { return CALL(pair); }
+
+af_err af_buffer_info_get_ptr(void** ptr, af_buffer_info pair) {
+    return CALL(ptr, pair);
+}
+
+af_err af_buffer_info_get_event(af_event* event, af_buffer_info pair) {
+    return CALL(event, pair);
+}
+
+af_err af_buffer_info_set_ptr(af_buffer_info pair, void* ptr) {
+    return CALL(pair, ptr);
+}
+
+af_err af_buffer_info_set_event(af_buffer_info pair, af_event event) {
+    return CALL(pair, event);
+}
+
+af_err af_unlock_buffer_info_event(af_event* event, af_buffer_info buf) {
+    return CALL(event, buf);
+}
+
+af_err af_unlock_buffer_info_ptr(void** ptr, af_buffer_info buf) {
+    return CALL(ptr, buf);
+}
+
+af_err af_create_memory_manager(af_memory_manager* out) { return CALL(out); }
+
+af_err af_release_memory_manager(af_memory_manager handle) {
+    return CALL(handle);
+}
+
+af_err af_set_memory_manager(af_memory_manager handle) { return CALL(handle); }
+
+af_err af_set_memory_manager_pinned(af_memory_manager handle) {
+    return CALL(handle);
+}
+
+af_err af_unset_memory_manager() { return CALL_NO_PARAMS(); }
+
+af_err af_unset_memory_manager_pinned() { return CALL_NO_PARAMS(); }
+
+af_err af_memory_manager_get_payload(af_memory_manager handle, void** payload) {
+    return CALL(handle, payload);
+}
+
+af_err af_memory_manager_set_payload(af_memory_manager handle, void* payload) {
+    return CALL(handle, payload);
+}
+
+af_err af_memory_manager_set_initialize_fn(af_memory_manager handle,
+                                           af_memory_manager_initialize_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_shutdown_fn(af_memory_manager handle,
+                                         af_memory_manager_shutdown_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_alloc_fn(af_memory_manager handle,
+                                      af_memory_manager_alloc_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_allocated_fn(af_memory_manager handle,
+                                          af_memory_manager_allocated_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_unlock_fn(af_memory_manager handle,
+                                       af_memory_manager_unlock_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_signal_memory_cleanup_fn(
+    af_memory_manager handle, af_memory_manager_signal_memory_cleanup_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_print_info_fn(af_memory_manager handle,
+                                           af_memory_manager_print_info_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_user_lock_fn(af_memory_manager handle,
+                                          af_memory_manager_user_lock_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_user_unlock_fn(
+    af_memory_manager handle, af_memory_manager_user_unlock_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_is_user_locked_fn(
+    af_memory_manager handle, af_memory_manager_is_user_locked_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_get_memory_pressure_fn(
+    af_memory_manager handle, af_memory_manager_get_memory_pressure_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn(
+    af_memory_manager handle,
+    af_memory_manager_jit_tree_exceeds_memory_pressure_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_add_memory_management_fn(
+    af_memory_manager handle, af_memory_manager_add_memory_management_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_set_remove_memory_management_fn(
+    af_memory_manager handle, af_memory_manager_remove_memory_management_fn fn) {
+    return CALL(handle, fn);
+}
+
+af_err af_memory_manager_get_active_device_id(af_memory_manager handle,
+                                              int* id) {
+    return CALL(handle, id);
+}
+
+af_err af_memory_manager_native_alloc(af_memory_manager handle, void** ptr,
+                                      size_t size) {
+    return CALL(handle, ptr, size);
+}
+
+af_err af_memory_manager_native_free(af_memory_manager handle, void* ptr) {
+    return CALL(handle, ptr);
+}
+
+af_err af_memory_manager_get_max_memory_size(af_memory_manager handle,
+                                             size_t* size, int id) {
+    return CALL(handle, size, id);
+}
+
+af_err af_memory_manager_get_memory_pressure_threshold(af_memory_manager handle,
+                                                       float* value) {
+    return CALL(handle, value);
+}
+
+af_err af_memory_manager_set_memory_pressure_threshold(af_memory_manager handle,
+                                                       float value) {
+    return CALL(handle, value);
+}
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 2c96ab097c..93119b4f77 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -29,7 +29,6 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
diff --git a/src/backend/common/EventBase.hpp b/src/backend/common/EventBase.hpp
index 55dfd706f4..786fb3aced 100644
--- a/src/backend/common/EventBase.hpp
+++ b/src/backend/common/EventBase.hpp
@@ -10,6 +10,7 @@
 #include <utility>
 
 namespace common {
+
 template<typename NativeEventPolicy>
 class EventBase {
     using QueueType = typename NativeEventPolicy::QueueType;
@@ -39,7 +40,9 @@ class EventBase {
     }
 
     /// \brief Creates the event object by calling the native create API
-    ErrorType create() noexcept { return NativeEventPolicy::createEvent(&e_); }
+    ErrorType create() noexcept {
+        return NativeEventPolicy::createAndMarkEvent(&e_);
+    }
 
     /// \brief Adds the event on the queue. Once this point on the program
     ///        is executed, the event is marked complete.
@@ -62,9 +65,7 @@ class EventBase {
 
     /// \brief This function will block the calling thread until the event has
     ///        completed
-    ErrorType block() const noexcept {
-        return NativeEventPolicy::syncForEvent();
-    }
+    ErrorType block() noexcept { return NativeEventPolicy::syncForEvent(&e_); }
 
     /// \brief Returns true if the event is a valid event.
     constexpr operator bool() const { return e_; }
diff --git a/src/backend/common/MemoryManager.hpp b/src/backend/common/MemoryManager.hpp
deleted file mode 100644
index 1da2f91f3b..0000000000
--- a/src/backend/common/MemoryManager.hpp
+++ /dev/null
@@ -1,159 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <Event.hpp>
-#include <backend.hpp>
-#include <common/dispatch.hpp>
-#include <common/err_common.hpp>
-#include <common/util.hpp>
-
-#include <algorithm>
-#include <functional>
-#include <iomanip>
-#include <memory>
-#include <mutex>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-namespace spdlog {
-class logger;
-}
-namespace common {
-using mutex_t      = std::mutex;
-using lock_guard_t = std::lock_guard<mutex_t>;
-
-constexpr unsigned MAX_BUFFERS = 1000;
-constexpr size_t ONE_GB        = 1 << 30;
-
-struct MemoryEventPair {
-    void *ptr;
-    detail::Event e;
-    MemoryEventPair(MemoryEventPair &other)  = delete;
-    MemoryEventPair(MemoryEventPair &&other) = default;
-    MemoryEventPair &operator=(MemoryEventPair &&other) = default;
-    MemoryEventPair &operator=(MemoryEventPair &other) = delete;
-};
-
-template<typename T>
-class MemoryManager {
-    struct locked_info {
-        bool manager_lock;
-        bool user_lock;
-        size_t bytes;
-    };
-
-    using locked_t    = typename std::unordered_map<void *, locked_info>;
-    using locked_iter = typename locked_t::iterator;
-
-    using free_t    = std::unordered_map<size_t, std::vector<MemoryEventPair>>;
-    using free_iter = typename free_t::iterator;
-
-    using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
-
-    struct memory_info {
-        locked_t locked_map;
-        free_t free_map;
-
-        size_t lock_bytes;
-        size_t lock_buffers;
-        size_t total_bytes;
-        size_t total_buffers;
-        size_t max_bytes;
-
-        memory_info()
-            // Calling getMaxMemorySize() here calls the virtual function
-            // that returns 0 Call it from outside the constructor.
-            : max_bytes(ONE_GB)
-            , total_bytes(0)
-            , total_buffers(0)
-            , lock_bytes(0)
-            , lock_buffers(0) {}
-
-        memory_info(memory_info &other)  = delete;
-        memory_info(memory_info &&other) = default;
-        memory_info &operator=(memory_info &other) = delete;
-        memory_info &operator=(memory_info &&other) = default;
-    };
-
-    size_t mem_step_size;
-    unsigned max_buffers;
-    std::vector<memory_info> memory;
-    std::shared_ptr<spdlog::logger> logger;
-    bool debug_mode;
-
-    memory_info &getCurrentMemoryInfo();
-
-    inline int getActiveDeviceId();
-    inline size_t getMaxMemorySize(int id);
-    void cleanDeviceMemoryManager(int device);
-
-   public:
-    MemoryManager(int num_devices, unsigned max_buffers, bool debug);
-
-    // Intended to be used with OpenCL backend, where
-    // users are allowed to add external devices(context, device pair)
-    // to the list of devices automatically detected by the library
-    void addMemoryManagement(int device);
-
-    // Intended to be used with OpenCL backend, where
-    // users are allowed to add external devices(context, device pair)
-    // to the list of devices automatically detected by the library
-    void removeMemoryManagement(int device);
-
-    void setMaxMemorySize();
-
-    /// Returns a pointer of size at least long
-    ///
-    /// This funciton will return a memory location of at least \p size
-    /// bytes. If there is already a free buffer available, it will use
-    /// that buffer. Otherwise, it will allocate a new buffer using the
-    /// nativeAlloc function.
-    MemoryEventPair alloc(const size_t size, bool user_lock);
-
-    /// returns the size of the buffer at the pointer allocated by the memory
-    /// manager.
-    size_t allocated(void *ptr);
-
-    /// Frees or marks the pointer for deletion during the next garbage
-    /// collection event
-    void unlock(void *ptr, detail::Event &&e, bool user_unlock);
-
-    /// Frees all buffers which are not locked by the user or not being
-    /// used.
-    void garbageCollect();
-
-    void printInfo(const char *msg, const int device);
-    void bufferInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                    size_t *lock_bytes, size_t *lock_buffers);
-    void userLock(const void *ptr);
-    void userUnlock(const void *ptr);
-    bool isUserLocked(const void *ptr);
-    size_t getMemStepSize();
-    size_t getMaxBytes();
-    unsigned getMaxBuffers();
-    void setMemStepSize(size_t new_step_size);
-    inline void *nativeAlloc(const size_t bytes);
-    inline void nativeFree(void *ptr);
-    bool checkMemoryLimit();
-
-   protected:
-    spdlog::logger *getLogger();
-    MemoryManager()                            = delete;
-    ~MemoryManager()                           = default;
-    MemoryManager(const MemoryManager &other)  = delete;
-    MemoryManager(const MemoryManager &&other) = delete;
-    MemoryManager &operator=(const MemoryManager &other) = delete;
-    MemoryManager &operator=(const MemoryManager &&other) = delete;
-    mutex_t memory_mutex;
-};
-
-}  // namespace common
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index e22c44f6b8..4f9c8f0533 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -13,6 +13,7 @@
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <common/traits.hpp>
 #include <copy.hpp>
@@ -24,7 +25,6 @@
 #include <queue.hpp>
 #include <traits.hpp>
 
-#include <common/half.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/seq.h>
@@ -221,15 +221,12 @@ Array<T> createEmptyArray(const dim4 &dims) {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) return kJITHeuristics::Pass;
-    if (root_node->getHeight() >= (int)getMaxJitSize()) { return kJITHeuristics::TreeHeight; }
-
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    if (root_node->getHeight() >= (int)getMaxJitSize()) {
+        return kJITHeuristics::TreeHeight;
+    }
 
     // Check if approaching the memory limit
-    if (lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers()) {
+    if (getMemoryPressure() >= getMemoryPressureThreshold()) {
         NodeIterator<jit::Node> it(root_node);
         NodeIterator<jit::Node> end_node;
         size_t bytes = accumulate(it, end_node, size_t(0),
@@ -240,7 +237,9 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
                                       return prev + n.getBytes();
                                   });
 
-        if (2 * bytes > lock_bytes) { return kJITHeuristics::MemoryPressure; }
+        if (jitTreeExceedsMemoryPressure(bytes)) {
+            return kJITHeuristics::MemoryPressure;
+        }
     }
     return kJITHeuristics::Pass;
 }
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index d983964656..994b916ee9 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -14,7 +14,6 @@
 #include <jit/Node.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
-#include <queue.hpp>
 
 #include <af/defines.h>
 #include <af/dim4.hpp>
diff --git a/src/backend/cpu/Event.cpp b/src/backend/cpu/Event.cpp
index f462444ec8..e8c62bafd5 100644
--- a/src/backend/cpu/Event.cpp
+++ b/src/backend/cpu/Event.cpp
@@ -8,13 +8,65 @@
  ********************************************************/
 
 #include <Event.hpp>
+
+#include <common/err_common.hpp>
+#include <events.hpp>
+#include <platform.hpp>
 #include <queue.hpp>
+#include <af/event.h>
+
+#include <memory>
 
 namespace cpu {
 /// \brief Creates a new event and marks it in the queue
-Event make_event(cpu::queue &queue) {
+Event makeEvent(cpu::queue& queue) {
     Event e;
     if (0 == e.create()) { e.mark(queue); }
     return e;
 }
+
+af_event createEvent() {
+    std::unique_ptr<Event> e;
+    e.reset(new Event());
+    // Ensure that the default queue is initialized
+    getQueue();
+    if (e->create() != 0) {
+        AF_ERROR("Could not create event", AF_ERR_RUNTIME);
+    }
+    Event& ref = *e.release();
+    return getHandle(ref);
+}
+
+void releaseEvent(af_event eventHandle) { delete (Event*)eventHandle; }
+
+void markEventOnActiveQueue(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active queue
+    if (event.mark(getQueue()) != 0) {
+        AF_ERROR("Could not mark event on active queue", AF_ERR_RUNTIME);
+    }
+}
+
+void enqueueWaitOnActiveQueue(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active queue
+    if (event.enqueueWait(getQueue()) != 0) {
+        AF_ERROR("Could not enqueue wait on active queue for event",
+                 AF_ERR_RUNTIME);
+    }
+}
+
+void block(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    if (event.block() != 0) {
+        AF_ERROR("Could not block on active queue for event", AF_ERR_RUNTIME);
+    }
+}
+
+af_event createAndMarkEvent() {
+    af_event handle = createEvent();
+    markEventOnActiveQueue(handle);
+    return handle;
+}
+
 }  // namespace cpu
diff --git a/src/backend/cpu/Event.hpp b/src/backend/cpu/Event.hpp
index 1ff0f0e678..c97c8af623 100644
--- a/src/backend/cpu/Event.hpp
+++ b/src/backend/cpu/Event.hpp
@@ -8,12 +8,9 @@
  ********************************************************/
 #pragma once
 
-#include <queue.hpp>
-
 #include <common/EventBase.hpp>
-
-#include <atomic>
-#include <thread>
+#include <queue.hpp>
+#include <af/event.h>
 
 namespace cpu {
 
@@ -23,7 +20,9 @@ class CPUEventPolicy {
     using QueueType = queue;
     using ErrorType = int;
 
-    static int createEvent(queue_event *e) noexcept { return e->create(); }
+    static int createAndMarkEvent(queue_event *e) noexcept {
+        return e->create();
+    }
 
     static int markEvent(queue_event *e, cpu::queue &stream) noexcept {
         return e->mark(stream);
@@ -44,6 +43,18 @@ class CPUEventPolicy {
 using Event = common::EventBase<CPUEventPolicy>;
 
 /// \brief Creates a new event and marks it in the queue
-Event make_event(cpu::queue &queue);
+Event makeEvent(cpu::queue &queue);
+
+af_event createEvent();
+
+void releaseEvent(af_event eventHandle);
+
+void markEventOnActiveQueue(af_event eventHandle);
+
+void enqueueWaitOnActiveQueue(af_event eventHandle);
+
+void block(af_event eventHandle);
+
+af_event createAndMarkEvent();
 
 }  // namespace cpu
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
index 5d48fbf03a..b9a1931a74 100644
--- a/src/backend/cpu/device_manager.cpp
+++ b/src/backend/cpu/device_manager.cpp
@@ -9,12 +9,13 @@
 
 #include <common/graphics_common.hpp>
 #include <device_manager.hpp>
-#include <af/version.h>
 #include <memory.hpp>
+#include <af/version.h>
 
 #include <cctype>
 #include <sstream>
 
+using common::memory::MemoryManagerBase;
 using std::string;
 
 #ifdef CPUID_CAPABLE
@@ -119,8 +120,15 @@ namespace cpu {
 
 DeviceManager::DeviceManager()
     : queues(MAX_QUEUES)
-    , memManager(new MemoryManager())
-    , fgMngr(new graphics::ForgeManager()) {}
+    , memManager(new common::DefaultMemoryManager(
+          getDeviceCount(), common::MAX_BUFFERS,
+          AF_MEM_DEBUG || AF_CPU_MEM_DEBUG))
+    , fgMngr(new graphics::ForgeManager()) {
+    // Use the default ArrayFire memory manager
+    std::unique_ptr<cpu::Allocator> deviceMemoryManager(new cpu::Allocator());
+    memManager->setAllocator(std::move(deviceMemoryManager));
+    memManager->initialize();
+}
 
 DeviceManager& DeviceManager::getInstance() {
     static DeviceManager* my_instance = new DeviceManager();
@@ -129,4 +137,41 @@ DeviceManager& DeviceManager::getInstance() {
 
 CPUInfo DeviceManager::getCPUInfo() const { return cinfo; }
 
+void DeviceManager::resetMemoryManager() {
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_CPU_MEM_DEBUG));
+    setMemoryManager(std::move(mgr));
+}
+
+void DeviceManager::setMemoryManager(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a memory manager and the default memory
+    // manager still hasn't been initialized, so initialize it anyways so we
+    // don't inadvertently reset to it when we first call memoryManager()
+    memoryManager();
+    // Calls shutdown() on the existing memory manager
+    if (memManager) { memManager->shutdownAllocator(); }
+    memManager = std::move(newMgr);
+    // Set the backend memory manager for this new manager to register native
+    // functions correctly.
+    std::unique_ptr<cpu::Allocator> deviceMemoryManager(new cpu::Allocator());
+    memManager->setAllocator(std::move(deviceMemoryManager));
+    memManager->initialize();
+}
+
+void DeviceManager::setMemoryManagerPinned(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    AF_ERROR("Using pinned memory with CPU is not supported",
+             AF_ERR_NOT_SUPPORTED);
+}
+
+void DeviceManager::resetMemoryManagerPinned() {
+    // This is a NOOP - we should never set a pinned memory manager in the first
+    // place for the CPU backend, but don't throw in case backend-agnostic
+    // functions that operate on all memory managers need to call this
+}
+
 }  // namespace cpu
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index 0a7d9d7828..6e2415398c 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -9,11 +9,19 @@
 
 #pragma once
 
+#include <memory_manager.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <memory>
+#include <mutex>
 #include <string>
 
+using common::memory::MemoryManagerBase;
+
+#ifndef AF_CPU_MEM_DEBUG
+#define AF_CPU_MEM_DEBUG 0
+#endif
+
 #if defined(AF_WITH_CPUID) &&                                       \
     (defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || \
      defined(_M_IX86) || defined(_WIN64))
@@ -89,16 +97,33 @@ class DeviceManager {
     static const bool IS_DOUBLE_SUPPORTED = true;
 
     // TODO(umar): Half is not supported for BLAS and FFT on x86_64
-    static const bool IS_HALF_SUPPORTED   = true;
+    static const bool IS_HALF_SUPPORTED = true;
 
     static DeviceManager& getInstance();
 
     friend queue& getQueue(int device);
 
-    friend MemoryManager& memoryManager();
+    friend MemoryManagerBase& memoryManager();
+
+    friend void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManager();
+
+    // Pinned memory not supported in CPU
+    friend void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManagerPinned();
+
+    void resetMemoryManagerPinned();
 
     friend graphics::ForgeManager& forgeManager();
 
+    void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void resetMemoryManager();
+
     CPUInfo getCPUInfo() const;
 
    private:
@@ -112,9 +137,10 @@ class DeviceManager {
 
     // Attributes
     std::vector<queue> queues;
-    std::unique_ptr<MemoryManager> memManager;
     std::unique_ptr<graphics::ForgeManager> fgMngr;
     const CPUInfo cinfo;
+    std::unique_ptr<MemoryManagerBase> memManager;
+    std::mutex mutex;
 };
 
 }  // namespace cpu
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index ac3010ecd7..f73ef60f35 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -10,26 +10,18 @@
 #include <memory.hpp>
 
 #include <common/Logger.hpp>
-#include <common/MemoryManagerImpl.hpp>
 #include <common/half.hpp>
 #include <err_cpu.hpp>
+#include <memory_manager_impl.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <spdlog/spdlog.h>
 #include <types.hpp>
+#include <af/dim4.hpp>
 
 #include <utility>
 
-template class common::MemoryManager<cpu::MemoryManager>;
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_CPU_MEM_DEBUG
-#define AF_CPU_MEM_DEBUG 0
-#endif
-
+using af::dim4;
 using common::bytesToString;
 using common::half;
 using std::function;
@@ -37,17 +29,24 @@ using std::move;
 using std::unique_ptr;
 
 namespace cpu {
+float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
+float getMemoryPressureThreshold() {
+    return memoryManager().getMemoryPressureThreshold();
+}
+
+bool jitTreeExceedsMemoryPressure(size_t bytes) {
+    return memoryManager().jitTreeExceedsMemoryPressure(bytes);
+}
+
 void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
 size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
 
-size_t getMaxBytes() { return memoryManager().getMaxBytes(); }
-
-unsigned getMaxBuffers() { return memoryManager().getMaxBuffers(); }
+void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
-void garbageCollect() { memoryManager().garbageCollect(); }
+void shutdownMemoryManager() { memoryManager().shutdown(); }
 
 void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
@@ -55,30 +54,39 @@ void printMemInfo(const char *msg, const int device) {
 
 template<typename T>
 unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements) {
-    T *ptr = nullptr;
-
-    common::MemoryEventPair me = memoryManager().alloc(elements * sizeof(T), false);
-    if(me.e) me.e.enqueueWait(getQueue());
-    ptr = (T *)me.ptr;
-    return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
+    // TODO: make memAlloc aware of array shapes
+    dim4 dims(elements);
+    af_buffer_info pair =
+        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue());
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return unique_ptr<T[], function<void(T *)>>((T *)ptr, memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
-    void *ptr = nullptr;
-    common::MemoryEventPair me = memoryManager().alloc(bytes, true);
-    if (me.e) me.e.enqueueWait(getQueue());
-    return me.ptr;
+    dim4 dims(bytes);
+    af_buffer_info pair = memoryManager().alloc(true, 1, dims.get(), 1);
+    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue());
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return ptr;
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    Event e = make_event(getQueue());
-    return memoryManager().unlock((void *)ptr, move(e), false);
+    return memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
+                                  false);
 }
 
 void memFreeUser(void *ptr) {
-    Event e = make_event(getQueue());
-    memoryManager().unlock((void *)ptr, move(e), true);
+    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), true);
 }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
@@ -91,25 +99,30 @@ void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers) {
-    memoryManager().bufferInfo(alloc_bytes, alloc_buffers, lock_bytes,
-                               lock_buffers);
+    memoryManager().usageInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                              lock_buffers);
 }
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    common::MemoryEventPair me = memoryManager().alloc(elements * sizeof(T), false);
-    if (me.e) me.e.enqueueWait(getQueue());
-    return (T*)me.ptr;
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    af_buffer_info pair =
+        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue());
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return (T *)ptr;
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    Event e = make_event(getQueue());
-    return memoryManager().unlock((void *)ptr, move(e), false);
+    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), false);
 }
 
-bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
-
 #define INSTANTIATE(T)                                                \
     template std::unique_ptr<T[], std::function<void(T *)>> memAlloc( \
         const size_t &elements);                                      \
@@ -131,38 +144,33 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 INSTANTIATE(half)
 
-MemoryManager::MemoryManager()
-    : common::MemoryManager<cpu::MemoryManager>(
-          getDeviceCount(), common::MAX_BUFFERS,
-          AF_MEM_DEBUG || AF_CPU_MEM_DEBUG) {
-    this->setMaxMemorySize();
-}
+Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
-MemoryManager::~MemoryManager() {
+void Allocator::shutdown() {
     for (int n = 0; n < cpu::getDeviceCount(); n++) {
         try {
             cpu::setDevice(n);
-            garbageCollect();
+            shutdownMemoryManager();
         } catch (AfError err) {
             continue;  // Do not throw any errors while shutting down
         }
     }
 }
 
-int MemoryManager::getActiveDeviceId() { return cpu::getActiveDeviceId(); }
+int Allocator::getActiveDeviceId() { return cpu::getActiveDeviceId(); }
 
-size_t MemoryManager::getMaxMemorySize(int id) {
+size_t Allocator::getMaxMemorySize(int id) {
     return cpu::getDeviceMemorySize(id);
 }
 
-void *MemoryManager::nativeAlloc(const size_t bytes) {
+void *Allocator::nativeAlloc(const size_t bytes) {
     void *ptr = malloc(bytes);
     AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
     return ptr;
 }
 
-void MemoryManager::nativeFree(void *ptr) {
+void Allocator::nativeFree(void *ptr) {
     AF_TRACE("nativeFree: {: >8} {}", " ", ptr);
     // Make sure this pointer is not being used on the queue before freeing the
     // memory.
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index af80156b42..2c19ada091 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -8,14 +8,13 @@
  ********************************************************/
 #pragma once
 
-#include <common/MemoryManager.hpp>
+#include <memory_manager.hpp>
 #include <af/defines.h>
 
 #include <functional>
 #include <memory>
 
 namespace cpu {
-
 template<typename T>
 using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
 
@@ -39,27 +38,29 @@ T *pinnedAlloc(const size_t &elements);
 template<typename T>
 void pinnedFree(T *ptr);
 
-size_t getMaxBytes();
-unsigned getMaxBuffers();
-
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers);
-void garbageCollect();
+void signalMemoryCleanup();
+void shutdownMemoryManager();
 void pinnedGarbageCollect();
 
 void printMemInfo(const char *msg, const int device);
 
+float getMemoryPressure();
+float getMemoryPressureThreshold();
+bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
-bool checkMemoryLimit();
 
-class MemoryManager : public common::MemoryManager<cpu::MemoryManager> {
+class Allocator final : public common::memory::AllocatorInterface {
    public:
-    MemoryManager();
-    ~MemoryManager();
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
+    Allocator();
+    ~Allocator() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
 };
+
 }  // namespace cpu
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 264fe2d7ab..2f6f4cd4e9 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -18,6 +18,7 @@
 #include <cctype>
 #include <sstream>
 
+using common::memory::MemoryManagerBase;
 using std::endl;
 using std::not1;
 using std::ostringstream;
@@ -149,11 +150,27 @@ bool& evalFlag() {
     return flag;
 }
 
-MemoryManager& memoryManager() {
+MemoryManagerBase& memoryManager() {
     DeviceManager& inst = DeviceManager::getInstance();
     return *(inst.memManager);
 }
 
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
+}
+
+void resetMemoryManager() {
+    return DeviceManager::getInstance().resetMemoryManager();
+}
+
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManagerPinned(std::move(mgr));
+}
+
+void resetMemoryManagerPinned() {
+    return DeviceManager::getInstance().resetMemoryManagerPinned();
+}
+
 graphics::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 0a3f8b9403..92ade6d3f2 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -16,9 +16,15 @@ namespace graphics {
 class ForgeManager;
 }
 
-namespace cpu {
+namespace common {
+namespace memory {
+class MemoryManagerBase;
+}
+}  // namespace common
 
-class MemoryManager;
+using common::memory::MemoryManagerBase;
+
+namespace cpu {
 
 int getBackend();
 
@@ -48,7 +54,16 @@ void sync(int device);
 
 bool& evalFlag();
 
-MemoryManager& memoryManager();
+MemoryManagerBase& memoryManager();
+
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+void resetMemoryManager();
+
+// Pinned memory not supported
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+void resetMemoryManagerPinned();
 
 graphics::ForgeManager& forgeManager();
 
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 55ee77e429..9290426810 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -10,6 +10,7 @@
 
 #include <Param.hpp>
 #include <common/util.hpp>
+#include <memory.hpp>
 
 #include <algorithm>
 
@@ -48,7 +49,6 @@ using event_impl = threads::event;
 #endif
 
 namespace cpu {
-  bool checkMemoryLimit();
 
 /// Wraps the async_queue class
 class queue {
@@ -59,7 +59,7 @@ class queue {
                      getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
 
     template<typename F, typename... Args>
-    void enqueue(const F func, Args&&... args) {
+    void enqueue(const F func, Args &&... args) {
         count++;
         if (sync_calls) {
             func(toParam(std::forward<Args>(args))...);
@@ -69,7 +69,9 @@ class queue {
 #ifndef NDEBUG
         sync();
 #else
-        if (checkMemoryLimit() || count >= 25) { sync(); }
+        if (getMemoryPressure() > getMemoryPressureThreshold() || count >= 25) {
+            sync();
+        }
 #endif
     }
 
@@ -83,15 +85,17 @@ class queue {
     }
 
     friend class queue_event;
+
    private:
     int count;
     const bool sync_calls;
     queue_impl aQueue;
 };
 
-  class queue_event {
+class queue_event {
     event_impl event_;
-  public:
+
+   public:
     queue_event() = default;
     queue_event(int val) : event_(val) {}
 
@@ -101,5 +105,5 @@ class queue {
     int wait(queue &q) { return event_.wait(q.aQueue); }
     int sync() noexcept { return event_.sync(); }
     operator bool() const noexcept { return event_; }
-  };
+};
 }  // namespace cpu
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index f0912b26f1..abd104359f 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -229,29 +229,25 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= (int)getMaxJitSize()) { return kJITHeuristics::TreeHeight; }
-
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
-
-    bool isBufferLimit =
-        lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
+    if (root_node->getHeight() >= (int)getMaxJitSize()) {
+        return kJITHeuristics::TreeHeight;
+    }
 
     // A lightweight check based on the height of the node. This is an
     // inexpensive operation and does not traverse the JIT tree.
-    if (root_node->getHeight() > 6 || isBufferLimit) {
+    if (root_node->getHeight() > 6 ||
+        getMemoryPressure() > getMemoryPressureThreshold()) {
         // The size of the parameters without any extra arguments from the
         // JIT tree. This includes one output Param object and 4 integers.
         constexpr size_t base_param_size =
             sizeof(Param<T>) + (4 * sizeof(uint));
 
         // extra padding for safety to avoid failure during compilation
-        constexpr size_t jit_padding_size = 256; //@umar dontfix!
+        constexpr size_t jit_padding_size = 256;  //@umar dontfix!
         // This is the maximum size of the params that can be allowed by the
         // CUDA platform.
-        constexpr size_t max_param_size = 4096 - base_param_size - jit_padding_size;
+        constexpr size_t max_param_size =
+            4096 - base_param_size - jit_padding_size;
 
         struct tree_info {
             size_t total_buffer_size;
@@ -285,7 +281,7 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
         if (param_size >= max_param_size) {
             return kJITHeuristics::KernelParameterSize;
         }
-        if (info.total_buffer_size * 2 > lock_bytes) {
+        if (jitTreeExceedsMemoryPressure(info.total_buffer_size)) {
             return kJITHeuristics::MemoryPressure;
         }
     }
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6a0064644b..10ce5acfba 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -48,7 +48,6 @@ cuda_include_directories(
   ${CMAKE_CURRENT_SOURCE_DIR}/cub
   ${ArrayFire_SOURCE_DIR}/src/api/c
   ${ArrayFire_SOURCE_DIR}/src/backend
-
   ${COMMON_INTERFACE_DIRS}
   )
 
@@ -522,6 +521,12 @@ if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
   list(REMOVE_ITEM CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_device_LIBRARY})
 endif()
 
+# Remove cublas_device library which is no longer included with the cuda
+# toolkit. Fixes issues with older CMake versions
+if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
+  list(REMOVE_ITEM CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_device_LIBRARY})
+endif()
+
 target_link_libraries(afcuda
   PRIVATE
     c_api_interface
diff --git a/src/backend/cuda/Event.cpp b/src/backend/cuda/Event.cpp
index 8ae1c6fab1..52a4865b5b 100644
--- a/src/backend/cuda/Event.cpp
+++ b/src/backend/cuda/Event.cpp
@@ -7,14 +7,68 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cuda_runtime_api.h>
 #include <Event.hpp>
 
+#include <common/err_common.hpp>
+#include <cuda_runtime_api.h>
+#include <events.hpp>
+#include <platform.hpp>
+#include <af/event.h>
+
+#include <memory>
+
 namespace cuda {
 /// \brief Creates a new event and marks it in the queue
-Event make_event(cudaStream_t queue) {
+Event makeEvent(cudaStream_t queue) {
     Event e;
     if (e.create() == CUDA_SUCCESS) { e.mark(queue); }
     return e;
 }
+
+af_event createEvent() {
+    // Default CUDA stream needs to be initialized to use the CUDA driver
+    // Ctx
+    getActiveStream();
+    std::unique_ptr<Event> e(new Event());
+    if (e->create() != CUDA_SUCCESS) {
+        AF_ERROR("Could not create event", AF_ERR_RUNTIME);
+    }
+    Event& ref = *e.release();
+    return getHandle(ref);
+}
+
+void releaseEvent(af_event eventHandle) { delete (Event*)eventHandle; }
+
+void markEventOnActiveQueue(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active stream
+    cudaStream_t stream = getActiveStream();
+    if (event.mark(stream) != CUDA_SUCCESS) {
+        AF_ERROR("Could not mark event on active stream", AF_ERR_RUNTIME);
+    }
+}
+
+void enqueueWaitOnActiveQueue(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active stream
+    cudaStream_t stream = getActiveStream();
+    if (event.enqueueWait(stream) != CUDA_SUCCESS) {
+        AF_ERROR("Could not enqueue wait on active stream for event",
+                 AF_ERR_RUNTIME);
+    }
+}
+
+void block(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    if (event.block() != CUDA_SUCCESS) {
+        AF_ERROR("Could not block on active stream for event", AF_ERR_RUNTIME);
+    }
+}
+
+af_event createAndMarkEvent() {
+    af_event handle = createEvent();
+    markEventOnActiveQueue(handle);
+    return handle;
+}
+
 }  // namespace cuda
diff --git a/src/backend/cuda/Event.hpp b/src/backend/cuda/Event.hpp
index be4a0b9551..f2b709ad03 100644
--- a/src/backend/cuda/Event.hpp
+++ b/src/backend/cuda/Event.hpp
@@ -11,6 +11,7 @@
 #include <common/EventBase.hpp>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
+#include <af/event.h>
 
 namespace cuda {
 
@@ -20,21 +21,19 @@ class CUDARuntimeEventPolicy {
     using QueueType = CUstream;
     using ErrorType = CUresult;
 
-    static ErrorType createEvent(CUevent *e) noexcept {
-        // Creating events with the CU_EVENT_BLOCKING_SYNC flag 
+    static ErrorType createAndMarkEvent(CUevent *e) noexcept {
+        // Creating events with the CU_EVENT_BLOCKING_SYNC flag
         // severly impacts the speed if/when creating many arrays
         auto err = cuEventCreate(e, CU_EVENT_DISABLE_TIMING);
         return err;
     }
 
-    static ErrorType markEvent(CUevent *e,
-                               QueueType &stream) noexcept {
+    static ErrorType markEvent(CUevent *e, QueueType &stream) noexcept {
         auto err = cuEventRecord(*e, stream);
         return err;
     }
 
-    static ErrorType waitForEvent(CUevent *e,
-                                  QueueType &stream) noexcept {
+    static ErrorType waitForEvent(CUevent *e, QueueType &stream) noexcept {
         auto err = cuStreamWaitEvent(stream, *e, 0);
         return err;
     }
@@ -52,6 +51,18 @@ class CUDARuntimeEventPolicy {
 using Event = common::EventBase<CUDARuntimeEventPolicy>;
 
 /// \brief Creates a new event and marks it in the stream
-Event make_event(cudaStream_t stream);
+Event makeEvent(cudaStream_t stream);
+
+af_event createEvent();
+
+void releaseEvent(af_event eventHandle);
+
+void markEventOnActiveQueue(af_event eventHandle);
+
+void enqueueWaitOnActiveQueue(af_event eventHandle);
+
+void block(af_event eventHandle);
+
+af_event createAndMarkEvent();
 
 }  // namespace cuda
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cpp
index ec85eae175..55fcdbb415 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cpp
@@ -104,7 +104,7 @@ SharedPlan findPlan(int rank, int *n, int *inembed, int istride, int idist,
 
     // If plan creation fails, clean up the memory we hold on to and try again
     if (res != CUFFT_SUCCESS) {
-        cuda::garbageCollect();
+        cuda::signalMemoryCleanup();
         CUFFT_CHECK(cufftPlanMany(temp, rank, n, inembed, istride, idist,
                                   onembed, ostride, odist, type, batch));
     }
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index b4ccaf4736..a2dd974365 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -16,6 +16,7 @@
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
+#include <cublas_v2.h>  // needed for af/cuda.h
 #include <device_manager.hpp>
 #include <driver.h>
 #include <err_cuda.hpp>
@@ -23,7 +24,6 @@
 #include <platform.hpp>
 #include <spdlog/spdlog.h>
 #include <version.hpp>
-#include <cublas_v2.h> // needed for af/cuda.h
 #include <af/cuda.h>
 #include <af/version.h>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
@@ -52,7 +52,7 @@ using std::stringstream;
 
 namespace cuda {
 
-void findJitDevCompute(pair<int, int>& prop) {
+void findJitDevCompute(pair<int, int> &prop) {
     struct cuNVRTCcompute {
         /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
         int cuda_version;
@@ -62,15 +62,8 @@ void findJitDevCompute(pair<int, int>& prop) {
         int minor;
     };
     static const cuNVRTCcompute Toolkit2Compute[] = {
-        {10010, 7, 5},
-        {10000, 7, 2},
-        {9020,  7, 2},
-        {9010,  7, 2},
-        {9000,  7, 2},
-        {8000,  5, 3},
-        {7050,  5, 3},
-        {7000,  5, 3}
-    };
+        {10010, 7, 5}, {10000, 7, 2}, {9020, 7, 2}, {9010, 7, 2},
+        {9000, 7, 2},  {8000, 5, 3},  {7050, 5, 3}, {7000, 5, 3}};
     int runtime_cuda_ver = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&runtime_cuda_ver));
     auto tkit_max_compute =
@@ -79,7 +72,7 @@ void findJitDevCompute(pair<int, int>& prop) {
                     return runtime_cuda_ver == v.cuda_version;
                 });
     if ((tkit_max_compute == end(Toolkit2Compute)) ||
-        (prop.first  > tkit_max_compute->major &&
+        (prop.first > tkit_max_compute->major &&
          prop.second > tkit_max_compute->minor)) {
         prop = make_pair(tkit_max_compute->major, tkit_max_compute->minor);
     }
@@ -202,6 +195,58 @@ DeviceManager &DeviceManager::getInstance() {
     return *my_instance;
 }
 
+void DeviceManager::setMemoryManager(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a memory manager and the default memory
+    // manager still hasn't been initialized, so initialize it anyways so we
+    // don't inadvertently reset to it when we first call memoryManager()
+    memoryManager();
+    // Calls shutdown() on the existing memory manager.
+    if (memManager) { memManager->shutdownAllocator(); }
+    memManager = std::move(newMgr);
+    // Set the backend memory manager for this new manager to register native
+    // functions correctly.
+    std::unique_ptr<cuda::Allocator> deviceMemoryManager(new cuda::Allocator());
+    memManager->setAllocator(std::move(deviceMemoryManager));
+    memManager->initialize();
+}
+
+void DeviceManager::resetMemoryManager() {
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG));
+    setMemoryManager(std::move(mgr));
+}
+
+void DeviceManager::setMemoryManagerPinned(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a pinned memory manager and the default
+    // memory manager still hasn't been initialized, so initialize it anyways so
+    // we don't inadvertently reset to it when we first call
+    // pinnedMemoryManager()
+    pinnedMemoryManager();
+    // Calls shutdown() on the existing memory manager.
+    if (pinnedMemoryManager) { pinnedMemManager->shutdownAllocator(); }
+    // Set the backend memory manager for this new manager to register native
+    // functions correctly.
+    pinnedMemManager = std::move(newMgr);
+    std::unique_ptr<cuda::AllocatorPinned> deviceMemoryManager(
+        new cuda::AllocatorPinned());
+    pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+    pinnedMemManager->initialize();
+}
+
+void DeviceManager::resetMemoryManagerPinned() {
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG));
+    setMemoryManagerPinned(std::move(mgr));
+}
+
 /// Struct represents the cuda toolkit version and its associated minimum
 /// required driver versions.
 struct ToolkitDriverVersions {
@@ -282,8 +327,7 @@ void debugRuntimeCheck(int runtime_version, int driver_version) {
             "request on the ArrayFire repository to update the "
             "CudaToDriverVersion variable with this version of the CUDA "
             "Toolkit.\n";
-        fprintf(stderr, err_msg,
-                int_version_to_string(driver_version).c_str());
+        fprintf(stderr, err_msg, int_version_to_string(driver_version).c_str());
     }
 #endif
 }
@@ -352,8 +396,7 @@ DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , cuDevices(0)
     , nDevices(0)
-    , fgMngr(new graphics::ForgeManager())
-    {
+    , fgMngr(new graphics::ForgeManager()) {
     checkCudaVsDriverVersion();
 
     CUDA_CHECK(cudaGetDeviceCount(&nDevices));
@@ -394,8 +437,8 @@ DeviceManager::DeviceManager()
     for (size_t i = 0; i < MAX_DEVICES; i++) {
         streams[i] = (cudaStream_t)0;
         if (i < nDevices) {
-            auto prop = make_pair(cuDevices[i].prop.major,
-                    cuDevices[i].prop.minor);
+            auto prop =
+                make_pair(cuDevices[i].prop.major, cuDevices[i].prop.minor);
             findJitDevCompute(prop);
             devJitComputes.emplace_back(prop);
         }
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index c2c73c89b1..e635a187ec 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -9,13 +9,21 @@
 
 #pragma once
 
+#include <memory_manager.hpp>
 #include <platform.hpp>
 
 #include <memory>
+#include <mutex>
 #include <string>
 #include <utility>
 #include <vector>
 
+using common::memory::MemoryManagerBase;
+
+#ifndef AF_CUDA_MEM_DEBUG
+#define AF_CUDA_MEM_DEBUG 0
+#endif
+
 namespace cuda {
 
 struct cudaDevice_t {
@@ -37,9 +45,25 @@ class DeviceManager {
 
     spdlog::logger* getLogger();
 
-    friend MemoryManager& memoryManager();
+    friend MemoryManagerBase& memoryManager();
+
+    friend void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManager();
+
+    void resetMemoryManager();
 
-    friend MemoryManagerPinned& pinnedMemoryManager();
+    friend MemoryManagerBase& pinnedMemoryManager();
+
+    friend void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManagerPinned();
+
+    void resetMemoryManagerPinned();
 
     friend graphics::ForgeManager& forgeManager();
 
@@ -97,11 +121,13 @@ class DeviceManager {
 
     std::unique_ptr<graphics::ForgeManager> fgMngr;
 
-    std::unique_ptr<MemoryManager> memManager;
+    std::unique_ptr<MemoryManagerBase> memManager;
 
-    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+    std::unique_ptr<MemoryManagerBase> pinnedMemManager;
 
     std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+
+    std::mutex mutex;
 };
 
 }  // namespace cuda
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 6da993bca8..c6cf5435cf 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -11,7 +11,6 @@
 
 #include <Event.hpp>
 #include <common/Logger.hpp>
-#include <common/MemoryManagerImpl.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/util.hpp>
@@ -19,41 +18,42 @@
 #include <cuda_runtime.h>
 #include <cuda_runtime_api.h>
 #include <err_cuda.hpp>
+#include <memory_manager_impl.hpp>
 #include <platform.hpp>
 #include <spdlog/spdlog.h>
 #include <types.hpp>
+#include <af/dim4.hpp>
 
 #include <mutex>
+#include <utility>
 
-template class common::MemoryManager<cuda::MemoryManager>;
-template class common::MemoryManager<cuda::MemoryManagerPinned>;
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_CUDA_MEM_DEBUG
-#define AF_CUDA_MEM_DEBUG 0
-#endif
-
+using af::dim4;
 using common::bytesToString;
-using common::MemoryEventPair;
 using common::half;
 
 using std::move;
 
 namespace cuda {
+float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
+float getMemoryPressureThreshold() {
+    return memoryManager().getMemoryPressureThreshold();
+}
+
+bool jitTreeExceedsMemoryPressure(size_t bytes) {
+    return memoryManager().jitTreeExceedsMemoryPressure(bytes);
+}
+
 void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
 size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
 
-size_t getMaxBytes() { return memoryManager().getMaxBytes(); }
+void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
-unsigned getMaxBuffers() { return memoryManager().getMaxBuffers(); }
+void shutdownMemoryManager() { memoryManager().shutdown(); }
 
-void garbageCollect() { memoryManager().garbageCollect(); }
+void shutdownPinnedMemoryManager() { pinnedMemoryManager().shutdown(); }
 
 void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
@@ -61,29 +61,41 @@ void printMemInfo(const char *msg, const int device) {
 
 template<typename T>
 uptr<T> memAlloc(const size_t &elements) {
-    size_t size                = elements * sizeof(T);
-    MemoryEventPair me = memoryManager().alloc(size, false);
-    cudaStream_t stream        = getActiveStream();
-    if (me.e) me.e.enqueueWait(stream);
-    return uptr<T>(static_cast<T *>(me.ptr), memFree<T>);
+    // TODO: make memAlloc aware of array shapes
+    dim4 dims(elements);
+    size_t size = elements * sizeof(T);
+    af_buffer_info pair =
+        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
+    cudaStream_t stream = getActiveStream();
+    if (e) e.enqueueWait(stream);
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return uptr<T>(static_cast<T *>(ptr), memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
-    MemoryEventPair me = memoryManager().alloc(bytes, true);
-    cudaStream_t stream        = getActiveStream();
-    if (me.e) me.e.enqueueWait(stream);
-    return me.ptr;
+    dim4 dims(bytes);
+    af_buffer_info pair = memoryManager().alloc(true, 1, dims.get(), 1);
+    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
+    cudaStream_t stream = getActiveStream();
+    if (e) e.enqueueWait(stream);
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return ptr;
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    Event e = make_event(getActiveStream());
-    memoryManager().unlock((void *)ptr, move(e), false);
+    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), false);
 }
 
 void memFreeUser(void *ptr) {
-    Event e = make_event(getActiveStream());
-    memoryManager().unlock((void *)ptr, move(e), true);
+    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), true);
 }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
@@ -96,27 +108,32 @@ bool isLocked(const void *ptr) {
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers) {
-    memoryManager().bufferInfo(alloc_bytes, alloc_buffers, lock_bytes,
-                               lock_buffers);
+    memoryManager().usageInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                              lock_buffers);
 }
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    MemoryEventPair me =
-        pinnedMemoryManager().alloc(elements * sizeof(T), false);
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    af_buffer_info pair =
+        pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
     cudaStream_t stream = getActiveStream();
-    if (me.e) me.e.enqueueWait(stream);
-    return (T *)me.ptr;
+    if (e) e.enqueueWait(stream);
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return (T *)ptr;
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    Event e = make_event(getActiveStream());
-    return pinnedMemoryManager().unlock((void *)ptr, move(e), false);
+    pinnedMemoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
+                                 false);
 }
 
-bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
-
 #define INSTANTIATE(T)                                 \
     template uptr<T> memAlloc(const size_t &elements); \
     template void memFree(T *ptr);                     \
@@ -137,68 +154,59 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 
-MemoryManager::MemoryManager()
-    : common::MemoryManager<cuda::MemoryManager>(
-          getDeviceCount(), common::MAX_BUFFERS,
-          AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG) {
-    this->setMaxMemorySize();
-}
+Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
-MemoryManager::~MemoryManager() {
+void Allocator::shutdown() {
     for (int n = 0; n < cuda::getDeviceCount(); n++) {
         try {
             cuda::setDevice(n);
-            garbageCollect();
+            shutdownMemoryManager();
         } catch (AfError err) {
             continue;  // Do not throw any errors while shutting down
         }
     }
 }
 
-int MemoryManager::getActiveDeviceId() { return cuda::getActiveDeviceId(); }
+int Allocator::getActiveDeviceId() { return cuda::getActiveDeviceId(); }
 
-size_t MemoryManager::getMaxMemorySize(int id) {
+size_t Allocator::getMaxMemorySize(int id) {
     return cuda::getDeviceMemorySize(id);
 }
 
-void *MemoryManager::nativeAlloc(const size_t bytes) {
+void *Allocator::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
     CUDA_CHECK(cudaMalloc(&ptr, bytes));
     AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
-void MemoryManager::nativeFree(void *ptr) {
+void Allocator::nativeFree(void *ptr) {
     AF_TRACE("nativeFree:          {}", ptr);
     cudaError_t err = cudaFree(ptr);
     if (err != cudaErrorCudartUnloading) { CUDA_CHECK(err); }
 }
 
-MemoryManagerPinned::MemoryManagerPinned()
-    : common::MemoryManager<MemoryManagerPinned>(
-          1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG) {
-    this->setMaxMemorySize();
-}
+AllocatorPinned::AllocatorPinned() { logger = common::loggerFactory("mem"); }
 
-MemoryManagerPinned::~MemoryManagerPinned() { garbageCollect(); }
+void AllocatorPinned::shutdown() { shutdownPinnedMemoryManager(); }
 
-int MemoryManagerPinned::getActiveDeviceId() {
+int AllocatorPinned::getActiveDeviceId() {
     return 0;  // pinned uses a single vector
 }
 
-size_t MemoryManagerPinned::getMaxMemorySize(int id) {
+size_t AllocatorPinned::getMaxMemorySize(int id) {
     UNUSED(id);
     return cuda::getHostMemorySize();
 }
 
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes) {
+void *AllocatorPinned::nativeAlloc(const size_t bytes) {
     void *ptr;
     CUDA_CHECK(cudaMallocHost(&ptr, bytes));
     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
-void MemoryManagerPinned::nativeFree(void *ptr) {
+void AllocatorPinned::nativeFree(void *ptr) {
     AF_TRACE("Pinned::nativeFree:          {}", ptr);
     cudaError_t err = cudaFreeHost(ptr);
     if (err != cudaErrorCudartUnloading) { CUDA_CHECK(err); }
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 4fa6e1562f..a2c397b7bb 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -8,13 +8,16 @@
  ********************************************************/
 #pragma once
 
-#include <common/MemoryManager.hpp>
+#include <memory_manager.hpp>
 
 #include <cstdlib>
 #include <functional>
 #include <memory>
 
 namespace cuda {
+float getMemoryPressure();
+float getMemoryPressureThreshold();
+
 template<typename T>
 void memFree(T *ptr);
 
@@ -41,42 +44,44 @@ T *pinnedAlloc(const size_t &elements);
 template<typename T>
 void pinnedFree(T *ptr);
 
-size_t getMaxBytes();
-unsigned getMaxBuffers();
-
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers);
-void garbageCollect();
+void signalMemoryCleanup();
+void shutdownMemoryManager();
 void pinnedGarbageCollect();
 
 void printMemInfo(const char *msg, const int device);
 
+float getMemoryPressure();
+float getMemoryPressureThreshold();
+bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 
-bool checkMemoryLimit();
-
-class MemoryManager : public common::MemoryManager<cuda::MemoryManager> {
+class Allocator final : public common::memory::AllocatorInterface {
    public:
-    MemoryManager();
-    ~MemoryManager();
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
+    Allocator();
+    ~Allocator() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
 };
 
 // CUDA Pinned Memory does not depend on device
 // So we pass 1 as numDevices to the constructor so that it creates 1 vector
 // of memory_info
 // When allocating and freeing, it doesn't really matter which device is active
-class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned> {
+class AllocatorPinned final : public common::memory::AllocatorInterface {
    public:
-    MemoryManagerPinned();
-    ~MemoryManagerPinned();
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
+    AllocatorPinned();
+    ~AllocatorPinned() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
 };
+
 }  // namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index aa1d59aa77..12a00a1119 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -56,6 +56,7 @@ using std::to_string;
 using std::unique_ptr;
 
 using common::unique_handle;
+using common::memory::MemoryManagerBase;
 
 namespace cuda {
 
@@ -356,28 +357,62 @@ cudaDeviceProp getDeviceProp(int device) {
     return DeviceManager::getInstance().cuDevices[0].prop;
 }
 
-MemoryManager &memoryManager() {
+MemoryManagerBase &memoryManager() {
     static std::once_flag flag;
 
     DeviceManager &inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() { inst.memManager.reset(new MemoryManager()); });
+    std::call_once(flag, [&]() {
+        // By default, create an instance of the default memory manager
+        inst.memManager.reset(new common::DefaultMemoryManager(
+            getDeviceCount(), common::MAX_BUFFERS,
+            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG));
+        // Set the memory manager's device memory manager
+        std::unique_ptr<cuda::Allocator> deviceMemoryManager(
+            new cuda::Allocator());
+        inst.memManager->setAllocator(std::move(deviceMemoryManager));
+        inst.memManager->initialize();
+    });
 
     return *(inst.memManager.get());
 }
 
-MemoryManagerPinned &pinnedMemoryManager() {
+MemoryManagerBase &pinnedMemoryManager() {
     static std::once_flag flag;
 
     DeviceManager &inst = DeviceManager::getInstance();
 
     std::call_once(flag, [&]() {
-        inst.pinnedMemManager.reset(new MemoryManagerPinned());
+        // By default, create an instance of the default memory manager
+        inst.pinnedMemManager.reset(new common::DefaultMemoryManager(
+            getDeviceCount(), common::MAX_BUFFERS,
+            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG));
+        // Set the memory manager's device memory manager
+        std::unique_ptr<cuda::AllocatorPinned> deviceMemoryManager(
+            new cuda::AllocatorPinned());
+        inst.pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+        inst.pinnedMemManager->initialize();
     });
 
     return *(inst.pinnedMemManager.get());
 }
 
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
+}
+
+void resetMemoryManager() {
+    return DeviceManager::getInstance().resetMemoryManager();
+}
+
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManagerPinned(std::move(mgr));
+}
+
+void resetMemoryManagerPinned() {
+    return DeviceManager::getInstance().resetMemoryManagerPinned();
+}
+
 graphics::ForgeManager &forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 6c1360621b..ec4e1219aa 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -12,6 +12,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 
+#include <memory>
 #include <string>
 #include <utility>
 
@@ -38,11 +39,17 @@ namespace graphics {
 class ForgeManager;
 }
 
+namespace common {
+namespace memory {
+class MemoryManagerBase;
+}
+}  // namespace common
+
+using common::memory::MemoryManagerBase;
+
 namespace cuda {
 
 class GraphicsResourceManager;
-class MemoryManager;
-class MemoryManagerPinned;
 class PlanCache;
 
 int getBackend();
@@ -94,11 +101,19 @@ std::pair<int, int> getComputeCapability(const int device);
 
 bool &evalFlag();
 
-MemoryManager& memoryManager();
+MemoryManagerBase& memoryManager();
+
+MemoryManagerBase& pinnedMemoryManager();
+
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+void resetMemoryManager();
+
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
 
-MemoryManagerPinned &pinnedMemoryManager();
+void resetMemoryManagerPinned();
 
-graphics::ForgeManager &forgeManager();
+graphics::ForgeManager& forgeManager();
 
 GraphicsResourceManager &interopManager();
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 79c9f91a3a..a3e67f447c 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -11,8 +11,8 @@
 
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
-#include <common/util.hpp>
 #include <common/traits.hpp>
+#include <common/util.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
 #include <jit/BufferNode.hpp>
@@ -123,7 +123,8 @@ Array<T>::Array(Param &tmp, bool owner_)
            dim4(tmp.info.strides[0], tmp.info.strides[1], tmp.info.strides[2],
                 tmp.info.strides[3]),
            (af_dtype)dtype_traits<T>::af_type)
-    , data(tmp.data, owner_ ? bufferFree : [](Buffer *) {})
+    , data(
+          tmp.data, owner_ ? bufferFree : [](Buffer *) {})
     , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
                      tmp.info.dims[3]))
     , node(bufferNodePtr<T>())
@@ -251,16 +252,12 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= (int)getMaxJitSize()) { return kJITHeuristics::TreeHeight; }
-
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    deviceMemoryInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+    if (root_node->getHeight() >= (int)getMaxJitSize()) {
+        return kJITHeuristics::TreeHeight;
+    }
 
-    bool isBufferLimit =
-        lock_bytes > getMaxBytes() || lock_buffers > getMaxBuffers();
-    auto platform = getActivePlatform();
+    bool isBufferLimit = getMemoryPressure() > getMemoryPressureThreshold();
+    auto platform      = getActivePlatform();
 
     // The Apple platform can have the nvidia card or the AMD card
     bool isNvidia =
@@ -312,19 +309,15 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
                            }
                            return prev;
                        });
-        isBufferLimit = 2 * info.total_buffer_size > lock_bytes;
+        isBufferLimit = jitTreeExceedsMemoryPressure(info.total_buffer_size);
 
         size_t param_size = (info.num_buffers * (sizeof(KParam) + sizeof(T *)) +
                              info.param_scalar_size);
 
         isParamLimit = param_size >= max_param_size;
 
-        if (isParamLimit) {
-            return kJITHeuristics::KernelParameterSize;
-        }
-        if (isBufferLimit) {
-            return kJITHeuristics::MemoryPressure;
-        }
+        if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
+        if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
     }
     return kJITHeuristics::Pass;
 }
diff --git a/src/backend/opencl/Event.cpp b/src/backend/opencl/Event.cpp
index fa7257e28f..1c7da40da8 100644
--- a/src/backend/opencl/Event.cpp
+++ b/src/backend/opencl/Event.cpp
@@ -9,11 +9,63 @@
 
 #include <Event.hpp>
 
+#include <common/err_common.hpp>
+#include <events.hpp>
+#include <platform.hpp>
+#include <af/event.h>
+
+#include <memory>
+
 namespace opencl {
 /// \brief Creates a new event and marks it in the queue
-Event make_event(cl::CommandQueue &queue) {
+Event makeEvent(cl::CommandQueue& queue) {
     Event e;
     if (e.create() == CL_SUCCESS) { e.mark(queue()); }
     return e;
 }
+
+af_event createEvent() {
+    std::unique_ptr<Event> e;
+    e.reset(new Event());
+    // Ensure the default CL command queue is initialized
+    getQueue()();
+    if (e->create() != CL_SUCCESS) {
+        AF_ERROR("Could not create event", AF_ERR_RUNTIME);
+    }
+    Event& ref = *e.release();
+    return getHandle(ref);
+}
+
+void releaseEvent(af_event eventHandle) { delete (Event*)eventHandle; }
+
+void markEventOnActiveQueue(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active stream
+    if (event.mark(getQueue()()) != CL_SUCCESS) {
+        AF_ERROR("Could not mark event on active queue", AF_ERR_RUNTIME);
+    }
+}
+
+void enqueueWaitOnActiveQueue(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active stream
+    if (event.enqueueWait(getQueue()()) != CL_SUCCESS) {
+        AF_ERROR("Could not enqueue wait on active queue for event",
+                 AF_ERR_RUNTIME);
+    }
+}
+
+void block(af_event eventHandle) {
+    Event& event = getEvent(eventHandle);
+    if (event.block() != CL_SUCCESS) {
+        AF_ERROR("Could not block on active queue for event", AF_ERR_RUNTIME);
+    }
+}
+
+af_event createAndMarkEvent() {
+    af_event handle = createEvent();
+    markEventOnActiveQueue(handle);
+    return handle;
+}
+
 }  // namespace opencl
diff --git a/src/backend/opencl/Event.hpp b/src/backend/opencl/Event.hpp
index bc3b4cf7ff..2f5c445f28 100644
--- a/src/backend/opencl/Event.hpp
+++ b/src/backend/opencl/Event.hpp
@@ -10,6 +10,7 @@
 
 #include <common/EventBase.hpp>
 #include <platform.hpp>
+#include <af/event.h>
 
 namespace opencl {
 class OpenCLEventPolicy {
@@ -18,7 +19,7 @@ class OpenCLEventPolicy {
     using QueueType = cl_command_queue;
     using ErrorType = cl_int;
 
-    static cl_int createEvent(cl_event *e) noexcept {
+    static cl_int createAndMarkEvent(cl_event *e) noexcept {
         // Events are created when you mark them
         return CL_SUCCESS;
     }
@@ -43,6 +44,18 @@ class OpenCLEventPolicy {
 using Event = common::EventBase<OpenCLEventPolicy>;
 
 /// \brief Creates a new event and marks it in the queue
-Event make_event(cl::CommandQueue &queue);
+Event makeEvent(cl::CommandQueue &queue);
+
+af_event createEvent();
+
+void releaseEvent(af_event eventHandle);
+
+void markEventOnActiveQueue(af_event eventHandle);
+
+void enqueueWaitOnActiveQueue(af_event eventHandle);
+
+void block(af_event eventHandle);
+
+af_event createAndMarkEvent();
 
 }  // namespace opencl
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
index eaf67f6c31..c593380e2d 100644
--- a/src/backend/opencl/clfft.hpp
+++ b/src/backend/opencl/clfft.hpp
@@ -39,7 +39,7 @@ class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
     do {                                                         \
         clfftStatus _clfft_st = fn;                              \
         if (_clfft_st != CLFFT_SUCCESS) {                        \
-            opencl::garbageCollect();                            \
+            opencl::signalMemoryCleanup();                            \
             _clfft_st = (fn);                                    \
         }                                                        \
         if (_clfft_st != CLFFT_SUCCESS) {                        \
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 42d58356a0..9e7d016614 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -299,6 +299,59 @@ DeviceManager& DeviceManager::getInstance() {
     return *my_instance;
 }
 
+void DeviceManager::setMemoryManager(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a memory manager and the default memory
+    // manager still hasn't been initialized, so initialize it anyways so we
+    // don't inadvertently reset to it when we first call memoryManager()
+    memoryManager();
+    // Calls shutdown() on the existing memory manager.
+    if (memManager) { memManager->shutdownAllocator(); }
+    memManager = std::move(newMgr);
+    // Set the backend memory manager for this new manager to register native
+    // functions correctly.
+    std::unique_ptr<opencl::Allocator> deviceMemoryManager(
+        new opencl::Allocator());
+    memManager->setAllocator(std::move(deviceMemoryManager));
+    memManager->initialize();
+}
+
+void DeviceManager::resetMemoryManager() {
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG));
+    setMemoryManager(std::move(mgr));
+}
+
+void DeviceManager::setMemoryManagerPinned(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a pinned memory manager and the default
+    // memory manager still hasn't been initialized, so initialize it anyways so
+    // we don't inadvertently reset to it when we first call
+    // pinnedMemoryManager()
+    pinnedMemoryManager();
+    // Calls shutdown() on the existing memory manager.
+    pinnedMemManager->shutdownAllocator();
+    pinnedMemManager = std::move(newMgr);
+    // Set the backend pinned memory manager for this new manager to register
+    // native functions correctly.
+    std::unique_ptr<opencl::AllocatorPinned> deviceMemoryManager(
+        new opencl::AllocatorPinned());
+    pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+    pinnedMemManager->initialize();
+}
+
+void DeviceManager::resetMemoryManagerPinned() {
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG));
+    setMemoryManagerPinned(std::move(mgr));
+}
+
 DeviceManager::~DeviceManager() {
     for (int i = 0; i < getDeviceCount(); ++i) {
         delete gfxManagers[i].release();
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 6ce0d7cca6..ddb6df5e11 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -10,21 +10,44 @@
 #pragma once
 
 #include <memory>
+#include <mutex>
 #include <string>
 #include <vector>
 
-#include <common/MemoryManager.hpp>
+#include <memory_manager.hpp>
 #include <platform.hpp>
 
+using common::memory::MemoryManagerBase;
+
+#ifndef AF_OPENCL_MEM_DEBUG
+#define AF_OPENCL_MEM_DEBUG 0
+#endif
+
 // Forward declaration from clFFT.h
 struct clfftSetupData_;
 
 namespace opencl {
 
 class DeviceManager {
-    friend MemoryManager& memoryManager();
+    friend MemoryManagerBase& memoryManager();
+
+    friend void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManager();
+
+    void resetMemoryManager();
+
+    friend MemoryManagerBase& pinnedMemoryManager();
+
+    friend void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManagerPinned();
 
-    friend MemoryManagerPinned& pinnedMemoryManager();
+    void resetMemoryManagerPinned();
 
     friend graphics::ForgeManager& forgeManager();
 
@@ -107,10 +130,11 @@ class DeviceManager {
     unsigned mUserDeviceOffset;
 
     std::unique_ptr<graphics::ForgeManager> fgMngr;
-    std::unique_ptr<MemoryManager> memManager;
-    std::unique_ptr<MemoryManagerPinned> pinnedMemManager;
+    std::unique_ptr<MemoryManagerBase> memManager;
+    std::unique_ptr<MemoryManagerBase> pinnedMemManager;
     std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
     std::unique_ptr<clfftSetupData> mFFTSetup;
+    std::mutex mutex;
 
     using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
     std::vector<BoostProgCache*> mBoostProgCacheVector;
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index c324a40331..33b11b58cc 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -11,41 +11,42 @@
 #include <memory.hpp>
 #include <platform.hpp>
 #include <types.hpp>
+#include <af/dim4.hpp>
 
 #include <common/Logger.hpp>
+#include <memory_manager_impl.hpp>
 #include <spdlog/spdlog.h>
 
-#include <common/MemoryManagerImpl.hpp>
-template class common::MemoryManager<opencl::MemoryManager>;
-template class common::MemoryManager<opencl::MemoryManagerPinned>;
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-#ifndef AF_OPENCL_MEM_DEBUG
-#define AF_OPENCL_MEM_DEBUG 0
-#endif
+#include <utility>
 
 using common::bytesToString;
-using common::MemoryEventPair;
 
+using af::dim4;
 using std::function;
 using std::move;
 using std::unique_ptr;
 
 namespace opencl {
+float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
+float getMemoryPressureThreshold() {
+    return memoryManager().getMemoryPressureThreshold();
+}
+
+bool jitTreeExceedsMemoryPressure(size_t bytes) {
+    return memoryManager().jitTreeExceedsMemoryPressure(bytes);
+}
+
 void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
 size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
 
-size_t getMaxBytes() { return memoryManager().getMaxBytes(); }
+void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
-unsigned getMaxBuffers() { return memoryManager().getMaxBuffers(); }
+void shutdownMemoryManager() { memoryManager().shutdown(); }
 
-void garbageCollect() { memoryManager().garbageCollect(); }
+void shutdownPinnedMemoryManager() { pinnedMemoryManager().shutdown(); }
 
 void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
@@ -54,38 +55,58 @@ void printMemInfo(const char *msg, const int device) {
 template<typename T>
 unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
     const size_t &elements) {
-    MemoryEventPair me = memoryManager().alloc(elements * sizeof(T), false);
-    if (me.e) me.e.enqueueWait(getQueue()());
-    cl::Buffer *ptr = static_cast<cl::Buffer *>(me.ptr);
+    // TODO: make memAlloc aware of array shapes
+    dim4 dims(elements);
+    af_buffer_info pair =
+        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue()());
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *rawPtr     = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    cl::Buffer *ptr = static_cast<cl::Buffer *>(rawPtr);
     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(ptr,
                                                                 bufferFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
-    MemoryEventPair me = memoryManager().alloc(bytes, true);
-    if (me.e) me.e.enqueueWait(getQueue()());
-    return me.ptr;
+    dim4 dims(bytes);
+    af_buffer_info pair = memoryManager().alloc(true, 1, dims.get(), 1);
+    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue()());
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return ptr;
 }
+
 template<typename T>
 void memFree(T *ptr) {
-    Event e = make_event(getQueue());
-    return memoryManager().unlock((void *)ptr, move(e), false);
+    return memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
+                                  false);
 }
 
 void memFreeUser(void *ptr) {
-    Event e = make_event(getQueue());
-    memoryManager().unlock((void *)ptr, move(e), true);
+    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), true);
 }
 
 cl::Buffer *bufferAlloc(const size_t &bytes) {
-    MemoryEventPair me = memoryManager().alloc(bytes, false);
-    if (me.e) me.e.enqueueWait(getQueue()());
-    return static_cast<cl::Buffer *>(me.ptr);
+    dim4 dims(bytes);
+    af_buffer_info pair = memoryManager().alloc(false, 1, dims.get(), 1);
+    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue()());
+    auto *bufferInfo = (BufferInfo *)pair;
+    void *ptr        = bufferInfo->ptr;
+    delete (detail::Event *)bufferInfo->event;
+    delete bufferInfo;
+    return static_cast<cl::Buffer *>(ptr);
 }
 
 void bufferFree(cl::Buffer *buf) {
-    Event e = make_event(getQueue());
-    return memoryManager().unlock((void *)buf, move(e), false);
+    return memoryManager().unlock((void *)buf, detail::createAndMarkEvent(),
+                                  false);
 }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
@@ -98,26 +119,30 @@ bool isLocked(const void *ptr) {
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers) {
-    memoryManager().bufferInfo(alloc_bytes, alloc_buffers, lock_bytes,
-                               lock_buffers);
+    memoryManager().usageInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                              lock_buffers);
 }
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    MemoryEventPair me =
-        pinnedMemoryManager().alloc(elements * sizeof(T), false);
-    if (me.e) me.e.enqueueWait(getQueue()());
-    return static_cast<T *>(me.ptr);
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    af_buffer_info pair =
+        pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
+    if (e) e.enqueueWait(getQueue()());
+    void *ptr;
+    af_unlock_buffer_info_ptr(&ptr, pair);
+    af_delete_buffer_info(pair);
+    return static_cast<T *>(ptr);
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    Event e = make_event(getQueue());
-    return pinnedMemoryManager().unlock((void *)ptr, move(e), false);
+    pinnedMemoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
+                                 false);
 }
 
-bool checkMemoryLimit() { return memoryManager().checkMemoryLimit(); }
-
 #define INSTANTIATE(T)                                                         \
     template unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc<T>( \
         const size_t &elements);                                               \
@@ -138,53 +163,44 @@ INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-MemoryManager::MemoryManager()
-    : common::MemoryManager<opencl::MemoryManager>(
-          getDeviceCount(), common::MAX_BUFFERS,
-          AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG) {
-    this->setMaxMemorySize();
-}
+Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
-MemoryManager::~MemoryManager() {
+void Allocator::shutdown() {
     for (int n = 0; n < opencl::getDeviceCount(); n++) {
         try {
             opencl::setDevice(n);
-            this->garbageCollect();
+            shutdownMemoryManager();
         } catch (AfError err) {
             continue;  // Do not throw any errors while shutting down
         }
     }
 }
 
-int MemoryManager::getActiveDeviceId() { return opencl::getActiveDeviceId(); }
+int Allocator::getActiveDeviceId() { return opencl::getActiveDeviceId(); }
 
-size_t MemoryManager::getMaxMemorySize(int id) {
+size_t Allocator::getMaxMemorySize(int id) {
     return opencl::getDeviceMemorySize(id);
 }
 
-void *MemoryManager::nativeAlloc(const size_t bytes) {
+void *Allocator::nativeAlloc(const size_t bytes) {
     auto ptr = (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
     AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
-void MemoryManager::nativeFree(void *ptr) {
+void Allocator::nativeFree(void *ptr) {
     AF_TRACE("nativeFree:          {}", ptr);
     delete (cl::Buffer *)ptr;
 }
 
-MemoryManagerPinned::MemoryManagerPinned()
-    : common::MemoryManager<MemoryManagerPinned>(
-          getDeviceCount(), common::MAX_BUFFERS,
-          AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG)
-    , pinnedMaps(getDeviceCount()) {
-    this->setMaxMemorySize();
+AllocatorPinned::AllocatorPinned() : pinnedMaps(opencl::getDeviceCount()) {
+    logger = common::loggerFactory("mem");
 }
 
-MemoryManagerPinned::~MemoryManagerPinned() {
+void AllocatorPinned::shutdown() {
     for (int n = 0; n < opencl::getDeviceCount(); n++) {
         opencl::setDevice(n);
-        this->garbageCollect();
+        shutdownPinnedMemoryManager();
         auto currIterator = pinnedMaps[n].begin();
         auto endIterator  = pinnedMaps[n].end();
         while (currIterator != endIterator) {
@@ -193,15 +209,13 @@ MemoryManagerPinned::~MemoryManagerPinned() {
     }
 }
 
-int MemoryManagerPinned::getActiveDeviceId() {
-    return opencl::getActiveDeviceId();
-}
+int AllocatorPinned::getActiveDeviceId() { return opencl::getActiveDeviceId(); }
 
-size_t MemoryManagerPinned::getMaxMemorySize(int id) {
+size_t AllocatorPinned::getMaxMemorySize(int id) {
     return opencl::getDeviceMemorySize(id);
 }
 
-void *MemoryManagerPinned::nativeAlloc(const size_t bytes) {
+void *AllocatorPinned::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
     cl::Buffer *buf =
         new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
@@ -212,7 +226,7 @@ void *MemoryManagerPinned::nativeAlloc(const size_t bytes) {
     return ptr;
 }
 
-void MemoryManagerPinned::nativeFree(void *ptr) {
+void AllocatorPinned::nativeFree(void *ptr) {
     AF_TRACE("Pinned::nativeFree:          {}", ptr);
     int n     = opencl::getActiveDeviceId();
     auto map  = pinnedMaps[n];
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index c4298b1404..a9d8ec3020 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 
-#include <common/MemoryManager.hpp>
+#include <memory_manager.hpp>
 
 #include <cstdlib>
 #include <functional>
@@ -45,40 +45,43 @@ T *pinnedAlloc(const size_t &elements);
 template<typename T>
 void pinnedFree(T *ptr);
 
-size_t getMaxBytes();
-unsigned getMaxBuffers();
-
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers);
-void garbageCollect();
+void signalMemoryCleanup();
+void shutdownMemoryManager();
 void pinnedGarbageCollect();
 
 void printMemInfo(const char *msg, const int device);
 
+float getMemoryPressure();
+float getMemoryPressureThreshold();
+bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
-bool checkMemoryLimit();
 
-class MemoryManager : public common::MemoryManager<opencl::MemoryManager> {
+class Allocator final : public common::memory::AllocatorInterface {
    public:
-    MemoryManager();
-    ~MemoryManager();
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
+    Allocator();
+    ~Allocator() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
 };
 
-class MemoryManagerPinned : public common::MemoryManager<MemoryManagerPinned> {
+class AllocatorPinned final : public common::memory::AllocatorInterface {
    public:
-    MemoryManagerPinned();
-    ~MemoryManagerPinned();
-    int getActiveDeviceId();
-    size_t getMaxMemorySize(int id);
-    void *nativeAlloc(const size_t bytes);
-    void nativeFree(void *ptr);
+    AllocatorPinned();
+    ~AllocatorPinned() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
 
    private:
     std::vector<std::map<void *, cl::Buffer *>> pinnedMaps;
 };
+
 }  // namespace opencl
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index a7f1e59394..68e199482e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -62,6 +62,8 @@ using std::string;
 using std::to_string;
 using std::vector;
 
+using common::memory::MemoryManagerBase;
+
 namespace opencl {
 
 static const string get_system(void) {
@@ -522,7 +524,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
 
         if (deleteIdx < (int)devId.first) {
             device_id_t newVals = make_pair(devId.first - 1, devId.second - 1);
-            devId = newVals;
+            devId               = newVals;
         }
     }
 }
@@ -536,7 +538,7 @@ unsigned getMaxJitSize() {
 #if defined(OS_MAC)
     const int MAX_JIT_LEN = 50;
 #else
-    const int MAX_JIT_LEN       = 100;
+    const int MAX_JIT_LEN = 100;
 #endif
 
     thread_local int length = 0;
@@ -556,27 +558,62 @@ bool& evalFlag() {
     return flag;
 }
 
-MemoryManager& memoryManager() {
+MemoryManagerBase& memoryManager() {
     static once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    call_once(flag, [&] { inst.memManager.reset(new MemoryManager()); });
+    std::call_once(flag, [&]() {
+        // By default, create an instance of the default memory manager
+        inst.memManager.reset(new common::DefaultMemoryManager(
+            getDeviceCount(), common::MAX_BUFFERS,
+            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG));
+        // Set the memory manager's device memory manager
+        std::unique_ptr<opencl::Allocator> deviceMemoryManager;
+        deviceMemoryManager.reset(new opencl::Allocator());
+        inst.memManager->setAllocator(std::move(deviceMemoryManager));
+        inst.memManager->initialize();
+    });
 
     return *(inst.memManager.get());
 }
 
-MemoryManagerPinned& pinnedMemoryManager() {
+MemoryManagerBase& pinnedMemoryManager() {
     static once_flag flag;
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    call_once(flag,
-              [&] { inst.pinnedMemManager.reset(new MemoryManagerPinned()); });
+    std::call_once(flag, [&]() {
+        // By default, create an instance of the default memory manager
+        inst.pinnedMemManager.reset(new common::DefaultMemoryManager(
+            getDeviceCount(), common::MAX_BUFFERS,
+            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG));
+        // Set the memory manager's device memory manager
+        std::unique_ptr<opencl::AllocatorPinned> deviceMemoryManager;
+        deviceMemoryManager.reset(new opencl::AllocatorPinned());
+        inst.pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+        inst.pinnedMemManager->initialize();
+    });
 
     return *(inst.pinnedMemManager.get());
 }
 
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
+}
+
+void resetMemoryManager() {
+    return DeviceManager::getInstance().resetMemoryManager();
+}
+
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManagerPinned(std::move(mgr));
+}
+
+void resetMemoryManagerPinned() {
+    return DeviceManager::getInstance().resetMemoryManagerPinned();
+}
+
 graphics::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 5ed0810135..bf51c364f6 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -18,6 +18,7 @@
 #pragma GCC diagnostic pop
 
 #include <af/opencl.h>
+#include <memory>
 #include <string>
 
 namespace boost {
@@ -33,14 +34,20 @@ namespace graphics {
 class ForgeManager;
 }
 
+namespace common {
+namespace memory {
+class MemoryManagerBase;
+}
+}  // namespace common
+
+using common::memory::MemoryManagerBase;
+
 namespace opencl {
 
 // Forward declarations
 class GraphicsResourceManager;
 struct kc_entry_t;  // kernel cache entry
-class MemoryManager;
-class MemoryManagerPinned;
-class PlanCache;  // clfft
+class PlanCache;    // clfft
 
 static inline bool verify_present(std::string pname, const char* ref) {
     return pname.find(ref) != std::string::npos;
@@ -101,9 +108,17 @@ int getActivePlatform();
 
 bool& evalFlag();
 
-MemoryManager& memoryManager();
+MemoryManagerBase& memoryManager();
+
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+void resetMemoryManager();
+
+MemoryManagerBase& pinnedMemoryManager();
+
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
 
-MemoryManagerPinned& pinnedMemoryManager();
+void resetMemoryManagerPinned();
 
 graphics::ForgeManager& forgeManager();
 
@@ -120,7 +135,8 @@ kc_entry_t kernelCache(int device, const std::string& key);
 
 static afcl::platform getPlatformEnum(cl::Device dev) {
     std::string pname = getPlatformName(dev);
-    if (verify_present(pname, "AMD")) return AFCL_PLATFORM_AMD;
+    if (verify_present(pname, "AMD"))
+        return AFCL_PLATFORM_AMD;
     else if (verify_present(pname, "NVIDIA"))
         return AFCL_PLATFORM_NVIDIA;
     else if (verify_present(pname, "INTEL"))
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 9236045bff..dc3f345868 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -193,6 +193,7 @@ make_test(SRC diff2.cpp)
 make_test(SRC dog.cpp)
 make_test(SRC dot.cpp)
 make_test(SRC empty.cpp)
+make_test(SRC event.cpp CXX11)
 make_test(SRC fast.cpp)
 make_test(SRC fft.cpp)
 make_test(SRC fft_large.cpp)
@@ -236,7 +237,7 @@ make_test(SRC meanshift.cpp)
 make_test(SRC meanvar.cpp CXX11)
 make_test(SRC medfilt.cpp)
 make_test(SRC median.cpp)
-make_test(SRC memory.cpp)
+make_test(SRC memory.cpp CXX11)
 make_test(SRC memory_lock.cpp)
 make_test(SRC missing.cpp)
 make_test(SRC moddims.cpp)
diff --git a/test/event.cpp b/test/event.cpp
new file mode 100644
index 0000000000..2fb932fabd
--- /dev/null
+++ b/test/event.cpp
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/event.h>
+
+#include <memory>
+#include <utility>
+
+#include <iostream>
+
+using af::event;
+
+TEST(EventTests, SimpleCreateRelease) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    ASSERT_SUCCESS(af_delete_event(event));
+}
+
+TEST(EventTests, MarkEnqueueAndBlock) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    ASSERT_SUCCESS(af_mark_event(event));
+    ASSERT_SUCCESS(af_enqueue_wait_event(event));
+    ASSERT_SUCCESS(af_block_event(event));
+    ASSERT_SUCCESS(af_delete_event(event));
+}
+
+TEST(EventTests, EventCreateAndMove) {
+    af_event eventHandle;
+    ASSERT_SUCCESS(af_create_event(&eventHandle));
+
+    std::unique_ptr<event> e;
+    e.reset(new event(eventHandle));
+    e->mark();
+    ASSERT_EQ(eventHandle, e->get());
+
+    auto otherEvent = std::move(e);
+    ASSERT_EQ(otherEvent->get(), eventHandle);
+
+    std::unique_ptr<event> f;
+    f.reset(new event());
+    af_event fE       = f->get();
+    auto anotherEvent = std::move(f);
+    ASSERT_EQ(fE, anotherEvent->get());
+}
diff --git a/test/memory.cpp b/test/memory.cpp
index abbe008199..c3893bd0f6 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -11,17 +11,25 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
+#include <af/event.h>
 #include <af/internal.h>
+#include <af/memory.h>
 #include <af/traits.hpp>
-#include <iostream>
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
 #include <vector>
 
 using af::alloc;
 using af::array;
+using af::buffer_info;
 using af::cdouble;
 using af::cfloat;
 using af::deviceGC;
 using af::deviceMemInfo;
+using af::dim4;
+using af::dtype;
 using af::dtype_traits;
 using af::randu;
 using af::seq;
@@ -239,7 +247,7 @@ TEST(Memory, LargeLoop) {
         // Verify that new buffers are being allocated
         deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
-        // Limit to 10 to check before garbage collection
+        // Limit to 10 to check before memory cleanup
         if (i < 10) {
             ASSERT_EQ(alloc_buffers, (size_t)(i + 2));  // i is zero based
             ASSERT_EQ(lock_buffers, 2u);
@@ -522,7 +530,6 @@ TEST(Memory, device) {
     ASSERT_EQ(lock_bytes, 0u);
 }
 
-
 TEST(Memory, Assign2D) {
     size_t alloc_bytes, alloc_buffers;
     size_t alloc_bytes_after, alloc_buffers_after;
@@ -531,10 +538,10 @@ TEST(Memory, Assign2D) {
 
     cleanSlate();  // Clean up everything done so far
     {
-        array a    = af::randu(10, 10, f32);
+        array a       = af::randu(10, 10, f32);
         unsigned hb[] = {3, 5, 6, 8, 9};
         array b(5, hb);
-        array c   = af::randu(5, f32);
+        array c = af::randu(5, f32);
         deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
         a(b) = c;
     }
@@ -627,3 +634,399 @@ TEST(Memory, IndexedDevice) {
         }
     }
 }
+
+TEST(BufferInfo, SimpleCreateDelete) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    af_buffer_info pair;
+
+    void *ptr = af::alloc(1, dtype::f32);
+    ASSERT_SUCCESS(af_create_buffer_info(&pair, ptr, event));
+    ASSERT_SUCCESS(af_delete_buffer_info(pair));
+}
+
+TEST(BufferInfo, Unlock) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    af_buffer_info pair;
+    void *ptr = af::alloc(1, dtype::f32);
+    ASSERT_SUCCESS(af_create_buffer_info(&pair, ptr, event));
+
+    void *curPtr;
+    ASSERT_SUCCESS(af_unlock_buffer_info_ptr(&curPtr, pair));
+    ASSERT_EQ(curPtr, ptr);
+    void *zeroPtr;
+    ASSERT_SUCCESS(af_buffer_info_get_ptr(&zeroPtr, pair));
+    ASSERT_EQ(zeroPtr, nullptr);
+    ASSERT_SUCCESS(af_unlock_buffer_info_ptr(&zeroPtr, pair));
+    ASSERT_EQ(zeroPtr, nullptr);
+
+    af_event curEvent;
+    ASSERT_SUCCESS(af_unlock_buffer_info_event(&curEvent, pair));
+    ASSERT_EQ(curEvent, event);
+    void *zeroEvent;
+    ASSERT_SUCCESS(af_buffer_info_get_ptr(&zeroEvent, pair));
+    ASSERT_EQ(zeroEvent, nullptr);
+    ASSERT_SUCCESS(af_unlock_buffer_info_ptr(&zeroEvent, pair));
+    ASSERT_EQ(zeroEvent, nullptr);
+
+    ASSERT_SUCCESS(af_delete_buffer_info(pair));
+    ASSERT_SUCCESS(af_delete_event(event));
+    af::free(ptr);
+}
+
+TEST(BufferInfo, EventAndPtrAttributes) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    void *ptr = af::alloc(1, dtype::f32);
+    af_buffer_info pair;
+    ASSERT_SUCCESS(af_create_buffer_info(&pair, ptr, event));
+    af_event anEvent;
+    ASSERT_SUCCESS(af_buffer_info_get_event(&anEvent, pair));
+    ASSERT_EQ(event, anEvent);
+    void *somePtr;
+    ASSERT_SUCCESS(af_buffer_info_get_ptr(&somePtr, pair));
+    ASSERT_EQ(ptr, somePtr);
+
+    af_event anotherEvent;
+    ASSERT_SUCCESS(af_create_event(&anotherEvent));
+    ASSERT_SUCCESS(af_buffer_info_set_event(pair, anotherEvent));
+    af_event yetAnotherEvent;
+    ASSERT_SUCCESS(af_buffer_info_get_event(&yetAnotherEvent, pair));
+    ASSERT_NE(yetAnotherEvent, event);
+    ASSERT_EQ(yetAnotherEvent, anotherEvent);
+
+    void *anotherPtr = af::alloc(1, dtype::f32);
+    ASSERT_SUCCESS(af_buffer_info_set_ptr(pair, anotherPtr));
+    void *yetAnotherPtr;
+    ASSERT_SUCCESS(af_buffer_info_get_ptr(&yetAnotherPtr, pair));
+    ASSERT_NE(yetAnotherPtr, ptr);
+    ASSERT_EQ(yetAnotherPtr, anotherPtr);
+
+    ASSERT_SUCCESS(af_delete_buffer_info(pair));
+    ASSERT_SUCCESS(af_delete_event(event));
+    af::free(ptr);
+}
+
+TEST(BufferInfo, BufferInfoCreateMove) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    void *ptr = af::alloc(1, dtype::f32);
+    std::unique_ptr<buffer_info> bufferInfo(new buffer_info(ptr, event));
+    ASSERT_EQ(bufferInfo->getEvent(), event);
+    ASSERT_EQ(bufferInfo->getPtr(), ptr);
+
+    void *anotherPtr = af::alloc(1, dtype::f32);
+    bufferInfo->setPtr(anotherPtr);
+    ASSERT_EQ(bufferInfo->getPtr(), anotherPtr);
+
+    af_event anotherEvent;
+    ASSERT_SUCCESS(af_create_event(&anotherEvent));
+    bufferInfo->setEvent(anotherEvent);
+    ASSERT_EQ(bufferInfo->getEvent(), anotherEvent);
+
+    auto anotherBufferInfo = std::move(bufferInfo);
+    ASSERT_EQ(anotherBufferInfo->getPtr(), anotherPtr);
+    ASSERT_EQ(anotherBufferInfo->getEvent(), anotherEvent);
+
+    af_delete_event(event);
+    af::free(ptr);
+}
+
+TEST(BufferInfo, UnlockCpp) {
+    af_event event;
+    ASSERT_SUCCESS(af_create_event(&event));
+    void *ptr = af::alloc(1, dtype::f32);
+    std::unique_ptr<buffer_info> bufferInfo(new buffer_info(ptr, event));
+
+    void *anotherPtr = bufferInfo->unlockPtr();
+    ASSERT_EQ(ptr, anotherPtr);
+    af_event anotherEvent = bufferInfo->unlockEvent();
+    ASSERT_EQ(event, anotherEvent);
+
+    ASSERT_SUCCESS(af_delete_event(anotherEvent));
+    af::free(ptr);
+}
+
+namespace {
+
+template<typename T>
+T *getMemoryManagerPayload(af_memory_manager manager) {
+    void *payloadPtr;
+    af_memory_manager_get_payload(manager, &payloadPtr);
+    return (T *)payloadPtr;
+}
+
+/**
+ * An extremely basic memory manager with a basic caching mechanism for testing
+ * purposes. It is not thread safe or optimized.
+ */
+struct E2ETestPayload {
+    int initializeCalledTimes{0};
+    int shutdownCalledTimes{0};
+    std::unordered_map<void *, size_t> table;
+    std::unordered_set<void *> locked;
+    size_t totalBytes{0};
+    size_t totalBuffers{0};
+    size_t lockedBytes{0};
+    unsigned lastNdims;
+    dim4 lastDims;
+    unsigned lastElementSize;
+
+    size_t maxBuffers{64};
+    size_t maxBytes{1024};
+    // Print info args
+    std::string printInfoStringArg;
+    int printInfoDevice{-1};
+};
+
+af_err allocated_fn(af_memory_manager manager, size_t *out, void *ptr) {
+    auto &table = getMemoryManagerPayload<E2ETestPayload>(manager)->table;
+    if (table.find(ptr) == table.end()) {
+        *out = 0;
+    } else {
+        *out = table[ptr];
+    }
+    return AF_SUCCESS;
+}
+
+af_err user_lock_fn(af_memory_manager manager, void *ptr) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    if (payload->locked.find(ptr) == payload->locked.end()) {
+        payload->locked.insert(ptr);
+        payload->lockedBytes += payload->table[ptr];
+    }
+    return AF_SUCCESS;
+}
+
+af_err is_user_locked_fn(af_memory_manager manager, int *out, void *ptr) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    *out          = payload->locked.find(ptr) != payload->locked.end();
+    return AF_SUCCESS;
+}
+
+af_err unlock_fn(af_memory_manager manager, void *ptr, af_event event,
+                 int userLock) {
+    af_delete_event(event);
+    if (!ptr) { return AF_SUCCESS; }
+
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+
+    if (payload->table.find(ptr) == payload->table.end()) {
+        return AF_SUCCESS;  // fast path
+    }
+
+    // For testing, treat user-allocated and AF-allocated memory identically
+    if (payload->locked.find(ptr) != payload->locked.end()) {
+        payload->locked.erase(ptr);
+        payload->lockedBytes -= payload->table[ptr];
+    }
+    return AF_SUCCESS;
+}
+
+af_err user_unlock_fn(af_memory_manager manager, void *ptr) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    af_event event;
+    af_create_event(&event);
+    af_mark_event(event);
+    af_err err = unlock_fn(manager, ptr, event, /* user */ 1);
+    payload->lockedBytes -= payload->table[ptr];
+    return err;
+}
+
+af_err signal_memory_cleanup_fn(af_memory_manager manager) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    // Free unlocked memory
+    std::vector<void *> freed;
+    for (auto &entry : payload->table) {
+        int isUserLocked;
+        is_user_locked_fn(manager, &isUserLocked, entry.first);
+        if (!isUserLocked) {
+            void *ptr = entry.first;
+            af_memory_manager_native_free(manager, ptr);
+            payload->totalBytes -= payload->table[entry.first];
+            freed.push_back(entry.first);
+        }
+    }
+    for (auto ptr : freed) { payload->table.erase(ptr); }
+    return AF_SUCCESS;
+}
+
+af_err print_info_fn(af_memory_manager manager, char *c, int b) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    payload->printInfoStringArg = std::string(c);
+    payload->printInfoDevice    = b;
+    return AF_SUCCESS;
+}
+
+af_err get_memory_pressure_fn(af_memory_manager manager, float *out) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    if (payload->totalBytes > payload->maxBytes ||
+        payload->totalBuffers > payload->maxBuffers) {
+        *out = 1.0;
+    } else {
+        *out = 0.0;
+    }
+    return AF_SUCCESS;
+}
+
+af_err jit_tree_exceeds_memory_pressure_fn(af_memory_manager manager, int *out,
+                                           size_t bytes) {
+    auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+    *out          = 2 * bytes > payload->totalBytes;
+    return AF_SUCCESS;
+}
+
+af_err alloc_fn(af_memory_manager manager, af_buffer_info *out,
+                /* bool */ int userLock, const unsigned ndims, dim_t *dims,
+                const unsigned element_size) {
+    af_event event;
+    af_create_event(&event);
+    af_mark_event(event);
+    af_buffer_info bufferInfo;
+    af_create_buffer_info(&bufferInfo, nullptr, event);
+
+    size_t size = element_size;
+    for (unsigned i = 0; i < ndims; ++i) { size *= dims[i]; }
+
+    if (size > 0) {
+        float pressure;
+        get_memory_pressure_fn(manager, &pressure);
+        float threshold;
+        af_memory_manager_get_memory_pressure_threshold(manager, &threshold);
+        if (pressure > threshold) { signal_memory_cleanup_fn(manager); }
+
+        void *piece;
+        af_memory_manager_native_alloc(manager, &piece, size);
+        af_buffer_info_set_ptr(bufferInfo, piece);
+
+        auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+        payload->table[piece] = size;
+        payload->totalBytes += size;
+        payload->totalBuffers++;
+
+        // Simple implementation: treat user and AF allocations the same
+        payload->locked.insert(piece);
+        payload->lockedBytes += size;
+
+        payload->lastNdims       = ndims;
+        payload->lastDims        = dim4(ndims, dims);
+        payload->lastElementSize = element_size;
+    }
+
+    *out = bufferInfo;
+    return AF_SUCCESS;
+}
+
+void add_memory_management_fn(af_memory_manager manager, int id) {}
+
+void remove_memory_management_fn(af_memory_manager manager, int id) {}
+
+}  // namespace
+
+TEST(MemoryManagerApi, E2ETest) {
+    af_memory_manager manager;
+    af_create_memory_manager(&manager);
+
+    // Set payload_fn
+    std::unique_ptr<E2ETestPayload> payload(new E2ETestPayload());
+    af_memory_manager_set_payload(manager, payload.get());
+
+    auto initialize_fn = [](af_memory_manager manager) {
+        auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+        payload->initializeCalledTimes++;
+        return AF_SUCCESS;
+    };
+    af_memory_manager_set_initialize_fn(manager, initialize_fn);
+
+    auto shutdown_fn = [](af_memory_manager manager) {
+        auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+        payload->shutdownCalledTimes++;
+        return AF_SUCCESS;
+    };
+    af_memory_manager_set_shutdown_fn(manager, shutdown_fn);
+
+    // alloc
+    af_memory_manager_set_alloc_fn(manager, alloc_fn);
+    af_memory_manager_set_allocated_fn(manager, allocated_fn);
+    af_memory_manager_set_unlock_fn(manager, unlock_fn);
+    // utils
+    af_memory_manager_set_signal_memory_cleanup_fn(manager,
+                                                   signal_memory_cleanup_fn);
+    af_memory_manager_set_print_info_fn(manager, print_info_fn);
+    // user lock/unlock
+    af_memory_manager_set_user_lock_fn(manager, user_lock_fn);
+    af_memory_manager_set_user_unlock_fn(manager, user_unlock_fn);
+    af_memory_manager_set_is_user_locked_fn(manager, is_user_locked_fn);
+    // memory pressure
+    af_memory_manager_set_get_memory_pressure_fn(manager,
+                                                 get_memory_pressure_fn);
+    af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn(
+        manager, jit_tree_exceeds_memory_pressure_fn);
+    // ocl
+    af_memory_manager_set_add_memory_management_fn(manager,
+                                                   add_memory_management_fn);
+    af_memory_manager_set_remove_memory_management_fn(
+        manager, remove_memory_management_fn);
+
+    af_set_memory_manager(manager);
+    {
+        size_t aSize = 8;
+
+        void *a = af::alloc(aSize, af::dtype::f32);
+        ASSERT_EQ(payload->table.size(), 1);
+        ASSERT_EQ(payload->table[a], aSize * sizeof(float));
+        ASSERT_EQ(payload->lastNdims, 1);
+        ASSERT_EQ(payload->lastDims, af::dim4(aSize * sizeof(float)));
+        ASSERT_EQ(payload->lastElementSize, 1);
+
+        dim_t bDim = 2;
+        auto b     = af::randu({bDim, bDim});
+
+        ASSERT_EQ(payload->totalBytes, aSize * sizeof(float) + b.bytes());
+        ASSERT_EQ(payload->totalBuffers, 2);
+        ASSERT_EQ(payload->lockedBytes, aSize * sizeof(float) + b.bytes());
+        ASSERT_EQ(payload->locked.size(), 2);
+        ASSERT_EQ(payload->lastNdims, 1);
+        // Some backends might alloc by number of bytes (OpenCL), others alloc
+        // by elements (CPU, CUDA)
+        if (payload->lastElementSize != 1) {
+            // alloced as floats
+            ASSERT_EQ(payload->lastDims, af::dim4(bDim * b.numdims()));
+            ASSERT_EQ(payload->lastElementSize, sizeof(float));
+        } else {
+            // alloced as bytes
+            ASSERT_EQ(payload->lastDims,
+                      af::dim4(bDim * b.numdims() * sizeof(float)));
+            ASSERT_EQ(payload->lastElementSize, 1);
+        }
+
+        af::free(a);
+
+        ASSERT_EQ(payload->totalBytes, aSize * sizeof(float) + b.bytes());
+        ASSERT_EQ(payload->totalBuffers, 2);
+        ASSERT_EQ(payload->lockedBytes, b.bytes());
+        ASSERT_EQ(payload->locked.size(), 1);
+    }
+
+    // gc
+    af::deviceGC();
+    ASSERT_EQ(payload->table.size(), 0);
+
+    // printInfo
+    std::string printInfoMsg = "testPrintInfo";
+    int printInfoDeviceId    = 0;
+    af::printMemInfo(printInfoMsg.c_str(), printInfoDeviceId);
+    ASSERT_EQ(printInfoMsg, payload->printInfoStringArg);
+    ASSERT_EQ(printInfoDeviceId, payload->printInfoDevice);
+
+    // step size (throws with a custom memory manager)
+    ASSERT_THROW(af::setMemStepSize(500), af::exception);
+    ASSERT_THROW(af::getMemStepSize(), af::exception);
+
+    ASSERT_EQ(payload->table.size(), 0);
+    af_unset_memory_manager();
+    af_release_memory_manager(manager);
+    ASSERT_EQ(payload->initializeCalledTimes, 1);
+    ASSERT_EQ(payload->shutdownCalledTimes, af::getDeviceCount());
+}

From 9defd6b87ccf22861cca875989ded4d39c7d203b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Dec 2019 09:22:50 +0530
Subject: [PATCH 1762/2677] Use FindcuDNN custom module to locate cudnn (#2661)

* Throw error from cmake when cudnn not found

* Use FindcuDNN cmake module for cuDNN framework
---
 CMakeLists.txt                  |   1 +
 CMakeModules/FindcuDNN.cmake    | 143 ++++++++++++++++++++++++++++++++
 src/backend/cuda/CMakeLists.txt |   6 +-
 3 files changed, 148 insertions(+), 2 deletions(-)
 create mode 100644 CMakeModules/FindcuDNN.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 312bd97058..c6bb17d1b3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -30,6 +30,7 @@ arrayfire_set_cmake_default_variables()
 set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
 
 find_package(CUDA 7.0)
+find_package(cuDNN 7.3)
 find_package(OpenCL 1.2)
 find_package(OpenGL)
 find_package(FreeImage)
diff --git a/CMakeModules/FindcuDNN.cmake b/CMakeModules/FindcuDNN.cmake
new file mode 100644
index 0000000000..fd49fbe96b
--- /dev/null
+++ b/CMakeModules/FindcuDNN.cmake
@@ -0,0 +1,143 @@
+# Fetched the original content of this file from
+# https://github.com/soumith/cudnn.torch
+#
+# Original Copyright:
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+#
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+#
+# FindcuDNN
+# -------
+#
+# Find cuDNN library
+#
+# This module creates imported target cuDNN::cuDNN upon successfull
+# lookup of cuDNN headers and libraries.
+#
+# Valiables that affect result:
+# <VERSION>, <REQUIRED>, <QUIET>: as usual
+#
+# Usage
+# -----
+# add_exectuable(helloworld main.cpp)
+# target_link_libraries(helloworld PRIVATE cuDNN::cuDNN)
+#
+# Note: It is recommended to avoid using variables set by the find module.
+#
+# Result variables
+# ----------------
+#
+# This module will set the following variables in your project:
+#
+# ``cuDNN_INCLUDE_DIRS``
+#   where to find cudnn.h.
+# ``cuDNN_LINK_LIBRARY``
+#   the libraries to link against to use cuDNN.
+# ``cuDNN_DLL_LIBRARY``
+#   Windows DLL of cuDNN
+# ``cuDNN_FOUND``
+#   If false, do not try to use cuDNN.
+# ``cuDNN_VERSION``
+#   Version of the cuDNN library we looked for
+
+find_package(PkgConfig)
+pkg_check_modules(PC_CUDNN QUIET cuDNN)
+
+find_package(CUDA QUIET)
+
+find_path(cuDNN_INCLUDE_DIRS
+  NAMES cudnn.h
+  HINTS
+    ${PC_CUDNN_INCLUDE_DIRS}
+    ${cuDNN_ROOT_DIR}
+    ${CUDA_TOOLKIT_INCLUDE}
+  PATH_SUFFIXES include
+  DOC "cuDNN include directory path." )
+
+if(cuDNN_INCLUDE_DIRS)
+  file(READ ${cuDNN_INCLUDE_DIRS}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
+  string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+    CUDNN_MAJOR_VERSION "${CUDNN_VERSION_FILE_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
+      CUDNN_MAJOR_VERSION "${CUDNN_MAJOR_VERSION}")
+  string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
+    CUDNN_MINOR_VERSION "${CUDNN_VERSION_FILE_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1"
+      CUDNN_MINOR_VERSION "${CUDNN_MINOR_VERSION}")
+  string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)"
+    CUDNN_PATCH_VERSION "${CUDNN_VERSION_FILE_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
+      CUDNN_PATCH_VERSION "${CUDNN_PATCH_VERSION}")
+  set(cuDNN_VERSION ${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION})
+endif()
+
+# Choose lib suffix to be exact major version if requested
+# otherwise, just pick the one read from cudnn.h header
+if(cuDNN_FIND_VERSION_EXACT)
+  set(cudnn_ver_suffix "${cuDNN_FIND_VERSION_MAJOR}")
+else()
+  set(cudnn_ver_suffix "${CUDNN_MAJOR_VERSION}")
+endif()
+
+if(cuDNN_INCLUDE_DIRS)
+  get_filename_component(libpath_cudart "${CUDA_CUDART_LIBRARY}" PATH)
+
+  find_library(cuDNN_LINK_LIBRARY
+    NAMES
+      libcudnn.so.${cudnn_ver_suffix}
+      libcudnn.${cudnn_ver_suffix}.dylib
+      cudnn
+    PATHS
+      $ENV{LD_LIBRARY_PATH}
+      ${libpath_cudart}
+      ${cuDNN_ROOT_DIR}
+      ${PC_CUDNN_LIBRARY_DIRS}
+      ${CMAKE_INSTALL_PREFIX}
+    PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
+    DOC "cuDNN link library." )
+
+  if(WIN32 AND cuDNN_LINK_LIBRARY)
+    find_file(cuDNN_DLL_LIBRARY
+    NAMES cudnn64_${cudnn_ver_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+    PATHS
+      $ENV{PATH}
+      ${libpath_cudart}
+      ${cuDNN_ROOT_DIR}
+      ${PC_CUDNN_LIBRARY_DIRS}
+      ${CMAKE_INSTALL_PREFIX}
+    PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
+    DOC "cuDNN Windows DLL." )
+  endif()
+endif()
+
+find_package_handle_standard_args(cuDNN
+  REQUIRED_VARS cuDNN_LINK_LIBRARY cuDNN_INCLUDE_DIRS
+  VERSION_VAR   cuDNN_VERSION)
+
+mark_as_advanced(cuDNN_LINK_LIBRARY cuDNN_INCLUDE_DIRS cuDNN_DLL_LIBRARY)
+
+if(cuDNN_FOUND)
+  add_library(cuDNN::cuDNN SHARED IMPORTED)
+  if(WIN32)
+    set_target_properties(cuDNN::cuDNN
+      PROPERTIES
+      IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+      INTERFACE_INCLUDE_DIRECTORIES "${cuDNN_INCLUDE_DIRS}"
+      IMPORTED_LOCATION "${cuDNN_DLL_LIBRARY}"
+      IMPORTED_IMPLIB "${cuDNN_LINK_LIBRARY}"
+    )
+  else(WIN32)
+    set_target_properties(cuDNN::cuDNN
+      PROPERTIES
+      IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+      INTERFACE_INCLUDE_DIRECTORIES "${cuDNN_INCLUDE_DIRS}"
+      IMPORTED_LOCATION "${cuDNN_LINK_LIBRARY}"
+    )
+  endif(WIN32)
+endif(cuDNN_FOUND)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 10ce5acfba..0b05fb5f9d 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -12,7 +12,9 @@ dependency_check(CUDA_FOUND "CUDA not found.")
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
-find_cuda_helper_libs(cudnn)
+if(NOT cuDNN_FOUND)
+  message(FATAL_ERROR "Atleast cuDNN version 7.3 is required, please install.")
+endif()
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
@@ -540,7 +542,7 @@ target_link_libraries(afcuda
     ${CUDA_CUFFT_LIBRARIES}
     ${CUDA_cusolver_LIBRARY}
     ${CUDA_cusparse_LIBRARY}
-    ${CUDA_cudnn_LIBRARY}
+    cuDNN::cuDNN
     ${CMAKE_DL_LIBS}
   )
 

From ccf8a3ea1136104a0d6814c819f4d4d5175215ca Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Dec 2019 09:23:30 +0530
Subject: [PATCH 1763/2677] Dump debug symbols into separate files from
 binaries (#2535)

* Dump debug symbols into separate files from binaries

* Linux systems will created separate `lib<target>`.debug
  files with debug symbols of respective backend binaries.
* Windows by default splits debug symbols into pdb files.
* OSX debug symbol files are not generated - DISABLED for now

OSX support will added later.

Component based installers will have *_debug_symbols component(s)
disabled(not-installed) by default. The user has to explicitly select
them during installation. The idea is to skip installation on these
components on deployment systems and have them installed on development
systems.

* Pass install destination as argument for split fn
---
 CMakeLists.txt                    |  1 +
 CMakeModules/CPackConfig.cmake    | 36 +++++++++++-
 CMakeModules/SplitDebugInfo.cmake | 94 +++++++++++++++++++++++++++++++
 src/api/unified/CMakeLists.txt    |  2 +
 src/backend/cpu/CMakeLists.txt    |  2 +
 src/backend/cuda/CMakeLists.txt   |  2 +
 src/backend/opencl/CMakeLists.txt |  2 +
 7 files changed, 138 insertions(+), 1 deletion(-)
 create mode 100644 CMakeModules/SplitDebugInfo.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c6bb17d1b3..cf57587403 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -19,6 +19,7 @@ include(build_cl2hpp)
 include(platform)
 include(GetPrerequisites)
 include(CheckCXXCompilerFlag)
+include(SplitDebugInfo)
 
 set_policies(
   TYPE NEW
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 9753c0f6b5..43b12e904f 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -166,12 +166,37 @@ cpack_add_component(opencl_dependencies
   DESCRIPTION "Libraries required by the OpenCL backend."
   GROUP opencl_backend
   INSTALL_TYPES All Development Runtime)
-  
+if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
+  cpack_add_component(afopencl_debug_symbols
+    DISPLAY_NAME "OpenCL Backend Debug Symbols"
+    DESCRIPTION "File containing debug symbols for afopencl dll/so/dylib file"
+    GROUP opencl_backend
+    DISABLED
+    INSTALL_TYPES Development)
+endif ()
+
 cpack_add_component(cuda_dependencies
   DISPLAY_NAME "CUDA Dependencies"
   DESCRIPTION "CUDA runtime and libraries required by the CUDA backend."
   GROUP cuda_backend
   INSTALL_TYPES All Development Runtime)
+if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
+  cpack_add_component(afcuda_debug_symbols
+    DISPLAY_NAME "CUDA Backend Debug Symbols"
+    DESCRIPTION "File containing debug symbols for afcuda dll/so/dylib file"
+    GROUP cuda_backend
+    DISABLED
+    INSTALL_TYPES Development)
+endif ()
+
+if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
+  cpack_add_component(afcpu_debug_symbols
+    DISPLAY_NAME "CPU Backend Debug Symbols"
+    DESCRIPTION "File containing debug symbols for afcpu dll/so/dylib file"
+    GROUP cpu_backend
+    DISABLED
+    INSTALL_TYPES Development)
+endif ()
 
 cpack_add_component(cuda
   DISPLAY_NAME "CUDA Backend"
@@ -206,11 +231,20 @@ cpack_add_component(opencl
   DEPENDS ${ocl_deps_comps}
   INSTALL_TYPES All Development Runtime)
 
+if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
+  cpack_add_component(af_debug_symbols
+    DISPLAY_NAME "Unified Backend Debug Symbols"
+    DESCRIPTION "File containing debug symbols for af dll/so/dylib file"
+    GROUP backends
+    DISABLED
+    INSTALL_TYPES Development)
+endif ()
 cpack_add_component(unified
   DISPLAY_NAME "Unified Backend"
   DESCRIPTION "The Unified backend allows you to choose between any of the installed backends (CUDA, OpenCL, or CPU) at runtime."
   GROUP backends
   INSTALL_TYPES All Development Runtime)
+
 cpack_add_component(headers
   DISPLAY_NAME "C/C++ Headers"
   DESCRIPTION "Headers for the ArrayFire libraries."
diff --git a/CMakeModules/SplitDebugInfo.cmake b/CMakeModules/SplitDebugInfo.cmake
new file mode 100644
index 0000000000..060fe84bda
--- /dev/null
+++ b/CMakeModules/SplitDebugInfo.cmake
@@ -0,0 +1,94 @@
+# Tailored after https://github.com/GerbilSoft/mcrecover/blob/master/cmake/macros/SplitDebugInformation.cmake
+# Minor modifications to original
+
+if (NOT WIN32)
+  include(CMakeFindBinUtils)
+  if (NOT APPLE AND NOT CMAKE_OBJCOPY)
+    message("'objcopy' tool not found; debug information will not be split.")
+  elseif (NOT CMAKE_STRIP)
+    message("'strip' tool not found; debug information will not be split.")
+  elseif (APPLE)
+    # TODO(pradeep) debug info splits on OSX are disabled
+    # this section of elseif will be removed when Apple support is added
+    message("Debug information is not split on OSX")
+  endif ()
+endif (NOT WIN32)
+
+function(af_split_debug_info _target _destination_dir)
+  set(SPLIT_TOOL_EXISTS ON)
+  if (WIN32)
+    set(SPLIT_TOOL_EXISTS OFF)
+    if (MSVC)
+      install(FILES
+        $<TARGET_PDB_FILE:${_target}>
+        DESTINATION ${_destination_dir}
+        COMPONENT "${_target}_debug_symbols"
+        )
+    endif()
+  elseif (NOT APPLE AND NOT CMAKE_OBJCOPY)
+    set(SPLIT_TOOL_EXISTS OFF)
+  elseif (NOT CMAKE_STRIP)
+    set(SPLIT_TOOL_EXISTS OFF)
+  elseif (APPLE)
+    # TODO(pradeep) debug info splits on OSX are disabled
+    # this section of elseif will be removed when Apple support is added
+    set(SPLIT_TOOL_EXISTS OFF)
+  endif ()
+
+  if (SPLIT_TOOL_EXISTS)
+    get_target_property(TARGET_TYPE ${_target} TYPE)
+    set(PREFIX_EXPR_1
+      "$<$<STREQUAL:$<TARGET_PROPERTY:${_target},PREFIX>,>:${CMAKE_${TARGET_TYPE}_PREFIX}>")
+    set(PREFIX_EXPR_2
+      "$<$<NOT:$<STREQUAL:$<TARGET_PROPERTY:${_target},PREFIX>,>>:$<TARGET_PROPERTY:${_target},PREFIX>>")
+    set(PREFIX_EXPR_FULL "${PREFIX_EXPR_1}${PREFIX_EXPR_2}")
+
+    # If a custom OUTPUT_NAME was specified, use it.
+    set(OUTPUT_NAME_EXPR_1
+        "$<$<STREQUAL:$<TARGET_PROPERTY:${_target},OUTPUT_NAME>,>:${_target}>")
+    set(OUTPUT_NAME_EXPR_2
+        "$<$<NOT:$<STREQUAL:$<TARGET_PROPERTY:${_target},OUTPUT_NAME>,>>:$<TARGET_PROPERTY:${_target},OUTPUT_NAME>>")
+    set(OUTPUT_NAME_EXPR "${OUTPUT_NAME_EXPR_1}${OUTPUT_NAME_EXPR_2}")
+    set(OUTPUT_NAME_FULL "${PREFIX_EXPR_FULL}${OUTPUT_NAME_EXPR}$<TARGET_PROPERTY:${_target},POSTFIX>")
+
+    set(SPLIT_DEBUG_TARGET_EXT ".debug")
+    if(APPLE)
+        set(SPLIT_DEBUG_TARGET_EXT ".dSYM")
+    endif()
+    set(SPLIT_DEBUG_SOURCE "$<TARGET_FILE:${_target}>")
+    set(SPLIT_DEBUG_TARGET_NAME
+        "$<TARGET_FILE_DIR:${_target}>/${OUTPUT_NAME_FULL}")
+    set(SPLIT_DEBUG_TARGET
+        "${SPLIT_DEBUG_TARGET_NAME}${SPLIT_DEBUG_TARGET_EXT}")
+
+    if(APPLE)
+      add_custom_command(TARGET ${_target} POST_BUILD
+          COMMAND dsymutil ${SPLIT_DEBUG_SOURCE} -o ${SPLIT_DEBUG_TARGET}
+          #TODO(pradeep) From initial research stripping debug info from
+          # is removing debug LC_ID_DYLIB command also which is make
+          # shared library unusable. Confirm this from OSX expert
+          # and remove these comments and below command
+          #COMMAND ${CMAKE_STRIP} --strip-debug ${SPLIT_DEBUG_SOURCE}
+        )
+    else(APPLE)
+      add_custom_command(TARGET ${_target} POST_BUILD
+        COMMAND ${CMAKE_OBJCOPY}
+          --only-keep-debug ${SPLIT_DEBUG_SOURCE} ${SPLIT_DEBUG_TARGET}
+        COMMAND ${CMAKE_STRIP}
+          --strip-debug ${SPLIT_DEBUG_SOURCE}
+        COMMAND ${CMAKE_OBJCOPY}
+          --add-gnu-debuglink=${SPLIT_DEBUG_TARGET} ${SPLIT_DEBUG_SOURCE}
+        )
+    endif()
+
+    install(FILES
+      ${SPLIT_DEBUG_TARGET}
+      DESTINATION ${_destination_dir}
+      COMPONENT "${OUTPUT_NAME_FULL}_debug_symbols"
+      )
+
+    # Make sure the file is deleted on `make clean`.
+    set_property(DIRECTORY APPEND
+      PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${SPLIT_DEBUG_TARGET})
+  endif(SPLIT_TOOL_EXISTS)
+endfunction(af_split_debug_info)
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index a931558e81..d644cf36c2 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -103,6 +103,8 @@ install(TARGETS af
   INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
   )
 
+af_split_debug_info(af ${AF_INSTALL_LIB_DIR})
+
 # install(TARGETS af EXPORT AF DESTINATION "${AF_INSTALL_LIB_DIR}"
 #   COMPONENT libraries)
 #
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 53a56e6b75..6492e0d857 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -351,6 +351,8 @@ if(LAPACK_FOUND OR MKL_FOUND)
       WITH_LINEAR_ALGEBRA)
 endif()
 
+af_split_debug_info(afcpu ${AF_INSTALL_LIB_DIR})
+
 install(TARGETS afcpu
   EXPORT ArrayFireCPUTargets
   COMPONENT cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 0b05fb5f9d..79e240c0da 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -573,6 +573,8 @@ if(APPLE)
   target_link_libraries(afcuda PUBLIC -Wl,-rpath,${CUDA_LIBRARIES_PATH})
 endif()
 
+af_split_debug_info(afcuda ${AF_INSTALL_LIB_DIR})
+
 install(TARGETS afcuda
   EXPORT ArrayFireCUDATargets
   COMPONENT cuda
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f11d9ab60a..a1d91bcfcc 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -516,6 +516,8 @@ if(LAPACK_FOUND OR MKL_FOUND)
       WITH_LINEAR_ALGEBRA)
 endif(LAPACK_FOUND OR MKL_FOUND)
 
+af_split_debug_info(afopencl ${AF_INSTALL_LIB_DIR})
+
 install(TARGETS afopencl
   EXPORT ArrayFireOpenCLTargets
   COMPONENT opencl

From e1ef255865afba21344bf555c7b0ba654dc3df95 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Dec 2019 09:23:59 +0530
Subject: [PATCH 1764/2677] Remove gl header dependency for cuda backend
 (#2663)

---
 src/backend/common/CMakeLists.txt            |  2 +-
 src/backend/cuda/CMakeLists.txt              | 12 +++++++++++-
 src/backend/cuda/GraphicsResourceManager.cpp |  9 +++------
 src/backend/cuda/device_manager.cpp          |  4 ++--
 src/backend/cuda/platform.cpp                |  3 ---
 src/backend/opencl/CMakeLists.txt            |  3 +--
 6 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 93119b4f77..bb184e00dc 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -75,7 +75,7 @@ target_include_directories(afcommon_interface
     ${ArrayFire_SOURCE_DIR}/src/backend
     ${ArrayFire_BINARY_DIR}
   SYSTEM INTERFACE
-    ${OPENGL_INCLUDE_DIR}
+    $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
     ${ArrayFire_SOURCE_DIR}/extern/forge/include
     ${ArrayFire_BINARY_DIR}/extern/forge/include
   )
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 79e240c0da..def5847fca 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -16,6 +16,17 @@ if(NOT cuDNN_FOUND)
   message(FATAL_ERROR "Atleast cuDNN version 7.3 is required, please install.")
 endif()
 
+if(NOT OPENGL_FOUND)
+  # create a dummy gl.h header to satisfy cuda_gl_interop.h requirement
+  # all opengl functionality is made available via glad third party code
+  # that is built along with arrayfire code base.
+  set(dummy_gl_root "${ArrayFire_BINARY_DIR}/include/GL")
+  if(APPLE)
+    set(dummy_gl_root "${ArrayFire_BINARY_DIR}/include/OpenGL")
+  endif()
+  file(WRITE "${dummy_gl_root}/gl.h" "// Dummy file to satisy cuda_gl_interop")
+endif()
+
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
 include(FileToString)
@@ -536,7 +547,6 @@ target_link_libraries(afcuda
     afcommon_interface
     cuda_scan_by_key
     cuda_thrust_sort_by_key
-    ${CUDA_LIBRARIES}
     ${CUDA_nvrtc_LIBRARY}
     ${CUDA_CUBLAS_LIBRARIES}
     ${CUDA_CUFFT_LIBRARIES}
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index de4e4dc71f..c2f45f488e 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -7,14 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if defined(OS_WIN)
-#include <windows.h>
-#endif
+#include <GraphicsResourceManager.hpp>
 
-// cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <common/graphics_common.hpp>
-#define __gl_h_  // FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
-#include <GraphicsResourceManager.hpp>
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+// __gl_h_ should be defined by glad.h inclusion
 #include <cuda_gl_interop.h>
 #include <err_cuda.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index a2dd974365..55a2991f6e 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -26,9 +26,9 @@
 #include <version.hpp>
 #include <af/cuda.h>
 #include <af/version.h>
-// cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <common/graphics_common.hpp>
-#define __gl_h_  // FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
+// cuda_gl_interop.h does not include OpenGL headers for ARM
+// __gl_h_ should be defined by glad.h inclusion
 #include <cuda_gl_interop.h>
 
 #include <algorithm>
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 12a00a1119..e8533ce023 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -31,10 +31,7 @@
 #include <af/cuda.h>
 #include <af/device.h>
 #include <af/version.h>
-// cuda_gl_interop.h does not include OpenGL headers for ARM
 #include <common/graphics_common.hpp>
-#define __gl_h_  // FIXME Hack to avoid gl.h inclusion by cuda_gl_interop.h
-#include <cuda_gl_interop.h>
 
 #include <algorithm>
 #include <array>
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a1d91bcfcc..0f36009599 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -447,8 +447,7 @@ if(AF_WITH_NONFREE)
 endif()
 
 if(APPLE)
-  target_link_libraries(afopencl
-    PRIVATE OpenGL::GL)
+  target_link_libraries(afopencl PRIVATE OpenGL::GL)
 endif()
 
 if(LAPACK_FOUND OR MKL_FOUND)

From 6f5812f14ef7f4ded0bc7428e3ad302e431c214a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 12 Nov 2019 13:00:45 +0530
Subject: [PATCH 1765/2677] Forward generator platform to external projects on
 windows

---
 CMakeModules/build_CLBlast.cmake | 9 ++++++++-
 CMakeModules/build_clBLAS.cmake  | 9 ++++++++-
 CMakeModules/build_clFFT.cmake   | 9 ++++++++-
 3 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index be2cfce794..e6e651d96d 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -37,6 +37,12 @@ index 9446499..786f7db 100644
   set(CLBLAST_PATCH_COMMAND ${GIT} apply ${ArrayFire_BINARY_DIR}/clblast.patch)
 endif()
 
+if(WIN32)
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
+else(WIN32)
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
+endif(WIN32)
+
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
@@ -46,7 +52,8 @@ ExternalProject_Add(
     UPDATE_COMMAND ""
     PATCH_COMMAND ${CLBLAST_PATCH_COMMAND}
     BUILD_BYPRODUCTS ${CLBlast_location}
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
+      -Wno-dev <SOURCE_DIR>
       -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
       "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
       -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 8de529e840..e535d6763a 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -12,6 +12,12 @@ set(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${C
 
 find_package(OpenCL)
 
+if(WIN32)
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
+else(WIN32)
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
+endif(WIN32)
+
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
@@ -21,7 +27,8 @@ ExternalProject_Add(
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
     DOWNLOAD_NO_PROGRESS 1
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/src
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
+      -Wno-dev <SOURCE_DIR>/src
       -DCMAKE_CXX_FLAGS:STRING="-fPIC"
       -DCMAKE_C_FLAGS:STRING="-fPIC"
       -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 28be38a3cb..9319d8498b 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -18,6 +18,12 @@ ELSE()
     SET(byproducts BUILD_BYPRODUCTS ${clFFT_location})
 ENDIF()
 
+if(WIN32)
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
+else(WIN32)
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
+endif(WIN32)
+
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
@@ -25,7 +31,8 @@ ExternalProject_Add(
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} "-G${CMAKE_GENERATOR}" -Wno-dev <SOURCE_DIR>/src
+    CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
+      -Wno-dev <SOURCE_DIR>/src
       -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
       "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
       -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}

From af665bc30b36502e0b164a59a124f43a21100daf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 Dec 2019 17:14:08 -0500
Subject: [PATCH 1766/2677] Refactor memory manager. Remove buffer_info.
 Simplify allocs. Disable Events (#2689)

* Refactor memory manager. Remove buffer_info. Simplify allocs

* Removed buffer_info from the allocation calls

The buffer_info object was created so that the event synchonization was
performed in the memAlloc call instead of in the memory manager. It did not have
significant benefit over performing the synchronization within the memory
manager and increased complexity of the API. Now that the events API is
exposed, it shouldn't be necessary to include this.

* Moved memory manager classes into separate files in the common directory
* Use memAlloc instead of bufferAlloc in OpenCL Array<T> initializtion to
  provide type context to the memory manager. Avoid inconsistencies between
  backends.
* Use objects instead of handles within the codebase
---
 include/af/memory.h                           | 145 +---------
 include/arrayfire.h                           |   4 -
 src/api/c/CMakeLists.txt                      |   2 -
 src/api/c/buffer_info.cpp                     | 119 ---------
 src/api/c/events.cpp                          |  14 +-
 src/api/c/events.hpp                          |   5 +-
 src/api/c/memory.cpp                          |  23 +-
 src/api/c/memory_manager.hpp                  | 252 ------------------
 src/api/c/memoryapi.hpp                       |  24 +-
 src/api/cpp/CMakeLists.txt                    |   1 -
 src/api/cpp/buffer_info.cpp                   |  74 -----
 src/api/cpp/device.cpp                        |   4 +-
 src/api/cpp/event.cpp                         |   2 +-
 src/api/unified/memory.cpp                    |  30 ---
 src/backend/common/AllocatorInterface.hpp     |  43 +++
 src/backend/common/CMakeLists.txt             |   4 +
 .../common/DefaultMemoryManager.cpp}          | 107 ++++----
 src/backend/common/DefaultMemoryManager.hpp   | 138 ++++++++++
 src/backend/common/MemoryManagerBase.hpp      |  93 +++++++
 src/backend/common/defines.hpp                |  10 +
 src/backend/common/jit/NaryNode.hpp           |   1 -
 src/backend/cpu/Array.hpp                     |   1 +
 src/backend/cpu/Event.cpp                     |   2 -
 src/backend/cpu/Event.hpp                     |   2 -
 src/backend/cpu/cholesky.cpp                  |   2 +
 src/backend/cpu/device_manager.cpp            |   2 +
 src/backend/cpu/device_manager.hpp            |   1 -
 src/backend/cpu/memory.cpp                    |  35 +--
 src/backend/cpu/memory.hpp                    |   2 +-
 src/backend/cpu/platform.cpp                  |   5 +-
 src/backend/cpu/sort_by_key.cpp               |   1 +
 src/backend/cpu/sort_index.cpp                |   2 +
 src/backend/cuda/Array.hpp                    |   4 +-
 src/backend/cuda/Event.cpp                    |   7 +-
 src/backend/cuda/Event.hpp                    |   2 -
 src/backend/cuda/device_manager.cpp           |   2 +
 src/backend/cuda/device_manager.hpp           |   1 -
 src/backend/cuda/jit.cpp                      |   1 +
 src/backend/cuda/memory.cpp                   |  42 +--
 src/backend/cuda/memory.hpp                   |   2 +-
 src/backend/cuda/platform.cpp                 |   3 +-
 src/backend/opencl/Array.cpp                  |  12 +-
 src/backend/opencl/Array.hpp                  |   1 +
 src/backend/opencl/Event.cpp                  |   2 -
 src/backend/opencl/Event.hpp                  |   2 -
 src/backend/opencl/device_manager.cpp         |   1 +
 src/backend/opencl/device_manager.hpp         |   7 +-
 src/backend/opencl/memory.cpp                 |  58 +---
 src/backend/opencl/memory.hpp                 |   2 +-
 src/backend/opencl/platform.cpp               |   1 +
 test/event.cpp                                |  21 +-
 test/memory.cpp                               | 155 +----------
 52 files changed, 455 insertions(+), 1021 deletions(-)
 delete mode 100644 src/api/c/buffer_info.cpp
 delete mode 100644 src/api/c/memory_manager.hpp
 delete mode 100644 src/api/cpp/buffer_info.cpp
 create mode 100644 src/backend/common/AllocatorInterface.hpp
 rename src/{api/c/memory_manager_impl.hpp => backend/common/DefaultMemoryManager.cpp} (76%)
 create mode 100644 src/backend/common/DefaultMemoryManager.hpp
 create mode 100644 src/backend/common/MemoryManagerBase.hpp

diff --git a/include/af/memory.h b/include/af/memory.h
index 0a9d631b8f..7ebd5ab905 100644
--- a/include/af/memory.h
+++ b/include/af/memory.h
@@ -16,147 +16,12 @@
 
 #if AF_API_VERSION >= 37
 
-typedef void* af_buffer_info;
-
 typedef void* af_memory_manager;
 
-#ifdef __cplusplus
-namespace af {
-
-/// A simple RAII wrapper for af_buffer_info
-class AFAPI buffer_info {
-    af_buffer_info p_;
-
-   public:
-    buffer_info(af_buffer_info p);
-    buffer_info(void* ptr, af_event event);
-    ~buffer_info();
-#if AF_COMPILER_CXX_RVALUE_REFERENCES
-    buffer_info(buffer_info&& other);
-    buffer_info& operator=(buffer_info&& other);
-#endif
-    void* getPtr() const;
-    void setPtr(void* ptr);
-    af_event getEvent() const;
-    void setEvent(af_event event);
-    af_buffer_info get() const;
-    af_event unlockEvent();
-    void* unlockPtr();
-
-   private:
-    buffer_info& operator=(const buffer_info& other);
-    buffer_info(const buffer_info& other);
-};
-
-}  // namespace af
-#endif  // __cplusplus
-
 #ifdef __cplusplus
 extern "C" {
 #endif  // __cplusplus
 
-/**
-   \brief Creates an \ref af_buffer_info handle from an \ref af_event event and
-   ptr
-
-   \param[in] buf The \ref af_buffer_info object to be created
-   \param[in] ptr A pointer
-   \param[in] event An \ref af_event
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_create_buffer_info(af_buffer_info* buf, void* ptr,
-                                   af_event event);
-
-/**
-   \brief deletes the \ref af_buffer_info and its resources
-
-   Deletes the \ref af_buffer_info object and its tracked resources. If buffer
-   still holds a pointer, that pointer is freed. Does NOT enqueue a wait on the
-   associated event
-
-   \param[in] buf The \ref af_buffer_info object that will be deleted
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_delete_buffer_info(af_buffer_info buf);
-
-/**
-   \brief Retrieves a pointer from an \ref af_buffer_info
-
-   \param[out] ptr The associated pointer
-   \param[in] buf The \ref af_buffer_info object
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_buffer_info_get_ptr(void** ptr, af_buffer_info buf);
-
-/**
-   \brief Retrieves an \ref af_event from an \ref af_buffer_info
-
-   \param[out] event The associated event
-   \param[in] buf The \ref af_buffer_info object
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_buffer_info_get_event(af_event* event, af_buffer_info buf);
-
-/**
-   \brief Sets a pointer on an \ref af_buffer_info
-
-   \param[in] buf The \ref af_buffer_info object
-   \param[in] ptr The pointer to set
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_buffer_info_set_ptr(af_buffer_info buf, void* ptr);
-
-/**
-   \brief Sets an \ref af_event on an \ref af_buffer_info
-
-   \param[in] buf The \ref af_buffer_info object
-   \param[in] event The \ref af_event to set
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_buffer_info_set_event(af_buffer_info buf, af_event event);
-
-/**
-   \brief Disassociates the \ref af_event from the \ref af_buffer_info object
-
-   Gets the \ref af_event and disassociated it from the af_buffer_info object.
-   Deleting the af_buffer_info object will not affect this event.
-
-   \param[out] event The \ref af_event that will be disassociated. If NULL no
-   event is returned and the event is NOT freed
-   \param[in] buf The target \ref af_buffer_info object
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_unlock_buffer_info_event(af_event* event, af_buffer_info buf);
-
-/**
-   \brief Disassociates the pointer from the \ref af_buffer_info object
-
-   Gets the pointer and disassociated it from the \ref af_buffer_info object.
-   Deleting the \ref af_buffer_info object will not affect this pointer.
-
-   \param[out] ptr The pointer that will be disassociated. If NULL no
-   pointer is returned and the data is NOT freed.
-   \param[in] buf The target \ref af_buffer_info object
-   \returns AF_SUCCESS
-
-   \ingroup buffer_info
-*/
-AFAPI af_err af_unlock_buffer_info_ptr(void** ptr, af_buffer_info buf);
-
 /**
    \brief Called after a memory manager is set and becomes active.
 
@@ -181,8 +46,7 @@ typedef af_err (*af_memory_manager_shutdown_fn)(af_memory_manager handle);
    \brief Function pointer that will be called by ArrayFire to allocate memory.
 
    \param[in] handle a pointer to the active \ref af_memory_manager handle
-   \param[out] buffer_info a pointer to a \ref af_buffer_info containing the
-   pointer to the allocated buffer and an associated \ref af_event
+   \param[out] ptr pointer to the allocated buffer
    \param[in] bytes number of bytes to allocate
    \param[in] user_lock a truthy value corresponding to whether or not the
    memory should have a user lock associated with it
@@ -198,7 +62,7 @@ typedef af_err (*af_memory_manager_shutdown_fn)(af_memory_manager handle);
    \ingroup memory_manager_api
 */
 typedef af_err (*af_memory_manager_alloc_fn)(af_memory_manager handle,
-                                             af_buffer_info* buffer_info,
+                                             void** ptr,
                                              /* bool */ int user_lock,
                                              const unsigned ndims, dim_t* dims,
                                              const unsigned element_size);
@@ -221,16 +85,13 @@ typedef af_err (*af_memory_manager_allocated_fn)(af_memory_manager handle,
 
    \param[in] handle a pointer to the active \ref af_memory_manager handle
    \param[out] ptr the pointer to query
-   \param[in] event a new \ref af_event which will be marked before the free is
-   executed such that enqueing a wait on this event
    \param[in] user_unlock frees the memory from user lock
    \returns AF_SUCCESS
 
    \ingroup memory_manager_api
 */
 typedef af_err (*af_memory_manager_unlock_fn)(af_memory_manager handle,
-                                              void* ptr, af_event event,
-                                              /* bool */ int user_unlock);
+                                              void* ptr, /* bool */ int user_unlock);
 
 /**
    \brief Called to signal the memory manager should free memory if possible
diff --git a/include/arrayfire.h b/include/arrayfire.h
index 56369b3642..aa378cb026 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -144,10 +144,6 @@ closures for each required function, for example:
       af_unset_memory_manager();
       \endcode
 
-      @defgroup buffer_info Buffer Info
-      \brief An interface for managing information about memory (pointers and
-\ref af_event)
-
       @defgroup native_memory_interface Native Memory Interface
       \brief Native alloc, native free, get device id, etc.
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 12dfe47862..09dccb2eab 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -57,7 +57,6 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/buffer_info.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/canny.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cast.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cholesky.cpp
@@ -112,7 +111,6 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/median.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/memoryapi.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/memory_manager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moddims.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/morph.cpp
diff --git a/src/api/c/buffer_info.cpp b/src/api/c/buffer_info.cpp
deleted file mode 100644
index 07c40d735e..0000000000
--- a/src/api/c/buffer_info.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-/*******************************************************
- * Copyright (c) 2019, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <memoryapi.hpp>
-
-#include <backend.hpp>
-#include <common/err_common.hpp>
-#include <af/device.h>
-#include <af/event.h>
-#include <af/memory.h>
-
-BufferInfo &getBufferInfo(const af_buffer_info handle) {
-    return *(BufferInfo *)handle;
-}
-
-af_buffer_info getHandle(BufferInfo &buf) {
-    BufferInfo *handle;
-    handle = &buf;
-    return (af_buffer_info)handle;
-}
-
-detail::Event &getEventFromBufferInfoHandle(const af_buffer_info handle) {
-    return getEvent(getBufferInfo(handle).event);
-}
-
-af_err af_create_buffer_info(af_buffer_info *handle, void *ptr,
-                             af_event event) {
-    try {
-        BufferInfo *buf = new BufferInfo({ptr, event});
-        *handle         = getHandle(*((BufferInfo *)buf));
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_delete_buffer_info(af_buffer_info handle) {
-    try {
-        /// NB: deleting a memory event buf does frees the associated memory
-        /// and deletes the associated event. Use unlock functions to free
-        /// resources individually
-        BufferInfo &buf = getBufferInfo(handle);
-        af_delete_event(buf.event);
-        if (buf.ptr) { af_free_device(buf.ptr); }
-
-        delete (BufferInfo *)handle;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_buffer_info_get_ptr(void **ptr, af_buffer_info handle) {
-    try {
-        BufferInfo &buf = getBufferInfo(handle);
-        *ptr            = buf.ptr;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_buffer_info_get_event(af_event *event, af_buffer_info handle) {
-    try {
-        BufferInfo &buf = getBufferInfo(handle);
-        *event          = buf.event;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_buffer_info_set_ptr(af_buffer_info handle, void *ptr) {
-    try {
-        BufferInfo &buf = getBufferInfo(handle);
-        buf.ptr         = ptr;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_buffer_info_set_event(af_buffer_info handle, af_event event) {
-    try {
-        BufferInfo &buf = getBufferInfo(handle);
-        buf.event       = event;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_unlock_buffer_info_event(af_event *event, af_buffer_info handle) {
-    try {
-        af_buffer_info_get_event(event, handle);
-        BufferInfo &buf = getBufferInfo(handle);
-        buf.event       = 0;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
-
-af_err af_unlock_buffer_info_ptr(void **ptr, af_buffer_info handle) {
-    try {
-        af_buffer_info_get_ptr(ptr, handle);
-        BufferInfo &buf = getBufferInfo(handle);
-        buf.ptr         = 0;
-    }
-    CATCHALL;
-
-    return AF_SUCCESS;
-}
diff --git a/src/api/c/events.cpp b/src/api/c/events.cpp
index 25bd9cb285..8dd8fc760d 100644
--- a/src/api/c/events.cpp
+++ b/src/api/c/events.cpp
@@ -16,12 +16,18 @@
 
 using namespace detail;
 
-Event &getEvent(const af_event handle) {
-    Event &event = *(Event *)handle;
+Event &getEvent(af_event &handle) {
+    Event &event = *static_cast<Event *>(handle);
     return event;
 }
 
-af_event getHandle(const Event &event) { return (af_event)&event; }
+const Event &getEvent(const af_event &handle) {
+    const Event &event = *static_cast<const Event *>(handle);
+    return event;
+}
+
+af_event getHandle(Event &event) { return static_cast<af_event>(&event); }
+
 
 af_err af_create_event(af_event *handle) {
     try {
@@ -35,7 +41,7 @@ af_err af_create_event(af_event *handle) {
 
 af_err af_delete_event(af_event handle) {
     try {
-        releaseEvent(handle);
+        delete &getEvent(handle);
     }
     CATCHALL;
 
diff --git a/src/api/c/events.hpp b/src/api/c/events.hpp
index 68cd0f5a96..aca2463e64 100644
--- a/src/api/c/events.hpp
+++ b/src/api/c/events.hpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <af/event.h>
 
-af_event getHandle(const detail::Event& event);
+af_event getHandle(detail::Event& event);
 
-detail::Event& getEvent(const af_event eventHandle);
+detail::Event& getEvent(af_event &eventHandle);
+const detail::Event& getEvent(const af_event &eventHandle);
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 53cda23f7d..4031a7cfbc 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -13,8 +13,8 @@
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <events.hpp>
 #include <handle.hpp>
-#include <memory_manager.hpp>
 #include <platform.hpp>
 #include <af/backend.h>
 #include <af/device.h>
@@ -27,6 +27,7 @@
 using namespace detail;
 
 using common::half;
+using std::move;
 
 af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
                        const dim_t *const dims, const af_dtype type) {
@@ -674,13 +675,14 @@ void MemoryManagerFunctionWrapper::shutdown() {
     AF_CHECK(getMemoryManager(handle_).shutdown_fn(handle_));
 }
 
-af_buffer_info MemoryManagerFunctionWrapper::alloc(
-    bool user_lock, const unsigned ndims, dim_t *dims,
-    const unsigned element_size) {
-    af_buffer_info bufferInfo;
-    AF_CHECK(getMemoryManager(handle_).alloc_fn(
-        handle_, &bufferInfo, (int)user_lock, ndims, dims, element_size));
-    return bufferInfo;
+void *MemoryManagerFunctionWrapper::alloc(bool user_lock, const unsigned ndims,
+                                          dim_t *dims,
+                                          const unsigned element_size) {
+    void *ptr;
+    AF_CHECK(getMemoryManager(handle_).alloc_fn(handle_, &ptr, (int)user_lock,
+                                                ndims, dims, element_size));
+
+    return ptr;
 }
 
 size_t MemoryManagerFunctionWrapper::allocated(void *ptr) {
@@ -689,10 +691,9 @@ size_t MemoryManagerFunctionWrapper::allocated(void *ptr) {
     return out;
 }
 
-void MemoryManagerFunctionWrapper::unlock(void *ptr, af_event e,
-                                          bool user_unlock) {
+void MemoryManagerFunctionWrapper::unlock(void *ptr, bool user_unlock) {
     AF_CHECK(
-        getMemoryManager(handle_).unlock_fn(handle_, ptr, e, (int)user_unlock));
+        getMemoryManager(handle_).unlock_fn(handle_, ptr, (int)user_unlock));
 }
 
 void MemoryManagerFunctionWrapper::signalMemoryCleanup() {
diff --git a/src/api/c/memory_manager.hpp b/src/api/c/memory_manager.hpp
deleted file mode 100644
index 4398f6f4fe..0000000000
--- a/src/api/c/memory_manager.hpp
+++ /dev/null
@@ -1,252 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <backend.hpp>
-#include <common/dispatch.hpp>
-#include <common/err_common.hpp>
-#include <common/util.hpp>
-#include <af/event.h>
-#include <af/memory.h>
-
-#include <algorithm>
-#include <functional>
-#include <iomanip>
-#include <memory>
-#include <mutex>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-#ifndef AF_MEM_DEBUG
-#define AF_MEM_DEBUG 0
-#endif
-
-namespace spdlog {
-class logger;
-}
-namespace common {
-using mutex_t      = std::mutex;
-using lock_guard_t = std::lock_guard<mutex_t>;
-
-constexpr unsigned MAX_BUFFERS = 1000;
-constexpr size_t ONE_GB        = 1 << 30;
-
-namespace memory {
-
-/**
- * An interface that provides backend-specific memory management functions,
- * typically calling a dedicated backend-specific native API. Stored, wrapped,
- * and called by a MemoryManagerBase, from which calls to its interface are
- * delegated.
- */
-class AllocatorInterface {
-   public:
-    AllocatorInterface() = default;
-    virtual ~AllocatorInterface() {}
-    virtual void shutdown()                       = 0;
-    virtual int getActiveDeviceId()               = 0;
-    virtual size_t getMaxMemorySize(int id)       = 0;
-    virtual void *nativeAlloc(const size_t bytes) = 0;
-    virtual void nativeFree(void *ptr)            = 0;
-    virtual spdlog::logger *getLogger() final { return this->logger.get(); }
-
-   protected:
-    std::shared_ptr<spdlog::logger> logger;
-};
-
-/**
- * A internal base interface for a memory manager which is exposed to AF
- * internals. Externally, both the default AF memory manager implementation and
- * custom memory manager implementations are wrapped in a derived implementation
- * of this interface.
- */
-class MemoryManagerBase {
-   public:
-    MemoryManagerBase()        = default;
-    MemoryManagerBase &operator=(const MemoryManagerBase &) = delete;
-    MemoryManagerBase(const MemoryManagerBase &)            = delete;
-    virtual ~MemoryManagerBase() {}
-    // Shuts down the allocator interface which calls shutdown on the subclassed
-    // memory manager with device-specific context
-    virtual void shutdownAllocator() {
-        if (nmi_) nmi_->shutdown();
-    }
-    virtual void initialize()                                              = 0;
-    virtual void shutdown()                                                = 0;
-    virtual af_buffer_info alloc(bool user_lock, const unsigned ndims,
-                                 dim_t *dims, const unsigned element_size) = 0;
-    virtual size_t allocated(void *ptr)                                    = 0;
-    virtual void unlock(void *ptr, af_event e, bool user_unlock)           = 0;
-    virtual void signalMemoryCleanup()                                     = 0;
-    virtual void printInfo(const char *msg, const int device)              = 0;
-    virtual void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                           size_t *lock_bytes, size_t *lock_buffers)       = 0;
-    virtual void userLock(const void *ptr)                                 = 0;
-    virtual void userUnlock(const void *ptr)                               = 0;
-    virtual bool isUserLocked(const void *ptr)                             = 0;
-    virtual size_t getMemStepSize()                                        = 0;
-    virtual void setMemStepSize(size_t new_step_size)                      = 0;
-
-    /// Backend-specific functions
-    // OpenCL
-    virtual void addMemoryManagement(int device)    = 0;
-    virtual void removeMemoryManagement(int device) = 0;
-
-    int getActiveDeviceId() { return nmi_->getActiveDeviceId(); }
-    size_t getMaxMemorySize(int id) { return nmi_->getMaxMemorySize(id); }
-    void *nativeAlloc(const size_t bytes) { return nmi_->nativeAlloc(bytes); }
-    void nativeFree(void *ptr) { nmi_->nativeFree(ptr); }
-    virtual spdlog::logger *getLogger() final { return nmi_->getLogger(); }
-    virtual void setAllocator(std::unique_ptr<AllocatorInterface> nmi) {
-        nmi_ = std::move(nmi);
-    }
-
-    // Memory pressure functions
-    void setMemoryPressureThreshold(float pressure) {
-        memoryPressureThreshold_ = pressure;
-    }
-    float getMemoryPressureThreshold() const {
-        return memoryPressureThreshold_;
-    }
-    virtual float getMemoryPressure()                       = 0;
-    virtual bool jitTreeExceedsMemoryPressure(size_t bytes) = 0;
-
-   private:
-    // A threshold at or above which JIT evaluations will be triggered due to
-    // memory pressure. Settable via a call to setMemoryPressureThreshold
-    float memoryPressureThreshold_{1.0};
-    // A backend-specific memory manager, containing backend-specific
-    // methods that call native memory manipulation functions in a device
-    // API. We need to wrap these since they are opaquely called by the
-    // memory manager.
-    std::unique_ptr<AllocatorInterface> nmi_;
-};
-
-/******************** Default memory manager implementation *******************/
-
-struct locked_info {
-    bool manager_lock;
-    bool user_lock;
-    size_t bytes;
-};
-
-using locked_t    = typename std::unordered_map<void *, memory::locked_info>;
-using locked_iter = typename locked_t::iterator;
-
-using free_t    = std::unordered_map<size_t, std::vector<af_buffer_info>>;
-using free_iter = typename free_t::iterator;
-
-using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
-
-struct memory_info {
-    locked_t locked_map;
-    free_t free_map;
-
-    size_t lock_bytes;
-    size_t lock_buffers;
-    size_t total_bytes;
-    size_t total_buffers;
-    size_t max_bytes;
-
-    memory_info()
-        // Calling getMaxMemorySize() here calls the virtual function
-        // that returns 0 Call it from outside the constructor.
-        : max_bytes(ONE_GB)
-        , total_bytes(0)
-        , total_buffers(0)
-        , lock_bytes(0)
-        , lock_buffers(0) {}
-
-    memory_info(memory_info &other)  = delete;
-    memory_info(memory_info &&other) = default;
-    memory_info &operator=(memory_info &other) = delete;
-    memory_info &operator=(memory_info &&other) = default;
-};
-
-}  // namespace memory
-
-class DefaultMemoryManager final : public memory::MemoryManagerBase {
-    size_t mem_step_size;
-    unsigned max_buffers;
-
-    bool debug_mode;
-
-    memory::memory_info &getCurrentMemoryInfo();
-
-   public:
-    DefaultMemoryManager(int num_devices, unsigned max_buffers, bool debug);
-
-    // Initializes the memory manager
-    virtual void initialize() override;
-
-    // Shuts down the memory manager
-    virtual void shutdown() override;
-
-    // Intended to be used with OpenCL backend, where
-    // users are allowed to add external devices(context, device pair)
-    // to the list of devices automatically detected by the library
-    void addMemoryManagement(int device) override;
-
-    // Intended to be used with OpenCL backend, where
-    // users are allowed to add external devices(context, device pair)
-    // to the list of devices automatically detected by the library
-    void removeMemoryManagement(int device) override;
-
-    void setMaxMemorySize();
-
-    /// Returns a pointer of size at least long
-    ///
-    /// This funciton will return a memory location of at least \p size
-    /// bytes. If there is already a free buffer available, it will use
-    /// that buffer. Otherwise, it will allocate a new buffer using the
-    /// nativeAlloc function.
-    af_buffer_info alloc(bool user_lock, const unsigned ndims, dim_t *dims,
-                         const unsigned element_size) override;
-
-    /// returns the size of the buffer at the pointer allocated by the memory
-    /// manager.
-    size_t allocated(void *ptr) override;
-
-    /// Frees or marks the pointer for deletion during the nex garbage
-    /// collection event
-    void unlock(void *ptr, af_event e, bool user_unlock) override;
-
-    /// Frees all buffers which are not locked by the user or not being
-    /// used.
-    void signalMemoryCleanup() override;
-
-    void printInfo(const char *msg, const int device) override;
-    void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                   size_t *lock_bytes, size_t *lock_buffers);
-    void userLock(const void *ptr) override;
-    void userUnlock(const void *ptr) override;
-    bool isUserLocked(const void *ptr) override;
-    size_t getMemStepSize() override;
-    void setMemStepSize(size_t new_step_size) override;
-    float getMemoryPressure() override;
-    bool jitTreeExceedsMemoryPressure(size_t bytes) override;
-
-   protected:
-    DefaultMemoryManager()                                  = delete;
-    ~DefaultMemoryManager()                                 = default;
-    DefaultMemoryManager(const DefaultMemoryManager &other) = delete;
-    // DefaultMemoryManager(const DefaultMemoryManager &&other) = default;
-    DefaultMemoryManager &operator=(const DefaultMemoryManager &other) = delete;
-    // DefaultMemoryManager &operator=(const DefaultMemoryManager &&other) =
-    //     default;
-    mutex_t memory_mutex;
-    // backend-specific
-    std::vector<common::memory::memory_info> memory;
-    // backend-agnostic
-    void cleanDeviceMemoryManager(int device);
-};
-
-}  // namespace common
diff --git a/src/api/c/memoryapi.hpp b/src/api/c/memoryapi.hpp
index 1453e2e148..dd5dcdfef2 100644
--- a/src/api/c/memoryapi.hpp
+++ b/src/api/c/memoryapi.hpp
@@ -9,26 +9,10 @@
 
 #pragma once
 
-#include <memory_manager.hpp>
+#include <common/MemoryManagerBase.hpp>
 
-#include <events.hpp>
-#include <af/event.h>
 #include <af/memory.h>
 
-////////////////////////////////////////////////////////////////////////////////
-// Buffer Info
-////////////////////////////////////////////////////////////////////////////////
-
-struct BufferInfo {
-    void *ptr;
-    af_event event;
-};
-
-BufferInfo &getBufferInfo(const af_buffer_info pair);
-
-af_buffer_info getHandle(BufferInfo &pairHandle);
-
-detail::Event &getEventFromBufferInfoHandle(const af_buffer_info handle);
 
 ////////////////////////////////////////////////////////////////////////////////
 // Memory Manager API
@@ -46,10 +30,10 @@ class MemoryManagerFunctionWrapper final : public common::memory::MemoryManagerB
     ~MemoryManagerFunctionWrapper();
     void initialize() override;
     void shutdown() override;
-    af_buffer_info alloc(bool user_lock, const unsigned ndims, dim_t *dims,
-                         const unsigned element_size) override;
+    void* alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+                const unsigned element_size) override;
     size_t allocated(void *ptr) override;
-    void unlock(void *ptr, af_event e, bool user_unlock) override;
+    void unlock(void *ptr, bool user_unlock) override;
     void signalMemoryCleanup() override;
     void printInfo(const char *msg, const int device) override;
     void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 8aaa67295a..14543fd921 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -11,7 +11,6 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/bilateral.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/buffer_info.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/canny.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/clamp.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/colorspace.cpp
diff --git a/src/api/cpp/buffer_info.cpp b/src/api/cpp/buffer_info.cpp
deleted file mode 100644
index 2d2e7f9f64..0000000000
--- a/src/api/cpp/buffer_info.cpp
+++ /dev/null
@@ -1,74 +0,0 @@
-/*******************************************************
- * Copyright (c) 2019, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <common/err_common.hpp>
-#include <af/event.h>
-#include <af/memory.h>
-
-namespace af {
-
-buffer_info::buffer_info(void* ptr, af_event event) {
-    AF_CHECK(af_create_buffer_info(&p_, ptr, event));
-}
-
-buffer_info::buffer_info(af_buffer_info p) : p_(p) {}
-
-buffer_info::buffer_info(buffer_info&& other) : p_(other.p_) { other.p_ = 0; }
-
-buffer_info& buffer_info::operator=(buffer_info&& other) {
-    af_delete_buffer_info(this->p_);
-    this->p_ = other.p_;
-    other.p_ = 0;
-    return *this;
-}
-
-buffer_info::~buffer_info() {
-    // No throw dtor
-    af_delete_buffer_info(p_);
-}
-
-void* buffer_info::getPtr() const {
-    void* ptr;
-    AF_CHECK(af_buffer_info_get_ptr(&ptr, p_));
-    return ptr;
-}
-
-af_event buffer_info::getEvent() const {
-    af_event e;
-    AF_CHECK(af_buffer_info_get_event(&e, p_));
-    return e;
-}
-
-void buffer_info::setPtr(void* ptr) {
-    AF_CHECK(af_buffer_info_set_ptr(p_, ptr));
-}
-
-void buffer_info::setEvent(af_event event) {
-    AF_CHECK(af_buffer_info_set_event(p_, event));
-}
-
-af_event buffer_info::unlockEvent() {
-    af_event event;
-    AF_CHECK(af_unlock_buffer_info_event(&event, p_));
-    // Zero out the event
-    AF_CHECK(af_buffer_info_set_event(p_, 0));
-    return event;
-}
-
-void* buffer_info::unlockPtr() {
-    void* ptr;
-    AF_CHECK(af_unlock_buffer_info_ptr(&ptr, p_));
-    // Zero out the ptr
-    AF_CHECK(af_buffer_info_set_ptr(p_, 0));
-    return ptr;
-}
-
-af_buffer_info buffer_info::get() const { return p_; }
-
-}  // namespace af
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 45076aa863..52f783e576 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -100,8 +100,7 @@ int deviceget() { return getDevice(); }
 
 void sync(int device) { AF_THROW(af_sync(device)); }
 
-///////////////////////////////////////////////////////////////////////////
-// Alloc and free host, pinned, zero copy
+// Alloc device memory
 void *alloc(const size_t elements, const af::dtype type) {
     void *ptr;
     AF_THROW(af_alloc_device(&ptr, elements * size_of(type)));
@@ -109,6 +108,7 @@ void *alloc(const size_t elements, const af::dtype type) {
     return ptr;
 }
 
+// Alloc pinned memory
 void *pinned(const size_t elements, const af::dtype type) {
     void *ptr;
     AF_THROW(af_alloc_pinned(&ptr, elements * size_of(type)));
diff --git a/src/api/cpp/event.cpp b/src/api/cpp/event.cpp
index b032d324d5..a43c893641 100644
--- a/src/api/cpp/event.cpp
+++ b/src/api/cpp/event.cpp
@@ -18,7 +18,7 @@ event::event(af_event e) : e_(e) {}
 
 event::~event() {
     // No dtor throw
-    af_delete_event(e_);
+    if(e_) af_delete_event(e_);
 }
 
 event::event(event&& other) : e_(other.e_) { other.e_ = 0; }
diff --git a/src/api/unified/memory.cpp b/src/api/unified/memory.cpp
index 6f47a461d9..d58dd68f90 100644
--- a/src/api/unified/memory.cpp
+++ b/src/api/unified/memory.cpp
@@ -10,36 +10,6 @@
 #include <af/memory.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_buffer_info(af_buffer_info* pair, void* ptr, af_event event) {
-    return CALL(pair, ptr, event);
-}
-
-af_err af_delete_buffer_info(af_buffer_info pair) { return CALL(pair); }
-
-af_err af_buffer_info_get_ptr(void** ptr, af_buffer_info pair) {
-    return CALL(ptr, pair);
-}
-
-af_err af_buffer_info_get_event(af_event* event, af_buffer_info pair) {
-    return CALL(event, pair);
-}
-
-af_err af_buffer_info_set_ptr(af_buffer_info pair, void* ptr) {
-    return CALL(pair, ptr);
-}
-
-af_err af_buffer_info_set_event(af_buffer_info pair, af_event event) {
-    return CALL(pair, event);
-}
-
-af_err af_unlock_buffer_info_event(af_event* event, af_buffer_info buf) {
-    return CALL(event, buf);
-}
-
-af_err af_unlock_buffer_info_ptr(void** ptr, af_buffer_info buf) {
-    return CALL(ptr, buf);
-}
-
 af_err af_create_memory_manager(af_memory_manager* out) { return CALL(out); }
 
 af_err af_release_memory_manager(af_memory_manager handle) {
diff --git a/src/backend/common/AllocatorInterface.hpp b/src/backend/common/AllocatorInterface.hpp
new file mode 100644
index 0000000000..499da73564
--- /dev/null
+++ b/src/backend/common/AllocatorInterface.hpp
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <cstddef>
+#include <memory>
+
+namespace spdlog {
+class logger;
+}
+namespace common {
+namespace memory {
+
+/**
+ * An interface that provides backend-specific memory management functions,
+ * typically calling a dedicated backend-specific native API. Stored, wrapped,
+ * and called by a MemoryManagerBase, from which calls to its interface are
+ * delegated.
+ */
+class AllocatorInterface {
+   public:
+    AllocatorInterface() = default;
+    virtual ~AllocatorInterface() {}
+    virtual void shutdown()                       = 0;
+    virtual int getActiveDeviceId()               = 0;
+    virtual size_t getMaxMemorySize(int id)       = 0;
+    virtual void *nativeAlloc(const size_t bytes) = 0;
+    virtual void nativeFree(void *ptr)            = 0;
+    virtual spdlog::logger *getLogger() final { return this->logger.get(); }
+
+  protected:
+    std::shared_ptr<spdlog::logger> logger;
+};
+
+}  // namespace memory
+}  // namespace common
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index bb184e00dc..00a3182294 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -20,8 +20,11 @@ target_sources(afcommon_interface
 
 target_sources(afcommon_interface
   INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/AllocatorInterface.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/DefaultMemoryManager.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/DefaultMemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
@@ -29,6 +32,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManagerBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
diff --git a/src/api/c/memory_manager_impl.hpp b/src/backend/common/DefaultMemoryManager.cpp
similarity index 76%
rename from src/api/c/memory_manager_impl.hpp
rename to src/backend/common/DefaultMemoryManager.cpp
index 9ec283ba18..f3921a6b69 100644
--- a/src/api/c/memory_manager_impl.hpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -7,18 +7,22 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Event.hpp>
+#include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
-#include <memory_manager.hpp>
+#include <common/dispatch.hpp>
+#include <common/err_common.hpp>
+#include <common/util.hpp>
 #include <memoryapi.hpp>
 #include <af/event.h>
 #include <af/memory.h>
+
+#include <memory>
 #include <string>
 #include <vector>
 
-using af::buffer_info;
-using af::event;
+using std::make_unique;
 using std::max;
+using std::move;
 using std::stoi;
 using std::string;
 using std::vector;
@@ -27,7 +31,8 @@ using spdlog::logger;
 
 namespace common {
 
-memory::memory_info &DefaultMemoryManager::getCurrentMemoryInfo() {
+DefaultMemoryManager::memory_info &
+DefaultMemoryManager::getCurrentMemoryInfo() {
     return memory[this->getActiveDeviceId()];
 }
 
@@ -37,20 +42,21 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
     // the lock is being held because the CPU backend calls sync.
-    vector<af_buffer_info> free_ptrs;
+    vector<void*> free_ptrs;
     size_t bytes_freed           = 0;
-    memory::memory_info &current = memory[device];
+    DefaultMemoryManager::memory_info &current = memory[device];
     {
         lock_guard_t lock(this->memory_mutex);
         // Return if all buffers are locked
         if (current.total_buffers == current.lock_buffers) return;
-        free_ptrs.reserve(32);
+        free_ptrs.reserve(current.free_map.size());
 
         for (auto &kv : current.free_map) {
             size_t num_ptrs = kv.second.size();
             // Free memory by pushing the last element into the free_ptrs
             // vector which will be freed once outside of the lock
-            for (auto &pair : kv.second) { free_ptrs.emplace_back(pair); }
+            //for (auto ptr : kv.second) { free_ptrs.emplace_back(pair); }
+            std::move(begin(kv.second), end(kv.second), back_inserter(free_ptrs));
             current.total_bytes -= num_ptrs * kv.first;
             bytes_freed += num_ptrs * kv.first;
             current.total_buffers -= num_ptrs;
@@ -61,13 +67,8 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(),
              bytesToString(bytes_freed));
     // Free memory outside of the lock
-    for (auto &pair : free_ptrs) {
-        auto *bufferInfo = (BufferInfo *)pair;
-        void *ptr        = bufferInfo->ptr;
+    for (auto ptr : free_ptrs) {
         this->nativeFree(ptr);
-        // Release resources
-        delete (detail::Event *)bufferInfo->event;
-        delete bufferInfo;
     }
 }
 
@@ -127,7 +128,7 @@ void DefaultMemoryManager::setMaxMemorySize() {
 
 float DefaultMemoryManager::getMemoryPressure() {
     lock_guard_t lock(this->memory_mutex);
-    memory::memory_info &current = this->getCurrentMemoryInfo();
+    memory_info &current = this->getCurrentMemoryInfo();
     if (current.lock_bytes > current.max_bytes ||
         current.lock_buffers > max_buffers) {
         return 1.0;
@@ -138,27 +139,24 @@ float DefaultMemoryManager::getMemoryPressure() {
 
 bool DefaultMemoryManager::jitTreeExceedsMemoryPressure(size_t bytes) {
     lock_guard_t lock(this->memory_mutex);
-    memory::memory_info &current = this->getCurrentMemoryInfo();
+    memory_info &current = this->getCurrentMemoryInfo();
     return 2 * bytes > current.lock_bytes;
 }
 
-af_buffer_info DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
-                                           dim_t *dims,
-                                           const unsigned element_size) {
+void* DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
+                                  dim_t *dims,
+                                  const unsigned element_size) {
     size_t bytes = element_size;
     for (unsigned i = 0; i < ndims; ++i) { bytes *= dims[i]; }
 
-    auto *event        = new detail::Event();
-    auto *bufferInfo   = new BufferInfo();
-    bufferInfo->ptr    = nullptr;
-    bufferInfo->event  = getHandle(*event);
+    void* ptr = nullptr;
     size_t alloc_bytes = this->debug_mode
                              ? bytes
                              : (divup(bytes, mem_step_size) * mem_step_size);
 
     if (bytes > 0) {
-        memory::memory_info &current = this->getCurrentMemoryInfo();
-        memory::locked_info info     = {!user_lock, user_lock, alloc_bytes};
+        memory_info &current = this->getCurrentMemoryInfo();
+        locked_info info     = {!user_lock, user_lock, alloc_bytes};
 
         // There is no memory cache in debug mode
         if (!this->debug_mode) {
@@ -169,36 +167,29 @@ af_buffer_info DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
             }
 
             lock_guard_t lock(this->memory_mutex);
-            memory::free_iter iter = current.free_map.find(alloc_bytes);
+            free_iter iter = current.free_map.find(alloc_bytes);
 
             if (iter != current.free_map.end() && !iter->second.empty()) {
                 // Delete existing buffer info and underlying event
-                delete event;
-                delete bufferInfo;
                 // Set to existing in from free map
-                bufferInfo = (BufferInfo *)iter->second.back();
-                event      = (detail::Event *)bufferInfo->event;
+                ptr = iter->second.back();
                 iter->second.pop_back();
-                void *ptrM               = bufferInfo->ptr;
-                current.locked_map[ptrM] = info;
+                current.locked_map[ptr] = info;
                 current.lock_bytes += alloc_bytes;
                 current.lock_buffers++;
             }
         }
 
-        void *ptr = bufferInfo->ptr;
         // Only comes here if buffer size not found or in debug mode
         if (ptr == nullptr) {
             // Perform garbage collection if memory can not be allocated
             try {
                 ptr             = this->nativeAlloc(alloc_bytes);
-                bufferInfo->ptr = ptr;
             } catch (const AfError &ex) {
                 // If out of memory, run garbage collect and try again
                 if (ex.getError() != AF_ERR_NO_MEM) throw;
                 this->signalMemoryCleanup();
                 ptr             = this->nativeAlloc(alloc_bytes);
-                bufferInfo->ptr = ptr;
             }
             lock_guard_t lock(this->memory_mutex);
             // Increment these two only when it succeeds to come here.
@@ -209,38 +200,37 @@ af_buffer_info DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
             current.lock_buffers++;
         }
     }
-    return (af_buffer_info)bufferInfo;
+
+    return ptr;
 }
 
 size_t DefaultMemoryManager::allocated(void *ptr) {
     if (!ptr) return 0;
-    memory::memory_info &current = this->getCurrentMemoryInfo();
-    memory::locked_iter iter     = current.locked_map.find((void *)ptr);
+    memory_info &current = this->getCurrentMemoryInfo();
+    locked_iter iter     = current.locked_map.find((void *)ptr);
     if (iter == current.locked_map.end()) return 0;
     return (iter->second).bytes;
 }
 
-void DefaultMemoryManager::unlock(void *ptr, af_event eventHandle,
+void DefaultMemoryManager::unlock(void *ptr,
                                   bool user_unlock) {
     // Shortcut for empty arrays
     if (!ptr) {
-        delete (detail::Event *)eventHandle;
         return;
     }
 
     // Frees the pointer outside the lock.
-    memory::uptr_t freed_ptr(nullptr, [this](void *p) { this->nativeFree(p); });
+    uptr_t freed_ptr(nullptr, [this](void *p) { this->nativeFree(p); });
     {
         lock_guard_t lock(this->memory_mutex);
-        memory::memory_info &current = this->getCurrentMemoryInfo();
+        memory_info &current = this->getCurrentMemoryInfo();
 
-        memory::locked_iter iter = current.locked_map.find((void *)ptr);
+        locked_iter iter = current.locked_map.find((void *)ptr);
 
         // Pointer not found in locked map
         if (iter == current.locked_map.end()) {
             // Probably came from user, just free it
             freed_ptr.reset(ptr);
-            delete (detail::Event *)eventHandle;
             return;
         }
 
@@ -252,7 +242,6 @@ void DefaultMemoryManager::unlock(void *ptr, af_event eventHandle,
 
         // Return early if either one is locked
         if ((iter->second).user_lock || (iter->second).manager_lock) {
-            delete (detail::Event *)eventHandle;
             return;
         }
 
@@ -267,12 +256,8 @@ void DefaultMemoryManager::unlock(void *ptr, af_event eventHandle,
                 current.total_buffers--;
                 current.total_bytes -= iter->second.bytes;
             }
-            delete (detail::Event *)eventHandle;
         } else {
-            auto *info  = new BufferInfo();
-            info->ptr   = ptr;
-            info->event = eventHandle;
-            current.free_map[bytes].emplace_back((af_buffer_info)info);
+            current.free_map[bytes].emplace_back(ptr);
         }
         current.locked_map.erase(iter);
     }
@@ -283,7 +268,7 @@ void DefaultMemoryManager::signalMemoryCleanup() {
 }
 
 void DefaultMemoryManager::printInfo(const char *msg, const int device) {
-    const memory::memory_info &current = this->getCurrentMemoryInfo();
+    const memory_info &current = this->getCurrentMemoryInfo();
 
     printf("%s\n", msg);
     printf(
@@ -322,8 +307,7 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
             unit = "MB";
         }
 
-        for (auto &pair : kv.second) {
-            void *ptr = ((BufferInfo *)pair)->ptr;
+        for (auto &ptr : kv.second) {
             printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr, size, unit,
                    status_mngr, status_user);
         }
@@ -334,7 +318,7 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
 
 void DefaultMemoryManager::usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                                      size_t *lock_bytes, size_t *lock_buffers) {
-    const memory::memory_info &current = this->getCurrentMemoryInfo();
+    const memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
     if (alloc_bytes) *alloc_bytes = current.total_bytes;
     if (alloc_buffers) *alloc_buffers = current.total_buffers;
@@ -343,15 +327,15 @@ void DefaultMemoryManager::usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 }
 
 void DefaultMemoryManager::userLock(const void *ptr) {
-    memory::memory_info &current = this->getCurrentMemoryInfo();
+    memory_info &current = this->getCurrentMemoryInfo();
 
     lock_guard_t lock(this->memory_mutex);
 
-    memory::locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
     if (iter != current.locked_map.end()) {
         iter->second.user_lock = true;
     } else {
-        memory::locked_info info = {false, true,
+        locked_info info = {false, true,
                                     100};  // This number is not relevant
 
         current.locked_map[(void *)ptr] = info;
@@ -359,14 +343,13 @@ void DefaultMemoryManager::userLock(const void *ptr) {
 }
 
 void DefaultMemoryManager::userUnlock(const void *ptr) {
-    auto *e = new detail::Event();
-    this->unlock(const_cast<void *>(ptr), getHandle(*e), true);
+    this->unlock(const_cast<void *>(ptr), true);
 }
 
 bool DefaultMemoryManager::isUserLocked(const void *ptr) {
-    memory::memory_info &current = this->getCurrentMemoryInfo();
+    memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
-    memory::locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
+    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
     if (iter != current.locked_map.end()) {
         return iter->second.user_lock;
     } else {
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
new file mode 100644
index 0000000000..4f87e25976
--- /dev/null
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -0,0 +1,138 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/MemoryManagerBase.hpp>
+#include <common/defines.hpp>
+
+#include <functional>
+#include <unordered_map>
+#include <vector>
+
+namespace common {
+
+constexpr unsigned MAX_BUFFERS = 1000;
+constexpr size_t ONE_GB        = 1 << 30;
+
+using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
+
+class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
+    size_t mem_step_size;
+    unsigned max_buffers;
+
+    bool debug_mode;
+
+    struct locked_info {
+        bool manager_lock;
+        bool user_lock;
+        size_t bytes;
+    };
+
+    using locked_t = typename std::unordered_map<void *, locked_info>;
+    using locked_iter = typename locked_t::iterator;
+
+    using free_t = std::unordered_map<size_t, std::vector<void*>>;
+    using free_iter = typename free_t::iterator;
+
+    struct memory_info {
+        locked_t locked_map;
+        free_t free_map;
+
+        size_t lock_bytes;
+        size_t lock_buffers;
+        size_t total_bytes;
+        size_t total_buffers;
+        size_t max_bytes;
+
+        memory_info()
+            // Calling getMaxMemorySize() here calls the virtual function
+            // that returns 0 Call it from outside the constructor.
+            : max_bytes(ONE_GB)
+            , total_bytes(0)
+            , total_buffers(0)
+            , lock_bytes(0)
+            , lock_buffers(0) {}
+
+        memory_info(memory_info &other)  = delete;
+        memory_info(memory_info &&other) = default;
+        memory_info &operator=(memory_info &other) = delete;
+        memory_info &operator=(memory_info &&other) = default;
+    };
+
+    memory_info &getCurrentMemoryInfo();
+
+   public:
+    DefaultMemoryManager(int num_devices, unsigned max_buffers, bool debug);
+
+    // Initializes the memory manager
+    virtual void initialize() override;
+
+    // Shuts down the memory manager
+    virtual void shutdown() override;
+
+    // Intended to be used with OpenCL backend, where
+    // users are allowed to add external devices(context, device pair)
+    // to the list of devices automatically detected by the library
+    void addMemoryManagement(int device) override;
+
+    // Intended to be used with OpenCL backend, where
+    // users are allowed to add external devices(context, device pair)
+    // to the list of devices automatically detected by the library
+    void removeMemoryManagement(int device) override;
+
+    void setMaxMemorySize();
+
+    /// Returns a pointer of size at least long
+    ///
+    /// This funciton will return a memory location of at least \p size
+    /// bytes. If there is already a free buffer available, it will use
+    /// that buffer. Otherwise, it will allocate a new buffer using the
+    /// nativeAlloc function.
+    void* alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+                const unsigned element_size) override;
+
+    /// returns the size of the buffer at the pointer allocated by the memory
+    /// manager.
+    size_t allocated(void *ptr) override;
+
+    /// Frees or marks the pointer for deletion during the nex garbage
+    /// collection event
+    void unlock(void *ptr, bool user_unlock) override;
+
+    /// Frees all buffers which are not locked by the user or not being
+    /// used.
+    void signalMemoryCleanup() override;
+
+    void printInfo(const char *msg, const int device) override;
+    void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                   size_t *lock_bytes, size_t *lock_buffers) override;
+    void userLock(const void *ptr) override;
+    void userUnlock(const void *ptr) override;
+    bool isUserLocked(const void *ptr) override;
+    size_t getMemStepSize() override;
+    void setMemStepSize(size_t new_step_size) override;
+    float getMemoryPressure() override;
+    bool jitTreeExceedsMemoryPressure(size_t bytes) override;
+
+   protected:
+    DefaultMemoryManager()                                  = delete;
+    ~DefaultMemoryManager()                                 = default;
+    DefaultMemoryManager(const DefaultMemoryManager &other) = delete;
+    DefaultMemoryManager(DefaultMemoryManager &&other) = default;
+    DefaultMemoryManager &operator=(const DefaultMemoryManager &other) = delete;
+    DefaultMemoryManager &operator=(DefaultMemoryManager &&other) = default;
+    common::mutex_t memory_mutex;
+    // backend-specific
+    std::vector<memory_info> memory;
+    // backend-agnostic
+    void cleanDeviceMemoryManager(int device);
+};
+
+}  // namespace common
diff --git a/src/backend/common/MemoryManagerBase.hpp b/src/backend/common/MemoryManagerBase.hpp
new file mode 100644
index 0000000000..5ba3281294
--- /dev/null
+++ b/src/backend/common/MemoryManagerBase.hpp
@@ -0,0 +1,93 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Event.hpp>
+#include <common/AllocatorInterface.hpp>
+
+#include <cstddef>
+#include <memory>
+
+namespace spdlog {
+class logger;
+}
+
+namespace common {
+namespace memory {
+/**
+ * A internal base interface for a memory manager which is exposed to AF
+ * internals. Externally, both the default AF memory manager implementation and
+ * custom memory manager implementations are wrapped in a derived implementation
+ * of this interface.
+ */
+class MemoryManagerBase {
+   public:
+    MemoryManagerBase()        = default;
+    MemoryManagerBase &operator=(const MemoryManagerBase &) = delete;
+    MemoryManagerBase(const MemoryManagerBase &)            = delete;
+    virtual ~MemoryManagerBase() {}
+    // Shuts down the allocator interface which calls shutdown on the subclassed
+    // memory manager with device-specific context
+    virtual void shutdownAllocator() {
+        if (nmi_) nmi_->shutdown();
+    }
+    virtual void initialize()                                        = 0;
+    virtual void shutdown()                                          = 0;
+    virtual void *alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+                        const unsigned element_size)                 = 0;
+    virtual size_t allocated(void *ptr)                              = 0;
+    virtual void unlock(void *ptr, bool user_unlock)                 = 0;
+    virtual void signalMemoryCleanup()                               = 0;
+    virtual void printInfo(const char *msg, const int device)        = 0;
+    virtual void usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                           size_t *lock_bytes, size_t *lock_buffers) = 0;
+    virtual void userLock(const void *ptr)                           = 0;
+    virtual void userUnlock(const void *ptr)                         = 0;
+    virtual bool isUserLocked(const void *ptr)                       = 0;
+    virtual size_t getMemStepSize()                                  = 0;
+    virtual void setMemStepSize(size_t new_step_size)                = 0;
+
+    /// Backend-specific functions
+    // OpenCL
+    virtual void addMemoryManagement(int device)    = 0;
+    virtual void removeMemoryManagement(int device) = 0;
+
+    int getActiveDeviceId() { return nmi_->getActiveDeviceId(); }
+    size_t getMaxMemorySize(int id) { return nmi_->getMaxMemorySize(id); }
+    void *nativeAlloc(const size_t bytes) { return nmi_->nativeAlloc(bytes); }
+    void nativeFree(void *ptr) { nmi_->nativeFree(ptr); }
+    virtual spdlog::logger *getLogger() final { return nmi_->getLogger(); }
+    virtual void setAllocator(std::unique_ptr<AllocatorInterface> nmi) {
+        nmi_ = std::move(nmi);
+    }
+
+    // Memory pressure functions
+    void setMemoryPressureThreshold(float pressure) {
+        memoryPressureThreshold_ = pressure;
+    }
+    float getMemoryPressureThreshold() const {
+        return memoryPressureThreshold_;
+    }
+    virtual float getMemoryPressure()                       = 0;
+    virtual bool jitTreeExceedsMemoryPressure(size_t bytes) = 0;
+
+   private:
+    // A threshold at or above which JIT evaluations will be triggered due to
+    // memory pressure. Settable via a call to setMemoryPressureThreshold
+    float memoryPressureThreshold_{1.0};
+    // A backend-specific memory manager, containing backend-specific
+    // methods that call native memory manipulation functions in a device
+    // API. We need to wrap these since they are opaquely called by the
+    // memory manager.
+    std::unique_ptr<AllocatorInterface> nmi_;
+};
+
+}  // namespace memory
+}  // namespace common
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index ec8bf97cec..4c78efbf8b 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <string>
+#include <mutex>
 
 inline std::string clipFilePath(std::string path, std::string str) {
     try {
@@ -69,3 +70,12 @@ using LibHandle = void*;
 #else
 #error "Unsupported platform"
 #endif
+
+#ifndef AF_MEM_DEBUG
+#define AF_MEM_DEBUG 0
+#endif
+
+namespace common {
+using mutex_t      = std::mutex;
+using lock_guard_t = std::lock_guard<mutex_t>;
+}
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index c8a6f084f3..47cf4d480e 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -98,7 +98,6 @@ common::Node_ptr createNaryNode(
             return ptr;
         }
     }
-    assert("MISSING HEURISTIC EVALUATION" && 1 == 0);
     return ptr;
 }
 }  // namespace common
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 994b916ee9..ad8816fa14 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -11,6 +11,7 @@
 #pragma once
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <jit/Node.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/Event.cpp b/src/backend/cpu/Event.cpp
index e8c62bafd5..83454529a6 100644
--- a/src/backend/cpu/Event.cpp
+++ b/src/backend/cpu/Event.cpp
@@ -37,8 +37,6 @@ af_event createEvent() {
     return getHandle(ref);
 }
 
-void releaseEvent(af_event eventHandle) { delete (Event*)eventHandle; }
-
 void markEventOnActiveQueue(af_event eventHandle) {
     Event& event = getEvent(eventHandle);
     // Use the currently-active queue
diff --git a/src/backend/cpu/Event.hpp b/src/backend/cpu/Event.hpp
index c97c8af623..bcd2ac31ef 100644
--- a/src/backend/cpu/Event.hpp
+++ b/src/backend/cpu/Event.hpp
@@ -47,8 +47,6 @@ Event makeEvent(cpu::queue &queue);
 
 af_event createEvent();
 
-void releaseEvent(af_event eventHandle);
-
 void markEventOnActiveQueue(af_event eventHandle);
 
 void enqueueWaitOnActiveQueue(af_event eventHandle);
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 34d73a5205..efe763583a 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -9,6 +9,8 @@
 
 #include <cholesky.hpp>
 
+#include <common/err_common.hpp>
+
 #if defined(WITH_LINEAR_ALGEBRA)
 
 #include <Array.hpp>
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
index b9a1931a74..dc00900161 100644
--- a/src/backend/cpu/device_manager.cpp
+++ b/src/backend/cpu/device_manager.cpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/DefaultMemoryManager.hpp>
+#include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <device_manager.hpp>
 #include <memory.hpp>
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index 6e2415398c..ffd983d048 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#include <memory_manager.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <memory>
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index f73ef60f35..2174080a43 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -9,10 +9,10 @@
 
 #include <memory.hpp>
 
+#include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/half.hpp>
 #include <err_cpu.hpp>
-#include <memory_manager_impl.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <spdlog/spdlog.h>
@@ -56,37 +56,23 @@ template<typename T>
 unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
-    af_buffer_info pair =
-        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue());
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
     return unique_ptr<T[], function<void(T *)>>((T *)ptr, memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
     dim4 dims(bytes);
-    af_buffer_info pair = memoryManager().alloc(true, 1, dims.get(), 1);
-    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue());
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
     return ptr;
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
-                                  false);
+    return memoryManager().unlock((void *)ptr, false);
 }
 
 void memFreeUser(void *ptr) {
-    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), true);
+    memoryManager().unlock(ptr, true);
 }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
@@ -107,20 +93,13 @@ template<typename T>
 T *pinnedAlloc(const size_t &elements) {
     // TODO: make pinnedAlloc aware of array shapes
     dim4 dims(elements);
-    af_buffer_info pair =
-        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue());
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
     return (T *)ptr;
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), false);
+    memoryManager().unlock((void *)ptr, false);
 }
 
 #define INSTANTIATE(T)                                                \
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index 2c19ada091..bdd7365559 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 
-#include <memory_manager.hpp>
+#include <common/AllocatorInterface.hpp>
 #include <af/defines.h>
 
 #include <functional>
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 2f6f4cd4e9..ccbc8ad021 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/MemoryManagerBase.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <device_manager.hpp>
@@ -16,6 +17,7 @@
 
 #include <algorithm>
 #include <cctype>
+#include <memory>
 #include <sstream>
 
 using common::memory::MemoryManagerBase;
@@ -25,6 +27,7 @@ using std::ostringstream;
 using std::ptr_fun;
 using std::stoi;
 using std::string;
+using std::unique_ptr;
 
 namespace cpu {
 
@@ -155,7 +158,7 @@ MemoryManagerBase& memoryManager() {
     return *(inst.memManager);
 }
 
-void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr) {
+void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
     return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
 }
 
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index f888758a12..ef1a1bdd2f 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <copy.hpp>
+#include <common/err_common.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index 4e1d07b559..bd2055bdb8 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <copy.hpp>
+#include <common/err_common.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -16,6 +17,7 @@
 #include <range.hpp>
 #include <reorder.hpp>
 #include <sort_index.hpp>
+
 #include <algorithm>
 #include <numeric>
 
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index af4f08384b..f29ef4a206 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <common/jit/Node.hpp>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
@@ -18,9 +19,10 @@
 #include <traits.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
-#include <vector>
 #include "traits.hpp"
 
+#include <vector>
+
 namespace cuda {
 using af::dim4;
 
diff --git a/src/backend/cuda/Event.cpp b/src/backend/cuda/Event.cpp
index 52a4865b5b..0b0d9618e8 100644
--- a/src/backend/cuda/Event.cpp
+++ b/src/backend/cuda/Event.cpp
@@ -29,16 +29,13 @@ af_event createEvent() {
     // Default CUDA stream needs to be initialized to use the CUDA driver
     // Ctx
     getActiveStream();
-    std::unique_ptr<Event> e(new Event());
+    auto e = std::make_unique<Event>();
     if (e->create() != CUDA_SUCCESS) {
         AF_ERROR("Could not create event", AF_ERR_RUNTIME);
     }
-    Event& ref = *e.release();
-    return getHandle(ref);
+    return getHandle(*(e.release()));
 }
 
-void releaseEvent(af_event eventHandle) { delete (Event*)eventHandle; }
-
 void markEventOnActiveQueue(af_event eventHandle) {
     Event& event = getEvent(eventHandle);
     // Use the currently-active stream
diff --git a/src/backend/cuda/Event.hpp b/src/backend/cuda/Event.hpp
index f2b709ad03..4d9cb7e295 100644
--- a/src/backend/cuda/Event.hpp
+++ b/src/backend/cuda/Event.hpp
@@ -55,8 +55,6 @@ Event makeEvent(cudaStream_t stream);
 
 af_event createEvent();
 
-void releaseEvent(af_event eventHandle);
-
 void markEventOnActiveQueue(af_event eventHandle);
 
 void enqueueWaitOnActiveQueue(af_event eventHandle);
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 55a2991f6e..7d5600419a 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -12,7 +12,9 @@
 #endif
 
 #include <GraphicsResourceManager.hpp>
+#include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index e635a187ec..883d71f6f5 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#include <memory_manager.hpp>
 #include <platform.hpp>
 
 #include <memory>
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index c8b1e5ba76..54a98e3c2e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -11,6 +11,7 @@
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/jit/Node.hpp>
+#include <common/util.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
 #include <device_manager.hpp>
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index c6cf5435cf..6e1fba9178 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -11,6 +11,7 @@
 
 #include <Event.hpp>
 #include <common/Logger.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/util.hpp>
@@ -18,7 +19,6 @@
 #include <cuda_runtime.h>
 #include <cuda_runtime_api.h>
 #include <err_cuda.hpp>
-#include <memory_manager_impl.hpp>
 #include <platform.hpp>
 #include <spdlog/spdlog.h>
 #include <types.hpp>
@@ -64,39 +64,22 @@ uptr<T> memAlloc(const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
     size_t size = elements * sizeof(T);
-    af_buffer_info pair =
-        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
-    cudaStream_t stream = getActiveStream();
-    if (e) e.enqueueWait(stream);
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr   = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
     return uptr<T>(static_cast<T *>(ptr), memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
     dim4 dims(bytes);
-    af_buffer_info pair = memoryManager().alloc(true, 1, dims.get(), 1);
-    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
-    cudaStream_t stream = getActiveStream();
-    if (e) e.enqueueWait(stream);
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
     return ptr;
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), false);
+    memoryManager().unlock((void *)ptr, false);
 }
 
-void memFreeUser(void *ptr) {
-    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
@@ -116,22 +99,13 @@ template<typename T>
 T *pinnedAlloc(const size_t &elements) {
     // TODO: make pinnedAlloc aware of array shapes
     dim4 dims(elements);
-    af_buffer_info pair =
-        pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
-    cudaStream_t stream = getActiveStream();
-    if (e) e.enqueueWait(stream);
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
-    return (T *)ptr;
+    void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    return static_cast<T *>(ptr);
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    pinnedMemoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
-                                 false);
+    pinnedMemoryManager().unlock((void *)ptr, false);
 }
 
 #define INSTANTIATE(T)                                 \
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index a2c397b7bb..d033ba0443 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 
-#include <memory_manager.hpp>
+#include <common/AllocatorInterface.hpp>
 
 #include <cstdlib>
 #include <functional>
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index e8533ce023..6053f3be73 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -12,8 +12,10 @@
 #endif
 
 #include <GraphicsResourceManager.hpp>
+#include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
+#include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
 #include <common/unique_handle.hpp>
 #include <common/util.hpp>
@@ -31,7 +33,6 @@
 #include <af/cuda.h>
 #include <af/device.h>
 #include <af/version.h>
-#include <common/graphics_common.hpp>
 
 #include <algorithm>
 #include <array>
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index a3e67f447c..82f0c1030b 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -51,7 +51,7 @@ template<typename T>
 Array<T>::Array(dim4 dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            (af_dtype)dtype_traits<T>::af_type)
-    , data(bufferAlloc(info.elements() * sizeof(T)), bufferFree)
+    , data(memAlloc<T>(info.elements()).release(), bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
     , ready(true)
@@ -71,7 +71,7 @@ template<typename T>
 Array<T>::Array(dim4 dims, const T *const in_data)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            (af_dtype)dtype_traits<T>::af_type)
-    , data(bufferAlloc(info.elements() * sizeof(T)), bufferFree)
+    , data(memAlloc<T>(info.elements()).release(), bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
     , ready(true)
@@ -89,7 +89,7 @@ template<typename T>
 Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            (af_dtype)dtype_traits<T>::af_type)
-    , data(copy ? bufferAlloc(info.elements() * sizeof(T)) : new Buffer(mem),
+    , data(copy ? memAlloc<T>(info.elements()).release() : new Buffer(mem),
            bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -137,7 +137,7 @@ Array<T>::Array(dim4 dims, dim4 strides, dim_t offset_, const T *const in_data,
     : info(getActiveDeviceId(), dims, offset_, strides,
            (af_dtype)dtype_traits<T>::af_type)
     , data(is_device ? (new Buffer((cl_mem)in_data))
-                     : (bufferAlloc(info.total() * sizeof(T))),
+                     : (memAlloc<T>(info.elements()).release()),
            bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -154,7 +154,7 @@ void Array<T>::eval() {
     if (isReady()) return;
 
     this->setId(getActiveDeviceId());
-    data = Buffer_ptr(bufferAlloc(elements() * sizeof(T)), bufferFree);
+    data = Buffer_ptr(memAlloc<T>(info.elements()).release(), bufferFree);
 
     // Do not replace this with cast operator
     KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
@@ -196,7 +196,7 @@ void evalMultiple(vector<Array<T> *> arrays) {
         array->ready = true;
         array->setId(getActiveDeviceId());
         array->data =
-            Buffer_ptr(bufferAlloc(info.elements() * sizeof(T)), bufferFree);
+            Buffer_ptr(memAlloc<T>(info.elements()).release(), bufferFree);
 
         // Do not replace this with cast operator
         KParam kInfo = {
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index a078df1195..261464f084 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -12,6 +12,7 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/jit/Node.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <err_opencl.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/opencl/Event.cpp b/src/backend/opencl/Event.cpp
index 1c7da40da8..9a8dc24061 100644
--- a/src/backend/opencl/Event.cpp
+++ b/src/backend/opencl/Event.cpp
@@ -36,8 +36,6 @@ af_event createEvent() {
     return getHandle(ref);
 }
 
-void releaseEvent(af_event eventHandle) { delete (Event*)eventHandle; }
-
 void markEventOnActiveQueue(af_event eventHandle) {
     Event& event = getEvent(eventHandle);
     // Use the currently-active stream
diff --git a/src/backend/opencl/Event.hpp b/src/backend/opencl/Event.hpp
index 2f5c445f28..b9797d8afa 100644
--- a/src/backend/opencl/Event.hpp
+++ b/src/backend/opencl/Event.hpp
@@ -48,8 +48,6 @@ Event makeEvent(cl::CommandQueue &queue);
 
 af_event createEvent();
 
-void releaseEvent(af_event eventHandle);
-
 void markEventOnActiveQueue(af_event eventHandle);
 
 void enqueueWaitOnActiveQueue(af_event eventHandle);
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 9e7d016614..a2e413469f 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -14,6 +14,7 @@
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
 #include <clfft.hpp>
+#include <common/DefaultMemoryManager.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index ddb6df5e11..04d76d638b 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -9,14 +9,13 @@
 
 #pragma once
 
+#include <platform.hpp>
+
 #include <memory>
 #include <mutex>
 #include <string>
 #include <vector>
 
-#include <memory_manager.hpp>
-#include <platform.hpp>
-
 using common::memory::MemoryManagerBase;
 
 #ifndef AF_OPENCL_MEM_DEBUG
@@ -120,7 +119,7 @@ class DeviceManager {
 
    private:
     // Attributes
-    common::mutex_t deviceMutex;
+    std::mutex deviceMutex;
     std::vector<cl::Device*> mDevices;
     std::vector<cl::Context*> mContexts;
     std::vector<cl::CommandQueue*> mQueues;
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 33b11b58cc..782a19b06a 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -7,16 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/Logger.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <err_opencl.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
+#include <spdlog/spdlog.h>
 #include <types.hpp>
 #include <af/dim4.hpp>
 
-#include <common/Logger.hpp>
-#include <memory_manager_impl.hpp>
-#include <spdlog/spdlog.h>
-
 #include <utility>
 
 using common::bytesToString;
@@ -57,56 +56,33 @@ unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
     const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
-    af_buffer_info pair =
-        memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue()());
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *rawPtr     = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
-    cl::Buffer *ptr = static_cast<cl::Buffer *>(rawPtr);
-    return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(ptr,
+    void *ptr       = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
+    return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(buf,
                                                                 bufferFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
     dim4 dims(bytes);
-    af_buffer_info pair = memoryManager().alloc(true, 1, dims.get(), 1);
-    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue()());
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
     return ptr;
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
-                                  false);
+    return memoryManager().unlock((void *)ptr, false);
 }
 
-void memFreeUser(void *ptr) {
-    memoryManager().unlock((void *)ptr, detail::createAndMarkEvent(), true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
 
 cl::Buffer *bufferAlloc(const size_t &bytes) {
     dim4 dims(bytes);
-    af_buffer_info pair = memoryManager().alloc(false, 1, dims.get(), 1);
-    detail::Event e     = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue()());
-    auto *bufferInfo = (BufferInfo *)pair;
-    void *ptr        = bufferInfo->ptr;
-    delete (detail::Event *)bufferInfo->event;
-    delete bufferInfo;
+    void *ptr = memoryManager().alloc(false, 1, dims.get(), 1);
     return static_cast<cl::Buffer *>(ptr);
 }
 
 void bufferFree(cl::Buffer *buf) {
-    return memoryManager().unlock((void *)buf, detail::createAndMarkEvent(),
-                                  false);
+    return memoryManager().unlock((void *)buf, false);
 }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
@@ -127,20 +103,13 @@ template<typename T>
 T *pinnedAlloc(const size_t &elements) {
     // TODO: make pinnedAlloc aware of array shapes
     dim4 dims(elements);
-    af_buffer_info pair =
-        pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    detail::Event e = std::move(getEventFromBufferInfoHandle(pair));
-    if (e) e.enqueueWait(getQueue()());
-    void *ptr;
-    af_unlock_buffer_info_ptr(&ptr, pair);
-    af_delete_buffer_info(pair);
+    void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
     return static_cast<T *>(ptr);
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    pinnedMemoryManager().unlock((void *)ptr, detail::createAndMarkEvent(),
-                                 false);
+    pinnedMemoryManager().unlock((void *)ptr, false);
 }
 
 #define INSTANTIATE(T)                                                         \
@@ -162,6 +131,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(common::half)
 
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index a9d8ec3020..35632a9d12 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 
-#include <memory_manager.hpp>
+#include <common/AllocatorInterface.hpp>
 
 #include <cstdlib>
 #include <functional>
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 68e199482e..a090aa686b 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -16,6 +16,7 @@
 #include <cache.hpp>
 #include <clfft.hpp>
 #include <common/host_memory.hpp>
+#include <common/DefaultMemoryManager.hpp>
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <err_opencl.hpp>
diff --git a/test/event.cpp b/test/event.cpp
index 2fb932fabd..5b98cbe433 100644
--- a/test/event.cpp
+++ b/test/event.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -38,17 +38,16 @@ TEST(EventTests, EventCreateAndMove) {
     af_event eventHandle;
     ASSERT_SUCCESS(af_create_event(&eventHandle));
 
-    std::unique_ptr<event> e;
-    e.reset(new event(eventHandle));
-    e->mark();
-    ASSERT_EQ(eventHandle, e->get());
+    event e(eventHandle);
+    e.mark();
+    ASSERT_EQ(eventHandle, e.get());
 
     auto otherEvent = std::move(e);
-    ASSERT_EQ(otherEvent->get(), eventHandle);
+    ASSERT_EQ(otherEvent.get(), eventHandle);
 
-    std::unique_ptr<event> f;
-    f.reset(new event());
-    af_event fE       = f->get();
-    auto anotherEvent = std::move(f);
-    ASSERT_EQ(fE, anotherEvent->get());
+    event f;
+    af_event fE       = f.get();
+    event anotherEvent = std::move(f);
+    ASSERT_EQ(fE, anotherEvent.get());
+    af::sync();
 }
diff --git a/test/memory.cpp b/test/memory.cpp
index c3893bd0f6..d0768850b6 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -15,6 +15,7 @@
 #include <af/internal.h>
 #include <af/memory.h>
 #include <af/traits.hpp>
+
 #include <memory>
 #include <unordered_map>
 #include <unordered_set>
@@ -23,7 +24,6 @@
 
 using af::alloc;
 using af::array;
-using af::buffer_info;
 using af::cdouble;
 using af::cfloat;
 using af::deviceGC;
@@ -635,119 +635,6 @@ TEST(Memory, IndexedDevice) {
     }
 }
 
-TEST(BufferInfo, SimpleCreateDelete) {
-    af_event event;
-    ASSERT_SUCCESS(af_create_event(&event));
-    af_buffer_info pair;
-
-    void *ptr = af::alloc(1, dtype::f32);
-    ASSERT_SUCCESS(af_create_buffer_info(&pair, ptr, event));
-    ASSERT_SUCCESS(af_delete_buffer_info(pair));
-}
-
-TEST(BufferInfo, Unlock) {
-    af_event event;
-    ASSERT_SUCCESS(af_create_event(&event));
-    af_buffer_info pair;
-    void *ptr = af::alloc(1, dtype::f32);
-    ASSERT_SUCCESS(af_create_buffer_info(&pair, ptr, event));
-
-    void *curPtr;
-    ASSERT_SUCCESS(af_unlock_buffer_info_ptr(&curPtr, pair));
-    ASSERT_EQ(curPtr, ptr);
-    void *zeroPtr;
-    ASSERT_SUCCESS(af_buffer_info_get_ptr(&zeroPtr, pair));
-    ASSERT_EQ(zeroPtr, nullptr);
-    ASSERT_SUCCESS(af_unlock_buffer_info_ptr(&zeroPtr, pair));
-    ASSERT_EQ(zeroPtr, nullptr);
-
-    af_event curEvent;
-    ASSERT_SUCCESS(af_unlock_buffer_info_event(&curEvent, pair));
-    ASSERT_EQ(curEvent, event);
-    void *zeroEvent;
-    ASSERT_SUCCESS(af_buffer_info_get_ptr(&zeroEvent, pair));
-    ASSERT_EQ(zeroEvent, nullptr);
-    ASSERT_SUCCESS(af_unlock_buffer_info_ptr(&zeroEvent, pair));
-    ASSERT_EQ(zeroEvent, nullptr);
-
-    ASSERT_SUCCESS(af_delete_buffer_info(pair));
-    ASSERT_SUCCESS(af_delete_event(event));
-    af::free(ptr);
-}
-
-TEST(BufferInfo, EventAndPtrAttributes) {
-    af_event event;
-    ASSERT_SUCCESS(af_create_event(&event));
-    void *ptr = af::alloc(1, dtype::f32);
-    af_buffer_info pair;
-    ASSERT_SUCCESS(af_create_buffer_info(&pair, ptr, event));
-    af_event anEvent;
-    ASSERT_SUCCESS(af_buffer_info_get_event(&anEvent, pair));
-    ASSERT_EQ(event, anEvent);
-    void *somePtr;
-    ASSERT_SUCCESS(af_buffer_info_get_ptr(&somePtr, pair));
-    ASSERT_EQ(ptr, somePtr);
-
-    af_event anotherEvent;
-    ASSERT_SUCCESS(af_create_event(&anotherEvent));
-    ASSERT_SUCCESS(af_buffer_info_set_event(pair, anotherEvent));
-    af_event yetAnotherEvent;
-    ASSERT_SUCCESS(af_buffer_info_get_event(&yetAnotherEvent, pair));
-    ASSERT_NE(yetAnotherEvent, event);
-    ASSERT_EQ(yetAnotherEvent, anotherEvent);
-
-    void *anotherPtr = af::alloc(1, dtype::f32);
-    ASSERT_SUCCESS(af_buffer_info_set_ptr(pair, anotherPtr));
-    void *yetAnotherPtr;
-    ASSERT_SUCCESS(af_buffer_info_get_ptr(&yetAnotherPtr, pair));
-    ASSERT_NE(yetAnotherPtr, ptr);
-    ASSERT_EQ(yetAnotherPtr, anotherPtr);
-
-    ASSERT_SUCCESS(af_delete_buffer_info(pair));
-    ASSERT_SUCCESS(af_delete_event(event));
-    af::free(ptr);
-}
-
-TEST(BufferInfo, BufferInfoCreateMove) {
-    af_event event;
-    ASSERT_SUCCESS(af_create_event(&event));
-    void *ptr = af::alloc(1, dtype::f32);
-    std::unique_ptr<buffer_info> bufferInfo(new buffer_info(ptr, event));
-    ASSERT_EQ(bufferInfo->getEvent(), event);
-    ASSERT_EQ(bufferInfo->getPtr(), ptr);
-
-    void *anotherPtr = af::alloc(1, dtype::f32);
-    bufferInfo->setPtr(anotherPtr);
-    ASSERT_EQ(bufferInfo->getPtr(), anotherPtr);
-
-    af_event anotherEvent;
-    ASSERT_SUCCESS(af_create_event(&anotherEvent));
-    bufferInfo->setEvent(anotherEvent);
-    ASSERT_EQ(bufferInfo->getEvent(), anotherEvent);
-
-    auto anotherBufferInfo = std::move(bufferInfo);
-    ASSERT_EQ(anotherBufferInfo->getPtr(), anotherPtr);
-    ASSERT_EQ(anotherBufferInfo->getEvent(), anotherEvent);
-
-    af_delete_event(event);
-    af::free(ptr);
-}
-
-TEST(BufferInfo, UnlockCpp) {
-    af_event event;
-    ASSERT_SUCCESS(af_create_event(&event));
-    void *ptr = af::alloc(1, dtype::f32);
-    std::unique_ptr<buffer_info> bufferInfo(new buffer_info(ptr, event));
-
-    void *anotherPtr = bufferInfo->unlockPtr();
-    ASSERT_EQ(ptr, anotherPtr);
-    af_event anotherEvent = bufferInfo->unlockEvent();
-    ASSERT_EQ(event, anotherEvent);
-
-    ASSERT_SUCCESS(af_delete_event(anotherEvent));
-    af::free(ptr);
-}
-
 namespace {
 
 template<typename T>
@@ -805,9 +692,7 @@ af_err is_user_locked_fn(af_memory_manager manager, int *out, void *ptr) {
     return AF_SUCCESS;
 }
 
-af_err unlock_fn(af_memory_manager manager, void *ptr, af_event event,
-                 int userLock) {
-    af_delete_event(event);
+af_err unlock_fn(af_memory_manager manager, void *ptr, int userLock) {
     if (!ptr) { return AF_SUCCESS; }
 
     auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
@@ -829,7 +714,7 @@ af_err user_unlock_fn(af_memory_manager manager, void *ptr) {
     af_event event;
     af_create_event(&event);
     af_mark_event(event);
-    af_err err = unlock_fn(manager, ptr, event, /* user */ 1);
+    af_err err = unlock_fn(manager, ptr, /* user */ 1);
     payload->lockedBytes -= payload->table[ptr];
     return err;
 }
@@ -877,15 +762,9 @@ af_err jit_tree_exceeds_memory_pressure_fn(af_memory_manager manager, int *out,
     return AF_SUCCESS;
 }
 
-af_err alloc_fn(af_memory_manager manager, af_buffer_info *out,
+af_err alloc_fn(af_memory_manager manager, void **ptr,
                 /* bool */ int userLock, const unsigned ndims, dim_t *dims,
                 const unsigned element_size) {
-    af_event event;
-    af_create_event(&event);
-    af_mark_event(event);
-    af_buffer_info bufferInfo;
-    af_create_buffer_info(&bufferInfo, nullptr, event);
-
     size_t size = element_size;
     for (unsigned i = 0; i < ndims; ++i) { size *= dims[i]; }
 
@@ -896,17 +775,15 @@ af_err alloc_fn(af_memory_manager manager, af_buffer_info *out,
         af_memory_manager_get_memory_pressure_threshold(manager, &threshold);
         if (pressure > threshold) { signal_memory_cleanup_fn(manager); }
 
-        void *piece;
-        af_memory_manager_native_alloc(manager, &piece, size);
-        af_buffer_info_set_ptr(bufferInfo, piece);
+        af_memory_manager_native_alloc(manager, ptr, size);
 
-        auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
-        payload->table[piece] = size;
+        auto *payload        = getMemoryManagerPayload<E2ETestPayload>(manager);
+        payload->table[*ptr] = size;
         payload->totalBytes += size;
         payload->totalBuffers++;
 
         // Simple implementation: treat user and AF allocations the same
-        payload->locked.insert(piece);
+        payload->locked.insert(*ptr);
         payload->lockedBytes += size;
 
         payload->lastNdims       = ndims;
@@ -914,7 +791,6 @@ af_err alloc_fn(af_memory_manager manager, af_buffer_info *out,
         payload->lastElementSize = element_size;
     }
 
-    *out = bufferInfo;
     return AF_SUCCESS;
 }
 
@@ -975,6 +851,7 @@ TEST(MemoryManagerApi, E2ETest) {
 
         void *a = af::alloc(aSize, af::dtype::f32);
         ASSERT_EQ(payload->table.size(), 1);
+
         ASSERT_EQ(payload->table[a], aSize * sizeof(float));
         ASSERT_EQ(payload->lastNdims, 1);
         ASSERT_EQ(payload->lastDims, af::dim4(aSize * sizeof(float)));
@@ -988,18 +865,8 @@ TEST(MemoryManagerApi, E2ETest) {
         ASSERT_EQ(payload->lockedBytes, aSize * sizeof(float) + b.bytes());
         ASSERT_EQ(payload->locked.size(), 2);
         ASSERT_EQ(payload->lastNdims, 1);
-        // Some backends might alloc by number of bytes (OpenCL), others alloc
-        // by elements (CPU, CUDA)
-        if (payload->lastElementSize != 1) {
-            // alloced as floats
-            ASSERT_EQ(payload->lastDims, af::dim4(bDim * b.numdims()));
-            ASSERT_EQ(payload->lastElementSize, sizeof(float));
-        } else {
-            // alloced as bytes
-            ASSERT_EQ(payload->lastDims,
-                      af::dim4(bDim * b.numdims() * sizeof(float)));
-            ASSERT_EQ(payload->lastElementSize, 1);
-        }
+        ASSERT_EQ(payload->lastDims, af::dim4(bDim * b.numdims()));
+        ASSERT_EQ(payload->lastElementSize, sizeof(float));
 
         af::free(a);
 

From e5c97009ce8be96b0c58f05250f3abdd3a4e933e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 12 Dec 2019 22:01:09 +0530
Subject: [PATCH 1767/2677] Log dynamic lib loading failures from unified API
 (#2670)

* Log dynamic lib loading failures from unified API
---
 src/api/unified/symbol_manager.cpp         | 26 +++++++++++++---------
 src/backend/common/module_loading_unix.cpp | 10 +++++++--
 2 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 696a2f5488..c268586d33 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -26,10 +26,10 @@
 #include <dlfcn.h>
 #endif
 
+using common::getErrorMessage;
 using common::getFunctionPointer;
 using common::loadLibrary;
 using common::loggerFactory;
-using common::unloadLibrary;
 
 using std::extent;
 using std::function;
@@ -105,8 +105,10 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
     // FIXME(umar): avoid this if at all possible
     auto getLogger = [&] { return spdlog::get("unified"); };
 
-    string paths[] = {
-        "",   // Default paths
+    string pathPrefixes[] = {
+        "",   // empty prefix i.e. just the library name will enable search in
+              // system default paths such as LD_LIBRARY_PATH, Program
+              // Files(Windows) etc.
         ".",  // Shared libraries in current directory
         // Running from the CMake Build directory
         join_path(".", "src", "backend", getBackendDirectoryName(bknd_idx)),
@@ -133,10 +135,11 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
     typedef af_err (*func)(int*);
 
     LibHandle retVal = nullptr;
-    for (size_t i = 0; i < extent<decltype(paths)>::value; i++) {
-        AF_TRACE("Attempting: {}", paths[i]);
-        if ((retVal = loadLibrary(join_path(paths[i], bkndLibName).c_str()))) {
-            AF_TRACE("Found: {}", join_path(paths[i], bkndLibName));
+    for (size_t i = 0; i < extent<decltype(pathPrefixes)>::value; i++) {
+        AF_TRACE("Attempting: {}", (pathPrefixes[i].empty() ? "Default System Paths" : pathPrefixes[i]));
+        if ((retVal = loadLibrary(
+                 join_path(pathPrefixes[i], bkndLibName).c_str()))) {
+            AF_TRACE("Found: {}", join_path(pathPrefixes[i], bkndLibName));
 
             func count_func =
                 (func)getFunctionPointer(retVal, "af_get_device_count");
@@ -145,6 +148,8 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
                 count_func(&count);
                 AF_TRACE("Device Count: {}.", count);
                 if (count == 0) {
+                    AF_TRACE("Skipping: No devices found for {}",
+                             bkndLibName);
                     retVal = nullptr;
                     continue;
                 }
@@ -152,14 +157,13 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
 
             if (show_load_path) { printf("Using %s\n", bkndLibName.c_str()); }
             break;
+        } else {
+            AF_TRACE("Failed to load {}", getErrorMessage());
         }
     }
-
     return retVal;
 }
 
-void closeDynLibrary(LibHandle handle) { unloadLibrary(handle); }
-
 AFSymbolManager& AFSymbolManager::getInstance() {
     thread_local AFSymbolManager symbolManager;
     return symbolManager;
@@ -202,7 +206,7 @@ AFSymbolManager::AFSymbolManager()
 
 AFSymbolManager::~AFSymbolManager() {
     for (int i = 0; i < NUM_BACKENDS; ++i) {
-        if (bkndHandles[i]) { closeDynLibrary(bkndHandles[i]); }
+        if (bkndHandles[i]) { common::unloadLibrary(bkndHandles[i]); }
     }
 }
 
diff --git a/src/backend/common/module_loading_unix.cpp b/src/backend/common/module_loading_unix.cpp
index cd9efab751..711ec1cfca 100644
--- a/src/backend/common/module_loading_unix.cpp
+++ b/src/backend/common/module_loading_unix.cpp
@@ -27,8 +27,14 @@ LibHandle loadLibrary(const char* library_name) {
 void unloadLibrary(LibHandle handle) { dlclose(handle); }
 
 string getErrorMessage() {
-    string error_message(dlerror());
-    return error_message;
+    char* errMsg = dlerror();
+    if (errMsg) {
+        return string(errMsg);
+    } else {
+        // constructing std::basic_string from NULL/0 address is
+        // invalid and has undefined behavior
+        return string("No Error");
+    }
 }
 
 }  // namespace common

From 1905d39d82219436775fe8155c7984479565d284 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 16 Dec 2019 22:15:25 +0530
Subject: [PATCH 1768/2677] Fix missing headers and windows+ninja combo (#2691)

* Add missing header inclusions

* Fix deps build scripts for Windows+ninja combo

* Fix missing headers in cuda backend
---
 CMakeModules/build_CLBlast.cmake                   | 6 +++---
 CMakeModules/build_clBLAS.cmake                    | 6 +++---
 CMakeModules/build_clFFT.cmake                     | 6 +++---
 src/api/c/memory.cpp                               | 1 +
 src/backend/cuda/kernel/scan_dim_by_key_impl.hpp   | 3 +++
 src/backend/cuda/kernel/scan_first_by_key_impl.hpp | 2 ++
 6 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index e6e651d96d..25197fb2cf 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -37,11 +37,11 @@ index 9446499..786f7db 100644
   set(CLBLAST_PATCH_COMMAND ${GIT} apply ${ArrayFire_BINARY_DIR}/clblast.patch)
 endif()
 
-if(WIN32)
+if(WIN32 AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
-else(WIN32)
+else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
-endif(WIN32)
+endif()
 
 ExternalProject_Add(
     CLBlast-ext
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index e535d6763a..c4ee52bf5c 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -12,11 +12,11 @@ set(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${C
 
 find_package(OpenCL)
 
-if(WIN32)
+if(WIN32 AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
-else(WIN32)
+else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
-endif(WIN32)
+endif()
 
 ExternalProject_Add(
     clBLAS-ext
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 9319d8498b..a72016972c 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -18,11 +18,11 @@ ELSE()
     SET(byproducts BUILD_BYPRODUCTS ${clFFT_location})
 ENDIF()
 
-if(WIN32)
+if(WIN32 AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
-else(WIN32)
+else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
-endif(WIN32)
+endif()
 
 ExternalProject_Add(
     clFFT-ext
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 4031a7cfbc..ff7a18f215 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -15,6 +15,7 @@
 #include <common/half.hpp>
 #include <events.hpp>
 #include <handle.hpp>
+#include <memory.hpp>
 #include <platform.hpp>
 #include <af/backend.h>
 #include <af/device.h>
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 91fcb1d0a9..df6c50ca79 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -20,6 +20,9 @@
 #include <traits.hpp>
 #include "config.hpp"
 
+#include <algorithm>
+#include <string>
+
 namespace cuda {
 namespace kernel {
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 8672a4e978..649208a251 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -19,6 +19,8 @@
 #include <optypes.hpp>
 #include "config.hpp"
 
+#include <algorithm>
+
 namespace cuda {
 namespace kernel {
 

From ba46c2ed46847af12b61f762ccc5996c3b9dd37c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 19 Dec 2019 12:36:23 +0530
Subject: [PATCH 1769/2677] Update dev props and driver arrays for cuda 10.2
 (#2686)

* Use min compute for toolkit versions not in lookup

* Update toolkit and driver arrays for cuda 10.2

* Address feedback
---
 src/backend/cuda/device_manager.cpp | 70 +++++++++++++++++++++--------
 1 file changed, 52 insertions(+), 18 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 7d5600419a..8debebb093 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -54,29 +54,62 @@ using std::stringstream;
 
 namespace cuda {
 
-void findJitDevCompute(pair<int, int> &prop) {
+/// Check for compatible compute version based on runtime cuda toolkit version
+void checkAndSetDevMaxCompute(pair<int, int> &prop) {
     struct cuNVRTCcompute {
         /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
-        int cuda_version;
-        /// Maximum major compute flag supported by cuda_version
+        int cudaVersion;
+        /// Maximum major compute flag supported by cudaVersion
         int major;
-        /// Maximum minor compute flag supported by cuda_version
+        /// Maximum minor compute flag supported by cudaVersion
         int minor;
     };
-    static const cuNVRTCcompute Toolkit2Compute[] = {
-        {10010, 7, 5}, {10000, 7, 2}, {9020, 7, 2}, {9010, 7, 2},
+    static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+        {10020, 7, 5}, {10010, 7, 5}, {10000, 7, 2}, {9020, 7, 2}, {9010, 7, 2},
         {9000, 7, 2},  {8000, 5, 3},  {7050, 5, 3}, {7000, 5, 3}};
-    int runtime_cuda_ver = 0;
-    CUDA_CHECK(cudaRuntimeGetVersion(&runtime_cuda_ver));
-    auto tkit_max_compute =
-        find_if(begin(Toolkit2Compute), end(Toolkit2Compute),
-                [runtime_cuda_ver](cuNVRTCcompute v) {
-                    return runtime_cuda_ver == v.cuda_version;
-                });
-    if ((tkit_max_compute == end(Toolkit2Compute)) ||
-        (prop.first > tkit_max_compute->major &&
-         prop.second > tkit_max_compute->minor)) {
-        prop = make_pair(tkit_max_compute->major, tkit_max_compute->minor);
+
+    auto originalCompute = prop;
+    int rtCudaVer = 0;
+    CUDA_CHECK(cudaRuntimeGetVersion(&rtCudaVer));
+    auto tkitMaxCompute = find_if(
+        begin(Toolkit2MaxCompute), end(Toolkit2MaxCompute),
+        [rtCudaVer](cuNVRTCcompute v) { return rtCudaVer == v.cudaVersion; });
+
+    // If runtime cuda version is found in toolkit array
+    // check for max possible compute for that cuda version
+    if (tkitMaxCompute != end(Toolkit2MaxCompute) &&
+        prop.first > tkitMaxCompute->major) {
+        prop = make_pair(tkitMaxCompute->major, tkitMaxCompute->minor);
+#ifndef NDEBUG
+        char errMsg[] =
+            "Current device compute version (%d.%d) exceeds supported maximum "
+            "cuda runtime compute version (%d.%d). Using %d.%d.";
+        fprintf(stderr, errMsg, originalCompute.first, originalCompute.second,
+                prop.first, prop.second, prop.first, prop.second);
+#endif
+    } else if (prop.first > Toolkit2MaxCompute[0].major) {
+        // If runtime cuda version is NOT found in toolkit array
+        // use the top most toolkit max compute
+        prop =
+            make_pair(Toolkit2MaxCompute[0].major, Toolkit2MaxCompute[0].minor);
+#ifndef NDEBUG
+        char errMsg[] =
+            "Runtime cuda version not found in toolkit info array."
+            "Current device compute version (%d.%d) exceeds supported maximum "
+            "runtime cuda compute version (%d.%d) of latest known cuda toolkit."
+            "Using %d.%d.";
+        fprintf(stderr, errMsg, originalCompute.first, originalCompute.second,
+                prop.first, prop.second, prop.first, prop.second);
+#endif
+    } else if (prop.first < 3) {
+        // all compute versions prior to Kepler, we don't support
+        // don't change the prop.
+#ifndef NDEBUG
+        char errMsg[] =
+            "Current device compute version (%d.%d) lower than the"
+            "minimum compute version ArrayFire supports.";
+        fprintf(stderr, errMsg, originalCompute.first, originalCompute.second);
+#endif
     }
 }
 
@@ -271,6 +304,7 @@ struct ToolkitDriverVersions {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {10020, 440.33f, 441.22f},
         {10010, 418.39f, 418.96f},
         {10000, 410.48f, 411.31f},
         {9020,  396.37f, 398.26f},
@@ -441,7 +475,7 @@ DeviceManager::DeviceManager()
         if (i < nDevices) {
             auto prop =
                 make_pair(cuDevices[i].prop.major, cuDevices[i].prop.minor);
-            findJitDevCompute(prop);
+            checkAndSetDevMaxCompute(prop);
             devJitComputes.emplace_back(prop);
         }
     }

From 7bfaeff1a56ea88e07dc7c8a72ab2e7468acd143 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Dec 2019 22:25:13 +0530
Subject: [PATCH 1770/2677] Fix buffer-overflow write in scan by kery cuda
 kernel

---
 src/backend/cuda/kernel/scan_first_by_key.cuh | 44 ++++++++++---------
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 26 +++++------
 .../opencl/kernel/scan_first_by_key.cl        |  4 +-
 test/scan_by_key.cpp                          | 18 ++++++++
 4 files changed, 54 insertions(+), 38 deletions(-)

diff --git a/src/backend/cuda/kernel/scan_first_by_key.cuh b/src/backend/cuda/kernel/scan_first_by_key.cuh
index 351f4b8bf2..349bb2d8ac 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_first_by_key.cuh
@@ -282,27 +282,29 @@ void scanbykey_first_bcast(Param<To> out, Param<To> tmp, Param<int> tlid,
     const int xid        = blockIdx_x * blockDim.x * lim + tidx;
     const int yid        = blockIdx_y * blockDim.y + tidy;
 
-    if (blockIdx_x == 0) return;
-
-    bool cond =
-        (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
-    if (!cond) return;
-
-    To *optr        = out.ptr;
-    const To *tptr  = tmp.ptr;
-    const int *iptr = tlid.ptr;
-
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-    tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-    iptr +=
-        wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
-
-    Binary<To, op> binop;
-    int boundary = iptr[blockIdx_x];
-    To accum     = tptr[blockIdx_x - 1];
-
-    for (int k = 0, id = xid; k < lim && id < boundary; k++, id += blockDim.x) {
-        optr[id] = binop(accum, optr[id]);
+    if (blockIdx_x != 0) {
+        bool cond = (yid < out.dims[1]) && (zid < out.dims[2]) &&
+                    (wid < out.dims[3]);
+        if (cond) {
+            To *optr        = out.ptr;
+            const To *tptr  = tmp.ptr;
+            const int *iptr = tlid.ptr;
+
+            optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+            tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
+            iptr +=
+                wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+
+            Binary<To, op> binop;
+            int boundary = iptr[blockIdx_x];
+            To accum     = tptr[blockIdx_x - 1];
+
+            for (int k = 0, id = xid;
+                 k < lim && id < out.dims[0] && id < boundary;
+                 k++, id += blockDim.x) {
+                optr[id] = binop(accum, optr[id]);
+            }
+        }
     }
 }
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 649208a251..fe4863cda6 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -99,24 +99,20 @@ void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key,
     if (blocks_x == 1) {
         scan_final_launcher<Ti, Tk, To, op>(out, in, key, blocks_x, blocks_y,
                                             threads_x, true, inclusive_scan);
-
     } else {
-        Param<To> tmp = out;
         Param<char> tmpflg;
         Param<int> tmpid;
-
-        tmp.dims[0]       = blocks_x;
-        tmpflg.dims[0]    = blocks_x;
-        tmpid.dims[0]     = blocks_x;
-        tmp.strides[0]    = 1;
-        tmpflg.strides[0] = 1;
-        tmpid.strides[0]  = 1;
-        for (int k = 1; k < 4; k++) {
-            tmpflg.dims[k]    = out.dims[k];
-            tmpid.dims[k]     = out.dims[k];
-            tmp.strides[k]    = tmp.strides[k - 1] * tmp.dims[k - 1];
-            tmpflg.strides[k] = tmpflg.strides[k - 1] * tmpflg.dims[k - 1];
-            tmpid.strides[k]  = tmpid.strides[k - 1] * tmpid.dims[k - 1];
+        Param<To> tmp  = out;
+        tmp.dims[0]    = blocks_x;
+        tmp.strides[0] = 1;
+        for (int k = 1; k < AF_MAX_DIMS; k++) {
+            tmp.strides[k] = tmp.strides[k - 1] * tmp.dims[k - 1];
+        }
+        for (int k = 0; k < AF_MAX_DIMS; k++) {
+            tmpflg.dims[k]    = tmp.dims[k];
+            tmpflg.strides[k] = tmp.strides[k];
+            tmpid.dims[k]     = tmp.dims[k];
+            tmpid.strides[k]  = tmp.strides[k];
         }
 
         int tmp_elements  = tmp.strides[3] * tmp.dims[3];
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index bce1eb8f9e..05a5712dcf 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -269,7 +269,6 @@ __kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
                                  uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
-    const int lid  = lidy * get_local_size(0) + lidx;
 
     const int zid       = get_group_id(0) / groups_x;
     const int wid       = get_group_id(1) / groups_y;
@@ -295,7 +294,8 @@ __kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
             int boundary = tiData[groupId_x];
             To accum     = tData[groupId_x - 1];
 
-            for (int k = 0, id = xid; k < lim && id < boundary;
+            for (int k = 0, id = xid;
+                 k < lim && id < oInfo.dims[0] && id < boundary;
                  k++, id += DIMX) {
                 oData[id] = binOp(accum, oData[id]);
             }
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
index 4aeba5d00e..783f9fee7c 100644
--- a/test/scan_by_key.cpp
+++ b/test/scan_by_key.cpp
@@ -218,3 +218,21 @@ TEST(ScanByKey, Test_Scan_By_key_Simple_1) {
     scanByKeyTest<int, int, AF_BINARY_ADD, false>(
         dims, scanDim, nodeLengths, keyStart, keyEnd, dataStart, dataEnd, 1e-5);
 }
+
+TEST(ScanByKey, FixOverflowWrite) {
+    const int SIZE = 41000;
+    vector<int> keys(SIZE, 0);
+    vector<float> vals(SIZE, 1.0f);
+
+    array someVals = array(SIZE, vals.data());
+    array keysAF = array(SIZE, s32);
+    array valsAF = array(SIZE, vals.data());
+
+    keysAF = array(SIZE, keys.data());
+
+    float prior = valsAF(0).scalar<float>();
+
+    array result = af::scanByKey(keysAF, someVals, 0, AF_BINARY_ADD, true);
+
+    ASSERT_EQ(prior, valsAF(0).scalar<float>());
+}

From ebf9a12ceb8ef053736eaccb5a7aaed745e11f17 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 19 Dec 2019 01:43:46 -0500
Subject: [PATCH 1771/2677] Optimize unified backend. Remove exceptions in
 af_get_backend_id.

* The unified backend calls af_get_backend_id which throw an exception
  if the af_array passed to it is not an initialized array. This occures
  when you create an af::array with a default constructor or if a move
  is performed. In those cases the af_get_backend_id was throwing an
  exception and catching it then returning the error code. This was
  causing the slowdown in the unified backend.
* Remove ARG_ASSERT calls on simple functions to avoid overhead
* Remove the unordered map from the call function.
* Optimize the CALL macro
* Move the backend handling code from the C API to the C++ API. The
  C API function call will behave like a regular function call.
---
 src/api/c/device.cpp               |  22 ++++--
 src/api/cpp/array.cpp              | 123 ++++++++++++++++++-----------
 src/api/unified/CMakeLists.txt     |   3 +
 src/api/unified/algorithm.cpp      |  30 +++----
 src/api/unified/arith.cpp          |   8 +-
 src/api/unified/array.cpp          |  44 +++++------
 src/api/unified/blas.cpp           |  19 +++--
 src/api/unified/cuda.cpp           |  20 ++---
 src/api/unified/data.cpp           |  48 +++++------
 src/api/unified/device.cpp         |  77 ++++++++++--------
 src/api/unified/event.cpp          |  16 ++--
 src/api/unified/features.cpp       |  12 +--
 src/api/unified/graphics.cpp       |  76 +++++++++++-------
 src/api/unified/image.cpp          |  92 +++++++++++----------
 src/api/unified/index.cpp          |  24 +++---
 src/api/unified/internal.cpp       |  15 ++--
 src/api/unified/lapack.cpp         |  32 ++++----
 src/api/unified/memory.cpp         |  67 +++++++++-------
 src/api/unified/ml.cpp             |  24 +++---
 src/api/unified/moments.cpp        |   4 +-
 src/api/unified/random.cpp         |  32 ++++----
 src/api/unified/signal.cpp         |  76 +++++++++---------
 src/api/unified/sparse.cpp         |  25 +++---
 src/api/unified/statistics.cpp     |  32 ++++----
 src/api/unified/symbol_manager.cpp |  37 +--------
 src/api/unified/symbol_manager.hpp |  86 ++++++++++----------
 src/api/unified/util.cpp           |  16 ++--
 src/api/unified/vision.cpp         |  31 ++++----
 28 files changed, 579 insertions(+), 512 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 629150d901..9da3d70798 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -27,7 +27,9 @@ using common::half;
 
 af_err af_set_backend(const af_backend bknd) {
     try {
-        ARG_ASSERT(0, bknd == getBackend());
+        if(bknd != getBackend()) {
+            return AF_ERR_ARG;
+        }
     }
     CATCHALL;
 
@@ -49,9 +51,12 @@ af_err af_get_available_backends(int* result) {
 
 af_err af_get_backend_id(af_backend* result, const af_array in) {
     try {
-        ARG_ASSERT(1, in != 0);
-        const ArrayInfo& info = getInfo(in, false, false);
-        *result               = info.getBackendId();
+        if(in) {
+            const ArrayInfo& info = getInfo(in, false, false);
+            *result               = info.getBackendId();
+        } else {
+            return AF_ERR_ARG;
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -59,9 +64,12 @@ af_err af_get_backend_id(af_backend* result, const af_array in) {
 
 af_err af_get_device_id(int* device, const af_array in) {
     try {
-        ARG_ASSERT(1, in != 0);
-        const ArrayInfo& info = getInfo(in, false, false);
-        *device               = info.getDevId();
+        if(in) {
+            const ArrayInfo& info = getInfo(in, false, false);
+            *device               = info.getDevId();
+        } else {
+            return AF_ERR_ARG;
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index b440a1d37e..cfd8398fa0 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -29,6 +29,11 @@
 #include <traits.hpp>
 #endif
 
+#ifdef AF_UNIFIED
+#include <symbol_manager.hpp>
+#include <af/backend.h>
+#endif
+
 #include <memory>
 #include <stdexcept>
 #include <vector>
@@ -99,28 +104,33 @@ dim4 getDims(const af_array arr) {
     return dim4(d0, d1, d2, d3);
 }
 
-void initEmptyArray(af_array *arr, af::dtype ty, dim_t d0, dim_t d1 = 1,
-                    dim_t d2 = 1, dim_t d3 = 1) {
+af_array initEmptyArray(af::dtype ty, dim_t d0, dim_t d1 = 1, dim_t d2 = 1,
+                        dim_t d3 = 1) {
+    af_array arr;
     dim_t my_dims[] = {d0, d1, d2, d3};
-    AF_THROW(af_create_handle(arr, AF_MAX_DIMS, my_dims, ty));
+    AF_THROW(af_create_handle(&arr, AF_MAX_DIMS, my_dims, ty));
+    return arr;
 }
 
-void initDataArray(af_array *arr, const void *ptr, af::dtype ty, af::source src,
-                   dim_t d0, dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
+af_array initDataArray(const void *ptr, int ty, af::source src, dim_t d0,
+                       dim_t d1 = 1, dim_t d2 = 1, dim_t d3 = 1) {
     dim_t my_dims[] = {d0, d1, d2, d3};
+    af_array arr;
     switch (src) {
         case afHost:
-            AF_THROW(af_create_array(arr, ptr, AF_MAX_DIMS, my_dims, ty));
+            AF_THROW(af_create_array(&arr, ptr, AF_MAX_DIMS, my_dims,
+                                     static_cast<af_dtype>(ty)));
             break;
         case afDevice:
-            AF_THROW(af_device_array(arr, const_cast<void *>(ptr), AF_MAX_DIMS,
-                                     my_dims, ty));
+            AF_THROW(af_device_array(&arr, const_cast<void *>(ptr), AF_MAX_DIMS,
+                                     my_dims, static_cast<af_dtype>(ty)));
             break;
         default:
             AF_THROW_ERR(
                 "Can not create array from the requested source pointer",
                 AF_ERR_ARG);
     }
+    return arr;
 }
 }  // namespace
 
@@ -156,7 +166,7 @@ struct array::array_proxy::array_proxy_impl {
 
 array::array(const af_array handle) : arr(handle) {}
 
-array::array() : arr(nullptr) { initEmptyArray(&arr, f32, 0, 1, 1, 1); }
+array::array() : arr(initEmptyArray(f32, 0, 1, 1, 1)) {}
 
 array::array(array &&other) noexcept : arr(other.arr) { other.arr = 0; }
 
@@ -167,26 +177,19 @@ array &array::operator=(array &&other) noexcept {
     return *this;
 }
 
-array::array(const dim4 &dims, af::dtype ty) : arr(nullptr) {
-    initEmptyArray(&arr, ty, dims[0], dims[1], dims[2], dims[3]);
-}
+array::array(const dim4 &dims, af::dtype ty)
+    : arr(initEmptyArray(ty, dims[0], dims[1], dims[2], dims[3])) {}
 
-array::array(dim_t dim0, af::dtype ty) : arr(nullptr) {
-    initEmptyArray(&arr, ty, dim0);
-}
+array::array(dim_t dim0, af::dtype ty) : arr(initEmptyArray(ty, dim0)) {}
 
-array::array(dim_t dim0, dim_t dim1, af::dtype ty) : arr(nullptr) {
-    initEmptyArray(&arr, ty, dim0, dim1);
-}
+array::array(dim_t dim0, dim_t dim1, af::dtype ty)
+    : arr(initEmptyArray(ty, dim0, dim1)) {}
 
-array::array(dim_t dim0, dim_t dim1, dim_t dim2, af::dtype ty) : arr(nullptr) {
-    initEmptyArray(&arr, ty, dim0, dim1, dim2);
-}
+array::array(dim_t dim0, dim_t dim1, dim_t dim2, af::dtype ty)
+    : arr(initEmptyArray(ty, dim0, dim1, dim2)) {}
 
 array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, af::dtype ty)
-    : arr(nullptr) {
-    initEmptyArray(&arr, ty, dim0, dim1, dim2, dim3);
-}
+    : arr(initEmptyArray(ty, dim0, dim1, dim2, dim3)) {}
 
 template<>
 struct dtype_traits<half_float::half> {
@@ -198,36 +201,25 @@ struct dtype_traits<half_float::half> {
 #define INSTANTIATE(T)                                                         \
     template<>                                                                 \
     AFAPI array::array(const dim4 &dims, const T *ptr, af::source src)         \
-        : arr(nullptr) {                                                       \
-        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
-        initDataArray(&arr, ptr, ty, src, dims[0], dims[1], dims[2], dims[3]); \
-    }                                                                          \
+        : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dims[0],       \
+                            dims[1], dims[2], dims[3])) {}                     \
     template<>                                                                 \
     AFAPI array::array(dim_t dim0, const T *ptr, af::source src)               \
-        : arr(nullptr) {                                                       \
-        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
-        initDataArray(&arr, ptr, ty, src, dim0);                               \
-    }                                                                          \
+        : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dim0)) {}      \
     template<>                                                                 \
     AFAPI array::array(dim_t dim0, dim_t dim1, const T *ptr, af::source src)   \
-        : arr(nullptr) {                                                       \
-        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
-        initDataArray(&arr, ptr, ty, src, dim0, dim1);                         \
+        : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dim0, dim1)) { \
     }                                                                          \
     template<>                                                                 \
     AFAPI array::array(dim_t dim0, dim_t dim1, dim_t dim2, const T *ptr,       \
                        af::source src)                                         \
-        : arr(nullptr) {                                                       \
-        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
-        initDataArray(&arr, ptr, ty, src, dim0, dim1, dim2);                   \
-    }                                                                          \
+        : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dim0, dim1,    \
+                            dim2)) {}                                          \
     template<>                                                                 \
     AFAPI array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,         \
                        const T *ptr, af::source src)                           \
-        : arr(nullptr) {                                                       \
-        af::dtype ty = static_cast<af::dtype>(dtype_traits<T>::af_type);       \
-        initDataArray(&arr, ptr, ty, src, dim0, dim1, dim2, dim3);             \
-    }
+        : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dim0, dim1,    \
+                            dim2, dim3)) {}
 
 INSTANTIATE(cdouble)
 INSTANTIATE(cfloat)
@@ -250,9 +242,50 @@ INSTANTIATE(__half);
 #undef INSTANTIATE
 
 array::~array() {
-    af_array tmp = get();
+#ifdef AF_UNIFIED
+    using af_release_array_ptr =
+        std::add_pointer<decltype(af_release_array)>::type;
+    static auto &instance = unified::AFSymbolManager::getInstance();
+
+    if (get()) {
+        af_backend backend = instance.getActiveBackend();
+        af_err err         = af_get_backend_id(&backend, get());
+        if (!err) {
+            switch (backend) {
+                case AF_BACKEND_CPU: {
+                    static auto cpu_handle = instance.getHandle();
+                    static af_release_array_ptr func =
+                        reinterpret_cast<af_release_array_ptr>(
+                            common::getFunctionPointer(cpu_handle,
+                                                       "af_release_array"));
+                    func(get());
+                    break;
+                }
+                case AF_BACKEND_OPENCL: {
+                    static auto opencl_handle = instance.getHandle();
+                    static af_release_array_ptr func =
+                        reinterpret_cast<af_release_array_ptr>(
+                            common::getFunctionPointer(opencl_handle,
+                                                       "af_release_array"));
+                    func(get());
+                    break;
+                }
+                case AF_BACKEND_CUDA: {
+                    static auto cuda_handle = instance.getHandle();
+                    static af_release_array_ptr func =
+                        reinterpret_cast<af_release_array_ptr>(
+                            common::getFunctionPointer(cuda_handle,
+                                                       "af_release_array"));
+                    func(get());
+                    break;
+                }
+            }
+        }
+    }
+#else
     // THOU SHALL NOT THROW IN DESTRUCTORS
-    af_release_array(tmp);
+    if (af_array arr = get()) af_release_array(arr);
+#endif
 }
 
 af::dtype array::type() const {
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index d644cf36c2..712b1f2dea 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -57,6 +57,8 @@ else()
       ${ArrayFire_SOURCE_DIR}/src/backend/common/module_loading_unix.cpp)
 endif()
 
+target_compile_definitions(af PRIVATE AF_UNIFIED)
+
 target_include_directories(af
   PUBLIC
     $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
@@ -64,6 +66,7 @@ target_include_directories(af
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c
+    ${ArrayFire_SOURCE_DIR}/src/api/unified
     $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
     ${CMAKE_BINARY_DIR}
   )
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index d0c77d402d..32fde88613 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -14,7 +14,7 @@
 #define ALGO_HAPI_DEF(af_func)                                        \
     af_err af_func(af_array *out, const af_array in, const int dim) { \
         CHECK_ARRAYS(in);                                             \
-        return CALL(out, in, dim);                                    \
+        CALL(af_func, out, in, dim);                                  \
     }
 
 ALGO_HAPI_DEF(af_sum)
@@ -34,7 +34,7 @@ ALGO_HAPI_DEF(af_diff2)
     af_err af_func_nan(af_array *out, const af_array in, const int dim, \
                        const double nanval) {                           \
         CHECK_ARRAYS(in);                                               \
-        return CALL(out, in, dim, nanval);                              \
+        CALL(af_func_nan, out, in, dim, nanval);                        \
     }
 
 ALGO_HAPI_DEF(af_sum_nan)
@@ -45,7 +45,7 @@ ALGO_HAPI_DEF(af_product_nan)
 #define ALGO_HAPI_DEF(af_func_all)                                      \
     af_err af_func_all(double *real, double *imag, const af_array in) { \
         CHECK_ARRAYS(in);                                               \
-        return CALL(real, imag, in);                                    \
+        CALL(af_func_all, real, imag, in);                              \
     }
 
 ALGO_HAPI_DEF(af_sum_all)
@@ -62,7 +62,7 @@ ALGO_HAPI_DEF(af_count_all)
     af_err af_func_nan_all(double *real, double *imag, const af_array in, \
                            const double nanval) {                         \
         CHECK_ARRAYS(in);                                                 \
-        return CALL(real, imag, in, nanval);                              \
+        CALL(af_func_nan_all, real, imag, in, nanval);                    \
     }
 
 ALGO_HAPI_DEF(af_sum_nan_all)
@@ -74,7 +74,7 @@ ALGO_HAPI_DEF(af_product_nan_all)
     af_err af_ifunc(af_array *out, af_array *idx, const af_array in, \
                     const int dim) {                                 \
         CHECK_ARRAYS(in);                                            \
-        return CALL(out, idx, in, dim);                              \
+        CALL(af_ifunc, out, idx, in, dim);                           \
     }
 
 ALGO_HAPI_DEF(af_imin)
@@ -86,7 +86,7 @@ ALGO_HAPI_DEF(af_imax)
     af_err af_ifunc_all(double *real, double *imag, unsigned *idx, \
                         const af_array in) {                       \
         CHECK_ARRAYS(in);                                          \
-        return CALL(real, imag, idx, in);                          \
+        CALL(af_ifunc_all, real, imag, idx, in);                   \
     }
 
 ALGO_HAPI_DEF(af_imin_all)
@@ -96,53 +96,53 @@ ALGO_HAPI_DEF(af_imax_all)
 
 af_err af_where(af_array *idx, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(idx, in);
+    CALL(af_where, idx, in);
 }
 
 af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op,
                bool inclusive_scan) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dim, op, inclusive_scan);
+    CALL(af_scan, out, in, dim, op, inclusive_scan);
 }
 
 af_err af_scan_by_key(af_array *out, const af_array key, const af_array in,
                       const int dim, af_binary_op op, bool inclusive_scan) {
     CHECK_ARRAYS(in, key);
-    return CALL(out, key, in, dim, op, inclusive_scan);
+    CALL(af_scan_by_key, out, key, in, dim, op, inclusive_scan);
 }
 
 af_err af_sort(af_array *out, const af_array in, const unsigned dim,
                const bool isAscending) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dim, isAscending);
+    CALL(af_sort, out, in, dim, isAscending);
 }
 
 af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
                      const unsigned dim, const bool isAscending) {
     CHECK_ARRAYS(in);
-    return CALL(out, indices, in, dim, isAscending);
+    CALL(af_sort_index, out, indices, in, dim, isAscending);
 }
 
 af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                       const af_array keys, const af_array values,
                       const unsigned dim, const bool isAscending) {
     CHECK_ARRAYS(keys, values);
-    return CALL(out_keys, out_values, keys, values, dim, isAscending);
+    CALL(af_sort_by_key, out_keys, out_values, keys, values, dim, isAscending);
 }
 
 af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, is_sorted);
+    CALL(af_set_unique, out, in, is_sorted);
 }
 
 af_err af_set_union(af_array *out, const af_array first, const af_array second,
                     const bool is_unique) {
     CHECK_ARRAYS(first, second);
-    return CALL(out, first, second, is_unique);
+    CALL(af_set_union, out, first, second, is_unique);
 }
 
 af_err af_set_intersect(af_array *out, const af_array first,
                         const af_array second, const bool is_unique) {
     CHECK_ARRAYS(first, second);
-    return CALL(out, first, second, is_unique);
+    CALL(af_set_intersect, out, first, second, is_unique);
 }
diff --git a/src/api/unified/arith.cpp b/src/api/unified/arith.cpp
index 9330373036..9798341c2b 100644
--- a/src/api/unified/arith.cpp
+++ b/src/api/unified/arith.cpp
@@ -15,7 +15,7 @@
     af_err af_func(af_array* out, const af_array lhs, const af_array rhs, \
                    const bool batchMode) {                                \
         CHECK_ARRAYS(lhs, rhs);                                           \
-        return CALL(out, lhs, rhs, batchMode);                            \
+        CALL(af_func, out, lhs, rhs, batchMode);                          \
     }
 
 BINARY_HAPI_DEF(af_add)
@@ -47,13 +47,13 @@ BINARY_HAPI_DEF(af_hypot)
 
 af_err af_cast(af_array* out, const af_array in, const af_dtype type) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, type);
+    CALL(af_cast, out, in, type);
 }
 
 #define UNARY_HAPI_DEF(af_func)                        \
     af_err af_func(af_array* out, const af_array in) { \
         CHECK_ARRAYS(in);                              \
-        return CALL(out, in);                          \
+        CALL(af_func, out, in);                        \
     }
 
 UNARY_HAPI_DEF(af_abs)
@@ -103,5 +103,5 @@ UNARY_HAPI_DEF(af_not)
 af_err af_clamp(af_array* out, const af_array in, const af_array lo,
                 const af_array hi, const bool batch) {
     CHECK_ARRAYS(in, lo, hi);
-    return CALL(out, in, lo, hi, batch);
+    CALL(af_clamp, out, in, lo, hi, batch);
 }
diff --git a/src/api/unified/array.cpp b/src/api/unified/array.cpp
index d90ae0d4ea..d68f54f84c 100644
--- a/src/api/unified/array.cpp
+++ b/src/api/unified/array.cpp
@@ -14,84 +14,78 @@
 af_err af_create_array(af_array *arr, const void *const data,
                        const unsigned ndims, const dim_t *const dims,
                        const af_dtype type) {
-    return CALL(arr, data, ndims, dims, type);
+    CALL(af_create_array, arr, data, ndims, dims, type);
 }
 
 af_err af_create_handle(af_array *arr, const unsigned ndims,
                         const dim_t *const dims, const af_dtype type) {
-    return CALL(arr, ndims, dims, type);
+    CALL(af_create_handle, arr, ndims, dims, type);
 }
 
 af_err af_copy_array(af_array *arr, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(arr, in);
+    CALL(af_copy_array, arr, in);
 }
 
 af_err af_write_array(af_array arr, const void *data, const size_t bytes,
                       af_source src) {
     CHECK_ARRAYS(arr);
-    return CALL(arr, data, bytes, src);
+    CALL(af_write_array, arr, data, bytes, src);
 }
 
 af_err af_get_data_ptr(void *data, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(data, arr);
+    CALL(af_get_data_ptr, data, arr);
 }
 
 af_err af_release_array(af_array arr) {
-    af_backend curr =
-        unified::AFSymbolManager::getInstance().getActiveBackend();
-    af_backend other = curr;
-
-    af_err err = af_get_backend_id(&other, arr);
-    if (err != AF_SUCCESS) return err;
-
-    unified::AFSymbolManager::getInstance().setBackend(other);
-    err = CALL(arr);
-    unified::AFSymbolManager::getInstance().setBackend(curr);
-    return err;
+    if (arr) {
+        CALL(af_release_array, arr);
+    } else {
+        return AF_SUCCESS;
+    }
 }
 
 af_err af_retain_array(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_retain_array, out, in);
 }
 
 af_err af_get_data_ref_count(int *use_count, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(use_count, in);
+    CALL(af_get_data_ref_count, use_count, in);
 }
 
 af_err af_eval(af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(in);
+    CALL(af_eval, in);
 }
 
 af_err af_get_elements(dim_t *elems, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(elems, arr);
+    CALL(af_get_elements, elems, arr);
 }
 
 af_err af_get_type(af_dtype *type, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(type, arr);
+    CALL(af_get_type, type, arr);
 }
 
 af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
                    const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(d0, d1, d2, d3, arr);
+    CALL(af_get_dims, d0, d1, d2, d3, arr);
 }
 
 af_err af_get_numdims(unsigned *result, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(result, arr);
+    CALL(af_get_numdims, result, arr);
 }
 
 #define ARRAY_HAPI_DEF(af_func)                        \
     af_err af_func(bool *result, const af_array arr) { \
         CHECK_ARRAYS(arr);                             \
-        return CALL(result, arr);                      \
+        CALL(af_func, result, arr);                    \
     }
 
 ARRAY_HAPI_DEF(af_is_empty)
@@ -112,5 +106,5 @@ ARRAY_HAPI_DEF(af_is_sparse)
 
 af_err af_get_scalar(void *output_value, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(output_value, arr);
+    CALL(af_get_scalar, output_value, arr);
 }
diff --git a/src/api/unified/blas.cpp b/src/api/unified/blas.cpp
index e82b9994ed..843fa8da35 100644
--- a/src/api/unified/blas.cpp
+++ b/src/api/unified/blas.cpp
@@ -10,39 +10,38 @@
 #include <af/blas.h>
 #include "symbol_manager.hpp"
 
-AFAPI af_err af_gemm(af_array *out,
-                     const af_mat_prop optLhs, const af_mat_prop optRhs,
-                     const void* alpha, const af_array lhs, const af_array rhs,
-                     const void* beta) {
+AFAPI af_err af_gemm(af_array *out, const af_mat_prop optLhs,
+                     const af_mat_prop optRhs, const void *alpha,
+                     const af_array lhs, const af_array rhs, const void *beta) {
     CHECK_ARRAYS(out, lhs, rhs);
-    return CALL(out, optLhs, optRhs, alpha, lhs, rhs, beta);
+    CALL(af_gemm, out, optLhs, optRhs, alpha, lhs, rhs, beta);
 }
 
 af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                  const af_mat_prop optLhs, const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
-    return CALL(out, lhs, rhs, optLhs, optRhs);
+    CALL(af_matmul, out, lhs, rhs, optLhs, optRhs);
 }
 
 af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
               const af_mat_prop optLhs, const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
-    return CALL(out, lhs, rhs, optLhs, optRhs);
+    CALL(af_dot, out, lhs, rhs, optLhs, optRhs);
 }
 
 af_err af_dot_all(double *rval, double *ival, const af_array lhs,
                   const af_array rhs, const af_mat_prop optLhs,
                   const af_mat_prop optRhs) {
     CHECK_ARRAYS(lhs, rhs);
-    return CALL(rval, ival, lhs, rhs, optLhs, optRhs);
+    CALL(af_dot_all, rval, ival, lhs, rhs, optLhs, optRhs);
 }
 
 af_err af_transpose(af_array *out, af_array in, const bool conjugate) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, conjugate);
+    CALL(af_transpose, out, in, conjugate);
 }
 
 af_err af_transpose_inplace(af_array in, const bool conjugate) {
     CHECK_ARRAYS(in);
-    return CALL(in, conjugate);
+    CALL(af_transpose_inplace, in, conjugate);
 }
diff --git a/src/api/unified/cuda.cpp b/src/api/unified/cuda.cpp
index 451b0ebf78..47d087e301 100644
--- a/src/api/unified/cuda.cpp
+++ b/src/api/unified/cuda.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "symbol_manager.hpp"
 #include <af/backend.h>
+#include "symbol_manager.hpp"
 
 #define AF_DEFINE_CUDA_TYPES
 #include <af/cuda.h>
@@ -16,37 +16,27 @@
 af_err afcu_get_stream(cudaStream_t* stream, int id) {
     af_backend backend;
     af_get_active_backend(&backend);
-    if(backend == AF_BACKEND_CUDA) {
-        return CALL(stream, id);
-    }
+    if (backend == AF_BACKEND_CUDA) { CALL(afcu_get_stream, stream, id); }
     return AF_ERR_NOT_SUPPORTED;
 }
 
 af_err afcu_get_native_id(int* nativeid, int id) {
     af_backend backend;
     af_get_active_backend(&backend);
-    if(backend == AF_BACKEND_CUDA) {
-        return CALL(nativeid, id);
-    }
+    if (backend == AF_BACKEND_CUDA) { CALL(afcu_get_native_id, nativeid, id); }
     return AF_ERR_NOT_SUPPORTED;
 }
 
-
 af_err afcu_set_native_id(int nativeid) {
     af_backend backend;
     af_get_active_backend(&backend);
-    if(backend == AF_BACKEND_CUDA) {
-        return CALL(nativeid);
-    }
+    if (backend == AF_BACKEND_CUDA) { CALL(afcu_set_native_id, nativeid); }
     return AF_ERR_NOT_SUPPORTED;
 }
 
-
 af_err afcu_cublasSetMathMode(cublasMath_t mode) {
     af_backend backend;
     af_get_active_backend(&backend);
-    if(backend == AF_BACKEND_CUDA) {
-        return CALL(mode);
-    }
+    if (backend == AF_BACKEND_CUDA) { CALL(afcu_cublasSetMathMode, mode); }
     return AF_ERR_NOT_SUPPORTED;
 }
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index df5b5accca..143c4209d5 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -13,131 +13,131 @@
 
 af_err af_constant(af_array *result, const double value, const unsigned ndims,
                    const dim_t *const dims, const af_dtype type) {
-    return CALL(result, value, ndims, dims, type);
+    CALL(af_constant, result, value, ndims, dims, type);
 }
 
 af_err af_constant_complex(af_array *arr, const double real, const double imag,
                            const unsigned ndims, const dim_t *const dims,
                            const af_dtype type) {
-    return CALL(arr, real, imag, ndims, dims, type);
+    CALL(af_constant_complex, arr, real, imag, ndims, dims, type);
 }
 
 af_err af_constant_long(af_array *arr, const long long val,
                         const unsigned ndims, const dim_t *const dims) {
-    return CALL(arr, val, ndims, dims);
+    CALL(af_constant_long, arr, val, ndims, dims);
 }
 
 af_err af_constant_ulong(af_array *arr, const unsigned long long val,
                          const unsigned ndims, const dim_t *const dims) {
-    return CALL(arr, val, ndims, dims);
+    CALL(af_constant_ulong, arr, val, ndims, dims);
 }
 
 af_err af_range(af_array *out, const unsigned ndims, const dim_t *const dims,
                 const int seq_dim, const af_dtype type) {
-    return CALL(out, ndims, dims, seq_dim, type);
+    CALL(af_range, out, ndims, dims, seq_dim, type);
 }
 
 af_err af_iota(af_array *out, const unsigned ndims, const dim_t *const dims,
                const unsigned t_ndims, const dim_t *const tdims,
                const af_dtype type) {
-    return CALL(out, ndims, dims, t_ndims, tdims, type);
+    CALL(af_iota, out, ndims, dims, t_ndims, tdims, type);
 }
 
 af_err af_identity(af_array *out, const unsigned ndims, const dim_t *const dims,
                    const af_dtype type) {
-    return CALL(out, ndims, dims, type);
+    CALL(af_identity, out, ndims, dims, type);
 }
 
 af_err af_diag_create(af_array *out, const af_array in, const int num) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, num);
+      CALL(af_diag_create, out, in, num);
 }
 
 af_err af_diag_extract(af_array *out, const af_array in, const int num) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, num);
+      CALL(af_diag_extract, out, in, num);
 }
 
 af_err af_join(af_array *out, const int dim, const af_array first,
                const af_array second) {
     CHECK_ARRAYS(first, second);
-    return CALL(out, dim, first, second);
+      CALL(af_join, out, dim, first, second);
 }
 
 af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
                     const af_array *inputs) {
     for (unsigned i = 0; i < n_arrays; i++) CHECK_ARRAYS(inputs[i]);
-    return CALL(out, dim, n_arrays, inputs);
+      CALL(af_join_many, out, dim, n_arrays, inputs);
 }
 
 af_err af_tile(af_array *out, const af_array in, const unsigned x,
                const unsigned y, const unsigned z, const unsigned w) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, x, y, z, w);
+      CALL(af_tile, out, in, x, y, z, w);
 }
 
 af_err af_reorder(af_array *out, const af_array in, const unsigned x,
                   const unsigned y, const unsigned z, const unsigned w) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, x, y, z, w);
+      CALL(af_reorder, out, in, x, y, z, w);
 }
 
 af_err af_shift(af_array *out, const af_array in, const int x, const int y,
                 const int z, const int w) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, x, y, z, w);
+      CALL(af_shift, out, in, x, y, z, w);
 }
 
 af_err af_moddims(af_array *out, const af_array in, const unsigned ndims,
                   const dim_t *const dims) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, ndims, dims);
+      CALL(af_moddims, out, in, ndims, dims);
 }
 
 af_err af_flat(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+      CALL(af_flat, out, in);
 }
 
 af_err af_flip(af_array *out, const af_array in, const unsigned dim) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dim);
+      CALL(af_flip, out, in, dim);
 }
 
 af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, is_unit_diag);
+      CALL(af_lower, out, in, is_unit_diag);
 }
 
 af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, is_unit_diag);
+      CALL(af_upper, out, in, is_unit_diag);
 }
 
 af_err af_select(af_array *out, const af_array cond, const af_array a,
                  const af_array b) {
     CHECK_ARRAYS(cond, a, b);
-    return CALL(out, cond, a, b);
+      CALL(af_select, out, cond, a, b);
 }
 
 af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a,
                           const double b) {
     CHECK_ARRAYS(cond, a);
-    return CALL(out, cond, a, b);
+      CALL(af_select_scalar_r, out, cond, a, b);
 }
 
 af_err af_select_scalar_l(af_array *out, const af_array cond, const double a,
                           const af_array b) {
     CHECK_ARRAYS(cond, b);
-    return CALL(out, cond, a, b);
+      CALL(af_select_scalar_l, out, cond, a, b);
 }
 
 af_err af_replace(af_array a, const af_array cond, const af_array b) {
     CHECK_ARRAYS(a, cond, b);
-    return CALL(a, cond, b);
+      CALL(af_replace, a, cond, b);
 }
 
 af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
     CHECK_ARRAYS(a, cond);
-    return CALL(a, cond, b);
+      CALL(af_replace_scalar, a, cond, b);
 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index c7629d59d1..cee81deed3 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -29,12 +29,12 @@ af_err af_get_available_backends(int *result) {
 af_err af_get_backend_id(af_backend *result, const af_array in) {
     // DO NOT CALL CHECK_ARRAYS HERE.
     // IT WILL RESULT IN AN INFINITE RECURSION
-    return CALL(result, in);
+    CALL(af_get_backend_id, result, in);
 }
 
 af_err af_get_device_id(int *device, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(device, in);
+    CALL(af_get_device_id, device, in);
 }
 
 af_err af_get_active_backend(af_backend *result) {
@@ -42,46 +42,48 @@ af_err af_get_active_backend(af_backend *result) {
     return AF_SUCCESS;
 }
 
-af_err af_info() { return CALL_NO_PARAMS(); }
+af_err af_info() { CALL_NO_PARAMS(af_info); }
 
-af_err af_init() { return CALL_NO_PARAMS(); }
+af_err af_init() { CALL_NO_PARAMS(af_init); }
 
 af_err af_info_string(char **str, const bool verbose) {
-    return CALL(str, verbose);
+    CALL(af_info_string, str, verbose);
 }
 
 af_err af_device_info(char *d_name, char *d_platform, char *d_toolkit,
                       char *d_compute) {
-    return CALL(d_name, d_platform, d_toolkit, d_compute);
+    CALL(af_device_info, d_name, d_platform, d_toolkit, d_compute);
 }
 
-af_err af_get_device_count(int *num_of_devices) { return CALL(num_of_devices); }
+af_err af_get_device_count(int *num_of_devices) {
+    CALL(af_get_device_count, num_of_devices);
+}
 
 af_err af_get_dbl_support(bool *available, const int device) {
-    return CALL(available, device);
+    CALL(af_get_dbl_support, available, device);
 }
 
 af_err af_get_half_support(bool *available, const int device) {
-    return CALL(available, device);
+    CALL(af_get_half_support, available, device);
 }
 
-af_err af_set_device(const int device) { return CALL(device); }
+af_err af_set_device(const int device) { CALL(af_set_device, device); }
 
-af_err af_get_device(int *device) { return CALL(device); }
+af_err af_get_device(int *device) { CALL(af_get_device, device); }
 
-af_err af_sync(const int device) { return CALL(device); }
+af_err af_sync(const int device) { CALL(af_sync, device); }
 
 af_err af_alloc_device(void **ptr, const dim_t bytes) {
-    return CALL(ptr, bytes);
+    CALL(af_alloc_device, ptr, bytes);
 }
 
 af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
-    return CALL(ptr, bytes);
+    CALL(af_alloc_pinned, ptr, bytes);
 }
 
-af_err af_free_device(void *ptr) { return CALL(ptr); }
+af_err af_free_device(void *ptr) { CALL(af_free_device, ptr); }
 
-af_err af_free_pinned(void *ptr) { return CALL(ptr); }
+af_err af_free_pinned(void *ptr) { CALL(af_free_pinned, ptr); }
 
 af_err af_alloc_host(void **ptr, const dim_t bytes) {
     *ptr = malloc(bytes);
@@ -95,61 +97,74 @@ af_err af_free_host(void *ptr) {
 
 af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
                        const dim_t *const dims, const af_dtype type) {
-    return CALL(arr, data, ndims, dims, type);
+    CALL(af_device_array, arr, data, ndims, dims, type);
 }
 
 af_err af_device_mem_info(size_t *alloc_bytes, size_t *alloc_buffers,
                           size_t *lock_bytes, size_t *lock_buffers) {
-    return CALL(alloc_bytes, alloc_buffers, lock_bytes, lock_buffers);
+    CALL(af_device_mem_info, alloc_bytes, alloc_buffers, lock_bytes,
+         lock_buffers);
 }
 
 af_err af_print_mem_info(const char *msg, const int device_id) {
-    return CALL(msg, device_id);
+    CALL(af_print_mem_info, msg, device_id);
 }
 
-af_err af_device_gc() { return CALL_NO_PARAMS(); }
+af_err af_device_gc() { CALL_NO_PARAMS(af_device_gc); }
 
 af_err af_set_mem_step_size(const size_t step_bytes) {
-    return CALL(step_bytes);
+    CALL(af_set_mem_step_size, step_bytes);
 }
 
-af_err af_get_mem_step_size(size_t *step_bytes) { return CALL(step_bytes); }
+af_err af_get_mem_step_size(size_t *step_bytes) {
+    CALL(af_get_mem_step_size, step_bytes);
+}
 
 af_err af_lock_device_ptr(const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(arr);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_lock_device_ptr, arr);
+#pragma GCC diagnostic pop
 }
 
 af_err af_unlock_device_ptr(const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(arr);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_unlock_device_ptr, arr);
+#pragma GCC diagnostic pop
 }
 
 af_err af_lock_array(const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(arr);
+    CALL(af_lock_array, arr);
 }
 
 af_err af_unlock_array(const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(arr);
+    CALL(af_unlock_array, arr);
 }
 
 af_err af_is_locked_array(bool *res, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(res, arr);
+    CALL(af_is_locked_array, res, arr);
 }
 
 af_err af_get_device_ptr(void **ptr, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(ptr, arr);
+    CALL(af_get_device_ptr, ptr, arr);
 }
 
 af_err af_eval_multiple(const int num, af_array *arrays) {
     for (int i = 0; i < num; i++) { CHECK_ARRAYS(arrays[i]); }
-    return CALL(num, arrays);
+    CALL(af_eval_multiple, num, arrays);
 }
 
-af_err af_set_manual_eval_flag(bool flag) { return CALL(flag); }
+af_err af_set_manual_eval_flag(bool flag) {
+    CALL(af_set_manual_eval_flag, flag);
+}
 
-af_err af_get_manual_eval_flag(bool *flag) { return CALL(flag); }
+af_err af_get_manual_eval_flag(bool *flag) {
+    CALL(af_get_manual_eval_flag, flag);
+}
diff --git a/src/api/unified/event.cpp b/src/api/unified/event.cpp
index 4439df362d..8e3c45f6c0 100644
--- a/src/api/unified/event.cpp
+++ b/src/api/unified/event.cpp
@@ -10,16 +10,22 @@
 #include <af/event.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_event(af_event* eventHandle) { return CALL(eventHandle); }
+af_err af_create_event(af_event* eventHandle) {
+    CALL(af_create_event, eventHandle);
+}
 
 af_err af_delete_event(af_event eventHandle) {
-    return CALL(eventHandle);
+    CALL(af_delete_event, eventHandle);
 }
 
-af_err af_mark_event(const af_event eventHandle) { return CALL(eventHandle); }
+af_err af_mark_event(const af_event eventHandle) {
+    CALL(af_mark_event, eventHandle);
+}
 
 af_err af_enqueue_wait_event(const af_event eventHandle) {
-    return CALL(eventHandle);
+    CALL(af_enqueue_wait_event, eventHandle);
 }
 
-af_err af_block_event(const af_event eventHandle) { return CALL(eventHandle); }
+af_err af_block_event(const af_event eventHandle) {
+    CALL(af_block_event, eventHandle);
+}
diff --git a/src/api/unified/features.cpp b/src/api/unified/features.cpp
index 98c8a3ca52..57c8d01982 100644
--- a/src/api/unified/features.cpp
+++ b/src/api/unified/features.cpp
@@ -12,20 +12,20 @@
 #include "symbol_manager.hpp"
 
 af_err af_create_features(af_features *feat, dim_t num) {
-    return CALL(feat, num);
+    CALL(af_create_features, feat, num);
 }
 
 af_err af_retain_features(af_features *out, const af_features feat) {
-    return CALL(out, feat);
+    CALL(af_retain_features, out, feat);
 }
 
 af_err af_get_features_num(dim_t *num, const af_features feat) {
-    return CALL(num, feat);
+    CALL(af_get_features_num, num, feat);
 }
 
 #define FEAT_HAPI_DEF(af_func)                              \
     af_err af_func(af_array *out, const af_features feat) { \
-        return CALL(out, feat);                             \
+        CALL(af_func, out, feat);                           \
     }
 
 FEAT_HAPI_DEF(af_get_features_xpos)
@@ -34,4 +34,6 @@ FEAT_HAPI_DEF(af_get_features_score)
 FEAT_HAPI_DEF(af_get_features_orientation)
 FEAT_HAPI_DEF(af_get_features_size)
 
-af_err af_release_features(af_features feat) { return CALL(feat); }
+af_err af_release_features(af_features feat) {
+    CALL(af_release_features, feat);
+}
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index 30181c4221..b1752ab859 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -13,84 +13,96 @@
 
 af_err af_create_window(af_window* out, const int width, const int height,
                         const char* const title) {
-    return CALL(out, width, height, title);
+    CALL(af_create_window, out, width, height, title);
 }
 
 af_err af_set_position(const af_window wind, const unsigned x,
                        const unsigned y) {
-    return CALL(wind, x, y);
+    CALL(af_set_position, wind, x, y);
 }
 
 af_err af_set_title(const af_window wind, const char* const title) {
-    return CALL(wind, title);
+    CALL(af_set_title, wind, title);
 }
 
 af_err af_set_size(const af_window wind, const unsigned w, const unsigned h) {
-    return CALL(wind, w, h);
+    CALL(af_set_size, wind, w, h);
 }
 
 af_err af_draw_image(const af_window wind, const af_array in,
                      const af_cell* const props) {
     CHECK_ARRAYS(in);
-    return CALL(wind, in, props);
+    CALL(af_draw_image, wind, in, props);
 }
 
 af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y,
                     const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
-    return CALL(wind, X, Y, props);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_draw_plot, wind, X, Y, props);
+#pragma GCC diagnostic pop
 }
 
 af_err af_draw_plot3(const af_window wind, const af_array P,
                      const af_cell* const props) {
     CHECK_ARRAYS(P);
-    return CALL(wind, P, props);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_draw_plot3, wind, P, props);
+#pragma GCC diagnostic pop
 }
 
 af_err af_draw_plot_nd(const af_window wind, const af_array in,
                        const af_cell* const props) {
     CHECK_ARRAYS(in);
-    return CALL(wind, in, props);
+    CALL(af_draw_plot_nd, wind, in, props);
 }
 
 af_err af_draw_plot_2d(const af_window wind, const af_array X, const af_array Y,
                        const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
-    return CALL(wind, X, Y, props);
+    CALL(af_draw_plot_2d, wind, X, Y, props);
 }
 
 af_err af_draw_plot_3d(const af_window wind, const af_array X, const af_array Y,
                        const af_array Z, const af_cell* const props) {
     CHECK_ARRAYS(X, Y, Z);
-    return CALL(wind, X, Y, Z, props);
+    CALL(af_draw_plot_3d, wind, X, Y, Z, props);
 }
 
 af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
                        const af_marker_type marker,
                        const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
-    return CALL(wind, X, Y, marker, props);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_draw_scatter, wind, X, Y, marker, props);
+#pragma GCC diagnostic pop
 }
 
 af_err af_draw_scatter3(const af_window wind, const af_array P,
                         const af_marker_type marker,
                         const af_cell* const props) {
     CHECK_ARRAYS(P);
-    return CALL(wind, P, marker, props);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_draw_scatter3, wind, P, marker, props);
+#pragma GCC diagnostic pop
 }
 
 af_err af_draw_scatter_nd(const af_window wind, const af_array in,
                           const af_marker_type marker,
                           const af_cell* const props) {
     CHECK_ARRAYS(in);
-    return CALL(wind, in, marker, props);
+    CALL(af_draw_scatter_nd, wind, in, marker, props);
 }
 
 af_err af_draw_scatter_2d(const af_window wind, const af_array X,
                           const af_array Y, const af_marker_type marker,
                           const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
-    return CALL(wind, X, Y, marker, props);
+    CALL(af_draw_scatter_2d, wind, X, Y, marker, props);
 }
 
 af_err af_draw_scatter_3d(const af_window wind, const af_array X,
@@ -98,27 +110,27 @@ af_err af_draw_scatter_3d(const af_window wind, const af_array X,
                           const af_marker_type marker,
                           const af_cell* const props) {
     CHECK_ARRAYS(X, Y, Z);
-    return CALL(wind, X, Y, Z, marker, props);
+    CALL(af_draw_scatter_3d, wind, X, Y, Z, marker, props);
 }
 
 af_err af_draw_hist(const af_window wind, const af_array X, const double minval,
                     const double maxval, const af_cell* const props) {
     CHECK_ARRAYS(X);
-    return CALL(wind, X, minval, maxval, props);
+    CALL(af_draw_hist, wind, X, minval, maxval, props);
 }
 
 af_err af_draw_surface(const af_window wind, const af_array xVals,
                        const af_array yVals, const af_array S,
                        const af_cell* const props) {
     CHECK_ARRAYS(xVals, yVals, S);
-    return CALL(wind, xVals, yVals, S, props);
+    CALL(af_draw_surface, wind, xVals, yVals, S, props);
 }
 
 af_err af_draw_vector_field_nd(const af_window wind, const af_array points,
                                const af_array directions,
                                const af_cell* const props) {
     CHECK_ARRAYS(points, directions);
-    return CALL(wind, points, directions, props);
+    CALL(af_draw_vector_field_nd, wind, points, directions, props);
 }
 
 af_err af_draw_vector_field_3d(const af_window wind, const af_array xPoints,
@@ -127,7 +139,8 @@ af_err af_draw_vector_field_3d(const af_window wind, const af_array xPoints,
                                const af_array zDirs,
                                const af_cell* const props) {
     CHECK_ARRAYS(xPoints, yPoints, zPoints, xDirs, yDirs, zDirs);
-    return CALL(wind, xPoints, yPoints, zPoints, xDirs, yDirs, zDirs, props);
+    CALL(af_draw_vector_field_3d, wind, xPoints, yPoints, zPoints, xDirs, yDirs,
+         zDirs, props);
 }
 
 af_err af_draw_vector_field_2d(const af_window wind, const af_array xPoints,
@@ -135,11 +148,11 @@ af_err af_draw_vector_field_2d(const af_window wind, const af_array xPoints,
                                const af_array yDirs,
                                const af_cell* const props) {
     CHECK_ARRAYS(xPoints, yPoints, xDirs, yDirs);
-    return CALL(wind, xPoints, yPoints, xDirs, yDirs, props);
+    CALL(af_draw_vector_field_2d, wind, xPoints, yPoints, xDirs, yDirs, props);
 }
 
 af_err af_grid(const af_window wind, const int rows, const int cols) {
-    return CALL(wind, rows, cols);
+    CALL(af_grid, wind, rows, cols);
 }
 
 af_err af_set_axes_limits_compute(const af_window wind, const af_array x,
@@ -148,14 +161,14 @@ af_err af_set_axes_limits_compute(const af_window wind, const af_array x,
                                   const af_cell* const props) {
     CHECK_ARRAYS(x, y);
     if (z) CHECK_ARRAYS(z);
-    return CALL(wind, x, y, z, exact, props);
+    CALL(af_set_axes_limits_compute, wind, x, y, z, exact, props);
 }
 
 af_err af_set_axes_limits_2d(const af_window wind, const float xmin,
                              const float xmax, const float ymin,
                              const float ymax, const bool exact,
                              const af_cell* const props) {
-    return CALL(wind, xmin, xmax, ymin, ymax, exact, props);
+    CALL(af_set_axes_limits_2d, wind, xmin, xmax, ymin, ymax, exact, props);
 }
 
 af_err af_set_axes_limits_3d(const af_window wind, const float xmin,
@@ -163,30 +176,33 @@ af_err af_set_axes_limits_3d(const af_window wind, const float xmin,
                              const float ymax, const float zmin,
                              const float zmax, const bool exact,
                              const af_cell* const props) {
-    return CALL(wind, xmin, xmax, ymin, ymax, zmin, zmax, exact, props);
+    CALL(af_set_axes_limits_3d, wind, xmin, xmax, ymin, ymax, zmin, zmax, exact,
+         props);
 }
 
 af_err af_set_axes_titles(const af_window wind, const char* const xtitle,
                           const char* const ytitle, const char* const ztitle,
                           const af_cell* const props) {
-    return CALL(wind, xtitle, ytitle, ztitle, props);
+    CALL(af_set_axes_titles, wind, xtitle, ytitle, ztitle, props);
 }
 
 af_err af_set_axes_label_format(const af_window wind, const char* const xformat,
                                 const char* const yformat,
                                 const char* const zformat,
                                 const af_cell* const props) {
-    return CALL(wind, xformat, yformat, zformat, props);
+    CALL(af_set_axes_label_format, wind, xformat, yformat, zformat, props);
 }
 
-af_err af_show(const af_window wind) { return CALL(wind); }
+af_err af_show(const af_window wind) { CALL(af_show, wind); }
 
 af_err af_is_window_closed(bool* out, const af_window wind) {
-    return CALL(out, wind);
+    CALL(af_is_window_closed, out, wind);
 }
 
 af_err af_set_visibility(const af_window wind, const bool is_visible) {
-    return CALL(wind, is_visible);
+    CALL(af_set_visibility, wind, is_visible);
 }
 
-af_err af_destroy_window(const af_window wind) { return CALL(wind); }
+af_err af_destroy_window(const af_window wind) {
+    CALL(af_destroy_window, wind);
+}
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index ade6308466..9c604590ae 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -14,224 +14,228 @@
 
 af_err af_gradient(af_array *dx, af_array *dy, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(dx, dy, in);
+      CALL(af_gradient, dx, dy, in);
 }
 
 af_err af_load_image(af_array *out, const char *filename, const bool isColor) {
-    return CALL(out, filename, isColor);
+    CALL(af_load_image, out, filename, isColor);
 }
 
 af_err af_save_image(const char *filename, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(filename, in);
+      CALL(af_save_image, filename, in);
 }
 
 af_err af_load_image_memory(af_array *out, const void *ptr) {
-    return CALL(out, ptr);
+    CALL(af_load_image_memory, out, ptr);
 }
 
 af_err af_save_image_memory(void **ptr, const af_array in,
                             const af_image_format format) {
     CHECK_ARRAYS(in);
-    return CALL(ptr, in, format);
+      CALL(af_save_image_memory, ptr, in, format);
 }
 
-af_err af_delete_image_memory(void *ptr) { return CALL(ptr); }
+af_err af_delete_image_memory(void *ptr) {
+    CALL(af_delete_image_memory, ptr);
+}
 
 af_err af_load_image_native(af_array *out, const char *filename) {
-    return CALL(out, filename);
+    CALL(af_load_image_native, out, filename);
 }
 
 af_err af_save_image_native(const char *filename, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(filename, in);
+      CALL(af_save_image_native, filename, in);
 }
 
-af_err af_is_image_io_available(bool *out) { return CALL(out); }
+af_err af_is_image_io_available(bool *out) {
+    CALL(af_is_image_io_available, out);
+}
 
 af_err af_resize(af_array *out, const af_array in, const dim_t odim0,
                  const dim_t odim1, const af_interp_type method) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, odim0, odim1, method);
+      CALL(af_resize, out, in, odim0, odim1, method);
 }
 
 af_err af_transform(af_array *out, const af_array in, const af_array transform,
                     const dim_t odim0, const dim_t odim1,
                     const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(in, transform);
-    return CALL(out, in, transform, odim0, odim1, method, inverse);
+      CALL(af_transform, out, in, transform, odim0, odim1, method, inverse);
 }
 
 af_err af_transform_coordinates(af_array *out, const af_array tf,
                                 const float d0, const float d1) {
     CHECK_ARRAYS(tf);
-    return CALL(out, tf, d0, d1);
+      CALL(af_transform_coordinates, out, tf, d0, d1);
 }
 
 af_err af_rotate(af_array *out, const af_array in, const float theta,
                  const bool crop, const af_interp_type method) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, theta, crop, method);
+      CALL(af_rotate, out, in, theta, crop, method);
 }
 
 af_err af_translate(af_array *out, const af_array in, const float trans0,
                     const float trans1, const dim_t odim0, const dim_t odim1,
                     const af_interp_type method) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, trans0, trans1, odim0, odim1, method);
+      CALL(af_translate, out, in, trans0, trans1, odim0, odim1, method);
 }
 
 af_err af_scale(af_array *out, const af_array in, const float scale0,
                 const float scale1, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, scale0, scale1, odim0, odim1, method);
+      CALL(af_scale, out, in, scale0, scale1, odim0, odim1, method);
 }
 
 af_err af_skew(af_array *out, const af_array in, const float skew0,
                const float skew1, const dim_t odim0, const dim_t odim1,
                const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, skew0, skew1, odim0, odim1, method, inverse);
+      CALL(af_skew, out, in, skew0, skew1, odim0, odim1, method, inverse);
 }
 
 af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
                     const double minval, const double maxval) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, nbins, minval, maxval);
+      CALL(af_histogram, out, in, nbins, minval, maxval);
 }
 
 af_err af_dilate(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-    return CALL(out, in, mask);
+      CALL(af_dilate, out, in, mask);
 }
 
 af_err af_dilate3(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-    return CALL(out, in, mask);
+      CALL(af_dilate3, out, in, mask);
 }
 
 af_err af_erode(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-    return CALL(out, in, mask);
+      CALL(af_erode, out, in, mask);
 }
 
 af_err af_erode3(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-    return CALL(out, in, mask);
+      CALL(af_erode3, out, in, mask);
 }
 
 af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma,
                     const float chromatic_sigma, const bool isColor) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, spatial_sigma, chromatic_sigma, isColor);
+      CALL(af_bilateral, out, in, spatial_sigma, chromatic_sigma, isColor);
 }
 
 af_err af_mean_shift(af_array *out, const af_array in,
                      const float spatial_sigma, const float chromatic_sigma,
                      const unsigned iter, const bool is_color) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, spatial_sigma, chromatic_sigma, iter, is_color);
+      CALL(af_mean_shift, out, in, spatial_sigma, chromatic_sigma, iter, is_color);
 }
 
 af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, wind_length, wind_width, edge_pad);
+      CALL(af_minfilt, out, in, wind_length, wind_width, edge_pad);
 }
 
 af_err af_maxfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, wind_length, wind_width, edge_pad);
+      CALL(af_maxfilt, out, in, wind_length, wind_width, edge_pad);
 }
 
 af_err af_regions(af_array *out, const af_array in,
                   const af_connectivity connectivity, const af_dtype ty) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, connectivity, ty);
+      CALL(af_regions, out, in, connectivity, ty);
 }
 
 af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img,
                          const unsigned ker_size) {
     CHECK_ARRAYS(img);
-    return CALL(dx, dy, img, ker_size);
+      CALL(af_sobel_operator, dx, dy, img, ker_size);
 }
 
 af_err af_rgb2gray(af_array *out, const af_array in, const float rPercent,
                    const float gPercent, const float bPercent) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, rPercent, gPercent, bPercent);
+      CALL(af_rgb2gray, out, in, rPercent, gPercent, bPercent);
 }
 
 af_err af_gray2rgb(af_array *out, const af_array in, const float rFactor,
                    const float gFactor, const float bFactor) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, rFactor, gFactor, bFactor);
+      CALL(af_gray2rgb, out, in, rFactor, gFactor, bFactor);
 }
 
 af_err af_hist_equal(af_array *out, const af_array in, const af_array hist) {
     CHECK_ARRAYS(in, hist);
-    return CALL(out, in, hist);
+      CALL(af_hist_equal, out, in, hist);
 }
 
 af_err af_gaussian_kernel(af_array *out, const int rows, const int cols,
                           const double sigma_r, const double sigma_c) {
-    return CALL(out, rows, cols, sigma_r, sigma_c);
+    CALL(af_gaussian_kernel, out, rows, cols, sigma_r, sigma_c);
 }
 
 af_err af_hsv2rgb(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+      CALL(af_hsv2rgb, out, in);
 }
 
 af_err af_rgb2hsv(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+      CALL(af_rgb2hsv, out, in);
 }
 
 af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to,
                       const af_cspace_t from) {
     CHECK_ARRAYS(image);
-    return CALL(out, image, to, from);
+      CALL(af_color_space, out, image, to, from);
 }
 
 af_err af_unwrap(af_array *out, const af_array in, const dim_t wx,
                  const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
                  const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, wx, wy, sx, sy, px, py, is_column);
+      CALL(af_unwrap, out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
 af_err af_wrap(af_array *out, const af_array in, const dim_t ox, const dim_t oy,
                const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
                const dim_t px, const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+      CALL(af_wrap, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
 }
 
 af_err af_sat(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+      CALL(af_sat, out, in);
 }
 
 af_err af_ycbcr2rgb(af_array *out, const af_array in,
                     const af_ycc_std standard) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, standard);
+      CALL(af_ycbcr2rgb, out, in, standard);
 }
 
 af_err af_rgb2ycbcr(af_array *out, const af_array in,
                     const af_ycc_std standard) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, standard);
+      CALL(af_rgb2ycbcr, out, in, standard);
 }
 
 af_err af_canny(af_array *out, const af_array in, const af_canny_threshold ct,
                 const float t1, const float t2, const unsigned sw,
                 const bool isf) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, ct, t1, t2, sw, isf);
+      CALL(af_canny, out, in, ct, t1, t2, sw, isf);
 }
 
 af_err af_anisotropic_diffusion(af_array *out, const af_array in,
@@ -240,18 +244,20 @@ af_err af_anisotropic_diffusion(af_array *out, const af_array in,
                                 const af_flux_function fftype,
                                 const af_diffusion_eq eq) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dt, K, iterations, fftype, eq);
+      CALL(af_anisotropic_diffusion, out, in, dt, K, iterations, fftype,
+                 eq);
 }
 
 af_err af_iterative_deconv(af_array *out, const af_array in, const af_array ker,
                            const unsigned iterations, const float relax_factor,
                            const af_iterative_deconv_algo algo) {
     CHECK_ARRAYS(in, ker);
-    return CALL(out, in, ker, iterations, relax_factor, algo);
+      CALL(af_iterative_deconv, out, in, ker, iterations, relax_factor,
+                 algo);
 }
 
 af_err af_inverse_deconv(af_array *out, const af_array in, const af_array psf,
                          const float gamma, const af_inverse_deconv_algo algo) {
     CHECK_ARRAYS(in, psf);
-    return CALL(out, in, psf, gamma, algo);
+      CALL(af_inverse_deconv, out, in, psf, gamma, algo);
 }
diff --git a/src/api/unified/index.cpp b/src/api/unified/index.cpp
index 975fc746c5..90ea9d4694 100644
--- a/src/api/unified/index.cpp
+++ b/src/api/unified/index.cpp
@@ -14,31 +14,31 @@
 af_err af_index(af_array* out, const af_array in, const unsigned ndims,
                 const af_seq* const index) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, ndims, index);
+    CALL(af_index, out, in, ndims, index);
 }
 
 af_err af_lookup(af_array* out, const af_array in, const af_array indices,
                  const unsigned dim) {
     CHECK_ARRAYS(in, indices);
-    return CALL(out, in, indices, dim);
+    CALL(af_lookup, out, in, indices, dim);
 }
 
 af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
                      const af_seq* const indices, const af_array rhs) {
     CHECK_ARRAYS(lhs, rhs);
-    return CALL(out, lhs, ndims, indices, rhs);
+    CALL(af_assign_seq, out, lhs, ndims, indices, rhs);
 }
 
 af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
                     const af_index_t* indices) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, ndims, indices);
+    CALL(af_index_gen, out, in, ndims, indices);
 }
 
 af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
                      const af_index_t* indices, const af_array rhs) {
     CHECK_ARRAYS(lhs, rhs);
-    return CALL(out, lhs, ndims, indices, rhs);
+    CALL(af_assign_gen, out, lhs, ndims, indices, rhs);
 }
 
 af_seq af_make_seq(double begin, double end, double step) {
@@ -46,23 +46,27 @@ af_seq af_make_seq(double begin, double end, double step) {
     return seq;
 }
 
-af_err af_create_indexers(af_index_t** indexers) { return CALL(indexers); }
+af_err af_create_indexers(af_index_t** indexers) {
+    CALL(af_create_indexers, indexers);
+}
 
 af_err af_set_array_indexer(af_index_t* indexer, const af_array idx,
                             const dim_t dim) {
     CHECK_ARRAYS(idx);
-    return CALL(indexer, idx, dim);
+    CALL(af_set_array_indexer, indexer, idx, dim);
 }
 
 af_err af_set_seq_indexer(af_index_t* indexer, const af_seq* idx,
                           const dim_t dim, const bool is_batch) {
-    return CALL(indexer, idx, dim, is_batch);
+    CALL(af_set_seq_indexer, indexer, idx, dim, is_batch);
 }
 
 af_err af_set_seq_param_indexer(af_index_t* indexer, const double begin,
                                 const double end, const double step,
                                 const dim_t dim, const bool is_batch) {
-    return CALL(indexer, begin, end, step, dim, is_batch);
+    CALL(af_set_seq_param_indexer, indexer, begin, end, step, dim, is_batch);
 }
 
-af_err af_release_indexers(af_index_t* indexers) { return CALL(indexers); }
+af_err af_release_indexers(af_index_t* indexers) {
+    CALL(af_release_indexers, indexers);
+}
diff --git a/src/api/unified/internal.cpp b/src/api/unified/internal.cpp
index c5dc3a9655..ab1d3be7ca 100644
--- a/src/api/unified/internal.cpp
+++ b/src/api/unified/internal.cpp
@@ -15,36 +15,37 @@ af_err af_create_strided_array(af_array *arr, const void *data,
                                const dim_t *const dims_,
                                const dim_t *const strides_, const af_dtype ty,
                                const af_source location) {
-    return CALL(arr, data, offset, ndims, dims_, strides_, ty, location);
+    CALL(af_create_strided_array, arr, data, offset, ndims, dims_, strides_, ty,
+         location);
 }
 
 af_err af_get_strides(dim_t *s0, dim_t *s1, dim_t *s2, dim_t *s3,
                       const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(s0, s1, s2, s3, in);
+    CALL(af_get_strides, s0, s1, s2, s3, in);
 }
 
 af_err af_get_offset(dim_t *offset, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(offset, arr);
+    CALL(af_get_offset, offset, arr);
 }
 
 af_err af_get_raw_ptr(void **ptr, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(ptr, arr);
+    CALL(af_get_raw_ptr, ptr, arr);
 }
 
 af_err af_is_linear(bool *result, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(result, arr);
+    CALL(af_is_linear, result, arr);
 }
 
 af_err af_is_owner(bool *result, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(result, arr);
+    CALL(af_is_owner, result, arr);
 }
 
 af_err af_get_allocated_bytes(size_t *bytes, const af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(bytes, arr);
+    CALL(af_get_allocated_bytes, bytes, arr);
 }
diff --git a/src/api/unified/lapack.cpp b/src/api/unified/lapack.cpp
index 7e22beaaf6..491e4e2763 100644
--- a/src/api/unified/lapack.cpp
+++ b/src/api/unified/lapack.cpp
@@ -13,83 +13,83 @@
 
 af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(u, s, vt, in);
+    CALL(af_svd, u, s, vt, in);
 }
 
 af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(u, s, vt, in);
+    CALL(af_svd_inplace, u, s, vt, in);
 }
 
 af_err af_lu(af_array *lower, af_array *upper, af_array *pivot,
              const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(lower, upper, pivot, in);
+    CALL(af_lu, lower, upper, pivot, in);
 }
 
 af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv) {
     CHECK_ARRAYS(in);
-    return CALL(pivot, in, is_lapack_piv);
+    CALL(af_lu_inplace, pivot, in, is_lapack_piv);
 }
 
 af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(q, r, tau, in);
+    CALL(af_qr, q, r, tau, in);
 }
 
 af_err af_qr_inplace(af_array *tau, af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(tau, in);
+    CALL(af_qr_inplace, tau, in);
 }
 
 af_err af_cholesky(af_array *out, int *info, const af_array in,
                    const bool is_upper) {
     CHECK_ARRAYS(in);
-    return CALL(out, info, in, is_upper);
+    CALL(af_cholesky, out, info, in, is_upper);
 }
 
 af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper) {
     CHECK_ARRAYS(in);
-    return CALL(info, in, is_upper);
+    CALL(af_cholesky_inplace, info, in, is_upper);
 }
 
 af_err af_solve(af_array *x, const af_array a, const af_array b,
                 const af_mat_prop options) {
     CHECK_ARRAYS(a, b);
-    return CALL(x, a, b, options);
+    CALL(af_solve, x, a, b, options);
 }
 
 af_err af_solve_lu(af_array *x, const af_array a, const af_array piv,
                    const af_array b, const af_mat_prop options) {
     CHECK_ARRAYS(a, piv, b);
-    return CALL(x, a, piv, b, options);
+    CALL(af_solve_lu, x, a, piv, b, options);
 }
 
 af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, options);
+    CALL(af_inverse, out, in, options);
 }
 
 af_err af_pinverse(af_array *out, const af_array in, const double tol,
                    const af_mat_prop options) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, tol, options);
+    CALL(af_pinverse, out, in, tol, options);
 }
 
 af_err af_rank(unsigned *rank, const af_array in, const double tol) {
     CHECK_ARRAYS(in);
-    return CALL(rank, in, tol);
+    CALL(af_rank, rank, in, tol);
 }
 
 af_err af_det(double *det_real, double *det_imag, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(det_real, det_imag, in);
+    CALL(af_det, det_real, det_imag, in);
 }
 
 af_err af_norm(double *out, const af_array in, const af_norm_type type,
                const double p, const double q) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, type, p, q);
+    CALL(af_norm, out, in, type, p, q);
 }
 
-af_err af_is_lapack_available(bool *out) { return CALL(out); }
+af_err af_is_lapack_available(bool *out) { CALL(af_is_lapack_available, out); }
diff --git a/src/api/unified/memory.cpp b/src/api/unified/memory.cpp
index d58dd68f90..45ab9bc623 100644
--- a/src/api/unified/memory.cpp
+++ b/src/api/unified/memory.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -10,126 +10,133 @@
 #include <af/memory.h>
 #include "symbol_manager.hpp"
 
-af_err af_create_memory_manager(af_memory_manager* out) { return CALL(out); }
+af_err af_create_memory_manager(af_memory_manager* out) {
+    CALL(af_create_memory_manager, out);
+}
 
 af_err af_release_memory_manager(af_memory_manager handle) {
-    return CALL(handle);
+    CALL(af_release_memory_manager, handle);
 }
 
-af_err af_set_memory_manager(af_memory_manager handle) { return CALL(handle); }
+af_err af_set_memory_manager(af_memory_manager handle) {
+    CALL(af_set_memory_manager, handle);
+}
 
 af_err af_set_memory_manager_pinned(af_memory_manager handle) {
-    return CALL(handle);
+    CALL(af_set_memory_manager_pinned, handle);
 }
 
-af_err af_unset_memory_manager() { return CALL_NO_PARAMS(); }
+af_err af_unset_memory_manager() { CALL_NO_PARAMS(af_unset_memory_manager); }
 
-af_err af_unset_memory_manager_pinned() { return CALL_NO_PARAMS(); }
+af_err af_unset_memory_manager_pinned() {
+    CALL_NO_PARAMS(af_unset_memory_manager_pinned);
+}
 
 af_err af_memory_manager_get_payload(af_memory_manager handle, void** payload) {
-    return CALL(handle, payload);
+    CALL(af_memory_manager_get_payload, handle, payload);
 }
 
 af_err af_memory_manager_set_payload(af_memory_manager handle, void* payload) {
-    return CALL(handle, payload);
+    CALL(af_memory_manager_set_payload, handle, payload);
 }
 
 af_err af_memory_manager_set_initialize_fn(af_memory_manager handle,
                                            af_memory_manager_initialize_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_initialize_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_shutdown_fn(af_memory_manager handle,
                                          af_memory_manager_shutdown_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_shutdown_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_alloc_fn(af_memory_manager handle,
                                       af_memory_manager_alloc_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_alloc_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_allocated_fn(af_memory_manager handle,
                                           af_memory_manager_allocated_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_allocated_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_unlock_fn(af_memory_manager handle,
                                        af_memory_manager_unlock_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_unlock_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_signal_memory_cleanup_fn(
     af_memory_manager handle, af_memory_manager_signal_memory_cleanup_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_signal_memory_cleanup_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_print_info_fn(af_memory_manager handle,
                                            af_memory_manager_print_info_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_print_info_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_user_lock_fn(af_memory_manager handle,
                                           af_memory_manager_user_lock_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_user_lock_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_user_unlock_fn(
     af_memory_manager handle, af_memory_manager_user_unlock_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_user_unlock_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_is_user_locked_fn(
     af_memory_manager handle, af_memory_manager_is_user_locked_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_is_user_locked_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_get_memory_pressure_fn(
     af_memory_manager handle, af_memory_manager_get_memory_pressure_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_get_memory_pressure_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn(
     af_memory_manager handle,
     af_memory_manager_jit_tree_exceeds_memory_pressure_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_add_memory_management_fn(
     af_memory_manager handle, af_memory_manager_add_memory_management_fn fn) {
-    return CALL(handle, fn);
+    CALL(af_memory_manager_set_add_memory_management_fn, handle, fn);
 }
 
 af_err af_memory_manager_set_remove_memory_management_fn(
-    af_memory_manager handle, af_memory_manager_remove_memory_management_fn fn) {
-    return CALL(handle, fn);
+    af_memory_manager handle,
+    af_memory_manager_remove_memory_management_fn fn) {
+    CALL(af_memory_manager_set_remove_memory_management_fn, handle, fn);
 }
 
 af_err af_memory_manager_get_active_device_id(af_memory_manager handle,
                                               int* id) {
-    return CALL(handle, id);
+    CALL(af_memory_manager_get_active_device_id, handle, id);
 }
 
 af_err af_memory_manager_native_alloc(af_memory_manager handle, void** ptr,
                                       size_t size) {
-    return CALL(handle, ptr, size);
+    CALL(af_memory_manager_native_alloc, handle, ptr, size);
 }
 
 af_err af_memory_manager_native_free(af_memory_manager handle, void* ptr) {
-    return CALL(handle, ptr);
+    CALL(af_memory_manager_native_free, handle, ptr);
 }
 
 af_err af_memory_manager_get_max_memory_size(af_memory_manager handle,
                                              size_t* size, int id) {
-    return CALL(handle, size, id);
+    CALL(af_memory_manager_get_max_memory_size, handle, size, id);
 }
 
 af_err af_memory_manager_get_memory_pressure_threshold(af_memory_manager handle,
                                                        float* value) {
-    return CALL(handle, value);
+    CALL(af_memory_manager_get_memory_pressure_threshold, handle, value);
 }
 
 af_err af_memory_manager_set_memory_pressure_threshold(af_memory_manager handle,
                                                        float value) {
-    return CALL(handle, value);
+    CALL(af_memory_manager_set_memory_pressure_threshold, handle, value);
 }
diff --git a/src/api/unified/ml.cpp b/src/api/unified/ml.cpp
index 1723cfc7a7..b91cc7a49d 100644
--- a/src/api/unified/ml.cpp
+++ b/src/api/unified/ml.cpp
@@ -6,22 +6,20 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <af/ml.h>
 #include <af/array.h>
+#include <af/ml.h>
 #include "symbol_manager.hpp"
 
-af_err af_convolve2_gradient_nn(af_array *out, const af_array incoming_gradient,
-                                const af_array original_signal,
-                                const af_array original_filter,
-                                const af_array convolved_output,
-                                const unsigned stride_dims, const dim_t *strides,
-                                const unsigned padding_dims, const dim_t *paddings,
-                                const unsigned dilation_dims,
-                                const dim_t *dilations,
-                                af_conv_gradient_type gradType) {
+af_err af_convolve2_gradient_nn(
+    af_array *out, const af_array incoming_gradient,
+    const af_array original_signal, const af_array original_filter,
+    const af_array convolved_output, const unsigned stride_dims,
+    const dim_t *strides, const unsigned padding_dims, const dim_t *paddings,
+    const unsigned dilation_dims, const dim_t *dilations,
+    af_conv_gradient_type gradType) {
     CHECK_ARRAYS(incoming_gradient, original_signal, original_filter,
                  convolved_output);
-    return CALL(out, incoming_gradient, original_signal, original_filter,
-                convolved_output, stride_dims, strides, padding_dims, paddings,
-                dilation_dims, dilations, gradType);
+    CALL(af_convolve2_gradient_nn, out, incoming_gradient, original_signal,
+         original_filter, convolved_output, stride_dims, strides, padding_dims,
+         paddings, dilation_dims, dilations, gradType);
 }
diff --git a/src/api/unified/moments.cpp b/src/api/unified/moments.cpp
index d79673fda9..5d709160e7 100644
--- a/src/api/unified/moments.cpp
+++ b/src/api/unified/moments.cpp
@@ -14,11 +14,11 @@
 af_err af_moments(af_array* out, const af_array in,
                   const af_moment_type moment) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, moment);
+    CALL(af_moments, out, in, moment);
 }
 
 af_err af_moments_all(double* out, const af_array in,
                       const af_moment_type moment) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, moment);
+    CALL(af_moments_all, out, in, moment);
 }
diff --git a/src/api/unified/random.cpp b/src/api/unified/random.cpp
index a40515a077..771839e9fc 100644
--- a/src/api/unified/random.cpp
+++ b/src/api/unified/random.cpp
@@ -11,69 +11,71 @@
 #include <af/random.h>
 #include "symbol_manager.hpp"
 
-af_err af_get_default_random_engine(af_random_engine *r) { return CALL(r); }
+af_err af_get_default_random_engine(af_random_engine *r) {
+    CALL(af_get_default_random_engine, r);
+}
 
 af_err af_create_random_engine(af_random_engine *engineHandle,
                                af_random_engine_type rtype,
                                unsigned long long seed) {
-    return CALL(engineHandle, rtype, seed);
+    CALL(af_create_random_engine, engineHandle, rtype, seed);
 }
 
 af_err af_retain_random_engine(af_random_engine *outHandle,
                                const af_random_engine engineHandle) {
-    return CALL(outHandle, engineHandle);
+    CALL(af_retain_random_engine, outHandle, engineHandle);
 }
 
 af_err af_random_engine_get_type(af_random_engine_type *rtype,
                                  const af_random_engine engine) {
-    return CALL(rtype, engine);
+    CALL(af_random_engine_get_type, rtype, engine);
 }
 
 af_err af_random_engine_set_type(af_random_engine *engine,
                                  const af_random_engine_type rtype) {
-    return CALL(engine, rtype);
+    CALL(af_random_engine_set_type, engine, rtype);
 }
 
 af_err af_set_default_random_engine_type(const af_random_engine_type rtype) {
-    return CALL(rtype);
+    CALL(af_set_default_random_engine_type, rtype);
 }
 
 af_err af_random_uniform(af_array *arr, const unsigned ndims,
                          const dim_t *const dims, const af_dtype type,
                          af_random_engine engine) {
-    return CALL(arr, ndims, dims, type, engine);
+    CALL(af_random_uniform, arr, ndims, dims, type, engine);
 }
 
 af_err af_random_normal(af_array *arr, const unsigned ndims,
                         const dim_t *const dims, const af_dtype type,
                         af_random_engine engine) {
-    return CALL(arr, ndims, dims, type, engine);
+    CALL(af_random_normal, arr, ndims, dims, type, engine);
 }
 
 af_err af_release_random_engine(af_random_engine engineHandle) {
-    return CALL(engineHandle);
+    CALL(af_release_random_engine, engineHandle);
 }
 
 af_err af_random_engine_set_seed(af_random_engine *engine,
                                  const unsigned long long seed) {
-    return CALL(engine, seed);
+    CALL(af_random_engine_set_seed, engine, seed);
 }
 
 af_err af_random_engine_get_seed(unsigned long long *const seed,
                                  af_random_engine engine) {
-    return CALL(seed, engine);
+    CALL(af_random_engine_get_seed, seed, engine);
 }
 
 af_err af_randu(af_array *out, const unsigned ndims, const dim_t *const dims,
                 const af_dtype type) {
-    return CALL(out, ndims, dims, type);
+    CALL(af_randu, out, ndims, dims, type);
 }
 
 af_err af_randn(af_array *out, const unsigned ndims, const dim_t *const dims,
                 const af_dtype type) {
-    return CALL(out, ndims, dims, type);
+    CALL(af_randn, out, ndims, dims, type);
 }
 
-af_err af_set_seed(const unsigned long long seed) { return CALL(seed); }
+af_err af_set_seed(const unsigned long long seed) { CALL(af_set_seed, seed); }
 
-af_err af_get_seed(unsigned long long *seed) { return CALL(seed); }
+af_err af_get_seed(unsigned long long *seed) { CALL(af_get_seed, seed); }
diff --git a/src/api/unified/signal.cpp b/src/api/unified/signal.cpp
index 10dd6a15a9..e491965acd 100644
--- a/src/api/unified/signal.cpp
+++ b/src/api/unified/signal.cpp
@@ -15,27 +15,27 @@
 af_err af_approx1(af_array *yo, const af_array yi, const af_array xo,
                   const af_interp_type method, const float offGrid) {
     CHECK_ARRAYS(yo, yi, xo);
-    return CALL(yo, yi, xo, method, offGrid);
+    CALL(af_approx1, yo, yi, xo, method, offGrid);
 }
 
 af_err af_approx1_v2(af_array *yo, const af_array yi, const af_array xo,
                      const af_interp_type method, const float offGrid) {
     CHECK_ARRAYS(yo, yi, xo);
-    return CALL(yo, yi, xo, method, offGrid);
+    CALL(af_approx1_v2, yo, yi, xo, method, offGrid);
 }
 
 af_err af_approx2(af_array *zo, const af_array zi, const af_array xo,
                   const af_array yo, const af_interp_type method,
                   const float offGrid) {
     CHECK_ARRAYS(zo, zi, xo, yo);
-    return CALL(zo, zi, xo, yo, method, offGrid);
+    CALL(af_approx2, zo, zi, xo, yo, method, offGrid);
 }
 
 af_err af_approx2_v2(af_array *zo, const af_array zi, const af_array xo,
                      const af_array yo, const af_interp_type method,
                      const float offGrid) {
     CHECK_ARRAYS(zo, zi, xo, yo);
-    return CALL(zo, zi, xo, yo, method, offGrid);
+    CALL(af_approx2_v2, zo, zi, xo, yo, method, offGrid);
 }
 
 af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
@@ -43,7 +43,8 @@ af_err af_approx1_uniform(af_array *yo, const af_array yi, const af_array xo,
                           const double xi_step, const af_interp_type method,
                           const float offGrid) {
     CHECK_ARRAYS(yo, yi, xo);
-    return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
+    CALL(af_approx1_uniform, yo, yi, xo, xdim, xi_beg, xi_step, method,
+         offGrid);
 }
 
 af_err af_approx1_uniform_v2(af_array *yo, const af_array yi, const af_array xo,
@@ -51,7 +52,8 @@ af_err af_approx1_uniform_v2(af_array *yo, const af_array yi, const af_array xo,
                              const double xi_step, const af_interp_type method,
                              const float offGrid) {
     CHECK_ARRAYS(yo, yi, xo);
-    return CALL(yo, yi, xo, xdim, xi_beg, xi_step, method, offGrid);
+    CALL(af_approx1_uniform_v2, yo, yi, xo, xdim, xi_beg, xi_step, method,
+         offGrid);
 }
 
 af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
@@ -61,8 +63,8 @@ af_err af_approx2_uniform(af_array *zo, const af_array zi, const af_array xo,
                           const double yi_step, const af_interp_type method,
                           const float offGrid) {
     CHECK_ARRAYS(zo, zi, xo, yo);
-    return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
-                method, offGrid);
+    CALL(af_approx2_uniform, zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+         yi_beg, yi_step, method, offGrid);
 }
 
 af_err af_approx2_uniform_v2(af_array *zo, const af_array zi, const af_array xo,
@@ -72,18 +74,18 @@ af_err af_approx2_uniform_v2(af_array *zo, const af_array zi, const af_array xo,
                              const double yi_step, const af_interp_type method,
                              const float offGrid) {
     CHECK_ARRAYS(zo, zi, xo, yo);
-    return CALL(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
-                method, offGrid);
+    CALL(af_approx2_uniform_v2, zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+         yi_beg, yi_step, method, offGrid);
 }
 
 af_err af_set_fft_plan_cache_size(size_t cache_size) {
-    return CALL(cache_size);
+    CALL(af_set_fft_plan_cache_size, cache_size);
 }
 
 #define FFT_HAPI_DEF(af_func)                               \
     af_err af_func(af_array in, const double norm_factor) { \
         CHECK_ARRAYS(in);                                   \
-        return CALL(in, norm_factor);                       \
+        CALL(af_func, in, norm_factor);                     \
     }
 
 FFT_HAPI_DEF(af_fft_inplace)
@@ -96,62 +98,62 @@ FFT_HAPI_DEF(af_ifft3_inplace)
 af_err af_fft(af_array *out, const af_array in, const double norm_factor,
               const dim_t odim0) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, odim0);
+    CALL(af_fft, out, in, norm_factor, odim0);
 }
 
 af_err af_fft2(af_array *out, const af_array in, const double norm_factor,
                const dim_t odim0, const dim_t odim1) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, odim0, odim1);
+    CALL(af_fft2, out, in, norm_factor, odim0, odim1);
 }
 
 af_err af_fft3(af_array *out, const af_array in, const double norm_factor,
                const dim_t odim0, const dim_t odim1, const dim_t odim2) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, odim0, odim1, odim2);
+    CALL(af_fft3, out, in, norm_factor, odim0, odim1, odim2);
 }
 
 af_err af_ifft(af_array *out, const af_array in, const double norm_factor,
                const dim_t odim0) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, odim0);
+    CALL(af_ifft, out, in, norm_factor, odim0);
 }
 
 af_err af_ifft2(af_array *out, const af_array in, const double norm_factor,
                 const dim_t odim0, const dim_t odim1) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, odim0, odim1);
+    CALL(af_ifft2, out, in, norm_factor, odim0, odim1);
 }
 
 af_err af_ifft3(af_array *out, const af_array in, const double norm_factor,
                 const dim_t odim0, const dim_t odim1, const dim_t odim2) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, odim0, odim1, odim2);
+    CALL(af_ifft3, out, in, norm_factor, odim0, odim1, odim2);
 }
 
 af_err af_fft_r2c(af_array *out, const af_array in, const double norm_factor,
                   const dim_t pad0) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, pad0);
+    CALL(af_fft_r2c, out, in, norm_factor, pad0);
 }
 
 af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_factor,
                    const dim_t pad0, const dim_t pad1) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, pad0, pad1);
+    CALL(af_fft2_r2c, out, in, norm_factor, pad0, pad1);
 }
 
 af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_factor,
                    const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, norm_factor, pad0, pad1, pad2);
+    CALL(af_fft3_r2c, out, in, norm_factor, pad0, pad1, pad2);
 }
 
 #define FFTC2R_HAPI_DEF(af_func)                                               \
     af_err af_func(af_array *out, const af_array in, const double norm_factor, \
                    const bool is_odd) {                                        \
         CHECK_ARRAYS(in);                                                      \
-        return CALL(out, in, norm_factor, is_odd);                             \
+        CALL(af_func, out, in, norm_factor, is_odd);                           \
     }
 
 FFTC2R_HAPI_DEF(af_fft_c2r)
@@ -163,7 +165,7 @@ FFTC2R_HAPI_DEF(af_fft3_c2r)
                    const af_array filter, const af_conv_mode mode, \
                    af_conv_domain domain) {                        \
         CHECK_ARRAYS(signal, filter);                              \
-        return CALL(out, signal, filter, mode, domain);            \
+        CALL(af_func, out, signal, filter, mode, domain);          \
     }
 
 CONV_HAPI_DEF(af_convolve1)
@@ -176,8 +178,8 @@ af_err af_convolve2_nn(af_array *out, const af_array signal,
                        const dim_t *paddings, const unsigned dilation_dims,
                        const dim_t *dilations) {
     CHECK_ARRAYS(signal, filter);
-    return CALL(out, signal, filter, stride_dims, strides, padding_dims,
-                paddings, dilation_dims, dilations);
+    CALL(af_convolve2_nn, out, signal, filter, stride_dims, strides,
+         padding_dims, paddings, dilation_dims, dilations);
 }
 
 af_err af_convolve2_gradient_nn(
@@ -187,18 +189,18 @@ af_err af_convolve2_gradient_nn(
     const dim_t *strides, const unsigned padding_dims, const dim_t *paddings,
     const unsigned dilation_dims, const dim_t *dilations,
     af_conv_gradient_type grad_type) {
-
-    CHECK_ARRAYS(incoming_gradient, original_signal, original_filter, convolved_output);
-    return CALL(out, incoming_gradient, original_signal, original_filter, convolved_output,
-                stride_dims, strides, padding_dims, paddings, dilation_dims, dilations, grad_type);
-
+    CHECK_ARRAYS(incoming_gradient, original_signal, original_filter,
+                 convolved_output);
+    CALL(af_convolve2_gradient_nn, out, incoming_gradient, original_signal,
+         original_filter, convolved_output, stride_dims, strides, padding_dims,
+         paddings, dilation_dims, dilations, grad_type);
 }
 
 #define FFT_CONV_HAPI_DEF(af_func)                                   \
     af_err af_func(af_array *out, const af_array signal,             \
                    const af_array filter, const af_conv_mode mode) { \
         CHECK_ARRAYS(signal, filter);                                \
-        return CALL(out, signal, filter, mode);                      \
+        CALL(af_func, out, signal, filter, mode);                    \
     }
 
 FFT_CONV_HAPI_DEF(af_fft_convolve1)
@@ -209,34 +211,34 @@ af_err af_convolve2_sep(af_array *out, const af_array col_filter,
                         const af_array row_filter, const af_array signal,
                         const af_conv_mode mode) {
     CHECK_ARRAYS(col_filter, row_filter, signal);
-    return CALL(out, col_filter, row_filter, signal, mode);
+    CALL(af_convolve2_sep, out, col_filter, row_filter, signal, mode);
 }
 
 af_err af_fir(af_array *y, const af_array b, const af_array x) {
     CHECK_ARRAYS(b, x);
-    return CALL(y, b, x);
+    CALL(af_fir, y, b, x);
 }
 
 af_err af_iir(af_array *y, const af_array b, const af_array a,
               const af_array x) {
     CHECK_ARRAYS(b, a, x);
-    return CALL(y, b, a, x);
+    CALL(af_iir, y, b, a, x);
 }
 
 af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, wind_length, wind_width, edge_pad);
+    CALL(af_medfilt, out, in, wind_length, wind_width, edge_pad);
 }
 
 af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
                    const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, wind_width, edge_pad);
+    CALL(af_medfilt1, out, in, wind_width, edge_pad);
 }
 
 af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length,
                    const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, wind_length, wind_width, edge_pad);
+    CALL(af_medfilt2, out, in, wind_length, wind_width, edge_pad);
 }
diff --git a/src/api/unified/sparse.cpp b/src/api/unified/sparse.cpp
index 0f723edd04..56ec71858a 100644
--- a/src/api/unified/sparse.cpp
+++ b/src/api/unified/sparse.cpp
@@ -15,61 +15,62 @@ af_err af_create_sparse_array(af_array *out, const dim_t nRows,
                               const af_array rowIdx, const af_array colIdx,
                               const af_storage stype) {
     CHECK_ARRAYS(values, rowIdx, colIdx);
-    return CALL(out, nRows, nCols, values, rowIdx, colIdx, stype);
+    CALL(af_create_sparse_array, out, nRows, nCols, values, rowIdx, colIdx,
+         stype);
 }
 
 af_err af_create_sparse_array_from_ptr(
     af_array *out, const dim_t nRows, const dim_t nCols, const dim_t nNZ,
     const void *const values, const int *const rowIdx, const int *const colIdx,
     const af_dtype type, const af_storage stype, const af_source source) {
-    return CALL(out, nRows, nCols, nNZ, values, rowIdx, colIdx, type, stype,
-                source);
+    CALL(af_create_sparse_array_from_ptr, out, nRows, nCols, nNZ, values,
+         rowIdx, colIdx, type, stype, source);
 }
 
 af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
                                          const af_storage stype) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, stype);
+    CALL(af_create_sparse_array_from_dense, out, in, stype);
 }
 
 af_err af_sparse_convert_to(af_array *out, const af_array in,
                             const af_storage destStorage) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, destStorage);
+    CALL(af_sparse_convert_to, out, in, destStorage);
 }
 
 af_err af_sparse_to_dense(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_sparse_to_dense, out, in);
 }
 
 af_err af_sparse_get_info(af_array *values, af_array *rowIdx, af_array *colIdx,
                           af_storage *stype, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(values, rowIdx, colIdx, stype, in);
+    CALL(af_sparse_get_info, values, rowIdx, colIdx, stype, in);
 }
 
 af_err af_sparse_get_values(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_sparse_get_values, out, in);
 }
 
 af_err af_sparse_get_row_idx(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_sparse_get_row_idx, out, in);
 }
 
 af_err af_sparse_get_col_idx(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_sparse_get_col_idx, out, in);
 }
 
 af_err af_sparse_get_nnz(dim_t *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_sparse_get_nnz, out, in);
 }
 
 af_err af_sparse_get_storage(af_storage *out, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(out, in);
+    CALL(af_sparse_get_storage, out, in);
 }
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index 8654aeb725..fadb506cb0 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -13,91 +13,91 @@
 
 af_err af_mean(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dim);
+    CALL(af_mean, out, in, dim);
 }
 
 af_err af_mean_weighted(af_array *out, const af_array in,
                         const af_array weights, const dim_t dim) {
     CHECK_ARRAYS(in, weights);
-    return CALL(out, in, weights, dim);
+    CALL(af_mean_weighted, out, in, weights, dim);
 }
 
 af_err af_var(af_array *out, const af_array in, const bool isbiased,
               const dim_t dim) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, isbiased, dim);
+    CALL(af_var, out, in, isbiased, dim);
 }
 
 af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
                        const dim_t dim) {
     CHECK_ARRAYS(in, weights);
-    return CALL(out, in, weights, dim);
+    CALL(af_var_weighted, out, in, weights, dim);
 }
 
 af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
                   const af_array weights, const af_var_bias bias,
                   const dim_t dim) {
     CHECK_ARRAYS(in, weights);
-    return CALL(mean, var, in, weights, bias, dim);
+    CALL(af_meanvar, mean, var, in, weights, bias, dim);
 }
 
 af_err af_stdev(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dim);
+    CALL(af_stdev, out, in, dim);
 }
 
 af_err af_cov(af_array *out, const af_array X, const af_array Y,
               const bool isbiased) {
     CHECK_ARRAYS(X, Y);
-    return CALL(out, X, Y, isbiased);
+    CALL(af_cov, out, X, Y, isbiased);
 }
 
 af_err af_median(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, dim);
+    CALL(af_median, out, in, dim);
 }
 
 af_err af_mean_all(double *real, double *imag, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(real, imag, in);
+    CALL(af_mean_all, real, imag, in);
 }
 
 af_err af_mean_all_weighted(double *real, double *imag, const af_array in,
                             const af_array weights) {
     CHECK_ARRAYS(in, weights);
-    return CALL(real, imag, in, weights);
+    CALL(af_mean_all_weighted, real, imag, in, weights);
 }
 
 af_err af_var_all(double *realVal, double *imagVal, const af_array in,
                   const bool isbiased) {
     CHECK_ARRAYS(in);
-    return CALL(realVal, imagVal, in, isbiased);
+    CALL(af_var_all, realVal, imagVal, in, isbiased);
 }
 
 af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in,
                            const af_array weights) {
     CHECK_ARRAYS(in, weights);
-    return CALL(realVal, imagVal, in, weights);
+    CALL(af_var_all_weighted, realVal, imagVal, in, weights);
 }
 
 af_err af_stdev_all(double *real, double *imag, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(real, imag, in);
+    CALL(af_stdev_all, real, imag, in);
 }
 
 af_err af_median_all(double *realVal, double *imagVal, const af_array in) {
     CHECK_ARRAYS(in);
-    return CALL(realVal, imagVal, in);
+    CALL(af_median_all, realVal, imagVal, in);
 }
 
 af_err af_corrcoef(double *realVal, double *imagVal, const af_array X,
                    const af_array Y) {
     CHECK_ARRAYS(X, Y);
-    return CALL(realVal, imagVal, X, Y);
+    CALL(af_corrcoef, realVal, imagVal, X, Y);
 }
 
 af_err af_topk(af_array *values, af_array *indices, const af_array in,
                const int k, const int dim, const af_topk_function order) {
     CHECK_ARRAYS(in);
-    return CALL(values, indices, in, k, dim, order);
+    CALL(af_topk, values, indices, in, k, dim, order);
 }
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index c268586d33..b3e229875c 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -136,7 +136,9 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
 
     LibHandle retVal = nullptr;
     for (size_t i = 0; i < extent<decltype(pathPrefixes)>::value; i++) {
-        AF_TRACE("Attempting: {}", (pathPrefixes[i].empty() ? "Default System Paths" : pathPrefixes[i]));
+        AF_TRACE("Attempting: {}",
+                 (pathPrefixes[i].empty() ? "Default System Paths"
+                                          : pathPrefixes[i]));
         if ((retVal = loadLibrary(
                  join_path(pathPrefixes[i], bkndLibName).c_str()))) {
             AF_TRACE("Found: {}", join_path(pathPrefixes[i], bkndLibName));
@@ -148,8 +150,7 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
                 count_func(&count);
                 AF_TRACE("Device Count: {}.", count);
                 if (count == 0) {
-                    AF_TRACE("Skipping: No devices found for {}",
-                             bkndLibName);
+                    AF_TRACE("Skipping: No devices found for {}", bkndLibName);
                     retVal = nullptr;
                     continue;
                 }
@@ -234,34 +235,4 @@ af_err AFSymbolManager::setBackend(af::Backend bknd) {
     }
 }
 
-bool checkArray(af_backend activeBackend, const af_array a) {
-    // Convert af_array into int to retrieve the backend info.
-    // See ArrayInfo.hpp for more
-    af_backend backend = (af_backend)0;
-
-    // This condition is required so that the invalid args tests for unified
-    // backend return the expected error rather than AF_ERR_ARR_BKND_MISMATCH
-    // Since a = 0, does not have a backend specified, it should be a
-    // AF_ERR_ARG instead of AF_ERR_ARR_BKND_MISMATCH
-    if (a == 0) return true;
-
-    unified::AFSymbolManager::getInstance().call("af_get_backend_id", &backend,
-                                                 a);
-    return backend == activeBackend;
-}
-
-bool checkArray(af_backend activeBackend, const af_array* a) {
-    if (a) {
-        return checkArray(activeBackend, *a);
-    } else {
-        return true;
-    }
-}
-
-bool checkArrays(af_backend activeBackend) {
-    UNUSED(activeBackend);
-    // Dummy
-    return true;
-}
-
 }  // namespace unified
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index da8d4a7a87..0bcb2d0ebb 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -12,6 +12,7 @@
 #include <common/err_common.hpp>
 #include <common/module_loading.hpp>
 #include <common/util.hpp>
+#include <af/backend.h>
 #include <af/defines.h>
 
 #include <spdlog/spdlog.h>
@@ -54,32 +55,6 @@ class AFSymbolManager {
 
     af::Backend getActiveBackend() { return activeBackend; }
 
-    template<typename... CalleeArgs>
-    af_err call(const char* symbolName, CalleeArgs... args) {
-        typedef af_err (*af_func)(CalleeArgs...);
-        if (!activeHandle) { UNIFIED_ERROR_LOAD_LIB(); }
-        thread_local std::array<std::unordered_map<const char*, af_func>,
-                                NUM_BACKENDS>
-            funcHandles;
-
-        int index           = backend_index(getActiveBackend());
-        af_func& funcHandle = funcHandles[index][symbolName];
-
-        if (!funcHandle) {
-            AF_TRACE("Loading: {}", symbolName);
-            funcHandle =
-                (af_func)common::getFunctionPointer(activeHandle, symbolName);
-        }
-        if (!funcHandle) {
-            AF_TRACE("Failed to load symbol: {}", symbolName);
-            std::string str = "Failed to load symbol: ";
-            str += symbolName;
-            AF_RETURN_ERROR(str.c_str(), AF_ERR_LOAD_SYM);
-        }
-
-        return funcHandle(args...);
-    }
-
     LibHandle getHandle() { return activeHandle; }
     spdlog::logger* getLogger();
 
@@ -105,10 +80,37 @@ class AFSymbolManager {
     std::shared_ptr<spdlog::logger> logger;
 };
 
-// Helper functions to ensure all the input arrays are on the active backend
-bool checkArray(af_backend activeBackend, const af_array a);
-bool checkArray(af_backend activeBackend, const af_array *a);
-bool checkArrays(af_backend activeBackend);
+namespace {
+bool checkArray(af_backend activeBackend, const af_array a) {
+    // Convert af_array into int to retrieve the backend info.
+    // See ArrayInfo.hpp for more
+    af_backend backend = (af_backend)0;
+
+    // This condition is required so that the invalid args tests for unified
+    // backend return the expected error rather than AF_ERR_ARR_BKND_MISMATCH
+    // Since a = 0, does not have a backend specified, it should be a
+    // AF_ERR_ARG instead of AF_ERR_ARR_BKND_MISMATCH
+    if (a == 0) return true;
+
+    af_get_backend_id(&backend, a);
+    return backend == activeBackend;
+}
+
+bool checkArray(af_backend activeBackend, const af_array* a) {
+    if (a) {
+        return checkArray(activeBackend, *a);
+    } else {
+        return true;
+    }
+}
+
+bool checkArrays(af_backend activeBackend) {
+    UNUSED(activeBackend);
+    // Dummy
+    return true;
+}
+
+}  // namespace
 
 template<typename T, typename... Args>
 bool checkArrays(af_backend activeBackend, T a, Args... arg) {
@@ -133,16 +135,20 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
                             AF_ERR_ARR_BKND_MISMATCH);                        \
     } while (0)
 
-#if defined(OS_WIN)
-#define CALL(...) \
-    unified::AFSymbolManager::getInstance().call(__FUNCTION__, __VA_ARGS__)
-#define CALL_NO_PARAMS() \
-    unified::AFSymbolManager::getInstance().call(__FUNCTION__)
-#else
-#define CALL(...) \
-    unified::AFSymbolManager::getInstance().call(__func__, __VA_ARGS__)
-#define CALL_NO_PARAMS() unified::AFSymbolManager::getInstance().call(__func__)
-#endif
+#define CALL(FUNCTION, ...)                                                      \
+    using af_func                  = std::add_pointer<decltype(FUNCTION)>::type; \
+    thread_local auto& instance    = unified::AFSymbolManager::getInstance();    \
+    thread_local af_backend index_ = instance.getActiveBackend();                \
+    thread_local af_func func =                                                  \
+        (af_func)common::getFunctionPointer(instance.getHandle(), __func__);     \
+    if (index_ != instance.getActiveBackend()) {                                 \
+        index_ = instance.getActiveBackend();                                    \
+        func   = (af_func)common::getFunctionPointer(instance.getHandle(),       \
+                                                   __func__);                    \
+    }                                                                            \
+    return func(__VA_ARGS__);
+
+#define CALL_NO_PARAMS(FUNCTION) CALL(FUNCTION)
 
 #define LOAD_SYMBOL()           \
     common::getFunctionPointer( \
diff --git a/src/api/unified/util.cpp b/src/api/unified/util.cpp
index 8223f0b29d..5833046a3f 100644
--- a/src/api/unified/util.cpp
+++ b/src/api/unified/util.cpp
@@ -13,43 +13,43 @@
 
 af_err af_print_array(af_array arr) {
     CHECK_ARRAYS(arr);
-    return CALL(arr);
+    CALL(af_print_array, arr);
 }
 
 af_err af_print_array_gen(const char *exp, const af_array arr,
                           const int precision) {
     CHECK_ARRAYS(arr);
-    return CALL(exp, arr, precision);
+    CALL(af_print_array_gen, exp, arr, precision);
 }
 
 af_err af_save_array(int *index, const char *key, const af_array arr,
                      const char *filename, const bool append) {
     CHECK_ARRAYS(arr);
-    return CALL(index, key, arr, filename, append);
+    CALL(af_save_array, index, key, arr, filename, append);
 }
 
 af_err af_read_array_index(af_array *out, const char *filename,
                            const unsigned index) {
-    return CALL(out, filename, index);
+    CALL(af_read_array_index, out, filename, index);
 }
 
 af_err af_read_array_key(af_array *out, const char *filename, const char *key) {
-    return CALL(out, filename, key);
+    CALL(af_read_array_key, out, filename, key);
 }
 
 af_err af_read_array_key_check(int *index, const char *filename,
                                const char *key) {
-    return CALL(index, filename, key);
+    CALL(af_read_array_key_check, index, filename, key);
 }
 
 af_err af_array_to_string(char **output, const char *exp, const af_array arr,
                           const int precision, const bool transpose) {
     CHECK_ARRAYS(arr);
-    return CALL(output, exp, arr, precision, transpose);
+    CALL(af_array_to_string, output, exp, arr, precision, transpose);
 }
 
 af_err af_example_function(af_array *out, const af_array a,
                            const af_someenum_t param) {
     CHECK_ARRAYS(a);
-    return CALL(out, a, param);
+    CALL(af_example_function, out, a, param);
 }
diff --git a/src/api/unified/vision.cpp b/src/api/unified/vision.cpp
index 50c6a69dff..b600c4355f 100644
--- a/src/api/unified/vision.cpp
+++ b/src/api/unified/vision.cpp
@@ -15,7 +15,7 @@ af_err af_fast(af_features *out, const af_array in, const float thr,
                const unsigned arc_length, const bool non_max,
                const float feature_ratio, const unsigned edge) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, thr, arc_length, non_max, feature_ratio, edge);
+    CALL(af_fast, out, in, thr, arc_length, non_max, feature_ratio, edge);
 }
 
 af_err af_harris(af_features *out, const af_array in,
@@ -23,7 +23,8 @@ af_err af_harris(af_features *out, const af_array in,
                  const float sigma, const unsigned block_size,
                  const float k_thr) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, max_corners, min_response, sigma, block_size, k_thr);
+    CALL(af_harris, out, in, max_corners, min_response, sigma, block_size,
+         k_thr);
 }
 
 af_err af_orb(af_features *feat, af_array *desc, const af_array in,
@@ -31,7 +32,8 @@ af_err af_orb(af_features *feat, af_array *desc, const af_array in,
               const float scl_fctr, const unsigned levels,
               const bool blur_img) {
     CHECK_ARRAYS(in);
-    return CALL(feat, desc, in, fast_thr, max_feat, scl_fctr, levels, blur_img);
+    CALL(af_orb, feat, desc, in, fast_thr, max_feat, scl_fctr, levels,
+         blur_img);
 }
 
 af_err af_sift(af_features *feat, af_array *desc, const af_array in,
@@ -40,8 +42,8 @@ af_err af_sift(af_features *feat, af_array *desc, const af_array in,
                const bool double_input, const float intensity_scale,
                const float feature_ratio) {
     CHECK_ARRAYS(in);
-    return CALL(feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma,
-                double_input, intensity_scale, feature_ratio);
+    CALL(af_sift, feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma,
+         double_input, intensity_scale, feature_ratio);
 }
 
 af_err af_gloh(af_features *feat, af_array *desc, const af_array in,
@@ -50,15 +52,15 @@ af_err af_gloh(af_features *feat, af_array *desc, const af_array in,
                const bool double_input, const float intensity_scale,
                const float feature_ratio) {
     CHECK_ARRAYS(in);
-    return CALL(feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma,
-                double_input, intensity_scale, feature_ratio);
+    CALL(af_gloh, feat, desc, in, n_layers, contrast_thr, edge_thr, init_sigma,
+         double_input, intensity_scale, feature_ratio);
 }
 
 af_err af_hamming_matcher(af_array *idx, af_array *dist, const af_array query,
                           const af_array train, const dim_t dist_dim,
                           const unsigned n_dist) {
     CHECK_ARRAYS(query, train);
-    return CALL(idx, dist, query, train, dist_dim, n_dist);
+    CALL(af_hamming_matcher, idx, dist, query, train, dist_dim, n_dist);
 }
 
 af_err af_nearest_neighbour(af_array *idx, af_array *dist, const af_array query,
@@ -66,27 +68,28 @@ af_err af_nearest_neighbour(af_array *idx, af_array *dist, const af_array query,
                             const unsigned n_dist,
                             const af_match_type dist_type) {
     CHECK_ARRAYS(query, train);
-    return CALL(idx, dist, query, train, dist_dim, n_dist, dist_type);
+    CALL(af_nearest_neighbour, idx, dist, query, train, dist_dim, n_dist,
+         dist_type);
 }
 
 af_err af_match_template(af_array *out, const af_array search_img,
                          const af_array template_img,
                          const af_match_type m_type) {
     CHECK_ARRAYS(search_img, template_img);
-    return CALL(out, search_img, template_img, m_type);
+    CALL(af_match_template, out, search_img, template_img, m_type);
 }
 
 af_err af_susan(af_features *out, const af_array in, const unsigned radius,
                 const float diff_thr, const float geom_thr,
                 const float feature_ratio, const unsigned edge) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, radius, diff_thr, geom_thr, feature_ratio, edge);
+    CALL(af_susan, out, in, radius, diff_thr, geom_thr, feature_ratio, edge);
 }
 
 af_err af_dog(af_array *out, const af_array in, const int radius1,
               const int radius2) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, radius1, radius2);
+    CALL(af_dog, out, in, radius1, radius2);
 }
 
 af_err af_homography(af_array *H, int *inliers, const af_array x_src,
@@ -95,6 +98,6 @@ af_err af_homography(af_array *H, int *inliers, const af_array x_src,
                      const float inlier_thr, const unsigned iterations,
                      const af_dtype type) {
     CHECK_ARRAYS(x_src, y_src, x_dst, y_dst);
-    return CALL(H, inliers, x_src, y_src, x_dst, y_dst, htype, inlier_thr,
-                iterations, type);
+    CALL(af_homography, H, inliers, x_src, y_src, x_dst, y_dst, htype,
+         inlier_thr, iterations, type);
 }

From 70ef19897e4cf639dd720f3083dd2c6c522ff076 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 19 Dec 2019 18:58:58 -0500
Subject: [PATCH 1772/2677] Add support for fp16 for several functions in the
 internal header

---
 src/api/c/internal.cpp | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 6a4c318a88..8a2d5cb84f 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
 #include <af/device.h>
@@ -19,6 +20,7 @@
 #include <cstring>
 
 using af::dim4;
+using common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createStridedArray;
@@ -103,6 +105,10 @@ af_err af_create_strided_array(af_array *arr, const void *data,
                 res = getHandle(createStridedArray<uchar>(
                     dims, strides, offset, (uchar *)data, isdev));
                 break;
+            case f16:
+                res = getHandle(createStridedArray<half>(
+                    dims, strides, offset, (half *)data, isdev));
+                break;
             default: TYPE_ERROR(6, ty);
         }
 
@@ -153,6 +159,7 @@ af_err af_get_raw_ptr(void **ptr, const af_array arr) {
             case s16: res = (void *)getRawPtr(getArray<short>(arr)); break;
             case b8: res = (void *)getRawPtr(getArray<char>(arr)); break;
             case u8: res = (void *)getRawPtr(getArray<uchar>(arr)); break;
+            case f16: res = (void *)getRawPtr(getArray<half>(arr)); break;
             default: TYPE_ERROR(6, ty);
         }
 
@@ -189,6 +196,7 @@ af_err af_is_owner(bool *result, const af_array arr) {
             case s16: res = (void *)getArray<short>(arr).isOwner(); break;
             case b8: res = (void *)getArray<char>(arr).isOwner(); break;
             case u8: res = (void *)getArray<uchar>(arr).isOwner(); break;
+            case f16: res = (void *)getArray<half>(arr).isOwner(); break;
             default: TYPE_ERROR(6, ty);
         }
 
@@ -217,6 +225,7 @@ af_err af_get_allocated_bytes(size_t *bytes, const af_array arr) {
             case s16: res = getArray<short>(arr).getAllocatedBytes(); break;
             case b8: res = getArray<char>(arr).getAllocatedBytes(); break;
             case u8: res = getArray<uchar>(arr).getAllocatedBytes(); break;
+            case f16: res = getArray<half>(arr).getAllocatedBytes(); break;
             default: TYPE_ERROR(6, ty);
         }
 

From ae09f4d9c31cc5cdad1d1004462d34652bd522eb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Jan 2020 15:08:57 -0500
Subject: [PATCH 1773/2677] Add f16 support for topk (#2702)

* Add f16 support for topk
---
 src/api/c/topk.cpp                            |  3 +
 src/backend/cpu/math.hpp                      |  2 +-
 src/backend/cpu/topk.cpp                      |  7 +-
 src/backend/cuda/kernel/topk.hpp              | 18 ++---
 src/backend/cuda/math.hpp                     |  4 +-
 src/backend/cuda/topk.cu                      |  4 ++
 src/backend/opencl/kernel/reduce.hpp          | 16 ++---
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  2 +-
 .../opencl/kernel/sort_by_key_impl.hpp        |  6 +-
 src/backend/opencl/kernel/sort_helper.hpp     | 25 ++++---
 src/backend/opencl/sort_index.cpp             |  4 ++
 src/backend/opencl/topk.cpp                   | 11 +--
 src/backend/opencl/types.cpp                  | 17 -----
 src/backend/opencl/types.hpp                  | 16 ++++-
 test/topk.cpp                                 | 71 +++++++++++++++----
 15 files changed, 137 insertions(+), 69 deletions(-)

diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index f72652a471..4d848eef9a 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -13,10 +13,12 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <topk.hpp>
 
 using namespace detail;
+using common::half;
 
 namespace {
 
@@ -77,6 +79,7 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
             case f64: topk<double>(values, indices, in, k, rdim, ord); break;
             case u32: topk<uint>(values, indices, in, k, rdim, ord); break;
             case s32: topk<int>(values, indices, in, k, rdim, ord); break;
+            case f16: topk<half>(values, indices, in, k, rdim, ord); break;
             default: TYPE_ERROR(1, type);
         }
     }
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 1ef0239dba..5761147151 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -64,7 +64,7 @@ STATIC_ T maxval() {
 }
 template<typename T>
 STATIC_ T minval() {
-    return std::numeric_limits<T>::min();
+    return std::numeric_limits<T>::lowest();
 }
 template<>
 STATIC_ float maxval() {
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index 4a5a5b56a4..8fd5393e25 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <index.hpp>
 #include <sort.hpp>
 #include <sort_index.hpp>
@@ -17,6 +18,7 @@
 #include <numeric>
 #include <vector>
 
+using common::half;
 using std::iota;
 using std::min;
 using std::partial_sort_copy;
@@ -60,13 +62,13 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
                 partial_sort_copy(
                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
                     [ptr](const uint lhs, const uint rhs) -> bool {
-                        return ptr[lhs] < ptr[rhs];
+                        return compute_t<T>(ptr[lhs]) < compute_t<T>(ptr[rhs]);
                     });
             } else {
                 partial_sort_copy(
                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
                     [ptr](const uint lhs, const uint rhs) -> bool {
-                        return ptr[lhs] >= ptr[rhs];
+                        return compute_t<T>(ptr[lhs]) >= compute_t<T>(ptr[rhs]);
                     });
             }
 
@@ -96,4 +98,5 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
+INSTANTIATE(half)
 }  // namespace cpu
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 803380d89a..4552ab0b97 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -16,6 +16,7 @@
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
+#include <types.hpp>
 
 #include <limits>
 
@@ -31,9 +32,9 @@ static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
                                    CParam<T> ivals, CParam<uint> iidxs,
                                    const int k, const af::topkFunction order,
                                    uint numLaunchBlocksY) {
-    using ValueType = uint;
-    using BlockRadixSortT =
-        BlockRadixSort<T, TOPK_THRDS_PER_BLK, TOPK_IDX_THRD_LOAD, ValueType>;
+    using ValueType       = uint;
+    using BlockRadixSortT = BlockRadixSort<compute_t<T>, TOPK_THRDS_PER_BLK,
+                                           TOPK_IDX_THRD_LOAD, ValueType>;
 
     __shared__ typename BlockRadixSortT::TempStorage smem;
 
@@ -45,8 +46,8 @@ static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
     const uint gxStride = blockDim.x * gridDim.x;
     const uint elements = ivals.dims[0];
 
-    const T* kdata = ivals.ptr + by * ivals.strides[1] + bz * ivals.strides[2] +
-                     bw * ivals.strides[3];
+    const data_t<T>* kdata = ivals.ptr + by * ivals.strides[1] +
+                             bz * ivals.strides[2] + bw * ivals.strides[3];
 
     const ValueType* idata = iidxs.ptr + by * iidxs.strides[1] +
                              bz * iidxs.strides[2] + bw * iidxs.strides[3];
@@ -56,15 +57,16 @@ static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
     uint* ires = oidxs.ptr + by * oidxs.strides[1] + bz * oidxs.strides[2] +
                  bw * oidxs.strides[3];
 
-    T keys[TOPK_IDX_THRD_LOAD];
+    compute_t<T> keys[TOPK_IDX_THRD_LOAD];
     ValueType vals[TOPK_IDX_THRD_LOAD];
 
     for (uint li = 0, i = gx; li < TOPK_IDX_THRD_LOAD; i += gxStride, li++) {
         if (i < elements) {
-            keys[li] = kdata[i];
+            keys[li] = static_cast<compute_t<T>>(kdata[i]);
             vals[li] = (READ_INDEX) ? idata[i] : i;
         } else {
-            keys[li] = (order == AF_TOPK_MAX) ? minval<T>() : maxval<T>();
+            keys[li] = (order == AF_TOPK_MAX) ? minval<compute_t<T>>()
+                                              : maxval<compute_t<T>>();
             vals[li] = maxval<ValueType>();
         }
     }
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 6a4660d86e..2b9b8fbf96 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -242,11 +242,11 @@ __device__ common::half minval<common::half>() {
 }
 template<>
 __device__ __half maxval<__half>() {
-    return __float2half(65537.f);
+    return __float2half(CUDART_INF);
 }
 template<>
 __device__ __half minval<__half>() {
-    return __float2half(-65537.f);
+    return __float2half(-CUDART_INF);
 }
 #endif
 
diff --git a/src/backend/cuda/topk.cu b/src/backend/cuda/topk.cu
index e6c5c0b366..5901c5e5b1 100644
--- a/src/backend/cuda/topk.cu
+++ b/src/backend/cuda/topk.cu
@@ -8,10 +8,13 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/topk.hpp>
 #include <topk.hpp>
 #include <af/dim4.hpp>
 
+using common::half;
+
 namespace cuda {
 template<typename T>
 void topk(Array<T>& ovals, Array<uint>& oidxs, const Array<T>& ivals,
@@ -35,4 +38,5 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
+INSTANTIATE(half)
 }  // namespace cuda
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 3affd06471..930bcd49eb 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -283,10 +283,10 @@ To reduce_all(Param in, int change_nan, double nanval) {
         getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
                                      sizeof(To) * tmp_elements, h_ptr.data());
 
-        Binary<To, op> reduce;
+        Binary<compute_t<To>, op> reduce;
         compute_t<To> out = Binary<To, op>::init();
         for (int i = 0; i < (int)tmp_elements; i++) {
-            out = reduce(out, h_ptr[i]);
+            out = reduce(out, compute_t<To>(h_ptr[i]));
         }
         return data_t<To>(out);
     } else {
@@ -295,15 +295,15 @@ To reduce_all(Param in, int change_nan, double nanval) {
                                      sizeof(Ti) * in.info.offset,
                                      sizeof(Ti) * in_elements, h_ptr.data());
 
-        Transform<Ti, To, op> transform;
-        Binary<To, op> reduce;
-        compute_t<To> out       = Binary<To, op>::init();
-        compute_t<To> nanval_to = scalar<To>(nanval);
+        Transform<Ti, compute_t<To>, op> transform;
+        Binary<compute_t<To>, op> reduce;
+        compute_t<To> out       = Binary<compute_t<To>, op>::init();
+        compute_t<To> nanval_to = scalar<compute_t<To>>(nanval);
 
         for (int i = 0; i < (int)in_elements; i++) {
-            To in_val = transform(h_ptr[i]);
+            compute_t<To> in_val = transform(h_ptr[i]);
             if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
-            out = reduce(out, in_val);
+            out = reduce(out, compute_t<To>(in_val));
         }
 
         return data_t<To>(out);
diff --git a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index 2a64f05b0a..893c3ecc88 100644
--- a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -9,7 +9,7 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
-// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar half
 
 namespace opencl {
 namespace kernel {
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 076b359ea8..24adb18f61 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <iota.hpp>
 #include <kernel/sort_by_key.hpp>
@@ -44,6 +45,7 @@ using cl::Kernel;
 using cl::KernelFunctor;
 using cl::NDRange;
 using cl::Program;
+using common::half;
 using std::string;
 
 template<typename Tk, typename Tv, bool isAscending>
@@ -256,7 +258,9 @@ void sort0ByKey(Param pKey, Param pVal, bool isAscending) {
     INSTANTIATE(Tk, char)    \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
-    INSTANTIATE(Tk, uintl)
+    INSTANTIATE(Tk, uintl)   \
+    INSTANTIATE(Tk, half)
+
 }  // namespace kernel
 }  // namespace opencl
 
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 7908801593..1c9db6cab7 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -10,32 +10,37 @@
 #pragma once
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <type_traits>
 
 namespace opencl {
 namespace kernel {
-using std::conditional;
-using std::is_same;
+
+template<typename T>
+using htype_t = typename std::conditional<std::is_same<T, common::half>::value,
+                                          cl_half, T>::type;
 
 // If type is cdouble, return std::complex<double>, else return T
 template<typename T>
-using ztype_t = typename conditional<is_same<T, cdouble>::value,
-                                     std::complex<double>, T>::type;
+using ztype_t =
+    typename std::conditional<std::is_same<T, cdouble>::value,
+                              std::complex<double>, htype_t<T>>::type;
 
 // If type is cfloat, return std::complex<float>, else return ztype_t
 template<typename T>
-using ctype_t = typename conditional<is_same<T, cfloat>::value,
-                                     std::complex<float>, ztype_t<T>>::type;
+using ctype_t =
+    typename std::conditional<std::is_same<T, cfloat>::value,
+                              std::complex<float>, ztype_t<T>>::type;
 
 // If type is intl, return cl_long, else return ctype_t
 template<typename T>
-using ltype_t =
-    typename conditional<is_same<T, intl>::value, cl_long, ctype_t<T>>::type;
+using ltype_t = typename std::conditional<std::is_same<T, intl>::value, cl_long,
+                                          ctype_t<T>>::type;
 
 // If type is uintl, return cl_ulong, else return ltype_t
 template<typename T>
-using type_t =
-    typename conditional<is_same<T, uintl>::value, cl_ulong, ltype_t<T>>::type;
+using type_t = typename std::conditional<std::is_same<T, uintl>::value,
+                                         cl_ulong, ltype_t<T>>::type;
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index a595e97f30..da70519840 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
 #include <kernel/sort_by_key.hpp>
@@ -17,6 +18,8 @@
 #include <sort_index.hpp>
 #include <stdexcept>
 
+using common::half;
+
 namespace opencl {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
@@ -71,5 +74,6 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index bdef1369f1..356811ddd5 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -8,10 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <index.hpp>
 #include <sort.hpp>
 #include <sort_index.hpp>
+#include <types.hpp>
 
 #include <algorithm>
 #include <cmath>
@@ -20,6 +22,7 @@
 
 using cl::Buffer;
 using cl::Event;
+using common::half;
 
 using std::iota;
 using std::min;
@@ -96,13 +99,13 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
                 partial_sort_copy(
                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
                     [ptr](const uint lhs, const uint rhs) -> bool {
-                        return ptr[lhs] < ptr[rhs];
+                        return compute_t<T>(ptr[lhs]) < compute_t<T>(ptr[rhs]);
                     });
             } else {
                 partial_sort_copy(
                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
                     [ptr](const uint lhs, const uint rhs) -> bool {
-                        return ptr[lhs] >= ptr[rhs];
+                        return compute_t<T>(ptr[lhs]) >= compute_t<T>(ptr[rhs]);
                     });
             }
             ev_val.wait();
@@ -125,8 +128,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
     } else {
         auto values  = createEmptyArray<T>(in.dims());
         auto indices = createEmptyArray<unsigned>(in.dims());
-        sort_index(values, indices, in, dim,
-                   (order == AF_TOPK_MIN ? true : false));
+        sort_index(values, indices, in, dim, order == AF_TOPK_MIN);
         auto indVec = indexForTopK(k);
         vals        = index<T>(values, indVec.data());
         idxs        = index<unsigned>(indices, indVec.data());
@@ -143,4 +145,5 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
+INSTANTIATE(half)
 }  // namespace opencl
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index 52f3a5a330..d9ec439f18 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -98,20 +98,3 @@ std::string ToNumStr<half>::operator()<float>(float val) {
 #undef INSTANTIATE
 
 }  // namespace opencl
-
-namespace common {
-template<typename T>
-class kernel_type;
-}
-
-namespace common {
-template<>
-struct kernel_type<common::half> {
-    using data = common::half;
-
-    using compute = cl_half;
-
-    // These are the types within a kernel
-    using native = cl_half;
-};
-}
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index f2d40b096a..96aa2bd72d 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -17,11 +17,25 @@
 #endif
 #pragma GCC diagnostic pop
 
-#include <common/traits.hpp>
 #include <common/kernel_type.hpp>
+#include <common/traits.hpp>
 
 #include <string>
 
+namespace common {
+/// This is a CPU based half which need to be converted into floats before they
+/// are used
+template<>
+struct kernel_type<common::half> {
+    using data = common::half;
+
+    // These are the types within a kernel
+    using native = float;
+
+    using compute = float;
+};
+}  // namespace common
+
 namespace opencl {
 using cdouble = cl_double2;
 using cfloat  = cl_float2;
diff --git a/test/topk.cpp b/test/topk.cpp
index a86150bb76..b2faab6ff5 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -9,6 +9,7 @@
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 
 #include <algorithm>
@@ -21,6 +22,7 @@
 #include <random>
 #include <sstream>
 #include <string>
+#include <type_traits>
 #include <utility>
 
 using af::array;
@@ -29,6 +31,7 @@ using af::dtype_traits;
 using af::iota;
 using af::topk;
 using af::topkFunction;
+using half_float::half;
 
 using std::iota;
 using std::make_pair;
@@ -45,13 +48,34 @@ using std::vector;
 template<typename T>
 class TopK : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint> TestTypes;
+typedef ::testing::Types<float, double, int, uint, half_float::half> TestTypes;
 
 TYPED_TEST_CASE(TopK, TestTypes);
 
+template<typename T>
+void increment_next(T& val,
+                    typename std::enable_if<std::is_floating_point<T>::value,
+                                            int>::type t = 0) {
+    val = std::nextafterf(val, std::numeric_limits<T>::max());
+}
+
+template<typename T>
+void increment_next(
+    T& val,
+    typename std::enable_if<std::is_integral<T>::value, int>::type t = 0) {
+    ++val;
+}
+
+void increment_next(half_float::half& val) {
+    half_float::half tmp = (half_float::half)half_float::nextafter(
+        val, std::numeric_limits<half_float::half>::max());
+    val = tmp;
+}
+
 template<typename T>
 void topkTest(const int ndims, const dim_t* dims, const unsigned k,
               const int dim, const af_topk_function order) {
+    SUPPORTED_TYPE_CHECK(T);
     af_dtype dtype = (af_dtype)dtype_traits<T>::af_type;
 
     af_array input, output, outindex;
@@ -68,7 +92,11 @@ void topkTest(const int ndims, const dim_t* dims, const unsigned k,
     size_t bSize  = dims[dim];
 
     vector<T> inData(ielems);
-    iota(begin(inData), end(inData), 0);
+    T val{std::numeric_limits<T>::lowest()};
+    generate(begin(inData), end(inData), [&]() {
+        increment_next(val);
+        return val;
+    });
 
     random_device rnd_device;
     mt19937 g(rnd_device());
@@ -132,43 +160,58 @@ void topkTest(const int ndims, const dim_t* dims, const unsigned k,
     ASSERT_SUCCESS(af_release_array(outindex));
 }
 
+int type_max(af_dtype type) {
+    switch (type) {
+        case f16: return 63000;
+        default: return 100000;
+    }
+}
+
 TYPED_TEST(TopK, Max1D0) {
-    dim_t dims[4] = {100000, 1, 1, 1};
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t), 1, 1, 1};
     topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MAX);
 }
 
 TYPED_TEST(TopK, Max2D0) {
-    dim_t dims[4] = {10000, 10, 1, 1};
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 10, 10, 1, 1};
     topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MAX);
 }
 
 TYPED_TEST(TopK, Max3D0) {
-    dim_t dims[4] = {10000, 10, 10, 1};
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 100, 10, 10, 1};
     topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
 }
 
 TYPED_TEST(TopK, Max4D0) {
-    dim_t dims[4] = {10000, 10, 10, 10};
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 1000, 10, 10, 10};
     topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MAX);
 }
 
-TYPED_TEST(TopK, MIN1D0) {
-    dim_t dims[4] = {100000, 1, 1, 1};
+TYPED_TEST(TopK, Min1D0) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t), 1, 1, 1};
     topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_MIN);
 }
 
-TYPED_TEST(TopK, MIN2D0) {
-    dim_t dims[4] = {10000, 10, 1, 1};
+TYPED_TEST(TopK, Min2D0) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 10, 10, 1, 1};
     topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_MIN);
 }
 
-TYPED_TEST(TopK, MIN3D0) {
-    dim_t dims[4] = {10000, 10, 10, 1};
+TYPED_TEST(TopK, Min3D0) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 100, 10, 10, 1};
     topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
 }
 
-TYPED_TEST(TopK, MIN4D0) {
-    dim_t dims[4] = {10000, 10, 10, 10};
+TYPED_TEST(TopK, Min4D0) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 1000, 10, 10, 10};
     topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_MIN);
 }
 

From 1a11988f1e4bc66e06d5465e337a939d06451612 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 9 Jan 2020 08:25:48 +0530
Subject: [PATCH 1774/2677] FEAT: function to pad array borders (#2682)

* FEAT: function to pad array borders

Add periodic padding support for pad fn
---
 docs/details/data.dox                         |  11 ++
 include/af/data.h                             |  36 ++++
 include/af/defines.h                          |   7 +-
 src/api/c/data.cpp                            |  54 +++++
 src/api/cpp/data.cpp                          |  12 ++
 src/api/unified/data.cpp                      |   7 +
 src/backend/cpu/kernel/pad_array_borders.hpp  |  15 +-
 src/backend/cpu/padarray.cpp                  |   2 +
 src/backend/cuda/CMakeLists.txt               |   1 +
 src/backend/cuda/kernel/pad_array_borders.cuh |  16 +-
 src/backend/cuda/nvrtc/cache.cpp              |   5 +
 src/backend/cuda/pad_array_borders.cpp        |   2 +
 src/backend/opencl/copy.hpp                   |   3 +
 .../opencl/kernel/pad_array_borders.cl        |  28 ++-
 .../opencl/kernel/pad_array_borders.hpp       |   1 +
 test/CMakeLists.txt                           |   1 +
 test/pad_borders.cpp                          | 187 ++++++++++++++++++
 17 files changed, 369 insertions(+), 19 deletions(-)
 create mode 100644 test/pad_borders.cpp

diff --git a/docs/details/data.dox b/docs/details/data.dox
index 9e35e3c1e7..f38ac8e93e 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -13,6 +13,17 @@ The array created has the same value at all locations
 
 =======================================================================
 
+\defgroup data_func_pad Padding
+
+\brief Pad an array
+
+Pad the input array using a constant or values from input along border
+
+\ingroup data_mat
+\ingroup arrayfire_func
+
+=======================================================================
+
 \defgroup data_func_identity identity
 
 \brief Create an identity array with diagonal values 1
diff --git a/include/af/data.h b/include/af/data.h
index 18961b8760..b6c6b35f5e 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -416,6 +416,23 @@ namespace af
     AFAPI void replace(array &a, const array  &cond, const double &b);
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+       \param[in] in is the input array to be padded
+       \param[in] beginPadding informs the number of elements to be
+                  padded at beginning of each dimension
+       \param[in] endPadding informs the number of elements to be
+                  padded at end of each dimension
+       \param[in] padFillType is indicates what values should fill padded region
+
+       \return the padded array
+
+       \ingroup data_func_pad
+    */
+    AFAPI array pad(const array &in, const dim4 &beginPadding,
+                    const dim4 &endPadding, const borderType padFillType);
+#endif
+
     /**
       @}
     */
@@ -709,6 +726,25 @@ extern "C" {
     AFAPI af_err af_replace_scalar(af_array a, const af_array cond, const double b);
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+       \param[out] out is the padded array
+       \param[in] in is the input array to be padded
+       \param[in] b_ndims is size of \p l_dims array
+       \param[in] b_dims array contains padding size at beginning of each
+       dimension \param[in] e_ndims is size of \p u_dims array \param[in] e_dims
+       array contains padding sizes at end of each dimension \param[in]
+       pad_fill_type is indicates what values should fill padded region
+
+       \ingroup data_func_pad
+    */
+    AFAPI af_err af_pad(af_array *out, const af_array in,
+                        const unsigned begin_ndims,
+                        const dim_t *const begin_dims, const unsigned end_ndims,
+                        const dim_t *const end_dims,
+                        const af_border_type pad_fill_type);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/include/af/defines.h b/include/af/defines.h
index d511b408ac..8477e227bc 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -277,7 +277,12 @@ typedef enum {
     ///
     /// Out of bound values are clamped to the edge
     ///
-    AF_PAD_CLAMP_TO_EDGE
+    AF_PAD_CLAMP_TO_EDGE,
+
+    ///
+    /// Out of bound values are mapped to range of the dimension in cyclic fashion
+    ///
+    AF_PAD_PERIODIC
 } af_border_type;
 
 typedef enum {
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 450ca73e29..b0d76e3fe7 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -427,3 +427,57 @@ af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
     CATCHALL
     return AF_SUCCESS;
 }
+
+template<typename T>
+inline af_array pad(const af_array in, const dim4 &lPad, const dim4 &uPad,
+                    const af::borderType ptype) {
+    return getHandle(padArrayBorders<T>(getArray<T>(in), lPad, uPad, ptype));
+}
+
+af_err af_pad(af_array *out, const af_array in, const unsigned begin_ndims,
+              const dim_t *const begin_dims, const unsigned end_ndims,
+              const dim_t *const end_dims, const af_border_type pad_type) {
+    try {
+        DIM_ASSERT(2, begin_ndims > 0 && begin_ndims <= 4);
+        DIM_ASSERT(4, end_ndims > 0 && end_ndims <= 4);
+        ARG_ASSERT(3, begin_dims != NULL);
+        ARG_ASSERT(5, end_dims != NULL);
+        ARG_ASSERT(6, (pad_type >= AF_PAD_ZERO && pad_type <= AF_PAD_PERIODIC));
+        for (unsigned i = 0; i < begin_ndims; i++) {
+            DIM_ASSERT(3, begin_dims[i] >= 0);
+        }
+        for (unsigned i = 0; i < end_ndims; i++) {
+            DIM_ASSERT(5, end_dims[i] >= 0);
+        }
+
+        dim4 lPad(begin_ndims, begin_dims);
+        dim4 uPad(end_ndims, end_dims);
+        for (unsigned i = begin_ndims; i < AF_MAX_DIMS; i++) { lPad[i] = 0; }
+        for (unsigned i = end_ndims; i < AF_MAX_DIMS; i++) { uPad[i] = 0; }
+
+        const ArrayInfo &info = getInfo(in);
+        af_dtype type         = info.getType();
+
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
+
+        af_array res = 0;
+        switch (type) {
+            case f32: res = pad<float>(in, lPad, uPad, pad_type); break;
+            case f64: res = pad<double>(in, lPad, uPad, pad_type); break;
+            case c32: res = pad<cfloat>(in, lPad, uPad, pad_type); break;
+            case c64: res = pad<cdouble>(in, lPad, uPad, pad_type); break;
+            case s32: res = pad<int>(in, lPad, uPad, pad_type); break;
+            case u32: res = pad<uint>(in, lPad, uPad, pad_type); break;
+            case s64: res = pad<intl>(in, lPad, uPad, pad_type); break;
+            case u64: res = pad<uintl>(in, lPad, uPad, pad_type); break;
+            case s16: res = pad<short>(in, lPad, uPad, pad_type); break;
+            case u16: res = pad<ushort>(in, lPad, uPad, pad_type); break;
+            case u8: res = pad<uchar>(in, lPad, uPad, pad_type); break;
+            case b8: res = pad<char>(in, lPad, uPad, pad_type); break;
+            case f16: res = pad<half>(in, lPad, uPad, pad_type); break;
+        }
+        std::swap(*out, res);
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 4d28b9371d..5be0130728 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -307,4 +307,16 @@ void replace(array &a, const array &cond, const array &b) {
 void replace(array &a, const array &cond, const double &b) {
     AF_THROW(af_replace_scalar(a.get(), cond.get(), b));
 }
+
+array pad(const array &in, const dim4 &beginPadding, const dim4 &endPadding,
+          const borderType padFillType) {
+    af_array out = 0;
+    // FIXME(pradeep) Cannot use dim4::ndims() since that will
+    //               always return 0 if any one of dimensions
+    //               has no padding completely
+    AF_THROW(af_pad(&out, in.get(), 4, beginPadding.get(), 4, endPadding.get(),
+                    padFillType));
+    return array(out);
+}
+
 }  // namespace af
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index 143c4209d5..7084485c01 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -141,3 +141,10 @@ af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
     CHECK_ARRAYS(a, cond);
       CALL(af_replace_scalar, a, cond, b);
 }
+
+af_err af_pad(af_array *out, const af_array in, const unsigned b_ndims,
+              const dim_t *const b_dims, const unsigned e_ndims,
+              const dim_t *const e_dims, const af_border_type ptype) {
+    CHECK_ARRAYS(in);
+    return CALL(out, in, b_ndims, b_dims, e_ndims, e_dims, ptype);
+}
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
index 2daa4c588d..98176ca481 100644
--- a/src/backend/cpu/kernel/pad_array_borders.hpp
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -8,7 +8,9 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
+#include <utility.hpp>
 
 #include <algorithm>
 
@@ -19,14 +21,15 @@ static inline dim_t idxByndEdge(const dim_t i, const dim_t lb, const dim_t len,
                                 const af::borderType btype) {
     dim_t retVal;
     switch (btype) {
-        case AF_PAD_SYM:
-            retVal =
-                ((i < lb || i >= (lb + len)) ? ((len - 1) - ((i - lb) % len))
-                                             : i - lb);
-            break;
+        case AF_PAD_SYM: retVal = trimIndex(i - lb, len); break;
         case AF_PAD_CLAMP_TO_EDGE:
             retVal = std::max(dim_t(0), std::min(i - lb, len - 1));
             break;
+        case AF_PAD_PERIODIC: {
+            dim_t rem = (i - lb) % len;
+            bool cond = rem < 0;
+            retVal    = cond * (rem + len) + (1 - cond) * rem;
+        } break;
         default: retVal = 0; break;
     }
     return retVal;
@@ -118,7 +121,7 @@ void padBorders(Param<T> out, CParam<T> in, const dim4 lBoundPadSize,
                                                          iDims[0], btype);
 
                     dst[oLOff + oKOff + oJOff + oIOff] =
-                        src[iLOff + iKOff + iJOff + iIOff];
+                       src[iLOff + iKOff + iJOff + iIOff];
 
                 }  // first dimension loop
             }      // second dimension loop
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
index a83d287448..0ffbb6c684 100644
--- a/src/backend/cpu/padarray.cpp
+++ b/src/backend/cpu/padarray.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
 #include <err_cpu.hpp>
 #include <kernel/copy.hpp>
@@ -101,6 +102,7 @@ INSTANTIATE_PAD_ARRAY(uchar)
 INSTANTIATE_PAD_ARRAY(char)
 INSTANTIATE_PAD_ARRAY(ushort)
 INSTANTIATE_PAD_ARRAY(short)
+INSTANTIATE_PAD_ARRAY(common::half)
 
 #define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
     template Array<cfloat> padArray<SRC_T, cfloat>(                       \
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index def5847fca..e81c3a18c5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -93,6 +93,7 @@ set(nvrtc_src
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/interp.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/math.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/utility.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/types.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/half.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/kernel_type.hpp
diff --git a/src/backend/cuda/kernel/pad_array_borders.cuh b/src/backend/cuda/kernel/pad_array_borders.cuh
index bff5f86af8..20e8ac6bc7 100644
--- a/src/backend/cuda/kernel/pad_array_borders.cuh
+++ b/src/backend/cuda/kernel/pad_array_borders.cuh
@@ -9,6 +9,7 @@
 
 #include <Param.hpp>
 #include <math.hpp>
+#include <utility.hpp>
 
 namespace  cuda {
 
@@ -17,15 +18,14 @@ __device__
 int idxByndEdge(const int i, const int lb, const int len) {
     uint retVal;
     switch (BType) {
-        case AF_PAD_SYM:
-            retVal =
-                ((i < lb || i >= (lb + len)) ? ((len - 1) - ((i - lb) % len))
-                                             : i - lb);
-            break;
+        case AF_PAD_SYM: retVal = trimIndex(i-lb, len); break;
         case AF_PAD_CLAMP_TO_EDGE: retVal = clamp(i - lb, 0, len - 1); break;
-        default:  // AF_PAD_ZERO
-            retVal = 0;
-            break;
+        case AF_PAD_PERIODIC: {
+            int rem   = (i - lb) % len;
+            bool cond = rem < 0;
+            retVal    = cond * (rem + len) + (1 - cond) * rem;
+        } break;
+        default: retVal = 0; break; // AF_PAD_ZERO
     }
     return retVal;
 }
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 30449d059b..6f874e38f4 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -27,6 +27,7 @@
 #include <nvrtc_kernel_headers/shared_hpp.hpp>
 #include <nvrtc_kernel_headers/traits_hpp.hpp>
 #include <nvrtc_kernel_headers/types_hpp.hpp>
+#include <nvrtc_kernel_headers/utility_hpp.hpp>
 #include <nvrtc_kernel_headers/version_h.hpp>
 #include <optypes.hpp>
 #include <platform.hpp>
@@ -166,6 +167,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
             "math_constants.h",
             "af/defines.h",
             "af/version.h",
+            "utility.hpp",
         };
 
         constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
@@ -192,6 +194,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
             string(math_constants_h, math_constants_h_len),
             string(defines_h, defines_h_len),
             string(version_h, version_h_len),
+            string(utility_hpp, utility_hpp_len),
         }};
 
         static const char *headers[] = {
@@ -206,6 +209,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
             sourceStrings[16].c_str(), sourceStrings[17].c_str(),
             sourceStrings[18].c_str(), sourceStrings[19].c_str(),
             sourceStrings[20].c_str(), sourceStrings[21].c_str(),
+            sourceStrings[22].c_str(),
         };
         NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
                                        NumHeaders, headers, includeNames));
@@ -448,6 +452,7 @@ string toString(af_border_type p) {
         CASE_STMT(AF_PAD_ZERO);
         CASE_STMT(AF_PAD_SYM);
         CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
+        CASE_STMT(AF_PAD_PERIODIC);
     }
 #undef CASE_STMT
     return retVal;
diff --git a/src/backend/cuda/pad_array_borders.cpp b/src/backend/cuda/pad_array_borders.cpp
index 369237d5d6..86d4c83982 100644
--- a/src/backend/cuda/pad_array_borders.cpp
+++ b/src/backend/cuda/pad_array_borders.cpp
@@ -10,6 +10,7 @@
 #include <copy.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/pad_array_borders.hpp>
 
@@ -48,4 +49,5 @@ INSTANTIATE_PAD_ARRAY_BORDERS(uchar)
 INSTANTIATE_PAD_ARRAY_BORDERS(char)
 INSTANTIATE_PAD_ARRAY_BORDERS(ushort)
 INSTANTIATE_PAD_ARRAY_BORDERS(short)
+INSTANTIATE_PAD_ARRAY_BORDERS(common::half)
 }  // namespace cuda
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index fdf32fc1ec..97be450a66 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -46,6 +46,9 @@ Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
             kernel::padBorders<T, AF_PAD_CLAMP_TO_EDGE>(ret, in,
                                                         lowerBoundPadding);
             break;
+        case AF_PAD_PERIODIC:
+            kernel::padBorders<T, AF_PAD_PERIODIC>(ret, in, lowerBoundPadding);
+            break;
         default:
             kernel::padBorders<T, AF_PAD_ZERO>(ret, in, lowerBoundPadding);
             break;
diff --git a/src/backend/opencl/kernel/pad_array_borders.cl b/src/backend/opencl/kernel/pad_array_borders.cl
index 766810b030..9ab2110749 100644
--- a/src/backend/opencl/kernel/pad_array_borders.cl
+++ b/src/backend/opencl/kernel/pad_array_borders.cl
@@ -9,11 +9,23 @@
 
 #if AF_BORDER_TYPE == AF_PAD_SYM
 
+int trimIndex(int idx, const int len) {
+    int ret_val = idx;
+    int offset  = abs(ret_val) % len;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
+    }
+    return ret_val;
+}
+
+//TODO(Pradeep) move trimindex from all locations into
+//              a single header after opencl cache is cleaned up
 int idxByndEdge(const int i, const int lb, const int len) {
-    if (i < lb || i >= (lb + len)) {
-        return (len - 1) - ((i - lb) % len);
-    } else
-        return i - lb;
+    return trimIndex(i-lb, len);
 }
 
 #elif AF_BORDER_TYPE == AF_PAD_CLAMP_TO_EDGE
@@ -22,6 +34,14 @@ int idxByndEdge(const int i, const int lb, const int len) {
     return clamp(i - lb, 0, len - 1);
 }
 
+#elif AF_BORDER_TYPE == AF_PAD_PERIODIC
+
+int idxByndEdge(const int i, const int lb, const int len) {
+    int rem   = (i - lb) % len;
+    int cond = rem < 0;
+    return cond * (rem + len) + (1 - cond) * rem;
+}
+
 #else
 
 #define DEFAULT_BORDER
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index cc82da65f0..97065eddc0 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -44,6 +44,7 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding) {
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D AF_BORDER_TYPE=" << BType
                 << " -D AF_PAD_SYM=" << AF_PAD_SYM
+                << " -D AF_PAD_PERIODIC=" << AF_PAD_PERIODIC
                 << " -D AF_PAD_CLAMP_TO_EDGE=" << AF_PAD_CLAMP_TO_EDGE;
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index dc3f345868..3753c08971 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -252,6 +252,7 @@ if(OpenCL_FOUND)
 endif()
 
 make_test(SRC orb.cpp)
+make_test(SRC pad_borders.cpp CXX11)
 make_test(SRC pinverse.cpp)
 make_test(SRC qr_dense.cpp SERIAL)
 make_test(SRC random.cpp)
diff --git a/test/pad_borders.cpp b/test/pad_borders.cpp
new file mode 100644
index 0000000000..663d349361
--- /dev/null
+++ b/test/pad_borders.cpp
@@ -0,0 +1,187 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <half.hpp>
+#include <testHelpers.hpp>
+#include <af/dim4.hpp>
+#include <af/traits.hpp>
+
+#include <vector>
+
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::dim4;
+using std::vector;
+
+template<typename T>
+class PadBorders : public ::testing::Test {};
+
+typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
+                         int, uint, intl, uintl, short,
+                         ushort /*, half_float::half*/>
+    TestTypes;
+
+TYPED_TEST_CASE(PadBorders, TestTypes);
+
+template<typename T>
+void testPad(const vector<T>& input, const dim4& inDims, const dim4& lbPadding,
+             const dim4& ubPadding, const af::borderType btype,
+             const vector<T>& gold, const dim4& outDims) {
+    SUPPORTED_TYPE_CHECK(T);
+    array in(inDims, input.data());
+    array out = af::pad(in, lbPadding, ubPadding, btype);
+    ASSERT_VEC_ARRAY_EQ(gold, outDims, out);
+}
+
+TYPED_TEST(PadBorders, Zero) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(5, 5), dim4(2, 2, 0, 0), dim4(2, 2, 0, 0), AF_PAD_ZERO,
+            vector<TypeParam>({
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
+                1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,
+                1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            dim4(9, 9));
+}
+
+TYPED_TEST(PadBorders, ClampToEdge) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2,
+                2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(5, 5), dim4(2, 2, 0, 0), dim4(2, 2, 0, 0),
+            AF_PAD_CLAMP_TO_EDGE,
+            vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2,
+                1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(9, 9));
+}
+
+TYPED_TEST(PadBorders, SymmetricOverEdge) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 0, 2, 3, 2, 2, 0, 3, 5, 2,
+                2, 0, 4, 7, 3, 3, 0, 5, 9, 1, 1, 0,
+            }),
+            dim4(5, 5), dim4(2, 2, 0, 0), dim4(2, 2, 0, 0), AF_PAD_SYM,
+            vector<TypeParam>({
+                3, 2, 2, 3, 2, 2, 0, 0, 2, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
+                1, 1, 1, 0, 0, 1, 3, 2, 2, 3, 2, 2, 0, 0, 2, 5, 3, 3, 5, 2, 2,
+                0, 0, 2, 7, 4, 4, 7, 3, 3, 0, 0, 3, 9, 5, 5, 9, 1, 1, 0, 0, 1,
+                9, 5, 5, 9, 1, 1, 0, 0, 1, 7, 4, 4, 7, 3, 3, 0, 0, 3,
+            }),
+            dim4(9, 9));
+}
+
+TYPED_TEST(PadBorders, Periodic) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 0, 2, 3, 2, 2, 0, 3, 5, 2,
+                2, 0, 4, 7, 3, 3, 0, 5, 9, 1, 1, 0,
+            }),
+            dim4(5, 5), dim4(2, 2, 0, 0), dim4(2, 2, 0, 0), AF_PAD_PERIODIC,
+            vector<TypeParam>({
+                3, 0, 4, 7, 3, 3, 0, 4, 7, 1, 0, 5, 9, 1, 1, 0, 5, 9, 1, 0, 1,
+                1, 1, 1, 0, 1, 1, 2, 0, 2, 3, 2, 2, 0, 2, 3, 2, 0, 3, 5, 2, 2,
+                0, 3, 5, 3, 0, 4, 7, 3, 3, 0, 4, 7, 1, 0, 5, 9, 1, 1, 0, 5, 9,
+                1, 0, 1, 1, 1, 1, 0, 1, 1, 2, 0, 2, 3, 2, 2, 0, 2, 3,
+            }),
+            dim4(9, 9));
+}
+
+TYPED_TEST(PadBorders, BeginOnly) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(5, 5), dim4(2, 2, 0, 0), dim4(0, 2, 0, 0), AF_PAD_ZERO,
+            vector<TypeParam>({
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
+                0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
+                0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            dim4(7, 9));
+}
+
+TYPED_TEST(PadBorders, EndOnly) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(5, 5), dim4(0, 2, 0, 0), dim4(2, 2, 0, 0), AF_PAD_ZERO,
+            vector<TypeParam>({
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
+                1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,
+                1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            dim4(7, 9));
+}
+
+TYPED_TEST(PadBorders, BeginCorner) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(5, 5), dim4(2, 2, 0, 0), dim4(0, 0, 0, 0), AF_PAD_ZERO,
+            vector<TypeParam>({
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
+                1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
+                1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
+            }),
+            dim4(7, 7));
+}
+
+TYPED_TEST(PadBorders, EndCorner) {
+    testPad(vector<TypeParam>({
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            }),
+            dim4(5, 5), dim4(0, 0, 0, 0), dim4(2, 2, 0, 0), AF_PAD_ZERO,
+            vector<TypeParam>({
+                1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
+                1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            dim4(7, 7));
+}
+
+TEST(PadBorders, NegativePadding) {
+    af_array dummyIn  = 0;
+    af_array dummyOut = 0;
+    dim_t ldims[4]    = {-1, 1, 0, 1};
+    dim_t udims[4]    = {-1, 1, 0, 1};
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_pad(&dummyOut, dummyIn, 4, ldims, 4, udims, AF_PAD_ZERO));
+}
+
+TEST(PadBorders, NegativeNDims) {
+    af_array dummyIn  = 0;
+    af_array dummyOut = 0;
+    dim_t ldims[4]    = {1, 1, 0, 1};
+    dim_t udims[4]    = {1, 1, 0, 1};
+    ASSERT_EQ(AF_ERR_SIZE,
+              af_pad(&dummyOut, dummyIn, -1, ldims, 4, udims, AF_PAD_ZERO));
+}
+
+TEST(PadBorders, InvalidPadType) {
+    af_array dummyIn  = 0;
+    af_array dummyOut = 0;
+    dim_t ldims[4]    = {1, 1, 0, 1};
+    dim_t udims[4]    = {1, 1, 0, 1};
+    ASSERT_EQ(AF_ERR_ARG, af_pad(&dummyOut, dummyIn, 4, ldims, 4, udims,
+                                 (af_border_type)4));
+}

From 8d55984264f118c06fa47655f45d4cf61dce4e40 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 9 Jan 2020 11:49:18 -0500
Subject: [PATCH 1775/2677] Fix conversion error in OpenCL reduce on Windows

---
 src/backend/opencl/kernel/reduce.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 930bcd49eb..1d0f77128e 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -284,7 +284,7 @@ To reduce_all(Param in, int change_nan, double nanval) {
                                      sizeof(To) * tmp_elements, h_ptr.data());
 
         Binary<compute_t<To>, op> reduce;
-        compute_t<To> out = Binary<To, op>::init();
+        compute_t<To> out = Binary<compute_t<To>, op>::init();
         for (int i = 0; i < (int)tmp_elements; i++) {
             out = reduce(out, compute_t<To>(h_ptr[i]));
         }

From 1d6fbd4c17b562d3adbb796e35c1683ff8455dc2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 6 Jan 2020 19:37:29 +0530
Subject: [PATCH 1776/2677] Fix doxygen warnings and invalid doxygen commands

---
 docs/details/signal.dox | 16 ++++++++--------
 docs/doxygen.mk         |  7 ++-----
 include/af/arith.h      |  3 +--
 include/af/array.h      | 18 +++++-------------
 include/af/blas.h       |  4 ----
 include/af/data.h       | 12 ------------
 6 files changed, 16 insertions(+), 44 deletions(-)

diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index ef50f296d4..de1feb5e97 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -179,7 +179,7 @@ below notation.
 
 Given below is an example of no batch mode.
 
-\image html "conv_docs_images/basic.png" "Single 2d convolution with 2d filter"
+\image html "basic.png" "Single 2d convolution with 2d filter"
 
 For input size \dims{M,N,1,1} and filter size \dims{A,B,1,1}, the following set-builder
 notation gives a formal definition of all convolutions performed in this mode.
@@ -191,7 +191,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of filter batch mode.
 
-\image html "conv_docs_images/filter.png" "Single signal convolved with many filters independently"
+\image html "filter.png" "Single signal convolved with many filters independently"
 
 For input size \dims{M,N,1,1} and filter size \dims{A,B,P,1}, the following set-builder
 notation gives a formal definition of all convolutions performed in this mode.
@@ -203,7 +203,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of signal batch mode.
 
-\image html "conv_docs_images/signal.png" "Single filter convolved with many signals independently"
+\image html "signal.png" "Single filter convolved with many signals independently"
 
 For input size \dims{M,N,P,1} and filter size \dims{A,B,1,1}, the following set-builder
 notation gives a formal definition of all convolutions performed in this mode.
@@ -215,7 +215,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of identical batch mode.
 
-\image html "conv_docs_images/identical.png" "Many signals convolved with many filters in one-on-one manner"
+\image html "identical.png" "Many signals convolved with many filters in one-on-one manner"
 
 For input size \dims{M,N,P,Q} and filter size \dims{A,B,P,Q}, the following set-builder
 notation gives a formal definition of all convolutions performed in this mode.
@@ -237,7 +237,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of this batch mode.
 
-\image html "conv_docs_images/non-overlapping_1.png"
+\image html "non-overlapping_1.png"
 
 #### Combination 2
 
@@ -248,7 +248,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of this batch mode.
 
-\image html "conv_docs_images/non-overlapping_2.png"
+\image html "non-overlapping_2.png"
 
 #### Combination 3
 
@@ -259,7 +259,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of this batch mode.
 
-\image html "conv_docs_images/non-overlapping_3.png"
+\image html "non-overlapping_3.png"
 
 #### Combination 4
 
@@ -270,7 +270,7 @@ notation gives a formal definition of all convolutions performed in this mode.
 
 Given below is an example of this batch mode.
 
-\image html "conv_docs_images/non-overlapping_4.png"
+\image html "non-overlapping_4.png"
 
 
 The batching behavior of convolve2NN functions(\ref af_convolve2_nn() and
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 85d362c3de..7a5a5ad229 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -906,7 +906,8 @@ EXAMPLE_RECURSIVE      = YES
 # that contain images that are to be included in the documentation (see the
 # \image command).
 
-IMAGE_PATH             = ${ASSETS_DIR}
+IMAGE_PATH             = ${ASSETS_DIR} \
+                         ${ASSETS_DIR}/conv_docs_images
 
 # The INPUT_FILTER tag can be used to specify a program that doxygen should
 # invoke to filter for each input file. Doxygen will invoke the filter program
@@ -2173,8 +2174,6 @@ EXTERNAL_PAGES         = YES
 # interpreter (i.e. the result of 'which perl').
 # The default file (with absolute path) is: /usr/bin/perl.
 
-PERL_PATH              = /usr/bin/perl
-
 #---------------------------------------------------------------------------
 # Configuration options related to the dot tool
 #---------------------------------------------------------------------------
@@ -2195,8 +2194,6 @@ CLASS_DIAGRAMS         = YES
 # the mscgen tool resides. If left empty the tool is assumed to be found in the
 # default search path.
 
-MSCGEN_PATH            =
-
 # You can include diagrams made with dia in doxygen documentation. Doxygen will
 # then run dia to produce the diagram and insert it in the documentation. The
 # DIA_PATH tag allows you to specify the directory where the dia binary resides.
diff --git a/include/af/arith.h b/include/af/arith.h
index 6089b3619d..c7d8812496 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -67,14 +67,13 @@ namespace af
 #if AF_API_VERSION >= 34
     /// \copydoc clamp(const array&, const array&, const array&)
     AFAPI array clamp(const array &in, const double lo, const array &hi);
-    /// @}
 #endif
 
 #if AF_API_VERSION >= 34
     /// \copydoc clamp(const array&, const array&, const array&)
     AFAPI array clamp(const array &in, const double lo, const double hi);
-    /// @}
 #endif
+    /// @}
 
     /// \ingroup arith_func_rem
     /// @{
diff --git a/include/af/array.h b/include/af/array.h
index caffaded3a..a445265b58 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -203,9 +203,9 @@ namespace af
 #endif
 #endif
         /**
-            Creates an array from an \ref af_array handle. Does not increment 
-            a reference counter: the array assumes ownership of the handle. To 
-            share the array between multiple objects, use this in conjunction 
+            Creates an array from an \ref af_array handle. Does not increment
+            a reference counter: the array assumes ownership of the handle. To
+            share the array between multiple objects, use this in conjunction
             with \ref af_retain_array.
             \param handle the af_array object.
          */
@@ -516,7 +516,6 @@ namespace af
 
            \param[in] input
            \param[in] dims total number of elements must not change.
-           \return same underlying array data with different dimensions
         */
         array(const array& input, const dim4& dims);
 
@@ -549,7 +548,6 @@ namespace af
            \param[in] dim1 second dimension
            \param[in] dim2 third dimension
            \param[in] dim3 fourth dimension
-           \return same underlying array data with different dimensions
         */
         array(  const array& input,
                 const dim_t dim0, const dim_t dim1 = 1,
@@ -928,7 +926,7 @@ namespace af
         ASSIGN_(OP)                                                                       \
           array& OP(const short  &val);              /**< \copydoc OP##(const array &) */ \
           array& OP(const unsigned short &val);
- 
+
 #else
 #define ASSIGN(OP) ASSIGN_(OP)
 #endif
@@ -1604,9 +1602,6 @@ extern "C" {
        Evaluate multiple arrays together
     */
     AFAPI af_err af_eval_multiple(const int num, af_array *arrays);
-    /**
-      @}
-    */
 #endif
 
 #if AF_API_VERSION >= 34
@@ -1614,9 +1609,6 @@ extern "C" {
        Turn the manual eval flag on or off
     */
     AFAPI af_err af_set_manual_eval_flag(bool flag);
-    /**
-      @}
-    */
 #endif
 
 #if AF_API_VERSION >= 34
@@ -1624,10 +1616,10 @@ extern "C" {
        Get the manual eval flag
     */
     AFAPI af_err af_get_manual_eval_flag(bool *flag);
+#endif
     /**
       @}
     */
-#endif
 
     /**
         \ingroup method_mat
diff --git a/include/af/blas.h b/include/af/blas.h
index 495697c635..f42e062a7f 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -158,8 +158,6 @@ namespace af
         \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run conjugate dot operation.
         \note This function is not supported in GFOR
 
-        \returns out = dot(lhs, rhs)
-
         \ingroup blas_func_dot
     */
     AFAPI array dot   (const array &lhs, const array &rhs,
@@ -190,8 +188,6 @@ namespace af
         \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run conjugate dot operation.
         \note This function is not supported in GFOR
 
-        \returns out = dot(lhs, rhs)
-
         \ingroup blas_func_dot
     */
     template<typename T> T dot(const array &lhs, const array &rhs,
diff --git a/include/af/data.h b/include/af/data.h
index b6c6b35f5e..9d359d69e3 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -432,18 +432,12 @@ namespace af
     AFAPI array pad(const array &in, const dim4 &beginPadding,
                     const dim4 &endPadding, const borderType padFillType);
 #endif
-
-    /**
-      @}
-    */
 }
 #endif
 
 #ifdef __cplusplus
 extern "C" {
 #endif
-
-
     /**
         \param[out] arr is the generated array of given type
         \param[in] val is the value of each element in the generated array
@@ -490,9 +484,6 @@ extern "C" {
     */
 
     AFAPI af_err af_constant_ulong(af_array *arr, const unsigned long long val, const unsigned ndims, const dim_t * const dims);
-    /**
-       @}
-    */
 
     /**
         \param[out] out is the generated array
@@ -660,9 +651,6 @@ extern "C" {
         \ingroup data_func_upper
     */
     AFAPI af_err af_upper(af_array *out, const af_array in, bool is_unit_diag);
-    /**
-      @}
-    */
 
 #if AF_API_VERSION >= 31
     /**

From 61c72d770ee9441b893f420630570bb954ee7aa6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 9 Jan 2020 11:22:56 +0530
Subject: [PATCH 1777/2677] Refactor pad unified CALL macro syntax

---
 src/api/unified/data.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index 7084485c01..aa27dec836 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -146,5 +146,5 @@ af_err af_pad(af_array *out, const af_array in, const unsigned b_ndims,
               const dim_t *const b_dims, const unsigned e_ndims,
               const dim_t *const e_dims, const af_border_type ptype) {
     CHECK_ARRAYS(in);
-    return CALL(out, in, b_ndims, b_dims, e_ndims, e_dims, ptype);
+    CALL(af_pad, out, in, b_ndims, b_dims, e_ndims, e_dims, ptype);
 }

From c2b47ffff83a7034c5197c530b9039b847cc34b1 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Mon, 6 Jan 2020 15:24:31 -0800
Subject: [PATCH 1778/2677] Add support for f16 flip

---
 src/api/c/flip.cpp |  8 +++++++-
 test/flip.cpp      | 15 +++++++++++----
 2 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 7e1acb5cdb..1f80fac6b5 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -11,17 +11,22 @@
 #include <vector>
 
 #include <Array.hpp>
+#include <optypes.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
-#include <handle.hpp>
+#include <common/half.hpp>
 #include <lookup.hpp>
+#include <af/defines.h>
 #include <af/array.h>
+#include <af/arith.h>
 #include <af/data.h>
 #include <af/index.h>
 #include <af/seq.h>
+#include <handle.hpp>
 
 using namespace detail;
+using common::half;
 using std::swap;
 using std::vector;
 
@@ -56,6 +61,7 @@ af_err af_flip(af_array *result, const af_array in, const unsigned dim) {
         af_dtype in_type = in_info.getType();
 
         switch (in_type) {
+            case f16: out = flipArray<half>(in, dim); break;
             case f32: out = flipArray<float>(in, dim); break;
             case c32: out = flipArray<cfloat>(in, dim); break;
             case f64: out = flipArray<double>(in, dim); break;
diff --git a/test/flip.cpp b/test/flip.cpp
index f29dbfc643..d8ac409e37 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -21,13 +21,13 @@ using af::randu;
 using af::seq;
 using af::span;
 
-TEST(FlipTests, Test_flip_1D) {
+void Test_flip_1D(const af::dtype dt) {
     const int num = 10000;
-    array in      = randu(num);
+    array in      = randu(num, dt);
     array out     = flip(in, 0);
 
-    float *h_in  = in.host<float>();
-    float *h_out = out.host<float>();
+    float *h_in  = in.as(f32).host<float>();
+    float *h_out = out.as(f32).host<float>();
 
     for (int i = 0; i < num; i++) {
         ASSERT_EQ(h_in[num - i - 1], h_out[i]) << "at (" << i << ")";
@@ -37,6 +37,13 @@ TEST(FlipTests, Test_flip_1D) {
     freeHost(h_out);
 }
 
+TEST(FlipTests, Test_flip_1D_f32) {
+  Test_flip_1D(f32);
+}
+TEST(FlipTests, Test_flip_1D_f16) {
+  Test_flip_1D(f16);
+}
+
 TEST(FlipTests, Test_flip_2D0) {
     const int nx = 200;
     const int ny = 200;

From 38c5003f9d129f068831ce3567590172a27d9101 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Jan 2020 13:57:07 -0500
Subject: [PATCH 1779/2677] Fix var bias parameter

---
 src/api/c/var.cpp | 2 +-
 test/var.cpp      | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 2e02319333..25ede1cf72 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -177,7 +177,7 @@ af_err af_var(af_array* out, const af_array in, const bool isbiased,
 
         af_array no_weights = 0;
         af_var_bias bias =
-            (isbiased) ? AF_VARIANCE_POPULATION : AF_VARIANCE_SAMPLE;
+            (isbiased) ? AF_VARIANCE_SAMPLE: AF_VARIANCE_POPULATION;
         switch (type) {
             case f32:
                 output = var_<float, float>(in, no_weights, bias, dim);
diff --git a/test/var.cpp b/test/var.cpp
index 60f71c0998..ab9c4bc38c 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -116,11 +116,11 @@ TYPED_TEST(Var, DimCPPSmall) {
     for (size_t i = 0; i < in.size(); i++) {
         array input(numDims[i], &in[i].front(), afHost);
 
-        array bout  = var(input, false);
-        array nbout = var(input, true);
+        array bout  = var(input, true);
+        array nbout = var(input, false);
 
-        array bout1  = var(input, false, 1);
-        array nbout1 = var(input, true, 1);
+        array bout1  = var(input, true, 1);
+        array nbout1 = var(input, false, 1);
 
         vector<vector<outType> > h_out(4);
 

From 2fdc387a30a20c1d0bae15660ef56b8fa4431a27 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Jan 2020 15:12:14 -0500
Subject: [PATCH 1780/2677] Add missing meanvar C++ function

---
 src/api/cpp/CMakeLists.txt |  1 +
 src/api/cpp/meanvar.cpp    | 25 ++++++++++++++++++++++++
 test/meanvar.cpp           | 40 ++++++++++++++++++++++++++++++++++++++
 3 files changed, 66 insertions(+)
 create mode 100644 src/api/cpp/meanvar.cpp

diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 14543fd921..2339da2477 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -47,6 +47,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/matchTemplate.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/mean.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/meanvar.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/meanshift.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/median.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
diff --git a/src/api/cpp/meanvar.cpp b/src/api/cpp/meanvar.cpp
new file mode 100644
index 0000000000..d62499bd32
--- /dev/null
+++ b/src/api/cpp/meanvar.cpp
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/statistics.h>
+#include "error.hpp"
+
+using af::array;
+
+namespace af {
+void meanvar(array& mean, array& var, const array& in, const array& weights,
+             const af_var_bias bias, const dim_t dim) {
+    af_array mean_ = mean.get();
+    af_array var_  = var.get();
+    AF_THROW(af_meanvar(&mean_, &var_, in.get(), weights.get(), bias, dim));
+    mean.set(mean_);
+    var.set(var_);
+}
+}  // namespace af
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 2f26c09d65..c46685cb3a 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -137,6 +137,38 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
         ASSERT_SUCCESS(af_release_array(mean));
         ASSERT_SUCCESS(af_release_array(var));
     }
+
+    void meanvar_cpp_test_function(const meanvar_test<T> &test) {
+        array mean, var;
+
+        // Cast to the expected type
+        af_array in_tmp = 0;
+        AF_SUCCESS(af_retain_array(&in_tmp, test.in_));
+        array in(in_tmp);
+        in = in.as((af_dtype)dtype_traits<T>::af_type);
+
+        af_array weights_tmp = test.weights_;
+        if (weights_tmp) {
+            AF_SUCCESS(af_retain_array(&weights_tmp, weights_tmp));
+        }
+        array weights(weights_tmp);
+        meanvar(mean, var, in, weights, test.bias_, test.dim_);
+
+        vector<outType<T> > h_mean(test.mean_.size()),
+            h_var(test.variance_.size());
+
+        dim4 outDim       = in.dims();
+        outDim[test.dim_] = 1;
+
+        if (is_same_type<float, outType<T> >::value ||
+            is_same_type<cfloat, outType<T> >::value) {
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
+            ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
+        } else {
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.00001f);
+            ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.0001f);
+        }
+    }
 };
 
 af_array empty = 0;
@@ -252,6 +284,10 @@ vector<meanvar_test<T> > large_test_values() {
     TEST_P(MeanVar##NAME, Testing) {                                          \
         const meanvar_test<TYPE> &test = GetParam();                          \
         meanvar_test_function(test);                                          \
+    }                                                                         \
+    TEST_P(MeanVar##NAME, TestingCPP) {                                       \
+        const meanvar_test<TYPE> &test = GetParam();                          \
+        meanvar_cpp_test_function(test);                                      \
     }
 
 MEANVAR_TEST(Float, float)
@@ -278,6 +314,10 @@ MEANVAR_TEST(ComplexDouble, af::af_cdouble)
     TEST_P(MeanVar##NAME, Testing) {                                          \
         const meanvar_test<TYPE> &test = GetParam();                          \
         meanvar_test_function(test);                                          \
+    }                                                                         \
+    TEST_P(MeanVar##NAME, TestingCPP) {                                       \
+        const meanvar_test<TYPE> &test = GetParam();                          \
+        meanvar_cpp_test_function(test);                                      \
     }
 
 // Only test small sizes because the range of the large arrays go out of bounds

From 489c1b3253a98fdbc0546078b6c2324eea11353c Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Thu, 9 Jan 2020 14:10:14 -0800
Subject: [PATCH 1781/2677] Fix test meanvar.cpp

---
 test/meanvar.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index c46685cb3a..631f1dedcf 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -143,13 +143,13 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
 
         // Cast to the expected type
         af_array in_tmp = 0;
-        AF_SUCCESS(af_retain_array(&in_tmp, test.in_));
+        ASSERT_SUCCESS(af_retain_array(&in_tmp, test.in_));
         array in(in_tmp);
         in = in.as((af_dtype)dtype_traits<T>::af_type);
 
         af_array weights_tmp = test.weights_;
         if (weights_tmp) {
-            AF_SUCCESS(af_retain_array(&weights_tmp, weights_tmp));
+            ASSERT_SUCCESS(af_retain_array(&weights_tmp, weights_tmp));
         }
         array weights(weights_tmp);
         meanvar(mean, var, in, weights, test.bias_, test.dim_);

From 928e77aed1db65680f9b6bfbfa4d7791bdb32511 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 10 Jan 2020 22:13:35 +0530
Subject: [PATCH 1782/2677] PERF: Anisotropic smoothing improvements (#2713)

This improves CUDA/OpenCL backend performance by about 24%
---
 .../cuda/kernel/anisotropic_diffusion.cuh     | 86 +++++++++----------
 .../cuda/kernel/anisotropic_diffusion.hpp     | 13 +--
 src/backend/cuda/nvrtc/cache.cpp              | 13 +++
 .../opencl/kernel/anisotropic_diffusion.cl    | 64 +++++++-------
 .../opencl/kernel/anisotropic_diffusion.hpp   | 22 ++---
 5 files changed, 108 insertions(+), 90 deletions(-)

diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.cuh b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
index 29e635870d..cdb5c59121 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.cuh
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
@@ -21,13 +21,13 @@ int index(const int x, const int y, const int dim0,
 __device__
 float quadratic(const float value) { return 1.0 / (1.0 + value); }
 
+template<af_flux_function FluxEnum>
 __device__
-float computeGradientBasedUpdate(const float mct, const float C,
-                                 const float S, const float N,
-                                 const float W, const float E,
-                                 const float SE, const float SW,
-                                 const float NE, const float NW,
-                                 const af::fluxFunction fftype) {
+float gradientUpdate(const float mct, const float C,
+                     const float S, const float N,
+                     const float W, const float E,
+                     const float SE, const float SW,
+                     const float NE, const float NW) {
     float delta = 0;
 
     float dx, dy, df, db, cx, cxd;
@@ -40,7 +40,7 @@ float computeGradientBasedUpdate(const float mct, const float C,
     df = E - C;
     db = C - W;
 
-    if (fftype == AF_FLUX_EXPONENTIAL) {
+    if (FluxEnum == AF_FLUX_EXPONENTIAL) {
         cx  = expf((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
         cxd = expf((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
     } else {
@@ -55,7 +55,7 @@ float computeGradientBasedUpdate(const float mct, const float C,
     df = S - C;
     db = C - N;
 
-    if (fftype == AF_FLUX_EXPONENTIAL) {
+    if (FluxEnum == AF_FLUX_EXPONENTIAL) {
         cx  = expf((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
         cxd = expf((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
     } else {
@@ -70,12 +70,10 @@ float computeGradientBasedUpdate(const float mct, const float C,
 }
 
 __device__
-float computeCurvatureBasedUpdate(const float mct, const float C,
-                                  const float S, const float N,
-                                  const float W, const float E,
-                                  const float SE, const float SW,
-                                  const float NE, const float NW,
-                                  const af::fluxFunction fftype) {
+float curvatureUpdate(const float mct, const float C, const float S,
+                      const float N, const float W, const float E,
+                      const float SE, const float SW, const float NE,
+                      const float NW) {
     float delta     = 0;
     float prop_grad = 0;
 
@@ -132,17 +130,21 @@ float computeCurvatureBasedUpdate(const float mct, const float C,
     return sqrtf(prop_grad) * delta;
 }
 
-template<typename T, bool isMCDE>
+template<typename T, af_flux_function FluxEnum, bool isMCDE>
 __global__
 void diffUpdate(Param<T> inout, const float dt, const float mct,
-                const af::fluxFunction fftype, const unsigned blkX,
-                const unsigned blkY) {
-    const unsigned RADIUS         = 1;
-    const unsigned SHRD_MEM_WIDTH = THREADS_X + 2 * RADIUS;   // Coloumns
-    const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2 * RADIUS;  // Rows
+                const unsigned blkX, const unsigned blkY) {
+    const unsigned RADIUS = 1;
+    const unsigned SHRD_MEM_WIDTH = THREADS_X + 2 * RADIUS;
+    const unsigned SHRD_MEM_HEIGHT = THREADS_Y * YDIM_LOAD + 2 * RADIUS;
 
     __shared__ float shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
+    const int l0 = inout.dims[0];
+    const int l1 = inout.dims[1];
+    const int s0 = inout.strides[0];
+    const int s1 = inout.strides[1];
+
     const int lx = threadIdx.x;
     const int ly = threadIdx.y;
 
@@ -150,44 +152,42 @@ void diffUpdate(Param<T> inout, const float dt, const float mct,
     const int b3 = blockIdx.y / blkY;
 
     const int gx = blockDim.x * (blockIdx.x - b2 * blkX) + lx;
-    const int gy = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
+          int gy = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
 
     T* img = (T*)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
 
 #pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+    for (int b = ly, gy2 = gy - RADIUS; b < SHRD_MEM_HEIGHT;
          b += blockDim.y, gy2 += blockDim.y) {
 #pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+        for (int a = lx, gx2 = gx - RADIUS; a < SHRD_MEM_WIDTH;
              a += blockDim.x, gx2 += blockDim.x) {
-            int idx       = index(gx2 - RADIUS, gy2 - RADIUS, inout.dims[0],
-                            inout.dims[1], inout.strides[0], inout.strides[1]);
-            shrdMem[b][a] = img[idx];
+            shrdMem[b][a] = img[ index(gx2, gy2, l0, l1, s0, s1) ];
         }
     }
-
     __syncthreads();
 
-    if (gx < inout.dims[0] && gy < inout.dims[1]) {
-        int i       = lx + RADIUS;
-        int j       = ly + RADIUS;
-        float C     = shrdMem[j][i];
-        float delta = 0;
+    int i = lx + RADIUS;
+    int j = ly + RADIUS;
 
+#pragma unroll
+    for (int ld = 0; ld < YDIM_LOAD; ++ld, j+= blockDim.y, gy += blockDim.y) {
+        float C = shrdMem[j][i];
+        float delta = 0.0f;
         if (isMCDE) {
-            delta = computeCurvatureBasedUpdate(
-                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
-                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
-                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
+            delta = curvatureUpdate(
+                    mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                    shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                    shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1]);
         } else {
-            delta = computeGradientBasedUpdate(
-                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
-                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
-                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1], fftype);
+            delta = gradientUpdate<FluxEnum>(
+                    mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                    shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                    shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1]);
+        }
+        if (gy < l1 && gx < l0) {
+            img[gx * s0 + gy * s1] = (T)(C + delta * dt);
         }
-
-        img[gx * inout.strides[0] + gy * inout.strides[1]] =
-            (T)(C + delta * dt);
     }
 }
 
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index bcff2c4989..acf798dcc9 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -21,8 +21,9 @@
 namespace cuda {
 namespace kernel {
 
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
+constexpr int THREADS_X = 32;
+constexpr int THREADS_Y = 8;
+constexpr int YDIM_LOAD = 2 * THREADS_X / THREADS_Y;
 
 template<typename T>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
@@ -30,13 +31,13 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
     static const std::string source(anisotropic_diffusion_cuh,
                                     anisotropic_diffusion_cuh_len);
     auto diffUpdate = getKernel("cuda::diffUpdate", source,
-                             {TemplateTypename<T>(), TemplateArg(isMCDE)},
-                             {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+                             {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
+                             {DefineValue(THREADS_X), DefineValue(THREADS_Y), DefineValue(YDIM_LOAD)});
 
     dim3 threads(THREADS_X, THREADS_Y, 1);
 
     int blkX = divup(inout.dims[0], threads.x);
-    int blkY = divup(inout.dims[1], threads.y);
+    int blkY = divup(inout.dims[1], threads.y * YDIM_LOAD);
 
     dim3 blocks(blkX * inout.dims[2], blkY * inout.dims[3], 1);
 
@@ -51,7 +52,7 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    diffUpdate(qArgs, inout, dt, mct, fftype, blkX, blkY);
+    diffUpdate(qArgs, inout, dt, mct, blkX, blkY);
 
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 6f874e38f4..a40c47b01c 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -493,6 +493,19 @@ string toString(af_match_type p) {
     return retVal;
 }
 
+template<>
+string toString(af_flux_function p) {
+    const char *retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_FLUX_QUADRATIC);
+        CASE_STMT(AF_FLUX_EXPONENTIAL);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
 Kernel getKernel(const string &nameExpr, const string &source,
                  const vector<TemplateArg> &targs,
                  const vector<string> &compileOpts) {
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.cl b/src/backend/opencl/kernel/anisotropic_diffusion.cl
index be867684b1..950a119323 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.cl
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.cl
@@ -16,10 +16,10 @@ int gIndex(const int x, const int y, const int dim0, const int dim1,
 
 float quadratic(const float value) { return 1.0f / (1.0f + value); }
 
-float computeGradientBasedUpdate(const float mct, const float C, const float S,
-                                 const float N, const float W, const float E,
-                                 const float SE, const float SW, const float NE,
-                                 const float NW, const int FLUX_FN) {
+float gradientUpdate(const float mct, const float C, const float S,
+                     const float N, const float W, const float E,
+                     const float SE, const float SW, const float NE,
+                     const float NW) {
     float delta = 0;
 
     float dx, dy, df, db, cx, cxd;
@@ -61,11 +61,10 @@ float computeGradientBasedUpdate(const float mct, const float C, const float S,
     return delta;
 }
 
-float computeCurvatureBasedUpdate(const float mct, const float C, const float S,
-                                  const float N, const float W, const float E,
-                                  const float SE, const float SW,
-                                  const float NE, const float NW,
-                                  const int FLUX_FN) {
+float curvatureUpdate(const float mct, const float C, const float S,
+                      const float N, const float W, const float E,
+                      const float SE, const float SW,
+                      const float NE, const float NW) {
     float delta     = 0;
     float prop_grad = 0;
 
@@ -120,54 +119,57 @@ float computeCurvatureBasedUpdate(const float mct, const float C, const float S,
 }
 
 kernel void diffUpdate(global T* inout, KParam info, const float dt,
-                       const float mct, const int FLUX_FN, unsigned blkX,
-                       unsigned blkY) {
-    // Beware of the integer value of FLUX_FN
-
+                       const float mct, unsigned blkX, unsigned blkY) {
     local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
+    const int l0 = info.dims[0];
+    const int l1 = info.dims[1];
+    const int s0 = info.strides[0];
+    const int s1 = info.strides[1];
+
     const int lx = get_local_id(0);
     const int ly = get_local_id(1);
 
-    const unsigned b2 = get_group_id(0) / blkX;
-    const unsigned b3 = get_group_id(1) / blkY;
+    const int b2 = get_group_id(0) / blkX;
+    const int b3 = get_group_id(1) / blkY;
 
     const int gx = get_local_size(0) * (get_group_id(0) - b2 * blkX) + lx;
-    const int gy = get_local_size(1) * (get_group_id(1) - b3 * blkY) + ly;
+          int gy = get_local_size(1) * (get_group_id(1) - b3 * blkY) + ly;
 
     global T* img =
         inout + (b3 * info.strides[3] + b2 * info.strides[2]) + info.offset;
 
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+    for (int b = ly, gy2 = gy - 1; b < SHRD_MEM_HEIGHT;
          b += get_local_size(1), gy2 += get_local_size(1)) {
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+        for (int a = lx, gx2 = gx - 1; a < SHRD_MEM_WIDTH;
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            int idx = gIndex(gx2 - 1, gy2 - 1, info.dims[0], info.dims[1],
-                             info.strides[0], info.strides[1]);
-            localMem[b][a] = img[idx];
+            localMem[b][a] = img[ gIndex(gx2, gy2, l0, l1, s0, s1) ];
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    if (gx < info.dims[0] && gy < info.dims[1]) {
-        int i       = lx + 1;
-        int j       = ly + 1;
+    int i       = lx + 1;
+    int j       = ly + 1;
+
+#pragma unroll
+    for (int ld = 0; ld < YDIM_LOAD;
+            ++ld, j+= get_local_size(1), gy += get_local_size(1)) {
         float C     = localMem[j][i];
         float delta = 0;
-
 #if IS_MCDE == 1
-        delta = computeCurvatureBasedUpdate(
+        delta = curvatureUpdate(
             mct, C, localMem[j][i + 1], localMem[j][i - 1], localMem[j - 1][i],
             localMem[j + 1][i], localMem[j + 1][i + 1], localMem[j - 1][i + 1],
-            localMem[j + 1][i - 1], localMem[j - 1][i - 1], FLUX_FN);
+            localMem[j + 1][i - 1], localMem[j - 1][i - 1]);
 #else
-        delta = computeGradientBasedUpdate(
+        delta = gradientUpdate(
             mct, C, localMem[j][i + 1], localMem[j][i - 1], localMem[j - 1][i],
             localMem[j + 1][i], localMem[j + 1][i + 1], localMem[j - 1][i + 1],
-            localMem[j + 1][i - 1], localMem[j - 1][i - 1], FLUX_FN);
+            localMem[j + 1][i - 1], localMem[j - 1][i - 1]);
 #endif
-
-        img[gx * info.strides[0] + gy * info.strides[1]] = (T)(C + delta * dt);
+        if (gx < l0 && gy < l1) {
+            img[gx * s0 + gy * s1] = (T)(C + delta * dt);
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index 26baecbf86..995a50a4e1 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -20,8 +20,9 @@
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
+constexpr int THREADS_X = 32;
+constexpr int THREADS_Y = 8;
+constexpr int YDIM_LOAD = 2 * THREADS_X / THREADS_Y;
 
 template<typename T, bool isMCDE>
 void anisotropicDiffusion(Param inout, const float dt, const float mct,
@@ -35,7 +36,8 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
 
     std::string kerKeyStr = std::string("anisotropic_diffusion_") +
                             std::string(dtype_traits<T>::getName()) + "_" +
-                            std::to_string(isMCDE);
+                            std::to_string(isMCDE) + "_" +
+                            std::to_string(fluxFnCode);
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, kerKeyStr);
@@ -43,9 +45,10 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
-                << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
-                << " -D IS_MCDE=" << isMCDE;
+                << " -D SHRD_MEM_HEIGHT=" << (THREADS_Y * YDIM_LOAD + 2)
+                << " -D SHRD_MEM_WIDTH=" << (THREADS_X + 2)
+                << " -D IS_MCDE=" << isMCDE << " -D FLUX_FN=" << fluxFnCode
+                << " -D YDIM_LOAD=" << YDIM_LOAD;
         if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {anisotropic_diffusion_cl};
@@ -58,20 +61,19 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     }
 
     auto diffUpdateOp =
-        KernelFunctor<Buffer, KParam, float, float, int, unsigned, unsigned>(
+        KernelFunctor<Buffer, KParam, float, float, unsigned, unsigned>(
             *entry.ker);
 
     NDRange threads(THREADS_X, THREADS_Y, 1);
 
     int blkX = divup(inout.info.dims[0], threads[0]);
-    int blkY = divup(inout.info.dims[1], threads[1]);
+    int blkY = divup(inout.info.dims[1], threads[1] * YDIM_LOAD);
 
     NDRange global(threads[0] * blkX * inout.info.dims[2],
                    threads[1] * blkY * inout.info.dims[3], 1);
 
     diffUpdateOp(EnqueueArgs(getQueue(), global, threads), *inout.data,
-                 inout.info, dt, mct, fluxFnCode, blkX, blkY);
-
+                 inout.info, dt, mct, blkX, blkY);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel

From c1155af9c76819cc60640c365fef3e446095e702 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 10 Jan 2020 12:33:47 +0530
Subject: [PATCH 1783/2677] Add github action for documentation build

---
 .github/workflows/cmake_doxygen_build.yml | 52 +++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 .github/workflows/cmake_doxygen_build.yml

diff --git a/.github/workflows/cmake_doxygen_build.yml b/.github/workflows/cmake_doxygen_build.yml
new file mode 100644
index 0000000000..2fa428ab88
--- /dev/null
+++ b/.github/workflows/cmake_doxygen_build.yml
@@ -0,0 +1,52 @@
+on:
+  push:
+    branches:
+    - master
+  pull_request:
+    branches:
+    - master
+
+name: Doxygen
+
+env:
+    NINJA_VER: 1.9.0
+    DOXYGEN_VER: 1.8.17
+
+jobs:
+    build_documentation:
+        name: Build Documentation
+        runs-on: ubuntu-18.04
+        steps:
+            - name: Checkout Repository
+              uses: actions/checkout@master
+
+            - name: Download Ninja
+              id: ninja
+              run: |
+                  wget --quiet "https://github.com/ninja-build/ninja/releases/download/v${NINJA_VER}/ninja-linux.zip"
+                  unzip ./ninja-linux.zip
+                  chmod +x ninja
+                  ${GITHUB_WORKSPACE}/ninja --version
+
+            - name: Install Doxygen
+              run: |
+                  wget --quiet http://doxygen.nl/files/doxygen-${DOXYGEN_VER}.linux.bin.tar.gz
+                  mkdir doxygen
+                  tar -xf doxygen-${DOXYGEN_VER}.linux.bin.tar.gz -C doxygen --strip 1
+
+            - name: Configure
+              run: |
+                  git submodule update --init --recursive
+                  mkdir build && cd build
+                  cmake -G Ninja \
+                      -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
+                      -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
+                      -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
+                      -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
+                      -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen \
+                      ..
+
+            - name: Build
+              run: |
+                  cd ${GITHUB_WORKSPACE}/build
+                  cmake --build . --target docs

From e557d7ec992e80b634d55e901bfb45d647e13764 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 10 Jan 2020 18:46:06 +0530
Subject: [PATCH 1784/2677] Correct indentations and doc param identifiers

---
 include/af/data.h | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/include/af/data.h b/include/af/data.h
index 9d359d69e3..05ef5f9f35 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -718,11 +718,13 @@ extern "C" {
     /**
        \param[out] out is the padded array
        \param[in] in is the input array to be padded
-       \param[in] b_ndims is size of \p l_dims array
-       \param[in] b_dims array contains padding size at beginning of each
-       dimension \param[in] e_ndims is size of \p u_dims array \param[in] e_dims
-       array contains padding sizes at end of each dimension \param[in]
-       pad_fill_type is indicates what values should fill padded region
+       \param[in] begin_ndims is size of \p l_dims array
+       \param[in] begin_dims array contains padding size at beginning of each
+                  dimension
+       \param[in] end_ndims is size of \p u_dims array
+       \param[in] end_dims array contains padding sizes at end of each dimension
+       \param[in] pad_fill_type is indicates what values should fill
+                  padded region
 
        \ingroup data_func_pad
     */

From 1442ab7f3af98c9c0bf4131ae9eb66315c6b1c3c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 14 Jan 2020 18:49:45 -0500
Subject: [PATCH 1785/2677] Refactor print_error. Remove default case from
 af_err_to_string

---
 src/backend/common/err_common.cpp | 33 ++++++++++++-------------------
 src/backend/common/err_common.hpp | 19 ++++++++----------
 2 files changed, 21 insertions(+), 31 deletions(-)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 7d51e842cb..d06aab3b78 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -93,12 +93,13 @@ const string &DimensionError::getExpectedCondition() const { return expected; }
 
 int DimensionError::getArgIndex() const { return argIndex; }
 
-void print_error(const string &msg) {
+af_err set_global_error_string(const string &msg, af_err err) {
     std::string perr = getEnvVar("AF_PRINT_ERRORS");
     if (!perr.empty()) {
         if (perr != "0") fprintf(stderr, "%s\n", msg.c_str());
     }
     get_global_error_string() = msg;
+    return err;
 }
 
 af_err processException() {
@@ -113,53 +114,44 @@ af_err processException() {
            << "Invalid dimension for argument " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
-        print_error(ss.str());
-        err = AF_ERR_SIZE;
+        err = set_global_error_string(ss.str(), AF_ERR_SIZE);
     } catch (const ArgumentError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid argument at index " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
-        print_error(ss.str());
-        err = AF_ERR_ARG;
+        err = set_global_error_string(ss.str(), AF_ERR_ARG);
     } catch (const SupportError &ex) {
         ss << ex.getFunctionName() << " not supported for "
            << ex.getBackendName() << " backend\n";
 
-        print_error(ss.str());
-        err = AF_ERR_NOT_SUPPORTED;
+        err = set_global_error_string(ss.str(), AF_ERR_NOT_SUPPORTED);
     } catch (const TypeError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid type for argument " << ex.getArgIndex() << "\n";
 
-        print_error(ss.str());
-        err = AF_ERR_TYPE;
+        err = set_global_error_string(ss.str(), AF_ERR_TYPE);
     } catch (const AfError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << ex.what() << "\n";
 
-        print_error(ss.str());
-        err = ex.getError();
+        err = set_global_error_string(ss.str(), ex.getError());
 #ifdef AF_OPENCL
     } catch (const cl::Error &ex) {
         char opencl_err_msg[1024];
         snprintf(opencl_err_msg, sizeof(opencl_err_msg),
                  "OpenCL Error (%d): %s when calling %s", ex.err(),
                  getErrorMessage(ex.err()).c_str(), ex.what());
-        print_error(opencl_err_msg);
         if (ex.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {
-            err = AF_ERR_NO_MEM;
+            err = set_global_error_string(opencl_err_msg, AF_ERR_NO_MEM);
         } else {
-            err = AF_ERR_INTERNAL;
+            err = set_global_error_string(opencl_err_msg, AF_ERR_INTERNAL);
         }
 #endif
-    } catch (...) {
-        print_error(ss.str());
-        err = AF_ERR_UNKNOWN;
-    }
+    } catch (...) { err = set_global_error_string(ss.str(), AF_ERR_UNKNOWN); }
 
     return err;
 }
@@ -199,7 +191,8 @@ const char *af_err_to_string(const af_err err) {
             return "There was a mismatch between an array and the current "
                    "backend";
         case AF_ERR_INTERNAL: return "Internal error";
-        case AF_ERR_UNKNOWN:
-        default: return "Unknown error";
+        case AF_ERR_UNKNOWN: return "Unknown error";
     }
+    return "Unknown error. Please open an issue and add this error code to the "
+           "case in af_err_to_string.";
 }
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index e042ff40fa..19b83d7a13 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -109,7 +109,7 @@ class DimensionError : public AfError {
 
 af_err processException();
 
-void print_error(const std::string& msg);
+af_err set_global_error_string(const std::string& msg, af_err err = AF_ERR_UNKNOWN);
 
 #define DIM_ASSERT(INDEX, COND)                                        \
     do {                                                               \
@@ -139,16 +139,13 @@ void print_error(const std::string& msg);
                       ERR_TYPE);                                           \
     } while (0)
 
-#define AF_RETURN_ERROR(MSG, ERR_TYPE)                                       \
-    do {                                                                     \
-        AfError err(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, MSG,     \
-                    ERR_TYPE);                                               \
-        std::stringstream s;                                                 \
-        s << "Error in " << err.getFunctionName() << "\n"                    \
-          << "In file " << err.getFileName() << ":" << err.getLine() << "\n" \
-          << err.what() << "\n";                                             \
-        print_error(s.str());                                                \
-        return ERR_TYPE;                                                     \
+#define AF_RETURN_ERROR(MSG, ERR_TYPE)                                \
+    do {                                                              \
+        std::stringstream s;                                          \
+        s << "Error in " << __PRETTY_FUNCTION__ << "\n"               \
+          << "In file " << __AF_FILENAME__ << ":" << __LINE__ << ": " \
+          << MSG;                                                     \
+        return set_global_error_string(s.str(), ERR_TYPE);            \
     } while (0)
 
 #define TYPE_ASSERT(COND)                                       \

From 89636bba50d894679d1a2f1d68f7ff0a19e57ac7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 14 Jan 2020 18:50:16 -0500
Subject: [PATCH 1786/2677] Add missing AF_ERR_DEVICE from af_err_to_string

---
 src/backend/common/err_common.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index d06aab3b78..db78c76e62 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -173,6 +173,8 @@ const char *af_err_to_string(const af_err err) {
         case AF_ERR_TYPE: return "Function does not support this data type";
         case AF_ERR_DIFF_TYPE: return "Input types are not the same";
         case AF_ERR_BATCH: return "Invalid batch configuration";
+        case AF_ERR_DEVICE:
+            return "Input does not belong to the current device.";
         case AF_ERR_NOT_SUPPORTED: return "Function not supported";
         case AF_ERR_NOT_CONFIGURED: return "Function not configured to build";
         case AF_ERR_NONFREE:

From 0acc75f6b17c75b2d5b94074c4a71bc9a5a44209 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 14 Jan 2020 18:50:51 -0500
Subject: [PATCH 1787/2677] Fix segfault in the CALL macro when no backends are
 found

---
 src/api/unified/symbol_manager.hpp | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 0bcb2d0ebb..0de1eda6de 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -139,14 +139,19 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
     using af_func                  = std::add_pointer<decltype(FUNCTION)>::type; \
     thread_local auto& instance    = unified::AFSymbolManager::getInstance();    \
     thread_local af_backend index_ = instance.getActiveBackend();                \
-    thread_local af_func func =                                                  \
-        (af_func)common::getFunctionPointer(instance.getHandle(), __func__);     \
-    if (index_ != instance.getActiveBackend()) {                                 \
-        index_ = instance.getActiveBackend();                                    \
-        func   = (af_func)common::getFunctionPointer(instance.getHandle(),       \
-                                                   __func__);                    \
-    }                                                                            \
-    return func(__VA_ARGS__);
+    if (instance.getHandle()) {                                                  \
+        thread_local af_func func = (af_func)common::getFunctionPointer(         \
+            instance.getHandle(), __func__);                                     \
+        if (index_ != instance.getActiveBackend()) {                             \
+            index_ = instance.getActiveBackend();                                \
+            func   = (af_func)common::getFunctionPointer(instance.getHandle(),   \
+                                                         __func__);              \
+        }                                                                        \
+        return func(__VA_ARGS__);                                                \
+    } else {                                                                     \
+        AF_RETURN_ERROR("ArrayFire couldn't locate any backends.",               \
+                        AF_ERR_LOAD_LIB);                                        \
+    }
 
 #define CALL_NO_PARAMS(FUNCTION) CALL(FUNCTION)
 

From 9b37b722d3194990e2ecad119e02c6b400da73da Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Tue, 14 Jan 2020 11:46:11 -0800
Subject: [PATCH 1788/2677] Add support for f16 replace

---
 src/api/c/replace.cpp | 5 +++++
 test/replace.cpp      | 3 ++-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 585219b4be..868a3d2081 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -9,6 +9,8 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
+#include <common/traits.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
 #include <optypes.hpp>
@@ -20,6 +22,7 @@
 #include <select.hpp>
 
 using namespace detail;
+using common::half;
 using af::dim4;
 
 template<typename T>
@@ -52,6 +55,7 @@ af_err af_replace(af_array a, const af_array cond, const af_array b) {
         }
 
         switch (ainfo.getType()) {
+            case f16: replace<half>(a, cond, b); break;
             case f32: replace<float>(a, cond, b); break;
             case f64: replace<double>(a, cond, b); break;
             case c32: replace<cfloat>(a, cond, b); break;
@@ -91,6 +95,7 @@ af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
         for (int i = 0; i < 4; i++) { DIM_ASSERT(1, cdims[i] == adims[i]); }
 
         switch (ainfo.getType()) {
+            case f16: replace_scalar<half>(a, cond, b); break;
             case f32: replace_scalar<float>(a, cond, b); break;
             case f64: replace_scalar<double>(a, cond, b); break;
             case c32: replace_scalar<cfloat>(a, cond, b); break;
diff --git a/test/replace.cpp b/test/replace.cpp
index 060993cfa2..aa91ec3e0f 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -12,6 +12,7 @@
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+#include <half.hpp>
 #include <iostream>
 #include <string>
 #include <vector>
@@ -31,7 +32,7 @@ using std::vector;
 template<typename T>
 class Replace : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
+typedef ::testing::Types<half_float::half, float, double, cfloat, cdouble, uint, int, intl, uintl,
                          uchar, char, short, ushort>
     TestTypes;
 

From 164976508ae8cebfe28c4411eb7bed626758c386 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Dec 2019 11:37:44 +0530
Subject: [PATCH 1789/2677] Add log trace support for cuda/opencl jit

* added platform logging from opencl backend
---
 .../configuring_arrayfire_environment.md      |  6 ++
 src/api/unified/symbol_manager.cpp            |  1 +
 src/backend/common/Logger.cpp                 |  9 ++-
 src/backend/common/Logger.hpp                 |  2 +
 src/backend/cuda/device_manager.cpp           | 19 +++--
 src/backend/cuda/nvrtc/cache.cpp              | 81 ++++++++++++++-----
 src/backend/opencl/device_manager.cpp         | 32 ++++++--
 src/backend/opencl/device_manager.hpp         |  3 +
 src/backend/opencl/jit.cpp                    | 26 ++++--
 src/backend/opencl/platform.cpp               |  1 +
 src/backend/opencl/platform.hpp               |  4 +
 11 files changed, 139 insertions(+), 45 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 0e7a6e1d7d..566cc6af44 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -164,7 +164,9 @@ list of modules to trace. If enabled, ArrayFire will print relevant information
 to stdout. Currently the following modules are supported:
 
 - all: All trace outputs
+- jit: Logs kernel fetch & respective compile options and any errors.
 - mem: Memory management allocation, free and garbage collection information
+- platform: Device management information
 - unified: Unified backend dynamic loading information
 
 Tracing displays the information that could be useful when debugging or
@@ -175,6 +177,10 @@ optimizing your application. Here is how you would use this variable:
 This will print information about memory operations such as allocations,
 deallocations, and garbage collection.
 
+All trace statements printed to the console have a suffix with the following
+pattern.
+
+**[category][Seconds since Epoch][Thread Id][source file relative path] <Message>**
 
 AF_MAX_BUFFERS {#af_max_buffers}
 -------------------------------------------------------------------------
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index b3e229875c..a4328fce55 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -135,6 +135,7 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
     typedef af_err (*func)(int*);
 
     LibHandle retVal = nullptr;
+
     for (size_t i = 0; i < extent<decltype(pathPrefixes)>::value; i++) {
         AF_TRACE("Attempting: {}",
                  (pathPrefixes[i].empty() ? "Default System Paths"
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index d08732f950..441e0f2546 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -18,6 +18,7 @@
 #include <array>
 #include <cstdlib>
 #include <memory>
+#include <mutex>
 #include <string>
 
 using std::array;
@@ -29,20 +30,22 @@ using std::to_string;
 using spdlog::get;
 using spdlog::logger;
 using spdlog::stdout_logger_mt;
-using spdlog::level::trace;
 
 namespace common {
+
 shared_ptr<logger> loggerFactory(string name) {
     shared_ptr<logger> logger;
     if (!(logger = get(name))) {
         logger = stdout_logger_mt(name);
-        logger->set_pattern("[%n][%t] %v");
+        logger->set_pattern("[%n][%E][%t] %v");
 
         // Log mode
         string env_var = getEnvVar("AF_TRACE");
         if (env_var.find("all") != string::npos ||
             env_var.find(name) != string::npos) {
-            logger->set_level(trace);
+            logger->set_level(spdlog::level::trace);
+        } else {
+            logger->set_level(spdlog::level::off);
         }
     }
     return logger;
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index 85c79a25be..ac627e81bb 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -11,6 +11,8 @@
 
 #include <memory>
 #include <string>
+#include <type_traits>
+
 #include <spdlog/spdlog.h>
 
 namespace common {
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 8debebb093..66cb210d75 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -326,7 +326,8 @@ static const ToolkitDriverVersions
 /// \param[in] driver_version   The version integer returned by
 ///                             cudaDriverGetVersion
 /// \note: only works in debug builds
-void debugRuntimeCheck(int runtime_version, int driver_version) {
+void debugRuntimeCheck(spdlog::logger *logger, int runtime_version,
+                       int driver_version) {
 #ifndef NDEBUG
     auto runtime_it =
         find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
@@ -339,31 +340,35 @@ void debugRuntimeCheck(int runtime_version, int driver_version) {
                     return driver_version == ver.version;
                 });
 
+    auto getLogger = [&logger]() -> spdlog::logger * { return logger; };
+
     // If the runtime version is not part of the CudaToDriverVersion array,
     // display a message in the trace. Do not throw an error unless this is
     // a debug build
     if (runtime_it == end(CudaToDriverVersion)) {
         char buf[1024];
         char err_msg[] =
-            "WARNING: CUDA runtime version(%s) not recognized. Please "
+            "CUDA runtime version(%s) not recognized. Please "
             "create an issue or a pull request on the ArrayFire repository to "
             "update the CudaToDriverVersion variable with this version of "
             "the CUDA Toolkit.\n";
         snprintf(buf, 1024, err_msg,
                  int_version_to_string(runtime_version).c_str());
-        fprintf(stderr, err_msg,
-                int_version_to_string(runtime_version).c_str());
+        AF_TRACE("{}", buf);
         AF_ERROR(buf, AF_ERR_RUNTIME);
     }
 
     if (driver_it == end(CudaToDriverVersion)) {
+        char buf[1024];
         char err_msg[] =
-            "WARNING: CUDA driver version(%s) not part of the "
+            "CUDA driver version(%s) not part of the "
             "CudaToDriverVersion array. Please create an issue or a pull "
             "request on the ArrayFire repository to update the "
             "CudaToDriverVersion variable with this version of the CUDA "
             "Toolkit.\n";
-        fprintf(stderr, err_msg, int_version_to_string(driver_version).c_str());
+        snprintf(buf, 1024, err_msg,
+                 int_version_to_string(driver_version).c_str());
+        AF_TRACE("{}", buf);
     }
 #endif
 }
@@ -381,7 +386,7 @@ void DeviceManager::checkCudaVsDriverVersion() {
     AF_TRACE("CUDA supported by the GPU Driver {} ArrayFire CUDA Runtime {}",
              int_version_to_string(driver), int_version_to_string(runtime));
 
-    debugRuntimeCheck(runtime, driver);
+    debugRuntimeCheck(getLogger(), runtime, driver);
 
     if (runtime > driver) {
         string msg =
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index a40c47b01c..d4435b6771 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -9,6 +9,7 @@
 
 #include <nvrtc/cache.hpp>
 
+#include <common/Logger.hpp>
 #include <device_manager.hpp>
 #include <kernel_headers/jit_cuh.hpp>
 #include <nvrtc_kernel_headers/Param_hpp.hpp>
@@ -36,14 +37,20 @@
 
 #include <algorithm>
 #include <array>
+#include <chrono>
 #include <iterator>
 #include <map>
 #include <memory>
+#include <numeric>
 #include <string>
 #include <type_traits>
 #include <utility>
 
 using std::array;
+using std::accumulate;
+using std::chrono::duration_cast;
+using std::chrono::high_resolution_clock;
+using std::chrono::milliseconds;
 using std::begin;
 using std::end;
 using std::extent;
@@ -57,39 +64,45 @@ using std::transform;
 using std::unique_ptr;
 using std::vector;
 
+spdlog::logger* getLogger() {
+    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
+    return logger.get();
+}
+
 namespace cuda {
 
 using kc_t = map<string, Kernel>;
 
 #ifdef NDEBUG
-#define CU_LINK_CHECK(fn)                                                 \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[1024];                                            \
-        const char *cu_err_name;                                          \
-        cuGetErrorName(res, &cu_err_name);                                \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), linkError);                     \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+#define CU_LINK_CHECK(fn)                                                    \
+    do {                                                                     \
+        CUresult res = fn;                                                   \
+        if (res == CUDA_SUCCESS) break;                                      \
+        char cu_err_msg[1024];                                               \
+        const char *cu_err_name;                                             \
+        cuGetErrorName(res, &cu_err_name);                                   \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n",    \
+                 cu_err_name, (int)(res), linkError);                        \
+        AF_TRACE("Driver API Call: {}\nError Message: {}", #fn, cu_err_msg); \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                               \
     } while (0)
 #else
 #define CU_LINK_CHECK(fn) CU_CHECK(fn)
 #endif
 
 #ifndef NDEBUG
-#define NVRTC_CHECK(fn)                                \
-    do {                                               \
-        nvrtcResult res = fn;                          \
-        if (res == NVRTC_SUCCESS) break;               \
-        size_t logSize;                                \
-        nvrtcGetProgramLogSize(prog, &logSize);        \
-        unique_ptr<char[]> log(new char[logSize + 1]); \
-        char *logptr = log.get();                      \
-        nvrtcGetProgramLog(prog, logptr);              \
-        logptr[logSize] = '\x0';                       \
-        puts(logptr);                                  \
-        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
+#define NVRTC_CHECK(fn)                                                 \
+    do {                                                                \
+        nvrtcResult res = fn;                                           \
+        if (res == NVRTC_SUCCESS) break;                                \
+        size_t logSize;                                                 \
+        nvrtcGetProgramLogSize(prog, &logSize);                         \
+        unique_ptr<char[]> log(new char[logSize + 1]);                  \
+        char *logptr = log.get();                                       \
+        nvrtcGetProgramLog(prog, logptr);                               \
+        logptr[logSize] = '\x0';                                        \
+        AF_TRACE("NVRTC API Call: {}\nError Message: {}", #fn, logptr); \
+        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);                       \
     } while (0)
 #else
 #define NVRTC_CHECK(fn)                                                   \
@@ -99,6 +112,7 @@ using kc_t = map<string, Kernel>;
         char nvrtc_err_msg[1024];                                         \
         snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
                  "NVRTC Error(%d): %s\n", res, nvrtcGetErrorString(res)); \
+        AF_TRACE("NVRTC Error Message: {}", nvrtc_err_msg);               \
         AF_ERROR(nvrtc_err_msg, AF_ERR_INTERNAL);                         \
     } while (0)
 #endif
@@ -233,8 +247,11 @@ Kernel buildKernel(const int device, const string &nameExpr,
         NVRTC_CHECK(nvrtcAddNameExpression(prog, ker_name));
     }
 
+    auto compile = high_resolution_clock::now();
     NVRTC_CHECK(nvrtcCompileProgram(prog, compiler_options.size(),
                                     compiler_options.data()));
+
+    auto compile_end = high_resolution_clock::now();
     size_t ptx_size;
     vector<char> ptx;
     NVRTC_CHECK(nvrtcGetPTXSize(prog, &ptx_size));
@@ -255,7 +272,10 @@ Kernel buildKernel(const int device, const string &nameExpr,
         linkInfo, reinterpret_cast<void *>(linkLogSize), linkError,
         reinterpret_cast<void *>(linkLogSize), reinterpret_cast<void *>(1)};
 
+    auto link = high_resolution_clock::now();
     CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
+
+    // cuLinkAddData accounts for most of the time spent linking
     CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void *)ptx.data(),
                                 ptx.size(), ker_name, 0, NULL, NULL));
 
@@ -266,6 +286,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
     CUfunction kernel;
     CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
     CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
+    auto link_end = high_resolution_clock::now();
 
     const char *name = ker_name;
     if (!isJIT) { NVRTC_CHECK(nvrtcGetLoweredName(prog, ker_name, &name)); }
@@ -276,6 +297,22 @@ Kernel buildKernel(const int device, const string &nameExpr,
     CU_LINK_CHECK(cuLinkDestroy(linkState));
     NVRTC_CHECK(nvrtcDestroyProgram(&prog));
 
+    // skip --std=c++14 because it will stay the same. It doesn't
+    // provide useful information
+    auto listOpts = [](vector<const char *> &in) {
+        return accumulate(
+            begin(in) + 2, end(in), string(in[0]),
+            [](const string &lhs, const string &rhs) {
+                return lhs + ", " + rhs;
+            });
+    };
+
+    AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}}}",
+             nameExpr,
+             duration_cast<milliseconds>(compile_end - compile).count(),
+             duration_cast<milliseconds>(link_end - link).count(),
+             listOpts(compiler_options), getDeviceProp(device).name);
+
     return entry;
 }
 
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index a2e413469f..f3960b2272 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -15,6 +15,7 @@
 #include <blas.hpp>
 #include <clfft.hpp>
 #include <common/DefaultMemoryManager.hpp>
+#include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
@@ -164,7 +165,8 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
 }
 
 DeviceManager::DeviceManager()
-    : mUserDeviceOffset(0)
+    : logger(common::loggerFactory("platform"))
+    , mUserDeviceOffset(0)
     , fgMngr(new graphics::ForgeManager())
     , mFFTSetup(new clfftSetupData) {
     vector<Platform> platforms;
@@ -187,6 +189,8 @@ DeviceManager::DeviceManager()
         DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
     }
 
+    AF_TRACE("Found {} OpenCL platforms", platforms.size());
+
     // Iterate through platforms, get all available devices and store them
     for (auto& platform : platforms) {
         vector<Device> current_devices;
@@ -196,14 +200,20 @@ DeviceManager::DeviceManager()
         } catch (const cl::Error& err) {
             if (err.err() != CL_DEVICE_NOT_FOUND) { throw; }
         }
+        AF_TRACE("Found {} devices on platform {}", current_devices.size(),
+                 platform.getInfo<CL_PLATFORM_NAME>());
         for (auto dev : current_devices) {
             mDevices.push_back(new Device(dev));
+            AF_TRACE("Found device {} on platform {}",
+                     dev.getInfo<CL_DEVICE_NAME>(),
+                     platform.getInfo<CL_PLATFORM_NAME>());
         }
     }
 
     int nDevices = mDevices.size();
+    AF_TRACE("Found {} OpenCL devices", nDevices);
 
-    if (nDevices == 0) AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME);
+    if (nDevices == 0) { AF_ERROR("No OpenCL devices found", AF_ERR_RUNTIME); }
 
     // Sort OpenCL devices based on default criteria
     stable_sort(mDevices.begin(), mDevices.end(), compare_default);
@@ -231,8 +241,10 @@ DeviceManager::DeviceManager()
         int def_device = -1;
         s >> def_device;
         if (def_device < 0 || def_device >= (int)nDevices) {
-            printf("WARNING: AF_OPENCL_DEFAULT_DEVICE is out of range\n");
-            printf("Setting default device as 0\n");
+            AF_TRACE(
+                "AF_OPENCL_DEFAULT_DEVICE ({}) \
+                   is out of range, Setting default device to 0",
+                def_device);
         } else {
             setActiveContext(def_device);
             default_device_set = true;
@@ -257,10 +269,10 @@ DeviceManager::DeviceManager()
             }
         }
         if (!default_device_set) {
-            printf(
-                "WARNING: AF_OPENCL_DEFAULT_DEVICE_TYPE=%s is not available\n",
-                deviceENV.c_str());
-            printf("Using default device as 0\n");
+            AF_TRACE(
+                "AF_OPENCL_DEFAULT_DEVICE_TYPE={} \
+                   is not available, Using default device as 0",
+                deviceENV);
         }
     }
 
@@ -293,8 +305,11 @@ DeviceManager::DeviceManager()
         BoostProgCache currCache = compute::program_cache::get_global_cache(c);
         mBoostProgCacheVector.emplace_back(new BoostProgCache(currCache));
     }
+    AF_TRACE("Default device: {}", getActiveDeviceId());
 }
 
+spdlog::logger* DeviceManager::getLogger() { return logger.get(); }
+
 DeviceManager& DeviceManager::getInstance() {
     static DeviceManager* my_instance = new DeviceManager();
     return *my_instance;
@@ -394,6 +409,7 @@ void DeviceManager::markDeviceForInterop(const int device,
     try {
         if (device >= (int)mQueues.size() ||
             device >= (int)DeviceManager::MAX_DEVICES) {
+            AF_TRACE("Invalid device (}) passed for CL-GL Interop", device);
             throw cl::Error(CL_INVALID_DEVICE,
                             "Invalid device passed for CL-GL Interop");
         } else {
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 04d76d638b..9602f52f4c 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -104,6 +104,8 @@ class DeviceManager {
 
     ~DeviceManager();
 
+    spdlog::logger* getLogger();
+
    protected:
     using clfftSetupData = clfftSetupData_;
 
@@ -119,6 +121,7 @@ class DeviceManager {
 
    private:
     // Attributes
+    std::shared_ptr<spdlog::logger> logger;
     std::mutex deviceMutex;
     std::vector<cl::Device*> mDevices;
     std::vector<cl::Context*> mContexts;
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 2f511d7ed9..50f513bf85 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/Logger.hpp>
 #include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
 #include <copy.hpp>
@@ -17,6 +18,7 @@
 #include <af/dim4.hpp>
 #include <af/opencl.h>
 
+#include <chrono>
 #include <functional>
 #include <stdexcept>
 #include <vector>
@@ -37,6 +39,14 @@ using std::hash;
 using std::string;
 using std::stringstream;
 using std::vector;
+using std::chrono::duration_cast;
+using std::chrono::high_resolution_clock;
+using std::chrono::milliseconds;
+
+spdlog::logger *getLogger() {
+    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
+    return logger.get();
+}
 
 namespace opencl {
 
@@ -181,16 +191,22 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
         const int ker_lens[]   = {jit_cl_len, (int)jit_ker.size()};
 
         Program prog;
-        buildProgram(
-            prog, 2, ker_strs, ker_lens,
-            (isDoubleSupported(device) ? string(" -D USE_DOUBLE") : string("")) +
-            (isHalfSupported(device) ? string(" -D USE_HALF") : string(""))
-                     );
+        string options =
+            (isDoubleSupported(device) ? string(" -D USE_DOUBLE")
+                                       : string("")) +
+            (isHalfSupported(device) ? string(" -D USE_HALF") : string(""));
+        auto compileBegin = high_resolution_clock::now();
+        buildProgram(prog, 2, ker_strs, ker_lens, options);
+        auto compileEnd = high_resolution_clock::now();
 
         entry.prog = new Program(prog);
         entry.ker  = new Kernel(*entry.prog, funcName.c_str());
 
         addKernelToCache(device, funcName, entry);
+
+        AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", funcName,
+                 duration_cast<milliseconds>(compileEnd - compileBegin).count(),
+                 options, getDevice(device).getInfo<CL_DEVICE_NAME>());
     }
 
     return *entry.ker;
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index a090aa686b..036122027f 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -15,6 +15,7 @@
 #include <blas.hpp>
 #include <cache.hpp>
 #include <clfft.hpp>
+#include <common/Logger.hpp>
 #include <common/host_memory.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/util.hpp>
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index bf51c364f6..c9024d4dc9 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -30,6 +30,10 @@ class program_cache;
 }
 }  // namespace boost
 
+namespace spdlog {
+class logger;
+}
+
 namespace graphics {
 class ForgeManager;
 }

From 8f767a026290ae2fbde975757b64032e9832c43c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 22 Jan 2020 01:24:32 -0500
Subject: [PATCH 1790/2677] Check for half support in flip tests

---
 test/flip.cpp        | 9 +++++----
 test/testHelpers.hpp | 2 +-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/test/flip.cpp b/test/flip.cpp
index d8ac409e37..b1839ce413 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -37,11 +37,12 @@ void Test_flip_1D(const af::dtype dt) {
     freeHost(h_out);
 }
 
-TEST(FlipTests, Test_flip_1D_f32) {
-  Test_flip_1D(f32);
-}
+TEST(FlipTests, Test_flip_1D_f32) { Test_flip_1D(f32); }
+
 TEST(FlipTests, Test_flip_1D_f16) {
-  Test_flip_1D(f16);
+    SUPPORTED_TYPE_CHECK(half_float::half);
+
+    Test_flip_1D(f16);
 }
 
 TEST(FlipTests, Test_flip_2D0) {
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 011dcfd450..95aa70f0f3 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -494,7 +494,7 @@ bool noHalfTests(af::dtype ty) {
 
 #define SUPPORTED_TYPE_CHECK(type)                                        \
     if (noDoubleTests((af_dtype)af::dtype_traits<type>::af_type)) return; \
-    if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type)) return
+    if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type)) return;
 
 inline bool noImageIOTests() {
     bool ret = !af::isImageIOAvailable();

From 05a4050cef222d6105bdecee1e8345c59abfa8f5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 17 Jan 2020 18:52:34 -0500
Subject: [PATCH 1791/2677] Avoid upcasting in operations involving fp16 and
 scalars

This commit prevents up-casting in operations that involve the f16
array and a scalar value. Normally you would want upcasts but it
is unlikely that the user expects this to occur with f16 because
there is no native f16 type.
---
 src/api/cpp/array.cpp |  5 +++++
 test/binary.cpp       | 51 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 56 insertions(+)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index cfd8398fa0..f85f21f0e0 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -861,6 +861,11 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type) {
         return array_type;
     }
 
+    // If the array is f16 then avoid upcasting to float or double
+    if ((scalar_type == f64 || scalar_type == f32) && (array_type == f16)) {
+        return f16;
+    }
+
     // Default to single precision by default when multiplying with scalar
     if ((scalar_type == f64 || scalar_type == c64) &&
         (array_type != f64 && array_type != c64)) {
diff --git a/test/binary.cpp b/test/binary.cpp
index 98aead1922..e1064a296f 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -9,6 +9,7 @@
 
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
@@ -386,3 +387,53 @@ INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestInt,
                         testing::Range<int>(-46340, 0, 10e3));
 INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestShort,
                         testing::Range<short>(-180, 0, 50));
+
+template<typename T>
+class ResultTypeScalar : public ::testing::Test {
+protected:
+    T scalar;
+    void SetUp() {
+      scalar = T(1);
+    }
+};
+
+typedef ::testing::Types<float, double, unsigned int, int, short,
+                         unsigned short, char, unsigned char, half_float::half>
+    TestTypes;
+TYPED_TEST_CASE(ResultTypeScalar, TestTypes);
+
+TYPED_TEST(ResultTypeScalar, HalfAddition) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    ASSERT_EQ(f16, (af::array(10, f16) + this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, HalfSubtraction) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    ASSERT_EQ(f16, (af::array(10, f16) - this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, HalfMultiplication) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    ASSERT_EQ(f16, (af::array(10, f16) * this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, HalfDivision) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    ASSERT_EQ(f16, (af::array(10, f16) / this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, FloatAddition) {
+    ASSERT_EQ(f32, (af::array(10, f32) + this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, FloatSubtraction) {
+    ASSERT_EQ(f32, (af::array(10, f32) - this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, FloatMultiplication) {
+    ASSERT_EQ(f32, (af::array(10, f32) * this->scalar).type());
+}
+
+TYPED_TEST(ResultTypeScalar, FloatDivision) {
+    ASSERT_EQ(f32, (af::array(10, f32) / this->scalar).type());
+}

From de0368e18f20ba527b44be6e0175dcd2c1113dca Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 17 Jan 2020 18:56:33 -0500
Subject: [PATCH 1792/2677] Add additional binary and array tests for f16 and
 types tests

---
 test/array.cpp  |  25 +++++++---
 test/binary.cpp | 122 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 141 insertions(+), 6 deletions(-)

diff --git a/test/array.cpp b/test/array.cpp
index 3e1b9a2d63..42c7d414df 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -10,6 +10,7 @@
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <cstddef>
 #include <cstdlib>
@@ -24,7 +25,7 @@ template<typename T>
 using ArrayDeathTest = Array<T>;
 
 typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
-                         int, uint, intl, uintl, short, ushort>
+                         int, uint, intl, uintl, short, ushort, half_float::half>
     TestTypes;
 
 TYPED_TEST_CASE(Array, TestTypes);
@@ -127,7 +128,7 @@ TYPED_TEST(Array, ConstructorHostPointer1D) {
 
     dtype type    = (dtype)dtype_traits<TypeParam>::af_type;
     size_t nelems = 10;
-    vector<TypeParam> data(nelems, 4);
+    vector<TypeParam> data(nelems, TypeParam(4));
     array a(nelems, &data.front(), afHost);
     EXPECT_EQ(1u, a.numdims());
     EXPECT_EQ(dim_t(nelems), a.dims(0));
@@ -149,7 +150,7 @@ TYPED_TEST(Array, ConstructorHostPointer2D) {
     size_t ndims    = 2;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size;
-    vector<TypeParam> data(nelems, 4);
+    vector<TypeParam> data(nelems, TypeParam(4));
     array a(dim_size, dim_size, &data.front(), afHost);
     EXPECT_EQ(ndims, a.numdims());
     EXPECT_EQ(dim_t(dim_size), a.dims(0));
@@ -171,7 +172,7 @@ TYPED_TEST(Array, ConstructorHostPointer3D) {
     size_t ndims    = 3;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size * dim_size;
-    vector<TypeParam> data(nelems, 4);
+    vector<TypeParam> data(nelems, TypeParam(4));
     array a(dim_size, dim_size, dim_size, &data.front(), afHost);
     EXPECT_EQ(ndims, a.numdims());
     EXPECT_EQ(dim_t(dim_size), a.dims(0));
@@ -193,7 +194,7 @@ TYPED_TEST(Array, ConstructorHostPointer4D) {
     size_t ndims    = 4;
     size_t dim_size = 10;
     size_t nelems   = dim_size * dim_size * dim_size * dim_size;
-    vector<TypeParam> data(nelems, 4);
+    vector<TypeParam> data(nelems, TypeParam(4));
     array a(dim_size, dim_size, dim_size, dim_size, &data.front(), afHost);
     EXPECT_EQ(ndims, a.numdims());
     EXPECT_EQ(dim_t(dim_size), a.dims(0));
@@ -223,6 +224,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
 
         case f64:
@@ -234,6 +236,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case c32:
             EXPECT_TRUE(one.isfloating());
@@ -244,6 +247,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_FALSE(one.isreal());
             EXPECT_TRUE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case c64:
             EXPECT_TRUE(one.isfloating());
@@ -254,6 +258,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_FALSE(one.isreal());
             EXPECT_TRUE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case s32:
             EXPECT_FALSE(one.isfloating());
@@ -264,6 +269,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case u32:
             EXPECT_FALSE(one.isfloating());
@@ -274,6 +280,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case s16:
             EXPECT_FALSE(one.isfloating());
@@ -284,6 +291,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case u16:
             EXPECT_FALSE(one.isfloating());
@@ -294,6 +302,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case u8:
             EXPECT_FALSE(one.isfloating());
@@ -304,6 +313,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case b8:
             EXPECT_FALSE(one.isfloating());
@@ -314,6 +324,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_TRUE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case s64:
             EXPECT_FALSE(one.isfloating());
@@ -324,6 +335,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case u64:
             EXPECT_FALSE(one.isfloating());
@@ -334,6 +346,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
             break;
         case f16:
             EXPECT_TRUE(one.isfloating());
@@ -344,7 +357,7 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_TRUE(one.isreal());
             EXPECT_FALSE(one.iscomplex());
             EXPECT_FALSE(one.isbool());
-            EXPECT_FALSE(one.ishalf());
+            EXPECT_TRUE(one.ishalf());
             break;
     }
 }
diff --git a/test/binary.cpp b/test/binary.cpp
index e1064a296f..15e39c9388 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -388,6 +388,128 @@ INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestInt,
 INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestShort,
                         testing::Range<short>(-180, 0, 50));
 
+struct result_type_param {
+    af_dtype result_;
+    af_dtype lhs_;
+    af_dtype rhs_;
+
+    result_type_param(af_dtype type) : result_(type), lhs_(type), rhs_(type) {}
+    result_type_param(af_dtype result, af_dtype lhs, af_dtype rhs)
+        : result_(result), lhs_(lhs), rhs_(rhs) {}
+};
+
+ostream &operator<<(ostream &os, const result_type_param &p) {
+    os << "{lhs_ = " << p.lhs_ << " rhs_ = " << p.rhs_
+       << " result_ = " << p.result_ << "}";
+    return os;
+}
+
+class ResultType : public testing::TestWithParam<result_type_param> {
+   protected:
+    af::array lhs;
+    af::array rhs;
+    af_dtype gold;
+    bool skip;
+
+    void SetUp() {
+        result_type_param params = GetParam();
+        gold                     = params.result_;
+        skip = false;
+        if (noHalfTests(params.result_) || noHalfTests(params.lhs_) ||
+            noHalfTests(params.rhs_)) {
+            skip = true;
+            return;
+        }
+        lhs                      = af::array(10, params.lhs_);
+        rhs                      = af::array(10, params.rhs_);
+    }
+};
+
+std::string print_types(
+    const ::testing::TestParamInfo<ResultType::ParamType> info) {
+    stringstream ss;
+    ss << "lhs_" << info.param.lhs_ << "_rhs_" << info.param.rhs_ << "_result_"
+       << info.param.result_;
+    return ss.str();
+}
+
+INSTANTIATE_TEST_CASE_P(
+    SameTypes, ResultType,
+    // clang-format off
+    ::testing::Values(result_type_param(f32),
+                      result_type_param(f64),
+                      result_type_param(c32),
+                      result_type_param(c64),
+                      result_type_param(b8),
+                      result_type_param(s32),
+                      result_type_param(u32),
+                      result_type_param(u8),
+                      result_type_param(s64),
+                      result_type_param(u64),
+                      result_type_param(s16),
+                      result_type_param(u16),
+                      result_type_param(f16)),
+    // clang-format on
+    print_types);
+
+INSTANTIATE_TEST_CASE_P(
+    Float, ResultType,
+    // clang-format off
+    ::testing::Values(result_type_param(f32),
+                      result_type_param(f64, f64, f32),
+                      result_type_param(c32, c32, f32),
+                      result_type_param(c64, c64, f32),
+                      result_type_param(f32, b8, f32),
+                      result_type_param(f32, s32, f32),
+                      result_type_param(f32, u32, f32),
+                      result_type_param(f32, u8, f32),
+                      result_type_param(f32, s64, f32),
+                      result_type_param(f32, u64, f32),
+                      result_type_param(f32, s16, f32),
+                      result_type_param(f32, u16, f32),
+                      result_type_param(f32, f16, f32)),
+    // clang-format on
+    print_types);
+
+INSTANTIATE_TEST_CASE_P(
+    Double, ResultType,
+    ::testing::Values(
+        // clang-format off
+                      result_type_param(f64, f32, f64),
+                      result_type_param(f64, f64, f64),
+                      result_type_param(c64, c32, f64),
+                      result_type_param(c64, c64, f64),
+                      result_type_param(f64, b8,  f64),
+                      result_type_param(f64, s32, f64),
+                      result_type_param(f64, u32, f64),
+                      result_type_param(f64, u8,  f64),
+                      result_type_param(f64, s64, f64),
+                      result_type_param(f64, u64, f64),
+                      result_type_param(f64, s16, f64),
+                      result_type_param(f64, u16, f64),
+                      result_type_param(f64, f16, f64)),
+    // clang-format on
+    print_types);
+
+// clang-format off
+TEST_P(ResultType, Addition)       {
+    if (skip) return;
+    ASSERT_EQ(gold, (lhs + rhs).type());
+}
+TEST_P(ResultType, Subtraction)    {
+    if (skip) return;
+    ASSERT_EQ(gold, (lhs - rhs).type());
+}
+TEST_P(ResultType, Multiplication) {
+    if (skip) return;
+    ASSERT_EQ(gold, (lhs * rhs).type());
+}
+TEST_P(ResultType, Division)       {
+    if (skip) return;
+    ASSERT_EQ(gold, (lhs / rhs).type());
+}
+// clang-format on
+
 template<typename T>
 class ResultTypeScalar : public ::testing::Test {
 protected:

From 5307e465774aeb81893bf5664e6cc1ac8ed4d183 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 24 Jan 2020 12:50:01 +0530
Subject: [PATCH 1793/2677] github action for cpu backend builds (#2718)

* github action for cpu backend builds

* Set fail-fast to false in matrix action strategy
* Three different blas-cum-fft backend jobs run
    * Atlas and fftw
    * MKL
    * OpenBLAS and fftw
* Test reports are submitted to arrayfire cdash

* Macos workflow paths

* try two exclusion entries

* Change workflow and job name

* Minor name changes and env setup moves

* Build CPU examples
---
 .github/workflows/cpu_build.yml               | 101 ++++++++++++++++++
 ...cmake_doxygen_build.yml => docs_build.yml} |  11 +-
 2 files changed, 106 insertions(+), 6 deletions(-)
 create mode 100644 .github/workflows/cpu_build.yml
 rename .github/workflows/{cmake_doxygen_build.yml => docs_build.yml} (93%)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
new file mode 100644
index 0000000000..c79ce52219
--- /dev/null
+++ b/.github/workflows/cpu_build.yml
@@ -0,0 +1,101 @@
+on:
+  push:
+    branches:
+    - master
+  pull_request:
+    branches:
+    - master
+
+name: ci
+
+jobs:
+    build_cpu:
+        name: CPU
+        runs-on: ${{ matrix.os }}
+        env:
+          NINJA_VER: 1.9.0
+        strategy:
+            fail-fast: false
+            matrix:
+                blas_backend: [Atlas, MKL, OpenBLAS]
+                os: [ubuntu-18.04, macos-latest]
+                exclude:
+                    - os: macos-latest
+                      blas_backend: Atlas
+                    - os: macos-latest
+                      blas_backend: MKL
+        steps:
+            - name: Checkout Repository
+              uses: actions/checkout@master
+
+            - name: Checkout Submodules
+              shell: bash
+              run: git submodule update --init --recursive
+
+            - name: Download Ninja
+              env:
+                  OS_NAME: ${{ matrix.os }}
+              run: |
+                  os_suffix=$(if [ $OS_NAME == 'macos-latest' ]; then echo "mac"; else echo "linux"; fi)
+                  wget --quiet "https://github.com/ninja-build/ninja/releases/download/v${NINJA_VER}/ninja-${os_suffix}.zip"
+                  unzip ./ninja-${os_suffix}.zip
+                  chmod +x ninja
+                  ${GITHUB_WORKSPACE}/ninja --version
+
+            - name: Install Common Dependencies for Macos
+              if: matrix.os == 'macos-latest'
+              run: |
+                  brew install fontconfig glfw freeimage boost fftw lapack openblas
+
+            - name: Install Common Dependencies for Ubuntu
+              if: matrix.os == 'ubuntu-18.04'
+              run: |
+                  sudo apt-get -qq update
+                  sudo apt-get install -y libfreeimage-dev \
+                                          libglfw3-dev \
+                                          libboost-dev \
+                                          libfftw3-dev \
+                                          liblapacke-dev
+
+            - name: Install Atlas for Ubuntu
+              if: matrix.os == 'ubuntu-18.04' && matrix.blas_backend == 'Atlas'
+              run: sudo apt-get install -y libatlas-base-dev
+
+            - name: Install MKL for Ubuntu
+              if: matrix.os == 'ubuntu-18.04' && matrix.blas_backend == 'MKL'
+              run: |
+                  wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
+                  sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
+                  sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
+                  sudo apt-get -qq update
+                  sudo apt-get install -y intel-mkl-64bit-2020.0-088
+
+            - name: Install OpenBLAS for Ubuntu
+              if: matrix.os == 'ubuntu-18.04' && matrix.blas_backend == 'OpenBLAS'
+              run: sudo apt-get install -y libopenblas-dev
+
+            - name: CMake Configure
+              env:
+                  USE_MKL: ${{ matrix.blas_backend == 'MKL' }}
+                  BLAS_BACKEND: ${{ matrix.blas_backend }}
+              run: |
+                  ref=$(echo ${GITHUB_REF} | awk '/refs\/pull\/[0-9]+\/merge/{print $0}')
+                  prnum=$(echo $ref | awk '{split($0, a, "/"); print a[3]}')
+                  branch=$(git rev-parse --abbrev-ref HEAD)
+                  buildname=$(if [ -z "$prnum" ]; then echo "$branch"; else echo "PR-$prnum"; fi)
+                  buildname="$buildname-cpu-$BLAS_BACKEND"
+                  mkdir build && cd build
+                  cmake -G Ninja \
+                      -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
+                      -DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo \
+                      -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
+                      -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
+                      -DAF_BUILD_FORGE:BOOL=ON \
+                      -DUSE_CPU_MKL:BOOL=$USE_MKL \
+                      -DBUILDNAME:STRING=${buildname} \
+                      ..
+
+            - name: Build and Test
+              run: |
+                  cd ${GITHUB_WORKSPACE}/build
+                  ctest -D Experimental -T Test -T Submit -R cpu -j2
diff --git a/.github/workflows/cmake_doxygen_build.yml b/.github/workflows/docs_build.yml
similarity index 93%
rename from .github/workflows/cmake_doxygen_build.yml
rename to .github/workflows/docs_build.yml
index 2fa428ab88..6a89ad7856 100644
--- a/.github/workflows/cmake_doxygen_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -6,16 +6,15 @@ on:
     branches:
     - master
 
-name: Doxygen
-
-env:
-    NINJA_VER: 1.9.0
-    DOXYGEN_VER: 1.8.17
+name: ci
 
 jobs:
     build_documentation:
-        name: Build Documentation
+        name: Documentation
         runs-on: ubuntu-18.04
+        env:
+            NINJA_VER: 1.9.0
+            DOXYGEN_VER: 1.8.17
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master

From 5a29e5a869168b7f53ca70b8f71605794f2247e4 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Mon, 14 Oct 2019 18:03:25 -0700
Subject: [PATCH 1794/2677] Enable support for f16 types for mean

* Add support for mean for f16 types
* Add tests for f16 types
---
 src/api/c/mean.cpp                 |   7 ++
 src/api/cpp/mean.cpp               |  28 ++++++-
 src/backend/cpu/kernel/mean.hpp    |  12 +--
 src/backend/cpu/mean.cpp           |  23 ++++--
 src/backend/cuda/kernel/mean.hpp   | 120 ++++++++++++++++++-----------
 src/backend/cuda/mean.cu           |   5 ++
 src/backend/opencl/kernel/mean.hpp |  54 +++++++++----
 src/backend/opencl/mean.cpp        |   8 +-
 test/data                          |   2 +-
 test/mean.cpp                      |  83 +++++++++++++++-----
 10 files changed, 243 insertions(+), 99 deletions(-)

diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 7e30ba3341..04a8523bf6 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <mean.hpp>
@@ -21,6 +22,8 @@
 
 #include "stats.h"
 
+using common::half;
+
 using namespace detail;
 
 template<typename Ti, typename To>
@@ -69,6 +72,7 @@ af_err af_mean(af_array *out, const af_array in, const dim_t dim) {
             case b8: output = mean<char, float>(in, dim); break;
             case c32: output = mean<cfloat, cfloat>(in, dim); break;
             case c64: output = mean<cdouble, cdouble>(in, dim); break;
+            case f16: output = mean<half, half>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -121,6 +125,7 @@ af_err af_mean_weighted(af_array *out, const af_array in,
             case b8: output = mean<float>(in, w, dim); break;
             case c32: output = mean<cfloat>(in, w, dim); break;
             case c64: output = mean<cdouble>(in, w, dim); break;
+            case f16: output = mean<half>(in, w, dim); break;
             default: TYPE_ERROR(1, iType);
         }
 
@@ -146,6 +151,7 @@ af_err af_mean_all(double *realVal, double *imagVal, const af_array in) {
             case u16: *realVal = mean<ushort, float>(in); break;
             case u8: *realVal = mean<uchar, float>(in); break;
             case b8: *realVal = mean<char, float>(in); break;
+            case f16: *realVal = mean<common::half, float>(in); break;
             case c32: {
                 cfloat tmp = mean<cfloat, cfloat>(in);
                 *realVal   = real(tmp);
@@ -188,6 +194,7 @@ af_err af_mean_all_weighted(double *realVal, double *imagVal, const af_array in,
             case u16: *realVal = mean<float>(in, weights); break;
             case u8: *realVal = mean<float>(in, weights); break;
             case b8: *realVal = mean<float>(in, weights); break;
+            case f16: *realVal = mean<float>(in, weights); break;
             case c32: {
                 cfloat tmp = mean<cfloat>(in, weights);
                 *realVal   = real(tmp);
diff --git a/src/api/cpp/mean.cpp b/src/api/cpp/mean.cpp
index 70f1772688..a8ba685b11 100644
--- a/src/api/cpp/mean.cpp
+++ b/src/api/cpp/mean.cpp
@@ -13,6 +13,12 @@
 #include <af/statistics.h>
 #include "common.hpp"
 #include "error.hpp"
+#include "half.hpp"
+#ifdef AF_CUDA
+// NOTE: Adding ifdef here to avoid copying code constructor in the cuda backend
+#include <cuda_fp16.h>
+#include <traits.hpp>
+#endif
 
 namespace af {
 
@@ -29,18 +35,31 @@ array mean(const array& in, const array& weights, const dim_t dim) {
     return array(temp);
 }
 
+template<typename To, typename T>
+To cast(T in) {
+    return static_cast<To>(in);
+}
+
+template<>
+af_half cast<af_half, double>(double in) {
+    half_float::half tmp = static_cast<half_float::half>(in);
+    af_half out;
+    memcpy(&out, &tmp, sizeof(af_half));
+    return out;
+}
+
 #define INSTANTIATE_MEAN(T)                                                  \
     template<>                                                               \
     AFAPI T mean(const array& in) {                                          \
         double ret_val;                                                      \
         AF_THROW(af_mean_all(&ret_val, NULL, in.get()));                     \
-        return (T)ret_val;                                                   \
+        return cast<T>(ret_val);                                      \
     }                                                                        \
     template<>                                                               \
     AFAPI T mean(const array& in, const array& wts) {                        \
         double ret_val;                                                      \
         AF_THROW(af_mean_all_weighted(&ret_val, NULL, in.get(), wts.get())); \
-        return (T)ret_val;                                                   \
+        return cast<T>(ret_val);                                             \
     }
 
 template<>
@@ -81,6 +100,11 @@ INSTANTIATE_MEAN(long long);
 INSTANTIATE_MEAN(unsigned long long);
 INSTANTIATE_MEAN(short);
 INSTANTIATE_MEAN(unsigned short);
+INSTANTIATE_MEAN(af_half);
+INSTANTIATE_MEAN(half_float::half); // Add support for public API
+#ifdef AF_CUDA
+INSTANTIATE_MEAN(__half);
+#endif
 
 #undef INSTANTIATE_MEAN
 
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index db4b12473b..2be3c7d017 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -71,9 +71,10 @@ struct mean_weighted_dim<T, Tw, 0> {
 
         dim_t istride = istrides[dim];
         dim_t wstride = wstrides[dim];
-        MeanOp<T, T, Tw> Op(0, 0);
+        MeanOp<compute_t<T>, compute_t<T>, compute_t<Tw>> Op(0, 0);
         for (dim_t i = 0; i < idims[dim]; i++) {
-            Op(in[inOffset + i * istride], wt[wtOffset + i * wstride]);
+            Op(compute_t<T>(in[inOffset + i * istride]),
+               compute_t<Tw>(wt[wtOffset + i * wstride]));
         }
 
         out[outOffset] = Op.runningMean;
@@ -108,9 +109,10 @@ struct mean_dim<Ti, Tw, To, 0> {
         To* out            = output.get();
 
         dim_t istride = istrides[dim];
-        MeanOp<Ti, To, Tw> Op(0, 0);
-        for (dim_t i = 0; i < idims[dim]; i++) {
-            Op(in[inOffset + i * istride], 1);
+        dim_t end     = inOffset + idims[dim] * istride;
+        MeanOp<compute_t<Ti>, compute_t<To>, compute_t<Tw>> Op(0, 0);
+        for (dim_t i = inOffset; i < end; i += istride) {
+            Op(compute_t<Ti>(in[i]), 1);
         }
 
         out[outOffset] = Op.runningMean;
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index 6298a76d4c..e38e90d0fe 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -12,10 +12,14 @@
 #include <mean.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
+#include <types.hpp>
 #include <af/dim4.hpp>
 #include <complex>
 
+#include <common/half.hpp>
+
 using af::dim4;
+using common::half;
 
 namespace cpu {
 
@@ -58,6 +62,7 @@ Array<T> mean(const Array<T> &in, const Array<Tw> &wt, const int dim) {
 
 template<typename T, typename Tw>
 T mean(const Array<T> &in, const Array<Tw> &wt) {
+    using MeanOpT = kernel::MeanOp<compute_t<T>, compute_t<T>, compute_t<Tw>>;
     in.eval();
     wt.eval();
     getQueue().sync();
@@ -67,7 +72,9 @@ T mean(const Array<T> &in, const Array<Tw> &wt) {
     const T *inPtr   = in.get();
     const Tw *wtPtr  = wt.get();
 
-    kernel::MeanOp<T, T, Tw> Op(inPtr[0], wtPtr[0]);
+    compute_t<T> i  = inPtr[0];
+    compute_t<Tw> w = wtPtr[0];
+    MeanOpT Op(i, w);
 
     for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
@@ -80,17 +87,18 @@ T mean(const Array<T> &in, const Array<Tw> &wt) {
 
                 for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
-                    Op(inPtr[idx], wtPtr[idx]);
+                    Op(compute_t<T>(inPtr[idx]), compute_t<Tw>(wtPtr[idx]));
                 }
             }
         }
     }
 
-    return Op.runningMean;
+    return T(Op.runningMean);
 }
 
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti> &in) {
+    using MeanOpT = kernel::MeanOp<compute_t<Ti>, compute_t<To>, compute_t<Tw>>;
     in.eval();
     getQueue().sync();
 
@@ -98,7 +106,7 @@ To mean(const Array<Ti> &in) {
     af::dim4 strides = in.strides();
     const Ti *inPtr  = in.get();
 
-    kernel::MeanOp<Ti, To, Tw> Op(0, 0);
+    MeanOpT Op(0, 0);
 
     for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
@@ -111,13 +119,13 @@ To mean(const Array<Ti> &in) {
 
                 for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
-                    Op(inPtr[idx], 1);
+                    Op(compute_t<Ti>(inPtr[idx]), 1);
                 }
             }
         }
     }
 
-    return Op.runningMean;
+    return To(Op.runningMean);
 }
 
 #define INSTANTIATE(Ti, Tw, To)                        \
@@ -136,6 +144,8 @@ INSTANTIATE(uchar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
 INSTANTIATE(cdouble, double, cdouble);
+INSTANTIATE(half, float, half);
+INSTANTIATE(half, float, float);
 
 #define INSTANTIATE_WGT(T, Tw)                                              \
     template T mean<T, Tw>(const Array<T> &in, const Array<Tw> &wts);       \
@@ -146,5 +156,6 @@ INSTANTIATE_WGT(double, double);
 INSTANTIATE_WGT(float, float);
 INSTANTIATE_WGT(cfloat, float);
 INSTANTIATE_WGT(cdouble, double);
+INSTANTIATE_WGT(half, float);
 
 }  // namespace cpu
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 393f09fa56..23db5baeec 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -10,7 +10,9 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <copy.hpp>
+#include <cuda_fp16.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
@@ -24,17 +26,26 @@
 using std::vector;
 
 namespace cuda {
+
+__host__ __device__ auto operator*(float lhs, __half rhs) -> __half {
+    return __float2half(lhs * __half2float(rhs));
+}
+
+__device__ auto operator/(__half lhs, float rhs) -> __half {
+    return __float2half(__half2float(lhs) / rhs);
+}
+
 namespace kernel {
 
 template<typename To, typename Tw>
 __device__ __host__ void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
-    if (((*l_wt) != 0) || (r_wt != 0)) {
+    if (((*l_wt) != (Tw)0) || (r_wt != (Tw)0)) {
         Tw l_scale = (*l_wt);
         (*l_wt) += r_wt;
         l_scale = l_scale / (*l_wt);
 
         Tw r_scale = r_wt / (*l_wt);
-        (*lhs)     = (l_scale * (*lhs)) + (r_scale * rhs);
+        (*lhs)     = (l_scale * *lhs) + (r_scale * rhs);
     }
 }
 
@@ -85,10 +96,10 @@ __global__ static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
     bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
                     (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
 
-    Transform<Ti, To, af_add_t> transform;
+    Transform<Ti, compute_t<To>, af_add_t> transform;
 
-    To val    = Binary<To, af_add_t>::init();
-    Tw weight = Binary<Tw, af_add_t>::init();
+    compute_t<To> val    = Binary<compute_t<To>, af_add_t>::init();
+    compute_t<Tw> weight = Binary<compute_t<Tw>, af_add_t>::init();
 
     if (is_valid && id_dim_in < in.dims[dim]) {
         val = transform(*iptr);
@@ -101,27 +112,27 @@ __global__ static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
 
     const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
 
-    __shared__ To s_val[THREADS_X * DIMY];
-    __shared__ Tw s_idx[THREADS_X * DIMY];
+    __shared__ compute_t<To> s_val[THREADS_X * DIMY];
+    __shared__ compute_t<Tw> s_idx[THREADS_X * DIMY];
 
     for (int id = id_dim_in_start; is_valid && (id < in.dims[dim]);
          id += offset_dim * blockDim.y) {
         iptr = iptr + offset_dim * blockDim.y * istride_dim;
         if (iwptr != NULL) {
             iwptr = iwptr + offset_dim * blockDim.y * istride_dim;
-            stable_mean(&val, &weight, transform(*iptr), *iwptr);
+            stable_mean(&val, &weight, transform(*iptr), compute_t<Tw>(*iwptr));
         } else {
             // Faster version of stable_mean when iwptr is NULL
-            val    = val + (transform(*iptr) - val) / (weight + 1);
-            weight = weight + 1;
+            val    = val + (transform(*iptr) - val) / (weight + (Tw)1);
+            weight = weight + (Tw)1;
         }
     }
 
     s_val[tid] = val;
     s_idx[tid] = weight;
 
-    To *s_vptr = s_val + tid;
-    Tw *s_iptr = s_idx + tid;
+    compute_t<To> *s_vptr = s_val + tid;
+    compute_t<Tw> *s_iptr = s_idx + tid;
     __syncthreads();
 
     if (DIMY == 8) {
@@ -271,10 +282,10 @@ __global__ static void mean_first_kernel(Param<To> out, Param<Tw> owt,
 
     int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-    Transform<Ti, To, af_add_t> transform;
+    Transform<Ti, compute_t<To>, af_add_t> transform;
 
-    To val    = Binary<To, af_add_t>::init();
-    Tw weight = Binary<Tw, af_add_t>::init();
+    compute_t<To> val    = Binary<compute_t<To>, af_add_t>::init();
+    compute_t<Tw> weight = Binary<compute_t<Tw>, af_add_t>::init();
 
     if (xid < lim) {
         val = transform(iptr[xid]);
@@ -285,18 +296,19 @@ __global__ static void mean_first_kernel(Param<To> out, Param<Tw> owt,
         }
     }
 
-    __shared__ To s_val[THREADS_PER_BLOCK];
-    __shared__ Tw s_idx[THREADS_PER_BLOCK];
+    __shared__ compute_t<To> s_val[THREADS_PER_BLOCK];
+    __shared__ compute_t<Tw> s_idx[THREADS_PER_BLOCK];
 
     if (iwptr != NULL) {
         for (int id = xid + DIMX; id < lim; id += DIMX) {
-            stable_mean(&val, &weight, transform(iptr[id]), iwptr[id]);
+            stable_mean(&val, &weight, transform(iptr[id]),
+                        compute_t<Tw>(iwptr[id]));
         }
     } else {
         for (int id = xid + DIMX; id < lim; id += DIMX) {
             // Faster version of stable_mean when iwptr is NULL
-            val    = val + (transform(iptr[id]) - val) / (weight + 1);
-            weight = weight + 1;
+            val    = val + (transform(iptr[id]) - val) / (weight + (Tw)1);
+            weight = weight + (Tw)1;
         }
     }
 
@@ -304,8 +316,8 @@ __global__ static void mean_first_kernel(Param<To> out, Param<Tw> owt,
     s_idx[tid] = weight;
     __syncthreads();
 
-    To *s_vptr = s_val + tidy * DIMX;
-    Tw *s_iptr = s_idx + tidy * DIMX;
+    compute_t<To> *s_vptr = s_val + tidy * DIMX;
+    compute_t<Tw> *s_iptr = s_idx + tidy * DIMX;
 
     if (DIMX == 256) {
         if (tidx < 128) {
@@ -331,7 +343,7 @@ __global__ static void mean_first_kernel(Param<To> out, Param<Tw> owt,
         __syncthreads();
     }
 
-    warp_reduce<To, Tw>(s_vptr, s_iptr, tidx);
+    warp_reduce<compute_t<To>, compute_t<Tw>>(s_vptr, s_iptr, tidx);
 
     if (tidx == 0) {
         optr[blockIdx_x] = s_vptr[0];
@@ -465,19 +477,26 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
         vector<T> h_ptr(tmp_elements);
         vector<Tw> h_wptr(tmp_elements);
 
-        copyData<T>(h_ptr.data(), tmpOut);
-        copyData<Tw>(h_wptr.data(), tmpWt);
+        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.get(),
+                                   tmp_elements * sizeof(T),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), tmpWt.get(),
+                                   tmp_elements * sizeof(Tw),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
         CUDA_CHECK(
             cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-        T val     = h_ptr[0];
-        Tw weight = h_wptr[0];
+        compute_t<T> val     = static_cast <compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast <compute_t<Tw>>(h_wptr[0]);
 
         for (int i = 1; i < tmp_elements; i++) {
-            stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
+            stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
+                        compute_t<Tw>(h_wptr[i]));
         }
 
-        return val;
+        return static_cast<T>(val);
     } else {
         vector<T> h_ptr(in_elements);
         vector<Tw> h_wptr(in_elements);
@@ -493,13 +512,14 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
         CUDA_CHECK(
             cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-        T val     = h_ptr[0];
-        Tw weight = h_wptr[0];
+        compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
         for (int i = 1; i < in_elements; i++) {
-            stable_mean(&val, &weight, h_ptr[i], h_wptr[i]);
+            stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
+                        compute_t<Tw>(h_wptr[i]));
         }
 
-        return val;
+        return static_cast<T>(val);
     }
 }
 
@@ -507,10 +527,9 @@ template<typename Ti, typename Tw, typename To>
 To mean_all(CParam<Ti> in) {
     using std::unique_ptr;
     int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
-    bool is_linear = (in.strides[0] == 1);
+    bool is_linear  = (in.strides[0] == 1);
     for (int k = 1; k < 4; k++) {
-        is_linear &=
-            (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+        is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
     }
 
     // FIXME: Use better heuristics to get to the optimum number
@@ -543,19 +562,26 @@ To mean_all(CParam<Ti> in) {
         vector<To> h_ptr(tmp_elements);
         vector<Tw> h_cptr(tmp_elements);
 
-        copyData<To>(h_ptr.data(), tmpOut);
-        copyData<Tw>(h_cptr.data(), tmpCt);
+        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.get(),
+                                   tmp_elements * sizeof(To),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(h_cptr.data(), tmpCt.get(),
+                                   tmp_elements * sizeof(Tw),
+                                   cudaMemcpyDeviceToHost,
+                                   cuda::getStream(cuda::getActiveDeviceId())));
         CUDA_CHECK(
             cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-        To val    = h_ptr[0];
-        Tw weight = h_cptr[0];
+        compute_t<To> val    = static_cast<compute_t<To>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_cptr[0]);
 
         for (int i = 1; i < tmp_elements; i++) {
-            stable_mean(&val, &weight, h_ptr[i], h_cptr[i]);
+            stable_mean(&val, &weight, compute_t<To>(h_ptr[i]),
+                        compute_t<Tw>(h_cptr[i]));
         }
 
-        return val;
+        return static_cast<To>(val);
     } else {
         vector<Ti> h_ptr(in_elements);
 
@@ -566,16 +592,16 @@ To mean_all(CParam<Ti> in) {
         CUDA_CHECK(
             cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-        Transform<Ti, To, af_add_t> transform;
-        Tw count = (Tw)1;
+        Transform<Ti, compute_t<To>, af_add_t> transform;
+        compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
 
-        To val    = transform(h_ptr[0]);
-        Tw weight = count;
+        compute_t<To> val    = transform(h_ptr[0]);
+        compute_t<Tw> weight = count;
         for (int i = 1; i < in_elements; i++) {
             stable_mean(&val, &weight, transform(h_ptr[i]), count);
         }
 
-        return val;
+        return static_cast<To>(val);
     }
 }
 
diff --git a/src/backend/cuda/mean.cu b/src/backend/cuda/mean.cu
index ecc649cc44..cf692ea48c 100644
--- a/src/backend/cuda/mean.cu
+++ b/src/backend/cuda/mean.cu
@@ -15,7 +15,9 @@
 #include <kernel/mean.hpp>
 #include <mean.hpp>
 #include <complex>
+#include <common/half.hpp>
 
+using common::half;
 using af::dim4;
 using std::swap;
 namespace cuda {
@@ -63,6 +65,8 @@ INSTANTIATE(uchar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
 INSTANTIATE(cdouble, double, cdouble);
+INSTANTIATE(half, float, half);
+INSTANTIATE(half, float, float);
 
 #define INSTANTIATE_WGT(T, Tw)                                              \
     template T mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts);       \
@@ -73,5 +77,6 @@ INSTANTIATE_WGT(double, double);
 INSTANTIATE_WGT(float, float);
 INSTANTIATE_WGT(cfloat, float);
 INSTANTIATE_WGT(cdouble, double);
+INSTANTIATE_WGT(half, float);
 
 }  // namespace cuda
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index b0ac5099fe..1a184d50f9 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/mean_dim.hpp>
 #include <kernel_headers/mean_first.hpp>
@@ -33,6 +34,7 @@ using cl::Kernel;
 using cl::KernelFunctor;
 using cl::NDRange;
 using cl::Program;
+using common::half;
 using std::string;
 using std::vector;
 
@@ -147,6 +149,10 @@ void mean_dim_launcher(Param out, Param owt, Param in, Param inWeight,
             options << " -D USE_DOUBLE";
         }
 
+        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
+            options << " -D USE_HALF";
+        }
+
         const char *ker_strs[] = {mean_ops_cl, mean_dim_cl};
         const int ker_lens[]   = {mean_ops_cl_len, mean_dim_cl_len};
         Program prog;
@@ -272,6 +278,10 @@ void mean_first_launcher(Param out, Param owt, Param in, Param inWeight,
             options << " -D USE_DOUBLE";
         }
 
+        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
+            options << " -D USE_HALF";
+        }
+
         const char *ker_strs[] = {mean_ops_cl, mean_first_cl};
         const int ker_lens[]   = {mean_ops_cl_len, mean_first_cl_len};
         Program prog;
@@ -429,13 +439,14 @@ T mean_all_weighted(Param in, Param inWeight) {
                                      sizeof(Tw) * tmpWeight.elements(),
                                      h_wptr.data());
 
-        MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
+        compute_t<T> initial = h_ptr[0];
+        compute_t<Tw> w      = h_wptr[0];
+        MeanOp<compute_t<T>, compute_t<Tw>> Op(initial, w);
         for (int i = 1; i < (int)tmpOut.elements(); i++) {
-            Op(h_ptr[i], h_wptr[i]);
+            Op(compute_t<T>(h_ptr[i]), compute_t<Tw>(h_wptr[i]));
         }
 
-        return Op.runningMean;
-
+        return static_cast<T>(Op.runningMean);
     } else {
         vector<T> h_ptr(in_elements);
         vector<Tw> h_wptr(in_elements);
@@ -447,10 +458,14 @@ T mean_all_weighted(Param in, Param inWeight) {
                                      sizeof(Tw) * inWeight.info.offset,
                                      sizeof(Tw) * in_elements, h_wptr.data());
 
-        MeanOp<T, Tw> Op(h_ptr[0], h_wptr[0]);
-        for (int i = 1; i < (int)in_elements; i++) { Op(h_ptr[i], h_wptr[i]); }
+        compute_t<T> initial = h_ptr[0];
+        compute_t<Tw> w      = h_wptr[0];
+        MeanOp<compute_t<T>, compute_t<Tw>> Op(initial, w);
+        for (int i = 1; i < (int)in_elements; i++) {
+            Op(compute_t<T>(h_ptr[i]), compute_t<Tw>(h_wptr[i]));
+        }
 
-        return Op.runningMean;
+        return static_cast<T>(Op.runningMean);
     }
 }
 
@@ -461,7 +476,7 @@ To mean_all(Param in) {
     bool is_linear = (in.info.strides[0] == 1);
     for (int k = 1; k < 4; k++) {
         is_linear &= (in.info.strides[k] ==
-                (in.info.strides[k - 1] * in.info.dims[k - 1]));
+                      (in.info.strides[k - 1] * in.info.dims[k - 1]));
     }
 
     // FIXME: Use better heuristics to get to the optimum number
@@ -481,8 +496,8 @@ To mean_all(Param in) {
         uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
         uint groups_y = divup(in.info.dims[1], threads_y);
 
-        dim4 outDims(groups_x, in.info.dims[1],
-                     in.info.dims[2], in.info.dims[3]);
+        dim4 outDims(groups_x, in.info.dims[1], in.info.dims[2],
+                     in.info.dims[3]);
         Array<To> tmpOut = createEmptyArray<To>(outDims);
         Array<Tw> tmpCt  = createEmptyArray<Tw>(outDims);
 
@@ -500,10 +515,14 @@ To mean_all(Param in) {
                                      sizeof(Tw) * tmpCt.elements(),
                                      h_cptr.data());
 
-        MeanOp<To, Tw> Op(h_ptr[0], h_cptr[0]);
-        for (int i = 1; i < (int)h_ptr.size(); i++) { Op(h_ptr[i], h_cptr[i]); }
+        compute_t<To> initial = h_ptr[0];
+        compute_t<Tw> w       = h_cptr[0];
+        MeanOp<compute_t<To>, compute_t<Tw>> Op(initial, w);
+        for (int i = 1; i < (int)h_ptr.size(); i++) {
+            Op(compute_t<To>(h_ptr[i]), compute_t<Tw>(h_cptr[i]));
+        }
 
-        return Op.runningMean;
+        return static_cast<To>(Op.runningMean);
     } else {
         vector<Ti> h_ptr(in_elements);
 
@@ -512,14 +531,15 @@ To mean_all(Param in) {
                                      sizeof(Ti) * in_elements, h_ptr.data());
 
         // TODO : MeanOp with (Tw)1
-        Transform<Ti, To, af_add_t> transform;
-        Transform<uint, Tw, af_add_t> transform_weight;
-        MeanOp<To, Tw> Op(transform(h_ptr[0]), transform_weight(1));
+        Transform<Ti, compute_t<To>, af_add_t> transform;
+        Transform<uint, compute_t<Tw>, af_add_t> transform_weight;
+        MeanOp<compute_t<To>, compute_t<Tw>> Op(transform(h_ptr[0]),
+                                                transform_weight(1));
         for (int i = 1; i < (int)in_elements; i++) {
             Op(transform(h_ptr[i]), transform_weight(1));
         }
 
-        return Op.runningMean;
+        return static_cast<To>(Op.runningMean);
     }
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
index 1f9cbbdcd6..0bd59b15b3 100644
--- a/src/backend/opencl/mean.cpp
+++ b/src/backend/opencl/mean.cpp
@@ -9,14 +9,17 @@
 
 #include <Array.hpp>
 #include <af/dim4.hpp>
-
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <kernel/mean.hpp>
 #include <mean.hpp>
+
 #include <complex>
 
 using af::dim4;
+using common::half;
 using std::swap;
+
 namespace opencl {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
@@ -62,6 +65,8 @@ INSTANTIATE(uchar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
 INSTANTIATE(cdouble, double, cdouble);
+INSTANTIATE(half, float, half);
+INSTANTIATE(half, float, float);
 
 #define INSTANTIATE_WGT(T, Tw)                                              \
     template T mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts);       \
@@ -72,5 +77,6 @@ INSTANTIATE_WGT(double, double);
 INSTANTIATE_WGT(float, float);
 INSTANTIATE_WGT(cfloat, float);
 INSTANTIATE_WGT(cdouble, double);
+INSTANTIATE_WGT(half, float);
 
 }  // namespace opencl
diff --git a/test/data b/test/data
index a7bbe5ee63..141759fe81 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit a7bbe5ee6376ba23ac50687866c28b94620104fd
+Subproject commit 141759fe815641da18e240fa955c99cf8a4b20ec
diff --git a/test/mean.cpp b/test/mean.cpp
index d8d90b194f..6de2e13242 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -17,6 +17,7 @@
 #include <iostream>
 #include <string>
 #include <vector>
+#include <half.hpp>
 
 using af::array;
 using af::cdouble;
@@ -24,6 +25,7 @@ using af::cfloat;
 using af::constant;
 using af::dim4;
 using af::randu;
+using half_float::half;
 using std::endl;
 using std::string;
 using std::vector;
@@ -35,8 +37,9 @@ class Mean : public ::testing::Test {
 };
 
 // create a list of types to be tested
+// This list does not allow to cleanly add the af_half/half_float type : at the moment half tested in some special unittests
 typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, intl, uintl,
-                         char, uchar, short, ushort>
+                         char, uchar, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
@@ -68,8 +71,8 @@ struct meanOutType {
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
             is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
             is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
-            is_same_type<T, char>::value,
-        float, typename elseType<T>::type>::type type;
+            is_same_type<T, char>::value , float, typename elseType<T>::type>::type
+        type;
 };
 
 template<typename T>
@@ -78,12 +81,16 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
     SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
 
+    double tol = 1.0e-3;
+    if((af_dtype)af::dtype_traits<T>::af_type == f16) tol = 4.e-3;
     vector<dim4> numDims;
     vector<vector<int> > in;
     vector<vector<float> > tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
+    dim4 goldDims = numDims[0];
+    goldDims[dim] = 1;
     if (!isWeighted) {
         dim4 dims = numDims[0];
         vector<T> input(in[0].begin(), in[0].end());
@@ -98,14 +105,10 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
-                        1.0e-3)
-                << "at: " << elIter << endl;
-            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
-                        1.0e-3)
-                << "at: " << elIter << endl;
-        }
+
+        dim4 goldDims = dims;
+        goldDims[dim] = 1;
+        ASSERT_VEC_ARRAY_NEAR(currGoldBar, goldDims, outArray, tol);
     } else {
         dim4 dims  = numDims[0];
         dim4 wdims = numDims[1];
@@ -126,14 +129,8 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
         size_t nElems = currGoldBar.size();
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_NEAR(::real(currGoldBar[elIter]), ::real(outData[elIter]),
-                        1.0e-3)
-                << "at: " << elIter << endl;
-            ASSERT_NEAR(::imag(currGoldBar[elIter]), ::imag(outData[elIter]),
-                        1.0e-3)
-                << "at: " << elIter << endl;
-        }
+
+        ASSERT_VEC_ARRAY_NEAR(currGoldBar, goldDims, outArray, tol);
     }
 }
 
@@ -173,9 +170,11 @@ TYPED_TEST(Mean, Wtd_Dim1Matrix) {
                            true);
 }
 
+
 template<typename T>
 void meanAllTest(T const_value, dim4 dims) {
     typedef typename meanOutType<T>::type outType;
+
     SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
 
@@ -196,10 +195,34 @@ void meanAllTest(T const_value, dim4 dims) {
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 }
 
+
+template<>
+void meanAllTest(half_float::half const_value, dim4 dims) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+
+    using af::array;
+    using af::mean;
+
+    vector<float> hundred(dims.elements(), const_value);
+
+    float gold = float(0);
+    for (int i = 0; i < (int)hundred.size(); i++) { gold = gold + hundred[i]; }
+    gold = gold / dims.elements();
+
+    array a = array(dims, &(hundred.front())).as(f16);
+    half output = mean<half>(a);
+    af_half output2 = mean<af_half>(a);
+
+    ASSERT_NEAR(output, gold, 1.0e-3);
+}
+
+
 TEST(MeanAll, f64) { meanAllTest<double>(2.1, dim4(10, 10, 1, 1)); }
 
 TEST(MeanAll, f32) { meanAllTest<float>(2.1f, dim4(10, 5, 2, 1)); }
 
+TEST(MeanAll, f16) { meanAllTest<half>((half)0.3f, dim4(10, 5, 2, 1)); }
+
 TEST(MeanAll, s32) { meanAllTest<int>(2, dim4(5, 5, 2, 2)); }
 
 TEST(MeanAll, u32) { meanAllTest<unsigned>(2, dim4(100, 1, 1, 1)); }
@@ -221,6 +244,14 @@ T random() {
     return T(std::rand() % 10);
 }
 
+template<>
+half random<half>() {
+    // create values from -0.5 to 0.5 to ensure sum does not deviate
+    // too far out of half's useful range
+    float r = static_cast<float>(rand()) / static_cast<float>(RAND_MAX)-0.5f;
+    return half(r);
+}
+
 template<>
 cfloat random<cfloat>() {
     return cfloat(float(std::rand() % 10), float(std::rand() % 10));
@@ -266,7 +297,7 @@ void weightedMeanAllTest(dim4 dims) {
         wtsSum = wtsSum + wts[i];
     }
 
-    outType gold = wtdSum / wtsSum;
+    outType gold = wtdSum / outType(wtsSum);
 
     array a(dims, &(data.front()));
     array w(dims, &(wts.front()));
@@ -333,3 +364,15 @@ TEST(MeanAll, SubArray) {
     size_t nElems = sub.elements();
     ASSERT_FLOAT_EQ(mean<float>(sub), sum<float>(sub)/nElems);
 }
+
+TEST(MeanHalf, dim0) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    // Keeping N low to be able to run on 6GB GPUs
+    int N = 1024;
+    const dim4 inDims(N, N, 1, 1);
+    array in  = randu(inDims, f16);
+    array m16 = af::mean(in, 0);
+    array m32 = af::mean(in.as(f32), 0);
+    // Some diffs appears at 0.0001 max diff : example: float: 0.507014 vs half: 0.506836
+    ASSERT_ARRAYS_NEAR(m16.as(f32), m32, 0.001f);
+}

From 83b1d6b5dc1228048e8695fde1b6d62b994db037 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 16 Jan 2020 20:48:54 -0500
Subject: [PATCH 1795/2677] Add support for f16 for var and meanvar

---
 src/api/c/var.cpp                  | 15 ++++++++++++
 src/api/cpp/common.hpp             | 19 +++++++++++++++
 src/api/cpp/mean.cpp               | 18 ++------------
 src/api/cpp/var.cpp                | 14 +++++++++--
 src/backend/cpu/mean.cpp           | 10 ++++----
 src/backend/opencl/kernel/mean.hpp | 12 ++++-----
 test/mean.cpp                      |  6 +++++
 test/meanvar.cpp                   | 39 ++++++++++++++++++++++++------
 test/var.cpp                       | 29 +++++++++-------------
 9 files changed, 109 insertions(+), 53 deletions(-)

diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 25ede1cf72..eabaa81364 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <mean.hpp>
@@ -26,6 +27,7 @@
 
 using namespace detail;
 
+using common::half;
 using std::ignore;
 using std::make_tuple;
 using std::tie;
@@ -215,6 +217,9 @@ af_err af_var(af_array* out, const af_array in, const bool isbiased,
             case c64:
                 output = var_<cdouble, cdouble>(in, no_weights, bias, dim);
                 break;
+            case f16:
+                output = var_<half, half>(in, no_weights, bias, dim);
+                break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -281,6 +286,10 @@ af_err af_var_weighted(af_array* out, const af_array in, const af_array weights,
                 output =
                     var_<char, float>(in, weights, AF_VARIANCE_POPULATION, dim);
                 break;
+            case f16:
+                output =
+                    var_<half, float>(in, weights, AF_VARIANCE_POPULATION, dim);
+                break;
             case c32:
                 output = var_<cfloat, cfloat>(in, weights,
                                               AF_VARIANCE_POPULATION, dim);
@@ -313,6 +322,7 @@ af_err af_var_all(double* realVal, double* imagVal, const af_array in,
             case u64: *realVal = varAll<uintl, double>(in, isbiased); break;
             case u8: *realVal = varAll<uchar, float>(in, isbiased); break;
             case b8: *realVal = varAll<char, float>(in, isbiased); break;
+            case f16: *realVal = varAll<half, float>(in, isbiased); break;
             case c32: {
                 cfloat tmp = varAll<cfloat, cfloat>(in, isbiased);
                 *realVal   = real(tmp);
@@ -355,6 +365,7 @@ af_err af_var_all_weighted(double* realVal, double* imagVal, const af_array in,
             case u64: *realVal = varAll<uintl, double>(in, weights); break;
             case u8: *realVal = varAll<uchar, float>(in, weights); break;
             case b8: *realVal = varAll<char, float>(in, weights); break;
+            case f16: *realVal = varAll<half, float>(in, weights); break;
             case c32: {
                 cfloat tmp = varAll<cfloat, cfloat>(in, weights);
                 *realVal   = real(tmp);
@@ -430,6 +441,10 @@ af_err af_meanvar(af_array* mean, af_array* var, const af_array in,
                 tie(*mean, *var) =
                     meanvar<cdouble, cdouble>(in, weights, bias, dim);
                 break;
+            case f16:
+                tie(*mean, *var) =
+                    meanvar<half, half>(in, weights, bias, dim);
+                break;
             default: TYPE_ERROR(1, iType);
         }
     }
diff --git a/src/api/cpp/common.hpp b/src/api/cpp/common.hpp
index 355972fc8a..61597ab989 100644
--- a/src/api/cpp/common.hpp
+++ b/src/api/cpp/common.hpp
@@ -8,6 +8,10 @@
  ********************************************************/
 
 #include <af/dim4.hpp>
+#include <af/half.h>
+#include "half.hpp"
+
+#include <cstring>
 
 namespace af {
 
@@ -25,4 +29,19 @@ static inline dim_t getFNSD(const int dim, af::dim4 dims) {
     return fNSD;
 }
 
+namespace {
+// casts from one type to another. Needed for af_half conversions specialization
+template<typename To, typename T>
+To cast(T in) {
+    return static_cast<To>(in);
+}
+
+template<>
+af_half cast<af_half, double>(double in) {
+    half_float::half tmp = static_cast<half_float::half>(in);
+    af_half out;
+    memcpy(&out, &tmp, sizeof(af_half));
+    return out;
+}
+}  // namespace
 }  // namespace af
diff --git a/src/api/cpp/mean.cpp b/src/api/cpp/mean.cpp
index a8ba685b11..55c0a02335 100644
--- a/src/api/cpp/mean.cpp
+++ b/src/api/cpp/mean.cpp
@@ -15,7 +15,6 @@
 #include "error.hpp"
 #include "half.hpp"
 #ifdef AF_CUDA
-// NOTE: Adding ifdef here to avoid copying code constructor in the cuda backend
 #include <cuda_fp16.h>
 #include <traits.hpp>
 #endif
@@ -35,25 +34,12 @@ array mean(const array& in, const array& weights, const dim_t dim) {
     return array(temp);
 }
 
-template<typename To, typename T>
-To cast(T in) {
-    return static_cast<To>(in);
-}
-
-template<>
-af_half cast<af_half, double>(double in) {
-    half_float::half tmp = static_cast<half_float::half>(in);
-    af_half out;
-    memcpy(&out, &tmp, sizeof(af_half));
-    return out;
-}
-
 #define INSTANTIATE_MEAN(T)                                                  \
     template<>                                                               \
     AFAPI T mean(const array& in) {                                          \
         double ret_val;                                                      \
         AF_THROW(af_mean_all(&ret_val, NULL, in.get()));                     \
-        return cast<T>(ret_val);                                      \
+        return cast<T>(ret_val);                                             \
     }                                                                        \
     template<>                                                               \
     AFAPI T mean(const array& in, const array& wts) {                        \
@@ -101,7 +87,7 @@ INSTANTIATE_MEAN(unsigned long long);
 INSTANTIATE_MEAN(short);
 INSTANTIATE_MEAN(unsigned short);
 INSTANTIATE_MEAN(af_half);
-INSTANTIATE_MEAN(half_float::half); // Add support for public API
+INSTANTIATE_MEAN(half_float::half);  // Add support for public API
 #ifdef AF_CUDA
 INSTANTIATE_MEAN(__half);
 #endif
diff --git a/src/api/cpp/var.cpp b/src/api/cpp/var.cpp
index 413c25a40a..534eb07f48 100644
--- a/src/api/cpp/var.cpp
+++ b/src/api/cpp/var.cpp
@@ -12,6 +12,11 @@
 #include <af/statistics.h>
 #include "common.hpp"
 #include "error.hpp"
+#include "half.hpp"
+#ifdef AF_CUDA
+#include <cuda_fp16.h>
+#include <traits.hpp>
+#endif
 
 namespace af {
 
@@ -33,7 +38,7 @@ array var(const array& in, const array& weights, const dim_t dim) {
     AFAPI T var(const array& in, const bool isbiased) {                    \
         double ret_val;                                                    \
         AF_THROW(af_var_all(&ret_val, NULL, in.get(), isbiased));          \
-        return (T)ret_val;                                                 \
+        return cast<T>(ret_val);                                           \
     }                                                                      \
                                                                            \
     template<>                                                             \
@@ -41,7 +46,7 @@ array var(const array& in, const array& weights, const dim_t dim) {
         double ret_val;                                                    \
         AF_THROW(                                                          \
             af_var_all_weighted(&ret_val, NULL, in.get(), weights.get())); \
-        return (T)ret_val;                                                 \
+        return cast<T>(ret_val);                                           \
     }
 
 template<>
@@ -82,6 +87,11 @@ INSTANTIATE_VAR(short);
 INSTANTIATE_VAR(unsigned short);
 INSTANTIATE_VAR(char);
 INSTANTIATE_VAR(unsigned char);
+INSTANTIATE_VAR(af_half);
+INSTANTIATE_VAR(half_float::half);
+#ifdef AF_CUDA
+INSTANTIATE_VAR(__half);
+#endif
 
 #undef INSTANTIATE_VAR
 
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index e38e90d0fe..c44b24a2cf 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -8,15 +8,15 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/mean.hpp>
 #include <mean.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
-#include <complex>
 
-#include <common/half.hpp>
+#include <complex>
 
 using af::dim4;
 using common::half;
@@ -72,9 +72,9 @@ T mean(const Array<T> &in, const Array<Tw> &wt) {
     const T *inPtr   = in.get();
     const Tw *wtPtr  = wt.get();
 
-    compute_t<T> i  = inPtr[0];
-    compute_t<Tw> w = wtPtr[0];
-    MeanOpT Op(i, w);
+    compute_t<T> input  = compute_t<T>(inPtr[0]);
+    compute_t<Tw> weight = compute_t<Tw>(wtPtr[0]);
+    MeanOpT Op(input, weight);
 
     for (dim_t l = 0; l < dims[3]; l++) {
         dim_t off3 = l * strides[3];
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 1a184d50f9..2922748748 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -439,8 +439,8 @@ T mean_all_weighted(Param in, Param inWeight) {
                                      sizeof(Tw) * tmpWeight.elements(),
                                      h_wptr.data());
 
-        compute_t<T> initial = h_ptr[0];
-        compute_t<Tw> w      = h_wptr[0];
+        compute_t<T> initial = static_cast<compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> w       = static_cast<compute_t<Tw>>(h_wptr[0]);
         MeanOp<compute_t<T>, compute_t<Tw>> Op(initial, w);
         for (int i = 1; i < (int)tmpOut.elements(); i++) {
             Op(compute_t<T>(h_ptr[i]), compute_t<Tw>(h_wptr[i]));
@@ -458,8 +458,8 @@ T mean_all_weighted(Param in, Param inWeight) {
                                      sizeof(Tw) * inWeight.info.offset,
                                      sizeof(Tw) * in_elements, h_wptr.data());
 
-        compute_t<T> initial = h_ptr[0];
-        compute_t<Tw> w      = h_wptr[0];
+        compute_t<T> initial = static_cast<compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> w      = static_cast<compute_t<Tw>>(h_wptr[0]);
         MeanOp<compute_t<T>, compute_t<Tw>> Op(initial, w);
         for (int i = 1; i < (int)in_elements; i++) {
             Op(compute_t<T>(h_ptr[i]), compute_t<Tw>(h_wptr[i]));
@@ -515,8 +515,8 @@ To mean_all(Param in) {
                                      sizeof(Tw) * tmpCt.elements(),
                                      h_cptr.data());
 
-        compute_t<To> initial = h_ptr[0];
-        compute_t<Tw> w       = h_cptr[0];
+        compute_t<To> initial = static_cast<compute_t<To>>(h_ptr[0]);
+        compute_t<Tw> w       = static_cast<compute_t<Tw>>(h_cptr[0]);
         MeanOp<compute_t<To>, compute_t<Tw>> Op(initial, w);
         for (int i = 1; i < (int)h_ptr.size(); i++) {
             Op(compute_t<To>(h_ptr[i]), compute_t<Tw>(h_cptr[i]));
diff --git a/test/mean.cpp b/test/mean.cpp
index 6de2e13242..a3a7a31558 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -213,6 +213,12 @@ void meanAllTest(half_float::half const_value, dim4 dims) {
     half output = mean<half>(a);
     af_half output2 = mean<af_half>(a);
 
+    // make sure output2 and output are binary equals. This is necessary
+    // because af_half is not a complete type
+    half output2_copy;
+    memcpy(&output2_copy, &output2, sizeof(af_half));
+    ASSERT_EQ(output, output2_copy);
+
     ASSERT_NEAR(output, gold, 1.0e-3);
 }
 
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 631f1dedcf..81cd680ee1 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -11,6 +11,7 @@
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 
+#include <half.hpp>
 #include <testHelpers.hpp>
 
 #include <iterator>
@@ -70,8 +71,8 @@ struct meanvar_test {
         if (weights) { af_retain_array(&weights_, weights); }
         mean_.reserve(mean.size());
         variance_.reserve(variance.size());
-        std::copy(begin(mean), end(mean), back_inserter(mean_));
-        std::copy(begin(variance), end(variance), back_inserter(variance_));
+        for (auto &v : mean) mean_.push_back((outType<T>)v);
+        for (auto &v : variance) variance_.push_back((outType<T>)v);
     }
     meanvar_test()                        = default;
     meanvar_test(meanvar_test<T> &&other) = default;
@@ -108,11 +109,12 @@ template<typename T>
 class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
    public:
     void meanvar_test_function(const meanvar_test<T> &test) {
+        SUPPORTED_TYPE_CHECK(T);
         af_array mean, var;
 
         // Cast to the expected type
         af_array in = 0;
-        af_cast(&in, test.in_, (af_dtype)dtype_traits<T>::af_type);
+        ASSERT_SUCCESS(af_cast(&in, test.in_, (af_dtype)dtype_traits<T>::af_type));
 
         EXPECT_EQ(AF_SUCCESS, af_meanvar(&mean, &var, in, test.weights_,
                                          test.bias_, test.dim_));
@@ -124,8 +126,11 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
         af_get_dims(&outDim[0], &outDim[1], &outDim[2], &outDim[3], in);
         outDim[test.dim_] = 1;
 
-        if (is_same_type<float, outType<T> >::value ||
-            is_same_type<cfloat, outType<T> >::value) {
+        if (is_same_type<half_float::half, outType<T> >::value) {
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 1.f);
+            ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.5f);
+        } else if (is_same_type<float, outType<T> >::value ||
+                   is_same_type<cfloat, outType<T> >::value) {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
         } else {
@@ -139,6 +144,7 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
     }
 
     void meanvar_cpp_test_function(const meanvar_test<T> &test) {
+        SUPPORTED_TYPE_CHECK(T);
         array mean, var;
 
         // Cast to the expected type
@@ -160,8 +166,11 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
         dim4 outDim       = in.dims();
         outDim[test.dim_] = 1;
 
-        if (is_same_type<float, outType<T> >::value ||
-            is_same_type<cfloat, outType<T> >::value) {
+        if (is_same_type<half_float::half, outType<T> >::value) {
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 1.f);
+            ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.5f);
+        } else if (is_same_type<float, outType<T> >::value ||
+                   is_same_type<cfloat, outType<T> >::value) {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
         } else {
@@ -303,6 +312,22 @@ MEANVAR_TEST(ComplexDouble, af::af_cdouble)
 
 #undef MEANVAR_TEST
 
+using MeanVarHalf = MeanVarTyped<half_float::half>;
+INSTANTIATE_TEST_CASE_P(
+    Small, MeanVarHalf,
+    ::testing::ValuesIn(small_test_values<half_float::half>()),
+    [](const ::testing::TestParamInfo<MeanVarHalf::ParamType> info) {
+        return info.param.test_description_;
+    });
+TEST_P(MeanVarHalf, Testing) {
+    const meanvar_test<half_float::half> &test = GetParam();
+    meanvar_test_function(test);
+}
+TEST_P(MeanVarHalf, TestingCPP) {
+    const meanvar_test<half_float::half> &test = GetParam();
+    meanvar_cpp_test_function(test);
+}
+
 #define MEANVAR_TEST(NAME, TYPE)                                              \
     using MeanVar##NAME = MeanVarTyped<TYPE>;                                 \
     INSTANTIATE_TEST_CASE_P(                                                  \
diff --git a/test/var.cpp b/test/var.cpp
index ab9c4bc38c..eb43e6c1eb 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -26,7 +26,7 @@ template<typename T>
 class Var : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, uintl, intl,
-                         char, uchar, short, ushort>
+                         char, uchar, short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_CASE(Var, TestTypes);
 
@@ -90,18 +90,22 @@ void testCPPVar(T const_value, dim4 dims) {
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 }
 
-TYPED_TEST(Var, AllCPPSmall) { testCPPVar<TypeParam>(2, dim4(10, 10, 1, 1)); }
+TYPED_TEST(Var, AllCPPSmall) {
+    testCPPVar<TypeParam>(TypeParam(2), dim4(10, 10, 1, 1));
+}
 
 TYPED_TEST(Var, AllCPPMedium) {
-    testCPPVar<TypeParam>(2, dim4(100, 100, 1, 1));
+    testCPPVar<TypeParam>(TypeParam(2), dim4(100, 100, 1, 1));
 }
 
 TYPED_TEST(Var, AllCPPLarge) {
-    testCPPVar<TypeParam>(2, dim4(1000, 1000, 1, 1));
+    testCPPVar<TypeParam>(TypeParam(2), dim4(1000, 1000, 1, 1));
 }
 
 TYPED_TEST(Var, DimCPPSmall) {
     typedef typename varOutType<TypeParam>::type outType;
+    float tol = 0.001f;
+    if ((af_dtype)af::dtype_traits<TypeParam>::af_type == f16) { tol = 0.6f; }
 
     SUPPORTED_TYPE_CHECK(TypeParam);
     SUPPORTED_TYPE_CHECK(outType);
@@ -134,19 +138,10 @@ TYPED_TEST(Var, DimCPPSmall) {
         bout1.host(&h_out[2].front());
         nbout1.host(&h_out[3].front());
 
-        for (size_t j = 0; j < tests.size(); j++) {
-            for (size_t jj = 0; jj < tests[j].size(); jj++) {
-                // NOTE: will work for all types
-                if (is_same_type<float, outType>::value ||
-                    is_same_type<cfloat, outType>::value) {
-                    ASSERT_FLOAT_EQ(real(h_out[j][jj]), real(tests[j][jj]));
-                    ASSERT_FLOAT_EQ(imag(h_out[j][jj]), imag(tests[j][jj]));
-                } else {
-                    ASSERT_DOUBLE_EQ(real(h_out[j][jj]), real(tests[j][jj]));
-                    ASSERT_DOUBLE_EQ(imag(h_out[j][jj]), imag(tests[j][jj]));
-                }
-            }
-        }
+        ASSERT_VEC_ARRAY_NEAR(tests[0], bout.dims(), bout, tol);
+        ASSERT_VEC_ARRAY_NEAR(tests[1], nbout.dims(), nbout, tol);
+        ASSERT_VEC_ARRAY_NEAR(tests[2], bout1.dims(), bout1, tol);
+        ASSERT_VEC_ARRAY_NEAR(tests[3], nbout1.dims(), nbout1, tol);
     }
 }
 

From 767646438a75d9f90d65d2835da68ae03e175664 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 25 Jan 2020 10:31:04 +0530
Subject: [PATCH 1796/2677] Change cfloat eps for inverse cfloat test

---
 test/inverse_dense.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index 21981061ec..cd39d0239e 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -62,7 +62,7 @@ double eps();
 
 template<>
 double eps<float>() {
-    return 0.01f;
+    return 0.01;
 }
 
 template<>
@@ -72,7 +72,7 @@ double eps<double>() {
 
 template<>
 double eps<cfloat>() {
-    return 0.01f;
+    return 0.015;
 }
 
 template<>

From 91e0af7019657f25627758105a810c6b83466cae Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 23 Jan 2020 01:12:24 -0500
Subject: [PATCH 1797/2677] Fix out of bound accesses in canny

---
 src/backend/cpu/kernel/canny.hpp               |  8 ++++----
 src/backend/cuda/kernel/canny.cuh              | 18 ++++++++----------
 .../opencl/kernel/nonmax_suppression.cl        | 11 +++++------
 src/backend/opencl/kernel/trace_edge.cl        | 12 ++++--------
 4 files changed, 21 insertions(+), 28 deletions(-)

diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
index 412d209b6b..55ff282db7 100644
--- a/src/backend/cpu/kernel/canny.hpp
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -32,8 +32,8 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dxParam,
 
             offset = dims[0] + 1;
 
-            for (dim_t j = 1; j < dims[1] - 1; ++j, offset += 2) {
-                for (dim_t i = 1; i < dims[0] - 1; ++i, ++offset) {
+            for (dim_t j = 2; j < dims[1]; ++j, offset += 2) {
+                for (dim_t i = 2; i < dims[0]; ++i, ++offset) {
                     T curr = mag[offset];
                     if (curr == 0) {
                         out[offset] = (T)0;
@@ -89,8 +89,8 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dxParam,
                             }
                         }
 
-                        float mag1 = (1 - alpha) * a1 + alpha * b1;
-                        float mag2 = (1 - alpha) * a2 + alpha * b2;
+                        float mag1 = (1.0f - alpha) * a1 + alpha * b1;
+                        float mag2 = (1.0f - alpha) * a2 + alpha * b2;
 
                         if (curr > mag1 && curr > mag2) {
                             out[offset] = curr;
diff --git a/src/backend/cuda/kernel/canny.cuh b/src/backend/cuda/kernel/canny.cuh
index d0cf25f582..bd96ae6d9e 100644
--- a/src/backend/cuda/kernel/canny.cuh
+++ b/src/backend/cuda/kernel/canny.cuh
@@ -47,8 +47,7 @@ void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
     const T* mag = (const T*)in.ptr +
-                   (b2 * in.strides[2] + b3 * in.strides[3]) + in.strides[1] +
-                   1;
+                   (b2 * in.strides[2] + b3 * in.strides[3]);
     const T* dX = (const T*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]) +
                   dx.strides[1] + 1;
     const T* dY = (const T*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]) +
@@ -59,13 +58,13 @@ void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
 
     // pull image to shared memory
 #pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT && gy2 < in.dims[1];
          b += blockDim.y, gy2 += blockDim.y)
 #pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH && gx2 < in.dims[0];
              a += blockDim.x, gx2 += blockDim.x)
             shrdMem[b][a] =
-                mag[lIdx(gx2 - 1, gy2 - 1, in.strides[0], in.strides[1])];
+                mag[lIdx(gx2, gy2, in.strides[0], in.strides[1])];
 
     int i = lx + 1;
     int j = ly + 1;
@@ -200,8 +199,7 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]) +
-              output.strides[1] + 1;
+    T* oPtr = output.ptr + (b2 * output.strides[2] + b3 * output.strides[3]);
 
     // pull image to shared memory
 #pragma unroll
@@ -210,8 +208,8 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
 #pragma unroll
         for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
              a += blockDim.x, gx2 += blockDim.x) {
-            int x = gx2 - 1;
-            int y = gy2 - 1;
+            int x = gx2;
+            int y = gy2;
             if (x >= 0 && x < output.dims[0] && y >= 0 && y < output.dims[1])
                 outMem[b][a] =
                     oPtr[lIdx(x, y, output.strides[0], output.strides[1])];
@@ -294,7 +292,7 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
 
     // Update output with shared memory result
     if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2))
-        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1])] = outMem[j][i];
+        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1]) + output.strides[1] + 1] = outMem[j][i];
 }
 
 template<typename T>
diff --git a/src/backend/opencl/kernel/nonmax_suppression.cl b/src/backend/opencl/kernel/nonmax_suppression.cl
index 7b56cc42ab..7c204a039b 100644
--- a/src/backend/opencl/kernel/nonmax_suppression.cl
+++ b/src/backend/opencl/kernel/nonmax_suppression.cl
@@ -27,8 +27,7 @@ __kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
     __local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
     __global const T* mag =
-        in + (b2 * inInfo.strides[2] + b3 * inInfo.strides[3] + inInfo.offset) +
-        inInfo.strides[1] + 1;
+        in + (b2 * inInfo.strides[2] + b3 * inInfo.strides[3] + inInfo.offset);
     __global const T* dX =
         dx + (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3] + dxInfo.offset) +
         dxInfo.strides[1] + 1;
@@ -39,13 +38,13 @@ __kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
                       oInfo.strides[1] + 1;
 
 #pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT && gy2 < inInfo.dims[1];
          b += get_local_size(1), gy2 += get_local_size(1)) {
 #pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH && gx2 < inInfo.dims[0];
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            localMem[b][a] = mag[(gx2 - 1) * inInfo.strides[0] +
-                                 (gy2 - 1) * inInfo.strides[1]];
+            localMem[b][a] = mag[(gx2) * inInfo.strides[0] +
+                                 (gy2) * inInfo.strides[1]];
         }
     }
     int i = lx + 1;
diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
index a72bfd554e..797d6d8d90 100644
--- a/src/backend/opencl/kernel/trace_edge.cl
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -78,8 +78,7 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
     __global T* oPtr = output +
-                       (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
-                       oInfo.strides[1] + 1;
+                       (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     // pull image to local memory
 #pragma unroll
@@ -88,11 +87,8 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 #pragma unroll
         for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            int x = gx2 - 1;
-            int y = gy2 - 1;
-            if (x >= 0 && x < oInfo.dims[0] && y >= 0 && y < oInfo.dims[1])
-                outMem[b][a] =
-                    oPtr[x * oInfo.strides[0] + y * oInfo.strides[1]];
+            if (gx2 >= 0 && gx2 < oInfo.dims[0] && gy2 >= 0 && gy2 < oInfo.dims[1] - 1)
+                outMem[b][a] = oPtr[gx2 * oInfo.strides[0] + gy2 * oInfo.strides[1]];
             else
                 outMem[b][a] = NOEDGE;
         }
@@ -191,7 +187,7 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
     // Update output with shared memory result
     if (gx < (oInfo.dims[0] - 2) && gy < (oInfo.dims[1] - 2))
-        oPtr[gx * oInfo.strides[0] + gy * oInfo.strides[1]] = outMem[j][i];
+        oPtr[(gx * oInfo.strides[0] + gy * oInfo.strides[1]) + oInfo.strides[1] + 1] = outMem[j][i];
 }
 #endif
 

From 6188435e05d528899a29d53dca4a07899147e353 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 24 Jan 2020 02:40:01 -0500
Subject: [PATCH 1798/2677] Optimize edgeTrack on opencl and cuda.

---
 src/backend/cuda/kernel/canny.cuh       | 57 ++++++++--------
 src/backend/opencl/kernel/canny.hpp     |  4 +-
 src/backend/opencl/kernel/trace_edge.cl | 87 ++++++++++++-------------
 test/canny.cpp                          | 10 +++
 4 files changed, 83 insertions(+), 75 deletions(-)

diff --git a/src/backend/cuda/kernel/canny.cuh b/src/backend/cuda/kernel/canny.cuh
index bd96ae6d9e..27c758d1c4 100644
--- a/src/backend/cuda/kernel/canny.cuh
+++ b/src/backend/cuda/kernel/canny.cuh
@@ -226,47 +226,46 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
     int continueIter = 1;
 
     while (continueIter) {
-        int cu = outMem[j][i];
-        int nw = outMem[j - 1][i - 1];
-        int no = outMem[j - 1][i];
-        int ne = outMem[j - 1][i + 1];
-        int ea = outMem[j][i + 1];
-        int se = outMem[j + 1][i + 1];
-        int so = outMem[j + 1][i];
-        int sw = outMem[j + 1][i - 1];
-        int we = outMem[j][i - 1];
+
+      int nw ,no ,ne ,we ,ea ,sw ,so ,se;
+
+      if(outMem[j][i] == WEAK) {
+        nw = outMem[j - 1][i - 1];
+        no = outMem[j - 1][i];
+        ne = outMem[j - 1][i + 1];
+        we = outMem[j    ][i - 1];
+        ea = outMem[j    ][i + 1];
+        sw = outMem[j + 1][i - 1];
+        so = outMem[j + 1][i];
+        se = outMem[j + 1][i + 1];
 
         bool hasStrongNeighbour =
             nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
             se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
 
-        if (cu == WEAK && hasStrongNeighbour) outMem[j][i] = STRONG;
+        if (hasStrongNeighbour) outMem[j][i] = STRONG;
+      }
 
         __syncthreads();
 
         // Check if there are any STRONG pixels with weak neighbours.
         // This search however ignores 1-pixel border encompassing the
         // shared memory tile region.
+        bool hasWeakNeighbour = false;
+        if(outMem[j][i] == STRONG) {
+          nw = outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
+          no = outMem[j - 1][i    ] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+          ne = outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
+          we = outMem[j    ][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+          ea = outMem[j    ][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+          sw = outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
+          so = outMem[j + 1][i    ] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+          se = outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
+
+          hasWeakNeighbour = nw || no || ne || ea || se || so || sw || we;
+        }
 
-        cu = outMem[j][i];
-
-        bool _nw =
-            outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
-        bool _no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
-        bool _ne =
-            outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
-        bool _ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
-        bool _se =
-            outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
-        bool _so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
-        bool _sw =
-            outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
-        bool _we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
-
-        bool hasWeakNeighbour =
-            _nw || _no || _ne || _ea || _se || _so || _sw || _we;
-
-        continueIter = __syncthreads_or(cu == STRONG && hasWeakNeighbour);
+        continueIter = __syncthreads_or(hasWeakNeighbour);
     };
 
     // Check if any 1-pixel border ring
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index d9dd2c6f3c..3133e500b8 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -213,9 +213,9 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     int notFinished        = 1;
     cl::Buffer *d_continue = bufferAlloc(sizeof(int));
 
-    while (notFinished) {
+    while (notFinished > 0) {
         notFinished = 0;
-        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
+        getQueue().enqueueWriteBuffer(*d_continue, CL_FALSE, 0, sizeof(int),
                                       &notFinished);
 
         edgeTraceOp(EnqueueArgs(getQueue(), global, threads), *output.data,
diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
index 797d6d8d90..e592b58f41 100644
--- a/src/backend/opencl/kernel/trace_edge.cl
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -61,7 +61,7 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
     // strong and weak images are binary(char) images thus,
     // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
     __local int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
-    __local int predicates[TOTAL_NUM_THREADS];
+    __local bool predicates[TOTAL_NUM_THREADS];
 
     // local thread indices
     const int lx = get_local_id(0);
@@ -82,12 +82,12 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
     // pull image to local memory
 #pragma unroll
-    for (int b = ly, gy2 = gy; b < SHRD_MEM_HEIGHT;
+    for (int b = ly, gy2 = gy-1; b < SHRD_MEM_HEIGHT;
          b += get_local_size(1), gy2 += get_local_size(1)) {
 #pragma unroll
-        for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH;
+        for (int a = lx, gx2 = gx-1; a < SHRD_MEM_WIDTH;
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            if (gx2 >= 0 && gx2 < oInfo.dims[0] && gy2 >= 0 && gy2 < oInfo.dims[1] - 1)
+            if (gx2 >= 0 && gx2 < oInfo.dims[0] && gy2 >= 0 && gy2 < oInfo.dims[1])
                 outMem[b][a] = oPtr[gx2 * oInfo.strides[0] + gy2 * oInfo.strides[1]];
             else
                 outMem[b][a] = NOEDGE;
@@ -99,57 +99,56 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    int tid = get_local_id(0) + get_local_size(0) * get_local_id(1);
+    int tid = lx + get_local_size(0) * ly;
 
-    int continueIter = 1;
+    bool continueIter = 1;
 
+    int mycounter = 0;
     while (continueIter) {
-        int cu = outMem[j][i];
-        int nw = outMem[j - 1][i - 1];
-        int no = outMem[j - 1][i];
-        int ne = outMem[j - 1][i + 1];
-        int ea = outMem[j][i + 1];
-        int se = outMem[j + 1][i + 1];
-        int so = outMem[j + 1][i];
-        int sw = outMem[j + 1][i - 1];
-        int we = outMem[j][i - 1];
-
-        bool hasStrongNeighbour =
-            nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
-            se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
-
-        if (cu == WEAK && hasStrongNeighbour) outMem[j][i] = STRONG;
+        int nw ,no ,ne ,we ,ea ,sw ,so ,se;
+
+        if(outMem[j][i] == WEAK) {
+            nw = outMem[j - 1][i - 1];
+            no = outMem[j - 1][i];
+            ne = outMem[j - 1][i + 1];
+            we = outMem[j    ][i - 1];
+            ea = outMem[j    ][i + 1];
+            sw = outMem[j + 1][i - 1];
+            so = outMem[j + 1][i];
+            se = outMem[j + 1][i + 1];
+
+            bool hasStrongNeighbour =
+                nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
+                se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
+
+            if (hasStrongNeighbour) outMem[j][i] = STRONG;
+        }
 
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        cu = outMem[j][i];
 
-        bool _nw =
-            outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
-        bool _no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
-        bool _ne =
-            outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
-        bool _ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
-        bool _se =
-            outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
-        bool _so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
-        bool _sw =
-            outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
-        bool _we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+        predicates[tid] = false;
+        if(outMem[j][i] == STRONG) {
+            nw = outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
+            no = outMem[j - 1][i    ] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+            ne = outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
+            we = outMem[j    ][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+            ea = outMem[j    ][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+            sw = outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
+            so = outMem[j + 1][i    ] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+            se = outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
 
-        bool hasWeakNeighbour =
-            _nw || _no || _ne || _ea || _se || _so || _sw || _we;
+            bool hasWeakNeighbour = nw || no || ne || ea || se || so || sw || we;
 
-        // Following Block is equivalent of __syncthreads_or in CUDA
-        predicates[tid] = cu == STRONG && hasWeakNeighbour;
+            predicates[tid] = hasWeakNeighbour;
+        }
         barrier(CLK_LOCAL_MEM_FENCE);
 
+        // Following Block is equivalent of __syncthreads_or in CUDA
         for (int nt = TOTAL_NUM_THREADS / 2; nt > 0; nt >>= 1) {
-            if (tid < nt)
-                predicates[tid] = predicates[tid] || predicates[tid + nt];
+            if (tid < nt) { predicates[tid] = predicates[tid] || predicates[tid + nt]; }
             barrier(CLK_LOCAL_MEM_FENCE);
         }
-        barrier(CLK_LOCAL_MEM_FENCE);
 
         continueIter = predicates[0];
     };
@@ -183,11 +182,11 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
     continueIter = predicates[0];
 
-    if (continueIter > 0 && lx == 0 && ly == 0) atomic_add(hasChanged, 1);
+    if (continueIter && lx == 0 && ly == 0) atomic_inc(hasChanged);
 
     // Update output with shared memory result
-    if (gx < (oInfo.dims[0] - 2) && gy < (oInfo.dims[1] - 2))
-        oPtr[(gx * oInfo.strides[0] + gy * oInfo.strides[1]) + oInfo.strides[1] + 1] = outMem[j][i];
+    if (gx < (oInfo.dims[0] - 1) && gy < (oInfo.dims[1] - 1))
+        oPtr[(gx * oInfo.strides[0] + gy * oInfo.strides[1])] = outMem[j][i];
 }
 #endif
 
diff --git a/test/canny.cpp b/test/canny.cpp
index 54fd55763e..9687d0a070 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -79,6 +79,16 @@ TYPED_TEST(CannyEdgeDetector, ArraySizeEqualBlockSize16x16) {
     cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast16x16.test"));
 }
 
+TEST(Canny, DISABLED_Exact) {
+    using namespace af;
+    array img = loadImage(TEST_DIR "/CannyEdgeDetector/woman.jpg", false);
+
+    array out = canny(img, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false);
+    array gold = loadImage(TEST_DIR "/CannyEdgeDetector/woman_edges.jpg", false) > 3;
+
+    ASSERT_ARRAYS_EQ(gold, out);
+}
+
 template<typename T>
 void cannyImageOtsuTest(string pTestFile, bool isColor) {
     SUPPORTED_TYPE_CHECK(T);

From ff6fa7c1ee13c66e0f2a2d2c31b3df496b17db3b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 25 Jan 2020 19:59:35 +0530
Subject: [PATCH 1799/2677] Set ctest dashboard based on ci build typer:push/pr

---
 .github/workflows/cpu_build.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index c79ce52219..fa721a9e58 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -83,6 +83,7 @@ jobs:
                   prnum=$(echo $ref | awk '{split($0, a, "/"); print a[3]}')
                   branch=$(git rev-parse --abbrev-ref HEAD)
                   buildname=$(if [ -z "$prnum" ]; then echo "$branch"; else echo "PR-$prnum"; fi)
+                  dashboard=$(if [ -z "$prnum" ]; then echo "Continuous"; else echo "Experimental"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
                   mkdir build && cd build
                   cmake -G Ninja \
@@ -94,8 +95,9 @@ jobs:
                       -DUSE_CPU_MKL:BOOL=$USE_MKL \
                       -DBUILDNAME:STRING=${buildname} \
                       ..
+                  echo "::set-env name=CTEST_DASHBOARD::${dashboard}"
 
             - name: Build and Test
               run: |
                   cd ${GITHUB_WORKSPACE}/build
-                  ctest -D Experimental -T Test -T Submit -R cpu -j2
+                  ctest -D ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2

From 8aca0996120f7dbc91f2feba28e889a0187ad72a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 26 Jan 2020 12:07:41 +0530
Subject: [PATCH 1800/2677] Use ctest track option to group build reports

---
 .github/workflows/cpu_build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index fa721a9e58..86851c3cad 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -100,4 +100,4 @@ jobs:
             - name: Build and Test
               run: |
                   cd ${GITHUB_WORKSPACE}/build
-                  ctest -D ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2
+                  ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2

From e9ce3f2a852f132aaaccc8cce42e3572753cc743 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 28 Jan 2020 11:36:34 +0530
Subject: [PATCH 1801/2677] Delegate all non-batch matmul to cuBLAS GEMM API

We have tried the following and compared the avg. runtimes:

- Use GEMV for special case
- Use JIT+Reduction for the above GEMV case instead of cuBLAS GEMV
- Directly call GEMM API for all gemv cases

GEMM API is better(performance) and simple solution.
---
 src/backend/cuda/blas.cpp | 49 ++++++++-------------------------------
 test/blas.cpp             | 34 +++++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 39 deletions(-)

diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index b80975aefc..2b7ff45d43 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -14,13 +14,17 @@
 #include <platform.hpp>
 
 #include <arith.hpp>
+#include <cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <complex.hpp>
+#include <copy.hpp>
 #include <cublas.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <reduce.hpp>
+#include <tile.hpp>
+#include <transpose.hpp>
 
 #include <cassert>
 #include <functional>
@@ -56,11 +60,6 @@ using gemmBatched_func_def = std::function<cublasStatus_t(
     cublasHandle_t, cublasOperation_t, cublasOperation_t, int, int, int,
     const T *, const T **, int, const T **, int, const T *, T **, int, int)>;
 
-template<typename T>
-using gemv_func_def = std::function<cublasStatus_t(
-    cublasHandle_t, cublasOperation_t, int, int, const T *, const T *, int,
-    const T *, int, const T *, T *, int)>;
-
 template<typename T>
 using trsm_func_def = std::function<cublasStatus_t(
     cublasHandle_t, cublasSideMode_t, cublasFillMode_t, cublasOperation_t,
@@ -90,20 +89,6 @@ BLAS_FUNC(gemmBatched, double, D)
 BLAS_FUNC(gemmBatched, cdouble, Z)
 BLAS_FUNC(gemmBatched, __half, H)
 
-BLAS_FUNC_DEF(gemv)
-BLAS_FUNC(gemv, float, S)
-BLAS_FUNC(gemv, cfloat, C)
-BLAS_FUNC(gemv, double, D)
-BLAS_FUNC(gemv, cdouble, Z)
-
-template<>
-gemv_func_def<half> gemv_func<half>() {
-    assert(1 != 1 && "GEMV for half is not available.");
-    return gemv_func_def<half>();
-}
-
-// BLAS_FUNC(gemv, __half, S)  // TODO(umar): Not implemented in CUDA
-
 BLAS_FUNC_DEF(trsm)
 BLAS_FUNC(trsm, float, S)
 BLAS_FUNC(trsm, cfloat, C)
@@ -311,22 +296,9 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     dim4 oStrides = out.strides();
 
     if (oDims.ndims() <= 2) {
-        if (rDims[bColDim] == 1) {
-            dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-            if (is_same<T, half>::value) {
-                AF_ERROR(
-                    "GEMV does not support half, Please create an issue on "
-                    "the GitHub repo.",
-                    AF_ERR_NOT_SUPPORTED);
-            }
-            CUBLAS_CHECK(gemv_func<T>()(blasHandle(), lOpts, lDims[0], lDims[1],
-                                        alpha, lhs.get(), lStrides[1],
-                                        rhs.get(), incr, beta, out.get(), 1));
-        } else {
-            CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K,
-                                         alpha, lhs, lStrides[1], rhs,
-                                         rStrides[1], beta, out, oStrides[1]));
-        }
+        CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K,
+                                     alpha, lhs, lStrides[1], rhs,
+                                     rStrides[1], beta, out, oStrides[1]));
     } else {
         int batchSize = oDims[2] * oDims[3];
         vector<const T *> lptrs(batchSize);
@@ -382,10 +354,9 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
-    const Array<T> lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
-    const Array<T> rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
-
-    const Array<T> temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
+    auto lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
+    auto rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
+    auto temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
     return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
diff --git a/test/blas.cpp b/test/blas.cpp
index 81d8d659c5..4a815f931d 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -31,6 +31,9 @@ using af::max;
 using af::randu;
 using af::setDevice;
 using af::span;
+using af::constant;
+using af::dot;
+using af::transpose;
 using std::copy;
 using std::cout;
 using std::endl;
@@ -328,6 +331,12 @@ TEST(MatrixMultiply, RhsBroadcastBatched) {
 float alpha = 1.f;
 float beta = 0.f;
 
+float h_gold_gemv[4] = {5, 5, 5, 5};
+float h_half_ones[20] = {1.f, 1.f, 1.f, 1.f, 1.f,
+                         1.f, 1.f, 1.f, 1.f, 1.f,
+                         1.f, 1.f, 1.f, 1.f, 1.f,
+                         1.f, 1.f, 1.f, 1.f, 1.f};
+
 float h_lhs[9] =         {1.f, 4.f, 7.f,
                           2.f, 5.f, 8.f,
                           3.f, 6.f, 9.f};
@@ -576,6 +585,11 @@ INSTANTIATE_TEST_CASE_P(
         test_params(AF_MAT_TRANS, AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_tall, h_gold_TN, dim4(3, 2), dim4(3, 2), dim4(2, 2), &beta, NULL_ARRAY),
         test_params(AF_MAT_TRANS, AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_wide, h_gold_TT, dim4(3, 2), dim4(2, 3), dim4(2, 2), &beta, NULL_ARRAY),
 
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_half_ones, h_half_ones, h_gold_gemv, dim4(4, 5), dim4(5, 1), dim4(4, 1), &beta, NULL_ARRAY),
+        test_params(AF_MAT_NONE, AF_MAT_NONE, &alpha, h_half_ones, h_half_ones, h_gold_gemv, dim4(1, 5), dim4(5, 1), dim4(1, 1), &beta, NULL_ARRAY),
+        test_params(AF_MAT_NONE, AF_MAT_TRANS, &alpha, h_half_ones, h_half_ones, h_gold_gemv, dim4(4, 5), dim4(1, 5), dim4(4, 1), &beta, NULL_ARRAY),
+        test_params(AF_MAT_TRANS, AF_MAT_NONE, &alpha, h_half_ones, h_half_ones, h_gold_gemv, dim4(5, 4), dim4(5, 1), dim4(4, 1), &beta, NULL_ARRAY),
+
         test_params(AF_MAT_NONE,  AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_wide, h_gold_NN, dim4(3, 2), dim4(2, 3), dim4(3, 3), &beta, FULL_ARRAY),
         test_params(AF_MAT_NONE,  AF_MAT_TRANS, &alpha, h_lhs_tall, h_rhs_tall, h_gold_NT, dim4(3, 2), dim4(3, 2), dim4(3, 3), &beta, FULL_ARRAY),
         test_params(AF_MAT_TRANS, AF_MAT_NONE,  &alpha, h_lhs_tall, h_rhs_tall, h_gold_TN, dim4(3, 2), dim4(3, 2), dim4(2, 2), &beta, FULL_ARRAY),
@@ -680,3 +694,23 @@ TEST(Gemm, DocSnippet) {
 
     ASSERT_VEC_ARRAY_EQ(gold2, dim4(5, 5, 2), c2);
 }
+
+TEST(Gemv, HalfScalarProduct) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+
+    const unsigned int sizeValue = 5;
+    array gold = constant(sizeValue, 4, 1, f16);
+    {
+        array a     = constant(1, 4, sizeValue, f16);
+        array b     = constant(1, sizeValue, 1, f16);
+        array mmRes = matmul(a, b);
+        ASSERT_ARRAYS_EQ(mmRes, gold);
+    }
+    {
+        array a     = constant(1, 1, sizeValue, f16);
+        array b     = constant(1, sizeValue, 1, f16);
+        array mmRes = matmul(a, b);
+        array dotRes = dot(transpose(a), b);
+        ASSERT_ARRAYS_EQ(mmRes, dotRes);
+    }
+}

From a2db4e83b11c3a318411018a6f9af263fc8e9245 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 27 Jan 2020 13:57:51 -0500
Subject: [PATCH 1802/2677] Return error codes from the unique_handle's create
 function

---
 src/backend/common/unique_handle.hpp | 44 ++++++++++++++++------------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index baf351597b..8c6e07ef91 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -27,7 +27,7 @@ void handle_deleter(T handle) noexcept;
 /// \param[in] handle the handle that will be initialzed by the create function
 /// \note This function will need to be specialized for each type of handle
 template<typename T>
-void handle_creator(T *handle) noexcept;
+int handle_creator(T *handle) noexcept;
 
 /// \brief A generic class to manage basic RAII lifetimes for C handles
 ///
@@ -49,8 +49,13 @@ class unique_handle {
     /// Default constructor. Initializes the handle to zero. Does not call the
     /// create function
     constexpr unique_handle() noexcept : handle_(0) {}
-    void create() {
-        if (!handle_) handle_creator(&handle_);
+    int create() {
+        if (!handle_) {
+            int error = handle_creator(&handle_);
+            if (error) { handle_ = 0; }
+            return error;
+        }
+        return 0;
     }
 
     /// \brief Takes ownership of a previously created handle
@@ -66,15 +71,15 @@ class unique_handle {
     /// \brief Implicit converter for the handle
     constexpr operator const T &() const noexcept { return handle_; }
 
-    unique_handle(const unique_handle &other)      noexcept = delete;
+    unique_handle(const unique_handle &other) noexcept = delete;
     constexpr unique_handle(unique_handle &&other) noexcept
-      : handle_(other.handle_) {
-      other.handle_ = 0;
+        : handle_(other.handle_) {
+        other.handle_ = 0;
     }
 
-    unique_handle &operator=(unique_handle &other)  noexcept = delete;
+    unique_handle &operator=(unique_handle &other) noexcept = delete;
     unique_handle &operator=(unique_handle &&other) noexcept {
-        handle_ = other.handle_;
+        handle_       = other.handle_;
         other.handle_ = 0;
     }
 
@@ -92,6 +97,9 @@ class unique_handle {
     constexpr bool operator==(T other) const noexcept {
         return handle_ == other;
     }
+
+    // Returns true if the handle was initialized correctly
+    constexpr operator bool() { return handle_ != 0; }
 };
 
 /// \brief Returns an initialized handle object. The create function on this
@@ -113,14 +121,14 @@ unique_handle<T> make_handle() {
 /// \param[in] DESTROY The destroy function for the handle
 /// \note Do not add this macro to another namespace, The macro provides a
 ///       namespace for the functions.
-#define CREATE_HANDLE(HANDLE, CREATE, DESTROY)              \
-    namespace common {                                      \
-    template<>                                              \
-    void handle_deleter<HANDLE>(HANDLE handle) noexcept {   \
-        DESTROY(handle);                                    \
-    }                                                       \
-    template<>                                              \
-    void handle_creator<HANDLE>(HANDLE * handle) noexcept { \
-        CREATE(handle);                                     \
-    }                                                       \
+#define CREATE_HANDLE(HANDLE, CREATE, DESTROY)             \
+    namespace common {                                     \
+    template<>                                             \
+    void handle_deleter<HANDLE>(HANDLE handle) noexcept {  \
+        DESTROY(handle);                                   \
+    }                                                      \
+    template<>                                             \
+    int handle_creator<HANDLE>(HANDLE * handle) noexcept { \
+        return CREATE(handle);                             \
+    }                                                      \
     }  // namespace common

From 29d83f1069faa92ced684d34262bc24be64203cd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 28 Jan 2020 17:49:39 -0500
Subject: [PATCH 1803/2677] Add the ability to search for library suffixes in
 DependencyModule

---
 src/backend/common/DependencyModule.cpp | 37 +++++++++++++++++++++++--
 src/backend/common/DependencyModule.hpp | 10 +++++++
 2 files changed, 45 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index 7fee45cdf8..b76f44bf29 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <common/DependencyModule.hpp>
+#include <common/Logger.hpp>
 #include <common/module_loading.hpp>
 #include <algorithm>
 #include <string>
@@ -33,6 +34,7 @@ static const char* libraryPrefix = "lib";
 #endif
 
 using std::string;
+using std::vector;
 
 namespace {
 
@@ -45,14 +47,42 @@ namespace common {
 
 DependencyModule::DependencyModule(const char* plugin_file_name,
                                    const char** paths)
-    : handle(nullptr) {
+    : handle(nullptr), logger(loggerFactory("platform")) {
     // TODO(umar): Implement handling of non-standard paths
     UNUSED(paths);
     if (plugin_file_name) {
-        handle = loadLibrary(libName(plugin_file_name).c_str());
+        string filename = libName(plugin_file_name);
+        AF_TRACE("Attempting to load: {}", filename);
+        handle = loadLibrary(filename.c_str());
+        if (handle) {
+            AF_TRACE("Found: {}", filename);
+        } else {
+            AF_TRACE("Unable to open {}", plugin_file_name);
+        }
     }
 }
 
+DependencyModule::DependencyModule(const vector<string> plugin_base_file_name,
+                                   const vector<string> suffixes,
+                                   const vector<string> paths)
+    : handle(nullptr), logger(common::loggerFactory("platform")) {
+    UNUSED(paths);
+    for (const string& base_name : plugin_base_file_name) {
+        for (const string& path : paths) {
+            for (const string& suffix : suffixes) {
+                string filename = libName(base_name + suffix);
+                AF_TRACE("Attempting to load: {}", filename);
+                handle = loadLibrary(filename.c_str());
+                if (handle) {
+                    AF_TRACE("Found: {}", filename);
+                    return;
+                }
+            }
+        }
+    }
+    AF_TRACE("Unable to open {}", plugin_base_file_name[0]);
+}
+
 DependencyModule::~DependencyModule() {
     if (handle) { unloadLibrary(handle); }
 }
@@ -65,4 +95,7 @@ bool DependencyModule::symbolsLoaded() {
 }
 
 string DependencyModule::getErrorMessage() { return common::getErrorMessage(); }
+
+spdlog::logger* DependencyModule::getLogger() { return logger.get(); }
+
 }  // namespace common
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 2122612712..14d2ee3f0a 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -10,7 +10,10 @@
 #pragma once
 #include <common/defines.hpp>
 #include <common/module_loading.hpp>
+#include <common/Logger.hpp>
 
+#include <memory>
+#include <string>
 #include <utility>
 #include <vector>
 
@@ -24,12 +27,17 @@ namespace common {
 /// we use in ArrayFire
 class DependencyModule {
     LibHandle handle;
+    std::shared_ptr<spdlog::logger> logger;
     std::vector<void*> functions;
 
    public:
     DependencyModule(const char* plugin_file_name,
                      const char** paths = nullptr);
 
+    DependencyModule(const std::vector<std::string> plugin_base_file_name,
+                     const std::vector<std::string> suffixes,
+                     const std::vector<std::string> paths);
+
     ~DependencyModule();
 
     /// Returns a function pointer to the function with the name symbol_name
@@ -48,6 +56,8 @@ class DependencyModule {
     /// Returns the last error message that occurred because of loading the
     /// library
     std::string getErrorMessage();
+
+    spdlog::logger* getLogger();
 };
 
 }  // namespace common

From 5a1c47d0911f89fff414f0845a6a71bb7f6fb3aa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 26 Jan 2020 05:34:12 -0500
Subject: [PATCH 1804/2677] Make cuDNN a runtime dependency rather than a link
 time dependency

* Load cuDNN at runtime
* Take into account different versions of cuDNN
* Add support for older cuDNN versions (Tested with 4, 6 and 7)
* Check loading runtime errors for cuDNN.
---
 CMakeLists.txt                          |   2 +-
 src/backend/common/DependencyModule.cpp |   3 +-
 src/backend/common/DependencyModule.hpp |  11 +-
 src/backend/common/util.hpp             |  10 +-
 src/backend/cuda/CMakeLists.txt         |   7 +-
 src/backend/cuda/convolve.cpp           |  52 +++++---
 src/backend/cuda/cudnn.cpp              | 160 +++++++++++++++++++++++-
 src/backend/cuda/cudnn.hpp              | 138 ++++++++++++++++++--
 src/backend/cuda/cudnnModule.cpp        | 137 ++++++++++++++++++++
 src/backend/cuda/cudnnModule.hpp        |  77 ++++++++++++
 src/backend/cuda/device_manager.cpp     |  68 +++++++---
 src/backend/cuda/device_manager.hpp     |   2 +
 src/backend/cuda/handle.cpp             |  11 +-
 src/backend/cuda/platform.cpp           |  45 +++++--
 src/backend/cuda/platform.hpp           |   1 -
 15 files changed, 646 insertions(+), 78 deletions(-)
 create mode 100644 src/backend/cuda/cudnnModule.cpp
 create mode 100644 src/backend/cuda/cudnnModule.hpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cf57587403..7bdb70ace7 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -31,7 +31,7 @@ arrayfire_set_cmake_default_variables()
 set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
 
 find_package(CUDA 7.0)
-find_package(cuDNN 7.3)
+find_package(cuDNN 4.0)
 find_package(OpenCL 1.2)
 find_package(OpenGL)
 find_package(FreeImage)
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index b76f44bf29..dcbbc9809e 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -47,7 +47,7 @@ namespace common {
 
 DependencyModule::DependencyModule(const char* plugin_file_name,
                                    const char** paths)
-    : handle(nullptr), logger(loggerFactory("platform")) {
+    : handle(nullptr), logger(common::loggerFactory("platform")) {
     // TODO(umar): Implement handling of non-standard paths
     UNUSED(paths);
     if (plugin_file_name) {
@@ -66,7 +66,6 @@ DependencyModule::DependencyModule(const vector<string> plugin_base_file_name,
                                    const vector<string> suffixes,
                                    const vector<string> paths)
     : handle(nullptr), logger(common::loggerFactory("platform")) {
-    UNUSED(paths);
     for (const string& base_name : plugin_base_file_name) {
         for (const string& path : paths) {
             for (const string& suffix : suffixes) {
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 14d2ee3f0a..a83850518b 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -8,15 +8,18 @@
  ********************************************************/
 
 #pragma once
+#include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/module_loading.hpp>
-#include <common/Logger.hpp>
 
 #include <memory>
 #include <string>
 #include <utility>
 #include <vector>
 
+namespace spdlog {
+class logger;
+}
 namespace common {
 
 /// Allows you to create classes which dynamically load dependencies at runtime
@@ -50,7 +53,7 @@ class DependencyModule {
     /// Returns true if the module was successfully loaded
     bool isLoaded();
 
-    /// Returns true if the module was successfully loaded
+    /// Returns true if all of the symbols for the module were loaded
     bool symbolsLoaded();
 
     /// Returns the last error message that occurred because of loading the
@@ -66,5 +69,5 @@ class DependencyModule {
 #define MODULE_MEMBER(NAME) decltype(&::NAME) NAME
 
 /// Dynamically loads the function pointer at runtime
-#define MODULE_FUNCTION_INIT(NAME) \
-    NAME = module.getSymbol<decltype(&::NAME)>(#NAME)
+#define MODULE_FUNCTION_INIT(NAME)                     \
+    NAME = module.getSymbol<decltype(&::NAME)>(#NAME);
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index a6ddefbb7c..23c4b9b606 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -17,5 +17,13 @@
 std::string getEnvVar(const std::string &key);
 
 // Dump the kernel sources only if the environment variable is defined
-static const char* saveJitKernelsEnvVarName = "AF_JIT_KERNEL_TRACE";
 void saveKernel(const std::string& funcName, const std::string& jit_ker, const std::string& ext);
+namespace {
+static constexpr const char* saveJitKernelsEnvVarName = "AF_JIT_KERNEL_TRACE";
+
+std::string int_version_to_string(int version) {
+    return std::to_string(version / 1000) + "." +
+           std::to_string((int)((version % 1000) / 10.));
+}
+
+}  // namespace
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e81c3a18c5..2438dcda8f 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -12,9 +12,6 @@ dependency_check(CUDA_FOUND "CUDA not found.")
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
-if(NOT cuDNN_FOUND)
-  message(FATAL_ERROR "Atleast cuDNN version 7.3 is required, please install.")
-endif()
 
 if(NOT OPENGL_FOUND)
   # create a dummy gl.h header to satisfy cuda_gl_interop.h requirement
@@ -383,6 +380,8 @@ cuda_add_library(afcuda
     cublas.hpp
     cudnn.cpp
     cudnn.hpp
+    cudnnModule.cpp
+    cudnnModule.hpp
     cufft.cpp
     cufft.hpp
     cusolverDn.cpp
@@ -525,6 +524,7 @@ target_include_directories (afcuda
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel
     ${CMAKE_CURRENT_SOURCE_DIR}/jit
     ${CMAKE_CURRENT_BINARY_DIR}
+    ${cuDNN_INCLUDE_DIRS}
 )
 
 set_target_properties(afcuda PROPERTIES POSITION_INDEPENDENT_CODE ON)
@@ -553,7 +553,6 @@ target_link_libraries(afcuda
     ${CUDA_CUFFT_LIBRARIES}
     ${CUDA_cusolver_LIBRARY}
     ${CUDA_cusparse_LIBRARY}
-    cuDNN::cuDNN
     ${CMAKE_DL_LIBS}
   )
 
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 3f9f0d4bd9..a8c48b343e 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -39,14 +39,21 @@ template<>
 cudnnDataType_t getCudnnDataType<double>() {
     return CUDNN_DATA_DOUBLE;
 }
+
+#if CUDNN_VERSION >= 6000
 template<>
 cudnnDataType_t getCudnnDataType<int>() {
     return CUDNN_DATA_INT32;
 }
+
+#if CUDNN_VERSION >= 7100
 template<>
 cudnnDataType_t getCudnnDataType<unsigned char>() {
     return CUDNN_DATA_UINT8;
 }
+#endif
+#endif
+
 template<>
 cudnnDataType_t getCudnnDataType<half>() {
     return CUDNN_DATA_HALF;
@@ -83,14 +90,16 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
 
 void cudnnSet(cudnnTensorDescriptor_t desc, cudnnDataType_t cudnn_dtype,
               dim4 dims) {
-    CUDNN_CHECK(cudnnSetTensor4dDescriptor(desc, CUDNN_TENSOR_NCHW, cudnn_dtype,
-                                           dims[3], dims[2], dims[1], dims[0]));
+    CUDNN_CHECK(cuda::cudnnSetTensor4dDescriptor(desc, CUDNN_TENSOR_NCHW,
+                                                 cudnn_dtype, dims[3], dims[2],
+                                                 dims[1], dims[0]));
 }
 
 void cudnnSet(cudnnFilterDescriptor_t desc, cudnnDataType_t cudnn_dtype,
               dim4 dims) {
-    CUDNN_CHECK(cudnnSetFilter4dDescriptor(desc, cudnn_dtype, CUDNN_TENSOR_NCHW,
-                                           dims[3], dims[2], dims[1], dims[0]));
+    CUDNN_CHECK(cuda::cudnnSetFilter4dDescriptor(desc, cudnn_dtype,
+                                                 CUDNN_TENSOR_NCHW, dims[3],
+                                                 dims[2], dims[1], dims[0]));
 }
 
 template<typename Desc, typename T>
@@ -179,7 +188,7 @@ template<typename T>
 Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
                          const dim4 stride, const dim4 padding,
                          const dim4 dilation) {
-    auto cudnn = nnHandle();
+    cudnnHandle_t cudnn = nnHandle();
 
     dim4 sDims = signal.dims();
     dim4 fDims = filter.dims();
@@ -195,14 +204,15 @@ Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
 
     // create convolution descriptor
     auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
-    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
+
+    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
         convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
         dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
 
     // get output dimensions
     const int tensorDims = 4;
     int convolved_output_dim[tensorDims];
-    CUDNN_CHECK(cudnnGetConvolutionNdForwardOutputDim(
+    CUDNN_CHECK(cuda::cudnnGetConvolutionNdForwardOutputDim(
         convolution_descriptor, input_descriptor, filter_descriptor, tensorDims,
         convolved_output_dim));
 
@@ -222,14 +232,14 @@ Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
     const int memory_limit =
         0;  // TODO: set to remaining space in memory manager?
     cudnnConvolutionFwdAlgo_t convolution_algorithm;
-    CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
+    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithm(
         cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
         output_descriptor, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, memory_limit,
         &convolution_algorithm));
 
     // figure out scratch space memory requirements
     size_t workspace_bytes;
-    CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
+    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardWorkspaceSize(
         cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
         output_descriptor, convolution_algorithm, &workspace_bytes));
 
@@ -238,7 +248,7 @@ Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
     // perform convolution
     scale_type<T> alpha = scalar<scale_type<T>>(1.0);
     scale_type<T> beta  = scalar<scale_type<T>>(0.0);
-    CUDNN_CHECK(cudnnConvolutionForward(
+    CUDNN_CHECK(cuda::cudnnConvolutionForward(
         cudnn, &alpha, input_descriptor, signal.device(), filter_descriptor,
         filter.device(), convolution_descriptor, convolution_algorithm,
         (void *)workspace_buffer.get(), workspace_bytes, &beta,
@@ -291,7 +301,7 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
 
     // create convolution descriptor
     auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
-    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
+    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
         convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
         dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
 
@@ -300,14 +310,14 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
 
     // determine algorithm to use
     cudnnConvolutionBwdFilterAlgo_t bwd_filt_convolution_algorithm;
-    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(
+    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithm(
         cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
         dw_descriptor, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0,
         &bwd_filt_convolution_algorithm));
 
     // figure out scratch space memory requirements
     size_t workspace_bytes;
-    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
+    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterWorkspaceSize(
         cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
         dw_descriptor, bwd_filt_convolution_algorithm, &workspace_bytes));
     // prepare output array and scratch space
@@ -318,7 +328,7 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
     // perform convolution
     scale_type<T> alpha = scalar<scale_type<T>>(1.0);
     scale_type<T> beta  = scalar<scale_type<T>>(0.0);
-    CUDNN_CHECK(cudnnConvolutionBackwardFilter(
+    CUDNN_CHECK(cuda::cudnnConvolutionBackwardFilter(
         cudnn, &alpha, x_descriptor, original_signal.device(), dy_descriptor,
         incoming_gradient.device(), convolution_descriptor,
         bwd_filt_convolution_algorithm, (void *)workspace_buffer.get(),
@@ -347,13 +357,15 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
 
     // create output filter gradient descriptor
     auto w_descriptor = make_handle<cudnnFilterDescriptor_t>();
-    CUDNN_CHECK(cudnnSetFilter4dDescriptor(w_descriptor, cudnn_dtype,
-                                           CUDNN_TENSOR_NCHW, fDims[3],
-                                           fDims[2], fDims[1], fDims[0]));
+
+    CUDNN_CHECK(cuda::cudnnSetFilter4dDescriptor(w_descriptor, cudnn_dtype,
+                                                 CUDNN_TENSOR_NCHW, fDims[3],
+                                                 fDims[2], fDims[1], fDims[0]));
 
     // create convolution descriptor
     auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
-    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(
+
+    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
         convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
         dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
 
@@ -366,7 +378,7 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
 
     // figure out scratch space memory requirements
     size_t workspace_bytes;
-    CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
+    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardDataWorkspaceSize(
         cudnn, w_descriptor, dy_descriptor, convolution_descriptor,
         dx_descriptor, bwd_data_convolution_algorithm, &workspace_bytes));
 
@@ -379,7 +391,7 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
     scale_type<T> alpha = scalar<scale_type<T>>(1.0);
     scale_type<T> beta  = scalar<scale_type<T>>(0.0);
 
-    CUDNN_CHECK(cudnnConvolutionBackwardData(
+    CUDNN_CHECK(cuda::cudnnConvolutionBackwardData(
         cudnn, &alpha, w_descriptor, original_filter.get(), dy_descriptor,
         incoming_gradient.get(), convolution_descriptor,
         bwd_data_convolution_algorithm, (void *)workspace_buffer.get(),
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index 06fffbbd4b..d4710b3886 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <cudnn.hpp>
+#include <err_cuda.hpp>
 
 namespace cuda {
 
@@ -27,14 +27,172 @@ const char *errorString(cudnnStatus_t err) {
             return "CUDNN_STATUS_EXECUTION_FAILED";
         case CUDNN_STATUS_NOT_SUPPORTED: return "CUDNN_STATUS_NOT_SUPPORTED";
         case CUDNN_STATUS_LICENSE_ERROR: return "CUDNN_STATUS_LICENSE_ERROR";
+#if CUDNN_VERSION >= 6000
         case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
             return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
+#if CUDNN_VERSION >= 7000
         case CUDNN_STATUS_RUNTIME_IN_PROGRESS:
             return "CUDNN_STATUS_RUNTIME_IN_PROGRESS";
         case CUDNN_STATUS_RUNTIME_FP_OVERFLOW:
             return "CUDNN_STATUS_RUNTIME_FP_OVERFLOW";
+#endif
+#endif
         default: return "UNKNOWN";
     }
 }
 
+cudnnStatus_t cudnnSetConvolution2dDescriptor(
+    cudnnConvolutionDescriptor_t convDesc,
+    int pad_h,     // zero-padding height
+    int pad_w,     // zero-padding width
+    int u,         // vertical filter stride
+    int v,         // horizontal filter stride
+    int upscalex,  // upscale the input in x-direction
+    int upscaley,  // upscale the input in y-direction
+    cudnnConvolutionMode_t mode, cudnnDataType_t computeType) {
+    return
+#if CUDNN_VERSION >= 6000
+        getCudnnPlugin().cudnnSetConvolution2dDescriptor(
+            convDesc, pad_h, pad_w, u, v, upscalex, upscaley, mode,
+            computeType);
+#elif CUDNN_VERSION >= 4000
+        getCudnnPlugin().cudnnSetConvolution2dDescriptor(
+            convDesc, pad_h, pad_w, u, v, upscalex, upscaley, mode);
+#else
+        static_assert(1 != 1, "cuDNN version not supported");
+#endif
 }
+
+cudnnStatus_t cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                                         cudnnDataType_t dataType,
+                                         cudnnTensorFormat_t format, int k,
+                                         int c, int h, int w) {
+#if CUDNN_VERSION >= 6000
+    int version = getCudnnPlugin().cudnnGetVersion();
+    if (version >= 6000) {
+        return getCudnnPlugin().cudnnSetFilter4dDescriptor(filterDesc, dataType,
+                                                           format, k, c, h, w);
+    } else if (version == 4000) {
+        return getCudnnPlugin().cudnnSetFilter4dDescriptor_v4(
+            filterDesc, dataType, format, k, c, h, w);
+    }
+    CUDA_NOT_SUPPORTED(
+        "cudnnSetFilter4dDescriptor not supported for the current version of cuDNN");
+#elif CUDNN_VERSION == 4000
+    return getCudnnPlugin().cudnnSetFilter4dDescriptor_v4(filterDesc, dataType,
+                                                          format, k, c, h, w);
+#else
+    static_assert(1 != 1, "cuDNN version not supported");
+#endif
+}
+
+cudnnStatus_t cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                                         cudnnTensorFormat_t format,
+                                         cudnnDataType_t dataType, int n, int c,
+                                         int h, int w) {
+    return getCudnnPlugin().cudnnSetTensor4dDescriptor(tensorDesc, format,
+                                                       dataType, n, c, h, w);
+}
+
+cudnnStatus_t cudnnGetConvolutionBackwardDataWorkspaceSize(
+    cudnnHandle_t handle, const cudnnFilterDescriptor_t wDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t dxDesc, cudnnConvolutionBwdDataAlgo_t algo,
+    size_t *sizeInBytes) {
+    return getCudnnPlugin().cudnnGetConvolutionBackwardDataWorkspaceSize(
+        handle, wDesc, dyDesc, convDesc, dxDesc, algo, sizeInBytes);
+}
+
+cudnnStatus_t cudnnConvolutionBackwardData(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnFilterDescriptor_t wDesc, const void *w,
+    const cudnnTensorDescriptor_t dyDesc, const void *dy,
+    const cudnnConvolutionDescriptor_t convDesc,
+    cudnnConvolutionBwdDataAlgo_t algo, void *workSpace,
+    size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnTensorDescriptor_t dxDesc, void *dx) {
+    return getCudnnPlugin().cudnnConvolutionBackwardData(
+        handle, alpha, wDesc, w, dyDesc, dy, convDesc, algo, workSpace,
+        workSpaceSizeInBytes, beta, dxDesc, dx);
+}
+
+cudnnStatus_t cudnnGetConvolutionNdForwardOutputDim(
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t inputTensorDesc,
+    const cudnnFilterDescriptor_t filterDesc, int nbDims,
+    int tensorOuputDimA[]) {
+    return getCudnnPlugin().cudnnGetConvolutionNdForwardOutputDim(
+        convDesc, inputTensorDesc, filterDesc, nbDims, tensorOuputDimA);
+}
+
+cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc,
+    cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionFwdAlgo_t *algo) {
+    return getCudnnPlugin().cudnnGetConvolutionForwardAlgorithm(
+        handle, xDesc, wDesc, convDesc, yDesc, preference, memoryLimitInBytes,
+        algo);
+}
+
+cudnnStatus_t cudnnGetConvolutionForwardWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc, cudnnConvolutionFwdAlgo_t algo,
+    size_t *sizeInBytes) {
+    return getCudnnPlugin().cudnnGetConvolutionForwardWorkspaceSize(
+        handle, xDesc, wDesc, convDesc, yDesc, algo, sizeInBytes);
+}
+
+cudnnStatus_t cudnnConvolutionForward(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnTensorDescriptor_t xDesc, const void *x,
+    const cudnnFilterDescriptor_t wDesc, const void *w,
+    const cudnnConvolutionDescriptor_t convDesc, cudnnConvolutionFwdAlgo_t algo,
+    void *workSpace, size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnTensorDescriptor_t yDesc, void *y) {
+    return getCudnnPlugin().cudnnConvolutionForward(
+        handle, alpha, xDesc, x, wDesc, w, convDesc, algo, workSpace,
+        workSpaceSizeInBytes, beta, yDesc, y);
+}
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t dwDesc,
+    cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionBwdFilterAlgo_t *algo) {
+    return getCudnnPlugin().cudnnGetConvolutionBackwardFilterAlgorithm(
+        handle, xDesc, dyDesc, convDesc, dwDesc, preference, memoryLimitInBytes,
+        algo);
+}
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t gradDesc,
+    cudnnConvolutionBwdFilterAlgo_t algo, size_t *sizeInBytes) {
+    return getCudnnPlugin().cudnnGetConvolutionBackwardFilterWorkspaceSize(
+        handle, xDesc, dyDesc, convDesc, gradDesc, algo, sizeInBytes);
+}
+
+cudnnStatus_t cudnnConvolutionBackwardFilter(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnTensorDescriptor_t xDesc, const void *x,
+    const cudnnTensorDescriptor_t dyDesc, const void *dy,
+    const cudnnConvolutionDescriptor_t convDesc,
+    cudnnConvolutionBwdFilterAlgo_t algo, void *workSpace,
+    size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnFilterDescriptor_t dwDesc, void *dw) {
+    return getCudnnPlugin().cudnnConvolutionBackwardFilter(
+        handle, alpha, xDesc, x, dyDesc, dy, convDesc, algo, workSpace,
+        workSpaceSizeInBytes, beta, dwDesc, dw);
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
index a7bc85499b..8a6b13b8fe 100644
--- a/src/backend/cuda/cudnn.hpp
+++ b/src/backend/cuda/cudnn.hpp
@@ -10,22 +10,136 @@
 #pragma once
 
 #include <common/defines.hpp>
-#include <cudnn.h>
+#include <cudnnModule.hpp>
 
 namespace cuda {
 
 const char *errorString(cudnnStatus_t err);
 
-#define CUDNN_CHECK(fn)                                                    \
-    do {                                                                   \
-        cudnnStatus_t _error = (fn);                                       \
-        if (_error != CUDNN_STATUS_SUCCESS) {                              \
-            char _err_msg[1024];                                           \
-            snprintf(_err_msg, sizeof(_err_msg), "CUDNN Error (%d): %s\n", \
-                     (int)(_error), errorString(_error));                  \
-                                                                           \
-            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                           \
-        }                                                                  \
+#define CUDNN_CHECK(fn)                                                     \
+    do {                                                                    \
+        cudnnStatus_t _error = (fn);                                        \
+        if (_error == CUDNN_STATUS_SUCCESS) {                               \
+            break;                                                          \
+        } else if (_error == CUDNN_STATUS_ALLOC_FAILED) {                   \
+            AF_ERROR(                                                       \
+                "CUDNN Error(CUDNN_STATUS_ALLOC_FAILED): Error allocating " \
+                "for function all ",                                        \
+                AF_ERR_NO_MEM);                                             \
+        } else if (_error == CUDNN_STATUS_NOT_SUPPORTED) {                  \
+            CUDA_NOT_SUPPORTED(                                             \
+                "CUDNN Error(CUDNN_STATUS_NOT_SUPPORTED): This version of " \
+                "CUDNN does not support the data type or the size of this " \
+                "operation");                                               \
+        } else {                                                            \
+            char _err_msg[1024];                                            \
+            snprintf(_err_msg, sizeof(_err_msg), "CUDNN Error(%s): \n",     \
+                     errorString(_error));                                  \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
+        }                                                                   \
     } while (0)
 
-}
+
+
+// cuDNN Wrappers
+//
+// cuDNN deprecates and releases function names often between releases. in order
+// to prevent locking arrayfire versions to specific cuDNN versions, we wrap all
+// cuDNN calls so that the main codebase is not full of ifdefs. The Following
+// functions are wrappers around cuDNN functions that abstract out the version
+// differences between older versions of cuDNN.
+//
+
+cudnnStatus_t cudnnSetConvolution2dDescriptor(
+    cudnnConvolutionDescriptor_t convDesc,
+    int pad_h,     // zero-padding height
+    int pad_w,     // zero-padding width
+    int u,         // vertical filter stride
+    int v,         // horizontal filter stride
+    int upscalex,  // upscale the input in x-direction
+    int upscaley,  // upscale the input in y-direction
+    cudnnConvolutionMode_t mode, cudnnDataType_t computeType);
+
+cudnnStatus_t cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                                         cudnnDataType_t dataType,
+                                         cudnnTensorFormat_t format, int k,
+                                         int c, int h, int w);
+
+cudnnStatus_t cudnnSetTensor4dDescriptor(
+    cudnnTensorDescriptor_t tensorDesc, cudnnTensorFormat_t format,
+    cudnnDataType_t dataType, /* image data type */
+    int n,                    /* number of inputs (batch size) */
+    int c,                    /* number of input feature maps */
+    int h,                    /* height of input section */
+    int w);                   /* width of input section */
+
+cudnnStatus_t cudnnGetConvolutionBackwardDataWorkspaceSize(
+    cudnnHandle_t handle, const cudnnFilterDescriptor_t wDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t dxDesc, cudnnConvolutionBwdDataAlgo_t algo,
+    size_t *sizeInBytes);
+
+cudnnStatus_t cudnnConvolutionBackwardData(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnFilterDescriptor_t wDesc, const void *w,
+    const cudnnTensorDescriptor_t dyDesc, const void *dy,
+    const cudnnConvolutionDescriptor_t convDesc,
+    cudnnConvolutionBwdDataAlgo_t algo, void *workSpace,
+    size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnTensorDescriptor_t dxDesc, void *dx);
+
+cudnnStatus_t cudnnGetConvolutionNdForwardOutputDim(
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t inputTensorDesc,
+    const cudnnFilterDescriptor_t filterDesc, int nbDims,
+    int tensorOuputDimA[]);
+
+cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc,
+    cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionFwdAlgo_t *algo);
+
+cudnnStatus_t cudnnGetConvolutionForwardWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc, cudnnConvolutionFwdAlgo_t algo,
+    size_t *sizeInBytes);
+
+cudnnStatus_t cudnnConvolutionForward(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnTensorDescriptor_t xDesc, const void *x,
+    const cudnnFilterDescriptor_t wDesc, const void *w,
+    const cudnnConvolutionDescriptor_t convDesc, cudnnConvolutionFwdAlgo_t algo,
+    void *workSpace, size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnTensorDescriptor_t yDesc, void *y);
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t dwDesc,
+    cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionBwdFilterAlgo_t *algo);
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t gradDesc,
+    cudnnConvolutionBwdFilterAlgo_t algo, size_t *sizeInBytes);
+
+cudnnStatus_t cudnnConvolutionBackwardFilter(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnTensorDescriptor_t xDesc, const void *x,
+    const cudnnTensorDescriptor_t dyDesc, const void *dy,
+    const cudnnConvolutionDescriptor_t convDesc,
+    cudnnConvolutionBwdFilterAlgo_t algo, void *workSpace,
+    size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnFilterDescriptor_t dwDesc, void *dw);
+
+}  // namespace cuda
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
new file mode 100644
index 0000000000..6607206ef9
--- /dev/null
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -0,0 +1,137 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/Logger.hpp>
+#include <common/err_common.hpp>
+#include <common/util.hpp>
+#include <cudnnModule.hpp>
+#include <device_manager.hpp>
+
+#include <string>
+#include <tuple>
+
+using std::string;
+
+namespace cuda {
+
+spdlog::logger* cudnnModule::getLogger() { return module.getLogger(); }
+
+auto cudnnVersionComponents(size_t version) {
+    int major = version / 1000;
+    int minor = (version - (major * 1000)) / 100;
+    int patch = (version - (major * 1000) - (minor * 100));
+    return std::tuple<int, int, int>(major, minor, patch);
+}
+
+cudnnModule::cudnnModule()
+    : module({"cudnn"}, {"", "64_7", "64_8", "64_6", "64_5", "64_4"}, {""}) {
+    if (!module.isLoaded()) {
+        string error_message =
+            "Error loading cuDNN: " + module.getErrorMessage() +
+            "\ncuDNN failed to load. Try installing cuDNN or check if cuDNN is "
+            "in the search path. On Linux, you can set the LD_DEBUG=libs "
+            "environment variable to debug loading issues.";
+        AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+    }
+
+    MODULE_FUNCTION_INIT(cudnnGetVersion);
+
+    int rtmajor, rtminor;
+    int cudnn_version             = this->cudnnGetVersion();
+    int cudnn_rtversion           = 0;
+    std::tie(major, minor, patch) = cudnnVersionComponents(cudnn_version);
+
+    if (cudnn_version >= 6000) {
+        MODULE_FUNCTION_INIT(cudnnGetCudartVersion);
+        cudnn_rtversion = this->cudnnGetCudartVersion();
+    } else {
+        AF_TRACE(
+            "Warning: This version of cuDNN({}.{}) does not support "
+            "cudnnGetCudartVersion. No runtime checks performed.",
+            major, minor);
+    }
+
+    std::tie(rtmajor, rtminor, std::ignore) =
+        cudnnVersionComponents(cudnn_rtversion);
+
+    AF_TRACE("cuDNN Version: {}.{}.{} cuDNN CUDA Runtime: {}.{}", major, minor,
+             patch, rtmajor, rtminor);
+
+    // Check to see if the version of cuDNN ArrayFire was compiled against
+    // is compatible with the version loaded at runtime
+    if (CUDNN_VERSION <= 6000 && cudnn_version > CUDNN_VERSION) {
+        string error_msg = fmt::format(
+            "ArrayFire was compiled with an older version of cuDNN({}.{}) that "
+            "does not support the version that was loaded at runtime({}.{}).",
+            CUDNN_MAJOR, CUDNN_MINOR, major, minor);
+        AF_ERROR(error_msg, AF_ERR_NOT_SUPPORTED);
+    }
+
+    int afcuda_runtime = 0;
+    cudaRuntimeGetVersion(&afcuda_runtime);
+    if (afcuda_runtime != cudnn_version) {
+        getLogger()->warn(
+            "WARNING: ArrayFire CUDA Runtime({}) and cuDNN CUDA "
+            "Runtime({}.{}) do not match. For maximum compatibility, make sure "
+            "the two versions match.(Ignoring check)",
+            // NOTE: the int version formats from CUDA and cuDNN are different
+            // so we are using int_version_to_string for the ArrayFire CUDA
+            // runtime
+            int_version_to_string(afcuda_runtime), rtmajor, rtminor);
+    }
+
+    MODULE_FUNCTION_INIT(cudnnConvolutionBackwardData);
+    MODULE_FUNCTION_INIT(cudnnConvolutionBackwardFilter);
+    MODULE_FUNCTION_INIT(cudnnConvolutionForward);
+    MODULE_FUNCTION_INIT(cudnnCreate);
+    MODULE_FUNCTION_INIT(cudnnCreateConvolutionDescriptor);
+    MODULE_FUNCTION_INIT(cudnnCreateFilterDescriptor);
+    MODULE_FUNCTION_INIT(cudnnCreateTensorDescriptor);
+    MODULE_FUNCTION_INIT(cudnnDestroy);
+    MODULE_FUNCTION_INIT(cudnnDestroyConvolutionDescriptor);
+    MODULE_FUNCTION_INIT(cudnnDestroyFilterDescriptor);
+    MODULE_FUNCTION_INIT(cudnnDestroyTensorDescriptor);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardDataWorkspaceSize);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterAlgorithm);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterWorkspaceSize);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardAlgorithm);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardWorkspaceSize);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionNdForwardOutputDim);
+    MODULE_FUNCTION_INIT(cudnnSetConvolution2dDescriptor);
+    MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor);
+    if (major == 4) { MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor_v4); }
+    MODULE_FUNCTION_INIT(cudnnSetStream);
+    MODULE_FUNCTION_INIT(cudnnSetTensor4dDescriptor);
+
+    // Check to see if the cuDNN runtime is compatible with the current device
+    cudaDeviceProp prop = getDeviceProp(getActiveDeviceId());
+    if (!checkDeviceWithRuntime(cudnn_rtversion, {prop.major, prop.minor})) {
+        string error_message = fmt::format(
+            "Error: cuDNN CUDA Runtime({}.{}) does not support the "
+            "current device's compute capability(sm_{}{}).",
+            rtmajor, rtminor, prop.major, prop.minor);
+        AF_ERROR(error_message, AF_ERR_RUNTIME);
+    }
+
+    if (!module.symbolsLoaded()) {
+        string error_message =
+            "Error loading cuDNN symbols. ArrayFire was unable to load some "
+            "symbols from the cuDNN library. Please create an issue on the "
+            "ArrayFire repository with information about the installed cuDNN "
+            "and ArrayFire on your system.";
+        AF_ERROR(error_message, AF_ERR_LOAD_LIB);
+    }
+}
+
+cudnnModule& getCudnnPlugin() {
+    static cudnnModule* plugin = new cudnnModule();
+    return *plugin;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
new file mode 100644
index 0000000000..b83ddf19be
--- /dev/null
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -0,0 +1,77 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/DependencyModule.hpp>
+
+#include <cudnn.h>
+
+#include <memory>
+#include <tuple>
+
+#if CUDNN_VERSION > 4000
+// This function is not available on versions greater than v4
+cudnnStatus_t
+cudnnSetFilter4dDescriptor_v4(cudnnFilterDescriptor_t filterDesc,
+                              cudnnDataType_t dataType,  // image data type
+                              cudnnTensorFormat_t format,
+                              int k,   // number of output feature maps
+                              int c,   // number of input feature maps
+                              int h,   // height of each input filter
+                              int w);  // width of  each input filter
+#else
+// This function is only available on newer versions of cudnn
+size_t cudnnGetCudartVersion(void);
+#endif
+
+namespace cuda {
+
+class cudnnModule {
+    common::DependencyModule module;
+    int major, minor, patch;
+
+   public:
+    cudnnModule();
+    MODULE_MEMBER(cudnnConvolutionBackwardData);
+    MODULE_MEMBER(cudnnConvolutionBackwardFilter);
+    MODULE_MEMBER(cudnnConvolutionForward);
+    MODULE_MEMBER(cudnnCreate);
+    MODULE_MEMBER(cudnnCreateConvolutionDescriptor);
+    MODULE_MEMBER(cudnnCreateFilterDescriptor);
+    MODULE_MEMBER(cudnnCreateTensorDescriptor);
+    MODULE_MEMBER(cudnnDestroy);
+    MODULE_MEMBER(cudnnDestroyConvolutionDescriptor);
+    MODULE_MEMBER(cudnnDestroyFilterDescriptor);
+    MODULE_MEMBER(cudnnDestroyTensorDescriptor);
+    MODULE_MEMBER(cudnnGetConvolutionBackwardDataWorkspaceSize);
+    MODULE_MEMBER(cudnnGetConvolutionBackwardFilterAlgorithm);
+    MODULE_MEMBER(cudnnGetConvolutionBackwardFilterWorkspaceSize);
+    MODULE_MEMBER(cudnnGetConvolutionForwardAlgorithm);
+    MODULE_MEMBER(cudnnGetConvolutionForwardWorkspaceSize);
+    MODULE_MEMBER(cudnnGetConvolutionNdForwardOutputDim);
+    MODULE_MEMBER(cudnnSetConvolution2dDescriptor);
+    MODULE_MEMBER(cudnnSetFilter4dDescriptor);
+    MODULE_MEMBER(cudnnSetFilter4dDescriptor_v4);
+    MODULE_MEMBER(cudnnGetVersion);
+    MODULE_MEMBER(cudnnGetCudartVersion);
+    MODULE_MEMBER(cudnnSetStream);
+    MODULE_MEMBER(cudnnSetTensor4dDescriptor);
+
+    spdlog::logger* getLogger();
+
+    /// Returns the version of the cuDNN loaded at runtime
+    std::tuple<int, int, int> getVersion() {
+        return { major, minor, patch };
+    }
+};
+
+cudnnModule& getCudnnPlugin();
+
+}  // namespace cuda
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 66cb210d75..c144d60862 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -16,6 +16,7 @@
 #include <common/Logger.hpp>
 #include <common/MemoryManagerBase.hpp>
 #include <common/defines.hpp>
+#include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
 #include <cublas_v2.h>  // needed for af/cuda.h
@@ -28,7 +29,6 @@
 #include <version.hpp>
 #include <af/cuda.h>
 #include <af/version.h>
-#include <common/graphics_common.hpp>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 // __gl_h_ should be defined by glad.h inclusion
 #include <cuda_gl_interop.h>
@@ -54,22 +54,57 @@ using std::stringstream;
 
 namespace cuda {
 
+struct cuNVRTCcompute {
+    /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
+    int cudaVersion;
+    /// Maximum major compute flag supported by cudaVersion
+    int major;
+    /// Maximum minor compute flag supported by cudaVersion
+    int minor;
+};
+
+// clang-format off
+static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {10020, 7, 5},
+    {10010, 7, 5},
+    {10000, 7, 2},
+    {9020, 7, 2},
+    {9010, 7, 2},
+    {9000, 7, 2},
+    {8000, 5, 3},
+    {7050, 5, 3},
+    {7000, 5, 3}};
+// clang-format on
+
+bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
+    auto rt = find_if(
+        begin(Toolkit2MaxCompute), end(Toolkit2MaxCompute),
+        [runtime](cuNVRTCcompute c) { return c.cudaVersion == runtime; });
+    if (rt == end(Toolkit2MaxCompute)) {
+        spdlog::get("platform")
+            ->warn(
+                "CUDA runtime version({}) not recognized. Please "
+                "create an issue or a pull request on the ArrayFire repository "
+                "to update the Toolkit2MaxCompute array with this version of "
+                "the CUDA Runtime. Continuing assuming everything is okay.",
+                int_version_to_string(runtime));
+        return true;
+    }
+
+    if (rt->major >= compute.first) {
+        if (rt->major == compute.first)
+            return rt->minor >= compute.second;
+        else
+            return true;
+    } else {
+        return false;
+    }
+}
+
 /// Check for compatible compute version based on runtime cuda toolkit version
 void checkAndSetDevMaxCompute(pair<int, int> &prop) {
-    struct cuNVRTCcompute {
-        /// The CUDA Toolkit version returned by cudaRuntimeGetVersion
-        int cudaVersion;
-        /// Maximum major compute flag supported by cudaVersion
-        int major;
-        /// Maximum minor compute flag supported by cudaVersion
-        int minor;
-    };
-    static const cuNVRTCcompute Toolkit2MaxCompute[] = {
-        {10020, 7, 5}, {10010, 7, 5}, {10000, 7, 2}, {9020, 7, 2}, {9010, 7, 2},
-        {9000, 7, 2},  {8000, 5, 3},  {7050, 5, 3}, {7000, 5, 3}};
-
     auto originalCompute = prop;
-    int rtCudaVer = 0;
+    int rtCudaVer        = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&rtCudaVer));
     auto tkitMaxCompute = find_if(
         begin(Toolkit2MaxCompute), end(Toolkit2MaxCompute),
@@ -383,7 +418,7 @@ void DeviceManager::checkCudaVsDriverVersion() {
     CUDA_CHECK(cudaDriverGetVersion(&driver));
     CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
 
-    AF_TRACE("CUDA supported by the GPU Driver {} ArrayFire CUDA Runtime {}",
+    AF_TRACE("CUDA Driver supports up to CUDA {} ArrayFire CUDA Runtime {}",
              int_version_to_string(driver), int_version_to_string(runtime));
 
     debugRuntimeCheck(getLogger(), runtime, driver);
@@ -503,7 +538,8 @@ DeviceManager::DeviceManager()
             setActiveDevice(def_device, cuDevices[def_device].nativeId);
         }
     }
-    AF_TRACE("Default device: {}", getActiveDeviceId());
+    AF_TRACE("Default device: {}({})", getActiveDeviceId(),
+             cuDevices[getActiveDeviceId()].prop.name);
 }
 
 spdlog::logger *DeviceManager::getLogger() { return logger.get(); }
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index 883d71f6f5..98ebe38696 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -33,6 +33,8 @@ struct cudaDevice_t {
 
 int& tlocalActiveDeviceId();
 
+bool checkDeviceWithRuntime(int runtime, std::pair<int, int> compute);
+
 class DeviceManager {
    public:
     static const size_t MAX_DEVICES = 16;
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
index 8dc6823a6b..18fc5d5b97 100644
--- a/src/backend/cuda/handle.cpp
+++ b/src/backend/cuda/handle.cpp
@@ -10,9 +10,10 @@
 #include <common/unique_handle.hpp>
 #include <cublas.hpp>
 #include <cudnn.hpp>
+#include <cudnnModule.hpp>
+#include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
-#include <cufft.hpp>
 
 // clang-format off
 CREATE_HANDLE(cusparseMatDescr_t, cusparseCreateMatDescr, cusparseDestroyMatDescr);
@@ -20,10 +21,10 @@ CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
 CREATE_HANDLE(cublasHandle_t, cublasCreate, cublasDestroy);
 CREATE_HANDLE(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
 CREATE_HANDLE(cufftHandle, cufftCreate, cufftDestroy);
-CREATE_HANDLE(cudnnHandle_t, cudnnCreate, cudnnDestroy);
-CREATE_HANDLE(cudnnTensorDescriptor_t, cudnnCreateTensorDescriptor, cudnnDestroyTensorDescriptor);
-CREATE_HANDLE(cudnnFilterDescriptor_t, cudnnCreateFilterDescriptor, cudnnDestroyFilterDescriptor);
-CREATE_HANDLE(cudnnConvolutionDescriptor_t, cudnnCreateConvolutionDescriptor, cudnnDestroyConvolutionDescriptor);
+CREATE_HANDLE(cudnnHandle_t, cuda::getCudnnPlugin().cudnnCreate, cuda::getCudnnPlugin().cudnnDestroy);
+CREATE_HANDLE(cudnnTensorDescriptor_t, cuda::getCudnnPlugin().cudnnCreateTensorDescriptor, cuda::getCudnnPlugin().cudnnDestroyTensorDescriptor);
+CREATE_HANDLE(cudnnFilterDescriptor_t, cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
+CREATE_HANDLE(cudnnConvolutionDescriptor_t, cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
 
 
 // clang-format on
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 6053f3be73..b2b48febf4 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -21,6 +21,7 @@
 #include <common/util.hpp>
 #include <cublas.hpp>
 #include <cudnn.hpp>
+#include <cudnnModule.hpp>
 #include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
@@ -104,13 +105,26 @@ unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
         cudnnHandles[DeviceManager::MAX_DEVICES];
     thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
 
-    std::call_once(initFlags[deviceId],
-                   [&] { cudnnHandles[deviceId].create(); });
+    auto *handle        = &cudnnHandles[deviceId];
+    cudnnStatus_t error = CUDNN_STATUS_SUCCESS;
+    std::call_once(initFlags[deviceId], [deviceId, handle, &error] {
+        auto getLogger = [&] { return spdlog::get("platform"); };
+        AF_TRACE("Initializing cuDNN");
+        error = static_cast<cudnnStatus_t>(handle->create());
 
-    CUDNN_CHECK(
-        cudnnSetStream(cudnnHandles[deviceId], cuda::getStream(deviceId)));
+        // Not throwing an AF_ERROR here because we are in a lambda that could
+        // be executing on another thread;
+        if (!(*handle)) getLogger()->error("Error initalizing cuDNN");
+    });
+    if (error) {
+        string error_msg = fmt::format("Error initializing cuDNN({}): {}.",
+                                       error, errorString(error));
+        AF_ERROR(error_msg, AF_ERR_RUNTIME);
+    }
+    CUDNN_CHECK(getCudnnPlugin().cudnnSetStream(cudnnHandles[deviceId],
+                                                cuda::getStream(deviceId)));
 
-    return &cudnnHandles[deviceId];
+    return handle;
 }
 
 unique_ptr<PlanCache> &cufftManager(const int deviceId) {
@@ -273,11 +287,6 @@ string getDriverVersion() {
     }
 }
 
-string int_version_to_string(int version) {
-    return to_string(version / 1000) + "." +
-           to_string((int)((version % 1000) / 10.));
-}
-
 string getCUDARuntimeVersion() {
     int runtime = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
@@ -435,7 +444,21 @@ PlanCache &fftManager() {
 
 BlasHandle blasHandle() { return *cublasManager(cuda::getActiveDeviceId()); }
 
-cudnnHandle_t nnHandle() { return *nnManager(cuda::getActiveDeviceId()); }
+cudnnHandle_t nnHandle() {
+    // Keep the getCudnnPlugin call here because module loading can throw an
+    // exception the first time its called. We want to avoid that because the
+    // unique handle object is marked noexcept and could terminate. if the
+    // module is not loaded correctly
+    static cudnnModule keep_me_to_avoid_exceptions_exceptions =
+        getCudnnPlugin();
+    static unique_handle<cudnnHandle_t> *handle =
+        nnManager(cuda::getActiveDeviceId());
+    if (*handle)
+        return *handle;
+    else {
+        AF_ERROR("Error Initializing cuDNN\n", AF_ERR_RUNTIME);
+    }
+}
 
 SolveHandle solverDnHandle() {
     return *cusolverManager(cuda::getActiveDeviceId());
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ec4e1219aa..68db32ca8b 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -59,7 +59,6 @@ std::string getDeviceInfo(int device);
 
 std::string getPlatformInfo();
 
-std::string int_version_to_string(int version);
 std::string getDriverVersion();
 
 std::string getCUDARuntimeVersion();

From 726faa09d3ff64810df1fb3f007d0530ac30022c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 30 Jan 2020 16:12:43 +0530
Subject: [PATCH 1805/2677] Fix cuda dependencies install collection macro for
 CUDA 10.*

---
 src/backend/cuda/CMakeLists.txt | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 2438dcda8f..9405c2a0c1 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -606,9 +606,17 @@ endif ()
 
 macro(afcu_collect_libs libname)
   if (WIN32)
-    install(FILES       "${dlib_path_prefix}/${PX}${libname}64_${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}${SX}"
-            DESTINATION ${AF_INSTALL_BIN_DIR}
-            COMPONENT   cuda_dependencies)
+    find_file(CUDA_${libname}_LIBRARY_DLL
+      NAMES
+        "${PX}${libname}64_${CUDA_VERSION_MAJOR}${SX}"
+        "${PX}${libname}64_${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}${SX}"
+        "${PX}${libname}64_${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}_0${SX}"
+      PATHS ${dlib_path_prefix}
+    )
+    mark_as_advanced(CUDA_${libname}_LIBRARY_DLL)
+    install(FILES "${CUDA_${libname}_LIBRARY_DLL}"
+      DESTINATION ${AF_INSTALL_BIN_DIR}
+      COMPONENT cuda_dependencies)
   elseif (APPLE)
     get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
     install(FILES       "${outpath}"
@@ -629,14 +637,7 @@ if(AF_INSTALL_STANDALONE)
   afcu_collect_libs(cublas)
   afcu_collect_libs(cusolver)
   afcu_collect_libs(cusparse)
-
-  if(WIN32 AND ${CUDA_VERSION_MAJOR} EQUAL 10 AND ${CUDA_VERSION_MINOR} EQUAL 0)
-    install(FILES       "${dlib_path_prefix}/${PX}nvrtc64_100_0${SX}"
-            DESTINATION ${AF_INSTALL_BIN_DIR}
-            COMPONENT   cuda_dependencies)
-  else()
-    afcu_collect_libs(nvrtc)
-  endif()
+  afcu_collect_libs(nvrtc)
 
   if(APPLE)
     afcu_collect_libs(cudart)

From 106b2585ad389d3239820dfefbfd5da112f44130 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Jan 2020 18:59:52 -0500
Subject: [PATCH 1806/2677] Print stacktraces with error messages (#2632)

* Prints the stacktrace with error messages.
* Relies on boost::stacktrace
* Adds the af_set_enable_stacktrace call to enable and disable
  stacktraces at runtime
* Adds the AF_WITH_STACKTRACE CMake variable to enable and disable
  stacktraces at compile time
---
 CMakeLists.txt                                | 26 ++++++++-
 CMakeModules/boost_package.cmake              | 20 ++++++-
 include/af/util.h                             |  9 +++
 src/api/c/error.cpp                           |  6 ++
 src/backend/common/CMakeLists.txt             |  1 +
 src/backend/common/err_common.cpp             | 49 ++++++++++++----
 src/backend/common/err_common.hpp             | 57 ++++++++++++-------
 src/backend/cpu/err_cpu.hpp                   |  2 +-
 .../cpu/kernel/sort_by_key/CMakeLists.txt     |  3 +-
 src/backend/cuda/err_cuda.hpp                 |  2 +-
 .../cuda/kernel/scan_by_key/CMakeLists.txt    |  5 +-
 .../kernel/thrust_sort_by_key/CMakeLists.txt  |  2 +
 src/backend/opencl/CMakeLists.txt             |  1 -
 src/backend/opencl/err_opencl.hpp             |  2 +-
 14 files changed, 144 insertions(+), 41 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7bdb70ace7..f0480d1843 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,7 +24,9 @@ include(SplitDebugInfo)
 set_policies(
   TYPE NEW
   POLICIES CMP0073
-           CMP0074)
+           CMP0074
+           CMP0077
+           CMP0079)
 arrayfire_set_cmake_default_variables()
 
 #Set Intel OpenMP as default MKL thread layer
@@ -55,6 +57,15 @@ option(AF_BUILD_FORGE
 
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
+option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
+
+if(WIN32)
+  set(AF_STACKTRACE_TYPE "Windbg" CACHE STRING "The type of backtrace features. Windbg(simple), None")
+  set_property(CACHE AF_STACKTRACE_TYPE PROPERTY STRINGS "Windbg" "None")
+else()
+  set(AF_STACKTRACE_TYPE "Basic" CACHE STRING "The type of backtrace features. Basic(simple), libbacktrace(fancy), addr2line(fancy), None")
+  set_property(CACHE AF_STACKTRACE_TYPE PROPERTY STRINGS "Basic" "libbacktrace" "addr2line" "None")
+endif()
 
 option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
 
@@ -89,7 +100,10 @@ mark_as_advanced(
   CUDA_USE_STATIC_CUDA_RUNTIME
   CUDA_rt_LIBRARY
   SPDLOG_BUILD_EXAMPLES
-  SPDLOG_BUILD_TESTING)
+  SPDLOG_BUILD_TESTING
+  ADDR2LINE_PROGRAM
+  Backtrace_LIBRARY
+  )
 
 #Configure forge submodule
 #forge is included in ALL target if AF_BUILD_FORGE is ON
@@ -139,7 +153,7 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
-set(SPDLOG_BUILD_TESTING OFF)
+set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
 add_subdirectory(extern/spdlog EXCLUDE_FROM_ALL)
 add_subdirectory(extern/glad)
 add_subdirectory(src/backend/common)
@@ -180,6 +194,12 @@ if(UNIX AND NOT APPLE AND CMAKE_CXX_COMPILER_ID MATCHES "GNU")
                         LINK_FLAGS "-Wl,--no-as-needed")
 endif()
 
+
+find_library(Backtrace_LIBRARY backtrace
+  DOC "libbacktrace.so file for more informative stacktraces. https://github.com/ianlancetaylor/libbacktrace")
+find_program(ADDR2LINE_PROGRAM addr2line
+  DOC "The path to the addr2line program for informative stacktraces")
+
 foreach(backend ${built_backends})
   target_compile_definitions(${backend} PRIVATE AFDLL)
   if(AF_WITH_LOGGING)
diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index bbd0fef57d..f76c6a059a 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -42,8 +42,26 @@ if("${Boost_VERSION}" VERSION_LESS 107000)
 endif()
 
 if(TARGET Boost::boost)
+  set(BOOST_DEFINITIONS "BOOST_CHRONO_HEADER_ONLY;BOOST_COMPUTE_THREAD_SAFE;BOOST_COMPUTE_HAVE_THREAD_LOCAL")
+
+  # NOTE: Basic and Windows options do not requre flags or libraries for
+  #       backtraces
+  if(AF_STACKTRACE_TYPE STREQUAL "libbacktrace")
+    list(APPEND BOOST_DEFINITIONS "BOOST_STACKTRACE_USE_BACKTRACE")
+    set_target_properties(Boost::boost PROPERTIES
+      INTERFACE_LINK_LIBRARIES ${Backtrace_LIBRARY})
+  elseif(AF_STACKTRACE_TYPE STREQUAL "addr2line")
+    list(APPEND BOOST_DEFINITIONS "BOOST_STACKTRACE_USE_ADDR2LINE")
+  elseif(AF_STACKTRACE_TYPE STREQUAL "None")
+      list(APPEND BOOST_DEFINITIONS "BOOST_STACKTRACE_USE_NOOP")
+  endif()
+
+  if(NOT AF_STACKTRACE_TYPE STREQUAL "None" AND APPLE)
+      list(APPEND BOOST_DEFINITIONS "BOOST_STACKTRACE_GNU_SOURCE_NOT_REQUIRED")
+  endif()
+
   # NOTE: BOOST_CHRONO_HEADER_ONLY is required for Windows because otherwise it
   # will try to link with libboost-chrono.
   set_target_properties(Boost::boost PROPERTIES INTERFACE_COMPILE_DEFINITIONS
-    "BOOST_CHRONO_HEADER_ONLY;BOOST_COMPUTE_THREAD_SAFE;BOOST_COMPUTE_HAVE_THREAD_LOCAL")
+      "${BOOST_DEFINITIONS}")
 endif()
diff --git a/include/af/util.h b/include/af/util.h
index 95a7eea693..6075625de5 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -278,6 +278,15 @@ extern "C" {
     AFAPI af_err af_get_size_of(size_t *size, af_dtype type);
 #endif
 
+#if AF_API_VERSION >= 37
+    /// Enable(default) or disable error messages that display the stacktrace.
+    ///
+    /// \param[in] is_enabled If zero stacktraces are not shown with the error
+    ///                       messages
+    /// \returns Always returns AF_SUCCESS
+    AFAPI af_err af_set_enable_stacktrace(int is_enabled);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 3747126a1f..3404161c36 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -31,3 +31,9 @@ void af_get_last_error(char **str, dim_t *len) {
 
     if (len) *len = slen;
 }
+
+af_err af_set_enable_stacktrace(int is_enabled) {
+    common::is_stacktrace_enabled() = is_enabled;
+
+    return AF_SUCCESS;
+}
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 00a3182294..90db1fc100 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -71,6 +71,7 @@ endif()
 target_link_libraries(afcommon_interface
   INTERFACE
     spdlog
+    Boost::boost
     af_glad_interface
     ${CMAKE_DL_LIBS})
 
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index db78c76e62..3d0605c286 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -28,21 +28,26 @@
 using std::string;
 using std::stringstream;
 
+using common::is_stacktrace_enabled;
+
 AfError::AfError(const char *const func, const char *const file, const int line,
-                 const char *const message, af_err err)
+                 const char *const message, af_err err,
+                 boost::stacktrace::stacktrace st)
     : logic_error(message)
     , functionName(func)
     , fileName(file)
     , lineNumber(line)
-    , error(err) {}
+    , error(err)
+    , st_(move(st)) {}
 
 AfError::AfError(string func, string file, const int line, string message,
-                 af_err err)
+                 af_err err, boost::stacktrace::stacktrace st)
     : logic_error(message)
     , functionName(func)
     , fileName(file)
     , lineNumber(line)
-    , error(err) {}
+    , error(err)
+    , st_(move(st)) {}
 
 const string &AfError::getFunctionName() const { return functionName; }
 
@@ -55,8 +60,9 @@ af_err AfError::getError() const { return error; }
 AfError::~AfError() throw() {}
 
 TypeError::TypeError(const char *const func, const char *const file,
-                     const int line, const int index, const af_dtype type)
-    : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE)
+                     const int line, const int index, const af_dtype type,
+                     boost::stacktrace::stacktrace st)
+    : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE, move(st))
     , argIndex(index)
     , errTypeName(getName(type)) {}
 
@@ -66,8 +72,9 @@ int TypeError::getArgIndex() const { return argIndex; }
 
 ArgumentError::ArgumentError(const char *const func, const char *const file,
                              const int line, const int index,
-                             const char *const expectString)
-    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG)
+                             const char *const expectString,
+                             boost::stacktrace::stacktrace st)
+    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG, move(st))
     , argIndex(index)
     , expected(expectString) {}
 
@@ -76,16 +83,19 @@ const string &ArgumentError::getExpectedCondition() const { return expected; }
 int ArgumentError::getArgIndex() const { return argIndex; }
 
 SupportError::SupportError(const char *const func, const char *const file,
-                           const int line, const char *const back)
-    : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED)
+                           const int line, const char *const back,
+                           boost::stacktrace::stacktrace st)
+    : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED,
+              move(st))
     , backend(back) {}
 
 const string &SupportError::getBackendName() const { return backend; }
 
 DimensionError::DimensionError(const char *const func, const char *const file,
                                const int line, const int index,
-                               const char *const expectString)
-    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE)
+                               const char *const expectString,
+                               const boost::stacktrace::stacktrace st)
+    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE, move(st))
     , argIndex(index)
     , expected(expectString) {}
 
@@ -113,6 +123,7 @@ af_err processException() {
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid dimension for argument " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
+        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
 
         err = set_global_error_string(ss.str(), AF_ERR_SIZE);
     } catch (const ArgumentError &ex) {
@@ -121,22 +132,26 @@ af_err processException() {
            << "Invalid argument at index " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
+        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
         err = set_global_error_string(ss.str(), AF_ERR_ARG);
     } catch (const SupportError &ex) {
         ss << ex.getFunctionName() << " not supported for "
            << ex.getBackendName() << " backend\n";
 
+        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
         err = set_global_error_string(ss.str(), AF_ERR_NOT_SUPPORTED);
     } catch (const TypeError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid type for argument " << ex.getArgIndex() << "\n";
 
+        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
         err = set_global_error_string(ss.str(), AF_ERR_TYPE);
     } catch (const AfError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << ex.what() << "\n";
+        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
 
         err = set_global_error_string(ss.str(), ex.getError());
 #ifdef AF_OPENCL
@@ -145,6 +160,7 @@ af_err processException() {
         snprintf(opencl_err_msg, sizeof(opencl_err_msg),
                  "OpenCL Error (%d): %s when calling %s", ex.err(),
                  getErrorMessage(ex.err()).c_str(), ex.what());
+
         if (ex.err() == CL_MEM_OBJECT_ALLOCATION_FAILURE) {
             err = set_global_error_string(opencl_err_msg, AF_ERR_NO_MEM);
         } else {
@@ -198,3 +214,12 @@ const char *af_err_to_string(const af_err err) {
     return "Unknown error. Please open an issue and add this error code to the "
            "case in af_err_to_string.";
 }
+
+namespace common {
+
+bool &is_stacktrace_enabled() {
+    static bool stacktrace_enabled = true;
+    return stacktrace_enabled;
+}
+
+}  // namespace common
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 19b83d7a13..42b144ef4b 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+#include <boost/stacktrace.hpp>
 #include <common/defines.hpp>
 #include <af/defines.h>
 
@@ -23,19 +24,23 @@ class AfError : public std::logic_error {
     std::string fileName;
     int lineNumber;
     af_err error;
+    boost::stacktrace::stacktrace st_;
     AfError();
 
    public:
     AfError(const char* const func, const char* const file, const int line,
-            const char* const message, af_err err);
+            const char* const message, af_err err,
+            boost::stacktrace::stacktrace st);
 
     AfError(std::string func, std::string file, const int line,
-            std::string message, af_err err);
+            std::string message, af_err err, boost::stacktrace::stacktrace st);
 
     const std::string& getFunctionName() const;
 
     const std::string& getFileName() const;
 
+    const boost::stacktrace::stacktrace& getStacktrace() const { return st_; };
+
     int getLine() const;
 
     af_err getError() const;
@@ -51,7 +56,8 @@ class TypeError : public AfError {
 
    public:
     TypeError(const char* const func, const char* const file, const int line,
-              const int index, const af_dtype type);
+              const int index, const af_dtype type,
+              const boost::stacktrace::stacktrace st);
 
     const std::string& getTypeName() const;
 
@@ -68,7 +74,8 @@ class ArgumentError : public AfError {
    public:
     ArgumentError(const char* const func, const char* const file,
                   const int line, const int index,
-                  const char* const expectString);
+                  const char* const expectString,
+                  const boost::stacktrace::stacktrace st);
 
     const std::string& getExpectedCondition() const;
 
@@ -83,7 +90,8 @@ class SupportError : public AfError {
 
    public:
     SupportError(const char* const func, const char* const file, const int line,
-                 const char* const back);
+                 const char* const back,
+                 const boost::stacktrace::stacktrace st);
 
     ~SupportError() throw() {}
 
@@ -98,7 +106,8 @@ class DimensionError : public AfError {
    public:
     DimensionError(const char* const func, const char* const file,
                    const int line, const int index,
-                   const char* const expectString);
+                   const char* const expectString,
+                   const boost::stacktrace::stacktrace st);
 
     const std::string& getExpectedCondition() const;
 
@@ -109,13 +118,15 @@ class DimensionError : public AfError {
 
 af_err processException();
 
-af_err set_global_error_string(const std::string& msg, af_err err = AF_ERR_UNKNOWN);
+af_err set_global_error_string(const std::string& msg,
+                               af_err err = AF_ERR_UNKNOWN);
 
 #define DIM_ASSERT(INDEX, COND)                                        \
     do {                                                               \
         if ((COND) == false) {                                         \
             throw DimensionError(__PRETTY_FUNCTION__, __AF_FILENAME__, \
-                                 __LINE__, INDEX, #COND);              \
+                                 __LINE__, INDEX, #COND,               \
+                                 boost::stacktrace::stacktrace());     \
         }                                                              \
     } while (0)
 
@@ -123,29 +134,31 @@ af_err set_global_error_string(const std::string& msg, af_err err = AF_ERR_UNKNO
     do {                                                              \
         if ((COND) == false) {                                        \
             throw ArgumentError(__PRETTY_FUNCTION__, __AF_FILENAME__, \
-                                __LINE__, INDEX, #COND);              \
+                                __LINE__, INDEX, #COND,               \
+                                boost::stacktrace::stacktrace());     \
         }                                                             \
     } while (0)
 
 #define TYPE_ERROR(INDEX, type)                                                \
     do {                                                                       \
         throw TypeError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, INDEX, \
-                        type);                                                 \
+                        type, boost::stacktrace::stacktrace());                \
     } while (0)
 
 #define AF_ERROR(MSG, ERR_TYPE)                                            \
     do {                                                                   \
         throw AfError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, MSG, \
-                      ERR_TYPE);                                           \
+                      ERR_TYPE, boost::stacktrace::stacktrace());          \
     } while (0)
 
-#define AF_RETURN_ERROR(MSG, ERR_TYPE)                                \
-    do {                                                              \
-        std::stringstream s;                                          \
-        s << "Error in " << __PRETTY_FUNCTION__ << "\n"               \
-          << "In file " << __AF_FILENAME__ << ":" << __LINE__ << ": " \
-          << MSG;                                                     \
-        return set_global_error_string(s.str(), ERR_TYPE);            \
+#define AF_RETURN_ERROR(MSG, ERR_TYPE)                                       \
+    do {                                                                     \
+        std::stringstream s;                                                 \
+        s << "Error in " << __PRETTY_FUNCTION__ << "\n"                      \
+          << "In file " << __AF_FILENAME__ << ":" << __LINE__ << ": " << MSG \
+          << "\n"                                                            \
+          << boost::stacktrace::stacktrace();                                \
+        return set_global_error_string(s.str(), ERR_TYPE);                   \
     } while (0)
 
 #define TYPE_ASSERT(COND)                                       \
@@ -167,8 +180,14 @@ af_err set_global_error_string(const std::string& msg, af_err err = AF_ERR_UNKNO
         af_err __err = fn;                                                  \
         if (__err == AF_SUCCESS) break;                                     \
         throw AfError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, "\n", \
-                      __err);                                               \
+                      __err, boost::stacktrace::stacktrace());              \
     } while (0)
 
 static const int MAX_ERR_SIZE = 1024;
 std::string& get_global_error_string();
+
+namespace common {
+
+bool& is_stacktrace_enabled();
+
+}  // namespace common
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index 966b403a35..3715c94988 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -12,5 +12,5 @@
 #define CPU_NOT_SUPPORTED(message)                                         \
     do {                                                                   \
         throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                           message);                                       \
+                           message, boost::stacktrace::stacktrace());      \
     } while (0)
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
index fd71ce54b7..9abd9b3f84 100644
--- a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -22,7 +22,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
     )
   set_target_properties(cpu_sort_by_key_${SBK_TYPE}
     PROPERTIES
-      COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL"
+      COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL;$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>"
       FOLDER "Generated Targets")
 
   arrayfire_set_default_cxx_flags(cpu_sort_by_key_${SBK_TYPE})
@@ -34,6 +34,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
       ../../api/c
       ${ArrayFire_SOURCE_DIR}/include
       ${ArrayFire_BINARY_DIR}/include
+      $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
     PRIVATE
       ../common
       ..
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index c53df653f3..061522aa4e 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -15,7 +15,7 @@
 #define CUDA_NOT_SUPPORTED(message)                                        \
     do {                                                                   \
         throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                           message);                                       \
+                           message, boost::stacktrace::stacktrace());      \
     } while (0)
 
 #define CUDA_CHECK(fn)                                               \
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index 55e7e1f234..e110bd8152 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -30,7 +30,10 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
     cuda_compile(scan_by_key_gen_files "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
-          OPTIONS -DSBK_BINARY_OP=${SBK_BINARY_OP} "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
+          OPTIONS
+          -I$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>, -I>
+          -D$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>, -D>
+          -DSBK_BINARY_OP=${SBK_BINARY_OP} "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
       )
 
     list(APPEND SCAN_OBJ ${scan_by_key_gen_files})
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 3772040761..654141948f 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -34,6 +34,8 @@ foreach(SBK_TYPE ${SBK_TYPES})
           ${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp
           OPTIONS
+            -I$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>, -I>
+            -D$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>, -D>
             -DSBK_TYPE=${SBK_TYPE}
             -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
             "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 0f36009599..6a7e839990 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -421,7 +421,6 @@ target_link_libraries(afopencl
     clFFT::clFFT
     opencl_scan_by_key
     opencl_sort_by_key
-    Boost::boost
     Threads::Threads
     )
 
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index a060ccd1b0..5e389285ea 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -18,7 +18,7 @@
 #define OPENCL_NOT_SUPPORTED(message)                                      \
     do {                                                                   \
         throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                           message);                                       \
+                           message, boost::stacktrace::stacktrace());      \
     } while (0)
 
 namespace opencl {

From 8ce5778b641c1b51bd1dc79b19a184a3f6518896 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 23 Nov 2019 22:09:06 -0500
Subject: [PATCH 1807/2677] Ensure query and info functions do not fail if
 there is no device

Some of the info and query functions failed when called on a system
with no OpenCL runtime or graphics drivers installed. These functions
should be resilient to these situations and provide feed back to the
user about possible next steps. The following functions are able to
function even if there is not device or driver installed.

af_info
af_info_string
af_get_device_count
af_get_size_of
af_get_backend_count
af_get_version
af_get_revision

This also includes their respective C++ functions.

Added unit tests.
---
 src/api/c/device.cpp                  | 19 ++++++-
 src/backend/cpu/platform.cpp          |  2 +-
 src/backend/cpu/platform.hpp          |  2 +-
 src/backend/cuda/device_manager.cpp   | 71 ++++++++++++++----------
 src/backend/cuda/device_manager.hpp   | 10 ++--
 src/backend/cuda/platform.cpp         | 29 +++++-----
 src/backend/cuda/platform.hpp         | 10 ++--
 src/backend/opencl/device_manager.cpp | 13 ++++-
 src/backend/opencl/device_manager.hpp |  4 +-
 src/backend/opencl/platform.cpp       | 77 +++++++++++++++------------
 src/backend/opencl/platform.hpp       |  4 +-
 test/CMakeLists.txt                   |  1 +
 test/nodevice.cpp                     | 73 +++++++++++++++++++++++++
 13 files changed, 226 insertions(+), 89 deletions(-)
 create mode 100644 test/nodevice.cpp

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 9da3d70798..ac6245e4a1 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -21,6 +21,7 @@
 #include <af/version.h>
 
 #include <cstring>
+#include <string>
 
 using namespace detail;
 using common::half;
@@ -158,7 +159,23 @@ af_err af_get_device(int* device) {
 af_err af_set_device(const int device) {
     try {
         ARG_ASSERT(0, device >= 0);
-        ARG_ASSERT(0, setDevice(device) >= 0);
+        if (setDevice(device) < 0) {
+            int ndevices = getDeviceCount();
+            if (ndevices == 0) {
+                AF_ERROR(
+                    "No devices were found on this system. Ensure "
+                    "you have installed the device driver as well as the "
+                    "necessary runtime libraries for your platform.",
+                    AF_ERR_RUNTIME);
+            } else {
+                char buf[512];
+                char err_msg[] =
+                    "The device index of %d is out of range. Use a value "
+                    "between 0 and %d.";
+                snprintf(buf, 512, err_msg, device, ndevices - 1);
+                AF_ERROR(buf, AF_ERR_ARG);
+            }
+        }
     }
     CATCHALL;
 
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index ccbc8ad021..d520d676ff 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -54,7 +54,7 @@ static inline string& ltrim(string& s) {
 
 int getBackend() { return AF_BACKEND_CPU; }
 
-string getDeviceInfo() {
+string getDeviceInfo() noexcept {
     const CPUInfo cinfo = DeviceManager::getInstance().getCPUInfo();
 
     ostringstream info;
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index 92ade6d3f2..dcd2c351a6 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -28,7 +28,7 @@ namespace cpu {
 
 int getBackend();
 
-std::string getDeviceInfo();
+std::string getDeviceInfo() noexcept;
 
 bool isDoubleSupported(int device);
 
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index c144d60862..515b37f938 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -473,35 +473,52 @@ DeviceManager::DeviceManager()
     , cuDevices(0)
     , nDevices(0)
     , fgMngr(new graphics::ForgeManager()) {
-    checkCudaVsDriverVersion();
+    try {
+        checkCudaVsDriverVersion();
 
-    CUDA_CHECK(cudaGetDeviceCount(&nDevices));
-    AF_TRACE("Found {} CUDA devices", nDevices);
-    if (nDevices == 0) {
-        AF_ERROR("No CUDA capable devices found", AF_ERR_DRIVER);
-    }
-    cuDevices.reserve(nDevices);
-
-    int cudaRtVer = 0;
-    CUDA_CHECK(cudaRuntimeGetVersion(&cudaRtVer));
-    int cudaMajorVer = cudaRtVer / 1000;
-
-    for (int i = 0; i < nDevices; i++) {
-        cudaDevice_t dev;
-        CUDA_CHECK(cudaGetDeviceProperties(&dev.prop, i));
-        if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
-            AF_TRACE("Unsuppored device: {}", dev.prop.name);
-            continue;
+        CUDA_CHECK(cudaGetDeviceCount(&nDevices));
+        AF_TRACE("Found {} CUDA devices", nDevices);
+        if (nDevices == 0) {
+            AF_ERROR("No CUDA capable devices found", AF_ERR_DRIVER);
+            return;
+        }
+        cuDevices.reserve(nDevices);
+
+        int cudaRtVer = 0;
+        CUDA_CHECK(cudaRuntimeGetVersion(&cudaRtVer));
+        int cudaMajorVer = cudaRtVer / 1000;
+
+        for (int i = 0; i < nDevices; i++) {
+            cudaDevice_t dev;
+            CUDA_CHECK(cudaGetDeviceProperties(&dev.prop, i));
+            if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
+                AF_TRACE("Unsuppored device: {}", dev.prop.name);
+                continue;
+            } else {
+                dev.flops = static_cast<size_t>(dev.prop.multiProcessorCount) *
+                            compute2cores(dev.prop.major, dev.prop.minor) *
+                            dev.prop.clockRate;
+                dev.nativeId = i;
+                AF_TRACE("Found device: {} ({:0.3} GB | ~{} GFLOPs | {} SMs)",
+                         dev.prop.name,
+                         dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
+                         dev.flops / 1024. / 1024. * 2,
+                         dev.prop.multiProcessorCount);
+                cuDevices.push_back(dev);
+            }
+        }
+    } catch (const AfError &err) {
+        // If one of the CUDA functions threw an exception. catch it and wrap it
+        // into a more informative ArrayFire exception.
+        if (err.getError() == AF_ERR_INTERNAL) {
+            AF_ERROR(
+                "Error initializing CUDA runtime. Check your CUDA device is "
+                "visible to the OS and you have installed the correct driver. "
+                "Try running the nvidia-smi utility to debug any driver "
+                "issues.",
+                AF_ERR_RUNTIME);
         } else {
-            dev.flops = static_cast<size_t>(dev.prop.multiProcessorCount) *
-                        compute2cores(dev.prop.major, dev.prop.minor) *
-                        dev.prop.clockRate;
-            dev.nativeId = i;
-            AF_TRACE(
-                "Found device: {} ({:0.3} GB | ~{} GFLOPs | {} SMs)",
-                dev.prop.name, dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
-                dev.flops / 1024. / 1024. * 2, dev.prop.multiProcessorCount);
-            cuDevices.push_back(dev);
+            throw;
         }
     }
     nDevices = cuDevices.size();
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index 98ebe38696..4594f21d8a 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -70,15 +70,15 @@ class DeviceManager {
 
     friend GraphicsResourceManager& interopManager();
 
-    friend std::string getDeviceInfo(int device);
+    friend std::string getDeviceInfo(int device) noexcept;
 
-    friend std::string getPlatformInfo();
+    friend std::string getPlatformInfo() noexcept;
 
     friend std::string getDriverVersion();
 
-    friend std::string getCUDARuntimeVersion();
+    friend std::string getCUDARuntimeVersion() noexcept;
 
-    friend std::string getDeviceInfo();
+    friend std::string getDeviceInfo() noexcept;
 
     friend int getDeviceCount();
 
@@ -107,6 +107,8 @@ class DeviceManager {
     // Attributes
     enum sort_mode { flops = 0, memory = 1, compute = 2, none = 3 };
 
+    // Checks if the Graphics driver is capable of running the CUDA toolkit
+    // version that ArrayFire was compiled against
     void checkCudaVsDriverVersion();
     void sortDevices(sort_mode mode = flops);
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index b2b48febf4..f4493433e8 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -187,7 +187,7 @@ DeviceManager::~DeviceManager() {
 
 int getBackend() { return AF_BACKEND_CUDA; }
 
-string getDeviceInfo(int device) {
+string getDeviceInfo(int device) noexcept {
     cudaDeviceProp dev = getDeviceProp(device);
 
     size_t mem_gpu_total = dev.totalGlobalMem;
@@ -209,7 +209,7 @@ string getDeviceInfo(int device) {
     return info;
 }
 
-string getDeviceInfo() {
+string getDeviceInfo() noexcept {
     ostringstream info;
     info << "ArrayFire v" << AF_VERSION << " (CUDA, " << get_system()
          << ", build " << AF_REVISION << ")" << std::endl;
@@ -218,10 +218,10 @@ string getDeviceInfo() {
     return info.str();
 }
 
-string getPlatformInfo() {
+string getPlatformInfo() noexcept {
     string driverVersion    = getDriverVersion();
     std::string cudaRuntime = getCUDARuntimeVersion();
-    string platform         = "Platform: CUDA Toolkit " + cudaRuntime;
+    string platform         = "Platform: CUDA Runtime " + cudaRuntime;
     if (!driverVersion.empty()) {
         platform.append(", Driver: ");
         platform.append(driverVersion);
@@ -269,15 +269,13 @@ void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
 }
 
 string getDriverVersion() {
-    char driverVersion[1024] = {
-        " ",
-    };
+    char driverVersion[1024] = {" "};
     int x = nvDriverVersion(driverVersion, sizeof(driverVersion));
     if (x != 1) {
 // Windows, OSX, Tegra Need a new way to fetch driver
 #if !defined(OS_WIN) && !defined(OS_MAC) && !defined(__arm__) && \
     !defined(__aarch64__)
-        throw runtime_error("Invalid driver");
+        return "N/A";
 #endif
         int driver = 0;
         CUDA_CHECK(cudaDriverGetVersion(&driver));
@@ -287,10 +285,13 @@ string getDriverVersion() {
     }
 }
 
-string getCUDARuntimeVersion() {
+string getCUDARuntimeVersion() noexcept {
     int runtime = 0;
-    CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
-    return int_version_to_string(runtime);
+    if (cudaSuccess == cudaRuntimeGetVersion(&runtime)) {
+        return int_version_to_string(runtime);
+    } else {
+        return int_version_to_string(CUDA_VERSION);
+    }
 }
 
 unsigned getMaxJitSize() {
@@ -315,7 +316,11 @@ int &tlocalActiveDeviceId() {
     return activeDeviceId;
 }
 
-int getDeviceCount() { return DeviceManager::getInstance().nDevices; }
+int getDeviceCount() {
+    int count = 0;
+    if (cudaGetDeviceCount(&count)) { return 0; }
+    else { return count; }
+}
 
 int getActiveDeviceId() { return tlocalActiveDeviceId(); }
 
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 68db32ca8b..a358bdcae9 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -54,14 +54,16 @@ class PlanCache;
 
 int getBackend();
 
-std::string getDeviceInfo();
-std::string getDeviceInfo(int device);
+std::string getDeviceInfo() noexcept;
+std::string getDeviceInfo(int device) noexcept;
 
-std::string getPlatformInfo();
+std::string getPlatformInfo() noexcept;
 
 std::string getDriverVersion();
 
-std::string getCUDARuntimeVersion();
+// Returns the cuda runtime version as a string for the current build. If no
+// runtime is found or an error occured, the string "N/A" is returned
+std::string getCUDARuntimeVersion() noexcept;
 
 // Returns true if double is supported by the device
 bool isDoubleSupported(int device);
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index f3960b2272..1fb78781c7 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -170,7 +170,18 @@ DeviceManager::DeviceManager()
     , fgMngr(new graphics::ForgeManager())
     , mFFTSetup(new clfftSetupData) {
     vector<Platform> platforms;
-    Platform::get(&platforms);
+    try {
+        Platform::get(&platforms);
+    } catch (const cl::Error& err) {
+        if (err.err() == CL_PLATFORM_NOT_FOUND_KHR) {
+            AF_ERROR(
+                "No OpenCL platforms found on this system. Ensure you have "
+                "installed the device driver as well as the OpenCL runtime and "
+                "ICD from your device vendor. You can use the clinfo utility "
+                "to debug OpenCL installation issues.",
+                AF_ERR_RUNTIME);
+        }
+    }
 
     // This is all we need because the sort takes care of the order of devices
 #ifdef OS_MAC
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 9602f52f4c..11cc5336c8 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -61,9 +61,9 @@ class DeviceManager {
 
     friend kc_entry_t kernelCache(int device, const std::string& key);
 
-    friend std::string getDeviceInfo();
+    friend std::string getDeviceInfo() noexcept;
 
-    friend int getDeviceCount();
+    friend int getDeviceCount() noexcept;
 
     friend int getDeviceIdFromNativeId(cl_device_id id);
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 036122027f..f10e1f0c56 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -111,48 +111,54 @@ static string platformMap(string& platStr) {
     }
 }
 
-string getDeviceInfo() {
-    DeviceManager& devMngr = DeviceManager::getInstance();
+string getDeviceInfo() noexcept {
+    ostringstream info;
+    info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
+         << ", build " << AF_REVISION << ")\n";
 
     vector<cl::Device*> devices;
-    {
+    try {
+        DeviceManager& devMngr = DeviceManager::getInstance();
+
         common::lock_guard_t lock(devMngr.deviceMutex);
         devices = devMngr.mDevices;
-    }
 
-    ostringstream info;
-    info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
-         << ", build " << AF_REVISION << ")\n";
-
-    unsigned nDevices = 0;
-    for (auto device : devices) {
-        const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
+        unsigned nDevices = 0;
+        for (auto device : devices) {
+            const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
-        string dstr      = device->getInfo<CL_DEVICE_NAME>();
-        bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
+            string dstr      = device->getInfo<CL_DEVICE_NAME>();
+            bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
 
-        string id = (show_braces ? string("[") : "-") + to_string(nDevices) +
-                    (show_braces ? string("]") : "-");
+            string id = (show_braces ? string("[") : "-") +
+                        to_string(nDevices) + (show_braces ? string("]") : "-");
 
-        size_t msize = device->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
-        info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
-             << ", " << msize / 1048576 << " MB";
+            size_t msize = device->getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
+            info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
+                 << ", " << msize / 1048576 << " MB";
 #ifndef NDEBUG
-        info << " -- ";
-        string devVersion = device->getInfo<CL_DEVICE_VERSION>();
-        string driVersion = device->getInfo<CL_DRIVER_VERSION>();
-        info << devVersion;
-        info << " -- Device driver " << driVersion;
-        info << " -- FP64 Support: "
-             << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0
-                     ? "True"
-                     : "False");
-        info << " -- Unified Memory ("
-             << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
+            info << " -- ";
+            string devVersion = device->getInfo<CL_DEVICE_VERSION>();
+            string driVersion = device->getInfo<CL_DRIVER_VERSION>();
+            info << devVersion;
+            info << " -- Device driver " << driVersion;
+            info
+                << " -- FP64 Support: "
+                << (device->getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() >
+                            0
+                        ? "True"
+                        : "False");
+            info << " -- Unified Memory ("
+                 << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif
-        info << endl;
+            info << endl;
 
-        nDevices++;
+            nDevices++;
+        }
+    } catch (const AfError& err) {
+        info << "No platforms found.\n";
+        // Don't throw an exception here. Info should pass even if the system
+        // doesn't have the correct drivers installed.
     }
     return info.str();
 }
@@ -177,13 +183,16 @@ void setActiveContext(int device) {
     tlocalActiveDeviceId() = make_pair(device, device);
 }
 
-int getDeviceCount() {
+int getDeviceCount() noexcept try {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
-
     return devMngr.mQueues.size();
-}
+} catch (const AfError& err) {
+    // If device manager threw an error then return 0 because no platforms
+    // were found
+    return 0;
+ }
 
 int getActiveDeviceId() {
     // Second element is the queue id, which is
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index c9024d4dc9..5ab5249e93 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -59,9 +59,9 @@ static inline bool verify_present(std::string pname, const char* ref) {
 
 int getBackend();
 
-std::string getDeviceInfo();
+std::string getDeviceInfo() noexcept;
 
-int getDeviceCount();
+int getDeviceCount() noexcept;
 
 int getActiveDeviceId();
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 3753c08971..b42ead6834 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -244,6 +244,7 @@ make_test(SRC moddims.cpp)
 make_test(SRC moments.cpp)
 make_test(SRC morph.cpp)
 make_test(SRC nearest_neighbour.cpp CXX11)
+make_test(SRC nodevice.cpp CXX11)
 
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
diff --git a/test/nodevice.cpp b/test/nodevice.cpp
new file mode 100644
index 0000000000..c37051b4ec
--- /dev/null
+++ b/test/nodevice.cpp
@@ -0,0 +1,73 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+// Include functions that provide information about the system and shouldn't
+// throw exceptions during runtime.
+
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
+TEST(NoDevice, Info) {
+    ASSERT_SUCCESS(af_info());
+}
+
+TEST(NoDevice, InfoCxx) {
+    af::info();
+}
+
+TEST(NoDevice, InfoString) {
+    char *str;
+    ASSERT_SUCCESS(af_info_string(&str, true));
+    ASSERT_SUCCESS(af_free_host((void*)str));
+}
+
+TEST(NoDevice, GetDeviceCount) {
+    int device = 0;
+    ASSERT_SUCCESS(af_get_device_count(&device));
+}
+
+TEST(NoDevice, GetDeviceCountCxx) {
+    int device = 0;
+    af::getDeviceCount();
+}
+
+TEST(NoDevice, GetSizeOf) {
+    size_t size;
+    ASSERT_SUCCESS(af_get_size_of(&size, f32));
+    ASSERT_EQ(4, size);
+}
+
+TEST(NoDevice, GetSizeOfCxx) {
+    size_t size = af::getSizeOf(f32);
+    ASSERT_EQ(4, size);
+}
+
+TEST(NoDevice, GetBackendCount) {
+    unsigned int nbackends;
+    ASSERT_SUCCESS(af_get_backend_count(&nbackends));
+}
+
+TEST(NoDevice, GetBackendCountCxx) {
+    unsigned int nbackends = af::getBackendCount();
+}
+
+TEST(NoDevice, GetVersion) {
+    int major = 0, minor = 0, patch = 0;
+
+    ASSERT_SUCCESS(af_get_version(&major, &minor, &patch));
+
+    ASSERT_EQ(AF_VERSION_MAJOR, major);
+    ASSERT_EQ(AF_VERSION_MINOR, minor);
+    ASSERT_EQ(AF_VERSION_PATCH, patch);
+}
+
+TEST(NoDevice, GetRevision) {
+    const char* revision = af_get_revision();
+}

From 868ca2798ea85499aa84e131b54e7d2f61a04b85 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Jan 2020 21:04:06 -0500
Subject: [PATCH 1808/2677] Fix boost version check from multiple versions of
 CMake

---
 CMakeModules/boost_package.cmake | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index f76c6a059a..39bb6ff47e 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -7,7 +7,9 @@
 
 find_package(Boost)
 
-if("${Boost_VERSION}" VERSION_LESS 107000)
+if(NOT (   Boost_VERSION        VERSION_GREATER_EQUAL 107000
+        OR Boost_VERSION_STRING VERSION_GREATER_EQUAL 1.70
+        OR Boost_VERSION_MACRO  VERSION_GREATER_EQUAL 107000))
   set(VER 1.70.0)
   set(MD5 e160ec0ff825fc2850ea4614323b1fb5)
   include(ExternalProject)

From e88457aee175bbbb21f2d69816cf65bb817fa26f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 31 Jan 2020 15:02:17 +0530
Subject: [PATCH 1809/2677] Fix build configs of mutli-config cmake generators

All upstream projects will use `Release` config on single config
cmake generators if the value of CMAKE_BUILD_TYPE is either Release
or RelWithDebInfo. In every other scenario, it will just use the
existing value of CMAKE_BUILD_TYPE variable.

Note: With current cmake min version as 3.5, which doesn't have ternary
generator expression, setting the build type using generator expressions
turned out to be too clumsy and cumbersone set of lines. Hence, used
simple if-else code-block.
---
 CMakeLists.txt                           |  7 ++++---
 CMakeModules/AFBuildConfigurations.cmake | 24 ++++++++++++++++++++++++
 CMakeModules/InternalUtils.cmake         | 10 ----------
 CMakeModules/build_CLBlast.cmake         |  8 +++++++-
 CMakeModules/build_clBLAS.cmake          |  8 +++++++-
 CMakeModules/build_clFFT.cmake           |  8 +++++++-
 6 files changed, 49 insertions(+), 16 deletions(-)
 create mode 100644 CMakeModules/AFBuildConfigurations.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f0480d1843..d4dbd85d4b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,11 +6,12 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.5)
-project(ArrayFire
-  VERSION 3.7.0
-  LANGUAGES C CXX )
+
+project(ArrayFire VERSION 3.7.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
+
+include(AFBuildConfigurations)
 include(AFInstallDirs)
 include(CMakeDependentOption)
 include(InternalUtils)
diff --git a/CMakeModules/AFBuildConfigurations.cmake b/CMakeModules/AFBuildConfigurations.cmake
new file mode 100644
index 0000000000..68d75fd34d
--- /dev/null
+++ b/CMakeModules/AFBuildConfigurations.cmake
@@ -0,0 +1,24 @@
+# CMake 3.9 or later provides a global property to whether we are multi-config
+# or single-config generator. Before 3.9, the defintion of CMAKE_CONFIGURATION_TYPES
+# variable indicated multi-config, but developers might modify.
+if(NOT CMAKE_VERSION VERSION_LESS 3.9)
+  get_property(_isMultiConfig GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
+elseif(CMAKE_CONFIGURATION_TYPES)
+  # CMAKE_CONFIGURATION_TYPES is set by project() call for multi-config generators
+  set(_isMultiConfig True)
+else()
+  set(_isMultiConfig False)
+endif()
+
+if(_isMultiConfig)
+  set(CMAKE_CONFIGURATION_TYPES
+    "Coverage;Debug;MinSizeRel;Release;RelWithDebInfo"
+    CACHE STRING "Configurations for Multi-Config CMake Generator" FORCE)
+else()
+  if(NOT CMAKE_BUILD_TYPE)
+    set(CMAKE_BUILD_TYPE "Release" CACHE STRING "Build Type" FORCE)
+  endif()
+  set_property(CACHE CMAKE_BUILD_TYPE
+    PROPERTY
+      STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "Coverage")
+endif()
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 7944926130..eb9b7f4d05 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -108,16 +108,6 @@ macro(arrayfire_set_cmake_default_variables)
   set(CMAKE_CXX_EXTENSIONS OFF)
   set(CMAKE_CXX_VISIBILITY_PRESET hidden)
 
-  # Set a default build type if none was specified
-  if(NOT CMAKE_BUILD_TYPE)
-      set(CMAKE_BUILD_TYPE Release CACHE STRING "The type of the build")
-  endif()
-
-  # Set the possible values of build type for cmake-gui
-  set_property(CACHE CMAKE_BUILD_TYPE
-    PROPERTY
-      STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "Coverage")
-
   set(CMAKE_CXX_FLAGS_COVERAGE
       "-g -O0"
       CACHE STRING "Flags used by the C++ compiler during coverage builds.")
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 25197fb2cf..80c82386c7 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -43,6 +43,12 @@ else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
 endif()
 
+if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
+  set(extproj_build_type "Release")
+else()
+  set(extproj_build_type ${CMAKE_BUILD_TYPE})
+endif()
+
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
@@ -59,7 +65,7 @@ ExternalProject_Add(
       -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
       -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
       "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
       -DCMAKE_INSTALL_LIBDIR:PATH=lib
       -DBUILD_SHARED_LIBS:BOOL=OFF
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index c4ee52bf5c..7d547b08b3 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -18,6 +18,12 @@ else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
 endif()
 
+if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
+  set(extproj_build_type "Release")
+else()
+  set(extproj_build_type ${CMAKE_BUILD_TYPE})
+endif()
+
 ExternalProject_Add(
     clBLAS-ext
     GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
@@ -31,7 +37,7 @@ ExternalProject_Add(
       -Wno-dev <SOURCE_DIR>/src
       -DCMAKE_CXX_FLAGS:STRING="-fPIC"
       -DCMAKE_C_FLAGS:STRING="-fPIC"
-      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
       -DBUILD_SHARED_LIBS:BOOL=OFF
       -DBUILD_CLIENT:BOOL=OFF
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index a72016972c..e92d9fd912 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -24,6 +24,12 @@ else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
 endif()
 
+if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
+  set(extproj_build_type "Release")
+else()
+  set(extproj_build_type ${CMAKE_BUILD_TYPE})
+endif()
+
 ExternalProject_Add(
     clFFT-ext
     GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
@@ -37,7 +43,7 @@ ExternalProject_Add(
       "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
       -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
       "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-      -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE}
+      -DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
       -DBUILD_SHARED_LIBS:BOOL=OFF
       -DBUILD_EXAMPLES:BOOL=OFF

From bf997ff9482ef7ef092c77dbc4f48bfff9afaedc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 1 Feb 2020 12:16:16 +0530
Subject: [PATCH 1810/2677] Use Default build type for github ci jobs

The default build type now is Release
---
 .github/workflows/cpu_build.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index 86851c3cad..438d59d9c2 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -88,7 +88,6 @@ jobs:
                   mkdir build && cd build
                   cmake -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
-                      -DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo \
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \

From 3723e287cab78d6cb9ffc095a0bcf90818e515e9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 2 Feb 2020 11:39:08 +0530
Subject: [PATCH 1811/2677] Fix Boost pkg condition for min cmake ver(3.5)
 (#2737)

* Fix Boost pkg condition for min cmake ver(3.5)
---
 CMakeModules/boost_package.cmake | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 39bb6ff47e..361b9d58a8 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -7,9 +7,16 @@
 
 find_package(Boost)
 
-if(NOT (   Boost_VERSION        VERSION_GREATER_EQUAL 107000
-        OR Boost_VERSION_STRING VERSION_GREATER_EQUAL 1.70
-        OR Boost_VERSION_MACRO  VERSION_GREATER_EQUAL 107000))
+set(Boost_MIN_VER 107000)
+set(Boost_MIN_VER_STR "1.70")
+
+if(NOT
+  ((Boost_VERSION VERSION_GREATER Boost_MIN_VER OR
+    Boost_VERSION VERSION_EQUAL Boost_MIN_VER) OR
+   (Boost_VERSION_STRING VERSION_GREATER Boost_MIN_VER_STR OR
+    Boost_VERSION_STRING VERSION_EQUAL Boost_MIN_VER_STR) OR
+   (Boost_VERSION_MACRO VERSION_GREATER Boost_MIN_VER OR
+    Boost_VERSION_MACRO VERSION_EQUAL Boost_MIN_VER)))
   set(VER 1.70.0)
   set(MD5 e160ec0ff825fc2850ea4614323b1fb5)
   include(ExternalProject)

From eb704fe2f4063a73436e0b4303ec6d8227c8b3e3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 31 Jan 2020 13:06:56 -0500
Subject: [PATCH 1812/2677] Improve FindMKL. Use LIBRARY_PATH set by mklvars
 script. Add STATIC support

* Add support for Static linking
* Use library path to find libiomp5
* Add better checks for missing libs
* Version support
---
 CMakeLists.txt                    |   4 +-
 CMakeModules/CPackConfig.cmake    |   2 +-
 CMakeModules/FindMKL.cmake        | 234 ++++++++++++++++++++++++------
 src/api/unified/CMakeLists.txt    |   4 +-
 src/backend/cpu/CMakeLists.txt    |  24 +--
 src/backend/opencl/CMakeLists.txt |   8 +-
 6 files changed, 199 insertions(+), 77 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d4dbd85d4b..5337ecdee7 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -292,7 +292,7 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL AND AF_INSTALL_STANDALONE)
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared AND AF_INSTALL_STANDALONE)
   if(TARGET MKL::ThreadingLibrary)
     install(FILES
       $<TARGET_FILE:MKL::ThreadingLibrary>
@@ -308,7 +308,7 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL AND AF_INSTALL_STANDALONE
   endif()
 
   install(FILES
-    $<TARGET_FILE:MKL::MKL>
+    $<TARGET_FILE:MKL::Shared>
     $<TARGET_FILE:MKL::ThreadLayer>
     ${MKL_RUNTIME_KERNEL_LIBRARIES}
 
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 43b12e904f..059d11c2db 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -146,7 +146,7 @@ cpack_add_component_group(opencl_backend
 
 set(PACKAGE_MKL_DEPS OFF)
 
-if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
+if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared)
   set(PACKAGE_MKL_DEPS ON)
   cpack_add_component(mkl_dependencies
     DISPLAY_NAME "Intel MKL"
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 1a1e5b8204..dab91ff0d0 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -17,7 +17,7 @@
 # find_package(MKL)
 #
 # add_executable(myapp main.cpp)
-# target_link_libraries(myapp PRIVATE MKL::MKL)
+# target_link_libraries(myapp PRIVATE MKL::Shared)
 #
 # This module bases its behavior based on the following variables:
 #
@@ -28,7 +28,13 @@
 #
 # This module provides the following :prop_tgt:'IMPORTED' targets:
 #
-# ``MKL::MKL``
+# ``MKL::Shared``
+#   Target used to define and link all MKL libraries required by Intel's Link
+#   Line Advisor. This usually the only thing you need to link against unless
+#   you want to link against the single dynamic library version of MKL
+#   (libmkl_rt.so)
+#
+# ``MKL::Static``
 #   Target used to define and link all MKL libraries required by Intel's Link
 #   Line Advisor. This usually the only thing you need to link against unless
 #   you want to link against the single dynamic library version of MKL
@@ -58,6 +64,7 @@
 #   Targets for MKL kernel libraries.
 
 include(CheckTypeSize)
+include(FindPackageHandleStandardArgs)
 
 check_type_size("int" INT_SIZE
   BUILTIN_TYPES_ONLY LANGUAGE C)
@@ -91,14 +98,59 @@ find_path(MKL_INCLUDE_DIR
     include
     IntelSWTools/compilers_and_libraries/windows/mkl/include
     )
-mark_as_advanced(MKL_INCLUDE_DIR)
+
+if(MKL_INCLUDE_DIR)
+  mark_as_advanced(MKL_INCLUDE_DIR)
+endif()
+
+function(find_version)
+  set(options "")
+  set(single_args VAR FILE REGEX)
+  set(multi_args "")
+  cmake_parse_arguments(find_version "${options}" "${single_args}" "${multi_args}" ${ARGN})
+
+  file(READ ${find_version_FILE} VERSION_FILE_CONTENTS)
+  string(REGEX MATCH ${find_version_REGEX}
+    VERSION_LINE "${VERSION_FILE_CONTENTS}")
+  set(${ARGV0} ${CMAKE_MATCH_1} PARENT_SCOPE)
+endfunction()
+
+if(MKL_INCLUDE_DIR)
+  find_file(MKL_VERSION_HEADER
+    NAMES
+      mkl_version.h
+    PATHS
+      ${MKL_INCLUDE_DIR})
+
+    find_version(MKL_MAJOR_VERSION
+      FILE ${MKL_VERSION_HEADER}
+      REGEX "__INTEL_MKL__ * ([0-9]+)")
+
+    find_version(MKL_MINOR_VERSION
+      FILE ${MKL_VERSION_HEADER}
+      REGEX "__INTEL_MKL_MINOR__ * ([0-9]+)")
+
+    find_version(MKL_UPDATE_VERSION
+      FILE ${MKL_VERSION_HEADER}
+      REGEX "__INTEL_MKL_UPDATE__ * ([0-9]+)")
+
+    find_version(MKL_VERSION_MACRO
+      FILE ${MKL_VERSION_HEADER}
+      REGEX "INTEL_MKL_VERSION * ([0-9]+)")
+
+  set(MKL_VERSION_STRING ${MKL_MAJOR_VERSION}.${MKL_MINOR_VERSION}.${MKL_UPDATE_VERSION})
+  mark_as_advanced(MKL_VERSION_HEADER)
+endif()
+
 
 find_path(MKL_FFTW_INCLUDE_DIR
   NAMES
     fftw3_mkl.h
   HINTS
     ${MKL_INCLUDE_DIR}/fftw)
-mark_as_advanced(MKL_FFTW_INCLUDE_DIR)
+if(MKL_FFTW_INCLUDE_DIR)
+  mark_as_advanced(MKL_FFTW_INCLUDE_DIR)
+endif()
 
 
 if(WIN32)
@@ -117,6 +169,12 @@ endif()
 #    NAME:         A variable name describing the library
 #    LIBRARY_NAME: The library that needs to be searched
 #
+# OPTIONS:
+#    DLL_ONLY     On Windows do not search for .lib files. Ignored in other
+#                 platforms
+#    SEARCH_STATIC Search for static versions of the libraries as well as the
+#                  dynamic libraries
+#
 # Output Libraries:
 #    MKL::${NAME}
 #    MKL::${NAME}_STATIC
@@ -128,39 +186,46 @@ endif()
 #    MKL_${NAME}_STATIC_LINK_LIBRARY: on Unix: *.a  on Windows *.lib
 #    MKL_${NAME}_DLL_LIBRARY:         on Unix: ""   on Windows *.dll
 function(find_mkl_library)
-  set(options "")
+  set(options "SEARCH_STATIC;DLL_ONLY")
   set(single_args NAME LIBRARY_NAME)
   set(multi_args "")
 
   cmake_parse_arguments(mkl_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
 
   add_library(MKL::${mkl_args_NAME}        SHARED IMPORTED)
-  add_library(MKL::${mkl_args_NAME}_STATIC SHARED IMPORTED)
-  find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
-    NAMES
-      ${mkl_args_LIBRARY_NAME}${shared_suffix}
-      ${mkl_args_LIBRARY_NAME}${md_suffix}
-      lib${mkl_args_LIBRARY_NAME}${md_suffix}
-      ${mkl_args_LIBRARY_NAME}
-    PATHS
-      /opt/intel/mkl/lib
-      /opt/intel/tbb/lib
-      /opt/intel/lib
-      $ENV{MKLROOT}/lib
-      /opt/intel/compilers_and_libraries/linux/mkl/lib
-    PATH_SUFFIXES
-      IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64
-      IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
-      IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
-      ""
-      intel64
-      intel64/gcc4.7)
-  mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
+  add_library(MKL::${mkl_args_NAME}_STATIC STATIC IMPORTED)
+
+  string(REGEX REPLACE ":" ";" ENV_LIBRARY_PATHS "$ENV{LIBRARY_PATH}")
+
+  if(NOT (WIN32 AND mkl_args_DLL_ONLY))
+    find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
+      NAMES
+        ${mkl_args_LIBRARY_NAME}${shared_suffix}
+        ${mkl_args_LIBRARY_NAME}${md_suffix}
+        lib${mkl_args_LIBRARY_NAME}${md_suffix}
+        ${mkl_args_LIBRARY_NAME}
+      PATHS
+        /opt/intel/mkl/lib
+        /opt/intel/tbb/lib
+        /opt/intel/lib
+        $ENV{MKLROOT}/lib
+        ${ENV_LIBRARY_PATHS}
+        /opt/intel/compilers_and_libraries/linux/mkl/lib
+      PATH_SUFFIXES
+        IntelSWTools/compilers_and_libraries/windows/mkl/lib/intel64
+        IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
+        IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
+        ""
+        intel64
+        intel64/gcc4.7)
+    if(MKL_${mkl_args_NAME}_LINK_LIBRARY)
+      mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
+    endif()
+  endif()
 
   #message(STATUS "NAME: ${mkl_args_NAME} LIBNAME: ${mkl_args_LIBRARY_NAME} MKL_${mkl_args_NAME}_LINK_LIBRARY  ${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
 
-  # The rt library does not have a static library
-  if(NOT ${mkl_args_NAME} STREQUAL "rt")
+  if(mkl_args_SEARCH_STATIC)
     find_library(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY
       NAMES
         ${CMAKE_STATIC_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}
@@ -169,6 +234,7 @@ function(find_mkl_library)
         /opt/intel/tbb/lib
         /opt/intel/lib
         $ENV{MKLROOT}/lib
+        ${ENV_LIBRARY_PATHS}
         /opt/intel/compilers_and_libraries/linux/mkl/lib
       PATH_SUFFIXES
         ""
@@ -178,7 +244,9 @@ function(find_mkl_library)
         IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
         IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
         )
-      mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+      if(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+        mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+      endif()
     endif()
 
     set_target_properties(MKL::${mkl_args_NAME}
@@ -186,12 +254,21 @@ function(find_mkl_library)
         INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
         IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_LINK_LIBRARY}"
         IMPORTED_NO_SONAME TRUE)
+
+    set_target_properties(MKL::${mkl_args_NAME}_STATIC
+        PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
+        IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}"
+        IMPORTED_NO_SONAME TRUE)
+
     if(WIN32)
       find_file(MKL_${mkl_args_NAME}_DLL_LIBRARY
         NAMES
           ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX}
           ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
           lib${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+          $ENV{LIB}
+          $ENV{LIBRARY_PATH}
         PATH_SUFFIXES
           IntelSWTools/compilers_and_libraries/windows/redist/intel64/mkl
           IntelSWTools/compilers_and_libraries/windows/redist/intel64/compiler
@@ -208,63 +285,86 @@ function(find_mkl_library)
 endfunction()
 
 
-find_mkl_library(NAME Core LIBRARY_NAME mkl_core)
+find_mkl_library(NAME Core LIBRARY_NAME mkl_core SEARCH_STATIC)
 find_mkl_library(NAME RT LIBRARY_NAME mkl_rt)
 
 # MKL can link against Intel OpenMP, GNU OpenMP, TBB, and Sequential
 if(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
-  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_intel_thread)
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_intel_thread SEARCH_STATIC)
   find_mkl_library(NAME ThreadingLibrary LIBRARY_NAME iomp5)
 elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
   find_package(OpenMP REQUIRED)
-  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_gnu_thread)
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_gnu_thread SEARCH_STATIC)
   add_library(MKL::ThreadingLibrary SHARED IMPORTED)
   set_target_properties(MKL::ThreadingLibrary
     PROPERTIES
       IMPORTED_LOCATION "${OpenMP_gomp_LIBRARY}"
       INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_CXX)
 elseif(MKL_THREAD_LAYER STREQUAL "TBB")
-  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_tbb_thread)
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_tbb_thread SEARCH_STATIC)
   find_mkl_library(NAME ThreadingLibrary LIBRARY_NAME tbb)
 elseif(MKL_THREAD_LAYER STREQUAL "Sequential")
-  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_sequential)
+  find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_sequential SEARCH_STATIC)
 endif()
 
 if("${INT_SIZE}" EQUAL 4)
-  find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_lp64)
+  find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_lp64 SEARCH_STATIC)
 else()
-  find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_ilp64)
+  find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_ilp64 SEARCH_STATIC)
 endif()
 
-set(MKL_RUNTIME_KERNEL_LIBRARIES "" CACHE FILEPATH "MKL kernel libraries targeting different CPU architectures")
 set(MKL_KernelLibraries "mkl_def;mkl_mc;mkl_mc3;mkl_avx;mkl_avx2;mkl_avx512")
 
 foreach(lib ${MKL_KernelLibraries})
-  find_mkl_library(NAME ${lib} LIBRARY_NAME ${lib})
-  if(MKL_${lib}_LINK_LIBRARY OR MKL_${lib}_DLL_LIBRARY)
-    list(APPEND MKL_RUNTIME_KERNEL_LIBRARIES $<TARGET_FILE:MKL::${lib}>)
+  find_mkl_library(NAME ${lib} LIBRARY_NAME ${lib} DLL_ONLY)
+
+  if(MKL_${lib}_LINK_LIBRARY)
+    list(APPEND MKL_RUNTIME_KERNEL_LIBRARIES_TMP ${MKL_${lib}_LINK_LIBRARY})
+  endif()
+
+  if(MKL_${lib}_DLL_LIBRARY)
+    list(APPEND MKL_RUNTIME_KERNEL_LIBRARIES_TMP ${MKL_${lib}_DLL_LIBRARY})
   endif()
 endforeach()
+
+set(MKL_RUNTIME_KERNEL_LIBRARIES "${MKL_RUNTIME_KERNEL_LIBRARIES_TMP}" CACHE STRING
+    "MKL kernel libraries targeting different CPU architectures")
 mark_as_advanced(MKL_RUNTIME_KERNEL_LIBRARIES)
 
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(MKL
-  REQUIRED_VARS MKL_INCLUDE_DIR MKL_Core_LINK_LIBRARY)
+find_package_handle_standard_args(MKL_Shared
+  FAIL_MESSAGE "Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. Looking in MKLROOT and LIBRARY_PATHS environment variables"
+  VERSION_VAR  MKL_VERSION_STRING
+  REQUIRED_VARS MKL_INCLUDE_DIR
+                MKL_Core_LINK_LIBRARY
+                MKL_Interface_LINK_LIBRARY
+                MKL_ThreadLayer_LINK_LIBRARY
+                MKL_ThreadingLibrary_LINK_LIBRARY)
+
+find_package_handle_standard_args(MKL_Static
+  FAIL_MESSAGE "Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. Looking in MKLROOT and LIBRARY_PATHS environment variables"
+  VERSION_VAR   MKL_VERSION_STRING
+  REQUIRED_VARS MKL_INCLUDE_DIR
+                MKL_Core_STATIC_LINK_LIBRARY
+                MKL_Interface_STATIC_LINK_LIBRARY
+                MKL_ThreadLayer_STATIC_LINK_LIBRARY
+                MKL_ThreadingLibrary_LINK_LIBRARY)
+
 if(NOT WIN32)
   find_library(M_LIB m)
   mark_as_advanced(M_LIB)
 endif()
-if(MKL_FOUND)
-  add_library(MKL::MKL SHARED IMPORTED)
+
+if(MKL_Shared_FOUND)
+  add_library(MKL::Shared SHARED IMPORTED)
   if(MKL_THREAD_LAYER STREQUAL "Sequential")
-    set_target_properties(MKL::MKL
+    set_target_properties(MKL::Shared
       PROPERTIES
         IMPORTED_LOCATION "${MKL_Core_LINK_LIBRARY}"
         INTERFACE_LINK_LIBRARIES "MKL::Interface;MKL::ThreadLayer;${CMAKE_DL_LIBS};${M_LIB}"
         INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
         IMPORTED_NO_SONAME TRUE)
   else()
-    set_target_properties(MKL::MKL
+    set_target_properties(MKL::Shared
       PROPERTIES
         IMPORTED_LOCATION "${MKL_Core_LINK_LIBRARY}"
         INTERFACE_LINK_LIBRARIES "MKL::Interface;MKL::ThreadLayer;MKL::ThreadingLibrary;${CMAKE_DL_LIBS};${M_LIB}"
@@ -272,9 +372,47 @@ if(MKL_FOUND)
         IMPORTED_NO_SONAME TRUE)
   endif()
   if(WIN32)
-    set_target_properties(MKL::MKL
+    set_target_properties(MKL::Shared
       PROPERTIES
         IMPORTED_LOCATION "${MKL_Core_DLL_LIBRARY}"
         IMPORTED_IMPLIB "${MKL_Core_LINK_LIBRARY}")
   endif()
 endif()
+
+if(MKL_Static_FOUND)
+  add_library(MKL::Static STATIC IMPORTED)
+
+  if(UNIX AND NOT APPLE)
+    if(MKL_THREAD_LAYER STREQUAL "Sequential")
+      set_target_properties(MKL::Static
+        PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_STATIC_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES "-Wl,--start-group;MKL::Core_STATIC;MKL::Interface_STATIC;MKL::ThreadLayer_STATIC;-Wl,--end-group;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
+        IMPORTED_NO_SONAME TRUE)
+    else()
+      set_target_properties(MKL::Static
+        PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_STATIC_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES "-Wl,--start-group;MKL::Core_STATIC;MKL::Interface_STATIC;MKL::ThreadLayer_STATIC;-Wl,--end-group;MKL::ThreadingLibrary;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
+        IMPORTED_NO_SONAME TRUE)
+    endif()
+  else()
+    if(MKL_THREAD_LAYER STREQUAL "Sequential")
+      set_target_properties(MKL::Static
+        PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_STATIC_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES "MKL::Core_STATIC;MKL::Interface_STATIC;MKL::ThreadLayer_STATIC;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
+        IMPORTED_NO_SONAME TRUE)
+    else()
+      set_target_properties(MKL::Static
+        PROPERTIES
+        IMPORTED_LOCATION "${MKL_Core_STATIC_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES "MKL::Core_STATIC;MKL::Interface_STATIC;MKL::ThreadLayer_STATIC;MKL::ThreadingLibrary;${CMAKE_DL_LIBS};${M_LIB}"
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}"
+        IMPORTED_NO_SONAME TRUE)
+    endif()
+  endif()
+endif()
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 712b1f2dea..b0489be4d1 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -88,10 +88,10 @@ target_link_libraries(af
 # pass the RTLD_GLOBAL flag to dlload, but that causes issues with the ArrayFire
 # libraries. To get around this we are also linking the unified backend with
 # the MKL library
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::MKL)
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared)
   target_link_libraries(af
     PRIVATE
-      MKL::MKL)
+      MKL::Shared)
 endif()
 
 
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 6492e0d857..973bf426c9 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -287,37 +287,21 @@ target_include_directories(afcpu
     ${CBLAS_INCLUDE_DIR}
   )
 
-# TODO(umar) Find a better way to determine BLAS selection
-if(USE_CPU_MKL)
-  dependency_check(MKL_FOUND "MKL not found")
-  target_compile_definitions(afcpu PRIVATE USE_MKL)
-
-  if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" OR
-    (CMAKE_CXX_COMPILER_ID STREQUAL "Intel" AND (UNIX AND NOT APPLE)))
-    # MKL requires multiple passes when linking with the static libs. This can be
-    # done in CMake using LINK_INTERFACE_MULTIPLICITY but that will require
-    # changine the way FindCBLAS works. This can also be done using the
-    # --start-group and --end-group linker around the libraries in the linking
-    # step.
-    #
-    # TODO(umar): Change the way CBLAS libraries are found and linked
-    set(CBLAS_LIBRARIES -Wl,--start-group ${CBLAS_LIBRARIES} -Wl,--end-group)
-  endif()
-endif()
-
 target_compile_definitions(afcpu
   PRIVATE
     AF_CPU
   )
 
 if(USE_CPU_MKL)
+  dependency_check(MKL_Shared_FOUND "MKL not found")
+  target_compile_definitions(afcpu PRIVATE USE_MKL)
   target_link_libraries(afcpu
     PRIVATE
       c_api_interface
       cpp_api_interface
       afcommon_interface
       cpu_sort_by_key
-      MKL::MKL
+      MKL::Shared
       Threads::Threads
     )
 else()
@@ -345,7 +329,7 @@ else()
   endif()
 endif()
 
-if(LAPACK_FOUND OR MKL_FOUND)
+if(LAPACK_FOUND OR MKL_Shared_FOUND)
   target_compile_definitions(afcpu
     PRIVATE
       WITH_LINEAR_ALGEBRA)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 6a7e839990..513959384d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -449,7 +449,7 @@ if(APPLE)
   target_link_libraries(afopencl PRIVATE OpenGL::GL)
 endif()
 
-if(LAPACK_FOUND OR MKL_FOUND)
+if(LAPACK_FOUND OR MKL_Shared_FOUND)
   target_sources(afopencl
     PRIVATE
       magma/gebrd.cpp
@@ -484,12 +484,12 @@ if(LAPACK_FOUND OR MKL_FOUND)
       )
 
   if(USE_OPENCL_MKL)
-    dependency_check(MKL_FOUND "MKL not found")
+    dependency_check(MKL_Shared_FOUND "MKL not found")
     target_compile_definitions(afopencl PRIVATE USE_MKL)
 
     target_link_libraries(afopencl
       PRIVATE
-        MKL::MKL)
+        MKL::Shared)
   else()
     dependency_check(OpenCL_FOUND "OpenCL not found.")
 
@@ -512,7 +512,7 @@ if(LAPACK_FOUND OR MKL_FOUND)
     afopencl
     PRIVATE
       WITH_LINEAR_ALGEBRA)
-endif(LAPACK_FOUND OR MKL_FOUND)
+endif(LAPACK_FOUND OR MKL_Shared_FOUND)
 
 af_split_debug_info(afopencl ${AF_INSTALL_LIB_DIR})
 

From 615e00b09ac552cf5ee4262fe7a6a08825fadd0b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 2 Feb 2020 02:11:15 -0500
Subject: [PATCH 1813/2677] Put print_info in the ArrayFire_BINARY_DIR for all
 Operating systems

The windows runtime binary directory is different than other operating
systems. This causes issues with the print_info command in the
CTEST_CUSTOM_POST_TEST command because it is located in a different
location on windows. This sets the runtime output directory to the
same location on all OSs for print_info
---
 CMakeModules/CTestCustom.cmake | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index 69e4e72d04..ad85c05075 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -7,7 +7,11 @@
 
 set(CTEST_CUSTOM_ERROR_POST_CONTEXT 50)
 set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 50)
-set(CTEST_CUSTOM_POST_TEST ./test/print_info)
+if(WIN32)
+  set(CTEST_CUSTOM_POST_TEST ./bin/print_info.exe)
+else()
+  set(CTEST_CUSTOM_POST_TEST ./test/print_info)
+endif()
 
 list(APPEND CTEST_CUSTOM_COVERAGE_EXCLUDE
   "test"

From 2a2d6ff52c7f66dec24caa15a0ee5e9ef1cef1f2 Mon Sep 17 00:00:00 2001
From: WilliamTambellini <william.tambellini@gmail.com>
Date: Tue, 29 Oct 2019 14:20:01 -0700
Subject: [PATCH 1814/2677] Add an option to run the neural network example at
 f16

---
 examples/machine_learning/neural_network.cpp | 65 +++++++++++++++-----
 1 file changed, 51 insertions(+), 14 deletions(-)

diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index 3c4996d971..8302fdb1bd 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2019, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -18,6 +18,14 @@
 using namespace af;
 using std::vector;
 
+std::string toStr(const dtype dt) { 
+    switch(dt) {
+        case f32: return "f32";
+        case f16: return "f16";
+        default: return std::to_string(dt);
+    }
+}
+
 float accuracy(const array &predicted, const array &target) {
     array val, plabels, tlabels;
     max(val, tlabels, target, 1);
@@ -37,6 +45,7 @@ double error(const array &out, const array &pred) {
 class ann {
    private:
     int num_layers;
+    dtype datatype;
     vector<array> weights;
 
     // Add bias input to the output from previous layer
@@ -49,12 +58,12 @@ class ann {
 
    public:
     // Create a network with given parameters
-    ann(vector<int> layers, double range = 0.05);
+    ann(vector<int> layers, double range, dtype dt = f32);
 
     // Output after single pass of forward propagation
     array predict(const array &input);
 
-    // Method to trian the neural net
+    // Method to train the neural net
     double train(const array &input, const array &target, double alpha = 1.0,
                  int max_epochs = 300, int batch_size = 100,
                  double maxerr = 1.0, bool verbose = false);
@@ -62,7 +71,7 @@ class ann {
 
 array ann::add_bias(const array &in) {
     // Bias input is added on top of given input
-    return join(1, constant(1, in.dims(0), 1), in);
+    return join(1, constant(1, in.dims(0), 1, datatype), in);
 }
 
 vector<array> ann::forward_propagate(const array &input) {
@@ -84,6 +93,7 @@ void ann::back_propagate(const vector<array> signal, const array &target,
     // Get error for output layer
     array out = signal[num_layers - 1];
     array err = (out - target);
+
     int m     = target.dims(0);
 
     for (int i = num_layers - 2; i >= 0; i--) {
@@ -91,11 +101,13 @@ void ann::back_propagate(const vector<array> signal, const array &target,
         array delta = (deriv(out) * err).T();
 
         // Adjust weights
-        array grad = -(alpha * matmul(delta, in)) / m;
+        array tg = alpha * matmul(delta, in);
+        array grad = -(tg) / m;
         weights[i] += grad.T();
 
         // Input to current layer is output of previous
         out = signal[i];
+
         err = matmulTT(delta, weights[i]);
 
         // Remove the error of bias and propagate backward
@@ -103,11 +115,14 @@ void ann::back_propagate(const vector<array> signal, const array &target,
     }
 }
 
-ann::ann(vector<int> layers, double range)
-    : num_layers(layers.size()), weights(layers.size() - 1) {
-    // Generate uniformly distributed random numbers between [-range/2,range/2]
+
+ann::ann(vector<int> layers, double range, dtype dt)
+    : num_layers(layers.size()), weights(layers.size() - 1), datatype(dt) {
+    std::cout << "Initializing weights using a random uniformly distribution between " << -range/2 << " and " << range/2 << " at precision " << toStr(datatype) << std::endl;
     for (int i = 0; i < num_layers - 1; i++) {
         weights[i] = range * randu(layers[i] + 1, layers[i + 1]) - range / 2;
+        if (datatype != f32)
+            weights[i] = weights[i].as(datatype);
     }
 }
 
@@ -121,7 +136,7 @@ double ann::train(const array &input, const array &target, double alpha,
                   int max_epochs, int batch_size, double maxerr, bool verbose) {
     const int num_samples = input.dims(0);
     const int num_batches = num_samples / batch_size;
-
+    
     double err = 0;
 
     // Training the entire network
@@ -161,7 +176,7 @@ double ann::train(const array &input, const array &target, double alpha,
     return err;
 }
 
-int ann_demo(bool console, int perc) {
+int ann_demo(bool console, int perc, const dtype dt) {
     printf("** ArrayFire ANN Demo **\n\n");
 
     array train_images, test_images;
@@ -172,6 +187,11 @@ int ann_demo(bool console, int perc) {
     float frac = (float)(perc) / 100.0;
     setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
                       test_images, train_target, test_target, frac);
+    if (dt != f32) {
+        train_images = train_images.as(dt);
+        test_images = test_images.as(dt);
+        train_target = train_target.as(dt);
+    }
 
     int feature_size = train_images.elements() / num_train;
 
@@ -189,8 +209,8 @@ int ann_demo(bool console, int perc) {
     layers.push_back(50);
     layers.push_back(num_classes);
 
-    // Create network
-    ann network(layers);
+    // Create network: architecture, range, datatype
+    ann network(layers, 0.05, dt);
 
     // Train network
     timer::start();
@@ -235,15 +255,32 @@ int ann_demo(bool console, int perc) {
 }
 
 int main(int argc, char **argv) {
+    // usage:  neural_network_xxx (device) (console on/off) (percentage training/test set) (f32|f16)
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
+	if (perc < 0 || perc > 100) {
+        std::cerr << "Bad perc arg: " << perc << std::endl;
+        return EXIT_FAILURE;
+    }
+    std::string dts = argc > 4 ? argv[4] : "f32";
+    dtype dt = f32;
+    if (dts == "f16") 
+        dt = f16;
+    else if (dts != "f32") {
+        std::cerr << "Unsupported datatype " << dts << ". Supported: f32 or f16" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    if (dts == "f16" && !af::isHalfAvailable(device)) {
+        std::cerr << "Half not available for device " << device << std::endl;
+        return EXIT_FAILURE;
+    }
 
     try {
         af::setDevice(device);
         af::info();
-        return ann_demo(console, perc);
-
+        return ann_demo(console, perc, dt);
     } catch (af::exception &ae) { std::cerr << ae.what() << std::endl; }
 
     return 0;

From 930cbdc7c41d92c83380aaee80b8d4cf5cf94162 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 3 Feb 2020 19:40:49 +0530
Subject: [PATCH 1815/2677] Move matrixmarket downloads to build stage

---
 test/CMakeLists.txt                           | 18 ++--
 .../download_sparse_datasets.cmake            | 88 +++++++------------
 test/matrixmarket.cpp                         |  6 +-
 test/sparse_arith.cpp                         | 20 ++---
 4 files changed, 58 insertions(+), 74 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b42ead6834..460fe4f47f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -87,7 +87,7 @@ include(CMakeParseArguments)
 # 'BACKENDS'    Backends to target for this test. If not set then the test will
 #               compiled againat all backends
 function(make_test)
-  set(options CXX11 SERIAL)
+  set(options CXX11 SERIAL USE_MMIO)
   set(single_args SRC)
   set(multi_args LIBRARIES DEFINITIONS BACKENDS)
   cmake_parse_arguments(mt_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
@@ -141,6 +141,14 @@ function(make_test)
         AF_$<UPPER_CASE:${backend}>
         ${mt_args_DEFINITIONS}
       )
+    if(AF_TEST_WITH_MTX_FILES AND ${mt_args_USE_MMIO})
+      target_link_libraries(${target} PRIVATE mmio)
+      add_dependencies(${target} mtxDownloads)
+      target_compile_definitions(${target}
+        PRIVATE
+          MTX_TEST_DIR="${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/"
+        )
+    endif()
     if(WIN32)
       target_compile_options(${target}
         PRIVATE
@@ -284,10 +292,8 @@ make_test(SRC solve_dense.cpp       CXX11 SERIAL)
 make_test(SRC sort.cpp)
 make_test(SRC sort_by_key.cpp)
 make_test(SRC sort_index.cpp)
-make_test(SRC sparse.cpp SERIAL
-    $<$<BOOL:${AF_TEST_WITH_MTX_FILES}>:LIBRARIES mmio>)
-make_test(SRC sparse_arith.cpp
-    $<$<BOOL:${AF_TEST_WITH_MTX_FILES}>:LIBRARIES mmio>)
+make_test(SRC sparse.cpp SERIAL)
+make_test(SRC sparse_arith.cpp      USE_MMIO)
 make_test(SRC sparse_convert.cpp)
 make_test(SRC stdev.cpp)
 make_test(SRC susan.cpp)
@@ -309,7 +315,7 @@ make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
 
 if(AF_TEST_WITH_MTX_FILES)
-    make_test(SRC matrixmarket.cpp LIBRARIES mmio)
+  make_test(SRC matrixmarket.cpp USE_MMIO)
 endif()
 
 add_executable(print_info print_info.cpp)
diff --git a/test/CMakeModules/download_sparse_datasets.cmake b/test/CMakeModules/download_sparse_datasets.cmake
index 2c58a7b1c7..b7748ea5bb 100644
--- a/test/CMakeModules/download_sparse_datasets.cmake
+++ b/test/CMakeModules/download_sparse_datasets.cmake
@@ -1,77 +1,55 @@
-# Copyright (c) 2018, ArrayFire
+# Copyright (c) 2020, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-set(URL "https://sparse.tamu.edu")
+include(ExternalProject)
+
+add_custom_target(mtxDownloads)
 
-function(download_mtx name group)
-  if(AF_TEST_WITH_MTX_FILES)
-    set(file_name "${group}/${name}.tar.gz")
-    if (NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
-      file(DOWNLOAD
-          "${URL}/MM/${file_name}"
-          ${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}
-          INACTIVITY_TIMEOUT 600
-          SHOW_PROGRESS
-          STATUS out_status
-          TLS_VERIFY ON
-          )
-      list(GET out_status 0 error_code)
-      list(GET out_status 1 error_string)
-      if (${error_code} EQUAL 0)
-        message("Downloaded ${name} file from sparse.tamu.edu")
-        file(MAKE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}")
-        execute_process(
-          COMMAND ${CMAKE_COMMAND} -E tar xzf "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}"
-          WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/data/matrixmarket/${group}"
-          )
-        message("Extracted mtx files to test data directory")
-      else ()
-        if (${error_code} EQUAL 503)
-            message(WARNING "${URL} service unavailable")
-        elseif (${error_code} EQUAL 504)
-            message(WARNING "Request to ${URL} timedout")
-        elseif (${error_code} EQUAL 521)
-          # CLOUDFLARE error code
-          message(WARNING "Request to ${URL} has been refused")
-        elseif (${error_code} EQUAL 523)
-          # CLOUDFLARE error code
-          message(WARNING "${URL} is unreachable")
-        else ()
-          message(WARNING  "Failed to download ${name} file from sparse.tamu.edu")
-          message(WARNING  "Failure message: ${error_string}")
-        endif ()
-        file(REMOVE "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/${file_name}")
+set(URL "https://sparse.tamu.edu")
+set(mtx_data_dir "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket")
+file(MAKE_DIRECTORY ${mtx_data_dir})
 
-        set(AF_TEST_WITH_MTX_FILES OFF CACHE BOOL
-            "Download and run tests on large matrices form sparse.tamu.edu"
-            FORCE)
-      endif ()
-    endif ()
-  endif ()
+function(mtxDownload name group)
+  set(extproj_name mtxDownload-${group}-${name})
+  set(path_prefix "${ArrayFire_BINARY_DIR}/mtx_datasets/${group}")
+  ExternalProject_Add(
+      ${extproj_name}
+      PREFIX "${path_prefix}"
+      URL "${URL}/MM/${group}/${name}.tar.gz"
+      DOWNLOAD_NO_EXTRACT False
+      DOWNLOAD_NO_PROGRESS False
+      LOG_DOWNLOAD True
+      LOG_DIR ${PREFIX}
+      CONFIGURE_COMMAND ${CMAKE_COMMAND} -E make_directory "${mtx_data_dir}/${group}"
+      BINARY_DIR "${mtx_data_dir}/${group}"
+      BUILD_COMMAND ${CMAKE_COMMAND} -E tar xzf "${path_prefix}/src/${name}.tar.gz"
+      INSTALL_COMMAND ""
+    )
+  add_dependencies(mtxDownloads mtxDownload-${group}-${name})
 endfunction()
 
 # Following files are used for testing mtx read fn
 # integer data
-download_mtx("Trec4" "JGD_Kocay")
+mtxDownload("Trec4" "JGD_Kocay")
 # real data
-download_mtx("bcsstm02" "HB")
+mtxDownload("bcsstm02" "HB")
 # complex data
-download_mtx("young4c" "HB")
+mtxDownload("young4c" "HB")
 
 #Following files are used for sparse-sparse arith
 # real data
 #linear programming problem
-download_mtx("lpi_vol1" "LPnetlib")
-download_mtx("lpi_qual" "LPnetlib")
+mtxDownload("lpi_vol1" "LPnetlib")
+mtxDownload("lpi_qual" "LPnetlib")
 #Subsequent Circuit Simulation problem
-download_mtx("oscil_dcop_12" "Sandia")
-download_mtx("oscil_dcop_42" "Sandia")
+mtxDownload("oscil_dcop_12" "Sandia")
+mtxDownload("oscil_dcop_42" "Sandia")
 
 # complex data
 #Quantum Chemistry problem
-download_mtx("conf6_0-4x4-20" "QCD")
-download_mtx("conf6_0-4x4-30" "QCD")
+mtxDownload("conf6_0-4x4-20" "QCD")
+mtxDownload("conf6_0-4x4-30" "QCD")
diff --git a/test/matrixmarket.cpp b/test/matrixmarket.cpp
index 71b8d5c86c..700b604d50 100644
--- a/test/matrixmarket.cpp
+++ b/test/matrixmarket.cpp
@@ -12,18 +12,18 @@
 
 TEST(Sparse, ReadRealMTXFile) {
     af::array out;
-    std::string file(TEST_DIR "/matrixmarket/HB/bcsstm02/bcsstm02.mtx");
+    std::string file(MTX_TEST_DIR "HB/bcsstm02/bcsstm02.mtx");
     ASSERT_TRUE(mtxReadSparseMatrix(out, file.c_str()));
 }
 
 TEST(Sparse, ReadComplexMTXFile) {
     af::array out;
-    std::string file(TEST_DIR "/matrixmarket/HB/young4c/young4c.mtx");
+    std::string file(MTX_TEST_DIR "HB/young4c/young4c.mtx");
     ASSERT_TRUE(mtxReadSparseMatrix(out, file.c_str()));
 }
 
 TEST(Sparse, FailIntegerMTXRead) {
     af::array out;
-    std::string file(TEST_DIR "/matrixmarket/JGD_Kocay/Trec4/Trec4.mtx");
+    std::string file(MTX_TEST_DIR "JGD_Kocay/Trec4/Trec4.mtx");
     ASSERT_FALSE(mtxReadSparseMatrix(out, file.c_str()));
 }
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index ecbd30ea46..daa4d144fc 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -385,24 +385,24 @@ void ssArithmeticMTX(const char* op1, const char* op2) {
 }
 
 TEST(SparseSparseArith, LinearProgrammingData) {
-    std::string file1(TEST_DIR "/matrixmarket/LPnetlib/lpi_vol1/lpi_vol1.mtx");
-    std::string file2(TEST_DIR "/matrixmarket/LPnetlib/lpi_qual/lpi_qual.mtx");
+    std::string file1(MTX_TEST_DIR "LPnetlib/lpi_vol1/lpi_vol1.mtx");
+    std::string file2(MTX_TEST_DIR "LPnetlib/lpi_qual/lpi_qual.mtx");
     ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
 }
 
 TEST(SparseSparseArith, SubsequentCircuitSimData) {
-    std::string file1(TEST_DIR
-                      "/matrixmarket/Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
-    std::string file2(TEST_DIR
-                      "/matrixmarket/Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
+    std::string file1(MTX_TEST_DIR
+                      "Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
+    std::string file2(MTX_TEST_DIR
+                      "Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
     ssArithmeticMTX<af_sub_t>(file1.c_str(), file2.c_str());
 }
 
 TEST(SparseSparseArith, QuantumChemistryData) {
-    std::string file1(TEST_DIR
-                      "/matrixmarket/QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
-    std::string file2(TEST_DIR
-                      "/matrixmarket/QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
+    std::string file1(MTX_TEST_DIR
+                      "QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
+    std::string file2(MTX_TEST_DIR
+                      "QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
     ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
 }
 #endif

From 55955ad5d36cc77b89846d43308556ff38fadb56 Mon Sep 17 00:00:00 2001
From: Mark Poscablo <mark@arrayfire.com>
Date: Thu, 6 Feb 2020 16:40:51 -0600
Subject: [PATCH 1816/2677] Relax pinverse tests' max error tolerance if it's
 too small

---
 test/pinverse.cpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 85979f4aba..7ba9aac20c 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -110,8 +110,12 @@ double eps<cdouble>() {
 template<typename T>
 double relEps(array in) {
     typedef typename af::dtype_traits<T>::base_type InBaseType;
-    return std::numeric_limits<InBaseType>::epsilon() *
-           std::max(in.dims(0), in.dims(1)) * af::max<double>(in);
+    double fixed_eps = eps<T>();
+    double calc_eps = std::numeric_limits<InBaseType>::epsilon() *
+                      std::max(in.dims(0), in.dims(1)) * af::max<double>(in);
+    // Use the fixed values above if calculated error tolerance is unnecessarily
+    // too small
+    return std::max(fixed_eps, calc_eps);
 }
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;

From ea08aa6e47c9a14c44f9e0b58cc6c6b8fc7e74a6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Feb 2020 14:33:05 -0500
Subject: [PATCH 1817/2677] Only set platform if CMAKE_GENERATOR_PLATFORM is
 set for extern libs

In Visual Studio the CMAKE_GENERATOR_PLATFORM is not set if you use
a generator that includes the arch flag in its name.
---
 CMakeModules/build_CLBlast.cmake | 2 +-
 CMakeModules/build_clBLAS.cmake  | 2 +-
 CMakeModules/build_clFFT.cmake   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 80c82386c7..3085aef139 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -37,7 +37,7 @@ index 9446499..786f7db 100644
   set(CLBLAST_PATCH_COMMAND ${GIT} apply ${ArrayFire_BINARY_DIR}/clblast.patch)
 endif()
 
-if(WIN32 AND NOT CMAKE_GENERATOR MATCHES "Ninja")
+if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
 else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index 7d547b08b3..c30f015f1c 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -12,7 +12,7 @@ set(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${C
 
 find_package(OpenCL)
 
-if(WIN32 AND NOT CMAKE_GENERATOR MATCHES "Ninja")
+if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
 else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index e92d9fd912..e0b7716553 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -18,7 +18,7 @@ ELSE()
     SET(byproducts BUILD_BYPRODUCTS ${clFFT_location})
 ENDIF()
 
-if(WIN32 AND NOT CMAKE_GENERATOR MATCHES "Ninja")
+if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
 else()
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}")

From d3c2d418f27fd3983b5ec9821bbbf714e0f2f84f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Feb 2020 14:35:42 -0500
Subject: [PATCH 1818/2677] Only install PDB files for configurations that
 create debug symbols

---
 CMakeModules/SplitDebugInfo.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/SplitDebugInfo.cmake b/CMakeModules/SplitDebugInfo.cmake
index 060fe84bda..560fa96c9e 100644
--- a/CMakeModules/SplitDebugInfo.cmake
+++ b/CMakeModules/SplitDebugInfo.cmake
@@ -20,7 +20,8 @@ function(af_split_debug_info _target _destination_dir)
     set(SPLIT_TOOL_EXISTS OFF)
     if (MSVC)
       install(FILES
-        $<TARGET_PDB_FILE:${_target}>
+        $<$<CONFIG:Debug>:$<TARGET_PDB_FILE:${_target}>>
+        $<$<CONFIG:RelWithDebInfo>:$<TARGET_PDB_FILE:${_target}>>
         DESTINATION ${_destination_dir}
         COMPONENT "${_target}_debug_symbols"
         )

From a23dfd1bca6915074d5b42aa0f6c22db6eb66ab0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Feb 2020 15:05:17 -0500
Subject: [PATCH 1819/2677] Fix MKL library path environment variable on
 Windows

On windows the compilevars.sh script sets the LIB variable to the
location of the *.lib files. This is LIBRARY_PATH on Linux
---
 CMakeModules/FindMKL.cmake | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index dab91ff0d0..75889d9bfa 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -36,9 +36,8 @@
 #
 # ``MKL::Static``
 #   Target used to define and link all MKL libraries required by Intel's Link
-#   Line Advisor. This usually the only thing you need to link against unless
-#   you want to link against the single dynamic library version of MKL
-#   (libmkl_rt.so)
+#   Line Advisor for a static build. This will still link the threading libraries
+#   using dynamic linking as advised by the Intel Link Advisor
 #
 #  Optional:
 #
@@ -180,8 +179,6 @@ endif()
 #    MKL::${NAME}_STATIC
 #
 # Output Variables
-#    MKL_INCLUDE_DIR:                Include directory for MKL
-#    MKL_FFTW_INCLUDE_DIR:           Include directory for the MKL FFTW interface
 #    MKL_${NAME}_LINK_LIBRARY:        on Unix: *.so on Windows *.lib
 #    MKL_${NAME}_STATIC_LINK_LIBRARY: on Unix: *.a  on Windows *.lib
 #    MKL_${NAME}_DLL_LIBRARY:         on Unix: ""   on Windows *.dll
@@ -195,7 +192,11 @@ function(find_mkl_library)
   add_library(MKL::${mkl_args_NAME}        SHARED IMPORTED)
   add_library(MKL::${mkl_args_NAME}_STATIC STATIC IMPORTED)
 
-  string(REGEX REPLACE ":" ";" ENV_LIBRARY_PATHS "$ENV{LIBRARY_PATH}")
+  if(WIN32)
+    set(ENV_LIBRARY_PATHS "$ENV{LIB}")
+  else()
+    string(REGEX REPLACE ":" ";" ENV_LIBRARY_PATHS "$ENV{LIBRARY_PATH}")
+  endif()
 
   if(NOT (WIN32 AND mkl_args_DLL_ONLY))
     find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
@@ -332,7 +333,7 @@ set(MKL_RUNTIME_KERNEL_LIBRARIES "${MKL_RUNTIME_KERNEL_LIBRARIES_TMP}" CACHE STR
 mark_as_advanced(MKL_RUNTIME_KERNEL_LIBRARIES)
 
 find_package_handle_standard_args(MKL_Shared
-  FAIL_MESSAGE "Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. Looking in MKLROOT and LIBRARY_PATHS environment variables"
+  FAIL_MESSAGE "Could NOT find MKL: Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. This script searches for the libraries in MKLROOT, LIBRARY_PATHS(Linux), and LIB(Windows) environment variables"
   VERSION_VAR  MKL_VERSION_STRING
   REQUIRED_VARS MKL_INCLUDE_DIR
                 MKL_Core_LINK_LIBRARY
@@ -341,7 +342,7 @@ find_package_handle_standard_args(MKL_Shared
                 MKL_ThreadingLibrary_LINK_LIBRARY)
 
 find_package_handle_standard_args(MKL_Static
-  FAIL_MESSAGE "Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. Looking in MKLROOT and LIBRARY_PATHS environment variables"
+  FAIL_MESSAGE "Could NOT find MKL: Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. This script searches for the libraries in MKLROOT, LIBRARY_PATHS(Linux), and LIB(Windows) environment variables"
   VERSION_VAR   MKL_VERSION_STRING
   REQUIRED_VARS MKL_INCLUDE_DIR
                 MKL_Core_STATIC_LINK_LIBRARY

From a054fffd402c3be13de4661ad253cd3fb2c28a80 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Feb 2020 15:06:53 -0500
Subject: [PATCH 1820/2677] Fix a ArrayFireConfig error caused by building
 multiple configs

In a multi-config build, CMake will generate multiple files targeting
different types of builds. If you targeted one build then switched
to another when building ArrayFire, the old ArrayFire CMake config
files were still there and parsed. When multiple configs are found
CMake will return a list of configs instead of one. This case
was not handled by the current ArrayFireConfig file. Fixed in this
commit.
---
 CMakeModules/ArrayFireConfig.cmake.in | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index 28cbf942f2..0d3cdda048 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -100,14 +100,24 @@ foreach(backend Unified CPU OpenCL CUDA)
     endif()
   endif()
   if(TARGET ArrayFire::af${lowerbackend})
-    get_property(config TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_CONFIGURATIONS)
-    if(NOT config)
-      set(config "NOCONFIG")
-    endif()
-    get_property(loc TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_LOCATION_${config})
+    get_property(all_config TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_CONFIGURATIONS)
+    foreach(config IN LISTS all_config)
+      if(NOT all_config)
+        set(all_config "NOCONFIG")
+      endif()
+      get_property(loc TARGET ArrayFire::af${lowerbackend} PROPERTY IMPORTED_LOCATION_${config})
+
+      # break if any of the imported configurations exist. All configs write to the same
+      # location so they are not working as CMake intended. Its fine for single config
+      # installers like ours.
+      if(EXISTS ${loc})
+        set(ArrayFire_${backend}_BINARY_EXISTS TRUE)
+        break()
+      endif()
+    endforeach()
   endif()
 
-  if((TARGET ArrayFire::af${lowerbackend} AND EXISTS ${loc}) OR TARGET af${lowerbackend})
+  if((TARGET ArrayFire::af${lowerbackend} AND ArrayFire_${backend}_BINARY_EXISTS) OR TARGET af${lowerbackend})
       set(ArrayFire_${backend}_FOUND ON)
       set(ArrayFire_${backend}_LIBRARIES ArrayFire::af${lowerbackend})
       set(ArrayFire_LIBRARIES ArrayFire::af${lowerbackend})

From 3b7141f99737094667978231e9d6a8c1508e9751 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 7 Feb 2020 06:20:36 -0600
Subject: [PATCH 1821/2677] Reduce by key (#2254)

* FEAT: ndim reduce by key
* add docs for each reduceByKey function

Co-authored-by: pradeep <pradeep@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 docs/details/algorithm.dox                    |  194 +++
 include/af/algorithm.h                        |  463 +++++++-
 src/api/c/reduce.cpp                          |  315 ++++-
 src/api/cpp/reduce.cpp                        |   81 ++
 src/api/unified/algorithm.cpp                 |   30 +
 src/backend/cpu/kernel/reduce.hpp             |  109 +-
 src/backend/cpu/reduce.cpp                    |   56 +-
 src/backend/cpu/reduce.hpp                    |    5 +
 src/backend/cpu/set.hpp                       |    7 +-
 src/backend/cuda/CMakeLists.txt               |    2 +
 src/backend/cuda/kernel/reduce.hpp            |    6 +-
 src/backend/cuda/kernel/reduce_by_key.hpp     |  636 ++++++++++
 src/backend/cuda/kernel/shfl_intrinsics.hpp   |  112 ++
 src/backend/cuda/reduce.hpp                   |    5 +
 src/backend/cuda/reduce_impl.hpp              |  318 ++++-
 .../opencl/kernel/reduce_blocks_by_key_dim.cl |  134 +++
 .../kernel/reduce_blocks_by_key_first.cl      |  120 ++
 src/backend/opencl/kernel/reduce_by_key.hpp   |  708 +++++++++++
 .../opencl/kernel/reduce_by_key_boundary.cl   |   36 +
 .../kernel/reduce_by_key_boundary_dim.cl      |   51 +
 .../opencl/kernel/reduce_by_key_compact.cl    |   42 +
 .../kernel/reduce_by_key_compact_dim.cl       |   56 +
 .../kernel/reduce_by_key_needs_reduction.cl   |   39 +
 src/backend/opencl/reduce.hpp                 |   11 +-
 src/backend/opencl/reduce_impl.hpp            |   23 +-
 test/CMakeLists.txt                           |    4 +-
 test/data                                     |    2 +-
 test/reduce.cpp                               | 1037 ++++++++++++++++-
 test/testHelpers.hpp                          |   18 +-
 29 files changed, 4516 insertions(+), 104 deletions(-)
 create mode 100644 src/backend/cuda/kernel/reduce_by_key.hpp
 create mode 100644 src/backend/cuda/kernel/shfl_intrinsics.hpp
 create mode 100644 src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
 create mode 100644 src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
 create mode 100644 src/backend/opencl/kernel/reduce_by_key.hpp
 create mode 100644 src/backend/opencl/kernel/reduce_by_key_boundary.cl
 create mode 100644 src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
 create mode 100644 src/backend/opencl/kernel/reduce_by_key_compact.cl
 create mode 100644 src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
 create mode 100644 src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index 15ddff9927..ab28da2740 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -26,6 +26,40 @@ u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
 
+\defgroup reduce_func_sum_by_key sumByKey
+
+\ingroup reduce_mat
+
+Finds the sum of an input array according to an array of keys.
+The values corresponding to each group of consecutive equal keys will be summed
+together.  Keys can repeat, however only consecutive key values will be
+considered for each reduction. If a key value is repeated somewhere else in the
+keys array it will be considered the start of a new reduction.  There are two
+outputs: the reduced set of consecutive keys and the corresponding final
+reduced values.  An example demonstrating the reduction behavior can be seen in
+the following snippet.
+
+\snippet test/reduce.cpp ex_reduce_sum_by_key
+
+The keys input type must be an integer type(s32 or u32).
+This table defines the return types for the corresponding values type
+
+Input Type          | Output Type
+--------------------|---------------------
+f32, f64, c32, c64  | same as input
+s32, u32, s64, u64  | same as input
+s16                 | s32
+u16, u8, b8         | u32
+f16                 | f32
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_sum_by_key_dim
+
+
 
 
 \defgroup reduce_func_product product
@@ -45,6 +79,40 @@ u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
 
+\defgroup reduce_func_product_by_key productByKey
+
+\ingroup reduce_mat
+
+Finds the product of an input array according to an array of keys.
+The values corresponding to each group of consecutive equal keys will be
+multiplied together.  Keys can repeat, however only consecutive key values will
+be considered for each reduction. If a key value is repeated somewhere else in
+the keys array it will be considered the start of a new reduction.  There are
+two outputs: the reduced set of consecutive keys and the corresponding final
+reduced values.  An example demonstrating the reduction behavior can be seen in
+the following snippet.
+
+\snippet test/reduce.cpp ex_reduce_product_by_key
+
+The keys input type must be an integer type(s32 or u32).
+This table defines the return types for the corresponding values type
+
+Input Type          | Output Type
+--------------------|---------------------
+f32, f64, c32, c64  | same as input
+s32, u32, s64, u64  | same as input
+s16                 | s32
+u16, u8, b8         | u32
+f16                 | f32
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_product_by_key_dim
+
+
 
 
 \defgroup reduce_func_min min
@@ -55,6 +123,30 @@ Find the minimum values and their locations
 
 \copydoc batch_detail_algo
 
+\defgroup reduce_func_min_by_key minByKey
+
+\ingroup reduce_mat
+
+Finds the min of an input array according to an array of keys.  The minimum
+will be found of all values corresponding to each group of consecutive equal
+keys.  Keys can repeat, however only consecutive key values will be considered
+for each reduction. If a key value is repeated somewhere else in the keys array
+it will be considered the start of a new reduction.  There are two outputs:
+the reduced set of consecutive keys and the corresponding final reduced
+values.  An example demonstrating the reduction behavior can be seen in the
+following snippet.
+
+\snippet test/reduce.cpp ex_reduce_min_by_key
+
+The keys input type must be an integer type(s32 or u32).
+The values return type will be the same as the values input type.
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_min_by_key_dim
 
 
 \defgroup reduce_func_max max
@@ -66,6 +158,32 @@ Find the maximum values and their locations
 \copydoc batch_detail_algo
 
 
+\defgroup reduce_func_max_by_key maxByKey
+
+\ingroup reduce_mat
+
+Finds the max of an input array according to an array of keys.  The maximum
+will be found of all values corresponding to each group of consecutive equal
+keys.  Keys can repeat, however only consecutive key values will be considered
+for each reduction. If a key value is repeated somewhere else in the keys array
+it will be considered the start of a new reduction.  There are two outputs:
+the reduced set of consecutive keys and the corresponding final reduced
+values.  An example demonstrating the reduction behavior can be seen in the
+following snippet.
+
+\snippet test/reduce.cpp ex_reduce_max_by_key
+
+The keys input type must be an integer type(s32 or u32).
+The values return type will be the same as the values input type.
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_max_by_key_dim
+
+
 
 \defgroup reduce_func_all_true alltrue
 
@@ -77,6 +195,32 @@ Return type is b8 for all input types
 
 \copydoc batch_detail_algo
 
+\defgroup reduce_func_all_true_by_key allTrueByKey
+
+\ingroup reduce_mat
+
+Finds if all of the values of an input array are true according to an array of
+keys.  All values corresponding to each group of consecutive equal keys will be
+tested to make sure all are true.  Keys can repeat, however only consecutive
+key values will be considered for each reduction. If a key value is repeated
+somewhere else in the keys array it will be considered the start of a new
+reduction.  There are two outputs: the reduced set of consecutive keys and the
+corresponding final reduced values.  An example demonstrating the reduction
+behavior can be seen in the following snippet.
+
+\snippet test/reduce.cpp ex_reduce_alltrue_by_key
+
+The keys input type must be an integer type(s32 or u32).
+The values return type will be of type b8.
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_alltrue_by_key_dim
+
+
 
 
 \defgroup reduce_func_any_true anytrue
@@ -89,6 +233,30 @@ Return type is b8 for all input types
 
 \copydoc batch_detail_algo
 
+\defgroup reduce_func_any_true_by_key anyTrueByKey
+
+\ingroup reduce_mat
+
+Finds if any of the values of an input array are true according to an array of
+keys.  All values corresponding to each group of consecutive equal keys will be
+tested to make sure any are true.  Keys can repeat, however only consecutive
+key values will be considered for each reduction. If a key value is repeated
+somewhere else in the keys array it will be considered the start of a new
+reduction.  There are two outputs: the reduced set of consecutive keys and the
+corresponding final reduced values.  An example demonstrating the reduction
+behavior can be seen in the following snippet.
+
+\snippet test/reduce.cpp ex_reduce_anytrue_by_key
+
+The keys input type must be an integer type(s32 or u32).
+The values return type will be of type u8.
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_anytrue_by_key_dim
 
 
 \defgroup reduce_func_count count
@@ -101,6 +269,32 @@ Return type is u32 for all input types
 
 \copydoc batch_detail_algo
 
+\defgroup reduce_func_count_by_key countByKey
+
+\ingroup reduce_mat
+
+Counts the non-zero values of an input array according to an array of keys.
+All non-zero values corresponding to each group of consecutive equal keys will
+be counted.  Keys can repeat, however only consecutive key values will be
+considered for each reduction. If a key value is repeated somewhere else in the
+keys array it will be considered the start of a new reduction.  There are two
+outputs: the reduced set of consecutive keys and the corresponding final
+reduced values.  An example demonstrating the reduction behavior can be seen in
+the following snippet.
+
+\snippet test/reduce.cpp ex_reduce_count_by_key
+
+The keys input type must be an integer type(s32 or u32).
+The values return type will be of type u32.
+
+The input keys must be a 1-D vector matching the size of the reduced dimension.
+In the case of multiple dimensions in the input values array, the dim parameter
+specifies which dimension to reduce along.  An example of multi-dimensional
+reduce by key can be seen below:
+
+\snippet test/reduce.cpp ex_reduce_count_by_key_dim
+
+
 
 
 \defgroup scan_func_accum accum
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 517d65dcff..a8372c9d3e 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -34,7 +34,7 @@ namespace af
 
        \param[in] in is the input array
        \param[in] dim The dimension along which the add operation occurs
-       \param[in] nanval Replace nans with the value passed to this function
+       \param[in]  nanval   The value that will replace the NaNs in \p in
        \return    result of sum all values along dimension \p dim
 
        \ingroup reduce_func_sum
@@ -43,11 +43,48 @@ namespace af
     AFAPI array sum(const array &in, const int dim, const double nanval);
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for sum of elements along given dimension by key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the sum of all values in \p vals along
+                            \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the add operation occurs
+
+       \ingroup reduce_func_sum_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+    */
+    AFAPI void sumByKey(array &keys_out, array &vals_out,
+                        const array &keys, const array &vals,
+                        const int dim=-1);
+
+    /**
+       C++ Interface for sum of elements along given dimension by key while replacing nan values
+
+       \param[out] keys_out Will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out Will contain the sum of all values in \p vals along
+                            \p dim according to \p keys
+       \param[in]  keys     Is the key array
+       \param[in]  vals     Is the array containing the values to be reduced
+       \param[in]  dim      The dimension along which the add operation occurs
+       \param[in]  nanval   The value that will replace the NaNs in \p vals
+
+       \ingroup reduce_func_sum_by_key
+    */
+    AFAPI void sumByKey(array &keys_out, array &vals_out,
+                        const array &keys, const array &vals,
+                        const int dim, const double nanval);
+#endif
+
     /**
        C++ Interface for product of elements in an array
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the multiply operation occurs
+       \param[in] in     The input array
+       \param[in] dim    The dimension along which the multiply operation occurs
        \return    result of product all values along dimension \p dim
 
        \ingroup reduce_func_product
@@ -58,19 +95,59 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for product of elements in an array while replacing nan values
+       C++ Interface for product of elements in an array while replacing nan
+       values
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the add operation occurs
-       \param[in] nanval Replace nans with the value passed to this function
+       \param[in] in      The input array
+       \param[in] dim     The dimension along which the multiply operation occurs
+       \param[in] nanval  The value that will replace the NaNs in \p in
        \return    result of product all values along dimension \p dim
 
        \ingroup reduce_func_product
-
     */
     AFAPI array product(const array &in, const int dim, const double nanval);
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for product of elements in an array according to a key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the product of all values in \p vals
+                            along \p dim according to \p keys
+       \param[in]  keys     The key array
+       \param[in]  vals     The array containing the values to be reduced
+       \param[in]  dim      The dimension along which the product operation occurs
+
+       \ingroup reduce_func_product_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+    */
+    AFAPI void productByKey(array &keys_out, array &vals_out,
+                            const array &keys, const array &vals,
+                            const int dim = -1);
+
+    /**
+       C++ Interface for product of elements in an array according to a key
+       while replacing nan values
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p
+                            dim
+       \param[out] vals_out will contain the product of all values in \p
+                            vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the product operation occurs
+       \param[in] nanval  The value that will replace the NaNs in \p vals
+
+       \ingroup reduce_func_product_by_key
+
+    */
+    AFAPI void productByKey(array &keys_out, array &vals_out,
+                            const array &keys, const array &vals,
+                            const int dim, const double nanval);
+#endif
+
     /**
        C++ Interface for minimum values in an array
 
@@ -85,6 +162,26 @@ namespace af
     */
     AFAPI array min(const array &in, const int dim = -1);
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for minimum values in an array according to a key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the minimum of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the min operation occurs
+
+       \ingroup reduce_func_min_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \note NaN values are ignored
+    */
+    AFAPI void minByKey(array &keys_out, array &vals_out,
+                        const array &keys, const array &vals,
+                        const int dim = -1);
+#endif
+
     /**
        C++ Interface for maximum values in an array
 
@@ -99,6 +196,26 @@ namespace af
     */
     AFAPI array max(const array &in, const int dim = -1);
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for maximum values in an array according to a key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the maximum of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the max operation occurs
+
+       \ingroup reduce_func_max_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \note NaN values are ignored
+    */
+    AFAPI void maxByKey(array &keys_out, array &vals_out,
+                        const array &keys, const array &vals,
+                        const int dim = -1);
+#endif
+
     /**
        C++ Interface for checking all true values in an array
 
@@ -113,6 +230,26 @@ namespace af
     */
     AFAPI array allTrue(const array &in, const int dim = -1);
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for checking all true values in an array according to a key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the reduced and of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the all true operation occurs
+
+       \ingroup reduce_func_alltrue_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \note NaN values are ignored
+    */
+    AFAPI void allTrueByKey(array &keys_out, array &vals_out,
+                            const array &keys, const array &vals,
+                            const int dim = -1);
+#endif
+
     /**
        C++ Interface for checking any true values in an array
 
@@ -127,6 +264,26 @@ namespace af
     */
     AFAPI array anyTrue(const array &in, const int dim = -1);
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for checking any true values in an array according to a key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the reduced or of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the any true operation occurs
+
+       \ingroup reduce_func_anytrue_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \note NaN values are ignored
+    */
+    AFAPI void anyTrueByKey(array &keys_out, array &vals_out,
+                            const array &keys, const array &vals,
+                            const int dim = -1);
+#endif
+
     /**
        C++ Interface for counting non-zero values in an array
 
@@ -141,6 +298,26 @@ namespace af
     */
     AFAPI array count(const array &in, const int dim = -1);
 
+#if AF_API_VERSION >= 37
+    /**
+       C++ Interface for counting non-zero values in an array according to a key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the count of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the count operation occurs
+
+       \ingroup reduce_func_count_by_key
+
+       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \note NaN values are treated as non zero.
+    */
+    AFAPI void countByKey(array &keys_out, array &vals_out,
+                          const array &keys, const array &vals,
+                          const int dim = -1);
+#endif
+
     /**
        C++ Interface for sum of all elements in an array
 
@@ -153,10 +330,11 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for sum of all elements in an array while replacing nan values
+       C++ Interface for sum of all elements in an array while replacing nan
+       values
 
        \param[in] in is the input array
-       \param[in] nanval  Replace nans with the value passed to this function
+       \param[in] nanval  The value that will replace the NaNs in \p in
        \return    the sum of all values of \p in
 
        \ingroup reduce_func_sum
@@ -176,10 +354,11 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for product of all elements in an array while replacing nan values
+       C++ Interface for product of all elements in an array while replacing nan
+       values
 
        \param[in] in is the input array
-       \param[in] nanval  Replace nans with the value passed to this function
+       \param[in] nanval  The value that will replace the NaNs in \p in
        \return    the product of all values of \p in
 
        \ingroup reduce_func_product
@@ -187,6 +366,7 @@ namespace af
     template<typename T> T product(const array &in, double nanval);
 #endif
 
+
     /**
        C++ Interface for getting minimum value of an array
 
@@ -328,7 +508,8 @@ namespace af
 
        \ingroup scan_func_scan
     */
-    AFAPI array scan(const array &in, const int dim = 0, binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
+    AFAPI array scan(const array &in, const int dim = 0,
+                     binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
 
     /**
        C++ Interface generalized scan by key of an array
@@ -342,7 +523,8 @@ namespace af
 
        \ingroup scan_func_scanbykey
     */
-    AFAPI array scanByKey(const array &key, const array& in, const int dim = 0, binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
+    AFAPI array scanByKey(const array &key, const array& in, const int dim = 0,
+                          binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
 #endif
 
     /**
@@ -387,7 +569,8 @@ namespace af
 
        \ingroup sort_func_sort
     */
-    AFAPI array sort(const array &in, const unsigned dim = 0, const bool isAscending = true);
+    AFAPI array sort(const array &in, const unsigned dim = 0,
+                     const bool isAscending = true);
 
     /**
        C++ Interface for sorting an array and getting original indices
@@ -414,8 +597,9 @@ namespace af
 
        \ingroup sort_func_sort_keys
     */
-    AFAPI void  sort(array &out_keys, array &out_values, const array &keys, const array &values,
-                     const unsigned dim = 0, const bool isAscending = true);
+    AFAPI void  sort(array &out_keys, array &out_values, const array &keys,
+                     const array &values, const unsigned dim = 0,
+                     const bool isAscending = true);
 
     /**
        C++ Interface for getting unique values
@@ -438,7 +622,8 @@ namespace af
 
        \ingroup set_func_union
     */
-    AFAPI array setUnion(const array &first, const array &second, const bool is_unique=false);
+    AFAPI array setUnion(const array &first, const array &second,
+                         const bool is_unique=false);
 
     /**
        C++ Interface for finding the intersection of two arrays
@@ -450,7 +635,8 @@ namespace af
 
        \ingroup set_func_intersect
     */
-    AFAPI array setIntersect(const array &first, const array &second, const bool is_unique=false);
+    AFAPI array setIntersect(const array &first, const array &second,
+                             const bool is_unique=false);
 }
 #endif
 
@@ -477,12 +663,52 @@ extern "C" {
        \param[out] out will contain the sum of all values in \p in along \p dim
        \param[in] in is the input array
        \param[in] dim The dimension along which the add operation occurs
-       \param[in] nanval Replace nans with the value passed to this function
+       \param[in] nanval  The value that will replace the NaNs in \p in
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup reduce_func_sum
     */
-    AFAPI af_err af_sum_nan(af_array *out, const af_array in, const int dim, const double nanval);
+    AFAPI af_err af_sum_nan(af_array *out, const af_array in,
+                            const int dim, const double nanval);
+#endif
+
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for sum of elements in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the sum of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the add operation occurs
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_sum_by_key
+    */
+    AFAPI af_err af_sum_by_key(af_array *keys_out, af_array *vals_out,
+                               const af_array keys, const af_array vals, const int dim);
+
+    /**
+       C Interface for sum of elements in an array according to key while
+       replacing nans
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p
+                            dim
+       \param[out] vals_out will contain the sum of all values in \p vals
+                            along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the add operation occurs
+       \param[in] nanval  The value that will replace the NaNs in \p vals
+
+
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_sum_by_key
+    */
+    AFAPI af_err af_sum_by_key_nan(af_array *keys_out, af_array *vals_out,
+                                   const af_array keys, const af_array vals,
+                                   const int dim, const double nanval);
 #endif
 
     /**
@@ -501,10 +727,11 @@ extern "C" {
     /**
        C Interface for product of elements in an array while replacing nans
 
-       \param[out] out will contain the product of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the add operation occurs
-       \param[in] nanval Replace nans with the value passed to this function
+       \param[out] out will contain the product of all values in \p in along \p
+                       dim
+       \param[in] in   is the input array
+       \param[in] dim  The dimension along which the product operation occurs
+       \param[in] nanval  The value that will replace the NaNs in \p in
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup reduce_func_product
@@ -512,6 +739,43 @@ extern "C" {
     AFAPI af_err af_product_nan(af_array *out, const af_array in, const int dim, const double nanval);
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for product of elements in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the product of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the product operation occurs
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_product_by_key
+    */
+    AFAPI af_err af_product_by_key(af_array *keys_out, af_array *vals_out,
+                                   const af_array keys, const af_array vals, const int dim);
+
+    /**
+       C Interface for product of elements in an array according to key while
+       replacing nans
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p
+                            dim
+       \param[out] vals_out will contain the product of all values in \p
+                            vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the product operation occurs
+       \param[in] nanval  The value that will replace the NaNs in \p vals
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_product_by_key
+    */
+    AFAPI af_err af_product_by_key_nan(af_array *keys_out, af_array *vals_out,
+                                       const af_array keys, const af_array vals,
+                                       const int dim, const double nanval);
+#endif
+
     /**
        C Interface for minimum values in an array
 
@@ -524,6 +788,24 @@ extern "C" {
     */
     AFAPI af_err af_min(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for minimum values in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the minimum of all values in \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the minimum value is extracted
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_min_by_key
+    */
+    AFAPI af_err af_min_by_key(af_array *keys_out, af_array *vals_out,
+                               const af_array keys, const af_array vals,
+                               const int dim);
+#endif
+
     /**
        C Interface for maximum values in an array
 
@@ -536,6 +818,26 @@ extern "C" {
     */
     AFAPI af_err af_max(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for maximum values in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p
+                            dim
+       \param[out] vals_out will contain the maximum of all values in \p
+                            vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the maximum value is extracted
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_max_by_key
+    */
+    AFAPI af_err af_max_by_key(af_array *keys_out, af_array *vals_out,
+                               const af_array keys, const af_array vals,
+                               const int dim);
+#endif
+
     /**
        C Interface for checking all true values in an array
 
@@ -548,6 +850,25 @@ extern "C" {
     */
     AFAPI af_err af_all_true(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for checking all true values in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the the reduced and of all values in
+                            \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the "and" operation occurs
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_alltrue_by_key
+    */
+    AFAPI af_err af_all_true_by_key(af_array *keys_out, af_array *vals_out,
+                                    const af_array keys, const af_array vals,
+                                    const int dim);
+#endif
+
     /**
        C Interface for checking any true values in an array
 
@@ -560,6 +881,25 @@ extern "C" {
     */
     AFAPI af_err af_any_true(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for checking any true values in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the reduced or of all values in
+                            \p vals along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the "or" operation occurs
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_anytrue_by_key
+    */
+    AFAPI af_err af_any_true_by_key(af_array *keys_out, af_array *vals_out,
+                                    const af_array keys, const af_array vals,
+                                    const int dim);
+#endif
+
     /**
        C Interface for counting non-zero values in an array
 
@@ -572,11 +912,32 @@ extern "C" {
     */
     AFAPI af_err af_count(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for counting non-zero values in an array according to key
+
+       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
+       \param[out] vals_out will contain the count of all values in \p vals
+                            along \p dim according to \p keys
+       \param[in] keys is the key array
+       \param[in] vals is the array containing the values to be reduced
+       \param[in] dim The dimension along which the non-zero values are counted
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_count_by_key
+    */
+    AFAPI af_err af_count_by_key(af_array *keys_out, af_array *vals_out,
+                                 const af_array keys, const af_array vals,
+                                 const int dim);
+#endif
+
     /**
        C Interface for sum of all elements in an array
 
-       \param[out] real will contain the real part of adding all elements in input \p in
-       \param[out] imag will contain the imaginary part of adding all elements in input \p in
+       \param[out] real will contain the real part of adding all elements in
+                        input \p in
+       \param[out] imag will contain the imaginary part of adding all elements
+                        in input \p in
        \param[in] in is the input array
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -590,8 +951,10 @@ extern "C" {
     /**
        C Interface for sum of all elements in an array while replacing nans
 
-       \param[out] real will contain the real part of adding all elements in input \p in
-       \param[out] imag will contain the imaginary part of adding all elements in input \p in
+       \param[out] real will contain the real part of adding all elements in
+                        input \p in
+       \param[out] imag will contain the imaginary part of adding all elements
+                        in input \p in
        \param[in] in is the input array
        \param[in] nanval is the value which replaces nan
        \return \ref AF_SUCCESS if the execution completes properly
@@ -600,7 +963,8 @@ extern "C" {
 
        \ingroup reduce_func_sum
     */
-    AFAPI af_err af_sum_nan_all(double *real, double *imag, const af_array in, const double nanval);
+    AFAPI af_err af_sum_nan_all(double *real, double *imag,
+                                const af_array in, const double nanval);
 #endif
 
     /**
@@ -621,17 +985,20 @@ extern "C" {
     /**
        C Interface for product of all elements in an array while replacing nans
 
-       \param[out] real will contain the real part of adding all elements in input \p in
-       \param[out] imag will contain the imaginary part of adding all elements in input \p in
-       \param[in] in is the input array
-       \param[in] nanval is the value which replaces nan
+       \param[out] real   will contain the real part of multiplication of all
+                          elements in input \p in
+       \param[out] imag   will contain the imaginary part of multiplication of
+                          all elements in input \p in
+       \param[in]  in     is the input array
+       \param[in]  nanval is the value which replaces nan
        \return \ref AF_SUCCESS if the execution completes properly
 
        \note \p imag is always set to 0 when \p in is real
 
        \ingroup reduce_func_product
     */
-    AFAPI af_err af_product_nan_all(double *real, double *imag, const af_array in, const double nanval);
+    AFAPI af_err af_product_nan_all(double *real, double *imag,
+                                    const af_array in, const double nanval);
 #endif
 
     /**
@@ -715,7 +1082,8 @@ extern "C" {
 
        \ingroup reduce_func_min
     */
-    AFAPI af_err af_imin(af_array *out, af_array *idx, const af_array in, const int dim);
+    AFAPI af_err af_imin(af_array *out, af_array *idx, const af_array in,
+                         const int dim);
 
     /**
        C Interface for getting maximum values and their locations in an array
@@ -728,7 +1096,8 @@ extern "C" {
 
        \ingroup reduce_func_max
     */
-    AFAPI af_err af_imax(af_array *out, af_array *idx, const af_array in, const int dim);
+    AFAPI af_err af_imax(af_array *out, af_array *idx, const af_array in,
+                         const int dim);
 
     /**
        C Interface for getting minimum value and its location from the entire array
@@ -743,7 +1112,8 @@ extern "C" {
 
        \ingroup reduce_func_min
     */
-    AFAPI af_err af_imin_all(double *real, double *imag, unsigned *idx, const af_array in);
+    AFAPI af_err af_imin_all(double *real, double *imag, unsigned *idx,
+                             const af_array in);
 
     /**
        C Interface for getting maximum value and it's location from the entire array
@@ -785,7 +1155,8 @@ extern "C" {
 
        \ingroup scan_func_scan
     */
-    AFAPI af_err af_scan(af_array *out, const af_array in, const int dim, af_binary_op op, bool inclusive_scan);
+    AFAPI af_err af_scan(af_array *out, const af_array in, const int dim,
+                         af_binary_op op, bool inclusive_scan);
 
     /**
        C Interface generalized scan by key of an array
@@ -800,7 +1171,10 @@ extern "C" {
 
        \ingroup scan_func_scanbykey
     */
-    AFAPI af_err af_scan_by_key(af_array *out, const af_array key, const af_array in, const int dim, af_binary_op op, bool inclusive_scan);
+    AFAPI af_err af_scan_by_key(af_array *out, const af_array key,
+                                const af_array in, const int dim,
+                                af_binary_op op, bool inclusive_scan);
+
 #endif
 
     /**
@@ -849,7 +1223,8 @@ extern "C" {
 
        \ingroup sort_func_sort
     */
-    AFAPI af_err af_sort(af_array *out, const af_array in, const unsigned dim, const bool isAscending);
+    AFAPI af_err af_sort(af_array *out, const af_array in, const unsigned dim,
+                         const bool isAscending);
 
     /**
        C Interface for sorting an array and getting original indices
@@ -905,7 +1280,8 @@ extern "C" {
 
        \ingroup set_func_union
     */
-    AFAPI af_err af_set_union(af_array *out, const af_array first, const af_array second, const bool is_unique);
+    AFAPI af_err af_set_union(af_array *out, const af_array first,
+                              const af_array second, const bool is_unique);
 
     /**
        C Interface for finding the intersection of two arrays
@@ -918,7 +1294,8 @@ extern "C" {
 
        \ingroup set_func_intersect
     */
-    AFAPI af_err af_set_intersect(af_array *out, const af_array first, const af_array second, const bool is_unique);
+    AFAPI af_err af_set_intersect(af_array *out, const af_array first,
+                                  const af_array second, const bool is_unique);
 
 #ifdef __cplusplus
 }
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 8fdb55996a..82909584bb 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -31,6 +31,42 @@ static inline af_array reduce(const af_array in, const int dim,
         reduce<op, Ti, To>(getArray<Ti>(in), dim, change_nan, nanval));
 }
 
+template<af_op_t op, typename Ti, typename Tk, typename To>
+static inline void reduce_by_key(af_array *keys_out, af_array *vals_out,
+                                 const af_array keys, const af_array vals,
+                                 const int dim, bool change_nan,
+                                 double nanval) {
+    Array<Tk> oKeyArray = createEmptyArray<Tk>(dim4());
+    Array<To> oValArray = createEmptyArray<To>(dim4());
+
+    reduce_by_key<op, Ti, Tk, To>(oKeyArray, oValArray, getArray<Tk>(keys),
+                                  getArray<Ti>(vals), dim, change_nan, nanval);
+
+    *keys_out = getHandle(oKeyArray);
+    *vals_out = getHandle(oValArray);
+}
+
+template<af_op_t op, typename Ti, typename To>
+static inline void reduce_key(af_array *keys_out, af_array *vals_out,
+                              const af_array keys, const af_array vals,
+                              const int dim, bool change_nan = false,
+                              double nanval = 0.0) {
+    const ArrayInfo &key_info = getInfo(keys);
+    af_dtype type             = key_info.getType();
+
+    switch (type) {
+        case s32:
+            reduce_by_key<op, Ti, int, To>(keys_out, vals_out, keys, vals, dim,
+                                           change_nan, nanval);
+            break;
+        case u32:
+            reduce_by_key<op, Ti, uint, To>(keys_out, vals_out, keys, vals, dim,
+                                            change_nan, nanval);
+            break;
+        default: TYPE_ERROR(2, type);
+    }
+}
+
 template<af_op_t op, typename To>
 static af_err reduce_type(af_array *out, const af_array in, const int dim) {
     try {
@@ -71,6 +107,70 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim) {
     return AF_SUCCESS;
 }
 
+template<af_op_t op, typename To>
+static af_err reduce_by_key_type(af_array *keys_out, af_array *vals_out,
+                                 const af_array keys, const af_array vals,
+                                 const int dim) {
+    try {
+        ARG_ASSERT(4, dim >= 0);
+        ARG_ASSERT(4, dim < 4);
+
+        const ArrayInfo &kinfo   = getInfo(keys);
+        const ArrayInfo &in_info = getInfo(vals);
+        af_dtype type            = in_info.getType();
+
+        ARG_ASSERT(2, kinfo.isVector());
+        ARG_ASSERT(2, in_info.dims()[dim] == kinfo.elements());
+
+        switch (type) {
+            case f32:
+                reduce_key<op, float, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case f64:
+                reduce_key<op, double, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case c32:
+                reduce_key<op, cfloat, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case c64:
+                reduce_key<op, cdouble, To>(keys_out, vals_out, keys, vals,
+                                            dim);
+                break;
+            case u32:
+                reduce_key<op, uint, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case s32:
+                reduce_key<op, int, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case u64:
+                reduce_key<op, uintl, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case s64:
+                reduce_key<op, intl, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case u16:
+                reduce_key<op, ushort, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case s16:
+                reduce_key<op, short, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case b8:
+                reduce_key<op, char, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case u8:
+                reduce_key<op, uchar, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case f16:
+                reduce_key<op, half, To>(keys_out, vals_out, keys, vals, dim);
+                break;
+            default: TYPE_ERROR(3, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 template<af_op_t op>
 static af_err reduce_common(af_array *out, const af_array in, const int dim) {
     try {
@@ -108,9 +208,79 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim) {
     return AF_SUCCESS;
 }
 
+template<af_op_t op>
+static af_err reduce_by_key_common(af_array *keys_out, af_array *vals_out,
+                                   const af_array keys, const af_array vals,
+                                   const int dim) {
+    try {
+        ARG_ASSERT(4, dim >= 0);
+        ARG_ASSERT(4, dim < 4);
+
+        const ArrayInfo &kinfo   = getInfo(keys);
+        const ArrayInfo &in_info = getInfo(vals);
+        af_dtype type            = in_info.getType();
+
+        ARG_ASSERT(2, kinfo.isVector());
+        ARG_ASSERT(2, in_info.dims()[dim] == kinfo.dims()[0]);
+
+        switch (type) {
+            case f32:
+                reduce_key<op, float, float>(keys_out, vals_out, keys, vals,
+                                             dim);
+                break;
+            case f64:
+                reduce_key<op, double, double>(keys_out, vals_out, keys, vals,
+                                               dim);
+                break;
+            case c32:
+                reduce_key<op, cfloat, cfloat>(keys_out, vals_out, keys, vals,
+                                               dim);
+                break;
+            case c64:
+                reduce_key<op, cdouble, cdouble>(keys_out, vals_out, keys, vals,
+                                                 dim);
+                break;
+            case u32:
+                reduce_key<op, uint, uint>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case s32:
+                reduce_key<op, int, int>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case u64:
+                reduce_key<op, uintl, uintl>(keys_out, vals_out, keys, vals,
+                                             dim);
+                break;
+            case s64:
+                reduce_key<op, intl, intl>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case u16:
+                reduce_key<op, ushort, ushort>(keys_out, vals_out, keys, vals,
+                                               dim);
+                break;
+            case s16:
+                reduce_key<op, short, short>(keys_out, vals_out, keys, vals,
+                                             dim);
+                break;
+            case b8:
+                reduce_key<op, char, char>(keys_out, vals_out, keys, vals, dim);
+                break;
+            case u8:
+                reduce_key<op, uchar, uchar>(keys_out, vals_out, keys, vals,
+                                             dim);
+            case f16:
+                reduce_key<op, half, half>(keys_out, vals_out, keys, vals, dim);
+                break;
+            default: TYPE_ERROR(1, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 template<af_op_t op>
 static af_err reduce_promote(af_array *out, const af_array in, const int dim,
-                             bool change_nan = false, double nanval = 0) {
+                             bool change_nan = false, double nanval = 0.0) {
     try {
         ARG_ASSERT(2, dim >= 0);
         ARG_ASSERT(2, dim < 4);
@@ -180,6 +350,83 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
     return AF_SUCCESS;
 }
 
+template<af_op_t op>
+static af_err reduce_promote_by_key(af_array *keys_out, af_array *vals_out,
+                                    const af_array keys, const af_array vals,
+                                    const int dim, bool change_nan = false,
+                                    double nanval = 0.0) {
+    try {
+        ARG_ASSERT(4, dim >= 0);
+        ARG_ASSERT(4, dim < 4);
+
+        const ArrayInfo &kinfo   = getInfo(keys);
+        const ArrayInfo &in_info = getInfo(vals);
+        af_dtype type            = in_info.getType();
+
+        ARG_ASSERT(2, kinfo.isVector());
+        ARG_ASSERT(2, in_info.dims()[dim] == kinfo.dims()[0]);
+
+        switch (type) {
+            case f32:
+                reduce_key<op, float, float>(keys_out, vals_out, keys, vals,
+                                             dim, change_nan, nanval);
+                break;
+            case f64:
+                reduce_key<op, double, double>(keys_out, vals_out, keys, vals,
+                                               dim, change_nan, nanval);
+                break;
+            case c32:
+                reduce_key<op, cfloat, cfloat>(keys_out, vals_out, keys, vals,
+                                               dim, change_nan, nanval);
+                break;
+            case c64:
+                reduce_key<op, cdouble, cdouble>(keys_out, vals_out, keys, vals,
+                                                 dim, change_nan, nanval);
+                break;
+            case u32:
+                reduce_key<op, uint, uint>(keys_out, vals_out, keys, vals, dim,
+                                           change_nan, nanval);
+                break;
+            case s32:
+                reduce_key<op, int, int>(keys_out, vals_out, keys, vals, dim,
+                                         change_nan, nanval);
+                break;
+            case u64:
+                reduce_key<op, uintl, uintl>(keys_out, vals_out, keys, vals,
+                                             dim, change_nan, nanval);
+                break;
+            case s64:
+                reduce_key<op, intl, intl>(keys_out, vals_out, keys, vals, dim,
+                                           change_nan, nanval);
+                break;
+            case u16:
+                reduce_key<op, ushort, uint>(keys_out, vals_out, keys, vals,
+                                             dim, change_nan, nanval);
+                break;
+            case s16:
+                reduce_key<op, short, int>(keys_out, vals_out, keys, vals, dim,
+                                           change_nan, nanval);
+                break;
+            case u8:
+                reduce_key<op, uchar, uint>(keys_out, vals_out, keys, vals, dim,
+                                            change_nan, nanval);
+                break;
+            case b8:
+                reduce_key<af_notzero_t, char, uint>(
+                    keys_out, vals_out, keys, vals, dim, change_nan, nanval);
+                break;
+            case f16:
+                reduce_key<op, half, float>(keys_out, vals_out, keys, vals, dim,
+                                            change_nan, nanval);
+                break;
+            default: TYPE_ERROR(3, type);
+        }
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 af_err af_min(af_array *out, const af_array in, const int dim) {
     return reduce_common<af_min_t>(out, in, dim);
 }
@@ -218,6 +465,63 @@ af_err af_any_true(af_array *out, const af_array in, const int dim) {
     return reduce_type<af_or_t, char>(out, in, dim);
 }
 
+// by key versions
+af_err af_min_by_key(af_array *keys_out, af_array *vals_out,
+                     const af_array keys, const af_array vals, const int dim) {
+    return reduce_by_key_common<af_min_t>(keys_out, vals_out, keys, vals, dim);
+}
+
+af_err af_max_by_key(af_array *keys_out, af_array *vals_out,
+                     const af_array keys, const af_array vals, const int dim) {
+    return reduce_by_key_common<af_max_t>(keys_out, vals_out, keys, vals, dim);
+}
+
+af_err af_sum_by_key(af_array *keys_out, af_array *vals_out,
+                     const af_array keys, const af_array vals, const int dim) {
+    return reduce_promote_by_key<af_add_t>(keys_out, vals_out, keys, vals, dim);
+}
+
+af_err af_product_by_key(af_array *keys_out, af_array *vals_out,
+                         const af_array keys, const af_array vals,
+                         const int dim) {
+    return reduce_promote_by_key<af_mul_t>(keys_out, vals_out, keys, vals, dim);
+}
+
+af_err af_sum_by_key_nan(af_array *keys_out, af_array *vals_out,
+                         const af_array keys, const af_array vals,
+                         const int dim, const double nanval) {
+    return reduce_promote_by_key<af_add_t>(keys_out, vals_out, keys, vals, dim,
+                                           true, nanval);
+}
+
+af_err af_product_by_key_nan(af_array *keys_out, af_array *vals_out,
+                             const af_array keys, const af_array vals,
+                             const int dim, const double nanval) {
+    return reduce_promote_by_key<af_mul_t>(keys_out, vals_out, keys, vals, dim,
+                                           true, nanval);
+}
+
+af_err af_count_by_key(af_array *keys_out, af_array *vals_out,
+                       const af_array keys, const af_array vals,
+                       const int dim) {
+    return reduce_by_key_type<af_notzero_t, uint>(keys_out, vals_out, keys,
+                                                  vals, dim);
+}
+
+af_err af_all_true_by_key(af_array *keys_out, af_array *vals_out,
+                          const af_array keys, const af_array vals,
+                          const int dim) {
+    return reduce_by_key_type<af_and_t, char>(keys_out, vals_out, keys, vals,
+                                              dim);
+}
+
+af_err af_any_true_by_key(af_array *keys_out, af_array *vals_out,
+                          const af_array keys, const af_array vals,
+                          const int dim) {
+    return reduce_by_key_type<af_or_t, char>(keys_out, vals_out, keys, vals,
+                                             dim);
+}
+
 template<af_op_t op, typename Ti, typename To>
 static inline To reduce_all(const af_array in, bool change_nan = false,
                             double nanval = 0) {
@@ -294,9 +598,7 @@ static af_err reduce_all_common(double *real_val, double *imag_val,
             case u8:
                 *real_val = (double)reduce_all<op, uchar, uchar>(in);
                 break;
-            case f16:
-                *real_val = (double)reduce_all<op, half, half>(in);
-                break;
+            case f16: *real_val = (double)reduce_all<op, half, half>(in); break;
 
             case c32:
                 cfval = reduce_all<op, cfloat, cfloat>(in);
@@ -381,7 +683,6 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
                         in, change_nan, nanval);
                 }
             } break;
-
             case c32:
                 cfval = reduce_all<op, cfloat, cfloat>(in);
                 ARG_ASSERT(1, imag_val != NULL);
@@ -396,8 +697,8 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
                 *imag_val = imag(cdval);
                 break;
             case f16:
-                *real_val = (double)reduce_all<op, half, float>(in, change_nan,
-                                                                nanval);
+                *real_val =
+                    (double)reduce_all<op, half, float>(in, change_nan, nanval);
                 break;
 
             default: TYPE_ERROR(1, type);
diff --git a/src/api/cpp/reduce.cpp b/src/api/cpp/reduce.cpp
index a7c3a91a02..15c16365f5 100644
--- a/src/api/cpp/reduce.cpp
+++ b/src/api/cpp/reduce.cpp
@@ -26,6 +26,24 @@ array sum(const array &in, const int dim, const double nanval) {
     return array(out);
 }
 
+void sumByKey(array &keys_out, array &vals_out, const array &keys,
+              const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_sum_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                           getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
+void sumByKey(array &keys_out, array &vals_out, const array &keys,
+              const array &vals, const int dim, const double nanval) {
+    af_array okeys, ovals;
+    AF_THROW(
+        af_sum_by_key_nan(&okeys, &ovals, keys.get(), vals.get(), dim, nanval));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 array product(const array &in, const int dim) {
     af_array out = 0;
     AF_THROW(af_product(&out, in.get(), getFNSD(dim, in.dims())));
@@ -38,6 +56,24 @@ array product(const array &in, const int dim, const double nanval) {
     return array(out);
 }
 
+void productByKey(array &keys_out, array &vals_out, const array &keys,
+                  const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_product_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                               getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
+void productByKey(array &keys_out, array &vals_out, const array &keys,
+                  const array &vals, const int dim, const double nanval) {
+    af_array okeys, ovals;
+    AF_THROW(af_product_by_key_nan(&okeys, &ovals, keys.get(), vals.get(), dim,
+                                   nanval));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 array mul(const array &in, const int dim) { return product(in, dim); }
 
 array min(const array &in, const int dim) {
@@ -46,12 +82,30 @@ array min(const array &in, const int dim) {
     return array(out);
 }
 
+void minByKey(array &keys_out, array &vals_out, const array &keys,
+              const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_min_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                           getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 array max(const array &in, const int dim) {
     af_array out = 0;
     AF_THROW(af_max(&out, in.get(), getFNSD(dim, in.dims())));
     return array(out);
 }
 
+void maxByKey(array &keys_out, array &vals_out, const array &keys,
+              const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_max_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                           getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 // 2.1 compatibility
 array alltrue(const array &in, const int dim) { return allTrue(in, dim); }
 array allTrue(const array &in, const int dim) {
@@ -60,6 +114,15 @@ array allTrue(const array &in, const int dim) {
     return array(out);
 }
 
+void allTrueByKey(array &keys_out, array &vals_out, const array &keys,
+                  const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_all_true_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                                getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 // 2.1 compatibility
 array anytrue(const array &in, const int dim) { return anyTrue(in, dim); }
 array anyTrue(const array &in, const int dim) {
@@ -68,12 +131,30 @@ array anyTrue(const array &in, const int dim) {
     return array(out);
 }
 
+void anyTrueByKey(array &keys_out, array &vals_out, const array &keys,
+                  const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_any_true_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                                getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 array count(const array &in, const int dim) {
     af_array out = 0;
     AF_THROW(af_count(&out, in.get(), getFNSD(dim, in.dims())));
     return array(out);
 }
 
+void countByKey(array &keys_out, array &vals_out, const array &keys,
+                const array &vals, const int dim) {
+    af_array okeys, ovals;
+    AF_THROW(af_count_by_key(&okeys, &ovals, keys.get(), vals.get(),
+                             getFNSD(dim, vals.dims())));
+    keys_out = array(okeys);
+    vals_out = array(ovals);
+}
+
 void min(array &val, array &idx, const array &in, const int dim) {
     af_array out = 0;
     af_array loc = 0;
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index 32fde88613..2e115e8470 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -30,6 +30,23 @@ ALGO_HAPI_DEF(af_diff2)
 
 #undef ALGO_HAPI_DEF
 
+#define ALGO_HAPI_DEF_BYKEY(af_func)                                          \
+    af_err af_func(af_array *keys_out, af_array *vals_out,                    \
+                   const af_array keys, const af_array vals, const int dim) { \
+        CHECK_ARRAYS(keys, vals);                                             \
+        CALL(af_func, keys_out, vals_out, keys, vals, dim);              \
+    }
+
+ALGO_HAPI_DEF_BYKEY(af_sum_by_key)
+ALGO_HAPI_DEF_BYKEY(af_product_by_key)
+ALGO_HAPI_DEF_BYKEY(af_min_by_key)
+ALGO_HAPI_DEF_BYKEY(af_max_by_key)
+ALGO_HAPI_DEF_BYKEY(af_all_true_by_key)
+ALGO_HAPI_DEF_BYKEY(af_any_true_by_key)
+ALGO_HAPI_DEF_BYKEY(af_count_by_key)
+
+#undef ALGO_HAPI_DEF_BYKEY
+
 #define ALGO_HAPI_DEF(af_func_nan)                                      \
     af_err af_func_nan(af_array *out, const af_array in, const int dim, \
                        const double nanval) {                           \
@@ -42,6 +59,19 @@ ALGO_HAPI_DEF(af_product_nan)
 
 #undef ALGO_HAPI_DEF
 
+#define ALGO_HAPI_DEF_BYKEY(af_func_nan)                         \
+    af_err af_func_nan(af_array *keys_out, af_array *vals_out,   \
+                       const af_array keys, const af_array vals, \
+                       const int dim, const double nanval) {     \
+        CHECK_ARRAYS(keys, vals);                                \
+        CALL(af_func_nan, keys_out, vals_out, keys, vals, dim, nanval);  \
+    }
+
+ALGO_HAPI_DEF_BYKEY(af_sum_by_key_nan)
+ALGO_HAPI_DEF_BYKEY(af_product_by_key_nan)
+
+#undef ALGO_HAPI_DEF_BYKEY
+
 #define ALGO_HAPI_DEF(af_func_all)                                      \
     af_err af_func_all(double *real, double *imag, const af_array in) { \
         CHECK_ARRAYS(in);                                               \
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index d1d8a71459..99f10970b8 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ops.hpp>
 #include <common/half.hpp>
+#include <ops.hpp>
 
 namespace cpu {
 namespace kernel {
@@ -45,9 +45,9 @@ struct reduce_dim<op, Ti, To, 0> {
         const af::dim4 istrides = in.strides();
         const af::dim4 idims    = in.dims();
 
-        data_t<To> * const outPtr      = out.get() + outOffset;
-        data_t<Ti> const* const inPtr = in.get() + inOffset;
-        dim_t stride          = istrides[dim];
+        data_t<To> *const outPtr      = out.get() + outOffset;
+        data_t<Ti> const *const inPtr = in.get() + inOffset;
+        dim_t stride                  = istrides[dim];
 
         compute_t<To> out_val = Binary<compute_t<To>, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
@@ -60,5 +60,106 @@ struct reduce_dim<op, Ti, To, 0> {
     }
 };
 
+template<typename Tk>
+void n_reduced_keys(Param<Tk> okeys, int *n_reduced, CParam<Tk> keys) {
+    const af::dim4 kstrides = keys.strides();
+    const af::dim4 kdims    = keys.dims();
+
+    Tk *const outKeysPtr      = okeys.get();
+    Tk const *const inKeysPtr = keys.get();
+
+    int nkeys      = 0;
+    Tk current_key = inKeysPtr[0];
+    for (dim_t i = 0; i < kdims[0]; i++) {
+        Tk keyval = inKeysPtr[i];
+
+        if (keyval != current_key) {
+            outKeysPtr[nkeys] = current_key;
+            current_key       = keyval;
+            ++nkeys;
+        }
+
+        if (i == (kdims[0] - 1)) { outKeysPtr[nkeys] = current_key; }
+    }
+
+    *n_reduced = nkeys + 1;
+}
+
+template<af_op_t op, typename Ti, typename Tk, typename To, int D>
+struct reduce_dim_by_key {
+    void operator()(Param<To> ovals, const dim_t ovOffset, CParam<Tk> keys,
+                    CParam<Ti> vals, const dim_t vOffset, int *n_reduced,
+                    const int dim, bool change_nan, double nanval) {
+        static const int D1 = D - 1;
+        reduce_dim_by_key<op, Ti, Tk, To, D1> reduce_by_key_dim_next;
+
+        const af::dim4 ovstrides = ovals.strides();
+        const af::dim4 vstrides  = vals.strides();
+        const af::dim4 vdims     = ovals.dims();
+
+        if (D1 == dim) {
+            reduce_by_key_dim_next(ovals, ovOffset, keys, vals, vOffset,
+                                   n_reduced, dim, change_nan, nanval);
+        } else {
+            for (dim_t i = 0; i < vdims[D1]; i++) {
+                reduce_by_key_dim_next(ovals, ovOffset + (i * ovstrides[D1]),
+                                       keys, vals, vOffset + (i * vstrides[D1]),
+                                       n_reduced, dim, change_nan, nanval);
+            }
+        }
+    }
+};
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+struct reduce_dim_by_key<op, Ti, Tk, To, 0> {
+    Transform<data_t<Ti>, compute_t<To>, op> transform;
+    Binary<compute_t<To>, op> reduce;
+    void operator()(Param<To> ovals, const dim_t ovOffset, CParam<Tk> keys,
+                    CParam<Ti> vals, const dim_t vOffset, int *n_reduced,
+                    const int dim, bool change_nan, double nanval) {
+        const af::dim4 kstrides = keys.strides();
+        const af::dim4 kdims    = keys.dims();
+
+        const af::dim4 vstrides = vals.strides();
+        const af::dim4 vdims    = vals.dims();
+
+        const af::dim4 ovstrides = ovals.strides();
+        const af::dim4 ovdims    = ovals.dims();
+
+        data_t<Tk> const *const inKeysPtr = keys.get();
+        data_t<Ti> const *const inValsPtr = vals.get();
+        data_t<To> *const outValsPtr      = ovals.get();
+
+        int keyidx                = 0;
+        compute_t<Tk> current_key = compute_t<Tk>(inKeysPtr[0]);
+        compute_t<To> out_val     = reduce.init();
+
+        dim_t istride = vstrides[dim];
+        dim_t ostride = ovstrides[dim];
+
+        for (dim_t i = 0; i < vdims[dim]; i++) {
+            dim_t off            = vOffset;
+            compute_t<Tk> keyval = inKeysPtr[i];
+
+            if (keyval == current_key) {
+                compute_t<To> in_val =
+                    transform(inValsPtr[vOffset + (i * istride)]);
+                if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
+                out_val = reduce(in_val, out_val);
+
+            } else {
+                outValsPtr[ovOffset + (keyidx * ostride)] = out_val;
+
+                current_key = keyval;
+                out_val     = transform(inValsPtr[vOffset + (i * istride)]);
+                ++keyidx;
+            }
+
+            if (i == (vdims[dim] - 1)) {
+                outValsPtr[ovOffset + (keyidx * ostride)] = out_val;
+            }
+        }
+    }
+};
 }  // namespace kernel
 }  // namespace cpu
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index cae71e102f..8795ce8ff7 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -56,6 +56,49 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
     return out;
 }
 
+template<af_op_t op, typename Ti, typename Tk, typename To>
+using reduce_dim_func_by_key =
+    std::function<void(Param<To> ovals, const dim_t ovOffset, CParam<Tk> keys,
+                       CParam<Ti> vals, const dim_t vOffset, int *n_reduced,
+                       const int dim, bool change_nan, double nanval)>;
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan, double nanval) {
+    dim4 okdims = keys.dims();
+    dim4 ovdims = vals.dims();
+
+    int n_reduced;
+    Array<Tk> fullsz_okeys = createEmptyArray<Tk>(okdims);
+    getQueue().enqueue(kernel::n_reduced_keys<Tk>, fullsz_okeys, &n_reduced,
+                       keys);
+    getQueue().sync();
+
+    okdims[0]   = n_reduced;
+    ovdims[dim] = n_reduced;
+
+    std::vector<af_seq> index;
+    for (int i = 0; i < keys.ndims(); ++i) {
+        af_seq s = {0.0, (double)okdims[i] - 1, 1.0};
+        index.push_back(s);
+    }
+    Array<Tk> okeys = createSubArray<Tk>(fullsz_okeys, index, true);
+    Array<To> ovals = createEmptyArray<To>(ovdims);
+
+    static const reduce_dim_func_by_key<op, Ti, Tk, To> reduce_funcs[4] = {
+        kernel::reduce_dim_by_key<op, Ti, Tk, To, 1>(),
+        kernel::reduce_dim_by_key<op, Ti, Tk, To, 2>(),
+        kernel::reduce_dim_by_key<op, Ti, Tk, To, 3>(),
+        kernel::reduce_dim_by_key<op, Ti, Tk, To, 4>()};
+
+    getQueue().enqueue(reduce_funcs[vals.ndims() - 1], ovals, 0, keys, vals, 0,
+                       &n_reduced, dim, change_nan, nanval);
+
+    keys_out = okeys;
+    vals_out = ovals;
+}
+
 template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     in.eval();
@@ -83,8 +126,7 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
                 for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
 
-                    compute_t<To> in_val =
-                        transform(inPtr[idx]);
+                    compute_t<To> in_val = transform(inPtr[idx]);
                     if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
                     out = reduce(in_val, out);
                 }
@@ -99,7 +141,13 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     template Array<To> reduce<ROp, Ti, To>(const Array<Ti> &in, const int dim, \
                                            bool change_nan, double nanval);    \
     template To reduce_all<ROp, Ti, To>(const Array<Ti> &in, bool change_nan,  \
-                                        double nanval);
+                                        double nanval);                        \
+    template void reduce_by_key<ROp, Ti, int, To>(                             \
+        Array<int> & keys_out, Array<To> & vals_out, const Array<int> &keys,   \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template void reduce_by_key<ROp, Ti, uint, To>(                            \
+        Array<uint> & keys_out, Array<To> & vals_out, const Array<uint> &keys, \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval);
 
 // min
 INSTANTIATE(af_min_t, float, float)
@@ -152,8 +200,8 @@ INSTANTIATE(af_add_t, short, int)
 INSTANTIATE(af_add_t, short, float)
 INSTANTIATE(af_add_t, ushort, uint)
 INSTANTIATE(af_add_t, ushort, float)
-INSTANTIATE(af_add_t, half, half)
 INSTANTIATE(af_add_t, half, float)
+INSTANTIATE(af_add_t, half, half)
 
 // mul
 INSTANTIATE(af_mul_t, float, float)
diff --git a/src/backend/cpu/reduce.hpp b/src/backend/cpu/reduce.hpp
index e8acbd9543..7a1d3381be 100644
--- a/src/backend/cpu/reduce.hpp
+++ b/src/backend/cpu/reduce.hpp
@@ -15,6 +15,11 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
                  double nanval = 0);
 
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan = false, double nanval = 0);
+
 template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
 }  // namespace cpu
diff --git a/src/backend/cpu/set.hpp b/src/backend/cpu/set.hpp
index eac24a6ba3..bddb668baf 100644
--- a/src/backend/cpu/set.hpp
+++ b/src/backend/cpu/set.hpp
@@ -7,17 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #include <Array.hpp>
 
 namespace cpu {
-template<typename T>
+template <typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted);
 
-template<typename T>
+template <typename T>
 Array<T> setUnion(const Array<T> &first, const Array<T> &second,
                   const bool is_unique);
 
-template<typename T>
+template <typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique);
 }  // namespace cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9405c2a0c1..b8956bbc17 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -326,6 +326,7 @@ cuda_add_library(afcuda
     kernel/random_engine_threefry.hpp
     kernel/range.hpp
     kernel/reduce.hpp
+    kernel/reduce_by_key.hpp
     kernel/regions.hpp
     kernel/reorder.hpp
     kernel/resize.hpp
@@ -338,6 +339,7 @@ cuda_add_library(afcuda
     kernel/scan_first_by_key_impl.hpp
     kernel/select.hpp
     kernel/shared.hpp
+    kernel/shfl_intrinsics.hpp
     kernel/sift_nonfree.hpp
     kernel/sobel.hpp
     kernel/sort.hpp
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index be5c59e975..bfd9fb56ea 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
@@ -25,6 +26,7 @@ using std::unique_ptr;
 
 namespace cuda {
 namespace kernel {
+
 template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
 __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
                                          uint blocks_x, uint blocks_y,
@@ -74,7 +76,8 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
     for (int id = id_dim_in; is_valid && (id < in.dims[dim]);
          id += offset_dim * blockDim.y) {
         compute_t<To> in_val = transform(*iptr);
-        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : compute_t<To>(nanval);
+        if (change_nan)
+            in_val = !IS_NAN(in_val) ? in_val : compute_t<To>(nanval);
         out_val = reduce(in_val, out_val);
         iptr    = iptr + offset_dim * blockDim.y * istride_dim;
     }
@@ -358,7 +361,6 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
                 in.strides[k] = in_elements;
             }
         }
-
         uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
         threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
         uint threads_y = THREADS_PER_BLOCK / threads_x;
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
new file mode 100644
index 0000000000..8eddecf490
--- /dev/null
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -0,0 +1,636 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/dispatch.hpp>
+#include <debug_cuda.hpp>
+#include <err_cuda.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <ops.hpp>
+#include <type_traits>
+#include "config.hpp"
+
+#include <cub/device/device_reduce.cuh>
+#include <kernel/shfl_intrinsics.hpp>
+
+using std::unique_ptr;
+
+const static unsigned int FULL_MASK = 0xFFFFFFFF;
+
+namespace cuda {
+namespace kernel {
+
+// Reduces keys across block boundaries
+template<typename Tk, typename To, af_op_t op>
+__global__ void final_boundary_reduce(int *reduced_block_sizes, Param<Tk> keys,
+                                      Param<To> vals, const int n) {
+    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    Binary<compute_t<To>, op> reduce;
+
+    if (tid == ((blockIdx.x + 1) * blockDim.x) - 1 &&
+        blockIdx.x < gridDim.x - 1) {
+        Tk k0 = keys.ptr[tid];
+        Tk k1 = keys.ptr[tid + 1];
+        if (k0 == k1) {
+            compute_t<To> v0                = compute_t<To>(vals.ptr[tid]);
+            compute_t<To> v1                = compute_t<To>(vals.ptr[tid + 1]);
+            vals.ptr[tid + 1]               = reduce(v0, v1);
+            reduced_block_sizes[blockIdx.x] = blockDim.x - 1;
+        } else {
+            reduced_block_sizes[blockIdx.x] = blockDim.x;
+        }
+    }
+
+    // if last block, set block size to difference between n and block boundary
+    if (threadIdx.x == 0 && blockIdx.x == gridDim.x - 1) {
+        reduced_block_sizes[blockIdx.x] = n - (blockIdx.x * blockDim.x);
+    }
+}
+
+// Tests if data needs further reduction, including across block boundaries
+template<typename Tk>
+__global__ void test_needs_reduction(int *needs_another_reduction,
+                                     int *needs_block_boundary_reduced,
+                                     CParam<Tk> keys_in, const int n) {
+    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    Tk k;
+
+    if (tid < n) { k = keys_in.ptr[tid]; }
+
+    int update_key = (k == shfl_down_sync(FULL_MASK, k, 1)) &&
+                     (tid < (n - 1)) && ((threadIdx.x % 32) < 31);
+    int remaining_updates = any_sync(FULL_MASK, update_key);
+
+    __syncthreads();
+
+    if (remaining_updates && (threadIdx.x % 32 == 0)) atomicOr(needs_another_reduction, remaining_updates);
+
+    // check across warp boundaries
+    if ((tid + 1) < n) { k = keys_in.ptr[tid + 1]; }
+
+    update_key = (k == shfl_down_sync(FULL_MASK, k, 1)) &&
+                 ((tid + 1) < (n - 1)) && ((threadIdx.x % 32) < 31);
+    remaining_updates = any_sync(FULL_MASK, update_key);
+
+    // TODO: single per warp? change to assignment rather than atomicOr
+    if (remaining_updates) atomicOr(needs_another_reduction, remaining_updates);
+
+    // last thread in each block checks if any inter-block keys need further
+    // reduction
+    if (tid == ((blockIdx.x + 1) * blockDim.x) - 1 &&
+        blockIdx.x < gridDim.x - 1) {
+        int k0 = keys_in.ptr[tid];
+        int k1 = keys_in.ptr[tid + 1];
+        if (k0 == k1) { atomicOr(needs_block_boundary_reduced, 1); }
+    }
+}
+
+// Compacts "incomplete" block-sized chunks of data in global memory
+template<typename Tk, typename To>
+__global__ void compact(int *reduced_block_sizes, Param<Tk> keys_out,
+                        Param<To> vals_out, CParam<Tk> keys_in,
+                        CParam<To> vals_in, const int nBlocksZ) {
+    const int tidx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int bidy = blockIdx.y;
+    const int bidz = blockIdx.z % nBlocksZ;
+    const int bidw = blockIdx.z / nBlocksZ;
+
+    Tk k;
+    To v;
+
+    // reduced_block_sizes should have inclusive sum of block sizes
+    int nwrite = (blockIdx.x == 0) ? reduced_block_sizes[0]
+                                   : reduced_block_sizes[blockIdx.x] -
+                                         reduced_block_sizes[blockIdx.x - 1];
+    int writeloc = (blockIdx.x == 0) ? 0 : reduced_block_sizes[blockIdx.x - 1];
+
+    const int bOffset = bidw * vals_in.strides[3] + bidz * vals_in.strides[2] +
+                        bidy * vals_in.strides[1];
+    k = keys_in.ptr[tidx];
+    v = vals_in.ptr[bOffset + tidx];
+
+    if (threadIdx.x < nwrite) {
+        keys_out.ptr[writeloc + threadIdx.x]           = k;
+        vals_out.ptr[bOffset + writeloc + threadIdx.x] = v;
+    }
+}
+
+// Compacts "incomplete" block-sized chunks of data in global memory
+template<typename Tk, typename To>
+__global__ void compact_dim(int *reduced_block_sizes, Param<Tk> keys_out,
+                            Param<To> vals_out, CParam<Tk> keys_in,
+                            CParam<To> vals_in, const int dim,
+                            const int nBlocksZ) {
+    __shared__ int dim_ordering[4];
+    if (threadIdx.x == 0) {
+        int d           = 1;
+        dim_ordering[0] = dim;
+        for (int i = 0; i < 4; ++i) {
+            if (i != dim) dim_ordering[d++] = i;
+        }
+    }
+    __syncthreads();
+
+    const int tidx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int bidy = blockIdx.y;
+    const int bidz = blockIdx.z % nBlocksZ;
+    const int bidw = blockIdx.z / nBlocksZ;
+
+    Tk k;
+    To v;
+
+    // reduced_block_sizes should have inclusive sum of block sizes
+    int nwrite = (blockIdx.x == 0) ? reduced_block_sizes[0]
+                                   : reduced_block_sizes[blockIdx.x] -
+                                         reduced_block_sizes[blockIdx.x - 1];
+    int writeloc = (blockIdx.x == 0) ? 0 : reduced_block_sizes[blockIdx.x - 1];
+
+    const int tid = bidw * vals_in.strides[dim_ordering[3]] +
+                    bidz * vals_in.strides[dim_ordering[2]] +
+                    bidy * vals_in.strides[dim_ordering[1]] +
+                    tidx * vals_in.strides[dim];
+    k = keys_in.ptr[tidx];
+    v = vals_in.ptr[tid];
+
+    if (threadIdx.x < nwrite) {
+        keys_out.ptr[writeloc + threadIdx.x] = k;
+        const int bOffset = bidw * vals_out.strides[dim_ordering[3]] +
+                            bidz * vals_out.strides[dim_ordering[2]] +
+                            bidy * vals_out.strides[dim_ordering[1]];
+        vals_out
+            .ptr[bOffset + (writeloc + threadIdx.x) * vals_in.strides[dim]] = v;
+    }
+}
+
+const static int maxResPerWarp = 32;  // assume dim 0, no NAN values
+
+// Reduces each block by key
+template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
+__global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
+                                            Param<Tk> reduced_keys,
+                                            Param<To> reduced_vals,
+                                            CParam<Tk> keys, CParam<Ti> vals,
+                                            int n, bool change_nan, To nanval,
+                                            const int nBlocksZ) {
+    const int tidx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int bidy = blockIdx.y;
+    const int bidz = blockIdx.z % nBlocksZ;
+    const int bidw = blockIdx.z / nBlocksZ;
+
+    const int laneid = tidx % 32;
+
+    const int nWarps = DIMX / 32;
+
+    //
+    // Allocate and initialize shared memory
+
+    __shared__ int
+        warpReduceSizes[nWarps];  // number of reduced elements in each warp
+
+    __shared__ compute_t<Tk> warpReduceKeys[nWarps]
+                                           [maxResPerWarp];  // reduced key
+                                                             // segments for
+                                                             // each warp
+    __shared__ compute_t<To> warpReduceVals[nWarps]
+                                           [maxResPerWarp];  // reduced values
+                                                             // for each warp
+                                                             // corresponding to
+                                                             // each key segment
+
+    // space to hold left/right-most keys of each reduced warp to check if
+    // reduction should happen across boundaries
+    __shared__ compute_t<Tk> warpReduceLeftBoundaryKeys[nWarps];
+    __shared__ compute_t<Tk> warpReduceRightBoundaryKeys[nWarps];
+
+    // space to hold right-most values of each reduced warp to check if
+    // reduction should happen across boundaries
+    __shared__ compute_t<To> warpReduceRightBoundaryVals[nWarps];
+
+    // space to compact and finalize all reductions within block
+    __shared__ compute_t<Tk> warpReduceKeysSmemFinal[nWarps * maxResPerWarp];
+    __shared__ compute_t<To> warpReduceValsSmemFinal[nWarps * maxResPerWarp];
+
+    //
+    // will hold final number of reduced elements in block
+    __shared__ int reducedBlockSize;
+
+    if (threadIdx.x == 0) { reducedBlockSize = 0; }
+    if (threadIdx.x < nWarps * maxResPerWarp)
+        warpReduceValsSmemFinal[threadIdx.x] = scalar<compute_t<To>>(0);
+    __syncthreads();
+
+    Binary<compute_t<To>, op> reduce;
+    Transform<compute_t<Ti>, compute_t<To>, op> transform;
+
+    // load keys and values to threads
+    compute_t<Tk> k;
+    compute_t<To> v;
+    if (tidx < n) {
+        const int tid = bidw * vals.strides[3] + bidz * vals.strides[2] +
+                        bidy * vals.strides[1] +
+                        tidx;  // index for batched inputs
+        k = keys.ptr[tidx];
+        v = transform(compute_t<Ti>(vals.ptr[tid]));
+        if (change_nan) v = IS_NAN(v) ? compute_t<To>(nanval) : v;
+    } else {
+        v = Binary<compute_t<To>, op>::init();
+    }
+
+    compute_t<Tk> eq_check = (k != shfl_up_sync(FULL_MASK, k, 1));
+    // mark threads containing unique keys
+    char unique_flag = (eq_check || (laneid == 0)) && (tidx < n);
+
+    // scan unique flags to enumerate unique keys
+    char unique_id = unique_flag;
+#pragma unroll
+    for (int offset = 1; offset < 32; offset <<= 1) {
+        char y = shfl_up_sync(FULL_MASK, unique_id, offset);
+        if (laneid >= offset) unique_id += y;
+    }
+
+    //
+    // Reduce each warp by key
+    char all_eq = (k == shfl_down_sync(FULL_MASK, k, 1));
+    if (all_sync(FULL_MASK,
+                 all_eq)) {  // check special case of single key per warp
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 1));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 2));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 4));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 8));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 16));
+    } else {
+        compute_t<To> init = Binary<compute_t<To>, op>::init();
+        int eq_check, update_key;
+        unsigned shflmask;
+        #pragma unroll
+        for (int delta = 1; delta < 32; delta <<= 1) {
+            eq_check = (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
+
+            // checks if this thread should perform a reduction
+            update_key = eq_check && (laneid < (32-delta)) && ((tidx + delta) < n);
+
+            // obtains mask of all threads that should be reduced
+            shflmask = ballot_sync(FULL_MASK, update_key);
+
+            // shifts mask to include source threads that should participate in _shfl
+            shflmask |= (shflmask << delta);
+
+            // shfls data from neighboring threads
+            compute_t<To> uval = shfl_down_sync(shflmask, v, delta);
+
+            // update if thread requires it
+            v = reduce(v, (update_key ? uval : init));
+        }
+    }
+
+    const int warpid = threadIdx.x / 32;
+
+    // last thread in warp has reduced warp size due to scan^
+    if (laneid == 31) { warpReduceSizes[warpid] = unique_id; }
+
+    // write left boundary values for each warp
+    if (unique_flag && unique_id == 1) {
+        warpReduceLeftBoundaryKeys[warpid] = k;
+    }
+
+    // write right boundary values for each warp
+    if (unique_flag && unique_id == warpReduceSizes[warpid]) {
+        warpReduceRightBoundaryKeys[warpid] = k;
+        warpReduceRightBoundaryVals[warpid] = v;
+    }
+
+    __syncthreads();
+
+    // if rightmost thread, check next warp's kv,
+    // invalidate self and change warpReduceSizes since first thread of next
+    // warp will update same key
+    // TODO: what if extra empty warps???
+    if (unique_flag && unique_id == warpReduceSizes[warpid] &&
+        warpid < nWarps - 1) {
+        int tid_next_warp = (blockIdx.x * blockDim.x + (warpid + 1) * 32);
+        // check within data range
+        if (tid_next_warp < n && k == warpReduceLeftBoundaryKeys[warpid + 1]) {
+            // disable writing from warps that need carry but aren't terminal
+            if (warpReduceSizes[warpid] > 1 || warpid > 0) { unique_flag = 0; }
+        }
+    }
+    __syncthreads();
+
+    // if leftmost thread, reduce carryover from previous warp(s) if needed
+    if (unique_flag && unique_id == 1 && warpid > 0) {
+        int test_wid = warpid - 1;
+        while (test_wid >= 0 && k == warpReduceRightBoundaryKeys[test_wid]) {
+            v = reduce(v, warpReduceRightBoundaryVals[test_wid]);
+            --warpReduceSizes[test_wid];
+            if (warpReduceSizes[test_wid] > 1) break;
+
+            --test_wid;
+        }
+    }
+
+    if (unique_flag) {
+        warpReduceKeys[warpid][unique_id - 1] = k;
+        warpReduceVals[warpid][unique_id - 1] = v;
+    }
+
+    __syncthreads();
+
+    // at this point, we have nWarps lists in shared memory with each list's
+    // size located in the warpReduceSizes[] array
+    // perform warp-scan to determine each warp's write location
+    int warpSzScan = 0;
+    if (warpid == 0 && laneid < nWarps) {
+        warpSzScan     = warpReduceSizes[laneid];
+        int activemask = 0xFFFFFFFF >> (32 - nWarps);
+#pragma unroll
+        for (int offset = 1; offset < 32; offset <<= 1) {
+            char y = __shfl_up_sync(activemask, warpSzScan, offset);
+            if (laneid >= offset) warpSzScan += y;
+        }
+        warpReduceSizes[laneid] = warpSzScan;
+        // final thread has final reduced size of block
+        if (laneid == nWarps - 1) reducedBlockSize = warpSzScan;
+    }
+    __syncthreads();
+
+    // write reduced block size to global memory
+    if (threadIdx.x == 0) {
+        reduced_block_sizes[blockIdx.x] = reducedBlockSize;
+    }
+
+    // compact reduced keys and values before writing to global memory
+    if (warpid > 0) {
+        int wsz = warpReduceSizes[warpid] - warpReduceSizes[warpid - 1];
+        if (laneid < wsz) {
+            int warpOffset = warpReduceSizes[warpid - 1];
+            warpReduceKeysSmemFinal[warpOffset + laneid] =
+                warpReduceKeys[warpid][laneid];
+            warpReduceValsSmemFinal[warpOffset + laneid] =
+                warpReduceVals[warpid][laneid];
+        }
+    } else {
+        int wsz = warpReduceSizes[warpid];
+        if (laneid < wsz) {
+            warpReduceKeysSmemFinal[laneid] = warpReduceKeys[0][laneid];
+            warpReduceValsSmemFinal[laneid] = warpReduceVals[0][laneid];
+        }
+    }
+    __syncthreads();
+
+    const int bOffset = bidw * reduced_vals.strides[3] +
+                        bidz * reduced_vals.strides[2] +
+                        bidy * reduced_vals.strides[1];
+    // write reduced keys/values per-block
+    if (threadIdx.x < reducedBlockSize) {
+        reduced_keys.ptr[(blockIdx.x * blockDim.x) + threadIdx.x] =
+            warpReduceKeysSmemFinal[threadIdx.x];
+        reduced_vals.ptr[bOffset + (blockIdx.x * blockDim.x) + threadIdx.x] =
+            warpReduceValsSmemFinal[threadIdx.x];
+    }
+}
+
+// Reduces each block by key
+template<typename Ti, typename Tk, typename To, af_op_t op, uint DIMX>
+__global__ static void reduce_blocks_dim_by_key(
+    int *reduced_block_sizes, Param<Tk> reduced_keys, Param<To> reduced_vals,
+    CParam<Tk> keys, CParam<Ti> vals, int n, bool change_nan, To nanval,
+    int dim, const int nBlocksZ) {
+    const int tidx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int bidy = blockIdx.y;
+    const int bidz = blockIdx.z % nBlocksZ;
+    const int bidw = blockIdx.z / nBlocksZ;
+
+    const int laneid = tidx % 32;
+    const int nWarps = DIMX / 32;
+
+    //
+    // Allocate and initialize shared memory
+
+    __shared__ int
+        warpReduceSizes[nWarps];  // number of reduced elements in each warp
+
+    __shared__ Tk warpReduceKeys[nWarps][maxResPerWarp];  // reduced key
+                                                          // segments for each
+                                                          // warp
+    __shared__ compute_t<To> warpReduceVals[nWarps]
+                                           [maxResPerWarp];  // reduced values
+                                                             // for each warp
+                                                             // corresponding to
+                                                             // each key segment
+
+    // space to hold left/right-most keys of each reduced warp to check if
+    // reduction should happen accros boundaries
+    __shared__ Tk warpReduceLeftBoundaryKeys[nWarps];
+    __shared__ Tk warpReduceRightBoundaryKeys[nWarps];
+
+    // space to hold right-most values of each reduced warp to check if
+    // reduction should happen accros boundaries
+    __shared__ compute_t<To> warpReduceRightBoundaryVals[nWarps];
+
+    // space to compact and finalize all reductions within block
+    __shared__ Tk warpReduceKeysSmemFinal[nWarps * maxResPerWarp];
+    __shared__ compute_t<To> warpReduceValsSmemFinal[nWarps * maxResPerWarp];
+
+    //
+    // will hold final number of reduced elements in block
+    __shared__ int reducedBlockSize;
+    __shared__ int dim_ordering[4];
+
+    compute_t<To> init = Binary<compute_t<To>, op>::init();
+
+    if (threadIdx.x == 0) {
+        reducedBlockSize = 0;
+        int d            = 1;
+        dim_ordering[0]  = dim;
+        for (int i = 0; i < 4; ++i) {
+            if (i != dim) dim_ordering[d++] = i;
+        }
+    }
+    if (threadIdx.x < nWarps * maxResPerWarp)
+        warpReduceValsSmemFinal[threadIdx.x] = init;
+    __syncthreads();
+
+    Binary<compute_t<To>, op> reduce;
+    Transform<compute_t<Ti>, compute_t<To>, op> transform;
+
+    // load keys and values to threads
+    Tk k;
+    compute_t<To> v;
+    if (tidx < n) {
+        const int tid = bidw * vals.strides[dim_ordering[3]] +
+                        bidz * vals.strides[dim_ordering[2]] +
+                        bidy * vals.strides[dim_ordering[1]] +
+                        tidx * vals.strides[dim];  // index for batched inputs
+
+        k = keys.ptr[tidx];
+        v = transform(compute_t<Ti>(vals.ptr[tid]));
+        if (change_nan) v = IS_NAN(v) ? compute_t<To>(nanval) : v;
+    } else {
+        v = init;
+    }
+
+    Tk eq_check = (k != shfl_up_sync(FULL_MASK, k, 1));
+    // mark threads containing unique keys
+    char unique_flag = (eq_check || (laneid == 0)) && (tidx < n);
+
+    // scan unique flags to enumerate unique keys
+    char unique_id = unique_flag;
+#pragma unroll
+    for (int offset = 1; offset < 32; offset <<= 1) {
+        char y = shfl_up_sync(FULL_MASK, unique_id, offset);
+        if (laneid >= offset) unique_id += y;
+    }
+
+    //
+    // Reduce each warp by key
+    char all_eq = (k == shfl_down_sync(FULL_MASK, k, 1));
+    if (all_sync(FULL_MASK,
+                 all_eq)) {  // check special case of single key per warp
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 1));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 2));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 4));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 8));
+        v = reduce(v, shfl_down_sync(FULL_MASK, v, 16));
+    } else {
+        compute_t<To> init = Binary<compute_t<To>, op>::init();
+        int eq_check, update_key;
+        unsigned shflmask;
+        #pragma unroll
+        for (int delta = 1; delta < 32; delta <<= 1) {
+            eq_check = (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
+
+            // checks if this thread should perform a reduction
+            update_key = eq_check && (laneid < (32-delta)) && ((tidx + delta) < n);
+
+            // obtains mask of all threads that should be reduced
+            shflmask = ballot_sync(FULL_MASK, update_key);
+
+            // shifts mask to include source threads that should participate in _shfl
+            shflmask |= (shflmask << delta);
+
+            // shfls data from neighboring threads
+            compute_t<To> uval = shfl_down_sync(shflmask, v, delta);
+
+            // update if thread requires it
+            v = reduce(v, (update_key ? uval : init));
+        }
+    }
+
+    const int warpid = threadIdx.x / 32;
+
+    // last thread in warp has reduced warp size due to scan^
+    if (laneid == 31) { warpReduceSizes[warpid] = unique_id; }
+
+    // write left boundary values for each warp
+    if (unique_flag && unique_id == 1) {
+        warpReduceLeftBoundaryKeys[warpid] = k;
+    }
+
+    // write right boundary values for each warp
+    if (unique_flag && unique_id == warpReduceSizes[warpid]) {
+        warpReduceRightBoundaryKeys[warpid] = k;
+        warpReduceRightBoundaryVals[warpid] = v;
+    }
+
+    __syncthreads();
+
+    // if rightmost thread, check next warp's kv,
+    // invalidate self and change warpReduceSizes since first thread of next
+    // warp will update same key
+    // TODO: what if extra empty warps???
+    if (unique_flag && unique_id == warpReduceSizes[warpid] &&
+        warpid < nWarps - 1) {
+        int tid_next_warp = (blockIdx.x * blockDim.x + (warpid + 1) * 32);
+        // check within data range
+        if (tid_next_warp < n && k == warpReduceLeftBoundaryKeys[warpid + 1]) {
+            // disable writing from warps that need carry but aren't terminal
+            if (warpReduceSizes[warpid] > 1 || warpid > 0) { unique_flag = 0; }
+        }
+    }
+    __syncthreads();
+
+    // if leftmost thread, reduce carryover from previous warp(s) if needed
+    if (unique_flag && unique_id == 1 && warpid > 0) {
+        int test_wid = warpid - 1;
+        while (test_wid >= 0 && k == warpReduceRightBoundaryKeys[test_wid]) {
+            v = reduce(v, warpReduceRightBoundaryVals[test_wid]);
+            --warpReduceSizes[test_wid];
+            if (warpReduceSizes[test_wid] > 1) break;
+
+            --test_wid;
+        }
+    }
+
+    if (unique_flag) {
+        warpReduceKeys[warpid][unique_id - 1] = k;
+        warpReduceVals[warpid][unique_id - 1] = v;
+    }
+
+    __syncthreads();
+
+    // at this point, we have nWarps lists in shared memory with each list's
+    // size located in the warpReduceSizes[] array
+    // perform warp-scan to determine each warp's write location
+    int warpSzScan = 0;
+    if (warpid == 0 && laneid < nWarps) {
+        warpSzScan     = warpReduceSizes[laneid];
+        int activemask = 0xFFFFFFFF >> (32 - nWarps);
+#pragma unroll
+        for (int offset = 1; offset < 32; offset <<= 1) {
+            char y = __shfl_up_sync(activemask, warpSzScan, offset);
+            if (laneid >= offset) warpSzScan += y;
+        }
+        warpReduceSizes[laneid] = warpSzScan;
+        // final thread has final reduced size of block
+        if (laneid == nWarps - 1) reducedBlockSize = warpSzScan;
+    }
+    __syncthreads();
+
+    // write reduced block size to global memory
+    if (threadIdx.x == 0) {
+        reduced_block_sizes[blockIdx.x] = reducedBlockSize;
+    }
+
+    // compact reduced keys and values before writing to global memory
+    if (warpid > 0) {
+        int wsz = warpReduceSizes[warpid] - warpReduceSizes[warpid - 1];
+        if (laneid < wsz) {
+            int warpOffset = warpReduceSizes[warpid - 1];
+            warpReduceKeysSmemFinal[warpOffset + laneid] =
+                warpReduceKeys[warpid][laneid];
+            warpReduceValsSmemFinal[warpOffset + laneid] =
+                warpReduceVals[warpid][laneid];
+        }
+    } else {
+        int wsz = warpReduceSizes[warpid];
+        if (laneid < wsz) {
+            warpReduceKeysSmemFinal[laneid] = warpReduceKeys[0][laneid];
+            warpReduceValsSmemFinal[laneid] = warpReduceVals[0][laneid];
+        }
+    }
+    __syncthreads();
+
+    // write reduced keys/values per-block
+    if (threadIdx.x < reducedBlockSize) {
+        const int bOffset = bidw * reduced_vals.strides[dim_ordering[3]] +
+                            bidz * reduced_vals.strides[dim_ordering[2]] +
+                            bidy * reduced_vals.strides[dim_ordering[1]];
+        reduced_keys.ptr[(blockIdx.x * blockDim.x) + threadIdx.x] =
+            warpReduceKeysSmemFinal[threadIdx.x];
+        reduced_vals.ptr[bOffset + ((blockIdx.x * blockDim.x) + threadIdx.x) *
+                                       reduced_vals.strides[dim]] =
+            warpReduceValsSmemFinal[threadIdx.x];
+    }
+}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/shfl_intrinsics.hpp b/src/backend/cuda/kernel/shfl_intrinsics.hpp
new file mode 100644
index 0000000000..ef12aafe29
--- /dev/null
+++ b/src/backend/cuda/kernel/shfl_intrinsics.hpp
@@ -0,0 +1,112 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace cuda {
+namespace kernel {
+
+//__all_sync wrapper
+template<typename T>
+__device__ T all_sync(unsigned mask, T var) {
+#if (CUDA_VERSION >= 9000)
+    return __all_sync(mask, var);
+#else
+    return __all(var);
+#endif
+}
+
+//__all_sync wrapper
+template<typename T>
+__device__ T any_sync(unsigned mask, T var) {
+#if (CUDA_VERSION >= 9000)
+    return __any_sync(mask, var);
+#else
+    return __any(var);
+#endif
+}
+
+//__shfl_down_sync wrapper
+template<typename T>
+__device__ T ballot_sync(unsigned mask, T var) {
+#if (CUDA_VERSION >= 9000)
+    return __ballot_sync(mask, var);
+#else
+    return __ballot(var);
+#endif
+}
+
+//__shfl_down_sync wrapper
+template<typename T>
+__device__ T shfl_down_sync(unsigned mask, T var, int delta) {
+#if (CUDA_VERSION >= 9000)
+    return __shfl_down_sync(mask, var, delta);
+#else
+    return __shfl_down(var, delta);
+#endif
+}
+// specialization for cfloat
+template<>
+inline __device__ cuda::cfloat shfl_down_sync(unsigned mask, cuda::cfloat var,
+                                              int delta) {
+#if (CUDA_VERSION >= 9000)
+    cuda::cfloat res = {__shfl_down_sync(mask, var.x, delta),
+                        __shfl_down_sync(mask, var.y, delta)};
+#else
+    cuda::cfloat res  = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
+#endif
+    return res;
+}
+// specialization for cdouble
+template<>
+inline __device__ cuda::cdouble shfl_down_sync(unsigned mask, cuda::cdouble var,
+                                               int delta) {
+#if (CUDA_VERSION >= 9000)
+    cuda::cdouble res = {__shfl_down_sync(mask, var.x, delta),
+                         __shfl_down_sync(mask, var.y, delta)};
+#else
+    cuda::cdouble res = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
+#endif
+    return res;
+}
+
+//__shfl_up_sync wrapper
+template<typename T>
+__device__ T shfl_up_sync(unsigned mask, T var, int delta) {
+#if (CUDA_VERSION >= 9000)
+    return __shfl_up_sync(mask, var, delta);
+#else
+    return __shfl_up(var, delta);
+#endif
+}
+// specialization for cfloat
+template<>
+inline __device__ cuda::cfloat shfl_up_sync(unsigned mask, cuda::cfloat var,
+                                            int delta) {
+#if (CUDA_VERSION >= 9000)
+    cuda::cfloat res = {__shfl_up_sync(mask, var.x, delta),
+                        __shfl_up_sync(mask, var.y, delta)};
+#else
+    cuda::cfloat res  = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
+#endif
+    return res;
+}
+// specialization for cdouble
+template<>
+inline __device__ cuda::cdouble shfl_up_sync(unsigned mask, cuda::cdouble var,
+                                             int delta) {
+#if (CUDA_VERSION >= 9000)
+    cuda::cdouble res = {__shfl_up_sync(mask, var.x, delta),
+                         __shfl_up_sync(mask, var.y, delta)};
+#else
+    cuda::cdouble res = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
+#endif
+    return res;
+}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/cuda/reduce.hpp b/src/backend/cuda/reduce.hpp
index af47866e8f..55bc47032a 100644
--- a/src/backend/cuda/reduce.hpp
+++ b/src/backend/cuda/reduce.hpp
@@ -15,6 +15,11 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
                  double nanval = 0);
 
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan = false, double nanval = 0);
+
 template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
 }  // namespace cuda
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 9ed2ddb60d..7b7785d402 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -13,11 +13,15 @@
 #undef _GLIBCXX_USE_INT128
 #include <err_cuda.hpp>
 #include <kernel/reduce.hpp>
+#include <kernel/reduce_by_key.hpp>
 #include <reduce.hpp>
+#include <set.hpp>
 #include <complex>
+#include <cub/device/device_scan.cuh>
 
 using af::dim4;
 using std::swap;
+
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
@@ -29,14 +33,320 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
     return out;
 }
 
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
+                       const Array<Tk> &keys, const Array<Ti> &vals,
+                       bool change_nan, double nanval, const int dim) {
+    std::vector<int> dim_ordering = {dim};
+    for (int i = 0; i < 4; ++i) {
+        if (i != dim) { dim_ordering.push_back(i); }
+    }
+
+    dim4 kdims = keys.dims();
+    dim4 odims = vals.dims();
+
+    // allocate space for output and temporary working arrays
+    Array<Tk> reduced_keys   = createEmptyArray<Tk>(kdims);
+    Array<To> reduced_vals   = createEmptyArray<To>(odims);
+    Array<Tk> t_reduced_keys = createEmptyArray<Tk>(kdims);
+    Array<To> t_reduced_vals = createEmptyArray<To>(odims);
+
+    // flags determining more reduction is necessary
+    auto needs_another_reduction        = memAlloc<int>(1);
+    auto needs_block_boundary_reduction = memAlloc<int>(1);
+
+    // reset flags
+    CUDA_CHECK(cudaMemsetAsync(needs_another_reduction.get(), 0, sizeof(int),
+                               getActiveStream()));
+    CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
+                               sizeof(int), getActiveStream()));
+
+    int nelems = kdims[0];
+
+    const unsigned int numThreads = 128;
+    int numBlocksD0               = divup(nelems, numThreads);
+
+    auto reduced_block_sizes = memAlloc<int>(numBlocksD0);
+
+    size_t temp_storage_bytes = 0;
+    cub::DeviceScan::InclusiveSum(NULL, temp_storage_bytes,
+                                  reduced_block_sizes.get(),
+                                  reduced_block_sizes.get(), numBlocksD0);
+    auto d_temp_storage = memAlloc<char>(temp_storage_bytes);
+
+    int n_reduced_host = nelems;
+    int needs_another_reduction_host;
+    int needs_block_boundary_reduction_host;
+
+    bool first_pass = true;
+    do {
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+        dim3 blocks(numBlocksD0, odims[dim_ordering[1]],
+                    odims[dim_ordering[2]] * odims[dim_ordering[3]]);
+
+        int folded_dim_sz = odims[dim_ordering[2]];
+        if (first_pass) {
+            CUDA_LAUNCH(
+                (kernel::reduce_blocks_dim_by_key<Ti, Tk, To, op, numThreads>),
+                blocks, numThreads, reduced_block_sizes.get(), reduced_keys,
+                reduced_vals, keys, vals, nelems, change_nan,
+                scalar<To>(nanval), dim, folded_dim_sz);
+            POST_LAUNCH_CHECK();
+            first_pass = false;
+        } else {
+            CUDA_LAUNCH(
+                (kernel::reduce_blocks_dim_by_key<To, Tk, To, op, numThreads>),
+                blocks, numThreads, reduced_block_sizes.get(), reduced_keys,
+                reduced_vals, t_reduced_keys, t_reduced_vals, n_reduced_host,
+                change_nan, scalar<To>(nanval), dim, folded_dim_sz);
+            POST_LAUNCH_CHECK();
+        }
+
+        cub::DeviceScan::InclusiveSum(
+            (void *)d_temp_storage.get(), temp_storage_bytes,
+            reduced_block_sizes.get(), reduced_block_sizes.get(), numBlocksD0);
+
+        CUDA_LAUNCH((kernel::compact_dim<Tk, To>), blocks, numThreads,
+                    reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
+                    reduced_keys, reduced_vals, dim, folded_dim_sz);
+        POST_LAUNCH_CHECK();
+
+        CUDA_CHECK(cudaMemcpyAsync(
+            &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
+            sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+
+        // reset flags
+        CUDA_CHECK(cudaMemsetAsync(needs_another_reduction.get(), 0,
+                                   sizeof(int), getActiveStream()));
+        CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
+                                   sizeof(int), getActiveStream()));
+
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        CUDA_LAUNCH((kernel::test_needs_reduction<Tk>), numBlocksD0, numThreads,
+                    needs_another_reduction.get(),
+                    needs_block_boundary_reduction.get(), t_reduced_keys,
+                    n_reduced_host);
+        POST_LAUNCH_CHECK();
+
+        CUDA_CHECK(cudaMemcpyAsync(&needs_another_reduction_host,
+                                   needs_another_reduction.get(), sizeof(int),
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&needs_block_boundary_reduction_host,
+                                   needs_block_boundary_reduction.get(),
+                                   sizeof(int), cudaMemcpyDeviceToHost,
+                                   getActiveStream()));
+
+        if (needs_block_boundary_reduction_host &&
+            !needs_another_reduction_host) {
+            dim3 blocks(numBlocksD0, odims[dim_ordering[1]],
+                        odims[dim_ordering[2]] * odims[dim_ordering[3]]);
+            CUDA_LAUNCH((kernel::final_boundary_reduce<Tk, To, op>), blocks,
+                        numThreads, reduced_block_sizes.get(), t_reduced_keys,
+                        t_reduced_vals, n_reduced_host);
+            POST_LAUNCH_CHECK();
+
+            cub::DeviceScan::InclusiveSum(
+                (void *)d_temp_storage.get(), temp_storage_bytes,
+                reduced_block_sizes.get(), reduced_block_sizes.get(),
+                numBlocksD0);
+
+            CUDA_CHECK(cudaMemcpyAsync(
+                &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
+                sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+
+            CUDA_LAUNCH((kernel::compact_dim<Tk, To>), blocks, numThreads,
+                        reduced_block_sizes.get(), reduced_keys, reduced_vals,
+                        t_reduced_keys, t_reduced_vals, dim, folded_dim_sz);
+            POST_LAUNCH_CHECK();
+
+            swap(t_reduced_keys, reduced_keys);
+            swap(t_reduced_vals, reduced_vals);
+        }
+    } while (needs_another_reduction_host ||
+             needs_block_boundary_reduction_host);
+
+    kdims[0]   = n_reduced_host;
+    odims[dim] = n_reduced_host;
+    std::vector<af_seq> kindex, vindex;
+    for (int i = 0; i < odims.ndims(); ++i) {
+        af_seq sk = {0.0, (double)kdims[i] - 1, 1.0};
+        af_seq sv = {0.0, (double)odims[i] - 1, 1.0};
+        kindex.push_back(sk);
+        vindex.push_back(sv);
+    }
+
+    keys_out = createSubArray<Tk>(t_reduced_keys, kindex, true);
+    vals_out = createSubArray<To>(t_reduced_vals, vindex, true);
+}
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
+                         const Array<Tk> &keys, const Array<Ti> &vals,
+                         bool change_nan, double nanval) {
+    dim4 kdims = keys.dims();
+    dim4 odims = vals.dims();
+
+    // allocate space for output and temporary working arrays
+    Array<Tk> reduced_keys   = createEmptyArray<Tk>(kdims);
+    Array<To> reduced_vals   = createEmptyArray<To>(odims);
+    Array<Tk> t_reduced_keys = createEmptyArray<Tk>(kdims);
+    Array<To> t_reduced_vals = createEmptyArray<To>(odims);
+
+    // flags determining more reduction is necessary
+    auto needs_another_reduction        = memAlloc<int>(1);
+    auto needs_block_boundary_reduction = memAlloc<int>(1);
+
+    // reset flags
+    CUDA_CHECK(cudaMemsetAsync(needs_another_reduction.get(), 0, sizeof(int),
+                               getActiveStream()));
+    CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
+                               sizeof(int), getActiveStream()));
+
+    int nelems = kdims[0];
+
+    const unsigned int numThreads = 128;
+    int numBlocksD0               = divup(nelems, numThreads);
+
+    auto reduced_block_sizes = memAlloc<int>(numBlocksD0);
+
+    size_t temp_storage_bytes = 0;
+    cub::DeviceScan::InclusiveSum(NULL, temp_storage_bytes,
+                                  reduced_block_sizes.get(),
+                                  reduced_block_sizes.get(), numBlocksD0);
+    auto d_temp_storage = memAlloc<char>(temp_storage_bytes);
+
+    int n_reduced_host = nelems;
+    int needs_another_reduction_host;
+    int needs_block_boundary_reduction_host;
+
+    bool first_pass = true;
+    do {
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+        dim3 blocks(numBlocksD0, odims[1], odims[2] * odims[3]);
+
+        if (first_pass) {
+            CUDA_LAUNCH(
+                (kernel::reduce_blocks_by_key<Ti, Tk, To, op, numThreads>),
+                blocks, numThreads, reduced_block_sizes.get(), reduced_keys,
+                reduced_vals, keys, vals, nelems, change_nan,
+                scalar<To>(nanval), odims[2]);
+            POST_LAUNCH_CHECK();
+            first_pass = false;
+        } else {
+            CUDA_LAUNCH(
+                (kernel::reduce_blocks_by_key<To, Tk, To, op, numThreads>),
+                blocks, numThreads, reduced_block_sizes.get(), reduced_keys,
+                reduced_vals, t_reduced_keys, t_reduced_vals, n_reduced_host,
+                change_nan, scalar<To>(nanval), odims[2]);
+            POST_LAUNCH_CHECK();
+        }
+
+        cub::DeviceScan::InclusiveSum(
+            (void *)d_temp_storage.get(), temp_storage_bytes,
+            reduced_block_sizes.get(), reduced_block_sizes.get(), numBlocksD0);
+
+        CUDA_LAUNCH((kernel::compact<Tk, To>), blocks, numThreads,
+                    reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
+                    reduced_keys, reduced_vals, odims[2]);
+        POST_LAUNCH_CHECK();
+
+        CUDA_CHECK(cudaMemcpyAsync(
+            &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
+            sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+
+        // reset flags
+        CUDA_CHECK(cudaMemsetAsync(needs_another_reduction.get(), 0,
+                                   sizeof(int), getActiveStream()));
+        CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
+                                   sizeof(int), getActiveStream()));
+
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        CUDA_LAUNCH((kernel::test_needs_reduction<Tk>), numBlocksD0, numThreads,
+                    needs_another_reduction.get(),
+                    needs_block_boundary_reduction.get(), t_reduced_keys,
+                    n_reduced_host);
+        POST_LAUNCH_CHECK();
+
+        CUDA_CHECK(cudaMemcpyAsync(&needs_another_reduction_host,
+                                   needs_another_reduction.get(), sizeof(int),
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(&needs_block_boundary_reduction_host,
+                                   needs_block_boundary_reduction.get(),
+                                   sizeof(int), cudaMemcpyDeviceToHost,
+                                   getActiveStream()));
+
+        if (needs_block_boundary_reduction_host &&
+            !needs_another_reduction_host) {
+            // TODO: fold 3,4 dimensions
+            blocks = dim3(numBlocksD0, odims[1], odims[2]);
+            CUDA_LAUNCH((kernel::final_boundary_reduce<Tk, To, op>), blocks,
+                        numThreads, reduced_block_sizes.get(), t_reduced_keys,
+                        t_reduced_vals, n_reduced_host);
+            POST_LAUNCH_CHECK();
+
+            cub::DeviceScan::InclusiveSum(
+                (void *)d_temp_storage.get(), temp_storage_bytes,
+                reduced_block_sizes.get(), reduced_block_sizes.get(),
+                numBlocksD0);
+
+            CUDA_CHECK(cudaMemcpyAsync(
+                &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
+                sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+
+            CUDA_LAUNCH((kernel::compact<Tk, To>), blocks, numThreads,
+                        reduced_block_sizes.get(), reduced_keys, reduced_vals,
+                        t_reduced_keys, t_reduced_vals, odims[2]);
+            POST_LAUNCH_CHECK();
+
+            swap(t_reduced_keys, reduced_keys);
+            swap(t_reduced_vals, reduced_vals);
+        }
+    } while (needs_another_reduction_host ||
+             needs_block_boundary_reduction_host);
+
+    kdims[0] = n_reduced_host;
+    odims[0] = n_reduced_host;
+    std::vector<af_seq> kindex, vindex;
+    for (int i = 0; i < odims.ndims(); ++i) {
+        af_seq sk = {0.0, (double)kdims[i] - 1, 1.0};
+        af_seq sv = {0.0, (double)odims[i] - 1, 1.0};
+        kindex.push_back(sk);
+        vindex.push_back(sv);
+    }
+
+    keys_out = createSubArray<Tk>(t_reduced_keys, kindex, true);
+    vals_out = createSubArray<To>(t_reduced_vals, vindex, true);
+}
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan, double nanval) {
+    if (dim == 0) {
+        reduce_by_key_first<op, Ti, Tk, To>(keys_out, vals_out, keys, vals,
+                                            change_nan, nanval);
+    } else {
+        reduce_by_key_dim<op, Ti, Tk, To>(keys_out, vals_out, keys, vals,
+                                          change_nan, nanval, dim);
+    }
+}
+
 template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
 }
 }  // namespace cuda
 
-#define INSTANTIATE(Op, Ti, To)                                               \
-    template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim, \
-                                          bool change_nan, double nanval);    \
-    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,  \
+#define INSTANTIATE(Op, Ti, To)                                                \
+    template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim,  \
+                                          bool change_nan, double nanval);     \
+    template void reduce_by_key<Op, Ti, int, To>(                              \
+        Array<int> & keys_out, Array<To> & vals_out, const Array<int> &keys,   \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template void reduce_by_key<Op, Ti, uint, To>(                             \
+        Array<uint> & keys_out, Array<To> & vals_out, const Array<uint> &keys, \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,   \
                                        double nanval);
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
new file mode 100644
index 0000000000..a82941b00c
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
@@ -0,0 +1,134 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+Tk work_group_scan_inclusive_add(__local Tk *arr) {
+    __local Tk tmp[DIMX];
+    __local int *l_val;
+
+    const int lid = get_local_id(0);
+    Tk val        = arr[lid];
+    l_val         = arr;
+
+    bool wbuf = 0;
+    for (int off = 1; off <= DIMX; off *= 2) {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if (lid >= off) val = val + l_val[lid - off];
+
+        wbuf       = 1 - wbuf;
+        l_val      = wbuf ? tmp : arr;
+        l_val[lid] = val;
+    }
+
+    Tk res = l_val[lid];
+    return res;
+}
+
+__kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
+                                       __global Tk *oKeys, KParam oKInfo,
+                                       __global To *oVals, KParam oVInfo,
+                                       const __global Tk *iKeys, KParam iKInfo,
+                                       const __global Ti *iVals, KParam iVInfo,
+                                       int change_nan, To nanval, int n,
+                                       const int nBlocksZ) {
+    const uint lid  = get_local_id(0);
+    const uint gidx = get_global_id(0);
+
+    const int bidy = get_group_id(1);
+    const int bidz = get_group_id(2) % nBlocksZ;
+    const int bidw = get_group_id(2) / nBlocksZ;
+
+    __local Tk keys[DIMX];
+    __local To vals[DIMX];
+
+    __local Tk reduced_keys[DIMX];
+    __local To reduced_vals[DIMX];
+
+    __local int unique_flags[DIMX];
+    __local int unique_ids[DIMX];
+
+    const To init_val = init;
+
+    //
+    // will hold final number of reduced elements in block
+    __local int reducedBlockSize;
+
+    __local int dims_ordering[4];
+    if (lid == 0) {
+        reducedBlockSize = 0;
+
+        int d            = 1;
+        dims_ordering[0] = DIM;
+        for (int i = 0; i < 4; ++i) {
+            if (i != DIM) dims_ordering[d++] = i;
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // load keys and values to threads
+    Tk k;
+    To v;
+    if (gidx < n) {
+        k             = iKeys[gidx];
+        const int gid = bidw * iVInfo.strides[dims_ordering[3]] +
+                        bidz * iVInfo.strides[dims_ordering[2]] +
+                        bidy * iVInfo.strides[dims_ordering[1]] +
+                        gidx * iVInfo.strides[DIM];
+        v                 = transform(iVals[gid]);
+        if (change_nan) v = IS_NAN(v) ? nanval : v;
+    } else {
+        v = init_val;
+    }
+
+    keys[lid] = k;
+    vals[lid] = v;
+
+    reduced_keys[lid] = k;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // mark threads containing unique keys
+    int eq_check      = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
+    int unique_flag   = (eq_check || (lid == 0)) && (gidx < n);
+    unique_flags[lid] = unique_flag;
+
+    int unique_id   = work_group_scan_inclusive_add(unique_flags);
+    unique_ids[lid] = unique_id;
+
+    if (lid == DIMX - 1) reducedBlockSize = unique_id;
+
+    for (int off = 1; off < DIMX; off *= 2) {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        int test_unique_id =
+            (lid + off < DIMX) ? unique_ids[lid + off] : ~unique_id;
+        eq_check = (unique_id == test_unique_id);
+        int update_key =
+            eq_check && (lid < (DIMX - off)) &&
+            ((gidx + off) <
+             n);  // checks if this thread should perform a reduction
+        To uval = (update_key) ? vals[lid + off] : init_val;
+        barrier(CLK_LOCAL_MEM_FENCE);
+        vals[lid] = binOp(vals[lid], uval);  // update if thread requires it
+    }
+
+    if (unique_flag) {
+        reduced_keys[unique_id - 1] = k;
+        reduced_vals[unique_id - 1] = vals[lid];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const int bid = get_group_id(0);
+    if (lid < reducedBlockSize) {
+        const int bOffset = bidw * oVInfo.strides[dims_ordering[3]] +
+                            bidz * oVInfo.strides[dims_ordering[2]] +
+                            bidy * oVInfo.strides[dims_ordering[1]];
+        oKeys[gidx]                                 = reduced_keys[lid];
+        oVals[bOffset + (gidx)*oVInfo.strides[DIM]] = reduced_vals[lid];
+    }
+
+    reduced_block_sizes[bid] = reducedBlockSize;
+}
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
new file mode 100644
index 0000000000..2912c53c7a
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -0,0 +1,120 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+Tk work_group_scan_inclusive_add(__local Tk *arr) {
+    __local Tk tmp[DIMX];
+    __local int *l_val;
+
+    const int lid = get_local_id(0);
+    Tk val        = arr[lid];
+    l_val         = arr;
+
+    bool wbuf = 0;
+    for (int off = 1; off <= DIMX; off *= 2) {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if (lid >= off) val = val + l_val[lid - off];
+
+        wbuf       = 1 - wbuf;
+        l_val      = wbuf ? tmp : arr;
+        l_val[lid] = val;
+    }
+
+    Tk res = l_val[lid];
+    return res;
+}
+
+__kernel void reduce_blocks_by_key_first(
+    __global int *reduced_block_sizes, __global Tk *oKeys, KParam oKInfo,
+    __global To *oVals, KParam oVInfo, const __global Tk *iKeys, KParam iKInfo,
+    const __global Ti *iVals, KParam iVInfo, int change_nan, To nanval, int n,
+    const int nBlocksZ) {
+    const uint lid = get_local_id(0);
+    const uint gid = get_global_id(0);
+
+    const int bidy = get_group_id(1);
+    const int bidz = get_group_id(2) % nBlocksZ;
+    const int bidw = get_group_id(2) / nBlocksZ;
+
+    __local Tk keys[DIMX];
+    __local To vals[DIMX];
+
+    __local Tk reduced_keys[DIMX];
+    __local To reduced_vals[DIMX];
+
+    __local int unique_flags[DIMX];
+    __local int unique_ids[DIMX];
+
+    const To init_val = init;
+
+    //
+    // will hold final number of reduced elements in block
+    __local int reducedBlockSize;
+
+    if (lid == 0) { reducedBlockSize = 0; }
+
+    // load keys and values to threads
+    Tk k;
+    To v;
+    if (gid < n) {
+        k                 = iKeys[gid];
+        const int bOffset = bidw * iVInfo.strides[3] +
+                            bidz * iVInfo.strides[2] + bidy * iVInfo.strides[1];
+        v                 = transform(iVals[bOffset + gid]);
+        if (change_nan) v = IS_NAN(v) ? nanval : v;
+    } else {
+        v = init_val;
+    }
+
+
+    keys[lid] = k;
+    vals[lid] = v;
+
+    reduced_keys[lid] = k;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // mark threads containing unique keys
+    int eq_check      = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
+    int unique_flag   = (eq_check || (lid == 0)) && (gid < n);
+    unique_flags[lid] = unique_flag;
+
+    int unique_id   = work_group_scan_inclusive_add(unique_flags);
+    unique_ids[lid] = unique_id;
+
+    if (lid == DIMX - 1) reducedBlockSize = unique_id;
+
+    for (int off = 1; off < DIMX; off *= 2) {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        int test_unique_id =
+            (lid + off < DIMX) ? unique_ids[lid + off] : ~unique_id;
+        eq_check = (unique_id == test_unique_id);
+        int update_key =
+            eq_check && (lid < (DIMX - off)) &&
+            ((gid + off) <
+             n);  // checks if this thread should perform a reduction
+        To uval = (update_key) ? vals[lid + off] : init_val;
+        barrier(CLK_LOCAL_MEM_FENCE);
+        vals[lid] = binOp(vals[lid], uval);  // update if thread requires it
+    }
+
+    if (unique_flag) {
+        reduced_keys[unique_id - 1] = k;
+        reduced_vals[unique_id - 1] = vals[lid];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const int bid = get_group_id(0);
+    if (lid < reducedBlockSize) {
+        const int bOffset = bidw * oVInfo.strides[3] +
+                            bidz * oVInfo.strides[2] + bidy * oVInfo.strides[1];
+        oKeys[bid * DIMX + lid]               = reduced_keys[lid];
+        oVals[bOffset + ((bid * DIMX) + lid)] = reduced_vals[lid];
+    }
+
+    reduced_block_sizes[bid] = reducedBlockSize;
+}
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
new file mode 100644
index 0000000000..856348f678
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -0,0 +1,708 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
+#include <kernel_headers/ops.hpp>
+#include <kernel_headers/reduce_blocks_by_key_dim.hpp>
+#include <kernel_headers/reduce_blocks_by_key_first.hpp>
+#include <kernel_headers/reduce_by_key_boundary.hpp>
+#include <kernel_headers/reduce_by_key_boundary_dim.hpp>
+#include <kernel_headers/reduce_by_key_compact.hpp>
+#include <kernel_headers/reduce_by_key_compact_dim.hpp>
+#include <kernel_headers/reduce_by_key_needs_reduction.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <type_util.hpp>
+#include <algorithm>
+#include <map>
+#include <memory>
+#include <mutex>
+#include <string>
+#include "config.hpp"
+#include "names.hpp"
+
+#include <boost/compute/algorithm/inclusive_scan.hpp>
+#include <boost/compute/core.hpp>
+#include <boost/compute/functional/operator.hpp>
+#include <boost/compute/iterator/buffer_iterator.hpp>
+
+namespace compute = boost::compute;
+
+using cl::Buffer;
+using cl::Program;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::EnqueueArgs;
+using cl::NDRange;
+using std::string;
+using std::unique_ptr;
+using std::vector;
+
+namespace opencl {
+
+namespace kernel {
+
+template <typename Ti, typename Tk, typename To, af_op_t op>
+void launch_reduce_blocks_dim_by_key(cl::Buffer *reduced_block_sizes,
+                                     Param keys_out, Param vals_out,
+                                     const Param keys, const Param vals,
+                                     int change_nan, double nanval, const int n,
+                                     const uint threads_x, const int dim,
+                                     vector<int> dim_ordering) {
+    std::string ref_name =
+        std::string("reduce_blocks_dim_by_key_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        Binary<To, op> reduce;
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x << " -D DIM=" << dim
+                << " -D init=" << toNumStr(reduce.init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
+
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {ops_cl, reduce_blocks_by_key_dim_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_blocks_by_key_dim_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "reduce_blocks_by_key_dim");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    int numBlocks = divup(n, threads_x);
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks, vals_out.info.dims[dim_ordering[1]],
+                   vals_out.info.dims[dim_ordering[2]] *
+                       vals_out.info.dims[dim_ordering[3]]);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                      Buffer, KParam, int, To, int, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+             *keys.data, keys.info, *vals.data, vals.info, change_nan,
+             scalar<To>(nanval), n, vals_out.info.dims[dim_ordering[2]]);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Ti, typename Tk, typename To, af_op_t op>
+void launch_reduce_blocks_by_key(cl::Buffer *reduced_block_sizes,
+                                 Param keys_out, Param vals_out,
+                                 const Param keys, const Param vals,
+                                 int change_nan, double nanval, const int n,
+                                 const uint threads_x) {
+    std::string ref_name =
+        std::string("reduce_blocks_by_key_0_") +
+        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
+        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        Binary<To, op> reduce;
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName()
+                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x
+                << " -D init=" << toNumStr(reduce.init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
+
+        if (std::is_same<Ti, double>::value ||
+            std::is_same<Ti, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {ops_cl, reduce_blocks_by_key_first_cl};
+        const int ker_lens[] = {ops_cl_len, reduce_blocks_by_key_first_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "reduce_blocks_by_key_first");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    int numBlocks = divup(n, threads_x);
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
+                   vals_out.info.dims[2] * vals_out.info.dims[3]);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                      Buffer, KParam, int, To, int, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+             *keys.data, keys.info, *vals.data, vals.info, change_nan,
+             scalar<To>(nanval), n, vals_out.info.dims[2]);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Tk, typename To, af_op_t op>
+void launch_final_boundary_reduce(cl::Buffer *reduced_block_sizes,
+                                  Param keys_out, Param vals_out, const int n,
+                                  const int numBlocks, const int threads_x) {
+    std::string ref_name =
+        std::string("final_boundary_reduce") +
+        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        Binary<To, op> reduce;
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<To>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x
+                << " -D init=" << toNumStr(reduce.init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<To>();
+
+        if (std::is_same<To, double>::value ||
+            std::is_same<To, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_by_key_boundary_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "final_boundary_reduce");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info, n);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Tk, typename To, af_op_t op>
+void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
+                                      Param keys_out, Param vals_out, const int n,
+                                      const int numBlocks, const int threads_x,
+                                      const int dim, vector<int> dim_ordering) {
+    std::string ref_name =
+        std::string("final_boundary_reduce") +
+        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
+        std::string(dtype_traits<To>::getName()) + std::string("_") +
+        std::to_string(op) + std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        Binary<To, op> reduce;
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Ti=" << dtype_traits<To>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x << " -D DIM=" << dim
+                << " -D init=" << toNumStr(reduce.init()) << " -D "
+                << binOpName<op>() << " -D CPLX=" << af::iscplx<To>();
+
+        if (std::is_same<To, double>::value ||
+            std::is_same<To, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_dim_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_by_key_boundary_dim_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "final_boundary_reduce_dim");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks, vals_out.info.dims[dim_ordering[1]],
+                   vals_out.info.dims[dim_ordering[2]] *
+                       vals_out.info.dims[dim_ordering[3]]);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, int, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info, n,
+             vals_out.info.dims[dim_ordering[2]]);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Tk, typename To>
+void launch_compact(cl::Buffer *reduced_block_sizes, Param keys_out,
+                    Param vals_out, const Param keys, const Param vals,
+                    const int numBlocks, const int threads_x) {
+    std::string ref_name =
+        std::string("compact_") + std::string(dtype_traits<Tk>::getName()) +
+        std::string("_") + std::string(dtype_traits<To>::getName()) +
+        std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x << " -D CPLX=" << af::iscplx<To>();
+
+        if (std::is_same<To, double>::value ||
+            std::is_same<To, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {ops_cl, reduce_by_key_compact_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_by_key_compact_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "compact");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
+                   vals_out.info.dims[2] * vals_out.info.dims[3]);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                      Buffer, KParam, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+             *keys.data, keys.info, *vals.data, vals.info,
+             vals_out.info.dims[2]);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Tk, typename To>
+void launch_compact_dim(cl::Buffer *reduced_block_sizes, Param keys_out,
+                        Param vals_out, const Param keys, const Param vals,
+                        const int numBlocks, const int threads_x, const int dim,
+                        vector<int> dim_ordering) {
+    std::string ref_name =
+        std::string("compact_dim_") + std::string(dtype_traits<Tk>::getName()) +
+        std::string("_") + std::string(dtype_traits<To>::getName()) +
+        std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        ToNumStr<To> toNumStr;
+
+        std::ostringstream options;
+        options << " -D To=" << dtype_traits<To>::getName()
+                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
+                << " -D DIMX=" << threads_x << " -D DIM=" << dim
+                << " -D CPLX=" << af::iscplx<To>();
+
+        if (std::is_same<To, double>::value ||
+            std::is_same<To, cdouble>::value) {
+            options << " -D USE_DOUBLE";
+        }
+
+        const char *ker_strs[] = {ops_cl, reduce_by_key_compact_dim_cl};
+        const int ker_lens[]   = {ops_cl_len, reduce_by_key_compact_dim_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "compact_dim");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks, vals_out.info.dims[dim_ordering[1]],
+                   vals_out.info.dims[dim_ordering[2]] *
+                       vals_out.info.dims[dim_ordering[3]]);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
+                      Buffer, KParam, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+             *keys.data, keys.info, *vals.data, vals.info,
+             vals_out.info.dims[dim_ordering[2]]);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Tk>
+void launch_test_needs_reduction(cl::Buffer needs_reduction,
+                                 cl::Buffer needs_boundary, const Param keys,
+                                 const int n, const int numBlocks,
+                                 const int threads_x) {
+    std::string ref_name = std::string("test_needs_reduction_") +
+                           std::string(dtype_traits<Tk>::getName()) +
+                           std::string("_") + std::to_string(threads_x);
+
+    int device = getActiveDeviceId();
+
+    kc_entry_t entry = kernelCache(device, ref_name);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D Tk=" << dtype_traits<Tk>::getName()
+                << " -D DIMX=" << threads_x;
+
+        const char *ker_strs[] = {ops_cl, reduce_by_key_needs_reduction_cl};
+        const int ker_lens[]   = {ops_cl_len,
+                                reduce_by_key_needs_reduction_cl_len};
+        Program prog;
+        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
+
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "test_needs_reduction");
+
+        addKernelToCache(device, ref_name, entry);
+    }
+
+    NDRange local(threads_x);
+    NDRange global(threads_x * numBlocks);
+
+    auto reduceOp =
+        KernelFunctor<Buffer, Buffer, Buffer, KParam, int>(*entry.ker);
+
+    reduceOp(EnqueueArgs(getQueue(), global, local), needs_reduction,
+             needs_boundary, *keys.data, keys.info, n);
+
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template <typename Ti, typename Tk, typename To, af_op_t op>
+int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
+                        const Param keys, const Param vals, bool change_nan,
+                        double nanval) {
+    dim4 kdims(4, keys.info.dims);
+    dim4 odims(4, vals.info.dims);
+
+    auto reduced_keys   = createEmptyArray<Tk>(kdims);
+    auto reduced_vals   = createEmptyArray<To>(odims);
+    auto t_reduced_keys = createEmptyArray<Tk>(kdims);
+    auto t_reduced_vals = createEmptyArray<To>(odims);
+
+    // flags determining more reduction is necessary
+    auto needs_another_reduction        = memAlloc<int>(1);
+    auto needs_block_boundary_reduction = memAlloc<int>(1);
+
+    int nelems = kdims[0];
+
+    const unsigned int numThreads = 128;
+    int numBlocksD0               = divup(nelems, numThreads);
+
+    auto reduced_block_sizes = memAlloc<int>(numBlocksD0);
+
+    compute::command_queue c_queue(getQueue()());
+    compute::buffer val_buf((*reduced_block_sizes.get())());
+
+    int n_reduced_host = nelems;
+    int needs_another_reduction_host;
+
+    int needs_block_boundary_reduction_host;
+    bool first_pass = true;
+    do {
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        if (first_pass) {
+            launch_reduce_blocks_by_key<Ti, Tk, To, op>(
+                reduced_block_sizes.get(), reduced_keys, reduced_vals, keys,
+                vals, change_nan, nanval, n_reduced_host, numThreads);
+            first_pass = false;
+        } else {
+            launch_reduce_blocks_by_key<To, Tk, To, op>(
+                reduced_block_sizes.get(), reduced_keys, reduced_vals,
+                t_reduced_keys, t_reduced_vals, change_nan, nanval,
+                n_reduced_host, numThreads);
+        }
+
+        compute::inclusive_scan(
+            compute::make_buffer_iterator<int>(val_buf),
+            compute::make_buffer_iterator<int>(val_buf, numBlocksD0),
+            compute::make_buffer_iterator<int>(val_buf), c_queue);
+
+        launch_compact<Tk, To>(reduced_block_sizes.get(), t_reduced_keys,
+                               t_reduced_vals, reduced_keys, reduced_vals,
+                               numBlocksD0, numThreads);
+
+        getQueue().enqueueReadBuffer(*reduced_block_sizes.get(), true,
+                                     (numBlocksD0 - 1) * sizeof(int),
+                                     sizeof(int), &n_reduced_host);
+
+        // reset flags
+        getQueue().enqueueFillBuffer<int>(*needs_another_reduction.get(), 0, 0,
+                                          sizeof(int));
+        getQueue().enqueueFillBuffer<int>(*needs_block_boundary_reduction.get(),
+                                          0, 0, sizeof(int));
+
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        launch_test_needs_reduction<Tk>(*needs_another_reduction.get(),
+                                        *needs_block_boundary_reduction.get(),
+                                        t_reduced_keys, n_reduced_host,
+                                        numBlocksD0, numThreads);
+
+        getQueue().enqueueReadBuffer(*needs_another_reduction.get(), true, 0,
+                                     sizeof(int),
+                                     &needs_another_reduction_host);
+        getQueue().enqueueReadBuffer(*needs_block_boundary_reduction.get(),
+                                     true, 0, sizeof(int),
+                                     &needs_block_boundary_reduction_host);
+
+        if (needs_block_boundary_reduction_host &&
+            !needs_another_reduction_host) {
+            launch_final_boundary_reduce<Tk, To, op>(
+                reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
+                n_reduced_host, numBlocksD0, numThreads);
+
+            compute::inclusive_scan(
+                compute::make_buffer_iterator<int>(val_buf),
+                compute::make_buffer_iterator<int>(val_buf, numBlocksD0),
+                compute::make_buffer_iterator<int>(val_buf), c_queue);
+
+            getQueue().enqueueReadBuffer(*reduced_block_sizes.get(), true,
+                                         (numBlocksD0 - 1) * sizeof(int),
+                                         sizeof(int), &n_reduced_host);
+
+            launch_compact<Tk, To>(reduced_block_sizes.get(), reduced_keys,
+                                   reduced_vals, t_reduced_keys, t_reduced_vals,
+                                   numBlocksD0, numThreads);
+
+            std::swap(t_reduced_keys, reduced_keys);
+            std::swap(t_reduced_vals, reduced_vals);
+        }
+    } while (needs_another_reduction_host ||
+             needs_block_boundary_reduction_host);
+
+    keys_out = t_reduced_keys;
+    vals_out = t_reduced_vals;
+
+    return n_reduced_host;
+}
+
+template <typename Ti, typename Tk, typename To, af_op_t op>
+int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
+                      const Param keys, const Param vals, bool change_nan,
+                      double nanval, const int dim) {
+    vector<int> dim_ordering = {dim};
+    for (int i = 0; i < 4; ++i) {
+        if (i != dim) { dim_ordering.push_back(i); }
+    }
+
+    dim4 kdims(4, keys.info.dims);
+    dim4 odims(4, vals.info.dims);
+
+    auto reduced_keys   = createEmptyArray<Tk>(kdims);
+    auto reduced_vals   = createEmptyArray<To>(odims);
+    auto t_reduced_keys = createEmptyArray<Tk>(kdims);
+    auto t_reduced_vals = createEmptyArray<To>(odims);
+
+    // flags determining more reduction is necessary
+    auto needs_another_reduction        = memAlloc<int>(1);
+    auto needs_block_boundary_reduction = memAlloc<int>(1);
+
+    int nelems = kdims[0];
+
+    const unsigned int numThreads = 128;
+    int numBlocksD0               = divup(nelems, numThreads);
+
+    auto reduced_block_sizes = memAlloc<int>(numBlocksD0);
+
+    compute::command_queue c_queue(getQueue()());
+    compute::buffer val_buf((*reduced_block_sizes.get())());
+
+    int n_reduced_host = nelems;
+    int needs_another_reduction_host;
+    int needs_block_boundary_reduction_host;
+
+    bool first_pass = true;
+    do {
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        if (first_pass) {
+            launch_reduce_blocks_dim_by_key<Ti, Tk, To, op>(
+                reduced_block_sizes.get(), reduced_keys, reduced_vals, keys,
+                vals, change_nan, nanval, n_reduced_host, numThreads, dim,
+                dim_ordering);
+            first_pass = false;
+        } else {
+            launch_reduce_blocks_dim_by_key<To, Tk, To, op>(
+                reduced_block_sizes.get(), reduced_keys, reduced_vals,
+                t_reduced_keys, t_reduced_vals, change_nan, nanval,
+                n_reduced_host, numThreads, dim, dim_ordering);
+        }
+
+        compute::inclusive_scan(
+            compute::make_buffer_iterator<int>(val_buf),
+            compute::make_buffer_iterator<int>(val_buf, numBlocksD0),
+            compute::make_buffer_iterator<int>(val_buf), c_queue);
+
+        launch_compact_dim<Tk, To>(reduced_block_sizes.get(), t_reduced_keys,
+                                   t_reduced_vals, reduced_keys, reduced_vals,
+                                   numBlocksD0, numThreads, dim, dim_ordering);
+
+        getQueue().enqueueReadBuffer(*reduced_block_sizes.get(), true,
+                                     (numBlocksD0 - 1) * sizeof(int),
+                                     sizeof(int), &n_reduced_host);
+
+        // reset flags
+        getQueue().enqueueFillBuffer<int>(*needs_another_reduction.get(), 0, 0,
+                                          sizeof(int));
+        getQueue().enqueueFillBuffer<int>(*needs_block_boundary_reduction.get(),
+                                          0, 0, sizeof(int));
+
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        launch_test_needs_reduction<Tk>(*needs_another_reduction.get(),
+                                        *needs_block_boundary_reduction.get(),
+                                        t_reduced_keys, n_reduced_host,
+                                        numBlocksD0, numThreads);
+
+        getQueue().enqueueReadBuffer(*needs_another_reduction.get(), true, 0,
+                                     sizeof(int),
+                                     &needs_another_reduction_host);
+        getQueue().enqueueReadBuffer(*needs_block_boundary_reduction.get(),
+                                     true, 0, sizeof(int),
+                                     &needs_block_boundary_reduction_host);
+
+        if (needs_block_boundary_reduction_host &&
+            !needs_another_reduction_host) {
+            launch_final_boundary_reduce_dim<Tk, To, op>(
+                reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
+                n_reduced_host, numBlocksD0, numThreads, dim, dim_ordering);
+
+            compute::inclusive_scan(
+                compute::make_buffer_iterator<int>(val_buf),
+                compute::make_buffer_iterator<int>(val_buf, numBlocksD0),
+                compute::make_buffer_iterator<int>(val_buf), c_queue);
+
+            getQueue().enqueueReadBuffer(*reduced_block_sizes.get(), true,
+                                         (numBlocksD0 - 1) * sizeof(int),
+                                         sizeof(int), &n_reduced_host);
+
+            launch_compact_dim<Tk, To>(reduced_block_sizes.get(), reduced_keys,
+                                       reduced_vals, t_reduced_keys,
+                                       t_reduced_vals, numBlocksD0, numThreads,
+                                       dim, dim_ordering);
+
+            std::swap(t_reduced_keys, reduced_keys);
+            std::swap(t_reduced_vals, reduced_vals);
+        }
+    } while (needs_another_reduction_host ||
+             needs_block_boundary_reduction_host);
+
+    keys_out = t_reduced_keys;
+    vals_out = t_reduced_vals;
+
+    return n_reduced_host;
+}
+
+template <af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, int dim,
+                   bool change_nan, double nanval) {
+    dim4 kdims = keys.dims();
+    dim4 odims = vals.dims();
+
+    // allocate space for output arrays
+    Array<Tk> reduced_keys = createEmptyArray<Tk>(dim4());
+    Array<To> reduced_vals = createEmptyArray<To>(dim4());
+
+    int n_reduced = 0;
+    if (dim == 0) {
+        n_reduced = reduce_by_key_first<Ti, Tk, To, op>(
+            reduced_keys, reduced_vals, keys, vals, change_nan, nanval);
+    } else {
+        n_reduced = reduce_by_key_dim<Ti, Tk, To, op>(
+            reduced_keys, reduced_vals, keys, vals, change_nan, nanval, dim);
+    }
+
+    kdims[0]   = n_reduced;
+    odims[dim] = n_reduced;
+    std::vector<af_seq> kindex, vindex;
+    for (int i = 0; i < odims.ndims(); ++i) {
+        af_seq sk = {0.0, (double)kdims[i] - 1, 1.0};
+        af_seq sv = {0.0, (double)odims[i] - 1, 1.0};
+        kindex.push_back(sk);
+        vindex.push_back(sv);
+    }
+
+    keys_out = createSubArray<Tk>(reduced_keys, kindex, true);
+    vals_out = createSubArray<To>(reduced_vals, vindex, true);
+}
+}
+}
diff --git a/src/backend/opencl/kernel/reduce_by_key_boundary.cl b/src/backend/opencl/kernel/reduce_by_key_boundary.cl
new file mode 100644
index 0000000000..e6f8c4e041
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_by_key_boundary.cl
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel void final_boundary_reduce(__global int *reduced_block_sizes,
+                                    __global Tk *oKeys, KParam oKInfo,
+                                    __global To *oVals, KParam oVInfo,
+                                    const int n) {
+    const uint lid = get_local_id(0);
+    const uint bid = get_group_id(0);
+    const uint gid = get_global_id(0);
+
+    if (gid == ((bid + 1) * get_local_size(0)) - 1 &&
+        bid < get_num_groups(0) - 1) {
+        Tk k0 = oKeys[gid];
+        Tk k1 = oKeys[gid + 1];
+        if (k0 == k1) {
+            To v0                    = oVals[gid];
+            To v1                    = oVals[gid + 1];
+            oVals[gid + 1]           = binOp(v0, v1);
+            reduced_block_sizes[bid] = get_local_size(0) - 1;
+        } else {
+            reduced_block_sizes[bid] = get_local_size(0);
+        }
+    }
+
+    // if last block, set block size to difference between n and block boundary
+    if (lid == 0 && bid == get_num_groups(0) - 1) {
+        reduced_block_sizes[bid] = n - (bid * get_local_size(0));
+    }
+}
diff --git a/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl b/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
new file mode 100644
index 0000000000..517277106b
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
@@ -0,0 +1,51 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel void final_boundary_reduce_dim(__global int *reduced_block_sizes,
+                                        __global Tk *oKeys, KParam oKInfo,
+                                        __global To *oVals, KParam oVInfo,
+                                        const int n, const int nBlocksZ) {
+    __local int dim_ordering[4];
+
+    const uint lid  = get_local_id(0);
+    const uint bid  = get_group_id(0);
+    const uint gidx = get_global_id(0);
+
+    const int bidy = get_group_id(1);
+    const int bidz = get_group_id(2) % nBlocksZ;
+    const int bidw = get_group_id(2) / nBlocksZ;
+
+    if (lid == 0) {
+        int d           = 1;
+        dim_ordering[0] = DIM;
+        for (int i = 0; i < 4; ++i) {
+            if (i != DIM) dim_ordering[d++] = i;
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (gid == ((bid + 1) * get_local_size(0)) - 1 &&
+        bid < get_num_groups(0) - 1) {
+        Tk k0 = oKeys[gid];
+        Tk k1 = oKeys[gid + 1];
+        if (k0 == k1) {
+            To v0                    = oVals[gid];
+            To v1                    = oVals[gid + 1];
+            oVals[gid + 1]           = binOp(v0, v1);
+            reduced_block_sizes[bid] = get_local_size(0) - 1;
+        } else {
+            reduced_block_sizes[bid] = get_local_size(0);
+        }
+    }
+
+    // if last block, set block size to difference between n and block boundary
+    if (lid == 0 && bid == get_num_groups(0) - 1) {
+        reduced_block_sizes[bid] = n - (bid * get_local_size(0));
+    }
+}
diff --git a/src/backend/opencl/kernel/reduce_by_key_compact.cl b/src/backend/opencl/kernel/reduce_by_key_compact.cl
new file mode 100644
index 0000000000..7751f5f673
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_by_key_compact.cl
@@ -0,0 +1,42 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel void compact(__global int *reduced_block_sizes, __global Tk *oKeys,
+                      KParam oKInfo, __global To *oVals, KParam oVInfo,
+                      const __global Tk *iKeys, KParam iKInfo,
+                      const __global To *iVals, KParam iVInfo,
+                      const int nBlocksZ) {
+    const uint lid = get_local_id(0);
+    const uint bid = get_group_id(0);
+    const uint gid = get_global_id(0);
+
+    const int bidy = get_group_id(1);
+    const int bidz = get_group_id(2) % nBlocksZ;
+    const int bidw = get_group_id(2) / nBlocksZ;
+
+    Tk k;
+    To v;
+
+    const int bOffset = bidw * oVInfo.strides[3] + bidz * oVInfo.strides[2] +
+                        bidy * oVInfo.strides[1];
+
+    // reduced_block_sizes should have inclusive sum of block sizes
+    int nwrite =
+        (bid == 0) ? reduced_block_sizes[0]
+                   : (reduced_block_sizes[bid] - reduced_block_sizes[bid - 1]);
+    int writeloc = (bid == 0) ? 0 : reduced_block_sizes[bid - 1];
+
+    k = iKeys[gid];
+    v = iVals[bOffset + gid];
+
+    if (lid < nwrite) {
+        oKeys[writeloc + lid]           = k;
+        oVals[bOffset + writeloc + lid] = v;
+    }
+}
diff --git a/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl b/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
new file mode 100644
index 0000000000..b7389e324f
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel void compact_dim(__global int *reduced_block_sizes, __global Tk *oKeys,
+                          KParam oKInfo, __global To *oVals, KParam oVInfo,
+                          const __global Tk *iKeys, KParam iKInfo,
+                          const __global To *iVals, KParam iVInfo,
+                          const int nBlocksZ) {
+    __local int dim_ordering[4];
+    const uint lid  = get_local_id(0);
+    const uint bid  = get_group_id(0);
+    const uint gidx = get_global_id(0);
+
+    const int bidy = get_group_id(1);
+    const int bidz = get_group_id(2) % nBlocksZ;
+    const int bidw = get_group_id(2) / nBlocksZ;
+
+    if (lid == 0) {
+        int d           = 1;
+        dim_ordering[0] = DIM;
+        for (int i = 0; i < 4; ++i) {
+            if (i != DIM) dim_ordering[d++] = i;
+        }
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    Tk k;
+    To v;
+
+    // reduced_block_sizes should have inclusive sum of block sizes
+    int nwrite =
+        (bid == 0) ? reduced_block_sizes[0]
+                   : (reduced_block_sizes[bid] - reduced_block_sizes[bid - 1]);
+    int writeloc = (bid == 0) ? 0 : reduced_block_sizes[bid - 1];
+
+    const int tid = bidw * iVInfo.strides[dim_ordering[3]] +
+                    bidz * iVInfo.strides[dim_ordering[2]] +
+                    bidy * iVInfo.strides[dim_ordering[1]] +
+                    gidx * iVInfo.strides[DIM];
+    k = iKeys[gidx];
+    v = iVals[tid];
+
+    if (lid < nwrite) {
+        oKeys[writeloc + lid] = k;
+        const int bOffset     = bidw * oVInfo.strides[dim_ordering[3]] +
+                            bidz * oVInfo.strides[dim_ordering[2]] +
+                            bidy * oVInfo.strides[dim_ordering[1]];
+        oVals[bOffset + (writeloc + lid) * oVInfo.strides[DIM]] = v;
+    }
+}
diff --git a/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl b/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
new file mode 100644
index 0000000000..3caf5bb939
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2018, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+__kernel void test_needs_reduction(__global int *needs_another_reduction,
+                                   __global int *needs_block_boundary_reduced,
+                                   const __global Tk *iKeys, KParam iKInfo,
+                                   int n) {
+    const uint lid = get_local_id(0);
+    const uint bid = get_group_id(0);
+    const uint gid = get_global_id(0);
+
+    Tk k;
+    if (gid < n) { k = iKeys[gid]; }
+
+    __local Tk keys[DIMX];
+    keys[lid] = k;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    int update_key =
+        (lid < DIMX - 2) && (k == keys[lid + 1]) && (gid < (n - 1));
+
+    if (update_key) { atomic_or(needs_another_reduction, update_key); }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // last thread in each block checks if any inter-block keys need further
+    // reduction
+    if (gid == ((bid + 1) * DIMX) - 1 && bid < get_num_groups(0) - 1) {
+        int k0 = iKeys[gid];
+        int k1 = iKeys[gid + 1];
+        if (k0 == k1) { atomic_or(needs_block_boundary_reduced, 1); }
+    }
+}
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 389038ca2e..0dc2c208a5 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -12,10 +12,15 @@
 #include <ops.hpp>
 
 namespace opencl {
-template<af_op_t op, typename Ti, typename To>
+template <af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
                  double nanval = 0);
 
-template<af_op_t op, typename Ti, typename To>
+template <af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan = false, double nanval = 0);
+
+template <af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
-}  // namespace opencl
+}
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index b7301912c6..15e2347abf 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <kernel/reduce.hpp>
+#include <kernel/reduce_by_key.hpp>
 #include <reduce.hpp>
 #include <af/dim4.hpp>
 #include <complex>
@@ -27,14 +28,28 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
     return out;
 }
 
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan, double nanval) {
+    kernel::reduce_by_key<op, Ti, Tk, To>(keys_out, vals_out, keys, vals, dim,
+                                          change_nan, nanval);
+}
+
 template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
 }
 }  // namespace opencl
 
-#define INSTANTIATE(Op, Ti, To)                                               \
-    template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim, \
-                                          bool change_nan, double nanval);    \
-    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,  \
+#define INSTANTIATE(Op, Ti, To)                                                \
+    template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim,  \
+                                          bool change_nan, double nanval);     \
+    template void reduce_by_key<Op, Ti, int, To>(                              \
+        Array<int> & keys_out, Array<To> & vals_out, const Array<int> &keys,   \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template void reduce_by_key<Op, Ti, uint, To>(                             \
+        Array<uint> & keys_out, Array<To> & vals_out, const Array<uint> &keys, \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,   \
                                        double nanval);
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 460fe4f47f..b9f6f81792 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -80,7 +80,7 @@ include(CMakeParseArguments)
 # Parameters
 # ----------
 # 'CXX11'       If set the tests will be compiled using c++11. Tests should strive
-#               to be C++98 compilient
+#               to be C++98 compliant
 # 'SRC'         The source files for the test
 # 'LIBRARIES'   Libraries other than ArrayFire that need to be linked
 # 'DEFINITIONS' Definitions that need to be defined
@@ -267,7 +267,7 @@ make_test(SRC qr_dense.cpp SERIAL)
 make_test(SRC random.cpp)
 make_test(SRC range.cpp)
 make_test(SRC rank_dense.cpp SERIAL)
-make_test(SRC reduce.cpp)
+make_test(SRC reduce.cpp CXX11)
 make_test(SRC regions.cpp)
 make_test(SRC reorder.cpp)
 make_test(SRC replace.cpp)
diff --git a/test/data b/test/data
index 141759fe81..e6ca2f3ab1 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 141759fe815641da18e240fa955c99cf8a4b20ec
+Subproject commit e6ca2f3ab19d4e8ca46317237ca26e48050160fc
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 0e04197efb..a799f05318 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -6,13 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
 #include <algorithm>
+#include <functional>
 #include <iostream>
 #include <string>
 #include <vector>
@@ -22,6 +25,7 @@ using af::cdouble;
 using af::cfloat;
 using af::dim4;
 using af::freeHost;
+using af::tile;
 using std::complex;
 using std::cout;
 using std::endl;
@@ -52,9 +56,7 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef = false,
     dim4 dims = numDims[0];
 
     vector<Ti> in(data[0].size());
-    transform(data[0].begin(), data[0].end(),
-              in.begin(),
-              convert_to<Ti, int>);
+    transform(data[0].begin(), data[0].end(), in.begin(), convert_to<Ti, int>);
 
     af_array inArray   = 0;
     af_array outArray  = 0;
@@ -221,9 +223,7 @@ void cppReduceTest(string pTestFile) {
     dim4 dims = numDims[0];
 
     vector<Ti> in(data[0].size());
-    transform(data[0].begin(), data[0].end(),
-              in.begin(),
-              convert_to<Ti, int>);
+    transform(data[0].begin(), data[0].end(), in.begin(), convert_to<Ti, int>);
 
     array input(dims, &in.front());
 
@@ -318,6 +318,570 @@ CPP_REDUCE_TESTS(anyTrue, any_true, float, unsigned char);
 CPP_REDUCE_TESTS(allTrue, all_true, float, unsigned char);
 CPP_REDUCE_TESTS(count, count, float, unsigned);
 
+struct reduce_by_key_params {
+    size_t iSize, oSize;
+    void *iKeys_;
+    void *iVals_;
+    void *oKeys_;
+    void *oVals_;
+    af_dtype kType_, vType_, oType_;
+    string testname_;
+    virtual ~reduce_by_key_params() {}
+};
+
+//
+// Reduce By Key tests
+//
+template<typename Tk, typename Tv, typename To>
+struct reduce_by_key_params_t : public reduce_by_key_params {
+    string testname_;
+    vector<Tk> iKeys_;
+    vector<Tv> iVals_;
+    vector<Tk> oKeys_;
+    vector<To> oVals_;
+
+    reduce_by_key_params_t(vector<Tk> ikeys, vector<Tv> ivals, vector<Tk> okeys,
+                           vector<To> ovals, string testname)
+        : iKeys_(ikeys)
+        , iVals_(ivals)
+        , oKeys_(okeys)
+        , oVals_(ovals)
+        , testname_(testname) {
+        reduce_by_key_params::iSize  = iKeys_.size();
+        reduce_by_key_params::oSize  = oKeys_.size();
+        reduce_by_key_params::iKeys_ = iKeys_.data();
+        reduce_by_key_params::iVals_ = iVals_.data();
+        reduce_by_key_params::oKeys_ = oKeys_.data();
+        reduce_by_key_params::oVals_ = oVals_.data();
+        reduce_by_key_params::vType_ = (af_dtype)af::dtype_traits<Tv>::af_type;
+        reduce_by_key_params::kType_ = (af_dtype)af::dtype_traits<Tk>::af_type;
+        reduce_by_key_params::oType_ = (af_dtype)af::dtype_traits<To>::af_type;
+        reduce_by_key_params::testname_ = testname_;
+    }
+    ~reduce_by_key_params_t() {}
+};
+
+array ptrToArray(size_t size, void *ptr, af_dtype type) {
+    array res;
+    switch (type) {
+        case f32: res = array(size, (float *)ptr); break;
+        case f64: res = array(size, (double *)ptr); break;
+        case c32: res = array(size, (cfloat *)ptr); break;
+        case c64: res = array(size, (cdouble *)ptr); break;
+        case u32: res = array(size, (unsigned *)ptr); break;
+        case s32: res = array(size, (int *)ptr); break;
+        case u64: res = array(size, (unsigned long long *)ptr); break;
+        case s64: res = array(size, (long long *)ptr); break;
+        case u16: res = array(size, (unsigned short *)ptr); break;
+        case s16: res = array(size, (short *)ptr); break;
+        case b8: res = array(size, (char *)ptr); break;
+        case u8: res = array(size, (unsigned char *)ptr); break;
+        case f16: res = array(size, (half_float::half *)ptr); break;
+    }
+    return res;
+}
+
+class ReduceByKeyP : public ::testing::TestWithParam<reduce_by_key_params *> {
+   public:
+    array keys, vals;
+    array keyResGold, valsReducedGold;
+
+    void SetUp() {
+        reduce_by_key_params *params = GetParam();
+        if (noHalfTests(params->vType_)) { return; }
+
+        keys = ptrToArray(params->iSize, params->iKeys_, params->kType_);
+        vals = ptrToArray(params->iSize, params->iVals_, params->vType_);
+
+        keyResGold = ptrToArray(params->oSize, params->oKeys_, params->kType_);
+        valsReducedGold =
+            ptrToArray(params->oSize, params->oVals_, params->oType_);
+    }
+
+    void TearDown() { delete GetParam(); }
+};
+
+template<typename T>
+struct generateConsq {
+    T vals;
+
+    generateConsq(T v_i = 0) : vals(v_i){};
+
+    T operator()() { return vals++; }
+};
+
+template<typename T>
+struct generateConst {
+    T vals;
+
+    generateConst(T v_i) : vals(v_i){};
+
+    T operator()() { return vals; }
+};
+
+template<typename Tk, typename Tv, typename To>
+reduce_by_key_params *rbk_unique_data(const string testname, const int testSz,
+                                      std::function<Tk()> k_gen,
+                                      std::function<Tv()> v_gen) {
+    vector<Tk> keys(testSz);
+    vector<Tv> vals(testSz);
+
+    generate(begin(keys), end(keys), k_gen);
+    generate(begin(vals), end(vals), v_gen);
+
+    vector<Tk> okeys(begin(keys), end(keys));
+    auto last = unique(begin(okeys), end(okeys));
+    okeys.resize(distance(begin(okeys), last));
+    vector<To> ovals(testSz, To(1));
+    return new reduce_by_key_params_t<Tk, Tv, To>(keys, vals, okeys, ovals,
+                                                  testname);
+}
+
+template<typename Tk, typename Tv, typename To>
+reduce_by_key_params *rbk_single_data(const string testname, const int testSz,
+                                      std::function<Tk()> k_gen,
+                                      std::function<Tv()> v_gen) {
+    vector<Tk> keys(testSz);
+    vector<Tv> vals(testSz);
+
+    generate(begin(keys), end(keys), k_gen);
+    generate(begin(vals), end(vals), v_gen);
+
+    vector<Tk> okeys(begin(keys), end(keys));
+    auto last = unique(begin(okeys), end(okeys));
+    okeys.resize(distance(begin(okeys), last));
+    vector<To> ovals(okeys.size(), To(keys.size()));
+    return new reduce_by_key_params_t<Tk, Tv, To>(keys, vals, okeys, ovals,
+                                                  testname);
+}
+
+// clang-format off
+template<typename Tk, typename Tv, typename To>
+vector<reduce_by_key_params*> genUniqueKeyTests() {
+  return {rbk_unique_data<Tk, Tv, To>("unique_key", 31,          generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 32,          generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 33,          generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 127,         generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 128,         generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 129,         generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 1024,        generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 1025,        generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_unique_data<Tk, Tv, To>("unique_key", 1024 * 1025, generateConsq<Tk>(0), generateConst<Tv>(Tv( 1 )))
+    };
+}
+
+template<typename Tk, typename Tv, typename To>
+vector<reduce_by_key_params*> genSingleKeyTests() {
+  return {rbk_single_data<Tk, Tv, To>("single_key", 31,         generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 32,         generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 33,         generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 127,        generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 128,        generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 129,        generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 1024,       generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 1025,       generateConst<Tk>(0), generateConst<Tv>(Tv( 1 ))),
+          rbk_single_data<Tk, Tv, To>("single_key", 128 * 1025, generateConst<Tk>(0), generateConst<Tv>(Tv( 1 )))
+    };
+}
+// clang-format on
+
+vector<reduce_by_key_params *> generateAllTypes() {
+    vector<reduce_by_key_params *> out;
+    vector<vector<reduce_by_key_params *> > tmp{
+        genUniqueKeyTests<int, float, float>(),
+        genSingleKeyTests<int, float, float>(),
+        genUniqueKeyTests<unsigned, float, float>(),
+        genSingleKeyTests<unsigned, float, float>(),
+        genUniqueKeyTests<int, double, double>(),
+        genSingleKeyTests<int, double, double>(),
+        genUniqueKeyTests<unsigned, double, double>(),
+        genSingleKeyTests<unsigned, double, double>(),
+        genUniqueKeyTests<int, cfloat, cfloat>(),
+        genSingleKeyTests<int, cfloat, cfloat>(),
+        genUniqueKeyTests<unsigned, cfloat, cfloat>(),
+        genSingleKeyTests<unsigned, cfloat, cfloat>(),
+        genUniqueKeyTests<int, cdouble, cdouble>(),
+        genSingleKeyTests<int, cdouble, cdouble>(),
+        genUniqueKeyTests<unsigned, cdouble, cdouble>(),
+        genSingleKeyTests<unsigned, cdouble, cdouble>(),
+        genUniqueKeyTests<int, half_float::half, float>(),
+        genSingleKeyTests<int, half_float::half, float>(),
+        genUniqueKeyTests<unsigned, half_float::half, float>(),
+        genSingleKeyTests<unsigned, half_float::half, float>(),
+    };
+
+    for (auto &v : tmp) { copy(begin(v), end(v), back_inserter(out)); }
+    return out;
+}
+
+template<typename TestClass>
+string testNameGenerator(
+    const ::testing::TestParamInfo<typename TestClass::ParamType> info) {
+    af_dtype kt = info.param->kType_;
+    af_dtype vt = info.param->vType_;
+    size_t size = info.param->iSize;
+    std::stringstream s;
+    s << info.param->testname_ << "_keyType_" << kt << "_valueType_" << vt
+      << "_size_" << size;
+    return s.str();
+}
+
+INSTANTIATE_TEST_CASE_P(UniqueKeyTests, ReduceByKeyP,
+                        ::testing::ValuesIn(generateAllTypes()),
+                        testNameGenerator<ReduceByKeyP>);
+
+TEST_P(ReduceByKeyP, SumDim0) {
+    if (noHalfTests(GetParam()->vType_)) { return; }
+    array keyRes, valsReduced;
+    sumByKey(keyRes, valsReduced, keys, vals, 0, 0);
+
+    ASSERT_ARRAYS_EQ(keyResGold, keyRes);
+    ASSERT_ARRAYS_NEAR(valsReducedGold, valsReduced, 1e-5);
+}
+
+TEST_P(ReduceByKeyP, SumDim2) {
+    if (noHalfTests(GetParam()->vType_)) { return; }
+    const int ntile = 2;
+    vals            = tile(vals, 1, ntile, 1, 1);
+    vals            = reorder(vals, 1, 2, 0, 3);
+
+    valsReducedGold = tile(valsReducedGold, 1, ntile, 1, 1);
+    valsReducedGold = reorder(valsReducedGold, 1, 2, 0, 3);
+
+    array keyRes, valsReduced;
+    const int dim       = 2;
+    const double nanval = 0.0;
+    sumByKey(keyRes, valsReduced, keys, vals, dim, nanval);
+
+    ASSERT_ARRAYS_EQ(keyResGold, keyRes);
+    ASSERT_ARRAYS_NEAR(valsReducedGold, valsReduced, 1e-5);
+}
+
+TEST(ReduceByKey, MultiBlockReduceSingleval) {
+    array keys = constant(0, 1024 * 1024, s32);
+    array vals = constant(1, 1024 * 1024, f32);
+
+    array keyResGold      = constant(0, 1);
+    array valsReducedGold = constant(1024 * 1024, 1, f32);
+
+    array keyRes, valsReduced;
+    sumByKey(keyRes, valsReduced, keys, vals);
+
+    ASSERT_TRUE(allTrue<bool>(keyResGold == keyRes));
+    ASSERT_ARRAYS_NEAR(valsReducedGold, valsReduced, 1e-5);
+}
+
+void reduce_by_key_test(std::string test_fn) {
+    vector<dim4> numDims;
+    vector<vector<float> > data;
+    vector<vector<float> > tests;
+    readTests<float, float, float>(test_fn, numDims, data, tests);
+
+    for (int t = 0; t < numDims.size() / 2; ++t) {
+        dim4 kdim = numDims[t * 2];
+        dim4 vdim = numDims[t * 2 + 1];
+
+        vector<int> in_keys(data[t * 2].begin(), data[t * 2].end());
+        vector<float> in_vals(data[t * 2 + 1].begin(), data[t * 2 + 1].end());
+
+        af_array inKeys  = 0;
+        af_array inVals  = 0;
+        af_array outKeys = 0;
+        af_array outVals = 0;
+        ASSERT_EQ(
+            AF_SUCCESS,
+            af_create_array(&inKeys, &in_keys.front(), kdim.ndims(), kdim.get(),
+                            (af_dtype)af::dtype_traits<int>::af_type));
+        ASSERT_EQ(
+            AF_SUCCESS,
+            af_create_array(&inVals, &in_vals.front(), vdim.ndims(), vdim.get(),
+                            (af_dtype)af::dtype_traits<float>::af_type));
+
+        vector<int> currGoldKeys(tests[t * 2].begin(), tests[t * 2].end());
+        vector<float> currGoldVals(tests[t * 2 + 1].begin(),
+                                   tests[t * 2 + 1].end());
+
+        // Run sum
+        ASSERT_EQ(AF_SUCCESS,
+                  af_sum_by_key(&outKeys, &outVals, inKeys, inVals, 0));
+
+        dim_t ok0, ok1, ok2, ok3;
+        dim_t ov0, ov1, ov2, ov3;
+        af_get_dims(&ok0, &ok1, &ok2, &ok3, outKeys);
+        af_get_dims(&ov0, &ov1, &ov2, &ov3, outVals);
+
+        // Get result
+        vector<int> outKeysVec(ok0 * ok1 * ok2 * ok3);
+        vector<float> outValsVec(ov0 * ov1 * ov2 * ov3);
+
+        ASSERT_EQ(AF_SUCCESS,
+                  af_get_data_ptr((void *)&outKeysVec.front(), outKeys));
+        ASSERT_EQ(AF_SUCCESS,
+                  af_get_data_ptr((void *)&outValsVec.front(), outVals));
+
+        size_t nElems = currGoldKeys.size();
+        if (std::equal(currGoldKeys.begin(), currGoldKeys.end(),
+                       outKeysVec.begin()) == false) {
+            for (size_t elIter = 0; elIter < nElems; ++elIter) {
+                EXPECT_NEAR(currGoldKeys[elIter], outKeysVec[elIter], 1e-4)
+                    << "at: " << elIter << endl;
+                EXPECT_NEAR(currGoldVals[elIter], outValsVec[elIter], 1e-4)
+                    << "at: " << elIter << endl;
+            }
+            for (int i = 0; i < (int)nElems; i++) {
+                cout << currGoldKeys[i] << ":" << currGoldVals[i] << ", ";
+            }
+
+            for (int i = 0; i < (int)nElems; i++) {
+                cout << outKeysVec[i] << ":" << outValsVec[i] << ", ";
+            }
+            FAIL();
+        }
+
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outKeys));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(outVals));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(inKeys));
+        ASSERT_EQ(AF_SUCCESS, af_release_array(inVals));
+    }
+}
+TEST(ReduceByKey, MultiBlockReduceContig10) {
+    reduce_by_key_test(string(TEST_DIR "/reduce/test_contig10_by_key.test"));
+}
+
+TEST(ReduceByKey, MultiBlockReduceRandom10) {
+    reduce_by_key_test(string(TEST_DIR "/reduce/test_random10_by_key.test"));
+}
+
+TEST(ReduceByKey, MultiBlockReduceContig500) {
+    reduce_by_key_test(string(TEST_DIR "/reduce/test_contig500_by_key.test"));
+}
+
+TEST(ReduceByKey, MultiBlockReduceByKeyRandom500) {
+    reduce_by_key_test(string(TEST_DIR "/reduce/test_random500_by_key.test"));
+}
+
+TEST(ReduceByKey, productReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    productByKey(reduced_keys, reduced_vals, keys, vals, 0, 1);
+
+    const int goldSz = 5;
+    const vector<float> gold_reduce{0, 7, 6, 30, 4};
+
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, minReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    minByKey(reduced_keys, reduced_vals, keys, vals);
+
+    const int goldSz = 5;
+    const vector<float> gold_reduce{0, 1, 6, 2, 4};
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, maxReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    maxByKey(reduced_keys, reduced_vals, keys, vals);
+
+    const int goldSz = 5;
+    const vector<float> gold_reduce{0, 7, 6, 5, 4};
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, allTrueReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 1, 1, 1, 0, 1, 1, 1};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    allTrueByKey(reduced_keys, reduced_vals, keys, vals);
+
+    const int goldSz = 5;
+    const vector<char> gold_reduce{0, 1, 1, 0, 1};
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, anyTrueReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 8, 8};
+    const float testVals[testSz] = {0, 1, 1, 1, 0, 1, 0, 0};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    anyTrueByKey(reduced_keys, reduced_vals, keys, vals);
+
+    const int goldSz = 5;
+    const vector<char> gold_reduce{0, 1, 1, 1, 0};
+
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, countReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 5};
+    const float testVals[testSz] = {0, 1, 1, 1, 0, 1, 1, 1};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    countByKey(reduced_keys, reduced_vals, keys, vals);
+
+    const int goldSz = 4;
+    const vector<unsigned> gold_reduce{0, 2, 1, 3};
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, ReduceByKeyNans) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, NAN, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    array reduced_keys, reduced_vals;
+    productByKey(reduced_keys, reduced_vals, keys, vals, 0, 1);
+
+    const int goldSz = 5;
+    const vector<float> gold_reduce{0, 7, 6, 30, 4};
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, nDim0ReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    const int ntile = 2;
+    vals            = tile(vals, af::dim4(1, ntile, ntile, ntile));
+
+    array reduced_keys, reduced_vals;
+    const int dim       = 0;
+    const double nanval = 0.0;
+    sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
+
+    const dim4 goldSz(5, 2, 2, 2);
+    const vector<float> gold_reduce{0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
+
+                                    0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
+
+                                    0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
+
+                                    0, 8, 6, 10, 4, 0, 8, 6, 10, 4};
+    ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
+}
+
+TEST(ReduceByKey, nDim1ReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    const int ntile = 2;
+    vals            = tile(vals, af::dim4(1, ntile, 1, 1));
+    vals            = transpose(vals);
+
+    array reduced_keys, reduced_vals;
+    const int dim       = 1;
+    const double nanval = 0.0;
+    sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
+
+    const int goldSz                = 5;
+    const float gold_reduce[goldSz] = {0, 8, 6, 10, 4};
+    vector<float> hreduce(reduced_vals.elements());
+    reduced_vals.host(hreduce.data());
+
+    for (int i = 0; i < goldSz * ntile; i++) {
+        ASSERT_EQ(gold_reduce[i / ntile], hreduce[i]);
+    }
+}
+
+TEST(ReduceByKey, nDim2ReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    const int ntile = 2;
+    vals            = tile(vals, af::dim4(1, ntile, 1, 1));
+    vals            = reorder(vals, 1, 2, 0, 3);
+
+    array reduced_keys, reduced_vals;
+    const int dim       = 2;
+    const double nanval = 0.0;
+    sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
+
+    const int goldSz                = 5;
+    const float gold_reduce[goldSz] = {0, 8, 6, 10, 4};
+    vector<float> h_a(reduced_vals.elements());
+    reduced_vals.host(h_a.data());
+
+    for (int i = 0; i < goldSz * ntile; i++) {
+        ASSERT_EQ(gold_reduce[i / ntile], h_a[i]);
+    }
+}
+
+TEST(ReduceByKey, nDim3ReduceByKey) {
+    const static int testSz      = 8;
+    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
+    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+
+    array keys(testSz, testKeys);
+    array vals(testSz, testVals);
+
+    const int ntile = 2;
+    vals            = tile(vals, af::dim4(1, ntile, 1, 1));
+    vals            = reorder(vals, 1, 2, 3, 0);
+
+    array reduced_keys, reduced_vals;
+    const int dim       = 3;
+    const double nanval = 0.0;
+    sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
+
+    const int goldSz                = 5;
+    const float gold_reduce[goldSz] = {0, 8, 6, 10, 4};
+    vector<float> h_a(reduced_vals.elements());
+    reduced_vals.host(h_a.data());
+
+    for (int i = 0; i < goldSz * ntile; i++) {
+        ASSERT_EQ(gold_reduce[i / ntile], h_a[i]);
+    }
+}
+
 TEST(Reduce, Test_Product_Global) {
     const int num = 100;
     array a       = 1 + round(5 * randu(num, 1)) / 100;
@@ -710,11 +1274,11 @@ struct reduce_params {
     dim4 arr_dim;
     dim4 result_dim;
     int reduce_dim;
-  reduce_params(double ev, dim4 ad, dim4 result_d, int red_dim)
-    : element_value(ev)
-    , arr_dim(ad)
-    , result_dim(result_d)
-    , reduce_dim(red_dim) {}
+    reduce_params(double ev, dim4 ad, dim4 result_d, int red_dim)
+        : element_value(ev)
+        , arr_dim(ad)
+        , result_dim(result_d)
+        , reduce_dim(red_dim) {}
 };
 
 class ReduceHalf : public ::testing::TestWithParam<reduce_params> {};
@@ -787,7 +1351,7 @@ TEST_P(ReduceHalf, Sum) {
     }
 
     double result_value = param.element_value * elements;
-    array gold = constant(result_value, param.result_dim, f32);
+    array gold          = constant(result_value, param.result_dim, f32);
 
     array result = sum(arr, param.reduce_dim);
     ASSERT_ARRAYS_EQ(gold, result);
@@ -797,7 +1361,7 @@ TEST_P(ReduceHalf, Product) {
     SUPPORTED_TYPE_CHECK(af_half);
     reduce_params param = GetParam();
 
-    array arr  = constant(param.element_value, param.arr_dim, f16);
+    array arr = constant(param.element_value, param.arr_dim, f16);
 
     size_t elements = 0;
     if (param.reduce_dim == -1) {
@@ -806,9 +1370,9 @@ TEST_P(ReduceHalf, Product) {
         elements = param.arr_dim[param.reduce_dim];
     }
 
-    double result_value = pow(param.element_value, elements);
+    float result_value = pow(param.element_value, elements);
 
-    if(isinf((float)result_value)) {
+    if (std::isinf(result_value)) {
         SUCCEED();
         return;
     }
@@ -821,9 +1385,9 @@ TEST_P(ReduceHalf, Product) {
 // TODO(umar): HalfMin
 TEST(ReduceHalf, Min) {
     SUPPORTED_TYPE_CHECK(af_half);
-    float harr[] = { 1, 2, 3, 4, 5, 6, 7 };
+    float harr[] = {1, 2, 3, 4, 5, 6, 7};
     array arr(7, harr);
-    arr = arr.as(f16);
+    arr       = arr.as(f16);
     array out = min(arr);
 
     array gold = constant(1, 1, f16);
@@ -877,3 +1441,442 @@ TEST(ReduceHalf, AllTrue) {
     array gold = constant(1, 1, b8);
     ASSERT_ARRAYS_EQ(gold, out);
 }
+
+//
+// Documentation Snippets
+
+TEST(Reduce, SNIPPET_sum_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+
+    //! [ex_reduce_sum_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+
+    array okeys, ovals;
+    sumByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0  1  0  2 ]
+    // ovals = [ 3 12 13 17 ]
+
+    //! [ex_reduce_sum_by_key]
+
+    vector<int> gold_keys   = { 0, 1, 0, 2 };
+    vector<float> gold_vals = { 3, 12, 13, 17 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
+}
+
+TEST(Reduce, SNIPPET_sum_by_key_dim) {
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 6,
+                     2, 7,
+                     3, 8,
+                     4, 9,
+                     5, 10};
+
+    //! [ex_reduce_sum_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 2 3 4 5  ]
+    //         [ 6 7 8 9 10 ]]
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    sumByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1  5  9 ],
+    //          [ 6 15 19 ]]
+
+    //! [ex_reduce_sum_by_key_dim]
+
+    vector<int> gold_keys   = { 1, 0, 2  };
+    vector<float> gold_vals = { 1, 6, 5, 15, 9, 19 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
+}
+
+TEST(Reduce, SNIPPET_product_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+
+    //! [ex_reduce_product_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+
+    array okeys, ovals;
+    productByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0  1  0  2 ]
+    // ovals = [ 2 60 42 72 ]
+
+    //! [ex_reduce_product_by_key]
+
+    vector<int> gold_keys   = { 0,  1,  0,  2 };
+    vector<float> gold_vals = { 2, 60, 42, 72 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
+}
+
+TEST(Reduce, SNIPPET_product_by_key_dim) {
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 6,
+                     2, 7,
+                     3, 8,
+                     4, 9,
+                     5, 10};
+
+    //! [ex_reduce_product_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 2 3 4 5  ]
+    //         [ 6 7 8 9 10 ]]
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    productByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1  6 20 ],
+    //          [ 6 56 90 ]]
+
+    //! [ex_reduce_product_by_key_dim]
+
+    vector<int> gold_keys   = { 1, 0, 2  };
+    vector<float> gold_vals = { 1, 6, 6, 56, 20, 90 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
+}
+
+TEST(Reduce, SNIPPET_min_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+
+    //! [ex_reduce_min_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+
+    array okeys, ovals;
+    minByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0 1 0 2 ]
+    // ovals = [ 1 3 6 8 ]
+
+    //! [ex_reduce_min_by_key]
+
+    vector<int> gold_keys   = { 0, 1, 0, 2 };
+    vector<float> gold_vals = { 1, 3, 6, 8 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
+}
+
+TEST(Reduce, SNIPPET_min_by_key_dim) {
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 6,
+                     2, 7,
+                     3, 8,
+                     4, 9,
+                     5, 10};
+
+    //! [ex_reduce_min_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 2 3 4 5  ]
+    //         [ 6 7 8 9 10 ]]
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    minByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1 2 4 ],
+    //          [ 6 7 9 ]]
+
+    //! [ex_reduce_min_by_key_dim]
+
+    vector<int> gold_keys   = { 1, 0, 2  };
+    vector<float> gold_vals = { 1, 6, 2, 7, 4, 9 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
+}
+
+TEST(Reduce, SNIPPET_max_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+
+    //! [ex_reduce_max_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+
+    array okeys, ovals;
+    maxByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0 1 0 2 ]
+    // ovals = [ 2 5 7 9 ]
+
+    //! [ex_reduce_max_by_key]
+
+    vector<int> gold_keys   = { 0, 1, 0, 2 };
+    vector<float> gold_vals = { 2, 5, 7, 9 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
+}
+
+TEST(Reduce, SNIPPET_max_by_key_dim) {
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 6,
+                     2, 7,
+                     3, 8,
+                     4, 9,
+                     5, 10};
+
+    //! [ex_reduce_max_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 2 3 4 5  ]
+    //         [ 6 7 8 9 10 ]]
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    maxByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1  3  5 ],
+    //          [ 6  8 10 ]]
+
+    //! [ex_reduce_max_by_key_dim]
+
+    vector<int> gold_keys   = { 1, 0, 2  };
+    vector<float> gold_vals = { 1, 6, 3, 8, 5, 10 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
+}
+
+TEST(Reduce, SNIPPET_alltrue_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 1, 0, 1, 1, 0, 0, 1, 0 };
+
+    //! [ex_reduce_alltrue_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 1 0 1 1 0 0 1 0 ];
+
+    array okeys, ovals;
+    allTrueByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0 1 0 2 ]
+    // ovals = [ 1 0 0 0 ]
+
+    //! [ex_reduce_alltrue_by_key]
+
+    vector<int>  gold_keys   = { 0, 1, 0, 2 };
+    vector<unsigned char> gold_vals = { 1, 0, 0, 0 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals.as(u8));
+}
+
+TEST(Reduce, SNIPPET_alltrue_by_key_dim) {
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 0,
+                     1, 1,
+                     1, 0,
+                     0, 1,
+                     1, 1};
+
+    //! [ex_reduce_alltrue_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 1 1 0 1 ]
+    //         [ 0 1 0 1 1 ]]
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    allTrueByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1 1 0 ],
+    //          [ 0 0 1 ]]
+
+    //! [ex_reduce_alltrue_by_key_dim]
+
+    vector<int> gold_keys   = { 1, 0, 2  };
+    vector<unsigned char> gold_vals = { 1, 0, 1, 0, 0, 1 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals.as(u8));
+}
+
+TEST(Reduce, SNIPPET_anytrue_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 1, 0, 1, 1, 0, 0, 1, 0 };
+
+    //! [ex_reduce_anytrue_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 1 0 1 1 0 0 1 0 ];
+
+    array okeys, ovals;
+    anyTrueByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0 1 0 2 ]
+    // ovals = [ 1 0 0 0 ]
+
+    //! [ex_reduce_anytrue_by_key]
+
+    vector<int> gold_keys   = { 0, 1, 0, 2 };
+    vector<unsigned char> gold_vals = { 1, 1, 0, 1 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals.as(u8));
+}
+
+TEST(Reduce, SNIPPET_anytrue_by_key_dim) {
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 0,
+                     1, 1,
+                     1, 0,
+                     0, 1,
+                     1, 1};
+
+    //! [ex_reduce_anytrue_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 1 1 0 1 ]
+    //         [ 0 1 0 1 1 ]]
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    anyTrueByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1 1 1 ],
+    //          [ 0 1 1 ]]
+
+    //! [ex_reduce_anytrue_by_key_dim]
+
+    vector<int> gold_keys   = { 1, 0, 2  };
+    vector<unsigned char> gold_vals = { 1, 0, 1, 1, 1, 1 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals.as(u8));
+}
+
+TEST(Reduce, SNIPPET_count_by_key) {
+
+    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
+    float hvals[] = { 1, 1, 0, 1, 1, 0, 0, 1, 0 };
+
+    //! [ex_reduce_count_by_key]
+
+    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals); // vals = [ 1 1 0 1 1 0 0 1 0 ];
+
+    array okeys, ovals;
+    countByKey(okeys, ovals, keys, vals);
+
+    // okeys = [ 0 1 0 2 ]
+    // ovals = [ 2 2 0 1 ]
+
+    //! [ex_reduce_count_by_key]
+
+    vector<int> gold_keys      = { 0, 1, 0, 2 };
+    vector<unsigned> gold_vals = { 2, 2, 0, 1 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
+}
+
+TEST(Reduce, SNIPPET_count_by_key_dim) {
+
+    int hkeys[]   = {1, 0, 0, 2, 2 };
+
+    float hvals[] = {1, 0,
+                     1, 1,
+                     1, 0,
+                     0, 1,
+                     1, 1};
+
+    //! [ex_reduce_count_by_key_dim]
+
+    array keys(5, hkeys);
+    array vals(2, 5, hvals);
+
+    // keys = [ 1 0 0 2 2 ]
+
+    // vals = [[ 1 1 1 0 1 ]
+    //         [ 0 1 0 1 1 ]]
+
+
+    const int reduce_dim = 1;
+    array okeys, ovals;
+    countByKey(okeys, ovals, keys, vals, reduce_dim);
+
+    // okeys = [ 1 0 2 ]
+
+    // ovals = [[ 1 2 1 ],
+    //          [ 0 1 2 ]]
+
+    //! [ex_reduce_count_by_key_dim]
+
+    vector<int> gold_keys      = { 1, 0, 2  };
+    vector<unsigned> gold_vals = { 1, 0, 2, 1, 1, 2 };
+
+    ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
+    ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 95aa70f0f3..ac368ead2a 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -135,7 +135,7 @@ float convert(af::half in) {
 template<>
 af_half convert(int in) {
     half_float::half h = half_float::half(in);
-    return *reinterpret_cast<af_half*>(&h);
+    return *reinterpret_cast<af_half *>(&h);
 }
 
 template<typename inType, typename outType, typename FileElementType>
@@ -485,9 +485,9 @@ bool noDoubleTests(af::dtype ty) {
 }
 
 bool noHalfTests(af::dtype ty) {
-    bool isTypeHalf        = (ty == f16);
-    int dev                = af::getDevice();
-    bool isHalfSupported   = af::isHalfAvailable(dev);
+    bool isTypeHalf      = (ty == f16);
+    int dev              = af::getDevice();
+    bool isHalfSupported = af::isHalfAvailable(dev);
 
     return ((isTypeHalf && !isHalfSupported) ? true : false);
 }
@@ -549,9 +549,9 @@ af::array cpu_randu(const af::dim4 dims) {
 
     bool isTypeCplx = is_same_type<T, af::cfloat>::value ||
                       is_same_type<T, af::cdouble>::value;
-    bool isTypeFloat =
-      is_same_type<BT, float>::value || is_same_type<BT, double>::value ||
-      is_same_type<BT, half_float::half>::value;
+    bool isTypeFloat = is_same_type<BT, float>::value ||
+                       is_same_type<BT, double>::value ||
+                       is_same_type<BT, half_float::half>::value;
 
     size_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
 
@@ -611,9 +611,7 @@ const af::cfloat &operator+(const af::cfloat &val) { return val; }
 
 const af::cdouble &operator+(const af::cdouble &val) { return val; }
 
-const af_half& operator+(const af_half& val) {
-    return val;
-}
+const af_half &operator+(const af_half &val) { return val; }
 
 // Calculate a multi-dimensional coordinates' linearized index
 dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {

From c9eb7e10ac70159cf41c5f989f1e64fab983fe5b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Feb 2020 15:39:12 -0500
Subject: [PATCH 1822/2677] Fix MKL not found error with OpenMP threading layer

---
 CMakeModules/FindMKL.cmake | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 75889d9bfa..f801650860 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -64,6 +64,7 @@
 
 include(CheckTypeSize)
 include(FindPackageHandleStandardArgs)
+find_package(OpenMP QUIET)
 
 check_type_size("int" INT_SIZE
   BUILTIN_TYPES_ONLY LANGUAGE C)
@@ -71,6 +72,8 @@ check_type_size("int" INT_SIZE
 set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
 set_property(CACHE MKL_THREAD_LAYER PROPERTY STRINGS "TBB" "GNU OpenMP" "Intel OpenMP" "Sequential")
 
+message(STATUS "MKL: Thread Layer(${MKL_THREAD_LAYER}) Interface(${INT_SIZE}-byte Integer)")
+
 if(NOT MKL_THREAD_LAYER STREQUAL MKL_THREAD_LAYER_LAST)
   unset(MKL::ThreadLayer CACHE)
   unset(MKL::ThreadingLibrary CACHE)
@@ -151,7 +154,6 @@ if(MKL_FFTW_INCLUDE_DIR)
   mark_as_advanced(MKL_FFTW_INCLUDE_DIR)
 endif()
 
-
 if(WIN32)
   if(${MSVC_VERSION} GREATER_EQUAL 1900)
     set(msvc_dir "vc_mt")
@@ -162,6 +164,15 @@ if(WIN32)
   endif()
 endif()
 
+
+if(WIN32)
+  set(ENV_LIBRARY_PATHS "$ENV{LIB}")
+  message(VERBOSE "MKL environment variable(LIB): ${ENV_LIBRARY_PATHS}")
+else()
+  string(REGEX REPLACE ":" ";" ENV_LIBRARY_PATHS "$ENV{LIBRARY_PATH}")
+  message(VERBOSE "MKL environment variable(LIBRARY_PATH): ${ENV_LIBRARY_PATHS}")
+endif()
+
 # Finds and creates libraries for MKL with the MKL:: prefix
 #
 # Parameters:
@@ -192,12 +203,6 @@ function(find_mkl_library)
   add_library(MKL::${mkl_args_NAME}        SHARED IMPORTED)
   add_library(MKL::${mkl_args_NAME}_STATIC STATIC IMPORTED)
 
-  if(WIN32)
-    set(ENV_LIBRARY_PATHS "$ENV{LIB}")
-  else()
-    string(REGEX REPLACE ":" ";" ENV_LIBRARY_PATHS "$ENV{LIBRARY_PATH}")
-  endif()
-
   if(NOT (WIN32 AND mkl_args_DLL_ONLY))
     find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
       NAMES
@@ -220,6 +225,7 @@ function(find_mkl_library)
         intel64
         intel64/gcc4.7)
     if(MKL_${mkl_args_NAME}_LINK_LIBRARY)
+      message(VERBOSE "MKL_${mkl_args_NAME}_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
       mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
     endif()
   endif()
@@ -246,6 +252,7 @@ function(find_mkl_library)
         IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
         )
       if(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+        message(VERBOSE "MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}")
         mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
       endif()
     endif()
@@ -296,10 +303,14 @@ if(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
 elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
   find_package(OpenMP REQUIRED)
   find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_gnu_thread SEARCH_STATIC)
+  set(MKL_ThreadingLibrary_LINK_LIBRARY ${OpenMP_gomp_LIBRARY})
+  if(MKL_ThreadingLibrary_LINK_LIBRARY)
+    mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
+  endif()
   add_library(MKL::ThreadingLibrary SHARED IMPORTED)
   set_target_properties(MKL::ThreadingLibrary
     PROPERTIES
-      IMPORTED_LOCATION "${OpenMP_gomp_LIBRARY}"
+      IMPORTED_LOCATION "${MKL_ThreadingLibrary_LINK_LIBRARY}"
       INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_CXX)
 elseif(MKL_THREAD_LAYER STREQUAL "TBB")
   find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_tbb_thread SEARCH_STATIC)

From eacfc5acf5cbcbcb4c3c6812ab0cc295181c2ceb Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Fri, 7 Feb 2020 21:37:02 -0600
Subject: [PATCH 1823/2677] Modified backend transform() to accept output arg
 (#2327)

* Modified backend transform() to accept output arg

* Testing transform.
* Refactor creating af_array test data from files into a function
* Add af_transform_v2 to image.h header, src/api/c and unified
* Refactor v2 test into classes
* Add more general setTestData version, move test before CPP tests
* Add guards for null output array pointer and input arrays

Co-authored-by: Mark Poscablo <mposcablo@gmail.com>
---
 include/af/image.h               |  46 ++++-
 src/api/c/transform.cpp          | 266 +++++++++++++-------------
 src/api/unified/image.cpp        |   7 +
 src/backend/cpu/transform.cpp    |  22 +--
 src/backend/cpu/transform.hpp    |   6 +-
 src/backend/cuda/transform.cpp   |  20 +-
 src/backend/cuda/transform.hpp   |   6 +-
 src/backend/opencl/transform.cpp |  21 +--
 src/backend/opencl/transform.hpp |   6 +-
 test/transform.cpp               | 311 +++++++++++++++++++++++++++++--
 10 files changed, 504 insertions(+), 207 deletions(-)

diff --git a/include/af/image.h b/include/af/image.h
index f69e3c004d..1d3d488a4f 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -973,14 +973,15 @@ extern "C" {
     /**
        C Interface for transforming an image
 
-       \param[out] out will contain the transformed image
-       \param[in] in is input image
-       \param[in] transform is transformation matrix
-       \param[in] odim0 is the first output dimension
-       \param[in] odim1 is the second output dimension
-       \param[in] method is the interpolation type (Nearest by default)
-       \param[in] inverse if true applies inverse transform, if false applies forward transoform
-       \return     \ref AF_SUCCESS if the color transformation is successful,
+       \param[out] out       will contain the transformed image
+       \param[in]  in        is input image
+       \param[in]  transform is transformation matrix
+       \param[in]  odim0     is the first output dimension
+       \param[in]  odim1     is the second output dimension
+       \param[in]  method    is the interpolation type (Nearest by default)
+       \param[in]  inverse   if true applies inverse transform, if false applies forward transoform
+
+       \return \ref AF_SUCCESS if the color transformation is successful,
        otherwise an appropriate error code is returned.
 
        \ingroup transform_func_transform
@@ -989,6 +990,35 @@ extern "C" {
                               const dim_t odim0, const dim_t odim1,
                               const af_interp_type method, const bool inverse);
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for the version of \ref af_transform that accepts a
+       preallocated output array
+
+       \param[out] out       will contain the transformed image
+       \param[in]  in        is input image
+       \param[in]  transform is transformation matrix
+       \param[in]  odim0     is the first output dimension
+       \param[in]  odim1     is the second output dimension
+       \param[in]  method    is the interpolation type (Nearest by default)
+       \param[in]  inverse   if true applies inverse transform, if false applies forward transoform
+
+       \return \ref AF_SUCCESS if the color transformation is successful,
+       otherwise an appropriate error code is returned.
+
+       \note \p out can either be a null or existing `af_array` object. If it is a
+             sub-array of an existing `af_array`, only the corresponding portion of
+             the `af_array` will be overwritten
+       \note Passing an `af_array` that has not been initialized to \p out will
+             cause undefined behavior.
+
+       \ingroup transform_func_transform
+    */
+    AFAPI af_err af_transform_v2(af_array *out, const af_array in, const af_array transform,
+                                 const dim_t odim0, const dim_t odim1,
+                                 const af_interp_type method, const bool inverse);
+#endif
+
 #if AF_API_VERSION >= 33
     /**
        C Interface for transforming an image
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 6c161d8877..fed87ba48b 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -19,12 +19,12 @@ using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array transform(const af_array in, const af_array tf,
-                                 const af::dim4 &odims,
-                                 const af_interp_type method,
-                                 const bool inverse, const bool perspective) {
-    return getHandle(transform<T>(getArray<T>(in), getArray<float>(tf), odims,
-                                  method, inverse, perspective));
+static inline void transform(af_array *out, const af_array in,
+                             const af_array tf, const dim4 &odims,
+                             const af_interp_type method, const bool inverse,
+                             const bool perspective) {
+    transform<T>(getArray<T>(*out), getArray<T>(in), getArray<float>(tf), odims,
+                 method, inverse, perspective);
 }
 
 AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims) {
@@ -53,141 +53,129 @@ AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims) {
         return AF_BATCH_UNSUPPORTED;
 }
 
+void af_transform_common(af_array *out, const af_array in, const af_array tf,
+                         const dim_t odim0, const dim_t odim1,
+                         const af_interp_type method, const bool inverse,
+                         bool allocate_out) {
+    ARG_ASSERT(0, out != 0);  // *out (the af_array) can be null, but not out
+    ARG_ASSERT(1, in  != 0);
+    ARG_ASSERT(2, tf  != 0);
+
+    const ArrayInfo &t_info = getInfo(tf);
+    const ArrayInfo &i_info = getInfo(in);
+
+    const dim4 idims     = i_info.dims();
+    const dim4 tdims     = t_info.dims();
+    const af_dtype itype = i_info.getType();
+
+    // Assert type and interpolation
+    ARG_ASSERT(2, t_info.getType() == f32);
+    ARG_ASSERT(5, method == AF_INTERP_NEAREST || method == AF_INTERP_BILINEAR ||
+                      method == AF_INTERP_BILINEAR_COSINE ||
+                      method == AF_INTERP_BICUBIC ||
+                      method == AF_INTERP_BICUBIC_SPLINE ||
+                      method == AF_INTERP_LOWER);
+
+    // Assert dimesions
+    // Image can be 2D or higher
+    DIM_ASSERT(1, idims.elements() > 0);
+    DIM_ASSERT(1, idims.ndims() >= 2);
+
+    // Transform can be 3x2 for affine transform or 3x3 for perspective
+    // transform
+    DIM_ASSERT(2, (tdims[0] == 3 && (tdims[1] == 2 || tdims[1] == 3)));
+
+    // If transform is batched, the output dimensions must be specified
+    if (tdims[2] * tdims[3] > 1) {
+        ARG_ASSERT(3, odim0 > 0);
+        ARG_ASSERT(4, odim1 > 0);
+    }
+
+    // If idims[2] > 1 and tdims[2] > 1, then both must be equal
+    // else at least one of them must be 1
+    if (tdims[2] != 1 && idims[2] != 1)
+        DIM_ASSERT(2, idims[2] == tdims[2]);
+    else
+        DIM_ASSERT(2, idims[2] == 1 || tdims[2] == 1);
+
+    // If idims[3] > 1 and tdims[3] > 1, then both must be equal
+    // else at least one of them must be 1
+    if (tdims[3] != 1 && idims[3] != 1)
+        DIM_ASSERT(2, idims[3] == tdims[3]);
+    else
+        DIM_ASSERT(2, idims[3] == 1 || tdims[3] == 1);
+
+    const bool perspective = (tdims[1] == 3);
+    dim_t o0 = odim0, o1 = odim1, o2 = 0, o3 = 0;
+    if (odim0 * odim1 == 0) {
+        o0 = idims[0];
+        o1 = idims[1];
+    }
+
+    switch (getTransformBatchKind(idims, tdims)) {
+        case AF_BATCH_NONE:  // Both are exactly 2D
+        case AF_BATCH_LHS:   // Image is 3/4D, transform is 2D
+        case AF_BATCH_SAME:  // Both are 3/4D and have the same dims
+            o2 = idims[2];
+            o3 = idims[3];
+            break;
+        case AF_BATCH_RHS:  // Image is 2D, transform is 3/4D
+            o2 = tdims[2];
+            o3 = tdims[3];
+            break;
+        case AF_BATCH_DIFF:  // Both are 3/4D, but have different dims
+            o2 = idims[2] == 1 ? tdims[2] : idims[2];
+            o3 = idims[3] == 1 ? tdims[3] : idims[3];
+            break;
+        case AF_BATCH_UNSUPPORTED:
+        default:
+            AF_ERROR(
+                "Unsupported combination of batching parameters in "
+                "transform",
+                AF_ERR_NOT_SUPPORTED);
+            break;
+    }
+
+    const dim4 odims(o0, o1, o2, o3);
+    if (allocate_out) { *out = createHandle(odims, itype); }
+
+    // clang-format off
+    switch(itype) {
+    case f32: transform<float  >(out, in, tf, odims, method, inverse, perspective);  break;
+    case f64: transform<double >(out, in, tf, odims, method, inverse, perspective);  break;
+    case c32: transform<cfloat >(out, in, tf, odims, method, inverse, perspective);  break;
+    case c64: transform<cdouble>(out, in, tf, odims, method, inverse, perspective);  break;
+    case s32: transform<int    >(out, in, tf, odims, method, inverse, perspective);  break;
+    case u32: transform<uint   >(out, in, tf, odims, method, inverse, perspective);  break;
+    case s64: transform<intl   >(out, in, tf, odims, method, inverse, perspective);  break;
+    case u64: transform<uintl  >(out, in, tf, odims, method, inverse, perspective);  break;
+    case s16: transform<short  >(out, in, tf, odims, method, inverse, perspective);  break;
+    case u16: transform<ushort >(out, in, tf, odims, method, inverse, perspective);  break;
+    case u8:  transform<uchar  >(out, in, tf, odims, method, inverse, perspective);  break;
+    case b8:  transform<char   >(out, in, tf, odims, method, inverse, perspective);  break;
+    default:  TYPE_ERROR(1, itype);
+    }
+    // clang-format on
+}
+
 af_err af_transform(af_array *out, const af_array in, const af_array tf,
                     const dim_t odim0, const dim_t odim1,
                     const af_interp_type method, const bool inverse) {
     try {
-        const ArrayInfo &t_info = getInfo(tf);
-        const ArrayInfo &i_info = getInfo(in);
-
-        af::dim4 idims = i_info.dims();
-        af::dim4 tdims = t_info.dims();
-        af_dtype itype = i_info.getType();
-
-        // Assert type and interpolation
-        ARG_ASSERT(2, t_info.getType() == f32);
-        ARG_ASSERT(5, method == AF_INTERP_NEAREST ||
-                          method == AF_INTERP_BILINEAR ||
-                          method == AF_INTERP_BILINEAR_COSINE ||
-                          method == AF_INTERP_BICUBIC ||
-                          method == AF_INTERP_BICUBIC_SPLINE ||
-                          method == AF_INTERP_LOWER);
-
-        // Assert dimesions
-        // Image can be 2D or higher
-        DIM_ASSERT(1, idims.elements() > 0);
-        DIM_ASSERT(1, idims.ndims() >= 2);
-
-        // Transform can be 3x2 for affine transform or 3x3 for perspective
-        // transform
-        DIM_ASSERT(2, (tdims[0] == 3 && (tdims[1] == 2 || tdims[1] == 3)));
-
-        // If transform is batched, the output dimensions must be specified
-        if (tdims[2] * tdims[3] > 1) {
-            ARG_ASSERT(3, odim0 > 0);
-            ARG_ASSERT(4, odim1 > 0);
-        }
-
-        // If idims[2] > 1 and tdims[2] > 1, then both must be equal
-        // else at least one of them must be 1
-        if (tdims[2] != 1 && idims[2] != 1)
-            DIM_ASSERT(2, idims[2] == tdims[2]);
-        else
-            DIM_ASSERT(2, idims[2] == 1 || tdims[2] == 1);
-
-        // If idims[3] > 1 and tdims[3] > 1, then both must be equal
-        // else at least one of them must be 1
-        if (tdims[3] != 1 && idims[3] != 1)
-            DIM_ASSERT(2, idims[3] == tdims[3]);
-        else
-            DIM_ASSERT(2, idims[3] == 1 || tdims[3] == 1);
-
-        const bool perspective = (tdims[1] == 3);
-        dim_t o0 = odim0, o1 = odim1, o2 = 0, o3 = 0;
-        if (odim0 * odim1 == 0) {
-            o0 = idims[0];
-            o1 = idims[1];
-        }
+        af_transform_common(out, in, tf, odim0, odim1, method, inverse, true);
+    }
+    CATCHALL;
 
-        switch (getTransformBatchKind(idims, tdims)) {
-            case AF_BATCH_NONE:  // Both are exactly 2D
-            case AF_BATCH_LHS:   // Image is 3/4D, transform is 2D
-            case AF_BATCH_SAME:  // Both are 3/4D and have the same dims
-                o2 = idims[2];
-                o3 = idims[3];
-                break;
-            case AF_BATCH_RHS:  // Image is 2D, transform is 3/4D
-                o2 = tdims[2];
-                o3 = tdims[3];
-                break;
-            case AF_BATCH_DIFF:  // Both are 3/4D, but have different dims
-                o2 = idims[2] == 1 ? tdims[2] : idims[2];
-                o3 = idims[3] == 1 ? tdims[3] : idims[3];
-                break;
-            case AF_BATCH_UNSUPPORTED:
-            default:
-                AF_ERROR(
-                    "Unsupported combination of batching parameters in "
-                    "transform",
-                    AF_ERR_NOT_SUPPORTED);
-                break;
-        }
+    return AF_SUCCESS;
+}
 
-        af::dim4 odims(o0, o1, o2, o3);
-
-        af_array output = 0;
-        switch (itype) {
-            case f32:
-                output = transform<float>(in, tf, odims, method, inverse,
-                                          perspective);
-                break;
-            case f64:
-                output = transform<double>(in, tf, odims, method, inverse,
-                                           perspective);
-                break;
-            case c32:
-                output = transform<cfloat>(in, tf, odims, method, inverse,
-                                           perspective);
-                break;
-            case c64:
-                output = transform<cdouble>(in, tf, odims, method, inverse,
-                                            perspective);
-                break;
-            case s32:
-                output =
-                    transform<int>(in, tf, odims, method, inverse, perspective);
-                break;
-            case u32:
-                output = transform<uint>(in, tf, odims, method, inverse,
-                                         perspective);
-                break;
-            case s64:
-                output = transform<intl>(in, tf, odims, method, inverse,
-                                         perspective);
-                break;
-            case u64:
-                output = transform<uintl>(in, tf, odims, method, inverse,
-                                          perspective);
-                break;
-            case s16:
-                output = transform<short>(in, tf, odims, method, inverse,
-                                          perspective);
-                break;
-            case u16:
-                output = transform<ushort>(in, tf, odims, method, inverse,
-                                           perspective);
-                break;
-            case u8:
-                output = transform<uchar>(in, tf, odims, method, inverse,
-                                          perspective);
-                break;
-            case b8:
-                output = transform<char>(in, tf, odims, method, inverse,
-                                         perspective);
-                break;
-            default: TYPE_ERROR(1, itype);
-        }
-        std::swap(*out, output);
+af_err af_transform_v2(af_array *out, const af_array in, const af_array tf,
+                       const dim_t odim0, const dim_t odim1,
+                       const af_interp_type method, const bool inverse) {
+    try {
+        ARG_ASSERT(0, out != 0);  // need to dereference out in next call
+        af_transform_common(out, in, tf, odim0, odim1, method, inverse,
+                            *out == 0);
     }
     CATCHALL;
 
@@ -202,7 +190,7 @@ af_err af_translate(af_array *out, const af_array in, const float trans0,
         trans_mat[2]       = trans0;
         trans_mat[5]       = trans1;
 
-        const af::dim4 tdims(3, 2, 1, 1);
+        const dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
 
         AF_CHECK(
@@ -220,7 +208,7 @@ af_err af_scale(af_array *out, const af_array in, const float scale0,
                 const af_interp_type method) {
     try {
         const ArrayInfo &i_info = getInfo(in);
-        af::dim4 idims          = i_info.dims();
+        dim4 idims              = i_info.dims();
 
         dim_t _odim0 = odim0, _odim1 = odim1;
         float sx, sy;
@@ -248,7 +236,7 @@ af_err af_scale(af_array *out, const af_array in, const float scale0,
         trans_mat[0]       = sx;
         trans_mat[4]       = sy;
 
-        const af::dim4 tdims(3, 2, 1, 1);
+        const dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
         AF_CHECK(
             af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
@@ -284,7 +272,7 @@ af_err af_skew(af_array *out, const af_array in, const float skew0,
                 trans_mat[4] = d;
             }
         }
-        const af::dim4 tdims(3, 2, 1, 1);
+        const dim4 tdims(3, 2, 1, 1);
         af_array t = 0;
         AF_CHECK(
             af_create_array(&t, trans_mat, tdims.ndims(), tdims.get(), f32));
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 9c604590ae..e7ebba93ee 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -66,6 +66,13 @@ af_err af_transform(af_array *out, const af_array in, const af_array transform,
       CALL(af_transform, out, in, transform, odim0, odim1, method, inverse);
 }
 
+af_err af_transform_v2(af_array *out, const af_array in, const af_array transform,
+                       const dim_t odim0, const dim_t odim1,
+                       const af_interp_type method, const bool inverse) {
+    CHECK_ARRAYS(out, in, transform);
+    return CALL(out, in, transform, odim0, odim1, method, inverse);
+}
+
 af_err af_transform_coordinates(af_array *out, const af_array tf,
                                 const float d0, const float d1) {
     CHECK_ARRAYS(tf);
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index e91301f85f..7f90f1a50d 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -16,10 +16,12 @@
 namespace cpu {
 
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af_interp_type method,
-                   const bool inverse, const bool perspective) {
-    Array<T> out = createEmptyArray<T>(odims);
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const dim4 &odims, const af_interp_type method,
+               const bool inverse, const bool perspective) {
+    out.eval();
+    in.eval();
+    tf.eval();
 
     switch (method) {
         case AF_INTERP_NEAREST:
@@ -39,15 +41,13 @@ Array<T> transform(const Array<T> &in, const Array<float> &tf,
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
-
-    return out;
 }
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> transform(const Array<T> &in, const Array<float> &tf, \
-                                const af::dim4 &odims,                      \
-                                const af_interp_type method,                \
-                                const bool inverse, const bool perspective);
+#define INSTANTIATE(T)                                                       \
+    template void transform(Array<T> &out, const Array<T> &in,               \
+                            const Array<float> &tf, const dim4 &odims,       \
+                            const af_interp_type method, const bool inverse, \
+                            const bool perspective);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/transform.hpp b/src/backend/cpu/transform.hpp
index 0d33e97ea2..1ddd73d4d6 100644
--- a/src/backend/cpu/transform.hpp
+++ b/src/backend/cpu/transform.hpp
@@ -11,7 +11,7 @@
 
 namespace cpu {
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af_interp_type method,
-                   const bool inverse, const bool perspective);
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const af::dim4 &odims, const af_interp_type method,
+               const bool inverse, const bool perspective);
 }
diff --git a/src/backend/cuda/transform.cpp b/src/backend/cuda/transform.cpp
index 513a378410..6ec97ebc8c 100644
--- a/src/backend/cuda/transform.cpp
+++ b/src/backend/cuda/transform.cpp
@@ -13,21 +13,20 @@
 #include <utility.hpp>
 
 namespace cuda {
+
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af::interpType method,
-                   const bool inverse, const bool perspective) {
-    Array<T> out = createEmptyArray<T>(odims);
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const af::dim4 &odims, const af::interpType method,
+               const bool inverse, const bool perspective) {
     kernel::transform<T>(out, in, tf, inverse, perspective, method,
                          interpOrder(method));
-    return out;
 }
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> transform(const Array<T> &in, const Array<float> &tf, \
-                                const af::dim4 &odims,                      \
-                                const af_interp_type method,                \
-                                const bool inverse, const bool perspective);
+#define INSTANTIATE(T)                                                       \
+    template void transform(Array<T> &out, const Array<T> &in,               \
+                            const Array<float> &tf, const af::dim4 &odims,   \
+                            const af_interp_type method, const bool inverse, \
+                            const bool perspective);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -41,4 +40,5 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+
 }  // namespace cuda
diff --git a/src/backend/cuda/transform.hpp b/src/backend/cuda/transform.hpp
index e814ee85b0..f0fd721226 100644
--- a/src/backend/cuda/transform.hpp
+++ b/src/backend/cuda/transform.hpp
@@ -11,7 +11,7 @@
 
 namespace cuda {
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af_interp_type method,
-                   const bool inverse, const bool perspective);
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const af::dim4 &odims, const af_interp_type method,
+               const bool inverse, const bool perspective);
 }
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 8311a7f656..b4b640e71b 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -14,12 +14,11 @@
 #include <stdexcept>
 
 namespace opencl {
-template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af_interp_type method,
-                   const bool inverse, const bool perspective) {
-    Array<T> out = createEmptyArray<T>(odims);
 
+template<typename T>
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const dim4 &odims, const af_interp_type method,
+               const bool inverse, const bool perspective) {
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -35,14 +34,13 @@ Array<T> transform(const Array<T> &in, const Array<float> &tf,
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
-    return out;
 }
 
-#define INSTANTIATE(T)                                                      \
-    template Array<T> transform(const Array<T> &in, const Array<float> &tf, \
-                                const af::dim4 &odims,                      \
-                                const af_interp_type method,                \
-                                const bool inverse, const bool perspective);
+#define INSTANTIATE(T)                                                       \
+    template void transform(Array<T> &out, const Array<T> &in,               \
+                            const Array<float> &tf, const dim4 &odims,       \
+                            const af_interp_type method, const bool inverse, \
+                            const bool perspective);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -56,4 +54,5 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+
 }  // namespace opencl
diff --git a/src/backend/opencl/transform.hpp b/src/backend/opencl/transform.hpp
index bafd56175a..847271f913 100644
--- a/src/backend/opencl/transform.hpp
+++ b/src/backend/opencl/transform.hpp
@@ -11,7 +11,7 @@
 
 namespace opencl {
 template<typename T>
-Array<T> transform(const Array<T> &in, const Array<float> &tf,
-                   const af::dim4 &odims, const af_interp_type method,
-                   const bool inverse, const bool perspective);
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const af::dim4 &odims, const af_interp_type method,
+               const bool inverse, const bool perspective);
 }
diff --git a/test/transform.cpp b/test/transform.cpp
index 254781b698..398400e7f9 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -18,6 +18,7 @@
 
 using af::array;
 using af::dim4;
+using af::dtype_traits;
 using af::loadImage;
 using std::abs;
 using std::endl;
@@ -44,11 +45,9 @@ TYPED_TEST_CASE(Transform, TestTypes);
 TYPED_TEST_CASE(TransformInt, TestTypesInt);
 
 template<typename T>
-void transformTest(string pTestFile, string pHomographyFile,
-                   const af_interp_type method, const bool invert) {
-    SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
-
+void genTestData(af_array *gold, af_array *in, af_array *transform,
+                 dim_t *odim0, dim_t *odim1, string pTestFile,
+                 string pHomographyFile) {
     vector<dim4> inNumDims;
     vector<string> inFiles;
     vector<dim_t> goldNumDims;
@@ -71,10 +70,8 @@ void transformTest(string pTestFile, string pHomographyFile,
 
     af_array sceneArray_f32 = 0;
     af_array goldArray_f32  = 0;
-    af_array outArray_f32   = 0;
     af_array sceneArray     = 0;
     af_array goldArray      = 0;
-    af_array outArray       = 0;
     af_array HArray         = 0;
 
     ASSERT_SUCCESS(af_load_image(&sceneArray_f32, inFiles[1].c_str(), false));
@@ -86,20 +83,47 @@ void transformTest(string pTestFile, string pHomographyFile,
     ASSERT_SUCCESS(af_create_array(&HArray, &(HIn[0].front()), HDims.ndims(),
                                    HDims.get(), f32));
 
-    ASSERT_SUCCESS(af_transform(&outArray, sceneArray, HArray, objDims[0],
-                                objDims[1], method, invert));
+    *gold      = goldArray;
+    *in        = sceneArray;
+    *transform = HArray;
+    *odim0     = objDims[0];
+    *odim1     = objDims[1];
+
+    if (goldArray_f32 != 0) af_release_array(goldArray_f32);
+    if (sceneArray_f32 != 0) af_release_array(sceneArray_f32);
+}
+
+template<typename T>
+void transformTest(string pTestFile, string pHomographyFile,
+                   const af_interp_type method, const bool invert) {
+    SUPPORTED_TYPE_CHECK(T);
+    if (noImageIOTests()) return;
+
+    af_array sceneArray = 0;
+    af_array goldArray  = 0;
+    af_array outArray   = 0;
+    af_array HArray     = 0;
+
+    dim_t odim0 = 0;
+    dim_t odim1 = 0;
+
+    genTestData<T>(&goldArray, &sceneArray, &HArray, &odim0, &odim1, pTestFile,
+                   pHomographyFile);
+
+    ASSERT_SUCCESS(af_transform(&outArray, sceneArray, HArray, odim0, odim1,
+                                method, invert));
 
     // Get gold data
     dim_t goldEl = 0;
     ASSERT_SUCCESS(af_get_elements(&goldEl, goldArray));
     vector<T> goldData(goldEl);
-    ASSERT_SUCCESS(af_get_data_ptr((void*)&goldData.front(), goldArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)&goldData.front(), goldArray));
 
     // Get result
     dim_t outEl = 0;
     ASSERT_SUCCESS(af_get_elements(&outEl, outArray));
     vector<T> outData(outEl);
-    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+    ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
     const float thr = 1.1f;
 
@@ -117,13 +141,10 @@ void transformTest(string pTestFile, string pHomographyFile,
         }
     }
 
-    if (sceneArray_f32 != 0) af_release_array(sceneArray_f32);
-    if (goldArray_f32 != 0) af_release_array(goldArray_f32);
-    if (outArray_f32 != 0) af_release_array(outArray_f32);
-    if (sceneArray != 0) af_release_array(sceneArray);
-    if (goldArray != 0) af_release_array(goldArray);
-    if (outArray != 0) af_release_array(outArray);
-    if (HArray != 0) af_release_array(HArray);
+    if (HArray != 0) { af_release_array(HArray); }
+    if (outArray != 0) { af_release_array(outArray); }
+    if (goldArray != 0) { af_release_array(goldArray); }
+    if (sceneArray != 0) { af_release_array(sceneArray); }
 }
 
 TYPED_TEST(Transform, PerspectiveNearest) {
@@ -204,6 +225,258 @@ TYPED_TEST(TransformInt, PerspectiveLowerInvert) {
         true);
 }
 
+template<typename T>
+class TransformV2 : public Transform<T> {
+   protected:
+    typedef typename dtype_traits<T>::base_type BT;
+
+    af_array gold;
+    af_array in;
+    af_array transform;
+
+    dim4 gold_dims;
+    dim4 in_dims;
+    dim4 transform_dims;
+
+    dim_t odim0;
+    dim_t odim1;
+
+    af_interp_type method;
+    bool invert;
+
+    TransformV2()
+        : gold(0)
+        , in(0)
+        , transform(0)
+        , odim0(0)
+        , odim1(0)
+        , method(AF_INTERP_NEAREST)
+        , invert(false) {}
+
+    void setInterpType(af_interp_type m) { method = m; }
+    void setInvertFlag(bool i) { invert = i; }
+
+    void SetUp() {}
+
+    void releaseArrays() {
+        if (transform != 0) { ASSERT_SUCCESS(af_release_array(transform)); }
+        if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
+        if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
+
+        gold      = 0;
+        in        = 0;
+        transform = 0;
+    }
+
+    void TearDown() { releaseArrays(); }
+
+    void setTestData(float *h_gold, dim4 gold_dims, float *h_in, dim4 in_dims,
+                     float *h_transform, dim4 transform_dims) {
+        releaseArrays();
+
+        this->gold_dims      = gold_dims;
+        this->in_dims        = in_dims;
+        this->transform_dims = transform_dims;
+
+        vector<T> h_gold_cast;
+        vector<T> h_in_cast;
+        vector<BT> h_transform_cast;
+
+        for (int i = 0; i < gold_dims.elements(); ++i) {
+            h_gold_cast.push_back(static_cast<T>(h_gold[i]));
+        }
+        for (int i = 0; i < in_dims.elements(); ++i) {
+            h_in_cast.push_back(static_cast<T>(h_in[i]));
+        }
+        for (int i = 0; i < transform_dims.elements(); ++i) {
+            h_transform_cast.push_back(static_cast<BT>(h_transform[i]));
+        }
+
+        ASSERT_SUCCESS(af_create_array(&gold, &h_gold_cast.front(),
+                                       gold_dims.ndims(), gold_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&in, &h_in_cast.front(), in_dims.ndims(),
+                                       in_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(
+            &transform, &h_transform_cast.front(), transform_dims.ndims(),
+            transform_dims.get(), (af_dtype)dtype_traits<BT>::af_type));
+    }
+
+    void setTestData(string pTestFile, string pHomographyFile) {
+        releaseArrays();
+
+        genTestData<T>(&gold, &in, &transform, &odim0, &odim1, pTestFile,
+                       pHomographyFile);
+
+        ASSERT_SUCCESS(af_get_dims(&gold_dims[0], &gold_dims[1], &gold_dims[2],
+                                   &gold_dims[3], gold));
+        ASSERT_SUCCESS(af_get_dims(&in_dims[0], &in_dims[1], &in_dims[2],
+                                   &in_dims[3], in));
+        ASSERT_SUCCESS(af_get_dims(&transform_dims[0], &transform_dims[1],
+                                   &transform_dims[2], &transform_dims[3],
+                                   transform));
+    }
+
+    void assertSpclArraysTransform(const af_array gold, const af_array out,
+                                   TestOutputArrayInfo *metadata) {
+        // In the case of NULL_ARRAY, the output array starts out as null.
+        // After the af_* function is called, it shouldn't be null anymore
+        if (metadata->getOutputArrayType() == NULL_ARRAY) {
+            if (out == 0) {
+                ASSERT_TRUE(out != 0) << "Output af_array is null";
+            }
+            metadata->setOutput(out);
+        }
+        // For every other case, must check if the af_array generated by
+        // genTestOutputArray was used by the af_* function as its output array
+        else {
+            if (metadata->getOutput() != out) {
+                ASSERT_TRUE(metadata->getOutput() != out)
+                    << "af_array POINTER MISMATCH:\n"
+                    << "  Actual: " << out << "\n"
+                    << "Expected: " << metadata->getOutput();
+            }
+        }
+
+        af_array out_  = 0;
+        af_array gold_ = 0;
+
+        if (metadata->getOutputArrayType() == SUB_ARRAY) {
+            // There are two full arrays. One will be injected with the gold
+            // subarray, the other should have already been injected with the
+            // af_* function's output. Then we compare the two full arrays
+            af_array gold_full_array = metadata->getFullOutputCopy();
+            af_assign_seq(&gold_full_array, gold_full_array,
+                          metadata->getSubArrayNumDims(),
+                          metadata->getSubArrayIdxs(), gold);
+
+            gold_ = metadata->getFullOutputCopy();
+            out_  = metadata->getFullOutput();
+        } else {
+            gold_ = gold;
+            out_  = out;
+        }
+
+        // Get gold data
+        dim_t goldEl = 0;
+        af_get_elements(&goldEl, gold_);
+        vector<T> goldData(goldEl);
+        af_get_data_ptr((void *)&goldData.front(), gold_);
+
+        // Get result
+        dim_t outEl = 0;
+        af_get_elements(&outEl, out_);
+        vector<T> outData(outEl);
+        af_get_data_ptr((void *)&outData.front(), out_);
+
+        const float thr = 1.1f;
+
+        // Maximum number of wrong pixels must be <= 0.01% of number of
+        // elements, this metric is necessary due to rounding errors between
+        // different backends for AF_INTERP_NEAREST and AF_INTERP_LOWER
+        const size_t maxErr = goldEl * 0.0001f;
+        size_t err          = 0;
+
+        for (dim_t elIter = 0; elIter < goldEl; elIter++) {
+            err += fabs((float)floor(outData[elIter]) -
+                        (float)floor(goldData[elIter])) > thr;
+            if (err > maxErr) {
+                ASSERT_LE(err, maxErr) << "at: " << elIter << endl;
+            }
+        }
+    }
+
+    void testSpclOutArray(TestOutputArrayType out_array_type) {
+        SUPPORTED_TYPE_CHECK(T);
+        if (noImageIOTests()) return;
+
+        af_array out = 0;
+        TestOutputArrayInfo metadata(out_array_type);
+        genTestOutputArray(&out, gold_dims.ndims(), gold_dims.get(),
+                           (af_dtype)dtype_traits<T>::af_type, &metadata);
+        ASSERT_SUCCESS(
+            af_transform_v2(&out, in, transform, odim0, odim1, method, invert));
+
+        assertSpclArraysTransform(gold, out, &metadata);
+    }
+};
+
+TYPED_TEST_CASE(TransformV2, TestTypes);
+
+template<typename T>
+class TransformV2TuxNearest : public TransformV2<T> {
+   protected:
+    void SetUp() {
+        this->setTestData(string(TEST_DIR "/transform/tux_nearest.test"),
+                          string(TEST_DIR "/transform/tux_tmat.test"));
+        this->setInterpType(AF_INTERP_NEAREST);
+        this->setInvertFlag(false);
+    }
+};
+
+TYPED_TEST_CASE(TransformV2TuxNearest, TestTypes);
+
+TYPED_TEST(TransformV2TuxNearest, UseNullOutputArray) {
+    this->testSpclOutArray(NULL_ARRAY);
+}
+
+TYPED_TEST(TransformV2TuxNearest, UseFullExistingOutputArray) {
+    this->testSpclOutArray(FULL_ARRAY);
+}
+
+TYPED_TEST(TransformV2TuxNearest, UseExistingOutputSubArray) {
+    this->testSpclOutArray(SUB_ARRAY);
+}
+
+TYPED_TEST(TransformV2TuxNearest, UseReorderedOutputArray) {
+    this->testSpclOutArray(REORDERED_ARRAY);
+}
+
+class TransformNullArgs : public TransformV2TuxNearest<float> {
+   protected:
+    af_array out;
+    TransformNullArgs() : out(0) {}
+};
+
+TEST_F(TransformNullArgs, NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG,
+              af_transform(out_ptr, this->in, this->transform, this->odim0,
+                           this->odim1, this->method, this->invert));
+}
+
+TEST_F(TransformNullArgs, NullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_transform(&this->out, 0, this->transform, this->odim0,
+                           this->odim1, this->method, this->invert));
+}
+
+TEST_F(TransformNullArgs, NullTransformArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_transform(&this->out, this->in, 0, this->odim0,
+                           this->odim1, this->method, this->invert));
+}
+
+TEST_F(TransformNullArgs, V2NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(AF_ERR_ARG,
+              af_transform_v2(out_ptr, this->in, this->transform, this->odim0,
+                              this->odim1, this->method, this->invert));
+}
+
+TEST_F(TransformNullArgs, V2NullInputArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_transform_v2(&this->out, 0, this->transform, this->odim0,
+                              this->odim1, this->method, this->invert));
+}
+
+TEST_F(TransformNullArgs, V2NullTransformArray) {
+    ASSERT_EQ(AF_ERR_ARG,
+              af_transform_v2(&this->out, this->in, 0, this->odim0,
+                              this->odim1, this->method, this->invert));
+}
+
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(Transform, CPP) {
@@ -337,7 +610,7 @@ TEST(TransformBatching, CPP) {
     for (int i = 0; i < (int)gold.size(); i++) {
         // Get result
         vector<float> outData(out[i].elements());
-        out[i].host((void*)&outData.front());
+        out[i].host((void *)&outData.front());
 
         for (int iter = 0; iter < (int)gold[i].size(); iter++) {
             ASSERT_EQ(gold[i][iter], outData[iter])

From a049d753a7388d0eff81301be95a7b402da20371 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Fri, 7 Feb 2020 22:35:30 -0600
Subject: [PATCH 1824/2677] Modified backend wrap() to accept output arg
 (#2328)

* Modified backend wrap() to accept output arg
* Used test fixture to improve wrap tests.
* Updated fill iterator. Removed ArrayIterator
* Integrate new test fixtures with existing ones

Co-authored-by: Umar Arshad <umar@arrayfire.com>
Co-authored-by: Mark Poscablo <mposcablo@gmail.com>
---
 include/af/image.h                |  46 +++++
 src/api/c/array.cpp               |  81 +++++---
 src/api/c/fft.cpp                 |   4 +-
 src/api/c/handle.hpp              |  10 +-
 src/api/c/wrap.cpp                | 165 ++++++++--------
 src/api/unified/image.cpp         |   8 +-
 src/backend/cpu/kernel/wrap.hpp   |   4 +
 src/backend/cpu/wrap.cpp          |  30 +--
 src/backend/cpu/wrap.hpp          |  12 +-
 src/backend/cuda/wrap.cu          |  26 +--
 src/backend/cuda/wrap.hpp         |   9 +-
 src/backend/opencl/kernel/wrap.cl |   2 +-
 src/backend/opencl/wrap.cpp       |  26 +--
 src/backend/opencl/wrap.hpp       |  23 ++-
 test/testHelpers.hpp              | 104 ++++++++++-
 test/wrap.cpp                     | 301 ++++++++++++++++++++++++++++++
 16 files changed, 679 insertions(+), 172 deletions(-)

diff --git a/include/af/image.h b/include/af/image.h
index 1d3d488a4f..f560072e67 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -1458,6 +1458,52 @@ extern "C" {
                          const bool is_column);
 #endif
 
+#if AF_API_VERSION >= 37
+    /**
+       C Interface for the version of \ref af_wrap that accepts a
+       preallocated output array
+
+       \param[out] out is an array with the input's columns (or rows) reshaped as
+                   patches
+       \param[in]  in is the input array
+       \param[in]  ox is the output's dimension 0 size
+       \param[in]  oy is the output's dimension 1 size
+       \param[in]  wx is the window size along dimension 0
+       \param[in]  wy is the window size along dimension 1
+       \param[in]  sx is the stride along dimension 0
+       \param[in]  sy is the stride along dimension 1
+       \param[in]  px is the padding along dimension 0
+       \param[in]  py is the padding along dimension 1
+       \param[in]  is_column determines whether an output patch is formed from a
+                   column (if true) or a row (if false)
+       \return     \ref AF_SUCCESS if the color transformation is successful,
+       otherwise an appropriate error code is returned.
+
+       \note Wrap is typically used to recompose an unwrapped image. If this is the
+             case, use the same parameters that were used in \ref unwrap(). Also
+             use the original image size (before unwrap) for \p ox and \p oy.
+       \note The window/patch size, \p wx \f$\times\f$ \p wy, must equal
+             `input.dims(0)` (or `input.dims(1)` if \p is_column is false).
+       \note \p sx and \p sy must be at least 1
+       \note \p px and \p py must be between [0, wx) and [0, wy), respectively
+       \note The number of patches, `input.dims(1)` (or `input.dims(0)` if
+             \p is_column is false), must equal \f$nx \times\ ny\f$, where
+             \f$\displaystyle nx = \frac{ox + 2px - wx}{sx} + 1\f$ and
+             \f$\displaystyle ny = \frac{oy + 2py - wy}{sy} + 1\f$
+       \note Batched wrap can be performed on multiple 2D slices at once if \p in
+             is three or four-dimensional
+
+       \ingroup image_func_wrap
+    */
+    AFAPI af_err af_wrap_v2(af_array *out,
+                            const af_array in,
+                            const dim_t ox, const dim_t oy,
+                            const dim_t wx, const dim_t wy,
+                            const dim_t sx, const dim_t sy,
+                            const dim_t px, const dim_t py,
+                            const bool is_column);
+#endif
+
 #if AF_API_VERSION >= 31
     /**
        C Interface wrapper for summed area tables
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index e4500c488c..bf390fdd05 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -21,48 +21,75 @@ using namespace detail;
 using common::half;
 using common::SparseArrayBase;
 
-af_array createHandle(af::dim4 d, af_dtype dtype) {
+af_array createHandle(const af::dim4 &d, af_dtype dtype) {
     using namespace detail;
 
+    // clang-format off
     switch (dtype) {
-        case f32: return createHandle<float>(d);
-        case c32: return createHandle<cfloat>(d);
-        case f64: return createHandle<double>(d);
+        case f32: return createHandle<float  >(d);
+        case c32: return createHandle<cfloat >(d);
+        case f64: return createHandle<double >(d);
         case c64: return createHandle<cdouble>(d);
-        case b8: return createHandle<char>(d);
-        case s32: return createHandle<int>(d);
-        case u32: return createHandle<uint>(d);
-        case u8: return createHandle<uchar>(d);
-        case s64: return createHandle<intl>(d);
-        case u64: return createHandle<uintl>(d);
-        case s16: return createHandle<short>(d);
-        case u16: return createHandle<ushort>(d);
-        case f16: return createHandle<half>(d);
+        case b8:  return createHandle<char   >(d);
+        case s32: return createHandle<int    >(d);
+        case u32: return createHandle<uint   >(d);
+        case u8:  return createHandle<uchar  >(d);
+        case s64: return createHandle<intl   >(d);
+        case u64: return createHandle<uintl  >(d);
+        case s16: return createHandle<short  >(d);
+        case u16: return createHandle<ushort >(d);
+        case f16: return createHandle<half   >(d);
         default: TYPE_ERROR(3, dtype);
     }
+    // clang-format on
+}
+
+af_array createHandleFromValue(const af::dim4 &d, double val, af_dtype dtype) {
+    using namespace detail;
+
+    // clang-format off
+    switch (dtype) {
+        case f32: return createHandleFromValue<float  >(d, val);
+        case c32: return createHandleFromValue<cfloat >(d, val);
+        case f64: return createHandleFromValue<double >(d, val);
+        case c64: return createHandleFromValue<cdouble>(d, val);
+        case b8:  return createHandleFromValue<char   >(d, val);
+        case s32: return createHandleFromValue<int    >(d, val);
+        case u32: return createHandleFromValue<uint   >(d, val);
+        case u8:  return createHandleFromValue<uchar  >(d, val);
+        case s64: return createHandleFromValue<intl   >(d, val);
+        case u64: return createHandleFromValue<uintl  >(d, val);
+        case s16: return createHandleFromValue<short  >(d, val);
+        case u16: return createHandleFromValue<ushort >(d, val);
+        case f16: return createHandleFromValue<half   >(d, val);
+        default: TYPE_ERROR(3, dtype);
+    }
+    // clang-format on
 }
 
 af_err af_get_data_ptr(void *data, const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
+        // clang-format off
         switch (type) {
-            case f32: copyData(static_cast<float *>(data), arr); break;
-            case c32: copyData(static_cast<cfloat *>(data), arr); break;
-            case f64: copyData(static_cast<double *>(data), arr); break;
-            case c64: copyData(static_cast<cdouble *>(data), arr); break;
-            case b8: copyData(static_cast<char *>(data), arr); break;
-            case s32: copyData(static_cast<int *>(data), arr); break;
-            case u32: copyData(static_cast<unsigned *>(data), arr); break;
-            case u8: copyData(static_cast<uchar *>(data), arr); break;
-            case s64: copyData(static_cast<intl *>(data), arr); break;
-            case u64: copyData(static_cast<uintl *>(data), arr); break;
-            case s16: copyData(static_cast<short *>(data), arr); break;
-            case u16: copyData(static_cast<ushort *>(data), arr); break;
-            case f16: copyData(static_cast<half *>(data), arr); break;
+            case f32: copyData(static_cast<float*   >(data), arr); break;
+            case c32: copyData(static_cast<cfloat*  >(data), arr); break;
+            case f64: copyData(static_cast<double*  >(data), arr); break;
+            case c64: copyData(static_cast<cdouble* >(data), arr); break;
+            case b8:  copyData(static_cast<char*    >(data), arr); break;
+            case s32: copyData(static_cast<int*     >(data), arr); break;
+            case u32: copyData(static_cast<unsigned*>(data), arr); break;
+            case u8:  copyData(static_cast<uchar*   >(data), arr); break;
+            case s64: copyData(static_cast<intl*    >(data), arr); break;
+            case u64: copyData(static_cast<uintl*   >(data), arr); break;
+            case s16: copyData(static_cast<short*   >(data), arr); break;
+            case u16: copyData(static_cast<ushort*  >(data), arr); break;
+            case f16: copyData(static_cast<half*    >(data), arr); break;
             default: TYPE_ERROR(1, type);
         }
+        // clang-format on
     }
-    CATCHALL
+    CATCHALL;
     return AF_SUCCESS;
 }
 
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index e5405ee47c..7a8283571d 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -195,7 +195,9 @@ static af_err fft_r2c(af_array *out, const af_array in,
                 output =
                     fft_r2c<double, cdouble, rank>(in, norm_factor, npad, pad);
                 break;
-            default: { TYPE_ERROR(1, type); }
+            default: {
+                TYPE_ERROR(1, type);
+            }
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index a97c2c422d..087fc1b2ed 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -28,7 +28,9 @@ af_array retain(const af_array in);
 
 af::dim4 verifyDims(const unsigned ndims, const dim_t *const dims);
 
-af_array createHandle(af::dim4 d, af_dtype dtype);
+af_array createHandle(const af::dim4 &d, af_dtype dtype);
+
+af_array createHandleFromValue(const af::dim4 &d, double val, af_dtype dtype);
 
 namespace {
 
@@ -124,17 +126,17 @@ af_array retainHandle(const af_array in) {
 }
 
 template<typename T>
-af_array createHandle(af::dim4 d) {
+af_array createHandle(const af::dim4 &d) {
     return getHandle(detail::createEmptyArray<T>(d));
 }
 
 template<typename T>
-af_array createHandleFromValue(af::dim4 d, double val) {
+af_array createHandleFromValue(const af::dim4 &d, double val) {
     return getHandle(detail::createValueArray<T>(d, detail::scalar<T>(val)));
 }
 
 template<typename T>
-af_array createHandleFromData(af::dim4 d, const T *const data) {
+af_array createHandleFromData(const af::dim4 &d, const T *const data) {
     return getHandle(detail::createHostDataArray<T>(d, data));
 }
 
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index 2ece64699d..1bba6194d2 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -19,90 +19,93 @@ using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline af_array wrap(const af_array in, const dim_t ox, const dim_t oy,
-                            const dim_t wx, const dim_t wy, const dim_t sx,
-                            const dim_t sy, const dim_t px, const dim_t py,
-                            const bool is_column) {
-    return getHandle(
-        wrap<T>(getArray<T>(in), ox, oy, wx, wy, sx, sy, px, py, is_column));
+static inline void wrap(af_array *out, const af_array in,
+                        const dim_t ox, const dim_t oy,
+                        const dim_t wx, const dim_t wy,
+                        const dim_t sx, const dim_t sy,
+                        const dim_t px, const dim_t py,
+                        const bool is_column) {
+    wrap<T>(getArray<T>(*out), getArray<T>(in), ox, oy, wx, wy, sx, sy, px, py,
+            is_column);
 }
 
-af_err af_wrap(af_array* out, const af_array in, const dim_t ox, const dim_t oy,
-               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-               const dim_t px, const dim_t py, const bool is_column) {
+void af_wrap_common(af_array *out, const af_array in,
+                      const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy,
+                      const dim_t sx, const dim_t sy,
+                      const dim_t px, const dim_t py,
+                      const bool is_column, bool allocate_out) {
+    ARG_ASSERT(0, out != 0);  // *out (the af_array) can be null, but not out
+    ARG_ASSERT(1, in != 0);
+
+    const ArrayInfo& info  = getInfo(in);
+    const af_dtype in_type = info.getType();
+    const dim4 in_dims     = info.dims();
+    const dim4 out_dims(ox, oy, in_dims[2], in_dims[3]);
+
+    ARG_ASSERT(4, wx > 0);
+    ARG_ASSERT(5, wy > 0);
+    ARG_ASSERT(6, sx > 0);
+    ARG_ASSERT(7, sy > 0);
+
+    const dim_t nx = (ox + 2 * px - wx) / sx + 1;
+    const dim_t ny = (oy + 2 * py - wy) / sy + 1;
+
+    const dim_t patch_size  = is_column ? in_dims[0] : in_dims[1];
+    const dim_t num_patches = is_column ? in_dims[1] : in_dims[0];
+
+    DIM_ASSERT(1, patch_size == wx * wy);
+    DIM_ASSERT(1, num_patches == nx * ny);
+
+    if (allocate_out) { *out = createHandleFromValue(out_dims, 0.0, in_type); }
+
+    // The out pointer can be passed in to the function by the user
+    DIM_ASSERT(0, getInfo(*out).dims() == out_dims);
+
+    // clang-format off
+    switch(in_type) {
+        case f32: wrap<float  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case f64: wrap<double >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case c32: wrap<cfloat >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case c64: wrap<cdouble>(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case s32: wrap<int    >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case u32: wrap<uint   >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case s64: wrap<intl   >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case u64: wrap<uintl  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case s16: wrap<short  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case u16: wrap<ushort >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case u8:  wrap<uchar  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case b8:  wrap<char   >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        default:  TYPE_ERROR(1, in_type);
+    }
+    // clang-format on
+}
+
+af_err af_wrap(af_array* out, const af_array in,
+               const dim_t ox, const dim_t oy,
+               const dim_t wx, const dim_t wy,
+               const dim_t sx, const dim_t sy,
+               const dim_t px, const dim_t py,
+               const bool is_column) {
+    try {
+        af_wrap_common(out, in, ox, oy, wx, wy, sx, sy, px, py,
+                              is_column, true);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_wrap_v2(af_array* out, const af_array in,
+                  const dim_t ox, const dim_t oy,
+                  const dim_t wx, const dim_t wy,
+                  const dim_t sx, const dim_t sy,
+                  const dim_t px, const dim_t py,
+                  const bool is_column) {
     try {
-        const ArrayInfo& info = getInfo(in);
-        af_dtype type         = info.getType();
-        af::dim4 idims        = info.dims();
-
-        ARG_ASSERT(2, wx > 0);
-        ARG_ASSERT(3, wx > 0);
-        ARG_ASSERT(4, sx > 0);
-        ARG_ASSERT(5, sy > 0);
-
-        dim_t nx = (ox + 2 * px - wx) / sx + 1;
-        dim_t ny = (oy + 2 * py - wy) / sy + 1;
-
-        dim_t patch_size  = is_column ? idims[0] : idims[1];
-        dim_t num_patches = is_column ? idims[1] : idims[0];
-
-        DIM_ASSERT(1, patch_size == wx * wy);
-        DIM_ASSERT(1, num_patches == nx * ny);
-
-        af_array output;
-
-        switch (type) {
-            case f32:
-                output =
-                    wrap<float>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case f64:
-                output =
-                    wrap<double>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case c32:
-                output =
-                    wrap<cfloat>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case c64:
-                output = wrap<cdouble>(in, ox, oy, wx, wy, sx, sy, px, py,
-                                       is_column);
-                break;
-            case s32:
-                output =
-                    wrap<int>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case u32:
-                output =
-                    wrap<uint>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case s64:
-                output =
-                    wrap<intl>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case u64:
-                output =
-                    wrap<uintl>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case s16:
-                output =
-                    wrap<short>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case u16:
-                output =
-                    wrap<ushort>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case u8:
-                output =
-                    wrap<uchar>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            case b8:
-                output =
-                    wrap<char>(in, ox, oy, wx, wy, sx, sy, px, py, is_column);
-                break;
-            default: TYPE_ERROR(1, type);
-        }
-        std::swap(*out, output);
+        ARG_ASSERT(0, out != 0);  // need to dereference out in next call
+        af_wrap_common(out, in, ox, oy, wx, wy, sx, sy, px, py,
+                              is_column, *out == 0);
     }
     CATCHALL;
 
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index e7ebba93ee..313ad1c8de 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -218,7 +218,13 @@ af_err af_wrap(af_array *out, const af_array in, const dim_t ox, const dim_t oy,
                const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
                const dim_t px, const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
-      CALL(af_wrap, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+      CALL(af_wrap, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);}
+
+af_err af_wrap_v2(af_array *out, const af_array in, const dim_t ox, const dim_t oy,
+                  const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+                  const dim_t px, const dim_t py, const bool is_column) {
+    CHECK_ARRAYS(out, in);
+    return CALL(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
 }
 
 af_err af_sat(af_array *out, const af_array in) {
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 5990fec6eb..22e9de017d 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -10,6 +10,10 @@
 #pragma once
 #include <Param.hpp>
 #include <err_cpu.hpp>
+#include <ParamIterator.hpp>
+#include <math.hpp>
+
+#include <algorithm>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index a92869ef0b..e0fffe10f3 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -19,14 +19,14 @@ using common::half;
 
 namespace cpu {
 
-template <typename T>
-Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py, const bool is_column) {
-    af::dim4 idims = in.dims();
-    af::dim4 odims(ox, oy, idims[2], idims[3]);
-
-    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
+template<typename T>
+void wrap(Array<T> &out, const Array<T> &in,
+          const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py,
+          const bool is_column) {
+    evalMultiple<T>(std::vector<Array<T>*>{const_cast<Array<T>*>(&in), &out});
 
     if (is_column) {
         getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px,
@@ -35,15 +35,15 @@ Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
         getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px,
                            py);
     }
-
-    return out;
 }
 
-#define INSTANTIATE(T)                                                        \
-    template Array<T> wrap<T>(const Array<T> &in, const dim_t ox,             \
-                              const dim_t oy, const dim_t wx, const dim_t wy, \
-                              const dim_t sx, const dim_t sy, const dim_t px, \
-                              const dim_t py, const bool is_column);
+#define INSTANTIATE(T)                                          \
+    template void wrap<T>(Array<T> & out, const Array<T> &in,   \
+                          const dim_t ox, const dim_t oy,       \
+                          const dim_t wx, const dim_t wy,       \
+                          const dim_t sx, const dim_t sy,       \
+                          const dim_t px, const dim_t py,       \
+                          const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/wrap.hpp b/src/backend/cpu/wrap.hpp
index ced3a74a4a..cbaac9ea50 100644
--- a/src/backend/cpu/wrap.hpp
+++ b/src/backend/cpu/wrap.hpp
@@ -10,10 +10,14 @@
 #include <Array.hpp>
 
 namespace cpu {
-template <typename T>
-Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py, const bool is_column);
+
+template<typename T>
+void wrap(Array<T> &out, const Array<T> &in,
+          const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py,
+          const bool is_column);
 
 template <typename T>
 Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
diff --git a/src/backend/cuda/wrap.cu b/src/backend/cuda/wrap.cu
index 13fc2aded1..aaf7d8f99f 100644
--- a/src/backend/cuda/wrap.cu
+++ b/src/backend/cuda/wrap.cu
@@ -18,22 +18,22 @@
 namespace cuda {
 
 template<typename T>
-Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py, const bool is_column) {
-    af::dim4 idims = in.dims();
-    af::dim4 odims(ox, oy, idims[2], idims[3]);
-    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
-
+void wrap(Array<T> &out, const Array<T> &in,
+          const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py,
+          const bool is_column) {
     kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
-    return out;
 }
 
-#define INSTANTIATE(T)                                                        \
-    template Array<T> wrap<T>(const Array<T> &in, const dim_t ox,             \
-                              const dim_t oy, const dim_t wx, const dim_t wy, \
-                              const dim_t sx, const dim_t sy, const dim_t px, \
-                              const dim_t py, const bool is_column);
+#define INSTANTIATE(T)                                          \
+    template void wrap<T> (Array<T> &out,  const Array<T> &in,  \
+                           const dim_t ox, const dim_t oy,      \
+                           const dim_t wx, const dim_t wy,      \
+                           const dim_t sx, const dim_t sy,      \
+                           const dim_t px, const dim_t py,      \
+                           const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/wrap.hpp b/src/backend/cuda/wrap.hpp
index 4beeb4fb5c..d03017b069 100644
--- a/src/backend/cuda/wrap.hpp
+++ b/src/backend/cuda/wrap.hpp
@@ -11,7 +11,10 @@
 
 namespace cuda {
 template<typename T>
-Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py, const bool is_column);
+void wrap(Array<T> &out,  const Array<T> &in,
+          const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py,
+          const bool is_column);
 }
diff --git a/src/backend/opencl/kernel/wrap.cl b/src/backend/opencl/kernel/wrap.cl
index 238eb28892..99da73c51d 100644
--- a/src/backend/opencl/kernel/wrap.cl
+++ b/src/backend/opencl/kernel/wrap.cl
@@ -21,7 +21,7 @@ __kernel void wrap_kernel(__global T *optr, KParam out, __global T *iptr,
     int oidx0 = get_local_id(0) + get_local_size(0) * groupId_x;
     int oidx1 = get_local_id(1) + get_local_size(1) * groupId_y;
 
-    optr += idx2 * out.strides[2] + idx3 * out.strides[3];
+    optr += idx2 * out.strides[2] + idx3 * out.strides[3] + out.offset;
     iptr += idx2 * in.strides[2] + idx3 * in.strides[3] + in.offset;
 
     if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1]) return;
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 73868e4fa4..7de960ff3a 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -21,22 +21,22 @@ using common::half;
 namespace opencl {
 
 template<typename T>
-Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py, const bool is_column) {
-    af::dim4 idims = in.dims();
-    af::dim4 odims(ox, oy, idims[2], idims[3]);
-    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
-
+void wrap(Array<T> &out, const Array<T> &in,
+          const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py,
+          const bool is_column) {
     kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
-    return out;
 }
 
-#define INSTANTIATE(T)                                                        \
-    template Array<T> wrap<T>(const Array<T> &in, const dim_t ox,             \
-                              const dim_t oy, const dim_t wx, const dim_t wy, \
-                              const dim_t sx, const dim_t sy, const dim_t px, \
-                              const dim_t py, const bool is_column);
+#define INSTANTIATE(T)                                          \
+    template void wrap<T> (Array<T> &out,  const Array<T> &in,  \
+                           const dim_t ox, const dim_t oy,      \
+                           const dim_t wx, const dim_t wy,      \
+                           const dim_t sx, const dim_t sy,      \
+                           const dim_t px, const dim_t py,      \
+                           const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/wrap.hpp b/src/backend/opencl/wrap.hpp
index bae5447dc1..35600be90a 100644
--- a/src/backend/opencl/wrap.hpp
+++ b/src/backend/opencl/wrap.hpp
@@ -10,14 +10,21 @@
 #include <Array.hpp>
 
 namespace opencl {
-template <typename T>
-Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
-              const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-              const dim_t px, const dim_t py, const bool is_column);
+
+template<typename T>
+void wrap(Array<T> &out,  const Array<T> &in,
+          const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py,
+          const bool is_column);
 
 template <typename T>
-Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
-                      const dim_t wx, const dim_t wy, const dim_t sx,
-                      const dim_t sy, const dim_t px, const dim_t py,
-                      const dim_t dx, const dim_t dy, const bool is_column);
+Array<T> wrap_dilated(const Array<T> &in,
+                      const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy,
+                      const dim_t sx, const dim_t sy,
+                      const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy,
+                      const bool is_column);
 }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index ac368ead2a..c60090c693 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -1253,7 +1253,39 @@ class TestOutputArrayInfo {
 
     void init(const unsigned ndims, const dim_t *const dims, const af_dtype ty,
               const af_seq *const subarr_idxs) {
-        ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
+        init(ndims, dims, ty);
+
+        ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
+        for (uint i = 0; i < ndims; ++i) {
+            out_subarr_idxs[i] = subarr_idxs[i];
+        }
+        out_subarr_ndims = ndims;
+
+        ASSERT_SUCCESS(af_index(&out_subarr, out_arr, ndims, subarr_idxs));
+    }
+
+    void init(double val, const unsigned ndims, const dim_t *const dims,
+              const af_dtype ty) {
+        switch (ty) {
+            case c32:
+            case c64:
+                af_constant_complex(&out_arr, val, 0.0, ndims, dims, ty);
+                break;
+            case s64:
+                af_constant_long(&out_arr, static_cast<intl>(val), ndims, dims);
+                break;
+            case u64:
+                af_constant_ulong(&out_arr, static_cast<uintl>(val), ndims,
+                                  dims);
+                break;
+            default: af_constant(&out_arr, val, ndims, dims, ty); break;
+        }
+    }
+
+    void init(double val, const unsigned ndims, const dim_t *const dims,
+              const af_dtype ty, const af_seq *const subarr_idxs) {
+        init(val, ndims, dims, ty);
+
         ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
         for (uint i = 0; i < ndims; ++i) {
             out_subarr_idxs[i] = subarr_idxs[i];
@@ -1290,6 +1322,12 @@ void genRegularArray(TestOutputArrayInfo *metadata, const unsigned ndims,
     metadata->init(ndims, dims, ty);
 }
 
+void genRegularArray(TestOutputArrayInfo *metadata, double val,
+                     const unsigned ndims, const dim_t *const dims,
+                     const af_dtype ty) {
+    metadata->init(val, ndims, dims, ty);
+}
+
 // Generates a large, random array, and extracts a subarray for the af_*
 // function to use. testWriteToOutputArray expects that the large array that it
 // receives is equal to the same large array with the gold array injected on the
@@ -1317,6 +1355,30 @@ void genSubArray(TestOutputArrayInfo *metadata, const unsigned ndims,
     metadata->init(ndims, full_arr_dims, ty, &subarr_idxs[0]);
 }
 
+void genSubArray(TestOutputArrayInfo *metadata, double val,
+                 const unsigned ndims, const dim_t *const dims,
+                 const af_dtype ty) {
+    const dim_t pad_size = 2;
+
+    // The large array is padded on both sides of each dimension
+    // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
+    dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
+    for (uint i = 0; i < ndims; ++i) {
+        full_arr_dims[i] = dims[i] + 2 * pad_size;
+    }
+
+    // Calculate index of sub-array. These will be used also by
+    // testWriteToOutputArray so that the gold sub array will be placed in the
+    // same location. Currently, this location is the center of the large array
+    af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
+    for (uint i = 0; i < ndims; ++i) {
+        af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
+        subarr_idxs[i] = idx;
+    }
+
+    metadata->init(val, ndims, full_arr_dims, ty, &subarr_idxs[0]);
+}
+
 // Generates a reordered array. testWriteToOutputArray expects that this array
 // will still have the correct output values from the af_* function, even though
 // the array was initially reordered.
@@ -1346,6 +1408,32 @@ void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
     metadata->setOutput(reordered);
 }
 
+void genReorderedArray(TestOutputArrayInfo *metadata, double val,
+                       const unsigned ndims, const dim_t *const dims,
+                       const af_dtype ty) {
+    // The rest of this function assumes that dims has 4 elements. Just in case
+    // dims has < 4 elements, use another dims array that is filled with 1s
+    dim_t all_dims[4] = {1, 1, 1, 1};
+    for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
+
+    // This reorder combination will not move data around, but will simply
+    // call modDims and modStrides (see src/api/c/reorder.cpp).
+    // The output will be checked if it is still correct even with the
+    // modified dims and strides "hack" with no data movement
+    uint reorder_idxs[4] = {0, 2, 1, 3};
+
+    // Shape the output array such that the reordered output array will have
+    // the correct dimensions that the test asks for (i.e. must match dims arg)
+    dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
+    for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
+    metadata->init(val, 4, init_dims, ty);
+
+    af_array reordered = 0;
+    ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
+                              reorder_idxs[0], reorder_idxs[1], reorder_idxs[2],
+                              reorder_idxs[3]));
+    metadata->setOutput(reordered);
+}
 // Partner function of testWriteToOutputArray. This generates the "special"
 // array that testWriteToOutputArray will use to check if the af_* function
 // correctly uses an existing array as its output
@@ -1363,6 +1451,20 @@ void genTestOutputArray(af_array *out_ptr, const unsigned ndims,
     *out_ptr = metadata->getOutput();
 }
 
+void genTestOutputArray(af_array *out_ptr, double val, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype ty,
+                        TestOutputArrayInfo *metadata) {
+    switch (metadata->getOutputArrayType()) {
+        case FULL_ARRAY: genRegularArray(metadata, val, ndims, dims, ty); break;
+        case SUB_ARRAY: genSubArray(metadata, val, ndims, dims, ty); break;
+        case REORDERED_ARRAY:
+            genReorderedArray(metadata, val, ndims, dims, ty);
+            break;
+        default: break;
+    }
+    *out_ptr = metadata->getOutput();
+}
+
 // Partner function of genTestOutputArray. This uses the same "special"
 // array that genTestOutputArray generates, and checks whether the
 // af_* function wrote to that array correctly
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 41d5d37af2..5eeb0c65ae 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
@@ -247,3 +248,303 @@ TEST(Wrap, DocSnippet) {
     array gold_B_wrapped(dim4(3, 3), gold_hB_wrapped);
     ASSERT_ARRAYS_EQ(gold_B_wrapped, B_wrapped);
 }
+
+static void getInput(af_array *data, const dim_t *dims) {
+    float h_data[16] = { 10, 20, 20, 30,
+                         30, 40, 40, 50,
+                         30, 40, 40, 50,
+                         50, 60, 60, 70 };
+    ASSERT_SUCCESS(af_create_array(data, &h_data[0], 2, dims, f32));
+}
+static void getGold(af_array *gold, const dim_t *dims) {
+    float h_gold[16]= { 10, 20, 30, 40,
+                        20, 30, 40, 50,
+                        30, 40, 50, 60,
+                        40, 50, 60, 70 };
+    ASSERT_SUCCESS(af_create_array(gold, &h_gold[0], 2, dims, f32));
+}
+
+class WrapCommon : virtual public ::testing::Test {
+   protected:
+    WrapCommon()
+        : in_(0)
+        , gold_(0)
+        , in_dims(4, 4)
+        , gold_dims(4, 4)
+        , win_len(2)
+        , strd_len(2)
+        , pad_len(0)
+        , is_column(true) {}
+
+    virtual void SetUp() {
+        ::getInput(&in_, &in_dims[0]);
+        ::getGold(&gold_, &in_dims[0]);
+    }
+
+    virtual void TearDown() {
+        if (in_ != 0) af_release_array(in_);
+        if (gold_ != 0) af_release_array(gold_);
+    }
+
+    af_array in_;
+    af_array gold_;
+    dim4 in_dims;
+    dim4 gold_dims;
+    dim_t win_len;
+    dim_t strd_len;
+    dim_t pad_len;
+    bool is_column;
+};
+
+template<typename T>
+class WrapV2 : public WrapCommon {
+   protected:
+    vector<T> h_gold_cast;
+    vector<T> h_in_cast;
+
+    WrapV2() {}
+
+    void setTestData(float *h_gold, dim4 gold_dims, float *h_in, dim4 in_dims) {
+        releaseArrays();
+
+        this->gold_ = 0;
+        this->in_   = 0;
+
+        this->gold_dims = gold_dims;
+        this->in_dims   = in_dims;
+
+        for (int i = 0; i < gold_dims.elements(); ++i) {
+            h_gold_cast.push_back(static_cast<T>(h_gold[i]));
+        }
+        for (int i = 0; i < in_dims.elements(); ++i) {
+            h_in_cast.push_back(static_cast<T>(h_in[i]));
+        }
+
+        ASSERT_SUCCESS(af_create_array(&this->gold_, &h_gold_cast.front(),
+                                       gold_dims.ndims(), gold_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(af_create_array(&this->in_, &h_in_cast.front(),
+                                       in_dims.ndims(), in_dims.get(),
+                                       (af_dtype)dtype_traits<T>::af_type));
+    }
+
+    void testSpclOutArray(TestOutputArrayType out_array_type) {
+        SUPPORTED_TYPE_CHECK(T);
+
+        af_array out = 0;
+        TestOutputArrayInfo metadata(out_array_type);
+        if (out_array_type == NULL_ARRAY) {
+            genTestOutputArray(&out, this->gold_dims.ndims(),
+                               this->gold_dims.get(),
+                               (af_dtype)dtype_traits<T>::af_type, &metadata);
+        } else {
+            genTestOutputArray(&out, 0.0, this->gold_dims.ndims(),
+                               this->gold_dims.get(),
+                               (af_dtype)dtype_traits<T>::af_type, &metadata);
+        }
+
+        // Taken from the Wrap.DocSnippet test
+        ASSERT_SUCCESS(af_wrap_v2(&out, this->in_,
+                                  4, 4,   // output dims
+                                  2, 2,   // window size
+                                  2, 2,   // stride
+                                  0, 0,   // padding
+                                  true)); // is_column
+
+        ASSERT_SPECIAL_ARRAYS_EQ(this->gold_, out, &metadata);
+    }
+
+    void releaseArrays() {
+        if (this->in_ != 0)   { ASSERT_SUCCESS(af_release_array(this->in_)); }
+        if (this->gold_ != 0) { ASSERT_SUCCESS(af_release_array(this->gold_)); }
+    }
+};
+
+TYPED_TEST_CASE(WrapV2, TestTypes);
+
+template<typename T>
+class WrapV2Simple : public WrapV2<T> {
+   protected:
+    void SetUp() {
+        this->releaseArrays();
+        this->in_   = 0;
+        this->gold_ = 0;
+
+        af_array tmp_in   = 0;
+        af_array tmp_gold = 0;
+
+        ::getInput(&tmp_in, this->in_dims.get());
+        ::getGold(&tmp_gold, this->gold_dims.get());
+
+        af_dtype dtype = (af_dtype)dtype_traits<T>::af_type;
+        ASSERT_SUCCESS(af_cast(&this->in_, tmp_in, dtype));
+        ASSERT_SUCCESS(af_cast(&this->gold_, tmp_gold, dtype));
+
+        ASSERT_SUCCESS(af_release_array(tmp_in));
+        ASSERT_SUCCESS(af_release_array(tmp_gold));
+    }
+};
+
+TYPED_TEST_CASE(WrapV2Simple, TestTypes);
+
+TYPED_TEST(WrapV2Simple, UseNullOutputArray) {
+    this->testSpclOutArray(NULL_ARRAY);
+}
+
+TYPED_TEST(WrapV2Simple, UseFullExistingOutputArray) {
+    this->testSpclOutArray(FULL_ARRAY);
+}
+
+TYPED_TEST(WrapV2Simple, UseExistingOutputSubArray) {
+    this->testSpclOutArray(SUB_ARRAY);
+}
+
+TYPED_TEST(WrapV2Simple, UseReorderedOutputArray) {
+    this->testSpclOutArray(REORDERED_ARRAY);
+}
+
+class WrapNullArgs : public WrapCommon {};
+
+TEST_F(WrapNullArgs, NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(af_wrap(out_ptr, this->in_,
+                      4, 4,  // output dims
+                      2, 2,  // window size
+                      2, 2,  // stride
+                      0, 0,  // padding
+                      true), // is_column
+              AF_ERR_ARG);
+}
+
+TEST_F(WrapNullArgs, NullInputArray) {
+    af_array out = 0;
+    ASSERT_EQ(af_wrap(&out, 0,
+                      4, 4,  // output dims
+                      2, 2,  // window size
+                      2, 2,  // stride
+                      0, 0,  // padding
+                      true), // is_column
+              AF_ERR_ARG);
+}
+
+TEST_F(WrapNullArgs, V2NullOutputPtr) {
+    af_array* out_ptr = 0;
+    ASSERT_EQ(af_wrap_v2(out_ptr, this->in_,
+                         4, 4,  // output dims
+                         2, 2,  // window size
+                         2, 2,  // stride
+                         0, 0,  // padding
+                         true), // is_column
+              AF_ERR_ARG);
+}
+
+TEST_F(WrapNullArgs, V2NullInputArray) {
+    af_array out = 0;
+    ASSERT_EQ(af_wrap_v2(&out, 0,
+                         4, 4,  // output dims
+                         2, 2,  // window size
+                         2, 2,  // stride
+                         0, 0,  // padding
+                         true), // is_column
+              AF_ERR_ARG);
+}
+
+struct ArgDim {
+    ArgDim(dim_t d0, dim_t d1) : dim0(d0), dim1(d1) {}
+    void get(dim_t *d0, dim_t *d1);
+
+    dim_t dim0;
+    dim_t dim1;
+};
+
+struct WindowDims : public ArgDim {
+    WindowDims() : ArgDim(1, 1) {}
+    WindowDims(dim_t d0, dim_t d1) : ArgDim(d0, d1) {}
+};
+
+struct StrideDims : public ArgDim {
+    StrideDims() : ArgDim(1, 1) {}
+    StrideDims(dim_t d0, dim_t d1) : ArgDim(d0, d1) {}
+};
+
+struct PadDims : public ArgDim {
+    PadDims() : ArgDim(0, 0) {}
+    PadDims(dim_t d0, dim_t d1) : ArgDim(d0, d1) {}
+};
+
+class WrapArgs {
+   public:
+    WindowDims wc_;
+    StrideDims sc_;
+    PadDims pc_;
+    bool is_column;
+    af_err err;
+
+    WrapArgs() : wc_(), sc_(), pc_(), is_column(true), err(af_err(999)) {}
+
+    WrapArgs(dim_t win_d0, dim_t win_d1, dim_t str_d0, dim_t str_d1,
+             dim_t pad_d0, dim_t pad_d1, bool is_col, af_err err)
+        : wc_(win_d0, win_d1)
+        , sc_(str_d0, str_d1)
+        , pc_(pad_d0, pad_d1)
+        , is_column(is_col)
+        , err(err) {}
+};
+
+class WrapAPITest
+    : public WrapCommon
+    , public ::testing::WithParamInterface<WrapArgs> {
+   public:
+    WrapAPITest() : input(), in_(0), in_dims(4, 4, 1, 1) {}
+
+    virtual void SetUp() {
+        input = GetParam();
+        ::getInput(&in_, in_dims.get());
+    }
+    virtual void TearDown() {
+        if (in_ != 0) af_release_array(in_);
+    }
+
+    WrapArgs input;
+    af_array in_;
+    dim4 in_dims;
+};
+
+TEST_P(WrapAPITest, CheckDifferentWrapArgs) {
+    dim_t win_d0 = input.wc_.dim0;
+    dim_t win_d1 = input.wc_.dim1;
+    dim_t str_d0 = input.sc_.dim0;
+    dim_t str_d1 = input.sc_.dim1;
+    dim_t pad_d0 = input.pc_.dim0;
+    dim_t pad_d1 = input.pc_.dim1;
+
+    af_array out_ = 0;
+    af_err err    = af_wrap(&out_, in_, in_dims[0], in_dims[1], win_d0, win_d1,
+                         str_d0, str_d1, pad_d0, pad_d1, input.is_column);
+
+    ASSERT_EQ(err, input.err);
+    if (out_ != 0) af_release_array(out_);
+}
+
+WrapArgs args[] = {
+    // clang-format off
+    //      | win_dim0 | win_dim1 | str_dim0 | str_dim1 | pad_dim0 | pad_dim1 | is_col |    err    |
+    WrapArgs(        2,         2,         2,         2,         0,         0,    true,  AF_SUCCESS),
+    WrapArgs(        2,         2,         2,         2,         0,         0,   false,  AF_SUCCESS),
+
+    WrapArgs(       -1,         2,         2,         2,         0,         0,    true,  AF_ERR_ARG),
+    WrapArgs(        2,        -1,         2,         2,         0,         0,    true,  AF_ERR_ARG),
+    WrapArgs(       -1,        -1,         2,         2,         0,         0,    true,  AF_ERR_ARG),
+
+    WrapArgs(        2,         2,        -1,         2,         0,         0,    true,  AF_ERR_ARG),
+    WrapArgs(        2,         2,         2,        -1,         0,         0,    true,  AF_ERR_ARG),
+    WrapArgs(        2,         2,        -1,        -1,         0,         0,    true,  AF_ERR_ARG),
+
+    WrapArgs(        2,         2,         2,         2,         1,         1,    true,  AF_ERR_SIZE),
+    WrapArgs(        2,         2,         2,         2,        -1,         1,    true,  AF_ERR_SIZE),
+    WrapArgs(        2,         2,         2,         2,         1,        -1,    true,  AF_ERR_SIZE),
+    WrapArgs(        2,         2,         2,         2,        -1,        -1,    true,  AF_ERR_SIZE),
+    // clang-format on
+};
+
+INSTANTIATE_TEST_CASE_P(BulkTest, WrapAPITest, ::testing::ValuesIn(args));

From 0497fd09350c16ba9c417f8db7713b87edbc7bdd Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 7 Feb 2020 16:55:27 -0500
Subject: [PATCH 1825/2677] move function definition of scale_type struct

---
 src/backend/cpu/blas.cpp | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index ae482e2744..4c3079eea8 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -123,13 +123,11 @@ struct scale_type {
                                                 const typename blas_base<T>::type *,
                                                 const typename conditional<batched,  const T*, const T>::type>::type;
 
-    api_type getScale() const;
+    api_type getScale() const {
+		return val;
+	}
 };
 
-template<typename T, bool batched>
-typename scale_type<T, batched>::api_type scale_type<T, batched>::getScale() const {
-    return val;
-}
 
 #define INSTANTIATE_BATCHED(TYPE)                                                    \
 template<>                                                                           \

From 9adc605f5438e6c887933d771b3ffbc06de1928a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 8 Feb 2020 15:47:36 +0530
Subject: [PATCH 1826/2677] Fix unified api CALL argument for wrap,transform_v2

---
 src/api/unified/image.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 313ad1c8de..47a3e601bc 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -70,7 +70,7 @@ af_err af_transform_v2(af_array *out, const af_array in, const af_array transfor
                        const dim_t odim0, const dim_t odim1,
                        const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(out, in, transform);
-    return CALL(out, in, transform, odim0, odim1, method, inverse);
+    CALL(af_transform_v2, out, in, transform, odim0, odim1, method, inverse);
 }
 
 af_err af_transform_coordinates(af_array *out, const af_array tf,
@@ -224,7 +224,7 @@ af_err af_wrap_v2(af_array *out, const af_array in, const dim_t ox, const dim_t
                   const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
                   const dim_t px, const dim_t py, const bool is_column) {
     CHECK_ARRAYS(out, in);
-    return CALL(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+    CALL(af_wrap_v2, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
 }
 
 af_err af_sat(af_array *out, const af_array in) {

From c1e141691187c4a56f17fffbc52b832d3fa3d2c9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 9 Feb 2020 01:46:07 +0530
Subject: [PATCH 1827/2677] Confidence Connected Components

- [x] Example showcasing confidence connected components
- [x] Update assets submodule for above example input image
- [x] Update test/data submodule hash
- [x] Unit tests
- [x] CPU Backend Implementation
- [x] CUDA Backend Implementation
- [x] OpenCL Backend Implementation - Has a known issue, API call errors
      out on this backend currently.
- [x] Documentation

- NeighborhoodIterator can be derived from ParamIterator, but at the moment
  not sure if it is good idea.
- Removed faulty post increment operator overload for ParamIterator. A
  correct version will be added later.
---
 assets                                        |   2 +-
 docs/details/image.dox                        |  37 +++
 examples/image_processing/CMakeLists.txt      |  13 +
 .../confidence_connected_components.cpp       |  62 +++++
 include/af/defines.h                          |   1 +
 include/af/image.h                            |  99 ++++++++
 src/api/c/CMakeLists.txt                      |   2 +
 src/api/c/confidence_connected.cpp            | 232 ++++++++++++++++++
 src/api/c/imgproc_common.hpp                  |  76 ++++++
 src/api/c/sat.cpp                             |  11 +-
 src/api/cpp/CMakeLists.txt                    |   1 +
 src/api/cpp/confidence_connected.cpp          |  49 ++++
 src/api/unified/image.cpp                     |   9 +
 src/backend/cpu/CMakeLists.txt                |   3 +
 src/backend/cpu/ParamIterator.hpp             | 224 ++++++++++++++---
 src/backend/cpu/arith.hpp                     |   2 +
 src/backend/cpu/flood_fill.cpp                |  41 ++++
 src/backend/cpu/flood_fill.hpp                |  21 ++
 src/backend/cpu/kernel/flood_fill.hpp         | 103 ++++++++
 src/backend/cuda/CMakeLists.txt               |   4 +
 src/backend/cuda/arith.hpp                    |   2 +
 src/backend/cuda/flood_fill.cpp               |  38 +++
 src/backend/cuda/flood_fill.hpp               |  21 ++
 src/backend/cuda/kernel/flood_fill.cuh        | 140 +++++++++++
 src/backend/cuda/kernel/flood_fill.hpp        |  82 +++++++
 src/backend/opencl/CMakeLists.txt             |   3 +
 src/backend/opencl/arith.hpp                  |   2 +
 src/backend/opencl/flood_fill.cpp             |  38 +++
 src/backend/opencl/flood_fill.hpp             |  21 ++
 src/backend/opencl/kernel/flood_fill.cl       | 136 ++++++++++
 src/backend/opencl/kernel/flood_fill.hpp      | 174 +++++++++++++
 test/CMakeLists.txt                           |   1 +
 test/confidence_connected.cpp                 | 206 ++++++++++++++++
 test/data                                     |   2 +-
 34 files changed, 1810 insertions(+), 48 deletions(-)
 create mode 100644 examples/image_processing/confidence_connected_components.cpp
 create mode 100644 src/api/c/confidence_connected.cpp
 create mode 100644 src/api/c/imgproc_common.hpp
 create mode 100644 src/api/cpp/confidence_connected.cpp
 create mode 100644 src/backend/cpu/flood_fill.cpp
 create mode 100644 src/backend/cpu/flood_fill.hpp
 create mode 100644 src/backend/cpu/kernel/flood_fill.hpp
 create mode 100644 src/backend/cuda/flood_fill.cpp
 create mode 100644 src/backend/cuda/flood_fill.hpp
 create mode 100644 src/backend/cuda/kernel/flood_fill.cuh
 create mode 100644 src/backend/cuda/kernel/flood_fill.hpp
 create mode 100644 src/backend/opencl/flood_fill.cpp
 create mode 100644 src/backend/opencl/flood_fill.hpp
 create mode 100644 src/backend/opencl/kernel/flood_fill.cl
 create mode 100644 src/backend/opencl/kernel/flood_fill.hpp
 create mode 100644 test/confidence_connected.cpp

diff --git a/assets b/assets
index fcc798248b..c53bfab909 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit fcc798248b9985dd4ded5b3adf51bb1af63cea90
+Subproject commit c53bfab909adfeed626f91ed419555711e20bca5
diff --git a/docs/details/image.dox b/docs/details/image.dox
index fe58584b98..94d97d5acf 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -1072,5 +1072,42 @@ explicitly.
 
 =======================================================================
 
+\defgroup image_func_confidence_cc Confidence Connected Components
+\ingroup connected_comps_mat
+
+\brief Segment image based on similar pixel characteristics
+
+This filter is similar to \ref regions() (connected components) with additional
+criteria for segmentation. In \ref regions(), all connected (\ref af_connectivity)
+pixels connected are considered to be a single component. In this
+variation of connected components, pixels having similar pixel statistics
+of the neighborhoods around a given set of seed points are grouped together.
+
+The parameter \p radius determines the size of neighborhood around a seed point.
+
+Mean (\f$ \mu \f$) and Variance (\f$ \sigma^2 \f$) are the pixel statistics that
+are computed across all neighborhoods around the given set of seed points. The
+pixels which are connected to seed points and lie in the confidence interval
+ (\f$ [\mu - \alpha * \sigma, \mu + \alpha * \sigma] \f$ where \f$ \alpha \f$
+is the parameter \p multiplier) are grouped. \p multiplier can be used to
+control the width of the confidence interval.
+
+This filter follows an iterative approach for fine tuning the segmentation.
+An initial segmenetation followed by a finite number (\p iter) of segmentations
+are performed. The user provided parameter \p iter is only a request and the
+algorithm can prempt the execution if \f$ \sigma^2 \f$ approaches zero. The
+initial segmentation uses the mean and variance calculated from the neighborhoods
+of all the seed points. For subsequent segmentations, all pixels in the previous
+segmentation are used to re-calculate the mean and variance (as opposed to using
+the pixels in the neighborhood of the seed point).
+
+Given below is a sample output for segmenting three different regions of a
+donut using single seed.
+
+<img src="ccc_sample_output.png" align="center" alt="Confidence Connected Components Example"
+width="60%" height="60%"/>
+
+
+
 @}
 */
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index 3e450911dd..ffffe17fa7 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -27,6 +27,11 @@ if(ArrayFire_CPU_FOUND)
   add_executable(brain_segmentation_cpu brain_segmentation.cpp)
   target_link_libraries(brain_segmentation_cpu ArrayFire::afcpu)
 
+  # Confidence Connected Components example
+  add_executable(confidence_connected_components_cpu
+      confidence_connected_components.cpp)
+  target_link_libraries(confidence_connected_components_cpu ArrayFire::afcpu)
+
   # Edge detection example
   add_executable(edge_cpu edge.cpp)
   target_link_libraries(edge_cpu ArrayFire::afcpu)
@@ -74,6 +79,10 @@ if(ArrayFire_CUDA_FOUND)
   add_executable(brain_segmentation_cuda brain_segmentation.cpp)
   target_link_libraries(brain_segmentation_cuda ArrayFire::afcuda)
 
+  add_executable(confidence_connected_components_cuda
+      confidence_connected_components.cpp)
+  target_link_libraries(confidence_connected_components_cuda ArrayFire::afcuda)
+
   add_executable(edge_cuda edge.cpp)
   target_link_libraries(edge_cuda ArrayFire::afcuda)
 
@@ -114,6 +123,10 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(brain_segmentation_opencl brain_segmentation.cpp)
   target_link_libraries(brain_segmentation_opencl ArrayFire::afopencl)
 
+  add_executable(confidence_connected_components_opencl
+      confidence_connected_components.cpp)
+  target_link_libraries(confidence_connected_components_opencl ArrayFire::afopencl)
+
   add_executable(edge_opencl edge.cpp)
   target_link_libraries(edge_opencl ArrayFire::afopencl)
 
diff --git a/examples/image_processing/confidence_connected_components.cpp b/examples/image_processing/confidence_connected_components.cpp
new file mode 100644
index 0000000000..0883c0d6be
--- /dev/null
+++ b/examples/image_processing/confidence_connected_components.cpp
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <cassert>
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+
+using namespace af;
+
+int main(int argc, char* argv[]) {
+    try {
+
+        unsigned s[1]       = {132};
+        unsigned radius     = 3;
+        unsigned multiplier = 3;
+        int iter            = 5;
+
+        array A = loadImage(ASSETS_DIR "/examples/images/donut.png", false);
+
+        unsigned seedx = 132;
+        unsigned seedy = 132;
+        array ring =
+            confidenceCC(A, 1, &seedx, &seedy, radius, multiplier, iter, 255);
+
+        seedx = 152;
+        seedy = 152;
+        array sxArr(dim4(1), &seedx);
+        array syArr(dim4(1), &seedy);
+        array core =
+            confidenceCC(A, sxArr, syArr, radius, multiplier, iter, 255);
+
+        seedx = 15;
+        seedy = 15;
+        unsigned seedcoords[]{15, 15};
+        array seeds(dim4(1, 2), seedcoords);
+        array background =
+            confidenceCC(A, seeds, radius, multiplier, iter, 255);
+
+        af::Window wnd("Confidence Connected Components demo");
+        while(!wnd.close()) {
+            wnd.grid(2, 2);
+            wnd(0, 0).image(A, "Input");
+            wnd(0, 1).image(ring, "Ring Component - Seed(132, 132)");
+            wnd(1, 0).image(core, "Center Black Hole - Seed(152, 152)");
+            wnd(1, 1).image(background, "Background - Seed(15, 15)");
+            wnd.show();
+        }
+    } catch (af::exception& e) {
+        fprintf(stderr, "%s\n", e.what());
+        throw;
+    }
+
+    return 0;
+}
diff --git a/include/af/defines.h b/include/af/defines.h
index 8477e227bc..bd58ec1f45 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -531,6 +531,7 @@ typedef enum {
     AF_INVERSE_DECONV_TIKHONOV       = 1,        ///< Tikhonov Inverse deconvolution
     AF_INVERSE_DECONV_DEFAULT        = 0         ///< Default is Tikhonov deconvolution
 } af_inverse_deconv_algo;
+
 #endif
 
 #if AF_API_VERSION >= 37
diff --git a/include/af/image.h b/include/af/image.h
index f560072e67..5e32b551a9 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -797,6 +797,78 @@ AFAPI array iterativeDeconv(const array& in, const array& ker,
  */
 AFAPI array inverseDeconv(const array& in, const array& psf,
                           const float gamma, const inverseDeconvAlgo algo);
+
+/**
+   C++ Interface for confidence connected components
+
+   \param[in] in is the input image, expects non-integral (float/double)
+              typed af_array
+   \param[in] seeds is an af::array of x & y coordinates of the seed points
+              with coordinate values along columns of this af::array i.e. they
+              are not stored in interleaved fashion.
+   \param[in] radius is the neighborhood region to be considered around
+              each seed point
+   \param[in] multiplier controls the threshold range computed from
+              the mean and variance of seed point neighborhoods
+   \param[in] iter is number of iterations
+   \param[in] segmentedValue is the value to which output array valid
+              pixels are set to.
+   \return out is the output af_array having the connected components
+
+   \ingroup image_func_confidence_cc
+*/
+AFAPI array confidenceCC(const array &in, const array &seeds,
+                         const unsigned radius,
+                         const unsigned multiplier, const int iter,
+                         const double segmentedValue);
+
+/**
+   C++ Interface for confidence connected components
+
+   \param[in] in is the input image, expects non-integral (float/double)
+              typed af_array
+   \param[in] seedx is an af::array of x coordinates of the seed points
+   \param[in] seedy is an af::array of y coordinates of the seed points
+   \param[in] radius is the neighborhood region to be considered around
+              each seed point
+   \param[in] multiplier controls the threshold range computed from
+              the mean and variance of seed point neighborhoods
+   \param[in] iter is number of iterations
+   \param[in] segmentedValue is the value to which output array valid
+              pixels are set to.
+   \return out is the output af_array having the connected components
+
+   \ingroup image_func_confidence_cc
+*/
+AFAPI array confidenceCC(const array &in, const array &seedx,
+                         const array &seedy, const unsigned radius,
+                         const unsigned multiplier, const int iter,
+                         const double segmentedValue);
+
+/**
+   C++ Interface for confidence connected components
+
+   \param[in] in is the input image, expects non-integral (float/double)
+              typed af_array
+   \param[in] num_seeds is the total number of seeds
+   \param[in] seedx is an array of x coordinates of the seed points
+   \param[in] seedy is an array of y coordinates of the seed points
+   \param[in] radius is the neighborhood region to be considered around
+              each seed point
+   \param[in] multiplier controls the threshold range computed from
+              the mean and variance of seed point neighborhoods
+   \param[in] iter is number of iterations
+   \param[in] segmentedValue is the value to which output array valid
+              pixels are set to.
+   \return out is the output af_array having the connected components
+
+   \ingroup image_func_confidence_cc
+*/
+AFAPI array confidenceCC(const array &in, const size_t num_seeds,
+                         const unsigned *seedx, const unsigned *seedy,
+                         const unsigned radius, const unsigned multiplier,
+                         const int iter, const double segmentedValue);
+
 #endif
 }
 #endif
@@ -1689,6 +1761,33 @@ extern "C" {
     AFAPI af_err af_inverse_deconv(af_array* out, const af_array in,
                                    const af_array psf, const float gamma,
                                    const af_inverse_deconv_algo algo);
+
+    /**
+       C Interface for confidence connected components
+
+       \param[out] out is the output af_array having the connected components
+       \param[in] in is the input image, expects non-integral (float/double)
+                  typed af_array
+       \param[in] seedx is an af_array of x coordinates of the seed points
+       \param[in] seedy is an af_array of y coordinates of the seed points
+       \param[in] radius is the neighborhood region to be considered around
+                  each seed point
+       \param[in] multiplier controls the threshold range computed from
+                  the mean and variance of seed point neighborhoods
+       \param[in] iter is number of iterations
+       \param[in] segmented_value is the value to which output array valid
+                  pixels are set to.
+       \return \ref AF_SUCCESS if the execution is successful, otherwise an
+       appropriate error code is returned.
+
+       \ingroup image_func_confidence_cc
+    */
+    AFAPI af_err af_confidence_cc(af_array *out, const af_array in,
+                                  const af_array seedx, const af_array seedy,
+                                  const unsigned radius,
+                                  const unsigned multiplier, const int iter,
+                                  const double segmented_value);
+
 #endif
 
 #ifdef __cplusplus
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 09dccb2eab..42fb56d29d 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -63,6 +63,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/clamp.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/colorspace.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/complex.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/confidence_connected.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/convolve.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/corrcoef.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/covariance.cpp
@@ -100,6 +101,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/imageio2.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/implicit.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/implicit.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/imgproc_common.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/inverse.cpp
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
new file mode 100644
index 0000000000..57411bf097
--- /dev/null
+++ b/src/api/c/confidence_connected.cpp
@@ -0,0 +1,232 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/image.h>
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <common/err_common.hpp>
+#include <flood_fill.hpp>
+#include <handle.hpp>
+#include <imgproc_common.hpp>
+#include <index.hpp>
+#include <indexing_common.hpp>
+#include <reduce.hpp>
+
+#include <array>
+#include <cmath>
+#include <type_traits>
+
+using af::dim4;
+using namespace detail;
+
+/// Index corner points of given seed points
+template<typename T>
+Array<T> pointList(const Array<T>& in,
+                   const Array<uint>& x, const Array<uint>& y) {
+    af_array xcoords = getHandle<uint>(x);
+    af_array ycoords = getHandle<uint>(y);
+    std::array<af_index_t, AF_MAX_DIMS> idxrs = {{
+        {xcoords, false, false}, {ycoords, false, false},
+        common::createSpanIndex(), common::createSpanIndex()
+    }};
+
+    Array<T> retVal = detail::index(in, idxrs.data());
+
+    // detail::index fn keeps a reference to detail::Array
+    // created from the xcoords/ycoords passed via idxrs.
+    // Hence, it is safe to release xcoords, ycoords
+    releaseHandle<uint>(xcoords);
+    releaseHandle<uint>(ycoords);
+
+    return retVal;
+}
+
+/// Returns the sum of all values given the four corner points of the region of
+/// interest in the integral-image/summed-area-table of an input image.
+///
+///  +-------------------------------------+
+///  |           |                |        |
+///  |  A(_x, _y)|       B(_x, y_)|        |
+///  |-----------+----------------+        |
+///  |           |@@@@@@@@@@@@@@@@|        |
+///  |           |@@@@@@@@@@@@@@@@|        |
+///  |           |@@@@@@@@@@@@@@@@|        |
+///  |           |@@@@@@@@@@@@@@@@|        |
+///  |-----------+----------------+        |
+///  |  C(x_, _y)        D(x_, y_)         |
+///  |                                     |
+///  +-------------------------------------+
+template<typename T>
+Array<T> sum(const Array<T>& sat, const Array<uint>& _x, const Array<uint>& x_,
+             const Array<uint>& _y, const Array<uint>& y_) {
+    Array<T> A  = pointList(sat, _x, _y);
+    Array<T> B  = pointList(sat, _x, y_);
+    Array<T> C  = pointList(sat, x_, _y);
+    Array<T> D  = pointList(sat, x_, y_);
+    Array<T> DA = arithOp<T, af_add_t>(D, A, D.dims());
+    Array<T> BC = arithOp<T, af_add_t>(B, C, B.dims());
+    return arithOp<T, af_sub_t>(DA, BC, DA.dims());
+}
+
+template<typename T>
+af_array ccHelper(const Array<T>& img, const Array<uint> &seedx,
+                  const Array<uint> &seedy, const unsigned radius, const unsigned mult,
+                  const unsigned iterations, const double segmentedValue) {
+    using CT   = typename std::conditional<std::is_same<T, double>::value,
+                                  double, float>::type;
+    constexpr CT epsilon = 1.0e-6;
+
+    auto calcVar = [](CT s2, CT s1, CT n) -> CT {
+        CT retVal = CT(0);
+        if (n > 1) {
+            retVal = (s2 - (s1 * s1 / n)) / (n - CT(1));
+        }
+        return retVal;
+    };
+
+    const dim4        inDims = img.dims();
+    const dim4      seedDims = seedx.dims();
+    const size_t    numSeeds = seedx.elements();
+    const unsigned  nhoodLen = 2*radius + 1;
+    const unsigned nhoodSize = nhoodLen * nhoodLen;
+
+    auto labelSegmented = [segmentedValue, inDims](const Array<CT>& segmented) {
+        Array<CT> newVals = createValueArray(inDims, CT(segmentedValue));
+        Array<CT> result  = arithOp<CT, af_mul_t>(newVals, segmented, inDims);
+        //cast final result to input type
+        return cast<T, CT>(result);
+    };
+
+    Array<uint> radiip = createValueArray<uint>(seedDims, radius + 1);
+    Array<uint> radii  = createValueArray<uint>(seedDims, radius);
+    Array<uint> _x     = arithOp<uint, af_sub_t>(seedx, radiip, seedDims);
+    Array<uint> x_     = arithOp<uint, af_add_t>(seedx, radii, seedDims);
+    Array<uint> _y     = arithOp<uint, af_sub_t>(seedy, radiip, seedDims);
+    Array<uint> y_     = arithOp<uint, af_add_t>(seedy, radii, seedDims);
+    Array<CT> in       = common::convRange<CT, T>(img, CT(1), CT(2));
+    Array<CT> in_2     = arithOp<CT, af_mul_t>(in, in, inDims);
+    Array<CT> I1       = common::integralImage<CT>(in);
+    Array<CT> I2       = common::integralImage<CT>(in_2);
+    Array<CT> S1       = sum(I1, _x, x_, _y, y_);
+    Array<CT> S2       = sum(I2, _x, x_, _y, y_);
+    CT totSum          = reduce_all<af_add_t, CT, CT>(S1);
+    CT totSumSq        = reduce_all<af_add_t, CT, CT>(S2);
+    CT totalNum        = numSeeds * nhoodSize;
+    CT mean            = totSum / totalNum;
+    CT var             = calcVar(totSumSq, totSum, totalNum);
+    CT stddev          = std::sqrt(var);
+    CT lower           = mean - mult * stddev;
+    CT upper           = mean + mult * stddev;
+
+    Array<CT> seedIntensities = pointList(in, seedx, seedy);
+    CT maxSeedIntensity  = reduce_all<af_max_t, CT, CT>(seedIntensities);
+    CT minSeedIntensity  = reduce_all<af_min_t, CT, CT>(seedIntensities);
+
+    if (lower > minSeedIntensity) { lower = minSeedIntensity; }
+    if (upper < maxSeedIntensity) { upper = maxSeedIntensity; }
+
+    Array<CT> segmented = floodFill(in, seedx, seedy, CT(1), lower, upper);
+
+    if (std::abs(var) < epsilon) {
+        // If variance is close to zero, stop after initial segmentation
+        return getHandle(labelSegmented(segmented));
+    }
+
+    bool continueLoop = true;
+    for (uint i = 0; (i < iterations) && continueLoop ; ++i) {
+        //Segmented images are set with 1's and 0's thus essentially
+        //making them into mask arrays for each iteration's input image
+
+        uint sampleCount = reduce_all<af_notzero_t, CT, uint>(segmented, true);
+        if (sampleCount == 0) {
+            // If no valid pixels are found, skip iterations
+            break;
+        }
+        Array<CT> valids = arithOp<CT, af_mul_t>(segmented, in, inDims);
+        Array<CT> vsqrd  = arithOp<CT, af_mul_t>(valids, valids, inDims);
+
+        CT sum      = reduce_all<af_add_t, CT, CT>(valids, true);
+        CT sumOfSqs = reduce_all<af_add_t, CT, CT>(vsqrd, true);
+        CT mean     = sum / sampleCount;
+        CT var      = calcVar(sumOfSqs, sum, CT(sampleCount));
+        CT stddev   = std::sqrt(var);
+        CT newLow   = mean - mult * stddev;
+        CT newHigh  = mean + mult * stddev;
+
+        if (newLow > minSeedIntensity) { newLow = minSeedIntensity; }
+        if (newHigh < maxSeedIntensity) { newHigh = maxSeedIntensity; }
+
+        if (std::abs(var) < epsilon) {
+            // If variance is close to zero, discontinue iterating.
+            continueLoop = false;
+        }
+        segmented = floodFill(in, seedx, seedy, CT(1), newLow, newHigh);
+    }
+
+    return getHandle(labelSegmented(segmented));
+}
+
+af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
+                        const af_array seedy, const unsigned radius,
+                        const unsigned multiplier, const int iter,
+                        const double segmented_value) {
+#if defined(AF_OPENCL)
+    // FIXME OpenCL backend keeps running into indefinte loop for
+    // short bit size(16,8) types very often and occasionally
+    // with 32 bit types.
+    AF_ERROR("There is a known issue for OpenCL implementation",
+            AF_ERR_NOT_SUPPORTED);
+#endif
+    try {
+        const ArrayInfo inInfo         = getInfo(in);
+        const ArrayInfo seedxInfo      = getInfo(seedx);
+        const ArrayInfo seedyInfo      = getInfo(seedy);
+        const af::dim4 inputDimensions = inInfo.dims();
+        const af::dtype inputArrayType = inInfo.getType();
+
+        //TODO(pradeep) handle case where seeds are towards border
+        //              and indexing may result in throwing exception
+        //TODO(pradeep) add batch support later
+        ARG_ASSERT(
+            1, (inputDimensions.ndims() > 0 && inputDimensions.ndims() <= 2));
+
+        ARG_ASSERT(2, (seedxInfo.ndims() == 1));
+        ARG_ASSERT(3, (seedyInfo.ndims() == 1));
+        ARG_ASSERT(2, (seedxInfo.elements() == seedyInfo.elements()));
+
+        af_array output = 0;
+        switch (inputArrayType) {
+            case f32:
+                output = ccHelper(getArray<float>(in), getArray<uint>(seedx),
+                                  getArray<uint>(seedy), radius, multiplier,
+                                  iter, segmented_value);
+                break;
+            case u32:
+                output = ccHelper(getArray<uint>(in), getArray<uint>(seedx),
+                                  getArray<uint>(seedy), radius, multiplier,
+                                  iter, segmented_value);
+                break;
+            case u16:
+                output = ccHelper(getArray<ushort>(in), getArray<uint>(seedx),
+                                  getArray<uint>(seedy), radius, multiplier,
+                                  iter, segmented_value);
+                break;
+            case u8:
+                output = ccHelper(getArray<uchar>(in), getArray<uint>(seedx),
+                                  getArray<uint>(seedy), radius, multiplier,
+                                  iter, segmented_value);
+                break;
+            default : TYPE_ERROR (0, inputArrayType);
+        }
+        std::swap(*out, output);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/imgproc_common.hpp b/src/api/c/imgproc_common.hpp
new file mode 100644
index 0000000000..0497d0e789
--- /dev/null
+++ b/src/api/c/imgproc_common.hpp
@@ -0,0 +1,76 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <arith.hpp>
+#include <cast.hpp>
+#include <logic.hpp>
+#include <reduce.hpp>
+#include <scan.hpp>
+
+#include <cmath>
+
+namespace common {
+
+template<typename To, typename Ti = To>
+detail::Array<To> integralImage(const detail::Array<Ti>& in) {
+    auto input = detail::cast<To, Ti>(in);
+    Array<To> horizontalScan = detail::scan<af_add_t, To, To>(input, 0);
+    return detail::scan<af_add_t, To, To>(horizontalScan, 1);
+}
+
+template<typename T>
+detail::Array<T> threshold(const Array<T>& in, T min, T max) {
+    const af::dim4 inDims = in.dims();
+
+    auto MN    = createValueArray(inDims, min);
+    auto MX    = createValueArray(inDims, max);
+    auto below = logicOp<T, af_le_t>(in, MX, inDims);
+    auto above = logicOp<T, af_ge_t>(in, MN, inDims);
+    auto valid = logicOp<char, af_and_t>(below, above, inDims);
+
+    return arithOp<T, af_mul_t>(in, cast<T, char>(valid), inDims);
+}
+
+template<typename To, typename Ti>
+detail::Array<To> convRange(const detail::Array<Ti>& in,
+                            const To newLow = To(0), const To newHigh = To(1)) {
+    auto dims  = in.dims();
+    auto input = detail::cast<To, Ti>(in);
+    To high    = reduce_all<af_max_t, To, To>(input);
+    To low     = reduce_all<af_min_t, To, To>(input);
+    To range   = high - low;
+
+    if (std::abs(range) < 1.0e-6) {
+        if (low == To(0) && newLow == To(0)) {
+            return input;
+        } else {
+            // Input is constant, use high as constant in converted range
+            return createValueArray(dims, newHigh);
+        }
+    }
+
+    auto minArray = createValueArray(dims, low);
+    auto invDen   = createValueArray(dims, To(1.0/range));
+    auto numer    = arithOp<To, af_sub_t>(input, minArray, dims);
+    auto result   = arithOp<To, af_mul_t>(numer, invDen, dims);
+
+    if (newLow != To(0) || newHigh != To(1)) {
+        To newRange    = newHigh - newLow;
+        auto newRngArr = createValueArray(dims, newRange);
+        auto newMinArr = createValueArray(dims, newLow);
+        auto scaledArr = arithOp<To, af_mul_t>(result, newRngArr, dims);
+
+        result = arithOp<To, af_add_t>(newMinArr, scaledArr, dims);
+    }
+    return result;
+}
+
+} // namespace common
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index 207b7b97f7..d63e2aa75d 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -9,7 +9,7 @@
 
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <scan.hpp>
+#include <imgproc_common.hpp>
 #include <af/defines.h>
 #include <af/image.h>
 
@@ -17,13 +17,8 @@ using af::dim4;
 using namespace detail;
 
 template<typename To, typename Ti>
-static af_array sat(const af_array& in) {
-    const Array<To> input = castArray<To>(in);
-
-    Array<To> hprefix_scan = scan<af_add_t, To, To>(input, 0);
-    Array<To> vprefix_scan = scan<af_add_t, To, To>(hprefix_scan, 1);
-
-    return getHandle<To>(vprefix_scan);
+inline af_array sat(const af_array& in) {
+    return getHandle<To>(common::integralImage<To, Ti>(getArray<Ti>(in)));
 }
 
 af_err af_sat(af_array* out, const af_array in) {
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 2339da2477..a714eeae4f 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -15,6 +15,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/clamp.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/colorspace.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/complex.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/confidence_connected.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/convolve.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/corrcoef.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/covariance.cpp
diff --git a/src/api/cpp/confidence_connected.cpp b/src/api/cpp/confidence_connected.cpp
new file mode 100644
index 0000000000..5410f0a334
--- /dev/null
+++ b/src/api/cpp/confidence_connected.cpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/array.h>
+#include <af/dim4.hpp>
+#include <af/image.h>
+#include "error.hpp"
+
+namespace af {
+
+array confidenceCC(const array &in, const size_t num_seeds,
+                   const unsigned *seedx, const unsigned *seedy,
+                   const unsigned radius, const unsigned multiplier,
+                   const int iter, const double segmentedValue) {
+    af::array xs(dim4(num_seeds), seedx);
+    af::array ys(dim4(num_seeds), seedy);
+    af_array temp = 0;
+    AF_THROW(af_confidence_cc(&temp, in.get(), xs.get(), ys.get(), radius,
+                              multiplier, iter, segmentedValue));
+    return array(temp);
+}
+
+array confidenceCC(const array &in, const array &seeds,
+                   const unsigned radius, const unsigned multiplier,
+                   const int iter, const double segmentedValue) {
+    af::array xcoords = seeds.col(0);
+    af::array ycoords = seeds.col(1);
+    af_array temp = 0;
+    AF_THROW(af_confidence_cc(&temp, in.get(), xcoords.get(), ycoords.get(), radius,
+                              multiplier, iter, segmentedValue));
+    return array(temp);
+}
+
+array confidenceCC(const array &in, const array &seedx, const array &seedy,
+                   const unsigned radius, const unsigned multiplier,
+                   const int iter, const double segmentedValue) {
+    af_array temp = 0;
+    AF_THROW(af_confidence_cc(&temp, in.get(), seedx.get(), seedy.get(), radius,
+                              multiplier, iter, segmentedValue));
+    return array(temp);
+}
+
+} // namespace af
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 47a3e601bc..0b079e1ab0 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -274,3 +274,12 @@ af_err af_inverse_deconv(af_array *out, const af_array in, const af_array psf,
     CHECK_ARRAYS(in, psf);
       CALL(af_inverse_deconv, out, in, psf, gamma, algo);
 }
+
+af_err af_confidence_cc(af_array *out, const af_array in, const af_array seedx,
+                        const af_array seedy, const unsigned radius,
+                        const unsigned multiplier, const int iter,
+                        const double segmented_value) {
+    CHECK_ARRAYS(in, seedx, seedy);
+    CALL(af_confidence_cc, out, in, seedx, seedy, radius, multiplier, iter,
+         segmented_value);
+}
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 973bf426c9..bdd205bca9 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -54,6 +54,8 @@ target_sources(afcpu
     fft.hpp
     fftconvolve.cpp
     fftconvolve.hpp
+    flood_fill.hpp
+    flood_fill.cpp
     gradient.cpp
     gradient.hpp
     harris.cpp
@@ -207,6 +209,7 @@ target_sources(afcpu
     kernel/exampleFunction.hpp
     kernel/fast.hpp
     kernel/fftconvolve.hpp
+    kernel/flood_fill.hpp
     kernel/gradient.hpp
     kernel/harris.hpp
     kernel/histogram.hpp
diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
index 2e750127ec..15e85d3249 100644
--- a/src/backend/cpu/ParamIterator.hpp
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -14,36 +14,24 @@
 #include <array>
 #include <cstddef>
 #include <iterator>
+#include <vector>
 
 namespace cpu {
 
+/// Calculates the iterator offsets.
+///
+/// These are different from the original offsets because they define
+/// the stride from the end of the last element in the previous dimension
+/// to the first element on the next dimension.
+static dim4 calcIteratorStrides(const dim4& dims, const dim4& stride) noexcept {
+    return dim4(stride[0], stride[1] - (stride[0] * dims[0]),
+                stride[2] - (stride[1] * dims[1]),
+                stride[3] - (stride[2] * dims[2]));
+}
+
 /// A Param iterator that iterates through a Param object
 template<typename T>
 class ParamIterator {
-    T* ptr;
-
-    // NOTE: This is not really the true coordinate of the iteration. It's
-    // values will go down as you move through the array.
-    std::array<dim_t, AF_MAX_DIMS> dim_index;
-
-    // The dimension of the array
-    const af::dim4 dims;
-
-    // The iterator's stride
-    const af::dim4 stride;
-
-    /// Calculates the iterator offsets. These are different from the original
-    /// offsets because they define the stride from the end of the last element
-    /// in the previous dimension to the first element on the next dimension.
-    static dim4 calculate_iterator_stride(const dim4& dims,
-                                          const dim4& stride) noexcept {
-        dim4 out(stride[0], stride[1] - (stride[0] * dims[0]),
-                 stride[2] - (stride[1] * dims[1]),
-                 stride[3] - (stride[2] * dims[2]));
-
-        return out;
-    }
-
    public:
     using difference_type   = ptrdiff_t;
     using value_type        = T;
@@ -54,22 +42,22 @@ class ParamIterator {
     /// Creates a sentinel iterator. This is equivalent to the end iterator
     ParamIterator() noexcept
         : ptr(nullptr)
-        , dim_index{dims[0], dims[1], dims[2], dims[3]}
         , dims(1)
-        , stride(1) {}
+        , stride(1)
+        , dim_index{dims[0], dims[1], dims[2], dims[3]} {}
 
     /// ParamIterator Constructor
     ParamIterator(cpu::Param<T>& in) noexcept
         : ptr(in.get())
-        , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]}
         , dims(in.dims())
-        , stride(calculate_iterator_stride(dims, in.strides())) {}
+        , stride(calcIteratorStrides(dims, in.strides()))
+        , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]} {}
 
     ParamIterator(cpu::CParam<typename std::remove_const<T>::type>& in) noexcept
         : ptr(in.get())
-        , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]}
         , dims(in.dims())
-        , stride(calculate_iterator_stride(dims, in.strides())) {}
+        , stride(calcIteratorStrides(dims, in.strides()))
+        , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]} {}
 
     /// The equality operator
     bool operator==(const ParamIterator& other) const noexcept {
@@ -81,7 +69,7 @@ class ParamIterator {
         return ptr != other.ptr;
     }
 
-    /// Advances the iterator
+    /// Advances the iterator, pre increment operator
     ParamIterator& operator++() noexcept {
         for (int i = 0; i < AF_MAX_DIMS; i++) {
             dim_index[i]--;
@@ -93,13 +81,6 @@ class ParamIterator {
         return *this;
     }
 
-    /// @copydoc operator++()
-    ParamIterator& operator++(int) noexcept {
-        ParamIterator before(*this);
-        operator++();
-        return before;
-    }
-
     /// Advances the iterator by count elements
     ParamIterator& operator+=(std::size_t count) noexcept {
         while (count-- > 0) { operator++(); }
@@ -116,6 +97,19 @@ class ParamIterator {
     ParamIterator<T>& operator=(const ParamIterator<T>& other) noexcept =
         default;
     ParamIterator<T>& operator=(ParamIterator<T>&& other) noexcept = default;
+
+   private:
+    T* ptr;
+
+    // The dimension of the array
+    const af::dim4 dims;
+
+    // The iterator's stride
+    const af::dim4 stride;
+
+    // NOTE: This is not really the true coordinate of the iteration. It's
+    // values will go down as you move through the array.
+    std::array<dim_t, AF_MAX_DIMS> dim_index;
 };
 
 template<typename T>
@@ -138,4 +132,158 @@ ParamIterator<const T> end(CParam<T>& param) {
     return ParamIterator<const T>();
 }
 
+/// Neighborhood iterator for Param data
+template<typename T>
+class NeighborhoodIterator {
+   public:
+    using difference_type   = ptrdiff_t;
+    using value_type        = T;
+    using pointer           = T*;
+    using reference         = T&;
+    using iterator_category = std::forward_iterator_tag;
+
+    using Self = NeighborhoodIterator;
+
+    /// Creates a sentinel iterator. This is equivalent to the end iterator
+    NeighborhoodIterator() noexcept
+        : nhoodRadius(0, 0, 0, 0)
+        , origDims(1)
+        , origStrides(1)
+        , iterDims(1)
+        , iterStrides(1)
+        , origPtr(nullptr)
+        , ptr(origPtr)
+        , nhoodIndex(0) {
+        calcOffsets();
+    }
+
+    /// NeighborhoodIterator Constructor
+    NeighborhoodIterator(cpu::Param<T>& in, const af::dim4 _radius) noexcept
+        : nhoodRadius(_radius)
+        , origDims(nhoodSize(nhoodRadius))
+        , origStrides(in.strides())
+        , iterDims(origDims)
+        , iterStrides(calcIteratorStrides(origDims, in.strides()))
+        , origPtr(in.get())
+        , ptr(origPtr)
+        , nhoodIndex(0) {
+        calcOffsets();
+    }
+
+    /// NeighborhoodIterator Constructor
+    NeighborhoodIterator(cpu::CParam<typename std::remove_const<T>::type>& in,
+                         const af::dim4 _radius) noexcept
+        : nhoodRadius(_radius)
+        , origDims(nhoodSize(nhoodRadius))
+        , origStrides(in.strides())
+        , iterDims(origDims)
+        , iterStrides(calcIteratorStrides(origDims, in.strides()))
+        , origPtr(const_cast<T*>(in.get()))
+        , ptr(origPtr)
+        , nhoodIndex(0) {
+        calcOffsets();
+    }
+
+    /// The equality operator
+    bool operator==(const Self& other) const noexcept {
+        return ptr == other.ptr;
+    }
+
+    /// The inequality operator
+    bool operator!=(const Self& other) const noexcept {
+        return ptr != other.ptr;
+    }
+
+    /// Set neighborhood center
+    ///
+    /// This method automatically resets iterator to starting point
+    /// of the neighborhood around the set center point
+    void setCenter(const af::dim4 center) noexcept {
+        ptr = origPtr;
+        for (dim_t d = 0; d < AF_MAX_DIMS; ++d) {
+            ptr += ((center[d] - nhoodRadius[d]) * origStrides[d]);
+        }
+        nhoodIndex = 0;
+    }
+
+    /// Advances the iterator, pre increment operator
+    Self& operator++() noexcept {
+        nhoodIndex++;
+        for (dim_t i = 0; i < AF_MAX_DIMS; i++) {
+            iterDims[i]--;
+            ptr += iterStrides[i];
+            if (iterDims[i]) { return *this; }
+            iterDims[i] = origDims[i];
+        }
+        ptr = nullptr;
+        return *this;
+    }
+
+    /// @copydoc operator++()
+    Self operator++(int) noexcept {
+        Self before(*this);
+        operator++();
+        return before;
+    }
+
+    reference operator*() const noexcept { return *ptr; }
+    pointer operator->() const noexcept { return ptr; }
+
+    /// Gets offsets of current position from center
+    const af::dim4 offset() const noexcept {
+        if (ptr) {
+            // Branch predictor almost always is a hit since,
+            // NeighborhoodIterator::offset is called only when iterator is
+            // valid i.e. it is not equal to END iterator
+            return offsets[nhoodIndex];
+        } else {
+            return af::dim4(0, 0, 0, 0);
+        }
+    }
+
+    NeighborhoodIterator(const NeighborhoodIterator<T>& other) = default;
+    NeighborhoodIterator(NeighborhoodIterator<T>&& other) = default;
+    ~NeighborhoodIterator() noexcept = default;
+    NeighborhoodIterator<T>& operator=(const Self& other) = default;
+    NeighborhoodIterator<T>& operator=(Self&& other) = default;
+
+   private:
+    const af::dim4 nhoodRadius;
+    const af::dim4 origDims;
+    const af::dim4 origStrides;
+    af::dim4 iterDims;
+    af::dim4 iterStrides;
+    pointer origPtr;
+    pointer ptr;
+    dim_t nhoodIndex;
+    std::vector<af::dim4> offsets;
+
+    af::dim4 nhoodSize(const af::dim4& radius) const noexcept {
+        return af::dim4(2 * radius[0] + 1, 2 * radius[1] + 1, 2 * radius[2] + 1,
+                        2 * radius[3] + 1);
+    }
+
+    void calcOffsets() noexcept {
+        auto linear2Coords = [this](const dim_t index) -> af::dim4 {
+            af::dim4 coords(0, 0, 0, 0);
+            for (dim_t i = 0, idx = index; i < AF_MAX_DIMS;
+                 ++i, idx /= origDims[i]) {
+                coords[i] = idx % origDims[i];
+            }
+            return coords;
+        };
+
+        offsets.clear();
+        size_t nElems = (2 * nhoodRadius[0] + 1) * (2 * nhoodRadius[1] + 1) *
+                        (2 * nhoodRadius[2] + 1) * (2 * nhoodRadius[3] + 1);
+        offsets.reserve(nElems);
+        for (size_t i = 0; i < nElems; ++i) {
+            auto coords = linear2Coords(i);
+            offsets.emplace_back(
+                coords[0] - nhoodRadius[0], coords[1] - nhoodRadius[1],
+                coords[2] - nhoodRadius[2], coords[3] - nhoodRadius[3]);
+        }
+    }
+};
+
 }  // namespace cpu
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 16c33c9100..7a095fc6bc 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <err_cpu.hpp>
 #include <jit/BinaryNode.hpp>
diff --git a/src/backend/cpu/flood_fill.cpp b/src/backend/cpu/flood_fill.cpp
new file mode 100644
index 0000000000..fc8830f08e
--- /dev/null
+++ b/src/backend/cpu/flood_fill.cpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <flood_fill.hpp>
+
+#include <err_cpu.hpp>
+#include <kernel/flood_fill.hpp>
+
+using af::connectivity;
+using af::dim4;
+
+namespace cpu {
+
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup) {
+    auto out = createValueArray(image.dims(), T(0));
+    getQueue().enqueue(kernel::floodFill<T>, out, image, seedsX, seedsY,
+                       newValue, lowValue, highValue, nlookup);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                    \
+    template Array<T> floodFill(                                          \
+        const Array<T>&, const Array<uint>&, const Array<uint>&, const T, \
+        const T, const T, const af::connectivity);
+
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/flood_fill.hpp b/src/backend/cpu/flood_fill.hpp
new file mode 100644
index 0000000000..8bd4623328
--- /dev/null
+++ b/src/backend/cpu/flood_fill.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <af/defines.h>
+
+namespace cpu {
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup = AF_CONNECTIVITY_8);
+}  // namespace cpu
diff --git a/src/backend/cpu/kernel/flood_fill.hpp b/src/backend/cpu/kernel/flood_fill.hpp
new file mode 100644
index 0000000000..1a0ef86ee0
--- /dev/null
+++ b/src/backend/cpu/kernel/flood_fill.hpp
@@ -0,0 +1,103 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <ParamIterator.hpp>
+#include <common/defines.hpp>
+
+#include <queue>
+#include <utility>
+
+namespace cpu {
+namespace kernel {
+
+// Output array is set to the following values during the progression
+// of the algorithm.
+//
+// 0 - not visited at all (default values in output because it was created
+//                         using createValueArray helper at level of the
+//                         functions caller)
+// 1 - not valid
+// 2 - valid (candidate for neighborhood walk, pushed onto the queue)
+//
+// Once, the algorithm is finished, output is reset
+// to either zero or \p newValue for all valid pixels.
+template<typename T>
+void floodFill(Param<T> out, CParam<T> in, CParam<uint> x, CParam<uint> y,
+               T newValue, T lower, T upper, af::connectivity connectivity) {
+    UNUSED(connectivity);
+
+    using af::dim4;
+    using Point      = std::pair<uint, uint>;
+    using Candidates = std::queue<Point>;
+
+    const size_t numSeeds = x.dims().elements();
+    const dim4 inDims     = in.dims();
+
+    auto isInside = [&inDims](uint x, uint y) -> bool {
+        return (x >= 0 && x < inDims[0] && y >= 0 && y < inDims[1]);
+    };
+
+    Candidates queue;
+    {
+        auto oit = begin(out);
+        for (auto xit = begin(x), yit = begin(y);
+             xit != end(x) && yit != end(y); ++xit, ++yit) {
+            if (isInside(*xit, *yit)) {
+                queue.emplace(*xit, *yit);
+                oit.operator->()[(*xit) + (*yit) * inDims[0]] = T(2);
+            }
+        }
+    }
+
+    NeighborhoodIterator<T> inNeighborhood(in, dim4(1, 1, 0, 0));
+    NeighborhoodIterator<T> endOfNeighborhood;
+    NeighborhoodIterator<T> outNeighborhood(out, dim4(1, 1, 0, 0));
+
+    while (!queue.empty()) {
+        auto p = queue.front();
+
+        inNeighborhood.setCenter(dim4(p.first, p.second, 0, 0));
+        outNeighborhood.setCenter(dim4(p.first, p.second, 0, 0));
+
+        while (inNeighborhood != endOfNeighborhood) {
+            const dim4 offsetP = inNeighborhood.offset();
+            const uint currx   = static_cast<uint>(p.first + offsetP[0]);
+            const uint curry   = static_cast<uint>(p.second + offsetP[1]);
+
+            if (isInside(currx, curry) && (*outNeighborhood == 0)) {
+                // Current point is inside image boundaries and hasn't been
+                // visited at all.
+                if (*inNeighborhood >= lower && *inNeighborhood <= upper) {
+                    // Current pixel is within threshold limits.
+                    // Mark as valid and push on to the queue
+                    *outNeighborhood = T(2);
+                    queue.emplace(currx, curry);
+                } else {
+                    // Not valid pixel
+                    *outNeighborhood = T(1);
+                }
+            }
+            // Both input and output neighborhood iterators
+            // should increment in lock step for this algorithm
+            // to work correctly
+            ++inNeighborhood;
+            ++outNeighborhood;
+        }
+        queue.pop();
+    }
+
+    for (auto outIter = begin(out); outIter != end(out); ++outIter) {
+        *outIter = (*outIter == T(2) ? newValue : T(0));
+    }
+}
+
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b8956bbc17..05850ff342 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -105,6 +105,7 @@ set(nvrtc_src
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve3.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve_separable.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/exampleFunction.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/flood_fill.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/histogram.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/hsv_rgb.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/match_template.cuh
@@ -295,6 +296,7 @@ cuda_add_library(afcuda
     kernel/fast_lut.hpp
     kernel/fast_pyramid.hpp
     kernel/fftconvolve.hpp
+    kernel/flood_fill.hpp
     kernel/gradient.hpp
     kernel/harris.hpp
     kernel/histogram.hpp
@@ -404,6 +406,8 @@ cuda_add_library(afcuda
     fft.cpp
     fft.hpp
     fftconvolve.hpp
+    flood_fill.cpp
+    flood_fill.hpp
     GraphicsResourceManager.cpp
     GraphicsResourceManager.hpp
     gradient.hpp
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index 8aa453ceb5..b245d2df71 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <binary.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/cuda/flood_fill.cpp b/src/backend/cuda/flood_fill.cpp
new file mode 100644
index 0000000000..ba7657182b
--- /dev/null
+++ b/src/backend/cuda/flood_fill.cpp
@@ -0,0 +1,38 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <flood_fill.hpp>
+
+#include <err_cuda.hpp>
+#include <kernel/flood_fill.hpp>
+
+namespace cuda {
+
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup) {
+    auto out = createValueArray(image.dims(), T(0));
+    kernel::floodFill<T>(out, image, seedsX, seedsY, newValue,
+                         lowValue, highValue, nlookup);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                    \
+    template Array<T> floodFill(                                          \
+        const Array<T>&, const Array<uint>&, const Array<uint>&, const T, \
+        const T, const T, const af::connectivity);
+
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/flood_fill.hpp b/src/backend/cuda/flood_fill.hpp
new file mode 100644
index 0000000000..b4d432feec
--- /dev/null
+++ b/src/backend/cuda/flood_fill.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <af/defines.h>
+
+namespace cuda {
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup = AF_CONNECTIVITY_8);
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/flood_fill.cuh b/src/backend/cuda/kernel/flood_fill.cuh
new file mode 100644
index 0000000000..bab68916ec
--- /dev/null
+++ b/src/backend/cuda/kernel/flood_fill.cuh
@@ -0,0 +1,140 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <af/defines.h>
+#include <math.hpp>
+
+/// doAnotherLaunch is a variable in kernel space
+/// used to track the convergence of
+/// the breath first search algorithm
+__device__ int doAnotherLaunch = 0;
+
+namespace cuda {
+
+/// Output array is set to the following values during the progression
+/// of the algorithm.
+///
+/// 0 - not processed
+/// 1 - not valid
+/// 2 - valid (candidate for neighborhood walk, pushed onto the queue)
+///
+/// Once, the algorithm is finished, output is reset
+/// to either zero or \p newValue for all valid pixels.
+template<typename T> constexpr T VALID() { return T(2); }
+template<typename T> constexpr T INVALID() { return T(1); }
+template<typename T> constexpr T ZERO() { return T(0); }
+
+template<typename T>
+__global__
+void initSeeds(Param<T> out, CParam<uint> seedsx, CParam<uint> seedsy) {
+    uint idx = blockDim.x * blockIdx.x + threadIdx.x;
+    if (idx < seedsx.elements()) {
+        uint x = seedsx.ptr[ idx ];
+        uint y = seedsy.ptr[ idx ];
+        out.ptr[ x + y * out.dims[0] ] = VALID<T>();
+    }
+}
+
+template<typename T>
+__global__
+void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
+    constexpr int RADIUS      = 1;
+    constexpr int SMEM_WIDTH  = THREADS_X + 2 * RADIUS;
+    constexpr int SMEM_HEIGHT = THREADS_Y + 2 * RADIUS;
+
+    __shared__ T smem[SMEM_HEIGHT][SMEM_WIDTH];
+
+    const int lx = threadIdx.x;
+    const int ly = threadIdx.y;
+    const int gx = blockDim.x * blockIdx.x + lx;
+    const int gy = blockDim.y * blockIdx.y + ly;
+    const int d0 = out.dims[0];
+    const int d1 = out.dims[1];
+    const int s0 = out.strides[0];
+    const int s1 = out.strides[1];
+
+    const T *iptr = (const T *)img.ptr;
+          T *optr = (T *)out.ptr;
+#pragma unroll
+    for (int b = ly, gy2 = gy; b < SMEM_HEIGHT;
+         b += blockDim.y, gy2 += blockDim.y) {
+#pragma unroll
+        for (int a = lx, gx2 = gx; a < SMEM_WIDTH;
+             a += blockDim.x, gx2 += blockDim.x) {
+            int x      = gx2 - RADIUS;
+            int y      = gy2 - RADIUS;
+            bool inROI = (x >= 0 && x < d0 && y >= 0 && y < d1);
+            smem[b][a] = (inROI ? optr[ x*s0+y*s1 ] : INVALID<T>());
+        }
+    }
+    int i = lx + RADIUS;
+    int j = ly + RADIUS;
+
+    T tImgVal = iptr[(clamp(gx, 0, int(img.dims[0]-1)) * img.strides[0] +
+                      clamp(gy, 0, int(img.dims[1]-1)) * img.strides[1])];
+    const int isPxBtwnThresholds =
+        (tImgVal >= lowValue && tImgVal <= highValue);
+    __syncthreads();
+
+    T origOutVal      = smem[j][i];
+    bool blockChanged = false;
+    bool isBorderPxl  = (lx == 0 || ly == 0 || lx == (blockDim.x - 1) ||
+                         ly == (blockDim.y - 1));
+    do {
+        int validNeighbors = 0;
+#pragma unroll
+        for (int no_j = -RADIUS; no_j <= RADIUS; ++no_j) {
+#pragma unroll
+            for (int no_i = -RADIUS; no_i <= RADIUS; ++no_i) {
+                T currVal = smem[j + no_j][i + no_i];
+                validNeighbors += (currVal == VALID<T>());
+            }
+        }
+        __syncthreads();
+
+        bool outChanged = (smem[j][i] == ZERO<T>() && (validNeighbors > 0));
+        if (outChanged) {
+            smem[j][i] = T(isPxBtwnThresholds + INVALID<T>());
+        }
+        blockChanged = __syncthreads_or(int(outChanged));
+    } while (blockChanged);
+
+    T newOutVal = smem[j][i];
+
+    bool borderChanged = (isBorderPxl &&
+                          newOutVal != origOutVal && newOutVal == VALID<T>());
+
+    borderChanged = __syncthreads_or(int(borderChanged));
+
+    if (borderChanged && lx == 0 && ly == 0) {
+        // Atleast one border pixel changed. Therefore, mark for
+        // another kernel launch to propogate changes beyond border
+        // of this block
+        doAnotherLaunch = 1;
+    }
+
+    if (gx < d0 && gy < d1) {
+        optr[ (gx*s0 + gy*s1) ] = smem[j][i];
+    }
+}
+
+template<typename T>
+__global__
+void finalizeOutput(Param<T> out, T newValue) {
+    uint gx = blockDim.x * blockIdx.x + threadIdx.x;
+    uint gy = blockDim.y * blockIdx.y + threadIdx.y;
+    if (gx < out.dims[0] && gy < out.dims[1]) {
+        uint idx = gx * out.strides[0] + gy * out.strides[1];
+        T val = out.ptr[idx];
+        out.ptr[idx] = (val == VALID<T>() ? newValue : ZERO<T>());
+    }
+}
+
+} // namespace cuda
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
new file mode 100644
index 0000000000..f1da489ace
--- /dev/null
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -0,0 +1,82 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/defines.hpp>
+#include <common/dispatch.hpp>
+#include <debug_cuda.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/flood_fill_cuh.hpp>
+
+#include <string>
+
+namespace cuda {
+namespace kernel {
+
+constexpr int THREADS   = 256;
+constexpr int TILE_DIM  = 32;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = THREADS / TILE_DIM;
+
+// Shared memory per block required by floodFill kernel
+template<typename T>
+constexpr size_t sharedMemRequiredByFloodFill() {
+    // 1-pixel border neighborhood
+    return sizeof(T) * ((THREADS_X + 2) * (THREADS_Y + 2));
+}
+
+template<typename T>
+void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
+               CParam<uint> seedsy, const T newValue, const T lowValue,
+               const T highValue, const af::connectivity nlookup) {
+    UNUSED(nlookup);
+    static const std::string source(flood_fill_cuh, flood_fill_cuh_len);
+
+    if (sharedMemRequiredByFloodFill<T>() >
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nCurrent thread's CUDA device doesn't have sufficient "
+                 "shared memory required by FloodFill\n");
+        CUDA_NOT_SUPPORTED(errMessage);
+    }
+
+    auto initSeeds = getKernel("cuda::initSeeds", source,
+            {TemplateTypename<T>()});
+    auto floodStep = getKernel("cuda::floodStep", source,
+            {TemplateTypename<T>()},
+            {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto finalizeOutput = getKernel("cuda::finalizeOutput", source,
+            {TemplateTypename<T>()});
+
+    EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)),
+                      dim3(THREADS), getActiveStream());
+    initSeeds(qArgs, out, seedsx, seedsy);
+    POST_LAUNCH_CHECK();
+
+    dim3 threads(THREADS_X, THREADS_Y);
+    dim3 blocks(divup(image.dims[0], threads.x),
+                divup(image.dims[1], threads.y));
+    EnqueueArgs fQArgs(blocks, threads, getActiveStream());
+
+    for (int doAnotherLaunch = 1; doAnotherLaunch > 0;) {
+        doAnotherLaunch = 0;
+        floodStep.setScalar("doAnotherLaunch", doAnotherLaunch);
+        floodStep(fQArgs, out, image, lowValue, highValue);
+        POST_LAUNCH_CHECK();
+        floodStep.getScalar(doAnotherLaunch, "doAnotherLaunch");
+    }
+    finalizeOutput(fQArgs, out, newValue);
+    POST_LAUNCH_CHECK();
+}
+
+}  // namespace kernel
+}  // namespace cuda
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 513959384d..b2cb7157f2 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -106,6 +106,8 @@ target_sources(afopencl
     fft.hpp
     fftconvolve.cpp
     fftconvolve.hpp
+    flood_fill.cpp
+    flood_fill.hpp
     GraphicsResourceManager.cpp
     GraphicsResourceManager.hpp
     gradient.cpp
@@ -273,6 +275,7 @@ target_sources(afopencl
     kernel/exampleFunction.hpp
     kernel/fast.hpp
     kernel/fftconvolve.hpp
+    kernel/flood_fill.hpp
     kernel/gradient.hpp
     kernel/harris.hpp
     kernel/histogram.hpp
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index 3c1e68d7e5..edc4749e35 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <binary.hpp>
 #include <optypes.hpp>
diff --git a/src/backend/opencl/flood_fill.cpp b/src/backend/opencl/flood_fill.cpp
new file mode 100644
index 0000000000..8a2e5da71c
--- /dev/null
+++ b/src/backend/opencl/flood_fill.cpp
@@ -0,0 +1,38 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <flood_fill.hpp>
+
+#include <err_opencl.hpp>
+#include <kernel/flood_fill.hpp>
+
+namespace opencl {
+
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup) {
+    auto out = createValueArray(image.dims(), T(0));
+    kernel::floodFill<T>(out, image, seedsX, seedsY, newValue,
+                         lowValue, highValue, nlookup);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                         \
+    template Array<T> floodFill(const Array<T>&, const Array<uint>&,           \
+                                const Array<uint>&, const T, const T, const T, \
+                                const af::connectivity);
+
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/flood_fill.hpp b/src/backend/opencl/flood_fill.hpp
new file mode 100644
index 0000000000..0cdea7fd62
--- /dev/null
+++ b/src/backend/opencl/flood_fill.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <af/defines.h>
+
+namespace opencl {
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup = AF_CONNECTIVITY_8);
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/flood_fill.cl b/src/backend/opencl/kernel/flood_fill.cl
new file mode 100644
index 0000000000..b74d4494c2
--- /dev/null
+++ b/src/backend/opencl/kernel/flood_fill.cl
@@ -0,0 +1,136 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/// Output array is set to the following values during the progression
+/// of the algorithm.
+///
+/// 0 - not processed
+/// 1 - not valid
+/// 2 - valid (candidate for neighborhood walk, pushed onto the queue)
+///
+/// Once, the algorithm is finished, output is reset
+/// to either zero or \p newValue for all valid pixels.
+
+#if defined(INIT_SEEDS)
+kernel
+void init_seeds(global T *out, KParam oInfo,
+                global const uint *seedsx, KParam sxInfo,
+                global const uint *seedsy, KParam syInfo) {
+    uint tid = get_global_id(0);
+    if (tid < sxInfo.dims[0]) {
+        uint x = seedsx[ tid ];
+        uint y = seedsy[ tid ];
+        out[ (x * oInfo.strides[0] + y * oInfo.strides[1]) ] = VALID;
+    }
+}
+#endif
+
+#if defined(FLOOD_FILL_STEP)
+
+int barrierOR(local int *predicates) {
+    int tid = get_local_id(0) + get_local_size(0) * get_local_id(1);
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (int nt = GROUP_SIZE / 2; nt > 0; nt >>= 1) {
+        if (tid < nt) {
+            predicates[tid] = (predicates[tid] | predicates[tid + nt]);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+    return predicates[0];
+}
+
+kernel
+void flood_step(global T *out, KParam oInfo, global const T *img, KParam iInfo,
+                T lowValue, T highValue, global volatile int *notFinished) {
+    local T lmem[LMEM_HEIGHT][LMEM_WIDTH];
+    local int predicates[GROUP_SIZE];
+
+    const int lx = get_local_id(0);
+    const int ly = get_local_id(1);
+    const int gx = get_global_id(0);
+    const int gy = get_global_id(1);
+    const int d0 = oInfo.dims[0];
+    const int d1 = oInfo.dims[1];
+    const int s0 = oInfo.strides[0];
+    const int s1 = oInfo.strides[1];
+
+    for (int b = ly, gy2 = gy; b < LMEM_HEIGHT;
+         b += get_local_size(1), gy2 += get_local_size(1)) {
+        for (int a = lx, gx2 = gx; a < LMEM_WIDTH;
+             a += get_local_size(0), gx2 += get_local_size(0)) {
+            int x      = gx2 - RADIUS;
+            int y      = gy2 - RADIUS;
+            bool inROI = (x >= 0 && x < d0 && y >= 0 && y < d1);
+            lmem[b][a] = (inROI ? out[ x*s0+y*s1 ] : INVALID);
+        }
+    }
+    int i = lx + RADIUS;
+    int j = ly + RADIUS;
+
+    T tImgVal = img[(clamp(gx, 0, (int)(iInfo.dims[0]-1)) * iInfo.strides[0] +
+                     clamp(gy, 0, (int)(iInfo.dims[1]-1)) * iInfo.strides[1])];
+    const int isPxBtwnThresholds =
+        (tImgVal >= lowValue && tImgVal <= highValue);
+
+    int tid = lx + get_local_size(0) * ly;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    T origOutVal     = lmem[j][i];
+    bool isBorderPxl = (lx == 0 || ly == 0 ||
+                        lx == (get_local_size(0) - 1) ||
+                        ly == (get_local_size(1) - 1));
+
+    for (bool blkChngd = true; blkChngd; blkChngd = barrierOR(predicates)) {
+        int validNeighbors = 0;
+        for (int no_j = -RADIUS; no_j <= RADIUS; ++no_j) {
+            for (int no_i = -RADIUS; no_i <= RADIUS; ++no_i) {
+                T currVal = lmem[j + no_j][i + no_i];
+                validNeighbors += (currVal == VALID);
+            }
+        }
+        bool outChanged = (lmem[j][i] == ZERO && (validNeighbors > 0));
+        predicates[tid] = outChanged;
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if (outChanged) { lmem[j][i] = (T)(isPxBtwnThresholds + INVALID); }
+    }
+
+    T newOutVal = lmem[j][i];
+
+    bool brdrChngd  = (isBorderPxl &&
+                       newOutVal != origOutVal && newOutVal == VALID);
+    predicates[tid] = brdrChngd;
+
+    brdrChngd = barrierOR(predicates) > 0;
+
+    if (gx < d0 && gy < d1) {
+        if (brdrChngd && lx == 0 && ly == 0) {
+            // Atleast one border pixel changed. Therefore, mark for
+            // another kernel launch to propogate changes beyond border
+            // of this block
+            atomic_inc(notFinished);
+        }
+        out[ (gx*s0 + gy*s1) ] = lmem[j][i];
+    }
+}
+#endif
+
+#if defined(FINALIZE_OUTPUT)
+kernel
+void finalize_output(global T* out, KParam oInfo, T newValue) {
+    uint gx = get_global_id(0);
+    uint gy = get_global_id(1);
+    if (gx < oInfo.dims[0] && gy < oInfo.dims[1]) {
+        uint idx = gx * oInfo.strides[0] + gy * oInfo.strides[1];
+        T val = out[idx];
+        out[idx] = (val == VALID ? newValue : ZERO);
+    }
+}
+#endif
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
new file mode 100644
index 0000000000..f5e417ba23
--- /dev/null
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -0,0 +1,174 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <cache.hpp>
+#include <common/dispatch.hpp>
+#include <debug_opencl.hpp>
+#include <kernel_headers/flood_fill.hpp>
+#include <memory.hpp>
+#include <program.hpp>
+#include <traits.hpp>
+#include <type_util.hpp>
+
+using cl::Buffer;
+using cl::EnqueueArgs;
+using cl::Kernel;
+using cl::KernelFunctor;
+using cl::NDRange;
+using cl::Program;
+using std::string;
+
+namespace opencl {
+namespace kernel {
+
+constexpr int THREADS   = 256;
+constexpr int TILE_DIM  = 16;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = THREADS / TILE_DIM;
+constexpr int VALID     = 2;
+constexpr int INVALID   = 1;
+constexpr int ZERO      = 0;
+
+template<typename T>
+void initSeeds(Param out, const Param seedsx, const Param seedsy) {
+    std::string refName = std::string("init_seeds_") +
+                          std::string(dtype_traits<T>::getName());
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D VALID=" << T(VALID)
+                << " -D INIT_SEEDS";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {flood_fill_cl};
+        const int ker_lens[]   = {flood_fill_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "init_seeds");
+        addKernelToCache(device, refName, entry);
+    }
+    auto initSeedsOp = KernelFunctor<Buffer, const KParam,
+                                     const Buffer, const KParam,
+                                     const Buffer, const KParam>(*entry.ker);
+    NDRange local(kernel::THREADS, 1, 1);
+    NDRange global( divup(seedsx.info.dims[0], local[0]) * local[0], 1 , 1);
+
+    initSeedsOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *seedsx.data, seedsx.info, *seedsy.data, seedsy.info);
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void finalizeOutput(Param out, const T newValue) {
+    std::string refName = std::string("finalize_output_") +
+                          std::string(dtype_traits<T>::getName());
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D VALID=" << T(VALID)
+                << " -D ZERO=" << T(ZERO)
+                << " -D FINALIZE_OUTPUT";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {flood_fill_cl};
+        const int ker_lens[]   = {flood_fill_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "finalize_output");
+        addKernelToCache(device, refName, entry);
+    }
+
+    auto finalizeOut = KernelFunctor<Buffer, const KParam, const T>(*entry.ker);
+
+    NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
+    NDRange global( divup(out.info.dims[0], local[0]) * local[0],
+                    divup(out.info.dims[1], local[1]) * local[1] ,
+                    1);
+    finalizeOut(EnqueueArgs(getQueue(), global, local),
+                *out.data, out.info, newValue);
+    CL_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void floodFill(Param out, const Param image, const Param seedsx,
+               const Param seedsy, const T newValue, const T lowValue,
+               const T highValue, const af::connectivity nlookup) {
+    constexpr int RADIUS = 1;
+    UNUSED(nlookup);
+    std::string refName = std::string("flood_step_") +
+                          std::string(dtype_traits<T>::getName());
+    int device       = getActiveDeviceId();
+    kc_entry_t entry = kernelCache(device, refName);
+
+    if (entry.prog == 0 && entry.ker == 0) {
+        std::ostringstream options;
+        options << " -D T=" << dtype_traits<T>::getName()
+                << " -D RADIUS=" << RADIUS
+                << " -D LMEM_WIDTH=" << (THREADS_X + 2 * RADIUS)
+                << " -D LMEM_HEIGHT=" << (THREADS_Y + 2 * RADIUS)
+                << " -D GROUP_SIZE=" << (THREADS_Y * THREADS_X)
+                << " -D VALID=" << T(VALID)
+                << " -D INVALID=" << T(INVALID)
+                << " -D ZERO=" << T(ZERO)
+                << " -D FLOOD_FILL_STEP";
+        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+
+        const char *ker_strs[] = {flood_fill_cl};
+        const int ker_lens[]   = {flood_fill_cl_len};
+        Program prog;
+        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
+        entry.prog = new Program(prog);
+        entry.ker  = new Kernel(*entry.prog, "flood_step");
+
+        addKernelToCache(device, refName, entry);
+    }
+    auto floodStep = KernelFunctor<Buffer, const KParam,
+                                   const Buffer, const KParam,
+                                   const T, const T, Buffer>(*entry.ker);
+    NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
+    NDRange global( divup(out.info.dims[0], local[0]) * local[0],
+                    divup(out.info.dims[1], local[1]) * local[1] ,
+                    1);
+
+    initSeeds<T>(out, seedsx, seedsy);
+
+    int notFinished       = 1;
+    cl::Buffer *dContinue = bufferAlloc(sizeof(int));
+
+    while (notFinished) {
+        notFinished = 0;
+        getQueue().enqueueWriteBuffer(*dContinue, CL_TRUE, 0, sizeof(int),
+                                      &notFinished);
+
+        floodStep(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                  *image.data, image.info, lowValue, highValue, *dContinue);
+        CL_DEBUG_FINISH(getQueue());
+
+        getQueue().enqueueReadBuffer(*dContinue, CL_TRUE, 0, sizeof(int),
+                                     &notFinished);
+    }
+
+    bufferFree(dContinue);
+
+    finalizeOutput<T>(out, newValue);
+}
+
+}
+}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b9f6f81792..0d3e4580bf 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -191,6 +191,7 @@ make_test(SRC cholesky_dense.cpp)
 make_test(SRC clamp.cpp)
 make_test(SRC compare.cpp)
 make_test(SRC complex.cpp)
+make_test(SRC confidence_connected.cpp CXX11)
 make_test(SRC constant.cpp)
 make_test(SRC convolve.cpp CXX11)
 make_test(SRC corrcoef.cpp)
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
new file mode 100644
index 0000000000..2c046fe193
--- /dev/null
+++ b/test/confidence_connected.cpp
@@ -0,0 +1,206 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/traits.hpp>
+
+#include <string>
+#include <sstream>
+#include <vector>
+
+using af::dim4;
+using std::abs;
+using std::string;
+using std::to_string;
+using std::stringstream;
+using std::vector;
+
+template<typename T>
+class ConfidenceConnectedImageTest : public testing::Test {
+   public:
+    virtual void SetUp() {}
+};
+
+typedef ::testing::Types<float, uint, ushort, uchar> TestTypes;
+
+TYPED_TEST_CASE(ConfidenceConnectedImageTest, TestTypes);
+
+struct CCCTestParams {
+    const char* prefix;
+    unsigned int radius;
+    unsigned int multiplier;
+    unsigned int iterations;
+    double replace;
+};
+
+void apiWrapper(af_array* out, const af_array in, const af_array seedx,
+        const af_array seedy, const CCCTestParams params) {
+    ASSERT_SUCCESS(
+            af_confidence_cc(out, in, seedx, seedy,
+                params.radius, params.multiplier,
+                params.iterations, params.replace));
+
+    int device = 0;
+    ASSERT_SUCCESS(af_get_device(&device));
+    ASSERT_SUCCESS(af_sync(device));
+}
+
+template<typename T>
+void testImage(const std::string pTestFile, const size_t numSeeds,
+        const unsigned *seedx, const unsigned *seedy, const int multiplier,
+        const unsigned neighborhood_radius, const int iter) {
+    SUPPORTED_TYPE_CHECK(T);
+    if (noImageIOTests()) return;
+
+    vector<af::dim4> inDims;
+    vector<string> inFiles;
+    vector<dim_t> outSizes;
+    vector<string> outFiles;
+
+    readImageTests(std::string(TEST_DIR)+"/confidence_cc/"+pTestFile,
+            inDims, inFiles, outSizes, outFiles);
+
+    size_t testCount = inDims.size();
+
+    af_array seedxArr = 0, seedyArr = 0;
+    dim4 seedDims(numSeeds);
+    ASSERT_SUCCESS(af_create_array(
+                &seedxArr, seedx, seedDims.ndims(), seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(
+                &seedyArr, seedy, seedDims.ndims(), seedDims.get(), u32));
+
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        af_array _inArray   = 0;
+        af_array inArray    = 0;
+        af_array outArray   = 0;
+        af_array _goldArray = 0;
+        af_array goldArray  = 0;
+        dim_t nElems        = 0;
+
+        inFiles[testId].insert(0, string(TEST_DIR "/confidence_cc/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/confidence_cc/"));
+
+        ASSERT_SUCCESS(
+                af_load_image(&_inArray, inFiles[testId].c_str(), false));
+        ASSERT_SUCCESS(
+                af_load_image(&_goldArray, outFiles[testId].c_str(), false));
+
+        // af_load_image always returns float array, so convert to output type
+        ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
+        ASSERT_SUCCESS(conv_image<T>(&goldArray, _goldArray));
+
+        CCCTestParams params;
+        params.prefix = "Image";
+        params.radius = neighborhood_radius;
+        params.multiplier = multiplier;
+        params.iterations = iter;
+        params.replace = 255.0;
+
+        apiWrapper(&outArray, inArray, seedxArr, seedyArr, params);
+
+        ASSERT_ARRAYS_EQ(outArray, goldArray);
+
+        ASSERT_SUCCESS(af_release_array(_inArray));
+        ASSERT_SUCCESS(af_release_array(inArray));
+        ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(_goldArray));
+        ASSERT_SUCCESS(af_release_array(goldArray));
+    }
+    ASSERT_SUCCESS(af_release_array(seedxArr));
+    ASSERT_SUCCESS(af_release_array(seedyArr));
+}
+
+template<typename T>
+void testData(CCCTestParams params) {
+    SUPPORTED_TYPE_CHECK(T);
+
+    vector<dim4> numDims;
+    vector<vector<T> > in;
+    vector<vector<T> > tests;
+
+    string file = string(TEST_DIR) + "/confidence_cc/" +
+                string(params.prefix) + "_" +
+                to_string(params.radius) + "_" +
+                to_string(params.multiplier) + ".test";
+    readTests<T, T, int>(file, numDims, in, tests);
+
+    dim4 dims         = numDims[0];
+    af_array inArray  = 0;
+    af_array seedxArr = 0, seedyArr = 0;
+
+    vector<uint> seedCoords(in[1].begin(), in[1].end());
+    const unsigned *seedxy = seedCoords.data();
+
+    dim4 seedDims(1);
+    ASSERT_SUCCESS(af_create_array(
+                &seedxArr, seedxy+0, seedDims.ndims(), seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(
+                &seedyArr, seedxy+1, seedDims.ndims(), seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
+                dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+
+    af_array outArray = 0;
+    apiWrapper(&outArray, inArray, seedxArr, seedyArr, params);
+
+    ASSERT_VEC_ARRAY_EQ(tests[0], dims, outArray);
+
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
+    ASSERT_SUCCESS(af_release_array(seedxArr));
+    ASSERT_SUCCESS(af_release_array(seedyArr));
+}
+
+class ConfidenceConnectedDataTest
+    : public testing::TestWithParam<CCCTestParams> {
+};
+
+#if !defined(AF_OPENCL)
+
+TYPED_TEST(ConfidenceConnectedImageTest, DonutBackgroundExtraction) {
+    const unsigned seedx = 10;
+    const unsigned seedy = 10;
+    testImage<TypeParam>(
+        std::string("donut_background.test"), 1, &seedx, &seedy, 3, 3, 25);
+}
+
+TYPED_TEST(ConfidenceConnectedImageTest, DonutRingExtraction) {
+    const unsigned seedx = 132;
+    const unsigned seedy = 132;
+    testImage<TypeParam>(
+        std::string("donut_ring.test"), 1, &seedx, &seedy, 3, 3, 25);
+}
+
+TYPED_TEST(ConfidenceConnectedImageTest, DonutKernelExtraction) {
+    const unsigned seedx = 150;
+    const unsigned seedy = 150;
+    testImage<TypeParam>(
+        std::string("donut_core.test"), 1, &seedx, &seedy, 3, 3, 25);
+}
+
+TEST_P(ConfidenceConnectedDataTest, SegmentARegion) {
+    testData<unsigned char>(GetParam());
+}
+
+INSTANTIATE_TEST_CASE_P(SingleSeed, ConfidenceConnectedDataTest,
+        testing::Values(CCCTestParams{"core", 0u, 1u, 5u, 255.0},
+            CCCTestParams{"background", 0u, 1u, 5u, 255.0},
+            CCCTestParams{"ring", 0u, 1u, 5u, 255.0}),
+        [](const ::testing::TestParamInfo<ConfidenceConnectedDataTest::ParamType> info) {
+            stringstream ss;
+            ss << "_prefix_" << info.param.prefix
+               << "_radius_" << info.param.radius
+               << "_multiplier_" << info.param.multiplier
+               << "_iterations_" << info.param.iterations
+               << "_replace_" << info.param.replace;
+            return ss.str();
+        });
+#endif
diff --git a/test/data b/test/data
index e6ca2f3ab1..6a48c88658 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit e6ca2f3ab19d4e8ca46317237ca26e48050160fc
+Subproject commit 6a48c88658bcd68392e99344714cb0dccd4ec285

From 3492fce256fc7ec85ce9e3a51f6731708080ee20 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Feb 2020 15:07:28 -0500
Subject: [PATCH 1828/2677] Add support for isInf, isNan, and iszero (#2751)

* Add support for isInf, isNan, and iszero
* Guard half calls in older compute architectures.
---
 src/api/c/unary.cpp             |  2 ++
 src/backend/cpu/unary.hpp       |  3 +-
 src/backend/cuda/kernel/jit.cuh | 40 +++++++++++++++++++++++-
 src/backend/cuda/unary.hpp      |  4 +--
 test/half.cpp                   | 54 +++++++++++++++++++++++++++++++++
 5 files changed, 99 insertions(+), 4 deletions(-)

diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index a921c4f5d5..26d75a06d8 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -669,10 +669,12 @@ static af_err af_check(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles / complex
         af_dtype type = implicit(in_type, f32);
+        if(in_type == f16) type = f16;
 
         switch (type) {
             case f32: res = checkOp<float, op>(in); break;
             case f64: res = checkOp<double, op>(in); break;
+            case f16: res = checkOp<half, op>(in); break;
             case c32: res = checkOpCplx<cfloat, float, op>(in); break;
             case c64: res = checkOpCplx<cdouble, double, op>(in); break;
             default: TYPE_ERROR(1, in_type); break;
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index a8c1e6518c..418510761b 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -96,7 +96,8 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 #define CHECK_FN(name, op)                                                   \
     template<typename T>                                                     \
     struct UnOp<char, T, af_##name##_t> {                                    \
-        void eval(jit::array<char> &out, const jit::array<T> &in, int lim) { \
+        void eval(jit::array<char> &out, const jit::array<compute_t<T>> &in, \
+                  int lim) {                                                 \
             for (int i = 0; i < lim; i++) { out[i] = op(in[i]); }            \
         }                                                                    \
     };
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 6909dc10a8..b613505647 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -71,7 +71,6 @@ typedef cuDoubleComplex cdouble;
 
 #define __convert_char(val) (char)((val) != 0)
 #define frem(lhs, rhs) remainder((lhs), (rhs))
-#define iszero(a) ((a) == 0)
 
 // ----------------------------------------------
 // COMPLEX FLOAT OPERATIONS
@@ -193,6 +192,45 @@ __device__ cdouble __cmin(cdouble lhs, cdouble rhs) {
 __device__ cdouble __cmax(cdouble lhs, cdouble rhs) {
     return __cabs(lhs) > __cabs(rhs) ? lhs : rhs;
 }
+
+template<typename T>
+static __device__ __inline__
+int iszero(T a) {
+  return a == T(0);
+}
+
+template<typename T>
+static __device__ __inline__
+int __isinf(const T in) {
+    return isinf(in);
+}
+
+template<>
+__device__ __inline__
+int __isinf<__half>(const __half in) {
+#if __CUDA_ARCH__ >= 530
+    return __hisinf(in);
+#else
+    return ::isinf(__half2float(in));
+#endif
+}
+
+template<typename T>
+static __device__ __inline__
+int __isnan(const T in) {
+    return isnan(in);
+}
+
+template<>
+__device__ __inline__
+int __isnan<__half>(const __half in) {
+#if __CUDA_ARCH__ >= 530
+    return __hisnan(in);
+#else
+    return ::isnan(__half2float(in));
+#endif
+}
+
 #define __cand(lhs, rhs) __cabs(lhs) && __cabs(rhs)
 #define __cor(lhs, rhs) __cabs(lhs) || __cabs(rhs)
 #define __ceq(lhs, rhs) (((lhs).x == (rhs).x) && ((lhs).y == (rhs).y))
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index f133140ab1..5e3f9fe92b 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -66,8 +66,8 @@ UNARY_FN(signbit)
 UNARY_FN(ceil)
 UNARY_FN(floor)
 
-UNARY_FN(isinf)
-UNARY_FN(isnan)
+UNARY_DECL(isinf, "__isinf")
+UNARY_DECL(isnan, "__isnan")
 UNARY_FN(iszero)
 UNARY_DECL(noop, "__noop")
 
diff --git a/test/half.cpp b/test/half.cpp
index 6ce640b83b..952a4197f7 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -86,3 +86,57 @@ TEST(Half, arith) {
 
     ASSERT_ARRAYS_EQ(gold, result);
 }
+
+TEST(Half, isInf) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    half_float::half hinf = std::numeric_limits<half_float::half>::infinity();
+
+    vector<half_float::half> input(2, half_float::half(0));
+    input[0] = hinf;
+
+    array infarr(2, &input.front());
+
+    array res = isInf(infarr);
+
+    vector<char> hgold(2, 0);
+    hgold[0] = 1;
+    array gold(2, &hgold.front());
+
+    ASSERT_ARRAYS_EQ(gold, res);
+}
+
+TEST(Half, isNan) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    half_float::half hnan = std::numeric_limits<half_float::half>::quiet_NaN();
+
+    vector<half_float::half> input(2, half_float::half(0));
+    input[0] = hnan;
+
+    array nanarr(2, &input.front());
+
+    array res = isNaN(nanarr);
+
+    vector<char> hgold(2, 0);
+    hgold[0] = 1;
+    array gold(2, &hgold.front());
+
+    ASSERT_ARRAYS_EQ(gold, res);
+}
+
+TEST(Half, isZero) {
+    SUPPORTED_TYPE_CHECK(af_half);
+    half_float::half hzero(0.f);
+
+    vector<half_float::half> input(2, half_float::half(1));
+    input[0] = hzero;
+
+    array nanarr(2, &input.front());
+
+    array res = iszero(nanarr);
+
+    vector<char> hgold(2, 0);
+    hgold[0] = 1;
+    array gold(2, &hgold.front());
+
+    ASSERT_ARRAYS_EQ(gold, res);
+}

From e6f153850e535f4cd6d6378cf5ef88c132f049fd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Feb 2020 02:49:20 -0500
Subject: [PATCH 1829/2677] Fix cast from f16 to u8 on CUDA backend

---
 src/backend/cuda/cast.hpp | 9 ++++++++-
 test/half.cpp             | 4 +---
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index dec6778293..e14aa9f352 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <Array.hpp>
-#include <common/jit/UnaryNode.hpp>
 #include <common/half.hpp>
+#include <common/jit/UnaryNode.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
@@ -74,6 +74,13 @@ struct CastOp<cdouble, cdouble> {
     const char *name() { return "__convert_z2z"; }
 };
 
+// Casting from half to unsigned char causes compilation issues. First convert
+// to short then to half
+template<>
+struct CastOp<unsigned char, common::half> {
+    const char *name() { return "(short)"; }
+};
+
 #undef CAST_FN
 #undef CAST_CFN
 
diff --git a/test/half.cpp b/test/half.cpp
index 952a4197f7..b07b738f6f 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -53,9 +53,7 @@ INSTANTIATE_TEST_CASE_P(FromF16, HalfConvert,
                                           convert_params(f16, f64, 10),
                                           convert_params(f16, s32, 10),
                                           convert_params(f16, u32, 10),
-                                          // causes compilation failures with
-                                          // nvrtc
-                                          // convert_params(f16, u8, 10),
+                                          convert_params(f16, u8, 10),
                                           convert_params(f16, s64, 10),
                                           convert_params(f16, u64, 10),
                                           convert_params(f16, s16, 10),

From 72e8ba39d3bfc5a3c69d4a48937a77b59bcf7169 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Feb 2020 15:08:10 -0500
Subject: [PATCH 1830/2677] Add v3.7.0 release notes (#2741)

* Add v3.7.0 release notes
---
 docs/pages/release_notes.md | 206 ++++++++++++++++++++++++++++++++++++
 1 file changed, 206 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 7bd99f2349..441f573467 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,212 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.7.0
+======
+
+Major Updates
+-------------
+
+- Added the ability to customize the memory manager(Thanks jacobkahn and flashlight) \PR{2461}
+- Added 16-bit floating point support for several functions \PR{2413} \PR{2587} \PR{2585} \PR{2587} \PR{2583}
+- Added sumByKey, productByKey, minByKey, maxByKey, allTrueByKey, anyTrueByKey, countByKey \PR{2254}
+- Added confidence connected components \PR{2748}
+- Added neural network based convolution and gradient functions \PR{2359}
+- Added a padding function \PR{2682}
+- Added pinverse for pseudo inverse \PR{2279}
+- Added support for uniform ranges in approx1 and approx2 functions. \PR{2297}
+- Added support to write to preallocated arrays for some functions \PR{2599} \PR{2481} \PR{2328} \PR{2327}
+- Added meanvar function \PR{2258}
+- Add support for sparse-sparse arithmetic support
+- Added rsqrt function for reciprocal square root
+- Added a lower level af_gemm function for general matrix multiplication \PR{2481}
+- Added a function to set the cuBLAS math mode for the CUDA backend \PR{2584}
+- Separate debug symbols into separate files \PR{2535}
+- Print stacktraces on errors \PR{2632}
+- Support move constructor for af::array \PR{2595}
+- Expose events in the public API \PR{2461}
+- Add setAxesLabelFormat to format labels on graphs \PR{2495}
+
+Improvements
+------------
+
+- Better error messages for systems with driver or device incompatibilities \PR{2678} \PR{2448}
+- Optimized unified backend function calls
+- Optimized anisotropic smoothing \PR{2713}
+- Optimized canny filter for CUDA and OpenCL
+- Better MKL search script
+- Better logging of different submodules in ArrayFire \PR{2670} \PR{2669}
+- Improve documentation \PR{2665} \PR{2620} \PR{2615} \PR{2639} \PR{2628} \PR{2633} \PR{2622} \PR{2617} \PR{2558} \PR{2326} \PR{2515}
+- Optimized af::array assignment \PR{2575}
+- Update the k-means example to display the result \PR{2521}
+
+
+Fixes
+-----
+
+- Fix multi-config generators
+- Fix access errors in canny
+- Fix segfault in the unified backend if no backends are available
+- Fix access errors in scan-by-key
+- Fix sobel operator
+- Fix an issue with the random number generator and s16
+- Fix issue with boolean product reduction
+- Fix array_proxy move constructor
+- Fix convolve3 launch configuration
+- Fix an issue where the fft function modified the input array \PR{2520}
+
+Contributions
+-------------
+Special thanks to our contributors:
+[Jacob Khan](https://github.com/jacobkahn)
+[William Tambellini](https://github.com/WilliamTambellini)
+[Alexey Kuleshevich](https://github.com/lehins)
+[Richard Barnes](https://github.com/r-barnes)
+[Gaika](https://github.com/gaika)
+[ShalokShalom](https://github.com/ShalokShalom)
+
+
+v3.6.4
+======
+
+Bug Fixes
+---------
+- Address a JIT performance regression due to moving kernel arguments to shared memory \PR{2501}
+- Fix the default parameter for setAxisTitle \PR{2491}
+
+v3.6.3
+======
+
+Improvements
+------------
+- Graphics are now a runtime dependency instead of a link time dependency \PR{2365}
+- Reduce the CUDA backend binary size using runtime compilation of kernels \PR{2437}
+- Improved batched matrix multiplication on the CPU backend by using Intel MKL's
+  `cblas_Xgemm_batched`\PR{2206}
+- Print JIT kernels to disk or stream using the `AF_JIT_KERNEL_TRACE`
+  environment variable \PR{2404}
+- `void*` pointers are now allowed as arguments to `af::array::write()` \PR{2367}
+- Slightly improve the efficiency of JITed tile operations \PR{2472}
+- Make the random number generation on the CPU backend to be consistent with
+  CUDA and OpenCL \PR{2435}
+- Handled very large JIT tree generations \PR{2484} \PR{2487}
+
+Bug Fixes
+---------
+- Fixed `af::array::array_proxy` move assignment operator \PR{2479}
+- Fixed input array dimensions validation in svdInplace() \PR{2331}
+- Fixed the typedef declaration for window resource handle \PR{2357}.
+- Increase compatibility with GCC 8 \PR{2379}
+- Fixed `af::write` tests \PR{2380}
+- Fixed a bug in broadcast step of 1D exclusive scan \PR{2366}
+- Fixed OpenGL related build errors on OSX \PR{2382}
+- Fixed multiple array evaluation. Performance improvement. \PR{2384}
+- Fixed buffer overflow and expected output of kNN SSD small test \PR{2445}
+- Fixed MKL linking order to enable threaded BLAS \PR{2444}
+- Added validations for forge module plugin availability before calling
+  resource cleanup \PR{2443}
+- Improve compatibility on MSVC toolchain(_MSC_VER > 1914) with the CUDA
+  backend \PR{2443}
+- Fixed BLAS gemm func generators for newest MSVC 19 on VS 2017 \PR{2464}
+- Fix errors on exits when using the cuda backend with unified \PR{2470}
+
+Documentation
+-------------
+- Updated svdInplace() documentation following a bugfix \PR{2331}
+- Fixed a typo in matrix multiplication documentation \PR{2358}
+- Fixed a code snippet demostrating C-API use \PR{2406}
+- Updated hamming matcher implementation limitation \PR{2434}
+- Added illustration for the rotate function \PR{2453}
+
+Misc
+----
+- Use cudaMemcpyAsync instead of cudaMemcpy throughout the codebase \PR{2362}
+- Display a more informative error message if CUDA driver is incomptible
+  \PR{2421} \PR{2448}
+- Changed forge resource managemenet to use smart pointers \PR{2452}
+- Deprecated intl and uintl typedefs in API \PR{2360}
+- Enabled graphics by default for all builds starting with v3.6.3 \PR{2365}
+- Fixed several warnings \PR{2344} \PR{2356} \PR{2361}
+- Refactored initArray() calls to use createEmptyArray(). initArray() is for
+  internal use only by Array class. \PR{2361}
+- Refactored `void*` memory allocations to use unsigned char type \PR{2459}
+- Replaced deprecated MKL API with in-house implementations for sparse
+  to sparse/dense conversions \PR{2312}
+- Reorganized and fixed some internal backend API \PR{2356}
+- Updated compilation order of cuda files to speed up compile time \PR{2368}
+- Removed conditional graphics support builds after enabling runtime
+  loading of graphics dependencies \PR{2365}
+- Marked graphics dependencies as optional in CPack RPM config \PR{2365}
+- Refactored a sparse arithmetic backend API \PR{2379}
+- Fixed const correctness of `af_device_array` API \PR{2396}
+- Update Forge to v1.0.4 \PR{2466}
+- Manage Forge resources from the DeviceManager class \PR{2381}
+- Fixed non-mkl & non-batch blas upstream call arguments \PR{2401}
+- Link MKL with OpenMP instead of TBB by default
+- use clang-format to format source code
+
+Contributions
+-------------
+Special thanks to our contributors:
+[Alessandro Bessi](https://github.com/alessandrobessi)
+[zhihaoy](https://github.com/zhihaoy)
+[Jacob Khan](https://github.com/jacobkahn)
+[William Tambellini](https://github.com/WilliamTambellini)
+
+v3.6.2
+======
+
+Features
+--------
+- Added support for batching on the `cond` argument in select() \PR{2243}
+- Added support for broadcasting batched matmul() \PR{2315}
+- Added support for multiple nearest neighbors in nearestNeighbour() \PR{2280}
+- Added support for clamp-to-edge padding as an `af_border_type` option \PR{2333}
+
+Improvements
+------------
+- Improved performance of morphological operations \PR{2238}
+- Fixed linking errors when compiling without Freeimage/Graphics \PR{2248}
+- Improved the usage of ArrayFire as a CMake subproject \PR{2290}
+- Enabled configuration of custom library path for loading dynamic backend
+  libraries \PR{2302}
+
+Bug Fixes
+---------
+- Fixed LAPACK definitions and linking errors \PR{2239}
+- Fixed overflow in dim4::ndims() \PR{2289}
+- Fixed pow() precision for integral types \PR{2305}
+- Fixed issues with tile() with a large repeat dimension \PR{2307}
+- Fixed svd() sub-array output on OpenCL \PR{2279}
+- Fixed grid-based indexing calculation in histogram() \PR{2230}
+- Fixed bug in indexing when used after reorder \PR{2311}
+- Fixed errors when exiting on Windows when using
+  [CLBlast](https://github.com/CNugteren/CLBlast) \PR{2222}
+- Fixed fallthrough error in medfilt1 \PR{2349}
+
+Documentation
+-------------
+- Improved unwrap() documentation \PR{2301}
+- Improved wrap() documentation \PR{2320}
+- Improved accum() documentation \PR{2298}
+- Improved tile() documentation \PR{2293}
+- Clarified approx1() and approx2() indexing in documentation \PR{2287}
+- Updated examples of [select()](@ref data_func_select) in detailed documentation
+  \PR{2277}
+- Updated lookup() examples \PR{2288}
+- Updated set operations' documentation \PR{2299}
+
+Misc
+----
+- `af*` libraries and dependencies directory changed to `lib64` \PR{2186}
+- Added new arrayfire ASSERT utility functions \PR{2249} \PR{2256} \PR{2257} \PR{2263}
+- Improved error messages in JIT \PR{2309}
+
+Contributions
+-------------
+Special thanks to our contributors: [Jacob Kahn](https://github.com/jacobkahn),
+[Vardan Akopian](https://github.com/vakopian)  
+
 v3.6.1
 ======
 

From 9b4eeafc48d85bc2a426c855f808fae142702542 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 9 Feb 2020 22:55:39 -0500
Subject: [PATCH 1831/2677] Fix several errors and warnings with documentation.
 Document classes

* Added an ArrayFire Classes section
* Document af::dim4, af::features, and other classes
* Fix issues with random engine documentation
* Fix issues with memory manager documentation
* Fix warning with dot
---
 docs/CMakeLists.txt                           |   2 +
 docs/arrayfire.css                            |   6 +-
 docs/details/algorithm.dox                    |   9 +-
 docs/details/arith.dox                        |  10 +-
 docs/details/data.dox                         |   2 +-
 docs/details/features.dox                     |  13 +
 docs/details/image.dox                        |  14 +-
 docs/details/index.dox                        |  27 +-
 docs/details/random.dox                       |  23 +-
 docs/details/signal.dox                       |  12 +-
 docs/details/vision.dox                       |   2 +-
 docs/doxygen.mk                               |  17 +-
 docs/header.htm                               |  49 ++--
 docs/layout.xml                               |   2 +-
 .../configuring_arrayfire_environment.md      |   2 +-
 include/af/arith.h                            |  16 +-
 include/af/array.h                            |   5 +-
 include/af/blas.h                             |  73 +++---
 include/af/dim4.hpp                           |  69 ++++-
 include/af/event.h                            |  19 ++
 include/af/exception.h                        |  24 +-
 include/af/features.h                         |  74 +++++-
 include/af/index.h                            |   9 +-
 include/af/lapack.h                           |   4 +-
 include/af/memory.h                           |   3 +
 include/af/random.h                           | 240 +++++++++---------
 include/af/seq.h                              |   9 +-
 include/arrayfire.h                           |  62 ++---
 28 files changed, 477 insertions(+), 320 deletions(-)
 create mode 100644 docs/details/features.dox

diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index f6951ae3e7..37938b3746 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -1,3 +1,5 @@
+
+include(Version)
 set(AF_DOCS_CONFIG "${CMAKE_CURRENT_SOURCE_DIR}/doxygen.mk")
 set(AF_DOCS_CONFIG_OUT "${CMAKE_CURRENT_BINARY_DIR}/doxygen.mk.out")
 
diff --git a/docs/arrayfire.css b/docs/arrayfire.css
index 9785b2b7ea..397e8089d5 100644
--- a/docs/arrayfire.css
+++ b/docs/arrayfire.css
@@ -123,7 +123,7 @@ div.support *
 
 #gsearch
 {
-    width       :   150px;
+    width       :   20%;
 }
 
 .tablist span
@@ -134,6 +134,10 @@ div.support *
     text-shadow     :   none;
 }
 
+#side-nav {
+    height: 100%
+}
+
 #nav-tree
 {
     background-color    : #F7F7F7;
diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index ab28da2740..38b3c26d5a 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -185,7 +185,8 @@ reduce by key can be seen below:
 
 
 
-\defgroup reduce_func_all_true alltrue
+\defgroup reduce_func_all_true allTrue
+\brief Test if all values in an array are true
 
 \ingroup reduce_mat
 
@@ -196,6 +197,7 @@ Return type is b8 for all input types
 \copydoc batch_detail_algo
 
 \defgroup reduce_func_all_true_by_key allTrueByKey
+\brief Calculate if all values that share the same consecutive keys are true
 
 \ingroup reduce_mat
 
@@ -224,6 +226,7 @@ reduce by key can be seen below:
 
 
 \defgroup reduce_func_any_true anytrue
+\brief Calculate if any values in an array are true
 
 \ingroup reduce_mat
 
@@ -233,7 +236,8 @@ Return type is b8 for all input types
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_any_true_by_key anyTrueByKey
+\defgroup reduce_func_anytrue_by_key anyTrueByKey
+\brief Calculate if any values that share the same consecutive keys are true
 
 \ingroup reduce_mat
 
@@ -298,6 +302,7 @@ reduce by key can be seen below:
 
 
 \defgroup scan_func_accum accum
+\brief Cumulative sum (inclusive). Also known as a scan
 
 \ingroup scan_mat
 
diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 49069c9567..2ad28273e2 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -118,13 +118,12 @@ Check if input two inputs are not equal
 
 
 \defgroup arith_func_and and
+\brief Logical AND
 
 \ingroup logic_mat
 
 Logical and of two inputs
 
-
-
 \defgroup arith_func_or or
 
 \ingroup logic_mat
@@ -213,6 +212,8 @@ Compute \f$x - n * y\f$ where n is quotient of \f$x / y\f$
 
 \defgroup arith_func_abs abs
 
+\brief Absolute value
+
 \ingroup numeric_mat
 
 Absolute value
@@ -220,10 +221,9 @@ Absolute value
 
 
 \defgroup arith_func_arg arg
-
 \ingroup numeric_mat
 
-Phase of a number in the complex plane
+\brief Phase of a number in the complex plane
 
 
 
@@ -319,6 +319,7 @@ arc sin of input
 
 
 \defgroup arith_func_acos acos
+\brief Inverse cosine.
 
 \ingroup trig_mat
 
@@ -373,6 +374,7 @@ asinh of input
 
 
 \defgroup arith_func_acosh acosh
+\brief Inverse hyperbolic cosine
 
 \ingroup hyper_mat
 
diff --git a/docs/details/data.dox b/docs/details/data.dox
index f38ac8e93e..f8db9586f0 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -13,7 +13,7 @@ The array created has the same value at all locations
 
 =======================================================================
 
-\defgroup data_func_pad Padding
+\defgroup data_func_pad pad
 
 \brief Pad an array
 
diff --git a/docs/details/features.dox b/docs/details/features.dox
new file mode 100644
index 0000000000..6fe1386060
--- /dev/null
+++ b/docs/details/features.dox
@@ -0,0 +1,13 @@
+/**
+\addtogroup arrayfire_func
+@{
+
+\defgroup features_group_features features
+
+\brief Lookup values of an array based on sequences and/or arrays
+
+===============================================================================
+
+
+@}
+*/
\ No newline at end of file
diff --git a/docs/details/image.dox b/docs/details/image.dox
index 94d97d5acf..554fc65db4 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -34,7 +34,7 @@ and red-difference chroma components.
 \addtogroup  arrayfire_func
 @{
 
-\defgroup image_func_colorspace colorspace
+\defgroup image_func_colorspace colorSpace
 \ingroup colorconv_mat
 
 Colorspace conversion function
@@ -262,7 +262,7 @@ discussion on it can be found [here](http://en.wikipedia.org/wiki/Sobel_operator
 
 =======================================================================
 
-\defgroup image_func_anisotropic_diffusion AnisotropicDiffusion
+\defgroup image_func_anisotropic_diffusion anisotropicDiffusion
 \ingroup imageflt_mat
 
 \brief Anisotropic Smoothing Filter
@@ -753,7 +753,7 @@ Affine transforms can be used for various purposes. \ref af::translate, \ref af:
 are specializations of the transform function.
 
 
-\defgroup transform_func_coordinates transformcoordinates
+\defgroup transform_func_coordinates transformCoordinates
 \ingroup transform_mat
 
 Transform input coordinates
@@ -766,7 +766,7 @@ transformed points.
 
 =======================================================================
 
-\defgroup image_func_sat SAT
+\defgroup image_func_sat sat
 \ingroup imageflt_mat
 
 \brief Summed Area Tables
@@ -970,7 +970,7 @@ wide range of edges in images. A more in depth discussion on it can be found [he
 
 =======================================================================
 
-\defgroup image_func_iterative_deconv Iterative Deconvolutions
+\defgroup image_func_iterative_deconv iterativeDeconv
 \ingroup imageflt_mat
 
 Iterative Deconvolution Algorithms
@@ -1022,7 +1022,7 @@ to be in a fixed range, that should be done by the caller explicitly.
 
 =======================================================================
 
-\defgroup image_func_inverse_deconv Inverse Deconvolution
+\defgroup image_func_inverse_deconv inverseDeconv
 \ingroup imageflt_mat
 
 Inverse deconvolution is an linear algorithm i.e. they are non-iterative in
@@ -1072,7 +1072,7 @@ explicitly.
 
 =======================================================================
 
-\defgroup image_func_confidence_cc Confidence Connected Components
+\defgroup image_func_confidence_cc confidenceCC
 \ingroup connected_comps_mat
 
 \brief Segment image based on similar pixel characteristics
diff --git a/docs/details/index.dox b/docs/details/index.dox
index 72a95da048..2e9d48eb0b 100644
--- a/docs/details/index.dox
+++ b/docs/details/index.dox
@@ -7,16 +7,18 @@
 
 \brief Lookup values of an array based on sequences and/or arrays
 
+===============================================================================
 
-
-\defgroup index_func_lookup Lookup
+\defgroup index_func_lookup lookup
 \ingroup index_mat
 
 \brief Lookup values of an array by indexing with another array.
 
-Will return an array with the values in the \p in array from the locations specified in the \p idx array.
-The resulting array contains values corresponding to each of the provided indices.
-Locations of the input data are assumed to be in the range [0, n). Indexing outside of this range will result in mirrored wrap-around behavior.
+Will return an array with the values in the \p in array from the locations
+specified in the \p idx array. The resulting array contains values corresponding
+to each of the provided indices. Locations of the input data are assumed to be
+in the range [0, n). Indexing outside of this range will result in mirrored
+wrap-around behavior.
 
 A simple example of one-dimension indexing can be seen in the following example.
 
@@ -26,12 +28,13 @@ Index locations can also be out of bounds.
 
 \snippet test/index.cpp ex_index_lookup_oob
 
-The dimensiong along which to query the indices can also be specified. The resulting array will be of the same size as the input, except for the queried dimension which will match the number of elements in the index array.
+The axis along which to query the indices can also be specified. The
+resulting array will be of the same size as the input, except for the queried
+dimension which will match the number of elements in the index array.
 
 \snippet test/index.cpp ex_index_lookup2d
 
-
-
+===============================================================================
 
 \defgroup index_func_assign assign
 \ingroup index_mat
@@ -39,13 +42,5 @@ The dimensiong along which to query the indices can also be specified. The resul
 \brief Copy and write values in the locations specified by the sequences
 
 
-
-
-\defgroup index_func_util util
-\ingroup index_mat
-
-\brief Utility functions to create objects of type \ref af_index_t
-
-
 @}
 */
diff --git a/docs/details/random.dox b/docs/details/random.dox
index 17bada453a..4da8fc7ec3 100644
--- a/docs/details/random.dox
+++ b/docs/details/random.dox
@@ -1,7 +1,5 @@
 
 /**
-\addtogroup arrayfire_func
-@{
 
 \defgroup random_mat Random Number Generation
 
@@ -13,26 +11,18 @@ Functions to generate and manage random numbers and random number engines
 
 ===============================================================================
 
-\defgroup random_engine_class randomEngine
-
-\brief Random Number Engine Generation Class
-
-The \ref af::randomEngine class is used to set the type and seed of random
-number generation engine based on \ref af::randomEngineType.
-
-\ingroup random_mat
-
-===============================================================================
+\addtogroup arrayfire_func
+@{
 
-\defgroup random_engine_func_constructor randomEngine Constructors
+\defgroup random_func_random_engine randomEngine
 
-\brief Create random number generator object
+\brief Functions to create, modify, use, and destroy randomEngine objects
 
 A \ref af::randomEngine object can be used to generate psuedo random numbers
 using various types of random number generation algorithms defined by \ref
 af::randomEngineType.
 
-\ingroup random_engine_class
+\ingroup random_mat
 
 ===============================================================================
 
@@ -60,7 +50,7 @@ The data is centered around 0.
 
 ===============================================================================
 
-\defgroup random_func_set_type setDefaultRandomEngineType
+\defgroup random_func_set_default_engine setDefaultRandomEngineType
 
 \brief Set the default random engine type.
 
@@ -99,5 +89,6 @@ Returns the seed for the current default random engine.
 
 \ingroup random_mat
 
+
 @}
 */
diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index de1feb5e97..fa1b3130c5 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -46,7 +46,7 @@ factor is calculated internally based on the input data provided.
 \addtogroup arrayfire_func
 @{
 
-\defgroup signal_func_convolve N-Dimensional Convolutions
+\defgroup signal_func_convolve convolve (Non-separable)
 \ingroup convolve_mat
 
 \brief Convolution Integral for any(one through three) dimensional data
@@ -85,7 +85,7 @@ documentation to find out more.
 
 
 
-\defgroup signal_func_convolve_sep Separable 2D Convolution
+\defgroup signal_func_convolve_sep convolve (Separable)
 \ingroup convolve_mat
 
 \brief Separable Convolution
@@ -120,7 +120,7 @@ can be decomposed into two vectors shown below.
 \f$
 
 
-\defgroup signal_func_convolve1 1D Convolutions
+\defgroup signal_func_convolve1 convolve1
 \ingroup convolve_mat
 
 \brief Convolution Integral for one dimensional data
@@ -148,7 +148,7 @@ non-overlapping batch mode, they should satisfy one of the following conditions.
 
 
 
-\defgroup signal_func_convolve2 2D Convolutions
+\defgroup signal_func_convolve2 convolve2
 \ingroup convolve_mat
 
 \brief Convolution Integral for two dimensional data
@@ -290,7 +290,7 @@ convolutional neural networks.
 
 
 
-\defgroup signal_func_convolve3 3D Convolutions
+\defgroup signal_func_convolve3 convolve3
 \ingroup convolve_mat
 
 \brief Convolution Integral for three dimensional data
@@ -373,6 +373,7 @@ respectively, given below are the possible batch operations.
 
 \defgroup signal_func_approx1 approx1
 \ingroup approx_mat
+\brief Interpolation across a single dimension
 
 Performs interpolation on data along a single dimension.
 
@@ -407,6 +408,7 @@ interpolation types.
 
 \defgroup signal_func_approx2 approx2
 \ingroup approx_mat
+\brief Interpolation along two dimensions
 
 Performs interpolation on data along two dimensions.
 
diff --git a/docs/details/vision.dox b/docs/details/vision.dox
index 619030a58c..d5d1c5fc06 100644
--- a/docs/details/vision.dox
+++ b/docs/details/vision.dox
@@ -195,7 +195,7 @@ input arrays to be at most 2-dimensional.
 
 =======================================================================
 
-\defgroup cv_func_dog DoG
+\defgroup cv_func_dog dog
 \ingroup featdetect_mat
 
 \brief Difference of Gaussians
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 7a5a5ad229..4a2801fa77 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -32,19 +32,19 @@ DOXYFILE_ENCODING      = UTF-8
 # title of most generated pages and in a few other places.
 # The default value is: My Project.
 
-PROJECT_NAME           = ""
+PROJECT_NAME           = "${PROJECT_NAME}"
 
 # The PROJECT_NUMBER tag can be used to enter a project or revision number. This
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = ""
+PROJECT_NUMBER         = "${AF_VERSION}"
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
 # quick idea about the purpose of the project. Keep the description short.
 
-PROJECT_BRIEF          = ""
+PROJECT_BRIEF          = "A high-performance general-purpose compute library"
 
 # With the PROJECT_LOGO tag one can specify a logo or an icon that is included
 # in the documentation. The maximum height of the logo should not exceed 55
@@ -245,7 +245,8 @@ ALIASES                = "support{1}=<DIV class=\"support\">\1</DIV>" \
                          "funcgroups{4}=\ingroup \3 \4 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "funcgroups{5}=\ingroup \3 \4 \5 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "funcgroups{6}=\ingroup \3 \4 \5 \6 \n @{ \n \defgroup \1 \2 \n @{ \n" \
-                         "endfuncgroups=@} \n @}"
+                         "endfuncgroups=@} \n @}" \
+                         "PR{1}=[[#\1](https://github.com/arrayfire/arrayfire/pull/\1)]"
 
 # Now add special commands for math equations. All of the following commands
 # are only expected to be used inside math mode
@@ -1244,7 +1245,7 @@ HTML_TIMESTAMP         = YES
 # The default value is: YES.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_DYNAMIC_MENUS     = NO
+HTML_DYNAMIC_MENUS     = YES
 
 # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
 # documentation will contain sections that can be hidden and shown after the
@@ -1252,7 +1253,7 @@ HTML_DYNAMIC_MENUS     = NO
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_DYNAMIC_SECTIONS  = NO
+HTML_DYNAMIC_SECTIONS  = YES
 
 # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
 # shown in the various tree structured indices initially; the user can expand
@@ -1936,7 +1937,7 @@ MAN_SUBDIR             =
 # The default value is: NO.
 # This tag requires that the tag GENERATE_MAN is set to YES.
 
-MAN_LINKS              = NO
+MAN_LINKS              = YES
 
 #---------------------------------------------------------------------------
 # Configuration options related to the XML output
@@ -2101,7 +2102,7 @@ INCLUDE_FILE_PATTERNS  =
 
 PREDEFINED             = __declspec(x)= \
                          __attribute__(x)= \
-                         __cplusplus = 99999999999999 \
+                         __cplusplus=99999999999999 \
                          AF_DOC \
                          AF_API_VERSION=${ArrayFire_API_VERSION_CURRENT}
 
diff --git a/docs/header.htm b/docs/header.htm
index 199e95b1b3..f7169bb870 100644
--- a/docs/header.htm
+++ b/docs/header.htm
@@ -1,16 +1,16 @@
-<!-- HTML header for doxygen 1.8.5-->
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<!-- HTML header for doxygen 1.8.17-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
 <html xmlns="http://www.w3.org/1999/xhtml">
 <head>
 <meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
 <meta http-equiv="X-UA-Compatible" content="IE=9"/>
 <meta name="generator" content="Doxygen $doxygenversion"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
 <!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME-->
 <!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME-->
 <link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/>
 <script type="text/javascript" src="$relpath^jquery.js"></script>
 <script type="text/javascript" src="$relpath^dynsections.js"></script>
-<script type="text/javascript" src="afw.js"></script>
 $treeview
 $search
 $mathjax
@@ -22,20 +22,13 @@
 
 <!--BEGIN TITLEAREA-->
 <div id="titlearea">
-<table width="100%">
+<table cellspacing="0" cellpadding="0" width="100%">
  <tbody>
  <tr style="height: 56px;">
   <!--BEGIN PROJECT_LOGO-->
-  <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/>
-  </td>
+  <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/></td>
   <!--END PROJECT_LOGO-->
   <!--BEGIN PROJECT_NAME-->
-  <td style="padding-left: 0.5em;">
-   <div id="projectname">$projectname
-   <!--BEGIN PROJECT_NUMBER-->&#160;<span id="projectnumber">$projectnumber</span><!--END PROJECT_NUMBER-->
-   </div>
-   <!--BEGIN PROJECT_BRIEF--><div id="projectbrief">$projectbrief</div><!--END PROJECT_BRIEF-->
-  </td>
   <!--END PROJECT_NAME-->
   <!--BEGIN !PROJECT_NAME-->
    <!--BEGIN PROJECT_BRIEF-->
@@ -49,22 +42,22 @@
    <td>$searchbox</td>
    <!--END SEARCHENGINE-->
   <!--END DISABLE_INDEX-->
-	 <td id="gsearch">
-   <div><script>
-	    (function() {
-        var cx = '004356362924927882526:zup3ehe-7bs';
-        var gcse = document.createElement('script');
-        gcse.type = 'text/javascript';
-        gcse.async = true;
-        gcse.src = (document.location.protocol == 'https:' ? 'https:' : 'http:') +
-        '//www.google.com/cse/cse.js?cx=' + cx;
-	    var s = document.getElementsByTagName('script')[0];
-	    s.parentNode.insertBefore(gcse, s);
-	  })();
-  </script>
-  <gcse:search></gcse:search>
-</div>
-	 </td>
+   <td id="gsearch">
+       <div><script>
+             (function() {
+                 var cx = '004356362924927882526:zup3ehe-7bs';
+                 var gcse = document.createElement('script');
+                 gcse.type = 'text/javascript';
+                 gcse.async = true;
+                 gcse.src = (document.location.protocol == 'https:' ? 'https:' : 'http:') +
+                            '//www.google.com/cse/cse.js?cx=' + cx;
+                 var s = document.getElementsByTagName('script')[0];
+                 s.parentNode.insertBefore(gcse, s);
+             })();
+       </script>
+       <gcse:search></gcse:search>
+       <div>
+   </td>
  </tr>
  </tbody>
 </table>
diff --git a/docs/layout.xml b/docs/layout.xml
index 69e8ec8da3..1f0db6af21 100644
--- a/docs/layout.xml
+++ b/docs/layout.xml
@@ -3,7 +3,7 @@
   <navindex>
     <tab type="mainpage" visible="yes" title="" />
     <tab type="user" url="\ref tutorials" visible="yes" title="Tutorials"/>
-    <tab type="modules" visible="yes" title="Functions" intro="Documentation grouped according to category:"/>
+    <tab type="modules" visible="yes" title="Functions" intro="Documentation grouped by category:"/>
     <tab type="user" url="\ref releasenotes" visible="yes" title="Release Notes"/>
     <tab type="examples" visible="yes" title="" intro=""/>
   </navindex>
diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 566cc6af44..3ea0ecaca6 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -180,7 +180,7 @@ deallocations, and garbage collection.
 All trace statements printed to the console have a suffix with the following
 pattern.
 
-**[category][Seconds since Epoch][Thread Id][source file relative path] <Message>**
+**[category][Seconds since Epoch][Thread Id][source file relative path] \<Message\>**
 
 AF_MAX_BUFFERS {#af_max_buffers}
 -------------------------------------------------------------------------
diff --git a/include/af/arith.h b/include/af/arith.h
index c7d8812496..d572f95359 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -268,19 +268,19 @@ namespace af
     /// function accepts two \ref af::array or one \ref af::array and a scalar
     /// as nputs.
     ///
-    /// \param[in] lhs is real value(s)
-    /// \param[in] rhs is imaginary value(s)
+    /// \param[in] real is real value(s)
+    /// \param[in] imaginary is imaginary value(s)
     /// \return complex array from inputs
     /// \ingroup arith_func_cplx
-    AFAPI array complex(const array &lhs, const array &rhs);
+    AFAPI array complex(const array &real, const array &imaginary);
 
     /// \copydoc complex(const array&, const array&)
     /// \ingroup arith_func_cplx
-    AFAPI array complex(const array &lhs, const double rhs);
+    AFAPI array complex(const array &real, const double imaginary);
 
     /// \copydoc complex(const array&, const array&)
     /// \ingroup arith_func_cplx
-    AFAPI array complex(const double lhs, const array &rhs);
+    AFAPI array complex(const double real, const array &imaginary);
 
     /// C++ Interface for creating complex array from real array
     ///
@@ -1064,14 +1064,14 @@ extern "C" {
        C Interface for creating complex array from two input arrays
 
        \param[out] out will contain the complex array generated from inputs
-       \param[in] lhs is real array
-       \param[in] rhs is imaginary array
+       \param[in] real is real array
+       \param[in] imaginary is imaginary array
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cplx
     */
-    AFAPI af_err af_cplx2 (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
+    AFAPI af_err af_cplx2 (af_array *out, const af_array real, const af_array imaginary, const bool batch);
 
     /**
        C Interface for creating complex array from real array
diff --git a/include/af/array.h b/include/af/array.h
index a445265b58..72869a7d89 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -24,7 +24,7 @@ namespace af
 
     ///
     /// \brief A multi dimensional data container
-    ///
+    /// \ingroup arrayfire_class
     class AFAPI array {
         af_array   arr;
 
@@ -725,14 +725,11 @@ namespace af
 
 
         /**
-           \defgroup device_func_device array::device<T>
-
            Get the device pointer from the array and lock the buffer in memory manager.
            @{
 
            The device memory returned by this function is not freed until unlock() is called.
 
-           \ingroup arrayfire_func
            \ingroup device_mat
         */
         template<typename T> T* device() const;
diff --git a/include/af/blas.h b/include/af/blas.h
index f42e062a7f..6023717d0e 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -134,67 +134,52 @@ namespace af
     */
     AFAPI array matmul(const array &a, const array &b, const array &c, const array &d);
 
-
+#if AF_API_VERSION >= 35
     /**
         \brief Dot Product
 
-        Scalar dot product between two vectors.  Also referred to as the inner
+        Scalar dot product between two vectors. Also referred to as the inner
         product.
 
         \code
-        // compute scalar dot product
-        array x = randu(100), y = randu(100);
-        af_print(dot(x,y));
-        \endcode
-
-        \param[in] lhs The array object on the left hand side
-        \param[in] rhs The array object on the right hand side
-        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
-                   AF_MAT_CONJ are supported.
-        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
-        \return The result of the dot product of lhs, rhs
-
-        \note optLhs and optRhs can only be one of \ref AF_MAT_NONE or \ref AF_MAT_CONJ
-        \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run conjugate dot operation.
-        \note This function is not supported in GFOR
-
-        \ingroup blas_func_dot
-    */
-    AFAPI array dot   (const array &lhs, const array &rhs,
-                       const matProp optLhs = AF_MAT_NONE,
-                       const matProp optRhs = AF_MAT_NONE);
+          // compute scalar dot product
+          array x = randu(100),
+          y = randu(100);
 
-#if AF_API_VERSION >= 35
-    /**
-        \brief Return the dot product of two vectors as a scalar
-
-        Scalar dot product between two vectors. Also referred to as the inner
-        product.
+          af_print(dot(x, y));
+          // OR
+          printf("%f\n", dot<float>(x, y));
 
-        \code
-        // compute scalar dot product
-        array x = randu(100), y = randu(100);
-        float h_dot = dot<float>(x,y);
         \endcode
 
+        \tparam T The type of the output
         \param[in] lhs The array object on the left hand side
         \param[in] rhs The array object on the right hand side
         \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
-                   AF_MAT_CONJ are supported.
-        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
-        \return The result of the dot product of lhs, rhs as a host scalar
-
-        \note optLhs and optRhs can only be one of \ref AF_MAT_NONE or \ref AF_MAT_CONJ
-        \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run conjugate dot operation.
+                  AF_MAT_CONJ are supported.
+        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and
+        AF_MAT_CONJ are supported \return The result of the dot product of lhs,
+        rhs
+
+        \note optLhs and optRhs can only be one of \ref AF_MAT_NONE or \ref
+              AF_MAT_CONJ
+        \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run
+              conjugate dot operation.
         \note This function is not supported in GFOR
 
         \ingroup blas_func_dot
     */
-    template<typename T> T dot(const array &lhs, const array &rhs,
-                               const matProp optLhs = AF_MAT_NONE,
-                               const matProp optRhs = AF_MAT_NONE);
+    template <typename T>
+    T dot(const array &lhs, const array &rhs,
+          const matProp optLhs = AF_MAT_NONE,
+          const matProp optRhs = AF_MAT_NONE);
 #endif
 
+    /// \ingroup blas_func_dot
+    AFAPI array dot(const array &lhs, const array &rhs,
+                    const matProp optLhs = AF_MAT_NONE,
+                    const matProp optRhs = AF_MAT_NONE);
+
     /**
         \brief Transposes a matrix
 
@@ -205,7 +190,7 @@ namespace af
         \return Transposed matrix
         \ingroup blas_func_transpose
     */
-    AFAPI array transpose(const array& in, const bool conjugate = false);
+    AFAPI array transpose(const array &in, const bool conjugate = false);
 
     /**
         \brief Transposes a matrix in-place
@@ -217,7 +202,7 @@ namespace af
 
         \ingroup blas_func_transpose
     */
-    AFAPI void transposeInPlace(array& in, const bool conjugate = false);
+    AFAPI void transposeInPlace(array &in, const bool conjugate = false);
 }
 #endif
 
diff --git a/include/af/dim4.hpp b/include/af/dim4.hpp
index 4ed4c56603..9a5bad3b33 100644
--- a/include/af/dim4.hpp
+++ b/include/af/dim4.hpp
@@ -20,37 +20,90 @@
 
 namespace af
 {
+/// \brief Generic object that represents size and shape
+/// \ingroup arrayfire_class
 class AFAPI dim4
 {
-    public:
-    dim_t dims[4]; //FIXME: Make this C compatible
-    dim4(); //deleted
 public:
+    dim_t dims[4];
+    /// Default constructor. Creates an invalid dim4 object
+    dim4();
+
+    /// Creates an new dim4 given a set of dimension
     dim4(   dim_t first,
             dim_t second = 1,
             dim_t third = 1,
             dim_t fourth = 1);
+
+    /// Copy constructor
+    ///
+    /// \param[in] other The dim4 that will be copied
     dim4(const dim4& other);
+
+    /// Constructs a dim4 object from a C array of dim_t objects
+    ///
+    /// Creates a new dim4 from a C array. If the C array is less than 4, all values
+    /// past \p ndims will be assigned the value 1.
+    ///
+    /// \param[in] ndims The number of elements in the C array. Must be less than 4
+    /// \param[in] dims  The values to assign to each element of dim4
     dim4(const unsigned ndims, const dim_t * const dims);
+
+    /// Returns the number of elements represented by this dim4
     dim_t elements();
+
+    /// Returns the number of elements represented by this dim4
     dim_t elements() const;
+
+    /// Returns the number of axis whose values are greater than one
     dim_t ndims();
+
+    /// Returns the number of axis whose values are greater than one
     dim_t ndims() const;
+
+    /// Returns true if the two dim4 represent the same shape
     bool operator==(const dim4& other) const;
+
+    /// Returns true if two dim4s store different values
     bool operator!=(const dim4& other) const;
+
+    /// Element-wise multiplication of the dim4 objects
     dim4& operator*=(const dim4& other);
+
+    /// Element-wise addition of the dim4 objects
     dim4& operator+=(const dim4& other);
+
+    /// Element-wise subtraction of the dim4 objects
     dim4& operator-=(const dim4& other);
+
+    /// Returns the reference to the element at a give index. (Must be less than 4)
     dim_t& operator[](const unsigned dim);
+
+    /// Returns the reference to the element at a give index. (Must be less than
+    /// 4)
     const dim_t& operator[](const unsigned dim) const;
-            dim_t* get()         { return dims; }
+
+    /// Returns the underlying pointer to the dim4 object
+    dim_t *get() { return dims; }
+
+    /// Returns the underlying pointer to the dim4 object
     const   dim_t* get() const   { return dims; }
 };
 
+/// Performs an element-wise addition of two dim4 objects
 AFAPI dim4 operator+(const dim4& first, const dim4& second);
+
+/// Performs an element-wise subtraction of two dim4 objects
 AFAPI dim4 operator-(const dim4& first, const dim4& second);
+
+/// Performs an element-wise multiplication of two dim4 objects
 AFAPI dim4 operator*(const dim4& first, const dim4& second);
 
+/// Prints the elements of the dim4 array separated by spaces
+///
+/// \param[inout] ostr An ostream object
+/// \param[in] dims The dim4 object to be printed
+/// \returns the reference to the \p ostr after the dim4 string as been streamed in
 static inline
 std::ostream&
 operator<<(std::ostream& ostr, const dim4& dims)
@@ -62,6 +115,11 @@ operator<<(std::ostream& ostr, const dim4& dims)
     return ostr;
 }
 
+/// Reads 4 dim_t values from an input stream and stores the results in a dim4
+///
+/// \param[inout] istr An istream object
+/// \param[in] dims The dim4 object that will store the values
+/// \return The \p istr object after 4 dim_t values have been read from the input
 static inline
 std::istream&
 operator>>(std::istream& istr, dim4& dims)
@@ -73,10 +131,13 @@ operator>>(std::istream& istr, dim4& dims)
     return istr;
 }
 
+/// Returns true if the af_seq object represents the entire range of an axis
 AFAPI bool isSpan(const af_seq &seq);
 
+/// Returns the number of elements that the af_seq object represents
 AFAPI size_t seqElements(const af_seq &seq);
 
+/// Returns the number of elements that will be represented by seq if applied on an array
 AFAPI dim_t calcDim(const af_seq &seq, const dim_t &parentDim);
 }
 
diff --git a/include/af/event.h b/include/af/event.h
index 5428cf3471..1a5b1718c0 100644
--- a/include/af/event.h
+++ b/include/af/event.h
@@ -13,6 +13,11 @@
 
 #if AF_API_VERSION >= 37
 
+/**
+    Handle to an event object
+
+    \ingroup event_api
+*/
 typedef void* af_event;
 
 #ifdef __cplusplus
@@ -20,25 +25,39 @@ namespace af {
 
 /**
     C++ RAII interface for manipulating events
+    \ingroup arrayfire_class
+    \ingroup event_api
 */
 class AFAPI event {
     af_event e_;
 
    public:
+    /// Create a new event using the C af_event handle
     event(af_event e);
 #if AF_COMPILER_CXX_RVALUE_REFERENCES
+    /// Move constructor
     event(event&& other);
+
+    /// Move assignment operator
     event& operator=(event&& other);
 #endif
+    /// Create a new event object
     event();
+
+    /// event Destructor
     ~event();
 
+    /// Return the underlying C af_event handle
     af_event get() const;
 
+    /// \brief Adds the event on the default ArrayFire queue. Once this point
+    ///        on the program is executed, the event is considered complete.
     void mark();
 
+    /// \brief Block the ArrayFire queue until this even has occurred
     void enqueue();
 
+    /// \brief block the calling thread until this event has occurred
     void block() const;
 
    private:
diff --git a/include/af/exception.h b/include/af/exception.h
index da8ccc554c..aaa566e9bc 100644
--- a/include/af/exception.h
+++ b/include/af/exception.h
@@ -16,6 +16,8 @@
 
 namespace af {
 
+/// An ArrayFire exception class
+/// \ingroup arrayfire_class
 class AFAPI exception : public std::exception
 {
 private:
@@ -24,19 +26,31 @@ class AFAPI exception : public std::exception
 public:
     af_err err() { return m_err; }
     exception();
+    /// Creates a new af::exception given a message. The error code is AF_ERR_UNKNOWN
     exception(const char *msg);
+
+    /// Creates a new exception with a formatted error message for a given file
+    /// and line number in the source code.
     exception(const char *file, unsigned line, af_err err);
+
+    /// Creates a new af::exception with a formatted error message for a given
+    /// an error code, file and line number in the source code.
     exception(const char *msg, const char *file, unsigned line, af_err err);
 #if AF_API_VERSION >= 33
+    /// Creates a new exception given a message, function name, file name, line number and
+    /// error code.
     exception(const char *msg, const char *func, const char *file, unsigned line, af_err err);
 #endif
     virtual ~exception() throw() {}
+    /// Returns an error message for the exception in a string format
     virtual const char *what() const throw() { return m_msg; }
+
+    /// Writes the exception to a stream
     friend inline std::ostream& operator<<(std::ostream &s, const exception &e)
     { return s << e.what(); }
 };
 
-}
+}  // namespace af
 
 #endif
 
@@ -44,7 +58,15 @@ class AFAPI exception : public std::exception
 extern "C" {
 #endif
 
+/// Returns the last error message that occurred and its error message
+///
+/// \param[out] msg The message of the previous error
+/// \param[out] len The number of characters in the msg object
 AFAPI void af_get_last_error(char **msg, dim_t *len);
+
+/// Converts the af_err error code to its string representation
+///
+/// \param[in] err The ArrayFire error code
 AFAPI const char *af_err_to_string(const af_err err);
 
 #ifdef __cplusplus
diff --git a/include/af/features.h b/include/af/features.h
index 69ee72489d..e387782ae6 100644
--- a/include/af/features.h
+++ b/include/af/features.h
@@ -17,25 +17,48 @@ namespace af
 {
     class array;
 
+    /// Represents a feature returned by a feature detector
+    ///
+    /// \ingroup arrayfire_class
+    /// \ingroup features_group_features
     class AFAPI features {
     private:
         af_features feat;
 
     public:
+        /// Default constructor. Creates a features object with new features
         features();
+
+        /// Creates a features object with n features with undefined locations
         features(const size_t n);
+
+        /// Creates a features object from a C af_features object
         features(af_features f);
 
         ~features();
 
+        /// Copy assignment operator
         features& operator= (const features& f);
 
+        /// Returns  the number of features represented by this object
         size_t getNumFeatures() const;
+
+        /// Returns an af::array which represents the x locations of a feature
         array getX() const;
+
+        /// Returns an af::array which represents the y locations of a feature
         array getY() const;
+
+        /// Returns an array with the score of the features
         array getScore() const;
+
+        /// Returns an array with the orientations of the features
         array getOrientation() const;
+
+        /// Returns an array that represents the size of the features
         array getSize() const;
+
+        /// Returns the underlying C af_features object
         af_features get() const;
     };
 
@@ -46,23 +69,72 @@ namespace af
 extern "C" {
 #endif
 
+    /// Creates a new af_feature object with \p num features
+    ///
+    /// \param[out] feat The new feature that will be created
+    /// \param[in] num The number of features that will be in the new features
+    ///                object
+    /// \returns AF_SUCCESS if successful
+    /// \ingroup features_group_features
     AFAPI af_err af_create_features(af_features *feat, dim_t num);
 
+    /// Increases the reference count of the feature and all of its associated
+    /// arrays
+    ///
+    /// \param[out] out The reference to the incremented array
+    /// \param[in] feat The features object whose will be incremented
+    ///                 object
+    /// \returns AF_SUCCESS if successful
+    /// \ingroup features_group_features
     AFAPI af_err af_retain_features(af_features *out, const af_features feat);
 
+    /// Returns the number of features associated with this object
+    ///
+    /// \param[out] num The number of features in the object
+    /// \param[in] feat The feature whose count will be returned
+    /// \ingroup features_group_features
     AFAPI af_err af_get_features_num(dim_t *num, const af_features feat);
 
+    /// Returns the x positions of the features
+    ///
+    /// \param[out] out An array with all x positions of the features
+    /// \param[in] feat The features object
+    /// \ingroup features_group_features
     AFAPI af_err af_get_features_xpos(af_array *out, const af_features feat);
 
+    /// Returns the y positions of the features
+    ///
+    /// \param[out] out An array with all y positions of the features
+    /// \param[in] feat The features object
+    /// \ingroup features_group_features
     AFAPI af_err af_get_features_ypos(af_array *out, const af_features feat);
 
+    /// Returns the scores of the features
+    ///
+    /// \param[out] score An array with scores of the features
+    /// \param[in] feat The features object
+    /// \ingroup features_group_features
     AFAPI af_err af_get_features_score(af_array *score, const af_features feat);
 
+    /// Returns the orientations of the features
+    ///
+    /// \param[out] orientation An array with the orientations of the features
+    /// \param[in] feat The features object
+    /// \ingroup features_group_features
     AFAPI af_err af_get_features_orientation(af_array *orientation, const af_features feat);
 
+    /// Returns the size of the features
+    ///
+    /// \param[out] size An array with the sizes of the features
+    /// \param[in] feat The features object
+    /// \ingroup features_group_features
     AFAPI af_err af_get_features_size(af_array *size, const af_features feat);
 
-    // Destroy af_features
+    /// Reduces the reference count of each of the features
+    ///
+    /// \param[in] feat The features object whose reference count will be
+    ///                 reduced
+    /// \ingroup features_group_features
     AFAPI af_err af_release_features(af_features feat);
 
 #ifdef __cplusplus
diff --git a/include/af/index.h b/include/af/index.h
index 513422c510..3bceb96cbf 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -12,14 +12,13 @@
 #include <af/seq.h>
 
 ///
-/// \brief Struct used while indexing af_array
+/// \brief Struct used to index an af_array
 ///
 /// This struct represents objects which can be used to index into an af_array
 /// Object. It contains a union object which can be an \ref af_seq or an
 /// \ref af_array. Indexing with an int can be represented using a \ref af_seq
 /// object with the same \ref af_seq::begin and \ref af_seq::end with an
 /// af_seq::step of 1
-///
 typedef struct af_index_t {
     union {
         af_array arr;   ///< The af_array used for indexing
@@ -49,6 +48,7 @@ class seq;
 /// \note This is a helper class and does not necessarily need to be created
 /// explicitly. It is used in the operator() overloads to simplify the API.
 ///
+/// \ingroup arrayfire_class
 class AFAPI index {
 
     af_index_t impl;
@@ -166,7 +166,6 @@ class AFAPI index {
 ///
 /// \ingroup index_func_lookup
 ///
-
 AFAPI array lookup(const array &in, const array &idx, const int dim = -1);
 
 #if AF_API_VERSION >= 31
@@ -181,7 +180,6 @@ AFAPI array lookup(const array &in, const array &idx, const int dim = -1);
 /// \param[in] idx3 The fourth index (defaults to \ref af::span)
 /// \ingroup index_func_index
 ///
-
 AFAPI void copy(array &dst, const array &src,
                 const index &idx0,
                 const index &idx1 = span,
@@ -206,7 +204,6 @@ extern "C" {
     /// \param[in] index is an array of sequences
     ///
     /// \ingroup index_func_index
-
     AFAPI af_err af_index(  af_array *out,
                             const af_array in,
                             const unsigned ndims, const af_seq* const index);
@@ -223,7 +220,6 @@ extern "C" {
     ///
     /// \ingroup index_func_lookup
     ///
-
     AFAPI af_err af_lookup( af_array *out,
                             const af_array in, const af_array indices,
                             const unsigned dim);
@@ -243,7 +239,6 @@ extern "C" {
     ///
     /// \ingroup index_func_assign
     ///
-
     AFAPI af_err af_assign_seq( af_array *out,
                                 const af_array lhs,
                                 const unsigned ndims, const af_seq* const indices,
diff --git a/include/af/lapack.h b/include/af/lapack.h
index 53386f7277..271d99cf4c 100644
--- a/include/af/lapack.h
+++ b/include/af/lapack.h
@@ -272,7 +272,7 @@ namespace af
 
        \returns true is LAPACK support is available, false otherwise
 
-       \ingroup lapack_ops_func_norm
+       \ingroup lapack_helper_func_available
     */
     AFAPI bool isLAPACKAvailable();
 #endif
@@ -503,7 +503,7 @@ extern "C" {
 
        \returns AF_SUCCESS if successful (does not depend on the value of out)
 
-       \ingroup lapack_ops_func_norm
+       \ingroup lapack_helper_func_available
     */
     AFAPI af_err af_is_lapack_available(bool *out);
 #endif
diff --git a/include/af/memory.h b/include/af/memory.h
index 7ebd5ab905..54e9833adc 100644
--- a/include/af/memory.h
+++ b/include/af/memory.h
@@ -16,6 +16,9 @@
 
 #if AF_API_VERSION >= 37
 
+/**
+   \ingroup memory_manager_api
+*/
 typedef void* af_memory_manager;
 
 #ifdef __cplusplus
diff --git a/include/af/random.h b/include/af/random.h
index 4940378709..347cdf84ed 100644
--- a/include/af/random.h
+++ b/include/af/random.h
@@ -15,6 +15,7 @@
 ///
 /// This handle is used to reference the internal random engine object.
 ///
+/// \ingroup random_mat
 typedef void * af_random_engine;
 
 #ifdef __cplusplus
@@ -23,127 +24,96 @@ namespace af
     class array;
     class dim4;
 #if AF_API_VERSION >= 34
+    /// \brief Random Number Generation Engine Class
     ///
-    /// \brief A random number generator class
-    /// \ingroup random_mat
+    /// The \ref af::randomEngine class is used to set the type and seed of
+    /// random number generation engine based on \ref af::randomEngineType.
     ///
-    class AFAPI randomEngine
-    {
-        private:
-            ///
-            /// \brief Handle to the interal random engine object
-            ///
-            /// \ingroup random_engine_class
-            ///
-            af_random_engine engine;
-        public:
-            /**
-                This function creates a \ref af::randomEngine object with a
-                \ref af::randomEngineType and a seed.
-
-                \code
-                randomEngine r(AF_RANDOM_ENGINE_DEFAULT, 1);   // creates random engine of default type with seed = 1
-                \endcode
-
-                \ingroup random_engine_func_constructor
-            */
-            explicit
-            randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT, unsigned long long seedIn = 0);
-
-            /**
-                Copy constructor for \ref af::randomEngine.
-
-                \param in The input random engine object
-
-                \ingroup random_engine_func_constructor
-            */
-            randomEngine(const randomEngine& in);
-
-            /**
-                Creates a copy of the random engine object from a \ref af_random_engine handle.
-
-                \param engine The input random engine object
-
-                \ingroup random_engine_func_constructor
-            */
-            randomEngine(af_random_engine engine);
-
-            /**
-                \defgroup random_engine_destructor ~randomEngine
-
-                \brief Destructor for \ref af::randomEngine
-
-                \ingroup random_engine_class
-            */
-            ~randomEngine();
-
-            /**
-                \defgroup random_engine_operator_eq operator=
-
-                \brief Assigns the internal state of randome engine
-
-                \param[in] in The object to be assigned to the random engine
-
-                \returns the reference to this
-
-                \ingroup random_engine_class
-            */
-            randomEngine& operator= (const randomEngine& in);
+    /// \ingroup arrayfire_class
+    /// \ingroup random_mat
+    class AFAPI randomEngine {
+    private:
+      ///
+      /// \brief Handle to the interal random engine object
+      af_random_engine engine;
 
-            /**
-                \defgroup random_engine_set_type setType
+    public:
+      /**
+          This function creates a \ref af::randomEngine object with a
+          \ref af::randomEngineType and a seed.
 
-                \brief Sets the random type of the random engine
+          \code
+          // creates random engine of default type with seed = 1
+          randomEngine r(AF_RANDOM_ENGINE_DEFAULT, 1);
+         \endcode
+      */
+      explicit randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT,
+                            unsigned long long seedIn = 0);
 
-                \param[in] type The type of the random number generator
+      /**
+          Copy constructor for \ref af::randomEngine.
 
-                \ingroup random_engine_class
-            */
-            void setType(const randomEngineType type);
+          \param[in] in The input random engine object
+      */
+      randomEngine(const randomEngine &in);
 
-            /**
-                \defgroup random_engine_get_type getType
+      /**
+          Creates a copy of the random engine object from a \ref
+          af_random_engine handle.
 
-                \brief Return the random type of the random engine
+          \param[in] engine The input random engine object
+      */
+      randomEngine(af_random_engine engine);
 
-                \returns the \ref af::randomEngineType associated with random engine
+      /**
+          \brief Destructor for \ref af::randomEngine
+      */
+      ~randomEngine();
 
-                \ingroup random_engine_class
-            */
-            randomEngineType getType(void);
+      /**
+          \brief Assigns the internal state of randome engine
 
-            /**
-                \defgroup random_engine_set_seed setSeed
+          \param[in] in The object to be assigned to the random engine
 
-                \brief Sets the seed of the random engine
+          \returns the reference to this
+      */
+      randomEngine &operator=(const randomEngine &in);
 
-                \param[in] seed The initializing seed of the random number generator
+      /**
+          \brief Sets the random type of the random engine
 
-                \ingroup random_engine_class
-            */
-            void setSeed(const unsigned long long seed);
+          \param[in] type The type of the random number generator
+      */
+      void setType(const randomEngineType type);
 
-            /**
-                \defgroup random_engine_get_seed getSeed
+      /**
+          \brief Return the random type of the random engine
 
-                \brief Returns the seed of the random engine
+          \returns the \ref af::randomEngineType associated with random engine
+      */
+      randomEngineType getType(void);
 
-                \returns the seed associated with random engine
+      /**
+          \brief Sets the seed of the random engine
 
-                \ingroup random_engine_class
-            */
-            unsigned long long getSeed(void) const;
+          \param[in] seed The initializing seed of the random number generator
+      */
+      void setSeed(const unsigned long long seed);
 
-            /**
-                \defgroup random_engine_get_handle get
+      /**
+          \brief Returns the seed of the random engine
 
-                \brief Returns the internal state of the random engine
+          \returns the seed associated with random engine
+      */
+      unsigned long long getSeed(void) const;
 
-                \returns the handle to the internal state associated with random engine
+      /**
+          \brief Returns the af_random_engine handle of this object
 
-                \ingroup random_engine_class
-            */
-            af_random_engine get(void) const;
+          \returns the handle to the af_random_engine associated with this
+                   random engine
+      */
+      af_random_engine get(void) const;
     };
 #endif
 
@@ -295,7 +265,7 @@ namespace af
     /**
         \param[in] rtype The type of the random number generator
 
-        \ingroup random_func_set_type
+        \ingroup random_func_set_default_engine
     */
     AFAPI void setDefaultRandomEngineType(randomEngineType rtype);
 #endif
@@ -310,15 +280,17 @@ namespace af
 #endif
 
     /**
-        \param[in] seed A 64 bit unsigned integer
+        \brief Sets the seed of the default random number generator
 
+        \param[in] seed A 64 bit unsigned integer
         \ingroup random_func_set_seed
     */
     AFAPI void setSeed(const unsigned long long seed);
 
     /**
-        \returns seed A 64 bit unsigned integer
+        \brief Gets the seed of the default random number generator
 
+        \returns seed A 64 bit unsigned integer
         \ingroup random_func_get_seed
     */
     AFAPI unsigned long long getSeed();
@@ -340,9 +312,11 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_func_constructor
+       \ingroup random_func_random_engine
     */
-    AFAPI af_err af_create_random_engine(af_random_engine *engine, af_random_engine_type rtype, unsigned long long seed);
+    AFAPI af_err af_create_random_engine(af_random_engine *engine,
+                                         af_random_engine_type rtype,
+                                         unsigned long long seed);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -354,9 +328,10 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_func_constructor
+       \ingroup random_func_random_engine
     */
-    AFAPI af_err af_retain_random_engine(af_random_engine *out, const af_random_engine engine);
+    AFAPI af_err af_retain_random_engine(af_random_engine *out,
+                                         const af_random_engine engine);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -368,9 +343,10 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_set_type
+       \ingroup random_func_random_engine
     */
-    AFAPI af_err af_random_engine_set_type(af_random_engine *engine, const af_random_engine_type rtype);
+    AFAPI af_err af_random_engine_set_type(af_random_engine *engine,
+                                           const af_random_engine_type rtype);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -382,18 +358,22 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_get_type
+       \ingroup random_func_random_engine
     */
-    AFAPI af_err af_random_engine_get_type(af_random_engine_type *rtype, const af_random_engine engine);
+    AFAPI af_err af_random_engine_get_type(af_random_engine_type *rtype,
+                                           const af_random_engine engine);
 #endif
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for creating an array of uniform numbers using a random engine
+       C Interface for creating an array of uniform numbers using a random
+       engine
 
        \param[out]  out The pointer to the returned object.
-       \param[in]   ndims The number of dimensions read from the \p dims parameter
-       \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
+       \param[in]   ndims The number of dimensions read from the \p dims
+                    parameter
+       \param[in]   dims A C pointer with \p ndims elements. Each value
+                    represents the size of that dimension
        \param[in]   type The type of the \ref af_array object
        \param[in]   engine The random engine object
 
@@ -401,7 +381,9 @@ extern "C" {
 
        \ingroup random_func_randu
     */
-    AFAPI af_err af_random_uniform(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
+    AFAPI af_err af_random_uniform(af_array *out, const unsigned ndims,
+                                   const dim_t * const dims, const af_dtype type,
+                                   af_random_engine engine);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -409,8 +391,10 @@ extern "C" {
        C Interface for creating an array of normal numbers using a random engine
 
        \param[out]  out The pointer to the returned object.
-       \param[in]   ndims The number of dimensions read from the \p dims parameter
-       \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
+       \param[in]   ndims The number of dimensions read from the \p dims
+                    parameter
+       \param[in]   dims A C pointer with \p ndims elements. Each value
+                    represents the size of that dimension
        \param[in]   type The type of the \ref af_array object
        \param[in]   engine The random engine object
 
@@ -418,7 +402,9 @@ extern "C" {
 
        \ingroup random_func_randn
     */
-    AFAPI af_err af_random_normal(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type, af_random_engine engine);
+    AFAPI af_err af_random_normal(af_array *out, const unsigned ndims,
+                                  const dim_t * const dims, const af_dtype type,
+                                  af_random_engine engine);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -430,9 +416,10 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_set_seed
+       \ingroup random_func_random_engine
     */
-    AFAPI af_err af_random_engine_set_seed(af_random_engine *engine, const unsigned long long seed);
+    AFAPI af_err af_random_engine_set_seed(af_random_engine *engine,
+                                           const unsigned long long seed);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -456,7 +443,7 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_func_set_type
+       \ingroup random_func_set_default_engine
     */
     AFAPI af_err af_set_default_random_engine_type(const af_random_engine_type rtype);
 #endif
@@ -470,9 +457,10 @@ extern "C" {
 
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_get_type
+       \ingroup random_func_random_engine
     */
-    AFAPI af_err af_random_engine_get_seed(unsigned long long * const seed, af_random_engine engine);
+    AFAPI af_err af_random_engine_get_seed(unsigned long long * const seed,
+                                           af_random_engine engine);
 #endif
 
 #if AF_API_VERSION >= 34
@@ -482,7 +470,7 @@ extern "C" {
        \param[in] engine The random engine object
        \returns \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup random_engine_destructor
+       \ingroup random_func_random_engine
     */
     AFAPI af_err af_release_random_engine(af_random_engine engine);
 #endif
@@ -495,7 +483,8 @@ extern "C" {
 
        \ingroup random_func_randu
     */
-    AFAPI af_err af_randu(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_randu(af_array *out, const unsigned ndims,
+                          const dim_t * const dims, const af_dtype type);
 
     /**
         \param[out] out The generated array
@@ -505,7 +494,8 @@ extern "C" {
 
        \ingroup random_func_randn
     */
-    AFAPI af_err af_randn(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+    AFAPI af_err af_randn(af_array *out, const unsigned ndims,
+                          const dim_t * const dims, const af_dtype type);
 
     /**
         \param[in] seed A 64 bit unsigned integer
diff --git a/include/af/seq.h b/include/af/seq.h
index 7ab36879a4..9f1600f005 100644
--- a/include/af/seq.h
+++ b/include/af/seq.h
@@ -38,9 +38,9 @@ class array;
 /**
     \class seq
 
-    \brief seq is used to create seq for indexing af::array
+    \brief seq is used to create sequences for indexing af::array
 
-    \ingroup index_mat
+    \ingroup arrayfire_class
 */
 class AFAPI seq
 {
@@ -225,7 +225,10 @@ class AFAPI seq
     void init(double begin, double end, double step);
 };
 
+/// A special value representing the last value of an axis
 extern AFAPI int end;
+
+/// A special value representing the entire axis of an af::array
 extern AFAPI seq span;
 
 }
@@ -234,6 +237,8 @@ extern AFAPI seq span;
 #ifdef __cplusplus
 extern "C" {
 #endif
+
+/// Create a new af_seq object.
 AFAPI af_seq af_make_seq(double begin, double end, double step);
 
 #ifdef __cplusplus
diff --git a/include/arrayfire.h b/include/arrayfire.h
index aa378cb026..d3b041001d 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -11,7 +11,11 @@
 
 /**
 
-\defgroup arrayfire_func Complete List of ArrayFire Functions
+\defgroup arrayfire_func ArrayFire Functions
+@{
+@}
+
+\defgroup arrayfire_class ArrayFire Classes
 @{
 @}
 
@@ -99,49 +103,48 @@
 
    @defgroup memory_manager Memory Management
    @{
-
-      \brief Interfaces for writing custom memory managers.
+      Interfaces for writing custom memory managers.
 
       Create and set a custom memory manager by first defining the relevant
-closures for each required function, for example:
+      closures for each required function, for example:
 
       \code{.cpp}
-      af_err my_initialize(af_memory_manager manager) {
-          void* myPayload = malloc(sizeof(MyPayload_t));
-          af_memory_manager_set_payload(manager, myPayload);
-          // ...
-      }
-
-      af_err my_allocated(af_memory_manager handle, size_t* size, void* ptr) {
-          void* myPayload;
-          af_memory_manager_get_payload(manager, &myPayload);
-          // ...
-      }
+          af_err my_initialize(af_memory_manager manager) {
+              void* myPayload = malloc(sizeof(MyPayload_t));
+              af_memory_manager_set_payload(manager, myPayload);
+              // ...
+          }
+
+          af_err my_allocated(af_memory_manager handle, size_t* size, void* ptr) {
+              void* myPayload;
+              af_memory_manager_get_payload(manager, &myPayload);
+              // ...
+          }
       \endcode
 
       Create an \ref af_memory_manager and attach relevant closures:
 
       \code{.cpp}
-      af_memory_manager manager;
-      af_create_memory_manager(&manager);
+          af_memory_manager manager;
+          af_create_memory_manager(&manager);
 
-      af_memory_manager_set_initialize_fn(manager, my_initialize);
-      af_memory_manager_set_allocated_fn(manager, my_allocated);
+          af_memory_manager_set_initialize_fn(manager, my_initialize);
+          af_memory_manager_set_allocated_fn(manager, my_allocated);
 
-      // ...
-       \endcode
+          // ...
+      \endcode
 
       Set the memory manager to be active, which shuts down the existing memory
-manager:
+      manager:
 
       \code{.cpp}
-      af_set_memory_manager(manager);
+          af_set_memory_manager(manager);
       \endcode
 
       Unset to re-create and reset an instance of the default memory manager:
 
       \code{.cpp}
-      af_unset_memory_manager();
+          af_unset_memory_manager();
       \endcode
 
       @defgroup native_memory_interface Native Memory Interface
@@ -149,23 +152,20 @@ closures for each required function, for example:
 
       @defgroup memory_manager_utils Memory Manager Utils
       \brief Set and unset memory managers, set and get manager payloads,
-function setters
+              function setters
 
       @defgroup memory_manager_api Memory Manager API
       \brief Functions for defining custom memory managers
-
    @}
 
    @defgroup event Events
    @{
 
       \brief Managing ArrayFire Events which allows manipulation of operations
-on computation queues.
-
-
+              on computation queues.
 
-      @defgroup event_api Event API
-      af_create_event, af_mark_event, etc.
+      \defgroup event_api Event API
+      \brief af_create_event, af_mark_event, etc.
    @}
 
    @defgroup linalg_mat Linear Algebra

From 0654c1cddf11f4d7e6da550f7c39d02e237f953c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 10 Feb 2020 17:26:52 +0530
Subject: [PATCH 1832/2677] Remove forge subproject target from ALL target

This will avoid forge target installation when arrayfire install
command runs.
---
 CMakeModules/AFconfigure_forge_submodule.cmake | 2 +-
 src/backend/common/CMakeLists.txt              | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index 748e1ba48d..4ee62909cd 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -14,7 +14,7 @@ if(AF_BUILD_FORGE)
   set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
   set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
-  add_subdirectory(extern/forge)
+  add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
 
   mark_as_advanced(
       FG_BUILD_EXAMPLES
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 90db1fc100..4e6747f28e 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -73,7 +73,9 @@ target_link_libraries(afcommon_interface
     spdlog
     Boost::boost
     af_glad_interface
-    ${CMAKE_DL_LIBS})
+    ${CMAKE_DL_LIBS}
+    $<$<BOOL:${AF_BUILD_FORGE}>:forge> #Making it a dependency only so that is built with ALL targets
+)
 
 target_include_directories(afcommon_interface
   INTERFACE

From d9fdd26b02f55115b2806ced87fb81b681b0e2e1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 10 Feb 2020 18:11:39 +0530
Subject: [PATCH 1833/2677] Updated forge submodule to v1.0.5 fix release

---
 extern/forge | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extern/forge b/extern/forge
index 650bf611de..1a0f0cb637 160000
--- a/extern/forge
+++ b/extern/forge
@@ -1 +1 @@
-Subproject commit 650bf611de102a2cc0c32dba7646f8128f0300c8
+Subproject commit 1a0f0cb6371a8c8053ab5eb7cbe3039c95132389

From 210981a3febf8c4dccfd3d3b13a63a24113717a2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 10 Feb 2020 14:15:43 -0500
Subject: [PATCH 1834/2677] Fix missing skip for imageio disabled builds

---
 test/transform.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/test/transform.cpp b/test/transform.cpp
index 398400e7f9..5618191cf0 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -304,6 +304,7 @@ class TransformV2 : public Transform<T> {
     }
 
     void setTestData(string pTestFile, string pHomographyFile) {
+        if (noImageIOTests()) return;
         releaseArrays();
 
         genTestData<T>(&gold, &in, &transform, &odim0, &odim1, pTestFile,

From 9ceda8e981f9dcd5f277a73e194deb3e2cba9a89 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Feb 2020 01:58:15 -0500
Subject: [PATCH 1835/2677] Remove direct linking to forge. Forge is linked at
 runtime

---
 CMakeModules/AFconfigure_forge_submodule.cmake | 1 +
 src/backend/common/CMakeLists.txt              | 1 -
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index 4ee62909cd..adc51037e7 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -15,6 +15,7 @@ if(AF_BUILD_FORGE)
   set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
   add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
+  set_target_properties(forge PROPERTIES EXCLUDE_FROM_ALL False)
 
   mark_as_advanced(
       FG_BUILD_EXAMPLES
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 4e6747f28e..81f3414b89 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -74,7 +74,6 @@ target_link_libraries(afcommon_interface
     Boost::boost
     af_glad_interface
     ${CMAKE_DL_LIBS}
-    $<$<BOOL:${AF_BUILD_FORGE}>:forge> #Making it a dependency only so that is built with ALL targets
 )
 
 target_include_directories(afcommon_interface

From 9a95cf5eda256fc509b2e4be3759c322b2cb73c0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Feb 2020 01:59:10 -0500
Subject: [PATCH 1836/2677] Use find_library to find the CUDA libraries

* On CentOS and Ubuntu all cuda libraries are not located in the
  CUDA toolkit lib directory. They are located in the standard library
  paths
---
 src/backend/cuda/CMakeLists.txt | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 05850ff342..3bf9b14a44 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -630,7 +630,12 @@ macro(afcu_collect_libs libname)
             RENAME      "${PX}${libname}.${CUDA_VERSION}${SX}"
             COMPONENT   cuda_dependencies)
   else () #UNIX
-    get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}${SX}" REALPATH)
+    find_library(CUDA_${libname}_LIBRARY
+      NAME ${libname}
+      PATH
+        ${dlib_path_prefix})
+
+    get_filename_component(outpath "${CUDA_${libname}_LIBRARY}" REALPATH)
     install(FILES       ${outpath}
             DESTINATION ${AF_INSTALL_LIB_DIR}
             RENAME      "${PX}${libname}${SX}.${CUDA_VERSION}"

From d17f08b8e2bb8f16fb16133faefa90f45205a4b4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Feb 2020 02:00:50 -0500
Subject: [PATCH 1837/2677] Set RUNPATH to $ORIGIN in standalone builds

Sets the install runpath to $ORIGIN in standalone builds. This allows
the linker to find the libraries in the same directory as the library
and avoids setting the LD_LIBRARY_PATH, and ld.so.conf.d
---
 CMakeLists.txt | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5337ecdee7..9729ee46c8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -186,6 +186,21 @@ set_target_properties(${built_backends} PROPERTIES
                       VERSION "${ArrayFire_VERSION}"
                       SOVERSION "${ArrayFire_VERSION_MAJOR}")
 
+if(AF_INSTALL_STANDALONE)
+
+  # This flag enables the use of RUNPATH instead of RPATH which is the
+  # preferred method to set the runtime lookup. Only doind this for
+  # standalone builds because we include all libraries with the installers
+  # and they are included in the same directory so the RUNPATH is set to
+  # $ORIGIN. This avoid setting the linker path in ld.so.conf.d
+  check_cxx_compiler_flag("-Wl,--enable-new-dtags" HAS_RUNPATH_FLAG)
+  if(HAS_RUNPATH_FLAG)
+    set_target_properties(${built_backends} PROPERTIES
+      INSTALL_RPATH "$ORIGIN"
+      LINK_OPTIONS "-Wl,--enable-new-dtags")
+  endif()
+endif()
+
 # On some distributions the linker will not add a library to the ELF header if
 # the symbols are not needed when the library was first parsed by the linker.
 # This causes undefined references issues when linking with libraries which have

From 961832f9c49ceb6e570ca7aa79bb9457b6fed24f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Feb 2020 04:04:37 -0500
Subject: [PATCH 1838/2677] Rename the nvrtc dll to the full version. Include
 cudnn library

---
 src/backend/cuda/CMakeLists.txt | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 3bf9b14a44..63704f0cfa 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -610,7 +610,12 @@ if (WIN32)
   set(dlib_path_prefix "${CUDA_TOOLKIT_ROOT_DIR}/bin")
 endif ()
 
-macro(afcu_collect_libs libname)
+function(afcu_collect_libs libname)
+  set(options "FULL_VERSION")
+  set(single_args "")
+  set(multi_args "")
+
+  cmake_parse_arguments(cuda_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
   if (WIN32)
     find_file(CUDA_${libname}_LIBRARY_DLL
       NAMES
@@ -636,19 +641,25 @@ macro(afcu_collect_libs libname)
         ${dlib_path_prefix})
 
     get_filename_component(outpath "${CUDA_${libname}_LIBRARY}" REALPATH)
+    if(cuda_args_FULL_VERSION)
+      set(library_install_name "${PX}${libname}${SX}.${CUDA_VERSION}")
+    else()
+      set(library_install_name "${PX}${libname}${SX}.${CUDA_VERSION_MAJOR}")
+    endif()
     install(FILES       ${outpath}
             DESTINATION ${AF_INSTALL_LIB_DIR}
-            RENAME      "${PX}${libname}${SX}.${CUDA_VERSION}"
+            RENAME      ${library_install_name}
             COMPONENT   cuda_dependencies)
   endif ()
-endmacro()
+endfunction()
 
 if(AF_INSTALL_STANDALONE)
   afcu_collect_libs(cufft)
+  afcu_collect_libs(cudnn)
   afcu_collect_libs(cublas)
   afcu_collect_libs(cusolver)
   afcu_collect_libs(cusparse)
-  afcu_collect_libs(nvrtc)
+  afcu_collect_libs(nvrtc FULL_VERSION)
 
   if(APPLE)
     afcu_collect_libs(cudart)

From e31c1cd0e29539a94cc762585406fdea3c09613e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 11 Feb 2020 12:31:46 +0530
Subject: [PATCH 1839/2677] add cublasLt to install dependencies

---
 src/backend/cuda/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 63704f0cfa..f6e81063a5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -657,6 +657,7 @@ if(AF_INSTALL_STANDALONE)
   afcu_collect_libs(cufft)
   afcu_collect_libs(cudnn)
   afcu_collect_libs(cublas)
+  afcu_collect_libs(cublasLt)
   afcu_collect_libs(cusolver)
   afcu_collect_libs(cusparse)
   afcu_collect_libs(nvrtc FULL_VERSION)

From 48d417721a1976ca6c9262204d6c2eef1cca6dbb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Feb 2020 05:01:51 -0500
Subject: [PATCH 1840/2677] Ensure examples are compatible with older versions
 of C++

---
 examples/CMakeLists.txt                       |  3 ++-
 .../confidence_connected_components.cpp       |  2 +-
 examples/machine_learning/kmeans.cpp          | 26 ++++++++++---------
 examples/machine_learning/neural_network.cpp  |  2 +-
 4 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 94b5b19c43..e6bf747554 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -8,9 +8,10 @@
 cmake_minimum_required(VERSION 3.0)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Examples
-  VERSION 3.5.0
+  VERSION 3.7.0
   LANGUAGES CXX)
 
+set(CMAKE_CXX_STANDARD 98)
 if(NOT EXISTS "${ArrayFire_SOURCE_DIR}/CMakeLists.txt")
   set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/..")
 endif()
diff --git a/examples/image_processing/confidence_connected_components.cpp b/examples/image_processing/confidence_connected_components.cpp
index 0883c0d6be..661b90652f 100644
--- a/examples/image_processing/confidence_connected_components.cpp
+++ b/examples/image_processing/confidence_connected_components.cpp
@@ -39,7 +39,7 @@ int main(int argc, char* argv[]) {
 
         seedx = 15;
         seedy = 15;
-        unsigned seedcoords[]{15, 15};
+        unsigned seedcoords[] = {15, 15};
         array seeds(dim4(1, 2), seedcoords);
         array background =
             confidenceCC(A, seeds, radius, multiplier, iter, 255);
diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index 43d2111bad..e40cc34368 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -113,8 +113,7 @@ int kmeans_demo(int k, bool console) {
     printf("** ArrayFire K-Means Demo (k = %d) **\n\n", k);
 
     array img =
-        loadImage(ASSETS_DIR "/examples/images/spider.jpg") /
-        255;  // [0-255]
+        loadImage(ASSETS_DIR "/examples/images/spider.jpg") / 255;  // [0-255]
 
     int w = img.dims(0), h = img.dims(1), c = img.dims(2);
     array vec = moddims(img, w * h, 1, c);
@@ -129,23 +128,26 @@ int kmeans_demo(int k, bool console) {
     kmeans(means_dbl, clusters_dbl, vec, k * 2);
 
     if (!console) {
-        array out_full = moddims(means_full(span, clusters_full, span), img.dims());
-        array out_half = moddims(means_half(span, clusters_half, span), img.dims());
-        array out_dbl  = moddims(means_dbl (span, clusters_dbl , span), img.dims());
+        array out_full =
+            moddims(means_full(span, clusters_full, span), img.dims());
+        array out_half =
+            moddims(means_half(span, clusters_half, span), img.dims());
+        array out_dbl =
+            moddims(means_dbl(span, clusters_dbl, span), img.dims());
 
         af::Window wnd(800, 800, "ArrayFire K-Means Demo");
         wnd.grid(2, 2);
-        std::string out_full_caption = "k = " + std::to_string(k);
-        std::string out_half_caption = "k = " + std::to_string(k / 2);
-        std::string out_dbl_caption = "k = " + std::to_string(k * 2);
+        std::stringstream out_full_caption, out_half_caption, out_dbl_caption;
+        out_full_caption << "k = " << k;
+        out_half_caption << "k = " << k / 2;
+        out_dbl_caption << "k = " << k * 2;
         while (!wnd.close()) {
             wnd(0, 0).image(img, "Input Image");
-            wnd(0, 1).image(out_full, out_full_caption.c_str());
-            wnd(1, 0).image(out_half, out_half_caption.c_str());
-            wnd(1, 1).image(out_dbl, out_dbl_caption.c_str());
+            wnd(0, 1).image(out_full, out_full_caption.str().c_str());
+            wnd(1, 0).image(out_half, out_half_caption.str().c_str());
+            wnd(1, 1).image(out_dbl, out_dbl_caption.str().c_str());
             wnd.show();
         }
-
     } else {
         means_full =
             moddims(means_full, means_full.dims(1), means_full.dims(2));
diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index 8302fdb1bd..c5fc857899 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -22,7 +22,7 @@ std::string toStr(const dtype dt) {
     switch(dt) {
         case f32: return "f32";
         case f16: return "f16";
-        default: return std::to_string(dt);
+        default: return "N/A";
     }
 }
 

From 39cc7679ff91fe5804ed08ca8eaa4d3190d681ff Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 12 Feb 2020 14:10:32 +0530
Subject: [PATCH 1841/2677] CMakeParseArguments include only needed for cmake
 <= 3.4

---
 CMakeModules/FileToString.cmake | 2 --
 test/CMakeLists.txt             | 2 --
 2 files changed, 4 deletions(-)

diff --git a/CMakeModules/FileToString.cmake b/CMakeModules/FileToString.cmake
index 061ddcced9..6092c9176c 100644
--- a/CMakeModules/FileToString.cmake
+++ b/CMakeModules/FileToString.cmake
@@ -23,8 +23,6 @@
 #
 # where ns is the contents of kernel.cl.namespace.
 
-include(CMakeParseArguments)
-
 set(BIN2CPP_PROGRAM "bin2cpp")
 
 function(FILE_TO_STRING)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 0d3e4580bf..a67e19ec91 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -70,8 +70,6 @@ if(AF_BUILD_UNIFIED)
   list(APPEND enabled_backends "unified")
 endif(AF_BUILD_UNIFIED)
 
-include(CMakeParseArguments)
-
 # Creates tests for all backends
 #
 # Creates a standard test for all backends. Most of the time you only need to

From 69ed0a883541951a6cd08a5099daa51d3e2374b3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 12 Feb 2020 15:29:47 +0530
Subject: [PATCH 1842/2677] Fix forge build dependency and install command

---
 CMakeLists.txt                                 | 2 --
 CMakeModules/AFconfigure_forge_submodule.cmake | 3 ++-
 src/backend/common/CMakeLists.txt              | 4 ++++
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9729ee46c8..62c70288d8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -251,8 +251,6 @@ install(FILES ${ArrayFire_BINARY_DIR}/include/af/version.h
         DESTINATION "${AF_INSTALL_INC_DIR}/af/"
         COMPONENT headers)
 
-#TODO(pradeep) install forge dependency for packaging - not required for builds
-
 # install the examples irrespective of the AF_BUILD_EXAMPLES value
 # only the examples source files are installed, so the installation of these
 # source files does not depend on AF_BUILD_EXAMPLES
diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_submodule.cmake
index adc51037e7..d16849f050 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_submodule.cmake
@@ -15,7 +15,6 @@ if(AF_BUILD_FORGE)
   set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
   add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
-  set_target_properties(forge PROPERTIES EXCLUDE_FROM_ALL False)
 
   mark_as_advanced(
       FG_BUILD_EXAMPLES
@@ -34,6 +33,8 @@ if(AF_BUILD_FORGE)
       $<TARGET_FILE:forge>
       $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
       $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
+      $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:forge>>
+      $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:forge>>
       DESTINATION "${AF_INSTALL_LIB_DIR}"
       COMPONENT common_backend_dependencies)
   set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 81f3414b89..7574e32d1d 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -76,6 +76,10 @@ target_link_libraries(afcommon_interface
     ${CMAKE_DL_LIBS}
 )
 
+if(AF_BUILD_FORGE)
+  add_dependencies(afcommon_interface forge)
+endif()
+
 target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend

From fbea2aeb6f7f2d277dcb0ab425a77bb18ed22291 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 13 Feb 2020 00:17:26 +0530
Subject: [PATCH 1843/2677] Workaround for nvidia OpenCL if forge dependencies
 are missing

Bug/root-cause found by Umar while testing examples on centos container setup
---
 src/backend/common/forge_loader.hpp    |  2 ++
 src/backend/common/graphics_common.cpp | 36 ++++++++++++++++----------
 src/backend/opencl/device_manager.cpp  |  3 ++-
 3 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index 15b8c81447..bf1cce8c5d 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -85,6 +85,8 @@ class ForgeModule : public common::DependencyModule {
     MODULE_MEMBER(fg_append_surface_to_chart);
     MODULE_MEMBER(fg_append_vector_field_to_chart);
     MODULE_MEMBER(fg_release_chart);
+
+    MODULE_MEMBER(fg_err_to_string);
 };
 
 namespace graphics {
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index a154041a46..8bca480253 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -90,6 +90,8 @@ ForgeModule::ForgeModule() : DependencyModule("forge", nullptr) {
         FG_MODULE_FUNCTION_INIT(fg_append_vector_field_to_chart);
         FG_MODULE_FUNCTION_INIT(fg_release_chart);
 
+        FG_MODULE_FUNCTION_INIT(fg_err_to_string);
+
         if (!DependencyModule::symbolsLoaded()) {
             string error_message =
                 "Error loading Forge: " + DependencyModule::getErrorMessage() +
@@ -241,27 +243,35 @@ fg_window ForgeManager::getMainWindow() {
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     std::string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
 
+    af_err error      = AF_SUCCESS;
+    fg_err forgeError = FG_ERR_NONE;
     if (noGraphicsENV.empty()) {  // If AF_DISABLE_GRAPHICS is not defined
-        std::call_once(flag, [this] {
+        std::call_once(flag, [this, &error, &forgeError] {
             if (!this->mPlugin->isLoaded()) {
-                string error_message =
-                    "Error loading Forge: " + this->mPlugin->getErrorMessage() +
-                    "\nForge or one of it's dependencies failed to "
-                    "load. Try installing Forge or check if Forge is in the "
-                    "search path.";
-                AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+                error = AF_ERR_LOAD_LIB;
+                return;
             }
             fg_window w = nullptr;
-            fg_err e    = this->mPlugin->fg_create_window(&w, WIDTH, HEIGHT,
-                                                       "ArrayFire", NULL, true);
-            if (e != FG_ERR_NONE) {
-                AF_ERROR("Graphics Window creation failed", AF_ERR_INTERNAL);
-            }
+            forgeError  = this->mPlugin->fg_create_window(
+                &w, WIDTH, HEIGHT, "ArrayFire", NULL, true);
+            if (forgeError != FG_ERR_NONE) { return; }
             this->setWindowChartGrid(w, 1, 1);
             this->mPlugin->fg_make_window_current(w);
             this->mMainWindow.reset(new Window({w}));
-            if (!gladLoadGL()) { AF_ERROR("GL Load Failed", AF_ERR_LOAD_LIB); }
+            if (!gladLoadGL()) { error = AF_ERR_LOAD_LIB; }
         });
+        if (error == AF_ERR_LOAD_LIB) {
+            string error_message =
+                "Error loading Forge: " + this->mPlugin->getErrorMessage() +
+                "\nForge or one of it's dependencies failed to "
+                "load. Try installing Forge or check if Forge is in the "
+                "search path.";
+            AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+        }
+        if (forgeError != FG_ERR_NONE) {
+            AF_ERROR(this->mPlugin->fg_err_to_string(forgeError),
+                     AF_ERR_RUNTIME);
+        }
     }
 
     return mMainWindow->handle;
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 1fb78781c7..cddf1b4c8c 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -167,7 +167,7 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
 DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , mUserDeviceOffset(0)
-    , fgMngr(new graphics::ForgeManager())
+    , fgMngr(nullptr)
     , mFFTSetup(new clfftSetupData) {
     vector<Platform> platforms;
     try {
@@ -182,6 +182,7 @@ DeviceManager::DeviceManager()
                 AF_ERR_RUNTIME);
         }
     }
+    fgMngr.reset(new graphics::ForgeManager());
 
     // This is all we need because the sort takes care of the order of devices
 #ifdef OS_MAC

From 2c819be95608b2e2f7888a7d7aaf50b530e40a50 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 14 Feb 2020 11:51:09 +0530
Subject: [PATCH 1844/2677] Format source files per clang-format style

---
 examples/benchmarks/blas.cpp                  |     3 +-
 examples/graphics/gravity_sim_init.h          | 14001 ++++++++--------
 .../confidence_connected_components.cpp       |     7 +-
 examples/machine_learning/neural_network.cpp  |    33 +-
 src/api/c/approx.cpp                          |    40 +-
 src/api/c/array.cpp                           |     2 +-
 src/api/c/assign.cpp                          |     3 +-
 src/api/c/blas.cpp                            |   146 +-
 src/api/c/clamp.cpp                           |     2 +-
 src/api/c/complex.cpp                         |     2 +-
 src/api/c/confidence_connected.cpp            |    59 +-
 src/api/c/device.cpp                          |     8 +-
 src/api/c/events.cpp                          |     1 -
 src/api/c/events.hpp                          |     4 +-
 src/api/c/features.hpp                        |     2 +-
 src/api/c/flip.cpp                            |     8 +-
 src/api/c/handle.hpp                          |     9 +-
 src/api/c/imgproc_common.hpp                  |     6 +-
 src/api/c/internal.cpp                        |     4 +-
 src/api/c/memoryapi.hpp                       |     6 +-
 src/api/c/pinverse.cpp                        |     8 +-
 src/api/c/random.cpp                          |    14 +-
 src/api/c/replace.cpp                         |     2 +-
 src/api/c/select.cpp                          |     4 +-
 src/api/c/transform.cpp                       |     4 +-
 src/api/c/transform_coordinates.cpp           |     5 +-
 src/api/c/unary.cpp                           |     2 +-
 src/api/c/var.cpp                             |     5 +-
 src/api/c/wrap.cpp                            |    47 +-
 src/api/cpp/confidence_connected.cpp          |    14 +-
 src/api/cpp/convolve.cpp                      |    16 +-
 src/api/cpp/data.cpp                          |     2 +-
 src/api/cpp/event.cpp                         |     2 +-
 src/api/unified/algorithm.cpp                 |    14 +-
 src/api/unified/data.cpp                      |    34 +-
 src/api/unified/image.cpp                     |    96 +-
 src/api/unified/symbol_manager.hpp            |     2 +-
 src/backend/common/AllocatorInterface.hpp     |     2 +-
 src/backend/common/ArrayInfo.cpp              |     4 +-
 src/backend/common/DefaultMemoryManager.cpp   |    38 +-
 src/backend/common/DefaultMemoryManager.hpp   |     8 +-
 src/backend/common/DependencyModule.hpp       |     2 +-
 src/backend/common/HandleBase.hpp             |     7 +-
 src/backend/common/defines.hpp                |     4 +-
 src/backend/common/graphics_common.cpp        |    51 +-
 src/backend/common/graphics_common.hpp        |    46 +-
 src/backend/common/half.hpp                   |     2 +-
 src/backend/common/jit/BufferNodeBase.hpp     |     2 +-
 src/backend/common/jit/NaryNode.hpp           |     7 +-
 src/backend/common/jit/ScalarNode.hpp         |     4 +-
 src/backend/common/kernel_type.hpp            |     2 +-
 src/backend/common/util.hpp                   |     5 +-
 src/backend/cpu/ParamIterator.hpp             |     4 +-
 src/backend/cpu/blas.cpp                      |   135 +-
 src/backend/cpu/convolve.cpp                  |     2 +-
 src/backend/cpu/convolve.hpp                  |    12 +-
 src/backend/cpu/flood_fill.cpp                |     8 +-
 src/backend/cpu/homography.cpp                |     2 +-
 src/backend/cpu/image.cpp                     |     2 +-
 src/backend/cpu/jit/BinaryNode.hpp            |     5 +-
 src/backend/cpu/jit/BufferNode.hpp            |     3 +-
 src/backend/cpu/join.cpp                      |     2 +-
 src/backend/cpu/kernel/copy.hpp               |     8 +-
 src/backend/cpu/kernel/iota.hpp               |     8 +-
 src/backend/cpu/kernel/pad_array_borders.hpp  |     2 +-
 src/backend/cpu/kernel/random_engine.hpp      |    14 +-
 src/backend/cpu/kernel/sobel.hpp              |    12 +-
 src/backend/cpu/kernel/wrap.hpp               |    17 +-
 src/backend/cpu/mean.cpp                      |     2 +-
 src/backend/cpu/memory.cpp                    |     4 +-
 src/backend/cpu/morph.cpp                     |     4 +-
 src/backend/cpu/set.cpp                       |     4 +-
 src/backend/cpu/set.hpp                       |     6 +-
 src/backend/cpu/solve.cpp                     |     3 +-
 src/backend/cpu/sort_by_key.cpp               |     2 +-
 src/backend/cpu/sort_index.cpp                |     2 +-
 src/backend/cpu/sparse_blas.cpp               |    13 +-
 src/backend/cpu/types.hpp                     |     2 +-
 src/backend/cpu/wrap.cpp                      |    25 +-
 src/backend/cpu/wrap.hpp                      |    13 +-
 src/backend/cuda/Array.hpp                    |     4 +-
 src/backend/cuda/binary.hpp                   |     3 +-
 src/backend/cuda/blas.cpp                     |     6 +-
 src/backend/cuda/convolve.hpp                 |    12 +-
 src/backend/cuda/cudnn.cpp                    |     3 +-
 src/backend/cuda/cudnn.hpp                    |     2 -
 src/backend/cuda/cudnnModule.hpp              |    20 +-
 src/backend/cuda/flood_fill.cpp               |    12 +-
 src/backend/cuda/handle.cpp                   |     1 -
 .../cuda/kernel/anisotropic_diffusion.hpp     |     8 +-
 src/backend/cuda/kernel/convolve.hpp          |    22 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |     6 +-
 src/backend/cuda/kernel/flood_fill.hpp        |    18 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |     5 +-
 src/backend/cuda/kernel/iota.hpp              |     4 +-
 src/backend/cuda/kernel/mean.hpp              |     4 +-
 src/backend/cuda/kernel/medfilt.hpp           |     6 +-
 src/backend/cuda/kernel/morph.hpp             |     2 +-
 src/backend/cuda/kernel/random_engine.hpp     |     3 +-
 src/backend/cuda/kernel/range.hpp             |     2 +-
 src/backend/cuda/kernel/reduce_by_key.hpp     |    27 +-
 src/backend/cuda/kernel/scan_dim.hpp          |     6 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |    17 +-
 src/backend/cuda/kernel/scan_first.hpp        |     5 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |     8 +-
 src/backend/cuda/kernel/sift_nonfree.hpp      |     4 +-
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |     2 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |     9 +-
 src/backend/cuda/math.hpp                     |     2 +-
 src/backend/cuda/nvrtc/cache.cpp              |    19 +-
 src/backend/cuda/nvrtc/cache.hpp              |     2 +-
 src/backend/cuda/platform.cpp                 |     7 +-
 src/backend/cuda/platform.hpp                 |     6 +-
 src/backend/cuda/scalar.hpp                   |     2 +-
 src/backend/cuda/transpose.cpp                |     2 +-
 src/backend/cuda/types.hpp                    |    11 +-
 src/backend/cuda/unary.hpp                    |     5 +-
 src/backend/cuda/wrap.hpp                     |     9 +-
 src/backend/opencl/Array.hpp                  |     2 +-
 src/backend/opencl/any.cpp                    |     2 +-
 src/backend/opencl/assign.cpp                 |     5 +-
 src/backend/opencl/blas.cpp                   |    39 +-
 src/backend/opencl/clfft.hpp                  |     2 +-
 src/backend/opencl/convolve.hpp               |    12 +-
 src/backend/opencl/cpu/cpu_blas.cpp           |    37 +-
 src/backend/opencl/cpu/cpu_blas.hpp           |     5 +-
 src/backend/opencl/cpu/cpu_sparse_blas.cpp    |    13 +-
 src/backend/opencl/flood_fill.cpp             |     4 +-
 src/backend/opencl/jit/kernel_generators.hpp  |     4 +-
 src/backend/opencl/kernel/exampleFunction.hpp |    10 +-
 src/backend/opencl/kernel/flood_fill.hpp      |    52 +-
 src/backend/opencl/kernel/identity.hpp        |     9 +-
 src/backend/opencl/kernel/laset.hpp           |     2 +-
 src/backend/opencl/kernel/lookup.hpp          |     9 +-
 src/backend/opencl/kernel/mean.hpp            |     2 +-
 src/backend/opencl/kernel/random_engine.hpp   |    20 +-
 src/backend/opencl/kernel/range.hpp           |     3 +-
 src/backend/opencl/kernel/reduce.hpp          |     2 +-
 src/backend/opencl/kernel/reduce_by_key.hpp   |    40 +-
 src/backend/opencl/kernel/transpose.hpp       |     6 +-
 src/backend/opencl/kernel/wrap.hpp            |     4 +-
 src/backend/opencl/magma/magma.h              |   135 +-
 src/backend/opencl/magma/magma_blas.h         |    24 +-
 src/backend/opencl/magma/magma_blas_clblast.h |   327 +-
 src/backend/opencl/magma/magma_helper.h       |    35 +-
 src/backend/opencl/magma/magma_types.h        |   645 +-
 src/backend/opencl/max.cpp                    |     2 +-
 src/backend/opencl/mean.cpp                   |     2 +-
 src/backend/opencl/platform.cpp               |     4 +-
 src/backend/opencl/product.cpp                |     2 +-
 src/backend/opencl/reduce.hpp                 |     8 +-
 src/backend/opencl/solve.cpp                  |     4 +-
 src/backend/opencl/sparse_blas.cpp            |     6 +-
 src/backend/opencl/triangle.cpp               |     2 +-
 src/backend/opencl/types.cpp                  |    34 +-
 src/backend/opencl/unwrap.cpp                 |     2 +-
 src/backend/opencl/wrap.cpp                   |    21 +-
 src/backend/opencl/wrap.hpp                   |    24 +-
 test/approx1.cpp                              |    11 +-
 test/approx2.cpp                              |     2 +-
 test/array.cpp                                |     3 +-
 test/binary.cpp                               |    12 +-
 test/blas.cpp                                 |   204 +-
 test/canny.cpp                                |     3 +-
 test/clamp.cpp                                |    10 +-
 test/compare.cpp                              |     2 +-
 test/confidence_connected.cpp                 |   103 +-
 test/convolve.cpp                             |    20 +-
 test/dot.cpp                                  |     6 +-
 test/event.cpp                                |     2 +-
 test/fft.cpp                                  |    59 +-
 test/flat.cpp                                 |     2 +-
 test/index.cpp                                |     8 +-
 test/jit.cpp                                  |     8 +-
 test/join.cpp                                 |     3 +-
 test/mean.cpp                                 |    32 +-
 test/meanvar.cpp                              |     3 +-
 test/nearest_neighbour.cpp                    |    19 +-
 test/nodevice.cpp                             |    14 +-
 test/pinverse.cpp                             |     2 +-
 test/range.cpp                                |     2 +-
 test/reduce.cpp                               |   177 +-
 test/replace.cpp                              |     6 +-
 test/scan.cpp                                 |     7 +-
 test/scan_by_key.cpp                          |     4 +-
 test/sobel.cpp                                |     1 -
 test/sort_index.cpp                           |     6 +-
 test/sparse.cpp                               |    91 +-
 test/sparse_arith.cpp                         |    12 +-
 test/stdev.cpp                                |     6 +-
 test/transform.cpp                            |    12 +-
 test/triangle.cpp                             |     2 +-
 test/where.cpp                                |     7 +-
 test/wrap.cpp                                 |    73 +-
 194 files changed, 8902 insertions(+), 8996 deletions(-)

diff --git a/examples/benchmarks/blas.cpp b/examples/benchmarks/blas.cpp
index ca41f8e220..ef0e2818cf 100644
--- a/examples/benchmarks/blas.cpp
+++ b/examples/benchmarks/blas.cpp
@@ -31,7 +31,8 @@ int main(int argc, char** argv) {
         const af_dtype dt = (dtype == "f16" ? f16 : f32);
 
         if (dt == f16)
-          printf("Device %d isHalfAvailable ? %s\n", device, isHalfAvailable(device) ? "yes" : "no");
+            printf("Device %d isHalfAvailable ? %s\n", device,
+                   isHalfAvailable(device) ? "yes" : "no");
 
         info();
 
diff --git a/examples/graphics/gravity_sim_init.h b/examples/graphics/gravity_sim_init.h
index 0c98115f0d..9b1af92cfa 100644
--- a/examples/graphics/gravity_sim_init.h
+++ b/examples/graphics/gravity_sim_init.h
@@ -1,7004 +1,7005 @@
 const int HBD_NUM_ELEMENTS = 4000 * 7;
 // halo, bulge, and disk particles
-float hbd[] = {4.9161855e-03f,  -1.5334119e+00f, -8.3381424e+00f, 4.4288845e+00f,
-               -2.3778248e-01f, 4.2592272e-02f,  -4.4895774e-01f, 4.9161855e-03f,
-               1.9886702e-02f,  6.0085773e+00f,  3.1188631e-01f,  8.1422836e-01f,
-               -1.4591325e-02f, 7.5382882e-01f,  4.9161855e-03f,  1.1676190e+00f,
-               -4.6193779e-01f, -5.0477743e-01f, -1.4803666e+00f, 5.6056118e-01f,
-               -2.9858449e-02f, 4.9161855e-03f,  -1.4250363e+00f, 1.0891747e+01f,
-               2.5225203e+00f,  -6.5798134e-02f, -3.5946497e-01f, 1.7471495e-01f,
-               4.9161855e-03f,  -3.7135857e-01f, 4.8796633e-01f,  -3.7898597e-01f,
-               8.5347527e-01f,  2.2493289e-01f,  -2.7678892e-01f, 4.9161855e-03f,
-               2.2072470e+00f,  -2.5046587e+00f, 2.6029270e+00f,  3.0826443e-01f,
-               5.8606583e-01f,  2.0105042e-01f,  4.9161855e-03f,  1.0779227e+00f,
-               -4.0834007e+00f, -3.3965745e+00f, -4.8430148e-01f, -7.1573091e-01f,
-               1.2384786e-01f,  4.9161855e-03f,  -3.8722844e+00f, -4.2357988e+00f,
-               -1.9723746e+00f, 3.5759529e-01f,  4.8990592e-01f,  -4.3040028e-01f,
-               4.9161855e-03f,  -1.3005282e-01f, -2.3483203e-01f, 1.3832784e-01f,
-               1.3746375e+00f,  -1.2947829e+00f, 6.1215276e-01f,  4.9161855e-03f,
-               3.6822948e-01f,  4.2760900e-01f,  1.1544695e+00f,  -2.3177411e-02f,
-               -6.9136995e-01f, -6.6200425e-03f, 4.9161855e-03f,  -1.2485707e+00f,
-               2.0474775e-01f,  -2.1652168e-01f, 2.7034196e-01f,  1.6398503e+00f,
-               -7.8224945e-01f, 4.9161855e-03f,  -3.3862705e+00f, 1.2049110e+00f,
-               1.0672448e+00f,  -1.6531572e-01f, -2.4370559e-01f, 8.7125647e-01f,
-               4.9161855e-03f,  3.4262960e+00f,  3.9102471e+00f,  6.6162848e-01f,
-               7.8005123e-01f,  -1.0415094e-01f, 5.0161743e-01f,  4.9161855e-03f,
-               1.5740298e-01f,  1.3008093e+00f,  7.8130345e+00f,  -1.6444305e-01f,
-               3.3037327e-03f,  1.9713788e-01f,  4.9161855e-03f,  5.6700945e-01f,
-               1.8889900e-01f,  2.7523971e+00f,  -3.4313673e-01f, -6.4287108e-01f,
-               -1.8927544e-01f, 4.9161855e-03f,  1.8354661e+00f,  1.3209668e+00f,
-               1.6966065e+00f,  5.3318393e-01f,  3.4129089e-01f,  -8.0587679e-01f,
-               4.9161855e-03f,  -7.8488460e+00f, 3.2376931e+00f,  2.6638079e+00f,
-               3.4405673e-01f,  -2.1986680e-01f, 1.6776933e-01f,  4.9161855e-03f,
-               3.2422847e-01f,  -1.2311785e+00f, 9.0597588e-01f,  3.6714745e-01f,
-               -1.3913552e-01f, 9.0002306e-02f,  4.9161855e-03f,  -1.9477528e-01f,
-               -2.3987198e+00f, -4.2354431e+00f, -2.1188869e-01f, -6.4195746e-01f,
-               1.5219630e-01f,  4.9161855e-03f,  3.2330542e+00f,  1.1787817e+00f,
-               -1.3654234e+00f, 1.9920348e-01f,  -1.0560199e+00f, -4.0022919e-01f,
-               4.9161855e-03f,  -2.2656450e+00f, 2.3343153e+00f,  3.0343585e+00f,
-               1.3909769e-01f,  -5.8018422e-01f, 7.7305830e-01f,  4.9161855e-03f,
-               1.0106117e+01f,  8.4062157e+00f,  -5.3659506e+00f, -3.3819172e-01f,
-               -5.7871189e-02f, -5.2655820e-02f, 4.9161855e-03f,  -8.4759682e-02f,
-               -2.4386784e-01f, 2.2389056e-01f,  -8.3496273e-01f, 1.1504352e+00f,
-               3.2196254e-03f,  4.9161855e-03f,  -4.8354459e+00f, -1.1709679e+01f,
-               -4.4684467e+00f, -3.7076837e-01f, 2.6136923e-01f,  -1.4268482e-01f,
-               4.9161855e-03f,  -1.3268198e+00f, -2.3238692e+00f, 6.7897618e-01f,
-               3.0518329e-01f,  6.8463421e-01f,  -7.1791840e-01f, 4.9161855e-03f,
-               -5.2054877e+00f, 2.0948052e+00f,  1.9656231e+00f,  7.4416548e-01f,
-               4.4825464e-01f,  -3.2727838e-01f, 4.9161855e-03f,  -8.2616639e-01f,
-               1.0700088e+00f,  3.5586545e+00f,  4.8024514e-01f,  1.1944018e-01f,
-               3.0837712e-01f,  4.9161855e-03f,  -2.9101398e+00f, -3.6366568e+00f,
-               8.7982547e-01f,  3.6643305e-01f,  -3.8197124e-01f, -1.1440479e-01f,
-               4.9161855e-03f,  3.5198438e-01f,  4.9096385e-01f,  -6.6494130e-02f,
-               -1.0383745e-01f, 3.9406076e-01f,  7.3723292e-01f,  4.9161855e-03f,
-               -6.9214082e+00f, -5.5405111e+00f, -2.3041859e+00f, 3.3985880e-01f,
-               1.0167535e-02f,  1.0593475e-01f,  4.9161855e-03f,  1.0908546e+00f,
-               -5.3155913e+00f, -4.5045247e+00f, 1.8077201e-01f,  -4.4904891e-01f,
-               4.7391072e-01f,  4.9161855e-03f,  -1.0766581e-01f, 6.7338924e+00f,
-               6.1174130e+00f,  -2.3362583e-01f, 7.6430768e-02f,  -2.4832390e-01f,
-               4.9161855e-03f,  -4.9775305e-01f, 1.6378751e+00f,  -2.6263945e+00f,
-               -3.0084690e-01f, -5.1551086e-01f, -6.6373748e-01f, 4.9161855e-03f,
-               -3.8946674e+00f, -1.4725525e+00f, 2.4148097e+00f,  -1.7075756e-01f,
-               5.3592271e-01f,  7.2393781e-01f,  4.9161855e-03f,  6.8583161e-02f,
-               -1.5991354e+00f, -3.0150402e-01f, 1.5219669e-01f,  -5.6440836e-01f,
-               1.5284424e+00f,  4.9161855e-03f,  -4.2822695e+00f, 4.0367408e+00f,
-               -2.2387395e+00f, 1.0239060e-01f,  3.2810995e-01f,  -1.4511149e-01f,
-               4.9161855e-03f,  5.3348875e-01f,  -3.6950427e-01f, 1.0364149e+00f,
-               7.8612208e-02f,  -2.7073494e-01f, 1.9663854e-01f,  4.9161855e-03f,
-               -3.3353384e+00f, 4.3220544e+00f,  -1.5343003e+00f, 6.7457032e-01f,
-               -1.8098858e-01f, 7.6241505e-01f,  4.9161855e-03f,  -8.8430309e+00f,
-               6.6101489e+00f,  2.2365890e+00f,  -2.9622875e-03f, -5.7892501e-01f,
-               2.3848678e-01f,  4.9161855e-03f,  -2.7121809e+00f, -3.7584829e+00f,
-               2.4702384e+00f,  3.9350358e-01f,  -6.7748266e-01f, -5.7142133e-01f,
-               4.9161855e-03f,  1.7517463e+00f,  -5.2237463e-01f, 1.2052536e+00f,
-               2.6133826e-01f,  -4.3084338e-01f, -2.8758329e-01f, 4.9161855e-03f,
-               -4.4221100e-01f, 2.4987850e-01f,  -9.0834004e-01f, -1.6435069e+00f,
-               -3.5537782e-01f, -5.6679737e-02f, 4.9161855e-03f,  9.5630264e+00f,
-               7.2472978e-01f,  -2.7188256e+00f, 4.1388586e-01f,  -2.7986884e-01f,
-               9.9171564e-02f,  4.9161855e-03f,  -2.5304942e+00f, -1.9891304e-01f,
-               -1.3565568e+00f, 1.6445565e-01f,  6.5720814e-01f,  8.8133616e-04f,
-               4.9161855e-03f,  -6.8739529e+00f, 6.0871582e+00f,  4.0246663e+00f,
-               -1.1313155e-01f, 2.6078510e-01f,  1.1052500e-02f,  4.9161855e-03f,
-               1.8411478e-01f,  6.3666153e-01f,  -1.7665352e+00f, 7.3893017e-01f,
-               8.2843482e-02f,  1.3584135e-01f,  4.9161855e-03f,  1.2281631e-01f,
-               -4.8358020e-01f, -4.2862403e-01f, -1.4062686e+00f, 2.6675841e-01f,
-               -5.2812093e-01f, 4.9161855e-03f,  -1.8010849e+00f, 2.5018549e+00f,
-               -1.1007906e+00f, -3.0198583e-01f, -2.5083411e-01f, -9.4572407e-01f,
-               4.9161855e-03f,  2.9228494e-02f,  2.8824418e+00f,  -7.7373713e-01f,
-               -8.9457905e-01f, -3.9830649e-01f, -8.2690775e-01f, 4.9161855e-03f,
-               -4.8449464e+00f, -3.5136631e+00f, 2.6319263e+00f,  2.3270021e-01f,
-               6.2155128e-01f,  -6.9675374e-01f, 4.9161855e-03f,  -2.4690704e-01f,
-               -3.6131024e+00f, 5.7440319e+00f,  -5.6087500e-01f, -2.9587632e-01f,
-               -7.5861102e-01f, 4.9161855e-03f,  5.2307582e+00f,  2.1941881e+00f,
-               -4.2112174e+00f, 2.3945954e-01f,  2.5676125e-01f,  3.2575151e-01f,
-               4.9161855e-03f,  4.8397323e-01f,  3.7831066e+00f,  4.4692445e+00f,
-               2.4802294e-02f,  6.5026706e-01f,  -1.1542060e-02f, 4.9161855e-03f,
-               7.9952207e+00f,  4.5379916e-01f,  1.4309001e-01f,  -2.2018740e-01f,
-               -2.1911193e-01f, -4.8267773e-01f, 4.9161855e-03f,  -2.0976503e+00f,
-               -2.4728169e-01f, 6.3614302e+00f,  -7.4839890e-02f, -4.1690156e-01f,
-               -1.7862423e-01f, 4.9161855e-03f,  3.4107253e-01f,  -1.2668414e+00f,
-               1.2606201e+00f,  3.6496368e-01f,  -3.5874972e-01f, -1.0340087e+00f,
-               4.9161855e-03f,  8.9313567e-01f,  3.6050075e-01f,  3.4469640e-01f,
-               -8.6372048e-01f, -6.3587260e-01f, 7.4591488e-01f,  4.9161855e-03f,
-               2.9728930e+00f,  -5.2957177e+00f, -7.3298526e+00f, -1.9522749e-01f,
-               -2.2528295e-01f, 1.9373624e-01f,  4.9161855e-03f,  -1.7334032e+00f,
-               1.9857804e+00f,  -4.9017177e+00f, -6.8124956e-01f, 8.3835334e-01f,
-               -7.8357399e-02f, 4.9161855e-03f,  2.0978465e+00f,  1.9166039e+00f,
-               1.0677823e+00f,  -2.6128739e-01f, -9.3216664e-01f, 8.0752736e-01f,
-               4.9161855e-03f,  -2.6831132e-01f, 1.6412498e-01f,  -5.8062166e-01f,
-               -3.9843372e-01f, 1.5403072e+00f,  -2.5054911e-01f, 4.9161855e-03f,
-               1.7003990e+00f,  3.3006930e+00f,  -1.7119979e+00f, -1.0552487e-01f,
-               -8.4340447e-01f, 9.8853576e-01f,  4.9161855e-03f,  -5.5339479e+00f,
-               4.8888919e-01f,  9.1028652e+00f,  4.6380356e-01f,  -4.4314775e-01f,
-               3.4938701e-03f,  4.9161855e-03f,  -3.9364102e+00f, -3.4606054e+00f,
-               2.2803564e+00f,  1.2712850e-01f,  -3.2586256e-01f, -6.5546811e-02f,
-               4.9161855e-03f,  -6.6842210e-01f, -8.6578093e-02f, -9.9518037e-01f,
-               3.0050567e-01f,  -1.3251954e+00f, -6.3900441e-01f, 4.9161855e-03f,
-               -1.7707565e+00f, -2.3981299e+00f, -2.8610508e+00f, 8.0815405e-02f,
-               2.6192275e-01f,  -4.4141706e-02f, 4.9161855e-03f,  5.2352209e+00f,
-               4.3753624e+00f,  5.2761130e+00f,  -3.6126247e-01f, -3.6049706e-01f,
-               -5.0132203e-01f, 4.9161855e-03f,  4.0741138e+00f,  -2.7320893e+00f,
-               -5.8015996e-01f, -3.3409804e-01f, -7.4342436e-01f, -8.1080115e-01f,
-               4.9161855e-03f,  1.0308882e+01f,  3.3621982e-01f,  -1.2449891e+01f,
-               -2.8561455e-01f, -1.0982110e-01f, -1.0319072e-02f, 4.9161855e-03f,
-               8.3470430e+00f,  -9.4488649e+00f, -6.6161261e+00f, -2.6525149e-01f,
-               5.0971325e-02f,  5.4980908e-02f,  4.9161855e-03f,  -4.8979187e-01f,
-               -2.1835434e+00f, 1.3237199e+00f,  -2.0376731e-01f, -4.8289922e-01f,
-               -1.9313942e-01f, 4.9161855e-03f,  3.8070815e+00f,  -4.1728072e+00f,
-               6.8302398e+00f,  2.1417937e-01f,  -5.6412149e-02f, 9.7045694e-03f,
-               4.9161855e-03f,  -1.7183731e+00f, 1.7611129e+00f,  5.8284336e-01f,
-               1.2992284e-01f,  -1.3527862e+00f, -4.3186599e-01f, 4.9161855e-03f,
-               -1.1291479e+01f, -3.0248559e+00f, -6.1554856e+00f, -6.8934292e-02f,
-               -3.0177805e-01f, -1.8667488e-01f, 4.9161855e-03f,  -2.3688557e+00f,
-               7.7071247e+00f,  -2.0670973e-01f, -2.1208389e-01f, 2.8578773e-01f,
-               2.0644853e-01f,  4.9161855e-03f,  8.2679868e-01f,  -2.1197610e+00f,
-               1.0767980e+00f,  2.4679126e-01f,  -4.0421063e-01f, -5.7845503e-01f,
-               4.9161855e-03f,  4.1475649e+00f,  -4.3077379e-01f, 5.4239964e+00f,
-               7.0667878e-02f,  4.9151066e-01f,  -5.2980289e-02f, 4.9161855e-03f,
-               -7.7668630e-02f, -4.1514721e+00f, -8.0719125e-01f, -4.2308268e-01f,
-               -5.9619360e-03f, -5.4758888e-01f, 4.9161855e-03f,  7.3864212e+00f,
-               -7.1388471e-01f, 4.2682199e+00f,  8.6512074e-02f,  -3.9517093e-01f,
-               3.4532326e-01f,  4.9161855e-03f,  3.1821191e+00f,  5.0156546e+00f,
-               -7.2775478e+00f, 3.8633448e-01f,  4.1517708e-01f,  -4.7167987e-01f,
-               4.9161855e-03f,  -5.5158086e+00f, -1.8736273e+00f, 1.2083918e+00f,
-               -5.2377588e-01f, -5.1698190e-01f, -1.7996560e-01f, 4.9161855e-03f,
-               -7.5245118e-01f, -5.0066152e+00f, -3.6176472e+00f, -1.4140940e-01f,
-               4.9951354e-01f,  -5.1893300e-01f, 4.9161855e-03f,  1.7928425e+00f,
-               2.7725005e+00f,  -2.2401933e-02f, -8.6086380e-01f, -3.3671090e-01f,
-               8.4016019e-01f,  4.9161855e-03f,  5.5359507e+00f,  -1.0514329e+01f,
-               3.6608188e+00f,  -1.5433036e-01f, -7.8473240e-03f, 2.5746456e-01f,
-               4.9161855e-03f,  1.8312926e+00f,  -6.6526437e-01f, -1.4381752e+00f,
-               -1.5768304e-01f, 4.5808712e-01f,  4.9162623e-01f,  4.9161855e-03f,
-               5.4815245e+00f,  -3.7619928e-01f, 3.7529993e-01f,  -3.4403029e-01f,
-               -1.9848712e-02f, 3.1211856e-01f,  4.9161855e-03f,  -2.8452486e-01f,
-               1.0852966e+00f,  -7.1417332e-01f, 8.5701519e-01f,  -1.9785182e-01f,
-               7.2242868e-01f,  4.9161855e-03f,  1.6400850e+00f,  6.0924044e+00f,
-               -6.7533379e+00f, -1.4117804e-01f, -2.7584502e-01f, 1.8720052e-01f,
-               4.9161855e-03f,  5.8992994e-01f,  -1.4057723e+00f, 1.7555045e+00f,
-               3.0828384e-01f,  -1.7618947e-01f, 5.7791591e-01f,  4.9161855e-03f,
-               3.2523406e+00f,  6.4261597e-01f,  -3.2577946e+00f, 4.3461993e-03f,
-               1.6368487e-01f,  -2.7604485e-01f, 4.9161855e-03f,  -4.4885483e+00f,
-               2.9889661e-01f,  7.7495706e-01f,  8.4083831e-01f,  -6.1657476e-01f,
-               -2.8107607e-01f, 4.9161855e-03f,  -8.8879662e+00f, 6.2833142e-01f,
-               -1.1011785e+01f, 4.1822538e-01f,  1.0211676e-01f,  -3.1296456e-01f,
-               4.9161855e-03f,  2.7859297e+00f,  -3.9616172e+00f, -9.8269482e+00f,
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-               4.5574255e-02f,  -2.9945102e-01f, 4.9161855e-03f,  -9.1713123e+00f,
-               -1.1326112e+01f, 8.7793245e+00f,  3.2807869e-01f,  3.1993087e-02f,
-               6.5704375e-03f,  4.9161855e-03f,  -6.3241405e+00f, 4.5917640e+00f,
-               5.2446551e+00f,  8.6806208e-02f,  -1.1900769e-01f, 3.7303127e-02f,
-               4.9161855e-03f,  1.8690332e+00f,  5.1850295e-01f,  -4.2205045e-01f,
-               5.1754210e-02f,  1.0277729e+00f,  -9.3673009e-01f, 4.9161855e-03f,
-               1.1749099e+00f,  1.8220998e+00f,  3.7768686e+00f,  3.2626029e-02f,
-               1.9230081e-01f,  -6.1840069e-01f, 4.9161855e-03f,  -6.4281154e+00f,
-               -3.2852066e+00f, -3.6263623e+00f, 4.3581065e-02f,  -9.3072295e-02f,
-               2.2059004e-01f,  4.9161855e-03f,  -2.8914037e+00f, -8.9913285e-01f,
-               -6.0291066e+00f, -7.3334366e-02f, -1.7908965e-01f, 2.4383314e-01f,
-               4.9161855e-03f,  3.5674961e+00f,  -1.9904513e+00f, -2.8840287e+00f,
-               -2.1585038e-01f, 2.6890549e-01f,  5.7695067e-01f,  4.9161855e-03f,
-               -4.5172372e+00f, -1.2764982e+01f, -6.5555286e+00f, -8.7975547e-02f,
-               -2.8868642e-02f, -2.4445239e-01f, 4.9161855e-03f,  1.1917623e+00f,
-               2.7240102e+00f,  -5.6969924e+00f, 1.5443534e-01f,  8.0268896e-01f,
-               7.6069735e-02f,  4.9161855e-03f,  1.8703443e+00f,  -1.6433734e+00f,
-               -3.6527286e+00f, 9.3277645e-01f,  -2.1267043e-01f, 1.9547650e-01f,
-               4.9161855e-03f,  3.5234538e-01f,  -3.5503694e-01f, -3.5764150e-02f,
-               -2.7299783e-01f, 2.0867128e+00f,  -4.0437704e-01f, 4.9161855e-03f,
-               7.0537286e+00f,  4.2256870e+00f,  -2.3376143e+00f, 1.0489196e-01f,
-               -2.2336484e-01f, -2.2279005e-01f, 4.9161855e-03f,  1.2876858e+00f,
-               7.2569623e+00f,  -2.2856178e+00f, -3.6533204e-01f, -2.2654597e-01f,
-               -3.9202511e-01f, 4.9161855e-03f,  -2.9575005e+00f, 4.0046115e+00f,
-               1.9336003e+00f,  7.7007276e-01f,  1.8195377e-01f,  5.0428671e-01f,
-               4.9161855e-03f,  3.6017182e+00f,  9.1012402e+00f,  -6.7456603e+00f,
-               -1.3861659e-01f, -2.6884264e-01f, -3.9056700e-01f, 4.9161855e-03f,
-               -1.1627531e+00f, 1.7062700e+00f,  -7.1475458e-01f, -1.5973236e-02f,
-               -5.2192539e-01f, 9.2492419e-01f,  4.9161855e-03f,  7.0983272e+00f,
-               4.3586853e-01f,  -3.5620954e+00f, 3.9555708e-01f,  5.6896615e-01f,
-               -3.9723828e-01f, 4.9161855e-03f,  1.4865612e+00f,  -1.0475974e+00f,
-               -8.4833641e+00f, -3.7397227e-01f, 1.3291334e-01f,  3.3054215e-01f,
-               4.9161855e-03f,  3.3097060e+00f,  -4.0853152e+00f, 2.3023739e+00f,
-               -7.3129189e-01f, 4.1393802e-01f,  2.4469729e-01f,  4.9161855e-03f,
-               -6.4677873e+00f, -1.6074709e+00f, 2.2694349e+00f,  2.4836297e-01f,
-               -4.7907314e-01f, -1.2783307e-02f, 4.9161855e-03f,  7.6441946e+00f,
-               -6.5884595e+00f, 8.2836065e+00f,  -6.5808132e-02f, -1.2891619e-01f,
-               -1.0536889e-01f, 4.9161855e-03f,  -6.1940775e+00f, -7.0686564e+00f,
-               2.8182077e+00f,  4.6267312e-02f,  2.1834882e-01f,  -2.8412163e-01f,
-               4.9161855e-03f,  7.5322211e-01f,  4.4226575e-01f,  8.6104780e-01f,
-               -4.5959395e-01f, -1.2565438e+00f, 1.0619931e+00f,  4.9161855e-03f,
-               -3.1116338e+00f, 5.5792129e-01f,  5.3073101e+00f,  3.0462223e-01f,
-               7.5853378e-02f,  -1.9224058e-01f, 4.9161855e-03f,  2.2643218e+00f,
-               2.0357387e+00f,  4.4502897e+00f,  -2.8496760e-01f, 1.2047067e-01f,
-               6.4417034e-01f,  4.9161855e-03f,  -1.4413284e+00f, 3.5867362e+00f,
-               -2.4204571e+00f, 4.2380524e-01f,  -2.1113880e-01f, -1.7703670e-01f,
-               4.9161855e-03f,  -6.8668759e-01f, -9.5317203e-01f, 1.5330289e-01f,
-               5.7356155e-01f,  6.3638610e-01f,  7.7120703e-01f,  4.9161855e-03f,
-               -1.0682197e+00f, -6.9213104e+00f, -5.8608122e+00f, 1.0352087e-01f,
-               -3.3730379e-01f, 1.9342881e-01f,  4.9161855e-03f,  -2.4783916e+00f,
-               1.2663845e+00f,  1.5080407e+00f,  3.5923757e-03f,  5.0929576e-01f,
-               3.1987467e-01f,  4.9161855e-03f,  6.2106740e-01f,  -8.0850184e-01f,
-               6.0432136e-01f,  1.0544959e+00f,  3.5460990e-02f,  7.1798617e-01f,
-               4.9161855e-03f,  5.7629764e-01f,  -4.1872951e-01f, 2.6883879e-01f,
-               -5.7401496e-01f, -5.2689475e-01f, -2.9298371e-01f, 4.9161855e-03f,
-               -6.0079894e+00f, -3.0357261e+00f, 1.1362796e+00f,  1.8514165e-01f,
-               -1.0868914e-02f, -2.6686630e-01f, 4.9161855e-03f,  -6.4743943e+00f,
-               5.0929122e+00f,  4.5632439e+00f,  -8.3602853e-03f, 1.3735165e-01f,
-               -3.0539981e-01f, 4.9161855e-03f,  -1.1718397e+00f, -4.3745694e+00f,
-               4.1264515e+00f,  3.4016520e-01f,  -2.4106152e-01f, -6.2656836e-03f,
-               4.9161855e-03f,  4.5977187e+00f,  9.2932510e-01f,  1.8005730e+00f,
-               7.5450696e-02f,  2.5778416e-01f,  -1.0443735e-01f, 4.9161855e-03f,
-               -1.2225604e+00f, 3.8227065e+00f,  -4.0077796e+00f, 3.7918901e-01f,
-               -3.4038458e-02f, -2.2999659e-01f, 4.9161855e-03f,  -1.6463979e+00f,
-               3.3725232e-01f,  -2.3585579e+00f, -7.5838506e-02f, 7.1057733e-03f,
-               2.9407086e-02f,  4.9161855e-03f,  5.4664793e+00f,  -3.7369993e-01f,
-               1.8591646e+00f,  6.9752198e-01f,  5.2111161e-01f,  -5.1446843e-01f,
-               4.9161855e-03f,  -2.0373304e+00f, 2.6609144e+00f,  -1.8289629e+00f,
-               5.7756305e-01f,  -3.7016757e-03f, -1.2520009e-01f, 4.9161855e-03f,
-               -4.3900475e-01f, 1.6747446e+00f,  4.9002385e+00f,  2.5009772e-01f,
-               -1.8630438e-01f, 3.6023688e-01f,  4.9161855e-03f,  -6.4800224e+00f,
-               1.0171971e+00f,  2.6008205e+00f,  7.6939821e-02f,  3.9370355e-01f,
-               1.5263109e-02f,  4.9161855e-03f,  7.7535975e-01f,  -6.5957302e-01f,
-               -1.4328420e-01f, 1.3423905e-01f,  -1.1076678e+00f, 2.9757038e-01f,
+float hbd[] = {
+    4.9161855e-03f,  -1.5334119e+00f, -8.3381424e+00f, 4.4288845e+00f,
+    -2.3778248e-01f, 4.2592272e-02f,  -4.4895774e-01f, 4.9161855e-03f,
+    1.9886702e-02f,  6.0085773e+00f,  3.1188631e-01f,  8.1422836e-01f,
+    -1.4591325e-02f, 7.5382882e-01f,  4.9161855e-03f,  1.1676190e+00f,
+    -4.6193779e-01f, -5.0477743e-01f, -1.4803666e+00f, 5.6056118e-01f,
+    -2.9858449e-02f, 4.9161855e-03f,  -1.4250363e+00f, 1.0891747e+01f,
+    2.5225203e+00f,  -6.5798134e-02f, -3.5946497e-01f, 1.7471495e-01f,
+    4.9161855e-03f,  -3.7135857e-01f, 4.8796633e-01f,  -3.7898597e-01f,
+    8.5347527e-01f,  2.2493289e-01f,  -2.7678892e-01f, 4.9161855e-03f,
+    2.2072470e+00f,  -2.5046587e+00f, 2.6029270e+00f,  3.0826443e-01f,
+    5.8606583e-01f,  2.0105042e-01f,  4.9161855e-03f,  1.0779227e+00f,
+    -4.0834007e+00f, -3.3965745e+00f, -4.8430148e-01f, -7.1573091e-01f,
+    1.2384786e-01f,  4.9161855e-03f,  -3.8722844e+00f, -4.2357988e+00f,
+    -1.9723746e+00f, 3.5759529e-01f,  4.8990592e-01f,  -4.3040028e-01f,
+    4.9161855e-03f,  -1.3005282e-01f, -2.3483203e-01f, 1.3832784e-01f,
+    1.3746375e+00f,  -1.2947829e+00f, 6.1215276e-01f,  4.9161855e-03f,
+    3.6822948e-01f,  4.2760900e-01f,  1.1544695e+00f,  -2.3177411e-02f,
+    -6.9136995e-01f, -6.6200425e-03f, 4.9161855e-03f,  -1.2485707e+00f,
+    2.0474775e-01f,  -2.1652168e-01f, 2.7034196e-01f,  1.6398503e+00f,
+    -7.8224945e-01f, 4.9161855e-03f,  -3.3862705e+00f, 1.2049110e+00f,
+    1.0672448e+00f,  -1.6531572e-01f, -2.4370559e-01f, 8.7125647e-01f,
+    4.9161855e-03f,  3.4262960e+00f,  3.9102471e+00f,  6.6162848e-01f,
+    7.8005123e-01f,  -1.0415094e-01f, 5.0161743e-01f,  4.9161855e-03f,
+    1.5740298e-01f,  1.3008093e+00f,  7.8130345e+00f,  -1.6444305e-01f,
+    3.3037327e-03f,  1.9713788e-01f,  4.9161855e-03f,  5.6700945e-01f,
+    1.8889900e-01f,  2.7523971e+00f,  -3.4313673e-01f, -6.4287108e-01f,
+    -1.8927544e-01f, 4.9161855e-03f,  1.8354661e+00f,  1.3209668e+00f,
+    1.6966065e+00f,  5.3318393e-01f,  3.4129089e-01f,  -8.0587679e-01f,
+    4.9161855e-03f,  -7.8488460e+00f, 3.2376931e+00f,  2.6638079e+00f,
+    3.4405673e-01f,  -2.1986680e-01f, 1.6776933e-01f,  4.9161855e-03f,
+    3.2422847e-01f,  -1.2311785e+00f, 9.0597588e-01f,  3.6714745e-01f,
+    -1.3913552e-01f, 9.0002306e-02f,  4.9161855e-03f,  -1.9477528e-01f,
+    -2.3987198e+00f, -4.2354431e+00f, -2.1188869e-01f, -6.4195746e-01f,
+    1.5219630e-01f,  4.9161855e-03f,  3.2330542e+00f,  1.1787817e+00f,
+    -1.3654234e+00f, 1.9920348e-01f,  -1.0560199e+00f, -4.0022919e-01f,
+    4.9161855e-03f,  -2.2656450e+00f, 2.3343153e+00f,  3.0343585e+00f,
+    1.3909769e-01f,  -5.8018422e-01f, 7.7305830e-01f,  4.9161855e-03f,
+    1.0106117e+01f,  8.4062157e+00f,  -5.3659506e+00f, -3.3819172e-01f,
+    -5.7871189e-02f, -5.2655820e-02f, 4.9161855e-03f,  -8.4759682e-02f,
+    -2.4386784e-01f, 2.2389056e-01f,  -8.3496273e-01f, 1.1504352e+00f,
+    3.2196254e-03f,  4.9161855e-03f,  -4.8354459e+00f, -1.1709679e+01f,
+    -4.4684467e+00f, -3.7076837e-01f, 2.6136923e-01f,  -1.4268482e-01f,
+    4.9161855e-03f,  -1.3268198e+00f, -2.3238692e+00f, 6.7897618e-01f,
+    3.0518329e-01f,  6.8463421e-01f,  -7.1791840e-01f, 4.9161855e-03f,
+    -5.2054877e+00f, 2.0948052e+00f,  1.9656231e+00f,  7.4416548e-01f,
+    4.4825464e-01f,  -3.2727838e-01f, 4.9161855e-03f,  -8.2616639e-01f,
+    1.0700088e+00f,  3.5586545e+00f,  4.8024514e-01f,  1.1944018e-01f,
+    3.0837712e-01f,  4.9161855e-03f,  -2.9101398e+00f, -3.6366568e+00f,
+    8.7982547e-01f,  3.6643305e-01f,  -3.8197124e-01f, -1.1440479e-01f,
+    4.9161855e-03f,  3.5198438e-01f,  4.9096385e-01f,  -6.6494130e-02f,
+    -1.0383745e-01f, 3.9406076e-01f,  7.3723292e-01f,  4.9161855e-03f,
+    -6.9214082e+00f, -5.5405111e+00f, -2.3041859e+00f, 3.3985880e-01f,
+    1.0167535e-02f,  1.0593475e-01f,  4.9161855e-03f,  1.0908546e+00f,
+    -5.3155913e+00f, -4.5045247e+00f, 1.8077201e-01f,  -4.4904891e-01f,
+    4.7391072e-01f,  4.9161855e-03f,  -1.0766581e-01f, 6.7338924e+00f,
+    6.1174130e+00f,  -2.3362583e-01f, 7.6430768e-02f,  -2.4832390e-01f,
+    4.9161855e-03f,  -4.9775305e-01f, 1.6378751e+00f,  -2.6263945e+00f,
+    -3.0084690e-01f, -5.1551086e-01f, -6.6373748e-01f, 4.9161855e-03f,
+    -3.8946674e+00f, -1.4725525e+00f, 2.4148097e+00f,  -1.7075756e-01f,
+    5.3592271e-01f,  7.2393781e-01f,  4.9161855e-03f,  6.8583161e-02f,
+    -1.5991354e+00f, -3.0150402e-01f, 1.5219669e-01f,  -5.6440836e-01f,
+    1.5284424e+00f,  4.9161855e-03f,  -4.2822695e+00f, 4.0367408e+00f,
+    -2.2387395e+00f, 1.0239060e-01f,  3.2810995e-01f,  -1.4511149e-01f,
+    4.9161855e-03f,  5.3348875e-01f,  -3.6950427e-01f, 1.0364149e+00f,
+    7.8612208e-02f,  -2.7073494e-01f, 1.9663854e-01f,  4.9161855e-03f,
+    -3.3353384e+00f, 4.3220544e+00f,  -1.5343003e+00f, 6.7457032e-01f,
+    -1.8098858e-01f, 7.6241505e-01f,  4.9161855e-03f,  -8.8430309e+00f,
+    6.6101489e+00f,  2.2365890e+00f,  -2.9622875e-03f, -5.7892501e-01f,
+    2.3848678e-01f,  4.9161855e-03f,  -2.7121809e+00f, -3.7584829e+00f,
+    2.4702384e+00f,  3.9350358e-01f,  -6.7748266e-01f, -5.7142133e-01f,
+    4.9161855e-03f,  1.7517463e+00f,  -5.2237463e-01f, 1.2052536e+00f,
+    2.6133826e-01f,  -4.3084338e-01f, -2.8758329e-01f, 4.9161855e-03f,
+    -4.4221100e-01f, 2.4987850e-01f,  -9.0834004e-01f, -1.6435069e+00f,
+    -3.5537782e-01f, -5.6679737e-02f, 4.9161855e-03f,  9.5630264e+00f,
+    7.2472978e-01f,  -2.7188256e+00f, 4.1388586e-01f,  -2.7986884e-01f,
+    9.9171564e-02f,  4.9161855e-03f,  -2.5304942e+00f, -1.9891304e-01f,
+    -1.3565568e+00f, 1.6445565e-01f,  6.5720814e-01f,  8.8133616e-04f,
+    4.9161855e-03f,  -6.8739529e+00f, 6.0871582e+00f,  4.0246663e+00f,
+    -1.1313155e-01f, 2.6078510e-01f,  1.1052500e-02f,  4.9161855e-03f,
+    1.8411478e-01f,  6.3666153e-01f,  -1.7665352e+00f, 7.3893017e-01f,
+    8.2843482e-02f,  1.3584135e-01f,  4.9161855e-03f,  1.2281631e-01f,
+    -4.8358020e-01f, -4.2862403e-01f, -1.4062686e+00f, 2.6675841e-01f,
+    -5.2812093e-01f, 4.9161855e-03f,  -1.8010849e+00f, 2.5018549e+00f,
+    -1.1007906e+00f, -3.0198583e-01f, -2.5083411e-01f, -9.4572407e-01f,
+    4.9161855e-03f,  2.9228494e-02f,  2.8824418e+00f,  -7.7373713e-01f,
+    -8.9457905e-01f, -3.9830649e-01f, -8.2690775e-01f, 4.9161855e-03f,
+    -4.8449464e+00f, -3.5136631e+00f, 2.6319263e+00f,  2.3270021e-01f,
+    6.2155128e-01f,  -6.9675374e-01f, 4.9161855e-03f,  -2.4690704e-01f,
+    -3.6131024e+00f, 5.7440319e+00f,  -5.6087500e-01f, -2.9587632e-01f,
+    -7.5861102e-01f, 4.9161855e-03f,  5.2307582e+00f,  2.1941881e+00f,
+    -4.2112174e+00f, 2.3945954e-01f,  2.5676125e-01f,  3.2575151e-01f,
+    4.9161855e-03f,  4.8397323e-01f,  3.7831066e+00f,  4.4692445e+00f,
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+    -1.1602592e-01f, 3.0858183e-02f,  4.6493343e-01f,  4.3753752e-01f,
+    1.5579678e-01f,  4.2524221e-04f,  -2.3568103e-01f, -3.1387237e-01f,
+    1.7740901e-01f,  -2.2428825e-01f, -7.9772305e-01f, 2.2299300e-01f,
+    4.2524221e-04f,  1.0266142e-01f,  -3.9200943e-02f, -1.6250725e-01f,
+    -2.1084811e-01f, 4.7313869e-01f,  7.5736183e-01f,  4.2524221e-04f,
+    -5.2503270e-01f, -2.5550249e-01f, 2.4210323e-01f,  4.2290211e-01f,
+    -1.1937749e-03f, -2.8803447e-01f, 4.2524221e-04f,  6.8656705e-02f,
+    2.3230983e-01f,  -1.0208790e-02f, -1.9244626e-01f, 8.1877112e-01f,
+    -2.5449389e-01f, 4.2524221e-04f,  -5.4129776e-02f, 2.9140076e-01f,
+    -4.6895444e-01f, -2.3883762e-02f, -1.9746602e-01f, -1.4508346e-02f,
+    4.2524221e-04f,  -3.0830520e-01f, -2.6217067e-01f, -2.6785174e-01f,
+    6.7281228e-01f,  3.7336886e-01f,  -1.4304060e-01f, 4.2524221e-04f,
+    1.5217099e-01f,  2.0078890e-01f,  7.7753231e-02f,  -3.3346283e-01f,
+    -1.2821050e-01f, -4.3130264e-01f, 4.2524221e-04f,  3.8476987e-04f,
+    -7.6562621e-02f, -4.8909627e-02f, -1.1036193e-01f, 2.4940021e-01f,
+    2.4720046e-01f,  4.2524221e-04f,  1.9815315e-01f,  1.9162391e-01f,
+    6.0125452e-02f,  -7.7126014e-01f, 4.2003978e-02f,  6.3951693e-02f,
+    4.2524221e-04f,  9.2402853e-02f,  -1.9484653e-01f, -1.4663309e-01f,
+    1.7251915e-01f,  -1.6592954e-01f, -3.1574631e-01f, 4.2524221e-04f,
+    1.4493692e-01f,  -3.1712703e-02f, -1.5764284e-01f, -1.6178896e-01f,
+    3.3917201e-01f,  -4.9173659e-01f, 4.2524221e-04f,  2.1914667e-01f,
+    -7.4241884e-02f, -9.9493600e-02f, -1.7168714e-01f, 1.7520438e-01f,
+    1.1748855e+00f,  4.2524221e-04f,  -1.6493322e-01f, 2.1094975e-01f,
+    2.6855225e-02f,  8.0839500e-02f,  6.4471591e-01f,  2.5444278e-01f,
+    4.2524221e-04f,  -1.0818439e-01f, 5.0222378e-02f,  1.0443858e-01f,
+    7.3543733e-01f,  -5.2923161e-01f, 2.3857592e-02f,  4.2524221e-04f,
+    -1.3066588e-01f, 3.3706114e-01f,  -6.5367684e-02f, -1.9584729e-01f,
+    -9.6636809e-02f, 5.7062846e-01f,  4.2524221e-04f,  8.9271449e-02f,
+    -1.5417366e-02f, -8.2307503e-02f, -5.0039625e-01f, 2.5350851e-01f,
+    -2.4847549e-01f, 4.2524221e-04f,  -2.8799692e-01f, -1.0268785e-01f,
+    -6.9768213e-02f, 1.9839688e-01f,  -9.6014850e-02f, 1.1959620e-02f,
+    4.2524221e-04f,  -7.6331727e-02f, 1.0289106e-01f,  2.5628258e-02f,
+    -9.5651820e-02f, -3.1599486e-01f, 3.4648609e-01f,  4.2524221e-04f,
+    -4.9910601e-02f, 8.5599929e-02f,  -3.1449606e-03f, -1.6781870e-01f,
+    1.0333546e+00f,  -6.6645592e-01f, 4.2524221e-04f,  8.2493991e-02f,
+    -9.5790043e-02f, 4.3036491e-02f,  1.8140252e-01f,  5.4385066e-01f,
+    3.2726720e-02f,  4.2524221e-04f,  2.2156011e-01f,  3.1133004e-02f,
+    -1.4379646e-01f, -5.9910184e-01f, 1.0038698e+00f,  -3.0557862e-01f,
+    4.2524221e-04f,  3.7525645e-01f,  7.0815518e-02f,  2.8620017e-01f,
+    6.9975668e-01f,  1.0616329e-01f,  1.8318458e-01f,  4.2524221e-04f,
+    9.5496923e-02f,  -3.8357295e-02f, 7.5472467e-02f,  1.4580189e-02f,
+    1.3419588e-01f,  -2.0312097e-02f, 4.2524221e-04f,  4.9029529e-02f,
+    1.7314212e-01f,  -4.9041037e-02f, -2.6927444e-01f, -2.4882385e-01f,
+    -2.5494534e-01f, 4.2524221e-04f,  -6.4100541e-02f, 2.6978979e-01f,
+    2.4858065e-02f,  -8.1361562e-01f, -3.7216064e-01f, 4.3392561e-02f,
+    4.2524221e-04f,  6.9799364e-02f,  -1.3860419e-01f, 1.0984455e-01f,
+    4.8301801e-01f,  5.5070144e-01f,  -3.3188796e-01f, 4.2524221e-04f,
+    -8.2801402e-02f, -6.8652697e-02f, -1.9647431e-02f, 1.8623030e-01f,
+    -1.3855183e-01f, 3.1506360e-01f,  4.2524221e-04f,  3.6300448e-01f,
+    -8.0298670e-02f, -3.1002939e-01f, -3.3787906e-01f, -3.0862695e-01f,
+    2.7613443e-01f,  4.2524221e-04f,  3.7739474e-01f,  1.1907437e-01f,
+    -3.9434172e-02f, 5.8045042e-01f,  4.5934165e-01f,  2.9962903e-01f,
+    4.2524221e-04f,  2.9385680e-02f,  1.1072745e-01f,  5.8579307e-02f,
+    -2.8264758e-01f, -1.0784884e-01f, 1.2321078e+00f,  4.2524221e-04f,
+    7.9958871e-02f,  1.2411897e-01f,  9.8061837e-02f,  3.3262360e-01f,
+    -8.3796644e-01f, 4.0548918e-01f,  4.2524221e-04f,  7.8290664e-02f,
+    4.5500584e-02f,  9.9731199e-02f,  -4.6239632e-01f, 3.0574635e-01f,
+    -4.3212789e-01f, 4.2524221e-04f,  3.6696273e-01f,  5.7200775e-03f,
+    5.3992327e-02f,  -1.6632666e-01f, -3.1065517e-03f, -1.1606836e-01f,
+    4.2524221e-04f,  2.3191632e-01f,  3.3108935e-01f,  2.0009531e-02f,
+    4.3141481e-01f,  7.1523404e-01f,  -4.0791895e-02f, 4.2524221e-04f,
+    -2.0644982e-01f, 3.2929885e-01f,  -2.1481182e-01f, 3.4483513e-01f,
+    8.7951744e-01f,  2.2883956e-01f,  4.2524221e-04f,  -2.4269024e-02f,
+    8.0496661e-02f,  -2.2875665e-02f, -4.7301382e-02f, -1.2039685e-01f,
+    -4.8519605e-01f, 4.2524221e-04f,  -3.5178763e-01f, -1.1468551e-01f,
+    -7.2022155e-02f, 7.1914357e-01f,  -1.8774068e-01f, 2.9152307e-01f,
+    4.2524221e-04f,  1.5231021e-01f,  2.1161540e-01f,  -1.1754553e-01f,
+    -7.1294534e-01f, -6.2154621e-01f, -1.9393834e-01f, 4.2524221e-04f,
+    -7.8070223e-02f, 1.7216440e-01f,  1.7939833e-01f,  4.8407644e-01f,
+    -1.7517121e-01f, 4.1451525e-02f,  4.2524221e-04f,  1.9436933e-02f,
+    4.3368284e-02f,  -3.5639319e-03f, 6.7544144e-01f,  5.4782498e-01f,
+    3.4879735e-01f,  4.2524221e-04f,  -1.3366042e-01f, -8.3979061e-03f,
+    -8.7891303e-02f, -9.8265654e-01f, -4.2677250e-02f, -1.1890029e-01f,
+    4.2524221e-04f,  1.2091810e-01f,  -1.8473221e-01f, 3.7591079e-01f,
+    1.7912203e-01f,  7.1378611e-03f,  5.6433028e-01f,  4.2524221e-04f,
+    -3.0588778e-02f, -8.0224700e-02f, 2.0911565e-01f,  1.7871276e-01f,
+    -4.5090526e-01f, 1.7313591e-01f,  4.2524221e-04f,  2.1592773e-01f,
+    -1.0682704e-01f, -1.4687291e-01f, -2.1309285e-01f, 3.2003528e-01f,
+    9.6824163e-01f,  4.2524221e-04f,  -7.1326107e-02f, -1.8375346e-01f,
+    1.6073698e-01f,  6.6706583e-02f,  -2.2058874e-01f, -1.6864805e-01f,
+    4.2524221e-04f,  -4.4198960e-02f, -1.1312663e-01f, 1.0822348e-01f,
+    1.3487945e-01f,  -7.0401341e-01f, -1.2007080e+00f, 4.2524221e-04f,
+    -2.9746767e-02f, -1.3425194e-01f, -2.5086749e-01f, -1.1511848e-01f,
+    -8.7276441e-01f, 1.6036594e-01f,  4.2524221e-04f,  1.7037044e-01f,
+    1.7299759e-01f,  4.6205060e-03f,  5.1056665e-01f,  1.0041865e+00f,
+    2.3419438e-01f,  4.2524221e-04f,  1.6252996e-01f,  1.1271755e-01f,
+    4.6216175e-02f,  5.6226152e-01f,  6.6637951e-01f,  5.3371119e-01f,
+    4.2524221e-04f,  -1.9546813e-01f, 1.3906172e-01f,  -5.5975009e-02f,
+    -1.0969467e-01f, -1.2633232e+00f, -4.3421894e-02f, 4.2524221e-04f,
+    -1.4044075e-01f, -2.6630515e-01f, 6.1962787e-02f,  4.6771467e-01f,
+    -6.9051319e-01f, 2.6465434e-01f,  4.2524221e-04f,  1.7195286e-01f,
+    -5.2851868e-01f, -1.6422449e-01f, 1.1703679e-01f,  7.2824037e-01f,
+    -3.6378372e-01f, 4.2524221e-04f,  1.0194746e-01f,  -9.7751893e-02f,
+    1.6529745e-01f,  2.4984296e-01f,  3.8181201e-02f,  2.7078211e-01f,
+    4.2524221e-04f,  2.0533490e-01f,  1.9480339e-01f,  -6.6993818e-02f,
+    3.9745870e-01f,  -7.9133675e-02f, -1.1942380e-01f, 4.2524221e-04f,
+    -3.9208923e-02f, 9.8150961e-02f,  1.0030308e-01f,  -5.7831265e-02f,
+    -6.4350224e-01f, 8.4775603e-01f,  4.2524221e-04f,  1.3816082e-01f,
+    -1.4092979e-02f, -1.0894109e-01f, 2.8519067e-01f,  5.8030725e-01f,
+    6.5652287e-01f,  4.2524221e-04f,  3.1362314e-02f,  -6.5740333e-03f,
+    6.7480214e-02f,  4.2265895e-01f,  -5.1995921e-01f, -2.8980300e-02f,
+    4.2524221e-04f,  -1.1953717e-01f, 1.5453845e-01f,  1.3720915e-01f,
+    -1.5399654e-01f, -1.2724885e-01f, 6.4902240e-01f,  4.2524221e-04f,
+    -2.4549389e-01f, -7.9987049e-02f, 8.9279823e-02f,  -9.2930816e-02f,
+    -6.1336237e-01f, 4.7973198e-01f,  4.2524221e-04f,  2.5360553e-02f,
+    -2.6513871e-02f, 5.4526389e-02f,  -9.8100655e-02f, 6.5327984e-01f,
+    -5.2721924e-01f, 4.2524221e-04f,  -1.0606319e-01f, -6.9447577e-02f,
+    4.3061398e-02f,  -1.0653659e+00f, 6.2340677e-01f,  4.6419606e-02f};
diff --git a/examples/image_processing/confidence_connected_components.cpp b/examples/image_processing/confidence_connected_components.cpp
index 661b90652f..368561dd1d 100644
--- a/examples/image_processing/confidence_connected_components.cpp
+++ b/examples/image_processing/confidence_connected_components.cpp
@@ -17,7 +17,6 @@ using namespace af;
 
 int main(int argc, char* argv[]) {
     try {
-
         unsigned s[1]       = {132};
         unsigned radius     = 3;
         unsigned multiplier = 3;
@@ -37,15 +36,15 @@ int main(int argc, char* argv[]) {
         array core =
             confidenceCC(A, sxArr, syArr, radius, multiplier, iter, 255);
 
-        seedx = 15;
-        seedy = 15;
+        seedx                 = 15;
+        seedy                 = 15;
         unsigned seedcoords[] = {15, 15};
         array seeds(dim4(1, 2), seedcoords);
         array background =
             confidenceCC(A, seeds, radius, multiplier, iter, 255);
 
         af::Window wnd("Confidence Connected Components demo");
-        while(!wnd.close()) {
+        while (!wnd.close()) {
             wnd.grid(2, 2);
             wnd(0, 0).image(A, "Input");
             wnd(0, 1).image(ring, "Ring Component - Seed(132, 132)");
diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index c5fc857899..d2b3466fa8 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -18,8 +18,8 @@
 using namespace af;
 using std::vector;
 
-std::string toStr(const dtype dt) { 
-    switch(dt) {
+std::string toStr(const dtype dt) {
+    switch (dt) {
         case f32: return "f32";
         case f16: return "f16";
         default: return "N/A";
@@ -94,14 +94,14 @@ void ann::back_propagate(const vector<array> signal, const array &target,
     array out = signal[num_layers - 1];
     array err = (out - target);
 
-    int m     = target.dims(0);
+    int m = target.dims(0);
 
     for (int i = num_layers - 2; i >= 0; i--) {
         array in    = add_bias(signal[i]);
         array delta = (deriv(out) * err).T();
 
         // Adjust weights
-        array tg = alpha * matmul(delta, in);
+        array tg   = alpha * matmul(delta, in);
         array grad = -(tg) / m;
         weights[i] += grad.T();
 
@@ -115,14 +115,15 @@ void ann::back_propagate(const vector<array> signal, const array &target,
     }
 }
 
-
 ann::ann(vector<int> layers, double range, dtype dt)
     : num_layers(layers.size()), weights(layers.size() - 1), datatype(dt) {
-    std::cout << "Initializing weights using a random uniformly distribution between " << -range/2 << " and " << range/2 << " at precision " << toStr(datatype) << std::endl;
+    std::cout
+        << "Initializing weights using a random uniformly distribution between "
+        << -range / 2 << " and " << range / 2 << " at precision "
+        << toStr(datatype) << std::endl;
     for (int i = 0; i < num_layers - 1; i++) {
         weights[i] = range * randu(layers[i] + 1, layers[i + 1]) - range / 2;
-        if (datatype != f32)
-            weights[i] = weights[i].as(datatype);
+        if (datatype != f32) weights[i] = weights[i].as(datatype);
     }
 }
 
@@ -136,7 +137,7 @@ double ann::train(const array &input, const array &target, double alpha,
                   int max_epochs, int batch_size, double maxerr, bool verbose) {
     const int num_samples = input.dims(0);
     const int num_batches = num_samples / batch_size;
-    
+
     double err = 0;
 
     // Training the entire network
@@ -189,7 +190,7 @@ int ann_demo(bool console, int perc, const dtype dt) {
                       test_images, train_target, test_target, frac);
     if (dt != f32) {
         train_images = train_images.as(dt);
-        test_images = test_images.as(dt);
+        test_images  = test_images.as(dt);
         train_target = train_target.as(dt);
     }
 
@@ -255,20 +256,22 @@ int ann_demo(bool console, int perc, const dtype dt) {
 }
 
 int main(int argc, char **argv) {
-    // usage:  neural_network_xxx (device) (console on/off) (percentage training/test set) (f32|f16)
+    // usage:  neural_network_xxx (device) (console on/off) (percentage
+    // training/test set) (f32|f16)
     int device   = argc > 1 ? atoi(argv[1]) : 0;
     bool console = argc > 2 ? argv[2][0] == '-' : false;
     int perc     = argc > 3 ? atoi(argv[3]) : 60;
-	if (perc < 0 || perc > 100) {
+    if (perc < 0 || perc > 100) {
         std::cerr << "Bad perc arg: " << perc << std::endl;
         return EXIT_FAILURE;
     }
     std::string dts = argc > 4 ? argv[4] : "f32";
-    dtype dt = f32;
-    if (dts == "f16") 
+    dtype dt        = f32;
+    if (dts == "f16")
         dt = f16;
     else if (dts != "f32") {
-        std::cerr << "Unsupported datatype " << dts << ". Supported: f32 or f16" << std::endl;
+        std::cerr << "Unsupported datatype " << dts << ". Supported: f32 or f16"
+                  << std::endl;
         return EXIT_FAILURE;
     }
 
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index d01e22a762..c13093b46e 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -58,13 +58,16 @@ void af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
     dim4 yo_dims       = yi_dims;
     yo_dims[xdim]      = xo_dims[xdim];
 
-    ARG_ASSERT(1, yi_info.isFloating());                        // Only floating and complex types
-    ARG_ASSERT(2, xo_info.isRealFloating()) ;                   // Only floating types
-    ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle());    // Must have same precision
-    ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble());    // Must have same precision
+    ARG_ASSERT(1, yi_info.isFloating());      // Only floating and complex types
+    ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
+    ARG_ASSERT(1, yi_info.isSingle() ==
+                      xo_info.isSingle());  // Must have same precision
+    ARG_ASSERT(1, yi_info.isDouble() ==
+                      xo_info.isDouble());  // Must have same precision
     ARG_ASSERT(3, xdim >= 0 && xdim < 4);
 
-    // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
+    // POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2],
+    // yi_dims[3])
     if (xo_dims[xdim] != xo_dims.elements()) {
         for (int i = 0; i < 4; i++) {
             if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
@@ -72,12 +75,10 @@ void af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
     }
 
     ARG_ASSERT(5, xi_step != 0);
-    ARG_ASSERT(6, (method == AF_INTERP_CUBIC         ||
-                   method == AF_INTERP_CUBIC_SPLINE  ||
-                   method == AF_INTERP_LINEAR        ||
-                   method == AF_INTERP_LINEAR_COSINE ||
-                   method == AF_INTERP_LOWER         ||
-                   method == AF_INTERP_NEAREST));
+    ARG_ASSERT(
+        6, (method == AF_INTERP_CUBIC || method == AF_INTERP_CUBIC_SPLINE ||
+            method == AF_INTERP_LINEAR || method == AF_INTERP_LINEAR_COSINE ||
+            method == AF_INTERP_LOWER || method == AF_INTERP_NEAREST));
 
     if (yi_dims.ndims() == 0 || xo_dims.ndims() == 0) {
         af_create_handle(yo, 0, nullptr, yi_info.getType());
@@ -176,13 +177,16 @@ void af_approx2_common(af_array *zo, const af_array zi, const af_array xo,
     dim4 xo_dims = xo_info.dims();
     dim4 yo_dims = yo_info.dims();
 
-    ARG_ASSERT(1, zi_info.isFloating());                      // Only floating and complex types
-    ARG_ASSERT(2, xo_info.isRealFloating());                  // Only floating types
-    ARG_ASSERT(4, yo_info.isRealFloating());                  // Only floating types
-    ARG_ASSERT(2, xo_info.getType() == yo_info.getType());    // Must have same type
-    ARG_ASSERT(1, zi_info.isSingle() == xo_info.isSingle());  // Must have same precision
-    ARG_ASSERT(1, zi_info.isDouble() == xo_info.isDouble());  // Must have same precision
-    DIM_ASSERT(2, xo_dims == yo_dims);                        // POS0 and POS1 must have same dims
+    ARG_ASSERT(1, zi_info.isFloating());      // Only floating and complex types
+    ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
+    ARG_ASSERT(4, yo_info.isRealFloating());  // Only floating types
+    ARG_ASSERT(2,
+               xo_info.getType() == yo_info.getType());  // Must have same type
+    ARG_ASSERT(1, zi_info.isSingle() ==
+                      xo_info.isSingle());  // Must have same precision
+    ARG_ASSERT(1, zi_info.isDouble() ==
+                      xo_info.isDouble());  // Must have same precision
+    DIM_ASSERT(2, xo_dims == yo_dims);      // POS0 and POS1 must have same dims
 
     ARG_ASSERT(3, xdim >= 0 && xdim < 4);
     ARG_ASSERT(5, ydim >= 0 && ydim < 4);
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index bf390fdd05..f0b58e6633 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -254,7 +254,7 @@ af_err af_get_data_ref_count(int *use_count, const af_array in) {
 
 af_err af_release_array(af_array arr) {
     try {
-        if(arr == 0) return AF_SUCCESS;
+        if (arr == 0) return AF_SUCCESS;
         const ArrayInfo &info = getInfo(arr, false, false);
         af_dtype type         = info.getType();
 
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 0211b72df1..7782170936 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -269,8 +269,7 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
             AF_CHECK(af_get_data_ref_count(&count, lhs));
             if (count > 1) {
                 AF_CHECK(af_copy_array(&output, lhs));
-	    }
-            else
+            } else
                 output = retain(lhs);
         } else {
             output = lhs;
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 3aa4d0a4a6..fe54e2f72d 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -19,11 +19,11 @@
 #include <sparse_blas.hpp>
 #include <sparse_handle.hpp>
 
+#include <type_util.hpp>
 #include <af/array.h>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <type_util.hpp>
 
 using common::half;
 
@@ -36,13 +36,10 @@ static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
 
 template<typename T>
 static inline void gemm(af_array *out, af_mat_prop optLhs, af_mat_prop optRhs,
-                        const T* alpha,
-                        const af_array lhs, const af_array rhs,
-                        const T* betas) {
-    detail::gemm<T>(getArray<T>(*out), optLhs, optRhs,
-                    alpha,
-                    getArray<T>(lhs), getArray<T>(rhs),
-                    betas);
+                        const T *alpha, const af_array lhs, const af_array rhs,
+                        const T *betas) {
+    detail::gemm<T>(getArray<T>(*out), optLhs, optRhs, alpha, getArray<T>(lhs),
+                    getArray<T>(rhs), betas);
 }
 
 template<typename T>
@@ -117,15 +114,14 @@ af_err af_sparse_matmul(af_array *out, const af_array lhs, const af_array rhs,
     return AF_SUCCESS;
 }
 
-af_err af_gemm(af_array *out,
-               const af_mat_prop optLhs, const af_mat_prop optRhs,
-               const void* alpha, const af_array lhs, const af_array rhs,
-               const void* beta) {
-    using namespace detail; // needed for cfloat and cdouble
+af_err af_gemm(af_array *out, const af_mat_prop optLhs,
+               const af_mat_prop optRhs, const void *alpha, const af_array lhs,
+               const af_array rhs, const void *beta) {
+    using namespace detail;  // needed for cfloat and cdouble
 
     try {
-        const ArrayInfo &lhsInfo    = getInfo(lhs, false, true);
-        const ArrayInfo &rhsInfo    = getInfo(rhs, true, true);
+        const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
+        const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
 
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
@@ -167,35 +163,44 @@ af_err af_gemm(af_array *out,
         af_array output = 0;
         if (*out) {
             output = *out;
-        }
-        else {
-            const int aRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
-            const int bColDim = (optRhs == AF_MAT_NONE) ? 1 : 0;
-            const int M = lDims[aRowDim];
-            const int N = rDims[bColDim];
-            const dim_t d2 = std::max(lDims[2], rDims[2]);
-            const dim_t d3 = std::max(lDims[3], rDims[3]);
+        } else {
+            const int aRowDim    = (optLhs == AF_MAT_NONE) ? 0 : 1;
+            const int bColDim    = (optRhs == AF_MAT_NONE) ? 1 : 0;
+            const int M          = lDims[aRowDim];
+            const int N          = rDims[bColDim];
+            const dim_t d2       = std::max(lDims[2], rDims[2]);
+            const dim_t d3       = std::max(lDims[3], rDims[3]);
             const af::dim4 oDims = af::dim4(M, N, d2, d3);
-            AF_CHECK(af_create_handle(&output, lhsInfo.ndims(),
-                                      oDims.get(), lhs_type));
+            AF_CHECK(af_create_handle(&output, lhsInfo.ndims(), oDims.get(),
+                                      lhs_type));
         }
 
         switch (lhs_type) {
-            case f32: gemm<float>  (&output, optLhs, optRhs,
-                                    static_cast<const float*  >(alpha), lhs, rhs,
-                                    static_cast<const float*  >(beta)); break;
-            case c32: gemm<cfloat> (&output, optLhs, optRhs,
-                                    static_cast<const cfloat* >(alpha), lhs, rhs,
-                                    static_cast<const cfloat* >(beta)); break;
-            case f64: gemm<double> (&output, optLhs, optRhs,
-                                    static_cast<const double* >(alpha), lhs, rhs,
-                                    static_cast<const double* >(beta)); break;
-            case c64: gemm<cdouble>(&output, optLhs, optRhs,
-                                    static_cast<const cdouble*>(alpha), lhs, rhs,
-                                    static_cast<const cdouble*>(beta)); break;
-            case f16: gemm<half>(&output, optLhs, optRhs,
-                                    static_cast<const half *>(alpha), lhs, rhs,
-                                    static_cast<const half *>(beta)); break;
+            case f32:
+                gemm<float>(&output, optLhs, optRhs,
+                            static_cast<const float *>(alpha), lhs, rhs,
+                            static_cast<const float *>(beta));
+                break;
+            case c32:
+                gemm<cfloat>(&output, optLhs, optRhs,
+                             static_cast<const cfloat *>(alpha), lhs, rhs,
+                             static_cast<const cfloat *>(beta));
+                break;
+            case f64:
+                gemm<double>(&output, optLhs, optRhs,
+                             static_cast<const double *>(alpha), lhs, rhs,
+                             static_cast<const double *>(beta));
+                break;
+            case c64:
+                gemm<cdouble>(&output, optLhs, optRhs,
+                              static_cast<const cdouble *>(alpha), lhs, rhs,
+                              static_cast<const cdouble *>(beta));
+                break;
+            case f16:
+                gemm<half>(&output, optLhs, optRhs,
+                           static_cast<const half *>(alpha), lhs, rhs,
+                           static_cast<const half *>(beta));
+                break;
             default: TYPE_ERROR(3, lhs_type);
         }
 
@@ -207,10 +212,9 @@ af_err af_gemm(af_array *out,
 
 af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                  const af_mat_prop optLhs, const af_mat_prop optRhs) {
-    using namespace detail; // needed for cfloat and cdouble
+    using namespace detail;  // needed for cfloat and cdouble
 
     try {
-
         const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
         const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
 
@@ -222,49 +226,55 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
 
         const af::dim4 lDims = lhsInfo.dims();
         const af::dim4 rDims = rhsInfo.dims();
-        const int M = lDims[aRowDim];
-        const int N = rDims[bColDim];
+        const int M          = lDims[aRowDim];
+        const int N          = rDims[bColDim];
 
-        const dim_t d2 = std::max(lDims[2], rDims[2]);
-        const dim_t d3 = std::max(lDims[3], rDims[3]);
+        const dim_t d2       = std::max(lDims[2], rDims[2]);
+        const dim_t d3       = std::max(lDims[3], rDims[3]);
         const af::dim4 oDims = af::dim4(M, N, d2, d3);
-        const int num_batch = oDims[2] * oDims[3];
+        const int num_batch  = oDims[2] * oDims[3];
 
         af_array gemm_out = 0;
-        AF_CHECK(af_create_handle(&gemm_out, oDims.ndims(), oDims.get(), lhsInfo.getType()));
+        AF_CHECK(af_create_handle(&gemm_out, oDims.ndims(), oDims.get(),
+                                  lhsInfo.getType()));
 
         af_dtype lhs_type = lhsInfo.getType();
         switch (lhs_type) {
             case f16: {
-                    static const half alpha(1.0f);
-                    static const half beta(0.0f);
-                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
-                    break;
+                static const half alpha(1.0f);
+                static const half beta(0.0f);
+                AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
+                                 &beta));
+                break;
             }
             case f32: {
-                    float alpha = 1.f;
-                    float beta  = 0.f;
-                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
-                    break;
+                float alpha = 1.f;
+                float beta  = 0.f;
+                AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
+                                 &beta));
+                break;
             }
             case c32: {
-                    cfloat alpha = {1.f, 0.f};
-                    cfloat beta  = {0.f, 0.f};
+                cfloat alpha = {1.f, 0.f};
+                cfloat beta  = {0.f, 0.f};
 
-                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
-                    break;
+                AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
+                                 &beta));
+                break;
             }
             case f64: {
-                    double alpha = 1.0;
-                    double beta  = 0.0;
-                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
-                    break;
+                double alpha = 1.0;
+                double beta  = 0.0;
+                AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
+                                 &beta));
+                break;
             }
             case c64: {
-                    cdouble alpha = {1.0, 0.0};
-                    cdouble beta  = {0.0, 0.0};
-                    AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs, &beta));
-                    break;
+                cdouble alpha = {1.0, 0.0};
+                cdouble beta  = {0.0, 0.0};
+                AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
+                                 &beta));
+                break;
             }
             default: TYPE_ERROR(1, lhs_type);
         }
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index 4312534903..df9629bc93 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -10,6 +10,7 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/half.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
 #include <optypes.hpp>
@@ -17,7 +18,6 @@
 #include <af/array.h>
 #include <af/data.h>
 #include <af/defines.h>
-#include <common/half.hpp>
 
 #include <arith.hpp>
 #include <logic.hpp>
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index e34b6fa13f..a14a6b16eb 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -173,7 +173,7 @@ af_err af_abs(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
-        if(in_type == f16) { type = f16; }
+        if (in_type == f16) { type = f16; }
 
         switch (type) {
             case f32:
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index 57411bf097..5a2910329f 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -28,14 +28,14 @@ using namespace detail;
 
 /// Index corner points of given seed points
 template<typename T>
-Array<T> pointList(const Array<T>& in,
-                   const Array<uint>& x, const Array<uint>& y) {
-    af_array xcoords = getHandle<uint>(x);
-    af_array ycoords = getHandle<uint>(y);
-    std::array<af_index_t, AF_MAX_DIMS> idxrs = {{
-        {xcoords, false, false}, {ycoords, false, false},
-        common::createSpanIndex(), common::createSpanIndex()
-    }};
+Array<T> pointList(const Array<T>& in, const Array<uint>& x,
+                   const Array<uint>& y) {
+    af_array xcoords                          = getHandle<uint>(x);
+    af_array ycoords                          = getHandle<uint>(y);
+    std::array<af_index_t, AF_MAX_DIMS> idxrs = {{{xcoords, false, false},
+                                                  {ycoords, false, false},
+                                                  common::createSpanIndex(),
+                                                  common::createSpanIndex()}};
 
     Array<T> retVal = detail::index(in, idxrs.data());
 
@@ -76,31 +76,30 @@ Array<T> sum(const Array<T>& sat, const Array<uint>& _x, const Array<uint>& x_,
 }
 
 template<typename T>
-af_array ccHelper(const Array<T>& img, const Array<uint> &seedx,
-                  const Array<uint> &seedy, const unsigned radius, const unsigned mult,
-                  const unsigned iterations, const double segmentedValue) {
-    using CT   = typename std::conditional<std::is_same<T, double>::value,
-                                  double, float>::type;
+af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
+                  const Array<uint>& seedy, const unsigned radius,
+                  const unsigned mult, const unsigned iterations,
+                  const double segmentedValue) {
+    using CT = typename std::conditional<std::is_same<T, double>::value, double,
+                                         float>::type;
     constexpr CT epsilon = 1.0e-6;
 
     auto calcVar = [](CT s2, CT s1, CT n) -> CT {
         CT retVal = CT(0);
-        if (n > 1) {
-            retVal = (s2 - (s1 * s1 / n)) / (n - CT(1));
-        }
+        if (n > 1) { retVal = (s2 - (s1 * s1 / n)) / (n - CT(1)); }
         return retVal;
     };
 
-    const dim4        inDims = img.dims();
-    const dim4      seedDims = seedx.dims();
-    const size_t    numSeeds = seedx.elements();
-    const unsigned  nhoodLen = 2*radius + 1;
+    const dim4 inDims        = img.dims();
+    const dim4 seedDims      = seedx.dims();
+    const size_t numSeeds    = seedx.elements();
+    const unsigned nhoodLen  = 2 * radius + 1;
     const unsigned nhoodSize = nhoodLen * nhoodLen;
 
     auto labelSegmented = [segmentedValue, inDims](const Array<CT>& segmented) {
         Array<CT> newVals = createValueArray(inDims, CT(segmentedValue));
         Array<CT> result  = arithOp<CT, af_mul_t>(newVals, segmented, inDims);
-        //cast final result to input type
+        // cast final result to input type
         return cast<T, CT>(result);
     };
 
@@ -126,8 +125,8 @@ af_array ccHelper(const Array<T>& img, const Array<uint> &seedx,
     CT upper           = mean + mult * stddev;
 
     Array<CT> seedIntensities = pointList(in, seedx, seedy);
-    CT maxSeedIntensity  = reduce_all<af_max_t, CT, CT>(seedIntensities);
-    CT minSeedIntensity  = reduce_all<af_min_t, CT, CT>(seedIntensities);
+    CT maxSeedIntensity       = reduce_all<af_max_t, CT, CT>(seedIntensities);
+    CT minSeedIntensity       = reduce_all<af_min_t, CT, CT>(seedIntensities);
 
     if (lower > minSeedIntensity) { lower = minSeedIntensity; }
     if (upper < maxSeedIntensity) { upper = maxSeedIntensity; }
@@ -140,9 +139,9 @@ af_array ccHelper(const Array<T>& img, const Array<uint> &seedx,
     }
 
     bool continueLoop = true;
-    for (uint i = 0; (i < iterations) && continueLoop ; ++i) {
-        //Segmented images are set with 1's and 0's thus essentially
-        //making them into mask arrays for each iteration's input image
+    for (uint i = 0; (i < iterations) && continueLoop; ++i) {
+        // Segmented images are set with 1's and 0's thus essentially
+        // making them into mask arrays for each iteration's input image
 
         uint sampleCount = reduce_all<af_notzero_t, CT, uint>(segmented, true);
         if (sampleCount == 0) {
@@ -182,7 +181,7 @@ af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
     // short bit size(16,8) types very often and occasionally
     // with 32 bit types.
     AF_ERROR("There is a known issue for OpenCL implementation",
-            AF_ERR_NOT_SUPPORTED);
+             AF_ERR_NOT_SUPPORTED);
 #endif
     try {
         const ArrayInfo inInfo         = getInfo(in);
@@ -191,9 +190,9 @@ af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
         const af::dim4 inputDimensions = inInfo.dims();
         const af::dtype inputArrayType = inInfo.getType();
 
-        //TODO(pradeep) handle case where seeds are towards border
+        // TODO(pradeep) handle case where seeds are towards border
         //              and indexing may result in throwing exception
-        //TODO(pradeep) add batch support later
+        // TODO(pradeep) add batch support later
         ARG_ASSERT(
             1, (inputDimensions.ndims() > 0 && inputDimensions.ndims() <= 2));
 
@@ -223,7 +222,7 @@ af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
                                   getArray<uint>(seedy), radius, multiplier,
                                   iter, segmented_value);
                 break;
-            default : TYPE_ERROR (0, inputArrayType);
+            default: TYPE_ERROR(0, inputArrayType);
         }
         std::swap(*out, output);
     }
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index ac6245e4a1..55ce3190a5 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -28,9 +28,7 @@ using common::half;
 
 af_err af_set_backend(const af_backend bknd) {
     try {
-        if(bknd != getBackend()) {
-            return AF_ERR_ARG;
-        }
+        if (bknd != getBackend()) { return AF_ERR_ARG; }
     }
     CATCHALL;
 
@@ -52,7 +50,7 @@ af_err af_get_available_backends(int* result) {
 
 af_err af_get_backend_id(af_backend* result, const af_array in) {
     try {
-        if(in) {
+        if (in) {
             const ArrayInfo& info = getInfo(in, false, false);
             *result               = info.getBackendId();
         } else {
@@ -65,7 +63,7 @@ af_err af_get_backend_id(af_backend* result, const af_array in) {
 
 af_err af_get_device_id(int* device, const af_array in) {
     try {
-        if(in) {
+        if (in) {
             const ArrayInfo& info = getInfo(in, false, false);
             *device               = info.getDevId();
         } else {
diff --git a/src/api/c/events.cpp b/src/api/c/events.cpp
index 8dd8fc760d..24aeed4421 100644
--- a/src/api/c/events.cpp
+++ b/src/api/c/events.cpp
@@ -28,7 +28,6 @@ const Event &getEvent(const af_event &handle) {
 
 af_event getHandle(Event &event) { return static_cast<af_event>(&event); }
 
-
 af_err af_create_event(af_event *handle) {
     try {
         AF_CHECK(af_init());
diff --git a/src/api/c/events.hpp b/src/api/c/events.hpp
index aca2463e64..b3d3eb398d 100644
--- a/src/api/c/events.hpp
+++ b/src/api/c/events.hpp
@@ -15,5 +15,5 @@
 
 af_event getHandle(detail::Event& event);
 
-detail::Event& getEvent(af_event &eventHandle);
-const detail::Event& getEvent(const af_event &eventHandle);
+detail::Event& getEvent(af_event& eventHandle);
+const detail::Event& getEvent(const af_event& eventHandle);
diff --git a/src/api/c/features.hpp b/src/api/c/features.hpp
index ab61cb5c8b..9cd977576a 100644
--- a/src/api/c/features.hpp
+++ b/src/api/c/features.hpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <cstddef>
 #include <af/array.h>
 #include <af/features.h>
+#include <cstddef>
 
 typedef struct {
     size_t n;
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 1f80fac6b5..e8c51d1db1 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -11,19 +11,19 @@
 #include <vector>
 
 #include <Array.hpp>
-#include <optypes.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <handle.hpp>
 #include <lookup.hpp>
-#include <af/defines.h>
-#include <af/array.h>
+#include <optypes.hpp>
 #include <af/arith.h>
+#include <af/array.h>
 #include <af/data.h>
+#include <af/defines.h>
 #include <af/index.h>
 #include <af/seq.h>
-#include <handle.hpp>
 
 using namespace detail;
 using common::half;
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 087fc1b2ed..4a94ffa1bb 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -61,8 +61,10 @@ const detail::Array<T> &getArray(const af_array &arr) {
 
 template<>
 const detail::Array<common::half> &getArray<common::half>(const af_array &arr) {
-  const detail::Array<common::half> *A = static_cast<const detail::Array<common::half> *>(arr);
-    if (f16 != A->getType()) AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+    const detail::Array<common::half> *A =
+        static_cast<const detail::Array<common::half> *>(arr);
+    if (f16 != A->getType())
+        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
     return *A;
 }
 
@@ -76,7 +78,8 @@ detail::Array<T> &getArray(af_array &arr) {
 
 template<>
 detail::Array<common::half> &getArray<common::half>(af_array &arr) {
-    detail::Array<common::half> *A = static_cast<detail::Array<common::half> *>(arr);
+    detail::Array<common::half> *A =
+        static_cast<detail::Array<common::half> *>(arr);
     if (f16 != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
     return *A;
diff --git a/src/api/c/imgproc_common.hpp b/src/api/c/imgproc_common.hpp
index 0497d0e789..210380bbed 100644
--- a/src/api/c/imgproc_common.hpp
+++ b/src/api/c/imgproc_common.hpp
@@ -21,7 +21,7 @@ namespace common {
 
 template<typename To, typename Ti = To>
 detail::Array<To> integralImage(const detail::Array<Ti>& in) {
-    auto input = detail::cast<To, Ti>(in);
+    auto input               = detail::cast<To, Ti>(in);
     Array<To> horizontalScan = detail::scan<af_add_t, To, To>(input, 0);
     return detail::scan<af_add_t, To, To>(horizontalScan, 1);
 }
@@ -58,7 +58,7 @@ detail::Array<To> convRange(const detail::Array<Ti>& in,
     }
 
     auto minArray = createValueArray(dims, low);
-    auto invDen   = createValueArray(dims, To(1.0/range));
+    auto invDen   = createValueArray(dims, To(1.0 / range));
     auto numer    = arithOp<To, af_sub_t>(input, minArray, dims);
     auto result   = arithOp<To, af_mul_t>(numer, invDen, dims);
 
@@ -73,4 +73,4 @@ detail::Array<To> convRange(const detail::Array<Ti>& in,
     return result;
 }
 
-} // namespace common
+}  // namespace common
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 8a2d5cb84f..82ab7f7a8b 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -106,8 +106,8 @@ af_err af_create_strided_array(af_array *arr, const void *data,
                     dims, strides, offset, (uchar *)data, isdev));
                 break;
             case f16:
-                res = getHandle(createStridedArray<half>(
-                    dims, strides, offset, (half *)data, isdev));
+                res = getHandle(createStridedArray<half>(dims, strides, offset,
+                                                         (half *)data, isdev));
                 break;
             default: TYPE_ERROR(6, ty);
         }
diff --git a/src/api/c/memoryapi.hpp b/src/api/c/memoryapi.hpp
index dd5dcdfef2..ab942e721d 100644
--- a/src/api/c/memoryapi.hpp
+++ b/src/api/c/memoryapi.hpp
@@ -13,7 +13,6 @@
 
 #include <af/memory.h>
 
-
 ////////////////////////////////////////////////////////////////////////////////
 // Memory Manager API
 ////////////////////////////////////////////////////////////////////////////////
@@ -22,7 +21,8 @@
  * An internal wrapper around an af_memory_manager which calls function pointers
  * on a af_memory_manager via calls to a MemoryManagerBase
  */
-class MemoryManagerFunctionWrapper final : public common::memory::MemoryManagerBase {
+class MemoryManagerFunctionWrapper final
+    : public common::memory::MemoryManagerBase {
     af_memory_manager handle_;
 
    public:
@@ -30,7 +30,7 @@ class MemoryManagerFunctionWrapper final : public common::memory::MemoryManagerB
     ~MemoryManagerFunctionWrapper();
     void initialize() override;
     void shutdown() override;
-    void* alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+    void *alloc(bool user_lock, const unsigned ndims, dim_t *dims,
                 const unsigned element_size) override;
     size_t allocated(void *ptr) override;
     void unlock(void *ptr, bool user_unlock) override;
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 418be4e6f5..6361d809f9 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -129,11 +129,13 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol) {
                          0, uT.dims()[2] - 1, 0, uT.dims()[3] - 1);
     }
 
-    Array<T> vsPinv = createEmptyArray<T>(dim4(v.dims()[0], sPinv.dims()[1], P, Q));
-    Array<T> out    = createEmptyArray<T>(dim4(vsPinv.dims()[0], uT.dims()[1], P, Q));
+    Array<T> vsPinv =
+        createEmptyArray<T>(dim4(v.dims()[0], sPinv.dims()[1], P, Q));
+    Array<T> out =
+        createEmptyArray<T>(dim4(vsPinv.dims()[0], uT.dims()[1], P, Q));
 
     T alpha = scalar<T>(1.0);
-    T beta = scalar<T>(0.0);
+    T beta  = scalar<T>(0.0);
 
     gemm<T>(vsPinv, AF_MAT_NONE, AF_MAT_NONE, &alpha, v, sPinv, &beta);
     gemm<T>(out, AF_MAT_NONE, AF_MAT_NONE, &alpha, vsPinv, uT, &beta);
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 862a0a0241..49a7eb13db 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -27,9 +27,7 @@ using namespace common;
 
 using af::dim4;
 
-Array<uint> emptyArray() {
-    return createEmptyArray<uint>(af::dim4(0));
-}
+Array<uint> emptyArray() { return createEmptyArray<uint>(af::dim4(0)); }
 
 struct RandomEngine {
     af_random_engine_type type;
@@ -71,8 +69,7 @@ RandomEngine *getRandomEngine(const af_random_engine engineHandle) {
 
 namespace {
 template<typename T>
-inline af_array uniformDistribution_(const af::dim4 &dims,
-                                            RandomEngine *e) {
+inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(uniformDistribution<T>(dims, e->pos, e->sh1, e->sh2,
                                                 e->mask, e->recursion_table,
@@ -84,8 +81,7 @@ inline af_array uniformDistribution_(const af::dim4 &dims,
 }
 
 template<typename T>
-inline af_array normalDistribution_(const af::dim4 &dims,
-                                           RandomEngine *e) {
+inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(normalDistribution<T>(dims, e->pos, e->sh1, e->sh2,
                                                e->mask, e->recursion_table,
@@ -107,14 +103,14 @@ void validateRandomType(const af_random_engine_type type) {
         AF_ERROR("Invalid random type", AF_ERR_ARG);
     }
 }
-}
+}  // namespace
 
 af_err af_get_default_random_engine(af_random_engine *r) {
     try {
         AF_CHECK(af_init());
 
         thread_local RandomEngine *re = new RandomEngine;
-        *r = static_cast<af_random_engine>(re);
+        *r                            = static_cast<af_random_engine>(re);
         return AF_SUCCESS;
     }
     CATCHALL;
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 868a3d2081..5f006d472d 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -22,8 +22,8 @@
 #include <select.hpp>
 
 using namespace detail;
-using common::half;
 using af::dim4;
+using common::half;
 
 template<typename T>
 void replace(af_array a, const af_array cond, const af_array b) {
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 2ee030c1b0..33cb129a0a 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -174,9 +174,7 @@ af_err af_select_scalar_l(af_array* out, const af_array cond, const double a,
         af_array res;
 
         switch (binfo.getType()) {
-            case f16:
-                res = select_scalar<half, true>(cond, b, a, odims);
-                break;
+            case f16: res = select_scalar<half, true>(cond, b, a, odims); break;
             case f32:
                 res = select_scalar<float, true>(cond, b, a, odims);
                 break;
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index fed87ba48b..bcd5563296 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -58,8 +58,8 @@ void af_transform_common(af_array *out, const af_array in, const af_array tf,
                          const af_interp_type method, const bool inverse,
                          bool allocate_out) {
     ARG_ASSERT(0, out != 0);  // *out (the af_array) can be null, but not out
-    ARG_ASSERT(1, in  != 0);
-    ARG_ASSERT(2, tf  != 0);
+    ARG_ASSERT(1, in != 0);
+    ARG_ASSERT(2, tf != 0);
 
     const ArrayInfo &t_info = getInfo(tf);
     const ArrayInfo &i_info = getInfo(in);
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index f1666b5b4e..979fa8da01 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -26,9 +26,10 @@ template<typename T>
 Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs,
                          std::vector<af_seq> idx) {
     Array<T> rhs_sub = createSubArray(rhs, idx);
-    Array<T> out     = createEmptyArray<T>(dim4(lhs.dims()[0], rhs_sub.dims()[1], lhs.dims()[2], lhs.dims()[3]));
+    Array<T> out     = createEmptyArray<T>(
+        dim4(lhs.dims()[0], rhs_sub.dims()[1], lhs.dims()[2], lhs.dims()[3]));
     T alpha = scalar<T>(1.0);
-    T beta = scalar<T>(0.0);
+    T beta  = scalar<T>(0.0);
     gemm(out, AF_MAT_NONE, AF_MAT_NONE, &alpha, lhs, rhs_sub, &beta);
     return out;
 }
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 26d75a06d8..d5435d1883 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -669,7 +669,7 @@ static af_err af_check(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles / complex
         af_dtype type = implicit(in_type, f32);
-        if(in_type == f16) type = f16;
+        if (in_type == f16) type = f16;
 
         switch (type) {
             case f32: res = checkOp<float, op>(in); break;
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index eabaa81364..1a8d2010f2 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -179,7 +179,7 @@ af_err af_var(af_array* out, const af_array in, const bool isbiased,
 
         af_array no_weights = 0;
         af_var_bias bias =
-            (isbiased) ? AF_VARIANCE_SAMPLE: AF_VARIANCE_POPULATION;
+            (isbiased) ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION;
         switch (type) {
             case f32:
                 output = var_<float, float>(in, no_weights, bias, dim);
@@ -442,8 +442,7 @@ af_err af_meanvar(af_array* mean, af_array* var, const af_array in,
                     meanvar<cdouble, cdouble>(in, weights, bias, dim);
                 break;
             case f16:
-                tie(*mean, *var) =
-                    meanvar<half, half>(in, weights, bias, dim);
+                tie(*mean, *var) = meanvar<half, half>(in, weights, bias, dim);
                 break;
             default: TYPE_ERROR(1, iType);
         }
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index 1bba6194d2..4736f14399 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -19,22 +19,18 @@ using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline void wrap(af_array *out, const af_array in,
-                        const dim_t ox, const dim_t oy,
-                        const dim_t wx, const dim_t wy,
-                        const dim_t sx, const dim_t sy,
-                        const dim_t px, const dim_t py,
-                        const bool is_column) {
+static inline void wrap(af_array* out, const af_array in, const dim_t ox,
+                        const dim_t oy, const dim_t wx, const dim_t wy,
+                        const dim_t sx, const dim_t sy, const dim_t px,
+                        const dim_t py, const bool is_column) {
     wrap<T>(getArray<T>(*out), getArray<T>(in), ox, oy, wx, wy, sx, sy, px, py,
             is_column);
 }
 
-void af_wrap_common(af_array *out, const af_array in,
-                      const dim_t ox, const dim_t oy,
-                      const dim_t wx, const dim_t wy,
-                      const dim_t sx, const dim_t sy,
-                      const dim_t px, const dim_t py,
-                      const bool is_column, bool allocate_out) {
+void af_wrap_common(af_array* out, const af_array in, const dim_t ox,
+                    const dim_t oy, const dim_t wx, const dim_t wy,
+                    const dim_t sx, const dim_t sy, const dim_t px,
+                    const dim_t py, const bool is_column, bool allocate_out) {
     ARG_ASSERT(0, out != 0);  // *out (the af_array) can be null, but not out
     ARG_ASSERT(1, in != 0);
 
@@ -81,31 +77,26 @@ void af_wrap_common(af_array *out, const af_array in,
     // clang-format on
 }
 
-af_err af_wrap(af_array* out, const af_array in,
-               const dim_t ox, const dim_t oy,
-               const dim_t wx, const dim_t wy,
-               const dim_t sx, const dim_t sy,
-               const dim_t px, const dim_t py,
-               const bool is_column) {
+af_err af_wrap(af_array* out, const af_array in, const dim_t ox, const dim_t oy,
+               const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+               const dim_t px, const dim_t py, const bool is_column) {
     try {
-        af_wrap_common(out, in, ox, oy, wx, wy, sx, sy, px, py,
-                              is_column, true);
+        af_wrap_common(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column,
+                       true);
     }
     CATCHALL;
 
     return AF_SUCCESS;
 }
 
-af_err af_wrap_v2(af_array* out, const af_array in,
-                  const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy,
-                  const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py,
-                  const bool is_column) {
+af_err af_wrap_v2(af_array* out, const af_array in, const dim_t ox,
+                  const dim_t oy, const dim_t wx, const dim_t wy,
+                  const dim_t sx, const dim_t sy, const dim_t px,
+                  const dim_t py, const bool is_column) {
     try {
         ARG_ASSERT(0, out != 0);  // need to dereference out in next call
-        af_wrap_common(out, in, ox, oy, wx, wy, sx, sy, px, py,
-                              is_column, *out == 0);
+        af_wrap_common(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column,
+                       *out == 0);
     }
     CATCHALL;
 
diff --git a/src/api/cpp/confidence_connected.cpp b/src/api/cpp/confidence_connected.cpp
index 5410f0a334..97e5209f8c 100644
--- a/src/api/cpp/confidence_connected.cpp
+++ b/src/api/cpp/confidence_connected.cpp
@@ -26,14 +26,14 @@ array confidenceCC(const array &in, const size_t num_seeds,
     return array(temp);
 }
 
-array confidenceCC(const array &in, const array &seeds,
-                   const unsigned radius, const unsigned multiplier,
-                   const int iter, const double segmentedValue) {
+array confidenceCC(const array &in, const array &seeds, const unsigned radius,
+                   const unsigned multiplier, const int iter,
+                   const double segmentedValue) {
     af::array xcoords = seeds.col(0);
     af::array ycoords = seeds.col(1);
-    af_array temp = 0;
-    AF_THROW(af_confidence_cc(&temp, in.get(), xcoords.get(), ycoords.get(), radius,
-                              multiplier, iter, segmentedValue));
+    af_array temp     = 0;
+    AF_THROW(af_confidence_cc(&temp, in.get(), xcoords.get(), ycoords.get(),
+                              radius, multiplier, iter, segmentedValue));
     return array(temp);
 }
 
@@ -46,4 +46,4 @@ array confidenceCC(const array &in, const array &seedx, const array &seedy,
     return array(temp);
 }
 
-} // namespace af
+}  // namespace af
diff --git a/src/api/cpp/convolve.cpp b/src/api/cpp/convolve.cpp
index 4b5ce62177..98dc315880 100644
--- a/src/api/cpp/convolve.cpp
+++ b/src/api/cpp/convolve.cpp
@@ -55,9 +55,9 @@ array convolve2(const array &signal, const array &filter, const convMode mode,
 array convolve2NN(const array &signal, const array &filter, const dim4 stride,
                   const dim4 padding, const dim4 dilation) {
     af_array out = 0;
-    AF_THROW(af_convolve2_nn(
-        &out, signal.get(), filter.get(), stride.ndims(), stride.get(),
-        padding.ndims(), padding.get(), dilation.ndims(), dilation.get()));
+    AF_THROW(af_convolve2_nn(&out, signal.get(), filter.get(), stride.ndims(),
+                             stride.get(), padding.ndims(), padding.get(),
+                             dilation.ndims(), dilation.get()));
     return array(out);
 }
 
@@ -68,11 +68,11 @@ array convolve2GradientNN(const array &incoming_gradient,
                           const dim4 padding, const dim4 dilation,
                           af_conv_gradient_type gradType) {
     af_array out = 0;
-    AF_THROW(af_convolve2_gradient_nn(&out, incoming_gradient.get(),
-                                      original_signal.get(), original_filter.get(),
-                                      convolved_output.get(), stride.ndims(),
-                                      stride.get(), padding.ndims(), padding.get(),
-                                      dilation.ndims(), dilation.get(), gradType));
+    AF_THROW(af_convolve2_gradient_nn(
+        &out, incoming_gradient.get(), original_signal.get(),
+        original_filter.get(), convolved_output.get(), stride.ndims(),
+        stride.get(), padding.ndims(), padding.get(), dilation.ndims(),
+        dilation.get(), gradType));
     return array(out);
 }
 
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 5be0130728..3c68386a11 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #include <af/data.h>
 
+#include <half.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/complex.h>
@@ -16,7 +17,6 @@
 #include <af/half.h>
 #include <af/traits.hpp>
 #include "error.hpp"
-#include <half.hpp>
 
 #include <type_traits>
 
diff --git a/src/api/cpp/event.cpp b/src/api/cpp/event.cpp
index a43c893641..577700399f 100644
--- a/src/api/cpp/event.cpp
+++ b/src/api/cpp/event.cpp
@@ -18,7 +18,7 @@ event::event(af_event e) : e_(e) {}
 
 event::~event() {
     // No dtor throw
-    if(e_) af_delete_event(e_);
+    if (e_) af_delete_event(e_);
 }
 
 event::event(event&& other) : e_(other.e_) { other.e_ = 0; }
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index 2e115e8470..8a18760867 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -34,7 +34,7 @@ ALGO_HAPI_DEF(af_diff2)
     af_err af_func(af_array *keys_out, af_array *vals_out,                    \
                    const af_array keys, const af_array vals, const int dim) { \
         CHECK_ARRAYS(keys, vals);                                             \
-        CALL(af_func, keys_out, vals_out, keys, vals, dim);              \
+        CALL(af_func, keys_out, vals_out, keys, vals, dim);                   \
     }
 
 ALGO_HAPI_DEF_BYKEY(af_sum_by_key)
@@ -59,12 +59,12 @@ ALGO_HAPI_DEF(af_product_nan)
 
 #undef ALGO_HAPI_DEF
 
-#define ALGO_HAPI_DEF_BYKEY(af_func_nan)                         \
-    af_err af_func_nan(af_array *keys_out, af_array *vals_out,   \
-                       const af_array keys, const af_array vals, \
-                       const int dim, const double nanval) {     \
-        CHECK_ARRAYS(keys, vals);                                \
-        CALL(af_func_nan, keys_out, vals_out, keys, vals, dim, nanval);  \
+#define ALGO_HAPI_DEF_BYKEY(af_func_nan)                                \
+    af_err af_func_nan(af_array *keys_out, af_array *vals_out,          \
+                       const af_array keys, const af_array vals,        \
+                       const int dim, const double nanval) {            \
+        CHECK_ARRAYS(keys, vals);                                       \
+        CALL(af_func_nan, keys_out, vals_out, keys, vals, dim, nanval); \
     }
 
 ALGO_HAPI_DEF_BYKEY(af_sum_by_key_nan)
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index aa27dec836..577a2cc950 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -50,96 +50,96 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t *const dims,
 
 af_err af_diag_create(af_array *out, const af_array in, const int num) {
     CHECK_ARRAYS(in);
-      CALL(af_diag_create, out, in, num);
+    CALL(af_diag_create, out, in, num);
 }
 
 af_err af_diag_extract(af_array *out, const af_array in, const int num) {
     CHECK_ARRAYS(in);
-      CALL(af_diag_extract, out, in, num);
+    CALL(af_diag_extract, out, in, num);
 }
 
 af_err af_join(af_array *out, const int dim, const af_array first,
                const af_array second) {
     CHECK_ARRAYS(first, second);
-      CALL(af_join, out, dim, first, second);
+    CALL(af_join, out, dim, first, second);
 }
 
 af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
                     const af_array *inputs) {
     for (unsigned i = 0; i < n_arrays; i++) CHECK_ARRAYS(inputs[i]);
-      CALL(af_join_many, out, dim, n_arrays, inputs);
+    CALL(af_join_many, out, dim, n_arrays, inputs);
 }
 
 af_err af_tile(af_array *out, const af_array in, const unsigned x,
                const unsigned y, const unsigned z, const unsigned w) {
     CHECK_ARRAYS(in);
-      CALL(af_tile, out, in, x, y, z, w);
+    CALL(af_tile, out, in, x, y, z, w);
 }
 
 af_err af_reorder(af_array *out, const af_array in, const unsigned x,
                   const unsigned y, const unsigned z, const unsigned w) {
     CHECK_ARRAYS(in);
-      CALL(af_reorder, out, in, x, y, z, w);
+    CALL(af_reorder, out, in, x, y, z, w);
 }
 
 af_err af_shift(af_array *out, const af_array in, const int x, const int y,
                 const int z, const int w) {
     CHECK_ARRAYS(in);
-      CALL(af_shift, out, in, x, y, z, w);
+    CALL(af_shift, out, in, x, y, z, w);
 }
 
 af_err af_moddims(af_array *out, const af_array in, const unsigned ndims,
                   const dim_t *const dims) {
     CHECK_ARRAYS(in);
-      CALL(af_moddims, out, in, ndims, dims);
+    CALL(af_moddims, out, in, ndims, dims);
 }
 
 af_err af_flat(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_flat, out, in);
+    CALL(af_flat, out, in);
 }
 
 af_err af_flip(af_array *out, const af_array in, const unsigned dim) {
     CHECK_ARRAYS(in);
-      CALL(af_flip, out, in, dim);
+    CALL(af_flip, out, in, dim);
 }
 
 af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
     CHECK_ARRAYS(in);
-      CALL(af_lower, out, in, is_unit_diag);
+    CALL(af_lower, out, in, is_unit_diag);
 }
 
 af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
     CHECK_ARRAYS(in);
-      CALL(af_upper, out, in, is_unit_diag);
+    CALL(af_upper, out, in, is_unit_diag);
 }
 
 af_err af_select(af_array *out, const af_array cond, const af_array a,
                  const af_array b) {
     CHECK_ARRAYS(cond, a, b);
-      CALL(af_select, out, cond, a, b);
+    CALL(af_select, out, cond, a, b);
 }
 
 af_err af_select_scalar_r(af_array *out, const af_array cond, const af_array a,
                           const double b) {
     CHECK_ARRAYS(cond, a);
-      CALL(af_select_scalar_r, out, cond, a, b);
+    CALL(af_select_scalar_r, out, cond, a, b);
 }
 
 af_err af_select_scalar_l(af_array *out, const af_array cond, const double a,
                           const af_array b) {
     CHECK_ARRAYS(cond, b);
-      CALL(af_select_scalar_l, out, cond, a, b);
+    CALL(af_select_scalar_l, out, cond, a, b);
 }
 
 af_err af_replace(af_array a, const af_array cond, const af_array b) {
     CHECK_ARRAYS(a, cond, b);
-      CALL(af_replace, a, cond, b);
+    CALL(af_replace, a, cond, b);
 }
 
 af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
     CHECK_ARRAYS(a, cond);
-      CALL(af_replace_scalar, a, cond, b);
+    CALL(af_replace_scalar, a, cond, b);
 }
 
 af_err af_pad(af_array *out, const af_array in, const unsigned b_ndims,
diff --git a/src/api/unified/image.cpp b/src/api/unified/image.cpp
index 0b079e1ab0..0459301f1a 100644
--- a/src/api/unified/image.cpp
+++ b/src/api/unified/image.cpp
@@ -14,7 +14,7 @@
 
 af_err af_gradient(af_array *dx, af_array *dy, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_gradient, dx, dy, in);
+    CALL(af_gradient, dx, dy, in);
 }
 
 af_err af_load_image(af_array *out, const char *filename, const bool isColor) {
@@ -23,7 +23,7 @@ af_err af_load_image(af_array *out, const char *filename, const bool isColor) {
 
 af_err af_save_image(const char *filename, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_save_image, filename, in);
+    CALL(af_save_image, filename, in);
 }
 
 af_err af_load_image_memory(af_array *out, const void *ptr) {
@@ -33,12 +33,10 @@ af_err af_load_image_memory(af_array *out, const void *ptr) {
 af_err af_save_image_memory(void **ptr, const af_array in,
                             const af_image_format format) {
     CHECK_ARRAYS(in);
-      CALL(af_save_image_memory, ptr, in, format);
+    CALL(af_save_image_memory, ptr, in, format);
 }
 
-af_err af_delete_image_memory(void *ptr) {
-    CALL(af_delete_image_memory, ptr);
-}
+af_err af_delete_image_memory(void *ptr) { CALL(af_delete_image_memory, ptr); }
 
 af_err af_load_image_native(af_array *out, const char *filename) {
     CALL(af_load_image_native, out, filename);
@@ -46,7 +44,7 @@ af_err af_load_image_native(af_array *out, const char *filename) {
 
 af_err af_save_image_native(const char *filename, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_save_image_native, filename, in);
+    CALL(af_save_image_native, filename, in);
 }
 
 af_err af_is_image_io_available(bool *out) {
@@ -56,19 +54,20 @@ af_err af_is_image_io_available(bool *out) {
 af_err af_resize(af_array *out, const af_array in, const dim_t odim0,
                  const dim_t odim1, const af_interp_type method) {
     CHECK_ARRAYS(in);
-      CALL(af_resize, out, in, odim0, odim1, method);
+    CALL(af_resize, out, in, odim0, odim1, method);
 }
 
 af_err af_transform(af_array *out, const af_array in, const af_array transform,
                     const dim_t odim0, const dim_t odim1,
                     const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(in, transform);
-      CALL(af_transform, out, in, transform, odim0, odim1, method, inverse);
+    CALL(af_transform, out, in, transform, odim0, odim1, method, inverse);
 }
 
-af_err af_transform_v2(af_array *out, const af_array in, const af_array transform,
-                       const dim_t odim0, const dim_t odim1,
-                       const af_interp_type method, const bool inverse) {
+af_err af_transform_v2(af_array *out, const af_array in,
+                       const af_array transform, const dim_t odim0,
+                       const dim_t odim1, const af_interp_type method,
+                       const bool inverse) {
     CHECK_ARRAYS(out, in, transform);
     CALL(af_transform_v2, out, in, transform, odim0, odim1, method, inverse);
 }
@@ -76,114 +75,115 @@ af_err af_transform_v2(af_array *out, const af_array in, const af_array transfor
 af_err af_transform_coordinates(af_array *out, const af_array tf,
                                 const float d0, const float d1) {
     CHECK_ARRAYS(tf);
-      CALL(af_transform_coordinates, out, tf, d0, d1);
+    CALL(af_transform_coordinates, out, tf, d0, d1);
 }
 
 af_err af_rotate(af_array *out, const af_array in, const float theta,
                  const bool crop, const af_interp_type method) {
     CHECK_ARRAYS(in);
-      CALL(af_rotate, out, in, theta, crop, method);
+    CALL(af_rotate, out, in, theta, crop, method);
 }
 
 af_err af_translate(af_array *out, const af_array in, const float trans0,
                     const float trans1, const dim_t odim0, const dim_t odim1,
                     const af_interp_type method) {
     CHECK_ARRAYS(in);
-      CALL(af_translate, out, in, trans0, trans1, odim0, odim1, method);
+    CALL(af_translate, out, in, trans0, trans1, odim0, odim1, method);
 }
 
 af_err af_scale(af_array *out, const af_array in, const float scale0,
                 const float scale1, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method) {
     CHECK_ARRAYS(in);
-      CALL(af_scale, out, in, scale0, scale1, odim0, odim1, method);
+    CALL(af_scale, out, in, scale0, scale1, odim0, odim1, method);
 }
 
 af_err af_skew(af_array *out, const af_array in, const float skew0,
                const float skew1, const dim_t odim0, const dim_t odim1,
                const af_interp_type method, const bool inverse) {
     CHECK_ARRAYS(in);
-      CALL(af_skew, out, in, skew0, skew1, odim0, odim1, method, inverse);
+    CALL(af_skew, out, in, skew0, skew1, odim0, odim1, method, inverse);
 }
 
 af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
                     const double minval, const double maxval) {
     CHECK_ARRAYS(in);
-      CALL(af_histogram, out, in, nbins, minval, maxval);
+    CALL(af_histogram, out, in, nbins, minval, maxval);
 }
 
 af_err af_dilate(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-      CALL(af_dilate, out, in, mask);
+    CALL(af_dilate, out, in, mask);
 }
 
 af_err af_dilate3(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-      CALL(af_dilate3, out, in, mask);
+    CALL(af_dilate3, out, in, mask);
 }
 
 af_err af_erode(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-      CALL(af_erode, out, in, mask);
+    CALL(af_erode, out, in, mask);
 }
 
 af_err af_erode3(af_array *out, const af_array in, const af_array mask) {
     CHECK_ARRAYS(in, mask);
-      CALL(af_erode3, out, in, mask);
+    CALL(af_erode3, out, in, mask);
 }
 
 af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma,
                     const float chromatic_sigma, const bool isColor) {
     CHECK_ARRAYS(in);
-      CALL(af_bilateral, out, in, spatial_sigma, chromatic_sigma, isColor);
+    CALL(af_bilateral, out, in, spatial_sigma, chromatic_sigma, isColor);
 }
 
 af_err af_mean_shift(af_array *out, const af_array in,
                      const float spatial_sigma, const float chromatic_sigma,
                      const unsigned iter, const bool is_color) {
     CHECK_ARRAYS(in);
-      CALL(af_mean_shift, out, in, spatial_sigma, chromatic_sigma, iter, is_color);
+    CALL(af_mean_shift, out, in, spatial_sigma, chromatic_sigma, iter,
+         is_color);
 }
 
 af_err af_minfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-      CALL(af_minfilt, out, in, wind_length, wind_width, edge_pad);
+    CALL(af_minfilt, out, in, wind_length, wind_width, edge_pad);
 }
 
 af_err af_maxfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad) {
     CHECK_ARRAYS(in);
-      CALL(af_maxfilt, out, in, wind_length, wind_width, edge_pad);
+    CALL(af_maxfilt, out, in, wind_length, wind_width, edge_pad);
 }
 
 af_err af_regions(af_array *out, const af_array in,
                   const af_connectivity connectivity, const af_dtype ty) {
     CHECK_ARRAYS(in);
-      CALL(af_regions, out, in, connectivity, ty);
+    CALL(af_regions, out, in, connectivity, ty);
 }
 
 af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img,
                          const unsigned ker_size) {
     CHECK_ARRAYS(img);
-      CALL(af_sobel_operator, dx, dy, img, ker_size);
+    CALL(af_sobel_operator, dx, dy, img, ker_size);
 }
 
 af_err af_rgb2gray(af_array *out, const af_array in, const float rPercent,
                    const float gPercent, const float bPercent) {
     CHECK_ARRAYS(in);
-      CALL(af_rgb2gray, out, in, rPercent, gPercent, bPercent);
+    CALL(af_rgb2gray, out, in, rPercent, gPercent, bPercent);
 }
 
 af_err af_gray2rgb(af_array *out, const af_array in, const float rFactor,
                    const float gFactor, const float bFactor) {
     CHECK_ARRAYS(in);
-      CALL(af_gray2rgb, out, in, rFactor, gFactor, bFactor);
+    CALL(af_gray2rgb, out, in, rFactor, gFactor, bFactor);
 }
 
 af_err af_hist_equal(af_array *out, const af_array in, const af_array hist) {
     CHECK_ARRAYS(in, hist);
-      CALL(af_hist_equal, out, in, hist);
+    CALL(af_hist_equal, out, in, hist);
 }
 
 af_err af_gaussian_kernel(af_array *out, const int rows, const int cols,
@@ -193,62 +193,64 @@ af_err af_gaussian_kernel(af_array *out, const int rows, const int cols,
 
 af_err af_hsv2rgb(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_hsv2rgb, out, in);
+    CALL(af_hsv2rgb, out, in);
 }
 
 af_err af_rgb2hsv(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_rgb2hsv, out, in);
+    CALL(af_rgb2hsv, out, in);
 }
 
 af_err af_color_space(af_array *out, const af_array image, const af_cspace_t to,
                       const af_cspace_t from) {
     CHECK_ARRAYS(image);
-      CALL(af_color_space, out, image, to, from);
+    CALL(af_color_space, out, image, to, from);
 }
 
 af_err af_unwrap(af_array *out, const af_array in, const dim_t wx,
                  const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,
                  const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
-      CALL(af_unwrap, out, in, wx, wy, sx, sy, px, py, is_column);
+    CALL(af_unwrap, out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
 af_err af_wrap(af_array *out, const af_array in, const dim_t ox, const dim_t oy,
                const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
                const dim_t px, const dim_t py, const bool is_column) {
     CHECK_ARRAYS(in);
-      CALL(af_wrap, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);}
+    CALL(af_wrap, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
+}
 
-af_err af_wrap_v2(af_array *out, const af_array in, const dim_t ox, const dim_t oy,
-                  const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-                  const dim_t px, const dim_t py, const bool is_column) {
+af_err af_wrap_v2(af_array *out, const af_array in, const dim_t ox,
+                  const dim_t oy, const dim_t wx, const dim_t wy,
+                  const dim_t sx, const dim_t sy, const dim_t px,
+                  const dim_t py, const bool is_column) {
     CHECK_ARRAYS(out, in);
     CALL(af_wrap_v2, out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);
 }
 
 af_err af_sat(af_array *out, const af_array in) {
     CHECK_ARRAYS(in);
-      CALL(af_sat, out, in);
+    CALL(af_sat, out, in);
 }
 
 af_err af_ycbcr2rgb(af_array *out, const af_array in,
                     const af_ycc_std standard) {
     CHECK_ARRAYS(in);
-      CALL(af_ycbcr2rgb, out, in, standard);
+    CALL(af_ycbcr2rgb, out, in, standard);
 }
 
 af_err af_rgb2ycbcr(af_array *out, const af_array in,
                     const af_ycc_std standard) {
     CHECK_ARRAYS(in);
-      CALL(af_rgb2ycbcr, out, in, standard);
+    CALL(af_rgb2ycbcr, out, in, standard);
 }
 
 af_err af_canny(af_array *out, const af_array in, const af_canny_threshold ct,
                 const float t1, const float t2, const unsigned sw,
                 const bool isf) {
     CHECK_ARRAYS(in);
-      CALL(af_canny, out, in, ct, t1, t2, sw, isf);
+    CALL(af_canny, out, in, ct, t1, t2, sw, isf);
 }
 
 af_err af_anisotropic_diffusion(af_array *out, const af_array in,
@@ -257,22 +259,20 @@ af_err af_anisotropic_diffusion(af_array *out, const af_array in,
                                 const af_flux_function fftype,
                                 const af_diffusion_eq eq) {
     CHECK_ARRAYS(in);
-      CALL(af_anisotropic_diffusion, out, in, dt, K, iterations, fftype,
-                 eq);
+    CALL(af_anisotropic_diffusion, out, in, dt, K, iterations, fftype, eq);
 }
 
 af_err af_iterative_deconv(af_array *out, const af_array in, const af_array ker,
                            const unsigned iterations, const float relax_factor,
                            const af_iterative_deconv_algo algo) {
     CHECK_ARRAYS(in, ker);
-      CALL(af_iterative_deconv, out, in, ker, iterations, relax_factor,
-                 algo);
+    CALL(af_iterative_deconv, out, in, ker, iterations, relax_factor, algo);
 }
 
 af_err af_inverse_deconv(af_array *out, const af_array in, const af_array psf,
                          const float gamma, const af_inverse_deconv_algo algo) {
     CHECK_ARRAYS(in, psf);
-      CALL(af_inverse_deconv, out, in, psf, gamma, algo);
+    CALL(af_inverse_deconv, out, in, psf, gamma, algo);
 }
 
 af_err af_confidence_cc(af_array *out, const af_array in, const af_array seedx,
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 0de1eda6de..6137370a4c 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -145,7 +145,7 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
         if (index_ != instance.getActiveBackend()) {                             \
             index_ = instance.getActiveBackend();                                \
             func   = (af_func)common::getFunctionPointer(instance.getHandle(),   \
-                                                         __func__);              \
+                                                       __func__);              \
         }                                                                        \
         return func(__VA_ARGS__);                                                \
     } else {                                                                     \
diff --git a/src/backend/common/AllocatorInterface.hpp b/src/backend/common/AllocatorInterface.hpp
index 499da73564..0a7d34393f 100644
--- a/src/backend/common/AllocatorInterface.hpp
+++ b/src/backend/common/AllocatorInterface.hpp
@@ -35,7 +35,7 @@ class AllocatorInterface {
     virtual void nativeFree(void *ptr)            = 0;
     virtual spdlog::logger *getLogger() final { return this->logger.get(); }
 
-  protected:
+   protected:
     std::shared_ptr<spdlog::logger> logger;
 };
 
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index bdade9d76e..d1a09f05fc 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -103,7 +103,9 @@ bool ArrayInfo::isSingle() const { return (type == f32 || type == c32); }
 
 bool ArrayInfo::isHalf() const { return (type == f16); }
 
-bool ArrayInfo::isRealFloating() const { return (type == f64 || type == f32 || type == f16); }
+bool ArrayInfo::isRealFloating() const {
+    return (type == f64 || type == f32 || type == f16);
+}
 
 bool ArrayInfo::isFloating() const { return (!isInteger() && !isBool()); }
 
diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index f3921a6b69..2f5ea29226 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -42,8 +42,8 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
     // the lock is being held because the CPU backend calls sync.
-    vector<void*> free_ptrs;
-    size_t bytes_freed           = 0;
+    vector<void *> free_ptrs;
+    size_t bytes_freed                         = 0;
     DefaultMemoryManager::memory_info &current = memory[device];
     {
         lock_guard_t lock(this->memory_mutex);
@@ -55,8 +55,9 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
             size_t num_ptrs = kv.second.size();
             // Free memory by pushing the last element into the free_ptrs
             // vector which will be freed once outside of the lock
-            //for (auto ptr : kv.second) { free_ptrs.emplace_back(pair); }
-            std::move(begin(kv.second), end(kv.second), back_inserter(free_ptrs));
+            // for (auto ptr : kv.second) { free_ptrs.emplace_back(pair); }
+            std::move(begin(kv.second), end(kv.second),
+                      back_inserter(free_ptrs));
             current.total_bytes -= num_ptrs * kv.first;
             bytes_freed += num_ptrs * kv.first;
             current.total_buffers -= num_ptrs;
@@ -67,9 +68,7 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(),
              bytesToString(bytes_freed));
     // Free memory outside of the lock
-    for (auto ptr : free_ptrs) {
-        this->nativeFree(ptr);
-    }
+    for (auto ptr : free_ptrs) { this->nativeFree(ptr); }
 }
 
 DefaultMemoryManager::DefaultMemoryManager(int num_devices,
@@ -143,13 +142,12 @@ bool DefaultMemoryManager::jitTreeExceedsMemoryPressure(size_t bytes) {
     return 2 * bytes > current.lock_bytes;
 }
 
-void* DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
-                                  dim_t *dims,
-                                  const unsigned element_size) {
+void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
+                                  dim_t *dims, const unsigned element_size) {
     size_t bytes = element_size;
     for (unsigned i = 0; i < ndims; ++i) { bytes *= dims[i]; }
 
-    void* ptr = nullptr;
+    void *ptr          = nullptr;
     size_t alloc_bytes = this->debug_mode
                              ? bytes
                              : (divup(bytes, mem_step_size) * mem_step_size);
@@ -184,12 +182,12 @@ void* DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
         if (ptr == nullptr) {
             // Perform garbage collection if memory can not be allocated
             try {
-                ptr             = this->nativeAlloc(alloc_bytes);
+                ptr = this->nativeAlloc(alloc_bytes);
             } catch (const AfError &ex) {
                 // If out of memory, run garbage collect and try again
                 if (ex.getError() != AF_ERR_NO_MEM) throw;
                 this->signalMemoryCleanup();
-                ptr             = this->nativeAlloc(alloc_bytes);
+                ptr = this->nativeAlloc(alloc_bytes);
             }
             lock_guard_t lock(this->memory_mutex);
             // Increment these two only when it succeeds to come here.
@@ -212,12 +210,9 @@ size_t DefaultMemoryManager::allocated(void *ptr) {
     return (iter->second).bytes;
 }
 
-void DefaultMemoryManager::unlock(void *ptr,
-                                  bool user_unlock) {
+void DefaultMemoryManager::unlock(void *ptr, bool user_unlock) {
     // Shortcut for empty arrays
-    if (!ptr) {
-        return;
-    }
+    if (!ptr) { return; }
 
     // Frees the pointer outside the lock.
     uptr_t freed_ptr(nullptr, [this](void *p) { this->nativeFree(p); });
@@ -241,9 +236,7 @@ void DefaultMemoryManager::unlock(void *ptr,
         }
 
         // Return early if either one is locked
-        if ((iter->second).user_lock || (iter->second).manager_lock) {
-            return;
-        }
+        if ((iter->second).user_lock || (iter->second).manager_lock) { return; }
 
         size_t bytes = iter->second.bytes;
         current.lock_bytes -= iter->second.bytes;
@@ -335,8 +328,7 @@ void DefaultMemoryManager::userLock(const void *ptr) {
     if (iter != current.locked_map.end()) {
         iter->second.user_lock = true;
     } else {
-        locked_info info = {false, true,
-                                    100};  // This number is not relevant
+        locked_info info = {false, true, 100};  // This number is not relevant
 
         current.locked_map[(void *)ptr] = info;
     }
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index 4f87e25976..3bb94cc0fb 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -35,10 +35,10 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
         size_t bytes;
     };
 
-    using locked_t = typename std::unordered_map<void *, locked_info>;
+    using locked_t    = typename std::unordered_map<void *, locked_info>;
     using locked_iter = typename locked_t::iterator;
 
-    using free_t = std::unordered_map<size_t, std::vector<void*>>;
+    using free_t    = std::unordered_map<size_t, std::vector<void *>>;
     using free_iter = typename free_t::iterator;
 
     struct memory_info {
@@ -95,7 +95,7 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
     /// bytes. If there is already a free buffer available, it will use
     /// that buffer. Otherwise, it will allocate a new buffer using the
     /// nativeAlloc function.
-    void* alloc(bool user_lock, const unsigned ndims, dim_t *dims,
+    void *alloc(bool user_lock, const unsigned ndims, dim_t *dims,
                 const unsigned element_size) override;
 
     /// returns the size of the buffer at the pointer allocated by the memory
@@ -125,7 +125,7 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
     DefaultMemoryManager()                                  = delete;
     ~DefaultMemoryManager()                                 = default;
     DefaultMemoryManager(const DefaultMemoryManager &other) = delete;
-    DefaultMemoryManager(DefaultMemoryManager &&other) = default;
+    DefaultMemoryManager(DefaultMemoryManager &&other)      = default;
     DefaultMemoryManager &operator=(const DefaultMemoryManager &other) = delete;
     DefaultMemoryManager &operator=(DefaultMemoryManager &&other) = default;
     common::mutex_t memory_mutex;
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index a83850518b..62eb16ce60 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -69,5 +69,5 @@ class DependencyModule {
 #define MODULE_MEMBER(NAME) decltype(&::NAME) NAME
 
 /// Dynamically loads the function pointer at runtime
-#define MODULE_FUNCTION_INIT(NAME)                     \
+#define MODULE_FUNCTION_INIT(NAME) \
     NAME = module.getSymbol<decltype(&::NAME)>(#NAME);
diff --git a/src/backend/common/HandleBase.hpp b/src/backend/common/HandleBase.hpp
index bcc2813c5c..bf7df20a20 100644
--- a/src/backend/common/HandleBase.hpp
+++ b/src/backend/common/HandleBase.hpp
@@ -24,12 +24,13 @@ class HandleBase {
     HandleBase(HandleBase const&) = delete;
     void operator=(HandleBase const&) = delete;
 
-    HandleBase(HandleBase &&h) = default;
-    HandleBase& operator=(HandleBase &&h) = default;
+    HandleBase(HandleBase&& h) = default;
+    HandleBase& operator=(HandleBase&& h) = default;
 };
 }  // namespace common
 
-#define CREATE_HANDLE(NAME, TYPE, CREATE_FUNCTION, DESTROY_FUNCTION, CHECK_FUNCTION) \
+#define CREATE_HANDLE(NAME, TYPE, CREATE_FUNCTION, DESTROY_FUNCTION,  \
+                      CHECK_FUNCTION)                                 \
     class NAME : public common::HandleBase<NAME, TYPE> {              \
        public:                                                        \
         void createHandle(TYPE* handle) {                             \
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 4c78efbf8b..1eb78964db 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#include <string>
 #include <mutex>
+#include <string>
 
 inline std::string clipFilePath(std::string path, std::string str) {
     try {
@@ -78,4 +78,4 @@ using LibHandle = void*;
 namespace common {
 using mutex_t      = std::mutex;
 using lock_guard_t = std::lock_guard<mutex_t>;
-}
+}  // namespace common
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 8bca480253..345e95d15a 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -281,30 +281,26 @@ fg_window ForgeManager::getWindow(const int w, const int h,
                                   const char* const title,
                                   const bool invisible) {
     fg_window retVal = 0;
-    FG_CHECK(mPlugin->fg_create_window(&retVal, w, h, title,
-                getMainWindow(), invisible));
-    if (retVal == 0) {
-        AF_ERROR("Window creation failed", AF_ERR_INTERNAL);
-    }
+    FG_CHECK(mPlugin->fg_create_window(&retVal, w, h, title, getMainWindow(),
+                                       invisible));
+    if (retVal == 0) { AF_ERROR("Window creation failed", AF_ERR_INTERNAL); }
     setWindowChartGrid(retVal, 1, 1);
     return retVal;
 }
 
-void ForgeManager::setWindowChartGrid(const fg_window window,
-                                      const int r, const int c) {
-    ChartMapIterator iter = mChartMap.find(window);
+void ForgeManager::setWindowChartGrid(const fg_window window, const int r,
+                                      const int c) {
+    ChartMapIterator iter     = mChartMap.find(window);
     WindGridMapIterator gIter = mWndGridMap.find(window);
 
     if (iter != mChartMap.end()) {
         // ChartVec found. Clear it.
         // This has to be cleared as there is no guarantee that existing
         // chart types(2D/3D) match the future grid requirements
-        for (const ChartPtr& c: iter->second) {
-            if (c) {
-                mChartAxesOverrideMap.erase(c->handle);
-            }
+        for (const ChartPtr& c : iter->second) {
+            if (c) { mChartAxesOverrideMap.erase(c->handle); }
         }
-        (iter->second).clear(); // Clear ChartList
+        (iter->second).clear();  // Clear ChartList
         gIter->second = std::make_pair<int, int>(1, 1);
     }
 
@@ -317,8 +313,8 @@ void ForgeManager::setWindowChartGrid(const fg_window window,
     }
 }
 
-ForgeManager::WindowGridDims
-ForgeManager::getWindowGrid(const fg_window window) {
+ForgeManager::WindowGridDims ForgeManager::getWindowGrid(
+    const fg_window window) {
     WindGridMapIterator gIter = mWndGridMap.find(window);
     if (gIter == mWndGridMap.end()) {
         mWndGridMap[window] = std::make_pair(1, 1);
@@ -328,7 +324,7 @@ ForgeManager::getWindowGrid(const fg_window window) {
 
 fg_chart ForgeManager::getChart(const fg_window window, const int r,
                                 const int c, const fg_chart_type ctype) {
-    ChartMapIterator iter = mChartMap.find(window);
+    ChartMapIterator iter     = mChartMap.find(window);
     WindGridMapIterator gIter = mWndGridMap.find(window);
 
     int rows = std::get<0>(gIter->second);
@@ -388,7 +384,7 @@ fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
                                 fg_channel_format mode, fg_dtype type) {
     auto key = genImageKey(w, h, mode, type);
 
-    ChartKey keypair = std::make_pair(key, chart);
+    ChartKey keypair      = std::make_pair(key, chart);
     ImageMapIterator iter = mImgMap.find(keypair);
 
     if (iter == mImgMap.end()) {
@@ -412,7 +408,7 @@ fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
     long long key = (((long long)(nPoints)&_48BIT) << 16);
     key |= (((dtype & _4BIT) << 12) | ((ptype & _4BIT) << 8) | (mtype & _8BIT));
 
-    ChartKey keypair = std::make_pair(key, chart);
+    ChartKey keypair     = std::make_pair(key, chart);
     PlotMapIterator iter = mPltMap.find(keypair);
 
     if (iter == mPltMap.end()) {
@@ -433,7 +429,7 @@ fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins,
                                         fg_dtype type) {
     long long key = (((long long)(nBins)&_48BIT) << 16) | (type & _16BIT);
 
-    ChartKey keypair = std::make_pair(key, chart);
+    ChartKey keypair          = std::make_pair(key, chart);
     HistogramMapIterator iter = mHstMap.find(keypair);
 
     if (iter == mHstMap.end()) {
@@ -451,13 +447,13 @@ fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins,
     return mHstMap[keypair]->handle;
 }
 
-fg_surface ForgeManager::getSurface(fg_chart chart,
-                                    int nX, int nY, fg_dtype type) {
+fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY,
+                                    fg_dtype type) {
     long long surfaceSize = nX * (long long)(nY);
     assert(surfaceSize <= 2ll << 48);
     long long key = ((surfaceSize & _48BIT) << 16) | (type & _16BIT);
 
-    ChartKey keypair = std::make_pair(key, chart);
+    ChartKey keypair        = std::make_pair(key, chart);
     SurfaceMapIterator iter = mSfcMap.find(keypair);
 
     if (iter == mSfcMap.end()) {
@@ -476,11 +472,11 @@ fg_surface ForgeManager::getSurface(fg_chart chart,
     return mSfcMap[keypair]->handle;
 }
 
-fg_vector_field ForgeManager::getVectorField(fg_chart chart,
-                                             int nPoints, fg_dtype type) {
+fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints,
+                                             fg_dtype type) {
     long long key = (((long long)(nPoints)&_48BIT) << 16) | (type & _16BIT);
 
-    ChartKey keypair = std::make_pair(key, chart);
+    ChartKey keypair         = std::make_pair(key, chart);
     VecFieldMapIterator iter = mVcfMap.find(keypair);
 
     if (iter == mVcfMap.end()) {
@@ -489,9 +485,8 @@ fg_vector_field ForgeManager::getVectorField(fg_chart chart,
 
         fg_vector_field vfield = nullptr;
         FG_CHECK(mPlugin->fg_create_vector_field(&vfield, nPoints, type,
-                    chart_type));
-        FG_CHECK(mPlugin->fg_append_vector_field_to_chart(chart,
-                    vfield));
+                                                 chart_type));
+        FG_CHECK(mPlugin->fg_append_vector_field_to_chart(chart, vfield));
         mVcfMap[keypair] = VectorFieldPtr(new VectorField({vfield}));
     }
     return mVcfMap[keypair]->handle;
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 432bd16f6c..911c1251a9 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -49,7 +49,7 @@ namespace graphics {
 ///      fg_vector_field
 ///
 class ForgeManager {
-  public:
+   public:
     using WindowGridDims = std::pair<int, int>;
 
     ForgeManager();
@@ -155,8 +155,8 @@ class ForgeManager {
     /// [0, 2^16] for the ForgeManager to correctly retrieve the necessary
     /// Forge Image object. This is an implementation limitation on how big
     /// of an image can be rendered using arrayfire graphics funtionality
-    fg_image getImage(fg_chart chart, int w, int h,
-                      fg_channel_format mode, fg_dtype type);
+    fg_image getImage(fg_chart chart, int w, int h, fg_channel_format mode,
+                      fg_dtype type);
 
     /// \brief Find/Create a Plot to render in a Chart
     ///
@@ -243,14 +243,14 @@ class ForgeManager {
     /// overriden \param[in] flag indicates if axes limits are overriden or not
     void setChartAxesOverride(const fg_chart chart, bool flag = true);
 
-  private:
-    constexpr static unsigned int WIDTH = 1280;
+   private:
+    constexpr static unsigned int WIDTH  = 1280;
     constexpr static unsigned int HEIGHT = 720;
     constexpr static long long _4BIT     = 0x000000000000000F;
     constexpr static long long _8BIT     = 0x00000000000000FF;
-    constexpr static long long _16BIT = 0x000000000000FFFF;
-    constexpr static long long _32BIT = 0x00000000FFFFFFFF;
-    constexpr static long long _48BIT = 0x0000FFFFFFFFFFFF;
+    constexpr static long long _16BIT    = 0x000000000000FFFF;
+    constexpr static long long _32BIT    = 0x00000000FFFFFFFF;
+    constexpr static long long _48BIT    = 0x0000FFFFFFFFFFFF;
 
     long long genImageKey(int w, int h, fg_channel_format mode, fg_dtype type);
 
@@ -274,14 +274,14 @@ class ForgeManager {
 
 #undef DEFINE_WRAPPER_OBJECT
 
-    using ImagePtr        = std::unique_ptr<Image      , Image::Deleter      >;
-    using ChartPtr        = std::unique_ptr<Chart      , Chart::Deleter      >;
-    using PlotPtr         = std::unique_ptr<Plot       , Plot::Deleter       >;
-    using SurfacePtr      = std::unique_ptr<Surface    , Surface::Deleter    >;
-    using HistogramPtr    = std::unique_ptr<Histogram  , Histogram::Deleter  >;
-    using VectorFieldPtr  = std::unique_ptr<VectorField, VectorField::Deleter>;
-    using ChartList       = std::vector<ChartPtr>;
-    using ChartKey        = std::pair<long long, fg_chart>;
+    using ImagePtr       = std::unique_ptr<Image, Image::Deleter>;
+    using ChartPtr       = std::unique_ptr<Chart, Chart::Deleter>;
+    using PlotPtr        = std::unique_ptr<Plot, Plot::Deleter>;
+    using SurfacePtr     = std::unique_ptr<Surface, Surface::Deleter>;
+    using HistogramPtr   = std::unique_ptr<Histogram, Histogram::Deleter>;
+    using VectorFieldPtr = std::unique_ptr<VectorField, VectorField::Deleter>;
+    using ChartList      = std::vector<ChartPtr>;
+    using ChartKey       = std::pair<long long, fg_chart>;
 
     using ChartMapIterator     = std::map<fg_window, ChartList>::iterator;
     using WindGridMapIterator  = std::map<fg_window, WindowGridDims>::iterator;
@@ -295,14 +295,14 @@ class ForgeManager {
     std::unique_ptr<ForgeModule> mPlugin;
     std::unique_ptr<Window, Window::Deleter> mMainWindow;
 
-    std::map<fg_window, ChartList     > mChartMap;
-    std::map< ChartKey, ImagePtr      > mImgMap;
-    std::map< ChartKey, PlotPtr       > mPltMap;
-    std::map< ChartKey, HistogramPtr  > mHstMap;
-    std::map< ChartKey, SurfacePtr    > mSfcMap;
-    std::map< ChartKey, VectorFieldPtr> mVcfMap;
+    std::map<fg_window, ChartList> mChartMap;
+    std::map<ChartKey, ImagePtr> mImgMap;
+    std::map<ChartKey, PlotPtr> mPltMap;
+    std::map<ChartKey, HistogramPtr> mHstMap;
+    std::map<ChartKey, SurfacePtr> mSfcMap;
+    std::map<ChartKey, VectorFieldPtr> mVcfMap;
     std::map<fg_window, WindowGridDims> mWndGridMap;
-    std::map< fg_chart, bool          > mChartAxesOverrideMap;
+    std::map<fg_chart, bool> mChartAxesOverrideMap;
 };
 
 }  // namespace graphics
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 5ce3afc2c7..8bb8348ff2 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -23,7 +23,7 @@
 
 #include <limits>
 #else
-using uint16_t = unsigned short;
+using uint16_t      = unsigned short;
 #endif
 
 #if AF_COMPILER_CXX_RELAXED_CONSTEXPR
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 1d8bf60361..29e70cf6cf 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <backend.hpp>
-#include <jit/kernel_generators.hpp>
 #include <common/jit/Node.hpp>
+#include <jit/kernel_generators.hpp>
 
 #include <iomanip>
 #include <mutex>
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 47cf4d480e..13265e7cfe 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -69,13 +69,14 @@ class NaryNode : public Node {
 template<typename Ti, int N, typename FUNC>
 common::Node_ptr createNaryNode(
     const af::dim4 &odims, FUNC createNode,
-    std::array<const detail::Array<Ti>*, N> &&children) {
+    std::array<const detail::Array<Ti> *, N> &&children) {
     std::array<common::Node_ptr, N> childNodes;
     for (int i = 0; i < N; i++) { childNodes[i] = children[i]->getNode(); }
 
     common::Node_ptr ptr = createNode(childNodes);
 
-    switch(static_cast<kJITHeuristics>(detail::passesJitHeuristics<Ti>(ptr.get()))) {
+    switch (static_cast<kJITHeuristics>(
+        detail::passesJitHeuristics<Ti>(ptr.get()))) {
         case kJITHeuristics::Pass: {
             return ptr;
         }
@@ -94,7 +95,7 @@ common::Node_ptr createNaryNode(
             return createNaryNode<Ti, N>(odims, createNode, move(children));
         }
         case kJITHeuristics::MemoryPressure: {
-            for (auto &c : children) { c->eval(); } //TODO: use evalMultiple()
+            for (auto &c : children) { c->eval(); }  // TODO: use evalMultiple()
             return ptr;
         }
     }
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 643804d218..35861103c7 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -54,9 +54,7 @@ class ScalarNode : public common::Node {
     }
 
     // Return the info for the params and the size of the buffers
-    virtual size_t getParamBytes() const final {
-        return sizeof(T);
-    }
+    virtual size_t getParamBytes() const final { return sizeof(T); }
 };
 
 }  // namespace common
diff --git a/src/backend/common/kernel_type.hpp b/src/backend/common/kernel_type.hpp
index 90cabb8c42..f38e481fca 100644
--- a/src/backend/common/kernel_type.hpp
+++ b/src/backend/common/kernel_type.hpp
@@ -30,4 +30,4 @@ struct kernel_type {
     /// The type defined by the compute framework for this type
     using native = compute;
 };
-}
+}  // namespace common
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 23c4b9b606..519c9c7caf 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -14,10 +14,11 @@
 
 #pragma once
 
-std::string getEnvVar(const std::string &key);
+std::string getEnvVar(const std::string& key);
 
 // Dump the kernel sources only if the environment variable is defined
-void saveKernel(const std::string& funcName, const std::string& jit_ker, const std::string& ext);
+void saveKernel(const std::string& funcName, const std::string& jit_ker,
+                const std::string& ext);
 namespace {
 static constexpr const char* saveJitKernelsEnvVarName = "AF_JIT_KERNEL_TRACE";
 
diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
index 15e85d3249..6c6f73b616 100644
--- a/src/backend/cpu/ParamIterator.hpp
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -242,8 +242,8 @@ class NeighborhoodIterator {
     }
 
     NeighborhoodIterator(const NeighborhoodIterator<T>& other) = default;
-    NeighborhoodIterator(NeighborhoodIterator<T>&& other) = default;
-    ~NeighborhoodIterator() noexcept = default;
+    NeighborhoodIterator(NeighborhoodIterator<T>&& other)      = default;
+    ~NeighborhoodIterator() noexcept                           = default;
     NeighborhoodIterator<T>& operator=(const Self& other) = default;
     NeighborhoodIterator<T>& operator=(Self&& other) = default;
 
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 4c3079eea8..3640c95af4 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -117,33 +117,31 @@ using ptr_type = typename conditional<is_complex<T>::value,
 template<typename T, bool batched = false>
 struct scale_type {
     const T val;
-    scale_type(const T* val_ptr)
-        : val(*val_ptr){}
-    using api_type = const typename conditional<is_complex<T>::value,
-                                                const typename blas_base<T>::type *,
-                                                const typename conditional<batched,  const T*, const T>::type>::type;
-
-    api_type getScale() const {
-		return val;
-	}
-};
+    scale_type(const T *val_ptr) : val(*val_ptr) {}
+    using api_type = const typename conditional<
+        is_complex<T>::value, const typename blas_base<T>::type *,
+        const typename conditional<batched, const T *, const T>::type>::type;
 
+    api_type getScale() const { return val; }
+};
 
-#define INSTANTIATE_BATCHED(TYPE)                                                    \
-template<>                                                                           \
-typename scale_type<TYPE, true>::api_type scale_type<TYPE, true>::getScale() const { \
-    return &val;                                                                     \
-}
+#define INSTANTIATE_BATCHED(TYPE)              \
+    template<>                                 \
+    typename scale_type<TYPE, true>::api_type  \
+    scale_type<TYPE, true>::getScale() const { \
+        return &val;                           \
+    }
 
 INSTANTIATE_BATCHED(float);
 INSTANTIATE_BATCHED(double);
 #undef INSTANTIATE_BATCHED
 
-#define INSTANTIATE_COMPLEX(TYPE, BATCHED)                                        \
-template<>                                                                        \
-scale_type<TYPE, BATCHED>::api_type scale_type<TYPE, BATCHED>::getScale() const { \
-    return reinterpret_cast<const blas_base<TYPE>::type * const>(&val);           \
-}
+#define INSTANTIATE_COMPLEX(TYPE, BATCHED)                                    \
+    template<>                                                                \
+    scale_type<TYPE, BATCHED>::api_type scale_type<TYPE, BATCHED>::getScale() \
+        const {                                                               \
+        return reinterpret_cast<const blas_base<TYPE>::type *const>(&val);    \
+    }
 
 INSTANTIATE_COMPLEX(cfloat, true);
 INSTANTIATE_COMPLEX(cfloat, false);
@@ -154,26 +152,28 @@ INSTANTIATE_COMPLEX(cdouble, false);
 template<typename T>
 using gemm_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
                                const CBLAS_TRANSPOSE, const blasint,
-                               const blasint, const blasint, typename scale_type<T>::api_type,
-                               cptr_type<T>, const blasint, cptr_type<T>,
-                               const blasint, typename scale_type<T>::api_type, ptr_type<T>,
+                               const blasint, const blasint,
+                               typename scale_type<T>::api_type, cptr_type<T>,
+                               const blasint, cptr_type<T>, const blasint,
+                               typename scale_type<T>::api_type, ptr_type<T>,
                                const blasint);
 
 template<typename T>
 using gemv_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
-                               const blasint, const blasint, typename scale_type<T>::api_type,
-                               cptr_type<T>, const blasint, cptr_type<T>,
-                               const blasint, typename scale_type<T>::api_type, ptr_type<T>,
+                               const blasint, const blasint,
+                               typename scale_type<T>::api_type, cptr_type<T>,
+                               const blasint, cptr_type<T>, const blasint,
+                               typename scale_type<T>::api_type, ptr_type<T>,
                                const blasint);
 
 #ifdef USE_MKL
 template<typename T>
 using gemm_batch_func_def = void (*)(
     const CBLAS_LAYOUT, const CBLAS_TRANSPOSE *, const CBLAS_TRANSPOSE *,
-    const MKL_INT *, const MKL_INT *, const MKL_INT *, typename scale_type<T, true>::api_type,
-    cptr_type<T> *, const MKL_INT *, cptr_type<T> *, const MKL_INT *,
-    typename scale_type<T, true>::api_type, ptr_type<T> *, const MKL_INT *, const MKL_INT,
-    const MKL_INT *);
+    const MKL_INT *, const MKL_INT *, const MKL_INT *,
+    typename scale_type<T, true>::api_type, cptr_type<T> *, const MKL_INT *,
+    cptr_type<T> *, const MKL_INT *, typename scale_type<T, true>::api_type,
+    ptr_type<T> *, const MKL_INT *, const MKL_INT, const MKL_INT *);
 #endif
 
 #define BLAS_FUNC_DEF(FUNC) \
@@ -219,10 +219,8 @@ toCblasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta) {
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
     const CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
     const CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
 
@@ -240,10 +238,10 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
     using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
-    auto alpha_ = scale_type<T, false>(alpha);
-    auto beta_ = scale_type<T, false>(beta);
+    auto alpha_        = scale_type<T, false>(alpha);
+    auto beta_         = scale_type<T, false>(beta);
     auto alpha_batched = scale_type<T, true>(alpha);
-    auto beta_batched = scale_type<T, true>(beta);
+    auto beta_batched  = scale_type<T, true>(beta);
 
     auto func = [=](Param<T> output, CParam<T> left, CParam<T> right) {
         dim4 lStrides = left.strides();
@@ -254,19 +252,19 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
             if (right.dims()[bColDim] == 1) {
                 dim_t incr =
                     (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                gemv_func<T>()(CblasColMajor, lOpts,
-                               lDims[0], lDims[1], alpha_.getScale(),
-                               reinterpret_cast<CBT*>(left.get()), lStrides[1],
-                               reinterpret_cast<CBT*>(right.get()), incr,
-                               beta_.getScale(),
-                               reinterpret_cast<BT*>(output.get()), oStrides[0]);
+                gemv_func<T>()(
+                    CblasColMajor, lOpts, lDims[0], lDims[1], alpha_.getScale(),
+                    reinterpret_cast<CBT *>(left.get()), lStrides[1],
+                    reinterpret_cast<CBT *>(right.get()), incr,
+                    beta_.getScale(), reinterpret_cast<BT *>(output.get()),
+                    oStrides[0]);
             } else {
-                gemm_func<T>()(CblasColMajor, lOpts, rOpts,
-                               M, N, K, alpha_.getScale(),
-                               reinterpret_cast<CBT *>(left.get()), lStrides[1],
-                               reinterpret_cast<CBT *>(right.get()), rStrides[1],
-                               beta_.getScale(),
-                               reinterpret_cast<BT *>(output.get()), oStrides[1]);
+                gemm_func<T>()(
+                    CblasColMajor, lOpts, rOpts, M, N, K, alpha_.getScale(),
+                    reinterpret_cast<CBT *>(left.get()), lStrides[1],
+                    reinterpret_cast<CBT *>(right.get()), rStrides[1],
+                    beta_.getScale(), reinterpret_cast<BT *>(output.get()),
+                    oStrides[1]);
             }
         } else {
             int batchSize = oDims[2] * oDims[3];
@@ -302,29 +300,23 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
             const MKL_INT ldb = rStrides[1];
             const MKL_INT ldc = oStrides[1];
 
-            gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts,
-                                 &M, &N, &K,
-                                 alpha_batched.getScale(),
-                                 lptrs.data(), &lda, rptrs.data(), &ldb,
-                                 beta_batched.getScale(),
+            gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts, &M, &N, &K,
+                                 alpha_batched.getScale(), lptrs.data(), &lda,
+                                 rptrs.data(), &ldb, beta_batched.getScale(),
                                  optrs.data(), &ldc, 1, &batchSize);
 #else
             for (int n = 0; n < batchSize; n++) {
                 if (rDims[bColDim] == 1) {
                     dim_t incr =
                         (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                    gemv_func<T>()(CblasColMajor, lOpts,
-                                   lDims[0], lDims[1],
-                                   alpha_.getScale(),
-                                   lptrs[n], lStrides[1], rptrs[n], incr,
-                                   beta_.getScale(),
-                                   optrs[n], oStrides[0]);
+                    gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1],
+                                   alpha_.getScale(), lptrs[n], lStrides[1],
+                                   rptrs[n], incr, beta_.getScale(), optrs[n],
+                                   oStrides[0]);
                 } else {
-                    gemm_func<T>()(CblasColMajor, lOpts, rOpts,
-                                   M, N, K,
-                                   alpha_.getScale(),
-                                   lptrs[n], lStrides[1], rptrs[n], rStrides[1],
-                                   beta_.getScale(),
+                    gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K,
+                                   alpha_.getScale(), lptrs[n], lStrides[1],
+                                   rptrs[n], rStrides[1], beta_.getScale(),
                                    optrs[n], oStrides[1]);
                 }
             }
@@ -367,8 +359,8 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
 }
 
 template<>
-Array<half> dot<half>(const Array<half> &lhs, const Array<half> &rhs, af_mat_prop optLhs,
-                      af_mat_prop optRhs) {
+Array<half> dot<half>(const Array<half> &lhs, const Array<half> &rhs,
+                      af_mat_prop optLhs, af_mat_prop optRhs) {
     Array<float> out = dot(cast<float>(lhs), cast<float>(rhs), optLhs, optRhs);
     return cast<half>(out);
 }
@@ -376,11 +368,10 @@ Array<half> dot<half>(const Array<half> &lhs, const Array<half> &rhs, af_mat_pro
 #undef BT
 #undef REINTEPRET_CAST
 
-#define INSTANTIATE_GEMM(TYPE)                                          \
-    template void gemm<TYPE>(Array<TYPE> &out,                          \
-                             af_mat_prop optLhs, af_mat_prop optRhs,    \
-                             const TYPE *alphas, const Array<TYPE> &lhs,\
-                             const Array<TYPE> &rhs,                    \
+#define INSTANTIATE_GEMM(TYPE)                                               \
+    template void gemm<TYPE>(Array<TYPE> & out, af_mat_prop optLhs,          \
+                             af_mat_prop optRhs, const TYPE *alphas,         \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs, \
                              const TYPE *beta)
 
 INSTANTIATE_GEMM(float);
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 3e3e8e730c..4011326fc7 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -11,8 +11,8 @@
 #include <arith.hpp>
 #include <blas.hpp>
 #include <common/defines.hpp>
-#include <common/indexing_helpers.hpp>
 #include <common/half.hpp>
+#include <common/indexing_helpers.hpp>
 #include <convolve.hpp>
 #include <handle.hpp>
 #include <kernel/convolve.hpp>
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index 7f882e4ce8..15f08c616b 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -12,29 +12,29 @@
 
 namespace cpu {
 
-template <typename T, typename accT, int baseDim, bool expand>
+template<typename T, typename accT, int baseDim, bool expand>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind);
 
-template <typename T, typename accT, bool expand>
+template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
                    Array<accT> const &r_filter);
 
-template <typename T>
+template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
                    const dim4 stride, const dim4 padding, const dim4 dilation);
 
-template <typename T>
+template<typename T>
 Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> &original_signal,
                            const Array<T> &original_filter,
                            const Array<T> &convolved_output, af::dim4 stride,
                            af::dim4 padding, af::dim4 dilation);
 
-template <typename T>
+template<typename T>
 Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &original_signal,
                              const Array<T> &original_filter,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
-}
+}  // namespace cpu
diff --git a/src/backend/cpu/flood_fill.cpp b/src/backend/cpu/flood_fill.cpp
index fc8830f08e..4b9f6d2de8 100644
--- a/src/backend/cpu/flood_fill.cpp
+++ b/src/backend/cpu/flood_fill.cpp
@@ -28,10 +28,10 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
     return out;
 }
 
-#define INSTANTIATE(T)                                                    \
-    template Array<T> floodFill(                                          \
-        const Array<T>&, const Array<uint>&, const Array<uint>&, const T, \
-        const T, const T, const af::connectivity);
+#define INSTANTIATE(T)                                                         \
+    template Array<T> floodFill(const Array<T>&, const Array<uint>&,           \
+                                const Array<uint>&, const T, const T, const T, \
+                                const af::connectivity);
 
 INSTANTIATE(float)
 INSTANTIATE(uint)
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 6dea1f25a3..ae856431a1 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -178,7 +178,7 @@ int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
     float src_scale = sqrt(2.0f) / sqrt(src_var);
     float dst_scale = sqrt(2.0f) / sqrt(dst_var);
 
-    Array<T> A = createValueArray<T>(af::dim4(9, 9), (T)0);
+    Array<T> A     = createValueArray<T>(af::dim4(9, 9), (T)0);
     af::dim4 Adims = A.dims();
     T* A_ptr       = A.get();
     getQueue().sync();
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 0336e9de1e..21b493c696 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -26,7 +26,7 @@ void copy_image(const Array<T> &in, fg_image image) {
     ForgeModule &_ = graphics::forgePlugin();
 
     CheckGL("Before CopyArrayToImage");
-    const T *d_X       = in.get();
+    const T *d_X = in.get();
     getQueue().sync();
 
     unsigned data_size = 0, buffer = 0;
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 05f23952df..70fa9ec4f7 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -36,8 +36,9 @@ class BinaryNode : public TNode<compute_t<To>> {
 
    public:
     BinaryNode(Node_ptr lhs, Node_ptr rhs)
-        : TNode<compute_t<To>>(compute_t<To>(0), std::max(lhs->getHeight(), rhs->getHeight()) + 1,
-                    {{lhs, rhs}})
+        : TNode<compute_t<To>>(compute_t<To>(0),
+                               std::max(lhs->getHeight(), rhs->getHeight()) + 1,
+                               {{lhs, rhs}})
         , m_lhs(reinterpret_cast<TNode<compute_t<Ti>> *>(lhs.get()))
         , m_rhs(reinterpret_cast<TNode<compute_t<Ti>> *>(rhs.get())) {}
 
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index 4caaa967ef..7404cd7ff3 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -57,7 +57,8 @@ class BufferNode : public TNode<T> {
         T *in_ptr   = m_ptr + l_off;
         Tc *out_ptr = this->m_val.data();
         for (int i = 0; i < lim; i++) {
-            out_ptr[i] = static_cast<Tc>(in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0]);
+            out_ptr[i] =
+                static_cast<Tc>(in_ptr[((x + i) < m_dims[0]) ? (x + i) : 0]);
         }
     }
 
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 94234101e1..79b6686680 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
-#include <common/half.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index b0bde70e6a..618d5deb22 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -75,9 +75,11 @@ void copyElemwise(Param<OutT> dst, CParam<InT> src, OutT default_value,
                     if (isLvalid && isKvalid && isJvalid && i < trgt_i) {
                         dim_t src_idx =
                             i * src_strides[0] + src_joff + src_koff + src_loff;
-                        // The conversions here are necessary because the half type does not convert to
-                        // complex automatically
-                        temp = compute_t<OutT>(compute_t<InT>(src_ptr[src_idx])) * compute_t<OutT>(factor);
+                        // The conversions here are necessary because the half
+                        // type does not convert to complex automatically
+                        temp =
+                            compute_t<OutT>(compute_t<InT>(src_ptr[src_idx])) *
+                            compute_t<OutT>(factor);
                     }
                     dim_t dst_idx =
                         i * dst_strides[0] + dst_joff + dst_koff + dst_loff;
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index 2c0044fdeb..e59151b82b 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -16,18 +16,18 @@ namespace kernel {
 template<typename T>
 void iota(Param<T> output, const af::dim4& sdims) {
     const af::dim4 dims    = output.dims();
-    data_t<T>* out      = output.get();
+    data_t<T>* out         = output.get();
     const af::dim4 strides = output.strides();
 
     for (dim_t w = 0; w < dims[3]; w++) {
         dim_t offW = w * strides[3];
-        dim_t valW     = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
+        dim_t valW = (w % sdims[3]) * sdims[0] * sdims[1] * sdims[2];
         for (dim_t z = 0; z < dims[2]; z++) {
             dim_t offWZ = offW + z * strides[2];
-            dim_t valZ      = valW + (z % sdims[2]) * sdims[0] * sdims[1];
+            dim_t valZ  = valW + (z % sdims[2]) * sdims[0] * sdims[1];
             for (dim_t y = 0; y < dims[1]; y++) {
                 dim_t offWZY = offWZ + y * strides[1];
-                dim_t valY       = valZ + (y % sdims[1]) * sdims[0];
+                dim_t valY   = valZ + (y % sdims[1]) * sdims[0];
                 for (dim_t x = 0; x < dims[0]; x++) {
                     dim_t id = offWZY + x;
                     out[id]  = valY + (x % sdims[0]);
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
index 98176ca481..5d9ea155a3 100644
--- a/src/backend/cpu/kernel/pad_array_borders.hpp
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -121,7 +121,7 @@ void padBorders(Param<T> out, CParam<T> in, const dim4 lBoundPadSize,
                                                          iDims[0], btype);
 
                     dst[oLOff + oKOff + oJOff + oIOff] =
-                       src[iLOff + iKOff + iJOff + iIOff];
+                        src[iLOff + iKOff + iJOff + iIOff];
 
                 }  // first dimension loop
             }      // second dimension loop
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 963d36db5d..b47ae0bd92 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -105,7 +105,8 @@ float transform<float>(uint *val, int index) {
 template<>
 common::half transform<common::half>(uint *val, int index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return static_cast<common::half>(1.f - (v * HALF_FACTOR + HALF_HALF_FACTOR));
+    return static_cast<common::half>(1.f -
+                                     (v * HALF_FACTOR + HALF_HALF_FACTOR));
 }
 
 // Generates rationals in [0, 1)
@@ -161,8 +162,7 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
                 for (size_t buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
                     size_t out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
                     if (out_idx < elements) {
-                        out[out_idx] =
-                            transform<T>(ctr, buf_idx);
+                        out[out_idx] = transform<T>(ctr, buf_idx);
                     }
                 }
             }
@@ -189,9 +189,7 @@ void threefryUniform(T *out, size_t elements, const uintl seed, uintl counter) {
         ++ctr[0];
         ctr[1] += (ctr[0] == 0);
         int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
-        for (int j = 0; j < lim; ++j) {
-            out[i + j] = transform<T>(val, j);
-        }
+        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(val, j); }
     }
 }
 
@@ -295,9 +293,7 @@ void uniformDistributionMT(T *out, size_t elements, uint *const state,
         mersenne(o, l_state, i, lpos, lsh1, lsh2, mask, recursion_table,
                  temper_table);
         int lim = (reset < (int)(elements - i)) ? reset : (int)(elements - i);
-        for (int j = 0; j < lim; ++j) {
-            out[i + j] = transform<T>(o, j);
-        }
+        for (int j = 0; j < lim; ++j) { out[i + j] = transform<T>(o, j); }
     }
 
     state_write(state, l_state);
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index 6a45f6e1c4..1bf3203874 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -33,16 +33,18 @@ void derivative(Param<To> output, CParam<Ti> input) {
         for (dim_t b2 = 0; b2 < dims[2]; ++b2) {
             for (dim_t j = 0; j < dims[1]; ++j) {
                 int joff    = j;
-                int _joff   = reflect101(j - 1, static_cast<int>(dims[1]-1));
-                int joff_   = reflect101(j + 1, static_cast<int>(dims[1]-1));
+                int _joff   = reflect101(j - 1, static_cast<int>(dims[1] - 1));
+                int joff_   = reflect101(j + 1, static_cast<int>(dims[1] - 1));
                 int joffset = j * ostrides[1];
 
                 for (dim_t i = 0; i < dims[0]; ++i) {
                     To accum = To(0);
 
-                    int ioff  = i;
-                    int _ioff = reflect101(i - 1, static_cast<int>(dims[0]-1));
-                    int ioff_ = reflect101(i + 1, static_cast<int>(dims[0]-1));
+                    int ioff = i;
+                    int _ioff =
+                        reflect101(i - 1, static_cast<int>(dims[0] - 1));
+                    int ioff_ =
+                        reflect101(i + 1, static_cast<int>(dims[0] - 1));
 
                     To NW = iptr[_joff * istrides[1] + _ioff * istrides[0]];
                     To SW = iptr[_joff * istrides[1] + ioff_ * istrides[0]];
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 22e9de017d..094c224d1a 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <Param.hpp>
-#include <err_cpu.hpp>
 #include <ParamIterator.hpp>
+#include <err_cpu.hpp>
 #include <math.hpp>
 
 #include <algorithm>
@@ -18,7 +18,7 @@
 namespace cpu {
 namespace kernel {
 
-template <typename T, int d>
+template<typename T, int d>
 void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
               const dim_t sx, const dim_t sy, const dim_t px, const dim_t py) {
     const T *inPtr = in.get();
@@ -79,7 +79,7 @@ void wrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
     }
 }
 
-template <typename T>
+template<typename T>
 void wrap_dim_dilated(Param<T> out, CParam<T> in, const dim_t wx,
                       const dim_t wy, const dim_t sx, const dim_t sy,
                       const dim_t px, const dim_t py, const dim_t dx,
@@ -96,8 +96,8 @@ void wrap_dim_dilated(Param<T> out, CParam<T> in, const dim_t wx,
 
     for (dim_t w = 0; w < idims[3]; w++) {
         for (dim_t z = 0; z < idims[2]; z++) {
-            dim_t cIn      = w * istrides[3] + z * istrides[2];
-            dim_t cOut     = w * ostrides[3] + z * ostrides[2];
+            dim_t cIn              = w * istrides[3] + z * istrides[2];
+            dim_t cOut             = w * ostrides[3] + z * ostrides[2];
             const data_t<T> *iptr_ = inPtr + cIn;
             data_t<T> *optr        = outPtr + cOut;
 
@@ -133,7 +133,8 @@ void wrap_dim_dilated(Param<T> out, CParam<T> in, const dim_t wx,
                             dim_t oloc =
                                 (ypad * ostrides[1] + xpad * ostrides[0]);
                             // FIXME: When using threads, atomize this
-                            optr[oloc] = static_cast<compute_t<T>>(optr[oloc]) + static_cast<compute_t<T>>(iptr[iloc]);
+                            optr[oloc] = static_cast<compute_t<T>>(optr[oloc]) +
+                                         static_cast<compute_t<T>>(iptr[iloc]);
                         }
                     }
                 }
@@ -142,5 +143,5 @@ void wrap_dim_dilated(Param<T> out, CParam<T> in, const dim_t wx,
     }
 }
 
-} // kernel namespace
-} // cpu namespace
+}  // namespace kernel
+}  // namespace cpu
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index c44b24a2cf..8d675d460a 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -72,7 +72,7 @@ T mean(const Array<T> &in, const Array<Tw> &wt) {
     const T *inPtr   = in.get();
     const Tw *wtPtr  = wt.get();
 
-    compute_t<T> input  = compute_t<T>(inPtr[0]);
+    compute_t<T> input   = compute_t<T>(inPtr[0]);
     compute_t<Tw> weight = compute_t<Tw>(wtPtr[0]);
     MeanOpT Op(input, weight);
 
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 2174080a43..98d9d23e79 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -71,9 +71,7 @@ void memFree(T *ptr) {
     return memoryManager().unlock((void *)ptr, false);
 }
 
-void memFreeUser(void *ptr) {
-    memoryManager().unlock(ptr, true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
 void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
 
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index ca0268917b..d109dbf022 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -22,8 +22,8 @@ namespace cpu {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     af::borderType padType = isDilation ? AF_PAD_ZERO : AF_PAD_CLAMP_TO_EDGE;
-    const af::dim4 idims = in.dims();
-    const af::dim4 mdims = mask.dims();
+    const af::dim4 idims   = in.dims();
+    const af::dim4 mdims   = mask.dims();
 
     const af::dim4 lpad(mdims[0] / 2, mdims[1] / 2, 0, 0);
     const af::dim4 upad(lpad);
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index b409634298..7a70238f92 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -66,7 +66,7 @@ Array<T> setUnion(const Array<T> &first, const Array<T> &second,
 
     Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
-    T *ptr = out.get();
+    T *ptr  = out.get();
     T *last = set_union(uFirst.get(), uFirst.get() + first_elements,
                         uSecond.get(), uSecond.get() + second_elements, ptr);
 
@@ -94,7 +94,7 @@ Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
 
     Array<T> out = createEmptyArray<T>(af::dim4(elements));
 
-    T *ptr  = out.get();
+    T *ptr = out.get();
     T *last =
         set_intersection(uFirst.get(), uFirst.get() + first_elements,
                          uSecond.get(), uSecond.get() + second_elements, ptr);
diff --git a/src/backend/cpu/set.hpp b/src/backend/cpu/set.hpp
index bddb668baf..762a7329db 100644
--- a/src/backend/cpu/set.hpp
+++ b/src/backend/cpu/set.hpp
@@ -11,14 +11,14 @@
 #include <Array.hpp>
 
 namespace cpu {
-template <typename T>
+template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted);
 
-template <typename T>
+template<typename T>
 Array<T> setUnion(const Array<T> &first, const Array<T> &second,
                   const bool is_unique);
 
-template <typename T>
+template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique);
 }  // namespace cpu
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 75553ca5b5..8a45b4919c 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -79,7 +79,8 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
     int NRHS   = b.dims()[1];
     Array<T> B = copyArray<T>(b);
 
-    auto func = [=](CParam<T> A, Param<T> B, CParam<int> pivot, int N, int NRHS) {
+    auto func = [=](CParam<T> A, Param<T> B, CParam<int> pivot, int N,
+                    int NRHS) {
         getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N', N, NRHS, A.get(),
                         A.strides(1), pivot.get(), B.get(), B.strides(1));
     };
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index ef1a1bdd2f..f4a18f6202 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <copy.hpp>
 #include <common/err_common.hpp>
+#include <copy.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index bd2055bdb8..4b8e84c2b6 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <copy.hpp>
 #include <common/err_common.hpp>
+#include <copy.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index 285805f636..edebaa4b1f 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -166,16 +166,15 @@ SPARSE_FUNC(create_csr, cdouble, z)
 
 template<typename T>
 using mv_func_def = sparse_status_t (*)(const sparse_operation_t, scale_type<T>,
-                                        const sparse_matrix_t,
-                                        matrix_descr, cptr_type<T>,
-                                        scale_type<T>, ptr_type<T>);
+                                        const sparse_matrix_t, matrix_descr,
+                                        cptr_type<T>, scale_type<T>,
+                                        ptr_type<T>);
 
 template<typename T>
 using mm_func_def = sparse_status_t (*)(const sparse_operation_t, scale_type<T>,
-                                        const sparse_matrix_t,
-                                        matrix_descr, sparse_layout_t,
-                                        cptr_type<T>, int, int, scale_type<T>,
-                                        ptr_type<T>, int);
+                                        const sparse_matrix_t, matrix_descr,
+                                        sparse_layout_t, cptr_type<T>, int, int,
+                                        scale_type<T>, ptr_type<T>, int);
 
 #define SPARSE_FUNC_DEF(FUNC) \
     template<typename T>      \
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index e88d46c208..79232a332b 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <complex>
 #include <common/kernel_type.hpp>
+#include <complex>
 
 namespace cpu {
 using cdouble = std::complex<double>;
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index e0fffe10f3..9010a306ba 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -20,13 +20,10 @@ using common::half;
 namespace cpu {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in,
-          const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy,
-          const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py,
-          const bool is_column) {
-    evalMultiple<T>(std::vector<Array<T>*>{const_cast<Array<T>*>(&in), &out});
+void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py, const bool is_column) {
+    evalMultiple<T>(std::vector<Array<T> *>{const_cast<Array<T> *>(&in), &out});
 
     if (is_column) {
         getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px,
@@ -37,13 +34,11 @@ void wrap(Array<T> &out, const Array<T> &in,
     }
 }
 
-#define INSTANTIATE(T)                                          \
-    template void wrap<T>(Array<T> & out, const Array<T> &in,   \
-                          const dim_t ox, const dim_t oy,       \
-                          const dim_t wx, const dim_t wy,       \
-                          const dim_t sx, const dim_t sy,       \
-                          const dim_t px, const dim_t py,       \
-                          const bool is_column);
+#define INSTANTIATE(T)                                                        \
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t ox, \
+                          const dim_t oy, const dim_t wx, const dim_t wy,     \
+                          const dim_t sx, const dim_t sy, const dim_t px,     \
+                          const dim_t py, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -59,7 +54,7 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 #undef INSTANTIATE
 
-template <typename T>
+template<typename T>
 Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
                       const dim_t wx, const dim_t wy, const dim_t sx,
                       const dim_t sy, const dim_t px, const dim_t py,
diff --git a/src/backend/cpu/wrap.hpp b/src/backend/cpu/wrap.hpp
index cbaac9ea50..c37d05c0ef 100644
--- a/src/backend/cpu/wrap.hpp
+++ b/src/backend/cpu/wrap.hpp
@@ -12,16 +12,13 @@
 namespace cpu {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in,
-          const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy,
-          const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py,
-          const bool is_column);
+void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py, const bool is_column);
 
-template <typename T>
+template<typename T>
 Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
                       const dim_t wx, const dim_t wy, const dim_t sx,
                       const dim_t sy, const dim_t px, const dim_t py,
                       const dim_t dx, const dim_t dy, const bool is_column);
-}
+}  // namespace cpu
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index f29ef4a206..33b2588672 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -225,12 +225,12 @@ class Array {
 
     operator Param<data_t<T>>() {
         return Param<data_t<T>>(this->get(), this->dims().get(),
-                                        this->strides().get());
+                                this->strides().get());
     }
 
     operator CParam<data_t<T>>() const {
         return CParam<data_t<T>>(this->get(), this->dims().get(),
-                                         this->strides().get());
+                                 this->strides().get());
     }
 
     common::Node_ptr getNode();
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index c6272ee545..bbdb390c51 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -143,7 +143,8 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
             operands[1], (int)(op)));
     };
 
-    Node_ptr out = common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
+    Node_ptr out =
+        common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
     return createNodeArray<To>(odims, out);
 }
 
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index 2b7ff45d43..bb005b1815 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -296,9 +296,9 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     dim4 oStrides = out.strides();
 
     if (oDims.ndims() <= 2) {
-        CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K,
-                                     alpha, lhs, lStrides[1], rhs,
-                                     rStrides[1], beta, out, oStrides[1]));
+        CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K, alpha,
+                                     lhs, lStrides[1], rhs, rStrides[1], beta,
+                                     out, oStrides[1]));
     } else {
         int batchSize = oDims[2] * oDims[3];
         vector<const T *> lptrs(batchSize);
diff --git a/src/backend/cuda/convolve.hpp b/src/backend/cuda/convolve.hpp
index 36b2c8b56d..bee4c77ea0 100644
--- a/src/backend/cuda/convolve.hpp
+++ b/src/backend/cuda/convolve.hpp
@@ -11,29 +11,29 @@
 
 namespace cuda {
 
-template <typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind);
 
-template <typename T, typename accT, bool expand>
+template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
                    Array<accT> const &r_filter);
 
-template <typename T>
+template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
                    const dim4 stride, const dim4 padding, const dim4 dilation);
 
-template <typename T>
+template<typename T>
 Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> &original_signal,
                            const Array<T> &original_filter,
                            const Array<T> &convolved_output, af::dim4 stride,
                            af::dim4 padding, af::dim4 dilation);
 
-template <typename T>
+template<typename T>
 Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &original_signal,
                              const Array<T> &original_filter,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index d4710b3886..cbfdf7ba9a 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -77,7 +77,8 @@ cudnnStatus_t cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
             filterDesc, dataType, format, k, c, h, w);
     }
     CUDA_NOT_SUPPORTED(
-        "cudnnSetFilter4dDescriptor not supported for the current version of cuDNN");
+        "cudnnSetFilter4dDescriptor not supported for the current version of "
+        "cuDNN");
 #elif CUDNN_VERSION == 4000
     return getCudnnPlugin().cudnnSetFilter4dDescriptor_v4(filterDesc, dataType,
                                                           format, k, c, h, w);
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
index 8a6b13b8fe..1538b5ca3b 100644
--- a/src/backend/cuda/cudnn.hpp
+++ b/src/backend/cuda/cudnn.hpp
@@ -39,8 +39,6 @@ const char *errorString(cudnnStatus_t err);
         }                                                                   \
     } while (0)
 
-
-
 // cuDNN Wrappers
 //
 // cuDNN deprecates and releases function names often between releases. in order
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index b83ddf19be..5d04e47f6c 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -18,14 +18,14 @@
 
 #if CUDNN_VERSION > 4000
 // This function is not available on versions greater than v4
-cudnnStatus_t
-cudnnSetFilter4dDescriptor_v4(cudnnFilterDescriptor_t filterDesc,
-                              cudnnDataType_t dataType,  // image data type
-                              cudnnTensorFormat_t format,
-                              int k,   // number of output feature maps
-                              int c,   // number of input feature maps
-                              int h,   // height of each input filter
-                              int w);  // width of  each input filter
+cudnnStatus_t cudnnSetFilter4dDescriptor_v4(
+    cudnnFilterDescriptor_t filterDesc,
+    cudnnDataType_t dataType,  // image data type
+    cudnnTensorFormat_t format,
+    int k,   // number of output feature maps
+    int c,   // number of input feature maps
+    int h,   // height of each input filter
+    int w);  // width of  each input filter
 #else
 // This function is only available on newer versions of cudnn
 size_t cudnnGetCudartVersion(void);
@@ -67,9 +67,7 @@ class cudnnModule {
     spdlog::logger* getLogger();
 
     /// Returns the version of the cuDNN loaded at runtime
-    std::tuple<int, int, int> getVersion() {
-        return { major, minor, patch };
-    }
+    std::tuple<int, int, int> getVersion() { return {major, minor, patch}; }
 };
 
 cudnnModule& getCudnnPlugin();
diff --git a/src/backend/cuda/flood_fill.cpp b/src/backend/cuda/flood_fill.cpp
index ba7657182b..1442ba2619 100644
--- a/src/backend/cuda/flood_fill.cpp
+++ b/src/backend/cuda/flood_fill.cpp
@@ -20,15 +20,15 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
                    const T lowValue, const T highValue,
                    const af::connectivity nlookup) {
     auto out = createValueArray(image.dims(), T(0));
-    kernel::floodFill<T>(out, image, seedsX, seedsY, newValue,
-                         lowValue, highValue, nlookup);
+    kernel::floodFill<T>(out, image, seedsX, seedsY, newValue, lowValue,
+                         highValue, nlookup);
     return out;
 }
 
-#define INSTANTIATE(T)                                                    \
-    template Array<T> floodFill(                                          \
-        const Array<T>&, const Array<uint>&, const Array<uint>&, const T, \
-        const T, const T, const af::connectivity);
+#define INSTANTIATE(T)                                                         \
+    template Array<T> floodFill(const Array<T>&, const Array<uint>&,           \
+                                const Array<uint>&, const T, const T, const T, \
+                                const af::connectivity);
 
 INSTANTIATE(float)
 INSTANTIATE(uint)
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
index 18fc5d5b97..7d8945a878 100644
--- a/src/backend/cuda/handle.cpp
+++ b/src/backend/cuda/handle.cpp
@@ -26,5 +26,4 @@ CREATE_HANDLE(cudnnTensorDescriptor_t, cuda::getCudnnPlugin().cudnnCreateTensorD
 CREATE_HANDLE(cudnnFilterDescriptor_t, cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
 CREATE_HANDLE(cudnnConvolutionDescriptor_t, cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
 
-
 // clang-format on
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index acf798dcc9..73b84072ba 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -30,9 +30,11 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
     static const std::string source(anisotropic_diffusion_cuh,
                                     anisotropic_diffusion_cuh_len);
-    auto diffUpdate = getKernel("cuda::diffUpdate", source,
-                             {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
-                             {DefineValue(THREADS_X), DefineValue(THREADS_Y), DefineValue(YDIM_LOAD)});
+    auto diffUpdate = getKernel(
+        "cuda::diffUpdate", source,
+        {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
+        {DefineValue(THREADS_X), DefineValue(THREADS_Y),
+         DefineValue(YDIM_LOAD)});
 
     dim3 threads(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 5589416f2a..74c9b208e6 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -127,8 +127,8 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
 
                 // FIXME: case where filter array is strided
                 convolve1.setConstant(conv_c_name,
-                                 reinterpret_cast<CUdeviceptr>(fptr),
-                                 filterSize);
+                                      reinterpret_cast<CUdeviceptr>(fptr),
+                                      filterSize);
 
                 p.o[0] = (p.outHasNoOffset ? 0 : b1);
                 p.o[1] = (p.outHasNoOffset ? 0 : b2);
@@ -139,8 +139,8 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
 
                 EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream(),
                                   p.mSharedSize);
-                convolve1(qArgs, out, sig, filt.dims[0], p.mBlk_x, p.mBlk_y, p.o[0],
-                     p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
+                convolve1(qArgs, out, sig, filt.dims[0], p.mBlk_x, p.mBlk_y,
+                          p.o[0], p.o[1], p.o[2], p.s[0], p.s[1], p.s[2]);
                 POST_LAUNCH_CHECK();
             }
         }
@@ -171,10 +171,11 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
 
     // FIXME: case where filter array is strided
     convolve2.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
-                     f0 * f1 * sizeof(aT));
+                          f0 * f1 * sizeof(aT));
 
     EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream());
-    convolve2(qArgs, out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1], p.s[2]);
+    convolve2(qArgs, out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1],
+              p.s[2]);
     POST_LAUNCH_CHECK();
 }
 
@@ -225,7 +226,7 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
 
         // FIXME: case where filter array is strided
         convolve3.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
-                         filterSize);
+                              filterSize);
 
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
         p.s[2] = (p.inHasNoOffset ? 0 : b3);
@@ -233,7 +234,7 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
         EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream(),
                           p.mSharedSize);
         convolve3(qArgs, out, sig, filt.dims[0], filt.dims[1], filt.dims[2],
-             p.mBlk_x, p.o[2], p.s[2]);
+                  p.mBlk_x, p.o[2], p.s[2]);
         POST_LAUNCH_CHECK();
     }
 }
@@ -327,8 +328,9 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
 
     // FIXME: case where filter array is strided
-    convolve2_separable.setConstant(sconv_c_name, reinterpret_cast<CUdeviceptr>(filter.ptr),
-                     fLen * sizeof(aT));
+    convolve2_separable.setConstant(sconv_c_name,
+                                    reinterpret_cast<CUdeviceptr>(filter.ptr),
+                                    fLen * sizeof(aT));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
     convolve2_separable(qArgs, out, signal, blk_x, blk_y);
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index be14157987..929a2251ff 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -32,9 +32,9 @@ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     static const std::string source(exampleFunction_cuh,
                                     exampleFunction_cuh_len);
     auto exampleFunc = getKernel("cuda::exampleFunc", source,
-                             {
-                                 TemplateTypename<T>(),
-                             });
+                                 {
+                                     TemplateTypename<T>(),
+                                 });
 
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index f1da489ace..60d6444f8d 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -49,16 +49,16 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds = getKernel("cuda::initSeeds", source,
-            {TemplateTypename<T>()});
-    auto floodStep = getKernel("cuda::floodStep", source,
-            {TemplateTypename<T>()},
-            {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto finalizeOutput = getKernel("cuda::finalizeOutput", source,
-            {TemplateTypename<T>()});
+    auto initSeeds =
+        getKernel("cuda::initSeeds", source, {TemplateTypename<T>()});
+    auto floodStep =
+        getKernel("cuda::floodStep", source, {TemplateTypename<T>()},
+                  {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto finalizeOutput =
+        getKernel("cuda::finalizeOutput", source, {TemplateTypename<T>()});
 
-    EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)),
-                      dim3(THREADS), getActiveStream());
+    EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
+                      getActiveStream());
     initSeeds(qArgs, out, seedsx, seedsy);
     POST_LAUNCH_CHECK();
 
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index ff143676d3..52ba48cc04 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -25,8 +25,9 @@ template<typename T>
 void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
     static const std::string source(hsv_rgb_cuh, hsv_rgb_cuh_len);
 
-    auto hsvrgbConverter = getKernel("cuda::hsvrgbConverter", source,
-                             {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
+    auto hsvrgbConverter =
+        getKernel("cuda::hsvrgbConverter", source,
+                  {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index a28cc72b07..01af4ee98e 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -48,8 +48,8 @@ __global__ void iota_kernel(Param<T> out, const int s0, const int s1,
     const int incx = blocksPerMatX * blockDim.x;
 
     for (int oy = yy; oy < out.dims[1]; oy += incy) {
-        int oyzw = ozw + oy * out.strides[1];
-        dim_t valY   = val + (oy % s1) * s0;
+        int oyzw   = ozw + oy * out.strides[1];
+        dim_t valY = val + (oy % s1) * s0;
         for (int ox = xx; ox < out.dims[0]; ox += incx) {
             int oidx = oyzw + ox;
 
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 23db5baeec..ca3044f9aa 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -488,8 +488,8 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
         CUDA_CHECK(
             cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-        compute_t<T> val     = static_cast <compute_t<T>>(h_ptr[0]);
-        compute_t<Tw> weight = static_cast <compute_t<Tw>>(h_wptr[0]);
+        compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
 
         for (int i = 1; i < tmp_elements; i++) {
             stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 8fa8c1ff79..6851e43f4b 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -31,9 +31,9 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
     static const std::string source(medfilt_cuh, medfilt_cuh_len);
 
     auto medfilt2 = getKernel("cuda::medfilt2", source,
-                            {TemplateTypename<T>(), TemplateArg(pad),
-                             TemplateArg(w_len), TemplateArg(w_wid)},
-                            {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+                              {TemplateTypename<T>(), TemplateArg(pad),
+                               TemplateArg(w_len), TemplateArg(w_wid)},
+                              {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 60207f1cfd..fe3434de75 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -75,7 +75,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
         });
 
     morph3D.setConstant("cFilter", reinterpret_cast<CUdeviceptr>(mask.ptr),
-                      mask.dims[0] * mask.dims[1] * mask.dims[2] * sizeof(T));
+                        mask.dims[0] * mask.dims[1] * mask.dims[2] * sizeof(T));
 
     dim3 threads(kernel::CUBE_X, kernel::CUBE_Y, kernel::CUBE_Z);
 
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 8e06bb56e6..ac1bdc4b7b 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -43,8 +43,7 @@ static const int THREADS = 256;
 // Generates rationals in (0, 1]
 __device__ static compute_t<common::half> getHalf(const uint &num) {
     ushort v = num;
-    return (compute_t<common::half>)(v * HALF_FACTOR +
-                                     HALF_HALF_FACTOR);
+    return (compute_t<common::half>)(v * HALF_FACTOR + HALF_HALF_FACTOR);
 }
 
 // Generates rationals in (0, 1]
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index c5d8bf1c41..f215f8df88 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -54,7 +54,7 @@ __global__ void range_kernel(Param<T> out, const int dim,
 
     for (int oy = yy; oy < out.dims[1]; oy += incy) {
         compute_t<T> valYZW = valZW + (mul1 * oy);
-        int oyzw     = ozw + oy * out.strides[1];
+        int oyzw            = ozw + oy * out.strides[1];
         for (int ox = xx; ox < out.dims[0]; ox += incx) {
             int oidx         = oyzw + ox;
             compute_t<T> val = valYZW + static_cast<compute_t<T>>(ox * mul0);
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index 8eddecf490..34481cfafb 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -19,8 +19,8 @@
 #include <type_traits>
 #include "config.hpp"
 
-#include <cub/device/device_reduce.cuh>
 #include <kernel/shfl_intrinsics.hpp>
+#include <cub/device/device_reduce.cuh>
 
 using std::unique_ptr;
 
@@ -72,7 +72,8 @@ __global__ void test_needs_reduction(int *needs_another_reduction,
 
     __syncthreads();
 
-    if (remaining_updates && (threadIdx.x % 32 == 0)) atomicOr(needs_another_reduction, remaining_updates);
+    if (remaining_updates && (threadIdx.x % 32 == 0))
+        atomicOr(needs_another_reduction, remaining_updates);
 
     // check across warp boundaries
     if ((tid + 1) < n) { k = keys_in.ptr[tid + 1]; }
@@ -271,17 +272,20 @@ __global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
         compute_t<To> init = Binary<compute_t<To>, op>::init();
         int eq_check, update_key;
         unsigned shflmask;
-        #pragma unroll
+#pragma unroll
         for (int delta = 1; delta < 32; delta <<= 1) {
-            eq_check = (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
+            eq_check =
+                (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
 
             // checks if this thread should perform a reduction
-            update_key = eq_check && (laneid < (32-delta)) && ((tidx + delta) < n);
+            update_key =
+                eq_check && (laneid < (32 - delta)) && ((tidx + delta) < n);
 
             // obtains mask of all threads that should be reduced
             shflmask = ballot_sync(FULL_MASK, update_key);
 
-            // shifts mask to include source threads that should participate in _shfl
+            // shifts mask to include source threads that should participate in
+            // _shfl
             shflmask |= (shflmask << delta);
 
             // shfls data from neighboring threads
@@ -504,17 +508,20 @@ __global__ static void reduce_blocks_dim_by_key(
         compute_t<To> init = Binary<compute_t<To>, op>::init();
         int eq_check, update_key;
         unsigned shflmask;
-        #pragma unroll
+#pragma unroll
         for (int delta = 1; delta < 32; delta <<= 1) {
-            eq_check = (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
+            eq_check =
+                (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
 
             // checks if this thread should perform a reduction
-            update_key = eq_check && (laneid < (32-delta)) && ((tidx + delta) < n);
+            update_key =
+                eq_check && (laneid < (32 - delta)) && ((tidx + delta) < n);
 
             // obtains mask of all threads that should be reduced
             shflmask = ballot_sync(FULL_MASK, update_key);
 
-            // shifts mask to include source threads that should participate in _shfl
+            // shifts mask to include source threads that should participate in
+            // _shfl
             shflmask |= (shflmask << delta);
 
             // shfls data from neighboring threads
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 5a9815ae7b..9de3b005ba 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -46,7 +46,7 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
     scan_dim(qArgs, out, tmp, in, blocks_all[0], blocks_all[1], blocks_all[dim],
-            lim);
+             lim);
     POST_LAUNCH_CHECK();
 }
 
@@ -70,8 +70,8 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scan_dim_bcast(qArgs, out, tmp, blocks_all[0], blocks_all[1], blocks_all[dim],
-             lim, inclusive_scan);
+    scan_dim_bcast(qArgs, out, tmp, blocks_all[0], blocks_all[1],
+                   blocks_all[dim], lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index df6c50ca79..cb44a4997a 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -49,8 +49,8 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanbykey_dim_nonfinal(qArgs, out, tmp, tflg, tlid, in, key, dim, blocks_all[0],
-                    blocks_all[1], lim, inclusive_scan);
+    scanbykey_dim_nonfinal(qArgs, out, tmp, tflg, tlid, in, key, dim,
+                           blocks_all[0], blocks_all[1], lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
@@ -73,8 +73,8 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanbykey_dim_final(qArgs, out, in, key, dim, blocks_all[0], blocks_all[1], lim,
-                 calculateFlags, inclusive_scan);
+    scanbykey_dim_final(qArgs, out, in, key, dim, blocks_all[0], blocks_all[1],
+                        lim, calculateFlags, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
@@ -82,16 +82,17 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
-    auto scanbykey_dim_bcast = getKernel("cuda::scanbykey_dim_bcast", ScanDimByKeySource,
-                              {TemplateTypename<To>(), TemplateArg(op)});
+    auto scanbykey_dim_bcast =
+        getKernel("cuda::scanbykey_dim_bcast", ScanDimByKeySource,
+                  {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanbykey_dim_bcast(qArgs, out, tmp, tlid, dim, blocks_all[0], blocks_all[1],
-             blocks_all[dim], lim);
+    scanbykey_dim_bcast(qArgs, out, tmp, tlid, dim, blocks_all[0],
+                        blocks_all[1], blocks_all[dim], lim);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index a339452caf..7704f29d54 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -53,8 +53,9 @@ template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
-    auto scan_first_bcast = getKernel("cuda::scan_first_bcast", ScanFirstSource,
-                                {TemplateTypename<To>(), TemplateArg(op)});
+    auto scan_first_bcast =
+        getKernel("cuda::scan_first_bcast", ScanFirstSource,
+                  {TemplateTypename<To>(), TemplateArg(op)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index fe4863cda6..3881aa3593 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -44,8 +44,8 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanbykey_first_nonfinal(qArgs, out, tmp, tflg, tlid, in, key, blocks_x, blocks_y, lim,
-                 inclusive_scan);
+    scanbykey_first_nonfinal(qArgs, out, tmp, tflg, tlid, in, key, blocks_x,
+                             blocks_y, lim, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
@@ -65,8 +65,8 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
-    scanbykey_first_final(qArgs, out, in, key, blocks_x, blocks_y, lim, calculateFlags,
-              inclusive_scan);
+    scanbykey_first_final(qArgs, out, in, key, blocks_x, blocks_y, lim,
+                          calculateFlags, inclusive_scan);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index 45723c6483..cab805ff9e 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -74,11 +74,11 @@
 
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
 #include <debug_thrust.hpp>
+#include <err_cuda.hpp>
 #include <memory.hpp>
-#include "shared.hpp"
 #include <af/defines.h>
+#include "shared.hpp"
 
 #include "convolve.hpp"
 #include "resize.hpp"
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 4a824e0a89..19108d285a 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <debug_thrust.hpp>
 #include <debug_cuda.hpp>
+#include <debug_thrust.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
 #include <thrust/sort.h>
 #include <types.hpp>
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index 303c5abbd6..4ae39da0bf 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -29,10 +29,11 @@ void transpose_inplace(Param<T> in, const bool conjugate,
                        const bool is32multiple) {
     static const std::string source(transpose_inplace_cuh,
                                     transpose_inplace_cuh_len);
-    auto transposeIP = getKernel("cuda::transposeIP", source,
-                               {TemplateTypename<T>(), TemplateArg(conjugate),
-                                TemplateArg(is32multiple)},
-                               {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+    auto transposeIP =
+        getKernel("cuda::transposeIP", source,
+                  {TemplateTypename<T>(), TemplateArg(conjugate),
+                   TemplateArg(is32multiple)},
+                  {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     // dimensions passed to this function should be input dimensions
     // any necessary transformations and dimension related calculations are
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 2b9b8fbf96..9ef463f4f7 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -27,9 +27,9 @@
 #endif  //__CUDACC_RTC__
 
 #include <backend.hpp>
+#include <common/half.hpp>
 #include <types.hpp>
 #include <af/defines.h>
-#include <common/half.hpp>
 
 #include <cuda_fp16.h>
 #include <math_constants.h>
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index d4435b6771..2aec0fb4e7 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -46,11 +46,8 @@
 #include <type_traits>
 #include <utility>
 
-using std::array;
 using std::accumulate;
-using std::chrono::duration_cast;
-using std::chrono::high_resolution_clock;
-using std::chrono::milliseconds;
+using std::array;
 using std::begin;
 using std::end;
 using std::extent;
@@ -63,8 +60,11 @@ using std::to_string;
 using std::transform;
 using std::unique_ptr;
 using std::vector;
+using std::chrono::duration_cast;
+using std::chrono::high_resolution_clock;
+using std::chrono::milliseconds;
 
-spdlog::logger* getLogger() {
+spdlog::logger *getLogger() {
     static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
     return logger.get();
 }
@@ -300,11 +300,10 @@ Kernel buildKernel(const int device, const string &nameExpr,
     // skip --std=c++14 because it will stay the same. It doesn't
     // provide useful information
     auto listOpts = [](vector<const char *> &in) {
-        return accumulate(
-            begin(in) + 2, end(in), string(in[0]),
-            [](const string &lhs, const string &rhs) {
-                return lhs + ", " + rhs;
-            });
+        return accumulate(begin(in) + 2, end(in), string(in[0]),
+                          [](const string &lhs, const string &rhs) {
+                              return lhs + ", " + rhs;
+                          });
     };
 
     AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}}}",
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index 00d11834a5..462161ff98 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -107,7 +107,7 @@ struct Kernel {
 Kernel buildKernel(const int device, const std::string& nameExpr,
                    const std::string& jitSourceString,
                    const std::vector<std::string>& opts = {},
-                   const bool isJIT = false);
+                   const bool isJIT                     = false);
 
 template<typename T>
 std::string toString(T value);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index f4493433e8..b0bc38ccfe 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -318,8 +318,11 @@ int &tlocalActiveDeviceId() {
 
 int getDeviceCount() {
     int count = 0;
-    if (cudaGetDeviceCount(&count)) { return 0; }
-    else { return count; }
+    if (cudaGetDeviceCount(&count)) {
+        return 0;
+    } else {
+        return count;
+    }
 }
 
 int getActiveDeviceId() { return tlocalActiveDeviceId(); }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index a358bdcae9..4e5d082884 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -100,7 +100,7 @@ cudaDeviceProp getDeviceProp(int device);
 
 std::pair<int, int> getComputeCapability(const int device);
 
-bool &evalFlag();
+bool& evalFlag();
 
 MemoryManagerBase& memoryManager();
 
@@ -116,9 +116,9 @@ void resetMemoryManagerPinned();
 
 graphics::ForgeManager& forgeManager();
 
-GraphicsResourceManager &interopManager();
+GraphicsResourceManager& interopManager();
 
-PlanCache &fftManager();
+PlanCache& fftManager();
 
 BlasHandle blasHandle();
 
diff --git a/src/backend/cuda/scalar.hpp b/src/backend/cuda/scalar.hpp
index eb2a0fbf3b..c08c201a73 100644
--- a/src/backend/cuda/scalar.hpp
+++ b/src/backend/cuda/scalar.hpp
@@ -23,7 +23,7 @@ Array<T> createScalarNode(const dim4 &size, const T val) {
     // Either this gaurd or we need to enable extended alignment
     // by defining _ENABLE_EXTENDED_ALIGNED_STORAGE before <type_traits>
     // header is included
-    using ScalarNode = common::ScalarNode<T>;
+    using ScalarNode    = common::ScalarNode<T>;
     using ScalarNodePtr = std::shared_ptr<ScalarNode>;
     return createNodeArray<T>(size, ScalarNodePtr(new ScalarNode(val)));
 #else
diff --git a/src/backend/cuda/transpose.cpp b/src/backend/cuda/transpose.cpp
index e48fb8f735..b891722f28 100644
--- a/src/backend/cuda/transpose.cpp
+++ b/src/backend/cuda/transpose.cpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <kernel/transpose.hpp>
 #include <transpose.hpp>
 #include <af/dim4.hpp>
-#include <common/half.hpp>
 
 using af::dim4;
 using common::half;
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index b0fbe9c935..93e1704ed7 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -14,7 +14,7 @@
 #include <cuda_fp16.h>
 
 namespace common {
-    class half;
+class half;
 }
 
 #ifdef __CUDACC_RTC__
@@ -133,14 +133,13 @@ const char *getFullName<common::half>() {
 }  // namespace
 #endif  //__CUDACC_RTC__
 
-  //#ifndef __CUDACC_RTC__
+//#ifndef __CUDACC_RTC__
 }  // namespace cuda
 //#endif  //__CUDACC_RTC__
 
-
 namespace common {
-  template<typename T>
-  class kernel_type;
+template<typename T>
+class kernel_type;
 }
 
 namespace common {
@@ -166,4 +165,4 @@ struct kernel_type<common::half> {
 #endif
 #endif
 };
-}
+}  // namespace common
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 5e3f9fe92b..b352930c81 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -81,8 +81,9 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using std::array;
 
     auto createUnary = [](array<Node_ptr, 1> &operands) {
-        return common::Node_ptr(new common::UnaryNode(
-            getFullName<T>(), shortname<T>(true), unaryName<op>(), operands[0], op));
+        return common::Node_ptr(
+            new common::UnaryNode(getFullName<T>(), shortname<T>(true),
+                                  unaryName<op>(), operands[0], op));
     };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
diff --git a/src/backend/cuda/wrap.hpp b/src/backend/cuda/wrap.hpp
index d03017b069..db923fc5cb 100644
--- a/src/backend/cuda/wrap.hpp
+++ b/src/backend/cuda/wrap.hpp
@@ -11,10 +11,7 @@
 
 namespace cuda {
 template<typename T>
-void wrap(Array<T> &out,  const Array<T> &in,
-          const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy,
-          const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py,
-          const bool is_column);
+void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py, const bool is_column);
 }
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 261464f084..e74abf5089 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -11,8 +11,8 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/jit/Node.hpp>
 #include <common/MemoryManagerBase.hpp>
+#include <common/jit/Node.hpp>
 #include <err_opencl.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/opencl/any.cpp b/src/backend/opencl/any.cpp
index 21ae5e6970..c9668f3451 100644
--- a/src/backend/opencl/any.cpp
+++ b/src/backend/opencl/any.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
 using common::half;
 
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 839bc06097..8bac7911a3 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -58,9 +58,8 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
             // alloc an 1-element buffer to avoid OpenCL from failing using
             // direct buffer allocation as opposed to mem manager to avoid
             // reference count desprepancies between different backends
-            static cl::Buffer *empty = new Buffer(getContext(),
-                                                   CL_MEM_READ_ONLY,
-                                                   sizeof(uint));
+            static cl::Buffer* empty =
+                new Buffer(getContext(), CL_MEM_READ_ONLY, sizeof(uint));
             bPtrs[x] = empty;
         }
     }
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index a71a774e71..6870da0e50 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -23,8 +23,8 @@
 #include <vector>
 
 // Includes one of the supported OpenCL BLAS back-ends (e.g. clBLAS, CLBlast)
-#include <magma/magma_blas.h>
 #include <cpu/cpu_blas.hpp>
+#include <magma/magma_blas.h>
 
 using common::half;
 
@@ -54,19 +54,16 @@ void gemm_fallback(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
 }
 
 template<>
-void gemm_fallback<half>(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-                          const half *alpha,
-                          const Array<half> &lhs, const Array<half> &rhs,
-                          const half *beta) {
+void gemm_fallback<half>(Array<half> &out, af_mat_prop optLhs,
+                         af_mat_prop optRhs, const half *alpha,
+                         const Array<half> &lhs, const Array<half> &rhs,
+                         const half *beta) {
     assert(false && "CPU fallback not implemented for f16");
 }
 
-
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta) {
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
 #if defined(WITH_LINEAR_ALGEBRA)
     // Do not force offload gemm on OSX Intel devices
     if (OpenCLCPUOffload(false) && (af_dtype)dtype_traits<T>::af_type != f16) {
@@ -119,15 +116,15 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
             OPENCL_BLAS_CHECK(gemv(lOpts, lDims[0], lDims[1], *alpha,
                                    (*lhs.get())(), lOffset, lStrides[1],
                                    (*rhs.get())(), rOffset, incr, *beta,
-                                   (*out.get())(), oOffset, oStrides[0], 1, &getQueue()(),
-                                   0, nullptr, &event()));
+                                   (*out.get())(), oOffset, oStrides[0], 1,
+                                   &getQueue()(), 0, nullptr, &event()));
         } else {
             gpu_blas_gemm_func<T> gemm;
-            OPENCL_BLAS_CHECK(gemm(lOpts, rOpts, M, N, K, *alpha, (*lhs.get())(),
-                                   lOffset, lStrides[1], (*rhs.get())(),
-                                   rOffset, rStrides[1], *beta, (*out.get())(),
-                                   oOffset, oStrides[1], 1, &getQueue()(), 0,
-                                   nullptr, &event()));
+            OPENCL_BLAS_CHECK(gemm(lOpts, rOpts, M, N, K, *alpha,
+                                   (*lhs.get())(), lOffset, lStrides[1],
+                                   (*rhs.get())(), rOffset, rStrides[1], *beta,
+                                   (*out.get())(), oOffset, oStrides[1], 1,
+                                   &getQueue()(), 0, nullptr, &event()));
         }
     }
 }
@@ -142,10 +139,10 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
-#define INSTANTIATE_GEMM(TYPE)                                                         \
-    template void gemm<TYPE>(Array<TYPE> &out, af_mat_prop optLhs, af_mat_prop optRhs, \
-                             const TYPE *alpha,                    \
-                             const Array<TYPE> &lhs, const Array<TYPE> &rhs,           \
+#define INSTANTIATE_GEMM(TYPE)                                               \
+    template void gemm<TYPE>(Array<TYPE> & out, af_mat_prop optLhs,          \
+                             af_mat_prop optRhs, const TYPE *alpha,          \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs, \
                              const TYPE *beta);
 
 INSTANTIATE_GEMM(float)
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
index c593380e2d..f0f1bc28f6 100644
--- a/src/backend/opencl/clfft.hpp
+++ b/src/backend/opencl/clfft.hpp
@@ -39,7 +39,7 @@ class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
     do {                                                         \
         clfftStatus _clfft_st = fn;                              \
         if (_clfft_st != CLFFT_SUCCESS) {                        \
-            opencl::signalMemoryCleanup();                            \
+            opencl::signalMemoryCleanup();                       \
             _clfft_st = (fn);                                    \
         }                                                        \
         if (_clfft_st != CLFFT_SUCCESS) {                        \
diff --git a/src/backend/opencl/convolve.hpp b/src/backend/opencl/convolve.hpp
index 59aafe7322..2ae65e561a 100644
--- a/src/backend/opencl/convolve.hpp
+++ b/src/backend/opencl/convolve.hpp
@@ -11,29 +11,29 @@
 
 namespace opencl {
 
-template <typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind);
 
-template <typename T, typename accT, bool expand>
+template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
                    Array<accT> const &r_filter);
 
-template <typename T>
+template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
                    const dim4 stride, const dim4 padding, const dim4 dilation);
 
-template <typename T>
+template<typename T>
 Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> &original_signal,
                            const Array<T> &original_filter,
                            const Array<T> &convolved_output, af::dim4 stride,
                            af::dim4 padding, af::dim4 dilation);
 
-template <typename T>
+template<typename T>
 Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &original_signal,
                              const Array<T> &original_filter,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 7739ba7502..28725d2e7f 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -92,17 +92,17 @@ using scale_type =
                          const typename blas_base<T>::type *, const T>::type;
 
 template<typename T>
-scale_type<T> getOneScalar(const T* const vals) {
+scale_type<T> getOneScalar(const T *const vals) {
     return vals[0];
 }
 
 template<>
-scale_type<cfloat> getOneScalar(const cfloat* const vals) {
+scale_type<cfloat> getOneScalar(const cfloat *const vals) {
     return reinterpret_cast<scale_type<cfloat>>(vals);
 }
 
 template<>
-scale_type<cdouble> getOneScalar(const cdouble* const vals) {
+scale_type<cdouble> getOneScalar(const cdouble *const vals) {
     return reinterpret_cast<scale_type<cdouble>>(vals);
 }
 
@@ -125,9 +125,9 @@ using gemv_func_def = void (*)(const CBLAS_ORDER, const CBLAS_TRANSPOSE,
     template<typename T>    \
     FUNC##_func_def<T> FUNC##_func();
 
-#define BLAS_FUNC(FUNC, TYPE, PREFIX)           \
-    template<>                                  \
-    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() { \
+#define BLAS_FUNC(FUNC, TYPE, PREFIX)                        \
+    template<>                                               \
+    FUNC##_func_def<TYPE> FUNC##_func<TYPE>() {              \
         return (FUNC##_func_def<TYPE>)&cblas_##PREFIX##FUNC; \
     }
 
@@ -168,9 +168,8 @@ toCblasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta) {
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
     using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
@@ -220,23 +219,21 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
         if (rDims[bColDim] == 1) {
             dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
             gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1],
-                           getOneScalar<T>(alpha),
-                           lptr, lStrides[1], rptr, incr,
-                           getOneScalar<T>(beta), optr, 1);
+                           getOneScalar<T>(alpha), lptr, lStrides[1], rptr,
+                           incr, getOneScalar<T>(beta), optr, 1);
         } else {
             gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K,
-                           getOneScalar<T>(alpha), lptr,
-                           lStrides[1], rptr, rStrides[1],
-                           getOneScalar<T>(beta),
-                           optr, oStrides[1]);
+                           getOneScalar<T>(alpha), lptr, lStrides[1], rptr,
+                           rStrides[1], getOneScalar<T>(beta), optr,
+                           oStrides[1]);
         }
     }
 }
 
-#define INSTANTIATE_GEMM(TYPE)                                                         \
-    template void gemm<TYPE>(Array<TYPE> &out, af_mat_prop optLhs, af_mat_prop optRhs, \
-                             const TYPE *alpha,                       \
-                             const Array<TYPE> &lhs, const Array<TYPE> &rhs,           \
+#define INSTANTIATE_GEMM(TYPE)                                               \
+    template void gemm<TYPE>(Array<TYPE> & out, af_mat_prop optLhs,          \
+                             af_mat_prop optRhs, const TYPE *alpha,          \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs, \
                              const TYPE *beta);
 
 INSTANTIATE_GEMM(float)
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
index 179ee8d633..b39d8ae205 100644
--- a/src/backend/opencl/cpu/cpu_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -13,8 +13,7 @@ namespace opencl {
 namespace cpu {
 
 template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
-          const T *beta);
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
 }
 }  // namespace opencl
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 35c0a1a2dd..6e48814d83 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -101,16 +101,15 @@ using create_csr_func_def = sparse_status_t (*)(sparse_matrix_t *,
 
 template<typename T>
 using mv_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
-                                        const sparse_matrix_t,
-                                        matrix_descr, cptr_type<T>,
-                                        scale_type<T>, ptr_type<T>);
+                                        const sparse_matrix_t, matrix_descr,
+                                        cptr_type<T>, scale_type<T>,
+                                        ptr_type<T>);
 
 template<typename T>
 using mm_func_def = sparse_status_t (*)(sparse_operation_t, scale_type<T>,
-                                        const sparse_matrix_t,
-                                        matrix_descr, sparse_layout_t,
-                                        cptr_type<T>, int, int, scale_type<T>,
-                                        ptr_type<T>, int);
+                                        const sparse_matrix_t, matrix_descr,
+                                        sparse_layout_t, cptr_type<T>, int, int,
+                                        scale_type<T>, ptr_type<T>, int);
 
 #define SPARSE_FUNC_DEF(FUNC) \
     template<typename T>      \
diff --git a/src/backend/opencl/flood_fill.cpp b/src/backend/opencl/flood_fill.cpp
index 8a2e5da71c..500a9219db 100644
--- a/src/backend/opencl/flood_fill.cpp
+++ b/src/backend/opencl/flood_fill.cpp
@@ -20,8 +20,8 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
                    const T lowValue, const T highValue,
                    const af::connectivity nlookup) {
     auto out = createValueArray(image.dims(), T(0));
-    kernel::floodFill<T>(out, image, seedsX, seedsY, newValue,
-                         lowValue, highValue, nlookup);
+    kernel::floodFill<T>(out, image, seedsX, seedsY, newValue, lowValue,
+                         highValue, nlookup);
     return out;
 }
 
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 56e2149f5b..54ebc69720 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -44,8 +44,8 @@ int setKernelArguments(
 }
 
 /// Generates the code to calculate the offsets for a buffer
-inline void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
-                                  const std::string& type_str) {
+inline void generateBufferOffsets(std::stringstream& kerStream, int id,
+                                  bool is_linear, const std::string& type_str) {
     UNUSED(type_str);
     std::string idx_str  = std::string("int idx") + std::to_string(id);
     std::string info_str = std::string("iInfo") + std::to_string(id);
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index c2c32c00bb..8a4391b11e 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <kernel_headers/example.hpp>  // This is the header that gets auto-generated
-          // from the .cl file you will create. We pre-process
-          // cl files to obfuscate code.
+// from the .cl file you will create. We pre-process
+// cl files to obfuscate code.
 
 #include <program.hpp>
 #include <traits.hpp>
@@ -21,9 +21,9 @@
 
 #include <cache.hpp>  // Has the definitions of functions such as the following
                       // used in caching and fetching kernels.
-          // * kernelCache - used to fetch existing kernel from cache
-          // if any
-          // * addKernelToCache - push new kernels into cache
+// * kernelCache - used to fetch existing kernel from cache
+// if any
+// * addKernelToCache - push new kernels into cache
 
 #include <common/dispatch.hpp>  // common utility header for CUDA & OpenCL backends
                                 // has the divup macro
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index f5e417ba23..a7ed4e3814 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -40,16 +40,15 @@ constexpr int ZERO      = 0;
 
 template<typename T>
 void initSeeds(Param out, const Param seedsx, const Param seedsy) {
-    std::string refName = std::string("init_seeds_") +
-                          std::string(dtype_traits<T>::getName());
+    std::string refName =
+        std::string("init_seeds_") + std::string(dtype_traits<T>::getName());
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-                << " -D VALID=" << T(VALID)
-                << " -D INIT_SEEDS";
+                << " -D VALID=" << T(VALID) << " -D INIT_SEEDS";
         if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {flood_fill_cl};
@@ -60,11 +59,11 @@ void initSeeds(Param out, const Param seedsx, const Param seedsy) {
         entry.ker  = new Kernel(*entry.prog, "init_seeds");
         addKernelToCache(device, refName, entry);
     }
-    auto initSeedsOp = KernelFunctor<Buffer, const KParam,
-                                     const Buffer, const KParam,
-                                     const Buffer, const KParam>(*entry.ker);
+    auto initSeedsOp =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
+                      const Buffer, const KParam>(*entry.ker);
     NDRange local(kernel::THREADS, 1, 1);
-    NDRange global( divup(seedsx.info.dims[0], local[0]) * local[0], 1 , 1);
+    NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
 
     initSeedsOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                 *seedsx.data, seedsx.info, *seedsy.data, seedsy.info);
@@ -81,8 +80,7 @@ void finalizeOutput(Param out, const T newValue) {
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
-                << " -D VALID=" << T(VALID)
-                << " -D ZERO=" << T(ZERO)
+                << " -D VALID=" << T(VALID) << " -D ZERO=" << T(ZERO)
                 << " -D FINALIZE_OUTPUT";
         if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
@@ -98,11 +96,10 @@ void finalizeOutput(Param out, const T newValue) {
     auto finalizeOut = KernelFunctor<Buffer, const KParam, const T>(*entry.ker);
 
     NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
-    NDRange global( divup(out.info.dims[0], local[0]) * local[0],
-                    divup(out.info.dims[1], local[1]) * local[1] ,
-                    1);
-    finalizeOut(EnqueueArgs(getQueue(), global, local),
-                *out.data, out.info, newValue);
+    NDRange global(divup(out.info.dims[0], local[0]) * local[0],
+                   divup(out.info.dims[1], local[1]) * local[1], 1);
+    finalizeOut(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                newValue);
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -112,8 +109,8 @@ void floodFill(Param out, const Param image, const Param seedsx,
                const T highValue, const af::connectivity nlookup) {
     constexpr int RADIUS = 1;
     UNUSED(nlookup);
-    std::string refName = std::string("flood_step_") +
-                          std::string(dtype_traits<T>::getName());
+    std::string refName =
+        std::string("flood_step_") + std::string(dtype_traits<T>::getName());
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
@@ -124,10 +121,8 @@ void floodFill(Param out, const Param image, const Param seedsx,
                 << " -D LMEM_WIDTH=" << (THREADS_X + 2 * RADIUS)
                 << " -D LMEM_HEIGHT=" << (THREADS_Y + 2 * RADIUS)
                 << " -D GROUP_SIZE=" << (THREADS_Y * THREADS_X)
-                << " -D VALID=" << T(VALID)
-                << " -D INVALID=" << T(INVALID)
-                << " -D ZERO=" << T(ZERO)
-                << " -D FLOOD_FILL_STEP";
+                << " -D VALID=" << T(VALID) << " -D INVALID=" << T(INVALID)
+                << " -D ZERO=" << T(ZERO) << " -D FLOOD_FILL_STEP";
         if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
 
         const char *ker_strs[] = {flood_fill_cl};
@@ -139,13 +134,12 @@ void floodFill(Param out, const Param image, const Param seedsx,
 
         addKernelToCache(device, refName, entry);
     }
-    auto floodStep = KernelFunctor<Buffer, const KParam,
-                                   const Buffer, const KParam,
-                                   const T, const T, Buffer>(*entry.ker);
+    auto floodStep =
+        KernelFunctor<Buffer, const KParam, const Buffer, const KParam, const T,
+                      const T, Buffer>(*entry.ker);
     NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
-    NDRange global( divup(out.info.dims[0], local[0]) * local[0],
-                    divup(out.info.dims[1], local[1]) * local[1] ,
-                    1);
+    NDRange global(divup(out.info.dims[0], local[0]) * local[0],
+                   divup(out.info.dims[1], local[1]) * local[1], 1);
 
     initSeeds<T>(out, seedsx, seedsy);
 
@@ -170,5 +164,5 @@ void floodFill(Param out, const Param image, const Param seedsx,
     finalizeOutput<T>(out, newValue);
 }
 
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index cb1ac8e0f6..998887b946 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -22,7 +22,6 @@ namespace opencl {
 namespace kernel {
 template<typename T>
 static void identity(Param out) {
-
     using af::scalar_to_option;
     using cl::Buffer;
     using cl::EnqueueArgs;
@@ -31,12 +30,12 @@ static void identity(Param out) {
     using cl::NDRange;
     using cl::Program;
     using common::half;
+    using std::is_same;
     using std::ostringstream;
     using std::string;
-    using std::is_same;
 
     string refName = std::string("identity_kernel") +
-                          std::string(dtype_traits<T>::getName());
+                     std::string(dtype_traits<T>::getName());
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
@@ -50,9 +49,7 @@ static void identity(Param out) {
             options << " -D USE_DOUBLE";
         }
 
-        if (is_same<T, half>::value) {
-          options << " -D USE_HALF";
-        }
+        if (is_same<T, half>::value) { options << " -D USE_HALF"; }
 
         const char* ker_strs[] = {identity_cl};
         const int ker_lens[]   = {identity_cl_len};
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index dec5615df9..76651d9b6f 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -13,11 +13,11 @@
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/laset.hpp>
+#include <magma_types.h>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
 #include <string>
-#include <magma_types.h>
 
 using cl::Buffer;
 using cl::EnqueueArgs;
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 4748da3cf6..561d670037 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -31,9 +31,9 @@ void lookup(Param out, const Param in, const Param indices) {
     using cl::KernelFunctor;
     using cl::NDRange;
     using cl::Program;
-    using std::string;
     using std::is_same;
     using std::ostringstream;
+    using std::string;
     using std::to_string;
 
     std::string refName =
@@ -49,15 +49,12 @@ void lookup(Param out, const Param in, const Param indices) {
                 << " -D idx_t=" << dtype_traits<idx_t>::getName()
                 << " -D DIM=" << dim;
 
-        if (is_same<in_t, double>::value ||
-            is_same<in_t, cdouble>::value ||
+        if (is_same<in_t, double>::value || is_same<in_t, cdouble>::value ||
             is_same<idx_t, double>::value) {
             options << " -D USE_DOUBLE";
         }
 
-        if (is_same<in_t, common::half>::value) {
-          options << " -D USE_HALF";
-        }
+        if (is_same<in_t, common::half>::value) { options << " -D USE_HALF"; }
 
         const char* ker_strs[] = {lookup_cl};
         const int ker_lens[]   = {lookup_cl_len};
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 2922748748..d119e997a7 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -440,7 +440,7 @@ T mean_all_weighted(Param in, Param inWeight) {
                                      h_wptr.data());
 
         compute_t<T> initial = static_cast<compute_t<T>>(h_ptr[0]);
-        compute_t<Tw> w       = static_cast<compute_t<Tw>>(h_wptr[0]);
+        compute_t<Tw> w      = static_cast<compute_t<Tw>>(h_wptr[0]);
         MeanOp<compute_t<T>, compute_t<Tw>> Op(initial, w);
         for (int i = 1; i < (int)tmpOut.elements(); i++) {
             Op(compute_t<T>(h_ptr[i]), compute_t<Tw>(h_wptr[i]));
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 62f678dff4..ed1f922b38 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -145,8 +145,8 @@ static void randomDistribution(cl::Buffer out, const size_t elements,
             get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
         auto randomEngineOp =
             cl::KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
-        randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out, elements,
-                       hic, loc, hi, lo);
+        randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out,
+                       elements, hic, loc, hi, lo);
     }
 
     counter += elements;
@@ -166,15 +166,15 @@ void randomDistribution(cl::Buffer out, const size_t elements, cl::Buffer state,
 
     cl::NDRange local(threads, 1);
     cl::NDRange global(threads * blocks, 1);
-    cl::Kernel ker = get_random_engine_kernel<T>(AF_RANDOM_ENGINE_MERSENNE_GP11213,
-                                             kerIdx, elementsPerBlock);
+    cl::Kernel ker = get_random_engine_kernel<T>(
+        AF_RANDOM_ENGINE_MERSENNE_GP11213, kerIdx, elementsPerBlock);
     auto randomEngineOp =
         cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
-                      cl::Buffer, uint, cl::Buffer, cl::Buffer, uint, uint>(
+                          cl::Buffer, uint, cl::Buffer, cl::Buffer, uint, uint>(
             ker);
-    randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out, state, pos, sh1,
-                   sh2, mask, recursion_table, temper_table, elementsPerBlock,
-                   elements);
+    randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out, state, pos,
+                   sh1, sh2, mask, recursion_table, temper_table,
+                   elementsPerBlock, elements);
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -215,8 +215,8 @@ void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
     cl::NDRange local(THREADS_PER_GROUP, 1);
     cl::NDRange global(local[0] * MAX_BLOCKS, 1);
 
-    cl::Kernel ker  = get_mersenne_init_kernel();
-    auto initOp = cl::KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
+    cl::Kernel ker = get_mersenne_init_kernel();
+    auto initOp    = cl::KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
     initOp(cl::EnqueueArgs(getQueue(), global, local), state, table, seed);
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index cf90221347..c4f3dcd37b 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -48,8 +48,7 @@ void range(Param out, const int dim) {
         if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
             options << " -D USE_DOUBLE";
 
-        if (std::is_same<T, common::half>::value)
-          options << " -D USE_HALF";
+        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
 
         const char* ker_strs[] = {range_cl};
         const int ker_lens[]   = {range_cl_len};
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 1d0f77128e..aa70c90dcb 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <Param.hpp>
 #include <Array.hpp>
+#include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 856348f678..c2189c4ba1 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -40,11 +40,11 @@
 namespace compute = boost::compute;
 
 using cl::Buffer;
-using cl::Program;
+using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::KernelFunctor;
-using cl::EnqueueArgs;
 using cl::NDRange;
+using cl::Program;
 using std::string;
 using std::unique_ptr;
 using std::vector;
@@ -53,7 +53,7 @@ namespace opencl {
 
 namespace kernel {
 
-template <typename Ti, typename Tk, typename To, af_op_t op>
+template<typename Ti, typename Tk, typename To, af_op_t op>
 void launch_reduce_blocks_dim_by_key(cl::Buffer *reduced_block_sizes,
                                      Param keys_out, Param vals_out,
                                      const Param keys, const Param vals,
@@ -118,7 +118,7 @@ void launch_reduce_blocks_dim_by_key(cl::Buffer *reduced_block_sizes,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Ti, typename Tk, typename To, af_op_t op>
+template<typename Ti, typename Tk, typename To, af_op_t op>
 void launch_reduce_blocks_by_key(cl::Buffer *reduced_block_sizes,
                                  Param keys_out, Param vals_out,
                                  const Param keys, const Param vals,
@@ -181,7 +181,7 @@ void launch_reduce_blocks_by_key(cl::Buffer *reduced_block_sizes,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Tk, typename To, af_op_t op>
+template<typename Tk, typename To, af_op_t op>
 void launch_final_boundary_reduce(cl::Buffer *reduced_block_sizes,
                                   Param keys_out, Param vals_out, const int n,
                                   const int numBlocks, const int threads_x) {
@@ -235,11 +235,12 @@ void launch_final_boundary_reduce(cl::Buffer *reduced_block_sizes,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Tk, typename To, af_op_t op>
+template<typename Tk, typename To, af_op_t op>
 void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
-                                      Param keys_out, Param vals_out, const int n,
-                                      const int numBlocks, const int threads_x,
-                                      const int dim, vector<int> dim_ordering) {
+                                      Param keys_out, Param vals_out,
+                                      const int n, const int numBlocks,
+                                      const int threads_x, const int dim,
+                                      vector<int> dim_ordering) {
     std::string ref_name =
         std::string("final_boundary_reduce") +
         std::string(dtype_traits<Tk>::getName()) + std::string("_") +
@@ -268,7 +269,7 @@ void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
         }
 
         const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_dim_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_by_key_boundary_dim_cl_len};
+        const int ker_lens[] = {ops_cl_len, reduce_by_key_boundary_dim_cl_len};
         Program prog;
         buildProgram(prog, 2, ker_strs, ker_lens, options.str());
 
@@ -284,7 +285,8 @@ void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
                        vals_out.info.dims[dim_ordering[3]]);
 
     auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, int, int>(*entry.ker);
+        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, int, int>(
+            *entry.ker);
 
     reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
              *keys_out.data, keys_out.info, *vals_out.data, vals_out.info, n,
@@ -293,7 +295,7 @@ void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Tk, typename To>
+template<typename Tk, typename To>
 void launch_compact(cl::Buffer *reduced_block_sizes, Param keys_out,
                     Param vals_out, const Param keys, const Param vals,
                     const int numBlocks, const int threads_x) {
@@ -346,7 +348,7 @@ void launch_compact(cl::Buffer *reduced_block_sizes, Param keys_out,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Tk, typename To>
+template<typename Tk, typename To>
 void launch_compact_dim(cl::Buffer *reduced_block_sizes, Param keys_out,
                         Param vals_out, const Param keys, const Param vals,
                         const int numBlocks, const int threads_x, const int dim,
@@ -402,7 +404,7 @@ void launch_compact_dim(cl::Buffer *reduced_block_sizes, Param keys_out,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Tk>
+template<typename Tk>
 void launch_test_needs_reduction(cl::Buffer needs_reduction,
                                  cl::Buffer needs_boundary, const Param keys,
                                  const int n, const int numBlocks,
@@ -444,7 +446,7 @@ void launch_test_needs_reduction(cl::Buffer needs_reduction,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename Ti, typename Tk, typename To, af_op_t op>
+template<typename Ti, typename Tk, typename To, af_op_t op>
 int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                         const Param keys, const Param vals, bool change_nan,
                         double nanval) {
@@ -554,7 +556,7 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
     return n_reduced_host;
 }
 
-template <typename Ti, typename Tk, typename To, af_op_t op>
+template<typename Ti, typename Tk, typename To, af_op_t op>
 int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                       const Param keys, const Param vals, bool change_nan,
                       double nanval, const int dim) {
@@ -671,7 +673,7 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
     return n_reduced_host;
 }
 
-template <af_op_t op, typename Ti, typename Tk, typename To>
+template<af_op_t op, typename Ti, typename Tk, typename To>
 void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    const Array<Tk> &keys, const Array<Ti> &vals, int dim,
                    bool change_nan, double nanval) {
@@ -704,5 +706,5 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
     keys_out = createSubArray<Tk>(reduced_keys, kindex, true);
     vals_out = createSubArray<To>(reduced_vals, vindex, true);
 }
-}
-}
+}  // namespace kernel
+}  // namespace opencl
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index d3263ebe8e..798bb87c99 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -34,9 +34,9 @@ void transpose(Param out, const Param in, cl::CommandQueue queue) {
     using cl::Program;
     using std::string;
 
-    string refName =
-        std::string("transpose_") + std::string(dtype_traits<T>::getName()) +
-        std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
+    string refName = std::string("transpose_") +
+                     std::string(dtype_traits<T>::getName()) +
+                     std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 3139a367a3..2fe5f2baa8 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -35,7 +35,7 @@ using std::string;
 namespace opencl {
 namespace kernel {
 
-template <typename T>
+template<typename T>
 void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
           const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
           const bool is_column) {
@@ -89,7 +89,7 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template <typename T>
+template<typename T>
 void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
                   const dim_t sx, const dim_t sy, const dim_t px,
                   const dim_t py, const dim_t dx, const dim_t dy,
diff --git a/src/backend/opencl/magma/magma.h b/src/backend/opencl/magma/magma.h
index 77977756d0..df1923b746 100644
--- a/src/backend/opencl/magma/magma.h
+++ b/src/backend/opencl/magma/magma.h
@@ -13,55 +13,45 @@
 #include "magma_common.h"
 
 template<typename Ty>
-magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n,
-                            cl_mem dA, size_t dA_offset, magma_int_t ldda,
-                            magma_int_t *ipiv,
-                            magma_queue_t queue,
+magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
+                            size_t dA_offset, magma_int_t ldda,
+                            magma_int_t *ipiv, magma_queue_t queue,
                             magma_int_t *info);
 
 template<typename Ty>
-magma_int_t magma_potrf_gpu(magma_uplo_t   uplo, magma_int_t    n,
-                            cl_mem dA, size_t dA_offset, magma_int_t ldda,
-                            magma_queue_t queue,
-                            magma_int_t*   info);
+magma_int_t magma_potrf_gpu(magma_uplo_t uplo, magma_int_t n, cl_mem dA,
+                            size_t dA_offset, magma_int_t ldda,
+                            magma_queue_t queue, magma_int_t *info);
 
-template<typename Ty> magma_int_t
-magma_larfb_gpu(
-    magma_side_t side, magma_trans_t trans, magma_direct_t direct, magma_storev_t storev,
-    magma_int_t m, magma_int_t n, magma_int_t k,
-    cl_mem dV   , size_t dV_offset,    magma_int_t lddv,
-    cl_mem dT   , size_t dT_offset,    magma_int_t lddt,
-    cl_mem dC   , size_t dC_offset,    magma_int_t lddc,
-    cl_mem dwork, size_t dwork_offset, magma_int_t ldwork,
-    magma_queue_t queue);
+template<typename Ty>
+magma_int_t magma_larfb_gpu(magma_side_t side, magma_trans_t trans,
+                            magma_direct_t direct, magma_storev_t storev,
+                            magma_int_t m, magma_int_t n, magma_int_t k,
+                            cl_mem dV, size_t dV_offset, magma_int_t lddv,
+                            cl_mem dT, size_t dT_offset, magma_int_t lddt,
+                            cl_mem dC, size_t dC_offset, magma_int_t lddc,
+                            cl_mem dwork, size_t dwork_offset,
+                            magma_int_t ldwork, magma_queue_t queue);
 
-template<typename Ty> magma_int_t
-magma_geqrf2_gpu(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    Ty *tau,
-    magma_queue_t* queue,
-    magma_int_t *info);
+template<typename Ty>
+magma_int_t magma_geqrf2_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
+                             size_t dA_offset, magma_int_t ldda, Ty *tau,
+                             magma_queue_t *queue, magma_int_t *info);
 
-template<typename Ty> magma_int_t
-magma_geqrf3_gpu(
-    magma_int_t m, magma_int_t n,
-    cl_mem dA, size_t dA_offset,  magma_int_t ldda,
-    Ty *tau, cl_mem dT, size_t dT_offset,
-    magma_queue_t queue,
-    magma_int_t *info);
+template<typename Ty>
+magma_int_t magma_geqrf3_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
+                             size_t dA_offset, magma_int_t ldda, Ty *tau,
+                             cl_mem dT, size_t dT_offset, magma_queue_t queue,
+                             magma_int_t *info);
 
-template<typename Ty>  magma_int_t
-magma_unmqr_gpu(
-    magma_side_t side, magma_trans_t trans,
-    magma_int_t m, magma_int_t n, magma_int_t k,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    Ty *tau,
-    cl_mem dC, size_t dC_offset, magma_int_t lddc,
-    Ty *hwork, magma_int_t lwork,
-    cl_mem dT, size_t dT_offset, magma_int_t nb,
-    magma_queue_t queue,
-    magma_int_t *info);
+template<typename Ty>
+magma_int_t magma_unmqr_gpu(magma_side_t side, magma_trans_t trans,
+                            magma_int_t m, magma_int_t n, magma_int_t k,
+                            cl_mem dA, size_t dA_offset, magma_int_t ldda,
+                            Ty *tau, cl_mem dC, size_t dC_offset,
+                            magma_int_t lddc, Ty *hwork, magma_int_t lwork,
+                            cl_mem dT, size_t dT_offset, magma_int_t nb,
+                            magma_queue_t queue, magma_int_t *info);
 
 #if 0  // Needs to be enabled when unmqr2 is enabled
 template<typename Ty> magma_int_t
@@ -76,42 +66,35 @@ magma_unmqr2_gpu(
     magma_int_t *info);
 #endif
 
-template<typename Ty>  magma_int_t
-magma_ungqr_gpu(
-    magma_int_t m, magma_int_t n, magma_int_t k,
-    cl_mem dA, size_t dA_offset, magma_int_t ldda,
-    Ty *tau,
-    cl_mem dT, size_t dT_offset, magma_int_t nb,
-    magma_queue_t queue,
-    magma_int_t *info);
+template<typename Ty>
+magma_int_t magma_ungqr_gpu(magma_int_t m, magma_int_t n, magma_int_t k,
+                            cl_mem dA, size_t dA_offset, magma_int_t ldda,
+                            Ty *tau, cl_mem dT, size_t dT_offset,
+                            magma_int_t nb, magma_queue_t queue,
+                            magma_int_t *info);
 
-template<typename Ty>  magma_int_t
-magma_getrs_gpu(magma_trans_t trans, magma_int_t n, magma_int_t nrhs,
-                cl_mem dA, size_t dA_offset, magma_int_t ldda,
-                magma_int_t *ipiv,
-                cl_mem dB, size_t dB_offset, magma_int_t lddb,
-                magma_queue_t queue,
-                magma_int_t *info);
+template<typename Ty>
+magma_int_t magma_getrs_gpu(magma_trans_t trans, magma_int_t n,
+                            magma_int_t nrhs, cl_mem dA, size_t dA_offset,
+                            magma_int_t ldda, magma_int_t *ipiv, cl_mem dB,
+                            size_t dB_offset, magma_int_t lddb,
+                            magma_queue_t queue, magma_int_t *info);
 
-template<typename Ty>  magma_int_t
-magma_labrd_gpu(magma_int_t m, magma_int_t n, magma_int_t nb,
-                Ty *a, magma_int_t lda,
-                cl_mem da, size_t da_offset, magma_int_t ldda,
-                void *_d, void *_e, Ty *tauq, Ty *taup,
-                Ty *x, magma_int_t ldx,
-                cl_mem dx, size_t dx_offset, magma_int_t lddx,
-                Ty *y, magma_int_t ldy,
-                cl_mem dy, size_t dy_offset, magma_int_t lddy,
-                magma_queue_t queue);
+template<typename Ty>
+magma_int_t magma_labrd_gpu(magma_int_t m, magma_int_t n, magma_int_t nb, Ty *a,
+                            magma_int_t lda, cl_mem da, size_t da_offset,
+                            magma_int_t ldda, void *_d, void *_e, Ty *tauq,
+                            Ty *taup, Ty *x, magma_int_t ldx, cl_mem dx,
+                            size_t dx_offset, magma_int_t lddx, Ty *y,
+                            magma_int_t ldy, cl_mem dy, size_t dy_offset,
+                            magma_int_t lddy, magma_queue_t queue);
 
-template<typename Ty> magma_int_t
-magma_gebrd_hybrid(magma_int_t m, magma_int_t n,
-                   Ty *a, magma_int_t lda,
-                   cl_mem da, size_t da_offset, magma_int_t ldda,
-                   void *_d, void *_e,
-                   Ty *tauq, Ty *taup,
-                   Ty *work, magma_int_t lwork,
-                   magma_queue_t queue,
-                   magma_int_t *info, bool copy);
+template<typename Ty>
+magma_int_t magma_gebrd_hybrid(magma_int_t m, magma_int_t n, Ty *a,
+                               magma_int_t lda, cl_mem da, size_t da_offset,
+                               magma_int_t ldda, void *_d, void *_e, Ty *tauq,
+                               Ty *taup, Ty *work, magma_int_t lwork,
+                               magma_queue_t queue, magma_int_t *info,
+                               bool copy);
 
 #endif
diff --git a/src/backend/opencl/magma/magma_blas.h b/src/backend/opencl/magma/magma_blas.h
index c937c0612c..7a1f341680 100644
--- a/src/backend/opencl/magma/magma_blas.h
+++ b/src/backend/opencl/magma/magma_blas.h
@@ -14,18 +14,24 @@
 // functions. They can be implemented in different back-ends,
 // such as CLBlast or clBLAS.
 
-#include "magma_common.h"
 #include <types.hpp>
+#include "magma_common.h"
 
-using opencl::cfloat;
 using opencl::cdouble;
+using opencl::cfloat;
 
-template<typename T> struct gpu_blas_gemm_func;
-template<typename T> struct gpu_blas_gemv_func;
-template<typename T> struct gpu_blas_trmm_func;
-template<typename T> struct gpu_blas_trsm_func;
-template<typename T> struct gpu_blas_trsv_func;
-template<typename T> struct gpu_blas_herk_func;
+template<typename T>
+struct gpu_blas_gemm_func;
+template<typename T>
+struct gpu_blas_gemv_func;
+template<typename T>
+struct gpu_blas_trmm_func;
+template<typename T>
+struct gpu_blas_trsm_func;
+template<typename T>
+struct gpu_blas_trsv_func;
+template<typename T>
+struct gpu_blas_herk_func;
 
 #if defined(USE_CLBLAST)
 #include "magma_blas_clblast.h"
@@ -35,4 +41,4 @@ template<typename T> struct gpu_blas_herk_func;
 #include "magma_blas_clblas.h"
 #endif
 
-#endif // __MAGMA_BLAS_H
+#endif  // __MAGMA_BLAS_H
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 573cb7b062..905b5fc723 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -18,23 +18,23 @@
 #include <err_clblast.hpp>
 
 // Convert MAGMA constants to CLBlast constants
-clblast::Layout         clblast_order_const( magma_order_t order );
-clblast::Transpose      clblast_trans_const( magma_trans_t trans );
-clblast::Triangle       clblast_uplo_const ( magma_uplo_t  uplo  );
-clblast::Diagonal       clblast_diag_const ( magma_diag_t  diag  );
-clblast::Side           clblast_side_const ( magma_side_t  side  );
+clblast::Layout clblast_order_const(magma_order_t order);
+clblast::Transpose clblast_trans_const(magma_trans_t trans);
+clblast::Triangle clblast_uplo_const(magma_uplo_t uplo);
+clblast::Diagonal clblast_diag_const(magma_diag_t diag);
+clblast::Side clblast_side_const(magma_side_t side);
 
 // Error checking
 #define OPENCL_BLAS_CHECK CLBLAST_CHECK
 
 // Transposing
-#define OPENCL_BLAS_TRANS_T clblast::Transpose // the type
+#define OPENCL_BLAS_TRANS_T clblast::Transpose  // the type
 #define OPENCL_BLAS_NO_TRANS clblast::Transpose::kNo
 #define OPENCL_BLAS_TRANS clblast::Transpose::kYes
 #define OPENCL_BLAS_CONJ_TRANS clblast::Transpose::kConjugate
 
 // Triangles
-#define OPENCL_BLAS_TRIANGLE_T clblast::Triangle // the type
+#define OPENCL_BLAS_TRIANGLE_T clblast::Triangle  // the type
 #define OPENCL_BLAS_TRIANGLE_UPPER clblast::Triangle::kUpper
 #define OPENCL_BLAS_TRIANGLE_LOWER clblast::Triangle::kLower
 
@@ -47,237 +47,280 @@ clblast::Side           clblast_side_const ( magma_side_t  side  );
 #define OPENCL_BLAS_NON_UNIT_DIAGONAL clblast::Diagonal::kNonUnit
 
 // Defines type conversions from ArrayFire (OpenCL) to CLBlast (C++ std)
-template <typename T> struct CLBlastType { using Type = T; };
-template <> struct CLBlastType<cfloat> { using Type = std::complex<float>; };
-template <> struct CLBlastType<cdouble> { using Type = std::complex<double>; };
-template <> struct CLBlastType<common::half> { using Type = cl_half; };
+template<typename T>
+struct CLBlastType {
+    using Type = T;
+};
+template<>
+struct CLBlastType<cfloat> {
+    using Type = std::complex<float>;
+};
+template<>
+struct CLBlastType<cdouble> {
+    using Type = std::complex<double>;
+};
+template<>
+struct CLBlastType<common::half> {
+    using Type = cl_half;
+};
 
 // Converts a constant from ArrayFire types (OpenCL) to CLBlast types (C++ std)
-template <typename T> typename CLBlastType<T>::Type inline toCLBlastConstant(const T val);
+template<typename T>
+typename CLBlastType<T>::Type inline toCLBlastConstant(const T val);
 
 // Specializations of the above function
-template <> float inline toCLBlastConstant(const float val) { return val; }
-template <> double inline toCLBlastConstant(const double val) { return val; }
-template <> cl_half inline toCLBlastConstant(const common::half val) {
+template<>
+float inline toCLBlastConstant(const float val) {
+    return val;
+}
+template<>
+double inline toCLBlastConstant(const double val) {
+    return val;
+}
+template<>
+cl_half inline toCLBlastConstant(const common::half val) {
     cl_half out;
     memcpy(&out, &val, sizeof(cl_half));
     return out;
 }
-template <> std::complex<float> inline toCLBlastConstant(cfloat val) { return {val.s[0], val.s[1]}; }
-template <> std::complex<double> inline toCLBlastConstant(cdouble val) { return {val.s[0], val.s[1]}; }
+template<>
+std::complex<float> inline toCLBlastConstant(cfloat val) {
+    return {val.s[0], val.s[1]};
+}
+template<>
+std::complex<double> inline toCLBlastConstant(cdouble val) {
+    return {val.s[0], val.s[1]};
+}
 
 // Conversions to CLBlast basic types
-template <typename T> struct CLBlastBasicType { using Type = T; };
-template <> struct CLBlastBasicType<common::half> { using Type = cl_half; };
-template <> struct CLBlastBasicType<cfloat> { using Type = float; };
-template <> struct CLBlastBasicType<cdouble> { using Type = double; };
+template<typename T>
+struct CLBlastBasicType {
+    using Type = T;
+};
+template<>
+struct CLBlastBasicType<common::half> {
+    using Type = cl_half;
+};
+template<>
+struct CLBlastBasicType<cfloat> {
+    using Type = float;
+};
+template<>
+struct CLBlastBasicType<cdouble> {
+    using Type = double;
+};
 
 // Initialization of the OpenCL BLAS library
 // Only meant to be once and from constructor
 // of DeviceManager singleton
 // DONT'T CALL FROM ANY OTHER LOCATION
-inline void gpu_blas_init()
-{
-  // Nothing to do here for CLBlast
+inline void gpu_blas_init() {
+    // Nothing to do here for CLBlast
 }
 
 // tear down of the OpenCL BLAS library
 // Only meant to be called from destructor
 // of DeviceManager singleton
 // DONT'T CALL FROM ANY OTHER LOCATION
-inline void gpu_blas_deinit()
-{
-  // Nothing to do here for CLBlast
+inline void gpu_blas_deinit() {
+    // Nothing to do here for CLBlast
 }
 
-template <typename T>
-struct gpu_blas_gemm_func
-{
-    clblast::StatusCode operator() (
-        const clblast::Transpose a_transpose, const clblast::Transpose b_transpose,
-        const size_t m, const size_t n, const size_t k, const T alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
-        const cl_mem b_buffer, const size_t b_offset, const size_t b_ld, const T beta,
-        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event wait_events, cl_event *events)
-    {
+template<typename T>
+struct gpu_blas_gemm_func {
+    clblast::StatusCode operator()(
+        const clblast::Transpose a_transpose,
+        const clblast::Transpose b_transpose, const size_t m, const size_t n,
+        const size_t k, const T alpha, const cl_mem a_buffer,
+        const size_t a_offset, const size_t a_ld, const cl_mem b_buffer,
+        const size_t b_offset, const size_t b_ld, const T beta, cl_mem c_buffer,
+        const size_t c_offset, const size_t c_ld, cl_uint num_queues,
+        cl_command_queue *queues, cl_uint num_wait_events,
+        const cl_event wait_events, cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        const auto beta_clblast = toCLBlastConstant(beta);
-        return clblast::Gemm(clblast::Layout::kColMajor, a_transpose, b_transpose, m, n, k, alpha_clblast,
-                             a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld, beta_clblast, c_buffer, c_offset, c_ld,
-                             queues, events);
+        const auto beta_clblast  = toCLBlastConstant(beta);
+        return clblast::Gemm(
+            clblast::Layout::kColMajor, a_transpose, b_transpose, m, n, k,
+            alpha_clblast, a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld,
+            beta_clblast, c_buffer, c_offset, c_ld, queues, events);
     }
 };
 
-template <typename T>
-struct gpu_blas_gemv_func
-{
-    clblast::StatusCode operator() (
-        const clblast::Transpose a_transpose,
-        const size_t m, const size_t n, const T alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
-        const cl_mem x_buffer, const size_t x_offset, const size_t x_inc, const T beta,
-        cl_mem y_buffer, const size_t y_offset, const size_t y_inc,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+template<typename T>
+struct gpu_blas_gemv_func {
+    clblast::StatusCode operator()(
+        const clblast::Transpose a_transpose, const size_t m, const size_t n,
+        const T alpha, const cl_mem a_buffer, const size_t a_offset,
+        const size_t a_ld, const cl_mem x_buffer, const size_t x_offset,
+        const size_t x_inc, const T beta, cl_mem y_buffer,
+        const size_t y_offset, const size_t y_inc, cl_uint num_queues,
+        cl_command_queue *queues, cl_uint num_wait_events,
+        const cl_event *wait_events, cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        const auto beta_clblast = toCLBlastConstant(beta);
-        return clblast::Gemv(clblast::Layout::kColMajor, a_transpose, m, n, alpha_clblast,
-                             a_buffer, a_offset, a_ld, x_buffer, x_offset, x_inc, beta_clblast, y_buffer, y_offset, y_inc,
-                             queues, events);
+        const auto beta_clblast  = toCLBlastConstant(beta);
+        return clblast::Gemv(clblast::Layout::kColMajor, a_transpose, m, n,
+                             alpha_clblast, a_buffer, a_offset, a_ld, x_buffer,
+                             x_offset, x_inc, beta_clblast, y_buffer, y_offset,
+                             y_inc, queues, events);
     }
 };
 
-template <typename T>
-struct gpu_blas_trmm_func
-{
-    clblast::StatusCode operator() (
-        const clblast::Side side, const clblast::Triangle triangle, const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
-        const size_t m, const size_t n, const T alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
-        cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+template<typename T>
+struct gpu_blas_trmm_func {
+    clblast::StatusCode operator()(
+        const clblast::Side side, const clblast::Triangle triangle,
+        const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
+        const size_t m, const size_t n, const T alpha, const cl_mem a_buffer,
+        const size_t a_offset, const size_t a_ld, cl_mem b_buffer,
+        const size_t b_offset, const size_t b_ld, cl_uint num_queues,
+        cl_command_queue *queues, cl_uint num_wait_events,
+        const cl_event *wait_events, cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        return clblast::Trmm(clblast::Layout::kColMajor, side, triangle, a_transpose, diagonal, m, n, alpha_clblast,
+        return clblast::Trmm(clblast::Layout::kColMajor, side, triangle,
+                             a_transpose, diagonal, m, n, alpha_clblast,
                              a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld,
                              queues, events);
     }
 };
 
-template <typename T>
-struct gpu_blas_trsm_func
-{
-    clblast::StatusCode operator() (
-        const clblast::Side side, const clblast::Triangle triangle, const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
-        const size_t m, const size_t n, const T alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
-        cl_mem b_buffer, const size_t b_offset, const size_t b_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+template<typename T>
+struct gpu_blas_trsm_func {
+    clblast::StatusCode operator()(
+        const clblast::Side side, const clblast::Triangle triangle,
+        const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
+        const size_t m, const size_t n, const T alpha, const cl_mem a_buffer,
+        const size_t a_offset, const size_t a_ld, cl_mem b_buffer,
+        const size_t b_offset, const size_t b_ld, cl_uint num_queues,
+        cl_command_queue *queues, cl_uint num_wait_events,
+        const cl_event *wait_events, cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        return clblast::Trsm(clblast::Layout::kColMajor, side, triangle, a_transpose, diagonal, m, n, alpha_clblast,
+        return clblast::Trsm(clblast::Layout::kColMajor, side, triangle,
+                             a_transpose, diagonal, m, n, alpha_clblast,
                              a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld,
                              queues, events);
     }
 };
 
-template <typename T>
-struct gpu_blas_trsv_func
-{
-    clblast::StatusCode operator() (
-        const clblast::Triangle triangle, const clblast::Transpose a_transpose, const clblast::Diagonal diagonal,
-        const size_t n,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
-        cl_mem x_buffer, const size_t x_offset, const size_t x_inc,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+template<typename T>
+struct gpu_blas_trsv_func {
+    clblast::StatusCode operator()(
+        const clblast::Triangle triangle, const clblast::Transpose a_transpose,
+        const clblast::Diagonal diagonal, const size_t n, const cl_mem a_buffer,
+        const size_t a_offset, const size_t a_ld, cl_mem x_buffer,
+        const size_t x_offset, const size_t x_inc, cl_uint num_queues,
+        cl_command_queue *queues, cl_uint num_wait_events,
+        const cl_event *wait_events, cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         return clblast::Trsv<typename CLBlastType<T>::Type>(
             clblast::Layout::kColMajor, triangle, a_transpose, diagonal, n,
-            a_buffer, a_offset, a_ld, x_buffer, x_offset, x_inc,
-            queues, events);
+            a_buffer, a_offset, a_ld, x_buffer, x_offset, x_inc, queues,
+            events);
     }
 };
 
-template <typename T>
-struct gpu_blas_herk_func
-{
+template<typename T>
+struct gpu_blas_herk_func {
     using BasicType = typename CLBlastBasicType<T>::Type;
 
-    clblast::StatusCode operator() (
+    clblast::StatusCode operator()(
         const clblast::Triangle triangle, const clblast::Transpose a_transpose,
         const size_t n, const size_t k, const BasicType alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const BasicType beta,
-        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        const BasicType beta, cl_mem c_buffer, const size_t c_offset,
+        const size_t c_ld, cl_uint num_queues, cl_command_queue *queues,
+        cl_uint num_wait_events, const cl_event *wait_events,
+        cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        const auto beta_clblast = toCLBlastConstant(beta);
-        return clblast::Herk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
-                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
-                             queues, events);
+        const auto beta_clblast  = toCLBlastConstant(beta);
+        return clblast::Herk(clblast::Layout::kColMajor, triangle, a_transpose,
+                             n, k, alpha_clblast, a_buffer, a_offset, a_ld,
+                             beta_clblast, c_buffer, c_offset, c_ld, queues,
+                             events);
     }
 };
 
-// Run syrk when calling non-complex herk function (specialisation of the above for 'float')
-template <>
-struct gpu_blas_herk_func<float>
-{
-    clblast::StatusCode operator() (
+// Run syrk when calling non-complex herk function (specialisation of the above
+// for 'float')
+template<>
+struct gpu_blas_herk_func<float> {
+    clblast::StatusCode operator()(
         const clblast::Triangle triangle, const clblast::Transpose a_transpose,
         const size_t n, const size_t k, const float alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const float beta,
-        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        const float beta, cl_mem c_buffer, const size_t c_offset,
+        const size_t c_ld, cl_uint num_queues, cl_command_queue *queues,
+        cl_uint num_wait_events, const cl_event *wait_events,
+        cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        const auto beta_clblast = toCLBlastConstant(beta);
-        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
-                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
-                             queues, events);
+        const auto beta_clblast  = toCLBlastConstant(beta);
+        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose,
+                             n, k, alpha_clblast, a_buffer, a_offset, a_ld,
+                             beta_clblast, c_buffer, c_offset, c_ld, queues,
+                             events);
     }
 };
 
-// Run syrk when calling non-complex herk function (specialisation of the above for 'double')
-template <>
-struct gpu_blas_herk_func<double>
-{
-    clblast::StatusCode operator() (
+// Run syrk when calling non-complex herk function (specialisation of the above
+// for 'double')
+template<>
+struct gpu_blas_herk_func<double> {
+    clblast::StatusCode operator()(
         const clblast::Triangle triangle, const clblast::Transpose a_transpose,
         const size_t n, const size_t k, const double alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const double beta,
-        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld,
+        const double beta, cl_mem c_buffer, const size_t c_offset,
+        const size_t c_ld, cl_uint num_queues, cl_command_queue *queues,
+        cl_uint num_wait_events, const cl_event *wait_events,
+        cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        const auto beta_clblast = toCLBlastConstant(beta);
-        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
-                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
-                             queues, events);
+        const auto beta_clblast  = toCLBlastConstant(beta);
+        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose,
+                             n, k, alpha_clblast, a_buffer, a_offset, a_ld,
+                             beta_clblast, c_buffer, c_offset, c_ld, queues,
+                             events);
     }
 };
 
-template <typename T>
-struct gpu_blas_syrk_func
-{
-    clblast::StatusCode operator() (
+template<typename T>
+struct gpu_blas_syrk_func {
+    clblast::StatusCode operator()(
         const clblast::Triangle triangle, const clblast::Transpose a_transpose,
-        const size_t n, const size_t k, const T alpha,
-        const cl_mem a_buffer, const size_t a_offset, const size_t a_ld, const T beta,
-        cl_mem c_buffer, const size_t c_offset, const size_t c_ld,
-        cl_uint num_queues, cl_command_queue *queues, cl_uint num_wait_events, const cl_event *wait_events, cl_event *events)
-    {
+        const size_t n, const size_t k, const T alpha, const cl_mem a_buffer,
+        const size_t a_offset, const size_t a_ld, const T beta, cl_mem c_buffer,
+        const size_t c_offset, const size_t c_ld, cl_uint num_queues,
+        cl_command_queue *queues, cl_uint num_wait_events,
+        const cl_event *wait_events, cl_event *events) {
         UNUSED(wait_events);
         assert(num_queues == 1);
         assert(num_wait_events == 0);
         const auto alpha_clblast = toCLBlastConstant(alpha);
-        const auto beta_clblast = toCLBlastConstant(beta);
-        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose, n, k, alpha_clblast,
-                             a_buffer, a_offset, a_ld, beta_clblast, c_buffer, c_offset, c_ld,
-                             queues, events);
+        const auto beta_clblast  = toCLBlastConstant(beta);
+        return clblast::Syrk(clblast::Layout::kColMajor, triangle, a_transpose,
+                             n, k, alpha_clblast, a_buffer, a_offset, a_ld,
+                             beta_clblast, c_buffer, c_offset, c_ld, queues,
+                             events);
     }
 };
diff --git a/src/backend/opencl/magma/magma_helper.h b/src/backend/opencl/magma/magma_helper.h
index 74b2d5ee19..6278761877 100644
--- a/src/backend/opencl/magma/magma_helper.h
+++ b/src/backend/opencl/magma/magma_helper.h
@@ -10,18 +10,31 @@
 #ifndef __MAGMA_HELPER_H
 #define __MAGMA_HELPER_H
 
-template<typename T> T magma_zero();
-template<typename T> T magma_one();
-template<typename T> T magma_neg_one();
-template<typename T> T magma_scalar(double val);
-template<typename T> double magma_real(T val);
-template<typename T> T magma_make(double r, double i);
+template<typename T>
+T magma_zero();
+template<typename T>
+T magma_one();
+template<typename T>
+T magma_neg_one();
+template<typename T>
+T magma_scalar(double val);
+template<typename T>
+double magma_real(T val);
+template<typename T>
+T magma_make(double r, double i);
 
-template<typename T> bool magma_is_real();
+template<typename T>
+bool magma_is_real();
 
-template<typename T> magma_int_t magma_get_getrf_nb(int num);
-template<typename T> magma_int_t magma_get_potrf_nb(int num);
-template<typename T> magma_int_t magma_get_geqrf_nb(int num);
-template<typename T> magma_int_t magma_get_gebrd_nb(int /*num*/) { return 32; }
+template<typename T>
+magma_int_t magma_get_getrf_nb(int num);
+template<typename T>
+magma_int_t magma_get_potrf_nb(int num);
+template<typename T>
+magma_int_t magma_get_geqrf_nb(int num);
+template<typename T>
+magma_int_t magma_get_gebrd_nb(int /*num*/) {
+    return 32;
+}
 
 #endif
diff --git a/src/backend/opencl/magma/magma_types.h b/src/backend/opencl/magma/magma_types.h
index b8e0bcca4d..90dcc6ab8d 100644
--- a/src/backend/opencl/magma/magma_types.h
+++ b/src/backend/opencl/magma/magma_types.h
@@ -29,22 +29,22 @@
  * * Redistributions  of  source  code  must  retain  the above copyright
  *   notice,  this  list  of  conditions  and  the  following  disclaimer.
  * * Redistributions  in  binary  form must reproduce the above copyright
- *   notice,  this list of conditions and the following disclaimer in the 
+ *   notice,  this list of conditions and the following disclaimer in the
  *   documentation  and/or other materials provided with the distribution.
- * * Neither  the  name of the University of Tennessee, Knoxville nor the 
+ * * Neither  the  name of the University of Tennessee, Knoxville nor the
  *   names of its contributors may be used to endorse or promote products
  *   derived from this software without specific prior written permission.
  *
  * THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
- * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+ * ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  * A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  * HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT 
+ * SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
  * LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 
+ * DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  * THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+ * (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  * OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *
  **********************************************************************/
@@ -52,99 +52,101 @@
 #ifndef MAGMA_TYPES_H
 #define MAGMA_TYPES_H
 
-#include <stdint.h>
 #include <assert.h>
+#include <stdint.h>
 typedef int magma_int_t;
 typedef int magma_index_t;
 
 // Define new type that the precision generator will not change (matches PLASMA)
 typedef double real_Double_t;
 
-typedef cl_command_queue  magma_queue_t;
-typedef cl_event          magma_event_t;
-typedef cl_device_id      magma_device_t;
+typedef cl_command_queue magma_queue_t;
+typedef cl_event magma_event_t;
+typedef cl_device_id magma_device_t;
 
 typedef cl_double2 magmaDoubleComplex;
-typedef cl_float2  magmaFloatComplex;
-
-#define MAGMA_Z_MAKE(r,i)     doubleComplex(r,i)
-#define MAGMA_Z_REAL(a)       (a).s[0]
-#define MAGMA_Z_IMAG(a)       (a).s[1]
-#define MAGMA_Z_ADD(a, b)     MAGMA_Z_MAKE((a).s[0]+(b).s[0], (a).s[1]+(b).s[1])
-#define MAGMA_Z_SUB(a, b)     MAGMA_Z_MAKE((a).s[0]-(b).s[0], (a).s[1]-(b).s[1])
-#define MAGMA_Z_DIV(a, b)     ((a)/(b))
-#define MAGMA_Z_ABS(a)        magma_cabs(a)
-#define MAGMA_Z_ABS1(a)       (fabs((a).s[0]) + fabs((a).s[1]))
-#define MAGMA_Z_CNJG(a)       MAGMA_Z_MAKE((a).s[0], -(a).s[1])
-
-#define MAGMA_C_MAKE(r,i)     floatComplex(r,i)
-#define MAGMA_C_REAL(a)       (a).s[0]
-#define MAGMA_C_IMAG(a)       (a).s[1]
-#define MAGMA_C_ADD(a, b)     MAGMA_C_MAKE((a).s[0]+(b).s[0], (a).s[1]+(b).s[1])
-#define MAGMA_C_SUB(a, b)     MAGMA_C_MAKE((a).s[0]-(b).s[0], (a).s[1]-(b).s[1])
-#define MAGMA_C_DIV(a, b)     ((a)/(b))
-#define MAGMA_C_ABS(a)        magma_cabsf(a)
-#define MAGMA_C_ABS1(a)       (fabsf((a).s[0]) + fabsf((a).s[1]))
-#define MAGMA_C_CNJG(a)       MAGMA_C_MAKE((a).s[0], -(a).s[1])
-
-#define MAGMA_Z_EQUAL(a,b)        (MAGMA_Z_REAL(a)==MAGMA_Z_REAL(b) && MAGMA_Z_IMAG(a)==MAGMA_Z_IMAG(b))
-#define MAGMA_Z_NEGATE(a)         MAGMA_Z_MAKE( -MAGMA_Z_REAL(a), -MAGMA_Z_IMAG(a))
-
-#define MAGMA_C_EQUAL(a,b)        (MAGMA_C_REAL(a)==MAGMA_C_REAL(b) && MAGMA_C_IMAG(a)==MAGMA_C_IMAG(b))
-#define MAGMA_C_NEGATE(a)         MAGMA_C_MAKE( -MAGMA_C_REAL(a), -MAGMA_C_IMAG(a))
-
-#define MAGMA_D_MAKE(r,i)         (r)
-#define MAGMA_D_REAL(x)           (x)
-#define MAGMA_D_IMAG(x)           (0.0)
-#define MAGMA_D_ADD(a, b)         ((a) + (b))
-#define MAGMA_D_SUB(a, b)         ((a) - (b))
-#define MAGMA_D_MUL(a, b)         ((a) * (b))
-#define MAGMA_D_DIV(a, b)         ((a) / (b))
-#define MAGMA_D_ABS(a)            ((a)>0 ? (a) : -(a))
-#define MAGMA_D_ABS1(a)           ((a)>0 ? (a) : -(a))
-#define MAGMA_D_CNJG(a)           (a)
-#define MAGMA_D_EQUAL(a,b)        ((a) == (b))
-#define MAGMA_D_NEGATE(a)         (-a)
-
-#define MAGMA_S_MAKE(r,i)         (r)
-#define MAGMA_S_REAL(x)           (x)
-#define MAGMA_S_IMAG(x)           (0.0)
-#define MAGMA_S_ADD(a, b)         ((a) + (b))
-#define MAGMA_S_SUB(a, b)         ((a) - (b))
-#define MAGMA_S_MUL(a, b)         ((a) * (b))
-#define MAGMA_S_DIV(a, b)         ((a) / (b))
-#define MAGMA_S_ABS(a)            ((a)>0 ? (a) : -(a))
-#define MAGMA_S_ABS1(a)           ((a)>0 ? (a) : -(a))
-#define MAGMA_S_CNJG(a)           (a)
-#define MAGMA_S_EQUAL(a,b)        ((a) == (b))
-#define MAGMA_S_NEGATE(a)         (-a)
-
-#define MAGMA_Z_ZERO              MAGMA_Z_MAKE( 0.0, 0.0)
-#define MAGMA_Z_ONE               MAGMA_Z_MAKE( 1.0, 0.0)
-#define MAGMA_Z_HALF              MAGMA_Z_MAKE( 0.5, 0.0)
-#define MAGMA_Z_NEG_ONE           MAGMA_Z_MAKE(-1.0, 0.0)
-#define MAGMA_Z_NEG_HALF          MAGMA_Z_MAKE(-0.5, 0.0)
-
-#define MAGMA_C_ZERO              MAGMA_C_MAKE( 0.0, 0.0)
-#define MAGMA_C_ONE               MAGMA_C_MAKE( 1.0, 0.0)
-#define MAGMA_C_HALF              MAGMA_C_MAKE( 0.5, 0.0)
-#define MAGMA_C_NEG_ONE           MAGMA_C_MAKE(-1.0, 0.0)
-#define MAGMA_C_NEG_HALF          MAGMA_C_MAKE(-0.5, 0.0)
-
-#define MAGMA_D_ZERO              ( 0.0)
-#define MAGMA_D_ONE               ( 1.0)
-#define MAGMA_D_HALF              ( 0.5)
-#define MAGMA_D_NEG_ONE           (-1.0)
-#define MAGMA_D_NEG_HALF          (-0.5)
-
-#define MAGMA_S_ZERO              ( 0.0)
-#define MAGMA_S_ONE               ( 1.0)
-#define MAGMA_S_HALF              ( 0.5)
-#define MAGMA_S_NEG_ONE           (-1.0)
-#define MAGMA_S_NEG_HALF          (-0.5)
+typedef cl_float2 magmaFloatComplex;
+
+#define MAGMA_Z_MAKE(r, i) doubleComplex(r, i)
+#define MAGMA_Z_REAL(a) (a).s[0]
+#define MAGMA_Z_IMAG(a) (a).s[1]
+#define MAGMA_Z_ADD(a, b) MAGMA_Z_MAKE((a).s[0] + (b).s[0], (a).s[1] + (b).s[1])
+#define MAGMA_Z_SUB(a, b) MAGMA_Z_MAKE((a).s[0] - (b).s[0], (a).s[1] - (b).s[1])
+#define MAGMA_Z_DIV(a, b) ((a) / (b))
+#define MAGMA_Z_ABS(a) magma_cabs(a)
+#define MAGMA_Z_ABS1(a) (fabs((a).s[0]) + fabs((a).s[1]))
+#define MAGMA_Z_CNJG(a) MAGMA_Z_MAKE((a).s[0], -(a).s[1])
+
+#define MAGMA_C_MAKE(r, i) floatComplex(r, i)
+#define MAGMA_C_REAL(a) (a).s[0]
+#define MAGMA_C_IMAG(a) (a).s[1]
+#define MAGMA_C_ADD(a, b) MAGMA_C_MAKE((a).s[0] + (b).s[0], (a).s[1] + (b).s[1])
+#define MAGMA_C_SUB(a, b) MAGMA_C_MAKE((a).s[0] - (b).s[0], (a).s[1] - (b).s[1])
+#define MAGMA_C_DIV(a, b) ((a) / (b))
+#define MAGMA_C_ABS(a) magma_cabsf(a)
+#define MAGMA_C_ABS1(a) (fabsf((a).s[0]) + fabsf((a).s[1]))
+#define MAGMA_C_CNJG(a) MAGMA_C_MAKE((a).s[0], -(a).s[1])
+
+#define MAGMA_Z_EQUAL(a, b) \
+    (MAGMA_Z_REAL(a) == MAGMA_Z_REAL(b) && MAGMA_Z_IMAG(a) == MAGMA_Z_IMAG(b))
+#define MAGMA_Z_NEGATE(a) MAGMA_Z_MAKE(-MAGMA_Z_REAL(a), -MAGMA_Z_IMAG(a))
+
+#define MAGMA_C_EQUAL(a, b) \
+    (MAGMA_C_REAL(a) == MAGMA_C_REAL(b) && MAGMA_C_IMAG(a) == MAGMA_C_IMAG(b))
+#define MAGMA_C_NEGATE(a) MAGMA_C_MAKE(-MAGMA_C_REAL(a), -MAGMA_C_IMAG(a))
+
+#define MAGMA_D_MAKE(r, i) (r)
+#define MAGMA_D_REAL(x) (x)
+#define MAGMA_D_IMAG(x) (0.0)
+#define MAGMA_D_ADD(a, b) ((a) + (b))
+#define MAGMA_D_SUB(a, b) ((a) - (b))
+#define MAGMA_D_MUL(a, b) ((a) * (b))
+#define MAGMA_D_DIV(a, b) ((a) / (b))
+#define MAGMA_D_ABS(a) ((a) > 0 ? (a) : -(a))
+#define MAGMA_D_ABS1(a) ((a) > 0 ? (a) : -(a))
+#define MAGMA_D_CNJG(a) (a)
+#define MAGMA_D_EQUAL(a, b) ((a) == (b))
+#define MAGMA_D_NEGATE(a) (-a)
+
+#define MAGMA_S_MAKE(r, i) (r)
+#define MAGMA_S_REAL(x) (x)
+#define MAGMA_S_IMAG(x) (0.0)
+#define MAGMA_S_ADD(a, b) ((a) + (b))
+#define MAGMA_S_SUB(a, b) ((a) - (b))
+#define MAGMA_S_MUL(a, b) ((a) * (b))
+#define MAGMA_S_DIV(a, b) ((a) / (b))
+#define MAGMA_S_ABS(a) ((a) > 0 ? (a) : -(a))
+#define MAGMA_S_ABS1(a) ((a) > 0 ? (a) : -(a))
+#define MAGMA_S_CNJG(a) (a)
+#define MAGMA_S_EQUAL(a, b) ((a) == (b))
+#define MAGMA_S_NEGATE(a) (-a)
+
+#define MAGMA_Z_ZERO MAGMA_Z_MAKE(0.0, 0.0)
+#define MAGMA_Z_ONE MAGMA_Z_MAKE(1.0, 0.0)
+#define MAGMA_Z_HALF MAGMA_Z_MAKE(0.5, 0.0)
+#define MAGMA_Z_NEG_ONE MAGMA_Z_MAKE(-1.0, 0.0)
+#define MAGMA_Z_NEG_HALF MAGMA_Z_MAKE(-0.5, 0.0)
+
+#define MAGMA_C_ZERO MAGMA_C_MAKE(0.0, 0.0)
+#define MAGMA_C_ONE MAGMA_C_MAKE(1.0, 0.0)
+#define MAGMA_C_HALF MAGMA_C_MAKE(0.5, 0.0)
+#define MAGMA_C_NEG_ONE MAGMA_C_MAKE(-1.0, 0.0)
+#define MAGMA_C_NEG_HALF MAGMA_C_MAKE(-0.5, 0.0)
+
+#define MAGMA_D_ZERO (0.0)
+#define MAGMA_D_ONE (1.0)
+#define MAGMA_D_HALF (0.5)
+#define MAGMA_D_NEG_ONE (-1.0)
+#define MAGMA_D_NEG_HALF (-0.5)
+
+#define MAGMA_S_ZERO (0.0)
+#define MAGMA_S_ONE (1.0)
+#define MAGMA_S_HALF (0.5)
+#define MAGMA_S_NEG_ONE (-1.0)
+#define MAGMA_S_NEG_HALF (-0.5)
 
 #ifndef CBLAS_SADDR
-#define CBLAS_SADDR(a)  &(a)
+#define CBLAS_SADDR(a) &(a)
 #endif
 
 // OpenCL uses opaque memory references on GPU
@@ -164,7 +166,6 @@ typedef cl_mem magmaDouble_const_ptr;
 typedef cl_mem magmaFloatComplex_const_ptr;
 typedef cl_mem magmaDoubleComplex_const_ptr;
 
-
 // ========================================
 // MAGMA constants
 
@@ -173,83 +174,74 @@ typedef cl_mem magmaDoubleComplex_const_ptr;
 #define MAGMA_VERSION_MINOR 0
 #define MAGMA_VERSION_MICRO 0
 
-// stage is "svn", "beta#", "rc#" (release candidate), or blank ("") for final release
+// stage is "svn", "beta#", "rc#" (release candidate), or blank ("") for final
+// release
 #define MAGMA_VERSION_STAGE "svn"
 
 #define MagmaMaxGPUs 8
 #define MagmaMaxSubs 16
 
-
 // ----------------------------------------
 // Return codes
 // LAPACK argument errors are < 0 but > MAGMA_ERR.
 // MAGMA errors are < MAGMA_ERR.
-#define MAGMA_SUCCESS               0
-#define MAGMA_ERR                  -100
-#define MAGMA_ERR_NOT_INITIALIZED  -101
-#define MAGMA_ERR_REINITIALIZED    -102
-#define MAGMA_ERR_NOT_SUPPORTED    -103
-#define MAGMA_ERR_ILLEGAL_VALUE    -104
-#define MAGMA_ERR_NOT_FOUND        -105
-#define MAGMA_ERR_ALLOCATION       -106
-#define MAGMA_ERR_INTERNAL_LIMIT   -107
-#define MAGMA_ERR_UNALLOCATED      -108
-#define MAGMA_ERR_FILESYSTEM       -109
-#define MAGMA_ERR_UNEXPECTED       -110
+#define MAGMA_SUCCESS 0
+#define MAGMA_ERR -100
+#define MAGMA_ERR_NOT_INITIALIZED -101
+#define MAGMA_ERR_REINITIALIZED -102
+#define MAGMA_ERR_NOT_SUPPORTED -103
+#define MAGMA_ERR_ILLEGAL_VALUE -104
+#define MAGMA_ERR_NOT_FOUND -105
+#define MAGMA_ERR_ALLOCATION -106
+#define MAGMA_ERR_INTERNAL_LIMIT -107
+#define MAGMA_ERR_UNALLOCATED -108
+#define MAGMA_ERR_FILESYSTEM -109
+#define MAGMA_ERR_UNEXPECTED -110
 #define MAGMA_ERR_SEQUENCE_FLUSHED -111
-#define MAGMA_ERR_HOST_ALLOC       -112
-#define MAGMA_ERR_DEVICE_ALLOC     -113
-#define MAGMA_ERR_CUDASTREAM       -114
-#define MAGMA_ERR_INVALID_PTR      -115
-#define MAGMA_ERR_UNKNOWN          -116
-#define MAGMA_ERR_NOT_IMPLEMENTED  -117
-
+#define MAGMA_ERR_HOST_ALLOC -112
+#define MAGMA_ERR_DEVICE_ALLOC -113
+#define MAGMA_ERR_CUDASTREAM -114
+#define MAGMA_ERR_INVALID_PTR -115
+#define MAGMA_ERR_UNKNOWN -116
+#define MAGMA_ERR_NOT_IMPLEMENTED -117
 
 // ----------------------------------------
 // parameter constants
 // numbering is consistent with CBLAS and PLASMA; see plasma/include/plasma.h
 // also with lapack_cwrapper/include/lapack_enum.h
-typedef enum {
-    MagmaFalse         = 0,
-    MagmaTrue          = 1
-} magma_bool_t;
+typedef enum { MagmaFalse = 0, MagmaTrue = 1 } magma_bool_t;
 
-typedef enum {
-    MagmaRowMajor      = 101,
-    MagmaColMajor      = 102
-} magma_order_t;
+typedef enum { MagmaRowMajor = 101, MagmaColMajor = 102 } magma_order_t;
 
 // Magma_ConjTrans is an alias for those rare occasions (zlarfb, zun*, zher*k)
-// where we want Magma_ConjTrans to convert to MagmaTrans in precision generation.
+// where we want Magma_ConjTrans to convert to MagmaTrans in precision
+// generation.
 typedef enum {
-    MagmaNoTrans       = 111,
-    MagmaTrans         = 112,
-    MagmaConjTrans     = 113,
-    Magma_ConjTrans    = MagmaConjTrans
+    MagmaNoTrans    = 111,
+    MagmaTrans      = 112,
+    MagmaConjTrans  = 113,
+    Magma_ConjTrans = MagmaConjTrans
 } magma_trans_t;
 
 typedef enum {
-    MagmaUpper         = 121,
-    MagmaLower         = 122,
-    MagmaUpperLower    = 123,
-    MagmaFull          = 123   /* lascl, laset */
+    MagmaUpper      = 121,
+    MagmaLower      = 122,
+    MagmaUpperLower = 123,
+    MagmaFull       = 123 /* lascl, laset */
 } magma_uplo_t;
 
-typedef magma_uplo_t magma_type_t;  /* lascl */
+typedef magma_uplo_t magma_type_t; /* lascl */
 
-typedef enum {
-    MagmaNonUnit       = 131,
-    MagmaUnit          = 132
-} magma_diag_t;
+typedef enum { MagmaNonUnit = 131, MagmaUnit = 132 } magma_diag_t;
 
 typedef enum {
-    MagmaLeft          = 141,
-    MagmaRight         = 142,
-    MagmaBothSides     = 143   /* trevc */
+    MagmaLeft      = 141,
+    MagmaRight     = 142,
+    MagmaBothSides = 143 /* trevc */
 } magma_side_t;
 
 typedef enum {
-    MagmaOneNorm       = 171,  /* lange, lanhe */
+    MagmaOneNorm       = 171, /* lange, lanhe */
     MagmaRealOneNorm   = 172,
     MagmaTwoNorm       = 173,
     MagmaFrobeniusNorm = 174,
@@ -260,20 +252,20 @@ typedef enum {
 } magma_norm_t;
 
 typedef enum {
-    MagmaDistUniform   = 201,  /* latms */
+    MagmaDistUniform   = 201, /* latms */
     MagmaDistSymmetric = 202,
     MagmaDistNormal    = 203
 } magma_dist_t;
 
 typedef enum {
-    MagmaHermGeev      = 241,  /* latms */
-    MagmaHermPoev      = 242,
-    MagmaNonsymPosv    = 243,
-    MagmaSymPosv       = 244
+    MagmaHermGeev   = 241, /* latms */
+    MagmaHermPoev   = 242,
+    MagmaNonsymPosv = 243,
+    MagmaSymPosv    = 244
 } magma_sym_t;
 
 typedef enum {
-    MagmaNoPacking     = 291,  /* latms */
+    MagmaNoPacking     = 291, /* latms */
     MagmaPackSubdiag   = 292,
     MagmaPackSupdiag   = 293,
     MagmaPackColumn    = 294,
@@ -284,170 +276,161 @@ typedef enum {
 } magma_pack_t;
 
 typedef enum {
-    MagmaNoVec         = 301,  /* geev, syev, gesvd */
-    MagmaVec           = 302,  /* geev, syev */
-    MagmaIVec          = 303,  /* stedc */
-    MagmaAllVec        = 304,  /* gesvd, trevc */
-    MagmaSomeVec       = 305,  /* gesvd, trevc */
-    MagmaOverwriteVec  = 306,  /* gesvd */
-    MagmaBacktransVec  = 307   /* trevc */
+    MagmaNoVec        = 301, /* geev, syev, gesvd */
+    MagmaVec          = 302, /* geev, syev */
+    MagmaIVec         = 303, /* stedc */
+    MagmaAllVec       = 304, /* gesvd, trevc */
+    MagmaSomeVec      = 305, /* gesvd, trevc */
+    MagmaOverwriteVec = 306, /* gesvd */
+    MagmaBacktransVec = 307  /* trevc */
 } magma_vec_t;
 
 typedef enum {
-    MagmaRangeAll      = 311,  /* syevx, etc. */
-    MagmaRangeV        = 312,
-    MagmaRangeI        = 313
+    MagmaRangeAll = 311, /* syevx, etc. */
+    MagmaRangeV   = 312,
+    MagmaRangeI   = 313
 } magma_range_t;
 
 typedef enum {
-    MagmaQ             = 322,  /* unmbr, ungbr */
-    MagmaP             = 323
+    MagmaQ = 322, /* unmbr, ungbr */
+    MagmaP = 323
 } magma_vect_t;
 
 typedef enum {
-    MagmaForward       = 391,  /* larfb */
-    MagmaBackward      = 392
+    MagmaForward  = 391, /* larfb */
+    MagmaBackward = 392
 } magma_direct_t;
 
 typedef enum {
-    MagmaColumnwise    = 401,  /* larfb */
-    MagmaRowwise       = 402
+    MagmaColumnwise = 401, /* larfb */
+    MagmaRowwise    = 402
 } magma_storev_t;
 
 // --------------------
 // sparse
 typedef enum {
-    Magma_CSR          = 411,
-    Magma_ELLPACK      = 412,
-    Magma_ELL          = 413,
-    Magma_DENSE        = 414,
-    Magma_BCSR         = 415,
-    Magma_CSC          = 416,
-    Magma_HYB          = 417,
-    Magma_COO          = 418,
-    Magma_ELLRT        = 419,
-    Magma_SELLC        = 420,
-    Magma_SELLP        = 421,
-    Magma_ELLD         = 422,
-    Magma_ELLDD        = 423,
-    Magma_CSRD         = 424,
-    Magma_CSRL         = 427,
-    Magma_CSRU         = 428,
-    Magma_CSRCOO       = 429
+    Magma_CSR     = 411,
+    Magma_ELLPACK = 412,
+    Magma_ELL     = 413,
+    Magma_DENSE   = 414,
+    Magma_BCSR    = 415,
+    Magma_CSC     = 416,
+    Magma_HYB     = 417,
+    Magma_COO     = 418,
+    Magma_ELLRT   = 419,
+    Magma_SELLC   = 420,
+    Magma_SELLP   = 421,
+    Magma_ELLD    = 422,
+    Magma_ELLDD   = 423,
+    Magma_CSRD    = 424,
+    Magma_CSRL    = 427,
+    Magma_CSRU    = 428,
+    Magma_CSRCOO  = 429
 } magma_storage_t;
 
-
 typedef enum {
-    Magma_CG           = 431,
-    Magma_CGMERGE      = 432,
-    Magma_GMRES        = 433,
-    Magma_BICGSTAB     = 434,
-  Magma_BICGSTABMERGE  = 435,
-  Magma_BICGSTABMERGE2 = 436,
-    Magma_JACOBI       = 437,
-    Magma_GS           = 438,
-    Magma_ITERREF      = 439,
-    Magma_BCSRLU       = 440,
-    Magma_PCG          = 441,
-    Magma_PGMRES       = 442,
-    Magma_PBICGSTAB    = 443,
-    Magma_PASTIX       = 444,
-    Magma_ILU          = 445,
-    Magma_ICC          = 446,
-    Magma_AILU         = 447,
-    Magma_AICC         = 448,
-    Magma_BAITER       = 449,
-    Magma_LOBPCG       = 450,
-    Magma_NONE         = 451
+    Magma_CG             = 431,
+    Magma_CGMERGE        = 432,
+    Magma_GMRES          = 433,
+    Magma_BICGSTAB       = 434,
+    Magma_BICGSTABMERGE  = 435,
+    Magma_BICGSTABMERGE2 = 436,
+    Magma_JACOBI         = 437,
+    Magma_GS             = 438,
+    Magma_ITERREF        = 439,
+    Magma_BCSRLU         = 440,
+    Magma_PCG            = 441,
+    Magma_PGMRES         = 442,
+    Magma_PBICGSTAB      = 443,
+    Magma_PASTIX         = 444,
+    Magma_ILU            = 445,
+    Magma_ICC            = 446,
+    Magma_AILU           = 447,
+    Magma_AICC           = 448,
+    Magma_BAITER         = 449,
+    Magma_LOBPCG         = 450,
+    Magma_NONE           = 451
 } magma_solver_type;
 
 typedef enum {
-    Magma_CGS          = 461,
-    Magma_FUSED_CGS    = 462,
-    Magma_MGS          = 463
+    Magma_CGS       = 461,
+    Magma_FUSED_CGS = 462,
+    Magma_MGS       = 463
 } magma_ortho_t;
 
-typedef enum {
-    Magma_CPU          = 471,
-    Magma_DEV          = 472
-} magma_location_t;
+typedef enum { Magma_CPU = 471, Magma_DEV = 472 } magma_location_t;
 
-typedef enum {
-    Magma_GENERAL      = 481,
-    Magma_SYMMETRIC    = 482
-} magma_symmetry_t;
+typedef enum { Magma_GENERAL = 481, Magma_SYMMETRIC = 482 } magma_symmetry_t;
 
 typedef enum {
-    Magma_ORDERED      = 491,
-    Magma_DIAGFIRST    = 492,
-    Magma_UNITY        = 493,
-    Magma_VALUE        = 494
+    Magma_ORDERED   = 491,
+    Magma_DIAGFIRST = 492,
+    Magma_UNITY     = 493,
+    Magma_VALUE     = 494
 } magma_diagorder_t;
 
 typedef enum {
-    Magma_DCOMPLEX     = 501,
-    Magma_FCOMPLEX     = 502,
-    Magma_DOUBLE       = 503,
-    Magma_FLOAT        = 504
+    Magma_DCOMPLEX = 501,
+    Magma_FCOMPLEX = 502,
+    Magma_DOUBLE   = 503,
+    Magma_FLOAT    = 504
 } magma_precision;
 
 typedef enum {
-    Magma_NOSCALE      = 511,
-    Magma_UNITROW      = 512,
-    Magma_UNITDIAG     = 513
+    Magma_NOSCALE  = 511,
+    Magma_UNITROW  = 512,
+    Magma_UNITDIAG = 513
 } magma_scale_t;
 
-
 // When adding constants, remember to do these steps as appropriate:
 // 1)  add magma_xxxx_const()  converter below and in control/constants.cpp
 // 2a) add to magma2lapack_constants[] in control/constants.cpp
-// 2b) update min & max here, which are used to check bounds for magma2lapack_constants[]
-// 2c) add lapack_xxxx_const() converter below and in control/constants.cpp
-#define Magma2lapack_Min  MagmaFalse     // 0
-#define Magma2lapack_Max  MagmaRowwise   // 402
-
+// 2b) update min & max here, which are used to check bounds for
+// magma2lapack_constants[] 2c) add lapack_xxxx_const() converter below and in
+// control/constants.cpp
+#define Magma2lapack_Min MagmaFalse    // 0
+#define Magma2lapack_Max MagmaRowwise  // 402
 
 // ----------------------------------------
 // string constants for calling Fortran BLAS and LAPACK
 // todo: use translators instead? lapack_const( MagmaUpper )
-#define MagmaRowMajorStr      "Row"
-#define MagmaColMajorStr      "Col"
+#define MagmaRowMajorStr "Row"
+#define MagmaColMajorStr "Col"
 
-#define MagmaNoTransStr       "NoTrans"
-#define MagmaTransStr         "Trans"
-#define MagmaConjTransStr     "ConjTrans"
+#define MagmaNoTransStr "NoTrans"
+#define MagmaTransStr "Trans"
+#define MagmaConjTransStr "ConjTrans"
 
-#define MagmaUpperStr         "Upper"
-#define MagmaLowerStr         "Lower"
-#define MagmaUpperLowerStr    "Full"
-#define MagmaFullStr          "Full"
+#define MagmaUpperStr "Upper"
+#define MagmaLowerStr "Lower"
+#define MagmaUpperLowerStr "Full"
+#define MagmaFullStr "Full"
 
-#define MagmaNonUnitStr       "NonUnit"
-#define MagmaUnitStr          "Unit"
+#define MagmaNonUnitStr "NonUnit"
+#define MagmaUnitStr "Unit"
 
-#define MagmaLeftStr          "Left"
-#define MagmaRightStr         "Right"
-#define MagmaBothSidesStr     "Both"
+#define MagmaLeftStr "Left"
+#define MagmaRightStr "Right"
+#define MagmaBothSidesStr "Both"
 
-#define MagmaOneNormStr       "1"
-#define MagmaTwoNormStr       "2"
+#define MagmaOneNormStr "1"
+#define MagmaTwoNormStr "2"
 #define MagmaFrobeniusNormStr "Fro"
-#define MagmaInfNormStr       "Inf"
-#define MagmaMaxNormStr       "Max"
+#define MagmaInfNormStr "Inf"
+#define MagmaMaxNormStr "Max"
 
-#define MagmaForwardStr       "Forward"
-#define MagmaBackwardStr      "Backward"
+#define MagmaForwardStr "Forward"
+#define MagmaBackwardStr "Backward"
 
-#define MagmaColumnwiseStr    "Columnwise"
-#define MagmaRowwiseStr       "Rowwise"
-
-#define MagmaNoVecStr         "NoVec"
-#define MagmaVecStr           "Vec"
-#define MagmaIVecStr          "IVec"
-#define MagmaAllVecStr        "All"
-#define MagmaSomeVecStr       "Some"
-#define MagmaOverwriteVecStr  "Overwrite"
+#define MagmaColumnwiseStr "Columnwise"
+#define MagmaRowwiseStr "Rowwise"
 
+#define MagmaNoVecStr "NoVec"
+#define MagmaVecStr "Vec"
+#define MagmaIVecStr "IVec"
+#define MagmaAllVecStr "All"
+#define MagmaSomeVecStr "Some"
+#define MagmaOverwriteVecStr "Overwrite"
 
 #ifdef __cplusplus
 extern "C" {
@@ -457,86 +440,114 @@ extern "C" {
 // Convert LAPACK character constants to MAGMA constants.
 // This is a one-to-many mapping, requiring multiple translators
 // (e.g., "N" can be NoTrans or NonUnit or NoVec).
-magma_bool_t   magma_bool_const  ( char lapack_char );
-magma_order_t  magma_order_const ( char lapack_char );
-magma_trans_t  magma_trans_const ( char lapack_char );
-magma_uplo_t   magma_uplo_const  ( char lapack_char );
-magma_diag_t   magma_diag_const  ( char lapack_char );
-magma_side_t   magma_side_const  ( char lapack_char );
-magma_norm_t   magma_norm_const  ( char lapack_char );
-magma_dist_t   magma_dist_const  ( char lapack_char );
-magma_sym_t    magma_sym_const   ( char lapack_char );
-magma_pack_t   magma_pack_const  ( char lapack_char );
-magma_vec_t    magma_vec_const   ( char lapack_char );
-magma_range_t  magma_range_const ( char lapack_char );
-magma_vect_t   magma_vect_const  ( char lapack_char );
-magma_direct_t magma_direct_const( char lapack_char );
-magma_storev_t magma_storev_const( char lapack_char );
-
+magma_bool_t magma_bool_const(char lapack_char);
+magma_order_t magma_order_const(char lapack_char);
+magma_trans_t magma_trans_const(char lapack_char);
+magma_uplo_t magma_uplo_const(char lapack_char);
+magma_diag_t magma_diag_const(char lapack_char);
+magma_side_t magma_side_const(char lapack_char);
+magma_norm_t magma_norm_const(char lapack_char);
+magma_dist_t magma_dist_const(char lapack_char);
+magma_sym_t magma_sym_const(char lapack_char);
+magma_pack_t magma_pack_const(char lapack_char);
+magma_vec_t magma_vec_const(char lapack_char);
+magma_range_t magma_range_const(char lapack_char);
+magma_vect_t magma_vect_const(char lapack_char);
+magma_direct_t magma_direct_const(char lapack_char);
+magma_storev_t magma_storev_const(char lapack_char);
 
 // --------------------
 // Convert MAGMA constants to LAPACK(E) constants.
 // The generic lapack_const works for all cases, but the specific routines
 // (e.g., lapack_trans_const) do better error checking.
-const char* lapack_const       ( int            magma_const );
-const char* lapack_bool_const  ( magma_bool_t   magma_const );
-const char* lapack_order_const ( magma_order_t  magma_const );
-const char* lapack_trans_const ( magma_trans_t  magma_const );
-const char* lapack_uplo_const  ( magma_uplo_t   magma_const );
-const char* lapack_diag_const  ( magma_diag_t   magma_const );
-const char* lapack_side_const  ( magma_side_t   magma_const );
-const char* lapack_norm_const  ( magma_norm_t   magma_const );
-const char* lapack_dist_const  ( magma_dist_t   magma_const );
-const char* lapack_sym_const   ( magma_sym_t    magma_const );
-const char* lapack_pack_const  ( magma_pack_t   magma_const );
-const char* lapack_vec_const   ( magma_vec_t    magma_const );
-const char* lapack_range_const ( magma_range_t  magma_const );
-const char* lapack_vect_const  ( magma_vect_t   magma_const );
-const char* lapack_direct_const( magma_direct_t magma_const );
-const char* lapack_storev_const( magma_storev_t magma_const );
-
-static inline char lapacke_const       ( int magma_const            ) { return *lapack_const       ( magma_const ); }
-static inline char lapacke_bool_const  ( magma_bool_t   magma_const ) { return *lapack_bool_const  ( magma_const ); }
-static inline char lapacke_order_const ( magma_order_t  magma_const ) { return *lapack_order_const ( magma_const ); }
-static inline char lapacke_trans_const ( magma_trans_t  magma_const ) { return *lapack_trans_const ( magma_const ); }
-static inline char lapacke_uplo_const  ( magma_uplo_t   magma_const ) { return *lapack_uplo_const  ( magma_const ); }
-static inline char lapacke_diag_const  ( magma_diag_t   magma_const ) { return *lapack_diag_const  ( magma_const ); }
-static inline char lapacke_side_const  ( magma_side_t   magma_const ) { return *lapack_side_const  ( magma_const ); }
-static inline char lapacke_norm_const  ( magma_norm_t   magma_const ) { return *lapack_norm_const  ( magma_const ); }
-static inline char lapacke_dist_const  ( magma_dist_t   magma_const ) { return *lapack_dist_const  ( magma_const ); }
-static inline char lapacke_sym_const   ( magma_sym_t    magma_const ) { return *lapack_sym_const   ( magma_const ); }
-static inline char lapacke_pack_const  ( magma_pack_t   magma_const ) { return *lapack_pack_const  ( magma_const ); }
-static inline char lapacke_vec_const   ( magma_vec_t    magma_const ) { return *lapack_vec_const   ( magma_const ); }
-static inline char lapacke_range_const ( magma_range_t  magma_const ) { return *lapack_range_const ( magma_const ); }
-static inline char lapacke_vect_const  ( magma_vect_t   magma_const ) { return *lapack_vect_const  ( magma_const ); }
-static inline char lapacke_direct_const( magma_direct_t magma_const ) { return *lapack_direct_const( magma_const ); }
-static inline char lapacke_storev_const( magma_storev_t magma_const ) { return *lapack_storev_const( magma_const ); }
-
+const char* lapack_const(int magma_const);
+const char* lapack_bool_const(magma_bool_t magma_const);
+const char* lapack_order_const(magma_order_t magma_const);
+const char* lapack_trans_const(magma_trans_t magma_const);
+const char* lapack_uplo_const(magma_uplo_t magma_const);
+const char* lapack_diag_const(magma_diag_t magma_const);
+const char* lapack_side_const(magma_side_t magma_const);
+const char* lapack_norm_const(magma_norm_t magma_const);
+const char* lapack_dist_const(magma_dist_t magma_const);
+const char* lapack_sym_const(magma_sym_t magma_const);
+const char* lapack_pack_const(magma_pack_t magma_const);
+const char* lapack_vec_const(magma_vec_t magma_const);
+const char* lapack_range_const(magma_range_t magma_const);
+const char* lapack_vect_const(magma_vect_t magma_const);
+const char* lapack_direct_const(magma_direct_t magma_const);
+const char* lapack_storev_const(magma_storev_t magma_const);
+
+static inline char lapacke_const(int magma_const) {
+    return *lapack_const(magma_const);
+}
+static inline char lapacke_bool_const(magma_bool_t magma_const) {
+    return *lapack_bool_const(magma_const);
+}
+static inline char lapacke_order_const(magma_order_t magma_const) {
+    return *lapack_order_const(magma_const);
+}
+static inline char lapacke_trans_const(magma_trans_t magma_const) {
+    return *lapack_trans_const(magma_const);
+}
+static inline char lapacke_uplo_const(magma_uplo_t magma_const) {
+    return *lapack_uplo_const(magma_const);
+}
+static inline char lapacke_diag_const(magma_diag_t magma_const) {
+    return *lapack_diag_const(magma_const);
+}
+static inline char lapacke_side_const(magma_side_t magma_const) {
+    return *lapack_side_const(magma_const);
+}
+static inline char lapacke_norm_const(magma_norm_t magma_const) {
+    return *lapack_norm_const(magma_const);
+}
+static inline char lapacke_dist_const(magma_dist_t magma_const) {
+    return *lapack_dist_const(magma_const);
+}
+static inline char lapacke_sym_const(magma_sym_t magma_const) {
+    return *lapack_sym_const(magma_const);
+}
+static inline char lapacke_pack_const(magma_pack_t magma_const) {
+    return *lapack_pack_const(magma_const);
+}
+static inline char lapacke_vec_const(magma_vec_t magma_const) {
+    return *lapack_vec_const(magma_const);
+}
+static inline char lapacke_range_const(magma_range_t magma_const) {
+    return *lapack_range_const(magma_const);
+}
+static inline char lapacke_vect_const(magma_vect_t magma_const) {
+    return *lapack_vect_const(magma_const);
+}
+static inline char lapacke_direct_const(magma_direct_t magma_const) {
+    return *lapack_direct_const(magma_const);
+}
+static inline char lapacke_storev_const(magma_storev_t magma_const) {
+    return *lapack_storev_const(magma_const);
+}
 
 // --------------------
 // Convert MAGMA constants to CUBLAS constants.
 #if defined(CUBLAS_V2_H_)
-cublasOperation_t    cublas_trans_const ( magma_trans_t trans );
-cublasFillMode_t     cublas_uplo_const  ( magma_uplo_t  uplo  );
-cublasDiagType_t     cublas_diag_const  ( magma_diag_t  diag  );
-cublasSideMode_t     cublas_side_const  ( magma_side_t  side  );
+cublasOperation_t cublas_trans_const(magma_trans_t trans);
+cublasFillMode_t cublas_uplo_const(magma_uplo_t uplo);
+cublasDiagType_t cublas_diag_const(magma_diag_t diag);
+cublasSideMode_t cublas_side_const(magma_side_t side);
 #endif
 
-
 // --------------------
 // Convert MAGMA constants to CBLAS constants.
 #if defined(HAVE_CBLAS)
 #include <cblas.h>
-enum CBLAS_ORDER     cblas_order_const  ( magma_order_t order );
-enum CBLAS_TRANSPOSE cblas_trans_const  ( magma_trans_t trans );
-enum CBLAS_UPLO      cblas_uplo_const   ( magma_uplo_t  uplo  );
-enum CBLAS_DIAG      cblas_diag_const   ( magma_diag_t  diag  );
-enum CBLAS_SIDE      cblas_side_const   ( magma_side_t  side  );
+enum CBLAS_ORDER cblas_order_const(magma_order_t order);
+enum CBLAS_TRANSPOSE cblas_trans_const(magma_trans_t trans);
+enum CBLAS_UPLO cblas_uplo_const(magma_uplo_t uplo);
+enum CBLAS_DIAG cblas_diag_const(magma_diag_t diag);
+enum CBLAS_SIDE cblas_side_const(magma_side_t side);
 #endif
 
-
 #ifdef __cplusplus
 }
 #endif
 
-#endif        //  #ifndef MAGMA_TYPES_H
+#endif  //  #ifndef MAGMA_TYPES_H
diff --git a/src/backend/opencl/max.cpp b/src/backend/opencl/max.cpp
index de8621427a..d4a7640acf 100644
--- a/src/backend/opencl/max.cpp
+++ b/src/backend/opencl/max.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
 using common::half;
 
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
index 0bd59b15b3..17315becb6 100644
--- a/src/backend/opencl/mean.cpp
+++ b/src/backend/opencl/mean.cpp
@@ -8,11 +8,11 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <af/dim4.hpp>
 #include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <kernel/mean.hpp>
 #include <mean.hpp>
+#include <af/dim4.hpp>
 
 #include <complex>
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index f10e1f0c56..5842fd4445 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -15,9 +15,9 @@
 #include <blas.hpp>
 #include <cache.hpp>
 #include <clfft.hpp>
+#include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/host_memory.hpp>
-#include <common/DefaultMemoryManager.hpp>
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <err_opencl.hpp>
@@ -192,7 +192,7 @@ int getDeviceCount() noexcept try {
     // If device manager threw an error then return 0 because no platforms
     // were found
     return 0;
- }
+}
 
 int getActiveDeviceId() {
     // Second element is the queue id, which is
diff --git a/src/backend/opencl/product.cpp b/src/backend/opencl/product.cpp
index 3bcd9fee9d..3ea554e2f6 100644
--- a/src/backend/opencl/product.cpp
+++ b/src/backend/opencl/product.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
 using common::half;
 
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 0dc2c208a5..28a99862c6 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -12,15 +12,15 @@
 #include <ops.hpp>
 
 namespace opencl {
-template <af_op_t op, typename Ti, typename To>
+template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
                  double nanval = 0);
 
-template <af_op_t op, typename Ti, typename Tk, typename To>
+template<af_op_t op, typename Ti, typename Tk, typename To>
 void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
                    bool change_nan = false, double nanval = 0);
 
-template <af_op_t op, typename Ti, typename To>
+template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
-}
+}  // namespace opencl
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index ad04d2cc1c..e890b57753 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -161,8 +161,8 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
                            tmp.getOffset(), NB, queue, &info);
 
         Array<T> B_new = createEmptyArray<T>(dim4(A.dims()[0], B.dims()[1]));
-        T alpha = scalar<T>(1.0);
-        T beta = scalar<T>(0.0);
+        T alpha        = scalar<T>(1.0);
+        T beta         = scalar<T>(0.0);
         gemm<T>(B_new, AF_MAT_NONE, AF_MAT_NONE, &alpha, A, B, &beta);
         B = B_new;
 #endif
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 5aaf396291..4b214e821e 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -62,9 +62,9 @@ Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
     static const T alpha = scalar<T>(1.0);
     static const T beta  = scalar<T>(0.0);
 
-    const Array<T> &values   = lhs.getValues();
-    const Array<int> &rowIdx = lhs.getRowIdx();
-    const Array<int> &colIdx = lhs.getColIdx();
+    const Array<T>& values   = lhs.getValues();
+    const Array<int>& rowIdx = lhs.getRowIdx();
+    const Array<int>& colIdx = lhs.getColIdx();
 
     if (optLhs == AF_MAT_NONE) {
         if (N == 1) {
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index 7c42555b91..dfb3209ab0 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -10,8 +10,8 @@
 #include <triangle.hpp>
 
 #include <Array.hpp>
-#include <af/dim4.hpp>
 #include <common/half.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using common::half;
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index d9ec439f18..775a3936b3 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -13,8 +13,8 @@
 #include <type_util.hpp>
 
 #include <cmath>
-#include <string>
 #include <sstream>
+#include <string>
 
 using common::half;
 
@@ -77,23 +77,21 @@ std::string ToNumStr<half>::operator()<float>(float val) {
     return std::to_string(val);
 }
 
-
-#define INSTANTIATE(TYPE)                       \
-  template struct ToNumStr<TYPE>
-
-  INSTANTIATE(float);
-  INSTANTIATE(double);
-  INSTANTIATE(cfloat);
-  INSTANTIATE(cdouble);
-  INSTANTIATE(short);
-  INSTANTIATE(ushort);
-  INSTANTIATE(int);
-  INSTANTIATE(uint);
-  INSTANTIATE(intl);
-  INSTANTIATE(uintl);
-  INSTANTIATE(uchar);
-  INSTANTIATE(char);
-  INSTANTIATE(half);
+#define INSTANTIATE(TYPE) template struct ToNumStr<TYPE>
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(cfloat);
+INSTANTIATE(cdouble);
+INSTANTIATE(short);
+INSTANTIATE(ushort);
+INSTANTIATE(int);
+INSTANTIATE(uint);
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(uchar);
+INSTANTIATE(char);
+INSTANTIATE(half);
 
 #undef INSTANTIATE
 
diff --git a/src/backend/opencl/unwrap.cpp b/src/backend/opencl/unwrap.cpp
index 08a7999788..26c720e3c1 100644
--- a/src/backend/opencl/unwrap.cpp
+++ b/src/backend/opencl/unwrap.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <err_opencl.hpp>
 #include <common/half.hpp>
+#include <err_opencl.hpp>
 #include <kernel/unwrap.hpp>
 #include <unwrap.hpp>
 #include <stdexcept>
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 7de960ff3a..41e841c5b5 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -21,22 +21,17 @@ using common::half;
 namespace opencl {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in,
-          const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy,
-          const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py,
-          const bool is_column) {
+void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py, const bool is_column) {
     kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
-#define INSTANTIATE(T)                                          \
-    template void wrap<T> (Array<T> &out,  const Array<T> &in,  \
-                           const dim_t ox, const dim_t oy,      \
-                           const dim_t wx, const dim_t wy,      \
-                           const dim_t sx, const dim_t sy,      \
-                           const dim_t px, const dim_t py,      \
-                           const bool is_column);
+#define INSTANTIATE(T)                                                        \
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t ox, \
+                          const dim_t oy, const dim_t wx, const dim_t wy,     \
+                          const dim_t sx, const dim_t sy, const dim_t px,     \
+                          const dim_t py, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/wrap.hpp b/src/backend/opencl/wrap.hpp
index 35600be90a..e28cc6e9d8 100644
--- a/src/backend/opencl/wrap.hpp
+++ b/src/backend/opencl/wrap.hpp
@@ -12,19 +12,13 @@
 namespace opencl {
 
 template<typename T>
-void wrap(Array<T> &out,  const Array<T> &in,
-          const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy,
-          const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py,
-          const bool is_column);
+void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py, const bool is_column);
 
-template <typename T>
-Array<T> wrap_dilated(const Array<T> &in,
-                      const dim_t ox, const dim_t oy,
-                      const dim_t wx, const dim_t wy,
-                      const dim_t sx, const dim_t sy,
-                      const dim_t px, const dim_t py,
-                      const dim_t dx, const dim_t dy,
-                      const bool is_column);
-}
+template<typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column);
+}  // namespace opencl
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 72542b773b..be8ce78c03 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -851,8 +851,8 @@ class Approx1V2 : public ::testing::Test {
     void SetUp() {}
 
     void releaseArrays() {
-        if (pos != 0)  { ASSERT_SUCCESS(af_release_array(pos)); }
-        if (in != 0)   { ASSERT_SUCCESS(af_release_array(in)); }
+        if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
+        if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
         if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
     }
 
@@ -938,9 +938,8 @@ class SimpleTestData {
                                        40.0f, 45.0f, 50.0f, 55.0f, 60.0f,
                                        70.0f, 75.0f, 80.0f, 85.0f, 90.0f};
 
-        float in_arr[h_in_size] = {10.0f, 20.0f, 30.0f,
-                                   40.0f, 50.0f, 60.0f,
-                                   70.0f, 80.0f, 90.0f};
+        float in_arr[h_in_size] = {10.0f, 20.0f, 30.0f, 40.0f, 50.0f,
+                                   60.0f, 70.0f, 80.0f, 90.0f};
 
         float pos_arr[h_pos_size] = {0.0f, 0.5f, 1.0f, 1.5f, 2.0f};
 
@@ -1016,7 +1015,7 @@ class Approx1NullArgs : public ::testing::Test {
 
     void TearDown() {
         if (pos != 0) { ASSERT_SUCCESS(af_release_array(pos)); }
-        if (in != 0)  { ASSERT_SUCCESS(af_release_array(in)); }
+        if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
     }
 };
 
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 7f840e3c5f..3528e66404 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -781,7 +781,7 @@ class Approx2V2 : public ::testing::Test {
     void releaseArrays() {
         if (pos2 != 0) { ASSERT_SUCCESS(af_release_array(pos2)); }
         if (pos1 != 0) { ASSERT_SUCCESS(af_release_array(pos1)); }
-        if (in != 0)   { ASSERT_SUCCESS(af_release_array(in)); }
+        if (in != 0) { ASSERT_SUCCESS(af_release_array(in)); }
         if (gold != 0) { ASSERT_SUCCESS(af_release_array(gold)); }
     }
 
diff --git a/test/array.cpp b/test/array.cpp
index 42c7d414df..c894dca30d 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -25,7 +25,8 @@ template<typename T>
 using ArrayDeathTest = Array<T>;
 
 typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
-                         int, uint, intl, uintl, short, ushort, half_float::half>
+                         int, uint, intl, uintl, short, ushort,
+                         half_float::half>
     TestTypes;
 
 TYPED_TEST_CASE(Array, TestTypes);
diff --git a/test/binary.cpp b/test/binary.cpp
index 15e39c9388..790b09002a 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -414,14 +414,14 @@ class ResultType : public testing::TestWithParam<result_type_param> {
     void SetUp() {
         result_type_param params = GetParam();
         gold                     = params.result_;
-        skip = false;
+        skip                     = false;
         if (noHalfTests(params.result_) || noHalfTests(params.lhs_) ||
             noHalfTests(params.rhs_)) {
             skip = true;
             return;
         }
-        lhs                      = af::array(10, params.lhs_);
-        rhs                      = af::array(10, params.rhs_);
+        lhs = af::array(10, params.lhs_);
+        rhs = af::array(10, params.rhs_);
     }
 };
 
@@ -512,11 +512,9 @@ TEST_P(ResultType, Division)       {
 
 template<typename T>
 class ResultTypeScalar : public ::testing::Test {
-protected:
+   protected:
     T scalar;
-    void SetUp() {
-      scalar = T(1);
-    }
+    void SetUp() { scalar = T(1); }
 };
 
 typedef ::testing::Types<float, double, unsigned int, int, short,
diff --git a/test/blas.cpp b/test/blas.cpp
index 4a815f931d..95582441db 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -13,16 +13,18 @@
 #include <testHelpers.hpp>
 #include <af/blas.h>
 #include <af/defines.h>
-#include <af/half.h>
 #include <af/dim4.hpp>
+#include <af/half.h>
 #include <af/traits.hpp>
-#include <string>
 #include <algorithm>
+#include <string>
 
 using af::array;
 using af::cdouble;
 using af::cfloat;
+using af::constant;
 using af::dim4;
+using af::dot;
 using af::dtype_traits;
 using af::getDevice;
 using af::getDeviceCount;
@@ -31,8 +33,6 @@ using af::max;
 using af::randu;
 using af::setDevice;
 using af::span;
-using af::constant;
-using af::dot;
 using af::transpose;
 using std::copy;
 using std::cout;
@@ -96,7 +96,7 @@ void MatMulCheck(string TestFile) {
 
     for (size_t i = 0; i < tests.size(); i++) {
         dim4 dd;
-        dim_t* d = dd.get();
+        dim_t *d = dd.get();
         af_get_dims(&d[0], &d[1], &d[2], &d[3], out[i]);
         ASSERT_VEC_ARRAY_NEAR(tests[i], dd, out[i], 1e-3);
     }
@@ -173,7 +173,7 @@ void cppMatMulCheck(string TestFile) {
     for (size_t i = 0; i < tests.size(); i++) {
         dim_t elems = out[i].elements();
         vector<T> h_out(elems);
-        out[i].host((void*)&h_out.front());
+        out[i].host((void *)&h_out.front());
 
         if (false == equal(h_out.begin(), h_out.end(), tests[i].begin())) {
             cout << "Failed test " << i << "\nCalculated: " << endl;
@@ -204,7 +204,7 @@ TYPED_TEST(MatrixMultiply, RectangleVector_CPP) {
 
 #define DEVICE_ITERATE(func)                             \
     do {                                                 \
-        const char* ENV = getenv("AF_MULTI_GPU_TESTS");  \
+        const char *ENV = getenv("AF_MULTI_GPU_TESTS");  \
         if (ENV && ENV[0] == '0') {                      \
             func;                                        \
         } else {                                         \
@@ -329,78 +329,51 @@ TEST(MatrixMultiply, RhsBroadcastBatched) {
 }
 
 float alpha = 1.f;
-float beta = 0.f;
-
-float h_gold_gemv[4] = {5, 5, 5, 5};
-float h_half_ones[20] = {1.f, 1.f, 1.f, 1.f, 1.f,
-                         1.f, 1.f, 1.f, 1.f, 1.f,
-                         1.f, 1.f, 1.f, 1.f, 1.f,
-                         1.f, 1.f, 1.f, 1.f, 1.f};
+float beta  = 0.f;
 
-float h_lhs[9] =         {1.f, 4.f, 7.f,
-                          2.f, 5.f, 8.f,
-                          3.f, 6.f, 9.f};
+float h_gold_gemv[4]  = {5, 5, 5, 5};
+float h_half_ones[20] = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f,
+                         1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f};
 
-float h_lhs_tall[6] =    {1.f, 3.f, 5.f,
-                          2.f, 4.f, 6.f};
+float h_lhs[9] = {1.f, 4.f, 7.f, 2.f, 5.f, 8.f, 3.f, 6.f, 9.f};
 
-float h_lhs_wide[6] =    {1.f, 4.f,
-                          2.f, 5.f,
-                          3.f, 6.f};
+float h_lhs_tall[6] = {1.f, 3.f, 5.f, 2.f, 4.f, 6.f};
 
-float h_lhs_batch[18] =  {1.f, 4.f, 7.f,
-                          2.f, 5.f, 8.f,
-                          3.f, 6.f, 9.f,
+float h_lhs_wide[6] = {1.f, 4.f, 2.f, 5.f, 3.f, 6.f};
 
-                          8.f, 2.f, 5.f,
-                          3.f, 4.f, 7.f,
-                          1.f, 0.f, 6.f};
+float h_lhs_batch[18] = {1.f, 4.f, 7.f, 2.f, 5.f, 8.f, 3.f, 6.f, 9.f,
 
-float h_rhs[9] =         {9.f, 6.f, 3.f,
-                          8.f, 5.f, 2.f,
-                          7.f, 4.f, 1.f};
+                         8.f, 2.f, 5.f, 3.f, 4.f, 7.f, 1.f, 0.f, 6.f};
 
-float h_rhs_tall[6] =    {9.f, 7.f, 5.f,
-                          8.f, 6.f, 4.f};
+float h_rhs[9] = {9.f, 6.f, 3.f, 8.f, 5.f, 2.f, 7.f, 4.f, 1.f};
 
-float h_rhs_wide[6] =    {9.f, 6.f,
-                          8.f, 5.f,
-                          7.f, 4.f};
+float h_rhs_tall[6] = {9.f, 7.f, 5.f, 8.f, 6.f, 4.f};
 
-float h_gold[9] =        {30.f, 84.f, 138.f,
-                          24.f, 69.f, 114.f,
-                          18.f, 54.f, 90.f};
+float h_rhs_wide[6] = {9.f, 6.f, 8.f, 5.f, 7.f, 4.f};
 
-float h_gold_NN[9] =     {21.f, 51.f, 81.f,
-                          18.f, 44.f, 70.f,
-                          15.f, 37.f, 59.f};
+float h_gold[9] = {30.f, 84.f, 138.f, 24.f, 69.f, 114.f, 18.f, 54.f, 90.f};
 
-float h_gold_NT[9] =     {25.f, 59.f, 93.f,
-                          19.f, 45.f, 71.f,
-                          13.f, 31.f, 49.f};
+float h_gold_NN[9] = {21.f, 51.f, 81.f, 18.f, 44.f, 70.f, 15.f, 37.f, 59.f};
 
-float h_gold_TN[4] =     {55.f, 76.f,
-                          46.f, 64.f};
+float h_gold_NT[9] = {25.f, 59.f, 93.f, 19.f, 45.f, 71.f, 13.f, 31.f, 49.f};
 
-float h_gold_TT[4] =     {68.f, 92.f,
-                          41.f, 56.f};
+float h_gold_TN[4] = {55.f, 76.f, 46.f, 64.f};
 
-float h_gold_batch[18] = {30.f, 84.f, 138.f,
-                          24.f, 69.f, 114.f,
-                          18.f, 54.f, 90.f,
+float h_gold_TT[4] = {68.f, 92.f, 41.f, 56.f};
 
-                          93.f, 42.f, 105.f,
-                          81.f, 36.f, 87.f,
-                          69.f, 30.f, 69.f};
+float h_gold_batch[18] = {
+    30.f, 84.f, 138.f, 24.f, 69.f, 114.f, 18.f, 54.f, 90.f,
 
+    93.f, 42.f, 105.f, 81.f, 36.f, 87.f,  69.f, 30.f, 69.f};
 
 TEST(MatrixMultiply, float) {
-    array A32 = array(3, 3, h_lhs);
-    array B32 = array(3, 3, h_rhs);
-    af_array C32 = 0;
+    array A32           = array(3, 3, h_lhs);
+    array B32           = array(3, 3, h_rhs);
+    af_array C32        = 0;
     const float alpha32 = 1.0f;
-    const float beta32 = 0.0f;
-    af_gemm(&C32, AF_MAT_NONE, AF_MAT_NONE, &alpha32, A32.get(), B32.get(), &beta32);
+    const float beta32  = 0.0f;
+    af_gemm(&C32, AF_MAT_NONE, AF_MAT_NONE, &alpha32, A32.get(), B32.get(),
+            &beta32);
     array expected32 = array(3, 3, h_gold);
     ASSERT_ARRAYS_NEAR(expected32, af::array(C32), 0.0001);
 }
@@ -408,15 +381,16 @@ TEST(MatrixMultiply, float) {
 TEST(MatrixMultiply, half) {
     SUPPORTED_TYPE_CHECK(af_half);
 
-    array A16 = array(3, 3, h_lhs).as(f16);
-    array B16 = array(3, 3, h_rhs).as(f16);
+    array A16        = array(3, 3, h_lhs).as(f16);
+    array B16        = array(3, 3, h_rhs).as(f16);
     array expected16 = array(3, 3, h_gold).as(f16);
 
     {
         af_array C16 = 0;
         const half_float::half alpha16(1.0f);
         const half_float::half beta16(0.0f);
-        ASSERT_SUCCESS(af_gemm(&C16, AF_MAT_NONE, AF_MAT_NONE, &alpha16, A16.get(), B16.get(), &beta16));
+        ASSERT_SUCCESS(af_gemm(&C16, AF_MAT_NONE, AF_MAT_NONE, &alpha16,
+                               A16.get(), B16.get(), &beta16));
         af::array C(C16);
         ASSERT_ARRAYS_NEAR(expected16, C, 0.00001);
     }
@@ -439,18 +413,20 @@ struct test_params {
     float *beta;
     TestOutputArrayType out_array_type;
 
-    test_params(af_mat_prop optl, af_mat_prop optr,
-                float *a,
-                float *l, float *r, float *g,
-                dim4 ldims, dim4 rdims, dim4 odims,
-                float *b,
-                TestOutputArrayType t)
-        :opt_lhs(optl), opt_rhs(optr),
-         alpha(a),
-         h_lhs(l), h_rhs(r), h_gold(g),
-         lhs_dims(ldims), rhs_dims(rdims), out_dims(odims),
-         beta(b),
-         out_array_type(t) {}
+    test_params(af_mat_prop optl, af_mat_prop optr, float *a, float *l,
+                float *r, float *g, dim4 ldims, dim4 rdims, dim4 odims,
+                float *b, TestOutputArrayType t)
+        : opt_lhs(optl)
+        , opt_rhs(optr)
+        , alpha(a)
+        , h_lhs(l)
+        , h_rhs(r)
+        , h_gold(g)
+        , lhs_dims(ldims)
+        , rhs_dims(rdims)
+        , out_dims(odims)
+        , beta(b)
+        , out_array_type(t) {}
 };
 
 class Gemm : public ::testing::TestWithParam<test_params> {
@@ -465,24 +441,28 @@ class Gemm : public ::testing::TestWithParam<test_params> {
         test_params params = GetParam();
 
         lhs  = 0;
-        rhs = 0;
-        out = 0;
+        rhs  = 0;
+        out  = 0;
         gold = 0;
 
-        ASSERT_SUCCESS(
-            af_create_array(&lhs, params.h_lhs, params.lhs_dims.ndims(), params.lhs_dims.get(), f32));
-        ASSERT_SUCCESS(
-            af_create_array(&rhs, params.h_rhs, params.rhs_dims.ndims(), params.rhs_dims.get(), f32));
-
-        dim_t gold_dim0 = params.opt_lhs == AF_MAT_TRANS ? params.lhs_dims[1] : params.lhs_dims[0];
-        dim_t gold_dim1 = params.opt_rhs == AF_MAT_TRANS ? params.rhs_dims[0] : params.rhs_dims[1];
+        ASSERT_SUCCESS(af_create_array(&lhs, params.h_lhs,
+                                       params.lhs_dims.ndims(),
+                                       params.lhs_dims.get(), f32));
+        ASSERT_SUCCESS(af_create_array(&rhs, params.h_rhs,
+                                       params.rhs_dims.ndims(),
+                                       params.rhs_dims.get(), f32));
+
+        dim_t gold_dim0 = params.opt_lhs == AF_MAT_TRANS ? params.lhs_dims[1]
+                                                         : params.lhs_dims[0];
+        dim_t gold_dim1 = params.opt_rhs == AF_MAT_TRANS ? params.rhs_dims[0]
+                                                         : params.rhs_dims[1];
         dim_t gold_dim2 = std::max(params.lhs_dims[2], params.rhs_dims[2]);
         dim_t gold_dim3 = std::max(params.lhs_dims[3], params.rhs_dims[3]);
         dim4 gold_dims(gold_dim0, gold_dim1, gold_dim2, gold_dim3);
 
         metadata = TestOutputArrayInfo(params.out_array_type);
-        genTestOutputArray(&out, params.out_dims.ndims(), params.out_dims.get(), f32,
-                           &metadata);
+        genTestOutputArray(&out, params.out_dims.ndims(), params.out_dims.get(),
+                           f32, &metadata);
 
         ASSERT_SUCCESS(af_create_array(&gold, params.h_gold, gold_dims.ndims(),
                                        gold_dims.get(), f32));
@@ -495,8 +475,8 @@ class Gemm : public ::testing::TestWithParam<test_params> {
     }
 };
 
-void replace_all(std::string& str, const std::string& oldStr,
-                 const std::string& newStr) {
+void replace_all(std::string &str, const std::string &oldStr,
+                 const std::string &newStr) {
     std::string::size_type pos = 0u;
     while ((pos = str.find(oldStr, pos)) != std::string::npos) {
         str.replace(pos, oldStr.length(), newStr);
@@ -517,32 +497,21 @@ string out_info(const ::testing::TestParamInfo<Gemm::ParamType> info) {
 
     stringstream ss;
     switch (params.out_array_type) {
-    case NULL_ARRAY:
-        ss << "NullOut";
-        break;
-    case FULL_ARRAY:
-        ss << "FullOut";
-        break;
-    case SUB_ARRAY:
-        ss << "SubarrayOut";
-        break;
-    case REORDERED_ARRAY:
-        ss << "ReorderedOut";
-        break;
-    default:
-        ss << "UnknownOutArrayType";
-        break;
+        case NULL_ARRAY: ss << "NullOut"; break;
+        case FULL_ARRAY: ss << "FullOut"; break;
+        case SUB_ARRAY: ss << "SubarrayOut"; break;
+        case REORDERED_ARRAY: ss << "ReorderedOut"; break;
+        default: ss << "UnknownOutArrayType"; break;
     }
 
-    ss << "_" << concat_dim4(params.lhs_dims) << "_" << concat_dim4(params.rhs_dims);
+    ss << "_" << concat_dim4(params.lhs_dims) << "_"
+       << concat_dim4(params.rhs_dims);
 
     ss << "_";
     ss << (params.opt_lhs == AF_MAT_TRANS ? "T" : "N");
     ss << (params.opt_rhs == AF_MAT_TRANS ? "T" : "N");
 
-    if (params.lhs_dims[2] > 1 || params.rhs_dims[2] > 1) {
-        ss << "_Batched";
-    }
+    if (params.lhs_dims[2] > 1 || params.rhs_dims[2] > 1) { ss << "_Batched"; }
 
     return ss.str();
 }
@@ -611,8 +580,8 @@ INSTANTIATE_TEST_CASE_P(
 
 TEST_P(Gemm, UsePreallocatedOutArray) {
     test_params params = GetParam();
-    ASSERT_SUCCESS(af_gemm(&out, params.opt_lhs, params.opt_rhs,
-                           params.alpha, lhs, rhs, params.beta));
+    ASSERT_SUCCESS(af_gemm(&out, params.opt_lhs, params.opt_rhs, params.alpha,
+                           lhs, rhs, params.beta));
 
     ASSERT_SPECIAL_ARRAYS_EQ(gold, out, &metadata);
 }
@@ -631,7 +600,8 @@ TEST(Gemm, DocSnippet) {
 
     // Undefined behavior!
     // af_array undef;
-    // af_gemm(&undef, AF_MAT_NONE, AF_MAT_NONE, &alpha, a.get(), b.get(), &beta);
+    // af_gemm(&undef, AF_MAT_NONE, AF_MAT_NONE, &alpha, a.get(), b.get(),
+    // &beta);
 
     af_array C = 0;
     af_gemm(&C, AF_MAT_NONE, AF_MAT_NONE, &alpha, A, B, &beta);
@@ -657,8 +627,8 @@ TEST(Gemm, DocSnippet) {
     ASSERT_ARRAYS_EQ(gold1, c1);
 
     //! [ex_af_gemm_overwrite]
-    alpha = 1.f;
-    beta  = 1.f;
+    alpha                = 1.f;
+    beta                 = 1.f;
     af_seq first_slice[] = {af_span, af_span, {0., 0., 1.}};
     af_array Asub, Bsub, Csub;
     af_index(&Asub, A, 3, first_slice);
@@ -682,7 +652,7 @@ TEST(Gemm, DocSnippet) {
     af_array c2_copy = 0;
     ASSERT_SUCCESS(af_retain_array(&c2_copy, C));
     af::array c2(c2_copy);
-    vector<float> gold2(5*5*2, 3);
+    vector<float> gold2(5 * 5 * 2, 3);
     fill(gold2.begin(), gold2.begin() + (5 * 5), 6);
 
     af_release_array(A);
@@ -699,7 +669,7 @@ TEST(Gemv, HalfScalarProduct) {
     SUPPORTED_TYPE_CHECK(half_float::half);
 
     const unsigned int sizeValue = 5;
-    array gold = constant(sizeValue, 4, 1, f16);
+    array gold                   = constant(sizeValue, 4, 1, f16);
     {
         array a     = constant(1, 4, sizeValue, f16);
         array b     = constant(1, sizeValue, 1, f16);
@@ -707,9 +677,9 @@ TEST(Gemv, HalfScalarProduct) {
         ASSERT_ARRAYS_EQ(mmRes, gold);
     }
     {
-        array a     = constant(1, 1, sizeValue, f16);
-        array b     = constant(1, sizeValue, 1, f16);
-        array mmRes = matmul(a, b);
+        array a      = constant(1, 1, sizeValue, f16);
+        array b      = constant(1, sizeValue, 1, f16);
+        array mmRes  = matmul(a, b);
         array dotRes = dot(transpose(a), b);
         ASSERT_ARRAYS_EQ(mmRes, dotRes);
     }
diff --git a/test/canny.cpp b/test/canny.cpp
index 9687d0a070..38df71e5f3 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -84,7 +84,8 @@ TEST(Canny, DISABLED_Exact) {
     array img = loadImage(TEST_DIR "/CannyEdgeDetector/woman.jpg", false);
 
     array out = canny(img, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false);
-    array gold = loadImage(TEST_DIR "/CannyEdgeDetector/woman_edges.jpg", false) > 3;
+    array gold =
+        loadImage(TEST_DIR "/CannyEdgeDetector/woman_edges.jpg", false) > 3;
 
     ASSERT_ARRAYS_EQ(gold, out);
 }
diff --git a/test/clamp.cpp b/test/clamp.cpp
index bd1227392c..3e885cf1f8 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -20,7 +20,6 @@
 #include <string>
 #include <vector>
 
-
 #include <iostream>
 
 using af::array;
@@ -70,9 +69,12 @@ class Clamp : public ::testing::TestWithParam<clamp_params> {
         hi_.as((dtype)af::dtype_traits<T>::af_type).host(&hhi[0]);
 
         for (int i = 0; i < num; i++) {
-            if (hin[i] < hlo[i])      hgold[i] = hlo[i];
-            else if (hin[i] > hhi[i]) hgold[i] = hhi[i];
-            else                      hgold[i] = hin[i];
+            if (hin[i] < hlo[i])
+                hgold[i] = hlo[i];
+            else if (hin[i] > hhi[i])
+                hgold[i] = hhi[i];
+            else
+                hgold[i] = hin[i];
         }
 
         gold_ = array(params.size_, &hgold[0]);
diff --git a/test/compare.cpp b/test/compare.cpp
index 2c1c4fa5a5..8e3d22acc5 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <gtest/gtest.h>
-#include <testHelpers.hpp>
 #include <half.hpp>
+#include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 2c046fe193..907eb63958 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -13,15 +13,15 @@
 #include <testHelpers.hpp>
 #include <af/traits.hpp>
 
-#include <string>
 #include <sstream>
+#include <string>
 #include <vector>
 
 using af::dim4;
 using std::abs;
 using std::string;
-using std::to_string;
 using std::stringstream;
+using std::to_string;
 using std::vector;
 
 template<typename T>
@@ -35,19 +35,18 @@ typedef ::testing::Types<float, uint, ushort, uchar> TestTypes;
 TYPED_TEST_CASE(ConfidenceConnectedImageTest, TestTypes);
 
 struct CCCTestParams {
-    const char* prefix;
+    const char *prefix;
     unsigned int radius;
     unsigned int multiplier;
     unsigned int iterations;
     double replace;
 };
 
-void apiWrapper(af_array* out, const af_array in, const af_array seedx,
-        const af_array seedy, const CCCTestParams params) {
-    ASSERT_SUCCESS(
-            af_confidence_cc(out, in, seedx, seedy,
-                params.radius, params.multiplier,
-                params.iterations, params.replace));
+void apiWrapper(af_array *out, const af_array in, const af_array seedx,
+                const af_array seedy, const CCCTestParams params) {
+    ASSERT_SUCCESS(af_confidence_cc(out, in, seedx, seedy, params.radius,
+                                    params.multiplier, params.iterations,
+                                    params.replace));
 
     int device = 0;
     ASSERT_SUCCESS(af_get_device(&device));
@@ -56,8 +55,9 @@ void apiWrapper(af_array* out, const af_array in, const af_array seedx,
 
 template<typename T>
 void testImage(const std::string pTestFile, const size_t numSeeds,
-        const unsigned *seedx, const unsigned *seedy, const int multiplier,
-        const unsigned neighborhood_radius, const int iter) {
+               const unsigned *seedx, const unsigned *seedy,
+               const int multiplier, const unsigned neighborhood_radius,
+               const int iter) {
     SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
@@ -66,17 +66,17 @@ void testImage(const std::string pTestFile, const size_t numSeeds,
     vector<dim_t> outSizes;
     vector<string> outFiles;
 
-    readImageTests(std::string(TEST_DIR)+"/confidence_cc/"+pTestFile,
-            inDims, inFiles, outSizes, outFiles);
+    readImageTests(std::string(TEST_DIR) + "/confidence_cc/" + pTestFile,
+                   inDims, inFiles, outSizes, outFiles);
 
     size_t testCount = inDims.size();
 
     af_array seedxArr = 0, seedyArr = 0;
     dim4 seedDims(numSeeds);
-    ASSERT_SUCCESS(af_create_array(
-                &seedxArr, seedx, seedDims.ndims(), seedDims.get(), u32));
-    ASSERT_SUCCESS(af_create_array(
-                &seedyArr, seedy, seedDims.ndims(), seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(&seedxArr, seedx, seedDims.ndims(),
+                                   seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(&seedyArr, seedy, seedDims.ndims(),
+                                   seedDims.get(), u32));
 
     for (size_t testId = 0; testId < testCount; ++testId) {
         af_array _inArray   = 0;
@@ -90,20 +90,20 @@ void testImage(const std::string pTestFile, const size_t numSeeds,
         outFiles[testId].insert(0, string(TEST_DIR "/confidence_cc/"));
 
         ASSERT_SUCCESS(
-                af_load_image(&_inArray, inFiles[testId].c_str(), false));
+            af_load_image(&_inArray, inFiles[testId].c_str(), false));
         ASSERT_SUCCESS(
-                af_load_image(&_goldArray, outFiles[testId].c_str(), false));
+            af_load_image(&_goldArray, outFiles[testId].c_str(), false));
 
         // af_load_image always returns float array, so convert to output type
         ASSERT_SUCCESS(conv_image<T>(&inArray, _inArray));
         ASSERT_SUCCESS(conv_image<T>(&goldArray, _goldArray));
 
         CCCTestParams params;
-        params.prefix = "Image";
-        params.radius = neighborhood_radius;
+        params.prefix     = "Image";
+        params.radius     = neighborhood_radius;
         params.multiplier = multiplier;
         params.iterations = iter;
-        params.replace = 255.0;
+        params.replace    = 255.0;
 
         apiWrapper(&outArray, inArray, seedxArr, seedyArr, params);
 
@@ -127,10 +127,9 @@ void testData(CCCTestParams params) {
     vector<vector<T> > in;
     vector<vector<T> > tests;
 
-    string file = string(TEST_DIR) + "/confidence_cc/" +
-                string(params.prefix) + "_" +
-                to_string(params.radius) + "_" +
-                to_string(params.multiplier) + ".test";
+    string file = string(TEST_DIR) + "/confidence_cc/" + string(params.prefix) +
+                  "_" + to_string(params.radius) + "_" +
+                  to_string(params.multiplier) + ".test";
     readTests<T, T, int>(file, numDims, in, tests);
 
     dim4 dims         = numDims[0];
@@ -141,12 +140,13 @@ void testData(CCCTestParams params) {
     const unsigned *seedxy = seedCoords.data();
 
     dim4 seedDims(1);
-    ASSERT_SUCCESS(af_create_array(
-                &seedxArr, seedxy+0, seedDims.ndims(), seedDims.get(), u32));
-    ASSERT_SUCCESS(af_create_array(
-                &seedyArr, seedxy+1, seedDims.ndims(), seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(&seedxArr, seedxy + 0, seedDims.ndims(),
+                                   seedDims.get(), u32));
+    ASSERT_SUCCESS(af_create_array(&seedyArr, seedxy + 1, seedDims.ndims(),
+                                   seedDims.get(), u32));
     ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims.ndims(),
-                dims.get(), (af_dtype)af::dtype_traits<T>::af_type));
+                                   dims.get(),
+                                   (af_dtype)af::dtype_traits<T>::af_type));
 
     af_array outArray = 0;
     apiWrapper(&outArray, inArray, seedxArr, seedyArr, params);
@@ -160,47 +160,46 @@ void testData(CCCTestParams params) {
 }
 
 class ConfidenceConnectedDataTest
-    : public testing::TestWithParam<CCCTestParams> {
-};
+    : public testing::TestWithParam<CCCTestParams> {};
 
 #if !defined(AF_OPENCL)
 
 TYPED_TEST(ConfidenceConnectedImageTest, DonutBackgroundExtraction) {
     const unsigned seedx = 10;
     const unsigned seedy = 10;
-    testImage<TypeParam>(
-        std::string("donut_background.test"), 1, &seedx, &seedy, 3, 3, 25);
+    testImage<TypeParam>(std::string("donut_background.test"), 1, &seedx,
+                         &seedy, 3, 3, 25);
 }
 
 TYPED_TEST(ConfidenceConnectedImageTest, DonutRingExtraction) {
     const unsigned seedx = 132;
     const unsigned seedy = 132;
-    testImage<TypeParam>(
-        std::string("donut_ring.test"), 1, &seedx, &seedy, 3, 3, 25);
+    testImage<TypeParam>(std::string("donut_ring.test"), 1, &seedx, &seedy, 3,
+                         3, 25);
 }
 
 TYPED_TEST(ConfidenceConnectedImageTest, DonutKernelExtraction) {
     const unsigned seedx = 150;
     const unsigned seedy = 150;
-    testImage<TypeParam>(
-        std::string("donut_core.test"), 1, &seedx, &seedy, 3, 3, 25);
+    testImage<TypeParam>(std::string("donut_core.test"), 1, &seedx, &seedy, 3,
+                         3, 25);
 }
 
 TEST_P(ConfidenceConnectedDataTest, SegmentARegion) {
     testData<unsigned char>(GetParam());
 }
 
-INSTANTIATE_TEST_CASE_P(SingleSeed, ConfidenceConnectedDataTest,
-        testing::Values(CCCTestParams{"core", 0u, 1u, 5u, 255.0},
-            CCCTestParams{"background", 0u, 1u, 5u, 255.0},
-            CCCTestParams{"ring", 0u, 1u, 5u, 255.0}),
-        [](const ::testing::TestParamInfo<ConfidenceConnectedDataTest::ParamType> info) {
-            stringstream ss;
-            ss << "_prefix_" << info.param.prefix
-               << "_radius_" << info.param.radius
-               << "_multiplier_" << info.param.multiplier
-               << "_iterations_" << info.param.iterations
-               << "_replace_" << info.param.replace;
-            return ss.str();
-        });
+INSTANTIATE_TEST_CASE_P(
+    SingleSeed, ConfidenceConnectedDataTest,
+    testing::Values(CCCTestParams{"core", 0u, 1u, 5u, 255.0},
+                    CCCTestParams{"background", 0u, 1u, 5u, 255.0},
+                    CCCTestParams{"ring", 0u, 1u, 5u, 255.0}),
+    [](const ::testing::TestParamInfo<ConfidenceConnectedDataTest::ParamType>
+           info) {
+        stringstream ss;
+        ss << "_prefix_" << info.param.prefix << "_radius_" << info.param.radius
+           << "_multiplier_" << info.param.multiplier << "_iterations_"
+           << info.param.iterations << "_replace_" << info.param.replace;
+        return ss.str();
+    });
 #endif
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 4b35cd2d4d..2768c63f9a 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -889,7 +889,7 @@ TEST_P(Conv2ConsistencyTest, RandomConvolutions) {
 
     array out_native = convolve2(signal, filter);
     array out = convolve2NN(signal, filter, params.stride_, params.padding_,
-                             params.dilation_);
+                            params.dilation_);
 
     ASSERT_ARRAYS_NEAR(out_native, out, 1e-5);
 }
@@ -898,13 +898,19 @@ template<typename T>
 float tolerance();
 
 template<>
-float tolerance<float>() { return 1e-4; }
+float tolerance<float>() {
+    return 1e-4;
+}
 
 template<>
-float tolerance<double>() { return 1e-4; }
+float tolerance<double>() {
+    return 1e-4;
+}
 
 template<>
-float tolerance<half_float::half>() { return 3e-2; }
+float tolerance<half_float::half>() {
+    return 3e-2;
+}
 
 template<typename T>
 void convolve2stridedTest(string pTestFile, dim4 stride, dim4 padding,
@@ -1011,10 +1017,12 @@ void convolve2GradientTest(string pTestFile, dim4 stride, dim4 padding,
         dilation.ndims(), dilation.get(), AF_CONV_GRADIENT_DATA));
 
     vector<T> &dataGradientGold = tests[1];
-    ASSERT_VEC_ARRAY_NEAR(dataGradientGold, sDims, data_gradient, tolerance<T>());
+    ASSERT_VEC_ARRAY_NEAR(dataGradientGold, sDims, data_gradient,
+                          tolerance<T>());
 
     vector<T> &filterGradientGold = tests[2];
-    ASSERT_VEC_ARRAY_NEAR(filterGradientGold, fDims, filter_gradient, tolerance<T>());
+    ASSERT_VEC_ARRAY_NEAR(filterGradientGold, fDims, filter_gradient,
+                          tolerance<T>());
 
     ASSERT_SUCCESS(af_release_array(incoming_gradient));
     ASSERT_SUCCESS(af_release_array(convolved));
diff --git a/test/dot.cpp b/test/dot.cpp
index f3cd11f251..53592e89c1 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -9,10 +9,10 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <half.hpp>
 #include <complex>
 #include <string>
 #include <vector>
@@ -89,7 +89,7 @@ void dotTest(string pTestFile, const int resultIdx,
 
     ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), out));
 
-    if(false == (isinf(outData.front()) && isinf(goldData[0]))) {
+    if (false == (isinf(outData.front()) && isinf(goldData[0]))) {
         for (size_t elIter = 0; elIter < nElems; ++elIter) {
             ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)
                 << "at: " << elIter << endl;
@@ -148,7 +148,7 @@ void dotAllTest(string pTestFile, const int resultIdx,
 
     vector<T> goldData = tests[resultIdx];
 
-    if(false == (isinf(rval) && isinf(goldData[0]))) {
+    if (false == (isinf(rval) && isinf(goldData[0]))) {
         compare<T>(rval, ival, goldData[0]);
     }
 
diff --git a/test/event.cpp b/test/event.cpp
index 5b98cbe433..e99bbf80c3 100644
--- a/test/event.cpp
+++ b/test/event.cpp
@@ -46,7 +46,7 @@ TEST(EventTests, EventCreateAndMove) {
     ASSERT_EQ(otherEvent.get(), eventHandle);
 
     event f;
-    af_event fE       = f.get();
+    af_event fE        = f.get();
     event anotherEvent = std::move(f);
     ASSERT_EQ(fE, anotherEvent.get());
     af::sync();
diff --git a/test/fft.cpp b/test/fft.cpp
index 204c1637a5..f289f3e600 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -743,13 +743,12 @@ string to_test_params(const ::testing::TestParamInfo<FFTBase::ParamType> info) {
     return out.replace(out.find("."), 1, "_");
 }
 
-INSTANTIATE_TEST_CASE_P(Inputs2D, FFTC2R2D,
-                        ::testing::Values(
-                            fft_params(dim4(513, 512), false, 0.5),
-                            fft_params(dim4(1025, 1024), false, 0.5),
-                            fft_params(dim4(2049, 2048), false, 0.5)
-                            ),
-                        to_test_params);
+INSTANTIATE_TEST_CASE_P(
+    Inputs2D, FFTC2R2D,
+    ::testing::Values(fft_params(dim4(513, 512), false, 0.5),
+                      fft_params(dim4(1025, 1024), false, 0.5),
+                      fft_params(dim4(2049, 2048), false, 0.5)),
+    to_test_params);
 
 INSTANTIATE_TEST_CASE_P(
     Inputs2D, FFT2D,
@@ -765,36 +764,35 @@ INSTANTIATE_TEST_CASE_P(
                       fft_params(dim4(2048, 2048, 3), false, 0.5)),
     to_test_params);
 
-INSTANTIATE_TEST_CASE_P(Inputs3D, FFT3D,
-                        ::testing::Values(
-                            fft_params(dim4(1024, 1024, 3), true, 0.5),
-                            fft_params(dim4(1024, 1024, 3), false, 0.5)),
-                        to_test_params);
-
-
-INSTANTIATE_TEST_CASE_P(InputsND, FFTND,
-                        ::testing::Values(
-                            fft_params(dim4(512), false, 0.5),
-                            fft_params(dim4(1024), false, 0.5),
-                            fft_params(dim4(1024, 1024), false, 0.5),
-                            fft_params(dim4(1024, 1024, 3), false, 0.5)),
-                        to_test_params);
+INSTANTIATE_TEST_CASE_P(
+    Inputs3D, FFT3D,
+    ::testing::Values(fft_params(dim4(1024, 1024, 3), true, 0.5),
+                      fft_params(dim4(1024, 1024, 3), false, 0.5)),
+    to_test_params);
 
+INSTANTIATE_TEST_CASE_P(
+    InputsND, FFTND,
+    ::testing::Values(fft_params(dim4(512), false, 0.5),
+                      fft_params(dim4(1024), false, 0.5),
+                      fft_params(dim4(1024, 1024), false, 0.5),
+                      fft_params(dim4(1024, 1024, 3), false, 0.5)),
+    to_test_params);
 
-INSTANTIATE_TEST_CASE_P(InputsND, FFTC2R,
-                        ::testing::Values(
-                            fft_params(dim4(513), false, 0.5),
-                            fft_params(dim4(1025), false, 0.5),
-                            fft_params(dim4(1025, 1024), false, 0.5),
-                            fft_params(dim4(1025, 1024, 3), false, 0.5)),
-                        to_test_params);
+INSTANTIATE_TEST_CASE_P(
+    InputsND, FFTC2R,
+    ::testing::Values(fft_params(dim4(513), false, 0.5),
+                      fft_params(dim4(1025), false, 0.5),
+                      fft_params(dim4(1025, 1024), false, 0.5),
+                      fft_params(dim4(1025, 1024, 3), false, 0.5)),
+    to_test_params);
 
 // Does not work well with CUDA 10.1
 // TEST_P(FFTC2R2D, Complex32ToRealInputsPreserved) {
 //     fft_params params = GetParam();
 //     af::array a       = af::randu(params.input_dims_, c32);
 //     af::array a_copy  = a.copy();
-//     af::array out     = af::fftC2R<2>(a, params.is_odd_, params.norm_factor_);
+//     af::array out     = af::fftC2R<2>(a, params.is_odd_,
+//     params.norm_factor_);
 //
 //     ASSERT_ARRAYS_EQ(a_copy, a);
 // }
@@ -803,7 +801,8 @@ INSTANTIATE_TEST_CASE_P(InputsND, FFTC2R,
 //     fft_params params = GetParam();
 //     af::array a       = af::randu(params.input_dims_, c64);
 //     af::array a_copy  = a.copy();
-//     af::array out     = af::fftC2R<2>(a, params.is_odd_, params.norm_factor_);
+//     af::array out     = af::fftC2R<2>(a, params.is_odd_,
+//     params.norm_factor_);
 //
 //     ASSERT_ARRAYS_EQ(a_copy, a);
 // }
diff --git a/test/flat.cpp b/test/flat.cpp
index 8df08f0346..4e0748b5eb 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -39,7 +39,7 @@ TEST(FlatTests, Test_flat_2D_Half) {
     array in      = randu(num, num, f16);
     array out     = flat(in);
 
-    vector<half_float::half> gold(num*num);
+    vector<half_float::half> gold(num * num);
     in.host(&gold[0]);
 
     ASSERT_VEC_ARRAY_EQ(gold, dim4(num * num), out);
diff --git a/test/index.cpp b/test/index.cpp
index ef5fd11b9b..36ce80387a 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1328,16 +1328,16 @@ TEST(Indexing, SNIPPET_indexing_first) {
     af_print(A(end));  // last element
     // 9.0000
 
-    af_print(A(-1));   // also last element
+    af_print(A(-1));  // also last element
     // 9.0000
 
     af_print(A(end - 1));  // second-to-last element
     // 8.0000
 
-    af_print(A(1, span));      // second row
+    af_print(A(1, span));  // second row
     // 2.0000     5.0000     8.0000
 
-    af_print(A.row(end));      // last row
+    af_print(A.row(end));  // last row
     // 3.0000     6.0000     9.0000
 
     af_print(A.cols(1, end));  // all but first column
@@ -1454,7 +1454,7 @@ TEST(Indexing, SNIPPET_indexing_set) {
     // 3.1415     4.0000     4.0000
 
     // copy in another matrix
-    array B  = constant(1, 4, 4, s32);
+    array B = constant(1, 4, 4, s32);
     af_print(B);
     //          1          1          1          1
     //          1          1          1          1
diff --git a/test/jit.cpp b/test/jit.cpp
index 3e315400ea..9f774c6a45 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -654,13 +654,11 @@ void testTwoLargeNonLinear(const af_dtype dt) {
     ASSERT_VEC_ARRAY_EQ(gold, a.dims(), c.as(f32));
 }
 
-TEST(JIT, TwoLargeNonLinear) {
-  testTwoLargeNonLinear(f32);
-}
+TEST(JIT, TwoLargeNonLinear) { testTwoLargeNonLinear(f32); }
 
 TEST(JIT, TwoLargeNonLinearHalf) {
-  if (noHalfTests(f16)) return;
-  testTwoLargeNonLinear(f16);
+    if (noHalfTests(f16)) return;
+    testTwoLargeNonLinear(f16);
 }
 
 std::string select_info(
diff --git a/test/join.cpp b/test/join.cpp
index f747d1a3c3..711c1efcb7 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -44,7 +44,8 @@ class Join : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort, af_half>
+                         intl, uintl, char, unsigned char, short, ushort,
+                         af_half>
     TestTypes;
 
 // register the type list
diff --git a/test/mean.cpp b/test/mean.cpp
index a3a7a31558..520d74c195 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -9,6 +9,7 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
@@ -17,7 +18,6 @@
 #include <iostream>
 #include <string>
 #include <vector>
-#include <half.hpp>
 
 using af::array;
 using af::cdouble;
@@ -37,7 +37,8 @@ class Mean : public ::testing::Test {
 };
 
 // create a list of types to be tested
-// This list does not allow to cleanly add the af_half/half_float type : at the moment half tested in some special unittests
+// This list does not allow to cleanly add the af_half/half_float type : at the
+// moment half tested in some special unittests
 typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, intl, uintl,
                          char, uchar, short, ushort, half_float::half>
     TestTypes;
@@ -71,8 +72,8 @@ struct meanOutType {
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
             is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
             is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
-            is_same_type<T, char>::value , float, typename elseType<T>::type>::type
-        type;
+            is_same_type<T, char>::value,
+        float, typename elseType<T>::type>::type type;
 };
 
 template<typename T>
@@ -82,7 +83,7 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
     SUPPORTED_TYPE_CHECK(outType);
 
     double tol = 1.0e-3;
-    if((af_dtype)af::dtype_traits<T>::af_type == f16) tol = 4.e-3;
+    if ((af_dtype)af::dtype_traits<T>::af_type == f16) tol = 4.e-3;
     vector<dim4> numDims;
     vector<vector<int> > in;
     vector<vector<float> > tests;
@@ -114,8 +115,7 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
         dim4 wdims = numDims[1];
         vector<T> input(in[0].begin(), in[0].end());
         vector<float> weights(in[1].size());
-        transform(in[1].begin(), in[1].end(),
-                  weights.begin(),
+        transform(in[1].begin(), in[1].end(), weights.begin(),
                   convert_to<float, int>);
 
         array inArray(dims, &(input.front()));
@@ -170,7 +170,6 @@ TYPED_TEST(Mean, Wtd_Dim1Matrix) {
                            true);
 }
 
-
 template<typename T>
 void meanAllTest(T const_value, dim4 dims) {
     typedef typename meanOutType<T>::type outType;
@@ -195,7 +194,6 @@ void meanAllTest(T const_value, dim4 dims) {
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 }
 
-
 template<>
 void meanAllTest(half_float::half const_value, dim4 dims) {
     SUPPORTED_TYPE_CHECK(half_float::half);
@@ -209,8 +207,8 @@ void meanAllTest(half_float::half const_value, dim4 dims) {
     for (int i = 0; i < (int)hundred.size(); i++) { gold = gold + hundred[i]; }
     gold = gold / dims.elements();
 
-    array a = array(dims, &(hundred.front())).as(f16);
-    half output = mean<half>(a);
+    array a         = array(dims, &(hundred.front())).as(f16);
+    half output     = mean<half>(a);
     af_half output2 = mean<af_half>(a);
 
     // make sure output2 and output are binary equals. This is necessary
@@ -222,7 +220,6 @@ void meanAllTest(half_float::half const_value, dim4 dims) {
     ASSERT_NEAR(output, gold, 1.0e-3);
 }
 
-
 TEST(MeanAll, f64) { meanAllTest<double>(2.1, dim4(10, 10, 1, 1)); }
 
 TEST(MeanAll, f32) { meanAllTest<float>(2.1f, dim4(10, 5, 2, 1)); }
@@ -254,7 +251,7 @@ template<>
 half random<half>() {
     // create values from -0.5 to 0.5 to ensure sum does not deviate
     // too far out of half's useful range
-    float r = static_cast<float>(rand()) / static_cast<float>(RAND_MAX)-0.5f;
+    float r = static_cast<float>(rand()) / static_cast<float>(RAND_MAX) - 0.5f;
     return half(r);
 }
 
@@ -357,9 +354,9 @@ TEST(Mean, Issue2093) {
 }
 
 TEST(MeanAll, SubArray) {
-    //Fixes Issue 2636
-    using af::span;
+    // Fixes Issue 2636
     using af::mean;
+    using af::span;
     using af::sum;
 
     const dim4 inDims(10, 10, 10, 10);
@@ -368,7 +365,7 @@ TEST(MeanAll, SubArray) {
     array sub = in(0, span, span, span);
 
     size_t nElems = sub.elements();
-    ASSERT_FLOAT_EQ(mean<float>(sub), sum<float>(sub)/nElems);
+    ASSERT_FLOAT_EQ(mean<float>(sub), sum<float>(sub) / nElems);
 }
 
 TEST(MeanHalf, dim0) {
@@ -379,6 +376,7 @@ TEST(MeanHalf, dim0) {
     array in  = randu(inDims, f16);
     array m16 = af::mean(in, 0);
     array m32 = af::mean(in.as(f32), 0);
-    // Some diffs appears at 0.0001 max diff : example: float: 0.507014 vs half: 0.506836
+    // Some diffs appears at 0.0001 max diff : example: float: 0.507014 vs half:
+    // 0.506836
     ASSERT_ARRAYS_NEAR(m16.as(f32), m32, 0.001f);
 }
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 81cd680ee1..fb280c058b 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -114,7 +114,8 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
 
         // Cast to the expected type
         af_array in = 0;
-        ASSERT_SUCCESS(af_cast(&in, test.in_, (af_dtype)dtype_traits<T>::af_type));
+        ASSERT_SUCCESS(
+            af_cast(&in, test.in_, (af_dtype)dtype_traits<T>::af_type));
 
         EXPECT_EQ(AF_SUCCESS, af_meanvar(&mean, &var, in, test.weights_,
                                          test.bias_, test.dim_));
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 9c4815c25a..1ae10acae5 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -530,7 +530,7 @@ TEST(NearestNeighbour, DocSnippet1) {
     //! [ex_nearest_1]
 
     unsigned int h_gold_idx[3] = {0, 1, 2};
-    float h_gold_dist[3] = {0.0625f, 0.5625f, 3.0625f};
+    float h_gold_dist[3]       = {0.0625f, 0.5625f, 3.0625f};
     array gold_idx(dim4(3), h_gold_idx);
     array gold_dist(dim4(3), h_gold_dist);
     ASSERT_ARRAYS_EQ(gold_idx, idx);
@@ -539,19 +539,14 @@ TEST(NearestNeighbour, DocSnippet1) {
 
 TEST(NearestNeighbour, DocSnippet2) {
     //! [ex_nearest_2]
-    float h_pts[18] = {0.f, 0.f, 0.f,
-                       1.f, 0.f, 0.f,
-                       0.f, 1.f, 0.f,
-                       8.f, 9.f, 1.f,
-                       9.f, 8.f, 1.f,
-                       9.f, 9.f, 1.f};
+    float h_pts[18] = {0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f, 0.f,
+                       8.f, 9.f, 1.f, 9.f, 8.f, 1.f, 9.f, 9.f, 1.f};
     array pts(dim4(3, 6), h_pts);
     //  0.    1.    0.    8.    9.    9.
     //  0.    0.    1.    9.    8.    9.
     //  0.    0.    0.    1.    1.    1.
 
-    float h_query[6] = {1.5f, 0.f, 0.f,
-                        7.5f, 9.f, 1.f};
+    float h_query[6] = {1.5f, 0.f, 0.f, 7.5f, 9.f, 1.f};
     array query(dim4(3, 2), h_query);
     //  1.5   7.5
     //  0.    9.
@@ -571,10 +566,8 @@ TEST(NearestNeighbour, DocSnippet2) {
     //  3.25  3.25
     //! [ex_nearest_2]
 
-    unsigned int h_gold_idx[6] = {1, 0, 2,
-                                  3, 5, 4};
-    float h_gold_dist[6] = {0.25f, 2.25f, 3.25f,
-                            0.25f, 2.25f, 3.25f};
+    unsigned int h_gold_idx[6] = {1, 0, 2, 3, 5, 4};
+    float h_gold_dist[6]       = {0.25f, 2.25f, 3.25f, 0.25f, 2.25f, 3.25f};
     array gold_idx(dim4(3, 2), h_gold_idx);
     array gold_dist(dim4(3, 2), h_gold_dist);
     ASSERT_ARRAYS_EQ(gold_idx, idx);
diff --git a/test/nodevice.cpp b/test/nodevice.cpp
index c37051b4ec..f81438b908 100644
--- a/test/nodevice.cpp
+++ b/test/nodevice.cpp
@@ -14,16 +14,12 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 
-TEST(NoDevice, Info) {
-    ASSERT_SUCCESS(af_info());
-}
+TEST(NoDevice, Info) { ASSERT_SUCCESS(af_info()); }
 
-TEST(NoDevice, InfoCxx) {
-    af::info();
-}
+TEST(NoDevice, InfoCxx) { af::info(); }
 
 TEST(NoDevice, InfoString) {
-    char *str;
+    char* str;
     ASSERT_SUCCESS(af_info_string(&str, true));
     ASSERT_SUCCESS(af_free_host((void*)str));
 }
@@ -68,6 +64,4 @@ TEST(NoDevice, GetVersion) {
     ASSERT_EQ(AF_VERSION_PATCH, patch);
 }
 
-TEST(NoDevice, GetRevision) {
-    const char* revision = af_get_revision();
-}
+TEST(NoDevice, GetRevision) { const char* revision = af_get_revision(); }
diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 7ba9aac20c..d6e27b20ee 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -111,7 +111,7 @@ template<typename T>
 double relEps(array in) {
     typedef typename af::dtype_traits<T>::base_type InBaseType;
     double fixed_eps = eps<T>();
-    double calc_eps = std::numeric_limits<InBaseType>::epsilon() *
+    double calc_eps  = std::numeric_limits<InBaseType>::epsilon() *
                       std::max(in.dims(0), in.dims(1)) * af::max<double>(in);
     // Use the fixed values above if calculated error tolerance is unnecessarily
     // too small
diff --git a/test/range.cpp b/test/range.cpp
index f3c4b0d5a0..78e7782379 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -9,8 +9,8 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
-#include <testHelpers.hpp>
 #include <half.hpp>
+#include <testHelpers.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
diff --git a/test/reduce.cpp b/test/reduce.cpp
index a799f05318..d7e2d129de 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -1446,14 +1446,13 @@ TEST(ReduceHalf, AllTrue) {
 // Documentation Snippets
 
 TEST(Reduce, SNIPPET_sum_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
 
     //! [ex_reduce_sum_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 2 3 4 5 6 7 8 9 ];
 
     array okeys, ovals;
     sumByKey(okeys, ovals, keys, vals);
@@ -1463,21 +1462,17 @@ TEST(Reduce, SNIPPET_sum_by_key) {
 
     //! [ex_reduce_sum_by_key]
 
-    vector<int> gold_keys   = { 0, 1, 0, 2 };
-    vector<float> gold_vals = { 3, 12, 13, 17 };
+    vector<int> gold_keys   = {0, 1, 0, 2};
+    vector<float> gold_vals = {3, 12, 13, 17};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
 }
 
 TEST(Reduce, SNIPPET_sum_by_key_dim) {
-    int hkeys[]   = {1, 0, 0, 2, 2 };
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    float hvals[] = {1, 6,
-                     2, 7,
-                     3, 8,
-                     4, 9,
-                     5, 10};
+    float hvals[] = {1, 6, 2, 7, 3, 8, 4, 9, 5, 10};
 
     //! [ex_reduce_sum_by_key_dim]
 
@@ -1500,22 +1495,21 @@ TEST(Reduce, SNIPPET_sum_by_key_dim) {
 
     //! [ex_reduce_sum_by_key_dim]
 
-    vector<int> gold_keys   = { 1, 0, 2  };
-    vector<float> gold_vals = { 1, 6, 5, 15, 9, 19 };
+    vector<int> gold_keys   = {1, 0, 2};
+    vector<float> gold_vals = {1, 6, 5, 15, 9, 19};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
 }
 
 TEST(Reduce, SNIPPET_product_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
 
     //! [ex_reduce_product_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 2 3 4 5 6 7 8 9 ];
 
     array okeys, ovals;
     productByKey(okeys, ovals, keys, vals);
@@ -1525,21 +1519,17 @@ TEST(Reduce, SNIPPET_product_by_key) {
 
     //! [ex_reduce_product_by_key]
 
-    vector<int> gold_keys   = { 0,  1,  0,  2 };
-    vector<float> gold_vals = { 2, 60, 42, 72 };
+    vector<int> gold_keys   = {0, 1, 0, 2};
+    vector<float> gold_vals = {2, 60, 42, 72};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
 }
 
 TEST(Reduce, SNIPPET_product_by_key_dim) {
-    int hkeys[]   = {1, 0, 0, 2, 2 };
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    float hvals[] = {1, 6,
-                     2, 7,
-                     3, 8,
-                     4, 9,
-                     5, 10};
+    float hvals[] = {1, 6, 2, 7, 3, 8, 4, 9, 5, 10};
 
     //! [ex_reduce_product_by_key_dim]
 
@@ -1562,22 +1552,21 @@ TEST(Reduce, SNIPPET_product_by_key_dim) {
 
     //! [ex_reduce_product_by_key_dim]
 
-    vector<int> gold_keys   = { 1, 0, 2  };
-    vector<float> gold_vals = { 1, 6, 6, 56, 20, 90 };
+    vector<int> gold_keys   = {1, 0, 2};
+    vector<float> gold_vals = {1, 6, 6, 56, 20, 90};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
 }
 
 TEST(Reduce, SNIPPET_min_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
 
     //! [ex_reduce_min_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 2 3 4 5 6 7 8 9 ];
 
     array okeys, ovals;
     minByKey(okeys, ovals, keys, vals);
@@ -1587,21 +1576,17 @@ TEST(Reduce, SNIPPET_min_by_key) {
 
     //! [ex_reduce_min_by_key]
 
-    vector<int> gold_keys   = { 0, 1, 0, 2 };
-    vector<float> gold_vals = { 1, 3, 6, 8 };
+    vector<int> gold_keys   = {0, 1, 0, 2};
+    vector<float> gold_vals = {1, 3, 6, 8};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
 }
 
 TEST(Reduce, SNIPPET_min_by_key_dim) {
-    int hkeys[]   = {1, 0, 0, 2, 2 };
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    float hvals[] = {1, 6,
-                     2, 7,
-                     3, 8,
-                     4, 9,
-                     5, 10};
+    float hvals[] = {1, 6, 2, 7, 3, 8, 4, 9, 5, 10};
 
     //! [ex_reduce_min_by_key_dim]
 
@@ -1624,22 +1609,21 @@ TEST(Reduce, SNIPPET_min_by_key_dim) {
 
     //! [ex_reduce_min_by_key_dim]
 
-    vector<int> gold_keys   = { 1, 0, 2  };
-    vector<float> gold_vals = { 1, 6, 2, 7, 4, 9 };
+    vector<int> gold_keys   = {1, 0, 2};
+    vector<float> gold_vals = {1, 6, 2, 7, 4, 9};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
 }
 
 TEST(Reduce, SNIPPET_max_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
 
     //! [ex_reduce_max_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 2 3 4 5 6 7 8 9 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 2 3 4 5 6 7 8 9 ];
 
     array okeys, ovals;
     maxByKey(okeys, ovals, keys, vals);
@@ -1649,21 +1633,17 @@ TEST(Reduce, SNIPPET_max_by_key) {
 
     //! [ex_reduce_max_by_key]
 
-    vector<int> gold_keys   = { 0, 1, 0, 2 };
-    vector<float> gold_vals = { 2, 5, 7, 9 };
+    vector<int> gold_keys   = {0, 1, 0, 2};
+    vector<float> gold_vals = {2, 5, 7, 9};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
 }
 
 TEST(Reduce, SNIPPET_max_by_key_dim) {
-    int hkeys[]   = {1, 0, 0, 2, 2 };
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    float hvals[] = {1, 6,
-                     2, 7,
-                     3, 8,
-                     4, 9,
-                     5, 10};
+    float hvals[] = {1, 6, 2, 7, 3, 8, 4, 9, 5, 10};
 
     //! [ex_reduce_max_by_key_dim]
 
@@ -1686,22 +1666,21 @@ TEST(Reduce, SNIPPET_max_by_key_dim) {
 
     //! [ex_reduce_max_by_key_dim]
 
-    vector<int> gold_keys   = { 1, 0, 2  };
-    vector<float> gold_vals = { 1, 6, 3, 8, 5, 10 };
+    vector<int> gold_keys   = {1, 0, 2};
+    vector<float> gold_vals = {1, 6, 3, 8, 5, 10};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
 }
 
 TEST(Reduce, SNIPPET_alltrue_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 1, 0, 1, 1, 0, 0, 1, 0 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 1, 0, 1, 1, 0, 0, 1, 0};
 
     //! [ex_reduce_alltrue_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 1 0 1 1 0 0 1 0 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 1 0 1 1 0 0 1 0 ];
 
     array okeys, ovals;
     allTrueByKey(okeys, ovals, keys, vals);
@@ -1711,21 +1690,17 @@ TEST(Reduce, SNIPPET_alltrue_by_key) {
 
     //! [ex_reduce_alltrue_by_key]
 
-    vector<int>  gold_keys   = { 0, 1, 0, 2 };
-    vector<unsigned char> gold_vals = { 1, 0, 0, 0 };
+    vector<int> gold_keys           = {0, 1, 0, 2};
+    vector<unsigned char> gold_vals = {1, 0, 0, 0};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals.as(u8));
 }
 
 TEST(Reduce, SNIPPET_alltrue_by_key_dim) {
-    int hkeys[]   = {1, 0, 0, 2, 2 };
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    float hvals[] = {1, 0,
-                     1, 1,
-                     1, 0,
-                     0, 1,
-                     1, 1};
+    float hvals[] = {1, 0, 1, 1, 1, 0, 0, 1, 1, 1};
 
     //! [ex_reduce_alltrue_by_key_dim]
 
@@ -1748,22 +1723,21 @@ TEST(Reduce, SNIPPET_alltrue_by_key_dim) {
 
     //! [ex_reduce_alltrue_by_key_dim]
 
-    vector<int> gold_keys   = { 1, 0, 2  };
-    vector<unsigned char> gold_vals = { 1, 0, 1, 0, 0, 1 };
+    vector<int> gold_keys           = {1, 0, 2};
+    vector<unsigned char> gold_vals = {1, 0, 1, 0, 0, 1};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals.as(u8));
 }
 
 TEST(Reduce, SNIPPET_anytrue_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 1, 0, 1, 1, 0, 0, 1, 0 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 1, 0, 1, 1, 0, 0, 1, 0};
 
     //! [ex_reduce_anytrue_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 1 0 1 1 0 0 1 0 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 1 0 1 1 0 0 1 0 ];
 
     array okeys, ovals;
     anyTrueByKey(okeys, ovals, keys, vals);
@@ -1773,21 +1747,17 @@ TEST(Reduce, SNIPPET_anytrue_by_key) {
 
     //! [ex_reduce_anytrue_by_key]
 
-    vector<int> gold_keys   = { 0, 1, 0, 2 };
-    vector<unsigned char> gold_vals = { 1, 1, 0, 1 };
+    vector<int> gold_keys           = {0, 1, 0, 2};
+    vector<unsigned char> gold_vals = {1, 1, 0, 1};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals.as(u8));
 }
 
 TEST(Reduce, SNIPPET_anytrue_by_key_dim) {
-    int hkeys[]   = {1, 0, 0, 2, 2 };
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    float hvals[] = {1, 0,
-                     1, 1,
-                     1, 0,
-                     0, 1,
-                     1, 1};
+    float hvals[] = {1, 0, 1, 1, 1, 0, 0, 1, 1, 1};
 
     //! [ex_reduce_anytrue_by_key_dim]
 
@@ -1810,22 +1780,21 @@ TEST(Reduce, SNIPPET_anytrue_by_key_dim) {
 
     //! [ex_reduce_anytrue_by_key_dim]
 
-    vector<int> gold_keys   = { 1, 0, 2  };
-    vector<unsigned char> gold_vals = { 1, 0, 1, 1, 1, 1 };
+    vector<int> gold_keys           = {1, 0, 2};
+    vector<unsigned char> gold_vals = {1, 0, 1, 1, 1, 1};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals.as(u8));
 }
 
 TEST(Reduce, SNIPPET_count_by_key) {
-
-    int hkeys[]   = { 0, 0, 1, 1, 1, 0, 0, 2, 2 };
-    float hvals[] = { 1, 1, 0, 1, 1, 0, 0, 1, 0 };
+    int hkeys[]   = {0, 0, 1, 1, 1, 0, 0, 2, 2};
+    float hvals[] = {1, 1, 0, 1, 1, 0, 0, 1, 0};
 
     //! [ex_reduce_count_by_key]
 
-    array keys(9, hkeys); // keys = [ 0 0 1 1 1 0 0 2 2 ]
-    array vals(9, hvals); // vals = [ 1 1 0 1 1 0 0 1 0 ];
+    array keys(9, hkeys);  // keys = [ 0 0 1 1 1 0 0 2 2 ]
+    array vals(9, hvals);  // vals = [ 1 1 0 1 1 0 0 1 0 ];
 
     array okeys, ovals;
     countByKey(okeys, ovals, keys, vals);
@@ -1835,22 +1804,17 @@ TEST(Reduce, SNIPPET_count_by_key) {
 
     //! [ex_reduce_count_by_key]
 
-    vector<int> gold_keys      = { 0, 1, 0, 2 };
-    vector<unsigned> gold_vals = { 2, 2, 0, 1 };
+    vector<int> gold_keys      = {0, 1, 0, 2};
+    vector<unsigned> gold_vals = {2, 2, 0, 1};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(4), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(4), ovals);
 }
 
 TEST(Reduce, SNIPPET_count_by_key_dim) {
+    int hkeys[] = {1, 0, 0, 2, 2};
 
-    int hkeys[]   = {1, 0, 0, 2, 2 };
-
-    float hvals[] = {1, 0,
-                     1, 1,
-                     1, 0,
-                     0, 1,
-                     1, 1};
+    float hvals[] = {1, 0, 1, 1, 1, 0, 0, 1, 1, 1};
 
     //! [ex_reduce_count_by_key_dim]
 
@@ -1862,7 +1826,6 @@ TEST(Reduce, SNIPPET_count_by_key_dim) {
     // vals = [[ 1 1 1 0 1 ]
     //         [ 0 1 0 1 1 ]]
 
-
     const int reduce_dim = 1;
     array okeys, ovals;
     countByKey(okeys, ovals, keys, vals, reduce_dim);
@@ -1874,8 +1837,8 @@ TEST(Reduce, SNIPPET_count_by_key_dim) {
 
     //! [ex_reduce_count_by_key_dim]
 
-    vector<int> gold_keys      = { 1, 0, 2  };
-    vector<unsigned> gold_vals = { 1, 0, 2, 1, 1, 2 };
+    vector<int> gold_keys      = {1, 0, 2};
+    vector<unsigned> gold_vals = {1, 0, 2, 1, 1, 2};
 
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
diff --git a/test/replace.cpp b/test/replace.cpp
index aa91ec3e0f..c8787dc5ee 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -9,10 +9,10 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <half.hpp>
 #include <iostream>
 #include <string>
 #include <vector>
@@ -32,8 +32,8 @@ using std::vector;
 template<typename T>
 class Replace : public ::testing::Test {};
 
-typedef ::testing::Types<half_float::half, float, double, cfloat, cdouble, uint, int, intl, uintl,
-                         uchar, char, short, ushort>
+typedef ::testing::Types<half_float::half, float, double, cfloat, cdouble, uint,
+                         int, intl, uintl, uchar, char, short, ushort>
     TestTypes;
 
 TYPED_TEST_CASE(Replace, TestTypes);
diff --git a/test/scan.cpp b/test/scan.cpp
index 580a4acd9e..cc42624ba9 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -54,9 +54,7 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef = false,
     dim4 dims = numDims[0];
 
     vector<Ti> in(data[0].size());
-    transform(data[0].begin(), data[0].end(),
-              in.begin(),
-              convert_to<Ti, int>);
+    transform(data[0].begin(), data[0].end(), in.begin(), convert_to<Ti, int>);
 
     af_array inArray   = 0;
     af_array outArray  = 0;
@@ -138,8 +136,7 @@ TEST(Accum, CPP) {
     dim4 dims = numDims[0];
 
     vector<float> in(data[0].size());
-    transform(data[0].begin(), data[0].end(),
-              in.begin(),
+    transform(data[0].begin(), data[0].end(), in.begin(),
               convert_to<float, int>);
 
     array input(dims, &(in.front()));
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
index 783f9fee7c..fe4d61d095 100644
--- a/test/scan_by_key.cpp
+++ b/test/scan_by_key.cpp
@@ -225,8 +225,8 @@ TEST(ScanByKey, FixOverflowWrite) {
     vector<float> vals(SIZE, 1.0f);
 
     array someVals = array(SIZE, vals.data());
-    array keysAF = array(SIZE, s32);
-    array valsAF = array(SIZE, vals.data());
+    array keysAF   = array(SIZE, s32);
+    array valsAF   = array(SIZE, vals.data());
 
     keysAF = array(SIZE, keys.data());
 
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 8acd873108..c1e7306b48 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -75,7 +75,6 @@ void testSobelDerivatives(string pTestFile) {
     ASSERT_SUCCESS(af_release_array(dyArray));
 }
 
-
 // rectangle test data is generated using opencv
 // border type is set to cv.BORDER_REFLECT_101 in opencv
 
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index f10623ba67..9eee997b29 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -82,8 +82,7 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
 
     vector<T> sxTest(tests[resultIdx0].size());
     transform(tests[resultIdx0].begin(), tests[resultIdx0].end(),
-              sxTest.begin(),
-              convert_to<T, float>);
+              sxTest.begin(), convert_to<T, float>);
 
     ASSERT_VEC_ARRAY_EQ(sxTest, idims, sxArray);
 
@@ -145,8 +144,7 @@ TEST(SortIndex, CPPDim0) {
 
     vector<unsigned> ixTest(tests[resultIdx1].size());
     transform(tests[resultIdx1].begin(), tests[resultIdx1].end(),
-              ixTest.begin(),
-              convert_to<unsigned, float>);
+              ixTest.begin(), convert_to<unsigned, float>);
 
     ASSERT_VEC_ARRAY_EQ(ixTest, idims, outIndices);
 }
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 1e92385536..75a577de56 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -260,18 +260,18 @@ TYPED_TEST(Sparse, EmptyDeepCopy) {
     EXPECT_EQ(0, sparseGetNNZ(b));
 }
 
-TEST(Sparse, CPPSparseFromHostArrays)
-{
+TEST(Sparse, CPPSparseFromHostArrays) {
     //! [ex_sparse_host_arrays]
 
-    float  vals[] = { 5, 8, 3, 6 };
-    int row_ptr[] = { 0, 0, 2, 3, 4 };
-    int col_idx[] = { 0, 1, 2, 1 };
+    float vals[]  = {5, 8, 3, 6};
+    int row_ptr[] = {0, 0, 2, 3, 4};
+    int col_idx[] = {0, 1, 2, 1};
     const int M = 4, N = 4, nnz = 4;
 
     // Create sparse array (CSR) from host pointers to values, row
     // pointers, and column indices.
-    array sparse = af::sparse(M, N, nnz, vals, row_ptr, col_idx, f32, AF_STORAGE_CSR, afHost);
+    array sparse = af::sparse(M, N, nnz, vals, row_ptr, col_idx, f32,
+                              AF_STORAGE_CSR, afHost);
 
     // sparse
     //     values:  [ 5.0, 8.0, 3.0, 6.0 ]
@@ -282,25 +282,25 @@ TEST(Sparse, CPPSparseFromHostArrays)
 
     array sparse_vals, sparse_row_ptr, sparse_col_idx;
     af::storage sparse_storage;
-    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage,
+                  sparse);
 
-    ASSERT_ARRAYS_EQ(sparse_vals   , array(dim4(nnz,1), vals));
-    ASSERT_ARRAYS_EQ(sparse_row_ptr, array(dim4(M+1,1), row_ptr));
-    ASSERT_ARRAYS_EQ(sparse_col_idx, array(dim4(nnz,1), col_idx));
+    ASSERT_ARRAYS_EQ(sparse_vals, array(dim4(nnz, 1), vals));
+    ASSERT_ARRAYS_EQ(sparse_row_ptr, array(dim4(M + 1, 1), row_ptr));
+    ASSERT_ARRAYS_EQ(sparse_col_idx, array(dim4(nnz, 1), col_idx));
     ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
     ASSERT_EQ(sparseGetNNZ(sparse), nnz);
 }
 
-TEST(Sparse, CPPSparseFromAFArrays)
-{
+TEST(Sparse, CPPSparseFromAFArrays) {
     //! [ex_sparse_af_arrays]
 
-    float v[] = { 5, 8, 3, 6 };
-    int   r[] = { 0, 0, 2, 3, 4 };
-    int   c[] = { 0, 1, 2, 1 };
+    float v[]   = {5, 8, 3, 6};
+    int r[]     = {0, 0, 2, 3, 4};
+    int c[]     = {0, 1, 2, 1};
     const int M = 4, N = 4, nnz = 4;
-    array    vals = array(dim4(nnz), v);
-    array row_ptr = array(dim4(M+1), r);
+    array vals    = array(dim4(nnz), v);
+    array row_ptr = array(dim4(M + 1), r);
     array col_idx = array(dim4(nnz), c);
 
     // Create sparse array (CSR) from af::arrays containing values,
@@ -316,23 +316,20 @@ TEST(Sparse, CPPSparseFromAFArrays)
 
     array sparse_vals, sparse_row_ptr, sparse_col_idx;
     af::storage sparse_storage;
-    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage,
+                  sparse);
 
-    ASSERT_ARRAYS_EQ(sparse_vals   , vals);
+    ASSERT_ARRAYS_EQ(sparse_vals, vals);
     ASSERT_ARRAYS_EQ(sparse_row_ptr, row_ptr);
     ASSERT_ARRAYS_EQ(sparse_col_idx, col_idx);
     ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
     ASSERT_EQ(sparseGetNNZ(sparse), nnz);
 }
 
-TEST(Sparse, CPPSparseFromDenseUsage)
-{
-    float dns[] = { 0, 5, 0, 0,
-                    0, 8, 0, 6,
-                    0, 0, 3, 0,
-                    0, 0, 0, 0 };
+TEST(Sparse, CPPSparseFromDenseUsage) {
+    float dns[] = {0, 5, 0, 0, 0, 8, 0, 6, 0, 0, 3, 0, 0, 0, 0, 0};
     const int M = 4, N = 4, nnz = 4;
-    array dense(dim4(M,N), dns);
+    array dense(dim4(M, N), dns);
 
     //! [ex_sparse_from_dense]
 
@@ -352,32 +349,29 @@ TEST(Sparse, CPPSparseFromDenseUsage)
 
     //! [ex_sparse_from_dense]
 
-    float v[] = { 5, 8, 3, 6 };
-    int   r[] = { 0, 0, 2, 3, 4 };
-    int   c[] = { 0, 1, 2, 1 };
-    array gold_vals(   dim4(nnz), v);
-    array gold_row_ptr(dim4(M+1), r);
+    float v[] = {5, 8, 3, 6};
+    int r[]   = {0, 0, 2, 3, 4};
+    int c[]   = {0, 1, 2, 1};
+    array gold_vals(dim4(nnz), v);
+    array gold_row_ptr(dim4(M + 1), r);
     array gold_col_idx(dim4(nnz), c);
 
     array sparse_vals, sparse_row_ptr, sparse_col_idx;
     af::storage sparse_storage;
-    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage,
+                  sparse);
 
-    ASSERT_ARRAYS_EQ(sparse_vals   , gold_vals);
+    ASSERT_ARRAYS_EQ(sparse_vals, gold_vals);
     ASSERT_ARRAYS_EQ(sparse_row_ptr, gold_row_ptr);
     ASSERT_ARRAYS_EQ(sparse_col_idx, gold_col_idx);
     ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
     ASSERT_EQ(sparseGetNNZ(sparse), nnz);
 }
 
-TEST(Sparse, CPPDenseToSparseToDenseUsage)
-{
-    float g[] = { 0, 5, 0, 0,
-                  0, 8, 0, 6,
-                  0, 0, 3, 0,
-                  0, 0, 0, 0 };
+TEST(Sparse, CPPDenseToSparseToDenseUsage) {
+    float g[]   = {0, 5, 0, 0, 0, 8, 0, 6, 0, 0, 3, 0, 0, 0, 0, 0};
     const int M = 4, N = 4;
-    array in(dim4(M,N), g);
+    array in(dim4(M, N), g);
     array sparse = af::sparse(in, AF_STORAGE_CSR);
 
     //! [ex_dense_from_sparse]
@@ -398,26 +392,27 @@ TEST(Sparse, CPPDenseToSparseToDenseUsage)
 
     //! [ex_dense_from_sparse]
 
-    float v[] = { 5, 8, 3, 6 };
-    int   r[] = { 0, 0, 2, 3, 4 };
-    int   c[] = { 0, 1, 2, 1 };
+    float v[]     = {5, 8, 3, 6};
+    int r[]       = {0, 0, 2, 3, 4};
+    int c[]       = {0, 1, 2, 1};
     const int nnz = 4;
-    array gold_vals(   dim4(nnz), v);
-    array gold_row_ptr(dim4(M+1), r);
+    array gold_vals(dim4(nnz), v);
+    array gold_row_ptr(dim4(M + 1), r);
     array gold_col_idx(dim4(nnz), c);
 
     array sparse_vals, sparse_row_ptr, sparse_col_idx;
     af::storage sparse_storage;
-    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage, sparse);
+    sparseGetInfo(sparse_vals, sparse_row_ptr, sparse_col_idx, sparse_storage,
+                  sparse);
 
-    ASSERT_ARRAYS_EQ(sparse_vals   , gold_vals);
+    ASSERT_ARRAYS_EQ(sparse_vals, gold_vals);
     ASSERT_ARRAYS_EQ(sparse_row_ptr, gold_row_ptr);
     ASSERT_ARRAYS_EQ(sparse_col_idx, gold_col_idx);
     ASSERT_EQ(sparse_storage, AF_STORAGE_CSR);
     ASSERT_EQ(sparseGetNNZ(sparse), nnz);
 
     // Check dense array
-    array gold(dim4(M,N), g);
+    array gold(dim4(M, N), g);
     ASSERT_ARRAYS_EQ(in, gold);
     ASSERT_ARRAYS_EQ(dense, gold);
 }
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index daa4d144fc..c8c36450ab 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -391,18 +391,14 @@ TEST(SparseSparseArith, LinearProgrammingData) {
 }
 
 TEST(SparseSparseArith, SubsequentCircuitSimData) {
-    std::string file1(MTX_TEST_DIR
-                      "Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
-    std::string file2(MTX_TEST_DIR
-                      "Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
+    std::string file1(MTX_TEST_DIR "Sandia/oscil_dcop_12/oscil_dcop_12.mtx");
+    std::string file2(MTX_TEST_DIR "Sandia/oscil_dcop_42/oscil_dcop_42.mtx");
     ssArithmeticMTX<af_sub_t>(file1.c_str(), file2.c_str());
 }
 
 TEST(SparseSparseArith, QuantumChemistryData) {
-    std::string file1(MTX_TEST_DIR
-                      "QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
-    std::string file2(MTX_TEST_DIR
-                      "QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
+    std::string file1(MTX_TEST_DIR "QCD/conf6_0-4x4-20/conf6_0-4x4-20.mtx");
+    std::string file2(MTX_TEST_DIR "QCD/conf6_0-4x4-30/conf6_0-4x4-30.mtx");
     ssArithmeticMTX<af_add_t>(file1.c_str(), file2.c_str());
 }
 #endif
diff --git a/test/stdev.cpp b/test/stdev.cpp
index 51879c6dff..aef4099886 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -194,16 +194,14 @@ TYPED_TEST(StandardDev, All) {
 
     dim4 dims = numDims[0];
     vector<TypeParam> input(in[0].size());
-    transform(in[0].begin(), in[0].end(),
-              input.begin(),
+    transform(in[0].begin(), in[0].end(), input.begin(),
               convert_to<TypeParam, int>);
 
     array a(dims, &(input.front()));
     outType b = stdev<outType>(a);
 
     vector<outType> currGoldBar(tests[0].size());
-    transform(tests[0].begin(), tests[0].end(),
-              currGoldBar.begin(),
+    transform(tests[0].begin(), tests[0].end(), currGoldBar.begin(),
               convert_to<outType, float>);
 
     ASSERT_NEAR(::real(currGoldBar[0]), ::real(b), 1.0e-3);
diff --git a/test/transform.cpp b/test/transform.cpp
index 5618191cf0..b5bf76f2ec 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -441,7 +441,7 @@ class TransformNullArgs : public TransformV2TuxNearest<float> {
 };
 
 TEST_F(TransformNullArgs, NullOutputPtr) {
-    af_array* out_ptr = 0;
+    af_array *out_ptr = 0;
     ASSERT_EQ(AF_ERR_ARG,
               af_transform(out_ptr, this->in, this->transform, this->odim0,
                            this->odim1, this->method, this->invert));
@@ -455,12 +455,12 @@ TEST_F(TransformNullArgs, NullInputArray) {
 
 TEST_F(TransformNullArgs, NullTransformArray) {
     ASSERT_EQ(AF_ERR_ARG,
-              af_transform(&this->out, this->in, 0, this->odim0,
-                           this->odim1, this->method, this->invert));
+              af_transform(&this->out, this->in, 0, this->odim0, this->odim1,
+                           this->method, this->invert));
 }
 
 TEST_F(TransformNullArgs, V2NullOutputPtr) {
-    af_array* out_ptr = 0;
+    af_array *out_ptr = 0;
     ASSERT_EQ(AF_ERR_ARG,
               af_transform_v2(out_ptr, this->in, this->transform, this->odim0,
                               this->odim1, this->method, this->invert));
@@ -474,8 +474,8 @@ TEST_F(TransformNullArgs, V2NullInputArray) {
 
 TEST_F(TransformNullArgs, V2NullTransformArray) {
     ASSERT_EQ(AF_ERR_ARG,
-              af_transform_v2(&this->out, this->in, 0, this->odim0,
-                              this->odim1, this->method, this->invert));
+              af_transform_v2(&this->out, this->in, 0, this->odim0, this->odim1,
+                              this->method, this->invert));
 }
 
 ///////////////////////////////////// CPP ////////////////////////////////
diff --git a/test/triangle.cpp b/test/triangle.cpp
index ab25d5f0ca..c7b9c7b029 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -9,8 +9,8 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
-#include <testHelpers.hpp>
 #include <half.hpp>
+#include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
diff --git a/test/where.cpp b/test/where.cpp
index caf9e80c7a..20913845a3 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -51,9 +51,7 @@ void whereTest(string pTestFile, bool isSubRef = false,
     dim4 dims = numDims[0];
 
     vector<T> in(data[0].size());
-    transform(data[0].begin(), data[0].end(),
-              in.begin(),
-              convert_to<T, int>);
+    transform(data[0].begin(), data[0].end(), in.begin(), convert_to<T, int>);
 
     af_array inArray   = 0;
     af_array outArray  = 0;
@@ -108,8 +106,7 @@ TYPED_TEST(Where, CPP) {
     dim4 dims = numDims[0];
 
     vector<float> in(data[0].size());
-    transform(data[0].begin(), data[0].end(),
-              in.begin(),
+    transform(data[0].begin(), data[0].end(), in.begin(),
               convert_to<float, int>);
 
     array input(dims, &in.front(), afHost);
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 5eeb0c65ae..7b6727bd5d 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -250,17 +250,13 @@ TEST(Wrap, DocSnippet) {
 }
 
 static void getInput(af_array *data, const dim_t *dims) {
-    float h_data[16] = { 10, 20, 20, 30,
-                         30, 40, 40, 50,
-                         30, 40, 40, 50,
-                         50, 60, 60, 70 };
+    float h_data[16] = {10, 20, 20, 30, 30, 40, 40, 50,
+                        30, 40, 40, 50, 50, 60, 60, 70};
     ASSERT_SUCCESS(af_create_array(data, &h_data[0], 2, dims, f32));
 }
 static void getGold(af_array *gold, const dim_t *dims) {
-    float h_gold[16]= { 10, 20, 30, 40,
-                        20, 30, 40, 50,
-                        30, 40, 50, 60,
-                        40, 50, 60, 70 };
+    float h_gold[16] = {10, 20, 30, 40, 20, 30, 40, 50,
+                        30, 40, 50, 60, 40, 50, 60, 70};
     ASSERT_SUCCESS(af_create_array(gold, &h_gold[0], 2, dims, f32));
 }
 
@@ -344,18 +340,17 @@ class WrapV2 : public WrapCommon {
         }
 
         // Taken from the Wrap.DocSnippet test
-        ASSERT_SUCCESS(af_wrap_v2(&out, this->in_,
-                                  4, 4,   // output dims
-                                  2, 2,   // window size
-                                  2, 2,   // stride
-                                  0, 0,   // padding
-                                  true)); // is_column
+        ASSERT_SUCCESS(af_wrap_v2(&out, this->in_, 4, 4,  // output dims
+                                  2, 2,                   // window size
+                                  2, 2,                   // stride
+                                  0, 0,                   // padding
+                                  true));                 // is_column
 
         ASSERT_SPECIAL_ARRAYS_EQ(this->gold_, out, &metadata);
     }
 
     void releaseArrays() {
-        if (this->in_ != 0)   { ASSERT_SUCCESS(af_release_array(this->in_)); }
+        if (this->in_ != 0) { ASSERT_SUCCESS(af_release_array(this->in_)); }
         if (this->gold_ != 0) { ASSERT_SUCCESS(af_release_array(this->gold_)); }
     }
 };
@@ -406,46 +401,42 @@ TYPED_TEST(WrapV2Simple, UseReorderedOutputArray) {
 class WrapNullArgs : public WrapCommon {};
 
 TEST_F(WrapNullArgs, NullOutputPtr) {
-    af_array* out_ptr = 0;
-    ASSERT_EQ(af_wrap(out_ptr, this->in_,
-                      4, 4,  // output dims
-                      2, 2,  // window size
-                      2, 2,  // stride
-                      0, 0,  // padding
-                      true), // is_column
+    af_array *out_ptr = 0;
+    ASSERT_EQ(af_wrap(out_ptr, this->in_, 4, 4,  // output dims
+                      2, 2,                      // window size
+                      2, 2,                      // stride
+                      0, 0,                      // padding
+                      true),                     // is_column
               AF_ERR_ARG);
 }
 
 TEST_F(WrapNullArgs, NullInputArray) {
     af_array out = 0;
-    ASSERT_EQ(af_wrap(&out, 0,
-                      4, 4,  // output dims
-                      2, 2,  // window size
-                      2, 2,  // stride
-                      0, 0,  // padding
-                      true), // is_column
+    ASSERT_EQ(af_wrap(&out, 0, 4, 4,  // output dims
+                      2, 2,           // window size
+                      2, 2,           // stride
+                      0, 0,           // padding
+                      true),          // is_column
               AF_ERR_ARG);
 }
 
 TEST_F(WrapNullArgs, V2NullOutputPtr) {
-    af_array* out_ptr = 0;
-    ASSERT_EQ(af_wrap_v2(out_ptr, this->in_,
-                         4, 4,  // output dims
-                         2, 2,  // window size
-                         2, 2,  // stride
-                         0, 0,  // padding
-                         true), // is_column
+    af_array *out_ptr = 0;
+    ASSERT_EQ(af_wrap_v2(out_ptr, this->in_, 4, 4,  // output dims
+                         2, 2,                      // window size
+                         2, 2,                      // stride
+                         0, 0,                      // padding
+                         true),                     // is_column
               AF_ERR_ARG);
 }
 
 TEST_F(WrapNullArgs, V2NullInputArray) {
     af_array out = 0;
-    ASSERT_EQ(af_wrap_v2(&out, 0,
-                         4, 4,  // output dims
-                         2, 2,  // window size
-                         2, 2,  // stride
-                         0, 0,  // padding
-                         true), // is_column
+    ASSERT_EQ(af_wrap_v2(&out, 0, 4, 4,  // output dims
+                         2, 2,           // window size
+                         2, 2,           // stride
+                         0, 0,           // padding
+                         true),          // is_column
               AF_ERR_ARG);
 }
 

From fd8ef2b86965adfb56ab66dacf172238855c6a45 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 14 Feb 2020 11:51:38 +0530
Subject: [PATCH 1845/2677] Clang format linter github action

---
 .github/workflows/clang-format-lint.yml | 38 +++++++++++++++++++++++++
 1 file changed, 38 insertions(+)
 create mode 100644 .github/workflows/clang-format-lint.yml

diff --git a/.github/workflows/clang-format-lint.yml b/.github/workflows/clang-format-lint.yml
new file mode 100644
index 0000000000..93a2957856
--- /dev/null
+++ b/.github/workflows/clang-format-lint.yml
@@ -0,0 +1,38 @@
+on:
+  push:
+    branches:
+    - master
+  pull_request:
+    branches:
+    - master
+
+name: ci
+
+jobs:
+  clang-format:
+      name: Clang Format Lint
+      runs-on: ubuntu-latest
+      steps:
+          - name: Checkout Respository
+            uses: actions/checkout@master
+
+          - name: Check Sources
+            uses: DoozyX/clang-format-lint-action@v0.5
+            with:
+              source: './src'
+              extensions: 'h,cpp,hpp'
+              clangFormatVersion: 9
+
+          - name: Check Tests
+            uses: DoozyX/clang-format-lint-action@v0.5
+            with:
+              source: './test'
+              extensions: 'h,cpp,hpp'
+              clangFormatVersion: 9
+
+          - name: Check Examples
+            uses: DoozyX/clang-format-lint-action@v0.5
+            with:
+              source: './examples'
+              extensions: 'h,cpp,hpp'
+              clangFormatVersion: 9

From 61da4c303a54c8d7e83836ab16a1ead2e8679185 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 14 Feb 2020 18:28:31 +0530
Subject: [PATCH 1846/2677] Remove gen expr use from COMPONENT prop of install

Generator expression based alternative seems to be working
from newer cmakes but not in older versions.
---
 CMakeModules/SplitDebugInfo.cmake | 65 +++++++++++++++++--------------
 1 file changed, 36 insertions(+), 29 deletions(-)

diff --git a/CMakeModules/SplitDebugInfo.cmake b/CMakeModules/SplitDebugInfo.cmake
index 560fa96c9e..3900c25a5d 100644
--- a/CMakeModules/SplitDebugInfo.cmake
+++ b/CMakeModules/SplitDebugInfo.cmake
@@ -37,59 +37,66 @@ function(af_split_debug_info _target _destination_dir)
   endif ()
 
   if (SPLIT_TOOL_EXISTS)
-    get_target_property(TARGET_TYPE ${_target} TYPE)
-    set(PREFIX_EXPR_1
-      "$<$<STREQUAL:$<TARGET_PROPERTY:${_target},PREFIX>,>:${CMAKE_${TARGET_TYPE}_PREFIX}>")
-    set(PREFIX_EXPR_2
-      "$<$<NOT:$<STREQUAL:$<TARGET_PROPERTY:${_target},PREFIX>,>>:$<TARGET_PROPERTY:${_target},PREFIX>>")
-    set(PREFIX_EXPR_FULL "${PREFIX_EXPR_1}${PREFIX_EXPR_2}")
+    get_target_property(TRGT_PREFIX ${_target} PREFIX)
+    if(TRGT_PREFIX)
+      set(prefix ${TRGT_PREFIX})
+    else()
+      get_target_property(TRGT_TYPE ${_target} TYPE)
+      set(prefix "${CMAKE_${TRGT_TYPE}_PREFIX}")
+    endif()
+
+    get_target_property(TRGT_OUT_NAME ${_target} OUTPUT_NAME)
+    if(TRGT_OUT_NAME)
+      set(outName ${TRGT_OUT_NAME})
+    else()
+      set(outName "${_target}")
+    endif()
 
-    # If a custom OUTPUT_NAME was specified, use it.
-    set(OUTPUT_NAME_EXPR_1
-        "$<$<STREQUAL:$<TARGET_PROPERTY:${_target},OUTPUT_NAME>,>:${_target}>")
-    set(OUTPUT_NAME_EXPR_2
-        "$<$<NOT:$<STREQUAL:$<TARGET_PROPERTY:${_target},OUTPUT_NAME>,>>:$<TARGET_PROPERTY:${_target},OUTPUT_NAME>>")
-    set(OUTPUT_NAME_EXPR "${OUTPUT_NAME_EXPR_1}${OUTPUT_NAME_EXPR_2}")
-    set(OUTPUT_NAME_FULL "${PREFIX_EXPR_FULL}${OUTPUT_NAME_EXPR}$<TARGET_PROPERTY:${_target},POSTFIX>")
+    get_target_property(TRGT_POSTFIX ${_target} POSTFIX)
+    if(TRGT_POSTFIX)
+      set(postfix ${TRGT_POSTFIX})
+    else()
+      get_target_property(TRGT_TYPE ${_target} TYPE)
+      set(postfix "${CMAKE_${TRGT_TYPE}_POSTFIX}")
+    endif()
 
-    set(SPLIT_DEBUG_TARGET_EXT ".debug")
+    set(OUT_NAME "${prefix}${outName}")
+    set(OUT_NAME_WE "${OUT_NAME}${postfix}")
+    set(SPLIT_DEBUG_OUT_FILE_EXT ".debug")
     if(APPLE)
-        set(SPLIT_DEBUG_TARGET_EXT ".dSYM")
+      set(SPLIT_DEBUG_OUT_FILE_EXT ".dSYM")
     endif()
-    set(SPLIT_DEBUG_SOURCE "$<TARGET_FILE:${_target}>")
-    set(SPLIT_DEBUG_TARGET_NAME
-        "$<TARGET_FILE_DIR:${_target}>/${OUTPUT_NAME_FULL}")
-    set(SPLIT_DEBUG_TARGET
-        "${SPLIT_DEBUG_TARGET_NAME}${SPLIT_DEBUG_TARGET_EXT}")
+    set(SPLIT_DEBUG_SRC_FILE "$<TARGET_FILE:${_target}>")
+    set(SPLIT_DEBUG_OUT_NAME "$<TARGET_FILE_DIR:${_target}>/${OUT_NAME_WE}")
+    set(SPLIT_DEBUG_OUT_FILE "${SPLIT_DEBUG_OUT_NAME}${SPLIT_DEBUG_OUT_FILE_EXT}")
 
     if(APPLE)
       add_custom_command(TARGET ${_target} POST_BUILD
-          COMMAND dsymutil ${SPLIT_DEBUG_SOURCE} -o ${SPLIT_DEBUG_TARGET}
+          COMMAND dsymutil ${SPLIT_DEBUG_SRC_FILE} -o ${SPLIT_DEBUG_OUT_FILE}
           #TODO(pradeep) From initial research stripping debug info from
           # is removing debug LC_ID_DYLIB command also which is make
           # shared library unusable. Confirm this from OSX expert
           # and remove these comments and below command
-          #COMMAND ${CMAKE_STRIP} --strip-debug ${SPLIT_DEBUG_SOURCE}
+          #COMMAND ${CMAKE_STRIP} --strip-debug ${SPLIT_DEBUG_SRC_FILE}
         )
     else(APPLE)
       add_custom_command(TARGET ${_target} POST_BUILD
         COMMAND ${CMAKE_OBJCOPY}
-          --only-keep-debug ${SPLIT_DEBUG_SOURCE} ${SPLIT_DEBUG_TARGET}
+          --only-keep-debug ${SPLIT_DEBUG_SRC_FILE} ${SPLIT_DEBUG_OUT_FILE}
         COMMAND ${CMAKE_STRIP}
-          --strip-debug ${SPLIT_DEBUG_SOURCE}
+          --strip-debug ${SPLIT_DEBUG_SRC_FILE}
         COMMAND ${CMAKE_OBJCOPY}
-          --add-gnu-debuglink=${SPLIT_DEBUG_TARGET} ${SPLIT_DEBUG_SOURCE}
+          --add-gnu-debuglink=${SPLIT_DEBUG_OUT_FILE} ${SPLIT_DEBUG_SRC_FILE}
         )
     endif()
 
-    install(FILES
-      ${SPLIT_DEBUG_TARGET}
+    install(FILES ${SPLIT_DEBUG_OUT_FILE}
       DESTINATION ${_destination_dir}
-      COMPONENT "${OUTPUT_NAME_FULL}_debug_symbols"
+      COMPONENT "${OUT_NAME}_debug_symbols"
       )
 
     # Make sure the file is deleted on `make clean`.
     set_property(DIRECTORY APPEND
-      PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${SPLIT_DEBUG_TARGET})
+      PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${SPLIT_DEBUG_OUT_FILE})
   endif(SPLIT_TOOL_EXISTS)
 endfunction(af_split_debug_info)

From fcf20a855bd421d3404ebf94dd414175c75e08c4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 14 Feb 2020 01:14:10 +0530
Subject: [PATCH 1847/2677] ci job to upload source archive to releases for
 tags

This job does a shallow clone since git history is not needed
---
 .github/workflows/release_src_artifact.yml | 50 ++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 .github/workflows/release_src_artifact.yml

diff --git a/.github/workflows/release_src_artifact.yml b/.github/workflows/release_src_artifact.yml
new file mode 100644
index 0000000000..0dee8ffea4
--- /dev/null
+++ b/.github/workflows/release_src_artifact.yml
@@ -0,0 +1,50 @@
+on:
+  push:
+    # Sequence of patterns matched against refs/tags
+    tags:
+    - 'v*' # Push events to tag names starting with v
+
+name: ci
+
+jobs:
+    upload_src_tarball:
+        name: Upload release source tarball
+        runs-on: ubuntu-18.04
+        steps:
+            - name: Fetch Repo Info
+              run: |
+                  tag=$(echo ${GITHUB_REF} | awk '{split($0, a, "/"); print a[3]}')
+                  ver=${tag:1}
+                  response=$(curl https://api.github.com/repos/${GITHUB_REPOSITORY}/releases/tags/${tag})
+                  id_line=$(echo "${response}" | grep -m 1 "id.:")
+                  rel_id=$(echo "${id_line}" | awk '{split($0, a, ":"); split(a[2], b, ","); print b[1]}')
+                  trimmed_rel_id=$(echo "${rel_id}" | awk '{gsub(/^[ \t]+/,""); print $0 }')
+                  echo "::set-env name=RELEASE_ID::${trimmed_rel_id}"
+                  echo "::set-env name=AF_TAG::${tag}"
+                  echo "::set-env name=AF_VER::${ver}"
+
+            - name: Checkout with Submodules
+              run: |
+                  cd ${GITHUB_WORKSPACE}
+                  clone_url="https://github.com/${GITHUB_REPOSITORY}"
+                  git clone --depth 1 --recursive -b ${AF_TAG} ${clone_url} arrayfire-full-${AF_VER}
+
+            - name: Create source tarball
+              id: create-src-tarball
+              run: |
+                  cd $GITHUB_WORKSPACE
+                  rm -rf arrayfire-full-${AF_VER}/.git
+                  rm -rf arrayfire-full-${AF_VER}/.github
+                  rm arrayfire-full-${AF_VER}/.gitmodules
+                  tar -cjf arrayfire-full-${AF_VER}.tar.bz2 arrayfire-full-${AF_VER}/
+                  echo "::set-env name=UPLOAD_FILE::arrayfire-full-${AF_VER}.tar.bz2"
+
+            - name: Upload source tarball
+              uses: actions/upload-release-asset@v1
+              env:
+                  GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+              with:
+                  upload_url: https://uploads.github.com/repos/${{ github.repository }}/releases/${{ env.RELEASE_ID }}/assets{?name,label}
+                  asset_path: ${{ env.UPLOAD_FILE }}
+                  asset_name: ${{ env.UPLOAD_FILE }}
+                  asset_content_type: application/x-bzip2

From 646b77bb6e50630c0c9850da8c8937c35d2ab0c9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Feb 2020 03:15:53 -0500
Subject: [PATCH 1848/2677] Fix evalMultiple when if the input array are not
 the same size

---
 src/backend/cpu/Array.cpp    | 13 ++++++++++
 src/backend/cuda/Array.cpp   | 12 +++++++++
 src/backend/cuda/join.cu     |  5 ++++
 src/backend/opencl/Array.cpp | 12 +++++++++
 src/backend/opencl/join.cpp  |  6 +++++
 test/join.cpp                | 47 ++++++++++++++++++++++++++++++++++++
 6 files changed, 95 insertions(+)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 4f9c8f0533..7c1d3a2de2 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -164,6 +164,19 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
     vector<Param<T>> params;
     if (getQueue().is_worker())
         AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+
+    // Check if all the arrays have the same dimension
+    auto it = std::adjacent_find(begin(array_ptrs), end(array_ptrs),
+                                 [](const Array<T> *l, const Array<T> *r) {
+                                     return l->dims() != r->dims();
+                                 });
+
+    // If they are not the same. eval individually
+    if (it != end(array_ptrs)) {
+        for (auto ptr : array_ptrs) { ptr->eval(); }
+        return;
+    }
+
     for (Array<T> *array : array_ptrs) {
         if (array->ready) continue;
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index abd104359f..9fba97aa65 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -172,6 +172,18 @@ void evalMultiple(std::vector<Array<T> *> arrays) {
     vector<Array<T> *> output_arrays;
     vector<Node *> nodes;
 
+    // Check if all the arrays have the same dimension
+    auto it = std::adjacent_find(begin(arrays), end(arrays),
+                                 [](const Array<T> *l, const Array<T> *r) {
+                                     return l->dims() != r->dims();
+                                 });
+
+    // If they are not the same. eval individually
+    if (it != end(arrays)) {
+        for (auto ptr : arrays) { ptr->eval(); }
+        return;
+    }
+
     for (Array<T> *array : arrays) {
         if (array->isReady()) { continue; }
 
diff --git a/src/backend/cuda/join.cu b/src/backend/cuda/join.cu
index 9096ed9434..c9293d9f36 100644
--- a/src/backend/cuda/join.cu
+++ b/src/backend/cuda/join.cu
@@ -129,6 +129,11 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
         }
     }
 
+    std::vector<Array<T> *> input_ptrs(inputs.size());
+    std::transform(
+        begin(inputs), end(inputs), begin(input_ptrs),
+        [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
+    evalMultiple(input_ptrs);
     Array<T> out = createEmptyArray<T>(odims);
 
     switch (n_arrays) {
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 82f0c1030b..8587741960 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -188,6 +188,18 @@ void evalMultiple(vector<Array<T> *> arrays) {
     vector<Array<T> *> output_arrays;
     vector<Node *> nodes;
 
+    // Check if all the arrays have the same dimension
+    auto it = std::adjacent_find(begin(arrays), end(arrays),
+                                 [](const Array<T> *l, const Array<T> *r) {
+                                     return l->dims() != r->dims();
+                                 });
+
+    // If they are not the same. eval individually
+    if (it != end(arrays)) {
+        for (auto ptr : arrays) { ptr->eval(); }
+        return;
+    }
+
     for (Array<T> *array : arrays) {
         if (array->isReady()) { continue; }
 
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 2936f7b228..b4f910abb6 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -130,6 +130,12 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
         }
     }
 
+    std::vector<Array<T> *> input_ptrs(inputs.size());
+    std::transform(
+        begin(inputs), end(inputs), begin(input_ptrs),
+        [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
+    evalMultiple(input_ptrs);
+    std::vector<Param> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
     switch (n_arrays) {
diff --git a/test/join.cpp b/test/join.cpp
index 711c1efcb7..630754b59e 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -14,8 +14,10 @@
 #include <af/dim4.hpp>
 #include <af/index.h>
 #include <af/traits.hpp>
+
 #include <complex>
 #include <iostream>
+#include <numeric>
 #include <string>
 #include <vector>
 
@@ -26,6 +28,7 @@ using af::dim4;
 using af::dtype_traits;
 using af::join;
 using af::randu;
+using af::seq;
 using af::sum;
 using std::endl;
 using std::string;
@@ -199,3 +202,47 @@ TEST(JoinMany1, CPP) {
     array gold   = join(dim, a0, join(dim, a1, join(dim, a2, a3)));
     ASSERT_EQ(sum<float>(output - gold), 0);
 }
+
+TEST(Join, DifferentSizes) {
+    array a = seq(10);
+    array b = seq(11);
+    array c = seq(12);
+
+    array d = join(0, a, b, c);
+
+    vector<float> ha(10);
+    vector<float> hb(11);
+    vector<float> hc(12);
+
+    for (int i = 0; i < ha.size(); i++) { ha[i] = i; }
+    for (int i = 0; i < hb.size(); i++) { hb[i] = i; }
+    for (int i = 0; i < hc.size(); i++) { hc[i] = i; }
+    vector<float> hgold(10 + 11 + 12);
+    vector<float>::iterator it = copy(ha.begin(), ha.end(), hgold.begin());
+    it                         = copy(hb.begin(), hb.end(), it);
+    it                         = copy(hc.begin(), hc.end(), it);
+
+    ASSERT_VEC_ARRAY_EQ(hgold, dim4(10 + 11 + 12), d);
+}
+
+TEST(Join, SameSize) {
+    array a = seq(10);
+    array b = seq(10);
+    array c = seq(10);
+
+    array d = join(0, a, b, c);
+
+    vector<float> ha(10);
+    vector<float> hb(10);
+    vector<float> hc(10);
+
+    for (int i = 0; i < ha.size(); i++) { ha[i] = i; }
+    for (int i = 0; i < hb.size(); i++) { hb[i] = i; }
+    for (int i = 0; i < hc.size(); i++) { hc[i] = i; }
+    vector<float> hgold(10 + 10 + 10);
+    vector<float>::iterator it = copy(ha.begin(), ha.end(), hgold.begin());
+    it                         = copy(hb.begin(), hb.end(), it);
+    it                         = copy(hc.begin(), hc.end(), it);
+
+    ASSERT_VEC_ARRAY_EQ(hgold, dim4(10 + 10 + 10), d);
+}

From eed71274f19e4a0f08e235ab7fe8b72d49a81319 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Feb 2020 11:20:21 -0500
Subject: [PATCH 1849/2677] Fix doxygen menus

---
 docs/doxygen.mk | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 4a2801fa77..5bbb39d3e9 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1245,7 +1245,7 @@ HTML_TIMESTAMP         = YES
 # The default value is: YES.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_DYNAMIC_MENUS     = YES
+HTML_DYNAMIC_MENUS     = NO
 
 # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
 # documentation will contain sections that can be hidden and shown after the
@@ -1253,7 +1253,7 @@ HTML_DYNAMIC_MENUS     = YES
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_DYNAMIC_SECTIONS  = YES
+HTML_DYNAMIC_SECTIONS  = NO
 
 # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
 # shown in the various tree structured indices initially; the user can expand

From f51a1eeda2c992d668c4c4973059feb38ed4478c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Feb 2020 15:04:08 +0530
Subject: [PATCH 1850/2677] Avoid new options from mtx downloads external
 project

---
 test/CMakeModules/download_sparse_datasets.cmake | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/test/CMakeModules/download_sparse_datasets.cmake b/test/CMakeModules/download_sparse_datasets.cmake
index b7748ea5bb..8d94b828d9 100644
--- a/test/CMakeModules/download_sparse_datasets.cmake
+++ b/test/CMakeModules/download_sparse_datasets.cmake
@@ -20,13 +20,9 @@ function(mtxDownload name group)
       ${extproj_name}
       PREFIX "${path_prefix}"
       URL "${URL}/MM/${group}/${name}.tar.gz"
-      DOWNLOAD_NO_EXTRACT False
-      DOWNLOAD_NO_PROGRESS False
-      LOG_DOWNLOAD True
-      LOG_DIR ${PREFIX}
-      CONFIGURE_COMMAND ${CMAKE_COMMAND} -E make_directory "${mtx_data_dir}/${group}"
-      BINARY_DIR "${mtx_data_dir}/${group}"
-      BUILD_COMMAND ${CMAKE_COMMAND} -E tar xzf "${path_prefix}/src/${name}.tar.gz"
+      SOURCE_DIR "${mtx_data_dir}/${group}/${name}"
+      CONFIGURE_COMMAND ""
+      BUILD_COMMAND ""
       INSTALL_COMMAND ""
     )
   add_dependencies(mtxDownloads mtxDownload-${group}-${name})

From ca72aef61baa58dbe9693fea59d9aa0ad5eda18e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 Feb 2020 09:50:56 +0530
Subject: [PATCH 1851/2677] Change github ci to xenial image for cmake 3.5.1

xenial however won't build/test CPU backend using ATLAS. There is
a known issue with atlas+lapacke on Ubuntu 16.04 as lapacke is broken.

OSX runner uses whatever the image provides.
---
 .github/workflows/cpu_build.yml | 33 +++++++++++++++++++++++++++------
 1 file changed, 27 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index 438d59d9c2..5fd4a67555 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -2,6 +2,7 @@ on:
   push:
     branches:
     - master
+    - cmake_3.5_fixes
   pull_request:
     branches:
     - master
@@ -14,12 +15,15 @@ jobs:
         runs-on: ${{ matrix.os }}
         env:
           NINJA_VER: 1.9.0
+          CMAKE_VER: 3.5.1
         strategy:
             fail-fast: false
             matrix:
                 blas_backend: [Atlas, MKL, OpenBLAS]
-                os: [ubuntu-18.04, macos-latest]
+                os: [ubuntu-16.04, ubuntu-18.04, macos-latest]
                 exclude:
+                    - os: ubuntu-16.04
+                      blas_backend: Atlas
                     - os: macos-latest
                       blas_backend: Atlas
                     - os: macos-latest
@@ -42,13 +46,30 @@ jobs:
                   chmod +x ninja
                   ${GITHUB_WORKSPACE}/ninja --version
 
-            - name: Install Common Dependencies for Macos
+            - name: Download CMake 3.5.1 for Linux
+              if: matrix.os != 'macos-latest'
+              env:
+                  OS_NAME: ${{ matrix.os }}
+              run: |
+                  cmake_suffix=$(if [ $OS_NAME == 'macos-latest' ]; then echo "Darwin-x86_64"; else echo "Linux-x86_64"; fi)
+                  cmake_url=$(echo "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VER}/cmake-${CMAKE_VER}-${cmake_suffix}.tar.gz")
+                  wget --quiet "${cmake_url}"
+                  tar -xf ./cmake-${CMAKE_VER}-${cmake_suffix}.tar.gz
+                  cmake_install_dir=$(echo "cmake-${CMAKE_VER}-x86_64")
+                  mv cmake-${CMAKE_VER}-${cmake_suffix} ${cmake_install_dir}
+                  cmake_lnx_dir=$(echo "${cmake_install_dir}/bin")
+                  cmake_osx_dir=$(echo "${cmake_install_dir}/CMake.app/Contents/bin")
+                  cmake_dir=$(if [ $OS_NAME == 'macos-latest' ]; then echo "${cmake_osx_dir}"; else echo "${cmake_lnx_dir}"; fi)
+                  echo "::set-env name=CMAKE_PROGRAM::$(pwd)/${cmake_dir}/cmake"
+
+            - name: Install Dependencies for Macos
               if: matrix.os == 'macos-latest'
               run: |
                   brew install fontconfig glfw freeimage boost fftw lapack openblas
+                  echo "::set-env name=CMAKE_PROGRAM::cmake"
 
             - name: Install Common Dependencies for Ubuntu
-              if: matrix.os == 'ubuntu-18.04'
+              if: matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04'
               run: |
                   sudo apt-get -qq update
                   sudo apt-get install -y libfreeimage-dev \
@@ -62,7 +83,7 @@ jobs:
               run: sudo apt-get install -y libatlas-base-dev
 
             - name: Install MKL for Ubuntu
-              if: matrix.os == 'ubuntu-18.04' && matrix.blas_backend == 'MKL'
+              if: (matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04') && matrix.blas_backend == 'MKL'
               run: |
                   wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
                   sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
@@ -71,7 +92,7 @@ jobs:
                   sudo apt-get install -y intel-mkl-64bit-2020.0-088
 
             - name: Install OpenBLAS for Ubuntu
-              if: matrix.os == 'ubuntu-18.04' && matrix.blas_backend == 'OpenBLAS'
+              if: (matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04') && matrix.blas_backend == 'OpenBLAS'
               run: sudo apt-get install -y libopenblas-dev
 
             - name: CMake Configure
@@ -86,7 +107,7 @@ jobs:
                   dashboard=$(if [ -z "$prnum" ]; then echo "Continuous"; else echo "Experimental"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
                   mkdir build && cd build
-                  cmake -G Ninja \
+                  ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \

From caf0c71a525406e28ff7eda3aec9e254bcdfd3a6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Feb 2020 02:40:24 -0500
Subject: [PATCH 1852/2677] Fix boost errors during configuration in CMake
 3.5.1

---
 CMakeModules/boost_package.cmake                       |  7 +++++++
 src/backend/cuda/CMakeLists.txt                        | 10 ++++++++++
 src/backend/cuda/kernel/scan_by_key/CMakeLists.txt     |  2 --
 .../cuda/kernel/thrust_sort_by_key/CMakeLists.txt      | 10 ++++------
 4 files changed, 21 insertions(+), 8 deletions(-)

diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 361b9d58a8..cf63452286 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -48,6 +48,13 @@ if(NOT
     INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
     INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
     )
+else()
+  if(NOT TARGET Boost::boost)
+    add_library(Boost::boost IMPORTED INTERFACE GLOBAL)
+    set_target_properties(Boost::boost PROPERTIES
+      INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR}"
+      INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR}")
+  endif()
 endif()
 
 if(TARGET Boost::boost)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f6e81063a5..53ce4cf2d1 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -184,6 +184,16 @@ endfunction()
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
 arrayfire_get_platform_definitions(platform_flags)
 
+
+get_property(boost_includes TARGET Boost::boost PROPERTY INTERFACE_INCLUDE_DIRECTORIES)
+get_property(boost_definitions TARGET Boost::boost PROPERTY INTERFACE_COMPILE_DEFINITIONS)
+
+string(REPLACE ";" ";-I" boost_includes "-I${boost_includes}")
+string(REPLACE ";" ";-D" boost_definitions "-D${boost_definitions}")
+
+set(cuda_cxx_flags "${cuda_cxx_flags};${boost_includes}")
+set(cuda_cxx_flags "${cuda_cxx_flags};${boost_definitions}")
+
 # This definition is required in addition to the definition below because in
 # an older verion of cmake definitions added using target_compile_definitions
 # were not added to the nvcc flags. This manually adds these definitions and
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index e110bd8152..55ba972de0 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -31,8 +31,6 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
           "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
           OPTIONS
-          -I$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>, -I>
-          -D$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>, -D>
           -DSBK_BINARY_OP=${SBK_BINARY_OP} "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
       )
 
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 654141948f..3a6f660098 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -9,11 +9,11 @@ file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_
 
 foreach(STR ${FILESTRINGS})
     if(${STR} MATCHES "// SBK_TYPES")
-        STRING(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_TYPES ${TEMP})
+        string(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_TYPES ${TEMP})
     elseif(${STR} MATCHES "// SBK_INSTS:")
-        STRING(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
-        STRING(REPLACE " " ";" SBK_INSTS ${TEMP})
+        string(REPLACE "// SBK_INSTS:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_INSTS ${TEMP})
     endif()
 endforeach()
 
@@ -34,8 +34,6 @@ foreach(SBK_TYPE ${SBK_TYPES})
           ${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp
           OPTIONS
-            -I$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>, -I>
-            -D$<JOIN:$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>, -D>
             -DSBK_TYPE=${SBK_TYPE}
             -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
             "${platform_flags} ${cuda_cxx_flags} -DAFDLL"

From 714430e8ea084fb9c4a738340fa60b63f61770cc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Feb 2020 14:10:04 -0500
Subject: [PATCH 1853/2677] Fix segfault on exit with nvrtc because not
 initialized on main thread

* nvrtc segfaults if first run on a child thread
* This commit works around this by creating a nvrtcProgram object
  in DeviceManager assuming the first call to ArrayFire will be
  called in the main thread.
* Consider modifying the af_init function to get this done.
---
 src/backend/cuda/device_manager.cpp |  13 +++
 test/threading.cpp                  | 172 ++++++++++++++--------------
 2 files changed, 102 insertions(+), 83 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 515b37f938..c055816808 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -33,6 +33,8 @@
 // __gl_h_ should be defined by glad.h inclusion
 #include <cuda_gl_interop.h>
 
+#include <nvrtc.h>
+
 #include <algorithm>
 #include <array>
 #include <cstdio>
@@ -468,6 +470,16 @@ void DeviceManager::checkCudaVsDriverVersion() {
     }
 }
 
+/// This function initializes and deletes a nvrtcProgram object. There seems to
+/// be a bug in nvrtc which fails if this is first done on a child thread. We
+/// are assuming that the initilization is done in the main thread.
+void initNvrtc() {
+    nvrtcProgram prog;
+    auto err = nvrtcCreateProgram(&prog, " ", "dummy", 0, nullptr, nullptr);
+    nvrtcDestroyProgram(&prog);
+    return;
+}
+
 DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , cuDevices(0)
@@ -555,6 +567,7 @@ DeviceManager::DeviceManager()
             setActiveDevice(def_device, cuDevices[def_device].nativeId);
         }
     }
+    initNvrtc();
     AF_TRACE("Default device: {}({})", getActiveDeviceId(),
              cuDevices[getActiveDeviceId()].prop.name);
 }
diff --git a/test/threading.cpp b/test/threading.cpp
index e0a4cd7cd6..d08b6965f0 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -35,6 +35,94 @@ static const unsigned ITERATION_COUNT = 10;
 static const unsigned ITERATION_COUNT = 1000;
 #endif
 
+enum ArithOp { ADD, SUB, DIV, MUL };
+
+void calc(ArithOp opcode, array op1, array op2, float outValue,
+          int iteration_count) {
+    setDevice(0);
+    array res;
+    for (unsigned i = 0; i < iteration_count; ++i) {
+        switch (opcode) {
+            case ADD: res = op1 + op2; break;
+            case SUB: res = op1 - op2; break;
+            case DIV: res = op1 / op2; break;
+            case MUL: res = op1 * op2; break;
+        }
+    }
+
+    vector<float> out(res.elements());
+    res.host((void*)out.data());
+
+    for (unsigned i = 0; i < out.size(); ++i) ASSERT_EQ(out[i], outValue);
+    af::sync();
+}
+
+TEST(Threading, SimultaneousRead) {
+    setDevice(0);
+
+    array A = constant(1.0, 100, 100);
+    array B = constant(1.0, 100, 100);
+
+    vector<std::thread> tests;
+
+    int thread_count    = 8;
+    int iteration_count = 30;
+    for (int t = 0; t < thread_count; ++t) {
+        ArithOp op;
+        float outValue;
+
+        switch (t % 4) {
+            case 0:
+                op       = ADD;
+                outValue = 2.0f;
+                break;
+            case 1:
+                op       = SUB;
+                outValue = 0.0f;
+                break;
+            case 2:
+                op       = DIV;
+                outValue = 1.0f;
+                break;
+            case 3:
+                op       = MUL;
+                outValue = 1.0f;
+                break;
+        }
+
+        tests.emplace_back(calc, op, A, B, outValue, iteration_count);
+    }
+
+    for (int t = 0; t < thread_count; ++t)
+        if (tests[t].joinable()) tests[t].join();
+}
+
+std::condition_variable cv;
+std::mutex cvMutex;
+size_t counter = THREAD_COUNT;
+
+void doubleAllocationTest() {
+    setDevice(0);
+
+    // Block until all threads are launched and the
+    // counter variable hits zero
+    std::unique_lock<std::mutex> lock(cvMutex);
+    // Check for current thread launch counter value
+    // if reached zero, notify others to continue
+    // otherwise block current thread
+    if (--counter == 0)
+        cv.notify_all();
+    else
+        cv.wait(lock, [] { return counter == 0; });
+    lock.unlock();
+
+    array a = randu(5, 5);
+
+    // Wait for for other threads to hit randu call
+    // while this thread's variable a is still in scope.
+    std::this_thread::sleep_for(std::chrono::seconds(2));
+}
+
 int nextTargetDeviceId() {
     static int nextId = 0;
     return nextId++;
@@ -119,90 +207,8 @@ TEST(Threading, SetPerThreadActiveDevice) {
         if (tests[testId].joinable()) tests[testId].join();
 }
 
-enum ArithOp { ADD, SUB, DIV, MUL };
-
-void calc(ArithOp opcode, array op1, array op2, float outValue) {
-    setDevice(0);
-    array res;
-    for (unsigned i = 0; i < ITERATION_COUNT; ++i) {
-        switch (opcode) {
-            case ADD: res = op1 + op2; break;
-            case SUB: res = op1 - op2; break;
-            case DIV: res = op1 / op2; break;
-            case MUL: res = op1 * op2; break;
-        }
-    }
-
-    vector<float> out(res.elements());
-    res.host((void*)out.data());
-
-    for (unsigned i = 0; i < out.size(); ++i) ASSERT_EQ(out[i], outValue);
-}
-
-TEST(Threading, SimultaneousRead) {
-    setDevice(0);
-    array A = constant(1.0, 100, 100);
-    array B = constant(1.0, 100, 100);
-
-    vector<std::thread> tests;
-
-    for (int t = 0; t < THREAD_COUNT; ++t) {
-        ArithOp op;
-        float outValue;
-
-        switch (t % 4) {
-            case 0:
-                op       = ADD;
-                outValue = 2.0f;
-                break;
-            case 1:
-                op       = SUB;
-                outValue = 0.0f;
-                break;
-            case 2:
-                op       = DIV;
-                outValue = 1.0f;
-                break;
-            case 3:
-                op       = MUL;
-                outValue = 1.0f;
-                break;
-        }
-
-        tests.emplace_back(calc, op, A, B, outValue);
-    }
-
-    for (int t = 0; t < THREAD_COUNT; ++t)
-        if (tests[t].joinable()) tests[t].join();
-}
-
-std::condition_variable cv;
-std::mutex cvMutex;
-size_t counter = THREAD_COUNT;
-
-void doubleAllocationTest() {
-    setDevice(0);
-
-    // Block until all threads are launched and the
-    // counter variable hits zero
-    std::unique_lock<std::mutex> lock(cvMutex);
-    // Check for current thread launch counter value
-    // if reached zero, notify others to continue
-    // otherwise block current thread
-    if (--counter == 0)
-        cv.notify_all();
-    else
-        cv.wait(lock, [] { return counter == 0; });
-    lock.unlock();
-
-    array a = randu(5, 5);
-
-    // Wait for for other threads to hit randu call
-    // while this thread's variable a is still in scope.
-    std::this_thread::sleep_for(std::chrono::seconds(2));
-}
-
 TEST(Threading, MemoryManagementScope) {
+    setDevice(0);
     cleanSlate();  // Clean up everything done so far
 
     vector<std::thread> tests;

From 59a00a64f7feffb152f794b47be0faabbbaa67c7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 17 Feb 2020 19:46:58 -0500
Subject: [PATCH 1854/2677] Avoid errors when AF_BACKEND_DEFAULT passed to
 direct linked backend

---
 src/api/c/device.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 55ce3190a5..99d6983f17 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -28,7 +28,9 @@ using common::half;
 
 af_err af_set_backend(const af_backend bknd) {
     try {
-        if (bknd != getBackend()) { return AF_ERR_ARG; }
+        if (bknd != getBackend() && bknd != AF_BACKEND_DEFAULT) {
+            return AF_ERR_ARG;
+        }
     }
     CATCHALL;
 

From 70a80514b6ca0f37d74727f0ebbf2c0a35909642 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 17 Feb 2020 01:10:54 -0500
Subject: [PATCH 1855/2677] Make the symbolManager a static pointer to avoid
 destruction on exit

* Avoid releasing symbol manager. Let OS release resources
* Does not affect leak sanitizer
* Use static instead of thread local to avoid loading the library
  multiple times
* Move activeBackend and activeHandle out of symbol manager since
  AFSymbolManager is a singleton class now. It didn't make sense
  to have those variables in there anyway because they do not do
  anything with the symbols. Added multi-threaded tests to make
  sure expected behavior when switching backends
---
 src/api/cpp/array.cpp              |  8 +--
 src/api/unified/device.cpp         |  4 +-
 src/api/unified/symbol_manager.cpp | 54 +++++++++++---------
 src/api/unified/symbol_manager.hpp | 46 +++++++++--------
 test/CMakeLists.txt                |  2 +-
 test/backend.cpp                   | 81 +++++++++++++++++++++++-------
 test/testHelpers.hpp               | 10 ++++
 7 files changed, 136 insertions(+), 69 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index f85f21f0e0..a0eabb17f7 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -248,12 +248,12 @@ array::~array() {
     static auto &instance = unified::AFSymbolManager::getInstance();
 
     if (get()) {
-        af_backend backend = instance.getActiveBackend();
+        af_backend backend = unified::getActiveBackend();
         af_err err         = af_get_backend_id(&backend, get());
         if (!err) {
             switch (backend) {
                 case AF_BACKEND_CPU: {
-                    static auto cpu_handle = instance.getHandle();
+                    static auto cpu_handle = unified::getActiveHandle();
                     static af_release_array_ptr func =
                         reinterpret_cast<af_release_array_ptr>(
                             common::getFunctionPointer(cpu_handle,
@@ -262,7 +262,7 @@ array::~array() {
                     break;
                 }
                 case AF_BACKEND_OPENCL: {
-                    static auto opencl_handle = instance.getHandle();
+                    static auto opencl_handle = unified::getActiveHandle();
                     static af_release_array_ptr func =
                         reinterpret_cast<af_release_array_ptr>(
                             common::getFunctionPointer(opencl_handle,
@@ -271,7 +271,7 @@ array::~array() {
                     break;
                 }
                 case AF_BACKEND_CUDA: {
-                    static auto cuda_handle = instance.getHandle();
+                    static auto cuda_handle = unified::getActiveHandle();
                     static af_release_array_ptr func =
                         reinterpret_cast<af_release_array_ptr>(
                             common::getFunctionPointer(cuda_handle,
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index cee81deed3..251d017676 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -13,7 +13,7 @@
 #include "symbol_manager.hpp"
 
 af_err af_set_backend(const af_backend bknd) {
-    return unified::AFSymbolManager::getInstance().setBackend(bknd);
+    return unified::setBackend(bknd);
 }
 
 af_err af_get_backend_count(unsigned *num_backends) {
@@ -38,7 +38,7 @@ af_err af_get_device_id(int *device, const af_array in) {
 }
 
 af_err af_get_active_backend(af_backend *result) {
-    *result = unified::AFSymbolManager::getInstance().getActiveBackend();
+    *result = unified::getActiveBackend();
     return AF_SUCCESS;
 }
 
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index a4328fce55..dc4a34e1b7 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -166,16 +166,22 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
     return retVal;
 }
 
-AFSymbolManager& AFSymbolManager::getInstance() {
-    thread_local AFSymbolManager symbolManager;
-    return symbolManager;
+spdlog::logger* AFSymbolManager::getLogger() { return logger.get(); }
+
+af::Backend& getActiveBackend() {
+    thread_local af_backend activeBackend =
+        AFSymbolManager::getInstance().getDefaultBackend();
+    return activeBackend;
 }
 
-spdlog::logger* AFSymbolManager::getLogger() { return logger.get(); }
+LibHandle& getActiveHandle() {
+    thread_local LibHandle activeHandle =
+        AFSymbolManager::getInstance().getDefaultHandle();
+    return activeHandle;
+}
 
 AFSymbolManager::AFSymbolManager()
-    : activeHandle(nullptr)
-    , defaultHandle(nullptr)
+    : defaultHandle(nullptr)
     , numBackends(0)
     , backendsAvailable(0)
     , logger(loggerFactory("unified")) {
@@ -183,27 +189,28 @@ AFSymbolManager::AFSymbolManager()
     static const af_backend order[] = {AF_BACKEND_CUDA, AF_BACKEND_OPENCL,
                                        AF_BACKEND_CPU};
 
+    LibHandle handle;
+    af::Backend backend;
     // Decremeting loop. The last successful backend loaded will be the most
     // prefered one.
     for (int i = NUM_BACKENDS - 1; i >= 0; i--) {
-        int backend          = order[i] >> 1;  // 2 4 1 -> 1 2 0
-        bkndHandles[backend] = openDynLibrary(order[i]);
-        if (bkndHandles[backend]) {
-            activeHandle  = bkndHandles[backend];
-            activeBackend = (af_backend)order[i];
+        int backend_index          = order[i] >> 1;  // 2 4 1 -> 1 2 0
+        bkndHandles[backend_index] = openDynLibrary(order[i]);
+        if (bkndHandles[backend_index]) {
+            handle  = bkndHandles[backend_index];
+            backend = (af_backend)order[i];
             numBackends++;
             backendsAvailable += order[i];
         }
     }
-    if (activeBackend) {
-        AF_TRACE("AF_DEFAULT_BACKEND: {}",
-                 getBackendDirectoryName(activeBackend));
+    if (backend) {
+        AF_TRACE("AF_DEFAULT_BACKEND: {}", getBackendDirectoryName(backend));
     }
 
     // Keep a copy of default order handle inorder to use it in ::setBackend
     // when the user passes AF_BACKEND_DEFAULT
-    defaultHandle  = activeHandle;
-    defaultBackend = activeBackend;
+    defaultHandle  = handle;
+    defaultBackend = backend;
 }
 
 AFSymbolManager::~AFSymbolManager() {
@@ -216,20 +223,21 @@ unsigned AFSymbolManager::getBackendCount() { return numBackends; }
 
 int AFSymbolManager::getAvailableBackends() { return backendsAvailable; }
 
-af_err AFSymbolManager::setBackend(af::Backend bknd) {
+af_err setBackend(af::Backend bknd) {
+    auto& instance = AFSymbolManager::getInstance();
     if (bknd == AF_BACKEND_DEFAULT) {
-        if (defaultHandle) {
-            activeHandle  = defaultHandle;
-            activeBackend = defaultBackend;
+        if (instance.getDefaultHandle()) {
+            getActiveHandle()  = instance.getDefaultHandle();
+            getActiveBackend() = instance.getDefaultBackend();
             return AF_SUCCESS;
         } else {
             UNIFIED_ERROR_LOAD_LIB();
         }
     }
     int idx = bknd >> 1;  // Convert 1, 2, 4 -> 0, 1, 2
-    if (bkndHandles[idx]) {
-        activeHandle  = bkndHandles[idx];
-        activeBackend = bknd;
+    if (instance.getHandle(idx)) {
+        getActiveHandle()  = instance.getHandle(idx);
+        getActiveBackend() = bknd;
         return AF_SUCCESS;
     } else {
         UNIFIED_ERROR_LOAD_LIB();
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 6137370a4c..bcb73b109c 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -43,20 +43,20 @@ static inline int backend_index(af::Backend be) {
 
 class AFSymbolManager {
    public:
-    static AFSymbolManager& getInstance();
+    static AFSymbolManager& getInstance() {
+        static AFSymbolManager* symbolManager = new AFSymbolManager();
+        return *symbolManager;
+    }
 
     ~AFSymbolManager();
 
     unsigned getBackendCount();
-
     int getAvailableBackends();
+    af::Backend getDefaultBackend() { return defaultBackend; }
+    LibHandle getDefaultHandle() { return defaultHandle; }
 
-    af_err setBackend(af::Backend bnkd);
-
-    af::Backend getActiveBackend() { return activeBackend; }
-
-    LibHandle getHandle() { return activeHandle; }
     spdlog::logger* getLogger();
+    LibHandle getHandle(int idx) { return bkndHandles[idx]; }
 
    protected:
     AFSymbolManager();
@@ -71,15 +71,19 @@ class AFSymbolManager {
    private:
     LibHandle bkndHandles[NUM_BACKENDS];
 
-    LibHandle activeHandle;
     LibHandle defaultHandle;
     unsigned numBackends;
     int backendsAvailable;
-    af_backend activeBackend;
     af_backend defaultBackend;
     std::shared_ptr<spdlog::logger> logger;
 };
 
+af_err setBackend(af::Backend bnkd);
+
+af::Backend& getActiveBackend();
+
+LibHandle& getActiveHandle();
+
 namespace {
 bool checkArray(af_backend activeBackend, const af_array a) {
     // Convert af_array into int to retrieve the backend info.
@@ -128,8 +132,7 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 /// \param[in] Any number of af_arrays or pointer to af_arrays
 #define CHECK_ARRAYS(...)                                                     \
     do {                                                                      \
-        af_backend backendId =                                                \
-            unified::AFSymbolManager::getInstance().getActiveBackend();       \
+        af_backend backendId = unified::getActiveBackend();                   \
         if (!unified::checkArrays(backendId, __VA_ARGS__))                    \
             AF_RETURN_ERROR("Input array does not belong to current backend", \
                             AF_ERR_ARR_BKND_MISMATCH);                        \
@@ -137,15 +140,15 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 
 #define CALL(FUNCTION, ...)                                                      \
     using af_func                  = std::add_pointer<decltype(FUNCTION)>::type; \
-    thread_local auto& instance    = unified::AFSymbolManager::getInstance();    \
-    thread_local af_backend index_ = instance.getActiveBackend();                \
-    if (instance.getHandle()) {                                                  \
+    static auto& instance          = unified::AFSymbolManager::getInstance();    \
+    thread_local af_backend index_ = unified::getActiveBackend();                \
+    if (unified::getActiveHandle()) {                                            \
         thread_local af_func func = (af_func)common::getFunctionPointer(         \
-            instance.getHandle(), __func__);                                     \
-        if (index_ != instance.getActiveBackend()) {                             \
-            index_ = instance.getActiveBackend();                                \
-            func   = (af_func)common::getFunctionPointer(instance.getHandle(),   \
-                                                       __func__);              \
+            unified::getActiveHandle(), __func__);                               \
+        if (index_ != unified::getActiveBackend()) {                             \
+            index_ = unified::getActiveBackend();                                \
+            func   = (af_func)common::getFunctionPointer(                        \
+                unified::getActiveHandle(), __func__);                         \
         }                                                                        \
         return func(__VA_ARGS__);                                                \
     } else {                                                                     \
@@ -155,6 +158,5 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 
 #define CALL_NO_PARAMS(FUNCTION) CALL(FUNCTION)
 
-#define LOAD_SYMBOL()           \
-    common::getFunctionPointer( \
-        unified::AFSymbolManager::getInstance().getHandle(), __FUNCTION__)
+#define LOAD_SYMBOL() \
+    common::getFunctionPointer(unified::getActiveHandle(), __FUNCTION__)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index a67e19ec91..6046c1b3a5 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -177,7 +177,7 @@ make_test(SRC approx2.cpp)
 make_test(SRC array.cpp CXX11)
 make_test(SRC arrayio.cpp)
 make_test(SRC assign.cpp CXX11)
-make_test(SRC backend.cpp)
+make_test(SRC backend.cpp CXX11)
 make_test(SRC basic.cpp)
 make_test(SRC basic_c.c)
 make_test(SRC bilateral.cpp)
diff --git a/test/backend.cpp b/test/backend.cpp
index c9d0abfa35..d6f9529c11 100644
--- a/test/backend.cpp
+++ b/test/backend.cpp
@@ -13,7 +13,10 @@
 #include <af/data.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
+#include <atomic>
 #include <string>
+#include <thread>
 #include <vector>
 
 #include <af/device.h>
@@ -24,7 +27,7 @@ using af::setBackend;
 using std::string;
 using std::vector;
 
-const char *getActiveBackendString(af_backend active) {
+const char* getActiveBackendString(af_backend active) {
     switch (active) {
         case AF_BACKEND_CPU: return "AF_BACKEND_CPU";
         case AF_BACKEND_CUDA: return "AF_BACKEND_CUDA";
@@ -33,19 +36,15 @@ const char *getActiveBackendString(af_backend active) {
     }
 }
 
-template<typename T>
-void testFunction() {
-    af_info();
-
+void testFunction(af_backend expected) {
     af_backend activeBackend = (af_backend)0;
     af_get_active_backend(&activeBackend);
 
-    printf("Active Backend Enum = %s\n", getActiveBackendString(activeBackend));
+    ASSERT_EQ(expected, activeBackend);
 
     af_array outArray = 0;
     dim_t dims[]      = {32, 32};
-    EXPECT_EQ(AF_SUCCESS,
-              af_randu(&outArray, 2, dims, (af_dtype)dtype_traits<T>::af_type));
+    EXPECT_EQ(AF_SUCCESS, af_randu(&outArray, 2, dims, f32));
 
     // Verify backends returned by array and by function are the same
     af_backend arrayBackend = (af_backend)0;
@@ -65,26 +64,74 @@ void backendTest() {
     bool cuda   = backends & AF_BACKEND_CUDA;
     bool opencl = backends & AF_BACKEND_OPENCL;
 
-    printf("\nRunning Default Backend...\n");
-    testFunction<float>();
-
     if (cpu) {
-        printf("\nRunning CPU Backend...\n");
         setBackend(AF_BACKEND_CPU);
-        testFunction<float>();
+        testFunction(AF_BACKEND_CPU);
     }
 
     if (cuda) {
-        printf("\nRunning CUDA Backend...\n");
         setBackend(AF_BACKEND_CUDA);
-        testFunction<float>();
+        testFunction(AF_BACKEND_CUDA);
     }
 
     if (opencl) {
-        printf("\nRunning OpenCL Backend...\n");
         setBackend(AF_BACKEND_OPENCL);
-        testFunction<float>();
+        testFunction(AF_BACKEND_OPENCL);
     }
 }
 
 TEST(BACKEND_TEST, Basic) { backendTest(); }
+
+using af::getActiveBackend;
+
+void test_backend(std::atomic<int>& counter, int ntests,
+                  af::Backend default_backend, af::Backend test_backend) {
+    auto ta_backend = getActiveBackend();
+    ASSERT_EQ(default_backend, ta_backend);
+
+    // Wait until all threads reach this point
+    counter++;
+    while (counter < ntests) {}
+
+    setBackend(test_backend);
+
+    // Wait until all threads reach this point
+    counter++;
+    while (counter < 2 * ntests) {}
+
+    ta_backend = getActiveBackend();
+    ASSERT_EQ(test_backend, ta_backend);
+}
+
+TEST(Backend, Threads) {
+    using std::thread;
+    std::atomic<int> count(0);
+
+    setBackend(AF_BACKEND_DEFAULT);
+    auto default_backend = getActiveBackend();
+
+    int numbk = af::getBackendCount();
+
+    thread a, b, c;
+    if (af::getAvailableBackends() & AF_BACKEND_CPU) {
+        a = thread([&]() {
+            test_backend(count, numbk, default_backend, AF_BACKEND_CPU);
+        });
+    }
+
+    if (af::getAvailableBackends() & AF_BACKEND_OPENCL) {
+        b = thread([&]() {
+            test_backend(count, numbk, default_backend, AF_BACKEND_OPENCL);
+        });
+    }
+
+    if (af::getAvailableBackends() & AF_BACKEND_CUDA) {
+        c = thread([&]() {
+            test_backend(count, numbk, default_backend, AF_BACKEND_CUDA);
+        });
+    }
+
+    if (a.joinable()) a.join();
+    if (b.joinable()) b.join();
+    if (c.joinable()) c.join();
+}
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index c60090c693..ca38518141 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -67,6 +67,16 @@ typedef uintl uintl;
 using aft::intl;
 using aft::uintl;
 
+std::ostream &operator<<(std::ostream &os, af::Backend bk) {
+    switch (bk) {
+        case AF_BACKEND_CPU: os << "AF_BACKEND_CPU"; break;
+        case AF_BACKEND_CUDA: os << "AF_BACKEND_CUDA"; break;
+        case AF_BACKEND_OPENCL: os << "AF_BACKEND_OPENCL"; break;
+        case AF_BACKEND_DEFAULT: os << "AF_BACKEND_DEFAULT"; break;
+    }
+    return os;
+}
+
 std::ostream &operator<<(std::ostream &os, af_err e) {
     return os << af_err_to_string(e);
 }

From 56b45fede23c26984c87e3d86c63107a2fa29336 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Feb 2020 13:56:25 -0500
Subject: [PATCH 1856/2677] Fix the check for f16 capability on hardware in
 OpenCL

---
 src/backend/opencl/platform.cpp | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 5842fd4445..14a3bb795f 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -332,7 +332,7 @@ bool isDoubleSupported(int device) {
         dev = *devMngr.mDevices[device];
     }
 
-    return (dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>() > 0);
+    return (dev.getInfo<CL_DEVICE_DOUBLE_FP_CONFIG>() > 0);
 }
 
 bool isHalfSupported(int device) {
@@ -343,7 +343,20 @@ bool isHalfSupported(int device) {
         common::lock_guard_t lock(devMngr.deviceMutex);
         dev = *devMngr.mDevices[device];
     }
-    return (dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_HALF>() > 0);
+    cl_device_fp_config config = 0;
+    size_t ret_size            = 0;
+    // NVIDIA OpenCL seems to return error codes for CL_DEVICE_HALF_FP_CONFIG.
+    // It seems to be a bug in their implementation. Assuming if this function
+    // fails that the implemenation does not support f16 type. Using the C API
+    // to avoid exceptions
+    cl_int err =
+        clGetDeviceInfo(dev(), CL_DEVICE_HALF_FP_CONFIG,
+                        sizeof(cl_device_fp_config), &config, &ret_size);
+
+    if (err)
+        return false;
+    else
+        return config > 0;
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {

From 78cc4067991cd232f2eb9176afe38684080e174a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Feb 2020 13:57:24 -0500
Subject: [PATCH 1857/2677] Move verifyTypeSupport to Array.cpp in OpenCL

---
 src/backend/opencl/Array.cpp      | 28 ++++++++++++++++++++++++++++
 src/backend/opencl/err_opencl.hpp | 13 -------------
 2 files changed, 28 insertions(+), 13 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 8587741960..6ceb9889c1 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -47,6 +47,34 @@ Node_ptr bufferNodePtr() {
                                    shortname<T>(true));
 }
 
+namespace {
+template<typename T>
+void verifyTypeSupport() {
+    return;
+}
+
+template<>
+void verifyTypeSupport<double>() {
+    if (!isDoubleSupported(getActiveDeviceId())) {
+        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
+    }
+}
+
+template<>
+void verifyTypeSupport<cdouble>() {
+    if (!isDoubleSupported(getActiveDeviceId())) {
+        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
+    }
+}
+
+template<>
+void verifyTypeSupport<common::half>() {
+    if (!isHalfSupported(getActiveDeviceId())) {
+        AF_ERROR("Half precision not supported", AF_ERR_NO_HALF);
+    }
+}
+}  // namespace
+
 template<typename T>
 Array<T>::Array(dim4 dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 5e389285ea..4e72ce1e84 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -20,16 +20,3 @@
         throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
                            message, boost::stacktrace::stacktrace());      \
     } while (0)
-
-namespace opencl {
-template<typename T>
-void verifyTypeSupport() {
-    if ((std::is_same<T, double>::value || std::is_same<T, cdouble>::value) &&
-        !isDoubleSupported(getActiveDeviceId())) {
-        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
-    } else if (std::is_same<T, common::half>::value &&
-               !isHalfSupported(getActiveDeviceId())) {
-        AF_ERROR("Half precision not supported", AF_ERR_NO_HALF);
-    }
-}
-}  // namespace opencl

From 67e759fd0bea2a6cd1ad3d56036b642fedc7536c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Feb 2020 13:57:56 -0500
Subject: [PATCH 1858/2677] Only print verbose messages in FindMKL if they are
 supported

---
 CMakeModules/FindMKL.cmake | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index f801650860..0b0505521e 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -167,10 +167,14 @@ endif()
 
 if(WIN32)
   set(ENV_LIBRARY_PATHS "$ENV{LIB}")
-  message(VERBOSE "MKL environment variable(LIB): ${ENV_LIBRARY_PATHS}")
+  if (${CMAKE_VERSION} VERSION_GREATER 3.14)
+    message(VERBOSE "MKL environment variable(LIB): ${ENV_LIBRARY_PATHS}")
+  endif()
 else()
   string(REGEX REPLACE ":" ";" ENV_LIBRARY_PATHS "$ENV{LIBRARY_PATH}")
-  message(VERBOSE "MKL environment variable(LIBRARY_PATH): ${ENV_LIBRARY_PATHS}")
+  if (${CMAKE_VERSION} VERSION_GREATER 3.14)
+    message(VERBOSE "MKL environment variable(LIBRARY_PATH): ${ENV_LIBRARY_PATHS}")
+  endif()
 endif()
 
 # Finds and creates libraries for MKL with the MKL:: prefix
@@ -225,7 +229,9 @@ function(find_mkl_library)
         intel64
         intel64/gcc4.7)
     if(MKL_${mkl_args_NAME}_LINK_LIBRARY)
-      message(VERBOSE "MKL_${mkl_args_NAME}_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
+      if (CMAKE_VERSION VERSION_GREATER 3.14)
+        message(VERBOSE "MKL_${mkl_args_NAME}_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
+      endif()
       mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
     endif()
   endif()
@@ -252,7 +258,9 @@ function(find_mkl_library)
         IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
         )
       if(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
-        message(VERBOSE "MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}")
+        if (CMAKE_VERSION VERSION_GREATER 3.14)
+          message(VERBOSE "MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}")
+        endif()
         mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
       endif()
     endif()

From 99bffc9a06c8e1279cdb6235238751a2686d477c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Feb 2020 21:24:30 -0500
Subject: [PATCH 1859/2677] Fix matmul on Intel OpenCL when passing same array
 as input

The Intel OpenCL mapping the same buffer for write access caused an
error. This caused the matmul operation to fail when the same
array was passed in. To fix this only the READ flag is passed into
the map function instead of the READ and WRITE flags
---
 src/backend/opencl/Array.hpp        | 21 +++++++++++++--------
 src/backend/opencl/cpu/cpu_blas.cpp | 10 +++++-----
 src/backend/opencl/cpu/cpu_lu.cpp   |  6 +++---
 test/blas.cpp                       | 23 +++++++++++++++++++++++
 4 files changed, 44 insertions(+), 16 deletions(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index e74abf5089..81641a5923 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -113,6 +113,9 @@ void *getRawPtr(const Array<T> &arr) {
     return (void *)mem;
 }
 
+template<typename T>
+using mapped_ptr = std::unique_ptr<T, std::function<void(void *)>>;
+
 template<typename T>
 class Array {
     ArrayInfo info;  // This must be the first element of Array<T>
@@ -245,23 +248,25 @@ class Array {
     common::Node_ptr getNode();
 
    public:
-    std::shared_ptr<T> getMappedPtr() const {
-        auto func = [=](void *ptr) {
+    mapped_ptr<T> getMappedPtr(cl_map_flags map_flags = CL_MAP_READ |
+                                                        CL_MAP_WRITE) const {
+        auto func = [this](void *ptr) {
             if (ptr != nullptr) {
-                getQueue().enqueueUnmapMemObject(*data, ptr);
-                ptr = nullptr;
+                cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);
+                ptr        = nullptr;
             }
         };
 
         T *ptr = nullptr;
         if (ptr == nullptr) {
+            cl_int err;
             ptr = (T *)getQueue().enqueueMapBuffer(
-                *const_cast<cl::Buffer *>(get()), true,
-                CL_MAP_READ | CL_MAP_WRITE, getOffset() * sizeof(T),
-                (getDataDims().elements() - getOffset()) * sizeof(T));
+                *const_cast<cl::Buffer *>(get()), CL_TRUE, map_flags,
+                getOffset() * sizeof(T), elements() * sizeof(T), nullptr,
+                nullptr, &err);
         }
 
-        return std::shared_ptr<T>(ptr, func);
+        return mapped_ptr<T>(ptr, func);
     }
 
     friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 28725d2e7f..7a35775d06 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -198,6 +198,11 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     bool is_r_d2_batched = (oDims[2] == rDims[2]);
     bool is_r_d3_batched = (oDims[3] == rDims[3]);
 
+    // get host pointers from mapped memory
+    mapped_ptr<T> lPtr = lhs.getMappedPtr(CL_MAP_READ);
+    mapped_ptr<T> rPtr = rhs.getMappedPtr(CL_MAP_READ);
+    mapped_ptr<T> oPtr = out.getMappedPtr(CL_MAP_READ | CL_MAP_WRITE);
+
     for (int n = 0; n < batchSize; ++n) {
         int w = n / rDims[2];
         int z = n - w * rDims[2];
@@ -207,11 +212,6 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         int roff = z * (is_r_d2_batched * rStrides[2]) +
                    w * (is_r_d3_batched * rStrides[3]);
 
-        // get host pointers from mapped memory
-        auto lPtr = lhs.getMappedPtr();
-        auto rPtr = rhs.getMappedPtr();
-        auto oPtr = out.getMappedPtr();
-
         CBT *lptr = (CBT *)(lPtr.get() + loff);
         CBT *rptr = (CBT *)(rPtr.get() + roff);
         BT *optr  = (BT *)(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index 39706c0b6a..7d0a2949bc 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -38,9 +38,9 @@ LU_FUNC(getrf, cdouble, z)
 
 template<typename T>
 void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in) {
-    std::shared_ptr<T> ls = lower.getMappedPtr();
-    std::shared_ptr<T> us = upper.getMappedPtr();
-    std::shared_ptr<T> is = in.getMappedPtr();
+    auto ls = lower.getMappedPtr();
+    auto us = upper.getMappedPtr();
+    auto is = in.getMappedPtr(CL_MAP_READ);
 
     T *l = ls.get();
     T *u = us.get();
diff --git a/test/blas.cpp b/test/blas.cpp
index 95582441db..d8d33005df 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -684,3 +684,26 @@ TEST(Gemv, HalfScalarProduct) {
         ASSERT_ARRAYS_EQ(mmRes, dotRes);
     }
 }
+
+TEST(MatrixMultiply, SameInput) {
+    // Tests for an error that occured in the Intel OpenCL GPU implementation
+    // that caused an error when you passed the same array as the lhs and the
+    // rhs. see #1711 and PR #2774. Caused by mapping the same buffer with
+    // CL_MEM_WRITE access
+    int dim = 10;
+    array a = randu(dim, dim);
+    vector<float> ha(dim * dim);
+    a.host(&ha.front());
+
+    vector<float> hgold(dim * dim, 0);
+
+    for (int i = 0; i < dim; i++) {
+        for (int j = 0; j < dim; j++) {
+            for (int k = 0; k < dim; k++) {
+                hgold[i * dim + j] += ha[k * dim + j] * ha[i * dim + k];
+            }
+        }
+    }
+    array out = matmul(a, a);
+    ASSERT_VEC_ARRAY_NEAR(hgold, dim4(dim, dim), out, 1e-4);
+}

From 501d09449f8304c70c7b6447af225dd76369ee1d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 21 Feb 2020 02:23:36 -0500
Subject: [PATCH 1860/2677] Replace all shared_ptr to mapped_ptr in OpenCL CPU

The new getMappedPtr is now (correctly) returning a unique_ptr.
This commit removes implicit conversions from unique_ptr to
shared ptr.
---
 src/backend/opencl/cpu/cpu_cholesky.cpp    |  4 +-
 src/backend/opencl/cpu/cpu_inverse.cpp     |  4 +-
 src/backend/opencl/cpu/cpu_lu.cpp          | 43 +++++++----------
 src/backend/opencl/cpu/cpu_qr.cpp          | 10 ++--
 src/backend/opencl/cpu/cpu_solve.cpp       | 14 +++---
 src/backend/opencl/cpu/cpu_sparse_blas.cpp | 54 +++++++++++-----------
 src/backend/opencl/cpu/cpu_svd.cpp         |  8 ++--
 7 files changed, 64 insertions(+), 73 deletions(-)

diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index 68d8415f18..c8bb0a5084 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -40,7 +40,7 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    std::shared_ptr<T> oPtr = out.getMappedPtr();
+    mapped_ptr<T> oPtr = out.getMappedPtr();
 
     if (is_upper)
         triangle<T, true, false>(oPtr.get(), oPtr.get(), out.dims(),
@@ -60,7 +60,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     char uplo = 'L';
     if (is_upper) uplo = 'U';
 
-    std::shared_ptr<T> inPtr = in.getMappedPtr();
+    mapped_ptr<T> inPtr = in.getMappedPtr();
 
     int info = potrf_func<T>()(AF_LAPACK_COL_MAJOR, uplo, N, inPtr.get(),
                                in.strides()[1]);
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index e7815659ba..7adcacc17c 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -50,8 +50,8 @@ Array<T> inverse(const Array<T> &in) {
 
     Array<int> pivot = cpu::lu_inplace<T>(A, false);
 
-    std::shared_ptr<T> aPtr   = A.getMappedPtr();
-    std::shared_ptr<int> pPtr = pivot.getMappedPtr();
+    mapped_ptr<T> aPtr   = A.getMappedPtr();
+    mapped_ptr<int> pPtr = pivot.getMappedPtr();
 
     getri_func<T>()(AF_LAPACK_COL_MAJOR, M, aPtr.get(), A.strides()[1],
                     pPtr.get());
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index 7d0a2949bc..30f7d4d64b 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -14,6 +14,8 @@
 #include <math.hpp>
 #include <range.hpp>
 
+#include <numeric>
+
 namespace opencl {
 namespace cpu {
 
@@ -38,9 +40,9 @@ LU_FUNC(getrf, cdouble, z)
 
 template<typename T>
 void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in) {
-    auto ls = lower.getMappedPtr();
-    auto us = upper.getMappedPtr();
-    auto is = in.getMappedPtr(CL_MAP_READ);
+    mapped_ptr<T> ls = lower.getMappedPtr();
+    mapped_ptr<T> us = upper.getMappedPtr();
+    mapped_ptr<T> is = in.getMappedPtr(CL_MAP_READ);
 
     T *l = ls.get();
     T *u = us.get();
@@ -89,26 +91,16 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in) {
     }
 }
 
-void convertPivot(Array<int> &pivot, int out_sz) {
-    Array<int> p = range<int>(dim4(out_sz), 0);  // Runs opencl
-
-    std::shared_ptr<int> pi = pivot.getMappedPtr();
-    std::shared_ptr<int> po = p.getMappedPtr();
-
-    int *d_pi = pi.get();
-    int *d_po = po.get();
+void convertPivot(int *pivot, int out_sz, size_t pivot_dim) {
+    std::vector<int> p(out_sz);
+    iota(begin(p), end(p), 0);
 
-    dim_t d0 = pivot.dims()[0];
-
-    for (int j = 0; j < (int)d0; j++) {
+    for (int j = 0; j < (int)pivot_dim; j++) {
         // 1 indexed in pivot
-        std::swap(d_po[j], d_po[d_pi[j] - 1]);
+        std::swap(p[j], p[pivot[j] - 1]);
     }
 
-    pi.reset();
-    po.reset();
-
-    pivot = p;
+    copy(begin(p), end(p), pivot);
 }
 
 template<typename T>
@@ -136,18 +128,17 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     int M      = iDims[0];
     int N      = iDims[1];
 
-    Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
+    int pivot_dim    = min(M, N);
+    Array<int> pivot = createEmptyArray<int>(af::dim4(pivot_dim, 1, 1, 1));
+    if (convert_pivot) { pivot = range<int>(af::dim4(M, 1, 1, 1)); }
 
-    std::shared_ptr<T> inPtr   = in.getMappedPtr();
-    std::shared_ptr<int> piPtr = pivot.getMappedPtr();
+    mapped_ptr<T> inPtr   = in.getMappedPtr();
+    mapped_ptr<int> piPtr = pivot.getMappedPtr();
 
     getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, inPtr.get(), in.strides()[1],
                     piPtr.get());
 
-    inPtr.reset();
-    piPtr.reset();
-
-    if (convert_pivot) convertPivot(pivot, M);
+    if (convert_pivot) convertPivot(piPtr.get(), M, min(M, N));
 
     return pivot;
 }
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index 199747e4e9..207134aa72 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -69,9 +69,9 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     dim4 rdims(M, N);
     r = createEmptyArray<T>(rdims);
 
-    std::shared_ptr<T> qPtr = q.getMappedPtr();
-    std::shared_ptr<T> rPtr = r.getMappedPtr();
-    std::shared_ptr<T> tPtr = t.getMappedPtr();
+    mapped_ptr<T> qPtr = q.getMappedPtr();
+    mapped_ptr<T> rPtr = r.getMappedPtr();
+    mapped_ptr<T> tPtr = t.getMappedPtr();
 
     triangle<T, true, false>(rPtr.get(), qPtr.get(), rdims, r.strides(),
                              q.strides());
@@ -90,8 +90,8 @@ Array<T> qr_inplace(Array<T> &in) {
 
     Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
 
-    std::shared_ptr<T> iPtr = in.getMappedPtr();
-    std::shared_ptr<T> tPtr = t.getMappedPtr();
+    mapped_ptr<T> iPtr = in.getMappedPtr();
+    mapped_ptr<T> tPtr = t.getMappedPtr();
 
     geqrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, iPtr.get(), in.strides()[1],
                     tPtr.get());
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 7ed2371b45..fb63f4c327 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -74,9 +74,9 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
 
     Array<T> B = copyArray<T>(b);
 
-    std::shared_ptr<T> aPtr   = A.getMappedPtr();
-    std::shared_ptr<T> bPtr   = B.getMappedPtr();
-    std::shared_ptr<int> pPtr = pivot.getMappedPtr();
+    mapped_ptr<T> aPtr   = A.getMappedPtr();
+    mapped_ptr<T> bPtr   = B.getMappedPtr();
+    mapped_ptr<int> pPtr = pivot.getMappedPtr();
 
     getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N', N, NRHS, aPtr.get(),
                     A.strides()[1], pPtr.get(), bPtr.get(), B.strides()[1]);
@@ -91,8 +91,8 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     int N      = B.dims()[0];
     int NRHS   = B.dims()[1];
 
-    std::shared_ptr<T> aPtr = A.getMappedPtr();
-    std::shared_ptr<T> bPtr = B.getMappedPtr();
+    mapped_ptr<T> aPtr = A.getMappedPtr();
+    mapped_ptr<T> bPtr = B.getMappedPtr();
 
     trtrs_func<T>()(AF_LAPACK_COL_MAJOR, options & AF_MAT_UPPER ? 'U' : 'L',
                     'N',  // transpose flag
@@ -116,8 +116,8 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
     Array<T> A = copyArray<T>(a);
     Array<T> B = padArray<T, T>(b, dim4(max(M, N), K), scalar<T>(0));
 
-    std::shared_ptr<T> aPtr = A.getMappedPtr();
-    std::shared_ptr<T> bPtr = B.getMappedPtr();
+    mapped_ptr<T> aPtr = A.getMappedPtr();
+    mapped_ptr<T> bPtr = B.getMappedPtr();
 
     if (M == N) {
         std::vector<int> pivot(N);
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 6e48814d83..dc08ef340d 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -223,18 +223,18 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
     int ldc = out.strides()[1];
 
     // get host pointers from mapped memory
-    auto rhsPtr = rhs.getMappedPtr();
-    auto outPtr = out.getMappedPtr();
+    mapped_ptr<T> rhsPtr = rhs.getMappedPtr(CL_MAP_READ);
+    mapped_ptr<T> outPtr = out.getMappedPtr();
 
     Array<T> values   = lhs.getValues();
     Array<int> rowIdx = lhs.getRowIdx();
     Array<int> colIdx = lhs.getColIdx();
 
-    auto vPtr = values.getMappedPtr();
-    auto rPtr = rowIdx.getMappedPtr();
-    auto cPtr = colIdx.getMappedPtr();
-    int *pB   = rPtr.get();
-    int *pE   = rPtr.get() + 1;
+    mapped_ptr<T> vPtr   = values.getMappedPtr();
+    mapped_ptr<int> rPtr = rowIdx.getMappedPtr();
+    mapped_ptr<int> cPtr = colIdx.getMappedPtr();
+    int *pB              = rPtr.get();
+    int *pE              = rPtr.get() + 1;
 
     sparse_matrix_t csrLhs;
     create_csr_func<T>()(&csrLhs, SPARSE_INDEX_BASE_ZERO, lhs.dims()[0],
@@ -293,11 +293,11 @@ template<typename T, bool conjugate>
 void mv(Array<T> output, const Array<T> values, const Array<int> rowIdx,
         const Array<int> colIdx, const Array<T> right, int M) {
     UNUSED(M);
-    auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right.getMappedPtr();
-    auto vPtr   = values.getMappedPtr();
-    auto rPtr   = rowIdx.getMappedPtr();
-    auto cPtr   = colIdx.getMappedPtr();
+    mapped_ptr<T> oPtr   = output.getMappedPtr();
+    mapped_ptr<T> rhtPtr = right.getMappedPtr();
+    mapped_ptr<T> vPtr   = values.getMappedPtr();
+    mapped_ptr<int> rPtr = rowIdx.getMappedPtr();
+    mapped_ptr<int> cPtr = colIdx.getMappedPtr();
 
     T const *const valPtr   = vPtr.get();
     int const *const rowPtr = rPtr.get();
@@ -322,11 +322,11 @@ void mv(Array<T> output, const Array<T> values, const Array<int> rowIdx,
 template<typename T, bool conjugate>
 void mtv(Array<T> output, const Array<T> values, const Array<int> rowIdx,
          const Array<int> colIdx, const Array<T> right, int M) {
-    auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right.getMappedPtr();
-    auto vPtr   = values.getMappedPtr();
-    auto rPtr   = rowIdx.getMappedPtr();
-    auto cPtr   = colIdx.getMappedPtr();
+    mapped_ptr<T> oPtr   = output.getMappedPtr();
+    mapped_ptr<T> rhtPtr = right.getMappedPtr();
+    mapped_ptr<T> vPtr   = values.getMappedPtr();
+    mapped_ptr<int> rPtr = rowIdx.getMappedPtr();
+    mapped_ptr<int> cPtr = colIdx.getMappedPtr();
 
     T const *const valPtr   = vPtr.get();
     int const *const rowPtr = rPtr.get();
@@ -354,11 +354,11 @@ void mm(Array<T> output, const Array<T> values, const Array<int> rowIdx,
         const Array<int> colIdx, const Array<T> right, int M, int N, int ldb,
         int ldc) {
     UNUSED(M);
-    auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right.getMappedPtr();
-    auto vPtr   = values.getMappedPtr();
-    auto rPtr   = rowIdx.getMappedPtr();
-    auto cPtr   = colIdx.getMappedPtr();
+    mapped_ptr<T> oPtr   = output.getMappedPtr();
+    mapped_ptr<T> rhtPtr = right.getMappedPtr();
+    mapped_ptr<T> vPtr   = values.getMappedPtr();
+    mapped_ptr<int> rPtr = rowIdx.getMappedPtr();
+    mapped_ptr<int> cPtr = colIdx.getMappedPtr();
 
     T const *const valPtr   = vPtr.get();
     int const *const rowPtr = rPtr.get();
@@ -388,11 +388,11 @@ template<typename T, bool conjugate>
 void mtm(Array<T> output, const Array<T> values, const Array<int> rowIdx,
          const Array<int> colIdx, const Array<T> right, int M, int N, int ldb,
          int ldc) {
-    auto oPtr   = output.getMappedPtr();
-    auto rhtPtr = right.getMappedPtr();
-    auto vPtr   = values.getMappedPtr();
-    auto rPtr   = rowIdx.getMappedPtr();
-    auto cPtr   = colIdx.getMappedPtr();
+    mapped_ptr<T> oPtr   = output.getMappedPtr();
+    mapped_ptr<T> rhtPtr = right.getMappedPtr();
+    mapped_ptr<T> vPtr   = values.getMappedPtr();
+    mapped_ptr<int> rPtr = rowIdx.getMappedPtr();
+    mapped_ptr<int> cPtr = colIdx.getMappedPtr();
 
     T const *const valPtr   = vPtr.get();
     int const *const rowPtr = rPtr.get();
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index a0f07a32d8..2b0e23db1e 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -58,10 +58,10 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     int M      = iDims[0];
     int N      = iDims[1];
 
-    std::shared_ptr<Tr> sPtr = s.getMappedPtr();
-    std::shared_ptr<T> uPtr  = u.getMappedPtr();
-    std::shared_ptr<T> vPtr  = vt.getMappedPtr();
-    std::shared_ptr<T> iPtr  = in.getMappedPtr();
+    mapped_ptr<Tr> sPtr = s.getMappedPtr();
+    mapped_ptr<T> uPtr  = u.getMappedPtr();
+    mapped_ptr<T> vPtr  = vt.getMappedPtr();
+    mapped_ptr<T> iPtr  = in.getMappedPtr();
 
 #if defined(USE_MKL) || defined(__APPLE__)
     svd_func<T, Tr>()(AF_LAPACK_COL_MAJOR, 'A', M, N, iPtr.get(),

From ca90115453359184797a8d54faa6af1fe1444594 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 21 Feb 2020 02:51:03 -0500
Subject: [PATCH 1861/2677] Fix CPU OpenCL blas batching

---
 src/backend/opencl/cpu/cpu_blas.cpp |   4 +-
 test/blas.cpp                       | 171 +++++++++++++++++++++-------
 2 files changed, 134 insertions(+), 41 deletions(-)

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 7a35775d06..ad8680cafe 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -204,8 +204,8 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     mapped_ptr<T> oPtr = out.getMappedPtr(CL_MAP_READ | CL_MAP_WRITE);
 
     for (int n = 0; n < batchSize; ++n) {
-        int w = n / rDims[2];
-        int z = n - w * rDims[2];
+        int w = n / oDims[2];
+        int z = n - w * oDims[2];
 
         int loff = z * (is_l_d2_batched * lStrides[2]) +
                    w * (is_l_d3_batched * lStrides[3]);
diff --git a/test/blas.cpp b/test/blas.cpp
index d8d33005df..317991973e 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -278,54 +278,147 @@ TEST(MatrixMultiply, ISSUE_1882) {
     ASSERT_ARRAYS_NEAR(res1, res2, 1E-5);
 }
 
-TEST(MatrixMultiply, LhsBroadcastBatched) {
-    const int M  = 512;
-    const int K  = 512;
-    const int N  = 10;
-    const int D2 = 2;
-    const int D3 = 3;
-
-    for (int d3 = 1; d3 <= D3; d3 *= D3) {
-        for (int d2 = 1; d2 <= D2; d2 *= D2) {
-            array a = randu(M, K);
-            array b = randu(K, N, d2, d3);
-            array c = matmul(a, b);
+struct blas_params {
+    int m, n, k, ld2, ld3, rd2, rd3;
+    af_dtype type;
+    blas_params(int m_, int n_, int k_, int ld2_, int ld3_, int rd2_, int rd3_,
+                af_dtype type_)
+        : m(m_)
+        , n(n_)
+        , k(k_)
+        , ld2(ld2_)
+        , ld3(ld3_)
+        , rd2(rd2_)
+        , rd3(rd3_)
+        , type(type_) {}
+};
 
-            for (int j = 0; j < d3; j++) {
-                for (int i = 0; i < d2; i++) {
-                    array b_ij = b(span, span, i, j);
-                    array c_ij = c(span, span, i, j);
-                    array res  = matmul(a, b_ij);
-                    ASSERT_ARRAYS_NEAR(c_ij, res, batch_tol);
+class MatrixMultiplyBatch : public ::testing::TestWithParam<blas_params> {
+   public:
+    array lhs, rhs, out;
+    void SetUp() {
+        blas_params params = GetParam();
+        lhs = randu(params.m, params.k, params.ld2, params.ld3, params.type);
+        rhs = randu(params.k, params.n, params.rd2, params.rd3, params.type);
+
+        array gold(params.m, params.n, std::max(params.ld2, params.rd2),
+                   std::max(params.ld3, params.rd3));
+
+        if (params.ld2 == params.rd2 && params.ld3 == params.rd3) {
+            for (int i = 0; i < params.ld2; i++) {
+                for (int j = 0; j < params.ld3; j++) {
+                    array lhs_sub          = lhs(span, span, i, j);
+                    array rhs_sub          = rhs(span, span, i, j);
+                    gold(span, span, i, j) = matmul(lhs_sub, rhs_sub);
+                }
+            }
+        } else {
+            for (int i = 0; i < params.ld2; i++) {
+                for (int j = 0; j < params.ld3; j++) {
+                    for (int k = 0; k < params.rd2; k++) {
+                        for (int l = 0; l < params.rd3; l++) {
+                            array lhs_sub = lhs(span, span, i, j);
+                            array rhs_sub = rhs(span, span, k, l);
+                            gold(span, span, std::max(i, k), std::max(j, l)) =
+                                matmul(lhs_sub, rhs_sub);
+                        }
+                    }
                 }
             }
         }
     }
+};
+
+std::string print_blas_params(
+    const ::testing::TestParamInfo<MatrixMultiplyBatch::ParamType> info) {
+    std::stringstream ss;
+
+    ss << "LHS_" << info.param.m << "x" << info.param.k << "x" << info.param.ld2
+       << "x" << info.param.ld3 << "__RHS" << info.param.k << "x"
+       << info.param.n << "x" << info.param.rd2 << "x" << info.param.rd3;
+
+    return ss.str();
 }
 
-TEST(MatrixMultiply, RhsBroadcastBatched) {
-    const int M  = 512;
-    const int K  = 512;
-    const int N  = 10;
-    const int D2 = 2;
-    const int D3 = 3;
+INSTANTIATE_TEST_CASE_P(
+    LHSBroadcast, MatrixMultiplyBatch,
+    ::testing::Values(
 
-    for (int d3 = 1; d3 <= D3; d3 *= D3) {
-        for (int d2 = 1; d2 <= D2; d2 *= D2) {
-            array a = randu(M, K, d2, d3);
-            array b = randu(K, N);
-            array c = matmul(a, b);
+        // clang-format off
+            //             M      N     K   ld2  ld3   rd2   rd3  type
+            blas_params( 32,     32,   10,    2,   1,    1,    1,  f32),
+            blas_params( 32,     32,   10,    1,   2,    1,    1,  f32),
+            blas_params( 32,     32,   10,    2,   2,    1,    1,  f32),
+            blas_params( 32,     32,   10,    3,   2,    1,    1,  f32),
+            blas_params( 32,     32,   10,    3,   3,    1,    1,  f32),
+            blas_params( 32,     32,   10,    4,   4,    1,    1,  f32),
+
+            blas_params(512,     32,  512,    4,   4,    1,    1,  f32),
+            blas_params(512,     32,  513,    4,   4,    1,    1,  f32),
+            blas_params(513,     32,  513,    4,   4,    1,    1,  f32),
+            blas_params(513,     33,  513,    4,   4,    1,    1,  f32),
+            blas_params(513,    511,   32,    4,   4,    1,    1,  f32),
+            blas_params(513,    511,   31,    4,   4,    1,    1,  f32),
+            blas_params(513,    511,   33,    4,   4,    1,    1,  f32),
+            blas_params(511,    511,   33,    4,   4,    1,    1,  f32)
+        // clang-format on
 
-            for (int j = 0; j < d3; j++) {
-                for (int i = 0; i < d2; i++) {
-                    array a_ij = a(span, span, i, j);
-                    array c_ij = c(span, span, i, j);
-                    array res  = matmul(a_ij, b);
-                    ASSERT_ARRAYS_NEAR(c_ij, res, batch_tol);
-                }
-            }
-        }
-    }
+        ),
+    print_blas_params);
+
+INSTANTIATE_TEST_CASE_P(
+    RHSBroadcast, MatrixMultiplyBatch,
+    ::testing::Values(
+        // clang-format off
+            //            M      N     K   ld2  ld3   rd2  rd3  type
+            blas_params( 32 ,    32,  10,    1,   1,    2,   1,  f32),
+            blas_params( 32 ,    32,  10,    1,   1,    1,   2,  f32),
+            blas_params( 32 ,    32,  10,    1,   1,    2,   2,  f32),
+            blas_params( 32 ,    32,  10,    1,   1,    3,   2,  f32),
+            blas_params( 32 ,    32,  10,    1,   1,    3,   3,  f32),
+            blas_params( 32 ,    32,  10,    1,   1,    4,   4,  f32),
+
+            blas_params(512 ,    32,  512,   1,   1,    4,   4,  f32),
+            blas_params(512 ,    32,  513,   1,   1,    4,   4,  f32),
+            blas_params(513 ,    32,  513,   1,   1,    4,   4,  f32),
+            blas_params(513 ,    33,  513,   1,   1,    4,   4,  f32),
+            blas_params(513 ,   511,   32,   1,   1,    4,   4,  f32),
+            blas_params(513 ,   511,   31,   1,   1,    4,   4,  f32),
+            blas_params(513 ,   511,   33,   1,   1,    4,   4,  f32),
+            blas_params(511 ,   511,   33,   1,   1,    4,   4,  f32)
+        // clang-format on
+        ),
+    print_blas_params);
+
+INSTANTIATE_TEST_CASE_P(
+    SameBatch, MatrixMultiplyBatch,
+    ::testing::Values(
+        // clang-format off
+            //          M      N     K   ld2  ld3   rd2  rd3  type
+            blas_params(32,   32,  10,     2,   1,    2,   1,  f32),
+            blas_params(32,   32,  10,     1,   2,    1,   2,  f32),
+            blas_params(32,   32,  10,     2,   2,    2,   2,  f32),
+            blas_params(32,   32,  10,     3,   2,    3,   2,  f32),
+            blas_params(32,   32,  10,     3,   3,    3,   3,  f32),
+            blas_params(32,   32,  10,     4,   4,    4,   4,  f32),
+
+            blas_params(512,  32, 512,     4,   4,    4,   4,  f32),
+            blas_params(512,  32, 513,     4,   4,    4,   4,  f32),
+            blas_params(513,  32, 513,     4,   4,    4,   4,  f32),
+            blas_params(513,  33, 513,     4,   4,    4,   4,  f32),
+            blas_params(513, 511,  32,     4,   4,    4,   4,  f32),
+            blas_params(513, 511,  31,     4,   4,    4,   4,  f32),
+            blas_params(513, 511,  33,     4,   4,    4,   4,  f32),
+            blas_params(511, 511,  33,     4,   4,    4,   4,  f32),
+
+            blas_params( 32,  32,  10,     1,   1,    1,   1, f32)
+        // clang-format on
+        ),
+    print_blas_params);
+
+TEST_P(MatrixMultiplyBatch, Batched) {
+    array out         = matmul(lhs, rhs);
+    blas_params param = GetParam();
 }
 
 float alpha = 1.f;

From 63c2d04d662a01c794e1c0ef1b587b1e8b66c270 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 21 Feb 2020 02:52:20 -0500
Subject: [PATCH 1862/2677] Convert EXPECT_PRED to ASSERT_PRED in testHelpers

---
 test/testHelpers.hpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index ca38518141..d4a449adf9 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -1059,7 +1059,7 @@ ::testing::AssertionResult assertArrayNear(
 /// \param[in] EXPECTED The expected array of the assertion
 /// \param[in] ACTUAL The actual resulting array from the calculation
 #define ASSERT_ARRAYS_EQ(EXPECTED, ACTUAL) \
-    EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
+    ASSERT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
 
 /// Same as ASSERT_ARRAYS_EQ, but for cases when a "special" output array is
 /// given to the function.
@@ -1069,7 +1069,7 @@ ::testing::AssertionResult assertArrayNear(
 /// \param[in] EXPECTED The expected array of the assertion
 /// \param[in] ACTUAL The actual resulting array from the calculation
 #define ASSERT_SPECIAL_ARRAYS_EQ(EXPECTED, ACTUAL, META) \
-    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED, ACTUAL, META)
+    ASSERT_PRED_FORMAT3(assertArrayEq, EXPECTED, ACTUAL, META)
 
 /// Compares a std::vector with an af::/af_array for their types, dims, and
 /// values (strict equality).
@@ -1078,7 +1078,7 @@ ::testing::AssertionResult assertArrayNear(
 /// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
 /// \param[in] ACTUAL_ARR The actual resulting array from the calculation
 #define ASSERT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
-    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
+    ASSERT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
                         ACTUAL_ARR)
 
 /// Compares two af::array or af_arrays for their type, dims, and values (with a
@@ -1091,7 +1091,7 @@ ::testing::AssertionResult assertArrayNear(
 ///
 /// \NOTE: This macro will deallocate the af_arrays after the call
 #define ASSERT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
-    EXPECT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
+    ASSERT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
 
 /// Compares a std::vector with an af::array for their dims and values (with a
 /// given tolerance).
@@ -1103,7 +1103,7 @@ ::testing::AssertionResult assertArrayNear(
 ///            elements of EXPECTED and ACTUAL
 #define ASSERT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
                               MAX_ABSDIFF)                                 \
-    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
+    ASSERT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
                         ACTUAL_ARR, MAX_ABSDIFF)
 
 #if defined(USE_MTX)

From 95038c3e3bda61b53a9c417fd931ba2cdd760f80 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 27 Feb 2020 04:17:01 -0500
Subject: [PATCH 1863/2677] Dot math opencl (#2775)

* Fix dot test to avoid large values for half. Adjust math precision

* Add missing GTEST_LINKED_AS_SHARED_LIBRARY in dot test.cpp

Co-authored-by: pradeep <pradeep@arrayfire.com>
---
 test/dot.cpp  | 54 ++++++++++++++++++++++++++++++++++++++-------------
 test/math.cpp |  2 +-
 2 files changed, 41 insertions(+), 15 deletions(-)

diff --git a/test/dot.cpp b/test/dot.cpp
index 53592e89c1..065f735d4c 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <half.hpp>
@@ -40,12 +41,7 @@ class DotC : public ::testing::Test {
     virtual void SetUp() {}
 };
 
-// create lists of types to be tested
-#ifdef AF_CPU
 typedef ::testing::Types<float, double> TestTypesF;
-#else
-typedef ::testing::Types<half_float::half, float, double> TestTypesF;
-#endif
 typedef ::testing::Types<cfloat, cdouble> TestTypesC;
 
 // register the type list
@@ -85,16 +81,8 @@ void dotTest(string pTestFile, const int resultIdx,
 
     vector<T> goldData = tests[resultIdx];
     size_t nElems      = goldData.size();
-    vector<T> outData(nElems);
 
-    ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), out));
-
-    if (false == (isinf(outData.front()) && isinf(goldData[0]))) {
-        for (size_t elIter = 0; elIter < nElems; ++elIter) {
-            ASSERT_NEAR(abs(goldData[elIter]), abs(outData[elIter]), 0.03)
-                << "at: " << elIter << endl;
-        }
-    }
+    ASSERT_VEC_ARRAY_NEAR(goldData, dim4(nElems), out, 0.03);
 
     ASSERT_SUCCESS(af_release_array(a));
     ASSERT_SUCCESS(af_release_array(b));
@@ -280,3 +268,41 @@ TEST(DotAllCCU, CPP) {
 
     ASSERT_EQ(goldData[0], out);
 }
+
+class Dot : public ::testing::TestWithParam<int> {
+   public:
+    array ha, hb, gold;
+
+    void SetUp() {
+        SUPPORTED_TYPE_CHECK(half_float::half);
+        int elems = GetParam();
+        array fa  = af::randu(elems) - 0.5f;
+        array fb  = af::randu(elems) - 0.5f;
+
+        ha = fa.as(f16);
+        hb = fb.as(f16);
+
+        gold = dot(fa, fb);
+    }
+};
+
+std::string print_dot(const ::testing::TestParamInfo<Dot::ParamType> info) {
+    std::stringstream ss;
+
+    ss << info.param;
+
+    return ss.str();
+}
+
+INSTANTIATE_TEST_CASE_P(Small, Dot,
+                        ::testing::Values(2, 4, 5, 10, 31, 32, 33, 100, 127,
+                                          128, 129, 200, 500, 511, 512, 513,
+                                          1000),
+                        print_dot);
+
+TEST_P(Dot, Half) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    array hc = dot(ha, hb);
+
+    ASSERT_ARRAYS_NEAR(gold, hc.as(f32), 1e-2);
+}
diff --git a/test/math.cpp b/test/math.cpp
index 8776220a21..e869c2bdde 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -27,7 +27,7 @@ using std::vector;
 const int num        = 10000;
 const float hlf_err  = 1e-2;
 const float flt_err  = 1e-3;
-const double dbl_err = 1e-10;
+const double dbl_err = 1e-6;
 
 typedef std::complex<float> complex_float;
 typedef std::complex<double> complex_double;

From e7bdb0f081068da4b088f589377f90bd72cbea9e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 2 Mar 2020 23:23:46 +0530
Subject: [PATCH 1864/2677] Rename cu to cpp where possible and cleanup CUDA
 fast/orb

cleanup scan by key source files and merge into afcuda target
---
 src/backend/cuda/CMakeLists.txt               | 33 ++++---
 src/backend/cuda/blas.cpp                     |  2 +-
 .../cuda/{cholesky.cu => cholesky.cpp}        |  0
 .../fast_pyramid.hpp => fast_pyramid.cpp}     | 86 ++++++++++---------
 src/backend/cuda/fast_pyramid.cu              | 51 -----------
 src/backend/cuda/fast_pyramid.hpp             | 21 ++---
 src/backend/cuda/{inverse.cu => inverse.cpp}  |  0
 src/backend/cuda/kernel/fast.hpp              |  2 +
 src/backend/cuda/kernel/fast_lut.hpp          |  2 +
 src/backend/cuda/kernel/orb.hpp               |  3 -
 src/backend/cuda/kernel/orb_patch.hpp         |  1 -
 .../cuda/kernel/scan_by_key/CMakeLists.txt    | 39 +++------
 ...an_by_key_impl.cu => scan_by_key_impl.cpp} |  6 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 17 ++--
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 19 ++--
 src/backend/cuda/math.hpp                     | 40 ++++-----
 src/backend/cuda/orb.cu                       | 18 +++-
 src/backend/cuda/reduce_impl.hpp              |  2 +
 src/backend/cuda/{solve.cu => solve.cpp}      |  0
 src/backend/cuda/{svd.cu => svd.cpp}          |  0
 20 files changed, 149 insertions(+), 193 deletions(-)
 rename src/backend/cuda/{cholesky.cu => cholesky.cpp} (100%)
 rename src/backend/cuda/{kernel/fast_pyramid.hpp => fast_pyramid.cpp} (55%)
 delete mode 100644 src/backend/cuda/fast_pyramid.cu
 rename src/backend/cuda/{inverse.cu => inverse.cpp} (100%)
 rename src/backend/cuda/kernel/scan_by_key/{scan_by_key_impl.cu => scan_by_key_impl.cpp} (83%)
 rename src/backend/cuda/{solve.cu => solve.cpp} (100%)
 rename src/backend/cuda/{svd.cu => svd.cpp} (100%)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 53ce4cf2d1..c8b769b2d0 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -38,9 +38,12 @@ set(CUDA_architecture_build_targets ${detected_gpus} CACHE
 cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
 message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets}")
 
-set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};
-  ${cuda_architecture_flags}
-  )
+set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
+if(${CUDA_SEPARABLE_COMPILATION})
+  # Enable relocatable device code generation for separable
+  # compilation which is in turn required for any device linking done.
+  set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-rdc=true)
+endif()
 
 mark_as_advanced(
     CUDA_LIBRARIES_PATH
@@ -177,7 +180,8 @@ function(cuda_add_library cuda_target)
   set_target_properties(${cuda_target}
     PROPERTIES
     LINKER_LANGUAGE ${CUDA_C_OR_CXX}
-    )
+    POSITION_INDEPENDENT_CODE ON
+  )
 
 endfunction()
 
@@ -221,7 +225,6 @@ cuda_add_library(afcuda
     assign.cu
     bilateral.cpp
     canny.cpp
-    cholesky.cu
     copy.cu
     count.cu
     diagonal.cu
@@ -234,7 +237,6 @@ cuda_add_library(afcuda
     Event.hpp
     exampleFunction.cpp
     fast.cu
-    fast_pyramid.cu
     fftconvolve.cu
     gradient.cu
     harris.cu
@@ -244,7 +246,6 @@ cuda_add_library(afcuda
     identity.cu
     iir.cu
     index.cu
-    inverse.cu
     iota.cu
     ireduce.cu
     join.cu
@@ -272,7 +273,6 @@ cuda_add_library(afcuda
     set.cu
     sift.cu
     sobel.cpp
-    solve.cu
     sort.cu
     sort_by_key.cu
     sort_index.cu
@@ -280,7 +280,6 @@ cuda_add_library(afcuda
     sparse_arith.cu
     sum.cu
     susan.cu
-    svd.cu
     tile.cu
     topk.cu
     transform.cpp
@@ -304,7 +303,6 @@ cuda_add_library(afcuda
     kernel/exampleFunction.hpp
     kernel/fast.hpp
     kernel/fast_lut.hpp
-    kernel/fast_pyramid.hpp
     kernel/fftconvolve.hpp
     kernel/flood_fill.hpp
     kernel/gradient.hpp
@@ -385,6 +383,7 @@ cuda_add_library(afcuda
     blas.hpp
     canny.hpp
     cast.hpp
+    cholesky.cpp
     cholesky.hpp
     complex.hpp
     convolve.cpp
@@ -412,6 +411,7 @@ cuda_add_library(afcuda
     err_cuda.hpp
     exampleFunction.hpp
     fast.hpp
+    fast_pyramid.cpp
     fast_pyramid.hpp
     fft.cpp
     fft.hpp
@@ -433,6 +433,7 @@ cuda_add_library(afcuda
     image.cpp
     image.hpp
     index.hpp
+    inverse.cpp
     inverse.hpp
     iota.hpp
     ireduce.hpp
@@ -479,6 +480,7 @@ cuda_add_library(afcuda
     shift.hpp
     sift.hpp
     sobel.hpp
+    solve.cpp
     solve.hpp
     sort_by_key.hpp
     sort_index.hpp
@@ -489,6 +491,7 @@ cuda_add_library(afcuda
     surface.cpp
     surface.hpp
     susan.hpp
+    svd.cpp
     svd.hpp
     tile.hpp
     topk.hpp
@@ -512,21 +515,18 @@ cuda_add_library(afcuda
 
     nvrtc/cache.cpp
 
+    ${scan_by_key_sources}
+
     OPTIONS ${platform_flags} ${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\"
   )
 
 arrayfire_set_default_cxx_flags(afcuda)
 
-# NOTE: Do not add additional CUDA specific definitions here. Add it to the
-# cxx_definitions variable above. cxx_definitions is used to propigate
-# definitions to the scan_by_key and thrust_sort_by_key targets as well as the
-# cuda library above.
 target_compile_options(afcuda PRIVATE ${cxx_definitions})
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
-add_dependencies(cuda_scan_by_key ${nvrtc_kernel_targets})
 
 target_include_directories (afcuda
   PUBLIC
@@ -543,8 +543,6 @@ target_include_directories (afcuda
     ${cuDNN_INCLUDE_DIRS}
 )
 
-set_target_properties(afcuda PROPERTIES POSITION_INDEPENDENT_CODE ON)
-
 # Remove cublas_device library which is no longer included with the cuda
 # toolkit. Fixes issues with older CMake versions
 if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
@@ -562,7 +560,6 @@ target_link_libraries(afcuda
     c_api_interface
     cpp_api_interface
     afcommon_interface
-    cuda_scan_by_key
     cuda_thrust_sort_by_key
     ${CUDA_nvrtc_LIBRARY}
     ${CUDA_CUBLAS_LIBRARIES}
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cpp
index bb005b1815..4d61e6439e 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cpp
@@ -199,7 +199,7 @@ cublasGemmAlgo_t selectGEMMAlgorithm() {
 }
 
 template<>
-cublasGemmAlgo_t selectGEMMAlgorithm<half>() {
+cublasGemmAlgo_t selectGEMMAlgorithm<common::half>() {
     auto dev              = getDeviceProp(getActiveDeviceId());
     cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
     if (dev.major >= 7) { algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP; }
diff --git a/src/backend/cuda/cholesky.cu b/src/backend/cuda/cholesky.cpp
similarity index 100%
rename from src/backend/cuda/cholesky.cu
rename to src/backend/cuda/cholesky.cpp
diff --git a/src/backend/cuda/kernel/fast_pyramid.hpp b/src/backend/cuda/fast_pyramid.cpp
similarity index 55%
rename from src/backend/cuda/kernel/fast_pyramid.hpp
rename to src/backend/cuda/fast_pyramid.cpp
index dbd33ec953..6bd2055097 100644
--- a/src/backend/cuda/kernel/fast_pyramid.hpp
+++ b/src/backend/cuda/fast_pyramid.cpp
@@ -7,23 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
-#include <debug_cuda.hpp>
+#include <fast_pyramid.hpp>
+
+#include <Array.hpp>
 #include <err_cuda.hpp>
-#include <memory.hpp>
+#include <fast.hpp>
+#include <resize.hpp>
+#include <af/dim4.hpp>
 
-#include "fast.hpp"
-#include "resize.hpp"
+using af::dim4;
+using std::vector;
 
 namespace cuda {
 
-namespace kernel {
-
 template<typename T>
-void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
-                  std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
-                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
-                  const Array<T>& in, const float fast_thr,
+void fast_pyramid(vector<unsigned> &feat_pyr, vector<Array<float>> &x_pyr,
+                  vector<Array<float>> &y_pyr, vector<unsigned> &lvl_best,
+                  vector<float> &lvl_scl, vector<Array<T>> &img_pyr,
+                  const Array<T> &in, const float fast_thr,
                   const unsigned max_feat, const float scl_fctr,
                   const unsigned levels, const unsigned patch_size) {
     dim4 indims         = in.dims();
@@ -72,48 +73,53 @@ void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
                       round(indims[1] / lvl_scl[i]));
 
             img_pyr.push_back(createEmptyArray<T>(dims));
-            resize<T>(img_pyr[i], img_pyr[i - 1], AF_INTERP_BILINEAR);
+            img_pyr[i] =
+                resize(img_pyr[i - 1], dims[0], dims[1], AF_INTERP_BILINEAR);
         }
     }
 
     feat_pyr.resize(max_levels);
-    d_x_pyr.resize(max_levels);
-    d_y_pyr.resize(max_levels);
 
-    for (unsigned i = 0; i < max_levels; i++) {
-        unsigned lvl_feat   = 0;
-        float* d_x_feat     = NULL;
-        float* d_y_feat     = NULL;
-        float* d_score_feat = NULL;
+    // Round feature size to nearest odd integer
+    float size = 2.f * floor(patch_size / 2.f) + 1.f;
 
-        // Round feature size to nearest odd integer
-        float size = 2.f * floor(patch_size / 2.f) + 1.f;
+    // Avoid keeping features that are too wide and might not fit the image,
+    // sqrt(2.f) is the radius when angle is 45 degrees and represents
+    // widest case possible
+    unsigned edge = ceil(size * sqrt(2.f) / 2.f);
 
-        // Avoid keeping features that are too wide and might not fit the image,
-        // sqrt(2.f) is the radius when angle is 45 degrees and represents
-        // widest case possible
-        unsigned edge = ceil(size * sqrt(2.f) / 2.f);
-
-        // Detects FAST features
-        fast(&lvl_feat, &d_x_feat, &d_y_feat, &d_score_feat, img_pyr[i],
-             fast_thr, 9, 1, 0.15f, edge);
+    for (unsigned i = 0; i < max_levels; i++) {
+        Array<float> x_out     = createEmptyArray<float>(dim4());
+        Array<float> y_out     = createEmptyArray<float>(dim4());
+        Array<float> score_out = createEmptyArray<float>(dim4());
 
-        // FAST score is not used
-        // TODO: should be handled by fast()
-        memFree(d_score_feat);
+        unsigned lvl_feat = fast(x_out, y_out, score_out, img_pyr[i], fast_thr,
+                                 9, 1, 0.14f, edge);
 
-        if (lvl_feat == 0) {
-            feat_pyr[i] = 0;
-            d_x_pyr[i]  = NULL;
-            d_x_pyr[i]  = NULL;
-        } else {
+        if (lvl_feat > 0) {
             feat_pyr[i] = lvl_feat;
-            d_x_pyr[i]  = d_x_feat;
-            d_y_pyr[i]  = d_y_feat;
+            x_pyr.push_back(x_out);
+            y_pyr.push_back(y_out);
+        } else {
+            feat_pyr[i] = 0;
         }
     }
 }
 
-}  // namespace kernel
+#define INSTANTIATE(T)                                                      \
+    template void fast_pyramid<T>(                                          \
+        vector<unsigned> &, vector<Array<float>> &, vector<Array<float>> &, \
+        vector<unsigned> &, vector<float> &, vector<Array<T>> &,            \
+        const Array<T> &, const float, const unsigned, const float,         \
+        const unsigned, const unsigned);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/fast_pyramid.cu b/src/backend/cuda/fast_pyramid.cu
deleted file mode 100644
index 9dab0988e2..0000000000
--- a/src/backend/cuda/fast_pyramid.cu
+++ /dev/null
@@ -1,51 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <err_cuda.hpp>
-#include <kernel/fast_pyramid.hpp>
-#include <af/dim4.hpp>
-#include <af/features.h>
-
-using af::dim4;
-using af::features;
-
-namespace cuda {
-
-template<typename T>
-void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
-                  std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
-                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
-                  const Array<T>& image, const float fast_thr,
-                  const unsigned max_feat, const float scl_fctr,
-                  const unsigned levels, const unsigned patch_size) {
-    kernel::fast_pyramid<T>(feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl,
-                            img_pyr, image, fast_thr, max_feat, scl_fctr,
-                            levels, patch_size);
-}
-
-#define INSTANTIATE(T)                                                        \
-    template void fast_pyramid<T>(                                            \
-        std::vector<unsigned> & feat_pyr, std::vector<float*> & d_x_pyr,      \
-        std::vector<float*> & d_y_pyr, std::vector<unsigned> & lvl_best,      \
-        std::vector<float> & lvl_scl, std::vector<Array<T>> & img_pyr,        \
-        const Array<T>& image, const float fast_thr, const unsigned max_feat, \
-        const float scl_fctr, const unsigned levels,                          \
-        const unsigned patch_size);
-
-INSTANTIATE(float)
-INSTANTIATE(double)
-INSTANTIATE(char)
-INSTANTIATE(int)
-INSTANTIATE(uint)
-INSTANTIATE(uchar)
-INSTANTIATE(short)
-INSTANTIATE(ushort)
-
-}  // namespace cuda
diff --git a/src/backend/cuda/fast_pyramid.hpp b/src/backend/cuda/fast_pyramid.hpp
index a7c9d79f86..762b61c011 100644
--- a/src/backend/cuda/fast_pyramid.hpp
+++ b/src/backend/cuda/fast_pyramid.hpp
@@ -7,19 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
-#include <af/features.h>
 
-using af::features;
+#include <vector>
 
 namespace cuda {
-
 template<typename T>
-void fast_pyramid(std::vector<unsigned>& feat_pyr, std::vector<float*>& d_x_pyr,
-                  std::vector<float*>& d_y_pyr, std::vector<unsigned>& lvl_best,
-                  std::vector<float>& lvl_scl, std::vector<Array<T>>& img_pyr,
-                  const Array<T>& image, const float fast_thr,
-                  const unsigned max_feat, const float scl_fctr,
-                  const unsigned levels, const unsigned patch_size);
-
+void fast_pyramid(std::vector<unsigned> &feat_pyr,
+                  std::vector<Array<float>> &d_x_pyr,
+                  std::vector<Array<float>> &d_y_pyr,
+                  std::vector<unsigned> &lvl_best, std::vector<float> &lvl_scl,
+                  std::vector<Array<T>> &img_pyr, const Array<T> &image,
+                  const float fast_thr, const unsigned max_feat,
+                  const float scl_fctr, const unsigned levels,
+                  const unsigned patch_size);
 }
diff --git a/src/backend/cuda/inverse.cu b/src/backend/cuda/inverse.cpp
similarity index 100%
rename from src/backend/cuda/inverse.cu
rename to src/backend/cuda/inverse.cpp
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 9cc96a464d..340f3ca94b 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
diff --git a/src/backend/cuda/kernel/fast_lut.hpp b/src/backend/cuda/kernel/fast_lut.hpp
index 55ebcc5de2..bbe926051d 100644
--- a/src/backend/cuda/kernel/fast_lut.hpp
+++ b/src/backend/cuda/kernel/fast_lut.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 __constant__ unsigned char FAST_LUT[] = {
     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 5765f8da18..cba1542400 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -352,9 +352,6 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
                     d_score_harris.get(), harris_idx.get(), NULL, feat_pyr[i]);
         POST_LAUNCH_CHECK();
 
-        memFree(d_x_pyr[i]);
-        memFree(d_y_pyr[i]);
-
         float* d_ori_lvl = memAlloc<float>(feat_pyr[i]).release();
 
         // Compute orientation of features
diff --git a/src/backend/cuda/kernel/orb_patch.hpp b/src/backend/cuda/kernel/orb_patch.hpp
index 8a6ec2633b..68a45e9c97 100644
--- a/src/backend/cuda/kernel/orb_patch.hpp
+++ b/src/backend/cuda/kernel/orb_patch.hpp
@@ -10,7 +10,6 @@
 #pragma once
 
 namespace cuda {
-
 namespace kernel {
 
 // Reference pattern, generated for a patch size of 31x31, as suggested by
diff --git a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
index 55ba972de0..8280fd4e74 100644
--- a/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/scan_by_key/CMakeLists.txt
@@ -1,11 +1,11 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2020, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu" FILESTRINGS)
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp" FILESTRINGS)
 
 foreach(STR ${FILESTRINGS})
     if(${STR} MATCHES "// SBK_BINARY_OPS")
@@ -14,32 +14,15 @@ foreach(STR ${FILESTRINGS})
     endif()
 endforeach()
 
-cuda_add_cuda_include_once()
-
 foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
-    # When using cuda_compile with older versions of FindCUDA. The generated targets
-    # have the same names as the source file. Since we are using the same file for
-    # the compilation of these targets we need to rename them before sending them
-    # to the cuda_compile command so that it doesn't generate multiple targets with
-    # the same name
-    file(COPY "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cu"
-      DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key")
-    file(RENAME "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl.cu"
-      "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu")
-
-    cuda_compile(scan_by_key_gen_files "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cu"
-          "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key_impl.hpp"
-          "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key_impl.hpp"
-          OPTIONS
-          -DSBK_BINARY_OP=${SBK_BINARY_OP} "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
-      )
-
-    list(APPEND SCAN_OBJ ${scan_by_key_gen_files})
-endforeach(SBK_BINARY_OP ${SBK_BINARY_OPS})
+  configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/scan_by_key_impl.cpp"
+    "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cpp"
+  )
 
-cuda_add_library(cuda_scan_by_key STATIC ${SCAN_OBJ})
-set_target_properties(cuda_scan_by_key
-  PROPERTIES
-    LINKER_LANGUAGE CXX
-    FOLDER "Generated Targets"
+  list(
+    APPEND
+    scan_by_key_sources
+    "${CMAKE_CURRENT_BINARY_DIR}/kernel/scan_by_key/scan_by_key_impl_${SBK_BINARY_OP}.cpp"
   )
+endforeach(SBK_BINARY_OP ${SBK_BINARY_OPS})
diff --git a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp
similarity index 83%
rename from src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
rename to src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp
index 39b0ae3a6f..6b88c5e8e0 100644
--- a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cu
+++ b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp
@@ -16,7 +16,9 @@
 
 namespace cuda {
 namespace kernel {
-INSTANTIATE_SCAN_FIRST_BY_KEY_OP(SBK_BINARY_OP)
-INSTANTIATE_SCAN_DIM_BY_KEY_OP(SBK_BINARY_OP)
+// clang-format off
+INSTANTIATE_SCAN_FIRST_BY_KEY_OP( @SBK_BINARY_OP@ )
+INSTANTIATE_SCAN_DIM_BY_KEY_OP( @SBK_BINARY_OP@ )
+// clang-format on
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index cb44a4997a..bfb9aade84 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -6,19 +6,18 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #pragma once
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
+#include <kernel/config.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/scan_dim_by_key_cuh.hpp>
 #include <optypes.hpp>
 #include <traits.hpp>
-#include "config.hpp"
 
 #include <algorithm>
 #include <string>
@@ -26,8 +25,10 @@
 namespace cuda {
 namespace kernel {
 
-static const std::string ScanDimByKeySource(scan_dim_by_key_cuh,
-                                            scan_dim_by_key_cuh_len);
+static inline std::string sbkDimSource() {
+    static const std::string src(scan_dim_by_key_cuh, scan_dim_by_key_cuh_len);
+    return src;
+}
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
 static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
@@ -37,7 +38,7 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
     auto scanbykey_dim_nonfinal =
-        getKernel("cuda::scanbykey_dim_nonfinal", ScanDimByKeySource,
+        getKernel("cuda::scanbykey_dim_nonfinal", sbkDimSource(),
                   {TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                    TemplateTypename<To>(), TemplateArg(op)},
                   {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
@@ -61,7 +62,7 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
     auto scanbykey_dim_final =
-        getKernel("cuda::scanbykey_dim_final", ScanDimByKeySource,
+        getKernel("cuda::scanbykey_dim_final", sbkDimSource(),
                   {TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                    TemplateTypename<To>(), TemplateArg(op)},
                   {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
@@ -83,7 +84,7 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
     auto scanbykey_dim_bcast =
-        getKernel("cuda::scanbykey_dim_bcast", ScanDimByKeySource,
+        getKernel("cuda::scanbykey_dim_bcast", sbkDimSource(),
                   {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 3881aa3593..bbf33e3b8c 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -6,26 +6,29 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #pragma once
 
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
+#include <kernel/config.hpp>
 #include <memory.hpp>
 #include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/scan_first_by_key_cuh.hpp>
 #include <optypes.hpp>
-#include "config.hpp"
 
 #include <algorithm>
+#include <string>
 
 namespace cuda {
 namespace kernel {
 
-static const std::string ScanFirstByKeySource(scan_first_by_key_cuh,
-                                              scan_first_by_key_cuh_len);
+static inline std::string sbkFirstSource() {
+    static const std::string src(scan_first_by_key_cuh,
+                                 scan_first_by_key_cuh_len);
+    return src;
+}
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
 static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
@@ -34,7 +37,7 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
     auto scanbykey_first_nonfinal = getKernel(
-        "cuda::scanbykey_first_nonfinal", ScanFirstByKeySource,
+        "cuda::scanbykey_first_nonfinal", sbkFirstSource(),
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
@@ -55,7 +58,7 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
     auto scanbykey_first_final = getKernel(
-        "cuda::scanbykey_first_final", ScanFirstByKeySource,
+        "cuda::scanbykey_first_final", sbkFirstSource(),
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
@@ -75,7 +78,7 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
     auto scanbykey_first_bcast =
-        getKernel("cuda::scanbykey_first_bcast", ScanFirstByKeySource,
+        getKernel("cuda::scanbykey_first_bcast", sbkFirstSource(),
                   {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 9ef463f4f7..5eadc9a449 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -168,84 +168,84 @@ STATIC_ double minval() {
 }
 #else
 template<typename T>
-__device__ T maxval() {
+STATIC_ __device__ T maxval() {
     return 1u << (8 * sizeof(T) - 1);
 }
 template<typename T>
-__device__ T minval() {
+STATIC_ __device__ T minval() {
     return scalar<T>(0);
 }
 
 template<>
-__device__ int maxval<int>() {
+STATIC_ __device__ int maxval<int>() {
     return 0x7fffffff;
 }
 template<>
-__device__ int minval<int>() {
+STATIC_ __device__ int minval<int>() {
     return 0x80000000;
 }
 template<>
-__device__ intl maxval<intl>() {
+STATIC_ __device__ intl maxval<intl>() {
     return 0x7fffffffffffffff;
 }
 template<>
-__device__ intl minval<intl>() {
+STATIC_ __device__ intl minval<intl>() {
     return 0x8000000000000000;
 }
 template<>
-__device__ uintl maxval<uintl>() {
+STATIC_ __device__ uintl maxval<uintl>() {
     return 1ULL << (8 * sizeof(uintl) - 1);
 }
 template<>
-__device__ char maxval<char>() {
+STATIC_ __device__ char maxval<char>() {
     return 0x7f;
 }
 template<>
-__device__ char minval<char>() {
+STATIC_ __device__ char minval<char>() {
     return 0x80;
 }
 template<>
-__device__ float maxval<float>() {
+STATIC_ __device__ float maxval<float>() {
     return CUDART_INF_F;
 }
 template<>
-__device__ float minval<float>() {
+STATIC_ __device__ float minval<float>() {
     return -CUDART_INF_F;
 }
 template<>
-__device__ double maxval<double>() {
+STATIC_ __device__ double maxval<double>() {
     return CUDART_INF;
 }
 template<>
-__device__ double minval<double>() {
+STATIC_ __device__ double minval<double>() {
     return -CUDART_INF;
 }
 template<>
-__device__ short maxval<short>() {
+STATIC_ __device__ short maxval<short>() {
     return 0x7fff;
 }
 template<>
-__device__ short minval<short>() {
+STATIC_ __device__ short minval<short>() {
     return 0x8000;
 }
 template<>
-__device__ ushort maxval<ushort>() {
+STATIC_ __device__ ushort maxval<ushort>() {
     return ((ushort)1) << (8 * sizeof(ushort) - 1);
 }
 template<>
-__device__ common::half maxval<common::half>() {
+STATIC_ __device__ common::half maxval<common::half>() {
     return common::half(65537.f);
 }
 template<>
-__device__ common::half minval<common::half>() {
+STATIC_ __device__ common::half minval<common::half>() {
     return common::half(-65537.f);
 }
 template<>
-__device__ __half maxval<__half>() {
+STATIC_ __device__ __half maxval<__half>() {
     return __float2half(CUDART_INF);
 }
 template<>
-__device__ __half minval<__half>() {
+STATIC_ __device__ __half minval<__half>() {
     return __float2half(-CUDART_INF);
 }
 #endif
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index 541df50d20..ec8691a899 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -26,11 +26,23 @@ unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              const unsigned levels, const bool blur_img) {
     std::vector<unsigned> feat_pyr, lvl_best;
     std::vector<float> lvl_scl;
-    std::vector<float *> d_x_pyr, d_y_pyr;
+    std::vector<Array<float>> x_pyr, y_pyr;
     std::vector<Array<T>> img_pyr;
 
-    fast_pyramid<T>(feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
-                    image, fast_thr, max_feat, scl_fctr, levels, REF_PAT_SIZE);
+    fast_pyramid<T>(feat_pyr, x_pyr, y_pyr, lvl_best, lvl_scl, img_pyr, image,
+                    fast_thr, max_feat, scl_fctr, levels, REF_PAT_SIZE);
+
+    const size_t num_levels = feat_pyr.size();
+
+    std::vector<float *> d_x_pyr(num_levels, nullptr),
+        d_y_pyr(num_levels, nullptr);
+
+    for (size_t i = 0; i < feat_pyr.size(); ++i) {
+        if (feat_pyr[i] > 0) {
+            d_x_pyr[i] = static_cast<float *>(x_pyr[i].get());
+            d_y_pyr[i] = static_cast<float *>(y_pyr[i].get());
+        }
+    }
 
     unsigned nfeat_out;
     float *x_out;
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 7b7785d402..6ff8d71e1f 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <af/dim4.hpp>
 
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cpp
similarity index 100%
rename from src/backend/cuda/solve.cu
rename to src/backend/cuda/solve.cpp
diff --git a/src/backend/cuda/svd.cu b/src/backend/cuda/svd.cpp
similarity index 100%
rename from src/backend/cuda/svd.cu
rename to src/backend/cuda/svd.cpp

From 928d19e6084091a668b28ec6139dca93a59fb1fd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 4 Mar 2020 03:19:52 +0530
Subject: [PATCH 1865/2677] move CUDA kernels to runtime(nvrtc) compilation

---
 src/backend/common/CMakeLists.txt             |   1 +
 src/backend/common/defines.hpp                |  18 +-
 src/backend/common/internal_enums.hpp         |  22 ++
 src/backend/common/jit/Node.hpp               |   7 +
 src/backend/cpu/Array.hpp                     |   1 +
 src/backend/cuda/CMakeLists.txt               |  80 ++--
 src/backend/cuda/{assign.cu => assign.cpp}    |   2 +-
 src/backend/cuda/assign_kernel_param.hpp      |  23 ++
 src/backend/cuda/{copy.cu => copy.cpp}        |   5 +-
 .../cuda/{diagonal.cu => diagonal.cpp}        |   0
 src/backend/cuda/{diff.cu => diff.cpp}        |  15 +-
 src/backend/cuda/dims_param.hpp               |  18 +
 .../cuda/{fftconvolve.cu => fftconvolve.cpp}  |   4 +-
 .../cuda/{gradient.cu => gradient.cpp}        |   4 +-
 .../cuda/{identity.cu => identity.cpp}        |   0
 src/backend/cuda/{iir.cu => iir.cpp}          |   0
 src/backend/cuda/{index.cu => index.cpp}      |   6 +-
 src/backend/cuda/{iota.cu => iota.cpp}        |   0
 src/backend/cuda/{ireduce.cu => ireduce.cpp}  |   0
 src/backend/cuda/{join.cu => join.cpp}        |  67 +---
 src/backend/cuda/kernel/assign.cuh            |  62 +++
 src/backend/cuda/kernel/assign.hpp            |  71 +---
 src/backend/cuda/kernel/copy.cuh              | 134 +++++++
 src/backend/cuda/kernel/diagonal.cuh          |  55 +++
 src/backend/cuda/kernel/diagonal.hpp          |  65 ++--
 src/backend/cuda/kernel/diff.cuh              |  60 +++
 src/backend/cuda/kernel/diff.hpp              |  75 +---
 src/backend/cuda/kernel/fftconvolve.cuh       | 220 +++++++++++
 src/backend/cuda/kernel/fftconvolve.hpp       | 271 ++-----------
 src/backend/cuda/kernel/gradient.cuh          |  92 +++++
 src/backend/cuda/kernel/gradient.hpp          |  99 +----
 src/backend/cuda/kernel/identity.cuh          |  41 ++
 src/backend/cuda/kernel/identity.hpp          |  41 +-
 src/backend/cuda/kernel/iir.cuh               |  69 ++++
 src/backend/cuda/kernel/iir.hpp               |  73 +---
 src/backend/cuda/kernel/index.cuh             |  62 +++
 src/backend/cuda/kernel/index.hpp             |  72 +---
 src/backend/cuda/kernel/iota.cuh              |  53 +++
 src/backend/cuda/kernel/iota.hpp              |  63 +--
 src/backend/cuda/kernel/ireduce.cuh           | 231 +++++++++++
 src/backend/cuda/kernel/ireduce.hpp           | 361 ++----------------
 src/backend/cuda/kernel/join.cuh              |  50 +++
 src/backend/cuda/kernel/join.hpp              |  66 +---
 src/backend/cuda/kernel/lookup.cuh            |  70 ++++
 src/backend/cuda/kernel/lookup.hpp            |  95 ++---
 src/backend/cuda/kernel/lu_split.cuh          |  64 ++++
 src/backend/cuda/kernel/lu_split.hpp          |  84 +---
 src/backend/cuda/kernel/memcopy.cuh           |  43 +++
 src/backend/cuda/kernel/memcopy.hpp           | 201 ++--------
 src/backend/cuda/kernel/range.cuh             |  58 +++
 src/backend/cuda/kernel/range.hpp             |  68 +---
 src/backend/cuda/kernel/reorder.cuh           |  58 +++
 src/backend/cuda/kernel/reorder.hpp           |  71 +---
 src/backend/cuda/kernel/select.cuh            | 101 +++++
 src/backend/cuda/kernel/select.hpp            | 126 ++----
 src/backend/cuda/kernel/sparse.cuh            |  35 ++
 src/backend/cuda/kernel/sparse.hpp            |  44 +--
 src/backend/cuda/kernel/sparse_arith.cuh      | 154 ++++++++
 src/backend/cuda/kernel/sparse_arith.hpp      | 180 ++-------
 src/backend/cuda/kernel/susan.cuh             | 123 ++++++
 src/backend/cuda/kernel/susan.hpp             | 139 ++-----
 src/backend/cuda/kernel/tile.cuh              |  54 +++
 src/backend/cuda/kernel/tile.hpp              |  63 +--
 src/backend/cuda/kernel/triangle.cuh          |  61 +++
 src/backend/cuda/kernel/triangle.hpp          |  75 +---
 src/backend/cuda/kernel/unwrap.cuh            |  81 ++++
 src/backend/cuda/kernel/unwrap.hpp            |  92 +----
 src/backend/cuda/kernel/wrap.cuh              |  75 ++++
 src/backend/cuda/kernel/wrap.hpp              |  89 +----
 src/backend/cuda/{lookup.cu => lookup.cpp}    |  15 +-
 src/backend/cuda/{lu.cu => lu.cpp}            |  11 +-
 src/backend/cuda/minmax_op.hpp                |  85 +++++
 src/backend/cuda/nvrtc/cache.cpp              |  34 +-
 src/backend/cuda/{qr.cu => qr.cpp}            |   2 +-
 src/backend/cuda/{range.cu => range.cpp}      |   3 +-
 src/backend/cuda/{reorder.cu => reorder.cpp}  |   5 +-
 src/backend/cuda/{select.cu => select.cpp}    |  10 +-
 src/backend/cuda/{sparse.cu => sparse.cpp}    |   2 +-
 .../{sparse_arith.cu => sparse_arith.cpp}     |   2 +-
 src/backend/cuda/{susan.cu => susan.cpp}      |   7 +-
 src/backend/cuda/{tile.cu => tile.cpp}        |   4 +-
 .../cuda/{triangle.cu => triangle.cpp}        |   8 +-
 src/backend/cuda/{unwrap.cu => unwrap.cpp}    |   4 +-
 src/backend/cuda/{wrap.cu => wrap.cpp}        |  26 +-
 84 files changed, 2931 insertions(+), 2250 deletions(-)
 create mode 100644 src/backend/common/internal_enums.hpp
 rename src/backend/cuda/{assign.cu => assign.cpp} (98%)
 create mode 100644 src/backend/cuda/assign_kernel_param.hpp
 rename src/backend/cuda/{copy.cu => copy.cpp} (97%)
 rename src/backend/cuda/{diagonal.cu => diagonal.cpp} (100%)
 rename src/backend/cuda/{diff.cu => diff.cpp} (73%)
 create mode 100644 src/backend/cuda/dims_param.hpp
 rename src/backend/cuda/{fftconvolve.cu => fftconvolve.cpp} (97%)
 rename src/backend/cuda/{gradient.cu => gradient.cpp} (99%)
 rename src/backend/cuda/{identity.cu => identity.cpp} (100%)
 rename src/backend/cuda/{iir.cu => iir.cpp} (100%)
 rename src/backend/cuda/{index.cu => index.cpp} (97%)
 rename src/backend/cuda/{iota.cu => iota.cpp} (100%)
 rename src/backend/cuda/{ireduce.cu => ireduce.cpp} (100%)
 rename src/backend/cuda/{join.cu => join.cpp} (65%)
 create mode 100644 src/backend/cuda/kernel/assign.cuh
 create mode 100644 src/backend/cuda/kernel/copy.cuh
 create mode 100644 src/backend/cuda/kernel/diagonal.cuh
 create mode 100644 src/backend/cuda/kernel/diff.cuh
 create mode 100644 src/backend/cuda/kernel/fftconvolve.cuh
 create mode 100644 src/backend/cuda/kernel/gradient.cuh
 create mode 100644 src/backend/cuda/kernel/identity.cuh
 create mode 100644 src/backend/cuda/kernel/iir.cuh
 create mode 100644 src/backend/cuda/kernel/index.cuh
 create mode 100644 src/backend/cuda/kernel/iota.cuh
 create mode 100644 src/backend/cuda/kernel/ireduce.cuh
 create mode 100644 src/backend/cuda/kernel/join.cuh
 create mode 100644 src/backend/cuda/kernel/lookup.cuh
 create mode 100644 src/backend/cuda/kernel/lu_split.cuh
 create mode 100644 src/backend/cuda/kernel/memcopy.cuh
 create mode 100644 src/backend/cuda/kernel/range.cuh
 create mode 100644 src/backend/cuda/kernel/reorder.cuh
 create mode 100644 src/backend/cuda/kernel/select.cuh
 create mode 100644 src/backend/cuda/kernel/sparse.cuh
 create mode 100644 src/backend/cuda/kernel/sparse_arith.cuh
 create mode 100644 src/backend/cuda/kernel/susan.cuh
 create mode 100644 src/backend/cuda/kernel/tile.cuh
 create mode 100644 src/backend/cuda/kernel/triangle.cuh
 create mode 100644 src/backend/cuda/kernel/unwrap.cuh
 create mode 100644 src/backend/cuda/kernel/wrap.cuh
 rename src/backend/cuda/{lookup.cu => lookup.cpp} (86%)
 rename src/backend/cuda/{lu.cu => lu.cpp} (96%)
 create mode 100644 src/backend/cuda/minmax_op.hpp
 rename src/backend/cuda/{qr.cu => qr.cpp} (99%)
 rename src/backend/cuda/{range.cu => range.cpp} (99%)
 rename src/backend/cuda/{reorder.cu => reorder.cpp} (99%)
 rename src/backend/cuda/{select.cu => select.cpp} (96%)
 rename src/backend/cuda/{sparse.cu => sparse.cpp} (100%)
 rename src/backend/cuda/{sparse_arith.cu => sparse_arith.cpp} (99%)
 rename src/backend/cuda/{susan.cu => susan.cpp} (96%)
 rename src/backend/cuda/{tile.cu => tile.cpp} (99%)
 rename src/backend/cuda/{triangle.cu => triangle.cpp} (97%)
 rename src/backend/cuda/{unwrap.cu => unwrap.cpp} (99%)
 rename src/backend/cuda/{wrap.cu => wrap.cpp} (58%)

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 7574e32d1d..33aa64e6d2 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -52,6 +52,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/half.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/internal_enums.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel_type.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 1eb78964db..658be6819a 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -9,6 +9,8 @@
 
 #pragma once
 
+#include <common/internal_enums.hpp>
+
 #include <mutex>
 #include <string>
 
@@ -41,22 +43,6 @@ inline std::string clipFilePath(std::string path, std::string str) {
 #define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
 #endif
 
-typedef enum {
-    AF_BATCH_UNSUPPORTED = -1, /* invalid inputs */
-    AF_BATCH_NONE,             /* one signal, one filter   */
-    AF_BATCH_LHS,              /* many signal, one filter  */
-    AF_BATCH_RHS,              /* one signal, many filter  */
-    AF_BATCH_SAME,             /* signal and filter have same batch size */
-    AF_BATCH_DIFF,             /* signal and filter have different batch size */
-} AF_BATCH_KIND;
-
-enum class kJITHeuristics {
-    Pass                = 0, /* no eval necessary */
-    TreeHeight          = 1, /* eval due to jit tree height */
-    KernelParameterSize = 2, /* eval due to many kernel parameters */
-    MemoryPressure      = 3  /* eval due to memory pressure */
-};
-
 #ifdef OS_WIN
 #include <Windows.h>
 using LibHandle = HMODULE;
diff --git a/src/backend/common/internal_enums.hpp b/src/backend/common/internal_enums.hpp
new file mode 100644
index 0000000000..c4e76f7b7c
--- /dev/null
+++ b/src/backend/common/internal_enums.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+// TODO AF_BATCH_UNSUPPORTED is not required and shouldn't happen
+//      Code changes are required to handle all cases properly
+//      and this enum value should be removed.
+typedef enum {
+    AF_BATCH_UNSUPPORTED = -1, /* invalid inputs */
+    AF_BATCH_NONE,             /* one signal, one filter   */
+    AF_BATCH_LHS,              /* many signal, one filter  */
+    AF_BATCH_RHS,              /* one signal, many filter  */
+    AF_BATCH_SAME,             /* signal and filter have same batch size */
+    AF_BATCH_DIFF,             /* signal and filter have different batch size */
+} AF_BATCH_KIND;
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index e31da4f7cd..afabb96219 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -19,6 +19,13 @@
 #include <unordered_map>
 #include <vector>
 
+enum class kJITHeuristics {
+    Pass                = 0, /* no eval necessary */
+    TreeHeight          = 1, /* eval due to jit tree height */
+    KernelParameterSize = 2, /* eval due to many kernel parameters */
+    MemoryPressure      = 3  /* eval due to memory pressure */
+};
+
 namespace common {
 class Node;
 struct Node_ids;
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index ad8816fa14..86a5af8d9d 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/MemoryManagerBase.hpp>
+#include <common/jit/Node.hpp>
 #include <jit/Node.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index c8b769b2d0..8d49ebed8e 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -89,45 +89,74 @@ set(nvrtc_src
   ${PROJECT_BINARY_DIR}/include/af/version.h
 
   ${CMAKE_CURRENT_SOURCE_DIR}/Param.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/assign_kernel_param.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/backend.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/dims_param.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/interp.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/shared.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/math.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/minmax_op.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/utility.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/types.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/half.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/../common/internal_enums.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/kernel_type.hpp
 
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/anisotropic_diffusion.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/approx1.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/approx2.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/assign.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/bilateral.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/canny.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve1.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve2.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve3.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve_separable.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/copy.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/diagonal.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/diff.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/exampleFunction.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/fftconvolve.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/flood_fill.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/gradient.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/histogram.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/hsv_rgb.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/identity.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/iir.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/index.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/iota.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/ireduce.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/join.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/lookup.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/lu_split.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/match_template.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/meanshift.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/medfilt.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/memcopy.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/moments.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/morph.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/pad_array_borders.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/range.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/resize.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reorder.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/rotate.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/select.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sobel.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sparse.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sparse_arith.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/susan.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/tile.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transform.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose_inplace.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/triangle.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/unwrap.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/where.cuh
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/wrap.cuh
   )
 
 file_to_string(
@@ -222,13 +251,9 @@ cuda_add_library(afcuda
     anisotropic_diffusion.cpp
     any.cu
     approx.cpp
-    assign.cu
     bilateral.cpp
     canny.cpp
-    copy.cu
     count.cu
-    diagonal.cu
-    diff.cu
     dilate.cpp
     dilate3d.cpp
     erode.cpp
@@ -237,20 +262,10 @@ cuda_add_library(afcuda
     Event.hpp
     exampleFunction.cpp
     fast.cu
-    fftconvolve.cu
-    gradient.cu
     harris.cu
     histogram.cpp
     homography.cu
     hsv_rgb.cpp
-    identity.cu
-    iir.cu
-    index.cu
-    iota.cu
-    ireduce.cu
-    join.cu
-    lookup.cu
-    lu.cu
     match_template.cpp
     max.cu
     mean.cu
@@ -262,32 +277,21 @@ cuda_add_library(afcuda
     orb.cu
     pad_array_borders.cpp
     product.cu
-    qr.cu
     random_engine.cu
-    range.cu
     regions.cu
-    reorder.cu
     resize.cpp
     rotate.cpp
-    select.cu
     set.cu
     sift.cu
     sobel.cpp
     sort.cu
     sort_by_key.cu
     sort_index.cu
-    sparse.cu
-    sparse_arith.cu
     sum.cu
-    susan.cu
-    tile.cu
     topk.cu
     transform.cpp
     transpose.cpp
     transpose_inplace.cpp
-    triangle.cu
-    unwrap.cu
-    wrap.cu
 
     kernel/anisotropic_diffusion.hpp
     kernel/approx.hpp
@@ -375,6 +379,7 @@ cuda_add_library(afcuda
     anisotropic_diffusion.hpp
     approx.hpp
     arith.hpp
+    assign.cpp
     assign.hpp
     backend.hpp
     bilateral.hpp
@@ -388,6 +393,7 @@ cuda_add_library(afcuda
     complex.hpp
     convolve.cpp
     convolve.hpp
+    copy.cpp
     copy.hpp
     cublas.cpp
     cublas.hpp
@@ -405,7 +411,9 @@ cuda_add_library(afcuda
     device_manager.hpp
     debug_cuda.hpp
     debug_thrust.hpp
+    diagonal.cpp
     diagonal.hpp
+    diff.cpp
     diff.hpp
     driver.cpp
     err_cuda.hpp
@@ -415,11 +423,13 @@ cuda_add_library(afcuda
     fast_pyramid.hpp
     fft.cpp
     fft.hpp
+    fftconvolve.cpp
     fftconvolve.hpp
     flood_fill.cpp
     flood_fill.hpp
     GraphicsResourceManager.cpp
     GraphicsResourceManager.hpp
+    gradient.cpp
     gradient.hpp
     handle.cpp
     harris.hpp
@@ -428,19 +438,27 @@ cuda_add_library(afcuda
     histogram.hpp
     homography.hpp
     hsv_rgb.hpp
+    identity.cpp
     identity.hpp
+    iir.cpp
     iir.hpp
     image.cpp
     image.hpp
+    index.cpp
     index.hpp
     inverse.cpp
     inverse.hpp
+    iota.cpp
     iota.hpp
+    ireduce.cpp
     ireduce.hpp
     jit.cpp
+    join.cpp
     join.hpp
     logic.hpp
+    lookup.cpp
     lookup.hpp
+    lu.cpp
     lu.hpp
     match_template.hpp
     math.hpp
@@ -449,6 +467,7 @@ cuda_add_library(afcuda
     medfilt.hpp
     memory.cpp
     memory.hpp
+    minmax_op.hpp
     moments.hpp
     morph.hpp
     morph3d_impl.hpp
@@ -460,12 +479,15 @@ cuda_add_library(afcuda
     plot.cpp
     plot.hpp
     print.hpp
+    qr.cpp
     qr.hpp
     random_engine.hpp
+    range.cpp
     range.hpp
     reduce.hpp
     reduce_impl.hpp
     regions.hpp
+    reorder.cpp
     reorder.hpp
     resize.hpp
     rotate.hpp
@@ -474,6 +496,7 @@ cuda_add_library(afcuda
     scan.hpp
     scan_by_key.cpp
     scan_by_key.hpp
+    select.cpp
     select.hpp
     set.hpp
     shift.cpp
@@ -484,23 +507,29 @@ cuda_add_library(afcuda
     solve.hpp
     sort_by_key.hpp
     sort_index.hpp
+    sparse.cpp
     sparse.hpp
+    sparse_arith.cpp
     sparse_arith.hpp
     sparse_blas.cpp
     sparse_blas.hpp
     surface.cpp
     surface.hpp
+    susan.cpp
     susan.hpp
     svd.cpp
     svd.hpp
+    tile.cpp
     tile.hpp
     topk.hpp
     traits.hpp
     transform.hpp
     transpose.hpp
+    triangle.cpp
     triangle.hpp
     types.hpp
     unary.hpp
+    unwrap.cpp
     unwrap.hpp
     utility.cpp
     utility.hpp
@@ -508,6 +537,7 @@ cuda_add_library(afcuda
     vector_field.hpp
     where.cpp
     where.hpp
+    wrap.cpp
     wrap.hpp
 
     jit/BufferNode.hpp
diff --git a/src/backend/cuda/assign.cu b/src/backend/cuda/assign.cpp
similarity index 98%
rename from src/backend/cuda/assign.cu
rename to src/backend/cuda/assign.cpp
index 06265efe32..8c910fceb6 100644
--- a/src/backend/cuda/assign.cu
+++ b/src/backend/cuda/assign.cpp
@@ -23,7 +23,7 @@ namespace cuda {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
-    kernel::AssignKernelParam_t p;
+    AssignKernelParam p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
diff --git a/src/backend/cuda/assign_kernel_param.hpp b/src/backend/cuda/assign_kernel_param.hpp
new file mode 100644
index 0000000000..6587465ce2
--- /dev/null
+++ b/src/backend/cuda/assign_kernel_param.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace cuda {
+
+typedef struct {
+    int offs[4];
+    int strds[4];
+    bool isSeq[4];
+    unsigned int* ptr[4];
+} AssignKernelParam;
+
+using IndexKernelParam = AssignKernelParam;
+
+}  // namespace cuda
diff --git a/src/backend/cuda/copy.cu b/src/backend/cuda/copy.cpp
similarity index 97%
rename from src/backend/cuda/copy.cu
rename to src/backend/cuda/copy.cpp
index 7ffd487a51..a570dab611 100644
--- a/src/backend/cuda/copy.cu
+++ b/src/backend/cuda/copy.cpp
@@ -56,10 +56,7 @@ Array<T> copyArray(const Array<T> &src) {
             cudaMemcpyAsync(out.get(), src.get(), src.elements() * sizeof(T),
                             cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
     } else {
-        // FIXME: Seems to fail when using Param<T>
-        kernel::memcopy(out.get(), out.strides().get(), src.get(),
-                        src.dims().get(), src.strides().get(),
-                        (uint)src.ndims());
+        kernel::memcopy<T>(out, src, src.ndims());
     }
     return out;
 }
diff --git a/src/backend/cuda/diagonal.cu b/src/backend/cuda/diagonal.cpp
similarity index 100%
rename from src/backend/cuda/diagonal.cu
rename to src/backend/cuda/diagonal.cpp
diff --git a/src/backend/cuda/diff.cu b/src/backend/cuda/diff.cpp
similarity index 73%
rename from src/backend/cuda/diff.cu
rename to src/backend/cuda/diff.cpp
index d0516286d5..21482bacec 100644
--- a/src/backend/cuda/diff.cu
+++ b/src/backend/cuda/diff.cpp
@@ -15,8 +15,8 @@
 
 namespace cuda {
 
-template<typename T, bool isDiff2>
-static Array<T> diff(const Array<T> &in, const int dim) {
+template<typename T>
+Array<T> diff(const Array<T> &in, const int dim, const bool isDiff2) {
     const af::dim4 iDims = in.dims();
     af::dim4 oDims       = iDims;
     oDims[dim] -= (isDiff2 + 1);
@@ -27,24 +27,19 @@ static Array<T> diff(const Array<T> &in, const int dim) {
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    switch (dim) {
-        case (0): kernel::diff<T, 0, isDiff2>(out, in, in.ndims()); break;
-        case (1): kernel::diff<T, 1, isDiff2>(out, in, in.ndims()); break;
-        case (2): kernel::diff<T, 2, isDiff2>(out, in, in.ndims()); break;
-        case (3): kernel::diff<T, 3, isDiff2>(out, in, in.ndims()); break;
-    }
+    kernel::diff<T>(out, in, in.ndims(), dim, isDiff2);
 
     return out;
 }
 
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim) {
-    return diff<T, false>(in, dim);
+    return diff<T>(in, dim, false);
 }
 
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim) {
-    return diff<T, true>(in, dim);
+    return diff<T>(in, dim, true);
 }
 
 #define INSTANTIATE(T)                                             \
diff --git a/src/backend/cuda/dims_param.hpp b/src/backend/cuda/dims_param.hpp
new file mode 100644
index 0000000000..3692a68838
--- /dev/null
+++ b/src/backend/cuda/dims_param.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace cuda {
+
+typedef struct {
+    int dim[4];
+} dims_t;
+
+}  // namespace cuda
diff --git a/src/backend/cuda/fftconvolve.cu b/src/backend/cuda/fftconvolve.cpp
similarity index 97%
rename from src/backend/cuda/fftconvolve.cu
rename to src/backend/cuda/fftconvolve.cpp
index 68d28f6f1e..33105b7a53 100644
--- a/src/backend/cuda/fftconvolve.cu
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -20,8 +20,8 @@ using af::dim4;
 namespace cuda {
 
 template<typename T>
-static const dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
-                                 const dim_t baseDim) {
+const dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
+                          const dim_t baseDim) {
     const dim4 i1d = i1.dims();
     const dim4 i2d = i2.dims();
 
diff --git a/src/backend/cuda/gradient.cu b/src/backend/cuda/gradient.cpp
similarity index 99%
rename from src/backend/cuda/gradient.cu
rename to src/backend/cuda/gradient.cpp
index 425fc91e3e..0fdd4941ee 100644
--- a/src/backend/cuda/gradient.cu
+++ b/src/backend/cuda/gradient.cpp
@@ -7,11 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <gradient.hpp>
+
 #include <Array.hpp>
 #include <err_cuda.hpp>
-#include <gradient.hpp>
 #include <kernel/gradient.hpp>
 #include <math.hpp>
+
 #include <stdexcept>
 
 namespace cuda {
diff --git a/src/backend/cuda/identity.cu b/src/backend/cuda/identity.cpp
similarity index 100%
rename from src/backend/cuda/identity.cu
rename to src/backend/cuda/identity.cpp
diff --git a/src/backend/cuda/iir.cu b/src/backend/cuda/iir.cpp
similarity index 100%
rename from src/backend/cuda/iir.cu
rename to src/backend/cuda/iir.cpp
diff --git a/src/backend/cuda/index.cu b/src/backend/cuda/index.cpp
similarity index 97%
rename from src/backend/cuda/index.cu
rename to src/backend/cuda/index.cpp
index 07743cf956..3d4b0c1b8d 100644
--- a/src/backend/cuda/index.cu
+++ b/src/backend/cuda/index.cpp
@@ -6,13 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #include <index.hpp>
-#include <kernel/index.hpp>
 
 #include <Array.hpp>
+#include <assign_kernel_param.hpp>
 #include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
+#include <kernel/index.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
@@ -22,7 +24,7 @@ namespace cuda {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
-    kernel::IndexKernelParam_t p;
+    IndexKernelParam p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
diff --git a/src/backend/cuda/iota.cu b/src/backend/cuda/iota.cpp
similarity index 100%
rename from src/backend/cuda/iota.cu
rename to src/backend/cuda/iota.cpp
diff --git a/src/backend/cuda/ireduce.cu b/src/backend/cuda/ireduce.cpp
similarity index 100%
rename from src/backend/cuda/ireduce.cu
rename to src/backend/cuda/ireduce.cpp
diff --git a/src/backend/cuda/join.cu b/src/backend/cuda/join.cpp
similarity index 65%
rename from src/backend/cuda/join.cu
rename to src/backend/cuda/join.cpp
index c9293d9f36..87d6a50123 100644
--- a/src/backend/cuda/join.cu
+++ b/src/backend/cuda/join.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <err_cuda.hpp>
 #include <common/half.hpp>
+#include <err_cuda.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
 #include <stdexcept>
@@ -17,13 +17,13 @@
 using common::half;
 
 namespace cuda {
-template<int dim>
-af::dim4 calcOffset(const af::dim4 dims) {
+
+af::dim4 calcOffset(const af::dim4 dims, const int dim) {
     af::dim4 offset;
-    offset[0] = (dim == 0) ? dims[0] : 0;
-    offset[1] = (dim == 1) ? dims[1] : 0;
-    offset[2] = (dim == 2) ? dims[2] : 0;
-    offset[3] = (dim == 3) ? dims[3] : 0;
+    offset[0] = (dim == 0) * dims[0];
+    offset[1] = (dim == 1) * dims[1];
+    offset[2] = (dim == 2) * dims[2];
+    offset[3] = (dim == 3) * dims[3];
     return offset;
 }
 
@@ -47,24 +47,8 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
 
     af::dim4 zero(0, 0, 0, 0);
 
-    switch (dim) {
-        case 0:
-            kernel::join<Tx, Tx, 0>(out, first, zero);
-            kernel::join<Tx, Ty, 0>(out, second, calcOffset<0>(fdims));
-            break;
-        case 1:
-            kernel::join<Tx, Tx, 1>(out, first, zero);
-            kernel::join<Tx, Ty, 1>(out, second, calcOffset<1>(fdims));
-            break;
-        case 2:
-            kernel::join<Tx, Tx, 2>(out, first, zero);
-            kernel::join<Tx, Ty, 2>(out, second, calcOffset<2>(fdims));
-            break;
-        case 3:
-            kernel::join<Tx, Tx, 3>(out, first, zero);
-            kernel::join<Tx, Ty, 3>(out, second, calcOffset<3>(fdims));
-            break;
-    }
+    kernel::join<Tx, Tx>(out, first, zero, dim);
+    kernel::join<Tx, Ty>(out, second, calcOffset(fdims, dim), dim);
 
     return out;
 }
@@ -75,35 +59,10 @@ void join_wrapper(const int dim, Array<T> &out,
     af::dim4 zero(0, 0, 0, 0);
     af::dim4 d = zero;
 
-    switch (dim) {
-        case 0:
-            kernel::join<T, T, 0>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 0>(out, inputs[i], calcOffset<0>(d));
-            }
-            break;
-        case 1:
-            kernel::join<T, T, 1>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 1>(out, inputs[i], calcOffset<1>(d));
-            }
-            break;
-        case 2:
-            kernel::join<T, T, 1>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 2>(out, inputs[i], calcOffset<2>(d));
-            }
-            break;
-        case 3:
-            kernel::join<T, T, 3>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 3>(out, inputs[i], calcOffset<3>(d));
-            }
-            break;
+    kernel::join<T, T>(out, inputs[0], zero, dim);
+    for (int i = 1; i < n_arrays; i++) {
+        d += inputs[i - 1].dims();
+        kernel::join<T, T>(out, inputs[i], calcOffset(d, dim), dim);
     }
 }
 
diff --git a/src/backend/cuda/kernel/assign.cuh b/src/backend/cuda/kernel/assign.cuh
new file mode 100644
index 0000000000..102d42ec99
--- /dev/null
+++ b/src/backend/cuda/kernel/assign.cuh
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <assign_kernel_param.hpp>
+#include <utility.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void assign(Param<T> out, CParam<T> in,
+                       const cuda::AssignKernelParam p, const int nBBS0,
+                       const int nBBS1) {
+    // retrieve index pointers
+    // these can be 0 where af_array index is not used
+    const uint* ptr0 = p.ptr[0];
+    const uint* ptr1 = p.ptr[1];
+    const uint* ptr2 = p.ptr[2];
+    const uint* ptr3 = p.ptr[3];
+    // retrive booleans that tell us which index to use
+    const bool s0 = p.isSeq[0];
+    const bool s1 = p.isSeq[1];
+    const bool s2 = p.isSeq[2];
+    const bool s3 = p.isSeq[3];
+
+    const int gz = blockIdx.x / nBBS0;
+    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
+    const int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + threadIdx.x;
+    const int gy =
+        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) +
+        threadIdx.y;
+
+    if (gx < in.dims[0] && gy < in.dims[1] && gz < in.dims[2] &&
+        gw < in.dims[3]) {
+        // calculate pointer offsets for input
+        int i =
+            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], out.dims[0]);
+        int j =
+            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], out.dims[1]);
+        int k =
+            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], out.dims[2]);
+        int l =
+            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], out.dims[3]);
+        // offset input and output pointers
+        const T* src =
+            (const T*)in.ptr + (gx * in.strides[0] + gy * in.strides[1] +
+                                gz * in.strides[2] + gw * in.strides[3]);
+        T* dst = (T*)out.ptr + (i + j + k + l);
+        // set the output
+        dst[0] = src[0];
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index a7e56b18ae..6a2a08a685 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -8,72 +8,26 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <backend.hpp>
+#include <assign_kernel_param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <utility.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/assign_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
-
-typedef struct {
-    int offs[4];
-    int strds[4];
-    bool isSeq[4];
-    uint* ptr[4];
-} AssignKernelParam_t;
-
 template<typename T>
-__global__ void AssignKernel(Param<T> out, CParam<T> in,
-                             const AssignKernelParam_t p, const int nBBS0,
-                             const int nBBS1) {
-    // retrieve index pointers
-    // these can be 0 where af_array index is not used
-    const uint* ptr0 = p.ptr[0];
-    const uint* ptr1 = p.ptr[1];
-    const uint* ptr2 = p.ptr[2];
-    const uint* ptr3 = p.ptr[3];
-    // retrive booleans that tell us which index to use
-    const bool s0 = p.isSeq[0];
-    const bool s1 = p.isSeq[1];
-    const bool s2 = p.isSeq[2];
-    const bool s3 = p.isSeq[3];
+void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
 
-    const int gz = blockIdx.x / nBBS0;
-    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
-    const int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + threadIdx.x;
-    const int gy =
-        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) +
-        threadIdx.y;
+    static const std::string src(assign_cuh, assign_cuh_len);
 
-    if (gx < in.dims[0] && gy < in.dims[1] && gz < in.dims[2] &&
-        gw < in.dims[3]) {
-        // calculate pointer offsets for input
-        int i =
-            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], out.dims[0]);
-        int j =
-            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], out.dims[1]);
-        int k =
-            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], out.dims[2]);
-        int l =
-            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], out.dims[3]);
-        // offset input and output pointers
-        const T* src =
-            (const T*)in.ptr + (gx * in.strides[0] + gy * in.strides[1] +
-                                gz * in.strides[2] + gw * in.strides[3]);
-        T* dst = (T*)out.ptr + (i + j + k + l);
-        // set the output
-        dst[0] = src[0];
-    }
-}
+    auto assignKer = getKernel("cuda::assign", src, {TemplateTypename<T>()});
 
-template<typename T>
-void assign(Param<T> out, CParam<T> in, const AssignKernelParam_t& p) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blks_x = divup(in.dims[0], threads.x);
@@ -86,11 +40,12 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam_t& p) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((AssignKernel<T>), blocks, threads, out, in, p, blks_x, blks_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    assignKer(qArgs, out, in, p, blks_x, blks_y);
 
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/copy.cuh b/src/backend/cuda/kernel/copy.cuh
new file mode 100644
index 0000000000..628a898904
--- /dev/null
+++ b/src/backend/cuda/kernel/copy.cuh
@@ -0,0 +1,134 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/half.hpp>
+#include <dims_param.hpp>
+#include <types.hpp>
+
+namespace cuda {
+
+template<typename T>
+__inline__ __device__ static T scale(T value, double factor) {
+    return (T)(double(value) * factor);
+}
+
+template<>
+__inline__ __device__ cfloat scale<cfloat>(cfloat value, double factor) {
+    return make_cuFloatComplex(value.x * factor, value.y * factor);
+}
+
+template<>
+__inline__ __device__ cdouble scale<cdouble>(cdouble value, double factor) {
+    return make_cuDoubleComplex(value.x * factor, value.y * factor);
+}
+
+template<typename inType, typename outType>
+__inline__ __device__ outType convertType(inType value) {
+    return static_cast<outType>(value);
+}
+
+template<>
+__inline__ __device__ char convertType<compute_t<common::half>, char>(
+    compute_t<common::half> value) {
+    return (char)((short)value);
+}
+
+template<>
+__inline__ __device__ compute_t<common::half>
+convertType<char, compute_t<common::half>>(char value) {
+    return compute_t<common::half>(value);
+}
+
+template<>
+__inline__ __device__ cuda::uchar
+convertType<compute_t<common::half>, cuda::uchar>(
+    compute_t<common::half> value) {
+    return (cuda::uchar)((short)value);
+}
+
+template<>
+__inline__ __device__ compute_t<common::half>
+convertType<cuda::uchar, compute_t<common::half>>(cuda::uchar value) {
+    return compute_t<common::half>(value);
+}
+
+template<>
+__inline__ __device__ cdouble convertType<cfloat, cdouble>(cfloat value) {
+    return cuComplexFloatToDouble(value);
+}
+
+template<>
+__inline__ __device__ cfloat convertType<cdouble, cfloat>(cdouble value) {
+    return cuComplexDoubleToFloat(value);
+}
+
+#define OTHER_SPECIALIZATIONS(IN_T)                                        \
+    template<>                                                             \
+    __inline__ __device__ cfloat convertType<IN_T, cfloat>(IN_T value) {   \
+        return make_cuFloatComplex(static_cast<float>(value), 0.0f);       \
+    }                                                                      \
+                                                                           \
+    template<>                                                             \
+    __inline__ __device__ cdouble convertType<IN_T, cdouble>(IN_T value) { \
+        return make_cuDoubleComplex(static_cast<double>(value), 0.0);      \
+    }
+
+OTHER_SPECIALIZATIONS(float)
+OTHER_SPECIALIZATIONS(double)
+OTHER_SPECIALIZATIONS(int)
+OTHER_SPECIALIZATIONS(uint)
+OTHER_SPECIALIZATIONS(intl)
+OTHER_SPECIALIZATIONS(uintl)
+OTHER_SPECIALIZATIONS(short)
+OTHER_SPECIALIZATIONS(ushort)
+OTHER_SPECIALIZATIONS(uchar)
+OTHER_SPECIALIZATIONS(char)
+OTHER_SPECIALIZATIONS(common::half)
+
+template<typename inType, typename outType, bool same_dims>
+__global__ void copy(Param<outType> dst, CParam<inType> src,
+                     outType default_value, double factor, const dims_t trgt,
+                     uint blk_x, uint blk_y) {
+    const uint lx = threadIdx.x;
+    const uint ly = threadIdx.y;
+
+    const uint gz         = blockIdx.x / blk_x;
+    const uint gw         = (blockIdx.y + (blockIdx.z * gridDim.y)) / blk_y;
+    const uint blockIdx_x = blockIdx.x - (blk_x)*gz;
+    const uint blockIdx_y =
+        (blockIdx.y + (blockIdx.z * gridDim.y)) - (blk_y)*gw;
+    const uint gx = blockIdx_x * blockDim.x + lx;
+    const uint gy = blockIdx_y * blockDim.y + ly;
+
+    const inType *in = src.ptr + (gw * src.strides[3] + gz * src.strides[2] +
+                                  gy * src.strides[1]);
+    outType *out     = dst.ptr + (gw * dst.strides[3] + gz * dst.strides[2] +
+                              gy * dst.strides[1]);
+
+    int istride0 = src.strides[0];
+    int ostride0 = dst.strides[0];
+
+    if (gy < dst.dims[1] && gz < dst.dims[2] && gw < dst.dims[3]) {
+        int loop_offset = blockDim.x * blk_x;
+        bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
+        for (int rep = gx; rep < dst.dims[0]; rep += loop_offset) {
+            outType temp = default_value;
+            if (same_dims || (rep < trgt.dim[0] && cond)) {
+                temp = convertType<inType, outType>(
+                    scale<inType>(in[rep * istride0], factor));
+            }
+            out[rep * ostride0] = temp;
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/diagonal.cuh b/src/backend/cuda/kernel/diagonal.cuh
new file mode 100644
index 0000000000..d337c8f2a1
--- /dev/null
+++ b/src/backend/cuda/kernel/diagonal.cuh
@@ -0,0 +1,55 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void createDiagonalMat(Param<T> out, CParam<T> in, int num,
+                                  int blocks_x) {
+    unsigned idz        = blockIdx.x / blocks_x;
+    unsigned blockIdx_x = blockIdx.x - idz * blocks_x;
+
+    unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
+    unsigned idy =
+        threadIdx.y + (blockIdx.y + blockIdx.z * gridDim.y) * blockDim.y;
+
+    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2]) return;
+
+    T *optr       = out.ptr + idz * out.strides[2] + idy * out.strides[1] + idx;
+    const T *iptr = in.ptr + idz * in.strides[1] + ((num > 0) ? idx : idy);
+
+    T val = (idx == (idy - num)) ? *iptr : scalar<T>(0);
+    *optr = val;
+}
+
+template<typename T>
+__global__ void extractDiagonal(Param<T> out, CParam<T> in, int num,
+                                int blocks_z) {
+    unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_z;
+    unsigned idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_z;
+
+    unsigned idx = threadIdx.x + blockIdx.x * blockDim.x;
+
+    if (idx >= out.dims[0] || idz >= out.dims[2] || idw >= out.dims[3]) return;
+
+    T *optr = out.ptr + idz * out.strides[2] + idw * out.strides[3] + idx;
+
+    if (idx >= in.dims[0] || idx >= in.dims[1]) *optr = scalar<T>(0);
+
+    int i_off     = (num > 0) ? (num * in.strides[1] + idx) : (idx - num);
+    const T *iptr = in.ptr + idz * in.strides[2] + idw * in.strides[3] + i_off;
+    *optr         = iptr[idx * in.strides[1]];
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index a5343a4052..a76d258fa9 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -7,36 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include <platform.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/diagonal_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-template<typename T>
-__global__ static void diagCreateKernel(Param<T> out, CParam<T> in, int num,
-                                        int blocks_x) {
-    unsigned idz        = blockIdx.x / blocks_x;
-    unsigned blockIdx_x = blockIdx.x - idz * blocks_x;
-
-    unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
-    unsigned idy =
-        threadIdx.y + (blockIdx.y + blockIdx.z * gridDim.y) * blockDim.y;
-
-    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2]) return;
 
-    T *optr       = out.ptr + idz * out.strides[2] + idy * out.strides[1] + idx;
-    const T *iptr = in.ptr + idz * in.strides[1] + ((num > 0) ? idx : idy);
+template<typename T>
+void diagCreate(Param<T> out, CParam<T> in, int num) {
+    static const std::string src(diagonal_cuh, diagonal_cuh_len);
 
-    T val = (idx == (idy - num)) ? *iptr : scalar<T>(0);
-    *optr = val;
-}
+    auto genDiagMat =
+        getKernel("cuda::createDiagonalMat", src, {TemplateTypename<T>()});
 
-template<typename T>
-static void diagCreate(Param<T> out, CParam<T> in, int num) {
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
     int blocks_y = divup(out.dims[1], threads.y);
@@ -50,31 +40,20 @@ static void diagCreate(Param<T> out, CParam<T> in, int num) {
         blocks.z = blocksPerMatZ;
     }
 
-    CUDA_LAUNCH((diagCreateKernel<T>), blocks, threads, out, in, num, blocks_x);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    genDiagMat(qArgs, out, in, num, blocks_x);
+
     POST_LAUNCH_CHECK();
 }
 
 template<typename T>
-__global__ static void diagExtractKernel(Param<T> out, CParam<T> in, int num,
-                                         int blocks_z) {
-    unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_z;
-    unsigned idz = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_z;
-
-    unsigned idx = threadIdx.x + blockIdx.x * blockDim.x;
-
-    if (idx >= out.dims[0] || idz >= out.dims[2] || idw >= out.dims[3]) return;
+void diagExtract(Param<T> out, CParam<T> in, int num) {
+    static const std::string src(diagonal_cuh, diagonal_cuh_len);
 
-    T *optr = out.ptr + idz * out.strides[2] + idw * out.strides[3] + idx;
+    auto extractDiag =
+        getKernel("cuda::extractDiagonal", src, {TemplateTypename<T>()});
 
-    if (idx >= in.dims[0] || idx >= in.dims[1]) *optr = scalar<T>(0);
-
-    int i_off     = (num > 0) ? (num * in.strides[1] + idx) : (idx - num);
-    const T *iptr = in.ptr + idz * in.strides[2] + idw * in.strides[3] + i_off;
-    *optr         = iptr[idx * in.strides[1]];
-}
-
-template<typename T>
-static void diagExtract(Param<T> out, CParam<T> in, int num) {
     dim3 threads(256, 1);
     int blocks_x = divup(out.dims[0], threads.x);
     int blocks_z = out.dims[2];
@@ -85,8 +64,10 @@ static void diagExtract(Param<T> out, CParam<T> in, int num) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((diagExtractKernel<T>), blocks, threads, out, in, num,
-                blocks_z);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    extractDiag(qArgs, out, in, num, blocks_z);
+
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/diff.cuh b/src/backend/cuda/kernel/diff.cuh
new file mode 100644
index 0000000000..2f6305eb0f
--- /dev/null
+++ b/src/backend/cuda/kernel/diff.cuh
@@ -0,0 +1,60 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool D>
+inline void diff_this(T* out, const T* in, const unsigned oMem,
+                      const unsigned iMem0, const unsigned iMem1,
+                      const unsigned iMem2) {
+    // iMem2 can never be 0
+    if (D == 0) {  // Diff1
+        out[oMem] = in[iMem1] - in[iMem0];
+    } else {  // Diff2
+        out[oMem] = in[iMem2] - in[iMem1] - in[iMem1] + in[iMem0];
+    }
+}
+
+template<typename T, unsigned dim, bool isDiff2>
+__global__ void diff(Param<T> out, CParam<T> in, const unsigned oElem,
+                     const unsigned blocksPerMatX,
+                     const unsigned blocksPerMatY) {
+    unsigned idz = blockIdx.x / blocksPerMatX;
+    unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    unsigned blockIdx_x = blockIdx.x - idz * blocksPerMatX;
+    unsigned blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
+
+    unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
+    unsigned idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2] ||
+        idw >= out.dims[3])
+        return;
+
+    unsigned iMem0 =
+        idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
+    unsigned iMem1 = iMem0 + in.strides[dim];
+    unsigned iMem2 = iMem1 + in.strides[dim];
+
+    unsigned oMem = idw * out.strides[3] + idz * out.strides[2] +
+                    idy * out.strides[1] + idx;
+
+    iMem2 *= isDiff2;
+
+    diff_this<T, isDiff2>(out.ptr, in.ptr, oMem, iMem0, iMem1, iMem2);
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index a3a23c546b..26e97929f2 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -7,71 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/diff_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX = 16;
-static const unsigned TY = 16;
-
-template<typename T, bool D>
-inline __host__ __device__ void diff_this(T* out, const T* in,
-                                          const unsigned oMem,
-                                          const unsigned iMem0,
-                                          const unsigned iMem1,
-                                          const unsigned iMem2) {
-    // iMem2 can never be 0
-    if (D == 0) {  // Diff1
-        out[oMem] = in[iMem1] - in[iMem0];
-    } else {  // Diff2
-        out[oMem] = in[iMem2] - in[iMem1] - in[iMem1] + in[iMem0];
-    }
-}
-
-/////////////////////////////////////////////////////////////////////////////
-// 1st and 2nd Order Differential for 4D along all dimensions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, unsigned dim, bool isDiff2>
-__global__ void diff_kernel(Param<T> out, CParam<T> in, const unsigned oElem,
-                            const unsigned blocksPerMatX,
-                            const unsigned blocksPerMatY) {
-    unsigned idz = blockIdx.x / blocksPerMatX;
-    unsigned idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-    unsigned blockIdx_x = blockIdx.x - idz * blocksPerMatX;
-    unsigned blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
 
-    unsigned idx = threadIdx.x + blockIdx_x * blockDim.x;
-    unsigned idy = threadIdx.y + blockIdx_y * blockDim.y;
+template<typename T>
+void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
+          const bool isDiff2) {
+    constexpr unsigned TX = 16;
+    constexpr unsigned TY = 16;
 
-    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2] ||
-        idw >= out.dims[3])
-        return;
+    static const std::string src(diff_cuh, diff_cuh_len);
 
-    unsigned iMem0 =
-        idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
-    unsigned iMem1 = iMem0 + in.strides[dim];
-    unsigned iMem2 = iMem1 + in.strides[dim];
+    auto diff = getKernel(
+        "cuda::diff", src,
+        {TemplateTypename<T>(), TemplateArg(dim), TemplateArg(isDiff2)});
 
-    unsigned oMem = idw * out.strides[3] + idz * out.strides[2] +
-                    idy * out.strides[1] + idx;
-
-    iMem2 *= isDiff2;
-
-    diff_this<T, isDiff2>(out.ptr, in.ptr, oMem, iMem0, iMem1, iMem2);
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, unsigned dim, bool isDiff2>
-void diff(Param<T> out, CParam<T> in, const int indims) {
     dim3 threads(TX, TY, 1);
 
     if (dim == 0 && indims == 1) { threads = dim3(TX * TY, 1, 1); }
@@ -87,8 +47,9 @@ void diff(Param<T> out, CParam<T> in, const int indims) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((diff_kernel<T, dim, isDiff2>), blocks, threads, out, in, oElem,
-                blocksPerMatX, blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    diff(qArgs, out, in, oElem, blocksPerMatX, blocksPerMatY);
 
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/kernel/fftconvolve.cuh b/src/backend/cuda/kernel/fftconvolve.cuh
new file mode 100644
index 0000000000..814e9b4621
--- /dev/null
+++ b/src/backend/cuda/kernel/fftconvolve.cuh
@@ -0,0 +1,220 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/internal_enums.hpp>
+
+namespace cuda {
+
+template<typename To, typename Ti>
+__global__ void packData(Param<To> out, CParam<Ti> in, const int di0_half,
+                         const bool odd_di0) {
+    const int t = blockDim.x * blockIdx.x + threadIdx.x;
+
+    const int tMax = out.strides[3] * out.dims[3];
+
+    if (t >= tMax) return;
+
+    const int do1 = out.dims[1];
+    const int do2 = out.dims[2];
+    const int so1 = out.strides[1];
+    const int so2 = out.strides[2];
+    const int so3 = out.strides[3];
+
+    const int to0 = t % so1;
+    const int to1 = (t / so1) % do1;
+    const int to2 = (t / so2) % do2;
+    const int to3 = t / so3;
+
+    const int di1 = in.dims[1];
+    const int di2 = in.dims[2];
+    const int si1 = in.strides[1];
+    const int si2 = in.strides[2];
+    const int si3 = in.strides[3];
+
+    const int ti0 = to0;
+    const int ti1 = to1 * si1;
+    const int ti2 = to2 * si2;
+    const int ti3 = to3 * si3;
+
+    const int iidx1 = ti3 + ti2 + ti1 + ti0;
+    const int iidx2 = iidx1 + di0_half;
+    const int oidx  = to3 * so3 + to2 * so2 + to1 * so1 + to0;
+
+    if (to0 < di0_half && to1 < di1 && to2 < di2) {
+        out.ptr[oidx].x = in.ptr[iidx1];
+        if (ti0 == di0_half - 1 && odd_di0)
+            out.ptr[oidx].y = 0;
+        else
+            out.ptr[oidx].y = in.ptr[iidx2];
+    } else {
+        // Pad remaining elements with 0s
+        out.ptr[oidx].x = 0;
+        out.ptr[oidx].y = 0;
+    }
+}
+
+template<typename To, typename Ti>
+__global__ void padArray(Param<To> out, CParam<Ti> in) {
+    const int t = blockDim.x * blockIdx.x + threadIdx.x;
+
+    const int tMax = out.strides[3] * out.dims[3];
+
+    if (t >= tMax) return;
+
+    const int do1 = out.dims[1];
+    const int do2 = out.dims[2];
+    const int so1 = out.strides[1];
+    const int so2 = out.strides[2];
+    const int so3 = out.strides[3];
+
+    const int to0 = t % so1;
+    const int to1 = (t / so1) % do1;
+    const int to2 = (t / so2) % do2;
+    const int to3 = (t / so3);
+
+    const int di0 = in.dims[0];
+    const int di1 = in.dims[1];
+    const int di2 = in.dims[2];
+    const int di3 = in.dims[3];
+    const int si1 = in.strides[1];
+    const int si2 = in.strides[2];
+    const int si3 = in.strides[3];
+
+    const int ti0 = to0;
+    const int ti1 = to1 * si1;
+    const int ti2 = to2 * si2;
+    const int ti3 = to3 * si3;
+
+    const int iidx = ti3 + ti2 + ti1 + ti0;
+
+    const int t2 = to3 * so3 + to2 * so2 + to1 * so1 + to0;
+
+    if (to0 < di0 && to1 < di1 && to2 < di2 && to3 < di3) {
+        // Copy input elements to real elements, set imaginary elements to 0
+        out.ptr[t2].x = in.ptr[iidx];
+        out.ptr[t2].y = 0;
+    } else {
+        // Pad remaining of the matrix to 0s
+        out.ptr[t2].x = 0;
+        out.ptr[t2].y = 0;
+    }
+}
+
+template<typename convT, AF_BATCH_KIND kind>
+__global__ void complexMultiply(Param<convT> out, Param<convT> in1,
+                                Param<convT> in2, const int nelem) {
+    const int t = blockDim.x * blockIdx.x + threadIdx.x;
+
+    if (t >= nelem) return;
+
+    if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
+        // Complex multiply each signal to equivalent filter
+        const int ridx = t;
+
+        convT c1 = in1.ptr[ridx];
+        convT c2 = in2.ptr[ridx];
+
+        out.ptr[ridx].x = c1.x * c2.x - c1.y * c2.y;
+        out.ptr[ridx].y = c1.x * c2.y + c1.y * c2.x;
+    } else if (kind == AF_BATCH_LHS) {
+        // Complex multiply all signals to filter
+        const int ridx1 = t;
+        const int ridx2 = t % (in2.strides[3] * in2.dims[3]);
+
+        convT c1 = in1.ptr[ridx1];
+        convT c2 = in2.ptr[ridx2];
+
+        out.ptr[ridx1].x = c1.x * c2.x - c1.y * c2.y;
+        out.ptr[ridx1].y = c1.x * c2.y + c1.y * c2.x;
+    } else if (kind == AF_BATCH_RHS) {
+        // Complex multiply signal to all filters
+        const int ridx1 = t % (in1.strides[3] * in1.dims[3]);
+        const int ridx2 = t;
+
+        convT c1 = in1.ptr[ridx1];
+        convT c2 = in2.ptr[ridx2];
+
+        out.ptr[ridx2].x = c1.x * c2.x - c1.y * c2.y;
+        out.ptr[ridx2].y = c1.x * c2.y + c1.y * c2.x;
+    }
+}
+
+template<typename To, typename Ti, bool expand, bool roundOut>
+__global__ void reorderOutput(Param<To> out, Param<Ti> in, CParam<To> filter,
+                              const int half_di0, const int baseDim,
+                              const int fftScale) {
+    const int t = blockIdx.x * blockDim.x + threadIdx.x;
+
+    const int tMax = out.strides[3] * out.dims[3];
+
+    if (t >= tMax) return;
+
+    const int do1 = out.dims[1];
+    const int do2 = out.dims[2];
+    const int so1 = out.strides[1];
+    const int so2 = out.strides[2];
+    const int so3 = out.strides[3];
+
+    const int si1 = in.strides[1];
+    const int si2 = in.strides[2];
+    const int si3 = in.strides[3];
+
+    const int to0 = t % so1;
+    const int to1 = (t / so1) % do1;
+    const int to2 = (t / so2) % do2;
+    const int to3 = (t / so3);
+
+    int oidx = to3 * so3 + to2 * so2 + to1 * so1 + to0;
+
+    int ti0, ti1, ti2, ti3;
+    if (expand) {
+        ti0 = to0;
+        ti1 = to1 * si1;
+        ti2 = to2 * si2;
+        ti3 = to3 * si3;
+    } else {
+        ti0 = to0 + filter.dims[0] / 2;
+        ti1 = (to1 + (baseDim > 1) * (filter.dims[1] / 2)) * si1;
+        ti2 = (to2 + (baseDim > 2) * (filter.dims[2] / 2)) * si2;
+        ti3 = to3 * si3;
+    }
+
+    // Divide output elements to cuFFT resulting scale, round result if output
+    // type is single or double precision floating-point
+    if (ti0 < half_di0) {
+        // Copy top elements
+        int iidx = ti3 + ti2 + ti1 + ti0;
+        if (roundOut)
+            out.ptr[oidx] = (To)roundf(in.ptr[iidx].x / fftScale);
+        else
+            out.ptr[oidx] = (To)(in.ptr[iidx].x / fftScale);
+    } else if (ti0 < half_di0 + filter.dims[0] - 1) {
+        // Add signal and filter elements to central part
+        int iidx1 = ti3 + ti2 + ti1 + ti0;
+        int iidx2 = ti3 + ti2 + ti1 + (ti0 - half_di0);
+        if (roundOut)
+            out.ptr[oidx] =
+                (To)roundf((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
+        else
+            out.ptr[oidx] =
+                (To)((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
+    } else {
+        // Copy bottom elements
+        const int iidx = ti3 + ti2 + ti1 + (ti0 - half_di0);
+        if (roundOut)
+            out.ptr[oidx] = (To)roundf(in.ptr[iidx].y / fftScale);
+        else
+            out.ptr[oidx] = (To)(in.ptr[iidx].y / fftScale);
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index cfa25ed76a..52fe80cb4d 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -7,225 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <memory.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/fftconvolve_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
 static const int THREADS = 256;
 
-template<typename To, typename Ti>
-__global__ void packData(Param<To> out, CParam<Ti> in, const int di0_half,
-                         const bool odd_di0) {
-    const int t = blockDim.x * blockIdx.x + threadIdx.x;
-
-    const int tMax = out.strides[3] * out.dims[3];
-
-    if (t >= tMax) return;
-
-    const int do1 = out.dims[1];
-    const int do2 = out.dims[2];
-    const int so1 = out.strides[1];
-    const int so2 = out.strides[2];
-    const int so3 = out.strides[3];
-
-    const int to0 = t % so1;
-    const int to1 = (t / so1) % do1;
-    const int to2 = (t / so2) % do2;
-    const int to3 = t / so3;
-
-    const int di1 = in.dims[1];
-    const int di2 = in.dims[2];
-    const int si1 = in.strides[1];
-    const int si2 = in.strides[2];
-    const int si3 = in.strides[3];
-
-    const int ti0 = to0;
-    const int ti1 = to1 * si1;
-    const int ti2 = to2 * si2;
-    const int ti3 = to3 * si3;
-
-    const int iidx1 = ti3 + ti2 + ti1 + ti0;
-    const int iidx2 = iidx1 + di0_half;
-    const int oidx  = to3 * so3 + to2 * so2 + to1 * so1 + to0;
-
-    if (to0 < di0_half && to1 < di1 && to2 < di2) {
-        out.ptr[oidx].x = in.ptr[iidx1];
-        if (ti0 == di0_half - 1 && odd_di0)
-            out.ptr[oidx].y = 0;
-        else
-            out.ptr[oidx].y = in.ptr[iidx2];
-    } else {
-        // Pad remaining elements with 0s
-        out.ptr[oidx].x = 0;
-        out.ptr[oidx].y = 0;
-    }
-}
-
-template<typename To, typename Ti>
-__global__ void padArray(Param<To> out, CParam<Ti> in) {
-    const int t = blockDim.x * blockIdx.x + threadIdx.x;
-
-    const int tMax = out.strides[3] * out.dims[3];
-
-    if (t >= tMax) return;
-
-    const int do1 = out.dims[1];
-    const int do2 = out.dims[2];
-    const int so1 = out.strides[1];
-    const int so2 = out.strides[2];
-    const int so3 = out.strides[3];
-
-    const int to0 = t % so1;
-    const int to1 = (t / so1) % do1;
-    const int to2 = (t / so2) % do2;
-    const int to3 = (t / so3);
-
-    const int di0 = in.dims[0];
-    const int di1 = in.dims[1];
-    const int di2 = in.dims[2];
-    const int di3 = in.dims[3];
-    const int si1 = in.strides[1];
-    const int si2 = in.strides[2];
-    const int si3 = in.strides[3];
-
-    const int ti0 = to0;
-    const int ti1 = to1 * si1;
-    const int ti2 = to2 * si2;
-    const int ti3 = to3 * si3;
-
-    const int iidx = ti3 + ti2 + ti1 + ti0;
-
-    const int t2 = to3 * so3 + to2 * so2 + to1 * so1 + to0;
-
-    if (to0 < di0 && to1 < di1 && to2 < di2 && to3 < di3) {
-        // Copy input elements to real elements, set imaginary elements to 0
-        out.ptr[t2].x = in.ptr[iidx];
-        out.ptr[t2].y = 0;
-    } else {
-        // Pad remaining of the matrix to 0s
-        out.ptr[t2].x = 0;
-        out.ptr[t2].y = 0;
-    }
-}
-
-template<typename convT, AF_BATCH_KIND kind>
-__global__ void complexMultiply(Param<convT> out, Param<convT> in1,
-                                Param<convT> in2, const int nelem) {
-    const int t = blockDim.x * blockIdx.x + threadIdx.x;
-
-    if (t >= nelem) return;
-
-    if (kind == AF_BATCH_NONE || kind == AF_BATCH_SAME) {
-        // Complex multiply each signal to equivalent filter
-        const int ridx = t;
-
-        convT c1 = in1.ptr[ridx];
-        convT c2 = in2.ptr[ridx];
-
-        out.ptr[ridx].x = c1.x * c2.x - c1.y * c2.y;
-        out.ptr[ridx].y = c1.x * c2.y + c1.y * c2.x;
-    } else if (kind == AF_BATCH_LHS) {
-        // Complex multiply all signals to filter
-        const int ridx1 = t;
-        const int ridx2 = t % (in2.strides[3] * in2.dims[3]);
-
-        convT c1 = in1.ptr[ridx1];
-        convT c2 = in2.ptr[ridx2];
-
-        out.ptr[ridx1].x = c1.x * c2.x - c1.y * c2.y;
-        out.ptr[ridx1].y = c1.x * c2.y + c1.y * c2.x;
-    } else if (kind == AF_BATCH_RHS) {
-        // Complex multiply signal to all filters
-        const int ridx1 = t % (in1.strides[3] * in1.dims[3]);
-        const int ridx2 = t;
-
-        convT c1 = in1.ptr[ridx1];
-        convT c2 = in2.ptr[ridx2];
-
-        out.ptr[ridx2].x = c1.x * c2.x - c1.y * c2.y;
-        out.ptr[ridx2].y = c1.x * c2.y + c1.y * c2.x;
-    }
-}
-
-template<typename To, typename Ti, bool expand, bool roundOut>
-__global__ void reorderOutput(Param<To> out, Param<Ti> in, CParam<To> filter,
-                              const int half_di0, const int baseDim,
-                              const int fftScale) {
-    const int t = blockIdx.x * blockDim.x + threadIdx.x;
-
-    const int tMax = out.strides[3] * out.dims[3];
-
-    if (t >= tMax) return;
-
-    const int do1 = out.dims[1];
-    const int do2 = out.dims[2];
-    const int so1 = out.strides[1];
-    const int so2 = out.strides[2];
-    const int so3 = out.strides[3];
-
-    const int si1 = in.strides[1];
-    const int si2 = in.strides[2];
-    const int si3 = in.strides[3];
-
-    const int to0 = t % so1;
-    const int to1 = (t / so1) % do1;
-    const int to2 = (t / so2) % do2;
-    const int to3 = (t / so3);
-
-    int oidx = to3 * so3 + to2 * so2 + to1 * so1 + to0;
-
-    int ti0, ti1, ti2, ti3;
-    if (expand) {
-        ti0 = to0;
-        ti1 = to1 * si1;
-        ti2 = to2 * si2;
-        ti3 = to3 * si3;
-    } else {
-        ti0 = to0 + filter.dims[0] / 2;
-        ti1 = (to1 + (baseDim > 1) * (filter.dims[1] / 2)) * si1;
-        ti2 = (to2 + (baseDim > 2) * (filter.dims[2] / 2)) * si2;
-        ti3 = to3 * si3;
-    }
-
-    // Divide output elements to cuFFT resulting scale, round result if output
-    // type is single or double precision floating-point
-    if (ti0 < half_di0) {
-        // Copy top elements
-        int iidx = ti3 + ti2 + ti1 + ti0;
-        if (roundOut)
-            out.ptr[oidx] = (To)roundf(in.ptr[iidx].x / fftScale);
-        else
-            out.ptr[oidx] = (To)(in.ptr[iidx].x / fftScale);
-    } else if (ti0 < half_di0 + filter.dims[0] - 1) {
-        // Add signal and filter elements to central part
-        int iidx1 = ti3 + ti2 + ti1 + ti0;
-        int iidx2 = ti3 + ti2 + ti1 + (ti0 - half_di0);
-        if (roundOut)
-            out.ptr[oidx] =
-                (To)roundf((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
-        else
-            out.ptr[oidx] =
-                (To)((in.ptr[iidx1].x + in.ptr[iidx2].y) / fftScale);
-    } else {
-        // Copy bottom elements
-        const int iidx = ti3 + ti2 + ti1 + (ti0 - half_di0);
-        if (roundOut)
-            out.ptr[oidx] = (To)roundf(in.ptr[iidx].y / fftScale);
-        else
-            out.ptr[oidx] = (To)(in.ptr[iidx].y / fftScale);
-    }
+static inline std::string fftConvSource() {
+    static const std::string src(fftconvolve_cuh, fftconvolve_cuh_len);
+    return src;
 }
 
 template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
+    auto packData =
+        getKernel("cuda::packData", fftConvSource(),
+                  {TemplateTypename<convT>(), TemplateTypename<T>()});
+    auto padArray =
+        getKernel("cuda::padArray", fftConvSource(),
+                  {TemplateTypename<convT>(), TemplateTypename<T>()});
+
     dim_t *sd = sig.dims;
 
     int sig_packed_elem    = 1;
@@ -243,16 +54,19 @@ void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
     dim3 threads(THREADS);
     dim3 blocks(divup(sig_packed_elem, threads.x));
 
+    EnqueueArgs packQArgs(blocks, threads, getActiveStream());
+
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    CUDA_LAUNCH((packData<convT, T>), blocks, threads, sig_packed, sig,
-                sig_half_d0, sig_half_d0_odd);
+    packData(packQArgs, sig_packed, sig, sig_half_d0, sig_half_d0_odd);
     POST_LAUNCH_CHECK();
 
     blocks = dim3(divup(filter_packed_elem, threads.x));
 
+    EnqueueArgs padQArgs(blocks, threads, getActiveStream());
+
     // Pad filter array with 0s
-    CUDA_LAUNCH((padArray<convT, T>), blocks, threads, filter_packed, filter);
+    padArray(padQArgs, filter_packed, filter);
     POST_LAUNCH_CHECK();
 }
 
@@ -260,6 +74,9 @@ void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
 template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
+    auto cplxMul = getKernel("cuda::complexMultiply", fftConvSource(),
+                             {TemplateTypename<convT>(), TemplateArg(kind)});
+
     int sig_packed_elem    = 1;
     int filter_packed_elem = 1;
 
@@ -275,28 +92,11 @@ void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                                                           : sig_packed_elem;
     blocks = dim3(divup(mul_elem, threads.x));
 
-    // Multiply filter and signal FFT arrays
-    switch (kind) {
-        case AF_BATCH_NONE:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_NONE>), blocks,
-                        threads, sig_packed, sig_packed, filter_packed,
-                        mul_elem);
-            break;
-        case AF_BATCH_LHS:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_LHS>), blocks, threads,
-                        sig_packed, sig_packed, filter_packed, mul_elem);
-            break;
-        case AF_BATCH_RHS:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_RHS>), blocks, threads,
-                        filter_packed, sig_packed, filter_packed, mul_elem);
-            break;
-        case AF_BATCH_SAME:
-            CUDA_LAUNCH((complexMultiply<convT, AF_BATCH_SAME>), blocks,
-                        threads, sig_packed, sig_packed, filter_packed,
-                        mul_elem);
-            break;
-        case AF_BATCH_UNSUPPORTED:
-        default: break;
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    if (kind == AF_BATCH_RHS) {
+        cplxMul(qArgs, filter_packed, sig_packed, filter_packed, mul_elem);
+    } else {
+        cplxMul(qArgs, sig_packed, sig_packed, filter_packed, mul_elem);
     }
     POST_LAUNCH_CHECK();
 }
@@ -304,6 +104,11 @@ void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
 template<typename T, typename convT, bool roundOut, int baseDim, bool expand>
 void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
                          CParam<T> filter) {
+    auto reorderOut =
+        getKernel("cuda::reorderOutput", fftConvSource(),
+                  {TemplateTypename<T>(), TemplateTypename<convT>(),
+                   TemplateArg(expand), TemplateArg(roundOut)});
+
     dim_t *sd    = sig.dims;
     int fftScale = 1;
 
@@ -316,11 +121,11 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     dim3 threads(THREADS);
     dim3 blocks(divup(out.strides[3] * out.dims[3], threads.x));
 
-    CUDA_LAUNCH((reorderOutput<T, convT, expand, roundOut>), blocks, threads,
-                out, packed, filter, sig_half_d0, baseDim, fftScale);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    reorderOut(qArgs, out, packed, filter, sig_half_d0, baseDim, fftScale);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/gradient.cuh b/src/backend/cuda/kernel/gradient.cuh
new file mode 100644
index 0000000000..94051dc6a8
--- /dev/null
+++ b/src/backend/cuda/kernel/gradient.cuh
@@ -0,0 +1,92 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+#define sidx(y, x) scratch[y + 1][x + 1]
+
+template<typename T>
+__global__ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in,
+                         const int blocksPerMatX,
+                         const int blocksPerMatY) {
+    const int idz = blockIdx.x / blocksPerMatX;
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - idz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
+
+    const int xB = blockIdx_x * blockDim.x;
+    const int yB = blockIdx_y * blockDim.y;
+
+    const int idx = threadIdx.x + xB;
+    const int idy = threadIdx.y + yB;
+
+    bool cond = (idx >= in.dims[0] || idy >= in.dims[1] || idz >= in.dims[2] ||
+                 idw >= in.dims[3]);
+
+    int xmax = (TX > (in.dims[0] - xB)) ? (in.dims[0] - xB) : TX;
+    int ymax = (TY > (in.dims[1] - yB)) ? (in.dims[1] - yB) : TY;
+
+    int iIdx =
+        idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
+
+    int g0dx = idw * grad0.strides[3] + idz * grad0.strides[2] +
+               idy * grad0.strides[1] + idx;
+
+    int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2] +
+               idy * grad1.strides[1] + idx;
+
+    __shared__ T scratch[TY + 2][TX + 2];
+
+    // Multipliers - 0.5 for interior, 1 for edge cases
+    float xf = 0.5 * (1 + (idx == 0 || idx >= (in.dims[0] - 1)));
+    float yf = 0.5 * (1 + (idy == 0 || idy >= (in.dims[1] - 1)));
+
+    // Copy data to scratch space
+    sidx(threadIdx.y, threadIdx.x) = cond ? scalar<T>(0) : in.ptr[iIdx];
+
+    __syncthreads();
+
+    // Copy buffer zone data. Corner (0,0) etc, are not used.
+    // Cols
+    if (threadIdx.y == 0) {
+        // Y-1
+        sidx(-1, threadIdx.x) = (cond || idy == 0)
+                                    ? sidx(0, threadIdx.x)
+                                    : in.ptr[iIdx - in.strides[1]];
+        sidx(ymax, threadIdx.x) = (cond || (idy + ymax) >= in.dims[1])
+                                      ? sidx(ymax - 1, threadIdx.x)
+                                      : in.ptr[iIdx + ymax * in.strides[1]];
+    }
+    // Rows
+    if (threadIdx.x == 0) {
+        sidx(threadIdx.y, -1) =
+            (cond || idx == 0) ? sidx(threadIdx.y, 0) : in.ptr[iIdx - 1];
+        sidx(threadIdx.y, xmax) = (cond || (idx + xmax) >= in.dims[0])
+                                      ? sidx(threadIdx.y, xmax - 1)
+                                      : in.ptr[iIdx + xmax];
+    }
+
+    __syncthreads();
+
+    if (cond) return;
+
+    grad0.ptr[g0dx] = xf * (sidx(threadIdx.y, threadIdx.x + 1) -
+                            sidx(threadIdx.y, threadIdx.x - 1));
+    grad1.ptr[g1dx] = yf * (sidx(threadIdx.y + 1, threadIdx.x) -
+                            sidx(threadIdx.y - 1, threadIdx.x));
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index a0a6a7299d..f6029af4c7 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -7,98 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/gradient_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX = 32;
-static const unsigned TY = 8;
-
-#define sidx(y, x) scratch[y + 1][x + 1]
 
 template<typename T>
-__global__ void gradient_kernel(Param<T> grad0, Param<T> grad1, CParam<T> in,
-                                const int blocksPerMatX,
-                                const int blocksPerMatY) {
-    const int idz = blockIdx.x / blocksPerMatX;
-    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-    const int blockIdx_x = blockIdx.x - idz * blocksPerMatX;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocksPerMatY;
-
-    const int xB = blockIdx_x * blockDim.x;
-    const int yB = blockIdx_y * blockDim.y;
-
-    const int idx = threadIdx.x + xB;
-    const int idy = threadIdx.y + yB;
-
-    bool cond = (idx >= in.dims[0] || idy >= in.dims[1] || idz >= in.dims[2] ||
-                 idw >= in.dims[3]);
-
-    int xmax = (TX > (in.dims[0] - xB)) ? (in.dims[0] - xB) : TX;
-    int ymax = (TY > (in.dims[1] - yB)) ? (in.dims[1] - yB) : TY;
-
-    int iIdx =
-        idw * in.strides[3] + idz * in.strides[2] + idy * in.strides[1] + idx;
-
-    int g0dx = idw * grad0.strides[3] + idz * grad0.strides[2] +
-               idy * grad0.strides[1] + idx;
-
-    int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2] +
-               idy * grad1.strides[1] + idx;
-
-    __shared__ T scratch[TY + 2][TX + 2];
-
-    // Multipliers - 0.5 for interior, 1 for edge cases
-    float xf = 0.5 * (1 + (idx == 0 || idx >= (in.dims[0] - 1)));
-    float yf = 0.5 * (1 + (idy == 0 || idy >= (in.dims[1] - 1)));
-
-    // Copy data to scratch space
-    sidx(threadIdx.y, threadIdx.x) = cond ? scalar<T>(0) : in.ptr[iIdx];
-
-    __syncthreads();
-
-    // Copy buffer zone data. Corner (0,0) etc, are not used.
-    // Cols
-    if (threadIdx.y == 0) {
-        // Y-1
-        sidx(-1, threadIdx.x) = (cond || idy == 0)
-                                    ? sidx(0, threadIdx.x)
-                                    : in.ptr[iIdx - in.strides[1]];
-        sidx(ymax, threadIdx.x) = (cond || (idy + ymax) >= in.dims[1])
-                                      ? sidx(ymax - 1, threadIdx.x)
-                                      : in.ptr[iIdx + ymax * in.strides[1]];
-    }
-    // Rows
-    if (threadIdx.x == 0) {
-        sidx(threadIdx.y, -1) =
-            (cond || idx == 0) ? sidx(threadIdx.y, 0) : in.ptr[iIdx - 1];
-        sidx(threadIdx.y, xmax) = (cond || (idx + xmax) >= in.dims[0])
-                                      ? sidx(threadIdx.y, xmax - 1)
-                                      : in.ptr[iIdx + xmax];
-    }
+void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
+    constexpr unsigned TX = 32;
+    constexpr unsigned TY = 8;
 
-    __syncthreads();
+    static const std::string source(gradient_cuh, gradient_cuh_len);
 
-    if (cond) return;
+    auto gradient = getKernel("cuda::gradient", source, {TemplateTypename<T>()},
+                              {DefineValue(TX), DefineValue(TY)});
 
-    grad0.ptr[g0dx] = xf * (sidx(threadIdx.y, threadIdx.x + 1) -
-                            sidx(threadIdx.y, threadIdx.x - 1));
-    grad1.ptr[g1dx] = yf * (sidx(threadIdx.y + 1, threadIdx.x) -
-                            sidx(threadIdx.y - 1, threadIdx.x));
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     dim3 threads(TX, TY, 1);
 
     int blocksPerMatX = divup(in.dims[0], TX);
@@ -110,9 +41,11 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((gradient_kernel<T>), blocks, threads, grad0, grad1, in,
-                blocksPerMatX, blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    gradient(qArgs, grad0, grad1, in, blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/identity.cuh b/src/backend/cuda/kernel/identity.cuh
new file mode 100644
index 0000000000..22ba3709d6
--- /dev/null
+++ b/src/backend/cuda/kernel/identity.cuh
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void identity(Param<T> out, int blocks_x, int blocks_y) {
+    const dim_t idz = blockIdx.x / blocks_x;
+    const dim_t idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+
+    const dim_t blockIdx_x = blockIdx.x - idz * blocks_x;
+    const dim_t blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_y;
+
+    const dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
+    const dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2] ||
+        idw >= out.dims[3])
+        return;
+
+    const T one  = scalar<T>(1);
+    const T zero = scalar<T>(0);
+
+    T *ptr = out.ptr + idz * out.strides[2] + idw * out.strides[3];
+    T val  = (idx == idy) ? one : zero;
+    ptr[idx + idy * out.strides[1]] = val;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index d6b42b3657..509356c5fb 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -7,43 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include <platform.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/identity_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
 
 template<typename T>
-__global__ static void identity_kernel(Param<T> out, int blocks_x,
-                                       int blocks_y) {
-    const dim_t idz = blockIdx.x / blocks_x;
-    const dim_t idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-
-    const dim_t blockIdx_x = blockIdx.x - idz * blocks_x;
-    const dim_t blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - idw * blocks_y;
-
-    const dim_t idx = threadIdx.x + blockIdx_x * blockDim.x;
-    const dim_t idy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (idx >= out.dims[0] || idy >= out.dims[1] || idz >= out.dims[2] ||
-        idw >= out.dims[3])
-        return;
+void identity(Param<T> out) {
+    static const std::string source(identity_cuh, identity_cuh_len);
 
-    const T one  = scalar<T>(1);
-    const T zero = scalar<T>(0);
+    auto identity =
+        getKernel("cuda::identity", source, {TemplateTypename<T>()});
 
-    T *ptr = out.ptr + idz * out.strides[2] + idw * out.strides[3];
-    T val  = (idx == idy) ? one : zero;
-    ptr[idx + idy * out.strides[1]] = val;
-}
-
-template<typename T>
-static void identity(Param<T> out) {
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
     int blocks_y = divup(out.dims[1], threads.y);
@@ -54,7 +37,9 @@ static void identity(Param<T> out) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((identity_kernel<T>), blocks, threads, out, blocks_x, blocks_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    identity(qArgs, out, blocks_x, blocks_y);
     POST_LAUNCH_CHECK();
 }
 }  // namespace kernel
diff --git a/src/backend/cuda/kernel/iir.cuh b/src/backend/cuda/kernel/iir.cuh
new file mode 100644
index 0000000000..edd18062eb
--- /dev/null
+++ b/src/backend/cuda/kernel/iir.cuh
@@ -0,0 +1,69 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool batch_a>
+__global__ void iir(Param<T> y, CParam<T> c, CParam<T> a, const int blocks_y) {
+    __shared__ T s_z[MAX_A_SIZE];
+    __shared__ T s_a[MAX_A_SIZE];
+    __shared__ T s_y;
+
+    const int idz = blockIdx.x;
+    const int idw = blockIdx.y / blocks_y;
+    const int idy = blockIdx.y - idw * blocks_y;
+
+    const int tx    = threadIdx.x;
+    const int num_a = a.dims[0];
+
+    int y_off = idw * y.strides[3] + idz * y.strides[2] + idy * y.strides[1];
+    int c_off = idw * c.strides[3] + idz * c.strides[2] + idy * c.strides[1];
+    int a_off = 0;
+
+    if (batch_a)
+        a_off = idw * a.strides[3] + idz * a.strides[2] + idy * a.strides[1];
+
+    T *d_y           = y.ptr + y_off;
+    const T *d_c     = c.ptr + c_off;
+    const T *d_a     = a.ptr + a_off;
+    const int repeat = (num_a + blockDim.x - 1) / blockDim.x;
+
+    for (int ii = 0; ii < MAX_A_SIZE / blockDim.x; ii++) {
+        int id  = ii * blockDim.x + tx;
+        s_z[id] = scalar<T>(0);
+        s_a[id] = (id < num_a) ? d_a[id] : scalar<T>(0);
+    }
+    __syncthreads();
+
+    for (int i = 0; i < y.dims[0]; i++) {
+        if (tx == 0) {
+            s_y    = (d_c[i] + s_z[0]) / s_a[0];
+            d_y[i] = s_y;
+        }
+        __syncthreads();
+
+#pragma unroll
+        for (int ii = 0; ii < repeat; ii++) {
+            int id = ii * blockDim.x + tx + 1;
+
+            T z = s_z[id] - s_a[id] * s_y;
+            __syncthreads();
+
+            s_z[id - 1] = z;
+            __syncthreads();
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index f54459a089..d1d52c5e68 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -7,73 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/iir_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
-static const int MAX_A_SIZE = 1024;
-
 template<typename T, bool batch_a>
-__global__ void iir_kernel(Param<T> y, CParam<T> c, CParam<T> a,
-                           const int blocks_y) {
-    __shared__ T s_z[MAX_A_SIZE];
-    __shared__ T s_a[MAX_A_SIZE];
-    __shared__ T s_y;
-
-    const int idz = blockIdx.x;
-    const int idw = blockIdx.y / blocks_y;
-    const int idy = blockIdx.y - idw * blocks_y;
-
-    const int tx    = threadIdx.x;
-    const int num_a = a.dims[0];
-
-    int y_off = idw * y.strides[3] + idz * y.strides[2] + idy * y.strides[1];
-    int c_off = idw * c.strides[3] + idz * c.strides[2] + idy * c.strides[1];
-    int a_off = 0;
-
-    if (batch_a)
-        a_off = idw * a.strides[3] + idz * a.strides[2] + idy * a.strides[1];
-
-    T *d_y           = y.ptr + y_off;
-    const T *d_c     = c.ptr + c_off;
-    const T *d_a     = a.ptr + a_off;
-    const int repeat = (num_a + blockDim.x - 1) / blockDim.x;
-
-    for (int ii = 0; ii < MAX_A_SIZE / blockDim.x; ii++) {
-        int id  = ii * blockDim.x + tx;
-        s_z[id] = scalar<T>(0);
-        s_a[id] = (id < num_a) ? d_a[id] : scalar<T>(0);
-    }
-    __syncthreads();
-
-    for (int i = 0; i < y.dims[0]; i++) {
-        if (tx == 0) {
-            s_y    = (d_c[i] + s_z[0]) / s_a[0];
-            d_y[i] = s_y;
-        }
-        __syncthreads();
-
-#pragma unroll
-        for (int ii = 0; ii < repeat; ii++) {
-            int id = ii * blockDim.x + tx + 1;
+void iir(Param<T> y, CParam<T> c, CParam<T> a) {
+    constexpr int MAX_A_SIZE = 1024;
 
-            T z = s_z[id] - s_a[id] * s_y;
-            __syncthreads();
+    static const std::string source(iir_cuh, iir_cuh_len);
 
-            s_z[id - 1] = z;
-            __syncthreads();
-        }
-    }
-}
+    auto iir = getKernel("cuda::iir", source,
+                         {TemplateTypename<T>(), TemplateArg(batch_a)},
+                         {DefineValue(MAX_A_SIZE)});
 
-template<typename T, bool batch_a>
-void iir(Param<T> y, CParam<T> c, CParam<T> a) {
     const int blocks_y = y.dims[1];
     const int blocks_x = y.dims[2];
 
@@ -82,7 +38,10 @@ void iir(Param<T> y, CParam<T> c, CParam<T> a) {
     int threads = 256;
     while (threads > y.dims[0] && threads > 32) threads /= 2;
 
-    CUDA_LAUNCH((iir_kernel<T, batch_a>), blocks, threads, y, c, a, blocks_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    iir(qArgs, y, c, a, blocks_y);
+    POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
diff --git a/src/backend/cuda/kernel/index.cuh b/src/backend/cuda/kernel/index.cuh
new file mode 100644
index 0000000000..643fe87837
--- /dev/null
+++ b/src/backend/cuda/kernel/index.cuh
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <assign_kernel_param.hpp>
+#include <utility.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void index(Param<T> out, CParam<T> in,
+                      const cuda::IndexKernelParam p, const int nBBS0,
+                      const int nBBS1) {
+    // retrieve index pointers
+    // these can be 0 where af_array index is not used
+    const uint* ptr0 = p.ptr[0];
+    const uint* ptr1 = p.ptr[1];
+    const uint* ptr2 = p.ptr[2];
+    const uint* ptr3 = p.ptr[3];
+    // retrive booleans that tell us which index to use
+    const bool s0 = p.isSeq[0];
+    const bool s1 = p.isSeq[1];
+    const bool s2 = p.isSeq[2];
+    const bool s3 = p.isSeq[3];
+
+    const int gz = blockIdx.x / nBBS0;
+    const int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + threadIdx.x;
+
+    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
+    const int gy =
+        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) +
+        threadIdx.y;
+
+    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2] &&
+        gw < out.dims[3]) {
+        // calculate pointer offsets for input
+        int i =
+            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], in.dims[0]);
+        int j =
+            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], in.dims[1]);
+        int k =
+            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], in.dims[2]);
+        int l =
+            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], in.dims[3]);
+        // offset input and output pointers
+        const T* src = (const T*)in.ptr + (i + j + k + l);
+        T* dst = (T*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
+                                gz * out.strides[2] + gw * out.strides[3]);
+        // set the output
+        dst[0] = src[0];
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 55de91119c..2ebdc5af72 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -7,73 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
+#include <assign_kernel_param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <utility.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/index_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
-
-typedef struct {
-    int offs[4];
-    int strds[4];
-    bool isSeq[4];
-    uint* ptr[4];
-} IndexKernelParam_t;
-
 template<typename T>
-__global__ void indexKernel(Param<T> out, CParam<T> in,
-                            const IndexKernelParam_t p, const int nBBS0,
-                            const int nBBS1) {
-    // retrieve index pointers
-    // these can be 0 where af_array index is not used
-    const uint* ptr0 = p.ptr[0];
-    const uint* ptr1 = p.ptr[1];
-    const uint* ptr2 = p.ptr[2];
-    const uint* ptr3 = p.ptr[3];
-    // retrive booleans that tell us which index to use
-    const bool s0 = p.isSeq[0];
-    const bool s1 = p.isSeq[1];
-    const bool s2 = p.isSeq[2];
-    const bool s3 = p.isSeq[3];
+void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
 
-    const int gz = blockIdx.x / nBBS0;
-    const int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + threadIdx.x;
+    static const std::string source(index_cuh, index_cuh_len);
 
-    const int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
-    const int gy =
-        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) +
-        threadIdx.y;
+    auto index = getKernel("cuda::index", source, {TemplateTypename<T>()});
 
-    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2] &&
-        gw < out.dims[3]) {
-        // calculate pointer offsets for input
-        int i =
-            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], in.dims[0]);
-        int j =
-            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], in.dims[1]);
-        int k =
-            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], in.dims[2]);
-        int l =
-            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], in.dims[3]);
-        // offset input and output pointers
-        const T* src = (const T*)in.ptr + (i + j + k + l);
-        T* dst = (T*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
-                                gz * out.strides[2] + gw * out.strides[3]);
-        // set the output
-        dst[0] = src[0];
-    }
-}
-
-template<typename T>
-void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p) {
     const dim3 threads(THREADS_X, THREADS_Y);
 
     int blks_x = divup(out.dims[0], threads.x);
@@ -86,11 +42,11 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam_t& p) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((indexKernel<T>), blocks, threads, out, in, p, blks_x, blks_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    index(qArgs, out, in, p, blks_x, blks_y);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/iota.cuh b/src/backend/cuda/kernel/iota.cuh
new file mode 100644
index 0000000000..1554e08096
--- /dev/null
+++ b/src/backend/cuda/kernel/iota.cuh
@@ -0,0 +1,53 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void iota(Param<T> out, const int s0, const int s1, const int s2,
+                     const int s3, const int blocksPerMatX,
+                     const int blocksPerMatY) {
+    const int oz         = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
+
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int ozw = ow * out.strides[3] + oz * out.strides[2];
+
+    dim_t val = (ow % s3) * s2 * s1 * s0;
+    val += (oz % s2) * s1 * s0;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        int oyzw   = ozw + oy * out.strides[1];
+        dim_t valY = val + (oy % s1) * s0;
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            int oidx = oyzw + ox;
+
+            out.ptr[oidx] = valY + (ox % s0);
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 01af4ee98e..4662fd5309 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -7,62 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/iota_cuh.hpp>
 #include <af/dim4.hpp>
 
+#include <string>
+
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned IOTA_TX = 32;
-static const unsigned IOTA_TY = 8;
-static const unsigned TILEX   = 512;
-static const unsigned TILEY   = 32;
 
 template<typename T>
-__global__ void iota_kernel(Param<T> out, const int s0, const int s1,
-                            const int s2, const int s3, const int blocksPerMatX,
-                            const int blocksPerMatY) {
-    const int oz         = blockIdx.x / blocksPerMatX;
-    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
-
-    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
-        ow >= out.dims[3])
-        return;
-
-    const int ozw = ow * out.strides[3] + oz * out.strides[2];
-
-    dim_t val = (ow % s3) * s2 * s1 * s0;
-    val += (oz % s2) * s1 * s0;
-
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
+void iota(Param<T> out, const af::dim4 &sdims) {
+    constexpr unsigned IOTA_TX = 32;
+    constexpr unsigned IOTA_TY = 8;
+    constexpr unsigned TILEX   = 512;
+    constexpr unsigned TILEY   = 32;
 
-    for (int oy = yy; oy < out.dims[1]; oy += incy) {
-        int oyzw   = ozw + oy * out.strides[1];
-        dim_t valY = val + (oy % s1) * s0;
-        for (int ox = xx; ox < out.dims[0]; ox += incx) {
-            int oidx = oyzw + ox;
+    static const std::string source(iota_cuh, iota_cuh_len);
 
-            out.ptr[oidx] = valY + (ox % s0);
-        }
-    }
-}
+    auto iota = getKernel("cuda::iota", source, {TemplateTypename<T>()});
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void iota(Param<T> out, const af::dim4 &sdims) {
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
     int blocksPerMatX = divup(out.dims[0], TILEX);
@@ -75,10 +44,12 @@ void iota(Param<T> out, const af::dim4 &sdims) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((iota_kernel<T>), blocks, threads, out, sdims[0], sdims[1],
-                sdims[2], sdims[3], blocksPerMatX, blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    iota(qArgs, out, sdims[0], sdims[1], sdims[2], sdims[3], blocksPerMatX,
+         blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/ireduce.cuh b/src/backend/cuda/kernel/ireduce.cuh
new file mode 100644
index 0000000000..865651e3ba
--- /dev/null
+++ b/src/backend/cuda/kernel/ireduce.cuh
@@ -0,0 +1,231 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <minmax_op.hpp>
+
+namespace cuda {
+
+template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
+__global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
+                                  const uint *ilptr, uint blocks_x,
+                                  uint blocks_y, uint offset_dim) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * THREADS_X + tidx;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint xid = blockIdx_x * blockDim.x + tidx;
+    const uint yid = blockIdx_y;  // yid  of output. updated for input later.
+
+    uint ids[4] = {xid, yid, zid, wid};
+
+    const T *iptr = in.ptr;
+    T *optr       = out.ptr;
+
+    // There is only one element per block for out
+    // There are blockDim.y elements per block for in
+    // Hence increment ids[dim] just after offseting out and before offsetting
+    // in
+    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+            ids[1] * out.strides[1] + ids[0];
+    olptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
+             ids[1] * out.strides[1] + ids[0];
+    const uint blockIdx_dim = ids[dim];
+
+    ids[dim] = ids[dim] * blockDim.y + tidy;
+    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+            ids[1] * in.strides[1] + ids[0];
+    if (!is_first)
+        ilptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+                 ids[1] * in.strides[1] + ids[0];
+    const uint id_dim_in = ids[dim];
+
+    const uint istride_dim = in.strides[dim];
+
+    bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
+                    (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
+
+    T val    = Binary<T, op>::init();
+    uint idx = id_dim_in;
+
+    if (is_valid && id_dim_in < in.dims[dim]) {
+        val = *iptr;
+        if (!is_first) idx = *ilptr;
+    }
+
+    MinMaxOp<op, T> Op(val, idx);
+
+    const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
+
+    __shared__ T s_val[THREADS_X * DIMY];
+    __shared__ uint s_idx[THREADS_X * DIMY];
+
+    for (int id = id_dim_in_start; is_valid && (id < in.dims[dim]);
+         id += offset_dim * blockDim.y) {
+        iptr = iptr + offset_dim * blockDim.y * istride_dim;
+        if (!is_first) {
+            ilptr = ilptr + offset_dim * blockDim.y * istride_dim;
+            Op(*iptr, *ilptr);
+        } else {
+            Op(*iptr, id);
+        }
+    }
+
+    s_val[tid] = Op.m_val;
+    s_idx[tid] = Op.m_idx;
+
+    T *s_vptr    = s_val + tid;
+    uint *s_iptr = s_idx + tid;
+    __syncthreads();
+
+    if (DIMY == 8) {
+        if (tidy < 4) {
+            Op(s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
+            *s_vptr = Op.m_val;
+            *s_iptr = Op.m_idx;
+        }
+        __syncthreads();
+    }
+
+    if (DIMY >= 4) {
+        if (tidy < 2) {
+            Op(s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
+            *s_vptr = Op.m_val;
+            *s_iptr = Op.m_idx;
+        }
+        __syncthreads();
+    }
+
+    if (DIMY >= 2) {
+        if (tidy < 1) {
+            Op(s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
+            *s_vptr = Op.m_val;
+            *s_iptr = Op.m_idx;
+        }
+        __syncthreads();
+    }
+
+    if (tidy == 0 && is_valid && (blockIdx_dim < out.dims[dim])) {
+        *optr  = *s_vptr;
+        *olptr = *s_iptr;
+    }
+}
+
+template<typename T, af_op_t op>
+__device__ void warp_reduce(T *s_ptr, uint *s_idx, uint tidx) {
+    MinMaxOp<op, T> Op(s_ptr[tidx], s_idx[tidx]);
+#pragma unroll
+    for (int n = 16; n >= 1; n >>= 1) {
+        if (tidx < n) {
+            Op(s_ptr[tidx + n], s_idx[tidx + n]);
+            s_ptr[tidx] = Op.m_val;
+            s_idx[tidx] = Op.m_idx;
+        }
+        __syncthreads();
+    }
+}
+
+template<typename T, af_op_t op, bool is_first, uint DIMX>
+__global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
+                                    const uint *ilptr, uint blocks_x,
+                                    uint blocks_y, uint repeat) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * blockDim.x + tidx;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
+    const uint yid = blockIdx_y * blockDim.y + tidy;
+
+    const data_t<T> *iptr = in.ptr;
+    data_t<T> *optr       = out.ptr;
+
+    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+    if (!is_first)
+        ilptr +=
+            wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
+    olptr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
+
+    if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
+
+    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+
+    compute_t<T> val = Binary<compute_t<T>, op>::init();
+    uint idx         = xid;
+
+    if (xid < lim) {
+        val = static_cast<compute_t<T>>(iptr[xid]);
+        if (!is_first) idx = ilptr[xid];
+    }
+
+    MinMaxOp<op, compute_t<T>> Op(val, idx);
+
+    __shared__ compute_t<T> s_val[THREADS_PER_BLOCK];
+    __shared__ uint s_idx[THREADS_PER_BLOCK];
+
+    for (int id = xid + DIMX; id < lim; id += DIMX) {
+        Op(static_cast<compute_t<T>>(iptr[id]), (!is_first) ? ilptr[id] : id);
+    }
+
+    s_val[tid] = Op.m_val;
+    s_idx[tid] = Op.m_idx;
+    __syncthreads();
+
+    compute_t<T> *s_vptr = s_val + tidy * DIMX;
+    uint *s_iptr         = s_idx + tidy * DIMX;
+
+    if (DIMX == 256) {
+        if (tidx < 128) {
+            Op(s_vptr[tidx + 128], s_iptr[tidx + 128]);
+            s_vptr[tidx] = Op.m_val;
+            s_iptr[tidx] = Op.m_idx;
+        }
+        __syncthreads();
+    }
+
+    if (DIMX >= 128) {
+        if (tidx < 64) {
+            Op(s_vptr[tidx + 64], s_iptr[tidx + 64]);
+            s_vptr[tidx] = Op.m_val;
+            s_iptr[tidx] = Op.m_idx;
+        }
+        __syncthreads();
+    }
+
+    if (DIMX >= 64) {
+        if (tidx < 32) {
+            Op(s_vptr[tidx + 32], s_iptr[tidx + 32]);
+            s_vptr[tidx] = Op.m_val;
+            s_iptr[tidx] = Op.m_idx;
+        }
+        __syncthreads();
+    }
+
+    warp_reduce<compute_t<T>, op>(s_vptr, s_iptr, tidx);
+
+    if (tidx == 0) {
+        optr[blockIdx_x]  = s_vptr[0];
+        olptr[blockIdx_x] = s_iptr[0];
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 8c16a7eb1f..5450be6be9 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -7,197 +7,26 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
-#include <memory>
+#include <minmax_op.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/ireduce_cuh.hpp>
 #include "config.hpp"
 
+#include <memory>
+#include <string>
+
 namespace cuda {
 namespace kernel {
-template<typename T>
-__host__ __device__ static double cabs(const T &in) {
-    return (double)in;
-}
-
-template<>
-__host__ __device__ double cabs<char>(const char &in) {
-    return (double)(in > 0);
-}
-
-template<>
-__host__ __device__ double cabs<cfloat>(const cfloat &in) {
-    return (double)abs(in);
-}
-
-template<>
-__host__ __device__ double cabs<cdouble>(const cdouble &in) {
-    return (double)abs(in);
-}
-
-template<typename T>
-__host__ __device__ static bool is_nan(const T &in) {
-    return in != in;
-}
-
-template<>
-__host__ __device__ bool is_nan<cfloat>(const cfloat &in) {
-    return in.x != in.x || in.y != in.y;
-}
-
-template<>
-__host__ __device__ bool is_nan<cdouble>(const cdouble &in) {
-    return in.x != in.x || in.y != in.y;
-}
-
-template<af_op_t op, typename T>
-struct MinMaxOp {
-    T m_val;
-    uint m_idx;
-    __host__ __device__ MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<compute_t<T>, op>::init(); }
-    }
-
-    __host__ __device__ void operator()(T val, uint idx) {
-        if ((cabs(val) < cabs(m_val) ||
-             (cabs(val) == cabs(m_val) && idx > m_idx))) {
-            m_val = val;
-            m_idx = idx;
-        }
-    }
-};
-
-template<typename T>
-struct MinMaxOp<af_max_t, T> {
-    T m_val;
-    uint m_idx;
-    __host__ __device__ MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<T, af_max_t>::init(); }
-    }
-
-    __host__ __device__ void operator()(T val, uint idx) {
-        if ((cabs(val) > cabs(m_val) ||
-             (cabs(val) == cabs(m_val) && idx <= m_idx))) {
-            m_val = val;
-            m_idx = idx;
-        }
-    }
-};
-
-template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
-__global__ static void ireduce_dim_kernel(Param<T> out, uint *olptr,
-                                          CParam<T> in, const uint *ilptr,
-                                          uint blocks_x, uint blocks_y,
-                                          uint offset_dim) {
-    const uint tidx = threadIdx.x;
-    const uint tidy = threadIdx.y;
-    const uint tid  = tidy * THREADS_X + tidx;
-
-    const uint zid        = blockIdx.x / blocks_x;
-    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const uint blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const uint xid = blockIdx_x * blockDim.x + tidx;
-    const uint yid = blockIdx_y;  // yid  of output. updated for input later.
-
-    uint ids[4] = {xid, yid, zid, wid};
-
-    const T *iptr = in.ptr;
-    T *optr       = out.ptr;
-
-    // There is only one element per block for out
-    // There are blockDim.y elements per block for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
-    // in
-    optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-            ids[1] * out.strides[1] + ids[0];
-    olptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
-             ids[1] * out.strides[1] + ids[0];
-    const uint blockIdx_dim = ids[dim];
-
-    ids[dim] = ids[dim] * blockDim.y + tidy;
-    iptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-            ids[1] * in.strides[1] + ids[0];
-    if (!is_first)
-        ilptr += ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-                 ids[1] * in.strides[1] + ids[0];
-    const uint id_dim_in = ids[dim];
-
-    const uint istride_dim = in.strides[dim];
-
-    bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
-                    (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
-
-    T val    = Binary<T, op>::init();
-    uint idx = id_dim_in;
-
-    if (is_valid && id_dim_in < in.dims[dim]) {
-        val = *iptr;
-        if (!is_first) idx = *ilptr;
-    }
-
-    MinMaxOp<op, T> Op(val, idx);
-
-    const uint id_dim_in_start = id_dim_in + offset_dim * blockDim.y;
-
-    __shared__ T s_val[THREADS_X * DIMY];
-    __shared__ uint s_idx[THREADS_X * DIMY];
-
-    for (int id = id_dim_in_start; is_valid && (id < in.dims[dim]);
-         id += offset_dim * blockDim.y) {
-        iptr = iptr + offset_dim * blockDim.y * istride_dim;
-        if (!is_first) {
-            ilptr = ilptr + offset_dim * blockDim.y * istride_dim;
-            Op(*iptr, *ilptr);
-        } else {
-            Op(*iptr, id);
-        }
-    }
-
-    s_val[tid] = Op.m_val;
-    s_idx[tid] = Op.m_idx;
-
-    T *s_vptr    = s_val + tid;
-    uint *s_iptr = s_idx + tid;
-    __syncthreads();
-
-    if (DIMY == 8) {
-        if (tidy < 4) {
-            Op(s_vptr[THREADS_X * 4], s_iptr[THREADS_X * 4]);
-            *s_vptr = Op.m_val;
-            *s_iptr = Op.m_idx;
-        }
-        __syncthreads();
-    }
-
-    if (DIMY >= 4) {
-        if (tidy < 2) {
-            Op(s_vptr[THREADS_X * 2], s_iptr[THREADS_X * 2]);
-            *s_vptr = Op.m_val;
-            *s_iptr = Op.m_idx;
-        }
-        __syncthreads();
-    }
-
-    if (DIMY >= 2) {
-        if (tidy < 1) {
-            Op(s_vptr[THREADS_X * 1], s_iptr[THREADS_X * 1]);
-            *s_vptr = Op.m_val;
-            *s_iptr = Op.m_idx;
-        }
-        __syncthreads();
-    }
 
-    if (tidy == 0 && is_valid && (blockIdx_dim < out.dims[dim])) {
-        *optr  = *s_vptr;
-        *olptr = *s_iptr;
-    }
+static inline std::string ireduceSource() {
+    static const std::string src(ireduce_cuh, ireduce_cuh_len);
+    return src;
 }
 
 template<typename T, af_op_t op, int dim, bool is_first>
@@ -213,28 +42,16 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    switch (threads_y) {
-        case 8:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 8>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim]);
-            break;
-        case 4:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 4>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim]);
-            break;
-        case 2:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 2>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim]);
-            break;
-        case 1:
-            CUDA_LAUNCH((ireduce_dim_kernel<T, op, dim, is_first, 1>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim]);
-            break;
-    }
+    auto ireduceDim =
+        getKernel("cuda::ireduceDim", ireduceSource(),
+                  {TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
+                   TemplateArg(is_first), TemplateArg(threads_y)},
+                  {DefineValue(THREADS_X)});
+
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    ireduceDim(qArgs, out, olptr, in, ilptr, blocks_dim[0], blocks_dim[1],
+               blocks_dim[dim]);
 
     POST_LAUNCH_CHECK();
 }
@@ -278,111 +95,6 @@ void ireduce_dim(Param<T> out, uint *olptr, CParam<T> in) {
     }
 }
 
-template<typename T, af_op_t op>
-__device__ void warp_reduce(T *s_ptr, uint *s_idx, uint tidx) {
-    MinMaxOp<op, T> Op(s_ptr[tidx], s_idx[tidx]);
-#pragma unroll
-    for (int n = 16; n >= 1; n >>= 1) {
-        if (tidx < n) {
-            Op(s_ptr[tidx + n], s_idx[tidx + n]);
-            s_ptr[tidx] = Op.m_val;
-            s_idx[tidx] = Op.m_idx;
-        }
-        __syncthreads();
-    }
-}
-
-template<typename T, af_op_t op, bool is_first, uint DIMX>
-__global__ static void ireduce_first_kernel(Param<T> out, uint *olptr,
-                                            CParam<T> in, const uint *ilptr,
-                                            uint blocks_x, uint blocks_y,
-                                            uint repeat) {
-    const uint tidx = threadIdx.x;
-    const uint tidy = threadIdx.y;
-    const uint tid  = tidy * blockDim.x + tidx;
-
-    const uint zid        = blockIdx.x / blocks_x;
-    const uint wid        = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const uint blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const uint xid = blockIdx_x * blockDim.x * repeat + tidx;
-    const uint yid = blockIdx_y * blockDim.y + tidy;
-
-    const data_t<T> *iptr = in.ptr;
-    data_t<T> *optr       = out.ptr;
-
-    iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
-    optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-
-    if (!is_first)
-        ilptr +=
-            wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
-    olptr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-
-    if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
-
-    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
-
-    compute_t<T> val = Binary<compute_t<T>, op>::init();
-    uint idx         = xid;
-
-    if (xid < lim) {
-        val = static_cast<compute_t<T>>(iptr[xid]);
-        if (!is_first) idx = ilptr[xid];
-    }
-
-    MinMaxOp<op, compute_t<T>> Op(val, idx);
-
-    __shared__ compute_t<T> s_val[THREADS_PER_BLOCK];
-    __shared__ uint s_idx[THREADS_PER_BLOCK];
-
-    for (int id = xid + DIMX; id < lim; id += DIMX) {
-        Op(static_cast<compute_t<T>>(iptr[id]), (!is_first) ? ilptr[id] : id);
-    }
-
-    s_val[tid] = Op.m_val;
-    s_idx[tid] = Op.m_idx;
-    __syncthreads();
-
-    compute_t<T> *s_vptr = s_val + tidy * DIMX;
-    uint *s_iptr         = s_idx + tidy * DIMX;
-
-    if (DIMX == 256) {
-        if (tidx < 128) {
-            Op(s_vptr[tidx + 128], s_iptr[tidx + 128]);
-            s_vptr[tidx] = Op.m_val;
-            s_iptr[tidx] = Op.m_idx;
-        }
-        __syncthreads();
-    }
-
-    if (DIMX >= 128) {
-        if (tidx < 64) {
-            Op(s_vptr[tidx + 64], s_iptr[tidx + 64]);
-            s_vptr[tidx] = Op.m_val;
-            s_iptr[tidx] = Op.m_idx;
-        }
-        __syncthreads();
-    }
-
-    if (DIMX >= 64) {
-        if (tidx < 32) {
-            Op(s_vptr[tidx + 32], s_iptr[tidx + 32]);
-            s_vptr[tidx] = Op.m_val;
-            s_iptr[tidx] = Op.m_idx;
-        }
-        __syncthreads();
-    }
-
-    warp_reduce<compute_t<T>, op>(s_vptr, s_iptr, tidx);
-
-    if (tidx == 0) {
-        optr[blockIdx_x]  = s_vptr[0];
-        olptr[blockIdx_x] = s_iptr[0];
-    }
-}
-
 template<typename T, af_op_t op, bool is_first>
 void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
                             const uint *ilptr, const uint blocks_x,
@@ -396,29 +108,16 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-    switch (threads_x) {
-        case 32:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 32>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
-                        repeat);
-            break;
-        case 64:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 64>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
-                        repeat);
-            break;
-        case 128:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 128>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
-                        repeat);
-            break;
-        case 256:
-            CUDA_LAUNCH((ireduce_first_kernel<T, op, is_first, 256>), blocks,
-                        threads, out, olptr, in, ilptr, blocks_x, blocks_y,
-                        repeat);
-            break;
-    }
+    // threads_x can take values 32, 64, 128, 256
+    auto ireduceFirst =
+        getKernel("cuda::ireduceFirst", ireduceSource(),
+                  {TemplateTypename<T>(), TemplateArg(op),
+                   TemplateArg(is_first), TemplateArg(threads_x)},
+                  {DefineValue(THREADS_PER_BLOCK)});
+
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    ireduceFirst(qArgs, out, olptr, in, ilptr, blocks_x, blocks_y, repeat);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/join.cuh b/src/backend/cuda/kernel/join.cuh
new file mode 100644
index 0000000000..c88ef1f422
--- /dev/null
+++ b/src/backend/cuda/kernel/join.cuh
@@ -0,0 +1,50 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename To, typename Ti, int dim>
+__global__ void join(Param<To> out, CParam<Ti> in, const int o0, const int o1,
+                     const int o2, const int o3, const int blocksPerMatX,
+                     const int blocksPerMatY) {
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    const int iz         = blockIdx.x / blocksPerMatX;
+    const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
+    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
+
+    To *d_out      = out.ptr;
+    Ti const *d_in = in.ptr;
+
+    const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
+    const int blockIdx_y =
+        (blockIdx.y + (blockIdx.z * gridDim.y)) - iw * blocksPerMatY;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (iz < in.dims[2] && iw < in.dims[3]) {
+        d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
+        d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
+
+        for (int iy = yy; iy < in.dims[1]; iy += incy) {
+            Ti const *d_in_ = d_in + iy * in.strides[1];
+            To *d_out_      = d_out + (iy + o1) * out.strides[1];
+
+            for (int ix = xx; ix < in.dims[0]; ix += incx) {
+                d_out_[ix + o0] = d_in_[ix];
+            }
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index e873c120e4..e9937a5287 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -7,59 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/join_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 256;
-static const unsigned TILEY = 32;
-
-template<typename To, typename Ti, int dim>
-__global__ void join_kernel(Param<To> out, CParam<Ti> in, const int o0,
-                            const int o1, const int o2, const int o3,
-                            const int blocksPerMatX, const int blocksPerMatY) {
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
-
-    const int iz         = blockIdx.x / blocksPerMatX;
-    const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
-    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
-
-    To *d_out      = out.ptr;
-    Ti const *d_in = in.ptr;
 
-    const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
-    const int blockIdx_y =
-        (blockIdx.y + (blockIdx.z * gridDim.y)) - iw * blocksPerMatY;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+template<typename To, typename Tx>
+void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset, int dim) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 256;
+    constexpr unsigned TILEY = 32;
 
-    if (iz < in.dims[2] && iw < in.dims[3]) {
-        d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
-        d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
+    static const std::string source(join_cuh, join_cuh_len);
 
-        for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            Ti const *d_in_ = d_in + iy * in.strides[1];
-            To *d_out_      = d_out + (iy + o1) * out.strides[1];
+    auto join = getKernel(
+        "cuda::join", source,
+        {TemplateTypename<To>(), TemplateTypename<Tx>(), TemplateArg(dim)});
 
-            for (int ix = xx; ix < in.dims[0]; ix += incx) {
-                d_out_[ix + o0] = d_in_[ix];
-            }
-        }
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename To, typename Tx, int dim>
-void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset) {
     dim3 threads(TX, TY, 1);
 
     int blocksPerMatX = divup(X.dims[0], TILEX);
@@ -72,9 +45,12 @@ void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((join_kernel<To, Tx, dim>), blocks, threads, out, X, offset[0],
-                offset[1], offset[2], offset[3], blocksPerMatX, blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    join(qArgs, out, X, offset[0], offset[1], offset[2], offset[3],
+         blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/lookup.cuh b/src/backend/cuda/kernel/lookup.cuh
new file mode 100644
index 0000000000..6613095ae6
--- /dev/null
+++ b/src/backend/cuda/kernel/lookup.cuh
@@ -0,0 +1,70 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <utility.hpp>
+
+namespace cuda {
+
+template<typename in_t, typename idx_t>
+__global__ void lookup1D(Param<in_t> out, CParam<in_t> in,
+                         CParam<idx_t> indices, int vDim) {
+    int idx = threadIdx.x + blockIdx.x * THREADS * THRD_LOAD;
+
+    const in_t* inPtr   = (const in_t*)in.ptr;
+    const idx_t* idxPtr = (const idx_t*)indices.ptr;
+
+    in_t* outPtr = (in_t*)out.ptr;
+
+    int en = min(out.dims[vDim], idx + THRD_LOAD * THREADS);
+
+    for (int oIdx = idx; oIdx < en; oIdx += THREADS) {
+        int iIdx     = trimIndex(static_cast<int>(idxPtr[oIdx]), in.dims[vDim]);
+        outPtr[oIdx] = inPtr[iIdx];
+    }
+}
+
+template<typename in_t, typename idx_t, unsigned dim>
+__global__ void lookupND(Param<in_t> out, CParam<in_t> in,
+                         CParam<idx_t> indices, int nBBS0, int nBBS1) {
+    int lx = threadIdx.x;
+    int ly = threadIdx.y;
+
+    int gz = blockIdx.x / nBBS0;
+    int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
+
+    int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + lx;
+    int gy =
+        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) + ly;
+
+    const idx_t* idxPtr = (const idx_t*)indices.ptr;
+
+    int i = in.strides[0] *
+            (dim == 0 ? trimIndex((int)idxPtr[gx], in.dims[0]) : gx);
+    int j = in.strides[1] *
+            (dim == 1 ? trimIndex((int)idxPtr[gy], in.dims[1]) : gy);
+    int k = in.strides[2] *
+            (dim == 2 ? trimIndex((int)idxPtr[gz], in.dims[2]) : gz);
+    int l = in.strides[3] *
+            (dim == 3 ? trimIndex((int)idxPtr[gw], in.dims[3]) : gw);
+
+    const in_t* inPtr = (const in_t*)in.ptr + (i + j + k + l);
+    in_t* outPtr = (in_t*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
+                                     gz * out.strides[2] + gw * out.strides[3]);
+
+    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2] &&
+        gw < out.dims[3]) {
+        outPtr[0] = inPtr[0];
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index e8dbe6a9d5..c036c044f9 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -7,75 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include <utility.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/lookup_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-static const int THREADS   = 256;
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
-static const int THRD_LOAD = THREADS_X / THREADS_Y;
-
-template<typename in_t, typename idx_t>
-__global__ void lookup1D(Param<in_t> out, CParam<in_t> in,
-                         CParam<idx_t> indices, int vDim) {
-    int idx = threadIdx.x + blockIdx.x * THREADS * THRD_LOAD;
-
-    const in_t* inPtr   = (const in_t*)in.ptr;
-    const idx_t* idxPtr = (const idx_t*)indices.ptr;
 
-    in_t* outPtr = (in_t*)out.ptr;
-
-    int en = min(out.dims[vDim], idx + THRD_LOAD * THREADS);
-
-    for (int oIdx = idx; oIdx < en; oIdx += THREADS) {
-        int iIdx     = trimIndex(static_cast<int>(idxPtr[oIdx]), in.dims[vDim]);
-        outPtr[oIdx] = inPtr[iIdx];
-    }
-}
+constexpr int THREADS   = 256;
+constexpr int THREADS_X = 32;
+constexpr int THREADS_Y = 8;
+constexpr int THRD_LOAD = THREADS_X / THREADS_Y;
 
-template<typename in_t, typename idx_t, unsigned dim>
-__global__ void lookupND(Param<in_t> out, CParam<in_t> in,
-                         CParam<idx_t> indices, int nBBS0, int nBBS1) {
-    int lx = threadIdx.x;
-    int ly = threadIdx.y;
-
-    int gz = blockIdx.x / nBBS0;
-    int gw = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
-
-    int gx = blockDim.x * (blockIdx.x - gz * nBBS0) + lx;
-    int gy =
-        blockDim.y * ((blockIdx.y + blockIdx.z * gridDim.y) - gw * nBBS1) + ly;
-
-    const idx_t* idxPtr = (const idx_t*)indices.ptr;
-
-    int i = in.strides[0] *
-            (dim == 0 ? trimIndex((int)idxPtr[gx], in.dims[0]) : gx);
-    int j = in.strides[1] *
-            (dim == 1 ? trimIndex((int)idxPtr[gy], in.dims[1]) : gy);
-    int k = in.strides[2] *
-            (dim == 2 ? trimIndex((int)idxPtr[gz], in.dims[2]) : gz);
-    int l = in.strides[3] *
-            (dim == 3 ? trimIndex((int)idxPtr[gw], in.dims[3]) : gw);
-
-    const in_t* inPtr = (const in_t*)in.ptr + (i + j + k + l);
-    in_t* outPtr = (in_t*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
-                                     gz * out.strides[2] + gw * out.strides[3]);
-
-    if (gx < out.dims[0] && gy < out.dims[1] && gz < out.dims[2] &&
-        gw < out.dims[3]) {
-        outPtr[0] = inPtr[0];
-    }
-}
+template<typename in_t, typename idx_t>
+void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
+            unsigned dim) {
+    static const std::string src(lookup_cuh, lookup_cuh_len);
 
-template<typename in_t, typename idx_t, unsigned dim>
-void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
-            int nDims) {
     /* find which dimension has non-zero # of elements */
     int vDim = 0;
     for (int i = 0; i < 4; i++) {
@@ -92,8 +46,14 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
 
         dim3 blocks(blks, 1);
 
-        CUDA_LAUNCH((lookup1D<in_t, idx_t>), blocks, threads, out, in, indices,
-                    vDim);
+        auto lookup1d =
+            getKernel("cuda::lookup1D", src,
+                      {TemplateTypename<in_t>(), TemplateTypename<idx_t>()},
+                      {DefineValue(THREADS), DefineValue(THRD_LOAD)});
+
+        EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+        lookup1d(qArgs, out, in, indices, vDim);
     } else {
         const dim3 threads(THREADS_X, THREADS_Y);
 
@@ -107,11 +67,16 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices,
         blocks.z = divup(blocks.y, maxBlocksY);
         blocks.y = divup(blocks.y, blocks.z);
 
-        CUDA_LAUNCH((lookupND<in_t, idx_t, dim>), blocks, threads, out, in,
-                    indices, blks_x, blks_y);
-    }
+        auto lookupnd =
+            getKernel("cuda::lookupND", src,
+                      {TemplateTypename<in_t>(), TemplateTypename<idx_t>(),
+                       TemplateArg(dim)});
+        EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+        lookupnd(qArgs, out, in, indices, blks_x, blks_y);
+    }
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/lu_split.cuh b/src/backend/cuda/kernel/lu_split.cuh
new file mode 100644
index 0000000000..4299419382
--- /dev/null
+++ b/src/backend/cuda/kernel/lu_split.cuh
@@ -0,0 +1,64 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool same_dims>
+__global__ void luSplit(Param<T> lower, Param<T> upper, Param<T> in,
+                        const int blocksPerMatX, const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = blockIdx.y / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    T *d_l = lower.ptr;
+    T *d_u = upper.ptr;
+    T *d_i = in.ptr;
+
+    if (oz < in.dims[2] && ow < in.dims[3]) {
+        d_i = d_i + oz * in.strides[2] + ow * in.strides[3];
+        d_l = d_l + oz * lower.strides[2] + ow * lower.strides[3];
+        d_u = d_u + oz * upper.strides[2] + ow * upper.strides[3];
+
+        for (int oy = yy; oy < in.dims[1]; oy += incy) {
+            T *Yd_i = d_i + oy * in.strides[1];
+            T *Yd_l = d_l + oy * lower.strides[1];
+            T *Yd_u = d_u + oy * upper.strides[1];
+            for (int ox = xx; ox < in.dims[0]; ox += incx) {
+                if (ox > oy) {
+                    if (same_dims || oy < lower.dims[1]) Yd_l[ox] = Yd_i[ox];
+                    if (!same_dims || ox < upper.dims[0])
+                        Yd_u[ox] = scalar<T>(0);
+                } else if (oy > ox) {
+                    if (same_dims || oy < lower.dims[1])
+                        Yd_l[ox] = scalar<T>(0);
+                    if (!same_dims || ox < upper.dims[0]) Yd_u[ox] = Yd_i[ox];
+                } else if (ox == oy) {
+                    if (same_dims || oy < lower.dims[1])
+                        Yd_l[ox] = scalar<T>(1.0);
+                    if (!same_dims || ox < upper.dims[0]) Yd_u[ox] = Yd_i[ox];
+                }
+            }
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index f9b95437bb..50e67459d9 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -7,87 +7,45 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/lu_split_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 128;
-static const unsigned TILEY = 32;
-
-template<typename T, bool same_dims>
-__global__ void lu_split_kernel(Param<T> lower, Param<T> upper, Param<T> in,
-                                const int blocksPerMatX,
-                                const int blocksPerMatY) {
-    const int oz = blockIdx.x / blocksPerMatX;
-    const int ow = blockIdx.y / blocksPerMatY;
 
-    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-    const int blockIdx_y = blockIdx.y - ow * blocksPerMatY;
-
-    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
+template<typename T>
+void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 128;
+    constexpr unsigned TILEY = 32;
 
-    T *d_l = lower.ptr;
-    T *d_u = upper.ptr;
-    T *d_i = in.ptr;
+    static const std::string src(lu_split_cuh, lu_split_cuh_len);
 
-    if (oz < in.dims[2] && ow < in.dims[3]) {
-        d_i = d_i + oz * in.strides[2] + ow * in.strides[3];
-        d_l = d_l + oz * lower.strides[2] + ow * lower.strides[3];
-        d_u = d_u + oz * upper.strides[2] + ow * upper.strides[3];
+    const bool sameDims =
+        lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
-        for (int oy = yy; oy < in.dims[1]; oy += incy) {
-            T *Yd_i = d_i + oy * in.strides[1];
-            T *Yd_l = d_l + oy * lower.strides[1];
-            T *Yd_u = d_u + oy * upper.strides[1];
-            for (int ox = xx; ox < in.dims[0]; ox += incx) {
-                if (ox > oy) {
-                    if (same_dims || oy < lower.dims[1]) Yd_l[ox] = Yd_i[ox];
-                    if (!same_dims || ox < upper.dims[0])
-                        Yd_u[ox] = scalar<T>(0);
-                } else if (oy > ox) {
-                    if (same_dims || oy < lower.dims[1])
-                        Yd_l[ox] = scalar<T>(0);
-                    if (!same_dims || ox < upper.dims[0]) Yd_u[ox] = Yd_i[ox];
-                } else if (ox == oy) {
-                    if (same_dims || oy < lower.dims[1])
-                        Yd_l[ox] = scalar<T>(1.0);
-                    if (!same_dims || ox < upper.dims[0]) Yd_u[ox] = Yd_i[ox];
-                }
-            }
-        }
-    }
-}
+    auto luSplit = getKernel("cuda::luSplit", src,
+                             {TemplateTypename<T>(), TemplateArg(sameDims)});
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
     dim3 threads(TX, TY, 1);
 
     int blocksPerMatX = divup(in.dims[0], TILEX);
     int blocksPerMatY = divup(in.dims[1], TILEY);
     dim3 blocks(blocksPerMatX * in.dims[2], blocksPerMatY * in.dims[3], 1);
 
-    if (lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1]) {
-        CUDA_LAUNCH((lu_split_kernel<T, true>), blocks, threads, lower, upper,
-                    in, blocksPerMatX, blocksPerMatY);
-    } else {
-        CUDA_LAUNCH((lu_split_kernel<T, false>), blocks, threads, lower, upper,
-                    in, blocksPerMatX, blocksPerMatY);
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    luSplit(qArgs, lower, upper, in, blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/memcopy.cuh b/src/backend/cuda/kernel/memcopy.cuh
new file mode 100644
index 0000000000..f22a013279
--- /dev/null
+++ b/src/backend/cuda/kernel/memcopy.cuh
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void memcopy(Param<T> out, CParam<T> in, uint blocks_x,
+                        uint blocks_y) {
+    const int tidx = threadIdx.x;
+    const int tidy = threadIdx.y;
+
+    const int zid        = blockIdx.x / blocks_x;
+    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const int xid        = blockIdx_x * blockDim.x + tidx;
+
+    const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const int yid = blockIdx_y * blockDim.y + tidy;
+    // FIXME: Do more work per block
+    T *const optr = out.ptr + wid * out.strides[3] + zid * out.strides[2] +
+                    yid * out.strides[1];
+    const T *iptr = in.ptr + wid * in.strides[3] + zid * in.strides[2] +
+                    yid * in.strides[1];
+
+    int istride0 = in.strides[0];
+    if (xid < in.dims[0] && yid < in.dims[1] && zid < in.dims[2] &&
+        wid < in.dims[3]) {
+        optr[xid] = iptr[xid * istride0];
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 724cf0b6bd..be51b0fe62 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -6,59 +6,33 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #pragma once
 
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
-#include <common/half.hpp>
 #include <debug_cuda.hpp>
-#include <types.hpp>
+#include <dims_param.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/copy_cuh.hpp>
+#include <nvrtc_kernel_headers/memcopy_cuh.hpp>
 
 #include <algorithm>
+#include <string>
 
 namespace cuda {
 namespace kernel {
 
-typedef struct {
-    int dim[4];
-} dims_t;
-
-static const uint DIMX = 32;
-static const uint DIMY = 8;
+constexpr uint DIMX = 32;
+constexpr uint DIMY = 8;
 
 template<typename T>
-__global__ static void memcopy_kernel(T *out, const dims_t ostrides,
-                                      const T *in, const dims_t idims,
-                                      const dims_t istrides, uint blocks_x,
-                                      uint blocks_y) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int xid        = blockIdx_x * blockDim.x + tidx;
-
-    const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const int yid = blockIdx_y * blockDim.y + tidy;
-    // FIXME: Do more work per block
-    T *const optr = out + wid * ostrides.dim[3] + zid * ostrides.dim[2] +
-                    yid * ostrides.dim[1];
-    const T *iptr = in + wid * istrides.dim[3] + zid * istrides.dim[2] +
-                    yid * istrides.dim[1];
-
-    int istride0 = istrides.dim[0];
-    if (xid < idims.dim[0] && yid < idims.dim[1] && zid < idims.dim[2] &&
-        wid < idims.dim[3]) {
-        optr[xid] = iptr[xid * istride0];
-    }
-}
+void memcopy(Param<T> out, CParam<T> in, const dim_t ndims) {
+    static const std::string src(memcopy_cuh, memcopy_cuh_len);
+
+    auto memCopy = getKernel("cuda::memcopy", src, {TemplateTypename<T>()});
 
-template<typename T>
-void memcopy(T *out, const dim_t *ostrides, const T *in, const dim_t *idims,
-             const dim_t *istrides, uint ndims) {
     dim3 threads(DIMX, DIMY);
 
     if (ndims == 1) {
@@ -67,149 +41,28 @@ void memcopy(T *out, const dim_t *ostrides, const T *in, const dim_t *idims,
     }
 
     // FIXME: DO more work per block
-    uint blocks_x = divup(idims[0], threads.x);
-    uint blocks_y = divup(idims[1], threads.y);
-
-    dim3 blocks(blocks_x * idims[2], blocks_y * idims[3]);
+    uint blocks_x = divup(in.dims[0], threads.x);
+    uint blocks_y = divup(in.dims[1], threads.y);
 
-    dims_t _ostrides = {{(int)ostrides[0], (int)ostrides[1], (int)ostrides[2],
-                         (int)ostrides[3]}};
-    dims_t _istrides = {{(int)istrides[0], (int)istrides[1], (int)istrides[2],
-                         (int)istrides[3]}};
-    dims_t _idims    = {
-        {(int)idims[0], (int)idims[1], (int)idims[2], (int)idims[3]}};
+    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
 
     const int maxBlocksY =
         cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((memcopy_kernel<T>), blocks, threads, out, _ostrides, in,
-                _idims, _istrides, blocks_x, blocks_y);
-    POST_LAUNCH_CHECK();
-}
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-///////////// BEGIN - templated help functions for copy_kernel /////////////////
-template<typename T>
-__inline__ __device__ static T scale(T value, double factor) {
-    return (T)(double(value) * factor);
-}
-
-template<>
-__inline__ __device__ cfloat scale<cfloat>(cfloat value, double factor) {
-    return make_cuFloatComplex(value.x * factor, value.y * factor);
-}
-
-template<>
-__inline__ __device__ cdouble scale<cdouble>(cdouble value, double factor) {
-    return make_cuDoubleComplex(value.x * factor, value.y * factor);
-}
+    memCopy(qArgs, out, in, blocks_x, blocks_y);
 
-template<typename inType, typename outType>
-__inline__ __device__ outType convertType(inType value) {
-    return static_cast<outType>(value);
-}
-
-template<>
-__inline__ __device__ char convertType<compute_t<common::half>, char>(
-    compute_t<common::half> value) {
-    return (char)((short)value);
-}
-
-template<>
-__inline__ __device__ compute_t<common::half>
-convertType<char, compute_t<common::half>>(char value) {
-    return compute_t<common::half>(value);
-}
-
-template<>
-__inline__ __device__ cuda::uchar
-convertType<compute_t<common::half>, cuda::uchar>(
-    compute_t<common::half> value) {
-    return (cuda::uchar)((short)value);
-}
-
-template<>
-__inline__ __device__ compute_t<common::half>
-convertType<cuda::uchar, compute_t<common::half>>(cuda::uchar value) {
-    return compute_t<common::half>(value);
-}
-
-template<>
-__inline__ __device__ cdouble convertType<cfloat, cdouble>(cfloat value) {
-    return cuComplexFloatToDouble(value);
-}
-
-template<>
-__inline__ __device__ cfloat convertType<cdouble, cfloat>(cdouble value) {
-    return cuComplexDoubleToFloat(value);
-}
-
-#define OTHER_SPECIALIZATIONS(IN_T)                                        \
-    template<>                                                             \
-    __inline__ __device__ cfloat convertType<IN_T, cfloat>(IN_T value) {   \
-        return make_cuFloatComplex(static_cast<float>(value), 0.0f);       \
-    }                                                                      \
-                                                                           \
-    template<>                                                             \
-    __inline__ __device__ cdouble convertType<IN_T, cdouble>(IN_T value) { \
-        return make_cuDoubleComplex(static_cast<double>(value), 0.0);      \
-    }
-
-OTHER_SPECIALIZATIONS(float)
-OTHER_SPECIALIZATIONS(double)
-OTHER_SPECIALIZATIONS(int)
-OTHER_SPECIALIZATIONS(uint)
-OTHER_SPECIALIZATIONS(intl)
-OTHER_SPECIALIZATIONS(uintl)
-OTHER_SPECIALIZATIONS(short)
-OTHER_SPECIALIZATIONS(ushort)
-OTHER_SPECIALIZATIONS(uchar)
-OTHER_SPECIALIZATIONS(char)
-OTHER_SPECIALIZATIONS(common::half)
-
-//////////// END - templated help functions for copy_kernel ////////////////////
-
-template<typename inType, typename outType, bool same_dims>
-__global__ static void copy_kernel(Param<outType> dst, CParam<inType> src,
-                                   outType default_value, double factor,
-                                   const dims_t trgt, uint blk_x, uint blk_y) {
-    const uint lx = threadIdx.x;
-    const uint ly = threadIdx.y;
-
-    const uint gz         = blockIdx.x / blk_x;
-    const uint gw         = (blockIdx.y + (blockIdx.z * gridDim.y)) / blk_y;
-    const uint blockIdx_x = blockIdx.x - (blk_x)*gz;
-    const uint blockIdx_y =
-        (blockIdx.y + (blockIdx.z * gridDim.y)) - (blk_y)*gw;
-    const uint gx = blockIdx_x * blockDim.x + lx;
-    const uint gy = blockIdx_y * blockDim.y + ly;
-
-    const inType *in = src.ptr + (gw * src.strides[3] + gz * src.strides[2] +
-                                  gy * src.strides[1]);
-    outType *out     = dst.ptr + (gw * dst.strides[3] + gz * dst.strides[2] +
-                              gy * dst.strides[1]);
-
-    int istride0 = src.strides[0];
-    int ostride0 = dst.strides[0];
-
-    if (gy < dst.dims[1] && gz < dst.dims[2] && gw < dst.dims[3]) {
-        int loop_offset = blockDim.x * blk_x;
-        bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
-        for (int rep = gx; rep < dst.dims[0]; rep += loop_offset) {
-            outType temp = default_value;
-            if (same_dims || (rep < trgt.dim[0] && cond)) {
-                temp = convertType<inType, outType>(
-                    scale<inType>(in[rep * istride0], factor));
-            }
-            out[rep * ostride0] = temp;
-        }
-    }
+    POST_LAUNCH_CHECK();
 }
 
 template<typename inType, typename outType>
 void copy(Param<outType> dst, CParam<inType> src, int ndims,
           outType default_value, double factor) {
+    static const std::string source(copy_cuh, copy_cuh_len);
+
     dim3 threads(DIMX, DIMY);
     size_t local_size[] = {DIMX, DIMY};
 
@@ -237,12 +90,14 @@ void copy(Param<outType> dst, CParam<inType> src, int ndims,
         ((src.dims[0] == dst.dims[0]) && (src.dims[1] == dst.dims[1]) &&
          (src.dims[2] == dst.dims[2]) && (src.dims[3] == dst.dims[3]));
 
-    if (same_dims)
-        CUDA_LAUNCH((copy_kernel<inType, outType, true>), blocks, threads, dst,
-                    src, default_value, factor, trgt_dims, blk_x, blk_y);
-    else
-        CUDA_LAUNCH((copy_kernel<inType, outType, false>), blocks, threads, dst,
-                    src, default_value, factor, trgt_dims, blk_x, blk_y);
+    auto copy =
+        getKernel("cuda::copy", source,
+                  {TemplateTypename<inType>(), TemplateTypename<outType>(),
+                   TemplateArg(same_dims)});
+
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    copy(qArgs, dst, src, default_value, factor, trgt_dims, blk_x, blk_y);
 
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/kernel/range.cuh b/src/backend/cuda/kernel/range.cuh
new file mode 100644
index 0000000000..8e703b356f
--- /dev/null
+++ b/src/backend/cuda/kernel/range.cuh
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void range(Param<T> out, const int dim, const int blocksPerMatX,
+                      const int blocksPerMatY) {
+    const int mul0 = (dim == 0);
+    const int mul1 = (dim == 1);
+    const int mul2 = (dim == 2);
+    const int mul3 = (dim == 3);
+
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int ozw = ow * out.strides[3] + oz * out.strides[2];
+
+    int valZW = (mul3 * ow) + (mul2 * oz);
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        compute_t<T> valYZW = valZW + (mul1 * oy);
+        int oyzw            = ozw + oy * out.strides[1];
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            int oidx         = oyzw + ox;
+            compute_t<T> val = valYZW + static_cast<compute_t<T>>(ox * mul0);
+
+            out.ptr[oidx] = val;
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index f215f8df88..61fab80462 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -7,68 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/range_cuh.hpp>
 
-#include <type_traits>
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned RANGE_TX    = 32;
-static const unsigned RANGE_TY    = 8;
-static const unsigned RANGE_TILEX = 512;
-static const unsigned RANGE_TILEY = 32;
 
 template<typename T>
-__global__ void range_kernel(Param<T> out, const int dim,
-                             const int blocksPerMatX, const int blocksPerMatY) {
-    const int mul0 = (dim == 0);
-    const int mul1 = (dim == 1);
-    const int mul2 = (dim == 2);
-    const int mul3 = (dim == 3);
-
-    const int oz = blockIdx.x / blocksPerMatX;
-    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
-        ow >= out.dims[3])
-        return;
-
-    const int ozw = ow * out.strides[3] + oz * out.strides[2];
-
-    int valZW = (mul3 * ow) + (mul2 * oz);
-
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
+void range(Param<T> out, const int dim) {
+    constexpr unsigned RANGE_TX    = 32;
+    constexpr unsigned RANGE_TY    = 8;
+    constexpr unsigned RANGE_TILEX = 512;
+    constexpr unsigned RANGE_TILEY = 32;
 
-    for (int oy = yy; oy < out.dims[1]; oy += incy) {
-        compute_t<T> valYZW = valZW + (mul1 * oy);
-        int oyzw            = ozw + oy * out.strides[1];
-        for (int ox = xx; ox < out.dims[0]; ox += incx) {
-            int oidx         = oyzw + ox;
-            compute_t<T> val = valYZW + static_cast<compute_t<T>>(ox * mul0);
+    static const std::string source(range_cuh, range_cuh_len);
 
-            out.ptr[oidx] = val;
-        }
-    }
-}
+    auto range = getKernel("cuda::range", source, {TemplateTypename<T>()});
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void range(Param<T> out, const int dim) {
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
     int blocksPerMatX = divup(out.dims[0], RANGE_TILEX);
@@ -80,9 +42,11 @@ void range(Param<T> out, const int dim) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((range_kernel<T>), blocks, threads, out, dim, blocksPerMatX,
-                blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    range(qArgs, out, dim, blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/reorder.cuh b/src/backend/cuda/kernel/reorder.cuh
new file mode 100644
index 0000000000..617943cc87
--- /dev/null
+++ b/src/backend/cuda/kernel/reorder.cuh
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void reorder(Param<T> out, CParam<T> in, const int d0, const int d1,
+                        const int d2, const int d3, const int blocksPerMatX,
+                        const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    const int rdims[] = {d0, d1, d2, d3};
+    const int o_off   = ow * out.strides[3] + oz * out.strides[2];
+    int ids[4]        = {0};
+    ids[rdims[3]]     = ow;
+    ids[rdims[2]]     = oz;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        ids[rdims[1]] = oy;
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            ids[rdims[0]] = ox;
+
+            const int oIdx = o_off + oy * out.strides[1] + ox;
+
+            const int iIdx = ids[3] * in.strides[3] + ids[2] * in.strides[2] +
+                             ids[1] * in.strides[1] + ids[0];
+
+            out.ptr[oIdx] = in.ptr[iIdx];
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 918cab33d0..72a6839449 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -7,68 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/reorder_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 512;
-static const unsigned TILEY = 32;
 
 template<typename T>
-__global__ void reorder_kernel(Param<T> out, CParam<T> in, const int d0,
-                               const int d1, const int d2, const int d3,
-                               const int blocksPerMatX,
-                               const int blocksPerMatY) {
-    const int oz = blockIdx.x / blocksPerMatX;
-    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
-        ow >= out.dims[3])
-        return;
-
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
-
-    const int rdims[] = {d0, d1, d2, d3};
-    const int o_off   = ow * out.strides[3] + oz * out.strides[2];
-    int ids[4]        = {0};
-    ids[rdims[3]]     = ow;
-    ids[rdims[2]]     = oz;
-
-    for (int oy = yy; oy < out.dims[1]; oy += incy) {
-        ids[rdims[1]] = oy;
-        for (int ox = xx; ox < out.dims[0]; ox += incx) {
-            ids[rdims[0]] = ox;
-
-            const int oIdx = o_off + oy * out.strides[1] + ox;
+void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 512;
+    constexpr unsigned TILEY = 32;
 
-            const int iIdx = ids[3] * in.strides[3] + ids[2] * in.strides[2] +
-                             ids[1] * in.strides[1] + ids[0];
+    static const std::string source(reorder_cuh, reorder_cuh_len);
 
-            out.ptr[oIdx] = in.ptr[iIdx];
-        }
-    }
-}
+    auto reorder = getKernel("cuda::reorder", source, {TemplateTypename<T>()});
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     dim3 threads(TX, TY, 1);
 
     int blocksPerMatX = divup(out.dims[0], TILEX);
@@ -80,9 +42,12 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((reorder_kernel<T>), blocks, threads, out, in, rdims[0],
-                rdims[1], rdims[2], rdims[3], blocksPerMatX, blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    reorder(qArgs, out, in, rdims[0], rdims[1], rdims[2], rdims[3],
+            blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/select.cuh b/src/backend/cuda/kernel/select.cuh
new file mode 100644
index 0000000000..36ab8e4991
--- /dev/null
+++ b/src/backend/cuda/kernel/select.cuh
@@ -0,0 +1,101 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace cuda {
+
+int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4]) {
+    int off = 0;
+    off += ids[3] * (dims[3] == refdims[3]) * strides[3];
+    off += ids[2] * (dims[2] == refdims[2]) * strides[2];
+    off += ids[1] * (dims[1] == refdims[1]) * strides[1];
+    return off;
+}
+
+template<typename T, bool is_same>
+__global__ void select(Param<T> out, CParam<char> cond, CParam<T> a,
+                       CParam<T> b, int blk_x, int blk_y) {
+    const int idz = blockIdx.x / blk_x;
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
+
+    const int blockIdx_x = blockIdx.x - idz * blk_x;
+    const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
+
+    const int idy  = blockIdx_y * blockDim.y + threadIdx.y;
+    const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
+
+    if (idw >= out.dims[3] || idz >= out.dims[2] || idy >= out.dims[1]) {
+        return;
+    }
+
+    const int off =
+        idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
+    T *optr = out.ptr + off;
+
+    const T *aptr    = a.ptr;
+    const T *bptr    = b.ptr;
+    const char *cptr = cond.ptr;
+
+    int ids[] = {idx0, idy, idz, idw};
+    aptr += getOffset(a.dims, a.strides, out.dims, ids);
+    bptr += getOffset(b.dims, b.strides, out.dims, ids);
+    cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
+
+    if (is_same) {
+        for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+            optr[idx] = cptr[idx] ? aptr[idx] : bptr[idx];
+        }
+    } else {
+        bool csame = cond.dims[0] == out.dims[0];
+        bool asame = a.dims[0] == out.dims[0];
+        bool bsame = b.dims[0] == out.dims[0];
+        for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+            optr[idx] =
+                cptr[csame * idx] ? aptr[asame * idx] : bptr[bsame * idx];
+        }
+    }
+}
+
+template<typename T, bool flip>
+__global__ void selectScalar(Param<T> out, CParam<char> cond, CParam<T> a, T b,
+                             int blk_x, int blk_y) {
+    const int idz = blockIdx.x / blk_x;
+    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
+
+    const int blockIdx_x = blockIdx.x - idz * blk_x;
+    const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
+
+    const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
+    const int idy  = blockIdx_y * blockDim.y + threadIdx.y;
+
+    const int off =
+        idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
+
+    T *optr = out.ptr + off;
+
+    const T *aptr    = a.ptr;
+    const char *cptr = cond.ptr;
+
+    int ids[] = {idx0, idy, idz, idw};
+    aptr += getOffset(a.dims, a.strides, out.dims, ids);
+    cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
+
+    if (idw >= out.dims[3] || idz >= out.dims[2] || idy >= out.dims[1]) {
+        return;
+    }
+
+    for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
+        optr[idx] = ((cptr[idx]) ^ flip) ? aptr[idx] : b;
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 51442e80b3..a19b88e89b 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -7,70 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
 #include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/select_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
 
-static const uint DIMX  = 32;
-static const uint DIMY  = 8;
-static const int REPEAT = 64;
-
-__device__ __host__ int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims,
-                                  int ids[4]) {
-    int off = 0;
-    off += ids[3] * (dims[3] == refdims[3]) * strides[3];
-    off += ids[2] * (dims[2] == refdims[2]) * strides[2];
-    off += ids[1] * (dims[1] == refdims[1]) * strides[1];
-    return off;
-}
-
-template<typename T, bool is_same>
-__global__ void select_kernel(Param<T> out, CParam<char> cond, CParam<T> a,
-                              CParam<T> b, int blk_x, int blk_y) {
-    const int idz = blockIdx.x / blk_x;
-    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
+constexpr uint DIMX  = 32;
+constexpr uint DIMY  = 8;
+constexpr int REPEAT = 64;
 
-    const int blockIdx_x = blockIdx.x - idz * blk_x;
-    const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
-
-    const int idy  = blockIdx_y * blockDim.y + threadIdx.y;
-    const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
-
-    if (idw >= out.dims[3] || idz >= out.dims[2] || idy >= out.dims[1]) {
-        return;
-    }
-
-    const int off =
-        idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
-    T *optr = out.ptr + off;
-
-    const T *aptr    = a.ptr;
-    const T *bptr    = b.ptr;
-    const char *cptr = cond.ptr;
-
-    int ids[] = {idx0, idy, idz, idw};
-    aptr += getOffset(a.dims, a.strides, out.dims, ids);
-    bptr += getOffset(b.dims, b.strides, out.dims, ids);
-    cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
-
-    if (is_same) {
-        for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
-            optr[idx] = cptr[idx] ? aptr[idx] : bptr[idx];
-        }
-    } else {
-        bool csame = cond.dims[0] == out.dims[0];
-        bool asame = a.dims[0] == out.dims[0];
-        bool bsame = b.dims[0] == out.dims[0];
-        for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
-            optr[idx] =
-                cptr[csame * idx] ? aptr[asame * idx] : bptr[bsame * idx];
-        }
-    }
+static inline std::string selectSource() {
+    static const std::string src(select_cuh, select_cuh_len);
+    return src;
 }
 
 template<typename T>
@@ -79,6 +36,9 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     bool is_same = true;
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
+    auto select = getKernel("cuda::select", selectSource(),
+                            {TemplateTypename<T>(), TemplateArg(is_same)});
+
     dim3 threads(DIMX, DIMY);
 
     if (ndims == 1) {
@@ -96,51 +56,18 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    if (is_same) {
-        CUDA_LAUNCH((select_kernel<T, true>), blocks, threads, out, cond, a, b,
-                    blk_x, blk_y);
-    } else {
-        CUDA_LAUNCH((select_kernel<T, false>), blocks, threads, out, cond, a, b,
-                    blk_x, blk_y);
-    }
-}
-
-template<typename T, bool flip>
-__global__ void select_scalar_kernel(Param<T> out, CParam<char> cond,
-                                     CParam<T> a, T b, int blk_x, int blk_y) {
-    const int idz = blockIdx.x / blk_x;
-    const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blk_y;
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    const int blockIdx_x = blockIdx.x - idz * blk_x;
-    const int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idw * blk_y;
-
-    const int idx0 = blockIdx_x * blockDim.x + threadIdx.x;
-    const int idy  = blockIdx_y * blockDim.y + threadIdx.y;
-
-    const int off =
-        idw * out.strides[3] + idz * out.strides[2] + idy * out.strides[1];
-
-    T *optr = out.ptr + off;
-
-    const T *aptr    = a.ptr;
-    const char *cptr = cond.ptr;
-
-    int ids[] = {idx0, idy, idz, idw};
-    aptr += getOffset(a.dims, a.strides, out.dims, ids);
-    cptr += getOffset(cond.dims, cond.strides, out.dims, ids);
-
-    if (idw >= out.dims[3] || idz >= out.dims[2] || idy >= out.dims[1]) {
-        return;
-    }
-
-    for (int idx = idx0; idx < out.dims[0]; idx += blockDim.x * blk_x) {
-        optr[idx] = ((cptr[idx]) ^ flip) ? aptr[idx] : b;
-    }
+    select(qArgs, out, cond, a, b, blk_x, blk_y);
+    POST_LAUNCH_CHECK();
 }
 
-template<typename T, bool flip>
+template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
-                   int ndims) {
+                   int ndims, bool flip) {
+    auto selectScalar = getKernel("cuda::selectScalar", selectSource(),
+                                  {TemplateTypename<T>(), TemplateArg(flip)});
+
     dim3 threads(DIMX, DIMY);
 
     if (ndims == 1) {
@@ -153,8 +80,11 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
 
     dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
 
-    CUDA_LAUNCH((select_scalar_kernel<T, flip>), blocks, threads, out, cond, a,
-                scalar<T>(b), blk_x, blk_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    selectScalar(qArgs, out, cond, a, scalar<T>(b), blk_x, blk_y);
+    POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/sparse.cuh b/src/backend/cuda/kernel/sparse.cuh
new file mode 100644
index 0000000000..81ad141f26
--- /dev/null
+++ b/src/backend/cuda/kernel/sparse.cuh
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void coo2Dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+                          CParam<int> colIdx) {
+    int id = blockIdx.x * blockDim.x * reps + threadIdx.x;
+    if (id >= values.dims[0]) return;
+
+    for (int i = threadIdx.x; i <= reps * blockDim.x; i += blockDim.x) {
+        if (i >= values.dims[0]) return;
+
+        T v   = values.ptr[i];
+        int r = rowIdx.ptr[i];
+        int c = colIdx.ptr[i];
+
+        int offset = r + c * output.strides[1];
+
+        output.ptr[offset] = v;
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 299d82eaf3..18b6efba30 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -7,52 +7,38 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/sparse_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-static const int reps = 4;
 
-/////////////////////////////////////////////////////////////////////////////
-// Kernel to convert COO into Dense
-///////////////////////////////////////////////////////////////////////////
 template<typename T>
-__global__ void coo2dense_kernel(Param<T> output, CParam<T> values,
-                                 CParam<int> rowIdx, CParam<int> colIdx) {
-    int id = blockIdx.x * blockDim.x * reps + threadIdx.x;
-    if (id >= values.dims[0]) return;
-
-    for (int i = threadIdx.x; i <= reps * blockDim.x; i += blockDim.x) {
-        if (i >= values.dims[0]) return;
+void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
+               CParam<int> colIdx) {
+    constexpr int reps = 4;
 
-        T v   = values.ptr[i];
-        int r = rowIdx.ptr[i];
-        int c = colIdx.ptr[i];
+    static const std::string source(sparse_cuh, sparse_cuh_len);
 
-        int offset = r + c * output.strides[1];
+    auto coo2Dense = getKernel("cuda::coo2Dense", source,
+                               {TemplateTypename<T>()}, {DefineValue(reps)});
 
-        output.ptr[offset] = v;
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
-               CParam<int> colIdx) {
     dim3 threads(256, 1, 1);
 
     dim3 blocks(divup(output.dims[0], threads.x * reps), 1, 1);
 
-    CUDA_LAUNCH((coo2dense_kernel<T>), blocks, threads, output, values, rowIdx,
-                colIdx);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    coo2Dense(qArgs, output, values, rowIdx, colIdx);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/sparse_arith.cuh b/src/backend/cuda/kernel/sparse_arith.cuh
new file mode 100644
index 0000000000..a5d51bc8cc
--- /dev/null
+++ b/src/backend/cuda/kernel/sparse_arith.cuh
@@ -0,0 +1,154 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
+
+namespace cuda {
+
+template<typename T, af_op_t op>
+struct arith_op {
+    T operator()(T v1, T v2) { return T(0); }
+};
+
+template<typename T>
+struct arith_op<T, af_add_t> {
+    T operator()(T v1, T v2) { return v1 + v2; }
+};
+
+template<typename T>
+struct arith_op<T, af_sub_t> {
+    T operator()(T v1, T v2) { return v1 - v2; }
+};
+
+template<typename T>
+struct arith_op<T, af_mul_t> {
+    T operator()(T v1, T v2) { return v1 * v2; }
+};
+
+template<typename T>
+struct arith_op<T, af_div_t> {
+    T operator()(T v1, T v2) { return v1 / v2; }
+};
+
+// All Kernels follow below naming convention
+// <format>ArithXYZ where
+// <format> is either csr or coo
+// X - D for Dense output, S for sparse output
+// Y - D for Dense lhs, S for sparse lhs
+// Z - D for Dense rhs, S for sparse rhs
+
+template<typename T, af_op_t op>
+__global__ void csrArithDSD(Param<T> out, CParam<T> values, CParam<int> rowIdx,
+                            CParam<int> colIdx, CParam<T> rhs,
+                            const bool reverse) {
+    const int row = blockIdx.x * TY + threadIdx.y;
+
+    if (row >= out.dims[0]) return;
+
+    const int rowStartIdx = rowIdx.ptr[row];
+    const int rowEndIdx   = rowIdx.ptr[row + 1];
+
+    // Repeat loop until all values in the row are computed
+    for (int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
+        const int col = colIdx.ptr[idx];
+
+        if (row >= out.dims[0] || col >= out.dims[1]) continue;  // Bad indices
+
+        // Get Values
+        const T val  = values.ptr[idx];
+        const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+        const int offset = col * out.strides[1] + row;
+        if (reverse)
+            out.ptr[offset] = arith_op<T, op>()(rval, val);
+        else
+            out.ptr[offset] = arith_op<T, op>()(val, rval);
+    }
+}
+
+template<typename T, af_op_t op>
+__global__ void cooArithDSD(Param<T> out, CParam<T> values, CParam<int> rowIdx,
+                            CParam<int> colIdx, CParam<T> rhs,
+                            const bool reverse) {
+    const int idx = blockIdx.x * THREADS + threadIdx.x;
+
+    if (idx >= values.dims[0]) return;
+
+    const int row = rowIdx.ptr[idx];
+    const int col = colIdx.ptr[idx];
+
+    if (row >= out.dims[0] || col >= out.dims[1]) return;  // Bad indices
+
+    // Get Values
+    const T val  = values.ptr[idx];
+    const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+    const int offset = col * out.strides[1] + row;
+    if (reverse)
+        out.ptr[offset] = arith_op<T, op>()(rval, val);
+    else
+        out.ptr[offset] = arith_op<T, op>()(val, rval);
+}
+
+template<typename T, af_op_t op>
+__global__ void csrArithSSD(Param<T> values, Param<int> rowIdx,
+                            Param<int> colIdx, CParam<T> rhs,
+                            const bool reverse) {
+    const int row = blockIdx.x * TY + threadIdx.y;
+
+    if (row >= rhs.dims[0]) return;
+
+    const int rowStartIdx = rowIdx.ptr[row];
+    const int rowEndIdx   = rowIdx.ptr[row + 1];
+
+    // Repeat loop until all values in the row are computed
+    for (int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
+        const int col = colIdx.ptr[idx];
+
+        if (row >= rhs.dims[0] || col >= rhs.dims[1]) continue;  // Bad indices
+
+        // Get Values
+        const T val  = values.ptr[idx];
+        const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+        if (reverse)
+            values.ptr[idx] = arith_op<T, op>()(rval, val);
+        else
+            values.ptr[idx] = arith_op<T, op>()(val, rval);
+    }
+}
+
+template<typename T, af_op_t op>
+__global__ void cooArithSSD(Param<T> values, Param<int> rowIdx,
+                            Param<int> colIdx, CParam<T> rhs,
+                            const bool reverse) {
+    const int idx = blockIdx.x * THREADS + threadIdx.x;
+
+    if (idx >= values.dims[0]) return;
+
+    const int row = rowIdx.ptr[idx];
+    const int col = colIdx.ptr[idx];
+
+    if (row >= rhs.dims[0] || col >= rhs.dims[1]) return;  // Bad indices
+
+    // Get Values
+    const T val  = values.ptr[idx];
+    const T rval = rhs.ptr[col * rhs.strides[1] + row];
+
+    if (reverse)
+        values.ptr[idx] = arith_op<T, op>()(rval, val);
+    else
+        values.ptr[idx] = arith_op<T, op>()(val, rval);
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index ebc9b4ec37..9fbb3f2ce7 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -7,212 +7,104 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <memory.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/sparse_arith_cuh.hpp>
 #include <optypes.hpp>
-#include <platform.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
-static const unsigned TX      = 32;
-static const unsigned TY      = 8;
-static const unsigned THREADS = TX * TY;
-
-template<typename T, af_op_t op>
-struct arith_op {
-    __DH__ T operator()(T v1, T v2) { return T(0); }
-};
-
-template<typename T>
-struct arith_op<T, af_add_t> {
-    __device__ T operator()(T v1, T v2) { return v1 + v2; }
-};
-
-template<typename T>
-struct arith_op<T, af_sub_t> {
-    __device__ T operator()(T v1, T v2) { return v1 - v2; }
-};
-
-template<typename T>
-struct arith_op<T, af_mul_t> {
-    __device__ T operator()(T v1, T v2) { return v1 * v2; }
-};
-
-template<typename T>
-struct arith_op<T, af_div_t> {
-    __device__ T operator()(T v1, T v2) { return v1 / v2; }
-};
-
-template<typename T, af_op_t op>
-__global__ void sparseArithCSRKernel(Param<T> out, CParam<T> values,
-                                     CParam<int> rowIdx, CParam<int> colIdx,
-                                     CParam<T> rhs, const bool reverse) {
-    const int row = blockIdx.x * TY + threadIdx.y;
-
-    if (row >= out.dims[0]) return;
-
-    const int rowStartIdx = rowIdx.ptr[row];
-    const int rowEndIdx   = rowIdx.ptr[row + 1];
-
-    // Repeat loop until all values in the row are computed
-    for (int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
-        const int col = colIdx.ptr[idx];
-
-        if (row >= out.dims[0] || col >= out.dims[1]) continue;  // Bad indices
+constexpr unsigned TX      = 32;
+constexpr unsigned TY      = 8;
+constexpr unsigned THREADS = TX * TY;
 
-        // Get Values
-        const T val  = values.ptr[idx];
-        const T rval = rhs.ptr[col * rhs.strides[1] + row];
-
-        const int offset = col * out.strides[1] + row;
-        if (reverse)
-            out.ptr[offset] = arith_op<T, op>()(rval, val);
-        else
-            out.ptr[offset] = arith_op<T, op>()(val, rval);
-    }
-}
-
-template<typename T, af_op_t op>
-__global__ void sparseArithCOOKernel(Param<T> out, CParam<T> values,
-                                     CParam<int> rowIdx, CParam<int> colIdx,
-                                     CParam<T> rhs, const bool reverse) {
-    const int idx = blockIdx.x * THREADS + threadIdx.x;
-
-    if (idx >= values.dims[0]) return;
-
-    const int row = rowIdx.ptr[idx];
-    const int col = colIdx.ptr[idx];
-
-    if (row >= out.dims[0] || col >= out.dims[1]) return;  // Bad indices
-
-    // Get Values
-    const T val  = values.ptr[idx];
-    const T rval = rhs.ptr[col * rhs.strides[1] + row];
-
-    const int offset = col * out.strides[1] + row;
-    if (reverse)
-        out.ptr[offset] = arith_op<T, op>()(rval, val);
-    else
-        out.ptr[offset] = arith_op<T, op>()(val, rval);
+static inline std::string sparseArithSrc() {
+    static const std::string src(sparse_arith_cuh, sparse_arith_cuh_len);
+    return src;
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
+    auto csrArithDSD = getKernel("cuda::csrArithDSD", sparseArithSrc(),
+                                 {TemplateTypename<T>(), TemplateArg(op)},
+                                 {DefineValue(TX), DefineValue(TY)});
+
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(out.dims[0], TY), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads, out, values,
-                rowIdx, colIdx, rhs, reverse);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    csrArithDSD(qArgs, out, values, rowIdx, colIdx, rhs, reverse);
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
+    auto cooArithDSD = getKernel("cuda::cooArithDSD", sparseArithSrc(),
+                                 {TemplateTypename<T>(), TemplateArg(op)},
+                                 {DefineValue(THREADS)});
+
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(values.dims[0], THREADS), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads, out, values,
-                rowIdx, colIdx, rhs, reverse);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    cooArithDSD(qArgs, out, values, rowIdx, colIdx, rhs, reverse);
     POST_LAUNCH_CHECK();
 }
 
-template<typename T, af_op_t op>
-__global__ void sparseArithCSRKernel(Param<T> values, Param<int> rowIdx,
-                                     Param<int> colIdx, CParam<T> rhs,
-                                     const bool reverse) {
-    const int row = blockIdx.x * TY + threadIdx.y;
-
-    if (row >= rhs.dims[0]) return;
-
-    const int rowStartIdx = rowIdx.ptr[row];
-    const int rowEndIdx   = rowIdx.ptr[row + 1];
-
-    // Repeat loop until all values in the row are computed
-    for (int idx = rowStartIdx + threadIdx.x; idx < rowEndIdx; idx += TX) {
-        const int col = colIdx.ptr[idx];
-
-        if (row >= rhs.dims[0] || col >= rhs.dims[1]) continue;  // Bad indices
-
-        // Get Values
-        const T val  = values.ptr[idx];
-        const T rval = rhs.ptr[col * rhs.strides[1] + row];
-
-        if (reverse)
-            values.ptr[idx] = arith_op<T, op>()(rval, val);
-        else
-            values.ptr[idx] = arith_op<T, op>()(val, rval);
-    }
-}
-
-template<typename T, af_op_t op>
-__global__ void sparseArithCOOKernel(Param<T> values, Param<int> rowIdx,
-                                     Param<int> colIdx, CParam<T> rhs,
-                                     const bool reverse) {
-    const int idx = blockIdx.x * THREADS + threadIdx.x;
-
-    if (idx >= values.dims[0]) return;
-
-    const int row = rowIdx.ptr[idx];
-    const int col = colIdx.ptr[idx];
-
-    if (row >= rhs.dims[0] || col >= rhs.dims[1]) return;  // Bad indices
-
-    // Get Values
-    const T val  = values.ptr[idx];
-    const T rval = rhs.ptr[col * rhs.strides[1] + row];
-
-    if (reverse)
-        values.ptr[idx] = arith_op<T, op>()(rval, val);
-    else
-        values.ptr[idx] = arith_op<T, op>()(val, rval);
-}
-
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
+    auto csrArithSSD = getKernel("cuda::csrArithSSD", sparseArithSrc(),
+                                 {TemplateTypename<T>(), TemplateArg(op)},
+                                 {DefineValue(TX), DefineValue(TY)});
+
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(rhs.dims[0], TY), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCSRKernel<T, op>), blocks, threads, values, rowIdx,
-                colIdx, rhs, reverse);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    csrArithSSD(qArgs, values, rowIdx, colIdx, rhs, reverse);
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
+    auto cooArithSSD = getKernel("cuda::cooArithSSD", sparseArithSrc(),
+                                 {TemplateTypename<T>(), TemplateArg(op)},
+                                 {DefineValue(THREADS)});
+
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
 
     // No. of blocks = divup(no. of rows / threads.y). No blocks on Y
     dim3 blocks(divup(values.dims[0], THREADS), 1, 1);
 
-    CUDA_LAUNCH((sparseArithCOOKernel<T, op>), blocks, threads, values, rowIdx,
-                colIdx, rhs, reverse);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    cooArithSSD(qArgs, values, rowIdx, colIdx, rhs, reverse);
     POST_LAUNCH_CHECK();
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/susan.cuh b/src/backend/cuda/kernel/susan.cuh
new file mode 100644
index 0000000000..0f23264454
--- /dev/null
+++ b/src/backend/cuda/kernel/susan.cuh
@@ -0,0 +1,123 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <shared.hpp>
+
+namespace cuda {
+
+inline __device__ int max_val(const int x, const int y) { return max(x, y); }
+inline __device__ unsigned max_val(const unsigned x, const unsigned y) {
+    return max(x, y);
+}
+inline __device__ float max_val(const float x, const float y) {
+    return fmax(x, y);
+}
+inline __device__ double max_val(const double x, const double y) {
+    return fmax(x, y);
+}
+
+template<typename T>
+__global__ void susan(T* out, const T* in, const unsigned idim0,
+                      const unsigned idim1, const unsigned radius,
+                      const float t, const float g, const unsigned edge) {
+    const int rSqrd   = radius * radius;
+    const int windLen = 2 * radius + 1;
+    const int shrdLen = BLOCK_X + windLen - 1;
+
+    SharedMemory<T> shared;
+    T* shrdMem = shared.getPointer();
+
+    const unsigned lx = threadIdx.x;
+    const unsigned ly = threadIdx.y;
+    const unsigned gx = blockDim.x * blockIdx.x + lx + edge;
+    const unsigned gy = blockDim.y * blockIdx.y + ly + edge;
+
+    const unsigned nucleusIdx = (ly + radius) * shrdLen + lx + radius;
+    shrdMem[nucleusIdx] = gx < idim0 && gy < idim1 ? in[gy * idim0 + gx] : 0;
+    T m_0               = shrdMem[nucleusIdx];
+
+#pragma unroll
+    for (int b = ly, gy2 = gy; b < shrdLen; b += BLOCK_Y, gy2 += BLOCK_Y) {
+        int j = gy2 - radius;
+#pragma unroll
+        for (int a = lx, gx2 = gx; a < shrdLen; a += BLOCK_X, gx2 += BLOCK_X) {
+            int i = gx2 - radius;
+            shrdMem[b * shrdLen + a] =
+                (i < idim0 && j < idim1 ? in[j * idim0 + i] : m_0);
+        }
+    }
+    __syncthreads();
+
+    if (gx < idim0 - edge && gy < idim1 - edge) {
+        unsigned idx = gy * idim0 + gx;
+        float nM     = 0.0f;
+#pragma unroll
+        for (int p = 0; p < windLen; ++p) {
+#pragma unroll
+            for (int q = 0; q < windLen; ++q) {
+                int i = p - radius;
+                int j = q - radius;
+                int a = lx + radius + i;
+                int b = ly + radius + j;
+                if (i * i + j * j < rSqrd) {
+                    float c       = m_0;
+                    float m       = shrdMem[b * shrdLen + a];
+                    float exp_pow = powf((m - c) / t, 6.0f);
+                    float cM      = expf(-exp_pow);
+                    nM += cM;
+                }
+            }
+        }
+        out[idx] = nM < g ? g - nM : T(0);
+    }
+}
+
+template<typename T>
+__global__ void nonMax(float* x_out, float* y_out, float* resp_out,
+                       unsigned* count, const unsigned idim0,
+                       const unsigned idim1, const T* resp_in,
+                       const unsigned edge, const unsigned max_corners) {
+    // Responses on the border don't have 8-neighbors to compare, discard them
+    const unsigned r = edge + 1;
+
+    const unsigned gx = blockDim.x * blockIdx.x + threadIdx.x + r;
+    const unsigned gy = blockDim.y * blockIdx.y + threadIdx.y + r;
+
+    if (gx < idim0 - r && gy < idim1 - r) {
+        const T v = resp_in[gy * idim0 + gx];
+
+        // Find maximum neighborhood response
+        T max_v;
+        max_v = max_val(resp_in[(gy - 1) * idim0 + gx - 1],
+                        resp_in[gy * idim0 + gx - 1]);
+        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx - 1]);
+        max_v = max_val(max_v, resp_in[(gy - 1) * idim0 + gx]);
+        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx]);
+        max_v = max_val(max_v, resp_in[(gy - 1) * idim0 + gx + 1]);
+        max_v = max_val(max_v, resp_in[(gy)*idim0 + gx + 1]);
+        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx + 1]);
+
+        // Stores corner to {x,y,resp}_out if it's response is maximum compared
+        // to its 8-neighborhood and greater or equal minimum response
+        if (v > max_v) {
+            unsigned idx = atomicAdd(count, 1u);
+            if (idx < max_corners) {
+                x_out[idx]    = (float)gx;
+                y_out[idx]    = (float)gy;
+                resp_out[idx] = (float)v;
+            }
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index f9e57793e4..bca29ecbc7 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -7,146 +7,54 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <math.hpp>
-#include "config.hpp"
-#include "shared.hpp"
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/susan_cuh.hpp>
 
-namespace cuda {
+#include <string>
 
+namespace cuda {
 namespace kernel {
 
-static const unsigned BLOCK_X = 16;
-static const unsigned BLOCK_Y = 16;
-
-inline __device__ int max_val(const int x, const int y) { return max(x, y); }
-inline __device__ unsigned max_val(const unsigned x, const unsigned y) {
-    return max(x, y);
-}
-inline __device__ float max_val(const float x, const float y) {
-    return fmax(x, y);
-}
-inline __device__ double max_val(const double x, const double y) {
-    return fmax(x, y);
-}
+constexpr unsigned BLOCK_X = 16;
+constexpr unsigned BLOCK_Y = 16;
 
-template<typename T>
-__global__ void susanKernel(T* out, const T* in, const unsigned idim0,
-                            const unsigned idim1, const unsigned radius,
-                            const float t, const float g, const unsigned edge) {
-    const int rSqrd   = radius * radius;
-    const int windLen = 2 * radius + 1;
-    const int shrdLen = BLOCK_X + windLen - 1;
-
-    SharedMemory<T> shared;
-    T* shrdMem = shared.getPointer();
-
-    const unsigned lx = threadIdx.x;
-    const unsigned ly = threadIdx.y;
-    const unsigned gx = blockDim.x * blockIdx.x + lx + edge;
-    const unsigned gy = blockDim.y * blockIdx.y + ly + edge;
-
-    const unsigned nucleusIdx = (ly + radius) * shrdLen + lx + radius;
-    shrdMem[nucleusIdx] = gx < idim0 && gy < idim1 ? in[gy * idim0 + gx] : 0;
-    T m_0               = shrdMem[nucleusIdx];
-
-#pragma unroll
-    for (int b = ly, gy2 = gy; b < shrdLen; b += BLOCK_Y, gy2 += BLOCK_Y) {
-        int j = gy2 - radius;
-#pragma unroll
-        for (int a = lx, gx2 = gx; a < shrdLen; a += BLOCK_X, gx2 += BLOCK_X) {
-            int i = gx2 - radius;
-            shrdMem[b * shrdLen + a] =
-                (i < idim0 && j < idim1 ? in[j * idim0 + i] : m_0);
-        }
-    }
-    __syncthreads();
-
-    if (gx < idim0 - edge && gy < idim1 - edge) {
-        unsigned idx = gy * idim0 + gx;
-        float nM     = 0.0f;
-#pragma unroll
-        for (int p = 0; p < windLen; ++p) {
-#pragma unroll
-            for (int q = 0; q < windLen; ++q) {
-                int i = p - radius;
-                int j = q - radius;
-                int a = lx + radius + i;
-                int b = ly + radius + j;
-                if (i * i + j * j < rSqrd) {
-                    float c       = m_0;
-                    float m       = shrdMem[b * shrdLen + a];
-                    float exp_pow = powf((m - c) / t, 6.0f);
-                    float cM      = expf(-exp_pow);
-                    nM += cM;
-                }
-            }
-        }
-        out[idx] = nM < g ? g - nM : T(0);
-    }
+static inline std::string susanSource() {
+    static const std::string src(susan_cuh, susan_cuh_len);
+    return src;
 }
 
 template<typename T>
 void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
+    auto susan =
+        getKernel("cuda::susan", susanSource(), {TemplateTypename<T>()},
+                  {DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
+
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
                 divup(idim1 - edge * 2, BLOCK_Y));
     const size_t SMEM_SIZE =
         (BLOCK_X + 2 * radius) * (BLOCK_Y + 2 * radius) * sizeof(T);
 
-    CUDA_LAUNCH_SMEM((susanKernel<T>), blocks, threads, SMEM_SIZE, out, in,
-                     idim0, idim1, radius, t, g, edge);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream(), SMEM_SIZE);
 
+    susan(qArgs, out, in, idim0, idim1, radius, t, g, edge);
     POST_LAUNCH_CHECK();
 }
 
-template<typename T>
-__global__ void nonMaxKernel(float* x_out, float* y_out, float* resp_out,
-                             unsigned* count, const unsigned idim0,
-                             const unsigned idim1, const T* resp_in,
-                             const unsigned edge, const unsigned max_corners) {
-    // Responses on the border don't have 8-neighbors to compare, discard them
-    const unsigned r = edge + 1;
-
-    const unsigned gx = blockDim.x * blockIdx.x + threadIdx.x + r;
-    const unsigned gy = blockDim.y * blockIdx.y + threadIdx.y + r;
-
-    if (gx < idim0 - r && gy < idim1 - r) {
-        const T v = resp_in[gy * idim0 + gx];
-
-        // Find maximum neighborhood response
-        T max_v;
-        max_v = max_val(resp_in[(gy - 1) * idim0 + gx - 1],
-                        resp_in[gy * idim0 + gx - 1]);
-        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx - 1]);
-        max_v = max_val(max_v, resp_in[(gy - 1) * idim0 + gx]);
-        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx]);
-        max_v = max_val(max_v, resp_in[(gy - 1) * idim0 + gx + 1]);
-        max_v = max_val(max_v, resp_in[(gy)*idim0 + gx + 1]);
-        max_v = max_val(max_v, resp_in[(gy + 1) * idim0 + gx + 1]);
-
-        // Stores corner to {x,y,resp}_out if it's response is maximum compared
-        // to its 8-neighborhood and greater or equal minimum response
-        if (v > max_v) {
-            unsigned idx = atomicAdd(count, 1u);
-            if (idx < max_corners) {
-                x_out[idx]    = (float)gx;
-                y_out[idx]    = (float)gy;
-                resp_out[idx] = (float)v;
-            }
-        }
-    }
-}
-
 template<typename T>
 void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
+    auto nonMax =
+        getKernel("cuda::nonMax", susanSource(), {TemplateTypename<T>()});
+
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
                 divup(idim1 - edge * 2, BLOCK_Y));
@@ -155,10 +63,10 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
     CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
                                cuda::getActiveStream()));
 
-    CUDA_LAUNCH((nonMaxKernel<T>), blocks, threads, x_out, y_out, resp_out,
-                d_corners_found.get(), idim0, idim1, resp_in, edge,
-                max_corners);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    nonMax(qArgs, x_out, y_out, resp_out, d_corners_found.get(), idim0, idim1,
+           resp_in, edge, max_corners);
     POST_LAUNCH_CHECK();
 
     CUDA_CHECK(cudaMemcpyAsync(count, d_corners_found.get(), sizeof(unsigned),
@@ -168,5 +76,4 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/tile.cuh b/src/backend/cuda/kernel/tile.cuh
new file mode 100644
index 0000000000..dd5047c46a
--- /dev/null
+++ b/src/backend/cuda/kernel/tile.cuh
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace cuda {
+
+template<typename T>
+__global__ void tile(Param<T> out, CParam<T> in, const int blocksPerMatX,
+                     const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
+        ow >= out.dims[3])
+        return;
+
+    const int iz  = oz % in.dims[2];
+    const int iw  = ow % in.dims[3];
+    const int izw = iw * in.strides[3] + iz * in.strides[2];
+    const int ozw = ow * out.strides[3] + oz * out.strides[2];
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    for (int oy = yy; oy < out.dims[1]; oy += incy) {
+        const int iy = oy % in.dims[1];
+        for (int ox = xx; ox < out.dims[0]; ox += incx) {
+            const int ix = ox % in.dims[0];
+
+            int iMem = izw + iy * in.strides[1] + ix;
+            int oMem = ozw + oy * out.strides[1] + ox;
+
+            out.ptr[oMem] = in.ptr[iMem];
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index d9d9740cc7..16d6a30a06 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -7,63 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/tile_cuh.hpp>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 512;
-static const unsigned TILEY = 32;
 
 template<typename T>
-__global__ void tile_kernel(Param<T> out, CParam<T> in, const int blocksPerMatX,
-                            const int blocksPerMatY) {
-    const int oz = blockIdx.x / blocksPerMatX;
-    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (xx >= out.dims[0] || yy >= out.dims[1] || oz >= out.dims[2] ||
-        ow >= out.dims[3])
-        return;
-
-    const int iz  = oz % in.dims[2];
-    const int iw  = ow % in.dims[3];
-    const int izw = iw * in.strides[3] + iz * in.strides[2];
-    const int ozw = ow * out.strides[3] + oz * out.strides[2];
-
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
-
-    for (int oy = yy; oy < out.dims[1]; oy += incy) {
-        const int iy = oy % in.dims[1];
-        for (int ox = xx; ox < out.dims[0]; ox += incx) {
-            const int ix = ox % in.dims[0];
+void tile(Param<T> out, CParam<T> in) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 512;
+    constexpr unsigned TILEY = 32;
 
-            int iMem = izw + iy * in.strides[1] + ix;
-            int oMem = ozw + oy * out.strides[1] + ox;
+    static const std::string source(tile_cuh, tile_cuh_len);
 
-            out.ptr[oMem] = in.ptr[iMem];
-        }
-    }
-}
+    auto tile = getKernel("cuda::tile", source, {TemplateTypename<T>()});
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T>
-void tile(Param<T> out, CParam<T> in) {
     dim3 threads(TX, TY, 1);
 
     int blocksPerMatX = divup(out.dims[0], TILEX);
@@ -75,9 +40,11 @@ void tile(Param<T> out, CParam<T> in) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((tile_kernel<T>), blocks, threads, out, in, blocksPerMatX,
-                blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    tile(qArgs, out, in, blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/triangle.cuh b/src/backend/cuda/kernel/triangle.cuh
new file mode 100644
index 0000000000..44d3342f2b
--- /dev/null
+++ b/src/backend/cuda/kernel/triangle.cuh
@@ -0,0 +1,61 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool is_upper, bool is_unit_diag>
+__global__ void triangle(Param<T> r, CParam<T> in, const int blocksPerMatX,
+                         const int blocksPerMatY) {
+    const int oz = blockIdx.x / blocksPerMatX;
+    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
+
+    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
+    const int blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
+
+    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
+    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
+
+    const int incy = blocksPerMatY * blockDim.y;
+    const int incx = blocksPerMatX * blockDim.x;
+
+    T *d_r       = r.ptr;
+    const T *d_i = in.ptr;
+
+    const T one  = scalar<T>(1);
+    const T zero = scalar<T>(0);
+
+    if (oz < r.dims[2] && ow < r.dims[3]) {
+        d_i = d_i + oz * in.strides[2] + ow * in.strides[3];
+        d_r = d_r + oz * r.strides[2] + ow * r.strides[3];
+
+        for (int oy = yy; oy < r.dims[1]; oy += incy) {
+            const T *Yd_i = d_i + oy * in.strides[1];
+            T *Yd_r       = d_r + oy * r.strides[1];
+
+            for (int ox = xx; ox < r.dims[0]; ox += incx) {
+                bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
+                bool do_unit_diag = is_unit_diag && (ox == oy);
+                if (cond) {
+                    // Change made because of compute 53 failing tests
+                    Yd_r[ox] = do_unit_diag ? one : Yd_i[ox];
+                } else {
+                    Yd_r[ox] = zero;
+                }
+            }
+        }
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 73bd145623..ac6b827321 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -7,70 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/triangle_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 128;
-static const unsigned TILEY = 32;
-
-template<typename T, bool is_upper, bool is_unit_diag>
-__global__ void triangle_kernel(Param<T> r, CParam<T> in,
-                                const int blocksPerMatX,
-                                const int blocksPerMatY) {
-    const int oz = blockIdx.x / blocksPerMatX;
-    const int ow = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
-
-    const int blockIdx_x = blockIdx.x - oz * blocksPerMatX;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - ow * blocksPerMatY;
-
-    const int xx = threadIdx.x + blockIdx_x * blockDim.x;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
 
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
+template<typename T>
+void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 128;
+    constexpr unsigned TILEY = 32;
 
-    T *d_r       = r.ptr;
-    const T *d_i = in.ptr;
+    static const std::string source(triangle_cuh, triangle_cuh_len);
 
-    const T one  = scalar<T>(1);
-    const T zero = scalar<T>(0);
+    auto triangle = getKernel("cuda::triangle", source,
+                              {TemplateTypename<T>(), TemplateArg(is_upper),
+                               TemplateArg(is_unit_diag)});
 
-    if (oz < r.dims[2] && ow < r.dims[3]) {
-        d_i = d_i + oz * in.strides[2] + ow * in.strides[3];
-        d_r = d_r + oz * r.strides[2] + ow * r.strides[3];
-
-        for (int oy = yy; oy < r.dims[1]; oy += incy) {
-            const T *Yd_i = d_i + oy * in.strides[1];
-            T *Yd_r       = d_r + oy * r.strides[1];
-
-            for (int ox = xx; ox < r.dims[0]; ox += incx) {
-                bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
-                bool do_unit_diag = is_unit_diag && (ox == oy);
-                if (cond) {
-                    // Change made because of compute 53 failing tests
-                    Yd_r[ox] = do_unit_diag ? one : Yd_i[ox];
-                } else {
-                    Yd_r[ox] = zero;
-                }
-            }
-        }
-    }
-}
-
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Param<T> r, CParam<T> in) {
     dim3 threads(TX, TY, 1);
 
     int blocksPerMatX = divup(r.dims[0], TILEX);
@@ -82,10 +44,11 @@ void triangle(Param<T> r, CParam<T> in) {
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    CUDA_LAUNCH((triangle_kernel<T, is_upper, is_unit_diag>), blocks, threads,
-                r, in, blocksPerMatX, blocksPerMatY);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    triangle(qArgs, r, in, blocksPerMatX, blocksPerMatY);
     POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/unwrap.cuh b/src/backend/cuda/kernel/unwrap.cuh
new file mode 100644
index 0000000000..b8668356b0
--- /dev/null
+++ b/src/backend/cuda/kernel/unwrap.cuh
@@ -0,0 +1,81 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool is_column>
+__global__ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
+                       const int sx, const int sy, const int px, const int py,
+                       const int dx, const int dy, const int nx, int reps) {
+    // Compute channel and volume
+    const int w = (blockIdx.y + blockIdx.z * gridDim.y) / in.dims[2];
+    const int z = (blockIdx.y + blockIdx.z * gridDim.y) % in.dims[2];
+
+    if (w >= in.dims[3] || z >= in.dims[2]) return;
+
+    // Compute offset for channel and volume
+    const int cOut = w * out.strides[3] + z * out.strides[2];
+    const int cIn  = w * in.strides[3] + z * in.strides[2];
+
+    // Compute the output column index
+    const int id = is_column ? (blockIdx.x * blockDim.y + threadIdx.y)
+                             : (blockIdx.x * blockDim.x + threadIdx.x);
+
+    if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
+
+    // Compute the starting index of window in x and y of input
+    const int startx = (id % nx) * sx;
+    const int starty = (id / nx) * sy;
+
+    const int spx = startx - px;
+    const int spy = starty - py;
+
+    // Offset the global pointers to the respective starting indices
+    T* optr       = out.ptr + cOut + id * (is_column ? out.strides[1] : 1);
+    const T* iptr = in.ptr + cIn;
+
+    // Compute output index local to column
+    int outIdx        = is_column ? threadIdx.x : threadIdx.y;
+    const int oStride = is_column ? blockDim.x : blockDim.y;
+    bool cond         = (spx >= 0 && spx + (wx * dx) < in.dims[0] && spy >= 0 &&
+                 spy + (wy * dy) < in.dims[1]);
+
+    for (int i = 0; i < reps; i++) {
+        if (outIdx >= (is_column ? out.dims[0] : out.dims[1])) return;
+
+        // Compute input index local to window
+        const int x = outIdx % wx;
+        const int y = outIdx / wx;
+
+        const int xpad = spx + x * dx;
+        const int ypad = spy + y * dy;
+
+        // Copy
+        T val = scalar<T>(0.0);
+        if (cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 &&
+                     ypad < in.dims[1])) {
+            const int inIdx = ypad * in.strides[1] + xpad * in.strides[0];
+            val             = iptr[inIdx];
+        }
+
+        if (is_column) {
+            optr[outIdx] = val;
+        } else {
+            optr[outIdx * out.strides[1]] = val;
+        }
+        outIdx += oStride;
+    }
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 8b08ab0099..c9d4fb5418 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -7,87 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include "config.hpp"
+#include <kernel/config.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/unwrap_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
-///////////////////////////////////////////////////////////////////////////
-// Unwrap Kernel
-///////////////////////////////////////////////////////////////////////////
-template<typename T, bool is_column>
-__global__ void unwrap_kernel(Param<T> out, CParam<T> in, const int wx,
-                              const int wy, const int sx, const int sy,
-                              const int px, const int py, const int dx,
-                              const int dy, const int nx, int reps) {
-    // Compute channel and volume
-    const int w = (blockIdx.y + blockIdx.z * gridDim.y) / in.dims[2];
-    const int z = (blockIdx.y + blockIdx.z * gridDim.y) % in.dims[2];
-
-    if (w >= in.dims[3] || z >= in.dims[2]) return;
-
-    // Compute offset for channel and volume
-    const int cOut = w * out.strides[3] + z * out.strides[2];
-    const int cIn  = w * in.strides[3] + z * in.strides[2];
-
-    // Compute the output column index
-    const int id = is_column ? (blockIdx.x * blockDim.y + threadIdx.y)
-                             : (blockIdx.x * blockDim.x + threadIdx.x);
-
-    if (id >= (is_column ? out.dims[1] : out.dims[0])) return;
-
-    // Compute the starting index of window in x and y of input
-    const int startx = (id % nx) * sx;
-    const int starty = (id / nx) * sy;
-
-    const int spx = startx - px;
-    const int spy = starty - py;
-
-    // Offset the global pointers to the respective starting indices
-    T* optr       = out.ptr + cOut + id * (is_column ? out.strides[1] : 1);
-    const T* iptr = in.ptr + cIn;
-
-    // Compute output index local to column
-    int outIdx        = is_column ? threadIdx.x : threadIdx.y;
-    const int oStride = is_column ? blockDim.x : blockDim.y;
-    bool cond         = (spx >= 0 && spx + (wx * dx) < in.dims[0] && spy >= 0 &&
-                 spy + (wy * dy) < in.dims[1]);
-
-    for (int i = 0; i < reps; i++) {
-        if (outIdx >= (is_column ? out.dims[0] : out.dims[1])) return;
-
-        // Compute input index local to window
-        const int x = outIdx % wx;
-        const int y = outIdx / wx;
-
-        const int xpad = spx + x * dx;
-        const int ypad = spy + y * dy;
-
-        // Copy
-        T val = scalar<T>(0.0);
-        if (cond || (xpad >= 0 && xpad < in.dims[0] && ypad >= 0 &&
-                     ypad < in.dims[1])) {
-            const int inIdx = ypad * in.strides[1] + xpad * in.strides[0];
-            val             = iptr[inIdx];
-        }
-
-        if (is_column) {
-            optr[outIdx] = val;
-        } else {
-            optr[outIdx * out.strides[1]] = val;
-        }
-        outIdx += oStride;
-    }
-}
 
 template<typename T>
 void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
             const int sx, const int sy, const int px, const int py,
             const int dx, const int dy, const int nx, const bool is_column) {
+    static const std::string source(unwrap_cuh, unwrap_cuh_len);
+
+    auto unwrap = getKernel("cuda::unwrap", source,
+                            {TemplateTypename<T>(), TemplateArg(is_column)});
+
     dim3 threads, blocks;
     int reps;
 
@@ -110,13 +52,9 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    if (is_column) {
-        CUDA_LAUNCH((unwrap_kernel<T, true>), blocks, threads, out, in, wx, wy,
-                    sx, sy, px, py, dx, dy, nx, reps);
-    } else {
-        CUDA_LAUNCH((unwrap_kernel<T, false>), blocks, threads, out, in, wx, wy,
-                    sx, sy, px, py, dx, dy, nx, reps);
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    unwrap(qArgs, out, in, wx, wy, sx, sy, px, py, dx, dy, nx, reps);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/wrap.cuh b/src/backend/cuda/kernel/wrap.cuh
new file mode 100644
index 0000000000..20bb97a985
--- /dev/null
+++ b/src/backend/cuda/kernel/wrap.cuh
@@ -0,0 +1,75 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <math.hpp>
+
+namespace cuda {
+
+template<typename T, bool is_column>
+__global__ void wrap(Param<T> out, CParam<T> in, const int wx,
+                     const int wy, const int sx, const int sy,
+                     const int px, const int py, const int nx,
+                     const int ny, int blocks_x, int blocks_y) {
+    int idx2 = blockIdx.x / blocks_x;
+    int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+
+    int blockIdx_x = blockIdx.x - idx2 * blocks_x;
+    int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idx3 * blocks_y;
+
+    int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
+    int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
+
+    T *optr       = out.ptr + idx2 * out.strides[2] + idx3 * out.strides[3];
+    const T *iptr = in.ptr + idx2 * in.strides[2] + idx3 * in.strides[3];
+
+    if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1] || idx2 >= out.dims[2] ||
+        idx3 >= out.dims[3])
+        return;
+
+    int pidx0 = oidx0 + px;
+    int pidx1 = oidx1 + py;
+
+    // The last time a value appears in the unwrapped index is padded_index /
+    // stride Each previous index has the value appear "stride" locations
+    // earlier We work our way back from the last index
+
+    const int x_end = min(pidx0 / sx, nx - 1);
+    const int y_end = min(pidx1 / sy, ny - 1);
+
+    const int x_off = pidx0 - sx * x_end;
+    const int y_off = pidx1 - sy * y_end;
+
+    T val   = scalar<T>(0);
+    int idx = 1;
+
+    for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
+        int win_end_y = yo * wx;
+        int dim_end_y = y * nx;
+
+        for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
+            int win_end = win_end_y + xo;
+            int dim_end = dim_end_y + x;
+
+            if (is_column) {
+                idx = dim_end * in.strides[1] + win_end;
+            } else {
+                idx = dim_end + win_end * in.strides[1];
+            }
+
+            val = val + iptr[idx];
+        }
+    }
+
+    optr[oidx1 * out.strides[1] + oidx0] = val;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 036ea4310d..6fd1a1577d 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -7,81 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <math.hpp>
-#include "atomics.hpp"
-#include "config.hpp"
+#include <kernel/config.hpp>
+#include <nvrtc/cache.hpp>
+#include <nvrtc_kernel_headers/wrap_cuh.hpp>
+
+#include <string>
 
 namespace cuda {
 namespace kernel {
 
-///////////////////////////////////////////////////////////////////////////
-// Wrap Kernel
-///////////////////////////////////////////////////////////////////////////
-template<typename T, bool is_column>
-__global__ void wrap_kernel(Param<T> out, CParam<T> in, const int wx,
-                            const int wy, const int sx, const int sy,
-                            const int px, const int py, const int nx,
-                            const int ny, int blocks_x, int blocks_y) {
-    int idx2 = blockIdx.x / blocks_x;
-    int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-
-    int blockIdx_x = blockIdx.x - idx2 * blocks_x;
-    int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idx3 * blocks_y;
-
-    int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
-    int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
-
-    T *optr       = out.ptr + idx2 * out.strides[2] + idx3 * out.strides[3];
-    const T *iptr = in.ptr + idx2 * in.strides[2] + idx3 * in.strides[3];
-
-    if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1] || idx2 >= out.dims[2] ||
-        idx3 >= out.dims[3])
-        return;
-
-    int pidx0 = oidx0 + px;
-    int pidx1 = oidx1 + py;
-
-    // The last time a value appears in the unwrapped index is padded_index /
-    // stride Each previous index has the value appear "stride" locations
-    // earlier We work our way back from the last index
-
-    const int x_end = min(pidx0 / sx, nx - 1);
-    const int y_end = min(pidx1 / sy, ny - 1);
-
-    const int x_off = pidx0 - sx * x_end;
-    const int y_off = pidx1 - sy * y_end;
-
-    T val   = scalar<T>(0);
-    int idx = 1;
-
-    for (int y = y_end, yo = y_off; y >= 0 && yo < wy; yo += sy, y--) {
-        int win_end_y = yo * wx;
-        int dim_end_y = y * nx;
-
-        for (int x = x_end, xo = x_off; x >= 0 && xo < wx; xo += sx, x--) {
-            int win_end = win_end_y + xo;
-            int dim_end = dim_end_y + x;
-
-            if (is_column) {
-                idx = dim_end * in.strides[1] + win_end;
-            } else {
-                idx = dim_end + win_end * in.strides[1];
-            }
-
-            val = val + iptr[idx];
-        }
-    }
-
-    optr[oidx1 * out.strides[1] + oidx0] = val;
-}
-
 template<typename T>
 void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
           const int sy, const int px, const int py, const bool is_column) {
+    static const std::string source(wrap_cuh, wrap_cuh_len);
+
+    auto wrap = getKernel("cuda::wrap", source,
+                          {TemplateTypename<T>(), TemplateArg(is_column)});
+
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
     int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
 
@@ -96,13 +43,11 @@ void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    if (is_column) {
-        CUDA_LAUNCH((wrap_kernel<T, true>), blocks, threads, out, in, wx, wy,
-                    sx, sy, px, py, nx, ny, blocks_x, blocks_y);
-    } else {
-        CUDA_LAUNCH((wrap_kernel<T, false>), blocks, threads, out, in, wx, wy,
-                    sx, sy, px, py, nx, ny, blocks_x, blocks_y);
-    }
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    wrap(qArgs, out, in, wx, wy, sx, sy, px, py, nx, ny, blocks_x, blocks_y);
+    POST_LAUNCH_CHECK();
 }
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/lookup.cu b/src/backend/cuda/lookup.cpp
similarity index 86%
rename from src/backend/cuda/lookup.cu
rename to src/backend/cuda/lookup.cpp
index e8ca726bca..0aadb8dbcb 100644
--- a/src/backend/cuda/lookup.cu
+++ b/src/backend/cuda/lookup.cpp
@@ -30,20 +30,7 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
 
     dim_t nDims = iDims.ndims();
 
-    switch (dim) {
-        case 0:
-            kernel::lookup<in_t, idx_t, 0>(out, input, indices, nDims);
-            break;
-        case 1:
-            kernel::lookup<in_t, idx_t, 1>(out, input, indices, nDims);
-            break;
-        case 2:
-            kernel::lookup<in_t, idx_t, 2>(out, input, indices, nDims);
-            break;
-        case 3:
-            kernel::lookup<in_t, idx_t, 3>(out, input, indices, nDims);
-            break;
-    }
+    kernel::lookup<in_t, idx_t>(out, input, indices, nDims, dim);
 
     return out;
 }
diff --git a/src/backend/cuda/lu.cu b/src/backend/cuda/lu.cpp
similarity index 96%
rename from src/backend/cuda/lu.cu
rename to src/backend/cuda/lu.cpp
index bc89874e10..5740522ab2 100644
--- a/src/backend/cuda/lu.cu
+++ b/src/backend/cuda/lu.cpp
@@ -7,17 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <lu.hpp>
 
 #include <common/err_common.hpp>
 #include <copy.hpp>
 #include <cusolverDn.hpp>
-#include <math.hpp>
+#include <kernel/lu_split.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 
-#include <kernel/lu_split.hpp>
+#include <algorithm>
 
 namespace cuda {
 
@@ -103,8 +102,8 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
     pivot            = lu_inplace(in_copy);
 
     // SPLIT into lower and upper
-    dim4 ldims(M, min(M, N));
-    dim4 udims(min(M, N), N);
+    dim4 ldims(M, std::min(M, N));
+    dim4 udims(std::min(M, N), N);
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
     kernel::lu_split<T>(lower, upper, in_copy);
@@ -116,7 +115,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     int M      = iDims[0];
     int N      = iDims[1];
 
-    Array<int> pivot = createEmptyArray<int>(af::dim4(min(M, N), 1, 1, 1));
+    Array<int> pivot = createEmptyArray<int>(af::dim4(std::min(M, N), 1, 1, 1));
 
     int lwork = 0;
 
diff --git a/src/backend/cuda/minmax_op.hpp b/src/backend/cuda/minmax_op.hpp
new file mode 100644
index 0000000000..b04c45b246
--- /dev/null
+++ b/src/backend/cuda/minmax_op.hpp
@@ -0,0 +1,85 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <ops.hpp>
+
+namespace cuda {
+
+template<typename T>
+static double cabs(const T &in) {
+    return (double)in;
+}
+
+template<>
+double cabs<char>(const char &in) {
+    return (double)(in > 0);
+}
+
+template<>
+double cabs<cfloat>(const cfloat &in) {
+    return (double)abs(in);
+}
+
+template<>
+double cabs<cdouble>(const cdouble &in) {
+    return (double)abs(in);
+}
+
+template<typename T>
+static bool is_nan(const T &in) {
+    return in != in;
+}
+
+template<>
+bool is_nan<cfloat>(const cfloat &in) {
+    return in.x != in.x || in.y != in.y;
+}
+
+template<>
+bool is_nan<cdouble>(const cdouble &in) {
+    return in.x != in.x || in.y != in.y;
+}
+
+template<af_op_t op, typename T>
+struct MinMaxOp {
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = Binary<compute_t<T>, op>::init(); }
+    }
+
+    void operator()(T val, uint idx) {
+        if ((cabs(val) < cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx > m_idx))) {
+            m_val = val;
+            m_idx = idx;
+        }
+    }
+};
+
+template<typename T>
+struct MinMaxOp<af_max_t, T> {
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = Binary<T, af_max_t>::init(); }
+    }
+
+    void operator()(T val, uint idx) {
+        if ((cabs(val) > cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx <= m_idx))) {
+            m_val = val;
+            m_idx = idx;
+        }
+    }
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 2aec0fb4e7..bfcefd2664 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -10,19 +10,24 @@
 #include <nvrtc/cache.hpp>
 
 #include <common/Logger.hpp>
+#include <common/internal_enums.hpp>
 #include <device_manager.hpp>
 #include <kernel_headers/jit_cuh.hpp>
 #include <nvrtc_kernel_headers/Param_hpp.hpp>
+#include <nvrtc_kernel_headers/assign_kernel_param_hpp.hpp>
 #include <nvrtc_kernel_headers/backend_hpp.hpp>
 #include <nvrtc_kernel_headers/cuComplex_h.hpp>
 #include <nvrtc_kernel_headers/cuda_fp16_h.hpp>
 #include <nvrtc_kernel_headers/cuda_fp16_hpp.hpp>
 #include <nvrtc_kernel_headers/defines_h.hpp>
+#include <nvrtc_kernel_headers/dims_param_hpp.hpp>
 #include <nvrtc_kernel_headers/half_hpp.hpp>
+#include <nvrtc_kernel_headers/internal_enums_hpp.hpp>
 #include <nvrtc_kernel_headers/interp_hpp.hpp>
 #include <nvrtc_kernel_headers/kernel_type_hpp.hpp>
 #include <nvrtc_kernel_headers/math_constants_h.hpp>
 #include <nvrtc_kernel_headers/math_hpp.hpp>
+#include <nvrtc_kernel_headers/minmax_op_hpp.hpp>
 #include <nvrtc_kernel_headers/ops_hpp.hpp>
 #include <nvrtc_kernel_headers/optypes_hpp.hpp>
 #include <nvrtc_kernel_headers/shared_hpp.hpp>
@@ -101,6 +106,7 @@ using kc_t = map<string, Kernel>;
         char *logptr = log.get();                                       \
         nvrtcGetProgramLog(prog, logptr);                               \
         logptr[logSize] = '\x0';                                        \
+        puts(logptr);                                                   \
         AF_TRACE("NVRTC API Call: {}\nError Message: {}", #fn, logptr); \
         AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);                       \
     } while (0)
@@ -182,6 +188,10 @@ Kernel buildKernel(const int device, const string &nameExpr,
             "af/defines.h",
             "af/version.h",
             "utility.hpp",
+            "assign_kernel_param.hpp",
+            "dims_param.hpp",
+            "common/internal_enums.hpp",
+            "minmax_op.hpp",
         };
 
         constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
@@ -209,6 +219,10 @@ Kernel buildKernel(const int device, const string &nameExpr,
             string(defines_h, defines_h_len),
             string(version_h, version_h_len),
             string(utility_hpp, utility_hpp_len),
+            string(assign_kernel_param_hpp, assign_kernel_param_hpp_len),
+            string(dims_param_hpp, dims_param_hpp_len),
+            string(internal_enums_hpp, internal_enums_hpp_len),
+            string(minmax_op_hpp, minmax_op_hpp_len),
         }};
 
         static const char *headers[] = {
@@ -223,7 +237,9 @@ Kernel buildKernel(const int device, const string &nameExpr,
             sourceStrings[16].c_str(), sourceStrings[17].c_str(),
             sourceStrings[18].c_str(), sourceStrings[19].c_str(),
             sourceStrings[20].c_str(), sourceStrings[21].c_str(),
-            sourceStrings[22].c_str(),
+            sourceStrings[22].c_str(), sourceStrings[23].c_str(),
+            sourceStrings[24].c_str(), sourceStrings[25].c_str(),
+            sourceStrings[26].c_str(),
         };
         NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
                                        NumHeaders, headers, includeNames));
@@ -542,6 +558,22 @@ string toString(af_flux_function p) {
     return retVal;
 }
 
+template<>
+string toString(AF_BATCH_KIND p) {
+    const char *retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_BATCH_NONE);
+        CASE_STMT(AF_BATCH_LHS);
+        CASE_STMT(AF_BATCH_RHS);
+        CASE_STMT(AF_BATCH_SAME);
+        CASE_STMT(AF_BATCH_DIFF);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
 Kernel getKernel(const string &nameExpr, const string &source,
                  const vector<TemplateArg> &targs,
                  const vector<string> &compileOpts) {
diff --git a/src/backend/cuda/qr.cu b/src/backend/cuda/qr.cpp
similarity index 99%
rename from src/backend/cuda/qr.cu
rename to src/backend/cuda/qr.cpp
index 48bee4f150..f9a5ea8e1d 100644
--- a/src/backend/cuda/qr.cu
+++ b/src/backend/cuda/qr.cpp
@@ -140,7 +140,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     dim4 rdims(M, N);
     r = createEmptyArray<T>(rdims);
 
-    kernel::triangle<T, true, false>(r, in_copy);
+    kernel::triangle<T>(r, in_copy, true, false);
 
     int mn = max(M, N);
     dim4 qdims(M, mn);
diff --git a/src/backend/cuda/range.cu b/src/backend/cuda/range.cpp
similarity index 99%
rename from src/backend/cuda/range.cu
rename to src/backend/cuda/range.cpp
index 1a10e28ab4..8380241e2c 100644
--- a/src/backend/cuda/range.cu
+++ b/src/backend/cuda/range.cpp
@@ -6,11 +6,12 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #include <range.hpp>
-#include <kernel/range.hpp>
 
 #include <Array.hpp>
 #include <err_cuda.hpp>
+#include <kernel/range.hpp>
 #include <math.hpp>
 
 #include <stdexcept>
diff --git a/src/backend/cuda/reorder.cu b/src/backend/cuda/reorder.cpp
similarity index 99%
rename from src/backend/cuda/reorder.cu
rename to src/backend/cuda/reorder.cpp
index 2d449d8a54..99485516fe 100644
--- a/src/backend/cuda/reorder.cu
+++ b/src/backend/cuda/reorder.cpp
@@ -7,16 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <reorder.hpp>
+
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/reorder.hpp>
-#include <reorder.hpp>
+
 #include <stdexcept>
 
 using common::half;
 
 namespace cuda {
+
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
     const af::dim4 iDims = in.dims();
diff --git a/src/backend/cuda/select.cu b/src/backend/cuda/select.cpp
similarity index 96%
rename from src/backend/cuda/select.cu
rename to src/backend/cuda/select.cpp
index 764f1997cf..e23917ce3b 100644
--- a/src/backend/cuda/select.cu
+++ b/src/backend/cuda/select.cpp
@@ -6,13 +6,15 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
+#include <select.hpp>
+
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <common/jit/NaryNode.hpp>
 #include <err_cuda.hpp>
 #include <kernel/select.hpp>
 #include <scalar.hpp>
-#include <select.hpp>
 
 #include <memory>
 
@@ -23,6 +25,7 @@ using std::make_shared;
 using std::max;
 
 namespace cuda {
+
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
             const Array<T> &b) {
@@ -32,7 +35,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
                    const double &b) {
-    kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
+    kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
 }
 
 template<typename T>
@@ -80,7 +83,8 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
     } else {
-        if(a_node->getHeight() > max(b_node->getHeight(), cond_node->getHeight())) {
+        if (a_node->getHeight() >
+            max(b_node->getHeight(), cond_node->getHeight())) {
             a.eval();
         } else {
             cond.eval();
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cpp
similarity index 100%
rename from src/backend/cuda/sparse.cu
rename to src/backend/cuda/sparse.cpp
index f34458f8fe..b7186085ba 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/sparse.hpp>
 #include <sparse.hpp>
 
 #include <arith.hpp>
@@ -16,6 +15,7 @@
 #include <complex.hpp>
 #include <copy.hpp>
 #include <cusparse.hpp>
+#include <kernel/sparse.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cpp
similarity index 99%
rename from src/backend/cuda/sparse_arith.cu
rename to src/backend/cuda/sparse_arith.cpp
index 64f395173a..a4fe734224 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cpp
@@ -142,7 +142,7 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
 
-    auto desc = make_handle<cusparseMatDescr_t>();
+    auto desc        = make_handle<cusparseMatDescr_t>();
     const dim4 ldims = lhs.dims();
 
     const int M = ldims[0];
diff --git a/src/backend/cuda/susan.cu b/src/backend/cuda/susan.cpp
similarity index 96%
rename from src/backend/cuda/susan.cu
rename to src/backend/cuda/susan.cpp
index 17bea453fb..e905daf854 100644
--- a/src/backend/cuda/susan.cu
+++ b/src/backend/cuda/susan.cpp
@@ -7,12 +7,15 @@
  * http://Arrayfire.com/licenses/bsd-3-clause
  ********************************************************/
 
+#include <susan.hpp>
+
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/susan.hpp>
-#include <susan.hpp>
 #include <af/features.h>
 
+#include <algorithm>
+
 using af::features;
 
 namespace cuda {
@@ -39,7 +42,7 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                           &corners_found, idims[0], idims[1], resp.get(), edge,
                           corner_lim);
 
-    const unsigned corners_out = min(corners_found, corner_lim);
+    const unsigned corners_out = std::min(corners_found, corner_lim);
     if (corners_out == 0) {
         x_out    = createEmptyArray<float>(dim4());
         y_out    = createEmptyArray<float>(dim4());
diff --git a/src/backend/cuda/tile.cu b/src/backend/cuda/tile.cpp
similarity index 99%
rename from src/backend/cuda/tile.cu
rename to src/backend/cuda/tile.cpp
index 174b609864..9457688e73 100644
--- a/src/backend/cuda/tile.cu
+++ b/src/backend/cuda/tile.cpp
@@ -7,11 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <tile.hpp>
+
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/tile.hpp>
-#include <tile.hpp>
+
 #include <stdexcept>
 
 using common::half;
diff --git a/src/backend/cuda/triangle.cu b/src/backend/cuda/triangle.cpp
similarity index 97%
rename from src/backend/cuda/triangle.cu
rename to src/backend/cuda/triangle.cpp
index 81e75337e5..cd0c270df0 100644
--- a/src/backend/cuda/triangle.cu
+++ b/src/backend/cuda/triangle.cpp
@@ -6,12 +6,13 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <kernel/triangle.hpp>
+
 #include <triangle.hpp>
 
 #include <Array.hpp>
-#include <af/dim4.hpp>
 #include <common/half.hpp>
+#include <kernel/triangle.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 using common::half;
@@ -20,7 +21,7 @@ namespace cuda {
 
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in) {
-    kernel::triangle<T, is_upper, is_unit_diag>(out, in);
+    kernel::triangle<T>(out, in, is_upper, is_unit_diag);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>
@@ -56,4 +57,5 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
+
 }  // namespace cuda
diff --git a/src/backend/cuda/unwrap.cu b/src/backend/cuda/unwrap.cpp
similarity index 99%
rename from src/backend/cuda/unwrap.cu
rename to src/backend/cuda/unwrap.cpp
index 6722c65bcd..6b989b3641 100644
--- a/src/backend/cuda/unwrap.cu
+++ b/src/backend/cuda/unwrap.cpp
@@ -7,10 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <unwrap.hpp>
+
 #include <Array.hpp>
 #include <err_cuda.hpp>
 #include <kernel/unwrap.hpp>
-#include <unwrap.hpp>
+
 #include <stdexcept>
 
 namespace cuda {
diff --git a/src/backend/cuda/wrap.cu b/src/backend/cuda/wrap.cpp
similarity index 58%
rename from src/backend/cuda/wrap.cu
rename to src/backend/cuda/wrap.cpp
index aaf7d8f99f..1cf57e8bde 100644
--- a/src/backend/cuda/wrap.cu
+++ b/src/backend/cuda/wrap.cpp
@@ -7,33 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <wrap.hpp>
+
 #include <Array.hpp>
 #include <common/dispatch.hpp>
 #include <err_cuda.hpp>
 #include <kernel/wrap.hpp>
-#include <math.hpp>
-#include <wrap.hpp>
+
 #include <stdexcept>
 
 namespace cuda {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in,
-          const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy,
-          const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py,
-          const bool is_column) {
+void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
+          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
+          const dim_t px, const dim_t py, const bool is_column) {
     kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
-#define INSTANTIATE(T)                                          \
-    template void wrap<T> (Array<T> &out,  const Array<T> &in,  \
-                           const dim_t ox, const dim_t oy,      \
-                           const dim_t wx, const dim_t wy,      \
-                           const dim_t sx, const dim_t sy,      \
-                           const dim_t px, const dim_t py,      \
-                           const bool is_column);
+#define INSTANTIATE(T)                                                        \
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t ox, \
+                          const dim_t oy, const dim_t wx, const dim_t wy,     \
+                          const dim_t sx, const dim_t sy, const dim_t px,     \
+                          const dim_t py, const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)

From 0a6ee6321af17e2fce7f0e97a6260f625243401d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 12 Mar 2020 16:01:38 +0530
Subject: [PATCH 1866/2677] Fix deconvolution documentation with existing algos

---
 docs/details/image.dox | 36 +++++++++---------------------------
 1 file changed, 9 insertions(+), 27 deletions(-)

diff --git a/docs/details/image.dox b/docs/details/image.dox
index 554fc65db4..73ae3239eb 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -973,31 +973,25 @@ wide range of edges in images. A more in depth discussion on it can be found [he
 \defgroup image_func_iterative_deconv iterativeDeconv
 \ingroup imageflt_mat
 
-Iterative Deconvolution Algorithms
+\brief Iterative Deconvolution
 
 The following table shows the iteration update equations of the respective
 deconvolution algorithms.
 
 <table>
 <tr><th>Algorithm</th><th>Update Equation</th></tr>
-<tr>
-  <td>VanCittert</td>
-  <td>
-    \f$ \hat{I}_{n} = \hat{I}_{n-1} + \alpha * (I - P \otimes \hat{I}_{n-1}) \f$
-  </td>
-</tr>
-<tr>
-  <td>Jansson-VanCittert</td>
-  <td>
-    \f$ \hat{I}_{n} = \hat{I}_{n-1} + \alpha * (1 - \frac{2*| \hat{I}_{n-1}-\frac{B}{2} |}{B}) * (I - P \otimes \hat{I}_{n-1}) \f$
-  </td>
-</tr>
 <tr>
     <td>LandWeber</td>
     <td>
         \f$ \hat{I}_{n} = \hat{I}_{n-1} + \alpha * P^T \otimes (I - P \otimes \hat{I}_{n-1}) \f$
     </td>
 </tr>
+<tr>
+  <td>Richardson-Lucy</td>
+  <td>
+    \f$ \hat{I}_{n} = \hat{I}_{n-1} . ( \frac{I}{\hat{I}_{n-1} \otimes P} \otimes P^T ) \f$
+  </td>
+</tr>
 </table>
 
 where
@@ -1025,6 +1019,8 @@ to be in a fixed range, that should be done by the caller explicitly.
 \defgroup image_func_inverse_deconv inverseDeconv
 \ingroup imageflt_mat
 
+\brief Inverse Deconvolution
+
 Inverse deconvolution is an linear algorithm i.e. they are non-iterative in
 nature and usually faster than iterative deconvolution algorithms.
 
@@ -1044,20 +1040,6 @@ where
     - \f$ P_{\omega} \f$ is the point spread function in frequency domain
     - \f$ \gamma \f$ is a user defined regularization constant
 
-#### Weiner's Deconvolution Method:
-
-The update equation for this algorithm is as follows:
-
-\f[
-\hat{I}_{\omega} = \frac{ I_{\omega} * P^{*}_{\omega} } { |P_{\omega}|^2 + \frac{\gamma}{|I_{\omega}|^2 - \gamma} }
-\f]
-
-where
-    - \f$ I_{\omega} \f$ is the input/blurred image in frequency domain
-    - \f$ P_{\omega} \f$ is the point spread function in frequency domain
-    - \f$ \gamma \f$ is a user defined noise variance constant
-
-
 Inverse deconvolution function excepts \ref af::array of the following types only:
     - \ref f32
     - \ref s16

From c71c6cbd18c5cd8e58aa1ea8590d57736b114c63 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 4 Mar 2020 14:30:12 +0530
Subject: [PATCH 1867/2677] Move fast LUT in CUDA backend to texture memory

cuda::kernel::locate_features is the CUDA kernel that uses the fast
lookup table. Shared below is performance of the kernel using constant
memory vs texture memory. There is neglible to no difference between two
versions. Hence, shifted to texture memory LUT to reduce global constant
memory usage.

Performance using constant memory LUT
-------------------------------------

Time(%)    Time   Calls      Avg       Min       Max  Name
1.48%  101.09us      3  33.696us  32.385us  34.976us  void cuda::kernel::locate_features<float, int=9>
1.34%  91.713us      2  45.856us  45.792us  45.921us  void cuda::kernel::locate_features<double, int=9>
1.02%  69.505us      2  34.752us  34.400us  35.105us  void cuda::kernel::locate_features<unsigned int, int=9>
0.99%  67.456us      2  33.728us  32.768us  34.688us  void cuda::kernel::locate_features<int, int=9>
0.95%  65.186us      2  32.593us  31.201us  33.985us  void cuda::kernel::locate_features<short, int=9>
0.93%  63.874us      2  31.937us  30.817us  33.057us  void cuda::kernel::locate_features<unsigned short, int=9>

Performance using texture LUT
-----------------------------

Time(%)    Time   Calls      Avg       Min       Max  Name
1.45%  99.776us      3  33.258us  32.896us  33.504us  void cuda::kernel::locate_features<float, int=9>
1.33%  91.105us      2  45.552us  44.961us  46.144us  void cuda::kernel::locate_features<double, int=9>
1.02%  70.017us      2  35.008us  34.273us  35.744us  void cuda::kernel::locate_features<unsigned int, int=9>
0.97%  66.689us      2  33.344us  32.065us  34.624us  void cuda::kernel::locate_features<int, int=9>
0.95%  65.249us      2  32.624us  31.585us  33.664us  void cuda::kernel::locate_features<short, int=9>
0.95%  65.025us      2  32.512us  30.945us  34.080us  void cuda::kernel::locate_features<unsigned short, int=9>
---
 src/backend/cuda/CMakeLists.txt      |  1 +
 src/backend/cuda/LookupTable1D.hpp   | 66 ++++++++++++++++++++++++++++
 src/backend/cuda/fast.cu             | 18 +++++---
 src/backend/cuda/kernel/fast.hpp     | 49 +++++++++++++--------
 src/backend/cuda/kernel/fast_lut.hpp |  4 +-
 5 files changed, 113 insertions(+), 25 deletions(-)
 create mode 100644 src/backend/cuda/LookupTable1D.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8d49ebed8e..cc78ee73cd 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -375,6 +375,7 @@ cuda_add_library(afcuda
 
     Array.cpp
     Array.hpp
+    LookupTable1D.hpp
     Param.hpp
     anisotropic_diffusion.hpp
     approx.hpp
diff --git a/src/backend/cuda/LookupTable1D.hpp b/src/backend/cuda/LookupTable1D.hpp
new file mode 100644
index 0000000000..746607d5d5
--- /dev/null
+++ b/src/backend/cuda/LookupTable1D.hpp
@@ -0,0 +1,66 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <err_cuda.hpp>
+
+#include <type_traits>
+
+namespace cuda {
+
+template<typename T>
+class LookupTable1D {
+   public:
+    LookupTable1D()                          = delete;
+    LookupTable1D(const LookupTable1D& arg)  = delete;
+    LookupTable1D(const LookupTable1D&& arg) = delete;
+    LookupTable1D& operator=(const LookupTable1D& arg) = delete;
+    LookupTable1D& operator=(const LookupTable1D&& arg) = delete;
+
+    LookupTable1D(const Array<T>& lutArray) : mTexture(0), mData(lutArray) {
+        cudaResourceDesc resDesc;
+        memset(&resDesc, 0, sizeof(resDesc));
+
+        cudaTextureDesc texDesc;
+        memset(&texDesc, 0, sizeof(texDesc));
+
+        resDesc.resType                = cudaResourceTypeLinear;
+        resDesc.res.linear.devPtr      = mData.get();
+        resDesc.res.linear.desc.x      = sizeof(T) * 8;
+        resDesc.res.linear.sizeInBytes = mData.elements() * sizeof(T);
+
+        if (std::is_signed<T>::value)
+            resDesc.res.linear.desc.f = cudaChannelFormatKindSigned;
+        else if (std::is_unsigned<T>::value)
+            resDesc.res.linear.desc.f = cudaChannelFormatKindUnsigned;
+        else
+            resDesc.res.linear.desc.f = cudaChannelFormatKindFloat;
+
+        texDesc.readMode = cudaReadModeElementType;
+
+        CUDA_CHECK(
+            cudaCreateTextureObject(&mTexture, &resDesc, &texDesc, NULL));
+    }
+
+    ~LookupTable1D() {
+        if (mTexture) { cudaDestroyTextureObject(mTexture); }
+    }
+
+    cudaTextureObject_t get() const noexcept { return mTexture; }
+
+   private:
+    // Keep a copy so that ref count doesn't go down to zero when
+    // original Array<T> goes out of scope before LookupTable1D object does.
+    Array<T> mData;
+    cudaTextureObject_t mTexture;
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index 538a59b1e1..d4f00274bc 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -7,11 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_cuda.hpp>
+#include <fast.hpp>
+
+#include <LookupTable1D.hpp>
 #include <kernel/fast.hpp>
+#include <kernel/fast_lut.hpp>
 #include <af/dim4.hpp>
-#include <af/features.h>
+
+#include <mutex>
 
 using af::dim4;
 using af::features;
@@ -28,8 +31,14 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     float *d_y_out;
     float *d_score_out;
 
+    // TODO(pradeep) Figure out a better way to create lut Array only once
+    const Array<unsigned char> lut = createHostDataArray(
+        af::dim4(sizeof(FAST_LUT) / sizeof(unsigned char)), FAST_LUT);
+
+    LookupTable1D<unsigned char> fastLUT(lut);
+
     kernel::fast<T>(&nfeat, &d_x_out, &d_y_out, &d_score_out, in, thr,
-                    arc_length, non_max, feature_ratio, edge);
+                    arc_length, non_max, feature_ratio, edge, fastLUT);
 
     if (nfeat > 0) {
         const dim4 out_dims(nfeat);
@@ -38,7 +47,6 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
         y_out     = createDeviceDataArray<float>(out_dims, d_y_out);
         score_out = createDeviceDataArray<float>(out_dims, d_score_out);
     }
-
     return nfeat;
 }
 
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 340f3ca94b..e88722c7bc 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -9,14 +9,13 @@
 
 #pragma once
 
+#include <LookupTable1D.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
-#include <kernel/fast_lut.hpp>
+#include <kernel/shared.hpp>
 #include <math.hpp>
 #include <memory.hpp>
 #include <cub/block/block_reduce.cuh>
-#include "shared.hpp"
 
 namespace cuda {
 namespace kernel {
@@ -102,11 +101,16 @@ inline __device__ double abs_diff(const double x, const double y) {
     return fabs(x - y);
 }
 
+inline __device__ int lookup(const int n, cudaTextureObject_t tex) {
+    return (int)tex1Dfetch<unsigned char>(tex, n);
+}
+
 template<typename T, int arc_length>
 __device__ void locate_features_core(T *local_image, float *score,
                                      const unsigned idim0, const unsigned idim1,
                                      const float thr, int x, int y,
-                                     const unsigned edge) {
+                                     const unsigned edge,
+                                     cudaTextureObject_t luTable) {
     if (x >= idim0 - edge || y >= idim1 - edge) return;
 
     score[y * idim0 + x] = 0.f;
@@ -159,8 +163,8 @@ __device__ void locate_features_core(T *local_image, float *score,
 
     // Checks LUT to verify if there is a segment for which all pixels are much
     // brighter or much darker than central pixel p.
-    if ((int)FAST_LUT[bright] >= arc_length ||
-        (int)FAST_LUT[dark] >= arc_length)
+    if (lookup(bright, luTable) >= arc_length ||
+        lookup(dark, luTable) >= arc_length)
         score[x + idim0 * y] = max_val(s_bright, s_dark);
 }
 
@@ -187,7 +191,8 @@ __device__ void load_shared_image(CParam<T> in, T *local_image, unsigned ix,
 
 template<typename T, int arc_length>
 __global__ void locate_features(CParam<T> in, float *score, const float thr,
-                                const unsigned edge) {
+                                const unsigned edge,
+                                cudaTextureObject_t luTable) {
     unsigned ix = threadIdx.x;
     unsigned iy = threadIdx.y;
     unsigned bx = blockDim.x;
@@ -202,7 +207,7 @@ __global__ void locate_features(CParam<T> in, float *score, const float thr,
     load_shared_image(in, local_image_curr, ix, iy, bx, by, x, y, lx, ly, edge);
     __syncthreads();
     locate_features_core<T, arc_length>(local_image_curr, score, in.dims[0],
-                                        in.dims[1], thr, x, y, edge);
+                                        in.dims[1], thr, x, y, edge, luTable);
 }
 
 template<bool nonmax>
@@ -316,8 +321,8 @@ __global__ void get_features(float *x_out, float *y_out, float *score_out,
 template<typename T>
 void fast(unsigned *out_feat, float **x_out, float **y_out, float **score_out,
           const Array<T> &in, const float thr, const unsigned arc_length,
-          const unsigned nonmax, const float feature_ratio,
-          const unsigned edge) {
+          const unsigned nonmax, const float feature_ratio, const unsigned edge,
+          const LookupTable1D<unsigned char> &luTable) {
     dim4 indims             = in.dims();
     const unsigned max_feat = ceil(indims[0] * indims[1] * feature_ratio);
 
@@ -342,35 +347,43 @@ void fast(unsigned *out_feat, float **x_out, float **y_out, float **score_out,
     switch (arc_length) {
         case 9:
             CUDA_LAUNCH_SMEM((locate_features<T, 9>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 10:
             CUDA_LAUNCH_SMEM((locate_features<T, 10>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 11:
             CUDA_LAUNCH_SMEM((locate_features<T, 11>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 12:
             CUDA_LAUNCH_SMEM((locate_features<T, 12>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 13:
             CUDA_LAUNCH_SMEM((locate_features<T, 13>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 14:
             CUDA_LAUNCH_SMEM((locate_features<T, 14>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 15:
             CUDA_LAUNCH_SMEM((locate_features<T, 15>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
         case 16:
             CUDA_LAUNCH_SMEM((locate_features<T, 16>), blocks, threads,
-                             shared_size, in, d_score.get(), thr, edge);
+                             shared_size, in, d_score.get(), thr, edge,
+                             luTable.get());
             break;
     }
 
diff --git a/src/backend/cuda/kernel/fast_lut.hpp b/src/backend/cuda/kernel/fast_lut.hpp
index bbe926051d..5ac82a67c7 100644
--- a/src/backend/cuda/kernel/fast_lut.hpp
+++ b/src/backend/cuda/kernel/fast_lut.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2020, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -9,7 +9,7 @@
 
 #pragma once
 
-__constant__ unsigned char FAST_LUT[] = {
+unsigned char FAST_LUT[] = {
     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
     0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,

From 0d61c6f37374dcaec6f7ba4343910b7502c5be06 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 4 Mar 2020 17:18:26 +0530
Subject: [PATCH 1868/2677] Move orb LUT in CUDA backend to texture memory

cuda::kernel::extract_orb is the CUDA kernel that uses the orb
lookup table. Shared below is performance of the kernel using constant
memory vs texture memory. There is neglible to no difference between two
versions. Hence, shifted to texture memory LUT to reduce global constant
memory usage.

Performance using constant memory LUT
-------------------------------------

Time(%)  Time   Calls      Avg       Min       Max  Name

3.02%  292.26us   24  12.177us  11.360us  14.528us  void cuda::kernel::extract_orb<float>
2.16%  209.00us   16  13.062us  11.616us  16.033us  void cuda::kernel::extract_orb<double>

Performance using texture LUT
-----------------------------

Time(%)    Time   Calls      Avg       Min       Max  Name

2.84%  270.63us     24  11.276us  9.6970us  15.040us  void cuda::kernel::extract_orb<float>
2.20%  209.28us     16  13.080us  10.688us  16.960us  void cuda::kernel::extract_orb<double>
---
 src/backend/cuda/kernel/orb.hpp       | 41 ++++++++++++++-------------
 src/backend/cuda/kernel/orb_patch.hpp | 13 ++++-----
 src/backend/cuda/orb.cu               | 19 ++++++++++---
 3 files changed, 42 insertions(+), 31 deletions(-)

diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index cba1542400..15ef584bb0 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -9,28 +9,26 @@
 
 #pragma once
 
+#include <LookupTable1D.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <err_cuda.hpp>
+#include <kernel/convolve.hpp>
+#include <kernel/orb_patch.hpp>
+#include <kernel/range.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <memory.hpp>
 
-#include "convolve.hpp"
-#include "orb_patch.hpp"
-#include "range.hpp"
-#include "sort_by_key.hpp"
-
 using std::unique_ptr;
 using std::vector;
 
 namespace cuda {
-
 namespace kernel {
 
-static const int THREADS   = 256;
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
+constexpr int THREADS   = 256;
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
 
-static const float PI_VAL = 3.14159265358979323846f;
+constexpr float PI_VAL = 3.14159265358979323846f;
 
 template<typename T>
 void gaussian1D(T* out, const int dim, double sigma = 0.0) {
@@ -213,12 +211,17 @@ inline __device__ T get_pixel(unsigned x, unsigned y, const float ori,
     return image.ptr[x * image.dims[0] + y];
 }
 
+inline __device__ int lookup(const int n, cudaTextureObject_t tex) {
+    return tex1Dfetch<int>(tex, n);
+}
+
 template<typename T>
 __global__ void extract_orb(unsigned* desc_out, const unsigned n_feat,
                             float* x_in_out, float* y_in_out,
                             const float* ori_in, float* size_out,
                             CParam<T> image, const float scl,
-                            const unsigned patch_size) {
+                            const unsigned patch_size,
+                            cudaTextureObject_t luTable) {
     unsigned f = blockDim.x * blockIdx.x + threadIdx.x;
 
     if (f < n_feat) {
@@ -240,13 +243,13 @@ __global__ void extract_orb(unsigned* desc_out, const unsigned n_feat,
             for (unsigned j = 0; j < 16; j++) {
                 // Get position from distribution pattern and values of points
                 // p1 and p2
-                int dist_x = d_ref_pat[i * 16 * 4 + j * 4];
-                int dist_y = d_ref_pat[i * 16 * 4 + j * 4 + 1];
+                int dist_x = lookup(i * 16 * 4 + j * 4, luTable);
+                int dist_y = lookup(i * 16 * 4 + j * 4 + 1, luTable);
                 T p1       = get_pixel(x, y, ori, size, dist_x, dist_y, image,
                                  patch_size);
 
-                dist_x = d_ref_pat[i * 16 * 4 + j * 4 + 2];
-                dist_y = d_ref_pat[i * 16 * 4 + j * 4 + 3];
+                dist_x = lookup(i * 16 * 4 + j * 4 + 2, luTable);
+                dist_y = lookup(i * 16 * 4 + j * 4 + 3, luTable);
                 T p2   = get_pixel(x, y, ori, size, dist_x, dist_y, image,
                                  patch_size);
 
@@ -274,7 +277,8 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
          vector<float*>& d_y_pyr, vector<unsigned>& lvl_best,
          vector<float>& lvl_scl, vector<Array<T>>& img_pyr,
          const float fast_thr, const unsigned max_feat, const float scl_fctr,
-         const unsigned levels, const bool blur_img) {
+         const unsigned levels, const bool blur_img,
+         const LookupTable1D<int>& luTable) {
     UNUSED(fast_thr);
     UNUSED(max_feat);
     UNUSED(scl_fctr);
@@ -381,7 +385,7 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
         blocks  = dim3(divup(feat_pyr[i], threads.x), 1);
         CUDA_LAUNCH((extract_orb<T>), blocks, threads, d_desc_lvl, feat_pyr[i],
                     d_x_lvl, d_y_lvl, d_ori_lvl, d_size_lvl, img_pyr[i],
-                    lvl_scl[i], patch_size);
+                    lvl_scl[i], patch_size, luTable.get());
         POST_LAUNCH_CHECK();
 
         // Store results to pyramids
@@ -446,5 +450,4 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/orb_patch.hpp b/src/backend/cuda/kernel/orb_patch.hpp
index 68a45e9c97..6dfe3fb037 100644
--- a/src/backend/cuda/kernel/orb_patch.hpp
+++ b/src/backend/cuda/kernel/orb_patch.hpp
@@ -10,19 +10,18 @@
 #pragma once
 
 namespace cuda {
-namespace kernel {
 
 // Reference pattern, generated for a patch size of 31x31, as suggested by
 // original ORB paper
-#define REF_PAT_SIZE 31
-#define REF_PAT_SAMPLES 256
-#define REF_PAT_COORDS 4
-#define REF_PAT_LENGTH (REF_PAT_SAMPLES * REF_PAT_COORDS)
+constexpr unsigned REF_PAT_SIZE    = 31;
+constexpr unsigned REF_PAT_SAMPLES = 256;
+constexpr unsigned REF_PAT_COORDS  = 4;
+constexpr unsigned REF_PAT_LENGTH  = (REF_PAT_SAMPLES * REF_PAT_COORDS);
 
 // Current reference pattern was borrowed from OpenCV, a randomly generated
 // pattern will not achieve same quality as it must be trained like described
 // in sections 4.2 and 4.3 of the original ORB paper.
-__constant__ int d_ref_pat[REF_PAT_LENGTH] = {
+int d_ref_pat[REF_PAT_LENGTH] = {
     8,   -3,  9,   5,   4,   2,   7,   -12, -11, 9,   -8,  2,   7,   -12, 12,
     -13, 2,   -13, 2,   12,  1,   -7,  1,   6,   -2,  -10, -2,  -4,  -13, -13,
     -11, -8,  -13, -3,  -12, -9,  10,  4,   11,  9,   -13, -8,  -8,  -9,  -11,
@@ -94,6 +93,4 @@ __constant__ int d_ref_pat[REF_PAT_LENGTH] = {
     -1,  -6,  0,   -11,
 };
 
-}  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index ec8691a899..86e463ed42 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -7,13 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <orb.hpp>
+
 #include <Array.hpp>
+#include <LookupTable1D.hpp>
 #include <err_cuda.hpp>
 #include <fast_pyramid.hpp>
 #include <kernel/orb.hpp>
 #include <kernel/orb_patch.hpp>
 #include <af/dim4.hpp>
 
+#include <type_traits>
+
 using af::dim4;
 
 namespace cuda {
@@ -52,10 +57,16 @@ unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
     float *size_out;
     unsigned *desc_out;
 
-    kernel::orb<T, convAccT>(&nfeat_out, &x_out, &y_out, &score_out,
-                             &orientation_out, &size_out, &desc_out, feat_pyr,
-                             d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
-                             fast_thr, max_feat, scl_fctr, levels, blur_img);
+    // TODO(pradeep) Figure out a better way to create lut Array only once
+    const Array<int> lut = createHostDataArray(
+        af::dim4(sizeof(d_ref_pat) / sizeof(int)), d_ref_pat);
+
+    LookupTable1D<int> orbLUT(lut);
+
+    kernel::orb<T, convAccT>(
+        &nfeat_out, &x_out, &y_out, &score_out, &orientation_out, &size_out,
+        &desc_out, feat_pyr, d_x_pyr, d_y_pyr, lvl_best, lvl_scl, img_pyr,
+        fast_thr, max_feat, scl_fctr, levels, blur_img, orbLUT);
 
     if (nfeat_out > 0) {
         if (x_out == NULL || y_out == NULL || score_out == NULL ||

From 778bef89899a7837c37d39b1470a72de3daeb8de Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 10 Mar 2020 16:59:06 -0400
Subject: [PATCH 1869/2677] update project version to 3.8

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 62c70288d8..0200ec9e45 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -7,7 +7,7 @@
 
 cmake_minimum_required(VERSION 3.5)
 
-project(ArrayFire VERSION 3.7.0 LANGUAGES C CXX)
+project(ArrayFire VERSION 3.8.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 

From bc37e8a9942b9af7507c87926b25e21298760aeb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 16 Mar 2020 20:38:11 +0530
Subject: [PATCH 1870/2677] Use std::make_tuple instead of explicit tuple
 constructor

the explicit tuple constructor that is invoked due to the following
statement is throwing error with gcc 5.4.

```c++
std::tuple<int, int> test = {2, 3};
```

However, the code using std::make_tuple is working on gcc 5.4 also.
---
 src/backend/cuda/cudnnModule.hpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index 5d04e47f6c..19f234d70b 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -67,7 +67,9 @@ class cudnnModule {
     spdlog::logger* getLogger();
 
     /// Returns the version of the cuDNN loaded at runtime
-    std::tuple<int, int, int> getVersion() { return {major, minor, patch}; }
+    std::tuple<int, int, int> getVersion() {
+        return std::make_tuple(major, minor, patch);
+    }
 };
 
 cudnnModule& getCudnnPlugin();

From c145e90c6fd2ba212eb826577be26cd94e68d71a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 16 Mar 2020 18:12:16 +0530
Subject: [PATCH 1871/2677] Remove debug puts left over accidentally

---
 src/backend/cuda/nvrtc/cache.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index bfcefd2664..e2cbdb37c6 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -105,8 +105,7 @@ using kc_t = map<string, Kernel>;
         unique_ptr<char[]> log(new char[logSize + 1]);                  \
         char *logptr = log.get();                                       \
         nvrtcGetProgramLog(prog, logptr);                               \
-        logptr[logSize] = '\x0';                                        \
-        puts(logptr);                                                   \
+        logptr[logSize] = '\0';                                         \
         AF_TRACE("NVRTC API Call: {}\nError Message: {}", #fn, logptr); \
         AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);                       \
     } while (0)

From 83e9aa7ab3c2c0778f7db4d9a6153b955ed22256 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Mar 2020 22:22:40 +0530
Subject: [PATCH 1872/2677] Move C API documentation into single doxygen group

Now that we have class based documentation available, it no longer makes
sense to retain the old approach. Hence, a new c_api_mat group is added
in the place of method_mat group that contains only the C API used to
manage af_arrays.
---
 docs/pages/README.md          |  6 ++--
 docs/pages/getting_started.md |  2 +-
 include/af/array.h            | 65 ++++-------------------------------
 include/af/device.h           |  6 ++--
 include/arrayfire.h           |  9 ++---
 5 files changed, 17 insertions(+), 71 deletions(-)

diff --git a/docs/pages/README.md b/docs/pages/README.md
index 8a395a70af..d20dc6b246 100644
--- a/docs/pages/README.md
+++ b/docs/pages/README.md
@@ -17,7 +17,7 @@ or Linux or download it from source:
 
 ## Easy to use
 
-The [array](\ref construct_mat) object is beautifully simple.
+The [array](\ref af::array) object is beautifully simple.
 
 Array-based notation effectively expresses computational algorithms in
 readable math-resembling notation. You _do not_ need expertise in
@@ -92,9 +92,9 @@ Read more about how [ArrayFire JIT](http://arrayfire.com/performance-of-arrayfir
 
 ## Simple Example
 
-Here's a live example to let you see ArrayFire code. You create [arrays](\ref construct_mat)
+Here's a live example to let you see ArrayFire code. You create [arrays](\ref af::array)
 which reside on CUDA or OpenCL devices. Then you can use
-[ArrayFire functions](modules.htm) on those [arrays](\ref construct_mat).
+[ArrayFire functions](modules.htm) on those [arrays](\ref af::array).
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 // sample 40 million points on the GPU
diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index 5db2f67150..d10142269b 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -48,7 +48,7 @@ which cannot freed until the `array` object goes out of scope. As device memory
 allocation can be expensive, ArrayFire also includes a memory manager which
 will re-use device memory whenever possible.
 
-Arrays can be created using one of the [array constructors](\ref #construct_mat).
+Arrays can be created using one of the [array constructors](\ref af::array).
 Below we show how to create 1D, 2D, and 3D arrays with uninitialized values:
 
 \snippet test/getting_started.cpp ex_getting_started_constructors
diff --git a/include/af/array.h b/include/af/array.h
index 72869a7d89..282b7aeb8c 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -163,11 +163,6 @@ namespace af
             const array::array_proxy slices(int first, int last) const;
         };
 
-        //array(af_array in, const array *par, af_index_t seqs[4]);
-        /**
-            \ingroup construct_mat
-            @{
-        */
         /**
             Create an uninitialized array (no data, undefined size)
 
@@ -553,15 +548,6 @@ namespace af
                 const dim_t dim0, const dim_t dim1 = 1,
                 const dim_t dim2 = 1, const dim_t dim3 = 1);
 
-        /**
-            @}
-        */
-
-        /**
-           \ingroup method_mat
-           @{
-        */
-
         /**
            get the \ref af_array handle
         */
@@ -720,22 +706,12 @@ namespace af
         template<typename T> T scalar() const;
 
         /**
-           @}
-        */
-
-
-        /**
-           Get the device pointer from the array and lock the buffer in memory manager.
-           @{
+           \brief Get the device pointer from the array and lock the buffer in memory manager.
 
            The device memory returned by this function is not freed until unlock() is called.
 
-           \ingroup device_mat
         */
         template<typename T> T* device() const;
-        /**
-           @}
-        */
 
         // INDEXING
         // Single arguments
@@ -884,7 +860,6 @@ namespace af
         ///
         ///  \param[in] type is the desired type(f32, s64, etc.)
         /// \returns an array with the type specified by \p type
-        /// \ingroup method_mat
         const array as(dtype type) const;
 
 
@@ -893,12 +868,10 @@ namespace af
         /// \brief Get the transposed the array
         ///
         /// \returns Transposed matrix
-        /// \ingroup method_mat
         array T() const;
         /// \brief Get the conjugate-transpose of the current array
         ///
         /// \returns conjugate-transpose matrix
-        /// \ingroup method_mat
         array H() const;
 
 #define ASSIGN_(OP2)                                                                      \
@@ -1366,7 +1339,7 @@ namespace af
 
     /// Evaluate an expression (nonblocking).
     /**
-       \ingroup method_mat
+       \ingroup data_mat
        @{
     */
     inline array &eval(array &a) { a.eval(); return a; }
@@ -1432,10 +1405,6 @@ namespace af
 #if AF_API_VERSION >= 37
 
     /// Evaluate an expression (nonblocking).
-    /**
-       \ingroup method_mat
-       @{
-    */
     inline const array &eval(const array &a) { a.eval(); return a; }
 
 #if AF_COMPILER_CXX_VARIADIC_TEMPLATES
@@ -1506,14 +1475,14 @@ extern "C" {
 #endif
 
     /**
-       \ingroup construct_mat
+       \ingroup c_api_mat
        @{
     */
 
     /**
        Create an \ref af_array handle initialized with user defined data
 
-       This function will create an \ref af_array handle from the memory provided in \p data
+       This function will create an \ref af_array handle from the memory provided in \p data.
 
        \param[out]  arr The pointer to the returned object.
        \param[in]   data The data which will be loaded into the array
@@ -1528,6 +1497,9 @@ extern "C" {
     /**
        Create af_array handle
 
+       To release the memory allocated by this call you would have to
+       call \ref af_release_array once your use of this \ref af_array is complete.
+
        \param[out]  arr The pointer to the retured object.
        \param[in]   ndims The number of dimensions read from the \p dims parameter
        \param[in]   dims A C pointer with \p ndims elements. Each value represents the size of that dimension
@@ -1538,13 +1510,6 @@ extern "C" {
     AFAPI af_err af_create_handle(af_array *arr, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
-    @}
-    */
-
-    /**
-       \ingroup method_mat
-       @{
-
        Deep copy an array to another
     */
     AFAPI af_err af_copy_array(af_array *arr, const af_array in);
@@ -1575,25 +1540,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \ingroup method_mat
-       @{
-
        Get the reference count of \ref af_array
     */
     AFAPI af_err af_get_data_ref_count(int *use_count, const af_array in);
 #endif
 
-
     /**
        Evaluate any expressions in the Array
     */
     AFAPI af_err af_eval(af_array in);
 
-    /**
-      @}
-    */
-
-
 #if AF_API_VERSION >= 34
     /**
        Evaluate multiple arrays together
@@ -1614,14 +1570,7 @@ extern "C" {
     */
     AFAPI af_err af_get_manual_eval_flag(bool *flag);
 #endif
-    /**
-      @}
-    */
 
-    /**
-        \ingroup method_mat
-        @{
-    */
     /**
         \brief Get the total number of elements across all dimensions of the array
 
diff --git a/include/af/device.h b/include/af/device.h
index 6c7db03e0c..41f336cf60 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -351,7 +351,7 @@ extern "C" {
 
     /**
        Create array from device memory
-       \ingroup construct_mat
+       \ingroup c_api_mat
     */
     AFAPI af_err af_device_array(af_array *arr, void *data, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
@@ -380,9 +380,9 @@ extern "C" {
        \param [in] msg A message to print before the table
        \param [in] device_id print the memory info of the specified device.
        -1 signifies active device.
-      
+
        \returns AF_SUCCESS if successful
-      
+
        \ingroup device_func_mem
     */
     AFAPI af_err af_print_mem_info(const char *msg, const int device_id);
diff --git a/include/arrayfire.h b/include/arrayfire.h
index d3b041001d..ed331aeb08 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -31,18 +31,15 @@
 
       Array constructors, random number generation, transpose, indexing, etc.
 
-      @defgroup construct_mat Constructors of array class
-      Construct an array object
-
-      @defgroup method_mat Methods of array class
-      Get information about the array object
-
       @defgroup device_mat Managing devices in ArrayFire
       getting device pointer, allocating and freeing memory
 
       @defgroup data_mat Functions to create arrays.
       constant, random, range, etc.
 
+      @defgroup c_api_mat C API to manage arrays
+      Create, release, copy, fetch-properties of \ref af_array
+
       @defgroup index_mat Assignment & Indexing operation on arrays
       Access sub regions of an array object
 

From 10a82db1809ffe4aff86f850681ad48c92206d7c Mon Sep 17 00:00:00 2001
From: padentomasello <pdtomasello@gmail.com>
Date: Mon, 23 Mar 2020 19:37:28 -0700
Subject: [PATCH 1873/2677] Fix memory pressure in DefaultMemoryManager (#2801)

* Fix getMemoryPressure comparison, and revert DefaultMemoryManager GC comparison.

Co-authored-by: Paden Tomasello <padentomasello@devfair049.maas>
---
 src/backend/common/DefaultMemoryManager.cpp | 3 ++-
 src/backend/cpu/queue.hpp                   | 3 ++-
 src/backend/cuda/Array.cpp                  | 2 +-
 src/backend/opencl/Array.cpp                | 2 +-
 4 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 2f5ea29226..a7a37a3dee 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -160,7 +160,8 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
         if (!this->debug_mode) {
             // FIXME: Add better checks for garbage collection
             // Perhaps look at total memory available as a metric
-            if (getMemoryPressure() > getMemoryPressureThreshold()) {
+            if (current.lock_bytes >= current.max_bytes ||
+                current.total_buffers >= this->max_buffers) {
                 this->signalMemoryCleanup();
             }
 
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 9290426810..213ccda892 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -69,7 +69,8 @@ class queue {
 #ifndef NDEBUG
         sync();
 #else
-        if (getMemoryPressure() > getMemoryPressureThreshold() || count >= 25) {
+        if (getMemoryPressure() >= getMemoryPressureThreshold() ||
+            count >= 25) {
             sync();
         }
 #endif
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 9fba97aa65..5a691af785 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -248,7 +248,7 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
     // A lightweight check based on the height of the node. This is an
     // inexpensive operation and does not traverse the JIT tree.
     if (root_node->getHeight() > 6 ||
-        getMemoryPressure() > getMemoryPressureThreshold()) {
+        getMemoryPressure() >= getMemoryPressureThreshold()) {
         // The size of the parameters without any extra arguments from the
         // JIT tree. This includes one output Param object and 4 integers.
         constexpr size_t base_param_size =
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 6ceb9889c1..7141f076a9 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -296,7 +296,7 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
         return kJITHeuristics::TreeHeight;
     }
 
-    bool isBufferLimit = getMemoryPressure() > getMemoryPressureThreshold();
+    bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
     auto platform      = getActivePlatform();
 
     // The Apple platform can have the nvidia card or the AMD card

From 1031fa9fbad4760b26dee2cdb1f1eabc78b5a723 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 23 Mar 2020 17:50:33 +0530
Subject: [PATCH 1874/2677] Move __local array declaration to opencl kernel
 scope

__local arrays can't be declared at non-kernel function scope in
OpenCL. Oddly, nvidia OpenCL implementation seems to work fine
although Clover OpenCL implementation throws an error.

This is a bug w.r.t implementation nevertheless. Hence, the fix.
---
 src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl | 10 +++++-----
 .../opencl/kernel/reduce_blocks_by_key_first.cl       | 11 +++++------
 2 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
index a82941b00c..15680e3321 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-Tk work_group_scan_inclusive_add(__local Tk *arr) {
-    __local Tk tmp[DIMX];
+Tk work_group_scan_inclusive_add(__local Tk *wg_tmp, __local Tk *arr) {
     __local int *l_val;
 
     const int lid = get_local_id(0);
@@ -21,7 +20,7 @@ Tk work_group_scan_inclusive_add(__local Tk *arr) {
         if (lid >= off) val = val + l_val[lid - off];
 
         wbuf       = 1 - wbuf;
-        l_val      = wbuf ? tmp : arr;
+        l_val      = wbuf ? wg_tmp : arr;
         l_val[lid] = val;
     }
 
@@ -45,6 +44,7 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
 
     __local Tk keys[DIMX];
     __local To vals[DIMX];
+    __local Tk wg_temp[DIMX];
 
     __local Tk reduced_keys[DIMX];
     __local To reduced_vals[DIMX];
@@ -79,7 +79,7 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
                         bidz * iVInfo.strides[dims_ordering[2]] +
                         bidy * iVInfo.strides[dims_ordering[1]] +
                         gidx * iVInfo.strides[DIM];
-        v                 = transform(iVals[gid]);
+        v = transform(iVals[gid]);
         if (change_nan) v = IS_NAN(v) ? nanval : v;
     } else {
         v = init_val;
@@ -96,7 +96,7 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
     int unique_flag   = (eq_check || (lid == 0)) && (gidx < n);
     unique_flags[lid] = unique_flag;
 
-    int unique_id   = work_group_scan_inclusive_add(unique_flags);
+    int unique_id   = work_group_scan_inclusive_add(wg_temp, unique_flags);
     unique_ids[lid] = unique_id;
 
     if (lid == DIMX - 1) reducedBlockSize = unique_id;
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
index 2912c53c7a..37e922c540 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-Tk work_group_scan_inclusive_add(__local Tk *arr) {
-    __local Tk tmp[DIMX];
+Tk work_group_scan_inclusive_add(__local Tk *wg_temp, __local Tk *arr) {
     __local int *l_val;
 
     const int lid = get_local_id(0);
@@ -21,7 +20,7 @@ Tk work_group_scan_inclusive_add(__local Tk *arr) {
         if (lid >= off) val = val + l_val[lid - off];
 
         wbuf       = 1 - wbuf;
-        l_val      = wbuf ? tmp : arr;
+        l_val      = wbuf ? wg_temp : arr;
         l_val[lid] = val;
     }
 
@@ -43,6 +42,7 @@ __kernel void reduce_blocks_by_key_first(
 
     __local Tk keys[DIMX];
     __local To vals[DIMX];
+    __local Tk wg_temp[DIMX];
 
     __local Tk reduced_keys[DIMX];
     __local To reduced_vals[DIMX];
@@ -65,13 +65,12 @@ __kernel void reduce_blocks_by_key_first(
         k                 = iKeys[gid];
         const int bOffset = bidw * iVInfo.strides[3] +
                             bidz * iVInfo.strides[2] + bidy * iVInfo.strides[1];
-        v                 = transform(iVals[bOffset + gid]);
+        v = transform(iVals[bOffset + gid]);
         if (change_nan) v = IS_NAN(v) ? nanval : v;
     } else {
         v = init_val;
     }
 
-
     keys[lid] = k;
     vals[lid] = v;
 
@@ -83,7 +82,7 @@ __kernel void reduce_blocks_by_key_first(
     int unique_flag   = (eq_check || (lid == 0)) && (gid < n);
     unique_flags[lid] = unique_flag;
 
-    int unique_id   = work_group_scan_inclusive_add(unique_flags);
+    int unique_id   = work_group_scan_inclusive_add(wg_temp, unique_flags);
     unique_ids[lid] = unique_id;
 
     if (lid == DIMX - 1) reducedBlockSize = unique_id;

From 5d62cbd258091fa8a467e0c17130094d323b2214 Mon Sep 17 00:00:00 2001
From: glavaux2 <42715101+glavaux2@users.noreply.github.com>
Date: Tue, 24 Mar 2020 19:54:25 +0100
Subject: [PATCH 1875/2677] Use kDim instead of dim to avoid name collision
 with AMD(#2802)

* Use kDim instead of dim to avoid name collision with some OpenCL implementations(AMD) in kernels.

Co-authored-by: LAVAUX Guilhem <lavaux@iap.fr>
---
 src/backend/opencl/kernel/ireduce.hpp         |  2 +-
 src/backend/opencl/kernel/ireduce_dim.cl      | 16 +++---
 src/backend/opencl/kernel/join.hpp            |  2 +-
 src/backend/opencl/kernel/mean.hpp            |  5 +-
 src/backend/opencl/kernel/mean_dim.cl         | 18 +++----
 src/backend/opencl/kernel/reduce.hpp          |  2 +-
 src/backend/opencl/kernel/reduce_dim.cl       | 14 ++---
 src/backend/opencl/kernel/scan_dim.cl         | 32 +++++------
 src/backend/opencl/kernel/scan_dim.hpp        |  2 +-
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 54 +++++++++----------
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  2 +-
 11 files changed, 75 insertions(+), 74 deletions(-)

diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 4994a006b5..070b384b4f 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -57,7 +57,7 @@ void ireduce_dim_launcher(Param out, cl::Buffer *oidx, Param in,
         ToNumStr<T> toNumStr;
 
         std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D dim=" << dim
+        options << " -D T=" << dtype_traits<T>::getName() << " -D kDim=" << dim
                 << " -D DIMY=" << threads_y << " -D THREADS_X=" << THREADS_X
                 << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
diff --git a/src/backend/opencl/kernel/ireduce_dim.cl b/src/backend/opencl/kernel/ireduce_dim.cl
index 35d29ea8f2..b7f98e2ddf 100644
--- a/src/backend/opencl/kernel/ireduce_dim.cl
+++ b/src/backend/opencl/kernel/ireduce_dim.cl
@@ -26,15 +26,15 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
 
     // There is only one element per group for out
     // There are get_local_size(1) elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
+    // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
     olData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
               ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
-    const uint id_dim_out = ids[dim];
+    const uint id_dim_out = ids[kDim];
 
-    ids[dim] = ids[dim] * get_local_size(1) + lidy;
+    ids[kDim] = ids[kDim] * get_local_size(1) + lidy;
 
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
              ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
@@ -44,8 +44,8 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
                   ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
     }
 
-    const uint id_dim_in   = ids[dim];
-    const uint istride_dim = iInfo.strides[dim];
+    const uint id_dim_in   = ids[kDim];
+    const uint istride_dim = iInfo.strides[kDim];
 
     bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
                     (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
@@ -56,14 +56,14 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
     T out_val    = init;
     uint out_idx = id_dim_in;
 
-    if (is_valid && id_dim_in < iInfo.dims[dim]) {
+    if (is_valid && id_dim_in < iInfo.dims[kDim]) {
         out_val = *iData;
         if (!IS_FIRST) out_idx = *ilData;
     }
 
     const uint id_dim_in_start = id_dim_in + group_dim * get_local_size(1);
 
-    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
+    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[kDim]);
          id += group_dim * get_local_size(1)) {
         iData = iData + group_dim * get_local_size(1) * istride_dim;
 
@@ -112,7 +112,7 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[dim])) {
+    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[kDim])) {
         *oData  = *s_vptr;
         *olData = *s_iptr;
     }
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index c33a7c4e51..1298978d05 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -48,7 +48,7 @@ void join(Param out, const Param in, const af::dim4 offset) {
         std::ostringstream options;
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D dim=" << dim;
+                << " -D kDim=" << dim;
 
         if (std::is_same<To, double>::value ||
             std::is_same<To, cdouble>::value) {
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index d119e997a7..120b5a560b 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -134,8 +134,9 @@ void mean_dim_launcher(Param out, Param owt, Param in, Param inWeight,
         std::ostringstream options;
         options << " -D Ti=" << dtype_traits<Ti>::getName()
                 << " -D Tw=" << dtype_traits<Tw>::getName()
-                << " -D To=" << dtype_traits<To>::getName() << " -D dim=" << dim
-                << " -D DIMY=" << threads_y << " -D THREADS_X=" << THREADS_X
+                << " -D To=" << dtype_traits<To>::getName()
+                << " -D kDim=" << dim << " -D DIMY=" << threads_y
+                << " -D THREADS_X=" << THREADS_X
                 << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
                 << " -D init_Tw=" << twNumStr(transform_weight(0))
                 << " -D one_Tw=" << twNumStr(transform_weight(1));
diff --git a/src/backend/opencl/kernel/mean_dim.cl b/src/backend/opencl/kernel/mean_dim.cl
index 59dfe7757a..60ed2fe0d6 100644
--- a/src/backend/opencl/kernel/mean_dim.cl
+++ b/src/backend/opencl/kernel/mean_dim.cl
@@ -31,7 +31,7 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
 
     // There is only one element per group for out
     // There are get_local_size(1) elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
+    // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
@@ -40,9 +40,9 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
     owData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
               ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
 #endif
-    const uint id_dim_out = ids[dim];
+    const uint id_dim_out = ids[kDim];
 
-    ids[dim] = ids[dim] * get_local_size(1) + lidy;
+    ids[kDim] = ids[kDim] * get_local_size(1) + lidy;
 
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
              ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
@@ -52,8 +52,8 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
               ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
 #endif
 
-    const uint id_dim_in   = ids[dim];
-    const uint istride_dim = iInfo.strides[dim];
+    const uint id_dim_in   = ids[kDim];
+    const uint istride_dim = iInfo.strides[kDim];
 
     bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
                     (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
@@ -64,7 +64,7 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
     To out_val = init_To;
     Tw out_wt  = init_Tw;
 
-    if (is_valid && id_dim_in < iInfo.dims[dim]) {
+    if (is_valid && id_dim_in < iInfo.dims[kDim]) {
         out_val = transform(*iData);
 #ifdef INPUT_WEIGHT
         out_wt = *iwData;
@@ -76,14 +76,14 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
     const uint id_dim_in_start = id_dim_in + group_dim * get_local_size(1);
 
 #ifdef INPUT_WEIGHT
-    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
+    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[kDim]);
          id += group_dim * get_local_size(1)) {
         iData  = iData + group_dim * get_local_size(1) * istride_dim;
         iwData = iwData + group_dim * get_local_size(1) * istride_dim;
         binOp(&out_val, &out_wt, transform(*iData), *iwData);
     }
 #else
-    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[dim]);
+    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[kDim]);
          id += group_dim * get_local_size(1)) {
         iData = iData + group_dim * get_local_size(1) * istride_dim;
         binOp(&out_val, &out_wt, transform(*iData), one_Tw);
@@ -127,7 +127,7 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[dim])) {
+    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[kDim])) {
         *oData = *s_vptr;
 #ifdef OUTPUT_WEIGHT
         *owData = *s_wptr;
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index aa70c90dcb..933a6390d5 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -60,7 +60,7 @@ void reduce_dim_launcher(Param out, Param in, const int dim,
         std::ostringstream options;
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D dim=" << dim << " -D DIMY=" << threads_y
+                << " -D kDim=" << dim << " -D DIMY=" << threads_y
                 << " -D THREADS_X=" << THREADS_X
                 << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
diff --git a/src/backend/opencl/kernel/reduce_dim.cl b/src/backend/opencl/kernel/reduce_dim.cl
index f2bbba5aa6..8c93a0fde3 100644
--- a/src/backend/opencl/kernel/reduce_dim.cl
+++ b/src/backend/opencl/kernel/reduce_dim.cl
@@ -26,18 +26,18 @@ __kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
 
     // There is only one element per group for out
     // There are get_local_size(1) elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
+    // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
-    const uint id_dim_out = ids[dim];
+    const uint id_dim_out = ids[kDim];
 
-    ids[dim] = ids[dim] * get_local_size(1) + lidy;
+    ids[kDim] = ids[kDim] * get_local_size(1) + lidy;
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
              ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
-    const uint id_dim_in = ids[dim];
+    const uint id_dim_in = ids[kDim];
 
-    const uint istride_dim = iInfo.strides[dim];
+    const uint istride_dim = iInfo.strides[kDim];
 
     bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
                     (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
@@ -45,7 +45,7 @@ __kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
     __local To s_val[THREADS_X * DIMY];
 
     To out_val = init;
-    for (int id = id_dim_in; is_valid && (id < iInfo.dims[dim]);
+    for (int id = id_dim_in; is_valid && (id < iInfo.dims[kDim]);
          id += group_dim * get_local_size(1)) {
         To in_val = transform(*iData);
         if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
@@ -73,7 +73,7 @@ __kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[dim])) {
+    if (lidy == 0 && is_valid && (id_dim_out < oInfo.dims[kDim])) {
         *oData = *s_ptr;
     }
 }
diff --git a/src/backend/opencl/kernel/scan_dim.cl b/src/backend/opencl/kernel/scan_dim.cl
index 53977f8d6c..cf59d1e8d7 100644
--- a/src/backend/opencl/kernel/scan_dim.cl
+++ b/src/backend/opencl/kernel/scan_dim.cl
@@ -27,27 +27,27 @@ __kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
 
     // There is only one element per group for out
     // There are DIMY elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
+    // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
     tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
              ids[1] * tInfo.strides[1] + ids[0];
-    const int groupId_dim = ids[dim];
+    const int groupId_dim = ids[kDim];
 
-    ids[dim] = ids[dim] * DIMY * lim + lidy;
+    ids[kDim] = ids[kDim] * DIMY * lim + lidy;
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0];
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
              ids[1] * iInfo.strides[1] + ids[0];
     iData += iInfo.offset;
 
-    int id_dim        = ids[dim];
-    const int out_dim = oInfo.dims[dim];
+    int id_dim        = ids[kDim];
+    const int out_dim = oInfo.dims[kDim];
 
     bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
                     (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
-    const int ostride_dim = oInfo.strides[dim];
-    const int istride_dim = iInfo.strides[dim];
+    const int ostride_dim = oInfo.strides[kDim];
+    const int istride_dim = iInfo.strides[kDim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
@@ -95,7 +95,7 @@ __kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (!isFinalPass && is_valid && (groupId_dim < tInfo.dims[dim]) && isLast) {
+    if (!isFinalPass && is_valid && (groupId_dim < tInfo.dims[kDim]) && isLast) {
         *tData = val;
     }
 }
@@ -116,34 +116,34 @@ __kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
     const int yid       = groupId_y;
 
     int ids[4]            = {xid, yid, zid, wid};
-    const int groupId_dim = ids[dim];
+    const int groupId_dim = ids[kDim];
 
     if (groupId_dim != 0) {
         // There is only one element per group for out
         // There are DIMY elements per group for in
-        // Hence increment ids[dim] just after offseting out and before
+        // Hence increment ids[kDim] just after offseting out and before
         // offsetting in
         tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
                  ids[1] * tInfo.strides[1] + ids[0];
 
-        ids[dim] = ids[dim] * DIMY * lim + lidy;
+        ids[kDim] = ids[kDim] * DIMY * lim + lidy;
         oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
                  ids[1] * oInfo.strides[1] + ids[0];
 
         // Shift broadcast one step to the right for exclusive scan (#2366)
-        int offset = inclusive_scan ? 0 : oInfo.strides[dim];
+        int offset = inclusive_scan ? 0 : oInfo.strides[kDim];
         oData += offset;
 
-        const int id_dim  = ids[dim];
-        const int out_dim = oInfo.dims[dim];
+        const int id_dim  = ids[kDim];
+        const int out_dim = oInfo.dims[kDim];
 
         bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
                         (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
         if (is_valid) {
-            To accum = *(tData - tInfo.strides[dim]);
+            To accum = *(tData - tInfo.strides[kDim]);
 
-            const int ostride_dim = oInfo.strides[dim];
+            const int ostride_dim = oInfo.strides[kDim];
 
             for (int k = 0, id = id_dim; is_valid && k < lim && (id < out_dim);
                  k++, id += DIMY) {
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index db7ca5d839..ff80763e4b 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -54,7 +54,7 @@ static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool isFinalPass,
         std::ostringstream options;
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D dim=" << dim << " -D DIMY=" << threads_y
+                << " -D kDim=" << dim << " -D DIMY=" << threads_y
                 << " -D THREADS_X=" << THREADS_X
                 << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index fbb5fe4ba2..94aa29688f 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -31,7 +31,7 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
 
     // There is only one element per group for out
     // There are DIMY elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
+    // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
     tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
              ids[1] * tInfo.strides[1] + ids[0];
@@ -39,9 +39,9 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
               ids[1] * tfInfo.strides[1] + ids[0];
     tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] +
               ids[1] * tiInfo.strides[1] + ids[0];
-    const int groupId_dim = ids[dim];
+    const int groupId_dim = ids[kDim];
 
-    ids[dim] = ids[dim] * DIMY * lim + lidy;
+    ids[kDim] = ids[kDim] * DIMY * lim + lidy;
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0];
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
@@ -50,14 +50,14 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
              ids[1] * kInfo.strides[1] + ids[0];
     iData += iInfo.offset;
 
-    int id_dim        = ids[dim];
-    const int out_dim = oInfo.dims[dim];
+    int id_dim        = ids[kDim];
+    const int out_dim = oInfo.dims[kDim];
 
     bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
                     (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
-    const int ostride_dim = oInfo.strides[dim];
-    const int istride_dim = iInfo.strides[dim];
+    const int ostride_dim = oInfo.strides[kDim];
+    const int istride_dim = iInfo.strides[kDim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
@@ -86,7 +86,7 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
         bool cond = (is_valid) && (id_dim < out_dim);
 
         if (cond) {
-            flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
+            flag = calculate_head_flags_dim(kData, id_dim, kInfo.strides[kDim]);
         } else {
             flag = 0;
         }
@@ -102,7 +102,7 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
             if ((id_dim == 0) || (!cond) || flag) {
                 val = init_val;
             } else {
-                val = transform(*(iData - iInfo.strides[dim]));
+                val = transform(*(iData - iInfo.strides[kDim]));
             }
         }
 
@@ -150,13 +150,13 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
             l_ftmp[lidx] = flag;
         }
         id_dim += DIMY;
-        kData += DIMY * kInfo.strides[dim];
+        kData += DIMY * kInfo.strides[kDim];
         iData += DIMY * istride_dim;
         oData += DIMY * ostride_dim;
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (is_valid && (groupId_dim < tInfo.dims[dim]) && isLast) {
+    if (is_valid && (groupId_dim < tInfo.dims[kDim]) && isLast) {
         *tData       = val;
         *tfData      = flag;
         int boundary = boundaryid[lidx];
@@ -183,11 +183,11 @@ __kernel void scan_dim_by_key_final_kernel(
 
     // There is only one element per group for out
     // There are DIMY elements per group for in
-    // Hence increment ids[dim] just after offseting out and before offsetting
+    // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
-    const int groupId_dim = ids[dim];
+    const int groupId_dim = ids[kDim];
 
-    ids[dim] = ids[dim] * DIMY * lim + lidy;
+    ids[kDim] = ids[kDim] * DIMY * lim + lidy;
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0];
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
@@ -196,14 +196,14 @@ __kernel void scan_dim_by_key_final_kernel(
              ids[1] * kInfo.strides[1] + ids[0];
     iData += iInfo.offset;
 
-    int id_dim        = ids[dim];
-    const int out_dim = oInfo.dims[dim];
+    int id_dim        = ids[kDim];
+    const int out_dim = oInfo.dims[kDim];
 
     bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
                     (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
-    const int ostride_dim = oInfo.strides[dim];
-    const int istride_dim = iInfo.strides[dim];
+    const int ostride_dim = oInfo.strides[kDim];
+    const int istride_dim = iInfo.strides[kDim];
 
     __local To l_val0[THREADS_X * DIMY];
     __local To l_val1[THREADS_X * DIMY];
@@ -232,7 +232,7 @@ __kernel void scan_dim_by_key_final_kernel(
         if (calculateFlags) {
             if (cond) {
                 flag =
-                    calculate_head_flags_dim(kData, id_dim, kInfo.strides[dim]);
+                    calculate_head_flags_dim(kData, id_dim, kInfo.strides[kDim]);
             } else {
                 flag = 0;
             }
@@ -251,7 +251,7 @@ __kernel void scan_dim_by_key_final_kernel(
             if ((id_dim == 0) || (!cond) || flag) {
                 val = init_val;
             } else {
-                val = transform(*(iData - iInfo.strides[dim]));
+                val = transform(*(iData - iInfo.strides[kDim]));
             }
         }
 
@@ -287,7 +287,7 @@ __kernel void scan_dim_by_key_final_kernel(
             l_ftmp[lidx] = flag;
         }
         id_dim += DIMY;
-        kData += DIMY * kInfo.strides[dim];
+        kData += DIMY * kInfo.strides[kDim];
         iData += DIMY * istride_dim;
         oData += DIMY * ostride_dim;
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -311,32 +311,32 @@ __kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
     const int yid       = groupId_y;
 
     int ids[4]            = {xid, yid, zid, wid};
-    const int groupId_dim = ids[dim];
+    const int groupId_dim = ids[kDim];
 
     if (groupId_dim != 0) {
         // There is only one element per group for out
         // There are DIMY elements per group for in
-        // Hence increment ids[dim] just after offseting out and before
+        // Hence increment ids[kDim] just after offseting out and before
         // offsetting in
         tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] +
                   ids[1] * tiInfo.strides[1] + ids[0];
         tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
                  ids[1] * tInfo.strides[1] + ids[0];
 
-        ids[dim] = ids[dim] * DIMY * lim + lidy;
+        ids[kDim] = ids[kDim] * DIMY * lim + lidy;
         oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
                  ids[1] * oInfo.strides[1] + ids[0];
 
-        const int id_dim = ids[dim];
+        const int id_dim = ids[kDim];
 
         bool is_valid = (ids[0] < oInfo.dims[0]) && (ids[1] < oInfo.dims[1]) &&
                         (ids[2] < oInfo.dims[2]) && (ids[3] < oInfo.dims[3]);
 
         if (is_valid) {
             int boundary = *tiData;
-            To accum     = *(tData - tInfo.strides[dim]);
+            To accum     = *(tData - tInfo.strides[kDim]);
 
-            const int ostride_dim = oInfo.strides[dim];
+            const int ostride_dim = oInfo.strides[kDim];
 
             for (int k = 0, id = id_dim; is_valid && k < lim && (id < boundary);
                  k++, id += DIMY) {
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 65ba414afa..9a5a8f9fd7 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -58,7 +58,7 @@ static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags,
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Ti=" << dtype_traits<Ti>::getName()
                 << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D dim=" << dim << " -D DIMY=" << threads_y
+                << " -D kDim=" << dim << " -D DIMY=" << threads_y
                 << " -D THREADS_X=" << THREADS_X
                 << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()

From 04d97ce151f7b5d309eec575497e7972705cda52 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 3 Mar 2020 12:09:28 -0500
Subject: [PATCH 1876/2677] adds missing print in array_to_string for f16

---
 src/api/c/print.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 642046c35a..8b9ddb4007 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -266,6 +266,9 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
                 case u16:
                     print<ushort>(exp, arr, precision, ss, transpose);
                     break;
+                case f16:
+                    print<half>(exp, arr, precision, ss, transpose);
+                    break;
                 default: TYPE_ERROR(1, type);
             }
         }

From cd3c107b9764ad64135102c196229a58afa4e4e8 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 4 Mar 2020 18:23:50 -0500
Subject: [PATCH 1877/2677] add type checks during array creation in cuda
 backend

---
 src/backend/cuda/Array.cpp    | 17 +++++++++++++++++
 src/backend/cuda/platform.cpp | 14 +++++++++++---
 2 files changed, 28 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 5a691af785..b75e809295 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -34,6 +34,18 @@ using std::shared_ptr;
 using std::vector;
 
 namespace cuda {
+
+template<typename T>
+void verifyTypeSupport() {
+    if ((std::is_same<T, double>::value || std::is_same<T, cdouble>::value) &&
+        !isDoubleSupported(getActiveDeviceId())) {
+        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
+    } else if (std::is_same<T, common::half>::value &&
+               !isHalfSupported(getActiveDeviceId())) {
+        AF_ERROR("Half precision not supported", AF_ERR_NO_HALF);
+    }
+}
+
 template<typename T>
 Node_ptr bufferNodePtr() {
     return Node_ptr(new BufferNode<T>(getFullName<T>(), shortname<T>(true)));
@@ -302,12 +314,14 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
 
 template<typename T>
 Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    verifyTypeSupport<T>();
     Array<T> out = Array<T>(dims, node);
     return out;
 }
 
 template<typename T>
 Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
+    verifyTypeSupport<T>();
     bool is_device   = false;
     bool copy_device = false;
     return Array<T>(dims, data, is_device, copy_device);
@@ -315,6 +329,7 @@ Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
 
 template<typename T>
 Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+    verifyTypeSupport<T>();
     bool is_device   = true;
     bool copy_device = false;
     return Array<T>(dims, static_cast<T *>(data), is_device, copy_device);
@@ -322,11 +337,13 @@ Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
 
 template<typename T>
 Array<T> createValueArray(const dim4 &dims, const T &value) {
+    verifyTypeSupport<T>();
     return createScalarNode<T>(dims, value);
 }
 
 template<typename T>
 Array<T> createEmptyArray(const dim4 &dims) {
+    verifyTypeSupport<T>();
     return Array<T>(dims);
 }
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index b0bc38ccfe..f9d438f67f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -236,9 +236,17 @@ bool isDoubleSupported(int device) {
 }
 
 bool isHalfSupported(int device) {
-    auto prop     = getDeviceProp(device);
-    float compute = prop.major * 1000 + prop.minor * 10;
-    return compute >= 5030;
+    std::array<bool, DeviceManager::MAX_DEVICES> half_supported = []() {
+        std::array<bool, DeviceManager::MAX_DEVICES> out;
+        int count = getDeviceCount();
+        for (int i = 0; i < count; i++) {
+            auto prop     = getDeviceProp(i);
+            float compute = prop.major * 1000 + prop.minor * 10;
+            out[i]        = compute >= 5030;
+        }
+        return out;
+    }();
+    return half_supported[device];
 }
 
 void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {

From 1196646c2f790df816e3f67025820b140d2e8636 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 25 Mar 2020 01:04:00 -0400
Subject: [PATCH 1878/2677] Create a thrust policy to intercept tmp buffer
 allocations (#2806)

* Create a thrust policy to intercept tmp buffer allocations

Thrust uses policies to perform certain operations in the backend. This
commit creates an ArrayFire policy for thrust which intercepts temporary
buffer allocations and frees to the memory manager. It also allows
you to specify the stream of the operation so the older approach to
specify the stream has been updated.
---
 src/backend/cuda/CMakeLists.txt               |  5 ++-
 src/backend/cuda/ThrustArrayFirePolicy.cpp    | 20 +++++++++
 src/backend/cuda/ThrustArrayFirePolicy.hpp    | 41 +++++++++++++++++++
 src/backend/cuda/kernel/regions.hpp           |  4 +-
 src/backend/cuda/kernel/sift_nonfree.hpp      |  2 +-
 src/backend/cuda/kernel/sort.hpp              |  2 +-
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |  2 +-
 src/backend/cuda/set.cu                       |  6 +--
 .../{debug_thrust.hpp => thrust_utils.hpp}    |  9 ++--
 9 files changed, 78 insertions(+), 13 deletions(-)
 create mode 100644 src/backend/cuda/ThrustArrayFirePolicy.cpp
 create mode 100644 src/backend/cuda/ThrustArrayFirePolicy.hpp
 rename src/backend/cuda/{debug_thrust.hpp => thrust_utils.hpp} (84%)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index cc78ee73cd..ae29c43d7a 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -377,6 +377,9 @@ cuda_add_library(afcuda
     Array.hpp
     LookupTable1D.hpp
     Param.hpp
+    ThrustAllocator.cuh
+    ThrustArrayFirePolicy.hpp
+    ThrustArrayFirePolicy.cpp
     anisotropic_diffusion.hpp
     approx.hpp
     arith.hpp
@@ -411,7 +414,7 @@ cuda_add_library(afcuda
     device_manager.cpp
     device_manager.hpp
     debug_cuda.hpp
-    debug_thrust.hpp
+    thrust_utils.hpp
     diagonal.cpp
     diagonal.hpp
     diff.cpp
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.cpp b/src/backend/cuda/ThrustArrayFirePolicy.cpp
new file mode 100644
index 0000000000..c67a4ac2e5
--- /dev/null
+++ b/src/backend/cuda/ThrustArrayFirePolicy.cpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <ThrustArrayFirePolicy.hpp>
+
+namespace cuda {
+
+cudaStream_t get_stream(ThrustArrayFirePolicy) { return getActiveStream(); }
+
+cudaError_t synchronize_stream(ThrustArrayFirePolicy) {
+    return cudaStreamSynchronize(getActiveStream());
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
new file mode 100644
index 0000000000..cd9c4e76e5
--- /dev/null
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <backend.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include <thrust/execution_policy.h>
+
+namespace cuda {
+struct ThrustArrayFirePolicy
+    : thrust::device_execution_policy<ThrustArrayFirePolicy> {};
+
+__DH__
+cudaStream_t get_stream(ThrustArrayFirePolicy);
+
+__DH__
+cudaError_t synchronize_stream(ThrustArrayFirePolicy);
+
+template<typename T>
+thrust::pair<thrust::pointer<T, ThrustArrayFirePolicy>, std::ptrdiff_t>
+get_temporary_buffer(ThrustArrayFirePolicy, std::ptrdiff_t n) {
+    thrust::pointer<T, ThrustArrayFirePolicy> result(
+        cuda::memAlloc<T>(n / sizeof(T)).release());
+
+    return thrust::make_pair(result, n);
+}
+
+template<typename Pointer>
+void return_temporary_buffer(ThrustArrayFirePolicy, Pointer p) {
+    memFree(p.get());
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 85a4556bde..4a9547ef35 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -9,11 +9,11 @@
 
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <debug_thrust.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <stdio.h>
+#include <thrust_utils.hpp>
+
 #include <thrust/adjacent_difference.h>
 #include <thrust/binary_search.h>
 #include <thrust/device_vector.h>
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift_nonfree.hpp
index cab805ff9e..8ede0fe412 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift_nonfree.hpp
@@ -74,9 +74,9 @@
 
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <debug_thrust.hpp>
 #include <err_cuda.hpp>
 #include <memory.hpp>
+#include <thrust_utils.hpp>
 #include <af/defines.h>
 #include "shared.hpp"
 
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index 14b2b57ed2..f99dcdf4ba 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -10,13 +10,13 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
-#include <debug_thrust.hpp>
 #include <err_cuda.hpp>
 #include <handle.hpp>
 #include <iota.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
 #include <math.hpp>
 #include <thrust/sort.h>
+#include <thrust_utils.hpp>
 
 namespace cuda {
 namespace kernel {
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 19108d285a..99d9ee7d9a 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -8,9 +8,9 @@
  ********************************************************/
 
 #include <debug_cuda.hpp>
-#include <debug_thrust.hpp>
 #include <kernel/thrust_sort_by_key.hpp>
 #include <thrust/sort.h>
+#include <thrust_utils.hpp>
 #include <types.hpp>
 
 namespace cuda {
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index 8e52eaec8d..a768c31e15 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -10,18 +10,18 @@
 #include <Array.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
-#include <debug_thrust.hpp>
+#include <thrust_utils.hpp>
 #include <set.hpp>
 #include <sort.hpp>
 #include <af/dim4.hpp>
 
-#include <algorithm>
-
 #include <thrust/device_ptr.h>
 #include <thrust/set_operations.h>
 #include <thrust/sort.h>
 #include <thrust/unique.h>
 
+#include <algorithm>
+
 namespace cuda {
 using af::dim4;
 
diff --git a/src/backend/cuda/debug_thrust.hpp b/src/backend/cuda/thrust_utils.hpp
similarity index 84%
rename from src/backend/cuda/debug_thrust.hpp
rename to src/backend/cuda/thrust_utils.hpp
index 02eb9b7ea8..ed468b74a5 100644
--- a/src/backend/cuda/debug_thrust.hpp
+++ b/src/backend/cuda/thrust_utils.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+#include <ThrustArrayFirePolicy.hpp>
 #include <thrust/system/cuda/detail/par.h>
 #include <thrust/version.h>
 #include <ThrustAllocator.cuh>
@@ -16,12 +18,11 @@ template<typename T>
 using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T>>;
 }
 
-#define THRUST_STREAM thrust::cuda::par.on(cuda::getActiveStream())
-
 #if THRUST_MAJOR_VERSION >= 1 && THRUST_MINOR_VERSION >= 8
 
-#define THRUST_SELECT(fn, ...) fn(THRUST_STREAM, __VA_ARGS__)
-#define THRUST_SELECT_OUT(res, fn, ...) res = fn(THRUST_STREAM, __VA_ARGS__)
+#define THRUST_SELECT(fn, ...) fn(cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
+#define THRUST_SELECT_OUT(res, fn, ...) \
+    res = fn(cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
 
 #else
 

From 1ecda96f278ca0eaca30baa8406e2c6706e684d0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 4 Mar 2020 22:44:52 +0530
Subject: [PATCH 1879/2677] Remove cuda_thrust_sort_by_key static dependency

Instead of creating a static library out of all separate instantiations
of thrust_sort_by_key sources, we now directly embed sources
generated(using cmake's configure_file command) into afcuda target.

This also fixed separable compilation.
Prior to this change, separate compilation failed (related to cuda device
linking - undefined references). I tried to fix that problem, but
couldn't get a break through. However, I realized that just directly
using the generated sources with afcuda target will do the job without
any additional static library.
---
 src/backend/cuda/CMakeLists.txt               | 12 ++++-
 .../kernel/thrust_sort_by_key/CMakeLists.txt  | 51 +++++++------------
 .../thrust_sort_by_key_impl.cu                |  6 ++-
 3 files changed, 32 insertions(+), 37 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ae29c43d7a..3e14227ddf 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -39,6 +39,7 @@ cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_ta
 message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets}")
 
 set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
+
 if(${CUDA_SEPARABLE_COMPILATION})
   # Enable relocatable device code generation for separable
   # compilation which is in turn required for any device linking done.
@@ -245,6 +246,7 @@ include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
 cuda_add_library(afcuda
+    ${thrust_sort_sources}
     sort.hpp
 
     all.cu
@@ -551,11 +553,18 @@ cuda_add_library(afcuda
 
     ${scan_by_key_sources}
 
-    OPTIONS ${platform_flags} ${cuda_cxx_flags} -Xcudafe \"--diag_suppress=1427\"
+    OPTIONS
+    ${platform_flags}
+    ${cuda_cxx_flags}
+    -Xcudafe \"--diag_suppress=1427\"
   )
 
 arrayfire_set_default_cxx_flags(afcuda)
 
+# NOTE: Do not add additional CUDA specific definitions here. Add it to the
+# cxx_definitions variable above. cxx_definitions is used to propigate
+# definitions to the scan_by_key and thrust_sort_by_key targets as well as the
+# cuda library above.
 target_compile_options(afcuda PRIVATE ${cxx_definitions})
 
 add_library(ArrayFire::afcuda ALIAS afcuda)
@@ -594,7 +603,6 @@ target_link_libraries(afcuda
     c_api_interface
     cpp_api_interface
     afcommon_interface
-    cuda_thrust_sort_by_key
     ${CUDA_nvrtc_LIBRARY}
     ${CUDA_CUBLAS_LIBRARIES}
     ${CUDA_CUFFT_LIBRARIES}
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
index 3a6f660098..6c2f7f3c49 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/CMakeLists.txt
@@ -1,11 +1,13 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2020, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu" FILESTRINGS)
+file(STRINGS
+    "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
+    FILESTRINGS)
 
 foreach(STR ${FILESTRINGS})
     if(${STR} MATCHES "// SBK_TYPES")
@@ -18,35 +20,18 @@ foreach(STR ${FILESTRINGS})
 endforeach()
 
 foreach(SBK_TYPE ${SBK_TYPES})
-    foreach(SBK_INST ${SBK_INSTS})
-
-      # When using cuda_compile with older versions of FindCUDA. The generated targets
-      # have the same names as the source file. Since we are using the same file for
-      # the compilation of these targets we need to rename them before sending them
-      # to the cuda_compile command so that it doesn't generate multiple targets with
-      # the same name
-      file(COPY "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
-        DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key")
-      file(RENAME "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
-          "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu")
-
-      cuda_compile(sort_by_key_gen_files
-          ${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key_impl.hpp
-          OPTIONS
-            -DSBK_TYPE=${SBK_TYPE}
-            -DINSTANTIATESBK_INST=INSTANTIATE${SBK_INST}
-            "${platform_flags} ${cuda_cxx_flags} -DAFDLL"
-      )
-
-      list(APPEND SORT_OBJ ${sort_by_key_gen_files})
-    endforeach(SBK_INST ${SBK_INSTS})
+  foreach(SBK_INST ${SBK_INSTS})
+    set(INSTANTIATESBK_INST "INSTANTIATE${SBK_INST}")
+
+    configure_file(
+      "${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu"
+      "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
+    )
+
+    list(
+      APPEND
+      thrust_sort_sources
+      "${CMAKE_CURRENT_BINARY_DIR}/kernel/thrust_sort_by_key/thrust_sort_by_key_impl_${SBK_TYPE}_${SBK_INST}.cu"
+    )
+  endforeach(SBK_INST ${SBK_INSTS})
 endforeach(SBK_TYPE ${SBK_TYPES})
-
-cuda_add_library(cuda_thrust_sort_by_key STATIC ${SORT_OBJ})
-
-set_target_properties(cuda_thrust_sort_by_key
-  PROPERTIES
-    LINKER_LANGUAGE CXX
-    FOLDER "Generated Targets"
-  )
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
index cf19942149..50996bb12e 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
@@ -16,6 +16,8 @@
 
 namespace cuda {
 namespace kernel {
-INSTANTIATESBK_INST(SBK_TYPE)
-}
+// clang-format off
+@INSTANTIATESBK_INST@ ( @SBK_TYPE@ )
+// clang-format on
+}  // namespace kernel
 }  // namespace cuda

From 08296d6f06d7eef2ef692542a0e9f284d902a055 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 26 Mar 2020 11:45:40 +0530
Subject: [PATCH 1880/2677] Use static cufft,cublas,cusolver and cusolver on
 Unix

thrust::stable_sort_by_key has known issue with device linking. The code
crashes with cudaInvalidValueError. It works as expected without any
changes with or without separable compilation otherwise.

https://github.com/thrust/thrust/wiki/Debugging#known-issues
https://github.com/thrust/thrust/blob/master/doc/changelog.md#known-issues-2

The above documents mention a known issue with device linking and thrust.
Although the documents say it happens in debug mode(with -G flag), I noticed
similar crashes in release configuration too in ArrayFire.

Due to the above issue, I have separated out the relevant source files
(fft,blas,sparse and solver) which require device linking into separate
static library. Once separated into a separate static library, sort_by_key
and all the other unit tests that use it are running as expected without
any crashes.
---
 CMakeModules/AFcuda_helpers.cmake             |  60 +++++
 src/backend/cuda/CMakeLists.txt               | 217 +++++++++++-------
 src/backend/cuda/{blas.cpp => blas.cu}        |   8 +-
 src/backend/cuda/cublas.cpp                   |   3 +-
 src/backend/cuda/{cufft.cpp => cufft.cu}      |   1 +
 src/backend/cuda/{fft.cpp => fft.cu}          |   3 +-
 src/backend/cuda/{solve.cpp => solve.cu}      |  13 +-
 src/backend/cuda/{sparse.cpp => sparse.cu}    |   0
 .../{sparse_arith.cpp => sparse_arith.cu}     |  11 +-
 .../cuda/{sparse_blas.cpp => sparse_blas.cu}  |   7 +-
 src/backend/cuda/types.hpp                    |   9 +-
 11 files changed, 227 insertions(+), 105 deletions(-)
 create mode 100644 CMakeModules/AFcuda_helpers.cmake
 rename src/backend/cuda/{blas.cpp => blas.cu} (99%)
 rename src/backend/cuda/{cufft.cpp => cufft.cu} (99%)
 rename src/backend/cuda/{fft.cpp => fft.cu} (99%)
 rename src/backend/cuda/{solve.cpp => solve.cu} (99%)
 rename src/backend/cuda/{sparse.cpp => sparse.cu} (100%)
 rename src/backend/cuda/{sparse_arith.cpp => sparse_arith.cu} (99%)
 rename src/backend/cuda/{sparse_blas.cpp => sparse_blas.cu} (99%)

diff --git a/CMakeModules/AFcuda_helpers.cmake b/CMakeModules/AFcuda_helpers.cmake
new file mode 100644
index 0000000000..4fde494df8
--- /dev/null
+++ b/CMakeModules/AFcuda_helpers.cmake
@@ -0,0 +1,60 @@
+# Copyright (c) 2020, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+
+# The following macro uses a macro defined by
+# FindCUDA module from cmake.
+function(af_find_static_cuda_libs libname)
+  set(search_name
+    "${CMAKE_STATIC_LIBRARY_PREFIX}${libname}${CMAKE_STATIC_LIBRARY_SUFFIX}")
+  cuda_find_library_local_first(CUDA_${libname}_LIBRARY
+    ${search_name} "${libname} static library")
+  mark_as_advanced(CUDA_${libname}_LIBRARY)
+endfunction()
+
+## Copied from FindCUDA.cmake
+## The target_link_library needs to link with the cuda libraries using
+## PRIVATE
+function(cuda_add_library cuda_target)
+  cuda_add_cuda_include_once()
+
+  # Separate the sources from the options
+  cuda_get_sources_and_options(_sources _cmake_options _options ${ARGN})
+  cuda_build_shared_library(_cuda_shared_flag ${ARGN})
+  # Create custom commands and targets for each file.
+  cuda_wrap_srcs( ${cuda_target} OBJ _generated_files ${_sources}
+    ${_cmake_options} ${_cuda_shared_flag}
+    OPTIONS ${_options} )
+
+  # Compute the file name of the intermedate link file used for separable
+  # compilation.
+  cuda_compute_separable_compilation_object_file_name(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  # Add the library.
+  add_library(${cuda_target} ${_cmake_options}
+    ${_generated_files}
+    ${_sources}
+    ${link_file}
+    )
+
+  # Add a link phase for the separable compilation if it has been enabled.  If
+  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
+  # variable will have been defined.
+  cuda_link_separable_compilation_objects("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  target_link_libraries(${cuda_target}
+      PRIVATE ${CUDA_LIBRARIES}
+    )
+
+  # We need to set the linker language based on what the expected generated file
+  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
+  set_target_properties(${cuda_target}
+    PROPERTIES
+    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
+    POSITION_INDEPENDENT_CODE ON
+  )
+endfunction()
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 3e14227ddf..b6059d2166 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -5,13 +5,18 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
+dependency_check(CUDA_FOUND "CUDA not found.")
+
+include(AFcuda_helpers)
+include(FileToString)
 include(InternalUtils)
 include(select_compute_arch)
 
-dependency_check(CUDA_FOUND "CUDA not found.")
-
-find_cuda_helper_libs(nvrtc)
-find_cuda_helper_libs(nvrtc-builtins)
+# Remove cublas_device library which is no longer included with the cuda
+# toolkit. Fixes issues with older CMake versions
+if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
+  list(REMOVE_ITEM CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_device_LIBRARY})
+endif()
 
 if(NOT OPENGL_FOUND)
   # create a dummy gl.h header to satisfy cuda_gl_interop.h requirement
@@ -24,9 +29,50 @@ if(NOT OPENGL_FOUND)
   file(WRITE "${dummy_gl_root}/gl.h" "// Dummy file to satisy cuda_gl_interop")
 endif()
 
-get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
+# Find if CUDA Toolkit is at least 10.0 to use static
+# lapack library. Otherwise, we have to use regular shared library
+if(UNIX AND CUDA_VERSION_MAJOR VERSION_GREATER 10 OR CUDA_VERSION_MAJOR VERSION_EQUAL 10)
+  set(use_static_cuda_lapack ON)
+else()
+  set(use_static_cuda_lapack OFF)
+endif()
 
-include(FileToString)
+find_cuda_helper_libs(nvrtc)
+find_cuda_helper_libs(nvrtc-builtins)
+if(UNIX)
+  af_find_static_cuda_libs(culibos)
+  af_find_static_cuda_libs(cublas_static)
+  af_find_static_cuda_libs(cublasLt_static)
+  af_find_static_cuda_libs(cufft_static)
+  af_find_static_cuda_libs(cusparse_static)
+
+  # FIXME When NVCC resolves this particular issue.
+  # NVCC doesn't like -l<full_path_static_lib>, hence we cannot
+  # use ${CMAKE_*_LIBRARY} variables in the following flags.
+  set(af_cuda_static_flags "-rdc=true;-dlink")
+  set(af_cuda_static_flags "${af_cuda_static_flags};-lculibos")
+  set(af_cuda_static_flags "${af_cuda_static_flags};-lcublas_static")
+  set(af_cuda_static_flags "${af_cuda_static_flags};-lcublasLt_static")
+  set(af_cuda_static_flags "${af_cuda_static_flags};-lcufft_static")
+  set(af_cuda_static_flags "${af_cuda_static_flags};-lcusparse_static")
+
+  if(${use_static_cuda_lapack})
+    af_find_static_cuda_libs(cusolver_static)
+    set(cusolver_static_lib "${CUDA_cusolver_static_LIBRARY}")
+
+    # NVIDIA LAPACK library liblapack_static.a is a subset of LAPACK and only
+    # contains GPU accelerated stedc and bdsqr. The user has to link
+    # libcusolver_static.a with liblapack_static.a in order to build
+    # successfully.
+    af_find_static_cuda_libs(lapack_static)
+
+    set(af_cuda_static_flags "${af_cuda_static_flags};-lcusolver_static")
+  else()
+    set(cusolver_lib "${CUDA_cusolver_LIBRARY}")
+  endif()
+endif()
+
+get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
 if(NOT CUDA_architecture_build_targets)
   cuda_detect_installed_gpus(detected_gpus)
@@ -171,54 +217,9 @@ file_to_string(
     NULLTERM
     )
 
-## Copied from FindCUDA.cmake
-## The target_link_library needs to link with the cuda libraries using
-## PRIVATE
-function(cuda_add_library cuda_target)
-  cuda_add_cuda_include_once()
-
-  # Separate the sources from the options
-  cuda_get_sources_and_options(_sources _cmake_options _options ${ARGN})
-  cuda_build_shared_library(_cuda_shared_flag ${ARGN})
-  # Create custom commands and targets for each file.
-  cuda_wrap_srcs( ${cuda_target} OBJ _generated_files ${_sources}
-    ${_cmake_options} ${_cuda_shared_flag}
-    OPTIONS ${_options} )
-
-  # Compute the file name of the intermedate link file used for separable
-  # compilation.
-  cuda_compute_separable_compilation_object_file_name(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
-
-  # Add the library.
-  add_library(${cuda_target} ${_cmake_options}
-    ${_generated_files}
-    ${_sources}
-    ${link_file}
-    )
-
-  # Add a link phase for the separable compilation if it has been enabled.  If
-  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
-  # variable will have been defined.
-  cuda_link_separable_compilation_objects("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
-
-  target_link_libraries(${cuda_target}
-      PRIVATE ${CUDA_LIBRARIES}
-    )
-
-  # We need to set the linker language based on what the expected generated file
-  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
-  set_target_properties(${cuda_target}
-    PROPERTIES
-    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
-    POSITION_INDEPENDENT_CODE ON
-  )
-
-endfunction()
-
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
 arrayfire_get_platform_definitions(platform_flags)
 
-
 get_property(boost_includes TARGET Boost::boost PROPERTY INTERFACE_INCLUDE_DIRECTORIES)
 get_property(boost_definitions TARGET Boost::boost PROPERTY INTERFACE_COMPILE_DEFINITIONS)
 
@@ -245,9 +246,78 @@ list(APPEND cuda_cxx_flags ${cxx_definitions})
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
+# CUDA static libraries require device linking to successfully link
+# against afcuda target. Device linking requires CUDA_SEPARABLE_COMPILATION
+# to be ON. Therefore, we turn on separable compilation for a subset of
+# source files while compiling af_cuda_static_cuda_library target. Once
+# this subset is compiled, separable compilation is reset to it's original
+# value.
+if(UNIX)
+  # Static linking cuda libs require device linking, which in turn
+  # requires separable compilation.
+  set(pior_val_CUDA_SEPARABLE_COMPILATION OFF)
+  if(DEFINED CUDA_SEPARABLE_COMPILATION)
+    set(pior_val_CUDA_SEPARABLE_COMPILATION ${CUDA_SEPARABLE_COMPILATION})
+  endif()
+  set(CUDA_SEPARABLE_COMPILATION ON)
+endif()
+
+cuda_add_library(af_cuda_static_cuda_library STATIC
+    blas.cu
+    blas.hpp
+    cufft.cu
+    cufft.hpp
+    fft.cu
+    sparse.cu
+    sparse.hpp
+    sparse_arith.cu
+    sparse_arith.hpp
+    sparse_blas.cu
+    sparse_blas.hpp
+    solve.cu
+    solve.hpp
+
+    OPTIONS
+    ${platform_flags} ${cuda_cxx_flags} ${af_cuda_static_flags}
+    -Xcudafe \"--diag_suppress=1427\" -DAFDLL
+)
+
+set_target_properties(af_cuda_static_cuda_library
+  PROPERTIES
+  LINKER_LANGUAGE CXX
+  FOLDER "Generated Targets"
+)
+
+if(UNIX)
+  target_link_libraries(af_cuda_static_cuda_library
+    PRIVATE
+      Boost::boost
+      ${CMAKE_DL_LIBS}
+      ${cusolver_lib}
+      -Wl,--start-group
+      ${CUDA_culibos_LIBRARY} #also a static libary
+      ${CUDA_cublas_static_LIBRARY}
+      ${CUDA_cublasLt_static_LIBRARY}
+      ${CUDA_cufft_static_LIBRARY}
+      ${CUDA_lapack_static_LIBRARY}
+      ${CUDA_cusparse_static_LIBRARY}
+      ${cusolver_static_lib}
+      -Wl,--end-group
+  )
+  set(CUDA_SEPARABLE_COMPILATION ${pior_val_CUDA_SEPARABLE_COMPILATION})
+else()
+  target_link_libraries(af_cuda_static_cuda_library
+    PRIVATE
+      Boost::boost
+      ${CUDA_CUBLAS_LIBRARIES}
+      ${CUDA_CUFFT_LIBRARIES}
+      ${CUDA_cusolver_LIBRARY}
+      ${CUDA_cusparse_LIBRARY}
+  )
+endif()
+
 cuda_add_library(afcuda
     ${thrust_sort_sources}
-    sort.hpp
 
     all.cu
     anisotropic_diffusion.cpp
@@ -390,7 +460,6 @@ cuda_add_library(afcuda
     backend.hpp
     bilateral.hpp
     binary.hpp
-    blas.cpp
     blas.hpp
     canny.hpp
     cast.hpp
@@ -407,7 +476,6 @@ cuda_add_library(afcuda
     cudnn.hpp
     cudnnModule.cpp
     cudnnModule.hpp
-    cufft.cpp
     cufft.hpp
     cusolverDn.cpp
     cusolverDn.hpp
@@ -427,7 +495,6 @@ cuda_add_library(afcuda
     fast.hpp
     fast_pyramid.cpp
     fast_pyramid.hpp
-    fft.cpp
     fft.hpp
     fftconvolve.cpp
     fftconvolve.hpp
@@ -509,15 +576,12 @@ cuda_add_library(afcuda
     shift.hpp
     sift.hpp
     sobel.hpp
-    solve.cpp
     solve.hpp
+    sort.hpp
     sort_by_key.hpp
     sort_index.hpp
-    sparse.cpp
     sparse.hpp
-    sparse_arith.cpp
     sparse_arith.hpp
-    sparse_blas.cpp
     sparse_blas.hpp
     surface.cpp
     surface.hpp
@@ -570,6 +634,8 @@ target_compile_options(afcuda PRIVATE ${cxx_definitions})
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
+add_dependencies(af_cuda_static_cuda_library ${nvrtc_kernel_targets})
+add_dependencies(afcuda af_cuda_static_cuda_library)
 
 target_include_directories (afcuda
   PUBLIC
@@ -586,29 +652,14 @@ target_include_directories (afcuda
     ${cuDNN_INCLUDE_DIRS}
 )
 
-# Remove cublas_device library which is no longer included with the cuda
-# toolkit. Fixes issues with older CMake versions
-if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
-  list(REMOVE_ITEM CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_device_LIBRARY})
-endif()
-
-# Remove cublas_device library which is no longer included with the cuda
-# toolkit. Fixes issues with older CMake versions
-if(DEFINED CUDA_cublas_device_LIBRARY AND NOT CUDA_cublas_device_LIBRARY)
-  list(REMOVE_ITEM CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_device_LIBRARY})
-endif()
-
 target_link_libraries(afcuda
   PRIVATE
     c_api_interface
     cpp_api_interface
     afcommon_interface
-    ${CUDA_nvrtc_LIBRARY}
-    ${CUDA_CUBLAS_LIBRARIES}
-    ${CUDA_CUFFT_LIBRARIES}
-    ${CUDA_cusolver_LIBRARY}
-    ${CUDA_cusparse_LIBRARY}
     ${CMAKE_DL_LIBS}
+    ${CUDA_nvrtc_LIBRARY}
+    af_cuda_static_cuda_library
   )
 
 # If the driver is not found the cuda driver api need to be linked against the
@@ -703,13 +754,17 @@ function(afcu_collect_libs libname)
 endfunction()
 
 if(AF_INSTALL_STANDALONE)
-  afcu_collect_libs(cufft)
   afcu_collect_libs(cudnn)
-  afcu_collect_libs(cublas)
-  afcu_collect_libs(cublasLt)
-  afcu_collect_libs(cusolver)
-  afcu_collect_libs(cusparse)
   afcu_collect_libs(nvrtc FULL_VERSION)
+  if(WIN32)
+    afcu_collect_libs(cufft)
+    afcu_collect_libs(cublas)
+    afcu_collect_libs(cublasLt)
+    afcu_collect_libs(cusolver)
+    afcu_collect_libs(cusparse)
+  elseif(NOT ${use_static_cuda_lapack})
+    afcu_collect_libs(cusolver)
+  endif()
 
   if(APPLE)
     afcu_collect_libs(cudart)
diff --git a/src/backend/cuda/blas.cpp b/src/backend/cuda/blas.cu
similarity index 99%
rename from src/backend/cuda/blas.cpp
rename to src/backend/cuda/blas.cu
index 4d61e6439e..188a426118 100644
--- a/src/backend/cuda/blas.cpp
+++ b/src/backend/cuda/blas.cu
@@ -7,11 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define NVCC
 #include <blas.hpp>
-#include <cublas_v2.h>
-#include <cuda_runtime.h>
-#include <platform.hpp>
 
 #include <arith.hpp>
 #include <cast.hpp>
@@ -20,11 +16,15 @@
 #include <complex.hpp>
 #include <copy.hpp>
 #include <cublas.hpp>
+#include <cublas_v2.h>
+#include <cuda_runtime.h>
 #include <err_cuda.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 #include <reduce.hpp>
 #include <tile.hpp>
 #include <transpose.hpp>
+#include <types.hpp>
 
 #include <cassert>
 #include <functional>
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 29a0023a18..4f024b8117 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <cublas.hpp>
+
+#include <common/err_common.hpp>
 #include <platform.hpp>
 
 namespace cuda {
diff --git a/src/backend/cuda/cufft.cpp b/src/backend/cuda/cufft.cu
similarity index 99%
rename from src/backend/cuda/cufft.cpp
rename to src/backend/cuda/cufft.cu
index 55fcdbb415..9dd976e9fe 100644
--- a/src/backend/cuda/cufft.cpp
+++ b/src/backend/cuda/cufft.cu
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <cufft.hpp>
+
 #include <memory.hpp>
 #include <platform.hpp>
 
diff --git a/src/backend/cuda/fft.cpp b/src/backend/cuda/fft.cu
similarity index 99%
rename from src/backend/cuda/fft.cpp
rename to src/backend/cuda/fft.cu
index bb1219171e..634f22daeb 100644
--- a/src/backend/cuda/fft.cpp
+++ b/src/backend/cuda/fft.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <fft.hpp>
+
 #include <Array.hpp>
 #include <copy.hpp>
 #include <cufft.hpp>
 #include <debug_cuda.hpp>
-#include <fft.hpp>
 #include <math.hpp>
 #include <memory.hpp>
 #include <af/dim4.hpp>
diff --git a/src/backend/cuda/solve.cpp b/src/backend/cuda/solve.cu
similarity index 99%
rename from src/backend/cuda/solve.cpp
rename to src/backend/cuda/solve.cu
index 4019170d2d..d45406a77c 100644
--- a/src/backend/cuda/solve.cpp
+++ b/src/backend/cuda/solve.cu
@@ -7,23 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <solve.hpp>
 
+#include <blas.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
 #include <cublas_v2.h>
 #include <cusolverDn.hpp>
 #include <identity.hpp>
+#include <lu.hpp>
+#include <math.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
-#include <transpose.hpp>
-
-#include <common/err_common.hpp>
-#include <math.hpp>
-
-#include <blas.hpp>
-#include <lu.hpp>
 #include <qr.hpp>
+#include <transpose.hpp>
 
 #include <cstdio>
 
diff --git a/src/backend/cuda/sparse.cpp b/src/backend/cuda/sparse.cu
similarity index 100%
rename from src/backend/cuda/sparse.cpp
rename to src/backend/cuda/sparse.cu
diff --git a/src/backend/cuda/sparse_arith.cpp b/src/backend/cuda/sparse_arith.cu
similarity index 99%
rename from src/backend/cuda/sparse_arith.cpp
rename to src/backend/cuda/sparse_arith.cu
index a4fe734224..66fad0bac2 100644
--- a/src/backend/cuda/sparse_arith.cpp
+++ b/src/backend/cuda/sparse_arith.cu
@@ -7,11 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <kernel/sparse_arith.hpp>
-#include <sparse.hpp>
-
-#include <stdexcept>
-#include <string>
+#include <sparse_arith.hpp>
 
 #include <arith.hpp>
 #include <cast.hpp>
@@ -20,11 +16,16 @@
 #include <complex.hpp>
 #include <copy.hpp>
 #include <cusparse.hpp>
+#include <kernel/sparse_arith.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <sparse.hpp>
 #include <where.hpp>
 
+#include <stdexcept>
+#include <string>
+
 namespace cuda {
 
 using namespace common;
diff --git a/src/backend/cuda/sparse_blas.cpp b/src/backend/cuda/sparse_blas.cu
similarity index 99%
rename from src/backend/cuda/sparse_blas.cpp
rename to src/backend/cuda/sparse_blas.cu
index 59d462780f..eb7378776c 100644
--- a/src/backend/cuda/sparse_blas.cpp
+++ b/src/backend/cuda/sparse_blas.cu
@@ -7,14 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cuda_runtime.h>
-#include <cusparse.hpp>
-#include <platform.hpp>
 #include <sparse_blas.hpp>
 
 #include <common/err_common.hpp>
 #include <complex.hpp>
+#include <cuda_runtime.h>
+#include <cusparse.hpp>
 #include <math.hpp>
+#include <platform.hpp>
+
 #include <stdexcept>
 #include <string>
 
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 93e1704ed7..d18d747db5 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -148,13 +148,17 @@ struct kernel_type<common::half> {
     using data = common::half;
 
 #ifdef __CUDA_ARCH__
+
     // These are the types within a kernel
 #if __CUDA_ARCH__ >= 530 && __CUDA_ARCH__ != 610
     using compute = __half;
 #else
     using compute = float;
 #endif
-#else
+    using native = compute;
+
+#else  // __CUDA_ARCH__
+
     // outside of a cuda kernel use float
     using compute = float;
 
@@ -163,6 +167,7 @@ struct kernel_type<common::half> {
 #else
     using native = common::half;
 #endif
-#endif
+
+#endif  // __CUDA_ARCH__
 };
 }  // namespace common

From 1ba5d242b36dab4a0741c44c9e7419aa91480b09 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 31 Mar 2020 15:52:09 +0530
Subject: [PATCH 1881/2677] Remove unsed header from wrap cpu kernel

---
 src/backend/cpu/kernel/wrap.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 094c224d1a..6b574ee158 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ParamIterator.hpp>
 #include <err_cpu.hpp>
 #include <math.hpp>
 

From d1370120eeb2161ef162acb7da2c80eb4048e2c4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 31 Mar 2020 16:25:41 +0530
Subject: [PATCH 1882/2677] Refactor cpu confidence cc to use ParamIterator

Removed a special neighborhood iterator which isn't necessary
---
 src/backend/cpu/ParamIterator.hpp     | 179 ++------------------------
 src/backend/cpu/kernel/flood_fill.hpp |  62 +++++----
 2 files changed, 53 insertions(+), 188 deletions(-)

diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
index 6c6f73b616..9b2ea78208 100644
--- a/src/backend/cpu/ParamIterator.hpp
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -18,17 +18,6 @@
 
 namespace cpu {
 
-/// Calculates the iterator offsets.
-///
-/// These are different from the original offsets because they define
-/// the stride from the end of the last element in the previous dimension
-/// to the first element on the next dimension.
-static dim4 calcIteratorStrides(const dim4& dims, const dim4& stride) noexcept {
-    return dim4(stride[0], stride[1] - (stride[0] * dims[0]),
-                stride[2] - (stride[1] * dims[1]),
-                stride[3] - (stride[2] * dims[2]));
-}
-
 /// A Param iterator that iterates through a Param object
 template<typename T>
 class ParamIterator {
@@ -54,7 +43,7 @@ class ParamIterator {
         , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]} {}
 
     ParamIterator(cpu::CParam<typename std::remove_const<T>::type>& in) noexcept
-        : ptr(in.get())
+        : ptr(const_cast<pointer>(in.get()))
         , dims(in.dims())
         , stride(calcIteratorStrides(dims, in.strides()))
         , dim_index{in.dims()[0], in.dims()[1], in.dims()[2], in.dims()[3]} {}
@@ -110,6 +99,18 @@ class ParamIterator {
     // NOTE: This is not really the true coordinate of the iteration. It's
     // values will go down as you move through the array.
     std::array<dim_t, AF_MAX_DIMS> dim_index;
+
+    /// Calculates the iterator offsets.
+    ///
+    /// These are different from the original offsets because they define
+    /// the stride from the end of the last element in the previous dimension
+    /// to the first element on the next dimension.
+    static dim4 calcIteratorStrides(const dim4& dims,
+                                    const dim4& stride) noexcept {
+        return dim4(stride[0], stride[1] - (stride[0] * dims[0]),
+                    stride[2] - (stride[1] * dims[1]),
+                    stride[3] - (stride[2] * dims[2]));
+    }
 };
 
 template<typename T>
@@ -132,158 +133,4 @@ ParamIterator<const T> end(CParam<T>& param) {
     return ParamIterator<const T>();
 }
 
-/// Neighborhood iterator for Param data
-template<typename T>
-class NeighborhoodIterator {
-   public:
-    using difference_type   = ptrdiff_t;
-    using value_type        = T;
-    using pointer           = T*;
-    using reference         = T&;
-    using iterator_category = std::forward_iterator_tag;
-
-    using Self = NeighborhoodIterator;
-
-    /// Creates a sentinel iterator. This is equivalent to the end iterator
-    NeighborhoodIterator() noexcept
-        : nhoodRadius(0, 0, 0, 0)
-        , origDims(1)
-        , origStrides(1)
-        , iterDims(1)
-        , iterStrides(1)
-        , origPtr(nullptr)
-        , ptr(origPtr)
-        , nhoodIndex(0) {
-        calcOffsets();
-    }
-
-    /// NeighborhoodIterator Constructor
-    NeighborhoodIterator(cpu::Param<T>& in, const af::dim4 _radius) noexcept
-        : nhoodRadius(_radius)
-        , origDims(nhoodSize(nhoodRadius))
-        , origStrides(in.strides())
-        , iterDims(origDims)
-        , iterStrides(calcIteratorStrides(origDims, in.strides()))
-        , origPtr(in.get())
-        , ptr(origPtr)
-        , nhoodIndex(0) {
-        calcOffsets();
-    }
-
-    /// NeighborhoodIterator Constructor
-    NeighborhoodIterator(cpu::CParam<typename std::remove_const<T>::type>& in,
-                         const af::dim4 _radius) noexcept
-        : nhoodRadius(_radius)
-        , origDims(nhoodSize(nhoodRadius))
-        , origStrides(in.strides())
-        , iterDims(origDims)
-        , iterStrides(calcIteratorStrides(origDims, in.strides()))
-        , origPtr(const_cast<T*>(in.get()))
-        , ptr(origPtr)
-        , nhoodIndex(0) {
-        calcOffsets();
-    }
-
-    /// The equality operator
-    bool operator==(const Self& other) const noexcept {
-        return ptr == other.ptr;
-    }
-
-    /// The inequality operator
-    bool operator!=(const Self& other) const noexcept {
-        return ptr != other.ptr;
-    }
-
-    /// Set neighborhood center
-    ///
-    /// This method automatically resets iterator to starting point
-    /// of the neighborhood around the set center point
-    void setCenter(const af::dim4 center) noexcept {
-        ptr = origPtr;
-        for (dim_t d = 0; d < AF_MAX_DIMS; ++d) {
-            ptr += ((center[d] - nhoodRadius[d]) * origStrides[d]);
-        }
-        nhoodIndex = 0;
-    }
-
-    /// Advances the iterator, pre increment operator
-    Self& operator++() noexcept {
-        nhoodIndex++;
-        for (dim_t i = 0; i < AF_MAX_DIMS; i++) {
-            iterDims[i]--;
-            ptr += iterStrides[i];
-            if (iterDims[i]) { return *this; }
-            iterDims[i] = origDims[i];
-        }
-        ptr = nullptr;
-        return *this;
-    }
-
-    /// @copydoc operator++()
-    Self operator++(int) noexcept {
-        Self before(*this);
-        operator++();
-        return before;
-    }
-
-    reference operator*() const noexcept { return *ptr; }
-    pointer operator->() const noexcept { return ptr; }
-
-    /// Gets offsets of current position from center
-    const af::dim4 offset() const noexcept {
-        if (ptr) {
-            // Branch predictor almost always is a hit since,
-            // NeighborhoodIterator::offset is called only when iterator is
-            // valid i.e. it is not equal to END iterator
-            return offsets[nhoodIndex];
-        } else {
-            return af::dim4(0, 0, 0, 0);
-        }
-    }
-
-    NeighborhoodIterator(const NeighborhoodIterator<T>& other) = default;
-    NeighborhoodIterator(NeighborhoodIterator<T>&& other)      = default;
-    ~NeighborhoodIterator() noexcept                           = default;
-    NeighborhoodIterator<T>& operator=(const Self& other) = default;
-    NeighborhoodIterator<T>& operator=(Self&& other) = default;
-
-   private:
-    const af::dim4 nhoodRadius;
-    const af::dim4 origDims;
-    const af::dim4 origStrides;
-    af::dim4 iterDims;
-    af::dim4 iterStrides;
-    pointer origPtr;
-    pointer ptr;
-    dim_t nhoodIndex;
-    std::vector<af::dim4> offsets;
-
-    af::dim4 nhoodSize(const af::dim4& radius) const noexcept {
-        return af::dim4(2 * radius[0] + 1, 2 * radius[1] + 1, 2 * radius[2] + 1,
-                        2 * radius[3] + 1);
-    }
-
-    void calcOffsets() noexcept {
-        auto linear2Coords = [this](const dim_t index) -> af::dim4 {
-            af::dim4 coords(0, 0, 0, 0);
-            for (dim_t i = 0, idx = index; i < AF_MAX_DIMS;
-                 ++i, idx /= origDims[i]) {
-                coords[i] = idx % origDims[i];
-            }
-            return coords;
-        };
-
-        offsets.clear();
-        size_t nElems = (2 * nhoodRadius[0] + 1) * (2 * nhoodRadius[1] + 1) *
-                        (2 * nhoodRadius[2] + 1) * (2 * nhoodRadius[3] + 1);
-        offsets.reserve(nElems);
-        for (size_t i = 0; i < nElems; ++i) {
-            auto coords = linear2Coords(i);
-            offsets.emplace_back(
-                coords[0] - nhoodRadius[0], coords[1] - nhoodRadius[1],
-                coords[2] - nhoodRadius[2], coords[3] - nhoodRadius[3]);
-        }
-    }
-};
-
 }  // namespace cpu
diff --git a/src/backend/cpu/kernel/flood_fill.hpp b/src/backend/cpu/kernel/flood_fill.hpp
index 1a0ef86ee0..045564ef44 100644
--- a/src/backend/cpu/kernel/flood_fill.hpp
+++ b/src/backend/cpu/kernel/flood_fill.hpp
@@ -35,16 +35,28 @@ void floodFill(Param<T> out, CParam<T> in, CParam<uint> x, CParam<uint> y,
     UNUSED(connectivity);
 
     using af::dim4;
+    using PtrDist    = typename ParamIterator<T>::difference_type;
     using Point      = std::pair<uint, uint>;
     using Candidates = std::queue<Point>;
 
-    const size_t numSeeds = x.dims().elements();
-    const dim4 inDims     = in.dims();
+    const dim4 dims    = in.dims();
+    const dim4 strides = in.strides();
 
-    auto isInside = [&inDims](uint x, uint y) -> bool {
-        return (x >= 0 && x < inDims[0] && y >= 0 && y < inDims[1]);
-    };
+    ParamIterator<T> endOfNeighborhood;
+    const dim4 nhoodRadii(1, 1, 0, 0);
+    const dim4 nhood(2 * nhoodRadii[0] + 1, 2 * nhoodRadii[1] + 1,
+                     2 * nhoodRadii[2] + 1, 2 * nhoodRadii[3] + 1);
 
+    auto isInside = [&dims](uint x, uint y) {
+        return (x >= 0 && x < dims[0] && y >= 0 && y < dims[1]);
+    };
+    auto leftTopPtr = [&strides, &nhoodRadii](T* ptr, const af::dim4& center) {
+        T* ltPtr = ptr;
+        for (dim_t d = 0; d < AF_MAX_DIMS; ++d) {
+            ltPtr += ((center[d] - nhoodRadii[d]) * strides[d]);
+        }
+        return ltPtr;
+    };
     Candidates queue;
     {
         auto oit = begin(out);
@@ -52,44 +64,50 @@ void floodFill(Param<T> out, CParam<T> in, CParam<uint> x, CParam<uint> y,
              xit != end(x) && yit != end(y); ++xit, ++yit) {
             if (isInside(*xit, *yit)) {
                 queue.emplace(*xit, *yit);
-                oit.operator->()[(*xit) + (*yit) * inDims[0]] = T(2);
+                oit.operator->()[(*xit) + (*yit) * dims[0]] = T(2);
             }
         }
     }
 
-    NeighborhoodIterator<T> inNeighborhood(in, dim4(1, 1, 0, 0));
-    NeighborhoodIterator<T> endOfNeighborhood;
-    NeighborhoodIterator<T> outNeighborhood(out, dim4(1, 1, 0, 0));
+    T* inPtr  = const_cast<T*>(in.get());
+    T* outPtr = out.get();
 
     while (!queue.empty()) {
-        auto p = queue.front();
+        Point& p = queue.front();
+
+        const dim4 center(p.first, p.second, 0, 0);
+
+        CParam<T> inNHood(const_cast<const T*>(leftTopPtr(inPtr, center)),
+                          nhood, strides);
+        Param<T> outNHood(leftTopPtr(outPtr, center), nhood, strides);
 
-        inNeighborhood.setCenter(dim4(p.first, p.second, 0, 0));
-        outNeighborhood.setCenter(dim4(p.first, p.second, 0, 0));
+        ParamIterator<T> inIter(inNHood);
+        ParamIterator<T> outIter(outNHood);
 
-        while (inNeighborhood != endOfNeighborhood) {
-            const dim4 offsetP = inNeighborhood.offset();
-            const uint currx   = static_cast<uint>(p.first + offsetP[0]);
-            const uint curry   = static_cast<uint>(p.second + offsetP[1]);
+        while (inIter != endOfNeighborhood) {
+            const T* ptr     = inIter.operator->();
+            PtrDist dist     = ptr - inPtr;
+            const uint currx = static_cast<uint>(dist % dims[0]);
+            const uint curry = static_cast<uint>(dist / dims[0]);
 
-            if (isInside(currx, curry) && (*outNeighborhood == 0)) {
+            if (isInside(currx, curry) && (*outIter == 0)) {
                 // Current point is inside image boundaries and hasn't been
                 // visited at all.
-                if (*inNeighborhood >= lower && *inNeighborhood <= upper) {
+                if (*inIter >= lower && *inIter <= upper) {
                     // Current pixel is within threshold limits.
                     // Mark as valid and push on to the queue
-                    *outNeighborhood = T(2);
+                    *outIter = T(2);
                     queue.emplace(currx, curry);
                 } else {
                     // Not valid pixel
-                    *outNeighborhood = T(1);
+                    *outIter = T(1);
                 }
             }
             // Both input and output neighborhood iterators
             // should increment in lock step for this algorithm
             // to work correctly
-            ++inNeighborhood;
-            ++outNeighborhood;
+            ++inIter;
+            ++outIter;
         }
         queue.pop();
     }

From 0a66851f4f646eb60638db27a9712e7bde508dd6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 7 Apr 2020 11:42:05 -0400
Subject: [PATCH 1883/2677] Ragged reduction (#2786)

* initial ragged max api and cuda implementation

* move ragged lengths into single ireduce kernel implementation

* adds opencl, cpu ragged max to ireduce

* fix issue with cuda bounds for higher dimensions, adds range based tests

* opencl kernel updates for higher dimensions

* check out of bounds access in lengths array

* fix incorrect nullptr for empty buffer in cl backend, clang-format

* update api

* remove old tests
---
 include/af/algorithm.h                     |  37 ++++
 src/api/c/reduce.cpp                       |  88 +++++++++
 src/api/cpp/reduce.cpp                     |   8 +
 src/api/unified/algorithm.cpp              |   6 +
 src/backend/cpu/ireduce.cpp                |  30 +++-
 src/backend/cpu/ireduce.hpp                |   4 +
 src/backend/cpu/kernel/ireduce.hpp         |  20 ++-
 src/backend/cuda/ireduce.cpp               |  12 +-
 src/backend/cuda/ireduce.hpp               |   4 +
 src/backend/cuda/kernel/ireduce.cuh        |  29 ++-
 src/backend/cuda/kernel/ireduce.hpp        |  37 ++--
 src/backend/opencl/ireduce.cpp             |  12 +-
 src/backend/opencl/ireduce.hpp             |   4 +
 src/backend/opencl/kernel/ireduce.hpp      |  51 ++++--
 src/backend/opencl/kernel/ireduce_dim.cl   |  17 +-
 src/backend/opencl/kernel/ireduce_first.cl |  11 +-
 test/reduce.cpp                            | 199 +++++++++++++++++++++
 17 files changed, 507 insertions(+), 62 deletions(-)

diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index a8372c9d3e..7c8cfdd393 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -216,6 +216,24 @@ namespace af
                         const int dim = -1);
 #endif
 
+#if AF_API_VERSION >= 38
+    /**
+       C++ Interface for ragged max values in an array
+       Uses an additional input array to determine the number of elements to use along the reduction axis.
+
+       \param[out] val will contain the maximum ragged values in \p in along \p dim according to \p ragged_len
+       \param[out] idx will contain the locations of the maximum ragged values in \p in along \p dim according to \p ragged_len
+       \param[in] in contains the input values to be reduced
+       \param[in] ragged_len array containing number of elements to use when reducing along \p dim
+       \param[in] dim The dimension along which the max operation occurs
+
+       \ingroup reduce_func_max
+
+       \note NaN values are ignored
+    */
+    AFAPI void max(array &val, array &idx, const array &in, const array &ragged_len, const int dim);
+#endif
+
     /**
        C++ Interface for checking all true values in an array
 
@@ -838,6 +856,25 @@ extern "C" {
                                const int dim);
 #endif
 
+#if AF_API_VERSION >= 38
+    /**
+       C Interface for finding ragged max values in an array
+       Uses an additional input array to determine the number of elements to use along the reduction axis.
+
+       \param[out] val will contain the maximum ragged values in \p in along \p dim according to \p ragged_len
+       \param[out] idx will contain the locations of the maximum ragged values in \p in along \p dim according to \p ragged_len
+       \param[in] in contains the input values to be reduced
+       \param[in] ragged_len array containing number of elements to use when reducing along \p dim
+       \param[in] dim The dimension along which the max operation occurs
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_max
+
+       \note NaN values are ignored
+    */
+    AFAPI af_err af_max_ragged(af_array *val, af_array *idx, const af_array in, const af_array ragged_len, const int dim);
+#endif
+
     /**
        C Interface for checking all true values in an array
 
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 82909584bb..1c5ef4c821 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -752,6 +752,22 @@ static inline void ireduce(af_array *res, af_array *loc, const af_array in,
     *loc = getHandle(Loc);
 }
 
+template<af_op_t op, typename T>
+static inline void rreduce(af_array *res, af_array *loc, const af_array in,
+                           const int dim, const af_array ragged_len) {
+    const Array<T> In     = getArray<T>(in);
+    const Array<uint> Len = getArray<uint>(ragged_len);
+    dim4 odims            = In.dims();
+    odims[dim]            = 1;
+
+    Array<T> Res    = createEmptyArray<T>(odims);
+    Array<uint> Loc = createEmptyArray<uint>(odims);
+    rreduce<op, T>(Res, Loc, In, dim, Len);
+
+    *res = getHandle(Res);
+    *loc = getHandle(Loc);
+}
+
 template<af_op_t op>
 static af_err ireduce_common(af_array *val, af_array *idx, const af_array in,
                              const int dim) {
@@ -804,6 +820,78 @@ af_err af_imax(af_array *val, af_array *idx, const af_array in, const int dim) {
     return ireduce_common<af_max_t>(val, idx, in, dim);
 }
 
+template<af_op_t op>
+static af_err rreduce_common(af_array *val, af_array *idx, const af_array in,
+                             const af_array ragged_len, const int dim) {
+    try {
+        ARG_ASSERT(3, dim >= 0);
+        ARG_ASSERT(3, dim < 4);
+
+        const ArrayInfo &in_info = getInfo(in);
+        ARG_ASSERT(2, in_info.ndims() > 0);
+
+        if (dim >= (int)in_info.ndims()) {
+            *val = retain(in);
+            *idx = createHandleFromValue<uint>(in_info.dims(), 0);
+            return AF_SUCCESS;
+        }
+
+        // TODO: make sure ragged_len.dims == in.dims(), except on reduced dim
+        const ArrayInfo &ragged_info = getInfo(ragged_len);
+        dim4 test_dim                = in_info.dims();
+        test_dim[dim]                = 1;
+        ARG_ASSERT(4, test_dim == ragged_info.dims());
+
+        af_dtype keytype = ragged_info.getType();
+        if (keytype != u32) { TYPE_ERROR(4, keytype); }
+
+        af_dtype type = in_info.getType();
+        af_array res, loc;
+
+        switch (type) {
+            case f32:
+                rreduce<op, float>(&res, &loc, in, dim, ragged_len);
+                break;
+            case f64:
+                rreduce<op, double>(&res, &loc, in, dim, ragged_len);
+                break;
+            case c32:
+                rreduce<op, cfloat>(&res, &loc, in, dim, ragged_len);
+                break;
+            case c64:
+                rreduce<op, cdouble>(&res, &loc, in, dim, ragged_len);
+                break;
+            case u32: rreduce<op, uint>(&res, &loc, in, dim, ragged_len); break;
+            case s32: rreduce<op, int>(&res, &loc, in, dim, ragged_len); break;
+            case u64:
+                rreduce<op, uintl>(&res, &loc, in, dim, ragged_len);
+                break;
+            case s64: rreduce<op, intl>(&res, &loc, in, dim, ragged_len); break;
+            case u16:
+                rreduce<op, ushort>(&res, &loc, in, dim, ragged_len);
+                break;
+            case s16:
+                rreduce<op, short>(&res, &loc, in, dim, ragged_len);
+                break;
+            case b8: rreduce<op, char>(&res, &loc, in, dim, ragged_len); break;
+            case u8: rreduce<op, uchar>(&res, &loc, in, dim, ragged_len); break;
+            case f16: rreduce<op, half>(&res, &loc, in, dim, ragged_len); break;
+            default: TYPE_ERROR(2, type);
+        }
+
+        std::swap(*val, res);
+        std::swap(*idx, loc);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
+af_err af_max_ragged(af_array *val, af_array *idx, const af_array in,
+                     const af_array ragged_len, const int dim) {
+    return rreduce_common<af_max_t>(val, idx, in, ragged_len, dim);
+}
+
 template<af_op_t op, typename T>
 static inline T ireduce_all(unsigned *loc, const af_array in) {
     return ireduce_all<op, T>(loc, getArray<T>(in));
diff --git a/src/api/cpp/reduce.cpp b/src/api/cpp/reduce.cpp
index 15c16365f5..44f981982d 100644
--- a/src/api/cpp/reduce.cpp
+++ b/src/api/cpp/reduce.cpp
@@ -106,6 +106,14 @@ void maxByKey(array &keys_out, array &vals_out, const array &keys,
     vals_out = array(ovals);
 }
 
+void max(array &val, array &idx, const array &in, const array &ragged_len,
+         const int dim) {
+    af_array oval, oidx;
+    AF_THROW(af_max_ragged(&oval, &oidx, in.get(), ragged_len.get(), dim));
+    val = array(oval);
+    idx = array(oidx);
+}
+
 // 2.1 compatibility
 array alltrue(const array &in, const int dim) { return allTrue(in, dim); }
 array allTrue(const array &in, const int dim) {
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index 8a18760867..87f03a053a 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -176,3 +176,9 @@ af_err af_set_intersect(af_array *out, const af_array first,
     CHECK_ARRAYS(first, second);
     CALL(af_set_intersect, out, first, second, is_unique);
 }
+
+af_err af_max_ragged(af_array *vals, af_array *idx, const af_array in,
+                     const af_array ragged_len, const int dim) {
+    CHECK_ARRAYS(in, ragged_len);
+    CALL(af_max_ragged, vals, idx, in, ragged_len, dim);
+}
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index e700c4b708..44b4b302be 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -23,19 +23,36 @@ using common::half;
 namespace cpu {
 
 template<af_op_t op, typename T>
-using ireduce_dim_func = std::function<void(Param<T>, Param<uint>, const dim_t,
-                                            CParam<T>, const dim_t, const int)>;
+using ireduce_dim_func =
+    std::function<void(Param<T>, Param<uint>, const dim_t, CParam<T>,
+                       const dim_t, const int, CParam<uint>)>;
 
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim) {
-    dim4 odims                                           = in.dims();
-    odims[dim]                                           = 1;
+    dim4 odims       = in.dims();
+    odims[dim]       = 1;
+    Array<uint> rlen = createEmptyArray<uint>(af::dim4(0));
     static const ireduce_dim_func<op, T> ireduce_funcs[] = {
         kernel::ireduce_dim<op, T, 1>(), kernel::ireduce_dim<op, T, 2>(),
         kernel::ireduce_dim<op, T, 3>(), kernel::ireduce_dim<op, T, 4>()};
 
-    getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim);
+    getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim,
+                       rlen);
+}
+
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen) {
+    dim4 odims = in.dims();
+    odims[dim] = 1;
+
+    static const ireduce_dim_func<op, T> ireduce_funcs[] = {
+        kernel::ireduce_dim<op, T, 1>(), kernel::ireduce_dim<op, T, 2>(),
+        kernel::ireduce_dim<op, T, 3>(), kernel::ireduce_dim<op, T, 4>()};
+
+    getQueue().enqueue(ireduce_funcs[in.ndims() - 1], out, loc, 0, in, 0, dim,
+                       rlen);
 }
 
 template<af_op_t op, typename T>
@@ -72,6 +89,9 @@ T ireduce_all(unsigned *loc, const Array<T> &in) {
 #define INSTANTIATE(ROp, T)                                           \
     template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
                                   const Array<T> &in, const int dim); \
+    template void rreduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim,  \
+                                  const Array<uint> &rlen);           \
     template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
 
 // min
diff --git a/src/backend/cpu/ireduce.hpp b/src/backend/cpu/ireduce.hpp
index 9efe8312f6..4861293c3c 100644
--- a/src/backend/cpu/ireduce.hpp
+++ b/src/backend/cpu/ireduce.hpp
@@ -15,6 +15,10 @@ template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim);
 
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen);
+
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace cpu
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index 74ef7ba60e..5517a6657b 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 #include <ops.hpp>
+#include <algorithm>
 
 namespace cpu {
 namespace kernel {
@@ -64,7 +65,7 @@ template<af_op_t op, typename T, int D>
 struct ireduce_dim {
     void operator()(Param<T> output, Param<uint> locParam,
                     const dim_t outOffset, CParam<T> input,
-                    const dim_t inOffset, const int dim) {
+                    const dim_t inOffset, const int dim, CParam<uint> rlen) {
         const af::dim4 odims    = output.dims();
         const af::dim4 ostrides = output.strides();
         const af::dim4 istrides = input.strides();
@@ -72,7 +73,7 @@ struct ireduce_dim {
         for (dim_t i = 0; i < odims[D1]; i++) {
             ireduce_dim<op, T, D1>()(output, locParam,
                                      outOffset + i * ostrides[D1], input,
-                                     inOffset + i * istrides[D1], dim);
+                                     inOffset + i * istrides[D1], dim, rlen);
         }
     }
 };
@@ -81,19 +82,20 @@ template<af_op_t op, typename T>
 struct ireduce_dim<op, T, 0> {
     void operator()(Param<T> output, Param<uint> locParam,
                     const dim_t outOffset, CParam<T> input,
-                    const dim_t inOffset, const int dim) {
+                    const dim_t inOffset, const int dim, CParam<uint> rlen) {
         const af::dim4 idims    = input.dims();
         const af::dim4 istrides = input.strides();
 
-        T const *const in = input.get();
-        T *out            = output.get();
-        uint *loc         = locParam.get();
+        T const *const in   = input.get();
+        T *out              = output.get();
+        uint *loc           = locParam.get();
+        const uint *rlenptr = (rlen.get()) ? rlen.get() + outOffset : nullptr;
 
         dim_t stride = istrides[dim];
         MinMaxOp<op, T> Op(in[inOffset], 0);
-        for (dim_t i = 0; i < idims[dim]; i++) {
-            Op(in[inOffset + i * stride], i);
-        }
+        int lim =
+            (rlenptr) ? std::min(idims[dim], (dim_t)*rlenptr) : idims[dim];
+        for (dim_t i = 0; i < lim; i++) { Op(in[inOffset + i * stride], i); }
 
         out[outOffset] = Op.m_val;
         loc[outOffset] = Op.m_idx;
diff --git a/src/backend/cuda/ireduce.cpp b/src/backend/cuda/ireduce.cpp
index 400fdf522b..abbea5514d 100644
--- a/src/backend/cuda/ireduce.cpp
+++ b/src/backend/cuda/ireduce.cpp
@@ -26,7 +26,14 @@ namespace cuda {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim) {
-    kernel::ireduce<T, op>(out, loc.get(), in, dim);
+    Array<uint> rlen = createEmptyArray<uint>(af::dim4(0));
+    kernel::ireduce<T, op>(out, loc.get(), in, dim, rlen);
+}
+
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen) {
+    kernel::ireduce<T, op>(out, loc.get(), in, dim, rlen);
 }
 
 template<af_op_t op, typename T>
@@ -37,6 +44,9 @@ T ireduce_all(unsigned *loc, const Array<T> &in) {
 #define INSTANTIATE(ROp, T)                                           \
     template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
                                   const Array<T> &in, const int dim); \
+    template void rreduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim,  \
+                                  const Array<uint> &rlen);           \
     template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
 
 // min
diff --git a/src/backend/cuda/ireduce.hpp b/src/backend/cuda/ireduce.hpp
index a41927cced..3fdfd3ee73 100644
--- a/src/backend/cuda/ireduce.hpp
+++ b/src/backend/cuda/ireduce.hpp
@@ -15,6 +15,10 @@ template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim);
 
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen);
+
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/ireduce.cuh b/src/backend/cuda/kernel/ireduce.cuh
index 865651e3ba..afdb5baec4 100644
--- a/src/backend/cuda/kernel/ireduce.cuh
+++ b/src/backend/cuda/kernel/ireduce.cuh
@@ -17,7 +17,7 @@ namespace cuda {
 template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
 __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
                                   const uint *ilptr, uint blocks_x,
-                                  uint blocks_y, uint offset_dim) {
+                                  uint blocks_y, uint offset_dim, CParam<uint> rlen) {
     const uint tidx = threadIdx.x;
     const uint tidy = threadIdx.y;
     const uint tid  = tidy * THREADS_X + tidx;
@@ -39,10 +39,18 @@ __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
     // There are blockDim.y elements per block for in
     // Hence increment ids[dim] just after offseting out and before offsetting
     // in
+    bool rlen_valid = (ids[0] < rlen.dims[0]) && (ids[1] < rlen.dims[1]) &&
+                      (ids[2] < rlen.dims[2]) && (ids[3] < rlen.dims[3]);
+    const uint *rlenptr   = (rlen.ptr && rlen_valid) ?
+             rlen.ptr + ids[3] * rlen.strides[3] + ids[2] * rlen.strides[2] +
+             ids[1] * rlen.strides[1] + ids[0] : nullptr;
+
     optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
             ids[1] * out.strides[1] + ids[0];
     olptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
              ids[1] * out.strides[1] + ids[0];
+
+
     const uint blockIdx_dim = ids[dim];
 
     ids[dim] = ids[dim] * blockDim.y + tidy;
@@ -61,7 +69,10 @@ __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
     T val    = Binary<T, op>::init();
     uint idx = id_dim_in;
 
-    if (is_valid && id_dim_in < in.dims[dim]) {
+    uint lim = (rlenptr) ? *rlenptr : in.dims[dim];
+    lim = (is_first) ? min((uint)in.dims[dim], lim) : lim;
+    bool within_ragged_bounds = (is_first) ? (idx < lim) : ((rlenptr)? ((is_valid) && (*ilptr < lim)) : true);
+    if (is_valid && id_dim_in < in.dims[dim] && within_ragged_bounds) {
         val = *iptr;
         if (!is_first) idx = *ilptr;
     }
@@ -73,7 +84,7 @@ __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
     __shared__ T s_val[THREADS_X * DIMY];
     __shared__ uint s_idx[THREADS_X * DIMY];
 
-    for (int id = id_dim_in_start; is_valid && (id < in.dims[dim]);
+    for (int id = id_dim_in_start; is_valid && (id < lim);
          id += offset_dim * blockDim.y) {
         iptr = iptr + offset_dim * blockDim.y * istride_dim;
         if (!is_first) {
@@ -139,9 +150,10 @@ __device__ void warp_reduce(T *s_ptr, uint *s_idx, uint tidx) {
 }
 
 template<typename T, af_op_t op, bool is_first, uint DIMX>
-__global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
-                                    const uint *ilptr, uint blocks_x,
-                                    uint blocks_y, uint repeat) {
+__global__ static void ireduceFirst(Param<T> out, uint *olptr,
+                                    CParam<T> in, const uint *ilptr,
+                                    uint blocks_x, uint blocks_y,
+                                    uint repeat, CParam<uint> rlen) {
     const uint tidx = threadIdx.x;
     const uint tidy = threadIdx.y;
     const uint tid  = tidy * blockDim.x + tidx;
@@ -156,6 +168,8 @@ __global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
 
     const data_t<T> *iptr = in.ptr;
     data_t<T> *optr       = out.ptr;
+    const uint *rlenptr   = (rlen.ptr) ?  rlen.ptr + wid * rlen.strides[3] +
+                        zid * rlen.strides[2] + yid * rlen.strides[1] : nullptr;
 
     iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
     optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
@@ -167,7 +181,8 @@ __global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
 
     if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
 
-    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+    int minlen = rlenptr ? min(*rlenptr, in.dims[0]) : in.dims[0];
+    int lim = min((int)(xid + repeat * DIMX), minlen);
 
     compute_t<T> val = Binary<compute_t<T>, op>::init();
     uint idx         = xid;
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 5450be6be9..ac502d0584 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -32,7 +32,7 @@ static inline std::string ireduceSource() {
 template<typename T, af_op_t op, int dim, bool is_first>
 void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
                           const uint *ilptr, const uint threads_y,
-                          const dim_t blocks_dim[4]) {
+                          const dim_t blocks_dim[4], CParam<uint> rlen) {
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_dim[0] * blocks_dim[2], blocks_dim[1] * blocks_dim[3]);
@@ -51,13 +51,13 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
     ireduceDim(qArgs, out, olptr, in, ilptr, blocks_dim[0], blocks_dim[1],
-               blocks_dim[dim]);
+               blocks_dim[dim], rlen);
 
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op, int dim>
-void ireduce_dim(Param<T> out, uint *olptr, CParam<T> in) {
+void ireduce_dim(Param<T> out, uint *olptr, CParam<T> in, CParam<uint> rlen) {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -85,20 +85,21 @@ void ireduce_dim(Param<T> out, uint *olptr, CParam<T> in) {
     }
 
     ireduce_dim_launcher<T, op, dim, true>(tmp, tlptr, in, NULL, threads_y,
-                                           blocks_dim);
+                                           blocks_dim, rlen);
 
     if (blocks_dim[dim] > 1) {
         blocks_dim[dim] = 1;
 
         ireduce_dim_launcher<T, op, dim, false>(out, olptr, tmp, tlptr,
-                                                threads_y, blocks_dim);
+                                                threads_y, blocks_dim, rlen);
     }
 }
 
 template<typename T, af_op_t op, bool is_first>
 void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
                             const uint *ilptr, const uint blocks_x,
-                            const uint blocks_y, const uint threads_x) {
+                            const uint blocks_y, const uint threads_x,
+                            CParam<uint> rlen) {
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
     const int maxBlocksY =
@@ -117,12 +118,13 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    ireduceFirst(qArgs, out, olptr, in, ilptr, blocks_x, blocks_y, repeat);
+    ireduceFirst(qArgs, out, olptr, in, ilptr, blocks_x, blocks_y, repeat,
+                 rlen);
     POST_LAUNCH_CHECK();
 }
 
 template<typename T, af_op_t op>
-void ireduce_first(Param<T> out, uint *olptr, CParam<T> in) {
+void ireduce_first(Param<T> out, uint *olptr, CParam<T> in, CParam<uint> rlen) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
     uint threads_y = THREADS_PER_BLOCK / threads_x;
@@ -146,21 +148,22 @@ void ireduce_first(Param<T> out, uint *olptr, CParam<T> in) {
     }
 
     ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x,
-                                        blocks_y, threads_x);
+                                        blocks_y, threads_x, rlen);
 
     if (blocks_x > 1) {
         ireduce_first_launcher<T, op, false>(out, olptr, tmp, tlptr, 1,
-                                             blocks_y, threads_x);
+                                             blocks_y, threads_x, rlen);
     }
 }
 
 template<typename T, af_op_t op>
-void ireduce(Param<T> out, uint *olptr, CParam<T> in, int dim) {
+void ireduce(Param<T> out, uint *olptr, CParam<T> in, int dim,
+             CParam<uint> rlen) {
     switch (dim) {
-        case 0: return ireduce_first<T, op>(out, olptr, in);
-        case 1: return ireduce_dim<T, op, 1>(out, olptr, in);
-        case 2: return ireduce_dim<T, op, 2>(out, olptr, in);
-        case 3: return ireduce_dim<T, op, 3>(out, olptr, in);
+        case 0: return ireduce_first<T, op>(out, olptr, in, rlen);
+        case 1: return ireduce_dim<T, op, 1>(out, olptr, in, rlen);
+        case 2: return ireduce_dim<T, op, 2>(out, olptr, in, rlen);
+        case 3: return ireduce_dim<T, op, 3>(out, olptr, in, rlen);
     }
 }
 
@@ -210,8 +213,10 @@ T ireduce_all(uint *idx, CParam<T> in) {
         auto tlptr_alloc = memAlloc<uint>(tmp_elements);
         tmp.ptr          = tmp_alloc.get();
         tlptr            = tlptr_alloc.get();
+        af::dim4 emptysz(0);
+        CParam<uint> rlen(nullptr, emptysz.get(), emptysz.get());
         ireduce_first_launcher<T, op, true>(tmp, tlptr, in, NULL, blocks_x,
-                                            blocks_y, threads_x);
+                                            blocks_y, threads_x, rlen);
 
         unique_ptr<T[]> h_ptr(new T[tmp_elements]);
         unique_ptr<uint[]> h_lptr(new uint[tmp_elements]);
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index fc79e6ef06..6a60cc0c97 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -24,7 +24,14 @@ namespace opencl {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim) {
-    kernel::ireduce<T, op>(out, loc.get(), in, dim);
+    Array<uint> rlen = createEmptyArray<uint>(af::dim4(0));
+    kernel::ireduce<T, op>(out, loc.get(), in, dim, rlen);
+}
+
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen) {
+    kernel::ireduce<T, op>(out, loc.get(), in, dim, rlen);
 }
 
 template<af_op_t op, typename T>
@@ -35,6 +42,9 @@ T ireduce_all(unsigned *loc, const Array<T> &in) {
 #define INSTANTIATE(ROp, T)                                           \
     template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
                                   const Array<T> &in, const int dim); \
+    template void rreduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim,  \
+                                  const Array<uint> &rlen);           \
     template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
 
 // min
diff --git a/src/backend/opencl/ireduce.hpp b/src/backend/opencl/ireduce.hpp
index 5af4b15001..108bd2dfeb 100644
--- a/src/backend/opencl/ireduce.hpp
+++ b/src/backend/opencl/ireduce.hpp
@@ -15,6 +15,10 @@ template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim);
 
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen);
+
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 070b384b4f..145171ad3d 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -42,7 +42,8 @@ namespace kernel {
 template<typename T, af_op_t op>
 void ireduce_dim_launcher(Param out, cl::Buffer *oidx, Param in,
                           cl::Buffer *iidx, const int dim, const int threads_y,
-                          const bool is_first, const uint groups_all[4]) {
+                          const bool is_first, const uint groups_all[4],
+                          Param rlen) {
     std::string ref_name =
         std::string("ireduce_") + std::to_string(dim) + std::string("_") +
         std::string(dtype_traits<T>::getName()) + std::string("_") +
@@ -81,18 +82,19 @@ void ireduce_dim_launcher(Param out, cl::Buffer *oidx, Param in,
     NDRange global(groups_all[0] * groups_all[2] * local[0],
                    groups_all[1] * groups_all[3] * local[1]);
 
-    auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam,
-                                   Buffer, uint, uint, uint>(*entry.ker);
+    auto ireduceOp =
+        KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam, Buffer, uint,
+                      uint, uint, Buffer, KParam>(*entry.ker);
 
     ireduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
               *oidx, *in.data, in.info, *iidx, groups_all[0], groups_all[1],
-              groups_all[dim]);
+              groups_all[dim], *rlen.data, rlen.info);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
-void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim) {
+void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -119,13 +121,13 @@ void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim) {
     }
 
     ireduce_dim_launcher<T, op>(tmp, tidx, in, tidx, dim, threads_y, true,
-                                groups_all);
+                                groups_all, rlen);
 
     if (groups_all[dim] > 1) {
         groups_all[dim] = 1;
 
         ireduce_dim_launcher<T, op>(out, oidx, tmp, tidx, dim, threads_y, false,
-                                    groups_all);
+                                    groups_all, rlen);
         bufferFree(tmp.data);
         bufferFree(tidx);
     }
@@ -135,7 +137,7 @@ template<typename T, af_op_t op>
 void ireduce_first_launcher(Param out, cl::Buffer *oidx, Param in,
                             cl::Buffer *iidx, const int threads_x,
                             const bool is_first, const uint groups_x,
-                            const uint groups_y) {
+                            const uint groups_y, Param rlen) {
     std::string ref_name =
         std::string("ireduce_0_") + std::string(dtype_traits<T>::getName()) +
         std::string("_") + std::to_string(op) + std::string("_") +
@@ -176,17 +178,19 @@ void ireduce_first_launcher(Param out, cl::Buffer *oidx, Param in,
 
     uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-    auto ireduceOp = KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam,
-                                   Buffer, uint, uint, uint>(*entry.ker);
+    auto ireduceOp =
+        KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam, Buffer, uint,
+                      uint, uint, Buffer, KParam>(*entry.ker);
 
     ireduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-              *oidx, *in.data, in.info, *iidx, groups_x, groups_y, repeat);
+              *oidx, *in.data, in.info, *iidx, groups_x, groups_y, repeat,
+              *rlen.data, rlen.info);
 
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
-void ireduce_first(Param out, cl::Buffer *oidx, Param in) {
+void ireduce_first(Param out, cl::Buffer *oidx, Param in, Param rlen) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
@@ -209,11 +213,11 @@ void ireduce_first(Param out, cl::Buffer *oidx, Param in) {
     }
 
     ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
-                                  groups_x, groups_y);
+                                  groups_x, groups_y, rlen);
 
     if (groups_x > 1) {
         ireduce_first_launcher<T, op>(out, oidx, tmp, tidx, threads_x, false, 1,
-                                      groups_y);
+                                      groups_y, rlen);
 
         bufferFree(tmp.data);
         bufferFree(tidx);
@@ -221,11 +225,19 @@ void ireduce_first(Param out, cl::Buffer *oidx, Param in) {
 }
 
 template<typename T, af_op_t op>
-void ireduce(Param out, cl::Buffer *oidx, Param in, int dim) {
+void ireduce(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
+    if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
+            rlen.info.dims[3] ==
+        0) {
+        // empty opencl::Param() does not have nullptr by default
+        // set to nullptr explicitly here for consequent kernel calls
+        // through cl::Buffer's constructor
+        rlen.data = new cl::Buffer();
+    }
     if (dim == 0)
-        return ireduce_first<T, op>(out, oidx, in);
+        return ireduce_first<T, op>(out, oidx, in, rlen);
     else
-        return ireduce_dim<T, op>(out, oidx, in, dim);
+        return ireduce_dim<T, op>(out, oidx, in, dim, rlen);
 }
 
 #if defined(__GNUC__) || defined(__GNUG__)
@@ -313,8 +325,10 @@ T ireduce_all(uint *loc, Param in) {
         int tmp_elements = tmp.elements();
         cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
 
+        Param rlen;
+        rlen.data = new cl::Buffer();
         ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
-                                      groups_x, groups_y);
+                                      groups_x, groups_y, rlen);
 
         unique_ptr<T[]> h_ptr(new T[tmp_elements]);
         unique_ptr<uint[]> h_iptr(new uint[tmp_elements]);
@@ -363,6 +377,7 @@ T ireduce_all(uint *loc, Param in) {
         return Op.m_val;
     }
 }
+
 }  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/ireduce_dim.cl b/src/backend/opencl/kernel/ireduce_dim.cl
index b7f98e2ddf..502df9c241 100644
--- a/src/backend/opencl/kernel/ireduce_dim.cl
+++ b/src/backend/opencl/kernel/ireduce_dim.cl
@@ -10,7 +10,8 @@
 __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
                                  __global uint *olData, const __global T *iData,
                                  KParam iInfo, const __global uint *ilData,
-                                 uint groups_x, uint groups_y, uint group_dim) {
+                                 uint groups_x, uint groups_y, uint group_dim,
+                                 __global uint *rlenptr, KParam rlen) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * THREADS_X + lidx;
@@ -28,10 +29,16 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
     // There are get_local_size(1) elements per group for in
     // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
+    bool rlen_valid = (ids[0] < rlen.dims[0]) && (ids[1] < rlen.dims[1]) &&
+                      (ids[2] < rlen.dims[2]) && (ids[3] < rlen.dims[3]);
+    rlenptr += (rlenptr && rlen_valid) ?  ids[3] * rlen.strides[3] + ids[2] * rlen.strides[2] +
+             ids[1] * rlen.strides[1] + ids[0] + rlen.offset : 0;
+
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
     olData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
               ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
+
     const uint id_dim_out = ids[kDim];
 
     ids[kDim] = ids[kDim] * get_local_size(1) + lidy;
@@ -56,14 +63,18 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
     T out_val    = init;
     uint out_idx = id_dim_in;
 
-    if (is_valid && id_dim_in < iInfo.dims[kDim]) {
+    uint lim = rlenptr ? *rlenptr : iInfo.dims[kDim];
+    lim = (IS_FIRST) ? min((uint)iInfo.dims[kDim], lim) : lim;
+    bool within_ragged_bounds = (IS_FIRST) ? (out_idx < lim) :
+                                ((rlenptr) ? (is_valid) && (*ilData < lim) : true);
+    if (is_valid && id_dim_in < iInfo.dims[kDim] && within_ragged_bounds) {
         out_val = *iData;
         if (!IS_FIRST) out_idx = *ilData;
     }
 
     const uint id_dim_in_start = id_dim_in + group_dim * get_local_size(1);
 
-    for (int id = id_dim_in_start; is_valid && (id < iInfo.dims[kDim]);
+    for (int id = id_dim_in_start; is_valid && (id < lim);
          id += group_dim * get_local_size(1)) {
         iData = iData + group_dim * get_local_size(1) * istride_dim;
 
diff --git a/src/backend/opencl/kernel/ireduce_first.cl b/src/backend/opencl/kernel/ireduce_first.cl
index 48f8826be5..784fb88641 100644
--- a/src/backend/opencl/kernel/ireduce_first.cl
+++ b/src/backend/opencl/kernel/ireduce_first.cl
@@ -11,7 +11,8 @@ __kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
                                    __global uint *olData,
                                    const __global T *iData, KParam iInfo,
                                    const __global uint *ilData, uint groups_x,
-                                   uint groups_y, uint repeat) {
+                                   uint groups_y, uint repeat,
+                                   __global uint *rlenptr, KParam rlen) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * get_local_size(0) + lidx;
@@ -37,6 +38,9 @@ __kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
     olData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
               yid * oInfo.strides[1] + oInfo.offset;
 
+    rlenptr += (rlenptr) ?  wid * rlen.strides[3] + zid * rlen.strides[2] +
+             yid * rlen.strides[1] + rlen.offset : 0;
+
     bool cond =
         (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
@@ -44,7 +48,10 @@ __kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
     __local uint s_idx[THREADS_PER_GROUP];
 
     int last     = (xid + repeat * DIMX);
-    int lim      = last > iInfo.dims[0] ? iInfo.dims[0] : last;
+
+    int minlen = rlenptr ? min(*rlenptr, (uint)iInfo.dims[0]) : iInfo.dims[0];
+
+    int lim      = last > minlen ? minlen : last;
     T out_val    = init;
     uint out_idx = xid;
 
diff --git a/test/reduce.cpp b/test/reduce.cpp
index d7e2d129de..71ed09d729 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -381,6 +381,26 @@ array ptrToArray(size_t size, void *ptr, af_dtype type) {
     return res;
 }
 
+array ptrToArray(af::dim4 size, void *ptr, af_dtype type) {
+    array res;
+    switch (type) {
+        case f32: res = array(size, (float *)ptr); break;
+        case f64: res = array(size, (double *)ptr); break;
+        case c32: res = array(size, (cfloat *)ptr); break;
+        case c64: res = array(size, (cdouble *)ptr); break;
+        case u32: res = array(size, (unsigned *)ptr); break;
+        case s32: res = array(size, (int *)ptr); break;
+        case u64: res = array(size, (unsigned long long *)ptr); break;
+        case s64: res = array(size, (long long *)ptr); break;
+        case u16: res = array(size, (unsigned short *)ptr); break;
+        case s16: res = array(size, (short *)ptr); break;
+        case b8: res = array(size, (char *)ptr); break;
+        case u8: res = array(size, (unsigned char *)ptr); break;
+        case f16: res = array(size, (half_float::half *)ptr); break;
+    }
+    return res;
+}
+
 class ReduceByKeyP : public ::testing::TestWithParam<reduce_by_key_params *> {
    public:
     array keys, vals;
@@ -1843,3 +1863,182 @@ TEST(Reduce, SNIPPET_count_by_key_dim) {
     ASSERT_VEC_ARRAY_EQ(gold_keys, dim4(3), okeys);
     ASSERT_VEC_ARRAY_EQ(gold_vals, dim4(2, 3), ovals);
 }
+
+TEST(RaggedMax, simple) {
+    const int testKeys[6]      = {1, 2, 3, 4, 5, 6};
+    const unsigned testVals[2] = {9, 2};
+
+    array arr(3, 2, testKeys);
+    array keys(1, 2, testVals);
+
+    array ragged_max, idx;
+    const int dim = 0;
+    max(ragged_max, idx, arr, keys, dim);
+
+    const dim4 goldSz(1, 2);
+    const vector<int> gold_reduced{3, 5};
+    const vector<unsigned> gold_idx{2, 1};
+
+    ASSERT_VEC_ARRAY_EQ(gold_reduced, goldSz, ragged_max);
+    ASSERT_VEC_ARRAY_EQ(gold_idx, goldSz, idx);
+}
+
+TEST(RaggedMax, simpleDim1) {
+    const int testKeys[8]      = {1, 2, 3, 4, 5, 6, 7, 8};
+    const unsigned testVals[2] = {8, 2};
+
+    array arr(2, 4, testKeys);
+    array keys(2, 1, testVals);
+
+    array ragged_max, idx;
+    const int dim = 1;
+    max(ragged_max, idx, arr, keys, dim);
+
+    const dim4 goldSz(2, 1);
+    const vector<int> gold_reduced{7, 4};
+    const vector<unsigned> gold_idx{3, 1};
+
+    ASSERT_VEC_ARRAY_EQ(gold_reduced, goldSz, ragged_max);
+    ASSERT_VEC_ARRAY_EQ(gold_idx, goldSz, idx);
+}
+
+struct ragged_params {
+    size_t reduceDimLen_;
+    int reduceDim_;
+    af_dtype lType_, vType_, oType_;
+    string testname_;
+
+    virtual ~ragged_params() {}
+};
+
+template<typename Tl, typename Tv, typename To>
+struct ragged_params_t : public ragged_params {
+    string testname_;
+
+    ragged_params_t(size_t reduce_dim_len, int reduce_dim, string testname)
+        : testname_(testname) {
+        ragged_params::reduceDim_    = reduce_dim;
+        ragged_params::reduceDimLen_ = reduce_dim_len;
+        ragged_params::lType_        = (af_dtype)af::dtype_traits<Tl>::af_type;
+        ragged_params::vType_        = (af_dtype)af::dtype_traits<Tv>::af_type;
+        ragged_params::oType_        = (af_dtype)af::dtype_traits<To>::af_type;
+        ragged_params::testname_     = testname_;
+    }
+    ~ragged_params_t() {}
+};
+
+class RaggedReduceMaxRangeP : public ::testing::TestWithParam<ragged_params *> {
+   public:
+    array vals, ragged_lens;
+    array valsReducedGold, idxsReducedGold;
+
+    void SetUp() {
+        ragged_params *params = GetParam();
+        if (noHalfTests(params->vType_)) { return; }
+
+        const size_t rdim_size = params->reduceDimLen_;
+        const int dim          = params->reduceDim_;
+
+        af::dim4 rdim(3, 3, 3, 3);
+        rdim[dim] = rdim_size;
+        vals      = af::range(rdim, dim, params->vType_);
+
+        rdim[dim]   = 1;
+        ragged_lens = af::range(rdim, (dim > 0) ? 0 : 1, params->lType_) + 1;
+
+        valsReducedGold = af::range(rdim, (dim > 0) ? 0 : 1, params->oType_);
+        idxsReducedGold = af::range(rdim, (dim > 0) ? 0 : 1, params->lType_);
+    }
+
+    void TearDown() { delete GetParam(); }
+};
+
+template<typename Tl, typename Tv, typename To>
+ragged_params *ragged_range_data(const string testname, const int testSz,
+                                 const int rdim) {
+    return new ragged_params_t<Tl, Tv, To>(testSz, rdim, testname);
+}
+
+// clang-format off
+template<typename Tv, typename To>
+vector<ragged_params *> genRaggedRangeTests() {
+  return {ragged_range_data<unsigned, Tv, To>("ragged_range", 31,          0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 32,          0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 33,          0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 255,         0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 256,         0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 257,         0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024,        0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1025,        0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024 * 1025, 0),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 31,          1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 32,          1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 33,          1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 255,         1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 256,         1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 257,         1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024,        1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1025,        1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024 * 1025, 1),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 31,          2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 32,          2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 33,          2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 255,         2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 256,         2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 257,         2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024,        2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1025,        2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024 * 1025, 2),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 31,          3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 32,          3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 33,          3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 255,         3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 256,         3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 257,         3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024,        3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1025,        3),
+          ragged_range_data<unsigned, Tv, To>("ragged_range", 1024 * 1025, 3),
+    };
+}
+
+vector<ragged_params *> generateAllTypesRagged() {
+    vector<ragged_params *> out;
+    vector<vector<ragged_params *> > tmp{
+        genRaggedRangeTests<int, int>(),
+        genRaggedRangeTests<float, float>(),
+        genRaggedRangeTests<double, double>(),
+        genRaggedRangeTests<half_float::half, half_float::half>()
+    };
+
+    for (auto &v : tmp) { copy(begin(v), end(v), back_inserter(out)); }
+    return out;
+}
+
+template<typename TestClass>
+string testNameGeneratorRagged(
+    const ::testing::TestParamInfo<typename TestClass::ParamType> info) {
+    af_dtype lt = info.param->lType_;
+    af_dtype vt = info.param->vType_;
+    size_t size = info.param->reduceDimLen_;
+    int rdim = info.param->reduceDim_;
+    std::stringstream s;
+    s << info.param->testname_ << "_lenType_" << lt << "_valueType_" << vt
+      << "_size_" << size << "_reduceDim_" << rdim;
+    return s.str();
+}
+
+INSTANTIATE_TEST_CASE_P(RaggedReduceTests, RaggedReduceMaxRangeP,
+                        ::testing::ValuesIn(generateAllTypesRagged()),
+                        testNameGeneratorRagged<RaggedReduceMaxRangeP>);
+
+TEST_P(RaggedReduceMaxRangeP, rangeMaxTest) {
+    if (noHalfTests(GetParam()->vType_)) { return; }
+
+    array ragged_max, idx;
+    const int dim = GetParam()->reduceDim_;
+    max(ragged_max, idx, vals, ragged_lens, dim);
+
+    ASSERT_ARRAYS_EQ(valsReducedGold, ragged_max);
+    ASSERT_ARRAYS_EQ(idxsReducedGold, idx);
+
+}

From 9cde12d9ead8d665d15e8f77fa0eb14b7ca754cd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 6 Apr 2020 22:52:34 -0400
Subject: [PATCH 1884/2677] Fix byteToString where the byte value is > a
 petabyte

---
 src/backend/common/Logger.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index 441e0f2546..d7c7d05323 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -52,13 +52,15 @@ shared_ptr<logger> loggerFactory(string name) {
 }
 
 string bytesToString(size_t bytes) {
-    static array<const char *, 5> units{{"B", "KB", "MB", "GB", "TB"}};
+    constexpr array<const char *, 7> units{
+        {"B", "KB", "MB", "GB", "TB", "PB", "EB"}};
     size_t count     = 0;
     double fbytes    = static_cast<double>(bytes);
     size_t num_units = units.size();
     for (count = 0; count < num_units && fbytes > 1000.0f; count++) {
         fbytes *= (1.0f / 1024.0f);
     }
+    if (count == units.size()) count--;
     return fmt::format("{:.3g} {}", fbytes, units[count]);
 }
 }  // namespace common

From ef1e37668ecf28d7bcbcf4aafdbcae4c7fee9bec Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 6 Apr 2020 22:53:12 -0400
Subject: [PATCH 1885/2677] Fix warning in boost stacktrace on newer gcc
 compilers

---
 src/backend/common/err_common.hpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 42b144ef4b..2371c1fc9f 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -9,7 +9,10 @@
 
 #pragma once
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wattributes"
 #include <boost/stacktrace.hpp>
+#pragma GCC diagnostic pop
 #include <common/defines.hpp>
 #include <af/defines.h>
 

From 61c4a0474e46b2083348b60d747964560946bf17 Mon Sep 17 00:00:00 2001
From: Paul Jurczak <pauljurczak@yahoo.com>
Date: Thu, 2 Apr 2020 04:40:06 -0600
Subject: [PATCH 1886/2677] Update forge_visualization.md

Added mouse manipulations
---
 docs/pages/forge_visualization.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/docs/pages/forge_visualization.md b/docs/pages/forge_visualization.md
index 72901dc681..01cffa07eb 100644
--- a/docs/pages/forge_visualization.md
+++ b/docs/pages/forge_visualization.md
@@ -16,6 +16,11 @@ particular is that instead of wasting time copying and reformatting data from
 the GPU to the host and back to the GPU, we can draw directly from GPU-data to
 GPU-framebuffers! This saves 2 memory copies.
 
+Visualizations can be manipulated with a mouse. The following actions are available:
+- zoom (Alt + Mouse Left Click, move up & down)
+- pan (Just left click and drag)
+- rotation (Mouse right click - track ball rotation).
+
 Let's see exactly what visuals we can illuminate with forge and how Arrayfire
 anneals the data between the two libraries.
 

From 3b251684fa95d1b583c5a791c1e5138df28c33c8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Apr 2020 12:52:37 +0530
Subject: [PATCH 1887/2677] Use boost env var on linux github ci jobs

Change ninja to 1.10.0
---
 .github/workflows/cpu_build.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index 5fd4a67555..d2e10f9d73 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -14,7 +14,7 @@ jobs:
         name: CPU
         runs-on: ${{ matrix.os }}
         env:
-          NINJA_VER: 1.9.0
+          NINJA_VER: 1.10.0
           CMAKE_VER: 3.5.1
         strategy:
             fail-fast: false
@@ -65,7 +65,7 @@ jobs:
             - name: Install Dependencies for Macos
               if: matrix.os == 'macos-latest'
               run: |
-                  brew install fontconfig glfw freeimage boost fftw lapack openblas
+                  brew install boost fontconfig glfw freeimage fftw lapack openblas
                   echo "::set-env name=CMAKE_PROGRAM::cmake"
 
             - name: Install Common Dependencies for Ubuntu
@@ -74,7 +74,6 @@ jobs:
                   sudo apt-get -qq update
                   sudo apt-get install -y libfreeimage-dev \
                                           libglfw3-dev \
-                                          libboost-dev \
                                           libfftw3-dev \
                                           liblapacke-dev
 
@@ -109,6 +108,7 @@ jobs:
                   mkdir build && cd build
                   ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
+                      -DBOOST_ROOT:PATH=${BOOST_ROOT_1_72_0} \
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \

From bb566113980a0a68d4661b48d08795f400a3f41b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 1 Apr 2020 13:27:34 +0530
Subject: [PATCH 1888/2677] Windows github action ci job for CPU backend

pinverse_cpu test is excluded as lapacke dependency is not taken care of
yet
---
 .github/workflows/cpu_build.yml | 64 ++++++++++++++++++++++++++++++++-
 CMakeModules/CTestCustom.cmake  |  6 +++-
 2 files changed, 68 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index d2e10f9d73..e22e9fa0f6 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -2,7 +2,6 @@ on:
   push:
     branches:
     - master
-    - cmake_3.5_fixes
   pull_request:
     branches:
     - master
@@ -121,3 +120,66 @@ jobs:
               run: |
                   cd ${GITHUB_WORKSPACE}/build
                   ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2
+
+    window_build_cpu:
+        name: CPU (OpenBLAS, windows-latest)
+        runs-on: windows-latest
+        env:
+          VCPKG_HASH: b79f7675aaa82eb6c5a96ae764fb1ce379a9d5d6 # March 29, 2020 - [hdf5] add tools and fortran feature
+          NINJA_VER: 1.10.0
+        steps:
+            - name: Checkout Repository
+              uses: actions/checkout@master
+
+            - name: Checkout Submodules
+              shell: bash
+              run: git submodule update --init --recursive
+
+            - name: VCPKG Cache
+              uses: actions/cache@v1
+              id: vcpkg-cache
+              with:
+                path: vcpkg
+                key: vcpkg-deps-${{ env.VCPKG_HASH }}
+
+            - name: Install VCPKG Common Deps
+              if: steps.vcpkg-cache.outputs.cache-hit != 'true'
+              run: |
+                  git clone --recursive https://github.com/microsoft/vcpkg
+                  Set-Location -Path .\vcpkg
+                  git reset --hard $env:VCPKG_HASH
+                  .\bootstrap-vcpkg.bat
+                  .\vcpkg.exe install --triplet x64-windows fftw3 freeimage freetype glfw3 openblas
+                  Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
+
+            - name: Download Ninja
+              run: |
+                  Invoke-WebRequest -Uri "https://github.com/ninja-build/ninja/releases/download/v$env:NINJA_VER/ninja-win.zip" -OutFile ninja.zip
+                  Expand-Archive -Path ninja.zip -DestinationPath .
+
+            - name: CMake Configure
+              run: |
+                  $cwd = (Get-Item -Path ".\").FullName
+                  $ref = $env:GITHUB_REF | %{ if ($_ -match "refs/pull/[0-9]+/merge") { $_;} }
+                  $prnum = $ref | %{$_.Split("/")[2]}
+                  $branch = git branch --show-current
+                  $buildname = if($prnum -eq $null) { $branch } else { "PR-$prnum" }
+                  $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
+                  $buildname = "$buildname-cpu-openblas"
+                  mkdir build && cd build
+                  cmake .. -G "Visual Studio 16 2019" -A x64 `
+                      -DCMAKE_TOOLCHAIN_FILE:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\scripts\buildsystems\vcpkg.cmake" `
+                      -DFFTW_INCLUDE_DIR:PATH="$env:GITHUB_WORKSPACE\vcpkg\installed/x64-windows\include" `
+                      -DFFTW_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3.lib" `
+                      -DFFTWF_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3f.lib" `
+                      -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
+                      -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
+                      -DBUILDNAME:STRING="$buildname"
+                  echo "::set-env name=CTEST_DASHBOARD::${dashboard}"
+
+            - name: Build and Test
+              run: |
+                  $cwd = (Get-Item -Path ".\").FullName
+                  $Env:PATH += ";$cwd/vcpkg/installed/x64-windows/bin"
+                  Set-Location -Path $cwd/build
+                  ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C Release -R cpu -E pinverse -j2
diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index ad85c05075..e9a4c35ba7 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -8,7 +8,11 @@
 set(CTEST_CUSTOM_ERROR_POST_CONTEXT 50)
 set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 50)
 if(WIN32)
-  set(CTEST_CUSTOM_POST_TEST ./bin/print_info.exe)
+  if(CMAKE_GENERATOR MATCHES "Ninja")
+    set(CTEST_CUSTOM_POST_TEST ./bin/print_info.exe)
+  else()
+    set(CTEST_CUSTOM_POST_TEST ./bin/Release/print_info.exe)
+  endif()
 else()
   set(CTEST_CUSTOM_POST_TEST ./test/print_info)
 endif()

From f5c65a6bdebf324441dc802e37c29c38dd348eb7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 7 Apr 2020 20:55:29 +0530
Subject: [PATCH 1889/2677] Avoid print_info as ctest post command for
 non-ninja win generators

---
 CMakeModules/CTestCustom.cmake | 2 --
 1 file changed, 2 deletions(-)

diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index e9a4c35ba7..514a5ee4d8 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -10,8 +10,6 @@ set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 50)
 if(WIN32)
   if(CMAKE_GENERATOR MATCHES "Ninja")
     set(CTEST_CUSTOM_POST_TEST ./bin/print_info.exe)
-  else()
-    set(CTEST_CUSTOM_POST_TEST ./bin/Release/print_info.exe)
   endif()
 else()
   set(CTEST_CUSTOM_POST_TEST ./test/print_info)

From decde4ec9a4e45c065ffbc0bf9c7428a58cc4482 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 8 Apr 2020 01:06:55 -0400
Subject: [PATCH 1890/2677] fix zero padding in convolve2NN (#2820)

* add tests for zero padding

* fix clang formatting
---
 include/af/ml.h          |  3 +++
 include/af/signal.h      |  3 +++
 src/api/cpp/convolve.cpp | 10 ++++------
 test/convolve.cpp        | 11 +++++++++++
 4 files changed, 21 insertions(+), 6 deletions(-)

diff --git a/include/af/ml.h b/include/af/ml.h
index c1581fe887..c341fd9a43 100644
--- a/include/af/ml.h
+++ b/include/af/ml.h
@@ -35,6 +35,9 @@ class dim4;
         \param[in]  grad_type specifies which gradient to return
         \return     gradient wrt/grad_type
 
+        \note Make sure you pass in both dim0, and dim1 in your dim4 arguments. The third
+        and fourth dimensions are currently ignored.
+
         \ingroup ml_convolution
     */
     AFAPI array convolve2GradientNN(const array& incoming_gradient,
diff --git a/include/af/signal.h b/include/af/signal.h
index 902e85e5c0..6b6720201d 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -612,6 +612,9 @@ AFAPI array convolve2(const array& signal, const array& filter, const convMode m
    \param[in]  dilation specifies the amount to dilate the filter before convolution
    \return              the convolved array
 
+   \note Make sure you pass in both dim0, and dim1 in your dim4 arguments. The third
+   and fourth dimensions are currently ignored.
+
    \ingroup signal_func_convolve2
  */
 AFAPI array convolve2NN(const array& signal, const array& filter,
diff --git a/src/api/cpp/convolve.cpp b/src/api/cpp/convolve.cpp
index 98dc315880..a74710d1d1 100644
--- a/src/api/cpp/convolve.cpp
+++ b/src/api/cpp/convolve.cpp
@@ -55,9 +55,8 @@ array convolve2(const array &signal, const array &filter, const convMode mode,
 array convolve2NN(const array &signal, const array &filter, const dim4 stride,
                   const dim4 padding, const dim4 dilation) {
     af_array out = 0;
-    AF_THROW(af_convolve2_nn(&out, signal.get(), filter.get(), stride.ndims(),
-                             stride.get(), padding.ndims(), padding.get(),
-                             dilation.ndims(), dilation.get()));
+    AF_THROW(af_convolve2_nn(&out, signal.get(), filter.get(), 2, stride.get(),
+                             2, padding.get(), 2, dilation.get()));
     return array(out);
 }
 
@@ -70,9 +69,8 @@ array convolve2GradientNN(const array &incoming_gradient,
     af_array out = 0;
     AF_THROW(af_convolve2_gradient_nn(
         &out, incoming_gradient.get(), original_signal.get(),
-        original_filter.get(), convolved_output.get(), stride.ndims(),
-        stride.get(), padding.ndims(), padding.get(), dilation.ndims(),
-        dilation.get(), gradType));
+        original_filter.get(), convolved_output.get(), 2, stride.get(), 2,
+        padding.get(), 2, dilation.get(), gradType));
     return array(out);
 }
 
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 2768c63f9a..d632071154 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -1169,3 +1169,14 @@ TYPED_TEST(ConvolveStrided, Gradient_sig81032_filt3334_s11_p11_d11) {
         string(TEST_DIR "/convolve/sig81032_filt3334_s11_p11_d11.test"),
         dim4(1, 1), dim4(1, 1), dim4(1, 1));
 }
+
+TEST(ConvolveNN, ZeroPadding_Issue2817) {
+    array signal = constant(1.f, 5, 5);
+    array filter = constant(1 / 9.f, 3, 3);
+    dim4 strides(1, 1), dilation(1, 1);
+    dim4 padding(0, 0, 1, 1);
+
+    array convolved = convolve2NN(signal, filter, strides, padding, dilation);
+    ASSERT_EQ(sum<float>(abs(signal(seq(1, 3), seq(1, 3)) - convolved)) < 1E-5,
+              true);
+}

From 612085fd93c5b7755d51af910dd5f5f70ee320ea Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 8 Apr 2020 20:27:50 +0530
Subject: [PATCH 1891/2677] std::initializer_list based constructors for
 af::array

updated compilers.h to check for generalized initializers feature
---
 CMakeModules/InternalUtils.cmake |  2 +-
 CMakeModules/compilers.h         | 30 ++++++++++++++++++++++++++++++
 include/af/array.h               | 15 +++++++++++++++
 src/api/cpp/array.cpp            | 10 +++++++++-
 test/array.cpp                   | 19 +++++++++++++++++++
 5 files changed, 74 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index eb9b7f4d05..92e269d8c0 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -172,7 +172,7 @@ macro(arrayfire_set_cmake_default_variables)
   #         PREFIX AF
   #         COMPILERS AppleClang Clang GNU Intel MSVC
   #         # NOTE: cxx_attribute_deprecated does not work well with C
-  #         FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas cxx_static_assert
+  #         FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas cxx_static_assert cxx_generalized_initializers
   #         ALLOW_UNKNOWN_COMPILERS
   #         #[VERSION <version>]
   #         #[PROLOG <prolog>]
diff --git a/CMakeModules/compilers.h b/CMakeModules/compilers.h
index 02851d18fb..cca330d4ca 100644
--- a/CMakeModules/compilers.h
+++ b/CMakeModules/compilers.h
@@ -196,6 +196,12 @@
 #      define AF_COMPILER_CXX_STATIC_ASSERT 0
 #    endif
 
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_generalized_initializers)
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 1
+#    else
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
+#    endif
+
 #  elif AF_COMPILER_IS_Clang
 
 #    if !(((__clang_major__ * 100) + __clang_minor__) >= 301)
@@ -241,6 +247,12 @@
 #      define AF_COMPILER_CXX_STATIC_ASSERT 0
 #    endif
 
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_generalized_initializers)
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 1
+#    else
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
+#    endif
+
 #  elif AF_COMPILER_IS_GNU
 
 #    if !((__GNUC__ * 100 + __GNUC_MINOR__) >= 404)
@@ -289,6 +301,12 @@
 #      define AF_COMPILER_CXX_STATIC_ASSERT 0
 #    endif
 
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 404 && (__cplusplus >= 201103L || (defined(__GXX_EXPERIMENTAL_CXX0X__) && __GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 1
+#    else
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
+#    endif
+
 #  elif AF_COMPILER_IS_Intel
 
 #    if !(__INTEL_COMPILER >= 1210)
@@ -354,6 +372,12 @@
 #      define AF_COMPILER_CXX_STATIC_ASSERT 0
 #    endif
 
+#    if __INTEL_COMPILER >= 1400 && ((__cplusplus >= 201103L) || defined(__INTEL_CXX11_MODE__) || defined(__GXX_EXPERIMENTAL_CXX0X__))
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 1
+#    else
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
+#    endif
+
 #  elif AF_COMPILER_IS_MSVC
 
 #    if !(_MSC_VER >= 1600)
@@ -406,6 +430,12 @@
 #      define AF_COMPILER_CXX_STATIC_ASSERT 0
 #    endif
 
+#    if _MSC_FULL_VER >= 180030723
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 1
+#    else
+#      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
+#    endif
+
 #  endif
 
 #  if defined(AF_COMPILER_CXX_NOEXCEPT) && AF_COMPILER_CXX_NOEXCEPT
diff --git a/include/af/array.h b/include/af/array.h
index 282b7aeb8c..438b4a99b4 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -17,6 +17,12 @@
 #ifdef __cplusplus
 #include <af/traits.hpp>
 
+#if AF_API_VERSION >= 38
+#if AF_COMPILER_CXX_GENERALIZED_INITIALIZERS
+#include <initializer_list>
+#endif
+#endif
+
 namespace af
 {
 
@@ -486,6 +492,15 @@ namespace af
         array(const dim4& dims,
               const T *pointer, af::source src=afHost);
 
+#if AF_API_VERSION >= 38
+#if AF_COMPILER_CXX_GENERALIZED_INITIALIZERS
+        template <typename T> array(std::initializer_list<T> list);
+
+        template <typename T>
+        array(const af::dim4 &dims, std::initializer_list<T> list);
+#endif
+#endif
+
         /**
            Adjust the dimensions of an N-D array (fast).
 
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index a0eabb17f7..2e75293867 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -219,7 +219,15 @@ struct dtype_traits<half_float::half> {
     AFAPI array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,         \
                        const T *ptr, af::source src)                           \
         : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dim0, dim1,    \
-                            dim2, dim3)) {}
+                            dim2, dim3)) {}                                    \
+    template<>                                                                 \
+    AFAPI array::array(std::initializer_list<T> list)                          \
+        : arr(initDataArray(list.begin(), dtype_traits<T>::af_type, afHost,    \
+                            list.size(), 1, 1, 1)) {}                          \
+    template<>                                                                 \
+    AFAPI array::array(const af::dim4 &dims, std::initializer_list<T> list)    \
+        : arr(initDataArray(list.begin(), dtype_traits<T>::af_type, afHost,    \
+                            dims[0], dims[1], dims[2], dims[3])) {}
 
 INSTANTIATE(cdouble)
 INSTANTIATE(cfloat)
diff --git a/test/array.cpp b/test/array.cpp
index c894dca30d..0b8f13c561 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -14,6 +14,7 @@
 #include <testHelpers.hpp>
 #include <cstddef>
 #include <cstdlib>
+#include <initializer_list>
 
 using namespace af;
 using std::vector;
@@ -565,3 +566,21 @@ void deathTest() {
 TEST(ArrayDeathTest, ProxyMoveAssignmentOperator) {
     EXPECT_EXIT(deathTest(), ::testing::ExitedWithCode(0), "");
 }
+
+TEST(Array, InitializerList) {
+    int h_buffer[] = {23, 34, 18, 99, 34};
+
+    array A(5, h_buffer);
+    array B({23, 34, 18, 99, 34});
+
+    ASSERT_ARRAYS_EQ(A, B);
+}
+
+TEST(Array, InitializerListAndDim4) {
+    int h_buffer[] = {23, 34, 18, 99, 34, 44};
+
+    array A(2, 3, h_buffer);
+    array B(dim4(2, 3), {23, 34, 18, 99, 34, 44});
+
+    ASSERT_ARRAYS_EQ(A, B);
+}

From 934da1bb3ea191f9a600743c4a0bff38069f0213 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Apr 2020 15:36:18 -0400
Subject: [PATCH 1892/2677] Fix the af_get_memory_pressure_threshold by
 assigning value parameter

---
 src/api/c/memory.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index ff7a18f215..1bffe37a05 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -476,7 +476,7 @@ af_err af_memory_manager_get_memory_pressure_threshold(af_memory_manager handle,
                                                        float *value) {
     try {
         MemoryManager &manager = getMemoryManager(handle);
-        manager.wrapper->getMemoryPressureThreshold();
+        *value                 = manager.wrapper->getMemoryPressureThreshold();
     }
     CATCHALL;
 

From 3cb51ab194773f0b0ddf118df2e6436a6ef9041f Mon Sep 17 00:00:00 2001
From: jacobkahn <jacobkahn1@gmail.com>
Date: Thu, 2 Apr 2020 16:16:51 -0700
Subject: [PATCH 1893/2677] Fix documentation to mem step size and clean up
 memory manager test

---
 include/af/device.h | 14 ++++++++++----
 test/memory.cpp     |  8 ++------
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/include/af/device.h b/include/af/device.h
index 41f336cf60..b798a6e80d 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -236,12 +236,14 @@ namespace af
     AFAPI void deviceGC();
     /// @}
 
-    /// \brief Set the resolution of memory chunks
+    /// \brief Set the resolution of memory chunks. Works only with the default
+    /// memory manager - throws if a custom memory manager is set.
     ///
     /// \ingroup device_func_mem
     AFAPI void setMemStepSize(const size_t size);
 
-    /// \brief Get the resolution of memory chunks
+    /// \brief Get the resolution of memory chunks. Works only with the default
+    /// memory manager - throws if a custom memory manager is set.
     ///
     /// \ingroup device_func_mem
     AFAPI size_t getMemStepSize();
@@ -395,13 +397,17 @@ extern "C" {
     AFAPI af_err af_device_gc();
 
     /**
-       Set the minimum memory chunk size
+       Set the minimum memory chunk size. Works only with the default
+       memory manager - returns an error if a custom memory manager is set.
+
        \ingroup device_func_mem
     */
     AFAPI af_err af_set_mem_step_size(const size_t step_bytes);
 
     /**
-       Get the minimum memory chunk size
+       Get the minimum memory chunk size. Works only with the default
+       memory manager - returns an error if a custom memory manager is set.
+
        \ingroup device_func_mem
     */
     AFAPI af_err af_get_mem_step_size(size_t *step_bytes);
diff --git a/test/memory.cpp b/test/memory.cpp
index d0768850b6..c1012c29ef 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -11,7 +11,6 @@
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
-#include <af/event.h>
 #include <af/internal.h>
 #include <af/memory.h>
 #include <af/traits.hpp>
@@ -711,9 +710,6 @@ af_err unlock_fn(af_memory_manager manager, void *ptr, int userLock) {
 
 af_err user_unlock_fn(af_memory_manager manager, void *ptr) {
     auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
-    af_event event;
-    af_create_event(&event);
-    af_mark_event(event);
     af_err err = unlock_fn(manager, ptr, /* user */ 1);
     payload->lockedBytes -= payload->table[ptr];
     return err;
@@ -746,7 +742,7 @@ af_err print_info_fn(af_memory_manager manager, char *c, int b) {
 
 af_err get_memory_pressure_fn(af_memory_manager manager, float *out) {
     auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
-    if (payload->totalBytes > payload->maxBytes ||
+    if (payload->lockedBytes > payload->maxBytes ||
         payload->totalBuffers > payload->maxBuffers) {
         *out = 1.0;
     } else {
@@ -773,7 +769,7 @@ af_err alloc_fn(af_memory_manager manager, void **ptr,
         get_memory_pressure_fn(manager, &pressure);
         float threshold;
         af_memory_manager_get_memory_pressure_threshold(manager, &threshold);
-        if (pressure > threshold) { signal_memory_cleanup_fn(manager); }
+        if (pressure >= threshold) { signal_memory_cleanup_fn(manager); }
 
         af_memory_manager_native_alloc(manager, ptr, size);
 

From 8b377e1a45eb8996b3a5462f066ab849873a7ee8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 10 Apr 2020 05:11:34 +0530
Subject: [PATCH 1894/2677] Fix constant mem declaration in CUDA morph kernel
 (#2835)

* Fix constant mem declaration in CUDA morph kernel

Global constant value of max filter length was not modified
after increasing filter support to 19 from 17 back originally.
---
 src/backend/cuda/kernel/morph.hpp | 2 +-
 src/backend/cuda/morph_impl.hpp   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index fe3434de75..0534fabcf4 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -19,7 +19,7 @@
 namespace cuda {
 namespace kernel {
 
-static const int MAX_MORPH_FILTER_LEN = 17;
+static const int MAX_MORPH_FILTER_LEN = 19;
 static const int THREADS_X            = 16;
 static const int THREADS_Y            = 16;
 static const int CUBE_X               = 8;
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
index a998fe7a6e..e155523897 100644
--- a/src/backend/cuda/morph_impl.hpp
+++ b/src/backend/cuda/morph_impl.hpp
@@ -22,7 +22,7 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     if (mdims[0] != mdims[1]) {
         CUDA_NOT_SUPPORTED("Rectangular masks are not supported");
     }
-    if (mdims[0] > 19) {
+    if (mdims[0] > kernel::MAX_MORPH_FILTER_LEN) {
         CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
     }
     Array<T> out = createEmptyArray<T>(in.dims());

From fc991933f59b2f87e78f3d309c146c7af64e94e2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 13 Apr 2020 06:13:18 -0400
Subject: [PATCH 1895/2677] Make cudnn dependency optional (#2836)

* adds fallback for convolveNN functions

* adds cudnn option, runtime fallback

* Noexcept and const many Dependency module functions

* Refactor cuDNN code in CMake

* Fix fallback logic. refactor cuDNN util functions. Fix f16 wrap

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 CMakeLists.txt                          |   1 +
 src/backend/common/DependencyModule.cpp |  14 +-
 src/backend/common/DependencyModule.hpp |  10 +-
 src/backend/cuda/CMakeLists.txt         |  28 +-
 src/backend/cuda/convolve.cpp           | 291 ---------------
 src/backend/cuda/convolveNN.cpp         | 459 ++++++++++++++++++++++++
 src/backend/cuda/cudnn.cpp              |  44 +++
 src/backend/cuda/cudnn.hpp              |  12 +
 src/backend/cuda/cudnnModule.cpp        |  17 +-
 src/backend/cuda/cudnnModule.hpp        |   8 +-
 src/backend/cuda/handle.cpp             |  10 +-
 src/backend/cuda/join.cpp               |   2 +
 src/backend/cuda/kernel/wrap.cuh        |  79 +++-
 src/backend/cuda/kernel/wrap.hpp        |  31 ++
 src/backend/cuda/platform.cpp           |  13 +-
 src/backend/cuda/platform.hpp           |   5 +
 src/backend/cuda/unwrap.cpp             |   4 +
 src/backend/cuda/wrap.cpp               |  30 ++
 src/backend/cuda/wrap.hpp               |   8 +-
 test/convolve.cpp                       |   2 +-
 20 files changed, 742 insertions(+), 326 deletions(-)
 create mode 100644 src/backend/cuda/convolveNN.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0200ec9e45..c518c818fd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -53,6 +53,7 @@ option(AF_BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FO
 option(AF_BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
 option(AF_BUILD_DOCS     "Create ArrayFire Documentation"            ${DOXYGEN_FOUND})
 option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
+option(AF_WITH_CUDNN     "Use cuDNN for convolveNN functions"        ${cuDNN_FOUND})
 option(AF_BUILD_FORGE
     "Forge libs are not built by default as it is not link time dependency" OFF)
 
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index dcbbc9809e..0176f9a84a 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -82,19 +82,23 @@ DependencyModule::DependencyModule(const vector<string> plugin_base_file_name,
     AF_TRACE("Unable to open {}", plugin_base_file_name[0]);
 }
 
-DependencyModule::~DependencyModule() {
+DependencyModule::~DependencyModule() noexcept {
     if (handle) { unloadLibrary(handle); }
 }
 
-bool DependencyModule::isLoaded() { return (bool)handle; }
+bool DependencyModule::isLoaded() const noexcept { return (bool)handle; }
 
-bool DependencyModule::symbolsLoaded() {
+bool DependencyModule::symbolsLoaded() const noexcept {
     return all_of(begin(functions), end(functions),
                   [](void* ptr) { return ptr != nullptr; });
 }
 
-string DependencyModule::getErrorMessage() { return common::getErrorMessage(); }
+string DependencyModule::getErrorMessage() const noexcept {
+    return common::getErrorMessage();
+}
 
-spdlog::logger* DependencyModule::getLogger() { return logger.get(); }
+spdlog::logger* DependencyModule::getLogger() const noexcept {
+    return logger.get();
+}
 
 }  // namespace common
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 62eb16ce60..d9a860a738 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -41,7 +41,7 @@ class DependencyModule {
                      const std::vector<std::string> suffixes,
                      const std::vector<std::string> paths);
 
-    ~DependencyModule();
+    ~DependencyModule() noexcept;
 
     /// Returns a function pointer to the function with the name symbol_name
     template<typename T>
@@ -51,16 +51,16 @@ class DependencyModule {
     }
 
     /// Returns true if the module was successfully loaded
-    bool isLoaded();
+    bool isLoaded() const noexcept;
 
     /// Returns true if all of the symbols for the module were loaded
-    bool symbolsLoaded();
+    bool symbolsLoaded() const noexcept;
 
     /// Returns the last error message that occurred because of loading the
     /// library
-    std::string getErrorMessage();
+    std::string getErrorMessage() const noexcept;
 
-    spdlog::logger* getLogger();
+    spdlog::logger* getLogger() const noexcept;
 };
 
 }  // namespace common
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b6059d2166..aa7caae368 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -6,6 +6,9 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 dependency_check(CUDA_FOUND "CUDA not found.")
+if(AF_WITH_CUDNN)
+  dependency_check(cuDNN_FOUND "CUDA not found.")
+endif()
 
 include(AFcuda_helpers)
 include(FileToString)
@@ -468,14 +471,11 @@ cuda_add_library(afcuda
     complex.hpp
     convolve.cpp
     convolve.hpp
+    convolveNN.cpp
     copy.cpp
     copy.hpp
     cublas.cpp
     cublas.hpp
-    cudnn.cpp
-    cudnn.hpp
-    cudnnModule.cpp
-    cudnnModule.hpp
     cufft.hpp
     cusolverDn.cpp
     cusolverDn.hpp
@@ -623,6 +623,20 @@ cuda_add_library(afcuda
     -Xcudafe \"--diag_suppress=1427\"
   )
 
+if(AF_WITH_CUDNN)
+  target_sources(afcuda PRIVATE
+    cudnn.cpp
+    cudnn.hpp
+    cudnnModule.cpp
+    cudnnModule.hpp)
+  target_compile_definitions(afcuda PRIVATE WITH_CUDNN)
+
+  target_include_directories (afcuda
+    PRIVATE
+      ${cuDNN_INCLUDE_DIRS}
+    )
+endif()
+
 arrayfire_set_default_cxx_flags(afcuda)
 
 # NOTE: Do not add additional CUDA specific definitions here. Add it to the
@@ -649,7 +663,6 @@ target_include_directories (afcuda
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel
     ${CMAKE_CURRENT_SOURCE_DIR}/jit
     ${CMAKE_CURRENT_BINARY_DIR}
-    ${cuDNN_INCLUDE_DIRS}
 )
 
 target_link_libraries(afcuda
@@ -754,7 +767,10 @@ function(afcu_collect_libs libname)
 endfunction()
 
 if(AF_INSTALL_STANDALONE)
-  afcu_collect_libs(cudnn)
+  if(AF_WITH_CUDNN)
+    afcu_collect_libs(cudnn)
+  endif()
+
   afcu_collect_libs(nvrtc FULL_VERSION)
   if(WIN32)
     afcu_collect_libs(cufft)
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index a8c48b343e..96e2b165a8 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -12,7 +12,6 @@
 #include <common/half.hpp>
 #include <common/unique_handle.hpp>
 #include <convolve.hpp>
-#include <cudnn.hpp>
 #include <err_cuda.hpp>
 #include <kernel/convolve.hpp>
 #include <platform.hpp>
@@ -28,37 +27,6 @@ using std::is_same;
 
 namespace cuda {
 
-template<typename T>
-cudnnDataType_t getCudnnDataType();
-
-template<>
-cudnnDataType_t getCudnnDataType<float>() {
-    return CUDNN_DATA_FLOAT;
-}
-template<>
-cudnnDataType_t getCudnnDataType<double>() {
-    return CUDNN_DATA_DOUBLE;
-}
-
-#if CUDNN_VERSION >= 6000
-template<>
-cudnnDataType_t getCudnnDataType<int>() {
-    return CUDNN_DATA_INT32;
-}
-
-#if CUDNN_VERSION >= 7100
-template<>
-cudnnDataType_t getCudnnDataType<unsigned char>() {
-    return CUDNN_DATA_UINT8;
-}
-#endif
-#endif
-
-template<>
-cudnnDataType_t getCudnnDataType<half>() {
-    return CUDNN_DATA_HALF;
-}
-
 template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind) {
@@ -88,34 +56,6 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
     return out;
 }
 
-void cudnnSet(cudnnTensorDescriptor_t desc, cudnnDataType_t cudnn_dtype,
-              dim4 dims) {
-    CUDNN_CHECK(cuda::cudnnSetTensor4dDescriptor(desc, CUDNN_TENSOR_NCHW,
-                                                 cudnn_dtype, dims[3], dims[2],
-                                                 dims[1], dims[0]));
-}
-
-void cudnnSet(cudnnFilterDescriptor_t desc, cudnnDataType_t cudnn_dtype,
-              dim4 dims) {
-    CUDNN_CHECK(cuda::cudnnSetFilter4dDescriptor(desc, cudnn_dtype,
-                                                 CUDNN_TENSOR_NCHW, dims[3],
-                                                 dims[2], dims[1], dims[0]));
-}
-
-template<typename Desc, typename T>
-unique_handle<Desc> toCudnn(Array<T> arr) {
-    dim4 dims = arr.dims();
-
-    auto descriptor             = make_handle<Desc>();
-    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
-    cudnnSet(descriptor, cudnn_dtype, dims);
-    return descriptor;
-}
-
-template<typename T>
-using scale_type =
-    typename conditional<is_same<T, double>::value, double, float>::type;
-
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
                    Array<accT> const &r_filter) {
@@ -184,235 +124,4 @@ INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
 #undef INSTANTIATE
 
-template<typename T>
-Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
-                         const dim4 stride, const dim4 padding,
-                         const dim4 dilation) {
-    cudnnHandle_t cudnn = nnHandle();
-
-    dim4 sDims = signal.dims();
-    dim4 fDims = filter.dims();
-
-    const int n = sDims[3];
-    const int c = sDims[2];
-    const int h = sDims[1];
-    const int w = sDims[0];
-
-    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
-    auto input_descriptor       = toCudnn<cudnnTensorDescriptor_t>(signal);
-    auto filter_descriptor      = toCudnn<cudnnFilterDescriptor_t>(filter);
-
-    // create convolution descriptor
-    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
-
-    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
-        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
-        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
-
-    // get output dimensions
-    const int tensorDims = 4;
-    int convolved_output_dim[tensorDims];
-    CUDNN_CHECK(cuda::cudnnGetConvolutionNdForwardOutputDim(
-        convolution_descriptor, input_descriptor, filter_descriptor, tensorDims,
-        convolved_output_dim));
-
-    // create output descriptor
-    const int n_out = convolved_output_dim[0];
-    const int c_out = convolved_output_dim[1];
-    const int h_out = convolved_output_dim[2];
-    const int w_out = convolved_output_dim[3];
-
-    // prepare output array and scratch space
-    dim4 odims(w_out, h_out, c_out, n_out);
-    Array<T> out = createEmptyArray<T>(odims);
-
-    auto output_descriptor = toCudnn<cudnnTensorDescriptor_t>(out);
-
-    // get convolution algorithm
-    const int memory_limit =
-        0;  // TODO: set to remaining space in memory manager?
-    cudnnConvolutionFwdAlgo_t convolution_algorithm;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithm(
-        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
-        output_descriptor, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, memory_limit,
-        &convolution_algorithm));
-
-    // figure out scratch space memory requirements
-    size_t workspace_bytes;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardWorkspaceSize(
-        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
-        output_descriptor, convolution_algorithm, &workspace_bytes));
-
-    auto workspace_buffer = memAlloc<char>(workspace_bytes);
-
-    // perform convolution
-    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
-    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
-    CUDNN_CHECK(cuda::cudnnConvolutionForward(
-        cudnn, &alpha, input_descriptor, signal.device(), filter_descriptor,
-        filter.device(), convolution_descriptor, convolution_algorithm,
-        (void *)workspace_buffer.get(), workspace_bytes, &beta,
-        output_descriptor, out.device()));
-
-    return out;
-}
-
-template<typename T>
-constexpr void checkTypeSupport() {
-    static_assert(std::is_same<float, T>::value ||
-                      std::is_same<double, T>::value ||
-                      std::is_same<half, T>::value,
-                  "Invalid CuDNN data type: only f64, f32, f16 are supported");
-}
-
-template<typename T>
-Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
-                   const dim4 stride, const dim4 padding, const dim4 dilation) {
-    checkTypeSupport<T>();
-    return convolve2_cudnn<T>(signal, filter, stride, padding, dilation);
-}
-
-#define INSTANTIATE(T)                                                        \
-    template Array<T> convolve2<T>(Array<T> const &signal,                    \
-                                   Array<T> const &filter, const dim4 stride, \
-                                   const dim4 padding, const dim4 dilation);
-
-INSTANTIATE(double)
-INSTANTIATE(float)
-INSTANTIATE(half)
-#undef INSTANTIATE
-
-template<typename T>
-Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
-                             const Array<T> &original_signal,
-                             const Array<T> &original_filter,
-                             const Array<T> &convolved_output, af::dim4 stride,
-                             af::dim4 padding, af::dim4 dilation) {
-    auto cudnn = nnHandle();
-
-    dim4 iDims = incoming_gradient.dims();
-    dim4 sDims = original_signal.dims();
-    dim4 fDims = original_filter.dims();
-
-    // create dx descriptor
-    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
-    auto x_descriptor  = toCudnn<cudnnTensorDescriptor_t>(original_signal);
-    auto dy_descriptor = toCudnn<cudnnTensorDescriptor_t>(incoming_gradient);
-
-    // create convolution descriptor
-    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
-    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
-        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
-        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
-
-    // create output filter gradient descriptor
-    auto dw_descriptor = toCudnn<cudnnFilterDescriptor_t>(original_filter);
-
-    // determine algorithm to use
-    cudnnConvolutionBwdFilterAlgo_t bwd_filt_convolution_algorithm;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithm(
-        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
-        dw_descriptor, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0,
-        &bwd_filt_convolution_algorithm));
-
-    // figure out scratch space memory requirements
-    size_t workspace_bytes;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterWorkspaceSize(
-        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
-        dw_descriptor, bwd_filt_convolution_algorithm, &workspace_bytes));
-    // prepare output array and scratch space
-    Array<T> out = createEmptyArray<T>(fDims);
-
-    auto workspace_buffer = memAlloc<char>(workspace_bytes);
-
-    // perform convolution
-    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
-    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
-    CUDNN_CHECK(cuda::cudnnConvolutionBackwardFilter(
-        cudnn, &alpha, x_descriptor, original_signal.device(), dy_descriptor,
-        incoming_gradient.device(), convolution_descriptor,
-        bwd_filt_convolution_algorithm, (void *)workspace_buffer.get(),
-        workspace_bytes, &beta, dw_descriptor, out.device()));
-
-    return out;
-}
-
-template<typename T>
-Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
-                           const Array<T> &original_signal,
-                           const Array<T> &original_filter,
-                           const Array<T> &convolved_output, af::dim4 stride,
-                           af::dim4 padding, af::dim4 dilation) {
-    auto cudnn = nnHandle();
-
-    dim4 iDims = incoming_gradient.dims();
-    dim4 sDims = original_signal.dims();
-    dim4 fDims = original_filter.dims();
-
-    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
-
-    // create x descriptor
-    auto dx_descriptor = toCudnn<cudnnTensorDescriptor_t>(original_signal);
-    auto dy_descriptor = toCudnn<cudnnTensorDescriptor_t>(incoming_gradient);
-
-    // create output filter gradient descriptor
-    auto w_descriptor = make_handle<cudnnFilterDescriptor_t>();
-
-    CUDNN_CHECK(cuda::cudnnSetFilter4dDescriptor(w_descriptor, cudnn_dtype,
-                                                 CUDNN_TENSOR_NCHW, fDims[3],
-                                                 fDims[2], fDims[1], fDims[0]));
-
-    // create convolution descriptor
-    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
-
-    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
-        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
-        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
-
-    cudnnConvolutionBwdDataAlgo_t bwd_data_convolution_algorithm;
-    if ((dilation[0] == 1 && dilation[1] == 1) || is_same<T, half>::value) {
-        bwd_data_convolution_algorithm = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
-    } else {
-        bwd_data_convolution_algorithm = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
-    }
-
-    // figure out scratch space memory requirements
-    size_t workspace_bytes;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardDataWorkspaceSize(
-        cudnn, w_descriptor, dy_descriptor, convolution_descriptor,
-        dx_descriptor, bwd_data_convolution_algorithm, &workspace_bytes));
-
-    dim4 odims(sDims[0], sDims[1], sDims[2], sDims[3]);
-    Array<T> out = createEmptyArray<T>(odims);
-
-    auto workspace_buffer = memAlloc<char>(workspace_bytes);
-
-    // perform convolution
-    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
-    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
-
-    CUDNN_CHECK(cuda::cudnnConvolutionBackwardData(
-        cudnn, &alpha, w_descriptor, original_filter.get(), dy_descriptor,
-        incoming_gradient.get(), convolution_descriptor,
-        bwd_data_convolution_algorithm, (void *)workspace_buffer.get(),
-        workspace_bytes, &beta, dx_descriptor, out.device()));
-
-    return out;
-}
-
-#define INSTANTIATE(T)                                                      \
-    template Array<T> conv2DataGradient<T>(                                 \
-        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
-        Array<T> const &original_filter, Array<T> const &convolved_output,  \
-        const dim4 stride, const dim4 padding, const dim4 dilation);        \
-    template Array<T> conv2FilterGradient<T>(                               \
-        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
-        Array<T> const &original_filter, Array<T> const &convolved_output,  \
-        const dim4 stride, const dim4 padding, const dim4 dilation);
-
-INSTANTIATE(double)
-INSTANTIATE(float)
-INSTANTIATE(half)
-#undef INSTANTIATE
-
 }  // namespace cuda
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
new file mode 100644
index 0000000000..9810ac6544
--- /dev/null
+++ b/src/backend/cuda/convolveNN.cpp
@@ -0,0 +1,459 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <convolve.hpp>
+
+#include <Array.hpp>
+#include <blas.hpp>
+#include <cast.hpp>
+#include <common/half.hpp>
+#include <common/indexing_helpers.hpp>
+#include <common/unique_handle.hpp>
+#include <cudnn.hpp>
+#include <err_cuda.hpp>
+#include <kernel/convolve.hpp>
+#include <platform.hpp>
+#include <reorder.hpp>
+#include <transpose.hpp>
+#include <unwrap.hpp>
+#include <wrap.hpp>
+#include <af/dim4.hpp>
+
+#include <type_traits>
+
+using af::dim4;
+using common::flip;
+using common::half;
+using common::make_handle;
+using common::unique_handle;
+using std::conditional;
+using std::is_same;
+
+namespace cuda {
+
+#ifdef WITH_CUDNN
+
+template<typename Desc, typename T>
+unique_handle<Desc> toCudnn(Array<T> arr) {
+    dim4 dims = arr.dims();
+
+    auto descriptor             = make_handle<Desc>();
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+    cudnnSet(descriptor, cudnn_dtype, dims);
+    return descriptor;
+}
+
+template<typename T>
+using scale_type =
+    typename conditional<is_same<T, double>::value, double, float>::type;
+
+template<typename T>
+Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
+                         const dim4 stride, const dim4 padding,
+                         const dim4 dilation) {
+    cudnnHandle_t cudnn = nnHandle();
+
+    dim4 sDims = signal.dims();
+    dim4 fDims = filter.dims();
+
+    const int n = sDims[3];
+    const int c = sDims[2];
+    const int h = sDims[1];
+    const int w = sDims[0];
+
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+    auto input_descriptor       = toCudnn<cudnnTensorDescriptor_t>(signal);
+    auto filter_descriptor      = toCudnn<cudnnFilterDescriptor_t>(filter);
+
+    // create convolution descriptor
+    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+
+    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
+        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
+        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
+
+    // get output dimensions
+    const int tensorDims = 4;
+    int convolved_output_dim[tensorDims];
+    CUDNN_CHECK(cuda::cudnnGetConvolutionNdForwardOutputDim(
+        convolution_descriptor, input_descriptor, filter_descriptor, tensorDims,
+        convolved_output_dim));
+
+    // create output descriptor
+    const int n_out = convolved_output_dim[0];
+    const int c_out = convolved_output_dim[1];
+    const int h_out = convolved_output_dim[2];
+    const int w_out = convolved_output_dim[3];
+
+    // prepare output array and scratch space
+    dim4 odims(w_out, h_out, c_out, n_out);
+    Array<T> out = createEmptyArray<T>(odims);
+
+    auto output_descriptor = toCudnn<cudnnTensorDescriptor_t>(out);
+
+    // get convolution algorithm
+    const int memory_limit =
+        0;  // TODO: set to remaining space in memory manager?
+    cudnnConvolutionFwdAlgo_t convolution_algorithm;
+    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithm(
+        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+        output_descriptor, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, memory_limit,
+        &convolution_algorithm));
+
+    // figure out scratch space memory requirements
+    size_t workspace_bytes;
+    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardWorkspaceSize(
+        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+        output_descriptor, convolution_algorithm, &workspace_bytes));
+
+    auto workspace_buffer = memAlloc<char>(workspace_bytes);
+
+    // perform convolution
+    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
+    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    CUDNN_CHECK(cuda::cudnnConvolutionForward(
+        cudnn, &alpha, input_descriptor, signal.device(), filter_descriptor,
+        filter.device(), convolution_descriptor, convolution_algorithm,
+        (void *)workspace_buffer.get(), workspace_bytes, &beta,
+        output_descriptor, out.device()));
+
+    return out;
+}
+
+template<typename T>
+constexpr void checkTypeSupport() {
+    static_assert(std::is_same<float, T>::value ||
+                      std::is_same<double, T>::value ||
+                      std::is_same<half, T>::value,
+                  "Invalid CuDNN data type: only f64, f32, f16 are supported");
+}
+
+#endif
+
+template<typename T>
+Array<T> convolve2_base(const Array<T> &signal, const Array<T> &filter,
+                        const dim4 stride, const dim4 padding,
+                        const dim4 dilation) {
+    dim4 sDims = signal.dims();
+    dim4 fDims = filter.dims();
+
+    dim_t outputWidth =
+        1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
+                stride[0];
+    dim_t outputHeight =
+        1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
+                stride[1];
+    dim4 oDims = dim4(outputWidth, outputHeight, fDims[3], sDims[3]);
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(signal, fDims[0], fDims[1], stride[0], stride[1], padding[0],
+               padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsedFilter = filter;
+
+    collapsedFilter = flip(collapsedFilter, {1, 1, 0, 0});
+    collapsedFilter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    T alpha        = scalar<T>(1.0);
+    T beta         = scalar<T>(0.0);
+    const int Mdim = 1;
+    const int Ndim = 1;
+    Array<T> res   = createEmptyArray<T>(
+        dim4(unwrapped.dims()[Mdim], collapsedFilter.dims()[Ndim],
+             unwrapped.dims()[2], unwrapped.dims()[3]));
+    gemm(res, AF_MAT_TRANS, AF_MAT_NONE, &alpha, unwrapped, collapsedFilter,
+         &beta);
+    res.modDims(dim4(outputWidth, outputHeight, signal.dims()[3],
+                     collapsedFilter.dims()[1]));
+    Array<T> out = reorder(res, dim4(0, 1, 3, 2));
+
+    return out;
+}
+
+template<typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation) {
+#ifdef WITH_CUDNN
+    if (getCudnnPlugin().isLoaded()) {
+        checkTypeSupport<T>();
+        return convolve2_cudnn<T>(signal, filter, stride, padding, dilation);
+    }
+#endif
+    return convolve2_base<T>(signal, filter, stride, padding, dilation);
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> convolve2<T>(Array<T> const &signal,                    \
+                                   Array<T> const &filter, const dim4 stride, \
+                                   const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> data_gradient_base(const Array<T> &incoming_gradient,
+                            const Array<T> &original_signal,
+                            const Array<T> &original_filter,
+                            const Array<T> &convolved_output, af::dim4 stride,
+                            af::dim4 padding, af::dim4 dilation) {
+    const dim4 cDims = incoming_gradient.dims();
+    const dim4 sDims = original_signal.dims();
+    const dim4 fDims = original_filter.dims();
+
+    Array<T> collapsed_filter = original_filter;
+
+    collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
+    collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    T alpha        = scalar<T>(1.0);
+    T beta         = scalar<T>(0.0);
+    const int Mdim = 0;
+    const int Ndim = 0;
+    Array<T> res   = createEmptyArray<T>(
+        dim4(collapsed_gradient.dims()[Mdim], collapsed_filter.dims()[Ndim],
+             collapsed_gradient.dims()[3], collapsed_gradient.dims()[3]));
+    gemm(res, AF_MAT_NONE, AF_MAT_TRANS, &alpha, collapsed_gradient,
+         collapsed_filter, &beta);
+    res.modDims(dim4(res.dims()[0] / sDims[3], sDims[3], fDims[0] * fDims[1],
+                     sDims[2]));
+    res = reorder(res, dim4(0, 2, 3, 1));
+
+    const bool retCols = false;
+    res = wrap_dilated(res, sDims[0], sDims[1], fDims[0], fDims[1], stride[0],
+                       stride[1], padding[0], padding[1], dilation[0],
+                       dilation[1], retCols);
+
+    return res;
+}
+
+#ifdef WITH_CUDNN
+template<typename T>
+Array<T> data_gradient_cudnn(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation) {
+    auto cudnn = nnHandle();
+
+    dim4 iDims = incoming_gradient.dims();
+    dim4 sDims = original_signal.dims();
+    dim4 fDims = original_filter.dims();
+
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+
+    // create x descriptor
+    auto dx_descriptor = toCudnn<cudnnTensorDescriptor_t>(original_signal);
+    auto dy_descriptor = toCudnn<cudnnTensorDescriptor_t>(incoming_gradient);
+
+    // create output filter gradient descriptor
+    auto w_descriptor = make_handle<cudnnFilterDescriptor_t>();
+
+    CUDNN_CHECK(cuda::cudnnSetFilter4dDescriptor(w_descriptor, cudnn_dtype,
+                                                 CUDNN_TENSOR_NCHW, fDims[3],
+                                                 fDims[2], fDims[1], fDims[0]));
+
+    // create convolution descriptor
+    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+
+    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
+        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
+        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
+
+    cudnnConvolutionBwdDataAlgo_t bwd_data_convolution_algorithm;
+    if ((dilation[0] == 1 && dilation[1] == 1) || is_same<T, half>::value) {
+        bwd_data_convolution_algorithm = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
+    } else {
+        bwd_data_convolution_algorithm = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
+    }
+
+    // figure out scratch space memory requirements
+    size_t workspace_bytes;
+    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardDataWorkspaceSize(
+        cudnn, w_descriptor, dy_descriptor, convolution_descriptor,
+        dx_descriptor, bwd_data_convolution_algorithm, &workspace_bytes));
+
+    dim4 odims(sDims[0], sDims[1], sDims[2], sDims[3]);
+    Array<T> out = createEmptyArray<T>(odims);
+
+    auto workspace_buffer = memAlloc<char>(workspace_bytes);
+
+    // perform convolution
+    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
+    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+
+    CUDNN_CHECK(cuda::cudnnConvolutionBackwardData(
+        cudnn, &alpha, w_descriptor, original_filter.get(), dy_descriptor,
+        incoming_gradient.get(), convolution_descriptor,
+        bwd_data_convolution_algorithm, (void *)workspace_buffer.get(),
+        workspace_bytes, &beta, dx_descriptor, out.device()));
+
+    return out;
+}
+#endif
+
+template<typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation) {
+#ifdef WITH_CUDNN
+    if (getCudnnPlugin().isLoaded()) {
+        checkTypeSupport<T>();
+        return data_gradient_cudnn<T>(incoming_gradient, original_signal,
+                                      original_filter, convolved_output, stride,
+                                      padding, dilation);
+    }
+#endif
+    return data_gradient_base<T>(incoming_gradient, original_signal,
+                                 original_filter, convolved_output, stride,
+                                 padding, dilation);
+}
+
+template<typename T>
+Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
+                              const Array<T> &original_signal,
+                              const Array<T> &original_filter,
+                              const Array<T> &convolved_output, af::dim4 stride,
+                              af::dim4 padding, af::dim4 dilation) {
+    const dim4 cDims = incoming_gradient.dims();
+    const dim4 sDims = original_signal.dims();
+    const dim4 fDims = original_filter.dims();
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(original_signal, fDims[0], fDims[1], stride[0], stride[1],
+               padding[0], padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    T alpha        = scalar<T>(1.0);
+    T beta         = scalar<T>(0.0);
+    const int Mdim = 0;
+    const int Ndim = 1;
+    Array<T> res   = createEmptyArray<T>(
+        dim4(unwrapped.dims()[Mdim], collapsed_gradient.dims()[Ndim],
+             unwrapped.dims()[2], unwrapped.dims()[3]));
+    gemm(res, AF_MAT_NONE, AF_MAT_NONE, &alpha, unwrapped, collapsed_gradient,
+         &beta);
+    res.modDims(dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+
+    return flip(res, {1, 1, 0, 0});
+}
+
+#ifdef WITH_CUDNN
+template<typename T>
+Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
+                               const Array<T> &original_signal,
+                               const Array<T> &original_filter,
+                               const Array<T> &convolved_output,
+                               af::dim4 stride, af::dim4 padding,
+                               af::dim4 dilation) {
+    auto cudnn = nnHandle();
+
+    dim4 iDims = incoming_gradient.dims();
+    dim4 sDims = original_signal.dims();
+    dim4 fDims = original_filter.dims();
+
+    // create dx descriptor
+    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
+    auto x_descriptor  = toCudnn<cudnnTensorDescriptor_t>(original_signal);
+    auto dy_descriptor = toCudnn<cudnnTensorDescriptor_t>(incoming_gradient);
+
+    // create convolution descriptor
+    auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+    CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
+        convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
+        dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
+
+    // create output filter gradient descriptor
+    auto dw_descriptor = toCudnn<cudnnFilterDescriptor_t>(original_filter);
+
+    // determine algorithm to use
+    cudnnConvolutionBwdFilterAlgo_t bwd_filt_convolution_algorithm;
+    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithm(
+        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+        dw_descriptor, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0,
+        &bwd_filt_convolution_algorithm));
+
+    // figure out scratch space memory requirements
+    size_t workspace_bytes;
+    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterWorkspaceSize(
+        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+        dw_descriptor, bwd_filt_convolution_algorithm, &workspace_bytes));
+    // prepare output array and scratch space
+    Array<T> out = createEmptyArray<T>(fDims);
+
+    auto workspace_buffer = memAlloc<char>(workspace_bytes);
+
+    // perform convolution
+    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
+    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    CUDNN_CHECK(cuda::cudnnConvolutionBackwardFilter(
+        cudnn, &alpha, x_descriptor, original_signal.device(), dy_descriptor,
+        incoming_gradient.device(), convolution_descriptor,
+        bwd_filt_convolution_algorithm, (void *)workspace_buffer.get(),
+        workspace_bytes, &beta, dw_descriptor, out.device()));
+
+    return out;
+}
+#endif
+
+template<typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation) {
+#ifdef WITH_CUDNN
+    if (getCudnnPlugin().isLoaded()) {
+        checkTypeSupport<T>();
+        return filter_gradient_cudnn<T>(incoming_gradient, original_signal,
+                                        original_filter, convolved_output,
+                                        stride, padding, dilation);
+    }
+#endif
+    return filter_gradient_base<T>(incoming_gradient, original_signal,
+                                   original_filter, convolved_output, stride,
+                                   padding, dilation);
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> conv2DataGradient<T>(                                 \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);        \
+    template Array<T> conv2FilterGradient<T>(                               \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+}  // namespace cuda
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index cbfdf7ba9a..5f3c7f982c 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -10,6 +10,8 @@
 #include <cudnn.hpp>
 #include <err_cuda.hpp>
 
+using af::dim4;
+
 namespace cuda {
 
 const char *errorString(cudnnStatus_t err) {
@@ -41,6 +43,48 @@ const char *errorString(cudnnStatus_t err) {
     }
 }
 
+template<>
+cudnnDataType_t getCudnnDataType<float>() {
+    return CUDNN_DATA_FLOAT;
+}
+template<>
+cudnnDataType_t getCudnnDataType<double>() {
+    return CUDNN_DATA_DOUBLE;
+}
+
+#if CUDNN_VERSION >= 6000
+template<>
+cudnnDataType_t getCudnnDataType<int>() {
+    return CUDNN_DATA_INT32;
+}
+
+#if CUDNN_VERSION >= 7100
+template<>
+cudnnDataType_t getCudnnDataType<unsigned char>() {
+    return CUDNN_DATA_UINT8;
+}
+#endif
+#endif
+
+template<>
+cudnnDataType_t getCudnnDataType<common::half>() {
+    return CUDNN_DATA_HALF;
+}
+
+void cudnnSet(cudnnTensorDescriptor_t desc, cudnnDataType_t cudnn_dtype,
+              dim4 dims) {
+    CUDNN_CHECK(cuda::cudnnSetTensor4dDescriptor(desc, CUDNN_TENSOR_NCHW,
+                                                 cudnn_dtype, dims[3], dims[2],
+                                                 dims[1], dims[0]));
+}
+
+void cudnnSet(cudnnFilterDescriptor_t desc, cudnnDataType_t cudnn_dtype,
+              dim4 dims) {
+    CUDNN_CHECK(cuda::cudnnSetFilter4dDescriptor(desc, cudnn_dtype,
+                                                 CUDNN_TENSOR_NCHW, dims[3],
+                                                 dims[2], dims[1], dims[0]));
+}
+
 cudnnStatus_t cudnnSetConvolution2dDescriptor(
     cudnnConvolutionDescriptor_t convDesc,
     int pad_h,     // zero-padding height
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
index 1538b5ca3b..60bd0fe1f1 100644
--- a/src/backend/cuda/cudnn.hpp
+++ b/src/backend/cuda/cudnn.hpp
@@ -10,7 +10,9 @@
 #pragma once
 
 #include <common/defines.hpp>
+#include <common/half.hpp>
 #include <cudnnModule.hpp>
+#include <af/dim4.hpp>
 
 namespace cuda {
 
@@ -39,6 +41,16 @@ const char *errorString(cudnnStatus_t err);
         }                                                                   \
     } while (0)
 
+/// Returns a cuDNN type based on the template parameter
+template<typename T>
+cudnnDataType_t getCudnnDataType();
+
+void cudnnSet(cudnnTensorDescriptor_t desc, cudnnDataType_t cudnn_dtype,
+              af::dim4 dims);
+
+void cudnnSet(cudnnFilterDescriptor_t desc, cudnnDataType_t cudnn_dtype,
+              af::dim4 dims);
+
 // cuDNN Wrappers
 //
 // cuDNN deprecates and releases function names often between releases. in order
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 6607206ef9..03a14942e3 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -20,7 +20,9 @@ using std::string;
 
 namespace cuda {
 
-spdlog::logger* cudnnModule::getLogger() { return module.getLogger(); }
+spdlog::logger* cudnnModule::getLogger() const noexcept {
+    return module.getLogger();
+}
 
 auto cudnnVersionComponents(size_t version) {
     int major = version / 1000;
@@ -32,12 +34,15 @@ auto cudnnVersionComponents(size_t version) {
 cudnnModule::cudnnModule()
     : module({"cudnn"}, {"", "64_7", "64_8", "64_6", "64_5", "64_4"}, {""}) {
     if (!module.isLoaded()) {
-        string error_message =
-            "Error loading cuDNN: " + module.getErrorMessage() +
+        AF_TRACE(
+            "WARNING: Unable to load cuDNN: {}"
             "\ncuDNN failed to load. Try installing cuDNN or check if cuDNN is "
             "in the search path. On Linux, you can set the LD_DEBUG=libs "
-            "environment variable to debug loading issues.";
-        AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
+            "environment variable to debug loading issues. Falling back to "
+            "matmul based implementation",
+            module.getErrorMessage());
+
+        return;
     }
 
     MODULE_FUNCTION_INIT(cudnnGetVersion);
@@ -129,7 +134,7 @@ cudnnModule::cudnnModule()
     }
 }
 
-cudnnModule& getCudnnPlugin() {
+cudnnModule& getCudnnPlugin() noexcept {
     static cudnnModule* plugin = new cudnnModule();
     return *plugin;
 }
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index 19f234d70b..c850185e40 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -64,14 +64,16 @@ class cudnnModule {
     MODULE_MEMBER(cudnnSetStream);
     MODULE_MEMBER(cudnnSetTensor4dDescriptor);
 
-    spdlog::logger* getLogger();
+    spdlog::logger* getLogger() const noexcept;
 
     /// Returns the version of the cuDNN loaded at runtime
-    std::tuple<int, int, int> getVersion() {
+    std::tuple<int, int, int> getVersion() const noexcept {
         return std::make_tuple(major, minor, patch);
     }
+
+    bool isLoaded() const noexcept { return module.isLoaded(); }
 };
 
-cudnnModule& getCudnnPlugin();
+cudnnModule& getCudnnPlugin() noexcept;
 
 }  // namespace cuda
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
index 7d8945a878..eb1ad7a167 100644
--- a/src/backend/cuda/handle.cpp
+++ b/src/backend/cuda/handle.cpp
@@ -9,8 +9,6 @@
 
 #include <common/unique_handle.hpp>
 #include <cublas.hpp>
-#include <cudnn.hpp>
-#include <cudnnModule.hpp>
 #include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
@@ -21,9 +19,17 @@ CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
 CREATE_HANDLE(cublasHandle_t, cublasCreate, cublasDestroy);
 CREATE_HANDLE(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
 CREATE_HANDLE(cufftHandle, cufftCreate, cufftDestroy);
+
+#ifdef WITH_CUDNN
+
+#include <cudnn.hpp>
+#include <cudnnModule.hpp>
+
 CREATE_HANDLE(cudnnHandle_t, cuda::getCudnnPlugin().cudnnCreate, cuda::getCudnnPlugin().cudnnDestroy);
 CREATE_HANDLE(cudnnTensorDescriptor_t, cuda::getCudnnPlugin().cudnnCreateTensorDescriptor, cuda::getCudnnPlugin().cudnnDestroyTensorDescriptor);
 CREATE_HANDLE(cudnnFilterDescriptor_t, cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
 CREATE_HANDLE(cudnnConvolutionDescriptor_t, cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
 
+#endif
+
 // clang-format on
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 87d6a50123..1cf0f51423 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -12,6 +12,8 @@
 #include <err_cuda.hpp>
 #include <join.hpp>
 #include <kernel/join.hpp>
+
+#include <algorithm>
 #include <stdexcept>
 
 using common::half;
diff --git a/src/backend/cuda/kernel/wrap.cuh b/src/backend/cuda/kernel/wrap.cuh
index 20bb97a985..f8f1db20ca 100644
--- a/src/backend/cuda/kernel/wrap.cuh
+++ b/src/backend/cuda/kernel/wrap.cuh
@@ -15,10 +15,9 @@
 namespace cuda {
 
 template<typename T, bool is_column>
-__global__ void wrap(Param<T> out, CParam<T> in, const int wx,
-                     const int wy, const int sx, const int sy,
-                     const int px, const int py, const int nx,
-                     const int ny, int blocks_x, int blocks_y) {
+__global__ void wrap(Param<T> out, CParam<T> in, const int wx, const int wy,
+                     const int sx, const int sy, const int px, const int py,
+                     const int nx, const int ny, int blocks_x, int blocks_y) {
     int idx2 = blockIdx.x / blocks_x;
     int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
 
@@ -72,4 +71,76 @@ __global__ void wrap(Param<T> out, CParam<T> in, const int wx,
     optr[oidx1 * out.strides[1] + oidx0] = val;
 }
 
+template<typename T, bool is_column>
+__global__ void wrap_dilated(Param<T> out, CParam<T> in, const int wx,
+                             const int wy, const int sx, const int sy,
+                             const int px, const int py, const int dx,
+                             const int dy, const int nx, const int ny,
+                             int blocks_x, int blocks_y) {
+    int idx2 = blockIdx.x / blocks_x;
+    int idx3 = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+
+    int blockIdx_x = blockIdx.x - idx2 * blocks_x;
+    int blockIdx_y = (blockIdx.y + blockIdx.z * gridDim.y) - idx3 * blocks_y;
+
+    int oidx0 = threadIdx.x + blockDim.x * blockIdx_x;
+    int oidx1 = threadIdx.y + blockDim.y * blockIdx_y;
+
+    T *optr       = out.ptr + idx2 * out.strides[2] + idx3 * out.strides[3];
+    const T *iptr = in.ptr + idx2 * in.strides[2] + idx3 * in.strides[3];
+
+    if (oidx0 >= out.dims[0] || oidx1 >= out.dims[1] || idx2 >= out.dims[2] ||
+        idx3 >= out.dims[3])
+        return;
+
+    int eff_wx = wx + (wx - 1) * (dx - 1);
+    int eff_wy = wy + (wy - 1) * (dy - 1);
+
+    int pidx0 = oidx0 + px;
+    int pidx1 = oidx1 + py;
+
+    // The last time a value appears in the unwrapped index is padded_index /
+    // stride Each previous index has the value appear "stride" locations
+    // earlier We work our way back from the last index
+
+    const int x_start = (pidx0 < eff_wx) ? 0 : (pidx0 - eff_wx) / sx + 1;
+    const int y_start = (pidx1 < eff_wy) ? 0 : (pidx1 - eff_wy) / sy + 1;
+
+    const int x_end = min(pidx0 / sx + 1, nx);
+    const int y_end = min(pidx1 / sy + 1, ny);
+
+    T val   = scalar<T>(0);
+    int idx = 1;
+
+    for (int y = y_start; y < y_end; y++) {
+        int fy      = (pidx1 - y * sy);
+        bool yvalid = (fy % dy == 0) && (y < ny);
+        fy /= dy;
+
+        int win_end_y = fy * wx;
+        int dim_end_y = y * nx;
+
+        for (int x = x_start; x < x_end; x++) {
+            int fx      = (pidx0 - x * sx);
+            bool xvalid = (fx % dx == 0) && (x < nx);
+            fx /= dx;
+
+            int win_end = win_end_y + fx;
+            int dim_end = dim_end_y + x;
+
+            if (is_column) {
+                idx = dim_end * in.strides[1] + win_end;
+            } else {
+                idx = dim_end + win_end * in.strides[1];
+            }
+
+            T ival;
+            ival = (yvalid && xvalid) ? iptr[idx] : T(0);
+            val  = val + ival;
+        }
+    }
+
+    optr[oidx1 * out.strides[1] + oidx0] = val;
+}
+
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 6fd1a1577d..cbbc7e77a6 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -49,5 +49,36 @@ void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
     POST_LAUNCH_CHECK();
 }
 
+template<typename T>
+void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
+                  const dim_t sx, const dim_t sy, const dim_t px,
+                  const dim_t py, const dim_t dx, const dim_t dy,
+                  const bool is_column) {
+    static const std::string source(wrap_cuh, wrap_cuh_len);
+
+    auto wrap = getKernel("cuda::wrap_dilated", source,
+                          {TemplateTypename<T>(), TemplateArg(is_column)});
+
+    int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    int ny = 1 + (out.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
+
+    dim3 threads(THREADS_X, THREADS_Y);
+    int blocks_x = divup(out.dims[0], threads.x);
+    int blocks_y = divup(out.dims[1], threads.y);
+
+    dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+
+    wrap(qArgs, out, in, wx, wy, sx, sy, px, py, dx, dy, nx, ny, blocks_x,
+         blocks_y);
+    POST_LAUNCH_CHECK();
+}
+
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index f9d438f67f..78e58fa8a1 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -11,6 +11,11 @@
 #include <windows.h>
 #endif
 
+#ifdef WITH_CUDNN
+#include <cudnn.hpp>
+#include <cudnnModule.hpp>
+#endif
+
 #include <GraphicsResourceManager.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
@@ -20,8 +25,6 @@
 #include <common/unique_handle.hpp>
 #include <common/util.hpp>
 #include <cublas.hpp>
-#include <cudnn.hpp>
-#include <cudnnModule.hpp>
 #include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
@@ -100,6 +103,7 @@ unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
     return &handles[deviceId];
 }
 
+#ifdef WITH_CUDNN
 unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
     thread_local unique_handle<cudnnHandle_t>
         cudnnHandles[DeviceManager::MAX_DEVICES];
@@ -126,6 +130,7 @@ unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
 
     return handle;
 }
+#endif
 
 unique_ptr<PlanCache> &cufftManager(const int deviceId) {
     thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
@@ -181,7 +186,9 @@ DeviceManager::~DeviceManager() {
         delete cusparseManager(i);
         cufftManager(i).reset();
         delete cublasManager(i);
+#ifdef WITH_CUDNN
         delete nnManager(i);
+#endif
     }
 }
 
@@ -460,6 +467,7 @@ PlanCache &fftManager() {
 
 BlasHandle blasHandle() { return *cublasManager(cuda::getActiveDeviceId()); }
 
+#ifdef WITH_CUDNN
 cudnnHandle_t nnHandle() {
     // Keep the getCudnnPlugin call here because module loading can throw an
     // exception the first time its called. We want to avoid that because the
@@ -475,6 +483,7 @@ cudnnHandle_t nnHandle() {
         AF_ERROR("Error Initializing cuDNN\n", AF_ERR_RUNTIME);
     }
 }
+#endif
 
 SolveHandle solverDnHandle() {
     return *cusolverManager(cuda::getActiveDeviceId());
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 4e5d082884..ce973bfd35 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -28,8 +28,11 @@ struct cusparseContext;
 typedef struct cusparseContext* SparseHandle;
 struct cusolverDnContext;
 typedef struct cusolverDnContext* SolveHandle;
+
+#ifdef WITH_CUDNN
 struct cudnnContext;
 typedef struct cudnnContext* cudnnHandle_t;
+#endif
 
 namespace spdlog {
 class logger;
@@ -122,7 +125,9 @@ PlanCache& fftManager();
 
 BlasHandle blasHandle();
 
+#ifdef WITH_CUDNN
 cudnnHandle_t nnHandle();
+#endif
 
 SolveHandle solverDnHandle();
 
diff --git a/src/backend/cuda/unwrap.cpp b/src/backend/cuda/unwrap.cpp
index 6b989b3641..0f9b4dd0c1 100644
--- a/src/backend/cuda/unwrap.cpp
+++ b/src/backend/cuda/unwrap.cpp
@@ -10,11 +10,14 @@
 #include <unwrap.hpp>
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/unwrap.hpp>
 
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 
 template<typename T>
@@ -55,6 +58,7 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cuda
diff --git a/src/backend/cuda/wrap.cpp b/src/backend/cuda/wrap.cpp
index 1cf57e8bde..9c4dcbaffc 100644
--- a/src/backend/cuda/wrap.cpp
+++ b/src/backend/cuda/wrap.cpp
@@ -11,11 +11,15 @@
 
 #include <Array.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/wrap.hpp>
+#include <math.hpp>
 
 #include <stdexcept>
 
+using common::half;
+
 namespace cuda {
 
 template<typename T>
@@ -43,4 +47,30 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column) {
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
+
+    kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy, is_column);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> wrap_dilated<T>(                                      \
+        const Array<T> &in, const dim_t ox, const dim_t oy, const dim_t wx, \
+        const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,     \
+        const dim_t py, const dim_t dx, const dim_t dy, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
 }  // namespace cuda
diff --git a/src/backend/cuda/wrap.hpp b/src/backend/cuda/wrap.hpp
index db923fc5cb..d0cc38bbfe 100644
--- a/src/backend/cuda/wrap.hpp
+++ b/src/backend/cuda/wrap.hpp
@@ -14,4 +14,10 @@ template<typename T>
 void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
           const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
           const dim_t px, const dim_t py, const bool is_column);
-}
+
+template<typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column);
+}  // namespace cuda
diff --git a/test/convolve.cpp b/test/convolve.cpp
index d632071154..b7a8fc0cc8 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -909,7 +909,7 @@ float tolerance<double>() {
 
 template<>
 float tolerance<half_float::half>() {
-    return 3e-2;
+    return 4e-2;
 }
 
 template<typename T>

From e2bd2940d24a701a284648025989628abd181d87 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 10 Apr 2020 12:21:30 -0400
Subject: [PATCH 1896/2677] Fix lu, rank and qr handling of empty arrays and
 check for nullptr

---
 src/api/c/lu.cpp    |  7 +++++--
 src/api/c/qr.cpp    |  7 +++++--
 src/api/c/rank.cpp  | 22 ++++++++++------------
 test/lu_dense.cpp   | 43 +++++++++++++++++++++++++++++++++++++++++++
 test/qr_dense.cpp   | 10 ++++++++++
 test/rank_dense.cpp | 10 ++++++++++
 6 files changed, 83 insertions(+), 16 deletions(-)

diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index cb5315588f..c9cef44e61 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -49,6 +49,9 @@ af_err af_lu(af_array *lower, af_array *upper, af_array *pivot,
 
         af_dtype type = i_info.getType();
 
+        ARG_ASSERT(0, lower != nullptr);
+        ARG_ASSERT(1, upper != nullptr);
+        ARG_ASSERT(2, pivot != nullptr);
         ARG_ASSERT(3, i_info.isFloating());  // Only floating and complex types
 
         if (i_info.ndims() == 0) {
@@ -81,13 +84,13 @@ af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv) {
         }
 
         ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(0, pivot != nullptr);
 
         if (i_info.ndims() == 0) {
             return af_create_handle(pivot, 0, nullptr, type);
         }
 
         af_array out;
-
         switch (type) {
             case f32: out = lu_inplace<float>(in, is_lapack_piv); break;
             case f64: out = lu_inplace<double>(in, is_lapack_piv); break;
@@ -95,7 +98,7 @@ af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv) {
             case c64: out = lu_inplace<cdouble>(in, is_lapack_piv); break;
             default: TYPE_ERROR(1, type);
         }
-        if (pivot != NULL) std::swap(*pivot, out);
+        std::swap(*pivot, out);
     }
     CATCHALL;
 
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index 3791ffc381..257b2b02ea 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -55,6 +55,9 @@ af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in) {
             return AF_SUCCESS;
         }
 
+        ARG_ASSERT(0, q != nullptr);
+        ARG_ASSERT(1, r != nullptr);
+        ARG_ASSERT(2, tau != nullptr);
         ARG_ASSERT(3, i_info.isFloating());  // Only floating and complex types
 
         switch (type) {
@@ -81,13 +84,13 @@ af_err af_qr_inplace(af_array *tau, af_array in) {
         af_dtype type = i_info.getType();
 
         ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(0, tau != nullptr);
 
         if (i_info.ndims() == 0) {
             return af_create_handle(tau, 0, nullptr, type);
         }
 
         af_array out;
-
         switch (type) {
             case f32: out = qr_inplace<float>(in); break;
             case f64: out = qr_inplace<double>(in); break;
@@ -95,7 +98,7 @@ af_err af_qr_inplace(af_array *tau, af_array in) {
             case c64: out = qr_inplace<cdouble>(in); break;
             default: TYPE_ERROR(1, type);
         }
-        if (tau != NULL) std::swap(*tau, out);
+        std::swap(*tau, out);
     }
     CATCHALL;
 
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 9816646e73..22b6b720c0 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -56,19 +56,17 @@ af_err af_rank(uint* out, const af_array in, const double tol) {
         af_dtype type = i_info.getType();
 
         ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
+        ARG_ASSERT(0, out != nullptr);
 
-        uint output;
-        if (i_info.ndims() == 0) {
-            output = 0;
-            return AF_SUCCESS;
-        }
-
-        switch (type) {
-            case f32: output = rank<float>(in, tol); break;
-            case f64: output = rank<double>(in, tol); break;
-            case c32: output = rank<cfloat>(in, tol); break;
-            case c64: output = rank<cdouble>(in, tol); break;
-            default: TYPE_ERROR(1, type);
+        uint output = 0;
+        if (i_info.ndims() != 0) {
+            switch (type) {
+                case f32: output = rank<float>(in, tol); break;
+                case f64: output = rank<double>(in, tol); break;
+                case c32: output = rank<cfloat>(in, tol); break;
+                case c64: output = rank<cdouble>(in, tol); break;
+                default: TYPE_ERROR(1, type);
+            }
         }
         std::swap(*out, output);
     }
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 3bd091bd49..88ed274112 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -235,3 +235,46 @@ TYPED_TEST(LU, RectangularLarge1) {
 TYPED_TEST(LU, RectangularMultipleOfTwoLarge1) {
     luTester<TypeParam>(512, 1024, eps<TypeParam>());
 }
+
+TEST(LU, NullLowerOutput) {
+    if (noLAPACKTests()) return;
+    dim4 dims(3, 3);
+    af_array in = 0;
+    ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
+
+    af_array upper, pivot;
+    ASSERT_EQ(AF_ERR_ARG, af_lu(NULL, &upper, &pivot, in));
+    ASSERT_SUCCESS(af_release_array(in));
+}
+
+TEST(LU, NullUpperOutput) {
+    if (noLAPACKTests()) return;
+    dim4 dims(3, 3);
+    af_array in = 0;
+    ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
+
+    af_array lower, pivot;
+    ASSERT_EQ(AF_ERR_ARG, af_lu(&lower, NULL, &pivot, in));
+    ASSERT_SUCCESS(af_release_array(in));
+}
+
+TEST(LU, NullPivotOutput) {
+    if (noLAPACKTests()) return;
+    dim4 dims(3, 3);
+    af_array in = 0;
+    ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
+
+    af_array lower, upper;
+    ASSERT_EQ(AF_ERR_ARG, af_lu(&lower, &upper, NULL, in));
+    ASSERT_SUCCESS(af_release_array(in));
+}
+
+TEST(LU, InPlaceNullOutput) {
+    if (noLAPACKTests()) return;
+    dim4 dims(3, 3);
+    af_array in = 0;
+    ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
+
+    ASSERT_EQ(AF_ERR_ARG, af_lu_inplace(NULL, in, true));
+    ASSERT_SUCCESS(af_release_array(in));
+}
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 17fdafa1a6..640171a754 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -179,3 +179,13 @@ TYPED_TEST(QR, RectangularLarge1) {
 TYPED_TEST(QR, RectangularMultipleOfTwoLarge1) {
     qrTester<TypeParam>(512, 1024, eps<TypeParam>());
 }
+
+TEST(QR, InPlaceNullOutput) {
+    if (noLAPACKTests()) return;
+    dim4 dims(3, 3);
+    af_array in = 0;
+    ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
+
+    ASSERT_EQ(AF_ERR_ARG, af_qr_inplace(NULL, in));
+    ASSERT_SUCCESS(af_release_array(in));
+}
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index 6f9879df16..003979ad62 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -112,3 +112,13 @@ void detTest() {
 }
 
 TYPED_TEST(Det, Small) { detTest<TypeParam>(); }
+
+TEST(Rank, NullOutput) {
+    if (noLAPACKTests()) return;
+    dim4 dims(3, 3);
+    af_array in = 0;
+    af_randu(&in, dims.ndims(), dims.get(), f32);
+
+    ASSERT_EQ(AF_ERR_ARG, af_rank(NULL, in, 1e-6));
+    ASSERT_SUCCESS(af_release_array(in));
+}

From f2112530c0021a4444f1a528a74500bc20726715 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 11 Apr 2020 01:38:57 -0400
Subject: [PATCH 1897/2677] Renamed a few variables in the default alloc and
 unlock funcitons

---
 src/backend/common/DefaultMemoryManager.cpp | 58 +++++++++++----------
 src/backend/common/DefaultMemoryManager.hpp |  7 +--
 2 files changed, 33 insertions(+), 32 deletions(-)

diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index a7a37a3dee..35a4dc58a9 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -166,13 +166,15 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
             }
 
             lock_guard_t lock(this->memory_mutex);
-            free_iter iter = current.free_map.find(alloc_bytes);
+            auto free_buffer_iter = current.free_map.find(alloc_bytes);
+            vector<void *> &free_buffer_vector = free_buffer_iter->second;
 
-            if (iter != current.free_map.end() && !iter->second.empty()) {
+            if (free_buffer_iter != current.free_map.end() &&
+                !free_buffer_vector.empty()) {
                 // Delete existing buffer info and underlying event
                 // Set to existing in from free map
-                ptr = iter->second.back();
-                iter->second.pop_back();
+                ptr = free_buffer_vector.back();
+                free_buffer_vector.pop_back();
                 current.locked_map[ptr] = info;
                 current.lock_bytes += alloc_bytes;
                 current.lock_buffers++;
@@ -206,9 +208,9 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
 size_t DefaultMemoryManager::allocated(void *ptr) {
     if (!ptr) return 0;
     memory_info &current = this->getCurrentMemoryInfo();
-    locked_iter iter     = current.locked_map.find((void *)ptr);
-    if (iter == current.locked_map.end()) return 0;
-    return (iter->second).bytes;
+    auto locked_iter     = current.locked_map.find(ptr);
+    if (locked_iter == current.locked_map.end()) { return 0; }
+    return (locked_iter->second).bytes;
 }
 
 void DefaultMemoryManager::unlock(void *ptr, bool user_unlock) {
@@ -221,39 +223,43 @@ void DefaultMemoryManager::unlock(void *ptr, bool user_unlock) {
         lock_guard_t lock(this->memory_mutex);
         memory_info &current = this->getCurrentMemoryInfo();
 
-        locked_iter iter = current.locked_map.find((void *)ptr);
+        auto locked_buffer_iter         = current.locked_map.find(ptr);
+        locked_info &locked_buffer_info = locked_buffer_iter->second;
+        void *locked_buffer_ptr         = locked_buffer_iter->first;
 
         // Pointer not found in locked map
-        if (iter == current.locked_map.end()) {
+        if (locked_buffer_iter == current.locked_map.end()) {
             // Probably came from user, just free it
             freed_ptr.reset(ptr);
             return;
         }
 
         if (user_unlock) {
-            (iter->second).user_lock = false;
+            locked_buffer_info.user_lock = false;
         } else {
-            (iter->second).manager_lock = false;
+            locked_buffer_info.manager_lock = false;
         }
 
         // Return early if either one is locked
-        if ((iter->second).user_lock || (iter->second).manager_lock) { return; }
+        if (locked_buffer_info.user_lock || locked_buffer_info.manager_lock) {
+            return;
+        }
 
-        size_t bytes = iter->second.bytes;
-        current.lock_bytes -= iter->second.bytes;
+        size_t bytes = locked_buffer_info.bytes;
+        current.lock_bytes -= locked_buffer_info.bytes;
         current.lock_buffers--;
 
         if (this->debug_mode) {
             // Just free memory in debug mode
-            if ((iter->second).bytes > 0) {
-                freed_ptr.reset(iter->first);
+            if (locked_buffer_info.bytes > 0) {
+                freed_ptr.reset(locked_buffer_ptr);
                 current.total_buffers--;
-                current.total_bytes -= iter->second.bytes;
+                current.total_bytes -= locked_buffer_info.bytes;
             }
         } else {
             current.free_map[bytes].emplace_back(ptr);
         }
-        current.locked_map.erase(iter);
+        current.locked_map.erase(locked_buffer_iter);
     }
 }
 
@@ -262,6 +268,7 @@ void DefaultMemoryManager::signalMemoryCleanup() {
 }
 
 void DefaultMemoryManager::printInfo(const char *msg, const int device) {
+    UNUSED(device);
     const memory_info &current = this->getCurrentMemoryInfo();
 
     printf("%s\n", msg);
@@ -325,9 +332,9 @@ void DefaultMemoryManager::userLock(const void *ptr) {
 
     lock_guard_t lock(this->memory_mutex);
 
-    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-    if (iter != current.locked_map.end()) {
-        iter->second.user_lock = true;
+    auto locked_iter = current.locked_map.find(const_cast<void *>(ptr));
+    if (locked_iter != current.locked_map.end()) {
+        locked_iter->second.user_lock = true;
     } else {
         locked_info info = {false, true, 100};  // This number is not relevant
 
@@ -342,12 +349,9 @@ void DefaultMemoryManager::userUnlock(const void *ptr) {
 bool DefaultMemoryManager::isUserLocked(const void *ptr) {
     memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
-    locked_iter iter = current.locked_map.find(const_cast<void *>(ptr));
-    if (iter != current.locked_map.end()) {
-        return iter->second.user_lock;
-    } else {
-        return false;
-    }
+    auto locked_iter = current.locked_map.find(const_cast<void *>(ptr));
+    if (locked_iter == current.locked_map.end()) { return false; }
+    return locked_iter->second.user_lock;
 }
 
 size_t DefaultMemoryManager::getMemStepSize() {
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index 3bb94cc0fb..d014a58fe5 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -35,11 +35,8 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
         size_t bytes;
     };
 
-    using locked_t    = typename std::unordered_map<void *, locked_info>;
-    using locked_iter = typename locked_t::iterator;
-
-    using free_t    = std::unordered_map<size_t, std::vector<void *>>;
-    using free_iter = typename free_t::iterator;
+    using locked_t = typename std::unordered_map<void *, locked_info>;
+    using free_t   = std::unordered_map<size_t, std::vector<void *>>;
 
     struct memory_info {
         locked_t locked_map;

From f61537855e565710a355c5c9b954beb4376da96f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 11 Apr 2020 01:41:23 -0400
Subject: [PATCH 1898/2677] Minor refactor in median. Add one and two element
 tests

---
 src/api/c/median.cpp | 24 ++++++++++--------------
 test/median.cpp      | 15 +++++++++++++++
 2 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 57d3ff05c1..fee958f06a 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -41,14 +41,12 @@ static double median(const af_array& in) {
     } else if (nElems == 2) {
         T result[2];
         AF_CHECK(af_get_data_ptr((void*)&result, in));
-        if (input.isFloating()) {
-            return division(result[0] + result[1], 2.0);
-        } else {
-            return division((float)result[0] + (float)result[1], 2.0);
-        }
+        return division(
+            (static_cast<double>(result[0]) + static_cast<double>(result[1])),
+            2.0);
     }
 
-    double mid       = (nElems + 1) / 2;
+    double mid       = static_cast<double>(nElems + 1) / 2.0;
     af_seq mdSpan[1] = {af_make_seq(mid - 1, mid, 1)};
 
     Array<T> sortedArr = sort<T>(input, 0, true);
@@ -68,11 +66,9 @@ static double median(const af_array& in) {
     if (nElems % 2 == 1) {
         result = resPtr[0];
     } else {
-        if (input.isFloating()) {
-            result = division(resPtr[0] + resPtr[1], 2);
-        } else {
-            result = division((float)resPtr[0] + (float)resPtr[1], 2);
-        }
+        result = division(
+            static_cast<double>(resPtr[0]) + static_cast<double>(resPtr[1]),
+            2.0);
     }
 
     return result;
@@ -90,9 +86,9 @@ static af_array median(const af_array& in, const dim_t dim) {
 
     Array<T> sortedIn = sort<T>(input, dim, true);
 
-    int dimLength = input.dims()[dim];
-    double mid    = (dimLength + 1) / 2;
-    af_array left = 0;
+    size_t dimLength = input.dims()[dim];
+    double mid       = static_cast<double>(dimLength + 1) / 2.0;
+    af_array left    = 0;
 
     af_seq slices[4] = {af_span, af_span, af_span, af_span};
     slices[dim]      = af_make_seq(mid - 1.0, mid - 1.0, 1.0);
diff --git a/test/median.cpp b/test/median.cpp
index 3c7e711b7f..36a71e3d3b 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -150,3 +150,18 @@ MEDIAN(float, uchar)
 MEDIAN(float, short)
 MEDIAN(float, ushort)
 MEDIAN(double, double)
+
+TEST(Median, OneElement) {
+    af::array in = randu(1, f32);
+
+    af::array out = median(in);
+    ASSERT_ARRAYS_EQ(in, out);
+}
+
+TEST(Median, TwoElements) {
+    af::array in = randu(2, f32);
+
+    af::array out  = median(in);
+    af::array gold = mean(in);
+    ASSERT_ARRAYS_EQ(gold, out);
+}

From 2e098d4d6972b4fdc6520d646f1d1aaa4debafe7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 12 Apr 2020 12:28:30 -0400
Subject: [PATCH 1899/2677] Fixed formatting issue in test/memory.cpp

---
 test/memory.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/memory.cpp b/test/memory.cpp
index c1012c29ef..a661700916 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -710,7 +710,7 @@ af_err unlock_fn(af_memory_manager manager, void *ptr, int userLock) {
 
 af_err user_unlock_fn(af_memory_manager manager, void *ptr) {
     auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
-    af_err err = unlock_fn(manager, ptr, /* user */ 1);
+    af_err err    = unlock_fn(manager, ptr, /* user */ 1);
     payload->lockedBytes -= payload->table[ptr];
     return err;
 }

From 6dd72beb19c8318c617f44c01e02a8a2ed523d00 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 14 Apr 2020 00:23:06 -0400
Subject: [PATCH 1900/2677] Remove MKL_ThreadingLibrary from required var.
 Sequential doesn have one

---
 CMakeModules/FindMKL.cmake | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 0b0505521e..0f215631c6 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -357,8 +357,7 @@ find_package_handle_standard_args(MKL_Shared
   REQUIRED_VARS MKL_INCLUDE_DIR
                 MKL_Core_LINK_LIBRARY
                 MKL_Interface_LINK_LIBRARY
-                MKL_ThreadLayer_LINK_LIBRARY
-                MKL_ThreadingLibrary_LINK_LIBRARY)
+                MKL_ThreadLayer_LINK_LIBRARY)
 
 find_package_handle_standard_args(MKL_Static
   FAIL_MESSAGE "Could NOT find MKL: Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. This script searches for the libraries in MKLROOT, LIBRARY_PATHS(Linux), and LIB(Windows) environment variables"
@@ -366,8 +365,7 @@ find_package_handle_standard_args(MKL_Static
   REQUIRED_VARS MKL_INCLUDE_DIR
                 MKL_Core_STATIC_LINK_LIBRARY
                 MKL_Interface_STATIC_LINK_LIBRARY
-                MKL_ThreadLayer_STATIC_LINK_LIBRARY
-                MKL_ThreadingLibrary_LINK_LIBRARY)
+                MKL_ThreadLayer_STATIC_LINK_LIBRARY)
 
 if(NOT WIN32)
   find_library(M_LIB m)

From 9d268cd23631f65cf344842f0f1baf9f411ee946 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 14 Apr 2020 00:27:28 -0400
Subject: [PATCH 1901/2677] Fix pinned memory manager check, was testing the
 function pointer

---
 src/backend/cuda/device_manager.cpp   | 2 +-
 src/backend/opencl/device_manager.cpp | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index c055816808..83aa9a0101 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -301,7 +301,7 @@ void DeviceManager::setMemoryManagerPinned(
     // pinnedMemoryManager()
     pinnedMemoryManager();
     // Calls shutdown() on the existing memory manager.
-    if (pinnedMemoryManager) { pinnedMemManager->shutdownAllocator(); }
+    if (pinnedMemManager) { pinnedMemManager->shutdownAllocator(); }
     // Set the backend memory manager for this new manager to register native
     // functions correctly.
     pinnedMemManager = std::move(newMgr);
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index cddf1b4c8c..11ed2238e4 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -362,10 +362,10 @@ void DeviceManager::setMemoryManagerPinned(
     // pinnedMemoryManager()
     pinnedMemoryManager();
     // Calls shutdown() on the existing memory manager.
-    pinnedMemManager->shutdownAllocator();
-    pinnedMemManager = std::move(newMgr);
+    if (pinnedMemManager) { pinnedMemManager->shutdownAllocator(); }
     // Set the backend pinned memory manager for this new manager to register
     // native functions correctly.
+    pinnedMemManager = std::move(newMgr);
     std::unique_ptr<opencl::AllocatorPinned> deviceMemoryManager(
         new opencl::AllocatorPinned());
     pinnedMemManager->setAllocator(std::move(deviceMemoryManager));

From 9404e33511ab69792abc02e1c88e90843c841cc2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 15 Apr 2020 09:27:15 -0400
Subject: [PATCH 1902/2677] Apply clang-tidy suggestions to all backends
 (#2839)

* Add clang-tidy configuration file

* Cleanup some exception code

* Add additional upstream directories to .gitignore

* Remove unused parameters from wrap and transform implementations

* Fix warnings and removed unused calls
---
 .gitignore                                    |   4 +
 include/af/features.h                         |   2 +-
 include/af/graphics.h                         |   5 +-
 include/af/random.h                           |   8 +-
 include/af/seq.h                              |   4 +-
 src/.clang-tidy                               | 391 ++++++++++++++++++
 src/api/c/anisotropic_diffusion.cpp           |  17 +-
 src/api/c/approx.cpp                          |  19 +-
 src/api/c/array.cpp                           |  76 ++--
 src/api/c/assign.cpp                          |  71 ++--
 src/api/c/bilateral.cpp                       |  16 +-
 src/api/c/binary.cpp                          |  81 ++--
 src/api/c/blas.cpp                            |  56 ++-
 src/api/c/canny.cpp                           |  17 +-
 src/api/c/cast.cpp                            |  21 +-
 src/api/c/cholesky.cpp                        |   7 +-
 src/api/c/clamp.cpp                           |  15 +-
 src/api/c/complex.cpp                         |  30 +-
 src/api/c/confidence_connected.cpp            |  66 +--
 src/api/c/convolve.cpp                        |  93 +++--
 src/api/c/corrcoef.cpp                        |  15 +-
 src/api/c/covariance.cpp                      |  10 +-
 src/api/c/data.cpp                            |  59 ++-
 src/api/c/deconvolution.cpp                   |  33 +-
 src/api/c/det.cpp                             |  11 +-
 src/api/c/device.cpp                          |  37 +-
 src/api/c/diff.cpp                            |   8 +-
 src/api/c/dog.cpp                             |   8 +-
 src/api/c/error.cpp                           |   8 +-
 src/api/c/events.cpp                          |   6 +-
 src/api/c/fast.cpp                            |   8 +-
 src/api/c/features.cpp                        |  23 +-
 src/api/c/fft.cpp                             |   9 +-
 src/api/c/fft_common.hpp                      |  36 +-
 src/api/c/fftconvolve.cpp                     |  73 ++--
 src/api/c/filters.cpp                         |   4 +-
 src/api/c/flip.cpp                            |  13 +-
 src/api/c/gaussian_kernel.cpp                 |  12 +-
 src/api/c/gradient.cpp                        |   3 +-
 src/api/c/harris.cpp                          |  16 +-
 src/api/c/hist.cpp                            |  32 +-
 src/api/c/histeq.cpp                          |   8 +-
 src/api/c/histogram.cpp                       |  18 +-
 src/api/c/homography.cpp                      |  10 +-
 src/api/c/hsv_rgb.cpp                         |   4 +-
 src/api/c/iir.cpp                             |   9 +-
 src/api/c/image.cpp                           |  20 +-
 src/api/c/imageio.cpp                         | 313 +++++++-------
 src/api/c/imageio2.cpp                        | 228 +++++-----
 src/api/c/implicit.cpp                        |  26 +-
 src/api/c/implicit.hpp                        |   2 -
 src/api/c/index.cpp                           |  21 +-
 src/api/c/internal.cpp                        | 108 ++---
 src/api/c/inverse.cpp                         |   1 -
 src/api/c/join.cpp                            |  10 +-
 src/api/c/match_template.cpp                  |  10 +-
 src/api/c/mean.cpp                            |  52 +--
 src/api/c/meanshift.cpp                       |   2 +-
 src/api/c/median.cpp                          |  19 +-
 src/api/c/memory.cpp                          |  67 ++-
 src/api/c/memoryapi.hpp                       |   2 +-
 src/api/c/moddims.cpp                         |   8 +-
 src/api/c/moments.cpp                         |   2 +-
 src/api/c/morph.cpp                           |  12 +-
 src/api/c/nearest_neighbour.cpp               |  10 +-
 src/api/c/norm.cpp                            |  10 +-
 src/api/c/pinverse.cpp                        |   2 +-
 src/api/c/plot.cpp                            |  43 +-
 src/api/c/print.cpp                           |   3 +-
 src/api/c/random.cpp                          |  42 +-
 src/api/c/rank.cpp                            |   4 +-
 src/api/c/reduce.cpp                          | 206 ++++-----
 src/api/c/reorder.cpp                         |   4 +-
 src/api/c/resize.cpp                          |   1 -
 src/api/c/rgb_gray.cpp                        |   5 +-
 src/api/c/rotate.cpp                          |  14 +-
 src/api/c/sat.cpp                             |   2 +-
 src/api/c/scan.cpp                            |  19 +-
 src/api/c/set.cpp                             |   3 +-
 src/api/c/shift.cpp                           |   1 -
 src/api/c/sobel.cpp                           |   6 +-
 src/api/c/sort.cpp                            |   2 -
 src/api/c/sparse.cpp                          |  31 +-
 src/api/c/sparse_handle.hpp                   |   2 +-
 src/api/c/stdev.cpp                           |  25 +-
 src/api/c/stream.cpp                          |  53 ++-
 src/api/c/surface.cpp                         |  19 +-
 src/api/c/svd.cpp                             |   4 +-
 src/api/c/tile.cpp                            |   8 +-
 src/api/c/topk.cpp                            |   5 +-
 src/api/c/transform.cpp                       |  63 +--
 src/api/c/transform_coordinates.cpp           |  11 +-
 src/api/c/transpose.cpp                       |   2 +-
 src/api/c/unary.cpp                           |  12 +-
 src/api/c/var.cpp                             |  19 +-
 src/api/c/vector_field.cpp                    |  48 +--
 src/api/c/where.cpp                           |   1 -
 src/api/c/window.cpp                          |  72 ++--
 src/api/c/wrap.cpp                            |  35 +-
 src/api/c/ycbcr_rgb.cpp                       |  51 ++-
 src/api/cpp/array.cpp                         |  34 +-
 src/api/cpp/blas.cpp                          |   8 +-
 src/api/cpp/convolve.cpp                      |  22 +-
 src/api/cpp/data.cpp                          |  11 +-
 src/api/cpp/device.cpp                        |   6 +-
 src/api/cpp/error.hpp                         |   2 +-
 src/api/cpp/event.cpp                         |   4 +-
 src/api/cpp/exception.cpp                     |  22 +-
 src/api/cpp/features.cpp                      |   4 +-
 src/api/cpp/fft.cpp                           |  26 +-
 src/api/cpp/fftconvolve.cpp                   |   2 +-
 src/api/cpp/gfor.cpp                          |   3 +-
 src/api/cpp/index.cpp                         |  31 +-
 src/api/cpp/internal.cpp                      |   8 +-
 src/api/cpp/mean.cpp                          |   8 +-
 src/api/cpp/random.cpp                        |  10 +-
 src/api/cpp/seq.cpp                           |  32 +-
 src/api/cpp/sparse.cpp                        |  25 +-
 src/api/cpp/stdev.cpp                         |   4 +-
 src/api/cpp/timing.cpp                        |  10 +-
 src/api/cpp/util.cpp                          |   3 -
 src/api/cpp/var.cpp                           |   8 +-
 src/backend/common/ArrayInfo.cpp              |  28 +-
 src/backend/common/ArrayInfo.hpp              |   4 +-
 src/backend/common/DefaultMemoryManager.cpp   |  46 +--
 src/backend/common/DefaultMemoryManager.hpp   |   3 +-
 src/backend/common/DependencyModule.cpp       |  14 +-
 src/backend/common/DependencyModule.hpp       |   8 +-
 src/backend/common/InteropManager.hpp         |   2 +-
 src/backend/common/Logger.cpp                 |  10 +-
 src/backend/common/Logger.hpp                 |   2 +-
 src/backend/common/SparseArray.cpp            |  56 +--
 src/backend/common/SparseArray.hpp            |  16 +-
 src/backend/common/dim4.cpp                   |  17 +-
 src/backend/common/dispatch.cpp               |  14 +-
 src/backend/common/err_common.cpp             |  61 +--
 src/backend/common/err_common.hpp             |  53 ++-
 src/backend/common/graphics_common.cpp        | 103 ++---
 src/backend/common/graphics_common.hpp        |  22 +-
 src/backend/common/half.hpp                   |   4 +
 src/backend/common/host_memory.cpp            |   3 +-
 src/backend/common/jit/Node.cpp               |   5 +-
 src/backend/common/module_loading_unix.cpp    |  11 +-
 src/backend/common/sparse_helpers.hpp         |   4 +-
 src/backend/common/util.cpp                   |   7 +-
 src/backend/cpu/Array.cpp                     |  51 +--
 src/backend/cpu/Array.hpp                     |   2 +-
 src/backend/cpu/Event.cpp                     |   6 +-
 src/backend/cpu/anisotropic_diffusion.cpp     |   5 +-
 src/backend/cpu/assign.cpp                    |   3 +-
 src/backend/cpu/bilateral.cpp                 |   2 +-
 src/backend/cpu/blas.cpp                      |  59 ++-
 src/backend/cpu/cholesky.cpp                  |   7 +-
 src/backend/cpu/convolve.cpp                  |  37 +-
 src/backend/cpu/copy.cpp                      |   2 +-
 src/backend/cpu/copy.hpp                      |   2 +-
 src/backend/cpu/device_manager.cpp            |  37 +-
 src/backend/cpu/device_manager.hpp            |   6 +-
 src/backend/cpu/diagonal.cpp                  |  12 +-
 src/backend/cpu/fast.cpp                      |   6 +-
 src/backend/cpu/fast.hpp                      |   2 +-
 src/backend/cpu/fft.cpp                       |  47 ++-
 src/backend/cpu/fftconvolve.cpp               |  65 +--
 src/backend/cpu/flood_fill.cpp                |   1 -
 src/backend/cpu/harris.cpp                    |  32 +-
 src/backend/cpu/histogram.cpp                 |   2 +-
 src/backend/cpu/homography.cpp                | 133 +++---
 src/backend/cpu/hsv_rgb.cpp                   |   2 -
 src/backend/cpu/identity.cpp                  |   2 +-
 src/backend/cpu/image.cpp                     |   2 -
 src/backend/cpu/index.cpp                     |   2 +-
 src/backend/cpu/iota.cpp                      |   2 +-
 src/backend/cpu/kernel/random_engine.hpp      |   5 +-
 src/backend/cpu/lookup.cpp                    |   5 +-
 src/backend/cpu/math.cpp                      |   2 +-
 src/backend/cpu/mean.cpp                      |   4 +-
 src/backend/cpu/meanshift.cpp                 |  11 +-
 src/backend/cpu/memory.cpp                    |  29 +-
 src/backend/cpu/moments.cpp                   |   8 +-
 src/backend/cpu/morph.cpp                     |   6 +-
 src/backend/cpu/nearest_neighbour.cpp         |   6 +-
 src/backend/cpu/orb.cpp                       |  77 ++--
 src/backend/cpu/platform.cpp                  |   7 +-
 src/backend/cpu/random_engine.cpp             |  10 +-
 src/backend/cpu/random_engine.hpp             |   4 +-
 src/backend/cpu/reduce.cpp                    |  20 +-
 src/backend/cpu/regions.cpp                   |   2 +-
 src/backend/cpu/reorder.cpp                   |   4 +-
 src/backend/cpu/resize.cpp                    |   2 +-
 src/backend/cpu/scan.cpp                      |   4 +-
 src/backend/cpu/scan_by_key.cpp               |   4 +-
 src/backend/cpu/set.cpp                       |  15 +-
 src/backend/cpu/sift.cpp                      |   5 +-
 src/backend/cpu/solve.cpp                     |   1 +
 src/backend/cpu/sort.cpp                      |   7 +-
 src/backend/cpu/sort_by_key.cpp               |   2 +-
 src/backend/cpu/sort_index.cpp                |   2 +-
 src/backend/cpu/sort_index.hpp                |   2 +-
 src/backend/cpu/sparse.cpp                    |  11 +-
 src/backend/cpu/sparse_arith.cpp              |  23 +-
 src/backend/cpu/sparse_blas.cpp               |  12 +-
 src/backend/cpu/tile.cpp                      |   4 +-
 src/backend/cpu/topk.cpp                      |   2 +-
 src/backend/cpu/transform.cpp                 |   6 +-
 src/backend/cpu/transform.hpp                 |   4 +-
 src/backend/cpu/transpose.cpp                 |   2 +-
 src/backend/cpu/types.hpp                     |   2 +-
 src/backend/cpu/vector_field.hpp              |   3 +-
 src/backend/cpu/wrap.cpp                      |  14 +-
 src/backend/cpu/wrap.hpp                      |   6 +-
 src/backend/cuda/Array.cpp                    |  69 ++--
 src/backend/cuda/Array.hpp                    |  41 +-
 src/backend/cuda/Event.hpp                    |   2 +-
 src/backend/cuda/GraphicsResourceManager.cpp  |   3 +-
 src/backend/cuda/GraphicsResourceManager.hpp  |   7 +-
 src/backend/cuda/ThrustArrayFirePolicy.cpp    |   6 +-
 src/backend/cuda/blas.cu                      |   6 +-
 src/backend/cuda/cholesky.cpp                 |  19 +-
 src/backend/cuda/convolve.cpp                 |  16 +-
 src/backend/cuda/convolveNN.cpp               |  54 +--
 src/backend/cuda/copy.cpp                     |   3 +-
 src/backend/cuda/cudnnModule.cpp              |  15 +-
 src/backend/cuda/cudnnModule.hpp              |   2 +-
 src/backend/cuda/device_manager.cpp           |  24 +-
 src/backend/cuda/device_manager.hpp           |   6 +-
 src/backend/cuda/diff.cpp                     |   4 +-
 src/backend/cuda/driver.cpp                   |  31 +-
 src/backend/cuda/driver.h                     |   2 +-
 src/backend/cuda/fast_pyramid.cpp             |   9 +-
 src/backend/cuda/fast_pyramid.hpp             |   2 +-
 src/backend/cuda/fftconvolve.cpp              |  29 +-
 src/backend/cuda/hist_graphics.cpp            |   3 +-
 src/backend/cuda/histogram.cpp                |   2 +-
 src/backend/cuda/iir.cpp                      |   2 +-
 src/backend/cuda/image.cpp                    |   4 +-
 src/backend/cuda/index.cpp                    |  10 +-
 src/backend/cuda/jit.cpp                      |  81 ++--
 src/backend/cuda/join.cpp                     |   4 +-
 src/backend/cuda/lookup.cpp                   |   5 +-
 src/backend/cuda/lu.cpp                       |  10 +-
 src/backend/cuda/math.hpp                     |   2 +-
 src/backend/cuda/meanshift.cpp                |   4 +-
 src/backend/cuda/medfilt.cpp                  |   8 +-
 src/backend/cuda/memory.cpp                   |  20 +-
 src/backend/cuda/moments.cpp                  |   8 +-
 src/backend/cuda/nvrtc/cache.cpp              |  52 ++-
 src/backend/cuda/nvrtc/cache.hpp              |   4 +-
 src/backend/cuda/platform.cpp                 |  94 +++--
 src/backend/cuda/platform.hpp                 |   2 +-
 src/backend/cuda/plot.cpp                     |   3 +-
 src/backend/cuda/qr.cpp                       |  18 +-
 src/backend/cuda/random_engine.cu             |   2 +-
 src/backend/cuda/random_engine.hpp            |   4 +-
 src/backend/cuda/range.cpp                    |   3 +-
 src/backend/cuda/reorder.cpp                  |   4 +-
 src/backend/cuda/resize.cpp                   |   2 +-
 src/backend/cuda/select.cpp                   |   8 +-
 src/backend/cuda/shift.cpp                    |   9 +-
 src/backend/cuda/surface.cpp                  |   3 +-
 src/backend/cuda/susan.cpp                    |  12 +-
 src/backend/cuda/susan.hpp                    |   6 +-
 src/backend/cuda/svd.cpp                      |  13 +-
 src/backend/cuda/tile.cpp                     |   4 +-
 src/backend/cuda/transform.cpp                |   6 +-
 src/backend/cuda/transform.hpp                |   4 +-
 src/backend/cuda/transpose.cpp                |   2 +-
 src/backend/cuda/types.hpp                    |   2 +-
 src/backend/cuda/vector_field.cpp             |   6 +-
 src/backend/cuda/vector_field.hpp             |   3 +-
 src/backend/cuda/wrap.cpp                     |  14 +-
 src/backend/cuda/wrap.hpp                     |   6 +-
 src/backend/opencl/Array.cpp                  | 105 +++--
 src/backend/opencl/Array.hpp                  |  29 +-
 src/backend/opencl/Event.cpp                  |   7 +-
 .../opencl/GraphicsResourceManager.cpp        |  11 +-
 .../opencl/GraphicsResourceManager.hpp        |   3 +-
 src/backend/opencl/Param.cpp                  |   2 +-
 src/backend/opencl/Param.hpp                  |   2 +-
 src/backend/opencl/anisotropic_diffusion.cpp  |   5 +-
 src/backend/opencl/api.cpp                    |   7 +-
 src/backend/opencl/assign.cpp                 |   7 +-
 src/backend/opencl/blas.cpp                   |  35 +-
 src/backend/opencl/cholesky.cpp               |   5 +-
 src/backend/opencl/clfft.cpp                  |  16 +-
 src/backend/opencl/convolve.cpp               |  40 +-
 src/backend/opencl/convolve_separable.cpp     |  14 +-
 src/backend/opencl/copy.cpp                   |  18 +-
 src/backend/opencl/cpu/cpu_blas.cpp           |  19 +-
 src/backend/opencl/cpu/cpu_cholesky.cpp       |   7 +-
 src/backend/opencl/cpu/cpu_lu.cpp             |  16 +-
 src/backend/opencl/cpu/cpu_sparse_blas.cpp    |  12 +-
 src/backend/opencl/device_manager.cpp         |  97 ++---
 src/backend/opencl/device_manager.hpp         |   4 +-
 src/backend/opencl/diff.cpp                   |  16 +-
 src/backend/opencl/fft.cpp                    |  43 +-
 src/backend/opencl/fftconvolve.cpp            |  40 +-
 src/backend/opencl/hist_graphics.cpp          |   3 +-
 src/backend/opencl/histogram.cpp              |   2 +-
 src/backend/opencl/homography.cpp             |  19 +-
 src/backend/opencl/iir.cpp                    |   2 +-
 src/backend/opencl/image.cpp                  |   4 +-
 src/backend/opencl/index.cpp                  |  14 +-
 src/backend/opencl/inverse.cpp                |   2 +-
 src/backend/opencl/jit.cpp                    |  22 +-
 src/backend/opencl/join.cpp                   |  42 +-
 src/backend/opencl/kernel/approx.hpp          |   4 +-
 .../opencl/kernel/convolve/conv2_b8.cpp       |   2 +-
 .../opencl/kernel/convolve/conv2_c32.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_c64.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_f32.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_f64.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_impl.hpp     |   4 +-
 .../opencl/kernel/convolve/conv2_s16.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_s32.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_s64.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_u16.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_u32.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_u64.cpp      |   2 +-
 .../opencl/kernel/convolve/conv2_u8.cpp       |   2 +-
 .../opencl/kernel/convolve/conv_common.hpp    |   4 +-
 .../opencl/kernel/convolve_separable.cpp      |   4 +-
 src/backend/opencl/kernel/fftconvolve.hpp     |  20 +-
 src/backend/opencl/kernel/gradient.hpp        |   4 +-
 src/backend/opencl/kernel/ireduce.hpp         |  17 +-
 src/backend/opencl/kernel/resize.hpp          |   4 +-
 src/backend/opencl/kernel/rotate.hpp          |   4 +-
 src/backend/opencl/kernel/sparse_arith.hpp    |  16 +-
 src/backend/opencl/kernel/transform.hpp       |   4 +-
 src/backend/opencl/lookup.cpp                 |   6 +-
 src/backend/opencl/lu.cpp                     |   2 +-
 src/backend/opencl/magma/gebrd.cpp            |   5 +-
 src/backend/opencl/magma/geqrf2.cpp           |   2 +-
 src/backend/opencl/magma/geqrf3.cpp           |   4 +-
 src/backend/opencl/magma/getrf.cpp            |  15 +-
 src/backend/opencl/magma/getrs.cpp            |   2 +-
 src/backend/opencl/magma/labrd.cpp            |   4 +-
 src/backend/opencl/magma/larfb.cpp            |   5 +-
 src/backend/opencl/magma/laset.cpp            |   9 +-
 src/backend/opencl/magma/laswp.cpp            |   9 +-
 src/backend/opencl/magma/magma_helper.cpp     |  58 +--
 src/backend/opencl/magma/transpose.cpp        |  11 +-
 .../opencl/magma/transpose_inplace.cpp        |   7 +-
 src/backend/opencl/magma/unmqr.cpp            |   4 +-
 src/backend/opencl/match_template.cpp         |   5 +-
 src/backend/opencl/math.cpp                   |  24 +-
 src/backend/opencl/math.hpp                   |  20 +-
 src/backend/opencl/meanshift.cpp              |   9 +-
 src/backend/opencl/medfilt.cpp                |   7 +-
 src/backend/opencl/memory.cpp                 |  35 +-
 src/backend/opencl/moments.cpp                |   8 +-
 src/backend/opencl/nearest_neighbour.cpp      |   6 +-
 src/backend/opencl/platform.cpp               |  76 ++--
 src/backend/opencl/platform.hpp               |   4 +-
 src/backend/opencl/plot.cpp                   |   3 +-
 src/backend/opencl/program.cpp                |  46 +--
 src/backend/opencl/program.hpp                |   4 +-
 src/backend/opencl/qr.hpp                     |   2 +-
 src/backend/opencl/random_engine.cpp          |   2 +-
 src/backend/opencl/random_engine.hpp          |   4 +-
 src/backend/opencl/range.cpp                  |   3 +-
 src/backend/opencl/regions.cpp                |   2 +-
 src/backend/opencl/reorder.cpp                |   4 +-
 src/backend/opencl/resize.cpp                 |   2 +-
 src/backend/opencl/scan.cpp                   |  10 +-
 src/backend/opencl/scan_by_key.cpp            |  10 +-
 src/backend/opencl/select.cpp                 |   8 +-
 src/backend/opencl/set.cpp                    |   6 +-
 src/backend/opencl/shift.cpp                  |   9 +-
 src/backend/opencl/sift.cpp                   |   5 +-
 src/backend/opencl/sort.cpp                   |   2 +-
 src/backend/opencl/sort_by_key.cpp            |   4 +-
 src/backend/opencl/sort_index.cpp             |   4 +-
 src/backend/opencl/sort_index.hpp             |   2 +-
 src/backend/opencl/sparse.cpp                 |  12 +-
 src/backend/opencl/sparse_arith.cpp           |   2 +-
 src/backend/opencl/surface.cpp                |   3 +-
 src/backend/opencl/svd.cpp                    |  30 +-
 src/backend/opencl/tile.cpp                   |   4 +-
 src/backend/opencl/topk.cpp                   |   2 +-
 src/backend/opencl/transform.cpp              |   6 +-
 src/backend/opencl/transform.hpp              |   4 +-
 src/backend/opencl/transpose.cpp              |  16 +-
 src/backend/opencl/transpose_inplace.cpp      |  10 +-
 src/backend/opencl/types.cpp                  |   2 +-
 src/backend/opencl/vector_field.cpp           |   6 +-
 src/backend/opencl/vector_field.hpp           |   3 +-
 src/backend/opencl/wrap.cpp                   |  14 +-
 src/backend/opencl/wrap.hpp                   |   6 +-
 388 files changed, 4009 insertions(+), 3009 deletions(-)
 create mode 100644 src/.clang-tidy

diff --git a/.gitignore b/.gitignore
index 9118753a0a..f332b57b56 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,4 +11,8 @@ GPATH
 docs/details/examples.dox
 /TAGS
 external/
+extern/
 compile_commands.json
+venv
+test/gtest
+src/backend/cuda/cub
diff --git a/include/af/features.h b/include/af/features.h
index e387782ae6..aa5e049a91 100644
--- a/include/af/features.h
+++ b/include/af/features.h
@@ -38,7 +38,7 @@ namespace af
         ~features();
 
         /// Copy assignment operator
-        features& operator= (const features& f);
+        features& operator= (const features& other);
 
         /// Returns  the number of features represented by this object
         size_t getNumFeatures() const;
diff --git a/include/af/graphics.h b/include/af/graphics.h
index df06c4b395..d6ffa208fb 100644
--- a/include/af/graphics.h
+++ b/include/af/graphics.h
@@ -83,12 +83,13 @@ class AFAPI Window {
            Creates a window object with default width
            and height with title set to "ArrayFire"
 
-           \param[in] wnd is an \ref af_window handle which can be retrieved by
+           \param[in] window is an \ref af_window handle which can be retrieved
+                             by
            doing a get call on any \ref Window object
 
            \ingroup gfx_func_window
          */
-        Window(const af_window wnd);
+        Window(const af_window window);
 
         /**
            Destroys the window handle
diff --git a/include/af/random.h b/include/af/random.h
index 347cdf84ed..bf81e9218e 100644
--- a/include/af/random.h
+++ b/include/af/random.h
@@ -53,9 +53,9 @@ namespace af
       /**
           Copy constructor for \ref af::randomEngine.
 
-          \param[in] in The input random engine object
+          \param[in] other The input random engine object
       */
-      randomEngine(const randomEngine &in);
+      randomEngine(const randomEngine &other);
 
       /**
           Creates a copy of the random engine object from a \ref
@@ -73,11 +73,11 @@ namespace af
       /**
           \brief Assigns the internal state of randome engine
 
-          \param[in] in The object to be assigned to the random engine
+          \param[in] other The object to be assigned to the random engine
 
           \returns the reference to this
       */
-      randomEngine &operator=(const randomEngine &in);
+      randomEngine &operator=(const randomEngine &other);
 
       /**
           \brief Sets the random type of the random engine
diff --git a/include/af/seq.h b/include/af/seq.h
index 9f1600f005..5a19921b1f 100644
--- a/include/af/seq.h
+++ b/include/af/seq.h
@@ -111,10 +111,10 @@ class AFAPI seq
 
         Creates a copy seq from another sequence.
 
-        \param[in] afs seqence to be copies
+        \param[in] other seqence to be copies
         \param[in] is_gfor is the gfor flag
     */
-    seq(seq afs, bool is_gfor);
+    seq(seq other, bool is_gfor);
 
     /**
         \brief Create a seq object from an \ref af_seq struct
diff --git a/src/.clang-tidy b/src/.clang-tidy
new file mode 100644
index 0000000000..c6a2c6577d
--- /dev/null
+++ b/src/.clang-tidy
@@ -0,0 +1,391 @@
+---
+Checks:          'clang-diagnostic-*,clang-analyzer-*,*,-fuchsia-*,-cppcoreguidelines-*,-misc-misplaced-const,-hicpp-no-array-decay,-readability-implicit-bool-conversion,bugprone-*,performance-*,modernize-*,-llvm-header-guard,-hicpp-use-auto,-modernize-use-trailing-return-type,-hicpp-uppercase-literal-suffix,-hicpp-use-nullptr,-modernize-use-nullptr,-google-runtime-int,-llvm-include-order,-google-runtime-references,-readability-magic-numbers,-readability-isolate-declaration,-hicpp-vararg,-google-readability-todo,-bugprone-macro-parentheses,-misc-unused-using-decls,-readability-else-after-return,-hicpp-avoid-c-arrays,-modernize-avoid-c-arrays'
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+HeaderFilterRegex: ''
+AnalyzeTemporaryDtors: true
+FormatStyle:     file
+User:            arrayfire
+CheckOptions:
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+    value:           llvm
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+    value:           '::std::basic_string'
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+    value:           '0'
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+    value:           assert
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+    value:           '0'
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+    value:           'std::basic_string_view;std::experimental::basic_string_view'
+  - key:             bugprone-exception-escape.FunctionsThatShouldNotThrow
+    value:           ''
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+    value:           ''
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+    value:           '0'
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+    value:           '1'
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+    value:           '1'
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+    value:           '0'
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+    value:           '1'
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+    value:           '8388608'
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+  - key:             cppcoreguidelines-macro-usage.CheckCapsOnly
+    value:           '0'
+  - key:             cppcoreguidelines-macro-usage.IgnoreCommandLineMacros
+    value:           '1'
+  - key:             cppcoreguidelines-no-malloc.Allocations
+    value:           '::malloc;::calloc'
+  - key:             cppcoreguidelines-no-malloc.Deallocations
+    value:           '::free'
+  - key:             cppcoreguidelines-no-malloc.Reallocations
+    value:           '::realloc'
+  - key:             cppcoreguidelines-non-private-member-variables-in-classes.IgnoreClassesWithAllMemberVariablesBeingPublic
+    value:           '1'
+  - key:             cppcoreguidelines-owning-memory.LegacyResourceConsumers
+    value:           '::free;::realloc;::freopen;::fclose'
+  - key:             cppcoreguidelines-owning-memory.LegacyResourceProducers
+    value:           '::malloc;::aligned_alloc;::realloc;::calloc;::fopen;::freopen;::tmpfile'
+  - key:             cppcoreguidelines-pro-bounds-constant-array-index.GslHeader
+    value:           ''
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+    value:           '0'
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+    value:           ',h,hh,hpp,hxx'
+  - key:             fuchsia-restrict-system-includes.Includes
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+  - key:             google-build-namespaces.HeaderFileExtensions
+    value:           ',h,hh,hpp,hxx'
+  - key:             google-global-names-in-headers.HeaderFileExtensions
+    value:           ',h,hh,hpp,hxx'
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+    value:           '1'
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+    value:           '4294967295'
+  - key:             google-readability-function-size.StatementThreshold
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+  - key:             google-readability-function-size.VariableThreshold
+    value:           '4294967295'
+  - key:             google-readability-namespace-comments.ShortNamespaceLines
+    value:           '10'
+  - key:             google-readability-namespace-comments.SpacesBeforeComments
+    value:           '2'
+  - key:             google-runtime-int.SignedTypePrefix
+    value:           int
+  - key:             google-runtime-int.TypeSuffix
+    value:           ''
+  - key:             google-runtime-int.UnsignedTypePrefix
+    value:           uint
+  - key:             google-runtime-references.WhiteListTypes
+    value:           ''
+  - key:             hicpp-braces-around-statements.ShortStatementLines
+    value:           '0'
+  - key:             hicpp-function-size.BranchThreshold
+    value:           '4294967295'
+  - key:             hicpp-function-size.LineThreshold
+    value:           '4294967295'
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+    value:           ''
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+    value:           'std::basic_string'
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+    value:           ''
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+    value:           'f,U,L,UL,LL,ULL'
+  - key:             zircon-temporary-objects.Names
+    value:           ''
+...
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index 9b560d28c0..6608ad10ab 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -17,23 +17,24 @@
 #include <gradient.hpp>
 #include <handle.hpp>
 #include <reduce.hpp>
+
 #include <af/dim4.hpp>
 #include <af/image.h>
 
 #include <type_traits>
 
 using af::dim4;
-using namespace detail;
 
 template<typename T>
-af_array diffusion(const Array<float> in, const float dt, const float K,
+af_array diffusion(const Array<float>& in, const float dt, const float K,
                    const unsigned iterations, const af_flux_function fftype,
                    const af::diffusionEq eq) {
-    auto out   = copyArray(in);
-    auto dims  = out.dims();
-    auto g0    = createEmptyArray<float>(dims);
-    auto g1    = createEmptyArray<float>(dims);
-    float cnst = -2.0f * K * K / dims.elements();
+    auto out  = copyArray(in);
+    auto dims = out.dims();
+    auto g0   = createEmptyArray<float>(dims);
+    auto g1   = createEmptyArray<float>(dims);
+    float cnst =
+        -2.0f * K * K / dims.elements();  // NOLINT(readability-magic-numbers)
 
     for (unsigned i = 0; i < iterations; ++i) {
         gradient<float>(g0, g1, out);
@@ -71,7 +72,7 @@ af_err af_anisotropic_diffusion(af_array* out, const af_array in,
 
         auto input = castArray<float>(in);
 
-        af_array output = 0;
+        af_array output = nullptr;
         switch (inputType) {
             case f64:
                 output = diffusion<double>(input, dt, K, iterations, F, eq);
diff --git a/src/api/c/approx.cpp b/src/api/c/approx.cpp
index c13093b46e..5d5f6acb00 100644
--- a/src/api/c/approx.cpp
+++ b/src/api/c/approx.cpp
@@ -19,7 +19,10 @@
 #include <af/signal.h>
 
 using af::dim4;
-using namespace detail;
+using detail::approx1;
+using detail::approx2;
+using detail::cdouble;
+using detail::cfloat;
 
 namespace {
 template<typename Ty, typename Tp>
@@ -53,10 +56,10 @@ void af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
     const ArrayInfo &yi_info = getInfo(yi);
     const ArrayInfo &xo_info = getInfo(xo);
 
-    const dim4 yi_dims = yi_info.dims();
-    const dim4 xo_dims = xo_info.dims();
-    dim4 yo_dims       = yi_dims;
-    yo_dims[xdim]      = xo_dims[xdim];
+    const dim4 &yi_dims = yi_info.dims();
+    const dim4 &xo_dims = xo_info.dims();
+    dim4 yo_dims        = yi_dims;
+    yo_dims[xdim]       = xo_dims[xdim];
 
     ARG_ASSERT(1, yi_info.isFloating());      // Only floating and complex types
     ARG_ASSERT(2, xo_info.isRealFloating());  // Only floating types
@@ -70,7 +73,7 @@ void af_approx1_common(af_array *yo, const af_array yi, const af_array xo,
     // yi_dims[3])
     if (xo_dims[xdim] != xo_dims.elements()) {
         for (int i = 0; i < 4; i++) {
-            if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
+            if (xdim != i) { DIM_ASSERT(2, xo_dims[i] == yi_dims[i]); }
         }
     }
 
@@ -196,7 +199,9 @@ void af_approx2_common(af_array *zo, const af_array zi, const af_array xo,
     // POS should either be (x, y, 1, 1) or (x, y, zi_dims[2], zi_dims[3])
     if (xo_dims[xdim] * xo_dims[ydim] != xo_dims.elements()) {
         for (int i = 0; i < 4; i++) {
-            if (xdim != i && ydim != i) DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
+            if (xdim != i && ydim != i) {
+                DIM_ASSERT(2, xo_dims[i] == zi_dims[i]);
+            }
         }
     }
 
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index f0b58e6633..d2bca69180 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -16,14 +16,18 @@
 #include <sparse_handle.hpp>
 #include <af/sparse.h>
 
-using namespace detail;
-
+using af::dim4;
 using common::half;
 using common::SparseArrayBase;
-
-af_array createHandle(const af::dim4 &d, af_dtype dtype) {
-    using namespace detail;
-
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+
+af_array createHandle(const dim4 &d, af_dtype dtype) {
     // clang-format off
     switch (dtype) {
         case f32: return createHandle<float  >(d);
@@ -44,9 +48,7 @@ af_array createHandle(const af::dim4 &d, af_dtype dtype) {
     // clang-format on
 }
 
-af_array createHandleFromValue(const af::dim4 &d, double val, af_dtype dtype) {
-    using namespace detail;
-
+af_array createHandleFromValue(const dim4 &d, double val, af_dtype dtype) {
     // clang-format off
     switch (dtype) {
         case f32: return createHandleFromValue<float  >(d, val);
@@ -161,7 +163,7 @@ af_err af_create_handle(af_array *result, const unsigned ndims,
     try {
         AF_CHECK(af_init());
 
-        if (ndims > 0) ARG_ASSERT(2, ndims > 0 && dims != NULL);
+        if (ndims > 0) { ARG_ASSERT(2, ndims > 0 && dims != NULL); }
 
         dim4 d(0);
         for (unsigned i = 0; i < ndims; i++) { d[i] = dims[i]; }
@@ -181,40 +183,39 @@ af_err af_copy_array(af_array *out, const af_array in) {
 
         af_array res = 0;
         if (info.isSparse()) {
-            SparseArrayBase sbase = getSparseArrayBase(in);
+            const SparseArrayBase sbase = getSparseArrayBase(in);
             if (info.ndims() == 0) {
                 return af_create_sparse_array_from_ptr(
                     out, info.dims()[0], info.dims()[1], 0, nullptr, nullptr,
                     nullptr, type, sbase.getStorage(), afDevice);
-            } else {
-                switch (type) {
-                    case f32: res = copySparseArray<float>(in); break;
-                    case f64: res = copySparseArray<double>(in); break;
-                    case c32: res = copySparseArray<cfloat>(in); break;
-                    case c64: res = copySparseArray<cdouble>(in); break;
-                    default: TYPE_ERROR(0, type);
-                }
             }
+            switch (type) {
+                case f32: res = copySparseArray<float>(in); break;
+                case f64: res = copySparseArray<double>(in); break;
+                case c32: res = copySparseArray<cfloat>(in); break;
+                case c64: res = copySparseArray<cdouble>(in); break;
+                default: TYPE_ERROR(0, type);
+            }
+
         } else {
             if (info.ndims() == 0) {
                 return af_create_handle(out, 0, nullptr, type);
-            } else {
-                switch (type) {
-                    case f32: res = copyArray<float>(in); break;
-                    case c32: res = copyArray<cfloat>(in); break;
-                    case f64: res = copyArray<double>(in); break;
-                    case c64: res = copyArray<cdouble>(in); break;
-                    case b8: res = copyArray<char>(in); break;
-                    case s32: res = copyArray<int>(in); break;
-                    case u32: res = copyArray<uint>(in); break;
-                    case u8: res = copyArray<uchar>(in); break;
-                    case s64: res = copyArray<intl>(in); break;
-                    case u64: res = copyArray<uintl>(in); break;
-                    case s16: res = copyArray<short>(in); break;
-                    case u16: res = copyArray<ushort>(in); break;
-                    case f16: res = copyArray<half>(in); break;
-                    default: TYPE_ERROR(1, type);
-                }
+            }
+            switch (type) {
+                case f32: res = copyArray<float>(in); break;
+                case c32: res = copyArray<cfloat>(in); break;
+                case f64: res = copyArray<double>(in); break;
+                case c64: res = copyArray<cdouble>(in); break;
+                case b8: res = copyArray<char>(in); break;
+                case s32: res = copyArray<int>(in); break;
+                case u32: res = copyArray<uint>(in); break;
+                case u8: res = copyArray<uchar>(in); break;
+                case s64: res = copyArray<intl>(in); break;
+                case u64: res = copyArray<uintl>(in); break;
+                case s16: res = copyArray<short>(in); break;
+                case u16: res = copyArray<ushort>(in); break;
+                case f16: res = copyArray<half>(in); break;
+                default: TYPE_ERROR(1, type);
             }
         }
         std::swap(*out, res);
@@ -254,7 +255,7 @@ af_err af_get_data_ref_count(int *use_count, const af_array in) {
 
 af_err af_release_array(af_array arr) {
     try {
-        if (arr == 0) return AF_SUCCESS;
+        if (arr == 0) { return AF_SUCCESS; }
         const ArrayInfo &info = getInfo(arr, false, false);
         af_dtype type         = info.getType();
 
@@ -338,7 +339,6 @@ void write_array(af_array arr, const T *const data, const size_t bytes,
     } else {
         writeDeviceDataArray(getArray<T>(arr), data, bytes);
     }
-    return;
 }
 
 af_err af_write_array(af_array arr, const void *data, const size_t bytes,
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 7782170936..ede1041ca1 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -24,18 +24,25 @@
 #include <af/dim4.hpp>
 #include <af/index.h>
 
-using namespace detail;
-
-using std::enable_if;
 using std::signbit;
 using std::swap;
 using std::vector;
 
+using af::dim4;
 using common::convert2Canonical;
 using common::createSpanIndex;
 using common::half;
 using common::if_complex;
 using common::if_real;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createSubArray;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename Tout, typename Tin>
 static void assign(Array<Tout>& out, const vector<af_seq> seqs,
@@ -44,23 +51,23 @@ static void assign(Array<Tout>& out, const vector<af_seq> seqs,
     const dim4& outDs = out.dims();
     const dim4& iDims = in.dims();
 
-    if (iDims.elements() == 0) return;
+    if (iDims.elements() == 0) { return; }
 
     out.eval();
 
     dim4 oDims = toDims(seqs, outDs);
 
     bool isVec = true;
-    for (int i = 0; isVec && i < (int)oDims.ndims() - 1; i++) {
+    for (int i = 0; isVec && i < static_cast<int>(oDims.ndims()) - 1; i++) {
         isVec &= oDims[i] == 1;
     }
 
     isVec &= in.isVector() || in.isScalar();
 
-    for (dim_t i = ndims; i < (int)in.ndims(); i++) { oDims[i] = 1; }
+    for (dim_t i = ndims; i < in.ndims(); i++) { oDims[i] = 1; }
 
     if (isVec) {
-        if (oDims.elements() != (dim_t)in.elements() && in.elements() != 1) {
+        if (oDims.elements() != in.elements() && in.elements() != 1) {
             AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
         }
 
@@ -73,8 +80,9 @@ static void assign(Array<Tout>& out, const vector<af_seq> seqs,
         copyArray<Tin, Tout>(dst, in_);
     } else {
         for (int i = 0; i < AF_MAX_DIMS; i++) {
-            if (oDims[i] != iDims[i])
+            if (oDims[i] != iDims[i]) {
                 AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
+            }
         }
         Array<Tout> dst = createSubArray<Tout>(out, seqs, false);
 
@@ -126,7 +134,8 @@ af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
         const ArrayInfo& lInfo = getInfo(lhs);
 
         if (ndims == 1 && ndims != lInfo.ndims()) {
-            af_array tmp_in, tmp_out;
+            af_array tmp_in;
+            af_array tmp_out;
             AF_CHECK(af_flat(&tmp_in, lhs));
             AF_CHECK(af_assign_seq(&tmp_out, tmp_in, ndims, index, rhs));
             AF_CHECK(
@@ -135,7 +144,7 @@ af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
             // This can run into a double free issue if tmp_in == tmp_out
             // The condition ensures release only if both are different
             // Issue found on Tegra X1
-            if (tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
+            if (tmp_in != tmp_out) { AF_CHECK(af_release_array(tmp_out)); }
             return AF_SUCCESS;
         }
 
@@ -144,10 +153,11 @@ af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
         if (*out != lhs) {
             int count = 0;
             AF_CHECK(af_get_data_ref_count(&count, lhs));
-            if (count > 1)
+            if (count > 1) {
                 AF_CHECK(af_copy_array(&res, lhs));
-            else
+            } else {
                 res = retain(lhs);
+            }
         } else {
             res = lhs;
         }
@@ -223,7 +233,7 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
         }
 
         af_array rhs = rhs_;
-        if (track == (int)ndims) {
+        if (track == static_cast<int>(ndims)) {
             // all indexs are sequences, redirecting to af_assign
             return af_assign_seq(out, lhs, ndims, seqs.data(), rhs);
         }
@@ -238,15 +248,17 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
         af_dtype lhsType       = lInfo.getType();
         af_dtype rhsType       = rInfo.getType();
 
-        if (rhsDims.ndims() == 0) return af_retain_array(out, lhs);
+        if (rhsDims.ndims() == 0) { return af_retain_array(out, lhs); }
 
-        if (lhsDims.ndims() == 0)
+        if (lhsDims.ndims() == 0) {
             return af_create_handle(out, 0, nullptr, lhsType);
+        }
 
         ARG_ASSERT(2, (ndims == 1) || (ndims == (dim_t)lInfo.ndims()));
 
-        if (ndims == 1 && ndims != (dim_t)lInfo.ndims()) {
-            af_array tmp_in = 0, tmp_out = 0;
+        if (ndims == 1 && ndims != static_cast<dim_t>(lInfo.ndims())) {
+            af_array tmp_in  = 0;
+            af_array tmp_out = 0;
             AF_CHECK(af_flat(&tmp_in, lhs));
             AF_CHECK(af_assign_gen(&tmp_out, tmp_in, ndims, indexs, rhs_));
             AF_CHECK(
@@ -255,7 +267,7 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
             // This can run into a double free issue if tmp_in == tmp_out
             // The condition ensures release only if both are different
             // Issue found on Tegra X1
-            if (tmp_in != tmp_out) AF_CHECK(af_release_array(tmp_out));
+            if (tmp_in != tmp_out) { AF_CHECK(af_release_array(tmp_out)); }
             return AF_SUCCESS;
         }
 
@@ -269,8 +281,9 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
             AF_CHECK(af_get_data_ref_count(&count, lhs));
             if (count > 1) {
                 AF_CHECK(af_copy_array(&output, lhs));
-            } else
+            } else {
                 output = retain(lhs);
+            }
         } else {
             output = lhs;
         }
@@ -280,21 +293,24 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
         // particular dimension, set the length of
         // that dimension accordingly before any checks
         for (dim_t i = 0; i < ndims; i++) {
-            if (!indexs[i].isSeq)
+            if (!indexs[i].isSeq) {
                 oDims[i] = getInfo(indexs[i].idx.arr).elements();
+            }
         }
 
-        for (dim_t i = ndims; i < (dim_t)lInfo.ndims(); i++) oDims[i] = 1;
+        for (dim_t i = ndims; i < static_cast<dim_t>(lInfo.ndims()); i++) {
+            oDims[i] = 1;
+        }
 
         bool isVec = true;
         for (int i = 0; isVec && i < oDims.ndims() - 1; i++) {
             isVec &= oDims[i] == 1;
         }
 
-        // TODO: Move logic out of this
+        // TODO(umar): Move logic out of this
         isVec &= rInfo.isVector() || rInfo.isScalar();
         if (isVec) {
-            if (oDims.elements() != (dim_t)rInfo.elements() &&
+            if (oDims.elements() != static_cast<dim_t>(rInfo.elements()) &&
                 rInfo.elements() != 1) {
                 AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
             }
@@ -308,13 +324,14 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
             }
         } else {
             for (int i = 0; i < AF_MAX_DIMS; i++) {
-                if (oDims[i] != rhsDims[i])
+                if (oDims[i] != rhsDims[i]) {
                     AF_ERROR("Size mismatch between input and output",
                              AF_ERR_SIZE);
+                }
             }
         }
 
-        std::array<af_index_t, AF_MAX_DIMS> idxrs;
+        std::array<af_index_t, AF_MAX_DIMS> idxrs{};
         for (dim_t i = 0; i < AF_MAX_DIMS; ++i) {
             if (i < ndims) {
                 bool isSeq = indexs[i].isSeq;
@@ -370,11 +387,11 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
         } catch (...) {
             if (*out != lhs) {
                 AF_CHECK(af_release_array(output));
-                if (isVec) AF_CHECK(af_release_array(rhs));
+                if (isVec) { AF_CHECK(af_release_array(rhs)); }
             }
             throw;
         }
-        if (isVec) AF_CHECK(af_release_array(rhs));
+        if (isVec) { AF_CHECK(af_release_array(rhs)); }
         swap(*out, output);
     }
     CATCHALL;
diff --git a/src/api/c/bilateral.cpp b/src/api/c/bilateral.cpp
index bb3beccb43..7d3427ee74 100644
--- a/src/api/c/bilateral.cpp
+++ b/src/api/c/bilateral.cpp
@@ -16,7 +16,10 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::bilateral;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename inType, typename outType, bool isColor>
 static inline af_array bilateral(const af_array &in, const float &sp_sig,
@@ -74,8 +77,11 @@ static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma,
 
 af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma,
                     const float chromatic_sigma, const bool isColor) {
-    if (isColor)
-        return bilateral<true>(out, in, spatial_sigma, chromatic_sigma);
-    else
-        return bilateral<false>(out, in, spatial_sigma, chromatic_sigma);
+    af_err err = AF_ERR_UNKNOWN;
+    if (isColor) {
+        err = bilateral<true>(out, in, spatial_sigma, chromatic_sigma);
+    } else {
+        err = bilateral<false>(out, in, spatial_sigma, chromatic_sigma);
+    }
+    return err;
 }
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index d4ddf3a211..1a2890f85b 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -26,9 +26,17 @@
 
 #include <common/half.hpp>
 
-using namespace detail;
 using af::dim4;
 using common::half;
+using detail::arithOp;
+using detail::arithOpD;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T, af_op_t op>
 static inline af_array arithOp(const af_array lhs, const af_array rhs,
@@ -48,12 +56,14 @@ template<typename T, af_op_t op>
 static inline af_array arithSparseDenseOp(const af_array lhs,
                                           const af_array rhs,
                                           const bool reverse) {
-    if (op == af_add_t || op == af_sub_t)
+    if (op == af_add_t || op == af_sub_t) {
         return getHandle(
             arithOpD<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
-    else if (op == af_mul_t || op == af_div_t)
+    }
+    if (op == af_mul_t || op == af_div_t) {
         return getHandle(
             arithOp<T, op>(castSparse<T>(lhs), castArray<T>(rhs), reverse));
+    }
 }
 
 template<af_op_t op>
@@ -115,7 +125,6 @@ static af_err af_arith_real(af_array *out, const af_array lhs,
             case f16: res = arithOp<half, op>(lhs, rhs, odims); break;
             default: TYPE_ERROR(0, otype);
         }
-
         std::swap(*out, res);
     }
     CATCHALL;
@@ -126,8 +135,8 @@ template<af_op_t op>
 static af_err af_arith_sparse(af_array *out, const af_array lhs,
                               const af_array rhs) {
     try {
-        common::SparseArrayBase linfo = getSparseArrayBase(lhs);
-        common::SparseArrayBase rinfo = getSparseArrayBase(rhs);
+        const common::SparseArrayBase linfo = getSparseArrayBase(lhs);
+        const common::SparseArrayBase rinfo = getSparseArrayBase(rhs);
 
         ARG_ASSERT(1, (linfo.getStorage() == rinfo.getStorage()));
         ARG_ASSERT(1, (linfo.dims() == rinfo.dims()));
@@ -153,10 +162,9 @@ template<af_op_t op>
 static af_err af_arith_sparse_dense(af_array *out, const af_array lhs,
                                     const af_array rhs,
                                     const bool reverse = false) {
-    using namespace common;
     try {
-        common::SparseArrayBase linfo = getSparseArrayBase(lhs);
-        ArrayInfo rinfo               = getInfo(rhs);
+        const common::SparseArrayBase linfo = getSparseArrayBase(lhs);
+        const ArrayInfo &rinfo              = getInfo(rhs);
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
@@ -185,82 +193,86 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs,
 af_err af_add(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     // Check if inputs are sparse
-    ArrayInfo linfo = getInfo(lhs, false, true);
-    ArrayInfo rinfo = getInfo(rhs, false, true);
+    const ArrayInfo &linfo = getInfo(lhs, false, true);
+    const ArrayInfo &rinfo = getInfo(rhs, false, true);
 
     if (linfo.isSparse() && rinfo.isSparse()) {
         return af_arith_sparse<af_add_t>(out, lhs, rhs);
-    } else if (linfo.isSparse() && !rinfo.isSparse()) {
+    }
+    if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_add_t>(out, lhs, rhs);
-    } else if (!linfo.isSparse() && rinfo.isSparse()) {
+    }
+    if (!linfo.isSparse() && rinfo.isSparse()) {
         // second operand(Array) of af_arith call should be dense
         return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true);
-    } else {
-        return af_arith<af_add_t>(out, lhs, rhs, batchMode);
     }
+    return af_arith<af_add_t>(out, lhs, rhs, batchMode);
 }
 
 af_err af_mul(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     // Check if inputs are sparse
-    ArrayInfo linfo = getInfo(lhs, false, true);
-    ArrayInfo rinfo = getInfo(rhs, false, true);
+    const ArrayInfo &linfo = getInfo(lhs, false, true);
+    const ArrayInfo &rinfo = getInfo(rhs, false, true);
 
     if (linfo.isSparse() && rinfo.isSparse()) {
         // return af_arith_sparse<af_mul_t>(out, lhs, rhs);
         // MKL doesn't have mul or div support yet, hence
         // this is commented out although alternative cpu code exists
         return AF_ERR_NOT_SUPPORTED;
-    } else if (linfo.isSparse() && !rinfo.isSparse()) {
+    }
+    if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_mul_t>(out, lhs, rhs);
-    } else if (!linfo.isSparse() && rinfo.isSparse()) {
+    }
+    if (!linfo.isSparse() && rinfo.isSparse()) {
         return af_arith_sparse_dense<af_mul_t>(out, rhs, lhs,
                                                true);  // dense should be rhs
-    } else {
-        return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
     }
+    return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
 }
 
 af_err af_sub(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     // Check if inputs are sparse
-    ArrayInfo linfo = getInfo(lhs, false, true);
-    ArrayInfo rinfo = getInfo(rhs, false, true);
+    const ArrayInfo &linfo = getInfo(lhs, false, true);
+    const ArrayInfo &rinfo = getInfo(rhs, false, true);
 
     if (linfo.isSparse() && rinfo.isSparse()) {
         return af_arith_sparse<af_sub_t>(out, lhs, rhs);
-    } else if (linfo.isSparse() && !rinfo.isSparse()) {
+    }
+    if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_sub_t>(out, lhs, rhs);
-    } else if (!linfo.isSparse() && rinfo.isSparse()) {
+    }
+    if (!linfo.isSparse() && rinfo.isSparse()) {
         return af_arith_sparse_dense<af_sub_t>(out, rhs, lhs,
                                                true);  // dense should be rhs
-    } else {
-        return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
     }
+    return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
 }
 
 af_err af_div(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     // Check if inputs are sparse
-    ArrayInfo linfo = getInfo(lhs, false, true);
-    ArrayInfo rinfo = getInfo(rhs, false, true);
+    const ArrayInfo &linfo = getInfo(lhs, false, true);
+    const ArrayInfo &rinfo = getInfo(rhs, false, true);
 
     if (linfo.isSparse() && rinfo.isSparse()) {
         // return af_arith_sparse<af_div_t>(out, lhs, rhs);
         // MKL doesn't have mul or div support yet, hence
         // this is commented out although alternative cpu code exists
         return AF_ERR_NOT_SUPPORTED;
-    } else if (linfo.isSparse() && !rinfo.isSparse()) {
+    }
+    if (linfo.isSparse() && !rinfo.isSparse()) {
         return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
-    } else if (!linfo.isSparse() && rinfo.isSparse()) {
+    }
+    if (!linfo.isSparse() && rinfo.isSparse()) {
         // Division by sparse is currently not allowed - for convinence of
         // dealing with division by 0
         // return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense
         // should be rhs
         return AF_ERR_NOT_SUPPORTED;
-    } else {
-        return af_arith<af_div_t>(out, lhs, rhs, batchMode);
     }
+    return af_arith<af_div_t>(out, lhs, rhs, batchMode);
 }
 
 af_err af_maxof(af_array *out, const af_array lhs, const af_array rhs,
@@ -298,7 +310,8 @@ af_err af_pow(af_array *out, const af_array lhs, const af_array rhs,
             AF_CHECK(af_release_array(log_res));
             std::swap(*out, res);
             return AF_SUCCESS;
-        } else if (linfo.isComplex()) {
+        }
+        if (linfo.isComplex()) {
             af_array mag, angle;
             af_array mag_res, angle_res;
             af_array real_res, imag_res, cplx_res;
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index fe54e2f72d..d34d55fd4a 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -26,36 +26,39 @@
 #include <af/dim4.hpp>
 
 using common::half;
+using common::SparseArrayBase;
+using detail::cdouble;
+using detail::cfloat;
+using detail::gemm;
+using detail::matmul;
 
 template<typename T>
 static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
                                     af_mat_prop optLhs, af_mat_prop optRhs) {
-    return getHandle(detail::matmul<T>(getSparseArray<T>(lhs), getArray<T>(rhs),
-                                       optLhs, optRhs));
+    return getHandle(
+        matmul<T>(getSparseArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
 template<typename T>
 static inline void gemm(af_array *out, af_mat_prop optLhs, af_mat_prop optRhs,
                         const T *alpha, const af_array lhs, const af_array rhs,
                         const T *betas) {
-    detail::gemm<T>(getArray<T>(*out), optLhs, optRhs, alpha, getArray<T>(lhs),
-                    getArray<T>(rhs), betas);
+    gemm<T>(getArray<T>(*out), optLhs, optRhs, alpha, getArray<T>(lhs),
+            getArray<T>(rhs), betas);
 }
 
 template<typename T>
 static inline af_array dot(const af_array lhs, const af_array rhs,
                            af_mat_prop optLhs, af_mat_prop optRhs) {
     return getHandle(
-        detail::dot<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
+        dot<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
 af_err af_sparse_matmul(af_array *out, const af_array lhs, const af_array rhs,
                         const af_mat_prop optLhs, const af_mat_prop optRhs) {
-    using namespace detail;
-
     try {
-        common::SparseArrayBase lhsBase = getSparseArrayBase(lhs);
-        const ArrayInfo &rhsInfo        = getInfo(rhs);
+        const SparseArrayBase lhsBase = getSparseArrayBase(lhs);
+        const ArrayInfo &rhsInfo      = getInfo(rhs);
 
         ARG_ASSERT(2,
                    lhsBase.isSparse() == true && rhsInfo.isSparse() == false);
@@ -117,8 +120,6 @@ af_err af_sparse_matmul(af_array *out, const af_array lhs, const af_array rhs,
 af_err af_gemm(af_array *out, const af_mat_prop optLhs,
                const af_mat_prop optRhs, const void *alpha, const af_array lhs,
                const af_array rhs, const void *beta) {
-    using namespace detail;  // needed for cfloat and cdouble
-
     try {
         const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
         const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
@@ -212,27 +213,25 @@ af_err af_gemm(af_array *out, const af_mat_prop optLhs,
 
 af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                  const af_mat_prop optLhs, const af_mat_prop optRhs) {
-    using namespace detail;  // needed for cfloat and cdouble
-
     try {
         const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
         const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
 
-        if (lhsInfo.isSparse())
+        if (lhsInfo.isSparse()) {
             return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
+        }
 
         const int aRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
         const int bColDim = (optRhs == AF_MAT_NONE) ? 1 : 0;
 
-        const af::dim4 lDims = lhsInfo.dims();
-        const af::dim4 rDims = rhsInfo.dims();
-        const int M          = lDims[aRowDim];
-        const int N          = rDims[bColDim];
+        const af::dim4 &lDims = lhsInfo.dims();
+        const af::dim4 &rDims = rhsInfo.dims();
+        const int M           = lDims[aRowDim];
+        const int N           = rDims[bColDim];
 
         const dim_t d2       = std::max(lDims[2], rDims[2]);
         const dim_t d3       = std::max(lDims[3], rDims[3]);
         const af::dim4 oDims = af::dim4(M, N, d2, d3);
-        const int num_batch  = oDims[2] * oDims[3];
 
         af_array gemm_out = 0;
         AF_CHECK(af_create_handle(&gemm_out, oDims.ndims(), oDims.get(),
@@ -287,8 +286,6 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
 
 af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
               const af_mat_prop optLhs, const af_mat_prop optRhs) {
-    using namespace detail;
-
     try {
         const ArrayInfo &lhsInfo = getInfo(lhs);
         const ArrayInfo &rhsInfo = getInfo(rhs);
@@ -332,7 +329,7 @@ af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
 
 template<typename T>
 static inline T dotAll(af_array out) {
-    T res;
+    T res{};
     AF_CHECK(af_eval(out));
     AF_CHECK(af_get_data_ptr((void *)&res, out));
     return res;
@@ -341,17 +338,18 @@ static inline T dotAll(af_array out) {
 af_err af_dot_all(double *rval, double *ival, const af_array lhs,
                   const af_array rhs, const af_mat_prop optLhs,
                   const af_mat_prop optRhs) {
-    using namespace detail;
+    using namespace detail;  // NOLINT needed for imag and real functions
+                             // name resolution
 
     try {
         *rval = 0;
-        if (ival) *ival = 0;
+        if (ival) { *ival = 0; }
 
         af_array out = 0;
         AF_CHECK(af_dot(&out, lhs, rhs, optLhs, optRhs));
 
-        ArrayInfo lhsInfo = getInfo(lhs);
-        af_dtype lhs_type = lhsInfo.getType();
+        const ArrayInfo &lhsInfo = getInfo(lhs);
+        af_dtype lhs_type        = lhsInfo.getType();
 
         switch (lhs_type) {
             case f16: *rval = static_cast<double>(dotAll<half>(out)); break;
@@ -360,17 +358,17 @@ af_err af_dot_all(double *rval, double *ival, const af_array lhs,
             case c32: {
                 cfloat temp = dotAll<cfloat>(out);
                 *rval       = real(temp);
-                if (ival) *ival = imag(temp);
+                if (ival) { *ival = imag(temp); }
             } break;
             case c64: {
                 cdouble temp = dotAll<cdouble>(out);
                 *rval        = real(temp);
-                if (ival) *ival = imag(temp);
+                if (ival) { *ival = imag(temp); }
             } break;
             default: TYPE_ERROR(1, lhs_type);
         }
 
-        if (out != 0) AF_CHECK(af_release_array(out));
+        if (out != 0) { AF_CHECK(af_release_array(out)); }
     }
     CATCHALL
     return AF_SUCCESS;
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 6c1341ff61..524c63f556 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -34,7 +34,6 @@
 
 using af::dim4;
 using std::vector;
-using namespace detail;
 
 Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy,
                                const bool& isf) {
@@ -56,7 +55,7 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
     Array<uint> hist =
         detail::histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
 
-    const af::dim4 hDims = hist.dims();
+    const af::dim4& hDims = hist.dims();
 
     // reduce along histogram dimension i.e. 0th dimension
     auto totals = reduce<af_add_t, uint, float>(hist, 0);
@@ -71,16 +70,16 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
     std::vector<af_seq> seqBegin(4, af_span);
     std::vector<af_seq> seqRest(4, af_span);
 
-    seqBegin[0] = af_make_seq(0, hDims[0] - 1, 1);
-    seqRest[0]  = af_make_seq(0, hDims[0] - 1, 1);
+    seqBegin[0] = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
+    seqRest[0]  = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
 
     const af::dim4& iDims = supEdges.dims();
 
     Array<float> sigmas = detail::createEmptyArray<float>(hDims);
 
     for (unsigned b = 0; b < (NUM_BINS - 1); ++b) {
-        seqBegin[0].end  = (double)b;
-        seqRest[0].begin = (double)(b + 1);
+        seqBegin[0].end  = static_cast<double>(b);
+        seqRest[0].begin = static_cast<double>(b + 1);
 
         auto frontPartition = createSubArray(probability, seqBegin, false);
         auto endPartition   = createSubArray(probability, seqRest, false);
@@ -139,12 +138,12 @@ Array<float> normalize(const Array<float>& supEdges, const float minVal,
 std::pair<Array<char>, Array<char>> computeCandidates(
     const Array<float>& supEdges, const float t1, const af_canny_threshold ct,
     const float t2) {
-    float maxVal = detail::reduce_all<af_max_t, float, float>(supEdges);
-    const unsigned NUM_BINS = static_cast<unsigned>(maxVal);
+    float maxVal  = detail::reduce_all<af_max_t, float, float>(supEdges);
+    auto NUM_BINS = static_cast<unsigned>(maxVal);
 
     auto lowRatio = createValueArray<float>(supEdges.dims(), t1);
 
-    switch (ct) {
+    switch (ct) {  // NOLINT(hicpp-multiway-paths-covered)
         case AF_CANNY_THRESHOLD_AUTO_OTSU: {
             auto T2 = otsuThreshold(supEdges, NUM_BINS, maxVal);
             auto T1 = arithOp<float, af_mul_t>(T2, lowRatio, T2.dims());
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 32ecf959f5..43ee4e9dad 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -7,22 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <backend.hpp>
+#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/err_common.hpp>
+#include <common/half.hpp>
+#include <handle.hpp>
 #include <optypes.hpp>
 #include <sparse.hpp>
 #include <sparse_handle.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 
-#include <backend.hpp>
-#include <cast.hpp>
-#include <common/err_common.hpp>
-#include <common/half.hpp>
-#include <handle.hpp>
-
-using namespace detail;
+using af::dim4;
 using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 static af_array cast(const af_array in, const af_dtype type) {
     const ArrayInfo& info = getInfo(in, false, true);
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index b83369d4dc..4dd8fdc20f 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <backend.hpp>
 #include <cholesky.hpp>
+
+#include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
@@ -16,8 +17,8 @@
 #include <af/defines.h>
 #include <af/lapack.h>
 
-using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
 
 template<typename T>
 static inline af_array cholesky(int *info, const af_array in,
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index df9629bc93..f0da3323eb 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -7,24 +7,31 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <arith.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
+#include <logic.hpp>
 #include <optypes.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
 #include <af/defines.h>
 
-#include <arith.hpp>
-#include <logic.hpp>
-
-using namespace detail;
 using af::dim4;
 using common::half;
+using detail::arithOp;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array clampOp(const af_array in, const af_array lo,
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index a14a6b16eb..1732aaf4bc 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -21,9 +21,13 @@
 
 #include <complex.hpp>
 
-using namespace detail;
 using af::dim4;
 using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::conj;
+using detail::imag;
+using detail::real;
 
 template<typename To, typename Ti>
 static inline af_array cplx(const af_array lhs, const af_array rhs,
@@ -42,7 +46,7 @@ af_err af_cplx2(af_array *out, const af_array lhs, const af_array rhs,
             AF_ERROR("Inputs to cplx2 can not be of complex type", AF_ERR_ARG);
         }
 
-        if (type != f64) type = f32;
+        if (type != f64) { type = f32; }
 
         dim4 odims =
             getOutDims(getInfo(lhs).dims(), getInfo(rhs).dims(), batchMode);
@@ -176,21 +180,13 @@ af_err af_abs(af_array *out, const af_array in) {
         if (in_type == f16) { type = f16; }
 
         switch (type) {
-            case f32:
-                res = getHandle(abs<float, float>(castArray<float>(in)));
-                break;
-            case f64:
-                res = getHandle(abs<double, double>(castArray<double>(in)));
-                break;
-            case c32:
-                res = getHandle(abs<float, cfloat>(castArray<cfloat>(in)));
-                break;
-            case c64:
-                res = getHandle(abs<double, cdouble>(castArray<cdouble>(in)));
-                break;
-            case f16:
-                res = getHandle(abs<half, half>(getArray<half>(in)));
-                break;
+            // clang-format off
+            case f32: res = getHandle(detail::abs<float, float>(castArray<float>(in))); break;
+            case f64: res = getHandle(detail::abs<double, double>(castArray<double>(in))); break;
+            case c32: res = getHandle(detail::abs<float, cfloat>(castArray<cfloat>(in))); break;
+            case c64: res = getHandle(detail::abs<double, cdouble>(castArray<cdouble>(in))); break;
+            case f16: res = getHandle(detail::abs<half, half>(getArray<half>(in))); break;
+            // clang-format on
             default: TYPE_ERROR(1, in_type); break;
         }
 
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index 5a2910329f..acf9e3bbd9 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -24,18 +24,22 @@
 #include <type_traits>
 
 using af::dim4;
-using namespace detail;
+using std::array;
+using std::conditional;
+using std::is_same;
+using std::sqrt;
+using std::swap;
 
 /// Index corner points of given seed points
 template<typename T>
 Array<T> pointList(const Array<T>& in, const Array<uint>& x,
                    const Array<uint>& y) {
-    af_array xcoords                          = getHandle<uint>(x);
-    af_array ycoords                          = getHandle<uint>(y);
-    std::array<af_index_t, AF_MAX_DIMS> idxrs = {{{xcoords, false, false},
-                                                  {ycoords, false, false},
-                                                  common::createSpanIndex(),
-                                                  common::createSpanIndex()}};
+    af_array xcoords                     = getHandle<uint>(x);
+    af_array ycoords                     = getHandle<uint>(y);
+    array<af_index_t, AF_MAX_DIMS> idxrs = {{{xcoords, false, false},
+                                             {ycoords, false, false},
+                                             common::createSpanIndex(),
+                                             common::createSpanIndex()}};
 
     Array<T> retVal = detail::index(in, idxrs.data());
 
@@ -80,8 +84,8 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
                   const Array<uint>& seedy, const unsigned radius,
                   const unsigned mult, const unsigned iterations,
                   const double segmentedValue) {
-    using CT = typename std::conditional<std::is_same<T, double>::value, double,
-                                         float>::type;
+    using CT =
+        typename conditional<is_same<T, double>::value, double, float>::type;
     constexpr CT epsilon = 1.0e-6;
 
     auto calcVar = [](CT s2, CT s1, CT n) -> CT {
@@ -90,8 +94,8 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
         return retVal;
     };
 
-    const dim4 inDims        = img.dims();
-    const dim4 seedDims      = seedx.dims();
+    const dim4& inDims       = img.dims();
+    const dim4& seedDims     = seedx.dims();
     const size_t numSeeds    = seedx.elements();
     const unsigned nhoodLen  = 2 * radius + 1;
     const unsigned nhoodSize = nhoodLen * nhoodLen;
@@ -118,11 +122,11 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
     CT totSum          = reduce_all<af_add_t, CT, CT>(S1);
     CT totSumSq        = reduce_all<af_add_t, CT, CT>(S2);
     CT totalNum        = numSeeds * nhoodSize;
-    CT mean            = totSum / totalNum;
-    CT var             = calcVar(totSumSq, totSum, totalNum);
-    CT stddev          = std::sqrt(var);
-    CT lower           = mean - mult * stddev;
-    CT upper           = mean + mult * stddev;
+    CT s1mean          = totSum / totalNum;
+    CT s1var           = calcVar(totSumSq, totSum, totalNum);
+    CT s1stddev        = sqrt(s1var);
+    CT lower           = s1mean - mult * s1stddev;
+    CT upper           = s1mean + mult * s1stddev;
 
     Array<CT> seedIntensities = pointList(in, seedx, seedy);
     CT maxSeedIntensity       = reduce_all<af_max_t, CT, CT>(seedIntensities);
@@ -133,7 +137,7 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
 
     Array<CT> segmented = floodFill(in, seedx, seedy, CT(1), lower, upper);
 
-    if (std::abs(var) < epsilon) {
+    if (std::abs<CT>(s1var) < epsilon) {
         // If variance is close to zero, stop after initial segmentation
         return getHandle(labelSegmented(segmented));
     }
@@ -151,18 +155,18 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
         Array<CT> valids = arithOp<CT, af_mul_t>(segmented, in, inDims);
         Array<CT> vsqrd  = arithOp<CT, af_mul_t>(valids, valids, inDims);
 
-        CT sum      = reduce_all<af_add_t, CT, CT>(valids, true);
-        CT sumOfSqs = reduce_all<af_add_t, CT, CT>(vsqrd, true);
-        CT mean     = sum / sampleCount;
-        CT var      = calcVar(sumOfSqs, sum, CT(sampleCount));
-        CT stddev   = std::sqrt(var);
-        CT newLow   = mean - mult * stddev;
-        CT newHigh  = mean + mult * stddev;
+        CT validsSum  = reduce_all<af_add_t, CT, CT>(valids, true);
+        CT sumOfSqs   = reduce_all<af_add_t, CT, CT>(vsqrd, true);
+        CT validsMean = validsSum / sampleCount;
+        CT validsVar  = calcVar(sumOfSqs, validsSum, CT(sampleCount));
+        CT stddev     = sqrt(validsVar);
+        CT newLow     = validsMean - mult * stddev;
+        CT newHigh    = validsMean + mult * stddev;
 
         if (newLow > minSeedIntensity) { newLow = minSeedIntensity; }
         if (newHigh < maxSeedIntensity) { newHigh = maxSeedIntensity; }
 
-        if (std::abs(var) < epsilon) {
+        if (std::abs<CT>(validsVar) < epsilon) {
             // If variance is close to zero, discontinue iterating.
             continueLoop = false;
         }
@@ -184,11 +188,11 @@ af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
              AF_ERR_NOT_SUPPORTED);
 #endif
     try {
-        const ArrayInfo inInfo         = getInfo(in);
-        const ArrayInfo seedxInfo      = getInfo(seedx);
-        const ArrayInfo seedyInfo      = getInfo(seedy);
-        const af::dim4 inputDimensions = inInfo.dims();
-        const af::dtype inputArrayType = inInfo.getType();
+        const ArrayInfo& inInfo         = getInfo(in);
+        const ArrayInfo& seedxInfo      = getInfo(seedx);
+        const ArrayInfo& seedyInfo      = getInfo(seedy);
+        const af::dim4& inputDimensions = inInfo.dims();
+        const af::dtype inputArrayType  = inInfo.getType();
 
         // TODO(pradeep) handle case where seeds are towards border
         //              and indexing may result in throwing exception
@@ -224,7 +228,7 @@ af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
                 break;
             default: TYPE_ERROR(0, inputArrayType);
         }
-        std::swap(*out, output);
+        swap(*out, output);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index e2f95fdd09..938808a648 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -6,16 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <convolve.hpp>
+
 #include <arith.hpp>
 #include <backend.hpp>
 #include <cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
-#include <convolve.hpp>
 #include <fftconvolve.hpp>
 #include <handle.hpp>
 #include <tile.hpp>
-
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -26,7 +26,17 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::cdouble;
+using detail::cfloat;
+using detail::convolve;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T, typename accT, dim_t baseDim, bool expand>
 inline static af_array convolve(const af_array &s, const af_array &f,
@@ -65,14 +75,15 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
-    if (sn == baseDim && fn == baseDim)
-        return AF_BATCH_NONE;
-    else if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS))
+    if (sn == baseDim && fn == baseDim) { return AF_BATCH_NONE; }
+    if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS)) {
         return AF_BATCH_RHS;
-    else if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim)
+    }
+    if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim) {
         return AF_BATCH_LHS;
-    else if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
-             (fn > baseDim && fn <= AF_MAX_DIMS)) {
+    }
+    if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
+        (fn > baseDim && fn <= AF_MAX_DIMS)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved       = true;
         for (dim_t i = baseDim; i < AF_MAX_DIMS; i++) {
@@ -80,10 +91,10 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
             isInterleaved &=
                 (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
         }
-        if (doesDimensionsMatch) return AF_BATCH_SAME;
+        if (doesDimensionsMatch) { return AF_BATCH_SAME; }
         return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
-    } else
-        return AF_BATCH_UNSUPPORTED;
+    }
+    return AF_BATCH_UNSUPPORTED;
 }
 
 template<dim_t baseDim, bool expand>
@@ -240,36 +251,38 @@ af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter,
 template<int baseDim>
 bool isFreqDomain(const af_array &signal, const af_array filter,
                   af_conv_domain domain) {
-    if (domain == AF_CONV_FREQ) return true;
-    if (domain != AF_CONV_AUTO) return false;
+    if (domain == AF_CONV_FREQ) { return true; }
+    if (domain != AF_CONV_AUTO) { return false; }
 
     const ArrayInfo &sInfo = getInfo(signal);
     const ArrayInfo &fInfo = getInfo(filter);
 
-    dim4 sdims = sInfo.dims();
-    dim4 fdims = fInfo.dims();
+    const dim4 &sdims = sInfo.dims();
+    dim4 fdims        = fInfo.dims();
 
-    if (identifyBatchKind<baseDim>(sdims, fdims) == AF_BATCH_DIFF) return true;
+    if (identifyBatchKind<baseDim>(sdims, fdims) == AF_BATCH_DIFF) {
+        return true;
+    }
 
     int kbatch = 1;
     for (int i = 3; i >= baseDim; i--) { kbatch *= fdims[i]; }
 
-    if (kbatch >= 10) return true;
+    if (kbatch >= 10) { return true; }
 
     if (baseDim == 1) {
-        if (fdims[0] > 128) return true;
+        if (fdims[0] > 128) { return true; }
     }
 
     if (baseDim == 2) {
         // maximum supported size in 2D domain
-        if (fdims[0] > 17 || fdims[1] > 17) return true;
+        if (fdims[0] > 17 || fdims[1] > 17) { return true; }
 
         // Maximum supported non square size
-        if (fdims[0] != fdims[1] && fdims[0] > 5) return true;
+        if (fdims[0] != fdims[1] && fdims[0] > 5) { return true; }
     }
 
     if (baseDim == 3) {
-        if (fdims[0] > 5 || fdims[1] > 5 || fdims[2] > 5) return true;
+        if (fdims[0] > 5 || fdims[1] > 5 || fdims[2] > 5) { return true; }
     }
 
     return false;
@@ -278,13 +291,14 @@ bool isFreqDomain(const af_array &signal, const af_array filter,
 af_err af_convolve1(af_array *out, const af_array signal, const af_array filter,
                     const af_conv_mode mode, af_conv_domain domain) {
     try {
-        if (isFreqDomain<1>(signal, filter, domain))
+        if (isFreqDomain<1>(signal, filter, domain)) {
             return af_fft_convolve1(out, signal, filter, mode);
+        }
 
-        if (mode == AF_CONV_EXPAND)
+        if (mode == AF_CONV_EXPAND) {
             return convolve<1, true>(out, signal, filter);
-        else
-            return convolve<1, false>(out, signal, filter);
+        }
+        { return convolve<1, false>(out, signal, filter); }
     }
     CATCHALL;
 }
@@ -297,13 +311,15 @@ af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
             return af_convolve1(out, signal, filter, mode, domain);
         }
 
-        if (isFreqDomain<2>(signal, filter, domain))
+        if (isFreqDomain<2>(signal, filter, domain)) {
             return af_fft_convolve2(out, signal, filter, mode);
+        }
 
-        if (mode == AF_CONV_EXPAND)
+        if (mode == AF_CONV_EXPAND) {
             return convolve<2, true>(out, signal, filter);
-        else
+        } else {
             return convolve<2, false>(out, signal, filter);
+        }
     }
     CATCHALL;
 }
@@ -371,13 +387,15 @@ af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
             return af_convolve2(out, signal, filter, mode, domain);
         }
 
-        if (isFreqDomain<3>(signal, filter, domain))
+        if (isFreqDomain<3>(signal, filter, domain)) {
             return af_fft_convolve3(out, signal, filter, mode);
+        }
 
-        if (mode == AF_CONV_EXPAND)
+        if (mode == AF_CONV_EXPAND) {
             return convolve<3, true>(out, signal, filter);
-        else
+        } else {
             return convolve<3, false>(out, signal, filter);
+        }
     }
     CATCHALL;
 }
@@ -386,10 +404,11 @@ af_err af_convolve2_sep(af_array *out, const af_array signal,
                         const af_array col_filter, const af_array row_filter,
                         const af_conv_mode mode) {
     try {
-        if (mode == AF_CONV_EXPAND)
+        if (mode == AF_CONV_EXPAND) {
             return convolve2_sep<true>(out, signal, col_filter, row_filter);
-        else
+        } else {
             return convolve2_sep<false>(out, signal, col_filter, row_filter);
+        }
     }
     CATCHALL;
 }
@@ -398,8 +417,8 @@ template<typename T>
 af_array conv2GradCall(const af_array incoming_gradient,
                        const af_array original_signal,
                        const af_array original_filter,
-                       const af_array convolved_output, af::dim4 stride,
-                       af::dim4 padding, af::dim4 dilation,
+                       const af_array convolved_output, const dim4 &stride,
+                       const dim4 &padding, const dim4 &dilation,
                        af_conv_gradient_type grad_type) {
     if (grad_type == AF_CONV_GRADIENT_FILTER) {
         return getHandle(detail::conv2FilterGradient<T>(
@@ -423,7 +442,7 @@ af_err af_convolve2_gradient_nn(
     af_conv_gradient_type grad_type) {
     try {
         const ArrayInfo &iinfo = getInfo(incoming_gradient);
-        af::dim4 iDims         = iinfo.dims();
+        const af::dim4 &iDims  = iinfo.dims();
 
         const ArrayInfo &sinfo = getInfo(original_signal);
         af::dim4 sDims         = sinfo.dims();
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index cb47e1d1df..00b67ab015 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -32,8 +32,8 @@ static To corrcoef(const af_array& X, const af_array& Y) {
     Array<To> xIn = cast<To>(getArray<Ti>(X));
     Array<To> yIn = cast<To>(getArray<Ti>(Y));
 
-    dim4 dims = xIn.dims();
-    dim_t n   = xIn.elements();
+    const dim4& dims = xIn.dims();
+    dim_t n          = xIn.elements();
 
     To xSum = detail::reduce_all<af_add_t, To, To>(xIn);
     To ySum = detail::reduce_all<af_add_t, To, To>(yIn);
@@ -46,15 +46,17 @@ static To corrcoef(const af_array& X, const af_array& Y) {
     To ySqSum = detail::reduce_all<af_add_t, To, To>(ySq);
     To xySum  = detail::reduce_all<af_add_t, To, To>(xy);
 
-    To result = (n * xySum - xSum * ySum) / (sqrt(n * xSqSum - xSum * xSum) *
-                                             sqrt(n * ySqSum - ySum * ySum));
+    To result =
+        (n * xySum - xSum * ySum) / (std::sqrt(n * xSqSum - xSum * xSum) *
+                                     std::sqrt(n * ySqSum - ySum * ySum));
 
     return result;
 }
 
+// NOLINTNEXTLINE
 af_err af_corrcoef(double* realVal, double* imagVal, const af_array X,
                    const af_array Y) {
-    UNUSED(imagVal);  // TODO: implement for complex types
+    UNUSED(imagVal);  // TODO(umar): implement for complex types
     try {
         const ArrayInfo& xInfo = getInfo(X);
         const ArrayInfo& yInfo = getInfo(Y);
@@ -66,8 +68,9 @@ af_err af_corrcoef(double* realVal, double* imagVal, const af_array X,
         ARG_ASSERT(2, (xType == yType));
         ARG_ASSERT(2, (xDims.ndims() == yDims.ndims()));
 
-        for (dim_t i = 0; i < xDims.ndims(); ++i)
+        for (dim_t i = 0; i < xDims.ndims(); ++i) {
             ARG_ASSERT(2, (xDims[i] == yDims[i]));
+        }
 
         switch (xType) {
             case f64: *realVal = corrcoef<double, double>(X, Y); break;
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index b250743ad1..df9c13e5ff 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -23,13 +23,13 @@
 #include "stats.h"
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
 
 template<typename T, typename cType>
-static af_array cov(const af_array& X, const af_array& Y, const bool isbiased) {
-    typedef typename baseOutType<cType>::type weightType;
-    Array<T> _x       = getArray<T>(X);
-    Array<T> _y       = getArray<T>(Y);
+static af_array cov(const af_array& X, const af_array& Y, bool isbiased) {
+    using weightType  = typename baseOutType<cType>::type;
+    const Array<T> _x = getArray<T>(X);
+    const Array<T> _y = getArray<T>(Y);
     Array<cType> xArr = cast<cType>(_x);
     Array<cType> yArr = cast<cType>(_y);
 
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index b0d76e3fe7..79a604173b 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -27,7 +27,18 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createValueArray;
+using detail::intl;
+using detail::iota;
+using detail::padArrayBorders;
+using detail::range;
+using detail::scalar;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
     DIM_ASSERT(1, ndims >= 1);
@@ -49,12 +60,8 @@ af_err af_constant(af_array *result, const double value, const unsigned ndims,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d(1, 1, 1, 1);
-        if (ndims <= 0) {
-            return af_create_handle(result, 0, nullptr, type);
-        } else {
-            d = verifyDims(ndims, dims);
-        }
+        if (ndims <= 0) { return af_create_handle(result, 0, nullptr, type); }
+        dim4 d = verifyDims(ndims, dims);
 
         switch (type) {
             case f32: out = createHandleFromValue<float>(d, value); break;
@@ -92,12 +99,8 @@ af_err af_constant_complex(af_array *result, const double real,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d(1, 1, 1, 1);
-        if (ndims <= 0) {
-            return af_create_handle(result, 0, nullptr, type);
-        } else {
-            d = verifyDims(ndims, dims);
-        }
+        if (ndims <= 0) { return af_create_handle(result, 0, nullptr, type); }
+        dim4 d = verifyDims(ndims, dims);
 
         switch (type) {
             case c32: out = createCplx<cfloat, float>(d, real, imag); break;
@@ -117,12 +120,8 @@ af_err af_constant_long(af_array *result, const intl val, const unsigned ndims,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d(1, 1, 1, 1);
-        if (ndims <= 0) {
-            return af_create_handle(result, 0, nullptr, s64);
-        } else {
-            d = verifyDims(ndims, dims);
-        }
+        if (ndims <= 0) { return af_create_handle(result, 0, nullptr, s64); }
+        dim4 d = verifyDims(ndims, dims);
 
         out = getHandle(createValueArray<intl>(d, val));
 
@@ -139,12 +138,9 @@ af_err af_constant_ulong(af_array *result, const uintl val,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d(1, 1, 1, 1);
-        if (ndims <= 0) {
-            return af_create_handle(result, 0, nullptr, u64);
-        } else {
-            d = verifyDims(ndims, dims);
-        }
+        if (ndims <= 0) { return af_create_handle(result, 0, nullptr, u64); }
+        dim4 d = verifyDims(ndims, dims);
+
         out = getHandle(createValueArray<uintl>(d, val));
 
         std::swap(*result, out);
@@ -207,12 +203,8 @@ af_err af_range(af_array *result, const unsigned ndims, const dim_t *const dims,
         af_array out;
         AF_CHECK(af_init());
 
-        dim4 d(0);
-        if (ndims <= 0) {
-            return af_create_handle(result, 0, nullptr, type);
-        } else {
-            d = verifyDims(ndims, dims);
-        }
+        if (ndims <= 0) { return af_create_handle(result, 0, nullptr, type); }
+        dim4 d = verifyDims(ndims, dims);
 
         switch (type) {
             case f32: out = range_<float>(d, seq_dim); break;
@@ -364,10 +356,11 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num) {
 
 template<typename T, bool is_upper>
 af_array triangle(const af_array in, bool is_unit_diag) {
-    if (is_unit_diag)
+    if (is_unit_diag) {
         return getHandle(triangle<T, is_upper, true>(getArray<T>(in)));
-    else
+    } else {
         return getHandle(triangle<T, is_upper, false>(getArray<T>(in)));
+    }
 }
 
 af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index 174843c03c..b86c9dca72 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -26,12 +26,16 @@
 #include <af/image.h>
 
 #include <algorithm>
+#include <array>
 #include <cmath>
 #include <type_traits>
 #include <vector>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::shift;
+using std::array;
+using std::vector;
 
 const int BASE_DIM = 2;
 
@@ -58,13 +62,13 @@ Array<T> complexNorm(const Array<CT>& input) {
 std::vector<af_seq> calcPadInfo(dim4& inLPad, dim4& psfLPad, dim4& inUPad,
                                 dim4& psfUPad, dim4& odims, dim_t nElems,
                                 const dim4& idims, const dim4& fdims) {
-    std::vector<af_seq> index(4);
+    vector<af_seq> index(4);
 
     for (int d = 0; d < 4; ++d) {
         if (d < BASE_DIM) {
             dim_t pad = idims[d] + fdims[d];
 
-            while (greatestPrimeFactor(pad) > GREATEST_PRIME_FACTOR) pad++;
+            while (greatestPrimeFactor(pad) > GREATEST_PRIME_FACTOR) { pad++; }
 
             dim_t diffLen  = pad - idims[d];
             inLPad[d]      = diffLen / 2;
@@ -137,7 +141,7 @@ void landweber(Array<T>& currentEstimate, const Array<T>& in,
 template<typename InputType, typename RealType = float>
 af_array iterDeconv(const af_array in, const af_array ker, const uint iters,
                     const float rfactor, const af_iterative_deconv_algo algo) {
-    typedef RealType T;
+    using T    = RealType;
     using CT   = typename std::conditional<std::is_same<T, double>::value,
                                          cdouble, cfloat>::type;
     auto input = castArray<T>(in);
@@ -154,24 +158,25 @@ af_array iterDeconv(const af_array in, const af_array ker, const uint iters,
         padArrayBorders<T>(input, inLPad, inUPad, AF_PAD_CLAMP_TO_EDGE);
     auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad, AF_PAD_ZERO);
 
-    const int shiftDims[4] = {-int(fdims[0] / 2), -int(fdims[1] / 2), 0, 0};
-    auto shiftedPsf        = shift(paddedPsf, shiftDims);
+    const std::array<int, 4> shiftDims = {-int(fdims[0] / 2),
+                                          -int(fdims[1] / 2), 0, 0};
+    auto shiftedPsf                    = shift(paddedPsf, shiftDims.data());
 
     auto P  = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
     auto Pc = conj(P);
 
     Array<T> currentEstimate = paddedIn;
-    const double normFactor  = 1 / (double)nElems;
+    const double normFactor  = 1 / static_cast<double>(nElems);
 
     switch (algo) {
         case AF_ITERATIVE_DECONV_RICHARDSONLUCY:
             richardsonLucy(currentEstimate, paddedIn, P, Pc, iters, normFactor,
                            odims);
             break;
+        case AF_ITERATIVE_DECONV_LANDWEBER:
         default:
             landweber(currentEstimate, paddedIn, P, Pc, iters, rfactor,
                       normFactor, odims);
-            break;
     }
     return getHandle(createSubArray<T>(currentEstimate, index));
 }
@@ -220,7 +225,7 @@ af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
 template<typename CT>
 Array<CT> denominator(const Array<CT>& I, const Array<CT>& P, const float gamma,
                       const af_inverse_deconv_algo algo) {
-    typedef typename af::dtype_traits<CT>::base_type T;
+    using T = typename af::dtype_traits<CT>::base_type;
 
     auto RCNST = createValueArray(I.dims(), scalar<T>(gamma));
 
@@ -245,7 +250,7 @@ Array<CT> denominator(const Array<CT>& I, const Array<CT>& P, const float gamma,
 template<typename InputType, typename RealType = float>
 af_array invDeconv(const af_array in, const af_array ker, const float gamma,
                    const af_inverse_deconv_algo algo) {
-    typedef RealType T;
+    using T    = RealType;
     using CT   = typename std::conditional<std::is_same<T, double>::value,
                                          cdouble, cfloat>::type;
     auto input = castArray<T>(in);
@@ -261,9 +266,10 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
     auto paddedIn =
         padArrayBorders<T>(input, inLPad, inUPad, AF_PAD_CLAMP_TO_EDGE);
     auto paddedPsf = padArrayBorders<T>(psf, psfLPad, psfUPad, AF_PAD_ZERO);
-    const int shiftDims[4] = {-int(fdims[0] / 2), -int(fdims[1] / 2), 0, 0};
+    const array<int, 4> shiftDims = {-int(fdims[0] / 2), -int(fdims[1] / 2), 0,
+                                     0};
 
-    auto shiftedPsf = shift(paddedPsf, shiftDims);
+    auto shiftedPsf = shift(paddedPsf, shiftDims.data());
 
     auto I      = fft_r2c<CT, T, BASE_DIM>(paddedIn);
     auto P      = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
@@ -277,7 +283,8 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
 
     select_scalar<CT, false>(val, cond, val, 0);
 
-    auto ival = fft_c2r<CT, T, BASE_DIM>(val, 1 / (double)nElems, odims);
+    auto ival =
+        fft_c2r<CT, T, BASE_DIM>(val, 1 / static_cast<double>(nElems), odims);
 
     return getHandle(createSubArray<T>(ival, index));
 }
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index 1cd6e76ac1..a5cc7154e8 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -20,7 +20,11 @@
 #include <af/lapack.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::scalar;
 
 template<typename T>
 T det(const af_array a) {
@@ -57,7 +61,7 @@ T det(const af_array a) {
         is_neg ^= (hP[i] != (i + 1));
     }
 
-    if (is_neg) res = res * scalar<T>(-1);
+    if (is_neg) { res = res * scalar<T>(-1); }
 
     return res;
 }
@@ -72,9 +76,10 @@ af_err af_det(double *real_val, double *imag_val, const af_array in) {
 
         af_dtype type = i_info.getType();
 
-        if (i_info.dims()[0])
+        if (i_info.dims()[0]) {
             DIM_ASSERT(1, i_info.dims()[0] ==
                               i_info.dims()[1]);  // Only square matrices
+        }
         ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
 
         *real_val = 0;
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 99d6983f17..9ea55f8dcb 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -14,7 +14,6 @@
 #include <handle.hpp>
 #include <platform.hpp>
 #include <sparse_handle.hpp>
-
 #include <af/backend.h>
 #include <af/device.h>
 #include <af/dim4.hpp>
@@ -23,8 +22,26 @@
 #include <cstring>
 #include <string>
 
-using namespace detail;
+using af::dim4;
 using common::half;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::devprop;
+using detail::evalFlag;
+using detail::getActiveDeviceId;
+using detail::getBackend;
+using detail::getDeviceCount;
+using detail::getDeviceInfo;
+using detail::intl;
+using detail::isDoubleSupported;
+using detail::isHalfSupported;
+using detail::setDevice;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 af_err af_set_backend(const af_backend bknd) {
     try {
@@ -67,7 +84,7 @@ af_err af_get_device_id(int* device, const af_array in) {
     try {
         if (in) {
             const ArrayInfo& info = getInfo(in, false, false);
-            *device               = info.getDevId();
+            *device               = static_cast<int>(info.getDevId());
         } else {
             return AF_ERR_ARG;
         }
@@ -77,7 +94,7 @@ af_err af_get_device_id(int* device, const af_array in) {
 }
 
 af_err af_get_active_backend(af_backend* result) {
-    *result = (af_backend)getBackend();
+    *result = static_cast<af_backend>(getBackend());
     return AF_SUCCESS;
 }
 
@@ -92,7 +109,7 @@ af_err af_init() {
 
 af_err af_info() {
     try {
-        printf("%s", getDeviceInfo().c_str());
+        printf("%s", getDeviceInfo().c_str());  // NOLINT
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -102,7 +119,8 @@ af_err af_info_string(char** str, const bool verbose) {
     UNUSED(verbose);  // TODO(umar): Add something useful
     try {
         std::string infoStr = getDeviceInfo();
-        af_alloc_host((void**)str, sizeof(char) * (infoStr.size() + 1));
+        af_alloc_host(reinterpret_cast<void**>(str),
+                      sizeof(char) * (infoStr.size() + 1));
 
         // Need to do a deep copy
         // str.c_str wont cut it
@@ -172,7 +190,7 @@ af_err af_set_device(const int device) {
                 char err_msg[] =
                     "The device index of %d is out of range. Use a value "
                     "between 0 and %d.";
-                snprintf(buf, 512, err_msg, device, ndevices - 1);
+                snprintf(buf, 512, err_msg, device, ndevices - 1);  // NOLINT
                 AF_ERROR(buf, AF_ERR_ARG);
             }
         }
@@ -194,13 +212,11 @@ af_err af_sync(const int device) {
 template<typename T>
 static inline void eval(af_array arr) {
     getArray<T>(arr).eval();
-    return;
 }
 
 template<typename T>
 static inline void sparseEval(af_array arr) {
     getSparseArray<T>(arr).eval();
-    return;
 }
 
 af_err af_eval(af_array arr) {
@@ -250,14 +266,13 @@ static inline void evalMultiple(int num, af_array* arrayPtrs) {
     }
 
     evalMultiple<T>(arrays);
-    return;
 }
 
 af_err af_eval_multiple(int num, af_array* arrays) {
     try {
         const ArrayInfo& info = getInfo(arrays[0]);
         af_dtype type         = info.getType();
-        dim4 dims             = info.dims();
+        const dim4& dims      = info.dims();
 
         for (int i = 1; i < num; i++) {
             const ArrayInfo& currInfo = getInfo(arrays[i]);
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index 1e2c024afe..3fb1cee150 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -16,7 +16,13 @@
 #include <af/defines.h>
 
 using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array diff1(const af_array in, const int dim) {
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index 7b932817a7..633f901409 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Array.hpp>
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
@@ -18,7 +19,12 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::convolve;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename T, typename accT>
 static af_array dog(const af_array& in, const int radius1, const int radius2) {
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 3404161c36..c818414eaa 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -10,12 +10,14 @@
 #include <common/err_common.hpp>
 #include <af/device.h>
 #include <af/exception.h>
+
 #include <algorithm>
 #include <string>
 
 void af_get_last_error(char **str, dim_t *len) {
     std::string &global_error_string = get_global_error_string();
-    dim_t slen = std::min(MAX_ERR_SIZE, (int)global_error_string.size());
+    dim_t slen =
+        std::min(MAX_ERR_SIZE, static_cast<int>(global_error_string.size()));
 
     if (len && slen == 0) {
         *len = 0;
@@ -23,13 +25,13 @@ void af_get_last_error(char **str, dim_t *len) {
         return;
     }
 
-    af_alloc_host((void **)str, sizeof(char) * (slen + 1));
+    af_alloc_host(reinterpret_cast<void **>(str), sizeof(char) * (slen + 1));
     global_error_string.copy(*str, slen);
 
     (*str)[slen]        = '\0';
     global_error_string = std::string("");
 
-    if (len) *len = slen;
+    if (len) { *len = slen; }
 }
 
 af_err af_set_enable_stacktrace(int is_enabled) {
diff --git a/src/api/c/events.cpp b/src/api/c/events.cpp
index 24aeed4421..c3d7d5a773 100644
--- a/src/api/c/events.cpp
+++ b/src/api/c/events.cpp
@@ -14,7 +14,11 @@
 #include <af/device.h>
 #include <af/event.h>
 
-using namespace detail;
+using detail::block;
+using detail::createEvent;
+using detail::enqueueWaitOnActiveQueue;
+using detail::Event;
+using detail::markEventOnActiveQueue;
 
 Event &getEvent(af_event &handle) {
     Event &event = *static_cast<Event *>(handle);
diff --git a/src/api/c/fast.cpp b/src/api/c/fast.cpp
index 742d68e21f..dbdd50c6a7 100644
--- a/src/api/c/fast.cpp
+++ b/src/api/c/fast.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <fast.hpp>
@@ -18,7 +19,12 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::createEmptyArray;
+using detail::createValueArray;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename T>
 static af_features fast(af_array const &in, const float thr,
diff --git a/src/api/c/features.cpp b/src/api/c/features.cpp
index 0c933aaa1c..06b048e830 100644
--- a/src/api/c/features.cpp
+++ b/src/api/c/features.cpp
@@ -14,26 +14,27 @@
 
 af_err af_release_features(af_features featHandle) {
     try {
-        af_features_t feat = *(af_features_t *)featHandle;
+        af_features_t feat = *static_cast<af_features_t *>(featHandle);
         if (feat.n > 0) {
-            if (feat.x != 0) AF_CHECK(af_release_array(feat.x));
-            if (feat.y != 0) AF_CHECK(af_release_array(feat.y));
-            if (feat.score != 0) AF_CHECK(af_release_array(feat.score));
-            if (feat.orientation != 0)
+            if (feat.x != 0) { AF_CHECK(af_release_array(feat.x)); }
+            if (feat.y != 0) { AF_CHECK(af_release_array(feat.y)); }
+            if (feat.score != 0) { AF_CHECK(af_release_array(feat.score)); }
+            if (feat.orientation != 0) {
                 AF_CHECK(af_release_array(feat.orientation));
-            if (feat.size != 0) AF_CHECK(af_release_array(feat.size));
+            }
+            if (feat.size != 0) { AF_CHECK(af_release_array(feat.size)); }
             feat.n = 0;
         }
-        delete (af_features_t *)featHandle;
+        delete static_cast<af_features_t *>(featHandle);
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
 af_features getFeaturesHandle(const af_features_t feat) {
-    af_features_t *featHandle = new af_features_t;
-    *featHandle               = feat;
-    return (af_features)featHandle;
+    auto *featHandle = new af_features_t;
+    *featHandle      = feat;
+    return static_cast<af_features>(featHandle);
 }
 
 af_err af_create_features(af_features *featHandle, dim_t num) {
@@ -58,7 +59,7 @@ af_err af_create_features(af_features *featHandle, dim_t num) {
 }
 
 af_features_t getFeatures(const af_features featHandle) {
-    return *(af_features_t *)featHandle;
+    return *static_cast<af_features_t *>(featHandle);
 }
 
 af_err af_retain_features(af_features *outHandle,
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index 7a8283571d..e68a4a4722 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -15,12 +15,15 @@
 #include <af/signal.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::multiply_inplace;
 
 void computePaddedDims(dim4 &pdims, const dim4 &idims, const dim_t npad,
                        dim_t const *const pad) {
     for (int i = 0; i < 4; i++) {
-        pdims[i] = (i < (int)npad) ? pad[i] : idims[i];
+        pdims[i] = (i < static_cast<int>(npad)) ? pad[i] : idims[i];
     }
 }
 
@@ -37,7 +40,7 @@ static af_err fft(af_array *out, const af_array in, const double norm_factor,
     try {
         const ArrayInfo &info = getInfo(in);
         af_dtype type         = info.getType();
-        af::dim4 dims         = info.dims();
+        const dim4 &dims      = info.dims();
 
         if (dims.ndims() == 0) { return af_retain_array(out, in); }
 
diff --git a/src/api/c/fft_common.hpp b/src/api/c/fft_common.hpp
index 76e4dc777e..a8bf7d06a3 100644
--- a/src/api/c/fft_common.hpp
+++ b/src/api/c/fft_common.hpp
@@ -10,38 +10,38 @@
 #include <fft.hpp>
 #include <handle.hpp>
 
-using namespace detail;
-
-void computePaddedDims(dim4 &pdims, const dim4 &idims, const dim_t npad,
+void computePaddedDims(af::dim4 &pdims, const af::dim4 &idims, const dim_t npad,
                        dim_t const *const pad);
 
 template<typename inType, typename outType, int rank, bool direction>
-Array<outType> fft(const Array<inType> input, const double norm_factor,
-                   const dim_t npad, const dim_t *const pad) {
-    dim4 pdims(1);
+detail::Array<outType> fft(const detail::Array<inType> input,
+                           const double norm_factor, const dim_t npad,
+                           const dim_t *const pad) {
+    af::dim4 pdims(1);
     computePaddedDims(pdims, input.dims(), npad, pad);
-    auto res = padArray(input, pdims, scalar<outType>(0));
+    auto res = padArray(input, pdims, detail::scalar<outType>(0));
 
-    fft_inplace<outType, rank, direction>(res);
+    detail::fft_inplace<outType, rank, direction>(res);
     if (norm_factor != 1.0) multiply_inplace(res, norm_factor);
 
     return res;
 }
 
 template<typename inType, typename outType, int rank>
-Array<outType> fft_r2c(const Array<inType> input, const double norm_factor,
-                       const dim_t npad, const dim_t *const pad) {
-    dim4 idims = input.dims();
+detail::Array<outType> fft_r2c(const detail::Array<inType> input,
+                               const double norm_factor, const dim_t npad,
+                               const dim_t *const pad) {
+    af::dim4 idims = input.dims();
 
     bool is_pad = false;
     for (int i = 0; i < npad; i++) { is_pad |= (pad[i] != idims[i]); }
 
-    Array<inType> tmp = input;
+    detail::Array<inType> tmp = input;
 
     if (is_pad) {
-        dim4 pdims(1);
+        af::dim4 pdims(1);
         computePaddedDims(pdims, input.dims(), npad, pad);
-        tmp = padArray(input, pdims, scalar<inType>(0));
+        tmp = padArray(input, pdims, detail::scalar<inType>(0));
     }
 
     auto res = fft_r2c<outType, inType, rank>(tmp);
@@ -51,9 +51,11 @@ Array<outType> fft_r2c(const Array<inType> input, const double norm_factor,
 }
 
 template<typename inType, typename outType, int rank>
-Array<outType> fft_c2r(const Array<inType> input, const double norm_factor,
-                       const dim4 &odims) {
-    Array<outType> output = fft_c2r<outType, inType, rank>(input, odims);
+detail::Array<outType> fft_c2r(const detail::Array<inType> input,
+                               const double norm_factor,
+                               const af::dim4 &odims) {
+    detail::Array<outType> output =
+        fft_c2r<outType, inType, rank>(input, odims);
 
     if (norm_factor != 1) {
         // Normalize input because tmp was not normalized
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 32694b11e7..87dae06c5c 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -19,7 +19,22 @@
 #include <af/signal.h>
 
 using af::dim4;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createSubArray;
+using detail::fftconvolve;
+using detail::intl;
+using detail::real;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+using std::max;
+using std::swap;
+using std::vector;
 
 template<typename T, typename convT, typename cT, int baseDim>
 static inline af_array fftconvolve_fallback(const af_array signal,
@@ -27,13 +42,13 @@ static inline af_array fftconvolve_fallback(const af_array signal,
                                             bool expand) {
     const Array<cT> S = castArray<cT>(signal);
     const Array<cT> F = castArray<cT>(filter);
-    const dim4 sdims  = S.dims();
-    const dim4 fdims  = F.dims();
+    const dim4 &sdims = S.dims();
+    const dim4 &fdims = F.dims();
     dim4 odims(1, 1, 1, 1);
     dim4 psdims(1, 1, 1, 1);
     dim4 pfdims(1, 1, 1, 1);
 
-    std::vector<af_seq> index(AF_MAX_DIMS);
+    vector<af_seq> index(AF_MAX_DIMS);
 
     int count = 1;
     for (int i = 0; i < baseDim; i++) {
@@ -49,17 +64,17 @@ static inline af_array fftconvolve_fallback(const af_array signal,
 
         // Get the indexing params for output
         if (expand) {
-            index[i].begin = 0;
-            index[i].end   = tdim_i - 1;
+            index[i].begin = 0.;
+            index[i].end   = static_cast<double>(tdim_i) - 1.;
         } else {
-            index[i].begin = fdims[i] / 2;
-            index[i].end   = index[i].begin + sdims[i] - 1;
+            index[i].begin = static_cast<double>(fdims[i]) / 2.0;
+            index[i].end = static_cast<double>(index[i].begin + sdims[i]) - 1.;
         }
-        index[i].step = 1;
+        index[i].step = 1.;
     }
 
     for (int i = baseDim; i < AF_MAX_DIMS; i++) {
-        odims[i]  = std::max(sdims[i], fdims[i]);
+        odims[i]  = max(sdims[i], fdims[i]);
         psdims[i] = sdims[i];
         pfdims[i] = fdims[i];
         index[i]  = af_span;
@@ -75,8 +90,8 @@ static inline af_array fftconvolve_fallback(const af_array signal,
     T1 = arithOp<cT, af_mul_t>(T1, T2, odims);
 
     // ifft(ffit(signal) * fft(filter))
-    T1 = fft<cT, cT, baseDim, false>(T1, 1.0 / (double)count, baseDim,
-                                     odims.get());
+    T1 = fft<cT, cT, baseDim, false>(T1, 1.0 / static_cast<double>(count),
+                                     baseDim, odims.get());
 
     // Index to proper offsets
     T1 = createSubArray<cT>(T1, index);
@@ -92,11 +107,12 @@ template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
          dim_t baseDim>
 inline static af_array fftconvolve(const af_array &s, const af_array &f,
                                    const bool expand, AF_BATCH_KIND kind) {
-    if (kind == AF_BATCH_DIFF)
+    if (kind == AF_BATCH_DIFF) {
         return fftconvolve_fallback<T, convT, cT, baseDim>(s, f, expand);
-    else
+    } else {
         return getHandle(fftconvolve<T, convT, cT, isDouble, roundOut, baseDim>(
             getArray<T>(s), castArray<T>(f), expand, kind));
+    }
 }
 
 template<dim_t baseDim>
@@ -104,14 +120,14 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
-    if (sn == baseDim && fn == baseDim)
-        return AF_BATCH_NONE;
-    else if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS))
+    if (sn == baseDim && fn == baseDim) { return AF_BATCH_NONE; }
+    if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS)) {
         return AF_BATCH_RHS;
-    else if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim)
+    }
+    if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim) {
         return AF_BATCH_LHS;
-    else if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
-             (fn > baseDim && fn <= AF_MAX_DIMS)) {
+    } else if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
+               (fn > baseDim && fn <= AF_MAX_DIMS)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved       = true;
         for (dim_t i = baseDim; i < AF_MAX_DIMS; i++) {
@@ -119,10 +135,11 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
             isInterleaved &=
                 (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
         }
-        if (doesDimensionsMatch) return AF_BATCH_SAME;
+        if (doesDimensionsMatch) { return AF_BATCH_SAME; }
         return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
-    } else
+    } else {
         return AF_BATCH_UNSUPPORTED;
+    }
 }
 
 template<dim_t baseDim>
@@ -134,8 +151,8 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
 
         af_dtype stype = sInfo.getType();
 
-        dim4 sdims = sInfo.dims();
-        dim4 fdims = fInfo.dims();
+        const dim4 &sdims = sInfo.dims();
+        const dim4 &fdims = fInfo.dims();
 
         AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
 
@@ -200,7 +217,7 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
                 break;
             default: TYPE_ERROR(1, stype);
         }
-        std::swap(*out, output);
+        swap(*out, output);
     }
     CATCHALL;
 
@@ -217,9 +234,8 @@ af_err af_fft_convolve2(af_array *out, const af_array signal,
     if (getInfo(signal).dims().ndims() < 2 &&
         getInfo(filter).dims().ndims() < 2) {
         return fft_convolve<1>(out, signal, filter, mode == AF_CONV_EXPAND);
-    } else {
-        return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
     }
+    return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
 }
 
 af_err af_fft_convolve3(af_array *out, const af_array signal,
@@ -227,7 +243,6 @@ af_err af_fft_convolve3(af_array *out, const af_array signal,
     if (getInfo(signal).dims().ndims() < 3 &&
         getInfo(filter).dims().ndims() < 3) {
         return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
-    } else {
-        return fft_convolve<3>(out, signal, filter, mode == AF_CONV_EXPAND);
     }
+    return fft_convolve<3>(out, signal, filter, mode == AF_CONV_EXPAND);
 }
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index 4ad1834904..c129c01710 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -18,7 +18,9 @@
 #include <af/signal.h>
 
 using af::dim4;
-using namespace detail;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length,
                   const dim_t wind_width, const af_border_type edge_pad) {
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index e8c51d1db1..d1a5159ea8 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -25,21 +25,28 @@
 #include <af/index.h>
 #include <af/seq.h>
 
-using namespace detail;
+using af::dim4;
 using common::half;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uintl;
+using detail::ushort;
 using std::swap;
 using std::vector;
 
 template<typename T>
 static af_array flipArray(const af_array in, const unsigned dim) {
-    const Array<T> &input = getArray<T>(in);
+    const Array<T> input = getArray<T>(in);
     vector<af_seq> index(4);
 
     for (int i = 0; i < 4; i++) { index[i] = af_span; }
 
     // Reverse "dim"
     dim4 in_dims = input.dims();
-    af_seq s     = {(double)(in_dims[dim] - 1), 0, -1};
+    af_seq s     = {static_cast<double>(in_dims[dim] - 1), 0, -1};
 
     index[dim] = s;
 
diff --git a/src/api/c/gaussian_kernel.cpp b/src/api/c/gaussian_kernel.cpp
index 0fb1bfefb6..b956dc8a69 100644
--- a/src/api/c/gaussian_kernel.cpp
+++ b/src/api/c/gaussian_kernel.cpp
@@ -20,7 +20,9 @@
 #include <af/dim4.hpp>
 #include <af/image.h>
 
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::createValueArray;
 
 template<typename T>
 Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r,
@@ -36,8 +38,8 @@ Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r,
         Array<T> wt = range<T>(dim4(cols, rows), 0);
         Array<T> w  = transpose<T>(wt, false);
 
-        Array<T> c =
-            createValueArray<T>(odims, scalar<T>((double)(cols - 1) / 2.0));
+        Array<T> c = createValueArray<T>(
+            odims, scalar<T>(static_cast<double>(cols - 1) / 2.0));
         w = arithOp<T, af_sub_t>(w, c, odims);
 
         sigma        = sigma_c > 0 ? sigma_c : 0.25 * cols;
@@ -51,8 +53,8 @@ Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r,
     if (rows > 1) {
         Array<T> w = range<T>(dim4(rows, cols), 0);
 
-        Array<T> r =
-            createValueArray<T>(odims, scalar<T>((double)(rows - 1) / 2.0));
+        Array<T> r = createValueArray<T>(
+            odims, scalar<T>(static_cast<double>(rows - 1) / 2.0));
         w = arithOp<T, af_sub_t>(w, r, odims);
 
         sigma        = sigma_r > 0 ? sigma_r : 0.25 * rows;
diff --git a/src/api/c/gradient.cpp b/src/api/c/gradient.cpp
index 857ad2f2b3..419039ad11 100644
--- a/src/api/c/gradient.cpp
+++ b/src/api/c/gradient.cpp
@@ -16,7 +16,8 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
 
 template<typename T>
 static inline void gradient(af_array *grad0, af_array *grad1,
diff --git a/src/api/c/harris.cpp b/src/api/c/harris.cpp
index ea2f00934f..c55beb3fc5 100644
--- a/src/api/c/harris.cpp
+++ b/src/api/c/harris.cpp
@@ -17,8 +17,13 @@
 #include <af/features.h>
 #include <af/vision.h>
 
+#include <cmath>
+
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::createEmptyArray;
+using detail::createValueArray;
+using std::floor;
 
 template<typename T, typename convAccT>
 static af_features harris(af_array const &in, const unsigned max_corners,
@@ -50,12 +55,13 @@ af_err af_harris(af_features *out, const af_array in,
                  const float k_thr) {
     try {
         const ArrayInfo &info = getInfo(in);
-        af::dim4 dims         = info.dims();
+        dim4 dims             = info.dims();
         dim_t in_ndims        = dims.ndims();
 
-        unsigned filter_len =
-            (block_size == 0) ? floor(6.f * sigma) : block_size;
-        if (block_size == 0 && filter_len % 2 == 0) filter_len--;
+        unsigned filter_len = (block_size == 0)
+                                  ? static_cast<unsigned>(floor(6.f * sigma))
+                                  : block_size;
+        if (block_size == 0 && filter_len % 2 == 0) { filter_len--; }
 
         const unsigned edge =
             (block_size > 0) ? block_size / 2 : filter_len / 2;
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 10d61963a0..756dd6b80e 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -17,9 +17,8 @@
 #include <reduce.hpp>
 #include <af/graphics.h>
 
-using af::dim4;
-using namespace detail;
-using namespace graphics;
+using detail::Array;
+using graphics::ForgeManager;
 
 template<typename T>
 fg_chart setup_histogram(fg_window const window, const af_array in,
@@ -27,18 +26,19 @@ fg_chart setup_histogram(fg_window const window, const af_array in,
                          const af_cell* const props) {
     ForgeModule& _ = graphics::forgePlugin();
 
-    Array<T> histogramInput = getArray<T>(in);
-    dim_t nBins             = histogramInput.elements();
+    const Array<T> histogramInput = getArray<T>(in);
+    dim_t nBins                   = histogramInput.elements();
 
     // Retrieve Forge Histogram with nBins and array type
     ForgeManager& fgMngr = forgeManager();
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
-    if (props->col > -1 && props->row > -1)
+    if (props->col > -1 && props->row > -1) {
         chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
-    else
+    } else {
         chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+    }
 
     // Create a histogram for the chart
     fg_histogram hist = fgMngr.getHistogram(chart, nBins, getGLType<T>());
@@ -56,15 +56,21 @@ fg_chart setup_histogram(fg_window const window, const af_array in,
 
         if (xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
             // No previous limits. Set without checking
-            xMin = step_round(minval, false);
-            xMax = step_round(maxval, true);
-            yMax = step_round(freqMax, true);
+            xMin = static_cast<float>(step_round(minval, false));
+            xMax = static_cast<float>(step_round(maxval, true));
+            yMax = static_cast<float>(step_round(freqMax, true));
             // For histogram, always set yMin to 0.
             yMin = 0;
         } else {
-            if (xMin > minval) xMin = step_round(minval, false);
-            if (xMax < maxval) xMax = step_round(maxval, true);
-            if (yMax < freqMax) yMax = step_round(freqMax, true);
+            if (xMin > minval) {
+                xMin = static_cast<float>(step_round(minval, false));
+            }
+            if (xMax < maxval) {
+                xMax = static_cast<float>(step_round(maxval, true));
+            }
+            if (yMax < freqMax) {
+                yMax = static_cast<float>(step_round(freqMax, true));
+            }
             // For histogram, always set yMin to 0.
             yMin = 0;
         }
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index a4447ac82e..050dd21fe7 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -20,7 +20,7 @@
 #include <af/image.h>
 #include <af/index.h>
 
-using namespace detail;
+using detail::Array;
 
 template<typename T, typename hType>
 static af_array hist_equal(const af_array& in, const af_array& hist) {
@@ -31,14 +31,14 @@ static af_array hist_equal(const af_array& in, const af_array& hist) {
 
     Array<float> fHist = cast<float>(getArray<hType>(hist));
 
-    dim4 hDims       = fHist.dims();
-    dim_t grayLevels = fHist.elements();
+    const dim4& hDims = fHist.dims();
+    dim_t grayLevels  = fHist.elements();
 
     Array<float> cdf = scan<af_add_t, float, float>(fHist, 0);
 
     float minCdf = reduce_all<af_min_t, float, float>(cdf);
     float maxCdf = reduce_all<af_max_t, float, float>(cdf);
-    float factor = (float)(grayLevels - 1) / (maxCdf - minCdf);
+    float factor = static_cast<float>(grayLevels - 1) / (maxCdf - minCdf);
 
     // constant array of min value from cdf
     Array<float> minCnst = createValueArray<float>(hDims, minCdf);
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index ad18aa63c7..f5c5c6497b 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -14,19 +14,25 @@
 #include <af/dim4.hpp>
 #include <af/image.h>
 
-using af::dim4;
-using namespace detail;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename inType, typename outType>
 static inline af_array histogram(const af_array in, const unsigned &nbins,
                                  const double &minval, const double &maxval,
                                  const bool islinear) {
-    if (islinear)
-        return getHandle(histogram<inType, outType, true>(
+    af_array out = nullptr;
+    if (islinear) {
+        out = getHandle(histogram<inType, outType, true>(
             getArray<inType>(in), nbins, minval, maxval));
-    else
-        return getHandle(histogram<inType, outType, false>(
+    } else {
+        out = getHandle(histogram<inType, outType, false>(
             getArray<inType>(in), nbins, minval, maxval));
+    }
+    return out;
 }
 
 af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index f888b4f92c..e929f1bd66 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -17,8 +17,12 @@
 #include <af/random.h>
 #include <af/vision.h>
 
+#include <utility>
+
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::createEmptyArray;
+using std::swap;
 
 template<typename T>
 static inline void homography(af_array& H, int& inliers, const af_array x_src,
@@ -89,8 +93,8 @@ af_err af_homography(af_array* H, int* inliers, const af_array x_src,
                 break;
             default: TYPE_ERROR(1, otype);
         }
-        std::swap(*H, outH);
-        std::swap(*inliers, outInl);
+        swap(*H, outH);
+        swap(*inliers, outInl);
     }
     CATCHALL;
 
diff --git a/src/api/c/hsv_rgb.cpp b/src/api/c/hsv_rgb.cpp
index e321125bc9..4661a255cc 100644
--- a/src/api/c/hsv_rgb.cpp
+++ b/src/api/c/hsv_rgb.cpp
@@ -16,7 +16,9 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::hsv2rgb;
+using detail::rgb2hsv;
 
 template<typename T, bool isHSV2RGB>
 static af_array convert(const af_array& in) {
diff --git a/src/api/c/iir.cpp b/src/api/c/iir.cpp
index 96dfc2b187..2c56011cc2 100644
--- a/src/api/c/iir.cpp
+++ b/src/api/c/iir.cpp
@@ -19,7 +19,8 @@
 #include <cstdio>
 
 using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
 
 af_err af_fir(af_array* y, const af_array b, const af_array x) {
     try {
@@ -28,9 +29,9 @@ af_err af_fir(af_array* y, const af_array b, const af_array x) {
 
         dim4 xdims    = getInfo(x).dims();
         af_seq seqs[] = {af_span, af_span, af_span, af_span};
-        seqs[0].begin = 0;
-        seqs[0].end   = xdims[0] - 1;
-        seqs[0].step  = 1;
+        seqs[0].begin = 0.;
+        seqs[0].end   = static_cast<double>(xdims[0]) - 1.;
+        seqs[0].step  = 1.;
         af_array res;
         AF_CHECK(af_index(&res, out, 4, seqs));
         AF_CHECK(af_release_array(out));
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 17505279b7..8f172a6762 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -27,8 +27,16 @@
 #include <limits>
 
 using af::dim4;
-using namespace detail;
-using namespace graphics;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::copy_image;
+using detail::createValueArray;
+using detail::forgeManager;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
+using graphics::ForgeManager;
 
 template<typename T>
 Array<T> normalizePerType(const Array<T>& in) {
@@ -58,10 +66,10 @@ static fg_image convert_and_copy_image(const af_array in) {
     ForgeManager& fgMngr = forgeManager();
 
     // The inDims[2] * 100 is a hack to convert to fg_channel_format
-    // TODO Write a proper conversion function
-    fg_image ret_val =
-        fgMngr.getImage(inDims[1], inDims[0],
-                        (fg_channel_format)(inDims[2] * 100), getGLType<T>());
+    // TODO(pradeep): Write a proper conversion function
+    fg_image ret_val = fgMngr.getImage(
+        inDims[1], inDims[0], static_cast<fg_channel_format>(inDims[2] * 100),
+        getGLType<T>());
     copy_image<T>(normalizePerType<T>(imgData), ret_val);
 
     return ret_val;
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index c44da9d0f8..ba0a024d9e 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -35,17 +35,20 @@
 #include <string>
 
 using af::dim4;
-using namespace detail;
+using detail::pinnedAlloc;
+using detail::pinnedFree;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using std::string;
 using std::swap;
-using std::unique_ptr;
 
 template<typename T, FI_CHANNELS fi_color, FI_CHANNELS fo_color>
 static af_err readImage(af_array* rImage, const uchar* pSrcLine,
                         const int nSrcPitch, const uint fi_w, const uint fi_h) {
     // create an array to receive the loaded image data.
     AF_CHECK(af_init());
-    float* pDst  = pinnedAlloc<float>(fi_w * fi_h * 4);  // 4 channels is max
+    auto* pDst   = pinnedAlloc<float>(fi_w * fi_h * 4);  // 4 channels is max
     float* pDst0 = pDst;
     float* pDst1 = pDst + (fi_w * fi_h * 1);
     float* pDst2 = pDst + (fi_w * fi_h * 2);
@@ -56,32 +59,37 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
 
     for (uint x = 0; x < fi_w; ++x) {
         for (uint y = 0; y < fi_h; ++y) {
-            const T* src = (T*)(pSrcLine - y * nSrcPitch);
+            const T* src = reinterpret_cast<const T*>(pSrcLine - y * nSrcPitch);
             if (fo_color == 1) {
-                pDst0[indx] = (T) * (src + (x * step));
+                pDst0[indx] = static_cast<T>(*(src + (x * step)));
             } else if (fo_color >= 3) {
-                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
-                    pDst0[indx] = (float)*(src + (x * step + FI_RGBA_RED));
-                    pDst1[indx] = (float)*(src + (x * step + FI_RGBA_GREEN));
-                    pDst2[indx] = (float)*(src + (x * step + FI_RGBA_BLUE));
-                    if (fo_color == 4)
-                        pDst3[indx] =
-                            (float)*(src + (x * step + FI_RGBA_ALPHA));
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                    pDst0[indx] =
+                        static_cast<float>(*(src + (x * step + FI_RGBA_RED)));
+                    pDst1[indx] =
+                        static_cast<float>(*(src + (x * step + FI_RGBA_GREEN)));
+                    pDst2[indx] =
+                        static_cast<float>(*(src + (x * step + FI_RGBA_BLUE)));
+                    if (fo_color == 4) {
+                        pDst3[indx] = static_cast<float>(
+                            *(src + (x * step + FI_RGBA_ALPHA)));
+                    }
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (float)*(src + (x * step + 0));
-                    pDst1[indx] = (float)*(src + (x * step + 1));
-                    pDst2[indx] = (float)*(src + (x * step + 2));
-                    if (fo_color == 4)
-                        pDst3[indx] = (float)*(src + (x * step + 3));
+                    pDst0[indx] = static_cast<float>(*(src + (x * step + 0)));
+                    pDst1[indx] = static_cast<float>(*(src + (x * step + 1)));
+                    pDst2[indx] = static_cast<float>(*(src + (x * step + 2)));
+                    if (fo_color == 4) {
+                        pDst3[indx] =
+                            static_cast<float>(*(src + (x * step + 3)));
+                    }
                 }
             }
             indx++;
         }
     }
 
-    // TODO
     af::dim4 dims(fi_h, fi_w, fo_color, 1);
     af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(),
                                  (af_dtype)af::dtype_traits<float>::af_type);
@@ -104,7 +112,8 @@ FreeImage_Module::FreeImage_Module() : module(nullptr, nullptr) {
 FreeImage_Module::FreeImage_Module() : module("freeimage", nullptr) {
     if (!module.isLoaded()) {
         string error_message =
-            "Error loading FreeImage: " + module.getErrorMessage() +
+            "Error loading FreeImage: " +
+            common::DependencyModule::getErrorMessage() +
             "\nFreeImage or one of it's dependencies failed to "
             "load. Try installing FreeImage or check if FreeImage is in the "
             "search path.";
@@ -139,7 +148,8 @@ FreeImage_Module::FreeImage_Module() : module("freeimage", nullptr) {
 #ifndef FREEIMAGE_STATIC
     if (!module.symbolsLoaded()) {
         string error_message =
-            "Error loading FreeImage: " + module.getErrorMessage() +
+            "Error loading FreeImage: " +
+            common::DependencyModule::getErrorMessage() +
             "\nThe installed version of FreeImage is not compatible with "
             "ArrayFire. Please create an issue on which this error message";
         AF_ERROR(error_message.c_str(), AF_ERR_LOAD_LIB);
@@ -147,14 +157,15 @@ FreeImage_Module::FreeImage_Module() : module("freeimage", nullptr) {
 #endif
 }
 
-FreeImage_Module::~FreeImage_Module() {
+FreeImage_Module::~FreeImage_Module() {  // NOLINT(hicpp-use-equals-default,
+                                         // modernize-use-equals-default)
 #ifdef FREEIMAGE_STATIC
     getFreeImagePlugin().FreeImage_DeInitialise();
 #endif
 }
 
 FreeImage_Module& getFreeImagePlugin() {
-    static FreeImage_Module* plugin = new FreeImage_Module();
+    static auto* plugin = new FreeImage_Module();
     return *plugin;
 }
 
@@ -167,27 +178,27 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
                         const int nSrcPitch, const uint fi_w, const uint fi_h) {
     // create an array to receive the loaded image data.
     AF_CHECK(af_init());
-    float* pDst = pinnedAlloc<float>(fi_w * fi_h);
+    auto* pDst = pinnedAlloc<float>(fi_w * fi_h);
 
     uint indx = 0;
     uint step = nSrcPitch / (fi_w * sizeof(T));
     T r, g, b;
     for (uint x = 0; x < fi_w; ++x) {
         for (uint y = 0; y < fi_h; ++y) {
-            const T* src = (T*)(pSrcLine - y * nSrcPitch);
+            const T* src = reinterpret_cast<const T*>(pSrcLine - y * nSrcPitch);
             if (fo_color == 1) {
-                pDst[indx] = (T) * (src + (x * step));
+                pDst[indx] = static_cast<T>(*(src + (x * step)));
             } else if (fo_color >= 3) {
-                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
-                    r = (T) * (src + (x * step + FI_RGBA_RED));
-                    g = (T) * (src + (x * step + FI_RGBA_GREEN));
-                    b = (T) * (src + (x * step + FI_RGBA_BLUE));
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                    r = *(src + (x * step + FI_RGBA_RED));
+                    g = *(src + (x * step + FI_RGBA_GREEN));
+                    b = *(src + (x * step + FI_RGBA_BLUE));
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    r = (T) * (src + (x * step + 0));
-                    g = (T) * (src + (x * step + 1));
-                    b = (T) * (src + (x * step + 2));
+                    r = *(src + (x * step + 0));
+                    g = *(src + (x * step + 1));
+                    b = *(src + (x * step + 2));
                 }
                 pDst[indx] = r * 0.2989f + g * 0.5870f + b * 0.1140f;
             }
@@ -226,16 +237,21 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        int flags = 0;
-        if (fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+        unsigned flags = 0;
+        if (fif == FIF_JPEG) {
+            flags = flags | static_cast<unsigned>(JPEG_ACCURATE);
+        }
 #ifdef JPEG_GREYSCALE
-        if (fif == FIF_JPEG && !isColor) flags = flags | JPEG_GREYSCALE;
+        if (fif == FIF_JPEG && !isColor) {
+            flags = flags | static_cast<unsigned>(JPEG_GREYSCALE);
+        }
 #endif
 
         // check that the plugin has reading capabilities ...
         bitmap_ptr pBitmap = make_bitmap_ptr(NULL);
         if (_.FreeImage_FIFSupportsReading(fif)) {
-            pBitmap.reset(_.FreeImage_Load(fif, filename, flags));
+            pBitmap.reset(
+                _.FreeImage_Load(fif, filename, static_cast<int>(flags)));
         }
 
         if (pBitmap == NULL) {
@@ -248,7 +264,7 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
         uint color_type   = _.FreeImage_GetColorType(pBitmap.get());
         const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
         // int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
-        int fi_color;
+        uint fi_color;
         switch (color_type) {
             case 0:  // FIC_MINISBLACK
             case 1:  // FIC_MINISWHITE
@@ -267,7 +283,7 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                 break;
         }
 
-        const int fi_bpc = fi_bpp / fi_color;
+        const uint fi_bpc = fi_bpp / fi_color;
         if (fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
             AF_ERROR("FreeImage Error: Bits per channel not supported",
                      AF_ERR_NOT_SUPPORTED);
@@ -289,19 +305,19 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
         af_array rImage;
         if (isColor) {
             if (fi_color == 4) {  // 4 channel image
-                if (fi_bpc == 8)
+                if (fi_bpc == 8) {
                     AF_CHECK((readImage<uchar, AFFI_RGBA, AFFI_RGBA>)(&rImage,
                                                                       pSrcLine,
                                                                       nSrcPitch,
                                                                       fi_w,
                                                                       fi_h));
-                else if (fi_bpc == 16)
+                } else if (fi_bpc == 16) {
                     AF_CHECK(
                         (readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage,
                                                                   pSrcLine,
                                                                   nSrcPitch,
                                                                   fi_w, fi_h));
-                else if (fi_bpc == 32)
+                } else if (fi_bpc == 32) {
                     switch (image_type) {
                         case FIT_UINT32:
                             AF_CHECK((readImage<uint, AFFI_RGBA,
@@ -328,20 +344,21 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                                      AF_ERR_NOT_SUPPORTED);
                             break;
                     }
+                }
             } else if (fi_color == 1) {
-                if (fi_bpc == 8)
+                if (fi_bpc == 8) {
                     AF_CHECK((readImage<uchar, AFFI_GRAY, AFFI_RGB>)(&rImage,
                                                                      pSrcLine,
                                                                      nSrcPitch,
                                                                      fi_w,
                                                                      fi_h));
-                else if (fi_bpc == 16)
+                } else if (fi_bpc == 16) {
                     AF_CHECK((readImage<ushort, AFFI_GRAY, AFFI_RGB>)(&rImage,
                                                                       pSrcLine,
                                                                       nSrcPitch,
                                                                       fi_w,
                                                                       fi_h));
-                else if (fi_bpc == 32)
+                } else if (fi_bpc == 32) {
                     switch (image_type) {
                         case FIT_UINT32:
                             AF_CHECK((
@@ -370,19 +387,20 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                                      AF_ERR_NOT_SUPPORTED);
                             break;
                     }
+                }
             } else {  // 3 channel image
-                if (fi_bpc == 8)
+                if (fi_bpc == 8) {
                     AF_CHECK((
                         readImage<uchar, AFFI_RGB, AFFI_RGB>)(&rImage, pSrcLine,
                                                               nSrcPitch, fi_w,
                                                               fi_h));
-                else if (fi_bpc == 16)
+                } else if (fi_bpc == 16) {
                     AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage,
                                                                      pSrcLine,
                                                                      nSrcPitch,
                                                                      fi_w,
                                                                      fi_h));
-                else if (fi_bpc == 32)
+                } else if (fi_bpc == 32) {
                     switch (image_type) {
                         case FIT_UINT32:
                             AF_CHECK(
@@ -413,18 +431,19 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                                      AF_ERR_NOT_SUPPORTED);
                             break;
                     }
+                }
             }
         } else {                  // output gray irrespective
             if (fi_color == 1) {  // 4 channel image
-                if (fi_bpc == 8)
+                if (fi_bpc == 8) {
                     AF_CHECK((readImage<uchar, AFFI_GRAY>)(&rImage, pSrcLine,
                                                            nSrcPitch, fi_w,
                                                            fi_h));
-                else if (fi_bpc == 16)
+                } else if (fi_bpc == 16) {
                     AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine,
                                                             nSrcPitch, fi_w,
                                                             fi_h));
-                else if (fi_bpc == 32)
+                } else if (fi_bpc == 32) {
                     switch (image_type) {
                         case FIT_UINT32:
                             AF_CHECK((readImage<uint, AFFI_GRAY>)(&rImage,
@@ -449,16 +468,17 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                                      AF_ERR_NOT_SUPPORTED);
                             break;
                     }
+                }
             } else if (fi_color == 3 || fi_color == 4) {
-                if (fi_bpc == 8)
+                if (fi_bpc == 8) {
                     AF_CHECK((readImage<uchar, AFFI_RGB>)(&rImage, pSrcLine,
                                                           nSrcPitch, fi_w,
                                                           fi_h));
-                else if (fi_bpc == 16)
+                } else if (fi_bpc == 16) {
                     AF_CHECK((readImage<ushort, AFFI_RGB>)(&rImage, pSrcLine,
                                                            nSrcPitch, fi_w,
                                                            fi_h));
-                else if (fi_bpc == 32)
+                } else if (fi_bpc == 32) {
                     switch (image_type) {
                         case FIT_UINT32:
                             AF_CHECK((readImage<uint, AFFI_RGB>)(&rImage,
@@ -483,6 +503,7 @@ af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
                                      AF_ERR_NOT_SUPPORTED);
                             break;
                     }
+                }
             }
         }
 
@@ -519,15 +540,15 @@ af_err af_save_image(const char* filename, const af_array in_) {
         DIM_ASSERT(1, channels <= 4);
         DIM_ASSERT(1, channels != 2);
 
-        int fi_bpp = channels * 8;
+        uint fi_bpp = channels * 8;
 
         // sizes
         uint fi_w = info.dims()[1];
         uint fi_h = info.dims()[0];
 
         // create the result image storage using FreeImage
-        bitmap_ptr pResultBitmap =
-            make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
+        bitmap_ptr pResultBitmap = make_bitmap_ptr(_.FreeImage_Allocate(
+            fi_w, fi_h, static_cast<int>(fi_bpp), 0, 0, 0));
         if (pResultBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error creating image or file",
                      AF_ERR_RUNTIME);
@@ -546,7 +567,7 @@ af_err af_save_image(const char* filename, const af_array in_) {
             AF_CHECK(af_mul(&in, in_, c255, false));
             AF_CHECK(af_release_array(c255));
             free_in = true;
-        } else if (max_real < 256) {
+        } else if (max_real < 256) {  // NOLINT(bugprone-branch-clone)
             in = in_;
         } else if (max_real < 65536) {
             af_array c255 = 0;
@@ -556,7 +577,7 @@ af_err af_save_image(const char* filename, const af_array in_) {
             AF_CHECK(af_release_array(c255));
             free_in = true;
         } else {
-            in = in_;
+            in = (in_);
         }
 
         // FI = row major | AF = column major
@@ -578,10 +599,11 @@ af_err af_save_image(const char* filename, const af_array in_) {
             AF_CHECK(af_transpose(&aaT, aa, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc3           = pinnedAlloc<float>(cinfo.elements());
+
+            auto* pSrc0 = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc1 = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc2 = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc3 = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -592,13 +614,13 @@ af_err af_save_image(const char* filename, const af_array in_) {
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
                     *(pDstLine + x * step + FI_RGBA_RED) =
-                        (uchar)pSrc0[indx];  // r
+                        static_cast<uchar>(pSrc0[indx]);  // r
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
-                        (uchar)pSrc1[indx];  // g
+                        static_cast<uchar>(pSrc1[indx]);  // g
                     *(pDstLine + x * step + FI_RGBA_BLUE) =
-                        (uchar)pSrc2[indx];  // b
+                        static_cast<uchar>(pSrc2[indx]);  // b
                     *(pDstLine + x * step + FI_RGBA_ALPHA) =
-                        (uchar)pSrc3[indx];  // a
+                        static_cast<uchar>(pSrc3[indx]);  // a
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -613,9 +635,10 @@ af_err af_save_image(const char* filename, const af_array in_) {
             AF_CHECK(af_transpose(&bbT, bb, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
+
+            auto* pSrc0 = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc1 = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc2 = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -625,11 +648,11 @@ af_err af_save_image(const char* filename, const af_array in_) {
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
                     *(pDstLine + x * step + FI_RGBA_RED) =
-                        (uchar)pSrc0[indx];  // r
+                        static_cast<uchar>(pSrc0[indx]);  // r
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
-                        (uchar)pSrc1[indx];  // g
+                        static_cast<uchar>(pSrc1[indx]);  // g
                     *(pDstLine + x * step + FI_RGBA_BLUE) =
-                        (uchar)pSrc2[indx];  // b
+                        static_cast<uchar>(pSrc2[indx]);  // b
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -640,12 +663,12 @@ af_err af_save_image(const char* filename, const af_array in_) {
         } else {
             AF_CHECK(af_transpose(&rrT, rr, false));
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc0            = pinnedAlloc<float>(cinfo.elements());
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
 
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step) = (uchar)pSrc0[indx];
+                    *(pDstLine + x * step) = static_cast<uchar>(pSrc0[indx]);
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -653,26 +676,28 @@ af_err af_save_image(const char* filename, const af_array in_) {
             pinnedFree(pSrc0);
         }
 
-        int flags = 0;
-        if (fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
+        unsigned flags = 0;
+        if (fif == FIF_JPEG) {
+            flags = flags | static_cast<unsigned>(JPEG_QUALITYSUPERB);
+        }
 
         // now save the result image
-        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) ==
-              TRUE)) {
+        if (_.FreeImage_Save(fif, pResultBitmap.get(), filename,
+                             static_cast<int>(flags)) == FALSE) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
-        if (free_in) AF_CHECK(af_release_array(in));
-        if (rr != 0) AF_CHECK(af_release_array(rr));
-        if (gg != 0) AF_CHECK(af_release_array(gg));
-        if (bb != 0) AF_CHECK(af_release_array(bb));
-        if (aa != 0) AF_CHECK(af_release_array(aa));
-        if (rrT != 0) AF_CHECK(af_release_array(rrT));
-        if (ggT != 0) AF_CHECK(af_release_array(ggT));
-        if (bbT != 0) AF_CHECK(af_release_array(bbT));
-        if (aaT != 0) AF_CHECK(af_release_array(aaT));
+        if (free_in) { AF_CHECK(af_release_array(in)); }
+        if (rr != 0) { AF_CHECK(af_release_array(rr)); }
+        if (gg != 0) { AF_CHECK(af_release_array(gg)); }
+        if (bb != 0) { AF_CHECK(af_release_array(bb)); }
+        if (aa != 0) { AF_CHECK(af_release_array(aa)); }
+        if (rrT != 0) { AF_CHECK(af_release_array(rrT)); }
+        if (ggT != 0) { AF_CHECK(af_release_array(ggT)); }
+        if (bbT != 0) { AF_CHECK(af_release_array(bbT)); }
+        if (aaT != 0) { AF_CHECK(af_release_array(aaT)); }
     }
-    CATCHALL
+    CATCHALL;
 
     return AF_SUCCESS;
 }
@@ -690,7 +715,7 @@ af_err af_load_image_memory(af_array* out, const void* ptr) {
         // set your own FreeImage error handler
         _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
-        FIMEMORY* stream = (FIMEMORY*)ptr;
+        auto* stream = static_cast<FIMEMORY*>(const_cast<void*>(ptr));
         _.FreeImage_SeekMemory(stream, 0L, SEEK_SET);
 
         // try to guess the file format from the file extension
@@ -704,13 +729,16 @@ af_err af_load_image_memory(af_array* out, const void* ptr) {
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        int flags = 0;
-        if (fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+        unsigned flags = 0;
+        if (fif == FIF_JPEG) {
+            flags = flags | static_cast<unsigned>(JPEG_ACCURATE);
+        }
 
         // check that the plugin has reading capabilities ...
         bitmap_ptr pBitmap = make_bitmap_ptr(NULL);
         if (_.FreeImage_FIFSupportsReading(fif)) {
-            pBitmap.reset(_.FreeImage_LoadFromMemory(fif, stream, flags));
+            pBitmap.reset(_.FreeImage_LoadFromMemory(fif, stream,
+                                                     static_cast<int>(flags)));
         }
 
         if (pBitmap == NULL) {
@@ -741,7 +769,7 @@ af_err af_load_image_memory(af_array* out, const void* ptr) {
                 fi_color = 3;
                 break;
         }
-        const int fi_bpc = fi_bpp / fi_color;
+        const uint fi_bpc = fi_bpp / fi_color;
         if (fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
             AF_ERROR("FreeImage Error: Bits per channel not supported",
                      AF_ERR_NOT_SUPPORTED);
@@ -759,47 +787,50 @@ af_err af_load_image_memory(af_array* out, const void* ptr) {
         // result image
         af_array rImage;
         if (fi_color == 4) {  // 4 channel image
-            if (fi_bpc == 8)
+            if (fi_bpc == 8) {
                 AF_CHECK((readImage<uchar, AFFI_RGBA, AFFI_RGBA>)(&rImage,
                                                                   pSrcLine,
                                                                   nSrcPitch,
                                                                   fi_w, fi_h));
-            else if (fi_bpc == 16)
+            } else if (fi_bpc == 16) {
                 AF_CHECK((readImage<ushort, AFFI_RGBA, AFFI_RGBA>)(&rImage,
                                                                    pSrcLine,
                                                                    nSrcPitch,
                                                                    fi_w, fi_h));
-            else if (fi_bpc == 32)
+            } else if (fi_bpc == 32) {
                 AF_CHECK((readImage<float, AFFI_RGBA, AFFI_RGBA>)(&rImage,
                                                                   pSrcLine,
                                                                   nSrcPitch,
                                                                   fi_w, fi_h));
+            }
         } else if (fi_color == 1) {  // 1 channel image
-            if (fi_bpc == 8)
+            if (fi_bpc == 8) {
                 AF_CHECK((readImage<uchar, AFFI_GRAY>)(&rImage, pSrcLine,
                                                        nSrcPitch, fi_w, fi_h));
-            else if (fi_bpc == 16)
+            } else if (fi_bpc == 16) {
                 AF_CHECK((readImage<ushort, AFFI_GRAY>)(&rImage, pSrcLine,
                                                         nSrcPitch, fi_w, fi_h));
-            else if (fi_bpc == 32)
+            } else if (fi_bpc == 32) {
                 AF_CHECK((readImage<float, AFFI_GRAY>)(&rImage, pSrcLine,
                                                        nSrcPitch, fi_w, fi_h));
+            }
         } else {  // 3 channel image
-            if (fi_bpc == 8)
+            if (fi_bpc == 8) {
                 AF_CHECK((readImage<uchar, AFFI_RGB, AFFI_RGB>)(&rImage,
                                                                 pSrcLine,
                                                                 nSrcPitch, fi_w,
                                                                 fi_h));
-            else if (fi_bpc == 16)
+            } else if (fi_bpc == 16) {
                 AF_CHECK((readImage<ushort, AFFI_RGB, AFFI_RGB>)(&rImage,
                                                                  pSrcLine,
                                                                  nSrcPitch,
                                                                  fi_w, fi_h));
-            else if (fi_bpc == 32)
+            } else if (fi_bpc == 32) {
                 AF_CHECK((readImage<float, AFFI_RGB, AFFI_RGB>)(&rImage,
                                                                 pSrcLine,
                                                                 nSrcPitch, fi_w,
                                                                 fi_h));
+            }
         }
 
         swap(*out, rImage);
@@ -819,7 +850,7 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
         _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
         // try to guess the file format from the file extension
-        FREE_IMAGE_FORMAT fif = (FREE_IMAGE_FORMAT)format;
+        auto fif = static_cast<FREE_IMAGE_FORMAT>(format);
 
         if (fif == FIF_UNKNOWN || fif > 34) {  // FreeImage FREE_IMAGE_FORMAT
                                                // has upto 34 enums as of 3.17
@@ -832,15 +863,15 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
         DIM_ASSERT(1, channels <= 4);
         DIM_ASSERT(1, channels != 2);
 
-        int fi_bpp = channels * 8;
+        uint fi_bpp = channels * 8;
 
         // sizes
         uint fi_w = info.dims()[1];
         uint fi_h = info.dims()[0];
 
         // create the result image storage using FreeImage
-        bitmap_ptr pResultBitmap =
-            make_bitmap_ptr(_.FreeImage_Allocate(fi_w, fi_h, fi_bpp, 0, 0, 0));
+        bitmap_ptr pResultBitmap = make_bitmap_ptr(_.FreeImage_Allocate(
+            fi_w, fi_h, static_cast<int>(fi_bpp), 0, 0, 0));
         if (pResultBitmap == NULL) {
             AF_ERROR("FreeImage Error: Error creating image or file",
                      AF_ERR_RUNTIME);
@@ -882,10 +913,10 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
             AF_CHECK(af_transpose(&aaT, aa, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc3           = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc0            = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc1            = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc2            = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc3            = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -896,13 +927,13 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
                     *(pDstLine + x * step + FI_RGBA_RED) =
-                        (uchar)pSrc0[indx];  // r
+                        static_cast<uchar>(pSrc0[indx]);  // r
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
-                        (uchar)pSrc1[indx];  // g
+                        static_cast<uchar>(pSrc1[indx]);  // g
                     *(pDstLine + x * step + FI_RGBA_BLUE) =
-                        (uchar)pSrc2[indx];  // b
+                        static_cast<uchar>(pSrc2[indx]);  // b
                     *(pDstLine + x * step + FI_RGBA_ALPHA) =
-                        (uchar)pSrc3[indx];  // a
+                        static_cast<uchar>(pSrc3[indx]);  // a
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -917,9 +948,9 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
             AF_CHECK(af_transpose(&bbT, bb, false));
 
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc1           = pinnedAlloc<float>(cinfo.elements());
-            float* pSrc2           = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc0            = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc1            = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc2            = pinnedAlloc<float>(cinfo.elements());
 
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
             AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
@@ -929,11 +960,11 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
                     *(pDstLine + x * step + FI_RGBA_RED) =
-                        (uchar)pSrc0[indx];  // r
+                        static_cast<uchar>(pSrc0[indx]);  // r
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
-                        (uchar)pSrc1[indx];  // g
+                        static_cast<uchar>(pSrc1[indx]);  // g
                     *(pDstLine + x * step + FI_RGBA_BLUE) =
-                        (uchar)pSrc2[indx];  // b
+                        static_cast<uchar>(pSrc2[indx]);  // b
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -944,12 +975,12 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
         } else {
             AF_CHECK(af_transpose(&rrT, rr, false));
             const ArrayInfo& cinfo = getInfo(rrT);
-            float* pSrc0           = pinnedAlloc<float>(cinfo.elements());
+            auto* pSrc0            = pinnedAlloc<float>(cinfo.elements());
             AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
 
             for (uint y = 0; y < fi_h; ++y) {
                 for (uint x = 0; x < fi_w; ++x) {
-                    *(pDstLine + x * step) = (uchar)pSrc0[indx];
+                    *(pDstLine + x * step) = static_cast<uchar>(pSrc0[indx]);
                     ++indx;
                 }
                 pDstLine -= nDstPitch;
@@ -961,28 +992,30 @@ af_err af_save_image_memory(void** ptr, const af_array in_,
         uint32_t size_in_bytes = 0;
         FIMEMORY* stream       = _.FreeImage_OpenMemory(data, size_in_bytes);
 
-        int flags = 0;
-        if (fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
+        unsigned flags = 0;
+        if (fif == FIF_JPEG) {
+            flags = flags | static_cast<unsigned>(JPEG_QUALITYSUPERB);
+        }
 
         // now save the result image
-        if (!(_.FreeImage_SaveToMemory(fif, pResultBitmap.get(), stream,
-                                       flags) == TRUE)) {
+        if (_.FreeImage_SaveToMemory(fif, pResultBitmap.get(), stream,
+                                     static_cast<int>(flags)) == FALSE) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
 
         *ptr = stream;
 
-        if (free_in) AF_CHECK(af_release_array(in));
-        if (rr != 0) AF_CHECK(af_release_array(rr));
-        if (gg != 0) AF_CHECK(af_release_array(gg));
-        if (bb != 0) AF_CHECK(af_release_array(bb));
-        if (aa != 0) AF_CHECK(af_release_array(aa));
-        if (rrT != 0) AF_CHECK(af_release_array(rrT));
-        if (ggT != 0) AF_CHECK(af_release_array(ggT));
-        if (bbT != 0) AF_CHECK(af_release_array(bbT));
-        if (aaT != 0) AF_CHECK(af_release_array(aaT));
+        if (free_in) { AF_CHECK(af_release_array(in)); }
+        if (rr != 0) { AF_CHECK(af_release_array(rr)); }
+        if (gg != 0) { AF_CHECK(af_release_array(gg)); }
+        if (bb != 0) { AF_CHECK(af_release_array(bb)); }
+        if (aa != 0) { AF_CHECK(af_release_array(aa)); }
+        if (rrT != 0) { AF_CHECK(af_release_array(rrT)); }
+        if (ggT != 0) { AF_CHECK(af_release_array(ggT)); }
+        if (bbT != 0) { AF_CHECK(af_release_array(bbT)); }
+        if (aaT != 0) { AF_CHECK(af_release_array(aaT)); }
     }
-    CATCHALL
+    CATCHALL;
 
     return AF_SUCCESS;
 }
@@ -996,19 +1029,19 @@ af_err af_delete_image_memory(void* ptr) {
         // set your own FreeImage error handler
         _.FreeImage_SetOutputMessage(FreeImageErrorHandler);
 
-        FIMEMORY* stream = (FIMEMORY*)ptr;
+        auto* stream = static_cast<FIMEMORY*>(ptr);
         _.FreeImage_SeekMemory(stream, 0L, SEEK_SET);
 
         // Ensure data is freeimage compatible
         FREE_IMAGE_FORMAT fif =
-            _.FreeImage_GetFileTypeFromMemory((FIMEMORY*)ptr, 0);
+            _.FreeImage_GetFileTypeFromMemory(static_cast<FIMEMORY*>(ptr), 0);
         if (fif == FIF_UNKNOWN) {
             AF_ERROR("FreeImage Error: Unknown Filetype", AF_ERR_NOT_SUPPORTED);
         }
 
-        _.FreeImage_CloseMemory((FIMEMORY*)ptr);
+        _.FreeImage_CloseMemory(static_cast<FIMEMORY*>(ptr));
     }
-    CATCHALL
+    CATCHALL;
 
     return AF_SUCCESS;
 }
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 13b7d0a3b7..f1edab6d7e 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -32,7 +32,11 @@
 #include <string>
 
 using af::dim4;
-using namespace detail;
+using detail::pinnedAlloc;
+using detail::pinnedFree;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename T, FI_CHANNELS fi_color>
 static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
@@ -51,60 +55,63 @@ static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
 
     for (uint x = 0; x < fi_w; ++x) {
         for (uint y = 0; y < fi_h; ++y) {
-            const T* src = (T*)((uchar*)pSrcLine - y * nSrcPitch);
+            const T* src = reinterpret_cast<T*>(const_cast<uchar*>(pSrcLine) -
+                                                y * nSrcPitch);
             if (fi_color == 1) {
-                pDst0[indx] = (T) * (src + (x * step));
+                pDst0[indx] = *(src + (x * step));
             } else if (fi_color >= 3) {
-                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
-                    pDst0[indx] = (T) * (src + (x * step + FI_RGBA_RED));
-                    pDst1[indx] = (T) * (src + (x * step + FI_RGBA_GREEN));
-                    pDst2[indx] = (T) * (src + (x * step + FI_RGBA_BLUE));
-                    if (fi_color == 4)
-                        pDst3[indx] = (T) * (src + (x * step + FI_RGBA_ALPHA));
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                    pDst0[indx] = *(src + (x * step + FI_RGBA_RED));
+                    pDst1[indx] = *(src + (x * step + FI_RGBA_GREEN));
+                    pDst2[indx] = *(src + (x * step + FI_RGBA_BLUE));
+                    if (fi_color == 4) {
+                        pDst3[indx] = *(src + (x * step + FI_RGBA_ALPHA));
+                    }
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    pDst0[indx] = (T) * (src + (x * step + 0));
-                    pDst1[indx] = (T) * (src + (x * step + 1));
-                    pDst2[indx] = (T) * (src + (x * step + 2));
-                    if (fi_color == 4)
-                        pDst3[indx] = (T) * (src + (x * step + 3));
+                    pDst0[indx] = *(src + (x * step + 0));
+                    pDst1[indx] = *(src + (x * step + 1));
+                    pDst2[indx] = *(src + (x * step + 2));
+                    if (fi_color == 4) {
+                        pDst3[indx] = *(src + (x * step + 3));
+                    }
                 }
             }
             indx++;
         }
     }
 
-    // TODO
     af::dim4 dims(fi_h, fi_w, fi_color, 1);
-    af_err err = af_create_array(rImage, pDst, dims.ndims(), dims.get(),
-                                 (af_dtype)af::dtype_traits<T>::af_type);
+    af_err err =
+        af_create_array(rImage, pDst, dims.ndims(), dims.get(),
+                        static_cast<af_dtype>(af::dtype_traits<T>::af_type));
     pinnedFree(pDst);
     return err;
 }
 
 FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type) {
     if (channels == AFFI_GRAY) {
-        if (type == u8)
-            return FIT_BITMAP;
-        else if (type == u16)
+        if (type == u8) { return FIT_BITMAP; }
+        if (type == u16) {
             return FIT_UINT16;
-        else if (type == f32)
+        } else if (type == f32) {
             return FIT_FLOAT;
+        }
     } else if (channels == AFFI_RGB) {
-        if (type == u8)
-            return FIT_BITMAP;
-        else if (type == u16)
+        if (type == u8) { return FIT_BITMAP; }
+        if (type == u16) {
             return FIT_RGB16;
-        else if (type == f32)
+        } else if (type == f32) {
             return FIT_RGBF;
+        }
     } else if (channels == AFFI_RGBA) {
-        if (type == u8)
-            return FIT_BITMAP;
-        else if (type == u16)
+        if (type == u8) { return FIT_BITMAP; }
+        if (type == u16) {
             return FIT_RGBA16;
-        else if (type == f32)
+        } else if (type == f32) {
             return FIT_RGBAF;
+        }
     }
     return FIT_BITMAP;
 }
@@ -133,13 +140,16 @@ af_err af_load_image_native(af_array* out, const char* filename) {
                      AF_ERR_NOT_SUPPORTED);
         }
 
-        int flags = 0;
-        if (fif == FIF_JPEG) flags = flags | JPEG_ACCURATE;
+        unsigned flags = 0;
+        if (fif == FIF_JPEG) {
+            flags = flags | static_cast<unsigned>(JPEG_ACCURATE);
+        }
 
         // check that the plugin has reading capabilities ...
         bitmap_ptr pBitmap = make_bitmap_ptr(nullptr);
         if (_.FreeImage_FIFSupportsReading(fif)) {
-            pBitmap.reset(_.FreeImage_Load(fif, filename, flags));
+            pBitmap.reset(
+                _.FreeImage_Load(fif, filename, static_cast<int>(flags)));
         }
 
         if (pBitmap == NULL) {
@@ -152,7 +162,7 @@ af_err af_load_image_native(af_array* out, const char* filename) {
         uint color_type   = _.FreeImage_GetColorType(pBitmap.get());
         const uint fi_bpp = _.FreeImage_GetBPP(pBitmap.get());
         // int fi_color = (int)((fi_bpp / 8.0) + 0.5);        //ceil
-        int fi_color;
+        uint fi_color;
         switch (color_type) {
             case 0:  // FIC_MINISBLACK
             case 1:  // FIC_MINISWHITE
@@ -171,7 +181,7 @@ af_err af_load_image_native(af_array* out, const char* filename) {
                 break;
         }
 
-        const int fi_bpc = fi_bpp / fi_color;
+        const uint fi_bpc = fi_bpp / fi_color;
         if (fi_bpc != 8 && fi_bpc != 16 && fi_bpc != 32) {
             AF_ERROR("FreeImage Error: Bits per channel not supported",
                      AF_ERR_NOT_SUPPORTED);
@@ -192,15 +202,15 @@ af_err af_load_image_native(af_array* out, const char* filename) {
         // result image
         af_array rImage;
         if (fi_color == 4) {  // 4 channel image
-            if (fi_bpc == 8)
+            if (fi_bpc == 8) {
                 AF_CHECK((readImage_t<uchar, AFFI_RGBA>)(&rImage, pSrcLine,
                                                          nSrcPitch, fi_w,
                                                          fi_h));
-            else if (fi_bpc == 16)
+            } else if (fi_bpc == 16) {
                 AF_CHECK((readImage_t<ushort, AFFI_RGBA>)(&rImage, pSrcLine,
                                                           nSrcPitch, fi_w,
                                                           fi_h));
-            else if (fi_bpc == 32)
+            } else if (fi_bpc == 32) {
                 switch (image_type) {
                     case FIT_UINT32:
                         AF_CHECK((readImage_t<uint, AFFI_RGBA>)(&rImage,
@@ -225,16 +235,17 @@ af_err af_load_image_native(af_array* out, const char* filename) {
                                  AF_ERR_NOT_SUPPORTED);
                         break;
                 }
+            }
         } else if (fi_color == 1) {
-            if (fi_bpc == 8)
+            if (fi_bpc == 8) {
                 AF_CHECK((readImage_t<uchar, AFFI_GRAY>)(&rImage, pSrcLine,
                                                          nSrcPitch, fi_w,
                                                          fi_h));
-            else if (fi_bpc == 16)
+            } else if (fi_bpc == 16) {
                 AF_CHECK((readImage_t<ushort, AFFI_GRAY>)(&rImage, pSrcLine,
                                                           nSrcPitch, fi_w,
                                                           fi_h));
-            else if (fi_bpc == 32)
+            } else if (fi_bpc == 32) {
                 switch (image_type) {
                     case FIT_UINT32:
                         AF_CHECK((readImage_t<uint, AFFI_GRAY>)(&rImage,
@@ -259,15 +270,16 @@ af_err af_load_image_native(af_array* out, const char* filename) {
                                  AF_ERR_NOT_SUPPORTED);
                         break;
                 }
+            }
         } else {  // 3 channel imag
-            if (fi_bpc == 8)
+            if (fi_bpc == 8) {
                 AF_CHECK((readImage_t<uchar, AFFI_RGB>)(&rImage, pSrcLine,
                                                         nSrcPitch, fi_w, fi_h));
-            else if (fi_bpc == 16)
+            } else if (fi_bpc == 16) {
                 AF_CHECK((readImage_t<ushort, AFFI_RGB>)(&rImage, pSrcLine,
                                                          nSrcPitch, fi_w,
                                                          fi_h));
-            else if (fi_bpc == 32)
+            } else if (fi_bpc == 32) {
                 switch (image_type) {
                     case FIT_UINT32:
                         AF_CHECK((readImage_t<uint, AFFI_RGB>)(&rImage,
@@ -291,6 +303,7 @@ af_err af_load_image_native(af_array* out, const char* filename) {
                                  AF_ERR_NOT_SUPPORTED);
                         break;
                 }
+            }
         }
 
         std::swap(*out, rImage);
@@ -301,7 +314,7 @@ af_err af_load_image_native(af_array* out, const char* filename) {
 }
 
 template<typename T, FI_CHANNELS channels>
-static void save_t(T* pDstLine, const af_array in, const dim4 dims,
+static void save_t(T* pDstLine, const af_array in, const dim4& dims,
                    uint nDstPitch) {
     af_array rr = 0, gg = 0, bb = 0, aa = 0;
     AF_CHECK(channel_split(in, dims, &rr, &gg, &bb,
@@ -314,20 +327,20 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims,
     uint indx = 0;
 
     AF_CHECK(af_transpose(&rrT, rr, false));
-    if (channels >= 3) AF_CHECK(af_transpose(&ggT, gg, false));
-    if (channels >= 3) AF_CHECK(af_transpose(&bbT, bb, false));
-    if (channels >= 4) AF_CHECK(af_transpose(&aaT, aa, false));
+    if (channels >= 3) { AF_CHECK(af_transpose(&ggT, gg, false)); }
+    if (channels >= 3) { AF_CHECK(af_transpose(&bbT, bb, false)); }
+    if (channels >= 4) { AF_CHECK(af_transpose(&aaT, aa, false)); }
 
     const ArrayInfo& cinfo = getInfo(rrT);
     pSrc0                  = pinnedAlloc<T>(cinfo.elements());
-    if (channels >= 3) pSrc1 = pinnedAlloc<T>(cinfo.elements());
-    if (channels >= 3) pSrc2 = pinnedAlloc<T>(cinfo.elements());
-    if (channels >= 4) pSrc3 = pinnedAlloc<T>(cinfo.elements());
+    if (channels >= 3) { pSrc1 = pinnedAlloc<T>(cinfo.elements()); }
+    if (channels >= 3) { pSrc2 = pinnedAlloc<T>(cinfo.elements()); }
+    if (channels >= 4) { pSrc3 = pinnedAlloc<T>(cinfo.elements()); }
 
     AF_CHECK(af_get_data_ptr((void*)pSrc0, rrT));
-    if (channels >= 3) AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT));
-    if (channels >= 3) AF_CHECK(af_get_data_ptr((void*)pSrc2, bbT));
-    if (channels >= 4) AF_CHECK(af_get_data_ptr((void*)pSrc3, aaT));
+    if (channels >= 3) { AF_CHECK(af_get_data_ptr((void*)pSrc1, ggT)); }
+    if (channels >= 3) { AF_CHECK(af_get_data_ptr((void*)pSrc2, bbT)); }
+    if (channels >= 4) { AF_CHECK(af_get_data_ptr((void*)pSrc3, aaT)); }
 
     const uint fi_w = dims[1];
     const uint fi_h = dims[0];
@@ -336,45 +349,48 @@ static void save_t(T* pDstLine, const af_array in, const dim4 dims,
     for (uint y = 0; y < fi_h; ++y) {
         for (uint x = 0; x < fi_w; ++x) {
             if (channels == 1) {
-                *(pDstLine + x * step) = (T)pSrc0[indx];  // r -> 0
+                *(pDstLine + x * step) = pSrc0[indx];  // r -> 0
             } else if (channels >= 3) {
-                if ((af_dtype)af::dtype_traits<T>::af_type == u8) {
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
                     *(pDstLine + x * step + FI_RGBA_RED) =
-                        (T)pSrc0[indx];  // r -> 0
+                        pSrc0[indx];  // r -> 0
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
-                        (T)pSrc1[indx];  // g -> 1
+                        pSrc1[indx];  // g -> 1
                     *(pDstLine + x * step + FI_RGBA_BLUE) =
-                        (T)pSrc2[indx];  // b -> 2
-                    if (channels >= 4)
+                        pSrc2[indx];  // b -> 2
+                    if (channels >= 4) {
                         *(pDstLine + x * step + FI_RGBA_ALPHA) =
-                            (T)pSrc3[indx];  // a
+                            pSrc3[indx];  // a
+                    }
                 } else {
                     // Non 8-bit types do not use ordering
                     // See Pixel Access Functions Chapter in FreeImage Doc
-                    *(pDstLine + x * step + 0) = (T)pSrc0[indx];  // r -> 0
-                    *(pDstLine + x * step + 1) = (T)pSrc1[indx];  // g -> 1
-                    *(pDstLine + x * step + 2) = (T)pSrc2[indx];  // b -> 2
-                    if (channels >= 4)
-                        *(pDstLine + x * step + 3) = (T)pSrc3[indx];  // a
+                    *(pDstLine + x * step + 0) = pSrc0[indx];  // r -> 0
+                    *(pDstLine + x * step + 1) = pSrc1[indx];  // g -> 1
+                    *(pDstLine + x * step + 2) = pSrc2[indx];  // b -> 2
+                    if (channels >= 4) {
+                        *(pDstLine + x * step + 3) = pSrc3[indx];  // a
+                    }
                 }
             }
             ++indx;
         }
-        pDstLine = (T*)(((uchar*)pDstLine) - nDstPitch);
+        pDstLine = reinterpret_cast<T*>(reinterpret_cast<uchar*>(pDstLine) -
+                                        nDstPitch);
     }
     pinnedFree(pSrc0);
-    if (channels >= 3) pinnedFree(pSrc1);
-    if (channels >= 3) pinnedFree(pSrc2);
-    if (channels >= 4) pinnedFree(pSrc3);
-
-    if (rr != 0) AF_CHECK(af_release_array(rr));
-    if (gg != 0) AF_CHECK(af_release_array(gg));
-    if (bb != 0) AF_CHECK(af_release_array(bb));
-    if (aa != 0) AF_CHECK(af_release_array(aa));
-    if (rrT != 0) AF_CHECK(af_release_array(rrT));
-    if (ggT != 0) AF_CHECK(af_release_array(ggT));
-    if (bbT != 0) AF_CHECK(af_release_array(bbT));
-    if (aaT != 0) AF_CHECK(af_release_array(aaT));
+    if (channels >= 3) { pinnedFree(pSrc1); }
+    if (channels >= 3) { pinnedFree(pSrc2); }
+    if (channels >= 4) { pinnedFree(pSrc3); }
+
+    if (rr != 0) { AF_CHECK(af_release_array(rr)); }
+    if (gg != 0) { AF_CHECK(af_release_array(gg)); }
+    if (bb != 0) { AF_CHECK(af_release_array(bb)); }
+    if (aa != 0) { AF_CHECK(af_release_array(aa)); }
+    if (rrT != 0) { AF_CHECK(af_release_array(rrT)); }
+    if (ggT != 0) { AF_CHECK(af_release_array(ggT)); }
+    if (bbT != 0) { AF_CHECK(af_release_array(bbT)); }
+    if (aaT != 0) { AF_CHECK(af_release_array(aaT)); }
 }
 
 // Save an image to disk.
@@ -399,7 +415,7 @@ af_err af_save_image_native(const char* filename, const af_array in) {
 
         const ArrayInfo& info = getInfo(in);
         // check image color type
-        FI_CHANNELS channels = (FI_CHANNELS)info.dims()[2];
+        auto channels = static_cast<FI_CHANNELS>(info.dims()[2]);
         DIM_ASSERT(1, channels <= 4);
         DIM_ASSERT(1, channels != 2);
 
@@ -426,13 +442,7 @@ af_err af_save_image_native(const char* filename, const af_array in) {
         bitmap_ptr pResultBitmap = make_bitmap_ptr(nullptr);
         switch (type) {
             case u8:
-                pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h,
-                                                          fi_bpp, 0, 0, 0));
-                break;
             case u16:
-                pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h,
-                                                          fi_bpp, 0, 0, 0));
-                break;
             case f32:
                 pResultBitmap.reset(_.FreeImage_AllocateT(fit_type, fi_w, fi_h,
                                                           fi_bpp, 0, 0, 0));
@@ -453,63 +463,65 @@ af_err af_save_image_native(const char* filename, const af_array in) {
         if (channels == AFFI_GRAY) {
             switch (type) {
                 case u8:
-                    save_t<uchar, AFFI_GRAY>((uchar*)pDstLine, in, info.dims(),
-                                             nDstPitch);
+                    save_t<uchar, AFFI_GRAY>(static_cast<uchar*>(pDstLine), in,
+                                             info.dims(), nDstPitch);
                     break;
                 case u16:
-                    save_t<ushort, AFFI_GRAY>((ushort*)pDstLine, in,
-                                              info.dims(), nDstPitch);
+                    save_t<ushort, AFFI_GRAY>(static_cast<ushort*>(pDstLine),
+                                              in, info.dims(), nDstPitch);
                     break;
                 case f32:
-                    save_t<float, AFFI_GRAY>((float*)pDstLine, in, info.dims(),
-                                             nDstPitch);
+                    save_t<float, AFFI_GRAY>(static_cast<float*>(pDstLine), in,
+                                             info.dims(), nDstPitch);
                     break;
                 default: TYPE_ERROR(1, type);
             }
         } else if (channels == AFFI_RGB) {
             switch (type) {
                 case u8:
-                    save_t<uchar, AFFI_RGB>((uchar*)pDstLine, in, info.dims(),
-                                            nDstPitch);
+                    save_t<uchar, AFFI_RGB>(static_cast<uchar*>(pDstLine), in,
+                                            info.dims(), nDstPitch);
                     break;
                 case u16:
-                    save_t<ushort, AFFI_RGB>((ushort*)pDstLine, in, info.dims(),
-                                             nDstPitch);
+                    save_t<ushort, AFFI_RGB>(static_cast<ushort*>(pDstLine), in,
+                                             info.dims(), nDstPitch);
                     break;
                 case f32:
-                    save_t<float, AFFI_RGB>((float*)pDstLine, in, info.dims(),
-                                            nDstPitch);
+                    save_t<float, AFFI_RGB>(static_cast<float*>(pDstLine), in,
+                                            info.dims(), nDstPitch);
                     break;
                 default: TYPE_ERROR(1, type);
             }
         } else {
             switch (type) {
                 case u8:
-                    save_t<uchar, AFFI_RGBA>((uchar*)pDstLine, in, info.dims(),
-                                             nDstPitch);
+                    save_t<uchar, AFFI_RGBA>(static_cast<uchar*>(pDstLine), in,
+                                             info.dims(), nDstPitch);
                     break;
                 case u16:
-                    save_t<ushort, AFFI_RGBA>((ushort*)pDstLine, in,
-                                              info.dims(), nDstPitch);
+                    save_t<ushort, AFFI_RGBA>(static_cast<ushort*>(pDstLine),
+                                              in, info.dims(), nDstPitch);
                     break;
                 case f32:
-                    save_t<float, AFFI_RGBA>((float*)pDstLine, in, info.dims(),
-                                             nDstPitch);
+                    save_t<float, AFFI_RGBA>(static_cast<float*>(pDstLine), in,
+                                             info.dims(), nDstPitch);
                     break;
                 default: TYPE_ERROR(1, type);
             }
         }
 
-        int flags = 0;
-        if (fif == FIF_JPEG) flags = flags | JPEG_QUALITYSUPERB;
+        unsigned flags = 0;
+        if (fif == FIF_JPEG) {
+            flags = flags | static_cast<unsigned>(JPEG_QUALITYSUPERB);
+        }
 
         // now save the result image
-        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename, flags) ==
-              TRUE)) {
+        if (!(_.FreeImage_Save(fif, pResultBitmap.get(), filename,
+                               static_cast<int>(flags)) == TRUE)) {
             AF_ERROR("FreeImage Error: Failed to save image", AF_ERR_RUNTIME);
         }
     }
-    CATCHALL
+    CATCHALL;
 
     return AF_SUCCESS;
 }
diff --git a/src/api/c/implicit.cpp b/src/api/c/implicit.cpp
index fbb6ba3262..f30afda7eb 100644
--- a/src/api/c/implicit.cpp
+++ b/src/api/c/implicit.cpp
@@ -23,22 +23,22 @@ af_dtype implicit(const af_dtype lty, const af_dtype rty) {
     if (lty == c64 || rty == c64) { return c64; }
 
     if (lty == c32 || rty == c32) {
-        if (lty == f64 || rty == f64) return c64;
+        if (lty == f64 || rty == f64) { return c64; }
         return c32;
     }
 
-    if (lty == f64 || rty == f64) return f64;
-    if (lty == f32 || rty == f32) return f32;
-    if ((lty == f16) || (rty == f16)) return f16;
-
-    if ((lty == u64) || (rty == u64)) return u64;
-    if ((lty == s64) || (rty == s64)) return s64;
-    if ((lty == u32) || (rty == u32)) return u32;
-    if ((lty == s32) || (rty == s32)) return s32;
-    if ((lty == u16) || (rty == u16)) return u16;
-    if ((lty == s16) || (rty == s16)) return s16;
-    if ((lty == u8) || (rty == u8)) return u8;
-    if ((lty == b8) && (rty == b8)) return b8;
+    if (lty == f64 || rty == f64) { return f64; }
+    if (lty == f32 || rty == f32) { return f32; }
+    if ((lty == f16) || (rty == f16)) { return f16; }
+
+    if ((lty == u64) || (rty == u64)) { return u64; }
+    if ((lty == s64) || (rty == s64)) { return s64; }
+    if ((lty == u32) || (rty == u32)) { return u32; }
+    if ((lty == s32) || (rty == s32)) { return s32; }
+    if ((lty == u16) || (rty == u16)) { return u16; }
+    if ((lty == s16) || (rty == s16)) { return s16; }
+    if ((lty == u8) || (rty == u8)) { return u8; }
+    if ((lty == b8) && (rty == b8)) { return b8; }
 
     return f32;
 }
diff --git a/src/api/c/implicit.hpp b/src/api/c/implicit.hpp
index d0bb51d62e..704e90a4f5 100644
--- a/src/api/c/implicit.hpp
+++ b/src/api/c/implicit.hpp
@@ -17,7 +17,5 @@
 #include <af/array.h>
 #include <af/defines.h>
 
-using namespace detail;
-
 af_dtype implicit(const af_array lhs, const af_array rhs);
 af_dtype implicit(const af_dtype lty, const af_dtype rty);
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 3ecdb64874..fcaca34f06 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -58,13 +58,14 @@ af_seq convert2Canonical(const af_seq s, const dim_t len) {
 template<typename T>
 static af_array indexBySeqs(const af_array& src,
                             const vector<af_seq> indicesV) {
-    size_t ndims = indicesV.size();
-    auto input   = getArray<T>(src);
+    size_t ndims      = indicesV.size();
+    const auto& input = getArray<T>(src);
 
-    if (ndims == 1 && ndims != input.ndims())
+    if (ndims == 1 && ndims != input.ndims()) {
         return getHandle(createSubArray(::flat(input), indicesV));
-    else
+    } else {
         return getHandle(createSubArray(input, indicesV));
+    }
 }
 
 af_err af_index(af_array* result, const af_array in, const unsigned ndims,
@@ -203,7 +204,7 @@ af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
             return AF_SUCCESS;
         }
 
-        if (ndims == 1 && ndims != (dim_t)iInfo.ndims()) {
+        if (ndims == 1 && ndims != static_cast<dim_t>(iInfo.ndims())) {
             af_array in_ = 0;
             AF_CHECK(af_flat(&in_, in));
             AF_CHECK(af_index_gen(out, in_, ndims, indexs));
@@ -212,7 +213,7 @@ af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
         }
 
         int track = 0;
-        std::array<af_seq, AF_MAX_DIMS> seqs;
+        std::array<af_seq, AF_MAX_DIMS> seqs{};
         seqs.fill(af_span);
         for (dim_t i = 0; i < ndims; i++) {
             if (indexs[i].isSeq) {
@@ -221,9 +222,11 @@ af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
             }
         }
 
-        if (track == (int)ndims) return af_index(out, in, ndims, seqs.data());
+        if (track == static_cast<int>(ndims)) {
+            return af_index(out, in, ndims, seqs.data());
+        }
 
-        std::array<af_index_t, AF_MAX_DIMS> idxrs;
+        std::array<af_index_t, AF_MAX_DIMS> idxrs{};
 
         for (dim_t i = 0; i < AF_MAX_DIMS; ++i) {
             if (i < ndims) {
@@ -289,7 +292,7 @@ af_seq af_make_seq(double begin, double end, double step) {
 
 af_err af_create_indexers(af_index_t** indexers) {
     try {
-        af_index_t* out = new af_index_t[AF_MAX_DIMS];
+        auto* out = new af_index_t[AF_MAX_DIMS];
         for (int i = 0; i < AF_MAX_DIMS; ++i) {
             out[i].idx.seq = af_span;
             out[i].isSeq   = true;
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 82ab7f7a8b..219942cc1e 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -42,12 +42,14 @@ af_err af_create_strided_array(af_array *arr, const void *data,
         ARG_ASSERT(5, strides_ != NULL);
         ARG_ASSERT(5, strides_[0] == 1);
 
-        for (int i = 1; i < (int)ndims; i++) { ARG_ASSERT(5, strides_[i] > 0); }
+        for (int i = 1; i < static_cast<int>(ndims); i++) {
+            ARG_ASSERT(5, strides_[i] > 0);
+        }
 
         dim4 dims(ndims, dims_);
         dim4 strides(ndims, strides_);
 
-        for (int i = ndims; i < 4; i++) {
+        for (int i = static_cast<int>(ndims); i < 4; i++) {
             strides[i] = strides[i - 1] * dims[i - 1];
         }
 
@@ -56,58 +58,72 @@ af_err af_create_strided_array(af_array *arr, const void *data,
         af_array res;
         AF_CHECK(af_init());
 
+        void *in_data = const_cast<void *>(
+            data);  // const cast because the api cannot change
         switch (ty) {
             case f32:
                 res = getHandle(createStridedArray<float>(
-                    dims, strides, offset, (float *)data, isdev));
+                    dims, strides, offset, static_cast<float *>(in_data),
+                    isdev));
                 break;
             case f64:
                 res = getHandle(createStridedArray<double>(
-                    dims, strides, offset, (double *)data, isdev));
+                    dims, strides, offset, static_cast<double *>(in_data),
+                    isdev));
                 break;
             case c32:
                 res = getHandle(createStridedArray<cfloat>(
-                    dims, strides, offset, (cfloat *)data, isdev));
+                    dims, strides, offset, static_cast<cfloat *>(in_data),
+                    isdev));
                 break;
             case c64:
                 res = getHandle(createStridedArray<cdouble>(
-                    dims, strides, offset, (cdouble *)data, isdev));
+                    dims, strides, offset, static_cast<cdouble *>(in_data),
+                    isdev));
                 break;
             case u32:
-                res = getHandle(createStridedArray<uint>(dims, strides, offset,
-                                                         (uint *)data, isdev));
+                res = getHandle(createStridedArray<uint>(
+                    dims, strides, offset, static_cast<uint *>(in_data),
+                    isdev));
                 break;
             case s32:
-                res = getHandle(createStridedArray<int>(dims, strides, offset,
-                                                        (int *)data, isdev));
+                res = getHandle(createStridedArray<int>(
+                    dims, strides, offset, static_cast<int *>(in_data), isdev));
                 break;
             case u64:
                 res = getHandle(createStridedArray<uintl>(
-                    dims, strides, offset, (uintl *)data, isdev));
+                    dims, strides, offset, static_cast<uintl *>(in_data),
+                    isdev));
                 break;
             case s64:
-                res = getHandle(createStridedArray<intl>(dims, strides, offset,
-                                                         (intl *)data, isdev));
+                res = getHandle(createStridedArray<intl>(
+                    dims, strides, offset, static_cast<intl *>(in_data),
+                    isdev));
                 break;
             case u16:
                 res = getHandle(createStridedArray<ushort>(
-                    dims, strides, offset, (ushort *)data, isdev));
+                    dims, strides, offset, static_cast<ushort *>(in_data),
+                    isdev));
                 break;
             case s16:
                 res = getHandle(createStridedArray<short>(
-                    dims, strides, offset, (short *)data, isdev));
+                    dims, strides, offset, static_cast<short *>(in_data),
+                    isdev));
                 break;
             case b8:
-                res = getHandle(createStridedArray<char>(dims, strides, offset,
-                                                         (char *)data, isdev));
+                res = getHandle(createStridedArray<char>(
+                    dims, strides, offset, static_cast<char *>(in_data),
+                    isdev));
                 break;
             case u8:
                 res = getHandle(createStridedArray<uchar>(
-                    dims, strides, offset, (uchar *)data, isdev));
+                    dims, strides, offset, static_cast<uchar *>(in_data),
+                    isdev));
                 break;
             case f16:
-                res = getHandle(createStridedArray<half>(dims, strides, offset,
-                                                         (half *)data, isdev));
+                res = getHandle(createStridedArray<half>(
+                    dims, strides, offset, static_cast<half *>(in_data),
+                    isdev));
                 break;
             default: TYPE_ERROR(6, ty);
         }
@@ -147,19 +163,19 @@ af_err af_get_raw_ptr(void **ptr, const af_array arr) {
         af_dtype ty = getInfo(arr).getType();
 
         switch (ty) {
-            case f32: res = (void *)getRawPtr(getArray<float>(arr)); break;
-            case f64: res = (void *)getRawPtr(getArray<double>(arr)); break;
-            case c32: res = (void *)getRawPtr(getArray<cfloat>(arr)); break;
-            case c64: res = (void *)getRawPtr(getArray<cdouble>(arr)); break;
-            case u32: res = (void *)getRawPtr(getArray<uint>(arr)); break;
-            case s32: res = (void *)getRawPtr(getArray<int>(arr)); break;
-            case u64: res = (void *)getRawPtr(getArray<uintl>(arr)); break;
-            case s64: res = (void *)getRawPtr(getArray<intl>(arr)); break;
-            case u16: res = (void *)getRawPtr(getArray<ushort>(arr)); break;
-            case s16: res = (void *)getRawPtr(getArray<short>(arr)); break;
-            case b8: res = (void *)getRawPtr(getArray<char>(arr)); break;
-            case u8: res = (void *)getRawPtr(getArray<uchar>(arr)); break;
-            case f16: res = (void *)getRawPtr(getArray<half>(arr)); break;
+            case f32: res = getRawPtr(getArray<float>(arr)); break;
+            case f64: res = getRawPtr(getArray<double>(arr)); break;
+            case c32: res = getRawPtr(getArray<cfloat>(arr)); break;
+            case c64: res = getRawPtr(getArray<cdouble>(arr)); break;
+            case u32: res = getRawPtr(getArray<uint>(arr)); break;
+            case s32: res = getRawPtr(getArray<int>(arr)); break;
+            case u64: res = getRawPtr(getArray<uintl>(arr)); break;
+            case s64: res = getRawPtr(getArray<intl>(arr)); break;
+            case u16: res = getRawPtr(getArray<ushort>(arr)); break;
+            case s16: res = getRawPtr(getArray<short>(arr)); break;
+            case b8: res = getRawPtr(getArray<char>(arr)); break;
+            case u8: res = getRawPtr(getArray<uchar>(arr)); break;
+            case f16: res = getRawPtr(getArray<half>(arr)); break;
             default: TYPE_ERROR(6, ty);
         }
 
@@ -184,19 +200,19 @@ af_err af_is_owner(bool *result, const af_array arr) {
         af_dtype ty = getInfo(arr).getType();
 
         switch (ty) {
-            case f32: res = (void *)getArray<float>(arr).isOwner(); break;
-            case f64: res = (void *)getArray<double>(arr).isOwner(); break;
-            case c32: res = (void *)getArray<cfloat>(arr).isOwner(); break;
-            case c64: res = (void *)getArray<cdouble>(arr).isOwner(); break;
-            case u32: res = (void *)getArray<uint>(arr).isOwner(); break;
-            case s32: res = (void *)getArray<int>(arr).isOwner(); break;
-            case u64: res = (void *)getArray<uintl>(arr).isOwner(); break;
-            case s64: res = (void *)getArray<intl>(arr).isOwner(); break;
-            case u16: res = (void *)getArray<ushort>(arr).isOwner(); break;
-            case s16: res = (void *)getArray<short>(arr).isOwner(); break;
-            case b8: res = (void *)getArray<char>(arr).isOwner(); break;
-            case u8: res = (void *)getArray<uchar>(arr).isOwner(); break;
-            case f16: res = (void *)getArray<half>(arr).isOwner(); break;
+            case f32: res = getArray<float>(arr).isOwner(); break;
+            case f64: res = getArray<double>(arr).isOwner(); break;
+            case c32: res = getArray<cfloat>(arr).isOwner(); break;
+            case c64: res = getArray<cdouble>(arr).isOwner(); break;
+            case u32: res = getArray<uint>(arr).isOwner(); break;
+            case s32: res = getArray<int>(arr).isOwner(); break;
+            case u64: res = getArray<uintl>(arr).isOwner(); break;
+            case s64: res = getArray<intl>(arr).isOwner(); break;
+            case u16: res = getArray<ushort>(arr).isOwner(); break;
+            case s16: res = getArray<short>(arr).isOwner(); break;
+            case b8: res = getArray<char>(arr).isOwner(); break;
+            case u8: res = getArray<uchar>(arr).isOwner(); break;
+            case f16: res = getArray<half>(arr).isOwner(); break;
             default: TYPE_ERROR(6, ty);
         }
 
diff --git a/src/api/c/inverse.cpp b/src/api/c/inverse.cpp
index 1eee6eeb12..fe6625d5c1 100644
--- a/src/api/c/inverse.cpp
+++ b/src/api/c/inverse.cpp
@@ -16,7 +16,6 @@
 #include <af/defines.h>
 #include <af/lapack.h>
 
-using af::dim4;
 using namespace detail;
 
 template<typename T>
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 34d6f7a12d..3fdfeb7036 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -33,7 +33,7 @@ static inline af_array join_many(const int dim, const unsigned n_arrays,
     std::vector<Array<T>> inputs_;
     inputs_.reserve(n_arrays);
 
-    for (int i = 0; i < (int)n_arrays; i++) {
+    for (unsigned i = 0; i < n_arrays; i++) {
         inputs_.push_back(getArray<T>(inputs[i]));
     }
     return getHandle(join<T>(dim, inputs_));
@@ -59,7 +59,7 @@ af_err af_join(af_array *out, const int dim, const af_array first,
         // All dimensions except join dimension must be equal
         // Compute output dims
         for (int i = 0; i < 4; i++) {
-            if (i != dim) DIM_ASSERT(2, fdims[i] == sdims[i]);
+            if (i != dim) { DIM_ASSERT(2, fdims[i] == sdims[i]); }
         }
 
         af_array output;
@@ -97,14 +97,14 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
         std::vector<ArrayInfo> info;
         info.reserve(n_arrays);
         std::vector<af::dim4> dims(n_arrays);
-        for (int i = 0; i < (int)n_arrays; i++) {
+        for (unsigned i = 0; i < n_arrays; i++) {
             info.push_back(getInfo(inputs[i]));
             dims[i] = info[i].dims();
         }
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
 
-        for (int i = 1; i < (int)n_arrays; i++) {
+        for (unsigned i = 1; i < n_arrays; i++) {
             ARG_ASSERT(3, info[0].getType() == info[i].getType());
             DIM_ASSERT(3, info[i].elements() > 0);
         }
@@ -113,7 +113,7 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
         // Compute output dims
         for (int i = 0; i < 4; i++) {
             if (i != dim) {
-                for (int j = 1; j < (int)n_arrays; j++) {
+                for (unsigned j = 1; j < n_arrays; j++) {
                     DIM_ASSERT(3, dims[0][i] == dims[j][i]);
                 }
             }
diff --git a/src/api/c/match_template.cpp b/src/api/c/match_template.cpp
index e5fbef6f4a..7e984b0c86 100644
--- a/src/api/c/match_template.cpp
+++ b/src/api/c/match_template.cpp
@@ -15,7 +15,11 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename inType, typename outType>
 static af_array match_template(const af_array& sImg, const af_array tImg,
@@ -63,8 +67,8 @@ af_err af_match_template(af_array* out, const af_array search_img,
         const ArrayInfo& sInfo = getInfo(search_img);
         const ArrayInfo& tInfo = getInfo(template_img);
 
-        dim4 const sDims = sInfo.dims();
-        dim4 const tDims = tInfo.dims();
+        dim4 const& sDims = sInfo.dims();
+        dim4 const& tDims = tInfo.dims();
 
         dim_t sNumDims = sDims.ndims();
         dim_t tNumDims = tDims.ndims();
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 04a8523bf6..9cef0f8cb1 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -23,31 +23,33 @@
 #include "stats.h"
 
 using common::half;
-
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::mean;
 
 template<typename Ti, typename To>
 static To mean(const af_array &in) {
-    typedef typename baseOutType<To>::type Tw;
+    using Tw = typename baseOutType<To>::type;
     return mean<Ti, Tw, To>(getArray<Ti>(in));
 }
 
 template<typename T>
 static T mean(const af_array &in, const af_array &weights) {
-    typedef typename baseOutType<T>::type Tw;
+    using Tw = typename baseOutType<T>::type;
     return mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights));
 }
 
 template<typename Ti, typename To>
 static af_array mean(const af_array &in, const dim_t dim) {
-    typedef typename baseOutType<To>::type Tw;
+    using Tw = typename baseOutType<To>::type;
     return getHandle<To>(mean<Ti, Tw, To>(getArray<Ti>(in), dim));
 }
 
 template<typename T>
 static af_array mean(const af_array &in, const af_array &weights,
                      const dim_t dim) {
-    typedef typename baseOutType<T>::type Tw;
+    using Tw = typename baseOutType<T>::type;
     return getHandle<T>(
         mean<T, Tw>(castArray<T>(in), castArray<Tw>(weights), dim));
 }
@@ -113,16 +115,16 @@ af_err af_mean_weighted(af_array *out, const af_array in,
         }
 
         switch (iType) {
-            case f64: output = mean<double>(in, w, dim); break;
-            case f32: output = mean<float>(in, w, dim); break;
-            case s32: output = mean<float>(in, w, dim); break;
-            case u32: output = mean<float>(in, w, dim); break;
-            case s64: output = mean<double>(in, w, dim); break;
-            case u64: output = mean<double>(in, w, dim); break;
-            case s16: output = mean<float>(in, w, dim); break;
-            case u16: output = mean<float>(in, w, dim); break;
-            case u8: output = mean<float>(in, w, dim); break;
+            case f32:
+            case s32:
+            case u32:
+            case s16:
+            case u16:
+            case u8:
             case b8: output = mean<float>(in, w, dim); break;
+            case f64:
+            case s64:
+            case u64: output = mean<double>(in, w, dim); break;
             case c32: output = mean<cfloat>(in, w, dim); break;
             case c64: output = mean<cdouble>(in, w, dim); break;
             case f16: output = mean<half>(in, w, dim); break;
@@ -184,17 +186,17 @@ af_err af_mean_all_weighted(double *realVal, double *imagVal, const af_array in,
                  f64)); /* verify that weights are non-complex real numbers */
 
         switch (iType) {
-            case f64: *realVal = mean<double>(in, weights); break;
-            case f32: *realVal = mean<float>(in, weights); break;
-            case s32: *realVal = mean<float>(in, weights); break;
-            case u32: *realVal = mean<float>(in, weights); break;
-            case s64: *realVal = mean<double>(in, weights); break;
-            case u64: *realVal = mean<double>(in, weights); break;
-            case s16: *realVal = mean<float>(in, weights); break;
-            case u16: *realVal = mean<float>(in, weights); break;
-            case u8: *realVal = mean<float>(in, weights); break;
-            case b8: *realVal = mean<float>(in, weights); break;
+            case f32:
+            case s32:
+            case u32:
+            case s16:
+            case u16:
+            case u8:
+            case b8:
             case f16: *realVal = mean<float>(in, weights); break;
+            case f64:
+            case s64:
+            case u64: *realVal = mean<double>(in, weights); break;
             case c32: {
                 cfloat tmp = mean<cfloat>(in, weights);
                 *realVal   = real(tmp);
diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index a6725f96d6..d69f11033d 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -39,7 +39,7 @@ af_err af_mean_shift(af_array *out, const af_array in,
         af::dim4 dims         = info.dims();
 
         DIM_ASSERT(1, (dims.ndims() >= 2));
-        if (is_color) DIM_ASSERT(1, (dims[2] == 3));
+        if (is_color) { DIM_ASSERT(1, (dims[2] == 3)); }
 
         af_array output;
         switch (type) {
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index fee958f06a..07652b121c 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -20,8 +20,13 @@
 #include <af/index.h>
 #include <af/statistics.h>
 
-using namespace detail;
 using af::dim4;
+using detail::Array;
+using detail::division;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
+using std::sort;
 
 template<typename T>
 static double median(const af_array& in) {
@@ -38,7 +43,8 @@ static double median(const af_array& in) {
         T result;
         AF_CHECK(af_get_data_ptr((void*)&result, in));
         return result;
-    } else if (nElems == 2) {
+    }
+    if (nElems == 2) {
         T result[2];
         AF_CHECK(af_get_data_ptr((void*)&result, in));
         return division(
@@ -96,6 +102,7 @@ static af_array median(const af_array& in, const dim_t dim) {
     af_array sortedIn_handle = getHandle<T>(sortedIn);
     AF_CHECK(af_index(&left, sortedIn_handle, input.ndims(), slices));
 
+    af_array out = nullptr;
     if (dimLength % 2 == 1) {
         // mid-1 is our guy
         if (input.isFloating()) {
@@ -119,7 +126,6 @@ static af_array median(const af_array& in, const dim_t dim) {
 
         af_array sumarr = 0;
         af_array carr   = 0;
-        af_array result = 0;
 
         dim4 cdims = dims;
         cdims[dim] = 1;
@@ -137,18 +143,19 @@ static af_array median(const af_array& in, const dim_t dim) {
         }
 
         AF_CHECK(af_add(&sumarr, left, right, false));
-        AF_CHECK(af_mul(&result, sumarr, carr, false));
+        AF_CHECK(af_mul(&out, sumarr, carr, false));
 
         AF_CHECK(af_release_array(left));
         AF_CHECK(af_release_array(right));
         AF_CHECK(af_release_array(sumarr));
         AF_CHECK(af_release_array(carr));
         AF_CHECK(af_release_array(sortedIn_handle));
-        return result;
     }
+    return out;
 }
 
-af_err af_median_all(double* realVal, double* imagVal, const af_array in) {
+af_err af_median_all(double* realVal, double* imagVal,  // NOLINT
+                     const af_array in) {
     UNUSED(imagVal);
     try {
         const ArrayInfo& info = getInfo(in);
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 1bffe37a05..818c2a96ae 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -25,10 +25,30 @@
 
 #include <utility>
 
-using namespace detail;
-
+using af::dim4;
 using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createDeviceDataArray;
+using detail::deviceMemoryInfo;
+using detail::getActiveDeviceId;
+using detail::getDeviceCount;
+using detail::intl;
+using detail::isLocked;
+using detail::memAllocUser;
+using detail::memFreeUser;
+using detail::memLock;
+using detail::memUnlock;
+using detail::pinnedAlloc;
+using detail::pinnedFree;
+using detail::printMemInfo;
+using detail::signalMemoryCleanup;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 using std::move;
+using std::swap;
 
 af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
                        const dim_t *const dims, const af_dtype type) {
@@ -87,7 +107,7 @@ af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
             default: TYPE_ERROR(4, type);
         }
 
-        std::swap(*arr, res);
+        swap(*arr, res);
     }
     CATCHALL;
 
@@ -127,7 +147,7 @@ inline void lockArray(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
-    memLock((void *)getArray<T>(arr).getData().get());
+    memLock(static_cast<void *>(getArray<T>(arr).getData().get()));
 }
 
 af_err af_lock_device_ptr(const af_array arr) { return af_lock_array(arr); }
@@ -163,7 +183,7 @@ inline bool checkUserLock(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
-    return isLocked((void *)getArray<T>(arr).getData().get());
+    return isLocked(static_cast<void *>(getArray<T>(arr).getData().get()));
 }
 
 af_err af_is_locked_array(bool *res, const af_array arr) {
@@ -197,7 +217,7 @@ inline void unlockArray(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
-    memUnlock((void *)getArray<T>(arr).getData().get());
+    memUnlock(static_cast<void *>(getArray<T>(arr).getData().get()));
 }
 
 af_err af_unlock_device_ptr(const af_array arr) { return af_unlock_array(arr); }
@@ -240,7 +260,7 @@ af_err af_alloc_device(void **ptr, const dim_t bytes) {
 af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
     try {
         AF_CHECK(af_init());
-        *ptr = (void *)pinnedAlloc<char>(bytes);
+        *ptr = static_cast<void *>(pinnedAlloc<char>(bytes));
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -256,19 +276,21 @@ af_err af_free_device(void *ptr) {
 
 af_err af_free_pinned(void *ptr) {
     try {
-        pinnedFree<char>((char *)ptr);
+        pinnedFree<char>(static_cast<char *>(ptr));
     }
     CATCHALL;
     return AF_SUCCESS;
 }
 
 af_err af_alloc_host(void **ptr, const dim_t bytes) {
-    if ((*ptr = malloc(bytes))) { return AF_SUCCESS; }
+    if ((*ptr = malloc(bytes))) {  // NOLINT(hicpp-no-malloc)
+        return AF_SUCCESS;
+    }
     return AF_ERR_NO_MEM;
 }
 
 af_err af_free_host(void *ptr) {
-    free(ptr);
+    free(ptr);  // NOLINT(hicpp-no-malloc)
     return AF_SUCCESS;
 }
 
@@ -277,8 +299,9 @@ af_err af_print_mem_info(const char *msg, const int device_id) {
         int device = device_id;
         if (device == -1) { device = getActiveDeviceId(); }
 
-        if (msg != NULL)
+        if (msg != nullptr) {
             ARG_ASSERT(0, strlen(msg) < 256);  // 256 character limit on msg
+        }
         ARG_ASSERT(1, device >= 0 && device < getDeviceCount());
 
         printMemInfo(msg ? msg : "", device);
@@ -325,21 +348,20 @@ af_err af_get_mem_step_size(size_t *step_bytes) {
 ////////////////////////////////////////////////////////////////////////////////
 
 MemoryManager &getMemoryManager(const af_memory_manager handle) {
-    return *(MemoryManager *)handle;
+    return *static_cast<MemoryManager *>(handle);
 }
 
 af_memory_manager getHandle(MemoryManager &manager) {
     MemoryManager *handle;
     handle = &manager;
-    return (af_memory_manager)handle;
+    return static_cast<af_memory_manager>(handle);
 }
 
 af_err af_create_memory_manager(af_memory_manager *manager) {
     try {
         AF_CHECK(af_init());
         std::unique_ptr<MemoryManager> m(new MemoryManager());
-        *manager = getHandle(*m);
-        m.release();
+        *manager = getHandle(*m.release());
     }
     CATCHALL;
 
@@ -351,7 +373,7 @@ af_err af_release_memory_manager(af_memory_manager handle) {
         // NB: does NOT reset the internal memory manager to be the default:
         // af_unset_memory_manager_pinned must be used to fully-reset with a new
         // AF default memory manager
-        delete (MemoryManager *)handle;
+        delete static_cast<MemoryManager *>(handle);
     }
     CATCHALL;
 
@@ -721,13 +743,13 @@ bool MemoryManagerFunctionWrapper::isUserLocked(const void *ptr) {
     int out;
     AF_CHECK(getMemoryManager(handle_).is_user_locked_fn(
         handle_, &out, const_cast<void *>(ptr)));
-    return (bool)out;
+    return static_cast<bool>(out);
 }
 
-void MemoryManagerFunctionWrapper::usageInfo(size_t *alloc_bytes,
-                                             size_t *alloc_buffers,
-                                             size_t *lock_bytes,
-                                             size_t *lock_buffers) {
+void MemoryManagerFunctionWrapper::usageInfo(size_t * /*alloc_bytes*/,
+                                             size_t * /*alloc_buffers*/,
+                                             size_t * /*lock_bytes*/,
+                                             size_t * /*lock_buffers*/) {
     // Not implemented in the public memory manager API, but for backward
     // compatibility reasons, needs to be in the common memory manager interface
     // so that it can be used with the default memory manager. Called from
@@ -748,7 +770,7 @@ bool MemoryManagerFunctionWrapper::jitTreeExceedsMemoryPressure(size_t bytes) {
     int out;
     AF_CHECK(getMemoryManager(handle_).jit_tree_exceeds_memory_pressure_fn(
         handle_, &out, bytes));
-    return (bool)out;
+    return static_cast<bool>(out);
 }
 
 size_t MemoryManagerFunctionWrapper::getMemStepSize() {
@@ -764,6 +786,7 @@ void MemoryManagerFunctionWrapper::setMemStepSize(size_t new_step_size) {
     // Not implemented in the public memory manager API, but for backward
     // compatibility reasons, needs to be in the common memory manager interface
     // so that it can be used with the default memory manager.
+    UNUSED(new_step_size);
     AF_ERROR("Memory step size API not implemented for custom memory manager ",
              AF_ERR_NOT_SUPPORTED);
 }
diff --git a/src/api/c/memoryapi.hpp b/src/api/c/memoryapi.hpp
index ab942e721d..945b0fb287 100644
--- a/src/api/c/memoryapi.hpp
+++ b/src/api/c/memoryapi.hpp
@@ -76,6 +76,6 @@ struct MemoryManager {
     MemoryManagerFunctionWrapper *wrapper;
 };
 
-MemoryManager &getMemoryManager(const af_memory_manager manager);
+MemoryManager &getMemoryManager(const af_memory_manager handle);
 
 af_memory_manager getHandle(MemoryManager &manager);
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index d368fc2e5b..07471692ca 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -18,7 +18,13 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 namespace {
 template<typename T>
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 379dd90edd..2584cf1123 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -62,7 +62,7 @@ af_err af_moments(af_array* out, const af_array in,
 
 template<typename T>
 static inline void moment_copy(double* out, const af_array moments) {
-    auto info = getInfo(moments);
+    const auto& info = getInfo(moments);
     vector<T> h_moments(info.elements());
     copyData(h_moments.data(), moments);
 
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index bec787d978..f318ed6486 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -16,11 +16,17 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename T, bool isDilation>
 static inline af_array morph(const af_array &in, const af_array &mask) {
-    const Array<T> &input  = getArray<T>(in);
+    const Array<T> input   = getArray<T>(in);
     const Array<T> &filter = castArray<T>(mask);
     Array<T> out           = morph<T, isDilation>(input, filter);
     return getHandle(out);
@@ -28,7 +34,7 @@ static inline af_array morph(const af_array &in, const af_array &mask) {
 
 template<typename T, bool isDilation>
 static inline af_array morph3d(const af_array &in, const af_array &mask) {
-    const Array<T> &input  = getArray<T>(in);
+    const Array<T> input   = getArray<T>(in);
     const Array<T> &filter = castArray<T>(mask);
     Array<T> out           = morph3d<T, isDilation>(input, filter);
     return getHandle(out);
diff --git a/src/api/c/nearest_neighbour.cpp b/src/api/c/nearest_neighbour.cpp
index 6c88b1357e..abc2a7b65b 100644
--- a/src/api/c/nearest_neighbour.cpp
+++ b/src/api/c/nearest_neighbour.cpp
@@ -16,7 +16,15 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename Ti, typename To>
 static void nearest_neighbour(af_array* idx, af_array* dist,
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index 42eccd23b6..06ea1b3a66 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -30,7 +30,8 @@ double matrixNorm(const Array<T> &A, double p) {
     if (p == 1) {
         Array<T> colSum = reduce<af_add_t, T, T>(A, 0);
         return reduce_all<af_max_t, T, T>(colSum);
-    } else if (p == af::Inf) {
+    }
+    if (p == af::Inf) {
         Array<T> rowSum = reduce<af_add_t, T, T>(A, 1);
         return reduce_all<af_max_t, T, T>(rowSum);
     }
@@ -41,9 +42,8 @@ double matrixNorm(const Array<T> &A, double p) {
 
 template<typename T>
 double vectorNorm(const Array<T> &A, double p) {
-    if (p == 1) {
-        return reduce_all<af_add_t, T, T>(A);
-    } else if (p == af::Inf) {
+    if (p == 1) { return reduce_all<af_add_t, T, T>(A); }
+    if (p == af::Inf) {
         return reduce_all<af_max_t, T, T>(A);
     } else if (p == 2) {
         Array<T> A_sq = arithOp<T, af_mul_t>(A, A, A.dims());
@@ -81,7 +81,7 @@ double LPQNorm(const Array<T> &A, double p, double q) {
 template<typename T>
 double norm(const af_array a, const af_norm_type type, const double p,
             const double q) {
-    typedef typename af::dtype_traits<T>::base_type BT;
+    using BT = typename af::dtype_traits<T>::base_type;
 
     const Array<BT> A = abs<BT, T>(getArray<T>(a));
 
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 6361d809f9..2c6ea88f0a 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -59,7 +59,7 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol) {
     dim_t Q = in.dims()[3];
 
     // Compute SVD
-    typedef typename dtype_traits<T>::base_type Tr;
+    using Tr = typename dtype_traits<T>::base_type;
     // Ideally, these initializations should use createEmptyArray(), but for
     // some reason, linux-opencl-k80 will produce wrong results for large arrays
     Array<T> u  = createValueArray<T>(dim4(M, M, P, Q), scalar<T>(0));
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 6d30820338..ddff3aa2bc 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -51,10 +51,11 @@ fg_chart setup_plot(fg_window window, const af_array in_,
     fg_chart chart      = NULL;
     fg_chart_type ctype = order == 2 ? FG_CHART_2D : FG_CHART_3D;
 
-    if (props->col > -1 && props->row > -1)
+    if (props->col > -1 && props->row > -1) {
         chart = fgMngr.getChart(window, props->row, props->col, ctype);
-    else
+    } else {
         chart = fgMngr.getChart(window, 0, 0, ctype);
+    }
 
     fg_plot plot =
         fgMngr.getPlot(chart, tdims[1], getGLType<T>(), ptype, mtype);
@@ -79,16 +80,16 @@ fg_chart setup_plot(fg_window window, const af_array in_,
             cmax[0] = step_round(dmax[0], true);
             cmin[1] = step_round(dmin[1], false);
             cmax[1] = step_round(dmax[1], true);
-            if (order == 3) cmin[2] = step_round(dmin[2], false);
-            if (order == 3) cmax[2] = step_round(dmax[2], true);
+            if (order == 3) { cmin[2] = step_round(dmin[2], false); }
+            if (order == 3) { cmax[2] = step_round(dmax[2], true); }
         } else {
-            if (cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
-            if (cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
-            if (cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
-            if (cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
+            if (cmin[0] > dmin[0]) { cmin[0] = step_round(dmin[0], false); }
+            if (cmax[0] < dmax[0]) { cmax[0] = step_round(dmax[0], true); }
+            if (cmin[1] > dmin[1]) { cmin[1] = step_round(dmin[1], false); }
+            if (cmax[1] < dmax[1]) { cmax[1] = step_round(dmax[1], true); }
             if (order == 3) {
-                if (cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
-                if (cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
+                if (cmin[2] > dmin[2]) { cmin[2] = step_round(dmin[2], false); }
+                if (cmax[2] < dmax[2]) { cmax[2] = step_round(dmax[2], true); }
             }
         }
         FG_CHECK(_.fg_set_chart_axes_limits(chart, cmin[0], cmax[0], cmin[1],
@@ -103,10 +104,12 @@ template<typename T>
 fg_chart setup_plot(fg_window window, const af_array in_, const int order,
                     const af_cell* const props, fg_plot_type ptype,
                     fg_marker_type mtype) {
-    if (order == 2)
+    if (order == 2) {
         return setup_plot<T, 2>(window, in_, props, ptype, mtype);
-    else if (order == 3)
+    }
+    if (order == 3) {
         return setup_plot<T, 3>(window, in_, props, ptype, mtype);
+    }
     // Dummy to avoid warnings
     return NULL;
 }
@@ -181,15 +184,15 @@ af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
         if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& xInfo = getInfo(X);
-        af::dim4 xDims         = xInfo.dims();
+        const af::dim4& xDims  = xInfo.dims();
         af_dtype xType         = xInfo.getType();
 
         const ArrayInfo& yInfo = getInfo(Y);
-        af::dim4 yDims         = yInfo.dims();
+        const af::dim4& yDims  = yInfo.dims();
         af_dtype yType         = yInfo.getType();
 
         const ArrayInfo& zInfo = getInfo(Z);
-        af::dim4 zDims         = zInfo.dims();
+        const af::dim4& zDims  = zInfo.dims();
         af_dtype zType         = zInfo.getType();
 
         DIM_ASSERT(0, xDims == yDims);
@@ -255,11 +258,11 @@ af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
         if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& xInfo = getInfo(X);
-        af::dim4 xDims         = xInfo.dims();
+        const af::dim4& xDims  = xInfo.dims();
         af_dtype xType         = xInfo.getType();
 
         const ArrayInfo& yInfo = getInfo(Y);
-        af::dim4 yDims         = yInfo.dims();
+        const af::dim4& yDims  = yInfo.dims();
         af_dtype yType         = yInfo.getType();
 
         DIM_ASSERT(0, xDims == yDims);
@@ -344,7 +347,8 @@ af_err af_draw_plot3(const af_window wind, const af_array P,
 
         if (dims.ndims() == 2 && dims[1] == 3) {
             return plotWrapper(wind, P, 1, props);
-        } else if (dims.ndims() == 2 && dims[0] == 3) {
+        }
+        if (dims.ndims() == 2 && dims[0] == 3) {
             return plotWrapper(wind, P, 0, props);
         } else if (dims.ndims() == 1 && dims[0] % 3 == 0) {
             dim4 rdims(dims.elements() / 3, 3, 1, 1);
@@ -405,7 +409,8 @@ af_err af_draw_scatter3(const af_window wind, const af_array P,
 
         if (dims.ndims() == 2 && dims[1] == 3) {
             return plotWrapper(wind, P, 1, props, FG_PLOT_SCATTER, fg_marker);
-        } else if (dims.ndims() == 2 && dims[0] == 3) {
+        }
+        if (dims.ndims() == 2 && dims[0] == 3) {
             return plotWrapper(wind, P, 0, props, FG_PLOT_SCATTER, fg_marker);
         } else if (dims.ndims() == 1 && dims[0] % 3 == 0) {
             dim4 rdims(dims.elements() / 3, 3, 1, 1);
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 8b9ddb4007..4a533b77c0 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -273,7 +273,8 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
             }
         }
         std::string str = ss.str();
-        af_alloc_host((void **)output, sizeof(char) * (str.size() + 1));
+        af_alloc_host(reinterpret_cast<void **>(output),
+                      sizeof(char) * (str.size() + 1));
         str.copy(*output, str.size());
         (*output)[str.size()] = '\0';  // don't forget the terminating 0
     }
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 49a7eb13db..744588680f 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -30,33 +30,33 @@ using af::dim4;
 Array<uint> emptyArray() { return createEmptyArray<uint>(af::dim4(0)); }
 
 struct RandomEngine {
-    af_random_engine_type type;
-    std::shared_ptr<uintl> seed;
-    std::shared_ptr<uintl> counter;
-    Array<uint> pos;
-    Array<uint> sh1;
-    Array<uint> sh2;
-    uint mask;
-    Array<uint> recursion_table;
-    Array<uint> temper_table;
-    Array<uint> state;
-
-    RandomEngine(void)
-        : type(AF_RANDOM_ENGINE_DEFAULT)
-        , seed(new uintl())
+    // clang-format off
+    af_random_engine_type type{AF_RANDOM_ENGINE_DEFAULT}; // NOLINT(misc-non-private-member-variables-in-classes)
+    std::shared_ptr<uintl> seed;                          // NOLINT(misc-non-private-member-variables-in-classes)
+    std::shared_ptr<uintl> counter;                       // NOLINT(misc-non-private-member-variables-in-classes)
+    Array<uint> pos;                                      // NOLINT(misc-non-private-member-variables-in-classes)
+    Array<uint> sh1;                                      // NOLINT(misc-non-private-member-variables-in-classes)
+    Array<uint> sh2;                                      // NOLINT(misc-non-private-member-variables-in-classes)
+    uint mask{0};                                         // NOLINT(misc-non-private-member-variables-in-classes)
+    Array<uint> recursion_table;                          // NOLINT(misc-non-private-member-variables-in-classes)
+    Array<uint> temper_table;                             // NOLINT(misc-non-private-member-variables-in-classes)
+    Array<uint> state;                                    // NOLINT(misc-non-private-member-variables-in-classes)
+    // clang-format on
+
+    RandomEngine()
+        : seed(new uintl())
         , counter(new uintl())
         , pos(emptyArray())
         , sh1(emptyArray())
         , sh2(emptyArray())
-        , mask(0)
         , recursion_table(emptyArray())
         , temper_table(emptyArray())
         , state(emptyArray()) {}
 };
 
-af_random_engine getRandomEngineHandle(const RandomEngine engine) {
-    RandomEngine *engineHandle = new RandomEngine;
-    *engineHandle              = engine;
+af_random_engine getRandomEngineHandle(const RandomEngine &engine) {
+    auto *engineHandle = new RandomEngine;
+    *engineHandle      = engine;
     return static_cast<af_random_engine>(engineHandle);
 }
 
@@ -64,7 +64,7 @@ RandomEngine *getRandomEngine(const af_random_engine engineHandle) {
     if (engineHandle == 0) {
         AF_ERROR("Uninitialized random engine", AF_ERR_ARG);
     }
-    return (RandomEngine *)engineHandle;
+    return static_cast<RandomEngine *>(engineHandle);
 }
 
 namespace {
@@ -109,8 +109,8 @@ af_err af_get_default_random_engine(af_random_engine *r) {
     try {
         AF_CHECK(af_init());
 
-        thread_local RandomEngine *re = new RandomEngine;
-        *r                            = static_cast<af_random_engine>(re);
+        thread_local auto *re = new RandomEngine;
+        *r                    = static_cast<af_random_engine>(re);
         return AF_SUCCESS;
     }
     CATCHALL;
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 22b6b720c0..6f0860a800 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -24,8 +24,8 @@ using namespace detail;
 
 template<typename T>
 static inline uint rank(const af_array in, double tol) {
-    typedef typename af::dtype_traits<T>::base_type BT;
-    Array<T> In = getArray<T>(in);
+    using BT          = typename af::dtype_traits<T>::base_type;
+    const Array<T> In = getArray<T>(in);
 
     Array<BT> R = createEmptyArray<BT>(dim4());
 
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 1c5ef4c821..e5088b8e5b 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -75,7 +75,7 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim) {
 
         const ArrayInfo &in_info = getInfo(in);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *out = retain(in);
             return AF_SUCCESS;
         }
@@ -179,7 +179,9 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim) {
 
         const ArrayInfo &in_info = getInfo(in);
 
-        if (dim >= (int)in_info.ndims()) { return af_retain_array(out, in); }
+        if (dim >= static_cast<int>(in_info.ndims())) {
+            return af_retain_array(out, in);
+        }
 
         af_dtype type = in_info.getType();
         af_array res;
@@ -287,7 +289,7 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
 
         const ArrayInfo &in_info = getInfo(in);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *out = retain(in);
             return AF_SUCCESS;
         }
@@ -522,10 +524,11 @@ af_err af_any_true_by_key(af_array *keys_out, af_array *vals_out,
                                              dim);
 }
 
-template<af_op_t op, typename Ti, typename To>
-static inline To reduce_all(const af_array in, bool change_nan = false,
-                            double nanval = 0) {
-    return reduce_all<op, Ti, To>(getArray<Ti>(in), change_nan, nanval);
+template<af_op_t op, typename Ti, typename Tacc, typename Tret = double>
+static inline Tret reduce_all(const af_array in, bool change_nan = false,
+                              double nanval = 0) {
+    return static_cast<Tret>(
+        reduce_all<op, Ti, Tacc>(getArray<Ti>(in), change_nan, nanval));
 }
 
 template<af_op_t op, typename To>
@@ -534,24 +537,26 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in) {
         const ArrayInfo &in_info = getInfo(in);
         af_dtype type            = in_info.getType();
 
-        ARG_ASSERT(0, real != NULL);
+        ARG_ASSERT(0, real != nullptr);
         *real = 0;
-        if (imag) *imag = 0;
+        if (imag) { *imag = 0; }
 
         switch (type) {
-            case f32: *real = (double)reduce_all<op, float, To>(in); break;
-            case f64: *real = (double)reduce_all<op, double, To>(in); break;
-            case c32: *real = (double)reduce_all<op, cfloat, To>(in); break;
-            case c64: *real = (double)reduce_all<op, cdouble, To>(in); break;
-            case u32: *real = (double)reduce_all<op, uint, To>(in); break;
-            case s32: *real = (double)reduce_all<op, int, To>(in); break;
-            case u64: *real = (double)reduce_all<op, uintl, To>(in); break;
-            case s64: *real = (double)reduce_all<op, intl, To>(in); break;
-            case u16: *real = (double)reduce_all<op, ushort, To>(in); break;
-            case s16: *real = (double)reduce_all<op, short, To>(in); break;
-            case b8: *real = (double)reduce_all<op, char, To>(in); break;
-            case u8: *real = (double)reduce_all<op, uchar, To>(in); break;
-            case f16: *real = (double)reduce_all<op, half, To>(in); break;
+            // clang-format off
+            case f32: *real = reduce_all<op, float,   To>(in); break;
+            case f64: *real = reduce_all<op, double,  To>(in); break;
+            case c32: *real = reduce_all<op, cfloat,  To>(in); break;
+            case c64: *real = reduce_all<op, cdouble, To>(in); break;
+            case u32: *real = reduce_all<op, uint,    To>(in); break;
+            case s32: *real = reduce_all<op, int,     To>(in); break;
+            case u64: *real = reduce_all<op, uintl,   To>(in); break;
+            case s64: *real = reduce_all<op, intl,    To>(in); break;
+            case u16: *real = reduce_all<op, ushort,  To>(in); break;
+            case s16: *real = reduce_all<op, short,   To>(in); break;
+            case b8:  *real = reduce_all<op, char,    To>(in); break;
+            case u8:  *real = reduce_all<op, uchar,   To>(in); break;
+            case f16: *real = reduce_all<op, half,    To>(in); break;
+            // clang-format on
             default: TYPE_ERROR(1, type);
         }
     }
@@ -568,48 +573,37 @@ static af_err reduce_all_common(double *real_val, double *imag_val,
         af_dtype type            = in_info.getType();
 
         ARG_ASSERT(2, in_info.ndims() > 0);
-        ARG_ASSERT(0, real_val != NULL);
+        ARG_ASSERT(0, real_val != nullptr);
         *real_val = 0;
-        if (imag_val != NULL) *imag_val = 0;
+        if (imag_val != nullptr) { *imag_val = 0; }
 
         cfloat cfval;
         cdouble cdval;
 
         switch (type) {
-            case f32:
-                *real_val = (double)reduce_all<op, float, float>(in);
-                break;
-            case f64:
-                *real_val = (double)reduce_all<op, double, double>(in);
-                break;
-            case u32: *real_val = (double)reduce_all<op, uint, uint>(in); break;
-            case s32: *real_val = (double)reduce_all<op, int, int>(in); break;
-            case u64:
-                *real_val = (double)reduce_all<op, uintl, uintl>(in);
-                break;
-            case s64: *real_val = (double)reduce_all<op, intl, intl>(in); break;
-            case u16:
-                *real_val = (double)reduce_all<op, ushort, ushort>(in);
-                break;
-            case s16:
-                *real_val = (double)reduce_all<op, short, short>(in);
-                break;
-            case b8: *real_val = (double)reduce_all<op, char, char>(in); break;
-            case u8:
-                *real_val = (double)reduce_all<op, uchar, uchar>(in);
-                break;
-            case f16: *real_val = (double)reduce_all<op, half, half>(in); break;
-
+            // clang-format off
+            case f32: *real_val = reduce_all<op, float,  float>(in); break;
+            case f64: *real_val = reduce_all<op, double, double>(in); break;
+            case u32: *real_val = reduce_all<op, uint,   uint>(in); break;
+            case s32: *real_val = reduce_all<op, int,    int>(in); break;
+            case u64: *real_val = reduce_all<op, uintl,  uintl>(in); break;
+            case s64: *real_val = reduce_all<op, intl,   intl>(in); break;
+            case u16: *real_val = reduce_all<op, ushort, ushort>(in); break;
+            case s16: *real_val = reduce_all<op, short,  short>(in); break;
+            case b8:  *real_val = reduce_all<op, char,   char>(in); break;
+            case u8:  *real_val = reduce_all<op, uchar,  uchar>(in); break;
+            case f16: *real_val = reduce_all<op, half,   half>(in); break;
+            // clang-format on
             case c32:
-                cfval = reduce_all<op, cfloat, cfloat>(in);
-                ARG_ASSERT(1, imag_val != NULL);
+                cfval = reduce_all<op, cfloat, cfloat, cfloat>(in);
+                ARG_ASSERT(1, imag_val != nullptr);
                 *real_val = real(cfval);
                 *imag_val = imag(cfval);
                 break;
 
             case c64:
-                cdval = reduce_all<op, cdouble, cdouble>(in);
-                ARG_ASSERT(1, imag_val != NULL);
+                cdval = reduce_all<op, cdouble, cdouble, cdouble>(in);
+                ARG_ASSERT(1, imag_val != nullptr);
                 *real_val = real(cdval);
                 *imag_val = imag(cdval);
                 break;
@@ -630,75 +624,49 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
         const ArrayInfo &in_info = getInfo(in);
         af_dtype type            = in_info.getType();
 
-        ARG_ASSERT(0, real_val != NULL);
+        ARG_ASSERT(0, real_val != nullptr);
         *real_val = 0;
-        if (imag_val) *imag_val = 0;
+        if (imag_val) { *imag_val = 0; }
 
         cfloat cfval;
         cdouble cdval;
 
         switch (type) {
-            case f32:
-                *real_val = (double)reduce_all<op, float, float>(in, change_nan,
-                                                                 nanval);
-                break;
-            case f64:
-                *real_val = (double)reduce_all<op, double, double>(
-                    in, change_nan, nanval);
-                break;
-            case u32:
-                *real_val =
-                    (double)reduce_all<op, uint, uint>(in, change_nan, nanval);
-                break;
-            case s32:
-                *real_val =
-                    (double)reduce_all<op, int, int>(in, change_nan, nanval);
-                break;
-            case u64:
-                *real_val = (double)reduce_all<op, uintl, uintl>(in, change_nan,
-                                                                 nanval);
-                break;
-            case s64:
-                *real_val =
-                    (double)reduce_all<op, intl, intl>(in, change_nan, nanval);
-                break;
-            case u16:
-                *real_val = (double)reduce_all<op, ushort, uint>(in, change_nan,
-                                                                 nanval);
-                break;
-            case s16:
-                *real_val =
-                    (double)reduce_all<op, short, int>(in, change_nan, nanval);
-                break;
-            case u8:
-                *real_val =
-                    (double)reduce_all<op, uchar, uint>(in, change_nan, nanval);
-                break;
+            // clang-format off
+            case f32: *real_val = reduce_all<op, float,   float>(in, change_nan, nanval); break;
+            case f64: *real_val = reduce_all<op, double, double>(in, change_nan, nanval); break;
+            case u32: *real_val = reduce_all<op, uint,     uint>(in, change_nan, nanval); break;
+            case s32: *real_val = reduce_all<op, int,       int>(in, change_nan, nanval); break;
+            case u64: *real_val = reduce_all<op, uintl,   uintl>(in, change_nan, nanval); break;
+            case s64: *real_val = reduce_all<op, intl,     intl>(in, change_nan, nanval); break;
+            case u16: *real_val = reduce_all<op, ushort,   uint>(in, change_nan, nanval); break;
+            case s16: *real_val = reduce_all<op, short,     int>(in, change_nan, nanval); break;
+            case u8:  *real_val = reduce_all<op, uchar,    uint>(in, change_nan, nanval); break;
+            // clang-format on
             case b8: {
                 if (op == af_mul_t) {
-                    *real_val = (double)reduce_all<af_and_t, char, char>(
-                        in, change_nan, nanval);
+                    *real_val = reduce_all<af_and_t, char, char>(in, change_nan,
+                                                                 nanval);
                 } else {
-                    *real_val = (double)reduce_all<af_notzero_t, char, uint>(
+                    *real_val = reduce_all<af_notzero_t, char, uint>(
                         in, change_nan, nanval);
                 }
             } break;
             case c32:
-                cfval = reduce_all<op, cfloat, cfloat>(in);
-                ARG_ASSERT(1, imag_val != NULL);
+                cfval = reduce_all<op, cfloat, cfloat, cfloat>(in);
+                ARG_ASSERT(1, imag_val != nullptr);
                 *real_val = real(cfval);
                 *imag_val = imag(cfval);
                 break;
 
             case c64:
-                cdval = reduce_all<op, cdouble, cdouble>(in);
-                ARG_ASSERT(1, imag_val != NULL);
+                cdval = reduce_all<op, cdouble, cdouble, cdouble>(in);
+                ARG_ASSERT(1, imag_val != nullptr);
                 *real_val = real(cdval);
                 *imag_val = imag(cdval);
                 break;
             case f16:
-                *real_val =
-                    (double)reduce_all<op, half, float>(in, change_nan, nanval);
+                *real_val = reduce_all<op, half, float>(in, change_nan, nanval);
                 break;
 
             default: TYPE_ERROR(1, type);
@@ -778,7 +746,7 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in,
         const ArrayInfo &in_info = getInfo(in);
         ARG_ASSERT(2, in_info.ndims() > 0);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *val = retain(in);
             *idx = createHandleFromValue<uint>(in_info.dims(), 0);
             return AF_SUCCESS;
@@ -830,13 +798,13 @@ static af_err rreduce_common(af_array *val, af_array *idx, const af_array in,
         const ArrayInfo &in_info = getInfo(in);
         ARG_ASSERT(2, in_info.ndims() > 0);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *val = retain(in);
             *idx = createHandleFromValue<uint>(in_info.dims(), 0);
             return AF_SUCCESS;
         }
 
-        // TODO: make sure ragged_len.dims == in.dims(), except on reduced dim
+        // Make sure ragged_len.dims == in.dims(), except on reduced dim
         const ArrayInfo &ragged_info = getInfo(ragged_len);
         dim4 test_dim                = in_info.dims();
         test_dim[dim]                = 1;
@@ -892,9 +860,9 @@ af_err af_max_ragged(af_array *val, af_array *idx, const af_array in,
     return rreduce_common<af_max_t>(val, idx, in, ragged_len, dim);
 }
 
-template<af_op_t op, typename T>
-static inline T ireduce_all(unsigned *loc, const af_array in) {
-    return ireduce_all<op, T>(loc, getArray<T>(in));
+template<af_op_t op, typename T, typename Tret = T>
+static inline Tret ireduce_all(unsigned *loc, const af_array in) {
+    return static_cast<Tret>(ireduce_all<op, T>(loc, getArray<T>(in)));
 }
 
 template<af_op_t op>
@@ -905,45 +873,45 @@ static af_err ireduce_all_common(double *real_val, double *imag_val,
         af_dtype type            = in_info.getType();
 
         ARG_ASSERT(3, in_info.ndims() > 0);
-        ARG_ASSERT(0, real_val != NULL);
+        ARG_ASSERT(0, real_val != nullptr);
         *real_val = 0;
-        if (imag_val) *imag_val = 0;
+        if (imag_val) { *imag_val = 0; }
 
         cfloat cfval;
         cdouble cdval;
 
         switch (type) {
             case f32:
-                *real_val = (double)ireduce_all<op, float>(loc, in);
+                *real_val = ireduce_all<op, float, double>(loc, in);
                 break;
             case f64:
-                *real_val = (double)ireduce_all<op, double>(loc, in);
+                *real_val = ireduce_all<op, double, double>(loc, in);
                 break;
-            case u32: *real_val = (double)ireduce_all<op, uint>(loc, in); break;
-            case s32: *real_val = (double)ireduce_all<op, int>(loc, in); break;
+            case u32: *real_val = ireduce_all<op, uint, double>(loc, in); break;
+            case s32: *real_val = ireduce_all<op, int, double>(loc, in); break;
             case u64:
-                *real_val = (double)ireduce_all<op, uintl>(loc, in);
+                *real_val = ireduce_all<op, uintl, double>(loc, in);
                 break;
-            case s64: *real_val = (double)ireduce_all<op, intl>(loc, in); break;
+            case s64: *real_val = ireduce_all<op, intl, double>(loc, in); break;
             case u16:
-                *real_val = (double)ireduce_all<op, ushort>(loc, in);
+                *real_val = ireduce_all<op, ushort, double>(loc, in);
                 break;
             case s16:
-                *real_val = (double)ireduce_all<op, short>(loc, in);
+                *real_val = ireduce_all<op, short, double>(loc, in);
                 break;
-            case b8: *real_val = (double)ireduce_all<op, char>(loc, in); break;
-            case u8: *real_val = (double)ireduce_all<op, uchar>(loc, in); break;
+            case b8: *real_val = ireduce_all<op, char, double>(loc, in); break;
+            case u8: *real_val = ireduce_all<op, uchar, double>(loc, in); break;
 
             case c32:
                 cfval = ireduce_all<op, cfloat>(loc, in);
-                ARG_ASSERT(1, imag_val != NULL);
+                ARG_ASSERT(1, imag_val != nullptr);
                 *real_val = real(cfval);
                 *imag_val = imag(cfval);
                 break;
 
             case c64:
                 cdval = ireduce_all<op, cdouble>(loc, in);
-                ARG_ASSERT(1, imag_val != NULL);
+                ARG_ASSERT(1, imag_val != nullptr);
                 *real_val = real(cdval);
                 *imag_val = imag(cdval);
                 break;
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 418d1180cf..bbd4431a5c 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -40,8 +40,8 @@ static inline af_array reorder(const af_array in, const af::dim4 &rdims0) {
 
     af_array out;
     if (rdims[0] == 0 && rdims[1] == 1 && rdims[2] == 2 && rdims[3] == 3) {
-        Array<T> Out = In;
-        out          = getHandle(Out);
+        const Array<T> &Out = In;
+        out                 = getHandle(Out);
     } else if (rdims[0] == 0) {
         dim4 odims    = dim4(1, 1, 1, 1);
         dim4 ostrides = dim4(1, 1, 1, 1);
diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index 9e912d6caf..6c783e0374 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -16,7 +16,6 @@
 #include <af/defines.h>
 #include <af/image.h>
 
-using af::dim4;
 using namespace detail;
 
 template<typename T>
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 0f308be153..ce4c2f6f57 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -117,10 +117,11 @@ af_err convert(af_array* out, const af_array in, const float r, const float g,
 
         // If RGB is input, then assert 3 channels
         // else 1 channel
-        if (isRGB2GRAY)
+        if (isRGB2GRAY) {
             ARG_ASSERT(1, (inputDims[2] == 3));
-        else
+        } else {
             ARG_ASSERT(1, (inputDims[2] == 1));
+        }
 
         af_array output = 0;
         switch (iType) {
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index fd2a9252e3..45b03c6796 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -13,8 +13,12 @@
 #include <handle.hpp>
 #include <rotate.hpp>
 #include <af/image.h>
+#include <cmath>
 
 using af::dim4;
+using std::cos;
+using std::fabs;
+using std::sin;
 using namespace detail;
 
 template<typename T>
@@ -27,16 +31,14 @@ static inline af_array rotate(const af_array in, const float theta,
 af_err af_rotate(af_array *out, const af_array in, const float theta,
                  const bool crop, const af_interp_type method) {
     try {
-        unsigned odims0 = 0, odims1 = 0;
+        dim_t odims0 = 0, odims1 = 0;
 
         const ArrayInfo &info = getInfo(in);
         af::dim4 idims        = info.dims();
 
         if (!crop) {
-            odims0 = idims[0] * fabs(std::cos(theta)) +
-                     idims[1] * fabs(std::sin(theta));
-            odims1 = idims[1] * fabs(std::cos(theta)) +
-                     idims[0] * fabs(std::sin(theta));
+            odims0 = idims[0] * fabs(cos(theta)) + idims[1] * fabs(sin(theta));
+            odims1 = idims[1] * fabs(cos(theta)) + idims[0] * fabs(sin(theta));
         } else {
             odims0 = idims[0];
             odims1 = idims[1];
@@ -68,7 +70,7 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
             case u64: output = rotate<uintl>(in, theta, odims, method); break;
             case s16: output = rotate<short>(in, theta, odims, method); break;
             case u16: output = rotate<ushort>(in, theta, odims, method); break;
-            case u8: output = rotate<uchar>(in, theta, odims, method); break;
+            case u8:
             case b8: output = rotate<uchar>(in, theta, odims, method); break;
             default: TYPE_ERROR(1, itype);
         }
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index d63e2aa75d..9b6231e0e6 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -24,7 +24,7 @@ inline af_array sat(const af_array& in) {
 af_err af_sat(af_array* out, const af_array in) {
     try {
         const ArrayInfo& info = getInfo(in);
-        const dim4 dims       = info.dims();
+        const dim4& dims      = info.dims();
 
         ARG_ASSERT(1, (dims.ndims() >= 2));
 
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 05811bae09..053ac0111a 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -18,7 +18,6 @@
 #include <af/dim4.hpp>
 #include <complex>
 
-using af::dim4;
 using namespace detail;
 
 template<af_op_t op, typename Ti, typename To>
@@ -116,7 +115,7 @@ af_err af_accum(af_array* out, const af_array in, const int dim) {
 
         const ArrayInfo& in_info = getInfo(in);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *out = retain(in);
             return AF_SUCCESS;
         }
@@ -157,7 +156,7 @@ af_err af_scan(af_array* out, const af_array in, const int dim, af_binary_op op,
 
         const ArrayInfo& in_info = getInfo(in);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *out = retain(in);
             return AF_SUCCESS;
         }
@@ -221,7 +220,7 @@ af_err af_scan_by_key(af_array* out, const af_array key, const af_array in,
         const ArrayInfo& in_info  = getInfo(in);
         const ArrayInfo& key_info = getInfo(key);
 
-        if (dim >= (int)in_info.ndims()) {
+        if (dim >= static_cast<int>(in_info.ndims())) {
             *out = retain(in);
             return AF_SUCCESS;
         }
@@ -245,9 +244,7 @@ af_err af_scan_by_key(af_array* out, const af_array key, const af_array in,
                 res =
                     scan_op<cdouble, cdouble>(key, in, dim, op, inclusive_scan);
                 break;
-            case u32:
-                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
-                break;
+            case s16:
             case s32:
                 res = scan_op<int, int>(key, in, dim, op, inclusive_scan);
                 break;
@@ -258,14 +255,8 @@ af_err af_scan_by_key(af_array* out, const af_array key, const af_array in,
                 res = scan_op<intl, intl>(key, in, dim, op, inclusive_scan);
                 break;
             case u16:
-                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
-                break;
-            case s16:
-                res = scan_op<int, int>(key, in, dim, op, inclusive_scan);
-                break;
+            case u32:
             case u8:
-                res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
-                break;
             case b8:
                 res = scan_op<uint, uint>(key, in, dim, op, inclusive_scan);
                 break;
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index df128f44ec..8bf9f8c4c4 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -15,7 +15,6 @@
 #include <af/defines.h>
 #include <complex>
 
-using af::dim4;
 using namespace detail;
 
 template<typename T>
@@ -117,7 +116,7 @@ af_err af_set_intersect(af_array* out, const af_array first,
         const ArrayInfo& first_info  = getInfo(first);
         const ArrayInfo& second_info = getInfo(second);
 
-        // TODO: fix for set intersect from union
+        // TODO(umar): fix for set intersect from union
         if (first_info.isEmpty()) { return af_retain_array(out, first); }
 
         if (second_info.isEmpty()) { return af_retain_array(out, second); }
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index 44da4d8b57..9b0a0f0170 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -14,7 +14,6 @@
 #include <shift.hpp>
 #include <af/data.h>
 
-using af::dim4;
 using namespace detail;
 
 template<typename T>
diff --git a/src/api/c/sobel.cpp b/src/api/c/sobel.cpp
index 7e7c35b2ea..9e70f3f257 100644
--- a/src/api/c/sobel.cpp
+++ b/src/api/c/sobel.cpp
@@ -19,11 +19,11 @@
 using af::dim4;
 using namespace detail;
 
-typedef std::pair<af_array, af_array> ArrayPair;
+using ArrayPair = std::pair<af_array, af_array>;
 template<typename Ti, typename To>
 ArrayPair sobelDerivatives(const af_array &in, const unsigned &ker_size) {
-    typedef std::pair<Array<To>, Array<To>> BAPair;
-    BAPair out = sobelDerivatives<Ti, To>(getArray<Ti>(in), ker_size);
+    using BAPair = std::pair<Array<To>, Array<To>>;
+    BAPair out   = sobelDerivatives<Ti, To>(getArray<Ti>(in), ker_size);
     return std::make_pair(getHandle<To>(out.first), getHandle<To>(out.second));
 }
 
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index ffefbb580c..62b2a37e2f 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -185,8 +185,6 @@ void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey,
             break;
         default: TYPE_ERROR(1, vtype);
     }
-
-    return;
 }
 
 af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index c093504db5..03331e472d 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -133,19 +133,22 @@ af_array createSparseArrayFromPtr(const af::dim4 &dims, const dim_t nNZ,
                                   const int *const colIdx,
                                   const af::storage stype,
                                   const af::source source) {
-    SparseArray<T> sparse = createEmptySparseArray<T>(dims, nNZ, stype);
-
     if (nNZ) {
-        if (source == afHost)
-            sparse = common::createHostDataSparseArray(dims, nNZ, values,
-                                                       rowIdx, colIdx, stype);
-        else if (source == afDevice)
-            sparse = common::createDeviceDataSparseArray(
-                dims, nNZ, const_cast<T *>(values), const_cast<int *>(rowIdx),
-                const_cast<int *>(colIdx), stype);
+        switch (source) {
+            case afHost:
+                return getHandle(common::createHostDataSparseArray(
+                    dims, nNZ, values, rowIdx, colIdx, stype));
+                break;
+            case afDevice:
+                return getHandle(common::createDeviceDataSparseArray(
+                    dims, nNZ, const_cast<T *>(values),
+                    const_cast<int *>(rowIdx), const_cast<int *>(colIdx),
+                    stype));
+                break;
+        }
     }
 
-    return getHandle(sparse);
+    return getHandle(createEmptySparseArray<T>(dims, nNZ, stype));
 }
 
 af_err af_create_sparse_array_from_ptr(
@@ -400,10 +403,10 @@ af_array getSparseValues(const af_array in) {
 af_err af_sparse_get_info(af_array *values, af_array *rows, af_array *cols,
                           af_storage *stype, const af_array in) {
     try {
-        if (values != NULL) AF_CHECK(af_sparse_get_values(values, in));
-        if (rows != NULL) AF_CHECK(af_sparse_get_row_idx(rows, in));
-        if (cols != NULL) AF_CHECK(af_sparse_get_col_idx(cols, in));
-        if (stype != NULL) AF_CHECK(af_sparse_get_storage(stype, in));
+        if (values != NULL) { AF_CHECK(af_sparse_get_values(values, in)); }
+        if (rows != NULL) { AF_CHECK(af_sparse_get_row_idx(rows, in)); }
+        if (cols != NULL) { AF_CHECK(af_sparse_get_col_idx(cols, in)); }
+        if (stype != NULL) { AF_CHECK(af_sparse_get_storage(stype, in)); }
     }
     CATCHALL;
 
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index c7afce5306..e3925b61d2 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -20,7 +20,7 @@
 
 #include <common/SparseArray.hpp>
 
-const common::SparseArrayBase &getSparseArrayBase(const af_array arr,
+const common::SparseArrayBase &getSparseArrayBase(const af_array in,
                                                   bool device_check = true);
 
 template<typename T>
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index b67c3c3dc4..11da858ca3 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -28,8 +28,8 @@ using namespace detail;
 
 template<typename inType, typename outType>
 static outType stdev(const af_array& in) {
-    typedef typename baseOutType<outType>::type weightType;
-    Array<inType> _in       = getArray<inType>(in);
+    using weightType        = typename baseOutType<outType>::type;
+    const Array<inType> _in = getArray<inType>(in);
     Array<outType> input    = cast<outType>(_in);
     Array<outType> meanCnst = createValueArray<outType>(
         input.dims(), mean<inType, weightType, outType>(_in));
@@ -45,10 +45,10 @@ static outType stdev(const af_array& in) {
 
 template<typename inType, typename outType>
 static af_array stdev(const af_array& in, int dim) {
-    typedef typename baseOutType<outType>::type weightType;
-    Array<inType> _in    = getArray<inType>(in);
-    Array<outType> input = cast<outType>(_in);
-    dim4 iDims           = input.dims();
+    using weightType        = typename baseOutType<outType>::type;
+    const Array<inType> _in = getArray<inType>(in);
+    Array<outType> input    = cast<outType>(_in);
+    dim4 iDims              = input.dims();
 
     Array<outType> meanArr = mean<inType, weightType, outType>(_in, dim);
 
@@ -63,7 +63,7 @@ static af_array stdev(const af_array& in, int dim) {
     Array<outType> diffSq =
         detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     Array<outType> redDiff = reduce<af_add_t, outType, outType>(diffSq, dim);
-    dim4 oDims             = redDiff.dims();
+    const dim4& oDims      = redDiff.dims();
 
     Array<outType> divArr =
         createValueArray<outType>(oDims, scalar<outType>(iDims[dim]));
@@ -74,6 +74,7 @@ static af_array stdev(const af_array& in, int dim) {
     return getHandle<outType>(result);
 }
 
+// NOLINTNEXTLINE(readability-non-const-parameter)
 af_err af_stdev_all(double* realVal, double* imagVal, const af_array in) {
     UNUSED(imagVal);  // TODO implement for complex values
     try {
@@ -90,8 +91,8 @@ af_err af_stdev_all(double* realVal, double* imagVal, const af_array in) {
             case u64: *realVal = stdev<uintl, double>(in); break;
             case u8: *realVal = stdev<uchar, float>(in); break;
             case b8: *realVal = stdev<char, float>(in); break;
-            // TODO: FIXME: sqrt(complex) is not present in cuda/opencl backend
-            // case c32: {
+            // TODO(umar): FIXME: sqrt(complex) is not present in cuda/opencl
+            // backend case c32: {
             //    cfloat tmp = stdev<cfloat,cfloat>(in);
             //    *realVal = real(tmp);
             //    *imagVal = imag(tmp);
@@ -126,9 +127,9 @@ af_err af_stdev(af_array* out, const af_array in, const dim_t dim) {
             case u64: output = stdev<uintl, double>(in, dim); break;
             case u8: output = stdev<uchar, float>(in, dim); break;
             case b8: output = stdev<char, float>(in, dim); break;
-            // TODO: FIXME: sqrt(complex) is not present in cuda/opencl backend
-            // case c32: output = stdev<cfloat,  cfloat>(in, dim); break;
-            // case c64: output = stdev<cdouble,cdouble>(in, dim); break;
+            // TODO(umar): FIXME: sqrt(complex) is not present in cuda/opencl
+            // backend case c32: output = stdev<cfloat,  cfloat>(in, dim);
+            // break; case c64: output = stdev<cdouble,cdouble>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index 1392df6db9..1be207c66d 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -80,7 +80,7 @@ static int save(const char *key, const af_array arr, const char *filename,
         }
 
         // Throw exception if file is not open
-        if (!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+        if (!fs.is_open()) { AF_ERROR("File failed to open", AF_ERR_ARG); }
 
         // Assert Version
         if (fs.peek() == std::fstream::traits_type::eof()) {
@@ -94,14 +94,14 @@ static int save(const char *key, const af_array arr, const char *filename,
                 prev_version == sfv_char,
                 "ArrayFire data format has changed. Can't append to file");
 
-            fs.read((char *)&n_arrays, sizeof(int));
+            fs.read(reinterpret_cast<char *>(&n_arrays), sizeof(int));
         }
     } else {
         fs.open(filename,
                 std::fstream::out | std::fstream::binary | std::fstream::trunc);
 
         // Throw exception if file is not open
-        if (!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+        if (!fs.is_open()) { AF_ERROR("File failed to open", AF_ERR_ARG); }
     }
 
     n_arrays++;
@@ -109,16 +109,16 @@ static int save(const char *key, const af_array arr, const char *filename,
     // Write version and n_arrays to top of file
     fs.seekp(0);
     fs.write(&sfv_char, 1);
-    fs.write((char *)&n_arrays, sizeof(int));
+    fs.write(reinterpret_cast<char *>(&n_arrays), sizeof(int));
 
     // Write array to end of file. Irrespective of new or append
     fs.seekp(0, std::ios_base::end);
-    fs.write((char *)&klen, sizeof(int));
+    fs.write(reinterpret_cast<char *>(&klen), sizeof(int));
     fs.write(k.c_str(), klen);
-    fs.write((char *)&offset, sizeof(intl));
+    fs.write(reinterpret_cast<char *>(&offset), sizeof(intl));
     fs.write(&type, sizeof(char));
-    fs.write((char *)&odims, sizeof(intl) * 4);
-    fs.write((char *)&data.front(), sizeof(T) * data.size());
+    fs.write(reinterpret_cast<char *>(&odims), sizeof(intl) * 4);
+    fs.write(reinterpret_cast<char *>(&data.front()), sizeof(T) * data.size());
     fs.close();
 
     return n_arrays - 1;
@@ -157,7 +157,7 @@ af_err af_save_array(int *index, const char *key, const af_array arr,
 template<typename T>
 static af_array readDataToArray(std::fstream &fs) {
     intl dims[4];
-    fs.read((char *)&dims, 4 * sizeof(intl));
+    fs.read(reinterpret_cast<char *>(&dims), 4 * sizeof(intl));
 
     dim4 d;
     for (int i = 0; i < 4; i++) { d[i] = dims[i]; }
@@ -165,7 +165,7 @@ static af_array readDataToArray(std::fstream &fs) {
     intl size = d.elements();
 
     std::vector<T> data(size);
-    fs.read((char *)&data.front(), size * sizeof(T));
+    fs.read(reinterpret_cast<char *>(&data.front()), size * sizeof(T));
 
     return getHandle(createHostDataArray<T>(d, &data.front()));
 }
@@ -177,18 +177,18 @@ static af_array readArrayV1(const char *filename, const unsigned index) {
     std::fstream fs(filename, std::fstream::in | std::fstream::binary);
 
     // Throw exception if file is not open
-    if (!fs.is_open()) AF_ERROR("File failed to open", AF_ERR_ARG);
+    if (!fs.is_open()) { AF_ERROR("File failed to open", AF_ERR_ARG); }
 
     if (fs.peek() == std::fstream::traits_type::eof()) {
         AF_ERROR("File is empty", AF_ERR_ARG);
     }
 
     fs.read(&version, sizeof(char));
-    fs.read((char *)&n_arrays, sizeof(int));
+    fs.read(reinterpret_cast<char *>(&n_arrays), sizeof(int));
 
     AF_ASSERT((int)index < n_arrays, "Index out of bounds");
 
-    for (int i = 0; i < (int)index; i++) {
+    for (unsigned i = 0; i < index; i++) {
         // (int    )   Length of the key
         // (cstring)   Key
         // (intl   )   Offset bytes to next array (type + dims + data)
@@ -196,7 +196,7 @@ static af_array readArrayV1(const char *filename, const unsigned index) {
         // (intl   )   dim4 (x 4)
         // (T      )   data (x elements)
         int klen = -1;
-        fs.read((char *)&klen, sizeof(int));
+        fs.read(reinterpret_cast<char *>(&klen), sizeof(int));
 
         // char* key = new char[klen];
         // fs.read((char*)&key, klen * sizeof(char));
@@ -206,14 +206,14 @@ static af_array readArrayV1(const char *filename, const unsigned index) {
 
         // Read data offset
         intl offset = -1;
-        fs.read((char *)&offset, sizeof(intl));
+        fs.read(reinterpret_cast<char *>(&offset), sizeof(intl));
 
         // Skip data
         fs.seekg(offset, std::ios_base::cur);
     }
 
     int klen = -1;
-    fs.read((char *)&klen, sizeof(int));
+    fs.read(reinterpret_cast<char *>(&klen), sizeof(int));
 
     // char* key = new char[klen];
     // fs.read((char*)&key, klen * sizeof(char));
@@ -223,13 +223,13 @@ static af_array readArrayV1(const char *filename, const unsigned index) {
 
     // Read data offset
     intl offset = -1;
-    fs.read((char *)&offset, sizeof(intl));
+    fs.read(reinterpret_cast<char *>(&offset), sizeof(intl));
 
     // Read type and dims
     char type_ = -1;
     fs.read(&type_, sizeof(char));
 
-    af_dtype type = (af_dtype)type_;
+    auto type = static_cast<af_dtype>(type_);
 
     af_array out;
     switch (type) {
@@ -272,7 +272,7 @@ static af_array checkVersionAndRead(const char *filename,
     }
     fs.close();
 
-    switch (version) {
+    switch (version) {  // NOLINT(hicpp-multiway-paths-covered)
         case 1: return readArrayV1(filename, index);
         default: AF_ERROR("Invalid version", AF_ERR_ARG);
     }
@@ -300,10 +300,10 @@ int checkVersionAndFindIndex(const char *filename, const char *k) {
     int index = -1;
     if (version == 1) {
         int n_arrays = -1;
-        fs.read((char *)&n_arrays, sizeof(int));
+        fs.read(reinterpret_cast<char *>(&n_arrays), sizeof(int));
         for (int i = 0; i < n_arrays; i++) {
             int klen = -1;
-            fs.read((char *)&klen, sizeof(int));
+            fs.read(reinterpret_cast<char *>(&klen), sizeof(int));
             string readKey;
             readKey.resize(klen);
             fs.read(&readKey.front(), klen);
@@ -312,12 +312,11 @@ int checkVersionAndFindIndex(const char *filename, const char *k) {
                 // Ket matches, break
                 index = i;
                 break;
-            } else {
-                // Key doesn't match. Skip the data
-                intl offset = -1;
-                fs.read((char *)&offset, sizeof(intl));
-                fs.seekg(offset, std::ios_base::cur);
             }
+            // Key doesn't match. Skip the data
+            intl offset = -1;
+            fs.read(reinterpret_cast<char *>(&offset), sizeof(intl));
+            fs.seekg(offset, std::ios_base::cur);
         }
     } else {
         AF_ERROR("Invalid version", AF_ERR_ARG);
@@ -350,7 +349,7 @@ af_err af_read_array_key(af_array *out, const char *filename, const char *key) {
         // Find index of key. Then call read by index
         int index = checkVersionAndFindIndex(filename, key);
 
-        if (index == -1) AF_ERROR("Key not found", AF_ERR_INVALID_ARRAY);
+        if (index == -1) { AF_ERROR("Key not found", AF_ERR_INVALID_ARRAY); }
 
         af_array output = checkVersionAndRead(filename, index);
         std::swap(*out, output);
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 8f325acb8e..6ca2c6d1a2 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -70,10 +70,11 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
 
     // Get the chart for the current grid position (if any)
     fg_chart chart = NULL;
-    if (props->col > -1 && props->row > -1)
+    if (props->col > -1 && props->row > -1) {
         chart = fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
-    else
+    } else {
         chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
+    }
 
     fg_surface surface =
         fgMngr.getSurface(chart, Z_dims[0], Z_dims[1], getGLType<T>());
@@ -104,12 +105,12 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
             cmin[2] = step_round(dmin[2], false);
             cmax[2] = step_round(dmax[2], true);
         } else {
-            if (cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
-            if (cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
-            if (cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
-            if (cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
-            if (cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
-            if (cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
+            if (cmin[0] > dmin[0]) { cmin[0] = step_round(dmin[0], false); }
+            if (cmax[0] < dmax[0]) { cmax[0] = step_round(dmax[0], true); }
+            if (cmin[1] > dmin[1]) { cmin[1] = step_round(dmin[1], false); }
+            if (cmax[1] < dmax[1]) { cmax[1] = step_round(dmax[1], true); }
+            if (cmin[2] > dmin[2]) { cmin[2] = step_round(dmin[2], false); }
+            if (cmax[2] < dmax[2]) { cmax[2] = step_round(dmax[2], true); }
         }
 
         FG_CHECK(_.fg_set_chart_axes_limits(chart, cmin[0], cmax[0], cmin[1],
@@ -135,7 +136,7 @@ af_err af_draw_surface(const af_window window, const af_array xVals,
         af_dtype Ytype         = Yinfo.getType();
 
         const ArrayInfo& Sinfo = getInfo(S);
-        af::dim4 S_dims        = Sinfo.dims();
+        const af::dim4& S_dims = Sinfo.dims();
         af_dtype Stype         = Sinfo.getType();
 
         TYPE_ASSERT(Xtype == Ytype);
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index cb208192fb..c1552a1e37 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -28,7 +28,7 @@ static inline void svd(af_array *s, af_array *u, af_array *vt,
     int M                 = dims[0];
     int N                 = dims[1];
 
-    typedef typename af::dtype_traits<T>::base_type Tr;
+    using Tr = typename af::dtype_traits<T>::base_type;
 
     // Allocate output arrays
     Array<Tr> sA = createEmptyArray<Tr>(af::dim4(min(M, N)));
@@ -50,7 +50,7 @@ static inline void svdInPlace(af_array *s, af_array *u, af_array *vt,
     int M                 = dims[0];
     int N                 = dims[1];
 
-    typedef typename af::dtype_traits<T>::base_type Tr;
+    using Tr = typename af::dtype_traits<T>::base_type;
 
     // Allocate output arrays
     Array<Tr> sA = createEmptyArray<Tr>(af::dim4(min(M, N)));
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index e59592c541..14d87559ba 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -26,7 +26,7 @@ using namespace detail;
 template<typename T>
 static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
     const Array<T> inArray = getArray<T>(in);
-    const dim4 inDims      = inArray.dims();
+    const dim4 &inDims     = inArray.dims();
 
     // FIXME: Always use JIT instead of checking for the condition.
     // The current limitation exists for performance reasons. it should change
@@ -42,11 +42,13 @@ static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
         outDims[i] = inDims[i] * tileDims[i];
     }
 
+    af_array out = nullptr;
     if (take_jit_path) {
-        return getHandle(unaryOp<T, af_noop_t>(inArray, outDims));
+        out = getHandle(unaryOp<T, af_noop_t>(inArray, outDims));
     } else {
-        return getHandle(tile<T>(inArray, tileDims));
+        out = getHandle(tile<T>(inArray, tileDims));
     }
+    return out;
 }
 
 af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims) {
diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index 4d848eef9a..0972f3b46e 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -41,7 +41,7 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
     try {
         af::topkFunction ord = (order == AF_TOPK_DEFAULT ? AF_TOPK_MAX : order);
 
-        ArrayInfo inInfo = getInfo(in);
+        const ArrayInfo &inInfo = getInfo(in);
 
         ARG_ASSERT(2, (inInfo.ndims() > 0));
 
@@ -67,9 +67,10 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
         ARG_ASSERT(2, (inInfo.dims()[rdim] >= k));
         ARG_ASSERT(4, (k <= 256));  // TODO(umar): Remove this limitation
 
-        if (rdim != 0)
+        if (rdim != 0) {
             AF_ERROR("topk is supported along dimenion 0 only.",
                      AF_ERR_NOT_SUPPORTED);
+        }
 
         af_dtype type = inInfo.getType();
 
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index bcd5563296..ff379f0b88 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -20,10 +20,9 @@ using namespace detail;
 
 template<typename T>
 static inline void transform(af_array *out, const af_array in,
-                             const af_array tf, const dim4 &odims,
-                             const af_interp_type method, const bool inverse,
-                             const bool perspective) {
-    transform<T>(getArray<T>(*out), getArray<T>(in), getArray<float>(tf), odims,
+                             const af_array tf, const af_interp_type method,
+                             const bool inverse, const bool perspective) {
+    transform<T>(getArray<T>(*out), getArray<T>(in), getArray<float>(tf),
                  method, inverse, perspective);
 }
 
@@ -33,13 +32,12 @@ AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims) {
     dim_t iNd = iDims.ndims();
     dim_t tNd = tDims.ndims();
 
-    if (iNd == baseDim && tNd == baseDim)
-        return AF_BATCH_NONE;
-    else if (iNd == baseDim && tNd <= 4)
+    if (iNd == baseDim && tNd == baseDim) { return AF_BATCH_NONE; }
+    if (iNd == baseDim && tNd <= 4) {
         return AF_BATCH_RHS;
-    else if (iNd <= 4 && tNd == baseDim)
+    } else if (iNd <= 4 && tNd == baseDim) {
         return AF_BATCH_LHS;
-    else if (iNd <= 4 && tNd <= 4) {
+    } else if (iNd <= 4 && tNd <= 4) {
         bool dimsMatch     = true;
         bool isInterleaved = true;
         for (dim_t i = baseDim; i < 4; i++) {
@@ -47,10 +45,11 @@ AF_BATCH_KIND getTransformBatchKind(const dim4 &iDims, const dim4 &tDims) {
             isInterleaved &=
                 (iDims[i] == 1 || tDims[i] == 1 || iDims[i] == tDims[i]);
         }
-        if (dimsMatch) return AF_BATCH_SAME;
+        if (dimsMatch) { return AF_BATCH_SAME; }
         return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
-    } else
+    } else {
         return AF_BATCH_UNSUPPORTED;
+    }
 }
 
 void af_transform_common(af_array *out, const af_array in, const af_array tf,
@@ -64,8 +63,8 @@ void af_transform_common(af_array *out, const af_array in, const af_array tf,
     const ArrayInfo &t_info = getInfo(tf);
     const ArrayInfo &i_info = getInfo(in);
 
-    const dim4 idims     = i_info.dims();
-    const dim4 tdims     = t_info.dims();
+    const dim4 &idims    = i_info.dims();
+    const dim4 &tdims    = t_info.dims();
     const af_dtype itype = i_info.getType();
 
     // Assert type and interpolation
@@ -93,17 +92,19 @@ void af_transform_common(af_array *out, const af_array in, const af_array tf,
 
     // If idims[2] > 1 and tdims[2] > 1, then both must be equal
     // else at least one of them must be 1
-    if (tdims[2] != 1 && idims[2] != 1)
+    if (tdims[2] != 1 && idims[2] != 1) {
         DIM_ASSERT(2, idims[2] == tdims[2]);
-    else
+    } else {
         DIM_ASSERT(2, idims[2] == 1 || tdims[2] == 1);
+    }
 
     // If idims[3] > 1 and tdims[3] > 1, then both must be equal
     // else at least one of them must be 1
-    if (tdims[3] != 1 && idims[3] != 1)
+    if (tdims[3] != 1 && idims[3] != 1) {
         DIM_ASSERT(2, idims[3] == tdims[3]);
-    else
+    } else {
         DIM_ASSERT(2, idims[3] == 1 || tdims[3] == 1);
+    }
 
     const bool perspective = (tdims[1] == 3);
     dim_t o0 = odim0, o1 = odim1, o2 = 0, o3 = 0;
@@ -141,18 +142,18 @@ void af_transform_common(af_array *out, const af_array in, const af_array tf,
 
     // clang-format off
     switch(itype) {
-    case f32: transform<float  >(out, in, tf, odims, method, inverse, perspective);  break;
-    case f64: transform<double >(out, in, tf, odims, method, inverse, perspective);  break;
-    case c32: transform<cfloat >(out, in, tf, odims, method, inverse, perspective);  break;
-    case c64: transform<cdouble>(out, in, tf, odims, method, inverse, perspective);  break;
-    case s32: transform<int    >(out, in, tf, odims, method, inverse, perspective);  break;
-    case u32: transform<uint   >(out, in, tf, odims, method, inverse, perspective);  break;
-    case s64: transform<intl   >(out, in, tf, odims, method, inverse, perspective);  break;
-    case u64: transform<uintl  >(out, in, tf, odims, method, inverse, perspective);  break;
-    case s16: transform<short  >(out, in, tf, odims, method, inverse, perspective);  break;
-    case u16: transform<ushort >(out, in, tf, odims, method, inverse, perspective);  break;
-    case u8:  transform<uchar  >(out, in, tf, odims, method, inverse, perspective);  break;
-    case b8:  transform<char   >(out, in, tf, odims, method, inverse, perspective);  break;
+    case f32: transform<float  >(out, in, tf, method, inverse, perspective);  break;
+    case f64: transform<double >(out, in, tf, method, inverse, perspective);  break;
+    case c32: transform<cfloat >(out, in, tf, method, inverse, perspective);  break;
+    case c64: transform<cdouble>(out, in, tf, method, inverse, perspective);  break;
+    case s32: transform<int    >(out, in, tf, method, inverse, perspective);  break;
+    case u32: transform<uint   >(out, in, tf, method, inverse, perspective);  break;
+    case s64: transform<intl   >(out, in, tf, method, inverse, perspective);  break;
+    case u64: transform<uintl  >(out, in, tf, method, inverse, perspective);  break;
+    case s16: transform<short  >(out, in, tf, method, inverse, perspective);  break;
+    case u16: transform<ushort >(out, in, tf, method, inverse, perspective);  break;
+    case u8:  transform<uchar  >(out, in, tf, method, inverse, perspective);  break;
+    case b8:  transform<char   >(out, in, tf, method, inverse, perspective);  break;
     default:  TYPE_ERROR(1, itype);
     }
     // clang-format on
@@ -225,8 +226,8 @@ af_err af_scale(af_array *out, const af_array in, const float scale0,
             DIM_ASSERT(4, odim0 != 0);
             DIM_ASSERT(5, odim1 != 0);
 
-            sx = idims[0] / (float)_odim0;
-            sy = idims[1] / (float)_odim1;
+            sx = idims[0] / static_cast<float>(_odim0);
+            sy = idims[1] / static_cast<float>(_odim1);
 
         } else {
             sx = 1.f / scale0, sy = 1.f / scale1;
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index 979fa8da01..4f27ac048d 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -38,8 +38,15 @@ template<typename T>
 static af_array transform_coordinates(const af_array &tf_, const float d0_,
                                       const float d1_) {
     af::dim4 h_dims(4, 3);
-    T h_in[4 * 3] = {(T)0,   (T)0, (T)d1_, (T)d1_, (T)0, (T)d0_,
-                     (T)d0_, (T)0, (T)1,   (T)1,   (T)1, (T)1};
+    T zero = 0;
+    T one  = 1;
+    T d0   = static_cast<T>(d0_);
+    T d1   = static_cast<T>(d1_);
+    // clang-format off
+    T h_in[4 * 3] = {zero, zero,  d1,   d1,
+                     zero,   d0,  d0, zero,
+                      one,  one, one,  one};
+    // clang-format on
 
     const Array<T> tf = getArray<T>(tf_);
     Array<T> in       = createHostDataArray<T>(h_dims, h_in);
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index 33140b9978..17553f191f 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -90,7 +90,7 @@ af_err af_transpose_inplace(af_array in, const bool conjugate) {
         DIM_ASSERT(0, dims[0] == dims[1]);
 
         // If singleton element
-        if (dims[0] == 1) return AF_SUCCESS;
+        if (dims[0] == 1) { return AF_SUCCESS; }
 
         switch (type) {
             case f32: transpose_inplace<float>(in, conjugate); break;
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index d5435d1883..c42cd4d4ff 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -201,7 +201,7 @@ struct unaryOpCplxFun<Tc, Tr, af_log_t> {
         // log(r)
         Array<Tr> a_out = unaryOp<Tr, af_log_t>(r);
         // phi
-        Array<Tr> b_out = phi;
+        const Array<Tr> &b_out = phi;
 
         // log(r) + i * phi
         return cplx<Tc, Tr>(a_out, b_out, a_out.dims());
@@ -631,14 +631,16 @@ static inline af_array checkOp(const af_array in) {
 
 template<af_op_t op>
 struct cplxLogicOp {
-    af_array operator()(Array<char> resR, Array<char> resI, dim4 dims) {
+    af_array operator()(const Array<char> &resR, const Array<char> &resI,
+                        const dim4 &dims) {
         return getHandle(logicOp<char, af_or_t>(resR, resI, dims));
     }
 };
 
 template<>
 struct cplxLogicOp<af_iszero_t> {
-    af_array operator()(Array<char> resR, Array<char> resI, dim4 dims) {
+    af_array operator()(const Array<char> &resR, const Array<char> &resI,
+                        const dim4 &dims) {
         return getHandle(logicOp<char, af_and_t>(resR, resI, dims));
     }
 };
@@ -652,7 +654,7 @@ static inline af_array checkOpCplx(const af_array in) {
     Array<char> resI = checkOp<BT, op>(I);
 
     const ArrayInfo &in_info = getInfo(in);
-    dim4 dims                = in_info.dims();
+    const dim4 &dims         = in_info.dims();
     cplxLogicOp<op> cplxLogic;
     af_array res = cplxLogic(resR, resI, dims);
 
@@ -669,7 +671,7 @@ static af_err af_check(af_array *out, const af_array in) {
 
         // Convert all inputs to floats / doubles / complex
         af_dtype type = implicit(in_type, f32);
-        if (in_type == f16) type = f16;
+        if (in_type == f16) { type = f16; }
 
         switch (type) {
             case f32: res = checkOp<float, op>(in); break;
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 1a8d2010f2..8ad68943d9 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -35,9 +35,9 @@ using std::tuple;
 
 template<typename inType, typename outType>
 static outType varAll(const af_array& in, const bool isbiased) {
-    typedef typename baseOutType<outType>::type weightType;
-    Array<inType> inArr  = getArray<inType>(in);
-    Array<outType> input = cast<outType>(inArr);
+    using weightType          = typename baseOutType<outType>::type;
+    const Array<inType> inArr = getArray<inType>(in);
+    Array<outType> input      = cast<outType>(inArr);
 
     Array<outType> meanCnst = createValueArray<outType>(
         input.dims(), mean<inType, weightType, outType>(inArr));
@@ -56,13 +56,13 @@ static outType varAll(const af_array& in, const bool isbiased) {
 
 template<typename inType, typename outType>
 static outType varAll(const af_array& in, const af_array weights) {
-    typedef typename baseOutType<outType>::type bType;
+    using bType = typename baseOutType<outType>::type;
 
     Array<outType> input = cast<outType>(getArray<inType>(in));
     Array<outType> wts   = cast<outType>(getArray<bType>(weights));
 
     bType wtsSum = reduce_all<af_add_t, bType, bType>(getArray<bType>(weights));
-    outType wtdMean = mean<outType, bType>(input, getArray<bType>(weights));
+    auto wtdMean = mean<outType, bType>(input, getArray<bType>(weights));
 
     Array<outType> meanArr = createValueArray<outType>(input.dims(), wtdMean);
     Array<outType> diff =
@@ -83,7 +83,7 @@ static tuple<Array<outType>, Array<outType>> meanvar(
     const Array<inType>& in,
     const Array<typename baseOutType<outType>::type>& weights,
     const af_var_bias bias, const dim_t dim) {
-    typedef typename baseOutType<outType>::type weightType;
+    using weightType     = typename baseOutType<outType>::type;
     Array<outType> input = cast<outType>(in);
     dim4 iDims           = input.dims();
 
@@ -129,7 +129,7 @@ static tuple<af_array, af_array> meanvar(const af_array& in,
                                          const af_array& weights,
                                          const af_var_bias bias,
                                          const dim_t dim) {
-    typedef typename baseOutType<outType>::type weightType;
+    using weightType    = typename baseOutType<outType>::type;
     Array<outType> mean = createEmptyArray<outType>({0}),
                    var  = createEmptyArray<outType>({0});
 
@@ -162,10 +162,9 @@ static af_array var_(const af_array& in, const af_array& weights,
         Array<bType> empty = createEmptyArray<bType>({0});
         return getHandle(
             var<inType, outType>(getArray<inType>(in), empty, bias, dim));
-    } else {
-        return getHandle(var<inType, outType>(
-            getArray<inType>(in), getArray<bType>(weights), bias, dim));
     }
+    return getHandle(var<inType, outType>(getArray<inType>(in),
+                                          getArray<bType>(weights), bias, dim));
 }
 
 af_err af_var(af_array* out, const af_array in, const bool isbiased,
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index bb6fdc1d3f..6dcd6d083d 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -57,17 +57,19 @@ fg_chart setup_vector_field(fg_window window, const vector<af_array>& points,
     fg_chart chart = NULL;
 
     if (pIn.dims()[0] == 2) {
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart =
                 fgMngr.getChart(window, props->row, props->col, FG_CHART_2D);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, FG_CHART_2D);
+        }
     } else {
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart =
                 fgMngr.getChart(window, props->row, props->col, FG_CHART_3D);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, FG_CHART_3D);
+        }
     }
 
     fg_vector_field vfield =
@@ -93,16 +95,16 @@ fg_chart setup_vector_field(fg_window window, const vector<af_array>& points,
             cmax[0] = step_round(dmax[0], true);
             cmin[1] = step_round(dmin[1], false);
             cmax[1] = step_round(dmax[1], true);
-            if (pIn.dims()[0] == 3) cmin[2] = step_round(dmin[2], false);
-            if (pIn.dims()[0] == 3) cmax[2] = step_round(dmax[2], true);
+            if (pIn.dims()[0] == 3) { cmin[2] = step_round(dmin[2], false); }
+            if (pIn.dims()[0] == 3) { cmax[2] = step_round(dmax[2], true); }
         } else {
-            if (cmin[0] > dmin[0]) cmin[0] = step_round(dmin[0], false);
-            if (cmax[0] < dmax[0]) cmax[0] = step_round(dmax[0], true);
-            if (cmin[1] > dmin[1]) cmin[1] = step_round(dmin[1], false);
-            if (cmax[1] < dmax[1]) cmax[1] = step_round(dmax[1], true);
+            if (cmin[0] > dmin[0]) { cmin[0] = step_round(dmin[0], false); }
+            if (cmax[0] < dmax[0]) { cmax[0] = step_round(dmax[0], true); }
+            if (cmin[1] > dmin[1]) { cmin[1] = step_round(dmin[1], false); }
+            if (cmax[1] < dmax[1]) { cmax[1] = step_round(dmax[1], true); }
             if (pIn.dims()[0] == 3) {
-                if (cmin[2] > dmin[2]) cmin[2] = step_round(dmin[2], false);
-                if (cmax[2] < dmax[2]) cmax[2] = step_round(dmax[2], true);
+                if (cmin[2] > dmin[2]) { cmin[2] = step_round(dmin[2], false); }
+                if (cmax[2] < dmax[2]) { cmax[2] = step_round(dmax[2], true); }
             }
         }
         FG_CHECK(_.fg_set_chart_axes_limits(chart, cmin[0], cmax[0], cmin[1],
@@ -124,7 +126,7 @@ af_err vectorFieldWrapper(const af_window window, const af_array points,
         af_dtype pType         = pInfo.getType();
 
         const ArrayInfo& dInfo = getInfo(directions);
-        af::dim4 dDims         = dInfo.dims();
+        const af::dim4& dDims  = dInfo.dims();
         af_dtype dType         = dInfo.getType();
 
         DIM_ASSERT(0, pDims == dDims);
@@ -193,9 +195,9 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
         const ArrayInfo& ypInfo = getInfo(yPoints);
         const ArrayInfo& zpInfo = getInfo(zPoints);
 
-        af::dim4 xpDims = xpInfo.dims();
-        af::dim4 ypDims = ypInfo.dims();
-        af::dim4 zpDims = zpInfo.dims();
+        af::dim4 xpDims        = xpInfo.dims();
+        const af::dim4& ypDims = ypInfo.dims();
+        const af::dim4& zpDims = zpInfo.dims();
 
         af_dtype xpType = xpInfo.getType();
         af_dtype ypType = ypInfo.getType();
@@ -205,9 +207,9 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
         const ArrayInfo& ydInfo = getInfo(yDirs);
         const ArrayInfo& zdInfo = getInfo(zDirs);
 
-        af::dim4 xdDims = xdInfo.dims();
-        af::dim4 ydDims = ydInfo.dims();
-        af::dim4 zdDims = zdInfo.dims();
+        const af::dim4& xdDims = xdInfo.dims();
+        const af::dim4& ydDims = ydInfo.dims();
+        const af::dim4& zdDims = zdInfo.dims();
 
         af_dtype xdType = xdInfo.getType();
         af_dtype ydType = ydInfo.getType();
@@ -298,8 +300,8 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
         const ArrayInfo& xpInfo = getInfo(xPoints);
         const ArrayInfo& ypInfo = getInfo(yPoints);
 
-        af::dim4 xpDims = xpInfo.dims();
-        af::dim4 ypDims = ypInfo.dims();
+        af::dim4 xpDims        = xpInfo.dims();
+        const af::dim4& ypDims = ypInfo.dims();
 
         af_dtype xpType = xpInfo.getType();
         af_dtype ypType = ypInfo.getType();
@@ -307,8 +309,8 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
         const ArrayInfo& xdInfo = getInfo(xDirs);
         const ArrayInfo& ydInfo = getInfo(yDirs);
 
-        af::dim4 xdDims = xdInfo.dims();
-        af::dim4 ydDims = ydInfo.dims();
+        const af::dim4& xdDims = xdInfo.dims();
+        const af::dim4& ydDims = ydInfo.dims();
 
         af_dtype xdType = xdInfo.getType();
         af_dtype ydType = ydInfo.getType();
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 8f2bf468fa..69b121323f 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -16,7 +16,6 @@
 #include <af/dim4.hpp>
 #include <complex>
 
-using af::dim4;
 using namespace detail;
 
 template<typename T>
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 92da1b35fe..bcde57658d 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -15,7 +15,6 @@
 #include <common/graphics_common.hpp>
 #include <platform.hpp>
 
-using af::dim4;
 using namespace detail;
 using namespace graphics;
 
@@ -75,26 +74,27 @@ af_err af_set_axes_limits_compute(const af_window window, const af_array x,
 
         ForgeManager& fgMngr = forgeManager();
 
-        fg_chart chart = NULL;
+        fg_chart chart = nullptr;
 
         fg_chart_type ctype = (z ? FG_CHART_3D : FG_CHART_2D);
 
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart = fgMngr.getChart(window, props->row, props->col, ctype);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, ctype);
+        }
 
-        double xmin = -1, xmax = 1;
-        double ymin = -1, ymax = 1;
-        double zmin = -1, zmax = 1;
-        AF_CHECK(af_min_all(&xmin, NULL, x));
-        AF_CHECK(af_max_all(&xmax, NULL, x));
-        AF_CHECK(af_min_all(&ymin, NULL, y));
-        AF_CHECK(af_max_all(&ymax, NULL, y));
+        double xmin = -1., xmax = 1.;
+        double ymin = -1., ymax = 1.;
+        double zmin = -1., zmax = 1.;
+        AF_CHECK(af_min_all(&xmin, nullptr, x));
+        AF_CHECK(af_max_all(&xmax, nullptr, x));
+        AF_CHECK(af_min_all(&ymin, nullptr, y));
+        AF_CHECK(af_max_all(&ymax, nullptr, y));
 
         if (ctype == FG_CHART_3D) {
-            AF_CHECK(af_min_all(&zmin, NULL, z));
-            AF_CHECK(af_max_all(&zmax, NULL, z));
+            AF_CHECK(af_min_all(&zmin, nullptr, z));
+            AF_CHECK(af_max_all(&zmax, nullptr, z));
         }
 
         if (!exact) {
@@ -123,21 +123,22 @@ af_err af_set_axes_limits_2d(const af_window window, const float xmin,
 
         ForgeManager& fgMngr = forgeManager();
 
-        fg_chart chart = NULL;
+        fg_chart chart = nullptr;
         // The ctype here below doesn't really matter as it is only fetching
         // the chart. It will not set it.
         // If this is actually being done, then it is extremely bad.
         fg_chart_type ctype = FG_CHART_2D;
 
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart = fgMngr.getChart(window, props->row, props->col, ctype);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, ctype);
+        }
 
-        float _xmin = xmin;
-        float _xmax = xmax;
-        float _ymin = ymin;
-        float _ymax = ymax;
+        double _xmin = xmin;
+        double _xmax = xmax;
+        double _ymin = ymin;
+        double _ymax = ymax;
         if (!exact) {
             _xmin = step_round(_xmin, false);
             _xmax = step_round(_xmax, true);
@@ -163,23 +164,24 @@ af_err af_set_axes_limits_3d(const af_window window, const float xmin,
 
         ForgeManager& fgMngr = forgeManager();
 
-        fg_chart chart = NULL;
+        fg_chart chart = nullptr;
         // The ctype here below doesn't really matter as it is only fetching
         // the chart. It will not set it.
         // If this is actually being done, then it is extremely bad.
         fg_chart_type ctype = FG_CHART_3D;
 
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart = fgMngr.getChart(window, props->row, props->col, ctype);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, ctype);
+        }
 
-        float _xmin = xmin;
-        float _xmax = xmax;
-        float _ymin = ymin;
-        float _ymax = ymax;
-        float _zmin = zmin;
-        float _zmax = zmax;
+        double _xmin = xmin;
+        double _xmax = xmax;
+        double _ymin = ymin;
+        double _ymax = ymax;
+        double _zmin = zmin;
+        double _zmax = zmax;
         if (!exact) {
             _xmin = step_round(_xmin, false);
             _xmax = step_round(_xmax, true);
@@ -205,14 +207,15 @@ af_err af_set_axes_titles(const af_window window, const char* const xtitle,
 
         ForgeManager& fgMngr = forgeManager();
 
-        fg_chart chart = NULL;
+        fg_chart chart = nullptr;
 
         fg_chart_type ctype = (ztitle ? FG_CHART_3D : FG_CHART_2D);
 
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart = fgMngr.getChart(window, props->row, props->col, ctype);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, ctype);
+        }
 
         FG_CHECK(forgePlugin().fg_set_chart_axes_titles(chart, xtitle, ytitle,
                                                         ztitle));
@@ -238,10 +241,11 @@ af_err af_set_axes_label_format(const af_window window,
 
         fg_chart_type ctype = (zformat ? FG_CHART_3D : FG_CHART_2D);
 
-        if (props->col > -1 && props->row > -1)
+        if (props->col > -1 && props->row > -1) {
             chart = fgMngr.getChart(window, props->row, props->col, ctype);
-        else
+        } else {
             chart = fgMngr.getChart(window, 0, 0, ctype);
+        }
 
         if (ctype == FG_CHART_2D) {
             FG_CHECK(forgePlugin().fg_set_chart_label_format(chart, xformat,
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index 4736f14399..011c86ca88 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -19,11 +19,10 @@ using af::dim4;
 using namespace detail;
 
 template<typename T>
-static inline void wrap(af_array* out, const af_array in, const dim_t ox,
-                        const dim_t oy, const dim_t wx, const dim_t wy,
-                        const dim_t sx, const dim_t sy, const dim_t px,
-                        const dim_t py, const bool is_column) {
-    wrap<T>(getArray<T>(*out), getArray<T>(in), ox, oy, wx, wy, sx, sy, px, py,
+static inline void wrap(af_array* out, const af_array in, const dim_t wx,
+                        const dim_t wy, const dim_t sx, const dim_t sy,
+                        const dim_t px, const dim_t py, const bool is_column) {
+    wrap<T>(getArray<T>(*out), getArray<T>(in), wx, wy, sx, sy, px, py,
             is_column);
 }
 
@@ -36,7 +35,7 @@ void af_wrap_common(af_array* out, const af_array in, const dim_t ox,
 
     const ArrayInfo& info  = getInfo(in);
     const af_dtype in_type = info.getType();
-    const dim4 in_dims     = info.dims();
+    const dim4& in_dims    = info.dims();
     const dim4 out_dims(ox, oy, in_dims[2], in_dims[3]);
 
     ARG_ASSERT(4, wx > 0);
@@ -60,18 +59,18 @@ void af_wrap_common(af_array* out, const af_array in, const dim_t ox,
 
     // clang-format off
     switch(in_type) {
-        case f32: wrap<float  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case f64: wrap<double >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case c32: wrap<cfloat >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case c64: wrap<cdouble>(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case s32: wrap<int    >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case u32: wrap<uint   >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case s64: wrap<intl   >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case u64: wrap<uintl  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case s16: wrap<short  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case u16: wrap<ushort >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case u8:  wrap<uchar  >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
-        case b8:  wrap<char   >(out, in, ox, oy, wx, wy, sx, sy, px, py, is_column);  break;
+        case f32: wrap<float  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case f64: wrap<double >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case c32: wrap<cfloat >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case c64: wrap<cdouble>(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case s32: wrap<int    >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case u32: wrap<uint   >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case s64: wrap<intl   >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case u64: wrap<uintl  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case s16: wrap<short  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case u16: wrap<ushort >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case u8:  wrap<uchar  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case b8:  wrap<char   >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
         default:  TYPE_ERROR(1, in_type);
     }
     // clang-format on
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index 1ee1065085..40ea20c8fd 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -23,7 +23,7 @@ using namespace detail;
 template<typename T>
 static Array<T> mix(const Array<T>& X, const Array<T>& Y, double xf,
                     double yf) {
-    dim4 dims        = X.dims();
+    const dim4& dims = X.dims();
     Array<T> xf_cnst = createValueArray<T>(dims, xf);
     Array<T> yf_cnst = createValueArray<T>(dims, yf);
 
@@ -36,7 +36,7 @@ static Array<T> mix(const Array<T>& X, const Array<T>& Y, double xf,
 template<typename T>
 static Array<T> mix(const Array<T>& X, const Array<T>& Y, const Array<T>& Z,
                     double xf, double yf, double zf) {
-    dim4 dims        = X.dims();
+    const dim4& dims = X.dims();
     Array<T> xf_cnst = createValueArray<T>(dims, xf);
     Array<T> yf_cnst = createValueArray<T>(dims, yf);
     Array<T> zf_cnst = createValueArray<T>(dims, zf);
@@ -52,10 +52,10 @@ static Array<T> mix(const Array<T>& X, const Array<T>& Y, const Array<T>& Z,
 template<typename T>
 static Array<T> digitize(const Array<T> ch, const double scale,
                          const double offset) {
-    dim4 dims     = ch.dims();
-    Array<T> base = createValueArray<T>(dims, scalar<T>(offset));
-    Array<T> cnst = createValueArray<T>(dims, scalar<T>(scale));
-    Array<T> scl  = arithOp<T, af_mul_t>(ch, cnst, dims);
+    const dim4& dims = ch.dims();
+    Array<T> base    = createValueArray<T>(dims, scalar<T>(offset));
+    Array<T> cnst    = createValueArray<T>(dims, scalar<T>(scale));
+    Array<T> scl     = arithOp<T, af_mul_t>(ch, cnst, dims);
     return arithOp<T, af_add_t>(scl, base, dims);
 }
 
@@ -79,7 +79,7 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
     // extract three channels as three slices
     // prepare sequence objects
     // get Array objects for corresponding channel views
-    const Array<T>& input = getArray<T>(in);
+    const Array<T> input = getArray<T>(in);
     std::vector<af_seq> indices(4, af_span);
 
     indices[2] = {0, 0, 1};
@@ -92,13 +92,13 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
     Array<T> Z = createSubArray(input, indices, false);
 
     if (isYCbCr2RGB) {
-        dim4 dims    = X.dims();
-        Array<T> yc  = createValueArray<T>(dims, 16);
-        Array<T> cc  = createValueArray<T>(dims, 128);
-        Array<T> Y_  = arithOp<T, af_sub_t>(X, yc, dims);
-        Array<T> Cb_ = arithOp<T, af_sub_t>(Y, cc, dims);
-        Array<T> Cr_ = arithOp<T, af_sub_t>(Z, cc, dims);
-        Array<T> R   = mix<T>(Y_, Cr_, INV_219, INV_112 * (1 - kr));
+        const dim4& dims = X.dims();
+        Array<T> yc      = createValueArray<T>(dims, 16);
+        Array<T> cc      = createValueArray<T>(dims, 128);
+        Array<T> Y_      = arithOp<T, af_sub_t>(X, yc, dims);
+        Array<T> Cb_     = arithOp<T, af_sub_t>(Y, cc, dims);
+        Array<T> Cr_     = arithOp<T, af_sub_t>(Z, cc, dims);
+        Array<T> R       = mix<T>(Y_, Cr_, INV_219, INV_112 * (1 - kr));
         Array<T> G =
             mix<T>(Y_, Cr_, Cb_, INV_219, INV_112 * (kr - 1) * kr * invKl,
                    INV_112 * (kb - 1) * kb * invKl);
@@ -106,19 +106,18 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
         // join channels
         Array<T> RG = join<T, T>(2, R, G);
         return getHandle(join<T, T>(2, RG, B));
-    } else {
-        Array<T> Ey = mix<T>(X, Y, Z, kr, kl, kb);
-        Array<T> Ecr =
-            mix<T>(X, Y, Z, 0.5, 0.5 * kl / (kr - 1), 0.5 * kb / (kr - 1));
-        Array<T> Ecb =
-            mix<T>(X, Y, Z, 0.5 * kr / (kb - 1), 0.5 * kl / (kb - 1), 0.5);
-        Array<T> Y  = digitize<T>(Ey, 219.0, 16.0);
-        Array<T> Cr = digitize<T>(Ecr, 224.0, 128.0);
-        Array<T> Cb = digitize<T>(Ecb, 224.0, 128.0);
-        // join channels
-        Array<T> YCb = join<T, T>(2, Y, Cb);
-        return getHandle(join<T, T>(2, YCb, Cr));
     }
+    Array<T> Ey = mix<T>(X, Y, Z, kr, kl, kb);
+    Array<T> Ecr =
+        mix<T>(X, Y, Z, 0.5, 0.5 * kl / (kr - 1), 0.5 * kb / (kr - 1));
+    Array<T> Ecb =
+        mix<T>(X, Y, Z, 0.5 * kr / (kb - 1), 0.5 * kl / (kb - 1), 0.5);
+    Array<T> Y_ = digitize<T>(Ey, 219.0, 16.0);
+    Array<T> Cr = digitize<T>(Ecr, 224.0, 128.0);
+    Array<T> Cb = digitize<T>(Ecb, 224.0, 128.0);
+    // join channels
+    Array<T> YCb = join<T, T>(2, Y_, Cb);
+    return getHandle(join<T, T>(2, YCb, Cr));
 }
 
 template<bool isYCbCr2RGB>
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 2e75293867..eff157bfd5 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -89,7 +89,7 @@ af::dim4 seqToDims(af_index_t *indices, af::dim4 parentDims,
             }
         }
         return odims;
-    } catch (logic_error &err) { AF_THROW_ERR(err.what(), AF_ERR_SIZE); }
+    } catch (const logic_error &err) { AF_THROW_ERR(err.what(), AF_ERR_SIZE); }
 }
 
 unsigned numDims(const af_array arr) {
@@ -137,12 +137,16 @@ af_array initDataArray(const void *ptr, int ty, af::source src, dim_t d0,
 namespace af {
 
 struct array::array_proxy::array_proxy_impl {
-    array *parent_;          //< The original array
+    // NOLINTNEXTLINE(misc-non-private-member-variables-in-classes)
+    array *parent_;  //< The original array
+    // NOLINTNEXTLINE(misc-non-private-member-variables-in-classes)
     af_index_t indices_[4];  //< Indexing array or seq objects
+    // NOLINTNEXTLINE(misc-non-private-member-variables-in-classes)
     bool is_linear_;
 
     // if true the parent_ object will be deleted on distruction. This is
     // necessary only when calling indexing functions in array_proxy objects.
+    // NOLINTNEXTLINE(misc-non-private-member-variables-in-classes)
     bool delete_on_destruction_;
     array_proxy_impl(array &parent, af_index_t *idx, bool linear)
         : parent_(&parent)
@@ -194,7 +198,7 @@ array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, af::dtype ty)
 template<>
 struct dtype_traits<half_float::half> {
     enum { af_type = f16, ctype = f16 };
-    typedef half base_type;
+    using base_type = half;
     static const char *getName() { return "half"; }
 };
 
@@ -292,7 +296,7 @@ array::~array() {
     }
 #else
     // THOU SHALL NOT THROW IN DESTRUCTORS
-    if (af_array arr = get()) af_release_array(arr);
+    if (af_array arr = get()) { af_release_array(arr); }
 #endif
 }
 
@@ -386,6 +390,7 @@ array::array_proxy array::operator()(const index &s0, const index &s1,
     return const_cast<const array *>(this)->operator()(s0, s1, s2, s3);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::operator()(const index &s0) const {
     index z = index(0);
     if (isvector()) {
@@ -401,12 +406,14 @@ const array::array_proxy array::operator()(const index &s0) const {
     }
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::operator()(const index &s0, const index &s1,
                                            const index &s2,
                                            const index &s3) const {
     return gen_indexing(*this, s0, s1, s2, s3);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::row(int index) const {
     return this->operator()(index, span, span, span);
 }
@@ -415,6 +422,7 @@ array::array_proxy array::row(int index) {
     return const_cast<const array *>(this)->row(index);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::col(int index) const {
     return this->operator()(span, index, span, span);
 }
@@ -423,6 +431,7 @@ array::array_proxy array::col(int index) {
     return const_cast<const array *>(this)->col(index);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::slice(int index) const {
     return this->operator()(span, span, index, span);
 }
@@ -431,6 +440,7 @@ array::array_proxy array::slice(int index) {
     return const_cast<const array *>(this)->slice(index);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::rows(int first, int last) const {
     seq idx(first, last, 1);
     return this->operator()(idx, span, span, span);
@@ -440,6 +450,7 @@ array::array_proxy array::rows(int first, int last) {
     return const_cast<const array *>(this)->rows(first, last);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::cols(int first, int last) const {
     seq idx(first, last, 1);
     return this->operator()(span, idx, span, span);
@@ -449,6 +460,7 @@ array::array_proxy array::cols(int first, int last) {
     return const_cast<const array *>(this)->cols(first, last);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array::array_proxy array::slices(int first, int last) const {
     seq idx(first, last, 1);
     return this->operator()(span, span, idx, span);
@@ -458,6 +470,7 @@ array::array_proxy array::slices(int first, int last) {
     return const_cast<const array *>(this)->slices(first, last);
 }
 
+// NOLINTNEXTLINE(readability-const-return-type)
 const array array::as(af::dtype type) const {
     af_array out;
     AF_THROW(af_cast(&out, this->get(), type));
@@ -576,6 +589,7 @@ array::array_proxy &af::array::array_proxy::operator=(const array &other) {
 
 array::array_proxy &af::array::array_proxy::operator=(
     const array::array_proxy &other) {
+    if (this == &other) { return *this; }
     array out = other;
     *this     = out;
     return *this;
@@ -588,6 +602,7 @@ af::array::array_proxy::array_proxy(const array_proxy &other)
     : impl(new array_proxy_impl(*other.impl->parent_, other.impl->indices_,
                                 other.impl->is_linear_)) {}
 
+// NOLINTNEXTLINE(hicpp-noexcept-move) too late to change public API
 af::array::array_proxy::array_proxy(array_proxy &&other) {
     impl       = other.impl;
     other.impl = nullptr;
@@ -758,12 +773,17 @@ array::array_proxy::operator array() {
         proxy.impl->delete_on_destruction(true);                  \
         return proxy;                                             \
     }
-
+// NOLINTNEXTLINE(readability-const-return-type)
 MEM_INDEX(row(int index), row(index));
+// NOLINTNEXTLINE(readability-const-return-type)
 MEM_INDEX(rows(int first, int last), rows(first, last));
+// NOLINTNEXTLINE(readability-const-return-type)
 MEM_INDEX(col(int index), col(index));
+// NOLINTNEXTLINE(readability-const-return-type)
 MEM_INDEX(cols(int first, int last), cols(first, last));
+// NOLINTNEXTLINE(readability-const-return-type)
 MEM_INDEX(slice(int index), slice(index));
+// NOLINTNEXTLINE(readability-const-return-type)
 MEM_INDEX(slices(int first, int last), slices(first, last));
 
 #undef MEM_INDEX
@@ -772,7 +792,7 @@ MEM_INDEX(slices(int first, int last), slices(first, last));
 // Operator =
 ///////////////////////////////////////////////////////////////////////////
 array &array::operator=(const array &other) {
-    if (this->get() == other.get()) { return *this; }
+    if (this == &other || this->get() == other.get()) { return *this; }
     // TODO(umar): Unsafe. loses data if af_weak_copy fails
     if (this->arr != nullptr) { AF_THROW(af_release_array(this->arr)); }
 
@@ -1067,6 +1087,8 @@ INSTANTIATE(half_float::half)
 // FIXME: These functions need to be implemented properly at a later point
 void array::array_proxy::unlock() const {}
 void array::array_proxy::lock() const {}
+
+// NOLINTNEXTLINE(readability-convert-member-functions-to-static)
 bool array::array_proxy::isLocked() const { return false; }
 
 int array::nonzeros() const { return count<int>(*this); }
diff --git a/src/api/cpp/blas.cpp b/src/api/cpp/blas.cpp
index b985dd863b..fbff177818 100644
--- a/src/api/cpp/blas.cpp
+++ b/src/api/cpp/blas.cpp
@@ -38,8 +38,8 @@ array matmulTT(const array &lhs, const array &rhs) {
 }
 
 array matmul(const array &a, const array &b, const array &c) {
-    int tmp1 = a.dims(0) * b.dims(1);
-    int tmp2 = b.dims(0) * c.dims(1);
+    dim_t tmp1 = a.dims(0) * b.dims(1);
+    dim_t tmp2 = b.dims(0) * c.dims(1);
 
     if (tmp1 < tmp2) {
         return matmul(matmul(a, b), c);
@@ -49,8 +49,8 @@ array matmul(const array &a, const array &b, const array &c) {
 }
 
 array matmul(const array &a, const array &b, const array &c, const array &d) {
-    int tmp1 = a.dims(0) * c.dims(1);
-    int tmp2 = b.dims(0) * d.dims(1);
+    dim_t tmp1 = a.dims(0) * c.dims(1);
+    dim_t tmp2 = b.dims(0) * d.dims(1);
 
     if (tmp1 < tmp2) {
         return matmul(matmul(a, b, c), d);
diff --git a/src/api/cpp/convolve.cpp b/src/api/cpp/convolve.cpp
index a74710d1d1..a69d26b9b4 100644
--- a/src/api/cpp/convolve.cpp
+++ b/src/api/cpp/convolve.cpp
@@ -25,8 +25,8 @@ array convolve(const array &signal, const array &filter, const convMode mode,
     switch (std::min(sN, fN)) {
         case 1: return convolve1(signal, filter, mode, domain);
         case 2: return convolve2(signal, filter, mode, domain);
+        default:
         case 3: return convolve3(signal, filter, mode, domain);
-        default: return convolve3(signal, filter, mode, domain);
     }
 }
 
@@ -52,20 +52,24 @@ array convolve2(const array &signal, const array &filter, const convMode mode,
     return array(out);
 }
 
-array convolve2NN(const array &signal, const array &filter, const dim4 stride,
-                  const dim4 padding, const dim4 dilation) {
+array convolve2NN(
+    const array &signal, const array &filter,
+    const dim4 stride,      // NOLINT(performance-unnecessary-value-param)
+    const dim4 padding,     // NOLINT(performance-unnecessary-value-param)
+    const dim4 dilation) {  // NOLINT(performance-unnecessary-value-param)
     af_array out = 0;
     AF_THROW(af_convolve2_nn(&out, signal.get(), filter.get(), 2, stride.get(),
                              2, padding.get(), 2, dilation.get()));
     return array(out);
 }
 
-array convolve2GradientNN(const array &incoming_gradient,
-                          const array &original_signal,
-                          const array &original_filter,
-                          const array &convolved_output, const dim4 stride,
-                          const dim4 padding, const dim4 dilation,
-                          af_conv_gradient_type gradType) {
+array convolve2GradientNN(
+    const array &incoming_gradient, const array &original_signal,
+    const array &original_filter, const array &convolved_output,
+    const dim4 stride,    // NOLINT(performance-unnecessary-value-param)
+    const dim4 padding,   // NOLINT(performance-unnecessary-value-param)
+    const dim4 dilation,  // NOLINT(performance-unnecessary-value-param)
+    af_conv_gradient_type gradType) {
     af_array out = 0;
     AF_THROW(af_convolve2_gradient_nn(
         &out, incoming_gradient.get(), original_signal.get(),
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 3c68386a11..126b10d990 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -44,14 +44,15 @@ struct is_complex<af::cdouble> {
 
 array constant(af_half val, const dim4 &dims, const dtype type) {
     af_array res;
+    UNUSED(val);
     AF_THROW(af_constant(&res, 0,  //(double)val,
                          dims.ndims(), dims.get(), type));
     return array(res);
 }
 
-template<typename T,
-         typename = typename enable_if<is_complex<T>::value == false, T>::type>
-array constant(T val, const dim4 &dims, const dtype type) {
+template<typename T, typename = typename enable_if<
+                         !static_cast<bool>(is_complex<T>::value), T>::type>
+array constant(T val, const dim4 &dims, dtype type) {
     af_array res;
     if (type != s64 && type != u64) {
         AF_THROW(
@@ -67,8 +68,8 @@ array constant(T val, const dim4 &dims, const dtype type) {
 }
 
 template<typename T>
-typename enable_if<is_complex<T>::value == true, array>::type constant(
-    T val, const dim4 &dims, const dtype type) {
+typename enable_if<static_cast<bool>(is_complex<T>::value), array>::type
+constant(T val, const dim4 &dims, const dtype type) {
     if (type != c32 && type != c64) {
         return ::constant(real(val), dims, type);
     }
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 52f783e576..524ebe0bb6 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -31,7 +31,7 @@ int getAvailableBackends() {
 }
 
 af::Backend getBackendId(const array &in) {
-    af::Backend result = (af::Backend)0;
+    auto result = static_cast<af::Backend>(0);
     AF_THROW(af_get_backend_id(&result, in.get()));
     return result;
 }
@@ -44,7 +44,7 @@ int getDeviceId(const array &in) {
 }
 
 af::Backend getActiveBackend() {
-    af::Backend result = (af::Backend)0;
+    auto result = static_cast<af::Backend>(0);
     AF_THROW(af_get_active_backend(&result));
     return result;
 }
@@ -54,7 +54,7 @@ void info() { AF_THROW(af_info()); }
 const char *infoString(const bool verbose) {
     char *str = NULL;
     AF_THROW(af_info_string(&str, verbose));
-    return (const char *)str;
+    return str;
 }
 
 void deviceprop(char *d_name, char *d_platform, char *d_toolkit,
diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index 4e4a464cce..37e03fc0e5 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -20,7 +20,7 @@
         af::exception ex(msg, __PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
                          __err);                                              \
         af_free_host(msg);                                                    \
-        throw ex; /* NOLINT(misc-throw-by-value-catch-by-reference)*/         \
+        throw std::move(ex);                                                  \
     } while (0)
 
 #define AF_THROW_ERR(__msg, __err)                                       \
diff --git a/src/api/cpp/event.cpp b/src/api/cpp/event.cpp
index 577700399f..47a70e3491 100644
--- a/src/api/cpp/event.cpp
+++ b/src/api/cpp/event.cpp
@@ -12,13 +12,13 @@
 
 namespace af {
 
-event::event() { AF_THROW(af_create_event(&e_)); }
+event::event() : e_{} { AF_THROW(af_create_event(&e_)); }
 
 event::event(af_event e) : e_(e) {}
 
 event::~event() {
     // No dtor throw
-    if (e_) af_delete_event(e_);
+    if (e_) { af_delete_event(e_); }
 }
 
 event::event(event&& other) : e_(other.e_) { other.e_ = 0; }
diff --git a/src/api/cpp/exception.cpp b/src/api/cpp/exception.cpp
index 523da68a84..8a56a48ea2 100644
--- a/src/api/cpp/exception.cpp
+++ b/src/api/cpp/exception.cpp
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <stdio.h>
-#include <string.h>  // strncpy
 #include <af/exception.h>
 #include <algorithm>
+#include <cstdio>
+#include <cstring>  // strncpy
 
 #ifdef OS_WIN
 #define snprintf _snprintf
@@ -18,38 +18,40 @@
 
 namespace af {
 
-exception::exception() : m_err(AF_ERR_UNKNOWN) {
+exception::exception() : m_msg{}, m_err(AF_ERR_UNKNOWN) {
     strncpy(m_msg, "unknown exception", sizeof(m_msg));
 }
 
-exception::exception(const char *msg) : m_err(AF_ERR_UNKNOWN) {
+exception::exception(const char *msg) : m_msg{}, m_err(AF_ERR_UNKNOWN) {
     strncpy(m_msg, msg, sizeof(m_msg));
     m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
-exception::exception(const char *file, unsigned line, af_err err) : m_err(err) {
+exception::exception(const char *file, unsigned line, af_err err)
+    : m_msg{}, m_err(err) {
     snprintf(m_msg, sizeof(m_msg) - 1, "ArrayFire Exception (%s:%d):\nIn %s:%u",
-             af_err_to_string(err), (int)err, file, line);
+             af_err_to_string(err), static_cast<int>(err), file, line);
 
     m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
 exception::exception(const char *msg, const char *file, unsigned line,
                      af_err err)
-    : m_err(err) {
+    : m_msg{}, m_err(err) {
     snprintf(m_msg, sizeof(m_msg) - 1,
              "ArrayFire Exception (%s:%d):\n%s\nIn %s:%u",
-             af_err_to_string(err), (int)(err), msg, file, line);
+             af_err_to_string(err), static_cast<int>(err), msg, file, line);
 
     m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
 exception::exception(const char *msg, const char *func, const char *file,
                      unsigned line, af_err err)
-    : m_err(err) {
+    : m_msg{}, m_err(err) {
     snprintf(m_msg, sizeof(m_msg) - 1,
              "ArrayFire Exception (%s:%d):\n%s\nIn function %s\nIn file %s:%u",
-             af_err_to_string(err), (int)(err), msg, func, file, line);
+             af_err_to_string(err), static_cast<int>(err), msg, func, file,
+             line);
 
     m_msg[sizeof(m_msg) - 1] = '\0';
 }
diff --git a/src/api/cpp/features.cpp b/src/api/cpp/features.cpp
index d84e39ff53..96a669b5ab 100644
--- a/src/api/cpp/features.cpp
+++ b/src/api/cpp/features.cpp
@@ -13,9 +13,9 @@
 
 namespace af {
 
-features::features() { AF_THROW(af_create_features(&feat, 0)); }
+features::features() : feat{} { AF_THROW(af_create_features(&feat, 0)); }
 
-features::features(const size_t n) {
+features::features(const size_t n) : feat{} {
     AF_THROW(af_create_features(&feat, (int)n));
 }
 
diff --git a/src/api/cpp/fft.cpp b/src/api/cpp/fft.cpp
index f72038a2f3..dbce09f488 100644
--- a/src/api/cpp/fft.cpp
+++ b/src/api/cpp/fft.cpp
@@ -12,6 +12,9 @@
 #include <af/signal.h>
 #include "error.hpp"
 
+using af::array;
+using af::dim4;
+
 namespace af {
 array fftNorm(const array& in, const double norm_factor, const dim_t odim0) {
     af_array out = 0;
@@ -46,6 +49,7 @@ array fft3(const array& in, const dim_t odim0, const dim_t odim1,
     return fft3Norm(in, 1.0, odim0, odim1, odim2);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array dft(const array& in, const double norm_factor, const dim4 outDims) {
     array temp;
     switch (in.dims().ndims()) {
@@ -60,6 +64,7 @@ array dft(const array& in, const double norm_factor, const dim4 outDims) {
     return temp;
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array dft(const array& in, const dim4 outDims) { return dft(in, 1.0, outDims); }
 
 array dft(const array& in) { return dft(in, 1.0, dim4(0, 0, 0, 0)); }
@@ -87,7 +92,7 @@ array ifft3Norm(const array& in, const double norm_factor, const dim_t odim0,
 array ifft(const array& in, const dim_t odim0) {
     const dim4 dims    = in.dims();
     dim_t dim0         = odim0 == 0 ? dims[0] : odim0;
-    double norm_factor = 1.0 / dim0;
+    double norm_factor = 1.0 / static_cast<double>(dim0);
     return ifftNorm(in, norm_factor, odim0);
 }
 
@@ -95,7 +100,7 @@ array ifft2(const array& in, const dim_t odim0, const dim_t odim1) {
     const dim4 dims    = in.dims();
     dim_t dim0         = odim0 == 0 ? dims[0] : odim0;
     dim_t dim1         = odim1 == 0 ? dims[1] : odim1;
-    double norm_factor = 1.0 / (dim0 * dim1);
+    double norm_factor = 1.0 / static_cast<double>(dim0 * dim1);
     return ifft2Norm(in, norm_factor, odim0, odim1);
 }
 
@@ -105,10 +110,11 @@ array ifft3(const array& in, const dim_t odim0, const dim_t odim1,
     dim_t dim0         = odim0 == 0 ? dims[0] : odim0;
     dim_t dim1         = odim1 == 0 ? dims[1] : odim1;
     dim_t dim2         = odim2 == 0 ? dims[2] : odim2;
-    double norm_factor = 1.0 / (dim0 * dim1 * dim2);
+    double norm_factor = 1.0 / static_cast<double>(dim0 * dim1 * dim2);
     return ifft3Norm(in, norm_factor, odim0, odim1, odim2);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array idft(const array& in, const double norm_factor, const dim4 outDims) {
     array temp;
     switch (in.dims().ndims()) {
@@ -125,6 +131,7 @@ array idft(const array& in, const double norm_factor, const dim4 outDims) {
     return temp;
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array idft(const array& in, const dim4 outDims) {
     return idft(in, 1.0, outDims);
 }
@@ -145,19 +152,20 @@ void fft3InPlace(array& in, const double norm_factor) {
 
 void ifftInPlace(array& in, const double norm_factor) {
     const dim4 dims = in.dims();
-    double norm     = norm_factor * (1.0 / dims[0]);
+    double norm     = norm_factor * (1.0 / static_cast<double>(dims[0]));
     AF_THROW(af_ifft_inplace(in.get(), norm));
 }
 
 void ifft2InPlace(array& in, const double norm_factor) {
     const dim4 dims = in.dims();
-    double norm     = norm_factor * (1.0 / (dims[0] * dims[1]));
+    double norm = norm_factor * (1.0 / static_cast<double>(dims[0] * dims[1]));
     AF_THROW(af_ifft2_inplace(in.get(), norm));
 }
 
 void ifft3InPlace(array& in, const double norm_factor) {
     const dim4 dims = in.dims();
-    double norm     = norm_factor * (1.0 / (dims[0] * dims[1] * dims[2]));
+    double norm =
+        norm_factor * (1.0 / static_cast<double>(dims[0] * dims[1] * dims[2]));
     AF_THROW(af_ifft3_inplace(in.get(), norm));
 }
 
@@ -200,7 +208,7 @@ AFAPI array fftC2R<1>(const array& in, const bool is_odd,
     if (norm == 0) {
         dim4 idims = in.dims();
         dim_t dim0 = getOrigDim(idims[0], is_odd);
-        norm       = 1.0 / dim0;
+        norm       = 1.0 / static_cast<double>(dim0);
     }
 
     af_array res;
@@ -217,7 +225,7 @@ AFAPI array fftC2R<2>(const array& in, const bool is_odd,
         dim4 idims = in.dims();
         dim_t dim0 = getOrigDim(idims[0], is_odd);
         dim_t dim1 = idims[1];
-        norm       = 1.0 / (dim0 * dim1);
+        norm       = 1.0 / static_cast<double>(dim0 * dim1);
     }
 
     af_array res;
@@ -235,7 +243,7 @@ AFAPI array fftC2R<3>(const array& in, const bool is_odd,
         dim_t dim0 = getOrigDim(idims[0], is_odd);
         dim_t dim1 = idims[1];
         dim_t dim2 = idims[2];
-        norm       = 1.0 / (dim0 * dim1 * dim2);
+        norm       = 1.0 / static_cast<double>(dim0 * dim1 * dim2);
     }
 
     af_array res;
diff --git a/src/api/cpp/fftconvolve.cpp b/src/api/cpp/fftconvolve.cpp
index 61fbf9937c..24f68b103b 100644
--- a/src/api/cpp/fftconvolve.cpp
+++ b/src/api/cpp/fftconvolve.cpp
@@ -22,8 +22,8 @@ array fftConvolve(const array& signal, const array& filter,
     switch (std::min(sN, fN)) {
         case 1: return fftConvolve1(signal, filter, mode);
         case 2: return fftConvolve2(signal, filter, mode);
+        default:
         case 3: return fftConvolve3(signal, filter, mode);
-        default: return fftConvolve3(signal, filter, mode);
     }
 }
 
diff --git a/src/api/cpp/gfor.cpp b/src/api/cpp/gfor.cpp
index fa37fd9ef1..f97ad1c34f 100644
--- a/src/api/cpp/gfor.cpp
+++ b/src/api/cpp/gfor.cpp
@@ -29,8 +29,9 @@ bool gforToggle() {
 }
 
 array batchFunc(const array &lhs, const array &rhs, batchFunc_t func) {
-    if (gforGet())
+    if (gforGet()) {
         AF_THROW_ERR("batchFunc can not be used inside GFOR", AF_ERR_ARG);
+    }
     gforSet(true);
     array res = func(lhs, rhs);
     gforSet(false);
diff --git a/src/api/cpp/index.cpp b/src/api/cpp/index.cpp
index bbc22bfdf0..68908c007c 100644
--- a/src/api/cpp/index.cpp
+++ b/src/api/cpp/index.cpp
@@ -32,31 +32,31 @@ void copy(array &dst, const array &src, const index &idx0, const index &idx1,
     AF_THROW(af_assign_gen(&lhs, lhs, nd, indices, rhs));
 }
 
-index::index() {
+index::index() : impl{} {
     impl.idx.seq = af_span;
     impl.isSeq   = true;
     impl.isBatch = false;
 }
 
-index::index(const int idx) {
+index::index(const int idx) : impl{} {
     impl.idx.seq = af_make_seq(idx, idx, 1);
     impl.isSeq   = true;
     impl.isBatch = false;
 }
 
-index::index(const af::seq &s0) {
+index::index(const af::seq &s0) : impl{} {
     impl.idx.seq = s0.s;
     impl.isSeq   = true;
     impl.isBatch = s0.m_gfor;
 }
 
-index::index(const af_seq &s0) {
+index::index(const af_seq &s0) : impl{} {
     impl.idx.seq = s0;
     impl.isSeq   = true;
     impl.isBatch = false;
 }
 
-index::index(const af::array &idx0) {
+index::index(const af::array &idx0) : impl{} {
     array idx    = idx0.isbool() ? where(idx0) : idx0;
     af_array arr = 0;
     AF_THROW(af_retain_array(&arr, idx.get()));
@@ -66,15 +66,20 @@ index::index(const af::array &idx0) {
     impl.isBatch = false;
 }
 
-index::index(const af::index &idx0) { *this = idx0; }
+index::index(const af::index &idx0) : impl{idx0.impl} {}  // NOLINT
+
+// NOLINTNEXTLINE(hicpp-noexcept-move)
+index::index(index &&idx0) : impl{idx0.impl} { idx0.impl.idx.arr = nullptr; }
 
 index::~index() {
-    if (!impl.isSeq && impl.idx.arr) af_release_array(impl.idx.arr);
+    if (!impl.isSeq && impl.idx.arr) { af_release_array(impl.idx.arr); }
 }
 
 index &index::operator=(const index &idx0) {
+    if (this == &idx0) { return *this; }
+
     impl = idx0.get();
-    if (impl.isSeq == false) {
+    if (!impl.isSeq) {
         // increment reference count to avoid double free
         // when/if idx0 is destroyed
         AF_THROW(af_retain_array(&impl.idx.arr, impl.idx.arr));
@@ -82,11 +87,7 @@ index &index::operator=(const index &idx0) {
     return *this;
 }
 
-index::index(index &&idx0) {
-    impl              = idx0.impl;
-    idx0.impl.idx.arr = nullptr;
-}
-
+// NOLINTNEXTLINE(hicpp-noexcept-move)
 index &index::operator=(index &&idx0) {
     impl              = idx0.impl;
     idx0.impl.idx.arr = nullptr;
@@ -97,9 +98,7 @@ static bool operator==(const af_seq &lhs, const af_seq &rhs) {
     return lhs.begin == rhs.begin && lhs.end == rhs.end && lhs.step == rhs.step;
 }
 
-bool index::isspan() const {
-    return impl.isSeq == true && impl.idx.seq == af_span;
-}
+bool index::isspan() const { return impl.isSeq && impl.idx.seq == af_span; }
 
 const af_index_t &index::get() const { return impl; }
 
diff --git a/src/api/cpp/internal.cpp b/src/api/cpp/internal.cpp
index b2d14360a2..e6760b7fe7 100644
--- a/src/api/cpp/internal.cpp
+++ b/src/api/cpp/internal.cpp
@@ -12,9 +12,11 @@
 #include "error.hpp"
 
 namespace af {
-array createStridedArray(const void *data, const dim_t offset, const dim4 dims,
-                         const dim4 strides, const af::dtype ty,
-                         const af::source location) {
+array createStridedArray(
+    const void *data, const dim_t offset,
+    const dim4 dims,     // NOLINT(performance-unnecessary-value-param)
+    const dim4 strides,  // NOLINT(performance-unnecessary-value-param)
+    const af::dtype ty, const af::source location) {
     af_array res;
     AF_THROW(af_create_strided_array(&res, data, offset, dims.ndims(),
                                      dims.get(), strides.get(), ty, location));
diff --git a/src/api/cpp/mean.cpp b/src/api/cpp/mean.cpp
index 55c0a02335..c03a83fa51 100644
--- a/src/api/cpp/mean.cpp
+++ b/src/api/cpp/mean.cpp
@@ -52,28 +52,28 @@ template<>
 AFAPI af_cfloat mean(const array& in) {
     double real, imag;
     AF_THROW(af_mean_all(&real, &imag, in.get()));
-    return af_cfloat((float)real, (float)imag);
+    return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
 AFAPI af_cdouble mean(const array& in) {
     double real, imag;
     AF_THROW(af_mean_all(&real, &imag, in.get()));
-    return af_cdouble(real, imag);
+    return {real, imag};
 }
 
 template<>
 AFAPI af_cfloat mean(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_mean_all_weighted(&real, &imag, in.get(), weights.get()));
-    return af_cfloat((float)real, (float)imag);
+    return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
 AFAPI af_cdouble mean(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_mean_all_weighted(&real, &imag, in.get(), weights.get()));
-    return af_cdouble(real, imag);
+    return {real, imag};
 }
 
 INSTANTIATE_MEAN(float);
diff --git a/src/api/cpp/random.cpp b/src/api/cpp/random.cpp
index 57751a2bec..821f5c70fe 100644
--- a/src/api/cpp/random.cpp
+++ b/src/api/cpp/random.cpp
@@ -25,7 +25,7 @@ randomEngine::randomEngine(const randomEngine &other) : engine(0) {
     }
 }
 
-randomEngine::randomEngine(af_random_engine handle) : engine(handle) {}
+randomEngine::randomEngine(af_random_engine engine) : engine(engine) {}
 
 randomEngine::~randomEngine() {
     if (engine) { af_release_random_engine(engine); }
@@ -39,7 +39,7 @@ randomEngine &randomEngine::operator=(const randomEngine &other) {
     return *this;
 }
 
-randomEngineType randomEngine::getType(void) {
+randomEngineType randomEngine::getType() {
     af_random_engine_type type;
     AF_THROW(af_random_engine_get_type(&type, engine));
     return type;
@@ -53,13 +53,13 @@ void randomEngine::setSeed(const unsigned long long seed) {
     AF_THROW(af_random_engine_set_seed(&engine, seed));
 }
 
-unsigned long long randomEngine::getSeed(void) const {
+unsigned long long randomEngine::getSeed() const {
     unsigned long long seed;
     AF_THROW(af_random_engine_get_seed(&seed, engine));
     return seed;
 }
 
-af_random_engine randomEngine::get(void) const { return engine; }
+af_random_engine randomEngine::get() const { return engine; }
 
 array randu(const dim4 &dims, const dtype ty, randomEngine &r) {
     af_array out;
@@ -121,7 +121,7 @@ void setDefaultRandomEngineType(randomEngineType rtype) {
     AF_THROW(af_set_default_random_engine_type(rtype));
 }
 
-randomEngine getDefaultRandomEngine(void) {
+randomEngine getDefaultRandomEngine() {
     af_random_engine internal_handle = 0;
     af_random_engine handle          = 0;
     AF_THROW(af_get_default_random_engine(&internal_handle));
diff --git a/src/api/cpp/seq.cpp b/src/api/cpp/seq.cpp
index 5f849a5acd..5d56a70f95 100644
--- a/src/api/cpp/seq.cpp
+++ b/src/api/cpp/seq.cpp
@@ -33,47 +33,51 @@ void seq::init(double begin, double end, double step) {
 #ifndef signbit
 // wtf windows?!
 inline int signbit(double x) {
-    if (x < 0) return -1;
+    if (x < 0) { return -1; }
     return 0;
 }
 #endif
 
-seq::~seq() {}
+seq::~seq() = default;
 
-seq::seq(double n) : m_gfor(false) {
-    if (n < 0) {
-        init(0, n, 1);
+seq::seq(double length) : s{}, size{}, m_gfor(false) {
+    if (length < 0) {
+        init(0, length, 1);
     } else {
-        init(0, n - 1, 1);
+        init(0, length - 1, 1);
     }
 }
 
-seq::seq(const af_seq& s_) : m_gfor(false) { init(s_.begin, s_.end, s_.step); }
+seq::seq(const af_seq& s_) : s{}, size{}, m_gfor(false) {
+    init(s_.begin, s_.end, s_.step);
+}
 
 seq& seq::operator=(const af_seq& s_) {
     init(s_.begin, s_.end, s_.step);
     return *this;
 }
 
-seq::seq(double begin, double end, double step) : m_gfor(false) {
+seq::seq(double begin, double end, double step) : s{}, size{}, m_gfor(false) {
     if (step == 0) {
-        if (begin != end)  // Span
+        if (begin != end) {  // Span
             AF_THROW_ERR("Invalid step size", AF_ERR_ARG);
+        }
     }
     if ((signbit(end) == signbit(begin)) &&
-        (signbit(end - begin) != signbit(step)))
+        (signbit(end - begin) != signbit(step))) {
         AF_THROW_ERR("Sequence is invalid", AF_ERR_ARG);
+    }
     init(begin, end, step);
 }
 
-seq::seq(seq other, bool is_gfor)
+seq::seq(seq other,  // NOLINT(performance-unnecessary-value-param)
+         bool is_gfor)
     : s(other.s), size(other.size), m_gfor(is_gfor) {}
 
 seq::operator array() const {
     double diff = s.end - s.begin;
-    dim_t len =
-        (int)((diff + std::fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) /
-              s.step);
+    dim_t len   = static_cast<int>(
+        (diff + std::fabs(s.step) * (signbit(diff) == 0 ? 1 : -1)) / s.step);
 
     array tmp = (m_gfor) ? range(1, 1, 1, len, 3) : range(len);
 
diff --git a/src/api/cpp/sparse.cpp b/src/api/cpp/sparse.cpp
index 1f9cabea4f..92486f873a 100644
--- a/src/api/cpp/sparse.cpp
+++ b/src/api/cpp/sparse.cpp
@@ -12,8 +12,11 @@
 #include "error.hpp"
 
 namespace af {
-array sparse(const dim_t nRows, const dim_t nCols, const array values,
-             const array rowIdx, const array colIdx, const af::storage stype) {
+array sparse(const dim_t nRows, const dim_t nCols,
+             const array values,  // NOLINT(performance-unnecessary-value-param)
+             const array rowIdx,  // NOLINT(performance-unnecessary-value-param)
+             const array colIdx,  // NOLINT(performance-unnecessary-value-param)
+             const af::storage stype) {
     af_array out = 0;
     AF_THROW(af_create_sparse_array(&out, nRows, nCols, values.get(),
                                     rowIdx.get(), colIdx.get(), stype));
@@ -21,8 +24,8 @@ array sparse(const dim_t nRows, const dim_t nCols, const array values,
 }
 
 array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
-             const void *const values, const int *const rowIdx,
-             const int *const colIdx, const dtype type, const af::storage stype,
+             const void* const values, const int* const rowIdx,
+             const int* const colIdx, const dtype type, const af::storage stype,
              const af::source src) {
     af_array out = 0;
     AF_THROW(af_create_sparse_array_from_ptr(&out, nRows, nCols, nNZ, values,
@@ -30,26 +33,30 @@ array sparse(const dim_t nRows, const dim_t nCols, const dim_t nNZ,
     return array(out);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array sparse(const array dense, const af::storage stype) {
     af_array out = 0;
     AF_THROW(af_create_sparse_array_from_dense(&out, dense.get(), stype));
     return array(out);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array sparseConvertTo(const array in, const af::storage stype) {
     af_array out = 0;
     AF_THROW(af_sparse_convert_to(&out, in.get(), stype));
     return array(out);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array dense(const array sparse) {
     af_array out = 0;
     AF_THROW(af_sparse_to_dense(&out, sparse.get()));
     return array(out);
 }
 
-void sparseGetInfo(array &values, array &rowIdx, array &colIdx, storage &stype,
-                   const array in) {
+void sparseGetInfo(
+    array& values, array& rowIdx, array& colIdx, storage& stype,
+    const array in) {  // NOLINT(performance-unnecessary-value-param)
     af_array values_ = 0, rowIdx_ = 0, colIdx_ = 0;
     af_storage stype_ = AF_STORAGE_DENSE;
     AF_THROW(
@@ -58,33 +65,37 @@ void sparseGetInfo(array &values, array &rowIdx, array &colIdx, storage &stype,
     rowIdx = array(rowIdx_);
     colIdx = array(colIdx_);
     stype  = stype_;
-    return;
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array sparseGetValues(const array in) {
     af_array out = 0;
     AF_THROW(af_sparse_get_values(&out, in.get()));
     return array(out);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array sparseGetRowIdx(const array in) {
     af_array out = 0;
     AF_THROW(af_sparse_get_row_idx(&out, in.get()));
     return array(out);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 array sparseGetColIdx(const array in) {
     af_array out = 0;
     AF_THROW(af_sparse_get_col_idx(&out, in.get()));
     return array(out);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 dim_t sparseGetNNZ(const array in) {
     dim_t out = 0;
     AF_THROW(af_sparse_get_nnz(&out, in.get()));
     return out;
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 af::storage sparseGetStorage(const array in) {
     af::storage out;
     AF_THROW(af_sparse_get_storage(&out, in.get()));
diff --git a/src/api/cpp/stdev.cpp b/src/api/cpp/stdev.cpp
index 7c8c116987..4031e53ba9 100644
--- a/src/api/cpp/stdev.cpp
+++ b/src/api/cpp/stdev.cpp
@@ -27,14 +27,14 @@ template<>
 AFAPI af_cfloat stdev(const array& in) {
     double real, imag;
     AF_THROW(af_stdev_all(&real, &imag, in.get()));
-    return af_cfloat((float)real, (float)imag);
+    return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
 AFAPI af_cdouble stdev(const array& in) {
     double real, imag;
     AF_THROW(af_stdev_all(&real, &imag, in.get()));
-    return af_cdouble(real, imag);
+    return {real, imag};
 }
 
 INSTANTIATE_STDEV(float);
diff --git a/src/api/cpp/timing.cpp b/src/api/cpp/timing.cpp
index c42ad90c87..847c8d7873 100644
--- a/src/api/cpp/timing.cpp
+++ b/src/api/cpp/timing.cpp
@@ -7,16 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <math.h>
 #include <af/device.h>
 #include <af/timing.h>
 #include <algorithm>
+#include <cmath>
 #include <vector>
 
 using namespace af;
 
 // get current time
-static inline timer time_now(void) {
+static inline timer time_now() {
 #if defined(OS_WIN)
     timer time;
     QueryPerformanceCounter(&time.val);
@@ -53,7 +53,7 @@ static inline double time_seconds(timer start, timer end) {
     double nano = (double)info.numer / (double)info.denom;
     return (end.val - start.val) * nano * 1e-9;
 #elif defined(OS_LNX)
-    struct timeval elapsed;
+    struct timeval elapsed {};
     timersub(&start.val, &end.val, &elapsed);
     long sec  = elapsed.tv_sec;
     long usec = elapsed.tv_usec;
@@ -98,12 +98,12 @@ double timeit(void (*fn)()) {
     //   then run (min time / (trials * median_time)) batches
     // else
     //   run 1 batch
-    int batches     = (int)ceilf(min_time / (trials * median_time));
+    int batches = static_cast<int>(ceilf(min_time / (trials * median_time)));
     double run_time = 0;
 
     for (int b = 0; b < batches; b++) {
         timer start = timer::start();
-        for (int i = 0; i < trials; ++i) fn();
+        for (int i = 0; i < trials; ++i) { fn(); }
         sync();
         run_time += timer::stop(start) / trials;
     }
diff --git a/src/api/cpp/util.cpp b/src/api/cpp/util.cpp
index b265fed161..c2bf0c05bf 100644
--- a/src/api/cpp/util.cpp
+++ b/src/api/cpp/util.cpp
@@ -17,12 +17,10 @@ using namespace std;
 namespace af {
 void print(const char *exp, const array &arr) {
     AF_THROW(af_print_array_gen(exp, arr.get(), 4));
-    return;
 }
 
 void print(const char *exp, const array &arr, const int precision) {
     AF_THROW(af_print_array_gen(exp, arr.get(), precision));
-    return;
 }
 
 int saveArray(const char *key, const array &arr, const char *filename,
@@ -53,7 +51,6 @@ int readArrayCheck(const char *filename, const char *key) {
 void toString(char **output, const char *exp, const array &arr,
               const int precision, const bool transpose) {
     AF_THROW(af_array_to_string(output, exp, arr.get(), precision, transpose));
-    return;
 }
 
 const char *toString(const char *exp, const array &arr, const int precision,
diff --git a/src/api/cpp/var.cpp b/src/api/cpp/var.cpp
index 534eb07f48..a5c563420a 100644
--- a/src/api/cpp/var.cpp
+++ b/src/api/cpp/var.cpp
@@ -53,28 +53,28 @@ template<>
 AFAPI af_cfloat var(const array& in, const bool isbiased) {
     double real, imag;
     AF_THROW(af_var_all(&real, &imag, in.get(), isbiased));
-    return af_cfloat((float)real, (float)imag);
+    return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
 AFAPI af_cdouble var(const array& in, const bool isbiased) {
     double real, imag;
     AF_THROW(af_var_all(&real, &imag, in.get(), isbiased));
-    return af_cdouble(real, imag);
+    return {real, imag};
 }
 
 template<>
 AFAPI af_cfloat var(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_var_all_weighted(&real, &imag, in.get(), weights.get()));
-    return af_cfloat((float)real, (float)imag);
+    return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
 AFAPI af_cdouble var(const array& in, const array& weights) {
     double real, imag;
     AF_THROW(af_var_all_weighted(&real, &imag, in.get(), weights.get()));
-    return af_cdouble(real, imag);
+    return {real, imag};
 }
 
 INSTANTIATE_VAR(float);
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index d1a09f05fc..0de280b89c 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -30,36 +30,36 @@ dim4 calcStrides(const dim4 &parentDim) {
     return out;
 }
 
-int ArrayInfo::getDevId() const {
+unsigned ArrayInfo::getDevId() const {
     // The actual device ID is only stored in the first 8 bits of devId
     // See ArrayInfo.hpp for more
-    return devId & 0xff;
+    return devId & 0xffU;
 }
 
 void ArrayInfo::setId(int id) const {
     // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
-    int backendId =
-        detail::getBackend() >> 1;  // Convert enums 1, 2, 4 to ints 0, 1, 2
-    const_cast<ArrayInfo *>(this)->setId(id | 1 << (backendId + 8));
+    unsigned backendId =
+        detail::getBackend() >> 1U;  // Convert enums 1, 2, 4 to ints 0, 1, 2
+    const_cast<ArrayInfo *>(this)->setId(id | 1 << (backendId + 8U));
 }
 
 void ArrayInfo::setId(int id) {
     // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
-    int backendId =
-        detail::getBackend() >> 1;  // Convert enums 1, 2, 4 to ints 0, 1, 2
-    devId = id | 1 << (backendId + 8);
+    unsigned backendId =
+        detail::getBackend() >> 1U;  // Convert enums 1, 2, 4 to ints 0, 1, 2
+    devId = id | 1U << (backendId + 8U);
 }
 
 af_backend ArrayInfo::getBackendId() const {
     // devId >> 8 converts the backend info to 1, 2, 4 which are enums
     // for CPU, CUDA and OpenCL respectively
     // See ArrayInfo.hpp for more
-    int backendId = devId >> 8;
-    return (af_backend)backendId;
+    unsigned backendId = devId >> 8U;
+    return static_cast<af_backend>(backendId);
 }
 
 void ArrayInfo::modStrides(const dim4 &newStrides) { dim_strides = newStrides; }
@@ -120,7 +120,7 @@ bool ArrayInfo::isLinear() const {
     if (ndims() == 1) { return dim_strides[0] == 1; }
 
     dim_t count = 1;
-    for (int i = 0; i < (int)ndims(); i++) {
+    for (size_t i = 0; i < ndims(); i++) {
         if (count != dim_strides[i]) { return false; }
         count *= dim_size[i];
     }
@@ -150,8 +150,9 @@ dim4 toDims(const vector<af_seq> &seqs, const dim4 &parentDims) {
     dim4 outDims(1, 1, 1, 1);
     for (unsigned i = 0; i < seqs.size(); i++) {
         outDims[i] = af::calcDim(seqs[i], parentDims[i]);
-        if (outDims[i] > parentDims[i])
+        if (outDims[i] > parentDims[i]) {
             AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
+        }
     }
     return outDims;
 }
@@ -167,8 +168,9 @@ dim4 toOffset(const vector<af_seq> &seqs, const dim4 &parentDims) {
             outOffsets[i] = 0;
         }
 
-        if (outOffsets[i] >= parentDims[i])
+        if (outOffsets[i] >= parentDims[i]) {
             AF_ERROR("Index out of range", AF_ERR_SIZE);
+        }
     }
     return outOffsets;
 }
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index 334556d4fa..d878d75fea 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -39,7 +39,7 @@ class ArrayInfo {
     // This can be changed in the future if the need arises for more devices as
     // this implementation is internal. Make sure to change the bit shift ops
     // when such a change is being made
-    int devId;
+    unsigned devId;
     af_dtype type;
     af::dim4 dim_size;
     dim_t offset;
@@ -95,7 +95,7 @@ class ArrayInfo {
     const af::dim4& dims() const { return dim_size; }
     size_t total() const { return offset + dim_strides[3] * dim_size[3]; }
 
-    int getDevId() const;
+    unsigned getDevId() const;
 
     void setId(int id) const;
 
diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 35a4dc58a9..030399bcb9 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -20,15 +20,12 @@
 #include <string>
 #include <vector>
 
-using std::make_unique;
 using std::max;
 using std::move;
 using std::stoi;
 using std::string;
 using std::vector;
 
-using spdlog::logger;
-
 namespace common {
 
 DefaultMemoryManager::memory_info &
@@ -37,7 +34,7 @@ DefaultMemoryManager::getCurrentMemoryInfo() {
 }
 
 void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
-    if (this->debug_mode) return;
+    if (this->debug_mode) { return; }
 
     // This vector is used to store the pointers which will be deleted by
     // the memory manager. We are using this to avoid calling free while
@@ -48,7 +45,7 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     {
         lock_guard_t lock(this->memory_mutex);
         // Return if all buffers are locked
-        if (current.total_buffers == current.lock_buffers) return;
+        if (current.total_buffers == current.lock_buffers) { return; }
         free_ptrs.reserve(current.free_map.size());
 
         for (auto &kv : current.free_map) {
@@ -81,12 +78,12 @@ DefaultMemoryManager::DefaultMemoryManager(int num_devices,
 
     // Debug mode
     string env_var = getEnvVar("AF_MEM_DEBUG");
-    if (!env_var.empty()) this->debug_mode = env_var[0] != '0';
-    if (this->debug_mode) mem_step_size = 1;
+    if (!env_var.empty()) { this->debug_mode = env_var[0] != '0'; }
+    if (this->debug_mode) { mem_step_size = 1; }
 
     // Max Buffer count
     env_var = getEnvVar("AF_MAX_BUFFERS");
-    if (!env_var.empty()) this->max_buffers = max(1, stoi(env_var));
+    if (!env_var.empty()) { this->max_buffers = max(1, stoi(env_var)); }
 }
 
 void DefaultMemoryManager::initialize() { this->setMaxMemorySize(); }
@@ -96,7 +93,7 @@ void DefaultMemoryManager::shutdown() { signalMemoryCleanup(); }
 void DefaultMemoryManager::addMemoryManagement(int device) {
     // If there is a memory manager allocated for this device id, we might
     // as well use it and the buffers allocated for it
-    if (static_cast<size_t>(device) < memory.size()) return;
+    if (static_cast<size_t>(device) < memory.size()) { return; }
 
     // Assuming, device need not be always the next device Lets resize to
     // current_size + device + 1 +1 is to account for device being 0-based
@@ -105,8 +102,9 @@ void DefaultMemoryManager::addMemoryManagement(int device) {
 }
 
 void DefaultMemoryManager::removeMemoryManagement(int device) {
-    if ((size_t)device >= memory.size())
+    if (static_cast<size_t>(device) >= memory.size()) {
         AF_ERROR("No matching device found", AF_ERR_ARG);
+    }
 
     // Do garbage collection for the device and leave the memory::memory_info
     // struct from the memory vector intact
@@ -120,8 +118,9 @@ void DefaultMemoryManager::setMaxMemorySize() {
         // memsize returned 0, then use 1GB
         size_t memsize = this->getMaxMemorySize(n);
         memory[n].max_bytes =
-            memsize == 0 ? ONE_GB
-                         : max(memsize * 0.75, (double)(memsize - ONE_GB));
+            memsize == 0
+                ? ONE_GB
+                : max(memsize * 0.75, static_cast<double>(memsize - ONE_GB));
     }
 }
 
@@ -188,7 +187,7 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
                 ptr = this->nativeAlloc(alloc_bytes);
             } catch (const AfError &ex) {
                 // If out of memory, run garbage collect and try again
-                if (ex.getError() != AF_ERR_NO_MEM) throw;
+                if (ex.getError() != AF_ERR_NO_MEM) { throw; }
                 this->signalMemoryCleanup();
                 ptr = this->nativeAlloc(alloc_bytes);
             }
@@ -206,7 +205,7 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
 }
 
 size_t DefaultMemoryManager::allocated(void *ptr) {
-    if (!ptr) return 0;
+    if (!ptr) { return 0; }
     memory_info &current = this->getCurrentMemoryInfo();
     auto locked_iter     = current.locked_map.find(ptr);
     if (locked_iter == current.locked_map.end()) { return 0; }
@@ -281,13 +280,14 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
     for (auto &kv : current.locked_map) {
         const char *status_mngr = "Yes";
         const char *status_user = "Unknown";
-        if (kv.second.user_lock)
+        if (kv.second.user_lock) {
             status_user = "Yes";
-        else
+        } else {
             status_user = " No";
+        }
 
         const char *unit = "KB";
-        double size      = (double)(kv.second.bytes) / 1024;
+        double size      = static_cast<double>(kv.second.bytes) / 1024;
         if (size >= 1024) {
             size = size / 1024;
             unit = "MB";
@@ -302,7 +302,7 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
         const char *status_user = "No";
 
         const char *unit = "KB";
-        double size      = (double)(kv.first) / 1024;
+        double size      = static_cast<double>(kv.first) / 1024;
         if (size >= 1024) {
             size = size / 1024;
             unit = "MB";
@@ -321,10 +321,10 @@ void DefaultMemoryManager::usageInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                                      size_t *lock_bytes, size_t *lock_buffers) {
     const memory_info &current = this->getCurrentMemoryInfo();
     lock_guard_t lock(this->memory_mutex);
-    if (alloc_bytes) *alloc_bytes = current.total_bytes;
-    if (alloc_buffers) *alloc_buffers = current.total_buffers;
-    if (lock_bytes) *lock_bytes = current.lock_bytes;
-    if (lock_buffers) *lock_buffers = current.lock_buffers;
+    if (alloc_bytes) { *alloc_bytes = current.total_bytes; }
+    if (alloc_buffers) { *alloc_buffers = current.total_buffers; }
+    if (lock_bytes) { *lock_bytes = current.lock_bytes; }
+    if (lock_buffers) { *lock_buffers = current.lock_buffers; }
 }
 
 void DefaultMemoryManager::userLock(const void *ptr) {
@@ -338,7 +338,7 @@ void DefaultMemoryManager::userLock(const void *ptr) {
     } else {
         locked_info info = {false, true, 100};  // This number is not relevant
 
-        current.locked_map[(void *)ptr] = info;
+        current.locked_map[const_cast<void *>(ptr)] = info;
     }
 }
 
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index d014a58fe5..6feda08bf2 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -118,9 +118,10 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
     float getMemoryPressure() override;
     bool jitTreeExceedsMemoryPressure(size_t bytes) override;
 
+    ~DefaultMemoryManager() = default;
+
    protected:
     DefaultMemoryManager()                                  = delete;
-    ~DefaultMemoryManager()                                 = default;
     DefaultMemoryManager(const DefaultMemoryManager &other) = delete;
     DefaultMemoryManager(DefaultMemoryManager &&other)      = default;
     DefaultMemoryManager &operator=(const DefaultMemoryManager &other) = delete;
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index 0176f9a84a..24bc53e4fb 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -38,7 +38,7 @@ using std::vector;
 
 namespace {
 
-std::string libName(std::string name) {
+std::string libName(const std::string& name) {
     return libraryPrefix + name + librarySuffix;
 }
 }  // namespace
@@ -62,9 +62,9 @@ DependencyModule::DependencyModule(const char* plugin_file_name,
     }
 }
 
-DependencyModule::DependencyModule(const vector<string> plugin_base_file_name,
-                                   const vector<string> suffixes,
-                                   const vector<string> paths)
+DependencyModule::DependencyModule(const vector<string>& plugin_base_file_name,
+                                   const vector<string>& suffixes,
+                                   const vector<string>& paths)
     : handle(nullptr), logger(common::loggerFactory("platform")) {
     for (const string& base_name : plugin_base_file_name) {
         for (const string& path : paths) {
@@ -86,14 +86,16 @@ DependencyModule::~DependencyModule() noexcept {
     if (handle) { unloadLibrary(handle); }
 }
 
-bool DependencyModule::isLoaded() const noexcept { return (bool)handle; }
+bool DependencyModule::isLoaded() const noexcept {
+    return static_cast<bool>(handle);
+}
 
 bool DependencyModule::symbolsLoaded() const noexcept {
     return all_of(begin(functions), end(functions),
                   [](void* ptr) { return ptr != nullptr; });
 }
 
-string DependencyModule::getErrorMessage() const noexcept {
+string DependencyModule::getErrorMessage() noexcept {
     return common::getErrorMessage();
 }
 
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index d9a860a738..9c2b00b53a 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -37,9 +37,9 @@ class DependencyModule {
     DependencyModule(const char* plugin_file_name,
                      const char** paths = nullptr);
 
-    DependencyModule(const std::vector<std::string> plugin_base_file_name,
-                     const std::vector<std::string> suffixes,
-                     const std::vector<std::string> paths);
+    DependencyModule(const std::vector<std::string>& plugin_base_file_name,
+                     const std::vector<std::string>& suffixes,
+                     const std::vector<std::string>& paths);
 
     ~DependencyModule() noexcept;
 
@@ -58,7 +58,7 @@ class DependencyModule {
 
     /// Returns the last error message that occurred because of loading the
     /// library
-    std::string getErrorMessage() const noexcept;
+    static std::string getErrorMessage() noexcept;
 
     spdlog::logger* getLogger() const noexcept;
 };
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index b3f95d5d2c..c784ae94aa 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -31,7 +31,7 @@ class InteropManager {
     ~InteropManager() {
         try {
             destroyResources();
-        } catch (AfError &ex) {
+        } catch (const AfError &ex) {
             std::string perr = getEnvVar("AF_PRINT_ERRORS");
             if (!perr.empty()) {
                 if (perr != "0") fprintf(stderr, "%s\n", ex.what());
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index d7c7d05323..ac488cd40b 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -22,10 +22,8 @@
 #include <string>
 
 using std::array;
-using std::make_shared;
 using std::shared_ptr;
 using std::string;
-using std::to_string;
 
 using spdlog::get;
 using spdlog::logger;
@@ -33,7 +31,7 @@ using spdlog::stdout_logger_mt;
 
 namespace common {
 
-shared_ptr<logger> loggerFactory(string name) {
+shared_ptr<logger> loggerFactory(const string& name) {
     shared_ptr<logger> logger;
     if (!(logger = get(name))) {
         logger = stdout_logger_mt(name);
@@ -52,15 +50,15 @@ shared_ptr<logger> loggerFactory(string name) {
 }
 
 string bytesToString(size_t bytes) {
-    constexpr array<const char *, 7> units{
+    constexpr array<const char*, 7> units{
         {"B", "KB", "MB", "GB", "TB", "PB", "EB"}};
     size_t count     = 0;
-    double fbytes    = static_cast<double>(bytes);
+    auto fbytes      = static_cast<double>(bytes);
     size_t num_units = units.size();
     for (count = 0; count < num_units && fbytes > 1000.0f; count++) {
         fbytes *= (1.0f / 1024.0f);
     }
-    if (count == units.size()) count--;
+    if (count == units.size()) { count--; }
     return fmt::format("{:.3g} {}", fbytes, units[count]);
 }
 }  // namespace common
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index ac627e81bb..aa56fc4ed0 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -16,7 +16,7 @@
 #include <spdlog/spdlog.h>
 
 namespace common {
-std::shared_ptr<spdlog::logger> loggerFactory(std::string name);
+std::shared_ptr<spdlog::logger> loggerFactory(const std::string& name);
 std::string bytesToString(size_t bytes);
 }  // namespace common
 
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index 8a56b4b851..deafcc9f06 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -71,7 +71,8 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
     }
 }
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims, const Array<int> &_rowIdx,
+SparseArrayBase::SparseArrayBase(const af::dim4 &_dims,
+                                 const Array<int> &_rowIdx,
                                  const Array<int> &_colIdx,
                                  const af::storage _storage, af_dtype _type,
                                  bool _copy)
@@ -90,13 +91,13 @@ SparseArrayBase::SparseArrayBase(const SparseArrayBase &base, bool copy)
     , rowIdx(copy ? copyArray<int>(base.rowIdx) : base.rowIdx)
     , colIdx(copy ? copyArray<int>(base.colIdx) : base.colIdx) {}
 
-SparseArrayBase::~SparseArrayBase() {}
+SparseArrayBase::~SparseArrayBase() = default;
 
 dim_t SparseArrayBase::getNNZ() const {
-    if (stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC)
+    if (stype == AF_STORAGE_COO || stype == AF_STORAGE_CSC) {
         return rowIdx.elements();
-    else if (stype == AF_STORAGE_CSR)
-        return colIdx.elements();
+    }
+    if (stype == AF_STORAGE_CSR) { return colIdx.elements(); }
 
     // This is to ensure future storages are properly configured
     return 0;
@@ -126,12 +127,11 @@ SparseArray<T> createHostDataSparseArray(const af::dim4 &_dims, const dim_t nNZ,
 }
 
 template<typename T>
-SparseArray<T> createDeviceDataSparseArray(const af::dim4 &_dims,
-                                           const dim_t nNZ, T *const _values,
-                                           int *const _rowIdx,
-                                           int *const _colIdx,
-                                           const af::storage _storage,
-                                           const bool _copy) {
+SparseArray<T> createDeviceDataSparseArray(
+    const af::dim4 &_dims, const dim_t nNZ, T *const _values,
+    int *const _rowIdx,  // NOLINT(readability-non-const-parameter)
+    int *const _colIdx,  // NOLINT(readability-non-const-parameter)
+    const af::storage _storage, const bool _copy) {
     return SparseArray<T>(_dims, nNZ, _values, _rowIdx, _colIdx, _storage, true,
                           _copy);
 }
@@ -162,8 +162,9 @@ void destroySparseArray(SparseArray<T> *sparse) {
 // Sparse Array Class Implementations
 ////////////////////////////////////////////////////////////////////////////
 template<typename T>
-SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage)
-    : base(_dims, _nNZ, _storage, (af_dtype)dtype_traits<T>::af_type)
+SparseArray<T>::SparseArray(const dim4 &_dims, dim_t _nNZ, af::storage _storage)
+    : base(_dims, _nNZ, _storage,
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , values(createValueArray<T>(dim4(_nNZ), scalar<T>(0))) {
     static_assert(std::is_standard_layout<SparseArray<T>>::value,
                   "SparseArray<T> must be a standard layout type");
@@ -173,12 +174,13 @@ SparseArray<T>::SparseArray(dim4 _dims, dim_t _nNZ, af::storage _storage)
 }
 
 template<typename T>
-SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, T *const _values,
+SparseArray<T>::SparseArray(const af::dim4 &_dims, dim_t _nNZ, T *const _values,
                             int *const _rowIdx, int *const _colIdx,
                             const af::storage _storage, bool _is_device,
                             bool _copy_device)
     : base(_dims, _nNZ, _rowIdx, _colIdx, _storage,
-           (af_dtype)dtype_traits<T>::af_type, _is_device, _copy_device)
+           static_cast<af_dtype>(dtype_traits<T>::af_type), _is_device,
+           _copy_device)
     , values(_is_device ? (!_copy_device
                                ? createDeviceDataArray<T>(dim4(_nNZ), _values)
                                : createValueArray<T>(dim4(_nNZ), scalar<T>(0)))
@@ -189,12 +191,12 @@ SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ, T *const _values,
 }
 
 template<typename T>
-SparseArray<T>::SparseArray(af::dim4 _dims, const Array<T> &_values,
+SparseArray<T>::SparseArray(const af::dim4 &_dims, const Array<T> &_values,
                             const Array<int> &_rowIdx,
                             const Array<int> &_colIdx,
                             const af::storage _storage, bool _copy)
     : base(_dims, _rowIdx, _colIdx, _storage,
-           (af_dtype)dtype_traits<T>::af_type, _copy)
+           static_cast<af_dtype>(dtype_traits<T>::af_type), _copy)
     , values(_copy ? copyArray<T>(_values) : _values) {}
 
 template<typename T>
@@ -202,9 +204,6 @@ SparseArray<T>::SparseArray(const SparseArray<T> &other, bool copy)
     : base(other.base, copy)
     , values(copy ? copyArray<T>(other.values) : other.values) {}
 
-template<typename T>
-SparseArray<T>::~SparseArray() {}
-
 #define INSTANTIATE(T)                                                       \
     template SparseArray<T> createEmptySparseArray<T>(                       \
         const af::dim4 &_dims, dim_t _nNZ, const af::storage _storage);      \
@@ -213,7 +212,8 @@ SparseArray<T>::~SparseArray() {}
         const int *const _rowIdx, const int *const _colIdx,                  \
         const af::storage _storage);                                         \
     template SparseArray<T> createDeviceDataSparseArray<T>(                  \
-        const af::dim4 &_dims, const dim_t _nNZ, T *const _values,           \
+        const af::dim4 &_dims, const dim_t _nNZ,                             \
+        T *const _values, /*  NOLINT */                                      \
         int *const _rowIdx, int *const _colIdx, const af::storage _storage,  \
         const bool _copy);                                                   \
     template SparseArray<T> createArrayDataSparseArray<T>(                   \
@@ -224,16 +224,16 @@ SparseArray<T>::~SparseArray() {}
     template SparseArray<T> copySparseArray<T>(const SparseArray<T> &other); \
     template void destroySparseArray<T>(SparseArray<T> * sparse);            \
                                                                              \
-    template SparseArray<T>::SparseArray(af::dim4 _dims, dim_t _nNZ,         \
+    template SparseArray<T>::SparseArray(const af::dim4 &_dims, dim_t _nNZ,  \
                                          af::storage _storage);              \
     template SparseArray<T>::SparseArray(                                    \
-        af::dim4 _dims, dim_t _nNZ, T *const _values, int *const _rowIdx,    \
-        int *const _colIdx, const af::storage _storage, bool _is_device,     \
-        bool _copy_device);                                                  \
+        const af::dim4 &_dims, dim_t _nNZ, T *const _values, /* NOLINT */    \
+        int *const _rowIdx, int *const _colIdx, const af::storage _storage,  \
+        bool _is_device, bool _copy_device);                                 \
     template SparseArray<T>::SparseArray(                                    \
-        af::dim4 _dims, const Array<T> &_values, const Array<int> &_rowIdx,  \
-        const Array<int> &_colIdx, const af::storage _storage, bool _copy);  \
-    template SparseArray<T>::~SparseArray();
+        const af::dim4 &_dims, const Array<T> &_values,                      \
+        const Array<int> &_rowIdx, const Array<int> &_colIdx,                \
+        const af::storage _storage, bool _copy)
 
 // Instantiate only floating types
 INSTANTIATE(float);
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 0f02922865..24144a29fe 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -48,7 +48,7 @@ class SparseArrayBase {
                     af_dtype _type, bool _is_device = false,
                     bool _copy_device = false);
 
-    SparseArrayBase(af::dim4 _dims, const Array<int> &_rowIdx,
+    SparseArrayBase(const af::dim4 &_dims, const Array<int> &_rowIdx,
                     const Array<int> &_colIdx, const af::storage _storage,
                     af_dtype _type, bool _copy = false);
 
@@ -59,7 +59,7 @@ class SparseArrayBase {
     ///
     /// \param[in] in         The array that will be copied
     /// \param[in] deep_copy  If true a deep copy is performed
-    SparseArrayBase(const SparseArrayBase &in, bool deep_copy = false);
+    SparseArrayBase(const SparseArrayBase &base, bool deep_copy = false);
 
     ~SparseArrayBase();
 
@@ -130,14 +130,14 @@ class SparseArray {
         base;         ///< This must be the first element of SparseArray<T>.
     Array<T> values;  ///< Linear array containing actual values
 
-    SparseArray(af::dim4 _dims, dim_t _nNZ, af::storage stype);
+    SparseArray(const af::dim4 &_dims, dim_t _nNZ, af::storage _storage);
 
-    explicit SparseArray(af::dim4 _dims, dim_t _nNZ, T *const _values,
+    explicit SparseArray(const af::dim4 &_dims, dim_t _nNZ, T *const _values,
                          int *const _rowIdx, int *const _colIdx,
                          const af::storage _storage, bool _is_device = false,
                          bool _copy_device = false);
 
-    SparseArray(af::dim4 _dims, const Array<T> &_values,
+    SparseArray(const af::dim4 &_dims, const Array<T> &_values,
                 const Array<int> &_rowIdx, const Array<int> &_colIdx,
                 const af::storage _storage, bool _copy = false);
 
@@ -146,12 +146,12 @@ class SparseArray {
     /// This constructor copies the \p in SparseArray and creates a new object
     /// from it. It can also perform a deep copy if the second argument is true.
     ///
-    /// \param[in] in         The array that will be copied
+    /// \param[in] other      The array that will be copied
     /// \param[in] deep_copy  If true a deep copy is performed
-    SparseArray(const SparseArray<T> &in, bool deep_copy);
+    SparseArray(const SparseArray<T> &other, bool deep_copy);
 
    public:
-    ~SparseArray();
+    ~SparseArray() noexcept = default;
 
 // Functions that call ArrayInfo object's functions
 #define INSTANTIATE_INFO(return_type, func) \
diff --git a/src/backend/common/dim4.cpp b/src/backend/common/dim4.cpp
index a17165451c..a83ed15457 100644
--- a/src/backend/common/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -23,7 +23,6 @@ static_assert(std::is_standard_layout<dim4>::value,
 
 using std::abs;
 using std::numeric_limits;
-using std::vector;
 
 dim4::dim4() : dims{0, 0, 0, 0} {}
 
@@ -33,7 +32,7 @@ dim4::dim4(dim_t first, dim_t second, dim_t third, dim_t fourth)
 dim4::dim4(const dim4& other)
     : dims{other.dims[0], other.dims[1], other.dims[2], other.dims[3]} {}
 
-dim4::dim4(const unsigned ndims_, const dim_t* const dims_) {
+dim4::dim4(const unsigned ndims_, const dim_t* const dims_) : dims{} {
     for (unsigned i = 0; i < 4; i++) { dims[i] = ndims_ > i ? dims_[i] : 1; }
 }
 
@@ -43,12 +42,12 @@ dim_t dim4::elements() { return static_cast<const dim4&>(*this).elements(); }
 
 dim_t dim4::ndims() const {
     dim_t num = elements();
-    if (num == 0) return 0;
-    if (num == 1) return 1;
+    if (num == 0) { return 0; }
+    if (num == 1) { return 1; }
 
-    if (dims[3] != 1) return 4;
-    if (dims[2] != 1) return 3;
-    if (dims[1] != 1) return 2;
+    if (dims[3] != 1) { return 4; }
+    if (dims[2] != 1) { return 3; }
+    if (dims[1] != 1) { return 2; }
 
     return 1;
 }
@@ -127,8 +126,8 @@ dim_t calcDim(const af_seq& seq, const dim_t& parentDim) {
         outDim = parentDim;
     } else if (hasEnd(seq)) {
         af_seq temp = {seq.begin, seq.end, seq.step};
-        if (seq.begin < 0) temp.begin += parentDim;
-        if (seq.end < 0) temp.end += parentDim;
+        if (seq.begin < 0) { temp.begin += parentDim; }
+        if (seq.end < 0) { temp.end += parentDim; }
         outDim = seqElements(temp);
     } else {
         DIM_ASSERT(1, seq.begin >= -DBL_MIN && seq.begin < parentDim);
diff --git a/src/backend/common/dispatch.cpp b/src/backend/common/dispatch.cpp
index 50d35da9bc..4cf5cbe6b7 100644
--- a/src/backend/common/dispatch.cpp
+++ b/src/backend/common/dispatch.cpp
@@ -10,11 +10,11 @@
 #include "dispatch.hpp"
 
 unsigned nextpow2(unsigned x) {
-    x = x - 1;
-    x = x | (x >> 1);
-    x = x | (x >> 2);
-    x = x | (x >> 4);
-    x = x | (x >> 8);
-    x = x | (x >> 16);
-    return x + 1;
+    x = x - 1U;
+    x = x | (x >> 1U);
+    x = x | (x >> 2U);
+    x = x | (x >> 4U);
+    x = x | (x >> 8U);
+    x = x | (x >> 16U);
+    return x + 1U;
 }
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 3d0605c286..21e7b7212b 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -19,12 +19,14 @@
 #include <cstring>
 #include <sstream>
 #include <string>
+#include <utility>
 
 #ifdef AF_OPENCL
 #include <errorcodes.hpp>
 #include <platform.hpp>
 #endif
 
+using std::move;
 using std::string;
 using std::stringstream;
 
@@ -40,24 +42,25 @@ AfError::AfError(const char *const func, const char *const file, const int line,
     , error(err)
     , st_(move(st)) {}
 
-AfError::AfError(string func, string file, const int line, string message,
-                 af_err err, boost::stacktrace::stacktrace st)
+AfError::AfError(string func, string file, const int line,
+                 const string &message, af_err err,
+                 boost::stacktrace::stacktrace st)
     : logic_error(message)
-    , functionName(func)
-    , fileName(file)
+    , functionName(move(func))
+    , fileName(move(file))
     , lineNumber(line)
     , error(err)
     , st_(move(st)) {}
 
-const string &AfError::getFunctionName() const { return functionName; }
+const string &AfError::getFunctionName() const noexcept { return functionName; }
 
-const string &AfError::getFileName() const { return fileName; }
+const string &AfError::getFileName() const noexcept { return fileName; }
 
-int AfError::getLine() const { return lineNumber; }
+int AfError::getLine() const noexcept { return lineNumber; }
 
-af_err AfError::getError() const { return error; }
+af_err AfError::getError() const noexcept { return error; }
 
-AfError::~AfError() throw() {}
+AfError::~AfError() noexcept = default;
 
 TypeError::TypeError(const char *const func, const char *const file,
                      const int line, const int index, const af_dtype type,
@@ -66,9 +69,9 @@ TypeError::TypeError(const char *const func, const char *const file,
     , argIndex(index)
     , errTypeName(getName(type)) {}
 
-const string &TypeError::getTypeName() const { return errTypeName; }
+const string &TypeError::getTypeName() const noexcept { return errTypeName; }
 
-int TypeError::getArgIndex() const { return argIndex; }
+int TypeError::getArgIndex() const noexcept { return argIndex; }
 
 ArgumentError::ArgumentError(const char *const func, const char *const file,
                              const int line, const int index,
@@ -78,9 +81,11 @@ ArgumentError::ArgumentError(const char *const func, const char *const file,
     , argIndex(index)
     , expected(expectString) {}
 
-const string &ArgumentError::getExpectedCondition() const { return expected; }
+const string &ArgumentError::getExpectedCondition() const noexcept {
+    return expected;
+}
 
-int ArgumentError::getArgIndex() const { return argIndex; }
+int ArgumentError::getArgIndex() const noexcept { return argIndex; }
 
 SupportError::SupportError(const char *const func, const char *const file,
                            const int line, const char *const back,
@@ -89,24 +94,26 @@ SupportError::SupportError(const char *const func, const char *const file,
               move(st))
     , backend(back) {}
 
-const string &SupportError::getBackendName() const { return backend; }
+const string &SupportError::getBackendName() const noexcept { return backend; }
 
 DimensionError::DimensionError(const char *const func, const char *const file,
                                const int line, const int index,
                                const char *const expectString,
-                               const boost::stacktrace::stacktrace st)
-    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE, move(st))
+                               const boost::stacktrace::stacktrace &st)
+    : AfError(func, file, line, "Invalid size", AF_ERR_SIZE, st)
     , argIndex(index)
     , expected(expectString) {}
 
-const string &DimensionError::getExpectedCondition() const { return expected; }
+const string &DimensionError::getExpectedCondition() const noexcept {
+    return expected;
+}
 
-int DimensionError::getArgIndex() const { return argIndex; }
+int DimensionError::getArgIndex() const noexcept { return argIndex; }
 
 af_err set_global_error_string(const string &msg, af_err err) {
     std::string perr = getEnvVar("AF_PRINT_ERRORS");
     if (!perr.empty()) {
-        if (perr != "0") fprintf(stderr, "%s\n", msg.c_str());
+        if (perr != "0") { fprintf(stderr, "%s\n", msg.c_str()); }
     }
     get_global_error_string() = msg;
     return err;
@@ -123,7 +130,7 @@ af_err processException() {
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid dimension for argument " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
-        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
+        if (is_stacktrace_enabled()) { ss << ex.getStacktrace(); }
 
         err = set_global_error_string(ss.str(), AF_ERR_SIZE);
     } catch (const ArgumentError &ex) {
@@ -132,26 +139,26 @@ af_err processException() {
            << "Invalid argument at index " << ex.getArgIndex() << "\n"
            << "Expected: " << ex.getExpectedCondition() << "\n";
 
-        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
+        if (is_stacktrace_enabled()) { ss << ex.getStacktrace(); }
         err = set_global_error_string(ss.str(), AF_ERR_ARG);
     } catch (const SupportError &ex) {
         ss << ex.getFunctionName() << " not supported for "
            << ex.getBackendName() << " backend\n";
 
-        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
+        if (is_stacktrace_enabled()) { ss << ex.getStacktrace(); }
         err = set_global_error_string(ss.str(), AF_ERR_NOT_SUPPORTED);
     } catch (const TypeError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << "Invalid type for argument " << ex.getArgIndex() << "\n";
 
-        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
+        if (is_stacktrace_enabled()) { ss << ex.getStacktrace(); }
         err = set_global_error_string(ss.str(), AF_ERR_TYPE);
     } catch (const AfError &ex) {
         ss << "In function " << ex.getFunctionName() << "\n"
            << "In file " << ex.getFileName() << ":" << ex.getLine() << "\n"
            << ex.what() << "\n";
-        if (is_stacktrace_enabled()) ss << ex.getStacktrace();
+        if (is_stacktrace_enabled()) { ss << ex.getStacktrace(); }
 
         err = set_global_error_string(ss.str(), ex.getError());
 #ifdef AF_OPENCL
@@ -172,8 +179,8 @@ af_err processException() {
     return err;
 }
 
-std::string &get_global_error_string() {
-    thread_local std::string *global_error_string = new std::string("");
+std::string &get_global_error_string() noexcept {
+    thread_local auto *global_error_string = new std::string("");
     return *global_error_string;
 }
 
@@ -217,7 +224,7 @@ const char *af_err_to_string(const af_err err) {
 
 namespace common {
 
-bool &is_stacktrace_enabled() {
+bool &is_stacktrace_enabled() noexcept {
     static bool stacktrace_enabled = true;
     return stacktrace_enabled;
 }
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 2371c1fc9f..f3d0132f04 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -36,19 +36,25 @@ class AfError : public std::logic_error {
             boost::stacktrace::stacktrace st);
 
     AfError(std::string func, std::string file, const int line,
-            std::string message, af_err err, boost::stacktrace::stacktrace st);
+            const std::string& message, af_err err,
+            boost::stacktrace::stacktrace st);
+
+    AfError(const AfError& other) noexcept = delete;
+    AfError(AfError&& other) noexcept      = default;
 
-    const std::string& getFunctionName() const;
+    const std::string& getFunctionName() const noexcept;
 
-    const std::string& getFileName() const;
+    const std::string& getFileName() const noexcept;
 
-    const boost::stacktrace::stacktrace& getStacktrace() const { return st_; };
+    const boost::stacktrace::stacktrace& getStacktrace() const noexcept {
+        return st_;
+    };
 
-    int getLine() const;
+    int getLine() const noexcept;
 
-    af_err getError() const;
+    af_err getError() const noexcept;
 
-    virtual ~AfError() throw();
+    virtual ~AfError() noexcept;
 };
 
 // TODO: Perhaps add a way to return supported types
@@ -62,11 +68,13 @@ class TypeError : public AfError {
               const int index, const af_dtype type,
               const boost::stacktrace::stacktrace st);
 
-    const std::string& getTypeName() const;
+    TypeError(TypeError&& other) noexcept = default;
+
+    const std::string& getTypeName() const noexcept;
 
-    int getArgIndex() const;
+    int getArgIndex() const noexcept;
 
-    ~TypeError() throw() {}
+    ~TypeError() noexcept {}
 };
 
 class ArgumentError : public AfError {
@@ -79,12 +87,13 @@ class ArgumentError : public AfError {
                   const int line, const int index,
                   const char* const expectString,
                   const boost::stacktrace::stacktrace st);
+    ArgumentError(ArgumentError&& other) noexcept = default;
 
-    const std::string& getExpectedCondition() const;
+    const std::string& getExpectedCondition() const noexcept;
 
-    int getArgIndex() const;
+    int getArgIndex() const noexcept;
 
-    ~ArgumentError() throw() {}
+    ~ArgumentError() noexcept {}
 };
 
 class SupportError : public AfError {
@@ -95,10 +104,11 @@ class SupportError : public AfError {
     SupportError(const char* const func, const char* const file, const int line,
                  const char* const back,
                  const boost::stacktrace::stacktrace st);
+    SupportError(SupportError&& other) noexcept = default;
 
-    ~SupportError() throw() {}
+    ~SupportError() noexcept {}
 
-    const std::string& getBackendName() const;
+    const std::string& getBackendName() const noexcept;
 };
 
 class DimensionError : public AfError {
@@ -110,13 +120,14 @@ class DimensionError : public AfError {
     DimensionError(const char* const func, const char* const file,
                    const int line, const int index,
                    const char* const expectString,
-                   const boost::stacktrace::stacktrace st);
+                   const boost::stacktrace::stacktrace& st);
+    DimensionError(DimensionError&& other) noexcept = default;
 
-    const std::string& getExpectedCondition() const;
+    const std::string& getExpectedCondition() const noexcept;
 
-    int getArgIndex() const;
+    int getArgIndex() const noexcept;
 
-    ~DimensionError() throw() {}
+    ~DimensionError() noexcept {}
 };
 
 af_err processException();
@@ -187,10 +198,10 @@ af_err set_global_error_string(const std::string& msg,
     } while (0)
 
 static const int MAX_ERR_SIZE = 1024;
-std::string& get_global_error_string();
+std::string& get_global_error_string() noexcept;
 
 namespace common {
 
-bool& is_stacktrace_enabled();
+bool& is_stacktrace_enabled() noexcept;
 
 }  // namespace common
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 345e95d15a..e8e24834b9 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -15,7 +15,8 @@
 #include <mutex>
 #include <utility>
 
-using namespace std;
+using std::make_pair;
+using std::string;
 
 /// Dynamically loads forge function pointer at runtime
 #define FG_MODULE_FUNCTION_INIT(NAME) \
@@ -138,6 +139,7 @@ INSTANTIATE_GET_FG_TYPE(unsigned char, FG_UINT8);
 INSTANTIATE_GET_FG_TYPE(unsigned short, FG_UINT16);
 INSTANTIATE_GET_FG_TYPE(short, FG_INT16);
 
+// NOLINTNEXTLINE(misc-unused-parameters)
 GLenum glErrorCheck(const char* msg, const char* file, int line) {
 // Skipped in release mode
 #ifndef NDEBUG
@@ -146,12 +148,15 @@ GLenum glErrorCheck(const char* msg, const char* file, int line) {
     if (x != GL_NO_ERROR) {
         char buf[1024];
         sprintf(buf, "GL Error at: %s:%d Message: %s Error Code: %d \"%s\"\n",
-                file, line, msg, (int)x, glGetString(x));
+                file, line, msg, static_cast<int>(x), glGetString(x));
         AF_ERROR(buf, AF_ERR_INTERNAL);
     }
     return x;
 #else
-    return (GLenum)0;
+    UNUSED(msg);
+    UNUSED(file);
+    UNUSED(line);
+    return static_cast<GLenum>(0);
 #endif
 }
 
@@ -175,7 +180,7 @@ void makeContextCurrent(fg_window window) {
 
 // dir -> true = round up, false = round down
 double step_round(const double in, const bool dir) {
-    if (in == 0) return 0;
+    if (in == 0) { return 0; }
 
     static const double __log2 = log10(2);
     static const double __log4 = log10(4);
@@ -192,7 +197,7 @@ double step_round(const double in, const bool dir) {
     const double dec = std::log10(in / mag);  // log of the fraction
 
     // This means in is of the for 10^n
-    if (dec == 0) return in;
+    if (dec == 0) { return in; }
 
     // For negative numbers, -ve round down = +ve round up and vice versa
     bool op_dir = in > 0 ? dir : !dir;
@@ -290,18 +295,18 @@ fg_window ForgeManager::getWindow(const int w, const int h,
 
 void ForgeManager::setWindowChartGrid(const fg_window window, const int r,
                                       const int c) {
-    ChartMapIterator iter     = mChartMap.find(window);
-    WindGridMapIterator gIter = mWndGridMap.find(window);
+    auto chart_iter = mChartMap.find(window);
 
-    if (iter != mChartMap.end()) {
+    if (chart_iter != mChartMap.end()) {
         // ChartVec found. Clear it.
         // This has to be cleared as there is no guarantee that existing
         // chart types(2D/3D) match the future grid requirements
-        for (const ChartPtr& c : iter->second) {
+        for (const ChartPtr& c : chart_iter->second) {
             if (c) { mChartAxesOverrideMap.erase(c->handle); }
         }
-        (iter->second).clear();  // Clear ChartList
-        gIter->second = std::make_pair<int, int>(1, 1);
+        (chart_iter->second).clear();  // Clear ChartList
+        auto gIter    = mWndGridMap.find(window);
+        gIter->second = make_pair(1, 1);
     }
 
     if (r == 0 || c == 0) {
@@ -315,26 +320,25 @@ void ForgeManager::setWindowChartGrid(const fg_window window, const int r,
 
 ForgeManager::WindowGridDims ForgeManager::getWindowGrid(
     const fg_window window) {
-    WindGridMapIterator gIter = mWndGridMap.find(window);
-    if (gIter == mWndGridMap.end()) {
-        mWndGridMap[window] = std::make_pair(1, 1);
-    }
+    auto gIter = mWndGridMap.find(window);
+    if (gIter == mWndGridMap.end()) { mWndGridMap[window] = make_pair(1, 1); }
     return mWndGridMap[window];
 }
 
 fg_chart ForgeManager::getChart(const fg_window window, const int r,
                                 const int c, const fg_chart_type ctype) {
-    ChartMapIterator iter     = mChartMap.find(window);
-    WindGridMapIterator gIter = mWndGridMap.find(window);
+    auto gIter = mWndGridMap.find(window);
 
     int rows = std::get<0>(gIter->second);
     int cols = std::get<1>(gIter->second);
 
-    if (c >= cols || r >= rows)
+    if (c >= cols || r >= rows) {
         AF_ERROR("Window Grid points are out of bounds", AF_ERR_TYPE);
+    }
 
     // upgrade to exclusive access to make changes
-    ChartPtr& chart = (iter->second)[c * rows + r];
+    auto chart_iter = mChartMap.find(window);
+    ChartPtr& chart = (chart_iter->second)[c * rows + r];
 
     if (!chart) {
         fg_chart temp = NULL;
@@ -356,12 +360,13 @@ fg_chart ForgeManager::getChart(const fg_window window, const int r,
     return chart->handle;
 }
 
-long long ForgeManager::genImageKey(int w, int h, fg_channel_format mode,
-                                    fg_dtype type) {
-    assert(w <= 2ll << 16);
-    assert(h <= 2ll << 16);
-    long long key = ((w & _16BIT) << 16) | (h & _16BIT);
-    key           = ((((key << 16) | (mode & _16BIT)) << 16) | (type | _16BIT));
+unsigned long long ForgeManager::genImageKey(unsigned w, unsigned h,
+                                             fg_channel_format mode,
+                                             fg_dtype type) {
+    assert(w <= 2U << 16U);
+    assert(h <= 2U << 16U);
+    unsigned long long key = ((w & _16BIT) << 16U) | (h & _16BIT);
+    key = ((((key << 16U) | (mode & _16BIT)) << 16U) | (type | _16BIT));
     return key;
 }
 
@@ -369,8 +374,8 @@ fg_image ForgeManager::getImage(int w, int h, fg_channel_format mode,
                                 fg_dtype type) {
     auto key = genImageKey(w, h, mode, type);
 
-    ChartKey keypair      = std::make_pair(key, nullptr);
-    ImageMapIterator iter = mImgMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, nullptr);
+    auto iter        = mImgMap.find(keypair);
 
     if (iter == mImgMap.end()) {
         fg_image img = nullptr;
@@ -384,8 +389,8 @@ fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
                                 fg_channel_format mode, fg_dtype type) {
     auto key = genImageKey(w, h, mode, type);
 
-    ChartKey keypair      = std::make_pair(key, chart);
-    ImageMapIterator iter = mImgMap.find(keypair);
+    ChartKey keypair = make_pair(key, chart);
+    auto iter        = mImgMap.find(keypair);
 
     if (iter == mImgMap.end()) {
         fg_chart_type chart_type;
@@ -405,11 +410,13 @@ fg_image ForgeManager::getImage(fg_chart chart, int w, int h,
 
 fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
                               fg_plot_type ptype, fg_marker_type mtype) {
-    long long key = (((long long)(nPoints)&_48BIT) << 16);
-    key |= (((dtype & _4BIT) << 12) | ((ptype & _4BIT) << 8) | (mtype & _8BIT));
+    unsigned long long key =
+        ((static_cast<unsigned long long>(nPoints) & _48BIT) << 16U);
+    key |=
+        (((dtype & _4BIT) << 12U) | ((ptype & _4BIT) << 8U) | (mtype & _8BIT));
 
-    ChartKey keypair     = std::make_pair(key, chart);
-    PlotMapIterator iter = mPltMap.find(keypair);
+    ChartKey keypair = std::make_pair(key, chart);
+    auto iter        = mPltMap.find(keypair);
 
     if (iter == mPltMap.end()) {
         fg_chart_type chart_type;
@@ -427,10 +434,12 @@ fg_plot ForgeManager::getPlot(fg_chart chart, int nPoints, fg_dtype dtype,
 
 fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins,
                                         fg_dtype type) {
-    long long key = (((long long)(nBins)&_48BIT) << 16) | (type & _16BIT);
+    unsigned long long key =
+        ((static_cast<unsigned long long>(nBins) & _48BIT) << 16U) |
+        (type & _16BIT);
 
-    ChartKey keypair          = std::make_pair(key, chart);
-    HistogramMapIterator iter = mHstMap.find(keypair);
+    ChartKey keypair = make_pair(key, chart);
+    auto iter        = mHstMap.find(keypair);
 
     if (iter == mHstMap.end()) {
         fg_chart_type chart_type;
@@ -449,12 +458,12 @@ fg_histogram ForgeManager::getHistogram(fg_chart chart, int nBins,
 
 fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY,
                                     fg_dtype type) {
-    long long surfaceSize = nX * (long long)(nY);
-    assert(surfaceSize <= 2ll << 48);
-    long long key = ((surfaceSize & _48BIT) << 16) | (type & _16BIT);
+    unsigned long long surfaceSize = nX * static_cast<unsigned long long>(nY);
+    assert(surfaceSize <= 2ULL << 48ULL);
+    unsigned long long key = ((surfaceSize & _48BIT) << 16U) | (type & _16BIT);
 
-    ChartKey keypair        = std::make_pair(key, chart);
-    SurfaceMapIterator iter = mSfcMap.find(keypair);
+    ChartKey keypair = make_pair(key, chart);
+    auto iter        = mSfcMap.find(keypair);
 
     if (iter == mSfcMap.end()) {
         fg_chart_type chart_type;
@@ -474,10 +483,12 @@ fg_surface ForgeManager::getSurface(fg_chart chart, int nX, int nY,
 
 fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints,
                                              fg_dtype type) {
-    long long key = (((long long)(nPoints)&_48BIT) << 16) | (type & _16BIT);
+    unsigned long long key =
+        ((static_cast<unsigned long long>(nPoints) & _48BIT) << 16U) |
+        (type & _16BIT);
 
-    ChartKey keypair         = std::make_pair(key, chart);
-    VecFieldMapIterator iter = mVcfMap.find(keypair);
+    ChartKey keypair = make_pair(key, chart);
+    auto iter        = mVcfMap.find(keypair);
 
     if (iter == mVcfMap.end()) {
         fg_chart_type chart_type;
@@ -493,7 +504,7 @@ fg_vector_field ForgeManager::getVectorField(fg_chart chart, int nPoints,
 }
 
 bool ForgeManager::getChartAxesOverride(const fg_chart chart) {
-    AxesOverrideIterator iter = mChartAxesOverrideMap.find(chart);
+    auto iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
@@ -501,7 +512,7 @@ bool ForgeManager::getChartAxesOverride(const fg_chart chart) {
 }
 
 void ForgeManager::setChartAxesOverride(const fg_chart chart, bool flag) {
-    AxesOverrideIterator iter = mChartAxesOverrideMap.find(chart);
+    auto iter = mChartAxesOverrideMap.find(chart);
     if (iter == mChartAxesOverrideMap.end()) {
         AF_ERROR("Chart Not Found!", AF_ERR_INTERNAL);
     }
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 911c1251a9..1f2b9f60b1 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -244,15 +244,17 @@ class ForgeManager {
     void setChartAxesOverride(const fg_chart chart, bool flag = true);
 
    private:
-    constexpr static unsigned int WIDTH  = 1280;
-    constexpr static unsigned int HEIGHT = 720;
-    constexpr static long long _4BIT     = 0x000000000000000F;
-    constexpr static long long _8BIT     = 0x00000000000000FF;
-    constexpr static long long _16BIT    = 0x000000000000FFFF;
-    constexpr static long long _32BIT    = 0x00000000FFFFFFFF;
-    constexpr static long long _48BIT    = 0x0000FFFFFFFFFFFF;
-
-    long long genImageKey(int w, int h, fg_channel_format mode, fg_dtype type);
+    constexpr static unsigned int WIDTH        = 1280;
+    constexpr static unsigned int HEIGHT       = 720;
+    constexpr static unsigned long long _4BIT  = 0x000000000000000F;
+    constexpr static unsigned long long _8BIT  = 0x00000000000000FF;
+    constexpr static unsigned long long _16BIT = 0x000000000000FFFF;
+    constexpr static unsigned long long _32BIT = 0x00000000FFFFFFFF;
+    constexpr static unsigned long long _48BIT = 0x0000FFFFFFFFFFFF;
+
+    static unsigned long long genImageKey(unsigned w, unsigned h,
+                                          fg_channel_format mode,
+                                          fg_dtype type);
 
 #define DEFINE_WRAPPER_OBJECT(OBJECT, RELEASE)                           \
     struct OBJECT {                                                      \
@@ -281,7 +283,7 @@ class ForgeManager {
     using HistogramPtr   = std::unique_ptr<Histogram, Histogram::Deleter>;
     using VectorFieldPtr = std::unique_ptr<VectorField, VectorField::Deleter>;
     using ChartList      = std::vector<ChartPtr>;
-    using ChartKey       = std::pair<long long, fg_chart>;
+    using ChartKey       = std::pair<unsigned long long, fg_chart>;
 
     using ChartMapIterator     = std::map<fg_window, ChartList>::iterator;
     using WindGridMapIterator  = std::map<fg_window, WindowGridDims>::iterator;
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 8bb8348ff2..1f29b517a1 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -991,6 +991,10 @@ CONSTEXPR_DH static inline bool operator<(common::half lhs,
 #ifndef __CUDA_ARCH__
 std::ostream& operator<<(std::ostream& os, const half& val);
 
+static inline std::string to_string(const half& val) {
+    return std::to_string(static_cast<float>(val));
+}
+
 static inline std::string to_string(const half&& val) {
     return std::to_string(static_cast<float>(val));
 }
diff --git a/src/backend/common/host_memory.cpp b/src/backend/common/host_memory.cpp
index a97aa12987..51a01e2164 100644
--- a/src/backend/common/host_memory.cpp
+++ b/src/backend/common/host_memory.cpp
@@ -80,7 +80,8 @@ size_t getHostMemorySize() {
 
 #elif defined(_SC_PHYS_PAGES) && defined(_SC_PAGESIZE)
     /* FreeBSD, Linux, OpenBSD, and Solaris. -------------------- */
-    return (size_t)sysconf(_SC_PHYS_PAGES) * (size_t)sysconf(_SC_PAGESIZE);
+    return static_cast<size_t>(sysconf(_SC_PHYS_PAGES)) *
+           static_cast<size_t>(sysconf(_SC_PAGESIZE));
 
 #elif defined(_SC_PHYS_PAGES) && defined(_SC_PAGE_SIZE)
     /* Legacy. -------------------------------------------------- */
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 9fdcfd72d2..bf17e2078e 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -12,7 +12,8 @@
 
 #include <string>
 #include <vector>
-using namespace std;
+
+using std::vector;
 
 namespace common {
 
@@ -20,7 +21,7 @@ int Node::getNodesMap(Node_map_t &node_map, vector<const Node *> &full_nodes,
                       vector<Node_ids> &full_ids) const {
     auto iter = node_map.find(this);
     if (iter == node_map.end()) {
-        Node_ids ids;
+        Node_ids ids{};
 
         for (int i = 0; i < kMaxChildren && m_children[i] != nullptr; i++) {
             ids.child_ids[i] =
diff --git a/src/backend/common/module_loading_unix.cpp b/src/backend/common/module_loading_unix.cpp
index 711ec1cfca..81dc4e391c 100644
--- a/src/backend/common/module_loading_unix.cpp
+++ b/src/backend/common/module_loading_unix.cpp
@@ -28,13 +28,10 @@ void unloadLibrary(LibHandle handle) { dlclose(handle); }
 
 string getErrorMessage() {
     char* errMsg = dlerror();
-    if (errMsg) {
-        return string(errMsg);
-    } else {
-        // constructing std::basic_string from NULL/0 address is
-        // invalid and has undefined behavior
-        return string("No Error");
-    }
+    if (errMsg) { return string(errMsg); }
+    // constructing std::basic_string from NULL/0 address is
+    // invalid and has undefined behavior
+    return string("No Error");
 }
 
 }  // namespace common
diff --git a/src/backend/common/sparse_helpers.hpp b/src/backend/common/sparse_helpers.hpp
index 3dda68b16e..2666cec978 100644
--- a/src/backend/common/sparse_helpers.hpp
+++ b/src/backend/common/sparse_helpers.hpp
@@ -56,9 +56,9 @@ void destroySparseArray(SparseArray<T> *sparse);
 
 /// Performs a deep copy of the \p input array.
 ///
-/// \param[in] input    The sparse array that is to be copied
+/// \param[in] other    The sparse array that is to be copied
 /// \returns A deep copy of the input sparse array
 template<typename T>
-SparseArray<T> copySparseArray(const SparseArray<T> &input);
+SparseArray<T> copySparseArray(const SparseArray<T> &other);
 
 }  // namespace common
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index a9f2941ca5..ee07d7fa7b 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -62,7 +62,7 @@ const char* getName(af_dtype type) {
 void saveKernel(const std::string& funcName, const std::string& jit_ker,
                 const std::string& ext) {
     static const char* jitKernelsOutput = getenv(saveJitKernelsEnvVarName);
-    if (!jitKernelsOutput) return;
+    if (!jitKernelsOutput) { return; }
     if (std::strcmp(jitKernelsOutput, "stdout") == 0) {
         fputs(jit_ker.c_str(), stdout);
         return;
@@ -74,12 +74,13 @@ void saveKernel(const std::string& funcName, const std::string& jit_ker,
     // Path to a folder
     const std::string ffp =
         std::string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
-    FILE* f = fopen(ffp.c_str(), "w");
+    FILE* f = fopen(ffp.c_str(), "we");
     if (!f) {
         fprintf(stderr, "Cannot open file %s\n", ffp.c_str());
         return;
     }
-    if (fputs(jit_ker.c_str(), f) == EOF)
+    if (fputs(jit_ker.c_str(), f) == EOF) {
         fprintf(stderr, "Failed to write kernel to file %s\n", ffp.c_str());
+    }
     fclose(f);
 }
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 7c1d3a2de2..92c058b036 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -34,6 +34,7 @@
 #include <cstddef>
 #include <cstring>
 #include <type_traits>
+#include <utility>
 
 using af::dim4;
 using common::half;
@@ -44,6 +45,7 @@ using cpu::jit::Node_map_t;
 using cpu::jit::Node_ptr;
 using std::copy;
 using std::is_standard_layout;
+using std::move;
 using std::vector;
 
 namespace cpu {
@@ -56,7 +58,7 @@ Node_ptr bufferNodePtr() {
 template<typename T>
 Array<T>::Array(dim4 dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(memAlloc<T>(dims.elements()).release(), memFree<T>)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -67,8 +69,8 @@ template<typename T>
 Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
                 bool copy_device)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
-    , data((is_device & !copy_device) ? (T *)in_data
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data((is_device & !copy_device) ? in_data
                                       : memAlloc<T>(dims.elements()).release(),
            memFree<T>)
     , data_dims(dims)
@@ -90,10 +92,10 @@ Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
 template<typename T>
 Array<T>::Array(const af::dim4 &dims, Node_ptr n)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data()
     , data_dims(dims)
-    , node(n)
+    , node(move(n))
     , ready(false)
     , owner(true) {}
 
@@ -101,7 +103,7 @@ template<typename T>
 Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
                 const dim4 &strides)
     : info(parent.getDevId(), dims, offset_, strides,
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(parent.getData())
     , data_dims(parent.getDataDims())
     , node(bufferNodePtr<T>())
@@ -112,7 +114,7 @@ template<typename T>
 Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
                 T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(is_device ? in_data : memAlloc<T>(info.total()).release(),
            memFree<T>)
     , data_dims(dims)
@@ -128,9 +130,10 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
 
 template<typename T>
 void Array<T>::eval() {
-    if (isReady()) return;
-    if (getQueue().is_worker())
+    if (isReady()) { return; }
+    if (getQueue().is_worker()) {
         AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+    }
 
     this->setId(getActiveDeviceId());
 
@@ -144,7 +147,7 @@ void Array<T>::eval() {
 
 template<typename T>
 void Array<T>::eval() const {
-    if (isReady()) return;
+    if (isReady()) { return; }
     const_cast<Array<T> *>(this)->eval();
 }
 
@@ -162,8 +165,9 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
     vector<Array<T> *> output_arrays;
     vector<Node_ptr> nodes;
     vector<Param<T>> params;
-    if (getQueue().is_worker())
+    if (getQueue().is_worker()) {
         AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
+    }
 
     // Check if all the arrays have the same dimension
     auto it = std::adjacent_find(begin(array_ptrs), end(array_ptrs),
@@ -178,7 +182,7 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
     }
 
     for (Array<T> *array : array_ptrs) {
-        if (array->ready) continue;
+        if (array->ready) { continue; }
 
         array->setId(getActiveDeviceId());
         array->data =
@@ -189,21 +193,20 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
         nodes.push_back(array->node);
     }
 
-    if (output_arrays.size() > 0) {
+    if (!output_arrays.empty()) {
         getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
         for (Array<T> *array : output_arrays) {
             array->ready = true;
             array->node  = bufferNodePtr<T>();
         }
     }
-    return;
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() const {
     if (node->isBuffer()) {
-        BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
-        unsigned bytes         = this->getDataDims().elements() * sizeof(T);
+        auto *bufNode  = reinterpret_cast<BufferNode<T> *>(node.get());
+        unsigned bytes = this->getDataDims().elements() * sizeof(T);
         bufNode->setData(data, bytes, getOffset(), dims().get(),
                          strides().get(), isLinear());
     }
@@ -233,8 +236,8 @@ Array<T> createEmptyArray(const dim4 &dims) {
 
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
-    if (!evalFlag()) return kJITHeuristics::Pass;
-    if (root_node->getHeight() >= (int)getMaxJitSize()) {
+    if (!evalFlag()) { return kJITHeuristics::Pass; }
+    if (root_node->getHeight() >= static_cast<int>(getMaxJitSize())) {
         return kJITHeuristics::TreeHeight;
     }
 
@@ -277,18 +280,18 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
         return createSubArray(parentCopy, index, copy);
     }
 
-    dim4 pDims   = parent.dims();
-    dim4 dims    = toDims(index, pDims);
-    dim4 strides = toStride(index, dDims);
+    const dim4 &pDims = parent.dims();
+    dim4 dims         = toDims(index, pDims);
+    dim4 strides      = toStride(index, dDims);
 
     // Find total offsets after indexing
     dim4 offsets = toOffset(index, pDims);
     dim_t offset = parent.getOffset();
-    for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+    for (int i = 0; i < 4; i++) { offset += offsets[i] * parent_strides[i]; }
 
     Array<T> out = Array<T>(parent, dims, offset, strides);
 
-    if (!copy) return out;
+    if (!copy) { return out; }
 
     if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
         out = copyArray(out);
@@ -316,7 +319,7 @@ template<typename T>
 void writeDeviceDataArray(Array<T> &arr, const void *const data,
                           const size_t bytes) {
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
-    memcpy(arr.get(), (const T *const)data, bytes);
+    memcpy(arr.get(), static_cast<const T *const>(data), bytes);
 }
 
 template<typename T>
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 86a5af8d9d..c722975e4e 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -43,7 +43,7 @@ using af::dim4;
 using std::shared_ptr;
 
 template<typename T>
-void evalMultiple(std::vector<Array<T> *> arrays);
+void evalMultiple(std::vector<Array<T> *> array_ptrs);
 
 // Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
diff --git a/src/backend/cpu/Event.cpp b/src/backend/cpu/Event.cpp
index 83454529a6..e0c67519d9 100644
--- a/src/backend/cpu/Event.cpp
+++ b/src/backend/cpu/Event.cpp
@@ -14,9 +14,10 @@
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/event.h>
-
 #include <memory>
 
+using std::make_unique;
+
 namespace cpu {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(cpu::queue& queue) {
@@ -26,8 +27,7 @@ Event makeEvent(cpu::queue& queue) {
 }
 
 af_event createEvent() {
-    std::unique_ptr<Event> e;
-    e.reset(new Event());
+    auto e = make_unique<Event>();
     // Ensure that the default queue is initialized
     getQueue();
     if (e->create() != 0) {
diff --git a/src/backend/cpu/anisotropic_diffusion.cpp b/src/backend/cpu/anisotropic_diffusion.cpp
index 3a7f518979..97818aea50 100644
--- a/src/backend/cpu/anisotropic_diffusion.cpp
+++ b/src/backend/cpu/anisotropic_diffusion.cpp
@@ -16,12 +16,13 @@ template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq) {
-    if (eq == AF_DIFFUSION_MCDE)
+    if (eq == AF_DIFFUSION_MCDE) {
         getQueue().enqueue(kernel::anisotropicDiffusion<T, true>, inout, dt,
                            mct, fftype);
-    else
+    } else {
         getQueue().enqueue(kernel::anisotropicDiffusion<T, false>, inout, dt,
                            mct, fftype);
+    }
 }
 
 #define INSTANTIATE(T)                                     \
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index d6f60c72db..0f32fab35d 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -26,7 +26,6 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
 using std::vector;
 
 namespace cpu {
@@ -70,6 +69,6 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
-INSTANTIATE(half)
+INSTANTIATE(common::half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 8198689a62..b70da95376 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -22,7 +22,7 @@ namespace cpu {
 template<typename inType, typename outType, bool isColor>
 Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
                          const float &c_sigma) {
-    const dim4 dims    = in.dims();
+    const dim4 &dims   = in.dims();
     Array<outType> out = createEmptyArray<outType>(dims);
     getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in,
                        s_sigma, c_sigma);
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 3640c95af4..bd516c209e 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -36,12 +36,7 @@
 using af::dtype_traits;
 using common::half;
 using common::is_complex;
-using std::add_const;
-using std::add_pointer;
 using std::conditional;
-using std::enable_if;
-using std::is_floating_point;
-using std::remove_const;
 using std::vector;
 
 namespace cpu {
@@ -115,14 +110,18 @@ using ptr_type = typename conditional<is_complex<T>::value,
                                       typename blas_base<T>::type *, T *>::type;
 
 template<typename T, bool batched = false>
-struct scale_type {
+class scale_type {
     const T val;
-    scale_type(const T *val_ptr) : val(*val_ptr) {}
+
+   public:
+    explicit scale_type(const T *val_ptr) : val(*val_ptr) {}
     using api_type = const typename conditional<
         is_complex<T>::value, const typename blas_base<T>::type *,
         const typename conditional<batched, const T *, const T>::type>::type;
 
-    api_type getScale() const { return val; }
+    api_type getScale() const {  // NOLINT(readability-const-return-type)
+        return val;
+    }
 };
 
 #define INSTANTIATE_BATCHED(TYPE)              \
@@ -132,8 +131,8 @@ struct scale_type {
         return &val;                           \
     }
 
-INSTANTIATE_BATCHED(float);
-INSTANTIATE_BATCHED(double);
+INSTANTIATE_BATCHED(float);   // NOLINT(readability-const-return-type)
+INSTANTIATE_BATCHED(double);  // NOLINT(readability-const-return-type)
 #undef INSTANTIATE_BATCHED
 
 #define INSTANTIATE_COMPLEX(TYPE, BATCHED)                                    \
@@ -143,10 +142,10 @@ INSTANTIATE_BATCHED(double);
         return reinterpret_cast<const blas_base<TYPE>::type *const>(&val);    \
     }
 
-INSTANTIATE_COMPLEX(cfloat, true);
-INSTANTIATE_COMPLEX(cfloat, false);
-INSTANTIATE_COMPLEX(cdouble, true);
-INSTANTIATE_COMPLEX(cdouble, false);
+INSTANTIATE_COMPLEX(cfloat, true);    // NOLINT(readability-const-return-type)
+INSTANTIATE_COMPLEX(cfloat, false);   // NOLINT(readability-const-return-type)
+INSTANTIATE_COMPLEX(cdouble, true);   // NOLINT(readability-const-return-type)
+INSTANTIATE_COMPLEX(cdouble, false);  // NOLINT(readability-const-return-type)
 #undef INSTANTIATE_COMPLEX
 
 template<typename T>
@@ -228,12 +227,12 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     const int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
     const int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
 
-    const dim4 lDims = lhs.dims();
-    const dim4 rDims = rhs.dims();
-    const int M      = lDims[aRowDim];
-    const int N      = rDims[bColDim];
-    const int K      = lDims[aColDim];
-    const dim4 oDims = out.dims();
+    const dim4 &lDims = lhs.dims();
+    const dim4 &rDims = rhs.dims();
+    const int M       = lDims[aRowDim];
+    const int N       = rDims[bColDim];
+    const int K       = lDims[aColDim];
+    const dim4 oDims  = out.dims();
 
     using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
@@ -267,7 +266,7 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
                     oStrides[1]);
             }
         } else {
-            int batchSize = oDims[2] * oDims[3];
+            int batchSize = static_cast<int>(oDims[2] * oDims[3]);
 
             const bool is_l_d2_batched = oDims[2] == lDims[2];
             const bool is_l_d3_batched = oDims[3] == lDims[3];
@@ -279,13 +278,13 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
             vector<BT *> optrs(batchSize);
 
             for (int n = 0; n < batchSize; n++) {
-                int w = n / oDims[2];
-                int z = n - w * oDims[2];
+                ptrdiff_t w = n / oDims[2];
+                ptrdiff_t z = n - w * oDims[2];
 
-                int loff = z * (is_l_d2_batched * lStrides[2]) +
-                           w * (is_l_d3_batched * lStrides[3]);
-                int roff = z * (is_r_d2_batched * rStrides[2]) +
-                           w * (is_r_d3_batched * rStrides[3]);
+                ptrdiff_t loff = z * (is_l_d2_batched * lStrides[2]) +
+                                 w * (is_l_d3_batched * lStrides[3]);
+                ptrdiff_t roff = z * (is_r_d2_batched * rStrides[2]) +
+                                 w * (is_r_d3_batched * rStrides[3]);
 
                 lptrs[n] = reinterpret_cast<CBT *>(left.get() + loff);
                 rptrs[n] = reinterpret_cast<CBT *>(right.get() + roff);
@@ -330,9 +329,9 @@ template<>
 void gemm<half>(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
                 const half *alpha, const Array<half> &lhs,
                 const Array<half> &rhs, const half *beta) {
-    Array<float> outArr     = createValueArray<float>(out.dims(), 0);
-    const float float_alpha = static_cast<float>(*alpha);
-    const float float_beta  = static_cast<float>(*beta);
+    Array<float> outArr    = createValueArray<float>(out.dims(), 0);
+    const auto float_alpha = static_cast<float>(*alpha);
+    const auto float_beta  = static_cast<float>(*beta);
     gemm<float>(outArr, optLhs, optRhs, &float_alpha, cast<float>(lhs),
                 cast<float>(rhs), &float_beta);
     copyArray(out, outArr);
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index efe763583a..90519cda3f 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -50,10 +50,11 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper)
+    if (is_upper) {
         triangle<T, true, false>(out, out);
-    else
+    } else {
         triangle<T, false, false>(out, out);
+    }
 
     return out;
 }
@@ -64,7 +65,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     int N      = iDims[0];
 
     char uplo = 'L';
-    if (is_upper) uplo = 'U';
+    if (is_upper) { uplo = 'U'; }
 
     int info  = 0;
     auto func = [&](int *info, Param<T> in) {
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 4011326fc7..efea6e08be 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -29,7 +29,6 @@
 using af::dim4;
 using common::flip;
 using common::half;
-using std::vector;
 
 namespace cpu {
 
@@ -51,7 +50,7 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
+            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
@@ -66,16 +65,16 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
                    Array<accT> const &r_filter) {
-    auto sDims = signal.dims();
-    dim4 tDims = sDims;
-    dim4 oDims = sDims;
+    const auto &sDims = signal.dims();
+    dim4 tDims        = sDims;
+    dim4 oDims        = sDims;
 
     if (expand) {
         auto cfDims = c_filter.dims();
         auto rfDims = r_filter.dims();
 
-        dim_t cflen = (dim_t)cfDims.elements();
-        dim_t rflen = (dim_t)rfDims.elements();
+        auto cflen = cfDims.elements();
+        auto rflen = rfDims.elements();
         // separable convolve only does AF_BATCH_NONE and standard
         // batch(AF_BATCH_LHS)
         tDims[0] += cflen - 1;
@@ -134,8 +133,8 @@ INSTANTIATE(intl, float)
 
 template<typename T>
 Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
-                          const dim4 stride, const dim4 padding,
-                          const dim4 dilation) {
+                          const dim4 &stride, const dim4 &padding,
+                          const dim4 &dilation) {
     dim4 sDims = signal.dims();
     dim4 fDims = filter.dims();
 
@@ -190,11 +189,12 @@ template<typename T>
 Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> &original_signal,
                            const Array<T> &original_filter,
-                           const Array<T> &convolved_output, af::dim4 stride,
-                           af::dim4 padding, af::dim4 dilation) {
-    const dim4 cDims = incoming_gradient.dims();
-    const dim4 sDims = original_signal.dims();
-    const dim4 fDims = original_filter.dims();
+                           const Array<T> & /*convolved_output*/,
+                           af::dim4 stride, af::dim4 padding,
+                           af::dim4 dilation) {
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
 
     Array<T> collapsed_filter = flip(original_filter, {1, 1, 0, 0});
     collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
@@ -221,10 +221,11 @@ template<typename T>
 Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &original_signal,
                              const Array<T> &original_filter,
-                             const Array<T> &convolved_output, af::dim4 stride,
-                             af::dim4 padding, af::dim4 dilation) {
-    const dim4 cDims = incoming_gradient.dims();
-    const dim4 fDims = original_filter.dims();
+                             const Array<T> & /*convolved_output*/,
+                             af::dim4 stride, af::dim4 padding,
+                             af::dim4 dilation) {
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &fDims = original_filter.dims();
 
     const bool retCols = false;
     Array<T> unwrapped =
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index f68713790d..359db199cc 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -23,7 +23,7 @@
 #include <cstdio>
 #include <cstring>
 
-using common::half;
+using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
 using common::is_complex;
 
 namespace cpu {
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 5b02711b63..46d7de9a27 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -20,7 +20,7 @@ class dim4;
 namespace cpu {
 
 template<typename T>
-void copyData(T *data, const Array<T> &A);
+void copyData(T *to, const Array<T> &from);
 
 template<typename T>
 Array<T> copyArray(const Array<T> &A);
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
index dc00900161..deb5fd0c3b 100644
--- a/src/backend/cpu/device_manager.cpp
+++ b/src/backend/cpu/device_manager.cpp
@@ -35,13 +35,14 @@ CPUInfo::CPUInfo()
 
     CPUID cpuID0(0, 0);
     uint32_t HFS = cpuID0.EAX();
-    mVendorId += string((const char*)&cpuID0.EBX(), 4);
-    mVendorId += string((const char*)&cpuID0.EDX(), 4);
-    mVendorId += string((const char*)&cpuID0.ECX(), 4);
+    mVendorId += string(reinterpret_cast<const char*>(&cpuID0.EBX()), 4);
+    mVendorId += string(reinterpret_cast<const char*>(&cpuID0.EDX()), 4);
+    mVendorId += string(reinterpret_cast<const char*>(&cpuID0.ECX()), 4);
 
     string upVId = mVendorId;
 
-    for_each(upVId.begin(), upVId.end(), [](char& in) { in = ::toupper(in); });
+    for_each(upVId.begin(), upVId.end(),
+             [](char& in) { in = static_cast<char>(::toupper(in)); });
 
     // Get num of cores
     if (upVId.find("INTEL") != std::string::npos) {
@@ -49,7 +50,7 @@ CPUInfo::CPUInfo()
         if (HFS >= 11) {
             for (int lvl = 0; lvl < MAX_INTEL_TOP_LVL; ++lvl) {
                 CPUID cpuID4(0x0B, lvl);
-                uint32_t currLevel = (LVL_TYPE & cpuID4.ECX()) >> 8;
+                uint32_t currLevel = (LVL_TYPE & cpuID4.ECX()) >> 8U;
                 switch (currLevel) {
                     case 0x01: mNumSMT = LVL_CORES & cpuID4.EBX(); break;
                     case 0x02: mNumLogCpus = LVL_CORES & cpuID4.EBX(); break;
@@ -61,15 +62,15 @@ CPUInfo::CPUInfo()
             mNumCores = mNumLogCpus / (mNumSMT == 0 ? 1 : mNumSMT);
         } else {
             if (HFS >= 1) {
-                mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
+                mNumLogCpus = (cpuID1.EBX() >> 16U) & 0xFFU;
                 if (HFS >= 4) {
-                    mNumCores = 1 + ((CPUID(4, 0).EAX() >> 26) & 0x3F);
+                    mNumCores = 1 + ((CPUID(4, 0).EAX() >> 26U) & 0x3FU);
                 }
             }
             if (mIsHTT) {
                 if (!(mNumCores > 1)) {
                     mNumCores   = 1;
-                    mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2);
+                    mNumLogCpus = (mNumLogCpus >= 2 ? mNumLogCpus : 2U);
                 }
             } else {
                 mNumCores = mNumLogCpus = 1;
@@ -78,9 +79,9 @@ CPUInfo::CPUInfo()
     } else if (upVId.find("AMD") != std::string::npos) {
         mVendorId = "AMD";
         if (HFS >= 1) {
-            mNumLogCpus = (cpuID1.EBX() >> 16) & 0xFF;
-            if (CPUID(0x80000000, 0).EAX() >= 8) {
-                mNumCores = 1 + ((CPUID(0x80000008, 0).ECX() & 0xFF));
+            mNumLogCpus = (cpuID1.EBX() >> 16U) & 0xFFU;
+            if (CPUID(0x80000000, 0).EAX() >= 8U) {
+                mNumCores = 1 + ((CPUID(0x80000008, 0).ECX() & 0xFFU));
             }
         }
         if (mIsHTT) {
@@ -98,12 +99,12 @@ CPUInfo::CPUInfo()
     // This seems to be working for both Intel & AMD vendors
     for (unsigned i = 0x80000002; i < 0x80000005; ++i) {
         CPUID cpuID(i, 0);
-        mModelName += string((const char*)&cpuID.EAX(), 4);
-        mModelName += string((const char*)&cpuID.EBX(), 4);
-        mModelName += string((const char*)&cpuID.ECX(), 4);
-        mModelName += string((const char*)&cpuID.EDX(), 4);
+        mModelName += string(reinterpret_cast<const char*>(&cpuID.EAX()), 4);
+        mModelName += string(reinterpret_cast<const char*>(&cpuID.EBX()), 4);
+        mModelName += string(reinterpret_cast<const char*>(&cpuID.ECX()), 4);
+        mModelName += string(reinterpret_cast<const char*>(&cpuID.EDX()), 4);
     }
-    mModelName = string(mModelName.c_str());
+    mModelName.shrink_to_fit();
 }
 
 #else
@@ -133,7 +134,7 @@ DeviceManager::DeviceManager()
 }
 
 DeviceManager& DeviceManager::getInstance() {
-    static DeviceManager* my_instance = new DeviceManager();
+    static auto* my_instance = new DeviceManager();
     return *my_instance;
 }
 
@@ -166,6 +167,8 @@ void DeviceManager::setMemoryManager(
 
 void DeviceManager::setMemoryManagerPinned(
     std::unique_ptr<MemoryManagerBase> newMgr) {
+    UNUSED(newMgr);
+    UNUSED(this);
     AF_ERROR("Using pinned memory with CPU is not supported",
              AF_ERR_NOT_SUPPORTED);
 }
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index ffd983d048..eeb027ca5e 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -80,9 +80,9 @@ class CPUInfo {
     // Attributes
     std::string mVendorId;
     std::string mModelName;
-    int mNumSMT;
-    int mNumCores;
-    int mNumLogCpus;
+    unsigned mNumSMT;
+    unsigned mNumCores;
+    unsigned mNumLogCpus;
     bool mIsHTT;
 };
 
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index e52b0d5c0c..9a8c61fc48 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -19,13 +19,15 @@
 #include <algorithm>
 #include <cstdlib>
 
-using common::half;
+using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using std::abs;      // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using std::min;      // NOLINT(misc-unused-using-decls) bug in clang-tidy
 
 namespace cpu {
 
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
-    int size     = in.dims()[0] + std::abs(num);
+    int size     = in.dims()[0] + abs(num);
     int batch    = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
 
@@ -36,9 +38,9 @@ Array<T> diagCreate(const Array<T> &in, const int num) {
 
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num) {
-    const dim4 idims = in.dims();
-    dim_t size       = std::min(idims[0], idims[1]) - std::abs(num);
-    Array<T> out     = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
+    const dim4 &idims = in.dims();
+    dim_t size        = min(idims[0], idims[1]) - abs(num);
+    Array<T> out      = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
     getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
 
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 91dc6bb19f..057cf96552 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -11,15 +11,17 @@
 #include <kernel/fast.hpp>
 
 #include <Array.hpp>
-#include <math.h>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
+#include <cmath>
 
 #include <algorithm>
+#include <cmath>
 #include <cstddef>
 
 using af::dim4;
+using std::ceil;
 
 namespace cpu {
 
@@ -38,7 +40,7 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     Array<float> V = createEmptyArray<float>(dim4());
     if (nonmax == 1) {
         dim4 V_dims(in_dims[0], in_dims[1]);
-        V = createValueArray<float>(V_dims, (float)0);
+        V = createValueArray<float>(V_dims, 0.f);
         V.eval();
     }
     getQueue().sync();
diff --git a/src/backend/cpu/fast.hpp b/src/backend/cpu/fast.hpp
index 21c0904c66..d588246916 100644
--- a/src/backend/cpu/fast.hpp
+++ b/src/backend/cpu/fast.hpp
@@ -14,7 +14,7 @@ class Array;
 template<typename T>
 unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const Array<T> &in, const float thr, const unsigned arc_length,
-              const bool non_max, const float feature_ratio,
+              const bool nonmax, const float feature_ratio,
               const unsigned edge);
 
 }  // namespace cpu
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 2b7f3158f5..26b1df7c00 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -84,7 +84,7 @@ void fft_inplace(Array<T> &in) {
 
         const af::dim4 istrides = in.strides();
 
-        typedef typename fftw_transform<T>::ctype_t ctype_t;
+        using ctype_t = typename fftw_transform<T>::ctype_t;
         typename fftw_transform<T>::plan_t plan;
 
         fftw_transform<T> transform;
@@ -93,10 +93,13 @@ void fft_inplace(Array<T> &in) {
         for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
         plan = transform.create(
-            rank, t_dims, (int)batch, (ctype_t *)in.get(), in_embed,
-            (int)istrides[0], (int)istrides[rank], (ctype_t *)in.get(),
-            in_embed, (int)istrides[0], (int)istrides[rank],
-            direction ? FFTW_FORWARD : FFTW_BACKWARD, FFTW_ESTIMATE);
+            rank, t_dims, batch, reinterpret_cast<ctype_t *>(in.get()),
+            in_embed, static_cast<int>(istrides[0]),
+            static_cast<int>(istrides[rank]),
+            reinterpret_cast<ctype_t *>(in.get()), in_embed,
+            static_cast<int>(istrides[0]), static_cast<int>(istrides[rank]),
+            direction ? FFTW_FORWARD : FFTW_BACKWARD,
+            FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
         transform.execute(plan);
         transform.destroy(plan);
@@ -125,8 +128,9 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
         const af::dim4 istrides = in.strides();
         const af::dim4 ostrides = out.strides();
 
-        typedef typename fftw_real_transform<Tc, Tr>::ctype_t ctype_t;
-        typename fftw_real_transform<Tc, Tr>::plan_t plan;
+        using ctype_t = typename fftw_real_transform<Tc, Tr>::ctype_t;
+        using plan_t  = typename fftw_real_transform<Tc, Tr>::plan_t;
+        plan_t plan;
 
         fftw_real_transform<Tc, Tr> transform;
 
@@ -134,9 +138,11 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
         for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
         plan = transform.create(
-            rank, t_dims, (int)batch, (Tr *)in.get(), in_embed,
-            (int)istrides[0], (int)istrides[rank], (ctype_t *)out.get(),
-            out_embed, (int)ostrides[0], (int)ostrides[rank], FFTW_ESTIMATE);
+            rank, t_dims, batch, const_cast<Tr *>(in.get()), in_embed,
+            static_cast<int>(istrides[0]), static_cast<int>(istrides[rank]),
+            reinterpret_cast<ctype_t *>(out.get()), out_embed,
+            static_cast<int>(ostrides[0]), static_cast<int>(ostrides[rank]),
+            FFTW_ESTIMATE);
 
         transform.execute(plan);
         transform.destroy(plan);
@@ -164,8 +170,9 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
         const af::dim4 istrides = in.strides();
         const af::dim4 ostrides = out.strides();
 
-        typedef typename fftw_real_transform<Tr, Tc>::ctype_t ctype_t;
-        typename fftw_real_transform<Tr, Tc>::plan_t plan;
+        using ctype_t = typename fftw_real_transform<Tr, Tc>::ctype_t;
+        using plan_t  = typename fftw_real_transform<Tr, Tc>::plan_t;
+        plan_t plan;
 
         fftw_real_transform<Tr, Tc> transform;
 
@@ -178,13 +185,17 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
         // FFTW_PRESERVE_INPUT also. This flag however only works for 1D
         // transforms and for higher level transformations, a copy of input
         // data is passed onto the upstream FFTW calls.
-        unsigned int flags = FFTW_ESTIMATE;
-        if (rank == 1) { flags |= FFTW_PRESERVE_INPUT; }
+        unsigned int flags = FFTW_ESTIMATE;  // NOLINT(hicpp-signed-bitwise)
+        if (rank == 1) {
+            flags |= FFTW_PRESERVE_INPUT;  // NOLINT(hicpp-signed-bitwise)
+        }
 
-        plan = transform.create(rank, t_dims, (int)batch, (ctype_t *)in.get(),
-                                in_embed, (int)istrides[0], (int)istrides[rank],
-                                (Tr *)out.get(), out_embed, (int)ostrides[0],
-                                (int)ostrides[rank], flags);
+        plan = transform.create(
+            rank, t_dims, batch,
+            reinterpret_cast<ctype_t *>(const_cast<Tc *>(in.get())), in_embed,
+            static_cast<int>(istrides[0]), static_cast<int>(istrides[rank]),
+            out.get(), out_embed, static_cast<int>(ostrides[0]),
+            static_cast<int>(ostrides[rank]), flags);
 
         transform.execute(plan);
         transform.destroy(plan);
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 93cc27227f..28eb5584eb 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -18,31 +18,34 @@
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
+using af::dim4;
+using std::ceil;
+
 namespace cpu {
 
 template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
          dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
-    const af::dim4 sd = signal.dims();
-    const af::dim4 fd = filter.dims();
+    const dim4& sd = signal.dims();
+    const dim4& fd = filter.dims();
 
     dim_t fftScale = 1;
 
-    af::dim4 packed_dims(1, 1, 1, 1);
+    dim4 packed_dims(1, 1, 1, 1);
     int fft_dims[baseDim];
-    af::dim4 sig_tmp_dims, sig_tmp_strides;
-    af::dim4 filter_tmp_dims, filter_tmp_strides;
+    dim4 sig_tmp_dims, sig_tmp_strides;
+    dim4 filter_tmp_dims, filter_tmp_strides;
 
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched FFT capabilities
-    fft_dims[baseDim - 1] =
-        nextpow2((unsigned)((int)ceil(sd[0] / 2.f) + fd[0] - 1));
+    fft_dims[baseDim - 1] = nextpow2(
+        static_cast<unsigned>(static_cast<int>(ceil(sd[0] / 2.f)) + fd[0] - 1));
     packed_dims[0] = 2 * fft_dims[baseDim - 1];
     fftScale *= fft_dims[baseDim - 1];
 
     for (dim_t k = 1; k < baseDim; k++) {
-        packed_dims[k]            = nextpow2((unsigned)(sd[k] + fd[k] - 1));
+        packed_dims[k] = nextpow2(static_cast<unsigned>(sd[k] + fd[k] - 1));
         fft_dims[baseDim - k - 1] = packed_dims[k];
         fftScale *= fft_dims[baseDim - k - 1];
     }
@@ -87,31 +90,34 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                        filter_tmp_strides, filter, offset);
 
     dim4 fftDims(1, 1, 1, 1);
-    for (int i = 0; i < baseDim; ++i) fftDims[i] = fft_dims[i];
+    for (int i = 0; i < baseDim; ++i) { fftDims[i] = fft_dims[i]; }
 
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
     auto upstream_dft = [=](Param<convT> packed, const dim4 fftDims) {
         int fft_dims[baseDim];
-        for (int i = 0; i < baseDim; ++i) fft_dims[i] = fftDims[i];
-        const dim4 packed_dims        = packed.dims();
-        const af::dim4 packed_strides = packed.strides();
+        for (int i = 0; i < baseDim; ++i) { fft_dims[i] = fftDims[i]; }
+        const dim4 packed_dims    = packed.dims();
+        const dim4 packed_strides = packed.strides();
         // Compute forward FFT
         if (isDouble) {
             fftw_plan plan = fftw_plan_many_dft(
                 baseDim, fft_dims, packed_dims[baseDim],
-                (fftw_complex*)packed.get(), NULL, packed_strides[0],
-                packed_strides[baseDim] / 2, (fftw_complex*)packed.get(), NULL,
+                reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
+                packed_strides[0], packed_strides[baseDim] / 2,
+                reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2, FFTW_FORWARD,
-                FFTW_ESTIMATE);
+                FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftw_execute(plan);
             fftw_destroy_plan(plan);
         } else {
             fftwf_plan plan = fftwf_plan_many_dft(
                 baseDim, fft_dims, packed_dims[baseDim],
-                (fftwf_complex*)packed.get(), NULL, packed_strides[0],
-                packed_strides[baseDim] / 2, (fftwf_complex*)packed.get(), NULL,
+                reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
+                packed_strides[0], packed_strides[baseDim] / 2,
+                reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2, FFTW_FORWARD,
-                FFTW_ESTIMATE);
+                FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftwf_execute(plan);
             fftwf_destroy_plan(plan);
@@ -124,29 +130,32 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                        sig_tmp_strides, filter_tmp_dims, filter_tmp_strides,
                        kind, offset);
 
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
     auto upstream_idft = [=](Param<convT> packed, const dim4 fftDims) {
         int fft_dims[baseDim];
-        for (int i = 0; i < baseDim; ++i) fft_dims[i] = fftDims[i];
-        const dim4 packed_dims        = packed.dims();
-        const af::dim4 packed_strides = packed.strides();
+        for (int i = 0; i < baseDim; ++i) { fft_dims[i] = fftDims[i]; }
+        const dim4 packed_dims    = packed.dims();
+        const dim4 packed_strides = packed.strides();
         // Compute inverse FFT
         if (isDouble) {
             fftw_plan plan = fftw_plan_many_dft(
                 baseDim, fft_dims, packed_dims[baseDim],
-                (fftw_complex*)packed.get(), NULL, packed_strides[0],
-                packed_strides[baseDim] / 2, (fftw_complex*)packed.get(), NULL,
+                reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
+                packed_strides[0], packed_strides[baseDim] / 2,
+                reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2, FFTW_BACKWARD,
-                FFTW_ESTIMATE);
+                FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftw_execute(plan);
             fftw_destroy_plan(plan);
         } else {
             fftwf_plan plan = fftwf_plan_many_dft(
                 baseDim, fft_dims, packed_dims[baseDim],
-                (fftwf_complex*)packed.get(), NULL, packed_strides[0],
-                packed_strides[baseDim] / 2, (fftwf_complex*)packed.get(), NULL,
+                reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
+                packed_strides[0], packed_strides[baseDim] / 2,
+                reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2, FFTW_BACKWARD,
-                FFTW_ESTIMATE);
+                FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftwf_execute(plan);
             fftwf_destroy_plan(plan);
@@ -167,7 +176,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     } else {
         oDims = sd;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fd[i];
+            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fd[i]; }
         }
     }
 
diff --git a/src/backend/cpu/flood_fill.cpp b/src/backend/cpu/flood_fill.cpp
index 4b9f6d2de8..7a08663ef3 100644
--- a/src/backend/cpu/flood_fill.cpp
+++ b/src/backend/cpu/flood_fill.cpp
@@ -13,7 +13,6 @@
 #include <kernel/flood_fill.hpp>
 
 using af::connectivity;
-using af::dim4;
 
 namespace cpu {
 
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 180a556943..1bc3a674e2 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -35,10 +35,12 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
     auto h_filter = memAlloc<convAccT>(filter_len);
     // Decide between rectangular or circular filter
     if (sigma < 0.5f) {
-        for (unsigned i = 0; i < filter_len; i++)
-            h_filter[i] = (T)1.f / (filter_len);
+        for (unsigned i = 0; i < filter_len; i++) {
+            h_filter[i] = static_cast<T>(1) / (filter_len);
+        }
     } else {
-        gaussian1D<convAccT>(h_filter.get(), (int)filter_len, sigma);
+        gaussian1D<convAccT>(h_filter.get(), static_cast<int>(filter_len),
+                             sigma);
     }
     Array<convAccT> filter =
         createDeviceDataArray<convAccT>(dim4(filter_len), h_filter.release());
@@ -74,7 +76,8 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
     Array<float> yCorners    = createEmptyArray<float>(dim4(corner_lim));
     Array<float> respCorners = createEmptyArray<float>(dim4(corner_lim));
 
-    const unsigned min_r = (max_corners > 0) ? 0.f : min_response;
+    const unsigned min_r =
+        (max_corners > 0) ? 0U : static_cast<unsigned>(min_response);
 
     // Performs non-maximal suppression
     getQueue().sync();
@@ -85,7 +88,7 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
 
     const unsigned corners_out =
         min(corners_found, (max_corners > 0) ? max_corners : corner_lim);
-    if (corners_out == 0) return 0;
+    if (corners_out == 0) { return 0; }
 
     if (max_corners > 0 && corners_found > corners_out) {
         respCorners.resetDims(dim4(corners_found));
@@ -110,15 +113,16 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
         y_out    = createEmptyArray<float>(dim4(corners_out));
         resp_out = createEmptyArray<float>(dim4(corners_out));
 
-        auto copyFunc = [=](Param<float> x_out, Param<float> y_out,
-                            Param<float> outResponses, CParam<float> x_crnrs,
-                            CParam<float> y_crnrs, CParam<float> inResponses,
-                            const unsigned corners_out) {
-            memcpy(x_out.get(), x_crnrs.get(), corners_out * sizeof(float));
-            memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
-            memcpy(outResponses.get(), inResponses.get(),
-                   corners_out * sizeof(float));
-        };
+        auto copyFunc =
+            [=](Param<float> x_out, Param<float> y_out,
+                Param<float> outResponses, const CParam<float> &x_crnrs,
+                const CParam<float> &y_crnrs, const CParam<float> &inResponses,
+                const unsigned corners_out) {
+                memcpy(x_out.get(), x_crnrs.get(), corners_out * sizeof(float));
+                memcpy(y_out.get(), y_crnrs.get(), corners_out * sizeof(float));
+                memcpy(outResponses.get(), inResponses.get(),
+                       corners_out * sizeof(float));
+            };
         getQueue().enqueue(copyFunc, x_out, y_out, resp_out, xCorners, yCorners,
                            respCorners, corners_out);
     } else {
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 4e05216ccd..a6292d951f 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -21,7 +21,7 @@ namespace cpu {
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
                          const double &minval, const double &maxval) {
-    const dim4 inDims  = in.dims();
+    const dim4 &inDims = in.dims();
     dim4 outDims       = dim4(nbins, 1, inDims[2], inDims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index ae856431a1..98e93f0f08 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -14,13 +14,23 @@
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
-#include <cfloat>
-#include <cstring>
 
 #include <array>
+#include <cfloat>
+#include <cmath>
+#include <cstring>
+#include <vector>
 
 using af::dim4;
+using std::abs;
 using std::array;
+using std::log;
+using std::max;
+using std::min;
+using std::pow;
+using std::round;
+using std::sqrt;
+using std::vector;
 
 namespace cpu {
 
@@ -53,7 +63,7 @@ struct EPS<double> {
 template<typename T, int M, int N>
 void JacobiSVD(T* S, T* V) {
     const int iterations = 30;
-    array<T, N> d;
+    array<T, N> d{};
 
     for (int i = 0; i < N; i++) {
         T sd = 0;
@@ -76,21 +86,22 @@ void JacobiSVD(T* S, T* V) {
                 T* Vi = V + i * N;
                 T* Vj = V + j * N;
 
-                T p = (T)0;
-                for (int k = 0; k < M; k++) p += Si[k] * Sj[k];
+                T p = static_cast<T>(0);
+                for (int k = 0; k < M; k++) { p += Si[k] * Sj[k]; }
 
-                if (std::abs(p) <= M * EPS<T>::eps() * std::sqrt(d[i] * d[j]))
+                if (abs(p) <= M * EPS<T>::eps() * sqrt(d[i] * d[j])) {
                     continue;
+                }
 
                 T y  = d[i] - d[j];
                 T r  = hypot(p * 2, y);
                 T r2 = r * 2;
                 T c, s;
                 if (y >= 0) {
-                    c = std::sqrt((r + y) / r2);
+                    c = sqrt((r + y) / r2);
                     s = p / (r2 * c);
                 } else {
-                    s = std::sqrt((r - y) / r2);
+                    s = sqrt((r - y) / r2);
                     c = p / (r2 * s);
                 }
 
@@ -117,44 +128,53 @@ void JacobiSVD(T* S, T* V) {
 
                 converged = true;
             }
-            if (!converged) break;
+            if (!converged) { break; }
         }
     }
 }
 
 unsigned updateIterations(float inlier_ratio, unsigned iter) {
-    float w  = std::min(std::max(inlier_ratio, 0.0f), 1.0f);
+    float w  = min(max(inlier_ratio, 0.0f), 1.0f);
     float wn = pow(1 - w, 4.f);
 
     float d = 1.f - wn;
-    if (d < FLT_MIN) return 0;
+    if (d < FLT_MIN) { return 0; }
 
     d = log(d);
 
-    float p = std::min(std::max(RANSACConfidence, 0.0f), 1.0f);
+    float p = min(max(RANSACConfidence, 0.0f), 1.0f);
     float n = log(1.f - p);
 
-    return n <= d * iter ? iter : (unsigned)round(n / d);
+    return n <= d * static_cast<float>(iter)
+               ? iter
+               : static_cast<unsigned>(round(n / d));
 }
 
 template<typename T>
 int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
                       const float* y_src_ptr, const float* x_dst_ptr,
                       const float* y_dst_ptr) {
-    if ((unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[1] ||
-        (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[2] ||
-        (unsigned)rnd_ptr[0] == (unsigned)rnd_ptr[3] ||
-        (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[2] ||
-        (unsigned)rnd_ptr[1] == (unsigned)rnd_ptr[3] ||
-        (unsigned)rnd_ptr[2] == (unsigned)rnd_ptr[3])
+    if (static_cast<unsigned>(rnd_ptr[0]) ==
+            static_cast<unsigned>(rnd_ptr[1]) ||
+        static_cast<unsigned>(rnd_ptr[0]) ==
+            static_cast<unsigned>(rnd_ptr[2]) ||
+        static_cast<unsigned>(rnd_ptr[0]) ==
+            static_cast<unsigned>(rnd_ptr[3]) ||
+        static_cast<unsigned>(rnd_ptr[1]) ==
+            static_cast<unsigned>(rnd_ptr[2]) ||
+        static_cast<unsigned>(rnd_ptr[1]) ==
+            static_cast<unsigned>(rnd_ptr[3]) ||
+        static_cast<unsigned>(rnd_ptr[2]) ==
+            static_cast<unsigned>(rnd_ptr[3])) {
         return 1;
+    }
 
     float src_pt_x[4], src_pt_y[4], dst_pt_x[4], dst_pt_y[4];
     for (unsigned j = 0; j < 4; j++) {
-        src_pt_x[j] = x_src_ptr[(unsigned)rnd_ptr[j]];
-        src_pt_y[j] = y_src_ptr[(unsigned)rnd_ptr[j]];
-        dst_pt_x[j] = x_dst_ptr[(unsigned)rnd_ptr[j]];
-        dst_pt_y[j] = y_dst_ptr[(unsigned)rnd_ptr[j]];
+        src_pt_x[j] = x_src_ptr[static_cast<unsigned>(rnd_ptr[j])];
+        src_pt_y[j] = y_src_ptr[static_cast<unsigned>(rnd_ptr[j])];
+        dst_pt_x[j] = x_dst_ptr[static_cast<unsigned>(rnd_ptr[j])];
+        dst_pt_y[j] = y_dst_ptr[static_cast<unsigned>(rnd_ptr[j])];
     }
 
     float x_src_mean =
@@ -178,7 +198,7 @@ int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
     float src_scale = sqrt(2.0f) / sqrt(src_var);
     float dst_scale = sqrt(2.0f) / sqrt(dst_var);
 
-    Array<T> A     = createValueArray<T>(af::dim4(9, 9), (T)0);
+    Array<T> A     = createValueArray<T>(af::dim4(9, 9), static_cast<T>(0));
     af::dim4 Adims = A.dims();
     T* A_ptr       = A.get();
     getQueue().sync();
@@ -204,7 +224,8 @@ int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
         APTR(8, j * 2 + 1) = -dstx;
     }
 
-    Array<T> V = createValueArray<T>(af::dim4(Adims[1], Adims[1]), (T)0);
+    Array<T> V =
+        createValueArray<T>(af::dim4(Adims[1], Adims[1]), static_cast<T>(0));
     V.eval();
     getQueue().sync();
     JacobiSVD<T, 9, 9>(A.get(), V.get());
@@ -212,8 +233,8 @@ int computeHomography(T* H_ptr, const float* rnd_ptr, const float* x_src_ptr,
     dim4 Vdims = V.dims();
     T* V_ptr   = V.get();
 
-    array<T, 9> vH;
-    for (unsigned j = 0; j < 9; j++) vH[j] = V_ptr[8 * Vdims[0] + j];
+    array<T, 9> vH{};
+    for (unsigned j = 0; j < 9; j++) { vH[j] = V_ptr[8 * Vdims[0] + j]; }
 
     H_ptr[0] = src_scale * x_dst_mean * vH[6] + src_scale * vH[0] / dst_scale;
     H_ptr[1] = src_scale * x_dst_mean * vH[7] + src_scale * vH[1] / dst_scale;
@@ -252,17 +273,18 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
     const float* x_dst_ptr = x_dst.get();
     const float* y_dst_ptr = y_dst.get();
 
-    Array<T> H = createValueArray<T>(af::dim4(9, iterations), (T)0);
+    Array<T> H =
+        createValueArray<T>(af::dim4(9, iterations), static_cast<T>(0));
     H.eval();
     getQueue().sync();
 
-    const af::dim4 rdims = rnd.dims();
-    const af::dim4 Hdims = H.dims();
+    const af::dim4& rdims = rnd.dims();
+    const af::dim4& Hdims = H.dims();
 
-    unsigned iter        = iterations;
-    unsigned bestIdx     = 0;
-    unsigned bestInliers = 0;
-    float minMedian      = FLT_MAX;
+    unsigned iter    = iterations;
+    unsigned bestIdx = 0;
+    int bestInliers  = 0;
+    float minMedian  = FLT_MAX;
 
     for (unsigned i = 0; i < iter; i++) {
         const unsigned Hidx = Hdims[0] * i;
@@ -272,11 +294,12 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
         const float* rnd_ptr = rnd.get() + ridx;
 
         if (computeHomography<T>(H_ptr, rnd_ptr, x_src_ptr, y_src_ptr,
-                                 x_dst_ptr, y_dst_ptr))
+                                 x_dst_ptr, y_dst_ptr)) {
             continue;
+        }
 
         if (htype == AF_HOMOGRAPHY_RANSAC) {
-            unsigned inliers_count = 0;
+            int inliers_count = 0;
             for (unsigned j = 0; j < nsamples; j++) {
                 float z = H_ptr[6] * x_src_ptr[j] + H_ptr[7] * y_src_ptr[j] +
                           H_ptr[8];
@@ -288,16 +311,18 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
                           z;
 
                 float dist = sq(x_dst_ptr[j] - x) + sq(y_dst_ptr[j] - y);
-                if (dist < (inlier_thr * inlier_thr)) inliers_count++;
+                if (dist < (inlier_thr * inlier_thr)) { inliers_count++; }
             }
-            iter = updateIterations(
-                (nsamples - inliers_count) / (float)nsamples, iter);
+            iter =
+                updateIterations(static_cast<float>(nsamples - inliers_count) /
+                                     static_cast<float>(nsamples),
+                                 iter);
             if (inliers_count > bestInliers) {
                 bestIdx     = i;
                 bestInliers = inliers_count;
             }
         } else if (htype == AF_HOMOGRAPHY_LMEDS) {
-            std::vector<float> err(nsamples);
+            vector<float> err(nsamples);
             for (unsigned j = 0; j < nsamples; j++) {
                 float z = H_ptr[6] * x_src_ptr[j] + H_ptr[7] * y_src_ptr[j] +
                           H_ptr[8];
@@ -312,11 +337,12 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
                 err[j]     = sqrt(dist);
             }
 
-            std::stable_sort(err.begin(), err.end());
+            stable_sort(err.begin(), err.end());
 
             float median = err[nsamples / 2];
-            if (nsamples % 2 == 0)
+            if (nsamples % 2 == 0) {
                 median = (median + err[nsamples / 2 - 1]) * 0.5f;
+            }
 
             if (median < minMedian && median > FLT_EPSILON) {
                 minMedian = median;
@@ -328,9 +354,10 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
     memcpy(bestH.get(), H.get() + bestIdx * 9, 9 * sizeof(T));
 
     if (htype == AF_HOMOGRAPHY_LMEDS) {
-        float sigma = std::max(
-            1.4826f * (1 + 5.f / (nsamples - 4)) * (float)sqrt(minMedian),
-            1e-6f);
+        float sigma =
+            max(1.4826f * (1.f + 5.f / (static_cast<float>(nsamples) - 4.f)) *
+                    static_cast<float>(sqrt(minMedian)),
+                1e-6f);
         float dist_thr = sq(2.5f * sigma);
         T* bestH_ptr   = bestH.get();
 
@@ -345,7 +372,7 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
                       z;
 
             float dist = sq(x_dst_ptr[j] - x) + sq(y_dst_ptr[j] - y);
-            if (dist <= dist_thr) bestInliers++;
+            if (dist <= dist_thr) { bestInliers++; }
         }
     }
 
@@ -358,18 +385,20 @@ int homography(Array<T>& bestH, const Array<float>& x_src,
                const Array<float>& y_dst, const Array<float>& initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations) {
-    const af::dim4 idims    = x_src.dims();
+    const dim4& idims       = x_src.dims();
     const unsigned nsamples = idims[0];
 
     unsigned iter = iterations;
-    if (htype == AF_HOMOGRAPHY_LMEDS)
-        iter = std::min(
-            iter, (unsigned)(log(1.f - LMEDSConfidence) /
+    if (htype == AF_HOMOGRAPHY_LMEDS) {
+        iter = min(iter, static_cast<unsigned>(
+                             log(1.f - LMEDSConfidence) /
                              log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
+    }
 
     af::dim4 rdims(4, iter);
-    Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd  = arithOp<float, af_mul_t>(initial, fctr, rdims);
+    Array<float> fctr =
+        createValueArray<float>(rdims, static_cast<float>(nsamples));
+    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
     rnd.eval();
     getQueue().sync();
 
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index eb37f3a118..da3cf25e54 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -14,8 +14,6 @@
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
-using af::dim4;
-
 namespace cpu {
 
 template<typename T>
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index c6a8af4dbb..ded01b348e 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -15,7 +15,7 @@
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
 
 namespace cpu {
 
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 21b493c696..4b5e3cd486 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -17,8 +17,6 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-using af::dim4;
-
 namespace cpu {
 
 template<typename T>
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index f9aa108ae6..9a2172569e 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -21,7 +21,7 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
+using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
 using std::vector;
 
 namespace cpu {
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index cb7b88d83d..38fb1c292b 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -15,7 +15,7 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-using common::half;
+using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
 
 namespace cpu {
 
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index b47ae0bd92..de70c8fef0 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -153,8 +153,9 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
                 // Recalculate key and ctr to emulate how the CUDA backend
                 // calculates these per thread
                 uint key[2] = {lo, hi};
-                uint ctr[4] = {loc + (uint)first_write_idx,
-                               hic + (ctr[0] < loc), (ctr[1] < hic), 0};
+                uint ctr[4] = {loc + (uint)first_write_idx, 0, 0, 0};
+                ctr[1]      = hic + (ctr[0] < loc);
+                ctr[2]      = (ctr[1] < hic);
                 philox(key, ctr);
 
                 // Use the same ctr array for each of the 4 locations,
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 10eb97b36a..9eda1f9253 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -20,11 +20,12 @@ namespace cpu {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim) {
-    const dim4 iDims = input.dims();
+    const dim4 &iDims = input.dims();
 
     dim4 oDims(1);
-    for (int d = 0; d < 4; ++d)
+    for (int d = 0; d < 4; ++d) {
         oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
+    }
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
     getQueue().enqueue(kernel::lookup<in_t, idx_t>, out, input, indices, dim);
diff --git a/src/backend/cpu/math.cpp b/src/backend/cpu/math.cpp
index b061c44b93..8310f12c57 100644
--- a/src/backend/cpu/math.cpp
+++ b/src/backend/cpu/math.cpp
@@ -16,7 +16,7 @@ uchar abs(uchar val) { return val; }
 uintl abs(uintl val) { return val; }
 
 cfloat scalar(float val) {
-    cfloat cval = {(float)val, 0};
+    cfloat cval = {val, 0};
     return cval;
 }
 
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index 8d675d460a..6da92b98e2 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -72,8 +72,8 @@ T mean(const Array<T> &in, const Array<Tw> &wt) {
     const T *inPtr   = in.get();
     const Tw *wtPtr  = wt.get();
 
-    compute_t<T> input   = compute_t<T>(inPtr[0]);
-    compute_t<Tw> weight = compute_t<Tw>(wtPtr[0]);
+    auto input  = compute_t<T>(inPtr[0]);
+    auto weight = compute_t<Tw>(wtPtr[0]);
     MeanOpT Op(input, weight);
 
     for (dim_t l = 0; l < dims[3]; l++) {
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index df326dd86c..e8a0f55ba4 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -24,16 +24,17 @@ using std::vector;
 namespace cpu {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
-                   const float &chromaticSigma, const unsigned &numInterations,
+                   const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor) {
     Array<T> out = createEmptyArray<T>(in.dims());
 
-    if (isColor)
+    if (isColor) {
         getQueue().enqueue(kernel::meanShift<T, true>, out, in, spatialSigma,
-                           chromaticSigma, numInterations);
-    else
+                           chromaticSigma, numIterations);
+    } else {
         getQueue().enqueue(kernel::meanShift<T, false>, out, in, spatialSigma,
-                           chromaticSigma, numInterations);
+                           chromaticSigma, numIterations);
+    }
 
     return out;
 }
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 98d9d23e79..e2dc906fd8 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -42,7 +42,7 @@ void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
-size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
+size_t getMemStepSize() { return memoryManager().getMemStepSize(); }
 
 void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
@@ -56,8 +56,9 @@ template<typename T>
 unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
-    void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    return unique_ptr<T[], function<void(T *)>>((T *)ptr, memFree<T>);
+    T *ptr = static_cast<T *>(
+        memoryManager().alloc(false, 1, dims.get(), sizeof(T)));
+    return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
 }
 
 void *memAllocUser(const size_t &bytes) {
@@ -68,18 +69,16 @@ void *memAllocUser(const size_t &bytes) {
 
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, false);
+    return memoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
 void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
-void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
+void memLock(const void *ptr) { memoryManager().userLock(ptr); }
 
-bool isLocked(const void *ptr) {
-    return memoryManager().isUserLocked((void *)ptr);
-}
+bool isLocked(const void *ptr) { return memoryManager().isUserLocked(ptr); }
 
-void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
+void memUnlock(const void *ptr) { memoryManager().userUnlock(ptr); }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers) {
@@ -92,12 +91,12 @@ T *pinnedAlloc(const size_t &elements) {
     // TODO: make pinnedAlloc aware of array shapes
     dim4 dims(elements);
     void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    return (T *)ptr;
+    return static_cast<T *>(ptr);
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    memoryManager().unlock((void *)ptr, false);
+    memoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
 #define INSTANTIATE(T)                                                \
@@ -128,7 +127,7 @@ void Allocator::shutdown() {
         try {
             cpu::setDevice(n);
             shutdownMemoryManager();
-        } catch (AfError err) {
+        } catch (const AfError &err) {
             continue;  // Do not throw any errors while shutting down
         }
     }
@@ -141,9 +140,9 @@ size_t Allocator::getMaxMemorySize(int id) {
 }
 
 void *Allocator::nativeAlloc(const size_t bytes) {
-    void *ptr = malloc(bytes);
+    void *ptr = malloc(bytes);  // NOLINT(hicpp-no-malloc)
     AF_TRACE("nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
-    if (!ptr) AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM);
+    if (!ptr) { AF_ERROR("Unable to allocate memory", AF_ERR_NO_MEM); }
     return ptr;
 }
 
@@ -152,6 +151,6 @@ void Allocator::nativeFree(void *ptr) {
     // Make sure this pointer is not being used on the queue before freeing the
     // memory.
     getQueue().sync();
-    return free((void *)ptr);
+    free(ptr);  // NOLINT(hicpp-no-malloc)
 }
 }  // namespace cpu
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index a1ddf7d333..aedb9bc214 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -16,10 +16,10 @@
 
 namespace cpu {
 
-static inline int bitCount(int v) {
-    v = v - ((v >> 1) & 0x55555555);
-    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
-    return (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
+static inline unsigned bitCount(unsigned v) {
+    v = v - ((v >> 1U) & 0x55555555U);
+    v = (v & 0x33333333U) + ((v >> 2U) & 0x33333333U);
+    return (((v + (v >> 4U)) & 0xF0F0F0FU) * 0x1010101U) >> 24U;
 }
 
 using af::dim4;
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index d109dbf022..c1d391996e 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -22,11 +22,11 @@ namespace cpu {
 template<typename T, bool isDilation>
 Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     af::borderType padType = isDilation ? AF_PAD_ZERO : AF_PAD_CLAMP_TO_EDGE;
-    const af::dim4 idims   = in.dims();
-    const af::dim4 mdims   = mask.dims();
+    const af::dim4 &idims  = in.dims();
+    const af::dim4 &mdims  = mask.dims();
 
     const af::dim4 lpad(mdims[0] / 2, mdims[1] / 2, 0, 0);
-    const af::dim4 upad(lpad);
+    const af::dim4 &upad(lpad);
     const af::dim4 odims(lpad[0] + idims[0] + upad[0],
                          lpad[1] + idims[1] + upad[1], idims[2], idims[3]);
 
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 4df5cd37f9..916d43d416 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -24,9 +24,9 @@ template<typename T, typename To>
 void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
                        const Array<T>& train, const uint dist_dim,
                        const uint n_dist, const af_match_type dist_type) {
-    uint sample_dim  = (dist_dim == 0) ? 1 : 0;
-    const dim4 qDims = query.dims();
-    const dim4 tDims = train.dims();
+    uint sample_dim   = (dist_dim == 0) ? 1 : 0;
+    const dim4& qDims = query.dims();
+    const dim4& tDims = train.dims();
     const dim4 outDims(n_dist, qDims[sample_dim]);
     const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
 
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 330fc42d7d..54fd77da4b 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -17,11 +17,23 @@
 #include <resize.hpp>
 #include <sort_index.hpp>
 #include <af/dim4.hpp>
+
+#include <cmath>
 #include <cstring>
+#include <functional>
+#include <memory>
+#include <utility>
+#include <vector>
 
 using af::dim4;
-
+using std::ceil;
+using std::floor;
 using std::function;
+using std::min;
+using std::move;
+using std::pow;
+using std::round;
+using std::sqrt;
 using std::unique_ptr;
 using std::vector;
 
@@ -36,21 +48,21 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
     image.eval();
     getQueue().sync();
 
-    unsigned patch_size = REF_PAT_SIZE;
+    float patch_size = REF_PAT_SIZE;
 
-    const af::dim4 idims = image.dims();
-    unsigned min_side    = std::min(idims[0], idims[1]);
-    unsigned max_levels  = 0;
-    float scl_sum        = 0.f;
+    const dim4& idims   = image.dims();
+    float min_side      = min(idims[0], idims[1]);
+    unsigned max_levels = 0;
+    float scl_sum       = 0.f;
 
     for (unsigned i = 0; i < levels; i++) {
         min_side /= scl_fctr;
 
         // Minimum image side for a descriptor to be computed
-        if (min_side < patch_size || max_levels == levels) break;
+        if (min_side < patch_size || max_levels == levels) { break; }
 
         max_levels++;
-        scl_sum += 1.f / (float)std::pow(scl_fctr, (float)i);
+        scl_sum += 1.f / pow(scl_fctr, static_cast<float>(i));
     }
 
     vector<unique_ptr<float[], function<void(float*)>>> h_x_pyr(max_levels);
@@ -61,31 +73,31 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
     vector<unique_ptr<unsigned[], function<void(unsigned*)>>> h_desc_pyr(
         max_levels);
 
-    std::vector<unsigned> feat_pyr(max_levels);
+    vector<unsigned> feat_pyr(max_levels);
     unsigned total_feat = 0;
 
     // Compute number of features to keep for each level
-    std::vector<unsigned> lvl_best(max_levels);
+    vector<unsigned> lvl_best(max_levels);
     unsigned feat_sum = 0;
     for (unsigned i = 0; i < max_levels - 1; i++) {
-        float lvl_scl = (float)std::pow(scl_fctr, (float)i);
-        lvl_best[i]   = ceil((max_feat / scl_sum) / lvl_scl);
+        auto lvl_scl = pow(scl_fctr, static_cast<float>(i));
+        lvl_best[i]  = ceil((static_cast<float>(max_feat) / scl_sum) / lvl_scl);
         feat_sum += lvl_best[i];
     }
     lvl_best[max_levels - 1] = max_feat - feat_sum;
 
     // Maintain a reference to previous level image
-    Array<T> prev_img = createEmptyArray<T>(af::dim4());
-    af::dim4 prev_ldims;
+    Array<T> prev_img = createEmptyArray<T>(dim4());
+    dim4 prev_ldims;
 
-    af::dim4 gauss_dims(9);
-    std::unique_ptr<T[], std::function<void(T*)>> h_gauss;
-    Array<T> gauss_filter = createEmptyArray<T>(af::dim4());
+    dim4 gauss_dims(9);
+    unique_ptr<T[], function<void(T*)>> h_gauss;
+    Array<T> gauss_filter = createEmptyArray<T>(dim4());
 
     for (unsigned i = 0; i < max_levels; i++) {
-        af::dim4 ldims;
-        const float lvl_scl = (float)std::pow(scl_fctr, (float)i);
-        Array<T> lvl_img    = createEmptyArray<T>(af::dim4());
+        dim4 ldims;
+        const auto lvl_scl = pow(scl_fctr, static_cast<float>(i));
+        Array<T> lvl_img   = createEmptyArray<T>(dim4());
 
         if (i == 0) {
             // First level is used in its original size
@@ -114,7 +126,7 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
         Array<float> score_feat = createEmptyArray<float>(dim4());
 
         // Round feature size to nearest odd integer
-        float size = 2.f * floor(patch_size / 2.f) + 1.f;
+        float size = 2.f * floor(static_cast<float>(patch_size) / 2.f) + 1.f;
 
         // Avoid keeping features that might be too wide and might not fit on
         // the image, sqrt(2.f) is the radius when angle is 45 degrees and
@@ -153,7 +165,7 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
         sort_index<float>(harris_sorted, harris_idx, score_harris, 0, false);
         getQueue().sync();
 
-        usable_feat = std::min(usable_feat, lvl_best[i]);
+        usable_feat = min(usable_feat, lvl_best[i]);
 
         if (usable_feat == 0) {
             h_score_harris.release();
@@ -201,26 +213,27 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
         // Compute ORB descriptors
         auto h_desc_lvl = memAlloc<unsigned>(usable_feat * 8);
         memset(h_desc_lvl.get(), 0, usable_feat * 8 * sizeof(unsigned));
-        if (blur_img)
+        if (blur_img) {
             kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat, h_x_lvl.get(),
                                    h_y_lvl.get(), h_ori_lvl.get(),
                                    h_size_lvl.get(), lvl_filt, lvl_scl,
                                    patch_size);
-        else
+        } else {
             kernel::extract_orb<T>(h_desc_lvl.get(), usable_feat, h_x_lvl.get(),
                                    h_y_lvl.get(), h_ori_lvl.get(),
                                    h_size_lvl.get(), lvl_img, lvl_scl,
                                    patch_size);
+        }
 
         // Store results to pyramids
         total_feat += usable_feat;
         feat_pyr[i]    = usable_feat;
-        h_x_pyr[i]     = std::move(h_x_lvl);
-        h_y_pyr[i]     = std::move(h_y_lvl);
-        h_score_pyr[i] = std::move(h_score_lvl);
-        h_ori_pyr[i]   = std::move(h_ori_lvl);
-        h_size_pyr[i]  = std::move(h_size_lvl);
-        h_desc_pyr[i]  = std::move(h_desc_lvl);
+        h_x_pyr[i]     = move(h_x_lvl);
+        h_y_pyr[i]     = move(h_y_lvl);
+        h_score_pyr[i] = move(h_score_lvl);
+        h_ori_pyr[i]   = move(h_ori_lvl);
+        h_size_pyr[i]  = move(h_size_lvl);
+        h_desc_pyr[i]  = move(h_desc_lvl);
         h_score_harris.release();
         h_gauss.release();
     }
@@ -247,9 +260,9 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
 
         unsigned offset = 0;
         for (unsigned i = 0; i < max_levels; i++) {
-            if (feat_pyr[i] == 0) continue;
+            if (feat_pyr[i] == 0) { continue; }
 
-            if (i > 0) offset += feat_pyr[i - 1];
+            if (i > 0) { offset += feat_pyr[i - 1]; }
 
             memcpy(h_x + offset, h_x_pyr[i].get(), feat_pyr[i] * sizeof(float));
             memcpy(h_y + offset, h_y_pyr[i].get(), feat_pyr[i] * sizeof(float));
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index d520d676ff..b10d168e9a 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -31,7 +31,7 @@ using std::unique_ptr;
 
 namespace cpu {
 
-static const string get_system(void) {
+static string get_system() {
     string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
@@ -68,10 +68,11 @@ string getDeviceInfo() noexcept {
 
     info << string("[0] ") << cinfo.vendor() << ": " << ltrim(model);
 
-    if (memMB)
+    if (memMB) {
         info << ", " << memMB << " MB, ";
-    else
+    } else {
         info << ", Unknown MB, ";
+    }
 
     info << "Max threads(" << cinfo.threads() << ") ";
 #ifndef NDEBUG
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 81aa060ac8..d6f6e7c792 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -16,7 +16,7 @@ using common::half;
 
 namespace cpu {
 void initMersenneState(Array<uint> &state, const uintl seed,
-                       const Array<uint> tbl) {
+                       const Array<uint> &tbl) {
     getQueue().enqueue(kernel::initMersenneState, state.get(), tbl.get(), seed);
 }
 
@@ -157,10 +157,10 @@ INSTANTIATE_NORMAL(float)
 INSTANTIATE_NORMAL(double)
 INSTANTIATE_NORMAL(half)
 
-COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)  // NOLINT
+COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)    // NOLINT
 
-COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)  // NOLINT
+COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)    // NOLINT
 
 }  // namespace cpu
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index bb50388e86..e2e490167d 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -14,10 +14,8 @@
 #include <af/defines.h>
 
 namespace cpu {
-Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
-
 void initMersenneState(Array<uint> &state, const uintl seed,
-                       const Array<uint> tbl);
+                       const Array<uint> &tbl);
 
 template<typename T>
 Array<T> uniformDistribution(const af::dim4 &dims,
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 8795ce8ff7..1e442714cc 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -80,7 +80,7 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
 
     std::vector<af_seq> index;
     for (int i = 0; i < keys.ndims(); ++i) {
-        af_seq s = {0.0, (double)okdims[i] - 1, 1.0};
+        af_seq s = {0.0, static_cast<double>(okdims[i]) - 1, 1.0};
         index.push_back(s);
     }
     Array<Tk> okeys = createSubArray<Tk>(fullsz_okeys, index, true);
@@ -99,15 +99,15 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
     vals_out = ovals;
 }
 
-template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+template<af_op_t op, typename Ti, typename Taccumulate>
+Taccumulate reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     in.eval();
     getQueue().sync();
 
-    Transform<Ti, compute_t<To>, op> transform;
-    Binary<compute_t<To>, op> reduce;
+    Transform<Ti, compute_t<Taccumulate>, op> transform;
+    Binary<compute_t<Taccumulate>, op> reduce;
 
-    compute_t<To> out = Binary<compute_t<To>, op>::init();
+    compute_t<Taccumulate> out = Binary<compute_t<Taccumulate>, op>::init();
 
     // Decrement dimension of select dimension
     af::dim4 dims           = in.dims();
@@ -126,15 +126,17 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
                 for (dim_t i = 0; i < dims[0]; i++) {
                     dim_t idx = i + off1 + off2 + off3;
 
-                    compute_t<To> in_val = transform(inPtr[idx]);
-                    if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
+                    compute_t<Taccumulate> in_val = transform(inPtr[idx]);
+                    if (change_nan) {
+                        in_val = IS_NAN(in_val) ? nanval : in_val;
+                    }
                     out = reduce(in_val, out);
                 }
             }
         }
     }
 
-    return data_t<To>(out);
+    return data_t<Taccumulate>(out);
 }
 
 #define INSTANTIATE(ROp, Ti, To)                                               \
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index 061358a4ec..0f6612768d 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -25,7 +25,7 @@ namespace cpu {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
-    Array<T> out = createValueArray(in.dims(), (T)0);
+    Array<T> out = createValueArray(in.dims(), static_cast<T>(0));
     getQueue().enqueue(kernel::regions<T>, out, in, connectivity);
 
     return out;
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 4bc4646e01..83d2038f38 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -20,9 +20,9 @@ namespace cpu {
 
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
-    const af::dim4 iDims = in.dims();
+    const af::dim4 &iDims = in.dims();
     af::dim4 oDims(0);
-    for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
+    for (int i = 0; i < 4; i++) { oDims[i] = iDims[rdims[i]]; }
 
     Array<T> out = createEmptyArray<T>(oDims);
     getQueue().enqueue(kernel::reorder<T>, out, in, oDims, rdims);
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 6049d0753c..f5850bb106 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -22,7 +22,7 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
     af::dim4 idims = in.dims();
     af::dim4 odims(odim0, odim1, idims[2], idims[3]);
     // Create output placeholder
-    Array<T> out = createValueArray(odims, (T)0);
+    Array<T> out = createValueArray(odims, static_cast<T>(0));
 
     switch (method) {
         case AF_INTERP_NEAREST:
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 4522c60799..0adb09b7b0 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -22,8 +22,8 @@ namespace cpu {
 
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
-    dim4 dims     = in.dims();
-    Array<To> out = createEmptyArray<To>(dims);
+    const dim4& dims = in.dims();
+    Array<To> out    = createEmptyArray<To>(dims);
 
     if (inclusive_scan) {
         switch (in.ndims()) {
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index d9a0e44bbe..9af16f2b33 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -22,8 +22,8 @@ namespace cpu {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
                bool inclusive_scan) {
-    dim4 dims     = in.dims();
-    Array<To> out = createEmptyArray<To>(dims);
+    const dim4& dims = in.dims();
+    Array<To> out    = createEmptyArray<To>(dims);
     kernel::scan_dim_by_key<op, Ti, Tk, To, 1> func1(inclusive_scan);
     kernel::scan_dim_by_key<op, Ti, Tk, To, 2> func2(inclusive_scan);
     kernel::scan_dim_by_key<op, Ti, Tk, To, 3> func3(inclusive_scan);
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 7a70238f92..d4bb1612e3 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -30,18 +30,19 @@ using std::unique;
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
     Array<T> out = createEmptyArray<T>(af::dim4());
-    if (is_sorted)
+    if (is_sorted) {
         out = copyArray<T>(in);
-    else
+    } else {
         out = sort<T>(in, 0, true);
+    }
 
     // Need to sync old jobs since we need to
     // operator on pointers directly in std::unique
     getQueue().sync();
 
-    T *ptr     = out.get();
-    T *last    = unique(ptr, ptr + in.elements());
-    dim_t dist = (dim_t)distance(ptr, last);
+    T *ptr    = out.get();
+    T *last   = unique(ptr, ptr + in.elements());
+    auto dist = static_cast<dim_t>(distance(ptr, last));
 
     dim4 dims(dist, 1, 1, 1);
     out.resetDims(dims);
@@ -70,7 +71,7 @@ Array<T> setUnion(const Array<T> &first, const Array<T> &second,
     T *last = set_union(uFirst.get(), uFirst.get() + first_elements,
                         uSecond.get(), uSecond.get() + second_elements, ptr);
 
-    dim_t dist = (dim_t)distance(ptr, last);
+    auto dist = static_cast<dim_t>(distance(ptr, last));
     dim4 dims(dist, 1, 1, 1);
     out.resetDims(dims);
 
@@ -99,7 +100,7 @@ Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
         set_intersection(uFirst.get(), uFirst.get() + first_elements,
                          uSecond.get(), uSecond.get() + second_elements, ptr);
 
-    dim_t dist = (dim_t)distance(ptr, last);
+    auto dist = static_cast<dim_t>(distance(ptr, last));
     dim4 dims(dist, 1, 1, 1);
     out.resetDims(dims);
 
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 15281c1a53..455f22c608 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -54,14 +54,15 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
     UNUSED(double_input);
     UNUSED(img_scale);
     UNUSED(feature_ratio);
-    if (compute_GLOH)
+    if (compute_GLOH) {
         AF_ERROR(
             "ArrayFire was not built with nonfree support, GLOH disabled\n",
             AF_ERR_NONFREE);
-    else
+    } else {
         AF_ERROR(
             "ArrayFire was not built with nonfree support, SIFT disabled\n",
             AF_ERR_NONFREE);
+    }
 #endif
 }
 
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 8a45b4919c..4f80d442e7 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -79,6 +79,7 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
     int NRHS   = b.dims()[1];
     Array<T> B = copyArray<T>(b);
 
+    // NOLINTNEXTLINE
     auto func = [=](CParam<T> A, Param<T> B, CParam<int> pivot, int N,
                     int NRHS) {
         getrs_func<T>()(AF_LAPACK_COL_MAJOR, 'N', N, NRHS, A.get(),
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 01c8e266da..50f44dcae9 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -52,10 +52,11 @@ template<typename T>
 void sort0(Array<T>& val, bool isAscending) {
     int higherDims = val.elements() / val.dims()[0];
     // TODO Make a better heurisitic
-    if (higherDims > 10)
+    if (higherDims > 10) {
         sortBatched<T, 0>(val, isAscending);
-    else
+    } else {
         getQueue().enqueue(kernel::sort0Iterative<T>, val, isAscending);
+    }
 }
 
 template<typename T>
@@ -74,7 +75,7 @@ Array<T> sort(const Array<T>& in, const unsigned dim, bool isAscending) {
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
         preorderDims[0]  = out.dims()[dim];
-        for (int i = 1; i <= (int)dim; i++) {
+        for (int i = 1; i <= static_cast<int>(dim); i++) {
             reorderDims[i - 1] = i;
             preorderDims[i]    = out.dims()[i - 1];
         }
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index f4a18f6202..e69672e6a4 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -44,7 +44,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
         preorderDims[0]  = okey.dims()[dim];
-        for (int i = 1; i <= (int)dim; i++) {
+        for (int i = 1; i <= static_cast<int>(dim); i++) {
             reorderDims[i - 1] = i;
             preorderDims[i]    = okey.dims()[i - 1];
         }
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index 4b8e84c2b6..c7ec0b8c05 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -49,7 +49,7 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
         af::dim4 reorderDims(0, 1, 2, 3);
         reorderDims[dim] = 0;
         preorderDims[0]  = okey.dims()[dim];
-        for (int i = 1; i <= (int)dim; i++) {
+        for (int i = 1; i <= static_cast<int>(dim); i++) {
             reorderDims[i - 1] = i;
             preorderDims[i]    = okey.dims()[i - 1];
         }
diff --git a/src/backend/cpu/sort_index.hpp b/src/backend/cpu/sort_index.hpp
index 001f152b95..e4a3cbf775 100644
--- a/src/backend/cpu/sort_index.hpp
+++ b/src/backend/cpu/sort_index.hpp
@@ -11,6 +11,6 @@
 
 namespace cpu {
 template<typename T>
-void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in,
+void sort_index(Array<T> &okey, Array<unsigned> &oval, const Array<T> &in,
                 const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 6409c0789b..7e490d0983 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -83,13 +83,14 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
     Array<int> rowIdx = in.getRowIdx();
     Array<int> colIdx = in.getColIdx();
 
-    if (stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR) {
         getQueue().enqueue(kernel::csr2dense<T>, dense, values, rowIdx, colIdx);
-    else if (stype == AF_STORAGE_COO)
+    } else if (stype == AF_STORAGE_COO) {
         getQueue().enqueue(kernel::coo2dense<T>, dense, values, rowIdx, colIdx);
-    else
+    } else {
         AF_ERROR("CPU Backend only supports CSR or COO to Dense",
                  AF_ERR_NOT_SUPPORTED);
+    }
 
     return dense;
 }
@@ -98,8 +99,8 @@ template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     in.eval();
 
-    auto converted =
-        createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    auto converted = createEmptySparseArray<T>(
+        in.dims(), static_cast<int>(in.getNNZ()), dest);
     converted.eval();
 
     function<void(Param<T>, Param<int>, Param<int>, CParam<T>, CParam<int>,
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index ec2383b244..f07d9c57c4 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -27,25 +27,28 @@
 #include <string>
 #include <vector>
 
-namespace cpu {
+using common::createArrayDataSparseArray;
+using common::createEmptySparseArray;
+using common::SparseArray;
+using std::numeric_limits;
 
-using namespace common;
+namespace cpu {
 
 template<typename T>
 T getInf() {
-    return scalar<T>(std::numeric_limits<T>::infinity());
+    return scalar<T>(numeric_limits<T>::infinity());
 }
 
 template<>
 cfloat getInf() {
-    return scalar<cfloat, float>(std::numeric_limits<float>::infinity(),
-                                 std::numeric_limits<float>::infinity());
+    return scalar<cfloat, float>(numeric_limits<float>::infinity(),
+                                 numeric_limits<float>::infinity());
 }
 
 template<>
 cdouble getInf() {
-    return scalar<cdouble, double>(std::numeric_limits<double>::infinity(),
-                                   std::numeric_limits<double>::infinity());
+    return scalar<cdouble, double>(numeric_limits<double>::infinity(),
+                                   numeric_limits<double>::infinity());
 }
 
 template<typename T, af_op_t op>
@@ -109,9 +112,9 @@ template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     af::storage sfmt = lhs.getStorage();
 
-    const dim4 dims = lhs.dims();
-    const uint M    = dims[0];
-    const uint N    = dims[1];
+    const dim4 &dims = lhs.dims();
+    const uint M     = dims[0];
+    const uint N     = dims[1];
 
     auto rowArr = createEmptyArray<int>(dim4(M + 1));
 
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index edebaa4b1f..bac8bba6ac 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -69,12 +69,12 @@ using scale_type =
                               const typename blas_base<T>::type, const T>::type;
 
 template<typename To, typename Ti>
-To getScaleValue(Ti val) {
-    return (To)(val);
+auto getScaleValue(Ti val) -> std::remove_cv_t<To> {
+    return static_cast<std::remove_cv_t<To>>(val);
 }
 
 template<typename T, int value>
-scale_type<T> getScale() {
+scale_type<T> getScale() {  // NOLINT(readability-const-return-type)
     static T val(value);
     return getScaleValue<scale_type<T>, T>(val);
 }
@@ -93,7 +93,7 @@ sparse_operation_t toSparseTranspose(af_mat_prop opt) {
 #ifdef USE_MKL
 
 template<>
-const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
+sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
     sp_cfloat ret;
     ret.real = val.real();
     ret.imag = val.imag();
@@ -101,7 +101,7 @@ const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
 }
 
 template<>
-const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val) {
+sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val) {
     sp_cdouble ret;
     ret.real = val.real();
     ret.imag = val.imag();
@@ -240,7 +240,7 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                              pE, const_cast<int *>(colIdx.get()),
                              reinterpret_cast<ptr_type<T>>(vptr));
 
-        struct matrix_descr descrLhs;
+        struct matrix_descr descrLhs {};
         descrLhs.type = SPARSE_MATRIX_TYPE_GENERAL;
 
         mkl_sparse_optimize(csrLhs);
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index ac9197f11b..9d951badf8 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -20,8 +20,8 @@ namespace cpu {
 
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
-    const af::dim4 iDims = in.dims();
-    af::dim4 oDims       = iDims;
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
     oDims *= tileDims;
 
     if (iDims.elements() == 0 || oDims.elements() == 0) {
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index 8fd5393e25..553013001b 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -34,7 +34,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
     int ndims = in.dims().ndims();
     for (int i = 0; i < ndims; i++) {
         if (i == dim) {
-            out_dims[i] = min(k, (int)in.dims()[i]);
+            out_dims[i] = min(k, static_cast<int>(in.dims()[i]));
         } else {
             out_dims[i] = in.dims()[i];
         }
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 7f90f1a50d..f03dd57919 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -17,8 +17,8 @@ namespace cpu {
 
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
-               const dim4 &odims, const af_interp_type method,
-               const bool inverse, const bool perspective) {
+               const af_interp_type method, const bool inverse,
+               const bool perspective) {
     out.eval();
     in.eval();
     tf.eval();
@@ -45,7 +45,7 @@ void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
 
 #define INSTANTIATE(T)                                                       \
     template void transform(Array<T> &out, const Array<T> &in,               \
-                            const Array<float> &tf, const dim4 &odims,       \
+                            const Array<float> &tf,                          \
                             const af_interp_type method, const bool inverse, \
                             const bool perspective);
 
diff --git a/src/backend/cpu/transform.hpp b/src/backend/cpu/transform.hpp
index 1ddd73d4d6..e00284980a 100644
--- a/src/backend/cpu/transform.hpp
+++ b/src/backend/cpu/transform.hpp
@@ -12,6 +12,6 @@
 namespace cpu {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
-               const af::dim4 &odims, const af_interp_type method,
-               const bool inverse, const bool perspective);
+               const af_interp_type method, const bool inverse,
+               const bool perspective);
 }
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index cd5a6b5c8e..4617f19b97 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -24,7 +24,7 @@ namespace cpu {
 
 template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate) {
-    const dim4 inDims  = in.dims();
+    const dim4 &inDims = in.dims();
     const dim4 outDims = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
     // create an array with first two dimensions swapped
     Array<T> out = createEmptyArray<T>(outDims);
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 79232a332b..58be372157 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -30,7 +30,7 @@ using data_t = typename common::kernel_type<T>::data;
 
 namespace common {
 template<typename T>
-class kernel_type;
+struct kernel_type;
 
 class half;
 
diff --git a/src/backend/cpu/vector_field.hpp b/src/backend/cpu/vector_field.hpp
index 45f5bb5929..c25a1501e4 100644
--- a/src/backend/cpu/vector_field.hpp
+++ b/src/backend/cpu/vector_field.hpp
@@ -14,6 +14,5 @@ namespace cpu {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vector_field);
-
+                       fg_vector_field vfield);
 }
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 9010a306ba..6a6c887faa 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -20,9 +20,9 @@ using common::half;
 namespace cpu {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py, const bool is_column) {
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column) {
     evalMultiple<T>(std::vector<Array<T> *>{const_cast<Array<T> *>(&in), &out});
 
     if (is_column) {
@@ -35,10 +35,10 @@ void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
 }
 
 #define INSTANTIATE(T)                                                        \
-    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t ox, \
-                          const dim_t oy, const dim_t wx, const dim_t wy,     \
-                          const dim_t sx, const dim_t sy, const dim_t px,     \
-                          const dim_t py, const bool is_column);
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t wx, \
+                          const dim_t wy, const dim_t sx, const dim_t sy,     \
+                          const dim_t px, const dim_t py,                     \
+                          const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/wrap.hpp b/src/backend/cpu/wrap.hpp
index c37d05c0ef..bcfe18ef5e 100644
--- a/src/backend/cpu/wrap.hpp
+++ b/src/backend/cpu/wrap.hpp
@@ -12,9 +12,9 @@
 namespace cpu {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py, const bool is_column);
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column);
 
 template<typename T>
 Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index b75e809295..6bfb45ff27 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -21,6 +21,7 @@
 #include <cstddef>
 #include <memory>
 #include <numeric>
+#include <utility>
 
 using af::dim4;
 using common::half;
@@ -30,6 +31,7 @@ using common::NodeIterator;
 using cuda::jit::BufferNode;
 
 using std::accumulate;
+using std::move;
 using std::shared_ptr;
 using std::vector;
 
@@ -52,9 +54,9 @@ Node_ptr bufferNodePtr() {
 }
 
 template<typename T>
-Array<T>::Array(af::dim4 dims)
+Array<T>::Array(const af::dim4 &dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data((dims.elements() ? memAlloc<T>(dims.elements()).release() : nullptr),
            memFree<T>)
     , data_dims(dims)
@@ -63,10 +65,10 @@ Array<T>::Array(af::dim4 dims)
     , owner(true) {}
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, const T *const in_data, bool is_device,
+Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
                 bool copy_device)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(
           ((is_device & !copy_device) ? const_cast<T *>(in_data)
                                       : memAlloc<T>(dims.elements()).release()),
@@ -99,7 +101,7 @@ template<typename T>
 Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
                 const dim4 &strides)
     : info(parent.getDevId(), dims, offset_, strides,
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(parent.getData())
     , data_dims(parent.getDataDims())
     , node(bufferNodePtr<T>())
@@ -112,30 +114,31 @@ Array<T>::Array(Param<T> &tmp, bool owner_)
            af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]), 0,
            af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2],
                     tmp.strides[3]),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(tmp.ptr, owner_ ? std::function<void(T *)>(memFree<T>)
-                           : std::function<void(T *)>([](T *) {}))
+                           : std::function<void(T *)>([](T * /*unused*/) {}))
     , data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]))
     , node(bufferNodePtr<T>())
     , ready(true)
     , owner(owner_) {}
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, common::Node_ptr n)
+Array<T>::Array(const af::dim4 &dims, common::Node_ptr n)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data()
     , data_dims(dims)
-    , node(n)
+    , node(move(n))
     , ready(false)
     , owner(true) {}
 
 template<typename T>
-Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
+Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset_,
                 const T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
-           (af_dtype)dtype_traits<T>::af_type)
-    , data(is_device ? (T *)in_data : memAlloc<T>(info.total()).release(),
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(is_device ? const_cast<T *>(in_data)
+                     : memAlloc<T>(info.total()).release(),
            memFree<T>)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -152,7 +155,7 @@ Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset_,
 
 template<typename T>
 void Array<T>::eval() {
-    if (isReady()) return;
+    if (isReady()) { return; }
 
     this->setId(getActiveDeviceId());
     this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
@@ -174,7 +177,7 @@ T *Array<T>::device() {
 
 template<typename T>
 void Array<T>::eval() const {
-    if (isReady()) return;
+    if (isReady()) { return; }
     const_cast<Array<T> *>(this)->eval();
 }
 
@@ -211,20 +214,18 @@ void evalMultiple(std::vector<Array<T> *> arrays) {
 
     evalNodes(outputs, nodes);
 
-    for (Array<T> *array : output_arrays) array->node = bufferNodePtr<T>();
-
-    return;
+    for (Array<T> *array : output_arrays) { array->node = bufferNodePtr<T>(); }
 }
 
 template<typename T>
-Array<T>::~Array() {}
+Array<T>::~Array() = default;
 
 template<typename T>
 Node_ptr Array<T>::getNode() {
     if (node->isBuffer()) {
-        unsigned bytes         = this->getDataDims().elements() * sizeof(T);
-        BufferNode<T> *bufNode = reinterpret_cast<BufferNode<T> *>(node.get());
-        Param<T> param         = *this;
+        unsigned bytes = this->getDataDims().elements() * sizeof(T);
+        auto *bufNode  = reinterpret_cast<BufferNode<T> *>(node.get());
+        Param<T> param = *this;
         bufNode->setData(param, data, bytes, isLinear());
     }
     return node;
@@ -253,7 +254,7 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= (int)getMaxJitSize()) {
+    if (root_node->getHeight() >= static_cast<int>(getMaxJitSize())) {
         return kJITHeuristics::TreeHeight;
     }
 
@@ -361,18 +362,18 @@ Array<T> createSubArray(const Array<T> &parent,
         return createSubArray(parentCopy, index, copy);
     }
 
-    dim4 pDims   = parent.dims();
-    dim4 dims    = toDims(index, pDims);
-    dim4 strides = toStride(index, dDims);
+    const dim4 &pDims = parent.dims();
+    dim4 dims         = toDims(index, pDims);
+    dim4 strides      = toStride(index, dDims);
 
     // Find total offsets after indexing
     dim4 offsets = toOffset(index, pDims);
     dim_t offset = parent.getOffset();
-    for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+    for (int i = 0; i < 4; i++) { offset += offsets[i] * parent_strides[i]; }
 
     Array<T> out = Array<T>(parent, dims, offset, strides);
 
-    if (!copy) return out;
+    if (!copy) { return out; }
 
     if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
         out = copyArray(out);
@@ -401,8 +402,6 @@ void writeHostDataArray(Array<T> &arr, const T *const data,
     CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice,
                                cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-    return;
 }
 
 template<typename T>
@@ -414,8 +413,6 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
     CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyDeviceToDevice,
                                cuda::getActiveStream()));
-
-    return;
 }
 
 template<typename T>
@@ -437,11 +434,11 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void destroyArray<T>(Array<T> * A);                              \
     template Array<T> createNodeArray<T>(const dim4 &size,                    \
                                          common::Node_ptr node);              \
-    template Array<T>::Array(af::dim4 dims, af::dim4 strides, dim_t offset,   \
-                             const T *const in_data, bool is_device);         \
-    template Array<T>::Array(af::dim4 dims, const T *const in_data,           \
+    template Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides,   \
+                             dim_t offset, const T *const in_data,            \
+                             bool is_device);                                 \
+    template Array<T>::Array(const af::dim4 &dims, const T *const in_data,    \
                              bool is_device, bool copy_device);               \
-    template Array<T>::~Array();                                              \
     template Node_ptr Array<T>::getNode() const;                              \
     template void Array<T>::eval();                                           \
     template void Array<T>::eval() const;                                     \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 33b2588672..887bbc4baa 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -33,16 +33,17 @@ template<typename T>
 void evalNodes(Param<T> out, common::Node *node);
 
 template<typename T>
-void evalNodes(std::vector<Param<T>> &out, std::vector<common::Node *> nodes);
+void evalNodes(std::vector<Param<T>> &out,
+               const std::vector<common::Node *> &nodes);
 
 template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays);
 
 template<typename T>
-Array<T> createNodeArray(const af::dim4 &size, common::Node_ptr node);
+Array<T> createNodeArray(const af::dim4 &dims, common::Node_ptr node);
 
 template<typename T>
-Array<T> createValueArray(const af::dim4 &size, const T &value);
+Array<T> createValueArray(const af::dim4 &dims, const T &value);
 
 // Creates an array and copies from the \p data pointer located in host memory
 //
@@ -52,11 +53,12 @@ template<typename T>
 Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &size, void *data);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
 
 template<typename T>
-Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
-                            const T *const in_data, bool is_device) {
+Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
+                            dim_t offset, const T *const in_data,
+                            bool is_device) {
     return Array<T>(dims, strides, offset, in_data, is_device);
 }
 
@@ -73,7 +75,7 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 ///
 /// \param[in] size The dimension of the output array
 template<typename T>
-Array<T> createEmptyArray(const af::dim4 &size);
+Array<T> createEmptyArray(const af::dim4 &dims);
 
 /// Create an Array object from Param<T> object.
 ///
@@ -82,7 +84,7 @@ Array<T> createEmptyArray(const af::dim4 &size);
 /// If false
 ///                  the Array<T> will not delete the object on destruction
 template<typename T>
-Array<T> createParamArray(Param<T> &in, bool owner);
+Array<T> createParamArray(Param<T> &tmp, bool owner);
 
 template<typename T>
 Array<T> createSubArray(const Array<T> &parent,
@@ -124,18 +126,18 @@ class Array {
     bool ready;
     bool owner;
 
-    Array(af::dim4 dims);
+    Array(const af::dim4 &dims);
 
-    explicit Array(af::dim4 dims, const T *const in_data,
+    explicit Array(const af::dim4 &dims, const T *const in_data,
                    bool is_device = false, bool copy_device = false);
-    Array(const Array<T> &parnt, const dim4 &dims, const dim_t &offset,
+    Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset,
           const dim4 &stride);
     Array(Param<T> &tmp, bool owner);
-    Array(af::dim4 dims, common::Node_ptr n);
+    Array(const af::dim4 &dims, common::Node_ptr n);
 
    public:
-    Array(af::dim4 dims, af::dim4 strides, dim_t offset, const T *const in_data,
-          bool is_device = false);
+    Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
+          const T *const in_data, bool is_device = false);
 
     void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
     void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
@@ -238,14 +240,15 @@ class Array {
 
     friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
     friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
-    friend Array<T> createHostDataArray<T>(const af::dim4 &size,
+    friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &size, void *data);
-    friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
-                                          dim_t offset, const T *const in_data,
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
+    friend Array<T> createStridedArray<T>(const af::dim4 &dims,
+                                          const af::dim4 &strides, dim_t offset,
+                                          const T *const in_data,
                                           bool is_device);
 
-    friend Array<T> createEmptyArray<T>(const af::dim4 &size);
+    friend Array<T> createEmptyArray<T>(const af::dim4 &dims);
     friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
     friend Array<T> createNodeArray<T>(const af::dim4 &dims,
                                        common::Node_ptr node);
diff --git a/src/backend/cuda/Event.hpp b/src/backend/cuda/Event.hpp
index 4d9cb7e295..b6600934e4 100644
--- a/src/backend/cuda/Event.hpp
+++ b/src/backend/cuda/Event.hpp
@@ -51,7 +51,7 @@ class CUDARuntimeEventPolicy {
 using Event = common::EventBase<CUDARuntimeEventPolicy>;
 
 /// \brief Creates a new event and marks it in the stream
-Event makeEvent(cudaStream_t stream);
+Event makeEvent(cudaStream_t queue);
 
 af_event createEvent();
 
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index c2f45f488e..5778f72658 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -18,7 +18,8 @@
 
 namespace cuda {
 GraphicsResourceManager::ShrdResVector
-GraphicsResourceManager::registerResources(std::vector<uint32_t> resources) {
+GraphicsResourceManager::registerResources(
+    const std::vector<uint32_t>& resources) {
     ShrdResVector output;
 
     auto deleter = [](cudaGraphicsResource_t* handle) {
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index ff6a261ba1..ba05c2dbe3 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -23,10 +23,11 @@ class GraphicsResourceManager
     using ShrdResVector = std::vector<std::shared_ptr<cudaGraphicsResource_t>>;
 
     GraphicsResourceManager() {}
-    ShrdResVector registerResources(std::vector<uint32_t> resources);
+    static ShrdResVector registerResources(
+        const std::vector<uint32_t> &resources);
 
    protected:
-    GraphicsResourceManager(GraphicsResourceManager const&);
-    void operator=(GraphicsResourceManager const&);
+    GraphicsResourceManager(GraphicsResourceManager const &);
+    void operator=(GraphicsResourceManager const &);
 };
 }  // namespace cuda
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.cpp b/src/backend/cuda/ThrustArrayFirePolicy.cpp
index c67a4ac2e5..6f21b96ed3 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.cpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.cpp
@@ -11,9 +11,11 @@
 
 namespace cuda {
 
-cudaStream_t get_stream(ThrustArrayFirePolicy) { return getActiveStream(); }
+cudaStream_t get_stream(ThrustArrayFirePolicy /*unused*/) {
+    return getActiveStream();
+}
 
-cudaError_t synchronize_stream(ThrustArrayFirePolicy) {
+cudaError_t synchronize_stream(ThrustArrayFirePolicy /*unused*/) {
     return cudaStreamSynchronize(getActiveStream());
 }
 
diff --git a/src/backend/cuda/blas.cu b/src/backend/cuda/blas.cu
index 188a426118..3f6dec1fa8 100644
--- a/src/backend/cuda/blas.cu
+++ b/src/backend/cuda/blas.cu
@@ -176,7 +176,6 @@ cudaDataType_t getComputeType() {
 
 template<>
 cudaDataType_t getComputeType<half>() {
-    auto dev            = getDeviceProp(getActiveDeviceId());
     cudaDataType_t algo = getType<half>();
     // There is probbaly a bug in nvidia cuda docs and/or drivers: According to
     // https://docs.nvidia.com/cuda/cublas/index.html#cublas-GemmEx computeType
@@ -186,6 +185,7 @@ cudaDataType_t getComputeType<half>() {
     // returns OK. At the moment let's comment out : the drawback is just that
     // the speed of f16 computation on these GPUs is very slow:
     //
+    // auto dev            = getDeviceProp(getActiveDeviceId());
     // if (dev.major == // 6 && dev.minor == 1) { algo = CUDA_R_32F; }
 
     return algo;
@@ -193,9 +193,7 @@ cudaDataType_t getComputeType<half>() {
 
 template<typename T>
 cublasGemmAlgo_t selectGEMMAlgorithm() {
-    auto dev              = getDeviceProp(getActiveDeviceId());
-    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
-    return algo;
+    return CUBLAS_GEMM_DEFAULT;
 }
 
 template<>
diff --git a/src/backend/cuda/cholesky.cpp b/src/backend/cuda/cholesky.cpp
index 9d824e1a10..973df87d83 100644
--- a/src/backend/cuda/cholesky.cpp
+++ b/src/backend/cuda/cholesky.cpp
@@ -41,16 +41,16 @@ namespace cuda {
 
 template<typename T>
 struct potrf_func_def_t {
-    typedef cusolverStatus_t (*potrf_func_def)(cusolverDnHandle_t,
-                                               cublasFillMode_t, int, T *, int,
-                                               T *, int, int *);
+    using potrf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t,
+                                                cublasFillMode_t, int, T *, int,
+                                                T *, int, int *);
 };
 
 template<typename T>
 struct potrf_buf_func_def_t {
-    typedef cusolverStatus_t (*potrf_buf_func_def)(cusolverDnHandle_t,
-                                                   cublasFillMode_t, int, T *,
-                                                   int, int *);
+    using potrf_buf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t,
+                                                    cublasFillMode_t, int, T *,
+                                                    int, int *);
 };
 
 #define CH_FUNC_DEF(FUNC)                                         \
@@ -85,10 +85,11 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper)
+    if (is_upper) {
         triangle<T, true, false>(out, out);
-    else
+    } else {
         triangle<T, false, false>(out, out);
+    }
 
     return out;
 }
@@ -101,7 +102,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     int lwork = 0;
 
     cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER;
-    if (is_upper) uplo = CUBLAS_FILL_MODE_UPPER;
+    if (is_upper) { uplo = CUBLAS_FILL_MODE_UPPER; }
 
     CUSOLVER_CHECK(potrf_buf_func<T>()(solverDnHandle(), uplo, N, in.get(),
                                        in.strides()[1], &lwork));
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 96e2b165a8..90141e2e7a 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -30,8 +30,8 @@ namespace cuda {
 template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind) {
-    const dim4 sDims = signal.dims();
-    const dim4 fDims = filter.dims();
+    const dim4 &sDims = signal.dims();
+    const dim4 &fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
@@ -45,7 +45,7 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
+            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
@@ -59,15 +59,15 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
                    Array<accT> const &r_filter) {
-    const dim4 cfDims = c_filter.dims();
-    const dim4 rfDims = r_filter.dims();
+    const dim4 &cfDims = c_filter.dims();
+    const dim4 &rfDims = r_filter.dims();
 
     const dim_t cfLen = cfDims.elements();
     const dim_t rfLen = rfDims.elements();
 
-    const dim4 sDims = signal.dims();
-    dim4 tDims       = sDims;
-    dim4 oDims       = sDims;
+    const dim4 &sDims = signal.dims();
+    dim4 tDims        = sDims;
+    dim4 oDims        = sDims;
 
     if (expand) {
         tDims[0] += cfLen - 1;
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 9810ac6544..e0db33264b 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -41,7 +41,7 @@ namespace cuda {
 
 template<typename Desc, typename T>
 unique_handle<Desc> toCudnn(Array<T> arr) {
-    dim4 dims = arr.dims();
+    const dim4 &dims = arr.dims();
 
     auto descriptor             = make_handle<Desc>();
     cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
@@ -55,12 +55,12 @@ using scale_type =
 
 template<typename T>
 Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
-                         const dim4 stride, const dim4 padding,
-                         const dim4 dilation) {
+                         const dim4 &stride, const dim4 &padding,
+                         const dim4 &dilation) {
     cudnnHandle_t cudnn = nnHandle();
 
-    dim4 sDims = signal.dims();
-    dim4 fDims = filter.dims();
+    dim4 sDims        = signal.dims();
+    const dim4 &fDims = filter.dims();
 
     const int n = sDims[3];
     const int c = sDims[2];
@@ -115,8 +115,8 @@ Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
     auto workspace_buffer = memAlloc<char>(workspace_bytes);
 
     // perform convolution
-    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
-    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    auto alpha = scalar<scale_type<T>>(1.0);
+    auto beta  = scalar<scale_type<T>>(0.0);
     CUDNN_CHECK(cuda::cudnnConvolutionForward(
         cudnn, &alpha, input_descriptor, signal.device(), filter_descriptor,
         filter.device(), convolution_descriptor, convolution_algorithm,
@@ -138,8 +138,8 @@ constexpr void checkTypeSupport() {
 
 template<typename T>
 Array<T> convolve2_base(const Array<T> &signal, const Array<T> &filter,
-                        const dim4 stride, const dim4 padding,
-                        const dim4 dilation) {
+                        const dim4 &stride, const dim4 &padding,
+                        const dim4 &dilation) {
     dim4 sDims = signal.dims();
     dim4 fDims = filter.dims();
 
@@ -209,9 +209,10 @@ Array<T> data_gradient_base(const Array<T> &incoming_gradient,
                             const Array<T> &original_filter,
                             const Array<T> &convolved_output, af::dim4 stride,
                             af::dim4 padding, af::dim4 dilation) {
-    const dim4 cDims = incoming_gradient.dims();
-    const dim4 sDims = original_signal.dims();
-    const dim4 fDims = original_filter.dims();
+    UNUSED(convolved_output);
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
 
     Array<T> collapsed_filter = original_filter;
 
@@ -250,11 +251,12 @@ Array<T> data_gradient_cudnn(const Array<T> &incoming_gradient,
                              const Array<T> &original_filter,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation) {
+    UNUSED(convolved_output);
     auto cudnn = nnHandle();
 
-    dim4 iDims = incoming_gradient.dims();
-    dim4 sDims = original_signal.dims();
-    dim4 fDims = original_filter.dims();
+    const dim4 &iDims = incoming_gradient.dims();
+    dim4 sDims        = original_signal.dims();
+    dim4 fDims        = original_filter.dims();
 
     cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
 
@@ -295,8 +297,8 @@ Array<T> data_gradient_cudnn(const Array<T> &incoming_gradient,
     auto workspace_buffer = memAlloc<char>(workspace_bytes);
 
     // perform convolution
-    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
-    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    auto alpha = scalar<scale_type<T>>(1.0);
+    auto beta  = scalar<scale_type<T>>(0.0);
 
     CUDNN_CHECK(cuda::cudnnConvolutionBackwardData(
         cudnn, &alpha, w_descriptor, original_filter.get(), dy_descriptor,
@@ -333,9 +335,10 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
                               const Array<T> &original_filter,
                               const Array<T> &convolved_output, af::dim4 stride,
                               af::dim4 padding, af::dim4 dilation) {
-    const dim4 cDims = incoming_gradient.dims();
-    const dim4 sDims = original_signal.dims();
-    const dim4 fDims = original_filter.dims();
+    UNUSED(convolved_output);
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
 
     const bool retCols = false;
     Array<T> unwrapped =
@@ -372,11 +375,12 @@ Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
                                const Array<T> &convolved_output,
                                af::dim4 stride, af::dim4 padding,
                                af::dim4 dilation) {
+    UNUSED(convolved_output);
     auto cudnn = nnHandle();
 
-    dim4 iDims = incoming_gradient.dims();
-    dim4 sDims = original_signal.dims();
-    dim4 fDims = original_filter.dims();
+    const dim4 &iDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
 
     // create dx descriptor
     cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
@@ -410,8 +414,8 @@ Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
     auto workspace_buffer = memAlloc<char>(workspace_bytes);
 
     // perform convolution
-    scale_type<T> alpha = scalar<scale_type<T>>(1.0);
-    scale_type<T> beta  = scalar<scale_type<T>>(0.0);
+    auto alpha = scalar<scale_type<T>>(1.0);
+    auto beta  = scalar<scale_type<T>>(0.0);
     CUDNN_CHECK(cuda::cudnnConvolutionBackwardFilter(
         cudnn, &alpha, x_descriptor, original_signal.device(), dy_descriptor,
         incoming_gradient.device(), convolution_descriptor,
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index a570dab611..6940382b69 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -44,7 +44,6 @@ void copyData(T *dst, const Array<T> &src) {
     CUDA_CHECK(cudaMemcpyAsync(dst, ptr, src.elements() * sizeof(T),
                                cudaMemcpyDeviceToHost, stream));
     CUDA_CHECK(cudaStreamSynchronize(stream));
-    return;
 }
 
 template<typename T>
@@ -221,7 +220,7 @@ INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
 T getScalar(const Array<T> &in) {
-    T retVal;
+    T retVal{};
     CUDA_CHECK(cudaMemcpyAsync(&retVal, in.get(), sizeof(T),
                                cudaMemcpyDeviceToHost,
                                cuda::getActiveStream()));
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 03a14942e3..210a1a6c03 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -16,6 +16,7 @@
 #include <string>
 #include <tuple>
 
+using std::make_tuple;
 using std::string;
 
 namespace cuda {
@@ -25,10 +26,10 @@ spdlog::logger* cudnnModule::getLogger() const noexcept {
 }
 
 auto cudnnVersionComponents(size_t version) {
-    int major = version / 1000;
-    int minor = (version - (major * 1000)) / 100;
-    int patch = (version - (major * 1000) - (minor * 100));
-    return std::tuple<int, int, int>(major, minor, patch);
+    size_t major = version / 1000;
+    size_t minor = (version - (major * 1000)) / 100;
+    size_t patch = (version - (major * 1000) - (minor * 100));
+    return make_tuple(major, minor, patch);
 }
 
 cudnnModule::cudnnModule()
@@ -48,8 +49,8 @@ cudnnModule::cudnnModule()
     MODULE_FUNCTION_INIT(cudnnGetVersion);
 
     int rtmajor, rtminor;
-    int cudnn_version             = this->cudnnGetVersion();
-    int cudnn_rtversion           = 0;
+    size_t cudnn_version          = this->cudnnGetVersion();
+    size_t cudnn_rtversion        = 0;
     std::tie(major, minor, patch) = cudnnVersionComponents(cudnn_version);
 
     if (cudnn_version >= 6000) {
@@ -135,7 +136,7 @@ cudnnModule::cudnnModule()
 }
 
 cudnnModule& getCudnnPlugin() noexcept {
-    static cudnnModule* plugin = new cudnnModule();
+    static auto* plugin = new cudnnModule();
     return *plugin;
 }
 
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index c850185e40..aa762e25fd 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -35,7 +35,7 @@ namespace cuda {
 
 class cudnnModule {
     common::DependencyModule module;
-    int major, minor, patch;
+    int major{}, minor{}, patch{};
 
    public:
     cudnnModule();
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 83aa9a0101..d2a23b7f1c 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -94,10 +94,11 @@ bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
     }
 
     if (rt->major >= compute.first) {
-        if (rt->major == compute.first)
+        if (rt->major == compute.first) {
             return rt->minor >= compute.second;
-        else
+        } else {
             return true;
+        }
     } else {
         return false;
     }
@@ -155,7 +156,7 @@ pair<int, int> getComputeCapability(const int device) {
 }
 
 // pulled from CUTIL from CUDA SDK
-static inline int compute2cores(int major, int minor) {
+static inline int compute2cores(unsigned major, unsigned minor) {
     struct {
         int compute;  // 0xMm (hex), M = major version, m = minor version
         int cores;
@@ -167,7 +168,7 @@ static inline int compute2cores(int major, int minor) {
     };
 
     for (int i = 0; gpus[i].compute != -1; ++i) {
-        if (gpus[i].compute == (major << 4) + minor) return gpus[i].cores;
+        if (gpus[i].compute == (major << 4U) + minor) { return gpus[i].cores; }
     }
     return 0;
 }
@@ -263,7 +264,7 @@ bool DeviceManager::checkGraphicsInteropCapability() {
 }
 
 DeviceManager &DeviceManager::getInstance() {
-    static DeviceManager *my_instance = new DeviceManager();
+    static auto *my_instance = new DeviceManager();
     return *my_instance;
 }
 
@@ -475,9 +476,8 @@ void DeviceManager::checkCudaVsDriverVersion() {
 /// are assuming that the initilization is done in the main thread.
 void initNvrtc() {
     nvrtcProgram prog;
-    auto err = nvrtcCreateProgram(&prog, " ", "dummy", 0, nullptr, nullptr);
+    nvrtcCreateProgram(&prog, " ", "dummy", 0, nullptr, nullptr);
     nvrtcDestroyProgram(&prog);
-    return;
 }
 
 DeviceManager::DeviceManager()
@@ -501,7 +501,7 @@ DeviceManager::DeviceManager()
         int cudaMajorVer = cudaRtVer / 1000;
 
         for (int i = 0; i < nDevices; i++) {
-            cudaDevice_t dev;
+            cudaDevice_t dev{};
             CUDA_CHECK(cudaGetDeviceProperties(&dev.prop, i));
             if (dev.prop.major < getMinSupportedCompute(cudaMajorVer)) {
                 AF_TRACE("Unsuppored device: {}", dev.prop.name);
@@ -540,7 +540,7 @@ DeviceManager::DeviceManager()
     // Initialize all streams to 0.
     // Streams will be created in setActiveDevice()
     for (size_t i = 0; i < MAX_DEVICES; i++) {
-        streams[i] = (cudaStream_t)0;
+        streams[i] = static_cast<cudaStream_t>(0);
         if (i < nDevices) {
             auto prop =
                 make_pair(cuDevices[i].prop.major, cuDevices[i].prop.minor);
@@ -601,11 +601,11 @@ int DeviceManager::setActiveDevice(int device, int nId) {
 
     int numDevices = cuDevices.size();
 
-    if (device >= numDevices) return -1;
+    if (device >= numDevices) { return -1; }
 
     int old = getActiveDeviceId();
 
-    if (nId == -1) nId = getDeviceNativeId(device);
+    if (nId == -1) { nId = getDeviceNativeId(device); }
 
     cudaError_t err = cudaSetDevice(nId);
 
@@ -645,7 +645,7 @@ int DeviceManager::setActiveDevice(int device, int nId) {
         // otherwise fails streamCreate with this error.
         // All other errors will error out
         device++;
-        if (device >= numDevices) break;
+        if (device >= numDevices) { break; }
 
         // Can't call getNativeId here as it will cause an infinite loop with
         // the constructor
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index 4594f21d8a..d661244bf4 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -74,7 +74,7 @@ class DeviceManager {
 
     friend std::string getPlatformInfo() noexcept;
 
-    friend std::string getDriverVersion();
+    friend std::string getDriverVersion() noexcept;
 
     friend std::string getCUDARuntimeVersion() noexcept;
 
@@ -112,7 +112,7 @@ class DeviceManager {
     void checkCudaVsDriverVersion();
     void sortDevices(sort_mode mode = flops);
 
-    int setActiveDevice(int device, int native = -1);
+    int setActiveDevice(int device, int nId = -1);
 
     std::shared_ptr<spdlog::logger> logger;
 
@@ -120,7 +120,7 @@ class DeviceManager {
     std::vector<std::pair<int, int>> devJitComputes;
 
     int nDevices;
-    cudaStream_t streams[MAX_DEVICES];
+    cudaStream_t streams[MAX_DEVICES]{};
 
     std::unique_ptr<graphics::ForgeManager> fgMngr;
 
diff --git a/src/backend/cuda/diff.cpp b/src/backend/cuda/diff.cpp
index 21482bacec..f67a0eabda 100644
--- a/src/backend/cuda/diff.cpp
+++ b/src/backend/cuda/diff.cpp
@@ -17,8 +17,8 @@ namespace cuda {
 
 template<typename T>
 Array<T> diff(const Array<T> &in, const int dim, const bool isDiff2) {
-    const af::dim4 iDims = in.dims();
-    af::dim4 oDims       = iDims;
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
     oDims[dim] -= (isDiff2 + 1);
 
     if (iDims.elements() == 0 || oDims.elements() == 0) {
diff --git a/src/backend/cuda/driver.cpp b/src/backend/cuda/driver.cpp
index 088f2f04de..4edcbf664f 100644
--- a/src/backend/cuda/driver.cpp
+++ b/src/backend/cuda/driver.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <driver.h>
-#include <stdio.h>
-#include <string.h>
+#include <cstdio>
+#include <cstring>
 
 #ifdef OS_WIN
 #include <stdlib.h>
@@ -59,34 +59,39 @@ int nvDriverVersion(char *result, int len) {
     char buffer[1024];
     FILE *f = NULL;
 
-    if (NULL == (f = fopen("/proc/driver/nvidia/version", "r"))) { return 0; }
+    if (NULL == (f = fopen("/proc/driver/nvidia/version", "re"))) { return 0; }
     if (fgets(buffer, 1024, f) == NULL) {
-        if (f) fclose(f);
+        if (f) { fclose(f); }
         return 0;
     }
 
     // just close it now since we've already read what we need
-    if (f) fclose(f);
+    if (f) { fclose(f); }
 
     for (i = 1; i < 8; i++) {
-        while (buffer[pos] != ' ' && buffer[pos] != '\t')
-            if (pos >= 1024 || buffer[pos] == '\0' || buffer[pos] == '\n')
+        while (buffer[pos] != ' ' && buffer[pos] != '\t') {
+            if (pos >= 1024 || buffer[pos] == '\0' || buffer[pos] == '\n') {
                 return 0;
-            else
+            } else {
                 pos++;
-        while (buffer[pos] == ' ' || buffer[pos] == '\t')
-            if (pos >= 1024 || buffer[pos] == '\0' || buffer[pos] == '\n')
+            }
+        }
+        while (buffer[pos] == ' ' || buffer[pos] == '\t') {
+            if (pos >= 1024 || buffer[pos] == '\0' || buffer[pos] == '\n') {
                 return 0;
-            else
+            } else {
                 pos++;
+            }
+        }
     }
 
     epos = pos;
     while (buffer[epos] != ' ' && buffer[epos] != '\t') {
-        if (epos >= 1024 || buffer[epos] == '\0' || buffer[epos] == '\n')
+        if (epos >= 1024 || buffer[epos] == '\0' || buffer[epos] == '\n') {
             return 0;
-        else
+        } else {
             epos++;
+        }
     }
 
     buffer[epos] = '\0';
diff --git a/src/backend/cuda/driver.h b/src/backend/cuda/driver.h
index 835c3fef17..fa828301f9 100644
--- a/src/backend/cuda/driver.h
+++ b/src/backend/cuda/driver.h
@@ -13,7 +13,7 @@
 extern "C" {
 #endif
 
-int nvDriverVersion(char *buffer, int len);
+int nvDriverVersion(char *result, int len);
 
 #ifdef __cplusplus
 }
diff --git a/src/backend/cuda/fast_pyramid.cpp b/src/backend/cuda/fast_pyramid.cpp
index 6bd2055097..8d14cf752c 100644
--- a/src/backend/cuda/fast_pyramid.cpp
+++ b/src/backend/cuda/fast_pyramid.cpp
@@ -36,10 +36,10 @@ void fast_pyramid(vector<unsigned> &feat_pyr, vector<Array<float>> &x_pyr,
         min_side /= scl_fctr;
 
         // Minimum image side for a descriptor to be computed
-        if (min_side < patch_size || max_levels == levels) break;
+        if (min_side < patch_size || max_levels == levels) { break; }
 
         max_levels++;
-        scl_sum += 1.f / (float)std::pow(scl_fctr, (float)i);
+        scl_sum += 1.f / std::pow(scl_fctr, static_cast<float>(i));
     }
 
     // Compute number of features to keep for each level
@@ -47,13 +47,14 @@ void fast_pyramid(vector<unsigned> &feat_pyr, vector<Array<float>> &x_pyr,
     lvl_scl.resize(max_levels);
     unsigned feat_sum = 0;
     for (unsigned i = 0; i < max_levels - 1; i++) {
-        float scl  = (float)std::pow(scl_fctr, (float)i);
+        auto scl   = std::pow(scl_fctr, static_cast<float>(i));
         lvl_scl[i] = scl;
 
         lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl[i]);
         feat_sum += lvl_best[i];
     }
-    lvl_scl[max_levels - 1]  = (float)std::pow(scl_fctr, (float)max_levels - 1);
+    lvl_scl[max_levels - 1] =
+        std::pow(scl_fctr, static_cast<float>(max_levels) - 1);
     lvl_best[max_levels - 1] = max_feat - feat_sum;
 
     // Hold multi-scale image pyramids
diff --git a/src/backend/cuda/fast_pyramid.hpp b/src/backend/cuda/fast_pyramid.hpp
index 762b61c011..ceac076d95 100644
--- a/src/backend/cuda/fast_pyramid.hpp
+++ b/src/backend/cuda/fast_pyramid.hpp
@@ -19,7 +19,7 @@ void fast_pyramid(std::vector<unsigned> &feat_pyr,
                   std::vector<Array<float>> &d_x_pyr,
                   std::vector<Array<float>> &d_y_pyr,
                   std::vector<unsigned> &lvl_best, std::vector<float> &lvl_scl,
-                  std::vector<Array<T>> &img_pyr, const Array<T> &image,
+                  std::vector<Array<T>> &img_pyr, const Array<T> &in,
                   const float fast_thr, const unsigned max_feat,
                   const float scl_fctr, const unsigned levels,
                   const unsigned patch_size);
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index 33105b7a53..3b6d38ce8a 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -20,20 +20,21 @@ using af::dim4;
 namespace cuda {
 
 template<typename T>
-const dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
-                          const dim_t baseDim) {
-    const dim4 i1d = i1.dims();
-    const dim4 i2d = i2.dims();
+dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
+                    const dim_t baseDim) {
+    const dim4& i1d = i1.dims();
+    const dim4& i2d = i2.dims();
 
     dim_t pd[4] = {1, 1, 1, 1};
 
     dim_t max_d0 = (i1d[0] > i2d[0]) ? i1d[0] : i2d[0];
     dim_t min_d0 = (i1d[0] < i2d[0]) ? i1d[0] : i2d[0];
-    pd[0]        = nextpow2((unsigned)((int)ceil(max_d0 / 2.f) + min_d0 - 1));
+    pd[0]        = nextpow2(static_cast<unsigned>(
+        static_cast<int>(ceil(max_d0 / 2.f)) + min_d0 - 1));
 
     for (dim_t k = 1; k < 4; k++) {
         if (k < baseDim) {
-            pd[k] = nextpow2((unsigned)(i1d[k] + i2d[k] - 1));
+            pd[k] = nextpow2(static_cast<unsigned>(i1d[k] + i2d[k] - 1));
         } else {
             pd[k] = i1d[k];
         }
@@ -46,8 +47,8 @@ template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
          dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
-    const dim4 sDims = signal.dims();
-    const dim4 fDims = filter.dims();
+    const dim4& sDims = signal.dims();
+    const dim4& fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
@@ -61,7 +62,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
+            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
@@ -81,20 +82,22 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     if (kind == AF_BATCH_RHS) {
         fft_inplace<cT, baseDim, false>(filter_packed);
-        if (expand)
+        if (expand) {
             kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true>(
                 out, filter_packed, signal, filter);
-        else
+        } else {
             kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(
                 out, filter_packed, signal, filter);
+        }
     } else {
         fft_inplace<cT, baseDim, false>(signal_packed);
-        if (expand)
+        if (expand) {
             kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true>(
                 out, signal_packed, signal, filter);
-        else
+        } else {
             kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(
                 out, signal_packed, signal, filter);
+        }
     }
 
     return out;
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 88feeed330..d415a12aad 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -43,7 +43,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist) {
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, data.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
diff --git a/src/backend/cuda/histogram.cpp b/src/backend/cuda/histogram.cpp
index 8e2b879d7a..5b3359e49a 100644
--- a/src/backend/cuda/histogram.cpp
+++ b/src/backend/cuda/histogram.cpp
@@ -22,7 +22,7 @@ namespace cuda {
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
                          const double &minval, const double &maxval) {
-    const dim4 dims    = in.dims();
+    const dim4 &dims   = in.dims();
     dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
diff --git a/src/backend/cuda/iir.cpp b/src/backend/cuda/iir.cpp
index d03653cb71..9951f4e2da 100644
--- a/src/backend/cuda/iir.cpp
+++ b/src/backend/cuda/iir.cpp
@@ -34,7 +34,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
 
     int num_a = a.dims()[0];
 
-    if (num_a == 1) return c;
+    if (num_a == 1) { return c; }
 
     dim4 ydims = c.dims();
     Array<T> y = createEmptyArray<T>(ydims);
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 996606888c..d247322201 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -47,8 +47,8 @@ void copy_image(const Array<T> &in, fg_image image) {
 
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
         glBufferData(GL_PIXEL_UNPACK_BUFFER, data_size, 0, GL_STREAM_DRAW);
-        GLubyte *ptr =
-            (GLubyte *)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
+        auto *ptr = static_cast<GLubyte *>(
+            glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, in.get(), data_size,
                                        cudaMemcpyDeviceToHost, stream));
diff --git a/src/backend/cuda/index.cpp b/src/backend/cuda/index.cpp
index 3d4b0c1b8d..0974e71dbb 100644
--- a/src/backend/cuda/index.cpp
+++ b/src/backend/cuda/index.cpp
@@ -33,11 +33,11 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     }
 
     // retrieve dimensions, strides and offsets
-    dim4 iDims  = in.dims();
-    dim4 dDims  = in.getDataDims();
-    dim4 oDims  = toDims(seqs, iDims);
-    dim4 iOffs  = toOffset(seqs, dDims);
-    dim4 iStrds = in.strides();
+    const dim4& iDims = in.dims();
+    dim4 dDims        = in.getDataDims();
+    dim4 oDims        = toDims(seqs, iDims);
+    dim4 iOffs        = toOffset(seqs, dDims);
+    dim4 iStrds       = in.strides();
 
     for (dim_t i = 0; i < 4; ++i) {
         p.isSeq[i] = idxrs[i].isSeq;
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 54a98e3c2e..16542cf09e 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -48,16 +48,17 @@ static string getFuncName(const vector<Node *> &output_nodes,
     stringstream funcName;
     stringstream hashName;
 
-    if (is_linear)
+    if (is_linear) {
         funcName << "L_";  // Kernel Linear
-    else
+    } else {
         funcName << "G_";  // Kernel General
+    }
 
     for (const auto &node : output_nodes) {
         funcName << node->getNameStr() << "_";
     }
 
-    for (int i = 0; i < (int)full_nodes.size(); i++) {
+    for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
@@ -68,7 +69,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
     return hashName.str();
 }
 
-static string getKernelString(const string funcName,
+static string getKernelString(const string &funcName,
                               const vector<const Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
                               const vector<int> &output_ids, bool is_linear) {
@@ -149,7 +150,7 @@ struct Param {
     stringstream opsStream;
     stringstream outrefstream;
 
-    for (int i = 0; i < (int)full_nodes.size(); i++) {
+    for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
         const auto &node     = full_nodes[i];
         const auto &ids_curr = full_ids[i];
         // Generate input parameters, only needs current id
@@ -163,8 +164,7 @@ struct Param {
     outrefstream << "const Param<" << full_nodes[output_ids[0]]->getTypeStr()
                  << "> &outref = out" << output_ids[0] << ";\n";
 
-    for (int i = 0; i < (int)output_ids.size(); i++) {
-        int id = output_ids[i];
+    for (int id : output_ids) {
         // Generate output parameters
         outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out"
                        << id << ", \n";
@@ -206,7 +206,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<const Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
-    typedef map<string, Kernel> kc_t;
+    using kc_t = map<string, Kernel>;
 
     thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
@@ -214,7 +214,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
     int device = getActiveDeviceId();
 
-    kc_t::iterator idx = kernelCaches[device].find(funcName);
+    auto idx = kernelCaches[device].find(funcName);
     Kernel entry{nullptr, nullptr};
 
     if (idx == kernelCaches[device].end()) {
@@ -231,11 +231,11 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
 }
 
 template<typename T>
-void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
-    int num_outputs = (int)outputs.size();
-    int device      = getActiveDeviceId();
+void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
+    size_t num_outputs = outputs.size();
+    int device         = getActiveDeviceId();
 
-    if (num_outputs == 0) return;
+    if (num_outputs == 0) { return; }
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
@@ -244,7 +244,7 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
     thread_local vector<int> output_ids;
 
     // Reserve some space to improve performance at smaller sizes
-    if (nodes.size() == 0) {
+    if (nodes.empty()) {
         nodes.reserve(1024);
         output_ids.reserve(output_nodes.size());
         full_nodes.reserve(1024);
@@ -274,10 +274,11 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
 
     int num_odims = 4;
     while (num_odims >= 1) {
-        if (outputs[0].dims[num_odims - 1] == 1)
+        if (outputs[0].dims[num_odims - 1] == 1) {
             num_odims--;
-        else
+        } else {
             break;
+        }
     }
 
     if (is_linear) {
@@ -317,14 +318,14 @@ void evalNodes(vector<Param<T>> &outputs, vector<Node *> output_nodes) {
                       });
     }
 
-    for (int i = 0; i < num_outputs; i++) {
-        args.push_back((void *)&outputs[i]);
+    for (size_t i = 0; i < num_outputs; i++) {
+        args.push_back(static_cast<void *>(&outputs[i]));
     }
 
-    args.push_back((void *)&blocks_x_);
-    args.push_back((void *)&blocks_y_);
-    args.push_back((void *)&blocks_x_total);
-    args.push_back((void *)&num_odims);
+    args.push_back(static_cast<void *>(&blocks_x_));
+    args.push_back(static_cast<void *>(&blocks_y_));
+    args.push_back(static_cast<void *>(&blocks_x_total));
+    args.push_back(static_cast<void *>(&num_odims));
 
     CU_CHECK(cuLaunchKernel(ker, blocks_x, blocks_y, blocks_z, threads_x,
                             threads_y, 1, 0, getActiveStream(), args.data(),
@@ -345,7 +346,6 @@ void evalNodes(Param<T> out, Node *node) {
     outputs.push_back(out);
     output_nodes.push_back(node);
     evalNodes(outputs, output_nodes);
-    return;
 }
 
 template void evalNodes<float>(Param<float> out, Node *node);
@@ -362,21 +362,30 @@ template void evalNodes<short>(Param<short> out, Node *node);
 template void evalNodes<ushort>(Param<ushort> out, Node *node);
 template void evalNodes<half>(Param<half> out, Node *node);
 
-template void evalNodes<float>(vector<Param<float>> &out, vector<Node *> node);
+template void evalNodes<float>(vector<Param<float>> &out,
+                               const vector<Node *> &node);
 template void evalNodes<double>(vector<Param<double>> &out,
-                                vector<Node *> node);
+                                const vector<Node *> &node);
 template void evalNodes<cfloat>(vector<Param<cfloat>> &out,
-                                vector<Node *> node);
+                                const vector<Node *> &node);
 template void evalNodes<cdouble>(vector<Param<cdouble>> &out,
-                                 vector<Node *> node);
-template void evalNodes<int>(vector<Param<int>> &out, vector<Node *> node);
-template void evalNodes<uint>(vector<Param<uint>> &out, vector<Node *> node);
-template void evalNodes<char>(vector<Param<char>> &out, vector<Node *> node);
-template void evalNodes<uchar>(vector<Param<uchar>> &out, vector<Node *> node);
-template void evalNodes<intl>(vector<Param<intl>> &out, vector<Node *> node);
-template void evalNodes<uintl>(vector<Param<uintl>> &out, vector<Node *> node);
-template void evalNodes<short>(vector<Param<short>> &out, vector<Node *> node);
+                                 const vector<Node *> &node);
+template void evalNodes<int>(vector<Param<int>> &out,
+                             const vector<Node *> &node);
+template void evalNodes<uint>(vector<Param<uint>> &out,
+                              const vector<Node *> &node);
+template void evalNodes<char>(vector<Param<char>> &out,
+                              const vector<Node *> &node);
+template void evalNodes<uchar>(vector<Param<uchar>> &out,
+                               const vector<Node *> &node);
+template void evalNodes<intl>(vector<Param<intl>> &out,
+                              const vector<Node *> &node);
+template void evalNodes<uintl>(vector<Param<uintl>> &out,
+                               const vector<Node *> &node);
+template void evalNodes<short>(vector<Param<short>> &out,
+                               const vector<Node *> &node);
 template void evalNodes<ushort>(vector<Param<ushort>> &out,
-                                vector<Node *> node);
-template void evalNodes<half>(vector<Param<half>> &out, vector<Node *> node);
+                                const vector<Node *> &node);
+template void evalNodes<half>(vector<Param<half>> &out,
+                              const vector<Node *> &node);
 }  // namespace cuda
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 1cf0f51423..6a94c8b644 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -20,7 +20,7 @@ using common::half;
 
 namespace cuda {
 
-af::dim4 calcOffset(const af::dim4 dims, const int dim) {
+af::dim4 calcOffset(const af::dim4 &dims, const int dim) {
     af::dim4 offset;
     offset[0] = (dim == 0) * dims[0];
     offset[1] = (dim == 1) * dims[1];
@@ -77,7 +77,7 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
     std::vector<af::dim4> idims(n_arrays);
 
     dim_t dim_size = 0;
-    for (int i = 0; i < (int)idims.size(); i++) {
+    for (int i = 0; i < static_cast<int>(idims.size()); i++) {
         idims[i] = inputs[i].dims();
         dim_size += idims[i][dim];
     }
diff --git a/src/backend/cuda/lookup.cpp b/src/backend/cuda/lookup.cpp
index 0aadb8dbcb..f5e6bebc69 100644
--- a/src/backend/cuda/lookup.cpp
+++ b/src/backend/cuda/lookup.cpp
@@ -20,11 +20,12 @@ namespace cuda {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim) {
-    const dim4 iDims = input.dims();
+    const dim4 &iDims = input.dims();
 
     dim4 oDims(1);
-    for (dim_t d = 0; d < 4; ++d)
+    for (dim_t d = 0; d < 4; ++d) {
         oDims[d] = (d == dim ? indices.elements() : iDims[d]);
+    }
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
diff --git a/src/backend/cuda/lu.cpp b/src/backend/cuda/lu.cpp
index 5740522ab2..cf3dcc11ea 100644
--- a/src/backend/cuda/lu.cpp
+++ b/src/backend/cuda/lu.cpp
@@ -37,14 +37,14 @@ namespace cuda {
 
 template<typename T>
 struct getrf_func_def_t {
-    typedef cusolverStatus_t (*getrf_func_def)(cusolverDnHandle_t, int, int,
-                                               T *, int, T *, int *, int *);
+    using getrf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t, int, int,
+                                                T *, int, T *, int *, int *);
 };
 
 template<typename T>
 struct getrf_buf_func_def_t {
-    typedef cusolverStatus_t (*getrf_buf_func_def)(cusolverDnHandle_t, int, int,
-                                                   T *, int, int *);
+    using getrf_buf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t, int,
+                                                    int, T *, int, int *);
 };
 
 #define LU_FUNC_DEF(FUNC)                                         \
@@ -129,7 +129,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
                                    in.strides()[1], workspace.get(),
                                    pivot.get(), info.get()));
 
-    if (convert_pivot) convertPivot(pivot, M);
+    if (convert_pivot) { convertPivot(pivot, M); }
 
     return pivot;
 }
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 5eadc9a449..a40a927807 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -38,7 +38,7 @@ namespace cuda {
 
 template<typename T>
 static inline __DH__ T abs(T val) {
-    return abs(val);
+    return ::abs(val);
 }
 static inline __DH__ int abs(int val) { return (val > 0 ? val : -val); }
 static inline __DH__ char abs(char val) { return (val > 0 ? val : -val); }
diff --git a/src/backend/cuda/meanshift.cpp b/src/backend/cuda/meanshift.cpp
index 3f22ab53dd..c2f552df2b 100644
--- a/src/backend/cuda/meanshift.cpp
+++ b/src/backend/cuda/meanshift.cpp
@@ -20,8 +20,8 @@ template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor) {
-    const dim4 dims = in.dims();
-    Array<T> out    = createEmptyArray<T>(dims);
+    const dim4 &dims = in.dims();
+    Array<T> out     = createEmptyArray<T>(dims);
     kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma, numIterations,
                          isColor);
     return out;
diff --git a/src/backend/cuda/medfilt.cpp b/src/backend/cuda/medfilt.cpp
index 41386203cc..fa8435ae80 100644
--- a/src/backend/cuda/medfilt.cpp
+++ b/src/backend/cuda/medfilt.cpp
@@ -23,8 +23,8 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
     ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
-    const dim4 dims = in.dims();
-    Array<T> out    = createEmptyArray<T>(dims);
+    const dim4 &dims = in.dims();
+    Array<T> out     = createEmptyArray<T>(dims);
 
     kernel::medfilt1<T>(out, in, pad, w_wid);
 
@@ -36,8 +36,8 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
     ARG_ASSERT(2, (w_len % 2 != 0));
 
-    const dim4 dims = in.dims();
-    Array<T> out    = createEmptyArray<T>(dims);
+    const dim4 &dims = in.dims();
+    Array<T> out     = createEmptyArray<T>(dims);
 
     kernel::medfilt2<T>(out, in, pad, w_len, w_wid);
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 6e1fba9178..d65122aff2 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -47,7 +47,7 @@ void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
-size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
+size_t getMemStepSize() { return memoryManager().getMemStepSize(); }
 
 void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
@@ -76,17 +76,21 @@ void *memAllocUser(const size_t &bytes) {
 
 template<typename T>
 void memFree(T *ptr) {
-    memoryManager().unlock((void *)ptr, false);
+    memoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
-void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
+void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
-void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
+void memLock(const void *ptr) {
+    memoryManager().userLock(const_cast<void *>(ptr));
+}
 
-void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
+void memUnlock(const void *ptr) {
+    memoryManager().userUnlock(const_cast<void *>(ptr));
+}
 
 bool isLocked(const void *ptr) {
-    return memoryManager().isUserLocked((void *)ptr);
+    return memoryManager().isUserLocked(const_cast<void *>(ptr));
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
@@ -105,7 +109,7 @@ T *pinnedAlloc(const size_t &elements) {
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    pinnedMemoryManager().unlock((void *)ptr, false);
+    pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
 #define INSTANTIATE(T)                                 \
@@ -135,7 +139,7 @@ void Allocator::shutdown() {
         try {
             cuda::setDevice(n);
             shutdownMemoryManager();
-        } catch (AfError err) {
+        } catch (const AfError &err) {
             continue;  // Do not throw any errors while shutting down
         }
     }
diff --git a/src/backend/cuda/moments.cpp b/src/backend/cuda/moments.cpp
index f963650148..a8c1a53ab7 100644
--- a/src/backend/cuda/moments.cpp
+++ b/src/backend/cuda/moments.cpp
@@ -16,10 +16,10 @@
 
 namespace cuda {
 
-static inline int bitCount(int v) {
-    v = v - ((v >> 1) & 0x55555555);
-    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
-    return (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
+static inline unsigned bitCount(unsigned v) {
+    v = v - ((v >> 1U) & 0x55555555U);
+    v = (v & 0x33333333U) + ((v >> 2U) & 0x33333333U);
+    return (((v + (v >> 4U)) & 0xF0F0F0FU) * 0x1010101U) >> 24U;
 }
 
 using af::dim4;
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index e2cbdb37c6..e3b28f325e 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -53,6 +53,7 @@
 
 using std::accumulate;
 using std::array;
+using std::back_insert_iterator;
 using std::begin;
 using std::end;
 using std::extent;
@@ -245,7 +246,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
     }
 
     auto computeFlag = getComputeCapability(device);
-    array<char, 32> arch;
+    array<char, 32> arch{};
     snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
              computeFlag.first, computeFlag.second);
     vector<const char *> compiler_options = {
@@ -257,7 +258,10 @@ Kernel buildKernel(const int device, const string &nameExpr,
 #endif
     };
     if (!isJIT) {
-        for (auto &s : opts) { compiler_options.push_back(&s[0]); }
+        transform(begin(opts), end(opts),
+                  back_insert_iterator<vector<const char *>>(compiler_options),
+                  [](const std::string &s) { return s.data(); });
+
         compiler_options.push_back("--device-as-default-execution-space");
         NVRTC_CHECK(nvrtcAddNameExpression(prog, ker_name));
     }
@@ -335,15 +339,15 @@ kc_t &getCache(int device) {
     return caches[device];
 }
 
-Kernel findKernel(int device, const string nameExpr) {
+Kernel findKernel(int device, const string &nameExpr) {
     kc_t &cache = getCache(device);
 
-    kc_t::iterator iter = cache.find(nameExpr);
+    auto iter = cache.find(nameExpr);
 
     return (iter == cache.end() ? Kernel{0, 0} : iter->second);
 }
 
-void addKernelToCache(int device, const string nameExpr, Kernel entry) {
+void addKernelToCache(int device, const string &nameExpr, Kernel entry) {
     getCache(device).emplace(nameExpr, entry);
 }
 
@@ -469,16 +473,16 @@ string toString(af_op_t val) {
 }
 
 template<>
-string toString(const char *str) {
-    return string(str);
+string toString(const char *val) {
+    return string(val);
 }
 
 template<>
-string toString(af_interp_type p) {
+string toString(af_interp_type val) {
     const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
-    switch (p) {
+    switch (val) {
         CASE_STMT(AF_INTERP_NEAREST);
         CASE_STMT(AF_INTERP_LINEAR);
         CASE_STMT(AF_INTERP_BILINEAR);
@@ -495,11 +499,11 @@ string toString(af_interp_type p) {
 }
 
 template<>
-string toString(af_border_type p) {
+string toString(af_border_type val) {
     const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
-    switch (p) {
+    switch (val) {
         CASE_STMT(AF_PAD_ZERO);
         CASE_STMT(AF_PAD_SYM);
         CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
@@ -510,11 +514,11 @@ string toString(af_border_type p) {
 }
 
 template<>
-string toString(af_moment_type p) {
+string toString(af_moment_type val) {
     const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
-    switch (p) {
+    switch (val) {
         CASE_STMT(AF_MOMENT_M00);
         CASE_STMT(AF_MOMENT_M01);
         CASE_STMT(AF_MOMENT_M10);
@@ -526,11 +530,11 @@ string toString(af_moment_type p) {
 }
 
 template<>
-string toString(af_match_type p) {
+string toString(af_match_type val) {
     const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
-    switch (p) {
+    switch (val) {
         CASE_STMT(AF_SAD);
         CASE_STMT(AF_ZSAD);
         CASE_STMT(AF_LSAD);
@@ -539,47 +543,51 @@ string toString(af_match_type p) {
         CASE_STMT(AF_LSSD);
         CASE_STMT(AF_NCC);
         CASE_STMT(AF_ZNCC);
+        CASE_STMT(AF_SHD);
     }
 #undef CASE_STMT
     return retVal;
 }
 
 template<>
-string toString(af_flux_function p) {
+string toString(af_flux_function val) {
     const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
-    switch (p) {
+    switch (val) {
         CASE_STMT(AF_FLUX_QUADRATIC);
         CASE_STMT(AF_FLUX_EXPONENTIAL);
+        CASE_STMT(AF_FLUX_DEFAULT);
     }
 #undef CASE_STMT
     return retVal;
 }
 
 template<>
-string toString(AF_BATCH_KIND p) {
+string toString(AF_BATCH_KIND val) {
     const char *retVal = NULL;
 #define CASE_STMT(v) \
     case v: retVal = #v; break
-    switch (p) {
+    switch (val) {
         CASE_STMT(AF_BATCH_NONE);
         CASE_STMT(AF_BATCH_LHS);
         CASE_STMT(AF_BATCH_RHS);
         CASE_STMT(AF_BATCH_SAME);
         CASE_STMT(AF_BATCH_DIFF);
+        CASE_STMT(AF_BATCH_UNSUPPORTED);
     }
 #undef CASE_STMT
     return retVal;
 }
 
 Kernel getKernel(const string &nameExpr, const string &source,
-                 const vector<TemplateArg> &targs,
+                 const vector<TemplateArg> &templateArgs,
                  const vector<string> &compileOpts) {
     vector<string> args;
-    args.reserve(targs.size());
+    args.reserve(templateArgs.size());
 
-    transform(targs.begin(), targs.end(), std::back_inserter(args),
+    transform(templateArgs.begin(), templateArgs.end(),
+              std::back_inserter(args),
               [](const TemplateArg &arg) -> string { return arg._tparam; });
 
     string tInstance = nameExpr + "<" + args[0];
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index 462161ff98..ebea991241 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -105,12 +105,12 @@ struct Kernel {
 
 // TODO(pradeep): remove this in API and merge JIT and nvrtc caches
 Kernel buildKernel(const int device, const std::string& nameExpr,
-                   const std::string& jitSourceString,
+                   const std::string& jit_ker,
                    const std::vector<std::string>& opts = {},
                    const bool isJIT                     = false);
 
 template<typename T>
-std::string toString(T value);
+std::string toString(T val);
 
 struct TemplateArg {
     std::string _tparam;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 78e58fa8a1..f6814254b4 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -20,6 +20,7 @@
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
+#include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
 #include <common/unique_handle.hpp>
@@ -37,6 +38,7 @@
 #include <af/cuda.h>
 #include <af/device.h>
 #include <af/version.h>
+#include <memory>
 
 #include <algorithm>
 #include <array>
@@ -62,7 +64,7 @@ using common::memory::MemoryManagerBase;
 
 namespace cuda {
 
-static const std::string get_system(void) {
+static std::string get_system() {
     std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
@@ -118,7 +120,7 @@ unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
 
         // Not throwing an AF_ERROR here because we are in a lambda that could
         // be executing on another thread;
-        if (!(*handle)) getLogger()->error("Error initalizing cuDNN");
+        if (!(*handle)) { getLogger()->error("Error initalizing cuDNN"); }
     });
     if (error) {
         string error_msg = fmt::format("Error initializing cuDNN({}): {}.",
@@ -136,7 +138,7 @@ unique_ptr<PlanCache> &cufftManager(const int deviceId) {
     thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId],
-              [&] { caches[deviceId].reset(new PlanCache()); });
+              [&] { caches[deviceId] = std::make_unique<PlanCache>(); });
     return caches[deviceId];
 }
 
@@ -178,17 +180,30 @@ unique_handle<cusparseHandle_t> *cusparseManager(const int deviceId) {
 }
 
 DeviceManager::~DeviceManager() {
-    // Reset unique_ptrs for all cu[BLAS | Sparse | Solver]
-    // handles of all devices
-    for (int i = 0; i < nDevices; ++i) {
-        setDevice(i);
-        delete cusolverManager(i);
-        delete cusparseManager(i);
-        cufftManager(i).reset();
-        delete cublasManager(i);
+    try {
+        // Reset unique_ptrs for all cu[BLAS | Sparse | Solver]
+        // handles of all devices
+        for (int i = 0; i < nDevices; ++i) {
+            setDevice(i);
+            delete cusolverManager(i);
+            delete cusparseManager(i);
+            cufftManager(i).reset();
+            delete cublasManager(i);
 #ifdef WITH_CUDNN
-        delete nnManager(i);
+            delete nnManager(i);
 #endif
+        }
+    } catch (const AfError &err) {
+        AF_TRACE(
+            "Exception thrown during destruction of DeviceManager(ignoring). "
+            "{}({}):{} "
+            "{}",
+            err.getFileName(), err.getLine(), err.getFunctionName(),
+            err.what());
+    } catch (...) {
+        AF_TRACE(
+            "Unknown exception thrown during destruction of "
+            "DeviceManager(ignoring)");
     }
 }
 
@@ -226,9 +241,9 @@ string getDeviceInfo() noexcept {
 }
 
 string getPlatformInfo() noexcept {
-    string driverVersion    = getDriverVersion();
-    std::string cudaRuntime = getCUDARuntimeVersion();
-    string platform         = "Platform: CUDA Runtime " + cudaRuntime;
+    string driverVersion = getDriverVersion();
+    string cudaRuntime   = getCUDARuntimeVersion();
+    string platform      = "Platform: CUDA Runtime " + cudaRuntime;
     if (!driverVersion.empty()) {
         platform.append(", Driver: ");
         platform.append(driverVersion);
@@ -244,12 +259,12 @@ bool isDoubleSupported(int device) {
 
 bool isHalfSupported(int device) {
     std::array<bool, DeviceManager::MAX_DEVICES> half_supported = []() {
-        std::array<bool, DeviceManager::MAX_DEVICES> out;
+        std::array<bool, DeviceManager::MAX_DEVICES> out{};
         int count = getDeviceCount();
         for (int i = 0; i < count; i++) {
-            auto prop     = getDeviceProp(i);
-            float compute = prop.major * 1000 + prop.minor * 10;
-            out[i]        = compute >= 5030;
+            auto prop   = getDeviceProp(i);
+            int compute = prop.major * 1000 + prop.minor * 10;
+            out[i]      = compute >= 5030;
         }
         return out;
     }();
@@ -275,15 +290,16 @@ void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
     // Sanitize input
     for (int i = 0; i < 256; i++) {
         if (d_name[i] == ' ') {
-            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ')
+            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ') {
                 d_name[i] = 0;
-            else
+            } else {
                 d_name[i] = '_';
+            }
         }
     }
 }
 
-string getDriverVersion() {
+string getDriverVersion() noexcept {
     char driverVersion[1024] = {" "};
     int x = nvDriverVersion(driverVersion, sizeof(driverVersion));
     if (x != 1) {
@@ -293,7 +309,7 @@ string getDriverVersion() {
         return "N/A";
 #endif
         int driver = 0;
-        CUDA_CHECK(cudaDriverGetVersion(&driver));
+        if (cudaDriverGetVersion(&driver)) { return "N/A"; }
         return to_string(driver);
     } else {
         return string(driverVersion);
@@ -343,8 +359,10 @@ int getDeviceCount() {
 int getActiveDeviceId() { return tlocalActiveDeviceId(); }
 
 int getDeviceNativeId(int device) {
-    if (device < (int)DeviceManager::getInstance().cuDevices.size())
+    if (device <
+        static_cast<int>(DeviceManager::getInstance().cuDevices.size())) {
         return DeviceManager::getInstance().cuDevices[device].nativeId;
+    }
     return -1;
 }
 
@@ -353,7 +371,7 @@ int getDeviceIdFromNativeId(int nativeId) {
 
     int devId = 0;
     for (devId = 0; devId < mngr.nDevices; ++devId) {
-        if (nativeId == mngr.cuDevices[devId].nativeId) break;
+        if (nativeId == mngr.cuDevices[devId].nativeId) { break; }
     }
     return devId;
 }
@@ -382,8 +400,10 @@ int setDevice(int device) {
 }
 
 cudaDeviceProp getDeviceProp(int device) {
-    if (device < (int)DeviceManager::getInstance().cuDevices.size())
+    if (device <
+        static_cast<int>(DeviceManager::getInstance().cuDevices.size())) {
         return DeviceManager::getInstance().cuDevices[device].prop;
+    }
     return DeviceManager::getInstance().cuDevices[0].prop;
 }
 
@@ -394,9 +414,9 @@ MemoryManagerBase &memoryManager() {
 
     std::call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.memManager.reset(new common::DefaultMemoryManager(
+        inst.memManager = std::make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
-            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG));
+            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG);
         // Set the memory manager's device memory manager
         std::unique_ptr<cuda::Allocator> deviceMemoryManager(
             new cuda::Allocator());
@@ -414,9 +434,9 @@ MemoryManagerBase &pinnedMemoryManager() {
 
     std::call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.pinnedMemManager.reset(new common::DefaultMemoryManager(
+        inst.pinnedMemManager = std::make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
-            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG));
+            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG);
         // Set the memory manager's device memory manager
         std::unique_ptr<cuda::AllocatorPinned> deviceMemoryManager(
             new cuda::AllocatorPinned());
@@ -455,7 +475,7 @@ GraphicsResourceManager &interopManager() {
     DeviceManager &inst = DeviceManager::getInstance();
 
     std::call_once(initFlags[id], [&] {
-        inst.gfxManagers[id].reset(new GraphicsResourceManager());
+        inst.gfxManagers[id] = std::make_unique<GraphicsResourceManager>();
     });
 
     return *(inst.gfxManagers[id].get());
@@ -470,16 +490,16 @@ BlasHandle blasHandle() { return *cublasManager(cuda::getActiveDeviceId()); }
 #ifdef WITH_CUDNN
 cudnnHandle_t nnHandle() {
     // Keep the getCudnnPlugin call here because module loading can throw an
-    // exception the first time its called. We want to avoid that because the
-    // unique handle object is marked noexcept and could terminate. if the
-    // module is not loaded correctly
+    // exception the first time its called. We want to avoid that because
+    // the unique handle object is marked noexcept and could terminate. if
+    // the module is not loaded correctly
     static cudnnModule keep_me_to_avoid_exceptions_exceptions =
         getCudnnPlugin();
     static unique_handle<cudnnHandle_t> *handle =
         nnManager(cuda::getActiveDeviceId());
-    if (*handle)
+    if (*handle) {
         return *handle;
-    else {
+    } else {
         AF_ERROR("Error Initializing cuDNN\n", AF_ERR_RUNTIME);
     }
 }
@@ -549,6 +569,6 @@ template<>
 __half *array::device<__half>() const {
     void *ptr = NULL;
     af_get_device_ptr(&ptr, get());
-    return (__half *)ptr;
+    return static_cast<__half *>(ptr);
 }
 }  // namespace af
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ce973bfd35..bfc67560f5 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -62,7 +62,7 @@ std::string getDeviceInfo(int device) noexcept;
 
 std::string getPlatformInfo() noexcept;
 
-std::string getDriverVersion();
+std::string getDriverVersion() noexcept;
 
 // Returns the cuda runtime version as a string for the current build. If no
 // runtime is found or an error occured, the string "N/A" is returned
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index 9d4128f98d..c454b0dff1 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -45,7 +45,8 @@ void copy_plot(const Array<T> &P, fg_plot plot) {
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
diff --git a/src/backend/cuda/qr.cpp b/src/backend/cuda/qr.cpp
index f9a5ea8e1d..4c02e60fd0 100644
--- a/src/backend/cuda/qr.cpp
+++ b/src/backend/cuda/qr.cpp
@@ -51,23 +51,23 @@ namespace cuda {
 
 template<typename T>
 struct geqrf_func_def_t {
-    typedef cusolverStatus_t (*geqrf_func_def)(cusolverDnHandle_t, int, int,
-                                               T *, int, T *, T *, int, int *);
+    using geqrf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t, int, int,
+                                                T *, int, T *, T *, int, int *);
 };
 
 template<typename T>
 struct geqrf_buf_func_def_t {
-    typedef cusolverStatus_t (*geqrf_buf_func_def)(cusolverDnHandle_t, int, int,
-                                                   T *, int, int *);
+    using geqrf_buf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t, int,
+                                                    int, T *, int, int *);
 };
 
 template<typename T>
 struct mqr_func_def_t {
-    typedef cusolverStatus_t (*mqr_func_def)(cusolverDnHandle_t,
-                                             cublasSideMode_t,
-                                             cublasOperation_t, int, int, int,
-                                             const T *, int, const T *, T *,
-                                             int, T *, int, int *);
+    using mqr_func_def = cusolverStatus_t (*)(cusolverDnHandle_t,
+                                              cublasSideMode_t,
+                                              cublasOperation_t, int, int, int,
+                                              const T *, int, const T *, T *,
+                                              int, T *, int, int *);
 };
 
 #define QR_FUNC_DEF(FUNC)                                         \
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index 46714825d3..d03eb51e91 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -17,7 +17,7 @@ using common::half;
 
 namespace cuda {
 void initMersenneState(Array<uint> &state, const uintl seed,
-                       const Array<uint> tbl) {
+                       const Array<uint> &tbl) {
     kernel::initMersenneState(state.get(), tbl.get(), seed);
 }
 
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index a5047d3429..ca7bd1a233 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -14,10 +14,8 @@
 #include <af/defines.h>
 
 namespace cuda {
-Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
-
 void initMersenneState(Array<uint> &state, const uintl seed,
-                       const Array<uint> tbl);
+                       const Array<uint> &tbl);
 
 template<typename T>
 Array<T> uniformDistribution(const af::dim4 &dims,
diff --git a/src/backend/cuda/range.cpp b/src/backend/cuda/range.cpp
index 8380241e2c..54cc76268e 100644
--- a/src/backend/cuda/range.cpp
+++ b/src/backend/cuda/range.cpp
@@ -28,8 +28,9 @@ Array<T> range(const dim4& dim, const int seq_dim) {
         _seq_dim = 0;  // column wise sequence
     }
 
-    if (_seq_dim < 0 || _seq_dim > 3)
+    if (_seq_dim < 0 || _seq_dim > 3) {
         AF_ERROR("Invalid rep selection", AF_ERR_ARG);
+    }
 
     Array<T> out = createEmptyArray<T>(dim);
     kernel::range<T>(out, _seq_dim);
diff --git a/src/backend/cuda/reorder.cpp b/src/backend/cuda/reorder.cpp
index 99485516fe..fcc0e6a830 100644
--- a/src/backend/cuda/reorder.cpp
+++ b/src/backend/cuda/reorder.cpp
@@ -22,9 +22,9 @@ namespace cuda {
 
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
-    const af::dim4 iDims = in.dims();
+    const af::dim4 &iDims = in.dims();
     af::dim4 oDims(0);
-    for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
+    for (int i = 0; i < 4; i++) { oDims[i] = iDims[rdims[i]]; }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
diff --git a/src/backend/cuda/resize.cpp b/src/backend/cuda/resize.cpp
index b7e882d31c..25678976e3 100644
--- a/src/backend/cuda/resize.cpp
+++ b/src/backend/cuda/resize.cpp
@@ -17,7 +17,7 @@ namespace cuda {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method) {
-    const af::dim4 iDims = in.dims();
+    const af::dim4 &iDims = in.dims();
     af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
 
     Array<T> out = createEmptyArray<T>(oDims);
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index e23917ce3b..7f0907d5d8 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -46,9 +46,9 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto b_node    = b.getNode();
     int height     = max(a_node->getHeight(), b_node->getHeight());
     height         = max(height, cond_node->getHeight()) + 1;
-    auto node      = make_shared<NaryNode>(
-        NaryNode(getFullName<T>(), shortname<T>(true), "__select", 3,
-                 {{cond_node, a_node, b_node}}, (int)af_select_t, height));
+    auto node      = make_shared<NaryNode>(NaryNode(
+        getFullName<T>(), shortname<T>(true), "__select", 3,
+        {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
@@ -78,7 +78,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto node = make_shared<NaryNode>(NaryNode(
         getFullName<T>(), shortname<T>(true),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
-        (int)(flip ? af_not_select_t : af_select_t), height));
+        static_cast<int>(flip ? af_not_select_t : af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index c5ab83248e..e66fe381fc 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -39,15 +39,16 @@ Array<T> shift(const Array<T> &in, const int sdims[4]) {
 
     string name_str("Sh");
     name_str += shortname<T>(true);
-    const dim4 iDims = in.dims();
-    dim4 oDims       = iDims;
+    const dim4 &iDims = in.dims();
+    dim4 oDims        = iDims;
 
-    array<int, 4> shifts;
+    array<int, 4> shifts{};
     for (int i = 0; i < 4; i++) {
         // sdims_[i] will always be positive and always [0, oDims[i]].
         // Negative shifts are converted to position by going the other way
         // round
-        shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+        shifts[i] = -(sdims[i] % static_cast<int>(oDims[i])) +
+                    oDims[i] * (sdims[i] > 0);
         assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
     }
 
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index 6644d22eb5..ca38716f39 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -45,7 +45,8 @@ void copy_surface(const Array<T> &P, fg_surface surface) {
 
         CheckGL("Begin CUDA fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, P.get(), bytes,
                                        cudaMemcpyDeviceToHost, stream));
diff --git a/src/backend/cuda/susan.cpp b/src/backend/cuda/susan.cpp
index e905daf854..1f2a367e88 100644
--- a/src/backend/cuda/susan.cpp
+++ b/src/backend/cuda/susan.cpp
@@ -49,12 +49,12 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
         resp_out = createEmptyArray<float>(dim4());
         return 0;
     } else {
-        x_out    = createDeviceDataArray<float>(dim4(corners_out),
-                                             (void *)x_corners.get());
-        y_out    = createDeviceDataArray<float>(dim4(corners_out),
-                                             (void *)y_corners.get());
-        resp_out = createDeviceDataArray<float>(dim4(corners_out),
-                                                (void *)resp_corners.get());
+        x_out = createDeviceDataArray<float>(
+            dim4(corners_out), static_cast<void *>(x_corners.get()));
+        y_out = createDeviceDataArray<float>(
+            dim4(corners_out), static_cast<void *>(y_corners.get()));
+        resp_out = createDeviceDataArray<float>(
+            dim4(corners_out), static_cast<void *>(resp_corners.get()));
         x_corners.release();
         y_corners.release();
         resp_corners.release();
diff --git a/src/backend/cuda/susan.hpp b/src/backend/cuda/susan.hpp
index 1d50a846be..bc27d5bc7f 100644
--- a/src/backend/cuda/susan.hpp
+++ b/src/backend/cuda/susan.hpp
@@ -15,10 +15,8 @@ using af::features;
 namespace cuda {
 
 template<typename T>
-unsigned susan(Array<float> &x_out, Array<float> &y_out,
-               Array<float> &score_out, const Array<T> &in,
-               const unsigned radius, const float diff_thr,
+unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
+               const Array<T> &in, const unsigned radius, const float diff_thr,
                const float geom_thr, const float feature_ratio,
                const unsigned edge);
-
 }
diff --git a/src/backend/cuda/svd.cpp b/src/backend/cuda/svd.cpp
index 012c04ece6..7c51fefc51 100644
--- a/src/backend/cuda/svd.cpp
+++ b/src/backend/cuda/svd.cpp
@@ -21,16 +21,17 @@
 
 namespace cuda {
 template<typename T>
-cusolverStatus_t gesvd_buf_func(cusolverDnHandle_t handle, int m, int n,
-                                int *Lwork) {
+cusolverStatus_t gesvd_buf_func(cusolverDnHandle_t /*handle*/, int /*m*/,
+                                int /*n*/, int * /*Lwork*/) {
     return CUSOLVER_STATUS_ARCH_MISMATCH;
 }
 
 template<typename T, typename Tr>
-cusolverStatus_t gesvd_func(cusolverDnHandle_t handle, char jobu, char jobvt,
-                            int m, int n, T *A, int lda, Tr *S, T *U, int ldu,
-                            T *VT, int ldvt, T *Work, int Lwork, Tr *rwork,
-                            int *devInfo) {
+cusolverStatus_t gesvd_func(cusolverDnHandle_t /*handle*/, char /*jobu*/,
+                            char /*jobvt*/, int /*m*/, int /*n*/, T * /*A*/,
+                            int /*lda*/, Tr * /*S*/, T * /*U*/, int /*ldu*/,
+                            T * /*VT*/, int /*ldvt*/, T * /*Work*/,
+                            int /*Lwork*/, Tr * /*rwork*/, int * /*devInfo*/) {
     return CUSOLVER_STATUS_ARCH_MISMATCH;
 }
 
diff --git a/src/backend/cuda/tile.cpp b/src/backend/cuda/tile.cpp
index 9457688e73..4b2839232e 100644
--- a/src/backend/cuda/tile.cpp
+++ b/src/backend/cuda/tile.cpp
@@ -21,8 +21,8 @@ using common::half;
 namespace cuda {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
-    const af::dim4 iDims = in.dims();
-    af::dim4 oDims       = iDims;
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
     oDims *= tileDims;
 
     if (iDims.elements() == 0 || oDims.elements() == 0) {
diff --git a/src/backend/cuda/transform.cpp b/src/backend/cuda/transform.cpp
index 6ec97ebc8c..a143d74963 100644
--- a/src/backend/cuda/transform.cpp
+++ b/src/backend/cuda/transform.cpp
@@ -16,15 +16,15 @@ namespace cuda {
 
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
-               const af::dim4 &odims, const af::interpType method,
-               const bool inverse, const bool perspective) {
+               const af::interpType method, const bool inverse,
+               const bool perspective) {
     kernel::transform<T>(out, in, tf, inverse, perspective, method,
                          interpOrder(method));
 }
 
 #define INSTANTIATE(T)                                                       \
     template void transform(Array<T> &out, const Array<T> &in,               \
-                            const Array<float> &tf, const af::dim4 &odims,   \
+                            const Array<float> &tf,                          \
                             const af_interp_type method, const bool inverse, \
                             const bool perspective);
 
diff --git a/src/backend/cuda/transform.hpp b/src/backend/cuda/transform.hpp
index f0fd721226..ee3596d3ef 100644
--- a/src/backend/cuda/transform.hpp
+++ b/src/backend/cuda/transform.hpp
@@ -12,6 +12,6 @@
 namespace cuda {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
-               const af::dim4 &odims, const af_interp_type method,
-               const bool inverse, const bool perspective);
+               const af_interp_type method, const bool inverse,
+               const bool perspective);
 }
diff --git a/src/backend/cuda/transpose.cpp b/src/backend/cuda/transpose.cpp
index b891722f28..25f882b667 100644
--- a/src/backend/cuda/transpose.cpp
+++ b/src/backend/cuda/transpose.cpp
@@ -20,7 +20,7 @@ namespace cuda {
 
 template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate) {
-    const dim4 inDims = in.dims();
+    const dim4 &inDims = in.dims();
 
     dim4 outDims = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
 
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index d18d747db5..97c9d91a16 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -139,7 +139,7 @@ const char *getFullName<common::half>() {
 
 namespace common {
 template<typename T>
-class kernel_type;
+struct kernel_type;
 }
 
 namespace common {
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 60506c4597..eba52ad532 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -65,7 +65,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Points
         glBindBuffer(GL_ARRAY_BUFFER, buff1);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, points.get(), size1,
                                        cudaMemcpyDeviceToHost, stream));
@@ -76,7 +77,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Directions
         glBindBuffer(GL_ARRAY_BUFFER, buff2);
-        ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             CUDA_CHECK(cudaMemcpyAsync(ptr, directions.get(), size2,
                                        cudaMemcpyDeviceToHost, stream));
diff --git a/src/backend/cuda/vector_field.hpp b/src/backend/cuda/vector_field.hpp
index f42a241b86..abb375bcbc 100644
--- a/src/backend/cuda/vector_field.hpp
+++ b/src/backend/cuda/vector_field.hpp
@@ -14,6 +14,5 @@ namespace cuda {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vector_field);
-
+                       fg_vector_field vfield);
 }
diff --git a/src/backend/cuda/wrap.cpp b/src/backend/cuda/wrap.cpp
index 9c4dcbaffc..76834e6a10 100644
--- a/src/backend/cuda/wrap.cpp
+++ b/src/backend/cuda/wrap.cpp
@@ -23,17 +23,17 @@ using common::half;
 namespace cuda {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py, const bool is_column) {
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column) {
     kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
 #define INSTANTIATE(T)                                                        \
-    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t ox, \
-                          const dim_t oy, const dim_t wx, const dim_t wy,     \
-                          const dim_t sx, const dim_t sy, const dim_t px,     \
-                          const dim_t py, const bool is_column);
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t wx, \
+                          const dim_t wy, const dim_t sx, const dim_t sy,     \
+                          const dim_t px, const dim_t py,                     \
+                          const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/wrap.hpp b/src/backend/cuda/wrap.hpp
index d0cc38bbfe..d324975379 100644
--- a/src/backend/cuda/wrap.hpp
+++ b/src/backend/cuda/wrap.hpp
@@ -11,9 +11,9 @@
 
 namespace cuda {
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py, const bool is_column);
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column);
 
 template<typename T>
 Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 7141f076a9..a01ac3071a 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -24,8 +24,10 @@
 
 #include <cstddef>
 #include <numeric>
+#include <utility>
 
 using af::dim4;
+using af::dtype_traits;
 
 using cl::Buffer;
 
@@ -49,9 +51,7 @@ Node_ptr bufferNodePtr() {
 
 namespace {
 template<typename T>
-void verifyTypeSupport() {
-    return;
-}
+void verifyTypeSupport() {}
 
 template<>
 void verifyTypeSupport<double>() {
@@ -76,9 +76,9 @@ void verifyTypeSupport<common::half>() {
 }  // namespace
 
 template<typename T>
-Array<T>::Array(dim4 dims)
+Array<T>::Array(const dim4 &dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(memAlloc<T>(info.elements()).release(), bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -86,19 +86,18 @@ Array<T>::Array(dim4 dims)
     , owner(true) {}
 
 template<typename T>
-Array<T>::Array(dim4 dims, Node_ptr n)
+Array<T>::Array(const dim4 &dims, Node_ptr n)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
-    , data()
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data_dims(dims)
-    , node(n)
+    , node(std::move(std::move(n)))
     , ready(false)
     , owner(true) {}
 
 template<typename T>
-Array<T>::Array(dim4 dims, const T *const in_data)
+Array<T>::Array(const dim4 &dims, const T *const in_data)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(memAlloc<T>(info.elements()).release(), bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
@@ -114,9 +113,9 @@ Array<T>::Array(dim4 dims, const T *const in_data)
 }
 
 template<typename T>
-Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy)
+Array<T>::Array(const dim4 &dims, cl_mem mem, size_t src_offset, bool copy)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(copy ? memAlloc<T>(info.elements()).release() : new Buffer(mem),
            bufferFree)
     , data_dims(dims)
@@ -125,7 +124,7 @@ Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset, bool copy)
     , owner(true) {
     if (copy) {
         clRetainMemObject(mem);
-        Buffer src_buf = Buffer((cl_mem)(mem));
+        Buffer src_buf = Buffer(mem);
         getQueue().enqueueCopyBuffer(src_buf, *data.get(), src_offset, 0,
                                      sizeof(T) * info.elements());
     }
@@ -135,7 +134,7 @@ template<typename T>
 Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
                 const dim4 &stride)
     : info(parent.getDevId(), dims, offset_, stride,
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(parent.getData())
     , data_dims(parent.getDataDims())
     , node(bufferNodePtr<T>())
@@ -150,9 +149,9 @@ Array<T>::Array(Param &tmp, bool owner_)
            0,
            dim4(tmp.info.strides[0], tmp.info.strides[1], tmp.info.strides[2],
                 tmp.info.strides[3]),
-           (af_dtype)dtype_traits<T>::af_type)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(
-          tmp.data, owner_ ? bufferFree : [](Buffer *) {})
+          tmp.data, owner_ ? bufferFree : [](Buffer * /*unused*/) {})
     , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
                      tmp.info.dims[3]))
     , node(bufferNodePtr<T>())
@@ -160,13 +159,15 @@ Array<T>::Array(Param &tmp, bool owner_)
     , owner(owner_) {}
 
 template<typename T>
-Array<T>::Array(dim4 dims, dim4 strides, dim_t offset_, const T *const in_data,
-                bool is_device)
+Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
+                const T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
-           (af_dtype)dtype_traits<T>::af_type)
-    , data(is_device ? (new Buffer((cl_mem)in_data))
-                     : (memAlloc<T>(info.elements()).release()),
-           bufferFree)
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(
+          is_device
+              ? (new Buffer(reinterpret_cast<cl_mem>(const_cast<T *>(in_data))))
+              : (memAlloc<T>(info.elements()).release()),
+          bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
     , ready(true)
@@ -179,7 +180,7 @@ Array<T>::Array(dim4 dims, dim4 strides, dim_t offset_, const T *const in_data,
 
 template<typename T>
 void Array<T>::eval() {
-    if (isReady()) return;
+    if (isReady()) { return; }
 
     this->setId(getActiveDeviceId());
     data = Buffer_ptr(memAlloc<T>(info.elements()).release(), bufferFree);
@@ -198,7 +199,7 @@ void Array<T>::eval() {
 
 template<typename T>
 void Array<T>::eval() const {
-    if (isReady()) return;
+    if (isReady()) { return; }
     const_cast<Array<T> *>(this)->eval();
 }
 
@@ -255,15 +256,12 @@ void evalMultiple(vector<Array<T> *> arrays) {
     for (Array<T> *array : output_arrays) { array->node = bufferNodePtr<T>(); }
 }
 
-template<typename T>
-Array<T>::~Array() {}
-
 template<typename T>
 Node_ptr Array<T>::getNode() {
     if (node->isBuffer()) {
-        KParam kinfo        = *this;
-        BufferNode *bufNode = reinterpret_cast<BufferNode *>(node.get());
-        unsigned bytes      = this->getDataDims().elements() * sizeof(T);
+        KParam kinfo   = *this;
+        auto *bufNode  = reinterpret_cast<BufferNode *>(node.get());
+        unsigned bytes = this->getDataDims().elements() * sizeof(T);
         bufNode->setData(kinfo, data, bytes, isLinear());
     }
     return node;
@@ -292,7 +290,7 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= (int)getMaxJitSize()) {
+    if (root_node->getHeight() >= static_cast<int>(getMaxJitSize())) {
         return kJITHeuristics::TreeHeight;
     }
 
@@ -383,7 +381,7 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
         return createSubArray(parentCopy, index, copy);
     }
 
-    dim4 pDims = parent.dims();
+    const dim4 &pDims = parent.dims();
 
     dim4 dims    = toDims(index, pDims);
     dim4 strides = toStride(index, dDims);
@@ -391,11 +389,11 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
     // Find total offsets after indexing
     dim4 offsets = toOffset(index, pDims);
     dim_t offset = parent.getOffset();
-    for (int i = 0; i < 4; i++) offset += offsets[i] * parent_strides[i];
+    for (int i = 0; i < 4; i++) { offset += offsets[i] * parent_strides[i]; }
 
     Array<T> out = Array<T>(parent, dims, offset, strides);
 
-    if (!copy) return out;
+    if (!copy) { return out; }
 
     if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
         out = copyArray(out);
@@ -405,29 +403,29 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
 }
 
 template<typename T>
-Array<T> createHostDataArray(const dim4 &size, const T *const data) {
+Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
     verifyTypeSupport<T>();
-    return Array<T>(size, data);
+    return Array<T>(dims, data);
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &size, void *data) {
+Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
     verifyTypeSupport<T>();
 
     bool copy_device = false;
-    return Array<T>(size, static_cast<cl_mem>(data), 0, copy_device);
+    return Array<T>(dims, static_cast<cl_mem>(data), 0, copy_device);
 }
 
 template<typename T>
-Array<T> createValueArray(const dim4 &size, const T &value) {
+Array<T> createValueArray(const dim4 &dims, const T &value) {
     verifyTypeSupport<T>();
-    return createScalarNode<T>(size, value);
+    return createScalarNode<T>(dims, value);
 }
 
 template<typename T>
-Array<T> createEmptyArray(const dim4 &size) {
+Array<T> createEmptyArray(const dim4 &dims) {
     verifyTypeSupport<T>();
-    return Array<T>(size);
+    return Array<T>(dims);
 }
 
 template<typename T>
@@ -448,8 +446,6 @@ void writeHostDataArray(Array<T> &arr, const T *const data,
 
     getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(), bytes,
                                   data);
-
-    return;
 }
 
 template<typename T>
@@ -459,13 +455,12 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
     Buffer &buf = *arr.get();
 
-    clRetainMemObject((cl_mem)(data));
-    Buffer data_buf = Buffer((cl_mem)(data));
-
-    getQueue().enqueueCopyBuffer(data_buf, buf, 0, (size_t)arr.getOffset(),
-                                 bytes);
+    clRetainMemObject(reinterpret_cast<cl_mem>(const_cast<void *>(data)));
+    Buffer data_buf =
+        Buffer(reinterpret_cast<cl_mem>(const_cast<void *>(data)));
 
-    return;
+    getQueue().enqueueCopyBuffer(data_buf, buf, 0,
+                                 static_cast<size_t>(arr.getOffset()), bytes);
 }
 
 template<typename T>
@@ -486,11 +481,11 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
         const Array<T> &parent, const vector<af_seq> &index, bool copy);      \
     template void destroyArray<T>(Array<T> * A);                              \
     template Array<T> createNodeArray<T>(const dim4 &dims, Node_ptr node);    \
-    template Array<T>::Array(dim4 dims, dim4 strides, dim_t offset,           \
-                             const T *const in_data, bool is_device);         \
-    template Array<T>::Array(dim4 dims, cl_mem mem, size_t src_offset,        \
+    template Array<T>::Array(const dim4 &dims, const dim4 &strides,           \
+                             dim_t offset, const T *const in_data,            \
+                             bool is_device);                                 \
+    template Array<T>::Array(const dim4 &dims, cl_mem mem, size_t src_offset, \
                              bool copy);                                      \
-    template Array<T>::~Array();                                              \
     template Node_ptr Array<T>::getNode() const;                              \
     template void Array<T>::eval();                                           \
     template void Array<T>::eval() const;                                     \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 81641a5923..e69e81578b 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -31,7 +31,8 @@ template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays);
 
 void evalNodes(Param &out, common::Node *node);
-void evalNodes(std::vector<Param> &outputs, std::vector<common::Node *> nodes);
+void evalNodes(std::vector<Param> &outputs,
+               const std::vector<common::Node *> &nodes);
 
 /// Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
@@ -49,8 +50,9 @@ template<typename T>
 Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
 
 template<typename T>
-Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
-                            const T *const in_data, bool is_device) {
+Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
+                            dim_t offset, const T *const in_data,
+                            bool is_device) {
     return Array<T>(dims, strides, offset, in_data, is_device);
 }
 
@@ -126,18 +128,18 @@ class Array {
     bool ready;
     bool owner;
 
-    Array(af::dim4 dims);
+    Array(const af::dim4 &dims);
 
-    Array(const Array<T> &parnt, const dim4 &dims, const dim_t &offset,
+    Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset,
           const dim4 &stride);
     Array(Param &tmp, bool owner);
-    explicit Array(af::dim4 dims, common::Node_ptr n);
-    explicit Array(af::dim4 dims, const T *const in_data);
-    explicit Array(af::dim4 dims, cl_mem mem, size_t offset, bool copy);
+    explicit Array(const af::dim4 &dims, common::Node_ptr n);
+    explicit Array(const af::dim4 &dims, const T *const in_data);
+    explicit Array(const af::dim4 &dims, cl_mem mem, size_t offset, bool copy);
 
    public:
-    Array(af::dim4 dims, af::dim4 strides, dim_t offset, const T *const in_data,
-          bool is_device = false);
+    Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
+          const T *const in_data, bool is_device = false);
 
     void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
     void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
@@ -178,7 +180,7 @@ class Array {
     INFO_IS_FUNC(isSparse);
 
 #undef INFO_IS_FUNC
-    ~Array();
+    ~Array() = default;
 
     bool isReady() const { return ready; }
     bool isOwner() const { return owner; }
@@ -275,8 +277,9 @@ class Array {
     friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
     friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
-    friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
-                                          dim_t offset, const T *const in_data,
+    friend Array<T> createStridedArray<T>(const af::dim4 &dims,
+                                          const af::dim4 &strides, dim_t offset,
+                                          const T *const in_data,
                                           bool is_device);
 
     friend Array<T> createEmptyArray<T>(const af::dim4 &dims);
diff --git a/src/backend/opencl/Event.cpp b/src/backend/opencl/Event.cpp
index 9a8dc24061..21523891d9 100644
--- a/src/backend/opencl/Event.cpp
+++ b/src/backend/opencl/Event.cpp
@@ -13,9 +13,13 @@
 #include <events.hpp>
 #include <platform.hpp>
 #include <af/event.h>
+#include <memory>
 
 #include <memory>
 
+using std::make_unique;
+using std::unique_ptr;
+
 namespace opencl {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(cl::CommandQueue& queue) {
@@ -25,8 +29,7 @@ Event makeEvent(cl::CommandQueue& queue) {
 }
 
 af_event createEvent() {
-    std::unique_ptr<Event> e;
-    e.reset(new Event());
+    auto e = make_unique<Event>();
     // Ensure the default CL command queue is initialized
     getQueue()();
     if (e->create() != CL_SUCCESS) {
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
index 954e9e2b6b..e2cd64150f 100644
--- a/src/backend/opencl/GraphicsResourceManager.cpp
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -12,12 +12,15 @@
 
 namespace opencl {
 GraphicsResourceManager::ShrdResVector
-GraphicsResourceManager::registerResources(std::vector<uint32_t> resources) {
+GraphicsResourceManager::registerResources(
+    const std::vector<uint32_t>& resources) {
     ShrdResVector output;
 
-    for (auto id : resources)
-        output.emplace_back(
-            new cl::BufferGL(getContext(), CL_MEM_WRITE_ONLY, id, NULL));
+    for (auto id : resources) {
+        output.emplace_back(new cl::BufferGL(
+            getContext(), CL_MEM_WRITE_ONLY,  // NOLINT(hicpp-signed-bitwise)
+            id, NULL));
+    }
 
     return output;
 }
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index 8924661572..618e46e2f4 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -25,7 +25,8 @@ class GraphicsResourceManager
     using ShrdResVector = std::vector<std::shared_ptr<cl::Buffer>>;
 
     GraphicsResourceManager() {}
-    ShrdResVector registerResources(std::vector<uint32_t> resources);
+    static ShrdResVector registerResources(
+        const std::vector<uint32_t>& resources);
 
    protected:
     GraphicsResourceManager(GraphicsResourceManager const&);
diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index 6be8d546ab..34a01f4a5d 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -16,7 +16,7 @@ namespace opencl {
 Param::Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
 Param::Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_) {}
 
-Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]) {
+Param makeParam(cl_mem mem, int off, const int dims[4], const int strides[4]) {
     Param out;
     out.data        = new cl::Buffer(mem);
     out.info.offset = off;
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 484ef71030..392c9d07b7 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -28,5 +28,5 @@ struct Param {
 };
 
 // AF_DEPRECATED("Use Array<T>")
-Param makeParam(cl_mem mem, int off, int dims[4], int strides[4]);
+Param makeParam(cl_mem mem, int off, const int dims[4], const int strides[4]);
 }  // namespace opencl
diff --git a/src/backend/opencl/anisotropic_diffusion.cpp b/src/backend/opencl/anisotropic_diffusion.cpp
index b5ce054750..e71a78cfc8 100644
--- a/src/backend/opencl/anisotropic_diffusion.cpp
+++ b/src/backend/opencl/anisotropic_diffusion.cpp
@@ -18,10 +18,11 @@ template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq) {
-    if (eq == AF_DIFFUSION_MCDE)
+    if (eq == AF_DIFFUSION_MCDE) {
         kernel::anisotropicDiffusion<T, true>(inout, dt, mct, fftype);
-    else
+    } else {
         kernel::anisotropicDiffusion<T, false>(inout, dt, mct, fftype);
+    }
 }
 
 #define INSTANTIATE(T)                                     \
diff --git a/src/backend/opencl/api.cpp b/src/backend/opencl/api.cpp
index ef8b9f9894..04b73eff4f 100644
--- a/src/backend/opencl/api.cpp
+++ b/src/backend/opencl/api.cpp
@@ -4,10 +4,11 @@
 namespace af {
 template<>
 AFAPI cl_mem *array::device() const {
-    cl_mem *mem_ptr = new cl_mem;
-    af_err err      = af_get_device_ptr((void **)mem_ptr, get());
-    if (err != AF_SUCCESS)
+    auto *mem_ptr = new cl_mem;
+    af_err err = af_get_device_ptr(reinterpret_cast<void **>(mem_ptr), get());
+    if (err != AF_SUCCESS) {
         throw af::exception("Failed to get cl_mem from array object");
+    }
     return mem_ptr;
 }
 }  // namespace af
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 8bac7911a3..541deac27f 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -33,7 +33,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
     }
 
     // retrieve dimensions, strides and offsets
-    dim4 dDims = out.dims();
+    const dim4& dDims = out.dims();
     // retrieve dimensions & strides for array
     // to which rhs is being copied to
     dim4 dstOffs  = toOffset(seqs, dDims);
@@ -58,8 +58,9 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
             // alloc an 1-element buffer to avoid OpenCL from failing using
             // direct buffer allocation as opposed to mem manager to avoid
             // reference count desprepancies between different backends
-            static cl::Buffer* empty =
-                new Buffer(getContext(), CL_MEM_READ_ONLY, sizeof(uint));
+            static auto* empty = new Buffer(
+                getContext(), CL_MEM_READ_ONLY,  // NOLINT(hicpp-signed-bitwise)
+                sizeof(uint));
             bPtrs[x] = empty;
         }
     }
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 6870da0e50..263d07bd9f 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -54,10 +54,10 @@ void gemm_fallback(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
 }
 
 template<>
-void gemm_fallback<half>(Array<half> &out, af_mat_prop optLhs,
-                         af_mat_prop optRhs, const half *alpha,
-                         const Array<half> &lhs, const Array<half> &rhs,
-                         const half *beta) {
+void gemm_fallback<half>(Array<half> & /*out*/, af_mat_prop /*optLhs*/,
+                         af_mat_prop /*optRhs*/, const half * /*alpha*/,
+                         const Array<half> & /*lhs*/,
+                         const Array<half> & /*rhs*/, const half * /*beta*/) {
     assert(false && "CPU fallback not implemented for f16");
 }
 
@@ -66,7 +66,8 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
           const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
 #if defined(WITH_LINEAR_ALGEBRA)
     // Do not force offload gemm on OSX Intel devices
-    if (OpenCLCPUOffload(false) && (af_dtype)dtype_traits<T>::af_type != f16) {
+    if (OpenCLCPUOffload(false) &&
+        static_cast<af_dtype>(dtype_traits<T>::af_type) != f16) {
         gemm_fallback(out, optLhs, optRhs, alpha, lhs, rhs, beta);
         return;
     }
@@ -78,18 +79,18 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     const auto aColDim = (lOpts == OPENCL_BLAS_NO_TRANS) ? 1 : 0;
     const auto bColDim = (rOpts == OPENCL_BLAS_NO_TRANS) ? 1 : 0;
 
-    const dim4 lDims = lhs.dims();
-    const dim4 rDims = rhs.dims();
-    const int M      = lDims[aRowDim];
-    const int N      = rDims[bColDim];
-    const int K      = lDims[aColDim];
-    const dim4 oDims = out.dims();
+    const dim4 &lDims = lhs.dims();
+    const dim4 &rDims = rhs.dims();
+    const int M       = lDims[aRowDim];
+    const int N       = rDims[bColDim];
+    const int K       = lDims[aColDim];
+    const dim4 oDims  = out.dims();
 
-    const dim4 lStrides = lhs.strides();
-    const dim4 rStrides = rhs.strides();
-    const dim4 oStrides = out.strides();
+    const dim4 &lStrides = lhs.strides();
+    const dim4 &rStrides = rhs.strides();
+    const dim4 oStrides  = out.strides();
 
-    int batchSize = oDims[2] * oDims[3];
+    int batchSize = static_cast<int>(oDims[2] * oDims[3]);
 
     bool is_l_d2_batched = oDims[2] == lDims[2];
     bool is_l_d3_batched = oDims[3] == lDims[3];
@@ -97,8 +98,8 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     bool is_r_d3_batched = oDims[3] == rDims[3];
 
     for (int n = 0; n < batchSize; n++) {
-        int w = n / oDims[2];
-        int z = n - w * oDims[2];
+        int w = static_cast<int>(n / oDims[2]);
+        int z = static_cast<int>(n - w * oDims[2]);
 
         int loff = z * (is_l_d2_batched * lStrides[2]) +
                    w * (is_l_d3_batched * lStrides[3]);
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index 963cf2299e..505ba2ea16 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -43,10 +43,11 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper)
+    if (is_upper) {
         triangle<T, true, false>(out, out);
-    else
+    } else {
         triangle<T, false, false>(out, out);
+    }
 
     return out;
 }
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index 49fd0fb430..e70a4a76db 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -11,8 +11,11 @@
 #include <common/err_common.hpp>
 #include <platform.hpp>
 #include <af/defines.h>
+
+#include <memory>
 #include <string>
 
+using std::make_unique;
 using std::string;
 
 namespace opencl {
@@ -122,20 +125,21 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
         key_string.append(std::string(key_str_temp));
     }
 
-    sprintf(key_str_temp, "%d:" SIZE_T_FRMT_SPECIFIER, (int)precision, batch);
+    sprintf(key_str_temp, "%d:" SIZE_T_FRMT_SPECIFIER,
+            static_cast<int>(precision), batch);
     key_string.append(std::string(key_str_temp));
 
     PlanCache &planner = opencl::fftManager();
     SharedPlan retVal  = planner.find(key_string);
 
-    if (retVal) return retVal;
+    if (retVal) { return retVal; }
 
-    PlanType *temp = (PlanType *)malloc(sizeof(PlanType));
+    auto temp = make_unique<PlanType>();
 
     // getContext() returns object of type Context
     // Context() returns the actual cl_context handle
-    CLFFT_CHECK(
-        clfftCreateDefaultPlan(temp, opencl::getContext()(), rank, clLengths));
+    CLFFT_CHECK(clfftCreateDefaultPlan(temp.get(), opencl::getContext()(), rank,
+                                       clLengths));
 
     // complex to complex
     if (iLayout == oLayout) {
@@ -156,7 +160,7 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
     // CommandQueue() returns the actual cl_command_queue handle
     CLFFT_CHECK(clfftBakePlan(*temp, 1, &(opencl::getQueue()()), NULL, NULL));
 
-    retVal.reset(temp, [](PlanType *p) {
+    retVal.reset(temp.release(), [](PlanType *p) {
 #ifndef OS_WIN
         // On Windows the resources that are released after the main function
         // have exited cause "Pure Virtual Function Called" errors. It seems
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 40a2895a95..eff48d262b 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -33,8 +33,8 @@ namespace opencl {
 template<typename T, typename accT, dim_t baseDim, bool expand>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind) {
-    const dim4 sDims = signal.dims();
-    const dim4 fDims = filter.dims();
+    const dim4 &sDims = signal.dims();
+    const dim4 &fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
@@ -48,7 +48,7 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
+            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
@@ -59,15 +59,17 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
     dim_t MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
     switch (baseDim) {
         case 1:
-            if (fDims[0] > kernel::MAX_CONV1_FILTER_LEN) callKernel = false;
+            if (fDims[0] > kernel::MAX_CONV1_FILTER_LEN) { callKernel = false; }
             break;
         case 2:
-            if ((fDims[0] * fDims[1]) > (MCFL2 * MCFL2)) callKernel = false;
+            if ((fDims[0] * fDims[1]) > (MCFL2 * MCFL2)) { callKernel = false; }
             break;
         case 3:
-            if ((fDims[0] * fDims[1] * fDims[2]) > (MCFL3 * MCFL3 * MCFL3))
+            if ((fDims[0] * fDims[1] * fDims[2]) > (MCFL3 * MCFL3 * MCFL3)) {
                 callKernel = false;
+            }
             break;
+        default: AF_ERROR("baseDim only supports values 1-3.", AF_ERR_UNKNOWN);
     }
 
     if (!callKernel) {
@@ -120,8 +122,8 @@ INSTANTIATE(intl, float)
 
 template<typename T>
 Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
-                          const dim4 stride, const dim4 padding,
-                          const dim4 dilation) {
+                          const dim4 &stride, const dim4 &padding,
+                          const dim4 &dilation) {
     dim4 sDims = signal.dims();
     dim4 fDims = filter.dims();
 
@@ -179,11 +181,12 @@ template<typename T>
 Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> &original_signal,
                            const Array<T> &original_filter,
-                           const Array<T> &convolved_output, af::dim4 stride,
-                           af::dim4 padding, af::dim4 dilation) {
-    const dim4 cDims = incoming_gradient.dims();
-    const dim4 sDims = original_signal.dims();
-    const dim4 fDims = original_filter.dims();
+                           const Array<T> & /*convolved_output*/,
+                           af::dim4 stride, af::dim4 padding,
+                           af::dim4 dilation) {
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
 
     Array<T> collapsed_filter = original_filter;
 
@@ -212,11 +215,12 @@ template<typename T>
 Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &original_signal,
                              const Array<T> &original_filter,
-                             const Array<T> &convolved_output, af::dim4 stride,
-                             af::dim4 padding, af::dim4 dilation) {
-    const dim4 cDims = incoming_gradient.dims();
-    const dim4 sDims = original_signal.dims();
-    const dim4 fDims = original_filter.dims();
+                             const Array<T> & /*convolved_output*/,
+                             af::dim4 stride, af::dim4 padding,
+                             af::dim4 dilation) {
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
 
     const bool retCols = false;
     Array<T> unwrapped =
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 08c5f57841..19b312b3af 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -20,23 +20,23 @@ namespace opencl {
 template<typename T, typename accT, bool expand>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
                    Array<accT> const& r_filter) {
-    const dim_t cflen = (dim_t)c_filter.elements();
-    const dim_t rflen = (dim_t)r_filter.elements();
+    const auto cflen = c_filter.elements();
+    const auto rflen = r_filter.elements();
 
     if ((cflen > kernel::MAX_SCONV_FILTER_LEN) ||
         (rflen > kernel::MAX_SCONV_FILTER_LEN)) {
         // TODO call upon fft
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nOpenCL Separable convolution doesn't support %lld(coloumn) "
-                 "%lld(row) filters\n",
+                 "\nOpenCL Separable convolution doesn't support %zu(coloumn) "
+                 "%zu(row) filters\n",
                  cflen, rflen);
         OPENCL_NOT_SUPPORTED(errMessage);
     }
 
-    const dim4 sDims = signal.dims();
-    dim4 tDims       = sDims;
-    dim4 oDims       = sDims;
+    const dim4& sDims = signal.dims();
+    dim4 tDims        = sDims;
+    dim4 oDims        = sDims;
 
     if (expand) {
         tDims[0] += cflen - 1;
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 7e43a19dd1..7be07316ed 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -44,7 +44,6 @@ void copyData(T *data, const Array<T> &A) {
     // FIXME: Add checks
     getQueue().enqueueReadBuffer(buf, CL_TRUE, sizeof(T) * offset,
                                  sizeof(T) * A.elements(), data);
-    return;
 }
 
 template<typename T>
@@ -69,12 +68,13 @@ Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
                         outType default_value, double factor) {
     Array<outType> ret = createEmptyArray<outType>(dims);
 
-    if (in.dims() == dims)
+    if (in.dims() == dims) {
         kernel::copy<inType, outType, true>(ret, in, in.ndims(), default_value,
                                             factor);
-    else
+    } else {
         kernel::copy<inType, outType, false>(ret, in, in.ndims(), default_value,
                                              factor);
+    }
     return ret;
 }
 
@@ -86,12 +86,13 @@ void multiply_inplace(Array<T> &in, double val) {
 template<typename inType, typename outType>
 struct copyWrapper {
     void operator()(Array<outType> &out, Array<inType> const &in) {
-        if (in.dims() == out.dims())
+        if (in.dims() == out.dims()) {
             kernel::copy<inType, outType, true>(out, in, in.ndims(),
                                                 scalar<outType>(0), 1);
-        else
+        } else {
             kernel::copy<inType, outType, false>(out, in, in.ndims(),
                                                  scalar<outType>(0), 1);
+        }
     }
 };
 
@@ -106,10 +107,11 @@ struct copyWrapper<T, T> {
             getQueue().enqueueCopyBuffer(*in.get(), *out.get(), in_offset,
                                          out_offset, in.elements() * sizeof(T));
         } else {
-            if (in.dims() == out.dims())
+            if (in.dims() == out.dims()) {
                 kernel::copy<T, T, true>(out, in, in.ndims(), scalar<T>(0), 1);
-            else
+            } else {
                 kernel::copy<T, T, false>(out, in, in.ndims(), scalar<T>(0), 1);
+            }
         }
     }
 };
@@ -237,7 +239,7 @@ INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
 T getScalar(const Array<T> &in) {
-    T retVal;
+    T retVal{};
     getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, sizeof(T) * in.getOffset(),
                                  sizeof(T), &retVal);
     return retVal;
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index ad8680cafe..6ae3f39c0f 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -180,12 +180,12 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     const int aColDim = (lOpts == CblasNoTrans) ? 1 : 0;
     const int bColDim = (rOpts == CblasNoTrans) ? 1 : 0;
 
-    const dim4 lDims = lhs.dims();
-    const dim4 rDims = rhs.dims();
-    const int M      = lDims[aRowDim];
-    const int N      = rDims[bColDim];
-    const int K      = lDims[aColDim];
-    const dim4 oDims = out.dims();
+    const dim4 &lDims = lhs.dims();
+    const dim4 &rDims = rhs.dims();
+    const int M       = lDims[aRowDim];
+    const int N       = rDims[bColDim];
+    const int K       = lDims[aColDim];
+    const dim4 &oDims = out.dims();
 
     dim4 lStrides = lhs.strides();
     dim4 rStrides = rhs.strides();
@@ -212,9 +212,10 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         int roff = z * (is_r_d2_batched * rStrides[2]) +
                    w * (is_r_d3_batched * rStrides[3]);
 
-        CBT *lptr = (CBT *)(lPtr.get() + loff);
-        CBT *rptr = (CBT *)(rPtr.get() + roff);
-        BT *optr  = (BT *)(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
+        CBT *lptr = static_cast<CBT *>(lPtr.get() + loff);
+        CBT *rptr = static_cast<CBT *>(rPtr.get() + roff);
+        BT *optr =
+            static_cast<BT *>(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
 
         if (rDims[bColDim] == 1) {
             dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index c8bb0a5084..fc066bd710 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -42,12 +42,13 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
 
     mapped_ptr<T> oPtr = out.getMappedPtr();
 
-    if (is_upper)
+    if (is_upper) {
         triangle<T, true, false>(oPtr.get(), oPtr.get(), out.dims(),
                                  out.strides(), out.strides());
-    else
+    } else {
         triangle<T, false, false>(oPtr.get(), oPtr.get(), out.dims(),
                                   out.strides(), out.strides());
+    }
 
     return out;
 }
@@ -58,7 +59,7 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     int N      = iDims[0];
 
     char uplo = 'L';
-    if (is_upper) uplo = 'U';
+    if (is_upper) { uplo = 'U'; }
 
     mapped_ptr<T> inPtr = in.getMappedPtr();
 
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index 30f7d4d64b..7793a3590e 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -76,14 +76,14 @@ void lu_split(Array<T> &lower, Array<T> &upper, const Array<T> &in) {
                     const dim_t uMem = uYZW + ox;
                     const dim_t iMem = iYZW + ox;
                     if (ox > oy) {
-                        if (oy < ldm[1]) l[lMem] = i[iMem];
-                        if (ox < udm[0]) u[uMem] = scalar<T>(0);
+                        if (oy < ldm[1]) { l[lMem] = i[iMem]; }
+                        if (ox < udm[0]) { u[uMem] = scalar<T>(0); }
                     } else if (oy > ox) {
-                        if (oy < ldm[1]) l[lMem] = scalar<T>(0);
-                        if (ox < udm[0]) u[uMem] = i[iMem];
+                        if (oy < ldm[1]) { l[lMem] = scalar<T>(0); }
+                        if (ox < udm[0]) { u[uMem] = i[iMem]; }
                     } else if (ox == oy) {
-                        if (oy < ldm[1]) l[lMem] = scalar<T>(1.0);
-                        if (ox < udm[0]) u[uMem] = i[iMem];
+                        if (oy < ldm[1]) { l[lMem] = scalar<T>(1.0); }
+                        if (ox < udm[0]) { u[uMem] = i[iMem]; }
                     }
                 }
             }
@@ -95,7 +95,7 @@ void convertPivot(int *pivot, int out_sz, size_t pivot_dim) {
     std::vector<int> p(out_sz);
     iota(begin(p), end(p), 0);
 
-    for (int j = 0; j < (int)pivot_dim; j++) {
+    for (int j = 0; j < static_cast<int>(pivot_dim); j++) {
         // 1 indexed in pivot
         std::swap(p[j], p[pivot[j] - 1]);
     }
@@ -138,7 +138,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     getrf_func<T>()(AF_LAPACK_COL_MAJOR, M, N, inPtr.get(), in.strides()[1],
                     piPtr.get());
 
-    if (convert_pivot) convertPivot(piPtr.get(), M, min(M, N));
+    if (convert_pivot) { convertPivot(piPtr.get(), M, min(M, N)); }
 
     return pivot;
 }
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index dc08ef340d..0699c44717 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -57,8 +57,8 @@ using scale_type =
                          const typename blas_base<T>::type, const T>::type;
 
 template<typename To, typename Ti>
-To getScaleValue(Ti val) {
-    return (To)(val);
+auto getScaleValue(Ti val) -> std::remove_cv_t<To> {
+    return static_cast<std::remove_cv_t<To>>(val);
 }
 
 #ifdef USE_MKL
@@ -143,7 +143,7 @@ SPARSE_FUNC(mm, cdouble, z)
 #undef SPARSE_FUNC_DEF
 
 template<>
-const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
+sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
     sp_cfloat ret;
     ret.real = val.s[0];
     ret.imag = val.s[1];
@@ -151,7 +151,7 @@ const sp_cfloat getScaleValue<const sp_cfloat, cfloat>(cfloat val) {
 }
 
 template<>
-const sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val) {
+sp_cdouble getScaleValue<const sp_cdouble, cdouble>(cdouble val) {
     sp_cdouble ret;
     ret.real = val.s[0];
     ret.imag = val.s[1];
@@ -181,7 +181,7 @@ sparse_operation_t toSparseTranspose(af_mat_prop opt) {
 }
 
 template<typename T, int value>
-scale_type<T> getScale() {
+scale_type<T> getScale() {  // NOLINT(readability-const-return-type)
     thread_local T val = scalar<T>(value);
     return getScaleValue<scale_type<T>, T>(val);
 }
@@ -241,7 +241,7 @@ Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                          lhs.dims()[1], pB, pE, cPtr.get(),
                          reinterpret_cast<ptr_type<T>>(vPtr.get()));
 
-    struct matrix_descr descrLhs;
+    struct matrix_descr descrLhs {};
     descrLhs.type = SPARSE_MATRIX_TYPE_GENERAL;
 
     mkl_sparse_optimize(csrLhs);
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 11ed2238e4..50a39ccdb6 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -25,6 +25,7 @@
 #include <version.hpp>
 #include <af/opencl.h>
 #include <af/version.h>
+#include <memory>
 
 #ifdef OS_MAC
 #include <OpenGL/CGLCurrent.h>
@@ -58,7 +59,7 @@ static const char* CL_GL_SHARING_EXT = "cl_APPLE_gl_sharing";
 static const char* CL_GL_SHARING_EXT = "cl_khr_gl_sharing";
 #endif
 
-bool checkExtnAvailability(const Device& pDevice, string pName) {
+bool checkExtnAvailability(const Device& pDevice, const string& pName) {
     bool ret_val = false;
     // find the extension required
     string exts = pDevice.getInfo<CL_DEVICE_EXTENSIONS>();
@@ -73,8 +74,8 @@ bool checkExtnAvailability(const Device& pDevice, string pName) {
     return ret_val;
 }
 
-static afcl::deviceType getDeviceTypeEnum(Device dev) {
-    return (afcl::deviceType)dev.getInfo<CL_DEVICE_TYPE>();
+static afcl::deviceType getDeviceTypeEnum(const Device& dev) {
+    return static_cast<afcl::deviceType>(dev.getInfo<CL_DEVICE_TYPE>());
 }
 
 static inline bool compare_default(const Device* ldev, const Device* rdev) {
@@ -89,16 +90,16 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
         auto is_l_curr_type = l_dev_type == current_type;
         auto is_r_curr_type = r_dev_type == current_type;
 
-        if (is_l_curr_type && !is_r_curr_type) return true;
-        if (!is_l_curr_type && is_r_curr_type) return false;
+        if (is_l_curr_type && !is_r_curr_type) { return true; }
+        if (!is_l_curr_type && is_r_curr_type) { return false; }
     }
 
     // For GPUs, this ensures discrete > integrated
     auto is_l_integrated = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
     auto is_r_integrated = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
 
-    if (!is_l_integrated && is_r_integrated) return true;
-    if (is_l_integrated && !is_r_integrated) return false;
+    if (!is_l_integrated && is_r_integrated) { return true; }
+    if (is_l_integrated && !is_r_integrated) { return false; }
 
     // At this point, the devices are of same type.
     // Sort based on emperical evidence of preferred platforms
@@ -114,12 +115,14 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
 
         for (auto ref_name : platforms) {
             if (verify_present(lPlatName, ref_name) &&
-                !verify_present(rPlatName, ref_name))
+                !verify_present(rPlatName, ref_name)) {
                 return true;
+            }
 
             if (!verify_present(lPlatName, ref_name) &&
-                verify_present(rPlatName, ref_name))
+                verify_present(rPlatName, ref_name)) {
                 return false;
+            }
         }
 
         // Intel falls back to compare based on memory
@@ -129,12 +132,14 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
 
         for (auto ref_name : platforms) {
             if (verify_present(lPlatName, ref_name) &&
-                !verify_present(rPlatName, ref_name))
+                !verify_present(rPlatName, ref_name)) {
                 return true;
+            }
 
             if (!verify_present(lPlatName, ref_name) &&
-                verify_present(rPlatName, ref_name))
+                verify_present(rPlatName, ref_name)) {
                 return false;
+            }
         }
     }
 
@@ -153,8 +158,8 @@ static inline bool compare_default(const Device* ldev, const Device* rdev) {
             (lversion[7] < rversion[7]) ||
             ((lversion[7] == rversion[7]) && (lversion[9] < rversion[9]));
 
-        if (lres) return true;
-        if (rres) return false;
+        if (lres) { return true; }
+        if (rres) { return false; }
     }
 
     // Default criteria, sort based on memory
@@ -182,7 +187,7 @@ DeviceManager::DeviceManager()
                 AF_ERR_RUNTIME);
         }
     }
-    fgMngr.reset(new graphics::ForgeManager());
+    fgMngr = std::make_unique<graphics::ForgeManager>();
 
     // This is all we need because the sort takes care of the order of devices
 #ifdef OS_MAC
@@ -193,9 +198,9 @@ DeviceManager::DeviceManager()
 
     string deviceENV = getEnvVar("AF_OPENCL_DEVICE_TYPE");
 
-    if (deviceENV.compare("GPU") == 0) {
+    if (deviceENV == "GPU") {
         DEVICE_TYPES = CL_DEVICE_TYPE_GPU;
-    } else if (deviceENV.compare("CPU") == 0) {
+    } else if (deviceENV == "CPU") {
         DEVICE_TYPES = CL_DEVICE_TYPE_CPU;
     } else if (deviceENV.compare("ACC") >= 0) {
         DEVICE_TYPES = CL_DEVICE_TYPE_ACCELERATOR;
@@ -214,7 +219,7 @@ DeviceManager::DeviceManager()
         }
         AF_TRACE("Found {} devices on platform {}", current_devices.size(),
                  platform.getInfo<CL_PLATFORM_NAME>());
-        for (auto dev : current_devices) {
+        for (const auto& dev : current_devices) {
             mDevices.push_back(new Device(dev));
             AF_TRACE("Found device {} on platform {}",
                      dev.getInfo<CL_DEVICE_NAME>(),
@@ -237,8 +242,8 @@ DeviceManager::DeviceManager()
         cl_context_properties cps[3] = {
             CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
 
-        Context* ctx     = new Context(*mDevices[i], cps);
-        CommandQueue* cq = new CommandQueue(*ctx, *mDevices[i]);
+        auto* ctx = new Context(*mDevices[i], cps);
+        auto* cq  = new CommandQueue(*ctx, *mDevices[i]);
         mContexts.push_back(ctx);
         mQueues.push_back(cq);
         mIsGLSharingOn.push_back(false);
@@ -252,7 +257,7 @@ DeviceManager::DeviceManager()
         stringstream s(deviceENV);
         int def_device = -1;
         s >> def_device;
-        if (def_device < 0 || def_device >= (int)nDevices) {
+        if (def_device < 0 || def_device >= nDevices) {
             AF_TRACE(
                 "AF_OPENCL_DEFAULT_DEVICE ({}) \
                    is out of range, Setting default device to 0",
@@ -266,7 +271,7 @@ DeviceManager::DeviceManager()
     deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
     if (!default_device_set && !deviceENV.empty()) {
         cl_device_type default_device_type = CL_DEVICE_TYPE_GPU;
-        if (deviceENV.compare("CPU") == 0) {
+        if (deviceENV == "CPU") {
             default_device_type = CL_DEVICE_TYPE_CPU;
         } else if (deviceENV.compare("ACC") >= 0) {
             default_device_type = CL_DEVICE_TYPE_ACCELERATOR;
@@ -298,7 +303,9 @@ DeviceManager::DeviceManager()
              * OpenGL shared contexts whereever applicable */
             int devCount      = mDevices.size();
             fg_window wHandle = fgMngr->getMainWindow();
-            for (int i = 0; i < devCount; ++i) markDeviceForInterop(i, wHandle);
+            for (int i = 0; i < devCount; ++i) {
+                markDeviceForInterop(i, wHandle);
+            }
         } catch (...) {}
     }
 
@@ -323,7 +330,7 @@ DeviceManager::DeviceManager()
 spdlog::logger* DeviceManager::getLogger() { return logger.get(); }
 
 DeviceManager& DeviceManager::getInstance() {
-    static DeviceManager* my_instance = new DeviceManager();
+    static auto* my_instance = new DeviceManager();
     return *my_instance;
 }
 
@@ -381,9 +388,7 @@ void DeviceManager::resetMemoryManagerPinned() {
 }
 
 DeviceManager::~DeviceManager() {
-    for (int i = 0; i < getDeviceCount(); ++i) {
-        delete gfxManagers[i].release();
-    }
+    for (int i = 0; i < getDeviceCount(); ++i) { gfxManagers[i] = nullptr; }
 #ifndef OS_WIN
     // TODO: FIXME:
     // clfftTeardown() causes a "Pure Virtual Function Called" crash on
@@ -395,12 +400,11 @@ DeviceManager::~DeviceManager() {
 
     // deCache Boost program_cache
 #ifndef OS_WIN
-    namespace compute = boost::compute;
-    for (auto bCache : mBoostProgCacheVector) delete bCache;
+    for (auto bCache : mBoostProgCacheVector) { delete bCache; }
 #endif
 
-    delete memManager.release();
-    delete pinnedMemManager.release();
+    memManager       = nullptr;
+    pinnedMemManager = nullptr;
 
     // TODO: FIXME:
     // OpenCL libs on Windows platforms
@@ -410,17 +414,17 @@ DeviceManager::~DeviceManager() {
     // doesn't seem to happen on Linux or MacOSX.
     // So, clean up OpenCL resources on non-Windows platforms
 #ifndef OS_WIN
-    for (auto q : mQueues) delete q;
-    for (auto c : mContexts) delete c;
-    for (auto d : mDevices) delete d;
+    for (auto q : mQueues) { delete q; }
+    for (auto c : mContexts) { delete c; }
+    for (auto d : mDevices) { delete d; }
 #endif
 }
 
 void DeviceManager::markDeviceForInterop(const int device,
                                          const void* wHandle) {
     try {
-        if (device >= (int)mQueues.size() ||
-            device >= (int)DeviceManager::MAX_DEVICES) {
+        if (device >= static_cast<int>(mQueues.size()) ||
+            device >= static_cast<int>(DeviceManager::MAX_DEVICES)) {
             AF_TRACE("Invalid device (}) passed for CL-GL Interop", device);
             throw cl::Error(CL_INVALID_DEVICE,
                             "Invalid device passed for CL-GL Interop");
@@ -455,13 +459,13 @@ void DeviceManager::markDeviceForInterop(const int device,
 #else
             cl_context_properties cps[] = {
                 CL_GL_CONTEXT_KHR,
-                (cl_context_properties)wnd_ctx,
+                static_cast<cl_context_properties>(wnd_ctx),
 #if defined(_WIN32) || defined(_MSC_VER)
                 CL_WGL_HDC_KHR,
                 (cl_context_properties)wnd_dsp,
 #else
                 CL_GLX_DISPLAY_KHR,
-                (cl_context_properties)wnd_dsp,
+                static_cast<cl_context_properties>(wnd_dsp),
 #endif
                 CL_CONTEXT_PLATFORM,
                 (cl_context_properties)plat(),
@@ -471,19 +475,20 @@ void DeviceManager::markDeviceForInterop(const int device,
             // Check if current OpenCL device is belongs to the OpenGL context
             {
                 cl_context_properties test_cps[] = {
-                    CL_GL_CONTEXT_KHR, (cl_context_properties)wnd_ctx,
+                    CL_GL_CONTEXT_KHR,
+                    static_cast<cl_context_properties>(wnd_ctx),
                     CL_CONTEXT_PLATFORM, (cl_context_properties)plat(), 0};
 
                 // Load the extension
                 // If cl_khr_gl_sharing is available, this function should be
                 // present This has been checked earlier, it comes to this point
                 // only if it is found
-                auto func = (clGetGLContextInfoKHR_fn)
+                auto func = reinterpret_cast<clGetGLContextInfoKHR_fn>(
                     clGetExtensionFunctionAddressForPlatform(
-                        plat(), "clGetGLContextInfoKHR");
+                        plat(), "clGetGLContextInfoKHR"));
 
                 // If the function doesn't load, bail early
-                if (!func) return;
+                if (!func) { return; }
 
                 // Get all devices associated with opengl context
                 vector<cl_device_id> devices(16);
@@ -491,21 +496,21 @@ void DeviceManager::markDeviceForInterop(const int device,
                 cl_int err = func(test_cps, CL_DEVICES_FOR_GL_CONTEXT_KHR,
                                   devices.size() * sizeof(cl_device_id),
                                   &devices[0], &ret);
-                if (err != CL_SUCCESS) return;
-                int num = ret / sizeof(cl_device_id);
+                if (err != CL_SUCCESS) { return; }
+                size_t num = ret / sizeof(cl_device_id);
                 devices.resize(num);
 
                 // Check if current device is present in the associated devices
                 cl_device_id current_device = (*mDevices[device])();
                 auto res = find(begin(devices), end(devices), current_device);
 
-                if (res == end(devices)) return;
+                if (res == end(devices)) { return; }
             }
 #endif
 
             // Change current device to use GL sharing
-            Context* ctx     = new Context(*mDevices[device], cps);
-            CommandQueue* cq = new CommandQueue(*ctx, *mDevices[device]);
+            auto* ctx = new Context(*mDevices[device], cps);
+            auto* cq  = new CommandQueue(*ctx, *mDevices[device]);
 
             // May be fixes the AMD GL issues we see on windows?
 #if !defined(_WIN32) && !defined(_MSC_VER)
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 11cc5336c8..6a6b125cea 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -86,10 +86,10 @@ class DeviceManager {
 
     friend int setDevice(int device);
 
-    friend void addDeviceContext(cl_device_id dev, cl_context cxt,
+    friend void addDeviceContext(cl_device_id dev, cl_context ctx,
                                  cl_command_queue que);
 
-    friend void setDeviceContext(cl_device_id dev, cl_context cxt);
+    friend void setDeviceContext(cl_device_id dev, cl_context ctx);
 
     friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
diff --git a/src/backend/opencl/diff.cpp b/src/backend/opencl/diff.cpp
index 2a556052da..e604404ee1 100644
--- a/src/backend/opencl/diff.cpp
+++ b/src/backend/opencl/diff.cpp
@@ -16,8 +16,8 @@
 namespace opencl {
 template<typename T, bool isDiff2>
 static Array<T> diff(const Array<T> &in, const int dim) {
-    const af::dim4 iDims = in.dims();
-    af::dim4 oDims       = iDims;
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
     oDims[dim] -= (isDiff2 + 1);
 
     if (iDims.elements() == 0 || oDims.elements() == 0) {
@@ -27,13 +27,11 @@ static Array<T> diff(const Array<T> &in, const int dim) {
     Array<T> out = createEmptyArray<T>(oDims);
 
     switch (dim) {
-        case (0): kernel::diff<T, 0, isDiff2>(out, in, in.ndims()); break;
-
-        case (1): kernel::diff<T, 1, isDiff2>(out, in, in.ndims()); break;
-
-        case (2): kernel::diff<T, 2, isDiff2>(out, in, in.ndims()); break;
-
-        case (3): kernel::diff<T, 3, isDiff2>(out, in, in.ndims()); break;
+        case 0: kernel::diff<T, 0, isDiff2>(out, in, in.ndims()); break;
+        case 1: kernel::diff<T, 1, isDiff2>(out, in, in.ndims()); break;
+        case 2: kernel::diff<T, 2, isDiff2>(out, in, in.ndims()); break;
+        case 3: kernel::diff<T, 3, isDiff2>(out, in, in.ndims()); break;
+        default: AF_ERROR("dim only supports values 0-3.", AF_ERR_UNKNOWN);
     }
 
     return out;
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index d0ae97d98b..466099dc92 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -37,32 +37,33 @@ struct Precision<cdouble> {
 };
 
 static void computeDims(size_t rdims[4], const dim4 &idims) {
-    for (int i = 0; i < 4; i++) { rdims[i] = (size_t)idims[i]; }
+    for (int i = 0; i < 4; i++) { rdims[i] = static_cast<size_t>(idims[i]); }
 }
 
 //(currently) true is in clFFT if length is a power of 2,3,5
 inline bool isSupLen(dim_t length) {
     while (length > 1) {
-        if (length % 2 == 0)
+        if (length % 2 == 0) {
             length /= 2;
-        else if (length % 3 == 0)
+        } else if (length % 3 == 0) {
             length /= 3;
-        else if (length % 5 == 0)
+        } else if (length % 5 == 0) {
             length /= 5;
-        else if (length % 7 == 0)
+        } else if (length % 7 == 0) {
             length /= 7;
-        else if (length % 11 == 0)
+        } else if (length % 11 == 0) {
             length /= 11;
-        else if (length % 13 == 0)
+        } else if (length % 13 == 0) {
             length /= 13;
-        else
+        } else {
             return false;
+        }
     }
     return true;
 }
 
 template<int rank>
-void verifySupported(const dim4 dims) {
+void verifySupported(const dim4 &dims) {
     for (int i = 0; i < rank; i++) { ARG_ASSERT(1, isSupLen(dims[i])); }
 }
 
@@ -77,10 +78,10 @@ void fft_inplace(Array<T> &in) {
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
 
-    SharedPlan plan =
-        findPlan(CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
-                 (clfftDim)rank, tdims, istrides, istrides[rank], istrides,
-                 istrides[rank], (clfftPrecision)Precision<T>::type, batch);
+    SharedPlan plan = findPlan(
+        CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
+        static_cast<clfftDim>(rank), tdims, istrides, istrides[rank], istrides,
+        istrides[rank], static_cast<clfftPrecision>(Precision<T>::type), batch);
 
     cl_mem imem            = (*in.get())();
     cl_command_queue queue = getQueue()();
@@ -108,10 +109,10 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
 
-    SharedPlan plan =
-        findPlan(CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED, (clfftDim)rank, tdims,
-                 istrides, istrides[rank], ostrides, ostrides[rank],
-                 (clfftPrecision)Precision<Tc>::type, batch);
+    SharedPlan plan = findPlan(
+        CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED, static_cast<clfftDim>(rank),
+        tdims, istrides, istrides[rank], ostrides, ostrides[rank],
+        static_cast<clfftPrecision>(Precision<Tc>::type), batch);
 
     cl_mem imem            = (*in.get())();
     cl_mem omem            = (*out.get())();
@@ -137,10 +138,10 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
 
-    SharedPlan plan =
-        findPlan(CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL, (clfftDim)rank, tdims,
-                 istrides, istrides[rank], ostrides, ostrides[rank],
-                 (clfftPrecision)Precision<Tc>::type, batch);
+    SharedPlan plan = findPlan(
+        CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL, static_cast<clfftDim>(rank),
+        tdims, istrides, istrides[rank], ostrides, ostrides[rank],
+        static_cast<clfftPrecision>(Precision<Tc>::type), batch);
 
     cl_mem imem            = (*in.get())();
     cl_mem omem            = (*out.get())();
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index e4b1e607d8..01707e5099 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -19,19 +19,20 @@ using af::dim4;
 namespace opencl {
 
 template<typename T>
-static const dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
-                                 const dim_t baseDim) {
-    const dim4 i1d = i1.dims();
-    const dim4 i2d = i2.dims();
+static dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
+                           const dim_t baseDim) {
+    const dim4& i1d = i1.dims();
+    const dim4& i2d = i2.dims();
 
     dim_t pd[4] = {1, 1, 1, 1};
 
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched cuFFT capabilities
-    pd[0] = nextpow2((unsigned)((int)ceil(i1d[0] / 2.f) + i2d[0] - 1));
+    pd[0] = nextpow2(static_cast<unsigned>(
+        static_cast<int>(std::ceil(i1d[0] / 2.f)) + i2d[0] - 1));
 
     for (dim_t k = 1; k < baseDim; k++) {
-        pd[k] = nextpow2((unsigned)(i1d[k] + i2d[k] - 1));
+        pd[k] = nextpow2(static_cast<unsigned>(i1d[k] + i2d[k] - 1));
     }
 
     dim_t i1batch = 1;
@@ -49,8 +50,8 @@ template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
          dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
-    const dim4 sDims = signal.dims();
-    const dim4 fDims = filter.dims();
+    const dim4& sDims = signal.dims();
+    const dim4& fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
@@ -64,7 +65,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) oDims[i] = fDims[i];
+            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
@@ -83,12 +84,13 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     if (kind == AF_BATCH_RHS) {
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
-            if (k < baseDim)
+            if (k < baseDim) {
                 seqs.push_back({0., static_cast<double>(pDims[k] - 1), 1.});
-            else if (k == baseDim)
+            } else if (k == baseDim) {
                 seqs.push_back({1., static_cast<double>(pDims[k] - 1), 1.});
-            else
+            } else {
                 seqs.push_back({0., 0., 1.});
+            }
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
@@ -96,12 +98,13 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     } else {
         std::vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
-            if (k < baseDim)
-                seqs.push_back({0., (double)pDims[k] - 1, 1.});
-            else if (k == baseDim)
+            if (k < baseDim) {
+                seqs.push_back({0., static_cast<double>(pDims[k]) - 1, 1.});
+            } else if (k == baseDim) {
                 seqs.push_back({0., static_cast<double>(pDims[k] - 2), 1.});
-            else
+            } else {
                 seqs.push_back({0., 0., 1.});
+            }
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
@@ -110,12 +113,13 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    if (expand)
+    if (expand) {
         kernel::reorderOutputHelper<T, cT, isDouble, roundOut, true, convT>(
             out, packed, signal, filter, baseDim, kind);
-    else
+    } else {
         kernel::reorderOutputHelper<T, cT, isDouble, roundOut, false, convT>(
             out, packed, signal, filter, baseDim, kind);
+    }
 
     return out;
 }
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index b83a73274f..a1875686bc 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -51,7 +51,8 @@ void copy_histogram(const Array<T> &data, fg_histogram hist) {
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             getQueue().enqueueReadBuffer(*data.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 7735803519..40f4621660 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -22,7 +22,7 @@ namespace opencl {
 template<typename inType, typename outType, bool isLinear>
 Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
                          const double &minval, const double &maxval) {
-    const dim4 dims    = in.dims();
+    const dim4 &dims   = in.dims();
     dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 8eaa3bf394..229678f700 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -30,15 +30,16 @@ int homography(Array<T> &bestH, const Array<float> &x_src,
                const Array<float> &y_dst, const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations) {
-    const af::dim4 idims    = x_src.dims();
+    const af::dim4 &idims   = x_src.dims();
     const unsigned nsamples = idims[0];
 
     unsigned iter    = iterations;
     Array<float> err = createEmptyArray<float>(af::dim4());
     if (htype == AF_HOMOGRAPHY_LMEDS) {
-        iter = ::std::min(
-            iter, (unsigned)(log(1.f - LMEDSConfidence) /
-                             log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
+        iter =
+            ::std::min(iter, static_cast<unsigned>(
+                                 log(1.f - LMEDSConfidence) /
+                                 log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
         err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
     } else {
         // Avoid passing "null" cl_mem object to kernels
@@ -48,12 +49,14 @@ int homography(Array<T> &bestH, const Array<float> &x_src,
     const size_t iter_sz = divup(iter, 256) * 256;
 
     af::dim4 rdims(4, iter_sz);
-    Array<float> fctr = createValueArray<float>(rdims, (float)nsamples);
-    Array<float> rnd  = arithOp<float, af_mul_t>(initial, fctr, rdims);
+    Array<float> fctr =
+        createValueArray<float>(rdims, static_cast<float>(nsamples));
+    Array<float> rnd = arithOp<float, af_mul_t>(initial, fctr, rdims);
 
-    Array<T> tmpH = createValueArray<T>(af::dim4(9, iter_sz), (T)0);
+    Array<T> tmpH =
+        createValueArray<T>(af::dim4(9, iter_sz), static_cast<T>(0));
 
-    bestH = createValueArray<T>(af::dim4(3, 3), (T)0);
+    bestH = createValueArray<T>(af::dim4(3, 3), static_cast<T>(0));
     switch (htype) {
         case AF_HOMOGRAPHY_RANSAC:
             return kernel::computeH<T, AF_HOMOGRAPHY_RANSAC>(
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index b2b7843459..3a70a3aa86 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -34,7 +34,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
 
     int num_a = a.dims()[0];
 
-    if (num_a == 1) return c;
+    if (num_a == 1) { return c; }
 
     dim4 ydims = c.dims();
     Array<T> y = createEmptyArray<T>(ydims);
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index f441f0d37f..15b6a614a6 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -57,8 +57,8 @@ void copy_image(const Array<T> &in, fg_image image) {
 
         glBindBuffer(GL_PIXEL_UNPACK_BUFFER, buffer);
         glBufferData(GL_PIXEL_UNPACK_BUFFER, bytes, 0, GL_STREAM_DRAW);
-        GLubyte *ptr =
-            (GLubyte *)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY);
+        auto *ptr = static_cast<GLubyte *>(
+            glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 4189d3ab4d..2478484977 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -31,14 +31,14 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     }
 
     // retrieve dimensions, strides and offsets
-    dim4 iDims  = in.dims();
-    dim4 dDims  = in.getDataDims();
-    dim4 oDims  = toDims(seqs, iDims);
-    dim4 iOffs  = toOffset(seqs, dDims);
-    dim4 iStrds = in.strides();
+    const dim4& iDims = in.dims();
+    dim4 dDims        = in.getDataDims();
+    dim4 oDims        = toDims(seqs, iDims);
+    dim4 iOffs        = toOffset(seqs, dDims);
+    dim4 iStrds       = in.strides();
 
     for (dim_t i = 0; i < 4; ++i) {
-        p.isSeq[i] = idxrs[i].isSeq;
+        p.isSeq[i] = idxrs[i].isSeq ? 1 : 0;
         p.offs[i]  = iOffs[i];
         p.strds[i] = iStrds[i];
     }
@@ -66,7 +66,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     kernel::index<T>(out, in, p, bPtrs);
 
     for (dim_t x = 0; x < 4; ++x) {
-        if (p.isSeq[x]) bufferFree(bPtrs[x]);
+        if (p.isSeq[x]) { bufferFree(bPtrs[x]); }
     }
 
     return out;
diff --git a/src/backend/opencl/inverse.cpp b/src/backend/opencl/inverse.cpp
index a6f141385b..c5b62a861f 100644
--- a/src/backend/opencl/inverse.cpp
+++ b/src/backend/opencl/inverse.cpp
@@ -20,7 +20,7 @@ namespace opencl {
 template<typename T>
 Array<T> inverse(const Array<T> &in) {
     if (OpenCLCPUOffload()) {
-        if (in.dims()[0] == in.dims()[1]) return cpu::inverse(in);
+        if (in.dims()[0] == in.dims()[1]) { return cpu::inverse(in); }
     }
     Array<T> I = identity<T>(in.dims());
     return solve<T>(in, I);
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 50f513bf85..09c6399d7a 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -64,7 +64,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
 
     for (auto node : output_nodes) { funcName << node->getNameStr() << "_"; }
 
-    for (int i = 0; i < (int)full_nodes.size(); i++) {
+    for (size_t i = 0; i < full_nodes.size(); i++) {
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
@@ -73,7 +73,7 @@ static string getFuncName(const vector<Node *> &output_nodes,
     return hashName.str();
 }
 
-static string getKernelString(const string funcName,
+static string getKernelString(const string &funcName,
                               const vector<const Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
                               const vector<int> &output_ids, bool is_linear) {
@@ -129,7 +129,7 @@ static string getKernelString(const string funcName,
     stringstream offsetsStream;
     stringstream opsStream;
 
-    for (int i = 0; i < (int)full_nodes.size(); i++) {
+    for (size_t i = 0; i < full_nodes.size(); i++) {
         const auto &node     = full_nodes[i];
         const auto &ids_curr = full_ids[i];
         // Generate input parameters, only needs current id
@@ -140,8 +140,7 @@ static string getKernelString(const string funcName,
         node->genFuncs(opsStream, ids_curr);
     }
 
-    for (int i = 0; i < (int)output_ids.size(); i++) {
-        int id = output_ids[i];
+    for (int id : output_ids) {
         // Generate output parameters
         outParamStream << "__global " << full_nodes[id]->getTypeStr() << " *out"
                        << id << ", \n";
@@ -188,7 +187,7 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
                                          output_ids, is_linear);
         saveKernel(funcName, jit_ker, ".cl");
         const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
-        const int ker_lens[]   = {jit_cl_len, (int)jit_ker.size()};
+        const int ker_lens[]   = {jit_cl_len, static_cast<int>(jit_ker.size())};
 
         Program prog;
         string options =
@@ -212,8 +211,8 @@ static Kernel getKernel(const vector<Node *> &output_nodes,
     return *entry.ker;
 }
 
-void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
-    if (outputs.size() == 0) return;
+void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
+    if (outputs.empty()) { return; }
 
     // Assume all ouputs are of same size
     // FIXME: Add assert to check if all outputs are same size?
@@ -226,7 +225,7 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
     thread_local vector<int> output_ids;
 
     // Reserve some space to improve performance at smaller sizes
-    if (nodes.size() == 0) {
+    if (nodes.empty()) {
         nodes.reserve(1024);
         output_ids.reserve(output_nodes.size());
         full_nodes.reserve(1024);
@@ -259,10 +258,11 @@ void evalNodes(vector<Param> &outputs, vector<Node *> output_nodes) {
         (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
 
     while (num_odims >= 1) {
-        if (out_info.dims[num_odims - 1] == 1)
+        if (out_info.dims[num_odims - 1] == 1) {
             num_odims--;
-        else
+        } else {
             break;
+        }
     }
 
     if (is_linear) {
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index b4f910abb6..b6e8ab7e2c 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -13,14 +13,19 @@
 #include <join.hpp>
 #include <kernel/join.hpp>
 
+#include <algorithm>
 #include <stdexcept>
+#include <vector>
 
+using af::dim4;
 using common::half;
+using std::transform;
+using std::vector;
 
 namespace opencl {
 template<int dim>
-af::dim4 calcOffset(const af::dim4 dims) {
-    af::dim4 offset;
+dim4 calcOffset(const dim4 &dims) {
+    dim4 offset;
     offset[0] = (dim == 0) ? dims[0] : 0;
     offset[1] = (dim == 1) ? dims[1] : 0;
     offset[2] = (dim == 2) ? dims[2] : 0;
@@ -32,9 +37,9 @@ template<typename Tx, typename Ty>
 Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
     // All dimensions except join dimension must be equal
     // Compute output dims
-    af::dim4 odims;
-    af::dim4 fdims = first.dims();
-    af::dim4 sdims = second.dims();
+    dim4 odims;
+    dim4 fdims = first.dims();
+    dim4 sdims = second.dims();
 
     for (int i = 0; i < 4; i++) {
         if (i == dim) {
@@ -46,7 +51,7 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
 
     Array<Tx> out = createEmptyArray<Tx>(odims);
 
-    af::dim4 zero(0, 0, 0, 0);
+    dim4 zero(0, 0, 0, 0);
 
     switch (dim) {
         case 0:
@@ -72,9 +77,9 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
 
 template<typename T, int n_arrays>
 void join_wrapper(const int dim, Array<T> &out,
-                  const std::vector<Array<T>> &inputs) {
-    af::dim4 zero(0, 0, 0, 0);
-    af::dim4 d = zero;
+                  const vector<Array<T>> &inputs) {
+    dim4 zero(0, 0, 0, 0);
+    dim4 d = zero;
 
     switch (dim) {
         case 0:
@@ -109,15 +114,15 @@ void join_wrapper(const int dim, Array<T> &out,
 }
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
+Array<T> join(const int dim, const vector<Array<T>> &inputs) {
     // All dimensions except join dimension must be equal
     // Compute output dims
-    af::dim4 odims;
+    dim4 odims;
     const dim_t n_arrays = inputs.size();
-    std::vector<af::dim4> idims(n_arrays);
+    vector<dim4> idims(n_arrays);
 
     dim_t dim_size = 0;
-    for (int i = 0; i < (int)idims.size(); i++) {
+    for (size_t i = 0; i < idims.size(); i++) {
         idims[i] = inputs[i].dims();
         dim_size += idims[i][dim];
     }
@@ -130,12 +135,12 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
         }
     }
 
-    std::vector<Array<T> *> input_ptrs(inputs.size());
-    std::transform(
+    vector<Array<T> *> input_ptrs(inputs.size());
+    transform(
         begin(inputs), end(inputs), begin(input_ptrs),
         [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
     evalMultiple(input_ptrs);
-    std::vector<Param> inputParams(inputs.begin(), inputs.end());
+    vector<Param> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
     switch (n_arrays) {
@@ -173,9 +178,8 @@ INSTANTIATE(half, half)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                       \
-    template Array<T> join<T>(const int dim, \
-                              const std::vector<Array<T>> &inputs);
+#define INSTANTIATE(T) \
+    template Array<T> join<T>(const int dim, const vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 9f1f8583a8..b31b68bc8d 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -50,8 +50,8 @@ std::string generateOptionsString() {
             << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
             << " -D ZERO=" << toNumStr(scalar<Ty>(0));
 
-    if ((af_dtype)dtype_traits<Ty>::af_type == c32 ||
-        (af_dtype)dtype_traits<Ty>::af_type == c64) {
+    if (static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c32 ||
+        static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c64) {
         options << " -D IS_CPLX=1";
     } else {
         options << " -D IS_CPLX=0";
diff --git a/src/backend/opencl/kernel/convolve/conv2_b8.cpp b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
index 2ddd478faf..75b34e5459 100644
--- a/src/backend/opencl/kernel/convolve/conv2_b8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(char, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_c32.cpp b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
index 253aeef4cb..d498dfeb7d 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(cfloat, cfloat)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_c64.cpp b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
index 9ba2ce1844..5996ce5e4f 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(cdouble, cdouble)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_f32.cpp b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
index b1567ac9d8..48bbc3f055 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(float, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_f64.cpp b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
index aff172d7db..50b3bcc2b7 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(double, double)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 7df69c2f60..404cd48fac 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -45,8 +45,8 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
                 << " -D EXPAND=" << expand << " -D C_SIZE=" << LOC_SIZE
                 << " -D " << binOpName<af_mul_t>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
diff --git a/src/backend/opencl/kernel/convolve/conv2_s16.cpp b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
index d8b7f33af0..30eccdf891 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(short, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s32.cpp b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
index 7b73459ec2..a8e2a4e8f7 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(int, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s64.cpp b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
index 39a06ae060..408b3a0df3 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(intl, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u16.cpp b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
index 8404825a23..26f46ae7d5 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(ushort, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u32.cpp b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
index 2dd7dfe3a4..6c87a7fbb2 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(uint, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u64.cpp b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
index 7c40aac13f..717b331628 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(uintl, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u8.cpp b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
index 4c0d2580a5..37f2e7f4cb 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
@@ -15,6 +15,6 @@ namespace kernel {
 
 INSTANTIATE(uchar, float)
 
-}
+}  // namespace kernel
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index f71f5ee0e1..7380f7dc1e 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -112,8 +112,8 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
                 << " -D BASE_DIM=" << bDim << " -D EXPAND=" << expand << " -D "
                 << binOpName<af_mul_t>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index e5b051f12e..cc5c20aaba 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -66,8 +66,8 @@ void convSep(Param out, const Param signal, const Param filter) {
                 << " -D FLEN=" << fLen << " -D LOCAL_MEM_SIZE=" << locSize
                 << " -D " << binOpName<af_mul_t>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index ac24c432d3..7494fc92dd 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -83,9 +83,10 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
 
         options << " -D T=" << dtype_traits<T>::getName();
 
-        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
+        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
             options << " -D CONVT=float";
-        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
+                   isDouble) {
             options << " -D CONVT=double"
                     << " -D USE_DOUBLE";
         }
@@ -140,9 +141,10 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
 
         options << " -D T=" << dtype_traits<T>::getName();
 
-        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
+        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
             options << " -D CONVT=float";
-        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
+                   isDouble) {
             options << " -D CONVT=double"
                     << " -D USE_DOUBLE";
         }
@@ -189,9 +191,10 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
                 << " -D AF_BATCH_RHS=" << (int)AF_BATCH_RHS
                 << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
 
-        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
+        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
             options << " -D CONVT=float";
-        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
+                   isDouble) {
             options << " -D CONVT=double"
                     << " -D USE_DOUBLE";
         }
@@ -251,9 +254,10 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
                 << " -D ROUND_OUT=" << (int)roundOut
                 << " -D EXPAND=" << (int)expand;
 
-        if ((af_dtype)dtype_traits<convT>::af_type == c32) {
+        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
             options << " -D CONVT=float";
-        } else if ((af_dtype)dtype_traits<convT>::af_type == c64 && isDouble) {
+        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
+                   isDouble) {
             options << " -D CONVT=double"
                     << " -D USE_DOUBLE";
         }
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 0fd5473937..19cf0ac7c1 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -48,8 +48,8 @@ void gradient(Param grad0, Param grad1, const Param in) {
         options << " -D T=" << dtype_traits<T>::getName() << " -D TX=" << TX
                 << " -D TY=" << TY << " -D ZERO=" << toNumStr(scalar<T>(0));
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D CPLX=1";
         } else {
             options << " -D CPLX=0";
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 145171ad3d..106b600aa4 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -326,20 +326,21 @@ T ireduce_all(uint *loc, Param in) {
         cl::Buffer *tidx = bufferAlloc(tmp_elements * sizeof(uint));
 
         Param rlen;
-        rlen.data = new cl::Buffer();
+        auto buff = std::make_unique<cl::Buffer>();
+        rlen.data = buff.get();
         ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
                                       groups_x, groups_y, rlen);
 
-        unique_ptr<T[]> h_ptr(new T[tmp_elements]);
-        unique_ptr<uint[]> h_iptr(new uint[tmp_elements]);
+        std::vector<T> h_ptr(tmp_elements);
+        std::vector<uint> h_iptr(tmp_elements);
 
         getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
-                                     sizeof(T) * tmp_elements, h_ptr.get());
-        getQueue().enqueueReadBuffer(*tidx, CL_TRUE, 0,
-                                     sizeof(uint) * tmp_elements, h_iptr.get());
+                                     sizeof(T) * tmp_elements, h_ptr.data());
+        getQueue().enqueueReadBuffer(
+            *tidx, CL_TRUE, 0, sizeof(uint) * tmp_elements, h_iptr.data());
 
-        T *h_ptr_raw     = h_ptr.get();
-        uint *h_iptr_raw = h_iptr.get();
+        T *h_ptr_raw     = h_ptr.data();
+        uint *h_iptr_raw = h_iptr.data();
 
         if (!is_linear) {
             // Converting n-d index into a linear index
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 3095eb562e..bc16d9ae18 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -55,8 +55,8 @@ void resize(Param out, const Param in) {
             default: break;
         }
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D CPLX=1";
             options << " -D TB=" << dtype_traits<BT>::getName();
         } else {
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index c69c9fa502..bc11a35b25 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -63,8 +63,8 @@ void rotate(Param out, const Param in, const float theta,
         options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
         options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D IS_CPLX=1";
             options << " -D TB=" << dtype_traits<BT>::getName();
         } else {
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index a1b7445ddc..14936b99b2 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -60,8 +60,8 @@ void sparseArithOpCSR(Param out, const Param values, const Param rowIdx,
         options << " -D T=" << dtype_traits<T>::getName();
         options << " -D OP=" << getOpString<op>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D IS_CPLX=1";
         } else {
             options << " -D IS_CPLX=0";
@@ -113,8 +113,8 @@ void sparseArithOpCOO(Param out, const Param values, const Param rowIdx,
         options << " -D T=" << dtype_traits<T>::getName();
         options << " -D OP=" << getOpString<op>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D IS_CPLX=1";
         } else {
             options << " -D IS_CPLX=0";
@@ -166,8 +166,8 @@ void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx, const Param rhs,
         options << " -D T=" << dtype_traits<T>::getName();
         options << " -D OP=" << getOpString<op>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D IS_CPLX=1";
         } else {
             options << " -D IS_CPLX=0";
@@ -218,8 +218,8 @@ void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx, const Param rhs,
         options << " -D T=" << dtype_traits<T>::getName();
         options << " -D OP=" << getOpString<op>();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D IS_CPLX=1";
         } else {
             options << " -D IS_CPLX=0";
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 9adc9d08ba..b42a94d446 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -65,8 +65,8 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
         options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
         options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
 
-        if ((af_dtype)dtype_traits<T>::af_type == c32 ||
-            (af_dtype)dtype_traits<T>::af_type == c64) {
+        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
             options << " -D IS_CPLX=1";
             options << " -D TB=" << dtype_traits<BT>::getName();
         } else {
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 692b26b768..ff71368e61 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -21,11 +21,12 @@ namespace opencl {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim) {
-    const dim4 iDims = input.dims();
+    const dim4 &iDims = input.dims();
 
     dim4 oDims(1);
-    for (int d = 0; d < 4; ++d)
+    for (int d = 0; d < 4; ++d) {
         oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
+    }
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
@@ -34,6 +35,7 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
         case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices); break;
         case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices); break;
         case 3: kernel::lookup<in_t, idx_t, 3>(out, input, indices); break;
+        default: AF_ERROR("dim only supports values 0-3.", AF_ERR_UNKNOWN);
     }
 
     return out;
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index 3c99dfd392..a06fc90939 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -71,7 +71,7 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     magma_getrf_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
                        &ipiv[0], getQueue()(), &info);
 
-    if (!convert_pivot) return createHostDataArray<int>(dim4(MN), &ipiv[0]);
+    if (!convert_pivot) { return createHostDataArray<int>(dim4(MN), &ipiv[0]); }
 
     Array<int> pivot = convertPivot(&ipiv[0], MN, M);
     return pivot;
diff --git a/src/backend/opencl/magma/gebrd.cpp b/src/backend/opencl/magma/gebrd.cpp
index 57bd505c31..4e88a498ae 100644
--- a/src/backend/opencl/magma/gebrd.cpp
+++ b/src/backend/opencl/magma/gebrd.cpp
@@ -190,7 +190,7 @@ magma_int_t magma_gebrd_hybrid(magma_int_t m, magma_int_t n, Ty *a,
         the vector defining G(i).
         ===================================================================== */
 
-    typedef typename af::dtype_traits<Ty>::base_type Tr;
+    using Tr = typename af::dtype_traits<Ty>::base_type;
 
     Tr *d = (Tr *)_d;
     Tr *e = (Tr *)_e;
@@ -228,8 +228,9 @@ magma_int_t magma_gebrd_hybrid(magma_int_t m, magma_int_t n, Ty *a,
     if (*info < 0) {
         // magma_xerbla(__func__, -(*info));
         return *info;
-    } else if (lquery)
+    } else if (lquery) {
         return *info;
+    }
 
     /* Quick return if possible */
     minmn = std::min(m, n);
diff --git a/src/backend/opencl/magma/geqrf2.cpp b/src/backend/opencl/magma/geqrf2.cpp
index 29dc4cf94c..2d09f0ba60 100644
--- a/src/backend/opencl/magma/geqrf2.cpp
+++ b/src/backend/opencl/magma/geqrf2.cpp
@@ -210,7 +210,7 @@ magma_int_t magma_geqrf2_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
     }
 
     k = std::min(m, n);
-    if (k == 0) return *info;
+    if (k == 0) { return *info; }
 
     nb = magma_get_geqrf_nb<Ty>(m);
 
diff --git a/src/backend/opencl/magma/geqrf3.cpp b/src/backend/opencl/magma/geqrf3.cpp
index 40bfd875db..ced1e01f4a 100644
--- a/src/backend/opencl/magma/geqrf3.cpp
+++ b/src/backend/opencl/magma/geqrf3.cpp
@@ -193,7 +193,7 @@ magma_int_t magma_geqrf3_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
     }
 
     k = minmn = std::min(m, n);
-    if (k == 0) return *info;
+    if (k == 0) { return *info; }
 
     nb = magma_get_geqrf_nb<Ty>(m);
 
@@ -252,7 +252,7 @@ magma_int_t magma_geqrf3_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
 
             /* Put 0s in the upper triangular part of a panel (and 1s on the
                diagonal); copy the upper triangular in ut and invert it. */
-            if (i > 0) magma_event_sync(event[0]);
+            if (i > 0) { magma_event_sync(event[0]); }
             // Change me
             split_diag_block<Ty>(ib, work_ref(i), ldwork, ut);
             magma_setmatrix<Ty>(rows, ib, work_ref(i), ldwork, a_ref(i, i),
diff --git a/src/backend/opencl/magma/getrf.cpp b/src/backend/opencl/magma/getrf.cpp
index f8b756e61b..4fa3960791 100644
--- a/src/backend/opencl/magma/getrf.cpp
+++ b/src/backend/opencl/magma/getrf.cpp
@@ -130,12 +130,13 @@ magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
 
     /* Check arguments */
     *info = 0;
-    if (m < 0)
+    if (m < 0) {
         *info = -1;
-    else if (n < 0)
+    } else if (n < 0) {
         *info = -2;
-    else if (ldda < std::max(1, m))
+    } else if (ldda < std::max(1, m)) {
         *info = -4;
+    }
 
     if (*info != 0) {
         // magma_xerbla(__func__, -(*info));
@@ -143,7 +144,7 @@ magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
     }
 
     /* Quick return if possible */
-    if (m == 0 || n == 0) return *info;
+    if (m == 0 || n == 0) { return *info; }
 
     gpu_blas_gemm_func<Ty> gpu_blas_gemm;
     gpu_blas_trsm_func<Ty> gpu_blas_trsm;
@@ -196,7 +197,7 @@ magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
         ldwork = maxm;
         if (MAGMA_SUCCESS != magma_malloc_cpu<Ty>(&work, ldwork * nb)) {
             magma_free(dAP);
-            if (dA != dAT) magma_free(dAT);
+            if (dA != dAT) { magma_free(dAT); }
 
             *info = MAGMA_ERR_HOST_ALLOC;
             return *info;
@@ -232,7 +233,7 @@ magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
             rows = m - j * nb;
             LAPACKE_CHECK(
                 cpu_lapack_getrf(rows, nb, work, ldwork, ipiv + j * nb));
-            if (*info == 0 && iinfo > 0) *info = iinfo + j * nb;
+            if (*info == 0 && iinfo > 0) { *info = iinfo + j * nb; }
 
             for (i = j * nb; i < j * nb + nb; ++i) { ipiv[i] += j * nb; }
             magmablas_laswp<Ty>(n, dAT(0, 0), lddat, j * nb + 1, j * nb + nb,
@@ -291,7 +292,7 @@ magma_int_t magma_getrf_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
             // do the cpu part
             LAPACKE_CHECK(
                 cpu_lapack_getrf(rows, nb0, work, ldwork, ipiv + s * nb));
-            if (*info == 0 && iinfo > 0) *info = iinfo + s * nb;
+            if (*info == 0 && iinfo > 0) { *info = iinfo + s * nb; }
 
             for (i = s * nb; i < s * nb + nb0; ++i) { ipiv[i] += s * nb; }
             magmablas_laswp<Ty>(n, dAT(0, 0), lddat, s * nb + 1, s * nb + nb0,
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index 829b909d2d..1f4578db6b 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -245,7 +245,7 @@ magma_int_t magma_getrs_gpu(magma_trans_t trans, magma_int_t n,
         magma_setmatrix<Ty>(n, nrhs, work, n, dB, dB_offset, lddb, queue);
     }
 
-    if (nrhs > 1 && dAT != 0) magma_free(dAT);
+    if (nrhs > 1 && dAT != 0) { magma_free(dAT); }
     magma_free_cpu(work);
     return *info;
 }
diff --git a/src/backend/opencl/magma/labrd.cpp b/src/backend/opencl/magma/labrd.cpp
index ed566f7956..010a3675a7 100644
--- a/src/backend/opencl/magma/labrd.cpp
+++ b/src/backend/opencl/magma/labrd.cpp
@@ -201,7 +201,7 @@ magma_int_t magma_labrd_gpu(magma_int_t m, magma_int_t n, magma_int_t nb, Ty *a,
         of the vector defining G(i).
         ===================================================================== */
 
-    typedef typename af::dtype_traits<Ty>::base_type Tr;
+    using Tr = typename af::dtype_traits<Ty>::base_type;
 
     constexpr bool is_cplx = common::is_complex<Ty>::value;
 
@@ -216,7 +216,7 @@ magma_int_t magma_labrd_gpu(magma_int_t m, magma_int_t n, magma_int_t nb, Ty *a,
     magma_int_t a_dim1, a_offset, x_dim1, x_offset, y_dim1, y_offset, i__2,
         i__3;
     magma_int_t i__;
-    Ty alpha;
+    Ty alpha{};
 
     a_dim1   = lda;
     a_offset = 1 + a_dim1;
diff --git a/src/backend/opencl/magma/larfb.cpp b/src/backend/opencl/magma/larfb.cpp
index abb8d7a60f..b7513bd971 100644
--- a/src/backend/opencl/magma/larfb.cpp
+++ b/src/backend/opencl/magma/larfb.cpp
@@ -237,10 +237,11 @@ magma_int_t magma_larfb_gpu(magma_side_t side, magma_trans_t trans,
 
     // whether T is upper or lower triangular
     OPENCL_BLAS_TRIANGLE_T uplo;
-    if (direct == MagmaForward)
+    if (direct == MagmaForward) {
         uplo = OPENCL_BLAS_TRIANGLE_UPPER;
-    else
+    } else {
         uplo = OPENCL_BLAS_TRIANGLE_LOWER;
+    }
 
     // whether V is stored transposed or not
     OPENCL_BLAS_TRANS_T notransV, transV;
diff --git a/src/backend/opencl/magma/laset.cpp b/src/backend/opencl/magma/laset.cpp
index 5af6d859e7..a08b7af2fa 100644
--- a/src/backend/opencl/magma/laset.cpp
+++ b/src/backend/opencl/magma/laset.cpp
@@ -61,14 +61,15 @@ void magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n, T offdiag,
                      T diag, cl_mem dA, size_t dA_offset, magma_int_t ldda,
                      magma_queue_t queue) {
     magma_int_t info = 0;
-    if (uplo != MagmaLower && uplo != MagmaUpper && uplo != MagmaFull)
+    if (uplo != MagmaLower && uplo != MagmaUpper && uplo != MagmaFull) {
         info = -1;
-    else if (m < 0)
+    } else if (m < 0) {
         info = -2;
-    else if (n < 0)
+    } else if (n < 0) {
         info = -3;
-    else if (ldda < std::max(1, m))
+    } else if (ldda < std::max(1, m)) {
         info = -7;
+    }
 
     if (info != 0) {
         return;  // info;
diff --git a/src/backend/opencl/magma/laswp.cpp b/src/backend/opencl/magma/laswp.cpp
index 62fdaff9c5..53f4cccbea 100644
--- a/src/backend/opencl/magma/laswp.cpp
+++ b/src/backend/opencl/magma/laswp.cpp
@@ -62,14 +62,15 @@ void magmablas_laswp(magma_int_t n, cl_mem dAT, size_t dAT_offset,
                      const magma_int_t *ipiv, magma_int_t inci,
                      magma_queue_t queue) {
     magma_int_t info = 0;
-    if (n < 0)
+    if (n < 0) {
         info = -1;
-    else if (k1 < 1)
+    } else if (k1 < 1) {
         info = -4;
-    else if (k2 < 1)
+    } else if (k2 < 1) {
         info = -5;
-    else if (inci <= 0)
+    } else if (inci <= 0) {
         info = -7;
+    }
 
     if (info != 0) {
         // magma_xerbla( __func__, -(info) );
diff --git a/src/backend/opencl/magma/magma_helper.cpp b/src/backend/opencl/magma/magma_helper.cpp
index a05d1d0fe9..19467d2277 100644
--- a/src/backend/opencl/magma/magma_helper.cpp
+++ b/src/backend/opencl/magma/magma_helper.cpp
@@ -63,11 +63,11 @@ template double magma_real<float>(float val);
 template double magma_real<double>(double val);
 template<>
 double magma_real<magmaFloatComplex>(magmaFloatComplex val) {
-    return (double)val.s[0];
+    return static_cast<double>(val.s[0]);
 }
 template<>
 double magma_real<magmaDoubleComplex>(magmaDoubleComplex val) {
-    return (double)val.s[0];
+    return static_cast<double>(val.s[0]);
 }
 
 #define INSTANTIATE_CPLX_SCALAR(T)  \
@@ -99,60 +99,66 @@ bool magma_is_real<magmaDoubleComplex>() {
 
 template<typename T>
 magma_int_t magma_get_getrf_nb(magma_int_t m) {
-    if (m <= 3200)
+    if (m <= 3200) {
         return 128;
-    else if (m < 9000)
+    } else if (m < 9000) {
         return 256;
-    else
+    } else {
         return 320;
+    }
 }
 
 template magma_int_t magma_get_getrf_nb<float>(magma_int_t m);
 
 template<>
 magma_int_t magma_get_getrf_nb<double>(magma_int_t m) {
-    if (m <= 2048)
+    if (m <= 2048) {
         return 64;
-    else if (m < 7200)
+    } else if (m < 7200) {
         return 192;
-    else
+    } else {
         return 256;
+    }
 }
 
 template<>
 magma_int_t magma_get_getrf_nb<magmaFloatComplex>(magma_int_t m) {
-    if (m <= 2048)
+    if (m <= 2048) {
         return 64;
-    else
+    } else {
         return 128;
+    }
 }
 
 template<>
 magma_int_t magma_get_getrf_nb<magmaDoubleComplex>(magma_int_t m) {
-    if (m <= 3072)
+    if (m <= 3072) {
         return 32;
-    else if (m <= 9024)
+    } else if (m <= 9024) {
         return 64;
-    else
+    } else {
         return 128;
+    }
 }
 
 template<typename T>
 magma_int_t magma_get_potrf_nb(magma_int_t m) {
-    if (m <= 1024)
+    if (m <= 1024) {
         return 128;
-    else
+    } else {
         return 320;
+    }
 }
 
 template magma_int_t magma_get_potrf_nb<float>(magma_int_t m);
 
 template<>
 magma_int_t magma_get_potrf_nb<double>(magma_int_t m) {
-    if (m <= 4256)
+    if (m <= 4256) {
         return 128;
-    else
+    } else {
         return 256;
+    }
 }
 
 template<>
@@ -177,28 +183,30 @@ template magma_int_t magma_get_geqrf_nb<float>(magma_int_t m);
 
 template<>
 magma_int_t magma_get_geqrf_nb<double>(magma_int_t m) {
-    if (m <= 2048) return 64;
+    if (m <= 2048) { return 64; }
     return 128;
 }
 
 template<>
 magma_int_t magma_get_geqrf_nb<magmaFloatComplex>(magma_int_t m) {
-    if (m <= 2048)
+    if (m <= 2048) {
         return 32;
-    else if (m <= 4032)
+    } else if (m <= 4032) {
         return 64;
-    else
+    } else {
         return 128;
+    }
 }
 
 template<>
 magma_int_t magma_get_geqrf_nb<magmaDoubleComplex>(magma_int_t m) {
-    if (m <= 2048)
+    if (m <= 2048) {
         return 32;
-    else if (m <= 4032)
+    } else if (m <= 4032) {
         return 64;
-    else
+    } else {
         return 128;
+    }
 }
 
 #if defined(__GNUC__) || defined(__GNUG__)
@@ -218,7 +226,7 @@ template float magma_make<float>(double r, double i);
 template double magma_make<double>(double r, double i);
 template<>
 magmaFloatComplex magma_make<magmaFloatComplex>(double r, double i) {
-    magmaFloatComplex tmp = {(float)r, (float)i};
+    magmaFloatComplex tmp = {static_cast<float>(r), static_cast<float>(i)};
     return tmp;
 }
 template<>
diff --git a/src/backend/opencl/magma/transpose.cpp b/src/backend/opencl/magma/transpose.cpp
index 5ccc6c3cbe..856679d3ca 100644
--- a/src/backend/opencl/magma/transpose.cpp
+++ b/src/backend/opencl/magma/transpose.cpp
@@ -60,14 +60,15 @@ void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
                          size_t dAT_offset, magma_int_t lddat,
                          magma_queue_t queue) {
     magma_int_t info = 0;
-    if (m < 0)
+    if (m < 0) {
         info = -1;
-    else if (n < 0)
+    } else if (n < 0) {
         info = -2;
-    else if (ldda < m)
+    } else if (ldda < m) {
         info = -4;
-    else if (lddat < n)
+    } else if (lddat < n) {
         info = -6;
+    }
 
     if (info != 0) {
         // magma_xerbla( __func__, -(info) );
@@ -75,7 +76,7 @@ void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
     }
 
     /* Quick return */
-    if ((m == 0) || (n == 0)) return;
+    if ((m == 0) || (n == 0)) { return; }
 
     int idims[]    = {m, n, 1, 1};
     int odims[]    = {n, m, 1, 1};
diff --git a/src/backend/opencl/magma/transpose_inplace.cpp b/src/backend/opencl/magma/transpose_inplace.cpp
index d99d727927..040a90ff22 100644
--- a/src/backend/opencl/magma/transpose_inplace.cpp
+++ b/src/backend/opencl/magma/transpose_inplace.cpp
@@ -58,17 +58,18 @@ template<typename T>
 void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
                                  magma_int_t ldda, magma_queue_t queue) {
     magma_int_t info = 0;
-    if (n < 0)
+    if (n < 0) {
         info = -1;
-    else if (ldda < n)
+    } else if (ldda < n) {
         info = -3;
+    }
 
     if (info != 0) {
         // magma_xerbla( __func__, -(info) );
         return;  // info;
     }
 
-    if (n == 0) return;
+    if (n == 0) { return; }
 
     int dims[]    = {n, n, 1, 1};
     int strides[] = {1, ldda, ldda * n, ldda * n};
diff --git a/src/backend/opencl/magma/unmqr.cpp b/src/backend/opencl/magma/unmqr.cpp
index 420c5a3572..81dae4a340 100644
--- a/src/backend/opencl/magma/unmqr.cpp
+++ b/src/backend/opencl/magma/unmqr.cpp
@@ -296,13 +296,13 @@ magma_int_t magma_unmqr_gpu(magma_side_t side, magma_trans_t trans,
                 jc = i;
             }
 
-            if (mi == 0 || ni == 0) break;
+            if (mi == 0 || ni == 0) { break; }
 
             ret = magma_larfb_gpu<Ty>(
                 MagmaLeft, is_real ? MagmaTrans : MagmaConjTrans, MagmaForward,
                 MagmaColumnwise, mi, ni, ib, a_ref(i, i), ldda, t_ref(i), nb,
                 c_ref(ic, jc), lddc, dwork, 0, nw, queue);
-            if (ret != MAGMA_SUCCESS) return ret;
+            if (ret != MAGMA_SUCCESS) { return ret; }
         }
     } else {
         i = i1;
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index c94b42770f..bbe01d5882 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -26,10 +26,11 @@ Array<outType> match_template(const Array<inType> &sImg,
     bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
                     mType == AF_LSSD || mType == AF_ZNCC;
 
-    if (needMean)
+    if (needMean) {
         kernel::matchTemplate<inType, outType, mType, true>(out, sImg, tImg);
-    else
+    } else {
         kernel::matchTemplate<inType, outType, mType, false>(out, sImg, tImg);
+    }
 
     return out;
 }
diff --git a/src/backend/opencl/math.cpp b/src/backend/opencl/math.cpp
index ff445a710a..82f03722f2 100644
--- a/src/backend/opencl/math.cpp
+++ b/src/backend/opencl/math.cpp
@@ -11,26 +11,26 @@
 #include <common/half.hpp>
 
 namespace opencl {
-bool operator==(cfloat a, cfloat b) {
-    return (a.s[0] == b.s[0]) && (a.s[1] == b.s[1]);
+bool operator==(cfloat lhs, cfloat rhs) {
+    return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
 }
-bool operator!=(cfloat a, cfloat b) { return !(a == b); }
-bool operator==(cdouble a, cdouble b) {
-    return (a.s[0] == b.s[0]) && (a.s[1] == b.s[1]);
+bool operator!=(cfloat lhs, cfloat rhs) { return !(lhs == rhs); }
+bool operator==(cdouble lhs, cdouble rhs) {
+    return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
 }
-bool operator!=(cdouble a, cdouble b) { return !(a == b); }
+bool operator!=(cdouble lhs, cdouble rhs) { return !(lhs == rhs); }
 
-cfloat operator+(cfloat a, cfloat b) {
-    cfloat res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
+cfloat operator+(cfloat lhs, cfloat rhs) {
+    cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
     return res;
 }
 
-common::half operator+(common::half a, common::half b) noexcept {
-    return common::half(static_cast<float>(a) + static_cast<float>(b));
+common::half operator+(common::half lhs, common::half rhs) noexcept {
+    return common::half(static_cast<float>(lhs) + static_cast<float>(rhs));
 }
 
-cdouble operator+(cdouble a, cdouble b) {
-    cdouble res = {{a.s[0] + b.s[0], a.s[1] + b.s[1]}};
+cdouble operator+(cdouble lhs, cdouble rhs) {
+    cdouble res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
     return res;
 }
 
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 06a728fac4..dd62930678 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -135,16 +135,16 @@ static inline float real(cfloat in) { return in.s[0]; }
 static inline double imag(cdouble in) { return in.s[1]; }
 static inline float imag(cfloat in) { return in.s[1]; }
 
-bool operator==(cfloat a, cfloat b);
-bool operator!=(cfloat a, cfloat b);
-bool operator==(cdouble a, cdouble b);
-bool operator!=(cdouble a, cdouble b);
-cfloat operator+(cfloat a, cfloat b);
-cfloat operator+(cfloat a);
-cdouble operator+(cdouble a, cdouble b);
-cdouble operator+(cdouble a);
-cfloat operator*(cfloat a, cfloat b);
-cdouble operator*(cdouble a, cdouble b);
+bool operator==(cfloat lhs, cfloat rhs);
+bool operator!=(cfloat lhs, cfloat rhs);
+bool operator==(cdouble lhs, cdouble rhs);
+bool operator!=(cdouble lhs, cdouble rhs);
+cfloat operator+(cfloat lhs, cfloat rhs);
+cfloat operator+(cfloat lhs);
+cdouble operator+(cdouble lhs, cdouble rhs);
+cdouble operator+(cdouble lhs);
+cfloat operator*(cfloat lhs, cfloat rhs);
+cdouble operator*(cdouble lhs, cdouble rhs);
 common::half operator+(common::half lhs, common::half rhs) noexcept;
 }  // namespace opencl
 
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index 5ab1d0ddc1..95257633de 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -20,14 +20,15 @@ template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor) {
-    const dim4 dims = in.dims();
-    Array<T> out    = createEmptyArray<T>(dims);
-    if (isColor)
+    const dim4 &dims = in.dims();
+    Array<T> out     = createEmptyArray<T>(dims);
+    if (isColor) {
         kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma,
                                    numIterations);
-    else
+    } else {
         kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma,
                                     numIterations);
+    }
     return out;
 }
 
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 72600dcb59..d2ab6674f3 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -22,7 +22,7 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
     ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
-    const dim4 dims = in.dims();
+    const dim4 &dims = in.dims();
 
     Array<T> out = createEmptyArray<T>(dims);
 
@@ -37,7 +37,7 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
     ARG_ASSERT(2, (w_len % 2 != 0));
 
-    const dim4 dims = in.dims();
+    const dim4 &dims = in.dims();
 
     Array<T> out = createEmptyArray<T>(dims);
 
@@ -49,6 +49,9 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
         case 11: kernel::medfilt2<T, pad, 11, 11>(out, in); break;
         case 13: kernel::medfilt2<T, pad, 13, 13>(out, in); break;
         case 15: kernel::medfilt2<T, pad, 15, 15>(out, in); break;
+        default:
+            AF_ERROR("w_len only supports values 3, 5, 7, 9, 11, 12, and 15.",
+                     AF_ERR_UNKNOWN);
     }
     return out;
 }
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 782a19b06a..b1051d29ec 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -39,7 +39,7 @@ void setMemStepSize(size_t step_bytes) {
     memoryManager().setMemStepSize(step_bytes);
 }
 
-size_t getMemStepSize(void) { return memoryManager().getMemStepSize(); }
+size_t getMemStepSize() { return memoryManager().getMemStepSize(); }
 
 void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
@@ -56,8 +56,8 @@ unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
     const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
-    void *ptr       = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
+    void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    auto *buf = static_cast<cl::Buffer *>(ptr);
     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(buf,
                                                                 bufferFree);
 }
@@ -70,10 +70,10 @@ void *memAllocUser(const size_t &bytes) {
 
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock((void *)ptr, false);
+    return memoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
-void memFreeUser(void *ptr) { memoryManager().unlock((void *)ptr, true); }
+void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
 cl::Buffer *bufferAlloc(const size_t &bytes) {
     dim4 dims(bytes);
@@ -82,15 +82,19 @@ cl::Buffer *bufferAlloc(const size_t &bytes) {
 }
 
 void bufferFree(cl::Buffer *buf) {
-    return memoryManager().unlock((void *)buf, false);
+    return memoryManager().unlock(static_cast<void *>(buf), false);
 }
 
-void memLock(const void *ptr) { memoryManager().userLock((void *)ptr); }
+void memLock(const void *ptr) {
+    memoryManager().userLock(const_cast<void *>(ptr));
+}
 
-void memUnlock(const void *ptr) { memoryManager().userUnlock((void *)ptr); }
+void memUnlock(const void *ptr) {
+    memoryManager().userUnlock(const_cast<void *>(ptr));
+}
 
 bool isLocked(const void *ptr) {
-    return memoryManager().isUserLocked((void *)ptr);
+    return memoryManager().isUserLocked(const_cast<void *>(ptr));
 }
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
@@ -109,7 +113,7 @@ T *pinnedAlloc(const size_t &elements) {
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    pinnedMemoryManager().unlock((void *)ptr, false);
+    pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
 #define INSTANTIATE(T)                                                         \
@@ -140,7 +144,7 @@ void Allocator::shutdown() {
         try {
             opencl::setDevice(n);
             shutdownMemoryManager();
-        } catch (AfError err) {
+        } catch (const AfError &err) {
             continue;  // Do not throw any errors while shutting down
         }
     }
@@ -153,14 +157,16 @@ size_t Allocator::getMaxMemorySize(int id) {
 }
 
 void *Allocator::nativeAlloc(const size_t bytes) {
-    auto ptr = (void *)(new cl::Buffer(getContext(), CL_MEM_READ_WRITE, bytes));
+    auto ptr = static_cast<void *>(new cl::Buffer(
+        getContext(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
+        bytes));
     AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
 void Allocator::nativeFree(void *ptr) {
     AF_TRACE("nativeFree:          {}", ptr);
-    delete (cl::Buffer *)ptr;
+    delete static_cast<cl::Buffer *>(ptr);
 }
 
 AllocatorPinned::AllocatorPinned() : pinnedMaps(opencl::getDeviceCount()) {
@@ -187,8 +193,7 @@ size_t AllocatorPinned::getMaxMemorySize(int id) {
 
 void *AllocatorPinned::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
-    cl::Buffer *buf =
-        new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
+    auto *buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
     ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ | CL_MAP_WRITE, 0,
                                       bytes);
     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index 8074c3ed4e..ef378762e2 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -14,10 +14,10 @@
 
 namespace opencl {
 
-static inline int bitCount(int v) {
-    v = v - ((v >> 1) & 0x55555555);
-    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
-    return (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
+static inline unsigned bitCount(unsigned v) {
+    v = v - ((v >> 1U) & 0x55555555U);
+    v = (v & 0x33333333U) + ((v >> 2U) & 0x33333333U);
+    return (((v + (v >> 4U)) & 0xF0F0F0FU) * 0x1010101U) >> 24U;
 }
 
 template<typename T>
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index f51a7336a1..3945077e68 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -24,9 +24,9 @@ template<typename T, typename To, af_match_type dist_type>
 void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
                         const Array<T>& query, const Array<T>& train,
                         const uint dist_dim, const uint n_dist) {
-    uint sample_dim  = (dist_dim == 0) ? 1 : 0;
-    const dim4 qDims = query.dims();
-    const dim4 tDims = train.dims();
+    uint sample_dim   = (dist_dim == 0) ? 1 : 0;
+    const dim4& qDims = query.dims();
+    const dim4& tDims = train.dims();
 
     const dim4 outDims(n_dist, qDims[sample_dim]);
     const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 14a3bb795f..fa1d29c111 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -24,6 +24,7 @@
 #include <errorcodes.hpp>
 #include <version.hpp>
 #include <af/version.h>
+#include <memory>
 
 #ifdef OS_MAC
 #include <OpenGL/CGLCurrent.h>
@@ -68,7 +69,7 @@ using common::memory::MemoryManagerBase;
 
 namespace opencl {
 
-static const string get_system(void) {
+static string get_system() {
     string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
@@ -92,7 +93,7 @@ static inline string& ltrim(string& s) {
 }
 
 static string platformMap(string& platStr) {
-    typedef map<string, string> strmap_t;
+    using strmap_t                = map<string, string>;
     static const strmap_t platMap = {
         make_pair("NVIDIA CUDA", "NVIDIA"),
         make_pair("Intel(R) OpenCL", "INTEL"),
@@ -127,8 +128,9 @@ string getDeviceInfo() noexcept {
         for (auto device : devices) {
             const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
-            string dstr      = device->getInfo<CL_DEVICE_NAME>();
-            bool show_braces = ((unsigned)getActiveDeviceId() == nDevices);
+            string dstr = device->getInfo<CL_DEVICE_NAME>();
+            bool show_braces =
+                (static_cast<unsigned>(getActiveDeviceId()) == nDevices);
 
             string id = (show_braces ? string("[") : "-") +
                         to_string(nDevices) + (show_braces ? string("]") : "-");
@@ -208,7 +210,7 @@ int getDeviceIdFromNativeId(cl_device_id id) {
     int nDevices = devMngr.mDevices.size();
     int devId    = 0;
     for (devId = 0; devId < nDevices; ++devId) {
-        if (id == devMngr.mDevices[devId]->operator()()) break;
+        if (id == devMngr.mDevices[devId]->operator()()) { break; }
     }
 
     return devId;
@@ -256,7 +258,7 @@ CommandQueue& getQueue() {
 const cl::Device& getDevice(int id) {
     device_id_t& devId = tlocalActiveDeviceId();
 
-    if (id == -1) id = get<1>(devId);
+    if (id == -1) { id = get<1>(devId); }
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -280,8 +282,8 @@ size_t getDeviceMemorySize(int device) {
 size_t getHostMemorySize() { return common::getHostMemorySize(); }
 
 cl_device_type getDeviceType() {
-    cl::Device device   = getDevice();
-    cl_device_type type = device.getInfo<CL_DEVICE_TYPE>();
+    const cl::Device& device = getDevice();
+    cl_device_type type      = device.getInfo<CL_DEVICE_TYPE>();
     return type;
 }
 
@@ -292,7 +294,7 @@ bool isHostUnifiedMemory(const cl::Device& device) {
 bool OpenCLCPUOffload(bool forceOffloadOSX) {
     static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") != "0";
     bool offload                 = false;
-    if (offloadEnv) offload = isHostUnifiedMemory(getDevice());
+    if (offloadEnv) { offload = isHostUnifiedMemory(getDevice()); }
 #if OS_MAC
     // FORCED OFFLOAD FOR LAPACK FUNCTIONS ON OSX UNIFIED MEMORY DEVICES
     //
@@ -353,16 +355,17 @@ bool isHalfSupported(int device) {
         clGetDeviceInfo(dev(), CL_DEVICE_HALF_FP_CONFIG,
                         sizeof(cl_device_fp_config), &config, &ret_size);
 
-    if (err)
+    if (err) {
         return false;
-    else
+    } else {
         return config > 0;
+    }
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
-    unsigned nDevices        = 0;
-    unsigned currActiveDevId = (unsigned)getActiveDeviceId();
-    bool devset              = false;
+    unsigned nDevices    = 0;
+    auto currActiveDevId = static_cast<unsigned>(getActiveDeviceId());
+    bool devset          = false;
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -399,19 +402,20 @@ void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
                 snprintf(d_compute, 10, "%s", com_str.c_str());
                 devset = true;
             }
-            if (devset) break;
+            if (devset) { break; }
             nDevices++;
         }
-        if (devset) break;
+        if (devset) { break; }
     }
 
     // Sanitize input
     for (int i = 0; i < 31; i++) {
         if (d_name[i] == ' ') {
-            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ')
+            if (d_name[i + 1] == 0 || d_name[i + 1] == ' ') {
                 d_name[i] = 0;
-            else
+            } else {
                 d_name[i] = '_';
+            }
         }
     }
 }
@@ -421,8 +425,8 @@ int setDevice(int device) {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    if (device >= (int)devMngr.mQueues.size() ||
-        device >= (int)DeviceManager::MAX_DEVICES) {
+    if (device >= static_cast<int>(devMngr.mQueues.size()) ||
+        device >= static_cast<int>(DeviceManager::MAX_DEVICES)) {
         return -1;
     } else {
         int old = getActiveDeviceId();
@@ -449,8 +453,8 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
 
-        cl::Device* tDevice   = new cl::Device(dev);
-        cl::Context* tContext = new cl::Context(ctx);
+        auto* tDevice  = new cl::Device(dev);
+        auto* tContext = new cl::Context(ctx);
         cl::CommandQueue* tQueue =
             (que == NULL ? new cl::CommandQueue(*tContext, *tDevice)
                          : new cl::CommandQueue(que));
@@ -514,7 +518,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
         }
     }
 
-    if (deleteIdx < (int)devMngr.mUserDeviceOffset) {
+    if (deleteIdx < static_cast<int>(devMngr.mUserDeviceOffset)) {
         AF_ERROR("Cannot pop ArrayFire internal devices", AF_ERR_ARG);
     } else if (deleteIdx == -1) {
         AF_ERROR("No matching device found", AF_ERR_ARG);
@@ -546,7 +550,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
         // OTHERWISE, update(decrement) the thread local active device ids
         device_id_t& devId = tlocalActiveDeviceId();
 
-        if (deleteIdx < (int)devId.first) {
+        if (deleteIdx < static_cast<int>(devId.first)) {
             device_id_t newVals = make_pair(devId.first - 1, devId.second - 1);
             devId               = newVals;
         }
@@ -589,12 +593,12 @@ MemoryManagerBase& memoryManager() {
 
     std::call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.memManager.reset(new common::DefaultMemoryManager(
+        inst.memManager = std::make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
-            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG));
+            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG);
         // Set the memory manager's device memory manager
         std::unique_ptr<opencl::Allocator> deviceMemoryManager;
-        deviceMemoryManager.reset(new opencl::Allocator());
+        deviceMemoryManager = std::make_unique<opencl::Allocator>();
         inst.memManager->setAllocator(std::move(deviceMemoryManager));
         inst.memManager->initialize();
     });
@@ -609,12 +613,12 @@ MemoryManagerBase& pinnedMemoryManager() {
 
     std::call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.pinnedMemManager.reset(new common::DefaultMemoryManager(
+        inst.pinnedMemManager = std::make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
-            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG));
+            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG);
         // Set the memory manager's device memory manager
         std::unique_ptr<opencl::AllocatorPinned> deviceMemoryManager;
-        deviceMemoryManager.reset(new opencl::AllocatorPinned());
+        deviceMemoryManager = std::make_unique<opencl::AllocatorPinned>();
         inst.pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
         inst.pinnedMemManager->initialize();
     });
@@ -650,7 +654,7 @@ GraphicsResourceManager& interopManager() {
     DeviceManager& inst = DeviceManager::getInstance();
 
     call_once(initFlags[id], [&] {
-        inst.gfxManagers[id].reset(new GraphicsResourceManager());
+        inst.gfxManagers[id] = std::make_unique<GraphicsResourceManager>();
     });
 
     return *(inst.gfxManagers[id].get());
@@ -679,7 +683,7 @@ void removeKernelFromCache(int device, const string& key) {
 kc_entry_t kernelCache(int device, const string& key) {
     kc_t& cache = getKernelCache(device);
 
-    kc_t::iterator iter = cache.find(key);
+    auto iter = cache.find(key);
 
     return (iter == cache.end() ? kc_entry_t{0, 0} : iter->second);
 }
@@ -690,7 +694,7 @@ using namespace opencl;
 
 af_err afcl_get_device_type(afcl_device_type* res) {
     try {
-        *res = (afcl_device_type)getActiveDeviceType();
+        *res = static_cast<afcl_device_type>(getActiveDeviceType());
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -698,7 +702,7 @@ af_err afcl_get_device_type(afcl_device_type* res) {
 
 af_err afcl_get_platform(afcl_platform* res) {
     try {
-        *res = (afcl_platform)getActivePlatform();
+        *res = static_cast<afcl_platform>(getActivePlatform());
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -707,7 +711,7 @@ af_err afcl_get_platform(afcl_platform* res) {
 af_err afcl_get_context(cl_context* ctx, const bool retain) {
     try {
         *ctx = getContext()();
-        if (retain) clRetainContext(*ctx);
+        if (retain) { clRetainContext(*ctx); }
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -716,7 +720,7 @@ af_err afcl_get_context(cl_context* ctx, const bool retain) {
 af_err afcl_get_queue(cl_command_queue* queue, const bool retain) {
     try {
         *queue = getQueue()();
-        if (retain) clRetainCommandQueue(*queue);
+        if (retain) { clRetainCommandQueue(*queue); }
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 5ab5249e93..5aeff25598 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -96,9 +96,9 @@ std::string getPlatformName(const cl::Device& device);
 
 int setDevice(int device);
 
-void addDeviceContext(cl_device_id dev, cl_context cxt, cl_command_queue que);
+void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que);
 
-void setDeviceContext(cl_device_id dev, cl_context cxt);
+void setDeviceContext(cl_device_id dev, cl_context ctx);
 
 void removeDeviceContext(cl_device_id dev, cl_context ctx);
 
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index 00da7e2bde..bf4a1e7370 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -53,7 +53,8 @@ void copy_plot(const Array<T> &P, fg_plot plot) {
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 586d2b3e33..e252fc0c4d 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -11,6 +11,7 @@
 #include <kernel_headers/KParam.hpp>
 #include <program.hpp>
 #include <traits.hpp>
+#include <utility>
 
 using cl::Buffer;
 using cl::EnqueueArgs;
@@ -20,32 +21,32 @@ using cl::Program;
 using std::string;
 
 namespace opencl {
-const static std::string DEFAULT_MACROS_STR(
-    "\n\
-                                           #ifdef USE_DOUBLE\n\
-                                           #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
-                                           #endif\n                     \
-                                           #ifdef USE_HALF\n\
-                                           #pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
-                                           #else\n                     \
-                                           #define half short\n          \
-                                           #endif\n                      \
-                                           #ifndef M_PI\n               \
-                                           #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
-                                           #endif\n                     \
-                                           ");
+
 void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
-                  std::string options) {
+                  const std::string &options) {
     buildProgram(prog, 1, &ker_str, &ker_len, options);
 }
 
 void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
-                  const int *ker_lens, std::string options) {
+                  const int *ker_lens, const std::string &options) {
     try {
-        Program::Sources setSrc;
-        setSrc.emplace_back(DEFAULT_MACROS_STR.c_str(),
-                            DEFAULT_MACROS_STR.length());
-        setSrc.emplace_back(KParam_hpp, KParam_hpp_len);
+        constexpr char kernel_header[] =
+            R"jit(#ifdef USE_DOUBLE
+#pragma OPENCL EXTENSION cl_khr_fp64 : enable
+#endif
+#ifdef USE_HALF
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#else
+#define half short
+#endif
+#ifndef M_PI
+#define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164
+#endif
+)jit";
+
+        Program::Sources setSrc{
+            {kernel_header, std::extent<decltype(kernel_header)>() - 1},
+            {KParam_hpp, KParam_hpp_len}};
 
         for (int i = 0; i < num_files; i++) {
             setSrc.emplace_back(ker_strs[i], ker_lens[i]);
@@ -55,8 +56,8 @@ void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
             std::string(" -D dim_t=") +
             std::string(dtype_traits<dim_t>::getName());
 
-        prog        = cl::Program(getContext(), setSrc);
-        auto device = getDevice();
+        prog               = cl::Program(getContext(), setSrc);
+        const auto &device = getDevice();
 
         std::string cl_std =
             std::string(" -cl-std=CL") +
@@ -64,7 +65,6 @@ void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
 
         // Braces needed to list initialize the vector for the first argument
         prog.build({device}, (cl_std + defaults + options).c_str());
-
     } catch (...) {
         SHOW_BUILD_INFO(prog);
         throw;
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 34eef3b8db..ba2ff9eb4d 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -45,8 +45,8 @@ class Program;
 
 namespace opencl {
 void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
-                  std::string options);
+                  const std::string &options);
 
 void buildProgram(cl::Program &prog, const int num_files, const char **ker_str,
-                  const int *ker_len, std::string options);
+                  const int *ker_len, const std::string &options);
 }  // namespace opencl
diff --git a/src/backend/opencl/qr.hpp b/src/backend/opencl/qr.hpp
index 26a877ba5a..b202aec88a 100644
--- a/src/backend/opencl/qr.hpp
+++ b/src/backend/opencl/qr.hpp
@@ -11,7 +11,7 @@
 
 namespace opencl {
 template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig);
 
 template<typename T>
 Array<T> qr_inplace(Array<T> &in);
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index 976b8a7cc2..c112df4196 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -16,7 +16,7 @@ using common::half;
 
 namespace opencl {
 void initMersenneState(Array<uint> &state, const uintl seed,
-                       const Array<uint> tbl) {
+                       const Array<uint> &tbl) {
     kernel::initMersenneState(*state.get(), *tbl.get(), seed);
 }
 
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index c3a692ec0b..279db75fc1 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -14,10 +14,8 @@
 #include <af/defines.h>
 
 namespace opencl {
-Array<uint> initMersenneState(const uintl seed, Array<uint> tbl);
-
 void initMersenneState(Array<uint> &state, const uintl seed,
-                       const Array<uint> tbl);
+                       const Array<uint> &tbl);
 
 template<typename T>
 Array<T> uniformDistribution(const af::dim4 &dims,
diff --git a/src/backend/opencl/range.cpp b/src/backend/opencl/range.cpp
index e6b4c76eaf..b98d9ba584 100644
--- a/src/backend/opencl/range.cpp
+++ b/src/backend/opencl/range.cpp
@@ -27,8 +27,9 @@ Array<T> range(const dim4& dim, const int seq_dim) {
         _seq_dim = 0;  // column wise sequence
     }
 
-    if (_seq_dim < 0 || _seq_dim > 3)
+    if (_seq_dim < 0 || _seq_dim > 3) {
         AF_ERROR("Invalid rep selection", AF_ERR_ARG);
+    }
 
     Array<T> out = createEmptyArray<T>(dim);
     kernel::range<T>(out, _seq_dim);
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index 9229d0005e..82d287508d 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -19,7 +19,7 @@ namespace opencl {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
-    const af::dim4 dims = in.dims();
+    const af::dim4 &dims = in.dims();
 
     Array<T> out = createEmptyArray<T>(dims);
 
diff --git a/src/backend/opencl/reorder.cpp b/src/backend/opencl/reorder.cpp
index 637654d49d..720d415883 100644
--- a/src/backend/opencl/reorder.cpp
+++ b/src/backend/opencl/reorder.cpp
@@ -19,9 +19,9 @@ using common::half;
 namespace opencl {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
-    const af::dim4 iDims = in.dims();
+    const af::dim4 &iDims = in.dims();
     af::dim4 oDims(0);
-    for (int i = 0; i < 4; i++) oDims[i] = iDims[rdims[i]];
+    for (int i = 0; i < 4; i++) { oDims[i] = iDims[rdims[i]]; }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
diff --git a/src/backend/opencl/resize.cpp b/src/backend/opencl/resize.cpp
index 4bb68a6a64..a911bacc6a 100644
--- a/src/backend/opencl/resize.cpp
+++ b/src/backend/opencl/resize.cpp
@@ -17,7 +17,7 @@ namespace opencl {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method) {
-    const af::dim4 iDims = in.dims();
+    const af::dim4 &iDims = in.dims();
     af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
 
     Array<T> out = createEmptyArray<T>(oDims);
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 6b75549773..c21c77badc 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -25,15 +25,17 @@ Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
     Param In  = in;
 
     if (inclusive_scan) {
-        if (dim == 0)
+        if (dim == 0) {
             kernel::scan_first<Ti, To, op, true>(Out, In);
-        else
+        } else {
             kernel::scan_dim<Ti, To, op, true>(Out, In, dim);
+        }
     } else {
-        if (dim == 0)
+        if (dim == 0) {
             kernel::scan_first<Ti, To, op, false>(Out, In);
-        else
+        } else {
             kernel::scan_dim<Ti, To, op, false>(Out, In, dim);
+        }
     }
 
     return out;
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index 0e63e52651..9d7cf450a7 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -27,15 +27,17 @@ Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
     Param In  = in;
 
     if (inclusive_scan) {
-        if (dim == 0)
+        if (dim == 0) {
             kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
-        else
+        } else {
             kernel::scan_dim<Ti, Tk, To, op, true>(Out, In, Key, dim);
+        }
     } else {
-        if (dim == 0)
+        if (dim == 0) {
             kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
-        else
+        } else {
             kernel::scan_dim<Ti, Tk, To, op, false>(Out, In, Key, dim);
+        }
     }
     return out;
 }
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 64006f6218..5a98433372 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -34,9 +34,9 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto b_node    = b.getNode();
     int height     = max(a_node->getHeight(), b_node->getHeight());
     height         = max(height, cond_node->getHeight()) + 1;
-    auto node      = make_shared<NaryNode>(
-        NaryNode(dtype_traits<T>::getName(), shortname<T>(true), "__select", 3,
-                 {{cond_node, a_node, b_node}}, (int)af_select_t, height));
+    auto node      = make_shared<NaryNode>(NaryNode(
+        dtype_traits<T>::getName(), shortname<T>(true), "__select", 3,
+        {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
@@ -66,7 +66,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto node = make_shared<NaryNode>(NaryNode(
         dtype_traits<T>::getName(), shortname<T>(true),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
-        (int)(flip ? af_not_select_t : af_select_t), height));
+        static_cast<int>(flip ? af_not_select_t : af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
         return createNodeArray<T>(odims, node);
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index 7afb23d95e..cb83765be2 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -56,7 +56,7 @@ Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
         out.resetDims(dim4(std::distance(begin, end), 1, 1, 1));
 
         return out;
-    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
 }
 
 template<typename T>
@@ -94,7 +94,7 @@ Array<T> setUnion(const Array<T> &first, const Array<T> &second,
         out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
         return out;
 
-    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
 }
 
 template<typename T>
@@ -132,7 +132,7 @@ Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
 
         out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
         return out;
-    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index da86c46cdf..f3e14270c4 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -37,15 +37,16 @@ Array<T> shift(const Array<T> &in, const int sdims[4]) {
 
     string name_str("Sh");
     name_str += shortname<T>(true);
-    const dim4 iDims = in.dims();
-    dim4 oDims       = iDims;
+    const dim4 &iDims = in.dims();
+    dim4 oDims        = iDims;
 
-    array<int, 4> shifts;
+    array<int, 4> shifts{};
     for (int i = 0; i < 4; i++) {
         // sdims_[i] will always be positive and always [0, oDims[i]].
         // Negative shifts are converted to position by going the other way
         // round
-        shifts[i] = -(sdims[i] % (int)oDims[i]) + oDims[i] * (sdims[i] > 0);
+        shifts[i] = -(sdims[i] % static_cast<int>(oDims[i])) +
+                    oDims[i] * (sdims[i] > 0);
         assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
     }
 
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index 35289495e1..626654c053 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -74,14 +74,15 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
     UNUSED(double_input);
     UNUSED(img_scale);
     UNUSED(feature_ratio);
-    if (compute_GLOH)
+    if (compute_GLOH) {
         AF_ERROR(
             "ArrayFire was not built with nonfree support, GLOH disabled\n",
             AF_ERR_NONFREE);
-    else
+    } else {
         AF_ERROR(
             "ArrayFire was not built with nonfree support, SIFT disabled\n",
             AF_ERR_NONFREE);
+    }
 #endif
 }
 
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 08f51faeaf..e73f4db312 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -34,7 +34,7 @@ Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
             af::dim4 reorderDims(0, 1, 2, 3);
             reorderDims[dim] = 0;
             preorderDims[0]  = out.dims()[dim];
-            for (int i = 1; i <= (int)dim; i++) {
+            for (int i = 1; i <= static_cast<int>(dim); i++) {
                 reorderDims[i - 1] = i;
                 preorderDims[i]    = out.dims()[i - 1];
             }
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
index f6cbb6158c..f98a70e057 100644
--- a/src/backend/opencl/sort_by_key.cpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -39,7 +39,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
             af::dim4 reorderDims(0, 1, 2, 3);
             reorderDims[dim] = 0;
             preorderDims[0]  = okey.dims()[dim];
-            for (int i = 1; i <= (int)dim; i++) {
+            for (unsigned i = 1; i <= dim; i++) {
                 reorderDims[i - 1] = i;
                 preorderDims[i]    = okey.dims()[i - 1];
             }
@@ -50,7 +50,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
             okey = reorder<Tk>(okey, reorderDims);
             oval = reorder<Tv>(oval, reorderDims);
         }
-    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
 }
 
 #define INSTANTIATE(Tk, Tv)                                        \
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index da70519840..869dd7bdc0 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -45,7 +45,7 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
             af::dim4 reorderDims(0, 1, 2, 3);
             reorderDims[dim] = 0;
             preorderDims[0]  = okey.dims()[dim];
-            for (int i = 1; i <= (int)dim; i++) {
+            for (uint i = 1; i <= dim; i++) {
                 reorderDims[i - 1] = i;
                 preorderDims[i]    = okey.dims()[i - 1];
             }
@@ -56,7 +56,7 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
             okey = reorder<T>(okey, reorderDims);
             oval = reorder<uint>(oval, reorderDims);
         }
-    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
 }
 
 #define INSTANTIATE(T)                                              \
diff --git a/src/backend/opencl/sort_index.hpp b/src/backend/opencl/sort_index.hpp
index 5b9560439d..573a61d247 100644
--- a/src/backend/opencl/sort_index.hpp
+++ b/src/backend/opencl/sort_index.hpp
@@ -11,6 +11,6 @@
 
 namespace opencl {
 template<typename T>
-void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in,
+void sort_index(Array<T> &okey, Array<unsigned> &oval, const Array<T> &in,
                 const unsigned dim, bool isAscending);
 }
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index c36e950ffe..2e79d558c2 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -94,9 +94,10 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
 
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
-    if (stype != AF_STORAGE_CSR)
+    if (stype != AF_STORAGE_CSR) {
         AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
                  AF_ERR_NOT_SUPPORTED);
+    }
 
     in_.eval();
 
@@ -107,11 +108,12 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
     const Array<int> &rowIdx = in_.getRowIdx();
     const Array<int> &colIdx = in_.getColIdx();
 
-    if (stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR) {
         kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
-    else
+    } else {
         AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
                  AF_ERR_NOT_SUPPORTED);
+    }
 
     return dense_;
 }
@@ -120,8 +122,8 @@ template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     in.eval();
 
-    SparseArray<T> converted =
-        createEmptySparseArray<T>(in.dims(), (int)in.getNNZ(), dest);
+    SparseArray<T> converted = createEmptySparseArray<T>(
+        in.dims(), static_cast<int>(in.getNNZ()), dest);
     converted.eval();
 
     if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index da376b3ee5..9e7545503d 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -115,7 +115,7 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     rhs.eval();
     af::storage sfmt = lhs.getStorage();
 
-    const dim4 ldims = lhs.dims();
+    const dim4 &ldims = lhs.dims();
 
     const uint M = ldims[0];
     const uint N = ldims[1];
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index 71a78589ab..abec7e6913 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -56,7 +56,8 @@ void copy_surface(const Array<T> &P, fg_surface surface) {
 
         CheckGL("Begin OpenCL fallback-resource copy");
         glBindBuffer(GL_ARRAY_BUFFER, buffer);
-        GLubyte *ptr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *ptr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (ptr) {
             getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
             glUnmapBuffer(GL_ARRAY_BUFFER);
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index ffdf69dfb3..2db7b17a5f 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -65,9 +65,9 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
     dim4 idims    = arrA.dims();
     dim4 istrides = arrA.strides();
 
-    const int m      = (int)idims[0];
-    const int n      = (int)idims[1];
-    const int ldda   = (int)istrides[1];
+    const int m      = static_cast<int>(idims[0]);
+    const int n      = static_cast<int>(idims[1]);
+    const int ldda   = static_cast<int>(istrides[1]);
     const int lda    = m;
     const int min_mn = std::min(m, n);
     const int ldu    = m;
@@ -92,12 +92,12 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
     static const int ione  = 1;
     static const int izero = 0;
 
-    bool iscl = 0;
+    bool iscl = false;
     if (anrm > 0. && anrm < smlnum) {
-        iscl  = 1;
+        iscl  = true;
         scale = scalar<T>(calc_scale<Tr>(anrm, smlnum));
     } else if (anrm > bignum) {
-        iscl  = 1;
+        iscl  = true;
         scale = scalar<T>(calc_scale<Tr>(anrm, bignum));
     }
 
@@ -109,9 +109,9 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
     // Instead of copying U, S, VT, and A to the host and copying the results
     // back to the device, create a pointer that's mapped to device memory where
     // the computation can directly happen
-    T *mappedA = (T *)getQueue().enqueueMapBuffer(
+    T *mappedA = static_cast<T *>(getQueue().enqueueMapBuffer(
         *arrA.get(), CL_FALSE, CL_MAP_READ, sizeof(T) * arrA.getOffset(),
-        sizeof(T) * arrA.elements());
+        sizeof(T) * arrA.elements()));
     std::vector<T> tauq(min_mn), taup(min_mn);
     std::vector<T> work(lwork);
     Tr *mappedS0 = (Tr *)getQueue().enqueueMapBuffer(
@@ -126,20 +126,20 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
     // (CWorkspace: need 2*N + M, prefer 2*N + (M + N)*NB)
     // (RWorkspace: need N)
     magma_gebrd_hybrid<T>(m, n, mappedA, lda, (*arrA.get())(), arrA.getOffset(),
-                          ldda, (void *)mappedS0, (void *)&s1[0], &tauq[0],
-                          &taup[0], &work[0], lwork, getQueue()(), &info,
-                          false);
+                          ldda, (void *)mappedS0, static_cast<void *>(&s1[0]),
+                          &tauq[0], &taup[0], &work[0], lwork, getQueue()(),
+                          &info, false);
 
     T *mappedU = nullptr, *mappedVT = nullptr;
     std::vector<T> cdummy(1);
 
     if (want_vectors) {
-        mappedU = (T *)getQueue().enqueueMapBuffer(
+        mappedU  = static_cast<T *>(getQueue().enqueueMapBuffer(
             *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
-            sizeof(T) * arrU.elements());
-        mappedVT = (T *)getQueue().enqueueMapBuffer(
+            sizeof(T) * arrU.elements()));
+        mappedVT = static_cast<T *>(getQueue().enqueueMapBuffer(
             *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
-            sizeof(T) * arrVT.elements());
+            sizeof(T) * arrVT.elements()));
 
         // If left singular vectors desired in U, copy result to U
         // and generate left bidiagonalizing vectors in U
diff --git a/src/backend/opencl/tile.cpp b/src/backend/opencl/tile.cpp
index 5c32c4582c..c3e2604970 100644
--- a/src/backend/opencl/tile.cpp
+++ b/src/backend/opencl/tile.cpp
@@ -18,8 +18,8 @@ using common::half;
 namespace opencl {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
-    const af::dim4 iDims = in.dims();
-    af::dim4 oDims       = iDims;
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
     oDims *= tileDims;
 
     Array<T> out = createEmptyArray<T>(oDims);
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 356811ddd5..5795ddd380 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -33,7 +33,7 @@ using std::vector;
 namespace opencl {
 vector<af_index_t> indexForTopK(const int k) {
     af_index_t idx;
-    idx.idx.seq = af_seq{0.0, (double)k - 1, 1.0};
+    idx.idx.seq = af_seq{0.0, static_cast<double>(k) - 1.0, 1.0};
     idx.isSeq   = true;
     idx.isBatch = false;
 
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index b4b640e71b..57103e9e90 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -17,8 +17,8 @@ namespace opencl {
 
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
-               const dim4 &odims, const af_interp_type method,
-               const bool inverse, const bool perspective) {
+               const af_interp_type method, const bool inverse,
+               const bool perspective) {
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
@@ -38,7 +38,7 @@ void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
 
 #define INSTANTIATE(T)                                                       \
     template void transform(Array<T> &out, const Array<T> &in,               \
-                            const Array<float> &tf, const dim4 &odims,       \
+                            const Array<float> &tf,                          \
                             const af_interp_type method, const bool inverse, \
                             const bool perspective);
 
diff --git a/src/backend/opencl/transform.hpp b/src/backend/opencl/transform.hpp
index 847271f913..809294fc6f 100644
--- a/src/backend/opencl/transform.hpp
+++ b/src/backend/opencl/transform.hpp
@@ -12,6 +12,6 @@
 namespace opencl {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
-               const af::dim4 &odims, const af_interp_type method,
-               const bool inverse, const bool perspective);
+               const af_interp_type method, const bool inverse,
+               const bool perspective);
 }
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index ce1760b26e..1881603dda 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -20,22 +20,24 @@ namespace opencl {
 
 template<typename T>
 Array<T> transpose(const Array<T> &in, const bool conjugate) {
-    const dim4 inDims = in.dims();
-    dim4 outDims      = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
-    Array<T> out      = createEmptyArray<T>(outDims);
+    const dim4 &inDims = in.dims();
+    dim4 outDims       = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
+    Array<T> out       = createEmptyArray<T>(outDims);
 
     if (conjugate) {
         if (inDims[0] % kernel::TILE_DIM == 0 &&
-            inDims[1] % kernel::TILE_DIM == 0)
+            inDims[1] % kernel::TILE_DIM == 0) {
             kernel::transpose<T, true, true>(out, in, getQueue());
-        else
+        } else {
             kernel::transpose<T, true, false>(out, in, getQueue());
+        }
     } else {
         if (inDims[0] % kernel::TILE_DIM == 0 &&
-            inDims[1] % kernel::TILE_DIM == 0)
+            inDims[1] % kernel::TILE_DIM == 0) {
             kernel::transpose<T, false, true>(out, in, getQueue());
-        else
+        } else {
             kernel::transpose<T, false, false>(out, in, getQueue());
+        }
     }
     return out;
 }
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index e36dedb0cb..bf3705e290 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -24,16 +24,18 @@ void transpose_inplace(Array<T> &in, const bool conjugate) {
 
     if (conjugate) {
         if (iDims[0] % kernel::TILE_DIM == 0 &&
-            iDims[1] % kernel::TILE_DIM == 0)
+            iDims[1] % kernel::TILE_DIM == 0) {
             kernel::transpose_inplace<T, true, true>(in, getQueue());
-        else
+        } else {
             kernel::transpose_inplace<T, true, false>(in, getQueue());
+        }
     } else {
         if (iDims[0] % kernel::TILE_DIM == 0 &&
-            iDims[1] % kernel::TILE_DIM == 0)
+            iDims[1] % kernel::TILE_DIM == 0) {
             kernel::transpose_inplace<T, false, true>(in, getQueue());
-        else
+        } else {
             kernel::transpose_inplace<T, false, false>(in, getQueue());
+        }
     }
 }
 
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index 775a3936b3..a7d255a987 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -65,7 +65,7 @@ std::string ToNumStr<half>::operator()(half val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
     if (common::isinf(val)) { return val < 0.f ? NINF : PINF; }
-    return to_string(move(val));
+    return common::to_string(val);
 }
 
 template<>
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index b8e8cd0318..508ff0ded9 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -65,7 +65,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Points
         glBindBuffer(GL_ARRAY_BUFFER, buff1);
-        GLubyte *pPtr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *pPtr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (pPtr) {
             getQueue().enqueueReadBuffer(*points.get(), CL_TRUE, 0, size1,
                                          pPtr);
@@ -75,7 +76,8 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         // Directions
         glBindBuffer(GL_ARRAY_BUFFER, buff2);
-        GLubyte *dPtr = (GLubyte *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
+        auto *dPtr =
+            static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
         if (dPtr) {
             getQueue().enqueueReadBuffer(*directions.get(), CL_TRUE, 0, size2,
                                          dPtr);
diff --git a/src/backend/opencl/vector_field.hpp b/src/backend/opencl/vector_field.hpp
index 62b5db39c0..2c3447aa4a 100644
--- a/src/backend/opencl/vector_field.hpp
+++ b/src/backend/opencl/vector_field.hpp
@@ -14,6 +14,5 @@ namespace opencl {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vector_field);
-
+                       fg_vector_field vfield);
 }
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 41e841c5b5..76847e1988 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -21,17 +21,17 @@ using common::half;
 namespace opencl {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py, const bool is_column) {
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column) {
     kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
 #define INSTANTIATE(T)                                                        \
-    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t ox, \
-                          const dim_t oy, const dim_t wx, const dim_t wy,     \
-                          const dim_t sx, const dim_t sy, const dim_t px,     \
-                          const dim_t py, const bool is_column);
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t wx, \
+                          const dim_t wy, const dim_t sx, const dim_t sy,     \
+                          const dim_t px, const dim_t py,                     \
+                          const bool is_column);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/wrap.hpp b/src/backend/opencl/wrap.hpp
index e28cc6e9d8..7a7815caa1 100644
--- a/src/backend/opencl/wrap.hpp
+++ b/src/backend/opencl/wrap.hpp
@@ -12,9 +12,9 @@
 namespace opencl {
 
 template<typename T>
-void wrap(Array<T> &out, const Array<T> &in, const dim_t ox, const dim_t oy,
-          const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
-          const dim_t px, const dim_t py, const bool is_column);
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column);
 
 template<typename T>
 Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,

From 09b18805c0628efca09909b7e7865938cbed3699 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 17 Apr 2020 08:11:08 +0530
Subject: [PATCH 1903/2677] Escape % character in windows install instructions

---
 docs/pages/install.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index 7166c48ebd..e24a61bc66 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -35,7 +35,7 @@ install the Visual Studio 2015 (x64) runtime libraries.
 Once you have downloaded the ArrayFire installer, execute the installer as you
 normally would on Windows. If you choose not to modify the path during the
 installation procedure, you'll need to manually add ArrayFire to the path for
-all users. Simply append `%AF_PATH%/lib` to the PATH variable so that the loader
+all users. Simply append `%%AF_PATH%/lib` to the PATH variable so that the loader
 can find ArrayFire DLLs.
 
 For more information on using ArrayFire on Windows, visit the following

From f8c674dd5873134e55350ef2cc7b7b5382ba2213 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 17 Apr 2020 08:11:33 +0530
Subject: [PATCH 1904/2677] Correct lib path suffix for linux install
 instructions

---
 docs/pages/install.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index e24a61bc66..5485c3a257 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -52,7 +52,7 @@ like to install ArrayFire to - we recommend `/opt`.
 Given sudo permissions, you can add the ArrayFire libraries via `ldconfig` like
 so:
 
-    echo /opt/arrayfire/lib > /etc/ld.so.conf.d/arrayfire.conf
+    echo /opt/arrayfire/lib64 > /etc/ld.so.conf.d/arrayfire.conf
     sudo ldconfig
 
 Otherwise, you will need to set the `LD_LIBRARY_PATH` environment variable in

From ce851753975fc4acd1098e8170216f0f18e6b38c Mon Sep 17 00:00:00 2001
From: Corentin Schreiber <54102755+cschreib-ibex@users.noreply.github.com>
Date: Mon, 20 Apr 2020 13:30:40 +0100
Subject: [PATCH 1905/2677] Remove constexpr not supported by VS2015 (#2850)

* Removed constexpr not supported by VS2015

* Fixed formatting
---
 src/backend/common/half.hpp          | 2 +-
 src/backend/common/unique_handle.hpp | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 1f29b517a1..2ea4b31cac 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -695,7 +695,7 @@ CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
 ///           value
 /// \param value The value to convert to integer
 template<std::float_round_style R, bool E, typename T>
-constexpr T half2int(native_half_t value) {
+T half2int(native_half_t value) {
     static_assert(std::is_integral<T>::value,
                   "half to int conversion only supports builtin integer types");
     unsigned int e = value & 0x7FFF;
diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index 8c6e07ef91..f6aa32e57b 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -72,8 +72,7 @@ class unique_handle {
     constexpr operator const T &() const noexcept { return handle_; }
 
     unique_handle(const unique_handle &other) noexcept = delete;
-    constexpr unique_handle(unique_handle &&other) noexcept
-        : handle_(other.handle_) {
+    unique_handle(unique_handle &&other) noexcept : handle_(other.handle_) {
         other.handle_ = 0;
     }
 

From 9f68819c001c7c63175cfc75230ee22a3582fa04 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 06:31:07 -0400
Subject: [PATCH 1906/2677] Fix dereference of memory_info iterator before
 check

---
 src/backend/common/DefaultMemoryManager.cpp | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 030399bcb9..740de509fe 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -166,12 +166,11 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
 
             lock_guard_t lock(this->memory_mutex);
             auto free_buffer_iter = current.free_map.find(alloc_bytes);
-            vector<void *> &free_buffer_vector = free_buffer_iter->second;
-
             if (free_buffer_iter != current.free_map.end() &&
-                !free_buffer_vector.empty()) {
+                !free_buffer_iter->second.empty()) {
                 // Delete existing buffer info and underlying event
                 // Set to existing in from free map
+                vector<void *> &free_buffer_vector = free_buffer_iter->second;
                 ptr = free_buffer_vector.back();
                 free_buffer_vector.pop_back();
                 current.locked_map[ptr] = info;
@@ -223,15 +222,14 @@ void DefaultMemoryManager::unlock(void *ptr, bool user_unlock) {
         memory_info &current = this->getCurrentMemoryInfo();
 
         auto locked_buffer_iter         = current.locked_map.find(ptr);
-        locked_info &locked_buffer_info = locked_buffer_iter->second;
-        void *locked_buffer_ptr         = locked_buffer_iter->first;
-
-        // Pointer not found in locked map
         if (locked_buffer_iter == current.locked_map.end()) {
+            // Pointer not found in locked map
             // Probably came from user, just free it
             freed_ptr.reset(ptr);
             return;
         }
+        locked_info &locked_buffer_info = locked_buffer_iter->second;
+        void *locked_buffer_ptr         = locked_buffer_iter->first;
 
         if (user_unlock) {
             locked_buffer_info.user_lock = false;

From 4be995a1d70da0e541647842d33a826b20ac6c47 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 06:46:40 -0400
Subject: [PATCH 1907/2677] Use double to calculate mean in random engine
 uniform tests if avialable

---
 test/random.cpp | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/test/random.cpp b/test/random.cpp
index 0a2dbf2a71..9c0b416be5 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -310,10 +310,22 @@ void testRandomEngineUniform(randomEngineType type) {
     int elem = 16 * 1024 * 1024;
     randomEngine r(type, 0);
     array A = randu(elem, ty, r);
-    T m     = mean<T>(A);
-    T s     = stdev<T>(A);
-    ASSERT_NEAR(m, 0.5, 1e-3);
-    ASSERT_NEAR(s, 0.2887, 1e-2);
+
+    // If double precision is available then perform the mean calculation using
+    // double because the A array is large and causes accuracy issues when using
+    // certain compiler flags (i.e. --march=native)
+    if (af::isDoubleAvailable(af::getDevice())) {
+        array Ad = A.as(f64);
+        double m = mean<double>(Ad);
+        double s = stdev<double>(Ad);
+        ASSERT_NEAR(m, 0.5, 1e-3);
+        ASSERT_NEAR(s, 0.2887, 1e-2);
+    } else {
+        T m = mean<T>(A);
+        T s = stdev<T>(A);
+        ASSERT_NEAR(m, 0.5, 1e-3);
+        ASSERT_NEAR(s, 0.2887, 1e-2);
+    }
 }
 
 template<typename T>

From e3a496264aa7dfb50a87f5c6855e0b7f37b6fd5c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 09:47:34 -0400
Subject: [PATCH 1908/2677] Prevent the optimizations in the MeanOp on cpu.

---
 src/backend/cpu/kernel/mean.hpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index 2be3c7d017..2683a69491 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -22,7 +22,9 @@ struct MeanOp {
     MeanOp(Ti mean, Tw count)
         : transform(), runningMean(transform(mean)), runningCount(count) {}
 
-    void operator()(Ti _newMean, Tw newCount) {
+    /// Prevents the optimzation of the mean calculation by some compiler flags
+    /// specifically -march=native.
+    [[gnu::optimize("01")]] void operator()(Ti _newMean, Tw newCount) {
         To newMean = transform(_newMean);
         if ((newCount != 0) || (runningCount != 0)) {
             Tw runningScale = runningCount;

From a70a00fde79b3241813a1d6ac64803d35f219e2c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 11:51:06 -0400
Subject: [PATCH 1909/2677] Fix the MatrixMultiplyBatch test so that we are
 testing the result

---
 test/blas.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/test/blas.cpp b/test/blas.cpp
index 317991973e..38fc5b0884 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -295,14 +295,14 @@ struct blas_params {
 
 class MatrixMultiplyBatch : public ::testing::TestWithParam<blas_params> {
    public:
-    array lhs, rhs, out;
+    array lhs, rhs, out, gold;
     void SetUp() {
         blas_params params = GetParam();
         lhs = randu(params.m, params.k, params.ld2, params.ld3, params.type);
         rhs = randu(params.k, params.n, params.rd2, params.rd3, params.type);
 
-        array gold(params.m, params.n, std::max(params.ld2, params.rd2),
-                   std::max(params.ld3, params.rd3));
+        gold = array(params.m, params.n, std::max(params.ld2, params.rd2),
+                     std::max(params.ld3, params.rd3));
 
         if (params.ld2 == params.rd2 && params.ld3 == params.rd3) {
             for (int i = 0; i < params.ld2; i++) {
@@ -418,7 +418,7 @@ INSTANTIATE_TEST_CASE_P(
 
 TEST_P(MatrixMultiplyBatch, Batched) {
     array out         = matmul(lhs, rhs);
-    blas_params param = GetParam();
+    ASSERT_ARRAYS_NEAR(gold, out, 1e-3);
 }
 
 float alpha = 1.f;

From e61ee65c5722470a70b6821f618b20c466ac2423 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 11:53:10 -0400
Subject: [PATCH 1910/2677] Remove unnecessary tile from var. Use arith output
 parameter instead

---
 src/api/c/var.cpp | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 8ad68943d9..1b9a70796f 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -16,7 +16,6 @@
 #include <math.hpp>
 #include <mean.hpp>
 #include <reduce.hpp>
-#include <tile.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/statistics.h>
@@ -107,14 +106,8 @@ static tuple<Array<outType>, Array<outType>> meanvar(
         normArr = arithOp<outType, af_div_t>(ones, wtsSum, meanArr.dims());
     }
 
-    /* now tile meanArr along dim and use it for variance computation */
-    dim4 tileDims(1);
-    tileDims[dim]           = iDims[dim];
-    Array<outType> tMeanArr = tile<outType>(meanArr, tileDims);
-    /* now mean array is ready */
-
     Array<outType> diff =
-        arithOp<outType, af_sub_t>(input, tMeanArr, tMeanArr.dims());
+        arithOp<outType, af_sub_t>(input, meanArr, input.dims());
     Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
     Array<outType> redDiff = reduce<af_add_t, outType, outType>(diffSq, dim);
 

From 3f56eb7459869d8cdf0d440e02cdceb4c946e8ef Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 20 Apr 2020 18:57:19 -0400
Subject: [PATCH 1911/2677] Address all warnings with -Wall flags in GCC 9.3

---
 examples/getting_started/vectorize.cpp        |  2 +-
 .../confidence_connected_components.cpp       |  1 -
 examples/machine_learning/neural_network.cpp  |  2 +-
 src/api/c/assign.cpp                          |  4 ++-
 src/api/c/handle.hpp                          |  3 +-
 src/api/c/index.cpp                           |  4 +--
 src/api/c/sparse.cpp                          |  5 +--
 src/api/c/var.cpp                             |  5 +--
 src/api/cpp/array.cpp                         |  9 +++++-
 src/api/cpp/common.hpp                        |  7 ++++-
 src/api/cpp/data.cpp                          |  4 +++
 src/api/cpp/device.cpp                        |  1 -
 src/api/cpp/exception.cpp                     |  2 +-
 src/api/unified/symbol_manager.cpp            | 11 ++++---
 src/api/unified/symbol_manager.hpp            |  5 ++-
 src/backend/common/ArrayInfo.cpp              |  2 +-
 src/backend/common/ArrayInfo.hpp              |  4 +--
 src/backend/common/DefaultMemoryManager.cpp   |  8 ++---
 src/backend/common/DefaultMemoryManager.hpp   |  6 ++--
 src/backend/common/DependencyModule.cpp       |  1 +
 src/backend/common/half.hpp                   | 12 +++----
 src/backend/common/util.cpp                   |  7 +++++
 src/backend/common/util.hpp                   |  9 +-----
 src/backend/cpu/Array.hpp                     |  4 +--
 src/backend/cpu/device_manager.cpp            |  4 +--
 src/backend/cpu/device_manager.hpp            |  8 ++---
 src/backend/cpu/kernel/reduce.hpp             |  8 +----
 src/backend/cpu/platform.cpp                  |  2 +-
 src/backend/cpu/platform.hpp                  |  2 +-
 src/backend/cuda/Array.hpp                    |  4 +--
 src/backend/cuda/Param.hpp                    |  2 +-
 src/backend/cuda/convolveNN.cpp               | 17 ++--------
 src/backend/cuda/copy.cpp                     |  4 +--
 src/backend/cuda/cudnnModule.cpp              |  3 +-
 src/backend/cuda/device_manager.cpp           | 11 ++++---
 src/backend/cuda/device_manager.hpp           |  2 +-
 src/backend/cuda/kernel/lookup.hpp            |  2 +-
 src/backend/cuda/memory.cpp                   |  3 +-
 src/backend/cuda/nvrtc/cache.cpp              |  2 +-
 src/backend/cuda/platform.cpp                 |  6 ++--
 src/backend/cuda/platform.hpp                 |  2 +-
 src/backend/cuda/utility.hpp                  |  3 +-
 src/backend/opencl/Array.hpp                  |  7 +++--
 src/backend/opencl/convolve.cpp               |  2 --
 src/backend/opencl/convolve_separable.cpp     |  4 +--
 src/backend/opencl/device_manager.hpp         |  2 +-
 src/backend/opencl/kernel/reduce_by_key.hpp   |  4 ---
 src/backend/opencl/platform.cpp               | 19 +++++++++++-
 src/backend/opencl/platform.hpp               | 20 ++----------
 test/array.cpp                                |  1 -
 test/binary.cpp                               |  1 -
 test/blas.cpp                                 |  2 +-
 test/clamp.cpp                                |  2 +-
 test/confidence_connected.cpp                 |  1 -
 test/convolve.cpp                             |  2 --
 test/gen_index.cpp                            |  6 ++--
 test/hsv_rgb.cpp                              |  2 +-
 test/index.cpp                                |  1 -
 test/jit.cpp                                  |  2 +-
 test/join.cpp                                 | 12 +++----
 test/math.cpp                                 | 31 +++++++++----------
 test/mean.cpp                                 |  4 +--
 test/meanvar.cpp                              |  3 +-
 test/nodevice.cpp                             | 11 ++++---
 test/pad_borders.cpp                          |  1 -
 test/reduce.cpp                               |  4 +--
 test/testHelpers.hpp                          | 17 +++++++---
 test/threading.cpp                            |  2 +-
 test/topk.cpp                                 |  1 -
 test/ycbcr_rgb.cpp                            |  2 +-
 70 files changed, 186 insertions(+), 183 deletions(-)

diff --git a/examples/getting_started/vectorize.cpp b/examples/getting_started/vectorize.cpp
index c94adba257..1d3bb4faaf 100644
--- a/examples/getting_started/vectorize.cpp
+++ b/examples/getting_started/vectorize.cpp
@@ -183,7 +183,7 @@ int main(int, char **) {
         printf("Time for dist_tile1: %2.2fms\n", 1000 * timeit(bench_tile1));
         printf("Time for dist_tile2: %2.2fms\n", 1000 * timeit(bench_tile2));
 
-    } catch (af::exception ex) {
+    } catch (const af::exception &ex) {
         fprintf(stderr, "%s\n", ex.what());
         throw;
     }
diff --git a/examples/image_processing/confidence_connected_components.cpp b/examples/image_processing/confidence_connected_components.cpp
index 368561dd1d..94617163bd 100644
--- a/examples/image_processing/confidence_connected_components.cpp
+++ b/examples/image_processing/confidence_connected_components.cpp
@@ -17,7 +17,6 @@ using namespace af;
 
 int main(int argc, char* argv[]) {
     try {
-        unsigned s[1]       = {132};
         unsigned radius     = 3;
         unsigned multiplier = 3;
         int iter            = 5;
diff --git a/examples/machine_learning/neural_network.cpp b/examples/machine_learning/neural_network.cpp
index d2b3466fa8..f480977706 100644
--- a/examples/machine_learning/neural_network.cpp
+++ b/examples/machine_learning/neural_network.cpp
@@ -45,8 +45,8 @@ double error(const array &out, const array &pred) {
 class ann {
    private:
     int num_layers;
-    dtype datatype;
     vector<array> weights;
+    dtype datatype;
 
     // Add bias input to the output from previous layer
     array add_bias(const array &in);
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index ede1041ca1..7dc6b6b437 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -64,7 +64,9 @@ static void assign(Array<Tout>& out, const vector<af_seq> seqs,
 
     isVec &= in.isVector() || in.isScalar();
 
-    for (dim_t i = ndims; i < in.ndims(); i++) { oDims[i] = 1; }
+    for (dim_t i = static_cast<dim_t>(ndims); i < in.ndims(); i++) {
+        oDims[i] = 1;
+    }
 
     if (isVec) {
         if (oDims.elements() != in.elements() && in.elements() != 1) {
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 4a94ffa1bb..087fd740f8 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -77,7 +77,8 @@ detail::Array<T> &getArray(af_array &arr) {
 }
 
 template<>
-detail::Array<common::half> &getArray<common::half>(af_array &arr) {
+[[gnu::unused]] detail::Array<common::half> &getArray<common::half>(
+    af_array &arr) {
     detail::Array<common::half> *A =
         static_cast<detail::Array<common::half> *>(arr);
     if (f16 != A->getType())
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index fcaca34f06..c97c7a404d 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -58,10 +58,10 @@ af_seq convert2Canonical(const af_seq s, const dim_t len) {
 template<typename T>
 static af_array indexBySeqs(const af_array& src,
                             const vector<af_seq> indicesV) {
-    size_t ndims      = indicesV.size();
+    dim_t ndims       = static_cast<dim_t>(indicesV.size());
     const auto& input = getArray<T>(src);
 
-    if (ndims == 1 && ndims != input.ndims()) {
+    if (ndims == 1U && ndims != input.ndims()) {
         return getHandle(createSubArray(::flat(input), indicesV));
     } else {
         return getHandle(createSubArray(input, indicesV));
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 03331e472d..e58e77de44 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -34,7 +34,8 @@ const SparseArrayBase &getSparseArrayBase(const af_array in,
             AF_ERR_ARG);
     }
 
-    if (device_check && base->getDevId() != detail::getActiveDeviceId()) {
+    if (device_check &&
+        base->getDevId() != static_cast<int>(detail::getActiveDeviceId())) {
         AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
     }
 
@@ -84,7 +85,7 @@ af_err af_create_sparse_array(af_array *out, const dim_t nRows,
         ARG_ASSERT(5, cInfo.getType() == s32);
         DIM_ASSERT(5, cInfo.isLinear());
 
-        const size_t nNZ = vInfo.elements();
+        const dim_t nNZ = vInfo.elements();
         if (stype == AF_STORAGE_COO) {
             DIM_ASSERT(4, rInfo.elements() == nNZ);
             DIM_ASSERT(5, cInfo.elements() == nNZ);
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 1b9a70796f..2efa032b1c 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -90,8 +90,9 @@ static tuple<Array<outType>, Array<outType>> meanvar(
     Array<outType> normArr = createEmptyArray<outType>({0});
     if (weights.isEmpty()) {
         meanArr  = mean<outType, weightType, outType>(input, dim);
-        auto val = 1.0 / (bias == AF_VARIANCE_POPULATION ? iDims[dim]
-                                                         : iDims[dim] - 1);
+        auto val = 1.0 / static_cast<double>(bias == AF_VARIANCE_POPULATION
+                                                 ? iDims[dim]
+                                                 : iDims[dim] - 1);
         normArr =
             createValueArray<outType>(meanArr.dims(), scalar<outType>(val));
     } else {
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index eff157bfd5..0612d33f16 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -21,7 +21,11 @@
 #include <af/traits.hpp>
 #include <af/util.h>
 #include "error.hpp"
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wparentheses"
 #include "half.hpp"  //note: NOT common. From extern/half/include/half.hpp
+#pragma GCC diagnostic pop
 
 #ifdef AF_CUDA
 // NOTE: Adding ifdef here to avoid copying code constructor in the cuda backend
@@ -257,7 +261,6 @@ array::~array() {
 #ifdef AF_UNIFIED
     using af_release_array_ptr =
         std::add_pointer<decltype(af_release_array)>::type;
-    static auto &instance = unified::AFSymbolManager::getInstance();
 
     if (get()) {
         af_backend backend = unified::getActiveBackend();
@@ -291,6 +294,10 @@ array::~array() {
                     func(get());
                     break;
                 }
+                case AF_BACKEND_DEFAULT:
+                    assert(1 != 1 &&
+                           "AF_BACKEND_DEFAULT cannot be set as a backend for "
+                           "an array");
             }
         }
     }
diff --git a/src/api/cpp/common.hpp b/src/api/cpp/common.hpp
index 61597ab989..39dec065e4 100644
--- a/src/api/cpp/common.hpp
+++ b/src/api/cpp/common.hpp
@@ -9,7 +9,11 @@
 
 #include <af/dim4.hpp>
 #include <af/half.h>
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wparentheses"
 #include "half.hpp"
+#pragma GCC diagnostic pop
 
 #include <cstring>
 
@@ -37,11 +41,12 @@ To cast(T in) {
 }
 
 template<>
-af_half cast<af_half, double>(double in) {
+[[gnu::unused]] af_half cast<af_half, double>(double in) {
     half_float::half tmp = static_cast<half_float::half>(in);
     af_half out;
     memcpy(&out, &tmp, sizeof(af_half));
     return out;
 }
+
 }  // namespace
 }  // namespace af
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 126b10d990..5ca5077b91 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -8,7 +8,11 @@
  ********************************************************/
 #include <af/data.h>
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wparentheses"
 #include <half.hpp>
+#pragma GCC diagnostic pop
+
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/complex.h>
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 524ebe0bb6..a393fa0d15 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -38,7 +38,6 @@ af::Backend getBackendId(const array &in) {
 
 int getDeviceId(const array &in) {
     int device = getDevice();
-    ;
     AF_THROW(af_get_device_id(&device, in.get()));
     return device;
 }
diff --git a/src/api/cpp/exception.cpp b/src/api/cpp/exception.cpp
index 8a56a48ea2..45efcf6b6a 100644
--- a/src/api/cpp/exception.cpp
+++ b/src/api/cpp/exception.cpp
@@ -23,7 +23,7 @@ exception::exception() : m_msg{}, m_err(AF_ERR_UNKNOWN) {
 }
 
 exception::exception(const char *msg) : m_msg{}, m_err(AF_ERR_UNKNOWN) {
-    strncpy(m_msg, msg, sizeof(m_msg));
+    strncpy(m_msg, msg, sizeof(m_msg) - 1);
     m_msg[sizeof(m_msg) - 1] = '\0';
 }
 
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index dc4a34e1b7..052ef7848f 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -189,8 +189,8 @@ AFSymbolManager::AFSymbolManager()
     static const af_backend order[] = {AF_BACKEND_CUDA, AF_BACKEND_OPENCL,
                                        AF_BACKEND_CPU};
 
-    LibHandle handle;
-    af::Backend backend;
+    LibHandle handle    = nullptr;
+    af::Backend backend = AF_BACKEND_DEFAULT;
     // Decremeting loop. The last successful backend loaded will be the most
     // prefered one.
     for (int i = NUM_BACKENDS - 1; i >= 0; i--) {
@@ -205,12 +205,15 @@ AFSymbolManager::AFSymbolManager()
     }
     if (backend) {
         AF_TRACE("AF_DEFAULT_BACKEND: {}", getBackendDirectoryName(backend));
+        defaultBackend = backend;
+    } else {
+        logger->error("Backend was not found");
+        defaultBackend = AF_BACKEND_DEFAULT;
     }
 
     // Keep a copy of default order handle inorder to use it in ::setBackend
     // when the user passes AF_BACKEND_DEFAULT
-    defaultHandle  = handle;
-    defaultBackend = backend;
+    defaultHandle = handle;
 }
 
 AFSymbolManager::~AFSymbolManager() {
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index bcb73b109c..7c7885d2a8 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -100,7 +100,7 @@ bool checkArray(af_backend activeBackend, const af_array a) {
     return backend == activeBackend;
 }
 
-bool checkArray(af_backend activeBackend, const af_array* a) {
+[[gnu::unused]] bool checkArray(af_backend activeBackend, const af_array* a) {
     if (a) {
         return checkArray(activeBackend, *a);
     } else {
@@ -108,7 +108,7 @@ bool checkArray(af_backend activeBackend, const af_array* a) {
     }
 }
 
-bool checkArrays(af_backend activeBackend) {
+[[gnu::unused]] bool checkArrays(af_backend activeBackend) {
     UNUSED(activeBackend);
     // Dummy
     return true;
@@ -140,7 +140,6 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 
 #define CALL(FUNCTION, ...)                                                      \
     using af_func                  = std::add_pointer<decltype(FUNCTION)>::type; \
-    static auto& instance          = unified::AFSymbolManager::getInstance();    \
     thread_local af_backend index_ = unified::getActiveBackend();                \
     if (unified::getActiveHandle()) {                                            \
         thread_local af_func func = (af_func)common::getFunctionPointer(         \
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index 0de280b89c..6cf55d20ea 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -120,7 +120,7 @@ bool ArrayInfo::isLinear() const {
     if (ndims() == 1) { return dim_strides[0] == 1; }
 
     dim_t count = 1;
-    for (size_t i = 0; i < ndims(); i++) {
+    for (dim_t i = 0; i < ndims(); i++) {
         if (count != dim_strides[i]) { return false; }
         count *= dim_size[i];
     }
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index d878d75fea..d543101c18 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -90,8 +90,8 @@ class ArrayInfo {
 
     const af::dim4& strides() const { return dim_strides; }
 
-    size_t elements() const { return dim_size.elements(); }
-    size_t ndims() const { return dim_size.ndims(); }
+    dim_t elements() const { return dim_size.elements(); }
+    dim_t ndims() const { return dim_size.ndims(); }
     const af::dim4& dims() const { return dim_size; }
     size_t total() const { return offset + dim_strides[3] * dim_size[3]; }
 
diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 740de509fe..10c5964a80 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -72,8 +72,8 @@ DefaultMemoryManager::DefaultMemoryManager(int num_devices,
                                            unsigned max_buffers, bool debug)
     : mem_step_size(1024)
     , max_buffers(max_buffers)
-    , memory(num_devices)
-    , debug_mode(debug) {
+    , debug_mode(debug)
+    , memory(num_devices) {
     // Check for environment variables
 
     // Debug mode
@@ -171,7 +171,7 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
                 // Delete existing buffer info and underlying event
                 // Set to existing in from free map
                 vector<void *> &free_buffer_vector = free_buffer_iter->second;
-                ptr = free_buffer_vector.back();
+                ptr                                = free_buffer_vector.back();
                 free_buffer_vector.pop_back();
                 current.locked_map[ptr] = info;
                 current.lock_bytes += alloc_bytes;
@@ -221,7 +221,7 @@ void DefaultMemoryManager::unlock(void *ptr, bool user_unlock) {
         lock_guard_t lock(this->memory_mutex);
         memory_info &current = this->getCurrentMemoryInfo();
 
-        auto locked_buffer_iter         = current.locked_map.find(ptr);
+        auto locked_buffer_iter = current.locked_map.find(ptr);
         if (locked_buffer_iter == current.locked_map.end()) {
             // Pointer not found in locked map
             // Probably came from user, just free it
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index 6feda08bf2..25eb4bd06a 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -42,11 +42,11 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
         locked_t locked_map;
         free_t free_map;
 
-        size_t lock_bytes;
-        size_t lock_buffers;
+        size_t max_bytes;
         size_t total_bytes;
         size_t total_buffers;
-        size_t max_bytes;
+        size_t lock_bytes;
+        size_t lock_buffers;
 
         memory_info()
             // Calling getMaxMemorySize() here calls the virtual function
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index 24bc53e4fb..ef99bc501b 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -68,6 +68,7 @@ DependencyModule::DependencyModule(const vector<string>& plugin_base_file_name,
     : handle(nullptr), logger(common::loggerFactory("platform")) {
     for (const string& base_name : plugin_base_file_name) {
         for (const string& path : paths) {
+            UNUSED(path);
             for (const string& suffix : suffixes) {
                 string filename = libName(base_name + suffix);
                 AF_TRACE("Attempting to load: {}", filename);
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 2ea4b31cac..0d378e2871 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -60,11 +60,11 @@ CONSTEXPR_DH native_half_t int2half_impl(T value) noexcept {
     uint16_t bits = S << 15;
     if (value > 0xFFFF) {
         if (R == std::round_toward_infinity)
-            bits |= 0x7C00 - S;
+            bits |= (0x7C00 - S);
         else if (R == std::round_toward_neg_infinity)
-            bits |= 0x7BFF + S;
+            bits |= (0x7BFF + S);
         else
-            bits |= 0x7BFF + (R != std::round_toward_zero);
+            bits |= (0x7BFF + (R != std::round_toward_zero));
     } else if (value) {
         uint32_t m = value, exp = 24;
         for (; m < 0x400; m <<= 1, --exp)
@@ -262,10 +262,10 @@ CONSTEXPR_DH native_half_t float2half_impl(double value) {
                (0x3FF & -static_cast<unsigned>((bits & 0xFFFFFFFFFFFFF) != 0));
     if (exp > 1038) {
         if (R == std::round_toward_infinity)
-            return hbits | 0x7C00 - (hbits >> 15);
+            return hbits | (0x7C00 - (hbits >> 15));
         if (R == std::round_toward_neg_infinity)
-            return hbits | 0x7BFF + (hbits >> 15);
-        return hbits | 0x7BFF + (R != std::round_toward_zero);
+            return hbits | (0x7BFF + (hbits >> 15));
+        return hbits | (0x7BFF + (R != std::round_toward_zero));
     }
     int g, s = lo != 0;
     if (exp > 1008) {
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index ee07d7fa7b..b786839d11 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -61,6 +61,8 @@ const char* getName(af_dtype type) {
 
 void saveKernel(const std::string& funcName, const std::string& jit_ker,
                 const std::string& ext) {
+    static constexpr const char* saveJitKernelsEnvVarName =
+        "AF_JIT_KERNEL_TRACE";
     static const char* jitKernelsOutput = getenv(saveJitKernelsEnvVarName);
     if (!jitKernelsOutput) { return; }
     if (std::strcmp(jitKernelsOutput, "stdout") == 0) {
@@ -84,3 +86,8 @@ void saveKernel(const std::string& funcName, const std::string& jit_ker,
     }
     fclose(f);
 }
+
+std::string int_version_to_string(int version) {
+    return std::to_string(version / 1000) + "." +
+           std::to_string((int)((version % 1000) / 10.));
+}
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 519c9c7caf..2df1ddd05a 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -19,12 +19,5 @@ std::string getEnvVar(const std::string& key);
 // Dump the kernel sources only if the environment variable is defined
 void saveKernel(const std::string& funcName, const std::string& jit_ker,
                 const std::string& ext);
-namespace {
-static constexpr const char* saveJitKernelsEnvVarName = "AF_JIT_KERNEL_TRACE";
 
-std::string int_version_to_string(int version) {
-    return std::to_string(version / 1000) + "." +
-           std::to_string((int)((version % 1000) / 10.));
-}
-
-}  // namespace
+std::string int_version_to_string(int version);
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index c722975e4e..c7d307b436 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -144,8 +144,8 @@ class Array {
 
     INFO_FUNC(const af_dtype &, getType)
     INFO_FUNC(const af::dim4 &, strides)
-    INFO_FUNC(size_t, elements)
-    INFO_FUNC(size_t, ndims)
+    INFO_FUNC(dim_t, elements)
+    INFO_FUNC(dim_t, ndims)
     INFO_FUNC(const af::dim4 &, dims)
     INFO_FUNC(int, getDevId)
 
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
index deb5fd0c3b..a95d9f5a5c 100644
--- a/src/backend/cpu/device_manager.cpp
+++ b/src/backend/cpu/device_manager.cpp
@@ -123,10 +123,10 @@ namespace cpu {
 
 DeviceManager::DeviceManager()
     : queues(MAX_QUEUES)
+    , fgMngr(new graphics::ForgeManager())
     , memManager(new common::DefaultMemoryManager(
           getDeviceCount(), common::MAX_BUFFERS,
-          AF_MEM_DEBUG || AF_CPU_MEM_DEBUG))
-    , fgMngr(new graphics::ForgeManager()) {
+          AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)) {
     // Use the default ArrayFire memory manager
     std::unique_ptr<cpu::Allocator> deviceMemoryManager(new cpu::Allocator());
     memManager->setAllocator(std::move(deviceMemoryManager));
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index eeb027ca5e..170f61df4b 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -90,10 +90,10 @@ namespace cpu {
 
 class DeviceManager {
    public:
-    static const int MAX_QUEUES           = 1;
-    static const int NUM_DEVICES          = 1;
-    static const int ACTIVE_DEVICE_ID     = 0;
-    static const bool IS_DOUBLE_SUPPORTED = true;
+    static const int MAX_QUEUES            = 1;
+    static const int NUM_DEVICES           = 1;
+    static const unsigned ACTIVE_DEVICE_ID = 0;
+    static const bool IS_DOUBLE_SUPPORTED  = true;
 
     // TODO(umar): Half is not supported for BLAS and FFT on x86_64
     static const bool IS_HALF_SUPPORTED = true;
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index 99f10970b8..db20b5213e 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -62,8 +62,7 @@ struct reduce_dim<op, Ti, To, 0> {
 
 template<typename Tk>
 void n_reduced_keys(Param<Tk> okeys, int *n_reduced, CParam<Tk> keys) {
-    const af::dim4 kstrides = keys.strides();
-    const af::dim4 kdims    = keys.dims();
+    const af::dim4 kdims = keys.dims();
 
     Tk *const outKeysPtr      = okeys.get();
     Tk const *const inKeysPtr = keys.get();
@@ -117,14 +116,10 @@ struct reduce_dim_by_key<op, Ti, Tk, To, 0> {
     void operator()(Param<To> ovals, const dim_t ovOffset, CParam<Tk> keys,
                     CParam<Ti> vals, const dim_t vOffset, int *n_reduced,
                     const int dim, bool change_nan, double nanval) {
-        const af::dim4 kstrides = keys.strides();
-        const af::dim4 kdims    = keys.dims();
-
         const af::dim4 vstrides = vals.strides();
         const af::dim4 vdims    = vals.dims();
 
         const af::dim4 ovstrides = ovals.strides();
-        const af::dim4 ovdims    = ovals.dims();
 
         data_t<Tk> const *const inKeysPtr = keys.get();
         data_t<Ti> const *const inValsPtr = vals.get();
@@ -138,7 +133,6 @@ struct reduce_dim_by_key<op, Ti, Tk, To, 0> {
         dim_t ostride = ovstrides[dim];
 
         for (dim_t i = 0; i < vdims[dim]; i++) {
-            dim_t off            = vOffset;
             compute_t<Tk> keyval = inKeysPtr[i];
 
             if (keyval == current_key) {
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index b10d168e9a..c44826447d 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -121,7 +121,7 @@ unsigned getMaxJitSize() {
 int getDeviceCount() { return DeviceManager::NUM_DEVICES; }
 
 // Get the currently active device id
-int getActiveDeviceId() { return DeviceManager::ACTIVE_DEVICE_ID; }
+unsigned getActiveDeviceId() { return DeviceManager::ACTIVE_DEVICE_ID; }
 
 size_t getDeviceMemorySize(int device) {
     UNUSED(device);
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index dcd2c351a6..f51691f741 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -40,7 +40,7 @@ unsigned getMaxJitSize();
 
 int getDeviceCount();
 
-int getActiveDeviceId();
+unsigned getActiveDeviceId();
 
 size_t getDeviceMemorySize(int device);
 
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 887bbc4baa..c528a8306a 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -150,8 +150,8 @@ class Array {
 
     INFO_FUNC(const af_dtype &, getType)
     INFO_FUNC(const af::dim4 &, strides)
-    INFO_FUNC(size_t, elements)
-    INFO_FUNC(size_t, ndims)
+    INFO_FUNC(dim_t, elements)
+    INFO_FUNC(dim_t, ndims)
     INFO_FUNC(const af::dim4 &, dims)
     INFO_FUNC(int, getDevId)
 
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 07f5376164..3b7476f7a5 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -22,7 +22,7 @@ class Param {
     dim_t strides[4];
     T *ptr;
 
-    __DH__ Param() noexcept : ptr(nullptr) {}
+    __DH__ Param() noexcept : dims(), strides(), ptr(nullptr) {}
 
     __DH__
     Param(T *iptr, const dim_t *idims, const dim_t *istrides) noexcept
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index e0db33264b..af192f5c74 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -59,14 +59,6 @@ Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
                          const dim4 &dilation) {
     cudnnHandle_t cudnn = nnHandle();
 
-    dim4 sDims        = signal.dims();
-    const dim4 &fDims = filter.dims();
-
-    const int n = sDims[3];
-    const int c = sDims[2];
-    const int h = sDims[1];
-    const int w = sDims[0];
-
     cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
     auto input_descriptor       = toCudnn<cudnnTensorDescriptor_t>(signal);
     auto filter_descriptor      = toCudnn<cudnnFilterDescriptor_t>(filter);
@@ -149,7 +141,6 @@ Array<T> convolve2_base(const Array<T> &signal, const Array<T> &filter,
     dim_t outputHeight =
         1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
                 stride[1];
-    dim4 oDims = dim4(outputWidth, outputHeight, fDims[3], sDims[3]);
 
     const bool retCols = false;
     Array<T> unwrapped =
@@ -254,9 +245,8 @@ Array<T> data_gradient_cudnn(const Array<T> &incoming_gradient,
     UNUSED(convolved_output);
     auto cudnn = nnHandle();
 
-    const dim4 &iDims = incoming_gradient.dims();
-    dim4 sDims        = original_signal.dims();
-    dim4 fDims        = original_filter.dims();
+    dim4 sDims = original_signal.dims();
+    dim4 fDims = original_filter.dims();
 
     cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
 
@@ -337,7 +327,6 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
                               af::dim4 padding, af::dim4 dilation) {
     UNUSED(convolved_output);
     const dim4 &cDims = incoming_gradient.dims();
-    const dim4 &sDims = original_signal.dims();
     const dim4 &fDims = original_filter.dims();
 
     const bool retCols = false;
@@ -378,8 +367,6 @@ Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
     UNUSED(convolved_output);
     auto cudnn = nnHandle();
 
-    const dim4 &iDims = incoming_gradient.dims();
-    const dim4 &sDims = original_signal.dims();
     const dim4 &fDims = original_filter.dims();
 
     // create dx descriptor
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index 6940382b69..5a4ad99642 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -63,7 +63,7 @@ Array<T> copyArray(const Array<T> &src) {
 template<typename inType, typename outType>
 Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
                         outType default_value, double factor) {
-    ARG_ASSERT(1, (in.ndims() == (size_t)dims.ndims()));
+    ARG_ASSERT(1, (in.ndims() == dims.ndims()));
     Array<outType> ret = createEmptyArray<outType>(dims);
     kernel::copy<inType, outType>(ret, in, in.ndims(), default_value, factor);
     return ret;
@@ -100,7 +100,7 @@ template<typename inType, typename outType>
 void copyArray(Array<outType> &out, Array<inType> const &in) {
     static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
                   "Cannot copy from complex value to a non complex value");
-    ARG_ASSERT(1, (in.ndims() == (size_t)out.dims().ndims()));
+    ARG_ASSERT(1, (in.ndims() == out.dims().ndims()));
     copyWrapper<inType, outType> copyFn;
     copyFn(out, in);
 }
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 210a1a6c03..5a37b6ead3 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -12,6 +12,7 @@
 #include <common/util.hpp>
 #include <cudnnModule.hpp>
 #include <device_manager.hpp>
+#include <utility.hpp>
 
 #include <string>
 #include <tuple>
@@ -81,7 +82,7 @@ cudnnModule::cudnnModule()
 
     int afcuda_runtime = 0;
     cudaRuntimeGetVersion(&afcuda_runtime);
-    if (afcuda_runtime != cudnn_version) {
+    if (afcuda_runtime != static_cast<int>(cudnn_version)) {
         getLogger()->warn(
             "WARNING: ArrayFire CUDA Runtime({}) and cuDNN CUDA "
             "Runtime({}.{}) do not match. For maximum compatibility, make sure "
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index d2a23b7f1c..b2921d7012 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -18,7 +18,6 @@
 #include <common/defines.hpp>
 #include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
-#include <common/util.hpp>
 #include <cublas_v2.h>  // needed for af/cuda.h
 #include <device_manager.hpp>
 #include <driver.h>
@@ -32,6 +31,7 @@
 // cuda_gl_interop.h does not include OpenGL headers for ARM
 // __gl_h_ should be defined by glad.h inclusion
 #include <cuda_gl_interop.h>
+#include <utility.hpp>
 
 #include <nvrtc.h>
 
@@ -107,7 +107,8 @@ bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
 /// Check for compatible compute version based on runtime cuda toolkit version
 void checkAndSetDevMaxCompute(pair<int, int> &prop) {
     auto originalCompute = prop;
-    int rtCudaVer        = 0;
+    UNUSED(originalCompute);
+    int rtCudaVer = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&rtCudaVer));
     auto tkitMaxCompute = find_if(
         begin(Toolkit2MaxCompute), end(Toolkit2MaxCompute),
@@ -168,7 +169,9 @@ static inline int compute2cores(unsigned major, unsigned minor) {
     };
 
     for (int i = 0; gpus[i].compute != -1; ++i) {
-        if (gpus[i].compute == (major << 4U) + minor) { return gpus[i].cores; }
+        if (static_cast<unsigned>(gpus[i].compute) == (major << 4U) + minor) {
+            return gpus[i].cores;
+        }
     }
     return 0;
 }
@@ -539,7 +542,7 @@ DeviceManager::DeviceManager()
 
     // Initialize all streams to 0.
     // Streams will be created in setActiveDevice()
-    for (size_t i = 0; i < MAX_DEVICES; i++) {
+    for (int i = 0; i < MAX_DEVICES; i++) {
         streams[i] = static_cast<cudaStream_t>(0);
         if (i < nDevices) {
             auto prop =
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index d661244bf4..c6009337d2 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -37,7 +37,7 @@ bool checkDeviceWithRuntime(int runtime, std::pair<int, int> compute);
 
 class DeviceManager {
    public:
-    static const size_t MAX_DEVICES = 16;
+    static const int MAX_DEVICES = 16;
 
     static bool checkGraphicsInteropCapability();
 
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index c036c044f9..02bbe69fba 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -31,7 +31,7 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
     static const std::string src(lookup_cuh, lookup_cuh_len);
 
     /* find which dimension has non-zero # of elements */
-    int vDim = 0;
+    unsigned vDim = 0;
     for (int i = 0; i < 4; i++) {
         if (in.dims[i] == 1)
             vDim++;
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index d65122aff2..9aa2d0c6c8 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -63,8 +63,7 @@ template<typename T>
 uptr<T> memAlloc(const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
-    size_t size = elements * sizeof(T);
-    void *ptr   = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
     return uptr<T>(static_cast<T *>(ptr), memFree<T>);
 }
 
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index e3b28f325e..aec0590c25 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -84,7 +84,7 @@ using kc_t = map<string, Kernel>;
     do {                                                                     \
         CUresult res = fn;                                                   \
         if (res == CUDA_SUCCESS) break;                                      \
-        char cu_err_msg[1024];                                               \
+        char cu_err_msg[1024 + 48];                                          \
         const char *cu_err_name;                                             \
         cuGetErrorName(res, &cu_err_name);                                   \
         snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n",    \
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index f6814254b4..33b2fe5a81 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -34,11 +34,11 @@
 #include <err_cuda.hpp>
 #include <memory.hpp>
 #include <spdlog/spdlog.h>
+#include <utility.hpp>
 #include <version.hpp>
 #include <af/cuda.h>
 #include <af/device.h>
 #include <af/version.h>
-#include <memory>
 
 #include <algorithm>
 #include <array>
@@ -215,7 +215,7 @@ string getDeviceInfo(int device) noexcept {
     size_t mem_gpu_total = dev.totalGlobalMem;
     // double cc = double(dev.major) + double(dev.minor) / 10;
 
-    bool show_braces = getActiveDeviceId() == device;
+    bool show_braces = getActiveDeviceId() == static_cast<unsigned>(device);
 
     string id = (show_braces ? string("[") : "-") + to_string(device) +
                 (show_braces ? string("]") : "-");
@@ -356,7 +356,7 @@ int getDeviceCount() {
     }
 }
 
-int getActiveDeviceId() { return tlocalActiveDeviceId(); }
+unsigned getActiveDeviceId() { return tlocalActiveDeviceId(); }
 
 int getDeviceNativeId(int device) {
     if (device <
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index bfc67560f5..ff73c5fcc3 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -80,7 +80,7 @@ unsigned getMaxJitSize();
 
 int getDeviceCount();
 
-int getActiveDeviceId();
+unsigned getActiveDeviceId();
 
 int getDeviceNativeId(int device);
 
diff --git a/src/backend/cuda/utility.hpp b/src/backend/cuda/utility.hpp
index f54435f484..bf602eacc9 100644
--- a/src/backend/cuda/utility.hpp
+++ b/src/backend/cuda/utility.hpp
@@ -14,7 +14,8 @@
 
 namespace cuda {
 
-static __DH__ dim_t trimIndex(const int &idx, const dim_t &len) {
+[[gnu::unused]] static __DH__ dim_t trimIndex(const int &idx,
+                                              const dim_t &len) {
     int ret_val = idx;
     if (ret_val < 0) {
         int offset = (abs(ret_val) - 1) % len;
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index e69e81578b..cb77569da7 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -152,8 +152,8 @@ class Array {
 
     INFO_FUNC(const af_dtype &, getType)
     INFO_FUNC(const af::dim4 &, strides)
-    INFO_FUNC(size_t, elements)
-    INFO_FUNC(size_t, ndims)
+    INFO_FUNC(dim_t, elements)
+    INFO_FUNC(dim_t, ndims)
     INFO_FUNC(const af::dim4 &, dims)
     INFO_FUNC(int, getDevId)
 
@@ -255,7 +255,8 @@ class Array {
         auto func = [this](void *ptr) {
             if (ptr != nullptr) {
                 cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);
-                ptr        = nullptr;
+                UNUSED(err);
+                ptr = nullptr;
             }
         };
 
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index eff48d262b..0382321306 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -133,7 +133,6 @@ Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
     dim_t outputHeight =
         1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
                 stride[1];
-    dim4 oDims = dim4(outputWidth, outputHeight, fDims[3], sDims[3]);
 
     const bool retCols = false;
     Array<T> unwrapped =
@@ -219,7 +218,6 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              af::dim4 stride, af::dim4 padding,
                              af::dim4 dilation) {
     const dim4 &cDims = incoming_gradient.dims();
-    const dim4 &sDims = original_signal.dims();
     const dim4 &fDims = original_filter.dims();
 
     const bool retCols = false;
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 19b312b3af..045a0a7e37 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -28,8 +28,8 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
         // TODO call upon fft
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
-                 "\nOpenCL Separable convolution doesn't support %zu(coloumn) "
-                 "%zu(row) filters\n",
+                 "\nOpenCL Separable convolution doesn't support %llu(coloumn) "
+                 "%llu(row) filters\n",
                  cflen, rflen);
         OPENCL_NOT_SUPPORTED(errMessage);
     }
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 6a6b125cea..8634092775 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -98,7 +98,7 @@ class DeviceManager {
     friend int getActivePlatform();
 
    public:
-    static const unsigned MAX_DEVICES = 32;
+    static const int MAX_DEVICES = 32;
 
     static DeviceManager& getInstance();
 
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index c2189c4ba1..6cca0ac6b1 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -309,8 +309,6 @@ void launch_compact(cl::Buffer *reduced_block_sizes, Param keys_out,
     kc_entry_t entry = kernelCache(device, ref_name);
 
     if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-
         std::ostringstream options;
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
@@ -363,8 +361,6 @@ void launch_compact_dim(cl::Buffer *reduced_block_sizes, Param keys_out,
     kc_entry_t entry = kernelCache(device, ref_name);
 
     if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-
         std::ostringstream options;
         options << " -D To=" << dtype_traits<To>::getName()
                 << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index fa1d29c111..1f02d15f4e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -112,6 +112,23 @@ static string platformMap(string& platStr) {
     }
 }
 
+afcl::platform getPlatformEnum(cl::Device dev) {
+    std::string pname = getPlatformName(dev);
+    if (verify_present(pname, "AMD"))
+        return AFCL_PLATFORM_AMD;
+    else if (verify_present(pname, "NVIDIA"))
+        return AFCL_PLATFORM_NVIDIA;
+    else if (verify_present(pname, "INTEL"))
+        return AFCL_PLATFORM_INTEL;
+    else if (verify_present(pname, "APPLE"))
+        return AFCL_PLATFORM_APPLE;
+    else if (verify_present(pname, "BEIGNET"))
+        return AFCL_PLATFORM_BEIGNET;
+    else if (verify_present(pname, "POCL"))
+        return AFCL_PLATFORM_POCL;
+    return AFCL_PLATFORM_UNKNOWN;
+}
+
 string getDeviceInfo() noexcept {
     ostringstream info;
     info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
@@ -196,7 +213,7 @@ int getDeviceCount() noexcept try {
     return 0;
 }
 
-int getActiveDeviceId() {
+unsigned getActiveDeviceId() {
     // Second element is the queue id, which is
     // what we mean by active device id in opencl backend
     return get<1>(tlocalActiveDeviceId());
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 5aeff25598..bb7d843fac 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -14,6 +14,7 @@
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 #pragma GCC diagnostic ignored "-Wignored-qualifiers"
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#pragma GCC diagnostic ignored "-Wcatch-value="
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
 
@@ -63,7 +64,7 @@ std::string getDeviceInfo() noexcept;
 
 int getDeviceCount() noexcept;
 
-int getActiveDeviceId();
+unsigned getActiveDeviceId();
 
 unsigned getMaxJitSize();
 
@@ -137,22 +138,7 @@ void removeKernelFromCache(int device, const std::string& key);
 
 kc_entry_t kernelCache(int device, const std::string& key);
 
-static afcl::platform getPlatformEnum(cl::Device dev) {
-    std::string pname = getPlatformName(dev);
-    if (verify_present(pname, "AMD"))
-        return AFCL_PLATFORM_AMD;
-    else if (verify_present(pname, "NVIDIA"))
-        return AFCL_PLATFORM_NVIDIA;
-    else if (verify_present(pname, "INTEL"))
-        return AFCL_PLATFORM_INTEL;
-    else if (verify_present(pname, "APPLE"))
-        return AFCL_PLATFORM_APPLE;
-    else if (verify_present(pname, "BEIGNET"))
-        return AFCL_PLATFORM_BEIGNET;
-    else if (verify_present(pname, "POCL"))
-        return AFCL_PLATFORM_POCL;
-    return AFCL_PLATFORM_UNKNOWN;
-}
+afcl::platform getPlatformEnum(cl::Device dev);
 
 void setActiveContext(int device);
 
diff --git a/test/array.cpp b/test/array.cpp
index 0b8f13c561..f8ebf7312c 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -10,7 +10,6 @@
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
-#include <half.hpp>
 #include <testHelpers.hpp>
 #include <cstddef>
 #include <cstdlib>
diff --git a/test/binary.cpp b/test/binary.cpp
index 790b09002a..a681e36b39 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -9,7 +9,6 @@
 
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
-#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
diff --git a/test/blas.cpp b/test/blas.cpp
index 38fc5b0884..0460f7de8d 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -417,7 +417,7 @@ INSTANTIATE_TEST_CASE_P(
     print_blas_params);
 
 TEST_P(MatrixMultiplyBatch, Batched) {
-    array out         = matmul(lhs, rhs);
+    array out = matmul(lhs, rhs);
     ASSERT_ARRAYS_NEAR(gold, out, 1e-3);
 }
 
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 3e885cf1f8..49025cf520 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -68,7 +68,7 @@ class Clamp : public ::testing::TestWithParam<clamp_params> {
         lo_.as((dtype)af::dtype_traits<T>::af_type).host(&hlo[0]);
         hi_.as((dtype)af::dtype_traits<T>::af_type).host(&hhi[0]);
 
-        for (int i = 0; i < num; i++) {
+        for (size_t i = 0; i < num; i++) {
             if (hin[i] < hlo[i])
                 hgold[i] = hlo[i];
             else if (hin[i] > hhi[i])
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 907eb63958..87ed52999b 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -84,7 +84,6 @@ void testImage(const std::string pTestFile, const size_t numSeeds,
         af_array outArray   = 0;
         af_array _goldArray = 0;
         af_array goldArray  = 0;
-        dim_t nElems        = 0;
 
         inFiles[testId].insert(0, string(TEST_DIR "/confidence_cc/"));
         outFiles[testId].insert(0, string(TEST_DIR "/confidence_cc/"));
diff --git a/test/convolve.cpp b/test/convolve.cpp
index b7a8fc0cc8..a62c0aa3c8 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -942,8 +942,6 @@ void convolve2stridedTest(string pTestFile, dim4 stride, dim4 padding,
 
     vector<T> &currGoldBar = tests[0];
 
-    size_t nElems = currGoldBar.size();
-
     dim_t expectedDim0 =
         1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
                 stride[0];
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index 5b8ea27765..f19510c24c 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -80,9 +80,9 @@ class IndexGeneralizedLegacy : public ::testing::TestWithParam<index_params> {
     }
 
     void TearDown() {
-        if (inArray_) ASSERT_SUCCESS(af_release_array(inArray_));
-        if (idxArray_) ASSERT_SUCCESS(af_release_array(idxArray_));
-        if (gold_) ASSERT_SUCCESS(af_release_array(gold_));
+        if (inArray_) { ASSERT_SUCCESS(af_release_array(inArray_)); }
+        if (idxArray_) { ASSERT_SUCCESS(af_release_array(idxArray_)); }
+        if (gold_) { ASSERT_SUCCESS(af_release_array(gold_)); }
     }
 
    public:
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index da484888c8..f00f5ab7f1 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -31,7 +31,7 @@ TEST(hsv_rgb, InvalidArray) {
     try {
         array output = hsv2rgb(input);
         ASSERT_EQ(true, false);
-    } catch (exception) {
+    } catch (const exception & /* ex */) {
         ASSERT_EQ(true, true);
         return;
     }
diff --git a/test/index.cpp b/test/index.cpp
index 36ce80387a..07dc5eac4f 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -9,7 +9,6 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
-#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/data.h>
 #include <af/defines.h>
diff --git a/test/jit.cpp b/test/jit.cpp
index 9f774c6a45..7afa1aab41 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -736,7 +736,7 @@ TEST(JIT, AllBuffers) {
 
   int inc = 2;
   for(int ii = buffers/2; ii > 2; ii/=2) {
-      for(int i = 0; i < arrs.size(); i += inc) {
+      for(size_t i = 0; i < arrs.size(); i += inc) {
           arrs[i] = arrs[i] + arrs[i + inc/2];
       }
       inc *= 2;
diff --git a/test/join.cpp b/test/join.cpp
index 630754b59e..24120c2b3f 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -214,9 +214,9 @@ TEST(Join, DifferentSizes) {
     vector<float> hb(11);
     vector<float> hc(12);
 
-    for (int i = 0; i < ha.size(); i++) { ha[i] = i; }
-    for (int i = 0; i < hb.size(); i++) { hb[i] = i; }
-    for (int i = 0; i < hc.size(); i++) { hc[i] = i; }
+    for (size_t i = 0; i < ha.size(); i++) { ha[i] = i; }
+    for (size_t i = 0; i < hb.size(); i++) { hb[i] = i; }
+    for (size_t i = 0; i < hc.size(); i++) { hc[i] = i; }
     vector<float> hgold(10 + 11 + 12);
     vector<float>::iterator it = copy(ha.begin(), ha.end(), hgold.begin());
     it                         = copy(hb.begin(), hb.end(), it);
@@ -236,9 +236,9 @@ TEST(Join, SameSize) {
     vector<float> hb(10);
     vector<float> hc(10);
 
-    for (int i = 0; i < ha.size(); i++) { ha[i] = i; }
-    for (int i = 0; i < hb.size(); i++) { hb[i] = i; }
-    for (int i = 0; i < hc.size(); i++) { hc[i] = i; }
+    for (size_t i = 0; i < ha.size(); i++) { ha[i] = i; }
+    for (size_t i = 0; i < hb.size(); i++) { hb[i] = i; }
+    for (size_t i = 0; i < hc.size(); i++) { hc[i] = i; }
     vector<float> hgold(10 + 10 + 10);
     vector<float>::iterator it = copy(ha.begin(), ha.end(), hgold.begin());
     it                         = copy(hb.begin(), hb.end(), it);
diff --git a/test/math.cpp b/test/math.cpp
index e869c2bdde..ed42d499b8 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <gtest/gtest.h>
-#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/data.h>
@@ -42,21 +41,21 @@ T rsqrt(T in) {
     return T(1.0 / sqrt(in));
 }
 
-#define MATH_TEST(T, func, err, lo, hi)                                     \
-    TEST(MathTests, Test_##func##_##T) {                                    \
-        try {                                                               \
-            SUPPORTED_TYPE_CHECK(T);                                        \
-            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;               \
-            array a     = (hi - lo) * randu(num, ty) + lo + err;            \
-            a           = a.as(ty);                                         \
-            eval(a);                                                        \
-            array b = func(a);                                              \
-            vector<T> h_a(a.elements());                                    \
-            a.host(&h_a[0]);                                                \
-            for (int i = 0; i < h_a.size(); i++) { h_a[i] = func(h_a[i]); } \
-                                                                            \
-            ASSERT_VEC_ARRAY_NEAR(h_a, dim4(h_a.size()), b, err);           \
-        } catch (exception & ex) { FAIL() << ex.what(); }                   \
+#define MATH_TEST(T, func, err, lo, hi)                                        \
+    TEST(MathTests, Test_##func##_##T) {                                       \
+        try {                                                                  \
+            SUPPORTED_TYPE_CHECK(T);                                           \
+            af_dtype ty = (af_dtype)dtype_traits<T>::af_type;                  \
+            array a     = (hi - lo) * randu(num, ty) + lo + err;               \
+            a           = a.as(ty);                                            \
+            eval(a);                                                           \
+            array b = func(a);                                                 \
+            vector<T> h_a(a.elements());                                       \
+            a.host(&h_a[0]);                                                   \
+            for (size_t i = 0; i < h_a.size(); i++) { h_a[i] = func(h_a[i]); } \
+                                                                               \
+            ASSERT_VEC_ARRAY_NEAR(h_a, dim4(h_a.size()), b, err);              \
+        } catch (exception & ex) { FAIL() << ex.what(); }                      \
     }
 
 #define MATH_TESTS_HALF(func) MATH_TEST(half, func, hlf_err, 0.05f, 0.95f)
diff --git a/test/mean.cpp b/test/mean.cpp
index 520d74c195..22b622c868 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -105,7 +105,6 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
         outArray.host((void*)outData.data());
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
-        size_t nElems = currGoldBar.size();
 
         dim4 goldDims = dims;
         goldDims[dim] = 1;
@@ -128,7 +127,6 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
         outArray.host((void*)outData.data());
 
         vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
-        size_t nElems = currGoldBar.size();
 
         ASSERT_VEC_ARRAY_NEAR(currGoldBar, goldDims, outArray, tol);
     }
@@ -214,7 +212,7 @@ void meanAllTest(half_float::half const_value, dim4 dims) {
     // make sure output2 and output are binary equals. This is necessary
     // because af_half is not a complete type
     half output2_copy;
-    memcpy(&output2_copy, &output2, sizeof(af_half));
+    memcpy(static_cast<void*>(&output2_copy), &output2, sizeof(af_half));
     ASSERT_EQ(output, output2_copy);
 
     ASSERT_NEAR(output, gold, 1.0e-3);
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index fb280c058b..e54268d3c7 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -11,7 +11,6 @@
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 
-#include <half.hpp>
 #include <testHelpers.hpp>
 
 #include <iterator>
@@ -266,7 +265,7 @@ template<typename T>
 vector<meanvar_test<T> > large_test_values() {
     return {
         // clang-format off
-        //               |           Name |     in_index | weight_index |                  bias |  dim | mean_index | var_index |
+        //                  |       Name |      in_index | weight_index |                  bias |  dim | mean_index | var_index |
         meanvar_test_gen<T>("Sample1Ddim0",             0,            -1,     AF_VARIANCE_SAMPLE,     0,           0,          1, MEANVAR_LARGE),
         meanvar_test_gen<T>("Sample1Ddim1",             1,            -1,     AF_VARIANCE_SAMPLE,     1,           0,          1, MEANVAR_LARGE),
         meanvar_test_gen<T>("Sample1Ddim2",             2,            -1,     AF_VARIANCE_SAMPLE,     2,           0,          1, MEANVAR_LARGE),
diff --git a/test/nodevice.cpp b/test/nodevice.cpp
index f81438b908..5674953c12 100644
--- a/test/nodevice.cpp
+++ b/test/nodevice.cpp
@@ -29,10 +29,7 @@ TEST(NoDevice, GetDeviceCount) {
     ASSERT_SUCCESS(af_get_device_count(&device));
 }
 
-TEST(NoDevice, GetDeviceCountCxx) {
-    int device = 0;
-    af::getDeviceCount();
-}
+TEST(NoDevice, GetDeviceCountCxx) { af::getDeviceCount(); }
 
 TEST(NoDevice, GetSizeOf) {
     size_t size;
@@ -52,6 +49,7 @@ TEST(NoDevice, GetBackendCount) {
 
 TEST(NoDevice, GetBackendCountCxx) {
     unsigned int nbackends = af::getBackendCount();
+    UNUSED(nbackends);
 }
 
 TEST(NoDevice, GetVersion) {
@@ -64,4 +62,7 @@ TEST(NoDevice, GetVersion) {
     ASSERT_EQ(AF_VERSION_PATCH, patch);
 }
 
-TEST(NoDevice, GetRevision) { const char* revision = af_get_revision(); }
+TEST(NoDevice, GetRevision) {
+    const char* revision = af_get_revision();
+    UNUSED(revision);
+}
diff --git a/test/pad_borders.cpp b/test/pad_borders.cpp
index 663d349361..33a977e03d 100644
--- a/test/pad_borders.cpp
+++ b/test/pad_borders.cpp
@@ -9,7 +9,6 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
-#include <half.hpp>
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 71ed09d729..f41fa897f5 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -334,11 +334,11 @@ struct reduce_by_key_params {
 //
 template<typename Tk, typename Tv, typename To>
 struct reduce_by_key_params_t : public reduce_by_key_params {
-    string testname_;
     vector<Tk> iKeys_;
     vector<Tv> iVals_;
     vector<Tk> oKeys_;
     vector<To> oVals_;
+    string testname_;
 
     reduce_by_key_params_t(vector<Tk> ikeys, vector<Tv> ivals, vector<Tk> okeys,
                            vector<To> ovals, string testname)
@@ -597,7 +597,7 @@ void reduce_by_key_test(std::string test_fn) {
     vector<vector<float> > tests;
     readTests<float, float, float>(test_fn, numDims, data, tests);
 
-    for (int t = 0; t < numDims.size() / 2; ++t) {
+    for (size_t t = 0; t < numDims.size() / 2; ++t) {
         dim4 kdim = numDims[t * 2];
         dim4 vdim = numDims[t * 2 + 1];
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index d4a449adf9..b35c099893 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -12,7 +12,11 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#pragma once
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wparentheses"
 #include <half.hpp>
+#pragma GCC diagnostic pop
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -145,7 +149,9 @@ float convert(af::half in) {
 template<>
 af_half convert(int in) {
     half_float::half h = half_float::half(in);
-    return *reinterpret_cast<af_half *>(&h);
+    af_half out;
+    memcpy(&out, &h, sizeof(af_half));
+    return out;
 }
 
 template<typename inType, typename outType, typename FileElementType>
@@ -599,7 +605,8 @@ void cleanSlate() {
 //  as numbers
 af_half abs(af_half in) {
     half_float::half in_;
-    memcpy(&in_, &in, sizeof(af_half));
+    // casting to void* to avoid class-memaccess warnings on windows
+    memcpy(static_cast<void *>(&in_), &in, sizeof(af_half));
     half_float::half out_ = abs(in_);
     af_half out;
     memcpy(&out, &out_, sizeof(af_half));
@@ -609,8 +616,10 @@ af_half abs(af_half in) {
 af_half operator-(af_half lhs, af_half rhs) {
     half_float::half lhs_;
     half_float::half rhs_;
-    memcpy(&lhs_, &lhs, sizeof(af_half));
-    memcpy(&rhs_, &rhs, sizeof(af_half));
+
+    // casting to void* to avoid class-memaccess warnings on windows
+    memcpy(static_cast<void *>(&lhs_), &lhs, sizeof(af_half));
+    memcpy(static_cast<void *>(&rhs_), &rhs, sizeof(af_half));
     half_float::half out = lhs_ - rhs_;
     af_half o;
     memcpy(&o, &out, sizeof(af_half));
diff --git a/test/threading.cpp b/test/threading.cpp
index d08b6965f0..99a789df49 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -41,7 +41,7 @@ void calc(ArithOp opcode, array op1, array op2, float outValue,
           int iteration_count) {
     setDevice(0);
     array res;
-    for (unsigned i = 0; i < iteration_count; ++i) {
+    for (int i = 0; i < iteration_count; ++i) {
         switch (opcode) {
             case ADD: res = op1 + op2; break;
             case SUB: res = op1 - op2; break;
diff --git a/test/topk.cpp b/test/topk.cpp
index b2faab6ff5..0e5c534949 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -9,7 +9,6 @@
 #define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
-#include <half.hpp>
 #include <testHelpers.hpp>
 
 #include <algorithm>
diff --git a/test/ycbcr_rgb.cpp b/test/ycbcr_rgb.cpp
index 8f5ea83a08..e137e1ede0 100644
--- a/test/ycbcr_rgb.cpp
+++ b/test/ycbcr_rgb.cpp
@@ -29,7 +29,7 @@ TEST(ycbcr_rgb, InvalidArray) {
     try {
         array output = hsv2rgb(input);
         ASSERT_EQ(true, false);
-    } catch (af::exception) {
+    } catch (const af::exception &ex) {
         ASSERT_EQ(true, true);
         return;
     }

From b5288b6bca59f0d74cbc76bf871726df58acd5e0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 12:08:47 -0400
Subject: [PATCH 1912/2677] Enable the -Wall flags if the compiler supports it

---
 CMakeModules/InternalUtils.cmake | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 92e269d8c0..1614f39f08 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -71,6 +71,12 @@ function(arrayfire_set_default_cxx_flags target)
         target_compile_options(${target}
           PRIVATE -Wno-ignored-attributes)
     endif()
+
+    check_cxx_compiler_flag(-Wall has_all_warnings_flag)
+    if(has_all_warnings_flag)
+      target_compile_options(${target}
+        PRIVATE -Wall)
+    endif()
   endif()
 endfunction()
 

From 13f1cbd4a76bd13535eabb3902574b97f1e84919 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 18:12:32 -0400
Subject: [PATCH 1913/2677] Speed up CPU transpose

---
 src/backend/cpu/kernel/transpose.hpp | 83 +++++++++++++++++++++++++---
 1 file changed, 75 insertions(+), 8 deletions(-)

diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
index 0851b4cd69..6ea41b65df 100644
--- a/src/backend/cpu/kernel/transpose.hpp
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -31,8 +31,17 @@ cdouble getConjugate(const cdouble &in) {
     return std::conj(in);
 }
 
-template<typename T, bool conjugate>
-void transpose(Param<T> output, CParam<T> input) {
+template<typename T, int M, int N>
+void transpose_kernel(T *output, const T *input, int ostride, int istride) {
+    for (int j = 0; j < N; j++) {
+        for (int i = 0; i < M; i++) { output[i * ostride] = input[i]; }
+        input += istride;
+        output++;
+    }
+}
+
+template<typename T>
+void transpose_real(Param<T> output, CParam<T> input) {
     const af::dim4 odims    = output.dims();
     const af::dim4 ostrides = output.strides();
     const af::dim4 istrides = input.strides();
@@ -40,21 +49,79 @@ void transpose(Param<T> output, CParam<T> input) {
     T *out            = output.get();
     T const *const in = input.get();
 
+    constexpr int M = 8;
+    constexpr int N = 8;
+
+    dim_t odims1_down = floor(odims[1] / N) * N;
+    dim_t odims0_down = floor(odims[0] / M) * M;
+
     for (dim_t l = 0; l < odims[3]; ++l) {
         for (dim_t k = 0; k < odims[2]; ++k) {
             // Outermost loop handles batch mode
             // if input has no data along third dimension
             // this loop runs only once
+            T *out_      = out + l * ostrides[3] + k * ostrides[2];
+            const T *in_ = in + l * istrides[3] + k * istrides[2];
+
+            if (odims1_down > 0) {
+                for (dim_t j = 0; j <= odims1_down; j += N) {
+                    for (dim_t i = 0; i < odims0_down; i += M) {
+                        transpose_kernel<T, M, N>(out_, in_, ostrides[1],
+                                                  istrides[1]);
+                        out_ += M;
+                        in_ += istrides[1] * N;
+                    }
+
+                    for (dim_t jj = 0; jj < N; jj++) {
+                        for (dim_t i = odims0_down; i < odims[0]; i++) {
+                            *out_ = *in_;
+                            out_++;
+                            in_ += istrides[1];
+                        }
+                        out_ += ostrides[1] - (odims[0] - odims0_down);
+                        in_ -= (odims[0] - odims0_down) * istrides[1] - 1;
+                    }
+                    out_ = out + l * ostrides[3] + k * ostrides[2] +
+                           j * ostrides[1];
+                    in_ = in + l * istrides[3] + k * istrides[2] + j;
+                }
+            }
+            for (dim_t j = odims1_down; j < odims[1]; j++) {
+                out_ =
+                    out + l * ostrides[3] + k * ostrides[2] + j * ostrides[1];
+                in_ = in + l * istrides[3] + k * istrides[2] + j;
+                for (dim_t i = 0; i < odims[0]; i++) {
+                    *out_ = *in_;
+                    out_++;
+                    in_ += istrides[1];
+                }
+            }
+        }
+    }
+}
+
+template<typename T>
+void transpose_conj(Param<T> output, CParam<T> input) {
+    const af::dim4 odims    = output.dims();
+    const af::dim4 ostrides = output.strides();
+    const af::dim4 istrides = input.strides();
+
+    T *out            = output.get();
+    T const *const in = input.get();
+
+    for (dim_t l = 0; l < odims[3]; ++l) {
+        for (dim_t k = 0; k < odims[2]; ++k) {
+            // Outermost loop handles batch mode
+            // if input has no data along third dimension
+            // this loop runs only once
+
             for (dim_t j = 0; j < odims[1]; ++j) {
                 for (dim_t i = 0; i < odims[0]; ++i) {
                     // calculate array indices based on offsets and strides
                     // the helper getIdx takes care of indices
                     const dim_t inIdx  = getIdx(istrides, j, i, k, l);
                     const dim_t outIdx = getIdx(ostrides, i, j, k, l);
-                    if (conjugate)
-                        out[outIdx] = getConjugate(in[inIdx]);
-                    else
-                        out[outIdx] = in[inIdx];
+                    out[outIdx]        = getConjugate(in[inIdx]);
                 }
             }
             // outData and inData pointers doesn't need to be
@@ -66,8 +133,8 @@ void transpose(Param<T> output, CParam<T> input) {
 
 template<typename T>
 void transpose(Param<T> out, CParam<T> in, const bool conjugate) {
-    return (conjugate ? transpose<T, true>(out, in)
-                      : transpose<T, false>(out, in));
+    return (conjugate ? transpose_conj<T>(out, in)
+                      : transpose_real<T>(out, in));
 }
 
 template<typename T, bool conjugate>

From cc4e1d60fafd23f2c8fa4a2b0be0464e23cc22c9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 19:08:46 -0400
Subject: [PATCH 1914/2677] Optimize join using memcpy

---
 src/backend/cpu/kernel/join.hpp | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index d23b9b757f..5899358fc9 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -39,11 +39,7 @@ void join_append(To *out, const Tx *X, const af::dim4 &offset,
                 const dim_t xYZW = xZW + oy * xst[1];
                 const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
 
-                for (dim_t ox = 0; ox < xdims[0]; ox++) {
-                    const dim_t iMem = xYZW + ox;
-                    const dim_t oMem = oYZW + (ox + offset[0]);
-                    out[oMem]        = X[iMem];
-                }
+                memcpy(out + oYZW + offset[0], X + xYZW, xdims[0] * sizeof(To));
             }
         }
     }

From c7f16cca120f722014458696e50aa292cb803e76 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 19 Apr 2020 20:31:09 -0400
Subject: [PATCH 1915/2677] Remove unnecessary instantiations of join in all
 backends

---
 src/api/c/join.cpp                 |  42 ++++++-----
 src/api/c/rgb_gray.cpp             |   4 +-
 src/api/c/ycbcr_rgb.cpp            |   8 +--
 src/backend/cpu/join.cpp           |  77 ++++++--------------
 src/backend/cpu/join.hpp           |   4 +-
 src/backend/cpu/kernel/join.hpp    | 103 ++++----------------------
 src/backend/cuda/join.cpp          |  67 ++++++++---------
 src/backend/cuda/join.hpp          |   4 +-
 src/backend/cuda/kernel/join.cuh   |  12 ++--
 src/backend/cuda/kernel/join.hpp   |   8 +--
 src/backend/opencl/join.cpp        | 111 ++++++++---------------------
 src/backend/opencl/join.hpp        |   4 +-
 src/backend/opencl/kernel/join.cl  |   8 +--
 src/backend/opencl/kernel/join.hpp |  18 ++---
 src/backend/opencl/surface.cpp     |   7 +-
 15 files changed, 150 insertions(+), 327 deletions(-)

diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 3fdfeb7036..2b7df25888 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -20,11 +20,10 @@ using af::dim4;
 using common::half;
 using namespace detail;
 
-template<typename Tx, typename Ty>
+template<typename T>
 static inline af_array join(const int dim, const af_array first,
                             const af_array second) {
-    return getHandle(
-        join<Tx, Ty>(dim, getArray<Tx>(first), getArray<Ty>(second)));
+    return getHandle(join<T>(dim, getArray<T>(first), getArray<T>(second)));
 }
 
 template<typename T>
@@ -65,21 +64,19 @@ af_err af_join(af_array *out, const int dim, const af_array first,
         af_array output;
 
         switch (finfo.getType()) {
-            case f32: output = join<float, float>(dim, first, second); break;
-            case c32: output = join<cfloat, cfloat>(dim, first, second); break;
-            case f64: output = join<double, double>(dim, first, second); break;
-            case c64:
-                output = join<cdouble, cdouble>(dim, first, second);
-                break;
-            case b8: output = join<char, char>(dim, first, second); break;
-            case s32: output = join<int, int>(dim, first, second); break;
-            case u32: output = join<uint, uint>(dim, first, second); break;
-            case s64: output = join<intl, intl>(dim, first, second); break;
-            case u64: output = join<uintl, uintl>(dim, first, second); break;
-            case s16: output = join<short, short>(dim, first, second); break;
-            case u16: output = join<ushort, ushort>(dim, first, second); break;
-            case u8: output = join<uchar, uchar>(dim, first, second); break;
-            case f16: output = join<half, half>(dim, first, second); break;
+            case f32: output = join<float>(dim, first, second); break;
+            case c32: output = join<cfloat>(dim, first, second); break;
+            case f64: output = join<double>(dim, first, second); break;
+            case c64: output = join<cdouble>(dim, first, second); break;
+            case b8: output = join<char>(dim, first, second); break;
+            case s32: output = join<int>(dim, first, second); break;
+            case u32: output = join<uint>(dim, first, second); break;
+            case s64: output = join<intl>(dim, first, second); break;
+            case u64: output = join<uintl>(dim, first, second); break;
+            case s16: output = join<short>(dim, first, second); break;
+            case u16: output = join<ushort>(dim, first, second); break;
+            case u8: output = join<uchar>(dim, first, second); break;
+            case f16: output = join<half>(dim, first, second); break;
             default: TYPE_ERROR(1, finfo.getType());
         }
         std::swap(*out, output);
@@ -92,7 +89,14 @@ af_err af_join(af_array *out, const int dim, const af_array first,
 af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
                     const af_array *inputs) {
     try {
-        ARG_ASSERT(3, n_arrays > 1 && n_arrays <= 10);
+        ARG_ASSERT(3, inputs != nullptr);
+
+        if (n_arrays == 1) {
+            af_array ret = nullptr;
+            AF_CHECK(af_retain_array(&ret, inputs[0]));
+            std::swap(*out, ret);
+            return AF_SUCCESS;
+        }
 
         std::vector<ArrayInfo> info;
         info.reserve(n_arrays);
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index ce4c2f6f57..e1d9732da6 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -88,8 +88,8 @@ static af_array gray2rgb(const af_array& in, const float r, const float g,
     AF_CHECK(af_release_array(mod_input));
 
     // join channels
-    Array<cType> expr4 = join<cType, cType>(2, expr1, expr2);
-    return getHandle(join<cType, cType>(2, expr3, expr4));
+    Array<cType> expr4 = join<cType>(2, expr1, expr2);
+    return getHandle(join<cType>(2, expr3, expr4));
 }
 
 template<typename T, typename cType, bool isRGB2GRAY>
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index 40ea20c8fd..2bf72a1474 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -104,8 +104,8 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
                    INV_112 * (kb - 1) * kb * invKl);
         Array<T> B = mix<T>(Y_, Cb_, INV_219, INV_112 * (1 - kb));
         // join channels
-        Array<T> RG = join<T, T>(2, R, G);
-        return getHandle(join<T, T>(2, RG, B));
+        Array<T> RG = join<T>(2, R, G);
+        return getHandle(join<T>(2, RG, B));
     }
     Array<T> Ey = mix<T>(X, Y, Z, kr, kl, kb);
     Array<T> Ecr =
@@ -116,8 +116,8 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
     Array<T> Cr = digitize<T>(Ecr, 224.0, 128.0);
     Array<T> Cb = digitize<T>(Ecb, 224.0, 128.0);
     // join channels
-    Array<T> YCb = join<T, T>(2, Y_, Cb);
-    return getHandle(join<T, T>(2, YCb, Cr));
+    Array<T> YCb = join<T>(2, Y_, Cb);
+    return getHandle(join<T>(2, YCb, Cr));
 }
 
 template<bool isYCbCr2RGB>
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 79b6686680..5b9382ee25 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -20,8 +20,8 @@ using common::half;
 
 namespace cpu {
 
-template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
     // All dimensions except join dimension must be equal
     // Compute output dims
     af::dim4 odims;
@@ -36,9 +36,9 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
         }
     }
 
-    Array<Tx> out = createEmptyArray<Tx>(odims);
-
-    getQueue().enqueue(kernel::join<Tx, Ty>, out, dim, first, second);
+    Array<T> out = createEmptyArray<T>(odims);
+    std::vector<CParam<T>> v{first, second};
+    getQueue().enqueue(kernel::join<T>, dim, out, v, 2);
 
     return out;
 }
@@ -73,59 +73,28 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
     std::vector<CParam<T>> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
-    switch (n_arrays) {
-        case 1:
-            getQueue().enqueue(kernel::join<T, 1>, dim, out, inputParams);
-            break;
-        case 2:
-            getQueue().enqueue(kernel::join<T, 2>, dim, out, inputParams);
-            break;
-        case 3:
-            getQueue().enqueue(kernel::join<T, 3>, dim, out, inputParams);
-            break;
-        case 4:
-            getQueue().enqueue(kernel::join<T, 4>, dim, out, inputParams);
-            break;
-        case 5:
-            getQueue().enqueue(kernel::join<T, 5>, dim, out, inputParams);
-            break;
-        case 6:
-            getQueue().enqueue(kernel::join<T, 6>, dim, out, inputParams);
-            break;
-        case 7:
-            getQueue().enqueue(kernel::join<T, 7>, dim, out, inputParams);
-            break;
-        case 8:
-            getQueue().enqueue(kernel::join<T, 8>, dim, out, inputParams);
-            break;
-        case 9:
-            getQueue().enqueue(kernel::join<T, 9>, dim, out, inputParams);
-            break;
-        case 10:
-            getQueue().enqueue(kernel::join<T, 10>, dim, out, inputParams);
-            break;
-    }
+    getQueue().enqueue(kernel::join<T>, dim, out, inputParams, n_arrays);
 
     return out;
 }
 
-#define INSTANTIATE(Tx, Ty)                                                \
-    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, \
-                                    const Array<Ty> &second);
-
-INSTANTIATE(float, float)
-INSTANTIATE(double, double)
-INSTANTIATE(cfloat, cfloat)
-INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(int, int)
-INSTANTIATE(uint, uint)
-INSTANTIATE(intl, intl)
-INSTANTIATE(uintl, uintl)
-INSTANTIATE(uchar, uchar)
-INSTANTIATE(char, char)
-INSTANTIATE(ushort, ushort)
-INSTANTIATE(short, short)
-INSTANTIATE(half, half)
+#define INSTANTIATE(T)                                              \
+    template Array<T> join<T>(const int dim, const Array<T> &first, \
+                              const Array<T> &second);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+INSTANTIATE(half)
 
 #undef INSTANTIATE
 
diff --git a/src/backend/cpu/join.hpp b/src/backend/cpu/join.hpp
index 847d6dc7eb..622e70c742 100644
--- a/src/backend/cpu/join.hpp
+++ b/src/backend/cpu/join.hpp
@@ -11,8 +11,8 @@
 #include <vector>
 
 namespace cpu {
-template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 
 template<typename T>
 Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index 5899358fc9..a81f8801fa 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -13,8 +13,7 @@
 namespace cpu {
 namespace kernel {
 
-template<int dim>
-af::dim4 calcOffset(const af::dim4 dims) {
+af::dim4 calcOffset(const af::dim4 dims, int dim) {
     af::dim4 offset;
     offset[0] = (dim == 0) ? dims[0] : 0;
     offset[1] = (dim == 1) ? dims[1] : 0;
@@ -23,8 +22,8 @@ af::dim4 calcOffset(const af::dim4 dims) {
     return offset;
 }
 
-template<typename To, typename Tx, int dim>
-void join_append(To *out, const Tx *X, const af::dim4 &offset,
+template<typename T>
+void join_append(T *out, const T *X, const af::dim4 &offset,
                  const af::dim4 &xdims, const af::dim4 &ost,
                  const af::dim4 &xst) {
     for (dim_t ow = 0; ow < xdims[3]; ow++) {
@@ -39,99 +38,23 @@ void join_append(To *out, const Tx *X, const af::dim4 &offset,
                 const dim_t xYZW = xZW + oy * xst[1];
                 const dim_t oYZW = oZW + (oy + offset[1]) * ost[1];
 
-                memcpy(out + oYZW + offset[0], X + xYZW, xdims[0] * sizeof(To));
+                memcpy(out + oYZW + offset[0], X + xYZW, xdims[0] * sizeof(T));
             }
         }
     }
 }
 
-template<typename Tx, typename Ty>
-void join(Param<Tx> out, const int dim, CParam<Tx> first, CParam<Ty> second) {
-    Tx *outPtr     = out.get();
-    const Tx *fptr = first.get();
-    const Ty *sptr = second.get();
-
-    af::dim4 zero(0, 0, 0, 0);
-    const af::dim4 fdims = first.dims();
-    const af::dim4 sdims = second.dims();
-
-    switch (dim) {
-        case 0:
-            join_append<Tx, Tx, 0>(outPtr, fptr, zero, fdims, out.strides(),
-                                   first.strides());
-            join_append<Tx, Ty, 0>(outPtr, sptr, calcOffset<0>(fdims), sdims,
-                                   out.strides(), second.strides());
-            break;
-        case 1:
-            join_append<Tx, Tx, 1>(outPtr, fptr, zero, fdims, out.strides(),
-                                   first.strides());
-            join_append<Tx, Ty, 1>(outPtr, sptr, calcOffset<1>(fdims), sdims,
-                                   out.strides(), second.strides());
-            break;
-        case 2:
-            join_append<Tx, Tx, 2>(outPtr, fptr, zero, fdims, out.strides(),
-                                   first.strides());
-            join_append<Tx, Ty, 2>(outPtr, sptr, calcOffset<2>(fdims), sdims,
-                                   out.strides(), second.strides());
-            break;
-        case 3:
-            join_append<Tx, Tx, 3>(outPtr, fptr, zero, fdims, out.strides(),
-                                   first.strides());
-            join_append<Tx, Ty, 3>(outPtr, sptr, calcOffset<3>(fdims), sdims,
-                                   out.strides(), second.strides());
-            break;
-    }
-}
-
-template<typename T, int n_arrays>
-void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs) {
+template<typename T>
+void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs,
+          int n_arrays) {
     af::dim4 zero(0, 0, 0, 0);
     af::dim4 d = zero;
-    switch (dim) {
-        case 0:
-            join_append<T, T, 0>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(),
-                                 inputs[0].strides());
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 0>(out.get(), inputs[i].get(),
-                                     calcOffset<0>(d), inputs[i].dims(),
-                                     out.strides(), inputs[i].strides());
-            }
-            break;
-        case 1:
-            join_append<T, T, 1>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(),
-                                 inputs[0].strides());
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 1>(out.get(), inputs[i].get(),
-                                     calcOffset<1>(d), inputs[i].dims(),
-                                     out.strides(), inputs[i].strides());
-            }
-            break;
-        case 2:
-            join_append<T, T, 2>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(),
-                                 inputs[0].strides());
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 2>(out.get(), inputs[i].get(),
-                                     calcOffset<2>(d), inputs[i].dims(),
-                                     out.strides(), inputs[i].strides());
-            }
-            break;
-        case 3:
-            join_append<T, T, 3>(out.get(), inputs[0].get(), zero,
-                                 inputs[0].dims(), out.strides(),
-                                 inputs[0].strides());
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                join_append<T, T, 3>(out.get(), inputs[i].get(),
-                                     calcOffset<3>(d), inputs[i].dims(),
-                                     out.strides(), inputs[i].strides());
-            }
-            break;
+    join_append<T>(out.get(), inputs[0].get(), zero, inputs[0].dims(),
+                   out.strides(), inputs[0].strides());
+    for (int i = 1; i < n_arrays; i++) {
+        d += inputs[i - 1].dims();
+        join_append<T>(out.get(), inputs[i].get(), calcOffset(d, dim),
+                       inputs[i].dims(), out.strides(), inputs[i].strides());
     }
 }
 
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 6a94c8b644..47f5a56205 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -29,8 +29,8 @@ af::dim4 calcOffset(const af::dim4 &dims, const int dim) {
     return offset;
 }
 
-template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
     // All dimensions except join dimension must be equal
     // Compute output dims
     af::dim4 odims;
@@ -45,26 +45,26 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
         }
     }
 
-    Array<Tx> out = createEmptyArray<Tx>(odims);
+    Array<T> out = createEmptyArray<T>(odims);
 
     af::dim4 zero(0, 0, 0, 0);
 
-    kernel::join<Tx, Tx>(out, first, zero, dim);
-    kernel::join<Tx, Ty>(out, second, calcOffset(fdims, dim), dim);
+    kernel::join<T>(out, first, zero, dim);
+    kernel::join<T>(out, second, calcOffset(fdims, dim), dim);
 
     return out;
 }
 
-template<typename T, int n_arrays>
+template<typename T>
 void join_wrapper(const int dim, Array<T> &out,
                   const std::vector<Array<T>> &inputs) {
     af::dim4 zero(0, 0, 0, 0);
     af::dim4 d = zero;
 
-    kernel::join<T, T>(out, inputs[0], zero, dim);
-    for (int i = 1; i < n_arrays; i++) {
+    kernel::join<T>(out, inputs[0], zero, dim);
+    for (size_t i = 1; i < inputs.size(); i++) {
         d += inputs[i - 1].dims();
-        kernel::join<T, T>(out, inputs[i], calcOffset(d, dim), dim);
+        kernel::join<T>(out, inputs[i], calcOffset(d, dim), dim);
     }
 }
 
@@ -77,7 +77,7 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
     std::vector<af::dim4> idims(n_arrays);
 
     dim_t dim_size = 0;
-    for (int i = 0; i < static_cast<int>(idims.size()); i++) {
+    for (size_t i = 0; i < idims.size(); i++) {
         idims[i] = inputs[i].dims();
         dim_size += idims[i][dim];
     }
@@ -97,38 +97,27 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
     evalMultiple(input_ptrs);
     Array<T> out = createEmptyArray<T>(odims);
 
-    switch (n_arrays) {
-        case 1: join_wrapper<T, 1>(dim, out, inputs); break;
-        case 2: join_wrapper<T, 2>(dim, out, inputs); break;
-        case 3: join_wrapper<T, 3>(dim, out, inputs); break;
-        case 4: join_wrapper<T, 4>(dim, out, inputs); break;
-        case 5: join_wrapper<T, 5>(dim, out, inputs); break;
-        case 6: join_wrapper<T, 6>(dim, out, inputs); break;
-        case 7: join_wrapper<T, 7>(dim, out, inputs); break;
-        case 8: join_wrapper<T, 8>(dim, out, inputs); break;
-        case 9: join_wrapper<T, 9>(dim, out, inputs); break;
-        case 10: join_wrapper<T, 10>(dim, out, inputs); break;
-    }
+    join_wrapper<T>(dim, out, inputs);
     return out;
 }
 
-#define INSTANTIATE(Tx, Ty)                                                \
-    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, \
-                                    const Array<Ty> &second);
-
-INSTANTIATE(float, float)
-INSTANTIATE(double, double)
-INSTANTIATE(cfloat, cfloat)
-INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(int, int)
-INSTANTIATE(uint, uint)
-INSTANTIATE(intl, intl)
-INSTANTIATE(uintl, uintl)
-INSTANTIATE(short, short)
-INSTANTIATE(ushort, ushort)
-INSTANTIATE(uchar, uchar)
-INSTANTIATE(char, char)
-INSTANTIATE(half, half)
+#define INSTANTIATE(T)                                              \
+    template Array<T> join<T>(const int dim, const Array<T> &first, \
+                              const Array<T> &second);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
 
 #undef INSTANTIATE
 
diff --git a/src/backend/cuda/join.hpp b/src/backend/cuda/join.hpp
index 3d0ecd760d..7f88e5cad1 100644
--- a/src/backend/cuda/join.hpp
+++ b/src/backend/cuda/join.hpp
@@ -10,8 +10,8 @@
 #include <Array.hpp>
 
 namespace cuda {
-template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 
 template<typename T>
 Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
diff --git a/src/backend/cuda/kernel/join.cuh b/src/backend/cuda/kernel/join.cuh
index c88ef1f422..666114e07b 100644
--- a/src/backend/cuda/kernel/join.cuh
+++ b/src/backend/cuda/kernel/join.cuh
@@ -13,8 +13,8 @@
 
 namespace cuda {
 
-template<typename To, typename Ti, int dim>
-__global__ void join(Param<To> out, CParam<Ti> in, const int o0, const int o1,
+template<typename T>
+__global__ void join(Param<T> out, CParam<T> in, const int o0, const int o1,
                      const int o2, const int o3, const int blocksPerMatX,
                      const int blocksPerMatY) {
     const int incy = blocksPerMatY * blockDim.y;
@@ -24,8 +24,8 @@ __global__ void join(Param<To> out, CParam<Ti> in, const int o0, const int o1,
     const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
     const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
 
-    To *d_out      = out.ptr;
-    Ti const *d_in = in.ptr;
+    T *d_out      = out.ptr;
+    T const *d_in = in.ptr;
 
     const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
     const int blockIdx_y =
@@ -37,8 +37,8 @@ __global__ void join(Param<To> out, CParam<Ti> in, const int o0, const int o1,
         d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
 
         for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            Ti const *d_in_ = d_in + iy * in.strides[1];
-            To *d_out_      = d_out + (iy + o1) * out.strides[1];
+            T const *d_in_ = d_in + iy * in.strides[1];
+            T *d_out_      = d_out + (iy + o1) * out.strides[1];
 
             for (int ix = xx; ix < in.dims[0]; ix += incx) {
                 d_out_[ix + o0] = d_in_[ix];
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index e9937a5287..f4a1645f52 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -20,8 +20,8 @@
 namespace cuda {
 namespace kernel {
 
-template<typename To, typename Tx>
-void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset, int dim) {
+template<typename T>
+void join(Param<T> out, CParam<T> X, const af::dim4 &offset, int dim) {
     constexpr unsigned TX    = 32;
     constexpr unsigned TY    = 8;
     constexpr unsigned TILEX = 256;
@@ -29,9 +29,7 @@ void join(Param<To> out, CParam<Tx> X, const af::dim4 &offset, int dim) {
 
     static const std::string source(join_cuh, join_cuh_len);
 
-    auto join = getKernel(
-        "cuda::join", source,
-        {TemplateTypename<To>(), TemplateTypename<Tx>(), TemplateArg(dim)});
+    auto join = getKernel("cuda::join", source, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index b6e8ab7e2c..162229af7f 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -23,8 +23,7 @@ using std::transform;
 using std::vector;
 
 namespace opencl {
-template<int dim>
-dim4 calcOffset(const dim4 &dims) {
+dim4 calcOffset(const dim4 &dims, int dim) {
     dim4 offset;
     offset[0] = (dim == 0) ? dims[0] : 0;
     offset[1] = (dim == 1) ? dims[1] : 0;
@@ -33,8 +32,8 @@ dim4 calcOffset(const dim4 &dims) {
     return offset;
 }
 
-template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
     // All dimensions except join dimension must be equal
     // Compute output dims
     dim4 odims;
@@ -49,67 +48,26 @@ Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second) {
         }
     }
 
-    Array<Tx> out = createEmptyArray<Tx>(odims);
+    Array<T> out = createEmptyArray<T>(odims);
 
     dim4 zero(0, 0, 0, 0);
 
-    switch (dim) {
-        case 0:
-            kernel::join<Tx, Tx, 0>(out, first, zero);
-            kernel::join<Tx, Ty, 0>(out, second, calcOffset<0>(fdims));
-            break;
-        case 1:
-            kernel::join<Tx, Tx, 1>(out, first, zero);
-            kernel::join<Tx, Ty, 1>(out, second, calcOffset<1>(fdims));
-            break;
-        case 2:
-            kernel::join<Tx, Tx, 2>(out, first, zero);
-            kernel::join<Tx, Ty, 2>(out, second, calcOffset<2>(fdims));
-            break;
-        case 3:
-            kernel::join<Tx, Tx, 3>(out, first, zero);
-            kernel::join<Tx, Ty, 3>(out, second, calcOffset<3>(fdims));
-            break;
-    }
+    kernel::join<T>(out, first, dim, zero);
+    kernel::join<T>(out, second, dim, calcOffset(fdims, dim));
 
     return out;
 }
 
-template<typename T, int n_arrays>
+template<typename T>
 void join_wrapper(const int dim, Array<T> &out,
                   const vector<Array<T>> &inputs) {
     dim4 zero(0, 0, 0, 0);
     dim4 d = zero;
 
-    switch (dim) {
-        case 0:
-            kernel::join<T, T, 0>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 0>(out, inputs[i], calcOffset<0>(d));
-            }
-            break;
-        case 1:
-            kernel::join<T, T, 1>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 1>(out, inputs[i], calcOffset<1>(d));
-            }
-            break;
-        case 2:
-            kernel::join<T, T, 1>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 2>(out, inputs[i], calcOffset<2>(d));
-            }
-            break;
-        case 3:
-            kernel::join<T, T, 3>(out, inputs[0], zero);
-            for (int i = 1; i < n_arrays; i++) {
-                d += inputs[i - 1].dims();
-                kernel::join<T, T, 3>(out, inputs[i], calcOffset<3>(d));
-            }
-            break;
+    kernel::join<T>(out, inputs[0], dim, zero);
+    for (size_t i = 1; i < inputs.size(); i++) {
+        d += inputs[i - 1].dims();
+        kernel::join<T>(out, inputs[i], dim, calcOffset(d, dim));
     }
 }
 
@@ -143,38 +101,27 @@ Array<T> join(const int dim, const vector<Array<T>> &inputs) {
     vector<Param> inputParams(inputs.begin(), inputs.end());
     Array<T> out = createEmptyArray<T>(odims);
 
-    switch (n_arrays) {
-        case 1: join_wrapper<T, 1>(dim, out, inputs); break;
-        case 2: join_wrapper<T, 2>(dim, out, inputs); break;
-        case 3: join_wrapper<T, 3>(dim, out, inputs); break;
-        case 4: join_wrapper<T, 4>(dim, out, inputs); break;
-        case 5: join_wrapper<T, 5>(dim, out, inputs); break;
-        case 6: join_wrapper<T, 6>(dim, out, inputs); break;
-        case 7: join_wrapper<T, 7>(dim, out, inputs); break;
-        case 8: join_wrapper<T, 8>(dim, out, inputs); break;
-        case 9: join_wrapper<T, 9>(dim, out, inputs); break;
-        case 10: join_wrapper<T, 10>(dim, out, inputs); break;
-    }
+    join_wrapper<T>(dim, out, inputs);
     return out;
 }
 
-#define INSTANTIATE(Tx, Ty)                                                \
-    template Array<Tx> join<Tx, Ty>(const int dim, const Array<Tx> &first, \
-                                    const Array<Ty> &second);
-
-INSTANTIATE(float, float)
-INSTANTIATE(double, double)
-INSTANTIATE(cfloat, cfloat)
-INSTANTIATE(cdouble, cdouble)
-INSTANTIATE(int, int)
-INSTANTIATE(uint, uint)
-INSTANTIATE(intl, intl)
-INSTANTIATE(uintl, uintl)
-INSTANTIATE(short, short)
-INSTANTIATE(ushort, ushort)
-INSTANTIATE(uchar, uchar)
-INSTANTIATE(char, char)
-INSTANTIATE(half, half)
+#define INSTANTIATE(T)                                              \
+    template Array<T> join<T>(const int dim, const Array<T> &first, \
+                              const Array<T> &second);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
 
 #undef INSTANTIATE
 
diff --git a/src/backend/opencl/join.hpp b/src/backend/opencl/join.hpp
index 63bd65b891..2f05a4fcf9 100644
--- a/src/backend/opencl/join.hpp
+++ b/src/backend/opencl/join.hpp
@@ -10,8 +10,8 @@
 #include <Array.hpp>
 
 namespace opencl {
-template<typename Tx, typename Ty>
-Array<Tx> join(const int dim, const Array<Tx> &first, const Array<Ty> &second);
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 
 template<typename T>
 Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
diff --git a/src/backend/opencl/kernel/join.cl b/src/backend/opencl/kernel/join.cl
index 71a1e16db7..b1e9de9112 100644
--- a/src/backend/opencl/kernel/join.cl
+++ b/src/backend/opencl/kernel/join.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void join_kernel(__global To *d_out, const KParam out,
-                          __global const Ti *d_in, const KParam in,
+__kernel void join_kernel(__global T *d_out, const KParam out,
+                          __global const T *d_in, const KParam in,
                           const int o0, const int o1, const int o2,
                           const int o3, const int blocksPerMatX,
                           const int blocksPerMatY) {
@@ -31,8 +31,8 @@ __kernel void join_kernel(__global To *d_out, const KParam out,
         d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
 
         for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            __global Ti *d_in_  = d_in + iy * in.strides[1];
-            __global To *d_out_ = d_out + (iy + o1) * out.strides[1];
+            __global T *d_in_  = d_in + iy * in.strides[1];
+            __global T *d_out_ = d_out + (iy + o1) * out.strides[1];
 
             for (int ix = xx; ix < in.dims[0]; ix += incx) {
                 d_out_[ix + o0] = d_in_[ix];
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 1298978d05..ac36696e1a 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -35,26 +35,20 @@ static const int TY    = 8;
 static const int TILEX = 256;
 static const int TILEY = 32;
 
-template<typename To, typename Ti, int dim>
-void join(Param out, const Param in, const af::dim4 offset) {
+template<typename T>
+void join(Param out, const Param in, dim_t dim, const af::dim4 offset) {
     std::string refName =
-        std::string("join_kernel_") + std::string(dtype_traits<To>::getName()) +
-        std::string(dtype_traits<Ti>::getName()) + std::to_string(dim);
+        std::string("join_kernel_") + std::string(dtype_traits<T>::getName()) +
+        std::string(dtype_traits<T>::getName()) + std::to_string(dim);
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
 
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D kDim=" << dim;
+        options << " -D T=" << dtype_traits<T>::getName();
 
-        if (std::is_same<To, double>::value ||
-            std::is_same<To, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        } else if (std::is_same<Ti, double>::value ||
-                   std::is_same<Ti, cdouble>::value) {
+        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
             options << " -D USE_DOUBLE";
         }
 
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index abec7e6913..d1ab53196d 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -11,12 +11,11 @@
 #include <GraphicsResourceManager.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
-#include <join.hpp>
-#include <reduce.hpp>
-#include <reorder.hpp>
 #include <surface.hpp>
 
 using af::dim4;
+using cl::Memory;
+using std::vector;
 
 namespace opencl {
 
@@ -31,7 +30,7 @@ void copy_surface(const Array<T> &P, fg_surface surface) {
 
         auto res = interopManager().getSurfaceResources(surface);
 
-        std::vector<cl::Memory> shared_objects;
+        vector<Memory> shared_objects;
         shared_objects.push_back(*(res[0].get()));
 
         glFinish();

From 56ded3c64e99a07f8e6ca642bdaaa10574d97729 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Apr 2020 12:59:16 +0530
Subject: [PATCH 1916/2677] Use builtin ocl work group scan when available in
 reduceByKey

---
 .../opencl/kernel/reduce_blocks_by_key_dim.cl | 39 ++++++++++--------
 .../kernel/reduce_blocks_by_key_first.cl      | 41 +++++++++++--------
 2 files changed, 47 insertions(+), 33 deletions(-)

diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
index 15680e3321..53aa60eb8b 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
@@ -7,26 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-Tk work_group_scan_inclusive_add(__local Tk *wg_tmp, __local Tk *arr) {
-    __local int *l_val;
+// Starting from OpenCL 2.0, core profile includes work group level
+// inclusive scan operations, hence skip defining custom one
+#if __OPENCL_VERSION__ < 200
+int work_group_scan_inclusive_add(__local int *wg_temp, __local int *arr) {
+    __local int *active_buf;
 
     const int lid = get_local_id(0);
-    Tk val        = arr[lid];
-    l_val         = arr;
+    int val       = arr[lid];
+    active_buf    = arr;
 
-    bool wbuf = 0;
+    bool swap_buffer = false;
     for (int off = 1; off <= DIMX; off *= 2) {
         barrier(CLK_LOCAL_MEM_FENCE);
-        if (lid >= off) val = val + l_val[lid - off];
-
-        wbuf       = 1 - wbuf;
-        l_val      = wbuf ? wg_tmp : arr;
-        l_val[lid] = val;
+        if (lid >= off) { val = val + active_buf[lid - off]; }
+        swap_buffer     = !swap_buffer;
+        active_buf      = swap_buffer ? wg_temp : arr;
+        active_buf[lid] = val;
     }
 
-    Tk res = l_val[lid];
+    int res = active_buf[lid];
     return res;
 }
+#endif  // __OPENCL_VERSION__ < 200
 
 __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
                                        __global Tk *oKeys, KParam oKInfo,
@@ -44,13 +47,13 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
 
     __local Tk keys[DIMX];
     __local To vals[DIMX];
-    __local Tk wg_temp[DIMX];
-
     __local Tk reduced_keys[DIMX];
     __local To reduced_vals[DIMX];
-
-    __local int unique_flags[DIMX];
     __local int unique_ids[DIMX];
+#if __OPENCL_VERSION__ < 200
+    __local int wg_temp[DIMX];
+    __local int unique_flags[DIMX];
+#endif
 
     const To init_val = init;
 
@@ -94,9 +97,13 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
     // mark threads containing unique keys
     int eq_check      = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
     int unique_flag   = (eq_check || (lid == 0)) && (gidx < n);
-    unique_flags[lid] = unique_flag;
 
+#if __OPENCL_VERSION__ < 200
+    unique_flags[lid] = unique_flag;
     int unique_id   = work_group_scan_inclusive_add(wg_temp, unique_flags);
+#else
+    int unique_id   = work_group_scan_inclusive_add(unique_flag);
+#endif
     unique_ids[lid] = unique_id;
 
     if (lid == DIMX - 1) reducedBlockSize = unique_id;
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
index 37e922c540..3ed23cd246 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -7,26 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-Tk work_group_scan_inclusive_add(__local Tk *wg_temp, __local Tk *arr) {
-    __local int *l_val;
+// Starting from OpenCL 2.0, core profile includes work group level
+// inclusive scan operations, hence skip defining custom one
+#if __OPENCL_VERSION__ < 200
+int work_group_scan_inclusive_add(__local int *wg_temp, __local int *arr) {
+    __local int *active_buf;
 
     const int lid = get_local_id(0);
-    Tk val        = arr[lid];
-    l_val         = arr;
+    int val       = arr[lid];
+    active_buf    = arr;
 
-    bool wbuf = 0;
+    bool swap_buffer = false;
     for (int off = 1; off <= DIMX; off *= 2) {
         barrier(CLK_LOCAL_MEM_FENCE);
-        if (lid >= off) val = val + l_val[lid - off];
-
-        wbuf       = 1 - wbuf;
-        l_val      = wbuf ? wg_temp : arr;
-        l_val[lid] = val;
+        if (lid >= off) { val = val + active_buf[lid - off]; }
+        swap_buffer     = !swap_buffer;
+        active_buf      = swap_buffer ? wg_temp : arr;
+        active_buf[lid] = val;
     }
 
-    Tk res = l_val[lid];
+    int res = active_buf[lid];
     return res;
 }
+#endif  // __OPENCL_VERSION__ < 200
 
 __kernel void reduce_blocks_by_key_first(
     __global int *reduced_block_sizes, __global Tk *oKeys, KParam oKInfo,
@@ -42,13 +45,13 @@ __kernel void reduce_blocks_by_key_first(
 
     __local Tk keys[DIMX];
     __local To vals[DIMX];
-    __local Tk wg_temp[DIMX];
-
     __local Tk reduced_keys[DIMX];
     __local To reduced_vals[DIMX];
-
-    __local int unique_flags[DIMX];
     __local int unique_ids[DIMX];
+#if __OPENCL_VERSION__ < 200
+    __local int wg_temp[DIMX];
+    __local int unique_flags[DIMX];
+#endif
 
     const To init_val = init;
 
@@ -80,9 +83,13 @@ __kernel void reduce_blocks_by_key_first(
     // mark threads containing unique keys
     int eq_check      = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
     int unique_flag   = (eq_check || (lid == 0)) && (gid < n);
-    unique_flags[lid] = unique_flag;
 
-    int unique_id   = work_group_scan_inclusive_add(wg_temp, unique_flags);
+#if __OPENCL_VERSION__ < 200
+    unique_flags[lid] = unique_flag;
+    int unique_id = work_group_scan_inclusive_add(wg_temp, unique_flags);
+#else
+    int unique_id = work_group_scan_inclusive_add(unique_flag);
+#endif
     unique_ids[lid] = unique_id;
 
     if (lid == DIMX - 1) reducedBlockSize = unique_id;

From 4c8312b4cabd6b5ec7494508ce6165cd21db73a5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Apr 2020 17:59:38 +0530
Subject: [PATCH 1917/2677] Use persistent boost env var in windows github ci
 job

---
 .github/workflows/cpu_build.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index e22e9fa0f6..ed74a7194a 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -172,6 +172,7 @@ jobs:
                       -DFFTW_INCLUDE_DIR:PATH="$env:GITHUB_WORKSPACE\vcpkg\installed/x64-windows\include" `
                       -DFFTW_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3.lib" `
                       -DFFTWF_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3f.lib" `
+                      -DBOOST_ROOT:PATH="$env:BOOST_ROOT_1_72_0" `
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
                       -DBUILDNAME:STRING="$buildname"

From c1283f67fd278a09f03c35e8c49076f41b2d0dd3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Apr 2020 21:29:00 +0530
Subject: [PATCH 1918/2677] Remove faulty cpu::BinOp struct implementation

---
 src/backend/cpu/jit/BinaryNode.hpp | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 70fa9ec4f7..4d199601ea 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <math.hpp>
 #include <optypes.hpp>
 #include <array>
@@ -17,14 +18,7 @@
 namespace cpu {
 
 template<typename To, typename Ti, af_op_t op>
-struct BinOp {
-    void eval(jit::array<To> &out, const jit::array<Ti> &lhs,
-              const jit::array<Ti> &rhs, int lim) const {
-        UNUSED(lhs);
-        UNUSED(rhs);
-        for (int i = 0; i < lim; i++) { out[i] = scalar<To>(0); }
-    }
-};
+struct BinOp;
 
 namespace jit {
 

From 44640688b785df5c4e284d60f7edf250e728fda7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 15 Apr 2020 21:11:29 +0530
Subject: [PATCH 1919/2677] Use backend agnostic flip at af_flip entry

This removes redundant flip implementation at src/api/c/ level again.
---
 src/api/c/flip.cpp                      | 60 +++++++++----------------
 src/backend/common/indexing_helpers.hpp | 13 ++++--
 2 files changed, 29 insertions(+), 44 deletions(-)

diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index d1a5159ea8..4b0bf15ef2 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -7,25 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cassert>
-#include <vector>
-
 #include <Array.hpp>
 #include <backend.hpp>
-#include <common/ArrayInfo.hpp>
-#include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <common/indexing_helpers.hpp>
 #include <handle.hpp>
-#include <lookup.hpp>
-#include <optypes.hpp>
-#include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
-#include <af/defines.h>
-#include <af/index.h>
-#include <af/seq.h>
+
+#include <cassert>
 
 using af::dim4;
+using common::flip;
 using common::half;
 using detail::Array;
 using detail::cdouble;
@@ -35,24 +28,11 @@ using detail::uchar;
 using detail::uintl;
 using detail::ushort;
 using std::swap;
-using std::vector;
 
 template<typename T>
-static af_array flipArray(const af_array in, const unsigned dim) {
-    const Array<T> input = getArray<T>(in);
-    vector<af_seq> index(4);
-
-    for (int i = 0; i < 4; i++) { index[i] = af_span; }
-
-    // Reverse "dim"
-    dim4 in_dims = input.dims();
-    af_seq s     = {static_cast<double>(in_dims[dim] - 1), 0, -1};
-
-    index[dim] = s;
-
-    Array<T> dst = createSubArray(input, index);
-
-    return getHandle(dst);
+static inline af_array flip(const af_array in, const unsigned dim) {
+    return getHandle(
+        flip(getArray<T>(in), {dim == 0, dim == 1, dim == 2, dim == 3}));
 }
 
 af_err af_flip(af_array *result, const af_array in, const unsigned dim) {
@@ -68,19 +48,19 @@ af_err af_flip(af_array *result, const af_array in, const unsigned dim) {
         af_dtype in_type = in_info.getType();
 
         switch (in_type) {
-            case f16: out = flipArray<half>(in, dim); break;
-            case f32: out = flipArray<float>(in, dim); break;
-            case c32: out = flipArray<cfloat>(in, dim); break;
-            case f64: out = flipArray<double>(in, dim); break;
-            case c64: out = flipArray<cdouble>(in, dim); break;
-            case b8: out = flipArray<char>(in, dim); break;
-            case s32: out = flipArray<int>(in, dim); break;
-            case u32: out = flipArray<unsigned>(in, dim); break;
-            case s64: out = flipArray<intl>(in, dim); break;
-            case u64: out = flipArray<uintl>(in, dim); break;
-            case s16: out = flipArray<short>(in, dim); break;
-            case u16: out = flipArray<ushort>(in, dim); break;
-            case u8: out = flipArray<uchar>(in, dim); break;
+            case f16: out = flip<half>(in, dim); break;
+            case f32: out = flip<float>(in, dim); break;
+            case c32: out = flip<cfloat>(in, dim); break;
+            case f64: out = flip<double>(in, dim); break;
+            case c64: out = flip<cdouble>(in, dim); break;
+            case b8: out = flip<char>(in, dim); break;
+            case s32: out = flip<int>(in, dim); break;
+            case u32: out = flip<unsigned>(in, dim); break;
+            case s64: out = flip<intl>(in, dim); break;
+            case u64: out = flip<uintl>(in, dim); break;
+            case s16: out = flip<short>(in, dim); break;
+            case u16: out = flip<ushort>(in, dim); break;
+            case u8: out = flip<uchar>(in, dim); break;
             default: TYPE_ERROR(1, in_type);
         }
         swap(*result, out);
diff --git a/src/backend/common/indexing_helpers.hpp b/src/backend/common/indexing_helpers.hpp
index 1808fabe43..46e33492bb 100644
--- a/src/backend/common/indexing_helpers.hpp
+++ b/src/backend/common/indexing_helpers.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Array.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -15,17 +16,21 @@
 #include <array>
 
 namespace common {
+
 // will generate indexes to flip input array
 // of size original dims according to axes specified in flip
 template<typename T>
-detail::Array<T> flip(const detail::Array<T> &in,
-                      const std::array<bool, AF_MAX_DIMS> flip) {
+static detail::Array<T> flip(const detail::Array<T>& in,
+                             const std::array<bool, AF_MAX_DIMS> flip) {
     std::vector<af_seq> index(4, af_span);
-    af::dim4 dims = in.dims();
+    const af::dim4& dims = in.dims();
 
     for (int i = 0; i < AF_MAX_DIMS; ++i) {
-        if (flip[i]) { index[i] = {(double)(dims[i] - 1), 0, -1}; }
+        if (flip[i]) {
+            index[i] = {static_cast<double>(dims[i] - 1), 0.0, -1.0};
+        }
     }
     return createSubArray(in, index);
 }
+
 }  // namespace common

From 6bfc3fc28e539e46020c994e0944d9642e64954a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 22 Apr 2020 15:02:59 +0530
Subject: [PATCH 1920/2677] Return input if pad output dims match input

---
 src/backend/cpu/copy.hpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 46d7de9a27..bd7671d082 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -44,6 +44,8 @@ Array<T> padArrayBorders(const Array<T> &in, const dim4 &lowerBoundPadding,
                lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
                lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
 
+    if (oDims == iDims) { return in; }
+
     auto ret = (btype == AF_PAD_ZERO ? createValueArray<T>(oDims, scalar<T>(0))
                                      : createEmptyArray<T>(oDims));
 

From 647cf394a1c8f9808a619fed81f85668b5bcf170 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 15 Apr 2020 21:17:37 +0530
Subject: [PATCH 1921/2677] Remove kernel size limit for b8 inputs of
 erode/dilate

b8(binary images) don't have any size limitations for
structuring-element/kernel starting with this change. For such
larger kernels, convolution(fft) based implementation is used.
---
 src/api/c/morph.cpp | 73 ++++++++++++++++++++++++++++++++++++++++++-
 test/data           |  2 +-
 test/morph.cpp      | 76 ++++++++++++++++++++++++++++++++++-----------
 3 files changed, 131 insertions(+), 20 deletions(-)

diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index f318ed6486..9a09f910a5 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -7,21 +7,36 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <arith.hpp>
 #include <backend.hpp>
+#include <cast.hpp>
 #include <common/err_common.hpp>
+#include <common/indexing_helpers.hpp>
+#include <copy.hpp>
+#include <fftconvolve.hpp>
 #include <handle.hpp>
+#include <logic.hpp>
+#include <math.hpp>
 #include <morph.hpp>
+#include <unary.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/image.h>
 
 using af::dim4;
+using common::flip;
+using detail::arithOp;
 using detail::Array;
+using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
+using detail::createValueArray;
+using detail::logicOp;
+using detail::scalar;
 using detail::uchar;
 using detail::uint;
+using detail::unaryOp;
 using detail::ushort;
 
 template<typename T, bool isDilation>
@@ -32,6 +47,62 @@ static inline af_array morph(const af_array &in, const af_array &mask) {
     return getHandle(out);
 }
 
+template<bool isDilation>
+static inline af_array morph(const af_array &input, const af_array &mask) {
+    using detail::fftconvolve;
+
+#if defined(AF_CPU)
+#if defined(USE_MKL)
+    constexpr unsigned fftMethodThreshold = 11;
+#else
+    constexpr unsigned fftMethodThreshold = 27;
+#endif  // defined(USE_MKL)
+#elif defined(AF_CUDA)
+    constexpr unsigned fftMethodThreshold = 17;
+#elif defined(AF_OPENCL)
+    constexpr unsigned fftMethodThreshold = 19;
+#endif  // defined(AF_CPU)
+
+    const Array<float> se = castArray<float>(mask);
+    const dim4 &seDims    = se.dims();
+
+    if (seDims[0] <= fftMethodThreshold) {
+        return morph<char, isDilation>(input, mask);
+    }
+
+    DIM_ASSERT(2, (seDims[0] == seDims[1]));
+
+    const Array<char> in = getArray<char>(input);
+    const dim4 &inDims   = in.dims();
+    const auto paddedSe =
+        padArrayBorders(se,
+                        {static_cast<dim_t>(seDims[0] % 2 == 0),
+                         static_cast<dim_t>(seDims[1] % 2 == 0), 0, 0},
+                        {0, 0, 0, 0}, AF_PAD_ZERO);
+
+    auto fftConv = fftconvolve<float, float, cfloat, false, false, 2>;
+
+    if (isDilation) {
+        Array<float> dft =
+            fftConv(cast<float>(in), paddedSe, false, AF_BATCH_LHS);
+
+        return getHandle(cast<char>(unaryOp<float, af_round_t>(dft)));
+    } else {
+        const Array<char> ONES   = createValueArray(inDims, scalar<char>(1));
+        const Array<float> ZEROS = createValueArray(inDims, scalar<float>(0));
+        const Array<char> inv    = arithOp<char, af_sub_t>(ONES, in, inDims);
+
+        Array<float> dft =
+            fftConv(cast<float>(inv), paddedSe, false, AF_BATCH_LHS);
+
+        Array<float> rounded = unaryOp<float, af_round_t>(dft);
+        Array<char> thrshd   = logicOp<float, af_gt_t>(rounded, ZEROS, inDims);
+        Array<char> inverted = arithOp<char, af_sub_t>(ONES, thrshd, inDims);
+
+        return getHandle(inverted);
+    }
+}
+
 template<typename T, bool isDilation>
 static inline af_array morph3d(const af_array &in, const af_array &mask) {
     const Array<T> input   = getArray<T>(in);
@@ -58,7 +129,7 @@ static af_err morph(af_array *out, const af_array &in, const af_array &mask) {
         switch (type) {
             case f32: output = morph<float, isDilation>(in, mask); break;
             case f64: output = morph<double, isDilation>(in, mask); break;
-            case b8: output = morph<char, isDilation>(in, mask); break;
+            case b8: output = morph<isDilation>(in, mask); break;
             case s32: output = morph<int, isDilation>(in, mask); break;
             case u32: output = morph<uint, isDilation>(in, mask); break;
             case s16: output = morph<short, isDilation>(in, mask); break;
diff --git a/test/data b/test/data
index 6a48c88658..408f440590 160000
--- a/test/data
+++ b/test/data
@@ -1 +1 @@
-Subproject commit 6a48c88658bcd68392e99344714cb0dccd4ec285
+Subproject commit 408f44059015c57a66e13b4c98df86ebcb427950
diff --git a/test/morph.cpp b/test/morph.cpp
index e91d8fe425..4558a50f42 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -134,7 +134,7 @@ TYPED_TEST(Morph, Erode4x4x4) {
 }
 
 template<typename T, bool isDilation, bool isColor>
-void morphImageTest(string pTestFile) {
+void morphImageTest(string pTestFile, dim_t seLen) {
     SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
@@ -148,29 +148,42 @@ void morphImageTest(string pTestFile) {
     size_t testCount = inDims.size();
 
     for (size_t testId = 0; testId < testCount; ++testId) {
-        af_array inArray   = 0;
-        af_array maskArray = 0;
-        af_array outArray  = 0;
-        af_array goldArray = 0;
-        dim_t nElems       = 0;
+        af_array _inArray   = 0;
+        af_array inArray    = 0;
+        af_array maskArray  = 0;
+        af_array outArray   = 0;
+        af_array _goldArray = 0;
+        af_array goldArray  = 0;
+        dim_t nElems        = 0;
 
         inFiles[testId].insert(0, string(TEST_DIR "/morph/"));
         outFiles[testId].insert(0, string(TEST_DIR "/morph/"));
 
-        dim4 mdims(3, 3, 1, 1);
+        af_dtype targetType = static_cast<af_dtype>(dtype_traits<T>::af_type);
+
+        dim4 mdims(seLen, seLen, 1, 1);
         ASSERT_SUCCESS(af_constant(&maskArray, 1.0, mdims.ndims(), mdims.get(),
-                                   (af_dtype)dtype_traits<T>::af_type));
+                                   targetType));
 
         ASSERT_SUCCESS(
-            af_load_image(&inArray, inFiles[testId].c_str(), isColor));
+            af_load_image(&_inArray, inFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_cast(&inArray, _inArray, targetType));
+
         ASSERT_SUCCESS(
-            af_load_image(&goldArray, outFiles[testId].c_str(), isColor));
+            af_load_image(&_goldArray, outFiles[testId].c_str(), isColor));
+        ASSERT_SUCCESS(af_cast(&goldArray, _goldArray, targetType));
+
         ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
 
-        if (isDilation)
-            ASSERT_SUCCESS(af_dilate(&outArray, inArray, maskArray));
-        else
-            ASSERT_SUCCESS(af_erode(&outArray, inArray, maskArray));
+        af_err error_code = AF_SUCCESS;
+        if (isDilation) {
+            error_code = af_dilate(&outArray, inArray, maskArray);
+        } else {
+            error_code = af_erode(&outArray, inArray, maskArray);
+        }
+
+#if defined(AF_CPU)
+        ASSERT_EQ(error_code, AF_SUCCESS);
 
         vector<T> outData(nElems);
         ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
@@ -180,20 +193,47 @@ void morphImageTest(string pTestFile) {
 
         ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
                                           outData.data(), 0.018f));
+#else
+        ASSERT_EQ(error_code,
+                  (targetType != b8 && seLen > 19 ? AF_ERR_NOT_SUPPORTED
+                                                  : AF_SUCCESS));
+#endif
 
+        ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(maskArray));
         ASSERT_SUCCESS(af_release_array(outArray));
+        ASSERT_SUCCESS(af_release_array(_goldArray));
         ASSERT_SUCCESS(af_release_array(goldArray));
     }
 }
 
-TEST(Morph, Grayscale) {
-    morphImageTest<float, true, false>(string(TEST_DIR "/morph/gray.test"));
+TEST(Morph, GrayscaleDilation3x3StructuringElement) {
+    morphImageTest<float, true, false>(string(TEST_DIR "/morph/gray.test"), 3);
+}
+
+TEST(Morph, ColorImageErosion3x3StructuringElement) {
+    morphImageTest<float, false, true>(string(TEST_DIR "/morph/color.test"), 3);
+}
+
+TEST(Morph, BinaryImageDilationBy33x33Kernel) {
+    morphImageTest<char, true, false>(
+        string(TEST_DIR "/morph/zag_dilation.test"), 33);
+}
+
+TEST(Morph, BinaryImageErosionBy33x33Kernel) {
+    morphImageTest<char, false, false>(
+        string(TEST_DIR "/morph/zag_erosion.test"), 33);
+}
+
+TEST(Morph, DilationBy33x33Kernel) {
+    morphImageTest<float, true, true>(
+        string(TEST_DIR "/morph/baboon_dilation.test"), 33);
 }
 
-TEST(Morph, ColorImage) {
-    morphImageTest<float, false, true>(string(TEST_DIR "/morph/color.test"));
+TEST(Morph, ErosionBy33x33Kernel) {
+    morphImageTest<float, false, true>(
+        string(TEST_DIR "/morph/baboon_erosion.test"), 33);
 }
 
 template<typename T, bool isDilation>

From 5b47079a3c866612f3e266a02b1fbb4448ff3ead Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 23 Apr 2020 01:22:55 -0400
Subject: [PATCH 1922/2677] Add minval and maxval for half in OpenCL and CPU
 backends

---
 src/backend/cpu/math.hpp    |  9 +++++++++
 src/backend/opencl/math.hpp | 12 ++++++++++++
 2 files changed, 21 insertions(+)

diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 5761147151..360750ca66 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/defines.hpp>
+#include <common/half.hpp>
 #include <types.hpp>
 #include <af/defines.h>
 
@@ -75,6 +76,10 @@ STATIC_ double maxval() {
     return std::numeric_limits<double>::infinity();
 }
 template<>
+STATIC_ common::half maxval() {
+    return std::numeric_limits<common::half>::infinity();
+}
+template<>
 STATIC_ float minval() {
     return -std::numeric_limits<float>::infinity();
 }
@@ -82,6 +87,10 @@ template<>
 STATIC_ double minval() {
     return -std::numeric_limits<double>::infinity();
 }
+template<>
+STATIC_ common::half minval() {
+    return -std::numeric_limits<common::half>::infinity();
+}
 
 template<typename T>
 static T scalar(double val) {
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index dd62930678..477cc039b9 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/defines.hpp>
+#include <common/half.hpp>
 #include <af/defines.h>
 
 #include <backend.hpp>
@@ -121,14 +122,25 @@ template<>
 STATIC_ double maxval() {
     return std::numeric_limits<double>::infinity();
 }
+
+template<>
+STATIC_ common::half maxval() {
+    return std::numeric_limits<common::half>::infinity();
+}
+
 template<>
 STATIC_ float minval() {
     return -std::numeric_limits<float>::infinity();
 }
+
 template<>
 STATIC_ double minval() {
     return -std::numeric_limits<double>::infinity();
 }
+template<>
+STATIC_ common::half minval() {
+    return -std::numeric_limits<common::half>::infinity();
+}
 
 static inline double real(cdouble in) { return in.s[0]; }
 static inline float real(cfloat in) { return in.s[0]; }

From 34db0d868a84237e20a45c8ad7287757d883b19c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 23 Apr 2020 01:37:47 -0400
Subject: [PATCH 1923/2677] Pass USE_(DOUBLE,HALF) definitions to OpenCL build
 in a uniform way

* Creates the getTypeBuildDefinition which will pass the USE_DOUBLE
  and USE_HALF types to the OpenCL build step in a consistent way
* The reduce by key step was not passing the USE_HALF flag so
  things weren't compiling on the Intel OpenCL GPU implementation
---
 .../opencl/kernel/anisotropic_diffusion.hpp   |  2 +-
 src/backend/opencl/kernel/approx.hpp          |  4 +--
 src/backend/opencl/kernel/assign.hpp          |  3 +-
 src/backend/opencl/kernel/bilateral.hpp       |  6 ++--
 src/backend/opencl/kernel/canny.hpp           |  8 ++---
 .../opencl/kernel/convolve/conv2_impl.hpp     |  3 +-
 .../opencl/kernel/convolve/conv_common.hpp    |  3 +-
 .../opencl/kernel/convolve_separable.cpp      |  4 +--
 src/backend/opencl/kernel/cscmm.hpp           |  4 +--
 src/backend/opencl/kernel/cscmv.hpp           |  5 ++--
 src/backend/opencl/kernel/csrmm.hpp           |  4 +--
 src/backend/opencl/kernel/csrmv.hpp           |  5 ++--
 src/backend/opencl/kernel/diagonal.hpp        |  5 ++--
 src/backend/opencl/kernel/diff.hpp            |  4 +--
 src/backend/opencl/kernel/exampleFunction.hpp |  4 +--
 src/backend/opencl/kernel/fast.hpp            |  4 +--
 src/backend/opencl/kernel/flood_fill.hpp      |  6 ++--
 src/backend/opencl/kernel/gradient.hpp        |  4 +--
 src/backend/opencl/kernel/harris.hpp          |  3 +-
 src/backend/opencl/kernel/histogram.hpp       |  5 +---
 src/backend/opencl/kernel/homography.hpp      |  2 +-
 src/backend/opencl/kernel/hsv_rgb.hpp         |  2 +-
 src/backend/opencl/kernel/identity.hpp        |  4 +--
 src/backend/opencl/kernel/iir.hpp             |  3 +-
 src/backend/opencl/kernel/index.hpp           |  3 +-
 src/backend/opencl/kernel/iota.hpp            |  5 +---
 src/backend/opencl/kernel/ireduce.hpp         | 10 ++-----
 src/backend/opencl/kernel/join.hpp            |  5 +---
 src/backend/opencl/kernel/laset.hpp           |  3 +-
 src/backend/opencl/kernel/laset_band.hpp      |  3 +-
 src/backend/opencl/kernel/laswp.hpp           |  3 +-
 src/backend/opencl/kernel/lookup.hpp          |  6 +---
 src/backend/opencl/kernel/lu_split.hpp        |  3 +-
 src/backend/opencl/kernel/match_template.hpp  |  2 +-
 src/backend/opencl/kernel/mean.hpp            | 22 ++------------
 src/backend/opencl/kernel/meanshift.hpp       |  3 +-
 src/backend/opencl/kernel/medfilt.hpp         |  6 ++--
 src/backend/opencl/kernel/memcopy.hpp         | 10 ++-----
 src/backend/opencl/kernel/moments.hpp         |  5 +---
 src/backend/opencl/kernel/morph.hpp           |  4 +--
 .../opencl/kernel/nearest_neighbour.hpp       |  4 +--
 src/backend/opencl/kernel/orb.hpp             |  4 +--
 .../opencl/kernel/pad_array_borders.hpp       |  3 +-
 src/backend/opencl/kernel/random_engine.hpp   |  3 +-
 src/backend/opencl/kernel/range.hpp           |  5 +---
 src/backend/opencl/kernel/reduce.hpp          | 18 ++---------
 src/backend/opencl/kernel/reduce_by_key.hpp   | 30 ++++---------------
 src/backend/opencl/kernel/regions.hpp         |  3 +-
 src/backend/opencl/kernel/reorder.hpp         |  3 +-
 src/backend/opencl/kernel/resize.hpp          |  4 +--
 src/backend/opencl/kernel/rotate.hpp          |  3 +-
 src/backend/opencl/kernel/scan_dim.hpp        |  5 +---
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  5 +---
 src/backend/opencl/kernel/scan_first.hpp      |  5 +---
 .../opencl/kernel/scan_first_by_key_impl.hpp  |  5 +---
 src/backend/opencl/kernel/select.hpp          |  6 ++--
 src/backend/opencl/kernel/sift_nonfree.hpp    |  3 +-
 src/backend/opencl/kernel/sobel.hpp           |  2 +-
 src/backend/opencl/kernel/sparse.hpp          | 29 ++++--------------
 src/backend/opencl/kernel/sparse_arith.hpp    | 20 ++++---------
 src/backend/opencl/kernel/susan.hpp           |  6 ++--
 src/backend/opencl/kernel/swapdblk.hpp        |  3 +-
 src/backend/opencl/kernel/tile.hpp            |  3 +-
 src/backend/opencl/kernel/transform.hpp       |  4 +--
 src/backend/opencl/kernel/transpose.hpp       |  6 +---
 .../opencl/kernel/transpose_inplace.hpp       |  4 +--
 src/backend/opencl/kernel/triangle.hpp        |  5 +---
 src/backend/opencl/kernel/unwrap.hpp          |  5 +---
 src/backend/opencl/kernel/where.hpp           |  3 +-
 src/backend/opencl/kernel/wrap.hpp            |  9 ++----
 src/backend/opencl/types.hpp                  | 30 +++++++++++++++++++
 71 files changed, 133 insertions(+), 295 deletions(-)

diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index 995a50a4e1..91cd393bce 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -49,7 +49,7 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
                 << " -D SHRD_MEM_WIDTH=" << (THREADS_X + 2)
                 << " -D IS_MCDE=" << isMCDE << " -D FLUX_FN=" << fluxFnCode
                 << " -D YDIM_LOAD=" << YDIM_LOAD;
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {anisotropic_diffusion_cl};
         const int ker_lens[]   = {anisotropic_diffusion_cl_len};
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index b31b68bc8d..44623c961e 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -56,9 +56,7 @@ std::string generateOptionsString() {
     } else {
         options << " -D IS_CPLX=0";
     }
-    if (std::is_same<Ty, double>::value || std::is_same<Ty, cdouble>::value) {
-        options << " -D USE_DOUBLE";
-    }
+    options << getTypeBuildDefinition<Ty>();
 
     options << " -D INTERP_ORDER=" << order;
     addInterpEnumOptions(options);
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 0caee37fd8..4f4b69b356 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -48,8 +48,7 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {assign_cl};
         const int ker_lens[]   = {assign_cl_len};
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 7aab2a5588..8b7c787982 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -47,10 +47,10 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma) {
         std::ostringstream options;
         options << " -D inType=" << dtype_traits<inType>::getName()
                 << " -D outType=" << dtype_traits<outType>::getName();
-        if (std::is_same<inType, double>::value ||
+
+        options << getTypeBuildDefinition<inType>();
+        if (!std::is_same<inType, double>::value ||
             std::is_same<inType, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        } else {
             options << " -D USE_NATIVE_EXP";
         }
 
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 3133e500b8..e49d5bf55d 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -47,7 +47,7 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
                 << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
                 << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
                 << " -D NON_MAX_SUPPRESSION";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {nonmax_suppression_cl};
         const int ker_lens[]   = {nonmax_suppression_cl_len};
@@ -92,7 +92,7 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D INIT_EDGE_OUT";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {trace_edge_cl};
         const int ker_lens[]   = {trace_edge_cl_len};
@@ -135,7 +135,7 @@ void suppressLeftOver(Param output) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D SUPPRESS_LEFT_OVER";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {trace_edge_cl};
         const int ker_lens[]   = {trace_edge_cl_len};
@@ -183,7 +183,7 @@ void edgeTrackingHysteresis(Param output, const Param strong,
                 << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
                 << " -D TOTAL_NUM_THREADS=" << (THREADS_X * THREADS_Y)
                 << " -D EDGE_TRACER";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {trace_edge_cl};
         const int ker_lens[]   = {trace_edge_cl_len};
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 404cd48fac..961ba3dc00 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -51,8 +51,7 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
         } else {
             options << " -D CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {ops_cl, convolve_cl};
         const int ker_lens[]   = {ops_cl_len, convolve_cl_len};
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 7380f7dc1e..d85c9ee819 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -118,8 +118,7 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
         } else {
             options << " -D CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {ops_cl, convolve_cl};
         const int ker_lens[]   = {ops_cl_len, convolve_cl_len};
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index cc5c20aaba..29b0fa1607 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -72,9 +72,7 @@ void convSep(Param out, const Param signal, const Param filter) {
         } else {
             options << " -D CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {ops_cl, convolve_separable_cl};
         const int ker_lens[]   = {ops_cl_len, convolve_separable_cl_len};
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 44e1e1a5e5..b97544a845 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -69,10 +69,8 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
         options << " -D THREADS=" << threads;
         options << " -D ROWS_PER_GROUP=" << rows_per_group;
         options << " -D COLS_PER_GROUP=" << cols_per_group;
+        options << getTypeBuildDefinition<T>();
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
         if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
             options << " -D IS_CPLX=1";
         } else {
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 0ac76a7bcd..49fde89c24 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -68,9 +68,8 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
         options << " -D THREADS=" << threads;
         options << " -D ROWS_PER_GROUP=" << rows_per_group;
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
+
         if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
             options << " -D IS_CPLX=1";
         } else {
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 69ea435524..7a0af07332 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -65,9 +65,7 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
         options << " -D USE_GREEDY=" << use_greedy;
         options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
         if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
             options << " -D IS_CPLX=1";
         } else {
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index e4c06ad39d..132b3e657d 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -68,9 +68,8 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
         options << " -D USE_GREEDY=" << use_greedy;
         options << " -D THREADS=" << threads;
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
+
         if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
             options << " -D IS_CPLX=1";
         } else {
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index afb860691a..8cd323f4d4 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -34,9 +34,8 @@ std::string generateOptionsString() {
     std::ostringstream options;
     options << " -D T=" << dtype_traits<T>::getName() << " -D ZERO=(T)("
             << scalar_to_option(scalar<T>(0)) << ")";
-    if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-        options << " -D USE_DOUBLE";
-    }
+    options << getTypeBuildDefinition<T>();
+
     return options.str();
 }
 
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 6fbf41a5c4..cf9c5c61f3 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -43,9 +43,7 @@ void diff(Param out, const Param in, const unsigned indims) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName() << " -D DIM=" << dim
                 << " -D isDiff2=" << isDiff2;
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {diff_cl};
         const int ker_lens[]   = {diff_cl_len};
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 8a4391b11e..fee67836f0 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -74,9 +74,7 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
         // The following option is passed to kernel compilation
         // if template parameter T is double or complex double
         // to enable FP64 extension
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {example_cl};
         const int ker_lens[]   = {example_cl_len};
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 434452c8e9..1abc7cc6ca 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -53,9 +53,7 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
                 << " -D ARC_LENGTH=" << arc_length
                 << " -D NONMAX=" << static_cast<unsigned>(nonmax);
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         cl::Program prog;
         buildProgram(prog, fast_cl, fast_cl_len, options.str());
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index a7ed4e3814..9faa2a8fe6 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -49,7 +49,7 @@ void initSeeds(Param out, const Param seedsx, const Param seedsy) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D VALID=" << T(VALID) << " -D INIT_SEEDS";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {flood_fill_cl};
         const int ker_lens[]   = {flood_fill_cl_len};
@@ -82,7 +82,7 @@ void finalizeOutput(Param out, const T newValue) {
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D VALID=" << T(VALID) << " -D ZERO=" << T(ZERO)
                 << " -D FINALIZE_OUTPUT";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {flood_fill_cl};
         const int ker_lens[]   = {flood_fill_cl_len};
@@ -123,7 +123,7 @@ void floodFill(Param out, const Param image, const Param seedsx,
                 << " -D GROUP_SIZE=" << (THREADS_Y * THREADS_X)
                 << " -D VALID=" << T(VALID) << " -D INVALID=" << T(INVALID)
                 << " -D ZERO=" << T(ZERO) << " -D FLOOD_FILL_STEP";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {flood_fill_cl};
         const int ker_lens[]   = {flood_fill_cl_len};
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 19cf0ac7c1..60bfac0b95 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -54,9 +54,7 @@ void gradient(Param grad0, Param grad1, const Param in) {
         } else {
             options << " -D CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {gradient_cl};
         const int ker_lens[]   = {gradient_cl_len};
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 026bb5150c..9f700d2aac 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -82,8 +82,7 @@ getHarrisKernels() {
     if (entries[0].prog == 0 && entries[0].ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {harris_cl};
         const int ker_lens[]   = {harris_cl_len};
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 43d18d7335..9a0568c2d8 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -47,10 +47,7 @@ void histogram(Param out, const Param in, int nbins, float minval,
                 << " -D outType=" << dtype_traits<outType>::getName()
                 << " -D THRD_LOAD=" << THRD_LOAD << " -D MAX_BINS=" << MAX_BINS;
         if (isLinear) options << " -D IS_LINEAR";
-        if (std::is_same<inType, double>::value ||
-            std::is_same<inType, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<inType>();
 
         const char* ker_strs[] = {histogram_cl};
         const int ker_lens[]   = {histogram_cl_len};
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 63a3e7213d..48b61d53f7 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -54,8 +54,8 @@ std::array<cl::Kernel*, 5> getHomographyKernels() {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
 
+        options << getTypeBuildDefinition<T>();
         if (std::is_same<T, double>::value) {
-            options << " -D USE_DOUBLE";
             options << " -D EPS=" << DBL_EPSILON;
         } else
             options << " -D EPS=" << FLT_EPSILON;
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index abff64a6e7..40ecbbcc03 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -44,7 +44,7 @@ void hsv2rgb_convert(Param out, const Param in) {
         options << " -D T=" << dtype_traits<T>::getName();
 
         if (isHSV2RGB) options << " -D isHSV2RGB";
-        if (std::is_same<T, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {hsv_rgb_cl};
         const int ker_lens[]   = {hsv_rgb_cl_len};
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 998887b946..a73b725518 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -45,9 +45,7 @@ static void identity(Param out) {
         options << " -D T=" << dtype_traits<T>::getName() << " -D ONE=(T)("
                 << scalar_to_option(scalar<T>(1)) << ")"
                 << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-        if (is_same<T, double>::value || is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         if (is_same<T, half>::value) { options << " -D USE_HALF"; }
 
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index c594fd3bc3..56c9af00a4 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -48,8 +48,7 @@ void iir(Param y, Param c, Param a) {
                 << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
                 << " -D T=" << dtype_traits<T>::getName();
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {iir_cl};
         const int ker_lens[]   = {iir_cl_len};
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 0f22da66cc..f9819325c8 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -49,8 +49,7 @@ void index(Param out, const Param in, const IndexKernelParam_t& p,
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {index_cl};
         const int ker_lens[]   = {index_cl_len};
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 2ce8ee04f5..0d4cf2ee5f 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -47,10 +47,7 @@ void iota(Param out, const af::dim4& sdims) {
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
-
-        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {iota_cl};
         const int ker_lens[]   = {iota_cl_len};
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 106b600aa4..9d8bcba263 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -63,10 +63,7 @@ void ireduce_dim_launcher(Param out, cl::Buffer *oidx, Param in,
                 << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
                 << " -D IS_FIRST=" << is_first;
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {iops_cl, ireduce_dim_cl};
         const int ker_lens[]   = {iops_cl_len, ireduce_dim_cl_len};
@@ -157,10 +154,7 @@ void ireduce_first_launcher(Param out, cl::Buffer *oidx, Param in,
                 << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
                 << " -D IS_FIRST=" << is_first;
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {iops_cl, ireduce_first_cl};
         const int ker_lens[]   = {iops_cl_len, ireduce_first_cl_len};
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index ac36696e1a..6dafbaa647 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -47,10 +47,7 @@ void join(Param out, const Param in, dim_t dim, const af::dim4 offset) {
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {join_cl};
         const int ker_lens[]   = {join_cl_len};
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 76651d9b6f..bae033a21c 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -65,8 +65,7 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
                 << " -D BLK_X=" << BLK_X << " -D BLK_Y=" << BLK_Y
                 << " -D IS_CPLX=" << af::iscplx<T>();
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {laset_cl};
         const int ker_lens[]   = {laset_cl_len};
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index e1e031705d..0c8da5eb47 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -53,8 +53,7 @@ void laset_band(int m, int  n, int k,
             << " -D NB=" << NB
             << " -D IS_CPLX=" << af::iscplx<T>();
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {laset_band_cl};
         const int   ker_lens[] = {laset_band_cl_len};
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 0a83f6b339..5b6281730a 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -50,8 +50,7 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D MAX_PIVOTS=" << MAX_PIVOTS;
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {laswp_cl};
         const int ker_lens[]   = {laswp_cl_len};
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 561d670037..a83af42953 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -48,11 +48,7 @@ void lookup(Param out, const Param in, const Param indices) {
         options << " -D in_t=" << dtype_traits<in_t>::getName()
                 << " -D idx_t=" << dtype_traits<idx_t>::getName()
                 << " -D DIM=" << dim;
-
-        if (is_same<in_t, double>::value || is_same<in_t, cdouble>::value ||
-            is_same<idx_t, double>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<in_t, idx_t>();
 
         if (is_same<in_t, common::half>::value) { options << " -D USE_HALF"; }
 
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 83c5395fd7..e993bc67c9 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -52,8 +52,7 @@ void lu_split_launcher(Param lower, Param upper, const Param in) {
                 << scalar_to_option(scalar<T>(0)) << ")"
                 << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {lu_split_cl};
         const int ker_lens[]   = {lu_split_cl_len};
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index d0c5f7b003..27f96bfb72 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -50,7 +50,7 @@ void matchTemplate(Param out, const Param srch, const Param tmplt) {
                 << " -D AF_ZSSD=" << AF_ZSSD << " -D AF_LSSD=" << AF_LSSD
                 << " -D AF_NCC=" << AF_NCC << " -D AF_ZNCC=" << AF_ZNCC
                 << " -D AF_SHD=" << AF_SHD;
-        if (std::is_same<outType, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<outType>();
 
         const char* ker_strs[] = {matchTemplate_cl};
         const int ker_lens[]   = {matchTemplate_cl_len};
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 120b5a560b..99bdef3bf7 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -143,16 +143,7 @@ void mean_dim_launcher(Param out, Param owt, Param in, Param inWeight,
 
         if (input_weight) { options << " -D INPUT_WEIGHT"; }
         if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
-
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value ||
-            std::is_same<To, double>::value) {
-            options << " -D USE_DOUBLE";
-        }
-
-        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
-            options << " -D USE_HALF";
-        }
+        options << getTypeBuildDefinition<Ti, To>();
 
         const char *ker_strs[] = {mean_ops_cl, mean_dim_cl};
         const int ker_lens[]   = {mean_ops_cl_len, mean_dim_cl_len};
@@ -272,16 +263,7 @@ void mean_first_launcher(Param out, Param owt, Param in, Param inWeight,
 
         if (input_weight) { options << " -D INPUT_WEIGHT"; }
         if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
-
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value ||
-            std::is_same<To, double>::value) {
-            options << " -D USE_DOUBLE";
-        }
-
-        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
-            options << " -D USE_HALF";
-        }
+        options << getTypeBuildDefinition<Ti, To>();
 
         const char *ker_strs[] = {mean_ops_cl, mean_first_cl};
         const int ker_lens[]   = {mean_ops_cl_len, mean_first_cl_len};
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index 534480d107..e237d99184 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -50,8 +50,7 @@ void meanshift(Param out, const Param in, const float spatialSigma,
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D AccType=" << dtype_traits<AccType>::getName()
                 << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {meanshift_cl};
         const int ker_lens[]   = {meanshift_cl_len};
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 81f69b082c..af758022df 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -51,8 +51,7 @@ void medfilt1(Param out, const Param in, unsigned w_wid) {
                 << " -D AF_PAD_ZERO=" << AF_PAD_ZERO
                 << " -D AF_PAD_SYM=" << AF_PAD_SYM
                 << " -D ARR_SIZE=" << ARR_SIZE << " -D w_wid=" << w_wid;
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {medfilt1_cl};
         const int ker_lens[]   = {medfilt1_cl_len};
@@ -101,8 +100,7 @@ void medfilt2(Param out, const Param in) {
                 << " -D AF_PAD_SYM=" << AF_PAD_SYM
                 << " -D ARR_SIZE=" << ARR_SIZE << " -D w_len=" << w_len
                 << " -D w_wid=" << w_wid;
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {medfilt2_cl};
         const int ker_lens[]   = {medfilt2_cl_len};
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 4c82a17bf7..75b4a1f6d0 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -53,8 +53,7 @@ void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {memcopy_cl};
         const int ker_lens[]   = {memcopy_cl_len};
@@ -112,12 +111,7 @@ void copy(Param dst, const Param src, int ndims, outType default_value,
                 << " -D inType_" << dtype_traits<inType>::getName()
                 << " -D outType_" << dtype_traits<outType>::getName()
                 << " -D SAME_DIMS=" << same_dims;
-
-        if (std::is_same<inType, double>::value ||
-            std::is_same<inType, cdouble>::value ||
-            std::is_same<outType, double>::value ||
-            std::is_same<outType, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<inType, outType>();
 
         const char *ker_strs[] = {copy_cl};
         const int ker_lens[]   = {copy_cl_len};
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index a64aa813c7..8ca90fb644 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -50,10 +50,7 @@ void moments(Param out, const Param in, af_moment_type moment) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         options << " -D MOMENTS_SZ=" << out.info.dims[0];
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         Program prog;
         buildProgram(prog, moments_cl, moments_cl_len, options.str());
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index a50c1e3fb8..f6945e4adb 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -47,8 +47,8 @@ std::string generateOptionsString() {
     options << " -D T=" << dtype_traits<T>::getName()
             << " -D isDilation=" << isDilation << " -D init=" << toNumStr(init)
             << " -D SeLength=" << SeLength;
-    if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-        options << " -D USE_DOUBLE";
+    options << getTypeBuildDefinition<T>();
+
     return options.str();
 }
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 795e08b3fc..bdf91b2c26 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -72,9 +72,7 @@ void all_distances(Param dist, Param query, Param train, const dim_t dist_dim) {
             default: break;
         }
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         if (use_lmem) options << " -D USE_LOCAL_MEM";
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index f19202027b..bbff55d9d6 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -98,9 +98,7 @@ std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*> getOrbKernels() {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D BLOCK_SIZE=" << ORB_THREADS_X;
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {orb_cl};
         const int ker_lens[]   = {orb_cl_len};
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index 97065eddc0..d40327bab8 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -46,8 +46,7 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding) {
                 << " -D AF_PAD_SYM=" << AF_PAD_SYM
                 << " -D AF_PAD_PERIODIC=" << AF_PAD_PERIODIC
                 << " -D AF_PAD_CLAMP_TO_EDGE=" << AF_PAD_CLAMP_TO_EDGE;
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {pad_array_borders_cl};
         const int ker_lens[]   = {pad_array_borders_cl_len};
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index ed1f922b38..f1cb1f7370 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -83,8 +83,7 @@ static cl::Kernel get_random_engine_kernel(const af_random_engine_type type,
         if (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) {
             options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
         }
-        if (std::is_same<T, double>::value) { options << " -D USE_DOUBLE"; }
-        if (std::is_same<T, common::half>::value) { options << " -D USE_HALF"; }
+        options << getTypeBuildDefinition<T>();
 #if defined(OS_MAC)  // Because apple is "special"
         options << " -D IS_APPLE"
                 << " -D log10_val=" << std::log(10.0);
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index c4f3dcd37b..d06223a9a4 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -45,10 +45,7 @@ void range(Param out, const int dim) {
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
-
-        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {range_cl};
         const int ker_lens[]   = {range_cl_len};
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 933a6390d5..d04cb651e2 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -64,14 +64,7 @@ void reduce_dim_launcher(Param out, Param in, const int dim,
                 << " -D THREADS_X=" << THREADS_X
                 << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
-
-        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
-            options << " -D USE_HALF";
-        }
+        options << getTypeBuildDefinition<Ti, To>();
 
         const char *ker_strs[] = {ops_cl, reduce_dim_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_dim_cl_len};
@@ -164,14 +157,7 @@ void reduce_first_launcher(Param out, Param in, const uint groups_x,
                 << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
                 << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
-
-        if (std::is_same<Ti, half>::value || std::is_same<To, half>::value) {
-            options << " -D USE_HALF";
-        }
+        options << getTypeBuildDefinition<Ti, To>();
 
         const char *ker_strs[] = {ops_cl, reduce_first_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_first_cl_len};
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 6cca0ac6b1..96b9d82a86 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -83,10 +83,7 @@ void launch_reduce_blocks_dim_by_key(cl::Buffer *reduced_block_sizes,
                 << " -D init=" << toNumStr(reduce.init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
 
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<Ti>();
 
         const char *ker_strs[] = {ops_cl, reduce_blocks_by_key_dim_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_blocks_by_key_dim_cl_len};
@@ -147,10 +144,7 @@ void launch_reduce_blocks_by_key(cl::Buffer *reduced_block_sizes,
                 << " -D init=" << toNumStr(reduce.init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
 
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<Ti>();
 
         const char *ker_strs[] = {ops_cl, reduce_blocks_by_key_first_cl};
         const int ker_lens[] = {ops_cl_len, reduce_blocks_by_key_first_cl_len};
@@ -207,10 +201,7 @@ void launch_final_boundary_reduce(cl::Buffer *reduced_block_sizes,
                 << " -D init=" << toNumStr(reduce.init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<To>();
 
-        if (std::is_same<To, double>::value ||
-            std::is_same<To, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<To>();
 
         const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_by_key_boundary_cl_len};
@@ -263,10 +254,7 @@ void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
                 << " -D init=" << toNumStr(reduce.init()) << " -D "
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<To>();
 
-        if (std::is_same<To, double>::value ||
-            std::is_same<To, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<To>();
 
         const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_dim_cl};
         const int ker_lens[] = {ops_cl_len, reduce_by_key_boundary_dim_cl_len};
@@ -314,10 +302,7 @@ void launch_compact(cl::Buffer *reduced_block_sizes, Param keys_out,
                 << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
                 << " -D DIMX=" << threads_x << " -D CPLX=" << af::iscplx<To>();
 
-        if (std::is_same<To, double>::value ||
-            std::is_same<To, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<To>();
 
         const char *ker_strs[] = {ops_cl, reduce_by_key_compact_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_by_key_compact_cl_len};
@@ -367,10 +352,7 @@ void launch_compact_dim(cl::Buffer *reduced_block_sizes, Param keys_out,
                 << " -D DIMX=" << threads_x << " -D DIM=" << dim
                 << " -D CPLX=" << af::iscplx<To>();
 
-        if (std::is_same<To, double>::value ||
-            std::is_same<To, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<To>();
 
         const char *ker_strs[] = {ops_cl, reduce_by_key_compact_dim_cl};
         const int ker_lens[]   = {ops_cl_len, reduce_by_key_compact_dim_cl_len};
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index d30800a615..6ab7449922 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -82,8 +82,7 @@ std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*> getRegionsKernels() {
                     << " -D N_PER_THREAD=" << n_per_thread
                     << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {regions_cl};
         const int ker_lens[]   = {regions_cl_len};
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index d7ef354238..517371c561 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -44,8 +44,7 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {reorder_cl};
         const int ker_lens[]   = {reorder_cl_len};
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index bc16d9ae18..b89221be45 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -62,9 +62,7 @@ void resize(Param out, const Param in) {
         } else {
             options << " -D CPLX=0";
         }
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {resize_cl};
         const int ker_lens[]   = {resize_cl_len};
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index bc11a35b25..20bf5546ab 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -70,8 +70,7 @@ void rotate(Param out, const Param in, const float theta,
         } else {
             options << " -D IS_CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         options << " -D INTERP_ORDER=" << order;
         addInterpEnumOptions(options);
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index ff80763e4b..4091e47147 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -60,10 +60,7 @@ static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool isFinalPass,
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
                 << " -D isFinalPass=" << (int)(isFinalPass)
                 << " -D inclusive_scan=" << inclusive_scan;
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<Ti>();
 
         const char *ker_strs[] = {ops_cl, scan_dim_cl};
         const int ker_lens[]   = {ops_cl_len, scan_dim_cl_len};
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 9a5a8f9fd7..953e2112ec 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -64,10 +64,7 @@ static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags,
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
                 << " -D calculateFlags=" << calculateFlags
                 << " -D inclusive_scan=" << inclusive_scan;
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<Ti>();
 
         const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
         const int ker_lens[]   = {ops_cl_len, scan_dim_by_key_cl_len};
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index a4e753aaac..f3f38a8121 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -65,10 +65,7 @@ static Kernel get_scan_first_kernels(int kerIdx, bool isFinalPass,
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
                 << " -D isFinalPass=" << (int)(isFinalPass)
                 << " -D inclusive_scan=" << inclusive_scan;
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<Ti>();
 
         const char *ker_strs[] = {ops_cl, scan_first_cl};
         const int ker_lens[]   = {ops_cl_len, scan_first_cl_len};
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 90bc212c24..f4962fe16d 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -66,10 +66,7 @@ static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags,
                 << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
                 << " -D calculateFlags=" << calculateFlags
                 << " -D inclusive_scan=" << inclusive_scan;
-        if (std::is_same<Ti, double>::value ||
-            std::is_same<Ti, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<Ti>();
 
         const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
         const int ker_lens[]   = {ops_cl_len, scan_first_by_key_cl_len};
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 019fb80ac7..2274fb5902 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -46,8 +46,7 @@ void select_launcher(Param out, Param cond, Param a, Param b, int ndims) {
         std::ostringstream options;
         options << " -D is_same=" << is_same
                 << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {select_cl};
         const int ker_lens[]   = {select_cl_len};
@@ -109,8 +108,7 @@ void select_scalar(Param out, Param cond, Param a, const double b, int ndims) {
         std::ostringstream options;
         options << " -D flip=" << flip
                 << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {select_cl};
         const int ker_lens[]   = {select_cl_len};
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index 17f3e064ee..ed8f8d6a84 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -438,8 +438,7 @@ std::array<cl::Kernel*, 7> getSiftKernels() {
     if (entries[0].prog == 0 && entries[0].ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         cl::Program prog;
         buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index b2a085b81c..6f4186c56b 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -44,7 +44,7 @@ void sobel(Param dx, Param dy, const Param in) {
         options << " -D Ti=" << dtype_traits<Ti>::getName()
                 << " -D To=" << dtype_traits<To>::getName()
                 << " -D KER_SIZE=" << ker_size;
-        if (std::is_same<Ti, double>::value) options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<Ti>();
 
         const char* ker_strs[] = {sobel_cl};
         const int ker_lens[]   = {sobel_cl_len};
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index dc9a5c2430..3854768027 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -52,10 +52,7 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D reps=" << REPEAT;
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         Program prog;
         buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
@@ -101,10 +98,7 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
         options << " -D THREADS=" << threads;
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {csr2dense_cl};
         const int ker_lens[]   = {csr2dense_cl_len};
@@ -159,9 +153,7 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
         if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
             options << " -D IS_CPLX=1";
         } else {
@@ -206,10 +198,7 @@ void swapIndex(Param ovalues, Param oindex, const Param ivalues,
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         Program prog;
         buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
@@ -247,10 +236,7 @@ void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {csr2coo_cl};
         const int ker_lens[]   = {csr2coo_cl_len};
@@ -308,10 +294,7 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         Program prog;
         buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 14936b99b2..5caadb558a 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -66,9 +66,7 @@ void sparseArithOpCSR(Param out, const Param values, const Param rowIdx,
         } else {
             options << " -D IS_CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_csr_cl};
         const int ker_lens[]   = {sparse_arith_common_cl_len,
@@ -119,9 +117,7 @@ void sparseArithOpCOO(Param out, const Param values, const Param rowIdx,
         } else {
             options << " -D IS_CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_coo_cl};
         const int ker_lens[]   = {sparse_arith_common_cl_len,
@@ -172,9 +168,7 @@ void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx, const Param rhs,
         } else {
             options << " -D IS_CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_csr_cl};
         const int ker_lens[]   = {sparse_arith_common_cl_len,
@@ -224,9 +218,7 @@ void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx, const Param rhs,
         } else {
             options << " -D IS_CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_coo_cl};
         const int ker_lens[]   = {sparse_arith_common_cl_len,
@@ -316,9 +308,7 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
                 << af::scalar_to_option(iden_val) << ")";
 
         options << " -D IS_CPLX=" << common::is_complex<T>::value;
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         const char *kerStrs[] = {sparse_arith_common_cl, sp_sp_arith_csr_cl};
         const int kerLens[]   = {sparse_arith_common_cl_len,
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 96105f1ca4..d2fa6032d7 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -51,8 +51,7 @@ void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
                 << " -D BLOCK_X=" << SUSAN_THREADS_X
                 << " -D BLOCK_Y=" << SUSAN_THREADS_Y << " -D RADIUS=" << radius
                 << " -D RESPONSE";
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {susan_cl};
         const int ker_lens[]   = {susan_cl_len};
@@ -91,8 +90,7 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName() << " -D NONMAX";
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {susan_cl};
         const int ker_lens[]   = {susan_cl_len};
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index b396423371..b6213b583a 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -42,8 +42,7 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {swapdblk_cl};
         const int ker_lens[]   = {swapdblk_cl_len};
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index d0e8467d26..c685973ca4 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -44,8 +44,7 @@ void tile(Param out, const Param in) {
     if (entry.prog == 0 && entry.ker == 0) {
         std::ostringstream options;
         options << " -D T=" << dtype_traits<T>::getName();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {tile_cl};
         const int ker_lens[]   = {tile_cl_len};
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index b42a94d446..0b81e0b5f9 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -72,9 +72,7 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
         } else {
             options << " -D IS_CPLX=0";
         }
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         options << " -D INTERP_ORDER=" << order;
         addInterpEnumOptions(options);
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 798bb87c99..e912b2d071 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -47,11 +47,7 @@ void transpose(Param out, const Param in, cl::CommandQueue queue) {
                 << " -D IS32MULTIPLE=" << IS32MULTIPLE
                 << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
                 << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
-
-        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {transpose_cl};
         const int ker_lens[]   = {transpose_cl_len};
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 761cd01335..800109a19f 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -48,9 +48,7 @@ void transpose_inplace(Param in, cl::CommandQueue& queue) {
                 << " -D IS32MULTIPLE=" << IS32MULTIPLE
                 << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
                 << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {transpose_inplace_cl};
         const int ker_lens[]   = {transpose_inplace_cl_len};
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index d0b05eb4b8..d11fff0371 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -53,10 +53,7 @@ void triangle(Param out, const Param in) {
                 << " -D is_unit_diag=" << is_unit_diag << " -D ZERO=(T)("
                 << scalar_to_option(scalar<T>(0)) << ")"
                 << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
-
-        if (std::is_same<T, common::half>::value) options << " -D USE_HALF";
+        options << getTypeBuildDefinition<T>();
 
         const char* ker_strs[] = {triangle_cl};
         const int ker_lens[]   = {triangle_cl_len};
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index d4d0ea96e1..ba1d602a49 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -52,10 +52,7 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
         options << " -D IS_COLUMN=" << is_column
                 << " -D ZERO=" << toNumStr(scalar<T>(0))
                 << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         Program prog;
         buildProgram(prog, unwrap_cl, unwrap_cl_len, options.str());
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 3ae2339d91..385a3604ff 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -47,8 +47,7 @@ static void get_out_idx(Buffer *out_data, Param &otmp, Param &rtmp, Param &in,
         options << " -D T=" << dtype_traits<T>::getName()
                 << " -D zero=" << toNumStr(scalar<T>(0))
                 << " -D CPLX=" << af::iscplx<T>();
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value)
-            options << " -D USE_DOUBLE";
+        options << getTypeBuildDefinition<T>();
 
         const char *ker_strs[] = {where_cl};
         const int ker_lens[]   = {where_cl_len};
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 2fe5f2baa8..34d9e2ec39 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -52,10 +52,8 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
         options << " -D is_column=" << is_column
                 << " -D ZERO=" << toNumStr(scalar<T>(0))
                 << " -D T=" << dtype_traits<T>::getName();
+        options << getTypeBuildDefinition<T>();
 
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
         Program prog;
         buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
 
@@ -107,10 +105,7 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
         options << " -D is_column=" << is_column
                 << " -D ZERO=" << toNumStr(scalar<T>(0))
                 << " -D T=" << dtype_traits<T>::getName();
-
-        if (std::is_same<T, double>::value || std::is_same<T, cdouble>::value) {
-            options << " -D USE_DOUBLE";
-        }
+        options << getTypeBuildDefinition<T>();
 
         Program prog;
         buildProgram(prog, wrap_dilated_cl, wrap_dilated_cl_len, options.str());
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 96aa2bd72d..8a3c9de00a 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -20,6 +20,8 @@
 #include <common/kernel_type.hpp>
 #include <common/traits.hpp>
 
+#include <algorithm>
+#include <array>
 #include <string>
 
 namespace common {
@@ -119,4 +121,32 @@ const char *getFullName() {
     return af::dtype_traits<T>::getName();
 }
 
+template<typename... ARGS>
+constexpr const char *getTypeBuildDefinition() {
+    using common::half;
+    using std::any_of;
+    using std::array;
+    using std::begin;
+    using std::end;
+    using std::is_same;
+    array<bool, sizeof...(ARGS)> is_half = {is_same<ARGS, half>::value...};
+    array<bool, sizeof...(ARGS) * 2> is_double = {
+        is_same<ARGS, double>::value..., is_same<ARGS, cdouble>::value...};
+
+    bool half_def =
+        any_of(begin(is_half), end(is_half), [](bool val) { return val; });
+    bool double_def =
+        any_of(begin(is_double), end(is_double), [](bool val) { return val; });
+
+    if (half_def && double_def) {
+        return " -D USE_HALF -D USE_DOUBLE";
+    } else if (half_def) {
+        return " -D USE_HALF";
+    } else if (double_def) {
+        return " -D USE_DOUBLE";
+    } else {
+        return "";
+    }
+}
+
 }  // namespace opencl

From 9a9f7e220dbbcf0af22638aecb97fea3f25dd0bb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 23 Apr 2020 03:31:38 -0400
Subject: [PATCH 1924/2677] Adjust JIT heuristics for the Intel GPU on the
 OpenCL backend

---
 src/backend/opencl/Array.cpp    | 16 +++++++++++++++-
 src/backend/opencl/binary.hpp   |  4 +---
 src/backend/opencl/platform.cpp | 11 +++++++++++
 src/backend/opencl/platform.hpp |  4 +---
 4 files changed, 28 insertions(+), 7 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index a01ac3071a..389ab47740 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -302,10 +302,16 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
         platform == AFCL_PLATFORM_NVIDIA || platform == AFCL_PLATFORM_APPLE;
     bool isAmd =
         platform == AFCL_PLATFORM_AMD || platform == AFCL_PLATFORM_APPLE;
+    bool isIntel = platform == AFCL_PLATFORM_INTEL;
+
+    /// Intels param_size limit is much smaller than the other platforms
+    /// so we need to start checking earlier with smaller trees
+    int heightCheckLimit =
+        isIntel && getDeviceType() == CL_DEVICE_TYPE_GPU ? 3 : 6;
 
     // A lightweight check based on the height of the node. This is
     // an inexpensive operation and does not traverse the JIT tree.
-    bool isParamLimit = (root_node->getHeight() > 6);
+    bool isParamLimit = (root_node->getHeight() >= heightCheckLimit);
     if (isParamLimit || isBufferLimit) {
         // This is the base parameter size if the kernel had no
         // arguments
@@ -317,11 +323,19 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
         constexpr size_t max_nvidia_param_size = (4096 - base_param_size);
         constexpr size_t max_amd_param_size    = (3520 - base_param_size);
 
+        // This value is really for the Intel HD Graphics platform. The CPU
+        // platform seems like it can handle unlimited parameters but the
+        // compile times become very large.
+        constexpr size_t max_intel_igpu_param_size =
+            (1024 - 256 - base_param_size);
+
         size_t max_param_size = 0;
         if (isNvidia) {
             max_param_size = max_nvidia_param_size;
         } else if (isAmd) {
             max_param_size = max_amd_param_size;
+        } else if (isIntel && getDeviceType() == CL_DEVICE_TYPE_GPU) {
+            max_param_size = max_intel_igpu_param_size;
         } else {
             max_param_size = 8192;
         }
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 6b6c9496b0..f26e408e3f 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -17,9 +17,7 @@
 namespace opencl {
 
 template<typename To, typename Ti, af_op_t op>
-struct BinOp {
-    const char *name() { return "__invalid"; }
-};
+struct BinOp;
 
 #define BINARY_TYPE_1(fn)                            \
     template<typename To, typename Ti>               \
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 1f02d15f4e..a985ce14ab 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -92,6 +92,16 @@ static inline string& ltrim(string& s) {
     return s;
 }
 
+bool verify_present(const std::string& pname, const std::string ref) {
+    auto iter = std::search(
+        begin(pname), end(pname), std::begin(ref), std::end(ref),
+        [](const std::string::value_type& l, const std::string::value_type& r) {
+            return tolower(l) == tolower(r);
+        });
+
+    return iter != end(pname);
+}
+
 static string platformMap(string& platStr) {
     using strmap_t                = map<string, string>;
     static const strmap_t platMap = {
@@ -99,6 +109,7 @@ static string platformMap(string& platStr) {
         make_pair("Intel(R) OpenCL", "INTEL"),
         make_pair("AMD Accelerated Parallel Processing", "AMD"),
         make_pair("Intel Gen OCL Driver", "BEIGNET"),
+        make_pair("Intel(R) OpenCL HD Graphics", "INTEL"),
         make_pair("Apple", "APPLE"),
         make_pair("Portable Computing Language", "POCL"),
     };
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index bb7d843fac..9bccbb428a 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -54,9 +54,7 @@ class GraphicsResourceManager;
 struct kc_entry_t;  // kernel cache entry
 class PlanCache;    // clfft
 
-static inline bool verify_present(std::string pname, const char* ref) {
-    return pname.find(ref) != std::string::npos;
-}
+bool verify_present(const std::string& pname, const std::string ref);
 
 int getBackend();
 

From e2ad39d68504582d5ebf575e1cc42c146aa43f9a Mon Sep 17 00:00:00 2001
From: Corentin Schreiber <54102755+cschreib-ibex@users.noreply.github.com>
Date: Fri, 24 Apr 2020 11:34:13 +0100
Subject: [PATCH 1925/2677] Auto cache compiled CUDA kernels on disk to speed
 up compilation (#2848)

* Adds CMake variable AF_CACHE_KERNELS_TO_DISK to enable kernel caching. It is turned ON by default.
* cuda::buildKernel() now dumps cubin to disk for reuse
* Adds cuda::loadKernel() for loading cached cubin files
* cuda::loadKernel() returns empty kernel on failure
* Uses XDG_CACHE_HOME as cache directory for Linux
* Adds common::deterministicHash() - This uses the FNV-1a hashing algorithm for fast and reproducible hashing of string or binary data. This is meant to replace the use of std::hash in some place, since std::hash does not guarantee its return value will be the same in subsequence executions of the program.
* Write cached kernel to temporary file before moving into final file. This prevents data races where two threads or two processes might write to the same file.
* Uses deterministicHash() for hashing kernel names and kernel binary data.
* Adds kernel binary data file integrity check upon loading from disk
---
 CMakeLists.txt                   |   5 ++
 src/backend/common/util.cpp      | 122 +++++++++++++++++++++++++++++++
 src/backend/common/util.hpp      |  31 ++++++++
 src/backend/cuda/jit.cpp         |  19 ++---
 src/backend/cuda/nvrtc/cache.cpp | 114 +++++++++++++++++++++++++++--
 src/backend/cuda/nvrtc/cache.hpp |   2 +
 6 files changed, 279 insertions(+), 14 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c518c818fd..2682dab9b3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -60,6 +60,7 @@ option(AF_BUILD_FORGE
 option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
 option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
+option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
 
 if(WIN32)
   set(AF_STACKTRACE_TYPE "Windbg" CACHE STRING "The type of backtrace features. Windbg(simple), None")
@@ -223,6 +224,10 @@ foreach(backend ${built_backends})
     target_compile_definitions(${backend}
       PRIVATE AF_WITH_LOGGING)
   endif()
+  if(AF_CACHE_KERNELS_TO_DISK)
+    target_compile_definitions(${backend}
+      PRIVATE AF_CACHE_KERNELS_TO_DISK)
+  endif()
 endforeach()
 
 if(AF_BUILD_FRAMEWORK)
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index b786839d11..555a3b3add 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -10,17 +10,28 @@
 /// This file contains platform independent utility functions
 #if defined(OS_WIN)
 #include <Windows.h>
+#else
+#include <pwd.h>
+#include <unistd.h>
 #endif
 
 #include <common/defines.hpp>
 #include <common/util.hpp>
 #include <af/defines.h>
 
+#include <sys/stat.h>
+#include <algorithm>
+#include <cstdio>
 #include <cstdlib>
 #include <cstring>
+#include <fstream>
+#include <numeric>
 #include <string>
+#include <thread>
+#include <vector>
 
 using std::string;
+using std::vector;
 
 string getEnvVar(const std::string& key) {
 #if defined(OS_WIN)
@@ -91,3 +102,114 @@ std::string int_version_to_string(int version) {
     return std::to_string(version / 1000) + "." +
            std::to_string((int)((version % 1000) / 10.));
 }
+
+#if defined(OS_WIN)
+string getTemporaryDirectory() {
+    DWORD bufSize = 261;  // limit according to GetTempPath documentation
+    string retVal;
+    retVal.resize(bufSize);
+    bufSize = GetTempPathA(bufSize, &retVal[0]);
+    retVal.resize(bufSize);
+    return retVal;
+}
+#else
+string getHomeDirectory() {
+    string home = getEnvVar("XDG_CACHE_HOME");
+    if (!home.empty()) return home;
+
+    home = getEnvVar("HOME");
+    if (!home.empty()) return home;
+
+    return getpwuid(getuid())->pw_dir;
+}
+#endif
+
+bool directoryExists(const string& path) {
+#if defined(OS_WIN)
+    struct _stat status;
+    return _stat(path.c_str(), &status) == 0 && (status.st_mode & S_IFDIR) != 0;
+#else
+    struct stat status;
+    return stat(path.c_str(), &status) == 0 && (status.st_mode & S_IFDIR) != 0;
+#endif
+}
+
+bool createDirectory(const string& path) {
+#if defined(OS_WIN)
+    return CreateDirectoryA(path.c_str(), NULL) != 0;
+#else
+    return mkdir(path.c_str(), 0777) == 0;
+#endif
+}
+
+bool removeFile(const string& path) {
+#if defined(OS_WIN)
+    return DeleteFileA(path.c_str()) != 0;
+#else
+    return unlink(path.c_str()) == 0;
+#endif
+}
+
+bool renameFile(const string& sourcePath, const string& destPath) {
+    return std::rename(sourcePath.c_str(), destPath.c_str()) == 0;
+}
+
+bool isDirectoryWritable(const string& path) {
+    if (!directoryExists(path) && !createDirectory(path)) return false;
+
+    const string testPath = path + AF_PATH_SEPARATOR + "test";
+    if (!std::ofstream(testPath).is_open()) return false;
+    removeFile(testPath);
+
+    return true;
+}
+
+const string& getCacheDirectory() {
+    static std::once_flag flag;
+    static string cacheDirectory;
+
+    std::call_once(flag, []() {
+        const vector<string> pathList = {
+#if defined(OS_WIN)
+            getTemporaryDirectory() + "\\ArrayFire"
+#else
+            getHomeDirectory() + "/.arrayfire",
+            "/tmp/arrayfire"
+#endif
+        };
+
+        auto iterDir =
+            std::find_if(pathList.begin(), pathList.end(), isDirectoryWritable);
+
+        cacheDirectory = iterDir != pathList.end() ? *iterDir : "";
+    });
+
+    return cacheDirectory;
+}
+
+string makeTempFilename() {
+    thread_local std::size_t fileCount = 0u;
+
+    ++fileCount;
+    const std::size_t threadID =
+        std::hash<std::thread::id>{}(std::this_thread::get_id());
+
+    return std::to_string(std::hash<string>{}(std::to_string(threadID) + "_" +
+                                              std::to_string(fileCount)));
+}
+
+std::size_t deterministicHash(const void* data, std::size_t byteSize) {
+    // Fowler-Noll-Vo "1a" 32 bit hash
+    // https://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function
+    constexpr std::size_t seed   = 0x811C9DC5;
+    constexpr std::size_t prime  = 0x01000193;
+    const std::uint8_t* byteData = static_cast<const std::uint8_t*>(data);
+    return std::accumulate(byteData, byteData + byteSize, seed,
+                           [&](std::size_t hash, std::uint8_t data) {
+                               return (hash ^ data) * prime;
+                           });
+}
+
+std::size_t deterministicHash(const std::string& data) {
+    return deterministicHash(data.data(), data.size());
+}
\ No newline at end of file
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 2df1ddd05a..5c4788315c 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -21,3 +21,34 @@ void saveKernel(const std::string& funcName, const std::string& jit_ker,
                 const std::string& ext);
 
 std::string int_version_to_string(int version);
+
+const std::string& getCacheDirectory();
+
+bool directoryExists(const std::string& path);
+
+bool createDirectory(const std::string& path);
+
+bool removeFile(const std::string& path);
+
+bool renameFile(const std::string& sourcePath, const std::string& destPath);
+
+bool isDirectoryWritable(const std::string& path);
+
+/// Return a string suitable for naming a temporary file.
+///
+/// Every call to this function will generate a new string with a very low
+/// probability of colliding with past or future outputs of this function,
+/// including calls from other threads or processes. The string contains
+/// no extension.
+std::string makeTempFilename();
+
+/// Return the FNV-1a hash of the provided bata.
+///
+/// \param[in] data Binary data to hash
+/// \param[in] byteSize Size of the data in bytes
+///
+/// \returns An unsigned integer representing the hash of the data
+std::size_t deterministicHash(const void* data, std::size_t byteSize);
+
+// This is just a wrapper around the above function.
+std::size_t deterministicHash(const std::string& data);
\ No newline at end of file
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 16542cf09e..7121401e50 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -23,7 +23,6 @@
 #include <af/dim4.hpp>
 
 #include <cstdio>
-#include <functional>
 #include <map>
 #include <stdexcept>
 #include <thread>
@@ -34,7 +33,6 @@ using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
 
-using std::hash;
 using std::map;
 using std::string;
 using std::stringstream;
@@ -62,10 +60,8 @@ static string getFuncName(const vector<Node *> &output_nodes,
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
-    hash<string> hash_fn;
-
     hashName << "KER";
-    hashName << hash_fn(funcName.str());
+    hashName << deterministicHash(funcName.str());
     return hashName.str();
 }
 
@@ -218,10 +214,15 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
     Kernel entry{nullptr, nullptr};
 
     if (idx == kernelCaches[device].end()) {
-        string jit_ker = getKernelString(funcName, full_nodes, full_ids,
-                                         output_ids, is_linear);
-        saveKernel(funcName, jit_ker, ".cu");
-        entry = buildKernel(device, funcName, jit_ker, {}, true);
+#ifdef AF_CACHE_KERNELS_TO_DISK
+        entry = loadKernel(device, funcName);
+#endif
+        if (entry.prog == nullptr || entry.ker == nullptr) {
+            string jit_ker = getKernelString(funcName, full_nodes, full_ids,
+                                             output_ids, is_linear);
+            saveKernel(funcName, jit_ker, ".cu");
+            entry = buildKernel(device, funcName, jit_ker, {}, true);
+        }
         kernelCaches[device][funcName] = entry;
     } else {
         entry = idx->second;
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index aec0590c25..93cda8a136 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -11,6 +11,7 @@
 
 #include <common/Logger.hpp>
 #include <common/internal_enums.hpp>
+#include <common/util.hpp>
 #include <device_manager.hpp>
 #include <kernel_headers/jit_cuh.hpp>
 #include <nvrtc_kernel_headers/Param_hpp.hpp>
@@ -43,6 +44,7 @@
 #include <algorithm>
 #include <array>
 #include <chrono>
+#include <fstream>
 #include <iterator>
 #include <map>
 #include <memory>
@@ -149,6 +151,17 @@ void Kernel::getScalar(T &out, const char *name) {
 template void Kernel::setScalar<int>(const char *, int);
 template void Kernel::getScalar<int>(int &, const char *);
 
+string getKernelCacheFilename(const int device, const string &nameExpr) {
+    const string mangledName = "KER" + to_string(deterministicHash(nameExpr));
+
+    const auto computeFlag = getComputeCapability(device);
+    const string computeVersion =
+        to_string(computeFlag.first) + to_string(computeFlag.second);
+
+    return mangledName + "_CU_" + computeVersion + "_AF_" +
+           to_string(AF_API_VERSION_CURRENT) + ".cubin";
+}
+
 Kernel buildKernel(const int device, const string &nameExpr,
                    const string &jit_ker, const vector<string> &opts,
                    const bool isJIT) {
@@ -313,6 +326,37 @@ Kernel buildKernel(const int device, const string &nameExpr,
     CU_CHECK(cuModuleGetFunction(&kernel, module, name));
     Kernel entry = {module, kernel};
 
+#ifdef AF_CACHE_KERNELS_TO_DISK
+    // save kernel in cache
+    const string &cacheDirectory = getCacheDirectory();
+    if (!cacheDirectory.empty()) {
+        const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
+                                 getKernelCacheFilename(device, nameExpr);
+        const string tempFile =
+            cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
+
+        // compute CUBIN hash
+        const size_t cubinHash = deterministicHash(cubin, cubinSize);
+
+        // write kernel function name and CUBIN binary data
+        std::ofstream out(tempFile, std::ios::binary);
+        const size_t nameSize = strlen(name);
+        out.write(reinterpret_cast<const char *>(&nameSize), sizeof(nameSize));
+        out.write(name, nameSize);
+        out.write(reinterpret_cast<const char *>(&cubinHash),
+                  sizeof(cubinHash));
+        out.write(reinterpret_cast<const char *>(&cubinSize),
+                  sizeof(cubinSize));
+        out.write(static_cast<const char *>(cubin), cubinSize);
+        out.close();
+
+        // try to rename temporary file into final cache file, if this fails
+        // this means another thread has finished compiling this kernel before
+        // the current thread.
+        if (!renameFile(tempFile, cacheFile)) { removeFile(tempFile); }
+    }
+#endif
+
     CU_LINK_CHECK(cuLinkDestroy(linkState));
     NVRTC_CHECK(nvrtcDestroyProgram(&prog));
 
@@ -334,21 +378,81 @@ Kernel buildKernel(const int device, const string &nameExpr,
     return entry;
 }
 
+Kernel loadKernel(const int device, const string &nameExpr) {
+    const string &cacheDirectory = getCacheDirectory();
+    if (cacheDirectory.empty()) return Kernel{nullptr, nullptr};
+
+    const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
+                             getKernelCacheFilename(device, nameExpr);
+
+    CUmodule module   = nullptr;
+    CUfunction kernel = nullptr;
+
+    try {
+        std::ifstream in(cacheFile, std::ios::binary);
+        if (!in.is_open()) return Kernel{nullptr, nullptr};
+
+        in.exceptions(std::ios::failbit | std::ios::badbit);
+
+        size_t nameSize = 0;
+        in.read(reinterpret_cast<char *>(&nameSize), sizeof(nameSize));
+        string name;
+        name.resize(nameSize);
+        in.read(&name[0], nameSize);
+
+        size_t cubinHash = 0;
+        in.read(reinterpret_cast<char *>(&cubinHash), sizeof(cubinHash));
+        size_t cubinSize = 0;
+        in.read(reinterpret_cast<char *>(&cubinSize), sizeof(cubinSize));
+        vector<char> cubin(cubinSize);
+        in.read(cubin.data(), cubinSize);
+        in.close();
+
+        // check CUBIN binary data has not been corrupted
+        const size_t recomputedHash =
+            deterministicHash(cubin.data(), cubinSize);
+        if (recomputedHash != cubinHash) {
+            AF_ERROR("cached kernel data is corrupted", AF_ERR_LOAD_SYM);
+        }
+
+        CU_CHECK(cuModuleLoadDataEx(&module, cubin.data(), 0, 0, 0));
+        CU_CHECK(cuModuleGetFunction(&kernel, module, name.c_str()));
+
+        AF_TRACE("{{{:<30} : loaded from {} for {} }}", nameExpr, cacheFile,
+                 getDeviceProp(device).name);
+
+        return Kernel{module, kernel};
+    } catch (...) {
+        if (module != nullptr) { CU_CHECK(cuModuleUnload(module)); }
+        removeFile(cacheFile);
+        return Kernel{nullptr, nullptr};
+    }
+}
+
 kc_t &getCache(int device) {
     thread_local kc_t caches[DeviceManager::MAX_DEVICES];
     return caches[device];
 }
 
+void addKernelToCache(int device, const string &nameExpr, Kernel entry) {
+    getCache(device).emplace(nameExpr, entry);
+}
+
 Kernel findKernel(int device, const string &nameExpr) {
     kc_t &cache = getCache(device);
 
     auto iter = cache.find(nameExpr);
+    if (iter != cache.end()) return iter->second;
 
-    return (iter == cache.end() ? Kernel{0, 0} : iter->second);
-}
+#ifdef AF_CACHE_KERNELS_TO_DISK
+    Kernel kernel = loadKernel(device, nameExpr);
+    if (kernel.prog != nullptr && kernel.ker != nullptr) {
+        addKernelToCache(device, nameExpr, kernel);
+        return kernel;
+    }
+#endif
 
-void addKernelToCache(int device, const string &nameExpr, Kernel entry) {
-    getCache(device).emplace(nameExpr, entry);
+    return Kernel{nullptr, nullptr};
 }
 
 string getOpEnumStr(af_op_t val) {
@@ -597,7 +701,7 @@ Kernel getKernel(const string &nameExpr, const string &source,
     int device    = getActiveDeviceId();
     Kernel kernel = findKernel(device, tInstance);
 
-    if (kernel.prog == 0 || kernel.ker == 0) {
+    if (kernel.prog == nullptr || kernel.ker == nullptr) {
         kernel = buildKernel(device, tInstance, source, compileOpts);
         addKernelToCache(device, tInstance, kernel);
     }
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index ebea991241..28163dac4f 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -109,6 +109,8 @@ Kernel buildKernel(const int device, const std::string& nameExpr,
                    const std::vector<std::string>& opts = {},
                    const bool isJIT                     = false);
 
+Kernel loadKernel(const int device, const std::string& nameExpr);
+
 template<typename T>
 std::string toString(T val);
 

From af2633b5addee622b3bbe16e81a7cdb4aa9b21ab Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 23 Apr 2020 22:03:37 +0530
Subject: [PATCH 1926/2677] Fix gfor third format type in gfor tutorial

---
 docs/pages/gfor.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/gfor.md b/docs/pages/gfor.md
index a7ed9a195d..e6886b5bb4 100644
--- a/docs/pages/gfor.md
+++ b/docs/pages/gfor.md
@@ -54,7 +54,7 @@ gfor (seq i, N)
 There are three formats for instantiating gfor-loops.
 -# gfor(var,n) Creates a sequence _{0, 1, ..., n-1}_
 -# gfor(var,first,last) Creates a sequence _{first, first+1, ..., last}_
--# gfor(var,first,incr,last) Creates a sequence _{first, first+inc, first+2*inc, ..., last}_
+-# gfor(var,first,last,incr) Creates a sequence _{first, first+inc, first+2*inc, ..., last}_
 
 So all of the following represent the equivalent sequence: _0,1,2,3,4_
 

From 6e85a40a04b85a4e3e7f8eed7c24374a9a1a2870 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 24 Apr 2020 17:27:35 +0530
Subject: [PATCH 1927/2677] Add tests for gfor-loop with non-unit step sequence

---
 test/gfor.cpp | 44 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/test/gfor.cpp b/test/gfor.cpp
index 70d6f0addd..b73d29fe5c 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -499,3 +499,47 @@ TEST(ASSIGN, ISSUE_1127) {
 
     ASSERT_ARRAYS_EQ(out0, out1);
 }
+
+TEST(GFOR, ArithLoopWithNonUnitIncrSeq) {
+    const int nx    = 10;
+    const int ny    = 10;
+    const int batch = 10;
+    const int start = 0;
+    const int end   = 8;
+    const int incr  = 2;
+
+    array A = randu(nx, ny, batch);
+    array B = randu(nx, ny);
+    array C = constant(0, nx, ny, batch);
+    array G = constant(0, nx, ny, batch);
+
+    for (int i = 0; i < batch; i += incr) {
+        G(span, span, i) = A(span, span, i) * B;
+    }
+    gfor(seq ii, start, end, incr) {
+        C(span, span, ii) = A(span, span, ii) * B;
+    }
+    ASSERT_ARRAYS_EQ(C, G);
+}
+
+TEST(GFOR, MatmulLoopWithNonUnitIncrSeq) {
+    const int nx    = 10;
+    const int ny    = 10;
+    const int batch = 10;
+    const int start = 0;
+    const int end   = 8;
+    const int incr  = 2;
+
+    array A = randu(nx, ny, batch);
+    array B = randu(nx, ny);
+    array C = constant(0, nx, ny, batch);
+    array G = constant(0, nx, ny, batch);
+
+    for (int i = 0; i < batch; i += incr) {
+        G(span, span, i) = matmul(A(span, span, i), B);
+    }
+    gfor(seq ii, start, end, incr) {
+        C(span, span, ii) = matmul(A(span, span, ii), B);
+    }
+    ASSERT_ARRAYS_NEAR(C, G, 1E-03);
+}

From 8ff13bb3a55047744566e8e272d331e6e7ab3e99 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 23 Apr 2020 11:59:45 +0530
Subject: [PATCH 1928/2677] Fix unused var warning by moving it to relevant
 build arm(#if)

---
 src/backend/cpu/blas.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index bd516c209e..6f59974a80 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -237,10 +237,12 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     using BT  = typename blas_base<T>::type;
     using CBT = const typename blas_base<T>::type;
 
-    auto alpha_        = scale_type<T, false>(alpha);
-    auto beta_         = scale_type<T, false>(beta);
+    auto alpha_ = scale_type<T, false>(alpha);
+    auto beta_  = scale_type<T, false>(beta);
+#ifdef USE_MKL
     auto alpha_batched = scale_type<T, true>(alpha);
     auto beta_batched  = scale_type<T, true>(beta);
+#endif
 
     auto func = [=](Param<T> output, CParam<T> left, CParam<T> right) {
         dim4 lStrides = left.strides();

From 4f5bee860bf264f3bc0cba9a757b995b8507371c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 27 Apr 2020 22:05:20 +0530
Subject: [PATCH 1929/2677] Add OpenCL show build log info to debugging docs
 page

---
 docs/pages/debugging.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/debugging.md b/docs/pages/debugging.md
index bf02679796..6712900f74 100644
--- a/docs/pages/debugging.md
+++ b/docs/pages/debugging.md
@@ -7,7 +7,7 @@ Using Environment Variables
  * [`AF_PRINT_ERRORS=1`](configuring_environment.htm#af_print_errors) : Makes exception's messages more helpful
  * [`AF_TRACE=all`](configuring_environment.htm#af_trace): Print ArrayFire message stream to console
  * [`AF_JIT_KERNEL_TRACE=stdout`](configuring_environment.htm#af_jit_kernel_trace): Writes out source code generated by ArrayFire's JIT to the specified target
-
+ * [`AF_OPENCL_SHOW_BUILD_INFO=1`](configuring_environment.htm#af_opencl_show_build_info): Print OpenCL kernel build log to console
 
 
 Tips in Language Bindings

From 6ede8c0975631cb42836b67763cfb21200d019f8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 27 Apr 2020 17:28:16 +0530
Subject: [PATCH 1930/2677] Clean packed dims calculation in cpu fftconvolve

Also made the following additional changes

- Refactored variable name to be consistent in cpu fftconvolve i.e. camelCase
- Fixed header inclusion as per convention used across library
- Removed unused header inclusions
---
 src/backend/cpu/fftconvolve.cpp    | 104 +++++++++++++----------------
 src/backend/cuda/fftconvolve.cpp   |   4 +-
 src/backend/opencl/fftconvolve.cpp |   3 +-
 3 files changed, 49 insertions(+), 62 deletions(-)

diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 28eb5584eb..191c806085 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -7,18 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <fftconvolve.hpp>
+
 #include <Array.hpp>
 #include <common/dispatch.hpp>
-#include <copy.hpp>
-#include <err_cpu.hpp>
-#include <fft.hpp>
 #include <fftw3.h>
 #include <kernel/fftconvolve.hpp>
-#include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
+#include <array>
+#include <cmath>
+
 using af::dim4;
+using std::array;
 using std::ceil;
 
 namespace cpu {
@@ -29,79 +31,64 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
     const dim4& sd = signal.dims();
     const dim4& fd = filter.dims();
-
     dim_t fftScale = 1;
 
-    dim4 packed_dims(1, 1, 1, 1);
-    int fft_dims[baseDim];
-    dim4 sig_tmp_dims, sig_tmp_strides;
-    dim4 filter_tmp_dims, filter_tmp_strides;
+    dim4 packedDims(1, 1, 1, 1);
+    array<int, baseDim> fftDims;
 
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched FFT capabilities
-    fft_dims[baseDim - 1] = nextpow2(
+    fftDims[baseDim - 1] = nextpow2(
         static_cast<unsigned>(static_cast<int>(ceil(sd[0] / 2.f)) + fd[0] - 1));
-    packed_dims[0] = 2 * fft_dims[baseDim - 1];
-    fftScale *= fft_dims[baseDim - 1];
+    packedDims[0] = 2 * fftDims[baseDim - 1];
+    fftScale *= fftDims[baseDim - 1];
 
     for (dim_t k = 1; k < baseDim; k++) {
-        packed_dims[k] = nextpow2(static_cast<unsigned>(sd[k] + fd[k] - 1));
-        fft_dims[baseDim - k - 1] = packed_dims[k];
-        fftScale *= fft_dims[baseDim - k - 1];
+        packedDims[k] = nextpow2(static_cast<unsigned>(sd[k] + fd[k] - 1));
+        fftDims[baseDim - k - 1] = packedDims[k];
+        fftScale *= fftDims[baseDim - k - 1];
     }
 
     dim_t sbatch = 1, fbatch = 1;
-    for (int k = baseDim; k < 4; k++) {
+    for (int k = baseDim; k < AF_MAX_DIMS; k++) {
         sbatch *= sd[k];
         fbatch *= fd[k];
     }
-    packed_dims[baseDim] = (sbatch + fbatch);
+    packedDims[baseDim] = (sbatch + fbatch);
 
-    Array<convT> packed = createEmptyArray<convT>(packed_dims);
+    Array<convT> packed = createEmptyArray<convT>(packedDims);
 
-    sig_tmp_dims[0] = filter_tmp_dims[0] = packed_dims[0];
-    sig_tmp_strides[0] = filter_tmp_strides[0] = 1;
-
-    for (dim_t k = 1; k < 4; k++) {
-        if (k < baseDim) {
-            sig_tmp_dims[k]    = packed_dims[k];
-            filter_tmp_dims[k] = packed_dims[k];
-        } else {
-            sig_tmp_dims[k]    = sd[k];
-            filter_tmp_dims[k] = fd[k];
-        }
-
-        sig_tmp_strides[k] = sig_tmp_strides[k - 1] * sig_tmp_dims[k - 1];
-        filter_tmp_strides[k] =
-            filter_tmp_strides[k - 1] * filter_tmp_dims[k - 1];
-    }
+    dim4 paddedSigDims(packedDims[0], (1 < baseDim ? packedDims[1] : sd[1]),
+                       (2 < baseDim ? packedDims[2] : sd[2]),
+                       (3 < baseDim ? packedDims[3] : sd[3]));
+    dim4 paddedFilDims(packedDims[0], (1 < baseDim ? packedDims[1] : fd[1]),
+                       (2 < baseDim ? packedDims[2] : fd[2]),
+                       (3 < baseDim ? packedDims[3] : fd[3]));
+    dim4 paddedSigStrides = calcStrides(paddedSigDims);
+    dim4 paddedFilStrides = calcStrides(paddedFilDims);
 
     // Number of packed complex elements in dimension 0
     dim_t sig_half_d0 = divup(sd[0], 2);
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    getQueue().enqueue(kernel::packData<convT, T>, packed, sig_tmp_dims,
-                       sig_tmp_strides, signal);
+    getQueue().enqueue(kernel::packData<convT, T>, packed, paddedSigDims,
+                       paddedSigStrides, signal);
 
     // Pad filter array with 0s
-    const dim_t offset = sig_tmp_strides[3] * sig_tmp_dims[3];
-    getQueue().enqueue(kernel::padArray<convT, T>, packed, filter_tmp_dims,
-                       filter_tmp_strides, filter, offset);
-
-    dim4 fftDims(1, 1, 1, 1);
-    for (int i = 0; i < baseDim; ++i) { fftDims[i] = fft_dims[i]; }
+    const dim_t offset = paddedSigStrides[3] * paddedSigDims[3];
+    getQueue().enqueue(kernel::padArray<convT, T>, packed, paddedFilDims,
+                       paddedFilStrides, filter, offset);
 
     // NOLINTNEXTLINE(performance-unnecessary-value-param)
-    auto upstream_dft = [=](Param<convT> packed, const dim4 fftDims) {
-        int fft_dims[baseDim];
-        for (int i = 0; i < baseDim; ++i) { fft_dims[i] = fftDims[i]; }
-        const dim4 packed_dims    = packed.dims();
+    auto upstream_dft = [=](Param<convT> packed,
+                            const array<int, baseDim> fftDims) {
+        const dim4 packedDims     = packed.dims();
         const dim4 packed_strides = packed.strides();
         // Compute forward FFT
         if (isDouble) {
             fftw_plan plan = fftw_plan_many_dft(
-                baseDim, fft_dims, packed_dims[baseDim],
+                baseDim, fftDims.data(), packedDims[baseDim],
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2,
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
@@ -112,7 +99,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
             fftw_destroy_plan(plan);
         } else {
             fftwf_plan plan = fftwf_plan_many_dft(
-                baseDim, fft_dims, packed_dims[baseDim],
+                baseDim, fftDims.data(), packedDims[baseDim],
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2,
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
@@ -126,20 +113,19 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     getQueue().enqueue(upstream_dft, packed, fftDims);
 
     // Multiply filter and signal FFT arrays
-    getQueue().enqueue(kernel::complexMultiply<convT>, packed, sig_tmp_dims,
-                       sig_tmp_strides, filter_tmp_dims, filter_tmp_strides,
-                       kind, offset);
+    getQueue().enqueue(kernel::complexMultiply<convT>, packed, paddedSigDims,
+                       paddedSigStrides, paddedFilDims, paddedFilStrides, kind,
+                       offset);
 
     // NOLINTNEXTLINE(performance-unnecessary-value-param)
-    auto upstream_idft = [=](Param<convT> packed, const dim4 fftDims) {
-        int fft_dims[baseDim];
-        for (int i = 0; i < baseDim; ++i) { fft_dims[i] = fftDims[i]; }
-        const dim4 packed_dims    = packed.dims();
+    auto upstream_idft = [=](Param<convT> packed,
+                             const array<int, baseDim> fftDims) {
+        const dim4 packedDims     = packed.dims();
         const dim4 packed_strides = packed.strides();
         // Compute inverse FFT
         if (isDouble) {
             fftw_plan plan = fftw_plan_many_dft(
-                baseDim, fft_dims, packed_dims[baseDim],
+                baseDim, fftDims.data(), packedDims[baseDim],
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2,
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
@@ -150,7 +136,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
             fftw_destroy_plan(plan);
         } else {
             fftwf_plan plan = fftwf_plan_many_dft(
-                baseDim, fft_dims, packed_dims[baseDim],
+                baseDim, fftDims.data(), packedDims[baseDim],
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
                 packed_strides[0], packed_strides[baseDim] / 2,
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
@@ -183,8 +169,8 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     Array<T> out = createEmptyArray<T>(oDims);
 
     getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out,
-                       packed, filter, sig_half_d0, fftScale, sig_tmp_dims,
-                       sig_tmp_strides, filter_tmp_dims, filter_tmp_strides,
+                       packed, filter, sig_half_d0, fftScale, paddedSigDims,
+                       paddedSigStrides, paddedFilDims, paddedFilStrides,
                        expand, kind);
 
     return out;
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index 3b6d38ce8a..8340c54757 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_cuda.hpp>
 #include <fftconvolve.hpp>
+
+#include <Array.hpp>
 #include <kernel/fftconvolve.hpp>
 #include <af/dim4.hpp>
 
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 01707e5099..cda5285064 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -7,10 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <fftconvolve.hpp>
+
 #include <Array.hpp>
 #include <err_opencl.hpp>
 #include <fft.hpp>
-#include <fftconvolve.hpp>
 #include <kernel/fftconvolve.hpp>
 #include <af/dim4.hpp>
 

From 34f0f42a32c679acf6e648b35bdba71e02ba89d2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 27 Apr 2020 22:01:03 +0530
Subject: [PATCH 1931/2677] Remove unnecessary template instantiations for
 fftconvolve

---
 src/api/c/fftconvolve.cpp                 | 78 +++++++++++++----------
 src/api/c/morph.cpp                       |  2 +-
 src/backend/cpu/fftconvolve.cpp           | 49 +++++++-------
 src/backend/cpu/fftconvolve.hpp           |  4 +-
 src/backend/cpu/kernel/fftconvolve.hpp    | 31 +++++----
 src/backend/cuda/fftconvolve.cpp          | 61 +++++++++---------
 src/backend/cuda/fftconvolve.hpp          |  4 +-
 src/backend/cuda/kernel/fftconvolve.hpp   |  8 ++-
 src/backend/opencl/fftconvolve.cpp        | 71 +++++++++++----------
 src/backend/opencl/fftconvolve.hpp        |  4 +-
 src/backend/opencl/kernel/fftconvolve.hpp | 73 ++++++++++++---------
 11 files changed, 206 insertions(+), 179 deletions(-)

diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 87dae06c5c..de756f6ff0 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
@@ -18,6 +19,10 @@
 #include <af/dim4.hpp>
 #include <af/signal.h>
 
+#include <algorithm>
+#include <type_traits>
+#include <vector>
+
 using af::dim4;
 using detail::arithOp;
 using detail::Array;
@@ -32,14 +37,23 @@ using detail::uchar;
 using detail::uint;
 using detail::uintl;
 using detail::ushort;
+using std::conditional;
+using std::is_integral;
+using std::is_same;
 using std::max;
 using std::swap;
 using std::vector;
 
-template<typename T, typename convT, typename cT, int baseDim>
+template<typename T, int baseDim>
 static inline af_array fftconvolve_fallback(const af_array signal,
                                             const af_array filter,
                                             bool expand) {
+    using convT =
+        typename conditional<is_integral<T>::value || is_same<T, float>::value,
+                             float, double>::type;
+    using cT = typename conditional<is_same<convT, float>::value, cfloat,
+                                    cdouble>::type;
+
     const Array<cT> S = castArray<cT>(signal);
     const Array<cT> F = castArray<cT>(filter);
     const dim4 &sdims = S.dims();
@@ -103,14 +117,13 @@ static inline af_array fftconvolve_fallback(const af_array signal,
     }
 }
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 inline static af_array fftconvolve(const af_array &s, const af_array &f,
                                    const bool expand, AF_BATCH_KIND kind) {
     if (kind == AF_BATCH_DIFF) {
-        return fftconvolve_fallback<T, convT, cT, baseDim>(s, f, expand);
+        return fftconvolve_fallback<T, baseDim>(s, f, expand);
     } else {
-        return getHandle(fftconvolve<T, convT, cT, isDouble, roundOut, baseDim>(
+        return getHandle(fftconvolve<T, baseDim>(
             getArray<T>(s), castArray<T>(f), expand, kind));
     }
 }
@@ -149,73 +162,68 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
         const ArrayInfo &sInfo = getInfo(signal);
         const ArrayInfo &fInfo = getInfo(filter);
 
-        af_dtype stype = sInfo.getType();
+        af_dtype signalType = sInfo.getType();
+        af_dtype filterType = fInfo.getType();
 
         const dim4 &sdims = sInfo.dims();
         const dim4 &fdims = fInfo.dims();
 
         AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
 
+        ARG_ASSERT(1, (signalType == filterType));
         ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED));
 
         af_array output;
-        switch (stype) {
+        switch (signalType) {
             case f64:
-                output =
-                    fftconvolve<double, double, cdouble, true, false, baseDim>(
-                        signal, filter, expand, convBT);
+                output = fftconvolve<double, baseDim>(signal, filter, expand,
+                                                      convBT);
                 break;
             case f32:
                 output =
-                    fftconvolve<float, float, cfloat, false, false, baseDim>(
-                        signal, filter, expand, convBT);
+                    fftconvolve<float, baseDim>(signal, filter, expand, convBT);
                 break;
             case u32:
-                output = fftconvolve<uint, float, cfloat, false, true, baseDim>(
-                    signal, filter, expand, convBT);
+                output =
+                    fftconvolve<uint, baseDim>(signal, filter, expand, convBT);
                 break;
             case s32:
-                output = fftconvolve<int, float, cfloat, false, true, baseDim>(
-                    signal, filter, expand, convBT);
+                output =
+                    fftconvolve<int, baseDim>(signal, filter, expand, convBT);
                 break;
             case u64:
                 output =
-                    fftconvolve<uintl, float, cfloat, false, true, baseDim>(
-                        signal, filter, expand, convBT);
+                    fftconvolve<uintl, baseDim>(signal, filter, expand, convBT);
                 break;
             case s64:
-                output = fftconvolve<intl, float, cfloat, false, true, baseDim>(
-                    signal, filter, expand, convBT);
+                output =
+                    fftconvolve<intl, baseDim>(signal, filter, expand, convBT);
                 break;
             case u16:
-                output =
-                    fftconvolve<ushort, float, cfloat, false, true, baseDim>(
-                        signal, filter, expand, convBT);
+                output = fftconvolve<ushort, baseDim>(signal, filter, expand,
+                                                      convBT);
                 break;
             case s16:
                 output =
-                    fftconvolve<short, float, cfloat, false, true, baseDim>(
-                        signal, filter, expand, convBT);
+                    fftconvolve<short, baseDim>(signal, filter, expand, convBT);
                 break;
             case u8:
                 output =
-                    fftconvolve<uchar, float, cfloat, false, true, baseDim>(
-                        signal, filter, expand, convBT);
+                    fftconvolve<uchar, baseDim>(signal, filter, expand, convBT);
                 break;
             case b8:
-                output = fftconvolve<char, float, cfloat, false, true, baseDim>(
-                    signal, filter, expand, convBT);
+                output =
+                    fftconvolve<char, baseDim>(signal, filter, expand, convBT);
                 break;
             case c32:
-                output = fftconvolve_fallback<cfloat, cfloat, cfloat, baseDim>(
-                    signal, filter, expand);
+                output = fftconvolve_fallback<cfloat, baseDim>(signal, filter,
+                                                               expand);
                 break;
             case c64:
-                output =
-                    fftconvolve_fallback<cdouble, cdouble, cdouble, baseDim>(
-                        signal, filter, expand);
+                output = fftconvolve_fallback<cdouble, baseDim>(signal, filter,
+                                                                expand);
                 break;
-            default: TYPE_ERROR(1, stype);
+            default: TYPE_ERROR(1, signalType);
         }
         swap(*out, output);
     }
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 9a09f910a5..771f0d651a 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -80,7 +80,7 @@ static inline af_array morph(const af_array &input, const af_array &mask) {
                          static_cast<dim_t>(seDims[1] % 2 == 0), 0, 0},
                         {0, 0, 0, 0}, AF_PAD_ZERO);
 
-    auto fftConv = fftconvolve<float, float, cfloat, false, false, 2>;
+    auto fftConv = fftconvolve<float, 2>;
 
     if (isDilation) {
         Array<float> dft =
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 191c806085..aa22112987 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -18,6 +18,7 @@
 
 #include <array>
 #include <cmath>
+#include <type_traits>
 
 using af::dim4;
 using std::array;
@@ -25,10 +26,15 @@ using std::ceil;
 
 namespace cpu {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
+    using convT = typename std::conditional<std::is_integral<T>::value ||
+                                                std::is_same<T, float>::value,
+                                            float, double>::type;
+
+    constexpr bool IsTypeDouble = std::is_same<T, double>::value;
+
     const dim4& sd = signal.dims();
     const dim4& fd = filter.dims();
     dim_t fftScale = 1;
@@ -86,7 +92,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         const dim4 packedDims     = packed.dims();
         const dim4 packed_strides = packed.strides();
         // Compute forward FFT
-        if (isDouble) {
+        if (IsTypeDouble) {
             fftw_plan plan = fftw_plan_many_dft(
                 baseDim, fftDims.data(), packedDims[baseDim],
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
@@ -123,7 +129,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         const dim4 packedDims     = packed.dims();
         const dim4 packed_strides = packed.strides();
         // Compute inverse FFT
-        if (isDouble) {
+        if (IsTypeDouble) {
             fftw_plan plan = fftw_plan_many_dft(
                 baseDim, fftDims.data(), packedDims[baseDim],
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
@@ -168,34 +174,33 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::reorder<T, convT, roundOut, baseDim>, out,
-                       packed, filter, sig_half_d0, fftScale, paddedSigDims,
-                       paddedSigStrides, paddedFilDims, paddedFilStrides,
-                       expand, kind);
+    getQueue().enqueue(kernel::reorder<T, convT, baseDim>, out, packed, filter,
+                       sig_half_d0, fftScale, paddedSigDims, paddedSigStrides,
+                       paddedFilDims, paddedFilStrides, expand, kind);
 
     return out;
 }
 
-#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                      \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 1>(    \
+#define INSTANTIATE(T)                                                     \
+    template Array<T> fftconvolve<T, 1>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 2>(    \
+    template Array<T> fftconvolve<T, 2>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 3>(    \
+    template Array<T> fftconvolve<T, 3>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);
 
-INSTANTIATE(double, double, cdouble, true, false)
-INSTANTIATE(float, float, cfloat, false, false)
-INSTANTIATE(uint, float, cfloat, false, true)
-INSTANTIATE(int, float, cfloat, false, true)
-INSTANTIATE(uchar, float, cfloat, false, true)
-INSTANTIATE(char, float, cfloat, false, true)
-INSTANTIATE(uintl, float, cfloat, false, true)
-INSTANTIATE(intl, float, cfloat, false, true)
-INSTANTIATE(ushort, float, cfloat, false, true)
-INSTANTIATE(short, float, cfloat, false, true)
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(int)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(uintl)
+INSTANTIATE(intl)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/fftconvolve.hpp b/src/backend/cpu/fftconvolve.hpp
index 671e27ac6b..196dec427a 100644
--- a/src/backend/cpu/fftconvolve.hpp
+++ b/src/backend/cpu/fftconvolve.hpp
@@ -11,9 +11,7 @@
 
 namespace cpu {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind);
-
 }
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 78205869c7..951ce33641 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -156,11 +156,13 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims,
     }
 }
 
-template<typename To, typename Ti, bool roundOut>
+template<typename To, typename Ti>
 void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                    const Ti* in_ptr, const af::dim4& id, const af::dim4& is,
                    const af::dim4& fd, const int half_di0, const int baseDim,
                    const int fftScale, const bool expand) {
+    constexpr bool RoundResult = std::is_integral<To>::value;
+
     UNUSED(id);
     for (int d3 = 0; d3 < (int)od[3]; d3++) {
         for (int d2 = 0; d2 < (int)od[2]; d2++) {
@@ -187,7 +189,7 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                     if (id0 < half_di0) {
                         // Copy top elements
                         int iidx = id3 + id2 + id1 + id0 * 2;
-                        if (roundOut)
+                        if (RoundResult)
                             out_ptr[oidx] =
                                 (To)roundf((float)(in_ptr[iidx] / fftScale));
                         else
@@ -196,7 +198,7 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                         // Add signal and filter elements to central part
                         int iidx1 = id3 + id2 + id1 + id0 * 2;
                         int iidx2 = id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
-                        if (roundOut)
+                        if (RoundResult)
                             out_ptr[oidx] = (To)roundf(
                                 (float)((in_ptr[iidx1] + in_ptr[iidx2]) /
                                         fftScale));
@@ -207,7 +209,7 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                         // Copy bottom elements
                         const int iidx =
                             id3 + id2 + id1 + (id0 - half_di0) * 2 + 1;
-                        if (roundOut)
+                        if (RoundResult)
                             out_ptr[oidx] =
                                 (To)roundf((float)(in_ptr[iidx] / fftScale));
                         else
@@ -219,12 +221,16 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
     }
 }
 
-template<typename T, typename convT, bool roundOut, int baseDim>
+template<typename T, typename convT, int baseDim>
 void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
              const dim_t sig_half_d0, const dim_t fftScale,
              const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
              const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
              bool expand, AF_BATCH_KIND kind) {
+    // TODO(pradeep) check if we can avoid convT template parameter also
+    // using convT = typename std::conditional<std::is_integral<T>::value,
+    // float, double>::type;
+
     T* out_ptr                 = out.get();
     const af::dim4 out_dims    = out.dims();
     const af::dim4 out_strides = out.strides();
@@ -237,15 +243,14 @@ void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
 
     // Reorder the output
     if (kind == AF_BATCH_RHS) {
-        reorderHelper<T, convT, roundOut>(
-            out_ptr, out_dims, out_strides, filter_tmp_ptr, filter_tmp_dims,
-            filter_tmp_strides, filter_dims, sig_half_d0, baseDim, fftScale,
-            expand);
+        reorderHelper<T, convT>(out_ptr, out_dims, out_strides, filter_tmp_ptr,
+                                filter_tmp_dims, filter_tmp_strides,
+                                filter_dims, sig_half_d0, baseDim, fftScale,
+                                expand);
     } else {
-        reorderHelper<T, convT, roundOut>(
-            out_ptr, out_dims, out_strides, sig_tmp_ptr, sig_tmp_dims,
-            sig_tmp_strides, filter_dims, sig_half_d0, baseDim, fftScale,
-            expand);
+        reorderHelper<T, convT>(out_ptr, out_dims, out_strides, sig_tmp_ptr,
+                                sig_tmp_dims, sig_tmp_strides, filter_dims,
+                                sig_half_d0, baseDim, fftScale, expand);
     }
 }
 
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index 8340c54757..8316ab26c3 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -10,12 +10,16 @@
 #include <fftconvolve.hpp>
 
 #include <Array.hpp>
+#include <fft.hpp>
 #include <kernel/fftconvolve.hpp>
 #include <af/dim4.hpp>
 
-#include <fft.hpp>
+#include <type_traits>
 
 using af::dim4;
+using std::conditional;
+using std::is_integral;
+using std::is_same;
 
 namespace cuda {
 
@@ -43,10 +47,15 @@ dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
     return dim4(pd[0], pd[1], pd[2], pd[3]);
 }
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
+    using convT =
+        typename conditional<is_integral<T>::value || is_same<T, float>::value,
+                             float, double>::type;
+    using cT = typename conditional<is_same<convT, float>::value, cfloat,
+                                    cdouble>::type;
+
     const dim4& sDims = signal.dims();
     const dim4& fDims = filter.dims();
 
@@ -82,47 +91,37 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     if (kind == AF_BATCH_RHS) {
         fft_inplace<cT, baseDim, false>(filter_packed);
-        if (expand) {
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true>(
-                out, filter_packed, signal, filter);
-        } else {
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(
-                out, filter_packed, signal, filter);
-        }
+        kernel::reorderOutputHelper<T, cT>(out, filter_packed, signal, filter,
+                                           expand, baseDim);
     } else {
         fft_inplace<cT, baseDim, false>(signal_packed);
-        if (expand) {
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, true>(
-                out, signal_packed, signal, filter);
-        } else {
-            kernel::reorderOutputHelper<T, cT, roundOut, baseDim, false>(
-                out, signal_packed, signal, filter);
-        }
+        kernel::reorderOutputHelper<T, cT>(out, signal_packed, signal, filter,
+                                           expand, baseDim);
     }
 
     return out;
 }
 
-#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                      \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 1>(    \
+#define INSTANTIATE(T)                                                     \
+    template Array<T> fftconvolve<T, 1>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 2>(    \
+    template Array<T> fftconvolve<T, 2>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 3>(    \
+    template Array<T> fftconvolve<T, 3>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);
 
-INSTANTIATE(double, double, cdouble, true, false)
-INSTANTIATE(float, float, cfloat, false, false)
-INSTANTIATE(uint, float, cfloat, false, true)
-INSTANTIATE(int, float, cfloat, false, true)
-INSTANTIATE(uchar, float, cfloat, false, true)
-INSTANTIATE(char, float, cfloat, false, true)
-INSTANTIATE(ushort, float, cfloat, false, true)
-INSTANTIATE(short, float, cfloat, false, true)
-INSTANTIATE(uintl, float, cfloat, false, true)
-INSTANTIATE(intl, float, cfloat, false, true)
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(int)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(uintl)
+INSTANTIATE(intl)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/fftconvolve.hpp b/src/backend/cuda/fftconvolve.hpp
index 86748ea16a..04df117831 100644
--- a/src/backend/cuda/fftconvolve.hpp
+++ b/src/backend/cuda/fftconvolve.hpp
@@ -11,9 +11,7 @@
 
 namespace cuda {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind);
-
 }
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 52fe80cb4d..eb147a5f64 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -101,13 +101,15 @@ void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
     POST_LAUNCH_CHECK();
 }
 
-template<typename T, typename convT, bool roundOut, int baseDim, bool expand>
+template<typename T, typename convT>
 void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
-                         CParam<T> filter) {
+                         CParam<T> filter, bool expand, int baseDim) {
+    constexpr bool RoundResult = std::is_integral<T>::value;
+
     auto reorderOut =
         getKernel("cuda::reorderOutput", fftConvSource(),
                   {TemplateTypename<T>(), TemplateTypename<convT>(),
-                   TemplateArg(expand), TemplateArg(roundOut)});
+                   TemplateArg(expand), TemplateArg(RoundResult)});
 
     dim_t *sd    = sig.dims;
     int fftScale = 1;
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index cda5285064..2d090a0b0e 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -10,12 +10,20 @@
 #include <fftconvolve.hpp>
 
 #include <Array.hpp>
-#include <err_opencl.hpp>
 #include <fft.hpp>
 #include <kernel/fftconvolve.hpp>
 #include <af/dim4.hpp>
 
+#include <cmath>
+#include <type_traits>
+#include <vector>
+
 using af::dim4;
+using std::ceil;
+using std::conditional;
+using std::is_integral;
+using std::is_same;
+using std::vector;
 
 namespace opencl {
 
@@ -30,7 +38,7 @@ static dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched cuFFT capabilities
     pd[0] = nextpow2(static_cast<unsigned>(
-        static_cast<int>(std::ceil(i1d[0] / 2.f)) + i2d[0] - 1));
+        static_cast<int>(ceil(i1d[0] / 2.f)) + i2d[0] - 1));
 
     for (dim_t k = 1; k < baseDim; k++) {
         pd[k] = nextpow2(static_cast<unsigned>(i1d[k] + i2d[k] - 1));
@@ -47,10 +55,15 @@ static dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
     return dim4(pd[0], pd[1], pd[2], pd[3]);
 }
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind) {
+    using convT =
+        typename conditional<is_integral<T>::value || is_same<T, float>::value,
+                             float, double>::type;
+    using cT = typename conditional<is_same<convT, float>::value, cfloat,
+                                    cdouble>::type;
+
     const dim4& sDims = signal.dims();
     const dim4& fDims = filter.dims();
 
@@ -73,17 +86,13 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     const dim4 pDims = calcPackedSize<T>(signal, filter, baseDim);
     Array<cT> packed = createEmptyArray<cT>(pDims);
 
-    kernel::packDataHelper<cT, T, isDouble, convT>(packed, signal, filter,
-                                                   baseDim, kind);
-
+    kernel::packDataHelper<cT, T>(packed, signal, filter, baseDim, kind);
     fft_inplace<cT, baseDim, true>(packed);
-
-    kernel::complexMultiplyHelper<cT, T, isDouble, convT>(
-        packed, signal, filter, baseDim, kind);
+    kernel::complexMultiplyHelper<cT, T>(packed, signal, filter, baseDim, kind);
 
     // Compute inverse FFT only on complex-multiplied data
     if (kind == AF_BATCH_RHS) {
-        std::vector<af_seq> seqs;
+        vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim) {
                 seqs.push_back({0., static_cast<double>(pDims[k] - 1), 1.});
@@ -97,7 +106,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
         fft_inplace<cT, baseDim, false>(subPacked);
     } else {
-        std::vector<af_seq> seqs;
+        vector<af_seq> seqs;
         for (dim_t k = 0; k < 4; k++) {
             if (k < baseDim) {
                 seqs.push_back({0., static_cast<double>(pDims[k]) - 1, 1.});
@@ -114,37 +123,31 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    if (expand) {
-        kernel::reorderOutputHelper<T, cT, isDouble, roundOut, true, convT>(
-            out, packed, signal, filter, baseDim, kind);
-    } else {
-        kernel::reorderOutputHelper<T, cT, isDouble, roundOut, false, convT>(
-            out, packed, signal, filter, baseDim, kind);
-    }
-
+    kernel::reorderOutputHelper<T, cT>(out, packed, signal, filter, baseDim,
+                                       kind, expand);
     return out;
 }
 
-#define INSTANTIATE(T, convT, cT, isDouble, roundOut)                      \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 1>(    \
+#define INSTANTIATE(T)                                                     \
+    template Array<T> fftconvolve<T, 1>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 2>(    \
+    template Array<T> fftconvolve<T, 2>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, convT, cT, isDouble, roundOut, 3>(    \
+    template Array<T> fftconvolve<T, 3>(                                   \
         Array<T> const& signal, Array<T> const& filter, const bool expand, \
         AF_BATCH_KIND kind);
 
-INSTANTIATE(double, double, cdouble, true, false)
-INSTANTIATE(float, float, cfloat, false, false)
-INSTANTIATE(uint, float, cfloat, false, true)
-INSTANTIATE(int, float, cfloat, false, true)
-INSTANTIATE(uchar, float, cfloat, false, true)
-INSTANTIATE(char, float, cfloat, false, true)
-INSTANTIATE(ushort, float, cfloat, false, true)
-INSTANTIATE(short, float, cfloat, false, true)
-INSTANTIATE(uintl, float, cfloat, false, true)
-INSTANTIATE(intl, float, cfloat, false, true)
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(int)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(uintl)
+INSTANTIATE(intl)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/fftconvolve.hpp b/src/backend/opencl/fftconvolve.hpp
index ca3d9defa0..0267ad6e85 100644
--- a/src/backend/opencl/fftconvolve.hpp
+++ b/src/backend/opencl/fftconvolve.hpp
@@ -11,9 +11,7 @@
 
 namespace opencl {
 
-template<typename T, typename convT, typename cT, bool isDouble, bool roundOut,
-         dim_t baseDim>
+template<typename T, dim_t baseDim>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind);
-
 }
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 7494fc92dd..535ee7c4cc 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <backend.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
@@ -17,8 +19,11 @@
 #include <kernel_headers/fftconvolve_reorder.hpp>
 #include <memory.hpp>
 #include <program.hpp>
+#include <types.hpp>
 #include <af/defines.h>
 
+#include <type_traits>
+
 using cl::Buffer;
 using cl::EnqueueArgs;
 using cl::Kernel;
@@ -67,13 +72,15 @@ void calcParamSizes(Param& sig_tmp, Param& filter_tmp, Param& packed,
     }
 }
 
-template<typename convT, typename T, bool isDouble, typename printT>
+template<typename convT, typename T>
 void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
                     AF_BATCH_KIND kind) {
+    constexpr bool IsTypeDouble = std::is_same<T, double>::value;
+
     std::string refName = std::string("pack_data_") +
                           std::string(dtype_traits<convT>::getName()) +
                           std::string(dtype_traits<T>::getName()) +
-                          std::to_string(isDouble);
+                          std::to_string(IsTypeDouble);
 
     int device          = getActiveDeviceId();
     kc_entry_t pdkEntry = kernelCache(device, refName);
@@ -82,13 +89,13 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
+        options << getTypeBuildDefinition<T, convT>();
 
-        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
+        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
+        if (ctDType == c32) {
             options << " -D CONVT=float";
-        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
-                   isDouble) {
-            options << " -D CONVT=double"
-                    << " -D USE_DOUBLE";
+        } else if (ctDType == c64 && IsTypeDouble) {
+            options << " -D CONVT=double";
         }
 
         const char* ker_strs[] = {fftconvolve_pack_cl};
@@ -132,7 +139,7 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
     refName = std::string("pack_array_") +
               std::string(dtype_traits<convT>::getName()) +
               std::string(dtype_traits<T>::getName()) +
-              std::to_string(isDouble);
+              std::to_string(IsTypeDouble);
 
     kc_entry_t pakEntry = kernelCache(device, refName);
 
@@ -140,13 +147,13 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName();
+        options << getTypeBuildDefinition<T, convT>();
 
-        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
+        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
+        if (ctDType == c32) {
             options << " -D CONVT=float";
-        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
-                   isDouble) {
-            options << " -D CONVT=double"
-                    << " -D USE_DOUBLE";
+        } else if (ctDType == c64 && IsTypeDouble) {
+            options << " -D CONVT=double";
         }
 
         const char* ker_strs[] = {fftconvolve_pack_cl};
@@ -171,13 +178,15 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename convT, typename T, bool isDouble, typename printT>
+template<typename convT, typename T>
 void complexMultiplyHelper(Param packed, Param sig, Param filter,
                            const int baseDim, AF_BATCH_KIND kind) {
+    constexpr bool IsTypeDouble = std::is_same<T, double>::value;
+
     std::string refName = std::string("complex_multiply_") +
                           std::string(dtype_traits<convT>::getName()) +
                           std::string(dtype_traits<T>::getName()) +
-                          std::to_string(isDouble);
+                          std::to_string(IsTypeDouble);
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
@@ -190,13 +199,13 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
                 << " -D AF_BATCH_LHS=" << (int)AF_BATCH_LHS
                 << " -D AF_BATCH_RHS=" << (int)AF_BATCH_RHS
                 << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
+        options << getTypeBuildDefinition<T, convT>();
 
-        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
+        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
+        if (ctDType == c32) {
             options << " -D CONVT=float";
-        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
-                   isDouble) {
-            options << " -D CONVT=double"
-                    << " -D USE_DOUBLE";
+        } else if (ctDType == c64 && IsTypeDouble) {
+            options << " -D CONVT=double";
         }
 
         const char* ker_strs[] = {fftconvolve_multiply_cl};
@@ -234,15 +243,17 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename T, typename convT, bool isDouble, bool roundOut, bool expand,
-         typename printT>
+template<typename T, typename convT>
 void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
-                         const int baseDim, AF_BATCH_KIND kind) {
+                         const int baseDim, AF_BATCH_KIND kind, bool expand) {
+    constexpr bool IsTypeDouble = std::is_same<T, double>::value;
+    constexpr bool RoundResult  = std::is_integral<T>::value;
+
     std::string refName = std::string("reorder_output_") +
                           std::string(dtype_traits<T>::getName()) +
                           std::string(dtype_traits<convT>::getName()) +
-                          std::to_string(isDouble) + std::to_string(roundOut) +
-                          std::to_string(expand);
+                          std::to_string(IsTypeDouble) +
+                          std::to_string(RoundResult) + std::to_string(expand);
 
     int device       = getActiveDeviceId();
     kc_entry_t entry = kernelCache(device, refName);
@@ -251,15 +262,15 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
         std::ostringstream options;
 
         options << " -D T=" << dtype_traits<T>::getName()
-                << " -D ROUND_OUT=" << (int)roundOut
+                << " -D ROUND_OUT=" << (int)RoundResult
                 << " -D EXPAND=" << (int)expand;
+        options << getTypeBuildDefinition<T, convT>();
 
-        if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c32) {
+        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
+        if (ctDType == c32) {
             options << " -D CONVT=float";
-        } else if (static_cast<af_dtype>(dtype_traits<convT>::af_type) == c64 &&
-                   isDouble) {
-            options << " -D CONVT=double"
-                    << " -D USE_DOUBLE";
+        } else if (ctDType == c64 && IsTypeDouble) {
+            options << " -D CONVT=double";
         }
 
         const char* ker_strs[] = {fftconvolve_reorder_cl};

From fd01d59dbfe81a950f4cd991a0bb6efc7dff6c80 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 26 Apr 2020 21:52:31 -0400
Subject: [PATCH 1932/2677] Create a test library to speed up test compilation

---
 test/CMakeLists.txt           |   54 +-
 test/approx1.cpp              |    9 +
 test/approx2.cpp              |    6 +
 test/array.cpp                |    1 -
 test/arrayfire_test.cpp       | 1522 +++++++++++++++++++++++++++++++++
 test/arrayio.cpp              |    1 -
 test/binary.cpp               |    3 +-
 test/blas.cpp                 |    1 -
 test/cast.cpp                 |    2 +
 test/clamp.cpp                |    4 +-
 test/compare.cpp              |    1 +
 test/complex.cpp              |    2 +
 test/confidence_connected.cpp |    1 -
 test/constant.cpp             |    2 +
 test/convolve.cpp             |    1 -
 test/dot.cpp                  |    1 -
 test/fft.cpp                  |    1 -
 test/flat.cpp                 |    2 +
 test/flip.cpp                 |    3 +
 test/gen_index.cpp            |    5 +-
 test/half.cpp                 |    2 +-
 test/hamming.cpp              |    4 +-
 test/index.cpp                |    1 +
 test/ireduce.cpp              |    5 +
 test/jit.cpp                  |    6 +-
 test/main.cpp                 |    6 -
 test/math.cpp                 |    4 +
 test/meanvar.cpp              |    1 -
 test/median.cpp               |    4 +
 test/missing.cpp              |    3 +
 test/nearest_neighbour.cpp    |    1 -
 test/reduce.cpp               |    1 -
 test/regions.cpp              |    6 +-
 test/rng_match.cpp            |    1 -
 test/select.cpp               |    1 -
 test/testHelpers.hpp          | 1185 ++-----------------------
 test/tile.cpp                 |    4 +-
 test/topk.cpp                 |    2 +-
 test/var.cpp                  |    4 +-
 test/wrap.cpp                 |    1 -
 40 files changed, 1718 insertions(+), 1146 deletions(-)
 create mode 100644 test/arrayfire_test.cpp
 delete mode 100644 test/main.cpp

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 6046c1b3a5..95bbbca80a 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -21,6 +21,7 @@ if(NOT TARGET gtest)
   if(WIN32)
     set(gtest_force_shared_crt ON
         CACHE INTERNAL "Required so that the libs Runtime is not set to MT DLL")
+    set(BUILD_SHARED_LIBS OFF)
   endif()
 
   add_subdirectory(gtest EXCLUDE_FROM_ALL)
@@ -70,6 +71,35 @@ if(AF_BUILD_UNIFIED)
   list(APPEND enabled_backends "unified")
 endif(AF_BUILD_UNIFIED)
 
+
+add_library(arrayfire_test OBJECT
+  testHelpers.hpp
+  arrayfire_test.cpp)
+
+target_include_directories(arrayfire_test
+  PRIVATE
+    .
+    ../include
+    ../build/include
+    ../extern/half/include
+    mmio
+    gtest/googletest/include)
+
+if(WIN32)
+  target_compile_options(arrayfire_test
+    PRIVATE
+      /bigobj
+      /EHsc)
+  target_compile_definitions(arrayfire_test
+    PRIVATE
+      WIN32_LEAN_AND_MEAN
+      NOMINMAX)
+endif()
+
+target_compile_definitions(arrayfire_test
+  PRIVATE
+    USE_MTX)
+
 # Creates tests for all backends
 #
 # Creates a standard test for all backends. Most of the time you only need to
@@ -97,7 +127,7 @@ function(make_test)
       continue()
     endif()
     set(target "test_${src_name}_${backend}")
-    add_executable(${target} ${mt_args_SRC})
+    add_executable(${target} ${mt_args_SRC} $<TARGET_OBJECTS:arrayfire_test>)
     target_include_directories(${target}
       PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
@@ -106,9 +136,8 @@ function(make_test)
       )
     target_link_libraries(${target}
       PRIVATE
-        gtest
-        gtest_main
         ${mt_args_LIBRARIES}
+        gtest
       )
 
     if(${backend} STREQUAL "unified")
@@ -139,8 +168,8 @@ function(make_test)
         AF_$<UPPER_CASE:${backend}>
         ${mt_args_DEFINITIONS}
       )
+    target_link_libraries(${target} PRIVATE mmio)
     if(AF_TEST_WITH_MTX_FILES AND ${mt_args_USE_MMIO})
-      target_link_libraries(${target} PRIVATE mmio)
       add_dependencies(${target} mtxDownloads)
       target_compile_definitions(${target}
         PRIVATE
@@ -179,7 +208,6 @@ make_test(SRC arrayio.cpp)
 make_test(SRC assign.cpp CXX11)
 make_test(SRC backend.cpp CXX11)
 make_test(SRC basic.cpp)
-make_test(SRC basic_c.c)
 make_test(SRC bilateral.cpp)
 make_test(SRC binary.cpp CXX11)
 make_test(SRC blas.cpp)
@@ -234,7 +262,6 @@ make_test(SRC iterative_deconv.cpp)
 make_test(SRC jit.cpp CXX11)
 make_test(SRC join.cpp)
 make_test(SRC lu_dense.cpp SERIAL)
-make_test(SRC main.cpp)
 #make_test(manual_memory_test.cpp)
 make_test(SRC match_template.cpp)
 make_test(SRC math.cpp CXX11)
@@ -313,6 +340,21 @@ make_test(SRC wrap.cpp)
 make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
 
+foreach(backend ${enabled_backends})
+  set(target "test_basic_c_${backend}")
+  add_executable(${target} basic_c.c)
+  if(${backend} STREQUAL "unified")
+    target_link_libraries(${target}
+      PRIVATE
+      ArrayFire::af)
+  else()
+    target_link_libraries(${target}
+      PRIVATE
+      ArrayFire::af${backend})
+  endif()
+  add_test(NAME ${target} COMMAND ${target})
+endforeach()
+
 if(AF_TEST_WITH_MTX_FILES)
   make_test(SRC matrixmarket.cpp USE_MMIO)
 endif()
diff --git a/test/approx1.cpp b/test/approx1.cpp
index be8ce78c03..a13c51c173 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -7,10 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/algorithm.h>
+#include <af/arith.h>
 #include <af/array.h>
+#include <af/blas.h>
 #include <af/complex.h>
+#include <af/constants.h>
+#include <af/data.h>
+#include <af/defines.h>
 #include <af/dim4.hpp>
+#include <af/exception.h>
+#include <af/gfor.h>
 #include <af/index.h>
+#include <af/random.h>
 #include <af/signal.h>
 #include <af/traits.hpp>
 
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 3528e66404..8ea4f5b8a4 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -7,8 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <af/algorithm.h>
+#include <af/arith.h>
+#include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
+#include <af/exception.h>
+#include <af/gfor.h>
+#include <af/random.h>
 #include <af/signal.h>
 #include <af/traits.hpp>
 
diff --git a/test/array.cpp b/test/array.cpp
index f8ebf7312c..23f7454ccc 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
new file mode 100644
index 0000000000..cf0d12b0b9
--- /dev/null
+++ b/test/arrayfire_test.cpp
@@ -0,0 +1,1522 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#define EXTERN_TEMPLATE
+#include <testHelpers.hpp>
+
+#include <arrayfire.h>
+#include <af/algorithm.h>
+#include <af/compatible.h>
+#include <af/internal.h>
+
+#include <gtest/gtest.h>
+
+#include <algorithm>
+#include <cfloat>
+#include <fstream>
+#include <iterator>
+#include <limits>
+#include <numeric>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <typeinfo>
+#include <utility>
+#include <vector>
+
+using af::af_cdouble;
+using af::af_cfloat;
+
+bool operator==(const af_half &lhs, const af_half &rhs) {
+    return lhs.data_ == rhs.data_;
+}
+
+std::ostream &operator<<(std::ostream &os, const af_half &val) {
+    float out = *reinterpret_cast<const half_float::half *>(&val);
+    os << out;
+    return os;
+}
+
+std::ostream &operator<<(std::ostream &os, af::Backend bk) {
+    switch (bk) {
+        case AF_BACKEND_CPU: os << "AF_BACKEND_CPU"; break;
+        case AF_BACKEND_CUDA: os << "AF_BACKEND_CUDA"; break;
+        case AF_BACKEND_OPENCL: os << "AF_BACKEND_OPENCL"; break;
+        case AF_BACKEND_DEFAULT: os << "AF_BACKEND_DEFAULT"; break;
+    }
+    return os;
+}
+
+std::ostream &operator<<(std::ostream &os, af_err e) {
+    return os << af_err_to_string(e);
+}
+
+std::ostream &operator<<(std::ostream &os, af::dtype type) {
+    std::string name;
+    switch (type) {
+        case f32: name = "f32"; break;
+        case c32: name = "c32"; break;
+        case f64: name = "f64"; break;
+        case c64: name = "c64"; break;
+        case b8: name = "b8"; break;
+        case s32: name = "s32"; break;
+        case u32: name = "u32"; break;
+        case u8: name = "u8"; break;
+        case s64: name = "s64"; break;
+        case u64: name = "u64"; break;
+        case s16: name = "s16"; break;
+        case u16: name = "u16"; break;
+        case f16: name = "f16"; break;
+        default: assert(false && "Invalid type");
+    }
+    return os << name;
+}
+
+std::string readNextNonEmptyLine(std::ifstream &file) {
+    std::string result = "";
+    // Using a for loop to read the next non empty line
+    for (std::string line; std::getline(file, line);) {
+        result += line;
+        if (result != "") break;
+    }
+    // If no file has been found, throw an exception
+    if (result == "") {
+        throw std::runtime_error("Non empty lines not found in the file");
+    }
+    return result;
+}
+
+namespace half_float {
+std::ostream &operator<<(std::ostream &os, half_float::half val) {
+    os << (float)val;
+    return os;
+}
+}  // namespace half_float
+
+// Called by ASSERT_ARRAYS_EQ
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         const af::array &a, const af::array &b,
+                                         float maxAbsDiff) {
+    af::dtype aType = a.type();
+    af::dtype bType = b.type();
+    if (aType != bType)
+        return ::testing::AssertionFailure()
+               << "TYPE MISMATCH: \n"
+               << "  Actual: " << bName << "(" << b.type() << ")\n"
+               << "Expected: " << aName << "(" << a.type() << ")";
+
+    af::dtype arrDtype = aType;
+    if (a.dims() != b.dims())
+        return ::testing::AssertionFailure()
+               << "SIZE MISMATCH: \n"
+               << "  Actual: " << bName << "([" << b.dims() << "])\n"
+               << "Expected: " << aName << "([" << a.dims() << "])";
+
+    switch (arrDtype) {
+        case f32:
+            return elemWiseEq<float>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case c32:
+            return elemWiseEq<af::cfloat>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case f64:
+            return elemWiseEq<double>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case c64:
+            return elemWiseEq<af::cdouble>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case b8: return elemWiseEq<char>(aName, bName, a, b, maxAbsDiff); break;
+        case s32: return elemWiseEq<int>(aName, bName, a, b, maxAbsDiff); break;
+        case u32:
+            return elemWiseEq<uint>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case u8:
+            return elemWiseEq<uchar>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case s64:
+            return elemWiseEq<long long>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case u64:
+            return elemWiseEq<unsigned long long>(aName, bName, a, b,
+                                                  maxAbsDiff);
+            break;
+        case s16:
+            return elemWiseEq<short>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case u16:
+            return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);
+            break;
+        case f16:
+            return elemWiseEq<af::half>(aName, bName, a, b, maxAbsDiff);
+            break;
+        default:
+            return ::testing::AssertionFailure()
+                   << "INVALID TYPE, see enum numbers: " << bName << "("
+                   << b.type() << ") and " << aName << "(" << a.type() << ")";
+    }
+
+    return ::testing::AssertionSuccess();
+}
+
+template<>
+float convert(af::half in) {
+    return static_cast<float>(half_float::half(in.data_));
+}
+
+template<>
+af_half convert(int in) {
+    half_float::half h = half_float::half(in);
+    af_half out;
+    memcpy(&out, &h, sizeof(af_half));
+    return out;
+}
+
+template<typename inType, typename outType, typename FileElementType>
+void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
+               std::vector<std::vector<inType> > &testInputs,
+               std::vector<std::vector<outType> > &testOutputs) {
+    using std::vector;
+
+    std::ifstream testFile(FileName.c_str());
+    if (testFile.good()) {
+        unsigned inputCount;
+        testFile >> inputCount;
+        inputDims.resize(inputCount);
+        for (unsigned i = 0; i < inputCount; i++) { testFile >> inputDims[i]; }
+
+        unsigned testCount;
+        testFile >> testCount;
+        testOutputs.resize(testCount);
+
+        vector<unsigned> testSizes(testCount);
+        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
+
+        testInputs.resize(inputCount, vector<inType>(0));
+        for (unsigned k = 0; k < inputCount; k++) {
+            dim_t nElems = inputDims[k].elements();
+            testInputs[k].resize(nElems);
+            FileElementType tmp;
+            for (unsigned i = 0; i < nElems; i++) {
+                testFile >> tmp;
+                testInputs[k][i] = convert<inType, FileElementType>(tmp);
+            }
+        }
+
+        testOutputs.resize(testCount, vector<outType>(0));
+        for (unsigned i = 0; i < testCount; i++) {
+            testOutputs[i].resize(testSizes[i]);
+            FileElementType tmp;
+            for (unsigned j = 0; j < testSizes[i]; j++) {
+                testFile >> tmp;
+                testOutputs[i][j] = convert<outType, FileElementType>(tmp);
+            }
+        }
+    } else {
+        FAIL() << "TEST FILE NOT FOUND";
+    }
+}
+
+#define INSTANTIATE(Tin, Tout, Tfile)                                  \
+    template void readTests<Tin, Tout, Tfile>(                         \
+        const std::string &FileName, std::vector<af::dim4> &inputDims, \
+        std::vector<std::vector<Tin> > &testInputs,                    \
+        std::vector<std::vector<Tout> > &testOutputs)
+
+INSTANTIATE(float, float, int);
+INSTANTIATE(double, float, int);
+INSTANTIATE(int, float, int);
+INSTANTIATE(unsigned int, float, int);
+INSTANTIATE(char, float, int);
+INSTANTIATE(unsigned char, float, int);
+INSTANTIATE(short, float, int);
+INSTANTIATE(unsigned short, float, int);
+INSTANTIATE(long long, float, int);
+INSTANTIATE(unsigned long long, float, int);
+INSTANTIATE(af_cfloat, af_cfloat, int);
+INSTANTIATE(double, double, int);
+INSTANTIATE(af_cdouble, af_cdouble, int);
+INSTANTIATE(int, int, int);
+INSTANTIATE(unsigned int, unsigned int, int);
+INSTANTIATE(unsigned int, unsigned int, unsigned int);
+INSTANTIATE(long long, long long, int);
+INSTANTIATE(unsigned long long, unsigned long long, int);
+INSTANTIATE(char, char, int);
+INSTANTIATE(unsigned char, unsigned char, int);
+INSTANTIATE(short, short, int);
+INSTANTIATE(unsigned short, unsigned short, int);
+INSTANTIATE(half_float::half, half_float::half, int);
+INSTANTIATE(af_half, af_half, int);
+INSTANTIATE(float, int, int);
+INSTANTIATE(unsigned int, int, int);
+INSTANTIATE(char, int, int);
+INSTANTIATE(unsigned char, int, int);
+INSTANTIATE(short, int, int);
+INSTANTIATE(unsigned short, int, int);
+
+INSTANTIATE(unsigned char, unsigned short, int);
+INSTANTIATE(unsigned char, short, int);
+INSTANTIATE(unsigned char, double, int);
+
+INSTANTIATE(long long, unsigned int, unsigned int);
+INSTANTIATE(unsigned long long, unsigned int, unsigned int);
+INSTANTIATE(int, unsigned int, unsigned int);
+INSTANTIATE(short, unsigned int, unsigned int);
+INSTANTIATE(unsigned short, unsigned int, unsigned int);
+INSTANTIATE(char, unsigned int, unsigned int);
+INSTANTIATE(unsigned char, unsigned int, unsigned int);
+INSTANTIATE(float, unsigned int, unsigned int);
+INSTANTIATE(double, unsigned int, unsigned int);
+
+INSTANTIATE(float, unsigned int, int);
+INSTANTIATE(double, unsigned int, int);
+INSTANTIATE(int, unsigned int, int);
+INSTANTIATE(long long, unsigned int, int);
+INSTANTIATE(unsigned long long, unsigned int, int);
+INSTANTIATE(char, unsigned int, int);
+INSTANTIATE(unsigned char, unsigned int, int);
+INSTANTIATE(short, unsigned int, int);
+INSTANTIATE(unsigned short, unsigned int, int);
+
+INSTANTIATE(float, char, int);
+INSTANTIATE(double, char, int);
+INSTANTIATE(unsigned char, char, int);
+INSTANTIATE(short, char, int);
+INSTANTIATE(unsigned short, char, int);
+INSTANTIATE(int, char, int);
+INSTANTIATE(unsigned int, char, int);
+
+INSTANTIATE(char, float, float);
+INSTANTIATE(int, float, float);
+INSTANTIATE(unsigned int, float, float);
+INSTANTIATE(short, float, float);
+INSTANTIATE(unsigned char, float, float);
+INSTANTIATE(unsigned short, float, float);
+INSTANTIATE(double, float, float);
+INSTANTIATE(af::af_cfloat, float, float);
+INSTANTIATE(af::af_cdouble, float, float);
+INSTANTIATE(long long, float, float);
+INSTANTIATE(long long, double, float);
+INSTANTIATE(unsigned long long, double, float);
+INSTANTIATE(float, float, float);
+INSTANTIATE(af_cfloat, af_cfloat, float);
+INSTANTIATE(af_cfloat, af_cfloat, af_cfloat);
+INSTANTIATE(af_cdouble, af_cdouble, af_cdouble);
+INSTANTIATE(double, double, float);
+INSTANTIATE(double, double, double);
+INSTANTIATE(af_cdouble, af_cdouble, float);
+INSTANTIATE(int, int, float);
+INSTANTIATE(unsigned int, unsigned int, float);
+INSTANTIATE(long long, long long, float);
+INSTANTIATE(unsigned long long, unsigned long long, float);
+INSTANTIATE(char, char, float);
+INSTANTIATE(unsigned char, unsigned char, float);
+INSTANTIATE(short, short, float);
+INSTANTIATE(unsigned short, unsigned short, float);
+INSTANTIATE(half_float::half, half_float::half, float);
+
+INSTANTIATE(double, af_cdouble, float);
+INSTANTIATE(float, af_cfloat, float);
+
+#undef INSTANTIATE
+
+bool noDoubleTests(af::dtype ty) {
+    bool isTypeDouble      = (ty == f64) || (ty == c64);
+    int dev                = af::getDevice();
+    bool isDoubleSupported = af::isDoubleAvailable(dev);
+
+    return ((isTypeDouble && !isDoubleSupported) ? true : false);
+}
+
+bool noHalfTests(af::dtype ty) {
+    bool isTypeHalf      = (ty == f16);
+    int dev              = af::getDevice();
+    bool isHalfSupported = af::isHalfAvailable(dev);
+
+    return ((isTypeHalf && !isHalfSupported) ? true : false);
+}
+
+af_half abs(af_half in) {
+    half_float::half in_;
+    // casting to void* to avoid class-memaccess warnings on windows
+    memcpy(static_cast<void *>(&in_), &in, sizeof(af_half));
+    half_float::half out_ = abs(in_);
+    af_half out;
+    memcpy(&out, &out_, sizeof(af_half));
+    return out;
+}
+
+af_half operator-(af_half lhs, af_half rhs) {
+    half_float::half lhs_;
+    half_float::half rhs_;
+
+    // casting to void* to avoid class-memaccess warnings on windows
+    memcpy(static_cast<void *>(&lhs_), &lhs, sizeof(af_half));
+    memcpy(static_cast<void *>(&rhs_), &rhs, sizeof(af_half));
+    half_float::half out = lhs_ - rhs_;
+    af_half o;
+    memcpy(&o, &out, sizeof(af_half));
+    return o;
+}
+
+const af::cfloat &operator+(const af::cfloat &val) { return val; }
+
+const af::cdouble &operator+(const af::cdouble &val) { return val; }
+
+const af_half &operator+(const af_half &val) { return val; }
+
+// Calculate a multi-dimensional coordinates' linearized index
+dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
+    return std::inner_product(coords.get(), coords.get() + 4, strides.get(),
+                              0LL);
+}
+
+// Calculate a linearized index's multi-dimensonal coordinates in an af::array,
+//  given its dimension sizes and strides
+af::dim4 unravelIdx(dim_t idx, af::dim4 dims, af::dim4 strides) {
+    af::dim4 coords;
+    coords[3] = idx / (strides[3]);
+    coords[2] = idx / (strides[2]) % dims[2];
+    coords[1] = idx / (strides[1]) % dims[1];
+    coords[0] = idx % dims[0];
+
+    return coords;
+}
+
+af::dim4 unravelIdx(dim_t idx, af::array arr) {
+    af::dim4 dims = arr.dims();
+    af::dim4 st   = af::getStrides(arr);
+    return unravelIdx(idx, dims, st);
+}
+
+af::dim4 calcStrides(const af::dim4 &parentDim) {
+    af::dim4 out(1, 1, 1, 1);
+    dim_t *out_dims          = out.get();
+    const dim_t *parent_dims = parentDim.get();
+
+    for (dim_t i = 1; i < 4; i++) {
+        out_dims[i] = out_dims[i - 1] * parent_dims[i - 1];
+    }
+
+    return out;
+}
+
+std::string minimalDim4(af::dim4 coords, af::dim4 dims) {
+    std::ostringstream os;
+    os << "(" << coords[0];
+    if (dims[1] > 1 || dims[2] > 1 || dims[3] > 1) { os << ", " << coords[1]; }
+    if (dims[2] > 1 || dims[3] > 1) { os << ", " << coords[2]; }
+    if (dims[3] > 1) { os << ", " << coords[3]; }
+    os << ")";
+
+    return os.str();
+}
+
+// Generates a random array. testWriteToOutputArray expects that it will receive
+// the same af_array that this generates after the af_* function is called
+void genRegularArray(TestOutputArrayInfo *metadata, const unsigned ndims,
+                     const dim_t *const dims, const af_dtype ty) {
+    metadata->init(ndims, dims, ty);
+}
+
+void genRegularArray(TestOutputArrayInfo *metadata, double val,
+                     const unsigned ndims, const dim_t *const dims,
+                     const af_dtype ty) {
+    metadata->init(val, ndims, dims, ty);
+}
+
+// Generates a large, random array, and extracts a subarray for the af_*
+// function to use. testWriteToOutputArray expects that the large array that it
+// receives is equal to the same large array with the gold array injected on the
+// same subarray location
+void genSubArray(TestOutputArrayInfo *metadata, const unsigned ndims,
+                 const dim_t *const dims, const af_dtype ty) {
+    const dim_t pad_size = 2;
+
+    // The large array is padded on both sides of each dimension
+    // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
+    dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
+    for (uint i = 0; i < ndims; ++i) {
+        full_arr_dims[i] = dims[i] + 2 * pad_size;
+    }
+
+    // Calculate index of sub-array. These will be used also by
+    // testWriteToOutputArray so that the gold sub array will be placed in the
+    // same location. Currently, this location is the center of the large array
+    af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
+    for (uint i = 0; i < ndims; ++i) {
+        af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
+        subarr_idxs[i] = idx;
+    }
+
+    metadata->init(ndims, full_arr_dims, ty, &subarr_idxs[0]);
+}
+
+void genSubArray(TestOutputArrayInfo *metadata, double val,
+                 const unsigned ndims, const dim_t *const dims,
+                 const af_dtype ty) {
+    const dim_t pad_size = 2;
+
+    // The large array is padded on both sides of each dimension
+    // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
+    dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
+    for (uint i = 0; i < ndims; ++i) {
+        full_arr_dims[i] = dims[i] + 2 * pad_size;
+    }
+
+    // Calculate index of sub-array. These will be used also by
+    // testWriteToOutputArray so that the gold sub array will be placed in the
+    // same location. Currently, this location is the center of the large array
+    af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
+    for (uint i = 0; i < ndims; ++i) {
+        af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
+        subarr_idxs[i] = idx;
+    }
+
+    metadata->init(val, ndims, full_arr_dims, ty, &subarr_idxs[0]);
+}
+
+// Generates a reordered array. testWriteToOutputArray expects that this array
+// will still have the correct output values from the af_* function, even though
+// the array was initially reordered.
+void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
+                       const dim_t *const dims, const af_dtype ty) {
+    // The rest of this function assumes that dims has 4 elements. Just in case
+    // dims has < 4 elements, use another dims array that is filled with 1s
+    dim_t all_dims[4] = {1, 1, 1, 1};
+    for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
+
+    // This reorder combination will not move data around, but will simply
+    // call modDims and modStrides (see src/api/c/reorder.cpp).
+    // The output will be checked if it is still correct even with the
+    // modified dims and strides "hack" with no data movement
+    uint reorder_idxs[4] = {0, 2, 1, 3};
+
+    // Shape the output array such that the reordered output array will have
+    // the correct dimensions that the test asks for (i.e. must match dims arg)
+    dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
+    for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
+    metadata->init(4, init_dims, ty);
+
+    af_array reordered = 0;
+    ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
+                              reorder_idxs[0], reorder_idxs[1], reorder_idxs[2],
+                              reorder_idxs[3]));
+    metadata->setOutput(reordered);
+}
+
+void genReorderedArray(TestOutputArrayInfo *metadata, double val,
+                       const unsigned ndims, const dim_t *const dims,
+                       const af_dtype ty) {
+    // The rest of this function assumes that dims has 4 elements. Just in case
+    // dims has < 4 elements, use another dims array that is filled with 1s
+    dim_t all_dims[4] = {1, 1, 1, 1};
+    for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
+
+    // This reorder combination will not move data around, but will simply
+    // call modDims and modStrides (see src/api/c/reorder.cpp).
+    // The output will be checked if it is still correct even with the
+    // modified dims and strides "hack" with no data movement
+    uint reorder_idxs[4] = {0, 2, 1, 3};
+
+    // Shape the output array such that the reordered output array will have
+    // the correct dimensions that the test asks for (i.e. must match dims arg)
+    dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
+    for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
+    metadata->init(val, 4, init_dims, ty);
+
+    af_array reordered = 0;
+    ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
+                              reorder_idxs[0], reorder_idxs[1], reorder_idxs[2],
+                              reorder_idxs[3]));
+    metadata->setOutput(reordered);
+}
+// Partner function of testWriteToOutputArray. This generates the "special"
+// array that testWriteToOutputArray will use to check if the af_* function
+// correctly uses an existing array as its output
+void genTestOutputArray(af_array *out_ptr, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype ty,
+                        TestOutputArrayInfo *metadata) {
+    switch (metadata->getOutputArrayType()) {
+        case FULL_ARRAY: genRegularArray(metadata, ndims, dims, ty); break;
+        case SUB_ARRAY: genSubArray(metadata, ndims, dims, ty); break;
+        case REORDERED_ARRAY:
+            genReorderedArray(metadata, ndims, dims, ty);
+            break;
+        default: break;
+    }
+    *out_ptr = metadata->getOutput();
+}
+
+void genTestOutputArray(af_array *out_ptr, double val, const unsigned ndims,
+                        const dim_t *const dims, const af_dtype ty,
+                        TestOutputArrayInfo *metadata) {
+    switch (metadata->getOutputArrayType()) {
+        case FULL_ARRAY: genRegularArray(metadata, val, ndims, dims, ty); break;
+        case SUB_ARRAY: genSubArray(metadata, val, ndims, dims, ty); break;
+        case REORDERED_ARRAY:
+            genReorderedArray(metadata, val, ndims, dims, ty);
+            break;
+        default: break;
+    }
+    *out_ptr = metadata->getOutput();
+}
+
+// Partner function of genTestOutputArray. This uses the same "special"
+// array that genTestOutputArray generates, and checks whether the
+// af_* function wrote to that array correctly
+::testing::AssertionResult testWriteToOutputArray(
+    std::string gold_name, std::string result_name, const af_array gold,
+    const af_array out, TestOutputArrayInfo *metadata) {
+    // In the case of NULL_ARRAY, the output array starts out as null.
+    // After the af_* function is called, it shouldn't be null anymore
+    if (metadata->getOutputArrayType() == NULL_ARRAY) {
+        if (out == 0) {
+            return ::testing::AssertionFailure()
+                   << "Output af_array " << result_name << " is null";
+        }
+        metadata->setOutput(out);
+    }
+    // For every other case, must check if the af_array generated by
+    // genTestOutputArray was used by the af_* function as its output array
+    else {
+        if (metadata->getOutput() != out) {
+            return ::testing::AssertionFailure()
+                   << "af_array POINTER MISMATCH:\n"
+                   << "  Actual: " << out << "\n"
+                   << "Expected: " << metadata->getOutput();
+        }
+    }
+
+    if (metadata->getOutputArrayType() == SUB_ARRAY) {
+        // There are two full arrays. One will be injected with the gold
+        // subarray, the other should have already been injected with the af_*
+        // function's output. Then we compare the two full arrays
+        af_array gold_full_array = metadata->getFullOutputCopy();
+        af_assign_seq(&gold_full_array, gold_full_array,
+                      metadata->getSubArrayNumDims(),
+                      metadata->getSubArrayIdxs(), gold);
+
+        return assertArrayEq(gold_name, result_name,
+                             metadata->getFullOutputCopy(),
+                             metadata->getFullOutput());
+    } else {
+        return assertArrayEq(gold_name, result_name, gold, out);
+    }
+}
+
+// Called by ASSERT_SPECIAL_ARRAYS_EQ
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         std::string metadataName,
+                                         const af_array a, const af_array b,
+                                         TestOutputArrayInfo *metadata) {
+    UNUSED(metadataName);
+    return testWriteToOutputArray(aName, bName, a, b, metadata);
+}
+
+// To support C API
+::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
+                                         const af_array a, const af_array b) {
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertArrayEq(aName, bName, aaa, bbb, 0.0f);
+}
+
+// Called by ASSERT_ARRAYS_NEAR
+::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           const af::array &a,
+                                           const af::array &b,
+                                           float maxAbsDiff) {
+    UNUSED(maxAbsDiffName);
+    return assertArrayEq(aName, bName, a, b, maxAbsDiff);
+}
+
+// To support C API
+::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           const af_array a, const af_array b,
+                                           float maxAbsDiff) {
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertArrayNear(aName, bName, maxAbsDiffName, aaa, bbb, maxAbsDiff);
+}
+
+void cleanSlate() {
+    const size_t step_bytes = 1024;
+
+    size_t alloc_bytes, alloc_buffers;
+    size_t lock_bytes, lock_buffers;
+
+    af::deviceGC();
+
+    af::deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
+
+    ASSERT_EQ(0u, alloc_buffers);
+    ASSERT_EQ(0u, lock_buffers);
+    ASSERT_EQ(0u, alloc_bytes);
+    ASSERT_EQ(0u, lock_bytes);
+
+    af::setMemStepSize(step_bytes);
+
+    ASSERT_EQ(af::getMemStepSize(), step_bytes);
+}
+
+bool noImageIOTests() {
+    bool ret = !af::isImageIOAvailable();
+    if (ret) printf("Image IO Not Configured. Test will exit\n");
+    return ret;
+}
+
+bool noLAPACKTests() {
+    bool ret = !af::isLAPACKAvailable();
+    if (ret) printf("LAPACK Not Configured. Test will exit\n");
+    return ret;
+}
+
+template<typename inType, typename outType>
+void readTestsFromFile(const std::string &FileName,
+                       std::vector<af::dim4> &inputDims,
+                       std::vector<std::vector<inType> > &testInputs,
+                       std::vector<std::vector<outType> > &testOutputs) {
+    using std::vector;
+
+    std::ifstream testFile(FileName.c_str());
+    if (testFile.good()) {
+        unsigned inputCount;
+        testFile >> inputCount;
+        for (unsigned i = 0; i < inputCount; i++) {
+            af::dim4 temp(1);
+            testFile >> temp;
+            inputDims.push_back(temp);
+        }
+
+        unsigned testCount;
+        testFile >> testCount;
+        testOutputs.resize(testCount);
+
+        vector<unsigned> testSizes(testCount);
+        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
+
+        testInputs.resize(inputCount, vector<inType>(0));
+        for (unsigned k = 0; k < inputCount; k++) {
+            dim_t nElems = inputDims[k].elements();
+            testInputs[k].resize(nElems);
+            inType tmp;
+            for (unsigned i = 0; i < nElems; i++) {
+                testFile >> tmp;
+                testInputs[k][i] = tmp;
+            }
+        }
+
+        testOutputs.resize(testCount, vector<outType>(0));
+        for (unsigned i = 0; i < testCount; i++) {
+            testOutputs[i].resize(testSizes[i]);
+            outType tmp;
+            for (unsigned j = 0; j < testSizes[i]; j++) {
+                testFile >> tmp;
+                testOutputs[i][j] = tmp;
+            }
+        }
+    } else {
+        FAIL() << "TEST FILE NOT FOUND";
+    }
+}
+
+#define INSTANTIATE(Ti, To)                                            \
+    template void readTestsFromFile<Ti, To>(                           \
+        const std::string &FileName, std::vector<af::dim4> &inputDims, \
+        std::vector<std::vector<Ti> > &testInputs,                     \
+        std::vector<std::vector<To> > &testOutputs)
+
+INSTANTIATE(float, float);
+INSTANTIATE(float, af_cfloat);
+INSTANTIATE(af_cfloat, af_cfloat);
+INSTANTIATE(double, double);
+INSTANTIATE(double, af_cdouble);
+INSTANTIATE(af_cdouble, af_cdouble);
+INSTANTIATE(int, float);
+
+#undef INSTANTIATE
+
+template<typename outType>
+void readImageTests(const std::string &pFileName,
+                    std::vector<af::dim4> &pInputDims,
+                    std::vector<std::string> &pTestInputs,
+                    std::vector<std::vector<outType> > &pTestOutputs) {
+    using std::vector;
+
+    std::ifstream testFile(pFileName.c_str());
+    if (testFile.good()) {
+        unsigned inputCount;
+        testFile >> inputCount;
+        for (unsigned i = 0; i < inputCount; i++) {
+            af::dim4 temp(1);
+            testFile >> temp;
+            pInputDims.push_back(temp);
+        }
+
+        unsigned testCount;
+        testFile >> testCount;
+        pTestOutputs.resize(testCount);
+
+        vector<unsigned> testSizes(testCount);
+        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
+
+        pTestInputs.resize(inputCount, "");
+        for (unsigned k = 0; k < inputCount; k++) {
+            pTestInputs[k] = readNextNonEmptyLine(testFile);
+        }
+
+        pTestOutputs.resize(testCount, vector<outType>(0));
+        for (unsigned i = 0; i < testCount; i++) {
+            pTestOutputs[i].resize(testSizes[i]);
+            outType tmp;
+            for (unsigned j = 0; j < testSizes[i]; j++) {
+                testFile >> tmp;
+                pTestOutputs[i][j] = tmp;
+            }
+        }
+    } else {
+        FAIL() << "TEST FILE NOT FOUND";
+    }
+}
+
+#define INSTANTIATE(To)                                                  \
+    template void readImageTests<To>(                                    \
+        const std::string &pFileName, std::vector<af::dim4> &pInputDims, \
+        std::vector<std::string> &pTestInputs,                           \
+        std::vector<std::vector<To> > &pTestOutputs)
+
+INSTANTIATE(float);
+#undef INSTANTIATE
+
+void readImageTests(const std::string &pFileName,
+                    std::vector<af::dim4> &pInputDims,
+                    std::vector<std::string> &pTestInputs,
+                    std::vector<dim_t> &pTestOutSizes,
+                    std::vector<std::string> &pTestOutputs) {
+    using std::vector;
+
+    std::ifstream testFile(pFileName.c_str());
+    if (testFile.good()) {
+        unsigned inputCount;
+        testFile >> inputCount;
+        for (unsigned i = 0; i < inputCount; i++) {
+            af::dim4 temp(1);
+            testFile >> temp;
+            pInputDims.push_back(temp);
+        }
+
+        unsigned testCount;
+        testFile >> testCount;
+        pTestOutputs.resize(testCount);
+
+        pTestOutSizes.resize(testCount);
+        for (unsigned i = 0; i < testCount; i++) {
+            testFile >> pTestOutSizes[i];
+        }
+
+        pTestInputs.resize(inputCount, "");
+        for (unsigned k = 0; k < inputCount; k++) {
+            pTestInputs[k] = readNextNonEmptyLine(testFile);
+        }
+
+        pTestOutputs.resize(testCount, "");
+        for (unsigned i = 0; i < testCount; i++) {
+            pTestOutputs[i] = readNextNonEmptyLine(testFile);
+        }
+    } else {
+        FAIL() << "TEST FILE NOT FOUND";
+    }
+}
+
+template<typename descType>
+void readImageFeaturesDescriptors(
+    const std::string &pFileName, std::vector<af::dim4> &pInputDims,
+    std::vector<std::string> &pTestInputs,
+    std::vector<std::vector<float> > &pTestFeats,
+    std::vector<std::vector<descType> > &pTestDescs) {
+    using std::vector;
+
+    std::ifstream testFile(pFileName.c_str());
+    if (testFile.good()) {
+        unsigned inputCount;
+        testFile >> inputCount;
+        for (unsigned i = 0; i < inputCount; i++) {
+            af::dim4 temp(1);
+            testFile >> temp;
+            pInputDims.push_back(temp);
+        }
+
+        unsigned attrCount, featCount, descLen;
+        testFile >> featCount;
+        testFile >> attrCount;
+        testFile >> descLen;
+        pTestFeats.resize(attrCount);
+
+        pTestInputs.resize(inputCount, "");
+        for (unsigned k = 0; k < inputCount; k++) {
+            pTestInputs[k] = readNextNonEmptyLine(testFile);
+        }
+
+        pTestFeats.resize(attrCount, vector<float>(0));
+        for (unsigned i = 0; i < attrCount; i++) {
+            pTestFeats[i].resize(featCount);
+            float tmp;
+            for (unsigned j = 0; j < featCount; j++) {
+                testFile >> tmp;
+                pTestFeats[i][j] = tmp;
+            }
+        }
+
+        pTestDescs.resize(featCount, vector<descType>(0));
+        for (unsigned i = 0; i < featCount; i++) {
+            pTestDescs[i].resize(descLen);
+            descType tmp;
+            for (unsigned j = 0; j < descLen; j++) {
+                testFile >> tmp;
+                pTestDescs[i][j] = tmp;
+            }
+        }
+    } else {
+        FAIL() << "TEST FILE NOT FOUND";
+    }
+}
+
+#define INSTANTIATE(TYPE)                                                \
+    template void readImageFeaturesDescriptors<TYPE>(                    \
+        const std::string &pFileName, std::vector<af::dim4> &pInputDims, \
+        std::vector<std::string> &pTestInputs,                           \
+        std::vector<std::vector<float> > &pTestFeats,                    \
+        std::vector<std::vector<TYPE> > &pTestDescs)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(unsigned int);
+#undef INSTANTIATE
+
+template<typename T>
+bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance) {
+    double accum  = 0.0;
+    double maxion = -FLT_MAX;  //(double)std::numeric_limits<T>::lowest();
+    double minion = FLT_MAX;   //(double)std::numeric_limits<T>::max();
+
+    for (dim_t i = 0; i < data_size; i++) {
+        double dTemp = (double)data[i];
+        double gTemp = (double)gold[i];
+        double diff  = gTemp - dTemp;
+        double err =
+            (std::isfinite(diff) && (std::abs(diff) > 1.0e-4)) ? diff : 0.0f;
+        accum += std::pow(err, 2.0);
+        maxion = std::max(maxion, dTemp);
+        minion = std::min(minion, dTemp);
+    }
+    accum /= data_size;
+    double NRMSD = std::sqrt(accum) / (maxion - minion);
+
+    if (std::isnan(NRMSD) || NRMSD > tolerance) {
+#ifndef NDEBUG
+        printf("Comparison failed, NRMSD value: %lf\n", NRMSD);
+#endif
+        return false;
+    }
+
+    return true;
+}
+
+#define INSTANTIATE(TYPE)                                               \
+    template bool compareArraysRMSD<TYPE>(dim_t data_size, TYPE * gold, \
+                                          TYPE * data, double tolerance)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(char);
+INSTANTIATE(unsigned char);
+#undef INSTANTIATE
+
+TestOutputArrayInfo::TestOutputArrayInfo()
+    : out_arr(0)
+    , out_arr_cpy(0)
+    , out_subarr(0)
+    , out_subarr_ndims(0)
+    , out_arr_type(NULL_ARRAY) {
+    for (uint i = 0; i < 4; ++i) { out_subarr_idxs[i] = af_span; }
+}
+
+TestOutputArrayInfo::TestOutputArrayInfo(TestOutputArrayType arr_type)
+    : out_arr(0)
+    , out_arr_cpy(0)
+    , out_subarr(0)
+    , out_subarr_ndims(0)
+    , out_arr_type(arr_type) {
+    for (uint i = 0; i < 4; ++i) { out_subarr_idxs[i] = af_span; }
+}
+
+TestOutputArrayInfo::~TestOutputArrayInfo() {
+    if (out_subarr) af_release_array(out_subarr);
+    if (out_arr_cpy) af_release_array(out_arr_cpy);
+    if (out_arr) af_release_array(out_arr);
+}
+
+void TestOutputArrayInfo::init(const unsigned ndims, const dim_t *const dims,
+                               const af_dtype ty) {
+    ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
+}
+
+void TestOutputArrayInfo::init(const unsigned ndims, const dim_t *const dims,
+                               const af_dtype ty,
+                               const af_seq *const subarr_idxs) {
+    init(ndims, dims, ty);
+
+    ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
+    for (uint i = 0; i < ndims; ++i) { out_subarr_idxs[i] = subarr_idxs[i]; }
+    out_subarr_ndims = ndims;
+
+    ASSERT_SUCCESS(af_index(&out_subarr, out_arr, ndims, subarr_idxs));
+}
+
+void TestOutputArrayInfo::init(double val, const unsigned ndims,
+                               const dim_t *const dims, const af_dtype ty) {
+    switch (ty) {
+        case c32:
+        case c64:
+            af_constant_complex(&out_arr, val, 0.0, ndims, dims, ty);
+            break;
+        case s64:
+            af_constant_long(&out_arr, static_cast<intl>(val), ndims, dims);
+            break;
+        case u64:
+            af_constant_ulong(&out_arr, static_cast<uintl>(val), ndims, dims);
+            break;
+        default: af_constant(&out_arr, val, ndims, dims, ty); break;
+    }
+}
+
+void TestOutputArrayInfo::init(double val, const unsigned ndims,
+                               const dim_t *const dims, const af_dtype ty,
+                               const af_seq *const subarr_idxs) {
+    init(val, ndims, dims, ty);
+
+    ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
+    for (uint i = 0; i < ndims; ++i) { out_subarr_idxs[i] = subarr_idxs[i]; }
+    out_subarr_ndims = ndims;
+
+    ASSERT_SUCCESS(af_index(&out_subarr, out_arr, ndims, subarr_idxs));
+}
+
+af_array TestOutputArrayInfo::getOutput() {
+    if (out_arr_type == SUB_ARRAY) {
+        return out_subarr;
+    } else {
+        return out_arr;
+    }
+}
+
+void TestOutputArrayInfo::setOutput(af_array array) {
+    if (out_arr != 0) { ASSERT_SUCCESS(af_release_array(out_arr)); }
+    out_arr = array;
+}
+
+af_array TestOutputArrayInfo::getFullOutput() { return out_arr; }
+af_array TestOutputArrayInfo::getFullOutputCopy() { return out_arr_cpy; }
+af_seq *TestOutputArrayInfo::getSubArrayIdxs() { return &out_subarr_idxs[0]; }
+dim_t TestOutputArrayInfo::getSubArrayNumDims() { return out_subarr_ndims; }
+TestOutputArrayType TestOutputArrayInfo::getOutputArrayType() {
+    return out_arr_type;
+}
+
+#if defined(USE_MTX)
+::testing::AssertionResult mtxReadSparseMatrix(af::array &out,
+                                               const char *fileName) {
+    FILE *fileHandle;
+
+    if ((fileHandle = fopen(fileName, "r")) == NULL) {
+        return ::testing::AssertionFailure()
+               << "Failed to open mtx file: " << fileName << "\n";
+    }
+
+    MM_typecode matcode;
+    if (mm_read_banner(fileHandle, &matcode)) {
+        return ::testing::AssertionFailure()
+               << "Could not process Matrix Market banner.\n";
+    }
+
+    if (!(mm_is_matrix(matcode) && mm_is_sparse(matcode))) {
+        return ::testing::AssertionFailure()
+               << "Input mtx doesn't have a sparse matrix.\n";
+    }
+
+    if (mm_is_integer(matcode)) {
+        return ::testing::AssertionFailure() << "MTX file has integer data. \
+                Integer sparse matrices are not supported in ArrayFire yet.\n";
+    }
+
+    int M = 0, N = 0, nz = 0;
+    if (mm_read_mtx_crd_size(fileHandle, &M, &N, &nz)) {
+        return ::testing::AssertionFailure()
+               << "Failed to read matrix dimensions.\n";
+    }
+
+    if (mm_is_real(matcode)) {
+        std::vector<int> I(nz);
+        std::vector<int> J(nz);
+        std::vector<float> V(nz);
+
+        for (int i = 0; i < nz; ++i) {
+            int c, r;
+            double v;
+            int readCount = fscanf(fileHandle, "%d %d %lg\n", &r, &c, &v);
+            if (readCount != 3) {
+                fclose(fileHandle);
+                return ::testing::AssertionFailure()
+                       << "\nEnd of file reached, expected more data, "
+                       << "following are some reasons this happens.\n"
+                       << "\t - use of template type that doesn't match data "
+                          "type\n"
+                       << "\t - the mtx file itself doesn't have enough data\n";
+            }
+            I[i] = r - 1;
+            J[i] = c - 1;
+            V[i] = (float)v;
+        }
+
+        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), f32,
+                         AF_STORAGE_COO);
+    } else if (mm_is_complex(matcode)) {
+        std::vector<int> I(nz);
+        std::vector<int> J(nz);
+        std::vector<af::cfloat> V(nz);
+
+        for (int i = 0; i < nz; ++i) {
+            int c, r;
+            double real, imag;
+            int readCount =
+                fscanf(fileHandle, "%d %d %lg %lg\n", &r, &c, &real, &imag);
+            if (readCount != 4) {
+                fclose(fileHandle);
+                return ::testing::AssertionFailure()
+                       << "\nEnd of file reached, expected more data, "
+                       << "following are some reasons this happens.\n"
+                       << "\t - use of template type that doesn't match data "
+                          "type\n"
+                       << "\t - the mtx file itself doesn't have enough data\n";
+            }
+            I[i] = r - 1;
+            J[i] = c - 1;
+            V[i] = af::cfloat(float(real), float(imag));
+        }
+
+        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), c32,
+                         AF_STORAGE_COO);
+    } else {
+        return ::testing::AssertionFailure()
+               << "Unknown matcode from MTX FILE\n";
+    }
+
+    fclose(fileHandle);
+    return ::testing::AssertionSuccess();
+}
+#endif  // USE_MTX
+
+// TODO: perform conversion on device for CUDA and OpenCL
+template<typename T>
+af_err conv_image(af_array *out, af_array in) {
+    af_array outArray;
+
+    dim_t d0, d1, d2, d3;
+    af_get_dims(&d0, &d1, &d2, &d3, in);
+    af::dim4 idims(d0, d1, d2, d3);
+
+    dim_t nElems = 0;
+    af_get_elements(&nElems, in);
+
+    float *in_data = new float[nElems];
+    af_get_data_ptr(in_data, in);
+
+    T *out_data = new T[nElems];
+
+    for (int i = 0; i < (int)nElems; i++) out_data[i] = (T)in_data[i];
+
+    af_create_array(&outArray, out_data, idims.ndims(), idims.get(),
+                    (af_dtype)af::dtype_traits<T>::af_type);
+
+    std::swap(*out, outArray);
+
+    delete[] in_data;
+    delete[] out_data;
+
+    return AF_SUCCESS;
+}
+
+#define INSTANTIATE(To) \
+    template af_err conv_image<To>(af_array * out, af_array in)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(unsigned char);
+INSTANTIATE(half_float::half);
+INSTANTIATE(unsigned int);
+INSTANTIATE(unsigned short);
+INSTANTIATE(int);
+INSTANTIATE(char);
+INSTANTIATE(short);
+INSTANTIATE(af_cdouble);
+INSTANTIATE(af_cfloat);
+INSTANTIATE(long long);
+INSTANTIATE(unsigned long long);
+#undef INSTANTIATE
+
+template<typename T>
+af::array cpu_randu(const af::dim4 dims) {
+    typedef typename af::dtype_traits<T>::base_type BT;
+
+    bool isTypeCplx = is_same_type<T, af::cfloat>::value ||
+                      is_same_type<T, af::cdouble>::value;
+    bool isTypeFloat = is_same_type<BT, float>::value ||
+                       is_same_type<BT, double>::value ||
+                       is_same_type<BT, half_float::half>::value;
+
+    size_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
+
+    std::vector<BT> out(elements);
+    for (size_t i = 0; i < elements; i++) {
+        out[i] = isTypeFloat ? (BT)(rand()) / RAND_MAX : rand() % 100;
+    }
+
+    return af::array(dims, (T *)&out[0]);
+}
+
+#define INSTANTIATE(To) template af::array cpu_randu<To>(const af::dim4 dims)
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(unsigned char);
+INSTANTIATE(half_float::half);
+INSTANTIATE(unsigned int);
+INSTANTIATE(unsigned short);
+INSTANTIATE(int);
+INSTANTIATE(char);
+INSTANTIATE(short);
+INSTANTIATE(af_cdouble);
+INSTANTIATE(af_cfloat);
+INSTANTIATE(long long);
+INSTANTIATE(unsigned long long);
+#undef INSTANTIATE
+
+template<typename T>
+std::string printContext(const std::vector<T> &hGold, std::string goldName,
+                         const std::vector<T> &hOut, std::string outName,
+                         af::dim4 arrDims, af::dim4 arrStrides, dim_t idx) {
+    std::ostringstream os;
+
+    af::dim4 coords = unravelIdx(idx, arrDims, arrStrides);
+    dim_t ctxWidth  = 5;
+
+    // Coordinates that span dim0
+    af::dim4 coordsMinBound = coords;
+    coordsMinBound[0]       = 0;
+    af::dim4 coordsMaxBound = coords;
+    coordsMaxBound[0]       = arrDims[0] - 1;
+
+    // dim0 positions that can be displayed
+    dim_t dim0Start = std::max<dim_t>(0LL, coords[0] - ctxWidth);
+    dim_t dim0End   = std::min<dim_t>(coords[0] + ctxWidth + 1LL, arrDims[0]);
+
+    // Linearized indices of values in vectors that can be displayed
+    dim_t vecStartIdx =
+        std::max<dim_t>(ravelIdx(coordsMinBound, arrStrides), idx - ctxWidth);
+
+    // Display as minimal coordinates as needed
+    // First value is the range of dim0 positions that will be displayed
+    os << "Viewing slice (" << dim0Start << ":" << dim0End - 1;
+    if (arrDims[1] > 1 || arrDims[2] > 1 || arrDims[3] > 1)
+        os << ", " << coords[1];
+    if (arrDims[2] > 1 || arrDims[3] > 1) os << ", " << coords[2];
+    if (arrDims[3] > 1) os << ", " << coords[3];
+    os << "), dims are (" << arrDims << ") strides: (" << arrStrides << ")\n";
+
+    dim_t ctxElems = dim0End - dim0Start;
+    std::vector<int> valFieldWidths(ctxElems);
+    std::vector<std::string> ctxDim0(ctxElems);
+    std::vector<std::string> ctxOutVals(ctxElems);
+    std::vector<std::string> ctxGoldVals(ctxElems);
+
+    // Get dim0 positions and out/reference values for the context window
+    //
+    // Also get the max string length between the position and out/ref values
+    // per item so that it can be used later as the field width for
+    // displaying each item in the context window
+    for (dim_t i = 0; i < ctxElems; ++i) {
+        std::ostringstream tmpOs;
+
+        dim_t dim0 = dim0Start + i;
+        if (dim0 == coords[0])
+            tmpOs << "[" << dim0 << "]";
+        else
+            tmpOs << dim0;
+        ctxDim0[i]     = tmpOs.str();
+        size_t dim0Len = tmpOs.str().length();
+        tmpOs.str(std::string());
+
+        dim_t valIdx = vecStartIdx + i;
+
+        if (valIdx == idx) {
+            tmpOs << "[" << +hOut[valIdx] << "]";
+        } else {
+            tmpOs << +hOut[valIdx];
+        }
+        ctxOutVals[i] = tmpOs.str();
+        size_t outLen = tmpOs.str().length();
+        tmpOs.str(std::string());
+
+        if (valIdx == idx) {
+            tmpOs << "[" << +hGold[valIdx] << "]";
+        } else {
+            tmpOs << +hGold[valIdx];
+        }
+        ctxGoldVals[i] = tmpOs.str();
+        size_t goldLen = tmpOs.str().length();
+        tmpOs.str(std::string());
+
+        int maxWidth      = std::max<int>(dim0Len, outLen);
+        maxWidth          = std::max<int>(maxWidth, goldLen);
+        valFieldWidths[i] = maxWidth;
+    }
+
+    size_t varNameWidth = std::max<size_t>(goldName.length(), outName.length());
+
+    // Display dim0 positions, output values, and reference values
+    os << std::right << std::setw(varNameWidth) << ""
+       << "   ";
+    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
+        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxDim0[i];
+    }
+    os << "\n";
+
+    os << std::right << std::setw(varNameWidth) << outName << ": {";
+    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
+        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxOutVals[i];
+    }
+    os << " }\n";
+
+    os << std::right << std::setw(varNameWidth) << goldName << ": {";
+    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
+        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxGoldVals[i];
+    }
+    os << " }";
+
+    return os.str();
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      const std::vector<T> &a, af::dim4 aDims,
+                                      const std::vector<T> &b, af::dim4 bDims,
+                                      float maxAbsDiff, IntegerTag) {
+    UNUSED(maxAbsDiff);
+    typedef typename std::vector<T>::const_iterator iter;
+    std::pair<iter, iter> mismatches =
+        std::mismatch(a.begin(), a.end(), b.begin());
+    iter bItr = mismatches.second;
+
+    if (bItr == b.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        dim_t idx         = std::distance(b.begin(), bItr);
+        af::dim4 aStrides = calcStrides(aDims);
+        af::dim4 bStrides = calcStrides(bDims);
+        af::dim4 coords   = unravelIdx(idx, bDims, bStrides);
+
+        return ::testing::AssertionFailure()
+               << "VALUE DIFFERS at " << minimalDim4(coords, aDims) << ":\n"
+               << printContext(a, aName, b, bName, aDims, aStrides, idx);
+    }
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      const std::vector<T> &a, af::dim4 aDims,
+                                      const std::vector<T> &b, af::dim4 bDims,
+                                      float maxAbsDiff, FloatTag) {
+    typedef typename std::vector<T>::const_iterator iter;
+    // TODO(mark): Modify equality for float
+    std::pair<iter, iter> mismatches =
+        std::mismatch(a.begin(), a.end(), b.begin(), absMatch(maxAbsDiff));
+
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
+
+    if (aItr == a.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        dim_t idx       = std::distance(b.begin(), bItr);
+        af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
+
+        af::dim4 aStrides = calcStrides(aDims);
+
+        ::testing::AssertionResult result =
+            ::testing::AssertionFailure()
+            << "VALUE DIFFERS at " << minimalDim4(coords, aDims) << ":\n"
+            << printContext(a, aName, b, bName, aDims, aStrides, idx);
+
+        if (maxAbsDiff > 0) {
+            using af::abs;
+            using std::abs;
+            double absdiff = abs(*aItr - *bItr);
+            result << "\n  Actual diff: " << absdiff << "\n"
+                   << "Expected diff: " << maxAbsDiff;
+        }
+
+        return result;
+    }
+}
+
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      const af::array &a, const af::array &b,
+                                      float maxAbsDiff) {
+    typedef typename cond_type<
+        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
+        FloatTag, IntegerTag>::type TagType;
+    TagType tag;
+
+    std::vector<T> hA(static_cast<size_t>(a.elements()));
+    a.host(hA.data());
+
+    std::vector<T> hB(static_cast<size_t>(b.elements()));
+    b.host(hB.data());
+    return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff,
+                         tag);
+}
+
+template<typename T>
+::testing::AssertionResult assertArrayEq(std::string aName,
+                                         std::string aDimsName,
+                                         std::string bName,
+                                         const std::vector<T> &hA,
+                                         af::dim4 aDims, const af::array &b,
+                                         float maxAbsDiff) {
+    af::dtype aDtype = (af::dtype)af::dtype_traits<T>::af_type;
+    if (aDtype != b.type()) {
+        return ::testing::AssertionFailure()
+               << "TYPE MISMATCH:\n"
+               << "  Actual: " << bName << "(" << b.type() << ")\n"
+               << "Expected: " << aName << "(" << aDtype << ")";
+    }
+
+    if (aDims != b.dims()) {
+        return ::testing::AssertionFailure()
+               << "SIZE MISMATCH:\n"
+               << "  Actual: " << bName << "([" << b.dims() << "])\n"
+               << "Expected: " << aDimsName << "([" << aDims << "])";
+    }
+
+    // In case vector<T> a.size() != aDims.elements()
+    if (hA.size() != static_cast<size_t>(aDims.elements()))
+        return ::testing::AssertionFailure()
+               << "SIZE MISMATCH:\n"
+               << "  Actual: " << aDimsName << "([" << aDims << "] => "
+               << aDims.elements() << ")\n"
+               << "Expected: " << aName << ".size()(" << hA.size() << ")";
+
+    typedef typename cond_type<
+        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
+        FloatTag, IntegerTag>::type TagType;
+    TagType tag;
+
+    std::vector<T> hB(b.elements());
+    b.host(&hB.front());
+    return elemWiseEq<T>(aName, bName, hA, aDims, hB, b.dims(), maxAbsDiff,
+                         tag);
+}
+
+// To support C API
+template<typename T>
+::testing::AssertionResult assertArrayEq(std::string hA_name,
+                                         std::string aDimsName,
+                                         std::string bName,
+                                         const std::vector<T> &hA,
+                                         af::dim4 aDims, const af_array b) {
+    af_array bb = 0;
+    af_retain_array(&bb, b);
+    af::array bbb(bb);
+    return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, bbb);
+}
+
+// Called by ASSERT_VEC_ARRAY_NEAR
+template<typename T>
+::testing::AssertionResult assertArrayNear(
+    std::string hA_name, std::string aDimsName, std::string bName,
+    std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
+    const af::array &b, float maxAbsDiff) {
+    UNUSED(maxAbsDiffName);
+    return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
+}
+
+// To support C API
+template<typename T>
+::testing::AssertionResult assertArrayNear(
+    std::string hA_name, std::string aDimsName, std::string bName,
+    std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
+    const af_array b, float maxAbsDiff) {
+    af_array bb = 0;
+    af_retain_array(&bb, b);
+    af::array bbb(bb);
+    return assertArrayNear(hA_name, aDimsName, bName, maxAbsDiffName, hA, aDims,
+                           bbb, maxAbsDiff);
+}
+
+#define INSTANTIATE(To)                                                        \
+    template std::string printContext(                                         \
+        const std::vector<To> &hGold, std::string goldName,                    \
+        const std::vector<To> &hOut, std::string outName, af::dim4 arrDims,    \
+        af::dim4 arrStrides, dim_t idx);                                       \
+    template ::testing::AssertionResult assertArrayEq<To>(                     \
+        std::string aName, std::string aDimsName, std::string bName,           \
+        const std::vector<To> &hA, af::dim4 aDims, const af::array &b,         \
+        float maxAbsDiff);                                                     \
+    template ::testing::AssertionResult assertArrayEq<To>(                     \
+        std::string hA_name, std::string aDimsName, std::string bName,         \
+        const std::vector<To> &hA, af::dim4 aDims, const af_array b);          \
+    template ::testing::AssertionResult assertArrayNear<To>(                   \
+        std::string hA_name, std::string aDimsName, std::string bName,         \
+        std::string maxAbsDiffName, const std::vector<To> &hA, af::dim4 aDims, \
+        const af_array b, float maxAbsDiff);                                   \
+    template ::testing::AssertionResult assertArrayNear<To>(                   \
+        std::string hA_name, std::string aDimsName, std::string bName,         \
+        std::string maxAbsDiffName, const std::vector<To> &hA, af::dim4 aDims, \
+        const af::array &b, float maxAbsDiff)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(unsigned char);
+INSTANTIATE(half_float::half);
+INSTANTIATE(unsigned int);
+INSTANTIATE(unsigned short);
+INSTANTIATE(int);
+INSTANTIATE(char);
+INSTANTIATE(short);
+INSTANTIATE(af_cdouble);
+INSTANTIATE(af_cfloat);
+INSTANTIATE(long long);
+INSTANTIATE(unsigned long long);
+INSTANTIATE(std::complex<float>);
+INSTANTIATE(std::complex<double>);
+INSTANTIATE(af_half);
+#undef INSTANTIATE
+
+int main(int argc, char **argv) {
+    ::testing::InitGoogleTest(&argc, argv);
+    return RUN_ALL_TESTS();
+}
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
index 2f175977dd..fbbb9c5030 100644
--- a/test/arrayio.cpp
+++ b/test/arrayio.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 
diff --git a/test/binary.cpp b/test/binary.cpp
index a681e36b39..2daad03a2b 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -7,12 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/random.h>
 
 #include <cfenv>
 #include <cmath>
diff --git a/test/blas.cpp b/test/blas.cpp
index 0460f7de8d..612f6dd97f 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/cast.cpp b/test/cast.cpp
index 39fd2155ca..75ff9aca42 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -9,9 +9,11 @@
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/random.h>
 
 using af::cdouble;
 using af::cfloat;
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 49025cf520..eb0b46a187 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -7,21 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
 #include <af/defines.h>
+#include <af/random.h>
 #include <af/traits.hpp>
 
 #include <sstream>
 #include <string>
 #include <vector>
 
-#include <iostream>
-
 using af::array;
 using af::dim4;
 using af::dtype;
diff --git a/test/compare.cpp b/test/compare.cpp
index 8e3d22acc5..576186d164 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -13,6 +13,7 @@
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/random.h>
 
 using af::array;
 using af::dtype_traits;
diff --git a/test/complex.cpp b/test/complex.cpp
index 498203ec44..93a5d47b18 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -12,6 +12,8 @@
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/random.h>
 
 using std::endl;
 using namespace af;
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 87ed52999b..5cac824b29 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/constant.cpp b/test/constant.cpp
index ce9541ff3c..e54a3d01f7 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -10,9 +10,11 @@
 #include <gtest/gtest.h>
 #include <half.hpp>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/exception.h>
 
 using af::array;
 using af::cdouble;
diff --git a/test/convolve.cpp b/test/convolve.cpp
index a62c0aa3c8..4a3e193b7a 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/dot.cpp b/test/dot.cpp
index 065f735d4c..8a1905397c 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <half.hpp>
diff --git a/test/fft.cpp b/test/fft.cpp
index f289f3e600..ce654d3c05 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -6,7 +6,6 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
diff --git a/test/flat.cpp b/test/flat.cpp
index 4e0748b5eb..c9258e865b 100644
--- a/test/flat.cpp
+++ b/test/flat.cpp
@@ -12,6 +12,8 @@
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/random.h>
 
 #include <vector>
 
diff --git a/test/flip.cpp b/test/flip.cpp
index b1839ce413..852a837f14 100644
--- a/test/flip.cpp
+++ b/test/flip.cpp
@@ -9,10 +9,13 @@
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
 #include <af/index.h>
+#include <af/random.h>
 
 using af::array;
 using af::flip;
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index f19510c24c..b8f041d47b 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -6,13 +6,16 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
+#include <af/arith.h>
 #include <af/data.h>
 #include <af/defines.h>
+#include <af/device.h>
 #include <af/dim4.hpp>
+#include <af/random.h>
 #include <af/traits.hpp>
 
 #include <algorithm>
diff --git a/test/half.cpp b/test/half.cpp
index b07b738f6f..541af826a9 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <iostream>
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 14ca3b53d9..6c0edd0618 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -50,7 +50,7 @@ void hammingMatcherTest(string pTestFile, int feat_dim) {
     vector<vector<uint> > in32;
     vector<vector<uint> > tests;
 
-    readTests<uint, uint, uint>(pTestFile, numDims, in32, tests);
+    readTests<uint, uint, int>(pTestFile, numDims, in32, tests);
 
     vector<vector<T> > in(in32.size());
     for (size_t i = 0; i < in32[0].size(); i++) in[0].push_back((T)in32[0][i]);
@@ -124,7 +124,7 @@ TEST(HammingMatcher, CPP) {
     vector<vector<uint> > in;
     vector<vector<uint> > tests;
 
-    readTests<uint, uint, uint>(
+    readTests<uint, uint, int>(
         TEST_DIR "/hamming/hamming_500_5000_dim0_u32.test", numDims, in, tests);
 
     dim4 qDims = numDims[0];
diff --git a/test/index.cpp b/test/index.cpp
index 07dc5eac4f..a2901ed830 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -18,6 +18,7 @@
 #include <algorithm>
 #include <functional>
 #include <iostream>
+#include <numeric>
 #include <string>
 #include <vector>
 
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 8908daf6ce..5c49e8c3e8 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -9,9 +9,14 @@
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+
+#include <af/algorithm.h>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/random.h>
+
 #include <algorithm>
 
 using af::allTrue;
diff --git a/test/jit.cpp b/test/jit.cpp
index 7afa1aab41..3fb73764b2 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -7,13 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/gfor.h>
+#include <af/random.h>
 
+#include <numeric>
 #include <tuple>
 
 using af::array;
diff --git a/test/main.cpp b/test/main.cpp
deleted file mode 100644
index 76f841f1b1..0000000000
--- a/test/main.cpp
+++ /dev/null
@@ -1,6 +0,0 @@
-#include <gtest/gtest.h>
-
-int main(int argc, char **argv) {
-    ::testing::InitGoogleTest(&argc, argv);
-    return RUN_ALL_TESTS();
-}
diff --git a/test/math.cpp b/test/math.cpp
index ed42d499b8..8e2243e13c 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -10,6 +10,10 @@
 #include <testHelpers.hpp>
 #include <af/arith.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/exception.h>
+#include <af/random.h>
+
 #include <complex>
 
 // This makes the macros cleaner
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index e54268d3c7..f7519aed47 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -6,7 +6,6 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
diff --git a/test/median.cpp b/test/median.cpp
index 36a71e3d3b..332dbe8d70 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -9,9 +9,13 @@
 
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/device.h>
+#include <af/random.h>
+#include <af/statistics.h>
 
 using af::array;
 using af::dtype;
diff --git a/test/missing.cpp b/test/missing.cpp
index 92eda5de4c..d76b035c91 100644
--- a/test/missing.cpp
+++ b/test/missing.cpp
@@ -12,6 +12,9 @@
 #include <af/arith.h>
 #include <af/array.h>
 #include <af/data.h>
+#include <af/image.h>
+#include <af/lapack.h>
+#include <af/random.h>
 
 using namespace af;
 
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 1ae10acae5..e2a09dc20d 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/reduce.cpp b/test/reduce.cpp
index f41fa897f5..8a6efff2be 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/regions.cpp b/test/regions.cpp
index 255fe20c37..7deae9f5a5 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -49,7 +49,7 @@ void regionsTest(string pTestFile, af_connectivity connectivity,
     vector<dim4> numDims;
     vector<vector<uchar> > in;
     vector<vector<T> > tests;
-    readTests<uchar, T, unsigned>(pTestFile, numDims, in, tests);
+    readTests<uchar, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
 
@@ -112,8 +112,8 @@ TEST(Regions, CPP) {
     vector<dim4> numDims;
     vector<vector<float> > in;
     vector<vector<float> > tests;
-    readTests<float, float, unsigned>(
-        string(TEST_DIR "/regions/regions_8x8_4.test"), numDims, in, tests);
+    readTests<float, float, int>(string(TEST_DIR "/regions/regions_8x8_4.test"),
+                                 numDims, in, tests);
 
     dim4 idims = numDims[0];
     array input(idims, (float*)&(in[0].front()));
diff --git a/test/rng_match.cpp b/test/rng_match.cpp
index 0d10c0d0fc..4e64ddf121 100644
--- a/test/rng_match.cpp
+++ b/test/rng_match.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/select.cpp b/test/select.cpp
index 730f37f6ee..9ee331dff2 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <half.hpp>
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index b35c099893..cd7425cbfc 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -10,9 +10,6 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 
-#include <arrayfire.h>
-#include <gtest/gtest.h>
-#pragma once
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wparentheses"
 #include <half.hpp>
@@ -20,42 +17,21 @@
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <af/internal.h>
 #include <af/traits.hpp>
 
-#include <algorithm>
+#include <gtest/gtest.h>
+
 #include <cfloat>
-#include <fstream>
-#include <iterator>
-#include <limits>
-#include <numeric>
-#include <sstream>
-#include <stdexcept>
 #include <string>
-#include <typeinfo>
-#include <utility>
 #include <vector>
 
 #if defined(USE_MTX)
 #include <mmio.h>
 #endif
 
-bool operator==(const af_half &lhs, const af_half &rhs) {
-    return lhs.data_ == rhs.data_;
-}
+bool operator==(const af_half &lhs, const af_half &rhs);
 
-std::ostream &operator<<(std::ostream &os, const af_half &val) {
-    float out = *reinterpret_cast<const half_float::half *>(&val);
-    os << out;
-    return os;
-}
-
-namespace half_float {
-std::ostream &operator<<(std::ostream &os, half_float::half val) {
-    os << (float)val;
-    return os;
-}
-}  // namespace half_float
+std::ostream &operator<<(std::ostream &os, const af_half &val);
 
 #define UNUSED(expr) \
     do { (void)(expr); } while (0)
@@ -71,40 +47,11 @@ typedef uintl uintl;
 using aft::intl;
 using aft::uintl;
 
-std::ostream &operator<<(std::ostream &os, af::Backend bk) {
-    switch (bk) {
-        case AF_BACKEND_CPU: os << "AF_BACKEND_CPU"; break;
-        case AF_BACKEND_CUDA: os << "AF_BACKEND_CUDA"; break;
-        case AF_BACKEND_OPENCL: os << "AF_BACKEND_OPENCL"; break;
-        case AF_BACKEND_DEFAULT: os << "AF_BACKEND_DEFAULT"; break;
-    }
-    return os;
-}
+std::ostream &operator<<(std::ostream &os, af::Backend bk);
 
-std::ostream &operator<<(std::ostream &os, af_err e) {
-    return os << af_err_to_string(e);
-}
+std::ostream &operator<<(std::ostream &os, af_err e);
 
-std::ostream &operator<<(std::ostream &os, af::dtype type) {
-    std::string name;
-    switch (type) {
-        case f32: name = "f32"; break;
-        case c32: name = "c32"; break;
-        case f64: name = "f64"; break;
-        case c64: name = "c64"; break;
-        case b8: name = "b8"; break;
-        case s32: name = "s32"; break;
-        case u32: name = "u32"; break;
-        case u8: name = "u8"; break;
-        case s64: name = "s64"; break;
-        case u64: name = "u64"; break;
-        case s16: name = "s16"; break;
-        case u16: name = "u16"; break;
-        case f16: name = "f16"; break;
-        default: assert(false && "Invalid type");
-    }
-    return os << name;
-}
+std::ostream &operator<<(std::ostream &os, af::dtype type);
 
 namespace af {
 template<>
@@ -116,273 +63,55 @@ struct dtype_traits<half_float::half> {
 
 }  // namespace af
 
-namespace {
-
 typedef unsigned char uchar;
 typedef unsigned int uint;
 typedef unsigned short ushort;
 
-std::string readNextNonEmptyLine(std::ifstream &file) {
-    std::string result = "";
-    // Using a for loop to read the next non empty line
-    for (std::string line; std::getline(file, line);) {
-        result += line;
-        if (result != "") break;
-    }
-    // If no file has been found, throw an exception
-    if (result == "") {
-        throw std::runtime_error("Non empty lines not found in the file");
-    }
-    return result;
-}
+std::string readNextNonEmptyLine(std::ifstream &file);
+
+namespace half_float {
+std::ostream &operator<<(std::ostream &os, half_float::half val);
+}  // namespace half_float
 
 template<typename To, typename Ti>
 To convert(Ti in) {
     return static_cast<To>(in);
 }
 
-template<>
-float convert(af::half in) {
-    return static_cast<float>(half_float::half(in.data_));
-}
-
-template<>
-af_half convert(int in) {
-    half_float::half h = half_float::half(in);
-    af_half out;
-    memcpy(&out, &h, sizeof(af_half));
-    return out;
-}
+#ifndef EXTERN_TEMPLATE
+extern template float convert(af::half in);
+extern template af_half convert(int in);
+#endif
 
 template<typename inType, typename outType, typename FileElementType>
 void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
                std::vector<std::vector<inType> > &testInputs,
-               std::vector<std::vector<outType> > &testOutputs) {
-    using std::vector;
-
-    std::ifstream testFile(FileName.c_str());
-    if (testFile.good()) {
-        unsigned inputCount;
-        testFile >> inputCount;
-        inputDims.resize(inputCount);
-        for (unsigned i = 0; i < inputCount; i++) { testFile >> inputDims[i]; }
-
-        unsigned testCount;
-        testFile >> testCount;
-        testOutputs.resize(testCount);
-
-        vector<unsigned> testSizes(testCount);
-        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
-
-        testInputs.resize(inputCount, vector<inType>(0));
-        for (unsigned k = 0; k < inputCount; k++) {
-            dim_t nElems = inputDims[k].elements();
-            testInputs[k].resize(nElems);
-            FileElementType tmp;
-            for (unsigned i = 0; i < nElems; i++) {
-                testFile >> tmp;
-                testInputs[k][i] = convert<inType, FileElementType>(tmp);
-            }
-        }
-
-        testOutputs.resize(testCount, vector<outType>(0));
-        for (unsigned i = 0; i < testCount; i++) {
-            testOutputs[i].resize(testSizes[i]);
-            FileElementType tmp;
-            for (unsigned j = 0; j < testSizes[i]; j++) {
-                testFile >> tmp;
-                testOutputs[i][j] = convert<outType, FileElementType>(tmp);
-            }
-        }
-    } else {
-        FAIL() << "TEST FILE NOT FOUND";
-    }
-}
+               std::vector<std::vector<outType> > &testOutputs);
 
 template<typename inType, typename outType>
 void readTestsFromFile(const std::string &FileName,
                        std::vector<af::dim4> &inputDims,
                        std::vector<std::vector<inType> > &testInputs,
-                       std::vector<std::vector<outType> > &testOutputs) {
-    using std::vector;
-
-    std::ifstream testFile(FileName.c_str());
-    if (testFile.good()) {
-        unsigned inputCount;
-        testFile >> inputCount;
-        for (unsigned i = 0; i < inputCount; i++) {
-            af::dim4 temp(1);
-            testFile >> temp;
-            inputDims.push_back(temp);
-        }
-
-        unsigned testCount;
-        testFile >> testCount;
-        testOutputs.resize(testCount);
-
-        vector<unsigned> testSizes(testCount);
-        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
-
-        testInputs.resize(inputCount, vector<inType>(0));
-        for (unsigned k = 0; k < inputCount; k++) {
-            dim_t nElems = inputDims[k].elements();
-            testInputs[k].resize(nElems);
-            inType tmp;
-            for (unsigned i = 0; i < nElems; i++) {
-                testFile >> tmp;
-                testInputs[k][i] = tmp;
-            }
-        }
-
-        testOutputs.resize(testCount, vector<outType>(0));
-        for (unsigned i = 0; i < testCount; i++) {
-            testOutputs[i].resize(testSizes[i]);
-            outType tmp;
-            for (unsigned j = 0; j < testSizes[i]; j++) {
-                testFile >> tmp;
-                testOutputs[i][j] = tmp;
-            }
-        }
-    } else {
-        FAIL() << "TEST FILE NOT FOUND";
-    }
-}
+                       std::vector<std::vector<outType> > &testOutputs);
 
-inline void readImageTests(const std::string &pFileName,
-                           std::vector<af::dim4> &pInputDims,
-                           std::vector<std::string> &pTestInputs,
-                           std::vector<dim_t> &pTestOutSizes,
-                           std::vector<std::string> &pTestOutputs) {
-    using std::vector;
-
-    std::ifstream testFile(pFileName.c_str());
-    if (testFile.good()) {
-        unsigned inputCount;
-        testFile >> inputCount;
-        for (unsigned i = 0; i < inputCount; i++) {
-            af::dim4 temp(1);
-            testFile >> temp;
-            pInputDims.push_back(temp);
-        }
-
-        unsigned testCount;
-        testFile >> testCount;
-        pTestOutputs.resize(testCount);
-
-        pTestOutSizes.resize(testCount);
-        for (unsigned i = 0; i < testCount; i++) {
-            testFile >> pTestOutSizes[i];
-        }
-
-        pTestInputs.resize(inputCount, "");
-        for (unsigned k = 0; k < inputCount; k++) {
-            pTestInputs[k] = readNextNonEmptyLine(testFile);
-        }
-
-        pTestOutputs.resize(testCount, "");
-        for (unsigned i = 0; i < testCount; i++) {
-            pTestOutputs[i] = readNextNonEmptyLine(testFile);
-        }
-    } else {
-        FAIL() << "TEST FILE NOT FOUND";
-    }
-}
+void readImageTests(const std::string &pFileName,
+                    std::vector<af::dim4> &pInputDims,
+                    std::vector<std::string> &pTestInputs,
+                    std::vector<dim_t> &pTestOutSizes,
+                    std::vector<std::string> &pTestOutputs);
 
 template<typename outType>
 void readImageTests(const std::string &pFileName,
                     std::vector<af::dim4> &pInputDims,
                     std::vector<std::string> &pTestInputs,
-                    std::vector<std::vector<outType> > &pTestOutputs) {
-    using std::vector;
-
-    std::ifstream testFile(pFileName.c_str());
-    if (testFile.good()) {
-        unsigned inputCount;
-        testFile >> inputCount;
-        for (unsigned i = 0; i < inputCount; i++) {
-            af::dim4 temp(1);
-            testFile >> temp;
-            pInputDims.push_back(temp);
-        }
-
-        unsigned testCount;
-        testFile >> testCount;
-        pTestOutputs.resize(testCount);
-
-        vector<unsigned> testSizes(testCount);
-        for (unsigned i = 0; i < testCount; i++) { testFile >> testSizes[i]; }
-
-        pTestInputs.resize(inputCount, "");
-        for (unsigned k = 0; k < inputCount; k++) {
-            pTestInputs[k] = readNextNonEmptyLine(testFile);
-        }
-
-        pTestOutputs.resize(testCount, vector<outType>(0));
-        for (unsigned i = 0; i < testCount; i++) {
-            pTestOutputs[i].resize(testSizes[i]);
-            outType tmp;
-            for (unsigned j = 0; j < testSizes[i]; j++) {
-                testFile >> tmp;
-                pTestOutputs[i][j] = tmp;
-            }
-        }
-    } else {
-        FAIL() << "TEST FILE NOT FOUND";
-    }
-}
+                    std::vector<std::vector<outType> > &pTestOutputs);
 
 template<typename descType>
 void readImageFeaturesDescriptors(
     const std::string &pFileName, std::vector<af::dim4> &pInputDims,
     std::vector<std::string> &pTestInputs,
     std::vector<std::vector<float> > &pTestFeats,
-    std::vector<std::vector<descType> > &pTestDescs) {
-    using std::vector;
-
-    std::ifstream testFile(pFileName.c_str());
-    if (testFile.good()) {
-        unsigned inputCount;
-        testFile >> inputCount;
-        for (unsigned i = 0; i < inputCount; i++) {
-            af::dim4 temp(1);
-            testFile >> temp;
-            pInputDims.push_back(temp);
-        }
-
-        unsigned attrCount, featCount, descLen;
-        testFile >> featCount;
-        testFile >> attrCount;
-        testFile >> descLen;
-        pTestFeats.resize(attrCount);
-
-        pTestInputs.resize(inputCount, "");
-        for (unsigned k = 0; k < inputCount; k++) {
-            pTestInputs[k] = readNextNonEmptyLine(testFile);
-        }
-
-        pTestFeats.resize(attrCount, vector<float>(0));
-        for (unsigned i = 0; i < attrCount; i++) {
-            pTestFeats[i].resize(featCount);
-            float tmp;
-            for (unsigned j = 0; j < featCount; j++) {
-                testFile >> tmp;
-                pTestFeats[i][j] = tmp;
-            }
-        }
-
-        pTestDescs.resize(featCount, vector<descType>(0));
-        for (unsigned i = 0; i < featCount; i++) {
-            pTestDescs[i].resize(descLen);
-            descType tmp;
-            for (unsigned j = 0; j < descLen; j++) {
-                testFile >> tmp;
-                pTestDescs[i][j] = tmp;
-            }
-        }
-    } else {
-        FAIL() << "TEST FILE NOT FOUND";
-    }
-}
+    std::vector<std::vector<descType> > &pTestDescs);
 
 /**
  * Below is not a pair wise comparition method, rather
@@ -399,33 +128,7 @@ void readImageFeaturesDescriptors(
  * value of NRMSD. Hence, the range of RMSD is [0,255] for image inputs.
  */
 template<typename T>
-bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance) {
-    double accum  = 0.0;
-    double maxion = -FLT_MAX;  //(double)std::numeric_limits<T>::lowest();
-    double minion = FLT_MAX;   //(double)std::numeric_limits<T>::max();
-
-    for (dim_t i = 0; i < data_size; i++) {
-        double dTemp = (double)data[i];
-        double gTemp = (double)gold[i];
-        double diff  = gTemp - dTemp;
-        double err =
-            (std::isfinite(diff) && (std::abs(diff) > 1.0e-4)) ? diff : 0.0f;
-        accum += std::pow(err, 2.0);
-        maxion = std::max(maxion, dTemp);
-        minion = std::min(minion, dTemp);
-    }
-    accum /= data_size;
-    double NRMSD = std::sqrt(accum) / (maxion - minion);
-
-    if (std::isnan(NRMSD) || NRMSD > tolerance) {
-#ifndef NDEBUG
-        printf("Comparison failed, NRMSD value: %lf\n", NRMSD);
-#endif
-        return false;
-    }
-
-    return true;
-}
+bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance);
 
 template<typename T, typename Other>
 struct is_same_type {
@@ -492,37 +195,17 @@ struct IsFloatingPoint {
                               is_same_type<long double, T>::value;
 };
 
-bool noDoubleTests(af::dtype ty) {
-    bool isTypeDouble      = (ty == f64) || (ty == c64);
-    int dev                = af::getDevice();
-    bool isDoubleSupported = af::isDoubleAvailable(dev);
+bool noDoubleTests(af::dtype ty);
 
-    return ((isTypeDouble && !isDoubleSupported) ? true : false);
-}
-
-bool noHalfTests(af::dtype ty) {
-    bool isTypeHalf      = (ty == f16);
-    int dev              = af::getDevice();
-    bool isHalfSupported = af::isHalfAvailable(dev);
-
-    return ((isTypeHalf && !isHalfSupported) ? true : false);
-}
+bool noHalfTests(af::dtype ty);
 
 #define SUPPORTED_TYPE_CHECK(type)                                        \
     if (noDoubleTests((af_dtype)af::dtype_traits<type>::af_type)) return; \
     if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type)) return;
 
-inline bool noImageIOTests() {
-    bool ret = !af::isImageIOAvailable();
-    if (ret) printf("Image IO Not Configured. Test will exit\n");
-    return ret;
-}
+bool noImageIOTests();
 
-inline bool noLAPACKTests() {
-    bool ret = !af::isLAPACKAvailable();
-    if (ret) printf("LAPACK Not Configured. Test will exit\n");
-    return ret;
-}
+bool noLAPACKTests();
 
 template<typename TO, typename FROM>
 TO convert_to(FROM in) {
@@ -531,258 +214,44 @@ TO convert_to(FROM in) {
 
 // TODO: perform conversion on device for CUDA and OpenCL
 template<typename T>
-af_err conv_image(af_array *out, af_array in) {
-    af_array outArray;
-
-    dim_t d0, d1, d2, d3;
-    af_get_dims(&d0, &d1, &d2, &d3, in);
-    af::dim4 idims(d0, d1, d2, d3);
-
-    dim_t nElems = 0;
-    af_get_elements(&nElems, in);
-
-    float *in_data = new float[nElems];
-    af_get_data_ptr(in_data, in);
-
-    T *out_data = new T[nElems];
-
-    for (int i = 0; i < (int)nElems; i++) out_data[i] = (T)in_data[i];
-
-    af_create_array(&outArray, out_data, idims.ndims(), idims.get(),
-                    (af_dtype)af::dtype_traits<T>::af_type);
-
-    std::swap(*out, outArray);
-
-    delete[] in_data;
-    delete[] out_data;
-
-    return AF_SUCCESS;
-}
+af_err conv_image(af_array *out, af_array in);
 
 template<typename T>
-af::array cpu_randu(const af::dim4 dims) {
-    typedef typename af::dtype_traits<T>::base_type BT;
+af::array cpu_randu(const af::dim4 dims);
 
-    bool isTypeCplx = is_same_type<T, af::cfloat>::value ||
-                      is_same_type<T, af::cdouble>::value;
-    bool isTypeFloat = is_same_type<BT, float>::value ||
-                       is_same_type<BT, double>::value ||
-                       is_same_type<BT, half_float::half>::value;
-
-    size_t elements = (isTypeCplx ? 2 : 1) * dims.elements();
-
-    std::vector<BT> out(elements);
-    for (size_t i = 0; i < elements; i++) {
-        out[i] = isTypeFloat ? (BT)(rand()) / RAND_MAX : rand() % 100;
-    }
-
-    return af::array(dims, (T *)&out[0]);
-}
-
-void cleanSlate() {
-    const size_t step_bytes = 1024;
-
-    size_t alloc_bytes, alloc_buffers;
-    size_t lock_bytes, lock_buffers;
-
-    af::deviceGC();
-
-    af::deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
-
-    ASSERT_EQ(0u, alloc_buffers);
-    ASSERT_EQ(0u, lock_buffers);
-    ASSERT_EQ(0u, alloc_bytes);
-    ASSERT_EQ(0u, lock_bytes);
-
-    af::setMemStepSize(step_bytes);
-
-    ASSERT_EQ(af::getMemStepSize(), step_bytes);
-}
+void cleanSlate();
 
 //********** arrayfire custom test asserts ***********
 
 // Overloading unary + op is needed to make unsigned char values printable
 //  as numbers
-af_half abs(af_half in) {
-    half_float::half in_;
-    // casting to void* to avoid class-memaccess warnings on windows
-    memcpy(static_cast<void *>(&in_), &in, sizeof(af_half));
-    half_float::half out_ = abs(in_);
-    af_half out;
-    memcpy(&out, &out_, sizeof(af_half));
-    return out;
-}
+af_half abs(af_half in);
 
-af_half operator-(af_half lhs, af_half rhs) {
-    half_float::half lhs_;
-    half_float::half rhs_;
-
-    // casting to void* to avoid class-memaccess warnings on windows
-    memcpy(static_cast<void *>(&lhs_), &lhs, sizeof(af_half));
-    memcpy(static_cast<void *>(&rhs_), &rhs, sizeof(af_half));
-    half_float::half out = lhs_ - rhs_;
-    af_half o;
-    memcpy(&o, &out, sizeof(af_half));
-    return o;
-}
+af_half operator-(af_half lhs, af_half rhs);
 
-const af::cfloat &operator+(const af::cfloat &val) { return val; }
+const af::cfloat &operator+(const af::cfloat &val);
 
-const af::cdouble &operator+(const af::cdouble &val) { return val; }
+const af::cdouble &operator+(const af::cdouble &val);
 
-const af_half &operator+(const af_half &val) { return val; }
+const af_half &operator+(const af_half &val);
 
 // Calculate a multi-dimensional coordinates' linearized index
-dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
-    return std::inner_product(coords.get(), coords.get() + 4, strides.get(),
-                              0LL);
-}
+dim_t ravelIdx(af::dim4 coords, af::dim4 strides);
 
 // Calculate a linearized index's multi-dimensonal coordinates in an af::array,
 //  given its dimension sizes and strides
-af::dim4 unravelIdx(dim_t idx, af::dim4 dims, af::dim4 strides) {
-    af::dim4 coords;
-    coords[3] = idx / (strides[3]);
-    coords[2] = idx / (strides[2]) % dims[2];
-    coords[1] = idx / (strides[1]) % dims[1];
-    coords[0] = idx % dims[0];
-
-    return coords;
-}
-
-af::dim4 unravelIdx(dim_t idx, af::array arr) {
-    af::dim4 dims = arr.dims();
-    af::dim4 st   = af::getStrides(arr);
-    return unravelIdx(idx, dims, st);
-}
-
-af::dim4 calcStrides(const af::dim4 &parentDim) {
-    af::dim4 out(1, 1, 1, 1);
-    dim_t *out_dims          = out.get();
-    const dim_t *parent_dims = parentDim.get();
-
-    for (dim_t i = 1; i < 4; i++) {
-        out_dims[i] = out_dims[i - 1] * parent_dims[i - 1];
-    }
+af::dim4 unravelIdx(dim_t idx, af::dim4 dims, af::dim4 strides);
 
-    return out;
-}
+af::dim4 unravelIdx(dim_t idx, af::array arr);
 
-std::string minimalDim4(af::dim4 coords, af::dim4 dims) {
-    std::ostringstream os;
-    os << "(" << coords[0];
-    if (dims[1] > 1 || dims[2] > 1 || dims[3] > 1) { os << ", " << coords[1]; }
-    if (dims[2] > 1 || dims[3] > 1) { os << ", " << coords[2]; }
-    if (dims[3] > 1) { os << ", " << coords[3]; }
-    os << ")";
+af::dim4 calcStrides(const af::dim4 &parentDim);
 
-    return os.str();
-}
+std::string minimalDim4(af::dim4 coords, af::dim4 dims);
 
 template<typename T>
 std::string printContext(const std::vector<T> &hGold, std::string goldName,
                          const std::vector<T> &hOut, std::string outName,
-                         af::dim4 arrDims, af::dim4 arrStrides, dim_t idx) {
-    std::ostringstream os;
-
-    af::dim4 coords = unravelIdx(idx, arrDims, arrStrides);
-    dim_t ctxWidth  = 5;
-
-    // Coordinates that span dim0
-    af::dim4 coordsMinBound = coords;
-    coordsMinBound[0]       = 0;
-    af::dim4 coordsMaxBound = coords;
-    coordsMaxBound[0]       = arrDims[0] - 1;
-
-    // dim0 positions that can be displayed
-    dim_t dim0Start = std::max<dim_t>(0LL, coords[0] - ctxWidth);
-    dim_t dim0End   = std::min<dim_t>(coords[0] + ctxWidth + 1LL, arrDims[0]);
-
-    // Linearized indices of values in vectors that can be displayed
-    dim_t vecStartIdx =
-        std::max<dim_t>(ravelIdx(coordsMinBound, arrStrides), idx - ctxWidth);
-
-    // Display as minimal coordinates as needed
-    // First value is the range of dim0 positions that will be displayed
-    os << "Viewing slice (" << dim0Start << ":" << dim0End - 1;
-    if (arrDims[1] > 1 || arrDims[2] > 1 || arrDims[3] > 1)
-        os << ", " << coords[1];
-    if (arrDims[2] > 1 || arrDims[3] > 1) os << ", " << coords[2];
-    if (arrDims[3] > 1) os << ", " << coords[3];
-    os << "), dims are (" << arrDims << ") strides: (" << arrStrides << ")\n";
-
-    dim_t ctxElems = dim0End - dim0Start;
-    std::vector<int> valFieldWidths(ctxElems);
-    std::vector<std::string> ctxDim0(ctxElems);
-    std::vector<std::string> ctxOutVals(ctxElems);
-    std::vector<std::string> ctxGoldVals(ctxElems);
-
-    // Get dim0 positions and out/reference values for the context window
-    //
-    // Also get the max string length between the position and out/ref values
-    // per item so that it can be used later as the field width for
-    // displaying each item in the context window
-    for (dim_t i = 0; i < ctxElems; ++i) {
-        std::ostringstream tmpOs;
-
-        dim_t dim0 = dim0Start + i;
-        if (dim0 == coords[0])
-            tmpOs << "[" << dim0 << "]";
-        else
-            tmpOs << dim0;
-        ctxDim0[i]     = tmpOs.str();
-        size_t dim0Len = tmpOs.str().length();
-        tmpOs.str(std::string());
-
-        dim_t valIdx = vecStartIdx + i;
-
-        if (valIdx == idx) {
-            tmpOs << "[" << +hOut[valIdx] << "]";
-        } else {
-            tmpOs << +hOut[valIdx];
-        }
-        ctxOutVals[i] = tmpOs.str();
-        size_t outLen = tmpOs.str().length();
-        tmpOs.str(std::string());
-
-        if (valIdx == idx) {
-            tmpOs << "[" << +hGold[valIdx] << "]";
-        } else {
-            tmpOs << +hGold[valIdx];
-        }
-        ctxGoldVals[i] = tmpOs.str();
-        size_t goldLen = tmpOs.str().length();
-        tmpOs.str(std::string());
-
-        int maxWidth      = std::max<int>(dim0Len, outLen);
-        maxWidth          = std::max<int>(maxWidth, goldLen);
-        valFieldWidths[i] = maxWidth;
-    }
-
-    size_t varNameWidth = std::max<size_t>(goldName.length(), outName.length());
-
-    // Display dim0 positions, output values, and reference values
-    os << std::right << std::setw(varNameWidth) << ""
-       << "   ";
-    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
-        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxDim0[i];
-    }
-    os << "\n";
-
-    os << std::right << std::setw(varNameWidth) << outName << ": {";
-    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
-        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxOutVals[i];
-    }
-    os << " }\n";
-
-    os << std::right << std::setw(varNameWidth) << goldName << ": {";
-    for (uint i = 0; i < (dim0End - dim0Start); ++i) {
-        os << std::setw(valFieldWidths[i] + 1) << std::right << ctxGoldVals[i];
-    }
-    os << " }";
-
-    return os.str();
-}
+                         af::dim4 arrDims, af::dim4 arrStrides, dim_t idx);
 
 struct FloatTag {};
 struct IntegerTag {};
@@ -791,26 +260,7 @@ template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T> &a, af::dim4 aDims,
                                       const std::vector<T> &b, af::dim4 bDims,
-                                      float maxAbsDiff, IntegerTag) {
-    UNUSED(maxAbsDiff);
-    typedef typename std::vector<T>::const_iterator iter;
-    std::pair<iter, iter> mismatches =
-        std::mismatch(a.begin(), a.end(), b.begin());
-    iter bItr = mismatches.second;
-
-    if (bItr == b.end()) {
-        return ::testing::AssertionSuccess();
-    } else {
-        dim_t idx         = std::distance(b.begin(), bItr);
-        af::dim4 aStrides = calcStrides(aDims);
-        af::dim4 bStrides = calcStrides(bDims);
-        af::dim4 coords   = unravelIdx(idx, bDims, bStrides);
-
-        return ::testing::AssertionFailure()
-               << "VALUE DIFFERS at " << minimalDim4(coords, aDims) << ":\n"
-               << printContext(a, aName, b, bName, aDims, aStrides, idx);
-    }
-}
+                                      float maxAbsDiff, IntegerTag);
 
 struct absMatch {
     float diff_;
@@ -819,6 +269,7 @@ struct absMatch {
     template<typename T>
     bool operator()(T lhs, T rhs) {
         using af::abs;
+        using half_float::abs;
         using std::abs;
         return abs(rhs - lhs) <= diff_;
     }
@@ -828,122 +279,16 @@ template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T> &a, af::dim4 aDims,
                                       const std::vector<T> &b, af::dim4 bDims,
-                                      float maxAbsDiff, FloatTag) {
-    typedef typename std::vector<T>::const_iterator iter;
-    // TODO(mark): Modify equality for float
-    std::pair<iter, iter> mismatches =
-        std::mismatch(a.begin(), a.end(), b.begin(), absMatch(maxAbsDiff));
-
-    iter aItr = mismatches.first;
-    iter bItr = mismatches.second;
-
-    if (aItr == a.end()) {
-        return ::testing::AssertionSuccess();
-    } else {
-        dim_t idx       = std::distance(b.begin(), bItr);
-        af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
-
-        af::dim4 aStrides = calcStrides(aDims);
-
-        ::testing::AssertionResult result =
-            ::testing::AssertionFailure()
-            << "VALUE DIFFERS at " << minimalDim4(coords, aDims) << ":\n"
-            << printContext(a, aName, b, bName, aDims, aStrides, idx);
-
-        if (maxAbsDiff > 0) {
-            using af::abs;
-            using std::abs;
-            double absdiff = abs(*aItr - *bItr);
-            result << "\n  Actual diff: " << absdiff << "\n"
-                   << "Expected diff: " << maxAbsDiff;
-        }
-
-        return result;
-    }
-}
+                                      float maxAbsDiff, FloatTag);
 
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const af::array &a, const af::array &b,
-                                      float maxAbsDiff) {
-    typedef typename cond_type<
-        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
-        FloatTag, IntegerTag>::type TagType;
-    TagType tag;
-
-    std::vector<T> hA(static_cast<size_t>(a.elements()));
-    a.host(hA.data());
-
-    std::vector<T> hB(static_cast<size_t>(b.elements()));
-    b.host(hB.data());
-    return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff,
-                         tag);
-}
+                                      float maxAbsDiff);
 
-// Called by ASSERT_ARRAYS_EQ
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          const af::array &a, const af::array &b,
-                                         float maxAbsDiff = 0.f) {
-    af::dtype aType = a.type();
-    af::dtype bType = b.type();
-    if (aType != bType)
-        return ::testing::AssertionFailure()
-               << "TYPE MISMATCH: \n"
-               << "  Actual: " << bName << "(" << b.type() << ")\n"
-               << "Expected: " << aName << "(" << a.type() << ")";
-
-    af::dtype arrDtype = aType;
-    if (a.dims() != b.dims())
-        return ::testing::AssertionFailure()
-               << "SIZE MISMATCH: \n"
-               << "  Actual: " << bName << "([" << b.dims() << "])\n"
-               << "Expected: " << aName << "([" << a.dims() << "])";
-
-    switch (arrDtype) {
-        case f32:
-            return elemWiseEq<float>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case c32:
-            return elemWiseEq<af::cfloat>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case f64:
-            return elemWiseEq<double>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case c64:
-            return elemWiseEq<af::cdouble>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case b8: return elemWiseEq<char>(aName, bName, a, b, maxAbsDiff); break;
-        case s32: return elemWiseEq<int>(aName, bName, a, b, maxAbsDiff); break;
-        case u32:
-            return elemWiseEq<uint>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case u8:
-            return elemWiseEq<uchar>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case s64:
-            return elemWiseEq<long long>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case u64:
-            return elemWiseEq<unsigned long long>(aName, bName, a, b,
-                                                  maxAbsDiff);
-            break;
-        case s16:
-            return elemWiseEq<short>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case u16:
-            return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);
-            break;
-        case f16:
-            return elemWiseEq<af::half>(aName, bName, a, b, maxAbsDiff);
-            break;
-        default:
-            return ::testing::AssertionFailure()
-                   << "INVALID TYPE, see enum numbers: " << bName << "("
-                   << b.type() << ") and " << aName << "(" << a.type() << ")";
-    }
-
-    return ::testing::AssertionSuccess();
-}
+                                         float maxAbsDiff = 0.f);
 
 // Called by ASSERT_VEC_ARRAY_EQ
 template<typename T>
@@ -952,51 +297,11 @@ ::testing::AssertionResult assertArrayEq(std::string aName,
                                          std::string bName,
                                          const std::vector<T> &hA,
                                          af::dim4 aDims, const af::array &b,
-                                         float maxAbsDiff = 0.0f) {
-    af::dtype aDtype = (af::dtype)af::dtype_traits<T>::af_type;
-    if (aDtype != b.type()) {
-        return ::testing::AssertionFailure()
-               << "TYPE MISMATCH:\n"
-               << "  Actual: " << bName << "(" << b.type() << ")\n"
-               << "Expected: " << aName << "(" << aDtype << ")";
-    }
-
-    if (aDims != b.dims()) {
-        return ::testing::AssertionFailure()
-               << "SIZE MISMATCH:\n"
-               << "  Actual: " << bName << "([" << b.dims() << "])\n"
-               << "Expected: " << aDimsName << "([" << aDims << "])";
-    }
-
-    // In case vector<T> a.size() != aDims.elements()
-    if (hA.size() != static_cast<size_t>(aDims.elements()))
-        return ::testing::AssertionFailure()
-               << "SIZE MISMATCH:\n"
-               << "  Actual: " << aDimsName << "([" << aDims << "] => "
-               << aDims.elements() << ")\n"
-               << "Expected: " << aName << ".size()(" << hA.size() << ")";
-
-    typedef typename cond_type<
-        IsFloatingPoint<typename af::dtype_traits<T>::base_type>::value,
-        FloatTag, IntegerTag>::type TagType;
-    TagType tag;
-
-    std::vector<T> hB(b.elements());
-    b.host(&hB.front());
-    return elemWiseEq<T>(aName, bName, hA, aDims, hB, b.dims(), maxAbsDiff,
-                         tag);
-}
+                                         float maxAbsDiff = 0.0f);
 
 // To support C API
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
-                                         const af_array a, const af_array b) {
-    af_array aa = 0, bb = 0;
-    af_retain_array(&aa, a);
-    af_retain_array(&bb, b);
-    af::array aaa(aa);
-    af::array bbb(bb);
-    return assertArrayEq(aName, bName, aaa, bbb, 0.0f);
-}
+                                         const af_array a, const af_array b);
 
 // To support C API
 template<typename T>
@@ -1004,58 +309,34 @@ ::testing::AssertionResult assertArrayEq(std::string hA_name,
                                          std::string aDimsName,
                                          std::string bName,
                                          const std::vector<T> &hA,
-                                         af::dim4 aDims, const af_array b) {
-    af_array bb = 0;
-    af_retain_array(&bb, b);
-    af::array bbb(bb);
-    return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, bbb);
-}
+                                         af::dim4 aDims, const af_array b);
 
 // Called by ASSERT_ARRAYS_NEAR
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
                                            const af::array &a,
                                            const af::array &b,
-                                           float maxAbsDiff) {
-    UNUSED(maxAbsDiffName);
-    return assertArrayEq(aName, bName, a, b, maxAbsDiff);
-}
+                                           float maxAbsDiff);
 
 // Called by ASSERT_VEC_ARRAY_NEAR
 template<typename T>
 ::testing::AssertionResult assertArrayNear(
     std::string hA_name, std::string aDimsName, std::string bName,
     std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
-    const af::array &b, float maxAbsDiff) {
-    UNUSED(maxAbsDiffName);
-    return assertArrayEq(hA_name, aDimsName, bName, hA, aDims, b, maxAbsDiff);
-}
+    const af::array &b, float maxAbsDiff);
 
 // To support C API
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
                                            const af_array a, const af_array b,
-                                           float maxAbsDiff) {
-    af_array aa = 0, bb = 0;
-    af_retain_array(&aa, a);
-    af_retain_array(&bb, b);
-    af::array aaa(aa);
-    af::array bbb(bb);
-    return assertArrayNear(aName, bName, maxAbsDiffName, aaa, bbb, maxAbsDiff);
-}
+                                           float maxAbsDiff);
 
 // To support C API
 template<typename T>
 ::testing::AssertionResult assertArrayNear(
     std::string hA_name, std::string aDimsName, std::string bName,
     std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
-    const af_array b, float maxAbsDiff) {
-    af_array bb = 0;
-    af_retain_array(&bb, b);
-    af::array bbb(bb);
-    return assertArrayNear(hA_name, aDimsName, bName, maxAbsDiffName, hA, aDims,
-                           bbb, maxAbsDiff);
-}
+    const af_array b, float maxAbsDiff);
 
 /// Checks if the C-API arrayfire function returns successfully
 ///
@@ -1117,99 +398,9 @@ ::testing::AssertionResult assertArrayNear(
 
 #if defined(USE_MTX)
 ::testing::AssertionResult mtxReadSparseMatrix(af::array &out,
-                                               const char *fileName) {
-    FILE *fileHandle;
-
-    if ((fileHandle = fopen(fileName, "r")) == NULL) {
-        return ::testing::AssertionFailure()
-               << "Failed to open mtx file: " << fileName << "\n";
-    }
-
-    MM_typecode matcode;
-    if (mm_read_banner(fileHandle, &matcode)) {
-        return ::testing::AssertionFailure()
-               << "Could not process Matrix Market banner.\n";
-    }
-
-    if (!(mm_is_matrix(matcode) && mm_is_sparse(matcode))) {
-        return ::testing::AssertionFailure()
-               << "Input mtx doesn't have a sparse matrix.\n";
-    }
-
-    if (mm_is_integer(matcode)) {
-        return ::testing::AssertionFailure() << "MTX file has integer data. \
-                Integer sparse matrices are not supported in ArrayFire yet.\n";
-    }
-
-    int M = 0, N = 0, nz = 0;
-    if (mm_read_mtx_crd_size(fileHandle, &M, &N, &nz)) {
-        return ::testing::AssertionFailure()
-               << "Failed to read matrix dimensions.\n";
-    }
-
-    if (mm_is_real(matcode)) {
-        std::vector<int> I(nz);
-        std::vector<int> J(nz);
-        std::vector<float> V(nz);
-
-        for (int i = 0; i < nz; ++i) {
-            int c, r;
-            double v;
-            int readCount = fscanf(fileHandle, "%d %d %lg\n", &r, &c, &v);
-            if (readCount != 3) {
-                fclose(fileHandle);
-                return ::testing::AssertionFailure()
-                       << "\nEnd of file reached, expected more data, "
-                       << "following are some reasons this happens.\n"
-                       << "\t - use of template type that doesn't match data "
-                          "type\n"
-                       << "\t - the mtx file itself doesn't have enough data\n";
-            }
-            I[i] = r - 1;
-            J[i] = c - 1;
-            V[i] = (float)v;
-        }
-
-        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), f32,
-                         AF_STORAGE_COO);
-    } else if (mm_is_complex(matcode)) {
-        std::vector<int> I(nz);
-        std::vector<int> J(nz);
-        std::vector<af::cfloat> V(nz);
-
-        for (int i = 0; i < nz; ++i) {
-            int c, r;
-            double real, imag;
-            int readCount =
-                fscanf(fileHandle, "%d %d %lg %lg\n", &r, &c, &real, &imag);
-            if (readCount != 4) {
-                fclose(fileHandle);
-                return ::testing::AssertionFailure()
-                       << "\nEnd of file reached, expected more data, "
-                       << "following are some reasons this happens.\n"
-                       << "\t - use of template type that doesn't match data "
-                          "type\n"
-                       << "\t - the mtx file itself doesn't have enough data\n";
-            }
-            I[i] = r - 1;
-            J[i] = c - 1;
-            V[i] = af::cfloat(float(real), float(imag));
-        }
-
-        out = af::sparse(M, N, nz, V.data(), I.data(), J.data(), c32,
-                         AF_STORAGE_COO);
-    } else {
-        return ::testing::AssertionFailure()
-               << "Unknown matcode from MTX FILE\n";
-    }
-
-    fclose(fileHandle);
-    return ::testing::AssertionSuccess();
-}
+                                               const char *fileName);
 #endif  // USE_MTX
 
-}  // namespace
-
 enum TestOutputArrayType {
     // Test af_* function when given a null array as its output
     NULL_ARRAY,
@@ -1241,299 +432,85 @@ class TestOutputArrayInfo {
     TestOutputArrayType out_arr_type;
 
    public:
-    TestOutputArrayInfo()
-        : out_arr(0)
-        , out_arr_cpy(0)
-        , out_subarr(0)
-        , out_subarr_ndims(0)
-        , out_arr_type(NULL_ARRAY) {
-        for (uint i = 0; i < 4; ++i) { out_subarr_idxs[i] = af_span; }
-    }
+    TestOutputArrayInfo();
 
-    TestOutputArrayInfo(TestOutputArrayType arr_type)
-        : out_arr(0)
-        , out_arr_cpy(0)
-        , out_subarr(0)
-        , out_subarr_ndims(0)
-        , out_arr_type(arr_type) {
-        for (uint i = 0; i < 4; ++i) { out_subarr_idxs[i] = af_span; }
-    }
+    TestOutputArrayInfo(TestOutputArrayType arr_type);
 
-    ~TestOutputArrayInfo() {
-        if (out_subarr) af_release_array(out_subarr);
-        if (out_arr_cpy) af_release_array(out_arr_cpy);
-        if (out_arr) af_release_array(out_arr);
-    }
+    ~TestOutputArrayInfo();
 
-    void init(const unsigned ndims, const dim_t *const dims,
-              const af_dtype ty) {
-        ASSERT_SUCCESS(af_randu(&out_arr, ndims, dims, ty));
-    }
+    void init(const unsigned ndims, const dim_t *const dims, const af_dtype ty);
 
     void init(const unsigned ndims, const dim_t *const dims, const af_dtype ty,
-              const af_seq *const subarr_idxs) {
-        init(ndims, dims, ty);
-
-        ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
-        for (uint i = 0; i < ndims; ++i) {
-            out_subarr_idxs[i] = subarr_idxs[i];
-        }
-        out_subarr_ndims = ndims;
-
-        ASSERT_SUCCESS(af_index(&out_subarr, out_arr, ndims, subarr_idxs));
-    }
+              const af_seq *const subarr_idxs);
 
     void init(double val, const unsigned ndims, const dim_t *const dims,
-              const af_dtype ty) {
-        switch (ty) {
-            case c32:
-            case c64:
-                af_constant_complex(&out_arr, val, 0.0, ndims, dims, ty);
-                break;
-            case s64:
-                af_constant_long(&out_arr, static_cast<intl>(val), ndims, dims);
-                break;
-            case u64:
-                af_constant_ulong(&out_arr, static_cast<uintl>(val), ndims,
-                                  dims);
-                break;
-            default: af_constant(&out_arr, val, ndims, dims, ty); break;
-        }
-    }
+              const af_dtype ty);
 
     void init(double val, const unsigned ndims, const dim_t *const dims,
-              const af_dtype ty, const af_seq *const subarr_idxs) {
-        init(val, ndims, dims, ty);
-
-        ASSERT_SUCCESS(af_copy_array(&out_arr_cpy, out_arr));
-        for (uint i = 0; i < ndims; ++i) {
-            out_subarr_idxs[i] = subarr_idxs[i];
-        }
-        out_subarr_ndims = ndims;
-
-        ASSERT_SUCCESS(af_index(&out_subarr, out_arr, ndims, subarr_idxs));
-    }
+              const af_dtype ty, const af_seq *const subarr_idxs);
 
-    af_array getOutput() {
-        if (out_arr_type == SUB_ARRAY) {
-            return out_subarr;
-        } else {
-            return out_arr;
-        }
-    }
+    af_array getOutput();
 
-    void setOutput(af_array array) {
-        if (out_arr != 0) { ASSERT_SUCCESS(af_release_array(out_arr)); }
-        out_arr = array;
-    }
+    void setOutput(af_array array);
 
-    af_array getFullOutput() { return out_arr; }
-    af_array getFullOutputCopy() { return out_arr_cpy; }
-    af_seq *getSubArrayIdxs() { return &out_subarr_idxs[0]; }
-    dim_t getSubArrayNumDims() { return out_subarr_ndims; }
-    TestOutputArrayType getOutputArrayType() { return out_arr_type; }
+    af_array getFullOutput();
+    af_array getFullOutputCopy();
+    af_seq *getSubArrayIdxs();
+    dim_t getSubArrayNumDims();
+    TestOutputArrayType getOutputArrayType();
 };
 
 // Generates a random array. testWriteToOutputArray expects that it will receive
 // the same af_array that this generates after the af_* function is called
 void genRegularArray(TestOutputArrayInfo *metadata, const unsigned ndims,
-                     const dim_t *const dims, const af_dtype ty) {
-    metadata->init(ndims, dims, ty);
-}
+                     const dim_t *const dims, const af_dtype ty);
 
 void genRegularArray(TestOutputArrayInfo *metadata, double val,
                      const unsigned ndims, const dim_t *const dims,
-                     const af_dtype ty) {
-    metadata->init(val, ndims, dims, ty);
-}
+                     const af_dtype ty);
 
 // Generates a large, random array, and extracts a subarray for the af_*
 // function to use. testWriteToOutputArray expects that the large array that it
 // receives is equal to the same large array with the gold array injected on the
 // same subarray location
 void genSubArray(TestOutputArrayInfo *metadata, const unsigned ndims,
-                 const dim_t *const dims, const af_dtype ty) {
-    const dim_t pad_size = 2;
-
-    // The large array is padded on both sides of each dimension
-    // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
-    dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
-    for (uint i = 0; i < ndims; ++i) {
-        full_arr_dims[i] = dims[i] + 2 * pad_size;
-    }
-
-    // Calculate index of sub-array. These will be used also by
-    // testWriteToOutputArray so that the gold sub array will be placed in the
-    // same location. Currently, this location is the center of the large array
-    af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
-    for (uint i = 0; i < ndims; ++i) {
-        af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
-        subarr_idxs[i] = idx;
-    }
-
-    metadata->init(ndims, full_arr_dims, ty, &subarr_idxs[0]);
-}
+                 const dim_t *const dims, const af_dtype ty);
 
 void genSubArray(TestOutputArrayInfo *metadata, double val,
                  const unsigned ndims, const dim_t *const dims,
-                 const af_dtype ty) {
-    const dim_t pad_size = 2;
-
-    // The large array is padded on both sides of each dimension
-    // Padding is only applied if the dimension is used, i.e. if dims[i] > 1
-    dim_t full_arr_dims[4] = {dims[0], dims[1], dims[2], dims[3]};
-    for (uint i = 0; i < ndims; ++i) {
-        full_arr_dims[i] = dims[i] + 2 * pad_size;
-    }
-
-    // Calculate index of sub-array. These will be used also by
-    // testWriteToOutputArray so that the gold sub array will be placed in the
-    // same location. Currently, this location is the center of the large array
-    af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
-    for (uint i = 0; i < ndims; ++i) {
-        af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
-        subarr_idxs[i] = idx;
-    }
-
-    metadata->init(val, ndims, full_arr_dims, ty, &subarr_idxs[0]);
-}
+                 const af_dtype ty);
 
 // Generates a reordered array. testWriteToOutputArray expects that this array
 // will still have the correct output values from the af_* function, even though
 // the array was initially reordered.
 void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
-                       const dim_t *const dims, const af_dtype ty) {
-    // The rest of this function assumes that dims has 4 elements. Just in case
-    // dims has < 4 elements, use another dims array that is filled with 1s
-    dim_t all_dims[4] = {1, 1, 1, 1};
-    for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
-
-    // This reorder combination will not move data around, but will simply
-    // call modDims and modStrides (see src/api/c/reorder.cpp).
-    // The output will be checked if it is still correct even with the
-    // modified dims and strides "hack" with no data movement
-    uint reorder_idxs[4] = {0, 2, 1, 3};
-
-    // Shape the output array such that the reordered output array will have
-    // the correct dimensions that the test asks for (i.e. must match dims arg)
-    dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
-    for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
-    metadata->init(4, init_dims, ty);
-
-    af_array reordered = 0;
-    ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
-                              reorder_idxs[0], reorder_idxs[1], reorder_idxs[2],
-                              reorder_idxs[3]));
-    metadata->setOutput(reordered);
-}
+                       const dim_t *const dims, const af_dtype ty);
 
 void genReorderedArray(TestOutputArrayInfo *metadata, double val,
                        const unsigned ndims, const dim_t *const dims,
-                       const af_dtype ty) {
-    // The rest of this function assumes that dims has 4 elements. Just in case
-    // dims has < 4 elements, use another dims array that is filled with 1s
-    dim_t all_dims[4] = {1, 1, 1, 1};
-    for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
-
-    // This reorder combination will not move data around, but will simply
-    // call modDims and modStrides (see src/api/c/reorder.cpp).
-    // The output will be checked if it is still correct even with the
-    // modified dims and strides "hack" with no data movement
-    uint reorder_idxs[4] = {0, 2, 1, 3};
-
-    // Shape the output array such that the reordered output array will have
-    // the correct dimensions that the test asks for (i.e. must match dims arg)
-    dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
-    for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
-    metadata->init(val, 4, init_dims, ty);
-
-    af_array reordered = 0;
-    ASSERT_SUCCESS(af_reorder(&reordered, metadata->getOutput(),
-                              reorder_idxs[0], reorder_idxs[1], reorder_idxs[2],
-                              reorder_idxs[3]));
-    metadata->setOutput(reordered);
-}
+                       const af_dtype ty);
 // Partner function of testWriteToOutputArray. This generates the "special"
 // array that testWriteToOutputArray will use to check if the af_* function
 // correctly uses an existing array as its output
 void genTestOutputArray(af_array *out_ptr, const unsigned ndims,
                         const dim_t *const dims, const af_dtype ty,
-                        TestOutputArrayInfo *metadata) {
-    switch (metadata->getOutputArrayType()) {
-        case FULL_ARRAY: genRegularArray(metadata, ndims, dims, ty); break;
-        case SUB_ARRAY: genSubArray(metadata, ndims, dims, ty); break;
-        case REORDERED_ARRAY:
-            genReorderedArray(metadata, ndims, dims, ty);
-            break;
-        default: break;
-    }
-    *out_ptr = metadata->getOutput();
-}
+                        TestOutputArrayInfo *metadata);
 
 void genTestOutputArray(af_array *out_ptr, double val, const unsigned ndims,
                         const dim_t *const dims, const af_dtype ty,
-                        TestOutputArrayInfo *metadata) {
-    switch (metadata->getOutputArrayType()) {
-        case FULL_ARRAY: genRegularArray(metadata, val, ndims, dims, ty); break;
-        case SUB_ARRAY: genSubArray(metadata, val, ndims, dims, ty); break;
-        case REORDERED_ARRAY:
-            genReorderedArray(metadata, val, ndims, dims, ty);
-            break;
-        default: break;
-    }
-    *out_ptr = metadata->getOutput();
-}
+                        TestOutputArrayInfo *metadata);
 
 // Partner function of genTestOutputArray. This uses the same "special"
 // array that genTestOutputArray generates, and checks whether the
 // af_* function wrote to that array correctly
 ::testing::AssertionResult testWriteToOutputArray(
     std::string gold_name, std::string result_name, const af_array gold,
-    const af_array out, TestOutputArrayInfo *metadata) {
-    // In the case of NULL_ARRAY, the output array starts out as null.
-    // After the af_* function is called, it shouldn't be null anymore
-    if (metadata->getOutputArrayType() == NULL_ARRAY) {
-        if (out == 0) {
-            return ::testing::AssertionFailure()
-                   << "Output af_array " << result_name << " is null";
-        }
-        metadata->setOutput(out);
-    }
-    // For every other case, must check if the af_array generated by
-    // genTestOutputArray was used by the af_* function as its output array
-    else {
-        if (metadata->getOutput() != out) {
-            return ::testing::AssertionFailure()
-                   << "af_array POINTER MISMATCH:\n"
-                   << "  Actual: " << out << "\n"
-                   << "Expected: " << metadata->getOutput();
-        }
-    }
-
-    if (metadata->getOutputArrayType() == SUB_ARRAY) {
-        // There are two full arrays. One will be injected with the gold
-        // subarray, the other should have already been injected with the af_*
-        // function's output. Then we compare the two full arrays
-        af_array gold_full_array = metadata->getFullOutputCopy();
-        af_assign_seq(&gold_full_array, gold_full_array,
-                      metadata->getSubArrayNumDims(),
-                      metadata->getSubArrayIdxs(), gold);
-
-        return assertArrayEq(gold_name, result_name,
-                             metadata->getFullOutputCopy(),
-                             metadata->getFullOutput());
-    } else {
-        return assertArrayEq(gold_name, result_name, gold, out);
-    }
-}
+    const af_array out, TestOutputArrayInfo *metadata);
 
 // Called by ASSERT_SPECIAL_ARRAYS_EQ
 ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          std::string metadataName,
                                          const af_array a, const af_array b,
-                                         TestOutputArrayInfo *metadata) {
-    UNUSED(metadataName);
-    return testWriteToOutputArray(aName, bName, a, b, metadata);
-}
+                                         TestOutputArrayInfo *metadata);
 
 #pragma GCC diagnostic pop
diff --git a/test/tile.cpp b/test/tile.cpp
index d7bcefbeef..8127379e78 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <af/data.h>
-
 #include <gtest/gtest.h>
 #include <half.hpp>
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
+#include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
diff --git a/test/topk.cpp b/test/topk.cpp
index 0e5c534949..8841303db1 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
+
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>
diff --git a/test/var.cpp b/test/var.cpp
index eb43e6c1eb..328a6b6277 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -114,8 +114,8 @@ TYPED_TEST(Var, DimCPPSmall) {
     vector<vector<TypeParam> > in;
     vector<vector<outType> > tests;
 
-    readTests<TypeParam, outType, double>(TEST_DIR "/var/var.data", numDims, in,
-                                          tests);
+    readTests<TypeParam, outType, float>(TEST_DIR "/var/var.data", numDims, in,
+                                         tests);
 
     for (size_t i = 0; i < in.size(); i++) {
         array input(numDims[i], &in[i].front(), afHost);
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 7b6727bd5d..92193bc88d 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -7,7 +7,6 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define GTEST_LINKED_AS_SHARED_LIBRARY 1
 #include <arrayfire.h>
 #include <gtest/gtest.h>
 #include <testHelpers.hpp>

From 91c70b0ae27fb8e1d69fb51f63b2812f1f771f9f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 28 Apr 2020 00:50:36 -0400
Subject: [PATCH 1933/2677] Remove unnecessary specilization for getArray for
 half. Other warnings

---
 src/api/c/confidence_connected.cpp |  4 ++--
 src/api/c/handle.hpp               | 20 +-------------------
 src/backend/opencl/platform.hpp    |  2 ++
 3 files changed, 5 insertions(+), 21 deletions(-)

diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index acf9e3bbd9..0294d90ca6 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -36,8 +36,8 @@ Array<T> pointList(const Array<T>& in, const Array<uint>& x,
                    const Array<uint>& y) {
     af_array xcoords                     = getHandle<uint>(x);
     af_array ycoords                     = getHandle<uint>(y);
-    array<af_index_t, AF_MAX_DIMS> idxrs = {{{xcoords, false, false},
-                                             {ycoords, false, false},
+    array<af_index_t, AF_MAX_DIMS> idxrs = {{{{xcoords}, false, false},
+                                             {{ycoords}, false, false},
                                              common::createSpanIndex(),
                                              common::createSpanIndex()}};
 
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 087fd740f8..27d4b558c6 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -13,6 +13,7 @@
 #include <cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <common/traits.hpp>
 #include <copy.hpp>
 #include <math.hpp>
 #include <types.hpp>
@@ -59,15 +60,6 @@ const detail::Array<T> &getArray(const af_array &arr) {
     return *A;
 }
 
-template<>
-const detail::Array<common::half> &getArray<common::half>(const af_array &arr) {
-    const detail::Array<common::half> *A =
-        static_cast<const detail::Array<common::half> *>(arr);
-    if (f16 != A->getType())
-        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
-    return *A;
-}
-
 template<typename T>
 detail::Array<T> &getArray(af_array &arr) {
     detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
@@ -76,16 +68,6 @@ detail::Array<T> &getArray(af_array &arr) {
     return *A;
 }
 
-template<>
-[[gnu::unused]] detail::Array<common::half> &getArray<common::half>(
-    af_array &arr) {
-    detail::Array<common::half> *A =
-        static_cast<detail::Array<common::half> *>(arr);
-    if (f16 != A->getType())
-        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
-    return *A;
-}
-
 template<typename To>
 detail::Array<To> castArray(const af_array &in) {
     using detail::cdouble;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 9bccbb428a..980807753e 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -14,7 +14,9 @@
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 #pragma GCC diagnostic ignored "-Wignored-qualifiers"
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#if __GNUC__ >= 8
 #pragma GCC diagnostic ignored "-Wcatch-value="
+#endif
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
 

From f664faad2e586d46dedaf139442ba36d5fdb4053 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 28 Apr 2020 14:46:14 +0530
Subject: [PATCH 1934/2677] Remove AF_TEST_WITH_MTX_FILES check for mmio
 project build

---
 test/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 95bbbca80a..3e38149a92 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -39,7 +39,7 @@ if(NOT TARGET gtest)
     gtest_hide_internal_symbols)
 endif()
 
-if(AF_TEST_WITH_MTX_FILES AND NOT TARGET mmio)
+if(NOT TARGET mmio)
   add_subdirectory(mmio)
 endif()
 

From 3ceff027ce6e3783ab1c55ced1e9c292efc5c3de Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 28 Apr 2020 23:24:29 +0530
Subject: [PATCH 1935/2677] Fix mtx tests macro check in sparse_arith test

---
 test/sparse_arith.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index c8c36450ab..5f08340530 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -353,7 +353,7 @@ SP_SP_ARITH_TESTS(cfloat,
                   1e-4)  // This is mostly for complex division in OpenCL
 SP_SP_ARITH_TESTS(cdouble, 1e-6)
 
-#if defined(USE_MTX)
+#if defined(USE_MTX) && defined(MTX_TEST_DIR)
 
 // Sparse-Sparse Arithmetic testing function using mtx files
 template<af_op_t op>

From 799103fd5cdb8098241ad9b9b5c3759b1b493618 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 29 Apr 2020 15:14:57 +0530
Subject: [PATCH 1936/2677] Refactor padArray to an apt name, reshape

Moved reshape implementations into separate source files
to speedup compilation further.
---
 src/api/c/fft_common.hpp          |  34 +++++--
 src/backend/cpu/Array.cpp         |   9 +-
 src/backend/cpu/CMakeLists.txt    |   2 +-
 src/backend/cpu/copy.hpp          |  19 +++-
 src/backend/cpu/padarray.cpp      | 117 -----------------------
 src/backend/cpu/reshape.cpp       |  95 +++++++++++++++++++
 src/backend/cuda/CMakeLists.txt   |   1 +
 src/backend/cuda/copy.cpp         | 146 ++++++++++-------------------
 src/backend/cuda/copy.hpp         |  18 +++-
 src/backend/cuda/reshape.cpp      |  77 ++++++++++++++++
 src/backend/opencl/CMakeLists.txt |   1 +
 src/backend/opencl/copy.cpp       | 148 +++++++++---------------------
 src/backend/opencl/copy.hpp       |  18 +++-
 src/backend/opencl/reshape.cpp    |  84 +++++++++++++++++
 14 files changed, 427 insertions(+), 342 deletions(-)
 delete mode 100644 src/backend/cpu/padarray.cpp
 create mode 100644 src/backend/cpu/reshape.cpp
 create mode 100644 src/backend/cuda/reshape.cpp
 create mode 100644 src/backend/opencl/reshape.cpp

diff --git a/src/api/c/fft_common.hpp b/src/api/c/fft_common.hpp
index a8bf7d06a3..992e71ac38 100644
--- a/src/api/c/fft_common.hpp
+++ b/src/api/c/fft_common.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #include <copy.hpp>
 #include <fft.hpp>
 #include <handle.hpp>
@@ -17,11 +18,16 @@ template<typename inType, typename outType, int rank, bool direction>
 detail::Array<outType> fft(const detail::Array<inType> input,
                            const double norm_factor, const dim_t npad,
                            const dim_t *const pad) {
-    af::dim4 pdims(1);
+    using af::dim4;
+    using detail::fft_inplace;
+    using detail::reshape;
+    using detail::scalar;
+
+    dim4 pdims(1);
     computePaddedDims(pdims, input.dims(), npad, pad);
-    auto res = padArray(input, pdims, detail::scalar<outType>(0));
+    auto res = reshape(input, pdims, scalar<outType>(0));
 
-    detail::fft_inplace<outType, rank, direction>(res);
+    fft_inplace<outType, rank, direction>(res);
     if (norm_factor != 1.0) multiply_inplace(res, norm_factor);
 
     return res;
@@ -31,17 +37,24 @@ template<typename inType, typename outType, int rank>
 detail::Array<outType> fft_r2c(const detail::Array<inType> input,
                                const double norm_factor, const dim_t npad,
                                const dim_t *const pad) {
-    af::dim4 idims = input.dims();
+    using af::dim4;
+    using detail::Array;
+    using detail::fft_r2c;
+    using detail::multiply_inplace;
+    using detail::reshape;
+    using detail::scalar;
+
+    const dim4 &idims = input.dims();
 
     bool is_pad = false;
     for (int i = 0; i < npad; i++) { is_pad |= (pad[i] != idims[i]); }
 
-    detail::Array<inType> tmp = input;
+    Array<inType> tmp = input;
 
     if (is_pad) {
-        af::dim4 pdims(1);
+        dim4 pdims(1);
         computePaddedDims(pdims, input.dims(), npad, pad);
-        tmp = padArray(input, pdims, detail::scalar<inType>(0));
+        tmp = reshape(input, pdims, scalar<inType>(0));
     }
 
     auto res = fft_r2c<outType, inType, rank>(tmp);
@@ -54,8 +67,11 @@ template<typename inType, typename outType, int rank>
 detail::Array<outType> fft_c2r(const detail::Array<inType> input,
                                const double norm_factor,
                                const af::dim4 &odims) {
-    detail::Array<outType> output =
-        fft_c2r<outType, inType, rank>(input, odims);
+    using detail::Array;
+    using detail::fft_c2r;
+    using detail::multiply_inplace;
+
+    Array<outType> output = fft_c2r<outType, inType, rank>(input, odims);
 
     if (norm_factor != 1) {
         // Normalize input because tmp was not normalized
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 92c058b036..4976bc2582 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -43,6 +43,7 @@ using cpu::jit::BufferNode;
 using cpu::jit::Node;
 using cpu::jit::Node_map_t;
 using cpu::jit::Node_ptr;
+using std::adjacent_find;
 using std::copy;
 using std::is_standard_layout;
 using std::move;
@@ -170,10 +171,10 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
     }
 
     // Check if all the arrays have the same dimension
-    auto it = std::adjacent_find(begin(array_ptrs), end(array_ptrs),
-                                 [](const Array<T> *l, const Array<T> *r) {
-                                     return l->dims() != r->dims();
-                                 });
+    auto it = adjacent_find(begin(array_ptrs), end(array_ptrs),
+                            [](const Array<T> *l, const Array<T> *r) {
+                                return l->dims() != r->dims();
+                            });
 
     // If they are not the same. eval individually
     if (it != end(array_ptrs)) {
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index bdd205bca9..25ef848f67 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -110,7 +110,6 @@ target_sources(afcpu
     nearest_neighbour.hpp
     orb.cpp
     orb.hpp
-    padarray.cpp
     ParamIterator.hpp
     platform.cpp
     platform.hpp
@@ -132,6 +131,7 @@ target_sources(afcpu
     reorder.hpp
     resize.cpp
     resize.hpp
+    reshape.cpp
     rotate.cpp
     rotate.hpp
     scan.cpp
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index bd7671d082..8aade1fe04 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -28,10 +28,23 @@ Array<T> copyArray(const Array<T> &A);
 template<typename inType, typename outType>
 void copyArray(Array<outType> &out, const Array<inType> &in);
 
+// Resize Array to target dimensions and convert type
+//
+// Depending on the \p outDims, the output Array can be either truncated
+// or padded (towards end of respective dimensions).
+//
+// While resizing copying, if output dimensions are larger than input, then
+// elements beyond the input dimensions are set to the \p defaultValue.
+//
+// \param[in] in is input Array
+// \param[in] outDims is the target output dimensions
+// \param[in] defaultValue is the value to which padded locations are set.
+// \param[in] scale is the value by which all output elements are scaled.
+//
+// \returns Array<outType>
 template<typename inType, typename outType>
-Array<outType> padArray(const Array<inType> &in, const dim4 &dims,
-                        outType default_value = outType(0),
-                        double factor         = 1.0);
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue = outType(0), double scale = 1.0);
 
 template<typename T>
 Array<T> padArrayBorders(const Array<T> &in, const dim4 &lowerBoundPadding,
diff --git a/src/backend/cpu/padarray.cpp b/src/backend/cpu/padarray.cpp
deleted file mode 100644
index 0ffbb6c684..0000000000
--- a/src/backend/cpu/padarray.cpp
+++ /dev/null
@@ -1,117 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <common/half.hpp>
-#include <copy.hpp>
-#include <err_cpu.hpp>
-#include <kernel/copy.hpp>
-#include <math.hpp>
-#include <platform.hpp>
-#include <queue.hpp>
-#include <algorithm>
-#include <cassert>
-#include <complex>
-#include <cstring>
-#include <type_traits>
-#include <vector>
-
-namespace cpu {
-template<typename T>
-void multiply_inplace(Array<T>& in, double val) {
-    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, static_cast<T>(0),
-                       val);
-}
-
-template<typename inType, typename outType>
-Array<outType> padArray(const Array<inType>& in, const dim4& dims,
-                        outType default_value, double factor) {
-    Array<outType> ret = createValueArray<outType>(dims, default_value);
-    getQueue().enqueue(kernel::copyElemwise<outType, inType>, ret, in,
-                       static_cast<outType>(default_value), factor);
-    return ret;
-}
-
-#define INSTANTIATE(T) \
-    template void multiply_inplace<T>(Array<T> & in, double norm);
-
-INSTANTIATE(float)
-INSTANTIATE(double)
-INSTANTIATE(cfloat)
-INSTANTIATE(cdouble)
-INSTANTIATE(int)
-INSTANTIATE(uint)
-INSTANTIATE(uchar)
-INSTANTIATE(char)
-INSTANTIATE(intl)
-INSTANTIATE(uintl)
-INSTANTIATE(short)
-INSTANTIATE(ushort)
-
-#define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
-    template Array<float> padArray<SRC_T, float>(                         \
-        const Array<SRC_T>& src, const dim4& dims, float default_value,   \
-        double factor);                                                   \
-    template Array<double> padArray<SRC_T, double>(                       \
-        const Array<SRC_T>& src, const dim4& dims, double default_value,  \
-        double factor);                                                   \
-    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
-        const Array<SRC_T>& src, const dim4& dims, cfloat default_value,  \
-        double factor);                                                   \
-    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
-        const Array<SRC_T>& src, const dim4& dims, cdouble default_value, \
-        double factor);                                                   \
-    template Array<int> padArray<SRC_T, int>(                             \
-        const Array<SRC_T>& src, const dim4& dims, int default_value,     \
-        double factor);                                                   \
-    template Array<uint> padArray<SRC_T, uint>(                           \
-        const Array<SRC_T>& src, const dim4& dims, uint default_value,    \
-        double factor);                                                   \
-    template Array<intl> padArray<SRC_T, intl>(                           \
-        const Array<SRC_T>& src, const dim4& dims, intl default_value,    \
-        double factor);                                                   \
-    template Array<uintl> padArray<SRC_T, uintl>(                         \
-        const Array<SRC_T>& src, const dim4& dims, uintl default_value,   \
-        double factor);                                                   \
-    template Array<short> padArray<SRC_T, short>(                         \
-        const Array<SRC_T>& src, const dim4& dims, short default_value,   \
-        double factor);                                                   \
-    template Array<ushort> padArray<SRC_T, ushort>(                       \
-        const Array<SRC_T>& src, const dim4& dims, ushort default_value,  \
-        double factor);                                                   \
-    template Array<uchar> padArray<SRC_T, uchar>(                         \
-        const Array<SRC_T>& src, const dim4& dims, uchar default_value,   \
-        double factor);                                                   \
-    template Array<char> padArray<SRC_T, char>(                           \
-        const Array<SRC_T>& src, const dim4& dims, char default_value,    \
-        double factor);
-
-INSTANTIATE_PAD_ARRAY(float)
-INSTANTIATE_PAD_ARRAY(double)
-INSTANTIATE_PAD_ARRAY(int)
-INSTANTIATE_PAD_ARRAY(uint)
-INSTANTIATE_PAD_ARRAY(intl)
-INSTANTIATE_PAD_ARRAY(uintl)
-INSTANTIATE_PAD_ARRAY(uchar)
-INSTANTIATE_PAD_ARRAY(char)
-INSTANTIATE_PAD_ARRAY(ushort)
-INSTANTIATE_PAD_ARRAY(short)
-INSTANTIATE_PAD_ARRAY(common::half)
-
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
-    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
-        const Array<SRC_T>& src, const dim4& dims, cfloat default_value,  \
-        double factor);                                                   \
-    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
-        const Array<SRC_T>& src, const dim4& dims, cdouble default_value, \
-        double factor);
-
-INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
-INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
-}  // namespace cpu
diff --git a/src/backend/cpu/reshape.cpp b/src/backend/cpu/reshape.cpp
new file mode 100644
index 0000000000..7844f3a596
--- /dev/null
+++ b/src/backend/cpu/reshape.cpp
@@ -0,0 +1,95 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <copy.hpp>
+
+#include <common/half.hpp>
+#include <kernel/copy.hpp>
+#include <platform.hpp>
+#include <queue.hpp>
+
+namespace cpu {
+template<typename T>
+void multiply_inplace(Array<T> &in, double val) {
+    getQueue().enqueue(kernel::copyElemwise<T, T>, in, in, static_cast<T>(0),
+                       val);
+}
+
+template<typename inType, typename outType>
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue, double scale) {
+    Array<outType> out = createValueArray(outDims, defaultValue);
+    getQueue().enqueue(kernel::copyElemwise<outType, inType>, out, in,
+                       defaultValue, scale);
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template void multiply_inplace<T>(Array<T> & in, double norm);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+#define INSTANTIATE_PAD_ARRAY(SRC_T)                                          \
+    template Array<float> reshape<SRC_T, float>(const Array<SRC_T> &,         \
+                                                const dim4 &, float, double); \
+    template Array<double> reshape<SRC_T, double>(                            \
+        const Array<SRC_T> &, const dim4 &, double, double);                  \
+    template Array<cfloat> reshape<SRC_T, cfloat>(                            \
+        const Array<SRC_T> &, const dim4 &, cfloat, double);                  \
+    template Array<cdouble> reshape<SRC_T, cdouble>(                          \
+        const Array<SRC_T> &, const dim4 &, cdouble, double);                 \
+    template Array<int> reshape<SRC_T, int>(const Array<SRC_T> &,             \
+                                            const dim4 &, int, double);       \
+    template Array<uint> reshape<SRC_T, uint>(const Array<SRC_T> &,           \
+                                              const dim4 &, uint, double);    \
+    template Array<intl> reshape<SRC_T, intl>(const Array<SRC_T> &,           \
+                                              const dim4 &, intl, double);    \
+    template Array<uintl> reshape<SRC_T, uintl>(const Array<SRC_T> &,         \
+                                                const dim4 &, uintl, double); \
+    template Array<short> reshape<SRC_T, short>(const Array<SRC_T> &,         \
+                                                const dim4 &, short, double); \
+    template Array<ushort> reshape<SRC_T, ushort>(                            \
+        const Array<SRC_T> &, const dim4 &, ushort, double);                  \
+    template Array<uchar> reshape<SRC_T, uchar>(const Array<SRC_T> &,         \
+                                                const dim4 &, uchar, double); \
+    template Array<char> reshape<SRC_T, char>(const Array<SRC_T> &,           \
+                                              const dim4 &, char, double);
+
+INSTANTIATE_PAD_ARRAY(float)
+INSTANTIATE_PAD_ARRAY(double)
+INSTANTIATE_PAD_ARRAY(int)
+INSTANTIATE_PAD_ARRAY(uint)
+INSTANTIATE_PAD_ARRAY(intl)
+INSTANTIATE_PAD_ARRAY(uintl)
+INSTANTIATE_PAD_ARRAY(uchar)
+INSTANTIATE_PAD_ARRAY(char)
+INSTANTIATE_PAD_ARRAY(ushort)
+INSTANTIATE_PAD_ARRAY(short)
+INSTANTIATE_PAD_ARRAY(common::half)
+
+#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                 \
+    template Array<cfloat> reshape<SRC_T, cfloat>(           \
+        const Array<SRC_T> &, const dim4 &, cfloat, double); \
+    template Array<cdouble> reshape<SRC_T, cdouble>(         \
+        const Array<SRC_T> &, const dim4 &, cdouble, double);
+
+INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
+INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+}  // namespace cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index aa7caae368..3decbf978e 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -563,6 +563,7 @@ cuda_add_library(afcuda
     reorder.cpp
     reorder.hpp
     resize.hpp
+    reshape.cpp
     rotate.hpp
     scalar.hpp
     scan.cpp
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index 5a4ad99642..17118b9058 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <copy.hpp>
+
 #include <Array.hpp>
 #include <common/complex.hpp>
 #include <common/half.hpp>
-#include <copy.hpp>
 #include <cuda_runtime_api.h>
-#include <err_cuda.hpp>
 #include <kernel/memcopy.hpp>
 #include <math.hpp>
 
@@ -60,15 +60,6 @@ Array<T> copyArray(const Array<T> &src) {
     return out;
 }
 
-template<typename inType, typename outType>
-Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                        outType default_value, double factor) {
-    ARG_ASSERT(1, (in.ndims() == dims.ndims()));
-    Array<outType> ret = createEmptyArray<outType>(dims);
-    kernel::copy<inType, outType>(ret, in, in.ndims(), default_value, factor);
-    return ret;
-}
-
 template<typename T>
 void multiply_inplace(Array<T> &in, double val) {
     kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val);
@@ -124,99 +115,54 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 
-#define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
-    template Array<float> padArray<SRC_T, float>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, float default_value,   \
-        double factor);                                                   \
-    template Array<double> padArray<SRC_T, double>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, double default_value,  \
-        double factor);                                                   \
-    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
-        double factor);                                                   \
-    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
-        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
-        double factor);                                                   \
-    template Array<int> padArray<SRC_T, int>(                             \
-        Array<SRC_T> const &src, dim4 const &dims, int default_value,     \
-        double factor);                                                   \
-    template Array<uint> padArray<SRC_T, uint>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, uint default_value,    \
-        double factor);                                                   \
-    template Array<intl> padArray<SRC_T, intl>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, intl default_value,    \
-        double factor);                                                   \
-    template Array<uintl> padArray<SRC_T, uintl>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, uintl default_value,   \
-        double factor);                                                   \
-    template Array<short> padArray<SRC_T, short>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, short default_value,   \
-        double factor);                                                   \
-    template Array<ushort> padArray<SRC_T, ushort>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, ushort default_value,  \
-        double factor);                                                   \
-    template Array<uchar> padArray<SRC_T, uchar>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, uchar default_value,   \
-        double factor);                                                   \
-    template Array<char> padArray<SRC_T, char>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, char default_value,    \
-        double factor);                                                   \
-    template Array<half> padArray<SRC_T, half>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, half default_value,    \
-        double factor);                                                   \
-    template void copyArray<SRC_T, float>(Array<float> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, double>(Array<double> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
-                                            Array<SRC_T> const &src);     \
-    template void copyArray<SRC_T, int>(Array<int> & dst,                 \
-                                        Array<SRC_T> const &src);         \
-    template void copyArray<SRC_T, uint>(Array<uint> & dst,               \
-                                         Array<SRC_T> const &src);        \
-    template void copyArray<SRC_T, intl>(Array<intl> & dst,               \
-                                         Array<SRC_T> const &src);        \
-    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, short>(Array<short> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, char>(Array<char> & dst,               \
-                                         Array<SRC_T> const &src);        \
-    template void copyArray<SRC_T, half>(Array<half> & dst,               \
+#define INSTANTIATE_COPY_ARRAY(SRC_T)                                 \
+    template void copyArray<SRC_T, float>(Array<float> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, double>(Array<double> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,     \
+                                            Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, int>(Array<int> & dst,             \
+                                        Array<SRC_T> const &src);     \
+    template void copyArray<SRC_T, uint>(Array<uint> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, intl>(Array<intl> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, short>(Array<short> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, char>(Array<char> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, half>(Array<half> & dst,           \
                                          Array<SRC_T> const &src);
 
-INSTANTIATE_PAD_ARRAY(float)
-INSTANTIATE_PAD_ARRAY(double)
-INSTANTIATE_PAD_ARRAY(int)
-INSTANTIATE_PAD_ARRAY(uint)
-INSTANTIATE_PAD_ARRAY(intl)
-INSTANTIATE_PAD_ARRAY(uintl)
-INSTANTIATE_PAD_ARRAY(short)
-INSTANTIATE_PAD_ARRAY(ushort)
-INSTANTIATE_PAD_ARRAY(uchar)
-INSTANTIATE_PAD_ARRAY(char)
-INSTANTIATE_PAD_ARRAY(half)
-
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
-    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
-        double factor);                                                   \
-    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
-        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
-        double factor);                                                   \
-    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
+INSTANTIATE_COPY_ARRAY(float)
+INSTANTIATE_COPY_ARRAY(double)
+INSTANTIATE_COPY_ARRAY(int)
+INSTANTIATE_COPY_ARRAY(uint)
+INSTANTIATE_COPY_ARRAY(intl)
+INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(short)
+INSTANTIATE_COPY_ARRAY(ushort)
+INSTANTIATE_COPY_ARRAY(uchar)
+INSTANTIATE_COPY_ARRAY(char)
+INSTANTIATE_COPY_ARRAY(half)
+
+#define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                        \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,      \
+                                           Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,    \
                                             Array<SRC_T> const &src);
 
-INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
-INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat)
+INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
 T getScalar(const Array<T> &in) {
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index be778832c4..143e6f0888 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -31,9 +31,23 @@ Array<T> copyArray(const Array<T> &src);
 template<typename inType, typename outType>
 void copyArray(Array<outType> &out, const Array<inType> &in);
 
+// Resize Array to target dimensions and convert type
+//
+// Depending on the \p outDims, the output Array can be either truncated
+// or padded (towards end of respective dimensions).
+//
+// While resizing copying, if output dimensions are larger than input, then
+// elements beyond the input dimensions are set to the \p defaultValue.
+//
+// \param[in] in is input Array
+// \param[in] outDims is the target output dimensions
+// \param[in] defaultValue is the value to which padded locations are set.
+// \param[in] scale is the value by which all output elements are scaled.
+//
+// \returns Array<outType>
 template<typename inType, typename outType>
-Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                        outType default_value, double factor = 1.0);
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue = outType(0), double scale = 1.0);
 
 template<typename T>
 Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
diff --git a/src/backend/cuda/reshape.cpp b/src/backend/cuda/reshape.cpp
new file mode 100644
index 0000000000..6e4c541adc
--- /dev/null
+++ b/src/backend/cuda/reshape.cpp
@@ -0,0 +1,77 @@
+
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <copy.hpp>
+
+#include <common/half.hpp>
+#include <kernel/memcopy.hpp>
+
+using common::half;
+
+namespace cuda {
+
+template<typename inType, typename outType>
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue, double scale) {
+    Array<outType> out = createEmptyArray<outType>(outDims);
+    kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale);
+    return out;
+}
+
+#define INSTANTIATE(SRC_T)                                                    \
+    template Array<float> reshape<SRC_T, float>(Array<SRC_T> const &,         \
+                                                dim4 const &, float, double); \
+    template Array<double> reshape<SRC_T, double>(                            \
+        Array<SRC_T> const &, dim4 const &, double, double);                  \
+    template Array<cfloat> reshape<SRC_T, cfloat>(                            \
+        Array<SRC_T> const &, dim4 const &, cfloat, double);                  \
+    template Array<cdouble> reshape<SRC_T, cdouble>(                          \
+        Array<SRC_T> const &, dim4 const &, cdouble, double);                 \
+    template Array<int> reshape<SRC_T, int>(Array<SRC_T> const &,             \
+                                            dim4 const &, int, double);       \
+    template Array<uint> reshape<SRC_T, uint>(Array<SRC_T> const &,           \
+                                              dim4 const &, uint, double);    \
+    template Array<intl> reshape<SRC_T, intl>(Array<SRC_T> const &,           \
+                                              dim4 const &, intl, double);    \
+    template Array<uintl> reshape<SRC_T, uintl>(Array<SRC_T> const &,         \
+                                                dim4 const &, uintl, double); \
+    template Array<short> reshape<SRC_T, short>(Array<SRC_T> const &,         \
+                                                dim4 const &, short, double); \
+    template Array<ushort> reshape<SRC_T, ushort>(                            \
+        Array<SRC_T> const &, dim4 const &, ushort, double);                  \
+    template Array<uchar> reshape<SRC_T, uchar>(Array<SRC_T> const &,         \
+                                                dim4 const &, uchar, double); \
+    template Array<char> reshape<SRC_T, char>(Array<SRC_T> const &,           \
+                                              dim4 const &, char, double);    \
+    template Array<half> reshape<SRC_T, half>(Array<SRC_T> const &,           \
+                                              dim4 const &, half, double);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
+
+#define INSTANTIATE_COMPLEX(SRC_T)                           \
+    template Array<cfloat> reshape<SRC_T, cfloat>(           \
+        Array<SRC_T> const &, dim4 const &, cfloat, double); \
+    template Array<cdouble> reshape<SRC_T, cdouble>(         \
+        Array<SRC_T> const &, dim4 const &, cdouble, double);
+
+INSTANTIATE_COMPLEX(cfloat)
+INSTANTIATE_COMPLEX(cdouble)
+
+}  // namespace cuda
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index b2cb7157f2..564f0af4ec 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -189,6 +189,7 @@ target_sources(afopencl
     reorder.hpp
     resize.cpp
     resize.hpp
+    reshape.cpp
     rotate.cpp
     rotate.hpp
     scalar.hpp
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 7be07316ed..20bf749a18 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -63,21 +63,6 @@ Array<T> copyArray(const Array<T> &A) {
     return out;
 }
 
-template<typename inType, typename outType>
-Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                        outType default_value, double factor) {
-    Array<outType> ret = createEmptyArray<outType>(dims);
-
-    if (in.dims() == dims) {
-        kernel::copy<inType, outType, true>(ret, in, in.ndims(), default_value,
-                                            factor);
-    } else {
-        kernel::copy<inType, outType, false>(ret, in, in.ndims(), default_value,
-                                             factor);
-    }
-    return ret;
-}
-
 template<typename T>
 void multiply_inplace(Array<T> &in, double val) {
     kernel::copy<T, T, true>(in, in, in.ndims(), scalar<T>(0), val);
@@ -143,99 +128,54 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 
-#define INSTANTIATE_PAD_ARRAY(SRC_T)                                      \
-    template Array<float> padArray<SRC_T, float>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, float default_value,   \
-        double factor);                                                   \
-    template Array<double> padArray<SRC_T, double>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, double default_value,  \
-        double factor);                                                   \
-    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
-        double factor);                                                   \
-    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
-        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
-        double factor);                                                   \
-    template Array<int> padArray<SRC_T, int>(                             \
-        Array<SRC_T> const &src, dim4 const &dims, int default_value,     \
-        double factor);                                                   \
-    template Array<uint> padArray<SRC_T, uint>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, uint default_value,    \
-        double factor);                                                   \
-    template Array<intl> padArray<SRC_T, intl>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, intl default_value,    \
-        double factor);                                                   \
-    template Array<uintl> padArray<SRC_T, uintl>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, uintl default_value,   \
-        double factor);                                                   \
-    template Array<short> padArray<SRC_T, short>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, short default_value,   \
-        double factor);                                                   \
-    template Array<ushort> padArray<SRC_T, ushort>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, ushort default_value,  \
-        double factor);                                                   \
-    template Array<uchar> padArray<SRC_T, uchar>(                         \
-        Array<SRC_T> const &src, dim4 const &dims, uchar default_value,   \
-        double factor);                                                   \
-    template Array<char> padArray<SRC_T, char>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, char default_value,    \
-        double factor);                                                   \
-    template Array<half> padArray<SRC_T, half>(                           \
-        Array<SRC_T> const &src, dim4 const &dims, half default_value,    \
-        double factor);                                                   \
-    template void copyArray<SRC_T, float>(Array<float> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, double>(Array<double> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
-                                            Array<SRC_T> const &src);     \
-    template void copyArray<SRC_T, int>(Array<int> & dst,                 \
-                                        Array<SRC_T> const &src);         \
-    template void copyArray<SRC_T, uint>(Array<uint> & dst,               \
-                                         Array<SRC_T> const &src);        \
-    template void copyArray<SRC_T, intl>(Array<intl> & dst,               \
-                                         Array<SRC_T> const &src);        \
-    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, short>(Array<short> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,             \
-                                          Array<SRC_T> const &src);       \
-    template void copyArray<SRC_T, char>(Array<char> & dst,               \
-                                         Array<SRC_T> const &src);        \
-    template void copyArray<SRC_T, half>(Array<half> & dst,               \
+#define INSTANTIATE_COPY_ARRAY(SRC_T)                                 \
+    template void copyArray<SRC_T, float>(Array<float> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, double>(Array<double> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,     \
+                                            Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, int>(Array<int> & dst,             \
+                                        Array<SRC_T> const &src);     \
+    template void copyArray<SRC_T, uint>(Array<uint> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, intl>(Array<intl> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, short>(Array<short> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, char>(Array<char> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, half>(Array<half> & dst,           \
                                          Array<SRC_T> const &src);
 
-INSTANTIATE_PAD_ARRAY(float)
-INSTANTIATE_PAD_ARRAY(double)
-INSTANTIATE_PAD_ARRAY(int)
-INSTANTIATE_PAD_ARRAY(uint)
-INSTANTIATE_PAD_ARRAY(intl)
-INSTANTIATE_PAD_ARRAY(uintl)
-INSTANTIATE_PAD_ARRAY(uchar)
-INSTANTIATE_PAD_ARRAY(char)
-INSTANTIATE_PAD_ARRAY(short)
-INSTANTIATE_PAD_ARRAY(ushort)
-INSTANTIATE_PAD_ARRAY(half)
-
-#define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                              \
-    template Array<cfloat> padArray<SRC_T, cfloat>(                       \
-        Array<SRC_T> const &src, dim4 const &dims, cfloat default_value,  \
-        double factor);                                                   \
-    template Array<cdouble> padArray<SRC_T, cdouble>(                     \
-        Array<SRC_T> const &src, dim4 const &dims, cdouble default_value, \
-        double factor);                                                   \
-    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,           \
-                                           Array<SRC_T> const &src);      \
-    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,         \
+INSTANTIATE_COPY_ARRAY(float)
+INSTANTIATE_COPY_ARRAY(double)
+INSTANTIATE_COPY_ARRAY(int)
+INSTANTIATE_COPY_ARRAY(uint)
+INSTANTIATE_COPY_ARRAY(intl)
+INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(uchar)
+INSTANTIATE_COPY_ARRAY(char)
+INSTANTIATE_COPY_ARRAY(short)
+INSTANTIATE_COPY_ARRAY(ushort)
+INSTANTIATE_COPY_ARRAY(half)
+
+#define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                        \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,      \
+                                           Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,    \
                                             Array<SRC_T> const &src);
 
-INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
-INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
+INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat)
+INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
 T getScalar(const Array<T> &in) {
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index 97be450a66..e02b8da3c0 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -21,9 +21,23 @@ Array<T> copyArray(const Array<T> &A);
 template<typename inType, typename outType>
 void copyArray(Array<outType> &out, const Array<inType> &in);
 
+// Resize Array to target dimensions and convert type
+//
+// Depending on the \p outDims, the output Array can be either truncated
+// or padded (towards end of respective dimensions).
+//
+// While resizing copying, if output dimensions are larger than input, then
+// elements beyond the input dimensions are set to the \p defaultValue.
+//
+// \param[in] in is input Array
+// \param[in] outDims is the target output dimensions
+// \param[in] defaultValue is the value to which padded locations are set.
+// \param[in] scale is the value by which all output elements are scaled.
+//
+// \returns Array<outType>
 template<typename inType, typename outType>
-Array<outType> padArray(Array<inType> const &in, dim4 const &dims,
-                        outType default_value, double factor = 1.0);
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue = outType(0), double scale = 1.0);
 
 template<typename T>
 Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
diff --git a/src/backend/opencl/reshape.cpp b/src/backend/opencl/reshape.cpp
new file mode 100644
index 0000000000..e3b752d351
--- /dev/null
+++ b/src/backend/opencl/reshape.cpp
@@ -0,0 +1,84 @@
+
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <copy.hpp>
+
+#include <common/half.hpp>
+#include <kernel/memcopy.hpp>
+
+using common::half;
+
+namespace opencl {
+
+template<typename inType, typename outType>
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue, double scale) {
+    Array<outType> out = createEmptyArray<outType>(outDims);
+
+    if (in.dims() == outDims) {
+        kernel::copy<inType, outType, true>(out, in, in.ndims(), defaultValue,
+                                            scale);
+    } else {
+        kernel::copy<inType, outType, false>(out, in, in.ndims(), defaultValue,
+                                             scale);
+    }
+    return out;
+}
+
+#define INSTANTIATE(SRC_T)                                                    \
+    template Array<float> reshape<SRC_T, float>(Array<SRC_T> const &,         \
+                                                dim4 const &, float, double); \
+    template Array<double> reshape<SRC_T, double>(                            \
+        Array<SRC_T> const &, dim4 const &, double, double);                  \
+    template Array<cfloat> reshape<SRC_T, cfloat>(                            \
+        Array<SRC_T> const &, dim4 const &, cfloat, double);                  \
+    template Array<cdouble> reshape<SRC_T, cdouble>(                          \
+        Array<SRC_T> const &, dim4 const &, cdouble, double);                 \
+    template Array<int> reshape<SRC_T, int>(Array<SRC_T> const &,             \
+                                            dim4 const &, int, double);       \
+    template Array<uint> reshape<SRC_T, uint>(Array<SRC_T> const &,           \
+                                              dim4 const &, uint, double);    \
+    template Array<intl> reshape<SRC_T, intl>(Array<SRC_T> const &,           \
+                                              dim4 const &, intl, double);    \
+    template Array<uintl> reshape<SRC_T, uintl>(Array<SRC_T> const &,         \
+                                                dim4 const &, uintl, double); \
+    template Array<short> reshape<SRC_T, short>(Array<SRC_T> const &,         \
+                                                dim4 const &, short, double); \
+    template Array<ushort> reshape<SRC_T, ushort>(                            \
+        Array<SRC_T> const &, dim4 const &, ushort, double);                  \
+    template Array<uchar> reshape<SRC_T, uchar>(Array<SRC_T> const &,         \
+                                                dim4 const &, uchar, double); \
+    template Array<char> reshape<SRC_T, char>(Array<SRC_T> const &,           \
+                                              dim4 const &, char, double);    \
+    template Array<half> reshape<SRC_T, half>(Array<SRC_T> const &,           \
+                                              dim4 const &, half, double);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
+
+#define INSTANTIATE_COMPLEX(SRC_T)                           \
+    template Array<cfloat> reshape<SRC_T, cfloat>(           \
+        Array<SRC_T> const &, dim4 const &, cfloat, double); \
+    template Array<cdouble> reshape<SRC_T, cdouble>(         \
+        Array<SRC_T> const &, dim4 const &, cdouble, double);
+
+INSTANTIATE_COMPLEX(cfloat)
+INSTANTIATE_COMPLEX(cdouble)
+
+}  // namespace opencl

From c089e0f334ef84fa5f6fbde0d165174130b37060 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 28 Apr 2020 23:26:25 +0530
Subject: [PATCH 1937/2677] Refactor qr,solve to use padArrayBorders

---
 src/backend/cpu/qr.cpp               | 16 +++++++++++-----
 src/backend/cpu/solve.cpp            | 18 ++++++++++++------
 src/backend/cuda/qr.cpp              |  8 ++------
 src/backend/cuda/solve.cu            |  9 ++++++---
 src/backend/opencl/cpu/cpu_qr.cpp    |  8 ++++++--
 src/backend/opencl/cpu/cpu_solve.cpp |  7 ++++++-
 src/backend/opencl/qr.cpp            | 15 ++++++++++-----
 src/backend/opencl/solve.cpp         | 18 +++++++++---------
 8 files changed, 62 insertions(+), 37 deletions(-)

diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index 5cdafa0481..a9d58303e1 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -7,19 +7,20 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <qr.hpp>
 
-#if defined(WITH_LINEAR_ALGEBRA)
 #include <err_cpu.hpp>
-#include <handle.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <triangle.hpp>
 #include <af/dim4.hpp>
-#include <cassert>
+
+using af::dim4;
 
 namespace cpu {
 
@@ -67,7 +68,12 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     int M      = iDims[0];
     int N      = iDims[1];
 
-    q = padArray<T, T>(in, dim4(M, max(M, N)));
+    const dim4 NullShape(0, 0, 0, 0);
+
+    dim4 endPadding(M - iDims[0], max(M, N) - iDims[1], 0, 0);
+    q = (endPadding == NullShape
+             ? copyArray(in)
+             : padArrayBorders(in, NullShape, endPadding, AF_PAD_ZERO));
     q.resetDims(iDims);
     t = qr_inplace(q);
 
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 4f80d442e7..d9fb586782 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -7,18 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <solve.hpp>
 
-#if defined(WITH_LINEAR_ALGEBRA)
 #include <err_cpu.hpp>
-#include <handle.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+#include <copy.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
-#include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
-#include <cassert>
+
+using af::dim4;
 
 namespace cpu {
 
@@ -116,12 +116,18 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
         return triangleSolve<T>(a, b, options);
     }
 
+    const dim4 NullShape(0, 0, 0, 0);
+
     int M = a.dims()[0];
     int N = a.dims()[1];
     int K = b.dims()[1];
 
     Array<T> A = copyArray<T>(a);
-    Array<T> B = padArray<T, T>(b, dim4(max(M, N), K));
+
+    dim4 endPadding(max(M, N) - b.dims()[0], K - b.dims()[1], 0, 0);
+    Array<T> B = (endPadding == NullShape
+                      ? copyArray(b)
+                      : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
 
     if (M == N) {
         Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
diff --git a/src/backend/cuda/qr.cpp b/src/backend/cuda/qr.cpp
index 4c02e60fd0..3663f43570 100644
--- a/src/backend/cuda/qr.cpp
+++ b/src/backend/cuda/qr.cpp
@@ -7,21 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/err_common.hpp>
 #include <qr.hpp>
 
 #include <copy.hpp>
 #include <cublas_v2.h>
 #include <cusolverDn.hpp>
 #include <identity.hpp>
+#include <kernel/triangle.hpp>
+#include <math.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 
-#include <common/err_common.hpp>
-#include <math.hpp>
-
-#include <kernel/triangle.hpp>
-
 namespace cuda {
 
 // cusolverStatus_t cusolverDn<>geqrf_bufferSize(
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index d45406a77c..92cdb64b2e 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -22,8 +22,6 @@
 #include <qr.hpp>
 #include <transpose.hpp>
 
-#include <cstdio>
-
 namespace cuda {
 
 // cusolverStatus_t cusolverDn<>getrs(
@@ -214,6 +212,8 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
     Array<T> B = createEmptyArray<T>(dim4());
 
     if (M < N) {
+        const dim4 NullShape(0, 0, 0, 0);
+
         // Least squres for this case is solved using the following
         // solve(A, B) == matmul(Q, Xpad);
         // Where:
@@ -224,7 +224,10 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
 
         // QR is performed on the transpose of A
         Array<T> A = transpose<T>(a, true);
-        B          = padArray<T, T>(b, dim4(N, K), scalar<T>(0));
+        dim4 endPadding(N - b.dims()[0], K - b.dims()[1], 0, 0);
+        B = (endPadding == NullShape
+                 ? copyArray(b)
+                 : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
 
         int lwork = 0;
 
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index 207134aa72..fd5526792d 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -60,8 +60,12 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     int M      = iDims[0];
     int N      = iDims[1];
 
-    dim4 padDims(M, max(M, N));
-    q = padArray<T, T>(in, padDims, scalar<T>(0));
+    const dim4 NullShape(0, 0, 0, 0);
+
+    dim4 endPadding(M - iDims[0], max(M, N) - iDims[1], 0, 0);
+    q = (endPadding == NullShape
+             ? copyArray(in)
+             : padArrayBorders(in, NullShape, endPadding, AF_PAD_ZERO));
     q.resetDims(iDims);
     t = qr_inplace(q);
 
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index fb63f4c327..b9f2fc9933 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -109,12 +109,17 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
         return triangleSolve<T>(a, b, options);
     }
 
+    const dim4 NullShape(0, 0, 0, 0);
+
     int M = a.dims()[0];
     int N = a.dims()[1];
     int K = b.dims()[1];
 
     Array<T> A = copyArray<T>(a);
-    Array<T> B = padArray<T, T>(b, dim4(max(M, N), K), scalar<T>(0));
+    dim4 endPadding(max(M, N) - b.dims()[0], K - b.dims()[1], 0, 0);
+    Array<T> B = (endPadding == NullShape
+                      ? copyArray(b)
+                      : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
 
     mapped_ptr<T> aPtr = A.getMappedPtr();
     mapped_ptr<T> bPtr = B.getMappedPtr();
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 3c6130d8e2..4187107383 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -7,13 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <blas.hpp>
-#include <copy.hpp>
-#include <err_opencl.hpp>
 #include <qr.hpp>
 
+#include <err_opencl.hpp>
+
 #if defined(WITH_LINEAR_ALGEBRA)
 
+#include <blas.hpp>
+#include <copy.hpp>
 #include <cpu/cpu_qr.hpp>
 #include <identity.hpp>
 #include <kernel/triangle.hpp>
@@ -28,13 +29,17 @@ template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig) {
     if (OpenCLCPUOffload()) { return cpu::qr(q, r, t, orig); }
 
+    const dim4 NullShape(0, 0, 0, 0);
+
     dim4 iDims = orig.dims();
     int M      = iDims[0];
     int N      = iDims[1];
 
-    dim4 pDims(M, std::max(M, N));
+    dim4 endPadding(M - iDims[0], max(M, N) - iDims[1], 0, 0);
     Array<T> in =
-        padArray<T, T>(orig, pDims, scalar<T>(0));  // copyArray<T>(orig);
+        (endPadding == NullShape
+             ? copyArray(orig)
+             : padArrayBorders(orig, NullShape, endPadding, AF_PAD_ZERO));
     in.resetDims(iDims);
 
     int MN = std::min(M, N);
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index e890b57753..bedd987287 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -7,28 +7,24 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <err_opencl.hpp>
 #include <solve.hpp>
 
+#include <err_opencl.hpp>
+
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <blas.hpp>
 #include <copy.hpp>
-#include <err_opencl.hpp>
+#include <cpu/cpu_solve.hpp>
 #include <lu.hpp>
 #include <magma/magma.h>
 #include <magma/magma_blas.h>
 #include <magma/magma_data.h>
 #include <magma/magma_helper.h>
 #include <math.hpp>
+#include <platform.hpp>
 #include <transpose.hpp>
 #include <af/opencl.h>
 
-#include <algorithm>
-#include <string>
-
-#include <cpu/cpu_solve.hpp>
-#include <platform.hpp>
-
 namespace opencl {
 
 template<typename T>
@@ -107,7 +103,11 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         Array<T> A = transpose<T>(a, true);
 
 #if UNMQR
-        B = padArray<T, T>(b, dim4(N, K), scalar<T>(0));
+        const dim4 NullShape(0, 0, 0, 0);
+        dim4 endPadding(N - b.dims()[0], K - b.dims()[1], 0, 0);
+        B = (endPadding == NullShape
+                 ? copyArray(b)
+                 : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
         B.resetDims(dim4(M, K));
 #else
         B = copyArray<T>(b);

From fbdf2d36e07297a892d178c3835e419b07f99e37 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 29 Apr 2020 18:53:30 +0530
Subject: [PATCH 1938/2677] Handle zero padding case in padArrayBorders

---
 src/backend/cuda/pad_array_borders.cpp | 2 ++
 src/backend/opencl/copy.hpp            | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/src/backend/cuda/pad_array_borders.cpp b/src/backend/cuda/pad_array_borders.cpp
index 86d4c83982..2250f7f363 100644
--- a/src/backend/cuda/pad_array_borders.cpp
+++ b/src/backend/cuda/pad_array_borders.cpp
@@ -26,6 +26,8 @@ Array<T> padArrayBorders(Array<T> const& in, dim4 const& lowerBoundPadding,
                lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
                lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
 
+    if (oDims == iDims) { return in; }
+
     auto ret = createEmptyArray<T>(oDims);
 
     kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index e02b8da3c0..347f2bc230 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -50,6 +50,8 @@ Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
                lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
                lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
 
+    if (oDims == iDims) { return in; }
+
     auto ret = createEmptyArray<T>(oDims);
 
     switch (btype) {

From 38800854712c38b946251b0efeb92c8639f282cb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 24 Jan 2020 20:01:11 +0530
Subject: [PATCH 1939/2677] Cleanup opencl backend header inclusions

---
 src/backend/common/util.hpp                   |  3 +-
 src/backend/opencl/Array.cpp                  | 24 ++++++++++-
 src/backend/opencl/Array.hpp                  | 20 ++--------
 src/backend/opencl/Event.hpp                  |  2 +-
 src/backend/opencl/Param.hpp                  |  3 +-
 src/backend/opencl/cast.hpp                   |  1 +
 src/backend/opencl/cholesky.cpp               |  1 -
 src/backend/opencl/cl2hpp.hpp                 | 21 ++++++++++
 src/backend/opencl/complex.hpp                |  1 +
 src/backend/opencl/cpu/cpu_blas.cpp           |  2 +
 src/backend/opencl/debug_opencl.hpp           |  9 +++--
 src/backend/opencl/device_manager.hpp         | 40 ++++++++++++++++---
 src/backend/opencl/err_opencl.hpp             |  6 +--
 src/backend/opencl/kernel/bilateral.hpp       |  2 +
 .../opencl/kernel/convolve/conv_common.hpp    |  1 +
 .../opencl/kernel/convolve_separable.cpp      |  1 +
 .../opencl/kernel/convolve_separable.hpp      |  3 +-
 src/backend/opencl/kernel/fast.hpp            |  2 +
 src/backend/opencl/kernel/fftconvolve.hpp     |  1 +
 src/backend/opencl/kernel/laset.hpp           |  2 +
 src/backend/opencl/kernel/laswp.hpp           |  2 +
 src/backend/opencl/kernel/lookup.hpp          |  3 ++
 .../opencl/kernel/nearest_neighbour.hpp       |  6 ++-
 src/backend/opencl/kernel/range.hpp           |  3 ++
 src/backend/opencl/kernel/regions.hpp         |  7 +++-
 src/backend/opencl/kernel/scan_dim.hpp        | 10 +++--
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  9 +++--
 src/backend/opencl/kernel/select.hpp          |  3 ++
 src/backend/opencl/kernel/sparse_arith.hpp    |  4 ++
 src/backend/opencl/kernel/susan.hpp           |  4 ++
 src/backend/opencl/kernel/swapdblk.hpp        |  2 +
 src/backend/opencl/kernel/tile.hpp            |  2 +
 src/backend/opencl/kernel/transpose.hpp       |  2 +
 .../opencl/kernel/transpose_inplace.hpp       |  3 ++
 src/backend/opencl/kernel/triangle.hpp        |  3 ++
 src/backend/opencl/magma/magma_common.h       |  6 +--
 src/backend/opencl/memory.cpp                 |  1 +
 src/backend/opencl/platform.hpp               | 14 ++-----
 src/backend/opencl/program.cpp                |  9 ++++-
 src/backend/opencl/program.hpp                |  2 +-
 src/backend/opencl/shift.cpp                  | 10 ++---
 src/backend/opencl/traits.hpp                 |  1 +
 src/backend/opencl/transform.cpp              |  6 ++-
 src/backend/opencl/types.hpp                  |  9 +----
 44 files changed, 180 insertions(+), 86 deletions(-)
 create mode 100644 src/backend/opencl/cl2hpp.hpp

diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 5c4788315c..35afef108e 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -8,12 +8,11 @@
  ********************************************************/
 
 /// This file contains platform independent utility functions
+#pragma once
 
 #include <iosfwd>
 #include <string>
 
-#pragma once
-
 std::string getEnvVar(const std::string& key);
 
 // Dump the kernel sources only if the environment variable is defined
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 389ab47740..2389a1b282 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -11,7 +11,6 @@
 
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
-#include <common/traits.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
@@ -19,6 +18,7 @@
 #include <memory.hpp>
 #include <platform.hpp>
 #include <scalar.hpp>
+#include <traits.hpp>
 #include <af/dim4.hpp>
 #include <af/opencl.h>
 
@@ -374,6 +374,15 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
     return kJITHeuristics::Pass;
 }
 
+template<typename T>
+void *getDevicePtr(const Array<T> &arr) {
+    const cl::Buffer *buf = arr.device();
+    if (!buf) return NULL;
+    memLock((T *)buf);
+    cl_mem mem = (*buf)();
+    return (void *)mem;
+}
+
 template<typename T>
 Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
     verifyTypeSupport<T>();
@@ -484,6 +493,15 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
+template<typename T>
+size_t Array<T>::getAllocatedBytes() const {
+    if (!isReady()) return 0;
+    size_t bytes = memoryManager().allocated(data.get());
+    // External device poitner
+    if (bytes == 0 && data.get()) { return data_dims.elements() * sizeof(T); }
+    return bytes;
+}
+
 #define INSTANTIATE(T)                                                        \
     template Array<T> createHostDataArray<T>(const dim4 &dims,                \
                                              const T *const data);            \
@@ -510,7 +528,9 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
     template kJITHeuristics passesJitHeuristics<T>(Node * node);              \
-    template void Array<T>::setDataDims(const dim4 &new_dims);
+    template void *getDevicePtr<T>(const Array<T> &arr);                      \
+    template void Array<T>::setDataDims(const dim4 &new_dims);                \
+    template size_t Array<T>::getAllocatedBytes() const;
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index cb77569da7..6262ae0048 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
@@ -19,6 +20,7 @@
 #include <traits.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
+
 #include <memory>
 
 namespace opencl {
@@ -99,13 +101,7 @@ template<typename T>
 kJITHeuristics passesJitHeuristics(common::Node *node);
 
 template<typename T>
-void *getDevicePtr(const Array<T> &arr) {
-    const cl::Buffer *buf = arr.device();
-    if (!buf) return NULL;
-    memLock((T *)buf);
-    cl_mem mem = (*buf)();
-    return (void *)mem;
-}
+void *getDevicePtr(const Array<T> &arr);
 
 template<typename T>
 void *getRawPtr(const Array<T> &arr) {
@@ -218,15 +214,7 @@ class Array {
 
     void setDataDims(const dim4 &new_dims);
 
-    size_t getAllocatedBytes() const {
-        if (!isReady()) return 0;
-        size_t bytes = memoryManager().allocated(data.get());
-        // External device poitner
-        if (bytes == 0 && data.get()) {
-            return data_dims.elements() * sizeof(T);
-        }
-        return bytes;
-    }
+    size_t getAllocatedBytes() const;
 
     operator Param() const {
         KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
diff --git a/src/backend/opencl/Event.hpp b/src/backend/opencl/Event.hpp
index b9797d8afa..51505d5489 100644
--- a/src/backend/opencl/Event.hpp
+++ b/src/backend/opencl/Event.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 #pragma once
 
+#include <cl2hpp.hpp>
 #include <common/EventBase.hpp>
-#include <platform.hpp>
 #include <af/event.h>
 
 namespace opencl {
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 392c9d07b7..85f010f2d2 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -8,8 +8,9 @@
  ********************************************************/
 
 #pragma once
+
+#include <cl2hpp.hpp>
 #include <kernel/KParam.hpp>
-#include <platform.hpp>
 
 namespace opencl {
 
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index a1817bfaff..aec21f7a3b 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -13,6 +13,7 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
+#include <traits.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
 #include <complex>
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index 505ba2ea16..e1c0314a33 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -15,7 +15,6 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <cpu/cpu_cholesky.hpp>
 #include <magma/magma.h>
-#include <platform.hpp>
 #include <triangle.hpp>
 
 namespace opencl {
diff --git a/src/backend/opencl/cl2hpp.hpp b/src/backend/opencl/cl2hpp.hpp
new file mode 100644
index 0000000000..f7a94d5391
--- /dev/null
+++ b/src/backend/opencl/cl2hpp.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Wignored-qualifiers"
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#if __GNUC__ >= 8
+#pragma GCC diagnostic ignored "-Wcatch-value="
+#endif
+#include <CL/cl2.hpp>
+#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index a17f0506bb..e403eaa996 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -11,6 +11,7 @@
 #include <binary.hpp>
 #include <common/jit/UnaryNode.hpp>
 #include <optypes.hpp>
+#include <traits.hpp>
 #include <af/dim4.hpp>
 
 namespace opencl {
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 6ae3f39c0f..7858905fce 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -10,9 +10,11 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <common/blas_headers.hpp>
 #include <common/complex.hpp>
+#include <common/err_common.hpp>
 #include <cpu/cpu_blas.hpp>
 #include <cpu/cpu_helper.hpp>
 #include <math.hpp>
+#include <traits.hpp>
 
 using common::is_complex;
 
diff --git a/src/backend/opencl/debug_opencl.hpp b/src/backend/opencl/debug_opencl.hpp
index e2e808d160..12e75a32dd 100644
--- a/src/backend/opencl/debug_opencl.hpp
+++ b/src/backend/opencl/debug_opencl.hpp
@@ -8,15 +8,18 @@
  ********************************************************/
 
 #pragma once
-#include <err_opencl.hpp>
-#include <errorcodes.hpp>
-#include <stdio.h>
 
 #ifndef NDEBUG
+
 #define CL_DEBUG_FINISH(Q) Q.finish()
+
 #else
+
+#include <platform.hpp>
+
 #define CL_DEBUG_FINISH(Q)                       \
     do {                                         \
         if (synchronize_calls()) { Q.finish(); } \
     } while (false);
+
 #endif
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 8634092775..c510eff687 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -9,24 +9,54 @@
 
 #pragma once
 
-#include <platform.hpp>
-
 #include <memory>
 #include <mutex>
 #include <string>
 #include <vector>
 
-using common::memory::MemoryManagerBase;
-
 #ifndef AF_OPENCL_MEM_DEBUG
 #define AF_OPENCL_MEM_DEBUG 0
 #endif
 
-// Forward declaration from clFFT.h
+// Forward declarations
 struct clfftSetupData_;
 
+namespace cl {
+class CommandQueue;
+class Context;
+class Device;
+}  // namespace cl
+
+namespace boost {
+template<typename T>
+class shared_ptr;
+
+namespace compute {
+class program_cache;
+}
+}  // namespace boost
+
+namespace spdlog {
+class logger;
+}
+
+namespace graphics {
+class ForgeManager;
+}
+
+namespace common {
+namespace memory {
+class MemoryManagerBase;
+}
+}  // namespace common
+
 namespace opencl {
 
+// opencl namespace forward declarations
+class GraphicsResourceManager;
+struct kc_entry_t;  // kernel cache entry
+class PlanCache;    // clfft
+
 class DeviceManager {
     friend MemoryManagerBase& memoryManager();
 
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 4e72ce1e84..7e715bbd77 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -8,12 +8,8 @@
  ********************************************************/
 
 #pragma once
+
 #include <common/err_common.hpp>
-#include <common/half.hpp>
-#include <errorcodes.hpp>
-#include <platform.hpp>
-#include <types.hpp>
-#include <cstdio>
 
 #define OPENCL_NOT_SUPPORTED(message)                                      \
     do {                                                                   \
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 8b7c787982..c69f2e7837 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -8,10 +8,12 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <err_opencl.hpp>
 #include <kernel_headers/bilateral.hpp>
 #include <program.hpp>
 #include <traits.hpp>
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index d85c9ee819..9b3e2b8006 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <af/defines.h>
 
 #include <kernel_headers/convolve.hpp>
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 29b0fa1607..73e0a3cfca 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -16,6 +16,7 @@
 #include <debug_opencl.hpp>
 #include <kernel/names.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <map>
diff --git a/src/backend/opencl/kernel/convolve_separable.hpp b/src/backend/opencl/kernel/convolve_separable.hpp
index 7794d830d0..de16973a4d 100644
--- a/src/backend/opencl/kernel/convolve_separable.hpp
+++ b/src/backend/opencl/kernel/convolve_separable.hpp
@@ -8,10 +8,10 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 // below shared MAX_*_LEN's are calculated based on
@@ -23,5 +23,4 @@ template<typename T, typename accT, int cDim, bool expand>
 void convSep(Param out, const Param sig, const Param filt);
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 1abc7cc6ca..b0ac0fa9cc 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -14,7 +14,9 @@
 #include <kernel_headers/fast.hpp>
 #include <memory.hpp>
 #include <program.hpp>
+#include <traits.hpp>
 #include <af/defines.h>
+
 #include <map>
 
 using cl::Buffer;
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 535ee7c4cc..648ad8c12a 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -19,6 +19,7 @@
 #include <kernel_headers/fftconvolve_reorder.hpp>
 #include <memory.hpp>
 #include <program.hpp>
+#include <traits.hpp>
 #include <types.hpp>
 #include <af/defines.h>
 
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index bae033a21c..dfbefdaf0e 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -8,12 +8,14 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/laset.hpp>
 #include <magma_types.h>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 5b6281730a..51b0d633fb 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/laswp.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index a83af42953..40d8da89bc 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -8,14 +8,17 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/lookup.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
+
 #include <string>
 
 namespace opencl {
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index bdf91b2c26..3b479432ba 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -7,14 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel_headers/nearest_neighbour.hpp>
 #include <math.hpp>
-#include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
+#include <traits.hpp>
 #include <af/defines.h>
 
 using cl::Buffer;
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index d06223a9a4..8e9202193b 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -8,14 +8,17 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/range.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
+
 #include <string>
 
 namespace opencl {
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 6ab7449922..da96f71019 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -8,15 +8,16 @@
  ********************************************************/
 
 #pragma once
+
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel_headers/regions.hpp>
 #include <math.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
-#include <stdio.h>
+#include <traits.hpp>
 #include <af/defines.h>
 
 #pragma GCC diagnostic push
@@ -33,6 +34,8 @@
 
 #pragma GCC diagnostic pop
 
+#include <cstdio>
+
 using cl::Buffer;
 using cl::EnqueueArgs;
 using cl::Kernel;
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 4091e47147..29acd4df23 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -8,20 +8,22 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_dim.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <type_util.hpp>
-#include <map>
-#include <mutex>
+
 #include <string>
-#include "config.hpp"
-#include "names.hpp"
 
 using cl::Buffer;
 using cl::EnqueueArgs;
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 953e2112ec..3f119c905e 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -8,22 +8,23 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_dim_by_key.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <type_util.hpp>
 #include <types.hpp>
-#include <map>
-#include <mutex>
+
 #include <string>
-#include "config.hpp"
-#include "names.hpp"
 
 using cl::Buffer;
 using cl::EnqueueArgs;
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 2274fb5902..7e77e16237 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -8,15 +8,18 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/select.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+
 #include <string>
 
 using cl::Buffer;
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 5caadb558a..a6e64c0368 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/complex.hpp>
@@ -19,10 +20,13 @@
 #include <kernel_headers/sparse_arith_csr.hpp>
 #include <kernel_headers/ssarith_calc_out_nnz.hpp>
 #include <math.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <type_util.hpp>
 #include <types.hpp>
+
 #include <map>
 #include <mutex>
 #include <string>
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index d2fa6032d7..4c2ddb44c8 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -7,13 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <kernel_headers/susan.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 #include <program.hpp>
+#include <traits.hpp>
 #include <af/defines.h>
 #include "config.hpp"
 
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index b6213b583a..b046575d39 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -8,9 +8,11 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/err_common.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/swapdblk.hpp>
 #include <program.hpp>
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index c685973ca4..8b29941727 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/tile.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <string>
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index e912b2d071..a47882d754 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/transpose.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 800109a19f..ba5286228f 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -8,14 +8,17 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/transpose_inplace.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+
 #include <string>
 
 using cl::Buffer;
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index d11fff0371..a1cfc4ee95 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <cache.hpp>
 #include <common/dispatch.hpp>
@@ -15,9 +16,11 @@
 #include <debug_opencl.hpp>
 #include <kernel_headers/triangle.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 #include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+
 #include <string>
 
 namespace opencl {
diff --git a/src/backend/opencl/magma/magma_common.h b/src/backend/opencl/magma/magma_common.h
index 83d3001e54..82365cadc5 100644
--- a/src/backend/opencl/magma/magma_common.h
+++ b/src/backend/opencl/magma/magma_common.h
@@ -10,11 +10,7 @@
 #ifndef __MAGMA_COMMON_H
 #define __MAGMA_COMMON_H
 
-#ifdef __APPLE__
-#include <OpenCL/cl.h>
-#else
-#include <CL/cl.h>
-#endif
+#include <cl2hpp.hpp>
 
 #include "magma_types.h"
 
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index b1051d29ec..e50dba24a1 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -9,6 +9,7 @@
 
 #include <common/Logger.hpp>
 #include <common/MemoryManagerBase.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 980807753e..97c3590e3a 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -9,21 +9,13 @@
 
 #pragma once
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wunused-function"
-#pragma GCC diagnostic ignored "-Wunused-parameter"
-#pragma GCC diagnostic ignored "-Wignored-qualifiers"
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#if __GNUC__ >= 8
-#pragma GCC diagnostic ignored "-Wcatch-value="
-#endif
-#include <CL/cl2.hpp>
-#pragma GCC diagnostic pop
-
+#include <cl2hpp.hpp>
 #include <af/opencl.h>
+
 #include <memory>
 #include <string>
 
+// Forward declarations
 namespace boost {
 template<typename T>
 class shared_ptr;
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index e252fc0c4d..6735b627a6 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -7,11 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <program.hpp>
+
+#include <cl2hpp.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/KParam.hpp>
-#include <program.hpp>
+#include <platform.hpp>
 #include <traits.hpp>
-#include <utility>
+#include <types.hpp>
+
+#include <string>
 
 using cl::Buffer;
 using cl::EnqueueArgs;
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index ba2ff9eb4d..514ce2376f 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #pragma once
+
 #include <common/util.hpp>
-#include <platform.hpp>
 
 #include <cstdio>
 #include <string>
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index f3e14270c4..e3ff7474fe 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -7,20 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <common/jit/ShiftNodeBase.hpp>
-#include <err_opencl.hpp>
 #include <shift.hpp>
 
-#include <memory>
-#include <stdexcept>
+#include <common/jit/ShiftNodeBase.hpp>
+#include <err_opencl.hpp>
+#include <traits.hpp>
 
 using af::dim4;
-
 using common::Node_ptr;
 using common::ShiftNodeBase;
 using opencl::jit::BufferNode;
-
 using std::array;
 using std::make_shared;
 using std::static_pointer_cast;
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 589ac4d625..e7e6921d77 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -12,6 +12,7 @@
 #include <common/defines.hpp>
 #include <common/traits.hpp>
 #include <types.hpp>
+
 #include <sstream>
 #include <string>
 
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 57103e9e90..8a49d30ec6 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -7,10 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <kernel/transform.hpp>
 #include <transform.hpp>
+
+#include <err_opencl.hpp>
+#include <kernel/transform.hpp>
 #include <af/dim4.hpp>
+
 #include <stdexcept>
 
 namespace opencl {
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 8a3c9de00a..e3d7970b78 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -8,15 +8,8 @@
  ********************************************************/
 
 #pragma once
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#if __APPLE__
-#include <OpenCL/cl.h>
-#else
-#include <CL/cl.h>
-#endif
-#pragma GCC diagnostic pop
 
+#include <cl2hpp.hpp>
 #include <common/kernel_type.hpp>
 #include <common/traits.hpp>
 

From 7e9171e6c68f058acf8e24c2f2dab566738f94f2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 17:14:16 -0400
Subject: [PATCH 1940/2677] Fix constness of operator* in ParamIterator

---
 src/backend/cpu/ParamIterator.hpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
index 9b2ea78208..ba2189bdeb 100644
--- a/src/backend/cpu/ParamIterator.hpp
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -26,6 +26,7 @@ class ParamIterator {
     using value_type        = T;
     using pointer           = T*;
     using reference         = T&;
+    using const_reference   = const T&;
     using iterator_category = std::forward_iterator_tag;
 
     /// Creates a sentinel iterator. This is equivalent to the end iterator
@@ -76,7 +77,9 @@ class ParamIterator {
         return *this;
     }
 
-    const reference operator*() const noexcept { return *ptr; }
+    reference operator*() noexcept { return *ptr; }
+
+    const_reference operator*() const noexcept { return *ptr; }
 
     const pointer operator->() const noexcept { return ptr; }
 

From 755651ffb1bfb334ad5741b30bacb032a5404ef9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 17:15:19 -0400
Subject: [PATCH 1941/2677] Fix warning in older versions of boost stacktrace

---
 src/backend/common/err_common.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index f3d0132f04..46697ec3ad 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -11,6 +11,7 @@
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wattributes"
+#pragma GCC diagnostic ignored "-Wparentheses"
 #include <boost/stacktrace.hpp>
 #pragma GCC diagnostic pop
 #include <common/defines.hpp>

From 9c35e874d9b00196d659838e35c5ce07b6541000 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 17:16:06 -0400
Subject: [PATCH 1942/2677] Prefer downloaded boost compute over system
 version. Set min version

---
 CMakeModules/boost_package.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index cf63452286..9f40409251 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-find_package(Boost)
+find_package(Boost 1.66 REQUIRED)
 
 set(Boost_MIN_VER 107000)
 set(Boost_MIN_VER_STR "1.70")
@@ -45,8 +45,8 @@ if(NOT
   add_dependencies(Boost::boost boost_compute)
 
   set_target_properties(Boost::boost PROPERTIES
-    INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
-    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIR};${source_dir}/include"
+    INTERFACE_INCLUDE_DIRECTORIES "${source_dir}/include;${Boost_INCLUDE_DIR}"
+    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${source_dir}/include;${Boost_INCLUDE_DIR}"
     )
 else()
   if(NOT TARGET Boost::boost)

From ffd322066bc22a99ddd63d1d9f26997583eee8bb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 17:16:44 -0400
Subject: [PATCH 1943/2677] Update CLBlast version to 1.5.1

---
 CMakeModules/build_CLBlast.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 3085aef139..76fd0ae1b0 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -52,7 +52,7 @@ endif()
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 1.5.0
+    GIT_TAG 1.5.1
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""

From b7ec3caf022f9dbc6c96239d22ae022266676bb9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 17:17:01 -0400
Subject: [PATCH 1944/2677] Remove deprecated variable from doxygen mk file

---
 docs/doxygen.mk | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 5bbb39d3e9..7994a8a315 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -258,12 +258,6 @@ ALIASES += "convolve_t{2}=\1 \ast \2"
 ALIASES += "set_eq{2}=\f$ \left\\{ \1 \ \Bigg\vert \ \2 \right\\} \f$"
 ALIASES += "set_t{2}=\left\\\{ \1 \ \Bigg\vert \ \2 \right\\\}"
 
-# This tag can be used to specify a number of word-keyword mappings (TCL only).
-# A mapping has the form "name=value". For example adding "class=itcl::class"
-# will allow you to use the command class in the itcl::class meaning.
-
-TCL_SUBST              =
-
 # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
 # only. Doxygen will then generate output that is more tailored for C. For
 # instance, some of the names that are used will be different. The list of all

From 876c1a28da5838c52e9abaf6b731e8456a572edd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 17:18:49 -0400
Subject: [PATCH 1945/2677] Set current device to array device before releasing
 array

* Arrays were being freed prematurely because the device associated
  with the array was not active. This change makes sure that we call
  setDevice before the memory is freed so we find the correct pointer
  in the memory manager.
---
 src/api/c/handle.hpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 27d4b558c6..de91cbfdc2 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -139,7 +139,16 @@ af_array copyArray(const af_array in) {
 
 template<typename T>
 void releaseHandle(const af_array arr) {
-    detail::destroyArray(static_cast<detail::Array<T> *>(arr));
+    auto &Arr      = getArray<T>(arr);
+    int old_device = detail::getActiveDeviceId();
+    int array_id   = Arr.getDevId();
+    if (array_id != old_device) {
+        detail::setDevice(array_id);
+        detail::destroyArray(static_cast<detail::Array<T> *>(arr));
+        detail::setDevice(old_device);
+    } else {
+        detail::destroyArray(static_cast<detail::Array<T> *>(arr));
+    }
 }
 
 template<typename T>

From db3a893e7d4b237a5371a11b6d09f9a796f1a6e5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 May 2020 18:01:41 -0400
Subject: [PATCH 1946/2677] Add boost to the doxygen ci jobs to pass minimum
 requirements

---
 .github/workflows/docs_build.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
index 6a89ad7856..7dec0803dc 100644
--- a/.github/workflows/docs_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -42,6 +42,7 @@ jobs:
                       -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
                       -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
                       -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
+                      -DBOOST_ROOT:PATH=${BOOST_ROOT_1_72_0} \
                       -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen \
                       ..
 

From e1d646cf834c0ac40c4678ec5825299b34b90758 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 29 Apr 2020 18:55:03 -0400
Subject: [PATCH 1947/2677] Add bitwise not operation to all backends

---
 include/af/arith.h               | 13 ++++++++++++
 include/af/array.h               |  9 ++++++++
 src/api/c/optypes.hpp            |  1 +
 src/api/c/unary.cpp              | 35 ++++++++++++++++++++++++++++++++
 src/api/cpp/array.cpp            |  7 +++++++
 src/api/unified/arith.cpp        |  1 +
 src/backend/cpu/unary.hpp        |  2 ++
 src/backend/cuda/kernel/jit.cuh  |  1 +
 src/backend/cuda/nvrtc/cache.cpp |  1 +
 src/backend/cuda/unary.hpp       |  1 +
 src/backend/opencl/kernel/jit.cl |  1 +
 src/backend/opencl/unary.hpp     |  2 ++
 test/binary.cpp                  | 20 ++++++++++++++++++
 13 files changed, 94 insertions(+)

diff --git a/include/af/arith.h b/include/af/arith.h
index d572f95359..6b0c08dea5 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -741,6 +741,19 @@ extern "C" {
     */
     AFAPI af_err af_not   (af_array *out, const af_array in);
 
+#if AF_API_VERSION >= 38
+    /**
+       C Interface for performing bitwise not on input
+
+       \param[out] out will contain result of bitwise not of \p in.
+       \param[in] in is the input
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup arith_func_bitnot
+    */
+    AFAPI af_err af_bitnot   (af_array *out, const af_array in);
+#endif
+
     /**
        C Interface for performing bitwise and on two arrays
 
diff --git a/include/af/array.h b/include/af/array.h
index 438b4a99b4..1b2325f7ac 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -996,6 +996,15 @@ namespace af
         /// \returns an \ref array with negated values
         array operator !() const;
 
+#if AF_API_VERSION >= 38
+        ///
+        /// \brief Performs a bitwise not operation on the values of the array
+        /// \ingroup arith_func_bitnot
+        ///
+        /// \returns an \ref array with inverted values
+        array operator ~() const;
+#endif
+
         ///
         /// \brief Get the count of non-zero elements in the array
         ///
diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index a20e52048a..c1ce3c0784 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -29,6 +29,7 @@ typedef enum {
     af_bitxor_t,
     af_bitshiftl_t,
     af_bitshiftr_t,
+    af_bitnot_t,
 
     af_min_t,
     af_max_t,
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index c42cd4d4ff..7d75b145a8 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -560,6 +560,41 @@ af_err af_not(af_array *out, const af_array in) {
     return AF_SUCCESS;
 }
 
+template<typename T>
+static inline af_array bitOpNot(const af_array in) {
+    return unaryOp<T, af_bitnot_t>(in);
+}
+
+af_err af_bitnot(af_array *out, const af_array in) {
+    try {
+        const ArrayInfo &iinfo = getInfo(in);
+        const af_dtype type    = iinfo.getType();
+
+        dim4 odims = iinfo.dims();
+
+        if (odims.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, type);
+        }
+
+        af_array res;
+        switch (type) {
+            case s32: res = bitOpNot<int>(in); break;
+            case u32: res = bitOpNot<uint>(in); break;
+            case u8: res = bitOpNot<uchar>(in); break;
+            case b8: res = bitOpNot<char>(in); break;
+            case s64: res = bitOpNot<intl>(in); break;
+            case u64: res = bitOpNot<uintl>(in); break;
+            case s16: res = bitOpNot<short>(in); break;
+            case u16: res = bitOpNot<ushort>(in); break;
+            default: TYPE_ERROR(0, type);
+        }
+
+        std::swap(*out, res);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_arg(af_array *out, const af_array in) {
     try {
         const ArrayInfo &in_info = getInfo(in);
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 0612d33f16..784ef605a6 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -988,6 +988,13 @@ array array::operator!() const {
     return array(out);
 }
 
+array array::operator~() const {
+    af_array lhs = this->get();
+    af_array out = nullptr;
+    AF_THROW(af_bitnot(&out, lhs));
+    return array(out);
+}
+
 void array::eval() const { AF_THROW(af_eval(get())); }
 
 // array instanciations
diff --git a/src/api/unified/arith.cpp b/src/api/unified/arith.cpp
index 9798341c2b..03638fdde3 100644
--- a/src/api/unified/arith.cpp
+++ b/src/api/unified/arith.cpp
@@ -99,6 +99,7 @@ UNARY_HAPI_DEF(af_iszero)
 UNARY_HAPI_DEF(af_isinf)
 UNARY_HAPI_DEF(af_isnan)
 UNARY_HAPI_DEF(af_not)
+UNARY_HAPI_DEF(af_bitnot)
 
 af_err af_clamp(af_array* out, const af_array in, const af_array lo,
                 const af_array hi, const bool batch) {
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 418510761b..87c3e12d3c 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -77,6 +77,8 @@ UNARY_OP(cbrt)
 UNARY_OP(tgamma)
 UNARY_OP(lgamma)
 
+UNARY_OP_FN(bitnot, ~)
+
 #undef UNARY_OP
 #undef UNARY_OP_FN
 
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index b613505647..4681c151ed 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -47,6 +47,7 @@ typedef cuDoubleComplex cdouble;
 #define __abs(in) abs(in)
 #define __sigmoid(in) (1.0 / (1 + exp(-(in))))
 
+#define __bitnot(in) (~(in))
 #define __bitor(lhs, rhs) ((lhs) | (rhs))
 #define __bitand(lhs, rhs) ((lhs) & (rhs))
 #define __bitxor(lhs, rhs) ((lhs) ^ (rhs))
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 93cda8a136..3e5c74e5b8 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -474,6 +474,7 @@ string getOpEnumStr(af_op_t val) {
         CASE_STMT(af_gt_t);
         CASE_STMT(af_ge_t);
 
+        CASE_STMT(af_bitnot_t);
         CASE_STMT(af_bitor_t);
         CASE_STMT(af_bitand_t);
         CASE_STMT(af_bitxor_t);
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index b352930c81..4183a91a2c 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -66,6 +66,7 @@ UNARY_FN(signbit)
 UNARY_FN(ceil)
 UNARY_FN(floor)
 
+UNARY_DECL(bitnot, "__bitnot")
 UNARY_DECL(isinf, "__isinf")
 UNARY_DECL(isnan, "__isnan")
 UNARY_FN(iszero)
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index ec6da04b6c..f3b6b0518e 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -95,6 +95,7 @@ float2 __cdivf(float2 lhs, float2 rhs) {
 #define __cgt(lhs, rhs) (__cabs(lhs) > __cabs(rhs))
 #define __cge(lhs, rhs) (__cabs(lhs) >= __cabs(rhs))
 
+#define __bitnot(in) (~(in))
 #define __bitor(lhs, rhs) ((lhs) | (rhs))
 #define __bitand(lhs, rhs) ((lhs) & (rhs))
 #define __bitxor(lhs, rhs) ((lhs) ^ (rhs))
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 66a2cf41a5..d0ee08537c 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -70,6 +70,8 @@ UNARY_FN(isnan)
 UNARY_FN(iszero)
 UNARY_DECL(noop, "__noop")
 
+UNARY_DECL(bitnot, "__bitnot")
+
 #undef UNARY_FN
 
 template<typename T, af_op_t op>
diff --git a/test/binary.cpp b/test/binary.cpp
index 2daad03a2b..4db3169693 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -289,6 +289,26 @@ BITOP(bitxor, uintl, ^)
 BITOP(bitshiftl, uintl, <<)
 BITOP(bitshiftr, uintl, >>)
 
+#define UBITOP(func, T)                                     \
+    TEST(BinaryTests, Test_##func##_##T) {                  \
+        af_dtype ty   = (af_dtype)dtype_traits<T>::af_type; \
+        const T vala  = 4095;                               \
+        const T valc  = ~vala;                              \
+        const int num = 10;                                 \
+        af::array a   = af::constant(vala, num, ty);        \
+        af::array b   = af::constant(valc, num, ty);        \
+        af::array c   = ~a;                                 \
+        ASSERT_ARRAYS_EQ(c, b);                             \
+    }
+
+UBITOP(bitnot, int)
+UBITOP(bitnot, uint)
+UBITOP(bitnot, intl)
+UBITOP(bitnot, uintl)
+UBITOP(bitnot, uchar)
+UBITOP(bitnot, short)
+UBITOP(bitnot, ushort)
+
 TEST(BinaryTests, Test_pow_cfloat_float) {
     af::array a        = randgen(num, c32);
     af::array b        = randgen(num, f32);

From d2db2833601e973a6048fd97158b55ea50cc61d3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 2 May 2020 19:49:18 +0530
Subject: [PATCH 1948/2677] Remove CUDA backend BinOP default implementation

---
 src/backend/cuda/binary.hpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index bbdb390c51..bcee0fa55f 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -18,9 +18,7 @@
 namespace cuda {
 
 template<typename To, typename Ti, af_op_t op>
-struct BinOp {
-    const char *name() { return "__invalid"; }
-};
+struct BinOp;
 
 #define BINARY_TYPE_1(fn)                            \
     template<typename To, typename Ti>               \

From 890e241a9b73bbc7604663f05830b8a8bd126073 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 4 May 2020 16:37:51 +0530
Subject: [PATCH 1949/2677] Use kernel source with instance name for hashing

This will ensure updated kernels are used/cached when their respective
source changes during development.

This change will however not change any behavior of programs using
arrayfire.
---
 src/backend/cuda/jit.cpp         |  6 +++---
 src/backend/cuda/nvrtc/cache.cpp | 22 +++++++++++++---------
 src/backend/cuda/nvrtc/cache.hpp |  3 ++-
 3 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 7121401e50..4ad9ee3546 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -214,12 +214,12 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
     Kernel entry{nullptr, nullptr};
 
     if (idx == kernelCaches[device].end()) {
+        string jit_ker = getKernelString(funcName, full_nodes, full_ids,
+                                         output_ids, is_linear);
 #ifdef AF_CACHE_KERNELS_TO_DISK
-        entry = loadKernel(device, funcName);
+        entry = loadKernel(device, funcName, jit_ker);
 #endif
         if (entry.prog == nullptr || entry.ker == nullptr) {
-            string jit_ker = getKernelString(funcName, full_nodes, full_ids,
-                                             output_ids, is_linear);
             saveKernel(funcName, jit_ker, ".cu");
             entry = buildKernel(device, funcName, jit_ker, {}, true);
         }
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/nvrtc/cache.cpp
index 3e5c74e5b8..18c4708d5c 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/nvrtc/cache.cpp
@@ -151,8 +151,10 @@ void Kernel::getScalar(T &out, const char *name) {
 template void Kernel::setScalar<int>(const char *, int);
 template void Kernel::getScalar<int>(int &, const char *);
 
-string getKernelCacheFilename(const int device, const string &nameExpr) {
-    const string mangledName = "KER" + to_string(deterministicHash(nameExpr));
+string getKernelCacheFilename(const int device, const string &nameExpr,
+                              const string &jitSource) {
+    const string mangledName =
+        "KER" + to_string(deterministicHash(nameExpr + jitSource));
 
     const auto computeFlag = getComputeCapability(device);
     const string computeVersion =
@@ -330,8 +332,9 @@ Kernel buildKernel(const int device, const string &nameExpr,
     // save kernel in cache
     const string &cacheDirectory = getCacheDirectory();
     if (!cacheDirectory.empty()) {
-        const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
-                                 getKernelCacheFilename(device, nameExpr);
+        const string cacheFile =
+            cacheDirectory + AF_PATH_SEPARATOR +
+            getKernelCacheFilename(device, nameExpr, jit_ker);
         const string tempFile =
             cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
 
@@ -378,12 +381,13 @@ Kernel buildKernel(const int device, const string &nameExpr,
     return entry;
 }
 
-Kernel loadKernel(const int device, const string &nameExpr) {
+Kernel loadKernel(const int device, const string &nameExpr,
+                  const string &source) {
     const string &cacheDirectory = getCacheDirectory();
     if (cacheDirectory.empty()) return Kernel{nullptr, nullptr};
 
     const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
-                             getKernelCacheFilename(device, nameExpr);
+                             getKernelCacheFilename(device, nameExpr, source);
 
     CUmodule module   = nullptr;
     CUfunction kernel = nullptr;
@@ -438,14 +442,14 @@ void addKernelToCache(int device, const string &nameExpr, Kernel entry) {
     getCache(device).emplace(nameExpr, entry);
 }
 
-Kernel findKernel(int device, const string &nameExpr) {
+Kernel findKernel(int device, const string &nameExpr, const string &source) {
     kc_t &cache = getCache(device);
 
     auto iter = cache.find(nameExpr);
     if (iter != cache.end()) return iter->second;
 
 #ifdef AF_CACHE_KERNELS_TO_DISK
-    Kernel kernel = loadKernel(device, nameExpr);
+    Kernel kernel = loadKernel(device, nameExpr, source);
     if (kernel.prog != nullptr && kernel.ker != nullptr) {
         addKernelToCache(device, nameExpr, kernel);
         return kernel;
@@ -700,7 +704,7 @@ Kernel getKernel(const string &nameExpr, const string &source,
     tInstance += ">";
 
     int device    = getActiveDeviceId();
-    Kernel kernel = findKernel(device, tInstance);
+    Kernel kernel = findKernel(device, tInstance, source);
 
     if (kernel.prog == nullptr || kernel.ker == nullptr) {
         kernel = buildKernel(device, tInstance, source, compileOpts);
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
index 28163dac4f..2380521908 100644
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ b/src/backend/cuda/nvrtc/cache.hpp
@@ -109,7 +109,8 @@ Kernel buildKernel(const int device, const std::string& nameExpr,
                    const std::vector<std::string>& opts = {},
                    const bool isJIT                     = false);
 
-Kernel loadKernel(const int device, const std::string& nameExpr);
+Kernel loadKernel(const int device, const std::string& nameExpr,
+                  const std::string& source);
 
 template<typename T>
 std::string toString(T val);

From 3086af05756b468c55ffe3978ac6124630827795 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 6 May 2020 01:44:19 -0400
Subject: [PATCH 1950/2677] Fix error in GCC 6.1 because of a noexcept move
 constructor in AfError

---
 src/backend/common/err_common.hpp | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 46697ec3ad..8da138d3a7 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -41,7 +41,17 @@ class AfError : public std::logic_error {
             boost::stacktrace::stacktrace st);
 
     AfError(const AfError& other) noexcept = delete;
-    AfError(AfError&& other) noexcept      = default;
+
+    /// This is the same as default but gcc 6.1 fails when noexcept is used
+    /// along with the default specifier. Expanded the default definition
+    /// to avoid this error
+    AfError(AfError&& other) noexcept
+        : std::logic_error(std::forward<std::logic_error>(other))
+        , functionName(std::forward<std::string>(other.functionName))
+        , fileName(std::forward<std::string>(other.fileName))
+        , lineNumber(std::forward<int>(other.lineNumber))
+        , error(std::forward<af_err>(other.error))
+        , st_(std::forward<boost::stacktrace::stacktrace>(other.st_)) {}
 
     const std::string& getFunctionName() const noexcept;
 

From 9d4cec2bc4d35cf4a00ed34e92a9ba22c73fba42 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 6 May 2020 01:45:55 -0400
Subject: [PATCH 1951/2677] Fix overflow warning. Make pinverse and cholesky
 tests SERIAL

---
 test/CMakeLists.txt | 4 ++--
 test/binary.cpp     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 3e38149a92..73ff944617 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -213,7 +213,7 @@ make_test(SRC binary.cpp CXX11)
 make_test(SRC blas.cpp)
 make_test(SRC canny.cpp)
 make_test(SRC cast.cpp)
-make_test(SRC cholesky_dense.cpp)
+make_test(SRC cholesky_dense.cpp SERIAL)
 make_test(SRC clamp.cpp)
 make_test(SRC compare.cpp)
 make_test(SRC complex.cpp)
@@ -288,7 +288,7 @@ endif()
 
 make_test(SRC orb.cpp)
 make_test(SRC pad_borders.cpp CXX11)
-make_test(SRC pinverse.cpp)
+make_test(SRC pinverse.cpp SERIAL)
 make_test(SRC qr_dense.cpp SERIAL)
 make_test(SRC random.cpp)
 make_test(SRC range.cpp)
diff --git a/test/binary.cpp b/test/binary.cpp
index 4db3169693..2bc2a1a62a 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -292,7 +292,7 @@ BITOP(bitshiftr, uintl, >>)
 #define UBITOP(func, T)                                     \
     TEST(BinaryTests, Test_##func##_##T) {                  \
         af_dtype ty   = (af_dtype)dtype_traits<T>::af_type; \
-        const T vala  = 4095;                               \
+        const T vala  = 127u;                               \
         const T valc  = ~vala;                              \
         const int num = 10;                                 \
         af::array a   = af::constant(vala, num, ty);        \

From abc8ddcad4b4c19fd58f29b5f64afc8d85dfc746 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 5 May 2020 13:02:35 -0400
Subject: [PATCH 1952/2677] Fix error in GCC 8.3: cl_float* -> float* using
 static_cast is invalid

* using renterpret_cast instead. This shouldn't be required but its
  an easy workaround
---
 src/backend/opencl/cpu/cpu_blas.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 7858905fce..8f80b044f3 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -214,10 +214,10 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         int roff = z * (is_r_d2_batched * rStrides[2]) +
                    w * (is_r_d3_batched * rStrides[3]);
 
-        CBT *lptr = static_cast<CBT *>(lPtr.get() + loff);
-        CBT *rptr = static_cast<CBT *>(rPtr.get() + roff);
-        BT *optr =
-            static_cast<BT *>(oPtr.get() + z * oStrides[2] + w * oStrides[3]);
+        CBT *lptr = reinterpret_cast<CBT *>(lPtr.get() + loff);
+        CBT *rptr = reinterpret_cast<CBT *>(rPtr.get() + roff);
+        BT *optr  = reinterpret_cast<BT *>(oPtr.get() + z * oStrides[2] +
+                                          w * oStrides[3]);
 
         if (rDims[bColDim] == 1) {
             dim_t incr = (rOpts == CblasNoTrans) ? rStrides[0] : rStrides[1];

From 5322673380bb5e9c0bdb89093590d2710b4379f5 Mon Sep 17 00:00:00 2001
From: Jacob Kahn <jacobkahn1@gmail.com>
Date: Wed, 6 May 2020 11:11:50 -0400
Subject: [PATCH 1953/2677] Add an ArrayFire conanfile.py that pulls from the
 linux binary installer (#2875)

* Add an ArrayFire conanfile.py that pulls from the linux binary installer

* Make backends and graphics opt-in/out, use variables for lib versioning
---
 .gitignore   |   4 ++
 conanfile.py | 126 +++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 130 insertions(+)
 create mode 100644 conanfile.py

diff --git a/.gitignore b/.gitignore
index f332b57b56..5762c63c5a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,3 +16,7 @@ compile_commands.json
 venv
 test/gtest
 src/backend/cuda/cub
+conanbuildinfo*
+conaninfo*
+conan.lock
+graph_info.json
\ No newline at end of file
diff --git a/conanfile.py b/conanfile.py
new file mode 100644
index 0000000000..13169b943b
--- /dev/null
+++ b/conanfile.py
@@ -0,0 +1,126 @@
+from conans import ConanFile, CMake, tools
+import os
+
+
+ARRAYFIRE_VERSION = "3.7.1"
+BINARY_INSTALLER_NAME_SUFFIX = "-1"
+BINARY_INSTALLER_NAME = f"ArrayFire-v{ARRAYFIRE_VERSION}{BINARY_INSTALLER_NAME_SUFFIX}_Linux_x86_64.sh"
+CUDA_TOOLKIT_VERSION = "10.0"
+
+class ArrayFireConan(ConanFile):
+    name = "arrayfire"
+    version = ARRAYFIRE_VERSION
+    license = "BSD"
+    author = "jacobkahn jacobkahn1@gmail.com"
+    url = "https://github.com/arrayfire/arrayfire"
+    requires = []
+    description = "ArrayFire: a general purpose GPU library"
+    topics = ("arrayfire", "gpu", "cuda", "opencl", "gpgpu",
+              "hpc", "performance", "scientific-computing")
+    settings = "os", "compiler", "build_type", "arch"
+    options = {
+        "cpu_backend": [True, False],
+        "cuda_backend": [True, False],
+        "opencl_backend": [True, False],
+        "unified_backend": [True, False],
+        "graphics": [True, False],
+    }
+    generators = "cmake"  # unused
+
+    def configure(self):
+        if self.settings.os == "Windows":
+            raise ConanInvalidConfiguration(
+                "Linux binary installer not compaible with Windows.")
+
+    def requirements(self):
+        if self.options.graphics:
+            self.requires('glfw/3.3.2@bincrafters/stable')
+
+    def _download_arrayfire(self):
+        self.af_installer_local_path = BINARY_INSTALLER_NAME
+        if not os.path.exists(self.af_installer_local_path):
+            self.output.info(
+                f"Downloading the ArrayFire {ARRAYFIRE_VERSION} binary installer...")
+            tools.download(
+                f"https://arrayfire.s3.amazonaws.com/{ARRAYFIRE_VERSION}/{BINARY_INSTALLER_NAME}", self.af_installer_local_path)
+            self.output.success(
+                f"ArrayFire {ARRAYFIRE_VERSION} binary installer successfully downloaded to {self.af_installer_local_path}")
+        else:
+            self.output.info(
+                f"ArrayFire {ARRAYFIRE_VERSION} binary installer already exists - skipping download.")
+
+    def _unpack_arrayfire(self):
+        if not os.path.exists(self.af_unpack_path):
+            os.mkdir(self.af_unpack_path)
+        self.output.info(
+            f"Unpacking ArrayFire {ARRAYFIRE_VERSION} binary installer...")
+        cmd = f"bash {self.af_installer_local_path} --prefix={self.af_unpack_path} --skip-license"
+        self.run(cmd)
+        self.output.success(
+            f"ArrayFire {ARRAYFIRE_VERSION} successfully unpacked.")
+
+    def _process_arrayfire(self):
+        # Install ArrayFire to requisite path
+        self.af_unpack_path = os.path.join(self.source_folder, 'arrayfire')
+
+        # Only proceed if missing
+        if os.path.exists(os.path.join(self.af_unpack_path, 'include', 'arrayfire.h')):
+            self.output.info(
+                f"ArrayFire {ARRAYFIRE_VERSION} already unpacked - skipping.")
+        else:
+            self._download_arrayfire()
+            self._unpack_arrayfire()
+
+    def build(self):
+        self._process_arrayfire()
+
+    def package(self):
+        # libs
+        self.copy("*.so", dst="lib", keep_path=False, symlinks=True)
+        self.copy("*.so.*", dst="lib", keep_path=False, symlinks=True)
+
+        # headers
+        self.copy("*.h", dst="include", src="arrayfire/include")
+        self.copy("*.hpp", dst="include", src="arrayfire/include")
+
+    def package_info(self):
+        self.cpp_info.libs = []
+        if self.options.unified_backend:
+            self.cpp_info.libs.extend([
+                f"libaf.so.{ARRAYFIRE_VERSION}",
+            ])
+        if self.options.graphics:
+            self.cpp_info.libs.extend([
+                "libforge.so.1.0.5",
+            ])
+        if self.options.cuda_backend:
+            self.cpp_info.libs.extend([
+                f"libafcuda.so.{ARRAYFIRE_VERSION}",
+                "libnvrtc-builtins.so",
+                f"libcudnn.so.{CUDA_TOOLKIT_VERSION}",
+                f"libcusparse.so.{CUDA_TOOLKIT_VERSION}",
+                f"libcublas.so.{CUDA_TOOLKIT_VERSION}",
+                f"libcusolver.so.{CUDA_TOOLKIT_VERSION}",
+                f"libnvrtc.so.{CUDA_TOOLKIT_VERSION}",
+                f"libcufft.so.{CUDA_TOOLKIT_VERSION}",
+            ])
+        if self.options.cpu_backend:
+            self.cpp_info.libs.extend([
+                f"libafcpu.so.{ARRAYFIRE_VERSION}",
+                "libmkl_avx2.so",
+                "libmkl_mc.so",
+                "libmkl_intel_lp64.so",
+                "libmkl_core.so",
+                "libmkl_avx.so",
+                "libmkl_def.so",
+                "libiomp5.so",
+                "libmkl_avx512.so",
+                "libmkl_intel_thread.so",
+                "libmkl_mc3.so",
+
+            ])
+        if self.options.opencl_backend:
+            self.cpp_info.libs.extend([
+                f"libafopencl.so.{ARRAYFIRE_VERSION}",
+                "libOpenCL.so.1",
+            ])

From d087e32b4f79da297ddb79666f17e80b4124471e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 6 May 2020 21:39:25 +0530
Subject: [PATCH 1954/2677] CMake support to link against static Intel MKL
 (#2877)

* CMake support to link against static Intel MKL

Static linking of Intel MKL is turned off by default.

Note that this however increases binary size of CPU backend by ~200MB and
OpenCL backend by ~100MB, respectively.

* remove generator expressions in favor of if-else
---
 CMakeLists.txt                    | 34 +++++++++++++++++--------------
 src/api/unified/CMakeLists.txt    |  6 ++----
 src/backend/cpu/CMakeLists.txt    |  6 +++++-
 src/backend/opencl/CMakeLists.txt |  8 +++++---
 4 files changed, 31 insertions(+), 23 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2682dab9b3..94b8560b8e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -61,6 +61,7 @@ option(AF_WITH_NONFREE  "Build ArrayFire nonfree algorithms"   OFF)
 option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
 option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
 option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
+option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
 
 if(WIN32)
   set(AF_STACKTRACE_TYPE "Windbg" CACHE STRING "The type of backtrace features. Windbg(simple), None")
@@ -106,6 +107,7 @@ mark_as_advanced(
   SPDLOG_BUILD_TESTING
   ADDR2LINE_PROGRAM
   Backtrace_LIBRARY
+  AF_WITH_STATIC_MKL
   )
 
 #Configure forge submodule
@@ -311,7 +313,7 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared AND AF_INSTALL_STANDALONE)
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND AF_INSTALL_STANDALONE)
   if(TARGET MKL::ThreadingLibrary)
     install(FILES
       $<TARGET_FILE:MKL::ThreadingLibrary>
@@ -319,24 +321,26 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared AND AF_INSTALL_STANDAL
       COMPONENT mkl_dependencies)
   endif()
 
-  if(NOT WIN32)
+  if(NOT AF_WITH_STATIC_MKL AND TARGET MKL::Shared)
+    if(NOT WIN32)
+      install(FILES
+        $<TARGET_FILE:MKL::Interface>
+        DESTINATION ${AF_INSTALL_LIB_DIR}
+        COMPONENT mkl_dependencies)
+    endif()
+
     install(FILES
-      $<TARGET_FILE:MKL::Interface>
+      $<TARGET_FILE:MKL::Shared>
+      $<TARGET_FILE:MKL::ThreadLayer>
+      ${MKL_RUNTIME_KERNEL_LIBRARIES}
+
+      # This variable is used to add tbb.so.2 library because the main lib
+      # is a linker script and not a symlink so it cant be resolved using
+      # get_filename_component
+      ${AF_ADDITIONAL_MKL_LIBRARIES}
       DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT mkl_dependencies)
   endif()
-
-  install(FILES
-    $<TARGET_FILE:MKL::Shared>
-    $<TARGET_FILE:MKL::ThreadLayer>
-    ${MKL_RUNTIME_KERNEL_LIBRARIES}
-
-    # This variable is used to add tbb.so.2 library because the main lib
-    # is a linker script and not a symlink so it cant be resolved using
-    # get_filename_component
-    ${AF_ADDITIONAL_MKL_LIBRARIES}
-    DESTINATION ${AF_INSTALL_LIB_DIR}
-    COMPONENT mkl_dependencies)
 endif()
 
 # This file will be used to create the config file for the build directory.
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index b0489be4d1..b103c11195 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -88,10 +88,8 @@ target_link_libraries(af
 # pass the RTLD_GLOBAL flag to dlload, but that causes issues with the ArrayFire
 # libraries. To get around this we are also linking the unified backend with
 # the MKL library
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared)
-  target_link_libraries(af
-    PRIVATE
-      MKL::Shared)
+if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared AND NOT AF_WITH_STATIC_MKL)
+  target_link_libraries(af PRIVATE MKL::Shared)
 endif()
 
 
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 25ef848f67..170bb0f3be 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -304,9 +304,13 @@ if(USE_CPU_MKL)
       cpp_api_interface
       afcommon_interface
       cpu_sort_by_key
-      MKL::Shared
       Threads::Threads
     )
+  if(AF_WITH_STATIC_MKL)
+      target_link_libraries(afcpu PRIVATE MKL::Static)
+  else()
+      target_link_libraries(afcpu PRIVATE MKL::Shared)
+  endif()
 else()
   dependency_check(FFTW_FOUND "FFTW not found")
   dependency_check(CBLAS_FOUND "CBLAS not found")
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 564f0af4ec..828414e547 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -491,9 +491,11 @@ if(LAPACK_FOUND OR MKL_Shared_FOUND)
     dependency_check(MKL_Shared_FOUND "MKL not found")
     target_compile_definitions(afopencl PRIVATE USE_MKL)
 
-    target_link_libraries(afopencl
-      PRIVATE
-        MKL::Shared)
+    if(AF_WITH_STATIC_MKL)
+        target_link_libraries(afopencl PRIVATE MKL::Static)
+    else()
+        target_link_libraries(afopencl PRIVATE MKL::Shared)
+    endif()
   else()
     dependency_check(OpenCL_FOUND "OpenCL not found.")
 

From 8144c4b963b3466dbdae765299445efba85ddcf8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 12 May 2020 02:42:05 +0530
Subject: [PATCH 1955/2677] Use read/write buffer in reduce by key instead of
 fill buffer (#2884)

* Use read/write buffer in reduce by key instead of fill buffer
---
 src/backend/opencl/kernel/reduce_by_key.hpp | 39 +++++++++++++--------
 1 file changed, 24 insertions(+), 15 deletions(-)

diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 96b9d82a86..be4df37b89 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -484,11 +484,15 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                                      sizeof(int), &n_reduced_host);
 
         // reset flags
-        getQueue().enqueueFillBuffer<int>(*needs_another_reduction.get(), 0, 0,
-                                          sizeof(int));
-        getQueue().enqueueFillBuffer<int>(*needs_block_boundary_reduction.get(),
-                                          0, 0, sizeof(int));
-
+        needs_block_boundary_reduction_host = 0;
+        needs_another_reduction_host        = 0;
+
+        getQueue().enqueueWriteBuffer(*needs_another_reduction.get(), CL_FALSE,
+                                      0, sizeof(int),
+                                      &needs_another_reduction_host);
+        getQueue().enqueueWriteBuffer(*needs_block_boundary_reduction.get(),
+                                      CL_FALSE, 0, sizeof(int),
+                                      &needs_block_boundary_reduction_host);
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
         launch_test_needs_reduction<Tk>(*needs_another_reduction.get(),
@@ -496,11 +500,11 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                                         t_reduced_keys, n_reduced_host,
                                         numBlocksD0, numThreads);
 
-        getQueue().enqueueReadBuffer(*needs_another_reduction.get(), true, 0,
-                                     sizeof(int),
+        getQueue().enqueueReadBuffer(*needs_another_reduction.get(), CL_FALSE,
+                                     0, sizeof(int),
                                      &needs_another_reduction_host);
         getQueue().enqueueReadBuffer(*needs_block_boundary_reduction.get(),
-                                     true, 0, sizeof(int),
+                                     CL_TRUE, 0, sizeof(int),
                                      &needs_block_boundary_reduction_host);
 
         if (needs_block_boundary_reduction_host &&
@@ -600,10 +604,15 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                                      sizeof(int), &n_reduced_host);
 
         // reset flags
-        getQueue().enqueueFillBuffer<int>(*needs_another_reduction.get(), 0, 0,
-                                          sizeof(int));
-        getQueue().enqueueFillBuffer<int>(*needs_block_boundary_reduction.get(),
-                                          0, 0, sizeof(int));
+        needs_block_boundary_reduction_host = 0;
+        needs_another_reduction_host        = 0;
+
+        getQueue().enqueueWriteBuffer(*needs_another_reduction.get(), CL_FALSE,
+                                      0, sizeof(int),
+                                      &needs_another_reduction_host);
+        getQueue().enqueueWriteBuffer(*needs_block_boundary_reduction.get(),
+                                      CL_FALSE, 0, sizeof(int),
+                                      &needs_block_boundary_reduction_host);
 
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
@@ -612,11 +621,11 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                                         t_reduced_keys, n_reduced_host,
                                         numBlocksD0, numThreads);
 
-        getQueue().enqueueReadBuffer(*needs_another_reduction.get(), true, 0,
-                                     sizeof(int),
+        getQueue().enqueueReadBuffer(*needs_another_reduction.get(), CL_FALSE,
+                                     0, sizeof(int),
                                      &needs_another_reduction_host);
         getQueue().enqueueReadBuffer(*needs_block_boundary_reduction.get(),
-                                     true, 0, sizeof(int),
+                                     CL_TRUE, 0, sizeof(int),
                                      &needs_block_boundary_reduction_host);
 
         if (needs_block_boundary_reduction_host &&

From fa2faab77cf0c904f706ab7623764cdcfb926077 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 11 May 2020 14:53:31 +0530
Subject: [PATCH 1956/2677] Fix input ndims validation in fast,orb,sift

---
 src/api/c/fast.cpp | 2 +-
 src/api/c/orb.cpp  | 2 +-
 src/api/c/sift.cpp | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/api/c/fast.cpp b/src/api/c/fast.cpp
index dbdd50c6a7..ed8822c402 100644
--- a/src/api/c/fast.cpp
+++ b/src/api/c/fast.cpp
@@ -64,7 +64,7 @@ af_err af_fast(af_features *out, const af_array in, const float thr,
         ARG_ASSERT(6, (feature_ratio > 0.0f && feature_ratio <= 1.0f));
 
         dim_t in_ndims = dims.ndims();
-        DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
+        DIM_ASSERT(1, (in_ndims == 2));
 
         af_dtype type = info.getType();
         switch (type) {
diff --git a/src/api/c/orb.cpp b/src/api/c/orb.cpp
index 2f984a6299..2007b255ac 100644
--- a/src/api/c/orb.cpp
+++ b/src/api/c/orb.cpp
@@ -63,7 +63,7 @@ af_err af_orb(af_features* feat, af_array* desc, const af_array in,
         ARG_ASSERT(6, levels > 0);
 
         dim_t in_ndims = dims.ndims();
-        DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
+        DIM_ASSERT(1, (in_ndims == 2));
 
         af_array tmp_desc;
         af_dtype type = info.getType();
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index 4f6aaf05bb..7d7cfa8bd4 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -129,7 +129,7 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in,
         ARG_ASSERT(9, feature_ratio > 0.0f);
 
         dim_t in_ndims = dims.ndims();
-        DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
+        DIM_ASSERT(1, (in_ndims == 2));
 
         af_array tmp_desc;
         af_dtype type = info.getType();

From 54b7031614bb6c16aef055d737297c325661a3db Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 24 Jan 2020 20:02:37 +0530
Subject: [PATCH 1957/2677] Simplify/Merge CUDA and OpenCL kernel caching API

* Moved common code required by CUDA and OpenCL caching algorithm into
  kernel_cache.[hpp|cpp]

* Added common/compile_kernel.hpp header that defines the signature
  of the function, compileKernel, that each backend has to implement.

* Each backend has to implement/satisfy the following requirements:

  - Provide compile_kernel.cpp source with compileKernel function that is
    used by common::findKernel

  - Provide Kernel.hpp/cpp that implements KernelInterface from
    common/KernelInterface.hpp

  - Kernel.hpp also provides a functor than helps launch backend kernels.

* Moved kernel utility helpers into separate header(s)/source:

  - TemplateArg.hpp/cpp contains the TemplateArg struct and some helper macros
    to convert template arguments to strings.

  - TemplateTypename.hpp contains the templated TemplateTypename struct that
    helps to convert backend kernel paramters to TemplateArg object.

* Refactored all CUDA kernels to use the new caching API

* Refactored only transpose, morph and canny to use new caching API from OpenCL

* Reduced lot of unnecessary instantiations for morphological functions
---
 CMakeLists.txt                                |   2 +
 src/api/c/morph.cpp                           |  75 ++--
 src/backend/common/CMakeLists.txt             |  11 +
 src/backend/common/KernelInterface.hpp        | 101 +++++
 src/backend/common/TemplateArg.cpp            | 273 ++++++++++++
 src/backend/common/TemplateArg.hpp            |  29 ++
 src/backend/common/TemplateTypename.hpp       |  38 ++
 src/backend/common/compile_kernel.hpp         |  50 +++
 src/backend/common/kernel_cache.cpp           |  87 ++++
 src/backend/common/kernel_cache.hpp           |  78 ++++
 src/backend/cpu/morph.cpp                     |  34 +-
 src/backend/cpu/morph.hpp                     |   8 +-
 src/backend/cuda/CMakeLists.txt               |  13 +-
 src/backend/cuda/{nvrtc => }/EnqueueArgs.hpp  |   1 -
 src/backend/cuda/Kernel.cpp                   |  42 ++
 src/backend/cuda/Kernel.hpp                   |  73 +++
 .../{nvrtc/cache.cpp => compile_kernel.cpp}   | 414 +++---------------
 src/backend/cuda/dilate.cpp                   |  23 -
 src/backend/cuda/dilate3d.cpp                 |  23 -
 src/backend/cuda/erode.cpp                    |  23 -
 src/backend/cuda/erode3d.cpp                  |  23 -
 src/backend/cuda/jit.cpp                      |  15 +-
 .../cuda/kernel/anisotropic_diffusion.hpp     |   6 +-
 src/backend/cuda/kernel/approx.hpp            |  10 +-
 src/backend/cuda/kernel/assign.hpp            |   5 +-
 src/backend/cuda/kernel/bilateral.hpp         |  10 +-
 src/backend/cuda/kernel/canny.hpp             |  40 +-
 src/backend/cuda/kernel/convolve.hpp          |  50 ++-
 src/backend/cuda/kernel/diagonal.hpp          |  10 +-
 src/backend/cuda/kernel/diff.hpp              |   6 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |  12 +-
 src/backend/cuda/kernel/fftconvolve.hpp       |  21 +-
 src/backend/cuda/kernel/flood_fill.hpp        |  20 +-
 src/backend/cuda/kernel/gradient.hpp          |   7 +-
 src/backend/cuda/kernel/histogram.hpp         |  10 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |   6 +-
 src/backend/cuda/kernel/identity.hpp          |   4 +-
 src/backend/cuda/kernel/iir.hpp               |   8 +-
 src/backend/cuda/kernel/index.hpp             |   5 +-
 src/backend/cuda/kernel/iota.hpp              |   5 +-
 src/backend/cuda/kernel/ireduce.hpp           |  20 +-
 src/backend/cuda/kernel/join.hpp              |   5 +-
 src/backend/cuda/kernel/lookup.hpp            |  16 +-
 src/backend/cuda/kernel/lu_split.hpp          |   6 +-
 src/backend/cuda/kernel/match_template.hpp    |  10 +-
 src/backend/cuda/kernel/meanshift.hpp         |  14 +-
 src/backend/cuda/kernel/medfilt.hpp           |  15 +-
 src/backend/cuda/kernel/memcopy.hpp           |  13 +-
 src/backend/cuda/kernel/moments.hpp           |   5 +-
 src/backend/cuda/kernel/morph.hpp             |  30 +-
 src/backend/cuda/kernel/pad_array_borders.hpp |   7 +-
 src/backend/cuda/kernel/range.hpp             |   5 +-
 src/backend/cuda/kernel/reorder.hpp           |   5 +-
 src/backend/cuda/kernel/resize.hpp            |   6 +-
 src/backend/cuda/kernel/rotate.hpp            |   6 +-
 src/backend/cuda/kernel/scan_dim.hpp          |  20 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |  26 +-
 src/backend/cuda/kernel/scan_first.hpp        |  18 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  14 +-
 src/backend/cuda/kernel/select.hpp            |  12 +-
 src/backend/cuda/kernel/sobel.hpp             |  15 +-
 src/backend/cuda/kernel/sparse.hpp            |   7 +-
 src/backend/cuda/kernel/sparse_arith.hpp      |  28 +-
 src/backend/cuda/kernel/susan.hpp             |  12 +-
 src/backend/cuda/kernel/tile.hpp              |   5 +-
 src/backend/cuda/kernel/transform.hpp         |  11 +-
 src/backend/cuda/kernel/transpose.hpp         |  11 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |  10 +-
 src/backend/cuda/kernel/triangle.hpp          |   9 +-
 src/backend/cuda/kernel/unwrap.hpp            |   7 +-
 src/backend/cuda/kernel/where.hpp             |   5 +-
 src/backend/cuda/kernel/wrap.hpp              |  12 +-
 src/backend/cuda/morph.cpp                    |  59 +++
 src/backend/cuda/morph.hpp                    |   8 +-
 src/backend/cuda/morph3d_impl.hpp             |  34 --
 src/backend/cuda/morph_impl.hpp               |  36 --
 src/backend/cuda/nvrtc/cache.hpp              | 208 ---------
 src/backend/opencl/Array.cpp                  |   2 +-
 src/backend/opencl/CMakeLists.txt             |  10 +-
 src/backend/opencl/Kernel.cpp                 |  35 ++
 src/backend/opencl/Kernel.hpp                 |  53 +++
 src/backend/opencl/compile_kernel.cpp         |  43 ++
 src/backend/opencl/debug_opencl.hpp           |   6 +-
 src/backend/opencl/device_manager.hpp         |   2 +
 src/backend/opencl/dilate.cpp                 |  23 -
 src/backend/opencl/dilate3d.cpp               |  23 -
 src/backend/opencl/erode.cpp                  |  23 -
 src/backend/opencl/erode3d.cpp                |  23 -
 src/backend/opencl/kernel/canny.hpp           | 199 +++------
 src/backend/opencl/kernel/morph.hpp           | 163 ++++---
 src/backend/opencl/kernel/transpose.hpp       |  70 ++-
 src/backend/opencl/magma/transpose.cpp        |  12 +-
 src/backend/opencl/morph.cpp                  |  63 +++
 src/backend/opencl/morph.hpp                  |   8 +-
 src/backend/opencl/morph3d_impl.hpp           |  50 ---
 src/backend/opencl/morph_impl.hpp             |  52 ---
 src/backend/opencl/program.cpp                |  53 +++
 src/backend/opencl/program.hpp                |  21 +-
 src/backend/opencl/transpose.cpp              |  20 +-
 99 files changed, 1770 insertions(+), 1585 deletions(-)
 create mode 100644 src/backend/common/KernelInterface.hpp
 create mode 100644 src/backend/common/TemplateArg.cpp
 create mode 100644 src/backend/common/TemplateArg.hpp
 create mode 100644 src/backend/common/TemplateTypename.hpp
 create mode 100644 src/backend/common/compile_kernel.hpp
 create mode 100644 src/backend/common/kernel_cache.cpp
 create mode 100644 src/backend/common/kernel_cache.hpp
 rename src/backend/cuda/{nvrtc => }/EnqueueArgs.hpp (98%)
 create mode 100644 src/backend/cuda/Kernel.cpp
 create mode 100644 src/backend/cuda/Kernel.hpp
 rename src/backend/cuda/{nvrtc/cache.cpp => compile_kernel.cpp} (58%)
 delete mode 100644 src/backend/cuda/dilate.cpp
 delete mode 100644 src/backend/cuda/dilate3d.cpp
 delete mode 100644 src/backend/cuda/erode.cpp
 delete mode 100644 src/backend/cuda/erode3d.cpp
 create mode 100644 src/backend/cuda/morph.cpp
 delete mode 100644 src/backend/cuda/morph3d_impl.hpp
 delete mode 100644 src/backend/cuda/morph_impl.hpp
 delete mode 100644 src/backend/cuda/nvrtc/cache.hpp
 create mode 100644 src/backend/opencl/Kernel.cpp
 create mode 100644 src/backend/opencl/Kernel.hpp
 create mode 100644 src/backend/opencl/compile_kernel.cpp
 delete mode 100644 src/backend/opencl/dilate.cpp
 delete mode 100644 src/backend/opencl/dilate3d.cpp
 delete mode 100644 src/backend/opencl/erode.cpp
 delete mode 100644 src/backend/opencl/erode3d.cpp
 create mode 100644 src/backend/opencl/morph.cpp
 delete mode 100644 src/backend/opencl/morph3d_impl.hpp
 delete mode 100644 src/backend/opencl/morph_impl.hpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 94b8560b8e..97dca3707b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -72,6 +72,7 @@ else()
 endif()
 
 option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
+option(AF_ENABLE_DEV_WARNINGS "Enable developer warnings such as attribute based" OFF)
 
 cmake_dependent_option(AF_WITH_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
   "BUILD_TESTING" OFF)
@@ -99,6 +100,7 @@ af_deprecate(USE_CPUID             AF_WITH_CPUID)
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
   AF_INSTALL_STANDALONE
+  AF_ENABLE_DEV_WARNINGS
   AF_WITH_CPUID
   CUDA_HOST_COMPILER
   CUDA_USE_STATIC_CUDA_RUNTIME
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 771f0d651a..084a26f551 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -39,16 +39,17 @@ using detail::uint;
 using detail::unaryOp;
 using detail::ushort;
 
-template<typename T, bool isDilation>
-static inline af_array morph(const af_array &in, const af_array &mask) {
-    const Array<T> input   = getArray<T>(in);
+template<typename T>
+af_array morph(const af_array &in, const af_array &mask, bool isDilation) {
+    const Array<T> &input  = getArray<T>(in);
     const Array<T> &filter = castArray<T>(mask);
-    Array<T> out           = morph<T, isDilation>(input, filter);
+    Array<T> out           = morph<T>(input, filter, isDilation);
     return getHandle(out);
 }
 
-template<bool isDilation>
-static inline af_array morph(const af_array &input, const af_array &mask) {
+template<>
+af_array morph<char>(const af_array &input, const af_array &mask,
+                     const bool isDilation) {
     using detail::fftconvolve;
 
 #if defined(AF_CPU)
@@ -67,7 +68,9 @@ static inline af_array morph(const af_array &input, const af_array &mask) {
     const dim4 &seDims    = se.dims();
 
     if (seDims[0] <= fftMethodThreshold) {
-        return morph<char, isDilation>(input, mask);
+        auto out =
+            morph(getArray<char>(input), castArray<char>(mask), isDilation);
+        return getHandle(out);
     }
 
     DIM_ASSERT(2, (seDims[0] == seDims[1]));
@@ -103,16 +106,17 @@ static inline af_array morph(const af_array &input, const af_array &mask) {
     }
 }
 
-template<typename T, bool isDilation>
-static inline af_array morph3d(const af_array &in, const af_array &mask) {
-    const Array<T> input   = getArray<T>(in);
+template<typename T>
+static inline af_array morph3d(const af_array &in, const af_array &mask,
+                               bool isDilation) {
+    const Array<T> &input  = getArray<T>(in);
     const Array<T> &filter = castArray<T>(mask);
-    Array<T> out           = morph3d<T, isDilation>(input, filter);
+    Array<T> out           = morph3d<T>(input, filter, isDilation);
     return getHandle(out);
 }
 
-template<bool isDilation>
-static af_err morph(af_array *out, const af_array &in, const af_array &mask) {
+af_err morph(af_array *out, const af_array &in, const af_array &mask,
+             bool isDilation) {
     try {
         const ArrayInfo &info  = getInfo(in);
         const ArrayInfo &mInfo = getInfo(mask);
@@ -127,14 +131,14 @@ static af_err morph(af_array *out, const af_array &in, const af_array &mask) {
         af_array output;
         af_dtype type = info.getType();
         switch (type) {
-            case f32: output = morph<float, isDilation>(in, mask); break;
-            case f64: output = morph<double, isDilation>(in, mask); break;
-            case b8: output = morph<isDilation>(in, mask); break;
-            case s32: output = morph<int, isDilation>(in, mask); break;
-            case u32: output = morph<uint, isDilation>(in, mask); break;
-            case s16: output = morph<short, isDilation>(in, mask); break;
-            case u16: output = morph<ushort, isDilation>(in, mask); break;
-            case u8: output = morph<uchar, isDilation>(in, mask); break;
+            case f32: output = morph<float>(in, mask, isDilation); break;
+            case f64: output = morph<double>(in, mask, isDilation); break;
+            case b8: output = morph<char>(in, mask, isDilation); break;
+            case s32: output = morph<int>(in, mask, isDilation); break;
+            case u32: output = morph<uint>(in, mask, isDilation); break;
+            case s16: output = morph<short>(in, mask, isDilation); break;
+            case u16: output = morph<ushort>(in, mask, isDilation); break;
+            case u8: output = morph<uchar>(in, mask, isDilation); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -144,8 +148,8 @@ static af_err morph(af_array *out, const af_array &in, const af_array &mask) {
     return AF_SUCCESS;
 }
 
-template<bool isDilation>
-static af_err morph3d(af_array *out, const af_array &in, const af_array &mask) {
+af_err morph3d(af_array *out, const af_array &in, const af_array &mask,
+               bool isDilation) {
     try {
         const ArrayInfo &info  = getInfo(in);
         const ArrayInfo &mInfo = getInfo(mask);
@@ -160,14 +164,14 @@ static af_err morph3d(af_array *out, const af_array &in, const af_array &mask) {
         af_array output;
         af_dtype type = info.getType();
         switch (type) {
-            case f32: output = morph3d<float, isDilation>(in, mask); break;
-            case f64: output = morph3d<double, isDilation>(in, mask); break;
-            case b8: output = morph3d<char, isDilation>(in, mask); break;
-            case s32: output = morph3d<int, isDilation>(in, mask); break;
-            case u32: output = morph3d<uint, isDilation>(in, mask); break;
-            case s16: output = morph3d<short, isDilation>(in, mask); break;
-            case u16: output = morph3d<ushort, isDilation>(in, mask); break;
-            case u8: output = morph3d<uchar, isDilation>(in, mask); break;
+            case f32: output = morph3d<float>(in, mask, isDilation); break;
+            case f64: output = morph3d<double>(in, mask, isDilation); break;
+            case b8: output = morph3d<char>(in, mask, isDilation); break;
+            case s32: output = morph3d<int>(in, mask, isDilation); break;
+            case u32: output = morph3d<uint>(in, mask, isDilation); break;
+            case s16: output = morph3d<short>(in, mask, isDilation); break;
+            case u16: output = morph3d<ushort>(in, mask, isDilation); break;
+            case u8: output = morph3d<uchar>(in, mask, isDilation); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -176,18 +180,19 @@ static af_err morph3d(af_array *out, const af_array &in, const af_array &mask) {
 
     return AF_SUCCESS;
 }
+
 af_err af_dilate(af_array *out, const af_array in, const af_array mask) {
-    return morph<true>(out, in, mask);
+    return morph(out, in, mask, true);
 }
 
 af_err af_erode(af_array *out, const af_array in, const af_array mask) {
-    return morph<false>(out, in, mask);
+    return morph(out, in, mask, false);
 }
 
 af_err af_dilate3(af_array *out, const af_array in, const af_array mask) {
-    return morph3d<true>(out, in, mask);
+    return morph3d(out, in, mask, true);
 }
 
 af_err af_erode3(af_array *out, const af_array in, const af_array mask) {
-    return morph3d<false>(out, in, mask);
+    return morph3d(out, in, mask, false);
 }
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 33aa64e6d2..684866120c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -30,14 +30,19 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/FFTPlanCache.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/HandleBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/InteropManager.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/KernelInterface.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManagerBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/TemplateTypename.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cblas.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/compile_kernel.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/complex.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/constants.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/defines.hpp
@@ -53,6 +58,8 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/host_memory.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal_enums.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/kernel_cache.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/kernel_cache.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel_type.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
@@ -69,6 +76,10 @@ else()
   target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_unix.cpp)
 endif()
 
+if(AF_ENABLE_DEV_WARNINGS)
+  target_compile_definitions(afcommon_interface INTERFACE AF_WITH_DEV_WARNINGS)
+endif()
+
 target_link_libraries(afcommon_interface
   INTERFACE
     spdlog
diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
new file mode 100644
index 0000000000..d2faa83b7d
--- /dev/null
+++ b/src/backend/common/KernelInterface.hpp
@@ -0,0 +1,101 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <cstddef>
+#include <utility>
+
+namespace common {
+
+/// Kernel Interface that should be implemented by each backend
+template<typename ModuleType, typename KernelType, typename EnqueuerType,
+         typename DevPtrType>
+class KernelInterface {
+   private:
+    ModuleType mProgram;
+    KernelType mKernel;
+
+   public:
+    KernelInterface(ModuleType mod, KernelType ker)
+        : mProgram(mod), mKernel(ker) {}
+
+    /// \brief Set module and kernel
+    ///
+    /// \param[in] mod is backend specific module handle
+    /// \param[in] ker is backend specific kernel handle
+    void set(ModuleType mod, KernelType ker) {
+        mProgram = mod;
+        mKernel  = ker;
+    }
+
+    /// \brief Get module
+    ///
+    /// \returns handle to backend specific module
+    inline ModuleType getModule() { return mProgram; }
+
+    /// \brief Get kernel
+    ///
+    /// \returns handle to backend specific kernel
+    inline KernelType getKernel() { return mKernel; }
+
+    /// \brief Get device pointer associated with name(label)
+    ///
+    /// This function is only useful with CUDA NVRTC based compilation
+    /// at the moment, calling this function for OpenCL backend build
+    /// will return a null pointer.
+    virtual DevPtrType get(const char* name) = 0;
+
+    /// \brief Copy data from device memory to read-only memory
+    ///
+    /// This function copies data of `bytes` size from the device pointer to a
+    /// read-only memory.
+    ///
+    /// \param[in] dst is the device pointer to which data will be copied
+    /// \param[in] src is the device pointer from which data will be copied
+    /// \param[in] bytes are the number of bytes of data to be copied
+    virtual void copyToReadOnly(DevPtrType dst, DevPtrType src,
+                                size_t bytes) = 0;
+
+    /// \brief Copy a single scalar to device memory
+    ///
+    /// This function copies a single value of type T from host variable
+    /// to the device memory pointed by `dst`
+    ///
+    /// \param[in] dst is the device pointer to which data will be copied
+    /// \param[in] value is the integer scalar to set at device pointer
+    virtual void setScalar(DevPtrType dst, int value) = 0;
+
+    /// \brief Fetch a scalar from device memory
+    ///
+    /// This function copies a single value of type T from device memory
+    ///
+    /// \param[in] src is the device pointer from which data will be copied
+    ///
+    /// \returns the integer scalar
+    virtual int getScalar(DevPtrType src) = 0;
+
+    /// \brief Enqueue Kernel per queueing criteria forwarding other parameters
+    ///
+    /// This operator overload enables Kernel object to work as functor that
+    /// internally executes the kernel stored in the Kernel object.
+    /// All parameters that are passed in after the EnqueueArgs object are
+    /// essentially forwarded to kenel launch API
+    ///
+    /// \param[in] qArgs is an object of type EnqueueArgsType like
+    //             cl::EnqueueArgs in OpenCL backend
+    /// \param[in] args is the placeholder for variadic arguments
+    template<typename EnqueueArgsType, typename... Args>
+    void operator()(const EnqueueArgsType& qArgs, Args... args) {
+        EnqueuerType launch;
+        launch(mKernel, qArgs, std::forward<Args>(args)...);
+    }
+};
+
+}  // namespace common
diff --git a/src/backend/common/TemplateArg.cpp b/src/backend/common/TemplateArg.cpp
new file mode 100644
index 0000000000..6c2066689f
--- /dev/null
+++ b/src/backend/common/TemplateArg.cpp
@@ -0,0 +1,273 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/TemplateArg.hpp>
+
+#include <common/internal_enums.hpp>
+#include <optypes.hpp>
+#include <af/defines.h>
+
+#include <string>
+
+using std::string;
+
+template<typename T>
+string toString(T value) {
+    return std::to_string(value);
+}
+
+template string toString<int>(int);
+template string toString<long>(long);
+template string toString<long long>(long long);
+template string toString<unsigned>(unsigned);
+template string toString<unsigned long>(unsigned long);
+template string toString<unsigned long long>(unsigned long long);
+template string toString<float>(float);
+template string toString<double>(double);
+template string toString<long double>(long double);
+
+template<>
+string toString(bool val) {
+    return string(val ? "true" : "false");
+}
+
+template<>
+string toString(const char* str) {
+    return string(str);
+}
+
+template<>
+string toString(const string str) {
+    return str;
+}
+
+template<>
+string toString(unsigned short val) {
+    return std::to_string((unsigned int)(val));
+}
+
+template<>
+string toString(short val) {
+    return std::to_string(int(val));
+}
+
+template<>
+string toString(unsigned char val) {
+    return std::to_string((unsigned int)(val));
+}
+
+template<>
+string toString(char val) {
+    return std::to_string(int(val));
+}
+
+string getOpEnumStr(af_op_t val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(af_add_t);
+        CASE_STMT(af_sub_t);
+        CASE_STMT(af_mul_t);
+        CASE_STMT(af_div_t);
+
+        CASE_STMT(af_and_t);
+        CASE_STMT(af_or_t);
+        CASE_STMT(af_eq_t);
+        CASE_STMT(af_neq_t);
+        CASE_STMT(af_lt_t);
+        CASE_STMT(af_le_t);
+        CASE_STMT(af_gt_t);
+        CASE_STMT(af_ge_t);
+
+        CASE_STMT(af_bitnot_t);
+        CASE_STMT(af_bitor_t);
+        CASE_STMT(af_bitand_t);
+        CASE_STMT(af_bitxor_t);
+        CASE_STMT(af_bitshiftl_t);
+        CASE_STMT(af_bitshiftr_t);
+
+        CASE_STMT(af_min_t);
+        CASE_STMT(af_max_t);
+        CASE_STMT(af_cplx2_t);
+        CASE_STMT(af_atan2_t);
+        CASE_STMT(af_pow_t);
+        CASE_STMT(af_hypot_t);
+
+        CASE_STMT(af_sin_t);
+        CASE_STMT(af_cos_t);
+        CASE_STMT(af_tan_t);
+        CASE_STMT(af_asin_t);
+        CASE_STMT(af_acos_t);
+        CASE_STMT(af_atan_t);
+
+        CASE_STMT(af_sinh_t);
+        CASE_STMT(af_cosh_t);
+        CASE_STMT(af_tanh_t);
+        CASE_STMT(af_asinh_t);
+        CASE_STMT(af_acosh_t);
+        CASE_STMT(af_atanh_t);
+
+        CASE_STMT(af_exp_t);
+        CASE_STMT(af_expm1_t);
+        CASE_STMT(af_erf_t);
+        CASE_STMT(af_erfc_t);
+
+        CASE_STMT(af_log_t);
+        CASE_STMT(af_log10_t);
+        CASE_STMT(af_log1p_t);
+        CASE_STMT(af_log2_t);
+
+        CASE_STMT(af_sqrt_t);
+        CASE_STMT(af_cbrt_t);
+
+        CASE_STMT(af_abs_t);
+        CASE_STMT(af_cast_t);
+        CASE_STMT(af_cplx_t);
+        CASE_STMT(af_real_t);
+        CASE_STMT(af_imag_t);
+        CASE_STMT(af_conj_t);
+
+        CASE_STMT(af_floor_t);
+        CASE_STMT(af_ceil_t);
+        CASE_STMT(af_round_t);
+        CASE_STMT(af_trunc_t);
+        CASE_STMT(af_signbit_t);
+
+        CASE_STMT(af_rem_t);
+        CASE_STMT(af_mod_t);
+
+        CASE_STMT(af_tgamma_t);
+        CASE_STMT(af_lgamma_t);
+
+        CASE_STMT(af_notzero_t);
+
+        CASE_STMT(af_iszero_t);
+        CASE_STMT(af_isinf_t);
+        CASE_STMT(af_isnan_t);
+
+        CASE_STMT(af_sigmoid_t);
+
+        CASE_STMT(af_noop_t);
+
+        CASE_STMT(af_select_t);
+        CASE_STMT(af_not_select_t);
+        CASE_STMT(af_rsqrt_t);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_op_t val) {
+    return getOpEnumStr(val);
+}
+
+template<>
+string toString(af_interp_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_INTERP_NEAREST);
+        CASE_STMT(AF_INTERP_LINEAR);
+        CASE_STMT(AF_INTERP_BILINEAR);
+        CASE_STMT(AF_INTERP_CUBIC);
+        CASE_STMT(AF_INTERP_LOWER);
+        CASE_STMT(AF_INTERP_LINEAR_COSINE);
+        CASE_STMT(AF_INTERP_BILINEAR_COSINE);
+        CASE_STMT(AF_INTERP_BICUBIC);
+        CASE_STMT(AF_INTERP_CUBIC_SPLINE);
+        CASE_STMT(AF_INTERP_BICUBIC_SPLINE);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_border_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_PAD_ZERO);
+        CASE_STMT(AF_PAD_SYM);
+        CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
+        CASE_STMT(AF_PAD_PERIODIC);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_moment_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_MOMENT_M00);
+        CASE_STMT(AF_MOMENT_M01);
+        CASE_STMT(AF_MOMENT_M10);
+        CASE_STMT(AF_MOMENT_M11);
+        CASE_STMT(AF_MOMENT_FIRST_ORDER);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_match_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_SAD);
+        CASE_STMT(AF_ZSAD);
+        CASE_STMT(AF_LSAD);
+        CASE_STMT(AF_SSD);
+        CASE_STMT(AF_ZSSD);
+        CASE_STMT(AF_LSSD);
+        CASE_STMT(AF_NCC);
+        CASE_STMT(AF_ZNCC);
+        CASE_STMT(AF_SHD);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_flux_function p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_FLUX_QUADRATIC);
+        CASE_STMT(AF_FLUX_EXPONENTIAL);
+        CASE_STMT(AF_FLUX_DEFAULT);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(AF_BATCH_KIND val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(AF_BATCH_NONE);
+        CASE_STMT(AF_BATCH_LHS);
+        CASE_STMT(AF_BATCH_RHS);
+        CASE_STMT(AF_BATCH_SAME);
+        CASE_STMT(AF_BATCH_DIFF);
+        CASE_STMT(AF_BATCH_UNSUPPORTED);
+    }
+#undef CASE_STMT
+    return retVal;
+}
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
new file mode 100644
index 0000000000..b38254d86d
--- /dev/null
+++ b/src/backend/common/TemplateArg.hpp
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <string>
+#include <utility>
+
+template<typename T>
+std::string toString(T value);
+
+struct TemplateArg {
+    std::string _tparam;
+
+    TemplateArg(std::string str) : _tparam(std::move(str)) {}
+
+    template<typename T>
+    constexpr TemplateArg(T value) noexcept : _tparam(toString(value)) {}
+};
+
+#define DefineKey(arg) " -D " #arg
+#define DefineValue(arg) " -D " #arg "=" + toString(arg)
+#define DefineKeyValue(key, arg) " -D " #key "=" + toString(arg)
diff --git a/src/backend/common/TemplateTypename.hpp b/src/backend/common/TemplateTypename.hpp
new file mode 100644
index 0000000000..6191348aae
--- /dev/null
+++ b/src/backend/common/TemplateTypename.hpp
@@ -0,0 +1,38 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/TemplateArg.hpp>
+#include <traits.hpp>
+
+#include <string>
+
+template<typename T>
+struct TemplateTypename {
+    operator TemplateArg() const noexcept {
+        return {std::string(dtype_traits<T>::getName())};
+    }
+};
+
+#define SPECIALIZE(TYPE, NAME)                      \
+    template<>                                      \
+    struct TemplateTypename<TYPE> {                 \
+        operator TemplateArg() const noexcept {     \
+            return TemplateArg(std::string(#NAME)); \
+        }                                           \
+    }
+
+SPECIALIZE(unsigned char, detail::uchar);
+SPECIALIZE(unsigned int, detail::uint);
+SPECIALIZE(unsigned short, detail::ushort);
+SPECIALIZE(long long, long long);
+SPECIALIZE(unsigned long long, unsigned long long);
+
+#undef SPECIALIZE
diff --git a/src/backend/common/compile_kernel.hpp b/src/backend/common/compile_kernel.hpp
new file mode 100644
index 0000000000..d66bc726a7
--- /dev/null
+++ b/src/backend/common/compile_kernel.hpp
@@ -0,0 +1,50 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#if !defined(AF_CPU)
+
+#include <Kernel.hpp>
+#include <backend.hpp>
+
+#include <string>
+#include <vector>
+
+namespace common {
+
+/// \brief Backend specific kernel compilation implementation
+///
+/// This function has to be implemented separately in each backend
+detail::Kernel compileKernel(const std::string& kernelName,
+                             const std::string& templateInstance,
+                             const std::vector<std::string>& sources,
+                             const std::vector<std::string>& compileOpts,
+                             const bool isJIT = false);
+
+/// \brief Load kernel from disk cache
+///
+/// Note that, this is for internal use by functions that get called from
+/// compileKernel. The reason it is exposed here is that, it's implementation
+/// is partly dependent on backend specifics like program binary loading etc.
+///
+/// \p kernelNameExpr can take following values depending on backend
+/// -  namespace qualified kernel template instantiation for CUDA
+/// -  simple kernel name for OpenCL
+/// -  encoded string with KER prefix for JIT
+///
+/// \param[in] device is the device index
+/// \param[in] kernelNameExpr is the name identifying the relevant kernel
+/// \param[in] sources is the list of kernel and helper source files
+detail::Kernel loadKernel(const int device, const std::string& kernelNameExpr,
+                          const std::vector<std::string>& sources);
+
+}  // namespace common
+
+#endif
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
new file mode 100644
index 0000000000..468919c64e
--- /dev/null
+++ b/src/backend/common/kernel_cache.cpp
@@ -0,0 +1,87 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#if !defined(AF_CPU)
+
+#include <common/kernel_cache.hpp>
+
+#include <common/compile_kernel.hpp>
+#include <device_manager.hpp>
+#include <platform.hpp>
+
+#include <algorithm>
+#include <map>
+#include <string>
+#include <vector>
+
+using detail::Kernel;
+using std::back_inserter;
+using std::map;
+using std::string;
+using std::transform;
+using std::vector;
+
+namespace common {
+
+using KernelMap = map<string, Kernel>;
+
+KernelMap& getCache(const int device) {
+    thread_local KernelMap caches[detail::DeviceManager::MAX_DEVICES];
+    return caches[device];
+}
+
+void cacheKernel(const int device, const string& nameExpr, const Kernel entry) {
+    getCache(device).emplace(nameExpr, entry);
+}
+
+Kernel lookupKernel(const int device, const string& nameExpr,
+                    const vector<string>& sources) {
+    auto& cache = getCache(device);
+    auto iter   = cache.find(nameExpr);
+
+    if (iter != cache.end()) return iter->second;
+
+#if defined(AF_CUDA) && defined(AF_CACHE_KERNELS_TO_DISK)
+    Kernel kernel = loadKernel(device, nameExpr, sources);
+    if (kernel.getModule() != nullptr && kernel.getKernel() != nullptr) {
+        cacheKernel(device, nameExpr, kernel);
+        return kernel;
+    }
+#endif
+
+    return Kernel{nullptr, nullptr};
+}
+
+Kernel findKernel(const string& kernelName, const vector<string>& sources,
+                  const vector<TemplateArg>& targs,
+                  const vector<string>& compileOpts) {
+    vector<string> args;
+    args.reserve(targs.size());
+
+    transform(targs.begin(), targs.end(), back_inserter(args),
+              [](const TemplateArg& arg) -> string { return arg._tparam; });
+
+    string tInstance = kernelName + "<" + args[0];
+    for (size_t i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
+    tInstance += ">";
+
+    int device    = detail::getActiveDeviceId();
+    Kernel kernel = lookupKernel(device, tInstance, sources);
+
+    if (kernel.getModule() == nullptr || kernel.getKernel() == nullptr) {
+        kernel = compileKernel(kernelName, tInstance, sources, compileOpts);
+        cacheKernel(device, tInstance, kernel);
+    }
+
+    return kernel;
+}
+
+}  // namespace common
+
+#endif
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
new file mode 100644
index 0000000000..b0dbad69e3
--- /dev/null
+++ b/src/backend/common/kernel_cache.hpp
@@ -0,0 +1,78 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#if !defined(AF_CPU)
+
+#include <Kernel.hpp>
+#include <backend.hpp>
+#include <common/TemplateTypename.hpp>
+
+#include <string>
+#include <vector>
+
+namespace common {
+
+/// \brief Find/Create-Cache a Kernel that fits the given criteria
+///
+/// This function takes in two vectors of strings apart from the main Kernel
+/// name, match criteria, to find a suitable kernel in the Kernel cache. It
+/// builds and caches a new Kernel object if one isn't found in the cache.
+///
+/// The paramter \p key has to be the unique name for a given kernel.
+/// The key has to be present in one of the entries of KernelMap defined in
+/// the header EnqueueArgs.hpp.
+///
+/// The parameter \p templateArgs is a list of stringified template arguments of
+/// the kernel. These strings are used to generate the template instantiation
+/// expression of the kernel during compilation stage. This string is used as
+/// key to kernel cache map. At some point in future, the idea is to use these
+/// instantiation strings to generate template instatiations in online compiler.
+///
+/// The paramter \p compileOpts is a list of strings that lets you add
+/// definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` to the compiler. To
+/// enable easy stringification of variables into their definition equation,
+/// three helper macros are provided: TemplateArg, DefineKey and DefineValue.
+///
+/// Example Usage: transpose
+///
+/// \code
+/// static const std::string src(transpose_cuh, transpose_cuh_len);
+/// auto transpose = getKernel("cuda::transpose", {src},
+///         {
+///           TemplateTypename<T>(),
+///           TemplateArg(conjugate),
+///           TemplateArg(is32multiple)
+///         },
+///         {
+///           DefineValue(THREADS_Y) // Results in a definition
+///                                  // "-D THREADS_Y=<Value of THREADS_Y>"
+///           DefineKeyValue(DIMY, threads_y)  // Results in a definition
+///                                            // "-D DIMY=<Value of threads_y>"
+///         }
+///         );
+/// \endcode
+///
+/// \param[in] kernelName is the name of the kernel qualified as kernel in code
+/// \param[in] sources is the list of source strings to be compiled if required
+/// \param[in] templateArgs is a vector of strings containing stringified names
+///            of the template arguments of kernel to be compiled.
+/// \param[in] compileOpts is a vector of strings that enables the user to
+///            add definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` for
+///            the kernel compilation.
+///
+detail::Kernel findKernel(const std::string& kernelName,
+                          const std::vector<std::string>& sources,
+                          const std::vector<TemplateArg>& templateArgs,
+                          const std::vector<std::string>& compileOpts = {});
+
+}  // namespace common
+
+#endif
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index c1d391996e..eca2424cb5 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -19,8 +19,8 @@
 using af::dim4;
 
 namespace cpu {
-template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask) {
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
     af::borderType padType = isDilation ? AF_PAD_ZERO : AF_PAD_CLAMP_TO_EDGE;
     const af::dim4 &idims  = in.dims();
     const af::dim4 &mdims  = mask.dims();
@@ -33,7 +33,11 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     auto out = createEmptyArray<T>(odims);
     auto inp = padArrayBorders(in, lpad, upad, padType);
 
-    getQueue().enqueue(kernel::morph<T, isDilation>, out, inp, mask);
+    if (isDilation) {
+        getQueue().enqueue(kernel::morph<T, true>, out, inp, mask);
+    } else {
+        getQueue().enqueue(kernel::morph<T, false>, out, inp, mask);
+    }
 
     std::vector<af_seq> idxs(4, af_span);
     idxs[0] = af_seq{double(lpad[0]), double(lpad[0] + idims[0] - 1), 1.0};
@@ -42,24 +46,20 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask) {
     return createSubArray(out, idxs);
 }
 
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    getQueue().enqueue(kernel::morph3d<T, isDilation>, out, in, mask);
-
+    if (isDilation) {
+        getQueue().enqueue(kernel::morph3d<T, true>, out, in, mask);
+    } else {
+        getQueue().enqueue(kernel::morph3d<T, false>, out, in, mask);
+    }
     return out;
 }
 
-#define INSTANTIATE(T)                                        \
-    template Array<T> morph<T, true>(const Array<T> &in,      \
-                                     const Array<T> &mask);   \
-    template Array<T> morph<T, false>(const Array<T> &in,     \
-                                      const Array<T> &mask);  \
-    template Array<T> morph3d<T, true>(const Array<T> &in,    \
-                                       const Array<T> &mask); \
-    template Array<T> morph3d<T, false>(const Array<T> &in,   \
-                                        const Array<T> &mask);
+#define INSTANTIATE(T)                                                    \
+    template Array<T> morph<T>(const Array<T> &, const Array<T> &, bool); \
+    template Array<T> morph3d<T>(const Array<T> &, const Array<T> &, bool);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/morph.hpp b/src/backend/cpu/morph.hpp
index a4ded63686..cf9e46bd9f 100644
--- a/src/backend/cpu/morph.hpp
+++ b/src/backend/cpu/morph.hpp
@@ -10,9 +10,9 @@
 #include <Array.hpp>
 
 namespace cpu {
-template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask);
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace cpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 3decbf978e..fa441ac8bf 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -322,6 +322,7 @@ endif()
 cuda_add_library(afcuda
     ${thrust_sort_sources}
 
+    EnqueueArgs.hpp
     all.cu
     anisotropic_diffusion.cpp
     any.cu
@@ -329,10 +330,6 @@ cuda_add_library(afcuda
     bilateral.cpp
     canny.cpp
     count.cu
-    dilate.cpp
-    dilate3d.cpp
-    erode.cpp
-    erode3d.cpp
     Event.cpp
     Event.hpp
     exampleFunction.cpp
@@ -450,6 +447,8 @@ cuda_add_library(afcuda
 
     Array.cpp
     Array.hpp
+    Kernel.cpp
+    Kernel.hpp
     LookupTable1D.hpp
     Param.hpp
     ThrustAllocator.cuh
@@ -469,6 +468,7 @@ cuda_add_library(afcuda
     cholesky.cpp
     cholesky.hpp
     complex.hpp
+    compile_kernel.cpp
     convolve.cpp
     convolve.hpp
     convolveNN.cpp
@@ -542,9 +542,8 @@ cuda_add_library(afcuda
     memory.hpp
     minmax_op.hpp
     moments.hpp
+    morph.cpp
     morph.hpp
-    morph3d_impl.hpp
-    morph_impl.hpp
     nearest_neighbour.hpp
     orb.hpp
     platform.cpp
@@ -614,8 +613,6 @@ cuda_add_library(afcuda
     jit/BufferNode.hpp
     jit/kernel_generators.hpp
 
-    nvrtc/cache.cpp
-
     ${scan_by_key_sources}
 
     OPTIONS
diff --git a/src/backend/cuda/nvrtc/EnqueueArgs.hpp b/src/backend/cuda/EnqueueArgs.hpp
similarity index 98%
rename from src/backend/cuda/nvrtc/EnqueueArgs.hpp
rename to src/backend/cuda/EnqueueArgs.hpp
index 0fd51ebdc5..9dbac7eaa7 100644
--- a/src/backend/cuda/nvrtc/EnqueueArgs.hpp
+++ b/src/backend/cuda/EnqueueArgs.hpp
@@ -11,7 +11,6 @@
 
 #include <cuda.h>
 #include <cuda_runtime.h>
-#include <nvrtc.h>
 
 #include <vector>
 
diff --git a/src/backend/cuda/Kernel.cpp b/src/backend/cuda/Kernel.cpp
new file mode 100644
index 0000000000..e1ffe672e0
--- /dev/null
+++ b/src/backend/cuda/Kernel.cpp
@@ -0,0 +1,42 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Kernel.hpp>
+
+#include <platform.hpp>
+
+namespace cuda {
+
+Kernel::DevPtrType Kernel::get(const char *name) {
+    Kernel::DevPtrType out = 0;
+    size_t size            = 0;
+    CU_CHECK(cuModuleGetGlobal(&out, &size, this->getModule(), name));
+    return out;
+}
+
+void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
+                            size_t bytes) {
+    CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, cuda::getActiveStream()));
+}
+
+void Kernel::setScalar(Kernel::DevPtrType dst, int value) {
+    CU_CHECK(
+        cuMemcpyHtoDAsync(dst, &value, sizeof(int), cuda::getActiveStream()));
+    CU_CHECK(cuStreamSynchronize(cuda::getActiveStream()));
+}
+
+int Kernel::getScalar(Kernel::DevPtrType src) {
+    int retVal = 0;
+    CU_CHECK(
+        cuMemcpyDtoHAsync(&retVal, src, sizeof(int), cuda::getActiveStream()));
+    CU_CHECK(cuStreamSynchronize(cuda::getActiveStream()));
+    return retVal;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
new file mode 100644
index 0000000000..accdf6b014
--- /dev/null
+++ b/src/backend/cuda/Kernel.hpp
@@ -0,0 +1,73 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/KernelInterface.hpp>
+
+#include <EnqueueArgs.hpp>
+#include <backend.hpp>
+#include <common/err_common.hpp>
+
+#include <cstdio>
+
+#define CU_CHECK(fn)                                                      \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[1024];                                            \
+        const char* cu_err_name;                                          \
+        const char* cu_err_string;                                        \
+        cuGetErrorName(res, &cu_err_name);                                \
+        cuGetErrorString(res, &cu_err_string);                            \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), cu_err_string);                 \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+    } while (0)
+
+namespace cuda {
+
+struct Enqueuer {
+    template<typename... Args>
+    void operator()(void* ker, const EnqueueArgs& qArgs, Args... args) {
+        void* params[] = {reinterpret_cast<void*>(&args)...};
+        for (auto& event : qArgs.mEvents) {
+            CU_CHECK(cuStreamWaitEvent(qArgs.mStream, event, 0));
+        }
+        CU_CHECK(cuLaunchKernel(static_cast<CUfunction>(ker), qArgs.mBlocks.x,
+                                qArgs.mBlocks.y, qArgs.mBlocks.z,
+                                qArgs.mThreads.x, qArgs.mThreads.y,
+                                qArgs.mThreads.z, qArgs.mSharedMemSize,
+                                qArgs.mStream, params, NULL));
+    }
+};
+
+class Kernel
+    : public common::KernelInterface<CUmodule, CUfunction, Enqueuer,
+                                     CUdeviceptr> {
+   public:
+    using ModuleType = CUmodule;
+    using KernelType = CUfunction;
+    using DevPtrType = CUdeviceptr;
+    using BaseClass =
+        common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType>;
+
+    Kernel() : BaseClass(nullptr, nullptr) {}
+    Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
+
+    DevPtrType get(const char* name) override;
+
+    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) override;
+
+    void setScalar(DevPtrType dst, int value) override;
+
+    int getScalar(DevPtrType src) override;
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/cache.cpp b/src/backend/cuda/compile_kernel.cpp
similarity index 58%
rename from src/backend/cuda/nvrtc/cache.cpp
rename to src/backend/cuda/compile_kernel.cpp
index 18c4708d5c..b0f8b2227b 100644
--- a/src/backend/cuda/nvrtc/cache.cpp
+++ b/src/backend/cuda/compile_kernel.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2019, ArrayFire
+ * Copyright (c) 2020, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <nvrtc/cache.hpp>
+#include <common/compile_kernel.hpp>
 
+#include <Kernel.hpp>
 #include <common/Logger.hpp>
 #include <common/internal_enums.hpp>
 #include <common/util.hpp>
@@ -41,6 +42,8 @@
 #include <af/defines.h>
 #include <af/version.h>
 
+#include <nvrtc.h>
+
 #include <algorithm>
 #include <array>
 #include <chrono>
@@ -53,6 +56,9 @@
 #include <type_traits>
 #include <utility>
 
+using namespace cuda;
+
+using detail::Kernel;
 using std::accumulate;
 using std::array;
 using std::back_insert_iterator;
@@ -62,6 +68,7 @@ using std::extent;
 using std::find_if;
 using std::make_pair;
 using std::map;
+using std::ofstream;
 using std::pair;
 using std::string;
 using std::to_string;
@@ -72,89 +79,59 @@ using std::chrono::duration_cast;
 using std::chrono::high_resolution_clock;
 using std::chrono::milliseconds;
 
-spdlog::logger *getLogger() {
-    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
-    return logger.get();
-}
-
-namespace cuda {
-
-using kc_t = map<string, Kernel>;
-
 #ifdef NDEBUG
-#define CU_LINK_CHECK(fn)                                                    \
-    do {                                                                     \
-        CUresult res = fn;                                                   \
-        if (res == CUDA_SUCCESS) break;                                      \
-        char cu_err_msg[1024 + 48];                                          \
-        const char *cu_err_name;                                             \
-        cuGetErrorName(res, &cu_err_name);                                   \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n",    \
-                 cu_err_name, (int)(res), linkError);                        \
-        AF_TRACE("Driver API Call: {}\nError Message: {}", #fn, cu_err_msg); \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                               \
+#define CU_LINK_CHECK(fn)                                                 \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[2048];                                            \
+        const char *cu_err_name;                                          \
+        cuGetErrorName(res, &cu_err_name);                                \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), linkError);                     \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
     } while (0)
 #else
 #define CU_LINK_CHECK(fn) CU_CHECK(fn)
 #endif
 
 #ifndef NDEBUG
-#define NVRTC_CHECK(fn)                                                 \
-    do {                                                                \
-        nvrtcResult res = fn;                                           \
-        if (res == NVRTC_SUCCESS) break;                                \
-        size_t logSize;                                                 \
-        nvrtcGetProgramLogSize(prog, &logSize);                         \
-        unique_ptr<char[]> log(new char[logSize + 1]);                  \
-        char *logptr = log.get();                                       \
-        nvrtcGetProgramLog(prog, logptr);                               \
-        logptr[logSize] = '\0';                                         \
-        AF_TRACE("NVRTC API Call: {}\nError Message: {}", #fn, logptr); \
-        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);                       \
+#define NVRTC_CHECK(fn)                                \
+    do {                                               \
+        nvrtcResult res = fn;                          \
+        if (res == NVRTC_SUCCESS) break;               \
+        size_t logSize;                                \
+        nvrtcGetProgramLogSize(prog, &logSize);        \
+        unique_ptr<char[]> log(new char[logSize + 1]); \
+        char *logptr = log.get();                      \
+        nvrtcGetProgramLog(prog, logptr);              \
+        logptr[logSize] = '\x0';                       \
+        puts(logptr);                                  \
+        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
     } while (0)
 #else
 #define NVRTC_CHECK(fn)                                                   \
     do {                                                                  \
         nvrtcResult res = (fn);                                           \
         if (res == NVRTC_SUCCESS) break;                                  \
-        char nvrtc_err_msg[1024];                                         \
+        char nvrtc_err_msg[2048];                                         \
         snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
                  "NVRTC Error(%d): %s\n", res, nvrtcGetErrorString(res)); \
-        AF_TRACE("NVRTC Error Message: {}", nvrtc_err_msg);               \
         AF_ERROR(nvrtc_err_msg, AF_ERR_INTERNAL);                         \
     } while (0)
 #endif
 
-void Kernel::setConstant(const char *name, CUdeviceptr src, size_t bytes) {
-    CUdeviceptr dst = 0;
-    size_t size     = 0;
-    CU_CHECK(cuModuleGetGlobal(&dst, &size, prog, name));
-    CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, getActiveStream()));
-}
-
-template<typename T>
-void Kernel::setScalar(const char *name, T value) {
-    CUdeviceptr dst = 0;
-    CU_CHECK(cuModuleGetGlobal(&dst, NULL, prog, name));
-    CU_CHECK(cuMemcpyHtoDAsync(dst, &value, sizeof(T), getActiveStream()));
-    CU_CHECK(cuStreamSynchronize(getActiveStream()));
-}
-
-template<typename T>
-void Kernel::getScalar(T &out, const char *name) {
-    CUdeviceptr src = 0;
-    CU_CHECK(cuModuleGetGlobal(&src, NULL, prog, name));
-    CU_CHECK(cuMemcpyDtoHAsync(&out, src, sizeof(T), getActiveStream()));
-    CU_CHECK(cuStreamSynchronize(getActiveStream()));
+spdlog::logger *getLogger() {
+    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
+    return logger.get();
 }
 
-template void Kernel::setScalar<int>(const char *, int);
-template void Kernel::getScalar<int>(int &, const char *);
-
 string getKernelCacheFilename(const int device, const string &nameExpr,
-                              const string &jitSource) {
+                              const vector<string> &sources) {
+    const string srcs =
+        accumulate(sources.begin(), sources.end(), std::string(""));
     const string mangledName =
-        "KER" + to_string(deterministicHash(nameExpr + jitSource));
+        "KER" + to_string(deterministicHash(nameExpr + srcs));
 
     const auto computeFlag = getComputeCapability(device);
     const string computeVersion =
@@ -164,9 +141,12 @@ string getKernelCacheFilename(const int device, const string &nameExpr,
            to_string(AF_API_VERSION_CURRENT) + ".cubin";
 }
 
-Kernel buildKernel(const int device, const string &nameExpr,
-                   const string &jit_ker, const vector<string> &opts,
-                   const bool isJIT) {
+namespace common {
+
+Kernel compileKernel(const string &kernelName, const string &nameExpr,
+                     const vector<string> &sources, const vector<string> &opts,
+                     const bool isJIT) {
+    auto &jit_ker        = sources[0];
     const char *ker_name = nameExpr.c_str();
 
     nvrtcProgram prog;
@@ -210,7 +190,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
         };
 
         constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
-        static const std::array<string, NumHeaders> sourceStrings = {{
+        static const array<string, NumHeaders> sourceStrings = {{
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY af/defines.h inclusion
@@ -260,8 +240,9 @@ Kernel buildKernel(const int device, const string &nameExpr,
                                        NumHeaders, headers, includeNames));
     }
 
-    auto computeFlag = getComputeCapability(device);
-    array<char, 32> arch{};
+    int device       = cuda::getActiveDeviceId();
+    auto computeFlag = cuda::getComputeCapability(device);
+    array<char, 32> arch;
     snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
              computeFlag.first, computeFlag.second);
     vector<const char *> compiler_options = {
@@ -275,7 +256,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
     if (!isJIT) {
         transform(begin(opts), end(opts),
                   back_insert_iterator<vector<const char *>>(compiler_options),
-                  [](const std::string &s) { return s.data(); });
+                  [](const string &s) { return s.data(); });
 
         compiler_options.push_back("--device-as-default-execution-space");
         NVRTC_CHECK(nvrtcAddNameExpression(prog, ker_name));
@@ -284,7 +265,6 @@ Kernel buildKernel(const int device, const string &nameExpr,
     auto compile = high_resolution_clock::now();
     NVRTC_CHECK(nvrtcCompileProgram(prog, compiler_options.size(),
                                     compiler_options.data()));
-
     auto compile_end = high_resolution_clock::now();
     size_t ptx_size;
     vector<char> ptx;
@@ -308,8 +288,6 @@ Kernel buildKernel(const int device, const string &nameExpr,
 
     auto link = high_resolution_clock::now();
     CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
-
-    // cuLinkAddData accounts for most of the time spent linking
     CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void *)ptx.data(),
                                 ptx.size(), ker_name, 0, NULL, NULL));
 
@@ -334,7 +312,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
     if (!cacheDirectory.empty()) {
         const string cacheFile =
             cacheDirectory + AF_PATH_SEPARATOR +
-            getKernelCacheFilename(device, nameExpr, jit_ker);
+            getKernelCacheFilename(device, nameExpr, sources);
         const string tempFile =
             cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
 
@@ -342,7 +320,7 @@ Kernel buildKernel(const int device, const string &nameExpr,
         const size_t cubinHash = deterministicHash(cubin, cubinSize);
 
         // write kernel function name and CUBIN binary data
-        std::ofstream out(tempFile, std::ios::binary);
+        ofstream out(tempFile, std::ios::binary);
         const size_t nameSize = strlen(name);
         out.write(reinterpret_cast<const char *>(&nameSize), sizeof(nameSize));
         out.write(name, nameSize);
@@ -377,17 +355,16 @@ Kernel buildKernel(const int device, const string &nameExpr,
              duration_cast<milliseconds>(compile_end - compile).count(),
              duration_cast<milliseconds>(link_end - link).count(),
              listOpts(compiler_options), getDeviceProp(device).name);
-
     return entry;
 }
 
 Kernel loadKernel(const int device, const string &nameExpr,
-                  const string &source) {
+                  const vector<string> &sources) {
     const string &cacheDirectory = getCacheDirectory();
     if (cacheDirectory.empty()) return Kernel{nullptr, nullptr};
 
     const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
-                             getKernelCacheFilename(device, nameExpr, source);
+                             getKernelCacheFilename(device, nameExpr, sources);
 
     CUmodule module   = nullptr;
     CUfunction kernel = nullptr;
@@ -433,285 +410,4 @@ Kernel loadKernel(const int device, const string &nameExpr,
     }
 }
 
-kc_t &getCache(int device) {
-    thread_local kc_t caches[DeviceManager::MAX_DEVICES];
-    return caches[device];
-}
-
-void addKernelToCache(int device, const string &nameExpr, Kernel entry) {
-    getCache(device).emplace(nameExpr, entry);
-}
-
-Kernel findKernel(int device, const string &nameExpr, const string &source) {
-    kc_t &cache = getCache(device);
-
-    auto iter = cache.find(nameExpr);
-    if (iter != cache.end()) return iter->second;
-
-#ifdef AF_CACHE_KERNELS_TO_DISK
-    Kernel kernel = loadKernel(device, nameExpr, source);
-    if (kernel.prog != nullptr && kernel.ker != nullptr) {
-        addKernelToCache(device, nameExpr, kernel);
-        return kernel;
-    }
-#endif
-
-    return Kernel{nullptr, nullptr};
-}
-
-string getOpEnumStr(af_op_t val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(af_add_t);
-        CASE_STMT(af_sub_t);
-        CASE_STMT(af_mul_t);
-        CASE_STMT(af_div_t);
-
-        CASE_STMT(af_and_t);
-        CASE_STMT(af_or_t);
-        CASE_STMT(af_eq_t);
-        CASE_STMT(af_neq_t);
-        CASE_STMT(af_lt_t);
-        CASE_STMT(af_le_t);
-        CASE_STMT(af_gt_t);
-        CASE_STMT(af_ge_t);
-
-        CASE_STMT(af_bitnot_t);
-        CASE_STMT(af_bitor_t);
-        CASE_STMT(af_bitand_t);
-        CASE_STMT(af_bitxor_t);
-        CASE_STMT(af_bitshiftl_t);
-        CASE_STMT(af_bitshiftr_t);
-
-        CASE_STMT(af_min_t);
-        CASE_STMT(af_max_t);
-        CASE_STMT(af_cplx2_t);
-        CASE_STMT(af_atan2_t);
-        CASE_STMT(af_pow_t);
-        CASE_STMT(af_hypot_t);
-
-        CASE_STMT(af_sin_t);
-        CASE_STMT(af_cos_t);
-        CASE_STMT(af_tan_t);
-        CASE_STMT(af_asin_t);
-        CASE_STMT(af_acos_t);
-        CASE_STMT(af_atan_t);
-
-        CASE_STMT(af_sinh_t);
-        CASE_STMT(af_cosh_t);
-        CASE_STMT(af_tanh_t);
-        CASE_STMT(af_asinh_t);
-        CASE_STMT(af_acosh_t);
-        CASE_STMT(af_atanh_t);
-
-        CASE_STMT(af_exp_t);
-        CASE_STMT(af_expm1_t);
-        CASE_STMT(af_erf_t);
-        CASE_STMT(af_erfc_t);
-
-        CASE_STMT(af_log_t);
-        CASE_STMT(af_log10_t);
-        CASE_STMT(af_log1p_t);
-        CASE_STMT(af_log2_t);
-
-        CASE_STMT(af_sqrt_t);
-        CASE_STMT(af_cbrt_t);
-
-        CASE_STMT(af_abs_t);
-        CASE_STMT(af_cast_t);
-        CASE_STMT(af_cplx_t);
-        CASE_STMT(af_real_t);
-        CASE_STMT(af_imag_t);
-        CASE_STMT(af_conj_t);
-
-        CASE_STMT(af_floor_t);
-        CASE_STMT(af_ceil_t);
-        CASE_STMT(af_round_t);
-        CASE_STMT(af_trunc_t);
-        CASE_STMT(af_signbit_t);
-
-        CASE_STMT(af_rem_t);
-        CASE_STMT(af_mod_t);
-
-        CASE_STMT(af_tgamma_t);
-        CASE_STMT(af_lgamma_t);
-
-        CASE_STMT(af_notzero_t);
-
-        CASE_STMT(af_iszero_t);
-        CASE_STMT(af_isinf_t);
-        CASE_STMT(af_isnan_t);
-
-        CASE_STMT(af_sigmoid_t);
-
-        CASE_STMT(af_noop_t);
-
-        CASE_STMT(af_select_t);
-        CASE_STMT(af_not_select_t);
-        CASE_STMT(af_rsqrt_t);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<typename T>
-string toString(T value) {
-    return to_string(value);
-}
-
-template string toString<int>(int);
-template string toString<long>(long);
-template string toString<long long>(long long);
-template string toString<unsigned>(unsigned);
-template string toString<unsigned long>(unsigned long);
-template string toString<unsigned long long>(unsigned long long);
-template string toString<float>(float);
-template string toString<double>(double);
-template string toString<long double>(long double);
-
-template<>
-string toString(bool val) {
-    return string(val ? "true" : "false");
-}
-
-template<>
-string toString(af_op_t val) {
-    return getOpEnumStr(val);
-}
-
-template<>
-string toString(const char *val) {
-    return string(val);
-}
-
-template<>
-string toString(af_interp_type val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_INTERP_NEAREST);
-        CASE_STMT(AF_INTERP_LINEAR);
-        CASE_STMT(AF_INTERP_BILINEAR);
-        CASE_STMT(AF_INTERP_CUBIC);
-        CASE_STMT(AF_INTERP_LOWER);
-        CASE_STMT(AF_INTERP_LINEAR_COSINE);
-        CASE_STMT(AF_INTERP_BILINEAR_COSINE);
-        CASE_STMT(AF_INTERP_BICUBIC);
-        CASE_STMT(AF_INTERP_CUBIC_SPLINE);
-        CASE_STMT(AF_INTERP_BICUBIC_SPLINE);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_border_type val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_PAD_ZERO);
-        CASE_STMT(AF_PAD_SYM);
-        CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
-        CASE_STMT(AF_PAD_PERIODIC);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_moment_type val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_MOMENT_M00);
-        CASE_STMT(AF_MOMENT_M01);
-        CASE_STMT(AF_MOMENT_M10);
-        CASE_STMT(AF_MOMENT_M11);
-        CASE_STMT(AF_MOMENT_FIRST_ORDER);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_match_type val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_SAD);
-        CASE_STMT(AF_ZSAD);
-        CASE_STMT(AF_LSAD);
-        CASE_STMT(AF_SSD);
-        CASE_STMT(AF_ZSSD);
-        CASE_STMT(AF_LSSD);
-        CASE_STMT(AF_NCC);
-        CASE_STMT(AF_ZNCC);
-        CASE_STMT(AF_SHD);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_flux_function val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_FLUX_QUADRATIC);
-        CASE_STMT(AF_FLUX_EXPONENTIAL);
-        CASE_STMT(AF_FLUX_DEFAULT);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(AF_BATCH_KIND val) {
-    const char *retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_BATCH_NONE);
-        CASE_STMT(AF_BATCH_LHS);
-        CASE_STMT(AF_BATCH_RHS);
-        CASE_STMT(AF_BATCH_SAME);
-        CASE_STMT(AF_BATCH_DIFF);
-        CASE_STMT(AF_BATCH_UNSUPPORTED);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-Kernel getKernel(const string &nameExpr, const string &source,
-                 const vector<TemplateArg> &templateArgs,
-                 const vector<string> &compileOpts) {
-    vector<string> args;
-    args.reserve(templateArgs.size());
-
-    transform(templateArgs.begin(), templateArgs.end(),
-              std::back_inserter(args),
-              [](const TemplateArg &arg) -> string { return arg._tparam; });
-
-    string tInstance = nameExpr + "<" + args[0];
-    for (size_t i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
-    tInstance += ">";
-
-    int device    = getActiveDeviceId();
-    Kernel kernel = findKernel(device, tInstance, source);
-
-    if (kernel.prog == nullptr || kernel.ker == nullptr) {
-        kernel = buildKernel(device, tInstance, source, compileOpts);
-        addKernelToCache(device, tInstance, kernel);
-    }
-
-    return kernel;
-}
-
-}  // namespace cuda
+}  // namespace common
diff --git a/src/backend/cuda/dilate.cpp b/src/backend/cuda/dilate.cpp
deleted file mode 100644
index ef7dc60b21..0000000000
--- a/src/backend/cuda/dilate.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph_impl.hpp"
-
-namespace cuda {
-
-INSTANTIATE(float, true)
-INSTANTIATE(double, true)
-INSTANTIATE(char, true)
-INSTANTIATE(int, true)
-INSTANTIATE(uint, true)
-INSTANTIATE(uchar, true)
-INSTANTIATE(short, true)
-INSTANTIATE(ushort, true)
-
-}  // namespace cuda
diff --git a/src/backend/cuda/dilate3d.cpp b/src/backend/cuda/dilate3d.cpp
deleted file mode 100644
index ba49e49f6e..0000000000
--- a/src/backend/cuda/dilate3d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph3d_impl.hpp"
-
-namespace cuda {
-
-INSTANTIATE(float, true)
-INSTANTIATE(double, true)
-INSTANTIATE(char, true)
-INSTANTIATE(int, true)
-INSTANTIATE(uint, true)
-INSTANTIATE(uchar, true)
-INSTANTIATE(short, true)
-INSTANTIATE(ushort, true)
-
-}  // namespace cuda
diff --git a/src/backend/cuda/erode.cpp b/src/backend/cuda/erode.cpp
deleted file mode 100644
index 9e0f41c42c..0000000000
--- a/src/backend/cuda/erode.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph_impl.hpp"
-
-namespace cuda {
-
-INSTANTIATE(float, false)
-INSTANTIATE(double, false)
-INSTANTIATE(char, false)
-INSTANTIATE(int, false)
-INSTANTIATE(uint, false)
-INSTANTIATE(uchar, false)
-INSTANTIATE(short, false)
-INSTANTIATE(ushort, false)
-
-}  // namespace cuda
diff --git a/src/backend/cuda/erode3d.cpp b/src/backend/cuda/erode3d.cpp
deleted file mode 100644
index 7c3128bc19..0000000000
--- a/src/backend/cuda/erode3d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph3d_impl.hpp"
-
-namespace cuda {
-
-INSTANTIATE(float, false)
-INSTANTIATE(double, false)
-INSTANTIATE(char, false)
-INSTANTIATE(int, false)
-INSTANTIATE(uint, false)
-INSTANTIATE(uchar, false)
-INSTANTIATE(short, false)
-INSTANTIATE(ushort, false)
-
-}  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 4ad9ee3546..9eee088e20 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -8,9 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <Kernel.hpp>
+#include <common/compile_kernel.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/jit/Node.hpp>
+#include <common/kernel_cache.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
@@ -18,7 +21,6 @@
 #include <err_cuda.hpp>
 #include <kernel_headers/jit_cuh.hpp>
 #include <math.hpp>
-#include <nvrtc/cache.hpp>
 #include <platform.hpp>
 #include <af/dim4.hpp>
 
@@ -28,6 +30,7 @@
 #include <thread>
 #include <vector>
 
+using common::compileKernel;
 using common::half;
 using common::Node;
 using common::Node_ids;
@@ -217,18 +220,20 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
         string jit_ker = getKernelString(funcName, full_nodes, full_ids,
                                          output_ids, is_linear);
 #ifdef AF_CACHE_KERNELS_TO_DISK
-        entry = loadKernel(device, funcName, jit_ker);
+        entry = common::loadKernel(device, funcName, {jit_ker});
 #endif
-        if (entry.prog == nullptr || entry.ker == nullptr) {
+        if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
             saveKernel(funcName, jit_ker, ".cu");
-            entry = buildKernel(device, funcName, jit_ker, {}, true);
+            // second argument, funcName, is important.
+            // From jit, first argument can be null as it is not used for CUDA
+            entry = compileKernel("", funcName, {jit_ker}, {}, true);
         }
         kernelCaches[device][funcName] = entry;
     } else {
         entry = idx->second;
     }
 
-    return entry.ker;
+    return entry.getKernel();
 }
 
 template<typename T>
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index 73b84072ba..1d14248306 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/anisotropic_diffusion_cuh.hpp>
 #include <af/defines.h>
 
@@ -30,8 +30,8 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
     static const std::string source(anisotropic_diffusion_cuh,
                                     anisotropic_diffusion_cuh_len);
-    auto diffUpdate = getKernel(
-        "cuda::diffUpdate", source,
+    auto diffUpdate = common::findKernel(
+        "cuda::diffUpdate", {source},
         {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
         {DefineValue(THREADS_X), DefineValue(THREADS_Y),
          DefineValue(YDIM_LOAD)});
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index d7716e90a8..c0525f12d3 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/approx1_cuh.hpp>
 #include <nvrtc_kernel_headers/approx2_cuh.hpp>
 #include <af/defines.h>
@@ -31,8 +31,8 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const af::interpType method, const int order) {
     static const std::string source(approx1_cuh, approx1_cuh_len);
 
-    auto approx1 = getKernel(
-        "cuda::approx1", source,
+    auto approx1 = common::findKernel(
+        "cuda::approx1", {source},
         {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
 
     dim3 threads(THREADS, 1, 1);
@@ -61,8 +61,8 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const af::interpType method, const int order) {
     static const std::string source(approx2_cuh, approx2_cuh_len);
 
-    auto approx2 = getKernel(
-        "cuda::approx2", source,
+    auto approx2 = common::findKernel(
+        "cuda::approx2", {source},
         {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 6a2a08a685..841ad6fef7 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -10,8 +10,8 @@
 #include <Param.hpp>
 #include <assign_kernel_param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/assign_cuh.hpp>
 
 #include <string>
@@ -26,7 +26,8 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
 
     static const std::string src(assign_cuh, assign_cuh_len);
 
-    auto assignKer = getKernel("cuda::assign", src, {TemplateTypename<T>()});
+    auto assignKer =
+        common::findKernel("cuda::assign", {src}, {TemplateTypename<T>()});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 7271e56757..a7bc4553d0 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/bilateral_cuh.hpp>
 
 #include <string>
@@ -26,10 +26,10 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
     static const std::string source(bilateral_cuh, bilateral_cuh_len);
 
-    auto bilateral =
-        getKernel("cuda::bilateral", source,
-                  {TemplateTypename<inType>(), TemplateTypename<outType>()},
-                  {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto bilateral = common::findKernel(
+        "cuda::bilateral", {source},
+        {TemplateTypename<inType>(), TemplateTypename<outType>()},
+        {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 85affc325b..1634104258 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/canny_cuh.hpp>
 
 #include <string>
@@ -30,10 +30,10 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
     static const std::string source(canny_cuh, canny_cuh_len);
 
-    auto nonMaxSuppress =
-        getKernel("cuda::nonMaxSuppression", source, {TemplateTypename<T>()},
-                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto nonMaxSuppress = common::findKernel(
+        "cuda::nonMaxSuppression", {source}, {TemplateTypename<T>()},
+        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
@@ -53,18 +53,18 @@ template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     static const std::string source(canny_cuh, canny_cuh_len);
 
-    auto initEdgeOut =
-        getKernel("cuda::initEdgeOut", source, {TemplateTypename<T>()},
-                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto edgeTrack =
-        getKernel("cuda::edgeTrack", source, {TemplateTypename<T>()},
-                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto suppressLeftOver =
-        getKernel("cuda::suppressLeftOver", source, {TemplateTypename<T>()},
-                  {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto initEdgeOut = common::findKernel(
+        "cuda::initEdgeOut", {source}, {TemplateTypename<T>()},
+        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto edgeTrack = common::findKernel(
+        "cuda::edgeTrack", {source}, {TemplateTypename<T>()},
+        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto suppressLeftOver = common::findKernel(
+        "cuda::suppressLeftOver", {source}, {TemplateTypename<T>()},
+        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
@@ -79,13 +79,15 @@ void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     initEdgeOut(qArgs, output, strong, weak, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 
+    auto flagPtr = edgeTrack.get("hasChanged");
+
     int notFinished = 1;
     while (notFinished) {
         notFinished = 0;
-        edgeTrack.setScalar("hasChanged", notFinished);
+        edgeTrack.setScalar(flagPtr, notFinished);
         edgeTrack(qArgs, output, blk_x, blk_y);
         POST_LAUNCH_CHECK();
-        edgeTrack.getScalar(notFinished, "hasChanged");
+        notFinished = edgeTrack.getScalar(flagPtr);
     }
     suppressLeftOver(qArgs, output, blk_x, blk_y);
     POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 74c9b208e6..7b0158f861 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -12,8 +12,8 @@
 #include <Param.hpp>
 #include <common/defines.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/convolve1_cuh.hpp>
 #include <nvrtc_kernel_headers/convolve2_cuh.hpp>
 #include <nvrtc_kernel_headers/convolve3_cuh.hpp>
@@ -106,8 +106,8 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     static const std::string src(convolve1_cuh, convolve1_cuh_len);
 
-    auto convolve1 = getKernel(
-        "cuda::convolve1", src,
+    auto convolve1 = common::findKernel(
+        "cuda::convolve1", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS)});
 
@@ -126,9 +126,10 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                 const aT* fptr = filt.ptr + (f1Off + f2Off + f3Off);
 
                 // FIXME: case where filter array is strided
-                convolve1.setConstant(conv_c_name,
-                                      reinterpret_cast<CUdeviceptr>(fptr),
-                                      filterSize);
+                auto constMemPtr = convolve1.get(conv_c_name);
+                convolve1.copyToReadOnly(constMemPtr,
+                                         reinterpret_cast<CUdeviceptr>(fptr),
+                                         filterSize);
 
                 p.o[0] = (p.outHasNoOffset ? 0 : b1);
                 p.o[1] = (p.outHasNoOffset ? 0 : b2);
@@ -162,16 +163,17 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
 
     static const std::string src(convolve2_cuh, convolve2_cuh_len);
 
-    auto convolve2 =
-        getKernel("cuda::convolve2", src,
-                  {TemplateTypename<T>(), TemplateTypename<aT>(),
-                   TemplateArg(expand), TemplateArg(f0), TemplateArg(f1)},
-                  {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
-                   DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
+    auto convolve2 = common::findKernel(
+        "cuda::convolve2", {src},
+        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand),
+         TemplateArg(f0), TemplateArg(f1)},
+        {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+         DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
 
     // FIXME: case where filter array is strided
-    convolve2.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
-                          f0 * f1 * sizeof(aT));
+    auto constMemPtr = convolve2.get(conv_c_name);
+    convolve2.copyToReadOnly(constMemPtr, reinterpret_cast<CUdeviceptr>(fptr),
+                             f0 * f1 * sizeof(aT));
 
     EnqueueArgs qArgs(p.mBlocks, p.mThreads, getActiveStream());
     convolve2(qArgs, out, sig, p.mBlk_x, p.mBlk_y, p.o[1], p.o[2], p.s[1],
@@ -208,8 +210,8 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     static const std::string src(convolve3_cuh, convolve3_cuh_len);
 
-    auto convolve3 = getKernel(
-        "cuda::convolve3", src,
+    auto convolve3 = common::findKernel(
+        "cuda::convolve3", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
          DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
@@ -225,8 +227,9 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
         const aT* fptr = filt.ptr + f3Off;
 
         // FIXME: case where filter array is strided
-        convolve3.setConstant(conv_c_name, reinterpret_cast<CUdeviceptr>(fptr),
-                              filterSize);
+        auto constMemPtr = convolve3.get(conv_c_name);
+        convolve3.copyToReadOnly(
+            constMemPtr, reinterpret_cast<CUdeviceptr>(fptr), filterSize);
 
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
         p.s[2] = (p.inHasNoOffset ? 0 : b3);
@@ -313,8 +316,8 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
 
     static const std::string src(convolve_separable_cuh,
                                  convolve_separable_cuh_len);
-    auto convolve2_separable = getKernel(
-        "cuda::convolve2_separable", src,
+    auto convolve2_separable = common::findKernel(
+        "cuda::convolve2_separable", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(conv_dim),
          TemplateArg(expand), TemplateArg(fLen)},
         {DefineValue(MAX_SCONV_FILTER_LEN), DefineValue(SCONV_THREADS_X),
@@ -328,9 +331,10 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
 
     // FIXME: case where filter array is strided
-    convolve2_separable.setConstant(sconv_c_name,
-                                    reinterpret_cast<CUdeviceptr>(filter.ptr),
-                                    fLen * sizeof(aT));
+    auto constMemPtr = convolve2_separable.get(sconv_c_name);
+    convolve2_separable.copyToReadOnly(
+        constMemPtr, reinterpret_cast<CUdeviceptr>(filter.ptr),
+        fLen * sizeof(aT));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
     convolve2_separable(qArgs, out, signal, blk_x, blk_y);
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index a76d258fa9..124f990027 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/diagonal_cuh.hpp>
 
 #include <string>
@@ -24,8 +24,8 @@ template<typename T>
 void diagCreate(Param<T> out, CParam<T> in, int num) {
     static const std::string src(diagonal_cuh, diagonal_cuh_len);
 
-    auto genDiagMat =
-        getKernel("cuda::createDiagonalMat", src, {TemplateTypename<T>()});
+    auto genDiagMat = common::findKernel("cuda::createDiagonalMat", {src},
+                                         {TemplateTypename<T>()});
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
@@ -51,8 +51,8 @@ template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int num) {
     static const std::string src(diagonal_cuh, diagonal_cuh_len);
 
-    auto extractDiag =
-        getKernel("cuda::extractDiagonal", src, {TemplateTypename<T>()});
+    auto extractDiag = common::findKernel("cuda::extractDiagonal", {src},
+                                          {TemplateTypename<T>()});
 
     dim3 threads(256, 1);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index 26e97929f2..1a890a46f2 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/diff_cuh.hpp>
 
 #include <string>
@@ -28,8 +28,8 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
 
     static const std::string src(diff_cuh, diff_cuh_len);
 
-    auto diff = getKernel(
-        "cuda::diff", src,
+    auto diff = common::findKernel(
+        "cuda::diff", {src},
         {TemplateTypename<T>(), TemplateArg(dim), TemplateArg(isDiff2)});
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 929a2251ff..1ee60f6fe7 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -14,7 +14,7 @@
 
 #include <debug_cuda.hpp>  // For Debug only related CUDA validations
 
-#include <nvrtc/cache.hpp>  // nvrtc cache mechanims API
+#include <common/kernel_cache.hpp>  // nvrtc cache mechanims API
 
 #include <nvrtc_kernel_headers/exampleFunction_cuh.hpp>  //kernel generated by nvrtc
 
@@ -31,10 +31,10 @@ template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     static const std::string source(exampleFunction_cuh,
                                     exampleFunction_cuh_len);
-    auto exampleFunc = getKernel("cuda::exampleFunc", source,
-                                 {
-                                     TemplateTypename<T>(),
-                                 });
+    auto exampleFunc = common::findKernel("cuda::exampleFunc", {source},
+                                          {
+                                              TemplateTypename<T>(),
+                                          });
 
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
@@ -48,7 +48,7 @@ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     // on your CUDA kernels needs such as shared memory etc.
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    // Call the kernel functor retrieved using getKernel
+    // Call the kernel functor retrieved using common::findKernel
     exampleFunc(qArgs, c, a, b, p);
 
     POST_LAUNCH_CHECK();  // Macro for post kernel launch checks
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index eb147a5f64..1c5194bea1 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/fftconvolve_cuh.hpp>
 
 #include <string>
@@ -31,11 +31,11 @@ template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
     auto packData =
-        getKernel("cuda::packData", fftConvSource(),
-                  {TemplateTypename<convT>(), TemplateTypename<T>()});
+        common::findKernel("cuda::packData", {fftConvSource()},
+                           {TemplateTypename<convT>(), TemplateTypename<T>()});
     auto padArray =
-        getKernel("cuda::padArray", fftConvSource(),
-                  {TemplateTypename<convT>(), TemplateTypename<T>()});
+        common::findKernel("cuda::padArray", {fftConvSource()},
+                           {TemplateTypename<convT>(), TemplateTypename<T>()});
 
     dim_t *sd = sig.dims;
 
@@ -74,8 +74,9 @@ void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
 template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
-    auto cplxMul = getKernel("cuda::complexMultiply", fftConvSource(),
-                             {TemplateTypename<convT>(), TemplateArg(kind)});
+    auto cplxMul =
+        common::findKernel("cuda::complexMultiply", {fftConvSource()},
+                           {TemplateTypename<convT>(), TemplateArg(kind)});
 
     int sig_packed_elem    = 1;
     int filter_packed_elem = 1;
@@ -107,9 +108,9 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     constexpr bool RoundResult = std::is_integral<T>::value;
 
     auto reorderOut =
-        getKernel("cuda::reorderOutput", fftConvSource(),
-                  {TemplateTypename<T>(), TemplateTypename<convT>(),
-                   TemplateArg(expand), TemplateArg(RoundResult)});
+        common::findKernel("cuda::reorderOutput", {fftConvSource()},
+                           {TemplateTypename<T>(), TemplateTypename<convT>(),
+                            TemplateArg(expand), TemplateArg(RoundResult)});
 
     dim_t *sd    = sig.dims;
     int fftScale = 1;
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index 60d6444f8d..d68490dcfb 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -12,8 +12,8 @@
 #include <Param.hpp>
 #include <common/defines.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/flood_fill_cuh.hpp>
 
 #include <string>
@@ -49,13 +49,13 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds =
-        getKernel("cuda::initSeeds", source, {TemplateTypename<T>()});
+    auto initSeeds = common::findKernel("cuda::initSeeds", {source},
+                                        {TemplateTypename<T>()});
     auto floodStep =
-        getKernel("cuda::floodStep", source, {TemplateTypename<T>()},
-                  {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto finalizeOutput =
-        getKernel("cuda::finalizeOutput", source, {TemplateTypename<T>()});
+        common::findKernel("cuda::floodStep", {source}, {TemplateTypename<T>()},
+                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto finalizeOutput = common::findKernel("cuda::finalizeOutput", {source},
+                                             {TemplateTypename<T>()});
 
     EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
                       getActiveStream());
@@ -67,12 +67,14 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
                 divup(image.dims[1], threads.y));
     EnqueueArgs fQArgs(blocks, threads, getActiveStream());
 
+    auto continueFlagPtr = floodStep.get("doAnotherLaunch");
+
     for (int doAnotherLaunch = 1; doAnotherLaunch > 0;) {
         doAnotherLaunch = 0;
-        floodStep.setScalar("doAnotherLaunch", doAnotherLaunch);
+        floodStep.setScalar(continueFlagPtr, doAnotherLaunch);
         floodStep(fQArgs, out, image, lowValue, highValue);
         POST_LAUNCH_CHECK();
-        floodStep.getScalar(doAnotherLaunch, "doAnotherLaunch");
+        doAnotherLaunch = floodStep.getScalar(continueFlagPtr);
     }
     finalizeOutput(fQArgs, out, newValue);
     POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index f6029af4c7..63324d385d 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/gradient_cuh.hpp>
 
 #include <string>
@@ -27,8 +27,9 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
 
     static const std::string source(gradient_cuh, gradient_cuh_len);
 
-    auto gradient = getKernel("cuda::gradient", source, {TemplateTypename<T>()},
-                              {DefineValue(TX), DefineValue(TY)});
+    auto gradient =
+        common::findKernel("cuda::gradient", {source}, {TemplateTypename<T>()},
+                           {DefineValue(TX), DefineValue(TY)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 580fa7c52a..047ffc6124 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/histogram_cuh.hpp>
 
 #include <string>
@@ -28,10 +28,10 @@ void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
     static const std::string source(histogram_cuh, histogram_cuh_len);
 
     auto histogram =
-        getKernel("cuda::histogram", source,
-                  {TemplateTypename<inType>(), TemplateTypename<outType>(),
-                   TemplateArg(isLinear)},
-                  {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
+        common::findKernel("cuda::histogram", {source},
+                           {TemplateTypename<inType>(),
+                            TemplateTypename<outType>(), TemplateArg(isLinear)},
+                           {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
     dim3 threads(kernel::THREADS_X, 1);
 
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index 52ba48cc04..b902c4e5ac 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/hsv_rgb_cuh.hpp>
 
 #include <string>
@@ -26,8 +26,8 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
     static const std::string source(hsv_rgb_cuh, hsv_rgb_cuh_len);
 
     auto hsvrgbConverter =
-        getKernel("cuda::hsvrgbConverter", source,
-                  {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
+        common::findKernel("cuda::hsvrgbConverter", {source},
+                           {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 509356c5fb..2c3b819a6a 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/identity_cuh.hpp>
 
 #include <string>
@@ -25,7 +25,7 @@ void identity(Param<T> out) {
     static const std::string source(identity_cuh, identity_cuh_len);
 
     auto identity =
-        getKernel("cuda::identity", source, {TemplateTypename<T>()});
+        common::findKernel("cuda::identity", {source}, {TemplateTypename<T>()});
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index d1d52c5e68..da72beeb40 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/iir_cuh.hpp>
 
 #include <string>
@@ -26,9 +26,9 @@ void iir(Param<T> y, CParam<T> c, CParam<T> a) {
 
     static const std::string source(iir_cuh, iir_cuh_len);
 
-    auto iir = getKernel("cuda::iir", source,
-                         {TemplateTypename<T>(), TemplateArg(batch_a)},
-                         {DefineValue(MAX_A_SIZE)});
+    auto iir = common::findKernel("cuda::iir", {source},
+                                  {TemplateTypename<T>(), TemplateArg(batch_a)},
+                                  {DefineValue(MAX_A_SIZE)});
 
     const int blocks_y = y.dims[1];
     const int blocks_x = y.dims[2];
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 2ebdc5af72..ad54c9d304 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -12,8 +12,8 @@
 #include <Param.hpp>
 #include <assign_kernel_param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/index_cuh.hpp>
 
 #include <string>
@@ -28,7 +28,8 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
 
     static const std::string source(index_cuh, index_cuh_len);
 
-    auto index = getKernel("cuda::index", source, {TemplateTypename<T>()});
+    auto index =
+        common::findKernel("cuda::index", {source}, {TemplateTypename<T>()});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 4662fd5309..eaa40b604b 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/iota_cuh.hpp>
 #include <af/dim4.hpp>
 
@@ -30,7 +30,8 @@ void iota(Param<T> out, const af::dim4 &sdims) {
 
     static const std::string source(iota_cuh, iota_cuh_len);
 
-    auto iota = getKernel("cuda::iota", source, {TemplateTypename<T>()});
+    auto iota =
+        common::findKernel("cuda::iota", {source}, {TemplateTypename<T>()});
 
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index ac502d0584..8fd47a9b34 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -11,10 +11,10 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <memory.hpp>
 #include <minmax_op.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/ireduce_cuh.hpp>
 #include "config.hpp"
 
@@ -42,11 +42,11 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    auto ireduceDim =
-        getKernel("cuda::ireduceDim", ireduceSource(),
-                  {TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
-                   TemplateArg(is_first), TemplateArg(threads_y)},
-                  {DefineValue(THREADS_X)});
+    auto ireduceDim = common::findKernel(
+        "cuda::ireduceDim", {ireduceSource()},
+        {TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
+         TemplateArg(is_first), TemplateArg(threads_y)},
+        {DefineValue(THREADS_X)});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -111,10 +111,10 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     // threads_x can take values 32, 64, 128, 256
     auto ireduceFirst =
-        getKernel("cuda::ireduceFirst", ireduceSource(),
-                  {TemplateTypename<T>(), TemplateArg(op),
-                   TemplateArg(is_first), TemplateArg(threads_x)},
-                  {DefineValue(THREADS_PER_BLOCK)});
+        common::findKernel("cuda::ireduceFirst", {ireduceSource()},
+                           {TemplateTypename<T>(), TemplateArg(op),
+                            TemplateArg(is_first), TemplateArg(threads_x)},
+                           {DefineValue(THREADS_PER_BLOCK)});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index f4a1645f52..7d2c7f2fbc 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/join_cuh.hpp>
 
 #include <string>
@@ -29,7 +29,8 @@ void join(Param<T> out, CParam<T> X, const af::dim4 &offset, int dim) {
 
     static const std::string source(join_cuh, join_cuh_len);
 
-    auto join = getKernel("cuda::join", source, {TemplateTypename<T>()});
+    auto join =
+        common::findKernel("cuda::join", {source}, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 02bbe69fba..02540f369f 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/lookup_cuh.hpp>
 
 #include <string>
@@ -46,10 +46,10 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
 
         dim3 blocks(blks, 1);
 
-        auto lookup1d =
-            getKernel("cuda::lookup1D", src,
-                      {TemplateTypename<in_t>(), TemplateTypename<idx_t>()},
-                      {DefineValue(THREADS), DefineValue(THRD_LOAD)});
+        auto lookup1d = common::findKernel(
+            "cuda::lookup1D", {src},
+            {TemplateTypename<in_t>(), TemplateTypename<idx_t>()},
+            {DefineValue(THREADS), DefineValue(THRD_LOAD)});
 
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -68,9 +68,9 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         blocks.y = divup(blocks.y, blocks.z);
 
         auto lookupnd =
-            getKernel("cuda::lookupND", src,
-                      {TemplateTypename<in_t>(), TemplateTypename<idx_t>(),
-                       TemplateArg(dim)});
+            common::findKernel("cuda::lookupND", {src},
+                               {TemplateTypename<in_t>(),
+                                TemplateTypename<idx_t>(), TemplateArg(dim)});
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
         lookupnd(qArgs, out, in, indices, blks_x, blks_y);
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 50e67459d9..543760097b 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/lu_split_cuh.hpp>
 
 #include <string>
@@ -32,8 +32,8 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
     const bool sameDims =
         lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
-    auto luSplit = getKernel("cuda::luSplit", src,
-                             {TemplateTypename<T>(), TemplateArg(sameDims)});
+    auto luSplit = common::findKernel(
+        "cuda::luSplit", {src}, {TemplateTypename<T>(), TemplateArg(sameDims)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 9fc9554866..1f3df97669 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/match_template_cuh.hpp>
 #include <af/defines.h>
 
@@ -28,10 +28,10 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
                    bool needMean) {
     static const std::string source(match_template_cuh, match_template_cuh_len);
 
-    auto matchTemplate =
-        getKernel("cuda::matchTemplate", source,
-                  {TemplateTypename<inType>(), TemplateTypename<outType>(),
-                   TemplateArg(mType), TemplateArg(needMean)});
+    auto matchTemplate = common::findKernel(
+        "cuda::matchTemplate", {source},
+        {TemplateTypename<inType>(), TemplateTypename<outType>(),
+         TemplateArg(mType), TemplateArg(needMean)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index 9f5988172a..ae753ca27a 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/meanshift_cuh.hpp>
 
 #include <string>
@@ -29,12 +29,12 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
                                       float>::type AccType;
     static const std::string source(meanshift_cuh, meanshift_cuh_len);
 
-    auto meanshift =
-        getKernel("cuda::meanshift", source,
-                  {
-                      TemplateTypename<AccType>(), TemplateTypename<T>(),
-                      TemplateArg((IsColor ? 3 : 1))  // channels
-                  });
+    auto meanshift = common::findKernel(
+        "cuda::meanshift", {source},
+        {
+            TemplateTypename<AccType>(), TemplateTypename<T>(),
+            TemplateArg((IsColor ? 3 : 1))  // channels
+        });
 
     static dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 6851e43f4b..7d8ba18721 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/medfilt_cuh.hpp>
 #include <af/defines.h>
 
@@ -30,10 +30,11 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
     UNUSED(w_wid);
     static const std::string source(medfilt_cuh, medfilt_cuh_len);
 
-    auto medfilt2 = getKernel("cuda::medfilt2", source,
-                              {TemplateTypename<T>(), TemplateArg(pad),
-                               TemplateArg(w_len), TemplateArg(w_wid)},
-                              {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto medfilt2 =
+        common::findKernel("cuda::medfilt2", {source},
+                           {TemplateTypename<T>(), TemplateArg(pad),
+                            TemplateArg(w_len), TemplateArg(w_wid)},
+                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
@@ -51,8 +52,8 @@ template<typename T>
 void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
     static const std::string source(medfilt_cuh, medfilt_cuh_len);
 
-    auto medfilt1 = getKernel(
-        "cuda::medfilt1", source,
+    auto medfilt1 = common::findKernel(
+        "cuda::medfilt1", {source},
         {TemplateTypename<T>(), TemplateArg(pad), TemplateArg(w_wid)});
 
     const dim3 threads(THREADS_X);
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index be51b0fe62..da0b099b5c 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -12,9 +12,9 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <dims_param.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/copy_cuh.hpp>
 #include <nvrtc_kernel_headers/memcopy_cuh.hpp>
 
@@ -31,7 +31,8 @@ template<typename T>
 void memcopy(Param<T> out, CParam<T> in, const dim_t ndims) {
     static const std::string src(memcopy_cuh, memcopy_cuh_len);
 
-    auto memCopy = getKernel("cuda::memcopy", src, {TemplateTypename<T>()});
+    auto memCopy =
+        common::findKernel("cuda::memcopy", {src}, {TemplateTypename<T>()});
 
     dim3 threads(DIMX, DIMY);
 
@@ -90,10 +91,10 @@ void copy(Param<outType> dst, CParam<inType> src, int ndims,
         ((src.dims[0] == dst.dims[0]) && (src.dims[1] == dst.dims[1]) &&
          (src.dims[2] == dst.dims[2]) && (src.dims[3] == dst.dims[3]));
 
-    auto copy =
-        getKernel("cuda::copy", source,
-                  {TemplateTypename<inType>(), TemplateTypename<outType>(),
-                   TemplateArg(same_dims)});
+    auto copy = common::findKernel(
+        "cuda::copy", {source},
+        {TemplateTypename<inType>(), TemplateTypename<outType>(),
+         TemplateArg(same_dims)});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 511ec9b3ea..4c5270a23f 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/moments_cuh.hpp>
 #include <af/defines.h>
 
@@ -25,7 +25,8 @@ template<typename T>
 void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
     static const std::string source(moments_cuh, moments_cuh_len);
 
-    auto moments = getKernel("cuda::moments", source, {TemplateTypename<T>()});
+    auto moments =
+        common::findKernel("cuda::moments", {source}, {TemplateTypename<T>()});
 
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 0534fabcf4..b3e6cca486 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/morph_cuh.hpp>
 
 #include <limits>
@@ -33,15 +33,16 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     const int windLen  = mask.dims[0];
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
-    auto morph = getKernel(
-        "cuda::morph", source,
+    auto morph = common::findKernel(
+        "cuda::morph", {source},
         {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(SeLength)},
         {
             DefineValue(MAX_MORPH_FILTER_LEN),
         });
 
-    morph.setConstant("cFilter", reinterpret_cast<CUdeviceptr>(mask.ptr),
-                      mask.dims[0] * mask.dims[1] * sizeof(T));
+    morph.copyToReadOnly(morph.get("cFilter"),
+                         reinterpret_cast<CUdeviceptr>(mask.ptr),
+                         mask.dims[0] * mask.dims[1] * sizeof(T));
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
@@ -67,15 +68,20 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
 
     const int windLen = mask.dims[0];
 
-    auto morph3D = getKernel(
-        "cuda::morph3D", source,
+    if (windLen > 7) {
+        CUDA_NOT_SUPPORTED("Morph 3D does not support kernels larger than 7.");
+    }
+
+    auto morph3D = common::findKernel(
+        "cuda::morph3D", {source},
         {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(windLen)},
         {
             DefineValue(MAX_MORPH_FILTER_LEN),
         });
 
-    morph3D.setConstant("cFilter", reinterpret_cast<CUdeviceptr>(mask.ptr),
-                        mask.dims[0] * mask.dims[1] * mask.dims[2] * sizeof(T));
+    morph3D.copyToReadOnly(
+        morph3D.get("cFilter"), reinterpret_cast<CUdeviceptr>(mask.ptr),
+        mask.dims[0] * mask.dims[1] * mask.dims[2] * sizeof(T));
 
     dim3 threads(kernel::CUBE_X, kernel::CUBE_Y, kernel::CUBE_Z);
 
@@ -92,11 +98,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
                    (kernel::CUBE_Z + padding) * sizeof(T);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream(), shrdSize);
-    if (windLen <= 7) {
-        morph3D(qArgs, out, in, blk_x);
-    } else {
-        CUDA_NOT_SUPPORTED("Morph 3D does not support kernels larger than 7.");
-    }
+    morph3D(qArgs, out, in, blk_x);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index e3aff9b25d..329d626a9b 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/pad_array_borders_cuh.hpp>
 #include <af/defines.h>
 
@@ -29,8 +29,9 @@ void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
                 const af::borderType btype) {
     static const std::string source(pad_array_borders_cuh,
                                     pad_array_borders_cuh_len);
-    auto padBorders = getKernel("cuda::padBorders", source,
-                                {TemplateTypename<T>(), TemplateArg(btype)});
+    auto padBorders =
+        common::findKernel("cuda::padBorders", {source},
+                           {TemplateTypename<T>(), TemplateArg(btype)});
 
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 61fab80462..d3ec29ab73 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/range_cuh.hpp>
 
 #include <string>
@@ -29,7 +29,8 @@ void range(Param<T> out, const int dim) {
 
     static const std::string source(range_cuh, range_cuh_len);
 
-    auto range = getKernel("cuda::range", source, {TemplateTypename<T>()});
+    auto range =
+        common::findKernel("cuda::range", {source}, {TemplateTypename<T>()});
 
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 72a6839449..3593a10ca4 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/reorder_cuh.hpp>
 
 #include <string>
@@ -29,7 +29,8 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
 
     static const std::string source(reorder_cuh, reorder_cuh_len);
 
-    auto reorder = getKernel("cuda::reorder", source, {TemplateTypename<T>()});
+    auto reorder =
+        common::findKernel("cuda::reorder", {source}, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index b3e96760cc..e6c3b45cc9 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -9,8 +9,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/resize_cuh.hpp>
 #include <af/defines.h>
 
@@ -27,8 +27,8 @@ template<typename T>
 void resize(Param<T> out, CParam<T> in, af_interp_type method) {
     static const std::string source(resize_cuh, resize_cuh_len);
 
-    auto resize = getKernel("cuda::resize", source,
-                            {TemplateTypename<T>(), TemplateArg(method)});
+    auto resize = common::findKernel(
+        "cuda::resize", {source}, {TemplateTypename<T>(), TemplateArg(method)});
 
     dim3 threads(TX, TY, 1);
     dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 0fd2273c32..7d98ed5b3e 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/rotate_cuh.hpp>
 #include <af/defines.h>
 
@@ -36,8 +36,8 @@ void rotate(Param<T> out, CParam<T> in, const float theta,
             const af::interpType method, const int order) {
     static const std::string source(rotate_cuh, rotate_cuh_len);
 
-    auto rotate = getKernel("cuda::rotate", source,
-                            {TemplateTypename<T>(), TemplateArg(order)});
+    auto rotate = common::findKernel(
+        "cuda::rotate", {source}, {TemplateTypename<T>(), TemplateArg(order)});
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 9de3b005ba..c3f555eece 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -10,10 +10,10 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <memory.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/scan_dim_cuh.hpp>
 #include "config.hpp"
 
@@ -26,12 +26,12 @@ template<typename Ti, typename To, af_op_t op>
 static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                               const uint threads_y, const dim_t blocks_all[4],
                               int dim, bool isFinalPass, bool inclusive_scan) {
-    auto scan_dim =
-        getKernel("cuda::scan_dim", ScanDimSource,
-                  {TemplateTypename<Ti>(), TemplateTypename<To>(),
-                   TemplateArg(op), TemplateArg(dim), TemplateArg(isFinalPass),
-                   TemplateArg(threads_y), TemplateArg(inclusive_scan)},
-                  {DefineValue(THREADS_X)});
+    auto scan_dim = common::findKernel(
+        "cuda::scan_dim", {ScanDimSource},
+        {TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(op),
+         TemplateArg(dim), TemplateArg(isFinalPass), TemplateArg(threads_y),
+         TemplateArg(inclusive_scan)},
+        {DefineValue(THREADS_X)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -54,9 +54,9 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
                                const uint threads_y, const dim_t blocks_all[4],
                                int dim, bool inclusive_scan) {
-    auto scan_dim_bcast =
-        getKernel("cuda::scan_dim_bcast", ScanDimSource,
-                  {TemplateTypename<To>(), TemplateArg(op), TemplateArg(dim)});
+    auto scan_dim_bcast = common::findKernel(
+        "cuda::scan_dim_bcast", {ScanDimSource},
+        {TemplateTypename<To>(), TemplateArg(op), TemplateArg(dim)});
 
     dim3 threads(THREADS_X, threads_y);
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index bfb9aade84..150bac33f9 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -11,10 +11,10 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <kernel/config.hpp>
 #include <memory.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/scan_dim_by_key_cuh.hpp>
 #include <optypes.hpp>
 #include <traits.hpp>
@@ -37,11 +37,11 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const int dim, const uint threads_y,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
-    auto scanbykey_dim_nonfinal =
-        getKernel("cuda::scanbykey_dim_nonfinal", sbkDimSource(),
-                  {TemplateTypename<Ti>(), TemplateTypename<Tk>(),
-                   TemplateTypename<To>(), TemplateArg(op)},
-                  {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+    auto scanbykey_dim_nonfinal = common::findKernel(
+        "cuda::scanbykey_dim_nonfinal", {sbkDimSource()},
+        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
+         TemplateArg(op)},
+        {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -61,11 +61,11 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const uint threads_y,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
-    auto scanbykey_dim_final =
-        getKernel("cuda::scanbykey_dim_final", sbkDimSource(),
-                  {TemplateTypename<Ti>(), TemplateTypename<Tk>(),
-                   TemplateTypename<To>(), TemplateArg(op)},
-                  {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+    auto scanbykey_dim_final = common::findKernel(
+        "cuda::scanbykey_dim_final", {sbkDimSource()},
+        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
+         TemplateArg(op)},
+        {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -84,8 +84,8 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
     auto scanbykey_dim_bcast =
-        getKernel("cuda::scanbykey_dim_bcast", sbkDimSource(),
-                  {TemplateTypename<To>(), TemplateArg(op)});
+        common::findKernel("cuda::scanbykey_dim_bcast", {sbkDimSource()},
+                           {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 7704f29d54..cbf49c0238 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -10,10 +10,10 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <memory.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/scan_first_cuh.hpp>
 #include "config.hpp"
 
@@ -27,12 +27,12 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
-    auto scan_first =
-        getKernel("cuda::scan_first", ScanFirstSource,
-                  {TemplateTypename<Ti>(), TemplateTypename<To>(),
-                   TemplateArg(op), TemplateArg(isFinalPass),
-                   TemplateArg(threads_x), TemplateArg(inclusive_scan)},
-                  {DefineValue(THREADS_PER_BLOCK)});
+    auto scan_first = common::findKernel(
+        "cuda::scan_first", {ScanFirstSource},
+        {TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(op),
+         TemplateArg(isFinalPass), TemplateArg(threads_x),
+         TemplateArg(inclusive_scan)},
+        {DefineValue(THREADS_PER_BLOCK)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -54,8 +54,8 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
     auto scan_first_bcast =
-        getKernel("cuda::scan_first_bcast", ScanFirstSource,
-                  {TemplateTypename<To>(), TemplateArg(op)});
+        common::findKernel("cuda::scan_first_bcast", {ScanFirstSource},
+                           {TemplateTypename<To>(), TemplateArg(op)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index bbf33e3b8c..249ed12bd1 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -11,10 +11,10 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <kernel/config.hpp>
 #include <memory.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/scan_first_by_key_cuh.hpp>
 #include <optypes.hpp>
 
@@ -36,8 +36,8 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    CParam<Ti> in, CParam<Tk> key,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
-    auto scanbykey_first_nonfinal = getKernel(
-        "cuda::scanbykey_first_nonfinal", sbkFirstSource(),
+    auto scanbykey_first_nonfinal = common::findKernel(
+        "cuda::scanbykey_first_nonfinal", {sbkFirstSource()},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
@@ -57,8 +57,8 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
-    auto scanbykey_first_final = getKernel(
-        "cuda::scanbykey_first_final", sbkFirstSource(),
+    auto scanbykey_first_final = common::findKernel(
+        "cuda::scanbykey_first_final", {sbkFirstSource()},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
@@ -78,8 +78,8 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
     auto scanbykey_first_bcast =
-        getKernel("cuda::scanbykey_first_bcast", sbkFirstSource(),
-                  {TemplateTypename<To>(), TemplateArg(op)});
+        common::findKernel("cuda::scanbykey_first_bcast", {sbkFirstSource()},
+                           {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index a19b88e89b..885562abd5 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -11,9 +11,9 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <math.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/select_cuh.hpp>
 
 #include <string>
@@ -36,8 +36,9 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     bool is_same = true;
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
-    auto select = getKernel("cuda::select", selectSource(),
-                            {TemplateTypename<T>(), TemplateArg(is_same)});
+    auto select =
+        common::findKernel("cuda::select", {selectSource()},
+                           {TemplateTypename<T>(), TemplateArg(is_same)});
 
     dim3 threads(DIMX, DIMY);
 
@@ -65,8 +66,9 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
 template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
                    int ndims, bool flip) {
-    auto selectScalar = getKernel("cuda::selectScalar", selectSource(),
-                                  {TemplateTypename<T>(), TemplateArg(flip)});
+    auto selectScalar =
+        common::findKernel("cuda::selectScalar", {selectSource()},
+                           {TemplateTypename<T>(), TemplateArg(flip)});
 
     dim3 threads(DIMX, DIMY);
 
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index b3a1cb6065..f3fd2b2f4b 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/sobel_cuh.hpp>
 
 #include <string>
@@ -29,12 +29,13 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
     UNUSED(ker_size);
     static const std::string source(sobel_cuh, sobel_cuh_len);
 
-    auto sobel3x3 = getKernel("cuda::sobel3x3", source,
-                              {
-                                  TemplateTypename<Ti>(),
-                                  TemplateTypename<To>(),
-                              },
-                              {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto sobel3x3 =
+        common::findKernel("cuda::sobel3x3", {source},
+                           {
+                               TemplateTypename<Ti>(),
+                               TemplateTypename<To>(),
+                           },
+                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 18b6efba30..aee05ce551 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/sparse_cuh.hpp>
 
 #include <string>
@@ -27,8 +27,9 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 
     static const std::string source(sparse_cuh, sparse_cuh_len);
 
-    auto coo2Dense = getKernel("cuda::coo2Dense", source,
-                               {TemplateTypename<T>()}, {DefineValue(reps)});
+    auto coo2Dense =
+        common::findKernel("cuda::coo2Dense", {source}, {TemplateTypename<T>()},
+                           {DefineValue(reps)});
 
     dim3 threads(256, 1, 1);
 
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 9fbb3f2ce7..17f2be3296 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/sparse_arith_cuh.hpp>
 #include <optypes.hpp>
 
@@ -33,9 +33,10 @@ static inline std::string sparseArithSrc() {
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
-    auto csrArithDSD = getKernel("cuda::csrArithDSD", sparseArithSrc(),
-                                 {TemplateTypename<T>(), TemplateArg(op)},
-                                 {DefineValue(TX), DefineValue(TY)});
+    auto csrArithDSD =
+        common::findKernel("cuda::csrArithDSD", {sparseArithSrc()},
+                           {TemplateTypename<T>(), TemplateArg(op)},
+                           {DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -52,9 +53,9 @@ void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
-    auto cooArithDSD = getKernel("cuda::cooArithDSD", sparseArithSrc(),
-                                 {TemplateTypename<T>(), TemplateArg(op)},
-                                 {DefineValue(THREADS)});
+    auto cooArithDSD = common::findKernel(
+        "cuda::cooArithDSD", {sparseArithSrc()},
+        {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
@@ -71,9 +72,10 @@ void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
-    auto csrArithSSD = getKernel("cuda::csrArithSSD", sparseArithSrc(),
-                                 {TemplateTypename<T>(), TemplateArg(op)},
-                                 {DefineValue(TX), DefineValue(TY)});
+    auto csrArithSSD =
+        common::findKernel("cuda::csrArithSSD", {sparseArithSrc()},
+                           {TemplateTypename<T>(), TemplateArg(op)},
+                           {DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -90,9 +92,9 @@ void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
-    auto cooArithSSD = getKernel("cuda::cooArithSSD", sparseArithSrc(),
-                                 {TemplateTypename<T>(), TemplateArg(op)},
-                                 {DefineValue(THREADS)});
+    auto cooArithSSD = common::findKernel(
+        "cuda::cooArithSSD", {sparseArithSrc()},
+        {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index bca29ecbc7..1f2ce38ba8 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/susan_cuh.hpp>
 
 #include <string>
@@ -32,9 +32,9 @@ template<typename T>
 void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
-    auto susan =
-        getKernel("cuda::susan", susanSource(), {TemplateTypename<T>()},
-                  {DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
+    auto susan = common::findKernel(
+        "cuda::susan", {susanSource()}, {TemplateTypename<T>()},
+        {DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
@@ -52,8 +52,8 @@ template<typename T>
 void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
-    auto nonMax =
-        getKernel("cuda::nonMax", susanSource(), {TemplateTypename<T>()});
+    auto nonMax = common::findKernel("cuda::nonMax", {susanSource()},
+                                     {TemplateTypename<T>()});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 16d6a30a06..66b33e8253 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/tile_cuh.hpp>
 
 namespace cuda {
@@ -27,7 +27,8 @@ void tile(Param<T> out, CParam<T> in) {
 
     static const std::string source(tile_cuh, tile_cuh_len);
 
-    auto tile = getKernel("cuda::tile", source, {TemplateTypename<T>()});
+    auto tile =
+        common::findKernel("cuda::tile", {source}, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index a749104f90..9fb5884dae 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/transform_cuh.hpp>
 #include <af/defines.h>
 
@@ -33,8 +33,8 @@ void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
                const bool perspective, const af::interpType method, int order) {
     static const std::string src(transform_cuh, transform_cuh_len);
 
-    auto transform = getKernel(
-        "cuda::transform", src,
+    auto transform = common::findKernel(
+        "cuda::transform", {src},
         {TemplateTypename<T>(), TemplateArg(inverse), TemplateArg(order)});
 
     const unsigned int nImg2  = in.dims[2];
@@ -44,8 +44,9 @@ void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
     const unsigned int tf_len = (perspective) ? 9 : 6;
 
     // Copy transform to constant memory.
-    transform.setConstant("c_tmat", reinterpret_cast<CUdeviceptr>(tf.ptr),
-                          nTfs2 * nTfs3 * tf_len * sizeof(float));
+    auto constPtr = transform.get("c_tmat");
+    transform.copyToReadOnly(constPtr, reinterpret_cast<CUdeviceptr>(tf.ptr),
+                             nTfs2 * nTfs3 * tf_len * sizeof(float));
 
     dim3 threads(TX, TY, 1);
     dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 5473ba128a..63b4ee6f30 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/transpose_cuh.hpp>
 
 #include <string>
@@ -29,10 +29,11 @@ void transpose(Param<T> out, CParam<T> in, const bool conjugate,
                const bool is32multiple) {
     static const std::string source(transpose_cuh, transpose_cuh_len);
 
-    auto transpose = getKernel("cuda::transpose", source,
-                               {TemplateTypename<T>(), TemplateArg(conjugate),
-                                TemplateArg(is32multiple)},
-                               {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+    auto transpose =
+        common::findKernel("cuda::transpose", {source},
+                           {TemplateTypename<T>(), TemplateArg(conjugate),
+                            TemplateArg(is32multiple)},
+                           {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index 4ae39da0bf..a40fd8df76 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/transpose_inplace_cuh.hpp>
 
 #include <string>
@@ -30,10 +30,10 @@ void transpose_inplace(Param<T> in, const bool conjugate,
     static const std::string source(transpose_inplace_cuh,
                                     transpose_inplace_cuh_len);
     auto transposeIP =
-        getKernel("cuda::transposeIP", source,
-                  {TemplateTypename<T>(), TemplateArg(conjugate),
-                   TemplateArg(is32multiple)},
-                  {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+        common::findKernel("cuda::transposeIP", {source},
+                           {TemplateTypename<T>(), TemplateArg(conjugate),
+                            TemplateArg(is32multiple)},
+                           {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     // dimensions passed to this function should be input dimensions
     // any necessary transformations and dimension related calculations are
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index ac6b827321..73fc3bae1a 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/triangle_cuh.hpp>
 
 #include <string>
@@ -29,9 +29,10 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
 
     static const std::string source(triangle_cuh, triangle_cuh_len);
 
-    auto triangle = getKernel("cuda::triangle", source,
-                              {TemplateTypename<T>(), TemplateArg(is_upper),
-                               TemplateArg(is_unit_diag)});
+    auto triangle =
+        common::findKernel("cuda::triangle", {source},
+                           {TemplateTypename<T>(), TemplateArg(is_upper),
+                            TemplateArg(is_unit_diag)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index c9d4fb5418..89776c343c 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -11,9 +11,9 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <kernel/config.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/unwrap_cuh.hpp>
 
 #include <string>
@@ -27,8 +27,9 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
             const int dx, const int dy, const int nx, const bool is_column) {
     static const std::string source(unwrap_cuh, unwrap_cuh_len);
 
-    auto unwrap = getKernel("cuda::unwrap", source,
-                            {TemplateTypename<T>(), TemplateArg(is_column)});
+    auto unwrap =
+        common::findKernel("cuda::unwrap", {source},
+                           {TemplateTypename<T>(), TemplateArg(is_column)});
 
     dim3 threads, blocks;
     int reps;
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 383c434870..2d8b9c5048 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -10,10 +10,10 @@
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <memory.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/where_cuh.hpp>
 #include "config.hpp"
 #include "scan_first.hpp"
@@ -24,7 +24,8 @@ namespace kernel {
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
     static const std::string src(where_cuh, where_cuh_len);
-    auto where = getKernel("cuda::where", src, {TemplateTypename<T>()});
+    auto where =
+        common::findKernel("cuda::where", {src}, {TemplateTypename<T>()});
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index cbbc7e77a6..3199d97ccb 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -11,9 +11,9 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <kernel/config.hpp>
-#include <nvrtc/cache.hpp>
 #include <nvrtc_kernel_headers/wrap_cuh.hpp>
 
 #include <string>
@@ -26,8 +26,9 @@ void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
           const int sy, const int px, const int py, const bool is_column) {
     static const std::string source(wrap_cuh, wrap_cuh_len);
 
-    auto wrap = getKernel("cuda::wrap", source,
-                          {TemplateTypename<T>(), TemplateArg(is_column)});
+    auto wrap =
+        common::findKernel("cuda::wrap", {source},
+                           {TemplateTypename<T>(), TemplateArg(is_column)});
 
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
     int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
@@ -56,8 +57,9 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                   const bool is_column) {
     static const std::string source(wrap_cuh, wrap_cuh_len);
 
-    auto wrap = getKernel("cuda::wrap_dilated", source,
-                          {TemplateTypename<T>(), TemplateArg(is_column)});
+    auto wrap =
+        common::findKernel("cuda::wrap_dilated", {source},
+                           {TemplateTypename<T>(), TemplateArg(is_column)});
 
     int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     int ny = 1 + (out.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
diff --git a/src/backend/cuda/morph.cpp b/src/backend/cuda/morph.cpp
new file mode 100644
index 0000000000..ba4cf98683
--- /dev/null
+++ b/src/backend/cuda/morph.cpp
@@ -0,0 +1,59 @@
+/*******************************************************
+ * Copyright (c) 2019, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_cuda.hpp>
+#include <kernel/morph.hpp>
+#include <morph.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace cuda {
+
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
+    const dim4 mdims = mask.dims();
+    if (mdims[0] != mdims[1]) {
+        CUDA_NOT_SUPPORTED("Rectangular masks are not supported");
+    }
+    if (mdims[0] > 19) {
+        CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
+    }
+    Array<T> out = createEmptyArray<T>(in.dims());
+    kernel::morph<T>(out, in, mask, isDilation);
+    return out;
+}
+
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation) {
+    const dim4 mdims = mask.dims();
+    if (mdims[0] != mdims[1] || mdims[0] != mdims[2]) {
+        CUDA_NOT_SUPPORTED("Only cubic masks are supported");
+    }
+    if (mdims[0] > 7) { CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported"); }
+    Array<T> out = createEmptyArray<T>(in.dims());
+    kernel::morph3d<T>(out, in, mask, isDilation);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                    \
+    template Array<T> morph<T>(const Array<T> &, const Array<T> &, bool); \
+    template Array<T> morph3d<T>(const Array<T> &, const Array<T> &, bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace cuda
diff --git a/src/backend/cuda/morph.hpp b/src/backend/cuda/morph.hpp
index 45abac1c95..b1276dfbf2 100644
--- a/src/backend/cuda/morph.hpp
+++ b/src/backend/cuda/morph.hpp
@@ -10,9 +10,9 @@
 #include <Array.hpp>
 
 namespace cuda {
-template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask);
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace cuda
diff --git a/src/backend/cuda/morph3d_impl.hpp b/src/backend/cuda/morph3d_impl.hpp
deleted file mode 100644
index 094bd815e8..0000000000
--- a/src/backend/cuda/morph3d_impl.hpp
+++ /dev/null
@@ -1,34 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <err_cuda.hpp>
-#include <kernel/morph.hpp>
-#include <morph.hpp>
-#include <af/dim4.hpp>
-
-using af::dim4;
-
-namespace cuda {
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
-    const dim4 mdims = mask.dims();
-    if (mdims[0] != mdims[1] || mdims[0] != mdims[2]) {
-        CUDA_NOT_SUPPORTED("Only cubic masks are supported");
-    }
-    if (mdims[0] > 7) { CUDA_NOT_SUPPORTED("Kernels > 7x7x7 not supported"); }
-    Array<T> out = createEmptyArray<T>(in.dims());
-    kernel::morph3d<T>(out, in, mask, isDilation);
-    return out;
-}
-
-#define INSTANTIATE(T, ISDILATE)                               \
-    template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, \
-                                           const Array<T> &mask);
-}  // namespace cuda
diff --git a/src/backend/cuda/morph_impl.hpp b/src/backend/cuda/morph_impl.hpp
deleted file mode 100644
index e155523897..0000000000
--- a/src/backend/cuda/morph_impl.hpp
+++ /dev/null
@@ -1,36 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <err_cuda.hpp>
-#include <kernel/morph.hpp>
-#include <morph.hpp>
-#include <af/dim4.hpp>
-
-using af::dim4;
-
-namespace cuda {
-template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask) {
-    const dim4 mdims = mask.dims();
-    if (mdims[0] != mdims[1]) {
-        CUDA_NOT_SUPPORTED("Rectangular masks are not supported");
-    }
-    if (mdims[0] > kernel::MAX_MORPH_FILTER_LEN) {
-        CUDA_NOT_SUPPORTED("Kernels > 19x19 are not supported");
-    }
-    Array<T> out = createEmptyArray<T>(in.dims());
-    kernel::morph<T>(out, in, mask, isDilation);
-    return out;
-}
-
-#define INSTANTIATE(T, ISDILATE)                             \
-    template Array<T> morph<T, ISDILATE>(const Array<T> &in, \
-                                         const Array<T> &mask);
-}  // namespace cuda
diff --git a/src/backend/cuda/nvrtc/cache.hpp b/src/backend/cuda/nvrtc/cache.hpp
deleted file mode 100644
index 2380521908..0000000000
--- a/src/backend/cuda/nvrtc/cache.hpp
+++ /dev/null
@@ -1,208 +0,0 @@
-/*******************************************************
- * Copyright (c) 2019, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <err_cuda.hpp>
-#include <nvrtc/EnqueueArgs.hpp>
-#include <traits.hpp>
-
-#include <cstdio>
-#include <string>
-#include <vector>
-
-#define CU_CHECK(fn)                                                      \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[1024];                                            \
-        const char* cu_err_name;                                          \
-        const char* cu_err_string;                                        \
-        cuGetErrorName(res, &cu_err_name);                                \
-        cuGetErrorString(res, &cu_err_string);                            \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), cu_err_string);                 \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
-    } while (0)
-
-namespace cuda {
-
-///
-/// \brief Kernel Functor that wraps CUDA nvrtc constructs
-///
-/// This struct encapsulates CUmodule and CUfunction pointers that are required
-/// to execution of CUDA C++ kernels compiled at runtime.
-///
-struct Kernel {
-    CUmodule prog;   ///< CUmodule helps acquire kernel attributes
-    CUfunction ker;  ///< CUfuntion is the actual kernel blob to run
-
-    ///
-    /// \brief Copy data to constant qualified global variable of kernel
-    ///
-    /// This function copies data of `bytes` size from the device pointer to a
-    /// global(__constant__) variable declared inside the kernel.
-    ///
-    /// \param[in] name is the name of the global variable inside kernel
-    /// \param[in] src is the device pointer from which data will be copied
-    /// \param[in] bytes are the number of bytes of data to be copied
-    ///
-    void setConstant(const char* name, CUdeviceptr src, size_t bytes);
-
-    ///
-    /// \brief Copy scalar to device qualified global variable of kernel
-    ///
-    /// This function copies a single value of type T from host variable
-    /// to a global(__device__) variable declared inside the kernel.
-    ///
-    /// \param[in] name is the name of the global variable inside kernel
-    /// \param[in] value is the value of type T
-    ///
-    template<typename T>
-    void setScalar(const char* name, T value);
-
-    ///
-    /// \brief Fetch scalar from device qualified global variable of kernel
-    ///
-    /// This function copies a single value of type T from a global(__device__)
-    /// variable declared inside the kernel to host.
-    ///
-    /// \param[in] name is the name of the global variable inside kernel
-    /// \param[in] value is the value of type T
-    ///
-    template<typename T>
-    void getScalar(T& out, const char* name);
-
-    ///
-    /// \brief Enqueue Kernel per queueing criteria forwarding other parameters
-    ///
-    /// This operator overload enables Kernel object to work as functor that
-    /// internally executes the CUDA kernel stored inside the Kernel object.
-    /// All parameters that are passed in after the EnqueueArgs object are
-    /// essentially forwarded to cuLaunchKernel driver API call.
-    ///
-    /// \param[in] qArgs is an object of struct \ref EnqueueArgs
-    /// \param[in] args is the placeholder for variadic arguments
-    ///
-    template<typename... Args>
-    void operator()(const EnqueueArgs& qArgs, Args... args) {
-        void* params[] = {reinterpret_cast<void*>(&args)...};
-        for (auto& event : qArgs.mEvents) {
-            CU_CHECK(cuStreamWaitEvent(qArgs.mStream, event, 0));
-        }
-        CU_CHECK(cuLaunchKernel(
-            ker, qArgs.mBlocks.x, qArgs.mBlocks.y, qArgs.mBlocks.z,
-            qArgs.mThreads.x, qArgs.mThreads.y, qArgs.mThreads.z,
-            qArgs.mSharedMemSize, qArgs.mStream, params, NULL));
-    }
-};
-
-// TODO(pradeep): remove this in API and merge JIT and nvrtc caches
-Kernel buildKernel(const int device, const std::string& nameExpr,
-                   const std::string& jit_ker,
-                   const std::vector<std::string>& opts = {},
-                   const bool isJIT                     = false);
-
-Kernel loadKernel(const int device, const std::string& nameExpr,
-                  const std::string& source);
-
-template<typename T>
-std::string toString(T val);
-
-struct TemplateArg {
-    std::string _tparam;
-
-    TemplateArg(std::string str) : _tparam(str) {}
-
-    template<typename T>
-    constexpr TemplateArg(T value) noexcept : _tparam(toString(value)) {}
-};
-
-template<typename T>
-struct TemplateTypename {
-    operator TemplateArg() const noexcept {
-        return {std::string(dtype_traits<T>::getName())};
-    }
-};
-
-#define SPECIALIZE(TYPE, NAME)                      \
-    template<>                                      \
-    struct TemplateTypename<TYPE> {                 \
-        operator TemplateArg() const noexcept {     \
-            return TemplateArg(std::string(#NAME)); \
-        }                                           \
-    }
-
-SPECIALIZE(unsigned char, cuda::uchar);
-SPECIALIZE(unsigned int, cuda::uint);
-SPECIALIZE(unsigned short, cuda::ushort);
-SPECIALIZE(long long, long long);
-SPECIALIZE(unsigned long long, unsigned long long);
-
-#undef SPECIALIZE
-
-#define DefineKey(arg) "-D " #arg
-#define DefineValue(arg) "-D " #arg "=" + toString(arg)
-#define DefineKeyValue(key, arg) "-D " #key "=" + toString(arg)
-
-///
-/// \brief Find/Create-Cache a Kernel that fits the given criteria
-///
-/// This function takes in two vectors of strings apart from the main Kernel
-/// name, match criteria, to find a suitable kernel in the Kernel cache. It
-/// builds and caches a new Kernel object if one isn't found in the cache.
-///
-/// The paramter \p key has to be the unique name for a given CUDA kernel.
-/// The key has to be present in one of the entries of KernelMap defined in
-/// the header EnqueueArgs.hpp.
-///
-/// The parameter \p templateArgs is a list of stringified template arguments of
-/// the CUDA kernel. These strings are used to generate the template
-/// instantiation expression of the CUDA kernel during compilation stage. It is
-/// critical that these strings are provided in correct format.
-///
-/// The paramter \p compileOpts is a list of strings that lets you add
-/// definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` to the compiler. To
-/// enable easy stringification of variables into their definition equation,
-/// three helper macros are provided: TemplateArg, DefineKey and DefineValue.
-///
-/// Example Usage: transpose
-///
-/// \code
-/// static const std::string src(transpose_cuh, transpose_cuh_len);
-/// auto transpose = getKernel("cuda::transpose", src,
-///         {
-///           TemplateTypename<T>(),
-///           TemplateArg(conjugate),
-///           TemplateArg(is32multiple)
-///         },
-///         {
-///           DefineValue(TILE_DIM), // Results in a definition
-///                                  // "-D TILE_DIME=<Value of TILE_DIM>"
-///           DefineValue(THREADS_Y) // Results in a definition
-///                                  // "-D THREADS_Y=<Value of THREADS_Y>"
-///           DefineKeyValue(DIMY, threads_y)  // Results in a definition
-///                                            // "-D DIMY=<Value of threads_y>"
-///         }
-///         );
-/// \endcode
-///
-/// \param[in] nameExpr is the of name expressions to be instantiated while
-///            compiling the kernel.
-/// \param[in] source is the kernel source code string
-/// \param[in] templateArgs is a vector of strings containing stringified names
-///            of the template arguments of CUDA kernel to be compiled.
-/// \param[in] compileOpts is a vector of strings that enables the user to
-///            add definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` for
-///            the kernel compilation.
-///
-Kernel getKernel(const std::string& nameExpr, const std::string& source,
-                 const std::vector<TemplateArg>& templateArgs,
-                 const std::vector<std::string>& compileOpts = {});
-}  // namespace cuda
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 2389a1b282..a390f6be0a 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -497,7 +497,7 @@ template<typename T>
 size_t Array<T>::getAllocatedBytes() const {
     if (!isReady()) return 0;
     size_t bytes = memoryManager().allocated(data.get());
-    // External device poitner
+    // External device pointer
     if (bytes == 0 && data.get()) { return data_dims.elements() * sizeof(T); }
     return bytes;
 }
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 828414e547..8dd0a74d12 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -47,6 +47,8 @@ target_sources(afopencl
   PRIVATE
     Array.cpp
     Array.hpp
+    Kernel.cpp
+    Kernel.hpp
     Param.cpp
     Param.hpp
     all.cpp
@@ -73,6 +75,7 @@ target_sources(afopencl
     cholesky.hpp
     clfft.cpp
     clfft.hpp
+    compile_kernel.cpp
     complex.hpp
     convolve.cpp
     convolve.hpp
@@ -87,10 +90,6 @@ target_sources(afopencl
     diagonal.hpp
     diff.cpp
     diff.hpp
-    dilate.cpp
-    dilate3d.cpp
-    erode.cpp
-    erode3d.cpp
     err_clblas.hpp
     err_clblast.hpp
     err_opencl.hpp
@@ -160,9 +159,8 @@ target_sources(afopencl
     min.cpp
     moments.cpp
     moments.hpp
+    morph.cpp
     morph.hpp
-    morph3d_impl.hpp
-    morph_impl.hpp
     nearest_neighbour.cpp
     nearest_neighbour.hpp
     orb.cpp
diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
new file mode 100644
index 0000000000..6b178e63e5
--- /dev/null
+++ b/src/backend/opencl/Kernel.cpp
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Kernel.hpp>
+
+#include <backend.hpp>
+#include <cl2hpp.hpp>
+#include <platform.hpp>
+
+namespace opencl {
+
+Kernel::DevPtrType Kernel::get(const char *name) { return nullptr; }
+
+void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
+                            size_t bytes) {
+    getQueue().enqueueCopyBuffer(*src, *dst, 0, 0, bytes);
+}
+
+void Kernel::setScalar(Kernel::DevPtrType dst, int value) {
+    getQueue().enqueueWriteBuffer(*dst, CL_FALSE, 0, sizeof(int), &value);
+}
+
+int Kernel::getScalar(Kernel::DevPtrType src) {
+    int retVal = 0;
+    getQueue().enqueueReadBuffer(*src, CL_TRUE, 0, sizeof(int), &retVal);
+    return retVal;
+}
+
+}  // namespace opencl
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
new file mode 100644
index 0000000000..1300a4e739
--- /dev/null
+++ b/src/backend/opencl/Kernel.hpp
@@ -0,0 +1,53 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/KernelInterface.hpp>
+
+#include <backend.hpp>
+#include <cl2hpp.hpp>
+
+namespace opencl {
+
+struct Enqueuer {
+    template<typename... Args>
+    void operator()(void* ker, const cl::EnqueueArgs& qArgs, Args... args) {
+        auto launchOp =
+            cl::KernelFunctor<Args...>(*static_cast<const cl::Kernel*>(ker));
+        launchOp(qArgs, std::forward<Args>(args)...);
+    }
+};
+
+class Kernel
+    : public common::KernelInterface<cl::Program*, cl::Kernel*, Enqueuer,
+                                     cl::Buffer*> {
+   public:
+    using ModuleType = cl::Program*;
+    using KernelType = cl::Kernel*;
+    using DevPtrType = cl::Buffer*;
+    using BaseClass =
+        common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType>;
+
+    Kernel() : BaseClass(nullptr, nullptr) {}
+    Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
+
+    // clang-format off
+    [[deprecated("OpenCL backend doesn't need Kernel::get method")]]
+    DevPtrType get(const char* name) override;
+    // clang-format on
+
+    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) override;
+
+    void setScalar(DevPtrType dst, int value) override;
+
+    int getScalar(DevPtrType src) override;
+};
+
+}  // namespace opencl
diff --git a/src/backend/opencl/compile_kernel.cpp b/src/backend/opencl/compile_kernel.cpp
new file mode 100644
index 0000000000..39f750db97
--- /dev/null
+++ b/src/backend/opencl/compile_kernel.cpp
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/compile_kernel.hpp>
+
+#include <cl2hpp.hpp>
+#include <common/defines.hpp>
+#include <err_opencl.hpp>
+#include <platform.hpp>
+#include <program.hpp>
+
+using detail::Kernel;
+using std::string;
+using std::vector;
+
+namespace common {
+
+Kernel compileKernel(const string &kernelName, const string &tInstance,
+                     const vector<string> &sources,
+                     const vector<string> &compileOpts, const bool isJIT) {
+    UNUSED(isJIT);
+    UNUSED(tInstance);
+
+    auto prog = detail::buildProgram(sources, compileOpts);
+    auto prg  = new cl::Program(prog);
+    auto krn =
+        new cl::Kernel(*static_cast<cl::Program *>(prg), kernelName.c_str());
+    return {prg, krn};
+}
+
+Kernel loadKernel(const int device, const string &nameExpr) {
+    OPENCL_NOT_SUPPORTED(
+        "Disk caching OpenCL kernel binaries is not yet supported");
+    return {nullptr, nullptr};
+}
+
+}  // namespace common
diff --git a/src/backend/opencl/debug_opencl.hpp b/src/backend/opencl/debug_opencl.hpp
index 12e75a32dd..078eacea72 100644
--- a/src/backend/opencl/debug_opencl.hpp
+++ b/src/backend/opencl/debug_opencl.hpp
@@ -17,9 +17,9 @@
 
 #include <platform.hpp>
 
-#define CL_DEBUG_FINISH(Q)                       \
-    do {                                         \
-        if (synchronize_calls()) { Q.finish(); } \
+#define CL_DEBUG_FINISH(Q)                               \
+    do {                                                 \
+        if (opencl::synchronize_calls()) { Q.finish(); } \
     } while (false);
 
 #endif
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index c510eff687..4be1595214 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -50,6 +50,8 @@ class MemoryManagerBase;
 }
 }  // namespace common
 
+using common::memory::MemoryManagerBase;
+
 namespace opencl {
 
 // opencl namespace forward declarations
diff --git a/src/backend/opencl/dilate.cpp b/src/backend/opencl/dilate.cpp
deleted file mode 100644
index 64a538ee76..0000000000
--- a/src/backend/opencl/dilate.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph_impl.hpp"
-
-namespace opencl {
-
-INSTANTIATE(float, true)
-INSTANTIATE(double, true)
-INSTANTIATE(char, true)
-INSTANTIATE(int, true)
-INSTANTIATE(uint, true)
-INSTANTIATE(uchar, true)
-INSTANTIATE(short, true)
-INSTANTIATE(ushort, true)
-
-}  // namespace opencl
diff --git a/src/backend/opencl/dilate3d.cpp b/src/backend/opencl/dilate3d.cpp
deleted file mode 100644
index 522fcbdc2b..0000000000
--- a/src/backend/opencl/dilate3d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph3d_impl.hpp"
-
-namespace opencl {
-
-INSTANTIATE(float, true)
-INSTANTIATE(double, true)
-INSTANTIATE(char, true)
-INSTANTIATE(int, true)
-INSTANTIATE(uint, true)
-INSTANTIATE(uchar, true)
-INSTANTIATE(short, true)
-INSTANTIATE(ushort, true)
-
-}  // namespace opencl
diff --git a/src/backend/opencl/erode.cpp b/src/backend/opencl/erode.cpp
deleted file mode 100644
index c5d6d84b84..0000000000
--- a/src/backend/opencl/erode.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph_impl.hpp"
-
-namespace opencl {
-
-INSTANTIATE(float, false)
-INSTANTIATE(double, false)
-INSTANTIATE(char, false)
-INSTANTIATE(int, false)
-INSTANTIATE(uint, false)
-INSTANTIATE(uchar, false)
-INSTANTIATE(short, false)
-INSTANTIATE(ushort, false)
-
-}  // namespace opencl
diff --git a/src/backend/opencl/erode3d.cpp b/src/backend/opencl/erode3d.cpp
deleted file mode 100644
index 73043c653d..0000000000
--- a/src/backend/opencl/erode3d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include "morph3d_impl.hpp"
-
-namespace opencl {
-
-INSTANTIATE(float, false)
-INSTANTIATE(double, false)
-INSTANTIATE(char, false)
-INSTANTIATE(int, false)
-INSTANTIATE(uint, false)
-INSTANTIATE(uchar, false)
-INSTANTIATE(short, false)
-INSTANTIATE(ushort, false)
-
-}  // namespace opencl
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index e49d5bf55d..2f4f3a44cd 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -8,60 +8,42 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/nonmax_suppression.hpp>
 #include <kernel_headers/trace_edge.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
 
 template<typename T>
 void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
                        const Param dy) {
-    std::string refName = std::string("non_max_suppression_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
-                << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
-                << " -D NON_MAX_SUPPRESSION";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {nonmax_suppression_cl};
-        const int ker_lens[]   = {nonmax_suppression_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "nonMaxSuppressionKernel");
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto nonMaxOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const Buffer, const KParam, const Buffer, const KParam,
-                      const unsigned, const unsigned>(*entry.ker);
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(nonmax_suppression_cl, nonmax_suppression_cl_len);
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(SHRD_MEM_HEIGHT, THREADS_X + 2),
+        DefineKeyValue(SHRD_MEM_WIDTH, THREADS_Y + 2),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto nonMaxOp = common::findKernel("nonMaxSuppressionKernel", {src},
+                                       {TemplateTypename<T>()}, compileOpts);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -76,36 +58,26 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     nonMaxOp(EnqueueArgs(getQueue(), global, threads), *output.data,
              output.info, *magnitude.data, magnitude.info, *dx.data, dx.info,
              *dy.data, dy.info, blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void initEdgeOut(Param output, const Param strong, const Param weak) {
-    std::string refName =
-        std::string("init_edge_out_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D INIT_EDGE_OUT";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {trace_edge_cl};
-        const int ker_lens[]   = {trace_edge_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "initEdgeOutKernel");
-        addKernelToCache(device, refName, entry);
-    }
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
 
-    auto initOp = KernelFunctor<Buffer, const KParam, const Buffer,
-                                const KParam, const Buffer, const KParam,
-                                const unsigned, const unsigned>(*entry.ker);
+    static const string src(trace_edge_cl, trace_edge_cl_len);
+
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKey(INIT_EDGE_OUT),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto initOp = common::findKernel("initEdgeOutKernel", {src},
+                                     {TemplateTypename<T>()}, compileOpts);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -125,31 +97,21 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
 
 template<typename T>
 void suppressLeftOver(Param output) {
-    std::string refName = std::string("suppress_left_over_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D SUPPRESS_LEFT_OVER";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {trace_edge_cl};
-        const int ker_lens[]   = {trace_edge_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "suppressLeftOverKernel");
-        addKernelToCache(device, refName, entry);
-    }
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(trace_edge_cl, trace_edge_cl_len);
+
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKey(SUPPRESS_LEFT_OVER),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalOp =
-        KernelFunctor<Buffer, const KParam, const unsigned, const unsigned>(
-            *entry.ker);
+    auto finalOp = common::findKernel("suppressLeftOverKernel", {src},
+                                      {TemplateTypename<T>()}, compileOpts);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -163,37 +125,30 @@ void suppressLeftOver(Param output) {
 
     finalOp(EnqueueArgs(getQueue(), global, threads), *output.data, output.info,
             blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void edgeTrackingHysteresis(Param output, const Param strong,
                             const Param weak) {
-    std::string refName =
-        std::string("edge_track_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT=" << (THREADS_X + 2)
-                << " -D SHRD_MEM_WIDTH=" << (THREADS_Y + 2)
-                << " -D TOTAL_NUM_THREADS=" << (THREADS_X * THREADS_Y)
-                << " -D EDGE_TRACER";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {trace_edge_cl};
-        const int ker_lens[]   = {trace_edge_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "edgeTrackKernel");
-        addKernelToCache(device, refName, entry);
-    }
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(trace_edge_cl, trace_edge_cl_len);
+
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKey(EDGE_TRACER),
+        DefineKeyValue(SHRD_MEM_HEIGHT, THREADS_X + 2),
+        DefineKeyValue(SHRD_MEM_WIDTH, THREADS_Y + 2),
+        DefineKeyValue(TOTAL_NUM_THREADS, THREADS_X * THREADS_Y),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto edgeTraceOp = common::findKernel("edgeTrackKernel", {src},
+                                          {TemplateTypename<T>()}, compileOpts);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
 
@@ -205,29 +160,19 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     NDRange global(blk_x * weak.info.dims[2] * threads[0],
                    blk_y * weak.info.dims[3] * threads[1], 1);
 
-    auto edgeTraceOp = KernelFunctor<Buffer, const KParam, const unsigned,
-                                     const unsigned, Buffer>(*entry.ker);
-
     initEdgeOut<T>(output, strong, weak);
 
-    int notFinished        = 1;
-    cl::Buffer *d_continue = bufferAlloc(sizeof(int));
+    int notFinished = 1;
+    auto dContinue  = memAlloc<T>(sizeof(int));
 
     while (notFinished > 0) {
         notFinished = 0;
-        getQueue().enqueueWriteBuffer(*d_continue, CL_FALSE, 0, sizeof(int),
-                                      &notFinished);
-
+        edgeTraceOp.setScalar(dContinue.get(), notFinished);
         edgeTraceOp(EnqueueArgs(getQueue(), global, threads), *output.data,
-                    output.info, blk_x, blk_y, *d_continue);
+                    output.info, blk_x, blk_y, *dContinue);
         CL_DEBUG_FINISH(getQueue());
-
-        getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
-                                     &notFinished);
+        notFinished = edgeTraceOp.getScalar(dContinue.get());
     }
-
-    bufferFree(d_continue);
-
     suppressLeftOver<T>(output);
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index f6945e4adb..29f78ea512 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -8,87 +8,74 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/morph.hpp>
 #include <memory.hpp>
 #include <ops.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <string>
+#include <types.hpp>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
+constexpr int CUBE_X    = 8;
+constexpr int CUBE_Y    = 8;
+constexpr int CUBE_Z    = 4;
+
+template<typename T>
+void morph(Param out, const Param in, const Param mask, bool isDilation) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::make_unique;
+    using std::string;
+    using std::vector;
 
-static const int CUBE_X = 8;
-static const int CUBE_Y = 8;
-static const int CUBE_Z = 4;
-
-template<typename T, bool isDilation, int SeLength>
-std::string generateOptionsString() {
     ToNumStr<T> toNumStr;
-    T init =
+    const T DefaultVal =
         isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
-    std::ostringstream options;
-    options << " -D T=" << dtype_traits<T>::getName()
-            << " -D isDilation=" << isDilation << " -D init=" << toNumStr(init)
-            << " -D SeLength=" << SeLength;
-    options << getTypeBuildDefinition<T>();
 
-    return options.str();
-}
+    static const string src(morph_cl, morph_cl_len);
 
-template<typename T, bool isDilation, int SeLength = 0>
-void morph(Param out, const Param in, const Param mask, int windLen = 0) {
-    std::string refName = std::string("morph_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(isDilation) + std::to_string(SeLength);
+    const int windLen  = mask.info.dims[0];
+    const int SeLength = (windLen <= 10 ? windLen : 0);
 
-    windLen = (SeLength > 0 ? SeLength : windLen);
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(isDilation),
+        TemplateArg(SeLength),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(isDilation),
+        DefineValue(SeLength),
+        DefineKeyValue(init, toNumStr(DefaultVal)),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string options = generateOptionsString<T, isDilation, SeLength>();
-        const char* ker_strs[] = {morph_cl};
-        const int ker_lens[]   = {morph_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "morph");
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto morphOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                 cl::LocalSpaceArg, int, int, int>(*entry.ker);
+    auto morphOp = common::findKernel("morph", {src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
-    // launch batch * blk_x blocks along x dimension
+
     NDRange global(blk_x * THREADS_X * in.info.dims[2],
                    blk_y * THREADS_Y * in.info.dims[3]);
 
-    // copy mask/filter to constant memory
-    cl_int se_size = sizeof(T) * windLen * windLen;
-    auto mBuff     = memAlloc<T>(windLen * windLen);
-    getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
+    // copy mask/filter to read-only memory
+    auto seBytes = windLen * windLen * sizeof(T);
+    auto mBuff =
+        make_unique<cl::Buffer>(getContext(), CL_MEM_READ_ONLY, seBytes);
+    morphOp.copyToReadOnly(mBuff.get(), mask.data, seBytes);
 
     // calculate shared memory size
     const int padding =
@@ -99,46 +86,54 @@ void morph(Param out, const Param in, const Param mask, int windLen = 0) {
     morphOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
             *in.data, in.info, *mBuff, cl::Local(locSize * sizeof(T)), blk_x,
             blk_y, windLen);
-
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename T, bool isDilation, int SeLength>
-void morph3d(Param out, const Param in, const Param mask) {
-    std::string refName = std::string("morph3d_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(isDilation) + std::to_string(SeLength);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string options = generateOptionsString<T, isDilation, SeLength>();
-        const char* ker_strs[] = {morph_cl};
-        const int ker_lens[]   = {morph_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "morph3d");
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto morphOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                 cl::LocalSpaceArg, int>(*entry.ker);
+template<typename T>
+void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::make_unique;
+    using std::string;
+    using std::vector;
+
+    ToNumStr<T> toNumStr;
+    const T DefaultVal =
+        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+
+    static const string src(morph_cl, morph_cl_len);
+
+    const int SeLength = mask.info.dims[0];
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(isDilation),
+        TemplateArg(SeLength),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(isDilation),
+        DefineValue(SeLength),
+        DefineKeyValue(init, toNumStr(DefaultVal)),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto morphOp = common::findKernel("morph3d", {src}, tmpltArgs, compileOpts);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
     int blk_x = divup(in.info.dims[0], CUBE_X);
     int blk_y = divup(in.info.dims[1], CUBE_Y);
     int blk_z = divup(in.info.dims[2], CUBE_Z);
-    // launch batch * blk_x blocks along x dimension
+
     NDRange global(blk_x * CUBE_X * in.info.dims[3], blk_y * CUBE_Y,
                    blk_z * CUBE_Z);
 
-    // copy mask/filter to constant memory
-    cl_int se_size    = sizeof(T) * SeLength * SeLength * SeLength;
-    cl::Buffer* mBuff = bufferAlloc(se_size);
-    getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
+    cl_int seBytes = sizeof(T) * SeLength * SeLength * SeLength;
+    auto mBuff =
+        make_unique<cl::Buffer>(getContext(), CL_MEM_READ_ONLY, seBytes);
+    morphOp.copyToReadOnly(mBuff.get(), mask.data, seBytes);
 
     // calculate shared memory size
     const int padding =
@@ -149,8 +144,6 @@ void morph3d(Param out, const Param in, const Param mask) {
 
     morphOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
             *in.data, in.info, *mBuff, cl::Local(locSize * sizeof(T)), blk_x);
-
-    bufferFree(mBuff);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index a47882d754..525e12664f 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -10,74 +10,62 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/transpose.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
+
 static const int TILE_DIM  = 32;
 static const int THREADS_X = TILE_DIM;
 static const int THREADS_Y = 256 / TILE_DIM;
 
-template<typename T, bool conjugate, bool IS32MULTIPLE>
-void transpose(Param out, const Param in, cl::CommandQueue queue) {
-    using cl::Buffer;
+template<typename T>
+void transpose(Param out, const Param in, cl::CommandQueue queue,
+               const bool conjugate, const bool IS32MULTIPLE) {
     using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
     using cl::NDRange;
-    using cl::Program;
     using std::string;
+    using std::vector;
 
-    string refName = std::string("transpose_") +
-                     std::string(dtype_traits<T>::getName()) +
-                     std::to_string(conjugate) + std::to_string(IS32MULTIPLE);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D TILE_DIM=" << TILE_DIM << " -D THREADS_Y=" << THREADS_Y
-                << " -D IS32MULTIPLE=" << IS32MULTIPLE
-                << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    static const string src(transpose_cl, transpose_cl_len);
 
-        const char* ker_strs[] = {transpose_cl};
-        const int ker_lens[]   = {transpose_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "transpose");
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(conjugate),
+        TemplateArg(IS32MULTIPLE),
+    };
+    vector<string> compileOpts = {
+        DefineValue(TILE_DIM),
+        DefineValue(THREADS_Y),
+        DefineValue(IS32MULTIPLE),
+        DefineKeyValue(DOCONJUGATE, (conjugate && af::iscplx<T>())),
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto transpose =
+        common::findKernel("transpose", {src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
-    int blk_x = divup(in.info.dims[0], TILE_DIM);
-    int blk_y = divup(in.info.dims[1], TILE_DIM);
+    const int blk_x = divup(in.info.dims[0], TILE_DIM);
+    const int blk_y = divup(in.info.dims[1], TILE_DIM);
 
-    // launch batch * blk_x blocks along x dimension
     NDRange global(blk_x * local[0] * in.info.dims[2],
                    blk_y * local[1] * in.info.dims[3]);
 
-    auto transposeOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const int, const int>(*entry.ker);
-
-    transposeOp(EnqueueArgs(queue, global, local), *out.data, out.info,
-                *in.data, in.info, blk_x, blk_y);
-
+    transpose(EnqueueArgs(queue, global, local), *out.data, out.info, *in.data,
+              in.info, blk_x, blk_y);
     CL_DEBUG_FINISH(queue);
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/magma/transpose.cpp b/src/backend/opencl/magma/transpose.cpp
index 856679d3ca..7ccb71eb4a 100644
--- a/src/backend/opencl/magma/transpose.cpp
+++ b/src/backend/opencl/magma/transpose.cpp
@@ -86,15 +86,9 @@ void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
     using namespace opencl;
 
     cl::CommandQueue q(queue, true);
-    if (m % 32 == 0 && n % 32 == 0) {
-        kernel::transpose<T, false, true>(
-            makeParam(dAT, dAT_offset, odims, ostrides),
-            makeParam(dA, dA_offset, idims, istrides), q);
-    } else {
-        kernel::transpose<T, false, false>(
-            makeParam(dAT, dAT_offset, odims, ostrides),
-            makeParam(dA, dA_offset, idims, istrides), q);
-    }
+    kernel::transpose<T>(makeParam(dAT, dAT_offset, odims, ostrides),
+                         makeParam(dA, dA_offset, idims, istrides), q, false,
+                         m % 32 == 0 && n % 32 == 0);
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/opencl/morph.cpp b/src/backend/opencl/morph.cpp
new file mode 100644
index 0000000000..10ac7397c5
--- /dev/null
+++ b/src/backend/opencl/morph.cpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_opencl.hpp>
+#include <kernel/morph.hpp>
+#include <math.hpp>
+#include <morph.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace opencl {
+
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
+    const dim4 mdims = mask.dims();
+    if (mdims[0] != mdims[1]) {
+        OPENCL_NOT_SUPPORTED("Rectangular masks are not suported");
+    }
+    if (mdims[0] > 19) {
+        OPENCL_NOT_SUPPORTED("Kernels > 19x19 are not supported");
+    }
+    const dim4 dims = in.dims();
+    Array<T> out    = createEmptyArray<T>(dims);
+    kernel::morph<T>(out, in, mask, isDilation);
+    return out;
+}
+
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation) {
+    const dim4 mdims = mask.dims();
+    if (mdims[0] != mdims[1] || mdims[0] != mdims[2]) {
+        OPENCL_NOT_SUPPORTED("Only cubic masks are supported");
+    }
+    if (mdims[0] > 7) {
+        OPENCL_NOT_SUPPORTED("Kernels > 7x7x7 masks are not supported");
+    }
+    Array<T> out = createEmptyArray<T>(in.dims());
+    kernel::morph3d<T>(out, in, mask, isDilation);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                    \
+    template Array<T> morph<T>(const Array<T> &, const Array<T> &, bool); \
+    template Array<T> morph3d<T>(const Array<T> &, const Array<T> &, bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/opencl/morph.hpp b/src/backend/opencl/morph.hpp
index 17b539d5e7..9435abef85 100644
--- a/src/backend/opencl/morph.hpp
+++ b/src/backend/opencl/morph.hpp
@@ -10,9 +10,9 @@
 #include <Array.hpp>
 
 namespace opencl {
-template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask);
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask);
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace opencl
diff --git a/src/backend/opencl/morph3d_impl.hpp b/src/backend/opencl/morph3d_impl.hpp
deleted file mode 100644
index ae7171ee27..0000000000
--- a/src/backend/opencl/morph3d_impl.hpp
+++ /dev/null
@@ -1,50 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <err_opencl.hpp>
-#include <kernel/morph.hpp>
-#include <math.hpp>
-#include <morph.hpp>
-#include <af/dim4.hpp>
-
-using af::dim4;
-
-namespace opencl {
-template<typename T, bool isDilation>
-Array<T> morph3d(const Array<T> &in, const Array<T> &mask) {
-    const dim4 mdims = mask.dims();
-
-    if (mdims[0] != mdims[1] || mdims[0] != mdims[2])
-        OPENCL_NOT_SUPPORTED("Only cubic masks are supported");
-
-    if (mdims[0] > 7)
-        OPENCL_NOT_SUPPORTED("Kernels > 7x7x7 masks are not supported");
-
-    const dim4 dims = in.dims();
-    Array<T> out    = createEmptyArray<T>(dims);
-
-    switch (mdims[0]) {
-        case 2: kernel::morph3d<T, isDilation, 2>(out, in, mask); break;
-        case 3: kernel::morph3d<T, isDilation, 3>(out, in, mask); break;
-        case 4: kernel::morph3d<T, isDilation, 4>(out, in, mask); break;
-        case 5: kernel::morph3d<T, isDilation, 5>(out, in, mask); break;
-        case 6: kernel::morph3d<T, isDilation, 6>(out, in, mask); break;
-        case 7: kernel::morph3d<T, isDilation, 7>(out, in, mask); break;
-        default:
-            assert(mdims[0] < 7 && "Kernel size should be haandled above.");
-    }
-
-    return out;
-}
-
-#define INSTANTIATE(T, ISDILATE)                               \
-    template Array<T> morph3d<T, ISDILATE>(const Array<T> &in, \
-                                           const Array<T> &mask);
-}  // namespace opencl
diff --git a/src/backend/opencl/morph_impl.hpp b/src/backend/opencl/morph_impl.hpp
deleted file mode 100644
index 1a79f6b338..0000000000
--- a/src/backend/opencl/morph_impl.hpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <Array.hpp>
-#include <err_opencl.hpp>
-#include <kernel/morph.hpp>
-#include <math.hpp>
-#include <morph.hpp>
-#include <af/dim4.hpp>
-
-using af::dim4;
-
-namespace opencl {
-template<typename T, bool isDilation>
-Array<T> morph(const Array<T> &in, const Array<T> &mask) {
-    const dim4 mdims = mask.dims();
-
-    if (mdims[0] != mdims[1])
-        OPENCL_NOT_SUPPORTED("Rectangular masks are not suported");
-
-    if (mdims[0] > 19)
-        OPENCL_NOT_SUPPORTED("Kernels > 19x19 are not supported");
-
-    const dim4 dims = in.dims();
-    Array<T> out    = createEmptyArray<T>(dims);
-
-    switch (mdims[0]) {
-        case 2: kernel::morph<T, isDilation, 2>(out, in, mask); break;
-        case 3: kernel::morph<T, isDilation, 3>(out, in, mask); break;
-        case 4: kernel::morph<T, isDilation, 4>(out, in, mask); break;
-        case 5: kernel::morph<T, isDilation, 5>(out, in, mask); break;
-        case 6: kernel::morph<T, isDilation, 6>(out, in, mask); break;
-        case 7: kernel::morph<T, isDilation, 7>(out, in, mask); break;
-        case 8: kernel::morph<T, isDilation, 8>(out, in, mask); break;
-        case 9: kernel::morph<T, isDilation, 9>(out, in, mask); break;
-        case 10: kernel::morph<T, isDilation, 10>(out, in, mask); break;
-        default: kernel::morph<T, isDilation>(out, in, mask, mdims[0]); break;
-    }
-
-    return out;
-}
-
-#define INSTANTIATE(T, ISDILATE)                             \
-    template Array<T> morph<T, ISDILATE>(const Array<T> &in, \
-                                         const Array<T> &mask);
-}  // namespace opencl
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
index 6735b627a6..fda0f6e86f 100644
--- a/src/backend/opencl/program.cpp
+++ b/src/backend/opencl/program.cpp
@@ -16,6 +16,7 @@
 #include <traits.hpp>
 #include <types.hpp>
 
+#include <sstream>
 #include <string>
 
 using cl::Buffer;
@@ -23,15 +24,33 @@ using cl::EnqueueArgs;
 using cl::Kernel;
 using cl::NDRange;
 using cl::Program;
+using std::ostringstream;
 using std::string;
 
 namespace opencl {
 
+const static std::string DEFAULT_MACROS_STR(
+    "\n\
+                                           #ifdef USE_DOUBLE\n\
+                                           #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
+                                           #endif\n                     \
+                                           #ifdef USE_HALF\n\
+                                           #pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
+                                           #else\n                     \
+                                           #define half short\n          \
+                                           #endif\n                      \
+                                           #ifndef M_PI\n               \
+                                           #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
+                                           #endif\n                     \
+                                           ");
+
+// TODO(pradeep) remove this version after porting to new cache interface
 void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
                   const std::string &options) {
     buildProgram(prog, 1, &ker_str, &ker_len, options);
 }
 
+// TODO(pradeep) remove this version after porting to new cache interface
 void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
                   const int *ker_lens, const std::string &options) {
     try {
@@ -75,4 +94,38 @@ void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
         throw;
     }
 }
+
+cl::Program buildProgram(const std::vector<std::string> &kernelSources,
+                         const std::vector<std::string> &compileOpts) {
+    cl::Program retVal;
+    try {
+        static const std::string defaults =
+            std::string(" -D dim_t=") +
+            std::string(dtype_traits<dim_t>::getName());
+
+        auto device = getDevice();
+
+        const std::string cl_std =
+            std::string(" -cl-std=CL") +
+            device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
+
+        Program::Sources sources;
+        sources.emplace_back(DEFAULT_MACROS_STR);
+        sources.emplace_back(KParam_hpp, KParam_hpp_len);
+
+        for (auto ksrc : kernelSources) { sources.emplace_back(ksrc); }
+
+        retVal = cl::Program(getContext(), sources);
+
+        ostringstream options;
+        for (auto &opt : compileOpts) { options << opt; }
+
+        retVal.build({device}, (cl_std + defaults + options.str()).c_str());
+    } catch (...) {
+        SHOW_BUILD_INFO(retVal);
+        throw;
+    }
+    return retVal;
+}
+
 }  // namespace opencl
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
index 514ce2376f..5f28fd5efe 100644
--- a/src/backend/opencl/program.hpp
+++ b/src/backend/opencl/program.hpp
@@ -9,10 +9,12 @@
 
 #pragma once
 
+#include <cl2hpp.hpp>
 #include <common/util.hpp>
 
 #include <cstdio>
 #include <string>
+#include <vector>
 
 #define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
     do {                                                                  \
@@ -32,21 +34,30 @@
 #define SHOW_BUILD_INFO(PROG)                                              \
     do {                                                                   \
         std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");         \
-        if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(prog); } \
+        if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(PROG); } \
     } while (0)
 
 #else
 #define SHOW_BUILD_INFO(PROG) SHOW_DEBUG_BUILD_INFO(PROG)
 #endif
 
-namespace cl {
-class Program;
-}
-
 namespace opencl {
+
+#if defined(AF_WITH_DEV_WARNINGS)
+// TODO(pradeep) remove this version after porting to new cache interface
+[[deprecated("use cl::Program buildProgram(vector<string>&, vector<string>&)")]]
+#endif
 void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
                   const std::string &options);
 
+#if defined(AF_WITH_DEV_WARNINGS)
+// TODO(pradeep) remove this version after porting to new cache interface
+[[deprecated("use cl::Program buildProgram(vector<string>&, vector<string>&)")]]
+#endif
 void buildProgram(cl::Program &prog, const int num_files, const char **ker_str,
                   const int *ker_len, const std::string &options);
+
+cl::Program buildProgram(const std::vector<std::string> &kernelSources,
+                         const std::vector<std::string> &options);
+
 }  // namespace opencl
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index 1881603dda..819e73fb29 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -24,21 +24,11 @@ Array<T> transpose(const Array<T> &in, const bool conjugate) {
     dim4 outDims       = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
     Array<T> out       = createEmptyArray<T>(outDims);
 
-    if (conjugate) {
-        if (inDims[0] % kernel::TILE_DIM == 0 &&
-            inDims[1] % kernel::TILE_DIM == 0) {
-            kernel::transpose<T, true, true>(out, in, getQueue());
-        } else {
-            kernel::transpose<T, true, false>(out, in, getQueue());
-        }
-    } else {
-        if (inDims[0] % kernel::TILE_DIM == 0 &&
-            inDims[1] % kernel::TILE_DIM == 0) {
-            kernel::transpose<T, false, true>(out, in, getQueue());
-        } else {
-            kernel::transpose<T, false, false>(out, in, getQueue());
-        }
-    }
+    const bool is32multiple =
+        inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+
+    kernel::transpose<T>(out, in, getQueue(), conjugate, is32multiple);
+
     return out;
 }
 

From 3ad4c0dada8daf4623da1c56fd1557999a5fc85b Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 14 May 2020 02:42:57 -0400
Subject: [PATCH 1958/2677] remove placeholder pooling docs

---
 include/arrayfire.h | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/include/arrayfire.h b/include/arrayfire.h
index ed331aeb08..4c9e50da47 100644
--- a/include/arrayfire.h
+++ b/include/arrayfire.h
@@ -364,9 +364,6 @@
 
      Machine learning functions
 
-     @defgroup ml_pool Pooling operations
-     Pool 2D, ND, maxpooling, minpooling, meanpooling
-
      @defgroup ml_convolution Convolutions
      Forward and backward convolution passes
    @}

From 8f9f410cd9ef9a0a4c9abe46ae082c61fa491163 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 19 May 2020 23:33:53 +0530
Subject: [PATCH 1959/2677] Refactor kernel wrappers to use new caching API
 (#2890)

* Refactor kernel wrappers to use new caching API

* Fix formatting
---
 CMakeLists.txt                                |   2 -
 src/api/c/homography.cpp                      |   2 +
 src/backend/common/CMakeLists.txt             |   4 -
 src/backend/common/TemplateArg.cpp            |  13 +
 src/backend/common/TemplateArg.hpp            |   1 +
 src/backend/common/kernel_cache.cpp           |  11 +-
 src/backend/opencl/CMakeLists.txt             |   3 -
 src/backend/opencl/approx.cpp                 |  28 +-
 src/backend/opencl/assign.cpp                 |   4 +-
 src/backend/opencl/bilateral.cpp              |   2 +-
 src/backend/opencl/cache.hpp                  |  27 -
 src/backend/opencl/compile_kernel.cpp         |  90 ++-
 src/backend/opencl/copy.cpp                   |  18 +-
 src/backend/opencl/copy.hpp                   |  16 +-
 src/backend/opencl/debug_opencl.hpp           |   4 +-
 src/backend/opencl/diff.cpp                   |  20 +-
 src/backend/opencl/fast.cpp                   |   4 +-
 src/backend/opencl/histogram.cpp              |   5 +-
 src/backend/opencl/homography.cpp             |  30 +-
 src/backend/opencl/hsv_rgb.cpp                |  14 +-
 src/backend/opencl/index.cpp                  |   2 +-
 src/backend/opencl/ireduce.cpp                |   2 +-
 src/backend/opencl/jit.cpp                    |  88 ++-
 .../opencl/kernel/anisotropic_diffusion.cl    |  34 +-
 .../opencl/kernel/anisotropic_diffusion.hpp   |  80 +-
 src/backend/opencl/kernel/approx.hpp          | 178 ++---
 src/backend/opencl/kernel/approx1.cl          |  11 +-
 src/backend/opencl/kernel/approx2.cl          |  13 +-
 src/backend/opencl/kernel/assign.hpp          |  63 +-
 src/backend/opencl/kernel/bilateral.cl        |  12 +-
 src/backend/opencl/kernel/bilateral.hpp       |  77 +-
 src/backend/opencl/kernel/canny.hpp           |  24 +-
 src/backend/opencl/kernel/convolve.hpp        |   6 +-
 src/backend/opencl/kernel/convolve/conv1.cpp  |   2 -
 .../opencl/kernel/convolve/conv2_b8.cpp       |   2 -
 .../opencl/kernel/convolve/conv2_c32.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_c64.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_f32.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_f64.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_impl.hpp     | 103 ++-
 .../opencl/kernel/convolve/conv2_s16.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_s32.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_s64.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_u16.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_u32.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_u64.cpp      |   2 -
 .../opencl/kernel/convolve/conv2_u8.cpp       |   2 -
 src/backend/opencl/kernel/convolve/conv3.cpp  |   2 -
 .../opencl/kernel/convolve/conv_common.hpp    | 121 ++-
 .../opencl/kernel/convolve_separable.cpp      | 120 ++-
 .../opencl/kernel/convolve_separable.hpp      |   2 +-
 src/backend/opencl/kernel/coo2dense.cl        |   8 +-
 src/backend/opencl/kernel/copy.cl             |  15 +-
 src/backend/opencl/kernel/cscmm.cl            |  14 +-
 src/backend/opencl/kernel/cscmm.hpp           | 117 ++-
 src/backend/opencl/kernel/cscmv.cl            |  16 +-
 src/backend/opencl/kernel/cscmv.hpp           | 111 +--
 src/backend/opencl/kernel/csr2coo.cl          |  18 +-
 src/backend/opencl/kernel/csr2dense.cl        |   6 +-
 src/backend/opencl/kernel/csrmm.cl            |  14 +-
 src/backend/opencl/kernel/csrmm.hpp           | 110 +--
 src/backend/opencl/kernel/csrmv.cl            |  24 +-
 src/backend/opencl/kernel/csrmv.hpp           | 113 +--
 src/backend/opencl/kernel/dense2csr.cl        |  10 +-
 src/backend/opencl/kernel/diag_create.cl      |  10 +-
 src/backend/opencl/kernel/diag_extract.cl     |  10 +-
 src/backend/opencl/kernel/diagonal.hpp        | 109 +--
 src/backend/opencl/kernel/diff.cl             |   4 +-
 src/backend/opencl/kernel/diff.hpp            |  70 +-
 src/backend/opencl/kernel/example.cl          |   4 +-
 src/backend/opencl/kernel/exampleFunction.hpp | 118 ++-
 src/backend/opencl/kernel/fast.cl             |  38 +-
 src/backend/opencl/kernel/fast.hpp            | 128 ++--
 src/backend/opencl/kernel/fftconvolve.hpp     | 277 +++----
 .../opencl/kernel/fftconvolve_multiply.cl     |   8 +-
 src/backend/opencl/kernel/fftconvolve_pack.cl |  10 +-
 .../opencl/kernel/fftconvolve_reorder.cl      |   8 +-
 src/backend/opencl/kernel/flood_fill.cl       |  40 +-
 src/backend/opencl/kernel/flood_fill.hpp      | 159 ++--
 src/backend/opencl/kernel/gradient.cl         |  12 +-
 src/backend/opencl/kernel/gradient.hpp        |  74 +-
 src/backend/opencl/kernel/harris.cl           |  39 +-
 src/backend/opencl/kernel/harris.hpp          | 111 +--
 src/backend/opencl/kernel/histogram.cl        |  10 +-
 src/backend/opencl/kernel/histogram.hpp       |  83 +--
 src/backend/opencl/kernel/homography.cl       |  96 +--
 src/backend/opencl/kernel/homography.hpp      | 155 ++--
 src/backend/opencl/kernel/hsv_rgb.cl          |   4 +-
 src/backend/opencl/kernel/hsv_rgb.hpp         |  65 +-
 src/backend/opencl/kernel/identity.cl         |   8 +-
 src/backend/opencl/kernel/identity.hpp        |  77 +-
 src/backend/opencl/kernel/iir.cl              |  18 +-
 src/backend/opencl/kernel/iir.hpp             |  69 +-
 src/backend/opencl/kernel/index.hpp           |  63 +-
 src/backend/opencl/kernel/interp.cl           |  24 +-
 src/backend/opencl/kernel/interp.hpp          |  37 +-
 src/backend/opencl/kernel/iota.cl             |   6 +-
 src/backend/opencl/kernel/iota.hpp            |  67 +-
 src/backend/opencl/kernel/ireduce.hpp         | 218 +++---
 src/backend/opencl/kernel/ireduce_dim.cl      |  29 +-
 src/backend/opencl/kernel/ireduce_first.cl    |  25 +-
 src/backend/opencl/kernel/jit.cl              |   4 +-
 src/backend/opencl/kernel/join.cl             |  13 +-
 src/backend/opencl/kernel/join.hpp            |  71 +-
 src/backend/opencl/kernel/laset.cl            |   6 +-
 src/backend/opencl/kernel/laset.hpp           |  68 +-
 src/backend/opencl/kernel/laset_band.cl       |   4 +-
 src/backend/opencl/kernel/laset_band.hpp      |  60 +-
 src/backend/opencl/kernel/laswp.cl            |   6 +-
 src/backend/opencl/kernel/laswp.hpp           |  60 +-
 src/backend/opencl/kernel/lookup.hpp          |  74 +-
 src/backend/opencl/kernel/lu_split.cl         |  25 +-
 src/backend/opencl/kernel/lu_split.hpp        |  96 +--
 src/backend/opencl/kernel/match_template.hpp  |  87 +--
 src/backend/opencl/kernel/mean.hpp            | 263 +++----
 src/backend/opencl/kernel/mean_dim.cl         |  18 +-
 src/backend/opencl/kernel/mean_first.cl       |  18 +-
 src/backend/opencl/kernel/meanshift.cl        |   8 +-
 src/backend/opencl/kernel/meanshift.hpp       |  81 +--
 src/backend/opencl/kernel/medfilt.hpp         | 150 ++--
 src/backend/opencl/kernel/medfilt1.cl         |  10 +-
 src/backend/opencl/kernel/medfilt2.cl         |  10 +-
 src/backend/opencl/kernel/memcopy.cl          |   7 +-
 src/backend/opencl/kernel/memcopy.hpp         | 134 ++--
 src/backend/opencl/kernel/moments.cl          |  22 +-
 src/backend/opencl/kernel/moments.hpp         |  69 +-
 src/backend/opencl/kernel/morph.cl            |  12 +-
 src/backend/opencl/kernel/morph.hpp           |  29 +-
 .../opencl/kernel/nearest_neighbour.cl        |  12 +-
 .../opencl/kernel/nearest_neighbour.hpp       | 107 ++-
 .../opencl/kernel/nonmax_suppression.cl       |  22 +-
 src/backend/opencl/kernel/orb.cl              |  46 +-
 src/backend/opencl/kernel/orb.hpp             | 154 ++--
 .../opencl/kernel/pad_array_borders.cl        |  12 +-
 .../opencl/kernel/pad_array_borders.hpp       |  75 +-
 src/backend/opencl/kernel/random_engine.hpp   | 139 ++--
 .../opencl/kernel/random_engine_mersenne.cl   |  41 +-
 .../kernel/random_engine_mersenne_init.cl     |  11 +-
 .../opencl/kernel/random_engine_philox.cl     |   4 +-
 .../opencl/kernel/random_engine_threefry.cl   |   4 +-
 .../opencl/kernel/random_engine_write.cl      | 122 ++--
 src/backend/opencl/kernel/range.cl            |   2 +-
 src/backend/opencl/kernel/range.hpp           |  61 +-
 src/backend/opencl/kernel/reduce.hpp          | 221 +++---
 .../opencl/kernel/reduce_blocks_by_key_dim.cl |  40 +-
 .../kernel/reduce_blocks_by_key_first.cl      |  34 +-
 src/backend/opencl/kernel/reduce_by_key.hpp   | 687 ++++++++----------
 .../opencl/kernel/reduce_by_key_boundary.cl   |   8 +-
 .../kernel/reduce_by_key_boundary_dim.cl      |  10 +-
 .../opencl/kernel/reduce_by_key_compact.cl    |   9 +-
 .../kernel/reduce_by_key_compact_dim.cl       |  12 +-
 .../kernel/reduce_by_key_needs_reduction.cl   |   8 +-
 src/backend/opencl/kernel/reduce_dim.cl       |   8 +-
 src/backend/opencl/kernel/reduce_first.cl     |   8 +-
 src/backend/opencl/kernel/regions.cl          |  12 +-
 src/backend/opencl/kernel/regions.hpp         | 147 ++--
 src/backend/opencl/kernel/reorder.cl          |   2 +-
 src/backend/opencl/kernel/reorder.hpp         |  71 +-
 src/backend/opencl/kernel/resize.cl           |  10 +-
 src/backend/opencl/kernel/resize.hpp          |  92 ++-
 src/backend/opencl/kernel/rotate.cl           |  10 +-
 src/backend/opencl/kernel/rotate.hpp          | 111 ++-
 src/backend/opencl/kernel/scan_dim.cl         |  30 +-
 src/backend/opencl/kernel/scan_dim.hpp        | 170 ++---
 src/backend/opencl/kernel/scan_dim_by_key.cl  |  73 +-
 src/backend/opencl/kernel/scan_dim_by_key.hpp |  10 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    | 211 +++---
 src/backend/opencl/kernel/scan_first.cl       |  26 +-
 src/backend/opencl/kernel/scan_first.hpp      | 177 ++---
 .../opencl/kernel/scan_first_by_key.cl        |  70 +-
 .../opencl/kernel/scan_first_by_key.hpp       |   9 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  | 228 +++---
 src/backend/opencl/kernel/select.cl           |  22 +-
 src/backend/opencl/kernel/select.hpp          | 134 ++--
 src/backend/opencl/kernel/sift_nonfree.cl     | 118 +--
 src/backend/opencl/kernel/sift_nonfree.hpp    | 247 +++----
 src/backend/opencl/kernel/sobel.cl            |   4 +-
 src/backend/opencl/kernel/sobel.hpp           |  68 +-
 src/backend/opencl/kernel/sort.hpp            |   9 +-
 .../opencl/kernel/sort_by_key_impl.hpp        |  11 +-
 src/backend/opencl/kernel/sp_sp_arith_csr.cl  |  10 +-
 src/backend/opencl/kernel/sparse.hpp          | 308 +++-----
 src/backend/opencl/kernel/sparse_arith.hpp    | 296 ++------
 src/backend/opencl/kernel/sparse_arith_coo.cl |  20 +-
 src/backend/opencl/kernel/sparse_arith_csr.cl |  20 +-
 src/backend/opencl/kernel/susan.hpp           | 150 ++--
 src/backend/opencl/kernel/swapdblk.cl         |   4 +-
 src/backend/opencl/kernel/swapdblk.hpp        |  62 +-
 src/backend/opencl/kernel/tile.cl             |   6 +-
 src/backend/opencl/kernel/tile.hpp            |  61 +-
 src/backend/opencl/kernel/trace_edge.cl       |  63 +-
 src/backend/opencl/kernel/transform.cl        |  18 +-
 src/backend/opencl/kernel/transform.hpp       | 118 ++-
 src/backend/opencl/kernel/transpose.cl        |   6 +-
 src/backend/opencl/kernel/transpose.hpp       |   7 +-
 .../opencl/kernel/transpose_inplace.cl        |   8 +-
 .../opencl/kernel/transpose_inplace.hpp       |  82 +--
 src/backend/opencl/kernel/triangle.cl         |  17 +-
 src/backend/opencl/kernel/triangle.hpp        |  70 +-
 src/backend/opencl/kernel/unwrap.cl           |  21 +-
 src/backend/opencl/kernel/unwrap.hpp          |  79 +-
 src/backend/opencl/kernel/where.cl            |   9 +-
 src/backend/opencl/kernel/where.hpp           |  86 +--
 src/backend/opencl/kernel/wrap.cl             |   9 +-
 src/backend/opencl/kernel/wrap.hpp            | 138 ++--
 src/backend/opencl/kernel/wrap_dilated.cl     |  11 +-
 src/backend/opencl/lookup.cpp                 |   8 +-
 src/backend/opencl/lu.cpp                     |   2 +-
 .../opencl/magma/transpose_inplace.cpp        |   9 +-
 src/backend/opencl/match_template.cpp         |  13 +-
 src/backend/opencl/match_template.hpp         |   1 +
 src/backend/opencl/mean.cpp                   |  13 +-
 src/backend/opencl/meanshift.cpp              |   9 +-
 src/backend/opencl/medfilt.cpp                |  23 +-
 src/backend/opencl/nearest_neighbour.cpp      |   2 +-
 src/backend/opencl/platform.cpp               |  23 -
 src/backend/opencl/platform.hpp               |  10 +-
 src/backend/opencl/program.cpp                | 131 ----
 src/backend/opencl/program.hpp                |  63 --
 src/backend/opencl/qr.cpp                     |   2 +-
 src/backend/opencl/reduce_impl.hpp            |   6 +-
 src/backend/opencl/regions.cpp                |  10 +-
 src/backend/opencl/reshape.cpp                |  10 +-
 src/backend/opencl/resize.cpp                 |  15 +-
 src/backend/opencl/rotate.cpp                 |  13 +-
 src/backend/opencl/scan.cpp                   |  25 +-
 src/backend/opencl/scan_by_key.cpp            |  14 +-
 src/backend/opencl/select.cpp                 |   2 +-
 src/backend/opencl/susan.cpp                  |  40 +-
 src/backend/opencl/transform.cpp              |  10 +-
 src/backend/opencl/transpose_inplace.cpp      |  23 +-
 src/backend/opencl/triangle.cpp               |   2 +-
 232 files changed, 4598 insertions(+), 7015 deletions(-)
 delete mode 100644 src/backend/opencl/cache.hpp
 delete mode 100644 src/backend/opencl/program.cpp
 delete mode 100644 src/backend/opencl/program.hpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 97dca3707b..94b8560b8e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -72,7 +72,6 @@ else()
 endif()
 
 option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
-option(AF_ENABLE_DEV_WARNINGS "Enable developer warnings such as attribute based" OFF)
 
 cmake_dependent_option(AF_WITH_RELATIVE_TEST_DIR "Use relative paths for the test data directory(For continious integration(CI) purposes only)" OFF
   "BUILD_TESTING" OFF)
@@ -100,7 +99,6 @@ af_deprecate(USE_CPUID             AF_WITH_CPUID)
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
   AF_INSTALL_STANDALONE
-  AF_ENABLE_DEV_WARNINGS
   AF_WITH_CPUID
   CUDA_HOST_COMPILER
   CUDA_USE_STATIC_CUDA_RUNTIME
diff --git a/src/api/c/homography.cpp b/src/api/c/homography.cpp
index e929f1bd66..9d6f0f9a39 100644
--- a/src/api/c/homography.cpp
+++ b/src/api/c/homography.cpp
@@ -78,6 +78,8 @@ af_err af_homography(af_array* H, int* inliers, const af_array x_src,
 
         ARG_ASSERT(5, (inlier_thr >= 0.1f));
         ARG_ASSERT(6, (iterations > 0));
+        ARG_ASSERT(
+            7, (htype == AF_HOMOGRAPHY_RANSAC || htype == AF_HOMOGRAPHY_LMEDS));
 
         af_array outH;
         int outInl;
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 684866120c..e3da6a898b 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -76,10 +76,6 @@ else()
   target_sources(afcommon_interface INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/module_loading_unix.cpp)
 endif()
 
-if(AF_ENABLE_DEV_WARNINGS)
-  target_compile_definitions(afcommon_interface INTERFACE AF_WITH_DEV_WARNINGS)
-endif()
-
 target_link_libraries(afcommon_interface
   INTERFACE
     spdlog
diff --git a/src/backend/common/TemplateArg.cpp b/src/backend/common/TemplateArg.cpp
index 6c2066689f..436099412b 100644
--- a/src/backend/common/TemplateArg.cpp
+++ b/src/backend/common/TemplateArg.cpp
@@ -271,3 +271,16 @@ string toString(AF_BATCH_KIND val) {
 #undef CASE_STMT
     return retVal;
 }
+
+template<>
+string toString(af_homography_type val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(AF_HOMOGRAPHY_RANSAC);
+        CASE_STMT(AF_HOMOGRAPHY_LMEDS);
+    }
+#undef CASE_STMT
+    return retVal;
+}
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
index b38254d86d..8239a5033f 100644
--- a/src/backend/common/TemplateArg.hpp
+++ b/src/backend/common/TemplateArg.hpp
@@ -27,3 +27,4 @@ struct TemplateArg {
 #define DefineKey(arg) " -D " #arg
 #define DefineValue(arg) " -D " #arg "=" + toString(arg)
 #define DefineKeyValue(key, arg) " -D " #key "=" + toString(arg)
+#define DefineKeyFromStr(arg) toString(" -D " + std::string(arg))
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 468919c64e..dce1b15049 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -67,9 +67,14 @@ Kernel findKernel(const string& kernelName, const vector<string>& sources,
     transform(targs.begin(), targs.end(), back_inserter(args),
               [](const TemplateArg& arg) -> string { return arg._tparam; });
 
-    string tInstance = kernelName + "<" + args[0];
-    for (size_t i = 1; i < args.size(); ++i) { tInstance += ("," + args[i]); }
-    tInstance += ">";
+    string tInstance = kernelName;
+    if (args.size() > 0) {
+        tInstance = kernelName + "<" + args[0];
+        for (size_t i = 1; i < args.size(); ++i) {
+            tInstance += ("," + args[i]);
+        }
+        tInstance += ">";
+    }
 
     int device    = detail::getActiveDeviceId();
     Kernel kernel = lookupKernel(device, tInstance, sources);
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 8dd0a74d12..60b80b2f37 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -67,7 +67,6 @@ target_sources(afopencl
     binary.hpp
     blas.cpp
     blas.hpp
-    cache.hpp
     canny.cpp
     canny.hpp
     cast.hpp
@@ -171,8 +170,6 @@ target_sources(afopencl
     plot.hpp
     print.hpp
     product.cpp
-    program.cpp
-    program.hpp
     qr.cpp
     qr.hpp
     random_engine.cpp
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index 462cc95cd3..dc4f851e4f 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -7,11 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <approx.hpp>
+
 #include <kernel/approx.hpp>
-#include <af/dim4.hpp>
-#include <stdexcept>
 
 namespace opencl {
 template<typename Ty, typename Tp>
@@ -21,18 +19,18 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp, 1>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       offGrid, method);
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp, 2>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       offGrid, method);
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp, 3>(yo, yi, xo, xdim, xi_beg, xi_step,
-                                       offGrid, method);
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 3);
             break;
         default: break;
     }
@@ -47,22 +45,22 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp, 1>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                       ydim, yi_beg, yi_step, offGrid, method);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp, 2>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                       ydim, yi_beg, yi_step, offGrid, method);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp, 3>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                       ydim, yi_beg, yi_step, offGrid, method);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 3);
             break;
         default: break;
     }
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 541deac27f..b11a2398a9 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -45,7 +45,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
         p.strds[i] = dstStrds[i];
     }
 
-    Buffer* bPtrs[4];
+    cl::Buffer* bPtrs[4];
 
     std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
@@ -58,7 +58,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
             // alloc an 1-element buffer to avoid OpenCL from failing using
             // direct buffer allocation as opposed to mem manager to avoid
             // reference count desprepancies between different backends
-            static auto* empty = new Buffer(
+            static auto* empty = new cl::Buffer(
                 getContext(), CL_MEM_READ_ONLY,  // NOLINT(hicpp-signed-bitwise)
                 sizeof(uint));
             bPtrs[x] = empty;
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index 523e32f1c9..77a45a9c11 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -20,7 +20,7 @@ template<typename inType, typename outType, bool isColor>
 Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
                          const float &c_sigma) {
     Array<outType> out = createEmptyArray<outType>(in.dims());
-    kernel::bilateral<inType, outType, isColor>(out, in, s_sigma, c_sigma);
+    kernel::bilateral<inType, outType>(out, in, s_sigma, c_sigma, isColor);
     return out;
 }
 
diff --git a/src/backend/opencl/cache.hpp b/src/backend/opencl/cache.hpp
deleted file mode 100644
index 1b870a68c4..0000000000
--- a/src/backend/opencl/cache.hpp
+++ /dev/null
@@ -1,27 +0,0 @@
-/*******************************************************
- * Copyright (c) 2015, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <program.hpp>
-#include <map>
-#include <string>
-
-namespace cl {
-class Program;
-class Kernel;
-}  // namespace cl
-
-namespace opencl {
-struct kc_entry_t {
-    cl::Program* prog;
-    cl::Kernel* ker;
-};
-
-typedef std::map<std::string, kc_entry_t> kc_t;
-}  // namespace opencl
diff --git a/src/backend/opencl/compile_kernel.cpp b/src/backend/opencl/compile_kernel.cpp
index 39f750db97..15bf080cb9 100644
--- a/src/backend/opencl/compile_kernel.cpp
+++ b/src/backend/opencl/compile_kernel.cpp
@@ -11,14 +11,102 @@
 
 #include <cl2hpp.hpp>
 #include <common/defines.hpp>
+#include <common/util.hpp>
+#include <debug_opencl.hpp>
 #include <err_opencl.hpp>
+#include <kernel_headers/KParam.hpp>
 #include <platform.hpp>
-#include <program.hpp>
+#include <traits.hpp>
+
+#include <cstdio>
+#include <sstream>
+#include <string>
+#include <vector>
 
 using detail::Kernel;
+using std::ostringstream;
 using std::string;
 using std::vector;
 
+#define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
+    do {                                                                  \
+        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();      \
+        for (unsigned int i = 0; i < numDevices; ++i) {                   \
+            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
+                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                 \
+            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
+                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                 \
+        }                                                                 \
+    } while (0)
+
+#if defined(NDEBUG)
+
+#define SHOW_BUILD_INFO(PROG)                                              \
+    do {                                                                   \
+        std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");         \
+        if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(PROG); } \
+    } while (0)
+
+#else
+#define SHOW_BUILD_INFO(PROG) SHOW_DEBUG_BUILD_INFO(PROG)
+#endif
+
+namespace opencl {
+
+const static std::string DEFAULT_MACROS_STR(
+    "\n\
+                                           #ifdef USE_DOUBLE\n\
+                                           #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
+                                           #endif\n                     \
+                                           #ifdef USE_HALF\n\
+                                           #pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
+                                           #else\n                     \
+                                           #define half short\n          \
+                                           #endif\n                      \
+                                           #ifndef M_PI\n               \
+                                           #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
+                                           #endif\n                     \
+                                           ");
+
+cl::Program buildProgram(const std::vector<std::string> &kernelSources,
+                         const std::vector<std::string> &compileOpts) {
+    using std::begin;
+    using std::end;
+
+    cl::Program retVal;
+    try {
+        static const std::string defaults =
+            std::string(" -D dim_t=") +
+            std::string(dtype_traits<dim_t>::getName());
+
+        auto device = getDevice();
+
+        const std::string cl_std =
+            std::string(" -cl-std=CL") +
+            device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
+
+        cl::Program::Sources sources;
+        sources.emplace_back(DEFAULT_MACROS_STR);
+        sources.emplace_back(KParam_hpp, KParam_hpp_len);
+        sources.insert(end(sources), begin(kernelSources), end(kernelSources));
+
+        retVal = cl::Program(getContext(), sources);
+
+        ostringstream options;
+        for (auto &opt : compileOpts) { options << opt; }
+
+        retVal.build({device}, (cl_std + defaults + options.str()).c_str());
+    } catch (...) {
+        SHOW_BUILD_INFO(retVal);
+        throw;
+    }
+    return retVal;
+}
+
+}  // namespace opencl
+
 namespace common {
 
 Kernel compileKernel(const string &kernelName, const string &tInstance,
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 20bf749a18..e6692541ae 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -65,19 +65,14 @@ Array<T> copyArray(const Array<T> &A) {
 
 template<typename T>
 void multiply_inplace(Array<T> &in, double val) {
-    kernel::copy<T, T, true>(in, in, in.ndims(), scalar<T>(0), val);
+    kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val, true);
 }
 
 template<typename inType, typename outType>
 struct copyWrapper {
     void operator()(Array<outType> &out, Array<inType> const &in) {
-        if (in.dims() == out.dims()) {
-            kernel::copy<inType, outType, true>(out, in, in.ndims(),
-                                                scalar<outType>(0), 1);
-        } else {
-            kernel::copy<inType, outType, false>(out, in, in.ndims(),
-                                                 scalar<outType>(0), 1);
-        }
+        kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
+                                      1, in.dims() == out.dims());
     }
 };
 
@@ -92,11 +87,8 @@ struct copyWrapper<T, T> {
             getQueue().enqueueCopyBuffer(*in.get(), *out.get(), in_offset,
                                          out_offset, in.elements() * sizeof(T));
         } else {
-            if (in.dims() == out.dims()) {
-                kernel::copy<T, T, true>(out, in, in.ndims(), scalar<T>(0), 1);
-            } else {
-                kernel::copy<T, T, false>(out, in, in.ndims(), scalar<T>(0), 1);
-            }
+            kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1,
+                               in.dims() == out.dims());
         }
     }
 };
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index 347f2bc230..9f6b19bcae 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -54,21 +54,7 @@ Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
 
     auto ret = createEmptyArray<T>(oDims);
 
-    switch (btype) {
-        case AF_PAD_SYM:
-            kernel::padBorders<T, AF_PAD_SYM>(ret, in, lowerBoundPadding);
-            break;
-        case AF_PAD_CLAMP_TO_EDGE:
-            kernel::padBorders<T, AF_PAD_CLAMP_TO_EDGE>(ret, in,
-                                                        lowerBoundPadding);
-            break;
-        case AF_PAD_PERIODIC:
-            kernel::padBorders<T, AF_PAD_PERIODIC>(ret, in, lowerBoundPadding);
-            break;
-        default:
-            kernel::padBorders<T, AF_PAD_ZERO>(ret, in, lowerBoundPadding);
-            break;
-    }
+    kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
 
     return ret;
 }
diff --git a/src/backend/opencl/debug_opencl.hpp b/src/backend/opencl/debug_opencl.hpp
index 078eacea72..81bc51dce0 100644
--- a/src/backend/opencl/debug_opencl.hpp
+++ b/src/backend/opencl/debug_opencl.hpp
@@ -9,14 +9,14 @@
 
 #pragma once
 
+#include <platform.hpp>
+
 #ifndef NDEBUG
 
 #define CL_DEBUG_FINISH(Q) Q.finish()
 
 #else
 
-#include <platform.hpp>
-
 #define CL_DEBUG_FINISH(Q)                               \
     do {                                                 \
         if (opencl::synchronize_calls()) { Q.finish(); } \
diff --git a/src/backend/opencl/diff.cpp b/src/backend/opencl/diff.cpp
index e604404ee1..8c99eee837 100644
--- a/src/backend/opencl/diff.cpp
+++ b/src/backend/opencl/diff.cpp
@@ -14,8 +14,9 @@
 #include <stdexcept>
 
 namespace opencl {
-template<typename T, bool isDiff2>
-static Array<T> diff(const Array<T> &in, const int dim) {
+
+template<typename T>
+Array<T> diff(const Array<T> &in, const int dim, const bool isDiff2) {
     const af::dim4 &iDims = in.dims();
     af::dim4 oDims        = iDims;
     oDims[dim] -= (isDiff2 + 1);
@@ -23,28 +24,19 @@ static Array<T> diff(const Array<T> &in, const int dim) {
     if (iDims.elements() == 0 || oDims.elements() == 0) {
         throw std::runtime_error("Elements are 0");
     }
-
     Array<T> out = createEmptyArray<T>(oDims);
-
-    switch (dim) {
-        case 0: kernel::diff<T, 0, isDiff2>(out, in, in.ndims()); break;
-        case 1: kernel::diff<T, 1, isDiff2>(out, in, in.ndims()); break;
-        case 2: kernel::diff<T, 2, isDiff2>(out, in, in.ndims()); break;
-        case 3: kernel::diff<T, 3, isDiff2>(out, in, in.ndims()); break;
-        default: AF_ERROR("dim only supports values 0-3.", AF_ERR_UNKNOWN);
-    }
-
+    kernel::diff<T>(out, in, in.ndims(), dim, isDiff2);
     return out;
 }
 
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim) {
-    return diff<T, false>(in, dim);
+    return diff<T>(in, dim, false);
 }
 
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim) {
-    return diff<T, true>(in, dim);
+    return diff<T>(in, dim, true);
 }
 
 #define INSTANTIATE(T)                                             \
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index f24bcced3f..faf9914b96 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -29,8 +29,8 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
     Param y;
     Param score;
 
-    kernel::fast_dispatch<T>(arc_length, non_max, &nfeat, x, y, score, in, thr,
-                             feature_ratio, edge);
+    kernel::fast<T>(arc_length, &nfeat, x, y, score, in, thr, feature_ratio,
+                    edge, non_max);
 
     if (nfeat > 0) {
         x_out     = createParamArray<float>(x, true);
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 40f4621660..a8eb53506e 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -26,9 +26,8 @@ Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
     dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
     Array<outType> out = createValueArray<outType>(outDims, outType(0));
 
-    kernel::histogram<inType, outType, isLinear>(out, in, nbins, minval,
-                                                 maxval);
-
+    kernel::histogram<inType, outType>(out, in, nbins, minval, maxval,
+                                       isLinear);
     return out;
 }
 
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 229678f700..3b598b0275 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -7,29 +7,28 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
+#include <homography.hpp>
+
 #include <arith.hpp>
-#include <err_opencl.hpp>
 #include <kernel/homography.hpp>
 #include <af/dim4.hpp>
-#include <algorithm>
 
-#include <cfloat>
+#include <algorithm>
 
 using af::dim4;
 
 namespace opencl {
 
-#define RANSACConfidence 0.99f
-#define LMEDSConfidence 0.99f
-#define LMEDSOutlierRatio 0.4f
-
 template<typename T>
 int homography(Array<T> &bestH, const Array<float> &x_src,
                const Array<float> &y_src, const Array<float> &x_dst,
                const Array<float> &y_dst, const Array<float> &initial,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations) {
+    // constexpr float RANSACConfidence  = 0.99f;
+    constexpr float LMEDSConfidence   = 0.99f;
+    constexpr float LMEDSOutlierRatio = 0.4f;
+
     const af::dim4 &idims   = x_src.dims();
     const unsigned nsamples = idims[0];
 
@@ -57,19 +56,8 @@ int homography(Array<T> &bestH, const Array<float> &x_src,
         createValueArray<T>(af::dim4(9, iter_sz), static_cast<T>(0));
 
     bestH = createValueArray<T>(af::dim4(3, 3), static_cast<T>(0));
-    switch (htype) {
-        case AF_HOMOGRAPHY_RANSAC:
-            return kernel::computeH<T, AF_HOMOGRAPHY_RANSAC>(
-                bestH, tmpH, err, x_src, y_src, x_dst, y_dst, rnd, iter,
-                nsamples, inlier_thr);
-            break;
-        case AF_HOMOGRAPHY_LMEDS:
-            return kernel::computeH<T, AF_HOMOGRAPHY_LMEDS>(
-                bestH, tmpH, err, x_src, y_src, x_dst, y_dst, rnd, iter,
-                nsamples, inlier_thr);
-            break;
-        default: return -1; break;
-    }
+    return kernel::computeH<T>(bestH, tmpH, err, x_src, y_src, x_dst, y_dst,
+                               rnd, iter, nsamples, inlier_thr, htype);
 }
 
 #define INSTANTIATE(T)                                                     \
diff --git a/src/backend/opencl/hsv_rgb.cpp b/src/backend/opencl/hsv_rgb.cpp
index 4af64ee10f..5ca8521236 100644
--- a/src/backend/opencl/hsv_rgb.cpp
+++ b/src/backend/opencl/hsv_rgb.cpp
@@ -7,31 +7,23 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_opencl.hpp>
 #include <hsv_rgb.hpp>
-#include <kernel/hsv_rgb.hpp>
-#include <af/dim4.hpp>
 
-using af::dim4;
+#include <kernel/hsv_rgb.hpp>
 
 namespace opencl {
 
 template<typename T>
 Array<T> hsv2rgb(const Array<T>& in) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    kernel::hsv2rgb_convert<T, true>(out, in);
-
+    kernel::hsv2rgb_convert<T>(out, in, true);
     return out;
 }
 
 template<typename T>
 Array<T> rgb2hsv(const Array<T>& in) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    kernel::hsv2rgb_convert<T, false>(out, in);
-
+    kernel::hsv2rgb_convert<T>(out, in, false);
     return out;
 }
 
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 2478484977..5433401387 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -43,7 +43,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
         p.strds[i] = iStrds[i];
     }
 
-    Buffer* bPtrs[4];
+    cl::Buffer* bPtrs[4];
 
     std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index 6a60cc0c97..04ce54aa56 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -36,7 +36,7 @@ void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
 
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in) {
-    return kernel::ireduce_all<T, op>(loc, in);
+    return kernel::ireduceAll<T, op>(loc, in);
 }
 
 #define INSTANTIATE(ROp, T)                                           \
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 09c6399d7a..67f1c025ab 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -9,20 +9,25 @@
 
 #include <Array.hpp>
 #include <common/Logger.hpp>
+#include <common/compile_kernel.hpp>
 #include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
+#include <common/kernel_cache.hpp>
+#include <common/util.hpp>
 #include <copy.hpp>
+#include <device_manager.hpp>
 #include <err_opencl.hpp>
 #include <kernel_headers/jit.hpp>
-#include <program.hpp>
 #include <af/dim4.hpp>
 #include <af/opencl.h>
 
 #include <chrono>
 #include <functional>
+#include <map>
 #include <stdexcept>
 #include <vector>
 
+using common::compileKernel;
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
@@ -36,6 +41,7 @@ using cl::NullRange;
 using cl::Program;
 
 using std::hash;
+using std::map;
 using std::string;
 using std::stringstream;
 using std::vector;
@@ -171,44 +177,60 @@ static string getKernelString(const string &funcName,
     return kerStream.str();
 }
 
-static Kernel getKernel(const vector<Node *> &output_nodes,
-                        const vector<int> &output_ids,
-                        const vector<const Node *> &full_nodes,
-                        const vector<Node_ids> &full_ids,
-                        const bool is_linear) {
-    string funcName =
-        getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    int device = getActiveDeviceId();
+static cl::Kernel getKernel(const vector<Node *> &output_nodes,
+                            const vector<int> &output_ids,
+                            const vector<const Node *> &full_nodes,
+                            const vector<Node_ids> &full_ids,
+                            const bool is_linear) {
+    using kc_t = map<string, Kernel>;
 
-    kc_entry_t entry = kernelCache(device, funcName);
+    static const string jit(jit_cl, jit_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        string jit_ker = getKernelString(funcName, full_nodes, full_ids,
-                                         output_ids, is_linear);
-        saveKernel(funcName, jit_ker, ".cl");
-        const char *ker_strs[] = {jit_cl, jit_ker.c_str()};
-        const int ker_lens[]   = {jit_cl_len, static_cast<int>(jit_ker.size())};
+    thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
 
-        Program prog;
-        string options =
-            (isDoubleSupported(device) ? string(" -D USE_DOUBLE")
-                                       : string("")) +
-            (isHalfSupported(device) ? string(" -D USE_HALF") : string(""));
-        auto compileBegin = high_resolution_clock::now();
-        buildProgram(prog, 2, ker_strs, ker_lens, options);
-        auto compileEnd = high_resolution_clock::now();
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, funcName.c_str());
+    string funcName =
+        getFuncName(output_nodes, full_nodes, full_ids, is_linear);
+    int device = getActiveDeviceId();
 
-        addKernelToCache(device, funcName, entry);
+    auto idx = kernelCaches[device].find(funcName);
+    Kernel entry{nullptr, nullptr};
+
+    if (idx == kernelCaches[device].end()) {
+        string jitKer = getKernelString(funcName, full_nodes, full_ids,
+                                        output_ids, is_linear);
+#ifdef AF_CACHE_KERNELS_TO_DISK
+        // TODO(pradeep) load jit kernels cached to disk
+#endif
+        if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
+            saveKernel(funcName, jitKer, ".cl");
+
+            vector<string> options;
+            if (isDoubleSupported(device)) {
+                options.emplace_back(DefineKey(USE_DOUBLE));
+            }
+            if (isHalfSupported(device)) {
+                options.emplace_back(DefineKey(USE_HALF));
+            }
 
-        AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", funcName,
-                 duration_cast<milliseconds>(compileEnd - compileBegin).count(),
-                 options, getDevice(device).getInfo<CL_DEVICE_NAME>());
+            auto compileBegin = high_resolution_clock::now();
+            // First argument, funcName, is important.
+            // From jit, second argument can be null as it is not used for
+            // OpenCL
+            entry = compileKernel(funcName, "", {jit, jitKer}, options, true);
+            auto compileEnd = high_resolution_clock::now();
+
+            AF_TRACE(
+                "{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", funcName,
+                duration_cast<milliseconds>(compileEnd - compileBegin).count(),
+                fmt::join(options, " "),
+                getDevice(device).getInfo<CL_DEVICE_NAME>());
+        }
+        kernelCaches[device][funcName] = entry;
+    } else {
+        entry = idx->second;
     }
 
-    return *entry.ker;
+    return *entry.getKernel();
 }
 
 void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
@@ -242,7 +264,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
         is_linear &= node->isLinear(outputs[0].info.dims);
     }
 
-    Kernel ker =
+    cl::Kernel ker =
         getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
 
     uint local_0   = 1;
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.cl b/src/backend/opencl/kernel/anisotropic_diffusion.cl
index 950a119323..82077791f6 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.cl
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.cl
@@ -63,8 +63,8 @@ float gradientUpdate(const float mct, const float C, const float S,
 
 float curvatureUpdate(const float mct, const float C, const float S,
                       const float N, const float W, const float E,
-                      const float SE, const float SW,
-                      const float NE, const float NW) {
+                      const float SE, const float SW, const float NE,
+                      const float NW) {
     float delta     = 0;
     float prop_grad = 0;
 
@@ -118,8 +118,8 @@ float curvatureUpdate(const float mct, const float C, const float S,
     return sqrt(prop_grad) * delta;
 }
 
-kernel void diffUpdate(global T* inout, KParam info, const float dt,
-                       const float mct, unsigned blkX, unsigned blkY) {
+kernel void aisoDiffUpdate(global T* inout, KParam info, const float dt,
+                           const float mct, unsigned blkX, unsigned blkY) {
     local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
     const int l0 = info.dims[0];
@@ -134,7 +134,7 @@ kernel void diffUpdate(global T* inout, KParam info, const float dt,
     const int b3 = get_group_id(1) / blkY;
 
     const int gx = get_local_size(0) * (get_group_id(0) - b2 * blkX) + lx;
-          int gy = get_local_size(1) * (get_group_id(1) - b3 * blkY) + ly;
+    int gy       = get_local_size(1) * (get_group_id(1) - b3 * blkY) + ly;
 
     global T* img =
         inout + (b3 * info.strides[3] + b2 * info.strides[2]) + info.offset;
@@ -143,30 +143,30 @@ kernel void diffUpdate(global T* inout, KParam info, const float dt,
          b += get_local_size(1), gy2 += get_local_size(1)) {
         for (int a = lx, gx2 = gx - 1; a < SHRD_MEM_WIDTH;
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            localMem[b][a] = img[ gIndex(gx2, gy2, l0, l1, s0, s1) ];
+            localMem[b][a] = img[gIndex(gx2, gy2, l0, l1, s0, s1)];
         }
     }
 
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    int i       = lx + 1;
-    int j       = ly + 1;
+    int i = lx + 1;
+    int j = ly + 1;
 
 #pragma unroll
     for (int ld = 0; ld < YDIM_LOAD;
-            ++ld, j+= get_local_size(1), gy += get_local_size(1)) {
+         ++ld, j += get_local_size(1), gy += get_local_size(1)) {
         float C     = localMem[j][i];
         float delta = 0;
 #if IS_MCDE == 1
-        delta = curvatureUpdate(
-            mct, C, localMem[j][i + 1], localMem[j][i - 1], localMem[j - 1][i],
-            localMem[j + 1][i], localMem[j + 1][i + 1], localMem[j - 1][i + 1],
-            localMem[j + 1][i - 1], localMem[j - 1][i - 1]);
+        delta = curvatureUpdate(mct, C, localMem[j][i + 1], localMem[j][i - 1],
+                                localMem[j - 1][i], localMem[j + 1][i],
+                                localMem[j + 1][i + 1], localMem[j - 1][i + 1],
+                                localMem[j + 1][i - 1], localMem[j - 1][i - 1]);
 #else
-        delta = gradientUpdate(
-            mct, C, localMem[j][i + 1], localMem[j][i - 1], localMem[j - 1][i],
-            localMem[j + 1][i], localMem[j + 1][i + 1], localMem[j - 1][i + 1],
-            localMem[j + 1][i - 1], localMem[j - 1][i - 1]);
+        delta = gradientUpdate(mct, C, localMem[j][i + 1], localMem[j][i - 1],
+                               localMem[j - 1][i], localMem[j + 1][i],
+                               localMem[j + 1][i + 1], localMem[j - 1][i + 1],
+                               localMem[j + 1][i - 1], localMem[j - 1][i - 1]);
 #endif
         if (gx < l0 && gy < l1) {
             img[gx * s0 + gy * s1] = (T)(C + delta * dt);
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index 91cd393bce..d1b725cfce 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -8,73 +8,65 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/anisotropic_diffusion.hpp>
-#include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
+
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-constexpr int THREADS_X = 32;
-constexpr int THREADS_Y = 8;
-constexpr int YDIM_LOAD = 2 * THREADS_X / THREADS_Y;
 
 template<typename T, bool isMCDE>
 void anisotropicDiffusion(Param inout, const float dt, const float mct,
                           const int fluxFnCode) {
-    using cl::Buffer;
     using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
     using cl::NDRange;
-    using cl::Program;
+    using std::string;
+    using std::vector;
 
-    std::string kerKeyStr = std::string("anisotropic_diffusion_") +
-                            std::string(dtype_traits<T>::getName()) + "_" +
-                            std::to_string(isMCDE) + "_" +
-                            std::to_string(fluxFnCode);
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
+    constexpr int YDIM_LOAD = 2 * THREADS_X / THREADS_Y;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, kerKeyStr);
+    static const string src(anisotropic_diffusion_cl,
+                            anisotropic_diffusion_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D SHRD_MEM_HEIGHT=" << (THREADS_Y * YDIM_LOAD + 2)
-                << " -D SHRD_MEM_WIDTH=" << (THREADS_X + 2)
-                << " -D IS_MCDE=" << isMCDE << " -D FLUX_FN=" << fluxFnCode
-                << " -D YDIM_LOAD=" << YDIM_LOAD;
-        options << getTypeBuildDefinition<T>();
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(isMCDE),
+        TemplateArg(fluxFnCode),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(SHRD_MEM_HEIGHT, (THREADS_Y * YDIM_LOAD + 2)),
+        DefineKeyValue(SHRD_MEM_WIDTH, (THREADS_X + 2)),
+        DefineKeyValue(IS_MCDE, isMCDE),
+        DefineKeyValue(FLUX_FN, fluxFnCode),
+        DefineValue(YDIM_LOAD),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        const char *ker_strs[] = {anisotropic_diffusion_cl};
-        const int ker_lens[]   = {anisotropic_diffusion_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "diffUpdate");
-        addKernelToCache(device, kerKeyStr, entry);
-    }
+    auto diffUpdate =
+        common::findKernel("aisoDiffUpdate", {src}, tmpltArgs, compileOpts);
 
-    auto diffUpdateOp =
-        KernelFunctor<Buffer, KParam, float, float, unsigned, unsigned>(
-            *entry.ker);
+    NDRange local(THREADS_X, THREADS_Y, 1);
 
-    NDRange threads(THREADS_X, THREADS_Y, 1);
+    int blkX = divup(inout.info.dims[0], local[0]);
+    int blkY = divup(inout.info.dims[1], local[1] * YDIM_LOAD);
 
-    int blkX = divup(inout.info.dims[0], threads[0]);
-    int blkY = divup(inout.info.dims[1], threads[1] * YDIM_LOAD);
+    NDRange global(local[0] * blkX * inout.info.dims[2],
+                   local[1] * blkY * inout.info.dims[3], 1);
 
-    NDRange global(threads[0] * blkX * inout.info.dims[2],
-                   threads[1] * blkY * inout.info.dims[3], 1);
-
-    diffUpdateOp(EnqueueArgs(getQueue(), global, threads), *inout.data,
-                 inout.info, dt, mct, blkX, blkY);
+    diffUpdate(EnqueueArgs(getQueue(), global, local), *inout.data, inout.info,
+               dt, mct, blkX, blkY);
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 44623c961e..dd71bbcf45 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -8,94 +8,76 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/interp.hpp>
 #include <kernel_headers/approx1.hpp>
 #include <kernel_headers/approx2.hpp>
 #include <kernel_headers/interp.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <string>
-#include "config.hpp"
-#include "interp.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int TX = 16;
-static const int TY = 16;
 
-static const int THREADS = 256;
+inline std::string interpSrc() {
+    static const std::string src(interp_cl, interp_cl_len);
+    return src;
+}
+
+template<typename Ty, typename Tp>
+auto genCompileOptions(const int order) {
+    constexpr bool isComplex =
+        static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c32 ||
+        static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c64;
 
-template<typename Ty, typename Tp, int order>
-std::string generateOptionsString() {
     ToNumStr<Ty> toNumStr;
 
-    std::ostringstream options;
-    options << " -D Ty=" << dtype_traits<Ty>::getName()
-            << " -D Tp=" << dtype_traits<Tp>::getName()
-            << " -D InterpInTy=" << dtype_traits<Ty>::getName()
-            << " -D InterpValTy=" << dtype_traits<Ty>::getName()
-            << " -D InterpPosTy=" << dtype_traits<Tp>::getName()
-            << " -D ZERO=" << toNumStr(scalar<Ty>(0));
-
-    if (static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c32 ||
-        static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c64) {
-        options << " -D IS_CPLX=1";
-    } else {
-        options << " -D IS_CPLX=0";
-    }
-    options << getTypeBuildDefinition<Ty>();
-
-    options << " -D INTERP_ORDER=" << order;
-    addInterpEnumOptions(options);
-
-    return options.str();
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Ty, dtype_traits<Ty>::getName()),
+        DefineKeyValue(Tp, dtype_traits<Tp>::getName()),
+        DefineKeyValue(InterpInTy, dtype_traits<Ty>::getName()),
+        DefineKeyValue(InterpValTy, dtype_traits<Ty>::getName()),
+        DefineKeyValue(InterpPosTy, dtype_traits<Tp>::getName()),
+        DefineKeyValue(ZERO, toNumStr(scalar<Ty>(0))),
+        DefineKeyValue(INTERP_ORDER, order),
+        DefineKeyValue(IS_CPLX, (isComplex ? 1 : 0)),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ty>());
+    addInterpEnumOptions(compileOpts);
+
+    return compileOpts;
 }
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
-template<typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp>
 void approx1(Param yo, const Param yi, const Param xo, const int xdim,
              const Tp xi_beg, const Tp xi_step, const float offGrid,
-             af_interp_type method) {
-    std::string refName = std::string("approx1_kernel_") +
-                          std::string(dtype_traits<Ty>::getName()) +
-                          std::string(dtype_traits<Tp>::getName()) +
-                          std::to_string(order);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+             const af_interp_type method, const int order) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string options = generateOptionsString<Ty, Tp, order>();
+    constexpr int THREADS = 256;
 
-        const char *ker_strs[] = {interp_cl, approx1_cl};
-        const int ker_lens[]   = {interp_cl_len, approx1_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "approx1_kernel");
+    static const string src(approx1_cl, approx1_cl_len);
 
-        addKernelToCache(device, refName, entry);
-    }
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ty>(),
+        TemplateTypename<Tp>(),
+        TemplateArg(order),
+    };
+    auto compileOpts = genCompileOptions<Ty, Tp>(order);
 
-    auto approx1Op =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const Buffer, const KParam, const int, const Tp, const Tp,
-                      const Ty, const int, const int, const int>(*entry.ker);
+    auto approx1 = common::findKernel("approx1", {interpSrc(), src}, tmpltArgs,
+                                      compileOpts);
 
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
@@ -106,45 +88,37 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
     bool batch =
         !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
-    approx1Op(EnqueueArgs(getQueue(), global, local), *yo.data, yo.info,
-              *yi.data, yi.info, *xo.data, xo.info, xdim, xi_beg, xi_step,
-              scalar<Ty>(offGrid), blocksPerMat, (int)batch, (int)method);
-
+    approx1(EnqueueArgs(getQueue(), global, local), *yo.data, yo.info, *yi.data,
+            yi.info, *xo.data, xo.info, xdim, xi_beg, xi_step,
+            scalar<Ty>(offGrid), (int)blocksPerMat, (int)batch, (int)method);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp>
 void approx2(Param zo, const Param zi, const Param xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, const Param yo,
              const int ydim, const Tp &yi_beg, const Tp &yi_step,
-             const float offGrid, af_interp_type method) {
-    std::string refName = std::string("approx2_kernel_") +
-                          std::string(dtype_traits<Ty>::getName()) +
-                          std::string(dtype_traits<Tp>::getName()) +
-                          std::to_string(order);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string options = generateOptionsString<Ty, Tp, order>();
-
-        const char *ker_strs[] = {interp_cl, approx2_cl};
-        const int ker_lens[]   = {interp_cl_len, approx2_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "approx2_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto approx2Op =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const Buffer, const KParam, const int, const Buffer,
-                      const KParam, const int, const Tp, const Tp, const Tp,
-                      const Tp, const Ty, const int, const int, const int,
-                      const int>(*entry.ker);
+             const float offGrid, const af_interp_type method,
+             const int order) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+
+    static const string src(approx2_cl, approx2_cl_len);
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ty>(),
+        TemplateTypename<Tp>(),
+        TemplateArg(order),
+    };
+    auto compileOpts = genCompileOptions<Ty, Tp>(order);
+
+    auto approx2 = common::findKernel("approx2", {interpSrc(), src}, tmpltArgs,
+                                      compileOpts);
 
     NDRange local(TX, TY, 1);
     dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
@@ -155,11 +129,11 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     // Passing bools to opencl kernels is not allowed
     bool batch = !(xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
-    approx2Op(EnqueueArgs(getQueue(), global, local), *zo.data, zo.info,
-              *zi.data, zi.info, *xo.data, xo.info, xdim, *yo.data, yo.info,
-              ydim, xi_beg, xi_step, yi_beg, yi_step, scalar<Ty>(offGrid),
-              blocksPerMatX, blocksPerMatY, (int)batch, (int)method);
-
+    approx2(EnqueueArgs(getQueue(), global, local), *zo.data, zo.info, *zi.data,
+            zi.info, *xo.data, xo.info, xdim, *yo.data, yo.info, ydim, xi_beg,
+            xi_step, yi_beg, yi_step, scalar<Ty>(offGrid),
+            static_cast<int>(blocksPerMatX), static_cast<int>(blocksPerMatY),
+            static_cast<int>(batch), static_cast<int>(method));
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index 1e7da75f18..2b22dc7313 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -7,12 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void approx1_kernel(__global Ty *d_yo, const KParam yo,
-                             __global const Ty *d_yi, const KParam yi,
-                             __global const Tp *d_xo, const KParam xo,
-                             const int xdim, const Tp xi_beg, const Tp xi_step,
-                             const Ty offGrid, const int blocksMatX,
-                             const int batch, const int method) {
+kernel void approx1(global Ty *d_yo, const KParam yo, global const Ty *d_yi,
+                    const KParam yi, global const Tp *d_xo, const KParam xo,
+                    const int xdim, const Tp xi_beg, const Tp xi_step,
+                    const Ty offGrid, const int blocksMatX, const int batch,
+                    const int method) {
     const int idw = get_group_id(1) / yo.dims[2];
     const int idz = get_group_id(1) - idw * yo.dims[2];
 
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index b22e6f9c04..bb544ce807 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -7,12 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void approx2_kernel(
-    __global Ty *d_zo, const KParam zo, __global const Ty *d_zi,
-    const KParam zi, __global const Tp *d_xo, const KParam xo, const int xdim,
-    __global const Tp *d_yo, const KParam yo, const int ydim, const Tp xi_beg,
-    const Tp xi_step, const Tp yi_beg, const Tp yi_step, const Ty offGrid,
-    const int blocksMatX, const int blocksMatY, const int batch, int method) {
+kernel void approx2(global Ty *d_zo, const KParam zo, global const Ty *d_zi,
+                    const KParam zi, global const Tp *d_xo, const KParam xo,
+                    const int xdim, global const Tp *d_yo, const KParam yo,
+                    const int ydim, const Tp xi_beg, const Tp xi_step,
+                    const Tp yi_beg, const Tp yi_step, const Ty offGrid,
+                    const int blocksMatX, const int blocksMatY, const int batch,
+                    int method) {
     const int idz = get_group_id(0) / blocksMatX;
     const int idw = get_group_id(1) / blocksMatY;
 
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 4f4b69b356..d1e60d4032 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -8,27 +8,19 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/assign.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
 
 typedef struct {
     int offs[4];
@@ -38,44 +30,33 @@ typedef struct {
 
 template<typename T>
 void assign(Param out, const Param in, const AssignKernelParam_t& p,
-            Buffer* bPtr[4]) {
-    std::string refName =
-        std::string("assignKernel_") + std::string(dtype_traits<T>::getName());
+            cl::Buffer* bPtr[4]) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(assign_cl, assign_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {assign_cl};
-        const int ker_lens[]   = {assign_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "assignKernel");
+    auto assign = common::findKernel("assignKernel", {src}, targs, options);
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                   blk_y * in.info.dims[3] * THREADS_Y);
-
-    auto assignOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, AssignKernelParam_t,
-                      Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
-
-    assignOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3],
-             blk_x, blk_y);
+    cl::NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                       blk_y * in.info.dims[3] * THREADS_Y);
 
+    assign(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x,
+           blk_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/bilateral.cl b/src/backend/opencl/kernel/bilateral.cl
index e435d15b0f..af6bb11143 100644
--- a/src/backend/opencl/kernel/bilateral.cl
+++ b/src/backend/opencl/kernel/bilateral.cl
@@ -17,7 +17,7 @@ int lIdx(int x, int y, int stride1, int stride0) {
     return (y * stride1 + x * stride0);
 }
 
-void load2LocalMem(__local outType* shrd, __global const inType* in, int lx,
+void load2LocalMem(local outType* shrd, global const inType* in, int lx,
                    int ly, int shrdStride, int dim0, int dim1, int gx, int gy,
                    int inStride1, int inStride0) {
     int gx_ = clamp(gx, 0, dim0 - 1);
@@ -26,9 +26,9 @@ void load2LocalMem(__local outType* shrd, __global const inType* in, int lx,
         (outType)in[lIdx(gx_, gy_, inStride1, inStride0)];
 }
 
-__kernel void bilateral(__global outType* d_dst, KParam oInfo,
-                        __global const inType* d_src, KParam iInfo,
-                        __local outType* localMem, __local outType* gauss2d,
+kernel void bilateral(global outType* d_dst, KParam oInfo,
+                        global const inType* d_src, KParam iInfo,
+                        local outType* localMem, __local outType* gauss2d,
                         float sigma_space, float sigma_color, int gaussOff,
                         int nBBS0, int nBBS1) {
     const int radius                    = max((int)(sigma_space * 1.5f), 1);
@@ -43,9 +43,9 @@ __kernel void bilateral(__global outType* d_dst, KParam oInfo,
     // gfor batch offsets
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
-    __global const inType* in =
+    global const inType* in =
         d_src + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
-    __global outType* out =
+    global outType* out =
         d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     int lx = get_local_id(0);
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index c69f2e7837..bf81091bcf 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -10,73 +10,51 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel_headers/bilateral.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <af/opencl.h>
+
 #include <algorithm>
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
 
-template<typename inType, typename outType, bool isColor>
-void bilateral(Param out, const Param in, float s_sigma, float c_sigma) {
-    std::string refName = std::string("bilateral_") +
-                          std::string(dtype_traits<inType>::getName()) +
-                          std::string(dtype_traits<outType>::getName()) +
-                          std::to_string(isColor);
+template<typename inType, typename outType>
+void bilateral(Param out, const Param in, const float s_sigma,
+               const float c_sigma, const bool isColor) {
+    constexpr int THREADS_X     = 16;
+    constexpr int THREADS_Y     = 16;
+    constexpr bool UseNativeExp = !std::is_same<inType, double>::value ||
+                                  std::is_same<inType, cdouble>::value;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(bilateral_cl, bilateral_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D inType=" << dtype_traits<inType>::getName()
-                << " -D outType=" << dtype_traits<outType>::getName();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<inType>(),
+        TemplateTypename<outType>(),
+        TemplateArg(isColor),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(inType, dtype_traits<inType>::getName()),
+        DefineKeyValue(outType, dtype_traits<outType>::getName()),
+    };
+    if (UseNativeExp) { options.emplace_back(DefineKey(USE_NATIVE_EXP)); }
+    options.emplace_back(getTypeBuildDefinition<inType>());
 
-        options << getTypeBuildDefinition<inType>();
-        if (!std::is_same<inType, double>::value ||
-            std::is_same<inType, cdouble>::value) {
-            options << " -D USE_NATIVE_EXP";
-        }
+    auto bilateralOp = common::findKernel("bilateral", {src}, targs, options);
 
-        const char* ker_strs[] = {bilateral_cl};
-        const int ker_lens[]   = {bilateral_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "bilateral");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto bilateralOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, LocalSpaceArg,
-                      LocalSpaceArg, float, float, int, int, int>(*entry.ker);
-
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                   blk_y * in.info.dims[3] * THREADS_Y);
+    cl::NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                       blk_y * in.info.dims[3] * THREADS_Y);
 
     // calculate local memory size
     int radius          = (int)std::max(s_sigma * 1.5f, 1.f);
@@ -93,11 +71,10 @@ void bilateral(Param out, const Param in, float s_sigma, float c_sigma) {
         OPENCL_NOT_SUPPORTED(errMessage);
     }
 
-    bilateralOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    bilateralOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                 *in.data, in.info, cl::Local(num_shrd_elems * sizeof(outType)),
                 cl::Local(num_gauss_elems * sizeof(outType)), s_sigma, c_sigma,
                 num_shrd_elems, blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 2f4f3a44cd..588356d065 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -35,15 +35,15 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     using std::vector;
 
     static const string src(nonmax_suppression_cl, nonmax_suppression_cl_len);
-    vector<string> compileOpts = {
+    vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(SHRD_MEM_HEIGHT, THREADS_X + 2),
         DefineKeyValue(SHRD_MEM_WIDTH, THREADS_Y + 2),
     };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(getTypeBuildDefinition<T>());
 
     auto nonMaxOp = common::findKernel("nonMaxSuppressionKernel", {src},
-                                       {TemplateTypename<T>()}, compileOpts);
+                                       {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -70,14 +70,14 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
 
     static const string src(trace_edge_cl, trace_edge_cl_len);
 
-    vector<string> compileOpts = {
+    vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKey(INIT_EDGE_OUT),
     };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(getTypeBuildDefinition<T>());
 
     auto initOp = common::findKernel("initEdgeOutKernel", {src},
-                                     {TemplateTypename<T>()}, compileOpts);
+                                     {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -104,14 +104,14 @@ void suppressLeftOver(Param output) {
 
     static const string src(trace_edge_cl, trace_edge_cl_len);
 
-    vector<string> compileOpts = {
+    vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKey(SUPPRESS_LEFT_OVER),
     };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(getTypeBuildDefinition<T>());
 
     auto finalOp = common::findKernel("suppressLeftOverKernel", {src},
-                                      {TemplateTypename<T>()}, compileOpts);
+                                      {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -138,17 +138,17 @@ void edgeTrackingHysteresis(Param output, const Param strong,
 
     static const string src(trace_edge_cl, trace_edge_cl_len);
 
-    vector<string> compileOpts = {
+    vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKey(EDGE_TRACER),
         DefineKeyValue(SHRD_MEM_HEIGHT, THREADS_X + 2),
         DefineKeyValue(SHRD_MEM_WIDTH, THREADS_Y + 2),
         DefineKeyValue(TOTAL_NUM_THREADS, THREADS_X * THREADS_Y),
     };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(getTypeBuildDefinition<T>());
 
     auto edgeTraceOp = common::findKernel("edgeTrackKernel", {src},
-                                          {TemplateTypename<T>()}, compileOpts);
+                                          {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index bd01a2eac2..06c620de20 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -17,9 +17,9 @@ namespace kernel {
 // below shared MAX_*_LEN's are calculated based on
 // a maximum shared memory configuration of 48KB per block
 // considering complex types as well
-static const int MAX_CONV1_FILTER_LEN = 129;
-static const int MAX_CONV2_FILTER_LEN = 17;
-static const int MAX_CONV3_FILTER_LEN = 5;
+constexpr int MAX_CONV1_FILTER_LEN = 129;
+constexpr int MAX_CONV2_FILTER_LEN = 17;
+constexpr int MAX_CONV3_FILTER_LEN = 5;
 
 /*
  * convolution kernel wrappers are split to multiple files to
diff --git a/src/backend/opencl/kernel/convolve/conv1.cpp b/src/backend/opencl/kernel/convolve/conv1.cpp
index 7a3b434c10..8992c9d5f5 100644
--- a/src/backend/opencl/kernel/convolve/conv1.cpp
+++ b/src/backend/opencl/kernel/convolve/conv1.cpp
@@ -10,7 +10,6 @@
 #include <kernel/convolve/conv_common.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 template<typename T, typename aT, bool expand>
@@ -67,5 +66,4 @@ INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_b8.cpp b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
index 75b34e5459..c9e61d1fee 100644
--- a/src/backend/opencl/kernel/convolve/conv2_b8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(char, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_c32.cpp b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
index d498dfeb7d..53b05d2cea 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(cfloat, cfloat)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_c64.cpp b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
index 5996ce5e4f..e8a5af8a4f 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(cdouble, cdouble)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_f32.cpp b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
index 48bbc3f055..2f92484942 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(float, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_f64.cpp b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
index 50b3bcc2b7..84dd2ac4bb 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(double, double)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 961ba3dc00..55ca7f7ae2 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -7,71 +7,59 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cache.hpp>
+#pragma once
+
+#include <common/kernel_cache.hpp>
 #include <kernel/convolve/conv_common.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 template<typename T, typename aT, bool expand>
 void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
                  const Param filter) {
-    int f0 = filter.info.dims[0];
-    int f1 = filter.info.dims[1];
-
-    std::string ref_name =
-        std::string("conv2_") + std::string(dtype_traits<T>::getName()) +
-        std::string("_") + std::string(dtype_traits<aT>::getName()) +
-        std::string("_") + std::to_string(expand) + std::string("_") +
-        std::to_string(f0) + std::string("_") + std::to_string(f1);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        size_t LOC_SIZE =
-            (THREADS_X + 2 * (f0 - 1)) * (THREADS_Y + 2 * (f1 - 1));
-
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D Ti=" << dtype_traits<T>::getName()
-                << " -D To=" << dtype_traits<aT>::getName()
-                << " -D accType=" << dtype_traits<aT>::getName()
-                << " -D BASE_DIM="
-                << 2 /* hard constant specific to this convolution type */
-                << " -D FLEN0=" << f0 << " -D FLEN1=" << f1
-                << " -D EXPAND=" << expand << " -D C_SIZE=" << LOC_SIZE
-                << " -D " << binOpName<af_mul_t>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D CPLX=1";
-        } else {
-            options << " -D CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {ops_cl, convolve_cl};
-        const int ker_lens[]   = {ops_cl_len, convolve_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "convolve");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto convOp =
-        cl::KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int,
-                          int, int, int, int, int>(*entry.ker);
-
-    convOp(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
-           out.info, *signal.data, signal.info, *param.impulse, filter.info,
-           param.nBBS0, param.nBBS1, param.o[1], param.o[2], param.s[1],
-           param.s[2]);
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    static const string src1(ops_cl, ops_cl_len);
+    static const string src2(convolve_cl, convolve_cl_len);
+
+    const int f0 = filter.info.dims[0];
+    const int f1 = filter.info.dims[1];
+    const size_t LOC_SIZE =
+        (THREADS_X + 2 * (f0 - 1)) * (THREADS_Y + 2 * (f1 - 1));
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand),
+        TemplateArg(f0),       TemplateArg(f1),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(Ti, dtype_traits<T>::getName()),
+        DefineKeyValue(To, dtype_traits<aT>::getName()),
+        DefineKeyValue(accType, dtype_traits<aT>::getName()),
+        DefineKeyValue(BASE_DIM, 2),
+        DefineKeyValue(FLEN0, f0),
+        DefineKeyValue(FLEN1, f1),
+        DefineKeyValue(EXPAND, (expand ? 1 : 0)),
+        DefineKeyValue(C_SIZE, LOC_SIZE),
+        DefineKeyFromStr(binOpName<af_mul_t>()),
+        DefineKeyValue(CPLX, (IsComplex ? 1 : 0)),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto convolve =
+        common::findKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+
+    convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
+             out.info, *signal.data, signal.info, *param.impulse, filter.info,
+             param.nBBS0, param.nBBS1, param.o[1], param.o[2], param.s[1],
+             param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
@@ -109,5 +97,4 @@ void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
                                         const Param& sig, const Param& filt);
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s16.cpp b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
index 30eccdf891..2a8b7866d3 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(short, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s32.cpp b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
index a8e2a4e8f7..4fa785d738 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(int, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_s64.cpp b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
index 408b3a0df3..93dca03a3b 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(intl, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u16.cpp b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
index 26f46ae7d5..ad06327135 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(ushort, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u32.cpp b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
index 6c87a7fbb2..6ad074843e 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(uint, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u64.cpp b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
index 717b331628..d682084197 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(uintl, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv2_u8.cpp b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
index 37f2e7f4cb..23879b269d 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
@@ -10,11 +10,9 @@
 #include <kernel/convolve/conv2_impl.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 INSTANTIATE(uchar, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv3.cpp b/src/backend/opencl/kernel/convolve/conv3.cpp
index 961d9f5ace..9baea7de83 100644
--- a/src/backend/opencl/kernel/convolve/conv3.cpp
+++ b/src/backend/opencl/kernel/convolve/conv3.cpp
@@ -10,7 +10,6 @@
 #include <kernel/convolve/conv_common.hpp>
 
 namespace opencl {
-
 namespace kernel {
 
 template<typename T, typename aT, bool expand>
@@ -54,5 +53,4 @@ INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 9b3e2b8006..6cfbd76837 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -9,44 +9,34 @@
 
 #pragma once
 
-#include <af/defines.h>
-
-#include <kernel_headers/convolve.hpp>
-#include <kernel_headers/ops.hpp>
-
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/names.hpp>
+#include <kernel_headers/convolve.hpp>
+#include <kernel_headers/ops.hpp>
 #include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <types.hpp>
-#include <string>
+#include <af/defines.h>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS = 256;
 
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-static const int CUBE_X = 8;
-static const int CUBE_Y = 8;
-static const int CUBE_Z = 4;
+constexpr int THREADS   = 256;
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
+constexpr int CUBE_X    = 8;
+constexpr int CUBE_Y    = 8;
+constexpr int CUBE_Z    = 4;
 
 struct conv_kparam_t {
-    NDRange global;
-    NDRange local;
+    cl::NDRange global;
+    cl::NDRange local;
     size_t loc_size;
     int nBBS0;
     int nBBS1;
@@ -61,6 +51,8 @@ struct conv_kparam_t {
 template<typename T>
 void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
                        int baseDim) {
+    using cl::NDRange;
+
     int batchDims[4] = {1, 1, 1, 1};
     for (int i = baseDim; i < 4; ++i) {
         batchDims[i] = (param.launchMoreBlocks ? 1 : oDims[i]);
@@ -95,51 +87,42 @@ void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
 template<typename T, typename aT, int bDim, bool expand>
 void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
                  const Param& filter) {
-    std::string ref_name = std::string("convolveND_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string(dtype_traits<aT>::getName()) +
-                           std::to_string(bDim) + std::to_string(expand);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D Ti=" << dtype_traits<T>::getName()
-                << " -D To=" << dtype_traits<aT>::getName()
-                << " -D accType=" << dtype_traits<aT>::getName()
-                << " -D BASE_DIM=" << bDim << " -D EXPAND=" << expand << " -D "
-                << binOpName<af_mul_t>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D CPLX=1";
-        } else {
-            options << " -D CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {ops_cl, convolve_cl};
-        const int ker_lens[]   = {ops_cl_len, convolve_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "convolve");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                    cl::LocalSpaceArg, Buffer, KParam, int, int,
-                                    int, int, int, int, int, int>(*entry.ker);
-
-    convOp(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
-           out.info, *signal.data, signal.info, cl::Local(param.loc_size),
-           *param.impulse, filter.info, param.nBBS0, param.nBBS1, param.o[0],
-           param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    static const string src1(ops_cl, ops_cl_len);
+    static const string src2(convolve_cl, convolve_cl_len);
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateTypename<aT>(),
+        TemplateArg(bDim),
+        TemplateArg(expand),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(Ti, dtype_traits<T>::getName()),
+        DefineKeyValue(To, dtype_traits<aT>::getName()),
+        DefineKeyValue(accType, dtype_traits<aT>::getName()),
+        DefineKeyValue(BASE_DIM, bDim),
+        DefineKeyValue(EXPAND, (expand ? 1 : 0)),
+        DefineKeyFromStr(binOpName<af_mul_t>()),
+        DefineKeyValue(CPLX, (IsComplex ? 1 : 0)),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto convolve =
+        common::findKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+
+    convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
+             out.info, *signal.data, signal.info, cl::Local(param.loc_size),
+             *param.impulse, filter.info, param.nBBS0, param.nBBS1, param.o[0],
+             param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
 }
 
 template<typename T, typename aT, bool expand>
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 73e0a3cfca..ef3b486063 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -8,104 +8,75 @@
  ********************************************************/
 
 #include <kernel_headers/convolve_separable.hpp>
-#include <kernel_headers/ops.hpp>
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/names.hpp>
+#include <kernel_headers/ops.hpp>
 #include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <map>
-#include <mutex>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
-
 namespace kernel {
 
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
 template<typename T, typename accType, int conv_dim, bool expand>
 void convSep(Param out, const Param signal, const Param filter) {
-    const int fLen = filter.info.dims[0] * filter.info.dims[1];
-
-    std::string ref_name =
-        std::string("convsep_") + std::to_string(conv_dim) + std::string("_") +
-        std::string(dtype_traits<T>::getName()) + std::string("_") +
-        std::string(dtype_traits<accType>::getName()) + std::string("_") +
-        std::to_string(expand) + std::string("_") + std::to_string(fLen);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        const size_t C0_SIZE = (THREADS_X + 2 * (fLen - 1)) * THREADS_Y;
-        const size_t C1_SIZE = (THREADS_Y + 2 * (fLen - 1)) * THREADS_X;
-
-        size_t locSize = (conv_dim == 0 ? C0_SIZE : C1_SIZE);
-
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D Ti=" << dtype_traits<T>::getName()
-                << " -D To=" << dtype_traits<accType>::getName()
-                << " -D accType=" << dtype_traits<accType>::getName()
-                << " -D CONV_DIM=" << conv_dim << " -D EXPAND=" << expand
-                << " -D FLEN=" << fLen << " -D LOCAL_MEM_SIZE=" << locSize
-                << " -D " << binOpName<af_mul_t>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D CPLX=1";
-        } else {
-            options << " -D CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {ops_cl, convolve_separable_cl};
-        const int ker_lens[]   = {ops_cl_len, convolve_separable_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "convolve");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto convOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, int, int>(
-            *entry.ker);
-
-    NDRange local(THREADS_X, THREADS_Y);
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(convolve_separable_cl,
+                                  convolve_separable_cl_len);
+
+    const int fLen       = filter.info.dims[0] * filter.info.dims[1];
+    const size_t C0_SIZE = (THREADS_X + 2 * (fLen - 1)) * THREADS_Y;
+    const size_t C1_SIZE = (THREADS_Y + 2 * (fLen - 1)) * THREADS_X;
+    size_t locSize       = (conv_dim == 0 ? C0_SIZE : C1_SIZE);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(), TemplateTypename<accType>(),
+        TemplateArg(conv_dim), TemplateArg(expand),
+        TemplateArg(fLen),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(Ti, dtype_traits<T>::getName()),
+        DefineKeyValue(To, dtype_traits<accType>::getName()),
+        DefineKeyValue(accType, dtype_traits<accType>::getName()),
+        DefineKeyValue(CONV_DIM, conv_dim),
+        DefineKeyValue(EXPAND, (expand ? 1 : 0)),
+        DefineKeyValue(FLEN, fLen),
+        DefineKeyFromStr(binOpName<af_mul_t>()),
+        DefineKeyValue(IS_CPLX, (IsComplex ? 1 : 0)),
+        DefineKeyValue(LOCAL_MEM_SIZE, locSize),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto conv =
+        common::findKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * signal.info.dims[2] * THREADS_X,
-                   blk_y * signal.info.dims[3] * THREADS_Y);
+    cl::NDRange global(blk_x * signal.info.dims[2] * THREADS_X,
+                       blk_y * signal.info.dims[3] * THREADS_Y);
 
     cl::Buffer *mBuff = bufferAlloc(fLen * sizeof(accType));
     // FIX ME: if the filter array is strided, direct might cause issues
     getQueue().enqueueCopyBuffer(*filter.data, *mBuff, 0, 0,
                                  fLen * sizeof(accType));
 
-    convOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *signal.data, signal.info, *mBuff, blk_x, blk_y);
-
+    conv(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+         *signal.data, signal.info, *mBuff, blk_x, blk_y);
     bufferFree(mBuff);
 }
 
@@ -133,5 +104,4 @@ INSTANTIATE(uintl, float)
 INSTANTIATE(intl, float)
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve_separable.hpp b/src/backend/opencl/kernel/convolve_separable.hpp
index de16973a4d..aaa23718c0 100644
--- a/src/backend/opencl/kernel/convolve_separable.hpp
+++ b/src/backend/opencl/kernel/convolve_separable.hpp
@@ -17,7 +17,7 @@ namespace kernel {
 // below shared MAX_*_LEN's are calculated based on
 // a maximum shared memory configuration of 48KB per block
 // considering complex types as well
-static const int MAX_SCONV_FILTER_LEN = 31;
+constexpr int MAX_SCONV_FILTER_LEN = 31;
 
 template<typename T, typename accT, int cDim, bool expand>
 void convSep(Param out, const Param sig, const Param filt);
diff --git a/src/backend/opencl/kernel/coo2dense.cl b/src/backend/opencl/kernel/coo2dense.cl
index 12580c027b..f86c073621 100644
--- a/src/backend/opencl/kernel/coo2dense.cl
+++ b/src/backend/opencl/kernel/coo2dense.cl
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void coo2dense_kernel(__global T *oPtr, const KParam output,
-                               __global const T *vPtr, const KParam values,
-                               __global const int *rPtr, const KParam rowIdx,
-                               __global const int *cPtr, const KParam colIdx) {
+kernel void coo2Dense(global T *oPtr, const KParam output, global const T *vPtr,
+                      const KParam values, global const int *rPtr,
+                      const KParam rowIdx, global const int *cPtr,
+                      const KParam colIdx) {
     const int id = get_group_id(0) * get_local_size(0) * reps + get_local_id(0);
 
     if (id >= values.dims[0]) return;
diff --git a/src/backend/opencl/kernel/copy.cl b/src/backend/opencl/kernel/copy.cl
index 3c4e883d51..308f177d94 100644
--- a/src/backend/opencl/kernel/copy.cl
+++ b/src/backend/opencl/kernel/copy.cl
@@ -47,10 +47,10 @@ inType scale(inType value, float factor) {
 
 #endif
 
-__kernel void copy(__global outType *dst, KParam oInfo,
-                   __global const inType *src, KParam iInfo,
-                   outType default_value, float factor, dims_t trgt, int blk_x,
-                   int blk_y) {
+kernel void reshapeCopy(global outType *dst, KParam oInfo,
+                        global const inType *src, KParam iInfo,
+                        outType default_value, float factor, dims_t trgt,
+                        int blk_x, int blk_y) {
     uint lx = get_local_id(0);
     uint ly = get_local_id(1);
 
@@ -61,12 +61,11 @@ __kernel void copy(__global outType *dst, KParam oInfo,
     uint gx         = blockIdx_x * get_local_size(0) + lx;
     uint gy         = blockIdx_y * get_local_size(1) + ly;
 
-    __global const inType *in =
+    global const inType *in =
         src + (gw * iInfo.strides[3] + gz * iInfo.strides[2] +
                gy * iInfo.strides[1] + iInfo.offset);
-    __global outType *out =
-        dst + (gw * oInfo.strides[3] + gz * oInfo.strides[2] +
-               gy * oInfo.strides[1] + oInfo.offset);
+    global outType *out = dst + (gw * oInfo.strides[3] + gz * oInfo.strides[2] +
+                                 gy * oInfo.strides[1] + oInfo.offset);
 
     uint istride0 = iInfo.strides[0];
     uint ostride0 = oInfo.strides[0];
diff --git a/src/backend/opencl/kernel/cscmm.cl b/src/backend/opencl/kernel/cscmm.cl
index 5d038e7506..4dd7a47514 100644
--- a/src/backend/opencl/kernel/cscmm.cl
+++ b/src/backend/opencl/kernel/cscmm.cl
@@ -35,7 +35,7 @@ T __ccmul(T lhs, T rhs) {
 #define CMUL(a, b) (a) * (b)
 #endif
 
-int binary_search(__global const int *ptr, int len, int val) {
+int binary_search(global const int *ptr, int len, int val) {
     int start = 0;
     int end   = len;
     while (end > start) {
@@ -55,14 +55,14 @@ int binary_search(__global const int *ptr, int len, int val) {
 // Each thread in a group maintains the partial outputs of size ROWS_PER_GROUP x
 // COLS_PER_GROUP The outputs from each thread are added up to generate the
 // final result.
-__kernel void cscmm_nn(
-    __global T *output, __global const T *values,
-    __global const int *colidx,  // rowidx from csr is colidx in csc
-    __global const int *rowidx,  // colidx from csr is rowidx in csc
+kernel void cscmm_nn(
+    global T *output, __global const T *values,
+    global const int *colidx,  // rowidx from csr is colidx in csc
+    global const int *rowidx,  // colidx from csr is rowidx in csc
     const int M,                 // K from csr is M in csc
     const int K,                 // M from csr is K in csc
     const int N,                 // N is number of columns in dense matrix
-    __global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
+    global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
     int lid = get_local_id(0);
 
     // Get the row offset for the current group in the uncompressed matrix
@@ -113,7 +113,7 @@ __kernel void cscmm_nn(
         }
     }
 
-    __local T s_outvals[THREADS];
+    local T s_outvals[THREADS];
 
     // For each row and col of output, copy registers to local memory, add
     // results, write to output.
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index b97544a845..cb02ff0b99 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -8,31 +8,21 @@
  ********************************************************/
 
 #pragma once
-#pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/reduce.hpp>
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
 #include <kernel_headers/cscmm.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 #include <af/opencl.h>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "reduce.hpp"
-#include "scan_dim.hpp"
-#include "scan_first.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
@@ -40,71 +30,48 @@ template<typename T>
 void cscmm_nn(Param out, const Param &values, const Param &colIdx,
               const Param &rowIdx, const Param &rhs, const T alpha,
               const T beta, bool is_conj) {
-    bool use_alpha = (alpha != scalar<T>(1.0));
-    bool use_beta  = (beta != scalar<T>(0.0));
-
-    int threads = 256;
+    constexpr int threads = 256;
     // TODO: Find a better way to tune these parameters
-    int rows_per_group = 8;
-    int cols_per_group = 8;
-
-    std::string ref_name =
-        std::string("cscmm_nn_") + std::string(dtype_traits<T>::getName()) +
-        std::string("_") + std::to_string(use_alpha) + std::string("_") +
-        std::to_string(use_beta) + std::string("_") + std::to_string(is_conj) +
-        std::string("_") + std::to_string(rows_per_group) + std::string("_") +
-        std::to_string(cols_per_group) + std::string("_") +
-        std::to_string(threads);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D USE_ALPHA=" << use_alpha;
-        options << " -D USE_BETA=" << use_beta;
-        options << " -D IS_CONJ=" << is_conj;
-        options << " -D THREADS=" << threads;
-        options << " -D ROWS_PER_GROUP=" << rows_per_group;
-        options << " -D COLS_PER_GROUP=" << cols_per_group;
-        options << getTypeBuildDefinition<T>();
-
-        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-
-        const char *ker_strs[] = {cscmm_cl};
-        const int ker_lens[]   = {cscmm_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "cscmm_nn");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto cscmm_kernel = *entry.ker;
-    auto cscmm_func   = KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, int,
-                                    int, Buffer, KParam, T, T>(cscmm_kernel);
-
-    NDRange local(threads, 1);
+    constexpr int rows_per_group = 8;
+    constexpr int cols_per_group = 8;
+
+    static const std::string src(cscmm_cl, cscmm_cl_len);
+
+    const bool use_alpha = (alpha != scalar<T>(1.0));
+    const bool use_beta  = (beta != scalar<T>(0.0));
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),       TemplateArg(use_alpha),
+        TemplateArg(use_beta),       TemplateArg(is_conj),
+        TemplateArg(rows_per_group), TemplateArg(cols_per_group),
+        TemplateArg(threads),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(USE_ALPHA, use_alpha),
+        DefineKeyValue(USE_BETA, use_beta),
+        DefineKeyValue(IS_CONJ, is_conj),
+        DefineKeyValue(THREADS, threads),
+        DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
+        DefineKeyValue(COLS_PER_GROUP, cols_per_group),
+        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto cscmmNN = common::findKernel("cscmm_nn", {src}, targs, options);
+
+    cl::NDRange local(threads, 1);
     int M = out.info.dims[0];
     int N = out.info.dims[1];
     int K = colIdx.info.dims[0] - 1;
 
     int groups_x = divup(M, rows_per_group);
     int groups_y = divup(N, cols_per_group);
-    NDRange global(local[0] * groups_x, local[1] * groups_y);
-
-    cscmm_func(EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
-               *colIdx.data, *rowIdx.data, M, K, N, *rhs.data, rhs.info, alpha,
-               beta);
+    cl::NDRange global(local[0] * groups_x, local[1] * groups_y);
 
+    cscmmNN(cl::EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
+            *colIdx.data, *rowIdx.data, M, K, N, *rhs.data, rhs.info, alpha,
+            beta);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/cscmv.cl b/src/backend/opencl/kernel/cscmv.cl
index cd698115c5..fab18301a1 100644
--- a/src/backend/opencl/kernel/cscmv.cl
+++ b/src/backend/opencl/kernel/cscmv.cl
@@ -35,7 +35,7 @@ T __ccmul(T lhs, T rhs) {
 #define CMUL(a, b) (a) * (b)
 #endif
 
-int binary_search(__global const int *ptr, int len, int val) {
+int binary_search(global const int *ptr, int len, int val) {
     int start = 0;
     int end   = len;
     while (end > start) {
@@ -55,13 +55,13 @@ int binary_search(__global const int *ptr, int len, int val) {
 // and (K / THREAD) columns. This generates a local output buffer of size
 // ROWS_PER_THREAD for each thread. The outputs from each thread are added up to
 // generate the final result.
-__kernel void cscmv_block(
-    __global T *output, __global const T *values,
-    __global const int *colidx,  // rowidx from csr is colidx in csc
-    __global const int *rowidx,  // colidx from csr is rowidx in csc
+kernel void cscmv_block(
+    global T *output, __global const T *values,
+    global const int *colidx,  // rowidx from csr is colidx in csc
+    global const int *rowidx,  // colidx from csr is rowidx in csc
     const int M,                 // K from csr is M in csc
     const int K,                 // M from csr is K in csc
-    __global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
+    global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
     int lid = get_local_id(0);
 
     // Get the row offset for the current group in the uncompressed matrix
@@ -93,10 +93,10 @@ __kernel void cscmv_block(
     }
 
     // s_outvals is used for reduction
-    __local T s_outvals[THREADS];
+    local T s_outvals[THREADS];
 
     // s_output is used to store the final output into local memory
-    __local T s_output[ROWS_PER_GROUP];
+    local T s_output[ROWS_PER_GROUP];
 
     // For each row of output, copy registers to local memory, add results,
     // write to output.
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 49fde89c24..01536c0985 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -8,31 +8,20 @@
  ********************************************************/
 
 #pragma once
-#pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/reduce.hpp>
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
 #include <kernel_headers/cscmv.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 #include <af/opencl.h>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "reduce.hpp"
-#include "scan_dim.hpp"
-#include "scan_first.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
 
 namespace opencl {
 namespace kernel {
@@ -40,67 +29,43 @@ template<typename T>
 void cscmv(Param out, const Param &values, const Param &colIdx,
            const Param &rowIdx, const Param &rhs, const T alpha, const T beta,
            bool is_conj) {
-    bool use_alpha = (alpha != scalar<T>(1.0));
-    bool use_beta  = (beta != scalar<T>(0.0));
-
-    int threads = 256;
+    constexpr int threads = 256;
     // TODO: rows_per_group limited by register pressure. Find better way to
     // handle this.
-    int rows_per_group = 64;
-
-    std::string ref_name =
-        std::string("cscmv_") + std::string(dtype_traits<T>::getName()) +
-        std::string("_") + std::to_string(use_alpha) + std::string("_") +
-        std::to_string(use_beta) + std::string("_") + std::to_string(is_conj) +
-        std::string("_") + std::to_string(rows_per_group) + std::string("_") +
-        std::to_string(threads);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D USE_ALPHA=" << use_alpha;
-        options << " -D USE_BETA=" << use_beta;
-        options << " -D IS_CONJ=" << is_conj;
-        options << " -D THREADS=" << threads;
-        options << " -D ROWS_PER_GROUP=" << rows_per_group;
-
-        options << getTypeBuildDefinition<T>();
-
-        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-
-        const char *ker_strs[] = {cscmv_cl};
-        const int ker_lens[]   = {cscmv_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "cscmv_block");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto cscmv_kernel = *entry.ker;
-    auto cscmv_func   = KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, int,
-                                    Buffer, KParam, T, T>(cscmv_kernel);
-
-    NDRange local(threads);
+    constexpr int rows_per_group = 64;
+
+    static const std::string src(cscmv_cl, cscmv_cl_len);
+
+    const bool use_alpha = (alpha != scalar<T>(1.0));
+    const bool use_beta  = (beta != scalar<T>(0.0));
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),       TemplateArg(use_alpha),
+        TemplateArg(use_beta),       TemplateArg(is_conj),
+        TemplateArg(rows_per_group), TemplateArg(threads),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(USE_ALPHA, use_alpha),
+        DefineKeyValue(USE_BETA, use_beta),
+        DefineKeyValue(IS_CONJ, is_conj),
+        DefineKeyValue(THREADS, threads),
+        DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
+        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto cscmvBlock = common::findKernel("cscmv_block", {src}, targs, options);
+
+    cl::NDRange local(threads);
     int K        = colIdx.info.dims[0] - 1;
     int M        = out.info.dims[0];
     int groups_x = divup(M, rows_per_group);
-    NDRange global(local[0] * groups_x, 1);
-
-    cscmv_func(EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
-               *colIdx.data, *rowIdx.data, M, K, *rhs.data, rhs.info, alpha,
-               beta);
+    cl::NDRange global(local[0] * groups_x, 1);
 
+    cscmvBlock(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+               *values.data, *colIdx.data, *rowIdx.data, M, K, *rhs.data,
+               rhs.info, alpha, beta);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/csr2coo.cl b/src/backend/opencl/kernel/csr2coo.cl
index 3268c8245b..d60766f96a 100644
--- a/src/backend/opencl/kernel/csr2coo.cl
+++ b/src/backend/opencl/kernel/csr2coo.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void csr2coo(__global int *orowidx, __global int *ocolidx,
-                      __global const int *irowidx, __global const int *icolidx,
-                      const int M) {
+kernel void csr2Coo(global int *orowidx, global int *ocolidx,
+                    global const int *irowidx, global const int *icolidx,
+                    const int M) {
     int lid = get_local_id(0);
     for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
         int colStart = irowidx[rowId];
@@ -22,10 +22,9 @@ __kernel void csr2coo(__global int *orowidx, __global int *ocolidx,
     }
 }
 
-__kernel void swapIndex_kernel(__global T *ovalues, __global int *oindex,
-                               __global const T *ivalues,
-                               __global const int *iindex,
-                               __global const int *swapIdx, const int nNZ) {
+kernel void swapIndex(global T *ovalues, global int *oindex,
+                      global const T *ivalues, global const int *iindex,
+                      global const int *swapIdx, const int nNZ) {
     int id = get_global_id(0);
     if (id >= nNZ) return;
 
@@ -35,9 +34,8 @@ __kernel void swapIndex_kernel(__global T *ovalues, __global int *oindex,
     oindex[id]  = iindex[idx];
 }
 
-__kernel void csrReduce_kernel(__global int *orowIdx,
-                               __global const int *irowIdx, const int M,
-                               const int nNZ) {
+kernel void csrReduce(global int *orowIdx, global const int *irowIdx,
+                      const int M, const int nNZ) {
     int id = get_global_id(0);
 
     if (id >= nNZ) return;
diff --git a/src/backend/opencl/kernel/csr2dense.cl b/src/backend/opencl/kernel/csr2dense.cl
index acd2ef454a..15a7c0c60d 100644
--- a/src/backend/opencl/kernel/csr2dense.cl
+++ b/src/backend/opencl/kernel/csr2dense.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void csr2dense(__global T *output, __global const T *values,
-                        __global const int *rowidx, __global const int *colidx,
-                        const int M) {
+kernel void csr2Dense(global T *output, global const T *values,
+                      global const int *rowidx, global const int *colidx,
+                      const int M) {
     int lid = get_local_id(0);
     for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
         int colStart = rowidx[rowId];
diff --git a/src/backend/opencl/kernel/csrmm.cl b/src/backend/opencl/kernel/csrmm.cl
index 1dd7d75972..750c97f8b5 100644
--- a/src/backend/opencl/kernel/csrmm.cl
+++ b/src/backend/opencl/kernel/csrmm.cl
@@ -43,11 +43,11 @@ T __ccmul(T lhs, T rhs) {
 // row, `THREADS_PER_GROUP` dense columns). The threads in the block load the
 // sparse row into local memmory and then perform individual "dot" operations.
 
-__kernel void csrmm_nt(__global T *output, __global const T *values,
-                       __global const int *rowidx, __global const int *colidx,
-                       const int M, const int N, __global const T *rhs,
+kernel void csrmm_nt(global T *output, __global const T *values,
+                       global const int *rowidx, __global const int *colidx,
+                       const int M, const int N, global const T *rhs,
                        const KParam rinfo, const T alpha, const T beta,
-                       __global int *counter) {
+                       global int *counter) {
     int gidx = get_global_id(0);
     int lid  = get_local_id(0);
 
@@ -56,11 +56,11 @@ __kernel void csrmm_nt(__global T *output, __global const T *values,
 
     bool within_N = (gidx < N);
 
-    __local T s_values[THREADS_PER_GROUP];
-    __local int s_colidx[THREADS_PER_GROUP];
+    local T s_values[THREADS_PER_GROUP];
+    local int s_colidx[THREADS_PER_GROUP];
 
     int rowNext = get_group_id(1);
-    __local int s_rowId;
+    local int s_rowId;
 
     // Each iteration writes `THREADS_PER_GROUP` columns from one row of the
     // output
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 7a0af07332..7f0e387664 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -8,106 +8,74 @@
  ********************************************************/
 
 #pragma once
-#pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/reduce.hpp>
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
 #include <kernel_headers/csrmm.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "reduce.hpp"
-#include "scan_dim.hpp"
-#include "scan_first.hpp"
+#include <af/opencl.h>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int MAX_CSRMM_GROUPS = 4096;
 template<typename T>
 void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
               const Param &colIdx, const Param &rhs, const T alpha,
               const T beta) {
-    bool use_alpha = (alpha != scalar<T>(1.0));
-    bool use_beta  = (beta != scalar<T>(0.0));
-
+    constexpr int MAX_CSRMM_GROUPS = 4096;
     // Using greedy indexing is causing performance issues on many platforms
     // FIXME: Figure out why
-    bool use_greedy = false;
-
-    std::string ref_name = std::string("csrmm_nt_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(use_alpha) +
-                           std::string("_") + std::to_string(use_beta) +
-                           std::string("_") + std::to_string(use_greedy);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D USE_ALPHA=" << use_alpha;
-        options << " -D USE_BETA=" << use_beta;
-        options << " -D USE_GREEDY=" << use_greedy;
-        options << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP;
-
-        options << getTypeBuildDefinition<T>();
-        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-
-        const char *ker_strs[] = {csrmm_cl};
-        const int ker_lens[]   = {csrmm_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog   = new Program(prog);
-        entry.ker    = new Kernel[2];
-        entry.ker[0] = Kernel(*entry.prog, "csrmm_nt");
-        // FIXME: Change this after adding another kernel
-        entry.ker[1] = Kernel(*entry.prog, "csrmm_nt");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto csrmm_nt_kernel = entry.ker[0];
-    auto csrmm_nt_func =
-        KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, int, Buffer, KParam,
-                      T, T, Buffer>(csrmm_nt_kernel);
-    NDRange local(THREADS_PER_GROUP, 1);
+    constexpr bool use_greedy = false;
+
+    static const std::string src(csrmm_cl, csrmm_cl_len);
+
+    const bool use_alpha = (alpha != scalar<T>(1.0));
+    const bool use_beta  = (beta != scalar<T>(0.0));
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(use_alpha),
+        TemplateArg(use_beta),
+        TemplateArg(use_greedy),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(USE_ALPHA, use_alpha),
+        DefineKeyValue(USE_BETA, use_beta),
+        DefineKeyValue(USE_GREEDY, use_greedy),
+        DefineValue(THREADS_PER_GROUP),
+        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    // FIXME: Switch to perf (thread vs block) baesd kernel
+    auto csrmm_nt_func = common::findKernel("csrmm_nt", {src}, targs, options);
+
+    cl::NDRange local(THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
     int N = rhs.info.dims[0];
 
     int groups_x = divup(N, local[0]);
     int groups_y = divup(M, REPEAT);
     groups_y     = std::min(groups_y, MAX_CSRMM_GROUPS);
-    NDRange global(local[0] * groups_x, local[1] * groups_y);
+    cl::NDRange global(local[0] * groups_x, local[1] * groups_y);
 
     std::vector<int> count(groups_x);
     cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
     getQueue().enqueueWriteBuffer(
         *counter, CL_TRUE, 0, count.size() * sizeof(int), (void *)count.data());
 
-    csrmm_nt_func(EnqueueArgs(getQueue(), global, local), *out.data,
+    csrmm_nt_func(cl::EnqueueArgs(getQueue(), global, local), *out.data,
                   *values.data, *rowIdx.data, *colIdx.data, M, N, *rhs.data,
                   rhs.info, alpha, beta, *counter);
-
     bufferFree(counter);
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index c37482cc55..b9655fc67a 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -39,11 +39,11 @@ T __ccmul(T lhs, T rhs) {
 // elements from one row and multiplying with the corresponding elements from
 // the dense vector to produce a single output value. This kernel should be used
 // when the number of nonzero elements per block is fairly small
-__kernel void csrmv_thread(__global T *output, __global const T *values,
-                           __global const int *rowidx,
-                           __global const int *colidx, const int M,
-                           __global const T *rhs, const KParam rinfo,
-                           const T alpha, const T beta, __global int *counter) {
+kernel void csrmv_thread(global T *output, __global const T *values,
+                           global const int *rowidx,
+                           global const int *colidx, const int M,
+                           global const T *rhs, const KParam rinfo,
+                           const T alpha, const T beta, global int *counter) {
     rhs += rinfo.offset;
     int rowNext = get_global_id(0);
 
@@ -91,18 +91,18 @@ __kernel void csrmv_thread(__global T *output, __global const T *values,
 // elements from dense vector to produce a local output values. Then the block
 // performs a reduction operation to produce a single output value. This kernel
 // should be used when the number of nonzero elements per block is large
-__kernel void csrmv_block(__global T *output, __global const T *values,
-                          __global const int *rowidx,
-                          __global const int *colidx, const int M,
-                          __global const T *rhs, const KParam rinfo,
-                          const T alpha, const T beta, __global int *counter) {
+kernel void csrmv_block(global T *output, __global const T *values,
+                          global const int *rowidx,
+                          global const int *colidx, const int M,
+                          global const T *rhs, const KParam rinfo,
+                          const T alpha, const T beta, global int *counter) {
     rhs += rinfo.offset;
     int lid     = get_local_id(0);
     int rowNext = get_group_id(0);
-    __local int s_rowId;
+    local int s_rowId;
 
     // Each thread stores part of the output result
-    __local T s_outval[THREADS];
+    local T s_outval[THREADS];
 
     // Each groups performs multiple "dot" operations
     while (true) {
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 132b3e657d..88b75e1b13 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -8,86 +8,56 @@
  ********************************************************/
 
 #pragma once
-#pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/reduce.hpp>
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
 #include <kernel_headers/csrmv.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 #include <af/opencl.h>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "reduce.hpp"
-#include "scan_dim.hpp"
-#include "scan_first.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int MAX_CSRMV_GROUPS = 4096;
 template<typename T>
 void csrmv(Param out, const Param &values, const Param &rowIdx,
            const Param &colIdx, const Param &rhs, const T alpha, const T beta) {
-    bool use_alpha = (alpha != scalar<T>(1.0));
-    bool use_beta  = (beta != scalar<T>(0.0));
-
+    constexpr int MAX_CSRMV_GROUPS = 4096;
     // Using greedy indexing is causing performance issues on many platforms
     // FIXME: Figure out why
-    bool use_greedy = false;
-
+    constexpr bool use_greedy = false;
     // FIXME: Find a better number based on average non zeros per row
-    int threads = 64;
-
-    std::string ref_name =
-        std::string("csrmv_") + std::string(dtype_traits<T>::getName()) +
-        std::string("_") + std::to_string(use_alpha) + std::string("_") +
-        std::to_string(use_beta) + std::string("_") +
-        std::to_string(use_greedy) + std::string("_") + std::to_string(threads);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D USE_ALPHA=" << use_alpha;
-        options << " -D USE_BETA=" << use_beta;
-        options << " -D USE_GREEDY=" << use_greedy;
-        options << " -D THREADS=" << threads;
-
-        options << getTypeBuildDefinition<T>();
-
-        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-
-        const char *ker_strs[] = {csrmv_cl};
-        const int ker_lens[]   = {csrmv_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog   = new Program(prog);
-        entry.ker    = new Kernel[2];
-        entry.ker[0] = Kernel(*entry.prog, "csrmv_thread");
-        entry.ker[1] = Kernel(*entry.prog, "csrmv_block");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    constexpr int threads = 64;
+
+    static const std::string src(csrmv_cl, csrmv_cl_len);
+
+    const bool use_alpha = (alpha != scalar<T>(1.0));
+    const bool use_beta  = (beta != scalar<T>(0.0));
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),   TemplateArg(use_alpha), TemplateArg(use_beta),
+        TemplateArg(use_greedy), TemplateArg(threads),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(USE_ALPHA, use_alpha),
+        DefineKeyValue(USE_BETA, use_beta),
+        DefineKeyValue(USE_GREEDY, use_greedy),
+        DefineKeyValue(THREADS, threads),
+        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto csrmvThread =
+        common::findKernel("csrmv_thread", {src}, targs, options);
+    auto csrmvBlock = common::findKernel("csrmv_block", {src}, targs, options);
 
     int count           = 0;
     cl::Buffer *counter = bufferAlloc(sizeof(int));
@@ -97,22 +67,19 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
     // TODO: Figure out the proper way to choose either csrmv_thread or
     // csrmv_block
     bool is_csrmv_block = true;
-    auto csrmv_kernel   = is_csrmv_block ? entry.ker[1] : entry.ker[0];
-    auto csrmv_func = KernelFunctor<Buffer, Buffer, Buffer, Buffer, int, Buffer,
-                                    KParam, T, T, Buffer>(csrmv_kernel);
+    auto csrmv          = is_csrmv_block ? csrmvBlock : csrmvThread;
 
-    NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
+    cl::NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
 
     int groups_x =
         is_csrmv_block ? divup(M, REPEAT) : divup(M, REPEAT * local[0]);
     groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
-    NDRange global(local[0] * groups_x, 1);
-
-    csrmv_func(EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
-               *rowIdx.data, *colIdx.data, M, *rhs.data, rhs.info, alpha, beta,
-               *counter);
+    cl::NDRange global(local[0] * groups_x, 1);
 
+    csrmv(cl::EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
+          *rowIdx.data, *colIdx.data, M, *rhs.data, rhs.info, alpha, beta,
+          *counter);
     CL_DEBUG_FINISH(getQueue());
     bufferFree(counter);
 }
diff --git a/src/backend/opencl/kernel/dense2csr.cl b/src/backend/opencl/kernel/dense2csr.cl
index c2ad83cc7e..7f10d2e022 100644
--- a/src/backend/opencl/kernel/dense2csr.cl
+++ b/src/backend/opencl/kernel/dense2csr.cl
@@ -13,12 +13,10 @@
 #define IS_ZERO(val) (val == 0)
 #endif
 
-__kernel void dense2csr_split_kernel(__global T *svalptr, __global int *scolptr,
-                                     __global const T *dvalptr,
-                                     const KParam valinfo,
-                                     __global const int *dcolptr,
-                                     const KParam colinfo,
-                                     __global const int *rowptr) {
+kernel void dense2Csr(global T *svalptr, global int *scolptr,
+                      global const T *dvalptr, const KParam valinfo,
+                      global const int *dcolptr, const KParam colinfo,
+                      global const int *rowptr) {
     int gidx = get_global_id(0);
     int gidy = get_global_id(1);
 
diff --git a/src/backend/opencl/kernel/diag_create.cl b/src/backend/opencl/kernel/diag_create.cl
index 3eb16ce3cc..9087133612 100644
--- a/src/backend/opencl/kernel/diag_create.cl
+++ b/src/backend/opencl/kernel/diag_create.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void diagCreateKernel(__global T *oData, KParam oInfo,
-                               const __global T *iData, KParam iInfo, int num,
+kernel void diagCreateKernel(global T *oData, KParam oInfo,
+                               const global T *iData, KParam iInfo, int num,
                                int groups_x) {
     unsigned idz       = get_group_id(0) / groups_x;
     unsigned groupId_x = get_group_id(0) - idz * groups_x;
@@ -19,11 +19,11 @@ __kernel void diagCreateKernel(__global T *oData, KParam oInfo,
     if (idx >= oInfo.dims[0] || idy >= oInfo.dims[1] || idz >= oInfo.dims[2])
         return;
 
-    __global T *optr =
+    global T *optr =
         oData + idz * oInfo.strides[2] + idy * oInfo.strides[1] + idx;
-    const __global T *iptr =
+    const global T *iptr =
         iData + idz * iInfo.strides[1] + ((num > 0) ? idx : idy) + iInfo.offset;
 
-    T val = (idx == (idy - num)) ? *iptr : ZERO;
+    T val = (idx == (idy - num)) ? *iptr : (T)(ZERO);
     *optr = val;
 }
diff --git a/src/backend/opencl/kernel/diag_extract.cl b/src/backend/opencl/kernel/diag_extract.cl
index c663923fd6..f873de5897 100644
--- a/src/backend/opencl/kernel/diag_extract.cl
+++ b/src/backend/opencl/kernel/diag_extract.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void diagExtractKernel(__global T *oData, KParam oInfo,
-                                const __global T *iData, KParam iInfo, int num,
+kernel void diagExtractKernel(global T *oData, KParam oInfo,
+                                const global T *iData, KParam iInfo, int num,
                                 int groups_z) {
     unsigned idw = get_group_id(1) / groups_z;
     unsigned idz = get_group_id(1) - idw * groups_z;
@@ -18,18 +18,18 @@ __kernel void diagExtractKernel(__global T *oData, KParam oInfo,
     if (idx >= oInfo.dims[0] || idz >= oInfo.dims[2] || idw >= oInfo.dims[3])
         return;
 
-    __global T *optr =
+    global T *optr =
         oData + idz * oInfo.strides[2] + idw * oInfo.strides[3] + idx;
 
     if (idx >= iInfo.dims[0] || idx >= iInfo.dims[1]) {
-        *optr = ZERO;
+        *optr = (T)(ZERO);
         return;
     }
 
     int i_off =
         (num > 0) ? (num * iInfo.strides[1] + idx) : (idx - num) + iInfo.offset;
 
-    const __global T *iptr =
+    const global T *iptr =
         iData + idz * iInfo.strides[2] + idw * iInfo.strides[3] + i_off;
 
     *optr = iptr[idx * iInfo.strides[1]];
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 8cd323f4d4..6a85c5a803 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -7,106 +7,77 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/diag_create.hpp>
 #include <kernel_headers/diag_extract.hpp>
 #include <math.hpp>
-#include <program.hpp>
-#include "../traits.hpp"
-#include "config.hpp"
+#include <traits.hpp>
 
-using af::scalar_to_option;
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-template<typename T>
-std::string generateOptionsString() {
-    std::ostringstream options;
-    options << " -D T=" << dtype_traits<T>::getName() << " -D ZERO=(T)("
-            << scalar_to_option(scalar<T>(0)) << ")";
-    options << getTypeBuildDefinition<T>();
-
-    return options.str();
-}
 
 template<typename T>
 static void diagCreate(Param out, Param in, int num) {
-    std::string refName = std::string("diagCreateKernel_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(diag_create_cl, diag_create_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string options    = generateOptionsString<T>();
-        const char* ker_strs[] = {diag_create_cl};
-        const int ker_lens[]   = {diag_create_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "diagCreateKernel");
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto diagCreate =
+        common::findKernel("diagCreateKernel", {src}, targs, options);
 
-    NDRange local(32, 8);
+    cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
     int groups_y = divup(out.info.dims[1], local[1]);
-    NDRange global(groups_x * local[0] * out.info.dims[2], groups_y * local[1]);
-
-    auto diagCreateOp =
-        KernelFunctor<Buffer, const KParam, Buffer, const KParam, int, int>(
-            *entry.ker);
-
-    diagCreateOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info,
-                 *(in.data), in.info, num, groups_x);
+    cl::NDRange global(groups_x * local[0] * out.info.dims[2],
+                       groups_y * local[1]);
 
+    diagCreate(cl::EnqueueArgs(getQueue(), global, local), *(out.data),
+               out.info, *(in.data), in.info, num, groups_x);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 static void diagExtract(Param out, Param in, int num) {
-    std::string refName = std::string("diagExtractKernel_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(diag_extract_cl, diag_extract_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string options    = generateOptionsString<T>();
-        const char* ker_strs[] = {diag_extract_cl};
-        const int ker_lens[]   = {diag_extract_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options);
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "diagExtractKernel");
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto diagExtract =
+        common::findKernel("diagExtractKernel", {src}, targs, options);
 
-    NDRange local(256, 1);
+    cl::NDRange local(256, 1);
     int groups_x = divup(out.info.dims[0], local[0]);
     int groups_z = out.info.dims[2];
-    NDRange global(groups_x * local[0], groups_z * local[1] * out.info.dims[3]);
-
-    auto diagExtractOp =
-        KernelFunctor<Buffer, const KParam, Buffer, const KParam, int, int>(
-            *entry.ker);
-
-    diagExtractOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info,
-                  *(in.data), in.info, num, groups_z);
+    cl::NDRange global(groups_x * local[0],
+                       groups_z * local[1] * out.info.dims[3]);
 
+    diagExtract(cl::EnqueueArgs(getQueue(), global, local), *(out.data),
+                out.info, *(in.data), in.info, num, groups_z);
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/diff.cl b/src/backend/opencl/kernel/diff.cl
index 89da8abd2c..aef7c0e86f 100644
--- a/src/backend/opencl/kernel/diff.cl
+++ b/src/backend/opencl/kernel/diff.cl
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-void diff_this(__global T* out, __global const T* in, const int oMem,
+void diff_this(global T* out, __global const T* in, const int oMem,
                const int iMem0, const int iMem1, const int iMem2) {
     if (isDiff2 == 0) {
         out[oMem] = in[iMem1] - in[iMem0];
@@ -16,7 +16,7 @@ void diff_this(__global T* out, __global const T* in, const int oMem,
     }
 }
 
-__kernel void diff_kernel(__global T* out, __global const T* in,
+kernel void diff_kernel(global T* out, __global const T* in,
                           const KParam op, const KParam ip, const int oElem,
                           const int blocksPerMatX, const int blocksPerMatY) {
     const int idz = get_group_id(0) / blocksPerMatX;
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index cf9c5c61f3..64a6f4ac15 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -8,71 +8,55 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/diff.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int TX = 16;
-static const int TY = 16;
 
-template<typename T, unsigned dim, bool isDiff2>
-void diff(Param out, const Param in, const unsigned indims) {
-    std::string refName = std::string("diff_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(dim) + std::to_string(isDiff2);
+template<typename T>
+void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
+          const bool isDiff2) {
+    constexpr int TX = 16;
+    constexpr int TY = 16;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(diff_cl, diff_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D DIM=" << dim
-                << " -D isDiff2=" << isDiff2;
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(dim),
+        TemplateArg(isDiff2),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(DIM, dim),
+        DefineKeyValue(isDiff2, (isDiff2 ? 1 : 0)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {diff_cl};
-        const int ker_lens[]   = {diff_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "diff_kernel");
+    auto diffOp = common::findKernel("diff_kernel", {src}, targs, options);
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto diffOp =
-        KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                      const int, const int, const int>(*entry.ker);
-
-    NDRange local(TX, TY, 1);
-    if (dim == 0 && indims == 1) { local = NDRange(TX * TY, 1, 1); }
+    cl::NDRange local(TX, TY, 1);
+    if (dim == 0 && indims == 1) { local = cl::NDRange(TX * TY, 1, 1); }
 
     int blocksPerMatX = divup(out.info.dims[0], local[0]);
     int blocksPerMatY = divup(out.info.dims[1], local[1]);
-    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                   local[1] * blocksPerMatY * out.info.dims[3], 1);
+    cl::NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                       local[1] * blocksPerMatY * out.info.dims[3], 1);
 
     const int oElem = out.info.dims[0] * out.info.dims[1] * out.info.dims[2] *
                       out.info.dims[3];
 
-    diffOp(EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
+    diffOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
            out.info, in.info, oElem, blocksPerMatX, blocksPerMatY);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/example.cl b/src/backend/opencl/kernel/example.cl
index 32be1bdd39..e946106326 100644
--- a/src/backend/opencl/kernel/example.cl
+++ b/src/backend/opencl/kernel/example.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void example(__global T* d_dst, KParam oInfo, __global const T* d_src1,
-                      KParam iInfo1, __global const T* d_src2, KParam iInfo2,
+kernel void example(global T* d_dst, KParam oInfo, __global const T* d_src1,
+                      KParam iInfo1, global const T* d_src2, KParam iInfo2,
                       int method);
 {
     // get current thread global identifiers along required dimensions
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index fee67836f0..894bc1f548 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -8,25 +8,6 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/example.hpp>  // This is the header that gets auto-generated
-// from the .cl file you will create. We pre-process
-// cl files to obfuscate code.
-
-#include <program.hpp>
-#include <traits.hpp>
-
-// Following c++ standard library headers are needed to maintain
-// OpenCL cl::Kernel & cl::Program objects
-#include <string>
-
-#include <cache.hpp>  // Has the definitions of functions such as the following
-                      // used in caching and fetching kernels.
-// * kernelCache - used to fetch existing kernel from cache
-// if any
-// * addKernelToCache - push new kernels into cache
-
-#include <common/dispatch.hpp>  // common utility header for CUDA & OpenCL backends
-                                // has the divup macro
 
 #include <Param.hpp>  // This header has the declaration of structures
                       // that are passed onto kernel. Operator overloads
@@ -35,80 +16,71 @@
                       // Hence, the OpenCL kernel wrapper function takes in
                       // Param instead of opencl::Array<T>
 
+#include <kernel_headers/example.hpp>  // This is the header that gets auto-generated
+// from the .cl file you will create. We pre-process
+// cl files to obfuscate code.
+
+#include <traits.hpp>
+
+#include <common/dispatch.hpp>      // common utility header for CUDA & OpenCL
+#include <common/kernel_cache.hpp>  // Has findKernel
+                                    // backends has the divup macro
+
 #include <debug_opencl.hpp>  // For Debug only related OpenCL validations
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+// Following c++ standard library headers are needed to create
+// the lists of parameters for common::findKernel function call
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
+
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
 
 template<typename T>
 void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
-    std::string refName = std::string("example_") +  //<kernel_function_name>_
-                          std::string(dtype_traits<T>::getName());
-    // std::string("encode template parameters one after one");
-    // If you have numericals, you can use std::to_string to convert
-    // them into std::strings
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    // Make sure OpenCL kernel isn't already available before
-    // compiling for given device and combination of template
-    // parameters to this kernel wrapper function 'exampleFunc<T>'
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        // You can pass any template parameters as compile options
-        // to kernel the compilation step. This is equivalent of
-        // having templated kernels in CUDA
-
-        // The following option is passed to kernel compilation
-        // if template parameter T is double or complex double
-        // to enable FP64 extension
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {example_cl};
-        const int ker_lens[]   = {example_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "example");
-
-        addKernelToCache(device, refName, entry);
-    }
+    static const std::string src(example_cl, example_cl_len);
+
+    // Compilation options for compiling OpenCL kernel.
+    // Go to common/kernel_cache.hpp to find details on this.
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+
+    // Compilation options for compiling OpenCL kernel.
+    // Go to common/kernel_cache.hpp to find details on this.
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+
+    // The following templated function can take variable
+    // number of template parameters and if one of them is double
+    // precision, it will enable necessary constants, flags, ops
+    // in opencl kernel compilation stage
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    // Fetch the Kernel functor, go to common/kernel_cache.hpp
+    // to find details of this function
+    auto exOp = common::findKernel("example", {src}, targs, options);
 
     // configure work group parameters
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(c.info.dims[0], THREADS_X);
     int blk_y = divup(c.info.dims[1], THREADS_Y);
 
     // configure global launch parameters
-    NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
-
-    // create a kernel functor from the cl::Kernel object
-    // corresponding to the device on which current execution
-    // is happending.
-    auto exampleFuncOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int>(
-            *entry.ker);
+    cl::NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
 
     // launch the kernel
-    exampleFuncOp(EnqueueArgs(getQueue(), global, local), *c.data, c.info,
-                  *a.data, a.info, *b.data, b.info, (int)p);
-
+    exOp(cl::EnqueueArgs(getQueue(), global, local), *c.data, c.info, *a.data,
+         a.info, *b.data, b.info, (int)p);
     // Below Macro activates validations ONLY in DEBUG
     // mode as its name indicates
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/fast.cl b/src/backend/opencl/kernel/fast.cl
index 3b34735e69..ef80350f01 100644
--- a/src/backend/opencl/kernel/fast.cl
+++ b/src/backend/opencl/kernel/fast.cl
@@ -38,13 +38,13 @@ inline int test_smaller(const float x, const float p, const float thr) {
 // Returns -1 when x < p - thr
 // Returns  0 when x >= p - thr && x <= p + thr
 // Returns  1 when x > p + thr
-inline int test_pixel(__local T* local_image, const float p, const float thr,
+inline int test_pixel(local T* local_image, const float p, const float thr,
                       const int x, const int y) {
     return -test_smaller((float)local_image[idx(x, y)], p, thr) +
            test_greater((float)local_image[idx(x, y)], p, thr);
 }
 
-void locate_features_core(__local T* local_image, __global float* score,
+void locate_features_core(local T* local_image, global float* score,
                           KParam iInfo, const float thr, int x, int y,
                           const unsigned edge) {
     if (x >= iInfo.dims[0] - edge || y >= iInfo.dims[1] - edge) return;
@@ -123,8 +123,8 @@ void locate_features_core(__local T* local_image, __global float* score,
     }
 }
 
-void load_shared_image(__global const T* in, KParam iInfo,
-                       __local T* local_image, unsigned ix, unsigned iy,
+void load_shared_image(global const T* in, KParam iInfo,
+                       local T* local_image, unsigned ix, unsigned iy,
                        unsigned bx, unsigned by, unsigned x, unsigned y,
                        unsigned lx, unsigned ly) {
     // Copy an image patch to shared memory, with a 3-pixel edge
@@ -143,9 +143,9 @@ void load_shared_image(__global const T* in, KParam iInfo,
     }
 }
 
-__kernel void locate_features(__global const T* in, KParam iInfo,
-                              __global float* score, const float thr,
-                              const unsigned edge, __local T* local_image) {
+kernel void locate_features(global const T* in, KParam iInfo,
+                              global float* score, const float thr,
+                              const unsigned edge, local T* local_image) {
     unsigned ix = get_local_id(0);
     unsigned iy = get_local_id(1);
     unsigned bx = get_local_size(0);
@@ -161,12 +161,12 @@ __kernel void locate_features(__global const T* in, KParam iInfo,
     locate_features_core(local_image, score, iInfo, thr, x, y, edge);
 }
 
-__kernel void non_max_counts(__global unsigned* d_counts,
-                             __global unsigned* d_offsets,
-                             __global unsigned* d_total, __global float* flags,
-                             __global const float* score, KParam iInfo,
+kernel void non_max_counts(global unsigned* d_counts,
+                             global unsigned* d_offsets,
+                             global unsigned* d_total, __global float* flags,
+                             global const float* score, KParam iInfo,
                              const unsigned edge) {
-    __local unsigned s_counts[256];
+    local unsigned s_counts[256];
 
     const int yid  = get_group_id(1) * get_local_size(1) * 8 + get_local_id(1);
     const int yend = (get_group_id(1) + 1) * get_local_size(1) * 8;
@@ -244,11 +244,11 @@ __kernel void non_max_counts(__global unsigned* d_counts,
     }
 }
 
-__kernel void get_features(__global float* x_out, __global float* y_out,
-                           __global float* score_out,
-                           __global const float* flags,
-                           __global const unsigned* d_counts,
-                           __global const unsigned* d_offsets, KParam iInfo,
+kernel void get_features(global float* x_out, __global float* y_out,
+                           global float* score_out,
+                           global const float* flags,
+                           global const unsigned* d_counts,
+                           global const unsigned* d_offsets, KParam iInfo,
                            const unsigned total, const unsigned edge) {
     const int xid = get_group_id(0) * get_local_size(0) * 2 + get_local_id(0);
     const int yid = get_group_id(1) * get_local_size(1) * 8 + get_local_id(1);
@@ -262,8 +262,8 @@ __kernel void get_features(__global float* x_out, __global float* y_out,
 
     const int bid = get_group_id(1) * get_num_groups(0) + get_group_id(0);
 
-    __local unsigned s_count;
-    __local unsigned s_idx;
+    local unsigned s_count;
+    local unsigned s_idx;
 
     if (tid == 0) {
         s_count = d_counts[bid];
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index b0ac0fa9cc..cd3a339642 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -7,67 +7,49 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cache.hpp>
+#pragma once
+
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel_headers/fast.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <af/defines.h>
 
-#include <map>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
+#include <string>
+#include <vector>
 
 namespace opencl {
-
 namespace kernel {
 
-static const int FAST_THREADS_X        = 16;
-static const int FAST_THREADS_Y        = 16;
-static const int FAST_THREADS_NONMAX_X = 32;
-static const int FAST_THREADS_NONMAX_Y = 8;
-
-template<typename T, const bool nonmax>
+template<typename T>
 void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
           Param &y_out, Param &score_out, Param in, const float thr,
-          const float feature_ratio, const unsigned edge) {
-    std::string ref_name = std::string("fast_") + std::to_string(arc_length) +
-                           std::string("_") + std::to_string(nonmax) +
-                           std::string("_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D ARC_LENGTH=" << arc_length
-                << " -D NONMAX=" << static_cast<unsigned>(nonmax);
-
-        options << getTypeBuildDefinition<T>();
-
-        cl::Program prog;
-        buildProgram(prog, fast_cl, fast_cl_len, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel[3];
-
-        entry.ker[0] = Kernel(*entry.prog, "locate_features");
-        entry.ker[1] = Kernel(*entry.prog, "non_max_counts");
-        entry.ker[2] = Kernel(*entry.prog, "get_features");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+          const float feature_ratio, const unsigned edge, const bool nonmax) {
+    constexpr int FAST_THREADS_X        = 16;
+    constexpr int FAST_THREADS_Y        = 16;
+    constexpr int FAST_THREADS_NONMAX_X = 32;
+    constexpr int FAST_THREADS_NONMAX_Y = 8;
+
+    static const std::string src(fast_cl, fast_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(arc_length),
+        TemplateArg(nonmax),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ARC_LENGTH, arc_length),
+        DefineKeyValue(NONMAX, static_cast<unsigned>(nonmax)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto locate  = common::findKernel("locate_features", {src}, targs, options);
+    auto nonMax  = common::findKernel("non_max_counts", {src}, targs, options);
+    auto getFeat = common::findKernel("get_features", {src}, targs, options);
 
     const unsigned max_feat =
         ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
@@ -91,24 +73,22 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     const int blk_y = divup(in.info.dims[1] - edge * 2, FAST_THREADS_Y);
 
     // Locate features kernel sizes
-    const NDRange local(FAST_THREADS_X, FAST_THREADS_Y);
-    const NDRange global(blk_x * FAST_THREADS_X, blk_y * FAST_THREADS_Y);
+    const cl::NDRange local(FAST_THREADS_X, FAST_THREADS_Y);
+    const cl::NDRange global(blk_x * FAST_THREADS_X, blk_y * FAST_THREADS_Y);
 
-    auto lfOp = KernelFunctor<Buffer, KParam, Buffer, const float,
-                              const unsigned, LocalSpaceArg>(entry.ker[0]);
-
-    lfOp(EnqueueArgs(getQueue(), global, local), *in.data, in.info, *d_score,
-         thr, edge,
-         cl::Local((FAST_THREADS_X + 6) * (FAST_THREADS_Y + 6) * sizeof(T)));
+    locate(cl::EnqueueArgs(getQueue(), global, local), *in.data, in.info,
+           *d_score, thr, edge,
+           cl::Local((FAST_THREADS_X + 6) * (FAST_THREADS_Y + 6) * sizeof(T)));
     CL_DEBUG_FINISH(getQueue());
 
     const int blk_nonmax_x = divup(in.info.dims[0], 64);
     const int blk_nonmax_y = divup(in.info.dims[1], 64);
 
     // Nonmax kernel sizes
-    const NDRange local_nonmax(FAST_THREADS_NONMAX_X, FAST_THREADS_NONMAX_Y);
-    const NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X,
-                                blk_nonmax_y * FAST_THREADS_NONMAX_Y);
+    const cl::NDRange local_nonmax(FAST_THREADS_NONMAX_X,
+                                   FAST_THREADS_NONMAX_Y);
+    const cl::NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X,
+                                    blk_nonmax_y * FAST_THREADS_NONMAX_Y);
 
     unsigned count_init = 0;
     cl::Buffer *d_total = bufferAlloc(sizeof(unsigned));
@@ -121,10 +101,8 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     cl::Buffer *d_counts  = bufferAlloc(blocks_sz);
     cl::Buffer *d_offsets = bufferAlloc(blocks_sz);
 
-    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, KParam,
-                              const unsigned>(entry.ker[1]);
-    nmOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax), *d_counts,
-         *d_offsets, *d_total, *d_flags, *d_score, in.info, edge);
+    nonMax(cl::EnqueueArgs(getQueue(), global_nonmax, local_nonmax), *d_counts,
+           *d_offsets, *d_total, *d_flags, *d_score, in.info, edge);
     CL_DEBUG_FINISH(getQueue());
 
     unsigned total;
@@ -138,12 +116,9 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
         y_out.data     = bufferAlloc(out_sz);
         score_out.data = bufferAlloc(out_sz);
 
-        auto gfOp =
-            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                          KParam, const unsigned, const unsigned>(entry.ker[2]);
-        gfOp(EnqueueArgs(getQueue(), global_nonmax, local_nonmax), *x_out.data,
-             *y_out.data, *score_out.data, *d_flags, *d_counts, *d_offsets,
-             in.info, total, edge);
+        getFeat(cl::EnqueueArgs(getQueue(), global_nonmax, local_nonmax),
+                *x_out.data, *y_out.data, *score_out.data, *d_flags, *d_counts,
+                *d_offsets, in.info, total, edge);
         CL_DEBUG_FINISH(getQueue());
     }
 
@@ -172,20 +147,5 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     bufferFree(d_offsets);
 }
 
-template<typename T>
-void fast_dispatch(const unsigned arc_length, const bool nonmax,
-                   unsigned *out_feat, Param &x_out, Param &y_out,
-                   Param &score_out, Param in, const float thr,
-                   const float feature_ratio, const unsigned edge) {
-    if (!nonmax) {
-        fast<T, 0>(arc_length, out_feat, x_out, y_out, score_out, in, thr,
-                   feature_ratio, edge);
-    } else {
-        fast<T, 1>(arc_length, out_feat, x_out, y_out, score_out, in, thr,
-                   feature_ratio, edge);
-    }
-}
-
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 648ad8c12a..9d7b76e1d1 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -9,33 +9,23 @@
 
 #pragma once
 
-#include <backend.hpp>
-#include <cache.hpp>
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel_headers/fftconvolve_multiply.hpp>
 #include <kernel_headers/fftconvolve_pack.hpp>
 #include <kernel_headers/fftconvolve_reorder.hpp>
-#include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
 #include <af/defines.h>
 
-#include <type_traits>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS = 256;
+
+constexpr int THREADS = 256;
 
 void calcParamSizes(Param& sig_tmp, Param& filter_tmp, Param& packed,
                     Param& sig, Param& filter, const int baseDim,
@@ -77,37 +67,28 @@ template<typename convT, typename T>
 void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
                     AF_BATCH_KIND kind) {
     constexpr bool IsTypeDouble = std::is_same<T, double>::value;
-
-    std::string refName = std::string("pack_data_") +
-                          std::string(dtype_traits<convT>::getName()) +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(IsTypeDouble);
-
-    int device          = getActiveDeviceId();
-    kc_entry_t pdkEntry = kernelCache(device, refName);
-
-    if (pdkEntry.prog == 0 && pdkEntry.ker == 0) {
-        std::ostringstream options;
-
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T, convT>();
-
-        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
-        if (ctDType == c32) {
-            options << " -D CONVT=float";
-        } else if (ctDType == c64 && IsTypeDouble) {
-            options << " -D CONVT=double";
-        }
-
-        const char* ker_strs[] = {fftconvolve_pack_cl};
-        const int ker_lens[]   = {fftconvolve_pack_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        pdkEntry.prog = new Program(prog);
-        pdkEntry.ker  = new Kernel(*pdkEntry.prog, "pack_data");
-
-        addKernelToCache(device, refName, pdkEntry);
+    constexpr auto ctDType =
+        static_cast<af_dtype>(dtype_traits<convT>::af_type);
+
+    static const std::string src(fftconvolve_pack_cl, fftconvolve_pack_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateTypename<convT>(),
+        TemplateArg(IsTypeDouble),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    if (ctDType == c32) {
+        options.emplace_back(DefineKeyValue(CONVT, "float"));
+    } else if (ctDType == c64 && IsTypeDouble) {
+        options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
+    options.emplace_back(getTypeBuildDefinition<T, convT>());
+
+    auto packData = common::findKernel("pack_data", {src}, targs, options);
+    auto padArray = common::findKernel("pad_array", {src}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -123,59 +104,21 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
     int blocks = divup(sig_packed_elem, THREADS);
 
     // Locate features kernel sizes
-    NDRange local(THREADS);
-    NDRange global(blocks * THREADS);
+    cl::NDRange local(THREADS);
+    cl::NDRange global(blocks * THREADS);
 
     // Pack signal in a complex matrix where first dimension is half the input
     // (allows faster FFT computation) and pad array to a power of 2 with 0s
-    auto pdOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, const int, const int>(
-            *pdkEntry.ker);
-
-    pdOp(EnqueueArgs(getQueue(), global, local), *sig_tmp.data, sig_tmp.info,
-         *sig.data, sig.info, sig_half_d0, sig_half_d0_odd);
-
+    packData(cl::EnqueueArgs(getQueue(), global, local), *sig_tmp.data,
+             sig_tmp.info, *sig.data, sig.info, sig_half_d0, sig_half_d0_odd);
     CL_DEBUG_FINISH(getQueue());
 
-    refName = std::string("pack_array_") +
-              std::string(dtype_traits<convT>::getName()) +
-              std::string(dtype_traits<T>::getName()) +
-              std::to_string(IsTypeDouble);
-
-    kc_entry_t pakEntry = kernelCache(device, refName);
-
-    if (pakEntry.prog == 0 && pakEntry.ker == 0) {
-        std::ostringstream options;
-
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T, convT>();
-
-        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
-        if (ctDType == c32) {
-            options << " -D CONVT=float";
-        } else if (ctDType == c64 && IsTypeDouble) {
-            options << " -D CONVT=double";
-        }
-
-        const char* ker_strs[] = {fftconvolve_pack_cl};
-        const int ker_lens[]   = {fftconvolve_pack_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        pakEntry.prog = new Program(prog);
-        pakEntry.ker  = new Kernel(*pakEntry.prog, "pad_array");
-
-        addKernelToCache(device, refName, pakEntry);
-    }
-
     blocks = divup(filter_packed_elem, THREADS);
-    global = NDRange(blocks * THREADS);
+    global = cl::NDRange(blocks * THREADS);
 
     // Pad filter array with 0s
-    auto paOp = KernelFunctor<Buffer, KParam, Buffer, KParam>(*pakEntry.ker);
-
-    paOp(EnqueueArgs(getQueue(), global, local), *filter_tmp.data,
-         filter_tmp.info, *filter.data, filter.info);
-
+    padArray(cl::EnqueueArgs(getQueue(), global, local), *filter_tmp.data,
+             filter_tmp.info, *filter.data, filter.info);
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -183,41 +126,32 @@ template<typename convT, typename T>
 void complexMultiplyHelper(Param packed, Param sig, Param filter,
                            const int baseDim, AF_BATCH_KIND kind) {
     constexpr bool IsTypeDouble = std::is_same<T, double>::value;
-
-    std::string refName = std::string("complex_multiply_") +
-                          std::string(dtype_traits<convT>::getName()) +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(IsTypeDouble);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D AF_BATCH_NONE=" << (int)AF_BATCH_NONE
-                << " -D AF_BATCH_LHS=" << (int)AF_BATCH_LHS
-                << " -D AF_BATCH_RHS=" << (int)AF_BATCH_RHS
-                << " -D AF_BATCH_SAME=" << (int)AF_BATCH_SAME;
-        options << getTypeBuildDefinition<T, convT>();
-
-        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
-        if (ctDType == c32) {
-            options << " -D CONVT=float";
-        } else if (ctDType == c64 && IsTypeDouble) {
-            options << " -D CONVT=double";
-        }
-
-        const char* ker_strs[] = {fftconvolve_multiply_cl};
-        const int ker_lens[]   = {fftconvolve_multiply_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "complex_multiply");
-
-        addKernelToCache(device, refName, entry);
+    constexpr auto ctDType =
+        static_cast<af_dtype>(dtype_traits<convT>::af_type);
+
+    static const std::string src(fftconvolve_multiply_cl,
+                                 fftconvolve_multiply_cl_len);
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateTypename<convT>(),
+        TemplateArg(IsTypeDouble),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(AF_BATCH_NONE, static_cast<int>(AF_BATCH_NONE)),
+        DefineKeyValue(AF_BATCH_LHS, static_cast<int>(AF_BATCH_LHS)),
+        DefineKeyValue(AF_BATCH_RHS, static_cast<int>(AF_BATCH_RHS)),
+        DefineKeyValue(AF_BATCH_SAME, static_cast<int>(AF_BATCH_SAME)),
+    };
+    if (ctDType == c32) {
+        options.emplace_back(DefineKeyValue(CONVT, "float"));
+    } else if (ctDType == c64 && IsTypeDouble) {
+        options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
+    options.emplace_back(getTypeBuildDefinition<T, convT>());
+
+    auto cplxMul =
+        common::findKernel("complex_multiply", {src}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -227,20 +161,15 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
         filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
     int mul_elem = (sig_packed_elem < filter_packed_elem) ? filter_packed_elem
                                                           : sig_packed_elem;
-
     int blocks = divup(mul_elem, THREADS);
 
-    NDRange local(THREADS);
-    NDRange global(blocks * THREADS);
+    cl::NDRange local(THREADS);
+    cl::NDRange global(blocks * THREADS);
 
     // Multiply filter and signal FFT arrays
-    auto cmOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                              const int, const int>(*entry.ker);
-
-    cmOp(EnqueueArgs(getQueue(), global, local), *packed.data, packed.info,
-         *sig_tmp.data, sig_tmp.info, *filter_tmp.data, filter_tmp.info,
-         mul_elem, (int)kind);
-
+    cplxMul(cl::EnqueueArgs(getQueue(), global, local), *packed.data,
+            packed.info, *sig_tmp.data, sig_tmp.info, *filter_tmp.data,
+            filter_tmp.info, mul_elem, (int)kind);
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -248,41 +177,31 @@ template<typename T, typename convT>
 void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
                          const int baseDim, AF_BATCH_KIND kind, bool expand) {
     constexpr bool IsTypeDouble = std::is_same<T, double>::value;
-    constexpr bool RoundResult  = std::is_integral<T>::value;
-
-    std::string refName = std::string("reorder_output_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::string(dtype_traits<convT>::getName()) +
-                          std::to_string(IsTypeDouble) +
-                          std::to_string(RoundResult) + std::to_string(expand);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D ROUND_OUT=" << (int)RoundResult
-                << " -D EXPAND=" << (int)expand;
-        options << getTypeBuildDefinition<T, convT>();
-
-        auto ctDType = static_cast<af_dtype>(dtype_traits<convT>::af_type);
-        if (ctDType == c32) {
-            options << " -D CONVT=float";
-        } else if (ctDType == c64 && IsTypeDouble) {
-            options << " -D CONVT=double";
-        }
-
-        const char* ker_strs[] = {fftconvolve_reorder_cl};
-        const int ker_lens[]   = {fftconvolve_reorder_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "reorder_output");
-
-        addKernelToCache(device, refName, entry);
+    constexpr auto ctDType =
+        static_cast<af_dtype>(dtype_traits<convT>::af_type);
+    constexpr bool RoundResult = std::is_integral<T>::value;
+
+    static const std::string src(fftconvolve_reorder_cl,
+                                 fftconvolve_reorder_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),     TemplateTypename<convT>(),
+        TemplateArg(IsTypeDouble), TemplateArg(RoundResult),
+        TemplateArg(expand),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ROUND_OUT, static_cast<int>(RoundResult)),
+        DefineKeyValue(EXPAND, static_cast<int>(expand)),
+    };
+    if (ctDType == c32) {
+        options.emplace_back(DefineKeyValue(CONVT, "float"));
+    } else if (ctDType == c64 && IsTypeDouble) {
+        options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
+    options.emplace_back(getTypeBuildDefinition<T, convT>());
+
+    auto reorder = common::findKernel("reorder_output", {src}, targs, options);
 
     int fftScale = 1;
 
@@ -297,22 +216,18 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
 
     int blocks = divup(out.info.strides[3] * out.info.dims[3], THREADS);
 
-    NDRange local(THREADS);
-    NDRange global(blocks * THREADS);
-
-    auto roOp = KernelFunctor<Buffer, KParam, Buffer, KParam, KParam, const int,
-                              const int, const int>(*entry.ker);
+    cl::NDRange local(THREADS);
+    cl::NDRange global(blocks * THREADS);
 
     if (kind == AF_BATCH_RHS) {
-        roOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *filter_tmp.data, filter_tmp.info, filter.info, sig_half_d0,
-             baseDim, fftScale);
+        reorder(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *filter_tmp.data, filter_tmp.info, filter.info, sig_half_d0,
+                baseDim, fftScale);
     } else {
-        roOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, baseDim,
-             fftScale);
+        reorder(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, baseDim,
+                fftScale);
     }
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/fftconvolve_multiply.cl b/src/backend/opencl/kernel/fftconvolve_multiply.cl
index f824b9ddc6..e0bd2ea6d9 100644
--- a/src/backend/opencl/kernel/fftconvolve_multiply.cl
+++ b/src/backend/opencl/kernel/fftconvolve_multiply.cl
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void complex_multiply(__global CONVT *d_out, KParam oInfo,
-                               __global const CONVT *d_in1, KParam i1Info,
-                               __global const CONVT *d_in2, KParam i2Info,
-                               const int nelem, const int kind) {
+kernel void complex_multiply(global CONVT *d_out, KParam oInfo,
+                             global const CONVT *d_in1, KParam i1Info,
+                             global const CONVT *d_in2, KParam i2Info,
+                             const int nelem, const int kind) {
     const int t = get_global_id(0);
 
     if (t >= nelem) return;
diff --git a/src/backend/opencl/kernel/fftconvolve_pack.cl b/src/backend/opencl/kernel/fftconvolve_pack.cl
index 99af5b592d..cc72bc8495 100644
--- a/src/backend/opencl/kernel/fftconvolve_pack.cl
+++ b/src/backend/opencl/kernel/fftconvolve_pack.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void pack_data(__global CONVT *d_out, KParam oInfo,
-                        __global const T *d_in, KParam iInfo,
-                        const int di0_half, const int odd_di0) {
+kernel void pack_data(global CONVT *d_out, KParam oInfo,
+                      global const T *d_in, KParam iInfo,
+                      const int di0_half, const int odd_di0) {
     const int t = get_global_id(0);
 
     const int tMax = oInfo.strides[3] * oInfo.dims[3];
@@ -64,8 +64,8 @@ __kernel void pack_data(__global CONVT *d_out, KParam oInfo,
     }
 }
 
-__kernel void pad_array(__global CONVT *d_out, KParam oInfo,
-                        __global const T *d_in, KParam iInfo) {
+kernel void pad_array(global CONVT *d_out, KParam oInfo,
+                        global const T *d_in, KParam iInfo) {
     const int t = get_global_id(0);
 
     const int tMax = oInfo.strides[3] * oInfo.dims[3];
diff --git a/src/backend/opencl/kernel/fftconvolve_reorder.cl b/src/backend/opencl/kernel/fftconvolve_reorder.cl
index 5ccfa75855..f0064392f0 100644
--- a/src/backend/opencl/kernel/fftconvolve_reorder.cl
+++ b/src/backend/opencl/kernel/fftconvolve_reorder.cl
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void reorder_output(__global T *d_out, KParam oInfo,
-                             __global const CONVT *d_in, KParam iInfo,
-                             KParam fInfo, const int half_di0,
-                             const int baseDim, const int fftScale) {
+kernel void reorder_output(global T *d_out, KParam oInfo,
+                           global const CONVT *d_in, KParam iInfo,
+                           KParam fInfo, const int half_di0,
+                           const int baseDim, const int fftScale) {
     const int t = get_global_id(0);
 
     const int tMax = oInfo.strides[3] * oInfo.dims[3];
diff --git a/src/backend/opencl/kernel/flood_fill.cl b/src/backend/opencl/kernel/flood_fill.cl
index b74d4494c2..24e39a15fb 100644
--- a/src/backend/opencl/kernel/flood_fill.cl
+++ b/src/backend/opencl/kernel/flood_fill.cl
@@ -18,15 +18,14 @@
 /// to either zero or \p newValue for all valid pixels.
 
 #if defined(INIT_SEEDS)
-kernel
-void init_seeds(global T *out, KParam oInfo,
-                global const uint *seedsx, KParam sxInfo,
-                global const uint *seedsy, KParam syInfo) {
+kernel void init_seeds(global T *out, KParam oInfo, global const uint *seedsx,
+                       KParam sxInfo, global const uint *seedsy,
+                       KParam syInfo) {
     uint tid = get_global_id(0);
     if (tid < sxInfo.dims[0]) {
-        uint x = seedsx[ tid ];
-        uint y = seedsy[ tid ];
-        out[ (x * oInfo.strides[0] + y * oInfo.strides[1]) ] = VALID;
+        uint x                                             = seedsx[tid];
+        uint y                                             = seedsy[tid];
+        out[(x * oInfo.strides[0] + y * oInfo.strides[1])] = VALID;
     }
 }
 #endif
@@ -46,9 +45,9 @@ int barrierOR(local int *predicates) {
     return predicates[0];
 }
 
-kernel
-void flood_step(global T *out, KParam oInfo, global const T *img, KParam iInfo,
-                T lowValue, T highValue, global volatile int *notFinished) {
+kernel void flood_step(global T *out, KParam oInfo, global const T *img,
+                       KParam iInfo, T lowValue, T highValue,
+                       global volatile int *notFinished) {
     local T lmem[LMEM_HEIGHT][LMEM_WIDTH];
     local int predicates[GROUP_SIZE];
 
@@ -68,14 +67,15 @@ void flood_step(global T *out, KParam oInfo, global const T *img, KParam iInfo,
             int x      = gx2 - RADIUS;
             int y      = gy2 - RADIUS;
             bool inROI = (x >= 0 && x < d0 && y >= 0 && y < d1);
-            lmem[b][a] = (inROI ? out[ x*s0+y*s1 ] : INVALID);
+            lmem[b][a] = (inROI ? out[x * s0 + y * s1] : INVALID);
         }
     }
     int i = lx + RADIUS;
     int j = ly + RADIUS;
 
-    T tImgVal = img[(clamp(gx, 0, (int)(iInfo.dims[0]-1)) * iInfo.strides[0] +
-                     clamp(gy, 0, (int)(iInfo.dims[1]-1)) * iInfo.strides[1])];
+    T tImgVal =
+        img[(clamp(gx, 0, (int)(iInfo.dims[0] - 1)) * iInfo.strides[0] +
+             clamp(gy, 0, (int)(iInfo.dims[1] - 1)) * iInfo.strides[1])];
     const int isPxBtwnThresholds =
         (tImgVal >= lowValue && tImgVal <= highValue);
 
@@ -84,8 +84,7 @@ void flood_step(global T *out, KParam oInfo, global const T *img, KParam iInfo,
     barrier(CLK_LOCAL_MEM_FENCE);
 
     T origOutVal     = lmem[j][i];
-    bool isBorderPxl = (lx == 0 || ly == 0 ||
-                        lx == (get_local_size(0) - 1) ||
+    bool isBorderPxl = (lx == 0 || ly == 0 || lx == (get_local_size(0) - 1) ||
                         ly == (get_local_size(1) - 1));
 
     for (bool blkChngd = true; blkChngd; blkChngd = barrierOR(predicates)) {
@@ -104,8 +103,8 @@ void flood_step(global T *out, KParam oInfo, global const T *img, KParam iInfo,
 
     T newOutVal = lmem[j][i];
 
-    bool brdrChngd  = (isBorderPxl &&
-                       newOutVal != origOutVal && newOutVal == VALID);
+    bool brdrChngd =
+        (isBorderPxl && newOutVal != origOutVal && newOutVal == VALID);
     predicates[tid] = brdrChngd;
 
     brdrChngd = barrierOR(predicates) > 0;
@@ -117,19 +116,18 @@ void flood_step(global T *out, KParam oInfo, global const T *img, KParam iInfo,
             // of this block
             atomic_inc(notFinished);
         }
-        out[ (gx*s0 + gy*s1) ] = lmem[j][i];
+        out[(gx * s0 + gy * s1)] = lmem[j][i];
     }
 }
 #endif
 
 #if defined(FINALIZE_OUTPUT)
-kernel
-void finalize_output(global T* out, KParam oInfo, T newValue) {
+kernel void finalize_output(global T *out, KParam oInfo, T newValue) {
     uint gx = get_global_id(0);
     uint gy = get_global_id(1);
     if (gx < oInfo.dims[0] && gy < oInfo.dims[1]) {
         uint idx = gx * oInfo.strides[0] + gy * oInfo.strides[1];
-        T val = out[idx];
+        T val    = out[idx];
         out[idx] = (val == VALID ? newValue : ZERO);
     }
 }
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 9faa2a8fe6..d643d8bf20 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -10,22 +10,15 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/flood_fill.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
@@ -38,67 +31,46 @@ constexpr int VALID     = 2;
 constexpr int INVALID   = 1;
 constexpr int ZERO      = 0;
 
+static inline std::string floodfillSrc() {
+    static const std::string src(flood_fill_cl, flood_fill_cl_len);
+    return src;
+}
+
 template<typename T>
 void initSeeds(Param out, const Param seedsx, const Param seedsy) {
-    std::string refName =
-        std::string("init_seeds_") + std::string(dtype_traits<T>::getName());
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D VALID=" << T(VALID) << " -D INIT_SEEDS";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {flood_fill_cl};
-        const int ker_lens[]   = {flood_fill_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "init_seeds");
-        addKernelToCache(device, refName, entry);
-    }
-    auto initSeedsOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const Buffer, const KParam>(*entry.ker);
-    NDRange local(kernel::THREADS, 1, 1);
-    NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
-
-    initSeedsOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-                *seedsx.data, seedsx.info, *seedsy.data, seedsy.info);
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(VALID),
+        DefineKey(INIT_SEEDS),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto initSeeds = common::findKernel("init_seeds", {floodfillSrc()},
+                                        {TemplateTypename<T>()}, options);
+    cl::NDRange local(kernel::THREADS, 1, 1);
+    cl::NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
+
+    initSeeds(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *seedsx.data, seedsx.info, *seedsy.data, seedsy.info);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void finalizeOutput(Param out, const T newValue) {
-    std::string refName = std::string("finalize_output_") +
-                          std::string(dtype_traits<T>::getName());
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D VALID=" << T(VALID) << " -D ZERO=" << T(ZERO)
-                << " -D FINALIZE_OUTPUT";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {flood_fill_cl};
-        const int ker_lens[]   = {flood_fill_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "finalize_output");
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto finalizeOut = KernelFunctor<Buffer, const KParam, const T>(*entry.ker);
-
-    NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
-    NDRange global(divup(out.info.dims[0], local[0]) * local[0],
-                   divup(out.info.dims[1], local[1]) * local[1], 1);
-    finalizeOut(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(VALID),
+        DefineValue(ZERO),
+        DefineKey(FINALIZE_OUTPUT),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto finalizeOut = common::findKernel("finalize_output", {floodfillSrc()},
+                                          {TemplateTypename<T>()}, options);
+    cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
+    cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
+                       divup(out.info.dims[1], local[1]) * local[1], 1);
+    finalizeOut(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                 newValue);
     CL_DEBUG_FINISH(getQueue());
 }
@@ -108,59 +80,46 @@ void floodFill(Param out, const Param image, const Param seedsx,
                const Param seedsy, const T newValue, const T lowValue,
                const T highValue, const af::connectivity nlookup) {
     constexpr int RADIUS = 1;
+
     UNUSED(nlookup);
-    std::string refName =
-        std::string("flood_step_") + std::string(dtype_traits<T>::getName());
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D RADIUS=" << RADIUS
-                << " -D LMEM_WIDTH=" << (THREADS_X + 2 * RADIUS)
-                << " -D LMEM_HEIGHT=" << (THREADS_Y + 2 * RADIUS)
-                << " -D GROUP_SIZE=" << (THREADS_Y * THREADS_X)
-                << " -D VALID=" << T(VALID) << " -D INVALID=" << T(INVALID)
-                << " -D ZERO=" << T(ZERO) << " -D FLOOD_FILL_STEP";
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {flood_fill_cl};
-        const int ker_lens[]   = {flood_fill_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "flood_step");
-
-        addKernelToCache(device, refName, entry);
-    }
-    auto floodStep =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam, const T,
-                      const T, Buffer>(*entry.ker);
-    NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
-    NDRange global(divup(out.info.dims[0], local[0]) * local[0],
-                   divup(out.info.dims[1], local[1]) * local[1], 1);
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(RADIUS),
+        DefineValue(VALID),
+        DefineValue(INVALID),
+        DefineValue(ZERO),
+        DefineKey(FLOOD_FILL_STEP),
+        DefineKeyValue(LMEM_WIDTH, (THREADS_X + 2 * RADIUS)),
+        DefineKeyValue(LMEM_HEIGHT, (THREADS_Y + 2 * RADIUS)),
+        DefineKeyValue(GROUP_SIZE, (THREADS_Y * THREADS_X)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto floodStep = common::findKernel("flood_step", {floodfillSrc()},
+                                        {TemplateTypename<T>()}, options);
+    cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
+    cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
+                       divup(out.info.dims[1], local[1]) * local[1], 1);
 
     initSeeds<T>(out, seedsx, seedsy);
 
     int notFinished       = 1;
-    cl::Buffer *dContinue = bufferAlloc(sizeof(int));
+    cl::Buffer* dContinue = bufferAlloc(sizeof(int));
 
     while (notFinished) {
         notFinished = 0;
         getQueue().enqueueWriteBuffer(*dContinue, CL_TRUE, 0, sizeof(int),
                                       &notFinished);
 
-        floodStep(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-                  *image.data, image.info, lowValue, highValue, *dContinue);
+        floodStep(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                  out.info, *image.data, image.info, lowValue, highValue,
+                  *dContinue);
         CL_DEBUG_FINISH(getQueue());
 
         getQueue().enqueueReadBuffer(*dContinue, CL_TRUE, 0, sizeof(int),
                                      &notFinished);
     }
-
     bufferFree(dContinue);
-
     finalizeOutput<T>(out, newValue);
 }
 
diff --git a/src/backend/opencl/kernel/gradient.cl b/src/backend/opencl/kernel/gradient.cl
index a378c84e2f..e3698ee9b8 100644
--- a/src/backend/opencl/kernel/gradient.cl
+++ b/src/backend/opencl/kernel/gradient.cl
@@ -24,11 +24,9 @@
 
 #define sidx(y, x) scratch[((y + 1) * (TX + 2)) + (x + 1)]
 
-__kernel void gradient_kernel(__global T *d_grad0, const KParam grad0,
-                              __global T *d_grad1, const KParam grad1,
-                              __global const T *d_in, const KParam in,
-                              const int blocksPerMatX,
-                              const int blocksPerMatY) {
+kernel void gradient(global T *d_grad0, const KParam grad0, global T *d_grad1,
+                     const KParam grad1, global const T *d_in, const KParam in,
+                     const int blocksPerMatX, const int blocksPerMatY) {
     const int idz = get_group_id(0) / blocksPerMatX;
     const int idw = get_group_id(1) / blocksPerMatY;
 
@@ -59,14 +57,14 @@ __kernel void gradient_kernel(__global T *d_grad0, const KParam grad0,
     int g1dx = idw * grad1.strides[3] + idz * grad1.strides[2] +
                idy * grad1.strides[1] + idx;
 
-    __local T scratch[(TY + 2) * (TX + 2)];
+    local T scratch[(TY + 2) * (TX + 2)];
 
     // Multipliers - 0.5 for interior, 1 for edge cases
     float xf = 0.5 * (1 + (idx == 0 || idx >= (in.dims[0] - 1)));
     float yf = 0.5 * (1 + (idy == 0 || idy >= (in.dims[1] - 1)));
 
     // Copy data to scratch space
-    T zero = ZERO;
+    T zero = (T)(ZERO);
     if (cond) {
         sidx(ty, tx) = zero;
     } else {
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 60bfac0b95..fddb319fe3 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -8,79 +8,53 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/gradient.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <string>
-#include "config.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const int TX = 32;
-static const int TY = 8;
 
 template<typename T>
 void gradient(Param grad0, Param grad1, const Param in) {
-    std::string refName = std::string("gradient_kernel_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr int TX = 32;
+    constexpr int TY = 8;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D TX=" << TX
-                << " -D TY=" << TY << " -D ZERO=" << toNumStr(scalar<T>(0));
+    static const std::string src(gradient_cl, gradient_cl_len);
 
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D CPLX=1";
-        } else {
-            options << " -D CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(TX),
+        DefineValue(TY),
+        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(CPLX, static_cast<int>(af::iscplx<T>())),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {gradient_cl};
-        const int ker_lens[]   = {gradient_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "gradient_kernel");
+    auto gradOp = common::findKernel("gradient", {src}, targs, options);
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto gradOp =
-        KernelFunctor<Buffer, const KParam, Buffer, const KParam, const Buffer,
-                      const KParam, const int, const int>(*entry.ker);
-
-    NDRange local(TX, TY, 1);
+    cl::NDRange local(TX, TY, 1);
 
     int blocksPerMatX = divup(in.info.dims[0], TX);
     int blocksPerMatY = divup(in.info.dims[1], TY);
-    NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                   local[1] * blocksPerMatY * in.info.dims[3], 1);
+    cl::NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                       local[1] * blocksPerMatY * in.info.dims[3], 1);
 
-    gradOp(EnqueueArgs(getQueue(), global, local), *grad0.data, grad0.info,
+    gradOp(cl::EnqueueArgs(getQueue(), global, local), *grad0.data, grad0.info,
            *grad1.data, grad1.info, *in.data, in.info, blocksPerMatX,
            blocksPerMatY);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/harris.cl b/src/backend/opencl/kernel/harris.cl
index 1c84a168b8..a849145a51 100644
--- a/src/backend/opencl/kernel/harris.cl
+++ b/src/backend/opencl/kernel/harris.cl
@@ -9,10 +9,9 @@
 
 #define MAX_VAL(A, B) (A) < (B) ? (B) : (A)
 
-__kernel void second_order_deriv(__global T* ixx_out, __global T* ixy_out,
-                                 __global T* iyy_out, const unsigned in_len,
-                                 __global const T* ix_in,
-                                 __global const T* iy_in) {
+kernel void second_order_deriv(global T* ixx_out, global T* ixy_out,
+                               global T* iyy_out, const dim_t in_len,
+                               global const T* ix_in, global const T* iy_in) {
     const unsigned x = get_global_id(0);
 
     if (x < in_len) {
@@ -22,11 +21,10 @@ __kernel void second_order_deriv(__global T* ixx_out, __global T* ixy_out,
     }
 }
 
-__kernel void harris_responses(__global T* resp_out, const unsigned idim0,
-                               const unsigned idim1, __global const T* ixx_in,
-                               __global const T* ixy_in,
-                               __global const T* iyy_in, const float k_thr,
-                               const unsigned border_len) {
+kernel void harris_responses(global T* resp_out, const unsigned idim0,
+                             const unsigned idim1, global const T* ixx_in,
+                             global const T* ixy_in, global const T* iyy_in,
+                             const float k_thr, const unsigned border_len) {
     const unsigned r = border_len;
 
     const unsigned x = get_global_id(0) + r;
@@ -44,12 +42,11 @@ __kernel void harris_responses(__global T* resp_out, const unsigned idim0,
     }
 }
 
-__kernel void non_maximal(__global float* x_out, __global float* y_out,
-                          __global float* resp_out, __global unsigned* count,
-                          __global const T* resp_in, const unsigned idim0,
-                          const unsigned idim1, const float min_resp,
-                          const unsigned border_len,
-                          const unsigned max_corners) {
+kernel void non_maximal(global float* x_out, global float* y_out,
+                        global float* resp_out, global unsigned* count,
+                        global const T* resp_in, const unsigned idim0,
+                        const unsigned idim1, const float min_resp,
+                        const unsigned border_len, const unsigned max_corners) {
     // Responses on the border don't have 8-neighbors to compare, discard them
     const unsigned r = border_len + 1;
 
@@ -83,13 +80,11 @@ __kernel void non_maximal(__global float* x_out, __global float* y_out,
     }
 }
 
-__kernel void keep_corners(__global float* x_out, __global float* y_out,
-                           __global float* score_out,
-                           __global const float* x_in,
-                           __global const float* y_in,
-                           __global const float* score_in,
-                           __global const unsigned* score_idx,
-                           const unsigned n_feat) {
+kernel void keep_corners(global float* x_out, global float* y_out,
+                         global float* score_out, global const float* x_in,
+                         global const float* y_in, global const float* score_in,
+                         global const unsigned* score_idx,
+                         const unsigned n_feat) {
     unsigned f = get_global_id(0);
 
     if (f < n_feat) {
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 9f700d2aac..d958155e6d 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -7,29 +7,27 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cache.hpp>
+#pragma once
+
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/gradient.hpp>
 #include <kernel/range.hpp>
 #include <kernel/sort_by_key.hpp>
 #include <kernel_headers/harris.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <af/constants.h>
 #include <af/defines.h>
 
-#include <tuple>
+#include <array>
+#include <string>
 #include <vector>
 
 namespace opencl {
 namespace kernel {
-static const unsigned HARRIS_THREADS_PER_GROUP = 256;
-static const unsigned HARRIS_THREADS_X         = 16;
-static const unsigned HARRIS_THREADS_Y =
-    HARRIS_THREADS_PER_GROUP / HARRIS_THREADS_X;
 
 template<typename T>
 void gaussian1D(T *out, const int dim, double sigma = 0.0) {
@@ -63,63 +61,44 @@ void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy,
 }
 
 template<typename T>
-std::tuple<cl::Kernel *, cl::Kernel *, cl::Kernel *, cl::Kernel *>
-getHarrisKernels() {
-    using cl::Kernel;
-    using cl::Program;
-    static const char *kernelNames[4] = {"second_order_deriv", "keep_corners",
-                                         "harris_responses", "non_maximal"};
-
-    kc_entry_t entries[4];
-
-    int device = getActiveDeviceId();
-
-    std::string checkName = kernelNames[0] + std::string("_") +
-                            std::string(dtype_traits<T>::getName());
-
-    entries[0] = kernelCache(device, checkName);
-
-    if (entries[0].prog == 0 && entries[0].ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {harris_cl};
-        const int ker_lens[]   = {harris_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-
-        for (int i = 0; i < 4; ++i) {
-            entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
-
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName());
-
-            addKernelToCache(device, name, entries[i]);
-        }
-    } else {
-        for (int i = 1; i < 4; ++i) {
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName());
-
-            entries[i] = kernelCache(device, name);
-        }
-    }
-
-    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker,
-                           entries[3].ker);
+std::array<Kernel, 4> getHarrisKernels() {
+    static const std::string src(harris_cl, harris_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    return {
+        common::findKernel("second_order_deriv", {src}, targs, options),
+        common::findKernel("keep_corners", {src}, targs, options),
+        common::findKernel("harris_responses", {src}, targs, options),
+        common::findKernel("non_maximal", {src}, targs, options),
+    };
 }
 
 template<typename T, typename convAccT>
 void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
             Param in, const unsigned max_corners, const float min_response,
             const float sigma, const unsigned filter_len, const float k_thr) {
-    auto kernels = getHarrisKernels<T>();
+    constexpr unsigned HARRIS_THREADS_PER_GROUP = 256;
+    constexpr unsigned HARRIS_THREADS_X         = 16;
+    constexpr unsigned HARRIS_THREADS_Y =
+        HARRIS_THREADS_PER_GROUP / HARRIS_THREADS_X;
+
     using cl::Buffer;
     using cl::EnqueueArgs;
     using cl::NDRange;
 
+    auto kernels = getHarrisKernels<T>();
+    auto soOp    = kernels[0];
+    auto kcOp    = kernels[1];
+    auto hrOp    = kernels[2];
+    auto nmOp    = kernels[3];
+
     // Window filter
     std::vector<convAccT> h_filter(filter_len);
     // Decide between rectangular or circular filter
@@ -151,9 +130,6 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
     const NDRange local_so(HARRIS_THREADS_PER_GROUP, 1);
     const NDRange global_so(blk_x_so * HARRIS_THREADS_PER_GROUP, 1);
 
-    auto soOp = KernelFunctor<Buffer, Buffer, Buffer, unsigned, Buffer, Buffer>(
-        *std::get<0>(kernels));
-
     // Compute second-order derivatives
     soOp(EnqueueArgs(getQueue(), global_so, local_so), *ixx.get(), *ixy.get(),
          *iyy.get(), in.info.dims[3] * in.info.strides[3], *ix.get(),
@@ -175,13 +151,10 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
     const NDRange global_hr(blk_x_hr * HARRIS_THREADS_X,
                             blk_y_hr * HARRIS_THREADS_Y);
 
-    auto hrOp = KernelFunctor<Buffer, unsigned, unsigned, Buffer, Buffer,
-                              Buffer, float, unsigned>(*std::get<2>(kernels));
-
     // Calculate Harris responses for all pixels
     hrOp(EnqueueArgs(getQueue(), global_hr, local_hr), *d_responses,
-         in.info.dims[0], in.info.dims[1], *ixx.get(), *ixy.get(), *iyy.get(),
-         k_thr, border_len);
+         static_cast<uint>(in.info.dims[0]), static_cast<uint>(in.info.dims[1]),
+         *ixx.get(), *ixy.get(), *iyy.get(), k_thr, border_len);
     CL_DEBUG_FINISH(getQueue());
 
     // Number of corners is not known a priori, limit maximum number of corners
@@ -199,14 +172,11 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
 
     const float min_r = (max_corners > 0) ? 0.f : min_response;
 
-    auto nmOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                              unsigned, float, unsigned, unsigned>(
-        *std::get<3>(kernels));
-
     // Perform non-maximal suppression
     nmOp(EnqueueArgs(getQueue(), global_hr, local_hr), *d_x_corners,
          *d_y_corners, *d_resp_corners, *d_corners_found, *d_responses,
-         in.info.dims[0], in.info.dims[1], min_r, border_len, corner_lim);
+         static_cast<uint>(in.info.dims[0]), static_cast<uint>(in.info.dims[1]),
+         min_r, border_len, corner_lim);
     CL_DEBUG_FINISH(getQueue());
 
     getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned),
@@ -269,10 +239,6 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
         const NDRange local_kc(HARRIS_THREADS_PER_GROUP, 1);
         const NDRange global_kc(blk_x_kc * HARRIS_THREADS_PER_GROUP, 1);
 
-        auto kcOp =
-            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                          Buffer, unsigned>(*std::get<1>(kernels));
-
         // Keep only the first corners_to_keep corners with higher Harris
         // responses
         kcOp(EnqueueArgs(getQueue(), global_kc, local_kc), *x_out.data,
@@ -304,5 +270,6 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
         resp_out.data = d_resp_corners;
     }
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/histogram.cl b/src/backend/opencl/kernel/histogram.cl
index 3821b985bf..8fb30fbb5d 100644
--- a/src/backend/opencl/kernel/histogram.cl
+++ b/src/backend/opencl/kernel/histogram.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void histogram(__global outType *d_dst, KParam oInfo,
-                        __global const inType *d_src, KParam iInfo,
-                        __local outType *localMem, int len, int nbins,
+kernel void histogram(global outType *d_dst, KParam oInfo,
+                        global const inType *d_src, KParam iInfo,
+                        local outType *localMem, int len, int nbins,
                         float minval, float maxval, int nBBS) {
     unsigned b2 = get_group_id(0) / nBBS;
     int start = (get_group_id(0) - b2 * nBBS) * THRD_LOAD * get_local_size(0) +
@@ -17,10 +17,10 @@ __kernel void histogram(__global outType *d_dst, KParam oInfo,
     int end = min((int)(start + THRD_LOAD * get_local_size(0)), len);
 
     // offset input and output to account for batch ops
-    __global const inType *in = d_src + b2 * iInfo.strides[2] +
+    global const inType *in = d_src + b2 * iInfo.strides[2] +
                                 get_group_id(1) * iInfo.strides[3] +
                                 iInfo.offset;
-    __global outType *out =
+    global outType *out =
         d_dst + b2 * oInfo.strides[2] + get_group_id(1) * oInfo.strides[3];
 
     float dx = (maxval - minval) / (float)nbins;
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 9a0568c2d8..0a53fd63b6 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -8,72 +8,55 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/histogram.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-constexpr int MAX_BINS  = 4000;
-constexpr int THREADS_X = 256;
-constexpr int THRD_LOAD = 16;
-
-template<typename inType, typename outType, bool isLinear>
-void histogram(Param out, const Param in, int nbins, float minval,
-               float maxval) {
-    std::string refName = std::string("histogram_") +
-                          std::string(dtype_traits<inType>::getName()) +
-                          std::string(dtype_traits<inType>::getName()) +
-                          std::to_string(isLinear);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D inType=" << dtype_traits<inType>::getName()
-                << " -D outType=" << dtype_traits<outType>::getName()
-                << " -D THRD_LOAD=" << THRD_LOAD << " -D MAX_BINS=" << MAX_BINS;
-        if (isLinear) options << " -D IS_LINEAR";
-        options << getTypeBuildDefinition<inType>();
-
-        const char* ker_strs[] = {histogram_cl};
-        const int ker_lens[]   = {histogram_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "histogram");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto histogramOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, int,
-                      int, float, float, int>(*entry.ker);
+template<typename inType, typename outType>
+void histogram(Param out, const Param in, int nbins, float minval, float maxval,
+               bool isLinear) {
+    constexpr int MAX_BINS  = 4000;
+    constexpr int THREADS_X = 256;
+    constexpr int THRD_LOAD = 16;
+
+    static const std::string src(histogram_cl, histogram_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<inType>(),
+        TemplateTypename<outType>(),
+        TemplateArg(isLinear),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(inType, dtype_traits<inType>::getName()),
+        DefineKeyValue(outType, dtype_traits<outType>::getName()),
+        DefineValue(THRD_LOAD),
+        DefineValue(MAX_BINS),
+    };
+    options.emplace_back(getTypeBuildDefinition<inType>());
+    if (isLinear) { options.emplace_back(DefineKey(IS_LINEAR)); }
+
+    auto histogram = common::findKernel("histogram", {src}, targs, options);
 
     int nElems  = in.info.dims[0] * in.info.dims[1];
     int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
     int locSize = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 1;
 
-    NDRange local(THREADS_X, 1);
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X, in.info.dims[3]);
-
-    histogramOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-                *in.data, in.info, cl::Local(locSize), nElems, nbins, minval,
-                maxval, blk_x);
+    cl::NDRange local(THREADS_X, 1);
+    cl::NDRange global(blk_x * in.info.dims[2] * THREADS_X, in.info.dims[3]);
 
+    histogram(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *in.data, in.info, cl::Local(locSize), nElems, nbins, minval,
+              maxval, blk_x);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/homography.cl b/src/backend/opencl/kernel/homography.cl
index fe01a3f926..07f9724147 100644
--- a/src/backend/opencl/kernel/homography.cl
+++ b/src/backend/opencl/kernel/homography.cl
@@ -9,8 +9,8 @@
 
 inline T sq(T a) { return a * a; }
 
-inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
-                       __local T* l_acc1, __local T* l_acc2, int m, int n) {
+inline void jacobi_svd(local T* l_V, __local T* l_S, __local T* l_d,
+                       local T* l_acc1, __local T* l_acc2, int m, int n) {
     const int iterations = 30;
 
     int tid_x = get_local_id(0);
@@ -47,11 +47,11 @@ inline void jacobi_svd(__local T* l_V, __local T* l_S, __local T* l_d,
     for (int it = 0; tcond && it < iterations; it++) {
         for (int i = 0; i < n - 1; i++) {
             for (int j = i + 1; j < n; j++) {
-                __local T* Si = l_S + soff + i * m;
-                __local T* Sj = l_S + soff + j * m;
+                local T* Si = l_S + soff + i * m;
+                local T* Sj = l_S + soff + j * m;
 
-                __local T* Vi = l_V + soff + i * n;
-                __local T* Vj = l_V + soff + j * n;
+                local T* Vi = l_V + soff + i * n;
+                local T* Vj = l_V + soff + j * n;
 
                 T p = (T)0;
                 for (int k = 0; k < m; k++) p += Si[k] * Sj[k];
@@ -119,11 +119,11 @@ inline int compute_mean_scale(float* x_src_mean, float* y_src_mean,
                               float* x_dst_mean, float* y_dst_mean,
                               float* src_scale, float* dst_scale,
                               float* src_pt_x, float* src_pt_y, float* dst_pt_x,
-                              float* dst_pt_y, __global const float* x_src,
-                              __global const float* y_src,
-                              __global const float* x_dst,
-                              __global const float* y_dst,
-                              __global const float* rnd, KParam rInfo, int i) {
+                              float* dst_pt_y, global const float* x_src,
+                              global const float* y_src,
+                              global const float* x_dst,
+                              global const float* y_dst,
+                              global const float* rnd, KParam rInfo, int i) {
     const unsigned ridx = rInfo.dims[0] * i;
     unsigned r[4]       = {(unsigned)rnd[ridx], (unsigned)rnd[ridx + 1],
                      (unsigned)rnd[ridx + 2], (unsigned)rnd[ridx + 3]};
@@ -164,12 +164,12 @@ inline int compute_mean_scale(float* x_src_mean, float* y_src_mean,
 
 #define LSPTR(Z, Y, X) (l_S[(Z)*81 + (Y)*9 + (X)])
 
-__kernel void compute_homography(__global T* H, KParam HInfo,
-                                 __global const float* x_src,
-                                 __global const float* y_src,
-                                 __global const float* x_dst,
-                                 __global const float* y_dst,
-                                 __global const float* rnd, KParam rInfo,
+kernel void compute_homography(global T* H, KParam HInfo,
+                                 global const float* x_src,
+                                 global const float* y_src,
+                                 global const float* x_dst,
+                                 global const float* y_dst,
+                                 global const float* rnd, KParam rInfo,
                                  const unsigned iterations) {
     unsigned i     = get_global_id(1);
     unsigned tid_y = get_local_id(1);
@@ -185,12 +185,12 @@ __kernel void compute_homography(__global T* H, KParam HInfo,
                            &src_scale, &dst_scale, src_pt_x, src_pt_y, dst_pt_x,
                            dst_pt_y, x_src, y_src, x_dst, y_dst, rnd, rInfo, i);
 
-    __local T l_acc1[256];
-    __local T l_acc2[256];
+    local T l_acc1[256];
+    local T l_acc2[256];
 
-    __local T l_S[16 * 81];
-    __local T l_V[16 * 81];
-    __local T l_d[16 * 9];
+    local T l_S[16 * 81];
+    local T l_V[16 * 81];
+    local T l_d[16 * 9];
 
     // Compute input matrix
     if (tid_x < 4) {
@@ -265,7 +265,7 @@ __kernel void compute_homography(__global T* H, KParam HInfo,
                    src_scale * x_src_mean * vH[6];
 
         const unsigned Hidx = HInfo.dims[0] * i;
-        __global T* H_ptr   = H + Hidx;
+        global T* H_ptr   = H + Hidx;
         for (int h = 0; h < 9; h++) H_ptr[h] = bad ? 0 : H_tmp[h];
     }
 }
@@ -274,18 +274,18 @@ __kernel void compute_homography(__global T* H, KParam HInfo,
 
 // LMedS:
 // http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node25.html
-__kernel void eval_homography(
-    __global unsigned* inliers, __global unsigned* idx, __global T* H,
-    KParam HInfo, __global float* err, KParam eInfo,
-    __global const float* x_src, __global const float* y_src,
-    __global const float* x_dst, __global const float* y_dst,
-    __global const float* rnd, const unsigned iterations,
+kernel void eval_homography(
+    global unsigned* inliers, __global unsigned* idx, __global T* H,
+    KParam HInfo, global float* err, KParam eInfo,
+    global const float* x_src, __global const float* y_src,
+    global const float* x_dst, __global const float* y_dst,
+    global const float* rnd, const unsigned iterations,
     const unsigned nsamples, const float inlier_thr) {
     unsigned tid_x = get_local_id(0);
     unsigned i     = get_global_id(0);
 
-    __local unsigned l_inliers[256];
-    __local unsigned l_idx[256];
+    local unsigned l_inliers[256];
+    local unsigned l_idx[256];
 
     l_inliers[tid_x] = 0;
     l_idx[tid_x]     = 0;
@@ -293,7 +293,7 @@ __kernel void eval_homography(
 
     if (i < iterations) {
         const unsigned Hidx = HInfo.dims[0] * i;
-        __global T* H_ptr   = H + Hidx;
+        global T* H_ptr   = H + Hidx;
         T H_tmp[9];
         for (int h = 0; h < 9; h++) H_tmp[h] = H_ptr[h];
 
@@ -351,15 +351,15 @@ __kernel void eval_homography(
 #endif
 }
 
-__kernel void compute_median(__global float* median, __global unsigned* idx,
-                             __global const float* err, KParam eInfo,
+kernel void compute_median(global float* median, __global unsigned* idx,
+                             global const float* err, KParam eInfo,
                              const unsigned iterations) {
     const unsigned tid = get_local_id(0);
     const unsigned bid = get_group_id(0);
     const unsigned i   = get_global_id(0);
 
-    __local float l_median[256];
-    __local unsigned l_idx[256];
+    local float l_median[256];
+    local unsigned l_idx[256];
 
     l_median[tid] = FLT_MAX;
     l_idx[tid]    = 0;
@@ -391,14 +391,14 @@ __kernel void compute_median(__global float* median, __global unsigned* idx,
 
 #define DIVUP(A, B) (((A) + (B)-1) / (B))
 
-__kernel void find_min_median(__global float* minMedian,
-                              __global unsigned* minIdx,
-                              __global const float* median, KParam mInfo,
-                              __global const unsigned* idx) {
+kernel void find_min_median(global float* minMedian,
+                              global unsigned* minIdx,
+                              global const float* median, KParam mInfo,
+                              global const unsigned* idx) {
     const unsigned tid = get_local_id(0);
 
-    __local float l_minMedian[256];
-    __local unsigned l_minIdx[256];
+    local float l_minMedian[256];
+    local unsigned l_minIdx[256];
 
     l_minMedian[tid] = FLT_MAX;
     l_minIdx[tid]    = 0;
@@ -431,17 +431,17 @@ __kernel void find_min_median(__global float* minMedian,
 
 #undef DIVUP
 
-__kernel void compute_lmeds_inliers(
-    __global unsigned* inliers, __global const T* H,
-    __global const float* x_src, __global const float* y_src,
-    __global const float* x_dst, __global const float* y_dst,
+kernel void compute_lmeds_inliers(
+    global unsigned* inliers, __global const T* H,
+    global const float* x_src, __global const float* y_src,
+    global const float* x_dst, __global const float* y_dst,
     const float minMedian, const unsigned nsamples) {
     unsigned tid = get_local_id(0);
     unsigned bid = get_group_id(0);
     unsigned i   = get_global_id(0);
 
-    __local T l_H[9];
-    __local unsigned l_inliers[256];
+    local T l_H[9];
+    local unsigned l_inliers[256];
 
     l_inliers[tid] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 48b61d53f7..79d1f1bba8 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -7,102 +7,72 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cache.hpp>
+#pragma once
+
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel/ireduce.hpp>
 #include <kernel/reduce.hpp>
 #include <kernel/sort.hpp>
 #include <kernel_headers/homography.hpp>
 #include <memory.hpp>
 #include <af/defines.h>
-#include <cfloat>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
-using std::vector;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-const int HG_THREADS_X = 16;
-const int HG_THREADS_Y = 16;
-const int HG_THREADS   = 256;
-
-template<typename T, af_homography_type htype>
-std::array<cl::Kernel*, 5> getHomographyKernels() {
-    static const unsigned NUM_KERNELS           = 5;
-    static const char* kernelNames[NUM_KERNELS] = {
-        "compute_homography", "eval_homography", "compute_median",
-        "find_min_median", "compute_lmeds_inliers"};
-
-    kc_entry_t entries[NUM_KERNELS];
-
-    int device = getActiveDeviceId();
-
-    std::string checkName = kernelNames[0] + std::string("_") +
-                            std::string(dtype_traits<T>::getName()) +
-                            std::to_string(htype);
-
-    entries[0] = kernelCache(device, checkName);
-
-    if (entries[0].prog == 0 && entries[0].ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-
-        options << getTypeBuildDefinition<T>();
-        if (std::is_same<T, double>::value) {
-            options << " -D EPS=" << DBL_EPSILON;
-        } else
-            options << " -D EPS=" << FLT_EPSILON;
-
-        if (htype == AF_HOMOGRAPHY_RANSAC)
-            options << " -D RANSAC";
-        else if (htype == AF_HOMOGRAPHY_LMEDS)
-            options << " -D LMEDS";
-
-        if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
-            options << " -D IS_CPU";
-        }
-
-        cl::Program prog;
-        buildProgram(prog, homography_cl, homography_cl_len, options.str());
-
-        for (unsigned i = 0; i < NUM_KERNELS; ++i) {
-            entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
-
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName()) +
-                               std::to_string(htype);
-
-            addKernelToCache(device, name, entries[i]);
-        }
-    } else {
-        for (unsigned i = 1; i < NUM_KERNELS; ++i) {
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName()) +
-                               std::to_string(htype);
-
-            entries[i] = kernelCache(device, name);
-        }
+constexpr int HG_THREADS_X = 16;
+constexpr int HG_THREADS_Y = 16;
+constexpr int HG_THREADS   = 256;
+
+template<typename T>
+std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
+    static const std::string src(homography_cl, homography_cl_len);
+
+    std::vector<TemplateArg> targs   = {TemplateTypename<T>(),
+                                      TemplateArg(htype)};
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(DefineKeyValue(
+        EPS, (std::is_same<T, double>::value ? DBL_EPSILON : FLT_EPSILON)));
+    if (htype == AF_HOMOGRAPHY_RANSAC) {
+        options.emplace_back(DefineKey(RANSAC));
     }
-
-    std::array<cl::Kernel*, NUM_KERNELS> retVal;
-    for (unsigned i = 0; i < NUM_KERNELS; ++i) retVal[i] = entries[i].ker;
-
-    return retVal;
+    if (htype == AF_HOMOGRAPHY_LMEDS) {
+        options.emplace_back(DefineKey(LMEDS));
+    }
+    if (getActiveDeviceType() == CL_DEVICE_TYPE_CPU) {
+        options.emplace_back(DefineKey(IS_CPU));
+    }
+    return {
+        common::findKernel("compute_homography", {src}, targs, options),
+        common::findKernel("eval_homography", {src}, targs, options),
+        common::findKernel("compute_median", {src}, targs, options),
+        common::findKernel("find_min_median", {src}, targs, options),
+        common::findKernel("compute_lmeds_inliers", {src}, targs, options),
+    };
 }
 
-template<typename T, af_homography_type htype>
+template<typename T>
 int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
              Param x_dst, Param y_dst, Param rnd, const unsigned iterations,
-             const unsigned nsamples, const float inlier_thr) {
-    auto kernels = getHomographyKernels<T, htype>();
+             const unsigned nsamples, const float inlier_thr,
+             const af_homography_type htype) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto kernels = getHomographyKernels<T>(htype);
+    auto chOp    = kernels[0];
+    auto ehOp    = kernels[1];
+    auto cmOp    = kernels[2];
+    auto fmOp    = kernels[3];
+    auto clOp    = kernels[4];
 
     const int blk_x_ch = 1;
     const int blk_y_ch = divup(iterations, HG_THREADS_Y);
@@ -110,13 +80,9 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
     const NDRange global_ch(blk_x_ch * HG_THREADS_X, blk_y_ch * HG_THREADS_Y);
 
     // Build linear system and solve SVD
-    auto chOp = KernelFunctor<Buffer, KParam, Buffer, Buffer, Buffer, Buffer,
-                              Buffer, KParam, unsigned>(*kernels[0]);
-
     chOp(EnqueueArgs(getQueue(), global_ch, local_ch), *H.data, H.info,
          *x_src.data, *y_src.data, *x_dst.data, *y_dst.data, *rnd.data,
          rnd.info, iterations);
-
     CL_DEBUG_FINISH(getQueue());
 
     const int blk_x_eh = divup(iterations, HG_THREADS);
@@ -151,14 +117,9 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
         median.data = bufferAlloc(sizeof(float));
 
     // Compute (and for RANSAC, evaluate) homographies
-    auto ehOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer, KParam,
-                              Buffer, Buffer, Buffer, Buffer, Buffer, unsigned,
-                              unsigned, float>(*kernels[1]);
-
     ehOp(EnqueueArgs(getQueue(), global_eh, local_eh), *inliers.data, *idx.data,
          *H.data, H.info, *err.data, err.info, *x_src.data, *y_src.data,
          *x_dst.data, *y_dst.data, *rnd.data, iterations, nsamples, inlier_thr);
-
     CL_DEBUG_FINISH(getQueue());
 
     unsigned inliersH, idxH;
@@ -171,12 +132,8 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
         float minMedian;
 
         // Compute median of every iteration
-        auto cmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, unsigned>(
-            *kernels[2]);
-
         cmOp(EnqueueArgs(getQueue(), global_eh, local_eh), *median.data,
              *idx.data, *err.data, err.info, iterations);
-
         CL_DEBUG_FINISH(getQueue());
 
         // Reduce medians, only in case iterations > 256
@@ -184,15 +141,11 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
             const NDRange local_fm(HG_THREADS);
             const NDRange global_fm(HG_THREADS);
 
-            cl::Buffer* finalMedian = bufferAlloc(sizeof(float));
-            cl::Buffer* finalIdx    = bufferAlloc(sizeof(unsigned));
-
-            auto fmOp = KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer>(
-                *kernels[3]);
+            Buffer* finalMedian = bufferAlloc(sizeof(float));
+            Buffer* finalIdx    = bufferAlloc(sizeof(unsigned));
 
             fmOp(EnqueueArgs(getQueue(), global_fm, local_fm), *finalMedian,
                  *finalIdx, *median.data, median.info, *idx.data);
-
             CL_DEBUG_FINISH(getQueue());
 
             getQueue().enqueueReadBuffer(*finalMedian, CL_TRUE, 0,
@@ -217,13 +170,9 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
         const NDRange local_cl(HG_THREADS);
         const NDRange global_cl(blk_x_cl * HG_THREADS);
 
-        auto clOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer,
-                                  Buffer, float, unsigned>(*kernels[4]);
-
         clOp(EnqueueArgs(getQueue(), global_cl, local_cl), *inliers.data,
              *bestH.data, *x_src.data, *y_src.data, *x_dst.data, *y_dst.data,
              minMedian, nsamples);
-
         CL_DEBUG_FINISH(getQueue());
 
         // Adds up the total number of inliers
@@ -242,7 +191,7 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
         bufferFree(totalInliers.data);
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
         unsigned blockIdx;
-        inliersH = kernel::ireduce_all<unsigned, af_max_t>(&blockIdx, inliers);
+        inliersH = kernel::ireduceAll<unsigned, af_max_t>(&blockIdx, inliers);
 
         // Copies back index and number of inliers of best homography estimation
         getQueue().enqueueReadBuffer(*idx.data, CL_TRUE,
diff --git a/src/backend/opencl/kernel/hsv_rgb.cl b/src/backend/opencl/kernel/hsv_rgb.cl
index d5308903c2..5fd7a060b4 100644
--- a/src/backend/opencl/kernel/hsv_rgb.cl
+++ b/src/backend/opencl/kernel/hsv_rgb.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-kernel void convert(global T* out, KParam oInfo, global const T* in,
-                    KParam iInfo, int nBBS) {
+kernel void hsvrgbConvert(global T* out, KParam oInfo, global const T* in,
+                          KParam iInfo, int nBBS) {
     // batch offsets
     unsigned batchId    = get_group_id(0) / nBBS;
     global const T* src = in + (batchId * iInfo.strides[3]);
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 40ecbbcc03..2257dc5ab9 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -8,69 +8,50 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/hsv_rgb.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-template<typename T, bool isHSV2RGB>
-void hsv2rgb_convert(Param out, const Param in) {
-    std::string refName = std::string("hsvrgb_convert_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(isHSV2RGB);
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+template<typename T>
+void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
+    static const std::string src(hsv_rgb_cl, hsv_rgb_cl_len);
 
-        if (isHSV2RGB) options << " -D isHSV2RGB";
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(isHSV2RGB),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+    if (isHSV2RGB) { options.emplace_back(DefineKey(isHSV2RGB)); }
 
-        const char* ker_strs[] = {hsv_rgb_cl};
-        const int ker_lens[]   = {hsv_rgb_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "convert");
+    auto convert = common::findKernel("hsvrgbConvert", {src}, targs, options);
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
     // all images are three channels, so batch
     // parameter would be along 4th dimension
-    NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
-
-    auto hsvrgbOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, int>(*entry.ker);
-
-    hsvrgbOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, blk_x);
+    cl::NDRange global(blk_x * in.info.dims[3] * THREADS_X, blk_y * THREADS_Y);
 
+    convert(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            *in.data, in.info, blk_x);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/identity.cl b/src/backend/opencl/kernel/identity.cl
index 0c0144c31f..383aee601b 100644
--- a/src/backend/opencl/kernel/identity.cl
+++ b/src/backend/opencl/kernel/identity.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void identity_kernel(__global T *oData, KParam oInfo, int groups_x,
-                              int groups_y) {
+kernel void identity_kernel(global T *oData, KParam oInfo, int groups_x,
+                            int groups_y) {
     unsigned idz = get_group_id(0) / groups_x;
     unsigned idw = get_group_id(1) / groups_y;
 
@@ -22,7 +22,7 @@ __kernel void identity_kernel(__global T *oData, KParam oInfo, int groups_x,
         idw >= oInfo.dims[3])
         return;
 
-    __global T *ptr = oData + idz * oInfo.strides[2] + idw * oInfo.strides[3];
-    T val           = (idx == idy) ? ONE : ZERO;
+    global T *ptr = oData + idz * oInfo.strides[2] + idw * oInfo.strides[3];
+    T val         = (idx == idy) ? (T)(ONE) : (T)(ZERO);
     ptr[idx + idy * oInfo.strides[1]] = val;
 }
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index a73b725518..ecebf34910 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -7,70 +7,51 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/identity.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include "config.hpp"
+
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
+
 template<typename T>
 static void identity(Param out) {
-    using af::scalar_to_option;
-    using cl::Buffer;
-    using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
-    using cl::NDRange;
-    using cl::Program;
-    using common::half;
-    using std::is_same;
-    using std::ostringstream;
-    using std::string;
-
-    string refName = std::string("identity_kernel") +
-                     std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D ONE=(T)("
-                << scalar_to_option(scalar<T>(1)) << ")"
-                << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")";
-        options << getTypeBuildDefinition<T>();
-
-        if (is_same<T, half>::value) { options << " -D USE_HALF"; }
-
-        const char* ker_strs[] = {identity_cl};
-        const int ker_lens[]   = {identity_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "identity_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(32, 8);
+    static const std::string src(identity_cl, identity_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ONE, af::scalar_to_option(scalar<T>(1))),
+        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto identityOp =
+        common::findKernel("identity_kernel", {src}, targs, options);
+
+    cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
     int groups_y = divup(out.info.dims[1], local[1]);
-    NDRange global(groups_x * out.info.dims[2] * local[0],
-                   groups_y * out.info.dims[3] * local[1]);
-
-    auto identityOp = KernelFunctor<Buffer, const KParam, int, int>(*entry.ker);
-
-    identityOp(EnqueueArgs(getQueue(), global, local), *(out.data), out.info,
-               groups_x, groups_y);
+    cl::NDRange global(groups_x * out.info.dims[2] * local[0],
+                       groups_y * out.info.dims[3] * local[1]);
 
+    identityOp(cl::EnqueueArgs(getQueue(), global, local), *(out.data),
+               out.info, groups_x, groups_y);
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/iir.cl b/src/backend/opencl/kernel/iir.cl
index 6a941c2e10..0292c6ba36 100644
--- a/src/backend/opencl/kernel/iir.cl
+++ b/src/backend/opencl/kernel/iir.cl
@@ -42,13 +42,13 @@ T __div(T lhs, T rhs) {
 #define __div(lhs, rhs) ((lhs) / (rhs))
 #endif
 
-__kernel void iir_kernel(__global T *yptr, const KParam yinfo,
-                         const __global T *cptr, const KParam cinfo,
-                         const __global T *aptr, const KParam ainfo,
+kernel void iir_kernel(global T *yptr, const KParam yinfo,
+                         const global T *cptr, const KParam cinfo,
+                         const global T *aptr, const KParam ainfo,
                          const int groups_y) {
-    __local T s_z[MAX_A_SIZE];
-    __local T s_a[MAX_A_SIZE];
-    __local T s_y;
+    local T s_z[MAX_A_SIZE];
+    local T s_a[MAX_A_SIZE];
+    local T s_y;
 
     const int idz = get_group_id(0);
     const int idw = get_group_id(1) / groups_y;
@@ -69,9 +69,9 @@ __kernel void iir_kernel(__global T *yptr, const KParam yinfo,
     int a_off = 0;
 #endif
 
-    __global T *d_y       = yptr + y_off;
-    const __global T *d_c = cptr + c_off + cinfo.offset;
-    const __global T *d_a = aptr + a_off + ainfo.offset;
+    global T *d_y       = yptr + y_off;
+    const global T *d_c = cptr + c_off + cinfo.offset;
+    const global T *d_a = aptr + a_off + ainfo.offset;
     const int repeat      = (num_a + get_local_size(0) - 1) / get_local_size(0);
 
     for (int ii = 0; ii < MAX_A_SIZE / get_local_size(0); ii++) {
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 56c9af00a4..4e6b1c0b7a 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -8,57 +8,41 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/iir.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
-#include <string>
 
-using af::scalar_to_option;
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
+
 template<typename T, bool batch_a>
 void iir(Param y, Param c, Param a) {
     // FIXME: This is a temporary fix. Ideally the local memory should be
     // allocted outside
-    static const int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
-
-    std::string refName = std::string("iir_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(batch_a);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D MAX_A_SIZE=" << MAX_A_SIZE << " -D BATCH_A=" << batch_a
-                << " -D ZERO=(T)(" << scalar_to_option(scalar<T>(0)) << ")"
-                << " -D T=" << dtype_traits<T>::getName();
+    static const std::string src(iir_cl, iir_cl_len);
 
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(batch_a),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(MAX_A_SIZE),
+        DefineKeyValue(BATCH_A, batch_a),
+        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {iir_cl};
-        const int ker_lens[]   = {iir_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "iir_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
+    auto iir = common::findKernel("iir_kernel", {src}, targs, options);
 
     const int groups_y = y.info.dims[1];
     const int groups_x = y.info.dims[2];
@@ -66,21 +50,18 @@ void iir(Param y, Param c, Param a) {
     int threads = 256;
     while (threads > (int)y.info.dims[0] && threads > 32) threads /= 2;
 
-    NDRange local(threads, 1);
-    NDRange global(groups_x * local[0], groups_y * y.info.dims[3] * local[1]);
-
-    auto iirOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int>(
-            *entry.ker);
+    cl::NDRange local(threads, 1);
+    cl::NDRange global(groups_x * local[0],
+                       groups_y * y.info.dims[3] * local[1]);
 
     try {
-        iirOp(EnqueueArgs(getQueue(), global, local), *y.data, y.info, *c.data,
-              c.info, *a.data, a.info, groups_y);
+        iir(cl::EnqueueArgs(getQueue(), global, local), *y.data, y.info,
+            *c.data, c.info, *a.data, a.info, groups_y);
     } catch (cl::Error& clerr) {
         AF_ERROR("Size of a too big for this datatype", AF_ERR_SIZE);
     }
-
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index f9819325c8..f780e528a2 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -8,27 +8,19 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/index.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
 
 typedef struct {
     int offs[4];
@@ -38,45 +30,30 @@ typedef struct {
 
 template<typename T>
 void index(Param out, const Param in, const IndexKernelParam_t& p,
-           Buffer* bPtr[4]) {
-    std::string refName =
-        std::string("indexKernel_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+           cl::Buffer* bPtr[4]) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
+    static const std::string src(index_cl, index_cl_len);
 
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {index_cl};
-        const int ker_lens[]   = {index_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "indexKernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(THREADS_X, THREADS_Y);
+    auto index = common::findKernel("indexKernel", {src},
+                                    {TemplateTypename<T>()}, options);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-                   blk_y * out.info.dims[3] * THREADS_Y);
-
-    auto indexOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
-                      Buffer, Buffer, Buffer, Buffer, int, int>(*entry.ker);
-
-    indexOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-            *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x,
-            blk_y);
+    cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
+                       blk_y * out.info.dims[3] * THREADS_Y);
 
+    index(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+          *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x,
+          blk_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index aa9c77ffde..5313ad8932 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -75,8 +75,8 @@ InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio,
 }
 
 #if INTERP_ORDER == 1
-void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
-                     __global const InterpInTy *d_in, KParam in, int ioff,
+void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
+                     global const InterpInTy *d_in, KParam in, int ioff,
                      InterpPosTy x, int method, int batch, bool clamp, int xdim,
                      int batch_dim) {
     InterpInTy zero = ZERO;
@@ -97,8 +97,8 @@ void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
     }
 }
 #elif INTERP_ORDER == 2
-void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
-                     __global const InterpInTy *d_in, KParam in, int ioff,
+void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
+                     global const InterpInTy *d_in, KParam in, int ioff,
                      InterpPosTy x, int method, int batch, bool clamp, int xdim,
                      int batch_dim) {
     const int grid_x        = floor(x);    // nearest grid
@@ -126,8 +126,8 @@ void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
     }
 }
 #elif INTERP_ORDER == 3
-void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
-                     __global const InterpInTy *d_in, KParam in, int ioff,
+void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
+                     global const InterpInTy *d_in, KParam in, int ioff,
                      InterpPosTy x, int method, int batch, bool clamp, int xdim,
                      int batch_dim) {
     const int grid_x        = floor(x);    // nearest grid
@@ -160,8 +160,8 @@ void interp1_general(__global InterpInTy *d_out, KParam out, int ooff,
 #endif
 
 #if INTERP_ORDER == 1
-void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
-                     __global const InterpInTy *d_in, KParam in, int ioff,
+void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
+                     global const InterpInTy *d_in, KParam in, int ioff,
                      InterpPosTy x, InterpPosTy y, int method, int batch,
                      bool clamp, int xdim, int ydim, int batch_dim) {
     int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
@@ -190,8 +190,8 @@ void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
     }
 }
 #elif INTERP_ORDER == 2
-void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
-                     __global const InterpInTy *d_in, KParam in, int ioff,
+void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
+                     global const InterpInTy *d_in, KParam in, int ioff,
                      InterpPosTy x, InterpPosTy y, int method, int batch,
                      bool clamp, int xdim, int ydim, int batch_dim) {
     const int grid_x        = floor(x);
@@ -233,8 +233,8 @@ void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
     }
 }
 #elif INTERP_ORDER == 3
-void interp2_general(__global InterpInTy *d_out, KParam out, int ooff,
-                     __global const InterpInTy *d_in, KParam in, int ioff,
+void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
+                     global const InterpInTy *d_in, KParam in, int ioff,
                      InterpPosTy x, InterpPosTy y, int method, int batch,
                      bool clamp, int xdim, int ydim, int batch_dim) {
     const int grid_x        = floor(x);
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
index 7b71d9395c..370e500322 100644
--- a/src/backend/opencl/kernel/interp.hpp
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -6,28 +6,37 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+
 #pragma once
 
+#include <common/TemplateArg.hpp>
 #include <af/defines.h>
-#include <string>
 
-#define ADD_ENUM_OPTION(options, name) \
-    do { options << " -D " #name "=" << name; } while (0)
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 
-static void addInterpEnumOptions(std::ostringstream &options) {
-    ADD_ENUM_OPTION(options, AF_INTERP_NEAREST);
-    ADD_ENUM_OPTION(options, AF_INTERP_LINEAR);
-    ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR);
-    ADD_ENUM_OPTION(options, AF_INTERP_CUBIC);
-    ADD_ENUM_OPTION(options, AF_INTERP_LOWER);
-    ADD_ENUM_OPTION(options, AF_INTERP_LINEAR_COSINE);
-    ADD_ENUM_OPTION(options, AF_INTERP_BILINEAR_COSINE);
-    ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC);
-    ADD_ENUM_OPTION(options, AF_INTERP_CUBIC_SPLINE);
-    ADD_ENUM_OPTION(options, AF_INTERP_BICUBIC_SPLINE);
+static void addInterpEnumOptions(std::vector<std::string>& options) {
+    std::vector<std::string> enOpts = {
+        DefineKeyValue(AF_INTERP_NEAREST, static_cast<int>(AF_INTERP_NEAREST)),
+        DefineKeyValue(AF_INTERP_LINEAR, static_cast<int>(AF_INTERP_LINEAR)),
+        DefineKeyValue(AF_INTERP_BILINEAR,
+                       static_cast<int>(AF_INTERP_BILINEAR)),
+        DefineKeyValue(AF_INTERP_CUBIC, static_cast<int>(AF_INTERP_CUBIC)),
+        DefineKeyValue(AF_INTERP_LOWER, static_cast<int>(AF_INTERP_LOWER)),
+        DefineKeyValue(AF_INTERP_LINEAR_COSINE,
+                       static_cast<int>(AF_INTERP_LINEAR_COSINE)),
+        DefineKeyValue(AF_INTERP_BILINEAR_COSINE,
+                       static_cast<int>(AF_INTERP_BILINEAR_COSINE)),
+        DefineKeyValue(AF_INTERP_BICUBIC, static_cast<int>(AF_INTERP_BICUBIC)),
+        DefineKeyValue(AF_INTERP_CUBIC_SPLINE,
+                       static_cast<int>(AF_INTERP_CUBIC_SPLINE)),
+        DefineKeyValue(AF_INTERP_BICUBIC_SPLINE,
+                       static_cast<int>(AF_INTERP_BICUBIC_SPLINE)),
+    };
+    options.insert(std::end(options), std::begin(enOpts), std::end(enOpts));
 }
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/iota.cl b/src/backend/opencl/kernel/iota.cl
index ef8ac16819..e7e5dccac4 100644
--- a/src/backend/opencl/kernel/iota.cl
+++ b/src/backend/opencl/kernel/iota.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void iota_kernel(__global T *out, const KParam op, const int s0,
-                          const int s1, const int s2, const int s3,
-                          const int blocksPerMatX, const int blocksPerMatY) {
+kernel void iota_kernel(global T *out, const KParam op, const int s0,
+                        const int s1, const int s2, const int s3,
+                        const int blocksPerMatX, const int blocksPerMatY) {
     const int oz = get_group_id(0) / blocksPerMatX;
     const int ow = get_group_id(1) / blocksPerMatY;
 
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 0d4cf2ee5f..2a1f784c1b 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -8,72 +8,49 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/iota.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <af/dim4.hpp>
+
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const int IOTA_TX = 32;
-static const int IOTA_TY = 8;
-static const int TILEX   = 512;
-static const int TILEY   = 32;
 
 template<typename T>
 void iota(Param out, const af::dim4& sdims) {
-    using cl::Buffer;
-    using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
-    using cl::NDRange;
-    using cl::Program;
-    using std::string;
-
-    std::string refName =
-        std::string("iota_kernel_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr int IOTA_TX = 32;
+    constexpr int IOTA_TY = 8;
+    constexpr int TILEX   = 512;
+    constexpr int TILEY   = 32;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
+    static const std::string src(iota_cl, iota_cl_len);
 
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {iota_cl};
-        const int ker_lens[]   = {iota_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "iota_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto iotaOp =
-        KernelFunctor<Buffer, const KParam, const int, const int, const int,
-                      const int, const int, const int>(*entry.ker);
-
-    NDRange local(IOTA_TX, IOTA_TY, 1);
+    auto iota = common::findKernel("iota_kernel", {src},
+                                   {TemplateTypename<T>()}, options);
+    cl::NDRange local(IOTA_TX, IOTA_TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], TILEX);
     int blocksPerMatY = divup(out.info.dims[1], TILEY);
-    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                   local[1] * blocksPerMatY * out.info.dims[3], 1);
-
-    iotaOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           sdims[0], sdims[1], sdims[2], sdims[3], blocksPerMatX,
-           blocksPerMatY);
+    cl::NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                       local[1] * blocksPerMatY * out.info.dims[3], 1);
 
+    iota(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+         static_cast<int>(sdims[0]), static_cast<int>(sdims[1]),
+         static_cast<int>(sdims[2]), static_cast<int>(sdims[3]), blocksPerMatX,
+         blocksPerMatY);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 9d8bcba263..92836e86e9 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -8,90 +8,64 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/iops.hpp>
 #include <kernel_headers/ireduce_dim.hpp>
 #include <kernel_headers/ireduce_first.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <memory>
-#include <mutex>
+
 #include <string>
-#include "config.hpp"
-#include "names.hpp"
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-using std::unique_ptr;
+#include <vector>
 
 namespace opencl {
-
 namespace kernel {
 
 template<typename T, af_op_t op>
-void ireduce_dim_launcher(Param out, cl::Buffer *oidx, Param in,
-                          cl::Buffer *iidx, const int dim, const int threads_y,
-                          const bool is_first, const uint groups_all[4],
-                          Param rlen) {
-    std::string ref_name =
-        std::string("ireduce_") + std::to_string(dim) + std::string("_") +
-        std::string(dtype_traits<T>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(is_first) +
-        std::string("_") + std::to_string(threads_y);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D kDim=" << dim
-                << " -D DIMY=" << threads_y << " -D THREADS_X=" << THREADS_X
-                << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
-                << " -D IS_FIRST=" << is_first;
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {iops_cl, ireduce_dim_cl};
-        const int ker_lens[]   = {iops_cl_len, ireduce_dim_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "ireduce_dim_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(THREADS_X, threads_y);
-    NDRange global(groups_all[0] * groups_all[2] * local[0],
-                   groups_all[1] * groups_all[3] * local[1]);
-
-    auto ireduceOp =
-        KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam, Buffer, uint,
-                      uint, uint, Buffer, KParam>(*entry.ker);
-
-    ireduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-              *oidx, *in.data, in.info, *iidx, groups_all[0], groups_all[1],
-              groups_all[dim], *rlen.data, rlen.info);
-
+void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
+                        const int dim, const int threads_y, const bool is_first,
+                        const uint groups_all[4], Param rlen) {
+    static const std::string src1(iops_cl, iops_cl_len);
+    static const std::string src2(ireduce_dim_cl, ireduce_dim_cl_len);
+
+    ToNumStr<T> toNumStr;
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(), TemplateArg(dim),       TemplateArg(op),
+        TemplateArg(is_first), TemplateArg(threads_y),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(kDim, dim),
+        DefineKeyValue(DIMY, threads_y),
+        DefineValue(THREADS_X),
+        DefineKeyValue(init, toNumStr(Binary<T, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<T>()),
+        DefineKeyValue(IS_FIRST, is_first),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto ireduceDim =
+        common::findKernel("ireduce_dim_kernel", {src1, src2}, targs, options);
+
+    cl::NDRange local(THREADS_X, threads_y);
+    cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
+                       groups_all[1] * groups_all[3] * local[1]);
+
+    ireduceDim(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+               *oidx, *in.data, in.info, *iidx, groups_all[0], groups_all[1],
+               groups_all[dim], *rlen.data, rlen.info);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
-void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
+void ireduceDim(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -117,74 +91,62 @@ void ireduce_dim(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
             tmp.info.strides[k] *= groups_all[dim];
     }
 
-    ireduce_dim_launcher<T, op>(tmp, tidx, in, tidx, dim, threads_y, true,
-                                groups_all, rlen);
+    ireduceDimLauncher<T, op>(tmp, tidx, in, tidx, dim, threads_y, true,
+                              groups_all, rlen);
 
     if (groups_all[dim] > 1) {
         groups_all[dim] = 1;
 
-        ireduce_dim_launcher<T, op>(out, oidx, tmp, tidx, dim, threads_y, false,
-                                    groups_all, rlen);
+        ireduceDimLauncher<T, op>(out, oidx, tmp, tidx, dim, threads_y, false,
+                                  groups_all, rlen);
         bufferFree(tmp.data);
         bufferFree(tidx);
     }
 }
 
 template<typename T, af_op_t op>
-void ireduce_first_launcher(Param out, cl::Buffer *oidx, Param in,
-                            cl::Buffer *iidx, const int threads_x,
-                            const bool is_first, const uint groups_x,
-                            const uint groups_y, Param rlen) {
-    std::string ref_name =
-        std::string("ireduce_0_") + std::string(dtype_traits<T>::getName()) +
-        std::string("_") + std::to_string(op) + std::string("_") +
-        std::to_string(is_first) + std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D DIMX=" << threads_x
-                << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                << " -D init=" << toNumStr(Binary<T, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<T>()
-                << " -D IS_FIRST=" << is_first;
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {iops_cl, ireduce_first_cl};
-        const int ker_lens[]   = {iops_cl_len, ireduce_first_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "ireduce_first_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-    NDRange global(groups_x * in.info.dims[2] * local[0],
-                   groups_y * in.info.dims[3] * local[1]);
+void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
+                          cl::Buffer *iidx, const int threads_x,
+                          const bool is_first, const uint groups_x,
+                          const uint groups_y, Param rlen) {
+    static const std::string src1(iops_cl, iops_cl_len);
+    static const std::string src2(ireduce_first_cl, ireduce_first_cl_len);
+
+    ToNumStr<T> toNumStr;
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(op),
+        TemplateArg(is_first),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(DIMX, threads_x),
+        DefineValue(THREADS_PER_GROUP),
+        DefineKeyValue(init, toNumStr(Binary<T, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<T>()),
+        DefineKeyValue(IS_FIRST, is_first),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto ireduceFirst = common::findKernel("ireduce_first_kernel", {src1, src2},
+                                           targs, options);
+
+    cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    cl::NDRange global(groups_x * in.info.dims[2] * local[0],
+                       groups_y * in.info.dims[3] * local[1]);
 
     uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-    auto ireduceOp =
-        KernelFunctor<Buffer, KParam, Buffer, Buffer, KParam, Buffer, uint,
-                      uint, uint, Buffer, KParam>(*entry.ker);
-
-    ireduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-              *oidx, *in.data, in.info, *iidx, groups_x, groups_y, repeat,
-              *rlen.data, rlen.info);
-
+    ireduceFirst(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                 out.info, *oidx, *in.data, in.info, *iidx, groups_x, groups_y,
+                 repeat, *rlen.data, rlen.info);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
-void ireduce_first(Param out, cl::Buffer *oidx, Param in, Param rlen) {
+void ireduceFirst(Param out, cl::Buffer *oidx, Param in, Param rlen) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
@@ -206,12 +168,12 @@ void ireduce_first(Param out, cl::Buffer *oidx, Param in, Param rlen) {
         for (int k = 1; k < 4; k++) tmp.info.strides[k] *= groups_x;
     }
 
-    ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
-                                  groups_x, groups_y, rlen);
+    ireduceFirstLauncher<T, op>(tmp, tidx, in, tidx, threads_x, true, groups_x,
+                                groups_y, rlen);
 
     if (groups_x > 1) {
-        ireduce_first_launcher<T, op>(out, oidx, tmp, tidx, threads_x, false, 1,
-                                      groups_y, rlen);
+        ireduceFirstLauncher<T, op>(out, oidx, tmp, tidx, threads_x, false, 1,
+                                    groups_y, rlen);
 
         bufferFree(tmp.data);
         bufferFree(tidx);
@@ -229,9 +191,9 @@ void ireduce(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
         rlen.data = new cl::Buffer();
     }
     if (dim == 0)
-        return ireduce_first<T, op>(out, oidx, in, rlen);
+        return ireduceFirst<T, op>(out, oidx, in, rlen);
     else
-        return ireduce_dim<T, op>(out, oidx, in, dim, rlen);
+        return ireduceDim<T, op>(out, oidx, in, dim, rlen);
 }
 
 #if defined(__GNUC__) || defined(__GNUG__)
@@ -287,7 +249,7 @@ struct MinMaxOp<af_max_t, T> {
 #endif
 
 template<typename T, af_op_t op>
-T ireduce_all(uint *loc, Param in) {
+T ireduceAll(uint *loc, Param in) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
@@ -298,7 +260,6 @@ T ireduce_all(uint *loc, Param in) {
             is_linear &= (in.info.strides[k] ==
                           (in.info.strides[k - 1] * in.info.dims[k - 1]));
         }
-
         if (is_linear) {
             in.info.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
@@ -322,8 +283,8 @@ T ireduce_all(uint *loc, Param in) {
         Param rlen;
         auto buff = std::make_unique<cl::Buffer>();
         rlen.data = buff.get();
-        ireduce_first_launcher<T, op>(tmp, tidx, in, tidx, threads_x, true,
-                                      groups_x, groups_y, rlen);
+        ireduceFirstLauncher<T, op>(tmp, tidx, in, tidx, threads_x, true,
+                                    groups_x, groups_y, rlen);
 
         std::vector<T> h_ptr(tmp_elements);
         std::vector<uint> h_iptr(tmp_elements);
@@ -358,7 +319,7 @@ T ireduce_all(uint *loc, Param in) {
         return Op.m_val;
 
     } else {
-        unique_ptr<T[]> h_ptr(new T[in_elements]);
+        std::unique_ptr<T[]> h_ptr(new T[in_elements]);
         T *h_ptr_raw = h_ptr.get();
 
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
@@ -374,5 +335,4 @@ T ireduce_all(uint *loc, Param in) {
 }
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/ireduce_dim.cl b/src/backend/opencl/kernel/ireduce_dim.cl
index 502df9c241..bf94c9c9a3 100644
--- a/src/backend/opencl/kernel/ireduce_dim.cl
+++ b/src/backend/opencl/kernel/ireduce_dim.cl
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
-                                 __global uint *olData, const __global T *iData,
-                                 KParam iInfo, const __global uint *ilData,
+kernel void ireduce_dim_kernel(global T *oData, KParam oInfo,
+                                 global uint *olData, const __global T *iData,
+                                 KParam iInfo, const global uint *ilData,
                                  uint groups_x, uint groups_y, uint group_dim,
-                                 __global uint *rlenptr, KParam rlen) {
+                                 global uint *rlenptr, KParam rlen) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * THREADS_X + lidx;
@@ -31,8 +31,10 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
     // in
     bool rlen_valid = (ids[0] < rlen.dims[0]) && (ids[1] < rlen.dims[1]) &&
                       (ids[2] < rlen.dims[2]) && (ids[3] < rlen.dims[3]);
-    rlenptr += (rlenptr && rlen_valid) ?  ids[3] * rlen.strides[3] + ids[2] * rlen.strides[2] +
-             ids[1] * rlen.strides[1] + ids[0] + rlen.offset : 0;
+    rlenptr += (rlenptr && rlen_valid)
+                   ? ids[3] * rlen.strides[3] + ids[2] * rlen.strides[2] +
+                         ids[1] * rlen.strides[1] + ids[0] + rlen.offset
+                   : 0;
 
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0] + oInfo.offset;
@@ -57,16 +59,17 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
     bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
                     (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
 
-    __local T s_val[THREADS_X * DIMY];
-    __local uint s_idx[THREADS_X * DIMY];
+    local T s_val[THREADS_X * DIMY];
+    local uint s_idx[THREADS_X * DIMY];
 
     T out_val    = init;
     uint out_idx = id_dim_in;
 
     uint lim = rlenptr ? *rlenptr : iInfo.dims[kDim];
-    lim = (IS_FIRST) ? min((uint)iInfo.dims[kDim], lim) : lim;
-    bool within_ragged_bounds = (IS_FIRST) ? (out_idx < lim) :
-                                ((rlenptr) ? (is_valid) && (*ilData < lim) : true);
+    lim      = (IS_FIRST) ? min((uint)iInfo.dims[kDim], lim) : lim;
+    bool within_ragged_bounds =
+        (IS_FIRST) ? (out_idx < lim)
+                   : ((rlenptr) ? (is_valid) && (*ilData < lim) : true);
     if (is_valid && id_dim_in < iInfo.dims[kDim] && within_ragged_bounds) {
         out_val = *iData;
         if (!IS_FIRST) out_idx = *ilData;
@@ -89,8 +92,8 @@ __kernel void ireduce_dim_kernel(__global T *oData, KParam oInfo,
     s_val[lid] = out_val;
     s_idx[lid] = out_idx;
 
-    __local T *s_vptr    = s_val + lid;
-    __local uint *s_iptr = s_idx + lid;
+    local T *s_vptr    = s_val + lid;
+    local uint *s_iptr = s_idx + lid;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (DIMY == 8) {
diff --git a/src/backend/opencl/kernel/ireduce_first.cl b/src/backend/opencl/kernel/ireduce_first.cl
index 784fb88641..428cc73b99 100644
--- a/src/backend/opencl/kernel/ireduce_first.cl
+++ b/src/backend/opencl/kernel/ireduce_first.cl
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
-                                   __global uint *olData,
-                                   const __global T *iData, KParam iInfo,
-                                   const __global uint *ilData, uint groups_x,
+kernel void ireduce_first_kernel(global T *oData, KParam oInfo,
+                                   global uint *olData,
+                                   const global T *iData, KParam iInfo,
+                                   const global uint *ilData, uint groups_x,
                                    uint groups_y, uint repeat,
-                                   __global uint *rlenptr, KParam rlen) {
+                                   global uint *rlenptr, KParam rlen) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * get_local_size(0) + lidx;
@@ -38,16 +38,17 @@ __kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
     olData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
               yid * oInfo.strides[1] + oInfo.offset;
 
-    rlenptr += (rlenptr) ?  wid * rlen.strides[3] + zid * rlen.strides[2] +
-             yid * rlen.strides[1] + rlen.offset : 0;
+    rlenptr += (rlenptr) ? wid * rlen.strides[3] + zid * rlen.strides[2] +
+                               yid * rlen.strides[1] + rlen.offset
+                         : 0;
 
     bool cond =
         (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
-    __local T s_val[THREADS_PER_GROUP];
-    __local uint s_idx[THREADS_PER_GROUP];
+    local T s_val[THREADS_PER_GROUP];
+    local uint s_idx[THREADS_PER_GROUP];
 
-    int last     = (xid + repeat * DIMX);
+    int last = (xid + repeat * DIMX);
 
     int minlen = rlenptr ? min(*rlenptr, (uint)iInfo.dims[0]) : iInfo.dims[0];
 
@@ -72,8 +73,8 @@ __kernel void ireduce_first_kernel(__global T *oData, KParam oInfo,
     s_idx[lid] = out_idx;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local T *s_vptr    = s_val + lidy * DIMX;
-    __local uint *s_iptr = s_idx + lidy * DIMX;
+    local T *s_vptr    = s_val + lidy * DIMX;
+    local uint *s_iptr = s_idx + lidy * DIMX;
 
     if (DIMX == 256) {
         if (lidx < 128) {
diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index f3b6b0518e..c9c3b7eb8c 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -27,8 +27,8 @@
 #define __neq(lhs, rhs) (lhs) != (rhs)
 
 #define __conj(in) (in)
-#define __real(in)(in)
-#define __imag(in)(0)
+#define __real(in) (in)
+#define __imag(in) (0)
 #define __abs(in) abs(in)
 
 #define __crealf(in) ((in).x)
diff --git a/src/backend/opencl/kernel/join.cl b/src/backend/opencl/kernel/join.cl
index b1e9de9112..884ec56d62 100644
--- a/src/backend/opencl/kernel/join.cl
+++ b/src/backend/opencl/kernel/join.cl
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void join_kernel(__global T *d_out, const KParam out,
-                          __global const T *d_in, const KParam in,
-                          const int o0, const int o1, const int o2,
-                          const int o3, const int blocksPerMatX,
-                          const int blocksPerMatY) {
+kernel void join_kernel(global T *d_out, const KParam out, global const T *d_in,
+                        const KParam in, const int o0, const int o1,
+                        const int o2, const int o3, const int blocksPerMatX,
+                        const int blocksPerMatY) {
     const int iz = get_group_id(0) / blocksPerMatX;
     const int iw = get_group_id(1) / blocksPerMatY;
 
@@ -31,8 +30,8 @@ __kernel void join_kernel(__global T *d_out, const KParam out,
         d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
 
         for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            __global T *d_in_  = d_in + iy * in.strides[1];
-            __global T *d_out_ = d_out + (iy + o1) * out.strides[1];
+            global T *d_in_  = d_in + iy * in.strides[1];
+            global T *d_out_ = d_out + (iy + o1) * out.strides[1];
 
             for (int ix = xx; ix < in.dims[0]; ix += incx) {
                 d_out_[ix + o0] = d_in_[ix];
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 6dafbaa647..9dbde81b5b 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -8,73 +8,50 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/join.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <map>
-#include <mutex>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const int TX    = 32;
-static const int TY    = 8;
-static const int TILEX = 256;
-static const int TILEY = 32;
 
 template<typename T>
 void join(Param out, const Param in, dim_t dim, const af::dim4 offset) {
-    std::string refName =
-        std::string("join_kernel_") + std::string(dtype_traits<T>::getName()) +
-        std::string(dtype_traits<T>::getName()) + std::to_string(dim);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr int TX    = 32;
+    constexpr int TY    = 8;
+    constexpr int TILEX = 256;
+    constexpr int TILEY = 32;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    static const std::string src(join_cl, join_cl_len);
 
-        const char* ker_strs[] = {join_cl};
-        const int ker_lens[]   = {join_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "join_kernel");
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto joinOp = KernelFunctor<Buffer, const KParam, const Buffer,
-                                const KParam, const int, const int, const int,
-                                const int, const int, const int>(*entry.ker);
-
-    NDRange local(TX, TY, 1);
+    auto join =
+        common::findKernel("join_kernel", {src},
+                           {TemplateTypename<T>(), TemplateArg(dim)}, options);
+    cl::NDRange local(TX, TY, 1);
 
     int blocksPerMatX = divup(in.info.dims[0], TILEX);
     int blocksPerMatY = divup(in.info.dims[1], TILEY);
-    NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                   local[1] * blocksPerMatY * in.info.dims[3], 1);
-
-    joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *in.data, in.info, offset[0], offset[1], offset[2], offset[3],
-           blocksPerMatX, blocksPerMatY);
+    cl::NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
+                       local[1] * blocksPerMatY * in.info.dims[3], 1);
 
+    join(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+         *in.data, in.info, static_cast<int>(offset[0]),
+         static_cast<int>(offset[1]), static_cast<int>(offset[2]),
+         static_cast<int>(offset[3]), blocksPerMatX, blocksPerMatY);
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/laset.cl b/src/backend/opencl/kernel/laset.cl
index 40c5933503..4efdbca814 100644
--- a/src/backend/opencl/kernel/laset.cl
+++ b/src/backend/opencl/kernel/laset.cl
@@ -69,7 +69,7 @@
 #define IS_EQUAL(lhs, rhs) ((rhs == lhs))
 #endif
 
-__kernel void laset_full(int m, int n, T offdiag, T diag, __global T *A,
+kernel void laset_full(int m, int n, T offdiag, T diag, global T *A,
                          unsigned long A_offset, int lda) {
     A += A_offset;
 
@@ -105,7 +105,7 @@ __kernel void laset_full(int m, int n, T offdiag, T diag, __global T *A,
 
     Code similar to zlacpy, zlat2c, clat2z.
 */
-__kernel void laset_lower(int m, int n, T offdiag, T diag, __global T *A,
+kernel void laset_lower(int m, int n, T offdiag, T diag, global T *A,
                           unsigned long A_offset, int lda) {
     A += A_offset;
 
@@ -138,7 +138,7 @@ __kernel void laset_lower(int m, int n, T offdiag, T diag, __global T *A,
 
     Code similar to zlacpy, zlat2c, clat2z.
 */
-__kernel void laset_upper(int m, int n, T offdiag, T diag, __global T *A,
+kernel void laset_upper(int m, int n, T offdiag, T diag, global T *A,
                           unsigned long A_offset, int lda) {
     A += A_offset;
 
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index dfbefdaf0e..dd4f04fa67 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -10,29 +10,18 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/laset.hpp>
 #include <magma_types.h>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int BLK_X = 64;
-static const int BLK_Y = 32;
 
 template<int num>
 const char *laset_name() {
@@ -54,46 +43,37 @@ const char *laset_name<2>() {
 template<typename T, int uplo>
 void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
            magma_int_t ldda, cl_command_queue queue) {
-    std::string refName = laset_name<uplo>() + std::string("_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(uplo);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D BLK_X=" << BLK_X << " -D BLK_Y=" << BLK_Y
-                << " -D IS_CPLX=" << af::iscplx<T>();
-
-        options << getTypeBuildDefinition<T>();
+    constexpr int BLK_X = 64;
+    constexpr int BLK_Y = 32;
+
+    static const std::string src(laset_cl, laset_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(uplo),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(BLK_X),
+        DefineValue(BLK_Y),
+        DefineKeyValue(IS_CPLX, static_cast<int>(af::iscplx<T>())),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char *ker_strs[] = {laset_cl};
-        const int ker_lens[]   = {laset_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, laset_name<uplo>());
-
-        addKernelToCache(device, refName, entry);
-    }
+    auto lasetOp =
+        common::findKernel(laset_name<uplo>(), {src}, targs, options);
 
     int groups_x = (m - 1) / BLK_X + 1;
     int groups_y = (n - 1) / BLK_Y + 1;
 
-    NDRange local(BLK_X, 1);
-    NDRange global(groups_x * local[0], groups_y * local[1]);
+    cl::NDRange local(BLK_X, 1);
+    cl::NDRange global(groups_x * local[0], groups_y * local[1]);
 
     // retain the cl_mem object during cl::Buffer creation
     cl::Buffer dAObj(dA, true);
 
-    auto lasetOp =
-        KernelFunctor<int, int, T, T, Buffer, unsigned long long, int>(
-            *entry.ker);
-
     cl::CommandQueue q(queue);
-    lasetOp(EnqueueArgs(q, global, local), m, n, offdiag, diag, dAObj,
+    lasetOp(cl::EnqueueArgs(q, global, local), m, n, offdiag, diag, dAObj,
             dA_offset, ldda);
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/laset_band.cl b/src/backend/opencl/kernel/laset_band.cl
index 01e3a6dacd..d3f0ddb683 100644
--- a/src/backend/opencl/kernel/laset_band.cl
+++ b/src/backend/opencl/kernel/laset_band.cl
@@ -40,7 +40,7 @@
     Thread assignment for m=10, n=12, k=4, nb=8. Each column is done in
  parallel.
 */
-__kernel void laset_band_upper(int m, int n, T offdiag, T diag, __global T *A,
+kernel void laset_band_upper(int m, int n, T offdiag, T diag, global T *A,
                                unsigned long off, int lda) {
     int k   = get_local_size(0);
     int ibx = get_group_id(0) * NB;
@@ -88,7 +88,7 @@ __kernel void laset_band_upper(int m, int n, T offdiag, T diag, __global T *A,
  parallel.
 */
 
-__kernel void laset_band_lower(int m, int n, T offdiag, T diag, __global T *A,
+kernel void laset_band_lower(int m, int n, T offdiag, T diag, global T *A,
                                unsigned long off, int lda) {
     // int k   = get_local_size(0);
     int ibx = get_group_id(0) * NB;
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 0c8da5eb47..0c80fc030d 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -8,26 +8,20 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/laset_band.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
+
 #if 0  // Needs to be enabled when unmqr2 is enabled
 static const int NB = 64;
 template<int num>
@@ -40,30 +34,19 @@ void laset_band(int m, int  n, int k,
                 T offdiag, T diag,
                 cl_mem dA, size_t dA_offset, magma_int_t ldda)
 {
-    std::string refName = laset_band_name<uplo>() + std::string("_") +
-        std::string(dtype_traits<T>::getName()) +
-        std::to_string(uplo);
-
-    int device = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(laset_band_cl, laset_band_cl_len);
 
-    if (entry.prog==0 && entry.ker==0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-            << " -D NB=" << NB
-            << " -D IS_CPLX=" << af::iscplx<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(), TemplateArg(uplo),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(NB),
+        DefineKeyValue(IS_CPLX, static_cast<int>(af::iscplx<T>())),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {laset_band_cl};
-        const int   ker_lens[] = {laset_band_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, laset_band_name<uplo>());
-
-        addKernelToCache(device, refName, entry);
-    }
+    auto lasetBandOp = common::findKernel(laset_band_name<uplo>(), {src}, targs, options);
 
     int threads = 1;
     int groups = 1;
@@ -76,13 +59,12 @@ void laset_band(int m, int  n, int k,
         groups = (std::min(m+k-1, n) - 1) / NB + 1;
     }
 
-    NDRange local(threads, 1);
-    NDRange global(threads * groups, 1);
+    cl::NDRange local(threads, 1);
+    cl::NDRange global(threads * groups, 1);
 
-    auto lasetBandOp = KernelFunctor<int, int, T, T, cl_mem, unsigned long long, int>(*entry.ker);
-
-    lasetBandOp(EnqueueArgs(getQueue(), global, local), m, n, offdiag, diag, dA, dA_offset, ldda);
+    lasetBandOp(cl::EnqueueArgs(getQueue(), global, local), m, n, offdiag, diag, dA, dA_offset, ldda);
 }
-#endif
+#endi
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/laswp.cl b/src/backend/opencl/kernel/laswp.cl
index 101fc39ab7..168ce52404 100644
--- a/src/backend/opencl/kernel/laswp.cl
+++ b/src/backend/opencl/kernel/laswp.cl
@@ -69,18 +69,18 @@ typedef struct {
 // Each GPU block processes one block-column of A.
 // Each thread goes down a column of A,
 // swapping rows according to pivots stored in params.
-__kernel void laswp(int n, __global T *dAT, unsigned long dAT_offset, int ldda,
+kernel void laswp(int n, global T *dAT, unsigned long dAT_offset, int ldda,
                     zlaswp_params_t params) {
     dAT += dAT_offset;
 
     int tid = get_local_id(0) + get_local_size(0) * get_group_id(0);
     if (tid < n) {
         dAT += tid;
-        __global T *A1 = dAT;
+        global T *A1 = dAT;
 
         for (int i1 = 0; i1 < params.npivots; ++i1) {
             int i2         = params.ipiv[i1];
-            __global T *A2 = dAT + i2 * ldda;
+            global T *A2 = dAT + i2 * ldda;
             T temp         = *A1;
             *A1            = *A2;
             *A2            = temp;
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 51b0d633fb..094ead3c07 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -10,28 +10,19 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/laswp.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int NTHREADS   = 256;
-static const int MAX_PIVOTS = 32;
+
+constexpr int MAX_PIVOTS = 32;
 
 typedef struct {
     int npivots;
@@ -41,41 +32,29 @@ typedef struct {
 template<typename T>
 void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
            const int *ipiv, int inci, cl::CommandQueue &queue) {
-    std::string refName =
-        std::string("laswp_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr int NTHREADS = 256;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D MAX_PIVOTS=" << MAX_PIVOTS;
+    static const std::string src(laswp_cl, laswp_cl_len);
 
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(MAX_PIVOTS),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        const char *ker_strs[] = {laswp_cl};
-        const int ker_lens[]   = {laswp_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "laswp");
-
-        addKernelToCache(device, refName, entry);
-    }
+    auto laswpOp = common::findKernel("laswp", {src}, targs, options);
 
     int groups = divup(n, NTHREADS);
-    NDRange local(NTHREADS);
-    NDRange global(groups * local[0]);
+    cl::NDRange local(NTHREADS);
+    cl::NDRange global(groups * local[0]);
     zlaswp_params_t params;
 
     // retain the cl_mem object during cl::Buffer creation
     cl::Buffer inObj(in, true);
 
-    auto laswpOp =
-        KernelFunctor<int, Buffer, unsigned long long, int, zlaswp_params_t>(
-            *entry.ker);
-
     for (int k = k1 - 1; k < k2; k += MAX_PIVOTS) {
         int pivots_left = k2 - k;
 
@@ -86,9 +65,10 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
 
         unsigned long long k_offset = offset + k * ldda;
 
-        laswpOp(EnqueueArgs(queue, global, local), n, inObj, k_offset, ldda,
+        laswpOp(cl::EnqueueArgs(queue, global, local), n, inObj, k_offset, ldda,
                 params);
     }
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 40d8da89bc..9a5b26abcf 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -10,77 +10,53 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/lookup.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 32;
-static const int THREADS_Y = 8;
 
-template<typename in_t, typename idx_t, unsigned dim>
-void lookup(Param out, const Param in, const Param indices) {
-    using cl::Buffer;
-    using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
-    using cl::NDRange;
-    using cl::Program;
-    using std::is_same;
-    using std::ostringstream;
-    using std::string;
-    using std::to_string;
+template<typename in_t, typename idx_t>
+void lookup(Param out, const Param in, const Param indices,
+            const unsigned dim) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
 
-    std::string refName =
-        string("lookupND_") + string(dtype_traits<in_t>::getName()) +
-        string(dtype_traits<idx_t>::getName()) + to_string(dim);
+    static const std::string src(lookup_cl, lookup_cl_len);
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<in_t>(),
+        TemplateTypename<idx_t>(),
+        TemplateArg(dim),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(in_t, dtype_traits<in_t>::getName()),
+        DefineKeyValue(idx_t, dtype_traits<idx_t>::getName()),
+        DefineKeyValue(DIM, dim),
+    };
+    options.emplace_back(getTypeBuildDefinition<in_t, idx_t>());
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        ostringstream options;
-        options << " -D in_t=" << dtype_traits<in_t>::getName()
-                << " -D idx_t=" << dtype_traits<idx_t>::getName()
-                << " -D DIM=" << dim;
-        options << getTypeBuildDefinition<in_t, idx_t>();
-
-        if (is_same<in_t, common::half>::value) { options << " -D USE_HALF"; }
-
-        const char* ker_strs[] = {lookup_cl};
-        const int ker_lens[]   = {lookup_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "lookupND");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
     int blk_y = divup(out.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-                   blk_y * out.info.dims[3] * THREADS_Y);
+    cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
+                       blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int, int>(
-            *entry.ker);
+    auto arrIdxOp = common::findKernel("lookupND", {src}, targs, options);
 
-    arrIdxOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    arrIdxOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/lu_split.cl b/src/backend/opencl/kernel/lu_split.cl
index 3a70ee668c..1b6986d4cf 100644
--- a/src/backend/opencl/kernel/lu_split.cl
+++ b/src/backend/opencl/kernel/lu_split.cl
@@ -7,10 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void lu_split_kernel(__global T *lptr, KParam linfo, __global T *uptr,
-                              KParam uinfo, const __global T *iptr,
-                              KParam iinfo, const int groups_x,
-                              const int groups_y) {
+kernel void luSplit(global T *lptr, KParam linfo, global T *uptr, KParam uinfo,
+                    const global T *iptr, KParam iinfo, const int groups_x,
+                    const int groups_y) {
     const int oz = get_group_id(0) / groups_x;
     const int ow = get_group_id(1) / groups_y;
 
@@ -23,9 +22,9 @@ __kernel void lu_split_kernel(__global T *lptr, KParam linfo, __global T *uptr,
     const int incy = groups_y * get_local_size(1);
     const int incx = groups_x * get_local_size(0);
 
-    __global T *d_l = lptr;
-    __global T *d_u = uptr;
-    __global T *d_i = iptr;
+    global T *d_l = lptr;
+    global T *d_u = uptr;
+    global T *d_i = iptr;
 
     if (oz < iinfo.dims[2] && ow < iinfo.dims[3]) {
         d_i = d_i + oz * iinfo.strides[2] + ow * iinfo.strides[3];
@@ -33,18 +32,18 @@ __kernel void lu_split_kernel(__global T *lptr, KParam linfo, __global T *uptr,
         d_u = d_u + oz * uinfo.strides[2] + ow * uinfo.strides[3];
 
         for (int oy = yy; oy < iinfo.dims[1]; oy += incy) {
-            __global T *Yd_i = d_i + oy * iinfo.strides[1];
-            __global T *Yd_l = d_l + oy * linfo.strides[1];
-            __global T *Yd_u = d_u + oy * uinfo.strides[1];
+            global T *Yd_i = d_i + oy * iinfo.strides[1];
+            global T *Yd_l = d_l + oy * linfo.strides[1];
+            global T *Yd_u = d_u + oy * uinfo.strides[1];
             for (int ox = xx; ox < iinfo.dims[0]; ox += incx) {
                 if (ox > oy) {
                     if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = Yd_i[ox];
-                    if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = ZERO;
+                    if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = (T)(ZERO);
                 } else if (oy > ox) {
-                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = ZERO;
+                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = (T)(ZERO);
                     if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = Yd_i[ox];
                 } else if (ox == oy) {
-                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = ONE;
+                    if (same_dims || oy < linfo.dims[1]) Yd_l[ox] = (T)(ONE);
                     if (!same_dims || ox < uinfo.dims[0]) Yd_u[ox] = Yd_i[ox];
                 }
             }
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index e993bc67c9..67107c1cc7 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -8,90 +8,62 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/lu_split.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
-#include <string>
 
-using af::scalar_to_option;
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 128;
-static const unsigned TILEY = 32;
-
-template<typename T, bool same_dims>
-void lu_split_launcher(Param lower, Param upper, const Param in) {
-    std::string refName = std::string("lu_split_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(same_dims);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D same_dims=" << same_dims << " -D ZERO=(T)("
-                << scalar_to_option(scalar<T>(0)) << ")"
-                << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
-
-        options << getTypeBuildDefinition<T>();
 
-        const char* ker_strs[] = {lu_split_cl};
-        const int ker_lens[]   = {lu_split_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "lu_split_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(TX, TY);
+template<typename T>
+void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 128;
+    constexpr unsigned TILEY = 32;
+
+    static const std::string src(lu_split_cl, lu_split_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(same_dims),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(same_dims),
+        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ONE, af::scalar_to_option(scalar<T>(1))),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto luSplit = common::findKernel("luSplit", {src}, targs, options);
+
+    cl::NDRange local(TX, TY);
 
     int groups_x = divup(in.info.dims[0], TILEX);
     int groups_y = divup(in.info.dims[1], TILEY);
 
-    NDRange global(groups_x * local[0] * in.info.dims[2],
-                   groups_y * local[1] * in.info.dims[3]);
-
-    auto lu_split_op =
-        KernelFunctor<Buffer, const KParam, Buffer, const KParam, const Buffer,
-                      const KParam, const int, const int>(*entry.ker);
-
-    lu_split_op(EnqueueArgs(getQueue(), global, local), *lower.data, lower.info,
-                *upper.data, upper.info, *in.data, in.info, groups_x, groups_y);
+    cl::NDRange global(groups_x * local[0] * in.info.dims[2],
+                       groups_y * local[1] * in.info.dims[3]);
 
+    luSplit(cl::EnqueueArgs(getQueue(), global, local), *lower.data, lower.info,
+            *upper.data, upper.info, *in.data, in.info, groups_x, groups_y);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
-void lu_split(Param lower, Param upper, const Param in) {
+void luSplit(Param lower, Param upper, const Param in) {
     bool same_dims = (lower.info.dims[0] == in.info.dims[0]) &&
                      (lower.info.dims[1] == in.info.dims[1]);
-
-    if (same_dims) {
-        lu_split_launcher<T, true>(lower, upper, in);
-    } else {
-        lu_split_launcher<T, false>(lower, upper, in);
-    }
+    luSplitLauncher<T>(lower, upper, in, same_dims);
 }
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index 27f96bfb72..ce8cd31dee 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -8,75 +8,64 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/matchTemplate.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-template<typename inType, typename outType, af_match_type mType, bool needMean>
-void matchTemplate(Param out, const Param srch, const Param tmplt) {
-    std::string refName = std::string("matchTemplate_") +
-                          std::string(dtype_traits<inType>::getName()) +
-                          std::string(dtype_traits<outType>::getName()) +
-                          std::to_string(mType) + std::to_string(needMean);
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+template<typename inType, typename outType>
+void matchTemplate(Param out, const Param srch, const Param tmplt,
+                   const af_match_type mType, const bool needMean) {
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D inType=" << dtype_traits<inType>::getName()
-                << " -D outType=" << dtype_traits<outType>::getName()
-                << " -D MATCH_T=" << mType << " -D NEEDMEAN=" << needMean
-                << " -D AF_SAD=" << AF_SAD << " -D AF_ZSAD=" << AF_ZSAD
-                << " -D AF_LSAD=" << AF_LSAD << " -D AF_SSD=" << AF_SSD
-                << " -D AF_ZSSD=" << AF_ZSSD << " -D AF_LSSD=" << AF_LSSD
-                << " -D AF_NCC=" << AF_NCC << " -D AF_ZNCC=" << AF_ZNCC
-                << " -D AF_SHD=" << AF_SHD;
-        options << getTypeBuildDefinition<outType>();
+    static const std::string src(matchTemplate_cl, matchTemplate_cl_len);
 
-        const char* ker_strs[] = {matchTemplate_cl};
-        const int ker_lens[]   = {matchTemplate_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "matchTemplate");
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<inType>(),
+        TemplateTypename<outType>(),
+        TemplateArg(mType),
+        TemplateArg(needMean),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(inType, dtype_traits<inType>::getName()),
+        DefineKeyValue(outType, dtype_traits<outType>::getName()),
+        DefineKeyValue(MATCH_T, static_cast<int>(mType)),
+        DefineKeyValue(NEEDMEAN, static_cast<int>(needMean)),
+        DefineKeyValue(AF_SAD, static_cast<int>(AF_SAD)),
+        DefineKeyValue(AF_ZSAD, static_cast<int>(AF_ZSAD)),
+        DefineKeyValue(AF_LSAD, static_cast<int>(AF_LSAD)),
+        DefineKeyValue(AF_SSD, static_cast<int>(AF_SSD)),
+        DefineKeyValue(AF_ZSSD, static_cast<int>(AF_ZSSD)),
+        DefineKeyValue(AF_LSSD, static_cast<int>(AF_LSSD)),
+        DefineKeyValue(AF_NCC, static_cast<int>(AF_NCC)),
+        DefineKeyValue(AF_ZNCC, static_cast<int>(AF_ZNCC)),
+        DefineKeyValue(AF_SHD, static_cast<int>(AF_SHD)),
+    };
+    options.emplace_back(getTypeBuildDefinition<outType>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto matchImgOp =
+        common::findKernel("matchTemplate", {src}, targs, options);
 
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(srch.info.dims[0], THREADS_X);
     int blk_y = divup(srch.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * srch.info.dims[2] * THREADS_X,
-                   blk_y * srch.info.dims[3] * THREADS_Y);
-
-    auto matchImgOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, int, int>(
-            *entry.ker);
+    cl::NDRange global(blk_x * srch.info.dims[2] * THREADS_X,
+                       blk_y * srch.info.dims[3] * THREADS_Y);
 
-    matchImgOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    matchImgOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 99bdef3bf7..7f2e417b5f 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -8,38 +8,24 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/mean_dim.hpp>
 #include <kernel_headers/mean_first.hpp>
 #include <kernel_headers/mean_ops.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include "config.hpp"
-#include "names.hpp"
 
-#include <map>
-#include <mutex>
 #include <string>
 #include <vector>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using common::half;
-using std::string;
-using std::vector;
-
 namespace opencl {
-
 namespace kernel {
 
 template<typename T, typename Tw>
@@ -104,98 +90,74 @@ struct MeanOp<cdouble, double> {
 };
 
 template<typename Ti, typename Tw, typename To>
-void mean_dim_launcher(Param out, Param owt, Param in, Param inWeight,
-                       const int dim, const int threads_y,
-                       const uint groups_all[4]) {
+void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
+                     const int dim, const int threads_y,
+                     const uint groups_all[4]) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
     bool input_weight = ((inWeight.info.dims[0] * inWeight.info.dims[1] *
                           inWeight.info.dims[2] * inWeight.info.dims[3]) != 0);
 
     bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                            owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-    std::string ref_name =
-        std::string("mean_") + std::to_string(dim) + std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<Tw>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(threads_y) + std::string("_") +
-        std::to_string(input_weight) + std::string("_") +
-        std::to_string(output_weight);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-        ToNumStr<Tw> twNumStr;
-        Transform<uint, Tw, af_add_t> transform_weight;
-
-        std::ostringstream options;
-        options << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D Tw=" << dtype_traits<Tw>::getName()
-                << " -D To=" << dtype_traits<To>::getName()
-                << " -D kDim=" << dim << " -D DIMY=" << threads_y
-                << " -D THREADS_X=" << THREADS_X
-                << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
-                << " -D init_Tw=" << twNumStr(transform_weight(0))
-                << " -D one_Tw=" << twNumStr(transform_weight(1));
-
-        if (input_weight) { options << " -D INPUT_WEIGHT"; }
-        if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
-        options << getTypeBuildDefinition<Ti, To>();
-
-        const char *ker_strs[] = {mean_ops_cl, mean_dim_cl};
-        const int ker_lens[]   = {mean_ops_cl_len, mean_dim_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "mean_dim_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    static const std::string src1(mean_ops_cl, mean_ops_cl_len);
+    static const std::string src2(mean_dim_cl, mean_dim_cl_len);
+
+    ToNumStr<To> toNumStr;
+    ToNumStr<Tw> twNumStr;
+    Transform<uint, Tw, af_add_t> transform_weight;
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<Ti>(),     TemplateTypename<To>(),
+        TemplateTypename<Tw>(),     TemplateArg(dim),
+        TemplateArg(threads_y),     TemplateArg(input_weight),
+        TemplateArg(output_weight),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(Tw, dtype_traits<Tw>::getName()),
+        DefineKeyValue(kDim, dim),
+        DefineKeyValue(DIMY, threads_y),
+        DefineValue(THREADS_X),
+        DefineKeyValue(init_To, toNumStr(Binary<To, af_add_t>::init())),
+        DefineKeyValue(init_Tw, twNumStr(transform_weight(0))),
+        DefineKeyValue(one_Tw, twNumStr(transform_weight(1))),
+    };
+    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+    if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
+    if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
+
+    auto meanOp = common::findKernel("meanDim", {src1, src2}, targs, options);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
                    groups_all[1] * groups_all[3] * local[1]);
 
     if (input_weight && output_weight) {
-        auto meanOp =
-            KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                          Buffer, KParam, uint, uint, uint>(*entry.ker);
-
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *owt.data, owt.info, *in.data, in.info, *inWeight.data,
                inWeight.info, groups_all[0], groups_all[1], groups_all[dim]);
     } else if (!input_weight && !output_weight) {
-        auto meanOp =
-            KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint>(
-                *entry.ker);
-
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *in.data, in.info, groups_all[0], groups_all[1],
                groups_all[dim]);
     } else if (input_weight && !output_weight) {
-        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                    KParam, uint, uint, uint>(*entry.ker);
-
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *in.data, in.info, *inWeight.data, inWeight.info, groups_all[0],
                groups_all[1], groups_all[dim]);
     } else if (!input_weight && output_weight) {
-        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                    KParam, uint, uint, uint>(*entry.ker);
-
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *owt.data, owt.info, *in.data, in.info, groups_all[0],
                groups_all[1], groups_all[dim]);
     }
-
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_dim(Param out, Param in, Param inWeight, int dim) {
+void meanDim(Param out, Param in, Param inWeight, int dim) {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -210,70 +172,60 @@ void mean_dim(Param out, Param in, Param inWeight, int dim) {
         d[dim]              = groups_all[dim];
         Array<To> tmpOut    = createEmptyArray<To>(d);
         Array<Tw> tmpWeight = createEmptyArray<Tw>(d);
-        mean_dim_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, dim,
-                                      threads_y, groups_all);
+        meanDimLauncher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, dim,
+                                    threads_y, groups_all);
 
         Param owt;
         groups_all[dim] = 1;
-        mean_dim_launcher<Ti, Tw, To>(out, owt, tmpOut, tmpWeight, dim,
-                                      threads_y, groups_all);
+        meanDimLauncher<Ti, Tw, To>(out, owt, tmpOut, tmpWeight, dim, threads_y,
+                                    groups_all);
     } else {
         Param tmpWeight;
-        mean_dim_launcher<Ti, Tw, To>(out, tmpWeight, in, inWeight, dim,
-                                      threads_y, groups_all);
+        meanDimLauncher<Ti, Tw, To>(out, tmpWeight, in, inWeight, dim,
+                                    threads_y, groups_all);
     }
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_first_launcher(Param out, Param owt, Param in, Param inWeight,
-                         const int threads_x, const uint groups_x,
-                         const uint groups_y) {
+void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
+                       const int threads_x, const uint groups_x,
+                       const uint groups_y) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
     bool input_weight = ((inWeight.info.dims[0] * inWeight.info.dims[1] *
                           inWeight.info.dims[2] * inWeight.info.dims[3]) != 0);
 
     bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                            owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-    std::string ref_name =
-        std::string("mean_0_") + std::string(dtype_traits<Ti>::getName()) +
-        std::string("_") + std::string(dtype_traits<Tw>::getName()) +
-        std::string("_") + std::string(dtype_traits<To>::getName()) +
-        std::string("_") + std::to_string(threads_x) + std::string("_") +
-        std::to_string(input_weight) + std::string("_") +
-        std::to_string(output_weight);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-        ToNumStr<Tw> twNumStr;
-        Transform<uint, Tw, af_add_t> transform_weight;
-
-        std::ostringstream options;
-        options << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D Tw=" << dtype_traits<Tw>::getName()
-                << " -D To=" << dtype_traits<To>::getName()
-                << " -D DIMX=" << threads_x
-                << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                << " -D init_To=" << toNumStr(Binary<To, af_add_t>::init())
-                << " -D init_Tw=" << twNumStr(transform_weight(0))
-                << " -D one_Tw=" << twNumStr(transform_weight(1));
-
-        if (input_weight) { options << " -D INPUT_WEIGHT"; }
-        if (output_weight) { options << " -D OUTPUT_WEIGHT"; }
-        options << getTypeBuildDefinition<Ti, To>();
-
-        const char *ker_strs[] = {mean_ops_cl, mean_first_cl};
-        const int ker_lens[]   = {mean_ops_cl_len, mean_first_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "mean_first_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    static const std::string src1(mean_ops_cl, mean_ops_cl_len);
+    static const std::string src2(mean_first_cl, mean_first_cl_len);
+
+    ToNumStr<To> toNumStr;
+    ToNumStr<Tw> twNumStr;
+    Transform<uint, Tw, af_add_t> transform_weight;
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<Ti>(),    TemplateTypename<To>(),
+        TemplateTypename<Tw>(),    TemplateArg(threads_x),
+        TemplateArg(input_weight), TemplateArg(output_weight),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(Tw, dtype_traits<Tw>::getName()),
+        DefineKeyValue(DIMX, threads_x),
+        DefineValue(THREADS_PER_GROUP),
+        DefineKeyValue(init_To, toNumStr(Binary<To, af_add_t>::init())),
+        DefineKeyValue(init_Tw, twNumStr(transform_weight(0))),
+        DefineKeyValue(one_Tw, twNumStr(transform_weight(1))),
+    };
+    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+    if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
+    if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
+
+    auto meanOp = common::findKernel("meanFirst", {src1, src2}, targs, options);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * in.info.dims[2] * local[0],
@@ -282,37 +234,26 @@ void mean_first_launcher(Param out, Param owt, Param in, Param inWeight,
     uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
     if (input_weight && output_weight) {
-        auto meanOp =
-            KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                          Buffer, KParam, uint, uint, uint>(*entry.ker);
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *owt.data, owt.info, *in.data, in.info, *inWeight.data,
                inWeight.info, groups_x, groups_y, repeat);
     } else if (!input_weight && !output_weight) {
-        auto meanOp =
-            KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint>(
-                *entry.ker);
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *in.data, in.info, groups_x, groups_y, repeat);
     } else if (input_weight && !output_weight) {
-        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                    KParam, uint, uint, uint>(*entry.ker);
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *in.data, in.info, *inWeight.data, inWeight.info, groups_x,
                groups_y, repeat);
     } else if (!input_weight && output_weight) {
-        auto meanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                    KParam, uint, uint, uint>(*entry.ker);
         meanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                *owt.data, owt.info, *in.data, in.info, groups_x, groups_y,
                repeat);
     }
-
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_first(Param out, Param in, Param inWeight) {
+void meanFirst(Param out, Param in, Param inWeight) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
@@ -346,13 +287,13 @@ void mean_first(Param out, Param in, Param inWeight) {
         tmpWeight.info = tmpOut.info;
     }
 
-    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, threads_x,
-                                    groups_x, groups_y);
+    meanFirstLauncher<Ti, Tw, To>(tmpOut, tmpWeight, in, inWeight, threads_x,
+                                  groups_x, groups_y);
 
     if (groups_x > 1) {
         // No Weight is needed when writing out the output.
-        mean_first_launcher<Ti, Tw, To>(out, noWeight, tmpOut, tmpWeight,
-                                        threads_x, 1, groups_y);
+        meanFirstLauncher<Ti, Tw, To>(out, noWeight, tmpOut, tmpWeight,
+                                      threads_x, 1, groups_y);
 
         bufferFree(tmpOut.data);
         bufferFree(tmpWeight.data);
@@ -360,21 +301,21 @@ void mean_first(Param out, Param in, Param inWeight) {
 }
 
 template<typename Ti, typename Tw, typename To>
-void mean_weighted(Param out, Param in, Param inWeight, int dim) {
+void meanWeighted(Param out, Param in, Param inWeight, int dim) {
     if (dim == 0)
-        return mean_first<Ti, Tw, To>(out, in, inWeight);
+        return meanFirst<Ti, Tw, To>(out, in, inWeight);
     else
-        return mean_dim<Ti, Tw, To>(out, in, inWeight, dim);
+        return meanDim<Ti, Tw, To>(out, in, inWeight, dim);
 }
 
 template<typename Ti, typename Tw, typename To>
 void mean(Param out, Param in, int dim) {
     Param noWeight;
-    mean_weighted<Ti, Tw, To>(out, in, noWeight, dim);
+    meanWeighted<Ti, Tw, To>(out, in, noWeight, dim);
 }
 
 template<typename T, typename Tw>
-T mean_all_weighted(Param in, Param inWeight) {
+T meanAllWeighted(Param in, Param inWeight) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
@@ -409,11 +350,11 @@ T mean_all_weighted(Param in, Param inWeight) {
         Array<T> tmpOut     = createEmptyArray<T>(groups_x);
         Array<Tw> tmpWeight = createEmptyArray<Tw>(groups_x);
 
-        mean_first_launcher<T, Tw, T>(tmpOut, tmpWeight, in, inWeight,
-                                      threads_x, groups_x, groups_y);
+        meanFirstLauncher<T, Tw, T>(tmpOut, tmpWeight, in, inWeight, threads_x,
+                                    groups_x, groups_y);
 
-        vector<T> h_ptr(tmpOut.elements());
-        vector<Tw> h_wptr(tmpWeight.elements());
+        std::vector<T> h_ptr(tmpOut.elements());
+        std::vector<Tw> h_wptr(tmpWeight.elements());
 
         getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0,
                                      sizeof(T) * tmpOut.elements(),
@@ -431,8 +372,8 @@ T mean_all_weighted(Param in, Param inWeight) {
 
         return static_cast<T>(Op.runningMean);
     } else {
-        vector<T> h_ptr(in_elements);
-        vector<Tw> h_wptr(in_elements);
+        std::vector<T> h_ptr(in_elements);
+        std::vector<Tw> h_wptr(in_elements);
 
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
                                      sizeof(T) * in.info.offset,
@@ -453,7 +394,7 @@ T mean_all_weighted(Param in, Param inWeight) {
 }
 
 template<typename Ti, typename Tw, typename To>
-To mean_all(Param in) {
+To meanAll(Param in) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
     bool is_linear = (in.info.strides[0] == 1);
@@ -485,11 +426,11 @@ To mean_all(Param in) {
         Array<Tw> tmpCt  = createEmptyArray<Tw>(outDims);
 
         Param iWt;
-        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x,
-                                        groups_x, groups_y);
+        meanFirstLauncher<Ti, Tw, To>(tmpOut, tmpCt, in, iWt, threads_x,
+                                      groups_x, groups_y);
 
-        vector<To> h_ptr(tmpOut.elements());
-        vector<Tw> h_cptr(tmpOut.elements());
+        std::vector<To> h_ptr(tmpOut.elements());
+        std::vector<Tw> h_cptr(tmpOut.elements());
 
         getQueue().enqueueReadBuffer(*tmpOut.get(), CL_TRUE, 0,
                                      sizeof(To) * tmpOut.elements(),
@@ -507,7 +448,7 @@ To mean_all(Param in) {
 
         return static_cast<To>(Op.runningMean);
     } else {
-        vector<Ti> h_ptr(in_elements);
+        std::vector<Ti> h_ptr(in_elements);
 
         getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
                                      sizeof(Ti) * in.info.offset,
@@ -525,6 +466,6 @@ To mean_all(Param in) {
         return static_cast<To>(Op.runningMean);
     }
 }
-}  // namespace kernel
 
+}  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/mean_dim.cl b/src/backend/opencl/kernel/mean_dim.cl
index 60ed2fe0d6..9448486391 100644
--- a/src/backend/opencl/kernel/mean_dim.cl
+++ b/src/backend/opencl/kernel/mean_dim.cl
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
+kernel void meanDim(global To *oData, KParam oInfo,
 #ifdef OUTPUT_WEIGHT
-                              __global Tw *owData, KParam owInfo,
+                    global Tw *owData, KParam owInfo,
 #endif
-                              const __global Ti *iData, KParam iInfo,
+                    const global Ti *iData, KParam iInfo,
 #ifdef INPUT_WEIGHT
-                              const __global Tw *iwData, KParam iwInfo,
+                    const global Tw *iwData, KParam iwInfo,
 #endif
-                              uint groups_x, uint groups_y, uint group_dim) {
+                    uint groups_x, uint groups_y, uint group_dim) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * THREADS_X + lidx;
@@ -58,8 +58,8 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
     bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
                     (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
 
-    __local To s_val[THREADS_X * DIMY];
-    __local Tw s_wt[THREADS_X * DIMY];
+    local To s_val[THREADS_X * DIMY];
+    local Tw s_wt[THREADS_X * DIMY];
 
     To out_val = init_To;
     Tw out_wt  = init_Tw;
@@ -93,8 +93,8 @@ __kernel void mean_dim_kernel(__global To *oData, KParam oInfo,
     s_val[lid] = out_val;
     s_wt[lid]  = out_wt;
 
-    __local To *s_vptr = s_val + lid;
-    __local Tw *s_wptr = s_wt + lid;
+    local To *s_vptr = s_val + lid;
+    local Tw *s_wptr = s_wt + lid;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (DIMY == 8) {
diff --git a/src/backend/opencl/kernel/mean_first.cl b/src/backend/opencl/kernel/mean_first.cl
index dbef188298..14b19827c9 100644
--- a/src/backend/opencl/kernel/mean_first.cl
+++ b/src/backend/opencl/kernel/mean_first.cl
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void mean_first_kernel(__global To *oData, KParam oInfo,
+kernel void meanFirst(global To *oData, KParam oInfo,
 #ifdef OUTPUT_WEIGHT
-                                __global Tw *owData, KParam owInfo,
+                      global Tw *owData, KParam owInfo,
 #endif
-                                const __global Ti *iData, KParam iInfo,
+                      const global Ti *iData, KParam iInfo,
 #ifdef INPUT_WEIGHT
-                                const __global Tw *iwData, KParam iwInfo,
+                      const global Tw *iwData, KParam iwInfo,
 #endif
-                                uint groups_x, uint groups_y, uint repeat) {
+                      uint groups_x, uint groups_y, uint repeat) {
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
     const uint lid  = lidy * get_local_size(0) + lidx;
@@ -46,8 +46,8 @@ __kernel void mean_first_kernel(__global To *oData, KParam oInfo,
     bool cond =
         (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
-    __local To s_val[THREADS_PER_GROUP];
-    __local Tw s_wt[THREADS_PER_GROUP];
+    local To s_val[THREADS_PER_GROUP];
+    local Tw s_wt[THREADS_PER_GROUP];
 
     int last   = (xid + repeat * DIMX);
     int lim    = last > iInfo.dims[0] ? iInfo.dims[0] : last;
@@ -77,8 +77,8 @@ __kernel void mean_first_kernel(__global To *oData, KParam oInfo,
     s_wt[lid]  = out_wt;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local To *s_vptr = s_val + lidy * DIMX;
-    __local Tw *s_wptr = s_wt + lidy * DIMX;
+    local To *s_vptr = s_val + lidy * DIMX;
+    local Tw *s_wptr = s_wt + lidy * DIMX;
 
     if (DIMX == 256) {
         if (lidx < 128) {
diff --git a/src/backend/opencl/kernel/meanshift.cl b/src/backend/opencl/kernel/meanshift.cl
index 0f8ae9355d..e80da6985a 100644
--- a/src/backend/opencl/kernel/meanshift.cl
+++ b/src/backend/opencl/kernel/meanshift.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void meanshift(__global T* d_dst, KParam oInfo,
-                        __global const T* d_src, KParam iInfo, int radius,
+kernel void meanshift(global T* d_dst, KParam oInfo,
+                        global const T* d_src, KParam iInfo, int radius,
                         float cvar, unsigned numIters, int nBBS0, int nBBS1) {
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
@@ -18,9 +18,9 @@ __kernel void meanshift(__global T* d_dst, KParam oInfo,
         get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + get_local_id(1);
 
     if (gx < iInfo.dims[0] && gy < iInfo.dims[1]) {
-        __global const T* iptr = d_src + (b2 * iInfo.strides[2] +
+        global const T* iptr = d_src + (b2 * iInfo.strides[2] +
                                           b3 * iInfo.strides[3] + iInfo.offset);
-        __global T* optr =
+        global T* optr =
             d_dst + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
         int meanPosI = gx;
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index e237d99184..affc26cf18 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -8,81 +8,62 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/meanshift.hpp>
-#include <program.hpp>
 #include <traits.hpp>
+
 #include <algorithm>
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
 
-template<typename T, bool is_color>
+template<typename T>
 void meanshift(Param out, const Param in, const float spatialSigma,
-               const float chromaticSigma, const uint numIters) {
-    typedef typename std::conditional<std::is_same<T, double>::value, double,
-                                      float>::type AccType;
-
-    std::string refName = std::string("meanshift_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(is_color);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D AccType=" << dtype_traits<AccType>::getName()
-                << " -D MAX_CHANNELS=" << (is_color ? 3 : 1);
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {meanshift_cl};
-        const int ker_lens[]   = {meanshift_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "meanshift");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto meanshiftOp = KernelFunctor<Buffer, KParam, Buffer, KParam, int, float,
-                                     unsigned, int, int>(*entry.ker);
-
-    NDRange local(THREADS_X, THREADS_Y);
+               const float chromaticSigma, const uint numIters,
+               const bool is_color) {
+    using AccType = typename std::conditional<std::is_same<T, double>::value,
+                                              double, float>::type;
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
+
+    static const std::string src(meanshift_cl, meanshift_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(is_color),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(AccType, dtype_traits<AccType>::getName()),
+        DefineKeyValue(MAX_CHANNELS, (is_color ? 3 : 1)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto meanshiftOp = common::findKernel("meanshift", {src}, targs, options);
+
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
     const int bCount = (is_color ? 1 : in.info.dims[2]);
 
-    NDRange global(bCount * blk_x * THREADS_X,
-                   in.info.dims[3] * blk_y * THREADS_Y);
+    cl::NDRange global(bCount * blk_x * THREADS_X,
+                       in.info.dims[3] * blk_y * THREADS_Y);
 
     // clamp spatical and chromatic sigma's
     int radius = std::max((int)(spatialSigma * 1.5f), 1);
 
     const float cvar = chromaticSigma * chromaticSigma;
 
-    meanshiftOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    meanshiftOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                 *in.data, in.info, radius, cvar, numIters, blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index af758022df..6e415b0d26 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -8,127 +8,101 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/medfilt1.hpp>
 #include <kernel_headers/medfilt2.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int MAX_MEDFILTER2_LEN = 15;
-static const int MAX_MEDFILTER1_LEN = 121;
-
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
 
-template<typename T, af_border_type pad>
-void medfilt1(Param out, const Param in, unsigned w_wid) {
-    std::string refName = std::string("medfilt1_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(pad);
+constexpr int MAX_MEDFILTER2_LEN = 15;
+constexpr int MAX_MEDFILTER1_LEN = 121;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        const int ARR_SIZE = (w_wid - w_wid / 2) + 1;
+template<typename T>
+void medfilt1(Param out, const Param in, const unsigned w_wid,
+              const af_border_type pad) {
+    static const std::string src(medfilt1_cl, medfilt1_cl_len);
 
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D pad=" << pad
-                << " -D AF_PAD_ZERO=" << AF_PAD_ZERO
-                << " -D AF_PAD_SYM=" << AF_PAD_SYM
-                << " -D ARR_SIZE=" << ARR_SIZE << " -D w_wid=" << w_wid;
-        options << getTypeBuildDefinition<T>();
+    const int ARR_SIZE = (w_wid - w_wid / 2) + 1;
+    size_t loc_size    = (THREADS_X + w_wid - 1) * sizeof(T);
 
-        const char* ker_strs[] = {medfilt1_cl};
-        const int ker_lens[]   = {medfilt1_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "medfilt1");
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(pad),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(pad, static_cast<int>(pad)),
+        DefineKeyValue(AF_PAD_ZERO, static_cast<int>(AF_PAD_ZERO)),
+        DefineKeyValue(AF_PAD_SYM, static_cast<int>(AF_PAD_SYM)),
+        DefineValue(ARR_SIZE),
+        DefineValue(w_wid),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto medfiltOp = common::findKernel("medfilt1", {src}, targs, options);
 
-    NDRange local(THREADS_X, 1, 1);
+    cl::NDRange local(THREADS_X, 1, 1);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
 
-    NDRange global(blk_x * in.info.dims[1] * THREADS_X, in.info.dims[2],
-                   in.info.dims[3]);
+    cl::NDRange global(blk_x * in.info.dims[1] * THREADS_X, in.info.dims[2],
+                       in.info.dims[3]);
 
-    auto medfiltOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, cl::LocalSpaceArg, int>(
-            *entry.ker);
-
-    size_t loc_size = (THREADS_X + w_wid - 1) * sizeof(T);
-
-    medfiltOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    medfiltOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
               *in.data, in.info, cl::Local(loc_size), blk_x);
-
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename T, af_border_type pad, unsigned w_len, unsigned w_wid>
-void medfilt2(Param out, const Param in) {
-    std::string refName =
-        std::string("medfilt2_") + std::string(dtype_traits<T>::getName()) +
-        std::to_string(pad) + std::to_string(w_len) + std::to_string(w_wid);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+template<typename T>
+void medfilt2(Param out, const Param in, const af_border_type pad,
+              const unsigned w_len, const unsigned w_wid) {
+    static const std::string src(medfilt2_cl, medfilt2_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
-
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D pad=" << pad
-                << " -D AF_PAD_ZERO=" << AF_PAD_ZERO
-                << " -D AF_PAD_SYM=" << AF_PAD_SYM
-                << " -D ARR_SIZE=" << ARR_SIZE << " -D w_len=" << w_len
-                << " -D w_wid=" << w_wid;
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {medfilt2_cl};
-        const int ker_lens[]   = {medfilt2_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "medfilt2");
-
-        addKernelToCache(device, refName, entry);
-    }
+    const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
+    const size_t loc_size =
+        (THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1) * sizeof(T);
 
-    NDRange local(THREADS_X, THREADS_Y);
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(pad),
+        TemplateArg(w_len),
+        TemplateArg(w_wid),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(pad, static_cast<int>(pad)),
+        DefineKeyValue(AF_PAD_ZERO, static_cast<int>(AF_PAD_ZERO)),
+        DefineKeyValue(AF_PAD_SYM, static_cast<int>(AF_PAD_SYM)),
+        DefineValue(ARR_SIZE),
+        DefineValue(w_wid),
+        DefineValue(w_len),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto medfiltOp = common::findKernel("medfilt2", {src}, targs, options);
+
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                   blk_y * in.info.dims[3] * THREADS_Y);
+    cl::NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                       blk_y * in.info.dims[3] * THREADS_Y);
 
-    auto medfiltOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
-                                   cl::LocalSpaceArg, int, int>(*entry.ker);
-
-    size_t loc_size =
-        (THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1) * sizeof(T);
-
-    medfiltOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    medfiltOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
               *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/medfilt1.cl b/src/backend/opencl/kernel/medfilt1.cl
index 1720da0d63..c547c60c3e 100644
--- a/src/backend/opencl/kernel/medfilt1.cl
+++ b/src/backend/opencl/kernel/medfilt1.cl
@@ -15,7 +15,7 @@
         b     = max(tmp, b); \
     }
 
-void load2ShrdMem_1d(__local T* shrd, __global const T* in, int lx, int dim0,
+void load2ShrdMem_1d(local T* shrd, global const T* in, int lx, int dim0,
                      int gx, int inStride0) {
     if (pad == AF_PAD_ZERO) {
         if (gx < 0 || gx >= dim0)
@@ -29,8 +29,8 @@ void load2ShrdMem_1d(__local T* shrd, __global const T* in, int lx, int dim0,
     }
 }
 
-__kernel void medfilt1(__global T* out, KParam oInfo, __global const T* in,
-                       KParam iInfo, __local T* localMem, int nBBS0) {
+kernel void medfilt1(global T* out, KParam oInfo, __global const T* in,
+                       KParam iInfo, local T* localMem, int nBBS0) {
     // calculate necessary offset and window parameters
     const int padding = w_wid - 1;
     const int halo    = padding / 2;
@@ -41,11 +41,11 @@ __kernel void medfilt1(__global T* out, KParam oInfo, __global const T* in,
     unsigned b0            = get_group_id(0) - b1 * nBBS0;
     unsigned b2            = get_group_id(1);
     unsigned b3            = get_group_id(2);
-    __global const T* iptr = in +
+    global const T* iptr = in +
                              (b1 * iInfo.strides[1] + b2 * iInfo.strides[2] +
                               b3 * iInfo.strides[3]) +
                              iInfo.offset;
-    __global T* optr = out +
+    global T* optr = out +
                        (b1 * oInfo.strides[1] + b2 * oInfo.strides[2] +
                         b3 * oInfo.strides[3]) +
                        oInfo.offset;
diff --git a/src/backend/opencl/kernel/medfilt2.cl b/src/backend/opencl/kernel/medfilt2.cl
index 87dd490381..bfb7109f7c 100644
--- a/src/backend/opencl/kernel/medfilt2.cl
+++ b/src/backend/opencl/kernel/medfilt2.cl
@@ -19,7 +19,7 @@ int lIdx(int x, int y, int stride1, int stride0) {
     return (y * stride1 + x * stride0);
 }
 
-void load2ShrdMem(__local T* shrd, __global const T* in, int lx, int ly,
+void load2ShrdMem(local T* shrd, global const T* in, int lx, int ly,
                   int shrdStride, int dim0, int dim1, int gx, int gy,
                   int inStride1, int inStride0) {
     if (pad == AF_PAD_ZERO) {
@@ -38,8 +38,8 @@ void load2ShrdMem(__local T* shrd, __global const T* in, int lx, int ly,
     }
 }
 
-__kernel void medfilt2(__global T* out, KParam oInfo, __global const T* in,
-                       KParam iInfo, __local T* localMem, int nBBS0,
+kernel void medfilt2(global T* out, KParam oInfo, __global const T* in,
+                       KParam iInfo, local T* localMem, int nBBS0,
                        int nBBS1) {
     // calculate necessary offset and window parameters
     const int padding = w_len - 1;
@@ -49,9 +49,9 @@ __kernel void medfilt2(__global T* out, KParam oInfo, __global const T* in,
     // batch offsets
     unsigned b2 = get_group_id(0) / nBBS0;
     unsigned b3 = get_group_id(1) / nBBS1;
-    __global const T* iptr =
+    global const T* iptr =
         in + (b2 * iInfo.strides[2] + b3 * iInfo.strides[3] + iInfo.offset);
-    __global T* optr = out + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+    global T* optr = out + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     // local neighborhood indices
     int lx = get_local_id(0);
diff --git a/src/backend/opencl/kernel/memcopy.cl b/src/backend/opencl/kernel/memcopy.cl
index 8219c8f211..912b5b028c 100644
--- a/src/backend/opencl/kernel/memcopy.cl
+++ b/src/backend/opencl/kernel/memcopy.cl
@@ -11,10 +11,9 @@ typedef struct {
     dim_t dim[4];
 } dims_t;
 
-__kernel void memcopy_kernel(__global T *out, dims_t ostrides,
-                             __global const T *in, dims_t idims,
-                             dims_t istrides, int offset, int groups_0,
-                             int groups_1) {
+kernel void memCopy(global T *out, dims_t ostrides, global const T *in,
+                    dims_t idims, dims_t istrides, int offset, int groups_0,
+                    int groups_1) {
     const int lid0 = get_local_id(0);
     const int lid1 = get_local_id(1);
 
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 75b4a1f6d0..751b608edc 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -8,27 +8,19 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <common/traits.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/copy.hpp>
 #include <kernel_headers/memcopy.hpp>
-#include <program.hpp>
 #include <traits.hpp>
+
 #include <algorithm>
-#include <sstream>
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
@@ -36,34 +28,24 @@ typedef struct {
     dim_t dim[4];
 } dims_t;
 
-static const uint DIM0 = 32;
-static const uint DIM1 = 8;
+constexpr uint DIM0 = 32;
+constexpr uint DIM1 = 8;
 
 template<typename T>
 void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
              const dim_t *idims, const dim_t *istrides, int offset,
              uint ndims) {
-    std::string refName =
-        std::string("memcopy_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    static const std::string source(memcopy_cl, memcopy_cl_len);
 
-        const char *ker_strs[] = {memcopy_cl};
-        const int ker_lens[]   = {memcopy_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "memcopy_kernel");
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto memCopy = common::findKernel("memCopy", {source}, targs, options);
 
     dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
     dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
@@ -78,52 +60,40 @@ void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
     int groups_0 = divup(idims[0], local_size[0]);
     int groups_1 = divup(idims[1], local_size[1]);
 
-    NDRange local(local_size[0], local_size[1]);
-    NDRange global(groups_0 * idims[2] * local_size[0],
-                   groups_1 * idims[3] * local_size[1]);
-
-    auto memCpyOp =
-        KernelFunctor<Buffer, dims_t, Buffer, dims_t, dims_t, int, int, int>(
-            *entry.ker);
-
-    memCpyOp(EnqueueArgs(getQueue(), global, local), out, _ostrides, in, _idims,
-             _istrides, offset, groups_0, groups_1);
+    cl::NDRange local(local_size[0], local_size[1]);
+    cl::NDRange global(groups_0 * idims[2] * local_size[0],
+                       groups_1 * idims[3] * local_size[1]);
 
+    memCopy(cl::EnqueueArgs(getQueue(), global, local), out, _ostrides, in,
+            _idims, _istrides, offset, groups_0, groups_1);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename inType, typename outType, bool same_dims>
-void copy(Param dst, const Param src, int ndims, outType default_value,
-          double factor) {
-    std::string refName = std::string("copy_") +
-                          std::string(dtype_traits<inType>::getName()) +
-                          std::string(dtype_traits<outType>::getName()) +
-                          std::to_string(same_dims);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-
-        options << " -D inType=" << dtype_traits<inType>::getName()
-                << " -D outType=" << dtype_traits<outType>::getName()
-                << " -D inType_" << dtype_traits<inType>::getName()
-                << " -D outType_" << dtype_traits<outType>::getName()
-                << " -D SAME_DIMS=" << same_dims;
-        options << getTypeBuildDefinition<inType, outType>();
-
-        const char *ker_strs[] = {copy_cl};
-        const int ker_lens[]   = {copy_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "copy");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(DIM0, DIM1);
+template<typename inType, typename outType>
+void copy(Param dst, const Param src, const int ndims,
+          const outType default_value, const double factor,
+          const bool same_dims) {
+    using std::string;
+
+    static const string source(copy_cl, copy_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<inType>(),
+        TemplateTypename<outType>(),
+        TemplateArg(same_dims),
+    };
+    std::vector<string> options = {
+        DefineKeyValue(inType, dtype_traits<inType>::getName()),
+        DefineKeyValue(outType, dtype_traits<outType>::getName()),
+        string(" -D inType_" + string(dtype_traits<inType>::getName())),
+        string(" -D outType_" + string(dtype_traits<outType>::getName())),
+        DefineKeyValue(SAME_DIMS, static_cast<int>(same_dims)),
+    };
+    options.emplace_back(getTypeBuildDefinition<inType, outType>());
+
+    auto copy = common::findKernel("reshapeCopy", {source}, targs, options);
+
+    cl::NDRange local(DIM0, DIM1);
     size_t local_size[] = {DIM0, DIM1};
 
     local_size[0] *= local_size[1];
@@ -132,8 +102,8 @@ void copy(Param dst, const Param src, int ndims, outType default_value,
     int blk_x = divup(dst.info.dims[0], local_size[0]);
     int blk_y = divup(dst.info.dims[1], local_size[1]);
 
-    NDRange global(blk_x * dst.info.dims[2] * DIM0,
-                   blk_y * dst.info.dims[3] * DIM1);
+    cl::NDRange global(blk_x * dst.info.dims[2] * DIM0,
+                       blk_y * dst.info.dims[3] * DIM1);
 
     dims_t trgt_dims;
     if (same_dims) {
@@ -147,13 +117,9 @@ void copy(Param dst, const Param src, int ndims, outType default_value,
         trgt_dims    = {{trgt_i, trgt_j, trgt_k, trgt_l}};
     }
 
-    auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam, outType, float,
-                                dims_t, int, int>(*entry.ker);
-
-    copyOp(EnqueueArgs(getQueue(), global, local), *dst.data, dst.info,
-           *src.data, src.info, default_value, (float)factor, trgt_dims, blk_x,
-           blk_y);
-
+    copy(cl::EnqueueArgs(getQueue(), global, local), *dst.data, dst.info,
+         *src.data, src.info, default_value, (float)factor, trgt_dims, blk_x,
+         blk_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/moments.cl b/src/backend/opencl/kernel/moments.cl
index 1afbaa2b0e..f9c8dc5031 100644
--- a/src/backend/opencl/kernel/moments.cl
+++ b/src/backend/opencl/kernel/moments.cl
@@ -12,10 +12,7 @@
 #define AF_MOMENT_M10 4
 #define AF_MOMENT_M11 8
 
-////////////////////////////////////////////////////////////////////////////////////
-// Helper Functions
-////////////////////////////////////////////////////////////////////////////////////
-inline void fatomic_add_l(volatile __local float *source, const float operand) {
+inline void fatomic_add_l(volatile local float *source, const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -25,13 +22,12 @@ inline void fatomic_add_l(volatile __local float *source, const float operand) {
     do {
         expVal.floatVal = prevVal.floatVal;
         newVal.floatVal = expVal.floatVal + operand;
-        prevVal.intVal = atomic_cmpxchg((volatile __local unsigned int *)source,
+        prevVal.intVal  = atomic_cmpxchg((volatile local unsigned int *)source,
                                         expVal.intVal, newVal.intVal);
     } while (expVal.intVal != prevVal.intVal);
 }
 
-inline void fatomic_add_g(volatile __global float *source,
-                          const float operand) {
+inline void fatomic_add_g(volatile global float *source, const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -41,15 +37,13 @@ inline void fatomic_add_g(volatile __global float *source,
     do {
         expVal.floatVal = prevVal.floatVal;
         newVal.floatVal = expVal.floatVal + operand;
-        prevVal.intVal =
-            atomic_cmpxchg((volatile __global unsigned int *)source,
-                           expVal.intVal, newVal.intVal);
+        prevVal.intVal  = atomic_cmpxchg((volatile global unsigned int *)source,
+                                        expVal.intVal, newVal.intVal);
     } while (expVal.intVal != prevVal.intVal);
 }
 
-__kernel void moments_kernel(__global float *d_out, const KParam out,
-                             __global const T *d_in, const KParam in,
-                             const int moment, const int pBatch) {
+kernel void moments(global float *d_out, const KParam out, global const T *d_in,
+                    const KParam in, const int moment, const int pBatch) {
     const dim_t idw = get_group_id(1) / in.dims[2];
     const dim_t idz = get_group_id(1) - idw * in.dims[2];
 
@@ -58,7 +52,7 @@ __kernel void moments_kernel(__global float *d_out, const KParam out,
 
     if (idy >= in.dims[1] || idz >= in.dims[2] || idw >= in.dims[3]) return;
 
-    __local float wkg_moment_sum[MOMENTS_SZ];
+    local float wkg_moment_sum[MOMENTS_SZ];
     if (get_local_id(0) < MOMENTS_SZ) { wkg_moment_sum[get_local_id(0)] = 0.f; }
     barrier(CLK_LOCAL_MEM_FENCE);
 
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 8ca90fb644..c3b2aa73a2 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -8,73 +8,50 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/moments.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS = 128;
 
-///////////////////////////////////////////////////////////////////////////
-// Wrapper functions
-///////////////////////////////////////////////////////////////////////////
 template<typename T>
 void moments(Param out, const Param in, af_moment_type moment) {
-    std::string ref_name = std::string("moments_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(out.info.dims[0]);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
+    constexpr int THREADS = 128;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D MOMENTS_SZ=" << out.info.dims[0];
-        options << getTypeBuildDefinition<T>();
+    static const std::string src(moments_cl, moments_cl_len);
 
-        Program prog;
-        buildProgram(prog, moments_cl, moments_cl_len, options.str());
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(out.info.dims[0]),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(MOMENTS_SZ, out.info.dims[0]),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "moments_kernel");
+    auto momentsOp = common::findKernel("moments", {src}, targs, options);
 
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    auto momentsp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const int, const int>(*entry.ker);
-
-    NDRange local(THREADS, 1, 1);
-    NDRange global(in.info.dims[1] * local[0],
-                   in.info.dims[2] * in.info.dims[3] * local[1]);
+    cl::NDRange local(THREADS, 1, 1);
+    cl::NDRange global(in.info.dims[1] * local[0],
+                       in.info.dims[2] * in.info.dims[3] * local[1]);
 
     bool pBatch = !(in.info.dims[2] == 1 && in.info.dims[3] == 1);
 
-    momentsp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, (int)moment, (int)pBatch);
-
+    momentsOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *in.data, in.info, (int)moment, (int)pBatch);
     CL_DEBUG_FINISH(getQueue());
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/morph.cl b/src/backend/opencl/kernel/morph.cl
index 22db54f0fa..993913628b 100644
--- a/src/backend/opencl/kernel/morph.cl
+++ b/src/backend/opencl/kernel/morph.cl
@@ -11,7 +11,7 @@ int lIdx(int x, int y, int stride1, int stride0) {
     return (y * stride1 + x * stride0);
 }
 
-void load2LocalMem(__local T* shrd, __global const T* in, int lx, int ly,
+void load2LocalMem(local T* shrd, global const T* in, int lx, int ly,
                    int shrdStride, int dim0, int dim1, int gx, int gy,
                    int inStride1, int inStride0) {
     T val = gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1
@@ -22,9 +22,9 @@ void load2LocalMem(__local T* shrd, __global const T* in, int lx, int ly,
 
 // kernel assumes four dimensions
 // doing this to reduce one uneccesary parameter
-__kernel void morph(__global T* out, KParam oInfo, __global const T* in,
+kernel void morph(global T* out, KParam oInfo, __global const T* in,
                     KParam iInfo, __constant const T* d_filt,
-                    __local T* localMem, int nBBS0, int nBBS1, int windLen) {
+                    local T* localMem, int nBBS0, int nBBS1, int windLen) {
     if (SeLength > 0) windLen = SeLength;
 
     const int halo = windLen / 2;
@@ -91,7 +91,7 @@ int lIdx3D(int x, int y, int z, int stride2, int stride1, int stride0) {
     return (z * stride2 + y * stride1 + x * stride0);
 }
 
-void load2LocVolume(__local T* shrd, __global const T* in, int lx, int ly,
+void load2LocVolume(local T* shrd, global const T* in, int lx, int ly,
                     int lz, int shrdStride1, int shrdStride2, int dim0,
                     int dim1, int dim2, int gx, int gy, int gz, int inStride2,
                     int inStride1, int inStride0) {
@@ -104,9 +104,9 @@ void load2LocVolume(__local T* shrd, __global const T* in, int lx, int ly,
     shrd[lx + ly * shrdStride1 + lz * shrdStride2] = val;
 }
 
-__kernel void morph3d(__global T* out, KParam oInfo, __global const T* in,
+kernel void morph3d(global T* out, KParam oInfo, __global const T* in,
                       KParam iInfo, __constant const T* d_filt,
-                      __local T* localMem, int nBBS) {
+                      local T* localMem, int nBBS) {
     const int halo = SeLength / 2;
     const int padding =
         (SeLength % 2 == 0 ? (SeLength - 1) : (2 * (SeLength / 2)));
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 29f78ea512..f170037824 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -17,18 +17,12 @@
 #include <memory.hpp>
 #include <ops.hpp>
 #include <traits.hpp>
-#include <types.hpp>
 
 #include <string>
 #include <vector>
 
 namespace opencl {
 namespace kernel {
-constexpr int THREADS_X = 16;
-constexpr int THREADS_Y = 16;
-constexpr int CUBE_X    = 8;
-constexpr int CUBE_Y    = 8;
-constexpr int CUBE_Z    = 4;
 
 template<typename T>
 void morph(Param out, const Param in, const Param mask, bool isDilation) {
@@ -39,6 +33,9 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     using std::string;
     using std::vector;
 
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
+
     ToNumStr<T> toNumStr;
     const T DefaultVal =
         isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
@@ -48,20 +45,20 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     const int windLen  = mask.info.dims[0];
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
-    std::vector<TemplateArg> tmpltArgs = {
+    std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(isDilation),
         TemplateArg(SeLength),
     };
-    vector<string> compileOpts = {
+    vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(isDilation),
         DefineValue(SeLength),
         DefineKeyValue(init, toNumStr(DefaultVal)),
     };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::findKernel("morph", {src}, tmpltArgs, compileOpts);
+    auto morphOp = common::findKernel("morph", {src}, targs, options);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -98,6 +95,10 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     using std::string;
     using std::vector;
 
+    constexpr int CUBE_X = 8;
+    constexpr int CUBE_Y = 8;
+    constexpr int CUBE_Z = 4;
+
     ToNumStr<T> toNumStr;
     const T DefaultVal =
         isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
@@ -106,20 +107,20 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
 
     const int SeLength = mask.info.dims[0];
 
-    std::vector<TemplateArg> tmpltArgs = {
+    std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(isDilation),
         TemplateArg(SeLength),
     };
-    vector<string> compileOpts = {
+    vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(isDilation),
         DefineValue(SeLength),
         DefineKeyValue(init, toNumStr(DefaultVal)),
     };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::findKernel("morph3d", {src}, tmpltArgs, compileOpts);
+    auto morphOp = common::findKernel("morph3d", {src}, targs, options);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.cl b/src/backend/opencl/kernel/nearest_neighbour.cl
index 8de72a611d..2c54b8d8af 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.cl
+++ b/src/backend/opencl/kernel/nearest_neighbour.cl
@@ -31,21 +31,21 @@ To _ssd_(T v1, T v2) { return (v1 - v2) * (v1 - v2); }
 unsigned _shd_(T v1, T v2) { return popcount(v1 ^ v2); }
 #endif
 
-__kernel void all_distances(__global To* out_dist, __global const T* query,
-                            KParam qInfo, __global const T* train, KParam tInfo,
+kernel void knnAllDistances(global To* out_dist, global const T* query,
+                            KParam qInfo, global const T* train, KParam tInfo,
                             const To max_dist, const unsigned feat_len,
                             const unsigned max_feat_len,
-                            const unsigned feat_offset, __local T* lmem) {
+                            const unsigned feat_offset, local T* lmem) {
     unsigned nquery = qInfo.dims[0];
     unsigned ntrain = tInfo.dims[0];
 
     unsigned f   = get_global_id(0);
     unsigned tid = get_local_id(0);
 
-    __local To l_dist[THREADS];
+    local To l_dist[THREADS];
 
-    __local T* l_query = lmem;
-    __local T* l_train = lmem + max_feat_len;
+    local T* l_query = lmem;
+    local T* l_train = lmem + max_feat_len;
 
     l_dist[tid] = max_dist;
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 3b479432ba..43b8c6566e 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -9,33 +9,27 @@
 
 #pragma once
 
-#include <cache.hpp>
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/nearest_neighbour.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <af/defines.h>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
+#include <string>
+#include <vector>
 
 namespace opencl {
-
 namespace kernel {
 
-static const unsigned THREADS = 256;
+template<typename T, typename To>
+void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
+                  af_match_type dist_type) {
+    constexpr unsigned THREADS = 256;
 
-template<typename T, typename To, af_match_type dist_type>
-void all_distances(Param dist, Param query, Param train, const dim_t dist_dim) {
-    const dim_t feat_len = query.info.dims[dist_dim];
+    const unsigned feat_len = static_cast<uint>(query.info.dims[dist_dim]);
     const unsigned max_kern_feat_len =
         min(THREADS, static_cast<unsigned>(feat_len));
     const To max_dist = maxval<To>();
@@ -51,68 +45,53 @@ void all_distances(Param dist, Param query, Param train, const dim_t dist_dim) {
     unsigned unroll_len = nextpow2(feat_len);
     if (unroll_len != feat_len) unroll_len = 0;
 
-    std::string ref_name = std::string("knn_") + std::to_string(dist_type) +
-                           std::string("_") + std::to_string(use_lmem) +
-                           std::string("_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(unroll_len);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D To=" << dtype_traits<To>::getName()
-                << " -D THREADS=" << THREADS << " -D FEAT_LEN=" << unroll_len;
-
-        switch (dist_type) {
-            case AF_SAD: options << " -D DISTOP=_sad_"; break;
-            case AF_SSD: options << " -D DISTOP=_ssd_"; break;
-            case AF_SHD: options << " -D DISTOP=_shd_ -D __SHD__"; break;
-            default: break;
-        }
-
-        options << getTypeBuildDefinition<T>();
-
-        if (use_lmem) options << " -D USE_LOCAL_MEM";
-
-        cl::Program prog;
-        buildProgram(prog, nearest_neighbour_cl, nearest_neighbour_cl_len,
-                     options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel;
-
-        *entry.ker = Kernel(*entry.prog, "all_distances");
-
-        addKernelToCache(device, ref_name, entry);
+    static const std::string src(nearest_neighbour_cl,
+                                 nearest_neighbour_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(dist_type),
+        TemplateArg(use_lmem),
+        TemplateArg(unroll_len),
+    };
+
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineValue(THREADS),
+        DefineKeyValue(FEAT_LEN, unroll_len),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+    if (use_lmem) { options.emplace_back(DefineKey(USE_LOCAL_MEM)); }
+    if (dist_type == AF_SAD) {
+        options.emplace_back(DefineKeyValue(DISTOP, "_sad_"));
     }
+    if (dist_type == AF_SSD) {
+        options.emplace_back(DefineKeyValue(DISTOP, "_ssd_"));
+    }
+    if (dist_type == AF_SHD) {
+        options.emplace_back(DefineKeyValue(DISTOP, "_shd_"));
+        options.emplace_back(DefineKey(__SHD__));
+    }
+    auto hmOp = common::findKernel("knnAllDistances", {src}, targs, options);
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
     const unsigned ntrain = train.info.dims[sample_dim];
 
     unsigned nblk = divup(ntrain, THREADS);
-    const NDRange local(THREADS, 1);
-    const NDRange global(nblk * THREADS, 1);
+    const cl::NDRange local(THREADS, 1);
+    const cl::NDRange global(nblk * THREADS, 1);
 
     // For each query vector, find training vector with smallest Hamming
     // distance per CUDA block
-    auto hmOp = KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, const To,
-                              const unsigned, const unsigned, const unsigned,
-                              LocalSpaceArg>(*entry.ker);
-
-    for (dim_t feat_offset = 0; feat_offset < feat_len;
-         feat_offset += THREADS) {
-        hmOp(EnqueueArgs(getQueue(), global, local), *dist.data, *query.data,
-             query.info, *train.data, train.info, max_dist, feat_len,
-             max_kern_feat_len, feat_offset, cl::Local(lmem_sz));
+    for (uint feat_offset = 0; feat_offset < feat_len; feat_offset += THREADS) {
+        hmOp(cl::EnqueueArgs(getQueue(), global, local), *dist.data,
+             *query.data, query.info, *train.data, train.info, max_dist,
+             feat_len, max_kern_feat_len, feat_offset, cl::Local(lmem_sz));
         CL_DEBUG_FINISH(getQueue());
     }
 }
 
 }  // namespace kernel
-
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/nonmax_suppression.cl b/src/backend/opencl/kernel/nonmax_suppression.cl
index 7c204a039b..e1c93f6add 100644
--- a/src/backend/opencl/kernel/nonmax_suppression.cl
+++ b/src/backend/opencl/kernel/nonmax_suppression.cl
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
-                                      __global const T* in, KParam inInfo,
-                                      __global const T* dx, KParam dxInfo,
-                                      __global const T* dy, KParam dyInfo,
+kernel void nonMaxSuppressionKernel(global T* output, KParam oInfo,
+                                      global const T* in, KParam inInfo,
+                                      global const T* dx, KParam dxInfo,
+                                      global const T* dy, KParam dyInfo,
                                       unsigned nBBS0, unsigned nBBS1) {
     // local thread indices
     const int lx = get_local_id(0);
@@ -24,17 +24,17 @@ __kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
     const int gx = get_local_size(0) * (get_group_id(0) - b2 * nBBS0) + lx;
     const int gy = get_local_size(1) * (get_group_id(1) - b3 * nBBS1) + ly;
 
-    __local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+    local T localMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
 
-    __global const T* mag =
+    global const T* mag =
         in + (b2 * inInfo.strides[2] + b3 * inInfo.strides[3] + inInfo.offset);
-    __global const T* dX =
+    global const T* dX =
         dx + (b2 * dxInfo.strides[2] + b3 * dxInfo.strides[3] + dxInfo.offset) +
         dxInfo.strides[1] + 1;
-    __global const T* dY =
+    global const T* dY =
         dy + (b2 * dyInfo.strides[2] + b3 * dyInfo.strides[3] + dyInfo.offset) +
         dyInfo.strides[1] + 1;
-    __global T* out = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
+    global T* out = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
                       oInfo.strides[1] + 1;
 
 #pragma unroll
@@ -43,8 +43,8 @@ __kernel void nonMaxSuppressionKernel(__global T* output, KParam oInfo,
 #pragma unroll
         for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH && gx2 < inInfo.dims[0];
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            localMem[b][a] = mag[(gx2) * inInfo.strides[0] +
-                                 (gy2) * inInfo.strides[1]];
+            localMem[b][a] =
+                mag[(gx2)*inInfo.strides[0] + (gy2)*inInfo.strides[1]];
         }
     }
     int i = lx + 1;
diff --git a/src/backend/opencl/kernel/orb.cl b/src/backend/opencl/kernel/orb.cl
index 0026f1410c..d8a31c81ec 100644
--- a/src/backend/opencl/kernel/orb.cl
+++ b/src/backend/opencl/kernel/orb.cl
@@ -88,7 +88,7 @@ __constant int ref_pat[] = {
     -1,  -6,  0,   -11,
 };
 
-float block_reduce_sum(float val, __local float* data) {
+float block_reduce_sum(float val, local float* data) {
     unsigned idx = get_local_id(0) * get_local_size(0) + get_local_id(1);
 
     data[idx] = val;
@@ -103,12 +103,12 @@ float block_reduce_sum(float val, __local float* data) {
     return data[get_local_id(0) * get_local_size(0)];
 }
 
-__kernel void keep_features(__global float* x_out, __global float* y_out,
-                            __global float* score_out,
-                            __global const float* x_in,
-                            __global const float* y_in,
-                            __global const float* score_in,
-                            __global const unsigned* score_idx,
+kernel void keep_features(global float* x_out, __global float* y_out,
+                            global float* score_out,
+                            global const float* x_in,
+                            global const float* y_in,
+                            global const float* score_in,
+                            global const unsigned* score_idx,
                             const unsigned n_feat) {
     unsigned f = get_global_id(0);
 
@@ -119,13 +119,13 @@ __kernel void keep_features(__global float* x_out, __global float* y_out,
     }
 }
 
-__kernel void harris_response(
-    __global float* x_out, __global float* y_out, __global float* score_out,
-    __global const float* x_in, __global const float* y_in,
-    const unsigned total_feat, __global unsigned* usable_feat,
-    __global const T* image, KParam iInfo, const unsigned block_size,
+kernel void harris_response(
+    global float* x_out, __global float* y_out, __global float* score_out,
+    global const float* x_in, __global const float* y_in,
+    const unsigned total_feat, global unsigned* usable_feat,
+    global const T* image, KParam iInfo, const unsigned block_size,
     const float k_thr, const unsigned patch_size) {
-    __local float data[BLOCK_SIZE * BLOCK_SIZE];
+    local float data[BLOCK_SIZE * BLOCK_SIZE];
 
     unsigned f = get_global_id(0);
 
@@ -194,12 +194,12 @@ __kernel void harris_response(
     }
 }
 
-__kernel void centroid_angle(__global const float* x_in,
-                             __global const float* y_in,
-                             __global float* orientation_out,
-                             const unsigned total_feat, __global const T* image,
+kernel void centroid_angle(global const float* x_in,
+                             global const float* y_in,
+                             global float* orientation_out,
+                             const unsigned total_feat, global const T* image,
                              KParam iInfo, const unsigned patch_size) {
-    __local float data[BLOCK_SIZE * BLOCK_SIZE];
+    local float data[BLOCK_SIZE * BLOCK_SIZE];
     unsigned f = get_global_id(0);
 
     T m01 = (T)0, m10 = (T)0;
@@ -237,7 +237,7 @@ __kernel void centroid_angle(__global const float* x_in,
 }
 
 inline T get_pixel(unsigned x, unsigned y, const float ori, const unsigned size,
-                   const int dist_x, const int dist_y, __global const T* image,
+                   const int dist_x, const int dist_y, global const T* image,
                    KParam iInfo, const unsigned patch_size) {
     float ori_sin   = sin(ori);
     float ori_cos   = cos(ori);
@@ -249,10 +249,10 @@ inline T get_pixel(unsigned x, unsigned y, const float ori, const unsigned size,
     return image[x * iInfo.dims[0] + y];
 }
 
-__kernel void extract_orb(__global unsigned* desc_out, const unsigned n_feat,
-                          __global float* x_in, __global float* y_in,
-                          __global float* ori_in, __global float* size_out,
-                          __global const T* image, KParam iInfo,
+kernel void extract_orb(global unsigned* desc_out, const unsigned n_feat,
+                          global float* x_in, __global float* y_in,
+                          global float* ori_in, __global float* size_out,
+                          global const T* image, KParam iInfo,
                           const float scl, const unsigned patch_size) {
     unsigned f = get_global_id(0);
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index bbff55d9d6..9c7dcdfee1 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -7,10 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cache.hpp>
+#pragma once
+
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <kernel/fast.hpp>
 #include <kernel/range.hpp>
@@ -18,17 +20,10 @@
 #include <kernel/sort_by_key.hpp>
 #include <kernel_headers/orb.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <af/defines.h>
-#include <vector>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
-using std::vector;
+#include <string>
+#include <vector>
 
 #if defined(__clang__)
 /* Clang/LLVM */
@@ -51,11 +46,11 @@ using std::vector;
 
 namespace opencl {
 namespace kernel {
-static const int ORB_THREADS   = 256;
-static const int ORB_THREADS_X = 16;
-static const int ORB_THREADS_Y = 16;
 
-static const float PI_VAL = 3.14159265358979323846f;
+constexpr int ORB_THREADS   = 256;
+constexpr int ORB_THREADS_X = 16;
+constexpr int ORB_THREADS_Y = 16;
+constexpr float PI_VAL      = 3.14159265358979323846f;
 
 // Reference pattern, generated for a patch size of 31x31, as suggested by
 // original ORB paper
@@ -81,50 +76,24 @@ void gaussian1D(T* out, const int dim, double sigma = 0.0) {
 }
 
 template<typename T>
-std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*, cl::Kernel*> getOrbKernels() {
-    static const char* kernelNames[4] = {"harris_response", "keep_features",
-                                         "centroid_angle", "extract_orb"};
-
-    kc_entry_t entries[4];
-
-    int device = getActiveDeviceId();
-
-    std::string checkName = kernelNames[0] + std::string("_") +
-                            std::string(dtype_traits<T>::getName());
-
-    entries[0] = kernelCache(device, checkName);
-
-    if (entries[0].prog == 0 && entries[0].ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D BLOCK_SIZE=" << ORB_THREADS_X;
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {orb_cl};
-        const int ker_lens[]   = {orb_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-
-        for (int i = 0; i < 4; ++i) {
-            entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
-
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName());
-
-            addKernelToCache(device, name, entries[i]);
-        }
-    } else {
-        for (int i = 1; i < 4; ++i) {
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName());
-
-            entries[i] = kernelCache(device, name);
-        }
-    }
-
-    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker,
-                           entries[3].ker);
+std::array<Kernel, 4> getOrbKernels() {
+    static const std::string src(orb_cl, orb_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(BLOCK_SIZE, ORB_THREADS_X),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    return {
+        common::findKernel("harris_response", {src}, targs, compileOpts),
+        common::findKernel("keep_features", {src}, targs, compileOpts),
+        common::findKernel("centroid_angle", {src}, targs, compileOpts),
+        common::findKernel("extract_orb", {src}, targs, compileOpts),
+    };
 }
 
 template<typename T, typename convAccT>
@@ -132,6 +101,11 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
          Param& ori_out, Param& size_out, Param& desc_out, Param image,
          const float fast_thr, const unsigned max_feat, const float scl_fctr,
          const unsigned levels, const bool blur_img) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::vector;
+
     auto kernels = getOrbKernels<T>();
 
     unsigned patch_size = REF_PAT_SIZE;
@@ -149,12 +123,12 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         scl_sum += 1.f / (float)pow(scl_fctr, (float)i);
     }
 
-    vector<cl::Buffer*> d_x_pyr(max_levels);
-    vector<cl::Buffer*> d_y_pyr(max_levels);
-    vector<cl::Buffer*> d_score_pyr(max_levels);
-    vector<cl::Buffer*> d_ori_pyr(max_levels);
-    vector<cl::Buffer*> d_size_pyr(max_levels);
-    vector<cl::Buffer*> d_desc_pyr(max_levels);
+    vector<Buffer*> d_x_pyr(max_levels);
+    vector<Buffer*> d_y_pyr(max_levels);
+    vector<Buffer*> d_score_pyr(max_levels);
+    vector<Buffer*> d_ori_pyr(max_levels);
+    vector<Buffer*> d_size_pyr(max_levels);
+    vector<Buffer*> d_desc_pyr(max_levels);
 
     vector<unsigned> feat_pyr(max_levels);
     unsigned total_feat = 0;
@@ -204,7 +178,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
             lvl_img.data        = bufferAlloc(lvl_img.info.dims[3] *
                                        lvl_img.info.strides[3] * sizeof(T));
 
-            resize<T, AF_INTERP_BILINEAR>(lvl_img, prev_img);
+            resize<T>(lvl_img, prev_img, AF_INTERP_BILINEAR);
 
             if (i > 1) bufferFree(prev_img.data);
             prev_img = lvl_img;
@@ -222,9 +196,8 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         unsigned edge = ceil(size * sqrt(2.f) / 2.f);
 
         // Detect FAST features
-        fast<T, true>(9, &lvl_feat, d_x_feat, d_y_feat, d_score_feat, lvl_img,
-                      fast_thr, 0.15f, edge);
-
+        fast<T>(9, &lvl_feat, d_x_feat, d_y_feat, d_score_feat, lvl_img,
+                fast_thr, 0.15f, edge, true);
         if (lvl_feat == 0) {
             feat_pyr[i] = 0;
 
@@ -235,14 +208,14 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         bufferFree(d_score_feat.data);
 
-        unsigned usable_feat      = 0;
-        cl::Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
+        unsigned usable_feat  = 0;
+        Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
         getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0,
                                       sizeof(unsigned), &usable_feat);
 
-        cl::Buffer* d_x_harris     = bufferAlloc(lvl_feat * sizeof(float));
-        cl::Buffer* d_y_harris     = bufferAlloc(lvl_feat * sizeof(float));
-        cl::Buffer* d_score_harris = bufferAlloc(lvl_feat * sizeof(float));
+        Buffer* d_x_harris     = bufferAlloc(lvl_feat * sizeof(float));
+        Buffer* d_y_harris     = bufferAlloc(lvl_feat * sizeof(float));
+        Buffer* d_score_harris = bufferAlloc(lvl_feat * sizeof(float));
 
         // Calculate Harris responses
         // Good block_size >= 7 (must be an odd number)
@@ -253,10 +226,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         unsigned block_size = 7;
         float k_thr         = 0.04f;
 
-        auto hrOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer,
-                                  const unsigned, Buffer, Buffer, KParam,
-                                  const unsigned, const float, const unsigned>(
-            *std::get<0>(kernels));
+        auto hrOp = kernels[0];
 
         hrOp(EnqueueArgs(getQueue(), global, local), *d_x_harris, *d_y_harris,
              *d_score_harris, *d_x_feat.data, *d_y_feat.data, lvl_feat,
@@ -314,9 +284,9 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         kernel::sort0ByKey<float, uint>(d_harris_sorted, d_harris_idx, false);
 
-        cl::Buffer* d_x_lvl     = bufferAlloc(usable_feat * sizeof(float));
-        cl::Buffer* d_y_lvl     = bufferAlloc(usable_feat * sizeof(float));
-        cl::Buffer* d_score_lvl = bufferAlloc(usable_feat * sizeof(float));
+        Buffer* d_x_lvl     = bufferAlloc(usable_feat * sizeof(float));
+        Buffer* d_y_lvl     = bufferAlloc(usable_feat * sizeof(float));
+        Buffer* d_score_lvl = bufferAlloc(usable_feat * sizeof(float));
 
         usable_feat = std::min(usable_feat, lvl_best[i]);
 
@@ -325,9 +295,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         const NDRange local_keep(ORB_THREADS, 1);
         const NDRange global_keep(keep_blk * ORB_THREADS, 1);
 
-        auto kfOp =
-            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                          Buffer, const unsigned>(*std::get<1>(kernels));
+        auto kfOp = kernels[1];
 
         kfOp(EnqueueArgs(getQueue(), global_keep, local_keep), *d_x_lvl,
              *d_y_lvl, *d_score_lvl, *d_x_harris, *d_y_harris,
@@ -339,8 +307,8 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         bufferFree(d_harris_sorted.data);
         bufferFree(d_harris_idx.data);
 
-        cl::Buffer* d_ori_lvl  = bufferAlloc(usable_feat * sizeof(float));
-        cl::Buffer* d_size_lvl = bufferAlloc(usable_feat * sizeof(float));
+        Buffer* d_ori_lvl  = bufferAlloc(usable_feat * sizeof(float));
+        Buffer* d_size_lvl = bufferAlloc(usable_feat * sizeof(float));
 
         // Compute orientation of features
         const int centroid_blk_x = divup(usable_feat, ORB_THREADS_X);
@@ -348,9 +316,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X,
                                       ORB_THREADS_Y);
 
-        auto caOp =
-            KernelFunctor<Buffer, Buffer, Buffer, const unsigned, Buffer,
-                          KParam, const unsigned>(*std::get<2>(kernels));
+        auto caOp = kernels[2];
 
         caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid), *d_x_lvl,
              *d_y_lvl, *d_ori_lvl, usable_feat, *lvl_img.data, lvl_img.info,
@@ -399,20 +365,14 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
         }
 
         // Compute ORB descriptors
-        cl::Buffer* d_desc_lvl =
-            bufferAlloc(usable_feat * 8 * sizeof(unsigned));
+        Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
         {
             vector<unsigned> h_desc_lvl(usable_feat * 8);
             getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0,
                                           usable_feat * 8 * sizeof(unsigned),
                                           h_desc_lvl.data());
         }
-
-        auto eoOp =
-            KernelFunctor<Buffer, const unsigned, Buffer, Buffer, Buffer,
-                          Buffer, Buffer, KParam, const float, const unsigned>(
-                *std::get<3>(kernels));
-
+        auto eoOp = kernels[3];
         if (blur_img) {
             eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
                  *d_desc_lvl, usable_feat, *d_x_lvl, *d_y_lvl, *d_ori_lvl,
diff --git a/src/backend/opencl/kernel/pad_array_borders.cl b/src/backend/opencl/kernel/pad_array_borders.cl
index 9ab2110749..f62111fb9d 100644
--- a/src/backend/opencl/kernel/pad_array_borders.cl
+++ b/src/backend/opencl/kernel/pad_array_borders.cl
@@ -22,10 +22,10 @@ int trimIndex(int idx, const int len) {
     return ret_val;
 }
 
-//TODO(Pradeep) move trimindex from all locations into
+// TODO(Pradeep) move trimindex from all locations into
 //              a single header after opencl cache is cleaned up
 int idxByndEdge(const int i, const int lb, const int len) {
-    return trimIndex(i-lb, len);
+    return trimIndex(i - lb, len);
 }
 
 #elif AF_BORDER_TYPE == AF_PAD_CLAMP_TO_EDGE
@@ -37,7 +37,7 @@ int idxByndEdge(const int i, const int lb, const int len) {
 #elif AF_BORDER_TYPE == AF_PAD_PERIODIC
 
 int idxByndEdge(const int i, const int lb, const int len) {
-    int rem   = (i - lb) % len;
+    int rem  = (i - lb) % len;
     int cond = rem < 0;
     return cond * (rem + len) + (1 - cond) * rem;
 }
@@ -48,7 +48,7 @@ int idxByndEdge(const int i, const int lb, const int len) {
 
 #endif
 
-__kernel void padBorders(__global T* out, KParam oInfo, __global const T* in,
+kernel void padBorders(global T* out, KParam oInfo, __global const T* in,
                          KParam iInfo, int l0, int l1, int l2, int l3,
                          unsigned blk_x, unsigned blk_y) {
     const int lx = get_local_id(0);
@@ -70,8 +70,8 @@ __kernel void padBorders(__global T* out, KParam oInfo, __global const T* in,
     const int s2 = iInfo.strides[2];
     const int s3 = iInfo.strides[3];
 
-    __global const T* src = in + iInfo.offset;
-    __global T* dst       = out;
+    global const T* src = in + iInfo.offset;
+    global T* dst       = out;
 
     bool isNotPadding =
         (l >= l3 && l < (d3 + l3)) && (k >= l2 && k < (d2 + l2)) &&
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index d40327bab8..be1d98c9de 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -8,72 +8,59 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/pad_array_borders.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 static const int PADB_THREADS_X = 16;
 static const int PADB_THREADS_Y = 16;
 
-template<typename T, af_border_type BType>
-void padBorders(Param out, const Param in, dim4 const& lBPadding) {
-    std::string refName = std::string("padBorders_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(BType);
+template<typename T>
+void padBorders(Param out, const Param in, dim4 const& lBPadding,
+                const af_border_type borderType) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const string src(pad_array_borders_cl, pad_array_borders_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D AF_BORDER_TYPE=" << BType
-                << " -D AF_PAD_SYM=" << AF_PAD_SYM
-                << " -D AF_PAD_PERIODIC=" << AF_PAD_PERIODIC
-                << " -D AF_PAD_CLAMP_TO_EDGE=" << AF_PAD_CLAMP_TO_EDGE;
-        options << getTypeBuildDefinition<T>();
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(borderType),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(AF_BORDER_TYPE, (int)borderType),
+        DefineKeyValue(AF_PAD_SYM, (int)AF_PAD_SYM),
+        DefineKeyValue(AF_PAD_PERIODIC, (int)AF_PAD_PERIODIC),
+        DefineKeyValue(AF_PAD_CLAMP_TO_EDGE, (int)AF_PAD_CLAMP_TO_EDGE),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {pad_array_borders_cl};
-        const int ker_lens[]   = {pad_array_borders_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "padBorders");
-
-        addKernelToCache(device, refName, entry);
-    }
+    auto pad = common::findKernel("padBorders", {src}, tmpltArgs, compileOpts);
 
     NDRange local(PADB_THREADS_X, PADB_THREADS_Y);
 
-    int blk_x = divup(out.info.dims[0], local[0]);
-    int blk_y = divup(out.info.dims[1], local[1]);
+    unsigned blk_x = divup(out.info.dims[0], local[0]);
+    unsigned blk_y = divup(out.info.dims[1], local[1]);
 
     NDRange global(blk_x * out.info.dims[2] * local[0],
                    blk_y * out.info.dims[3] * local[1]);
 
-    auto padOP =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, unsigned, unsigned,
-                      unsigned, unsigned, int, int>(*entry.ker);
-
-    padOP(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
-          in.info, lBPadding[0], lBPadding[1], lBPadding[2], lBPadding[3],
-          blk_x, blk_y);
-
+    pad(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+        in.info, static_cast<int>(lBPadding[0]), static_cast<int>(lBPadding[1]),
+        static_cast<int>(lBPadding[2]), static_cast<int>(lBPadding[3]), blk_x,
+        blk_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index f1cb1f7370..1b45726774 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -9,25 +9,21 @@
 
 #pragma once
 
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel_headers/random_engine_mersenne.hpp>
+#include <kernel_headers/random_engine_mersenne_init.hpp>
 #include <kernel_headers/random_engine_philox.hpp>
 #include <kernel_headers/random_engine_threefry.hpp>
 #include <kernel_headers/random_engine_write.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <random_engine.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 #include <af/defines.h>
-#include <sstream>
-#include <string>
-#include "config.hpp"
 
-#include <kernel_headers/random_engine_mersenne.hpp>
-#include <kernel_headers/random_engine_mersenne_init.hpp>
+#include <string>
+#include <vector>
 
 static const int N          = 351;
 static const int TABLE_SIZE = 16;
@@ -39,88 +35,57 @@ namespace kernel {
 static const uint THREADS = 256;
 
 template<typename T>
-static cl::Kernel get_random_engine_kernel(const af_random_engine_type type,
-                                           const int kerIdx,
-                                           const uint elementsPerBlock) {
-    using std::string;
-    using std::to_string;
-    string engineName;
-    const char *ker_strs[2];
-    int ker_lens[2];
-    ker_strs[0] = random_engine_write_cl;
-    ker_lens[0] = random_engine_write_cl_len;
+static Kernel getRandomEngineKernel(const af_random_engine_type type,
+                                    const int kerIdx,
+                                    const uint elementsPerBlock) {
+    std::string key;
+    std::vector<std::string> sources = {
+        std::string(random_engine_write_cl, random_engine_write_cl_len)};
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
-            engineName  = "Philox";
-            ker_strs[1] = random_engine_philox_cl;
-            ker_lens[1] = random_engine_philox_cl_len;
+            key = "philoxGenerator";
+            sources.emplace_back(random_engine_philox_cl,
+                                 random_engine_philox_cl_len);
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
-            engineName  = "Threefry";
-            ker_strs[1] = random_engine_threefry_cl;
-            ker_lens[1] = random_engine_threefry_cl_len;
+            key = "threefryGenerator";
+            sources.emplace_back(random_engine_threefry_cl,
+                                 random_engine_threefry_cl_len);
             break;
         case AF_RANDOM_ENGINE_MERSENNE_GP11213:
-            engineName  = "Mersenne";
-            ker_strs[1] = random_engine_mersenne_cl;
-            ker_lens[1] = random_engine_mersenne_cl_len;
+            key = "mersenneGenerator";
+            sources.emplace_back(random_engine_mersenne_cl,
+                                 random_engine_mersenne_cl_len);
             break;
         default:
             AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
     }
-
-    string ref_name = "random_engine_kernel_" + engineName + "_" +
-                      string(dtype_traits<T>::getName()) + "_" +
-                      to_string(kerIdx);
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D THREADS=" << THREADS << " -D RAND_DIST=" << kerIdx;
-        if (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-            options << " -D ELEMENTS_PER_BLOCK=" << elementsPerBlock;
-        }
-        options << getTypeBuildDefinition<T>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(kerIdx),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(THREADS),
+        DefineKeyValue(RAND_DIST, kerIdx),
+    };
+    if (type != AF_RANDOM_ENGINE_MERSENNE_GP11213) {
+        options.emplace_back(
+            DefineKeyValue(ELEMENTS_PER_BLOCK, elementsPerBlock));
+    }
 #if defined(OS_MAC)  // Because apple is "special"
-        options << " -D IS_APPLE"
-                << " -D log10_val=" << std::log(10.0);
+    options.emplace_back(DefineKey(IS_APPLE));
+    options.emplace_back(DefineKeyValue(log10_val, std::log(10.0)));
 #endif
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "generate");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-    return *entry.ker;
+    return common::findKernel(key, sources, targs, options);
 }
 
-static cl::Kernel get_mersenne_init_kernel(void) {
-    using std::string;
-    using std::to_string;
-    string engineName;
-    const char *ker_str = random_engine_mersenne_init_cl;
-    int ker_len         = random_engine_mersenne_init_cl_len;
-    string ref_name     = "mersenne_init";
-    int device          = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::string emptyOptionString;
-        cl::Program prog;
-        buildProgram(prog, 1, &ker_str, &ker_len, emptyOptionString);
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "initState");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    return *entry.ker;
+static Kernel getMersenneInitKernel(void) {
+    static const std::string src(random_engine_mersenne_init_cl,
+                                 random_engine_mersenne_init_cl_len);
+    return common::findKernel("mersenneInitState", {src}, {});
 }
 
 template<typename T>
@@ -140,14 +105,11 @@ static void randomDistribution(cl::Buffer out, const size_t elements,
 
     if ((type == AF_RANDOM_ENGINE_PHILOX_4X32_10) ||
         (type == AF_RANDOM_ENGINE_THREEFRY_2X32_16)) {
-        cl::Kernel ker =
-            get_random_engine_kernel<T>(type, kerIdx, elementsPerBlock);
         auto randomEngineOp =
-            cl::KernelFunctor<cl::Buffer, uint, uint, uint, uint, uint>(ker);
+            getRandomEngineKernel<T>(type, kerIdx, elementsPerBlock);
         randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out,
-                       elements, hic, loc, hi, lo);
+                       static_cast<unsigned>(elements), hic, loc, hi, lo);
     }
-
     counter += elements;
     CL_DEBUG_FINISH(getQueue());
 }
@@ -161,19 +123,15 @@ void randomDistribution(cl::Buffer out, const size_t elements, cl::Buffer state,
     int min_elements_per_block = 32 * THREADS * 4 * sizeof(uint) / sizeof(T);
     int blocks                 = divup(elements, min_elements_per_block);
     blocks                     = (blocks > MAX_BLOCKS) ? MAX_BLOCKS : blocks;
-    int elementsPerBlock       = divup(elements, blocks);
+    uint elementsPerBlock      = divup(elements, blocks);
 
     cl::NDRange local(threads, 1);
     cl::NDRange global(threads * blocks, 1);
-    cl::Kernel ker = get_random_engine_kernel<T>(
+    auto randomEngineOp = getRandomEngineKernel<T>(
         AF_RANDOM_ENGINE_MERSENNE_GP11213, kerIdx, elementsPerBlock);
-    auto randomEngineOp =
-        cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, cl::Buffer,
-                          cl::Buffer, uint, cl::Buffer, cl::Buffer, uint, uint>(
-            ker);
     randomEngineOp(cl::EnqueueArgs(getQueue(), global, local), out, state, pos,
                    sh1, sh2, mask, recursion_table, temper_table,
-                   elementsPerBlock, elements);
+                   elementsPerBlock, static_cast<uint>(elements));
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -214,8 +172,7 @@ void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
     cl::NDRange local(THREADS_PER_GROUP, 1);
     cl::NDRange global(local[0] * MAX_BLOCKS, 1);
 
-    cl::Kernel ker = get_mersenne_init_kernel();
-    auto initOp    = cl::KernelFunctor<cl::Buffer, cl::Buffer, uintl>(ker);
+    auto initOp = getMersenneInitKernel();
     initOp(cl::EnqueueArgs(getQueue(), global, local), state, table, seed);
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index 24be51e47d..ec06ba74f4 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -48,17 +48,15 @@
 
 #define divup(NUM, DEN) (((NUM) + (DEN)-1) / (DEN));
 
-void read_table(__local uint *const localTable,
-                __global const uint *const table) {
-    __global const uint *const t = table + (get_group_id(0) * TABLE_SIZE);
+void read_table(local uint *const localTable, global const uint *const table) {
+    global const uint *const t = table + (get_group_id(0) * TABLE_SIZE);
     if (get_local_id(0) < TABLE_SIZE) {
         localTable[get_local_id(0)] = t[get_local_id(0)];
     }
 }
 
-void state_read(__local uint *const localState,
-                __global const uint *const state) {
-    __global const uint *const g = state + (get_group_id(0) * N);
+void state_read(local uint *const localState, global const uint *const state) {
+    global const uint *const g = state + (get_group_id(0) * N);
     localState[STATE_SIZE - N + get_local_id(0)] = g[get_local_id(0)];
     if (get_local_id(0) < N - THREADS) {
         localState[STATE_SIZE - N + THREADS + get_local_id(0)] =
@@ -66,17 +64,16 @@ void state_read(__local uint *const localState,
     }
 }
 
-void state_write(__global uint *const state,
-                 __local const uint *const localState) {
-    __global uint *const g = state + (get_group_id(0) * N);
-    g[get_local_id(0)]     = localState[STATE_SIZE - N + get_local_id(0)];
+void state_write(global uint *const state, local const uint *const localState) {
+    global uint *const g = state + (get_group_id(0) * N);
+    g[get_local_id(0)]   = localState[STATE_SIZE - N + get_local_id(0)];
     if (get_local_id(0) < N - THREADS) {
         g[THREADS + get_local_id(0)] =
             localState[STATE_SIZE - N + THREADS + get_local_id(0)];
     }
 }
 
-uint recursion(__local const uint *const recursion_table, const uint mask,
+uint recursion(local const uint *const recursion_table, const uint mask,
                const uint sh1, const uint sh2, const uint x1, const uint x2,
                uint y) {
     uint x = (x1 & mask) ^ x2;
@@ -86,23 +83,23 @@ uint recursion(__local const uint *const recursion_table, const uint mask,
     return y ^ mat;
 }
 
-uint temper(__local const uint *const temper_table, const uint v, uint t) {
+uint temper(local const uint *const temper_table, const uint v, uint t) {
     t ^= t >> 16;
     t ^= t >> 8;
     uint mat = temper_table[t & 0x0f];
     return v ^ mat;
 }
 
-__kernel void generate(__global T *output, __global uint *const state,
-                       __global const uint *const pos_tbl,
-                       __global const uint *const sh1_tbl,
-                       __global const uint *const sh2_tbl, uint mask,
-                       __global const uint *const recursion_table,
-                       __global const uint *const temper_table,
-                       uint elements_per_block, uint elements) {
-    __local uint l_state[STATE_SIZE];
-    __local uint l_recursion_table[TABLE_SIZE];
-    __local uint l_temper_table[TABLE_SIZE];
+kernel void mersenneGenerator(global T *output, global uint *const state,
+                              global const uint *const pos_tbl,
+                              global const uint *const sh1_tbl,
+                              global const uint *const sh2_tbl, uint mask,
+                              global const uint *const recursion_table,
+                              global const uint *const temper_table,
+                              uint elements_per_block, uint elements) {
+    local uint l_state[STATE_SIZE];
+    local uint l_recursion_table[TABLE_SIZE];
+    local uint l_temper_table[TABLE_SIZE];
     uint start = get_group_id(0) * elements_per_block;
     uint end   = start + elements_per_block;
     end        = (end > elements) ? elements : end;
diff --git a/src/backend/opencl/kernel/random_engine_mersenne_init.cl b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
index de4db1a03e..af8435356a 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne_init.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne_init.cl
@@ -45,14 +45,15 @@
 #define N 351
 #define TABLE_SIZE 16
 
-__kernel void initState(__global uint *state, __global uint *tbl, ulong seed) {
+kernel void mersenneInitState(global uint *state, global uint *tbl,
+                              ulong seed) {
     int tid      = get_local_id(0);
     int nthreads = get_local_size(0);
     int gid      = get_group_id(0);
-    __local uint lstate[N];
-    const __global uint *ltbl = tbl + (TABLE_SIZE * gid);
-    uint hidden_seed          = ltbl[4] ^ (ltbl[8] << 16);
-    uint tmp                  = hidden_seed;
+    local uint lstate[N];
+    const global uint *ltbl = tbl + (TABLE_SIZE * gid);
+    uint hidden_seed        = ltbl[4] ^ (ltbl[8] << 16);
+    uint tmp                = hidden_seed;
     tmp += tmp >> 16;
     tmp += tmp >> 8;
     tmp &= 0xff;
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index 46bd9964cf..76990141f7 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -97,8 +97,8 @@ void philox(uint key[2], uint ctr[4]) {
     philoxRound(key, ctr);
 }
 
-__kernel void generate(__global T *output, unsigned elements, unsigned hic,
-                       unsigned loc, unsigned hi, unsigned lo) {
+kernel void philoxGenerator(global T *output, unsigned elements, unsigned hic,
+                            unsigned loc, unsigned hi, unsigned lo) {
     unsigned gid   = get_group_id(0);
     unsigned off   = get_local_size(0);
     unsigned index = gid * ELEMENTS_PER_BLOCK + get_local_id(0);
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index 6482b4b92e..ef6aca3ab1 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -151,8 +151,8 @@ inline void threefry(uint k[2], uint c[2], uint X[2]) {
     X[1] += 4;
 }
 
-__kernel void generate(__global T *output, unsigned elements, unsigned hic,
-                       unsigned loc, unsigned hi, unsigned lo) {
+kernel void threefryGenerator(global T *output, unsigned elements, unsigned hic,
+                              unsigned loc, unsigned hi, unsigned lo) {
     unsigned gid   = get_group_id(0);
     unsigned off   = get_local_size(0);
     unsigned index = gid * ELEMENTS_PER_BLOCK + get_local_id(0);
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 4aa2a9722f..e558fe1d16 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -22,7 +22,7 @@ float getFloat(const uint *const num) {
 
 // Writes without boundary checking
 
-void writeOut128Bytes_uchar(__global uchar *out, const uint *const index,
+void writeOut128Bytes_uchar(global uchar *out, const uint *const index,
                             const uint *const r1, const uint *const r2,
                             const uint *const r3, const uint *const r4) {
     out[*index]                = *r1;
@@ -43,7 +43,7 @@ void writeOut128Bytes_uchar(__global uchar *out, const uint *const index,
     out[*index + 15 * THREADS] = *r4 >> 24;
 }
 
-void writeOut128Bytes_char(__global char *out, const uint *const index,
+void writeOut128Bytes_char(global char *out, const uint *const index,
                            const uint *const r1, const uint *const r2,
                            const uint *const r3, const uint *const r4) {
     out[*index]                = (*r1) & 0x1;
@@ -64,7 +64,7 @@ void writeOut128Bytes_char(__global char *out, const uint *const index,
     out[*index + 15 * THREADS] = (*r4 >> 3) & 0x1;
 }
 
-void writeOut128Bytes_short(__global short *out, const uint *const index,
+void writeOut128Bytes_short(global short *out, const uint *const index,
                             const uint *const r1, const uint *const r2,
                             const uint *const r3, const uint *const r4) {
     out[*index]               = *r1;
@@ -77,7 +77,7 @@ void writeOut128Bytes_short(__global short *out, const uint *const index,
     out[*index + 7 * THREADS] = *r4 >> 16;
 }
 
-void writeOut128Bytes_ushort(__global ushort *out, const uint *const index,
+void writeOut128Bytes_ushort(global ushort *out, const uint *const index,
                              const uint *const r1, const uint *const r2,
                              const uint *const r3, const uint *const r4) {
     out[*index]               = *r1;
@@ -90,7 +90,7 @@ void writeOut128Bytes_ushort(__global ushort *out, const uint *const index,
     out[*index + 7 * THREADS] = *r4 >> 16;
 }
 
-void writeOut128Bytes_int(__global int *out, const uint *const index,
+void writeOut128Bytes_int(global int *out, const uint *const index,
                           const uint *const r1, const uint *const r2,
                           const uint *const r3, const uint *const r4) {
     out[*index]               = *r1;
@@ -99,7 +99,7 @@ void writeOut128Bytes_int(__global int *out, const uint *const index,
     out[*index + 3 * THREADS] = *r4;
 }
 
-void writeOut128Bytes_uint(__global uint *out, const uint *const index,
+void writeOut128Bytes_uint(global uint *out, const uint *const index,
                            const uint *const r1, const uint *const r2,
                            const uint *const r3, const uint *const r4) {
     out[*index]               = *r1;
@@ -108,7 +108,7 @@ void writeOut128Bytes_uint(__global uint *out, const uint *const index,
     out[*index + 3 * THREADS] = *r4;
 }
 
-void writeOut128Bytes_long(__global long *out, const uint *const index,
+void writeOut128Bytes_long(global long *out, const uint *const index,
                            const uint *const r1, const uint *const r2,
                            const uint *const r3, const uint *const r4) {
     long c1               = *r2;
@@ -119,7 +119,7 @@ void writeOut128Bytes_long(__global long *out, const uint *const index,
     out[*index + THREADS] = c2;
 }
 
-void writeOut128Bytes_ulong(__global ulong *out, const uint *const index,
+void writeOut128Bytes_ulong(global ulong *out, const uint *const index,
                             const uint *const r1, const uint *const r2,
                             const uint *const r3, const uint *const r4) {
     long c1               = *r2;
@@ -130,7 +130,7 @@ void writeOut128Bytes_ulong(__global ulong *out, const uint *const index,
     out[*index + THREADS] = c2;
 }
 
-void writeOut128Bytes_float(__global float *out, const uint *const index,
+void writeOut128Bytes_float(global float *out, const uint *const index,
                             const uint *const r1, const uint *const r2,
                             const uint *const r3, const uint *const r4) {
     out[*index]               = 1.f - getFloat(r1);
@@ -144,7 +144,7 @@ void writeOut128Bytes_float(__global float *out, const uint *const index,
 
 // Writes with boundary checking
 
-void partialWriteOut128Bytes_uchar(__global uchar *out, const uint *const index,
+void partialWriteOut128Bytes_uchar(global uchar *out, const uint *const index,
                                    const uint *const r1, const uint *const r2,
                                    const uint *const r3, const uint *const r4,
                                    const uint *const elements) {
@@ -188,7 +188,7 @@ void partialWriteOut128Bytes_uchar(__global uchar *out, const uint *const index,
     }
 }
 
-void partialWriteOut128Bytes_char(__global char *out, const uint *const index,
+void partialWriteOut128Bytes_char(global char *out, const uint *const index,
                                   const uint *const r1, const uint *const r2,
                                   const uint *const r3, const uint *const r4,
                                   const uint *const elements) {
@@ -240,7 +240,7 @@ void partialWriteOut128Bytes_char(__global char *out, const uint *const index,
     }
 }
 
-void partialWriteOut128Bytes_short(__global short *out, const uint *const index,
+void partialWriteOut128Bytes_short(global short *out, const uint *const index,
                                    const uint *const r1, const uint *const r2,
                                    const uint *const r3, const uint *const r4,
                                    const uint *const elements) {
@@ -260,7 +260,7 @@ void partialWriteOut128Bytes_short(__global short *out, const uint *const index,
     }
 }
 
-void partialWriteOut128Bytes_ushort(__global ushort *out,
+void partialWriteOut128Bytes_ushort(global ushort *out,
                                     const uint *const index,
                                     const uint *const r1, const uint *const r2,
                                     const uint *const r3, const uint *const r4,
@@ -281,7 +281,7 @@ void partialWriteOut128Bytes_ushort(__global ushort *out,
     }
 }
 
-void partialWriteOut128Bytes_int(__global int *out, const uint *const index,
+void partialWriteOut128Bytes_int(global int *out, const uint *const index,
                                  const uint *const r1, const uint *const r2,
                                  const uint *const r3, const uint *const r4,
                                  const uint *const elements) {
@@ -291,7 +291,7 @@ void partialWriteOut128Bytes_int(__global int *out, const uint *const index,
     if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = *r4; }
 }
 
-void partialWriteOut128Bytes_uint(__global uint *out, const uint *const index,
+void partialWriteOut128Bytes_uint(global uint *out, const uint *const index,
                                   const uint *const r1, const uint *const r2,
                                   const uint *const r3, const uint *const r4,
                                   const uint *const elements) {
@@ -301,7 +301,7 @@ void partialWriteOut128Bytes_uint(__global uint *out, const uint *const index,
     if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = *r4; }
 }
 
-void partialWriteOut128Bytes_long(__global long *out, const uint *const index,
+void partialWriteOut128Bytes_long(global long *out, const uint *const index,
                                   const uint *const r1, const uint *const r2,
                                   const uint *const r3, const uint *const r4,
                                   const uint *const elements) {
@@ -313,7 +313,7 @@ void partialWriteOut128Bytes_long(__global long *out, const uint *const index,
     if (*index + THREADS < *elements) { out[*index + THREADS] = c2; }
 }
 
-void partialWriteOut128Bytes_ulong(__global ulong *out, const uint *const index,
+void partialWriteOut128Bytes_ulong(global ulong *out, const uint *const index,
                                    const uint *const r1, const uint *const r2,
                                    const uint *const r3, const uint *const r4,
                                    const uint *const elements) {
@@ -325,7 +325,7 @@ void partialWriteOut128Bytes_ulong(__global ulong *out, const uint *const index,
     if (*index + THREADS < *elements) { out[*index + THREADS] = c2; }
 }
 
-void partialWriteOut128Bytes_float(__global float *out, const uint *const index,
+void partialWriteOut128Bytes_float(global float *out, const uint *const index,
                                    const uint *const r1, const uint *const r2,
                                    const uint *const r3, const uint *const r4,
                                    const uint *const elements) {
@@ -357,14 +357,14 @@ void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
 }
 
 // BoxMuller writes without boundary checking
-void boxMullerWriteOut128Bytes_float(__global float *out,
+void boxMullerWriteOut128Bytes_float(global float *out,
                                      const uint *const index,
                                      const uint *const r1, const uint *const r2,
                                      const uint *const r3,
                                      const uint *const r4) {
     float n1, n2, n3, n4;
-    boxMullerTransform((T*)&n1, (T*)&n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform((T*)&n3, (T*)&n4, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T *)&n1, (T *)&n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T *)&n3, (T *)&n4, getFloat(r1), getFloat(r2));
     out[*index]               = n1;
     out[*index + THREADS]     = n2;
     out[*index + 2 * THREADS] = n3;
@@ -373,12 +373,12 @@ void boxMullerWriteOut128Bytes_float(__global float *out,
 
 // BoxMuller writes with boundary checking
 void partialBoxMullerWriteOut128Bytes_float(
-    __global float *out, const uint *const index, const uint *const r1,
+    global float *out, const uint *const index, const uint *const r1,
     const uint *const r2, const uint *const r3, const uint *const r4,
     const uint *const elements) {
     float n1, n2, n3, n4;
-    boxMullerTransform((T*)&n1, (T*)&n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform((T*)&n3, (T*)&n4, getFloat(r3), getFloat(r4));
+    boxMullerTransform((T *)&n1, (T *)&n2, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T *)&n3, (T *)&n4, getFloat(r3), getFloat(r4));
     if (*index < *elements) { out[*index] = n1; }
     if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
     if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = n3; }
@@ -399,14 +399,14 @@ double getDouble(const uint *const num1, const uint *const num2) {
     return (num * DBL_FACTOR + HALF_DBL_FACTOR);
 }
 
-void writeOut128Bytes_double(__global double *out, const uint *const index,
+void writeOut128Bytes_double(global double *out, const uint *const index,
                              const uint *const r1, const uint *const r2,
                              const uint *const r3, const uint *const r4) {
     out[*index]           = 1.0 - getDouble(r1, r2);
     out[*index + THREADS] = 1.0 - getDouble(r3, r4);
 }
 
-void partialWriteOut128Bytes_double(__global double *out,
+void partialWriteOut128Bytes_double(global double *out,
                                     const uint *const index,
                                     const uint *const r1, const uint *const r2,
                                     const uint *const r3, const uint *const r4,
@@ -419,7 +419,7 @@ void partialWriteOut128Bytes_double(__global double *out,
 
 #if RAND_DIST == 1
 void boxMullerWriteOut128Bytes_double(
-    __global double *out, const uint *const index, const uint *const r1,
+    global double *out, const uint *const index, const uint *const r1,
     const uint *const r2, const uint *const r3, const uint *const r4) {
     double n1, n2;
     boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
@@ -428,7 +428,7 @@ void boxMullerWriteOut128Bytes_double(
 }
 
 void partialBoxMullerWriteOut128Bytes_double(
-    __global double *out, const uint *const index, const uint *const r1,
+    global double *out, const uint *const index, const uint *const r1,
     const uint *const r2, const uint *const r3, const uint *const r4,
     const uint *const elements) {
     double n1, n2;
@@ -452,9 +452,9 @@ half getHalf(const uint *const num, int index) {
     return 1.0f - (v * HALF_FACTOR + HALF_HALF_FACTOR);
 }
 
-void writeOut128Bytes_half(__global half *out, const uint *const index,
-                             const uint *const r1, const uint *const r2,
-                             const uint *const r3, const uint *const r4) {
+void writeOut128Bytes_half(global half *out, const uint *const index,
+                           const uint *const r1, const uint *const r2,
+                           const uint *const r3, const uint *const r4) {
     out[*index]               = getHalf(r1, 0);
     out[*index + THREADS]     = getHalf(r1, 1);
     out[*index + 2 * THREADS] = getHalf(r2, 0);
@@ -465,33 +465,51 @@ void writeOut128Bytes_half(__global half *out, const uint *const index,
     out[*index + 7 * THREADS] = getHalf(r4, 1);
 }
 
-void partialWriteOut128Bytes_half(__global half *out,
-                                    const uint *const index,
-                                    const uint *const r1, const uint *const r2,
-                                    const uint *const r3, const uint *const r4,
-                                    const uint *const elements) {
-    if (*index               < *elements) { out[*index              ] = getHalf(r1, 0); }
-    if (*index +     THREADS < *elements) { out[*index +     THREADS] = getHalf(r1, 1); }
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = getHalf(r2, 0); }
-    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = getHalf(r2, 1); }
-    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = getHalf(r3, 0); }
-    if (*index + 5 * THREADS < *elements) { out[*index + 5 * THREADS] = getHalf(r3, 1); }
-    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = getHalf(r4, 0); }
-    if (*index + 7 * THREADS < *elements) { out[*index + 7 * THREADS] = getHalf(r4, 1); }
+void partialWriteOut128Bytes_half(global half *out, const uint *const index,
+                                  const uint *const r1, const uint *const r2,
+                                  const uint *const r3, const uint *const r4,
+                                  const uint *const elements) {
+    if (*index < *elements) { out[*index] = getHalf(r1, 0); }
+    if (*index + THREADS < *elements) {
+        out[*index + THREADS] = getHalf(r1, 1);
+    }
+    if (*index + 2 * THREADS < *elements) {
+        out[*index + 2 * THREADS] = getHalf(r2, 0);
+    }
+    if (*index + 3 * THREADS < *elements) {
+        out[*index + 3 * THREADS] = getHalf(r2, 1);
+    }
+    if (*index + 4 * THREADS < *elements) {
+        out[*index + 4 * THREADS] = getHalf(r3, 0);
+    }
+    if (*index + 5 * THREADS < *elements) {
+        out[*index + 5 * THREADS] = getHalf(r3, 1);
+    }
+    if (*index + 6 * THREADS < *elements) {
+        out[*index + 6 * THREADS] = getHalf(r4, 0);
+    }
+    if (*index + 7 * THREADS < *elements) {
+        out[*index + 7 * THREADS] = getHalf(r4, 1);
+    }
 }
 
 #if RAND_DIST == 1
-void boxMullerWriteOut128Bytes_half(
-    __global half *out, const uint *const index, const uint *const r1,
-    const uint *const r2, const uint *const r3, const uint *const r4) {
-    boxMullerTransform(&out[*index], &out[*index + THREADS], getHalf(r1, 0), getHalf(r1, 1));
-    boxMullerTransform(&out[*index + 2 * THREADS], &out[*index + 3 * THREADS], getHalf(r2, 0), getHalf(r2, 1));
-    boxMullerTransform(&out[*index + 4 * THREADS], &out[*index + 5 * THREADS], getHalf(r3, 0), getHalf(r3, 1));
-    boxMullerTransform(&out[*index + 6 * THREADS], &out[*index + 7 * THREADS], getHalf(r4, 0), getHalf(r4, 1));
+void boxMullerWriteOut128Bytes_half(global half *out, const uint *const index,
+                                    const uint *const r1, const uint *const r2,
+                                    const uint *const r3,
+                                    const uint *const r4) {
+    boxMullerTransform(&out[*index], &out[*index + THREADS], getHalf(r1, 0),
+                       getHalf(r1, 1));
+    boxMullerTransform(&out[*index + 2 * THREADS], &out[*index + 3 * THREADS],
+                       getHalf(r2, 0), getHalf(r2, 1));
+    boxMullerTransform(&out[*index + 4 * THREADS], &out[*index + 5 * THREADS],
+                       getHalf(r3, 0), getHalf(r3, 1));
+    boxMullerTransform(&out[*index + 6 * THREADS], &out[*index + 7 * THREADS],
+                       getHalf(r4, 0), getHalf(r4, 1));
 }
 
 void partialBoxMullerWriteOut128Bytes_half(
-    __global half *out, const uint *const index, const uint *const r1,
+    global half *out, const uint *const index, const uint *const r1,
     const uint *const r2, const uint *const r3, const uint *const r4,
     const uint *const elements) {
     half n1, n2;
diff --git a/src/backend/opencl/kernel/range.cl b/src/backend/opencl/kernel/range.cl
index 102cda92cf..80fbdda90f 100644
--- a/src/backend/opencl/kernel/range.cl
+++ b/src/backend/opencl/kernel/range.cl
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void range_kernel(__global T *out, const KParam op, const int dim,
+kernel void range_kernel(global T *out, const KParam op, const int dim,
                            const int blocksPerMatX, const int blocksPerMatY) {
     const int mul0 = (dim == 0);
     const int mul1 = (dim == 1);
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 8e9202193b..46a78d04c1 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -10,70 +10,45 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/range.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const int RANGE_TX    = 32;
-static const int RANGE_TY    = 8;
-static const int RANGE_TILEX = 512;
-static const int RANGE_TILEY = 32;
 
 template<typename T>
 void range(Param out, const int dim) {
-    using cl::Buffer;
-    using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
-    using cl::NDRange;
-    using cl::Program;
-    using std::string;
+    constexpr int RANGE_TX    = 32;
+    constexpr int RANGE_TY    = 8;
+    constexpr int RANGE_TILEX = 512;
+    constexpr int RANGE_TILEY = 32;
 
-    std::string refName =
-        std::string("range_kernel_") + std::string(dtype_traits<T>::getName());
+    static const std::string src(range_cl, range_cl_len);
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    std::vector<TemplateArg> targs   = {TemplateTypename<T>()};
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    auto rangeOp = common::findKernel("range_kernel", {src}, targs, options);
 
-        const char* ker_strs[] = {range_cl};
-        const int ker_lens[]   = {range_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "range_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto rangeOp =
-        KernelFunctor<Buffer, const KParam, const int, const int, const int>(
-            *entry.ker);
-
-    NDRange local(RANGE_TX, RANGE_TY, 1);
+    cl::NDRange local(RANGE_TX, RANGE_TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
     int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
-    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                   local[1] * blocksPerMatY * out.info.dims[3], 1);
-
-    rangeOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, dim,
-            blocksPerMatX, blocksPerMatY);
+    cl::NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                       local[1] * blocksPerMatY * out.info.dims[3], 1);
 
+    rangeOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+            dim, blocksPerMatX, blocksPerMatY);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index d04cb651e2..5c3ef15a7a 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -8,91 +8,67 @@
  ********************************************************/
 
 #pragma once
+
 #include <Array.hpp>
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/reduce_dim.hpp>
 #include <kernel_headers/reduce_first.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <memory>
-#include <mutex>
+
 #include <string>
-#include "config.hpp"
-#include "names.hpp"
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using common::half;
-using std::string;
-using std::unique_ptr;
-
 template<typename Ti, typename To, af_op_t op>
-void reduce_dim_launcher(Param out, Param in, const int dim,
-                         const uint threads_y, const uint groups_all[4],
-                         int change_nan, double nanval) {
-    std::string ref_name =
-        std::string("reduce_") + std::to_string(dim) + std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_y);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D kDim=" << dim << " -D DIMY=" << threads_y
-                << " -D THREADS_X=" << THREADS_X
-                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
-        options << getTypeBuildDefinition<Ti, To>();
-
-        const char *ker_strs[] = {ops_cl, reduce_dim_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_dim_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "reduce_dim_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(THREADS_X, threads_y);
-    NDRange global(groups_all[0] * groups_all[2] * local[0],
-                   groups_all[1] * groups_all[3] * local[1]);
-
-    auto reduceOp = KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint,
-                                  uint, int, To>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, groups_all[0], groups_all[1], groups_all[dim],
-             change_nan, scalar<To>(nanval));
-
+void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
+                       const uint groups_all[4], int change_nan,
+                       double nanval) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_dim_cl, reduce_dim_cl_len);
+
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(dim),
+        TemplateArg(op),        TemplateArg(threads_y),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(kDim, dim),
+        DefineKeyValue(DIMY, threads_y),
+        DefineValue(THREADS_X),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+    };
+    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+
+    auto reduceDim =
+        common::findKernel("reduce_dim_kernel", {src1, src2}, targs, options);
+
+    cl::NDRange local(THREADS_X, threads_y);
+    cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
+                       groups_all[1] * groups_all[3] * local[1]);
+
+    reduceDim(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *in.data, in.info, groups_all[0], groups_all[1], groups_all[dim],
+              change_nan, scalar<To>(nanval));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename To, af_op_t op>
-void reduce_dim(Param out, Param in, int change_nan, double nanval, int dim) {
+void reduceDim(Param out, Param in, int change_nan, double nanval, int dim) {
     uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -116,78 +92,66 @@ void reduce_dim(Param out, Param in, int change_nan, double nanval, int dim) {
             tmp.info.strides[k] *= groups_all[dim];
     }
 
-    reduce_dim_launcher<Ti, To, op>(tmp, in, dim, threads_y, groups_all,
-                                    change_nan, nanval);
+    reduceDimLauncher<Ti, To, op>(tmp, in, dim, threads_y, groups_all,
+                                  change_nan, nanval);
 
     if (groups_all[dim] > 1) {
         groups_all[dim] = 1;
 
         if (op == af_notzero_t) {
-            reduce_dim_launcher<To, To, af_add_t>(
-                out, tmp, dim, threads_y, groups_all, change_nan, nanval);
+            reduceDimLauncher<To, To, af_add_t>(out, tmp, dim, threads_y,
+                                                groups_all, change_nan, nanval);
         } else {
-            reduce_dim_launcher<To, To, op>(out, tmp, dim, threads_y,
-                                            groups_all, change_nan, nanval);
+            reduceDimLauncher<To, To, op>(out, tmp, dim, threads_y, groups_all,
+                                          change_nan, nanval);
         }
         bufferFree(tmp.data);
     }
 }
 
 template<typename Ti, typename To, af_op_t op>
-void reduce_first_launcher(Param out, Param in, const uint groups_x,
-                           const uint groups_y, const uint threads_x,
-                           int change_nan, double nanval) {
-    std::string ref_name =
-        std::string("reduce_0_") + std::string(dtype_traits<Ti>::getName()) +
-        std::string("_") + std::string(dtype_traits<To>::getName()) +
-        std::string("_") + std::to_string(op) + std::string("_") +
-        std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x
-                << " -D THREADS_PER_GROUP=" << THREADS_PER_GROUP
-                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
-        options << getTypeBuildDefinition<Ti, To>();
-
-        const char *ker_strs[] = {ops_cl, reduce_first_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_first_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "reduce_first_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
-    NDRange global(groups_x * in.info.dims[2] * local[0],
-                   groups_y * in.info.dims[3] * local[1]);
+void reduceFirstLauncher(Param out, Param in, const uint groups_x,
+                         const uint groups_y, const uint threads_x,
+                         int change_nan, double nanval) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_first_cl, reduce_first_cl_len);
+
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<Ti>(),
+        TemplateTypename<To>(),
+        TemplateArg(op),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineValue(THREADS_PER_GROUP),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+    };
+    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+
+    auto reduceFirst =
+        common::findKernel("reduce_first_kernel", {src1, src2}, targs, options);
+
+    cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    cl::NDRange global(groups_x * in.info.dims[2] * local[0],
+                       groups_y * in.info.dims[3] * local[1]);
 
     uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
 
-    auto reduceOp = KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint,
-                                  uint, int, To>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, groups_x, groups_y, repeat, change_nan,
-             scalar<To>(nanval));
-
+    reduceFirst(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *in.data, in.info, groups_x, groups_y, repeat, change_nan,
+                scalar<To>(nanval));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename To, af_op_t op>
-void reduce_first(Param out, Param in, int change_nan, double nanval) {
+void reduceFirst(Param out, Param in, int change_nan, double nanval) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
@@ -205,19 +169,18 @@ void reduce_first(Param out, Param in, int change_nan, double nanval) {
         for (int k = 1; k < 4; k++) tmp.info.strides[k] *= groups_x;
     }
 
-    reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x,
-                                      change_nan, nanval);
+    reduceFirstLauncher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x,
+                                    change_nan, nanval);
 
     if (groups_x > 1) {
         // FIXME: Is there an alternative to the if condition ?
         if (op == af_notzero_t) {
-            reduce_first_launcher<To, To, af_add_t>(
+            reduceFirstLauncher<To, To, af_add_t>(
                 out, tmp, 1, groups_y, threads_x, change_nan, nanval);
         } else {
-            reduce_first_launcher<To, To, op>(out, tmp, 1, groups_y, threads_x,
-                                              change_nan, nanval);
+            reduceFirstLauncher<To, To, op>(out, tmp, 1, groups_y, threads_x,
+                                            change_nan, nanval);
         }
-
         bufferFree(tmp.data);
     }
 }
@@ -225,13 +188,13 @@ void reduce_first(Param out, Param in, int change_nan, double nanval) {
 template<typename Ti, typename To, af_op_t op>
 void reduce(Param out, Param in, int dim, int change_nan, double nanval) {
     if (dim == 0)
-        return reduce_first<Ti, To, op>(out, in, change_nan, nanval);
+        return reduceFirst<Ti, To, op>(out, in, change_nan, nanval);
     else
-        return reduce_dim<Ti, To, op>(out, in, change_nan, nanval, dim);
+        return reduceDim<Ti, To, op>(out, in, change_nan, nanval, dim);
 }
 
 template<typename Ti, typename To, af_op_t op>
-To reduce_all(Param in, int change_nan, double nanval) {
+To reduceAll(Param in, int change_nan, double nanval) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
@@ -262,8 +225,8 @@ To reduce_all(Param in, int change_nan, double nanval) {
 
         int tmp_elements = tmp.elements();
 
-        reduce_first_launcher<Ti, To, op>(tmp, in, groups_x, groups_y,
-                                          threads_x, change_nan, nanval);
+        reduceFirstLauncher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x,
+                                        change_nan, nanval);
 
         std::vector<To> h_ptr(tmp_elements);
         getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
index 53aa60eb8b..1fbd594e0a 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
@@ -10,8 +10,8 @@
 // Starting from OpenCL 2.0, core profile includes work group level
 // inclusive scan operations, hence skip defining custom one
 #if __OPENCL_VERSION__ < 200
-int work_group_scan_inclusive_add(__local int *wg_temp, __local int *arr) {
-    __local int *active_buf;
+int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
+    local int *active_buf;
 
     const int lid = get_local_id(0);
     int val       = arr[lid];
@@ -31,11 +31,11 @@ int work_group_scan_inclusive_add(__local int *wg_temp, __local int *arr) {
 }
 #endif  // __OPENCL_VERSION__ < 200
 
-__kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
-                                       __global Tk *oKeys, KParam oKInfo,
-                                       __global To *oVals, KParam oVInfo,
-                                       const __global Tk *iKeys, KParam iKInfo,
-                                       const __global Ti *iVals, KParam iVInfo,
+kernel void reduce_blocks_by_key_dim(global int *reduced_block_sizes,
+                                       global Tk *oKeys, KParam oKInfo,
+                                       global To *oVals, KParam oVInfo,
+                                       const global Tk *iKeys, KParam iKInfo,
+                                       const global Ti *iVals, KParam iVInfo,
                                        int change_nan, To nanval, int n,
                                        const int nBlocksZ) {
     const uint lid  = get_local_id(0);
@@ -45,23 +45,23 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
     const int bidz = get_group_id(2) % nBlocksZ;
     const int bidw = get_group_id(2) / nBlocksZ;
 
-    __local Tk keys[DIMX];
-    __local To vals[DIMX];
-    __local Tk reduced_keys[DIMX];
-    __local To reduced_vals[DIMX];
-    __local int unique_ids[DIMX];
+    local Tk keys[DIMX];
+    local To vals[DIMX];
+    local Tk reduced_keys[DIMX];
+    local To reduced_vals[DIMX];
+    local int unique_ids[DIMX];
 #if __OPENCL_VERSION__ < 200
-    __local int wg_temp[DIMX];
-    __local int unique_flags[DIMX];
+    local int wg_temp[DIMX];
+    local int unique_flags[DIMX];
 #endif
 
     const To init_val = init;
 
     //
     // will hold final number of reduced elements in block
-    __local int reducedBlockSize;
+    local int reducedBlockSize;
 
-    __local int dims_ordering[4];
+    local int dims_ordering[4];
     if (lid == 0) {
         reducedBlockSize = 0;
 
@@ -95,14 +95,14 @@ __kernel void reduce_blocks_by_key_dim(__global int *reduced_block_sizes,
     barrier(CLK_LOCAL_MEM_FENCE);
 
     // mark threads containing unique keys
-    int eq_check      = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
-    int unique_flag   = (eq_check || (lid == 0)) && (gidx < n);
+    int eq_check    = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
+    int unique_flag = (eq_check || (lid == 0)) && (gidx < n);
 
 #if __OPENCL_VERSION__ < 200
     unique_flags[lid] = unique_flag;
-    int unique_id   = work_group_scan_inclusive_add(wg_temp, unique_flags);
+    int unique_id     = work_group_scan_inclusive_add(wg_temp, unique_flags);
 #else
-    int unique_id   = work_group_scan_inclusive_add(unique_flag);
+    int unique_id = work_group_scan_inclusive_add(unique_flag);
 #endif
     unique_ids[lid] = unique_id;
 
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
index 3ed23cd246..5889288f82 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -10,8 +10,8 @@
 // Starting from OpenCL 2.0, core profile includes work group level
 // inclusive scan operations, hence skip defining custom one
 #if __OPENCL_VERSION__ < 200
-int work_group_scan_inclusive_add(__local int *wg_temp, __local int *arr) {
-    __local int *active_buf;
+int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
+    local int *active_buf;
 
     const int lid = get_local_id(0);
     int val       = arr[lid];
@@ -31,10 +31,10 @@ int work_group_scan_inclusive_add(__local int *wg_temp, __local int *arr) {
 }
 #endif  // __OPENCL_VERSION__ < 200
 
-__kernel void reduce_blocks_by_key_first(
-    __global int *reduced_block_sizes, __global Tk *oKeys, KParam oKInfo,
-    __global To *oVals, KParam oVInfo, const __global Tk *iKeys, KParam iKInfo,
-    const __global Ti *iVals, KParam iVInfo, int change_nan, To nanval, int n,
+kernel void reduce_blocks_by_key_first(
+    global int *reduced_block_sizes, __global Tk *oKeys, KParam oKInfo,
+    global To *oVals, KParam oVInfo, const __global Tk *iKeys, KParam iKInfo,
+    const global Ti *iVals, KParam iVInfo, int change_nan, To nanval, int n,
     const int nBlocksZ) {
     const uint lid = get_local_id(0);
     const uint gid = get_global_id(0);
@@ -43,21 +43,21 @@ __kernel void reduce_blocks_by_key_first(
     const int bidz = get_group_id(2) % nBlocksZ;
     const int bidw = get_group_id(2) / nBlocksZ;
 
-    __local Tk keys[DIMX];
-    __local To vals[DIMX];
-    __local Tk reduced_keys[DIMX];
-    __local To reduced_vals[DIMX];
-    __local int unique_ids[DIMX];
+    local Tk keys[DIMX];
+    local To vals[DIMX];
+    local Tk reduced_keys[DIMX];
+    local To reduced_vals[DIMX];
+    local int unique_ids[DIMX];
 #if __OPENCL_VERSION__ < 200
-    __local int wg_temp[DIMX];
-    __local int unique_flags[DIMX];
+    local int wg_temp[DIMX];
+    local int unique_flags[DIMX];
 #endif
 
     const To init_val = init;
 
     //
     // will hold final number of reduced elements in block
-    __local int reducedBlockSize;
+    local int reducedBlockSize;
 
     if (lid == 0) { reducedBlockSize = 0; }
 
@@ -81,12 +81,12 @@ __kernel void reduce_blocks_by_key_first(
     barrier(CLK_LOCAL_MEM_FENCE);
 
     // mark threads containing unique keys
-    int eq_check      = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
-    int unique_flag   = (eq_check || (lid == 0)) && (gid < n);
+    int eq_check    = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
+    int unique_flag = (eq_check || (lid == 0)) && (gid < n);
 
 #if __OPENCL_VERSION__ < 200
     unique_flags[lid] = unique_flag;
-    int unique_id = work_group_scan_inclusive_add(wg_temp, unique_flags);
+    int unique_id     = work_group_scan_inclusive_add(wg_temp, unique_flags);
 #else
     int unique_id = work_group_scan_inclusive_add(unique_flag);
 #endif
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index be4df37b89..16234fa811 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -8,10 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/reduce_blocks_by_key_dim.hpp>
 #include <kernel_headers/reduce_blocks_by_key_first.hpp>
@@ -21,413 +24,302 @@
 #include <kernel_headers/reduce_by_key_compact_dim.hpp>
 #include <kernel_headers/reduce_by_key_needs_reduction.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <algorithm>
-#include <map>
-#include <memory>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "names.hpp"
 
 #include <boost/compute/algorithm/inclusive_scan.hpp>
 #include <boost/compute/core.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 
-namespace compute = boost::compute;
+#include <string>
+#include <vector>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
-using std::unique_ptr;
-using std::vector;
+namespace compute = boost::compute;
 
 namespace opencl {
-
 namespace kernel {
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-void launch_reduce_blocks_dim_by_key(cl::Buffer *reduced_block_sizes,
-                                     Param keys_out, Param vals_out,
-                                     const Param keys, const Param vals,
-                                     int change_nan, double nanval, const int n,
-                                     const uint threads_x, const int dim,
-                                     vector<int> dim_ordering) {
-    std::string ref_name =
-        std::string("reduce_blocks_dim_by_key_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        Binary<To, op> reduce;
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x << " -D DIM=" << dim
-                << " -D init=" << toNumStr(reduce.init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
-
-        options << getTypeBuildDefinition<Ti>();
-
-        const char *ker_strs[] = {ops_cl, reduce_blocks_by_key_dim_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_blocks_by_key_dim_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "reduce_blocks_by_key_dim");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
+void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
+                          Param vals_out, const Param keys, const Param vals,
+                          int change_nan, double nanval, const int n,
+                          const uint threads_x, const int dim,
+                          std::vector<int> dim_ordering) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_blocks_by_key_dim_cl,
+                                  reduce_blocks_by_key_dim_cl_len);
+
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateTypename<Tk>(),
+        TemplateArg(op),        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(DIM, dim),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
+
+    auto reduceBlocksByKeyDim = common::findKernel(
+        "reduce_blocks_by_key_dim", {src1, src2}, tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks, vals_out.info.dims[dim_ordering[1]],
-                   vals_out.info.dims[dim_ordering[2]] *
-                       vals_out.info.dims[dim_ordering[3]]);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                      Buffer, KParam, int, To, int, int>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
-             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
-             *keys.data, keys.info, *vals.data, vals.info, change_nan,
-             scalar<To>(nanval), n, vals_out.info.dims[dim_ordering[2]]);
-
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks,
+                       vals_out.info.dims[dim_ordering[1]],
+                       vals_out.info.dims[dim_ordering[2]] *
+                           vals_out.info.dims[dim_ordering[3]]);
+
+    reduceBlocksByKeyDim(cl::EnqueueArgs(getQueue(), global, local),
+                         *reduced_block_sizes, *keys_out.data, keys_out.info,
+                         *vals_out.data, vals_out.info, *keys.data, keys.info,
+                         *vals.data, vals.info, change_nan, scalar<To>(nanval),
+                         n,
+                         static_cast<int>(vals_out.info.dims[dim_ordering[2]]));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-void launch_reduce_blocks_by_key(cl::Buffer *reduced_block_sizes,
-                                 Param keys_out, Param vals_out,
-                                 const Param keys, const Param vals,
-                                 int change_nan, double nanval, const int n,
-                                 const uint threads_x) {
-    std::string ref_name =
-        std::string("reduce_blocks_by_key_0_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        Binary<To, op> reduce;
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x
-                << " -D init=" << toNumStr(reduce.init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>();
-
-        options << getTypeBuildDefinition<Ti>();
-
-        const char *ker_strs[] = {ops_cl, reduce_blocks_by_key_first_cl};
-        const int ker_lens[] = {ops_cl_len, reduce_blocks_by_key_first_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "reduce_blocks_by_key_first");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
+void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
+                       Param vals_out, const Param keys, const Param vals,
+                       int change_nan, double nanval, const int n,
+                       const uint threads_x) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_blocks_by_key_first_cl,
+                                  reduce_blocks_by_key_first_cl_len);
+
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateTypename<Tk>(),
+        TemplateArg(op),        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
+
+    auto reduceBlocksByKeyFirst = common::findKernel(
+        "reduce_blocks_by_key_first", {src1, src2}, tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
-                   vals_out.info.dims[2] * vals_out.info.dims[3]);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                      Buffer, KParam, int, To, int, int>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
-             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
-             *keys.data, keys.info, *vals.data, vals.info, change_nan,
-             scalar<To>(nanval), n, vals_out.info.dims[2]);
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
+                       vals_out.info.dims[2] * vals_out.info.dims[3]);
 
+    reduceBlocksByKeyFirst(
+        cl::EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+        *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+        *keys.data, keys.info, *vals.data, vals.info, change_nan,
+        scalar<To>(nanval), n, static_cast<int>(vals_out.info.dims[2]));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Tk, typename To, af_op_t op>
-void launch_final_boundary_reduce(cl::Buffer *reduced_block_sizes,
-                                  Param keys_out, Param vals_out, const int n,
-                                  const int numBlocks, const int threads_x) {
-    std::string ref_name =
-        std::string("final_boundary_reduce") +
-        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        Binary<To, op> reduce;
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<To>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x
-                << " -D init=" << toNumStr(reduce.init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<To>();
-
-        options << getTypeBuildDefinition<To>();
-
-        const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_by_key_boundary_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "final_boundary_reduce");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, int>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
-             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info, n);
-
+void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
+                         Param vals_out, const int n, const int numBlocks,
+                         const int threads_x) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_by_key_boundary_cl,
+                                  reduce_by_key_boundary_cl_len);
+
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<To>(),
+        TemplateTypename<Tk>(),
+        TemplateArg(op),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(Ti, dtype_traits<To>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<To>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<To>());
+
+    auto finalBoundaryReduce = common::findKernel(
+        "final_boundary_reduce", {src1, src2}, tmpltArgs, compileOpts);
+
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks);
+
+    finalBoundaryReduce(cl::EnqueueArgs(getQueue(), global, local),
+                        *reduced_block_sizes, *keys_out.data, keys_out.info,
+                        *vals_out.data, vals_out.info, n);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Tk, typename To, af_op_t op>
-void launch_final_boundary_reduce_dim(cl::Buffer *reduced_block_sizes,
-                                      Param keys_out, Param vals_out,
-                                      const int n, const int numBlocks,
-                                      const int threads_x, const int dim,
-                                      vector<int> dim_ordering) {
-    std::string ref_name =
-        std::string("final_boundary_reduce") +
-        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        Binary<To, op> reduce;
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<To>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x << " -D DIM=" << dim
-                << " -D init=" << toNumStr(reduce.init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<To>();
-
-        options << getTypeBuildDefinition<To>();
-
-        const char *ker_strs[] = {ops_cl, reduce_by_key_boundary_dim_cl};
-        const int ker_lens[] = {ops_cl_len, reduce_by_key_boundary_dim_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "final_boundary_reduce_dim");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks, vals_out.info.dims[dim_ordering[1]],
-                   vals_out.info.dims[dim_ordering[2]] *
-                       vals_out.info.dims[dim_ordering[3]]);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, int, int>(
-            *entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
-             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info, n,
-             vals_out.info.dims[dim_ordering[2]]);
-
+void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
+                            Param vals_out, const int n, const int numBlocks,
+                            const int threads_x, const int dim,
+                            std::vector<int> dim_ordering) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_by_key_boundary_dim_cl,
+                                  reduce_by_key_boundary_dim_cl_len);
+
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<To>(),
+        TemplateTypename<Tk>(),
+        TemplateArg(op),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(Ti, dtype_traits<To>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(DIM, dim),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<To>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<To>());
+
+    auto finalBoundaryReduceDim = common::findKernel(
+        "final_boundary_reduce_dim", {src1, src2}, tmpltArgs, compileOpts);
+
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks,
+                       vals_out.info.dims[dim_ordering[1]],
+                       vals_out.info.dims[dim_ordering[2]] *
+                           vals_out.info.dims[dim_ordering[3]]);
+
+    finalBoundaryReduceDim(
+        cl::EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+        *keys_out.data, keys_out.info, *vals_out.data, vals_out.info, n,
+        static_cast<int>(vals_out.info.dims[dim_ordering[2]]));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Tk, typename To>
-void launch_compact(cl::Buffer *reduced_block_sizes, Param keys_out,
-                    Param vals_out, const Param keys, const Param vals,
-                    const int numBlocks, const int threads_x) {
-    std::string ref_name =
-        std::string("compact_") + std::string(dtype_traits<Tk>::getName()) +
-        std::string("_") + std::string(dtype_traits<To>::getName()) +
-        std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x << " -D CPLX=" << af::iscplx<To>();
-
-        options << getTypeBuildDefinition<To>();
-
-        const char *ker_strs[] = {ops_cl, reduce_by_key_compact_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_by_key_compact_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "compact");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
-                   vals_out.info.dims[2] * vals_out.info.dims[3]);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                      Buffer, KParam, int>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
-             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
-             *keys.data, keys.info, *vals.data, vals.info,
-             vals_out.info.dims[2]);
-
+void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
+             const Param keys, const Param vals, const int numBlocks,
+             const int threads_x) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_by_key_compact_cl,
+                                  reduce_by_key_compact_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<To>(),
+        TemplateTypename<Tk>(),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(CPLX, af::iscplx<To>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<To>());
+
+    auto compact =
+        common::findKernel("compact", {src1, src2}, tmpltArgs, compileOpts);
+
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
+                       vals_out.info.dims[2] * vals_out.info.dims[3]);
+
+    compact(cl::EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+            *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+            *keys.data, keys.info, *vals.data, vals.info,
+            static_cast<int>(vals_out.info.dims[2]));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Tk, typename To>
-void launch_compact_dim(cl::Buffer *reduced_block_sizes, Param keys_out,
-                        Param vals_out, const Param keys, const Param vals,
-                        const int numBlocks, const int threads_x, const int dim,
-                        vector<int> dim_ordering) {
-    std::string ref_name =
-        std::string("compact_dim_") + std::string(dtype_traits<Tk>::getName()) +
-        std::string("_") + std::string(dtype_traits<To>::getName()) +
-        std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x << " -D DIM=" << dim
-                << " -D CPLX=" << af::iscplx<To>();
-
-        options << getTypeBuildDefinition<To>();
-
-        const char *ker_strs[] = {ops_cl, reduce_by_key_compact_dim_cl};
-        const int ker_lens[]   = {ops_cl_len, reduce_by_key_compact_dim_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "compact_dim");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks, vals_out.info.dims[dim_ordering[1]],
-                   vals_out.info.dims[dim_ordering[2]] *
-                       vals_out.info.dims[dim_ordering[3]]);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                      Buffer, KParam, int>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
-             *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
-             *keys.data, keys.info, *vals.data, vals.info,
-             vals_out.info.dims[dim_ordering[2]]);
-
+void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
+                const Param keys, const Param vals, const int numBlocks,
+                const int threads_x, const int dim,
+                std::vector<int> dim_ordering) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_by_key_compact_dim_cl,
+                                  reduce_by_key_compact_dim_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<To>(),
+        TemplateTypename<Tk>(),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(DIM, dim),
+        DefineKeyValue(CPLX, af::iscplx<To>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<To>());
+
+    auto compactDim =
+        common::findKernel("compact_dim", {src1, src2}, tmpltArgs, compileOpts);
+
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks,
+                       vals_out.info.dims[dim_ordering[1]],
+                       vals_out.info.dims[dim_ordering[2]] *
+                           vals_out.info.dims[dim_ordering[3]]);
+
+    compactDim(cl::EnqueueArgs(getQueue(), global, local), *reduced_block_sizes,
+               *keys_out.data, keys_out.info, *vals_out.data, vals_out.info,
+               *keys.data, keys.info, *vals.data, vals.info,
+               static_cast<int>(vals_out.info.dims[dim_ordering[2]]));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Tk>
-void launch_test_needs_reduction(cl::Buffer needs_reduction,
-                                 cl::Buffer needs_boundary, const Param keys,
-                                 const int n, const int numBlocks,
-                                 const int threads_x) {
-    std::string ref_name = std::string("test_needs_reduction_") +
-                           std::string(dtype_traits<Tk>::getName()) +
-                           std::string("_") + std::to_string(threads_x);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D Tk=" << dtype_traits<Tk>::getName()
-                << " -D DIMX=" << threads_x;
-
-        const char *ker_strs[] = {ops_cl, reduce_by_key_needs_reduction_cl};
-        const int ker_lens[]   = {ops_cl_len,
-                                reduce_by_key_needs_reduction_cl_len};
-        Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "test_needs_reduction");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(threads_x);
-    NDRange global(threads_x * numBlocks);
-
-    auto reduceOp =
-        KernelFunctor<Buffer, Buffer, Buffer, KParam, int>(*entry.ker);
-
-    reduceOp(EnqueueArgs(getQueue(), global, local), needs_reduction,
-             needs_boundary, *keys.data, keys.info, n);
-
+void testNeedsReduction(cl::Buffer needs_reduction, cl::Buffer needs_boundary,
+                        const Param keys, const int n, const int numBlocks,
+                        const int threads_x) {
+    static const std::string src1(ops_cl, ops_cl_len);
+    static const std::string src2(reduce_by_key_needs_reduction_cl,
+                                  reduce_by_key_needs_reduction_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Tk>(),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(DIMX, threads_x),
+    };
+
+    auto testIfNeedsReduction = common::findKernel(
+        "test_needs_reduction", {src1, src2}, tmpltArgs, compileOpts);
+
+    cl::NDRange local(threads_x);
+    cl::NDRange global(threads_x * numBlocks);
+
+    testIfNeedsReduction(cl::EnqueueArgs(getQueue(), global, local),
+                         needs_reduction, needs_boundary, *keys.data, keys.info,
+                         n);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
-                        const Param keys, const Param vals, bool change_nan,
-                        double nanval) {
+int reduceByKeyFirst(Array<Tk> &keys_out, Array<To> &vals_out, const Param keys,
+                     const Param vals, bool change_nan, double nanval) {
     dim4 kdims(4, keys.info.dims);
     dim4 odims(4, vals.info.dims);
 
@@ -459,12 +351,12 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
         if (first_pass) {
-            launch_reduce_blocks_by_key<Ti, Tk, To, op>(
+            reduceBlocksByKey<Ti, Tk, To, op>(
                 reduced_block_sizes.get(), reduced_keys, reduced_vals, keys,
                 vals, change_nan, nanval, n_reduced_host, numThreads);
             first_pass = false;
         } else {
-            launch_reduce_blocks_by_key<To, Tk, To, op>(
+            reduceBlocksByKey<To, Tk, To, op>(
                 reduced_block_sizes.get(), reduced_keys, reduced_vals,
                 t_reduced_keys, t_reduced_vals, change_nan, nanval,
                 n_reduced_host, numThreads);
@@ -475,9 +367,9 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
             compute::make_buffer_iterator<int>(val_buf, numBlocksD0),
             compute::make_buffer_iterator<int>(val_buf), c_queue);
 
-        launch_compact<Tk, To>(reduced_block_sizes.get(), t_reduced_keys,
-                               t_reduced_vals, reduced_keys, reduced_vals,
-                               numBlocksD0, numThreads);
+        compact<Tk, To>(reduced_block_sizes.get(), t_reduced_keys,
+                        t_reduced_vals, reduced_keys, reduced_vals, numBlocksD0,
+                        numThreads);
 
         getQueue().enqueueReadBuffer(*reduced_block_sizes.get(), true,
                                      (numBlocksD0 - 1) * sizeof(int),
@@ -495,10 +387,10 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                                       &needs_block_boundary_reduction_host);
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
-        launch_test_needs_reduction<Tk>(*needs_another_reduction.get(),
-                                        *needs_block_boundary_reduction.get(),
-                                        t_reduced_keys, n_reduced_host,
-                                        numBlocksD0, numThreads);
+        testNeedsReduction<Tk>(*needs_another_reduction.get(),
+                               *needs_block_boundary_reduction.get(),
+                               t_reduced_keys, n_reduced_host, numBlocksD0,
+                               numThreads);
 
         getQueue().enqueueReadBuffer(*needs_another_reduction.get(), CL_FALSE,
                                      0, sizeof(int),
@@ -509,7 +401,7 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
 
         if (needs_block_boundary_reduction_host &&
             !needs_another_reduction_host) {
-            launch_final_boundary_reduce<Tk, To, op>(
+            finalBoundaryReduce<Tk, To, op>(
                 reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
                 n_reduced_host, numBlocksD0, numThreads);
 
@@ -522,9 +414,9 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                                          (numBlocksD0 - 1) * sizeof(int),
                                          sizeof(int), &n_reduced_host);
 
-            launch_compact<Tk, To>(reduced_block_sizes.get(), reduced_keys,
-                                   reduced_vals, t_reduced_keys, t_reduced_vals,
-                                   numBlocksD0, numThreads);
+            compact<Tk, To>(reduced_block_sizes.get(), reduced_keys,
+                            reduced_vals, t_reduced_keys, t_reduced_vals,
+                            numBlocksD0, numThreads);
 
             std::swap(t_reduced_keys, reduced_keys);
             std::swap(t_reduced_vals, reduced_vals);
@@ -539,10 +431,10 @@ int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
-                      const Param keys, const Param vals, bool change_nan,
-                      double nanval, const int dim) {
-    vector<int> dim_ordering = {dim};
+int reduceByKeyDim(Array<Tk> &keys_out, Array<To> &vals_out, const Param keys,
+                   const Param vals, bool change_nan, double nanval,
+                   const int dim) {
+    std::vector<int> dim_ordering = {dim};
     for (int i = 0; i < 4; ++i) {
         if (i != dim) { dim_ordering.push_back(i); }
     }
@@ -578,13 +470,13 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
         if (first_pass) {
-            launch_reduce_blocks_dim_by_key<Ti, Tk, To, op>(
+            reduceBlocksByKeyDim<Ti, Tk, To, op>(
                 reduced_block_sizes.get(), reduced_keys, reduced_vals, keys,
                 vals, change_nan, nanval, n_reduced_host, numThreads, dim,
                 dim_ordering);
             first_pass = false;
         } else {
-            launch_reduce_blocks_dim_by_key<To, Tk, To, op>(
+            reduceBlocksByKeyDim<To, Tk, To, op>(
                 reduced_block_sizes.get(), reduced_keys, reduced_vals,
                 t_reduced_keys, t_reduced_vals, change_nan, nanval,
                 n_reduced_host, numThreads, dim, dim_ordering);
@@ -595,9 +487,9 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
             compute::make_buffer_iterator<int>(val_buf, numBlocksD0),
             compute::make_buffer_iterator<int>(val_buf), c_queue);
 
-        launch_compact_dim<Tk, To>(reduced_block_sizes.get(), t_reduced_keys,
-                                   t_reduced_vals, reduced_keys, reduced_vals,
-                                   numBlocksD0, numThreads, dim, dim_ordering);
+        compactDim<Tk, To>(reduced_block_sizes.get(), t_reduced_keys,
+                           t_reduced_vals, reduced_keys, reduced_vals,
+                           numBlocksD0, numThreads, dim, dim_ordering);
 
         getQueue().enqueueReadBuffer(*reduced_block_sizes.get(), true,
                                      (numBlocksD0 - 1) * sizeof(int),
@@ -616,10 +508,10 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
 
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
-        launch_test_needs_reduction<Tk>(*needs_another_reduction.get(),
-                                        *needs_block_boundary_reduction.get(),
-                                        t_reduced_keys, n_reduced_host,
-                                        numBlocksD0, numThreads);
+        testNeedsReduction<Tk>(*needs_another_reduction.get(),
+                               *needs_block_boundary_reduction.get(),
+                               t_reduced_keys, n_reduced_host, numBlocksD0,
+                               numThreads);
 
         getQueue().enqueueReadBuffer(*needs_another_reduction.get(), CL_FALSE,
                                      0, sizeof(int),
@@ -630,7 +522,7 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
 
         if (needs_block_boundary_reduction_host &&
             !needs_another_reduction_host) {
-            launch_final_boundary_reduce_dim<Tk, To, op>(
+            finalBoundaryReduceDim<Tk, To, op>(
                 reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
                 n_reduced_host, numBlocksD0, numThreads, dim, dim_ordering);
 
@@ -643,10 +535,9 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                                          (numBlocksD0 - 1) * sizeof(int),
                                          sizeof(int), &n_reduced_host);
 
-            launch_compact_dim<Tk, To>(reduced_block_sizes.get(), reduced_keys,
-                                       reduced_vals, t_reduced_keys,
-                                       t_reduced_vals, numBlocksD0, numThreads,
-                                       dim, dim_ordering);
+            compactDim<Tk, To>(reduced_block_sizes.get(), reduced_keys,
+                               reduced_vals, t_reduced_keys, t_reduced_vals,
+                               numBlocksD0, numThreads, dim, dim_ordering);
 
             std::swap(t_reduced_keys, reduced_keys);
             std::swap(t_reduced_vals, reduced_vals);
@@ -661,9 +552,9 @@ int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
 }
 
 template<af_op_t op, typename Ti, typename Tk, typename To>
-void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
-                   const Array<Tk> &keys, const Array<Ti> &vals, int dim,
-                   bool change_nan, double nanval) {
+void reduceByKey(Array<Tk> &keys_out, Array<To> &vals_out,
+                 const Array<Tk> &keys, const Array<Ti> &vals, int dim,
+                 bool change_nan, double nanval) {
     dim4 kdims = keys.dims();
     dim4 odims = vals.dims();
 
@@ -673,10 +564,10 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
 
     int n_reduced = 0;
     if (dim == 0) {
-        n_reduced = reduce_by_key_first<Ti, Tk, To, op>(
+        n_reduced = reduceByKeyFirst<Ti, Tk, To, op>(
             reduced_keys, reduced_vals, keys, vals, change_nan, nanval);
     } else {
-        n_reduced = reduce_by_key_dim<Ti, Tk, To, op>(
+        n_reduced = reduceByKeyDim<Ti, Tk, To, op>(
             reduced_keys, reduced_vals, keys, vals, change_nan, nanval, dim);
     }
 
diff --git a/src/backend/opencl/kernel/reduce_by_key_boundary.cl b/src/backend/opencl/kernel/reduce_by_key_boundary.cl
index e6f8c4e041..300e95de54 100644
--- a/src/backend/opencl/kernel/reduce_by_key_boundary.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_boundary.cl
@@ -7,10 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void final_boundary_reduce(__global int *reduced_block_sizes,
-                                    __global Tk *oKeys, KParam oKInfo,
-                                    __global To *oVals, KParam oVInfo,
-                                    const int n) {
+kernel void final_boundary_reduce(global int *reduced_block_sizes,
+                                  global Tk *oKeys, KParam oKInfo,
+                                  global To *oVals, KParam oVInfo,
+                                  const int n) {
     const uint lid = get_local_id(0);
     const uint bid = get_group_id(0);
     const uint gid = get_global_id(0);
diff --git a/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl b/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
index 517277106b..4d97b98390 100644
--- a/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
@@ -7,11 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void final_boundary_reduce_dim(__global int *reduced_block_sizes,
-                                        __global Tk *oKeys, KParam oKInfo,
-                                        __global To *oVals, KParam oVInfo,
-                                        const int n, const int nBlocksZ) {
-    __local int dim_ordering[4];
+kernel void final_boundary_reduce_dim(global int *reduced_block_sizes,
+                                      global Tk *oKeys, KParam oKInfo,
+                                      global To *oVals, KParam oVInfo,
+                                      const int n, const int nBlocksZ) {
+    local int dim_ordering[4];
 
     const uint lid  = get_local_id(0);
     const uint bid  = get_group_id(0);
diff --git a/src/backend/opencl/kernel/reduce_by_key_compact.cl b/src/backend/opencl/kernel/reduce_by_key_compact.cl
index 7751f5f673..c8081e45e9 100644
--- a/src/backend/opencl/kernel/reduce_by_key_compact.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_compact.cl
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void compact(__global int *reduced_block_sizes, __global Tk *oKeys,
-                      KParam oKInfo, __global To *oVals, KParam oVInfo,
-                      const __global Tk *iKeys, KParam iKInfo,
-                      const __global To *iVals, KParam iVInfo,
-                      const int nBlocksZ) {
+kernel void compact(global int *reduced_block_sizes, global Tk *oKeys,
+                    KParam oKInfo, global To *oVals, KParam oVInfo,
+                    const global Tk *iKeys, KParam iKInfo,
+                    const global To *iVals, KParam iVInfo, const int nBlocksZ) {
     const uint lid = get_local_id(0);
     const uint bid = get_group_id(0);
     const uint gid = get_global_id(0);
diff --git a/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl b/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
index b7389e324f..285d4cc20c 100644
--- a/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void compact_dim(__global int *reduced_block_sizes, __global Tk *oKeys,
-                          KParam oKInfo, __global To *oVals, KParam oVInfo,
-                          const __global Tk *iKeys, KParam iKInfo,
-                          const __global To *iVals, KParam iVInfo,
-                          const int nBlocksZ) {
-    __local int dim_ordering[4];
+kernel void compact_dim(global int *reduced_block_sizes, global Tk *oKeys,
+                        KParam oKInfo, global To *oVals, KParam oVInfo,
+                        const global Tk *iKeys, KParam iKInfo,
+                        const global To *iVals, KParam iVInfo,
+                        const int nBlocksZ) {
+    local int dim_ordering[4];
     const uint lid  = get_local_id(0);
     const uint bid  = get_group_id(0);
     const uint gidx = get_global_id(0);
diff --git a/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl b/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
index 3caf5bb939..4b12830aaf 100644
--- a/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void test_needs_reduction(__global int *needs_another_reduction,
-                                   __global int *needs_block_boundary_reduced,
-                                   const __global Tk *iKeys, KParam iKInfo,
+kernel void test_needs_reduction(global int *needs_another_reduction,
+                                   global int *needs_block_boundary_reduced,
+                                   const global Tk *iKeys, KParam iKInfo,
                                    int n) {
     const uint lid = get_local_id(0);
     const uint bid = get_group_id(0);
@@ -18,7 +18,7 @@ __kernel void test_needs_reduction(__global int *needs_another_reduction,
     Tk k;
     if (gid < n) { k = iKeys[gid]; }
 
-    __local Tk keys[DIMX];
+    local Tk keys[DIMX];
     keys[lid] = k;
     barrier(CLK_LOCAL_MEM_FENCE);
 
diff --git a/src/backend/opencl/kernel/reduce_dim.cl b/src/backend/opencl/kernel/reduce_dim.cl
index 8c93a0fde3..7b1397ce87 100644
--- a/src/backend/opencl/kernel/reduce_dim.cl
+++ b/src/backend/opencl/kernel/reduce_dim.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
-                                const __global Ti *iData, KParam iInfo,
+kernel void reduce_dim_kernel(global To *oData, KParam oInfo,
+                                const global Ti *iData, KParam iInfo,
                                 uint groups_x, uint groups_y, uint group_dim,
                                 int change_nan, To nanval) {
     const uint lidx = get_local_id(0);
@@ -42,7 +42,7 @@ __kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
     bool is_valid = (ids[0] < iInfo.dims[0]) && (ids[1] < iInfo.dims[1]) &&
                     (ids[2] < iInfo.dims[2]) && (ids[3] < iInfo.dims[3]);
 
-    __local To s_val[THREADS_X * DIMY];
+    local To s_val[THREADS_X * DIMY];
 
     To out_val = init;
     for (int id = id_dim_in; is_valid && (id < iInfo.dims[kDim]);
@@ -55,7 +55,7 @@ __kernel void reduce_dim_kernel(__global To *oData, KParam oInfo,
 
     s_val[lid] = out_val;
 
-    __local To *s_ptr = s_val + lid;
+    local To *s_ptr = s_val + lid;
     barrier(CLK_LOCAL_MEM_FENCE);
 
     if (DIMY == 8) {
diff --git a/src/backend/opencl/kernel/reduce_first.cl b/src/backend/opencl/kernel/reduce_first.cl
index 06edf09b38..1dcf8ba91a 100644
--- a/src/backend/opencl/kernel/reduce_first.cl
+++ b/src/backend/opencl/kernel/reduce_first.cl
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void reduce_first_kernel(__global To *oData, KParam oInfo,
-                                  const __global Ti *iData, KParam iInfo,
+kernel void reduce_first_kernel(global To *oData, KParam oInfo,
+                                  const global Ti *iData, KParam iInfo,
                                   uint groups_x, uint groups_y, uint repeat,
                                   int change_nan, To nanval) {
     const uint lidx = get_local_id(0);
@@ -30,7 +30,7 @@ __kernel void reduce_first_kernel(__global To *oData, KParam oInfo,
     bool cond =
         (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
 
-    __local To s_val[THREADS_PER_GROUP];
+    local To s_val[THREADS_PER_GROUP];
 
     int last   = (xid + repeat * DIMX);
     int lim    = last > iInfo.dims[0] ? iInfo.dims[0] : last;
@@ -44,7 +44,7 @@ __kernel void reduce_first_kernel(__global To *oData, KParam oInfo,
 
     s_val[lid] = out_val;
     barrier(CLK_LOCAL_MEM_FENCE);
-    __local To *s_ptr = s_val + lidy * DIMX;
+    local To *s_ptr = s_val + lidy * DIMX;
 
     if (DIMX == 256) {
         if (lidx < 128) s_ptr[lidx] = binOp(s_ptr[lidx], s_ptr[lidx + 128]);
diff --git a/src/backend/opencl/kernel/regions.cl b/src/backend/opencl/kernel/regions.cl
index 0183696382..0a6235935e 100644
--- a/src/backend/opencl/kernel/regions.cl
+++ b/src/backend/opencl/kernel/regions.cl
@@ -9,7 +9,7 @@
 
 // The initial label kernel distinguishes between valid (nonzero)
 // pixels and "background" (zero) pixels.
-__kernel void initial_label(global T* equiv_map, KParam eInfo,
+kernel void initial_label(global T* equiv_map, KParam eInfo,
                             global char* bin_, KParam bInfo) {
     global char* bin = bin_ + bInfo.offset;
     const int base_x =
@@ -32,7 +32,7 @@ __kernel void initial_label(global T* equiv_map, KParam eInfo,
     }
 }
 
-__kernel void final_relabel(global T* equiv_map, KParam eInfo,
+kernel void final_relabel(global T* equiv_map, KParam eInfo,
                             global char* bin_, KParam bInfo,
                             global const T* d_tmp) {
     global char* bin = bin_ + bInfo.offset;
@@ -75,7 +75,7 @@ static inline T relabel(const T a, const T b) {
 // NUM_WARPS = 8; // (Could compute this from block dim)
 // Number of elements to handle per thread in each dimension
 // N_PER_THREAD = 2; // 2x2 per thread = 4 total elems per thread
-__kernel void update_equiv(global T* equiv_map, KParam eInfo,
+kernel void update_equiv(global T* equiv_map, KParam eInfo,
                            global int* continue_flag) {
     // Basic coordinates
     const int base_x =
@@ -97,10 +97,10 @@ __kernel void update_equiv(global T* equiv_map, KParam eInfo,
     }
 
     // Cached tile of the equivalency map
-    __local T s_tile[N_PER_THREAD * BLOCK_DIM][(N_PER_THREAD * BLOCK_DIM)];
+    local T s_tile[N_PER_THREAD * BLOCK_DIM][(N_PER_THREAD * BLOCK_DIM)];
 
     // Space to track ballot funcs to track convergence
-    __local int s_changed[NUM_WARPS];
+    local int s_changed[NUM_WARPS];
 
     const int tn = (get_local_id(1) * get_local_size(0)) + get_local_id(0);
 
@@ -109,7 +109,7 @@ __kernel void update_equiv(global T* equiv_map, KParam eInfo,
     s_changed[warpIdx] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
-    __local int tid_changed[NUM_WARPS];
+    local int tid_changed[NUM_WARPS];
     tid_changed[warpIdx] = 0;
     barrier(CLK_LOCAL_MEM_FENCE);
 
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index da96f71019..200fec8433 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -9,14 +9,13 @@
 
 #pragma once
 
-#include <cache.hpp>
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/regions.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <af/defines.h>
 
@@ -34,92 +33,60 @@
 
 #pragma GCC diagnostic pop
 
-#include <cstdio>
+#include <array>
+#include <string>
+#include <vector>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
 namespace compute = boost::compute;
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
-template<typename T, bool full_conn, int n_per_thread>
-std::tuple<cl::Kernel*, cl::Kernel*, cl::Kernel*> getRegionsKernels() {
-    static const int block_dim                  = 16;
-    static const int num_warps                  = 8;
-    static const unsigned NUM_KERNELS           = 3;
-    static const char* kernelNames[NUM_KERNELS] = {
-        "initial_label", "final_relabel", "update_equiv"};
-
-    kc_entry_t entries[NUM_KERNELS];
-
-    int device = getActiveDeviceId();
-
-    std::string checkName = kernelNames[0] + std::string("_") +
-                            std::string(dtype_traits<T>::getName()) +
-                            std::to_string(full_conn) +
-                            std::to_string(n_per_thread);
-
-    entries[0] = kernelCache(device, checkName);
-
-    if (entries[0].prog == 0 && entries[0].ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        if (full_conn) {
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D BLOCK_DIM=" << block_dim
-                    << " -D NUM_WARPS=" << num_warps
-                    << " -D N_PER_THREAD=" << n_per_thread
-                    << " -D LIMIT_MAX=" << toNumStr(maxval<T>())
-                    << " -D FULL_CONN";
-        } else {
-            options << " -D T=" << dtype_traits<T>::getName()
-                    << " -D BLOCK_DIM=" << block_dim
-                    << " -D NUM_WARPS=" << num_warps
-                    << " -D N_PER_THREAD=" << n_per_thread
-                    << " -D LIMIT_MAX=" << toNumStr(maxval<T>());
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {regions_cl};
-        const int ker_lens[]   = {regions_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
 
-        for (unsigned i = 0; i < NUM_KERNELS; ++i) {
-            entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
-
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName()) +
-                               std::to_string(full_conn) +
-                               std::to_string(n_per_thread);
+template<typename T>
+std::array<Kernel, 3> getRegionsKernels(const bool full_conn,
+                                        const int n_per_thread) {
+    using std::string;
+    using std::vector;
+
+    constexpr int block_dim = 16;
+    constexpr int num_warps = 8;
+
+    static const std::string src(regions_cl, regions_cl_len);
+
+    ToNumStr<T> toNumStr;
+    vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(full_conn),
+        TemplateArg(n_per_thread),
+    };
+    vector<string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(BLOCK_DIM, block_dim),
+        DefineKeyValue(NUM_WARPS, num_warps),
+        DefineKeyValue(N_PER_THREAD, n_per_thread),
+        DefineKeyValue(LIMIT_MAX, toNumStr(maxval<T>())),
+    };
+    if (full_conn) { options.emplace_back(DefineKey(FULL_CONN)); }
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    return {
+        common::findKernel("initial_label", {src}, targs, options),
+        common::findKernel("final_relabel", {src}, targs, options),
+        common::findKernel("update_equiv", {src}, targs, options),
+    };
+}
 
-            addKernelToCache(device, name, entries[i]);
-        }
-    } else {
-        for (unsigned i = 1; i < NUM_KERNELS; ++i) {
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName()) +
-                               std::to_string(full_conn) +
-                               std::to_string(n_per_thread);
-
-            entries[i] = kernelCache(device, name);
-        }
-    }
+template<typename T>
+void regions(Param out, Param in, const bool full_conn,
+             const int n_per_thread) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
 
-    return std::make_tuple(entries[0].ker, entries[1].ker, entries[2].ker);
-}
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
 
-template<typename T, bool full_conn, int n_per_thread>
-void regions(Param out, Param in) {
-    auto kernels = getRegionsKernels<T, full_conn, n_per_thread>();
+    auto kernels = getRegionsKernels<T>(full_conn, n_per_thread);
 
     const NDRange local(THREADS_X, THREADS_Y);
 
@@ -128,33 +95,27 @@ void regions(Param out, Param in) {
 
     const NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
 
-    auto ilOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam>(*std::get<0>(kernels));
+    auto ilOp = kernels[0];
+    auto ueOp = kernels[2];
+    auto frOp = kernels[1];
 
     ilOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
          in.info);
-
     CL_DEBUG_FINISH(getQueue());
 
-    int h_continue         = 1;
-    cl::Buffer* d_continue = bufferAlloc(sizeof(int));
+    int h_continue     = 1;
+    Buffer* d_continue = bufferAlloc(sizeof(int));
 
     while (h_continue) {
         h_continue = 0;
         getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
                                       &h_continue);
-
-        auto ueOp =
-            KernelFunctor<Buffer, KParam, Buffer>(*std::get<2>(kernels));
-
         ueOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *d_continue);
         CL_DEBUG_FINISH(getQueue());
-
         getQueue().enqueueReadBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
                                      &h_continue);
     }
-
     bufferFree(d_continue);
 
     // Now, perform the final relabeling.  This converts the equivalency
@@ -229,13 +190,9 @@ void regions(Param out, Param in) {
     compute::exclusive_scan(labels_begin, labels_end, labels_begin, c_queue);
 
     // Apply the correct labels to the equivalency map
-    auto frOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer>(
-        *std::get<1>(kernels));
-
     // Buffer labels_buf(tmp.get_buffer().get());
     frOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
          in.info, labels);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/reorder.cl b/src/backend/opencl/kernel/reorder.cl
index 52a1bfdff5..07b99a123b 100644
--- a/src/backend/opencl/kernel/reorder.cl
+++ b/src/backend/opencl/kernel/reorder.cl
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void reorder_kernel(__global T *out, __global const T *in,
+kernel void reorder_kernel(global T *out, __global const T *in,
                              const KParam op, const KParam ip, const int d0,
                              const int d1, const int d2, const int d3,
                              const int blocksPerMatX, const int blocksPerMatY) {
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 517371c561..05695ab4f4 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -8,70 +8,49 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/reorder.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const int TX    = 32;
-static const int TY    = 8;
-static const int TILEX = 512;
-static const int TILEY = 32;
-
 template<typename T>
 void reorder(Param out, const Param in, const dim_t* rdims) {
-    std::string refName = std::string("reorder_kernel_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {reorder_cl};
-        const int ker_lens[]   = {reorder_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "reorder_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
+    constexpr int TX    = 32;
+    constexpr int TY    = 8;
+    constexpr int TILEX = 512;
+    constexpr int TILEY = 32;
+
+    static const std::string src(reorder_cl, reorder_cl_len);
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
 
     auto reorderOp =
-        KernelFunctor<Buffer, const Buffer, const KParam, const KParam,
-                      const int, const int, const int, const int, const int,
-                      const int>(*entry.ker);
+        common::findKernel("reorder_kernel", {src}, targs, options);
 
-    NDRange local(TX, TY, 1);
+    cl::NDRange local(TX, TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], TILEX);
     int blocksPerMatY = divup(out.info.dims[1], TILEY);
-    NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
-                   local[1] * blocksPerMatY * out.info.dims[3], 1);
-
-    reorderOp(EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
-              out.info, in.info, rdims[0], rdims[1], rdims[2], rdims[3],
-              blocksPerMatX, blocksPerMatY);
+    cl::NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
+                       local[1] * blocksPerMatY * out.info.dims[3], 1);
 
+    reorderOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
+              out.info, in.info, static_cast<int>(rdims[0]),
+              static_cast<int>(rdims[1]), static_cast<int>(rdims[2]),
+              static_cast<int>(rdims[3]), blocksPerMatX, blocksPerMatY);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/resize.cl b/src/backend/opencl/kernel/resize.cl
index e69e53a50b..ab2d7a1d3f 100644
--- a/src/backend/opencl/kernel/resize.cl
+++ b/src/backend/opencl/kernel/resize.cl
@@ -28,7 +28,7 @@
 
 ////////////////////////////////////////////////////////////////////////////////////
 // nearest-neighbor resampling
-void resize_n_(__global T* d_out, const KParam out, __global const T* d_in,
+void resize_n_(global T* d_out, const KParam out, __global const T* d_in,
                const KParam in, const int blockIdx_x, const int blockIdx_y,
                const float xf, const float yf) {
     int const ox = get_local_id(0) + blockIdx_x * get_local_size(0);
@@ -48,7 +48,7 @@ void resize_n_(__global T* d_out, const KParam out, __global const T* d_in,
 
 ////////////////////////////////////////////////////////////////////////////////////
 // bilinear resampling
-void resize_b_(__global T* d_out, const KParam out, __global const T* d_in,
+void resize_b_(global T* d_out, const KParam out, __global const T* d_in,
                const KParam in, const int blockIdx_x, const int blockIdx_y,
                const float xf_, const float yf_) {
     int const ox = get_local_id(0) + blockIdx_x * get_local_size(0);
@@ -82,7 +82,7 @@ void resize_b_(__global T* d_out, const KParam out, __global const T* d_in,
 
 ////////////////////////////////////////////////////////////////////////////////////
 // lower resampling
-void resize_l_(__global T* d_out, const KParam out, __global const T* d_in,
+void resize_l_(global T* d_out, const KParam out, __global const T* d_in,
                const KParam in, const int blockIdx_x, const int blockIdx_y,
                const float xf, const float yf) {
     int const ox = get_local_id(0) + blockIdx_x * get_local_size(0);
@@ -100,8 +100,8 @@ void resize_l_(__global T* d_out, const KParam out, __global const T* d_in,
 
 ////////////////////////////////////////////////////////////////////////////////////
 // Wrapper Kernel
-__kernel void resize_kernel(__global T* d_out, const KParam out,
-                            __global const T* d_in, const KParam in,
+kernel void resize_kernel(global T* d_out, const KParam out,
+                            global const T* d_in, const KParam in,
                             const int b0, const int b1, const float xf,
                             const float yf) {
     int bIdx = get_group_id(0) / b0;
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index b89221be45..012d22ae88 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -8,20 +8,20 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/resize.hpp>
-#include <program.hpp>
 #include <traits.hpp>
+
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int RESIZE_TX = 16;
-static const int RESIZE_TY = 16;
 
 template<typename T>
 using wtype_t = typename std::conditional<std::is_same<T, double>::value,
@@ -31,53 +31,46 @@ template<typename T>
 using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
                                           wtype_t<T>>::type;
 
-template<typename T, af_interp_type method>
-void resize(Param out, const Param in) {
-    typedef typename dtype_traits<T>::base_type BT;
-
-    std::string refName = std::string("reorder_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(method);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D VT=" << dtype_traits<vtype_t<T>>::getName();
-        options << " -D WT=" << dtype_traits<wtype_t<BT>>::getName();
-
-        switch (method) {
-            case AF_INTERP_NEAREST: options << " -D INTERP=NEAREST"; break;
-            case AF_INTERP_BILINEAR: options << " -D INTERP=BILINEAR"; break;
-            case AF_INTERP_LOWER: options << " -D INTERP=LOWER"; break;
-            default: break;
-        }
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D CPLX=1";
-            options << " -D TB=" << dtype_traits<BT>::getName();
-        } else {
-            options << " -D CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {resize_cl};
-        const int ker_lens[]   = {resize_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "resize_kernel");
-
-        addKernelToCache(device, refName, entry);
+template<typename T>
+void resize(Param out, const Param in, const af_interp_type method) {
+    using BT = typename dtype_traits<T>::base_type;
+
+    constexpr int RESIZE_TX = 16;
+    constexpr int RESIZE_TY = 16;
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    static const std::string src(resize_cl, resize_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(method),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(VT, dtype_traits<vtype_t<T>>::getName()),
+        DefineKeyValue(WT, dtype_traits<wtype_t<BT>>::getName()),
+        DefineKeyValue(CPLX, (IsComplex ? 1 : 0)),
+    };
+    if (IsComplex) {
+        options.emplace_back(DefineKeyValue(TB, dtype_traits<BT>::getName()));
+    }
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    switch (method) {
+        case AF_INTERP_NEAREST:
+            options.emplace_back(DefineKeyValue(INTERP, "NEAREST"));
+            break;
+        case AF_INTERP_BILINEAR:
+            options.emplace_back(DefineKeyValue(INTERP, "BILINEAR"));
+            break;
+        case AF_INTERP_LOWER:
+            options.emplace_back(DefineKeyValue(INTERP, "LOWER"));
+            break;
+        default: break;
     }
 
-    auto resizeOp =
-        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
-                          const KParam, const int, const int, const float,
-                          const float>(*entry.ker);
+    auto resizeOp = common::findKernel("resize_kernel", {src}, targs, options);
 
     cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
@@ -93,7 +86,6 @@ void resize(Param out, const Param in) {
 
     resizeOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *in.data, in.info, blocksPerMatX, blocksPerMatY, xf, yf);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/rotate.cl b/src/backend/opencl/kernel/rotate.cl
index 835ce0c5ae..354e2e2d22 100644
--- a/src/backend/opencl/kernel/rotate.cl
+++ b/src/backend/opencl/kernel/rotate.cl
@@ -15,11 +15,11 @@ typedef struct {
     float tmat[6];
 } tmat_t;
 
-__kernel void rotate_kernel(__global T *d_out, const KParam out,
-                            __global const T *d_in, const KParam in,
-                            const tmat_t t, const int nimages,
-                            const int batches, const int blocksXPerImage,
-                            const int blocksYPerImage, int method) {
+kernel void rotateKernel(global T *d_out, const KParam out,
+                         global const T *d_in, const KParam in,
+                         const tmat_t t, const int nimages, const int batches,
+                         const int blocksXPerImage, const int blocksYPerImage,
+                         int method) {
     // Compute which image set
     const int setId      = get_group_id(0) / blocksXPerImage;
     const int blockIdx_x = get_group_id(0) - setId * blocksXPerImage;
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 20bf5546ab..aaa8a1929e 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -8,27 +8,24 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/interp.hpp>
 #include <kernel_headers/interp.hpp>
 #include <kernel_headers/rotate.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
+
 #include <string>
-#include "config.hpp"
-#include "interp.hpp"
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int TX = 16;
-static const int TY = 16;
-// Used for batching images
-static const int TI = 4;
 
 typedef struct {
     float tmat[6];
@@ -42,53 +39,49 @@ template<typename T>
 using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
                                           wtype_t<T>>::type;
 
-template<typename T, int order>
-void rotate(Param out, const Param in, const float theta,
-            af_interp_type method) {
-    typedef typename dtype_traits<T>::base_type BT;
-
-    std::string refName = std::string("rotate_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(order);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D ZERO=" << toNumStr(scalar<T>(0));
-        options << " -D InterpInTy=" << dtype_traits<T>::getName();
-        options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
-        options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D IS_CPLX=1";
-            options << " -D TB=" << dtype_traits<BT>::getName();
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        options << " -D INTERP_ORDER=" << order;
-        addInterpEnumOptions(options);
-
-        const char *ker_strs[] = {interp_cl, rotate_cl};
-        const int ker_lens[]   = {interp_cl_len, rotate_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "rotate_kernel");
-
-        addKernelToCache(device, refName, entry);
+template<typename T>
+void rotate(Param out, const Param in, const float theta, af_interp_type method,
+            int order) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+    using BT = typename dtype_traits<T>::base_type;
+
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+    // Used for batching images
+    constexpr int TI = 4;
+    constexpr bool isComplex =
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
+
+    static const std::string src1(interp_cl, interp_cl_len);
+    static const std::string src2(rotate_cl, rotate_cl_len);
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(order),
+    };
+    ToNumStr<T> toNumStr;
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
+        DefineKeyValue(InterpInTy, dtype_traits<T>::getName()),
+        DefineKeyValue(InterpValTy, dtype_traits<vtype_t<T>>::getName()),
+        DefineKeyValue(InterpPosTy, dtype_traits<wtype_t<BT>>::getName()),
+        DefineKeyValue(INTERP_ORDER, order),
+        DefineKeyValue(IS_CPLX, (isComplex ? 1 : 0)),
+    };
+    if (isComplex) {
+        compileOpts.emplace_back(
+            DefineKeyValue(TB, dtype_traits<BT>::getName()));
     }
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    addInterpEnumOptions(compileOpts);
 
-    auto rotateOp =
-        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
-                          const KParam, const tmat_t, const int, const int,
-                          const int, const int, const int>(*entry.ker);
+    auto rotate = common::findKernel("rotateKernel", {src1, src2}, tmpltArgs,
+                                     compileOpts);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
@@ -112,7 +105,7 @@ void rotate(Param out, const Param in, const float theta,
     t.tmat[4] = round(c * 1000) / 1000.0f;
     t.tmat[5] = round(ty * 1000) / 1000.0f;
 
-    cl::NDRange local(TX, TY, 1);
+    NDRange local(TX, TY, 1);
 
     int nimages               = in.info.dims[2];
     int nbatches              = in.info.dims[3];
@@ -128,11 +121,11 @@ void rotate(Param out, const Param in, const float theta,
     }
     global_y *= nbatches;
 
-    cl::NDRange global(global_x, global_y, 1);
+    NDRange global(global_x, global_y, 1);
 
-    rotateOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, t, nimages, nbatches, blocksXPerImage,
-             blocksYPerImage, (int)method);
+    rotate(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, t, nimages, nbatches, blocksXPerImage,
+           blocksYPerImage, (int)method);
 
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/scan_dim.cl b/src/backend/opencl/kernel/scan_dim.cl
index cf59d1e8d7..f6e86081e4 100644
--- a/src/backend/opencl/kernel/scan_dim.cl
+++ b/src/backend/opencl/kernel/scan_dim.cl
@@ -7,11 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
-                              __global To *tData, KParam tInfo,
-                              const __global Ti *iData, KParam iInfo,
-                              uint groups_x, uint groups_y, uint groups_dim,
-                              uint lim) {
+kernel void scanDim(global To *oData, KParam oInfo, global To *tData,
+                    KParam tInfo, const global Ti *iData, KParam iInfo,
+                    uint groups_x, uint groups_y, uint groups_dim, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
@@ -49,10 +47,10 @@ __kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
     const int ostride_dim = oInfo.strides[kDim];
     const int istride_dim = iInfo.strides[kDim];
 
-    __local To l_val0[THREADS_X * DIMY];
-    __local To l_val1[THREADS_X * DIMY];
-    __local To *l_val = l_val0;
-    __local To l_tmp[THREADS_X];
+    local To l_val0[THREADS_X * DIMY];
+    local To l_val1[THREADS_X * DIMY];
+    local To *l_val = l_val0;
+    local To l_tmp[THREADS_X];
 
     bool flip         = 0;
     const To init_val = init;
@@ -79,7 +77,7 @@ __kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
 
         val = binOp(val, l_tmp[lidx]);
 
-        if (inclusive_scan != 0) {
+        if (INCLUSIVE_SCAN != 0) {
             if (cond) { *oData = val; }
         } else if (is_valid) {
             if (id_dim == (out_dim - 1)) {
@@ -95,15 +93,15 @@ __kernel void scan_dim_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (!isFinalPass && is_valid && (groupId_dim < tInfo.dims[kDim]) && isLast) {
+    if (!IS_FINAL_PASS && is_valid && (groupId_dim < tInfo.dims[kDim]) &&
+        isLast) {
         *tData = val;
     }
 }
 
-__kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
-                               const __global To *tData, KParam tInfo,
-                               uint groups_x, uint groups_y, uint groups_dim,
-                               uint lim) {
+kernel void bcastDim(global To *oData, KParam oInfo, const global To *tData,
+                     KParam tInfo, uint groups_x, uint groups_y,
+                     uint groups_dim, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
@@ -131,7 +129,7 @@ __kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
                  ids[1] * oInfo.strides[1] + ids[0];
 
         // Shift broadcast one step to the right for exclusive scan (#2366)
-        int offset = inclusive_scan ? 0 : oInfo.strides[kDim];
+        int offset = INCLUSIVE_SCAN ? 0 : oInfo.strides[kDim];
         oData += offset;
 
         const int id_dim  = ids[kDim];
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 29acd4df23..5c1776d3f5 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -10,83 +10,67 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/config.hpp>
 #include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_dim.hpp>
-#include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
 
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool isFinalPass,
-                                   uint threads_y) {
-    std::string ref_name =
-        std::string("scan_") + std::to_string(dim) + std::string("_") +
-        std::to_string(isFinalPass) + std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_y) +
-        std::string("_") + std::to_string(int(inclusive_scan));
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D kDim=" << dim << " -D DIMY=" << threads_y
-                << " -D THREADS_X=" << THREADS_X
-                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
-                << " -D isFinalPass=" << (int)(isFinalPass)
-                << " -D inclusive_scan=" << inclusive_scan;
-        options << getTypeBuildDefinition<Ti>();
-
-        const char *ker_strs[] = {ops_cl, scan_dim_cl};
-        const int ker_lens[]   = {ops_cl_len, scan_dim_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel[2];
-
-        entry.ker[0] = Kernel(*entry.prog, "scan_dim_kernel");
-        entry.ker[1] = Kernel(*entry.prog, "bcast_dim_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    return entry.ker[kerIdx];
+template<typename Ti, typename To, af_op_t op>
+static opencl::Kernel getScanDimKernel(const std::string key, int dim,
+                                       bool isFinalPass, uint threads_y,
+                                       bool inclusiveScan) {
+    using std::string;
+    using std::vector;
+
+    static const string src1(ops_cl, ops_cl_len);
+    static const string src2(scan_dim_cl, scan_dim_cl_len);
+
+    ToNumStr<To> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ti>(),
+        TemplateTypename<To>(),
+        TemplateArg(dim),
+        TemplateArg(isFinalPass),
+        TemplateArg(op),
+        TemplateArg(threads_y),
+        TemplateArg(inclusiveScan),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(kDim, dim),
+        DefineKeyValue(DIMY, threads_y),
+        DefineValue(THREADS_X),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(IS_FINAL_PASS, (isFinalPass ? 1 : 0)),
+        DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
+
+    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-static void scan_dim_launcher(Param out, Param tmp, const Param in, int dim,
-                              bool isFinalPass, uint threads_y,
-                              const uint groups_all[4]) {
-    Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(
-        0, dim, isFinalPass, threads_y);
+template<typename Ti, typename To, af_op_t op>
+static void scanDimLauncher(Param out, Param tmp, const Param in, int dim,
+                            bool isFinalPass, uint threads_y,
+                            const uint groups_all[4], bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto scan = getScanDimKernel<Ti, To, op>("scanDim", dim, isFinalPass,
+                                             threads_y, inclusiveScan);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -94,21 +78,21 @@ static void scan_dim_launcher(Param out, Param tmp, const Param in, int dim,
 
     uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                uint, uint, uint, uint>(ker);
-
-    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *tmp.data, tmp.info, *in.data, in.info, groups_all[0], groups_all[1],
-           groups_all[dim], lim);
-
+    scan(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *tmp.data,
+         tmp.info, *in.data, in.info, groups_all[0], groups_all[1],
+         groups_all[dim], lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-static void bcast_dim_launcher(Param out, Param tmp, int dim, bool isFinalPass,
-                               uint threads_y, const uint groups_all[4]) {
-    Kernel ker = get_scan_dim_kernels<Ti, To, op, inclusive_scan>(
-        1, dim, isFinalPass, threads_y);
+template<typename Ti, typename To, af_op_t op>
+static void bcastDimLauncher(Param out, Param tmp, int dim, bool isFinalPass,
+                             uint threads_y, const uint groups_all[4],
+                             const bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto bcast = getScanDimKernel<Ti, To, op>("bcastDim", dim, isFinalPass,
+                                              threads_y, inclusiveScan);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -116,19 +100,15 @@ static void bcast_dim_launcher(Param out, Param tmp, int dim, bool isFinalPass,
 
     uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-    auto bcastOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint, uint>(
-            ker);
-
-    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-            *tmp.data, tmp.info, groups_all[0], groups_all[1], groups_all[dim],
-            lim);
-
+    bcast(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+          *tmp.data, tmp.info, groups_all[0], groups_all[1], groups_all[dim],
+          lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-static void scan_dim(Param out, const Param in, int dim) {
+template<typename Ti, typename To, af_op_t op>
+static void scanDim(Param out, const Param in, const int dim,
+                    const bool inclusiveScan = true) {
     uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -139,8 +119,8 @@ static void scan_dim(Param out, const Param in, int dim) {
     groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
 
     if (groups_all[dim] == 1) {
-        scan_dim_launcher<Ti, To, op, inclusive_scan>(out, out, in, dim, true,
-                                                      threads_y, groups_all);
+        scanDimLauncher<Ti, To, op>(out, out, in, dim, true, threads_y,
+                                    groups_all, inclusiveScan);
     } else {
         Param tmp = out;
 
@@ -155,23 +135,23 @@ static void scan_dim(Param out, const Param in, int dim) {
         // FIXME: Do I need to free this ?
         tmp.data = bufferAlloc(tmp_elements * sizeof(To));
 
-        scan_dim_launcher<Ti, To, op, inclusive_scan>(out, tmp, in, dim, false,
-                                                      threads_y, groups_all);
+        scanDimLauncher<Ti, To, op>(out, tmp, in, dim, false, threads_y,
+                                    groups_all, inclusiveScan);
 
         int gdim        = groups_all[dim];
         groups_all[dim] = 1;
 
         if (op == af_notzero_t) {
-            scan_dim_launcher<To, To, af_add_t, true>(tmp, tmp, tmp, dim, true,
-                                                      threads_y, groups_all);
+            scanDimLauncher<To, To, af_add_t>(tmp, tmp, tmp, dim, true,
+                                              threads_y, groups_all, true);
         } else {
-            scan_dim_launcher<To, To, op, true>(tmp, tmp, tmp, dim, true,
-                                                threads_y, groups_all);
+            scanDimLauncher<To, To, op>(tmp, tmp, tmp, dim, true, threads_y,
+                                        groups_all, true);
         }
 
         groups_all[dim] = gdim;
-        bcast_dim_launcher<To, To, op, inclusive_scan>(out, tmp, dim, true,
-                                                       threads_y, groups_all);
+        bcastDimLauncher<To, To, op>(out, tmp, dim, true, threads_y, groups_all,
+                                     inclusiveScan);
         bufferFree(tmp.data);
     }
 }
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index 94aa29688f..5446b28e29 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -7,15 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-char calculate_head_flags_dim(const __global Tk *kptr, int id, int stride) {
+char calculate_head_flags_dim(const global Tk *kptr, int id, int stride) {
     return (id == 0) ? 1 : ((*kptr) != (*(kptr - stride)));
 }
 
-__kernel void scan_dim_by_key_nonfinal_kernel(
-    __global To *oData, KParam oInfo, __global To *tData, KParam tInfo,
-    __global char *tfData, KParam tfInfo, __global int *tiData, KParam tiInfo,
-    const __global Ti *iData, KParam iInfo, const __global Tk *kData,
-    KParam kInfo, uint groups_x, uint groups_y, uint groups_dim, uint lim) {
+kernel void scanDimByKeyNonfinal(
+    global To *oData, KParam oInfo, global To *tData, KParam tInfo,
+    global char *tfData, KParam tfInfo, global int *tiData, KParam tiInfo,
+    const global Ti *iData, KParam iInfo, const global Tk *kData, KParam kInfo,
+    uint groups_x, uint groups_y, uint groups_dim, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
@@ -59,15 +59,15 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
     const int ostride_dim = oInfo.strides[kDim];
     const int istride_dim = iInfo.strides[kDim];
 
-    __local To l_val0[THREADS_X * DIMY];
-    __local To l_val1[THREADS_X * DIMY];
-    __local char l_flg0[THREADS_X * DIMY];
-    __local char l_flg1[THREADS_X * DIMY];
-    __local To *l_val   = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[THREADS_X];
-    __local char l_ftmp[THREADS_X];
-    __local int boundaryid[THREADS_X];
+    local To l_val0[THREADS_X * DIMY];
+    local To l_val1[THREADS_X * DIMY];
+    local char l_flg0[THREADS_X * DIMY];
+    local char l_flg1[THREADS_X * DIMY];
+    local To *l_val   = l_val0;
+    local char *l_flg = l_flg0;
+    local To l_tmp[THREADS_X];
+    local char l_ftmp[THREADS_X];
+    local int boundaryid[THREADS_X];
 
     bool flip         = 0;
     const To init_val = init;
@@ -92,7 +92,7 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
         }
 
         // Load val from global in
-        if (inclusive_scan) {
+        if (INCLUSIVE_SCAN) {
             if (!cond) {
                 val = init_val;
             } else {
@@ -164,10 +164,11 @@ __kernel void scan_dim_by_key_nonfinal_kernel(
     }
 }
 
-__kernel void scan_dim_by_key_final_kernel(
-    __global To *oData, KParam oInfo, const __global Ti *iData, KParam iInfo,
-    const __global Tk *kData, KParam kInfo, uint groups_x, uint groups_y,
-    uint groups_dim, uint lim) {
+kernel void scanDimByKeyFinal(global To *oData, KParam oInfo,
+                              const global Ti *iData, KParam iInfo,
+                              const global Tk *kData, KParam kInfo,
+                              uint groups_x, uint groups_y, uint groups_dim,
+                              uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
@@ -205,14 +206,14 @@ __kernel void scan_dim_by_key_final_kernel(
     const int ostride_dim = oInfo.strides[kDim];
     const int istride_dim = iInfo.strides[kDim];
 
-    __local To l_val0[THREADS_X * DIMY];
-    __local To l_val1[THREADS_X * DIMY];
-    __local char l_flg0[THREADS_X * DIMY];
-    __local char l_flg1[THREADS_X * DIMY];
-    __local To *l_val   = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[THREADS_X];
-    __local char l_ftmp[THREADS_X];
+    local To l_val0[THREADS_X * DIMY];
+    local To l_val1[THREADS_X * DIMY];
+    local char l_flg0[THREADS_X * DIMY];
+    local char l_flg1[THREADS_X * DIMY];
+    local To *l_val   = l_val0;
+    local char *l_flg = l_flg0;
+    local To l_tmp[THREADS_X];
+    local char l_ftmp[THREADS_X];
 
     bool flip         = 0;
     const To init_val = init;
@@ -231,8 +232,8 @@ __kernel void scan_dim_by_key_final_kernel(
 
         if (calculateFlags) {
             if (cond) {
-                flag =
-                    calculate_head_flags_dim(kData, id_dim, kInfo.strides[kDim]);
+                flag = calculate_head_flags_dim(kData, id_dim,
+                                                kInfo.strides[kDim]);
             } else {
                 flag = 0;
             }
@@ -241,7 +242,7 @@ __kernel void scan_dim_by_key_final_kernel(
         }
 
         // Load val from global in
-        if (inclusive_scan) {
+        if (INCLUSIVE_SCAN) {
             if (!cond) {
                 val = init_val;
             } else {
@@ -294,11 +295,11 @@ __kernel void scan_dim_by_key_final_kernel(
     }
 }
 
-__kernel void bcast_dim_kernel(__global To *oData, KParam oInfo,
-                               const __global To *tData, KParam tInfo,
-                               const __global int *tiData, KParam tiInfo,
-                               uint groups_x, uint groups_y, uint groups_dim,
-                               uint lim) {
+kernel void bcastDimByKey(global To *oData, KParam oInfo,
+                          const global To *tData, KParam tInfo,
+                          const global int *tiData, KParam tiInfo,
+                          uint groups_x, uint groups_y, uint groups_dim,
+                          uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * THREADS_X + lidx;
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
index 3f441192cb..d975fbe03e 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -8,13 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <common/dispatch.hpp>
-#include <debug_opencl.hpp>
-#include <traits.hpp>
+
 namespace opencl {
 namespace kernel {
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-void scan_dim(Param out, const Param in, const Param key, int dim);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scanDimByKey(Param out, const Param in, const Param key, int dim,
+                  const bool inclusive_scan);
 }
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 3f119c905e..1935ad2465 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -10,88 +10,68 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/config.hpp>
 #include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_dim_by_key.hpp>
 #include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <types.hpp>
 
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-static Kernel get_scan_dim_kernels(int kerIdx, int dim, bool calculateFlags,
-                                   uint threads_y) {
-    std::string ref_name =
-        std::string("scan_") + std::to_string(dim) + std::string("_") +
-        std::to_string(calculateFlags) + std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_y) +
-        std::string("_") + std::to_string(int(inclusive_scan));
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D kDim=" << dim << " -D DIMY=" << threads_y
-                << " -D THREADS_X=" << THREADS_X
-                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
-                << " -D calculateFlags=" << calculateFlags
-                << " -D inclusive_scan=" << inclusive_scan;
-        options << getTypeBuildDefinition<Ti>();
-
-        const char *ker_strs[] = {ops_cl, scan_dim_by_key_cl};
-        const int ker_lens[]   = {ops_cl_len, scan_dim_by_key_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel[3];
-
-        entry.ker[0] = Kernel(*entry.prog, "scan_dim_by_key_final_kernel");
-        entry.ker[1] = Kernel(*entry.prog, "scan_dim_by_key_nonfinal_kernel");
-        entry.ker[2] = Kernel(*entry.prog, "bcast_dim_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    return entry.ker[kerIdx];
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static opencl::Kernel getScanDimKernel(const std::string key, int dim,
+                                       bool calculateFlags, uint threads_y,
+                                       bool inclusiveScan) {
+    using std::string;
+    using std::vector;
+
+    static const string src1(ops_cl, ops_cl_len);
+    static const string src2(scan_dim_by_key_cl, scan_dim_by_key_cl_len);
+
+    ToNumStr<To> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ti>(),      TemplateTypename<To>(),
+        TemplateTypename<Tk>(),      TemplateArg(dim),
+        TemplateArg(calculateFlags), TemplateArg(op),
+        TemplateArg(threads_y),      TemplateArg(inclusiveScan),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(kDim, dim),
+        DefineKeyValue(DIMY, threads_y),
+        DefineValue(THREADS_X),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(calculateFlags, (calculateFlags ? 1 : 0)),
+        DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
+
+    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-static void scan_dim_nonfinal_launcher(Param out, Param tmp, Param tmpflg,
-                                       Param tmpid, const Param in,
-                                       const Param key, int dim, uint threads_y,
-                                       const uint groups_all[4]) {
-    Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(
-        1, dim, false, threads_y);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scanDimNonfinalLauncher(Param out, Param tmp, Param tmpflg,
+                                    Param tmpid, const Param in,
+                                    const Param key, int dim, uint threads_y,
+                                    const uint groups_all[4],
+                                    bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto scan = getScanDimKernel<Ti, Tk, To, op>(
+        "scanDimByKeyNonfinal", dim, false, threads_y, inclusiveScan);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -99,24 +79,23 @@ static void scan_dim_nonfinal_launcher(Param out, Param tmp, Param tmpflg,
 
     uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                uint, uint, uint, uint>(ker);
-
-    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *tmp.data, tmp.info, *tmpflg.data, tmpflg.info, *tmpid.data,
-           tmpid.info, *in.data, in.info, *key.data, key.info, groups_all[0],
-           groups_all[1], groups_all[dim], lim);
-
+    scan(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *tmp.data,
+         tmp.info, *tmpflg.data, tmpflg.info, *tmpid.data, tmpid.info, *in.data,
+         in.info, *key.data, key.info, groups_all[0], groups_all[1],
+         groups_all[dim], lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-static void scan_dim_final_launcher(Param out, const Param in, const Param key,
-                                    int dim, const bool calculateFlags,
-                                    uint threads_y, const uint groups_all[4]) {
-    Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(
-        0, dim, calculateFlags, threads_y);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scanDimFinalLauncher(Param out, const Param in, const Param key,
+                                 int dim, const bool calculateFlags,
+                                 uint threads_y, const uint groups_all[4],
+                                 bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto scan = getScanDimKernel<Ti, Tk, To, op>(
+        "scanDimByKeyFinal", dim, calculateFlags, threads_y, inclusiveScan);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -124,21 +103,21 @@ static void scan_dim_final_launcher(Param out, const Param in, const Param key,
 
     uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                uint, uint, uint, uint>(ker);
-
-    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *in.data, in.info, *key.data, key.info, groups_all[0], groups_all[1],
-           groups_all[dim], lim);
-
+    scan(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+         in.info, *key.data, key.info, groups_all[0], groups_all[1],
+         groups_all[dim], lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-static void bcast_dim_launcher(Param out, Param tmp, Param tmpid, int dim,
-                               uint threads_y, const uint groups_all[4]) {
-    Kernel ker = get_scan_dim_kernels<Ti, Tk, To, op, inclusive_scan>(
-        2, dim, false, threads_y);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void bcastDimLauncher(Param out, Param tmp, Param tmpid, int dim,
+                             uint threads_y, const uint groups_all[4],
+                             bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto bcast = getScanDimKernel<Ti, Tk, To, op>("bcastDimByKey", dim, false,
+                                                  threads_y, inclusiveScan);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -146,18 +125,15 @@ static void bcast_dim_launcher(Param out, Param tmp, Param tmpid, int dim,
 
     uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
 
-    auto bcastOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                 uint, uint, uint, uint>(ker);
-
-    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-            *tmp.data, tmp.info, *tmpid.data, tmpid.info, groups_all[0],
-            groups_all[1], groups_all[dim], lim);
-
+    bcast(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+          *tmp.data, tmp.info, *tmpid.data, tmpid.info, groups_all[0],
+          groups_all[1], groups_all[dim], lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-void scan_dim(Param out, const Param in, const Param key, int dim) {
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scanDimByKey(Param out, const Param in, const Param key, int dim,
+                  const bool inclusiveScan) {
     uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
     uint threads_x = THREADS_X;
 
@@ -168,8 +144,8 @@ void scan_dim(Param out, const Param in, const Param key, int dim) {
     groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
 
     if (groups_all[dim] == 1) {
-        scan_dim_final_launcher<Ti, Tk, To, op, inclusive_scan>(
-            out, in, key, dim, true, threads_y, groups_all);
+        scanDimFinalLauncher<Ti, Tk, To, op>(out, in, key, dim, true, threads_y,
+                                             groups_all, inclusiveScan);
     } else {
         Param tmp = out;
 
@@ -188,23 +164,24 @@ void scan_dim(Param out, const Param in, const Param key, int dim) {
         tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
         tmpid.data  = bufferAlloc(tmp_elements * sizeof(int));
 
-        scan_dim_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(
-            out, tmp, tmpflg, tmpid, in, key, dim, threads_y, groups_all);
+        scanDimNonfinalLauncher<Ti, Tk, To, op>(out, tmp, tmpflg, tmpid, in,
+                                                key, dim, threads_y, groups_all,
+                                                inclusiveScan);
 
         int gdim        = groups_all[dim];
         groups_all[dim] = 1;
 
         if (op == af_notzero_t) {
-            scan_dim_final_launcher<To, char, To, af_add_t, true>(
-                tmp, tmp, tmpflg, dim, false, threads_y, groups_all);
+            scanDimFinalLauncher<To, char, To, af_add_t>(
+                tmp, tmp, tmpflg, dim, false, threads_y, groups_all, true);
         } else {
-            scan_dim_final_launcher<To, char, To, op, true>(
-                tmp, tmp, tmpflg, dim, false, threads_y, groups_all);
+            scanDimFinalLauncher<To, char, To, op>(tmp, tmp, tmpflg, dim, false,
+                                                   threads_y, groups_all, true);
         }
 
         groups_all[dim] = gdim;
-        bcast_dim_launcher<To, Tk, To, op, inclusive_scan>(
-            out, tmp, tmpid, dim, threads_y, groups_all);
+        bcastDimLauncher<To, Tk, To, op>(out, tmp, tmpid, dim, threads_y,
+                                         groups_all, inclusiveScan);
         bufferFree(tmp.data);
         bufferFree(tmpflg.data);
         bufferFree(tmpid.data);
@@ -212,11 +189,9 @@ void scan_dim(Param out, const Param in, const Param key, int dim) {
 }
 }  // namespace kernel
 
-#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To)                          \
-    template void scan_dim<Ti, Tk, To, ROp, true>(Param out, const Param in,  \
-                                                  const Param key, int dim);  \
-    template void scan_dim<Ti, Tk, To, ROp, false>(Param out, const Param in, \
-                                                   const Param key, int dim);
+#define INSTANTIATE_SCAN_DIM_BY_KEY(ROp, Ti, Tk, To) \
+    template void scanDimByKey<Ti, Tk, To, ROp>(     \
+        Param out, const Param in, const Param key, int dim, const bool);
 
 #define INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, Tk)         \
     INSTANTIATE_SCAN_DIM_BY_KEY(ROp, float, Tk, float)     \
diff --git a/src/backend/opencl/kernel/scan_first.cl b/src/backend/opencl/kernel/scan_first.cl
index 3d4da2e0fd..f84dfc6294 100644
--- a/src/backend/opencl/kernel/scan_first.cl
+++ b/src/backend/opencl/kernel/scan_first.cl
@@ -7,10 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void scan_first_kernel(__global To *oData, KParam oInfo,
-                                __global To *tData, KParam tInfo,
-                                const __global Ti *iData, KParam iInfo,
-                                uint groups_x, uint groups_y, uint lim) {
+kernel void scanFirst(global To *oData, KParam oInfo, global To *tData,
+                      KParam tInfo, const global Ti *iData, KParam iInfo,
+                      uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
@@ -34,10 +33,10 @@ __kernel void scan_first_kernel(__global To *oData, KParam oInfo,
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
              yid * oInfo.strides[1] + oInfo.offset;
 
-    __local To l_val0[SHARED_MEM_SIZE];
-    __local To l_val1[SHARED_MEM_SIZE];
-    __local To *l_val = l_val0;
-    __local To l_tmp[DIMY];
+    local To l_val0[SHARED_MEM_SIZE];
+    local To l_val1[SHARED_MEM_SIZE];
+    local To *l_val = l_val0;
+    local To l_tmp[DIMY];
 
     bool flip = 0;
 
@@ -65,7 +64,7 @@ __kernel void scan_first_kernel(__global To *oData, KParam oInfo,
         }
 
         val = binOp(val, l_tmp[lidy]);
-        if (inclusive_scan != 0) {
+        if (INCLUSIVE_SCAN != 0) {
             if (cond) { oData[id] = val; }
         } else {
             if (id == (oInfo.dims[0] - 1)) {
@@ -78,12 +77,11 @@ __kernel void scan_first_kernel(__global To *oData, KParam oInfo,
         barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (!isFinalPass && isLast && cond_yzw) { tData[groupId_x] = val; }
+    if (!IS_FINAL_PASS && isLast && cond_yzw) { tData[groupId_x] = val; }
 }
 
-__kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
-                                 const __global To *tData, KParam tInfo,
-                                 uint groups_x, uint groups_y, uint lim) {
+kernel void bcastFirst(global To *oData, KParam oInfo, const global To *tData,
+                       KParam tInfo, uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
@@ -109,7 +107,7 @@ __kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
             To accum = tData[groupId_x - 1];
 
             // Shift broadcast one step to the right for exclusive scan (#2366)
-            int offset = !inclusive_scan;
+            int offset = !INCLUSIVE_SCAN;
             for (int k = 0, id = xid + offset; k < lim && id < oInfo.dims[0];
                  k++, id += DIMX) {
                 oData[id] = binOp(accum, oData[id]);
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index f3f38a8121..cd9ba2a53f 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -8,88 +8,71 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_first.hpp>
-#include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "names.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-static Kernel get_scan_first_kernels(int kerIdx, bool isFinalPass,
-                                     uint threads_x) {
-    std::string ref_name =
-        std::string("scan_0_") + std::string("_") +
-        std::to_string(isFinalPass) + std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_x) +
-        std::string("_") + std::to_string(int(inclusive_scan));
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        const uint threads_y       = THREADS_PER_GROUP / threads_x;
-        const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
-
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x << " -D DIMY=" << threads_y
-                << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
-                << " -D isFinalPass=" << (int)(isFinalPass)
-                << " -D inclusive_scan=" << inclusive_scan;
-        options << getTypeBuildDefinition<Ti>();
-
-        const char *ker_strs[] = {ops_cl, scan_first_cl};
-        const int ker_lens[]   = {ops_cl_len, scan_first_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel[2];
-
-        entry.ker[0] = Kernel(*entry.prog, "scan_first_kernel");
-        entry.ker[1] = Kernel(*entry.prog, "bcast_first_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    return entry.ker[kerIdx];
+template<typename Ti, typename To, af_op_t op>
+static opencl::Kernel getScanFirstKernel(const std::string key,
+                                         const bool isFinalPass,
+                                         const uint threads_x,
+                                         const bool inclusiveScan) {
+    using std::string;
+    using std::vector;
+
+    static const string src1(ops_cl, ops_cl_len);
+    static const string src2(scan_first_cl, scan_first_cl_len);
+
+    const uint threads_y       = THREADS_PER_GROUP / threads_x;
+    const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
+    ToNumStr<To> toNumStr;
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ti>(),   TemplateTypename<To>(),
+        TemplateArg(isFinalPass), TemplateArg(op),
+        TemplateArg(threads_x),   TemplateArg(inclusiveScan),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(DIMY, threads_y),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineValue(SHARED_MEM_SIZE),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(IS_FINAL_PASS, (isFinalPass ? 1 : 0)),
+        DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
+
+    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-static void scan_first_launcher(Param &out, Param &tmp, const Param &in,
-                                const bool isFinalPass, const uint groups_x,
-                                const uint groups_y, const uint threads_x) {
-    Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(
-        0, isFinalPass, threads_x);
+template<typename Ti, typename To, af_op_t op>
+static void scanFirstLauncher(Param &out, Param &tmp, const Param &in,
+                              const bool isFinalPass, const uint groups_x,
+                              const uint groups_y, const uint threads_x,
+                              const bool inclusiveScan = true) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto scan = getScanFirstKernel<Ti, To, op>("scanFirst", isFinalPass,
+                                               threads_x, inclusiveScan);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -97,21 +80,20 @@ static void scan_first_launcher(Param &out, Param &tmp, const Param &in,
 
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                uint, uint, uint>(ker);
-
-    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *tmp.data, tmp.info, *in.data, in.info, groups_x, groups_y, lim);
-
+    scan(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *tmp.data,
+         tmp.info, *in.data, in.info, groups_x, groups_y, lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan>
-static void bcast_first_launcher(Param &out, Param &tmp, const bool isFinalPass,
-                                 const uint groups_x, const uint groups_y,
-                                 const uint threads_x) {
-    Kernel ker = get_scan_first_kernels<Ti, To, op, inclusive_scan>(
-        1, isFinalPass, threads_x);
+template<typename Ti, typename To, af_op_t op>
+static void bcastFirstLauncher(Param &out, Param &tmp, const bool isFinalPass,
+                               const uint groups_x, const uint groups_y,
+                               const uint threads_x, const bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto bcast = getScanFirstKernel<Ti, To, op>("bcastFirst", isFinalPass,
+                                                threads_x, inclusiveScan);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -119,17 +101,14 @@ static void bcast_first_launcher(Param &out, Param &tmp, const bool isFinalPass,
 
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    auto bcastOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, uint, uint, uint>(ker);
-
-    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-            *tmp.data, tmp.info, groups_x, groups_y, lim);
-
+    bcast(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+          *tmp.data, tmp.info, groups_x, groups_y, lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename To, af_op_t op, bool inclusive_scan = true>
-static void scan_first(Param &out, const Param &in) {
+template<typename Ti, typename To, af_op_t op>
+static void scanFirst(Param &out, const Param &in,
+                      const bool inclusiveScan = true) {
     uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
@@ -138,8 +117,8 @@ static void scan_first(Param &out, const Param &in) {
     uint groups_y = divup(out.info.dims[1], threads_y);
 
     if (groups_x == 1) {
-        scan_first_launcher<Ti, To, op, inclusive_scan>(
-            out, out, in, true, groups_x, groups_y, threads_x);
+        scanFirstLauncher<Ti, To, op>(out, out, in, true, groups_x, groups_y,
+                                      threads_x, inclusiveScan);
 
     } else {
         Param tmp           = out;
@@ -154,19 +133,19 @@ static void scan_first(Param &out, const Param &in) {
 
         tmp.data = bufferAlloc(tmp_elements * sizeof(To));
 
-        scan_first_launcher<Ti, To, op, inclusive_scan>(
-            out, tmp, in, false, groups_x, groups_y, threads_x);
+        scanFirstLauncher<Ti, To, op>(out, tmp, in, false, groups_x, groups_y,
+                                      threads_x, inclusiveScan);
 
         if (op == af_notzero_t) {
-            scan_first_launcher<To, To, af_add_t, true>(tmp, tmp, tmp, true, 1,
-                                                        groups_y, threads_x);
+            scanFirstLauncher<To, To, af_add_t>(tmp, tmp, tmp, true, 1,
+                                                groups_y, threads_x, true);
         } else {
-            scan_first_launcher<To, To, op, true>(tmp, tmp, tmp, true, 1,
-                                                  groups_y, threads_x);
+            scanFirstLauncher<To, To, op>(tmp, tmp, tmp, true, 1, groups_y,
+                                          threads_x, true);
         }
 
-        bcast_first_launcher<To, To, op, inclusive_scan>(
-            out, tmp, true, groups_x, groups_y, threads_x);
+        bcastFirstLauncher<To, To, op>(out, tmp, true, groups_x, groups_y,
+                                       threads_x, inclusiveScan);
 
         bufferFree(tmp.data);
     }
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 05a5712dcf..54d572d965 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -7,15 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-char calculate_head_flags(const __global Tk *kptr, int id, int previd) {
+char calculate_head_flags(const global Tk *kptr, int id, int previd) {
     return (id == 0) ? 1 : (kptr[id] != kptr[previd]);
 }
 
-__kernel void scan_first_by_key_nonfinal_kernel(
-    __global To *oData, KParam oInfo, __global To *tData, KParam tInfo,
-    __global char *tfData, KParam tfInfo, __global int *tiData, KParam tiInfo,
-    const __global Ti *iData, KParam iInfo, const __global Tk *kData,
-    KParam kInfo, uint groups_x, uint groups_y, uint lim) {
+kernel void scanFirstByKeyNonfinal(global To *oData, KParam oInfo,
+                                   global To *tData, KParam tInfo,
+                                   global char *tfData, KParam tfInfo,
+                                   global int *tiData, KParam tiInfo,
+                                   const global Ti *iData, KParam iInfo,
+                                   const global Tk *kData, KParam kInfo,
+                                   uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
@@ -48,15 +50,15 @@ __kernel void scan_first_by_key_nonfinal_kernel(
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
              yid * oInfo.strides[1] + oInfo.offset;
 
-    __local To l_val0[SHARED_MEM_SIZE];
-    __local To l_val1[SHARED_MEM_SIZE];
-    __local char l_flg0[SHARED_MEM_SIZE];
-    __local char l_flg1[SHARED_MEM_SIZE];
-    __local To *l_val   = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[DIMY];
-    __local char l_ftmp[DIMY];
-    __local int boundaryid[DIMY];
+    local To l_val0[SHARED_MEM_SIZE];
+    local To l_val1[SHARED_MEM_SIZE];
+    local char l_flg0[SHARED_MEM_SIZE];
+    local char l_flg1[SHARED_MEM_SIZE];
+    local To *l_val   = l_val0;
+    local char *l_flg = l_flg0;
+    local To l_tmp[DIMY];
+    local char l_ftmp[DIMY];
+    local int boundaryid[DIMY];
 
     bool flip = 0;
 
@@ -84,7 +86,7 @@ __kernel void scan_first_by_key_nonfinal_kernel(
         }
 
         // Load val from global in
-        if (inclusive_scan) {
+        if (INCLUSIVE_SCAN) {
             if (!cond) {
                 val = init_val;
             } else {
@@ -152,12 +154,10 @@ __kernel void scan_first_by_key_nonfinal_kernel(
     }
 }
 
-__kernel void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
-                                             const __global Ti *iData,
-                                             KParam iInfo,
-                                             const __global Tk *kData,
-                                             KParam kInfo, uint groups_x,
-                                             uint groups_y, uint lim) {
+kernel void scanFirstByKeyFinal(global To *oData, KParam oInfo,
+                                const global Ti *iData, KParam iInfo,
+                                const global Tk *kData, KParam kInfo,
+                                uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
     const int lid  = lidy * get_local_size(0) + lidx;
@@ -181,14 +181,14 @@ __kernel void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
              yid * oInfo.strides[1] + oInfo.offset;
 
-    __local To l_val0[SHARED_MEM_SIZE];
-    __local To l_val1[SHARED_MEM_SIZE];
-    __local char l_flg0[SHARED_MEM_SIZE];
-    __local char l_flg1[SHARED_MEM_SIZE];
-    __local To *l_val   = l_val0;
-    __local char *l_flg = l_flg0;
-    __local To l_tmp[DIMY];
-    __local char l_ftmp[DIMY];
+    local To l_val0[SHARED_MEM_SIZE];
+    local To l_val1[SHARED_MEM_SIZE];
+    local char l_flg0[SHARED_MEM_SIZE];
+    local char l_flg1[SHARED_MEM_SIZE];
+    local To *l_val   = l_val0;
+    local char *l_flg = l_flg0;
+    local To l_tmp[DIMY];
+    local char l_ftmp[DIMY];
 
     bool flip = 0;
 
@@ -214,7 +214,7 @@ __kernel void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
         }
 
         // Load val from global in
-        if (inclusive_scan) {
+        if (INCLUSIVE_SCAN) {
             if (!cond) {
                 val = init_val;
             } else {
@@ -263,10 +263,10 @@ __kernel void scan_first_by_key_final_kernel(__global To *oData, KParam oInfo,
     }
 }
 
-__kernel void bcast_first_kernel(__global To *oData, KParam oInfo,
-                                 const __global To *tData, KParam tInfo,
-                                 const __global int *tiData, KParam tiInfo,
-                                 uint groups_x, uint groups_y, uint lim) {
+kernel void bcastFirstByKey(global To *oData, KParam oInfo,
+                            const global To *tData, KParam tInfo,
+                            const global int *tiData, KParam tiInfo,
+                            uint groups_x, uint groups_y, uint lim) {
     const int lidx = get_local_id(0);
     const int lidy = get_local_id(1);
 
diff --git a/src/backend/opencl/kernel/scan_first_by_key.hpp b/src/backend/opencl/kernel/scan_first_by_key.hpp
index c94e22a526..609e918f56 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key.hpp
@@ -8,14 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <common/dispatch.hpp>
-#include <debug_opencl.hpp>
-#include <traits.hpp>
 
 namespace opencl {
 namespace kernel {
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-void scan_first(Param &out, const Param &in, const Param &key);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scanFirstByKey(Param &out, const Param &in, const Param &key,
+                    const bool inclusive_scan);
 }
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index f4962fe16d..f54f0b00d4 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -8,93 +8,75 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_first_by_key.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
-#include "names.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-static Kernel get_scan_first_kernels(int kerIdx, bool calculateFlags,
-                                     uint threads_x) {
-    std::string ref_name =
-        std::string("scan_0_") + std::string("_") +
-        std::to_string(calculateFlags) + std::string("_") +
-        std::string(dtype_traits<Ti>::getName()) + std::string("_") +
-        std::string(dtype_traits<Tk>::getName()) + std::string("_") +
-        std::string(dtype_traits<To>::getName()) + std::string("_") +
-        std::to_string(op) + std::string("_") + std::to_string(threads_x) +
-        std::string("_") + std::to_string(int(inclusive_scan));
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        const uint threads_y       = THREADS_PER_GROUP / threads_x;
-        const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
-
-        ToNumStr<To> toNumStr;
-
-        std::ostringstream options;
-        options << " -D To=" << dtype_traits<To>::getName()
-                << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D Tk=" << dtype_traits<Tk>::getName() << " -D T=To"
-                << " -D DIMX=" << threads_x << " -D DIMY=" << threads_y
-                << " -D SHARED_MEM_SIZE=" << SHARED_MEM_SIZE
-                << " -D init=" << toNumStr(Binary<To, op>::init()) << " -D "
-                << binOpName<op>() << " -D CPLX=" << af::iscplx<Ti>()
-                << " -D calculateFlags=" << calculateFlags
-                << " -D inclusive_scan=" << inclusive_scan;
-        options << getTypeBuildDefinition<Ti>();
-
-        const char *ker_strs[] = {ops_cl, scan_first_by_key_cl};
-        const int ker_lens[]   = {ops_cl_len, scan_first_by_key_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel[3];
-
-        entry.ker[0] = Kernel(*entry.prog, "scan_first_by_key_final_kernel");
-        entry.ker[1] = Kernel(*entry.prog, "scan_first_by_key_nonfinal_kernel");
-        entry.ker[2] = Kernel(*entry.prog, "bcast_first_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    return entry.ker[kerIdx];
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static opencl::Kernel getScanFirstKernel(const std::string key,
+                                         bool calculateFlags, uint threads_x,
+                                         const bool inclusiveScan) {
+    using std::string;
+    using std::vector;
+
+    static const string src1(ops_cl, ops_cl_len);
+    static const string src2(scan_first_by_key_cl, scan_first_by_key_cl_len);
+
+    const uint threads_y       = THREADS_PER_GROUP / threads_x;
+    const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
+    ToNumStr<To> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<Ti>(),
+        TemplateTypename<To>(),
+        TemplateTypename<Tk>(),
+        TemplateArg(calculateFlags),
+        TemplateArg(op),
+        TemplateArg(threads_x),
+        TemplateArg(inclusiveScan),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(Tk, dtype_traits<Tk>::getName()),
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineKeyValue(DIMY, threads_y),
+        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineValue(SHARED_MEM_SIZE),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(calculateFlags, (calculateFlags ? 1 : 0)),
+        DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
+
+    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op,
-         bool inclusive_scan = true>
-static void scan_first_nonfinal_launcher(Param &out, Param &tmp, Param &tmpflg,
-                                         Param &tmpid, const Param &in,
-                                         const Param &key, const uint groups_x,
-                                         const uint groups_y,
-                                         const uint threads_x) {
-    Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(
-        1, false, threads_x);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scanFirstByKeyNonfinalLauncher(
+    Param &out, Param &tmp, Param &tmpflg, Param &tmpid, const Param &in,
+    const Param &key, const uint groups_x, const uint groups_y,
+    const uint threads_x, const bool inclusiveScan = true) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto scan = getScanFirstKernel<Ti, Tk, To, op>(
+        "scanFirstByKeyNonfinal", false, threads_x, inclusiveScan);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -102,28 +84,22 @@ static void scan_first_nonfinal_launcher(Param &out, Param &tmp, Param &tmpflg,
 
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    auto scanOp =
-        KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam, Buffer,
-                      KParam, Buffer, KParam, Buffer, KParam, uint, uint, uint>(
-            ker);
-
-    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *tmp.data, tmp.info, *tmpflg.data, tmpflg.info, *tmpid.data,
-           tmpid.info, *in.data, in.info, *key.data, key.info, groups_x,
-           groups_y, lim);
-
+    scan(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *tmp.data,
+         tmp.info, *tmpflg.data, tmpflg.info, *tmpid.data, tmpid.info, *in.data,
+         in.info, *key.data, key.info, groups_x, groups_y, lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op,
-         bool inclusive_scan = true>
-static void scan_first_final_launcher(Param &out, const Param &in,
-                                      const Param &key,
-                                      const bool calculateFlags,
-                                      const uint groups_x, const uint groups_y,
-                                      const uint threads_x) {
-    Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(
-        0, calculateFlags, threads_x);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void scanFirstByKeyFinalLauncher(
+    Param &out, const Param &in, const Param &key, const bool calculateFlags,
+    const uint groups_x, const uint groups_y, const uint threads_x,
+    const bool inclusiveScan = true) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto scan = getScanFirstKernel<Ti, Tk, To, op>(
+        "scanFirstByKeyFinal", calculateFlags, threads_x, inclusiveScan);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -131,21 +107,20 @@ static void scan_first_final_launcher(Param &out, const Param &in,
 
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    auto scanOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                uint, uint, uint>(ker);
-
-    scanOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *in.data, in.info, *key.data, key.info, groups_x, groups_y, lim);
-
+    scan(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+         in.info, *key.data, key.info, groups_x, groups_y, lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-static void bcast_first_launcher(Param &out, Param &tmp, Param &tmpid,
-                                 const uint groups_x, const uint groups_y,
-                                 const uint threads_x) {
-    Kernel ker = get_scan_first_kernels<Ti, Tk, To, op, inclusive_scan>(
-        2, false, threads_x);
+template<typename Ti, typename Tk, typename To, af_op_t op>
+static void bcastFirstByKeyLauncher(Param &out, Param &tmp, Param &tmpid,
+                                    const uint groups_x, const uint groups_y,
+                                    const uint threads_x, bool inclusiveScan) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+
+    auto bcast = getScanFirstKernel<Ti, Tk, To, op>("bcastFirstByKey", false,
+                                                    threads_x, inclusiveScan);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * out.info.dims[2] * local[0],
@@ -153,18 +128,15 @@ static void bcast_first_launcher(Param &out, Param &tmp, Param &tmpid,
 
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    auto bcastOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                 uint, uint, uint>(ker);
-
-    bcastOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-            *tmp.data, tmp.info, *tmpid.data, tmpid.info, groups_x, groups_y,
-            lim);
-
+    bcast(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+          *tmp.data, tmp.info, *tmpid.data, tmpid.info, groups_x, groups_y,
+          lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
-template<typename Ti, typename Tk, typename To, af_op_t op, bool inclusive_scan>
-void scan_first(Param &out, const Param &in, const Param &key) {
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void scanFirstByKey(Param &out, const Param &in, const Param &key,
+                    const bool inclusiveScan) {
     uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_GROUP);
     uint threads_y = THREADS_PER_GROUP / threads_x;
@@ -173,8 +145,8 @@ void scan_first(Param &out, const Param &in, const Param &key) {
     uint groups_y = divup(out.info.dims[1], threads_y);
 
     if (groups_x == 1) {
-        scan_first_final_launcher<Ti, Tk, To, op, inclusive_scan>(
-            out, in, key, true, groups_x, groups_y, threads_x);
+        scanFirstByKeyFinalLauncher<Ti, Tk, To, op>(
+            out, in, key, true, groups_x, groups_y, threads_x, inclusiveScan);
 
     } else {
         Param tmp           = out;
@@ -193,33 +165,31 @@ void scan_first(Param &out, const Param &in, const Param &key) {
         tmpflg.data = bufferAlloc(tmp_elements * sizeof(char));
         tmpid.data  = bufferAlloc(tmp_elements * sizeof(int));
 
-        scan_first_nonfinal_launcher<Ti, Tk, To, op, inclusive_scan>(
-            out, tmp, tmpflg, tmpid, in, key, groups_x, groups_y, threads_x);
+        scanFirstByKeyNonfinalLauncher<Ti, Tk, To, op>(
+            out, tmp, tmpflg, tmpid, in, key, groups_x, groups_y, threads_x,
+            inclusiveScan);
 
         if (op == af_notzero_t) {
-            scan_first_final_launcher<To, char, To, af_add_t, true>(
-                tmp, tmp, tmpflg, false, 1, groups_y, threads_x);
+            scanFirstByKeyFinalLauncher<To, char, To, af_add_t>(
+                tmp, tmp, tmpflg, false, 1, groups_y, threads_x, true);
         } else {
-            scan_first_final_launcher<To, char, To, op, true>(
-                tmp, tmp, tmpflg, false, 1, groups_y, threads_x);
+            scanFirstByKeyFinalLauncher<To, char, To, op>(
+                tmp, tmp, tmpflg, false, 1, groups_y, threads_x, true);
         }
 
-        bcast_first_launcher<To, Tk, To, op, inclusive_scan>(
-            out, tmp, tmpid, groups_x, groups_y, threads_x);
+        bcastFirstByKeyLauncher<To, Tk, To, op>(
+            out, tmp, tmpid, groups_x, groups_y, threads_x, inclusiveScan);
 
         bufferFree(tmp.data);
         bufferFree(tmpflg.data);
         bufferFree(tmpid.data);
     }
 }
-
 }  // namespace kernel
 
-#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To)   \
-    template void scan_first<Ti, Tk, To, ROp, true>(     \
-        Param & out, const Param &in, const Param &key); \
-    template void scan_first<Ti, Tk, To, ROp, false>(    \
-        Param & out, const Param &in, const Param &key);
+#define INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, Ti, Tk, To) \
+    template void scanFirstByKey<Ti, Tk, To, ROp>(     \
+        Param & out, const Param &in, const Param &key, const bool);
 
 #define INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, Tk)         \
     INSTANTIATE_SCAN_FIRST_BY_KEY(ROp, float, Tk, float)     \
diff --git a/src/backend/opencl/kernel/select.cl b/src/backend/opencl/kernel/select.cl
index e498aafbf5..02d113f3f8 100644
--- a/src/backend/opencl/kernel/select.cl
+++ b/src/backend/opencl/kernel/select.cl
@@ -23,13 +23,13 @@ int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4]) {
     return off;
 }
 
-__kernel void select_kernel(__global T *optr, KParam oinfo,
-                            __global char *cptr_, KParam cinfo,
-                            __global T *aptr_, KParam ainfo, __global T *bptr_,
+kernel void select_kernel(global T *optr, KParam oinfo,
+                            global char *cptr_, KParam cinfo,
+                            global T *aptr_, KParam ainfo, __global T *bptr_,
                             KParam binfo, int groups_0, int groups_1) {
-    __global char *cptr = cptr_ + cinfo.offset;
-    __global T *aptr    = aptr_ + ainfo.offset;
-    __global T *bptr    = bptr_ + binfo.offset;
+    global char *cptr = cptr_ + cinfo.offset;
+    global T *aptr    = aptr_ + ainfo.offset;
+    global T *bptr    = bptr_ + binfo.offset;
 
     const int idz = get_group_id(0) / groups_0;
     const int idw = get_group_id(1) / groups_1;
@@ -71,12 +71,12 @@ __kernel void select_kernel(__global T *optr, KParam oinfo,
     }
 }
 
-__kernel void select_scalar_kernel(__global T *optr, KParam oinfo,
-                                   __global char *cptr_, KParam cinfo,
-                                   __global T *aptr_, KParam ainfo, T b,
+kernel void select_scalar_kernel(global T *optr, KParam oinfo,
+                                   global char *cptr_, KParam cinfo,
+                                   global T *aptr_, KParam ainfo, T b,
                                    int groups_0, int groups_1) {
-    __global char *cptr = cptr_ + cinfo.offset;
-    __global T *aptr    = aptr_ + ainfo.offset;
+    global char *cptr = cptr_ + cinfo.offset;
+    global T *aptr    = aptr_ + ainfo.offset;
 
     const int idz = get_group_id(0) / groups_0;
     const int idw = get_group_id(1) / groups_1;
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 7e77e16237..9878a4f868 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -10,56 +10,42 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/select.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
 
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const uint DIMX  = 32;
-static const uint DIMY  = 8;
-static const int REPEAT = 64;
-
-template<typename T, bool is_same>
-void select_launcher(Param out, Param cond, Param a, Param b, int ndims) {
-    std::string refName = std::string("select_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(is_same);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D is_same=" << is_same
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {select_cl};
-        const int ker_lens[]   = {select_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "select_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
+constexpr uint DIMX  = 32;
+constexpr uint DIMY  = 8;
+constexpr int REPEAT = 64;
+
+static inline auto selectSrc() {
+    static const std::string src(select_cl, select_cl_len);
+    return src;
+};
+
+template<typename T>
+void selectLauncher(Param out, Param cond, Param a, Param b, const int ndims,
+                    const bool is_same) {
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(is_same),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(is_same),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto selectOp =
+        common::findKernel("select_kernel", {selectSrc()}, targs, options);
 
     int threads[] = {DIMX, DIMY};
 
@@ -68,18 +54,15 @@ void select_launcher(Param out, Param cond, Param a, Param b, int ndims) {
         threads[1] = 1;
     }
 
-    NDRange local(threads[0], threads[1]);
+    cl::NDRange local(threads[0], threads[1]);
 
     int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
     int groups_1 = divup(out.info.dims[1], local[1]);
 
-    NDRange global(groups_0 * out.info.dims[2] * local[0],
-                   groups_1 * out.info.dims[3] * local[1]);
-
-    auto selectOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                  KParam, Buffer, KParam, int, int>(*entry.ker);
+    cl::NDRange global(groups_0 * out.info.dims[2] * local[0],
+                       groups_1 * out.info.dims[3] * local[1]);
 
-    selectOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    selectOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *cond.data, cond.info, *a.data, a.info, *b.data, b.info, groups_0,
              groups_1);
 }
@@ -90,38 +73,24 @@ void select(Param out, Param cond, Param a, Param b, int ndims) {
     for (int i = 0; i < 4; i++) {
         is_same &= (a.info.dims[i] == b.info.dims[i]);
     }
-
-    if (is_same) {
-        select_launcher<T, true>(out, cond, a, b, ndims);
-    } else {
-        select_launcher<T, false>(out, cond, a, b, ndims);
-    }
+    selectLauncher<T>(out, cond, a, b, ndims, is_same);
 }
 
-template<typename T, bool flip>
-void select_scalar(Param out, Param cond, Param a, const double b, int ndims) {
-    std::string refName = std::string("select_scalar_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(flip);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D flip=" << flip
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {select_cl};
-        const int ker_lens[]   = {select_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "select_scalar_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
+template<typename T>
+void select_scalar(Param out, Param cond, Param a, const double b,
+                   const int ndims, const bool flip) {
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(flip),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(flip),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+
+    auto selectOp = common::findKernel("select_scalar_kernel", {selectSrc()},
+                                       targs, options);
 
     int threads[] = {DIMX, DIMY};
 
@@ -130,18 +99,15 @@ void select_scalar(Param out, Param cond, Param a, const double b, int ndims) {
         threads[1] = 1;
     }
 
-    NDRange local(threads[0], threads[1]);
+    cl::NDRange local(threads[0], threads[1]);
 
     int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
     int groups_1 = divup(out.info.dims[1], local[1]);
 
-    NDRange global(groups_0 * out.info.dims[2] * local[0],
-                   groups_1 * out.info.dims[3] * local[1]);
-
-    auto selectOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer,
-                                  KParam, T, int, int>(*entry.ker);
+    cl::NDRange global(groups_0 * out.info.dims[2] * local[0],
+                       groups_1 * out.info.dims[3] * local[1]);
 
-    selectOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+    selectOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *cond.data, cond.info, *a.data, a.info, scalar<T>(b), groups_0,
              groups_1);
 }
diff --git a/src/backend/opencl/kernel/sift_nonfree.cl b/src/backend/opencl/kernel/sift_nonfree.cl
index c31f3bf6af..e17403ed53 100644
--- a/src/backend/opencl/kernel/sift_nonfree.cl
+++ b/src/backend/opencl/kernel/sift_nonfree.cl
@@ -128,7 +128,7 @@ void gaussianElimination(float* A, float* b, float* x, const int n) {
     }
 }
 
-inline void fatomic_add(volatile __local float* source, const float operand) {
+inline void fatomic_add(volatile local float* source, const float operand) {
     union {
         unsigned int intVal;
         float floatVal;
@@ -140,11 +140,11 @@ inline void fatomic_add(volatile __local float* source, const float operand) {
     do {
         prevVal.floatVal = *source;
         newVal.floatVal  = prevVal.floatVal + operand;
-    } while (atomic_cmpxchg((volatile __local unsigned int*)source,
+    } while (atomic_cmpxchg((volatile local unsigned int*)source,
                             prevVal.intVal, newVal.intVal) != prevVal.intVal);
 }
 
-inline void normalizeDesc(__local float* desc, __local float* accum,
+inline void normalizeDesc(local float* desc, __local float* accum,
                           const int histlen, int lid_x, int lid_y, int lsz_x) {
     for (int i = lid_x; i < histlen; i += lsz_x)
         accum[i] = desc[lid_y * histlen + i] * desc[lid_y * histlen + i];
@@ -179,7 +179,7 @@ inline void normalizeDesc(__local float* desc, __local float* accum,
     barrier(CLK_LOCAL_MEM_FENCE);
 }
 
-inline void normalizeGLOHDesc(__local float* desc, __local float* accum,
+inline void normalizeGLOHDesc(local float* desc, __local float* accum,
                               const int histlen, int lid_x, int lid_y,
                               int lsz_x) {
     for (int i = lid_x; i < histlen; i += lsz_x)
@@ -219,7 +219,7 @@ inline void normalizeGLOHDesc(__local float* desc, __local float* accum,
     barrier(CLK_LOCAL_MEM_FENCE);
 }
 
-__kernel void sub(__global T* out, __global const T* in, unsigned nel,
+kernel void sub(global T* out, __global const T* in, unsigned nel,
                   unsigned n_layers) {
     unsigned i = get_global_id(0);
 
@@ -235,11 +235,11 @@ __kernel void sub(__global T* out, __global const T* in, unsigned nel,
 
 // Determines whether a pixel is a scale-space extremum by comparing it to its
 // 3x3x3 pixel neighborhood.
-__kernel void detectExtrema(__global float* x_out, __global float* y_out,
-                            __global unsigned* layer_out,
-                            __global unsigned* counter, __global const T* dog,
+kernel void detectExtrema(global float* x_out, __global float* y_out,
+                            global unsigned* layer_out,
+                            global unsigned* counter, __global const T* dog,
                             KParam iDoG, const unsigned max_feat,
-                            const float threshold, __local float* l_mem) {
+                            const float threshold, local float* l_mem) {
     const int dim0 = iDoG.dims[0];
     const int dim1 = iDoG.dims[1];
     const int imel = iDoG.dims[0] * iDoG.dims[1];
@@ -255,9 +255,9 @@ __kernel void detectExtrema(__global float* x_out, __global float* y_out,
     const int l_i = lsz_i + 2;
     const int l_j = lsz_j + 2;
 
-    __local float* l_prev   = l_mem;
-    __local float* l_center = l_mem + l_i * l_j;
-    __local float* l_next   = l_mem + l_i * l_j * 2;
+    local float* l_prev   = l_mem;
+    local float* l_center = l_mem + l_i * l_j;
+    local float* l_next   = l_mem + l_i * l_j * 2;
 
     const int x = lid_i + 1;
     const int y = lid_j + 1;
@@ -352,12 +352,12 @@ __kernel void detectExtrema(__global float* x_out, __global float* y_out,
 // Interpolates a scale-space extremum's location and scale to subpixel
 // accuracy to form an image feature. Rejects features with low contrast.
 // Based on Section 4 of Lowe's paper.
-__kernel void interpolateExtrema(
-    __global float* x_out, __global float* y_out, __global unsigned* layer_out,
-    __global float* response_out, __global float* size_out,
-    __global unsigned* counter, __global const float* x_in,
-    __global const float* y_in, __global const unsigned* layer_in,
-    const unsigned extrema_feat, __global const T* dog_octave, KParam iDoG,
+kernel void interpolateExtrema(
+    global float* x_out, __global float* y_out, __global unsigned* layer_out,
+    global float* response_out, __global float* size_out,
+    global unsigned* counter, __global const float* x_in,
+    global const float* y_in, __global const unsigned* layer_in,
+    const unsigned extrema_feat, global const T* dog_octave, KParam iDoG,
     const unsigned max_feat, const unsigned octave, const unsigned n_layers,
     const float contrast_thr, const float edge_thr, const float sigma,
     const float img_scale) {
@@ -379,9 +379,9 @@ __kernel void interpolateExtrema(
         const int dim1 = iDoG.dims[1];
         const int imel = dim0 * dim1;
 
-        __global const T* prev   = dog_octave + (int)((layer - 1) * imel);
-        __global const T* center = dog_octave + (int)((layer)*imel);
-        __global const T* next   = dog_octave + (int)((layer + 1) * imel);
+        global const T* prev   = dog_octave + (int)((layer - 1) * imel);
+        global const T* center = dog_octave + (int)((layer)*imel);
+        global const T* next   = dog_octave + (int)((layer + 1) * imel);
 
         for (i = 0; i < MAX_INTERP_STEPS; i++) {
             float dD[3] = {
@@ -474,12 +474,12 @@ __kernel void interpolateExtrema(
 #undef NPTR
 
 // Remove duplicate keypoints
-__kernel void removeDuplicates(
-    __global float* x_out, __global float* y_out, __global unsigned* layer_out,
-    __global float* response_out, __global float* size_out,
-    __global unsigned* counter, __global const float* x_in,
-    __global const float* y_in, __global const unsigned* layer_in,
-    __global const float* response_in, __global const float* size_in,
+kernel void removeDuplicates(
+    global float* x_out, __global float* y_out, __global unsigned* layer_out,
+    global float* response_out, __global float* size_out,
+    global unsigned* counter, __global const float* x_in,
+    global const float* y_in, __global const unsigned* layer_in,
+    global const float* response_in, __global const float* size_in,
     const unsigned total_feat) {
     const unsigned f = get_global_id(0);
 
@@ -515,15 +515,15 @@ __kernel void removeDuplicates(
 // Computes a canonical orientation for each image feature in an array.  Based
 // on Section 5 of Lowe's paper.  This function adds features to the array when
 // there is more than one dominant orientation at a given feature location.
-__kernel void calcOrientation(
-    __global float* x_out, __global float* y_out, __global unsigned* layer_out,
-    __global float* response_out, __global float* size_out,
-    __global float* ori_out, __global unsigned* counter,
-    __global const float* x_in, __global const float* y_in,
-    __global const unsigned* layer_in, __global const float* response_in,
-    __global const float* size_in, const unsigned total_feat,
-    __global const T* gauss_octave, KParam iGauss, const unsigned max_feat,
-    const unsigned octave, const int double_input, __local float* l_mem) {
+kernel void calcOrientation(
+    global float* x_out, __global float* y_out, __global unsigned* layer_out,
+    global float* response_out, __global float* size_out,
+    global float* ori_out, __global unsigned* counter,
+    global const float* x_in, __global const float* y_in,
+    global const unsigned* layer_in, __global const float* response_in,
+    global const float* size_in, const unsigned total_feat,
+    global const T* gauss_octave, KParam iGauss, const unsigned max_feat,
+    const unsigned octave, const int double_input, local float* l_mem) {
     const int lid_x = get_local_id(0);
     const int lid_y = get_local_id(1);
     const int lsz_x = get_local_size(0);
@@ -532,8 +532,8 @@ __kernel void calcOrientation(
 
     const int n = ORI_HIST_BINS;
 
-    __local float* hist     = l_mem;
-    __local float* temphist = l_mem + n * 8;
+    local float* hist     = l_mem;
+    local float* temphist = l_mem + n * 8;
 
     // Initialize temporary histogram
     for (int i = lid_x; i < n; i += lsz_x) { hist[lid_y * n + i] = 0.f; }
@@ -565,7 +565,7 @@ __kernel void calcOrientation(
 
         // Calculate layer offset
         const int layer_offset = layer * dim0 * dim1;
-        __global const T* img  = gauss_octave + layer_offset;
+        global const T* img  = gauss_octave + layer_offset;
 
         // Calculate orientation histogram
         for (int l = lid_x; l < len * len; l += lsz_x) {
@@ -683,22 +683,22 @@ __kernel void calcOrientation(
 
 // Computes feature descriptors for features in an array.  Based on Section 6
 // of Lowe's paper.
-__kernel void computeDescriptor(
-    __global float* desc_out, const unsigned desc_len, const unsigned histsz,
-    __global const float* x_in, __global const float* y_in,
-    __global const unsigned* layer_in, __global const float* response_in,
-    __global const float* size_in, __global const float* ori_in,
-    const unsigned total_feat, __global const T* gauss_octave, KParam iGauss,
+kernel void computeDescriptor(
+    global float* desc_out, const unsigned desc_len, const unsigned histsz,
+    global const float* x_in, __global const float* y_in,
+    global const unsigned* layer_in, __global const float* response_in,
+    global const float* size_in, __global const float* ori_in,
+    const unsigned total_feat, global const T* gauss_octave, KParam iGauss,
     const int d, const int n, const float scale, const int n_layers,
-    __local float* l_mem) {
+    local float* l_mem) {
     const int lid_x = get_local_id(0);
     const int lid_y = get_local_id(1);
     const int lsz_x = get_local_size(0);
 
     const int f = get_global_id(1);
 
-    __local float* desc  = l_mem;
-    __local float* accum = l_mem + desc_len * histsz;
+    local float* desc  = l_mem;
+    local float* accum = l_mem + desc_len * histsz;
 
     for (int i = lid_x; i < desc_len * histsz; i += lsz_x)
         desc[lid_y * desc_len + i] = 0.f;
@@ -715,7 +715,7 @@ __kernel void computeDescriptor(
         // Points img to correct Gaussian pyramid layer
         const int dim0        = iGauss.dims[0];
         const int dim1        = iGauss.dims[1];
-        __global const T* img = gauss_octave + (layer * dim0 * dim1);
+        global const T* img = gauss_octave + (layer * dim0 * dim1);
 
         float cos_t        = cos(ori);
         float sin_t        = sin(ori);
@@ -815,22 +815,22 @@ __kernel void computeDescriptor(
     }
 }
 
-__kernel void computeGLOHDescriptor(
-    __global float* desc_out, const unsigned desc_len, const unsigned histsz,
-    __global const float* x_in, __global const float* y_in,
-    __global const unsigned* layer_in, __global const float* response_in,
-    __global const float* size_in, __global const float* ori_in,
-    const unsigned total_feat, __global const T* gauss_octave, KParam iGauss,
+kernel void computeGLOHDescriptor(
+    global float* desc_out, const unsigned desc_len, const unsigned histsz,
+    global const float* x_in, __global const float* y_in,
+    global const unsigned* layer_in, __global const float* response_in,
+    global const float* size_in, __global const float* ori_in,
+    const unsigned total_feat, global const T* gauss_octave, KParam iGauss,
     const int d, const unsigned rb, const unsigned ab, const unsigned hb,
-    const float scale, const int n_layers, __local float* l_mem) {
+    const float scale, const int n_layers, local float* l_mem) {
     const int lid_x = get_local_id(0);
     const int lid_y = get_local_id(1);
     const int lsz_x = get_local_size(0);
 
     const int f = get_global_id(1);
 
-    __local float* desc  = l_mem;
-    __local float* accum = l_mem + desc_len * histsz;
+    local float* desc  = l_mem;
+    local float* accum = l_mem + desc_len * histsz;
 
     for (int i = lid_x; i < desc_len * histsz; i += lsz_x)
         desc[lid_y * desc_len + i] = 0.f;
@@ -847,7 +847,7 @@ __kernel void computeGLOHDescriptor(
         // Points img to correct Gaussian pyramid layer
         const int dim0        = iGauss.dims[0];
         const int dim1        = iGauss.dims[1];
-        __global const T* img = gauss_octave + (layer * dim0 * dim1);
+        global const T* img = gauss_octave + (layer * dim0 * dim1);
 
         float cos_t              = cos(ori);
         float sin_t              = sin(ori);
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index ed8f8d6a84..fc14d9f7d8 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -71,9 +71,13 @@
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
-#include <program.hpp>
+#include <kernel/convolve_separable.hpp>
+#include <kernel/fast.hpp>
+#include <kernel/resize.hpp>
+#include <kernel_headers/sift_nonfree.hpp>
+#include <memory.hpp>
 #include <af/defines.h>
 
 #pragma GCC diagnostic push
@@ -87,55 +91,42 @@
 
 #pragma GCC diagnostic pop
 
-#include <cache.hpp>
-#include <kernel/convolve_separable.hpp>
-#include <kernel/fast.hpp>
-#include <kernel/resize.hpp>
-#include <kernel_headers/sift_nonfree.hpp>
-#include <memory.hpp>
 #include <vector>
 
 namespace compute = boost::compute;
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
-using std::vector;
-
 namespace opencl {
 namespace kernel {
-static const int SIFT_THREADS   = 256;
-static const int SIFT_THREADS_X = 32;
-static const int SIFT_THREADS_Y = 8;
+
+constexpr int SIFT_THREADS   = 256;
+constexpr int SIFT_THREADS_X = 32;
+constexpr int SIFT_THREADS_Y = 8;
 
 // assumed gaussian blur for input image
-static const float InitSigma = 0.5f;
+constexpr float InitSigma = 0.5f;
 
 // width of border in which to ignore keypoints
-static const int ImgBorder = 5;
+constexpr int ImgBorder = 5;
 
 // default width of descriptor histogram array
-static const int DescrWidth = 4;
+constexpr int DescrWidth = 4;
 
 // default number of bins per histogram in descriptor array
-static const int DescrHistBins = 8;
+constexpr int DescrHistBins = 8;
 
 // default number of bins in histogram for orientation assignment
-static const int OriHistBins = 36;
+constexpr int OriHistBins = 36;
 
 // Number of GLOH bins in radial direction
-static const unsigned GLOHRadialBins = 3;
+constexpr unsigned GLOHRadialBins = 3;
 
 // Number of GLOH angular bins (excluding the inner-most radial section)
-static const unsigned GLOHAngularBins = 8;
+constexpr unsigned GLOHAngularBins = 8;
 
 // Number of GLOH bins per histogram in descriptor
-static const unsigned GLOHHistBins = 16;
+constexpr unsigned GLOHHistBins = 16;
 
-static const float PI_VAL = 3.14159265358979323846f;
+constexpr float PI_VAL = 3.14159265358979323846f;
 
 template<typename T>
 void gaussian1D(T* out, const int dim, double sigma = 0.0) {
@@ -231,7 +222,7 @@ Param createInitialImage(Param img, const float init_sigma,
 
     const Param filter = gaussFilter<convAccT>(s);
 
-    if (double_input) resize<T, AF_INTERP_BILINEAR>(init_img, img);
+    if (double_input) resize<T>(init_img, img, AF_INTERP_BILINEAR);
 
     convSepFull<T, convAccT>(init_img, (double_input) ? init_img : img, filter);
 
@@ -310,7 +301,7 @@ std::vector<Param> buildGaussPyr(Param init_img, const unsigned n_octaves,
                     tmp_pyr[idx].info.strides[3] * tmp_pyr[idx].info.dims[3];
                 tmp_pyr[idx].data = bufferAlloc(lvl_el * sizeof(T));
 
-                resize<T, AF_INTERP_BILINEAR>(tmp_pyr[idx], tmp_pyr[src_idx]);
+                resize<T>(tmp_pyr[idx], tmp_pyr[src_idx], AF_INTERP_BILINEAR);
             } else {
                 for (int k = 0; k < 4; k++) {
                     tmp_pyr[idx].info.dims[k] = tmp_pyr[src_idx].info.dims[k];
@@ -352,7 +343,7 @@ std::vector<Param> buildGaussPyr(Param init_img, const unsigned n_octaves,
 template<typename T>
 std::vector<Param> buildDoGPyr(std::vector<Param> gauss_pyr,
                                const unsigned n_octaves,
-                               const unsigned n_layers, Kernel* suKernel) {
+                               const unsigned n_layers, Kernel suOp) {
     // DoG Pyramid
     std::vector<Param> dog_pyr(n_octaves);
     for (unsigned o = 0; o < n_octaves; o++) {
@@ -368,23 +359,18 @@ std::vector<Param> buildDoGPyr(std::vector<Param> gauss_pyr,
 
         dog_pyr[o].data = bufferAlloc(dog_pyr[o].info.dims[3] *
                                       dog_pyr[o].info.strides[3] * sizeof(T));
-
         const unsigned nel =
             dog_pyr[o].info.dims[1] * dog_pyr[o].info.strides[1];
         const unsigned dog_layers = n_layers + 2;
 
         const int blk_x = divup(nel, SIFT_THREADS);
-        const NDRange local(SIFT_THREADS, 1);
-        const NDRange global(blk_x * SIFT_THREADS, 1);
+        const cl::NDRange local(SIFT_THREADS, 1);
+        const cl::NDRange global(blk_x * SIFT_THREADS, 1);
 
-        auto suOp =
-            KernelFunctor<Buffer, Buffer, unsigned, unsigned>(*suKernel);
-
-        suOp(EnqueueArgs(getQueue(), global, local), *dog_pyr[o].data,
+        suOp(cl::EnqueueArgs(getQueue(), global, local), *dog_pyr[o].data,
              *gauss_pyr[o].data, nel, dog_layers);
         CL_DEBUG_FINISH(getQueue());
     }
-
     return dog_pyr;
 }
 
@@ -416,55 +402,26 @@ void apply_permutation(compute::buffer_iterator<T>& keys,
 }
 
 template<typename T>
-std::array<cl::Kernel*, 7> getSiftKernels() {
-    static const unsigned NUM_KERNELS           = 7;
-    static const char* kernelNames[NUM_KERNELS] = {"sub",
-                                                   "detectExtrema",
-                                                   "interpolateExtrema",
-                                                   "calcOrientation",
-                                                   "removeDuplicates",
-                                                   "computeDescriptor",
-                                                   "computeGLOHDescriptor"};
-
-    kc_entry_t entries[NUM_KERNELS];
-
-    int device = getActiveDeviceId();
-
-    std::string checkName = kernelNames[0] + std::string("_") +
-                            std::string(dtype_traits<T>::getName());
-
-    entries[0] = kernelCache(device, checkName);
-
-    if (entries[0].prog == 0 && entries[0].ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        cl::Program prog;
-        buildProgram(prog, sift_nonfree_cl, sift_nonfree_cl_len, options.str());
-
-        for (unsigned i = 0; i < NUM_KERNELS; ++i) {
-            entries[i].prog = new Program(prog);
-            entries[i].ker  = new Kernel(*entries[i].prog, kernelNames[i]);
-
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName());
-
-            addKernelToCache(device, name, entries[i]);
-        }
-    } else {
-        for (unsigned i = 1; i < NUM_KERNELS; ++i) {
-            std::string name = kernelNames[i] + std::string("_") +
-                               std::string(dtype_traits<T>::getName());
-
-            entries[i] = kernelCache(device, name);
-        }
-    }
-
-    std::array<cl::Kernel*, NUM_KERNELS> retVal;
-    for (unsigned i = 0; i < NUM_KERNELS; ++i) retVal[i] = entries[i].ker;
-
-    return retVal;
+std::array<Kernel, 7> getSiftKernels() {
+    static const std::string src(sift_nonfree_cl, sift_nonfree_cl_len);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    return {
+        common::findKernel("sub", {src}, targs, compileOpts),
+        common::findKernel("detectExtrema", {src}, targs, compileOpts),
+        common::findKernel("interpolateExtrema", {src}, targs, compileOpts),
+        common::findKernel("calcOrientation", {src}, targs, compileOpts),
+        common::findKernel("removeDuplicates", {src}, targs, compileOpts),
+        common::findKernel("computeDescriptor", {src}, targs, compileOpts),
+        common::findKernel("computeGLOHDescriptor", {src}, targs, compileOpts),
+    };
 }
 
 template<typename T, typename convAccT>
@@ -474,6 +431,12 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
           const float edge_thr, const float init_sigma, const bool double_input,
           const float img_scale, const float feature_ratio,
           const bool compute_GLOH) {
+    using cl::Buffer;
+    using cl::EnqueueArgs;
+    using cl::Local;
+    using cl::NDRange;
+    using std::vector;
+
     auto kernels = getSiftKernels<T>();
 
     unsigned min_dim = min(img.info.dims[0], img.info.dims[1]);
@@ -484,19 +447,19 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     Param init_img =
         createInitialImage<T, convAccT>(img, init_sigma, double_input);
 
-    std::vector<Param> gauss_pyr =
+    vector<Param> gauss_pyr =
         buildGaussPyr<T, convAccT>(init_img, n_octaves, n_layers, init_sigma);
 
-    std::vector<Param> dog_pyr =
+    vector<Param> dog_pyr =
         buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, kernels[0]);
 
-    std::vector<cl::Buffer*> d_x_pyr(n_octaves, NULL);
-    std::vector<cl::Buffer*> d_y_pyr(n_octaves, NULL);
-    std::vector<cl::Buffer*> d_response_pyr(n_octaves, NULL);
-    std::vector<cl::Buffer*> d_size_pyr(n_octaves, NULL);
-    std::vector<cl::Buffer*> d_ori_pyr(n_octaves, NULL);
-    std::vector<cl::Buffer*> d_desc_pyr(n_octaves, NULL);
-    std::vector<unsigned> feat_pyr(n_octaves, 0);
+    vector<Buffer*> d_x_pyr(n_octaves, NULL);
+    vector<Buffer*> d_y_pyr(n_octaves, NULL);
+    vector<Buffer*> d_response_pyr(n_octaves, NULL);
+    vector<Buffer*> d_size_pyr(n_octaves, NULL);
+    vector<Buffer*> d_ori_pyr(n_octaves, NULL);
+    vector<Buffer*> d_desc_pyr(n_octaves, NULL);
+    vector<unsigned> feat_pyr(n_octaves, 0);
     unsigned total_feat = 0;
 
     const unsigned d  = DescrWidth;
@@ -507,7 +470,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     const unsigned desc_len =
         (compute_GLOH) ? (1 + (rb - 1) * ab) * hb : d * d * n;
 
-    cl::Buffer* d_count = bufferAlloc(sizeof(unsigned));
+    Buffer* d_count = bufferAlloc(sizeof(unsigned));
 
     for (unsigned o = 0; o < n_octaves; o++) {
         if (dog_pyr[o].info.dims[0] - 2 * ImgBorder < 1 ||
@@ -517,9 +480,9 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         const unsigned imel = dog_pyr[o].info.dims[0] * dog_pyr[o].info.dims[1];
         const unsigned max_feat = ceil(imel * feature_ratio);
 
-        cl::Buffer* d_extrema_x     = bufferAlloc(max_feat * sizeof(float));
-        cl::Buffer* d_extrema_y     = bufferAlloc(max_feat * sizeof(float));
-        cl::Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
+        Buffer* d_extrema_x     = bufferAlloc(max_feat * sizeof(float));
+        Buffer* d_extrema_y     = bufferAlloc(max_feat * sizeof(float));
+        Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
 
         unsigned extrema_feat = 0;
         getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
@@ -535,15 +498,13 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
         float extrema_thr = 0.5f * contrast_thr / n_layers;
 
-        auto deOp =
-            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, KParam,
-                          unsigned, float, LocalSpaceArg>(*kernels[1]);
+        auto deOp = kernels[1];
 
         deOp(EnqueueArgs(getQueue(), global, local), *d_extrema_x, *d_extrema_y,
              *d_extrema_layer, *d_count, *dog_pyr[o].data, dog_pyr[o].info,
              max_feat, extrema_thr,
-             cl::Local((SIFT_THREADS_X + 2) * (SIFT_THREADS_Y + 2) * 3 *
-                       sizeof(float)));
+             Local((SIFT_THREADS_X + 2) * (SIFT_THREADS_Y + 2) * 3 *
+                   sizeof(float)));
         CL_DEBUG_FINISH(getQueue());
 
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
@@ -562,22 +523,17 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                       &interp_feat);
 
-        cl::Buffer* d_interp_x = bufferAlloc(extrema_feat * sizeof(float));
-        cl::Buffer* d_interp_y = bufferAlloc(extrema_feat * sizeof(float));
-        cl::Buffer* d_interp_layer =
-            bufferAlloc(extrema_feat * sizeof(unsigned));
-        cl::Buffer* d_interp_response =
-            bufferAlloc(extrema_feat * sizeof(float));
-        cl::Buffer* d_interp_size = bufferAlloc(extrema_feat * sizeof(float));
+        Buffer* d_interp_x     = bufferAlloc(extrema_feat * sizeof(float));
+        Buffer* d_interp_y     = bufferAlloc(extrema_feat * sizeof(float));
+        Buffer* d_interp_layer = bufferAlloc(extrema_feat * sizeof(unsigned));
+        Buffer* d_interp_response = bufferAlloc(extrema_feat * sizeof(float));
+        Buffer* d_interp_size     = bufferAlloc(extrema_feat * sizeof(float));
 
         const int blk_x_interp = divup(extrema_feat, SIFT_THREADS);
         const NDRange local_interp(SIFT_THREADS, 1);
         const NDRange global_interp(blk_x_interp * SIFT_THREADS, 1);
 
-        auto ieOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer,
-                                  Buffer, Buffer, Buffer, Buffer, unsigned,
-                                  Buffer, KParam, unsigned, unsigned, unsigned,
-                                  float, float, float, float>(*kernels[2]);
+        auto ieOp = kernels[2];
 
         ieOp(EnqueueArgs(getQueue(), global_interp, local_interp), *d_interp_x,
              *d_interp_y, *d_interp_layer, *d_interp_response, *d_interp_size,
@@ -643,20 +599,17 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                       &nodup_feat);
 
-        cl::Buffer* d_nodup_x     = bufferAlloc(interp_feat * sizeof(float));
-        cl::Buffer* d_nodup_y     = bufferAlloc(interp_feat * sizeof(float));
-        cl::Buffer* d_nodup_layer = bufferAlloc(interp_feat * sizeof(unsigned));
-        cl::Buffer* d_nodup_response = bufferAlloc(interp_feat * sizeof(float));
-        cl::Buffer* d_nodup_size     = bufferAlloc(interp_feat * sizeof(float));
+        Buffer* d_nodup_x        = bufferAlloc(interp_feat * sizeof(float));
+        Buffer* d_nodup_y        = bufferAlloc(interp_feat * sizeof(float));
+        Buffer* d_nodup_layer    = bufferAlloc(interp_feat * sizeof(unsigned));
+        Buffer* d_nodup_response = bufferAlloc(interp_feat * sizeof(float));
+        Buffer* d_nodup_size     = bufferAlloc(interp_feat * sizeof(float));
 
         const int blk_x_nodup = divup(extrema_feat, SIFT_THREADS);
         const NDRange local_nodup(SIFT_THREADS, 1);
         const NDRange global_nodup(blk_x_nodup * SIFT_THREADS, 1);
 
-        auto rdOp =
-            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                          Buffer, Buffer, Buffer, Buffer, Buffer, unsigned>(
-                *kernels[4]);
+        auto rdOp = kernels[4];
 
         rdOp(EnqueueArgs(getQueue(), global_nodup, local_nodup), *d_nodup_x,
              *d_nodup_y, *d_nodup_layer, *d_nodup_response, *d_nodup_size,
@@ -679,28 +632,21 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
                                       &oriented_feat);
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        cl::Buffer* d_oriented_x =
-            bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_y =
-            bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_layer =
+        Buffer* d_oriented_x = bufferAlloc(max_oriented_feat * sizeof(float));
+        Buffer* d_oriented_y = bufferAlloc(max_oriented_feat * sizeof(float));
+        Buffer* d_oriented_layer =
             bufferAlloc(max_oriented_feat * sizeof(unsigned));
-        cl::Buffer* d_oriented_response =
-            bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_size =
+        Buffer* d_oriented_response =
             bufferAlloc(max_oriented_feat * sizeof(float));
-        cl::Buffer* d_oriented_ori =
+        Buffer* d_oriented_size =
             bufferAlloc(max_oriented_feat * sizeof(float));
+        Buffer* d_oriented_ori = bufferAlloc(max_oriented_feat * sizeof(float));
 
         const int blk_x_ori = divup(nodup_feat, SIFT_THREADS_Y);
         const NDRange local_ori(SIFT_THREADS_X, SIFT_THREADS_Y);
         const NDRange global_ori(SIFT_THREADS_X, blk_x_ori * SIFT_THREADS_Y);
 
-        auto coOp =
-            KernelFunctor<Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                          Buffer, Buffer, Buffer, Buffer, Buffer, Buffer,
-                          unsigned, Buffer, KParam, unsigned, unsigned, int,
-                          LocalSpaceArg>(*kernels[3]);
+        auto coOp = kernels[3];
 
         coOp(EnqueueArgs(getQueue(), global_ori, local_ori), *d_oriented_x,
              *d_oriented_y, *d_oriented_layer, *d_oriented_response,
@@ -708,7 +654,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
              *d_nodup_y, *d_nodup_layer, *d_nodup_response, *d_nodup_size,
              nodup_feat, *gauss_pyr[o].data, gauss_pyr[o].info,
              max_oriented_feat, o, (int)double_input,
-             cl::Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
+             Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
         CL_DEBUG_FINISH(getQueue());
 
         bufferFree(d_nodup_x);
@@ -731,8 +677,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
             continue;
         }
 
-        cl::Buffer* d_desc =
-            bufferAlloc(oriented_feat * desc_len * sizeof(float));
+        Buffer* d_desc = bufferAlloc(oriented_feat * desc_len * sizeof(float));
 
         float scale = 1.f / (1 << o);
         if (double_input) scale *= 2.f;
@@ -744,31 +689,23 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         const unsigned histsz = 8;
 
         if (compute_GLOH) {
-            auto cgOp =
-                KernelFunctor<Buffer, unsigned, unsigned, Buffer, Buffer,
-                              Buffer, Buffer, Buffer, Buffer, unsigned, Buffer,
-                              KParam, int, unsigned, unsigned, unsigned, float,
-                              int, LocalSpaceArg>(*kernels[6]);
+            auto cgOp = kernels[6];
 
             cgOp(EnqueueArgs(getQueue(), global_desc, local_desc), *d_desc,
                  desc_len, histsz, *d_oriented_x, *d_oriented_y,
                  *d_oriented_layer, *d_oriented_response, *d_oriented_size,
                  *d_oriented_ori, oriented_feat, *gauss_pyr[o].data,
                  gauss_pyr[o].info, d, rb, ab, hb, scale, n_layers,
-                 cl::Local(desc_len * (histsz + 1) * sizeof(float)));
+                 Local(desc_len * (histsz + 1) * sizeof(float)));
         } else {
-            auto cdOp =
-                KernelFunctor<Buffer, unsigned, unsigned, Buffer, Buffer,
-                              Buffer, Buffer, Buffer, Buffer, unsigned, Buffer,
-                              KParam, int, int, float, int, LocalSpaceArg>(
-                    *kernels[5]);
+            auto cdOp = kernels[5];
 
             cdOp(EnqueueArgs(getQueue(), global_desc, local_desc), *d_desc,
                  desc_len, histsz, *d_oriented_x, *d_oriented_y,
                  *d_oriented_layer, *d_oriented_response, *d_oriented_size,
                  *d_oriented_ori, oriented_feat, *gauss_pyr[o].data,
                  gauss_pyr[o].info, d, n, scale, n_layers,
-                 cl::Local(desc_len * (histsz + 1) * sizeof(float)));
+                 Local(desc_len * (histsz + 1) * sizeof(float)));
         }
         CL_DEBUG_FINISH(getQueue());
 
diff --git a/src/backend/opencl/kernel/sobel.cl b/src/backend/opencl/kernel/sobel.cl
index 9ef11d9e2f..04bc2565f0 100644
--- a/src/backend/opencl/kernel/sobel.cl
+++ b/src/backend/opencl/kernel/sobel.cl
@@ -13,8 +13,8 @@ int reflect101(int index, int endIndex) {
 
 Ti load2LocalMem(global const Ti* in, int d0, int d1, int gx, int gy,
                  int inStride1, int inStride0) {
-    int idx = reflect101(gx, d0-1) * inStride0 +
-              reflect101(gy, d1-1) * inStride1;
+    int idx =
+        reflect101(gx, d0 - 1) * inStride0 + reflect101(gy, d1 - 1) * inStride1;
     return in[idx];
 }
 
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 6f4186c56b..74683e265c 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -8,71 +8,53 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/sobel.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int THREADS_X = 16;
-static const int THREADS_Y = 16;
-
 template<typename Ti, typename To, unsigned ker_size>
 void sobel(Param dx, Param dy, const Param in) {
-    std::string refName =
-        std::string("sobel3x3_") + std::string(dtype_traits<Ti>::getName()) +
-        std::string(dtype_traits<To>::getName()) + std::to_string(ker_size);
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    static const std::string src(sobel_cl, sobel_cl_len);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D Ti=" << dtype_traits<Ti>::getName()
-                << " -D To=" << dtype_traits<To>::getName()
-                << " -D KER_SIZE=" << ker_size;
-        options << getTypeBuildDefinition<Ti>();
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<Ti>(),
+        TemplateTypename<To>(),
+        TemplateArg(ker_size),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(KER_SIZE, ker_size),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-        const char* ker_strs[] = {sobel_cl};
-        const int ker_lens[]   = {sobel_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "sobel3x3");
+    auto sobel = common::findKernel("sobel3x3", {src}, targs, compileOpts);
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(in.info.dims[0], THREADS_X);
     int blk_y = divup(in.info.dims[1], THREADS_Y);
 
-    NDRange global(blk_x * in.info.dims[2] * THREADS_X,
-                   blk_y * in.info.dims[3] * THREADS_Y);
-
-    auto sobelOp = KernelFunctor<Buffer, KParam, Buffer, KParam, Buffer, KParam,
-                                 cl::LocalSpaceArg, int, int>(*entry.ker);
-
+    cl::NDRange global(blk_x * in.info.dims[2] * THREADS_X,
+                       blk_y * in.info.dims[3] * THREADS_Y);
     size_t loc_size =
         (THREADS_X + ker_size - 1) * (THREADS_Y + ker_size - 1) * sizeof(Ti);
 
-    sobelOp(EnqueueArgs(getQueue(), global, local), *dx.data, dx.info, *dy.data,
-            dy.info, *in.data, in.info, cl::Local(loc_size), blk_x, blk_y);
-
+    sobel(cl::EnqueueArgs(getQueue(), global, local), *dx.data, dx.info,
+          *dy.data, dy.info, *in.data, in.info, cl::Local(loc_size), blk_x,
+          blk_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 8fed30aa41..6250ef454a 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -8,12 +8,12 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_opencl.hpp>
 #include <iota.hpp>
 #include <kernel/sort_helper.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 
 #pragma GCC diagnostic push
@@ -27,13 +27,6 @@
 
 namespace compute = boost::compute;
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-
 namespace opencl {
 namespace kernel {
 template<typename T>
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 24adb18f61..2c7f9b9822 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Array.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
@@ -18,10 +19,7 @@
 #include <kernel/sort_helper.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <mutex>
-#include <string>
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
@@ -39,14 +37,7 @@
 
 namespace compute = boost::compute;
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
 using common::half;
-using std::string;
 
 template<typename Tk, typename Tv, bool isAscending>
 inline boost::compute::function<bool(const std::pair<Tk, Tv>,
diff --git a/src/backend/opencl/kernel/sp_sp_arith_csr.cl b/src/backend/opencl/kernel/sp_sp_arith_csr.cl
index df589ee0f4..e9b54a755c 100644
--- a/src/backend/opencl/kernel/sp_sp_arith_csr.cl
+++ b/src/backend/opencl/kernel/sp_sp_arith_csr.cl
@@ -8,7 +8,7 @@
  ********************************************************/
 
 // TODO_PERF(pradeep) More performance improvements are possible
-__attribute__((reqd_work_group_size(256, 1, 1))) kernel void ssarith_csr_kernel(
+__attribute__((reqd_work_group_size(256, 1, 1))) kernel void ssarith_csr(
     global T *oVals, global int *oColIdx, global const int *oRowIdx, uint M,
     uint N, uint nnza, global const T *lVals, global const int *lRowIdx,
     global const int *lColIdx, uint nnzb, global const T *rVals,
@@ -32,8 +32,8 @@ __attribute__((reqd_work_group_size(256, 1, 1))) kernel void ssarith_csr_kernel(
         uint lci = lColIdx[l];
         uint rci = rColIdx[r];
 
-        T lhs = (lci <= rci ? lVals[l] : IDENTITY_VALUE);
-        T rhs = (lci >= rci ? rVals[r] : IDENTITY_VALUE);
+        T lhs = (lci <= rci ? lVals[l] : (T)(IDENTITY_VALUE));
+        T rhs = (lci >= rci ? rVals[r] : (T)(IDENTITY_VALUE));
 
         ovPtr[nnz] = OP(lhs, rhs);
         ocPtr[nnz] = (lci <= rci) ? lci : rci;
@@ -43,13 +43,13 @@ __attribute__((reqd_work_group_size(256, 1, 1))) kernel void ssarith_csr_kernel(
         nnz++;
     }
     while (l < lEnd) {
-        ovPtr[nnz] = OP(lVals[l], IDENTITY_VALUE);
+        ovPtr[nnz] = OP(lVals[l], (T)(IDENTITY_VALUE));
         ocPtr[nnz] = lColIdx[l];
         l++;
         nnz++;
     }
     while (r < rEnd) {
-        ovPtr[nnz] = OP(IDENTITY_VALUE, rVals[r]);
+        ovPtr[nnz] = OP((T)(IDENTITY_VALUE), rVals[r]);
         ocPtr[nnz] = rColIdx[r];
         r++;
         nnz++;
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 3854768027..d3a42564fe 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -8,124 +8,108 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/reduce.hpp>
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <kernel_headers/coo2dense.hpp>
 #include <kernel_headers/csr2coo.hpp>
 #include <kernel_headers/csr2dense.hpp>
 #include <kernel_headers/dense2csr.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <mutex>
+
 #include <string>
-#include "config.hpp"
-#include "reduce.hpp"
-#include "scan_dim.hpp"
-#include "scan_first.hpp"
-#include "sort_by_key.hpp"
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 template<typename T>
 void coo2dense(Param out, const Param values, const Param rowIdx,
                const Param colIdx) {
-    std::string ref_name = std::string("coo2dense_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(REPEAT);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D reps=" << REPEAT;
-        options << getTypeBuildDefinition<T>();
+    static const std::string src(coo2dense_cl, coo2dense_cl_len);
 
-        Program prog;
-        buildProgram(prog, coo2dense_cl, coo2dense_cl_len, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "coo2dense_kernel");
-
-        addKernelToCache(device, ref_name, entry);
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(REPEAT),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(resp, REPEAT),
     };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto coo2denseOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const Buffer, const KParam, const Buffer, const KParam>(
-            *entry.ker);
+    auto coo2dense =
+        common::findKernel("coo2Dense", {src}, tmpltArgs, compileOpts);
 
-    NDRange local(THREADS_PER_GROUP, 1, 1);
+    cl::NDRange local(THREADS_PER_GROUP, 1, 1);
 
-    NDRange global(
+    cl::NDRange global(
         divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
 
-    coo2denseOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-                *values.data, values.info, *rowIdx.data, rowIdx.info,
-                *colIdx.data, colIdx.info);
-
+    coo2dense(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *values.data, values.info, *rowIdx.data, rowIdx.info,
+              *colIdx.data, colIdx.info);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void csr2dense(Param output, const Param values, const Param rowIdx,
                const Param colIdx) {
-    const int MAX_GROUPS = 4096;
-    int M                = rowIdx.info.dims[0] - 1;
+    constexpr int MAX_GROUPS = 4096;
     // FIXME: This needs to be based non nonzeros per row
-    int threads = 64;
-
-    std::string ref_name = std::string("csr2dense_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(threads);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
+    constexpr int threads = 64;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D THREADS=" << threads;
-        options << getTypeBuildDefinition<T>();
+    static const std::string src(csr2dense_cl, csr2dense_cl_len);
 
-        const char *ker_strs[] = {csr2dense_cl};
-        const int ker_lens[]   = {csr2dense_cl_len};
+    const int M = rowIdx.info.dims[0] - 1;
 
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "csr2dense");
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(threads),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(THREADS, threads),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, ref_name, entry);
-    }
+    auto csr2dense =
+        common::findKernel("csr2Dense", {src}, tmpltArgs, compileOpts);
 
-    NDRange local(threads, 1);
+    cl::NDRange local(threads, 1);
     int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
-    NDRange global(local[0] * groups_x, 1);
-    auto csr2dense_kernel = *entry.ker;
-    auto csr2dense_func =
-        KernelFunctor<Buffer, Buffer, Buffer, Buffer, int>(csr2dense_kernel);
-
-    csr2dense_func(EnqueueArgs(getQueue(), global, local), *output.data,
-                   *values.data, *rowIdx.data, *colIdx.data, M);
+    cl::NDRange global(local[0] * groups_x, 1);
 
+    csr2dense(cl::EnqueueArgs(getQueue(), global, local), *output.data,
+              *values.data, *rowIdx.data, *colIdx.data, M);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    static const std::string src(dense2csr_cl, dense2csr_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(IS_CPLX, (IsComplex ? 1 : 0)),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto dense2Csr =
+        common::findKernel("dense2Csr", {src}, tmpltArgs, compileOpts);
+
     int num_rows = dense.info.dims[0];
     int num_cols = dense.info.dims[1];
 
@@ -134,9 +118,9 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     // rd1 contains output of nonzero count along dim 1 along dense
     Array<int> rd1 = createEmptyArray<int>(num_rows);
 
-    scan_dim<T, int, af_notzero_t, true>(sd1, dense, 1);
-    reduce_dim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
-    scan_first<int, int, af_add_t, false>(rowIdx, rd1);
+    scanDim<T, int, af_notzero_t>(sd1, dense, 1, true);
+    reduceDim<T, int, af_notzero_t>(rd1, dense, 0, 0, 1);
+    scanFirst<int, int, af_add_t>(rowIdx, rd1, false);
 
     int nnz = values.info.dims[0];
     getQueue().enqueueWriteBuffer(
@@ -144,123 +128,71 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
         rowIdx.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
         sizeof(int), (void *)&nnz);
 
-    std::string ref_name =
-        std::string("dense2csr_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-        if (std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-
-        const char *ker_strs[] = {dense2csr_cl};
-        const int ker_lens[]   = {dense2csr_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "dense2csr_split_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
-    NDRange local(THREADS_X, THREADS_Y);
+    cl::NDRange local(THREADS_X, THREADS_Y);
     int groups_x = divup(dense.info.dims[0], local[0]);
     int groups_y = divup(dense.info.dims[1], local[1]);
-    NDRange global(groups_x * local[0], groups_y * local[1]);
-    auto dense2csr_split =
-        KernelFunctor<Buffer, Buffer, Buffer, KParam, Buffer, KParam, Buffer>(
-            *entry.ker);
+    cl::NDRange global(groups_x * local[0], groups_y * local[1]);
 
-    dense2csr_split(EnqueueArgs(getQueue(), global, local), *values.data,
-                    *colIdx.data, *dense.data, dense.info, *sd1.get(), sd1,
-                    *rowIdx.data);
+    const Param sdParam = sd1;
 
+    dense2Csr(cl::EnqueueArgs(getQueue(), global, local), *values.data,
+              *colIdx.data, *dense.data, dense.info, *sdParam.data,
+              sdParam.info, *rowIdx.data);
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void swapIndex(Param ovalues, Param oindex, const Param ivalues,
                const cl::Buffer *iindex, const Param swapIdx) {
-    std::string ref_name = std::string("swapIndex_kernel_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    static const std::string src(csr2coo_cl, csr2coo_cl_len);
 
-        Program prog;
-        buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "swapIndex_kernel");
-
-        addKernelToCache(device, ref_name, entry);
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
     };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapIndexOp = KernelFunctor<Buffer, Buffer, const Buffer, const Buffer,
-                                     const Buffer, const int>(*entry.ker);
-
-    NDRange global(ovalues.info.dims[0], 1, 1);
+    auto swapIndex =
+        common::findKernel("swapIndex", {src}, tmpltArgs, compileOpts);
 
-    swapIndexOp(EnqueueArgs(getQueue(), global), *ovalues.data, *oindex.data,
-                *ivalues.data, *iindex, *swapIdx.data, ovalues.info.dims[0]);
+    cl::NDRange global(ovalues.info.dims[0], 1, 1);
 
+    swapIndex(cl::EnqueueArgs(getQueue(), global), *ovalues.data, *oindex.data,
+              *ivalues.data, *iindex, *swapIdx.data,
+              static_cast<int>(ovalues.info.dims[0]));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
              const Param irowIdx, const Param icolIdx, Param index) {
+    static const std::string src(csr2coo_cl, csr2coo_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto csr2coo = common::findKernel("csr2Coo", {src}, tmpltArgs, compileOpts);
+
     const int MAX_GROUPS = 4096;
     int M                = irowIdx.info.dims[0] - 1;
     // FIXME: This needs to be based non nonzeros per row
     int threads = 64;
 
-    std::string ref_name =
-        std::string("csr2coo_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {csr2coo_cl};
-        const int ker_lens[]   = {csr2coo_cl_len};
-
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "csr2coo");
-
-        addKernelToCache(device, ref_name, entry);
-    }
-
     cl::Buffer *scratch = bufferAlloc(orowIdx.info.dims[0] * sizeof(int));
 
-    NDRange local(threads, 1);
+    cl::NDRange local(threads, 1);
     int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
-    NDRange global(local[0] * groups_x, 1);
-    auto csr2coo_kernel = *entry.ker;
-    auto csr2coo_func =
-        KernelFunctor<Buffer, Buffer, const Buffer, const Buffer, int>(
-            csr2coo_kernel);
+    cl::NDRange global(local[0] * groups_x, 1);
 
-    csr2coo_func(EnqueueArgs(getQueue(), global, local), *scratch,
-                 *ocolIdx.data, *irowIdx.data, *icolIdx.data, M);
+    csr2coo(cl::EnqueueArgs(getQueue(), global, local), *scratch, *ocolIdx.data,
+            *irowIdx.data, *icolIdx.data, M);
 
     // Now we need to sort this into column major
     kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
@@ -277,6 +209,19 @@ template<typename T>
 void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
              const Param irowIdx, const Param icolIdx, Param index,
              Param rowCopy, const int M) {
+    static const std::string src(csr2coo_cl, csr2coo_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto csrReduce =
+        common::findKernel("csrReduce", {src}, tmpltArgs, compileOpts);
+
     // Now we need to sort this into column major
     kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
 
@@ -285,33 +230,10 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
 
     CL_DEBUG_FINISH(getQueue());
 
-    std::string ref_name = std::string("csrReduce_kernel_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        Program prog;
-        buildProgram(prog, csr2coo_cl, csr2coo_cl_len, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "csrReduce_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    };
-
-    auto csrReduceOp =
-        KernelFunctor<Buffer, const Buffer, const int, const int>(*entry.ker);
-
-    NDRange global(irowIdx.info.dims[0], 1, 1);
-
-    csrReduceOp(EnqueueArgs(getQueue(), global), *orowIdx.data, *rowCopy.data,
-                M, ovalues.info.dims[0]);
+    cl::NDRange global(irowIdx.info.dims[0], 1, 1);
 
+    csrReduce(cl::EnqueueArgs(getQueue(), global), *orowIdx.data, *rowCopy.data,
+              M, static_cast<int>(ovalues.info.dims[0]));
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index a6e64c0368..90a0b33303 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -10,9 +10,9 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/sp_sp_arith_csr.hpp>
 #include <kernel_headers/sparse_arith_common.hpp>
@@ -20,25 +20,20 @@
 #include <kernel_headers/sparse_arith_csr.hpp>
 #include <kernel_headers/ssarith_calc_out_nnz.hpp>
 #include <math.hpp>
-#include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <types.hpp>
 
-#include <map>
-#include <mutex>
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const unsigned TX      = 32;
-static const unsigned TY      = 8;
-static const unsigned THREADS = TX * TY;
+
+constexpr unsigned TX      = 32;
+constexpr unsigned TY      = 8;
+constexpr unsigned THREADS = TX * TY;
 
 template<af_op_t op>
-std::string getOpString() {
+constexpr std::string getOpString() {
     switch (op) {
         case af_add_t: return "ADD";
         case af_sub_t: return "SUB";
@@ -49,205 +44,95 @@ std::string getOpString() {
     return "";
 }
 
+template<typename T, af_op_t op>
+auto fetchKernel(const std::string key, const std::string &additionalSrc,
+                 const std::vector<std::string> additionalOptions = {}) {
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    static const std::string src(sparse_arith_common_cl,
+                                 sparse_arith_common_cl_len);
+
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(op),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(OP, getOpString<op>()),
+        DefineKeyValue(IS_CPLX, (IsComplex ? 1 : 0)),
+    };
+    options.emplace_back(getTypeBuildDefinition<T>());
+    options.insert(std::end(options), std::begin(additionalOptions),
+                   std::end(additionalOptions));
+    return common::findKernel(key, {src, additionalSrc}, tmpltArgs, options);
+}
+
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param out, const Param values, const Param rowIdx,
                       const Param colIdx, const Param rhs, const bool reverse) {
-    std::string ref_name = std::string("sparseArithOpCSR_") +
-                           getOpString<op>() + std::string("_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D OP=" << getOpString<op>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_csr_cl};
-        const int ker_lens[]   = {sparse_arith_common_cl_len,
-                                sparse_arith_csr_cl_len};
-
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    static const std::string src(sparse_arith_csr_cl, sparse_arith_csr_cl_len);
 
-    auto sparseArithCSROp =
-        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
-                          const cl::Buffer, const cl::Buffer, const int,
-                          const cl::Buffer, const KParam, const int>(
-            *entry.ker);
+    auto sparseArithCSR = fetchKernel<T, op>("sparseArithCSR", src);
 
     cl::NDRange local(TX, TY, 1);
     cl::NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
 
-    sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local), *out.data,
-                     out.info, *values.data, *rowIdx.data, *colIdx.data,
-                     values.info.dims[0], *rhs.data, rhs.info, reverse);
-
+    sparseArithCSR(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                   out.info, *values.data, *rowIdx.data, *colIdx.data,
+                   static_cast<int>(values.info.dims[0]), *rhs.data, rhs.info,
+                   static_cast<int>(reverse));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param out, const Param values, const Param rowIdx,
                       const Param colIdx, const Param rhs, const bool reverse) {
-    std::string ref_name = std::string("sparseArithOpCOO_") +
-                           getOpString<op>() + std::string("_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D OP=" << getOpString<op>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_coo_cl};
-        const int ker_lens[]   = {sparse_arith_common_cl_len,
-                                sparse_arith_coo_cl_len};
-
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    static const std::string src(sparse_arith_coo_cl, sparse_arith_coo_cl_len);
 
-    auto sparseArithCOOOp =
-        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
-                          const cl::Buffer, const cl::Buffer, const int,
-                          const cl::Buffer, const KParam, const int>(
-            *entry.ker);
+    auto sparseArithCOO = fetchKernel<T, op>("sparseArithCOO", src);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
 
-    sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local), *out.data,
-                     out.info, *values.data, *rowIdx.data, *colIdx.data,
-                     values.info.dims[0], *rhs.data, rhs.info, reverse);
-
+    sparseArithCOO(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                   out.info, *values.data, *rowIdx.data, *colIdx.data,
+                   static_cast<int>(values.info.dims[0]), *rhs.data, rhs.info,
+                   static_cast<int>(reverse));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx, const Param rhs,
                       const bool reverse) {
-    std::string ref_name = std::string("sparseArithOpSCSR_") +
-                           getOpString<op>() + std::string("_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D OP=" << getOpString<op>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_csr_cl};
-        const int ker_lens[]   = {sparse_arith_common_cl_len,
-                                sparse_arith_csr_cl_len};
-
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_csr_kernel_S");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    static const std::string src(sparse_arith_csr_cl, sparse_arith_csr_cl_len);
 
-    auto sparseArithCSROp =
-        cl::KernelFunctor<const cl::Buffer, const cl::Buffer, const cl::Buffer,
-                          const int, const cl::Buffer, const KParam, const int>(
-            *entry.ker);
+    auto sparseArithCSR = fetchKernel<T, op>("sparseArithCSR2", src);
 
     cl::NDRange local(TX, TY, 1);
     cl::NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
 
-    sparseArithCSROp(cl::EnqueueArgs(getQueue(), global, local), *values.data,
-                     *rowIdx.data, *colIdx.data, values.info.dims[0], *rhs.data,
-                     rhs.info, reverse);
-
+    sparseArithCSR(cl::EnqueueArgs(getQueue(), global, local), *values.data,
+                   *rowIdx.data, *colIdx.data,
+                   static_cast<int>(values.info.dims[0]), *rhs.data, rhs.info,
+                   static_cast<int>(reverse));
     CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx, const Param rhs,
                       const bool reverse) {
-    std::string ref_name = std::string("sparseArithOpSCOO_") +
-                           getOpString<op>() + std::string("_") +
-                           std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << " -D OP=" << getOpString<op>();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D IS_CPLX=1";
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {sparse_arith_common_cl, sparse_arith_coo_cl};
-        const int ker_lens[]   = {sparse_arith_common_cl_len,
-                                sparse_arith_coo_cl_len};
-
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "sparse_arith_coo_kernel_S");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    static const std::string src(sparse_arith_coo_cl, sparse_arith_coo_cl_len);
 
-    auto sparseArithCOOOp =
-        cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, const int,
-                          const cl::Buffer, const KParam, const int>(
-            *entry.ker);
+    auto sparseArithCOO = fetchKernel<T, op>("sparseArithCOO2", src);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
 
-    sparseArithCOOOp(cl::EnqueueArgs(getQueue(), global, local), *values.data,
-                     *rowIdx.data, *colIdx.data, values.info.dims[0], *rhs.data,
-                     rhs.info, reverse);
-
+    sparseArithCOO(cl::EnqueueArgs(getQueue(), global, local), *values.data,
+                   *rowIdx.data, *colIdx.data,
+                   static_cast<int>(values.info.dims[0]), *rhs.data, rhs.info,
+                   static_cast<int>(reverse));
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -258,25 +143,15 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
     UNUSED(N);
     UNUSED(nnzA);
     UNUSED(nnzB);
-    std::string refName = std::string("csr_calc_output_NNZ");
-    int device          = getActiveDeviceId();
-    kc_entry_t entry    = kernelCache(device, refName);
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        const char *kerStrs[] = {ssarith_calc_out_nnz_cl};
-        const int kerLens[]   = {ssarith_calc_out_nnz_cl_len};
+    static const std::string src(ssarith_calc_out_nnz_cl,
+                                 ssarith_calc_out_nnz_cl_len);
 
-        cl::Program prog;
-        buildProgram(prog, 1, kerStrs, kerLens, std::string(""));
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "csr_calc_out_nnz");
+    std::vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<uint>(),
+    };
 
-        addKernelToCache(device, refName, entry);
-    }
-    auto calcNNZop =
-        cl::KernelFunctor<cl::Buffer, cl::Buffer, unsigned, const cl::Buffer,
-                          const cl::Buffer, const cl::Buffer, const cl::Buffer,
-                          cl::LocalSpaceArg>(*entry.ker);
+    auto calcNNZ = common::findKernel("csr_calc_out_nnz", {src}, tmpltArgs, {});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
@@ -285,11 +160,10 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
     cl::Buffer *out = bufferAlloc(sizeof(unsigned));
     getQueue().enqueueWriteBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
 
-    calcNNZop(cl::EnqueueArgs(getQueue(), global, local), *out, *outRowIdx.data,
-              M, *lrowIdx.data, *lcolIdx.data, *rrowIdx.data, *rcolIdx.data,
-              cl::Local(local[0] * sizeof(unsigned int)));
+    calcNNZ(cl::EnqueueArgs(getQueue(), global, local), *out, *outRowIdx.data,
+            M, *lrowIdx.data, *lcolIdx.data, *rrowIdx.data, *rcolIdx.data,
+            cl::Local(local[0] * sizeof(unsigned int)));
     getQueue().enqueueReadBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
-
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -298,39 +172,14 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
                 const uint N, unsigned nnzA, const Param lVals,
                 const Param lRowIdx, const Param lColIdx, unsigned nnzB,
                 const Param rVals, const Param rRowIdx, const Param rColIdx) {
-    std::string refName = std::string("ss_arith_csr_") + getOpString<op>() +
-                          "_" + std::string(dtype_traits<T>::getName());
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        const T iden_val =
-            (op == af_mul_t || op == af_div_t ? scalar<T>(1) : scalar<T>(0));
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D OP=" << getOpString<op>() << " -D IDENTITY_VALUE=(T)("
-                << af::scalar_to_option(iden_val) << ")";
-
-        options << " -D IS_CPLX=" << common::is_complex<T>::value;
-        options << getTypeBuildDefinition<T>();
-
-        const char *kerStrs[] = {sparse_arith_common_cl, sp_sp_arith_csr_cl};
-        const int kerLens[]   = {sparse_arith_common_cl_len,
-                               sp_sp_arith_csr_cl_len};
-
-        cl::Program prog;
-        buildProgram(prog, 2, kerStrs, kerLens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "ssarith_csr_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
-    auto arithOp =
-        cl::KernelFunctor<cl::Buffer, cl::Buffer, cl::Buffer, unsigned,
-                          unsigned, unsigned, const cl::Buffer,
-                          const cl::Buffer, const cl::Buffer, unsigned,
-                          const cl::Buffer, const cl::Buffer, const cl::Buffer>(
-            *entry.ker);
+    static const std::string src(sp_sp_arith_csr_cl, sp_sp_arith_csr_cl_len);
+
+    const T iden_val =
+        (op == af_mul_t || op == af_div_t ? scalar<T>(1) : scalar<T>(0));
+
+    auto arithOp = fetchKernel<T, op>(
+        "ssarith_csr", src,
+        {DefineKeyValue(IDENTITY_VALUE, af::scalar_to_option(iden_val))});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
@@ -339,7 +188,6 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
             *oColIdx.data, *oRowIdx.data, M, N, nnzA, *lVals.data,
             *lRowIdx.data, *lColIdx.data, nnzB, *rVals.data, *rRowIdx.data,
             *rColIdx.data);
-
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/sparse_arith_coo.cl b/src/backend/opencl/kernel/sparse_arith_coo.cl
index 7d6c084a1d..07186f7a68 100644
--- a/src/backend/opencl/kernel/sparse_arith_coo.cl
+++ b/src/backend/opencl/kernel/sparse_arith_coo.cl
@@ -7,12 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void sparse_arith_coo_kernel(__global T *oPtr, const KParam out,
-                                      __global const T *values,
-                                      __global const int *rowIdx,
-                                      __global const int *colIdx, const int nNZ,
-                                      __global const T *rPtr, const KParam rhs,
-                                      const int reverse) {
+kernel void sparseArithCOO(global T *oPtr, const KParam out,
+                           global const T *values, global const int *rowIdx,
+                           global const int *colIdx, const int nNZ,
+                           global const T *rPtr, const KParam rhs,
+                           const int reverse) {
     const int idx = get_global_id(0);
 
     if (idx >= nNZ) return;
@@ -33,11 +32,10 @@ __kernel void sparse_arith_coo_kernel(__global T *oPtr, const KParam out,
         oPtr[offset] = OP(val, rval);
 }
 
-__kernel void sparse_arith_coo_kernel_S(__global T *values,
-                                        __global int *rowIdx,
-                                        __global int *colIdx, const int nNZ,
-                                        __global const T *rPtr,
-                                        const KParam rhs, const int reverse) {
+kernel void sparseArithCOO2(global T *values, global int *rowIdx,
+                            global int *colIdx, const int nNZ,
+                            global const T *rPtr, const KParam rhs,
+                            const int reverse) {
     const int idx = get_global_id(0);
 
     if (idx >= nNZ) return;
diff --git a/src/backend/opencl/kernel/sparse_arith_csr.cl b/src/backend/opencl/kernel/sparse_arith_csr.cl
index 80255cc462..165db256a4 100644
--- a/src/backend/opencl/kernel/sparse_arith_csr.cl
+++ b/src/backend/opencl/kernel/sparse_arith_csr.cl
@@ -7,12 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void sparse_arith_csr_kernel(__global T *oPtr, const KParam out,
-                                      __global const T *values,
-                                      __global const int *rowIdx,
-                                      __global const int *colIdx, const int nNZ,
-                                      __global const T *rPtr, const KParam rhs,
-                                      const int reverse) {
+kernel void sparseArithCSR(global T *oPtr, const KParam out,
+                           global const T *values, global const int *rowIdx,
+                           global const int *colIdx, const int nNZ,
+                           global const T *rPtr, const KParam rhs,
+                           const int reverse) {
     const int row = get_group_id(0) * get_local_size(1) + get_local_id(1);
 
     if (row >= out.dims[0]) return;
@@ -39,11 +38,10 @@ __kernel void sparse_arith_csr_kernel(__global T *oPtr, const KParam out,
     }
 }
 
-__kernel void sparse_arith_csr_kernel_S(__global T *values,
-                                        __global int *rowIdx,
-                                        __global int *colIdx, const int nNZ,
-                                        __global const T *rPtr,
-                                        const KParam rhs, const int reverse) {
+kernel void sparseArithCSR2(global T *values, global int *rowIdx,
+                            global int *colIdx, const int nNZ,
+                            global const T *rPtr, const KParam rhs,
+                            const int reverse) {
     const int row = get_group_id(0) * get_local_size(1) + get_local_id(1);
 
     if (row >= rhs.dims[0]) return;
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 4c2ddb44c8..35410b5564 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -9,73 +9,59 @@
 
 #pragma once
 
-#include <cache.hpp>
+#include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
-#include <err_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/susan.hpp>
-#include <memory.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
 #include <af/defines.h>
-#include "config.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::LocalSpaceArg;
-using cl::NDRange;
-using cl::Program;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const unsigned THREADS_PER_BLOCK = 256;
-static const unsigned SUSAN_THREADS_X   = 16;
-static const unsigned SUSAN_THREADS_Y   = 16;
+constexpr unsigned SUSAN_THREADS_X = 16;
+constexpr unsigned SUSAN_THREADS_Y = 16;
 
-template<typename T, unsigned radius>
+static inline std::string susanSrc() {
+    static const std::string src(susan_cl, susan_cl_len);
+    return src;
+}
+
+template<typename T>
 void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
            const unsigned idim0, const unsigned idim1, const float t,
-           const float g, const unsigned edge) {
-    std::string refName = std::string("susan_responses_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(radius);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        const size_t LOCAL_MEM_SIZE =
-            (SUSAN_THREADS_X + 2 * radius) * (SUSAN_THREADS_Y + 2 * radius);
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D LOCAL_MEM_SIZE=" << LOCAL_MEM_SIZE
-                << " -D BLOCK_X=" << SUSAN_THREADS_X
-                << " -D BLOCK_Y=" << SUSAN_THREADS_Y << " -D RADIUS=" << radius
-                << " -D RESPONSE";
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {susan_cl};
-        const int ker_lens[]   = {susan_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "susan_responses");
-
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto susanOp = KernelFunctor<Buffer, Buffer, unsigned, unsigned, unsigned,
-                                 float, float, unsigned>(*entry.ker);
-
-    NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-    NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
-                   divup(idim1 - 2 * edge, local[1]) * local[1]);
-
-    susanOp(EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0,
-            idim1, t, g, edge);
+           const float g, const unsigned edge, const unsigned radius) {
+    const size_t LOCAL_MEM_SIZE =
+        (SUSAN_THREADS_X + 2 * radius) * (SUSAN_THREADS_Y + 2 * radius);
+
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+        TemplateArg(radius),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineValue(LOCAL_MEM_SIZE),
+        DefineKeyValue(BLOCK_X, SUSAN_THREADS_X),
+        DefineKeyValue(BLOCK_Y, SUSAN_THREADS_Y),
+        DefineKeyValue(RADIUS, radius),
+        DefineKey(RESPONSE),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto susan =
+        common::findKernel("susan_responses", {susanSrc()}, targs, compileOpts);
+
+    cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
+    cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
+                       divup(idim1 - 2 * edge, local[1]) * local[1]);
+
+    susan(cl::EnqueueArgs(getQueue(), global, local), *out, *in, in_off, idim0,
+          idim1, t, g, edge);
+    CL_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
@@ -83,49 +69,33 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
                     const unsigned idim0, const unsigned idim1,
                     const cl::Buffer* resp_in, const unsigned edge,
                     const unsigned max_corners) {
-    unsigned corners_found = 0;
-
-    std::string refName =
-        std::string("non_maximal_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName() << " -D NONMAX";
-        options << getTypeBuildDefinition<T>();
-
-        const char* ker_strs[] = {susan_cl};
-        const int ker_lens[]   = {susan_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "non_maximal");
-
-        addKernelToCache(device, refName, entry);
-    }
-
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    std::vector<std::string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKey(NONMAX),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto nonMax =
+        common::findKernel("non_maximal", {susanSrc()}, targs, compileOpts);
+
+    unsigned corners_found      = 0;
     cl::Buffer* d_corners_found = bufferAlloc(sizeof(unsigned));
     getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0,
                                   sizeof(unsigned), &corners_found);
 
-    auto nonMaximalOp =
-        KernelFunctor<Buffer, Buffer, Buffer, Buffer, unsigned, unsigned,
-                      Buffer, unsigned, unsigned>(*entry.ker);
-
-    NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
-    NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
-                   divup(idim1 - 2 * edge, local[1]) * local[1]);
-
-    nonMaximalOp(EnqueueArgs(getQueue(), global, local), *x_out, *y_out,
-                 *resp_out, *d_corners_found, idim0, idim1, *resp_in, edge,
-                 max_corners);
+    cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
+    cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
+                       divup(idim1 - 2 * edge, local[1]) * local[1]);
 
+    nonMax(cl::EnqueueArgs(getQueue(), global, local), *x_out, *y_out,
+           *resp_out, *d_corners_found, idim0, idim1, *resp_in, edge,
+           max_corners);
     getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned),
                                  &corners_found);
     bufferFree(d_corners_found);
-
     return corners_found;
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/swapdblk.cl b/src/backend/opencl/kernel/swapdblk.cl
index f4be35a9b8..35c61c8889 100644
--- a/src/backend/opencl/kernel/swapdblk.cl
+++ b/src/backend/opencl/kernel/swapdblk.cl
@@ -49,8 +49,8 @@
  *
  **********************************************************************/
 
-__kernel void swapdblk(int nb, __global T *dA, unsigned long dA_offset,
-                       int ldda, int inca, __global T *dB,
+kernel void swapdblk(int nb, global T *dA, unsigned long dA_offset,
+                       int ldda, int inca, global T *dB,
                        unsigned long dB_offset, int lddb, int incb) {
     const int tx = get_local_id(0);
     const int bx = get_group_id(0);
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index b046575d39..857b49aa3b 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -10,23 +10,15 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/err_common.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/swapdblk.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
@@ -34,27 +26,24 @@ template<typename T>
 void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
               cl_mem dB, size_t dB_offset, int lddb, int incb,
               cl_command_queue queue) {
-    std::string refName =
-        std::string("swapdblk_") + std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    using cl::Buffer;
+    using cl::CommandQueue;
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
+    static const string src(swapdblk_cl, swapdblk_cl_len);
 
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        const char* ker_strs[] = {swapdblk_cl};
-        const int ker_lens[]   = {swapdblk_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "swapdblk");
-
-        addKernelToCache(device, refName, entry);
-    }
+    auto swapdblk = common::findKernel("swapdblk", {src}, targs, compileOpts);
 
     int nblocks = n / nb;
 
@@ -83,16 +72,13 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     NDRange local(nb);
     NDRange global(nblocks * nb);
 
-    cl::Buffer dAObj(dA, true);
-    cl::Buffer dBObj(dB, true);
-
-    auto swapdOp =
-        KernelFunctor<int, Buffer, unsigned long long, int, int, Buffer,
-                      unsigned long long, int, int>(*entry.ker);
+    Buffer dAObj(dA, true);
+    Buffer dBObj(dB, true);
 
-    cl::CommandQueue q(queue);
-    swapdOp(EnqueueArgs(q, global, local), nb, dAObj, dA_offset, ldda, inca,
-            dBObj, dB_offset, lddb, incb);
+    CommandQueue q(queue);
+    swapdblk(EnqueueArgs(q, global, local), nb, dAObj, dA_offset, ldda, inca,
+             dBObj, dB_offset, lddb, incb);
+    CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/tile.cl b/src/backend/opencl/kernel/tile.cl
index 3ecf2a1396..89323294db 100644
--- a/src/backend/opencl/kernel/tile.cl
+++ b/src/backend/opencl/kernel/tile.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void tile_kernel(__global T *out, __global const T *in,
-                          const KParam op, const KParam ip,
-                          const int blocksPerMatX, const int blocksPerMatY) {
+kernel void tile(global T *out, global const T *in, const KParam op,
+                 const KParam ip, const int blocksPerMatX,
+                 const int blocksPerMatY) {
     const int oz = get_group_id(0) / blocksPerMatX;
     const int ow = get_group_id(1) / blocksPerMatY;
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index 8b29941727..f931594ca4 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -10,56 +10,40 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/tile.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <string>
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const int TX    = 32;
-static const int TY    = 8;
-static const int TILEX = 512;
-static const int TILEY = 32;
-
 template<typename T>
 void tile(Param out, const Param in) {
-    std::string refName =
-        std::string("tile_kernel_") + std::string(dtype_traits<T>::getName());
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr int TX    = 32;
+    constexpr int TY    = 8;
+    constexpr int TILEX = 512;
+    constexpr int TILEY = 32;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
+    static const string src(tile_cl, tile_cl_len);
 
-        const char* ker_strs[] = {tile_cl};
-        const int ker_lens[]   = {tile_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "tile_kernel");
+    vector<TemplateArg> targs = {
+        TemplateTypename<T>(),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
-
-    auto tileOp = KernelFunctor<Buffer, const Buffer, const KParam,
-                                const KParam, const int, const int>(*entry.ker);
+    auto tile = common::findKernel("tile", {src}, targs, compileOpts);
 
     NDRange local(TX, TY, 1);
 
@@ -68,9 +52,8 @@ void tile(Param out, const Param in) {
     NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
                    local[1] * blocksPerMatY * out.info.dims[3], 1);
 
-    tileOp(EnqueueArgs(getQueue(), global, local), *out.data, *in.data,
-           out.info, in.info, blocksPerMatX, blocksPerMatY);
-
+    tile(EnqueueArgs(getQueue(), global, local), *out.data, *in.data, out.info,
+         in.info, blocksPerMatX, blocksPerMatY);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
index e592b58f41..d92e95a117 100644
--- a/src/backend/opencl/kernel/trace_edge.cl
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -12,9 +12,9 @@ __constant int WEAK   = 2;
 __constant int NOEDGE = 0;
 
 #if defined(INIT_EDGE_OUT)
-__kernel void initEdgeOutKernel(__global T* output, KParam oInfo,
-                                __global const T* strong, KParam sInfo,
-                                __global const T* weak, KParam wInfo,
+kernel void initEdgeOutKernel(global T* output, KParam oInfo,
+                                global const T* strong, KParam sInfo,
+                                global const T* weak, KParam wInfo,
                                 unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = get_group_id(0) / nBBS0;
@@ -28,16 +28,16 @@ __kernel void initEdgeOutKernel(__global T* output, KParam oInfo,
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    __global const T* wPtr =
+    global const T* wPtr =
         weak + (b2 * wInfo.strides[2] + b3 * wInfo.strides[3] + wInfo.offset) +
         wInfo.strides[1] + 1;
 
-    __global const T* sPtr =
+    global const T* sPtr =
         strong +
         (b2 * sInfo.strides[2] + b3 * sInfo.strides[3] + sInfo.offset) +
         sInfo.strides[1] + 1;
 
-    __global T* oPtr =
+    global T* oPtr =
         output +
         (b2 * oInfo.strides[2] + b3 * oInfo.strides[3] + oInfo.offset) +
         oInfo.strides[1] + 1;
@@ -54,14 +54,14 @@ __kernel void initEdgeOutKernel(__global T* output, KParam oInfo,
      (i) < (SHRD_MEM_WIDTH - 1))
 
 #if defined(EDGE_TRACER)
-__kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
+kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
                               unsigned nBBS1,
-                              __global volatile int* hasChanged) {
+                              global volatile int* hasChanged) {
     // shared memory with 1 pixel border
     // strong and weak images are binary(char) images thus,
     // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
-    __local int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
-    __local bool predicates[TOTAL_NUM_THREADS];
+    local int outMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
+    local bool predicates[TOTAL_NUM_THREADS];
 
     // local thread indices
     const int lx = get_local_id(0);
@@ -77,18 +77,19 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    __global T* oPtr = output +
-                       (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
+    global T* oPtr = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]);
 
     // pull image to local memory
 #pragma unroll
-    for (int b = ly, gy2 = gy-1; b < SHRD_MEM_HEIGHT;
+    for (int b = ly, gy2 = gy - 1; b < SHRD_MEM_HEIGHT;
          b += get_local_size(1), gy2 += get_local_size(1)) {
 #pragma unroll
-        for (int a = lx, gx2 = gx-1; a < SHRD_MEM_WIDTH;
+        for (int a = lx, gx2 = gx - 1; a < SHRD_MEM_WIDTH;
              a += get_local_size(0), gx2 += get_local_size(0)) {
-            if (gx2 >= 0 && gx2 < oInfo.dims[0] && gy2 >= 0 && gy2 < oInfo.dims[1])
-                outMem[b][a] = oPtr[gx2 * oInfo.strides[0] + gy2 * oInfo.strides[1]];
+            if (gx2 >= 0 && gx2 < oInfo.dims[0] && gy2 >= 0 &&
+                gy2 < oInfo.dims[1])
+                outMem[b][a] =
+                    oPtr[gx2 * oInfo.strides[0] + gy2 * oInfo.strides[1]];
             else
                 outMem[b][a] = NOEDGE;
         }
@@ -105,14 +106,14 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
     int mycounter = 0;
     while (continueIter) {
-        int nw ,no ,ne ,we ,ea ,sw ,so ,se;
+        int nw, no, ne, we, ea, sw, so, se;
 
-        if(outMem[j][i] == WEAK) {
+        if (outMem[j][i] == WEAK) {
             nw = outMem[j - 1][i - 1];
             no = outMem[j - 1][i];
             ne = outMem[j - 1][i + 1];
-            we = outMem[j    ][i - 1];
-            ea = outMem[j    ][i + 1];
+            we = outMem[j][i - 1];
+            ea = outMem[j][i + 1];
             sw = outMem[j + 1][i - 1];
             so = outMem[j + 1][i];
             se = outMem[j + 1][i + 1];
@@ -126,19 +127,19 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
         barrier(CLK_LOCAL_MEM_FENCE);
 
-
         predicates[tid] = false;
-        if(outMem[j][i] == STRONG) {
+        if (outMem[j][i] == STRONG) {
             nw = outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
-            no = outMem[j - 1][i    ] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+            no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
             ne = outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
-            we = outMem[j    ][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
-            ea = outMem[j    ][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+            we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+            ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
             sw = outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
-            so = outMem[j + 1][i    ] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+            so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
             se = outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
 
-            bool hasWeakNeighbour = nw || no || ne || ea || se || so || sw || we;
+            bool hasWeakNeighbour =
+                nw || no || ne || ea || se || so || sw || we;
 
             predicates[tid] = hasWeakNeighbour;
         }
@@ -146,7 +147,9 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 
         // Following Block is equivalent of __syncthreads_or in CUDA
         for (int nt = TOTAL_NUM_THREADS / 2; nt > 0; nt >>= 1) {
-            if (tid < nt) { predicates[tid] = predicates[tid] || predicates[tid + nt]; }
+            if (tid < nt) {
+                predicates[tid] = predicates[tid] || predicates[tid + nt];
+            }
             barrier(CLK_LOCAL_MEM_FENCE);
         }
 
@@ -191,7 +194,7 @@ __kernel void edgeTrackKernel(__global T* output, KParam oInfo, unsigned nBBS0,
 #endif
 
 #if defined(SUPPRESS_LEFT_OVER)
-__kernel void suppressLeftOverKernel(__global T* output, KParam oInfo,
+kernel void suppressLeftOverKernel(global T* output, KParam oInfo,
                                      unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = get_group_id(0) / nBBS0;
@@ -205,7 +208,7 @@ __kernel void suppressLeftOverKernel(__global T* output, KParam oInfo,
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    __global T* oPtr = output +
+    global T* oPtr = output +
                        (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
                        oInfo.strides[1] + 1;
 
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 2e4cc7a2a7..7651b35f29 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -11,7 +11,7 @@
 #define BILINEAR transform_b
 #define LOWER transform_l
 
-void calc_transf_inverse(float *txo, __global const float *txi) {
+void calc_transf_inverse(float *txo, global const float *txi) {
 #if PERSPECTIVE
     txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
     txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
@@ -49,13 +49,13 @@ void calc_transf_inverse(float *txo, __global const float *txi) {
 #endif
 }
 
-__kernel void transform_kernel(__global T *d_out, const KParam out,
-                               __global const T *d_in, const KParam in,
-                               __global const float *c_tmat, const KParam tf,
-                               const int nImg2, const int nImg3,
-                               const int nTfs2, const int nTfs3,
-                               const int batchImg2, const int blocksXPerImage,
-                               const int blocksYPerImage, const int method) {
+kernel void transformKernel(global T *d_out, const KParam out,
+                            global const T *d_in, const KParam in,
+                            global const float *c_tmat, const KParam tf,
+                            const int nImg2, const int nImg3, const int nTfs2,
+                            const int nTfs3, const int batchImg2,
+                            const int blocksXPerImage,
+                            const int blocksYPerImage, const int method) {
     // Image Ids
     const int imgId2 = get_group_id(0) / blocksXPerImage;
     const int imgId3 = get_group_id(1) / blocksYPerImage;
@@ -133,7 +133,7 @@ __kernel void transform_kernel(__global T *d_out, const KParam out,
     const int transf_len = 6;
     float tmat[6];
 #endif
-    __global const float *tmat_ptr = c_tmat + t_idx * transf_len;
+    global const float *tmat_ptr = c_tmat + t_idx * transf_len;
 
     // We expect a inverse transform matrix by default
     // If it is an forward transform, then we need its inverse
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 0b81e0b5f9..b1c0f3b8ea 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -8,29 +8,24 @@
  ********************************************************/
 
 #pragma once
-#include <kernel_headers/interp.hpp>
-#include <kernel_headers/transform.hpp>
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/interp.hpp>
+#include <kernel_headers/interp.hpp>
+#include <kernel_headers/transform.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include "config.hpp"
-#include "interp.hpp"
 
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int TX = 16;
-static const int TY = 16;
-// Used for batching images
-static const int TI = 4;
 
 template<typename T>
 using wtype_t = typename std::conditional<std::is_same<T, double>::value,
@@ -40,65 +35,60 @@ template<typename T>
 using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
                                           wtype_t<T>>::type;
 
-template<typename T, int order>
+template<typename T>
 void transform(Param out, const Param in, const Param tf, bool isInverse,
-               bool isPerspective, af_interp_type method) {
+               bool isPerspective, af_interp_type method, int order) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
     using BT = typename dtype_traits<T>::base_type;
 
-    std::string ref_name = std::string("transform_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(isInverse) +
-                           std::string("_") + std::to_string(isPerspective) +
-                           std::string("_") + std::to_string(order);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D INVERSE=" << (isInverse ? 1 : 0)
-                << " -D PERSPECTIVE=" << (isPerspective ? 1 : 0)
-                << " -D ZERO=" << toNumStr(scalar<T>(0));
-        options << " -D InterpInTy=" << dtype_traits<T>::getName();
-        options << " -D InterpValTy=" << dtype_traits<vtype_t<T>>::getName();
-        options << " -D InterpPosTy=" << dtype_traits<wtype_t<BT>>::getName();
-
-        if (static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-            static_cast<af_dtype>(dtype_traits<T>::af_type) == c64) {
-            options << " -D IS_CPLX=1";
-            options << " -D TB=" << dtype_traits<BT>::getName();
-        } else {
-            options << " -D IS_CPLX=0";
-        }
-        options << getTypeBuildDefinition<T>();
-
-        options << " -D INTERP_ORDER=" << order;
-        addInterpEnumOptions(options);
-
-        const char *ker_strs[] = {interp_cl, transform_cl};
-        const int ker_lens[]   = {interp_cl_len, transform_cl_len};
-        cl::Program prog;
-        buildProgram(prog, 2, ker_strs, ker_lens, options.str());
-        entry.prog = new cl::Program(prog);
-        entry.ker  = new cl::Kernel(*entry.prog, "transform_kernel");
-
-        addKernelToCache(device, ref_name, entry);
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+    // Used for batching images
+    constexpr int TI = 4;
+    constexpr bool isComplex =
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
+
+    static const std::string src1(interp_cl, interp_cl_len);
+    static const std::string src2(transform_cl, transform_cl_len);
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(isInverse),
+        TemplateArg(isPerspective),
+        TemplateArg(order),
+    };
+    ToNumStr<T> toNumStr;
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(INVERSE, (isInverse ? 1 : 0)),
+        DefineKeyValue(PERSPECTIVE, (isPerspective ? 1 : 0)),
+        DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
+        DefineKeyValue(InterpInTy, dtype_traits<T>::getName()),
+        DefineKeyValue(InterpValTy, dtype_traits<vtype_t<T>>::getName()),
+        DefineKeyValue(InterpPosTy, dtype_traits<wtype_t<BT>>::getName()),
+        DefineKeyValue(INTERP_ORDER, order),
+        DefineKeyValue(IS_CPLX, (isComplex ? 1 : 0)),
+    };
+    if (isComplex) {
+        compileOpts.emplace_back(
+            DefineKeyValue(TB, dtype_traits<BT>::getName()));
     }
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+    addInterpEnumOptions(compileOpts);
 
-    auto transformOp =
-        cl::KernelFunctor<cl::Buffer, const KParam, const cl::Buffer,
-                          const KParam, const cl::Buffer, const KParam,
-                          const int, const int, const int, const int, const int,
-                          const int, const int, const int>(*entry.ker);
+    auto transform = common::findKernel("transformKernel", {src1, src2},
+                                        tmpltArgs, compileOpts);
 
     const int nImg2 = in.info.dims[2];
     const int nImg3 = in.info.dims[3];
     const int nTfs2 = tf.info.dims[2];
     const int nTfs3 = tf.info.dims[3];
 
-    cl::NDRange local(TX, TY, 1);
+    NDRange local(TX, TY, 1);
 
     int batchImg2 = 1;
     if (nImg2 != nTfs2) batchImg2 = min(nImg2, TI);
@@ -110,13 +100,11 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
     int global_y = local[1] * blocksYPerImage * nImg3;
     int global_z = local[2] * max((nTfs2 / nImg2), 1) * max((nTfs3 / nImg3), 1);
 
-    cl::NDRange global(global_x, global_y, global_z);
-
-    transformOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-                *in.data, in.info, *tf.data, tf.info, nImg2, nImg3, nTfs2,
-                nTfs3, batchImg2, blocksXPerImage, blocksYPerImage,
-                (int)method);
+    NDRange global(global_x, global_y, global_z);
 
+    transform(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *in.data, in.info, *tf.data, tf.info, nImg2, nImg3, nTfs2, nTfs3,
+              batchImg2, blocksXPerImage, blocksYPerImage, (int)method);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/transpose.cl b/src/backend/opencl/kernel/transpose.cl
index 7b486f49fc..ea3075f3fd 100644
--- a/src/backend/opencl/kernel/transpose.cl
+++ b/src/backend/opencl/kernel/transpose.cl
@@ -15,10 +15,10 @@ T doOp(T in) {
 #define doOp(in) in
 #endif
 
-__kernel void transpose(__global T *oData, const KParam out,
-                        const __global T *iData, const KParam in,
+kernel void transpose(global T *oData, const KParam out,
+                        const global T *iData, const KParam in,
                         const int blocksPerMatX, const int blocksPerMatY) {
-    __local T shrdMem[TILE_DIM * (TILE_DIM + 1)];
+    local T shrdMem[TILE_DIM * (TILE_DIM + 1)];
 
     const int shrdStride = TILE_DIM + 1;
     // create variables to hold output dimensions
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 525e12664f..c7e40320b3 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -15,7 +15,6 @@
 #include <debug_opencl.hpp>
 #include <kernel_headers/transpose.hpp>
 #include <traits.hpp>
-#include <types.hpp>
 
 #include <string>
 #include <vector>
@@ -23,9 +22,9 @@
 namespace opencl {
 namespace kernel {
 
-static const int TILE_DIM  = 32;
-static const int THREADS_X = TILE_DIM;
-static const int THREADS_Y = 256 / TILE_DIM;
+constexpr int TILE_DIM  = 32;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = 256 / TILE_DIM;
 
 template<typename T>
 void transpose(Param out, const Param in, cl::CommandQueue queue,
diff --git a/src/backend/opencl/kernel/transpose_inplace.cl b/src/backend/opencl/kernel/transpose_inplace.cl
index ee9c7edf3a..db444b8bc4 100644
--- a/src/backend/opencl/kernel/transpose_inplace.cl
+++ b/src/backend/opencl/kernel/transpose_inplace.cl
@@ -15,11 +15,11 @@ T doOp(T in) {
 #define doOp(in) in
 #endif
 
-__kernel void transpose_inplace(__global T *iData, const KParam in,
+kernel void transpose_inplace(global T *iData, const KParam in,
                                 const int blocksPerMatX,
                                 const int blocksPerMatY) {
-    __local T shrdMem_s[TILE_DIM * (TILE_DIM + 1)];
-    __local T shrdMem_d[TILE_DIM * (TILE_DIM + 1)];
+    local T shrdMem_s[TILE_DIM * (TILE_DIM + 1)];
+    local T shrdMem_d[TILE_DIM * (TILE_DIM + 1)];
 
     const int shrdStride = TILE_DIM + 1;
 
@@ -43,7 +43,7 @@ __kernel void transpose_inplace(__global T *iData, const KParam in,
     const int x0 = TILE_DIM * blockIdx_x;
     const int y0 = TILE_DIM * blockIdx_y;
 
-    __global T *iptr = iData + batchId_x * in.strides[2] +
+    global T *iptr = iData + batchId_x * in.strides[2] +
                        batchId_y * in.strides[3] + in.offset;
 
     if (blockIdx_y > blockIdx_x) {
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index ba5286228f..300a7eec40 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -10,58 +10,48 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/transpose_inplace.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
 
 #include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-static const int TILE_DIM  = 16;
-static const int THREADS_X = TILE_DIM;
-static const int THREADS_Y = 256 / TILE_DIM;
-
-template<typename T, bool conjugate, bool IS32MULTIPLE>
-void transpose_inplace(Param in, cl::CommandQueue& queue) {
-    std::string refName = std::string("transpose_inplace_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(conjugate) +
-                          std::to_string(IS32MULTIPLE);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D TILE_DIM=" << TILE_DIM << " -D THREADS_Y=" << THREADS_Y
-                << " -D IS32MULTIPLE=" << IS32MULTIPLE
-                << " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
 
-        const char* ker_strs[] = {transpose_inplace_cl};
-        const int ker_lens[]   = {transpose_inplace_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "transpose_inplace");
-
-        addKernelToCache(device, refName, entry);
-    }
+constexpr int TILE_DIM  = 16;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = 256 / TILE_DIM;
+
+template<typename T>
+void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
+                       const bool IS32MULTIPLE) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(transpose_inplace_cl, transpose_inplace_cl_len);
+
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(conjugate),
+        TemplateArg(IS32MULTIPLE),
+    };
+    vector<string> compileOpts = {
+        DefineValue(TILE_DIM),
+        DefineValue(THREADS_Y),
+        DefineValue(IS32MULTIPLE),
+        DefineKeyValue(DOCONJUGATE, (conjugate && af::iscplx<T>())),
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto transpose =
+        common::findKernel("transpose_inplace", {src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -72,13 +62,11 @@ void transpose_inplace(Param in, cl::CommandQueue& queue) {
     NDRange global(blk_x * local[0] * in.info.dims[2],
                    blk_y * local[1] * in.info.dims[3]);
 
-    auto transposeOp =
-        KernelFunctor<Buffer, const KParam, const int, const int>(*entry.ker);
-
-    transposeOp(EnqueueArgs(queue, global, local), *in.data, in.info, blk_x,
-                blk_y);
+    transpose(EnqueueArgs(queue, global, local), *in.data, in.info, blk_x,
+              blk_y);
 
     CL_DEBUG_FINISH(queue);
 }
+
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/triangle.cl b/src/backend/opencl/kernel/triangle.cl
index c3dddffd44..536e074f2b 100644
--- a/src/backend/opencl/kernel/triangle.cl
+++ b/src/backend/opencl/kernel/triangle.cl
@@ -7,9 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void triangle_kernel(__global T *rptr, KParam rinfo,
-                              const __global T *iptr, KParam iinfo,
-                              const int groups_x, const int groups_y) {
+kernel void triangle(global T *rptr, KParam rinfo, const global T *iptr,
+                     KParam iinfo, const int groups_x, const int groups_y) {
     const int oz = get_group_id(0) / groups_x;
     const int ow = get_group_id(1) / groups_y;
 
@@ -22,24 +21,24 @@ __kernel void triangle_kernel(__global T *rptr, KParam rinfo,
     const int incy = groups_y * get_local_size(1);
     const int incx = groups_x * get_local_size(0);
 
-    __global T *d_r       = rptr;
-    const __global T *d_i = iptr + iinfo.offset;
+    global T *d_r       = rptr;
+    const global T *d_i = iptr + iinfo.offset;
 
     if (oz < rinfo.dims[2] && ow < rinfo.dims[3]) {
         d_i = d_i + oz * iinfo.strides[2] + ow * iinfo.strides[3];
         d_r = d_r + oz * rinfo.strides[2] + ow * rinfo.strides[3];
 
         for (int oy = yy; oy < rinfo.dims[1]; oy += incy) {
-            const __global T *Yd_i = d_i + oy * iinfo.strides[1];
-            __global T *Yd_r       = d_r + oy * rinfo.strides[1];
+            const global T *Yd_i = d_i + oy * iinfo.strides[1];
+            global T *Yd_r       = d_r + oy * rinfo.strides[1];
 
             for (int ox = xx; ox < rinfo.dims[0]; ox += incx) {
                 bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
                 bool do_unit_diag = is_unit_diag && (oy == ox);
                 if (cond) {
-                    Yd_r[ox] = do_unit_diag ? ONE : Yd_i[ox];
+                    Yd_r[ox] = do_unit_diag ? (T)(ONE) : Yd_i[ox];
                 } else {
-                    Yd_r[ox] = ZERO;
+                    Yd_r[ox] = (T)(ZERO);
                 }
             }
         }
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index a1cfc4ee95..dc3a50b35a 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -10,63 +10,51 @@
 #pragma once
 
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/triangle.hpp>
 #include <math.hpp>
-#include <platform.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <types.hpp>
 
 #include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
-// Kernel Launch Config Values
-static const unsigned TX    = 32;
-static const unsigned TY    = 8;
-static const unsigned TILEX = 128;
-static const unsigned TILEY = 32;
 
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Param out, const Param in) {
-    std::string refName = std::string("triangle_kernel_") +
-                          std::string(dtype_traits<T>::getName()) +
-                          std::to_string(is_upper) +
-                          std::to_string(is_unit_diag);
+template<typename T>
+void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
     using af::scalar_to_option;
-    using cl::Buffer;
     using cl::EnqueueArgs;
-    using cl::Kernel;
-    using cl::KernelFunctor;
     using cl::NDRange;
-    using cl::Program;
     using std::string;
+    using std::vector;
 
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 128;
+    constexpr unsigned TILEY = 32;
 
-    if (entry.prog == 0 && entry.ker == 0) {
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D is_upper=" << is_upper
-                << " -D is_unit_diag=" << is_unit_diag << " -D ZERO=(T)("
-                << scalar_to_option(scalar<T>(0)) << ")"
-                << " -D ONE=(T)(" << scalar_to_option(scalar<T>(1)) << ")";
-        options << getTypeBuildDefinition<T>();
+    static const string src(triangle_cl, triangle_cl_len);
 
-        const char* ker_strs[] = {triangle_cl};
-        const int ker_lens[]   = {triangle_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "triangle_kernel");
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(is_upper),
+        TemplateArg(is_unit_diag),
+    };
+    vector<string> compileOpts = {
+        DefineValue(is_upper),
+        DefineValue(is_unit_diag),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ONE, scalar_to_option(scalar<T>(1))),
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-        addKernelToCache(device, refName, entry);
-    }
+    auto triangle =
+        common::findKernel("triangle", {src}, tmpltArgs, compileOpts);
 
     NDRange local(TX, TY);
 
@@ -76,12 +64,8 @@ void triangle(Param out, const Param in) {
     NDRange global(groups_x * out.info.dims[2] * local[0],
                    groups_y * out.info.dims[3] * local[1]);
 
-    auto triangleOp = KernelFunctor<Buffer, KParam, const Buffer, KParam,
-                                    const int, const int>(*entry.ker);
-
-    triangleOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-               *in.data, in.info, groups_x, groups_y);
-
+    triangle(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+             *in.data, in.info, groups_x, groups_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/unwrap.cl b/src/backend/opencl/kernel/unwrap.cl
index 92bddc6c5f..2d67fb68ac 100644
--- a/src/backend/opencl/kernel/unwrap.cl
+++ b/src/backend/opencl/kernel/unwrap.cl
@@ -7,15 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void unwrap_kernel(__global T *d_out, const KParam out,
-                            __global const T *d_in, const KParam in,
-                            const int wx, const int wy, const int sx,
-                            const int sy, const int px, const int py,
-                            const int dx, const int dy, const int nx,
-                            const int reps) {
+kernel void unwrap(global T *d_out, const KParam out, global const T *d_in,
+                   const KParam in, const int wx, const int wy, const int sx,
+                   const int sy, const int px, const int py, const int dx,
+                   const int dy, const int nx, const int reps) {
     // Compute channel and volume
     const int w = get_group_id(1) / in.dims[2];
-    const int z = get_group_id(1) - w * in.dims[2];  // get_group_id(1) % in.dims[2];
+    const int z =
+        get_group_id(1) - w * in.dims[2];  // get_group_id(1) % in.dims[2];
 
     if (w >= in.dims[3] || z >= in.dims[2]) return;
 
@@ -38,17 +37,17 @@ __kernel void unwrap_kernel(__global T *d_out, const KParam out,
     const int spy = starty - py;
 
     // Offset the global pointers to the respective starting indices
-    __global T *optr = d_out + cOut + id * (IS_COLUMN ? out.strides[1] : 1);
-    __global const T *iptr = d_in + cIn + in.offset;
+    global T *optr       = d_out + cOut + id * (IS_COLUMN ? out.strides[1] : 1);
+    global const T *iptr = d_in + cIn + in.offset;
 
     bool cond = (spx >= 0 && spx + (wx * dx) < in.dims[0] && spy >= 0 &&
                  spy + (wy * dy) < in.dims[1]);
 
     // Compute output index local to column
-    int outIdx = IS_COLUMN ? get_local_id(0) : get_local_id(1);
+    int outIdx        = IS_COLUMN ? get_local_id(0) : get_local_id(1);
     const int oStride = IS_COLUMN ? get_local_size(0) : get_local_size(1);
 
-    for(int i = 0; i < reps; i++) {
+    for (int i = 0; i < reps; i++) {
         if (outIdx >= (IS_COLUMN ? out.dims[0] : out.dims[1])) return;
 
         // Compute input index local to window
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index ba1d602a49..908f318d9d 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -8,27 +8,18 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/unwrap.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
@@ -38,35 +29,31 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
             const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
             const dim_t dx, const dim_t dy, const dim_t nx,
             const bool is_column) {
-    std::string ref_name = std::string("unwrap_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(is_column);
-
-    int device = getActiveDeviceId();
-
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D IS_COLUMN=" << is_column
-                << " -D ZERO=" << toNumStr(scalar<T>(0))
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        Program prog;
-        buildProgram(prog, unwrap_cl, unwrap_cl_len, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "unwrap_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(unwrap_cl, unwrap_cl_len);
+
+    ToNumStr<T> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(is_column),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(IS_COLUMN, is_column),
+        DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto unwrap = common::findKernel("unwrap", {src}, tmpltArgs, compileOpts);
 
     dim_t TX = 1, TY = 1;
     dim_t BX       = 1;
     const dim_t BY = out.info.dims[2] * out.info.dims[3];
-    dim_t reps     = 1;
+    int reps       = 1;
 
     if (is_column) {
         TX   = std::min(THREADS_PER_GROUP, nextpow2(out.info.dims[0]));
@@ -83,15 +70,11 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     NDRange local(TX, TY);
     NDRange global(local[0] * BX, local[1] * BY);
 
-    auto unwrapOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const int, const int, const int, const int, const int,
-                      const int, const int, const int, const int, const int>(
-            *entry.ker);
-
-    unwrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *in.data, in.info, wx, wy, sx, sy, px, py, dx, dy, nx, reps);
-
+    unwrap(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+           *in.data, in.info, static_cast<int>(wx), static_cast<int>(wy),
+           static_cast<int>(sx), static_cast<int>(sy), static_cast<int>(px),
+           static_cast<int>(py), static_cast<int>(dx), static_cast<int>(dy),
+           static_cast<int>(nx), reps);
     CL_DEBUG_FINISH(getQueue());
 }
 
diff --git a/src/backend/opencl/kernel/where.cl b/src/backend/opencl/kernel/where.cl
index f3d5091916..4e5298012e 100644
--- a/src/backend/opencl/kernel/where.cl
+++ b/src/backend/opencl/kernel/where.cl
@@ -13,11 +13,10 @@
 #define isZero(val) ((val == 0))
 #endif
 
-__kernel void get_out_idx_kernel(__global uint *oData, __global uint *otData,
-                                 KParam otInfo, __global uint *rtData,
-                                 KParam rtInfo, __global T *iData, KParam iInfo,
-                                 uint groups_x, uint groups_y, uint lim) {
-    T Zero = zero;
+kernel void get_out_idx(global uint *oData, global uint *otData, KParam otInfo,
+                        global uint *rtData, KParam rtInfo, global T *iData,
+                        KParam iInfo, uint groups_x, uint groups_y, uint lim) {
+    T Zero = ZERO;
 
     const uint lidx = get_local_id(0);
     const uint lidy = get_local_id(1);
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 385a3604ff..63785bfd91 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -8,56 +8,46 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
+#include <kernel/names.hpp>
+#include <kernel/scan_first.hpp>
 #include <kernel_headers/where.hpp>
-#include <memory.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
+
 #include <string>
-#include "config.hpp"
-#include "names.hpp"
-#include "scan_first.hpp"
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <vector>
 
 namespace opencl {
 namespace kernel {
 template<typename T>
-static void get_out_idx(Buffer *out_data, Param &otmp, Param &rtmp, Param &in,
-                        uint threads_x, uint groups_x, uint groups_y) {
-    std::string refName = std::string("get_out_idx_kernel_") +
-                          std::string(dtype_traits<T>::getName());
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, refName);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D T=" << dtype_traits<T>::getName()
-                << " -D zero=" << toNumStr(scalar<T>(0))
-                << " -D CPLX=" << af::iscplx<T>();
-        options << getTypeBuildDefinition<T>();
-
-        const char *ker_strs[] = {where_cl};
-        const int ker_lens[]   = {where_cl_len};
-        Program prog;
-        buildProgram(prog, 1, ker_strs, ker_lens, options.str());
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "get_out_idx_kernel");
-
-        addKernelToCache(device, refName, entry);
-    }
+static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
+                        Param &in, uint threads_x, uint groups_x,
+                        uint groups_y) {
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(where_cl, where_cl_len);
+
+    ToNumStr<T> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+    };
+    vector<string> compileOpts = {
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+        DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
+        DefineKeyValue(CPLX, af::iscplx<T>()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto getIdx =
+        common::findKernel("get_out_idx", {src}, tmpltArgs, compileOpts);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(local[0] * groups_x * in.info.dims[2],
@@ -65,13 +55,9 @@ static void get_out_idx(Buffer *out_data, Param &otmp, Param &rtmp, Param &in,
 
     uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
 
-    auto whereOp = KernelFunctor<Buffer, Buffer, KParam, Buffer, KParam, Buffer,
-                                 KParam, uint, uint, uint>(*entry.ker);
-
-    whereOp(EnqueueArgs(getQueue(), global, local), *out_data, *otmp.data,
-            otmp.info, *rtmp.data, rtmp.info, *in.data, in.info, groups_x,
-            groups_y, lim);
-
+    getIdx(EnqueueArgs(getQueue(), global, local), *out_data, *otmp.data,
+           otmp.info, *rtmp.data, rtmp.info, *in.data, in.info, groups_x,
+           groups_y, lim);
     CL_DEBUG_FINISH(getQueue());
 }
 
@@ -110,8 +96,8 @@ static void where(Param &out, Param &in) {
     int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
     otmp.data         = bufferAlloc(otmp_elements * sizeof(uint));
 
-    scan_first_launcher<T, uint, af_notzero_t>(otmp, rtmp, in, false, groups_x,
-                                               groups_y, threads_x);
+    scanFirstLauncher<T, uint, af_notzero_t>(otmp, rtmp, in, false, groups_x,
+                                             groups_y, threads_x);
 
     // Linearize the dimensions and perform scan
     Param ltmp        = rtmp;
@@ -122,7 +108,7 @@ static void where(Param &out, Param &in) {
         ltmp.info.strides[k] = rtmp_elements;
     }
 
-    scan_first<uint, uint, af_add_t>(ltmp, ltmp);
+    scanFirst<uint, uint, af_add_t>(ltmp, ltmp);
 
     // Get output size and allocate output
     uint total;
diff --git a/src/backend/opencl/kernel/wrap.cl b/src/backend/opencl/kernel/wrap.cl
index 99da73c51d..3b2b1faf38 100644
--- a/src/backend/opencl/kernel/wrap.cl
+++ b/src/backend/opencl/kernel/wrap.cl
@@ -7,11 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void wrap_kernel(__global T *optr, KParam out, __global T *iptr,
-                          KParam in, const int wx, const int wy, const int sx,
-                          const int sy, const int px, const int py,
-                          const int nx, const int ny, int groups_x,
-                          int groups_y) {
+kernel void wrap(global T *optr, KParam out, global T *iptr, KParam in,
+                 const int wx, const int wy, const int sx, const int sy,
+                 const int px, const int py, const int nx, const int ny,
+                 int groups_x, int groups_y) {
     int idx2 = get_group_id(0) / groups_x;
     int idx3 = get_group_id(1) / groups_y;
 
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 34d9e2ec39..bf9b63762b 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -8,29 +8,19 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
+
 #include <Param.hpp>
-#include <cache.hpp>
 #include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
+#include <kernel/config.hpp>
 #include <kernel_headers/wrap.hpp>
 #include <kernel_headers/wrap_dilated.hpp>
 #include <math.hpp>
-#include <program.hpp>
 #include <traits.hpp>
-#include <type_util.hpp>
-#include <map>
-#include <mutex>
-#include <string>
-#include "config.hpp"
 
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::KernelFunctor;
-using cl::NDRange;
-using cl::Program;
-using std::string;
+#include <string>
+#include <vector>
 
 namespace opencl {
 namespace kernel {
@@ -39,29 +29,26 @@ template<typename T>
 void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
           const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
           const bool is_column) {
-    std::string ref_name = std::string("wrap_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(is_column);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D is_column=" << is_column
-                << " -D ZERO=" << toNumStr(scalar<T>(0))
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        Program prog;
-        buildProgram(prog, wrap_cl, wrap_cl_len, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "wrap_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(wrap_cl, wrap_cl_len);
+
+    ToNumStr<T> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(is_column),
+    };
+    vector<string> compileOpts = {
+        DefineValue(is_column),
+        DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto wrap = common::findKernel("wrap", {src}, tmpltArgs, compileOpts);
 
     dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
     dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
@@ -74,15 +61,11 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     NDRange global(local[0] * groups_x * out.info.dims[2],
                    local[1] * groups_y * out.info.dims[3]);
 
-    auto wrapOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const int, const int, const int, const int, const int,
-                      const int, const int, const int, const int, const int>(
-            *entry.ker);
-
-    wrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *in.data, in.info, wx, wy, sx, sy, px, py, nx, ny, groups_x,
-           groups_y);
+    wrap(EnqueueArgs(getQueue(), global, local), *out.data, out.info, *in.data,
+         in.info, static_cast<int>(wx), static_cast<int>(wy),
+         static_cast<int>(sx), static_cast<int>(sy), static_cast<int>(px),
+         static_cast<int>(py), static_cast<int>(nx), static_cast<int>(ny),
+         static_cast<int>(groups_x), static_cast<int>(groups_y));
 
     CL_DEBUG_FINISH(getQueue());
 }
@@ -92,29 +75,27 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
                   const dim_t sx, const dim_t sy, const dim_t px,
                   const dim_t py, const dim_t dx, const dim_t dy,
                   const bool is_column) {
-    std::string ref_name = std::string("wrap_dilated_") +
-                           std::string(dtype_traits<T>::getName()) +
-                           std::string("_") + std::to_string(is_column);
-
-    int device       = getActiveDeviceId();
-    kc_entry_t entry = kernelCache(device, ref_name);
-
-    if (entry.prog == 0 && entry.ker == 0) {
-        ToNumStr<T> toNumStr;
-        std::ostringstream options;
-        options << " -D is_column=" << is_column
-                << " -D ZERO=" << toNumStr(scalar<T>(0))
-                << " -D T=" << dtype_traits<T>::getName();
-        options << getTypeBuildDefinition<T>();
-
-        Program prog;
-        buildProgram(prog, wrap_dilated_cl, wrap_dilated_cl_len, options.str());
-
-        entry.prog = new Program(prog);
-        entry.ker  = new Kernel(*entry.prog, "wrap_dilated_kernel");
-
-        addKernelToCache(device, ref_name, entry);
-    }
+    using cl::EnqueueArgs;
+    using cl::NDRange;
+    using std::string;
+    using std::vector;
+
+    static const string src(wrap_dilated_cl, wrap_dilated_cl_len);
+
+    ToNumStr<T> toNumStr;
+    vector<TemplateArg> tmpltArgs = {
+        TemplateTypename<T>(),
+        TemplateArg(is_column),
+    };
+    vector<string> compileOpts = {
+        DefineValue(is_column),
+        DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
+        DefineKeyValue(T, dtype_traits<T>::getName()),
+    };
+    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+
+    auto dilatedWrap =
+        common::findKernel("wrap_dilated", {src}, tmpltArgs, compileOpts);
 
     dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
@@ -127,16 +108,13 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
     NDRange global(local[0] * groups_x * out.info.dims[2],
                    local[1] * groups_y * out.info.dims[3]);
 
-    auto wrapOp =
-        KernelFunctor<Buffer, const KParam, const Buffer, const KParam,
-                      const int, const int, const int, const int, const int,
-                      const int, const int, const int, const int, const int,
-                      const int, const int>(*entry.ker);
-
-    wrapOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-           *in.data, in.info, wx, wy, sx, sy, px, py, dx, dy, nx, ny, groups_x,
-           groups_y);
-
+    dilatedWrap(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+                *in.data, in.info, static_cast<int>(wx), static_cast<int>(wy),
+                static_cast<int>(sx), static_cast<int>(sy),
+                static_cast<int>(px), static_cast<int>(py),
+                static_cast<int>(dx), static_cast<int>(dy),
+                static_cast<int>(nx), static_cast<int>(ny),
+                static_cast<int>(groups_x), static_cast<int>(groups_y));
     CL_DEBUG_FINISH(getQueue());
 }
 
diff --git a/src/backend/opencl/kernel/wrap_dilated.cl b/src/backend/opencl/kernel/wrap_dilated.cl
index e3f81ac4dc..fee950eb24 100644
--- a/src/backend/opencl/kernel/wrap_dilated.cl
+++ b/src/backend/opencl/kernel/wrap_dilated.cl
@@ -7,12 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__kernel void wrap_dilated_kernel(__global T *optr, KParam out,
-                                  __global T *iptr, KParam in, const int wx,
-                                  const int wy, const int sx, const int sy,
-                                  const int px, const int py, const int dx,
-                                  const int dy, const int nx, const int ny,
-                                  int groups_x, int groups_y) {
+kernel void wrap_dilated(global T *optr, KParam out, global T *iptr, KParam in,
+                         const int wx, const int wy, const int sx, const int sy,
+                         const int px, const int py, const int dx, const int dy,
+                         const int nx, const int ny, int groups_x,
+                         int groups_y) {
     int idx2 = get_group_id(0) / groups_x;
     int idx3 = get_group_id(1) / groups_y;
 
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index ff71368e61..724538604e 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -30,13 +30,7 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);
 
-    switch (dim) {
-        case 0: kernel::lookup<in_t, idx_t, 0>(out, input, indices); break;
-        case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices); break;
-        case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices); break;
-        case 3: kernel::lookup<in_t, idx_t, 3>(out, input, indices); break;
-        default: AF_ERROR("dim only supports values 0-3.", AF_ERR_UNKNOWN);
-    }
+    kernel::lookup<in_t, idx_t>(out, input, indices, dim);
 
     return out;
 }
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index a06fc90939..8fe05b3bf6 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -53,7 +53,7 @@ void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
     dim4 udims(MN, N);
     lower = createEmptyArray<T>(ldims);
     upper = createEmptyArray<T>(udims);
-    kernel::lu_split<T>(lower, upper, in_copy);
+    kernel::luSplit<T>(lower, upper, in_copy);
 }
 
 template<typename T>
diff --git a/src/backend/opencl/magma/transpose_inplace.cpp b/src/backend/opencl/magma/transpose_inplace.cpp
index 040a90ff22..6f649f55bb 100644
--- a/src/backend/opencl/magma/transpose_inplace.cpp
+++ b/src/backend/opencl/magma/transpose_inplace.cpp
@@ -77,13 +77,8 @@ void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
     using namespace opencl;
 
     cl::CommandQueue q(queue, true);
-    if (n % 32 == 0) {
-        kernel::transpose_inplace<T, false, true>(
-            makeParam(dA, dA_offset, dims, strides), q);
-    } else {
-        kernel::transpose_inplace<T, false, false>(
-            makeParam(dA, dA_offset, dims, strides), q);
-    }
+    kernel::transpose_inplace<T>(makeParam(dA, dA_offset, dims, strides), q,
+                                 false, n % 32 == 0);
 }
 
 #define INSTANTIATE(T)                                                \
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index bbe01d5882..da5b6f3ef0 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -7,14 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_opencl.hpp>
-#include <kernel/match_template.hpp>
 #include <match_template.hpp>
-#include <af/defines.h>
-#include <af/dim4.hpp>
 
-using af::dim4;
+#include <kernel/match_template.hpp>
 
 namespace opencl {
 
@@ -26,11 +21,7 @@ Array<outType> match_template(const Array<inType> &sImg,
     bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
                     mType == AF_LSSD || mType == AF_ZNCC;
 
-    if (needMean) {
-        kernel::matchTemplate<inType, outType, mType, true>(out, sImg, tImg);
-    } else {
-        kernel::matchTemplate<inType, outType, mType, false>(out, sImg, tImg);
-    }
+    kernel::matchTemplate<inType, outType>(out, sImg, tImg, mType, needMean);
 
     return out;
 }
diff --git a/src/backend/opencl/match_template.hpp b/src/backend/opencl/match_template.hpp
index 2b82aeac03..8a83e1ac92 100644
--- a/src/backend/opencl/match_template.hpp
+++ b/src/backend/opencl/match_template.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <af/defines.h>
 
 namespace opencl {
 
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
index 17315becb6..adce4be841 100644
--- a/src/backend/opencl/mean.cpp
+++ b/src/backend/opencl/mean.cpp
@@ -7,15 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
+#include <mean.hpp>
+
 #include <common/half.hpp>
-#include <err_opencl.hpp>
 #include <kernel/mean.hpp>
-#include <mean.hpp>
 #include <af/dim4.hpp>
 
-#include <complex>
-
 using af::dim4;
 using common::half;
 using std::swap;
@@ -23,12 +20,12 @@ using std::swap;
 namespace opencl {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
-    return kernel::mean_all<Ti, Tw, To>(in);
+    return kernel::meanAll<Ti, Tw, To>(in);
 }
 
 template<typename T, typename Tw>
 T mean(const Array<T>& in, const Array<Tw>& wts) {
-    return kernel::mean_all_weighted<T, Tw>(in, wts);
+    return kernel::meanAllWeighted<T, Tw>(in, wts);
 }
 
 template<typename Ti, typename Tw, typename To>
@@ -45,7 +42,7 @@ Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim) {
     dim4 odims   = in.dims();
     odims[dim]   = 1;
     Array<T> out = createEmptyArray<T>(odims);
-    kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
+    kernel::meanWeighted<T, Tw, T>(out, in, wts, dim);
     return out;
 }
 
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index 95257633de..bceed64bb1 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -22,13 +22,8 @@ Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const bool &isColor) {
     const dim4 &dims = in.dims();
     Array<T> out     = createEmptyArray<T>(dims);
-    if (isColor) {
-        kernel::meanshift<T, true>(out, in, spatialSigma, chromaticSigma,
-                                   numIterations);
-    } else {
-        kernel::meanshift<T, false>(out, in, spatialSigma, chromaticSigma,
-                                    numIterations);
-    }
+    kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma, numIterations,
+                         isColor);
     return out;
 }
 
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index d2ab6674f3..34860b47ac 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -26,7 +26,7 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
 
     Array<T> out = createEmptyArray<T>(dims);
 
-    kernel::medfilt1<T, pad>(out, in, w_wid);
+    kernel::medfilt1<T>(out, in, w_wid, pad);
 
     return out;
 }
@@ -34,25 +34,12 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
 template<typename T, af_border_type pad>
 Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     UNUSED(w_wid);
-    ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
+    ARG_ASSERT(2, (w_len == w_wid));
     ARG_ASSERT(2, (w_len % 2 != 0));
+    ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
 
-    const dim4 &dims = in.dims();
-
-    Array<T> out = createEmptyArray<T>(dims);
-
-    switch (w_len) {
-        case 3: kernel::medfilt2<T, pad, 3, 3>(out, in); break;
-        case 5: kernel::medfilt2<T, pad, 5, 5>(out, in); break;
-        case 7: kernel::medfilt2<T, pad, 7, 7>(out, in); break;
-        case 9: kernel::medfilt2<T, pad, 9, 9>(out, in); break;
-        case 11: kernel::medfilt2<T, pad, 11, 11>(out, in); break;
-        case 13: kernel::medfilt2<T, pad, 13, 13>(out, in); break;
-        case 15: kernel::medfilt2<T, pad, 15, 15>(out, in); break;
-        default:
-            AF_ERROR("w_len only supports values 3, 5, 7, 9, 11, 12, and 15.",
-                     AF_ERR_UNKNOWN);
-    }
+    Array<T> out = createEmptyArray<T>(in.dims());
+    kernel::medfilt2<T>(out, in, pad, w_len, w_wid);
     return out;
 }
 
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 3945077e68..fc3727b860 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -39,7 +39,7 @@ void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
     Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
     Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
 
-    kernel::all_distances<T, To, dist_type>(tmp_dists, queryT, trainT, 1);
+    kernel::allDistances<T, To>(tmp_dists, queryT, trainT, 1, dist_type);
 
     topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
 }
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index a985ce14ab..b49c57716e 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -13,7 +13,6 @@
 
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
-#include <cache.hpp>
 #include <clfft.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
@@ -694,28 +693,6 @@ PlanCache& fftManager() {
     return clfftManagers[getActiveDeviceId()];
 }
 
-kc_t& getKernelCache(int device) {
-    thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
-
-    return kernelCaches[device];
-}
-
-void addKernelToCache(int device, const string& key, const kc_entry_t entry) {
-    getKernelCache(device).emplace(key, entry);
-}
-
-void removeKernelFromCache(int device, const string& key) {
-    getKernelCache(device).erase(key);
-}
-
-kc_entry_t kernelCache(int device, const string& key) {
-    kc_t& cache = getKernelCache(device);
-
-    auto iter = cache.find(key);
-
-    return (iter == cache.end() ? kc_entry_t{0, 0} : iter->second);
-}
-
 }  // namespace opencl
 
 using namespace opencl;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 97c3590e3a..82848bf000 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -45,8 +45,7 @@ namespace opencl {
 
 // Forward declarations
 class GraphicsResourceManager;
-struct kc_entry_t;  // kernel cache entry
-class PlanCache;    // clfft
+class PlanCache;  // clfft
 
 bool verify_present(const std::string& pname, const std::string ref);
 
@@ -123,13 +122,6 @@ GraphicsResourceManager& interopManager();
 
 PlanCache& fftManager();
 
-void addKernelToCache(int device, const std::string& key,
-                      const kc_entry_t entry);
-
-void removeKernelFromCache(int device, const std::string& key);
-
-kc_entry_t kernelCache(int device, const std::string& key);
-
 afcl::platform getPlatformEnum(cl::Device dev);
 
 void setActiveContext(int device);
diff --git a/src/backend/opencl/program.cpp b/src/backend/opencl/program.cpp
deleted file mode 100644
index fda0f6e86f..0000000000
--- a/src/backend/opencl/program.cpp
+++ /dev/null
@@ -1,131 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <program.hpp>
-
-#include <cl2hpp.hpp>
-#include <debug_opencl.hpp>
-#include <kernel_headers/KParam.hpp>
-#include <platform.hpp>
-#include <traits.hpp>
-#include <types.hpp>
-
-#include <sstream>
-#include <string>
-
-using cl::Buffer;
-using cl::EnqueueArgs;
-using cl::Kernel;
-using cl::NDRange;
-using cl::Program;
-using std::ostringstream;
-using std::string;
-
-namespace opencl {
-
-const static std::string DEFAULT_MACROS_STR(
-    "\n\
-                                           #ifdef USE_DOUBLE\n\
-                                           #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
-                                           #endif\n                     \
-                                           #ifdef USE_HALF\n\
-                                           #pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
-                                           #else\n                     \
-                                           #define half short\n          \
-                                           #endif\n                      \
-                                           #ifndef M_PI\n               \
-                                           #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
-                                           #endif\n                     \
-                                           ");
-
-// TODO(pradeep) remove this version after porting to new cache interface
-void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
-                  const std::string &options) {
-    buildProgram(prog, 1, &ker_str, &ker_len, options);
-}
-
-// TODO(pradeep) remove this version after porting to new cache interface
-void buildProgram(cl::Program &prog, const int num_files, const char **ker_strs,
-                  const int *ker_lens, const std::string &options) {
-    try {
-        constexpr char kernel_header[] =
-            R"jit(#ifdef USE_DOUBLE
-#pragma OPENCL EXTENSION cl_khr_fp64 : enable
-#endif
-#ifdef USE_HALF
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-#else
-#define half short
-#endif
-#ifndef M_PI
-#define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164
-#endif
-)jit";
-
-        Program::Sources setSrc{
-            {kernel_header, std::extent<decltype(kernel_header)>() - 1},
-            {KParam_hpp, KParam_hpp_len}};
-
-        for (int i = 0; i < num_files; i++) {
-            setSrc.emplace_back(ker_strs[i], ker_lens[i]);
-        }
-
-        const std::string defaults =
-            std::string(" -D dim_t=") +
-            std::string(dtype_traits<dim_t>::getName());
-
-        prog               = cl::Program(getContext(), setSrc);
-        const auto &device = getDevice();
-
-        std::string cl_std =
-            std::string(" -cl-std=CL") +
-            device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
-
-        // Braces needed to list initialize the vector for the first argument
-        prog.build({device}, (cl_std + defaults + options).c_str());
-    } catch (...) {
-        SHOW_BUILD_INFO(prog);
-        throw;
-    }
-}
-
-cl::Program buildProgram(const std::vector<std::string> &kernelSources,
-                         const std::vector<std::string> &compileOpts) {
-    cl::Program retVal;
-    try {
-        static const std::string defaults =
-            std::string(" -D dim_t=") +
-            std::string(dtype_traits<dim_t>::getName());
-
-        auto device = getDevice();
-
-        const std::string cl_std =
-            std::string(" -cl-std=CL") +
-            device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
-
-        Program::Sources sources;
-        sources.emplace_back(DEFAULT_MACROS_STR);
-        sources.emplace_back(KParam_hpp, KParam_hpp_len);
-
-        for (auto ksrc : kernelSources) { sources.emplace_back(ksrc); }
-
-        retVal = cl::Program(getContext(), sources);
-
-        ostringstream options;
-        for (auto &opt : compileOpts) { options << opt; }
-
-        retVal.build({device}, (cl_std + defaults + options.str()).c_str());
-    } catch (...) {
-        SHOW_BUILD_INFO(retVal);
-        throw;
-    }
-    return retVal;
-}
-
-}  // namespace opencl
diff --git a/src/backend/opencl/program.hpp b/src/backend/opencl/program.hpp
deleted file mode 100644
index 5f28fd5efe..0000000000
--- a/src/backend/opencl/program.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <cl2hpp.hpp>
-#include <common/util.hpp>
-
-#include <cstdio>
-#include <string>
-#include <vector>
-
-#define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
-    do {                                                                  \
-        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();      \
-        for (unsigned int i = 0; i < numDevices; ++i) {                   \
-            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
-                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                 \
-            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
-                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                 \
-        }                                                                 \
-    } while (0)
-
-#if defined(NDEBUG)
-
-#define SHOW_BUILD_INFO(PROG)                                              \
-    do {                                                                   \
-        std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");         \
-        if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(PROG); } \
-    } while (0)
-
-#else
-#define SHOW_BUILD_INFO(PROG) SHOW_DEBUG_BUILD_INFO(PROG)
-#endif
-
-namespace opencl {
-
-#if defined(AF_WITH_DEV_WARNINGS)
-// TODO(pradeep) remove this version after porting to new cache interface
-[[deprecated("use cl::Program buildProgram(vector<string>&, vector<string>&)")]]
-#endif
-void buildProgram(cl::Program &prog, const char *ker_str, const int ker_len,
-                  const std::string &options);
-
-#if defined(AF_WITH_DEV_WARNINGS)
-// TODO(pradeep) remove this version after porting to new cache interface
-[[deprecated("use cl::Program buildProgram(vector<string>&, vector<string>&)")]]
-#endif
-void buildProgram(cl::Program &prog, const int num_files, const char **ker_str,
-                  const int *ker_len, const std::string &options);
-
-cl::Program buildProgram(const std::vector<std::string> &kernelSources,
-                         const std::vector<std::string> &options);
-
-}  // namespace opencl
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 4187107383..3588147aed 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -59,7 +59,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig) {
                         &info);
 
     r = createEmptyArray<T>(in.dims());
-    kernel::triangle<T, true, false>(r, in);
+    kernel::triangle<T>(r, in, true, false);
 
     cl::Buffer *r_buf = r.get();
     magmablas_swapdblk<T>(MN - 1, NB, (*r_buf)(), r.getOffset(), r.strides()[1],
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index 15e2347abf..f7c8c675b6 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -32,13 +32,13 @@ template<af_op_t op, typename Ti, typename Tk, typename To>
 void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
                    bool change_nan, double nanval) {
-    kernel::reduce_by_key<op, Ti, Tk, To>(keys_out, vals_out, keys, vals, dim,
-                                          change_nan, nanval);
+    kernel::reduceByKey<op, Ti, Tk, To>(keys_out, vals_out, keys, vals, dim,
+                                        change_nan, nanval);
 }
 
 template<af_op_t op, typename Ti, typename To>
 To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
-    return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
+    return kernel::reduceAll<Ti, To, op>(in, change_nan, nanval);
 }
 }  // namespace opencl
 
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index 82d287508d..66d67ee448 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -20,14 +20,8 @@ namespace opencl {
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
     const af::dim4 &dims = in.dims();
-
-    Array<T> out = createEmptyArray<T>(dims);
-
-    switch (connectivity) {
-        case AF_CONNECTIVITY_4: kernel::regions<T, false, 2>(out, in); break;
-        case AF_CONNECTIVITY_8: kernel::regions<T, true, 2>(out, in); break;
-    }
-
+    Array<T> out         = createEmptyArray<T>(dims);
+    kernel::regions<T>(out, in, connectivity == AF_CONNECTIVITY_8, 2);
     return out;
 }
 
diff --git a/src/backend/opencl/reshape.cpp b/src/backend/opencl/reshape.cpp
index e3b752d351..6eb8862e28 100644
--- a/src/backend/opencl/reshape.cpp
+++ b/src/backend/opencl/reshape.cpp
@@ -21,14 +21,8 @@ template<typename inType, typename outType>
 Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                        outType defaultValue, double scale) {
     Array<outType> out = createEmptyArray<outType>(outDims);
-
-    if (in.dims() == outDims) {
-        kernel::copy<inType, outType, true>(out, in, in.ndims(), defaultValue,
-                                            scale);
-    } else {
-        kernel::copy<inType, outType, false>(out, in, in.ndims(), defaultValue,
-                                             scale);
-    }
+    kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale,
+                                  in.dims() == outDims);
     return out;
 }
 
diff --git a/src/backend/opencl/resize.cpp b/src/backend/opencl/resize.cpp
index a911bacc6a..67257cc214 100644
--- a/src/backend/opencl/resize.cpp
+++ b/src/backend/opencl/resize.cpp
@@ -19,21 +19,8 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method) {
     const af::dim4 &iDims = in.dims();
     af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
-
     Array<T> out = createEmptyArray<T>(oDims);
-
-    switch (method) {
-        case AF_INTERP_NEAREST:
-            kernel::resize<T, AF_INTERP_NEAREST>(out, in);
-            break;
-        case AF_INTERP_BILINEAR:
-            kernel::resize<T, AF_INTERP_BILINEAR>(out, in);
-            break;
-        case AF_INTERP_LOWER:
-            kernel::resize<T, AF_INTERP_LOWER>(out, in);
-            break;
-        default: break;
-    }
+    kernel::resize<T>(out, in, method);
     return out;
 }
 
diff --git a/src/backend/opencl/rotate.cpp b/src/backend/opencl/rotate.cpp
index 210a14e292..a7f969e55e 100644
--- a/src/backend/opencl/rotate.cpp
+++ b/src/backend/opencl/rotate.cpp
@@ -7,11 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_opencl.hpp>
-#include <kernel/rotate.hpp>
 #include <rotate.hpp>
-#include <stdexcept>
+
+#include <kernel/rotate.hpp>
 
 namespace opencl {
 template<typename T>
@@ -22,19 +20,18 @@ Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::rotate<T, 1>(out, in, theta, method);
+            kernel::rotate<T>(out, in, theta, method, 1);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::rotate<T, 2>(out, in, theta, method);
+            kernel::rotate<T>(out, in, theta, method, 2);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::rotate<T, 3>(out, in, theta, method);
+            kernel::rotate<T>(out, in, theta, method, 3);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
-
     return out;
 }
 
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index c21c77badc..c069beb537 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -7,43 +7,30 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_opencl.hpp>
 #include <scan.hpp>
-#include <af/dim4.hpp>
-#include <complex>
 
 #include <kernel/scan_dim.hpp>
 #include <kernel/scan_first.hpp>
 
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
-Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
     Array<To> out = createEmptyArray<To>(in.dims());
 
     Param Out = out;
     Param In  = in;
 
-    if (inclusive_scan) {
-        if (dim == 0) {
-            kernel::scan_first<Ti, To, op, true>(Out, In);
-        } else {
-            kernel::scan_dim<Ti, To, op, true>(Out, In, dim);
-        }
+    if (dim == 0) {
+        kernel::scanFirst<Ti, To, op>(Out, In, inclusiveScan);
     } else {
-        if (dim == 0) {
-            kernel::scan_first<Ti, To, op, false>(Out, In);
-        } else {
-            kernel::scan_dim<Ti, To, op, false>(Out, In, dim);
-        }
+        kernel::scanDim<Ti, To, op>(Out, In, dim, inclusiveScan);
     }
 
     return out;
 }
 
-#define INSTANTIATE_SCAN(ROp, Ti, To)                                        \
-    template Array<To> scan<ROp, Ti, To>(const Array<Ti>& in, const int dim, \
-                                         bool inclusive_scan);
+#define INSTANTIATE_SCAN(ROp, Ti, To) \
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti>&, const int, bool);
 
 #define INSTANTIATE_SCAN_ALL(ROp)           \
     INSTANTIATE_SCAN(ROp, float, float)     \
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index 9d7cf450a7..606a1b00f9 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -26,18 +26,10 @@ Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
     Param Key = key;
     Param In  = in;
 
-    if (inclusive_scan) {
-        if (dim == 0) {
-            kernel::scan_first<Ti, Tk, To, op, true>(Out, In, Key);
-        } else {
-            kernel::scan_dim<Ti, Tk, To, op, true>(Out, In, Key, dim);
-        }
+    if (dim == 0) {
+        kernel::scanFirstByKey<Ti, Tk, To, op>(Out, In, Key, inclusive_scan);
     } else {
-        if (dim == 0) {
-            kernel::scan_first<Ti, Tk, To, op, false>(Out, In, Key);
-        } else {
-            kernel::scan_dim<Ti, Tk, To, op, false>(Out, In, Key, dim);
-        }
+        kernel::scanDimByKey<Ti, Tk, To, op>(Out, In, Key, dim, inclusive_scan);
     }
     return out;
 }
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 5a98433372..49718969c5 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -90,7 +90,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
                    const double &b) {
-    kernel::select_scalar<T, flip>(out, cond, a, b, out.ndims());
+    kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/opencl/susan.cpp b/src/backend/opencl/susan.cpp
index d481c6aaf1..6b5cc5e1f3 100644
--- a/src/backend/opencl/susan.cpp
+++ b/src/backend/opencl/susan.cpp
@@ -32,44 +32,8 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
 
     cl::Buffer *resp = bufferAlloc(in.elements() * sizeof(float));
 
-    switch (radius) {
-        case 1:
-            kernel::susan<T, 1>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 2:
-            kernel::susan<T, 2>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 3:
-            kernel::susan<T, 3>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 4:
-            kernel::susan<T, 4>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 5:
-            kernel::susan<T, 5>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 6:
-            kernel::susan<T, 6>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 7:
-            kernel::susan<T, 7>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 8:
-            kernel::susan<T, 8>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-        case 9:
-            kernel::susan<T, 9>(resp, in.get(), in.getOffset(), idims[0],
-                                idims[1], diff_thr, geom_thr, edge);
-            break;
-    }
+    kernel::susan<T>(resp, in.get(), in.getOffset(), idims[0], idims[1],
+                     diff_thr, geom_thr, edge, radius);
 
     unsigned corners_found =
         kernel::nonMaximal<T>(x_corners, y_corners, resp_corners, idims[0],
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 8a49d30ec6..253ff6ccb4 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -9,11 +9,7 @@
 
 #include <transform.hpp>
 
-#include <err_opencl.hpp>
 #include <kernel/transform.hpp>
-#include <af/dim4.hpp>
-
-#include <stdexcept>
 
 namespace opencl {
 
@@ -24,15 +20,15 @@ void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::transform<T, 1>(out, in, tf, inverse, perspective, method);
+            kernel::transform<T>(out, in, tf, inverse, perspective, method, 1);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::transform<T, 2>(out, in, tf, inverse, perspective, method);
+            kernel::transform<T>(out, in, tf, inverse, perspective, method, 2);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::transform<T, 3>(out, in, tf, inverse, perspective, method);
+            kernel::transform<T>(out, in, tf, inverse, perspective, method, 3);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index bf3705e290..4ee4a740cd 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -20,23 +20,12 @@ namespace opencl {
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate) {
-    dim4 iDims = in.dims();
-
-    if (conjugate) {
-        if (iDims[0] % kernel::TILE_DIM == 0 &&
-            iDims[1] % kernel::TILE_DIM == 0) {
-            kernel::transpose_inplace<T, true, true>(in, getQueue());
-        } else {
-            kernel::transpose_inplace<T, true, false>(in, getQueue());
-        }
-    } else {
-        if (iDims[0] % kernel::TILE_DIM == 0 &&
-            iDims[1] % kernel::TILE_DIM == 0) {
-            kernel::transpose_inplace<T, false, true>(in, getQueue());
-        } else {
-            kernel::transpose_inplace<T, false, false>(in, getQueue());
-        }
-    }
+    const dim4 &inDims = in.dims();
+
+    const bool is32multiple =
+        inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+
+    kernel::transpose_inplace<T>(in, getQueue(), conjugate, is32multiple);
 }
 
 #define INSTANTIATE(T) \
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index dfb3209ab0..cb22d75965 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -20,7 +20,7 @@ namespace opencl {
 
 template<typename T, bool is_upper, bool is_unit_diag>
 void triangle(Array<T> &out, const Array<T> &in) {
-    kernel::triangle<T, is_upper, is_unit_diag>(out, in);
+    kernel::triangle<T>(out, in, is_upper, is_unit_diag);
 }
 
 template<typename T, bool is_upper, bool is_unit_diag>

From 2b93203929e09503de7ac819e38d7f10da680225 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 18 May 2020 12:25:14 -0400
Subject: [PATCH 1960/2677] Refactor common::Node and remove cpu::jit::Node

* Remove backend specific cpu::jit::Node and use common::Node as a
  base class for all backends.
* Remove std::string for types in the node class in favor of
  the enum af::dtype.
* Remove UnaryOp's default implementation
---
 src/backend/common/jit/BinaryNode.hpp     |   7 +-
 src/backend/common/jit/BufferNodeBase.hpp |  12 +--
 src/backend/common/jit/NaryNode.hpp       |   8 +-
 src/backend/common/jit/Node.cpp           |  31 ++++++-
 src/backend/common/jit/Node.hpp           | 104 +++++++++++++++++++---
 src/backend/common/jit/ScalarNode.hpp     |  12 ++-
 src/backend/common/jit/ShiftNodeBase.hpp  |  18 ++--
 src/backend/common/jit/UnaryNode.hpp      |   6 +-
 src/backend/cpu/Array.cpp                 |  14 +--
 src/backend/cpu/Array.hpp                 |  21 ++---
 src/backend/cpu/arith.hpp                 |   6 +-
 src/backend/cpu/cast.hpp                  |   5 +-
 src/backend/cpu/complex.hpp               |  30 +++----
 src/backend/cpu/jit/BinaryNode.hpp        |  36 +++++++-
 src/backend/cpu/jit/BufferNode.hpp        |  39 +++++++-
 src/backend/cpu/jit/Node.hpp              |  80 ++---------------
 src/backend/cpu/jit/ScalarNode.hpp        |  36 +++++++-
 src/backend/cpu/jit/UnaryNode.hpp         |  18 +++-
 src/backend/cpu/kernel/Array.hpp          |  15 ++--
 src/backend/cpu/logic.hpp                 |  12 +--
 src/backend/cpu/types.hpp                 |  14 +++
 src/backend/cpu/unary.hpp                 |  11 +--
 src/backend/cuda/Array.cpp                |   3 +-
 src/backend/cuda/binary.hpp               |   4 +-
 src/backend/cuda/cast.hpp                 |   6 +-
 src/backend/cuda/complex.hpp              |  14 +--
 src/backend/cuda/jit.cpp                  |  32 +------
 src/backend/cuda/select.cpp               |   4 +-
 src/backend/cuda/shift.cpp                |   2 +-
 src/backend/cuda/unary.hpp                |  12 +--
 src/backend/opencl/Array.cpp              |   4 +-
 src/backend/opencl/binary.hpp             |   4 +-
 src/backend/opencl/cast.hpp               |   2 +-
 src/backend/opencl/complex.hpp            |   8 +-
 src/backend/opencl/jit.cpp                |  30 +------
 src/backend/opencl/select.cpp             |   4 +-
 src/backend/opencl/shift.cpp              |   2 +-
 src/backend/opencl/types.hpp              |  16 +++-
 src/backend/opencl/unary.hpp              |  12 +--
 39 files changed, 420 insertions(+), 274 deletions(-)

diff --git a/src/backend/common/jit/BinaryNode.hpp b/src/backend/common/jit/BinaryNode.hpp
index 066dc9ac33..636deda7ad 100644
--- a/src/backend/common/jit/BinaryNode.hpp
+++ b/src/backend/common/jit/BinaryNode.hpp
@@ -14,10 +14,9 @@
 namespace common {
 class BinaryNode : public NaryNode {
    public:
-    BinaryNode(const char *out_type_str, const char *name_str,
-               const char *op_str, common::Node_ptr lhs, common::Node_ptr rhs,
-               int op)
-        : NaryNode(out_type_str, name_str, op_str, 2, {{lhs, rhs}}, op,
+    BinaryNode(const af::dtype type, const char *op_str, common::Node_ptr lhs,
+               common::Node_ptr rhs, int op)
+        : NaryNode(type, op_str, 2, {{lhs, rhs}}, op,
                    std::max(lhs->getHeight(), rhs->getHeight()) + 1) {}
 };
 }  // namespace common
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 29e70cf6cf..c5a444dbbe 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -28,8 +28,7 @@ class BufferNodeBase : public common::Node {
     bool m_linear_buffer;
 
    public:
-    BufferNodeBase(const char *type_str, const char *name_str)
-        : Node(type_str, name_str, 0, {}) {}
+    BufferNodeBase(af::dtype type) : Node(type, 0, {}) {}
 
     bool isBuffer() const final { return true; }
 
@@ -54,14 +53,15 @@ class BufferNodeBase : public common::Node {
 
     void genKerName(std::stringstream &kerStream,
                     const common::Node_ids &ids) const final {
-        kerStream << "_" << m_name_str;
+        kerStream << "_" << getNameStr();
         kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
                   << std::dec;
     }
 
     void genParams(std::stringstream &kerStream, int id,
                    bool is_linear) const final {
-        detail::generateParamDeclaration(kerStream, id, is_linear, m_type_str);
+        detail::generateParamDeclaration(kerStream, id, is_linear,
+                                         getTypeStr());
     }
 
     int setArgs(int start_id, bool is_linear,
@@ -73,12 +73,12 @@ class BufferNodeBase : public common::Node {
 
     void genOffsets(std::stringstream &kerStream, int id,
                     bool is_linear) const final {
-        detail::generateBufferOffsets(kerStream, id, is_linear, m_type_str);
+        detail::generateBufferOffsets(kerStream, id, is_linear, getTypeStr());
     }
 
     void genFuncs(std::stringstream &kerStream,
                   const common::Node_ids &ids) const final {
-        detail::generateBufferRead(kerStream, ids.id, m_type_str);
+        detail::generateBufferRead(kerStream, ids.id, getTypeStr());
     }
 
     void getInfo(unsigned &len, unsigned &buf_count,
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 13265e7cfe..0c18a72353 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -29,12 +29,11 @@ class NaryNode : public Node {
     const std::string m_op_str;
 
    public:
-    NaryNode(const char *out_type_str, const char *name_str, const char *op_str,
-             const int num_children,
+    NaryNode(const af::dtype type, const char *op_str, const int num_children,
              const std::array<common::Node_ptr, Node::kMaxChildren> &&children,
              const int op, const int height)
         : common::Node(
-              out_type_str, name_str, height,
+              type, height,
               std::forward<
                   const std::array<common::Node_ptr, Node::kMaxChildren>>(
                   children))
@@ -57,7 +56,8 @@ class NaryNode : public Node {
 
     void genFuncs(std::stringstream &kerStream,
                   const common::Node_ids &ids) const final {
-        kerStream << m_type_str << " val" << ids.id << " = " << m_op_str << "(";
+        kerStream << getTypeStr() << " val" << ids.id << " = " << m_op_str
+                  << "(";
         for (int i = 0; i < m_num_children; i++) {
             if (i > 0) kerStream << ", ";
             kerStream << "val" << ids.child_ids[i];
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index bf17e2078e..8b1b8736b8 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -9,7 +9,9 @@
 
 #include <common/defines.hpp>
 #include <common/jit/Node.hpp>
+#include <common/util.hpp>
 
+#include <sstream>
 #include <string>
 #include <vector>
 
@@ -17,8 +19,8 @@ using std::vector;
 
 namespace common {
 
-int Node::getNodesMap(Node_map_t &node_map, vector<const Node *> &full_nodes,
-                      vector<Node_ids> &full_ids) const {
+int Node::getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes,
+                      vector<Node_ids> &full_ids) {
     auto iter = node_map.find(this);
     if (iter == node_map.end()) {
         Node_ids ids{};
@@ -36,4 +38,29 @@ int Node::getNodesMap(Node_map_t &node_map, vector<const Node *> &full_nodes,
     return iter->second;
 }
 
+std::string getFuncName(const vector<Node *> &output_nodes,
+                        const vector<Node *> &full_nodes,
+                        const vector<Node_ids> &full_ids, bool is_linear) {
+    std::stringstream funcName;
+    std::stringstream hashName;
+
+    if (is_linear) {
+        funcName << "L_";  // Kernel Linear
+    } else {
+        funcName << "G_";  // Kernel General
+    }
+
+    for (const auto &node : output_nodes) {
+        funcName << node->getNameStr() << "_";
+    }
+
+    for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
+        full_nodes[i]->genKerName(funcName, full_ids[i]);
+    }
+
+    hashName << "KER";
+    hashName << deterministicHash(funcName.str());
+    return hashName.str();
+}
+
 }  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index afabb96219..b656b92ac4 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -8,8 +8,11 @@
  ********************************************************/
 
 #pragma once
+#include <backend.hpp>
+#include <common/defines.hpp>
 #include <optypes.hpp>
 #include <platform.hpp>
+#include <types.hpp>
 #include <af/defines.h>
 
 #include <array>
@@ -31,31 +34,78 @@ class Node;
 struct Node_ids;
 
 using Node_ptr      = std::shared_ptr<Node>;
-using Node_map_t    = std::unordered_map<const Node *, int>;
+using Node_map_t    = std::unordered_map<Node *, int>;
 using Node_map_iter = Node_map_t::iterator;
 
+static const char *getFullName(af::dtype type) {
+    switch (type) {
+        case f32: return detail::getFullName<float>();
+        case f64: return detail::getFullName<double>();
+        case c32: return detail::getFullName<detail::cfloat>();
+        case c64: return detail::getFullName<detail::cdouble>();
+        case u32: return detail::getFullName<unsigned>();
+        case s32: return detail::getFullName<int>();
+        case u64: return detail::getFullName<unsigned long long>();
+        case s64: return detail::getFullName<long long>();
+        case u16: return detail::getFullName<unsigned short>();
+        case s16: return detail::getFullName<short>();
+        case b8: return detail::getFullName<char>();
+        case u8: return detail::getFullName<unsigned char>();
+        case f16: return "half";
+    }
+    return "";
+}
+
+static const char *getShortName(af::dtype type) {
+    switch (type) {
+        case f32: return detail::shortname<float>();
+        case f64: return detail::shortname<double>();
+        case c32: return detail::shortname<detail::cfloat>();
+        case c64: return detail::shortname<detail::cdouble>();
+        case u32: return detail::shortname<unsigned>();
+        case s32: return detail::shortname<int>();
+        case u64: return detail::shortname<unsigned long long>();
+        case s64: return detail::shortname<long long>();
+        case u16: return detail::shortname<unsigned short>();
+        case s16: return detail::shortname<short>();
+        case b8: return detail::shortname<char>();
+        case u8: return detail::shortname<unsigned char>();
+        case f16: return "h";
+    }
+    return "";
+}
+
 class Node {
    public:
     static const int kMaxChildren = 3;
 
    protected:
     const std::array<Node_ptr, kMaxChildren> m_children;
-    const std::string m_type_str;
-    const std::string m_name_str;
+    const af::dtype m_type;
     const int m_height;
+
     template<typename T>
     friend class NodeIterator;
 
    public:
-    Node(const char *type_str, const char *name_str, const int height,
+    Node(const af::dtype type, const int height,
          const std::array<Node_ptr, kMaxChildren> children)
-        : m_children(children)
-        , m_type_str(type_str)
-        , m_name_str(name_str)
-        , m_height(height) {}
+        : m_children(children), m_type(type), m_height(height) {}
+
+    /// Default copy constructor
+    Node(Node &node) = default;
 
-    int getNodesMap(Node_map_t &node_map, std::vector<const Node *> &full_nodes,
-                    std::vector<Node_ids> &full_ids) const;
+    /// Default move constructor
+    Node(Node &&node) = default;
+
+    /// Default copy assignment operator
+    Node &operator=(const Node &node) = default;
+
+    /// Default move assignment operator
+    Node &operator=(Node &&node) = default;
+
+    int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes,
+                    std::vector<Node_ids> &full_ids);
 
     /// Generates the string that will be used to hash the kernel
     virtual void genKerName(std::stringstream &kerStream,
@@ -73,6 +123,18 @@ class Node {
         UNUSED(is_linear);
     }
 
+    virtual void calc(int x, int y, int z, int w, int lim) {
+        UNUSED(x);
+        UNUSED(y);
+        UNUSED(z);
+        UNUSED(w);
+    }
+
+    virtual void calc(int idx, int lim) {
+        UNUSED(idx);
+        UNUSED(lim);
+    }
+
     /// Generates the variable that stores the thread's/work-item's offset into
     /// the memory.
     ///
@@ -132,19 +194,35 @@ class Node {
 
     // Returns true if this node is a Buffer
     virtual bool isBuffer() const { return false; }
+
+    /// Returns true if the buffer is linear
     virtual bool isLinear(dim_t dims[4]) const {
         UNUSED(dims);
         return true;
     }
-    std::string getTypeStr() const { return m_type_str; }
+
+    /// Returns the string representation of the type
+    std::string getTypeStr() const { return getFullName(m_type); }
+
+    /// Returns the height of the JIT tree from this node
     int getHeight() const { return m_height; }
-    std::string getNameStr() const { return m_name_str; }
 
-    virtual ~Node() {}
+    /// Returns the short name for this type
+    /// \note For the shift node this is "Sh" appended by the short name of the
+    ///       type
+    virtual std::string getNameStr() const { return getShortName(m_type); }
+
+    /// Default destructor
+    virtual ~Node() = default;
 };
 
 struct Node_ids {
     std::array<int, Node::kMaxChildren> child_ids;
     int id;
 };
+
+std::string getFuncName(const std::vector<Node *> &output_nodes,
+                        const std::vector<Node *> &full_nodes,
+                        const std::vector<Node_ids> &full_ids, bool is_linear);
+
 }  // namespace common
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 35861103c7..e4ff5664f0 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -8,7 +8,9 @@
  ********************************************************/
 
 #pragma once
+#include <backend.hpp>
 #include <common/jit/Node.hpp>
+#include <af/traits.hpp>
 
 #include <math.hpp>
 #include <types.hpp>
@@ -23,12 +25,12 @@ class ScalarNode : public common::Node {
 
    public:
     ScalarNode(T val)
-        : Node(detail::getFullName<T>(), detail::shortname<T>(false), 0, {})
+        : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), 0, {})
         , m_val(val) {}
 
     void genKerName(std::stringstream& kerStream,
                     const common::Node_ids& ids) const final {
-        kerStream << "_" << m_name_str;
+        kerStream << "_" << getTypeStr();
         kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
                   << std::dec;
     }
@@ -36,7 +38,7 @@ class ScalarNode : public common::Node {
     void genParams(std::stringstream& kerStream, int id,
                    bool is_linear) const final {
         UNUSED(is_linear);
-        kerStream << m_type_str << " scalar" << id << ", \n";
+        kerStream << getTypeStr() << " scalar" << id << ", \n";
     }
 
     int setArgs(int start_id, bool is_linear,
@@ -49,10 +51,12 @@ class ScalarNode : public common::Node {
 
     void genFuncs(std::stringstream& kerStream,
                   const common::Node_ids& ids) const final {
-        kerStream << m_type_str << " val" << ids.id << " = scalar" << ids.id
+        kerStream << getTypeStr() << " val" << ids.id << " = scalar" << ids.id
                   << ";\n";
     }
 
+    std::string getNameStr() const final { return detail::shortname<T>(false); }
+
     // Return the info for the params and the size of the buffers
     virtual size_t getParamBytes() const final { return sizeof(T); }
 };
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index d02ebab0e2..68ca54354b 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -29,12 +29,9 @@ class ShiftNodeBase : public Node {
     const std::array<int, 4> m_shifts;
 
    public:
-    ShiftNodeBase(const char *type_str, const char *name_str,
-                  std::shared_ptr<BufferNode> buffer_node,
+    ShiftNodeBase(const af::dtype type, std::shared_ptr<BufferNode> buffer_node,
                   const std::array<int, 4> shifts)
-        : Node(type_str, name_str, 0, {})
-        , m_buffer_node(buffer_node)
-        , m_shifts(shifts) {}
+        : Node(type, 0, {}), m_buffer_node(buffer_node), m_shifts(shifts) {}
 
     bool isLinear(dim_t dims[4]) const final {
         UNUSED(dims);
@@ -43,7 +40,7 @@ class ShiftNodeBase : public Node {
 
     void genKerName(std::stringstream &kerStream,
                     const common::Node_ids &ids) const final {
-        kerStream << "_" << m_name_str;
+        kerStream << "_" << getNameStr();
         kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
                   << std::dec;
     }
@@ -69,17 +66,22 @@ class ShiftNodeBase : public Node {
 
     void genOffsets(std::stringstream &kerStream, int id,
                     bool is_linear) const final {
-        detail::generateShiftNodeOffsets(kerStream, id, is_linear, m_type_str);
+        detail::generateShiftNodeOffsets(kerStream, id, is_linear,
+                                         getTypeStr());
     }
 
     void genFuncs(std::stringstream &kerStream,
                   const common::Node_ids &ids) const final {
-        detail::generateShiftNodeRead(kerStream, ids.id, m_type_str);
+        detail::generateShiftNodeRead(kerStream, ids.id, getTypeStr());
     }
 
     void getInfo(unsigned &len, unsigned &buf_count,
                  unsigned &bytes) const final {
         m_buffer_node->getInfo(len, buf_count, bytes);
     }
+
+    std::string getNameStr() const final {
+        return std::string("Sh") + getShortName(m_type);
+    }
 };
 }  // namespace common
diff --git a/src/backend/common/jit/UnaryNode.hpp b/src/backend/common/jit/UnaryNode.hpp
index c169675148..c0588f4cee 100644
--- a/src/backend/common/jit/UnaryNode.hpp
+++ b/src/backend/common/jit/UnaryNode.hpp
@@ -14,9 +14,7 @@ namespace common {
 
 class UnaryNode : public NaryNode {
    public:
-    UnaryNode(const char *out_type_str, const char *name_str,
-              const char *op_str, Node_ptr child, int op)
-        : NaryNode(out_type_str, name_str, op_str, 1, {{child}}, op,
-                   child->getHeight() + 1) {}
+    UnaryNode(const af::dtype type, const char *op_str, Node_ptr child, int op)
+        : NaryNode(type, op_str, 1, {{child}}, op, child->getHeight() + 1) {}
 };
 }  // namespace common
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 4976bc2582..ffd0576b26 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -38,11 +38,11 @@
 
 using af::dim4;
 using common::half;
+using common::Node;
+using common::Node_map_t;
+using common::Node_ptr;
 using common::NodeIterator;
 using cpu::jit::BufferNode;
-using cpu::jit::Node;
-using cpu::jit::Node_map_t;
-using cpu::jit::Node_ptr;
 using std::adjacent_find;
 using std::copy;
 using std::is_standard_layout;
@@ -163,7 +163,7 @@ T *Array<T>::device() {
 
 template<typename T>
 void evalMultiple(vector<Array<T> *> array_ptrs) {
-    vector<Array<T> *> output_arrays;
+    vector<Array<T> *> outputs;
     vector<Node_ptr> nodes;
     vector<Param<T>> params;
     if (getQueue().is_worker()) {
@@ -189,14 +189,14 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
         array->data =
             shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
-        output_arrays.push_back(array);
+        outputs.push_back(array);
         params.push_back(*array);
         nodes.push_back(array->node);
     }
 
-    if (!output_arrays.empty()) {
+    if (!outputs.empty()) {
         getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
-        for (Array<T> *array : output_arrays) {
+        for (Array<T> *array : outputs) {
             array->ready = true;
             array->node  = bufferNodePtr<T>();
         }
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index c7d307b436..037db5c58b 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -9,6 +9,7 @@
 
 // This is the array implementation class.
 #pragma once
+
 #include <Param.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/MemoryManagerBase.hpp>
@@ -28,11 +29,11 @@
 namespace cpu {
 namespace kernel {
 template<typename T>
-void evalArray(Param<T> in, jit::Node_ptr node);
+void evalArray(Param<T> in, common::Node_ptr node);
 
 template<typename T>
 void evalMultiple(std::vector<Param<T>> arrays,
-                  std::vector<jit::Node_ptr> nodes);
+                  std::vector<common::Node_ptr> nodes);
 
 }  // namespace kernel
 
@@ -47,7 +48,7 @@ void evalMultiple(std::vector<Array<T> *> array_ptrs);
 
 // Creates a new Array object on the heap and returns a reference to it.
 template<typename T>
-Array<T> createNodeArray(const af::dim4 &dims, jit::Node_ptr node);
+Array<T> createNodeArray(const af::dim4 &dims, common::Node_ptr node);
 
 template<typename T>
 Array<T> createValueArray(const af::dim4 &dims, const T &value);
@@ -92,7 +93,7 @@ template<typename T>
 void destroyArray(Array<T> *A);
 
 template<typename T>
-kJITHeuristics passesJitHeuristics(jit::Node *node);
+kJITHeuristics passesJitHeuristics(common::Node *node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
@@ -116,7 +117,7 @@ class Array {
     // data if parent. empty if child
     std::shared_ptr<T> data;
     af::dim4 data_dims;
-    jit::Node_ptr node;
+    common::Node_ptr node;
 
     bool ready;
     bool owner;
@@ -128,7 +129,7 @@ class Array {
                    bool copy_device = false);
     Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset,
           const dim4 &stride);
-    explicit Array(const af::dim4 &dims, jit::Node_ptr n);
+    explicit Array(const af::dim4 &dims, common::Node_ptr n);
     Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
           T *const in_data, bool is_device = false);
 
@@ -226,7 +227,7 @@ class Array {
         return CParam<T>(this->get(), this->dims(), this->strides());
     }
 
-    jit::Node_ptr getNode() const;
+    common::Node_ptr getNode() const;
 
     friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
@@ -240,15 +241,15 @@ class Array {
 
     friend Array<T> createEmptyArray<T>(const af::dim4 &dims);
     friend Array<T> createNodeArray<T>(const af::dim4 &dims,
-                                       jit::Node_ptr node);
+                                       common::Node_ptr node);
 
     friend Array<T> createSubArray<T>(const Array<T> &parent,
                                       const std::vector<af_seq> &index,
                                       bool copy);
 
-    friend void kernel::evalArray<T>(Param<T> in, jit::Node_ptr node);
+    friend void kernel::evalArray<T>(Param<T> in, common::Node_ptr node);
     friend void kernel::evalMultiple<T>(std::vector<Param<T>> arrays,
-                                        std::vector<jit::Node_ptr> nodes);
+                                        std::vector<common::Node_ptr> nodes);
 
     friend void destroyArray<T>(Array<T> *arr);
     friend void *getDevicePtr<T>(const Array<T> &arr);
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 7a095fc6bc..cf0a94e40b 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -84,13 +84,13 @@ NUMERIC_FN(af_hypot_t, hypot)
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
-    jit::Node_ptr lhs_node = lhs.getNode();
-    jit::Node_ptr rhs_node = rhs.getNode();
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
 
     jit::BinaryNode<T, T, op> *node =
         new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-    return createNodeArray<T>(odims, jit::Node_ptr(node));
+    return createNodeArray<T>(odims, common::Node_ptr(node));
 }
 
 }  // namespace cpu
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index ad919405d2..5098d8b109 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -155,11 +155,12 @@ CAST_B8(char)
 template<typename To, typename Ti>
 struct CastWrapper {
     Array<To> operator()(const Array<Ti> &in) {
-        jit::Node_ptr in_node = in.getNode();
+        common::Node_ptr in_node = in.getNode();
         jit::UnaryNode<To, Ti, af_cast_t> *node =
             new jit::UnaryNode<To, Ti, af_cast_t>(in_node);
         return createNodeArray<To>(
-            in.dims(), jit::Node_ptr(reinterpret_cast<jit::Node *>(node)));
+            in.dims(),
+            common::Node_ptr(reinterpret_cast<common::Node *>(node)));
     }
 };
 
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index 2659c3c811..61b10f49e1 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -28,13 +28,13 @@ struct BinOp<To, Ti, af_cplx2_t> {
 template<typename To, typename Ti>
 Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
                const af::dim4 &odims) {
-    jit::Node_ptr lhs_node = lhs.getNode();
-    jit::Node_ptr rhs_node = rhs.getNode();
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
 
     jit::BinaryNode<To, Ti, af_cplx2_t> *node =
         new jit::BinaryNode<To, Ti, af_cplx2_t>(lhs_node, rhs_node);
 
-    return createNodeArray<To>(odims, jit::Node_ptr(node));
+    return createNodeArray<To>(odims, common::Node_ptr(node));
 }
 
 #define CPLX_UNARY_FN(op)                                              \
@@ -53,41 +53,41 @@ CPLX_UNARY_FN(abs)
 
 template<typename To, typename Ti>
 Array<To> real(const Array<Ti> &in) {
-    jit::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
     jit::UnaryNode<To, Ti, af_real_t> *node =
         new jit::UnaryNode<To, Ti, af_real_t>(in_node);
 
-    return createNodeArray<To>(in.dims(),
-                               jit::Node_ptr(static_cast<jit::Node *>(node)));
+    return createNodeArray<To>(
+        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
 }
 
 template<typename To, typename Ti>
 Array<To> imag(const Array<Ti> &in) {
-    jit::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
     jit::UnaryNode<To, Ti, af_imag_t> *node =
         new jit::UnaryNode<To, Ti, af_imag_t>(in_node);
 
-    return createNodeArray<To>(in.dims(),
-                               jit::Node_ptr(static_cast<jit::Node *>(node)));
+    return createNodeArray<To>(
+        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
 }
 
 template<typename To, typename Ti>
 Array<To> abs(const Array<Ti> &in) {
-    jit::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
     jit::UnaryNode<To, Ti, af_abs_t> *node =
         new jit::UnaryNode<To, Ti, af_abs_t>(in_node);
 
-    return createNodeArray<To>(in.dims(),
-                               jit::Node_ptr(static_cast<jit::Node *>(node)));
+    return createNodeArray<To>(
+        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
 }
 
 template<typename T>
 Array<T> conj(const Array<T> &in) {
-    jit::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
     jit::UnaryNode<T, T, af_conj_t> *node =
         new jit::UnaryNode<T, T, af_conj_t>(in_node);
 
-    return createNodeArray<T>(in.dims(),
-                              jit::Node_ptr(static_cast<jit::Node *>(node)));
+    return createNodeArray<T>(
+        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
 }
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 4d199601ea..f82172c97a 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -29,7 +29,7 @@ class BinaryNode : public TNode<compute_t<To>> {
     TNode<compute_t<Ti>> *m_lhs, *m_rhs;
 
    public:
-    BinaryNode(Node_ptr lhs, Node_ptr rhs)
+    BinaryNode(common::Node_ptr lhs, common::Node_ptr rhs)
         : TNode<compute_t<To>>(compute_t<To>(0),
                                std::max(lhs->getHeight(), rhs->getHeight()) + 1,
                                {{lhs, rhs}})
@@ -48,6 +48,40 @@ class BinaryNode : public TNode<compute_t<To>> {
         UNUSED(idx);
         m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
     }
+
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
+
+    void genParams(std::stringstream &kerStream, int id,
+                   bool is_linear) const final {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+
+    int setArgs(int start_id, bool is_linear,
+                std::function<void(int id, const void *ptr, size_t arg_size)>
+                    setArg) const override {
+        UNUSED(is_linear);
+        UNUSED(setArg);
+        return start_id++;
+    }
+
+    void genOffsets(std::stringstream &kerStream, int id,
+                    bool is_linear) const final {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
 };
 
 }  // namespace jit
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index 7404cd7ff3..d4360393cb 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -8,7 +8,10 @@
  ********************************************************/
 
 #pragma once
+
 #include <optypes.hpp>
+#include <af/defines.h>
+
 #include <mutex>
 #include <vector>
 #include "Node.hpp"
@@ -82,7 +85,41 @@ class BufferNode : public TNode<T> {
 
     size_t getBytes() const final { return m_bytes; }
 
-    bool isLinear(const dim_t *dims) const final {
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
+
+    void genParams(std::stringstream &kerStream, int id,
+                   bool is_linear) const final {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+
+    int setArgs(int start_id, bool is_linear,
+                std::function<void(int id, const void *ptr, size_t arg_size)>
+                    setArg) const override {
+        UNUSED(is_linear);
+        UNUSED(setArg);
+        return start_id++;
+    }
+
+    void genOffsets(std::stringstream &kerStream, int id,
+                    bool is_linear) const final {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
+
+    bool isLinear(dim_t *dims) const final {
         return m_linear_buffer && dims[0] == m_dims[0] &&
                dims[1] == m_dims[1] && dims[2] == m_dims[2] &&
                dims[3] == m_dims[3];
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index 5b309be338..5524bb75dc 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -10,7 +10,10 @@
 #pragma once
 #include <common/defines.hpp>
 #include <common/half.hpp>
+#include <common/jit/Node.hpp>
+#include <common/traits.hpp>
 #include <optypes.hpp>
+#include <af/traits.hpp>
 
 #include <array>
 #include <memory>
@@ -28,90 +31,25 @@ namespace jit {
 class Node;
 constexpr int VECTOR_LENGTH = 256;
 
-using Node_ptr      = std::shared_ptr<Node>;
-using Node_map_t    = std::unordered_map<Node *, int>;
-using Node_map_iter = Node_map_t::iterator;
-
 template<typename T>
 using array = std::array<T, VECTOR_LENGTH>;
 
-class Node {
-   public:
-    static const int kMaxChildren = 2;
-
-   protected:
-    const int m_height;
-    const std::array<Node_ptr, kMaxChildren> m_children;
-    template<typename T>
-    friend class common::NodeIterator;
-
-   public:
-    Node(const int height, const std::array<Node_ptr, kMaxChildren> children)
-        : m_height(height), m_children(children) {}
-
-    int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes) {
-        auto iter = node_map.find(this);
-        if (iter == node_map.end()) {
-            for (auto &child : m_children) {
-                if (child == nullptr) break;
-                child->getNodesMap(node_map, full_nodes);
-            }
-            int id         = static_cast<int>(node_map.size());
-            node_map[this] = id;
-            full_nodes.push_back(this);
-            return id;
-        }
-        return iter->second;
-    }
-
-    int getHeight() { return m_height; }
-
-    virtual void calc(int x, int y, int z, int w, int lim) {
-        UNUSED(x);
-        UNUSED(y);
-        UNUSED(z);
-        UNUSED(w);
-        UNUSED(lim);
-    }
-
-    virtual void calc(int idx, int lim) {
-        UNUSED(idx);
-        UNUSED(lim);
-    }
-
-    virtual void getInfo(unsigned &len, unsigned &buf_count,
-                         unsigned &bytes) const {
-        UNUSED(buf_count);
-        UNUSED(bytes);
-        len++;
-    }
-
-    virtual bool isLinear(const dim_t *dims) const {
-        UNUSED(dims);
-        return true;
-    }
-    virtual bool isBuffer() const { return false; }
-    virtual ~Node() {}
-
-    virtual size_t getBytes() const { return 0; }
-};
+}  // namespace jit
 
 template<typename T>
-class TNode : public Node {
+class TNode : public common::Node {
    public:
     alignas(16) jit::array<compute_t<T>> m_val;
 
    public:
     TNode(T val, const int height,
-          const std::array<Node_ptr, kMaxChildren> children)
-        : Node(height, children) {
+          const std::array<common::Node_ptr, kMaxChildren> children)
+        : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), height,
+               children) {
         using namespace common;
         m_val.fill(static_cast<compute_t<T>>(val));
     }
+    virtual ~TNode() = default;
 };
 
-template<typename T>
-using TNode_ptr = std::shared_ptr<TNode<T>>;
-
-}  // namespace jit
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index afb4ca8768..86dbea3998 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -18,8 +18,42 @@ namespace jit {
 
 template<typename T>
 class ScalarNode : public TNode<T> {
-   public:
+  public:
     ScalarNode(T val) : TNode<T>(val, 0, {}) {}
+
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
+
+    void genParams(std::stringstream &kerStream, int id,
+                   bool is_linear) const final {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+
+    int setArgs(int start_id, bool is_linear,
+                std::function<void(int id, const void *ptr, size_t arg_size)>
+                    setArg) const override {
+        UNUSED(is_linear);
+        UNUSED(setArg);
+        return start_id++;
+    }
+
+    void genOffsets(std::stringstream &kerStream, int id,
+                    bool is_linear) const final {
+        UNUSED(kerStream);
+        UNUSED(id);
+        UNUSED(is_linear);
+    }
+
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
 };
 }  // namespace jit
 
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 0cf6f2f83c..87dd911ba8 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -19,9 +19,7 @@ namespace cpu {
 template<typename To, typename Ti, af_op_t op>
 struct UnOp {
     void eval(jit::array<compute_t<To>> &out,
-              const jit::array<compute_t<Ti>> &in, int lim) const {
-        for (int i = 0; i < lim; i++) { out[i] = in[i]; }
-    }
+              const jit::array<compute_t<Ti>> &in, int lim) const;
 };
 
 namespace jit {
@@ -33,7 +31,7 @@ class UnaryNode : public TNode<To> {
     TNode<Ti> *m_child;
 
    public:
-    UnaryNode(Node_ptr child)
+    UnaryNode(common::Node_ptr child)
         : TNode<To>(To(0), child->getHeight() + 1, {{child}})
         , m_child(reinterpret_cast<TNode<Ti> *>(child.get())) {}
 
@@ -49,6 +47,18 @@ class UnaryNode : public TNode<To> {
         UNUSED(idx);
         m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
     }
+
+    void genKerName(std::stringstream &kerStream,
+                    const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
+
+    void genFuncs(std::stringstream &kerStream,
+                  const common::Node_ids &ids) const final {
+        UNUSED(kerStream);
+        UNUSED(ids);
+    }
 };
 
 }  // namespace jit
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index a8b3fbb512..bc320f6285 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -18,21 +18,22 @@ namespace kernel {
 
 template<typename T>
 void evalMultiple(std::vector<Param<T>> arrays,
-                  std::vector<jit::Node_ptr> output_nodes_) {
+                  std::vector<common::Node_ptr> output_nodes_) {
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
-    jit::Node_map_t nodes;
+    common::Node_map_t nodes;
     std::vector<T *> ptrs;
-    std::vector<jit::TNode<T> *> output_nodes;
-    std::vector<jit::Node *> full_nodes;
+    std::vector<TNode<T> *> output_nodes;
+    std::vector<common::Node *> full_nodes;
+    std::vector<common::Node_ids> ids;
 
     int narrays = static_cast<int>(arrays.size());
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
         output_nodes.push_back(
-            reinterpret_cast<jit::TNode<T> *>(output_nodes_[i].get()));
-        output_nodes_[i]->getNodesMap(nodes, full_nodes);
+            reinterpret_cast<TNode<T> *>(output_nodes_[i].get()));
+        output_nodes_[i]->getNodesMap(nodes, full_nodes, ids);
     }
 
     bool is_linear = true;
@@ -85,7 +86,7 @@ void evalMultiple(std::vector<Param<T>> arrays,
 }
 
 template<typename T>
-void evalArray(Param<T> arr, jit::Node_ptr node) {
+void evalArray(Param<T> arr, common::Node_ptr node) {
     evalMultiple<T>({arr}, {node});
 }
 
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index f356eaf6fa..0ea4222d81 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -69,13 +69,13 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
                     const af::dim4 &odims) {
-    jit::Node_ptr lhs_node = lhs.getNode();
-    jit::Node_ptr rhs_node = rhs.getNode();
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
 
     jit::BinaryNode<char, T, op> *node =
         new jit::BinaryNode<char, T, op>(lhs_node, rhs_node);
 
-    return createNodeArray<char>(odims, jit::Node_ptr(node));
+    return createNodeArray<char>(odims, common::Node_ptr(node));
 }
 
 #define BITWISE_FN(OP, op)                                               \
@@ -98,12 +98,12 @@ BITWISE_FN(af_bitshiftr_t, >>)
 template<typename T, af_op_t op>
 Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
                const af::dim4 &odims) {
-    jit::Node_ptr lhs_node = lhs.getNode();
-    jit::Node_ptr rhs_node = rhs.getNode();
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
 
     jit::BinaryNode<T, T, op> *node =
         new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
 
-    return createNodeArray<T>(odims, jit::Node_ptr(node));
+    return createNodeArray<T>(odims, common::Node_ptr(node));
 }
 }  // namespace cpu
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 58be372157..d0263fbf0b 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -12,6 +12,20 @@
 #include <complex>
 
 namespace cpu {
+
+namespace {
+template<typename T>
+const char *shortname(bool caps = false) {
+    return caps ? "?" : "?";
+}
+
+template<typename T>
+const char *getFullName() {
+    return "N/A";
+}
+
+}  // namespace
+
 using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
 using intl    = long long;
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 87c3e12d3c..46bbb23e2d 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -76,6 +76,7 @@ UNARY_OP(cbrt)
 
 UNARY_OP(tgamma)
 UNARY_OP(lgamma)
+UNARY_OP_FN(noop, )  /// Empty second parameter so it does nothing
 
 UNARY_OP_FN(bitnot, ~)
 
@@ -86,11 +87,11 @@ template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using UnaryNode = jit::UnaryNode<T, T, op>;
 
-    jit::Node_ptr in_node = in.getNode();
-    UnaryNode *node       = new UnaryNode(in_node);
+    common::Node_ptr in_node = in.getNode();
+    UnaryNode *node          = new UnaryNode(in_node);
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
-    return createNodeArray<T>(outDim, jit::Node_ptr(node));
+    return createNodeArray<T>(outDim, common::Node_ptr(node));
 }
 
 #define iszero(a) ((a) == 0)
@@ -111,12 +112,12 @@ CHECK_FN(iszero, iszero)
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    jit::Node_ptr in_node = in.getNode();
+    common::Node_ptr in_node = in.getNode();
     jit::UnaryNode<char, T, op> *node =
         new jit::UnaryNode<char, T, op>(in_node);
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
-    return createNodeArray<char>(outDim, jit::Node_ptr(node));
+    return createNodeArray<char>(outDim, common::Node_ptr(node));
 }
 
 }  // namespace cpu
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 6bfb45ff27..8ade10a592 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -50,7 +50,8 @@ void verifyTypeSupport() {
 
 template<typename T>
 Node_ptr bufferNodePtr() {
-    return Node_ptr(new BufferNode<T>(getFullName<T>(), shortname<T>(true)));
+    return Node_ptr(
+        new BufferNode<T>(static_cast<af::dtype>(dtype_traits<T>::af_type)));
 }
 
 template<typename T>
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index bcee0fa55f..61e4bceefb 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -137,8 +137,8 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
     auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
         BinOp<To, Ti, op> bop;
         return Node_ptr(new common::BinaryNode(
-            getFullName<To>(), shortname<To>(true), bop.name(), operands[0],
-            operands[1], (int)(op)));
+            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(),
+            operands[0], operands[1], (int)(op)));
     };
 
     Node_ptr out =
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index e14aa9f352..1dc8c3ae06 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -89,9 +89,9 @@ struct CastWrapper {
     Array<To> operator()(const Array<Ti> &in) {
         CastOp<To, Ti> cop;
         common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node =
-            new common::UnaryNode(getFullName<To>(), shortname<To>(true),
-                                  cop.name(), in_node, af_cast_t);
+        common::UnaryNode *node  = new common::UnaryNode(
+            static_cast<af::dtype>(dtype_traits<To>::af_type), cop.name(),
+            in_node, af_cast_t);
         return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 };
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index e0eba61c8a..f86a6fb027 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -23,8 +23,9 @@ Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
 template<typename To, typename Ti>
 Array<To> real(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
-    common::UnaryNode *node  = new common::UnaryNode(
-        getFullName<To>(), shortname<To>(true), "__creal", in_node, af_real_t);
+    common::UnaryNode *node =
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
+                              "__creal", in_node, af_real_t);
 
     return createNodeArray<To>(in.dims(), common::Node_ptr(node));
 }
@@ -32,8 +33,9 @@ Array<To> real(const Array<Ti> &in) {
 template<typename To, typename Ti>
 Array<To> imag(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
-    common::UnaryNode *node  = new common::UnaryNode(
-        getFullName<To>(), shortname<To>(true), "__cimag", in_node, af_imag_t);
+    common::UnaryNode *node =
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
+                              "__cimag", in_node, af_imag_t);
 
     return createNodeArray<To>(in.dims(), common::Node_ptr(node));
 }
@@ -55,7 +57,7 @@ template<typename To, typename Ti>
 Array<To> abs(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
     common::UnaryNode *node =
-        new common::UnaryNode(getFullName<To>(), shortname<To>(true),
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
                               abs_name<Ti>(), in_node, af_abs_t);
 
     return createNodeArray<To>(in.dims(), common::Node_ptr(node));
@@ -78,7 +80,7 @@ template<typename T>
 Array<T> conj(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
     common::UnaryNode *node =
-        new common::UnaryNode(getFullName<T>(), shortname<T>(true),
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type),
                               conj_name<T>(), in_node, af_conj_t);
 
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 9eee088e20..a31ca6aa1a 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -31,6 +31,7 @@
 #include <vector>
 
 using common::compileKernel;
+using common::getFuncName;
 using common::half;
 using common::Node;
 using common::Node_ids;
@@ -43,33 +44,8 @@ using std::vector;
 
 namespace cuda {
 
-static string getFuncName(const vector<Node *> &output_nodes,
-                          const vector<const Node *> &full_nodes,
-                          const vector<Node_ids> &full_ids, bool is_linear) {
-    stringstream funcName;
-    stringstream hashName;
-
-    if (is_linear) {
-        funcName << "L_";  // Kernel Linear
-    } else {
-        funcName << "G_";  // Kernel General
-    }
-
-    for (const auto &node : output_nodes) {
-        funcName << node->getNameStr() << "_";
-    }
-
-    for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
-        full_nodes[i]->genKerName(funcName, full_ids[i]);
-    }
-
-    hashName << "KER";
-    hashName << deterministicHash(funcName.str());
-    return hashName.str();
-}
-
 static string getKernelString(const string &funcName,
-                              const vector<const Node *> &full_nodes,
+                              const vector<Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
                               const vector<int> &output_ids, bool is_linear) {
     const std::string includeFileStr(jit_cuh, jit_cuh_len);
@@ -202,7 +178,7 @@ struct Param {
 
 static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<int> &output_ids,
-                            const vector<const Node *> &full_nodes,
+                            const vector<Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
     using kc_t = map<string, Kernel>;
@@ -245,7 +221,7 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
-    thread_local vector<const Node *> full_nodes;
+    thread_local vector<Node *> full_nodes;
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index 7f0907d5d8..47123f1156 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -47,7 +47,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     int height     = max(a_node->getHeight(), b_node->getHeight());
     height         = max(height, cond_node->getHeight()) + 1;
     auto node      = make_shared<NaryNode>(NaryNode(
-        getFullName<T>(), shortname<T>(true), "__select", 3,
+        static_cast<af::dtype>(dtype_traits<T>::af_type), "__select", 3,
         {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
@@ -76,7 +76,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     height         = max(height, cond_node->getHeight()) + 1;
 
     auto node = make_shared<NaryNode>(NaryNode(
-        getFullName<T>(), shortname<T>(true),
+        static_cast<af::dtype>(dtype_traits<T>::af_type),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         static_cast<int>(flip ? af_not_select_t : af_select_t), height));
 
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index e66fe381fc..f83bba9802 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -53,7 +53,7 @@ Array<T> shift(const Array<T> &in, const int sdims[4]) {
     }
 
     auto node = make_shared<ShiftNode<T>>(
-        getFullName<T>(), name_str.c_str(),
+        static_cast<af::dtype>(af::dtype_traits<T>::af_type),
         static_pointer_cast<BufferNode<T>>(in.getNode()), shifts);
     return createNodeArray<T>(oDims, Node_ptr(node));
 }
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 4183a91a2c..4c87932cf7 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -82,9 +82,9 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using std::array;
 
     auto createUnary = [](array<Node_ptr, 1> &operands) {
-        return common::Node_ptr(
-            new common::UnaryNode(getFullName<T>(), shortname<T>(true),
-                                  unaryName<op>(), operands[0], op));
+        return common::Node_ptr(new common::UnaryNode(
+            static_cast<af::dtype>(af::dtype_traits<T>::af_type),
+            unaryName<op>(), operands[0], op));
     };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
@@ -97,9 +97,9 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using common::Node_ptr;
 
     auto createUnary = [](std::array<Node_ptr, 1> &operands) {
-        return Node_ptr(
-            new common::UnaryNode(getFullName<char>(), shortname<char>(true),
-                                  unaryName<op>(), operands[0], op));
+        return Node_ptr(new common::UnaryNode(
+            static_cast<af::dtype>(dtype_traits<char>::af_type),
+            unaryName<op>(), operands[0], op));
     };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index a390f6be0a..6b65807755 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -45,8 +45,8 @@ using std::vector;
 namespace opencl {
 template<typename T>
 Node_ptr bufferNodePtr() {
-    return make_shared<BufferNode>(dtype_traits<T>::getName(),
-                                   shortname<T>(true));
+    return make_shared<BufferNode>(
+        static_cast<af::dtype>(dtype_traits<T>::af_type));
 }
 
 namespace {
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index f26e408e3f..28eeb98380 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -137,8 +137,8 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
     auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
         BinOp<To, Ti, op> bop;
         return Node_ptr(new common::BinaryNode(
-            getFullName<To>(), shortname<To>(true), bop.name(), operands[0],
-            operands[1], (int)(op)));
+            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(), operands[0], operands[1],
+            (int)(op)));
     };
 
     Node_ptr out =
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index aec21f7a3b..2ce6f5fc7b 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -76,7 +76,7 @@ struct CastWrapper {
         CastOp<To, Ti> cop;
         common::Node_ptr in_node = in.getNode();
         common::UnaryNode *node  = new common::UnaryNode(
-            dtype_traits<To>::getName(), shortname<To>(true), cop.name(),
+            static_cast<af::dtype>(dtype_traits<To>::af_type), cop.name(),
             in_node, af_cast_t);
         return createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index e403eaa996..d927005ef2 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -25,7 +25,7 @@ template<typename To, typename Ti>
 Array<To> real(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
     common::UnaryNode *node =
-        new common::UnaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
                               "__creal", in_node, af_real_t);
 
     return createNodeArray<To>(in.dims(), common::Node_ptr(node));
@@ -35,7 +35,7 @@ template<typename To, typename Ti>
 Array<To> imag(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
     common::UnaryNode *node =
-        new common::UnaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
                               "__cimag", in_node, af_imag_t);
 
     return createNodeArray<To>(in.dims(), common::Node_ptr(node));
@@ -58,7 +58,7 @@ template<typename To, typename Ti>
 Array<To> abs(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
     common::UnaryNode *node =
-        new common::UnaryNode(dtype_traits<To>::getName(), shortname<To>(true),
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
                               abs_name<Ti>(), in_node, af_abs_t);
 
     return createNodeArray<To>(in.dims(), common::Node_ptr(node));
@@ -81,7 +81,7 @@ template<typename T>
 Array<T> conj(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
     common::UnaryNode *node =
-        new common::UnaryNode(dtype_traits<T>::getName(), shortname<T>(true),
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type),
                               conj_name<T>(), in_node, af_conj_t);
 
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 67f1c025ab..9f6ab0a798 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -28,6 +28,7 @@
 #include <vector>
 
 using common::compileKernel;
+using common::getFuncName;
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
@@ -56,31 +57,8 @@ spdlog::logger *getLogger() {
 
 namespace opencl {
 
-static string getFuncName(const vector<Node *> &output_nodes,
-                          const vector<const Node *> &full_nodes,
-                          const vector<Node_ids> &full_ids, bool is_linear) {
-    stringstream hashName;
-    stringstream funcName;
-
-    if (is_linear) {
-        funcName << "L_";
-    } else {
-        funcName << "G_";
-    }
-
-    for (auto node : output_nodes) { funcName << node->getNameStr() << "_"; }
-
-    for (size_t i = 0; i < full_nodes.size(); i++) {
-        full_nodes[i]->genKerName(funcName, full_ids[i]);
-    }
-
-    hash<string> hash_fn;
-    hashName << "KER" << hash_fn(funcName.str());
-    return hashName.str();
-}
-
 static string getKernelString(const string &funcName,
-                              const vector<const Node *> &full_nodes,
+                              const vector<Node *> &full_nodes,
                               const vector<Node_ids> &full_ids,
                               const vector<int> &output_ids, bool is_linear) {
     // Common OpenCL code
@@ -179,7 +157,7 @@ static string getKernelString(const string &funcName,
 
 static cl::Kernel getKernel(const vector<Node *> &output_nodes,
                             const vector<int> &output_ids,
-                            const vector<const Node *> &full_nodes,
+                            const vector<Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
     using kc_t = map<string, Kernel>;
@@ -242,7 +220,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
-    thread_local vector<const Node *> full_nodes;
+    thread_local vector<Node *> full_nodes;
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 49718969c5..2721a04bab 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -35,7 +35,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     int height     = max(a_node->getHeight(), b_node->getHeight());
     height         = max(height, cond_node->getHeight()) + 1;
     auto node      = make_shared<NaryNode>(NaryNode(
-        dtype_traits<T>::getName(), shortname<T>(true), "__select", 3,
+        static_cast<af::dtype>(dtype_traits<T>::af_type), "__select", 3,
         {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
@@ -64,7 +64,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     height         = max(height, cond_node->getHeight()) + 1;
 
     auto node = make_shared<NaryNode>(NaryNode(
-        dtype_traits<T>::getName(), shortname<T>(true),
+        static_cast<af::dtype>(dtype_traits<T>::af_type),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         static_cast<int>(flip ? af_not_select_t : af_select_t), height));
 
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index e3ff7474fe..0266c5e6d5 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -47,7 +47,7 @@ Array<T> shift(const Array<T> &in, const int sdims[4]) {
     }
 
     auto node = make_shared<ShiftNode>(
-        dtype_traits<T>::getName(), name_str.c_str(),
+        static_cast<af::dtype>(dtype_traits<T>::af_type),
         static_pointer_cast<BufferNode>(in.getNode()), shifts);
     return createNodeArray<T>(oDims, common::Node_ptr(node));
 }
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index e3d7970b78..83a5d624cc 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -55,7 +55,7 @@ struct ToNumStr {
 
 namespace {
 template<typename T>
-inline const char *shortname(bool caps) {
+inline const char *shortname(bool caps = false) {
     return caps ? "X" : "x";
 }
 
@@ -107,13 +107,23 @@ template<>
 inline const char *shortname<ushort>(bool caps) {
     return caps ? "Q" : "q";
 }
-}  // namespace
 
 template<typename T>
-const char *getFullName() {
+inline const char *getFullName() {
     return af::dtype_traits<T>::getName();
 }
 
+template<>
+inline const char *getFullName<cfloat>() {
+    return "float2";
+}
+
+template<>
+inline const char *getFullName<cdouble>() {
+    return "double2";
+}
+}  // namespace
+
 template<typename... ARGS>
 constexpr const char *getTypeBuildDefinition() {
     using common::half;
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index d0ee08537c..803b5943f3 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -81,9 +81,9 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using std::array;
 
     auto createUnary = [](array<Node_ptr, 1> &operands) {
-        return common::Node_ptr(
-            new common::UnaryNode(getFullName<T>(), shortname<T>(true),
-                                  unaryName<op>(), operands[0], op));
+        return common::Node_ptr(new common::UnaryNode(
+            static_cast<af::dtype>(dtype_traits<T>::af_type), unaryName<op>(),
+            operands[0], op));
     };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
@@ -96,9 +96,9 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using common::Node_ptr;
 
     auto createUnary = [](std::array<Node_ptr, 1> &operands) {
-        return Node_ptr(
-            new common::UnaryNode(getFullName<char>(), shortname<char>(true),
-                                  unaryName<op>(), operands[0], op));
+        return Node_ptr(new common::UnaryNode(
+            static_cast<af::dtype>(dtype_traits<char>::af_type), unaryName<op>(),
+            operands[0], op));
     };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }

From 9128218884b266196ed1d8c923cdbb42f804eaad Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 May 2020 15:19:28 -0400
Subject: [PATCH 1961/2677] More clang-tidy fixes.

---
 src/.clang-tidy                               |  2 +-
 src/api/c/anisotropic_diffusion.cpp           |  6 ++
 src/api/c/assign.cpp                          |  2 +-
 src/api/c/canny.cpp                           | 83 ++++++++++-------
 src/api/c/confidence_connected.cpp            | 22 +++--
 src/api/c/corrcoef.cpp                        | 22 +++--
 src/api/c/covariance.cpp                      | 21 ++++-
 src/api/c/deconvolution.cpp                   | 17 +++-
 src/api/c/det.cpp                             |  3 +
 src/api/c/device.cpp                          |  4 +-
 src/api/c/exampleFunction.cpp                 |  1 +
 src/api/c/gaussian_kernel.cpp                 |  6 ++
 src/api/c/hist.cpp                            |  5 +
 src/api/c/histeq.cpp                          | 12 +++
 src/api/c/imgproc_common.hpp                  | 42 +++++----
 src/api/c/index.cpp                           | 14 ++-
 src/api/c/inverse.cpp                         |  3 +-
 src/api/c/join.cpp                            | 23 +++--
 src/api/c/lu.cpp                              |  6 +-
 src/api/c/mean.cpp                            |  7 ++
 src/api/c/meanshift.cpp                       |  7 +-
 src/api/c/memory.cpp                          |  2 +-
 src/api/c/moments.cpp                         |  2 +-
 src/api/c/norm.cpp                            | 12 ++-
 src/api/c/ops.hpp                             | 57 ++++++------
 src/api/c/orb.cpp                             |  5 +-
 src/api/c/pinverse.cpp                        | 21 ++++-
 src/api/c/plot.cpp                            |  8 +-
 src/api/c/print.cpp                           | 10 +-
 src/api/c/qr.cpp                              | 14 ++-
 src/api/c/random.cpp                          | 69 +++++++++-----
 src/api/c/rank.cpp                            | 12 ++-
 src/api/c/reduce.cpp                          | 13 ++-
 src/api/c/regions.cpp                         |  4 +-
 src/api/c/reorder.cpp                         | 12 ++-
 src/api/c/replace.cpp                         |  9 +-
 src/api/c/resize.cpp                          |  8 +-
 src/api/c/rgb_gray.cpp                        | 10 +-
 src/api/c/rotate.cpp                          |  8 +-
 src/api/c/sat.cpp                             |  8 +-
 src/api/c/scan.cpp                            |  8 +-
 src/api/c/select.cpp                          | 10 +-
 src/api/c/set.cpp                             |  8 +-
 src/api/c/shift.cpp                           |  8 +-
 src/api/c/sift.cpp                            |  3 +-
 src/api/c/sobel.cpp                           |  9 +-
 src/api/c/solve.cpp                           |  5 +-
 src/api/c/sort.cpp                            | 10 +-
 src/api/c/sparse.cpp                          |  9 +-
 src/api/c/sparse_handle.hpp                   |  6 +-
 src/api/c/stats.h                             | 30 +-----
 src/api/c/stdev.cpp                           | 17 +++-
 src/api/c/surface.cpp                         | 22 +++--
 src/api/c/susan.cpp                           | 10 +-
 src/api/c/svd.cpp                             | 32 ++++---
 src/api/c/tile.cpp                            | 10 +-
 src/api/c/topk.cpp                            |  7 +-
 src/api/c/transform.cpp                       |  8 +-
 src/api/c/transform_coordinates.cpp           |  7 +-
 src/api/c/transpose.cpp                       |  9 +-
 src/api/c/unary.cpp                           | 21 ++++-
 src/api/c/unwrap.cpp                          |  9 +-
 src/api/c/var.cpp                             | 22 ++++-
 src/api/c/vector_field.cpp                    | 10 +-
 src/api/c/where.cpp                           |  8 +-
 src/api/c/window.cpp                          |  2 +-
 src/api/c/wrap.cpp                            |  8 +-
 src/api/c/ycbcr_rgb.cpp                       |  6 +-
 src/api/cpp/array.cpp                         | 93 ++++++++++---------
 src/api/cpp/complex.cpp                       | 12 +--
 src/api/cpp/event.cpp                         |  2 +
 src/api/cpp/gfor.cpp                          |  2 +-
 src/api/cpp/graphics.cpp                      |  6 ++
 src/api/cpp/index.cpp                         |  4 +-
 src/api/unified/data.cpp                      |  2 +-
 src/api/unified/device.cpp                    |  4 +-
 src/api/unified/error.cpp                     | 10 +-
 src/api/unified/graphics.cpp                  |  2 +-
 src/api/unified/symbol_manager.cpp            | 33 ++++---
 src/api/unified/symbol_manager.hpp            |  8 +-
 src/backend/common/ArrayInfo.hpp              |  4 +-
 src/backend/common/DefaultMemoryManager.cpp   | 10 +-
 src/backend/common/SparseArray.cpp            | 24 +++--
 src/backend/common/SparseArray.hpp            | 54 +++++------
 src/backend/common/sparse_helpers.hpp         |  8 +-
 src/backend/common/util.cpp                   | 21 +++--
 src/backend/common/util.hpp                   |  2 +-
 src/backend/cpu/Array.cpp                     |  4 +-
 src/backend/cpu/fftconvolve.cpp               |  2 +-
 src/backend/cpu/jit/Node.hpp                  |  1 -
 src/backend/cpu/jit/ScalarNode.hpp            |  2 +-
 src/backend/cpu/kernel/ireduce.hpp            |  4 +-
 src/backend/cpu/kernel/mean.hpp               |  2 +-
 src/backend/cpu/kernel/morph.hpp              |  8 +-
 src/backend/cpu/kernel/reduce.hpp             | 10 +-
 src/backend/cpu/kernel/scan.hpp               | 20 ++--
 src/backend/cpu/kernel/scan_by_key.hpp        | 27 +++---
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  2 +-
 src/backend/cpu/math.cpp                      |  1 +
 src/backend/cpu/math.hpp                      |  6 ++
 src/backend/cpu/memory.cpp                    |  4 +-
 src/backend/cpu/platform.cpp                  |  3 +-
 src/backend/cpu/reduce.cpp                    |  7 ++
 src/backend/cpu/topk.cpp                      |  4 +-
 src/backend/cuda/kernel/ireduce.cuh           | 38 ++++----
 src/backend/cuda/kernel/mean.hpp              | 14 +--
 src/backend/cuda/kernel/morph.cuh             | 50 +++++-----
 src/backend/cuda/kernel/reduce.hpp            | 22 ++---
 src/backend/cuda/kernel/reduce_by_key.hpp     | 18 ++--
 src/backend/cuda/kernel/scan_dim.cuh          | 26 +++---
 src/backend/cuda/kernel/scan_dim_by_key.cuh   | 45 +++++----
 src/backend/cuda/kernel/scan_first.cuh        | 24 +++--
 src/backend/cuda/kernel/scan_first_by_key.cuh | 59 ++++++------
 src/backend/cuda/math.hpp                     | 18 ++--
 src/backend/cuda/minmax_op.hpp                |  4 +-
 src/backend/cuda/types.hpp                    |  3 +-
 src/backend/opencl/Array.cpp                  |  4 +-
 src/backend/opencl/Kernel.cpp                 |  6 +-
 src/backend/opencl/binary.hpp                 |  4 +-
 src/backend/opencl/clfft.cpp                  |  1 +
 src/backend/opencl/device_manager.hpp         |  4 +-
 src/backend/opencl/kernel/ireduce.hpp         |  4 +-
 src/backend/opencl/kernel/mean.hpp            | 12 +--
 src/backend/opencl/kernel/morph.hpp           |  8 +-
 src/backend/opencl/kernel/reduce.hpp          | 15 +--
 src/backend/opencl/kernel/reduce_by_key.hpp   |  8 +-
 src/backend/opencl/kernel/scan_dim.hpp        |  2 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  2 +-
 src/backend/opencl/kernel/scan_first.hpp      |  2 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |  2 +-
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  2 +-
 src/backend/opencl/math.cpp                   |  9 --
 src/backend/opencl/math.hpp                   | 19 +++-
 src/backend/opencl/platform.cpp               |  4 +-
 src/backend/opencl/platform.hpp               |  4 +-
 src/backend/opencl/traits.hpp                 |  8 +-
 src/backend/opencl/unary.hpp                  |  4 +-
 137 files changed, 1104 insertions(+), 655 deletions(-)

diff --git a/src/.clang-tidy b/src/.clang-tidy
index c6a2c6577d..a3e8a261dd 100644
--- a/src/.clang-tidy
+++ b/src/.clang-tidy
@@ -1,5 +1,5 @@
 ---
-Checks:          'clang-diagnostic-*,clang-analyzer-*,*,-fuchsia-*,-cppcoreguidelines-*,-misc-misplaced-const,-hicpp-no-array-decay,-readability-implicit-bool-conversion,bugprone-*,performance-*,modernize-*,-llvm-header-guard,-hicpp-use-auto,-modernize-use-trailing-return-type,-hicpp-uppercase-literal-suffix,-hicpp-use-nullptr,-modernize-use-nullptr,-google-runtime-int,-llvm-include-order,-google-runtime-references,-readability-magic-numbers,-readability-isolate-declaration,-hicpp-vararg,-google-readability-todo,-bugprone-macro-parentheses,-misc-unused-using-decls,-readability-else-after-return,-hicpp-avoid-c-arrays,-modernize-avoid-c-arrays'
+Checks:          'clang-diagnostic-*,clang-analyzer-*,*,-fuchsia-*,-cppcoreguidelines-*,-misc-misplaced-const,-hicpp-no-array-decay,-readability-implicit-bool-conversion,bugprone-*,performance-*,modernize-*,-llvm-header-guard,-hicpp-use-auto,-modernize-use-trailing-return-type,-hicpp-uppercase-literal-suffix,-hicpp-use-nullptr,-modernize-use-nullptr,-google-runtime-int,-llvm-include-order,-google-runtime-references,-readability-magic-numbers,-readability-isolate-declaration,-hicpp-vararg,-google-readability-todo,-bugprone-macro-parentheses,-misc-unused-using-decls,-readability-else-after-return,-hicpp-avoid-c-arrays,-modernize-avoid-c-arrays,-hicpp-braces-around-statements,-hicpp-noexcept-move'
 WarningsAsErrors: ''
 HeaderFilterRegex: ''
 AnalyzeTemporaryDtors: true
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index 6608ad10ab..ceed210548 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -24,6 +24,12 @@
 #include <type_traits>
 
 using af::dim4;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::createEmptyArray;
+using detail::gradient;
+using detail::reduce_all;
 
 template<typename T>
 af_array diffusion(const Array<float>& in, const float dt, const float K,
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 7dc6b6b437..2e357b6ab0 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -64,7 +64,7 @@ static void assign(Array<Tout>& out, const vector<af_seq> seqs,
 
     isVec &= in.isVector() || in.isScalar();
 
-    for (dim_t i = static_cast<dim_t>(ndims); i < in.ndims(); i++) {
+    for (auto i = static_cast<dim_t>(ndims); i < in.ndims(); i++) {
         oDims[i] = 1;
     }
 
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 524c63f556..21010de1e8 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -33,49 +33,70 @@
 #include <vector>
 
 using af::dim4;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::convolve2;
+using detail::createEmptyArray;
+using detail::createHostDataArray;
+using detail::createSubArray;
+using detail::createValueArray;
+using detail::histogram;
+using detail::iota;
+using detail::ireduce;
+using detail::logicOp;
+using detail::reduce;
+using detail::reduce_all;
+using detail::sobelDerivatives;
+using detail::uchar;
+using detail::uint;
+using detail::unaryOp;
+using detail::ushort;
+using std::make_pair;
+using std::pair;
 using std::vector;
 
 Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy,
                                const bool& isf) {
+    using detail::abs;
     if (isf) {
-        Array<float> gx2 = detail::abs<float, float>(gx);
-        Array<float> gy2 = detail::abs<float, float>(gy);
-        return detail::arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
+        Array<float> gx2 = abs<float, float>(gx);
+        Array<float> gy2 = abs<float, float>(gy);
+        return arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
     } else {
-        Array<float> gx2 = detail::arithOp<float, af_mul_t>(gx, gx, gx.dims());
-        Array<float> gy2 = detail::arithOp<float, af_mul_t>(gy, gy, gy.dims());
-        Array<float> sg =
-            detail::arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
-        return detail::unaryOp<float, af_sqrt_t>(sg);
+        Array<float> gx2 = arithOp<float, af_mul_t>(gx, gx, gx.dims());
+        Array<float> gy2 = arithOp<float, af_mul_t>(gy, gy, gy.dims());
+        Array<float> sg  = arithOp<float, af_add_t>(gx2, gy2, gx2.dims());
+        return unaryOp<float, af_sqrt_t>(sg);
     }
 }
 
 Array<float> otsuThreshold(const Array<float>& supEdges,
                            const unsigned NUM_BINS, const float maxVal) {
     Array<uint> hist =
-        detail::histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
+        histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
 
-    const af::dim4& hDims = hist.dims();
+    const dim4& hDims = hist.dims();
 
     // reduce along histogram dimension i.e. 0th dimension
     auto totals = reduce<af_add_t, uint, float>(hist, 0);
 
     // tile histogram total along 0th dimension
-    auto ttotals = tile(totals, af::dim4(hDims[0]));
+    auto ttotals = tile(totals, dim4(hDims[0]));
 
     // pixel frequency probabilities
     auto probability =
         arithOp<float, af_div_t>(cast<float, uint>(hist), ttotals, hDims);
 
-    std::vector<af_seq> seqBegin(4, af_span);
-    std::vector<af_seq> seqRest(4, af_span);
+    vector<af_seq> seqBegin(4, af_span);
+    vector<af_seq> seqRest(4, af_span);
 
     seqBegin[0] = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
     seqRest[0]  = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
 
-    const af::dim4& iDims = supEdges.dims();
+    const dim4& iDims = supEdges.dims();
 
-    Array<float> sigmas = detail::createEmptyArray<float>(hDims);
+    Array<float> sigmas = createEmptyArray<float>(hDims);
 
     for (unsigned b = 0; b < (NUM_BINS - 1); ++b) {
         seqBegin[0].end  = static_cast<double>(b);
@@ -109,7 +130,7 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
         auto op2   = arithOp<float, af_mul_t>(qL, qH, tdims);
         auto sigma = arithOp<float, af_mul_t>(sqrd, op2, tdims);
 
-        std::vector<af_seq> sliceIndex(4, af_span);
+        vector<af_seq> sliceIndex(4, af_span);
         sliceIndex[0] = {double(b), double(b), 1};
 
         auto binRes = createSubArray<float>(sigmas, sliceIndex, false);
@@ -135,10 +156,11 @@ Array<float> normalize(const Array<float>& supEdges, const float minVal,
     return arithOp<float, af_div_t>(diff, denom, supEdges.dims());
 }
 
-std::pair<Array<char>, Array<char>> computeCandidates(
-    const Array<float>& supEdges, const float t1, const af_canny_threshold ct,
-    const float t2) {
-    float maxVal  = detail::reduce_all<af_max_t, float, float>(supEdges);
+pair<Array<char>, Array<char>> computeCandidates(const Array<float>& supEdges,
+                                                 const float t1,
+                                                 const af_canny_threshold ct,
+                                                 const float t2) {
+    float maxVal  = reduce_all<af_max_t, float, float>(supEdges);
     auto NUM_BINS = static_cast<unsigned>(maxVal);
 
     auto lowRatio = createValueArray<float>(supEdges.dims(), t1);
@@ -155,10 +177,10 @@ std::pair<Array<char>, Array<char>> computeCandidates(
                 logicOp<char, af_and_t>(weak1, weak2, weak1.dims());
             Array<char> strong =
                 logicOp<float, af_ge_t>(supEdges, T2, supEdges.dims());
-            return std::make_pair(strong, weak);
+            return make_pair(strong, weak);
         };
         default: {
-            float minVal = detail::reduce_all<af_min_t, float, float>(supEdges);
+            float minVal = reduce_all<af_min_t, float, float>(supEdges);
             auto normG   = normalize(supEdges, minVal, maxVal);
             auto T2      = createValueArray<float>(supEdges.dims(), t2);
             auto T1      = createValueArray<float>(supEdges.dims(), t1);
@@ -181,27 +203,24 @@ af_array cannyHelper(const Array<T>& in, const float t1,
                      const unsigned sw, const bool isf) {
     static const vector<float> v{-0.11021f, -0.23691f, -0.30576f, -0.23691f,
                                  -0.11021f};
-    Array<float> cFilter =
-        detail::createHostDataArray<float>(dim4(5, 1), v.data());
-    Array<float> rFilter =
-        detail::createHostDataArray<float>(dim4(1, 5), v.data());
+    Array<float> cFilter = createHostDataArray<float>(dim4(5, 1), v.data());
+    Array<float> rFilter = createHostDataArray<float>(dim4(1, 5), v.data());
 
     // Run separable convolution to smooth the input image
-    Array<float> smt = detail::convolve2<float, float, false>(
-        cast<float, T>(in), cFilter, rFilter);
+    Array<float> smt =
+        convolve2<float, float, false>(cast<float, T>(in), cFilter, rFilter);
 
-    auto g          = detail::sobelDerivatives<float, float>(smt, sw);
+    auto g          = sobelDerivatives<float, float>(smt, sw);
     Array<float> gx = g.first;
     Array<float> gy = g.second;
 
     Array<float> gmag = gradientMagnitude(gx, gy, isf);
 
-    Array<float> supEdges = detail::nonMaximumSuppression(gmag, gx, gy);
+    Array<float> supEdges = nonMaximumSuppression(gmag, gx, gy);
 
     auto swpair = computeCandidates(supEdges, t1, ct, t2);
 
-    return getHandle(
-        detail::edgeTrackingByHysteresis(swpair.first, swpair.second));
+    return getHandle(edgeTrackingByHysteresis(swpair.first, swpair.second));
 }
 
 af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index 0294d90ca6..74b00cb0ea 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -24,7 +24,15 @@
 #include <type_traits>
 
 using af::dim4;
-using std::array;
+using common::createSpanIndex;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::createValueArray;
+using detail::reduce_all;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using std::conditional;
 using std::is_same;
 using std::sqrt;
@@ -34,12 +42,12 @@ using std::swap;
 template<typename T>
 Array<T> pointList(const Array<T>& in, const Array<uint>& x,
                    const Array<uint>& y) {
-    af_array xcoords                     = getHandle<uint>(x);
-    af_array ycoords                     = getHandle<uint>(y);
-    array<af_index_t, AF_MAX_DIMS> idxrs = {{{{xcoords}, false, false},
-                                             {{ycoords}, false, false},
-                                             common::createSpanIndex(),
-                                             common::createSpanIndex()}};
+    af_array xcoords                          = getHandle<uint>(x);
+    af_array ycoords                          = getHandle<uint>(y);
+    std::array<af_index_t, AF_MAX_DIMS> idxrs = {{{{xcoords}, false, false},
+                                                  {{ycoords}, false, false},
+                                                  createSpanIndex(),
+                                                  createSpanIndex()}};
 
     Array<T> retVal = detail::index(in, idxrs.data());
 
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index 00b67ab015..462d8897ce 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -22,10 +22,16 @@
 
 #include <cmath>
 
+using af::dim4;
 using detail::arithOp;
+using detail::Array;
+using detail::cast;
 using detail::intl;
 using detail::reduce_all;
+using detail::uchar;
+using detail::uint;
 using detail::uintl;
+using detail::ushort;
 
 template<typename Ti, typename To>
 static To corrcoef(const af_array& X, const af_array& Y) {
@@ -35,16 +41,16 @@ static To corrcoef(const af_array& X, const af_array& Y) {
     const dim4& dims = xIn.dims();
     dim_t n          = xIn.elements();
 
-    To xSum = detail::reduce_all<af_add_t, To, To>(xIn);
-    To ySum = detail::reduce_all<af_add_t, To, To>(yIn);
+    To xSum = reduce_all<af_add_t, To, To>(xIn);
+    To ySum = reduce_all<af_add_t, To, To>(yIn);
 
-    Array<To> xSq = detail::arithOp<To, af_mul_t>(xIn, xIn, dims);
-    Array<To> ySq = detail::arithOp<To, af_mul_t>(yIn, yIn, dims);
-    Array<To> xy  = detail::arithOp<To, af_mul_t>(xIn, yIn, dims);
+    Array<To> xSq = arithOp<To, af_mul_t>(xIn, xIn, dims);
+    Array<To> ySq = arithOp<To, af_mul_t>(yIn, yIn, dims);
+    Array<To> xy  = arithOp<To, af_mul_t>(xIn, yIn, dims);
 
-    To xSqSum = detail::reduce_all<af_add_t, To, To>(xSq);
-    To ySqSum = detail::reduce_all<af_add_t, To, To>(ySq);
-    To xySum  = detail::reduce_all<af_add_t, To, To>(xy);
+    To xSqSum = reduce_all<af_add_t, To, To>(xSq);
+    To ySqSum = reduce_all<af_add_t, To, To>(ySq);
+    To xySum  = reduce_all<af_add_t, To, To>(xy);
 
     To result =
         (n * xySum - xSum * ySum) / (std::sqrt(n * xSqSum - xSum * xSum) *
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index df9c13e5ff..bbacb71977 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -23,7 +23,18 @@
 #include "stats.h"
 
 using af::dim4;
+using detail::arithOp;
 using detail::Array;
+using detail::cast;
+using detail::createValueArray;
+using detail::intl;
+using detail::mean;
+using detail::reduce;
+using detail::scalar;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T, typename cType>
 static af_array cov(const af_array& X, const af_array& Y, bool isbiased) {
@@ -42,12 +53,12 @@ static af_array cov(const af_array& X, const af_array& Y, bool isbiased) {
         createValueArray<cType>(xDims, mean<T, weightType, cType>(_y));
     Array<cType> nArr = createValueArray<cType>(xDims, scalar<cType>(N));
 
-    Array<cType> diffX  = detail::arithOp<cType, af_sub_t>(xArr, xmArr, xDims);
-    Array<cType> diffY  = detail::arithOp<cType, af_sub_t>(yArr, ymArr, xDims);
-    Array<cType> mulXY  = detail::arithOp<cType, af_mul_t>(diffX, diffY, xDims);
-    Array<cType> redArr = detail::reduce<af_add_t, cType, cType>(mulXY, 0);
+    Array<cType> diffX  = arithOp<cType, af_sub_t>(xArr, xmArr, xDims);
+    Array<cType> diffY  = arithOp<cType, af_sub_t>(yArr, ymArr, xDims);
+    Array<cType> mulXY  = arithOp<cType, af_mul_t>(diffX, diffY, xDims);
+    Array<cType> redArr = reduce<af_add_t, cType, cType>(mulXY, 0);
     xDims[0]            = 1;
-    Array<cType> result = detail::arithOp<cType, af_div_t>(redArr, nArr, xDims);
+    Array<cType> result = arithOp<cType, af_div_t>(redArr, nArr, xDims);
 
     return getHandle<cType>(result);
 }
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index b86c9dca72..7ce24001b9 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -32,8 +32,21 @@
 #include <vector>
 
 using af::dim4;
+using detail::arithOp;
 using detail::Array;
+using detail::cast;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createSubArray;
+using detail::createValueArray;
+using detail::logicOp;
+using detail::padArrayBorders;
+using detail::scalar;
+using detail::select_scalar;
 using detail::shift;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using std::array;
 using std::vector;
 
@@ -54,7 +67,7 @@ const dim_t GREATEST_PRIME_FACTOR = 7;
 
 template<typename T, typename CT>
 Array<T> complexNorm(const Array<CT>& input) {
-    auto mag  = abs<T, CT>(input);
+    auto mag  = detail::abs<T, CT>(input);
     auto TWOS = createValueArray(input.dims(), scalar<T>(2));
     return arithOp<T, af_pow_t>(mag, TWOS, input.dims());
 }
@@ -276,7 +289,7 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
     auto Pc     = conj(P);
     auto numer  = arithOp<CT, af_mul_t>(I, Pc, I.dims());
     auto denom  = denominator(I, P, gamma, algo);
-    auto absVal = abs<T, CT>(denom);
+    auto absVal = detail::abs<T, CT>(denom);
     auto THRESH = createValueArray(I.dims(), scalar<T>(gamma));
     auto cond   = logicOp<T, af_ge_t>(absVal, THRESH, absVal.dims());
     auto val    = arithOp<CT, af_div_t>(numer, denom, numer.dims());
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index a5cc7154e8..8507675b85 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -24,10 +24,13 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
+using detail::imag;
+using detail::real;
 using detail::scalar;
 
 template<typename T>
 T det(const af_array a) {
+    using namespace detail;
     const Array<T> A = getArray<T>(a);
 
     const int num = A.dims()[0];
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 9ea55f8dcb..b82319d030 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -168,7 +168,7 @@ af_err af_get_device_count(int* nDevices) {
 
 af_err af_get_device(int* device) {
     try {
-        *device = getActiveDeviceId();
+        *device = static_cast<int>(getActiveDeviceId());
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -202,7 +202,7 @@ af_err af_set_device(const int device) {
 
 af_err af_sync(const int device) {
     try {
-        int dev = device == -1 ? getActiveDeviceId() : device;
+        int dev = device == -1 ? static_cast<int>(getActiveDeviceId()) : device;
         detail::sync(dev);
     }
     CATCHALL;
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index b86186245e..a304a6d963 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -30,6 +30,7 @@
                                 // where your new function declaration
                                 // is written
 
+// NOLINTNEXTLINE(google-build-using-namespace)
 using namespace detail;  // detail is an alias to appropriate backend
                          // defined in backend.hpp. You don't need to
                          // change this
diff --git a/src/api/c/gaussian_kernel.cpp b/src/api/c/gaussian_kernel.cpp
index b956dc8a69..79492f87ea 100644
--- a/src/api/c/gaussian_kernel.cpp
+++ b/src/api/c/gaussian_kernel.cpp
@@ -20,9 +20,15 @@
 #include <af/dim4.hpp>
 #include <af/image.h>
 
+using af::dim4;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
+using detail::range;
+using detail::reduce_all;
+using detail::scalar;
+using detail::transpose;
+using detail::unaryOp;
 
 template<typename T>
 Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r,
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 756dd6b80e..ae93108e79 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -18,6 +18,11 @@
 #include <af/graphics.h>
 
 using detail::Array;
+using detail::copy_histogram;
+using detail::forgeManager;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using graphics::ForgeManager;
 
 template<typename T>
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 050dd21fe7..6b1e57cf49 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -20,7 +20,19 @@
 #include <af/image.h>
 #include <af/index.h>
 
+using af::dim4;
+using detail::arithOp;
 using detail::Array;
+using detail::cast;
+using detail::createValueArray;
+using detail::intl;
+using detail::lookup;
+using detail::reduce_all;
+using detail::scan;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T, typename hType>
 static af_array hist_equal(const af_array& in, const af_array& hist) {
diff --git a/src/api/c/imgproc_common.hpp b/src/api/c/imgproc_common.hpp
index 210380bbed..818d11c763 100644
--- a/src/api/c/imgproc_common.hpp
+++ b/src/api/c/imgproc_common.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <arith.hpp>
+#include <backend.hpp>
 #include <cast.hpp>
 #include <logic.hpp>
 #include <reduce.hpp>
@@ -21,22 +22,23 @@ namespace common {
 
 template<typename To, typename Ti = To>
 detail::Array<To> integralImage(const detail::Array<Ti>& in) {
-    auto input               = detail::cast<To, Ti>(in);
-    Array<To> horizontalScan = detail::scan<af_add_t, To, To>(input, 0);
+    auto input                       = detail::cast<To, Ti>(in);
+    detail::Array<To> horizontalScan = detail::scan<af_add_t, To, To>(input, 0);
     return detail::scan<af_add_t, To, To>(horizontalScan, 1);
 }
 
 template<typename T>
-detail::Array<T> threshold(const Array<T>& in, T min, T max) {
+detail::Array<T> threshold(const detail::Array<T>& in, T min, T max) {
     const af::dim4 inDims = in.dims();
 
-    auto MN    = createValueArray(inDims, min);
-    auto MX    = createValueArray(inDims, max);
-    auto below = logicOp<T, af_le_t>(in, MX, inDims);
-    auto above = logicOp<T, af_ge_t>(in, MN, inDims);
-    auto valid = logicOp<char, af_and_t>(below, above, inDims);
+    auto MN    = detail::createValueArray(inDims, min);
+    auto MX    = detail::createValueArray(inDims, max);
+    auto below = detail::logicOp<T, af_le_t>(in, MX, inDims);
+    auto above = detail::logicOp<T, af_ge_t>(in, MN, inDims);
+    auto valid = detail::logicOp<char, af_and_t>(below, above, inDims);
 
-    return arithOp<T, af_mul_t>(in, cast<T, char>(valid), inDims);
+    return detail::arithOp<T, af_mul_t>(in, detail::cast<T, char>(valid),
+                                        inDims);
 }
 
 template<typename To, typename Ti>
@@ -44,8 +46,8 @@ detail::Array<To> convRange(const detail::Array<Ti>& in,
                             const To newLow = To(0), const To newHigh = To(1)) {
     auto dims  = in.dims();
     auto input = detail::cast<To, Ti>(in);
-    To high    = reduce_all<af_max_t, To, To>(input);
-    To low     = reduce_all<af_min_t, To, To>(input);
+    To high    = detail::reduce_all<af_max_t, To, To>(input);
+    To low     = detail::reduce_all<af_min_t, To, To>(input);
     To range   = high - low;
 
     if (std::abs(range) < 1.0e-6) {
@@ -53,22 +55,22 @@ detail::Array<To> convRange(const detail::Array<Ti>& in,
             return input;
         } else {
             // Input is constant, use high as constant in converted range
-            return createValueArray(dims, newHigh);
+            return detail::createValueArray(dims, newHigh);
         }
     }
 
-    auto minArray = createValueArray(dims, low);
-    auto invDen   = createValueArray(dims, To(1.0 / range));
-    auto numer    = arithOp<To, af_sub_t>(input, minArray, dims);
-    auto result   = arithOp<To, af_mul_t>(numer, invDen, dims);
+    auto minArray = detail::createValueArray(dims, low);
+    auto invDen   = detail::createValueArray(dims, To(1.0 / range));
+    auto numer    = detail::arithOp<To, af_sub_t>(input, minArray, dims);
+    auto result   = detail::arithOp<To, af_mul_t>(numer, invDen, dims);
 
     if (newLow != To(0) || newHigh != To(1)) {
         To newRange    = newHigh - newLow;
-        auto newRngArr = createValueArray(dims, newRange);
-        auto newMinArr = createValueArray(dims, newLow);
-        auto scaledArr = arithOp<To, af_mul_t>(result, newRngArr, dims);
+        auto newRngArr = detail::createValueArray(dims, newRange);
+        auto newMinArr = detail::createValueArray(dims, newLow);
+        auto scaledArr = detail::arithOp<To, af_mul_t>(result, newRngArr, dims);
 
-        result = arithOp<To, af_add_t>(newMinArr, scaledArr, dims);
+        result = detail::arithOp<To, af_add_t>(newMinArr, scaledArr, dims);
     }
     return result;
 }
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index c97c7a404d..292550a66a 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -26,14 +26,22 @@
 #include <cmath>
 #include <vector>
 
-using namespace detail;
 using std::signbit;
 using std::swap;
 using std::vector;
 
+using af::dim4;
 using common::convert2Canonical;
 using common::createSpanIndex;
 using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::index;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 namespace common {
 af_index_t createSpanIndex() {
@@ -57,8 +65,8 @@ af_seq convert2Canonical(const af_seq s, const dim_t len) {
 
 template<typename T>
 static af_array indexBySeqs(const af_array& src,
-                            const vector<af_seq> indicesV) {
-    dim_t ndims       = static_cast<dim_t>(indicesV.size());
+                            const vector<af_seq>& indicesV) {
+    auto ndims        = static_cast<dim_t>(indicesV.size());
     const auto& input = getArray<T>(src);
 
     if (ndims == 1U && ndims != input.ndims()) {
diff --git a/src/api/c/inverse.cpp b/src/api/c/inverse.cpp
index fe6625d5c1..a2b9b5c90b 100644
--- a/src/api/c/inverse.cpp
+++ b/src/api/c/inverse.cpp
@@ -16,7 +16,8 @@
 #include <af/defines.h>
 #include <af/lapack.h>
 
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
 
 template<typename T>
 static inline af_array inverse(const af_array in) {
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 2b7df25888..79e45d3f9f 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -18,7 +18,16 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+using std::swap;
+using std::vector;
 
 template<typename T>
 static inline af_array join(const int dim, const af_array first,
@@ -29,7 +38,7 @@ static inline af_array join(const int dim, const af_array first,
 template<typename T>
 static inline af_array join_many(const int dim, const unsigned n_arrays,
                                  const af_array *inputs) {
-    std::vector<Array<T>> inputs_;
+    vector<Array<T>> inputs_;
     inputs_.reserve(n_arrays);
 
     for (unsigned i = 0; i < n_arrays; i++) {
@@ -43,8 +52,8 @@ af_err af_join(af_array *out, const int dim, const af_array first,
     try {
         const ArrayInfo &finfo = getInfo(first);
         const ArrayInfo &sinfo = getInfo(second);
-        af::dim4 fdims         = finfo.dims();
-        af::dim4 sdims         = sinfo.dims();
+        dim4 fdims             = finfo.dims();
+        dim4 sdims             = sinfo.dims();
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
         ARG_ASSERT(2, finfo.getType() == sinfo.getType());
@@ -98,9 +107,9 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
             return AF_SUCCESS;
         }
 
-        std::vector<ArrayInfo> info;
+        vector<ArrayInfo> info;
         info.reserve(n_arrays);
-        std::vector<af::dim4> dims(n_arrays);
+        vector<af::dim4> dims(n_arrays);
         for (unsigned i = 0; i < n_arrays; i++) {
             info.push_back(getInfo(inputs[i]));
             dims[i] = info[i].dims();
@@ -141,7 +150,7 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
             case f16: output = join_many<half>(dim, n_arrays, inputs); break;
             default: TYPE_ERROR(1, info[0].getType());
         }
-        std::swap(*out, output);
+        swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/lu.cpp b/src/api/c/lu.cpp
index c9cef44e61..761f7b3dcd 100644
--- a/src/api/c/lu.cpp
+++ b/src/api/c/lu.cpp
@@ -17,7 +17,11 @@
 #include <af/lapack.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::isLAPACKAvailable;
 
 template<typename T>
 static inline void lu(af_array *lower, af_array *upper, af_array *pivot,
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 9cef0f8cb1..28c41eb334 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -22,11 +22,18 @@
 
 #include "stats.h"
 
+using af::dim4;
 using common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
+using detail::imag;
+using detail::intl;
 using detail::mean;
+using detail::real;
+using detail::uchar;
+using detail::uintl;
+using detail::ushort;
 
 template<typename Ti, typename To>
 static To mean(const af_array &in) {
diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index d69f11033d..0c8322cafe 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -16,7 +16,12 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::intl;
+using detail::meanshift;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array mean_shift(const af_array &in, const float &s_sigma,
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 818c2a96ae..a880b7dbcf 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -297,7 +297,7 @@ af_err af_free_host(void *ptr) {
 af_err af_print_mem_info(const char *msg, const int device_id) {
     try {
         int device = device_id;
-        if (device == -1) { device = getActiveDeviceId(); }
+        if (device == -1) { device = static_cast<int>(getActiveDeviceId()); }
 
         if (msg != nullptr) {
             ARG_ASSERT(0, strlen(msg) < 256);  // 256 character limit on msg
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 2584cf1123..985c1e6e60 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -28,8 +28,8 @@
 
 using af::dim4;
 
+using detail::Array;
 using std::vector;
-using namespace detail;
 
 template<typename T>
 static inline void moments(af_array* out, const af_array in,
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index 06ea1b3a66..79f064ebb7 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -23,7 +23,15 @@
 #include <af/traits.hpp>
 
 using af::dim4;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::createValueArray;
+using detail::reduce;
+using detail::reduce_all;
+using detail::scalar;
 
 template<typename T>
 double matrixNorm(const Array<T> &A, double p) {
@@ -83,7 +91,7 @@ double norm(const af_array a, const af_norm_type type, const double p,
             const double q) {
     using BT = typename af::dtype_traits<T>::base_type;
 
-    const Array<BT> A = abs<BT, T>(getArray<T>(a));
+    const Array<BT> A = detail::abs<BT, T>(getArray<T>(a));
 
     switch (type) {
         case AF_NORM_EUCLID: return vectorNorm(A, 2);
diff --git a/src/api/c/ops.hpp b/src/api/c/ops.hpp
index db9187e05a..edee76b384 100644
--- a/src/api/c/ops.hpp
+++ b/src/api/c/ops.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <backend.hpp>
 #include <math.hpp>
+#include <types.hpp>
 
 #ifndef __DH__
 #define __DH__
@@ -17,7 +18,9 @@
 
 #include "optypes.hpp"
 
-using namespace detail;
+namespace common {
+
+using namespace detail;  // NOLINT
 
 // Because isnan(cfloat) and isnan(cdouble) is not defined
 #define IS_NAN(val) !((val) == (val))
@@ -31,63 +34,59 @@ struct Binary {
 
 template<typename T>
 struct Binary<T, af_add_t> {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ T init() { return scalar<T>(0); }
 
     __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_mul_t> {
-    static __DH__ T init() { return detail::scalar<T>(1); }
+    static __DH__ T init() { return scalar<T>(1); }
 
     __DH__ T operator()(T lhs, T rhs) { return lhs * rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_or_t> {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ T init() { return scalar<T>(0); }
 
     __DH__ T operator()(T lhs, T rhs) { return lhs || rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_and_t> {
-    static __DH__ T init() { return detail::scalar<T>(1); }
+    static __DH__ T init() { return scalar<T>(1); }
 
     __DH__ T operator()(T lhs, T rhs) { return lhs && rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_notzero_t> {
-    static __DH__ T init() { return detail::scalar<T>(0); }
+    static __DH__ T init() { return scalar<T>(0); }
 
     __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
 template<typename T>
 struct Binary<T, af_min_t> {
-    static __DH__ T init() { return detail::maxval<T>(); }
+    static __DH__ T init() { return maxval<T>(); }
 
-    __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); }
+    __DH__ T operator()(T lhs, T rhs) { return min(lhs, rhs); }
 };
 
 template<>
 struct Binary<char, af_min_t> {
     static __DH__ char init() { return 1; }
 
-    __DH__ char operator()(char lhs, char rhs) {
-        return detail::min(lhs > 0, rhs > 0);
-    }
+    __DH__ char operator()(char lhs, char rhs) { return min(lhs > 0, rhs > 0); }
 };
 
-#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                       \
-    template<>                                                              \
-    struct Binary<T, af_min_t> {                                            \
-        static __DH__ T init() {                                            \
-            return detail::scalar<T>(detail::maxval<Tr>());                 \
-        }                                                                   \
-                                                                            \
-        __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); } \
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)                               \
+    template<>                                                      \
+    struct Binary<T, af_min_t> {                                    \
+        static __DH__ T init() { return scalar<T>(maxval<Tr>()); }  \
+                                                                    \
+        __DH__ T operator()(T lhs, T rhs) { return min(lhs, rhs); } \
     };
 
 SPECIALIZE_COMPLEX_MIN(cfloat, float)
@@ -97,26 +96,22 @@ SPECIALIZE_COMPLEX_MIN(cdouble, double)
 
 template<typename T>
 struct Binary<T, af_max_t> {
-    static __DH__ T init() { return detail::minval<T>(); }
+    static __DH__ T init() { return minval<T>(); }
 
-    __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); }
+    __DH__ T operator()(T lhs, T rhs) { return max(lhs, rhs); }
 };
 
 template<>
 struct Binary<char, af_max_t> {
     static __DH__ char init() { return 0; }
 
-    __DH__ char operator()(char lhs, char rhs) {
-        return detail::max(lhs > 0, rhs > 0);
-    }
+    __DH__ char operator()(char lhs, char rhs) { return max(lhs > 0, rhs > 0); }
 };
 
 #define SPECIALIZE_COMPLEX_MAX(T, Tr)                                       \
     template<>                                                              \
     struct Binary<T, af_max_t> {                                            \
-        static __DH__ T init() {                                            \
-            return detail::scalar<T>(detail::scalar<Tr>(0));                \
-        }                                                                   \
+        static __DH__ T init() { return scalar<T>(detail::scalar<Tr>(0)); } \
                                                                             \
         __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); } \
     };
@@ -147,15 +142,17 @@ struct Transform<Ti, To, af_max_t> {
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_or_t> {
-    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0.)); }
+    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_and_t> {
-    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0.)); }
+    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
 };
 
 template<typename Ti, typename To>
 struct Transform<Ti, To, af_notzero_t> {
-    __DH__ To operator()(Ti in) { return (in != detail::scalar<Ti>(0.)); }
+    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
 };
+
+}  // namespace common
diff --git a/src/api/c/orb.cpp b/src/api/c/orb.cpp
index 2007b255ac..7608553170 100644
--- a/src/api/c/orb.cpp
+++ b/src/api/c/orb.cpp
@@ -18,7 +18,10 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+
+using detail::Array;
+using detail::createEmptyArray;
+using detail::uint;
 
 template<typename T, typename convAccT>
 static void orb(af_features& feat_, af_array& descriptor, const af_array& in,
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 2c6ea88f0a..0d0c8496af 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -31,11 +31,28 @@
 
 using af::dim4;
 using af::dtype_traits;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::createSelectNode;
+using detail::createSubArray;
+using detail::createValueArray;
+using detail::diagCreate;
+using detail::gemm;
+using detail::logicOp;
+using detail::max;
+using detail::min;
+using detail::reduce;
+using detail::scalar;
+using detail::svd;
+using detail::tile;
+using detail::uint;
 using std::swap;
 using std::vector;
 
-using namespace detail;
-
 template<typename T>
 Array<T> getSubArray(const Array<T> &in, const bool copy, uint dim0begin = 0,
                      uint dim0end = 0, uint dim1begin = 0, uint dim1end = 0,
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index ddff3aa2bc..677fda370a 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -23,7 +23,13 @@
 #include <transpose.hpp>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::copy_plot;
+using detail::forgeManager;
+using detail::reduce;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using namespace graphics;
 
 // Requires in_ to be in either [order, n] or [n, order] format
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 4a533b77c0..ef749e970f 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -30,9 +30,14 @@
 
 #include <af/index.h>
 
-using namespace detail;
-
 using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 using std::cout;
 using std::endl;
 using std::ostream;
@@ -44,6 +49,7 @@ static void printer(ostream &out, const T *ptr, const ArrayInfo &info,
     dim_t stride = info.strides()[dim];
     dim_t d      = info.dims()[dim];
     ToNum<T> toNum;
+    using namespace detail;  // NOLINT
 
     if (dim == 0) {
         for (dim_t i = 0, j = 0; i < d; i++, j += stride) {
diff --git a/src/api/c/qr.cpp b/src/api/c/qr.cpp
index 257b2b02ea..8d74a0d3f9 100644
--- a/src/api/c/qr.cpp
+++ b/src/api/c/qr.cpp
@@ -17,14 +17,18 @@
 #include <af/lapack.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using std::swap;
 
 template<typename T>
 static inline void qr(af_array *q, af_array *r, af_array *tau,
                       const af_array in) {
-    Array<T> qArray = createEmptyArray<T>(af::dim4());
-    Array<T> rArray = createEmptyArray<T>(af::dim4());
-    Array<T> tArray = createEmptyArray<T>(af::dim4());
+    Array<T> qArray = createEmptyArray<T>(dim4());
+    Array<T> rArray = createEmptyArray<T>(dim4());
+    Array<T> tArray = createEmptyArray<T>(dim4());
 
     qr<T>(qArray, rArray, tArray, getArray<T>(in));
 
@@ -98,7 +102,7 @@ af_err af_qr_inplace(af_array *tau, af_array in) {
             case c64: out = qr_inplace<cdouble>(in); break;
             default: TYPE_ERROR(1, type);
         }
-        std::swap(*tau, out);
+        swap(*tau, out);
     }
     CATCHALL;
 
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 744588680f..8d65c4b718 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -22,12 +22,31 @@
 #include <af/dim4.hpp>
 #include <memory>
 
-using namespace detail;
-using namespace common;
-
 using af::dim4;
-
-Array<uint> emptyArray() { return createEmptyArray<uint>(af::dim4(0)); }
+using common::half;
+using common::mask;
+using common::MaxBlocks;
+using common::MtStateLength;
+using common::pos;
+using common::recursion_tbl;
+using common::sh1;
+using common::sh2;
+using common::TableLength;
+using common::temper_tbl;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::createHostDataArray;
+using detail::intl;
+using detail::normalDistribution;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::uniformDistribution;
+using detail::ushort;
+
+Array<uint> emptyArray() { return createEmptyArray<uint>(dim4(0)); }
 
 struct RandomEngine {
     // clang-format off
@@ -69,7 +88,7 @@ RandomEngine *getRandomEngine(const af_random_engine engineHandle) {
 
 namespace {
 template<typename T>
-inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e) {
+inline af_array uniformDistribution_(const dim4 &dims, RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(uniformDistribution<T>(dims, e->pos, e->sh1, e->sh2,
                                                 e->mask, e->recursion_table,
@@ -81,7 +100,7 @@ inline af_array uniformDistribution_(const af::dim4 &dims, RandomEngine *e) {
 }
 
 template<typename T>
-inline af_array normalDistribution_(const af::dim4 &dims, RandomEngine *e) {
+inline af_array normalDistribution_(const dim4 &dims, RandomEngine *e) {
     if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
         return getHandle(normalDistribution<T>(dims, e->pos, e->sh1, e->sh2,
                                                e->mask, e->recursion_table,
@@ -128,16 +147,16 @@ af_err af_create_random_engine(af_random_engine *engineHandle,
         *e.counter = 0;
 
         if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-            e.pos  = createHostDataArray<uint>(af::dim4(MaxBlocks), pos);
-            e.sh1  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh1);
-            e.sh2  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh2);
+            e.pos  = createHostDataArray<uint>(dim4(MaxBlocks), pos);
+            e.sh1  = createHostDataArray<uint>(dim4(MaxBlocks), sh1);
+            e.sh2  = createHostDataArray<uint>(dim4(MaxBlocks), sh2);
             e.mask = mask;
 
             e.recursion_table =
-                createHostDataArray<uint>(af::dim4(TableLength), recursion_tbl);
+                createHostDataArray<uint>(dim4(TableLength), recursion_tbl);
             e.temper_table =
-                createHostDataArray<uint>(af::dim4(TableLength), temper_tbl);
-            e.state = createEmptyArray<uint>(af::dim4(MtStateLength));
+                createHostDataArray<uint>(dim4(TableLength), temper_tbl);
+            e.state = createEmptyArray<uint>(dim4(MtStateLength));
 
             initMersenneState(e.state, seed, e.recursion_table);
         }
@@ -167,16 +186,16 @@ af_err af_random_engine_set_type(af_random_engine *engine,
         RandomEngine *e = getRandomEngine(*engine);
         if (rtype != e->type) {
             if (rtype == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
-                e->pos  = createHostDataArray<uint>(af::dim4(MaxBlocks), pos);
-                e->sh1  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh1);
-                e->sh2  = createHostDataArray<uint>(af::dim4(MaxBlocks), sh2);
+                e->pos  = createHostDataArray<uint>(dim4(MaxBlocks), pos);
+                e->sh1  = createHostDataArray<uint>(dim4(MaxBlocks), sh1);
+                e->sh2  = createHostDataArray<uint>(dim4(MaxBlocks), sh2);
                 e->mask = mask;
 
-                e->recursion_table = createHostDataArray<uint>(
-                    af::dim4(TableLength), recursion_tbl);
-                e->temper_table = createHostDataArray<uint>(
-                    af::dim4(TableLength), temper_tbl);
-                e->state = createEmptyArray<uint>(af::dim4(MtStateLength));
+                e->recursion_table =
+                    createHostDataArray<uint>(dim4(TableLength), recursion_tbl);
+                e->temper_table =
+                    createHostDataArray<uint>(dim4(TableLength), temper_tbl);
+                e->state = createEmptyArray<uint>(dim4(MtStateLength));
 
                 initMersenneState(e->state, *(e->seed), e->recursion_table);
             } else if (e->type == AF_RANDOM_ENGINE_MERSENNE_GP11213) {
@@ -249,7 +268,7 @@ af_err af_random_uniform(af_array *out, const unsigned ndims,
         AF_CHECK(af_init());
         af_array result;
 
-        af::dim4 d      = verifyDims(ndims, dims);
+        dim4 d          = verifyDims(ndims, dims);
         RandomEngine *e = getRandomEngine(engine);
 
         switch (type) {
@@ -281,7 +300,7 @@ af_err af_random_normal(af_array *out, const unsigned ndims,
         AF_CHECK(af_init());
         af_array result;
 
-        af::dim4 d      = verifyDims(ndims, dims);
+        dim4 d          = verifyDims(ndims, dims);
         RandomEngine *e = getRandomEngine(engine);
 
         switch (type) {
@@ -316,7 +335,7 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t *const dims,
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
         RandomEngine *e = getRandomEngine(engine);
-        af::dim4 d      = verifyDims(ndims, dims);
+        dim4 d          = verifyDims(ndims, dims);
 
         switch (type) {
             case f32: result = uniformDistribution_<float>(d, e); break;
@@ -349,7 +368,7 @@ af_err af_randn(af_array *out, const unsigned ndims, const dim_t *const dims,
         af_random_engine engine;
         AF_CHECK(af_get_default_random_engine(&engine));
         RandomEngine *e = getRandomEngine(engine);
-        af::dim4 d      = verifyDims(ndims, dims);
+        dim4 d          = verifyDims(ndims, dims);
 
         switch (type) {
             case f32: result = normalDistribution_<float>(d, e); break;
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 6f0860a800..8880814a82 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -20,7 +20,16 @@
 #include <af/lapack.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::createValueArray;
+using detail::logicOp;
+using detail::reduce;
+using detail::reduce_all;
+using detail::scalar;
+using detail::uint;
 
 template<typename T>
 static inline uint rank(const af_array in, double tol) {
@@ -35,6 +44,7 @@ static inline uint rank(const af_array in, double tol) {
         Array<T> r = createEmptyArray<T>(dim4());
         Array<T> t = createEmptyArray<T>(dim4());
         qr(q, r, t, In);
+        using detail::abs;
 
         R = abs<BT, T>(r);
     }
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index e5088b8e5b..2668b93543 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -18,11 +18,20 @@
 #include <af/algorithm.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <complex>
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::imag;
+using detail::intl;
+using detail::real;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<af_op_t op, typename Ti, typename To>
 static inline af_array reduce(const af_array in, const int dim,
diff --git a/src/api/c/regions.cpp b/src/api/c/regions.cpp
index a106993569..8009527f90 100644
--- a/src/api/c/regions.cpp
+++ b/src/api/c/regions.cpp
@@ -11,12 +11,14 @@
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <regions.hpp>
+#include <types.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::uint;
+using detail::ushort;
 
 template<typename T>
 static af_array regions(af_array const &in, af_connectivity connectivity) {
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index bbd4431a5c..c367430809 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -21,7 +21,15 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+using std::swap;
 
 template<typename T>
 static inline af_array reorder(const af_array in, const af::dim4 &rdims0) {
@@ -107,7 +115,7 @@ af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims) {
             case f16: output = reorder<half>(in, rdims); break;
             default: TYPE_ERROR(1, type);
         }
-        std::swap(*out, output);
+        swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 5f006d472d..27455982e9 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -21,9 +21,16 @@
 
 #include <select.hpp>
 
-using namespace detail;
 using af::dim4;
 using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::select_scalar;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 void replace(af_array a, const af_array cond, const af_array b) {
diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index 6c783e0374..8b6df743da 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -16,7 +16,13 @@
 #include <af/defines.h>
 #include <af/image.h>
 
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array resize(const af_array in, const dim_t odim0,
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index e1d9732da6..73717cdd46 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -23,7 +23,15 @@
 #include <tile.hpp>
 
 using af::dim4;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::createValueArray;
+using detail::join;
+using detail::scalar;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename T, typename cType>
 static af_array rgb2gray(const af_array& in, const float r, const float g,
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index 45b03c6796..762f77d7f4 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -16,10 +16,16 @@
 #include <cmath>
 
 using af::dim4;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 using std::cos;
 using std::fabs;
 using std::sin;
-using namespace detail;
 
 template<typename T>
 static inline af_array rotate(const af_array in, const float theta,
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index 9b6231e0e6..8012cfaaba 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -14,7 +14,13 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename To, typename Ti>
 inline af_array sat(const af_array& in) {
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index 053ac0111a..f207a302db 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -18,7 +18,13 @@
 #include <af/dim4.hpp>
 #include <complex>
 
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<af_op_t op, typename Ti, typename To>
 static inline af_array scan(const af_array in, const int dim,
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 33cb129a0a..952a8568fa 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -19,9 +19,17 @@
 #include <af/data.h>
 #include <af/defines.h>
 
-using namespace detail;
 using af::dim4;
 using common::half;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createSelectNode;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 af_array select(const af_array cond, const af_array a, const af_array b,
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index 8bf9f8c4c4..bf8b66e3c8 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -15,7 +15,13 @@
 #include <af/defines.h>
 #include <complex>
 
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array setUnique(const af_array in, const bool is_sorted) {
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index 9b0a0f0170..42052fbfbc 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -14,7 +14,13 @@
 #include <shift.hpp>
 #include <af/data.h>
 
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array shift(const af_array in, const int sdims[4]) {
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index 7d7cfa8bd4..7ce4028897 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -18,7 +18,8 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::createEmptyArray;
 
 template<typename T, typename convAccT>
 static void sift(af_features& feat_, af_array& descriptors, const af_array& in,
diff --git a/src/api/c/sobel.cpp b/src/api/c/sobel.cpp
index 9e70f3f257..6184d5502a 100644
--- a/src/api/c/sobel.cpp
+++ b/src/api/c/sobel.cpp
@@ -17,7 +17,14 @@
 #include <utility>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 using ArrayPair = std::pair<af_array, af_array>;
 template<typename Ti, typename To>
diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index 93c9459154..6328e90f01 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -17,7 +17,10 @@
 #include <af/lapack.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::solveLU;
 
 template<typename T>
 static inline af_array solve(const af_array a, const af_array b,
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 62b2a37e2f..4ec1c0a466 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -22,7 +22,15 @@
 #include <cstdio>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array sort(const af_array in, const unsigned dim,
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index e58e77de44..d1a737f488 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -19,9 +19,14 @@
 #include <af/array.h>
 #include <af/sparse.h>
 
-using namespace detail;
-using namespace common;
 using af::dim4;
+using common::createEmptySparseArray;
+using common::SparseArray;
+using common::SparseArrayBase;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::sparseConvertDenseToStorage;
 
 const SparseArrayBase &getSparseArrayBase(const af_array in,
                                           bool device_check) {
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index e3925b61d2..3356be24cb 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -66,7 +66,7 @@ common::SparseArray<To> castSparse(const af_array &in) {
 #define CAST_SPARSE(Ti)                                                          \
     do {                                                                         \
         const SparseArray<Ti> sparse = getSparseArray<Ti>(in);                   \
-        Array<To> values             = detail::cast<To, Ti>(sparse.getValues()); \
+        detail::Array<To> values     = detail::cast<To, Ti>(sparse.getValues()); \
         return createArrayDataSparseArray(                                       \
             sparse.dims(), values, sparse.getRowIdx(), sparse.getColIdx(),       \
             sparse.getStorage());                                                \
@@ -75,8 +75,8 @@ common::SparseArray<To> castSparse(const af_array &in) {
     switch (info.getType()) {
         case f32: CAST_SPARSE(float);
         case f64: CAST_SPARSE(double);
-        case c32: CAST_SPARSE(cfloat);
-        case c64: CAST_SPARSE(cdouble);
+        case c32: CAST_SPARSE(detail::cfloat);
+        case c64: CAST_SPARSE(detail::cdouble);
         default: TYPE_ERROR(1, info.getType());
     }
 }
diff --git a/src/api/c/stats.h b/src/api/c/stats.h
index d7e5c6f390..cde5b1621b 100644
--- a/src/api/c/stats.h
+++ b/src/api/c/stats.h
@@ -9,32 +9,12 @@
 
 #pragma once
 
-template<typename T, typename Other>
-struct is_same {
-    static const bool value = false;
-};
-
-template<typename T>
-struct is_same<T, T> {
-    static const bool value = true;
-};
-
-template<bool, typename T, typename O>
-struct cond_type;
-
-template<typename T, typename Other>
-struct cond_type<true, T, Other> {
-    typedef T type;
-};
-
-template<typename T, typename Other>
-struct cond_type<false, T, Other> {
-    typedef Other type;
-};
+#include <backend.hpp>
+#include <type_traits>
 
 template<typename T>
 struct baseOutType {
-    typedef typename cond_type<is_same<T, cdouble>::value ||
-                                   is_same<T, double>::value,
-                               double, float>::type type;
+    typedef typename std::conditional<std::is_same<T, detail::cdouble>::value ||
+                                          std::is_same<T, double>::value,
+                                      double, float>::type type;
 };
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 11da858ca3..8620f00bd4 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -24,7 +24,22 @@
 
 #include "stats.h"
 
-using namespace detail;
+using af::dim4;
+using detail::Array;
+using detail::cast;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createValueArray;
+using detail::division;
+using detail::intl;
+using detail::mean;
+using detail::reduce;
+using detail::reduce_all;
+using detail::scalar;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename inType, typename outType>
 static outType stdev(const af_array& in) {
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 6ca2c6d1a2..e8361c8c49 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -22,7 +22,13 @@
 #include <tile.hpp>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::copy_surface;
+using detail::forgeManager;
+using detail::reduce_all;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using namespace graphics;
 
 template<typename T>
@@ -38,9 +44,9 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
     const ArrayInfo& Yinfo = getInfo(yVals);
     const ArrayInfo& Zinfo = getInfo(zVals);
 
-    af::dim4 X_dims = Xinfo.dims();
-    af::dim4 Y_dims = Yinfo.dims();
-    af::dim4 Z_dims = Zinfo.dims();
+    dim4 X_dims = Xinfo.dims();
+    dim4 Y_dims = Yinfo.dims();
+    dim4 Z_dims = Zinfo.dims();
 
     if (Xinfo.isVector()) {
         // Convert xIn is a column vector
@@ -50,7 +56,7 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
         xIn = tile(xIn, x_tdims);
 
         // Convert yIn to a row vector
-        yIn = modDims(yIn, af::dim4(1, yIn.elements()));
+        yIn = modDims(yIn, dim4(1, yIn.elements()));
         // Now tile along first dimension
         dim4 y_tdims(X_dims[0], 1, 1, 1);
         yIn = tile(yIn, y_tdims);
@@ -128,15 +134,15 @@ af_err af_draw_surface(const af_window window, const af_array xVals,
         if (window == 0) { AF_ERROR("Not a valid window", AF_ERR_INTERNAL); }
 
         const ArrayInfo& Xinfo = getInfo(xVals);
-        af::dim4 X_dims        = Xinfo.dims();
+        dim4 X_dims            = Xinfo.dims();
         af_dtype Xtype         = Xinfo.getType();
 
         const ArrayInfo& Yinfo = getInfo(yVals);
-        af::dim4 Y_dims        = Yinfo.dims();
+        dim4 Y_dims            = Yinfo.dims();
         af_dtype Ytype         = Yinfo.getType();
 
         const ArrayInfo& Sinfo = getInfo(S);
-        const af::dim4& S_dims = Sinfo.dims();
+        const dim4& S_dims     = Sinfo.dims();
         af_dtype Stype         = Sinfo.getType();
 
         TYPE_ASSERT(Xtype == Ytype);
diff --git a/src/api/c/susan.cpp b/src/api/c/susan.cpp
index 6d630f5eff..0621f7eb16 100644
--- a/src/api/c/susan.cpp
+++ b/src/api/c/susan.cpp
@@ -18,7 +18,15 @@
 #include <af/vision.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::createValueArray;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 
 template<typename T>
 static af_features susan(af_array const& in, const unsigned radius,
diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index c1552a1e37..268b68cb26 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -18,22 +18,28 @@
 #include <svd.hpp>
 #include <af/defines.h>
 
-using namespace detail;
+using af::dim4;
+using af::dtype_traits;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using std::min;
 
 template<typename T>
 static inline void svd(af_array *s, af_array *u, af_array *vt,
                        const af_array in) {
     const ArrayInfo &info = getInfo(in);  // ArrayInfo is the base class which
-    af::dim4 dims         = info.dims();
+    dim4 dims             = info.dims();
     int M                 = dims[0];
     int N                 = dims[1];
 
-    using Tr = typename af::dtype_traits<T>::base_type;
+    using Tr = typename dtype_traits<T>::base_type;
 
     // Allocate output arrays
-    Array<Tr> sA = createEmptyArray<Tr>(af::dim4(min(M, N)));
-    Array<T> uA  = createEmptyArray<T>(af::dim4(M, M));
-    Array<T> vtA = createEmptyArray<T>(af::dim4(N, N));
+    Array<Tr> sA = createEmptyArray<Tr>(dim4(min(M, N)));
+    Array<T> uA  = createEmptyArray<T>(dim4(M, M));
+    Array<T> vtA = createEmptyArray<T>(dim4(N, N));
 
     svd<T, Tr>(sA, uA, vtA, getArray<T>(in));
 
@@ -46,16 +52,16 @@ template<typename T>
 static inline void svdInPlace(af_array *s, af_array *u, af_array *vt,
                               af_array in) {
     const ArrayInfo &info = getInfo(in);  // ArrayInfo is the base class which
-    af::dim4 dims         = info.dims();
+    dim4 dims             = info.dims();
     int M                 = dims[0];
     int N                 = dims[1];
 
-    using Tr = typename af::dtype_traits<T>::base_type;
+    using Tr = typename dtype_traits<T>::base_type;
 
     // Allocate output arrays
-    Array<Tr> sA = createEmptyArray<Tr>(af::dim4(min(M, N)));
-    Array<T> uA  = createEmptyArray<T>(af::dim4(M, M));
-    Array<T> vtA = createEmptyArray<T>(af::dim4(N, N));
+    Array<Tr> sA = createEmptyArray<Tr>(dim4(min(M, N)));
+    Array<T> uA  = createEmptyArray<T>(dim4(M, M));
+    Array<T> vtA = createEmptyArray<T>(dim4(N, N));
 
     svdInPlace<T, Tr>(sA, uA, vtA, getArray<T>(in));
 
@@ -67,7 +73,7 @@ static inline void svdInPlace(af_array *s, af_array *u, af_array *vt,
 af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in) {
     try {
         const ArrayInfo &info = getInfo(in);
-        af::dim4 dims         = info.dims();
+        dim4 dims             = info.dims();
 
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
         af_dtype type = info.getType();
@@ -94,7 +100,7 @@ af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in) {
 af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in) {
     try {
         const ArrayInfo &info = getInfo(in);
-        af::dim4 dims         = info.dims();
+        dim4 dims             = info.dims();
 
         ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
         af_dtype type = info.getType();
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 14d87559ba..db3d456691 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -21,7 +21,15 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::unaryOp;
+using detail::ushort;
 
 template<typename T>
 static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index 0972f3b46e..93445883f4 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -17,8 +17,9 @@
 #include <handle.hpp>
 #include <topk.hpp>
 
-using namespace detail;
 using common::half;
+using detail::createEmptyArray;
+using detail::uint;
 
 namespace {
 
@@ -52,8 +53,8 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
                                           : errValue;
         }
 
-        int rdim     = dim;
-        auto &inDims = inInfo.dims();
+        int rdim           = dim;
+        const auto &inDims = inInfo.dims();
 
         if (rdim == -1) {
             for (dim_t d = 0; d < 4; d++) {
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index ff379f0b88..9bdaceb149 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -16,7 +16,13 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline void transform(af_array *out, const af_array in,
diff --git a/src/api/c/transform_coordinates.cpp b/src/api/c/transform_coordinates.cpp
index 4f27ac048d..8bec381b6c 100644
--- a/src/api/c/transform_coordinates.cpp
+++ b/src/api/c/transform_coordinates.cpp
@@ -20,7 +20,12 @@
 #include <vector>
 
 using af::dim4;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::createEmptyArray;
+using detail::createHostDataArray;
+using detail::createSubArray;
+using detail::scalar;
 
 template<typename T>
 Array<T> multiplyIndexed(const Array<T> &lhs, const Array<T> &rhs,
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index 17553f191f..a92fe77e91 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -20,7 +20,14 @@
 
 using af::dim4;
 using common::half;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array trs(const af_array in, const bool conjugate) {
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 7d75b145a8..8ea0abe3c5 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -30,8 +30,23 @@
 #include <af/data.h>
 #include <af/defines.h>
 
+using af::dim4;
 using common::half;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::cplx;
+using detail::createValueArray;
+using detail::imag;
+using detail::intl;
+using detail::logicOp;
+using detail::real;
+using detail::scalar;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T, af_op_t op>
 static inline af_array unaryOp(const af_array in) {
@@ -195,7 +210,7 @@ struct unaryOpCplxFun<Tc, Tr, af_log_t> {
         // --> phi = atan2(b, a)
         Array<Tr> phi = arithOp<Tr, af_atan2_t>(b, a, b.dims());
 
-        Array<Tr> r = abs<Tr>(z);
+        Array<Tr> r = detail::abs<Tr>(z);
 
         // compute log
         // log(r)
@@ -515,7 +530,7 @@ struct unaryOpCplxFun<Tc, Tr, af_sqrt_t> {
         // phi = arg(a + ib)
         // --> phi = atan2(b, a)
         Array<Tr> phi = arithOp<Tr, af_atan2_t>(b, a, b.dims());
-        Array<Tr> r   = abs<Tr>(z);
+        Array<Tr> r   = detail::abs<Tr>(z);
 
         // compute sqrt
         Array<Tr> two = createValueArray<Tr>(phi.dims(), 2.0);
diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index 4636adb389..ee0ac2a16e 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -16,7 +16,14 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array unwrap(const af_array in, const dim_t wx, const dim_t wy,
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 2efa032b1c..ca68512cd7 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -24,9 +24,27 @@
 
 #include <tuple>
 
-using namespace detail;
-
+using af::dim4;
 using common::half;
+using detail::arithOp;
+using detail::Array;
+using detail::cast;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createEmptyArray;
+using detail::createValueArray;
+using detail::division;
+using detail::imag;
+using detail::intl;
+using detail::mean;
+using detail::real;
+using detail::reduce;
+using detail::reduce_all;
+using detail::scalar;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 using std::ignore;
 using std::make_tuple;
 using std::tie;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 6dcd6d083d..c2f764c5c7 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -23,8 +23,16 @@
 #include <vector>
 
 using af::dim4;
+using detail::Array;
+using detail::copy_vector_field;
+using detail::forgeManager;
+using detail::reduce;
+using detail::transpose;
+using detail::uchar;
+using detail::uint;
+using detail::ushort;
 using std::vector;
-using namespace detail;
+
 using namespace graphics;
 
 template<typename T>
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 69b121323f..f850787cbb 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -16,7 +16,13 @@
 #include <af/dim4.hpp>
 #include <complex>
 
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline af_array where(const af_array in) {
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index bcde57658d..5f9d6e1c43 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -15,7 +15,7 @@
 #include <common/graphics_common.hpp>
 #include <platform.hpp>
 
-using namespace detail;
+using detail::forgeManager;
 using namespace graphics;
 
 af_err af_create_window(af_window* out, const int width, const int height,
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index 011c86ca88..f436f37350 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -16,7 +16,13 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
 
 template<typename T>
 static inline void wrap(af_array* out, const af_array in, const dim_t wx,
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index 2bf72a1474..3e4238d28e 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -18,7 +18,11 @@
 #include <af/image.h>
 
 using af::dim4;
-using namespace detail;
+using detail::arithOp;
+using detail::Array;
+using detail::createValueArray;
+using detail::join;
+using detail::scalar;
 
 template<typename T>
 static Array<T> mix(const Array<T>& X, const Array<T>& Y, double xf,
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 784ef605a6..95497a0e4d 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -268,30 +268,30 @@ array::~array() {
         if (!err) {
             switch (backend) {
                 case AF_BACKEND_CPU: {
-                    static auto cpu_handle = unified::getActiveHandle();
-                    static af_release_array_ptr func =
+                    static auto *cpu_handle = unified::getActiveHandle();
+                    static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
                             common::getFunctionPointer(cpu_handle,
                                                        "af_release_array"));
-                    func(get());
+                    release_func(get());
                     break;
                 }
                 case AF_BACKEND_OPENCL: {
-                    static auto opencl_handle = unified::getActiveHandle();
-                    static af_release_array_ptr func =
+                    static auto *opencl_handle = unified::getActiveHandle();
+                    static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
                             common::getFunctionPointer(opencl_handle,
                                                        "af_release_array"));
-                    func(get());
+                    release_func(get());
                     break;
                 }
                 case AF_BACKEND_CUDA: {
-                    static auto cuda_handle = unified::getActiveHandle();
-                    static af_release_array_ptr func =
+                    static auto *cuda_handle = unified::getActiveHandle();
+                    static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
                             common::getFunctionPointer(cuda_handle,
                                                        "af_release_array"));
-                    func(get());
+                    release_func(get());
                     break;
                 }
                 case AF_BACKEND_DEFAULT:
@@ -609,12 +609,13 @@ af::array::array_proxy::array_proxy(const array_proxy &other)
     : impl(new array_proxy_impl(*other.impl->parent_, other.impl->indices_,
                                 other.impl->is_linear_)) {}
 
-// NOLINTNEXTLINE(hicpp-noexcept-move) too late to change public API
+// NOLINTNEXTLINE(performance-noexcept-move-constructor,hicpp-noexcept-move)
 af::array::array_proxy::array_proxy(array_proxy &&other) {
     impl       = other.impl;
     other.impl = nullptr;
 }
 
+// NOLINTNEXTLINE(performance-noexcept-move-constructor,hicpp-noexcept-move)
 array::array_proxy &af::array::array_proxy::operator=(array_proxy &&other) {
     array out = other;
     *this     = out;
@@ -911,44 +912,44 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type) {
     return scalar_type;
 }
 
-#define BINARY_TYPE(TY, OP, func, dty)                          \
-    array operator OP(const array &plhs, const TY &value) {     \
-        af_array out;                                           \
-        af::dtype cty = implicit_dtype(dty, plhs.type());       \
-        array cst     = constant(value, plhs.dims(), cty);      \
-        AF_THROW(func(&out, plhs.get(), cst.get(), gforGet())); \
-        return array(out);                                      \
-    }                                                           \
-    array operator OP(const TY &value, const array &other) {    \
-        const af_array rhs = other.get();                       \
-        af_array out;                                           \
-        af::dtype cty = implicit_dtype(dty, other.type());      \
-        array cst     = constant(value, other.dims(), cty);     \
-        AF_THROW(func(&out, cst.get(), rhs, gforGet()));        \
-        return array(out);                                      \
+#define BINARY_TYPE(TY, OP, release_func, dty)                          \
+    array operator OP(const array &plhs, const TY &value) {             \
+        af_array out;                                                   \
+        af::dtype cty = implicit_dtype(dty, plhs.type());               \
+        array cst     = constant(value, plhs.dims(), cty);              \
+        AF_THROW(release_func(&out, plhs.get(), cst.get(), gforGet())); \
+        return array(out);                                              \
+    }                                                                   \
+    array operator OP(const TY &value, const array &other) {            \
+        const af_array rhs = other.get();                               \
+        af_array out;                                                   \
+        af::dtype cty = implicit_dtype(dty, other.type());              \
+        array cst     = constant(value, other.dims(), cty);             \
+        AF_THROW(release_func(&out, cst.get(), rhs, gforGet()));        \
+        return array(out);                                              \
     }
 
-#define BINARY_OP(OP, func)                                    \
-    array operator OP(const array &lhs, const array &rhs) {    \
-        af_array out;                                          \
-        AF_THROW(func(&out, lhs.get(), rhs.get(), gforGet())); \
-        return array(out);                                     \
-    }                                                          \
-    BINARY_TYPE(double, OP, func, f64)                         \
-    BINARY_TYPE(float, OP, func, f32)                          \
-    BINARY_TYPE(cdouble, OP, func, c64)                        \
-    BINARY_TYPE(cfloat, OP, func, c32)                         \
-    BINARY_TYPE(int, OP, func, s32)                            \
-    BINARY_TYPE(unsigned, OP, func, u32)                       \
-    BINARY_TYPE(long, OP, func, s64)                           \
-    BINARY_TYPE(unsigned long, OP, func, u64)                  \
-    BINARY_TYPE(long long, OP, func, s64)                      \
-    BINARY_TYPE(unsigned long long, OP, func, u64)             \
-    BINARY_TYPE(char, OP, func, b8)                            \
-    BINARY_TYPE(unsigned char, OP, func, u8)                   \
-    BINARY_TYPE(bool, OP, func, b8)                            \
-    BINARY_TYPE(short, OP, func, s16)                          \
-    BINARY_TYPE(unsigned short, OP, func, u16)
+#define BINARY_OP(OP, release_func)                                    \
+    array operator OP(const array &lhs, const array &rhs) {            \
+        af_array out;                                                  \
+        AF_THROW(release_func(&out, lhs.get(), rhs.get(), gforGet())); \
+        return array(out);                                             \
+    }                                                                  \
+    BINARY_TYPE(double, OP, release_func, f64)                         \
+    BINARY_TYPE(float, OP, release_func, f32)                          \
+    BINARY_TYPE(cdouble, OP, release_func, c64)                        \
+    BINARY_TYPE(cfloat, OP, release_func, c32)                         \
+    BINARY_TYPE(int, OP, release_func, s32)                            \
+    BINARY_TYPE(unsigned, OP, release_func, u32)                       \
+    BINARY_TYPE(long, OP, release_func, s64)                           \
+    BINARY_TYPE(unsigned long, OP, release_func, u64)                  \
+    BINARY_TYPE(long long, OP, release_func, s64)                      \
+    BINARY_TYPE(unsigned long long, OP, release_func, u64)             \
+    BINARY_TYPE(char, OP, release_func, b8)                            \
+    BINARY_TYPE(unsigned char, OP, release_func, u8)                   \
+    BINARY_TYPE(bool, OP, release_func, b8)                            \
+    BINARY_TYPE(short, OP, release_func, s16)                          \
+    BINARY_TYPE(unsigned short, OP, release_func, u16)
 
 BINARY_OP(+, af_add)
 BINARY_OP(-, af_sub)
diff --git a/src/api/cpp/complex.cpp b/src/api/cpp/complex.cpp
index e1d4ada43b..e058536b36 100644
--- a/src/api/cpp/complex.cpp
+++ b/src/api/cpp/complex.cpp
@@ -35,14 +35,14 @@ cfloat operator*(const cfloat &lhs, const cfloat &rhs) {
     complex<float> clhs(lhs.real, lhs.imag);
     complex<float> crhs(rhs.real, rhs.imag);
     complex<float> out = clhs * crhs;
-    return cfloat(out.real(), out.imag());
+    return {out.real(), out.imag()};
 }
 
 cdouble operator*(const cdouble &lhs, const cdouble &rhs) {
     complex<double> clhs(lhs.real, lhs.imag);
     complex<double> crhs(rhs.real, rhs.imag);
     complex<double> out = clhs * crhs;
-    return cdouble(out.real(), out.imag());
+    return {out.real(), out.imag()};
 }
 
 cfloat operator-(const cfloat &lhs, const cfloat &rhs) {
@@ -59,14 +59,14 @@ cfloat operator/(const cfloat &lhs, const cfloat &rhs) {
     complex<float> clhs(lhs.real, lhs.imag);
     complex<float> crhs(rhs.real, rhs.imag);
     complex<float> out = clhs / crhs;
-    return cfloat(out.real(), out.imag());
+    return {out.real(), out.imag()};
 }
 
 cdouble operator/(const cdouble &lhs, const cdouble &rhs) {
     complex<double> clhs(lhs.real, lhs.imag);
     complex<double> crhs(rhs.real, rhs.imag);
     complex<double> out = clhs / crhs;
-    return cdouble(out.real(), out.imag());
+    return {out.real(), out.imag()};
 }
 
 #define IMPL_OP(OP)                                              \
@@ -120,9 +120,9 @@ double abs(const cdouble &val) {
     return abs(out);
 }
 
-cfloat conj(const cfloat &val) { return cfloat(val.real, -val.imag); }
+cfloat conj(const cfloat &val) { return {val.real, -val.imag}; }
 
-cdouble conj(const cdouble &val) { return cdouble(val.real, -val.imag); }
+cdouble conj(const cdouble &val) { return {val.real, -val.imag}; }
 
 std::ostream &operator<<(std::ostream &os, const cfloat &in) {
     os << "(" << in.real << ", " << in.imag << ")";
diff --git a/src/api/cpp/event.cpp b/src/api/cpp/event.cpp
index 47a70e3491..02d1e8fd73 100644
--- a/src/api/cpp/event.cpp
+++ b/src/api/cpp/event.cpp
@@ -21,8 +21,10 @@ event::~event() {
     if (e_) { af_delete_event(e_); }
 }
 
+// NOLINTNEXTLINE(performance-noexcept-move-constructor) we can't change the API
 event::event(event&& other) : e_(other.e_) { other.e_ = 0; }
 
+// NOLINTNEXTLINE(performance-noexcept-move-constructor) we can't change the API
 event& event::operator=(event&& other) {
     af_delete_event(this->e_);
     this->e_ = other.e_;
diff --git a/src/api/cpp/gfor.cpp b/src/api/cpp/gfor.cpp
index f97ad1c34f..51d36b3e12 100644
--- a/src/api/cpp/gfor.cpp
+++ b/src/api/cpp/gfor.cpp
@@ -23,7 +23,7 @@ void gforSet(bool val) { gforStatus = val; }
 
 bool gforToggle() {
     bool status = gforGet();
-    status ^= 1;
+    status ^= 1U;
     gforSet(status);
     return status;
 }
diff --git a/src/api/cpp/graphics.cpp b/src/api/cpp/graphics.cpp
index dff95979c8..c5f0ae2e20 100644
--- a/src/api/cpp/graphics.cpp
+++ b/src/api/cpp/graphics.cpp
@@ -41,14 +41,17 @@ Window::~Window() {
     if (wnd) { af_destroy_window(wnd); }
 }
 
+// NOLINTNEXTLINE(readability-make-member-function-const)
 void Window::setPos(const unsigned x, const unsigned y) {
     AF_THROW(af_set_position(get(), x, y));
 }
 
+// NOLINTNEXTLINE(readability-make-member-function-const)
 void Window::setTitle(const char* const title) {
     AF_THROW(af_set_title(get(), title));
 }
 
+// NOLINTNEXTLINE(readability-make-member-function-const)
 void Window::setSize(const unsigned w, const unsigned h) {
     AF_THROW(af_set_size(get(), w, h));
 }
@@ -151,6 +154,7 @@ void Window::vectorField(const array& xPoints, const array& yPoints,
                                      xDirs.get(), yDirs.get(), &temp));
 }
 
+// NOLINTNEXTLINE(readability-make-member-function-const)
 void Window::grid(const int rows, const int cols) {
     AF_THROW(af_grid(get(), rows, cols));
 }
@@ -202,12 +206,14 @@ void Window::show() {
     _c = -1;
 }
 
+// NOLINTNEXTLINE(readability-make-member-function-const)
 bool Window::close() {
     bool temp = true;
     AF_THROW(af_is_window_closed(&temp, get()));
     return temp;
 }
 
+// NOLINTNEXTLINE(readability-make-member-function-const)
 void Window::setVisibility(const bool isVisible) {
     AF_THROW(af_set_visibility(get(), isVisible));
 }
diff --git a/src/api/cpp/index.cpp b/src/api/cpp/index.cpp
index 68908c007c..134c58f0cb 100644
--- a/src/api/cpp/index.cpp
+++ b/src/api/cpp/index.cpp
@@ -68,7 +68,7 @@ index::index(const af::array &idx0) : impl{} {
 
 index::index(const af::index &idx0) : impl{idx0.impl} {}  // NOLINT
 
-// NOLINTNEXTLINE(hicpp-noexcept-move)
+// NOLINTNEXTLINE(hicpp-noexcept-move, performance-noexcept-move-constructor)
 index::index(index &&idx0) : impl{idx0.impl} { idx0.impl.idx.arr = nullptr; }
 
 index::~index() {
@@ -87,7 +87,7 @@ index &index::operator=(const index &idx0) {
     return *this;
 }
 
-// NOLINTNEXTLINE(hicpp-noexcept-move)
+// NOLINTNEXTLINE(hicpp-noexcept-move, performance-noexcept-move-constructor)
 index &index::operator=(index &&idx0) {
     impl              = idx0.impl;
     idx0.impl.idx.arr = nullptr;
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index 577a2cc950..b67868d181 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -66,7 +66,7 @@ af_err af_join(af_array *out, const int dim, const af_array first,
 
 af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
                     const af_array *inputs) {
-    for (unsigned i = 0; i < n_arrays; i++) CHECK_ARRAYS(inputs[i]);
+    for (unsigned i = 0; i < n_arrays; i++) { CHECK_ARRAYS(inputs[i]); }
     CALL(af_join_many, out, dim, n_arrays, inputs);
 }
 
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 251d017676..be384d3e11 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -86,12 +86,12 @@ af_err af_free_device(void *ptr) { CALL(af_free_device, ptr); }
 af_err af_free_pinned(void *ptr) { CALL(af_free_pinned, ptr); }
 
 af_err af_alloc_host(void **ptr, const dim_t bytes) {
-    *ptr = malloc(bytes);
+    *ptr = malloc(bytes);  // NOLINT(hicpp-no-malloc)
     return (*ptr == NULL) ? AF_ERR_NO_MEM : AF_SUCCESS;
 }
 
 af_err af_free_host(void *ptr) {
-    free(ptr);
+    free(ptr);  // NOLINT(hicpp-no-malloc)
     return AF_SUCCESS;
 }
 
diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index 23a90c4fb3..2e2d51642f 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -16,7 +16,8 @@
 void af_get_last_error(char **str, dim_t *len) {
     // Set error message from unified backend
     std::string &global_error_string = get_global_error_string();
-    dim_t slen = std::min(MAX_ERR_SIZE, (int)global_error_string.size());
+    dim_t slen =
+        std::min(MAX_ERR_SIZE, static_cast<int>(global_error_string.size()));
 
     // If this is true, the error is coming from the unified backend.
     if (slen != 0) {
@@ -26,17 +27,18 @@ void af_get_last_error(char **str, dim_t *len) {
             return;
         }
 
-        af_alloc_host((void **)str, sizeof(char) * (slen + 1));
+        af_alloc_host(reinterpret_cast<void **>(str),
+                      sizeof(char) * (slen + 1));
         global_error_string.copy(*str, slen);
 
         (*str)[slen]        = '\0';
         global_error_string = std::string("");
 
-        if (len) *len = slen;
+        if (len) { *len = slen; }
     } else {
         // If false, the error is coming from active backend.
         typedef void (*af_func)(char **, dim_t *);
-        af_func func = (af_func)LOAD_SYMBOL();
+        auto func = reinterpret_cast<af_func>(LOAD_SYMBOL());
         func(str, len);
     }
 }
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index b1752ab859..f3808091ed 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -160,7 +160,7 @@ af_err af_set_axes_limits_compute(const af_window wind, const af_array x,
                                   const bool exact,
                                   const af_cell* const props) {
     CHECK_ARRAYS(x, y);
-    if (z) CHECK_ARRAYS(z);
+    if (z) { CHECK_ARRAYS(z); }
     CALL(af_set_axes_limits_compute, wind, x, y, z, exact, props);
 }
 
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 052ef7848f..8e1f846c54 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -86,7 +86,7 @@ string getBackendDirectoryName(const af_backend backend) {
 string join_path(string first) { return first; }
 
 template<typename... ARGS>
-string join_path(string first, ARGS... args) {
+string join_path(const string& first, ARGS... args) {
     if (first.empty()) {
         return join_path(args...);
     } else {
@@ -136,16 +136,15 @@ LibHandle openDynLibrary(const af_backend bknd_idx) {
 
     LibHandle retVal = nullptr;
 
-    for (size_t i = 0; i < extent<decltype(pathPrefixes)>::value; i++) {
+    for (auto& pathPrefixe : pathPrefixes) {
         AF_TRACE("Attempting: {}",
-                 (pathPrefixes[i].empty() ? "Default System Paths"
-                                          : pathPrefixes[i]));
-        if ((retVal = loadLibrary(
-                 join_path(pathPrefixes[i], bkndLibName).c_str()))) {
-            AF_TRACE("Found: {}", join_path(pathPrefixes[i], bkndLibName));
-
-            func count_func =
-                (func)getFunctionPointer(retVal, "af_get_device_count");
+                 (pathPrefixe.empty() ? "Default System Paths" : pathPrefixe));
+        if ((retVal =
+                 loadLibrary(join_path(pathPrefixe, bkndLibName).c_str()))) {
+            AF_TRACE("Found: {}", join_path(pathPrefixe, bkndLibName));
+
+            func count_func = reinterpret_cast<func>(
+                getFunctionPointer(retVal, "af_get_device_count"));
             if (count_func) {
                 int count = 0;
                 count_func(&count);
@@ -194,11 +193,11 @@ AFSymbolManager::AFSymbolManager()
     // Decremeting loop. The last successful backend loaded will be the most
     // prefered one.
     for (int i = NUM_BACKENDS - 1; i >= 0; i--) {
-        int backend_index          = order[i] >> 1;  // 2 4 1 -> 1 2 0
+        int backend_index          = order[i] >> 1U;  // 2 4 1 -> 1 2 0
         bkndHandles[backend_index] = openDynLibrary(order[i]);
         if (bkndHandles[backend_index]) {
             handle  = bkndHandles[backend_index];
-            backend = (af_backend)order[i];
+            backend = order[i];
             numBackends++;
             backendsAvailable += order[i];
         }
@@ -217,14 +216,14 @@ AFSymbolManager::AFSymbolManager()
 }
 
 AFSymbolManager::~AFSymbolManager() {
-    for (int i = 0; i < NUM_BACKENDS; ++i) {
-        if (bkndHandles[i]) { common::unloadLibrary(bkndHandles[i]); }
+    for (auto& bkndHandle : bkndHandles) {
+        if (bkndHandle) { common::unloadLibrary(bkndHandle); }
     }
 }
 
-unsigned AFSymbolManager::getBackendCount() { return numBackends; }
+unsigned AFSymbolManager::getBackendCount() const { return numBackends; }
 
-int AFSymbolManager::getAvailableBackends() { return backendsAvailable; }
+int AFSymbolManager::getAvailableBackends() const { return backendsAvailable; }
 
 af_err setBackend(af::Backend bknd) {
     auto& instance = AFSymbolManager::getInstance();
@@ -237,7 +236,7 @@ af_err setBackend(af::Backend bknd) {
             UNIFIED_ERROR_LOAD_LIB();
         }
     }
-    int idx = bknd >> 1;  // Convert 1, 2, 4 -> 0, 1, 2
+    int idx = bknd >> 1U;  // Convert 1, 2, 4 -> 0, 1, 2
     if (instance.getHandle(idx)) {
         getActiveHandle()  = instance.getHandle(idx);
         getActiveBackend() = bknd;
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 7c7885d2a8..aeed23a415 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -50,8 +50,8 @@ class AFSymbolManager {
 
     ~AFSymbolManager();
 
-    unsigned getBackendCount();
-    int getAvailableBackends();
+    unsigned getBackendCount() const;
+    int getAvailableBackends() const;
     af::Backend getDefaultBackend() { return defaultBackend; }
     LibHandle getDefaultHandle() { return defaultHandle; }
 
@@ -69,7 +69,7 @@ class AFSymbolManager {
     void operator=(AFSymbolManager const&);
 
    private:
-    LibHandle bkndHandles[NUM_BACKENDS];
+    LibHandle bkndHandles[NUM_BACKENDS]{};
 
     LibHandle defaultHandle;
     unsigned numBackends;
@@ -78,7 +78,7 @@ class AFSymbolManager {
     std::shared_ptr<spdlog::logger> logger;
 };
 
-af_err setBackend(af::Backend bnkd);
+af_err setBackend(af::Backend bknd);
 
 af::Backend& getActiveBackend();
 
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index d543101c18..4dec5c3966 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -47,7 +47,7 @@ class ArrayInfo {
     bool is_sparse;
 
    public:
-    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride,
+    ArrayInfo(unsigned id, af::dim4 size, dim_t offset_, af::dim4 stride,
               af_dtype af_type)
         : devId(id)
         , type(af_type)
@@ -63,7 +63,7 @@ class ArrayInfo {
                    This is then used in the unified backend to check mismatched arrays.");
     }
 
-    ArrayInfo(int id, af::dim4 size, dim_t offset_, af::dim4 stride,
+    ArrayInfo(unsigned id, af::dim4 size, dim_t offset_, af::dim4 stride,
               af_dtype af_type, bool sparse)
         : devId(id)
         , type(af_type)
diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 10c5964a80..65ed9dc191 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -65,7 +65,7 @@ void DefaultMemoryManager::cleanDeviceMemoryManager(int device) {
     AF_TRACE("GC: Clearing {} buffers {}", free_ptrs.size(),
              bytesToString(bytes_freed));
     // Free memory outside of the lock
-    for (auto ptr : free_ptrs) { this->nativeFree(ptr); }
+    for (auto *ptr : free_ptrs) { this->nativeFree(ptr); }
 }
 
 DefaultMemoryManager::DefaultMemoryManager(int num_devices,
@@ -116,7 +116,7 @@ void DefaultMemoryManager::setMaxMemorySize() {
         // Calls garbage collection when: total_bytes > memsize * 0.75 when
         // memsize < 4GB total_bytes > memsize - 1 GB when memsize >= 4GB If
         // memsize returned 0, then use 1GB
-        size_t memsize = this->getMaxMemorySize(n);
+        size_t memsize = this->getMaxMemorySize(static_cast<int>(n));
         memory[n].max_bytes =
             memsize == 0
                 ? ONE_GB
@@ -275,7 +275,7 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
         "---------------------------------------------------------\n");
 
     lock_guard_t lock(this->memory_mutex);
-    for (auto &kv : current.locked_map) {
+    for (const auto &kv : current.locked_map) {
         const char *status_mngr = "Yes";
         const char *status_user = "Unknown";
         if (kv.second.user_lock) {
@@ -295,7 +295,7 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
                status_mngr, status_user);
     }
 
-    for (auto &kv : current.free_map) {
+    for (const auto &kv : current.free_map) {
         const char *status_mngr = "No";
         const char *status_user = "No";
 
@@ -306,7 +306,7 @@ void DefaultMemoryManager::printInfo(const char *msg, const int device) {
             unit = "MB";
         }
 
-        for (auto &ptr : kv.second) {
+        for (const auto &ptr : kv.second) {
             printf("|  %14p  |  %6.f %s | %9s | %9s |\n", ptr, size, unit,
                    status_mngr, status_user);
         }
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index deafcc9f06..350bb02789 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -14,12 +14,20 @@
 #include <platform.hpp>
 #include <af/traits.hpp>
 
+using af::dim4;
 using af::dtype_traits;
+using detail::Array;
+using detail::cdouble;
+using detail::cfloat;
+using detail::copyArray;
+using detail::createDeviceDataArray;
+using detail::createHostDataArray;
+using detail::createValueArray;
+using detail::getActiveDeviceId;
+using detail::scalar;
+using detail::writeDeviceDataArray;
 
 namespace common {
-
-using namespace detail;
-
 ////////////////////////////////////////////////////////////////////////////
 // Sparse Array Base Implementations
 ////////////////////////////////////////////////////////////////////////////
@@ -35,7 +43,7 @@ using namespace detail;
     ((stype == AF_STORAGE_COO || stype == AF_STORAGE_CSR) ? _nNZ \
                                                           : (_dims[1] + 1))
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
+SparseArrayBase::SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ,
                                  af::storage _storage, af_dtype _type)
     : info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true)
     , stype(_storage)
@@ -46,10 +54,10 @@ SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ,
                   "SparseArrayBase.");
 }
 
-SparseArrayBase::SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
-                                 int *const _colIdx, const af::storage _storage,
-                                 af_dtype _type, bool _is_device,
-                                 bool _copy_device)
+SparseArrayBase::SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ,
+                                 int *const _rowIdx, int *const _colIdx,
+                                 const af::storage _storage, af_dtype _type,
+                                 bool _is_device, bool _copy_device)
     : info(getActiveDeviceId(), _dims, 0, calcStrides(_dims), _type, true)
     , stype(_storage)
     , rowIdx(_is_device
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 24144a29fe..2e8c78c99c 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -18,8 +18,6 @@
 
 namespace common {
 
-using namespace detail;
-
 template<typename T>
 class SparseArray;
 
@@ -35,22 +33,23 @@ class SparseArrayBase {
    private:
     ArrayInfo
         info;  ///< NOTE: This must be the first element of SparseArray<T>.
-    af::storage stype;  ///< Storage format: CSR, CSC, COO
-    Array<int> rowIdx;  ///< Linear array containing row indices
-    Array<int> colIdx;  ///< Linear array containing col indices
+    af::storage stype;          ///< Storage format: CSR, CSC, COO
+    detail::Array<int> rowIdx;  ///< Linear array containing row indices
+    detail::Array<int> colIdx;  ///< Linear array containing col indices
 
    public:
-    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, af::storage _storage,
+    SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ, af::storage _storage,
                     af_dtype _type);
 
-    SparseArrayBase(af::dim4 _dims, dim_t _nNZ, int *const _rowIdx,
+    SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ, int *const _rowIdx,
                     int *const _colIdx, const af::storage _storage,
                     af_dtype _type, bool _is_device = false,
                     bool _copy_device = false);
 
-    SparseArrayBase(const af::dim4 &_dims, const Array<int> &_rowIdx,
-                    const Array<int> &_colIdx, const af::storage _storage,
-                    af_dtype _type, bool _copy = false);
+    SparseArrayBase(const af::dim4 &_dims, const detail::Array<int> &_rowIdx,
+                    const detail::Array<int> &_colIdx,
+                    const af::storage _storage, af_dtype _type,
+                    bool _copy = false);
 
     /// A copy constructor for SparseArray
     ///
@@ -103,13 +102,13 @@ class SparseArrayBase {
     }
 
     /// Returns the row indices for the corresponding values in the SparseArray
-    Array<int> &getRowIdx() { return rowIdx; }
-    const Array<int> &getRowIdx() const { return rowIdx; }
+    detail::Array<int> &getRowIdx() { return rowIdx; }
+    const detail::Array<int> &getRowIdx() const { return rowIdx; }
 
     /// Returns the column indices for the corresponding values in the
     /// SparseArray
-    Array<int> &getColIdx() { return colIdx; }
-    const Array<int> &getColIdx() const { return colIdx; }
+    detail::Array<int> &getColIdx() { return colIdx; }
+    const detail::Array<int> &getColIdx() const { return colIdx; }
 
     /// Returns the number of non-zero elements in the array.
     dim_t getNNZ() const;
@@ -127,8 +126,8 @@ template<typename T>
 class SparseArray {
    private:
     SparseArrayBase
-        base;         ///< This must be the first element of SparseArray<T>.
-    Array<T> values;  ///< Linear array containing actual values
+        base;  ///< This must be the first element of SparseArray<T>.
+    detail::Array<T> values;  ///< Linear array containing actual values
 
     SparseArray(const af::dim4 &_dims, dim_t _nNZ, af::storage _storage);
 
@@ -137,9 +136,10 @@ class SparseArray {
                          const af::storage _storage, bool _is_device = false,
                          bool _copy_device = false);
 
-    SparseArray(const af::dim4 &_dims, const Array<T> &_values,
-                const Array<int> &_rowIdx, const Array<int> &_colIdx,
-                const af::storage _storage, bool _copy = false);
+    SparseArray(const af::dim4 &_dims, const detail::Array<T> &_values,
+                const detail::Array<int> &_rowIdx,
+                const detail::Array<int> &_colIdx, const af::storage _storage,
+                bool _copy = false);
 
     /// A copy constructor for SparseArray
     ///
@@ -185,10 +185,10 @@ class SparseArray {
     INSTANTIATE_INFO(dim_t, getNNZ)
     INSTANTIATE_INFO(af::storage, getStorage)
 
-    Array<int> &getRowIdx() { return base.getRowIdx(); }
-    Array<int> &getColIdx() { return base.getColIdx(); }
-    const Array<int> &getRowIdx() const { return base.getRowIdx(); }
-    const Array<int> &getColIdx() const { return base.getColIdx(); }
+    detail::Array<int> &getRowIdx() { return base.getRowIdx(); }
+    detail::Array<int> &getColIdx() { return base.getColIdx(); }
+    const detail::Array<int> &getRowIdx() const { return base.getRowIdx(); }
+    const detail::Array<int> &getColIdx() const { return base.getColIdx(); }
 
 #undef INSTANTIATE_INFO
 
@@ -198,8 +198,8 @@ class SparseArray {
     }
 
     // Return the values array
-    Array<T> &getValues() { return values; }
-    const Array<T> &getValues() const { return values; }
+    detail::Array<T> &getValues() { return values; }
+    const detail::Array<T> &getValues() const { return values; }
 
     void eval() const {
         getValues().eval();
@@ -223,8 +223,8 @@ class SparseArray {
         const bool _copy);
 
     friend SparseArray<T> createArrayDataSparseArray<T>(
-        const af::dim4 &_dims, const Array<T> &_values,
-        const Array<int> &_rowIdx, const Array<int> &_colIdx,
+        const af::dim4 &_dims, const detail::Array<T> &_values,
+        const detail::Array<int> &_rowIdx, const detail::Array<int> &_colIdx,
         const af::storage _storage, const bool _copy);
 
     friend SparseArray<T> *initSparseArray<T>();
diff --git a/src/backend/common/sparse_helpers.hpp b/src/backend/common/sparse_helpers.hpp
index 2666cec978..7a370bc38c 100644
--- a/src/backend/common/sparse_helpers.hpp
+++ b/src/backend/common/sparse_helpers.hpp
@@ -12,8 +12,6 @@
 
 namespace common {
 
-using namespace detail;
-
 class SparseArrayBase;
 template<typename T>
 class SparseArray;
@@ -42,9 +40,9 @@ SparseArray<T> createDeviceDataSparseArray(const af::dim4 &_dims,
 
 template<typename T>
 SparseArray<T> createArrayDataSparseArray(const af::dim4 &_dims,
-                                          const Array<T> &_values,
-                                          const Array<int> &_rowIdx,
-                                          const Array<int> &_colIdx,
+                                          const detail::Array<T> &_values,
+                                          const detail::Array<int> &_rowIdx,
+                                          const detail::Array<int> &_colIdx,
                                           const af::storage _storage,
                                           const bool _copy = false);
 
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index 555a3b3add..cdf48e31d1 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -100,7 +100,7 @@ void saveKernel(const std::string& funcName, const std::string& jit_ker,
 
 std::string int_version_to_string(int version) {
     return std::to_string(version / 1000) + "." +
-           std::to_string((int)((version % 1000) / 10.));
+           std::to_string(static_cast<int>((version % 1000) / 10.));
 }
 
 #if defined(OS_WIN)
@@ -115,10 +115,10 @@ string getTemporaryDirectory() {
 #else
 string getHomeDirectory() {
     string home = getEnvVar("XDG_CACHE_HOME");
-    if (!home.empty()) return home;
+    if (!home.empty()) { return home; }
 
     home = getEnvVar("HOME");
-    if (!home.empty()) return home;
+    if (!home.empty()) { return home; }
 
     return getpwuid(getuid())->pw_dir;
 }
@@ -129,7 +129,8 @@ bool directoryExists(const string& path) {
     struct _stat status;
     return _stat(path.c_str(), &status) == 0 && (status.st_mode & S_IFDIR) != 0;
 #else
-    struct stat status;
+    struct stat status {};
+    // NOLINTNEXTLINE(hicpp-signed-bitwise)
     return stat(path.c_str(), &status) == 0 && (status.st_mode & S_IFDIR) != 0;
 #endif
 }
@@ -155,10 +156,10 @@ bool renameFile(const string& sourcePath, const string& destPath) {
 }
 
 bool isDirectoryWritable(const string& path) {
-    if (!directoryExists(path) && !createDirectory(path)) return false;
+    if (!directoryExists(path) && !createDirectory(path)) { return false; }
 
     const string testPath = path + AF_PATH_SEPARATOR + "test";
-    if (!std::ofstream(testPath).is_open()) return false;
+    if (!std::ofstream(testPath).is_open()) { return false; }
     removeFile(testPath);
 
     return true;
@@ -201,9 +202,9 @@ string makeTempFilename() {
 std::size_t deterministicHash(const void* data, std::size_t byteSize) {
     // Fowler-Noll-Vo "1a" 32 bit hash
     // https://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function
-    constexpr std::size_t seed   = 0x811C9DC5;
-    constexpr std::size_t prime  = 0x01000193;
-    const std::uint8_t* byteData = static_cast<const std::uint8_t*>(data);
+    constexpr std::size_t seed  = 0x811C9DC5;
+    constexpr std::size_t prime = 0x01000193;
+    const auto* byteData        = static_cast<const std::uint8_t*>(data);
     return std::accumulate(byteData, byteData + byteSize, seed,
                            [&](std::size_t hash, std::uint8_t data) {
                                return (hash ^ data) * prime;
@@ -212,4 +213,4 @@ std::size_t deterministicHash(const void* data, std::size_t byteSize) {
 
 std::size_t deterministicHash(const std::string& data) {
     return deterministicHash(data.data(), data.size());
-}
\ No newline at end of file
+}
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 35afef108e..369a0c4bb4 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -50,4 +50,4 @@ std::string makeTempFilename();
 std::size_t deterministicHash(const void* data, std::size_t byteSize);
 
 // This is just a wrapper around the above function.
-std::size_t deterministicHash(const std::string& data);
\ No newline at end of file
+std::size_t deterministicHash(const std::string& data);
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index ffd0576b26..c7b7439295 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -244,8 +244,8 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
 
     // Check if approaching the memory limit
     if (getMemoryPressure() >= getMemoryPressureThreshold()) {
-        NodeIterator<jit::Node> it(root_node);
-        NodeIterator<jit::Node> end_node;
+        NodeIterator<Node> it(root_node);
+        NodeIterator<Node> end_node;
         size_t bytes = accumulate(it, end_node, size_t(0),
                                   [=](const size_t prev, const Node &n) {
                                       // getBytes returns the size of the data
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index aa22112987..3dd1cae2cc 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -40,7 +40,7 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     dim_t fftScale = 1;
 
     dim4 packedDims(1, 1, 1, 1);
-    array<int, baseDim> fftDims;
+    array<int, baseDim> fftDims{};
 
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched FFT capabilities
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index 5524bb75dc..174489274c 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -28,7 +28,6 @@ class NodeIterator;
 namespace cpu {
 
 namespace jit {
-class Node;
 constexpr int VECTOR_LENGTH = 256;
 
 template<typename T>
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index 86dbea3998..196ce6a08c 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -18,7 +18,7 @@ namespace jit {
 
 template<typename T>
 class ScalarNode : public TNode<T> {
-  public:
+   public:
     ScalarNode(T val) : TNode<T>(val, 0, {}) {}
 
     void genKerName(std::stringstream &kerStream,
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index 5517a6657b..e6ea00ed93 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -32,7 +32,7 @@ struct MinMaxOp {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<T, op>::init(); }
+        if (is_nan(val)) { m_val = common::Binary<T, op>::init(); }
     }
 
     void operator()(T val, uint idx) {
@@ -49,7 +49,7 @@ struct MinMaxOp<af_max_t, T> {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<T, af_max_t>::init(); }
+        if (is_nan(val)) { m_val = common::Binary<T, af_max_t>::init(); }
     }
 
     void operator()(T val, uint idx) {
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index 2683a69491..966197a059 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -16,7 +16,7 @@ namespace kernel {
 
 template<typename Ti, typename To, typename Tw>
 struct MeanOp {
-    Transform<Ti, To, af_add_t> transform;
+    common::Transform<Ti, To, af_add_t> transform;
     To runningMean;
     Tw runningCount;
     MeanOp(Ti mean, Tw count)
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 56104e089a..e04a47b1af 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -45,8 +45,8 @@ struct MorphFilterOp {
 template<typename T, bool IsDilation>
 void morph(Param<T> paddedOut, CParam<T> paddedIn, CParam<T> mask) {
     MorphFilterOp<T, IsDilation> filterOp;
-    T init =
-        IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T init = IsDilation ? common::Binary<T, af_max_t>::init()
+                        : common::Binary<T, af_min_t>::init();
 
     const af::dim4 ostrides = paddedOut.strides();
     T* outData              = paddedOut.get();
@@ -89,8 +89,8 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask) {
     const T* inData         = in.get();
     const T* filter         = mask.get();
 
-    T init =
-        IsDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T init = IsDilation ? common::Binary<T, af_max_t>::init()
+                        : common::Binary<T, af_min_t>::init();
 
     for (dim_t batchId = 0; batchId < bCount; ++batchId) {
         // either channels or batch is handled by outer most loop
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index db20b5213e..61206b097f 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -37,8 +37,8 @@ struct reduce_dim {
 
 template<af_op_t op, typename Ti, typename To>
 struct reduce_dim<op, Ti, To, 0> {
-    Transform<data_t<Ti>, compute_t<To>, op> transform;
-    Binary<compute_t<To>, op> reduce;
+    common::Transform<data_t<Ti>, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
     void operator()(Param<To> out, const dim_t outOffset, CParam<Ti> in,
                     const dim_t inOffset, const int dim, bool change_nan,
                     double nanval) {
@@ -49,7 +49,7 @@ struct reduce_dim<op, Ti, To, 0> {
         data_t<Ti> const *const inPtr = in.get() + inOffset;
         dim_t stride                  = istrides[dim];
 
-        compute_t<To> out_val = Binary<compute_t<To>, op>::init();
+        compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
             compute_t<To> in_val = transform(inPtr[i * stride]);
             if (change_nan) in_val = IS_NAN(in_val) ? nanval : in_val;
@@ -111,8 +111,8 @@ struct reduce_dim_by_key {
 
 template<af_op_t op, typename Ti, typename Tk, typename To>
 struct reduce_dim_by_key<op, Ti, Tk, To, 0> {
-    Transform<data_t<Ti>, compute_t<To>, op> transform;
-    Binary<compute_t<To>, op> reduce;
+    common::Transform<data_t<Ti>, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
     void operator()(Param<To> ovals, const dim_t ovOffset, CParam<Tk> keys,
                     CParam<Ti> vals, const dim_t vOffset, int *n_reduced,
                     const int dim, bool change_nan, double nanval) {
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index f721e5a8d9..be9dd73392 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -18,9 +18,9 @@ template<af_op_t op, typename Ti, typename To, int D, bool inclusive_scan>
 struct scan_dim {
     void operator()(Param<To> out, dim_t outOffset, CParam<Ti> in,
                     dim_t inOffset, const int dim) const {
-        const dim4 odims    = out.dims();
-        const dim4 ostrides = out.strides();
-        const dim4 istrides = in.strides();
+        const af::dim4 odims    = out.dims();
+        const af::dim4 ostrides = out.strides();
+        const af::dim4 istrides = in.strides();
 
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
@@ -39,18 +39,18 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan> {
         const Ti* in = input.get() + inOffset;
         To* out      = output.get() + outOffset;
 
-        const dim4 ostrides = output.strides();
-        const dim4 istrides = input.strides();
-        const dim4 idims    = input.dims();
+        const af::dim4 ostrides = output.strides();
+        const af::dim4 istrides = input.strides();
+        const af::dim4 idims    = input.dims();
 
         dim_t istride = istrides[dim];
         dim_t ostride = ostrides[dim];
 
-        Transform<Ti, To, op> transform;
+        common::Transform<Ti, To, op> transform;
         // FIXME: Change the name to something better
-        Binary<To, op> scan;
+        common::Binary<To, op> scan;
 
-        To out_val = Binary<To, op>::init();
+        To out_val = common::Binary<To, op>::init();
         for (dim_t i = 0; i < idims[dim]; i++) {
             To in_val = transform(in[i * istride]);
             out_val   = scan(in_val, out_val);
@@ -58,7 +58,7 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan> {
                 // The loop shifts the output index by 1.
                 // The last index wraps around and writes the first element.
                 if (i == (idims[dim] - 1)) {
-                    out[0] = Binary<To, op>::init();
+                    out[0] = common::Binary<To, op>::init();
                 } else {
                     out[(i + 1) * ostride] = out_val;
                 }
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index bd9c3e627a..720b8d65d8 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -22,10 +22,10 @@ struct scan_dim_by_key {
     void operator()(Param<To> out, dim_t outOffset, CParam<Tk> key,
                     dim_t keyOffset, CParam<Ti> in, dim_t inOffset,
                     const int dim) const {
-        const dim4 odims    = out.dims();
-        const dim4 ostrides = out.strides();
-        const dim4 kstrides = key.strides();
-        const dim4 istrides = in.strides();
+        const af::dim4 odims    = out.dims();
+        const af::dim4 ostrides = out.strides();
+        const af::dim4 kstrides = key.strides();
+        const af::dim4 istrides = in.strides();
 
         const int D1 = D - 1;
         for (dim_t i = 0; i < odims[D1]; i++) {
@@ -50,29 +50,30 @@ struct scan_dim_by_key<op, Ti, Tk, To, 0> {
         const Tk* key = keyinput.get() + keyOffset;
         To* out       = output.get() + outOffset;
 
-        const dim4 ostrides = output.strides();
-        const dim4 kstrides = keyinput.strides();
-        const dim4 istrides = input.strides();
-        const dim4 idims    = input.dims();
+        const af::dim4 ostrides = output.strides();
+        const af::dim4 kstrides = keyinput.strides();
+        const af::dim4 istrides = input.strides();
+        const af::dim4 idims    = input.dims();
 
         dim_t istride = istrides[dim];
         dim_t kstride = kstrides[dim];
         dim_t ostride = ostrides[dim];
 
-        Transform<Ti, To, op> transform;
+        common::Transform<Ti, To, op> transform;
         // FIXME: Change the name to something better
-        Binary<To, op> scan;
+        common::Binary<To, op> scan;
 
-        To out_val = Binary<To, op>::init();
+        To out_val = common::Binary<To, op>::init();
         Tk key_val = key[0];
 
         dim_t k = !inclusive_scan;
-        if (!inclusive_scan) { out[0] = Binary<To, op>::init(); }
+        if (!inclusive_scan) { out[0] = common::Binary<To, op>::init(); }
 
         for (dim_t i = 0; i < idims[dim] - (!inclusive_scan); i++, k++) {
             To in_val = transform(in[i * istride]);
             if (key[k * kstride] != key_val) {
-                out_val = !inclusive_scan ? Binary<To, op>::init() : in_val;
+                out_val =
+                    !inclusive_scan ? common::Binary<To, op>::init() : in_val;
                 key_val = key[k * kstride];
             } else {
                 out_val = scan(in_val, out_val);
diff --git a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index 05d6709bda..c1ae75110e 100644
--- a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -14,5 +14,5 @@
 namespace cpu {
 namespace kernel {
 INSTANTIATE1(TYPE)
-}
+}  // namespace kernel
 }  // namespace cpu
diff --git a/src/backend/cpu/math.cpp b/src/backend/cpu/math.cpp
index 8310f12c57..04e426e48a 100644
--- a/src/backend/cpu/math.cpp
+++ b/src/backend/cpu/math.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #include <common/defines.hpp>
 #include <math.hpp>
+#include <complex>
 
 namespace cpu {
 
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 360750ca66..2142604095 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -113,4 +113,10 @@ static inline T clamp(const T value, const T lo, const T hi) {
     return (value < lo ? lo : (value > hi ? hi : value));
 }
 #endif
+
+inline double real(cdouble in) noexcept { return std::real(in); }
+inline float real(cfloat in) noexcept { return std::real(in); }
+inline double imag(cdouble in) noexcept { return std::imag(in); }
+inline float imag(cfloat in) noexcept { return std::imag(in); }
+
 }  // namespace cpu
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index e2dc906fd8..f64bed56ff 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -133,7 +133,9 @@ void Allocator::shutdown() {
     }
 }
 
-int Allocator::getActiveDeviceId() { return cpu::getActiveDeviceId(); }
+int Allocator::getActiveDeviceId() {
+    return static_cast<int>(cpu::getActiveDeviceId());
+}
 
 size_t Allocator::getMaxMemorySize(int id) {
     return cpu::getDeviceMemorySize(id);
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index c44826447d..da634b0d82 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -64,7 +64,8 @@ string getDeviceInfo() noexcept {
 
     string model = cinfo.model();
 
-    size_t memMB = getDeviceMemorySize(getActiveDeviceId()) / 1048576;
+    size_t memMB =
+        getDeviceMemorySize(static_cast<int>(getActiveDeviceId())) / 1048576;
 
     info << string("[0] ") << cinfo.vendor() << ": " << ltrim(model);
 
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 1e442714cc..ab0c782db9 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -20,7 +20,12 @@
 #include <functional>
 
 using af::dim4;
+using common::Binary;
 using common::half;
+using common::Transform;
+using cpu::cdouble;
+
+namespace common {
 
 template<>
 struct Binary<cdouble, af_add_t> {
@@ -31,6 +36,8 @@ struct Binary<cdouble, af_add_t> {
     }
 };
 
+}  // namespace common
+
 namespace cpu {
 
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index 553013001b..645e48d2e2 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -55,7 +55,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
         for (int i = 0; i < iter; i++) {
             auto idx_itr = begin(idx) + i * in.strides()[1];
-            auto kiptr   = iptr + k * i;
+            auto* kiptr  = iptr + k * i;
 
             if (order == AF_TOPK_MIN) {
                 // Sort the top k values in each column
@@ -72,7 +72,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
                     });
             }
 
-            auto kvptr = vptr + k * i;
+            auto* kvptr = vptr + k * i;
             for (int j = 0; j < k; j++) {
                 // Update the value arrays with the original values
                 kvptr[j] = ptr[kiptr[j]];
diff --git a/src/backend/cuda/kernel/ireduce.cuh b/src/backend/cuda/kernel/ireduce.cuh
index afdb5baec4..bd91b08e60 100644
--- a/src/backend/cuda/kernel/ireduce.cuh
+++ b/src/backend/cuda/kernel/ireduce.cuh
@@ -17,7 +17,8 @@ namespace cuda {
 template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
 __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
                                   const uint *ilptr, uint blocks_x,
-                                  uint blocks_y, uint offset_dim, CParam<uint> rlen) {
+                                  uint blocks_y, uint offset_dim,
+                                  CParam<uint> rlen) {
     const uint tidx = threadIdx.x;
     const uint tidy = threadIdx.y;
     const uint tid  = tidy * THREADS_X + tidx;
@@ -41,16 +42,17 @@ __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
     // in
     bool rlen_valid = (ids[0] < rlen.dims[0]) && (ids[1] < rlen.dims[1]) &&
                       (ids[2] < rlen.dims[2]) && (ids[3] < rlen.dims[3]);
-    const uint *rlenptr   = (rlen.ptr && rlen_valid) ?
-             rlen.ptr + ids[3] * rlen.strides[3] + ids[2] * rlen.strides[2] +
-             ids[1] * rlen.strides[1] + ids[0] : nullptr;
+    const uint *rlenptr = (rlen.ptr && rlen_valid)
+                              ? rlen.ptr + ids[3] * rlen.strides[3] +
+                                    ids[2] * rlen.strides[2] +
+                                    ids[1] * rlen.strides[1] + ids[0]
+                              : nullptr;
 
     optr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
             ids[1] * out.strides[1] + ids[0];
     olptr += ids[3] * out.strides[3] + ids[2] * out.strides[2] +
              ids[1] * out.strides[1] + ids[0];
 
-
     const uint blockIdx_dim = ids[dim];
 
     ids[dim] = ids[dim] * blockDim.y + tidy;
@@ -66,12 +68,14 @@ __global__ static void ireduceDim(Param<T> out, uint *olptr, CParam<T> in,
     bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
                     (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
 
-    T val    = Binary<T, op>::init();
+    T val    = common::Binary<T, op>::init();
     uint idx = id_dim_in;
 
     uint lim = (rlenptr) ? *rlenptr : in.dims[dim];
-    lim = (is_first) ? min((uint)in.dims[dim], lim) : lim;
-    bool within_ragged_bounds = (is_first) ? (idx < lim) : ((rlenptr)? ((is_valid) && (*ilptr < lim)) : true);
+    lim      = (is_first) ? min((uint)in.dims[dim], lim) : lim;
+    bool within_ragged_bounds =
+        (is_first) ? (idx < lim)
+                   : ((rlenptr) ? ((is_valid) && (*ilptr < lim)) : true);
     if (is_valid && id_dim_in < in.dims[dim] && within_ragged_bounds) {
         val = *iptr;
         if (!is_first) idx = *ilptr;
@@ -150,10 +154,10 @@ __device__ void warp_reduce(T *s_ptr, uint *s_idx, uint tidx) {
 }
 
 template<typename T, af_op_t op, bool is_first, uint DIMX>
-__global__ static void ireduceFirst(Param<T> out, uint *olptr,
-                                    CParam<T> in, const uint *ilptr,
-                                    uint blocks_x, uint blocks_y,
-                                    uint repeat, CParam<uint> rlen) {
+__global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
+                                    const uint *ilptr, uint blocks_x,
+                                    uint blocks_y, uint repeat,
+                                    CParam<uint> rlen) {
     const uint tidx = threadIdx.x;
     const uint tidy = threadIdx.y;
     const uint tid  = tidy * blockDim.x + tidx;
@@ -168,8 +172,10 @@ __global__ static void ireduceFirst(Param<T> out, uint *olptr,
 
     const data_t<T> *iptr = in.ptr;
     data_t<T> *optr       = out.ptr;
-    const uint *rlenptr   = (rlen.ptr) ?  rlen.ptr + wid * rlen.strides[3] +
-                        zid * rlen.strides[2] + yid * rlen.strides[1] : nullptr;
+    const uint *rlenptr   = (rlen.ptr) ? rlen.ptr + wid * rlen.strides[3] +
+                                           zid * rlen.strides[2] +
+                                           yid * rlen.strides[1]
+                                     : nullptr;
 
     iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
     optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
@@ -182,9 +188,9 @@ __global__ static void ireduceFirst(Param<T> out, uint *olptr,
     if (yid >= in.dims[1] || zid >= in.dims[2] || wid >= in.dims[3]) return;
 
     int minlen = rlenptr ? min(*rlenptr, in.dims[0]) : in.dims[0];
-    int lim = min((int)(xid + repeat * DIMX), minlen);
+    int lim    = min((int)(xid + repeat * DIMX), minlen);
 
-    compute_t<T> val = Binary<compute_t<T>, op>::init();
+    compute_t<T> val = common::Binary<compute_t<T>, op>::init();
     uint idx         = xid;
 
     if (xid < lim) {
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index ca3044f9aa..993b31d73f 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -96,10 +96,10 @@ __global__ static void mean_dim_kernel(Param<To> out, Param<Tw> owt,
     bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
                     (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
 
-    Transform<Ti, compute_t<To>, af_add_t> transform;
+    common::Transform<Ti, compute_t<To>, af_add_t> transform;
 
-    compute_t<To> val    = Binary<compute_t<To>, af_add_t>::init();
-    compute_t<Tw> weight = Binary<compute_t<Tw>, af_add_t>::init();
+    compute_t<To> val    = common::Binary<compute_t<To>, af_add_t>::init();
+    compute_t<Tw> weight = common::Binary<compute_t<Tw>, af_add_t>::init();
 
     if (is_valid && id_dim_in < in.dims[dim]) {
         val = transform(*iptr);
@@ -282,10 +282,10 @@ __global__ static void mean_first_kernel(Param<To> out, Param<Tw> owt,
 
     int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-    Transform<Ti, compute_t<To>, af_add_t> transform;
+    common::Transform<Ti, compute_t<To>, af_add_t> transform;
 
-    compute_t<To> val    = Binary<compute_t<To>, af_add_t>::init();
-    compute_t<Tw> weight = Binary<compute_t<Tw>, af_add_t>::init();
+    compute_t<To> val    = common::Binary<compute_t<To>, af_add_t>::init();
+    compute_t<Tw> weight = common::Binary<compute_t<Tw>, af_add_t>::init();
 
     if (xid < lim) {
         val = transform(iptr[xid]);
@@ -592,7 +592,7 @@ To mean_all(CParam<Ti> in) {
         CUDA_CHECK(
             cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
 
-        Transform<Ti, compute_t<To>, af_add_t> transform;
+        common::Transform<Ti, compute_t<To>, af_add_t> transform;
         compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
 
         compute_t<To> val    = transform(h_ptr[0]);
diff --git a/src/backend/cuda/kernel/morph.cuh b/src/backend/cuda/kernel/morph.cuh
index fbe62487d4..7525318c12 100644
--- a/src/backend/cuda/kernel/morph.cuh
+++ b/src/backend/cuda/kernel/morph.cuh
@@ -22,18 +22,16 @@ __constant__ char
 
 namespace cuda {
 
-__forceinline__ __device__
-int lIdx(int x, int y, int stride1, int stride0) {
+__forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
     return (y * stride1 + x * stride0);
 }
 
 template<typename T, bool isDilation>
-inline __device__
-void load2ShrdMem(T* shrd, const T* const in, int lx, int ly, int shrdStride,
-                  int dim0, int dim1, int gx, int gy,
-                  int inStride1, int inStride0) {
-    T val =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+inline __device__ void load2ShrdMem(T* shrd, const T* const in, int lx, int ly,
+                                    int shrdStride, int dim0, int dim1, int gx,
+                                    int gy, int inStride1, int inStride0) {
+    T val = isDilation ? common::Binary<T, af_max_t>::init()
+                       : common::Binary<T, af_min_t>::init();
     if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1) {
         val = in[lIdx(gx, gy, inStride1, inStride0)];
     }
@@ -56,8 +54,8 @@ void load2ShrdMem(T* shrd, const T* const in, int lx, int ly, int shrdStride,
 //  * windLen
 // If SeLength is > 0, then that will override the kernel argument.
 template<typename T, bool isDilation, int SeLength = 0>
-__global__
-void morph(Param<T> out, CParam<T> in, int nBBS0, int nBBS1, int windLen = 0) {
+__global__ void morph(Param<T> out, CParam<T> in, int nBBS0, int nBBS1,
+                      int windLen = 0) {
     windLen = (SeLength > 0 ? SeLength : windLen);
 
     SharedMemory<T> shared;
@@ -102,8 +100,8 @@ void morph(Param<T> out, CParam<T> in, int nBBS0, int nBBS1, int windLen = 0) {
     __syncthreads();
 
     const T* d_filt = (const T*)cFilter;
-    T acc =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T acc           = isDilation ? common::Binary<T, af_max_t>::init()
+                       : common::Binary<T, af_min_t>::init();
 #pragma unroll
     for (int wj = 0; wj < windLen; ++wj) {
         int joff   = wj * windLen;
@@ -126,19 +124,20 @@ void morph(Param<T> out, CParam<T> in, int nBBS0, int nBBS1, int windLen = 0) {
     }
 }
 
-__forceinline__ __device__
-int lIdx3D(int x, int y, int z, int stride2, int stride1, int stride0) {
+__forceinline__ __device__ int lIdx3D(int x, int y, int z, int stride2,
+                                      int stride1, int stride0) {
     return (z * stride2 + y * stride1 + x * stride0);
 }
 
 template<typename T, bool isDilation>
-inline __device__
-void load2ShrdVolume(T* shrd, const T* const in, int lx, int ly, int lz,
-                     int shrdStride1, int shrdStride2, int dim0, int dim1,
-                     int dim2, int gx, int gy, int gz,
-                     int inStride2, int inStride1, int inStride0) {
-    T val =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+inline __device__ void load2ShrdVolume(T* shrd, const T* const in, int lx,
+                                       int ly, int lz, int shrdStride1,
+                                       int shrdStride2, int dim0, int dim1,
+                                       int dim2, int gx, int gy, int gz,
+                                       int inStride2, int inStride1,
+                                       int inStride0) {
+    T val = isDilation ? common::Binary<T, af_max_t>::init()
+                       : common::Binary<T, af_min_t>::init();
     if (gx >= 0 && gx < dim0 && gy >= 0 && gy < dim1 && gz >= 0 && gz < dim2) {
         val = in[gx * inStride0 + gy * inStride1 + gz * inStride2];
     }
@@ -148,8 +147,7 @@ void load2ShrdVolume(T* shrd, const T* const in, int lx, int ly, int lz,
 // kernel assumes mask/filter is square and hence does the
 // necessary operations accordingly.
 template<typename T, bool isDilation, int windLen>
-__global__
-void morph3D(Param<T> out, CParam<T> in, int nBBS) {
+__global__ void morph3D(Param<T> out, CParam<T> in, int nBBS) {
     SharedMemory<T> shared;
     T* shrdMem = shared.getPointer();
 
@@ -198,8 +196,8 @@ void morph3D(Param<T> out, CParam<T> in, int nBBS) {
     int k = lz + halo;
 
     const T* d_filt = (const T*)cFilter;
-    T acc =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    T acc           = isDilation ? common::Binary<T, af_max_t>::init()
+                       : common::Binary<T, af_min_t>::init();
 #pragma unroll
     for (int wk = 0; wk < windLen; ++wk) {
         int koff   = wk * se_area;
@@ -228,4 +226,4 @@ void morph3D(Param<T> out, CParam<T> in, int nBBS) {
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index bfd9fb56ea..21204f6221 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -70,9 +70,9 @@ __global__ static void reduce_dim_kernel(Param<To> out, CParam<Ti> in,
     bool is_valid = (ids[0] < in.dims[0]) && (ids[1] < in.dims[1]) &&
                     (ids[2] < in.dims[2]) && (ids[3] < in.dims[3]);
 
-    Transform<Ti, compute_t<To>, op> transform;
-    Binary<compute_t<To>, op> reduce;
-    compute_t<To> out_val = Binary<compute_t<To>, op>::init();
+    common::Transform<Ti, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
+    compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
     for (int id = id_dim_in; is_valid && (id < in.dims[dim]);
          id += offset_dim * blockDim.y) {
         compute_t<To> in_val = transform(*iptr);
@@ -198,8 +198,8 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
     const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
     const uint xid        = blockIdx_x * blockDim.x * repeat + tidx;
 
-    Binary<compute_t<To>, op> reduce;
-    Transform<Ti, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
+    common::Transform<Ti, compute_t<To>, op> transform;
 
     __shared__ compute_t<To> s_val[THREADS_PER_BLOCK];
 
@@ -216,7 +216,7 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
 
     int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
 
-    compute_t<To> out_val = Binary<compute_t<To>, op>::init();
+    compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
     for (int id = xid; id < lim; id += DIMX) {
         compute_t<To> in_val = transform(iptr[id]);
         if (change_nan)
@@ -391,8 +391,8 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
                             cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-        Binary<compute_t<To>, op> reduce;
-        compute_t<To> out = Binary<compute_t<To>, op>::init();
+        common::Binary<compute_t<To>, op> reduce;
+        compute_t<To> out = common::Binary<compute_t<To>, op>::init();
         for (int i = 0; i < tmp_elements; i++) {
             out = reduce(out, compute_t<To>(h_data[i]));
         }
@@ -405,9 +405,9 @@ To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
                             cudaMemcpyDeviceToHost, cuda::getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
-        Transform<Ti, compute_t<To>, op> transform;
-        Binary<compute_t<To>, op> reduce;
-        compute_t<To> out       = Binary<compute_t<To>, op>::init();
+        common::Transform<Ti, compute_t<To>, op> transform;
+        common::Binary<compute_t<To>, op> reduce;
+        compute_t<To> out       = common::Binary<compute_t<To>, op>::init();
         compute_t<To> nanval_to = scalar<compute_t<To>>(nanval);
 
         for (int i = 0; i < in_elements; i++) {
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index 34481cfafb..49f29d7cc5 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -34,7 +34,7 @@ template<typename Tk, typename To, af_op_t op>
 __global__ void final_boundary_reduce(int *reduced_block_sizes, Param<Tk> keys,
                                       Param<To> vals, const int n) {
     const int tid = blockIdx.x * blockDim.x + threadIdx.x;
-    Binary<compute_t<To>, op> reduce;
+    common::Binary<compute_t<To>, op> reduce;
 
     if (tid == ((blockIdx.x + 1) * blockDim.x) - 1 &&
         blockIdx.x < gridDim.x - 1) {
@@ -229,8 +229,8 @@ __global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
         warpReduceValsSmemFinal[threadIdx.x] = scalar<compute_t<To>>(0);
     __syncthreads();
 
-    Binary<compute_t<To>, op> reduce;
-    Transform<compute_t<Ti>, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
+    common::Transform<compute_t<Ti>, compute_t<To>, op> transform;
 
     // load keys and values to threads
     compute_t<Tk> k;
@@ -243,7 +243,7 @@ __global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
         v = transform(compute_t<Ti>(vals.ptr[tid]));
         if (change_nan) v = IS_NAN(v) ? compute_t<To>(nanval) : v;
     } else {
-        v = Binary<compute_t<To>, op>::init();
+        v = common::Binary<compute_t<To>, op>::init();
     }
 
     compute_t<Tk> eq_check = (k != shfl_up_sync(FULL_MASK, k, 1));
@@ -269,7 +269,7 @@ __global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
         v = reduce(v, shfl_down_sync(FULL_MASK, v, 8));
         v = reduce(v, shfl_down_sync(FULL_MASK, v, 16));
     } else {
-        compute_t<To> init = Binary<compute_t<To>, op>::init();
+        compute_t<To> init = common::Binary<compute_t<To>, op>::init();
         int eq_check, update_key;
         unsigned shflmask;
 #pragma unroll
@@ -449,7 +449,7 @@ __global__ static void reduce_blocks_dim_by_key(
     __shared__ int reducedBlockSize;
     __shared__ int dim_ordering[4];
 
-    compute_t<To> init = Binary<compute_t<To>, op>::init();
+    compute_t<To> init = common::Binary<compute_t<To>, op>::init();
 
     if (threadIdx.x == 0) {
         reducedBlockSize = 0;
@@ -463,8 +463,8 @@ __global__ static void reduce_blocks_dim_by_key(
         warpReduceValsSmemFinal[threadIdx.x] = init;
     __syncthreads();
 
-    Binary<compute_t<To>, op> reduce;
-    Transform<compute_t<Ti>, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
+    common::Transform<compute_t<Ti>, compute_t<To>, op> transform;
 
     // load keys and values to threads
     Tk k;
@@ -505,7 +505,7 @@ __global__ static void reduce_blocks_dim_by_key(
         v = reduce(v, shfl_down_sync(FULL_MASK, v, 8));
         v = reduce(v, shfl_down_sync(FULL_MASK, v, 16));
     } else {
-        compute_t<To> init = Binary<compute_t<To>, op>::init();
+        compute_t<To> init = common::Binary<compute_t<To>, op>::init();
         int eq_check, update_key;
         unsigned shflmask;
 #pragma unroll
diff --git a/src/backend/cuda/kernel/scan_dim.cuh b/src/backend/cuda/kernel/scan_dim.cuh
index aa71f1bba9..bb67e35913 100644
--- a/src/backend/cuda/kernel/scan_dim.cuh
+++ b/src/backend/cuda/kernel/scan_dim.cuh
@@ -14,11 +14,11 @@
 
 namespace cuda {
 
-template<typename Ti, typename To, af_op_t op, int dim,
-         bool isFinalPass, uint DIMY, bool inclusive_scan>
-__global__
-void scan_dim(Param<To> out, Param<To> tmp, CParam<Ti> in,
-              uint blocks_x, uint blocks_y, uint blocks_dim, uint lim) {
+template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass,
+         uint DIMY, bool inclusive_scan>
+__global__ void scan_dim(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                         uint blocks_x, uint blocks_y, uint blocks_dim,
+                         uint lim) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
     const int tid  = tidy * THREADS_X + tidx;
@@ -63,10 +63,10 @@ void scan_dim(Param<To> out, Param<To> tmp, CParam<Ti> in,
     __shared__ To s_tmp[THREADS_X];
     To *sptr = s_val + tid;
 
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
+    common::Transform<Ti, To, op> transform;
+    common::Binary<To, op> binop;
 
-    const To init = Binary<To, op>::init();
+    const To init = common::Binary<To, op>::init();
     To val        = init;
 
     const bool isLast = (tidy == (DIMY - 1));
@@ -111,9 +111,9 @@ void scan_dim(Param<To> out, Param<To> tmp, CParam<Ti> in,
 }
 
 template<typename To, af_op_t op, int dim>
-__global__
-void scan_dim_bcast(Param<To> out, CParam<To> tmp, uint blocks_x, uint blocks_y,
-                    uint blocks_dim, uint lim, bool inclusive_scan) {
+__global__ void scan_dim_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
+                               uint blocks_y, uint blocks_dim, uint lim,
+                               bool inclusive_scan) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
 
@@ -156,7 +156,7 @@ void scan_dim_bcast(Param<To> out, CParam<To> tmp, uint blocks_x, uint blocks_y,
 
     To accum = *(tptr - tmp.strides[dim]);
 
-    Binary<To, op> binop;
+    common::Binary<To, op> binop;
     const int ostride_dim = out.strides[dim];
 
     for (int k = 0, id = id_dim; is_valid && k < lim && (id < out_dim);
@@ -166,4 +166,4 @@ void scan_dim_bcast(Param<To> out, CParam<To> tmp, uint blocks_x, uint blocks_y,
     }
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.cuh b/src/backend/cuda/kernel/scan_dim_by_key.cuh
index d1aac13cfe..905dce9e4a 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_dim_by_key.cuh
@@ -14,17 +14,17 @@
 namespace cuda {
 
 template<typename Tk>
-__device__ inline
-char calculate_head_flags_dim(const Tk *kptr, int id, int stride) {
+__device__ inline char calculate_head_flags_dim(const Tk *kptr, int id,
+                                                int stride) {
     return (id == 0) ? 1 : ((*kptr) != (*(kptr - stride)));
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-__global__
-void scanbykey_dim_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
-                            Param<int> tlid, CParam<Ti> in, CParam<Tk> key,
-                            int dim, uint blocks_x, uint blocks_y, uint lim,
-                            bool inclusive_scan) {
+__global__ void scanbykey_dim_nonfinal(Param<To> out, Param<To> tmp,
+                                       Param<char> tflg, Param<int> tlid,
+                                       CParam<Ti> in, CParam<Tk> key, int dim,
+                                       uint blocks_x, uint blocks_y, uint lim,
+                                       bool inclusive_scan) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
     const int tid  = tidy * THREADS_X + tidx;
@@ -81,10 +81,10 @@ void scanbykey_dim_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
     To *sptr    = s_val + tid;
     char *sfptr = s_flg + tid;
 
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
+    common::Transform<Ti, To, op> transform;
+    common::Binary<To, op> binop;
 
-    const To init = Binary<To, op>::init();
+    const To init = common::Binary<To, op>::init();
     To val        = init;
 
     const bool isLast = (tidy == (DIMY - 1));
@@ -181,10 +181,10 @@ void scanbykey_dim_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-__global__
-void scanbykey_dim_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
-                         int dim, uint blocks_x, uint blocks_y, uint lim,
-                         bool calculateFlags, bool inclusive_scan) {
+__global__ void scanbykey_dim_final(Param<To> out, CParam<Ti> in,
+                                    CParam<Tk> key, int dim, uint blocks_x,
+                                    uint blocks_y, uint lim,
+                                    bool calculateFlags, bool inclusive_scan) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
     const int tid  = tidy * THREADS_X + tidx;
@@ -230,10 +230,10 @@ void scanbykey_dim_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
     To *sptr    = s_val + tid;
     char *sfptr = s_flg + tid;
 
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
+    common::Transform<Ti, To, op> transform;
+    common::Binary<To, op> binop;
 
-    const To init = Binary<To, op>::init();
+    const To init = common::Binary<To, op>::init();
     To val        = init;
 
     const bool isLast = (tidy == (DIMY - 1));
@@ -313,10 +313,9 @@ void scanbykey_dim_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
 }
 
 template<typename To, af_op_t op>
-__global__
-void scanbykey_dim_bcast(Param<To> out, CParam<To> tmp, Param<int> tlid,
-                         int dim, uint blocks_x, uint blocks_y,
-                         uint blocks_dim, uint lim) {
+__global__ void scanbykey_dim_bcast(Param<To> out, CParam<To> tmp,
+                                    Param<int> tlid, int dim, uint blocks_x,
+                                    uint blocks_y, uint blocks_dim, uint lim) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
 
@@ -357,7 +356,7 @@ void scanbykey_dim_bcast(Param<To> out, CParam<To> tmp, Param<int> tlid,
     int boundary = *iptr;
     To accum     = *(tptr - tmp.strides[dim]);
 
-    Binary<To, op> binop;
+    common::Binary<To, op> binop;
     const int ostride_dim = out.strides[dim];
 
     for (int k = 0, id = id_dim; is_valid && k < lim && (id < boundary);
@@ -367,4 +366,4 @@ void scanbykey_dim_bcast(Param<To> out, CParam<To> tmp, Param<int> tlid,
     }
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_first.cuh b/src/backend/cuda/kernel/scan_first.cuh
index e12e126d5e..dcabc59a77 100644
--- a/src/backend/cuda/kernel/scan_first.cuh
+++ b/src/backend/cuda/kernel/scan_first.cuh
@@ -14,11 +14,10 @@
 
 namespace cuda {
 
-template<typename Ti, typename To, af_op_t op,
-         bool isFinalPass, uint DIMX, bool inclusive_scan>
-__global__
-void scan_first(Param<To> out, Param<To> tmp, CParam<Ti> in,
-                uint blocks_x, uint blocks_y, uint lim) {
+template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX,
+         bool inclusive_scan>
+__global__ void scan_first(Param<To> out, Param<To> tmp, CParam<Ti> in,
+                           uint blocks_x, uint blocks_y, uint lim) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
 
@@ -51,10 +50,10 @@ void scan_first(Param<To> out, Param<To> tmp, CParam<Ti> in,
 
     To *sptr = s_val + tidy * (2 * DIMX + 1);
 
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
+    common::Transform<Ti, To, op> transform;
+    common::Binary<To, op> binop;
 
-    const To init = Binary<To, op>::init();
+    const To init = common::Binary<To, op>::init();
     int id        = xid;
     To val        = init;
 
@@ -97,9 +96,8 @@ void scan_first(Param<To> out, Param<To> tmp, CParam<Ti> in,
 }
 
 template<typename To, af_op_t op>
-__global__
-void scan_first_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
-        uint blocks_y, uint lim, bool inclusive_scan) {
+__global__ void scan_first_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
+                                 uint blocks_y, uint lim, bool inclusive_scan) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
 
@@ -123,7 +121,7 @@ void scan_first_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
     optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
     tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
 
-    Binary<To, op> binop;
+    common::Binary<To, op> binop;
     To accum = tptr[blockIdx_x - 1];
 
     // Shift broadcast one step to the right for exclusive scan (#2366)
@@ -134,4 +132,4 @@ void scan_first_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
     }
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/scan_first_by_key.cuh b/src/backend/cuda/kernel/scan_first_by_key.cuh
index 349bb2d8ac..49d9f9ea09 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_first_by_key.cuh
@@ -14,20 +14,20 @@
 namespace cuda {
 
 template<typename Tk>
-__device__ inline
-char calculate_head_flags(const Tk *kptr, int id, int previd) {
+__device__ inline char calculate_head_flags(const Tk *kptr, int id,
+                                            int previd) {
     return (id == 0) ? 1 : (kptr[id] != kptr[previd]);
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-__global__
-void scanbykey_first_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
-                              Param<int> tlid, CParam<Ti> in, CParam<Tk> key,
-                              uint blocks_x, uint blocks_y, uint lim,
-                              bool inclusive_scan) {
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-    const To init = Binary<To, op>::init();
+__global__ void scanbykey_first_nonfinal(Param<To> out, Param<To> tmp,
+                                         Param<char> tflg, Param<int> tlid,
+                                         CParam<Ti> in, CParam<Tk> key,
+                                         uint blocks_x, uint blocks_y, uint lim,
+                                         bool inclusive_scan) {
+    common::Transform<Ti, To, op> transform;
+    common::Binary<To, op> binop;
+    const To init = common::Binary<To, op>::init();
     To val        = init;
 
     const int istride         = in.strides[0];
@@ -158,13 +158,14 @@ void scanbykey_first_nonfinal(Param<To> out, Param<To> tmp, Param<char> tflg,
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
-__global__
-void scanbykey_first_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
-                           uint blocks_x, uint blocks_y, uint lim,
-                           bool calculateFlags, bool inclusive_scan) {
-    Transform<Ti, To, op> transform;
-    Binary<To, op> binop;
-    const To init = Binary<To, op>::init();
+__global__ void scanbykey_first_final(Param<To> out, CParam<Ti> in,
+                                      CParam<Tk> key, uint blocks_x,
+                                      uint blocks_y, uint lim,
+                                      bool calculateFlags,
+                                      bool inclusive_scan) {
+    common::Transform<Ti, To, op> transform;
+    common::Binary<To, op> binop;
+    const To init = common::Binary<To, op>::init();
     To val        = init;
 
     const int istride         = in.strides[0];
@@ -269,9 +270,9 @@ void scanbykey_first_final(Param<To> out, CParam<Ti> in, CParam<Tk> key,
 }
 
 template<typename To, af_op_t op>
-__global__
-void scanbykey_first_bcast(Param<To> out, Param<To> tmp, Param<int> tlid,
-                           uint blocks_x, uint blocks_y, uint lim) {
+__global__ void scanbykey_first_bcast(Param<To> out, Param<To> tmp,
+                                      Param<int> tlid, uint blocks_x,
+                                      uint blocks_y, uint lim) {
     const int tidx = threadIdx.x;
     const int tidy = threadIdx.y;
 
@@ -283,19 +284,21 @@ void scanbykey_first_bcast(Param<To> out, Param<To> tmp, Param<int> tlid,
     const int yid        = blockIdx_y * blockDim.y + tidy;
 
     if (blockIdx_x != 0) {
-        bool cond = (yid < out.dims[1]) && (zid < out.dims[2]) &&
-                    (wid < out.dims[3]);
+        bool cond =
+            (yid < out.dims[1]) && (zid < out.dims[2]) && (wid < out.dims[3]);
         if (cond) {
             To *optr        = out.ptr;
             const To *tptr  = tmp.ptr;
             const int *iptr = tlid.ptr;
 
-            optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
-            tptr += wid * tmp.strides[3] + zid * tmp.strides[2] + yid * tmp.strides[1];
-            iptr +=
-                wid * tlid.strides[3] + zid * tlid.strides[2] + yid * tlid.strides[1];
+            optr += wid * out.strides[3] + zid * out.strides[2] +
+                    yid * out.strides[1];
+            tptr += wid * tmp.strides[3] + zid * tmp.strides[2] +
+                    yid * tmp.strides[1];
+            iptr += wid * tlid.strides[3] + zid * tlid.strides[2] +
+                    yid * tlid.strides[1];
 
-            Binary<To, op> binop;
+            common::Binary<To, op> binop;
             int boundary = iptr[blockIdx_x];
             To accum     = tptr[blockIdx_x - 1];
 
@@ -308,4 +311,4 @@ void scanbykey_first_bcast(Param<To> out, Param<To> tmp, Param<int> tlid,
     }
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index a40a927807..5f01395997 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -369,15 +369,6 @@ BINOP_SCALAR(cdouble, double, cdouble)
 
 #undef BINOP_SCALAR
 
-__SDH__ bool operator==(cfloat a, cfloat b) {
-    return (a.x == b.x) && (a.y == b.y);
-}
-__SDH__ bool operator!=(cfloat a, cfloat b) { return !(a == b); }
-__SDH__ bool operator==(cdouble a, cdouble b) {
-    return (a.x == b.x) && (a.y == b.y);
-}
-__SDH__ bool operator!=(cdouble a, cdouble b) { return !(a == b); }
-
 template<typename T>
 static inline T division(T lhs, double rhs) {
     return lhs / rhs;
@@ -403,3 +394,12 @@ static inline __DH__ T clamp(const T value, const T lo, const T hi) {
 }
 
 }  // namespace cuda
+
+__SDH__ bool operator==(cuda::cfloat a, cuda::cfloat b) {
+    return (a.x == b.x) && (a.y == b.y);
+}
+__SDH__ bool operator!=(cuda::cfloat a, cuda::cfloat b) { return !(a == b); }
+__SDH__ bool operator==(cuda::cdouble a, cuda::cdouble b) {
+    return (a.x == b.x) && (a.y == b.y);
+}
+__SDH__ bool operator!=(cuda::cdouble a, cuda::cdouble b) { return !(a == b); }
diff --git a/src/backend/cuda/minmax_op.hpp b/src/backend/cuda/minmax_op.hpp
index b04c45b246..12a2546595 100644
--- a/src/backend/cuda/minmax_op.hpp
+++ b/src/backend/cuda/minmax_op.hpp
@@ -53,7 +53,7 @@ struct MinMaxOp {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<compute_t<T>, op>::init(); }
+        if (is_nan(val)) { m_val = common::Binary<compute_t<T>, op>::init(); }
     }
 
     void operator()(T val, uint idx) {
@@ -70,7 +70,7 @@ struct MinMaxOp<af_max_t, T> {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
-        if (is_nan(val)) { m_val = Binary<T, af_max_t>::init(); }
+        if (is_nan(val)) { m_val = common::Binary<T, af_max_t>::init(); }
     }
 
     void operator()(T val, uint idx) {
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 97c9d91a16..5cab8d2edc 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -138,11 +138,10 @@ const char *getFullName<common::half>() {
 //#endif  //__CUDACC_RTC__
 
 namespace common {
+
 template<typename T>
 struct kernel_type;
-}
 
-namespace common {
 template<>
 struct kernel_type<common::half> {
     using data = common::half;
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 6b65807755..b40f999f26 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -377,7 +377,7 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const cl::Buffer *buf = arr.device();
-    if (!buf) return NULL;
+    if (!buf) { return NULL; }
     memLock((T *)buf);
     cl_mem mem = (*buf)();
     return (void *)mem;
@@ -495,7 +495,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
 
 template<typename T>
 size_t Array<T>::getAllocatedBytes() const {
-    if (!isReady()) return 0;
+    if (!isReady()) { return 0; }
     size_t bytes = memoryManager().allocated(data.get());
     // External device pointer
     if (bytes == 0 && data.get()) { return data_dims.elements() * sizeof(T); }
diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
index 6b178e63e5..7a5a432bb2 100644
--- a/src/backend/opencl/Kernel.cpp
+++ b/src/backend/opencl/Kernel.cpp
@@ -11,11 +11,15 @@
 
 #include <backend.hpp>
 #include <cl2hpp.hpp>
+#include <common/defines.hpp>
 #include <platform.hpp>
 
 namespace opencl {
 
-Kernel::DevPtrType Kernel::get(const char *name) { return nullptr; }
+Kernel::DevPtrType Kernel::get(const char* name) {
+    UNUSED(name);
+    return nullptr;
+}
 
 void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
                             size_t bytes) {
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 28eeb98380..8623fcce7a 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -137,8 +137,8 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
     auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
         BinOp<To, Ti, op> bop;
         return Node_ptr(new common::BinaryNode(
-            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(), operands[0], operands[1],
-            (int)(op)));
+            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(),
+            operands[0], operands[1], (int)(op)));
     };
 
     Node_ptr out =
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index e70a4a76db..1ae27c85cf 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -169,6 +169,7 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
         // thrown. This is related to
         // https://github.com/arrayfire/arrayfire/pull/1899
         CLFFT_CHECK(clfftDestroyPlan(p));
+        // NOLINTNEXTLINE(hicpp-no-malloc)
         free(p);
 #endif
     });
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 4be1595214..58a7d54678 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -109,9 +109,9 @@ class DeviceManager {
 
     friend bool isGLSharingSupported();
 
-    friend bool isDoubleSupported(int device);
+    friend bool isDoubleSupported(unsigned device);
 
-    friend bool isHalfSupported(int device);
+    friend bool isHalfSupported(unsigned device);
 
     friend void devprop(char* d_name, char* d_platform, char* d_toolkit,
                         char* d_compute);
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 92836e86e9..6aeb624a00 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -44,7 +44,7 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
         DefineKeyValue(kDim, dim),
         DefineKeyValue(DIMY, threads_y),
         DefineValue(THREADS_X),
-        DefineKeyValue(init, toNumStr(Binary<T, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<T, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<T>()),
         DefineKeyValue(IS_FIRST, is_first),
@@ -123,7 +123,7 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(DIMX, threads_x),
         DefineValue(THREADS_PER_GROUP),
-        DefineKeyValue(init, toNumStr(Binary<T, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<T, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<T>()),
         DefineKeyValue(IS_FIRST, is_first),
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 7f2e417b5f..01fcbc1263 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -107,7 +107,7 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
 
     ToNumStr<To> toNumStr;
     ToNumStr<Tw> twNumStr;
-    Transform<uint, Tw, af_add_t> transform_weight;
+    common::Transform<uint, Tw, af_add_t> transform_weight;
 
     std::vector<TemplateArg> targs = {
         TemplateTypename<Ti>(),     TemplateTypename<To>(),
@@ -122,7 +122,7 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
         DefineKeyValue(kDim, dim),
         DefineKeyValue(DIMY, threads_y),
         DefineValue(THREADS_X),
-        DefineKeyValue(init_To, toNumStr(Binary<To, af_add_t>::init())),
+        DefineKeyValue(init_To, toNumStr(common::Binary<To, af_add_t>::init())),
         DefineKeyValue(init_Tw, twNumStr(transform_weight(0))),
         DefineKeyValue(one_Tw, twNumStr(transform_weight(1))),
     };
@@ -204,7 +204,7 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
 
     ToNumStr<To> toNumStr;
     ToNumStr<Tw> twNumStr;
-    Transform<uint, Tw, af_add_t> transform_weight;
+    common::Transform<uint, Tw, af_add_t> transform_weight;
 
     std::vector<TemplateArg> targs = {
         TemplateTypename<Ti>(),    TemplateTypename<To>(),
@@ -217,7 +217,7 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
         DefineKeyValue(Tw, dtype_traits<Tw>::getName()),
         DefineKeyValue(DIMX, threads_x),
         DefineValue(THREADS_PER_GROUP),
-        DefineKeyValue(init_To, toNumStr(Binary<To, af_add_t>::init())),
+        DefineKeyValue(init_To, toNumStr(common::Binary<To, af_add_t>::init())),
         DefineKeyValue(init_Tw, twNumStr(transform_weight(0))),
         DefineKeyValue(one_Tw, twNumStr(transform_weight(1))),
     };
@@ -455,8 +455,8 @@ To meanAll(Param in) {
                                      sizeof(Ti) * in_elements, h_ptr.data());
 
         // TODO : MeanOp with (Tw)1
-        Transform<Ti, compute_t<To>, af_add_t> transform;
-        Transform<uint, compute_t<Tw>, af_add_t> transform_weight;
+        common::Transform<Ti, compute_t<To>, af_add_t> transform;
+        common::Transform<uint, compute_t<Tw>, af_add_t> transform_weight;
         MeanOp<compute_t<To>, compute_t<Tw>> Op(transform(h_ptr[0]),
                                                 transform_weight(1));
         for (int i = 1; i < (int)in_elements; i++) {
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index f170037824..863034c83c 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -37,8 +37,8 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     constexpr int THREADS_Y = 16;
 
     ToNumStr<T> toNumStr;
-    const T DefaultVal =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    const T DefaultVal = isDilation ? common::Binary<T, af_max_t>::init()
+                                    : common::Binary<T, af_min_t>::init();
 
     static const string src(morph_cl, morph_cl_len);
 
@@ -100,8 +100,8 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     constexpr int CUBE_Z = 4;
 
     ToNumStr<T> toNumStr;
-    const T DefaultVal =
-        isDilation ? Binary<T, af_max_t>::init() : Binary<T, af_min_t>::init();
+    const T DefaultVal = isDilation ? common::Binary<T, af_max_t>::init()
+                                    : common::Binary<T, af_min_t>::init();
 
     static const string src(morph_cl, morph_cl_len);
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 5c3ef15a7a..15a9b4429c 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -20,6 +20,7 @@
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/reduce_dim.hpp>
 #include <kernel_headers/reduce_first.hpp>
+#include <math.hpp>
 #include <memory.hpp>
 #include <traits.hpp>
 
@@ -48,7 +49,7 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
         DefineKeyValue(kDim, dim),
         DefineKeyValue(DIMY, threads_y),
         DefineValue(THREADS_X),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
     };
@@ -129,7 +130,7 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
         DefineValue(THREADS_PER_GROUP),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
     };
@@ -232,8 +233,8 @@ To reduceAll(Param in, int change_nan, double nanval) {
         getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
                                      sizeof(To) * tmp_elements, h_ptr.data());
 
-        Binary<compute_t<To>, op> reduce;
-        compute_t<To> out = Binary<compute_t<To>, op>::init();
+        common::Binary<compute_t<To>, op> reduce;
+        compute_t<To> out = common::Binary<compute_t<To>, op>::init();
         for (int i = 0; i < (int)tmp_elements; i++) {
             out = reduce(out, compute_t<To>(h_ptr[i]));
         }
@@ -244,9 +245,9 @@ To reduceAll(Param in, int change_nan, double nanval) {
                                      sizeof(Ti) * in.info.offset,
                                      sizeof(Ti) * in_elements, h_ptr.data());
 
-        Transform<Ti, compute_t<To>, op> transform;
-        Binary<compute_t<To>, op> reduce;
-        compute_t<To> out       = Binary<compute_t<To>, op>::init();
+        common::Transform<Ti, compute_t<To>, op> transform;
+        common::Binary<compute_t<To>, op> reduce;
+        compute_t<To> out       = common::Binary<compute_t<To>, op>::init();
         compute_t<To> nanval_to = scalar<compute_t<To>>(nanval);
 
         for (int i = 0; i < (int)in_elements; i++) {
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 16234fa811..9f9167ec95 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -61,7 +61,7 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
         DefineKeyValue(DIM, dim),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
     };
@@ -106,7 +106,7 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(To, dtype_traits<To>::getName()),
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
     };
@@ -149,7 +149,7 @@ void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(To, dtype_traits<To>::getName()),
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<To>()),
     };
@@ -190,7 +190,7 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
         DefineKeyValue(DIM, dim),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<To>()),
     };
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 5c1776d3f5..6c1d6196fa 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -51,7 +51,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
         DefineKeyValue(kDim, dim),
         DefineKeyValue(DIMY, threads_y),
         DefineValue(THREADS_X),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
         DefineKeyValue(IS_FINAL_PASS, (isFinalPass ? 1 : 0)),
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 1935ad2465..8e4728842e 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -50,7 +50,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
         DefineKeyValue(kDim, dim),
         DefineKeyValue(DIMY, threads_y),
         DefineValue(THREADS_X),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
         DefineKeyValue(calculateFlags, (calculateFlags ? 1 : 0)),
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index cd9ba2a53f..f00369484c 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -53,7 +53,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
         DefineKeyValue(DIMY, threads_y),
         DefineKeyFromStr(binOpName<op>()),
         DefineValue(SHARED_MEM_SIZE),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
         DefineKeyValue(IS_FINAL_PASS, (isFinalPass ? 1 : 0)),
         DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index f54f0b00d4..6e36b048af 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -55,7 +55,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
         DefineKeyValue(DIMY, threads_y),
-        DefineKeyValue(init, toNumStr(Binary<To, op>::init())),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineValue(SHARED_MEM_SIZE),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
diff --git a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index 893c3ecc88..ab20be6a33 100644
--- a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -14,5 +14,5 @@
 namespace opencl {
 namespace kernel {
 INSTANTIATE1(TYPE)
-}
+}  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/math.cpp b/src/backend/opencl/math.cpp
index 82f03722f2..31c09c3b96 100644
--- a/src/backend/opencl/math.cpp
+++ b/src/backend/opencl/math.cpp
@@ -11,15 +11,6 @@
 #include <common/half.hpp>
 
 namespace opencl {
-bool operator==(cfloat lhs, cfloat rhs) {
-    return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
-}
-bool operator!=(cfloat lhs, cfloat rhs) { return !(lhs == rhs); }
-bool operator==(cdouble lhs, cdouble rhs) {
-    return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
-}
-bool operator!=(cdouble lhs, cdouble rhs) { return !(lhs == rhs); }
-
 cfloat operator+(cfloat lhs, cfloat rhs) {
     cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
     return res;
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 477cc039b9..86ee50556d 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -147,10 +147,6 @@ static inline float real(cfloat in) { return in.s[0]; }
 static inline double imag(cdouble in) { return in.s[1]; }
 static inline float imag(cfloat in) { return in.s[1]; }
 
-bool operator==(cfloat lhs, cfloat rhs);
-bool operator!=(cfloat lhs, cfloat rhs);
-bool operator==(cdouble lhs, cdouble rhs);
-bool operator!=(cdouble lhs, cdouble rhs);
 cfloat operator+(cfloat lhs, cfloat rhs);
 cfloat operator+(cfloat lhs);
 cdouble operator+(cdouble lhs, cdouble rhs);
@@ -160,6 +156,21 @@ cdouble operator*(cdouble lhs, cdouble rhs);
 common::half operator+(common::half lhs, common::half rhs) noexcept;
 }  // namespace opencl
 
+static inline bool operator==(opencl::cfloat lhs, opencl::cfloat rhs) noexcept {
+    return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
+}
+static inline bool operator!=(opencl::cfloat lhs, opencl::cfloat rhs) noexcept {
+    return !(lhs == rhs);
+}
+static inline bool operator==(opencl::cdouble lhs,
+                              opencl::cdouble rhs) noexcept {
+    return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
+}
+static inline bool operator!=(opencl::cdouble lhs,
+                              opencl::cdouble rhs) noexcept {
+    return !(lhs == rhs);
+}
+
 #if defined(__GNUC__) || defined(__GNUG__)
 /* GCC/G++, Clang/LLVM, Intel ICC */
 #pragma GCC diagnostic pop
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index b49c57716e..3f6e37a733 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -352,7 +352,7 @@ bool isGLSharingSupported() {
     return devMngr.mIsGLSharingOn[get<1>(devId)];
 }
 
-bool isDoubleSupported(int device) {
+bool isDoubleSupported(unsigned device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     cl::Device dev;
@@ -364,7 +364,7 @@ bool isDoubleSupported(int device) {
     return (dev.getInfo<CL_DEVICE_DOUBLE_FP_CONFIG>() > 0);
 }
 
-bool isHalfSupported(int device) {
+bool isHalfSupported(unsigned device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     cl::Device dev;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 82848bf000..94d5d37120 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -77,10 +77,10 @@ bool OpenCLCPUOffload(bool forceOffloadOSX = true);
 
 bool isGLSharingSupported();
 
-bool isDoubleSupported(int device);
+bool isDoubleSupported(unsigned device);
 
 // Returns true if 16-bit precision floats are supported by the device
-bool isHalfSupported(int device);
+bool isHalfSupported(unsigned device);
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index e7e6921d77..60a08831e7 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -19,14 +19,14 @@
 namespace af {
 
 template<>
-struct dtype_traits<cl_float2> {
+struct dtype_traits<opencl::cfloat> {
     enum { af_type = c32 };
     typedef float base_type;
     static const char *getName() { return "float2"; }
 };
 
 template<>
-struct dtype_traits<cl_double2> {
+struct dtype_traits<opencl::cdouble> {
     enum { af_type = c64 };
     typedef double base_type;
     static const char *getName() { return "double2"; }
@@ -37,11 +37,11 @@ static bool iscplx() {
     return false;
 }
 template<>
-STATIC_ bool iscplx<cl_float2>() {
+STATIC_ bool iscplx<opencl::cfloat>() {
     return true;
 }
 template<>
-STATIC_ bool iscplx<cl_double2>() {
+STATIC_ bool iscplx<opencl::cdouble>() {
     return true;
 }
 
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 803b5943f3..a07cc5b0a2 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -97,8 +97,8 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 
     auto createUnary = [](std::array<Node_ptr, 1> &operands) {
         return Node_ptr(new common::UnaryNode(
-            static_cast<af::dtype>(dtype_traits<char>::af_type), unaryName<op>(),
-            operands[0], op));
+            static_cast<af::dtype>(dtype_traits<char>::af_type),
+            unaryName<op>(), operands[0], op));
     };
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }

From 2d1920db5ab0a790773efbe5145f02a721c563ec Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 May 2020 16:52:53 -0400
Subject: [PATCH 1962/2677] Move Binary and Transform to src/backend/common

---
 src/api/c/CMakeLists.txt                      |  1 -
 src/api/c/reduce.cpp                          |  2 +-
 src/api/c/scan.cpp                            |  2 +-
 src/api/c/where.cpp                           |  4 +-
 .../c/ops.hpp => backend/common/Binary.hpp}   | 40 +-----------
 src/backend/common/Transform.hpp              | 63 +++++++++++++++++++
 src/backend/cpu/ireduce.hpp                   |  2 +-
 src/backend/cpu/kernel/ireduce.hpp            |  2 +-
 src/backend/cpu/kernel/mean.hpp               |  2 +-
 src/backend/cpu/kernel/morph.hpp              |  2 +-
 src/backend/cpu/kernel/reduce.hpp             |  3 +-
 src/backend/cpu/kernel/scan.hpp               |  3 +-
 src/backend/cpu/kernel/scan_by_key.hpp        |  3 +-
 src/backend/cpu/kernel/triangle.hpp           |  2 +-
 src/backend/cpu/kernel/unwrap.hpp             |  2 +-
 src/backend/cpu/mean.hpp                      |  1 -
 src/backend/cpu/reduce.cpp                    |  3 +-
 src/backend/cpu/reduce.hpp                    |  2 +-
 src/backend/cpu/scan.cpp                      |  2 +-
 src/backend/cpu/scan.hpp                      |  2 +-
 src/backend/cpu/scan_by_key.cpp               |  1 -
 src/backend/cpu/scan_by_key.hpp               |  2 +-
 src/backend/cpu/where.cpp                     |  5 +-
 src/backend/cuda/CMakeLists.txt               |  3 +-
 src/backend/cuda/compile_kernel.cpp           | 13 ++--
 src/backend/cuda/diagonal.hpp                 |  1 -
 src/backend/cuda/ireduce.hpp                  |  2 +-
 src/backend/cuda/kernel/ireduce.cuh           |  1 +
 src/backend/cuda/kernel/mean.hpp              | 19 +++---
 src/backend/cuda/kernel/morph.cuh             |  2 +-
 src/backend/cuda/kernel/reduce.hpp            |  3 +-
 src/backend/cuda/kernel/reduce_by_key.hpp     |  3 +-
 src/backend/cuda/kernel/scan_dim.cuh          |  3 +-
 src/backend/cuda/kernel/scan_dim_by_key.cuh   |  3 +-
 src/backend/cuda/kernel/scan_dim_by_key.hpp   |  2 +-
 src/backend/cuda/kernel/scan_first.cuh        |  3 +-
 src/backend/cuda/kernel/scan_first_by_key.cuh |  3 +-
 src/backend/cuda/kernel/scan_first_by_key.hpp |  2 +-
 src/backend/cuda/mean.hpp                     |  1 -
 src/backend/cuda/minmax_op.hpp                |  2 +-
 src/backend/cuda/reduce.hpp                   |  2 +-
 src/backend/cuda/scan.hpp                     |  2 +-
 src/backend/cuda/scan_by_key.cpp              |  2 +-
 src/backend/cuda/scan_by_key.hpp              |  2 +-
 src/backend/opencl/diagonal.hpp               |  1 -
 src/backend/opencl/ireduce.cpp                |  2 +-
 src/backend/opencl/ireduce.hpp                |  2 +-
 src/backend/opencl/kernel/ireduce.hpp         |  1 +
 src/backend/opencl/kernel/mean.hpp            |  2 +
 src/backend/opencl/kernel/morph.hpp           |  2 +-
 src/backend/opencl/kernel/names.hpp           |  4 +-
 src/backend/opencl/kernel/reduce.hpp          |  2 +
 .../kernel/scan_by_key/scan_by_key_impl.cpp   |  1 -
 src/backend/opencl/kernel/scan_dim.hpp        |  1 +
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  2 +
 src/backend/opencl/kernel/scan_first.hpp      |  1 +
 src/backend/opencl/kernel/where.hpp           |  1 +
 src/backend/opencl/mean.hpp                   |  1 -
 src/backend/opencl/reduce.hpp                 |  2 +-
 src/backend/opencl/scan.hpp                   |  2 +-
 src/backend/opencl/scan_by_key.hpp            |  2 +-
 src/backend/opencl/svd.cpp                    |  1 +
 62 files changed, 154 insertions(+), 101 deletions(-)
 rename src/{api/c/ops.hpp => backend/common/Binary.hpp} (74%)
 create mode 100644 src/backend/common/Transform.hpp

diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 42fb56d29d..e76dd02d80 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -118,7 +118,6 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/morph.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/nearest_neighbour.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/norm.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/ops.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/optypes.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/orb.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/pinverse.cpp
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 2668b93543..544ced2368 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -13,7 +13,7 @@
 #include <handle.hpp>
 #include <ireduce.hpp>
 #include <math.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 #include <reduce.hpp>
 #include <af/algorithm.h>
 #include <af/defines.h>
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index f207a302db..d8a3a7a95d 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -10,7 +10,7 @@
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 #include <scan.hpp>
 #include <scan_by_key.hpp>
 #include <af/algorithm.h>
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index f850787cbb..4aeb7b60ba 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -10,7 +10,6 @@
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
-#include <ops.hpp>
 #include <where.hpp>
 #include <af/algorithm.h>
 #include <af/dim4.hpp>
@@ -23,6 +22,7 @@ using detail::uchar;
 using detail::uint;
 using detail::uintl;
 using detail::ushort;
+using std::swap;
 
 template<typename T>
 static inline af_array where(const af_array in) {
@@ -55,7 +55,7 @@ af_err af_where(af_array* idx, const af_array in) {
             case b8: res = where<char>(in); break;
             default: TYPE_ERROR(1, type);
         }
-        std::swap(*idx, res);
+        swap(*idx, res);
     }
     CATCHALL
 
diff --git a/src/api/c/ops.hpp b/src/backend/common/Binary.hpp
similarity index 74%
rename from src/api/c/ops.hpp
rename to src/backend/common/Binary.hpp
index edee76b384..6eeaad2058 100644
--- a/src/api/c/ops.hpp
+++ b/src/backend/common/Binary.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2020, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -71,7 +71,7 @@ template<typename T>
 struct Binary<T, af_min_t> {
     static __DH__ T init() { return maxval<T>(); }
 
-    __DH__ T operator()(T lhs, T rhs) { return min(lhs, rhs); }
+    __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); }
 };
 
 template<>
@@ -98,7 +98,7 @@ template<typename T>
 struct Binary<T, af_max_t> {
     static __DH__ T init() { return minval<T>(); }
 
-    __DH__ T operator()(T lhs, T rhs) { return max(lhs, rhs); }
+    __DH__ T operator()(T lhs, T rhs) { return detail::max(lhs, rhs); }
 };
 
 template<>
@@ -121,38 +121,4 @@ SPECIALIZE_COMPLEX_MAX(cdouble, double)
 
 #undef SPECIALIZE_COMPLEX_MAX
 
-template<typename Ti, typename To, af_op_t op>
-struct Transform {
-    __DH__ To operator()(Ti in) { return static_cast<To>(in); }
-};
-
-template<typename Ti, typename To>
-struct Transform<Ti, To, af_min_t> {
-    __DH__ To operator()(Ti in) {
-        return IS_NAN(in) ? Binary<To, af_min_t>::init() : To(in);
-    }
-};
-
-template<typename Ti, typename To>
-struct Transform<Ti, To, af_max_t> {
-    __DH__ To operator()(Ti in) {
-        return IS_NAN(in) ? Binary<To, af_max_t>::init() : To(in);
-    }
-};
-
-template<typename Ti, typename To>
-struct Transform<Ti, To, af_or_t> {
-    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
-};
-
-template<typename Ti, typename To>
-struct Transform<Ti, To, af_and_t> {
-    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
-};
-
-template<typename Ti, typename To>
-struct Transform<Ti, To, af_notzero_t> {
-    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
-};
-
 }  // namespace common
diff --git a/src/backend/common/Transform.hpp b/src/backend/common/Transform.hpp
new file mode 100644
index 0000000000..4fb2a127f1
--- /dev/null
+++ b/src/backend/common/Transform.hpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <math.hpp>
+#include <types.hpp>
+
+#ifndef __DH__
+#define __DH__
+#endif
+
+#include "optypes.hpp"
+
+namespace common {
+
+using namespace detail;  // NOLINT
+
+// Because isnan(cfloat) and isnan(cdouble) is not defined
+#define IS_NAN(val) !((val) == (val))
+
+template<typename Ti, typename To, af_op_t op>
+struct Transform {
+    __DH__ To operator()(Ti in) { return static_cast<To>(in); }
+};
+
+template<typename Ti, typename To>
+struct Transform<Ti, To, af_min_t> {
+    __DH__ To operator()(Ti in) {
+        return IS_NAN(in) ? Binary<To, af_min_t>::init() : To(in);
+    }
+};
+
+template<typename Ti, typename To>
+struct Transform<Ti, To, af_max_t> {
+    __DH__ To operator()(Ti in) {
+        return IS_NAN(in) ? Binary<To, af_max_t>::init() : To(in);
+    }
+};
+
+template<typename Ti, typename To>
+struct Transform<Ti, To, af_or_t> {
+    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
+};
+
+template<typename Ti, typename To>
+struct Transform<Ti, To, af_and_t> {
+    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
+};
+
+template<typename Ti, typename To>
+struct Transform<Ti, To, af_notzero_t> {
+    __DH__ To operator()(Ti in) { return (in != scalar<Ti>(0.)); }
+};
+
+}  // namespace common
diff --git a/src/backend/cpu/ireduce.hpp b/src/backend/cpu/ireduce.hpp
index 4861293c3c..39258a284e 100644
--- a/src/backend/cpu/ireduce.hpp
+++ b/src/backend/cpu/ireduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cpu {
 template<af_op_t op, typename T>
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index e6ea00ed93..c04cbc7409 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ops.hpp>
+#include <common/Binary.hpp>
 #include <algorithm>
 
 namespace cpu {
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index 966197a059..86f30e515c 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <Array.hpp>
-#include <ops.hpp>
+#include <common/Transform.hpp>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index e04a47b1af..1142940ba6 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ops.hpp>
+#include <common/Binary.hpp>
 #include <utility.hpp>
 #include <limits>
 
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index 61206b097f..cd8678edda 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -9,8 +9,9 @@
 
 #pragma once
 #include <Param.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/half.hpp>
-#include <ops.hpp>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index be9dd73392..6e6cc84d54 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -9,7 +9,8 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ops.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index 720b8d65d8..d4546377e0 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -9,7 +9,8 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ops.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index 6bab5e7693..617b74ca0b 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
-#include <ops.hpp>
+#include <math.hpp>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
index cade2cb0b7..2b4e4f662d 100644
--- a/src/backend/cpu/kernel/unwrap.hpp
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <Param.hpp>
 #include <err_cpu.hpp>
-#include <ops.hpp>
+#include <math.hpp>
 
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cpu/mean.hpp b/src/backend/cpu/mean.hpp
index d51a71bd2d..ecc481c203 100644
--- a/src/backend/cpu/mean.hpp
+++ b/src/backend/cpu/mean.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
 
 namespace cpu {
 template<typename Ti, typename Tw, typename To>
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index ab0c782db9..795390a04e 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -8,9 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/half.hpp>
 #include <kernel/reduce.hpp>
-#include <ops.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <reduce.hpp>
diff --git a/src/backend/cpu/reduce.hpp b/src/backend/cpu/reduce.hpp
index 7a1d3381be..9923d2aef3 100644
--- a/src/backend/cpu/reduce.hpp
+++ b/src/backend/cpu/reduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cpu {
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index 0adb09b7b0..f4412168d1 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <kernel/scan.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <scan.hpp>
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index f00f75e82d..431c46b1f9 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cpu {
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index 9af16f2b33..ef7a9d3036 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -9,7 +9,6 @@
 
 #include <Array.hpp>
 #include <kernel/scan_by_key.hpp>
-#include <ops.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <scan_by_key.hpp>
diff --git a/src/backend/cpu/scan_by_key.hpp b/src/backend/cpu/scan_by_key.hpp
index f239189136..3bc934d529 100644
--- a/src/backend/cpu/scan_by_key.hpp
+++ b/src/backend/cpu/scan_by_key.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cpu {
 template<af_op_t op, typename Ti, typename Tk, typename To>
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 7d76a98aa5..14dbdddfa5 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -8,11 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
 #include <platform.hpp>
 #include <where.hpp>
 #include <af/dim4.hpp>
+
 #include <complex>
 #include <vector>
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index fa441ac8bf..576e4b3582 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -132,7 +132,6 @@ set(nvrtc_src
   ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
   ${CUDA_TOOLKIT_ROOT_DIR}/include/math_constants.h
 
-  ${PROJECT_SOURCE_DIR}/src/api/c/ops.hpp
   ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
   ${PROJECT_SOURCE_DIR}/include/af/defines.h
   ${PROJECT_SOURCE_DIR}/include/af/traits.hpp
@@ -148,6 +147,8 @@ set(nvrtc_src
   ${CMAKE_CURRENT_SOURCE_DIR}/minmax_op.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/utility.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/types.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/../common/Binary.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/../common/Transform.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/half.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/internal_enums.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../common/kernel_type.hpp
diff --git a/src/backend/cuda/compile_kernel.cpp b/src/backend/cuda/compile_kernel.cpp
index b0f8b2227b..8a55f6e0c4 100644
--- a/src/backend/cuda/compile_kernel.cpp
+++ b/src/backend/cuda/compile_kernel.cpp
@@ -15,7 +15,9 @@
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <kernel_headers/jit_cuh.hpp>
+#include <nvrtc_kernel_headers/Binary_hpp.hpp>
 #include <nvrtc_kernel_headers/Param_hpp.hpp>
+#include <nvrtc_kernel_headers/Transform_hpp.hpp>
 #include <nvrtc_kernel_headers/assign_kernel_param_hpp.hpp>
 #include <nvrtc_kernel_headers/backend_hpp.hpp>
 #include <nvrtc_kernel_headers/cuComplex_h.hpp>
@@ -30,7 +32,6 @@
 #include <nvrtc_kernel_headers/math_constants_h.hpp>
 #include <nvrtc_kernel_headers/math_hpp.hpp>
 #include <nvrtc_kernel_headers/minmax_op_hpp.hpp>
-#include <nvrtc_kernel_headers/ops_hpp.hpp>
 #include <nvrtc_kernel_headers/optypes_hpp.hpp>
 #include <nvrtc_kernel_headers/shared_hpp.hpp>
 #include <nvrtc_kernel_headers/traits_hpp.hpp>
@@ -168,13 +169,14 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
             "cuComplex.h",
             "jit.cuh",
             "math.hpp",
-            "ops.hpp",
             "optypes.hpp",
             "Param.hpp",
             "shared.hpp",
             "types.hpp",
             "cuda_fp16.hpp",
             "cuda_fp16.h",
+            "common/Binary.hpp",
+            "common/Transform.hpp",
             "common/half.hpp",
             "common/kernel_type.hpp",
             "af/traits.hpp",
@@ -199,13 +201,14 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
             string(cuComplex_h, cuComplex_h_len),
             string(jit_cuh, jit_cuh_len),
             string(math_hpp, math_hpp_len),
-            string(ops_hpp, ops_hpp_len),
             string(optypes_hpp, optypes_hpp_len),
             string(Param_hpp, Param_hpp_len),
             string(shared_hpp, shared_hpp_len),
             string(types_hpp, types_hpp_len),
             string(cuda_fp16_hpp, cuda_fp16_hpp_len),
             string(cuda_fp16_h, cuda_fp16_h_len),
+            string(Binary_hpp, Binary_hpp_len),
+            string(Transform_hpp, Transform_hpp_len),
             string(half_hpp, half_hpp_len),
             string(kernel_type_hpp, kernel_type_hpp_len),
             string(traits_hpp, traits_hpp_len),
@@ -234,8 +237,10 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
             sourceStrings[20].c_str(), sourceStrings[21].c_str(),
             sourceStrings[22].c_str(), sourceStrings[23].c_str(),
             sourceStrings[24].c_str(), sourceStrings[25].c_str(),
-            sourceStrings[26].c_str(),
+            sourceStrings[26].c_str(), sourceStrings[27].c_str(),
         };
+        static_assert(extent<decltype(headers)>::value == NumHeaders,
+                      "headers array contains fewer sources than includeNames");
         NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
                                        NumHeaders, headers, includeNames));
     }
diff --git a/src/backend/cuda/diagonal.hpp b/src/backend/cuda/diagonal.hpp
index b36c1d181f..c6e2aff5fd 100644
--- a/src/backend/cuda/diagonal.hpp
+++ b/src/backend/cuda/diagonal.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
 
 namespace cuda {
 template<typename T>
diff --git a/src/backend/cuda/ireduce.hpp b/src/backend/cuda/ireduce.hpp
index 3fdfd3ee73..69f25be476 100644
--- a/src/backend/cuda/ireduce.hpp
+++ b/src/backend/cuda/ireduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cuda {
 template<af_op_t op, typename T>
diff --git a/src/backend/cuda/kernel/ireduce.cuh b/src/backend/cuda/kernel/ireduce.cuh
index bd91b08e60..1c6cd63b60 100644
--- a/src/backend/cuda/kernel/ireduce.cuh
+++ b/src/backend/cuda/kernel/ireduce.cuh
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <minmax_op.hpp>
 
 namespace cuda {
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index 993b31d73f..d6beffd43e 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -9,6 +9,8 @@
 
 #include <Param.hpp>
 #include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <copy.hpp>
@@ -17,14 +19,11 @@
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
 #include "config.hpp"
 
 #include <memory>
 #include <vector>
 
-using std::vector;
-
 namespace cuda {
 
 __host__ __device__ auto operator*(float lhs, __half rhs) -> __half {
@@ -474,8 +473,8 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
         mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x,
                                       blocks_y, threads_x);
 
-        vector<T> h_ptr(tmp_elements);
-        vector<Tw> h_wptr(tmp_elements);
+        std::vector<T> h_ptr(tmp_elements);
+        std::vector<Tw> h_wptr(tmp_elements);
 
         CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.get(),
                                    tmp_elements * sizeof(T),
@@ -498,8 +497,8 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
 
         return static_cast<T>(val);
     } else {
-        vector<T> h_ptr(in_elements);
-        vector<Tw> h_wptr(in_elements);
+        std::vector<T> h_ptr(in_elements);
+        std::vector<Tw> h_wptr(in_elements);
 
         CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr,
                                    in_elements * sizeof(T),
@@ -559,8 +558,8 @@ To mean_all(CParam<Ti> in) {
                                         blocks_y, threads_x);
 
         int tmp_elements = tmpOut.elements();
-        vector<To> h_ptr(tmp_elements);
-        vector<Tw> h_cptr(tmp_elements);
+        std::vector<To> h_ptr(tmp_elements);
+        std::vector<Tw> h_cptr(tmp_elements);
 
         CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.get(),
                                    tmp_elements * sizeof(To),
@@ -583,7 +582,7 @@ To mean_all(CParam<Ti> in) {
 
         return static_cast<To>(val);
     } else {
-        vector<Ti> h_ptr(in_elements);
+        std::vector<Ti> h_ptr(in_elements);
 
         CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr,
                                    in_elements * sizeof(Ti),
diff --git a/src/backend/cuda/kernel/morph.cuh b/src/backend/cuda/kernel/morph.cuh
index 7525318c12..086c4508ea 100644
--- a/src/backend/cuda/kernel/morph.cuh
+++ b/src/backend/cuda/kernel/morph.cuh
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <math.hpp>
-#include <ops.hpp>
 #include <shared.hpp>
 
 // cFilter is used by both 2d morph and 3d morph
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 21204f6221..02eedb4237 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -10,12 +10,13 @@
 #pragma once
 #include <Param.hpp>
 #include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
 #include "config.hpp"
 
 #include <cub/warp/warp_reduce.cuh>
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index 49f29d7cc5..247bbdd606 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -10,12 +10,13 @@
 #pragma once
 #include <Param.hpp>
 #include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/dispatch.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <math.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
 #include <type_traits>
 #include "config.hpp"
 
diff --git a/src/backend/cuda/kernel/scan_dim.cuh b/src/backend/cuda/kernel/scan_dim.cuh
index bb67e35913..3f019bb084 100644
--- a/src/backend/cuda/kernel/scan_dim.cuh
+++ b/src/backend/cuda/kernel/scan_dim.cuh
@@ -9,8 +9,9 @@
 
 #include <Param.hpp>
 #include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <math.hpp>
-#include <ops.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.cuh b/src/backend/cuda/kernel/scan_dim_by_key.cuh
index 905dce9e4a..0c5875c2e1 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_dim_by_key.cuh
@@ -8,8 +8,9 @@
  ********************************************************/
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <math.hpp>
-#include <ops.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.hpp b/src/backend/cuda/kernel/scan_dim_by_key.hpp
index 2b6ba16149..a36b95be39 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key.hpp
@@ -9,7 +9,7 @@
 #pragma once
 
 #include <Param.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cuda {
 namespace kernel {
diff --git a/src/backend/cuda/kernel/scan_first.cuh b/src/backend/cuda/kernel/scan_first.cuh
index dcabc59a77..1bd3b52a53 100644
--- a/src/backend/cuda/kernel/scan_first.cuh
+++ b/src/backend/cuda/kernel/scan_first.cuh
@@ -9,8 +9,9 @@
 
 #include <Param.hpp>
 #include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <math.hpp>
-#include <ops.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key.cuh b/src/backend/cuda/kernel/scan_first_by_key.cuh
index 49d9f9ea09..ec894127a0 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_first_by_key.cuh
@@ -8,8 +8,9 @@
  ********************************************************/
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <math.hpp>
-#include <ops.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/kernel/scan_first_by_key.hpp b/src/backend/cuda/kernel/scan_first_by_key.hpp
index 8b758810c1..41ae8d83c5 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key.hpp
@@ -9,7 +9,7 @@
 #pragma once
 
 #include <Param.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cuda {
 namespace kernel {
diff --git a/src/backend/cuda/mean.hpp b/src/backend/cuda/mean.hpp
index c97e78c896..7871bb2aab 100644
--- a/src/backend/cuda/mean.hpp
+++ b/src/backend/cuda/mean.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <Array.hpp>
-#include <ops.hpp>
 
 namespace cuda {
 template<typename Ti, typename Tw, typename To>
diff --git a/src/backend/cuda/minmax_op.hpp b/src/backend/cuda/minmax_op.hpp
index 12a2546595..83040d7248 100644
--- a/src/backend/cuda/minmax_op.hpp
+++ b/src/backend/cuda/minmax_op.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <ops.hpp>
+#include <common/Binary.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/reduce.hpp b/src/backend/cuda/reduce.hpp
index 55bc47032a..8f3ad82898 100644
--- a/src/backend/cuda/reduce.hpp
+++ b/src/backend/cuda/reduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 #pragma once
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/cuda/scan.hpp b/src/backend/cuda/scan.hpp
index 523e0ce432..4ee9e84d5c 100644
--- a/src/backend/cuda/scan.hpp
+++ b/src/backend/cuda/scan.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/cuda/scan_by_key.cpp b/src/backend/cuda/scan_by_key.cpp
index 715a719c3a..30ae778a3d 100644
--- a/src/backend/cuda/scan_by_key.cpp
+++ b/src/backend/cuda/scan_by_key.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 #undef _GLIBCXX_USE_INT128
 #include <kernel/scan_dim_by_key.hpp>
diff --git a/src/backend/cuda/scan_by_key.hpp b/src/backend/cuda/scan_by_key.hpp
index ffb2945a81..366453b3ad 100644
--- a/src/backend/cuda/scan_by_key.hpp
+++ b/src/backend/cuda/scan_by_key.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace cuda {
 template<af_op_t op, typename Ti, typename Tk, typename To>
diff --git a/src/backend/opencl/diagonal.hpp b/src/backend/opencl/diagonal.hpp
index df2a4d4ff9..2d08df817e 100644
--- a/src/backend/opencl/diagonal.hpp
+++ b/src/backend/opencl/diagonal.hpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
 
 namespace opencl {
 template<typename T>
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index 04ce54aa56..86ff0fd1db 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -12,7 +12,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_opencl.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 #include <af/dim4.hpp>
 #include <complex>
 
diff --git a/src/backend/opencl/ireduce.hpp b/src/backend/opencl/ireduce.hpp
index 108bd2dfeb..05bea7bd19 100644
--- a/src/backend/opencl/ireduce.hpp
+++ b/src/backend/opencl/ireduce.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace opencl {
 template<af_op_t op, typename T>
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 6aeb624a00..6ed9cea472 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 01fcbc1263..00d240b894 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -10,6 +10,8 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/kernel_cache.hpp>
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 863034c83c..f0eb10b472 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -10,12 +10,12 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/morph.hpp>
 #include <memory.hpp>
-#include <ops.hpp>
 #include <traits.hpp>
 
 #include <string>
diff --git a/src/backend/opencl/kernel/names.hpp b/src/backend/opencl/kernel/names.hpp
index acafade34c..73489b1e10 100644
--- a/src/backend/opencl/kernel/names.hpp
+++ b/src/backend/opencl/kernel/names.hpp
@@ -8,7 +8,9 @@
  ********************************************************/
 
 #pragma once
-#include <ops.hpp>
+#include <common/defines.hpp>
+#include <optypes.hpp>
+
 template<af_op_t T>
 static const char *binOpName() {
     return "ADD_OP";
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 15a9b4429c..a0c10c39e8 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -11,6 +11,8 @@
 
 #include <Array.hpp>
 #include <Param.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/kernel_cache.hpp>
diff --git a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
index 3cead6f2bb..db44fb59c7 100644
--- a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
@@ -10,7 +10,6 @@
 #include <backend.hpp>
 #include <kernel/scan_dim_by_key_impl.hpp>
 #include <kernel/scan_first_by_key_impl.hpp>
-#include <ops.hpp>
 
 // This file instantiates scan_dim_by_key as separate object files from CMake
 // The line below is read by CMake to determenine the instantiations
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 6c1d6196fa..bc5cba6732 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 8e4728842e..d018f31360 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
@@ -18,6 +19,7 @@
 #include <kernel_headers/ops.hpp>
 #include <kernel_headers/scan_dim_by_key.hpp>
 #include <memory.hpp>
+#include <optypes.hpp>
 #include <traits.hpp>
 
 #include <string>
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index f00369484c..be53559583 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/Binary.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 63785bfd91..799bd471fb 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -17,6 +17,7 @@
 #include <kernel/names.hpp>
 #include <kernel/scan_first.hpp>
 #include <kernel_headers/where.hpp>
+#include <math.hpp>
 #include <traits.hpp>
 
 #include <string>
diff --git a/src/backend/opencl/mean.hpp b/src/backend/opencl/mean.hpp
index 60a03e297c..7f98f439d8 100644
--- a/src/backend/opencl/mean.hpp
+++ b/src/backend/opencl/mean.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <Array.hpp>
-#include <ops.hpp>
 
 namespace opencl {
 template<typename Ti, typename Tw, typename To>
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 28a99862c6..4da84d10df 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index 9e6a71763e..d72f86dc64 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
diff --git a/src/backend/opencl/scan_by_key.hpp b/src/backend/opencl/scan_by_key.hpp
index 5a4b449312..58fb5cacdd 100644
--- a/src/backend/opencl/scan_by_key.hpp
+++ b/src/backend/opencl/scan_by_key.hpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <ops.hpp>
+#include <optypes.hpp>
 
 namespace opencl {
 template<af_op_t op, typename Ti, typename Tk, typename To>
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 2db7b17a5f..2d76c46961 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -11,6 +11,7 @@
 #include <blas.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>  // error check functions and Macros
+#include <math.hpp>
 #include <reduce.hpp>
 #include <svd.hpp>  // opencl backend function header
 #include <transpose.hpp>

From 4df0a51f3ee37fff47c6de36514a4bb0ce600e3e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 20 May 2020 18:38:11 +0530
Subject: [PATCH 1963/2677] Merge all jit caches into a single one

---
 src/backend/common/kernel_cache.cpp   | 24 +++++----
 src/backend/common/kernel_cache.hpp   | 15 +++++-
 src/backend/cuda/jit.cpp              | 38 ++++----------
 src/backend/opencl/compile_kernel.cpp | 25 +++++++++-
 src/backend/opencl/jit.cpp            | 72 ++++++---------------------
 5 files changed, 76 insertions(+), 98 deletions(-)

diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index dce1b15049..10b346461a 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -21,6 +21,7 @@
 #include <vector>
 
 using detail::Kernel;
+
 using std::back_inserter;
 using std::map;
 using std::string;
@@ -47,20 +48,25 @@ Kernel lookupKernel(const int device, const string& nameExpr,
 
     if (iter != cache.end()) return iter->second;
 
+    if (sources.size() > 0) {
 #if defined(AF_CUDA) && defined(AF_CACHE_KERNELS_TO_DISK)
-    Kernel kernel = loadKernel(device, nameExpr, sources);
-    if (kernel.getModule() != nullptr && kernel.getKernel() != nullptr) {
-        cacheKernel(device, nameExpr, kernel);
-        return kernel;
-    }
+        Kernel kernel = loadKernel(device, nameExpr, sources);
+        if (kernel.getModule() != nullptr && kernel.getKernel() != nullptr) {
+            cacheKernel(device, nameExpr, kernel);
+            return kernel;
+        }
 #endif
-
+    }
     return Kernel{nullptr, nullptr};
 }
 
+Kernel lookupKernel(const int device, const string& key) {
+    return lookupKernel(device, key, {});
+}
+
 Kernel findKernel(const string& kernelName, const vector<string>& sources,
                   const vector<TemplateArg>& targs,
-                  const vector<string>& compileOpts) {
+                  const vector<string>& compileOpts, const bool isKernelJIT) {
     vector<string> args;
     args.reserve(targs.size());
 
@@ -80,10 +86,10 @@ Kernel findKernel(const string& kernelName, const vector<string>& sources,
     Kernel kernel = lookupKernel(device, tInstance, sources);
 
     if (kernel.getModule() == nullptr || kernel.getKernel() == nullptr) {
-        kernel = compileKernel(kernelName, tInstance, sources, compileOpts);
+        kernel = compileKernel(kernelName, tInstance, sources, compileOpts,
+                               isKernelJIT);
         cacheKernel(device, tInstance, kernel);
     }
-
     return kernel;
 }
 
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index b0dbad69e3..78d78816b3 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -71,7 +71,20 @@ namespace common {
 detail::Kernel findKernel(const std::string& kernelName,
                           const std::vector<std::string>& sources,
                           const std::vector<TemplateArg>& templateArgs,
-                          const std::vector<std::string>& compileOpts = {});
+                          const std::vector<std::string>& compileOpts = {},
+                          const bool isKernelJIT                      = false);
+
+/// \brief Lookup a Kernel that matches the given key
+///
+/// This function is intended to be used by JIT only. Usage in other
+/// places will most likely result in Kernel{nullptr, nullptr}. If by
+/// chance you do get a match for non-jit usage, it is accidental and
+/// such kernel will not work as expected.
+///
+/// \param[in] device is index of device in given backend for which
+///            the kernel look up has to be done
+/// \param[in] key is kernel name generated by JIT getFuncName function
+detail::Kernel lookupKernel(const int device, const std::string& key);
 
 }  // namespace common
 
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index a31ca6aa1a..3854ba7862 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -9,7 +9,6 @@
 
 #include <Array.hpp>
 #include <Kernel.hpp>
-#include <common/compile_kernel.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/jit/Node.hpp>
@@ -25,19 +24,16 @@
 #include <af/dim4.hpp>
 
 #include <cstdio>
-#include <map>
 #include <stdexcept>
 #include <thread>
 #include <vector>
 
-using common::compileKernel;
 using common::getFuncName;
 using common::half;
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
 
-using std::map;
 using std::string;
 using std::stringstream;
 using std::vector;
@@ -181,34 +177,18 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
-    using kc_t = map<string, Kernel>;
-
-    thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
-
     string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    int device = getActiveDeviceId();
-
-    auto idx = kernelCaches[device].find(funcName);
-    Kernel entry{nullptr, nullptr};
-
-    if (idx == kernelCaches[device].end()) {
-        string jit_ker = getKernelString(funcName, full_nodes, full_ids,
-                                         output_ids, is_linear);
-#ifdef AF_CACHE_KERNELS_TO_DISK
-        entry = common::loadKernel(device, funcName, {jit_ker});
-#endif
-        if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
-            saveKernel(funcName, jit_ker, ".cu");
-            // second argument, funcName, is important.
-            // From jit, first argument can be null as it is not used for CUDA
-            entry = compileKernel("", funcName, {jit_ker}, {}, true);
-        }
-        kernelCaches[device][funcName] = entry;
-    } else {
-        entry = idx->second;
-    }
 
+    auto entry = common::lookupKernel(getActiveDeviceId(), funcName);
+
+    if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
+        string jitKer = getKernelString(funcName, full_nodes, full_ids,
+                                        output_ids, is_linear);
+        saveKernel(funcName, jitKer, ".cu");
+
+        entry = common::findKernel(funcName, {jitKer}, {}, {}, true);
+    }
     return entry.getKernel();
 }
 
diff --git a/src/backend/opencl/compile_kernel.cpp b/src/backend/opencl/compile_kernel.cpp
index 15bf080cb9..b77235bc18 100644
--- a/src/backend/opencl/compile_kernel.cpp
+++ b/src/backend/opencl/compile_kernel.cpp
@@ -10,6 +10,7 @@
 #include <common/compile_kernel.hpp>
 
 #include <cl2hpp.hpp>
+#include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/util.hpp>
 #include <debug_opencl.hpp>
@@ -24,9 +25,18 @@
 #include <vector>
 
 using detail::Kernel;
+
 using std::ostringstream;
 using std::string;
 using std::vector;
+using std::chrono::duration_cast;
+using std::chrono::high_resolution_clock;
+using std::chrono::milliseconds;
+
+spdlog::logger *getLogger() {
+    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
+    return logger.get();
+}
 
 #define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
     do {                                                                  \
@@ -112,13 +122,24 @@ namespace common {
 Kernel compileKernel(const string &kernelName, const string &tInstance,
                      const vector<string> &sources,
                      const vector<string> &compileOpts, const bool isJIT) {
+    using opencl::getActiveDeviceId;
+    using opencl::getDevice;
+
     UNUSED(isJIT);
     UNUSED(tInstance);
 
-    auto prog = detail::buildProgram(sources, compileOpts);
-    auto prg  = new cl::Program(prog);
+    auto compileBegin = high_resolution_clock::now();
+    auto prog         = detail::buildProgram(sources, compileOpts);
+    auto prg          = new cl::Program(prog);
     auto krn =
         new cl::Kernel(*static_cast<cl::Program *>(prg), kernelName.c_str());
+    auto compileEnd = high_resolution_clock::now();
+
+    AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", kernelName,
+             duration_cast<milliseconds>(compileEnd - compileBegin).count(),
+             fmt::join(compileOpts, " "),
+             getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_NAME>());
+
     return {prg, krn};
 }
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 9f6ab0a798..6d73f1d98d 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -8,7 +8,6 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <common/Logger.hpp>
 #include <common/compile_kernel.hpp>
 #include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
@@ -21,9 +20,7 @@
 #include <af/dim4.hpp>
 #include <af/opencl.h>
 
-#include <chrono>
 #include <functional>
-#include <map>
 #include <stdexcept>
 #include <vector>
 
@@ -33,27 +30,13 @@ using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
 
-using cl::Buffer;
-using cl::EnqueueArgs;
 using cl::Kernel;
-using cl::KernelFunctor;
 using cl::NDRange;
 using cl::NullRange;
-using cl::Program;
 
-using std::hash;
-using std::map;
 using std::string;
 using std::stringstream;
 using std::vector;
-using std::chrono::duration_cast;
-using std::chrono::high_resolution_clock;
-using std::chrono::milliseconds;
-
-spdlog::logger *getLogger() {
-    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
-    return logger.get();
-}
 
 namespace opencl {
 
@@ -160,54 +143,29 @@ static cl::Kernel getKernel(const vector<Node *> &output_nodes,
                             const vector<Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
-    using kc_t = map<string, Kernel>;
-
-    static const string jit(jit_cl, jit_cl_len);
-
-    thread_local kc_t kernelCaches[DeviceManager::MAX_DEVICES];
-
     string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    int device = getActiveDeviceId();
 
-    auto idx = kernelCaches[device].find(funcName);
-    Kernel entry{nullptr, nullptr};
+    auto entry = common::lookupKernel(getActiveDeviceId(), funcName);
+
+    if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
+        static const string jit(jit_cl, jit_cl_len);
 
-    if (idx == kernelCaches[device].end()) {
         string jitKer = getKernelString(funcName, full_nodes, full_ids,
                                         output_ids, is_linear);
-#ifdef AF_CACHE_KERNELS_TO_DISK
-        // TODO(pradeep) load jit kernels cached to disk
-#endif
-        if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
-            saveKernel(funcName, jitKer, ".cl");
-
-            vector<string> options;
-            if (isDoubleSupported(device)) {
-                options.emplace_back(DefineKey(USE_DOUBLE));
-            }
-            if (isHalfSupported(device)) {
-                options.emplace_back(DefineKey(USE_HALF));
-            }
-
-            auto compileBegin = high_resolution_clock::now();
-            // First argument, funcName, is important.
-            // From jit, second argument can be null as it is not used for
-            // OpenCL
-            entry = compileKernel(funcName, "", {jit, jitKer}, options, true);
-            auto compileEnd = high_resolution_clock::now();
-
-            AF_TRACE(
-                "{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", funcName,
-                duration_cast<milliseconds>(compileEnd - compileBegin).count(),
-                fmt::join(options, " "),
-                getDevice(device).getInfo<CL_DEVICE_NAME>());
+        int device    = getActiveDeviceId();
+        vector<string> options;
+        if (isDoubleSupported(device)) {
+            options.emplace_back(DefineKey(USE_DOUBLE));
         }
-        kernelCaches[device][funcName] = entry;
-    } else {
-        entry = idx->second;
-    }
+        if (isHalfSupported(device)) {
+            options.emplace_back(DefineKey(USE_HALF));
+        }
+
+        saveKernel(funcName, jitKer, ".cl");
 
+        entry = common::findKernel(funcName, {jit, jitKer}, {}, options, true);
+    }
     return *entry.getKernel();
 }
 

From c214dcd95095591470e15f7aa78eed0974c2dd3b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 20 May 2020 20:01:22 +0530
Subject: [PATCH 1964/2677] Refactor loadKernel fn name as loadKernelFromDisk

---
 src/backend/common/compile_kernel.hpp | 5 +++--
 src/backend/common/kernel_cache.cpp   | 2 +-
 src/backend/cuda/compile_kernel.cpp   | 4 ++--
 src/backend/opencl/compile_kernel.cpp | 2 +-
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/backend/common/compile_kernel.hpp b/src/backend/common/compile_kernel.hpp
index d66bc726a7..84f7570b45 100644
--- a/src/backend/common/compile_kernel.hpp
+++ b/src/backend/common/compile_kernel.hpp
@@ -42,8 +42,9 @@ detail::Kernel compileKernel(const std::string& kernelName,
 /// \param[in] device is the device index
 /// \param[in] kernelNameExpr is the name identifying the relevant kernel
 /// \param[in] sources is the list of kernel and helper source files
-detail::Kernel loadKernel(const int device, const std::string& kernelNameExpr,
-                          const std::vector<std::string>& sources);
+detail::Kernel loadKernelFromDisk(const int device,
+                                  const std::string& kernelNameExpr,
+                                  const std::vector<std::string>& sources);
 
 }  // namespace common
 
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 10b346461a..e4801feb9e 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -50,7 +50,7 @@ Kernel lookupKernel(const int device, const string& nameExpr,
 
     if (sources.size() > 0) {
 #if defined(AF_CUDA) && defined(AF_CACHE_KERNELS_TO_DISK)
-        Kernel kernel = loadKernel(device, nameExpr, sources);
+        Kernel kernel = loadKernelFromDisk(device, nameExpr, sources);
         if (kernel.getModule() != nullptr && kernel.getKernel() != nullptr) {
             cacheKernel(device, nameExpr, kernel);
             return kernel;
diff --git a/src/backend/cuda/compile_kernel.cpp b/src/backend/cuda/compile_kernel.cpp
index 8a55f6e0c4..04e8796679 100644
--- a/src/backend/cuda/compile_kernel.cpp
+++ b/src/backend/cuda/compile_kernel.cpp
@@ -363,8 +363,8 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
     return entry;
 }
 
-Kernel loadKernel(const int device, const string &nameExpr,
-                  const vector<string> &sources) {
+Kernel loadKernelFromDisk(const int device, const string &nameExpr,
+                          const vector<string> &sources) {
     const string &cacheDirectory = getCacheDirectory();
     if (cacheDirectory.empty()) return Kernel{nullptr, nullptr};
 
diff --git a/src/backend/opencl/compile_kernel.cpp b/src/backend/opencl/compile_kernel.cpp
index b77235bc18..3b876dcc6e 100644
--- a/src/backend/opencl/compile_kernel.cpp
+++ b/src/backend/opencl/compile_kernel.cpp
@@ -143,7 +143,7 @@ Kernel compileKernel(const string &kernelName, const string &tInstance,
     return {prg, krn};
 }
 
-Kernel loadKernel(const int device, const string &nameExpr) {
+Kernel loadKernelFromDisk(const int device, const string &nameExpr) {
     OPENCL_NOT_SUPPORTED(
         "Disk caching OpenCL kernel binaries is not yet supported");
     return {nullptr, nullptr};

From 6c35d8f7bb245f2aefc91b6231eae4dcad134274 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 21 May 2020 00:40:35 +0530
Subject: [PATCH 1965/2677] Enable ccache based compilation when it is
 available (#2893)

* Enable ccache based compilation when it is available automatically. If not present, no change in behavior.

* The user can turn off using ccache using the cmake option `AF_USE_CCACHE`. Note that this is an advanced cmake option.
---
 CMakeLists.txt                   |  1 +
 CMakeModules/config_ccache.cmake | 38 ++++++++++++++++++++++++++++++++
 CMakeModules/launch-c.in         | 10 +++++++++
 CMakeModules/launch-cxx.in       | 10 +++++++++
 4 files changed, 59 insertions(+)
 create mode 100644 CMakeModules/config_ccache.cmake
 create mode 100644 CMakeModules/launch-c.in
 create mode 100644 CMakeModules/launch-cxx.in

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 94b8560b8e..ccf3a755cc 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -11,6 +11,7 @@ project(ArrayFire VERSION 3.8.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 
+include(config_ccache)
 include(AFBuildConfigurations)
 include(AFInstallDirs)
 include(CMakeDependentOption)
diff --git a/CMakeModules/config_ccache.cmake b/CMakeModules/config_ccache.cmake
new file mode 100644
index 0000000000..b112787d76
--- /dev/null
+++ b/CMakeModules/config_ccache.cmake
@@ -0,0 +1,38 @@
+# picked up original content from https://crascit.com/2016/04/09/using-ccache-with-cmake/
+
+if (UNIX)
+  find_program(CCACHE_PROGRAM ccache)
+
+  set(CCACHE_FOUND OFF)
+  if(CCACHE_PROGRAM)
+    set(CCACHE_FOUND ON)
+  endif()
+
+  option(AF_USE_CCACHE "Use ccache when compiling" ${CCACHE_FOUND})
+
+  if(${AF_USE_CCACHE})
+    # Set up wrapper scripts
+    set(C_LAUNCHER   "${CCACHE_PROGRAM}")
+    set(CXX_LAUNCHER "${CCACHE_PROGRAM}")
+    configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-c.in   launch-c)
+    configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-cxx.in launch-cxx)
+    execute_process(COMMAND chmod a+rx
+        "${ArrayFire_BINARY_DIR}/launch-c"
+        "${ArrayFire_BINARY_DIR}/launch-cxx"
+      )
+    if(CMAKE_GENERATOR STREQUAL "Xcode")
+      # Set Xcode project attributes to route compilation and linking
+      # through our scripts
+      set(CMAKE_XCODE_ATTRIBUTE_CC         "${ArrayFire_BINARY_DIR}/launch-c")
+      set(CMAKE_XCODE_ATTRIBUTE_CXX        "${ArrayFire_BINARY_DIR}/launch-cxx")
+      set(CMAKE_XCODE_ATTRIBUTE_LD         "${ArrayFire_BINARY_DIR}/launch-c")
+      set(CMAKE_XCODE_ATTRIBUTE_LDPLUSPLUS "${ArrayFire_BINARY_DIR}/launch-cxx")
+    else()
+      # Support Unix Makefiles and Ninja
+      set(CMAKE_C_COMPILER_LAUNCHER   "${ArrayFire_BINARY_DIR}/launch-c")
+      set(CMAKE_CXX_COMPILER_LAUNCHER "${ArrayFire_BINARY_DIR}/launch-cxx")
+    endif()
+  endif()
+  mark_as_advanced(CCACHE_PROGRAM)
+  mark_as_advanced(AF_USE_CCACHE)
+endif()
diff --git a/CMakeModules/launch-c.in b/CMakeModules/launch-c.in
new file mode 100644
index 0000000000..a033af6cf1
--- /dev/null
+++ b/CMakeModules/launch-c.in
@@ -0,0 +1,10 @@
+#!/bin/sh
+
+# Xcode generator doesn't include the compiler as the
+# first argument, Ninja and Makefiles do. Handle both cases.
+if [[ "$1" = "${CMAKE_C_COMPILER}" ]] ; then
+    shift
+fi
+
+export CCACHE_CPP2=true
+exec "${C_LAUNCHER}" "${CMAKE_C_COMPILER}" "$@"
diff --git a/CMakeModules/launch-cxx.in b/CMakeModules/launch-cxx.in
new file mode 100644
index 0000000000..457660f5a1
--- /dev/null
+++ b/CMakeModules/launch-cxx.in
@@ -0,0 +1,10 @@
+#!/bin/sh
+
+# Xcode generator doesn't include the compiler as the
+# first argument, Ninja and Makefiles do. Handle both cases.
+if [[ "$1" = "${CMAKE_CXX_COMPILER}" ]] ; then
+    shift
+fi
+
+export CCACHE_CPP2=true
+exec "${CXX_LAUNCHER}" "${CMAKE_CXX_COMPILER}" "$@"

From 74c879bdb7efcb4c09f836b83a4e8a16773f6124 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 21 May 2020 12:44:48 +0530
Subject: [PATCH 1966/2677] Fix hardcoded include paths in arrayfire_test
 library

---
 test/CMakeLists.txt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 73ff944617..957800b2bd 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -78,10 +78,10 @@ add_library(arrayfire_test OBJECT
 
 target_include_directories(arrayfire_test
   PRIVATE
-    .
-    ../include
-    ../build/include
-    ../extern/half/include
+    ${CMAKE_CURRENT_LIST_DIR}
+    ${ArrayFire_SOURCE_DIR}/include
+    ${ArrayFire_BINARY_DIR}/include
+    ${ArrayFire_SOURCE_DIR}/extern/half/include
     mmio
     gtest/googletest/include)
 

From cc11ab6ef04e06f822fdd89933e1fb728a755a8c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 25 May 2020 17:24:28 -0400
Subject: [PATCH 1967/2677] Fix leak of the device, context and queue OpenCLHPP
 objects

* The OpenCL Wrapper objects were being leaked by OpenCL when calling the
  removeDeviceContext function. The handles were decremented correctly but
  the objects were not released so it caused a very small leak in the
  binary.
---
 src/backend/opencl/compile_kernel.cpp |  5 +-
 src/backend/opencl/device_manager.cpp | 40 ++++++------
 src/backend/opencl/device_manager.hpp |  6 +-
 src/backend/opencl/platform.cpp       | 90 ++++++++++++---------------
 4 files changed, 63 insertions(+), 78 deletions(-)

diff --git a/src/backend/opencl/compile_kernel.cpp b/src/backend/opencl/compile_kernel.cpp
index 3b876dcc6e..b62abe0ac4 100644
--- a/src/backend/opencl/compile_kernel.cpp
+++ b/src/backend/opencl/compile_kernel.cpp
@@ -131,9 +131,8 @@ Kernel compileKernel(const string &kernelName, const string &tInstance,
     auto compileBegin = high_resolution_clock::now();
     auto prog         = detail::buildProgram(sources, compileOpts);
     auto prg          = new cl::Program(prog);
-    auto krn =
-        new cl::Kernel(*static_cast<cl::Program *>(prg), kernelName.c_str());
-    auto compileEnd = high_resolution_clock::now();
+    auto krn          = new cl::Kernel(*prg, kernelName.c_str());
+    auto compileEnd   = high_resolution_clock::now();
 
     AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", kernelName,
              duration_cast<milliseconds>(compileEnd - compileBegin).count(),
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 50a39ccdb6..5286928150 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -47,8 +47,10 @@ using cl::Platform;
 using std::begin;
 using std::end;
 using std::find;
+using std::make_unique;
 using std::string;
 using std::stringstream;
+using std::unique_ptr;
 using std::vector;
 
 namespace opencl {
@@ -78,7 +80,8 @@ static afcl::deviceType getDeviceTypeEnum(const Device& dev) {
     return static_cast<afcl::deviceType>(dev.getInfo<CL_DEVICE_TYPE>());
 }
 
-static inline bool compare_default(const Device* ldev, const Device* rdev) {
+static inline bool compare_default(const unique_ptr<Device>& ldev,
+                                   const unique_ptr<Device>& rdev) {
     const cl_device_type device_types[] = {CL_DEVICE_TYPE_GPU,
                                            CL_DEVICE_TYPE_ACCELERATOR};
 
@@ -219,8 +222,8 @@ DeviceManager::DeviceManager()
         }
         AF_TRACE("Found {} devices on platform {}", current_devices.size(),
                  platform.getInfo<CL_PLATFORM_NAME>());
-        for (const auto& dev : current_devices) {
-            mDevices.push_back(new Device(dev));
+        for (auto& dev : current_devices) {
+            mDevices.emplace_back(make_unique<Device>(dev));
             AF_TRACE("Found device {} on platform {}",
                      dev.getInfo<CL_DEVICE_NAME>(),
                      platform.getInfo<CL_PLATFORM_NAME>());
@@ -242,10 +245,9 @@ DeviceManager::DeviceManager()
         cl_context_properties cps[3] = {
             CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
 
-        auto* ctx = new Context(*mDevices[i], cps);
-        auto* cq  = new CommandQueue(*ctx, *mDevices[i]);
-        mContexts.push_back(ctx);
-        mQueues.push_back(cq);
+        mContexts.push_back(make_unique<Context>(*mDevices[i], cps));
+        mQueues.push_back(make_unique<CommandQueue>(
+            *mContexts.back(), *mDevices[i], cl::QueueProperties::None));
         mIsGLSharingOn.push_back(false);
         mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
         mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
@@ -319,7 +321,7 @@ DeviceManager::DeviceManager()
 
     // Cache Boost program_cache
     namespace compute = boost::compute;
-    for (auto ctx : mContexts) {
+    for (auto& ctx : mContexts) {
         compute::context c(ctx->get());
         BoostProgCache currCache = compute::program_cache::get_global_cache(c);
         mBoostProgCacheVector.emplace_back(new BoostProgCache(currCache));
@@ -413,10 +415,10 @@ DeviceManager::~DeviceManager() {
     // on the investigation done so far. This problem
     // doesn't seem to happen on Linux or MacOSX.
     // So, clean up OpenCL resources on non-Windows platforms
-#ifndef OS_WIN
-    for (auto q : mQueues) { delete q; }
-    for (auto c : mContexts) { delete c; }
-    for (auto d : mDevices) { delete d; }
+#ifdef OS_WIN
+    for (auto& q : mQueues) { q.release(); }
+    for (auto& c : mContexts) { c.release(); }
+    for (auto& d : mDevices) { d.release(); }
 #endif
 }
 
@@ -509,17 +511,11 @@ void DeviceManager::markDeviceForInterop(const int device,
 #endif
 
             // Change current device to use GL sharing
-            auto* ctx = new Context(*mDevices[device], cps);
-            auto* cq  = new CommandQueue(*ctx, *mDevices[device]);
-
-            // May be fixes the AMD GL issues we see on windows?
-#if !defined(_WIN32) && !defined(_MSC_VER)
-            delete mContexts[device];
-            delete mQueues[device];
-#endif
+            auto ctx = make_unique<Context>(*mDevices[device], cps);
+            auto cq  = make_unique<CommandQueue>(*ctx, *mDevices[device]);
 
-            mContexts[device]      = ctx;
-            mQueues[device]        = cq;
+            mQueues[device]        = move(cq);
+            mContexts[device]      = move(ctx);
             mIsGLSharingOn[device] = true;
         }
     } catch (const cl::Error& ex) {
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 58a7d54678..b68297b511 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -155,9 +155,9 @@ class DeviceManager {
     // Attributes
     std::shared_ptr<spdlog::logger> logger;
     std::mutex deviceMutex;
-    std::vector<cl::Device*> mDevices;
-    std::vector<cl::Context*> mContexts;
-    std::vector<cl::CommandQueue*> mQueues;
+    std::vector<std::unique_ptr<cl::Device>> mDevices;
+    std::vector<std::unique_ptr<cl::Context>> mContexts;
+    std::vector<std::unique_ptr<cl::CommandQueue>> mQueues;
     std::vector<bool> mIsGLSharingOn;
     std::vector<int> mDeviceTypes;
     std::vector<int> mPlatforms;
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 3f6e37a733..d8af15f2fd 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -55,6 +55,7 @@ using std::endl;
 using std::find_if;
 using std::get;
 using std::make_pair;
+using std::make_unique;
 using std::map;
 using std::once_flag;
 using std::ostringstream;
@@ -149,10 +150,8 @@ string getDeviceInfo() noexcept {
         DeviceManager& devMngr = DeviceManager::getInstance();
 
         common::lock_guard_t lock(devMngr.deviceMutex);
-        devices = devMngr.mDevices;
-
         unsigned nDevices = 0;
-        for (auto device : devices) {
+        for (auto& device : devMngr.mDevices) {
             const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
             string dstr = device->getInfo<CL_DEVICE_NAME>();
@@ -396,43 +395,41 @@ void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    vector<cl::Context*> contexts;
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
-        contexts = devMngr.mContexts;  // NOTE: copy, not a reference
-    }
 
-    for (auto context : contexts) {
-        vector<Device> devices = context->getInfo<CL_CONTEXT_DEVICES>();
-
-        for (auto& device : devices) {
-            const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
-            string platStr = platform.getInfo<CL_PLATFORM_NAME>();
-
-            if (currActiveDevId == nDevices) {
-                string dev_str;
-                device.getInfo(CL_DEVICE_NAME, &dev_str);
-                string com_str = device.getInfo<CL_DEVICE_VERSION>();
-                com_str        = com_str.substr(7, 3);
-
-                // strip out whitespace from the device string:
-                const string& whitespace = " \t";
-                const auto strBegin = dev_str.find_first_not_of(whitespace);
-                const auto strEnd   = dev_str.find_last_not_of(whitespace);
-                const auto strRange = strEnd - strBegin + 1;
-                dev_str             = dev_str.substr(strBegin, strRange);
-
-                // copy to output
-                snprintf(d_name, 64, "%s", dev_str.c_str());
-                snprintf(d_platform, 10, "OpenCL");
-                snprintf(d_toolkit, 64, "%s", platStr.c_str());
-                snprintf(d_compute, 10, "%s", com_str.c_str());
-                devset = true;
+        for (auto& context : devMngr.mContexts) {
+            vector<Device> devices = context->getInfo<CL_CONTEXT_DEVICES>();
+
+            for (auto& device : devices) {
+                const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
+                string platStr = platform.getInfo<CL_PLATFORM_NAME>();
+
+                if (currActiveDevId == nDevices) {
+                    string dev_str;
+                    device.getInfo(CL_DEVICE_NAME, &dev_str);
+                    string com_str = device.getInfo<CL_DEVICE_VERSION>();
+                    com_str        = com_str.substr(7, 3);
+
+                    // strip out whitespace from the device string:
+                    const string& whitespace = " \t";
+                    const auto strBegin = dev_str.find_first_not_of(whitespace);
+                    const auto strEnd   = dev_str.find_last_not_of(whitespace);
+                    const auto strRange = strEnd - strBegin + 1;
+                    dev_str             = dev_str.substr(strBegin, strRange);
+
+                    // copy to output
+                    snprintf(d_name, 64, "%s", dev_str.c_str());
+                    snprintf(d_platform, 10, "OpenCL");
+                    snprintf(d_toolkit, 64, "%s", platStr.c_str());
+                    snprintf(d_compute, 10, "%s", com_str.c_str());
+                    devset = true;
+                }
+                if (devset) { break; }
+                nDevices++;
             }
             if (devset) { break; }
-            nDevices++;
         }
-        if (devset) { break; }
     }
 
     // Sanitize input
@@ -470,28 +467,25 @@ void sync(int device) {
 }
 
 void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
-    clRetainDevice(dev);
-    clRetainContext(ctx);
-    clRetainCommandQueue(que);
-
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     int nDevices = 0;
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
 
-        auto* tDevice  = new cl::Device(dev);
-        auto* tContext = new cl::Context(ctx);
-        cl::CommandQueue* tQueue =
-            (que == NULL ? new cl::CommandQueue(*tContext, *tDevice)
-                         : new cl::CommandQueue(que));
-        devMngr.mDevices.push_back(tDevice);
-        devMngr.mContexts.push_back(tContext);
-        devMngr.mQueues.push_back(tQueue);
+        auto tDevice  = make_unique<cl::Device>(dev, true);
+        auto tContext = make_unique<cl::Context>(ctx, true);
+        auto tQueue =
+            (que == NULL ? make_unique<cl::CommandQueue>(*tContext, *tDevice)
+                         : make_unique<cl::CommandQueue>(que, true));
         devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
         devMngr.mDeviceTypes.push_back(tDevice->getInfo<CL_DEVICE_TYPE>());
+
+        devMngr.mDevices.push_back(move(tDevice));
+        devMngr.mContexts.push_back(move(tContext));
+        devMngr.mQueues.push_back(move(tQueue));
         nDevices = devMngr.mDevices.size() - 1;
 
         // cache the boost program_cache object, clean up done on program exit
@@ -554,10 +548,6 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
         memoryManager().removeMemoryManagement(deleteIdx);
 
         common::lock_guard_t lock(devMngr.deviceMutex);
-        clReleaseDevice((*devMngr.mDevices[deleteIdx])());
-        clReleaseContext((*devMngr.mContexts[deleteIdx])());
-        clReleaseCommandQueue((*devMngr.mQueues[deleteIdx])());
-
         // FIXME: this case can potentially cause issues due to the
         // modification of the device pool stl containers.
 

From 52f747f27662aa274bcee9cdc6c640ffc4c776fa Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 21 May 2020 00:41:50 +0530
Subject: [PATCH 1968/2677] Refactor kernel cache to store CUmodule/cl_program

- Rename common::compileKernel to common::compileModule
- Rename common::loadKernelFromDisk to common::loadModuleFromDisk
- Rename common::findKernel to common::getKernel
- Rename common::lookupKernel to common::findModule

With this change, kernels are cached using respective backend's context handle,
CUmodule for CUDA; cl_program for OpenCL, a.k.a module in arrayfire jargon.
This required renaming the relevant files to appropriate names like
compile_module.[hpp|cpp]. Each backend has to implement common::compileModule
that handles the backend specific compilation of jit source. They also
have to implement common::loadModuleFromDisk to handle loading already
cached(should be taken care of by compileModule) modules from disk.
Loading modules from disk help with quick repopulation of cache without
the need of remcompiling the kernels.

Disk caching modules is not yet implemented in OpenCL.
---
 src/backend/common/CMakeLists.txt             |   3 +-
 src/backend/common/KernelInterface.hpp        |  30 ++--
 src/backend/common/ModuleInterface.hpp        |  34 ++++
 src/backend/common/compile_kernel.hpp         |  51 ------
 src/backend/common/compile_module.hpp         |  65 ++++++++
 src/backend/common/kernel_cache.cpp           |  83 +++++-----
 src/backend/common/kernel_cache.hpp           |  40 +++--
 src/backend/common/util.cpp                   |   6 +
 src/backend/common/util.hpp                   |   4 +
 src/backend/cuda/CMakeLists.txt               |   3 +-
 src/backend/cuda/Kernel.cpp                   |   4 +-
 src/backend/cuda/Kernel.hpp                   |   8 +-
 src/backend/cuda/Module.hpp                   |  50 ++++++
 ...{compile_kernel.cpp => compile_module.cpp} | 153 ++++++++++--------
 src/backend/cuda/jit.cpp                      |  21 ++-
 .../cuda/kernel/anisotropic_diffusion.hpp     |   2 +-
 src/backend/cuda/kernel/approx.hpp            |   4 +-
 src/backend/cuda/kernel/assign.hpp            |   2 +-
 src/backend/cuda/kernel/bilateral.hpp         |   2 +-
 src/backend/cuda/kernel/canny.hpp             |  10 +-
 src/backend/cuda/kernel/convolve.hpp          |  16 +-
 src/backend/cuda/kernel/diagonal.hpp          |   8 +-
 src/backend/cuda/kernel/diff.hpp              |   2 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |  10 +-
 src/backend/cuda/kernel/fftconvolve.hpp       |  18 +--
 src/backend/cuda/kernel/flood_fill.hpp        |  14 +-
 src/backend/cuda/kernel/gradient.hpp          |   4 +-
 src/backend/cuda/kernel/histogram.hpp         |   8 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |   4 +-
 src/backend/cuda/kernel/identity.hpp          |   2 +-
 src/backend/cuda/kernel/iir.hpp               |   6 +-
 src/backend/cuda/kernel/index.hpp             |   2 +-
 src/backend/cuda/kernel/iota.hpp              |   2 +-
 src/backend/cuda/kernel/ireduce.hpp           |  10 +-
 src/backend/cuda/kernel/join.hpp              |   2 +-
 src/backend/cuda/kernel/lookup.hpp            |   8 +-
 src/backend/cuda/kernel/lu_split.hpp          |   2 +-
 src/backend/cuda/kernel/match_template.hpp    |   2 +-
 src/backend/cuda/kernel/meanshift.hpp         |   2 +-
 src/backend/cuda/kernel/medfilt.hpp           |  10 +-
 src/backend/cuda/kernel/memcopy.hpp           |   4 +-
 src/backend/cuda/kernel/moments.hpp           |   2 +-
 src/backend/cuda/kernel/morph.hpp             |   8 +-
 src/backend/cuda/kernel/pad_array_borders.hpp |   4 +-
 src/backend/cuda/kernel/range.hpp             |   2 +-
 src/backend/cuda/kernel/reorder.hpp           |   2 +-
 src/backend/cuda/kernel/resize.hpp            |   2 +-
 src/backend/cuda/kernel/rotate.hpp            |   2 +-
 src/backend/cuda/kernel/scan_dim.hpp          |   4 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |   8 +-
 src/backend/cuda/kernel/scan_first.hpp        |  16 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |   8 +-
 src/backend/cuda/kernel/select.hpp            |   8 +-
 src/backend/cuda/kernel/sobel.hpp             |  12 +-
 src/backend/cuda/kernel/sparse.hpp            |   4 +-
 src/backend/cuda/kernel/sparse_arith.hpp      |  16 +-
 src/backend/cuda/kernel/susan.hpp             |   6 +-
 src/backend/cuda/kernel/tile.hpp              |   2 +-
 src/backend/cuda/kernel/transform.hpp         |   4 +-
 src/backend/cuda/kernel/transpose.hpp         |   8 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |   8 +-
 src/backend/cuda/kernel/triangle.hpp          |   6 +-
 src/backend/cuda/kernel/unwrap.hpp            |   4 +-
 src/backend/cuda/kernel/where.hpp             |   2 +-
 src/backend/cuda/kernel/wrap.hpp              |   8 +-
 src/backend/opencl/CMakeLists.txt             |   3 +-
 src/backend/opencl/Kernel.cpp                 |   2 +-
 src/backend/opencl/Kernel.hpp                 |   4 +-
 src/backend/opencl/Module.hpp                 |  27 ++++
 ...{compile_kernel.cpp => compile_module.cpp} |  70 ++++----
 src/backend/opencl/jit.cpp                    |  51 +++---
 .../opencl/kernel/anisotropic_diffusion.hpp   |   2 +-
 src/backend/opencl/kernel/approx.hpp          |   8 +-
 src/backend/opencl/kernel/assign.hpp          |   2 +-
 src/backend/opencl/kernel/bilateral.hpp       |   2 +-
 src/backend/opencl/kernel/canny.hpp           |  16 +-
 .../opencl/kernel/convolve/conv2_impl.hpp     |   2 +-
 .../opencl/kernel/convolve/conv_common.hpp    |   2 +-
 .../opencl/kernel/convolve_separable.cpp      |   2 +-
 src/backend/opencl/kernel/cscmm.hpp           |   2 +-
 src/backend/opencl/kernel/cscmv.hpp           |   2 +-
 src/backend/opencl/kernel/csrmm.hpp           |   2 +-
 src/backend/opencl/kernel/csrmv.hpp           |   5 +-
 src/backend/opencl/kernel/diagonal.hpp        |   4 +-
 src/backend/opencl/kernel/diff.hpp            |   2 +-
 src/backend/opencl/kernel/exampleFunction.hpp |   6 +-
 src/backend/opencl/kernel/fast.hpp            |   6 +-
 src/backend/opencl/kernel/fftconvolve.hpp     |   9 +-
 src/backend/opencl/kernel/flood_fill.hpp      |  12 +-
 src/backend/opencl/kernel/gradient.hpp        |   2 +-
 src/backend/opencl/kernel/harris.hpp          |   8 +-
 src/backend/opencl/kernel/histogram.hpp       |   2 +-
 src/backend/opencl/kernel/homography.hpp      |  10 +-
 src/backend/opencl/kernel/hsv_rgb.hpp         |   2 +-
 src/backend/opencl/kernel/identity.hpp        |   2 +-
 src/backend/opencl/kernel/iir.hpp             |   2 +-
 src/backend/opencl/kernel/index.hpp           |   4 +-
 src/backend/opencl/kernel/iota.hpp            |   4 +-
 src/backend/opencl/kernel/ireduce.hpp         |   6 +-
 src/backend/opencl/kernel/join.hpp            |   4 +-
 src/backend/opencl/kernel/laset.hpp           |   3 +-
 src/backend/opencl/kernel/laset_band.hpp      |   2 +-
 src/backend/opencl/kernel/laswp.hpp           |   2 +-
 src/backend/opencl/kernel/lookup.hpp          |   2 +-
 src/backend/opencl/kernel/lu_split.hpp        |   2 +-
 src/backend/opencl/kernel/match_template.hpp  |   3 +-
 src/backend/opencl/kernel/mean.hpp            |   4 +-
 src/backend/opencl/kernel/meanshift.hpp       |   2 +-
 src/backend/opencl/kernel/medfilt.hpp         |   4 +-
 src/backend/opencl/kernel/memcopy.hpp         |   4 +-
 src/backend/opencl/kernel/moments.hpp         |   2 +-
 src/backend/opencl/kernel/morph.hpp           |   4 +-
 .../opencl/kernel/nearest_neighbour.hpp       |   2 +-
 src/backend/opencl/kernel/orb.hpp             |   8 +-
 .../opencl/kernel/pad_array_borders.hpp       |   2 +-
 src/backend/opencl/kernel/random_engine.hpp   |   4 +-
 src/backend/opencl/kernel/range.hpp           |   2 +-
 src/backend/opencl/kernel/reduce.hpp          |   4 +-
 src/backend/opencl/kernel/reduce_by_key.hpp   |  14 +-
 src/backend/opencl/kernel/regions.hpp         |   6 +-
 src/backend/opencl/kernel/reorder.hpp         |   3 +-
 src/backend/opencl/kernel/resize.hpp          |   2 +-
 src/backend/opencl/kernel/rotate.hpp          |   4 +-
 src/backend/opencl/kernel/scan_dim.hpp        |   2 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   2 +-
 src/backend/opencl/kernel/scan_first.hpp      |   2 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   2 +-
 src/backend/opencl/kernel/select.hpp          |   6 +-
 src/backend/opencl/kernel/sift_nonfree.hpp    |  14 +-
 src/backend/opencl/kernel/sobel.hpp           |   2 +-
 src/backend/opencl/kernel/sparse.hpp          |  12 +-
 src/backend/opencl/kernel/sparse_arith.hpp    |   4 +-
 src/backend/opencl/kernel/susan.hpp           |   4 +-
 src/backend/opencl/kernel/swapdblk.hpp        |   2 +-
 src/backend/opencl/kernel/tile.hpp            |   2 +-
 src/backend/opencl/kernel/transform.hpp       |   4 +-
 src/backend/opencl/kernel/transpose.hpp       |   2 +-
 .../opencl/kernel/transpose_inplace.hpp       |   2 +-
 src/backend/opencl/kernel/triangle.hpp        |   2 +-
 src/backend/opencl/kernel/unwrap.hpp          |   2 +-
 src/backend/opencl/kernel/where.hpp           |   2 +-
 src/backend/opencl/kernel/wrap.hpp            |   4 +-
 142 files changed, 746 insertions(+), 557 deletions(-)
 create mode 100644 src/backend/common/ModuleInterface.hpp
 delete mode 100644 src/backend/common/compile_kernel.hpp
 create mode 100644 src/backend/common/compile_module.hpp
 create mode 100644 src/backend/cuda/Module.hpp
 rename src/backend/cuda/{compile_kernel.cpp => compile_module.cpp} (76%)
 create mode 100644 src/backend/opencl/Module.hpp
 rename src/backend/opencl/{compile_kernel.cpp => compile_module.cpp} (70%)

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index e3da6a898b..c9fe0889c5 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -35,6 +35,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/Logger.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManagerBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ModuleInterface.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.cpp
@@ -42,7 +43,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateTypename.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cblas.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/compile_kernel.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/compile_module.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/complex.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/constants.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/defines.hpp
diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
index d2faa83b7d..5027255c4a 100644
--- a/src/backend/common/KernelInterface.hpp
+++ b/src/backend/common/KernelInterface.hpp
@@ -19,38 +19,34 @@ template<typename ModuleType, typename KernelType, typename EnqueuerType,
          typename DevPtrType>
 class KernelInterface {
    private:
-    ModuleType mProgram;
-    KernelType mKernel;
+    ModuleType mModuleHandle;
+    KernelType mKernelHandle;
 
    public:
     KernelInterface(ModuleType mod, KernelType ker)
-        : mProgram(mod), mKernel(ker) {}
+        : mModuleHandle(mod), mKernelHandle(ker) {}
 
-    /// \brief Set module and kernel
+    /// \brief Set kernel
     ///
-    /// \param[in] mod is backend specific module handle
     /// \param[in] ker is backend specific kernel handle
-    void set(ModuleType mod, KernelType ker) {
-        mProgram = mod;
-        mKernel  = ker;
-    }
-
-    /// \brief Get module
-    ///
-    /// \returns handle to backend specific module
-    inline ModuleType getModule() { return mProgram; }
+    inline void set(KernelType ker) { mKernelHandle = ker; }
 
     /// \brief Get kernel
     ///
     /// \returns handle to backend specific kernel
-    inline KernelType getKernel() { return mKernel; }
+    inline KernelType get() const { return mKernelHandle; }
+
+    /// \brief Get module
+    ///
+    /// \returns handle to backend specific module
+    inline ModuleType getModuleHandle() { return mModuleHandle; }
 
     /// \brief Get device pointer associated with name(label)
     ///
     /// This function is only useful with CUDA NVRTC based compilation
     /// at the moment, calling this function for OpenCL backend build
     /// will return a null pointer.
-    virtual DevPtrType get(const char* name) = 0;
+    virtual DevPtrType getDevPtr(const char* name) = 0;
 
     /// \brief Copy data from device memory to read-only memory
     ///
@@ -94,7 +90,7 @@ class KernelInterface {
     template<typename EnqueueArgsType, typename... Args>
     void operator()(const EnqueueArgsType& qArgs, Args... args) {
         EnqueuerType launch;
-        launch(mKernel, qArgs, std::forward<Args>(args)...);
+        launch(mKernelHandle, qArgs, std::forward<Args>(args)...);
     }
 };
 
diff --git a/src/backend/common/ModuleInterface.hpp b/src/backend/common/ModuleInterface.hpp
new file mode 100644
index 0000000000..0147176277
--- /dev/null
+++ b/src/backend/common/ModuleInterface.hpp
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace common {
+
+/// Instances of this object are stored in jit kernel cache
+template<typename ModuleType>
+class ModuleInterface {
+   private:
+    ModuleType mModuleHandle;
+
+   public:
+    ModuleInterface(ModuleType mod) : mModuleHandle(mod) {}
+
+    /// \brief Set module
+    ///
+    /// \param[in] mod is backend specific module handle
+    inline void set(ModuleType mod) { mModuleHandle = mod; }
+
+    /// \brief Get module
+    ///
+    /// \returns handle to backend specific module
+    inline ModuleType get() const { return mModuleHandle; }
+};
+
+}  // namespace common
diff --git a/src/backend/common/compile_kernel.hpp b/src/backend/common/compile_kernel.hpp
deleted file mode 100644
index 84f7570b45..0000000000
--- a/src/backend/common/compile_kernel.hpp
+++ /dev/null
@@ -1,51 +0,0 @@
-/*******************************************************
- * Copyright (c) 2020, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#if !defined(AF_CPU)
-
-#include <Kernel.hpp>
-#include <backend.hpp>
-
-#include <string>
-#include <vector>
-
-namespace common {
-
-/// \brief Backend specific kernel compilation implementation
-///
-/// This function has to be implemented separately in each backend
-detail::Kernel compileKernel(const std::string& kernelName,
-                             const std::string& templateInstance,
-                             const std::vector<std::string>& sources,
-                             const std::vector<std::string>& compileOpts,
-                             const bool isJIT = false);
-
-/// \brief Load kernel from disk cache
-///
-/// Note that, this is for internal use by functions that get called from
-/// compileKernel. The reason it is exposed here is that, it's implementation
-/// is partly dependent on backend specifics like program binary loading etc.
-///
-/// \p kernelNameExpr can take following values depending on backend
-/// -  namespace qualified kernel template instantiation for CUDA
-/// -  simple kernel name for OpenCL
-/// -  encoded string with KER prefix for JIT
-///
-/// \param[in] device is the device index
-/// \param[in] kernelNameExpr is the name identifying the relevant kernel
-/// \param[in] sources is the list of kernel and helper source files
-detail::Kernel loadKernelFromDisk(const int device,
-                                  const std::string& kernelNameExpr,
-                                  const std::vector<std::string>& sources);
-
-}  // namespace common
-
-#endif
diff --git a/src/backend/common/compile_module.hpp b/src/backend/common/compile_module.hpp
new file mode 100644
index 0000000000..dcf3985f7c
--- /dev/null
+++ b/src/backend/common/compile_module.hpp
@@ -0,0 +1,65 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#if !defined(AF_CPU)
+
+#include <Module.hpp>
+#include <backend.hpp>
+
+#include <string>
+#include <vector>
+
+namespace common {
+
+/// \brief Backend specific source compilation implementation
+///
+/// This function has to be implemented separately in each backend
+///
+/// \p kInstances can take of the following two forms depending on backend.
+/// - CUDA
+///     - A template instantiation style string like transpose<float, true, 1>
+///     - The \p kInstances is of size one in almost all cases. These strings
+///       are used to generate template instantiations of CUDA kernels while
+///       compiling the \p sources.
+/// - OpenCL
+///     - The \p kInstances parameter is not used.
+///
+/// \param[in] moduleKey is hash of code+options+instantiations. This is
+///            provided by caller to avoid recomputation.
+/// \param[in] sources is the list of source code to compile
+/// \param[in] options is the list of preprocessor definitions to be passed
+///            to the backend compilation function
+/// \param[in] kInstances is the name list of kernels in the \p sources
+/// \param[in] isJIT is identify if the module being compiled is not
+///            hand-written kernel
+///
+/// \returns Backend specific binary module that contains associated kernel
+detail::Module compileModule(const std::string& moduleKey,
+                             const std::vector<std::string>& sources,
+                             const std::vector<std::string>& options,
+                             const std::vector<std::string>& kInstances,
+                             const bool isJIT);
+
+/// \brief Load module binary from disk cache
+///
+/// Note that, this is for internal use by functions that get called from
+/// compileModule. The reason it is exposed here is that, it's implementation
+/// is partly dependent on backend specifics like program binary loading etc.
+/// Exposing this enables each backend to implement it's specifics.
+///
+/// \param[in] device is the device index
+/// \param[in] moduleKey is hash of code+options+instantiations
+detail::Module loadModuleFromDisk(const int device,
+                                  const std::string& moduleKey);
+
+}  // namespace common
+
+#endif
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index e4801feb9e..52bb0bc6c9 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -11,62 +11,44 @@
 
 #include <common/kernel_cache.hpp>
 
-#include <common/compile_kernel.hpp>
+#include <common/compile_module.hpp>
+#include <common/util.hpp>
 #include <device_manager.hpp>
 #include <platform.hpp>
 
 #include <algorithm>
-#include <map>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 using detail::Kernel;
+using detail::Module;
 
 using std::back_inserter;
-using std::map;
 using std::string;
 using std::transform;
+using std::unordered_map;
 using std::vector;
 
 namespace common {
 
-using KernelMap = map<string, Kernel>;
+using ModuleMap = unordered_map<string, Module>;
 
-KernelMap& getCache(const int device) {
-    thread_local KernelMap caches[detail::DeviceManager::MAX_DEVICES];
+ModuleMap& getCache(const int device) {
+    thread_local ModuleMap caches[detail::DeviceManager::MAX_DEVICES];
     return caches[device];
 }
 
-void cacheKernel(const int device, const string& nameExpr, const Kernel entry) {
-    getCache(device).emplace(nameExpr, entry);
-}
-
-Kernel lookupKernel(const int device, const string& nameExpr,
-                    const vector<string>& sources) {
+Module findModule(const int device, const string& key) {
     auto& cache = getCache(device);
-    auto iter   = cache.find(nameExpr);
-
-    if (iter != cache.end()) return iter->second;
-
-    if (sources.size() > 0) {
-#if defined(AF_CUDA) && defined(AF_CACHE_KERNELS_TO_DISK)
-        Kernel kernel = loadKernelFromDisk(device, nameExpr, sources);
-        if (kernel.getModule() != nullptr && kernel.getKernel() != nullptr) {
-            cacheKernel(device, nameExpr, kernel);
-            return kernel;
-        }
-#endif
-    }
-    return Kernel{nullptr, nullptr};
+    auto iter   = cache.find(key);
+    if (iter != cache.end()) { return iter->second; }
+    return Module{nullptr};
 }
 
-Kernel lookupKernel(const int device, const string& key) {
-    return lookupKernel(device, key, {});
-}
-
-Kernel findKernel(const string& kernelName, const vector<string>& sources,
-                  const vector<TemplateArg>& targs,
-                  const vector<string>& compileOpts, const bool isKernelJIT) {
+Kernel getKernel(const string& kernelName, const vector<string>& sources,
+                 const vector<TemplateArg>& targs,
+                 const vector<string>& options, const bool sourceIsJIT) {
     vector<string> args;
     args.reserve(targs.size());
 
@@ -82,15 +64,36 @@ Kernel findKernel(const string& kernelName, const vector<string>& sources,
         tInstance += ">";
     }
 
-    int device    = detail::getActiveDeviceId();
-    Kernel kernel = lookupKernel(device, tInstance, sources);
+    const bool notJIT = !sourceIsJIT;
+
+    vector<string> hashingVals;
+    hashingVals.reserve(1 + (notJIT * (sources.size() + options.size())));
+    hashingVals.push_back(tInstance);
+    if (notJIT) {
+        // This code path is only used for regular kernel compilation
+        // since, jit funcName(kernelName) is unique to use it's hash
+        // for caching the relevant compiled/linked module
+        hashingVals.insert(hashingVals.end(), sources.begin(), sources.end());
+        hashingVals.insert(hashingVals.end(), options.begin(), options.end());
+    }
+
+    const string moduleKey = std::to_string(deterministicHash(hashingVals));
+    const int device       = detail::getActiveDeviceId();
+    Module currModule      = findModule(device, moduleKey);
 
-    if (kernel.getModule() == nullptr || kernel.getKernel() == nullptr) {
-        kernel = compileKernel(kernelName, tInstance, sources, compileOpts,
-                               isKernelJIT);
-        cacheKernel(device, tInstance, kernel);
+    if (currModule.get() == nullptr) {
+        currModule = loadModuleFromDisk(device, moduleKey);
+        if (currModule.get() == nullptr) {
+            currModule = compileModule(moduleKey, sources, options, {tInstance},
+                                       sourceIsJIT);
+        }
+        getCache(device).emplace(moduleKey, currModule);
     }
-    return kernel;
+#if defined(AF_CUDA)
+    return getKernel(currModule, tInstance, sourceIsJIT);
+#elif defined(AF_OPENCL)
+    return getKernel(currModule, kernelName, sourceIsJIT);
+#endif
 }
 
 }  // namespace common
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index 78d78816b3..3ac04081a1 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -12,6 +12,7 @@
 #if !defined(AF_CPU)
 
 #include <Kernel.hpp>
+#include <Module.hpp>
 #include <backend.hpp>
 #include <common/TemplateTypename.hpp>
 
@@ -36,7 +37,7 @@ namespace common {
 /// key to kernel cache map. At some point in future, the idea is to use these
 /// instantiation strings to generate template instatiations in online compiler.
 ///
-/// The paramter \p compileOpts is a list of strings that lets you add
+/// The paramter \p options is a list of strings that lets you add
 /// definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` to the compiler. To
 /// enable easy stringification of variables into their definition equation,
 /// three helper macros are provided: TemplateArg, DefineKey and DefineValue.
@@ -64,27 +65,40 @@ namespace common {
 /// \param[in] sources is the list of source strings to be compiled if required
 /// \param[in] templateArgs is a vector of strings containing stringified names
 ///            of the template arguments of kernel to be compiled.
-/// \param[in] compileOpts is a vector of strings that enables the user to
+/// \param[in] options is a vector of strings that enables the user to
 ///            add definitions such as `-D<NAME>` or `-D<NAME>=<VALUE>` for
 ///            the kernel compilation.
 ///
-detail::Kernel findKernel(const std::string& kernelName,
-                          const std::vector<std::string>& sources,
-                          const std::vector<TemplateArg>& templateArgs,
-                          const std::vector<std::string>& compileOpts = {},
-                          const bool isKernelJIT                      = false);
+detail::Kernel getKernel(const std::string& kernelName,
+                         const std::vector<std::string>& sources,
+                         const std::vector<TemplateArg>& templateArgs,
+                         const std::vector<std::string>& options = {},
+                         const bool sourceIsJIT                  = false);
 
-/// \brief Lookup a Kernel that matches the given key
+/// \brief Lookup a Module that matches the given key
 ///
 /// This function is intended to be used by JIT only. Usage in other
-/// places will most likely result in Kernel{nullptr, nullptr}. If by
+/// places will most likely result in Module{nullptr}. If by
 /// chance you do get a match for non-jit usage, it is accidental and
-/// such kernel will not work as expected.
+/// such Module will not work as expected.
 ///
 /// \param[in] device is index of device in given backend for which
-///            the kernel look up has to be done
-/// \param[in] key is kernel name generated by JIT getFuncName function
-detail::Kernel lookupKernel(const int device, const std::string& key);
+///            the module look up has to be done
+/// \param[in] key is hash generated from code + options + kernel_name
+///            at caller scope
+detail::Module findModule(const int device, const std::string& key);
+
+/// \brief Get Kernel object for given name from given Module
+///
+/// This function is intended to be used by JIT and compileKernel only.
+/// Usage in other places may have undefined behaviour.
+///
+/// \param[in] mod is cache entry from module map.
+/// \param[in] name is actual kernel name or it's template instantiation
+/// \param[in] sourceWasJIT is used to fetch mangled name for given module
+///            associated with \p name
+detail::Kernel getKernel(const detail::Module& mod, const std::string& name,
+                         const bool sourceWasJIT);
 
 }  // namespace common
 
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index cdf48e31d1..125ff535ef 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -30,6 +30,7 @@
 #include <thread>
 #include <vector>
 
+using std::accumulate;
 using std::string;
 using std::vector;
 
@@ -214,3 +215,8 @@ std::size_t deterministicHash(const void* data, std::size_t byteSize) {
 std::size_t deterministicHash(const std::string& data) {
     return deterministicHash(data.data(), data.size());
 }
+
+std::size_t deterministicHash(const vector<string>& list) {
+    string accumStr = accumulate(list.begin(), list.end(), string(""));
+    return deterministicHash(accumStr.data(), accumStr.size());
+}
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 369a0c4bb4..9d49f8524f 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -12,6 +12,7 @@
 
 #include <iosfwd>
 #include <string>
+#include <vector>
 
 std::string getEnvVar(const std::string& key);
 
@@ -51,3 +52,6 @@ std::size_t deterministicHash(const void* data, std::size_t byteSize);
 
 // This is just a wrapper around the above function.
 std::size_t deterministicHash(const std::string& data);
+
+// This concatenates strings in the vector and computes hash
+std::size_t deterministicHash(const std::vector<std::string>& list);
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 576e4b3582..9773257c2b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -451,6 +451,7 @@ cuda_add_library(afcuda
     Kernel.cpp
     Kernel.hpp
     LookupTable1D.hpp
+    Module.hpp
     Param.hpp
     ThrustAllocator.cuh
     ThrustArrayFirePolicy.hpp
@@ -469,7 +470,7 @@ cuda_add_library(afcuda
     cholesky.cpp
     cholesky.hpp
     complex.hpp
-    compile_kernel.cpp
+    compile_module.cpp
     convolve.cpp
     convolve.hpp
     convolveNN.cpp
diff --git a/src/backend/cuda/Kernel.cpp b/src/backend/cuda/Kernel.cpp
index e1ffe672e0..eb0dc63e4b 100644
--- a/src/backend/cuda/Kernel.cpp
+++ b/src/backend/cuda/Kernel.cpp
@@ -13,10 +13,10 @@
 
 namespace cuda {
 
-Kernel::DevPtrType Kernel::get(const char *name) {
+Kernel::DevPtrType Kernel::getDevPtr(const char *name) {
     Kernel::DevPtrType out = 0;
     size_t size            = 0;
-    CU_CHECK(cuModuleGetGlobal(&out, &size, this->getModule(), name));
+    CU_CHECK(cuModuleGetGlobal(&out, &size, this->getModuleHandle(), name));
     return out;
 }
 
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index accdf6b014..c0e7fb310f 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -61,13 +61,13 @@ class Kernel
     Kernel() : BaseClass(nullptr, nullptr) {}
     Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
 
-    DevPtrType get(const char* name) override;
+    DevPtrType getDevPtr(const char* name) final;
 
-    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) override;
+    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) final;
 
-    void setScalar(DevPtrType dst, int value) override;
+    void setScalar(DevPtrType dst, int value) final;
 
-    int getScalar(DevPtrType src) override;
+    int getScalar(DevPtrType src) final;
 };
 
 }  // namespace cuda
diff --git a/src/backend/cuda/Module.hpp b/src/backend/cuda/Module.hpp
new file mode 100644
index 0000000000..cb6e16591d
--- /dev/null
+++ b/src/backend/cuda/Module.hpp
@@ -0,0 +1,50 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/ModuleInterface.hpp>
+
+#include <cuda.h>
+
+#include <string>
+#include <unordered_map>
+
+namespace cuda {
+
+/// CUDA backend wrapper for CUmodule
+class Module : public common::ModuleInterface<CUmodule> {
+   private:
+    std::unordered_map<std::string, std::string> mInstanceMangledNames;
+
+   public:
+    using ModuleType = CUmodule;
+    using BaseClass  = common::ModuleInterface<ModuleType>;
+
+    Module(ModuleType mod) : BaseClass(mod) {
+        mInstanceMangledNames.reserve(1);
+    }
+
+    const std::string mangledName(const std::string& instantiation) const {
+        auto iter = mInstanceMangledNames.find(instantiation);
+        if (iter != mInstanceMangledNames.end()) {
+            return iter->second;
+        } else {
+            return std::string("");
+        }
+    }
+
+    void add(const std::string& instantiation, const std::string& mangledName) {
+        mInstanceMangledNames.emplace(instantiation, mangledName);
+    }
+
+    const auto& map() const { return mInstanceMangledNames; }
+};
+
+}  // namespace cuda
diff --git a/src/backend/cuda/compile_kernel.cpp b/src/backend/cuda/compile_module.cpp
similarity index 76%
rename from src/backend/cuda/compile_kernel.cpp
rename to src/backend/cuda/compile_module.cpp
index 04e8796679..455044d259 100644
--- a/src/backend/cuda/compile_kernel.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -7,9 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/compile_kernel.hpp>
+#include <common/compile_module.hpp>  //compileModule & loadModuleFromDisk
+#include <common/kernel_cache.hpp>    //getKernel(Module&, ...)
 
-#include <Kernel.hpp>
+#include <Module.hpp>
 #include <common/Logger.hpp>
 #include <common/internal_enums.hpp>
 #include <common/util.hpp>
@@ -59,7 +60,7 @@
 
 using namespace cuda;
 
-using detail::Kernel;
+using detail::Module;
 using std::accumulate;
 using std::array;
 using std::back_insert_iterator;
@@ -127,38 +128,30 @@ spdlog::logger *getLogger() {
     return logger.get();
 }
 
-string getKernelCacheFilename(const int device, const string &nameExpr,
-                              const vector<string> &sources) {
-    const string srcs =
-        accumulate(sources.begin(), sources.end(), std::string(""));
-    const string mangledName =
-        "KER" + to_string(deterministicHash(nameExpr + srcs));
-
+string getKernelCacheFilename(const int device, const string &key) {
     const auto computeFlag = getComputeCapability(device);
     const string computeVersion =
         to_string(computeFlag.first) + to_string(computeFlag.second);
 
-    return mangledName + "_CU_" + computeVersion + "_AF_" +
+    return "KER" + key + "_CU_" + computeVersion + "_AF_" +
            to_string(AF_API_VERSION_CURRENT) + ".cubin";
 }
 
 namespace common {
 
-Kernel compileKernel(const string &kernelName, const string &nameExpr,
-                     const vector<string> &sources, const vector<string> &opts,
-                     const bool isJIT) {
-    auto &jit_ker        = sources[0];
-    const char *ker_name = nameExpr.c_str();
-
+Module compileModule(const string &moduleKey, const vector<string> &sources,
+                     const vector<string> &opts,
+                     const vector<string> &kInstances, const bool sourceIsJIT) {
     nvrtcProgram prog;
-    if (isJIT) {
+    if (sourceIsJIT) {
         array<const char *, 2> headers = {
             cuda_fp16_hpp,
             cuda_fp16_h,
         };
         array<const char *, 2> header_names = {"cuda_fp16.hpp", "cuda_fp16.h"};
-        NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name, 2,
-                                       headers.data(), header_names.data()));
+        NVRTC_CHECK(nvrtcCreateProgram(&prog, sources[0].c_str(),
+                                       moduleKey.c_str(), 2, headers.data(),
+                                       header_names.data()));
     } else {
         constexpr static const char *includeNames[] = {
             "math.h",          // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
@@ -241,8 +234,9 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
         };
         static_assert(extent<decltype(headers)>::value == NumHeaders,
                       "headers array contains fewer sources than includeNames");
-        NVRTC_CHECK(nvrtcCreateProgram(&prog, jit_ker.c_str(), ker_name,
-                                       NumHeaders, headers, includeNames));
+        NVRTC_CHECK(nvrtcCreateProgram(&prog, sources[0].c_str(),
+                                       moduleKey.c_str(), NumHeaders, headers,
+                                       includeNames));
     }
 
     int device       = cuda::getActiveDeviceId();
@@ -258,13 +252,15 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
         "--generate-line-info"
 #endif
     };
-    if (!isJIT) {
+    if (!sourceIsJIT) {
         transform(begin(opts), end(opts),
                   back_insert_iterator<vector<const char *>>(compiler_options),
                   [](const string &s) { return s.data(); });
 
         compiler_options.push_back("--device-as-default-execution-space");
-        NVRTC_CHECK(nvrtcAddNameExpression(prog, ker_name));
+        for (auto &instantiation : kInstances) {
+            NVRTC_CHECK(nvrtcAddNameExpression(prog, instantiation.c_str()));
+        }
     }
 
     auto compile = high_resolution_clock::now();
@@ -294,41 +290,54 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
     auto link = high_resolution_clock::now();
     CU_LINK_CHECK(cuLinkCreate(5, linkOptions, linkOptionValues, &linkState));
     CU_LINK_CHECK(cuLinkAddData(linkState, CU_JIT_INPUT_PTX, (void *)ptx.data(),
-                                ptx.size(), ker_name, 0, NULL, NULL));
+                                ptx.size(), moduleKey.c_str(), 0, NULL, NULL));
 
     void *cubin = nullptr;
     size_t cubinSize;
 
-    CUmodule module;
-    CUfunction kernel;
+    CUmodule modOut = nullptr;
     CU_LINK_CHECK(cuLinkComplete(linkState, &cubin, &cubinSize));
-    CU_CHECK(cuModuleLoadDataEx(&module, cubin, 0, 0, 0));
+    CU_CHECK(cuModuleLoadData(&modOut, cubin));
     auto link_end = high_resolution_clock::now();
 
-    const char *name = ker_name;
-    if (!isJIT) { NVRTC_CHECK(nvrtcGetLoweredName(prog, ker_name, &name)); }
-
-    CU_CHECK(cuModuleGetFunction(&kernel, module, name));
-    Kernel entry = {module, kernel};
+    Module retVal(modOut);
+    if (!sourceIsJIT) {
+        for (auto &instantiation : kInstances) {
+            // memory allocated & destroyed by nvrtcProgram for below var
+            const char *name = nullptr;
+            NVRTC_CHECK(
+                nvrtcGetLoweredName(prog, instantiation.c_str(), &name));
+            retVal.add(instantiation, string(name, strlen(name)));
+        }
+    }
 
 #ifdef AF_CACHE_KERNELS_TO_DISK
     // save kernel in cache
     const string &cacheDirectory = getCacheDirectory();
     if (!cacheDirectory.empty()) {
-        const string cacheFile =
-            cacheDirectory + AF_PATH_SEPARATOR +
-            getKernelCacheFilename(device, nameExpr, sources);
+        const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
+                                 getKernelCacheFilename(device, moduleKey);
         const string tempFile =
             cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
 
         // compute CUBIN hash
         const size_t cubinHash = deterministicHash(cubin, cubinSize);
 
-        // write kernel function name and CUBIN binary data
+        // write module hash(everything: names, code & options) and CUBIN data
         ofstream out(tempFile, std::ios::binary);
-        const size_t nameSize = strlen(name);
-        out.write(reinterpret_cast<const char *>(&nameSize), sizeof(nameSize));
-        out.write(name, nameSize);
+        size_t mangledNamesListSize = retVal.map().size();
+        out.write(reinterpret_cast<const char *>(&cubinHash),
+                  sizeof(mangledNamesListSize));
+        for (auto &iter : retVal.map()) {
+            size_t kySize   = iter.first.size();
+            size_t vlSize   = iter.second.size();
+            const char *key = iter.first.c_str();
+            const char *val = iter.second.c_str();
+            out.write(reinterpret_cast<const char *>(&kySize), sizeof(kySize));
+            out.write(key, iter.first.size());
+            out.write(reinterpret_cast<const char *>(&vlSize), sizeof(vlSize));
+            out.write(val, iter.second.size());
+        }
         out.write(reinterpret_cast<const char *>(&cubinHash),
                   sizeof(cubinHash));
         out.write(reinterpret_cast<const char *>(&cubinSize),
@@ -354,37 +363,48 @@ Kernel compileKernel(const string &kernelName, const string &nameExpr,
                               return lhs + ", " + rhs;
                           });
     };
-
     AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}}}",
-             nameExpr,
+             sources[0],
              duration_cast<milliseconds>(compile_end - compile).count(),
              duration_cast<milliseconds>(link_end - link).count(),
              listOpts(compiler_options), getDeviceProp(device).name);
-    return entry;
+    return retVal;
 }
 
-Kernel loadKernelFromDisk(const int device, const string &nameExpr,
-                          const vector<string> &sources) {
+Module loadModuleFromDisk(const int device, const string &moduleKey) {
     const string &cacheDirectory = getCacheDirectory();
-    if (cacheDirectory.empty()) return Kernel{nullptr, nullptr};
+    if (cacheDirectory.empty()) return Module{nullptr};
 
     const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
-                             getKernelCacheFilename(device, nameExpr, sources);
-
-    CUmodule module   = nullptr;
-    CUfunction kernel = nullptr;
+                             getKernelCacheFilename(device, moduleKey);
 
+    CUmodule modOut = nullptr;
+    Module retVal{nullptr};
     try {
         std::ifstream in(cacheFile, std::ios::binary);
-        if (!in.is_open()) return Kernel{nullptr, nullptr};
+        if (!in.is_open()) return Module{nullptr};
 
         in.exceptions(std::ios::failbit | std::ios::badbit);
 
-        size_t nameSize = 0;
-        in.read(reinterpret_cast<char *>(&nameSize), sizeof(nameSize));
-        string name;
-        name.resize(nameSize);
-        in.read(&name[0], nameSize);
+        size_t mangledListSize = 0;
+        in.read(reinterpret_cast<char *>(&mangledListSize),
+                sizeof(mangledListSize));
+        for (size_t i = 0; i < mangledListSize; ++i) {
+            size_t keySize = 0;
+            in.read(reinterpret_cast<char *>(&keySize), sizeof(keySize));
+            vector<char> key;
+            key.reserve(keySize);
+            in.read(key.data(), keySize);
+
+            size_t itemSize = 0;
+            in.read(reinterpret_cast<char *>(&itemSize), sizeof(itemSize));
+            vector<char> item;
+            item.reserve(itemSize);
+            in.read(item.data(), itemSize);
+
+            retVal.add(string(key.data(), keySize),
+                       string(item.data(), itemSize));
+        }
 
         size_t cubinHash = 0;
         in.read(reinterpret_cast<char *>(&cubinHash), sizeof(cubinHash));
@@ -398,21 +418,28 @@ Kernel loadKernelFromDisk(const int device, const string &nameExpr,
         const size_t recomputedHash =
             deterministicHash(cubin.data(), cubinSize);
         if (recomputedHash != cubinHash) {
-            AF_ERROR("cached kernel data is corrupted", AF_ERR_LOAD_SYM);
+            AF_ERROR("Module on disk seems to be corrupted", AF_ERR_LOAD_SYM);
         }
 
-        CU_CHECK(cuModuleLoadDataEx(&module, cubin.data(), 0, 0, 0));
-        CU_CHECK(cuModuleGetFunction(&kernel, module, name.c_str()));
+        CU_CHECK(cuModuleLoadData(&modOut, cubin.data()));
 
-        AF_TRACE("{{{:<30} : loaded from {} for {} }}", nameExpr, cacheFile,
+        AF_TRACE("{{{:<30} : loaded from {} for {} }}", moduleKey, cacheFile,
                  getDeviceProp(device).name);
 
-        return Kernel{module, kernel};
+        retVal.set(modOut);
     } catch (...) {
-        if (module != nullptr) { CU_CHECK(cuModuleUnload(module)); }
+        if (modOut != nullptr) { CU_CHECK(cuModuleUnload(modOut)); }
         removeFile(cacheFile);
-        return Kernel{nullptr, nullptr};
     }
+    return retVal;
+}
+
+Kernel getKernel(const Module &mod, const string &nameExpr,
+                 const bool sourceWasJIT) {
+    std::string name  = (sourceWasJIT ? nameExpr : mod.mangledName(nameExpr));
+    CUfunction kernel = nullptr;
+    CU_CHECK(cuModuleGetFunction(&kernel, mod.get(), name.c_str()));
+    return {mod.get(), kernel};
 }
 
 }  // namespace common
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 3854ba7862..0298e6fdfa 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -25,9 +25,11 @@
 
 #include <cstdio>
 #include <stdexcept>
+#include <string>
 #include <thread>
 #include <vector>
 
+using common::findModule;
 using common::getFuncName;
 using common::half;
 using common::Node;
@@ -36,6 +38,7 @@ using common::Node_map_t;
 
 using std::string;
 using std::stringstream;
+using std::to_string;
 using std::vector;
 
 namespace cuda {
@@ -177,19 +180,23 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const vector<Node *> &full_nodes,
                             const vector<Node_ids> &full_ids,
                             const bool is_linear) {
-    string funcName =
+    const string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
+    const string moduleKey = to_string(deterministicHash(funcName));
 
-    auto entry = common::lookupKernel(getActiveDeviceId(), funcName);
+    // A forward lookup in module cache helps avoid recompiling the jit
+    // source generated from identical jit-trees. It also enables us
+    // with a way to save jit kernels to disk only once
+    auto entry = findModule(getActiveDeviceId(), moduleKey);
 
-    if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
-        string jitKer = getKernelString(funcName, full_nodes, full_ids,
-                                        output_ids, is_linear);
+    if (entry.get() == nullptr) {
+        const string jitKer = getKernelString(funcName, full_nodes, full_ids,
+                                              output_ids, is_linear);
         saveKernel(funcName, jitKer, ".cu");
 
-        entry = common::findKernel(funcName, {jitKer}, {}, {}, true);
+        return common::getKernel(funcName, {jitKer}, {}, {}, true).get();
     }
-    return entry.getKernel();
+    return common::getKernel(entry, funcName, true).get();
 }
 
 template<typename T>
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index 1d14248306..c8b7e06bbb 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -30,7 +30,7 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
     static const std::string source(anisotropic_diffusion_cuh,
                                     anisotropic_diffusion_cuh_len);
-    auto diffUpdate = common::findKernel(
+    auto diffUpdate = common::getKernel(
         "cuda::diffUpdate", {source},
         {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
         {DefineValue(THREADS_X), DefineValue(THREADS_Y),
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index c0525f12d3..46057e6d3c 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -31,7 +31,7 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const af::interpType method, const int order) {
     static const std::string source(approx1_cuh, approx1_cuh_len);
 
-    auto approx1 = common::findKernel(
+    auto approx1 = common::getKernel(
         "cuda::approx1", {source},
         {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
 
@@ -61,7 +61,7 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const af::interpType method, const int order) {
     static const std::string source(approx2_cuh, approx2_cuh_len);
 
-    auto approx2 = common::findKernel(
+    auto approx2 = common::getKernel(
         "cuda::approx2", {source},
         {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
 
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 841ad6fef7..9de3cdbfe2 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -27,7 +27,7 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
     static const std::string src(assign_cuh, assign_cuh_len);
 
     auto assignKer =
-        common::findKernel("cuda::assign", {src}, {TemplateTypename<T>()});
+        common::getKernel("cuda::assign", {src}, {TemplateTypename<T>()});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index a7bc4553d0..0f1995c87c 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -26,7 +26,7 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
     static const std::string source(bilateral_cuh, bilateral_cuh_len);
 
-    auto bilateral = common::findKernel(
+    auto bilateral = common::getKernel(
         "cuda::bilateral", {source},
         {TemplateTypename<inType>(), TemplateTypename<outType>()},
         {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 1634104258..ab3e838314 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -30,7 +30,7 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
     static const std::string source(canny_cuh, canny_cuh_len);
 
-    auto nonMaxSuppress = common::findKernel(
+    auto nonMaxSuppress = common::getKernel(
         "cuda::nonMaxSuppression", {source}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
@@ -53,15 +53,15 @@ template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     static const std::string source(canny_cuh, canny_cuh_len);
 
-    auto initEdgeOut = common::findKernel(
+    auto initEdgeOut = common::getKernel(
         "cuda::initEdgeOut", {source}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto edgeTrack = common::findKernel(
+    auto edgeTrack = common::getKernel(
         "cuda::edgeTrack", {source}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto suppressLeftOver = common::findKernel(
+    auto suppressLeftOver = common::getKernel(
         "cuda::suppressLeftOver", {source}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
@@ -79,7 +79,7 @@ void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     initEdgeOut(qArgs, output, strong, weak, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 
-    auto flagPtr = edgeTrack.get("hasChanged");
+    auto flagPtr = edgeTrack.getDevPtr("hasChanged");
 
     int notFinished = 1;
     while (notFinished) {
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 7b0158f861..b2829b3af8 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -106,7 +106,7 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     static const std::string src(convolve1_cuh, convolve1_cuh_len);
 
-    auto convolve1 = common::findKernel(
+    auto convolve1 = common::getKernel(
         "cuda::convolve1", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS)});
@@ -126,7 +126,7 @@ void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                 const aT* fptr = filt.ptr + (f1Off + f2Off + f3Off);
 
                 // FIXME: case where filter array is strided
-                auto constMemPtr = convolve1.get(conv_c_name);
+                auto constMemPtr = convolve1.getDevPtr(conv_c_name);
                 convolve1.copyToReadOnly(constMemPtr,
                                          reinterpret_cast<CUdeviceptr>(fptr),
                                          filterSize);
@@ -163,7 +163,7 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
 
     static const std::string src(convolve2_cuh, convolve2_cuh_len);
 
-    auto convolve2 = common::findKernel(
+    auto convolve2 = common::getKernel(
         "cuda::convolve2", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand),
          TemplateArg(f0), TemplateArg(f1)},
@@ -171,7 +171,7 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
          DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
 
     // FIXME: case where filter array is strided
-    auto constMemPtr = convolve2.get(conv_c_name);
+    auto constMemPtr = convolve2.getDevPtr(conv_c_name);
     convolve2.copyToReadOnly(constMemPtr, reinterpret_cast<CUdeviceptr>(fptr),
                              f0 * f1 * sizeof(aT));
 
@@ -210,7 +210,7 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     static const std::string src(convolve3_cuh, convolve3_cuh_len);
 
-    auto convolve3 = common::findKernel(
+    auto convolve3 = common::getKernel(
         "cuda::convolve3", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
@@ -227,7 +227,7 @@ void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
         const aT* fptr = filt.ptr + f3Off;
 
         // FIXME: case where filter array is strided
-        auto constMemPtr = convolve3.get(conv_c_name);
+        auto constMemPtr = convolve3.getDevPtr(conv_c_name);
         convolve3.copyToReadOnly(
             constMemPtr, reinterpret_cast<CUdeviceptr>(fptr), filterSize);
 
@@ -316,7 +316,7 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
 
     static const std::string src(convolve_separable_cuh,
                                  convolve_separable_cuh_len);
-    auto convolve2_separable = common::findKernel(
+    auto convolve2_separable = common::getKernel(
         "cuda::convolve2_separable", {src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(conv_dim),
          TemplateArg(expand), TemplateArg(fLen)},
@@ -331,7 +331,7 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     dim3 blocks(blk_x * signal.dims[2], blk_y * signal.dims[3]);
 
     // FIXME: case where filter array is strided
-    auto constMemPtr = convolve2_separable.get(sconv_c_name);
+    auto constMemPtr = convolve2_separable.getDevPtr(sconv_c_name);
     convolve2_separable.copyToReadOnly(
         constMemPtr, reinterpret_cast<CUdeviceptr>(filter.ptr),
         fLen * sizeof(aT));
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index 124f990027..d356b5d1bb 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -24,8 +24,8 @@ template<typename T>
 void diagCreate(Param<T> out, CParam<T> in, int num) {
     static const std::string src(diagonal_cuh, diagonal_cuh_len);
 
-    auto genDiagMat = common::findKernel("cuda::createDiagonalMat", {src},
-                                         {TemplateTypename<T>()});
+    auto genDiagMat = common::getKernel("cuda::createDiagonalMat", {src},
+                                        {TemplateTypename<T>()});
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
@@ -51,8 +51,8 @@ template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int num) {
     static const std::string src(diagonal_cuh, diagonal_cuh_len);
 
-    auto extractDiag = common::findKernel("cuda::extractDiagonal", {src},
-                                          {TemplateTypename<T>()});
+    auto extractDiag = common::getKernel("cuda::extractDiagonal", {src},
+                                         {TemplateTypename<T>()});
 
     dim3 threads(256, 1);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index 1a890a46f2..d8450a3085 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -28,7 +28,7 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
 
     static const std::string src(diff_cuh, diff_cuh_len);
 
-    auto diff = common::findKernel(
+    auto diff = common::getKernel(
         "cuda::diff", {src},
         {TemplateTypename<T>(), TemplateArg(dim), TemplateArg(isDiff2)});
 
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 1ee60f6fe7..9f6825f206 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -31,10 +31,10 @@ template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     static const std::string source(exampleFunction_cuh,
                                     exampleFunction_cuh_len);
-    auto exampleFunc = common::findKernel("cuda::exampleFunc", {source},
-                                          {
-                                              TemplateTypename<T>(),
-                                          });
+    auto exampleFunc = common::getKernel("cuda::exampleFunc", {source},
+                                         {
+                                             TemplateTypename<T>(),
+                                         });
 
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
@@ -48,7 +48,7 @@ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     // on your CUDA kernels needs such as shared memory etc.
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    // Call the kernel functor retrieved using common::findKernel
+    // Call the kernel functor retrieved using common::getKernel
     exampleFunc(qArgs, c, a, b, p);
 
     POST_LAUNCH_CHECK();  // Macro for post kernel launch checks
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 1c5194bea1..356ebb46bf 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -31,11 +31,11 @@ template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
     auto packData =
-        common::findKernel("cuda::packData", {fftConvSource()},
-                           {TemplateTypename<convT>(), TemplateTypename<T>()});
+        common::getKernel("cuda::packData", {fftConvSource()},
+                          {TemplateTypename<convT>(), TemplateTypename<T>()});
     auto padArray =
-        common::findKernel("cuda::padArray", {fftConvSource()},
-                           {TemplateTypename<convT>(), TemplateTypename<T>()});
+        common::getKernel("cuda::padArray", {fftConvSource()},
+                          {TemplateTypename<convT>(), TemplateTypename<T>()});
 
     dim_t *sd = sig.dims;
 
@@ -75,8 +75,8 @@ template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
     auto cplxMul =
-        common::findKernel("cuda::complexMultiply", {fftConvSource()},
-                           {TemplateTypename<convT>(), TemplateArg(kind)});
+        common::getKernel("cuda::complexMultiply", {fftConvSource()},
+                          {TemplateTypename<convT>(), TemplateArg(kind)});
 
     int sig_packed_elem    = 1;
     int filter_packed_elem = 1;
@@ -108,9 +108,9 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     constexpr bool RoundResult = std::is_integral<T>::value;
 
     auto reorderOut =
-        common::findKernel("cuda::reorderOutput", {fftConvSource()},
-                           {TemplateTypename<T>(), TemplateTypename<convT>(),
-                            TemplateArg(expand), TemplateArg(RoundResult)});
+        common::getKernel("cuda::reorderOutput", {fftConvSource()},
+                          {TemplateTypename<T>(), TemplateTypename<convT>(),
+                           TemplateArg(expand), TemplateArg(RoundResult)});
 
     dim_t *sd    = sig.dims;
     int fftScale = 1;
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index d68490dcfb..4967d570f4 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -49,13 +49,13 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds = common::findKernel("cuda::initSeeds", {source},
-                                        {TemplateTypename<T>()});
+    auto initSeeds =
+        common::getKernel("cuda::initSeeds", {source}, {TemplateTypename<T>()});
     auto floodStep =
-        common::findKernel("cuda::floodStep", {source}, {TemplateTypename<T>()},
-                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto finalizeOutput = common::findKernel("cuda::finalizeOutput", {source},
-                                             {TemplateTypename<T>()});
+        common::getKernel("cuda::floodStep", {source}, {TemplateTypename<T>()},
+                          {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto finalizeOutput = common::getKernel("cuda::finalizeOutput", {source},
+                                            {TemplateTypename<T>()});
 
     EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
                       getActiveStream());
@@ -67,7 +67,7 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
                 divup(image.dims[1], threads.y));
     EnqueueArgs fQArgs(blocks, threads, getActiveStream());
 
-    auto continueFlagPtr = floodStep.get("doAnotherLaunch");
+    auto continueFlagPtr = floodStep.getDevPtr("doAnotherLaunch");
 
     for (int doAnotherLaunch = 1; doAnotherLaunch > 0;) {
         doAnotherLaunch = 0;
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index 63324d385d..59bd37b6dd 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -28,8 +28,8 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     static const std::string source(gradient_cuh, gradient_cuh_len);
 
     auto gradient =
-        common::findKernel("cuda::gradient", {source}, {TemplateTypename<T>()},
-                           {DefineValue(TX), DefineValue(TY)});
+        common::getKernel("cuda::gradient", {source}, {TemplateTypename<T>()},
+                          {DefineValue(TX), DefineValue(TY)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 047ffc6124..76efb87597 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -28,10 +28,10 @@ void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
     static const std::string source(histogram_cuh, histogram_cuh_len);
 
     auto histogram =
-        common::findKernel("cuda::histogram", {source},
-                           {TemplateTypename<inType>(),
-                            TemplateTypename<outType>(), TemplateArg(isLinear)},
-                           {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
+        common::getKernel("cuda::histogram", {source},
+                          {TemplateTypename<inType>(),
+                           TemplateTypename<outType>(), TemplateArg(isLinear)},
+                          {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
     dim3 threads(kernel::THREADS_X, 1);
 
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index b902c4e5ac..a959853e6f 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -26,8 +26,8 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
     static const std::string source(hsv_rgb_cuh, hsv_rgb_cuh_len);
 
     auto hsvrgbConverter =
-        common::findKernel("cuda::hsvrgbConverter", {source},
-                           {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
+        common::getKernel("cuda::hsvrgbConverter", {source},
+                          {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 2c3b819a6a..2bcac932b1 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -25,7 +25,7 @@ void identity(Param<T> out) {
     static const std::string source(identity_cuh, identity_cuh_len);
 
     auto identity =
-        common::findKernel("cuda::identity", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::identity", {source}, {TemplateTypename<T>()});
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index da72beeb40..bfce16993a 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -26,9 +26,9 @@ void iir(Param<T> y, CParam<T> c, CParam<T> a) {
 
     static const std::string source(iir_cuh, iir_cuh_len);
 
-    auto iir = common::findKernel("cuda::iir", {source},
-                                  {TemplateTypename<T>(), TemplateArg(batch_a)},
-                                  {DefineValue(MAX_A_SIZE)});
+    auto iir = common::getKernel("cuda::iir", {source},
+                                 {TemplateTypename<T>(), TemplateArg(batch_a)},
+                                 {DefineValue(MAX_A_SIZE)});
 
     const int blocks_y = y.dims[1];
     const int blocks_x = y.dims[2];
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index ad54c9d304..590ef87acd 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -29,7 +29,7 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
     static const std::string source(index_cuh, index_cuh_len);
 
     auto index =
-        common::findKernel("cuda::index", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::index", {source}, {TemplateTypename<T>()});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index eaa40b604b..18dc0716fc 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -31,7 +31,7 @@ void iota(Param<T> out, const af::dim4 &sdims) {
     static const std::string source(iota_cuh, iota_cuh_len);
 
     auto iota =
-        common::findKernel("cuda::iota", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::iota", {source}, {TemplateTypename<T>()});
 
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 8fd47a9b34..091081170a 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -42,7 +42,7 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     blocks.z = divup(blocks.y, maxBlocksY);
     blocks.y = divup(blocks.y, blocks.z);
 
-    auto ireduceDim = common::findKernel(
+    auto ireduceDim = common::getKernel(
         "cuda::ireduceDim", {ireduceSource()},
         {TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
          TemplateArg(is_first), TemplateArg(threads_y)},
@@ -111,10 +111,10 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     // threads_x can take values 32, 64, 128, 256
     auto ireduceFirst =
-        common::findKernel("cuda::ireduceFirst", {ireduceSource()},
-                           {TemplateTypename<T>(), TemplateArg(op),
-                            TemplateArg(is_first), TemplateArg(threads_x)},
-                           {DefineValue(THREADS_PER_BLOCK)});
+        common::getKernel("cuda::ireduceFirst", {ireduceSource()},
+                          {TemplateTypename<T>(), TemplateArg(op),
+                           TemplateArg(is_first), TemplateArg(threads_x)},
+                          {DefineValue(THREADS_PER_BLOCK)});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index 7d2c7f2fbc..e65cc95b20 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -30,7 +30,7 @@ void join(Param<T> out, CParam<T> X, const af::dim4 &offset, int dim) {
     static const std::string source(join_cuh, join_cuh_len);
 
     auto join =
-        common::findKernel("cuda::join", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::join", {source}, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 02540f369f..afa7df98cb 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -46,7 +46,7 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
 
         dim3 blocks(blks, 1);
 
-        auto lookup1d = common::findKernel(
+        auto lookup1d = common::getKernel(
             "cuda::lookup1D", {src},
             {TemplateTypename<in_t>(), TemplateTypename<idx_t>()},
             {DefineValue(THREADS), DefineValue(THRD_LOAD)});
@@ -68,9 +68,9 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         blocks.y = divup(blocks.y, blocks.z);
 
         auto lookupnd =
-            common::findKernel("cuda::lookupND", {src},
-                               {TemplateTypename<in_t>(),
-                                TemplateTypename<idx_t>(), TemplateArg(dim)});
+            common::getKernel("cuda::lookupND", {src},
+                              {TemplateTypename<in_t>(),
+                               TemplateTypename<idx_t>(), TemplateArg(dim)});
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
         lookupnd(qArgs, out, in, indices, blks_x, blks_y);
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 543760097b..84fabaf18e 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -32,7 +32,7 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
     const bool sameDims =
         lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
-    auto luSplit = common::findKernel(
+    auto luSplit = common::getKernel(
         "cuda::luSplit", {src}, {TemplateTypename<T>(), TemplateArg(sameDims)});
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 1f3df97669..58cc99d118 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -28,7 +28,7 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
                    bool needMean) {
     static const std::string source(match_template_cuh, match_template_cuh_len);
 
-    auto matchTemplate = common::findKernel(
+    auto matchTemplate = common::getKernel(
         "cuda::matchTemplate", {source},
         {TemplateTypename<inType>(), TemplateTypename<outType>(),
          TemplateArg(mType), TemplateArg(needMean)});
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index ae753ca27a..a082f0a5d3 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -29,7 +29,7 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
                                       float>::type AccType;
     static const std::string source(meanshift_cuh, meanshift_cuh_len);
 
-    auto meanshift = common::findKernel(
+    auto meanshift = common::getKernel(
         "cuda::meanshift", {source},
         {
             TemplateTypename<AccType>(), TemplateTypename<T>(),
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 7d8ba18721..c1ab6d50d3 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -31,10 +31,10 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
     static const std::string source(medfilt_cuh, medfilt_cuh_len);
 
     auto medfilt2 =
-        common::findKernel("cuda::medfilt2", {source},
-                           {TemplateTypename<T>(), TemplateArg(pad),
-                            TemplateArg(w_len), TemplateArg(w_wid)},
-                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        common::getKernel("cuda::medfilt2", {source},
+                          {TemplateTypename<T>(), TemplateArg(pad),
+                           TemplateArg(w_len), TemplateArg(w_wid)},
+                          {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
@@ -52,7 +52,7 @@ template<typename T>
 void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
     static const std::string source(medfilt_cuh, medfilt_cuh_len);
 
-    auto medfilt1 = common::findKernel(
+    auto medfilt1 = common::getKernel(
         "cuda::medfilt1", {source},
         {TemplateTypename<T>(), TemplateArg(pad), TemplateArg(w_wid)});
 
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index da0b099b5c..e966d69490 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -32,7 +32,7 @@ void memcopy(Param<T> out, CParam<T> in, const dim_t ndims) {
     static const std::string src(memcopy_cuh, memcopy_cuh_len);
 
     auto memCopy =
-        common::findKernel("cuda::memcopy", {src}, {TemplateTypename<T>()});
+        common::getKernel("cuda::memcopy", {src}, {TemplateTypename<T>()});
 
     dim3 threads(DIMX, DIMY);
 
@@ -91,7 +91,7 @@ void copy(Param<outType> dst, CParam<inType> src, int ndims,
         ((src.dims[0] == dst.dims[0]) && (src.dims[1] == dst.dims[1]) &&
          (src.dims[2] == dst.dims[2]) && (src.dims[3] == dst.dims[3]));
 
-    auto copy = common::findKernel(
+    auto copy = common::getKernel(
         "cuda::copy", {source},
         {TemplateTypename<inType>(), TemplateTypename<outType>(),
          TemplateArg(same_dims)});
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 4c5270a23f..f1d7909942 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -26,7 +26,7 @@ void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
     static const std::string source(moments_cuh, moments_cuh_len);
 
     auto moments =
-        common::findKernel("cuda::moments", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::moments", {source}, {TemplateTypename<T>()});
 
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index b3e6cca486..3853a020ad 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -33,14 +33,14 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     const int windLen  = mask.dims[0];
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
-    auto morph = common::findKernel(
+    auto morph = common::getKernel(
         "cuda::morph", {source},
         {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(SeLength)},
         {
             DefineValue(MAX_MORPH_FILTER_LEN),
         });
 
-    morph.copyToReadOnly(morph.get("cFilter"),
+    morph.copyToReadOnly(morph.getDevPtr("cFilter"),
                          reinterpret_cast<CUdeviceptr>(mask.ptr),
                          mask.dims[0] * mask.dims[1] * sizeof(T));
 
@@ -72,7 +72,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
         CUDA_NOT_SUPPORTED("Morph 3D does not support kernels larger than 7.");
     }
 
-    auto morph3D = common::findKernel(
+    auto morph3D = common::getKernel(
         "cuda::morph3D", {source},
         {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(windLen)},
         {
@@ -80,7 +80,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
         });
 
     morph3D.copyToReadOnly(
-        morph3D.get("cFilter"), reinterpret_cast<CUdeviceptr>(mask.ptr),
+        morph3D.getDevPtr("cFilter"), reinterpret_cast<CUdeviceptr>(mask.ptr),
         mask.dims[0] * mask.dims[1] * mask.dims[2] * sizeof(T));
 
     dim3 threads(kernel::CUBE_X, kernel::CUBE_Y, kernel::CUBE_Z);
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index 329d626a9b..daf6fc9c53 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -30,8 +30,8 @@ void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
     static const std::string source(pad_array_borders_cuh,
                                     pad_array_borders_cuh_len);
     auto padBorders =
-        common::findKernel("cuda::padBorders", {source},
-                           {TemplateTypename<T>(), TemplateArg(btype)});
+        common::getKernel("cuda::padBorders", {source},
+                          {TemplateTypename<T>(), TemplateArg(btype)});
 
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index d3ec29ab73..1bd88ccd70 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -30,7 +30,7 @@ void range(Param<T> out, const int dim) {
     static const std::string source(range_cuh, range_cuh_len);
 
     auto range =
-        common::findKernel("cuda::range", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::range", {source}, {TemplateTypename<T>()});
 
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 3593a10ca4..2cac3be7d5 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -30,7 +30,7 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     static const std::string source(reorder_cuh, reorder_cuh_len);
 
     auto reorder =
-        common::findKernel("cuda::reorder", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::reorder", {source}, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index e6c3b45cc9..5964bcf11b 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -27,7 +27,7 @@ template<typename T>
 void resize(Param<T> out, CParam<T> in, af_interp_type method) {
     static const std::string source(resize_cuh, resize_cuh_len);
 
-    auto resize = common::findKernel(
+    auto resize = common::getKernel(
         "cuda::resize", {source}, {TemplateTypename<T>(), TemplateArg(method)});
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 7d98ed5b3e..1af65b67be 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -36,7 +36,7 @@ void rotate(Param<T> out, CParam<T> in, const float theta,
             const af::interpType method, const int order) {
     static const std::string source(rotate_cuh, rotate_cuh_len);
 
-    auto rotate = common::findKernel(
+    auto rotate = common::getKernel(
         "cuda::rotate", {source}, {TemplateTypename<T>(), TemplateArg(order)});
 
     const float c = cos(-theta), s = sin(-theta);
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index c3f555eece..1282ad415b 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -26,7 +26,7 @@ template<typename Ti, typename To, af_op_t op>
 static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                               const uint threads_y, const dim_t blocks_all[4],
                               int dim, bool isFinalPass, bool inclusive_scan) {
-    auto scan_dim = common::findKernel(
+    auto scan_dim = common::getKernel(
         "cuda::scan_dim", {ScanDimSource},
         {TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(op),
          TemplateArg(dim), TemplateArg(isFinalPass), TemplateArg(threads_y),
@@ -54,7 +54,7 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
                                const uint threads_y, const dim_t blocks_all[4],
                                int dim, bool inclusive_scan) {
-    auto scan_dim_bcast = common::findKernel(
+    auto scan_dim_bcast = common::getKernel(
         "cuda::scan_dim_bcast", {ScanDimSource},
         {TemplateTypename<To>(), TemplateArg(op), TemplateArg(dim)});
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 150bac33f9..04c4bd8925 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -37,7 +37,7 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const int dim, const uint threads_y,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
-    auto scanbykey_dim_nonfinal = common::findKernel(
+    auto scanbykey_dim_nonfinal = common::getKernel(
         "cuda::scanbykey_dim_nonfinal", {sbkDimSource()},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
@@ -61,7 +61,7 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const uint threads_y,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
-    auto scanbykey_dim_final = common::findKernel(
+    auto scanbykey_dim_final = common::getKernel(
         "cuda::scanbykey_dim_final", {sbkDimSource()},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
@@ -84,8 +84,8 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
     auto scanbykey_dim_bcast =
-        common::findKernel("cuda::scanbykey_dim_bcast", {sbkDimSource()},
-                           {TemplateTypename<To>(), TemplateArg(op)});
+        common::getKernel("cuda::scanbykey_dim_bcast", {sbkDimSource()},
+                          {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index cbf49c0238..14ff57df61 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -27,12 +27,12 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
-    auto scan_first = common::findKernel(
-        "cuda::scan_first", {ScanFirstSource},
-        {TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(op),
-         TemplateArg(isFinalPass), TemplateArg(threads_x),
-         TemplateArg(inclusive_scan)},
-        {DefineValue(THREADS_PER_BLOCK)});
+    auto scan_first =
+        common::getKernel("cuda::scan_first", {ScanFirstSource},
+                          {TemplateTypename<Ti>(), TemplateTypename<To>(),
+                           TemplateArg(op), TemplateArg(isFinalPass),
+                           TemplateArg(threads_x), TemplateArg(inclusive_scan)},
+                          {DefineValue(THREADS_PER_BLOCK)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -54,8 +54,8 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
     auto scan_first_bcast =
-        common::findKernel("cuda::scan_first_bcast", {ScanFirstSource},
-                           {TemplateTypename<To>(), TemplateArg(op)});
+        common::getKernel("cuda::scan_first_bcast", {ScanFirstSource},
+                          {TemplateTypename<To>(), TemplateArg(op)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 249ed12bd1..89bda149d0 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -36,7 +36,7 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    CParam<Ti> in, CParam<Tk> key,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
-    auto scanbykey_first_nonfinal = common::findKernel(
+    auto scanbykey_first_nonfinal = common::getKernel(
         "cuda::scanbykey_first_nonfinal", {sbkFirstSource()},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
@@ -57,7 +57,7 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
-    auto scanbykey_first_final = common::findKernel(
+    auto scanbykey_first_final = common::getKernel(
         "cuda::scanbykey_first_final", {sbkFirstSource()},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
@@ -78,8 +78,8 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
     auto scanbykey_first_bcast =
-        common::findKernel("cuda::scanbykey_first_bcast", {sbkFirstSource()},
-                           {TemplateTypename<To>(), TemplateArg(op)});
+        common::getKernel("cuda::scanbykey_first_bcast", {sbkFirstSource()},
+                          {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 885562abd5..547c2adf05 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -37,8 +37,8 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
     auto select =
-        common::findKernel("cuda::select", {selectSource()},
-                           {TemplateTypename<T>(), TemplateArg(is_same)});
+        common::getKernel("cuda::select", {selectSource()},
+                          {TemplateTypename<T>(), TemplateArg(is_same)});
 
     dim3 threads(DIMX, DIMY);
 
@@ -67,8 +67,8 @@ template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
                    int ndims, bool flip) {
     auto selectScalar =
-        common::findKernel("cuda::selectScalar", {selectSource()},
-                           {TemplateTypename<T>(), TemplateArg(flip)});
+        common::getKernel("cuda::selectScalar", {selectSource()},
+                          {TemplateTypename<T>(), TemplateArg(flip)});
 
     dim3 threads(DIMX, DIMY);
 
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index f3fd2b2f4b..d00649598c 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -30,12 +30,12 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
     static const std::string source(sobel_cuh, sobel_cuh_len);
 
     auto sobel3x3 =
-        common::findKernel("cuda::sobel3x3", {source},
-                           {
-                               TemplateTypename<Ti>(),
-                               TemplateTypename<To>(),
-                           },
-                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        common::getKernel("cuda::sobel3x3", {source},
+                          {
+                              TemplateTypename<Ti>(),
+                              TemplateTypename<To>(),
+                          },
+                          {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index aee05ce551..0147bc165e 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -28,8 +28,8 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
     static const std::string source(sparse_cuh, sparse_cuh_len);
 
     auto coo2Dense =
-        common::findKernel("cuda::coo2Dense", {source}, {TemplateTypename<T>()},
-                           {DefineValue(reps)});
+        common::getKernel("cuda::coo2Dense", {source}, {TemplateTypename<T>()},
+                          {DefineValue(reps)});
 
     dim3 threads(256, 1, 1);
 
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 17f2be3296..7544c2ab04 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -34,9 +34,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     auto csrArithDSD =
-        common::findKernel("cuda::csrArithDSD", {sparseArithSrc()},
-                           {TemplateTypename<T>(), TemplateArg(op)},
-                           {DefineValue(TX), DefineValue(TY)});
+        common::getKernel("cuda::csrArithDSD", {sparseArithSrc()},
+                          {TemplateTypename<T>(), TemplateArg(op)},
+                          {DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -53,7 +53,7 @@ void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
-    auto cooArithDSD = common::findKernel(
+    auto cooArithDSD = common::getKernel(
         "cuda::cooArithDSD", {sparseArithSrc()},
         {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
 
@@ -73,9 +73,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
     auto csrArithSSD =
-        common::findKernel("cuda::csrArithSSD", {sparseArithSrc()},
-                           {TemplateTypename<T>(), TemplateArg(op)},
-                           {DefineValue(TX), DefineValue(TY)});
+        common::getKernel("cuda::csrArithSSD", {sparseArithSrc()},
+                          {TemplateTypename<T>(), TemplateArg(op)},
+                          {DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -92,7 +92,7 @@ void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
-    auto cooArithSSD = common::findKernel(
+    auto cooArithSSD = common::getKernel(
         "cuda::cooArithSSD", {sparseArithSrc()},
         {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
 
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 1f2ce38ba8..ab767e67d3 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -32,7 +32,7 @@ template<typename T>
 void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
-    auto susan = common::findKernel(
+    auto susan = common::getKernel(
         "cuda::susan", {susanSource()}, {TemplateTypename<T>()},
         {DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
 
@@ -52,8 +52,8 @@ template<typename T>
 void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
-    auto nonMax = common::findKernel("cuda::nonMax", {susanSource()},
-                                     {TemplateTypename<T>()});
+    auto nonMax = common::getKernel("cuda::nonMax", {susanSource()},
+                                    {TemplateTypename<T>()});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 66b33e8253..e6f34d616a 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -28,7 +28,7 @@ void tile(Param<T> out, CParam<T> in) {
     static const std::string source(tile_cuh, tile_cuh_len);
 
     auto tile =
-        common::findKernel("cuda::tile", {source}, {TemplateTypename<T>()});
+        common::getKernel("cuda::tile", {source}, {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 9fb5884dae..78182d18ab 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -33,7 +33,7 @@ void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
                const bool perspective, const af::interpType method, int order) {
     static const std::string src(transform_cuh, transform_cuh_len);
 
-    auto transform = common::findKernel(
+    auto transform = common::getKernel(
         "cuda::transform", {src},
         {TemplateTypename<T>(), TemplateArg(inverse), TemplateArg(order)});
 
@@ -44,7 +44,7 @@ void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
     const unsigned int tf_len = (perspective) ? 9 : 6;
 
     // Copy transform to constant memory.
-    auto constPtr = transform.get("c_tmat");
+    auto constPtr = transform.getDevPtr("c_tmat");
     transform.copyToReadOnly(constPtr, reinterpret_cast<CUdeviceptr>(tf.ptr),
                              nTfs2 * nTfs3 * tf_len * sizeof(float));
 
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 63b4ee6f30..518ecb77da 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -30,10 +30,10 @@ void transpose(Param<T> out, CParam<T> in, const bool conjugate,
     static const std::string source(transpose_cuh, transpose_cuh_len);
 
     auto transpose =
-        common::findKernel("cuda::transpose", {source},
-                           {TemplateTypename<T>(), TemplateArg(conjugate),
-                            TemplateArg(is32multiple)},
-                           {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+        common::getKernel("cuda::transpose", {source},
+                          {TemplateTypename<T>(), TemplateArg(conjugate),
+                           TemplateArg(is32multiple)},
+                          {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index a40fd8df76..5452a7c19c 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -30,10 +30,10 @@ void transpose_inplace(Param<T> in, const bool conjugate,
     static const std::string source(transpose_inplace_cuh,
                                     transpose_inplace_cuh_len);
     auto transposeIP =
-        common::findKernel("cuda::transposeIP", {source},
-                           {TemplateTypename<T>(), TemplateArg(conjugate),
-                            TemplateArg(is32multiple)},
-                           {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+        common::getKernel("cuda::transposeIP", {source},
+                          {TemplateTypename<T>(), TemplateArg(conjugate),
+                           TemplateArg(is32multiple)},
+                          {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     // dimensions passed to this function should be input dimensions
     // any necessary transformations and dimension related calculations are
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 73fc3bae1a..00451e1ec7 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -30,9 +30,9 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
     static const std::string source(triangle_cuh, triangle_cuh_len);
 
     auto triangle =
-        common::findKernel("cuda::triangle", {source},
-                           {TemplateTypename<T>(), TemplateArg(is_upper),
-                            TemplateArg(is_unit_diag)});
+        common::getKernel("cuda::triangle", {source},
+                          {TemplateTypename<T>(), TemplateArg(is_upper),
+                           TemplateArg(is_unit_diag)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 89776c343c..5cb267a7f2 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -28,8 +28,8 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
     static const std::string source(unwrap_cuh, unwrap_cuh_len);
 
     auto unwrap =
-        common::findKernel("cuda::unwrap", {source},
-                           {TemplateTypename<T>(), TemplateArg(is_column)});
+        common::getKernel("cuda::unwrap", {source},
+                          {TemplateTypename<T>(), TemplateArg(is_column)});
 
     dim3 threads, blocks;
     int reps;
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 2d8b9c5048..380f05786a 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -25,7 +25,7 @@ template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
     static const std::string src(where_cuh, where_cuh_len);
     auto where =
-        common::findKernel("cuda::where", {src}, {TemplateTypename<T>()});
+        common::getKernel("cuda::where", {src}, {TemplateTypename<T>()});
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 3199d97ccb..be0cacef19 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -27,8 +27,8 @@ void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
     static const std::string source(wrap_cuh, wrap_cuh_len);
 
     auto wrap =
-        common::findKernel("cuda::wrap", {source},
-                           {TemplateTypename<T>(), TemplateArg(is_column)});
+        common::getKernel("cuda::wrap", {source},
+                          {TemplateTypename<T>(), TemplateArg(is_column)});
 
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
     int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
@@ -58,8 +58,8 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
     static const std::string source(wrap_cuh, wrap_cuh_len);
 
     auto wrap =
-        common::findKernel("cuda::wrap_dilated", {source},
-                           {TemplateTypename<T>(), TemplateArg(is_column)});
+        common::getKernel("cuda::wrap_dilated", {source},
+                          {TemplateTypename<T>(), TemplateArg(is_column)});
 
     int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     int ny = 1 + (out.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 60b80b2f37..f970da06b4 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -49,6 +49,7 @@ target_sources(afopencl
     Array.hpp
     Kernel.cpp
     Kernel.hpp
+    Module.hpp
     Param.cpp
     Param.hpp
     all.cpp
@@ -74,7 +75,7 @@ target_sources(afopencl
     cholesky.hpp
     clfft.cpp
     clfft.hpp
-    compile_kernel.cpp
+    compile_module.cpp
     complex.hpp
     convolve.cpp
     convolve.hpp
diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
index 7a5a432bb2..e59366ef13 100644
--- a/src/backend/opencl/Kernel.cpp
+++ b/src/backend/opencl/Kernel.cpp
@@ -16,7 +16,7 @@
 
 namespace opencl {
 
-Kernel::DevPtrType Kernel::get(const char* name) {
+Kernel::DevPtrType Kernel::getDevPtr(const char* name) {
     UNUSED(name);
     return nullptr;
 }
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index 1300a4e739..3284fea367 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -39,8 +39,8 @@ class Kernel
     Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
 
     // clang-format off
-    [[deprecated("OpenCL backend doesn't need Kernel::get method")]]
-    DevPtrType get(const char* name) override;
+    [[deprecated("OpenCL backend doesn't need Kernel::getDevPtr method")]]
+    DevPtrType getDevPtr(const char* name) override;
     // clang-format on
 
     void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) override;
diff --git a/src/backend/opencl/Module.hpp b/src/backend/opencl/Module.hpp
new file mode 100644
index 0000000000..2af60a51b4
--- /dev/null
+++ b/src/backend/opencl/Module.hpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/ModuleInterface.hpp>
+
+#include <cl2hpp.hpp>
+
+namespace opencl {
+
+/// OpenCL backend wrapper for cl::Program object
+class Module : public common::ModuleInterface<cl::Program*> {
+   public:
+    using ModuleType = cl::Program*;
+    using BaseClass  = common::ModuleInterface<ModuleType>;
+
+    Module(ModuleType mod) : BaseClass(mod) {}
+};
+
+}  // namespace opencl
diff --git a/src/backend/opencl/compile_kernel.cpp b/src/backend/opencl/compile_module.cpp
similarity index 70%
rename from src/backend/opencl/compile_kernel.cpp
rename to src/backend/opencl/compile_module.cpp
index b62abe0ac4..21146b38ec 100644
--- a/src/backend/opencl/compile_kernel.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -7,7 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/compile_kernel.hpp>
+#include <common/compile_module.hpp>  //compileModule & loadModuleFromDisk
+#include <common/kernel_cache.hpp>    //getKernel(Module&, ...)
 
 #include <cl2hpp.hpp>
 #include <common/Logger.hpp>
@@ -25,6 +26,7 @@
 #include <vector>
 
 using detail::Kernel;
+using detail::Module;
 
 using std::ostringstream;
 using std::string;
@@ -38,17 +40,17 @@ spdlog::logger *getLogger() {
     return logger.get();
 }
 
-#define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
-    do {                                                                  \
-        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();      \
-        for (unsigned int i = 0; i < numDevices; ++i) {                   \
-            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
-                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                 \
-            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
-                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                 \
-        }                                                                 \
+#define SHOW_DEBUG_BUILD_INFO(PROG)                                        \
+    do {                                                                   \
+        cl_uint numDevices = PROG->getInfo<CL_PROGRAM_NUM_DEVICES>();      \
+        for (unsigned int i = 0; i < numDevices; ++i) {                    \
+            printf("%s\n", PROG->getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
+                                   PROG->getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                  \
+            printf("%s\n", PROG->getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
+                                   PROG->getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                  \
+        }                                                                  \
     } while (0)
 
 #if defined(NDEBUG)
@@ -80,12 +82,12 @@ const static std::string DEFAULT_MACROS_STR(
                                            #endif\n                     \
                                            ");
 
-cl::Program buildProgram(const std::vector<std::string> &kernelSources,
-                         const std::vector<std::string> &compileOpts) {
+cl::Program *buildProgram(const std::vector<std::string> &kernelSources,
+                          const std::vector<std::string> &compileOpts) {
     using std::begin;
     using std::end;
 
-    cl::Program retVal;
+    cl::Program *retVal = nullptr;
     try {
         static const std::string defaults =
             std::string(" -D dim_t=") +
@@ -102,14 +104,14 @@ cl::Program buildProgram(const std::vector<std::string> &kernelSources,
         sources.emplace_back(KParam_hpp, KParam_hpp_len);
         sources.insert(end(sources), begin(kernelSources), end(kernelSources));
 
-        retVal = cl::Program(getContext(), sources);
+        retVal = new cl::Program(getContext(), sources);
 
         ostringstream options;
         for (auto &opt : compileOpts) { options << opt; }
 
-        retVal.build({device}, (cl_std + defaults + options.str()).c_str());
+        retVal->build({device}, (cl_std + defaults + options.str()).c_str());
     } catch (...) {
-        SHOW_BUILD_INFO(retVal);
+        if (retVal) { SHOW_BUILD_INFO(retVal); }
         throw;
     }
     return retVal;
@@ -119,33 +121,37 @@ cl::Program buildProgram(const std::vector<std::string> &kernelSources,
 
 namespace common {
 
-Kernel compileKernel(const string &kernelName, const string &tInstance,
-                     const vector<string> &sources,
-                     const vector<string> &compileOpts, const bool isJIT) {
+Module compileModule(const string &moduleKey, const vector<string> &sources,
+                     const vector<string> &options,
+                     const vector<string> &kInstances, const bool isJIT) {
     using opencl::getActiveDeviceId;
     using opencl::getDevice;
 
+    UNUSED(kInstances);
     UNUSED(isJIT);
-    UNUSED(tInstance);
 
     auto compileBegin = high_resolution_clock::now();
-    auto prog         = detail::buildProgram(sources, compileOpts);
-    auto prg          = new cl::Program(prog);
-    auto krn          = new cl::Kernel(*prg, kernelName.c_str());
+    auto program      = detail::buildProgram(sources, options);
     auto compileEnd   = high_resolution_clock::now();
 
-    AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", kernelName,
+    AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", moduleKey,
              duration_cast<milliseconds>(compileEnd - compileBegin).count(),
-             fmt::join(compileOpts, " "),
+             fmt::join(options, " "),
              getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_NAME>());
 
-    return {prg, krn};
+    return {program};
+}
+
+Module loadModuleFromDisk(const int device, const string &moduleKey) {
+    UNUSED(device);
+    UNUSED(moduleKey);
+    return {nullptr};
 }
 
-Kernel loadKernelFromDisk(const int device, const string &nameExpr) {
-    OPENCL_NOT_SUPPORTED(
-        "Disk caching OpenCL kernel binaries is not yet supported");
-    return {nullptr, nullptr};
+Kernel getKernel(const Module &mod, const string &nameExpr,
+                 const bool sourceWasJIT) {
+    UNUSED(sourceWasJIT);
+    return {mod.get(), new cl::Kernel(*mod.get(), nameExpr.c_str())};
 }
 
 }  // namespace common
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 6d73f1d98d..ac28c3f50f 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <common/compile_kernel.hpp>
+#include <common/compile_module.hpp>
 #include <common/dispatch.hpp>
 #include <common/jit/Node.hpp>
 #include <common/kernel_cache.hpp>
@@ -22,9 +22,9 @@
 
 #include <functional>
 #include <stdexcept>
+#include <string>
 #include <vector>
 
-using common::compileKernel;
 using common::getFuncName;
 using common::Node;
 using common::Node_ids;
@@ -40,10 +40,9 @@ using std::vector;
 
 namespace opencl {
 
-static string getKernelString(const string &funcName,
-                              const vector<Node *> &full_nodes,
-                              const vector<Node_ids> &full_ids,
-                              const vector<int> &output_ids, bool is_linear) {
+string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
+                       const vector<Node_ids> &full_ids,
+                       const vector<int> &output_ids, bool is_linear) {
     // Common OpenCL code
     // This part of the code does not change with the kernel.
 
@@ -138,17 +137,20 @@ static string getKernelString(const string &funcName,
     return kerStream.str();
 }
 
-static cl::Kernel getKernel(const vector<Node *> &output_nodes,
-                            const vector<int> &output_ids,
-                            const vector<Node *> &full_nodes,
-                            const vector<Node_ids> &full_ids,
-                            const bool is_linear) {
-    string funcName =
+cl::Kernel *getKernel(const vector<Node *> &output_nodes,
+                      const vector<int> &output_ids,
+                      const vector<Node *> &full_nodes,
+                      const vector<Node_ids> &full_ids, const bool is_linear) {
+    const string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
+    const string moduleKey = std::to_string(deterministicHash(funcName));
 
-    auto entry = common::lookupKernel(getActiveDeviceId(), funcName);
+    // A forward lookup in module cache helps avoid recompiling the jit
+    // source generated from identical jit-trees. It also enables us
+    // with a way to save jit kernels to disk only once
+    auto entry = common::findModule(getActiveDeviceId(), moduleKey);
 
-    if (entry.getModule() == nullptr || entry.getKernel() == nullptr) {
+    if (entry.get() == nullptr) {
         static const string jit(jit_cl, jit_cl_len);
 
         string jitKer = getKernelString(funcName, full_nodes, full_ids,
@@ -164,9 +166,10 @@ static cl::Kernel getKernel(const vector<Node *> &output_nodes,
 
         saveKernel(funcName, jitKer, ".cl");
 
-        entry = common::findKernel(funcName, {jit, jitKer}, {}, options, true);
+        return common::getKernel(funcName, {jit, jitKer}, {}, options, true)
+            .get();
     }
-    return *entry.getKernel();
+    return common::getKernel(entry, funcName, true).get();
 }
 
 void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
@@ -200,7 +203,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
         is_linear &= node->isLinear(outputs[0].info.dims);
     }
 
-    cl::Kernel ker =
+    auto ker =
         getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
 
     uint local_0   = 1;
@@ -249,25 +252,25 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
     for (const auto &node : full_nodes) {
         nargs = node->setArgs(nargs, is_linear,
                               [&](int id, const void *ptr, size_t arg_size) {
-                                  ker.setArg(id, arg_size, ptr);
+                                  ker->setArg(id, arg_size, ptr);
                               });
     }
 
     // Set output parameters
     for (auto output : outputs) {
-        ker.setArg(nargs, *(output.data));
+        ker->setArg(nargs, *(output.data));
         ++nargs;
     }
 
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
-    ker.setArg(nargs + 0, out_info);
-    ker.setArg(nargs + 1, groups_0);
-    ker.setArg(nargs + 2, groups_1);
-    ker.setArg(nargs + 3, num_odims);
+    ker->setArg(nargs + 0, out_info);
+    ker->setArg(nargs + 1, groups_0);
+    ker->setArg(nargs + 2, groups_1);
+    ker->setArg(nargs + 3, num_odims);
 
-    getQueue().enqueueNDRangeKernel(ker, NullRange, global, local);
+    getQueue().enqueueNDRangeKernel(*ker, NullRange, global, local);
 
     // Reset the thread local vectors
     nodes.clear();
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index d1b725cfce..61fdde34b3 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -53,7 +53,7 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto diffUpdate =
-        common::findKernel("aisoDiffUpdate", {src}, tmpltArgs, compileOpts);
+        common::getKernel("aisoDiffUpdate", {src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index dd71bbcf45..85cfe2310f 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -76,8 +76,8 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order);
 
-    auto approx1 = common::findKernel("approx1", {interpSrc(), src}, tmpltArgs,
-                                      compileOpts);
+    auto approx1 = common::getKernel("approx1", {interpSrc(), src}, tmpltArgs,
+                                     compileOpts);
 
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
@@ -117,8 +117,8 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order);
 
-    auto approx2 = common::findKernel("approx2", {interpSrc(), src}, tmpltArgs,
-                                      compileOpts);
+    auto approx2 = common::getKernel("approx2", {interpSrc(), src}, tmpltArgs,
+                                     compileOpts);
 
     NDRange local(TX, TY, 1);
     dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index d1e60d4032..83943d5b7d 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -44,7 +44,7 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto assign = common::findKernel("assignKernel", {src}, targs, options);
+    auto assign = common::getKernel("assignKernel", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index bf81091bcf..86f7b74519 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -46,7 +46,7 @@ void bilateral(Param out, const Param in, const float s_sigma,
     if (UseNativeExp) { options.emplace_back(DefineKey(USE_NATIVE_EXP)); }
     options.emplace_back(getTypeBuildDefinition<inType>());
 
-    auto bilateralOp = common::findKernel("bilateral", {src}, targs, options);
+    auto bilateralOp = common::getKernel("bilateral", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 588356d065..de90488303 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -42,8 +42,8 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto nonMaxOp = common::findKernel("nonMaxSuppressionKernel", {src},
-                                       {TemplateTypename<T>()}, options);
+    auto nonMaxOp = common::getKernel("nonMaxSuppressionKernel", {src},
+                                      {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -76,8 +76,8 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto initOp = common::findKernel("initEdgeOutKernel", {src},
-                                     {TemplateTypename<T>()}, options);
+    auto initOp = common::getKernel("initEdgeOutKernel", {src},
+                                    {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -110,8 +110,8 @@ void suppressLeftOver(Param output) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalOp = common::findKernel("suppressLeftOverKernel", {src},
-                                      {TemplateTypename<T>()}, options);
+    auto finalOp = common::getKernel("suppressLeftOverKernel", {src},
+                                     {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -147,8 +147,8 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto edgeTraceOp = common::findKernel("edgeTrackKernel", {src},
-                                          {TemplateTypename<T>()}, options);
+    auto edgeTraceOp = common::getKernel("edgeTrackKernel", {src},
+                                         {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 55ca7f7ae2..ea9a704701 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -54,7 +54,7 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto convolve =
-        common::findKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, *param.impulse, filter.info,
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 6cfbd76837..2d8aa9a5fd 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -117,7 +117,7 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto convolve =
-        common::findKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, cl::Local(param.loc_size),
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index ef3b486063..d348524c13 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -60,7 +60,7 @@ void convSep(Param out, const Param signal, const Param filter) {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto conv =
-        common::findKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index cb02ff0b99..54c52d35fe 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -58,7 +58,7 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto cscmmNN = common::findKernel("cscmm_nn", {src}, targs, options);
+    auto cscmmNN = common::getKernel("cscmm_nn", {src}, targs, options);
 
     cl::NDRange local(threads, 1);
     int M = out.info.dims[0];
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 01536c0985..9d91fafb19 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -55,7 +55,7 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto cscmvBlock = common::findKernel("cscmv_block", {src}, targs, options);
+    auto cscmvBlock = common::getKernel("cscmv_block", {src}, targs, options);
 
     cl::NDRange local(threads);
     int K        = colIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 7f0e387664..c5e742daa5 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -57,7 +57,7 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     // FIXME: Switch to perf (thread vs block) baesd kernel
-    auto csrmm_nt_func = common::findKernel("csrmm_nt", {src}, targs, options);
+    auto csrmm_nt_func = common::getKernel("csrmm_nt", {src}, targs, options);
 
     cl::NDRange local(THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 88b75e1b13..56af2d05f6 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -55,9 +55,8 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csrmvThread =
-        common::findKernel("csrmv_thread", {src}, targs, options);
-    auto csrmvBlock = common::findKernel("csrmv_block", {src}, targs, options);
+    auto csrmvThread = common::getKernel("csrmv_thread", {src}, targs, options);
+    auto csrmvBlock  = common::getKernel("csrmv_block", {src}, targs, options);
 
     int count           = 0;
     cl::Buffer *counter = bufferAlloc(sizeof(int));
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 6a85c5a803..3de60858e7 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -39,7 +39,7 @@ static void diagCreate(Param out, Param in, int num) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto diagCreate =
-        common::findKernel("diagCreateKernel", {src}, targs, options);
+        common::getKernel("diagCreateKernel", {src}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
@@ -66,7 +66,7 @@ static void diagExtract(Param out, Param in, int num) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto diagExtract =
-        common::findKernel("diagExtractKernel", {src}, targs, options);
+        common::getKernel("diagExtractKernel", {src}, targs, options);
 
     cl::NDRange local(256, 1);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 64a6f4ac15..bc04be7dc8 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -42,7 +42,7 @@ void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto diffOp = common::findKernel("diff_kernel", {src}, targs, options);
+    auto diffOp = common::getKernel("diff_kernel", {src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
     if (dim == 0 && indims == 1) { local = cl::NDRange(TX * TY, 1, 1); }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 894bc1f548..3473145aa8 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -23,13 +23,13 @@
 #include <traits.hpp>
 
 #include <common/dispatch.hpp>      // common utility header for CUDA & OpenCL
-#include <common/kernel_cache.hpp>  // Has findKernel
+#include <common/kernel_cache.hpp>  // Has getKernel
                                     // backends has the divup macro
 
 #include <debug_opencl.hpp>  // For Debug only related OpenCL validations
 
 // Following c++ standard library headers are needed to create
-// the lists of parameters for common::findKernel function call
+// the lists of parameters for common::getKernel function call
 #include <string>
 #include <vector>
 
@@ -63,7 +63,7 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
 
     // Fetch the Kernel functor, go to common/kernel_cache.hpp
     // to find details of this function
-    auto exOp = common::findKernel("example", {src}, targs, options);
+    auto exOp = common::getKernel("example", {src}, targs, options);
 
     // configure work group parameters
     cl::NDRange local(THREADS_X, THREADS_Y);
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index cd3a339642..64eb65f2b2 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -47,9 +47,9 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto locate  = common::findKernel("locate_features", {src}, targs, options);
-    auto nonMax  = common::findKernel("non_max_counts", {src}, targs, options);
-    auto getFeat = common::findKernel("get_features", {src}, targs, options);
+    auto locate  = common::getKernel("locate_features", {src}, targs, options);
+    auto nonMax  = common::getKernel("non_max_counts", {src}, targs, options);
+    auto getFeat = common::getKernel("get_features", {src}, targs, options);
 
     const unsigned max_feat =
         ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 9d7b76e1d1..62cf03cbfc 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -87,8 +87,8 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
     }
     options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto packData = common::findKernel("pack_data", {src}, targs, options);
-    auto padArray = common::findKernel("pad_array", {src}, targs, options);
+    auto packData = common::getKernel("pack_data", {src}, targs, options);
+    auto padArray = common::getKernel("pad_array", {src}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -150,8 +150,7 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
     }
     options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto cplxMul =
-        common::findKernel("complex_multiply", {src}, targs, options);
+    auto cplxMul = common::getKernel("complex_multiply", {src}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
@@ -201,7 +200,7 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
     }
     options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto reorder = common::findKernel("reorder_output", {src}, targs, options);
+    auto reorder = common::getKernel("reorder_output", {src}, targs, options);
 
     int fftScale = 1;
 
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index d643d8bf20..79310cf7d0 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -45,8 +45,8 @@ void initSeeds(Param out, const Param seedsx, const Param seedsy) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto initSeeds = common::findKernel("init_seeds", {floodfillSrc()},
-                                        {TemplateTypename<T>()}, options);
+    auto initSeeds = common::getKernel("init_seeds", {floodfillSrc()},
+                                       {TemplateTypename<T>()}, options);
     cl::NDRange local(kernel::THREADS, 1, 1);
     cl::NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
 
@@ -65,8 +65,8 @@ void finalizeOutput(Param out, const T newValue) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalizeOut = common::findKernel("finalize_output", {floodfillSrc()},
-                                          {TemplateTypename<T>()}, options);
+    auto finalizeOut = common::getKernel("finalize_output", {floodfillSrc()},
+                                         {TemplateTypename<T>()}, options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
                        divup(out.info.dims[1], local[1]) * local[1], 1);
@@ -95,8 +95,8 @@ void floodFill(Param out, const Param image, const Param seedsx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto floodStep = common::findKernel("flood_step", {floodfillSrc()},
-                                        {TemplateTypename<T>()}, options);
+    auto floodStep = common::getKernel("flood_step", {floodfillSrc()},
+                                       {TemplateTypename<T>()}, options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
                        divup(out.info.dims[1], local[1]) * local[1], 1);
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index fddb319fe3..0f9239d457 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -43,7 +43,7 @@ void gradient(Param grad0, Param grad1, const Param in) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto gradOp = common::findKernel("gradient", {src}, targs, options);
+    auto gradOp = common::getKernel("gradient", {src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index d958155e6d..89b1e8e32d 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -73,10 +73,10 @@ std::array<Kernel, 4> getHarrisKernels() {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::findKernel("second_order_deriv", {src}, targs, options),
-        common::findKernel("keep_corners", {src}, targs, options),
-        common::findKernel("harris_responses", {src}, targs, options),
-        common::findKernel("non_maximal", {src}, targs, options),
+        common::getKernel("second_order_deriv", {src}, targs, options),
+        common::getKernel("keep_corners", {src}, targs, options),
+        common::getKernel("harris_responses", {src}, targs, options),
+        common::getKernel("non_maximal", {src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 0a53fd63b6..bfab05b004 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -45,7 +45,7 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     options.emplace_back(getTypeBuildDefinition<inType>());
     if (isLinear) { options.emplace_back(DefineKey(IS_LINEAR)); }
 
-    auto histogram = common::findKernel("histogram", {src}, targs, options);
+    auto histogram = common::getKernel("histogram", {src}, targs, options);
 
     int nElems  = in.info.dims[0] * in.info.dims[1];
     int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 79d1f1bba8..b84e599fa1 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -50,11 +50,11 @@ std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
         options.emplace_back(DefineKey(IS_CPU));
     }
     return {
-        common::findKernel("compute_homography", {src}, targs, options),
-        common::findKernel("eval_homography", {src}, targs, options),
-        common::findKernel("compute_median", {src}, targs, options),
-        common::findKernel("find_min_median", {src}, targs, options),
-        common::findKernel("compute_lmeds_inliers", {src}, targs, options),
+        common::getKernel("compute_homography", {src}, targs, options),
+        common::getKernel("eval_homography", {src}, targs, options),
+        common::getKernel("compute_median", {src}, targs, options),
+        common::getKernel("find_min_median", {src}, targs, options),
+        common::getKernel("compute_lmeds_inliers", {src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 2257dc5ab9..a00d33ed10 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -39,7 +39,7 @@ void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
     options.emplace_back(getTypeBuildDefinition<T>());
     if (isHSV2RGB) { options.emplace_back(DefineKey(isHSV2RGB)); }
 
-    auto convert = common::findKernel("hsvrgbConvert", {src}, targs, options);
+    auto convert = common::getKernel("hsvrgbConvert", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index ecebf34910..e570f482eb 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -40,7 +40,7 @@ static void identity(Param out) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto identityOp =
-        common::findKernel("identity_kernel", {src}, targs, options);
+        common::getKernel("identity_kernel", {src}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 4e6b1c0b7a..42996a80e0 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -42,7 +42,7 @@ void iir(Param y, Param c, Param a) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto iir = common::findKernel("iir_kernel", {src}, targs, options);
+    auto iir = common::getKernel("iir_kernel", {src}, targs, options);
 
     const int groups_y = y.info.dims[1];
     const int groups_x = y.info.dims[2];
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index f780e528a2..481be5a9df 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -41,8 +41,8 @@ void index(Param out, const Param in, const IndexKernelParam_t& p,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto index = common::findKernel("indexKernel", {src},
-                                    {TemplateTypename<T>()}, options);
+    auto index = common::getKernel("indexKernel", {src},
+                                   {TemplateTypename<T>()}, options);
     cl::NDRange local(THREADS_X, THREADS_Y);
 
     int blk_x = divup(out.info.dims[0], THREADS_X);
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 2a1f784c1b..8650bfff0b 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -38,8 +38,8 @@ void iota(Param out, const af::dim4& sdims) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto iota = common::findKernel("iota_kernel", {src},
-                                   {TemplateTypename<T>()}, options);
+    auto iota = common::getKernel("iota_kernel", {src}, {TemplateTypename<T>()},
+                                  options);
     cl::NDRange local(IOTA_TX, IOTA_TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], TILEX);
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 6ed9cea472..3fb8a1633b 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -53,7 +53,7 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto ireduceDim =
-        common::findKernel("ireduce_dim_kernel", {src1, src2}, targs, options);
+        common::getKernel("ireduce_dim_kernel", {src1, src2}, targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -131,8 +131,8 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto ireduceFirst = common::findKernel("ireduce_first_kernel", {src1, src2},
-                                           targs, options);
+    auto ireduceFirst =
+        common::getKernel("ireduce_first_kernel", {src1, src2}, targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 9dbde81b5b..0a7b4c8d8a 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -37,8 +37,8 @@ void join(Param out, const Param in, dim_t dim, const af::dim4 offset) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto join =
-        common::findKernel("join_kernel", {src},
-                           {TemplateTypename<T>(), TemplateArg(dim)}, options);
+        common::getKernel("join_kernel", {src},
+                          {TemplateTypename<T>(), TemplateArg(dim)}, options);
     cl::NDRange local(TX, TY, 1);
 
     int blocksPerMatX = divup(in.info.dims[0], TILEX);
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index dd4f04fa67..95af3ba329 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -60,8 +60,7 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto lasetOp =
-        common::findKernel(laset_name<uplo>(), {src}, targs, options);
+    auto lasetOp = common::getKernel(laset_name<uplo>(), {src}, targs, options);
 
     int groups_x = (m - 1) / BLK_X + 1;
     int groups_y = (n - 1) / BLK_Y + 1;
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 0c80fc030d..1043310f70 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -46,7 +46,7 @@ void laset_band(int m, int  n, int k,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto lasetBandOp = common::findKernel(laset_band_name<uplo>(), {src}, targs, options);
+    auto lasetBandOp = common::getKernel(laset_band_name<uplo>(), {src}, targs, options);
 
     int threads = 1;
     int groups = 1;
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 094ead3c07..49c192babd 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -45,7 +45,7 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto laswpOp = common::findKernel("laswp", {src}, targs, options);
+    auto laswpOp = common::getKernel("laswp", {src}, targs, options);
 
     int groups = divup(n, NTHREADS);
     cl::NDRange local(NTHREADS);
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 9a5b26abcf..ecbacc3f42 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -51,7 +51,7 @@ void lookup(Param out, const Param in, const Param indices,
     cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
                        blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp = common::findKernel("lookupND", {src}, targs, options);
+    auto arrIdxOp = common::getKernel("lookupND", {src}, targs, options);
 
     arrIdxOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 67107c1cc7..5f34afed4e 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -44,7 +44,7 @@ void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto luSplit = common::findKernel("luSplit", {src}, targs, options);
+    auto luSplit = common::getKernel("luSplit", {src}, targs, options);
 
     cl::NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index ce8cd31dee..b109bcf16a 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -53,8 +53,7 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
     };
     options.emplace_back(getTypeBuildDefinition<outType>());
 
-    auto matchImgOp =
-        common::findKernel("matchTemplate", {src}, targs, options);
+    auto matchImgOp = common::getKernel("matchTemplate", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 00d240b894..649f427b8f 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -132,7 +132,7 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
     if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
-    auto meanOp = common::findKernel("meanDim", {src1, src2}, targs, options);
+    auto meanOp = common::getKernel("meanDim", {src1, src2}, targs, options);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -227,7 +227,7 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
     if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
-    auto meanOp = common::findKernel("meanFirst", {src1, src2}, targs, options);
+    auto meanOp = common::getKernel("meanFirst", {src1, src2}, targs, options);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index affc26cf18..c39b58daf8 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -45,7 +45,7 @@ void meanshift(Param out, const Param in, const float spatialSigma,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto meanshiftOp = common::findKernel("meanshift", {src}, targs, options);
+    auto meanshiftOp = common::getKernel("meanshift", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 6e415b0d26..2b3237dd93 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -51,7 +51,7 @@ void medfilt1(Param out, const Param in, const unsigned w_wid,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto medfiltOp = common::findKernel("medfilt1", {src}, targs, options);
+    auto medfiltOp = common::getKernel("medfilt1", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, 1, 1);
 
@@ -91,7 +91,7 @@ void medfilt2(Param out, const Param in, const af_border_type pad,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto medfiltOp = common::findKernel("medfilt2", {src}, targs, options);
+    auto medfiltOp = common::getKernel("medfilt2", {src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 751b608edc..94abc8ffe6 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -45,7 +45,7 @@ void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto memCopy = common::findKernel("memCopy", {source}, targs, options);
+    auto memCopy = common::getKernel("memCopy", {source}, targs, options);
 
     dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
     dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
@@ -91,7 +91,7 @@ void copy(Param dst, const Param src, const int ndims,
     };
     options.emplace_back(getTypeBuildDefinition<inType, outType>());
 
-    auto copy = common::findKernel("reshapeCopy", {source}, targs, options);
+    auto copy = common::getKernel("reshapeCopy", {source}, targs, options);
 
     cl::NDRange local(DIM0, DIM1);
     size_t local_size[] = {DIM0, DIM1};
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index c3b2aa73a2..cbe787f2e0 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -40,7 +40,7 @@ void moments(Param out, const Param in, af_moment_type moment) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto momentsOp = common::findKernel("moments", {src}, targs, options);
+    auto momentsOp = common::getKernel("moments", {src}, targs, options);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(in.info.dims[1] * local[0],
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index f0eb10b472..fc401f87cb 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -58,7 +58,7 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::findKernel("morph", {src}, targs, options);
+    auto morphOp = common::getKernel("morph", {src}, targs, options);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -120,7 +120,7 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::findKernel("morph3d", {src}, targs, options);
+    auto morphOp = common::getKernel("morph3d", {src}, targs, options);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 43b8c6566e..bc4343a1c6 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -73,7 +73,7 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
         options.emplace_back(DefineKeyValue(DISTOP, "_shd_"));
         options.emplace_back(DefineKey(__SHD__));
     }
-    auto hmOp = common::findKernel("knnAllDistances", {src}, targs, options);
+    auto hmOp = common::getKernel("knnAllDistances", {src}, targs, options);
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 9c7dcdfee1..2f49fb0e41 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -89,10 +89,10 @@ std::array<Kernel, 4> getOrbKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::findKernel("harris_response", {src}, targs, compileOpts),
-        common::findKernel("keep_features", {src}, targs, compileOpts),
-        common::findKernel("centroid_angle", {src}, targs, compileOpts),
-        common::findKernel("extract_orb", {src}, targs, compileOpts),
+        common::getKernel("harris_response", {src}, targs, compileOpts),
+        common::getKernel("keep_features", {src}, targs, compileOpts),
+        common::getKernel("centroid_angle", {src}, targs, compileOpts),
+        common::getKernel("extract_orb", {src}, targs, compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index be1d98c9de..87b7a23049 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -47,7 +47,7 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto pad = common::findKernel("padBorders", {src}, tmpltArgs, compileOpts);
+    auto pad = common::getKernel("padBorders", {src}, tmpltArgs, compileOpts);
 
     NDRange local(PADB_THREADS_X, PADB_THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 1b45726774..44a1903347 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -79,13 +79,13 @@ static Kernel getRandomEngineKernel(const af_random_engine_type type,
 #endif
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    return common::findKernel(key, sources, targs, options);
+    return common::getKernel(key, sources, targs, options);
 }
 
 static Kernel getMersenneInitKernel(void) {
     static const std::string src(random_engine_mersenne_init_cl,
                                  random_engine_mersenne_init_cl_len);
-    return common::findKernel("mersenneInitState", {src}, {});
+    return common::getKernel("mersenneInitState", {src}, {});
 }
 
 template<typename T>
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 46a78d04c1..82087a390b 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -38,7 +38,7 @@ void range(Param out, const int dim) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto rangeOp = common::findKernel("range_kernel", {src}, targs, options);
+    auto rangeOp = common::getKernel("range_kernel", {src}, targs, options);
 
     cl::NDRange local(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index a0c10c39e8..c5a0347ad8 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -58,7 +58,7 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
     options.emplace_back(getTypeBuildDefinition<Ti, To>());
 
     auto reduceDim =
-        common::findKernel("reduce_dim_kernel", {src1, src2}, targs, options);
+        common::getKernel("reduce_dim_kernel", {src1, src2}, targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -139,7 +139,7 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
     options.emplace_back(getTypeBuildDefinition<Ti, To>());
 
     auto reduceFirst =
-        common::findKernel("reduce_first_kernel", {src1, src2}, targs, options);
+        common::getKernel("reduce_first_kernel", {src1, src2}, targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 9f9167ec95..429081b976 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -67,7 +67,7 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto reduceBlocksByKeyDim = common::findKernel(
+    auto reduceBlocksByKeyDim = common::getKernel(
         "reduce_blocks_by_key_dim", {src1, src2}, tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
@@ -112,7 +112,7 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto reduceBlocksByKeyFirst = common::findKernel(
+    auto reduceBlocksByKeyFirst = common::getKernel(
         "reduce_blocks_by_key_first", {src1, src2}, tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
@@ -155,7 +155,7 @@ void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduce = common::findKernel(
+    auto finalBoundaryReduce = common::getKernel(
         "final_boundary_reduce", {src1, src2}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
@@ -196,7 +196,7 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduceDim = common::findKernel(
+    auto finalBoundaryReduceDim = common::getKernel(
         "final_boundary_reduce_dim", {src1, src2}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
@@ -235,7 +235,7 @@ void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto compact =
-        common::findKernel("compact", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("compact", {src1, src2}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
@@ -273,7 +273,7 @@ void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto compactDim =
-        common::findKernel("compact_dim", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("compact_dim", {src1, src2}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks,
@@ -305,7 +305,7 @@ void testNeedsReduction(cl::Buffer needs_reduction, cl::Buffer needs_boundary,
         DefineKeyValue(DIMX, threads_x),
     };
 
-    auto testIfNeedsReduction = common::findKernel(
+    auto testIfNeedsReduction = common::getKernel(
         "test_needs_reduction", {src1, src2}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 200fec8433..d7fbee0730 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -70,9 +70,9 @@ std::array<Kernel, 3> getRegionsKernels(const bool full_conn,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::findKernel("initial_label", {src}, targs, options),
-        common::findKernel("final_relabel", {src}, targs, options),
-        common::findKernel("update_equiv", {src}, targs, options),
+        common::getKernel("initial_label", {src}, targs, options),
+        common::getKernel("final_relabel", {src}, targs, options),
+        common::getKernel("update_equiv", {src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 05695ab4f4..a164d64e7f 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -37,8 +37,7 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto reorderOp =
-        common::findKernel("reorder_kernel", {src}, targs, options);
+    auto reorderOp = common::getKernel("reorder_kernel", {src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 012d22ae88..598737009b 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -70,7 +70,7 @@ void resize(Param out, const Param in, const af_interp_type method) {
         default: break;
     }
 
-    auto resizeOp = common::findKernel("resize_kernel", {src}, targs, options);
+    auto resizeOp = common::getKernel("resize_kernel", {src}, targs, options);
 
     cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index aaa8a1929e..42733fee85 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -80,8 +80,8 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto rotate = common::findKernel("rotateKernel", {src1, src2}, tmpltArgs,
-                                     compileOpts);
+    auto rotate =
+        common::getKernel("rotateKernel", {src1, src2}, tmpltArgs, compileOpts);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index bc5cba6732..76efa76131 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -60,7 +60,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index d018f31360..8a7e931e85 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -60,7 +60,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index be53559583..3cf29ae8c2 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -61,7 +61,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 6e36b048af..a4f1f3ac6b 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -64,7 +64,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::findKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 9878a4f868..38f378b795 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -45,7 +45,7 @@ void selectLauncher(Param out, Param cond, Param a, Param b, const int ndims,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto selectOp =
-        common::findKernel("select_kernel", {selectSrc()}, targs, options);
+        common::getKernel("select_kernel", {selectSrc()}, targs, options);
 
     int threads[] = {DIMX, DIMY};
 
@@ -89,8 +89,8 @@ void select_scalar(Param out, Param cond, Param a, const double b,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto selectOp = common::findKernel("select_scalar_kernel", {selectSrc()},
-                                       targs, options);
+    auto selectOp = common::getKernel("select_scalar_kernel", {selectSrc()},
+                                      targs, options);
 
     int threads[] = {DIMX, DIMY};
 
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index fc14d9f7d8..aa7388fe1d 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -414,13 +414,13 @@ std::array<Kernel, 7> getSiftKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::findKernel("sub", {src}, targs, compileOpts),
-        common::findKernel("detectExtrema", {src}, targs, compileOpts),
-        common::findKernel("interpolateExtrema", {src}, targs, compileOpts),
-        common::findKernel("calcOrientation", {src}, targs, compileOpts),
-        common::findKernel("removeDuplicates", {src}, targs, compileOpts),
-        common::findKernel("computeDescriptor", {src}, targs, compileOpts),
-        common::findKernel("computeGLOHDescriptor", {src}, targs, compileOpts),
+        common::getKernel("sub", {src}, targs, compileOpts),
+        common::getKernel("detectExtrema", {src}, targs, compileOpts),
+        common::getKernel("interpolateExtrema", {src}, targs, compileOpts),
+        common::getKernel("calcOrientation", {src}, targs, compileOpts),
+        common::getKernel("removeDuplicates", {src}, targs, compileOpts),
+        common::getKernel("computeDescriptor", {src}, targs, compileOpts),
+        common::getKernel("computeGLOHDescriptor", {src}, targs, compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 74683e265c..eb13187e2a 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -40,7 +40,7 @@ void sobel(Param dx, Param dy, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto sobel = common::findKernel("sobel3x3", {src}, targs, compileOpts);
+    auto sobel = common::getKernel("sobel3x3", {src}, targs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index d3a42564fe..6ef8e0973c 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -45,7 +45,7 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto coo2dense =
-        common::findKernel("coo2Dense", {src}, tmpltArgs, compileOpts);
+        common::getKernel("coo2Dense", {src}, tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_PER_GROUP, 1, 1);
 
@@ -80,7 +80,7 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto csr2dense =
-        common::findKernel("csr2Dense", {src}, tmpltArgs, compileOpts);
+        common::getKernel("csr2Dense", {src}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads, 1);
     int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
@@ -108,7 +108,7 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto dense2Csr =
-        common::findKernel("dense2Csr", {src}, tmpltArgs, compileOpts);
+        common::getKernel("dense2Csr", {src}, tmpltArgs, compileOpts);
 
     int num_rows = dense.info.dims[0];
     int num_cols = dense.info.dims[1];
@@ -155,7 +155,7 @@ void swapIndex(Param ovalues, Param oindex, const Param ivalues,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto swapIndex =
-        common::findKernel("swapIndex", {src}, tmpltArgs, compileOpts);
+        common::getKernel("swapIndex", {src}, tmpltArgs, compileOpts);
 
     cl::NDRange global(ovalues.info.dims[0], 1, 1);
 
@@ -178,7 +178,7 @@ void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2coo = common::findKernel("csr2Coo", {src}, tmpltArgs, compileOpts);
+    auto csr2coo = common::getKernel("csr2Coo", {src}, tmpltArgs, compileOpts);
 
     const int MAX_GROUPS = 4096;
     int M                = irowIdx.info.dims[0] - 1;
@@ -220,7 +220,7 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto csrReduce =
-        common::findKernel("csrReduce", {src}, tmpltArgs, compileOpts);
+        common::getKernel("csrReduce", {src}, tmpltArgs, compileOpts);
 
     // Now we need to sort this into column major
     kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 90a0b33303..8e42e0b96f 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -65,7 +65,7 @@ auto fetchKernel(const std::string key, const std::string &additionalSrc,
     options.emplace_back(getTypeBuildDefinition<T>());
     options.insert(std::end(options), std::begin(additionalOptions),
                    std::end(additionalOptions));
-    return common::findKernel(key, {src, additionalSrc}, tmpltArgs, options);
+    return common::getKernel(key, {src, additionalSrc}, tmpltArgs, options);
 }
 
 template<typename T, af_op_t op>
@@ -151,7 +151,7 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
         TemplateTypename<uint>(),
     };
 
-    auto calcNNZ = common::findKernel("csr_calc_out_nnz", {src}, tmpltArgs, {});
+    auto calcNNZ = common::getKernel("csr_calc_out_nnz", {src}, tmpltArgs, {});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 35410b5564..0d4b1576a6 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -53,7 +53,7 @@ void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto susan =
-        common::findKernel("susan_responses", {susanSrc()}, targs, compileOpts);
+        common::getKernel("susan_responses", {susanSrc()}, targs, compileOpts);
 
     cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
     cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
@@ -79,7 +79,7 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto nonMax =
-        common::findKernel("non_maximal", {susanSrc()}, targs, compileOpts);
+        common::getKernel("non_maximal", {susanSrc()}, targs, compileOpts);
 
     unsigned corners_found      = 0;
     cl::Buffer* d_corners_found = bufferAlloc(sizeof(unsigned));
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 857b49aa3b..ab5a4db4be 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -43,7 +43,7 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapdblk = common::findKernel("swapdblk", {src}, targs, compileOpts);
+    auto swapdblk = common::getKernel("swapdblk", {src}, targs, compileOpts);
 
     int nblocks = n / nb;
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index f931594ca4..287550e0db 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -43,7 +43,7 @@ void tile(Param out, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto tile = common::findKernel("tile", {src}, targs, compileOpts);
+    auto tile = common::getKernel("tile", {src}, targs, compileOpts);
 
     NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index b1c0f3b8ea..ab9055a703 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -80,8 +80,8 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto transform = common::findKernel("transformKernel", {src1, src2},
-                                        tmpltArgs, compileOpts);
+    auto transform = common::getKernel("transformKernel", {src1, src2},
+                                       tmpltArgs, compileOpts);
 
     const int nImg2 = in.info.dims[2];
     const int nImg3 = in.info.dims[3];
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index c7e40320b3..ec5c8c9eb1 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -51,7 +51,7 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto transpose =
-        common::findKernel("transpose", {src}, tmpltArgs, compileOpts);
+        common::getKernel("transpose", {src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 300a7eec40..73ecf2b8a5 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -51,7 +51,7 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto transpose =
-        common::findKernel("transpose_inplace", {src}, tmpltArgs, compileOpts);
+        common::getKernel("transpose_inplace", {src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index dc3a50b35a..031ce1e744 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -54,7 +54,7 @@ void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto triangle =
-        common::findKernel("triangle", {src}, tmpltArgs, compileOpts);
+        common::getKernel("triangle", {src}, tmpltArgs, compileOpts);
 
     NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 908f318d9d..64205178e4 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -48,7 +48,7 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto unwrap = common::findKernel("unwrap", {src}, tmpltArgs, compileOpts);
+    auto unwrap = common::getKernel("unwrap", {src}, tmpltArgs, compileOpts);
 
     dim_t TX = 1, TY = 1;
     dim_t BX       = 1;
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 799bd471fb..1fbceb1fa7 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -48,7 +48,7 @@ static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto getIdx =
-        common::findKernel("get_out_idx", {src}, tmpltArgs, compileOpts);
+        common::getKernel("get_out_idx", {src}, tmpltArgs, compileOpts);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(local[0] * groups_x * in.info.dims[2],
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index bf9b63762b..32c4695c78 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -48,7 +48,7 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto wrap = common::findKernel("wrap", {src}, tmpltArgs, compileOpts);
+    auto wrap = common::getKernel("wrap", {src}, tmpltArgs, compileOpts);
 
     dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
     dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
@@ -95,7 +95,7 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto dilatedWrap =
-        common::findKernel("wrap_dilated", {src}, tmpltArgs, compileOpts);
+        common::getKernel("wrap_dilated", {src}, tmpltArgs, compileOpts);
 
     dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;

From 7f512ac9a68f700c4fa05b376652ebb1b32d4a22 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 27 May 2020 17:37:44 +0530
Subject: [PATCH 1969/2677] Change kernel cache to mutex protected static
 storage

---
 src/backend/common/ModuleInterface.hpp |  3 ++
 src/backend/common/compile_module.hpp  |  3 +-
 src/backend/common/kernel_cache.cpp    | 25 ++++++++--
 src/backend/cuda/CMakeLists.txt        |  1 +
 src/backend/cuda/Kernel.hpp            | 18 +------
 src/backend/cuda/Module.hpp            |  6 +++
 src/backend/cuda/compile_module.cpp    | 69 ++++++++++++++------------
 src/backend/cuda/cu_check_macro.hpp    | 30 +++++++++++
 src/backend/opencl/Module.hpp          |  5 ++
 src/backend/opencl/compile_module.cpp  |  4 +-
 10 files changed, 111 insertions(+), 53 deletions(-)
 create mode 100644 src/backend/cuda/cu_check_macro.hpp

diff --git a/src/backend/common/ModuleInterface.hpp b/src/backend/common/ModuleInterface.hpp
index 0147176277..052a661916 100644
--- a/src/backend/common/ModuleInterface.hpp
+++ b/src/backend/common/ModuleInterface.hpp
@@ -29,6 +29,9 @@ class ModuleInterface {
     ///
     /// \returns handle to backend specific module
     inline ModuleType get() const { return mModuleHandle; }
+
+    /// \brief Unload module
+    virtual void unload() = 0;
 };
 
 }  // namespace common
diff --git a/src/backend/common/compile_module.hpp b/src/backend/common/compile_module.hpp
index dcf3985f7c..dc8a0b7dd0 100644
--- a/src/backend/common/compile_module.hpp
+++ b/src/backend/common/compile_module.hpp
@@ -58,7 +58,8 @@ detail::Module compileModule(const std::string& moduleKey,
 /// \param[in] device is the device index
 /// \param[in] moduleKey is hash of code+options+instantiations
 detail::Module loadModuleFromDisk(const int device,
-                                  const std::string& moduleKey);
+                                  const std::string& moduleKey,
+                                  const bool isJIT);
 
 }  // namespace common
 
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 52bb0bc6c9..0c879070a1 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -17,6 +17,7 @@
 #include <platform.hpp>
 
 #include <algorithm>
+#include <shared_mutex>
 #include <string>
 #include <unordered_map>
 #include <vector>
@@ -25,6 +26,7 @@ using detail::Kernel;
 using detail::Module;
 
 using std::back_inserter;
+using std::shared_timed_mutex;
 using std::string;
 using std::transform;
 using std::unordered_map;
@@ -34,12 +36,18 @@ namespace common {
 
 using ModuleMap = unordered_map<string, Module>;
 
+shared_timed_mutex& getCacheMutex(const int device) {
+    static shared_timed_mutex mutexes[detail::DeviceManager::MAX_DEVICES];
+    return mutexes[device];
+}
+
 ModuleMap& getCache(const int device) {
-    thread_local ModuleMap caches[detail::DeviceManager::MAX_DEVICES];
+    static ModuleMap caches[detail::DeviceManager::MAX_DEVICES];
     return caches[device];
 }
 
 Module findModule(const int device, const string& key) {
+    std::shared_lock<shared_timed_mutex> readLock(getCacheMutex(device));
     auto& cache = getCache(device);
     auto iter   = cache.find(key);
     if (iter != cache.end()) { return iter->second; }
@@ -82,12 +90,23 @@ Kernel getKernel(const string& kernelName, const vector<string>& sources,
     Module currModule      = findModule(device, moduleKey);
 
     if (currModule.get() == nullptr) {
-        currModule = loadModuleFromDisk(device, moduleKey);
+        currModule = loadModuleFromDisk(device, moduleKey, sourceIsJIT);
         if (currModule.get() == nullptr) {
             currModule = compileModule(moduleKey, sources, options, {tInstance},
                                        sourceIsJIT);
         }
-        getCache(device).emplace(moduleKey, currModule);
+
+        std::unique_lock<shared_timed_mutex> writeLock(getCacheMutex(device));
+        auto& cache = getCache(device);
+        auto iter   = cache.find(moduleKey);
+        if (iter == cache.end()) {
+            // If not found, this thread is the first one to compile this
+            // kernel. Keep the generated module.
+            getCache(device).emplace(moduleKey, currModule);
+        } else {
+            currModule.unload();  // dump the current threads extra compilation
+            currModule = iter->second;
+        }
     }
 #if defined(AF_CUDA)
     return getKernel(currModule, tInstance, sourceIsJIT);
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 9773257c2b..a1c6b7a0b0 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -476,6 +476,7 @@ cuda_add_library(afcuda
     convolveNN.cpp
     copy.cpp
     copy.hpp
+    cu_check_macro.hpp
     cublas.cpp
     cublas.hpp
     cufft.hpp
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index c0e7fb310f..180157b069 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -13,23 +13,7 @@
 
 #include <EnqueueArgs.hpp>
 #include <backend.hpp>
-#include <common/err_common.hpp>
-
-#include <cstdio>
-
-#define CU_CHECK(fn)                                                      \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[1024];                                            \
-        const char* cu_err_name;                                          \
-        const char* cu_err_string;                                        \
-        cuGetErrorName(res, &cu_err_name);                                \
-        cuGetErrorString(res, &cu_err_string);                            \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), cu_err_string);                 \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
-    } while (0)
+#include <cu_check_macro.hpp>
 
 namespace cuda {
 
diff --git a/src/backend/cuda/Module.hpp b/src/backend/cuda/Module.hpp
index cb6e16591d..d910d1f90c 100644
--- a/src/backend/cuda/Module.hpp
+++ b/src/backend/cuda/Module.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/ModuleInterface.hpp>
+#include <cu_check_macro.hpp>
 
 #include <cuda.h>
 
@@ -31,6 +32,11 @@ class Module : public common::ModuleInterface<CUmodule> {
         mInstanceMangledNames.reserve(1);
     }
 
+    void unload() final {
+        CU_CHECK(cuModuleUnload(get()));
+        set(nullptr);
+    }
+
     const std::string mangledName(const std::string& instantiation) const {
         auto iter = mInstanceMangledNames.find(instantiation);
         if (iter != mInstanceMangledNames.end()) {
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 455044d259..ee4ce27e49 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -325,18 +325,22 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
 
         // write module hash(everything: names, code & options) and CUBIN data
         ofstream out(tempFile, std::ios::binary);
-        size_t mangledNamesListSize = retVal.map().size();
-        out.write(reinterpret_cast<const char *>(&cubinHash),
-                  sizeof(mangledNamesListSize));
-        for (auto &iter : retVal.map()) {
-            size_t kySize   = iter.first.size();
-            size_t vlSize   = iter.second.size();
-            const char *key = iter.first.c_str();
-            const char *val = iter.second.c_str();
-            out.write(reinterpret_cast<const char *>(&kySize), sizeof(kySize));
-            out.write(key, iter.first.size());
-            out.write(reinterpret_cast<const char *>(&vlSize), sizeof(vlSize));
-            out.write(val, iter.second.size());
+        if (!sourceIsJIT) {
+            size_t mangledNamesListSize = retVal.map().size();
+            out.write(reinterpret_cast<const char *>(&mangledNamesListSize),
+                      sizeof(mangledNamesListSize));
+            for (auto &iter : retVal.map()) {
+                size_t kySize   = iter.first.size();
+                size_t vlSize   = iter.second.size();
+                const char *key = iter.first.c_str();
+                const char *val = iter.second.c_str();
+                out.write(reinterpret_cast<const char *>(&kySize),
+                          sizeof(kySize));
+                out.write(key, iter.first.size());
+                out.write(reinterpret_cast<const char *>(&vlSize),
+                          sizeof(vlSize));
+                out.write(val, iter.second.size());
+            }
         }
         out.write(reinterpret_cast<const char *>(&cubinHash),
                   sizeof(cubinHash));
@@ -371,7 +375,8 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     return retVal;
 }
 
-Module loadModuleFromDisk(const int device, const string &moduleKey) {
+Module loadModuleFromDisk(const int device, const string &moduleKey,
+                          const bool isJIT) {
     const string &cacheDirectory = getCacheDirectory();
     if (cacheDirectory.empty()) return Module{nullptr};
 
@@ -386,24 +391,26 @@ Module loadModuleFromDisk(const int device, const string &moduleKey) {
 
         in.exceptions(std::ios::failbit | std::ios::badbit);
 
-        size_t mangledListSize = 0;
-        in.read(reinterpret_cast<char *>(&mangledListSize),
-                sizeof(mangledListSize));
-        for (size_t i = 0; i < mangledListSize; ++i) {
-            size_t keySize = 0;
-            in.read(reinterpret_cast<char *>(&keySize), sizeof(keySize));
-            vector<char> key;
-            key.reserve(keySize);
-            in.read(key.data(), keySize);
-
-            size_t itemSize = 0;
-            in.read(reinterpret_cast<char *>(&itemSize), sizeof(itemSize));
-            vector<char> item;
-            item.reserve(itemSize);
-            in.read(item.data(), itemSize);
-
-            retVal.add(string(key.data(), keySize),
-                       string(item.data(), itemSize));
+        if (!isJIT) {
+            size_t mangledListSize = 0;
+            in.read(reinterpret_cast<char *>(&mangledListSize),
+                    sizeof(mangledListSize));
+            for (size_t i = 0; i < mangledListSize; ++i) {
+                size_t keySize = 0;
+                in.read(reinterpret_cast<char *>(&keySize), sizeof(keySize));
+                vector<char> key;
+                key.reserve(keySize);
+                in.read(key.data(), keySize);
+
+                size_t itemSize = 0;
+                in.read(reinterpret_cast<char *>(&itemSize), sizeof(itemSize));
+                vector<char> item;
+                item.reserve(itemSize);
+                in.read(item.data(), itemSize);
+
+                retVal.add(string(key.data(), keySize),
+                           string(item.data(), itemSize));
+            }
         }
 
         size_t cubinHash = 0;
diff --git a/src/backend/cuda/cu_check_macro.hpp b/src/backend/cuda/cu_check_macro.hpp
new file mode 100644
index 0000000000..a6b8d3f3e1
--- /dev/null
+++ b/src/backend/cuda/cu_check_macro.hpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/err_common.hpp>
+
+#include <cuda.h>
+
+#include <cstdio>
+
+#define CU_CHECK(fn)                                                      \
+    do {                                                                  \
+        CUresult res = fn;                                                \
+        if (res == CUDA_SUCCESS) break;                                   \
+        char cu_err_msg[1024];                                            \
+        const char* cu_err_name;                                          \
+        const char* cu_err_string;                                        \
+        cuGetErrorName(res, &cu_err_name);                                \
+        cuGetErrorString(res, &cu_err_string);                            \
+        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                 cu_err_name, (int)(res), cu_err_string);                 \
+        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+    } while (0)
diff --git a/src/backend/opencl/Module.hpp b/src/backend/opencl/Module.hpp
index 2af60a51b4..c0bafeadec 100644
--- a/src/backend/opencl/Module.hpp
+++ b/src/backend/opencl/Module.hpp
@@ -22,6 +22,11 @@ class Module : public common::ModuleInterface<cl::Program*> {
     using BaseClass  = common::ModuleInterface<ModuleType>;
 
     Module(ModuleType mod) : BaseClass(mod) {}
+
+    void unload() final {
+        delete get();
+        set(nullptr);
+    }
 };
 
 }  // namespace opencl
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 21146b38ec..69f4414eb6 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -142,9 +142,11 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     return {program};
 }
 
-Module loadModuleFromDisk(const int device, const string &moduleKey) {
+Module loadModuleFromDisk(const int device, const string &moduleKey,
+                          const bool isJIT) {
     UNUSED(device);
     UNUSED(moduleKey);
+    UNUSED(isJIT);
     return {nullptr};
 }
 

From 782979bb142686f767e820bacad8ae90308d2566 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 27 May 2020 15:47:37 +0530
Subject: [PATCH 1970/2677] Reduce unnecessary instantiations across backends

---
 src/api/c/bilateral.cpp                       |  70 ++--
 src/api/c/canny.cpp                           |   5 +-
 src/api/c/convolve.cpp                        | 340 ++++++++----------
 src/api/c/data.cpp                            |  63 ++--
 src/api/c/deconvolution.cpp                   |  20 +-
 src/api/c/dog.cpp                             |   4 +-
 src/api/c/fft.cpp                             | 145 ++++----
 src/api/c/fft_common.hpp                      |  21 +-
 src/api/c/fftconvolve.cpp                     |  79 ++--
 src/api/c/filters.cpp                         | 156 +++-----
 src/api/c/histogram.cpp                       |  59 ++-
 src/api/c/match_template.cpp                  |  72 ++--
 src/api/c/morph.cpp                           |   7 +-
 src/backend/cpu/bilateral.cpp                 |  21 +-
 src/backend/cpu/bilateral.hpp                 |   8 +-
 src/backend/cpu/cholesky.cpp                  |   6 +-
 src/backend/cpu/convolve.cpp                  |  53 +--
 src/backend/cpu/convolve.hpp                  |   8 +-
 src/backend/cpu/fft.cpp                       |  95 ++---
 src/backend/cpu/fft.hpp                       |  12 +-
 src/backend/cpu/fftconvolve.cpp               |  85 ++---
 src/backend/cpu/fftconvolve.hpp               |   4 +-
 src/backend/cpu/harris.cpp                    |   6 +-
 src/backend/cpu/histogram.cpp                 |  46 ++-
 src/backend/cpu/histogram.hpp                 |   9 +-
 src/backend/cpu/iir.cpp                       |   2 +-
 src/backend/cpu/kernel/bilateral.hpp          |   2 +-
 src/backend/cpu/kernel/convolve.hpp           |  96 ++---
 src/backend/cpu/kernel/fftconvolve.hpp        |  14 +-
 src/backend/cpu/kernel/histogram.hpp          |  10 +-
 src/backend/cpu/kernel/match_template.hpp     |  13 +-
 src/backend/cpu/kernel/medfilt.hpp            |  42 ++-
 src/backend/cpu/kernel/sift_nonfree.hpp       |   8 +-
 src/backend/cpu/kernel/triangle.hpp           |   5 +-
 src/backend/cpu/match_template.cpp            |  35 +-
 src/backend/cpu/match_template.hpp            |   7 +-
 src/backend/cpu/medfilt.cpp                   |  32 +-
 src/backend/cpu/medfilt.hpp                   |  10 +-
 src/backend/cpu/orb.cpp                       |   4 +-
 src/backend/cpu/qr.cpp                        |   2 +-
 src/backend/cpu/triangle.cpp                  |  30 +-
 src/backend/cpu/triangle.hpp                  |  10 +-
 src/backend/cuda/bilateral.cpp                |  17 +-
 src/backend/cuda/bilateral.hpp                |   8 +-
 src/backend/cuda/cholesky.cpp                 |   6 +-
 src/backend/cuda/convolve.cpp                 |  47 +--
 src/backend/cuda/convolve.hpp                 |   8 +-
 src/backend/cuda/fft.cu                       |  86 ++---
 src/backend/cuda/fft.hpp                      |  12 +-
 src/backend/cuda/fftconvolve.cpp              |  48 ++-
 src/backend/cuda/fftconvolve.hpp              |   4 +-
 src/backend/cuda/histogram.cpp                |  52 ++-
 src/backend/cuda/histogram.hpp                |   9 +-
 src/backend/cuda/iir.cpp                      |   2 +-
 src/backend/cuda/kernel/fftconvolve.cuh       |   6 +-
 src/backend/cuda/kernel/fftconvolve.hpp       |   6 +-
 src/backend/cuda/kernel/histogram.cuh         |  20 +-
 src/backend/cuda/kernel/histogram.hpp         |   9 +-
 src/backend/cuda/match_template.cpp           |  27 +-
 src/backend/cuda/match_template.hpp           |   7 +-
 src/backend/cuda/medfilt.cpp                  |  24 +-
 src/backend/cuda/medfilt.hpp                  |  10 +-
 src/backend/cuda/triangle.cpp                 |  28 +-
 src/backend/cuda/triangle.hpp                 |  10 +-
 src/backend/opencl/bilateral.cpp              |  16 +-
 src/backend/opencl/bilateral.hpp              |   8 +-
 src/backend/opencl/cholesky.cpp               |   6 +-
 src/backend/opencl/convolve.cpp               |  38 +-
 src/backend/opencl/convolve.hpp               |   8 +-
 src/backend/opencl/convolve_separable.cpp     |  21 +-
 src/backend/opencl/fft.cpp                    |  67 ++--
 src/backend/opencl/fft.hpp                    |  12 +-
 src/backend/opencl/fftconvolve.cpp            |  60 ++--
 src/backend/opencl/fftconvolve.hpp            |   5 +-
 src/backend/opencl/histogram.cpp              |  52 ++-
 src/backend/opencl/histogram.hpp              |   9 +-
 src/backend/opencl/iir.cpp                    |   2 +-
 src/backend/opencl/kernel/bilateral.hpp       |   3 +-
 src/backend/opencl/kernel/convolve.cl         |   6 +-
 src/backend/opencl/kernel/convolve.hpp        |  14 +-
 src/backend/opencl/kernel/convolve/conv1.cpp  |  15 +-
 .../opencl/kernel/convolve/conv2_impl.hpp     |  21 +-
 src/backend/opencl/kernel/convolve/conv3.cpp  |  15 +-
 .../opencl/kernel/convolve/conv_common.hpp    |  33 +-
 .../opencl/kernel/convolve_separable.cpp      |  22 +-
 .../opencl/kernel/convolve_separable.hpp      |   5 +-
 src/backend/opencl/kernel/fftconvolve.hpp     |  22 +-
 src/backend/opencl/kernel/harris.hpp          |  12 +-
 src/backend/opencl/kernel/histogram.cl        |  14 +-
 src/backend/opencl/kernel/histogram.hpp       |  12 +-
 src/backend/opencl/kernel/orb.hpp             |   4 +-
 src/backend/opencl/kernel/sift_nonfree.hpp    |   4 +-
 src/backend/opencl/match_template.cpp         |  27 +-
 src/backend/opencl/match_template.hpp         |   7 +-
 src/backend/opencl/medfilt.cpp                |  26 +-
 src/backend/opencl/medfilt.hpp                |  10 +-
 src/backend/opencl/triangle.cpp               |  28 +-
 src/backend/opencl/triangle.hpp               |  10 +-
 98 files changed, 1213 insertions(+), 1563 deletions(-)

diff --git a/src/api/c/bilateral.cpp b/src/api/c/bilateral.cpp
index 7d3427ee74..44e15c725c 100644
--- a/src/api/c/bilateral.cpp
+++ b/src/api/c/bilateral.cpp
@@ -15,22 +15,27 @@
 #include <af/dim4.hpp>
 #include <af/image.h>
 
+#include <type_traits>
+
 using af::dim4;
 using detail::bilateral;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
-
-template<typename inType, typename outType, bool isColor>
-static inline af_array bilateral(const af_array &in, const float &sp_sig,
-                                 const float &chr_sig) {
-    return getHandle(bilateral<inType, outType, isColor>(getArray<inType>(in),
-                                                         sp_sig, chr_sig));
+using std::conditional;
+using std::is_same;
+
+template<typename T>
+inline af_array bilateral(const af_array &in, const float &sp_sig,
+                          const float &chr_sig) {
+    using OutType =
+        typename conditional<is_same<T, double>::value, double, float>::type;
+    return getHandle(bilateral<T, OutType>(getArray<T>(in), sp_sig, chr_sig));
 }
 
-template<bool isColor>
-static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma,
-                        const float &c_sigma) {
+af_err af_bilateral(af_array *out, const af_array in, const float ssigma,
+                    const float csigma, const bool iscolor) {
+    UNUSED(iscolor);
     try {
         const ArrayInfo &info = getInfo(in);
         af_dtype type         = info.getType();
@@ -38,34 +43,16 @@ static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma,
 
         DIM_ASSERT(1, (dims.ndims() >= 2));
 
-        af_array output;
+        af_array output = nullptr;
         switch (type) {
-            case f64:
-                output =
-                    bilateral<double, double, isColor>(in, s_sigma, c_sigma);
-                break;
-            case f32:
-                output = bilateral<float, float, isColor>(in, s_sigma, c_sigma);
-                break;
-            case b8:
-                output = bilateral<char, float, isColor>(in, s_sigma, c_sigma);
-                break;
-            case s32:
-                output = bilateral<int, float, isColor>(in, s_sigma, c_sigma);
-                break;
-            case u32:
-                output = bilateral<uint, float, isColor>(in, s_sigma, c_sigma);
-                break;
-            case u8:
-                output = bilateral<uchar, float, isColor>(in, s_sigma, c_sigma);
-                break;
-            case s16:
-                output = bilateral<short, float, isColor>(in, s_sigma, c_sigma);
-                break;
-            case u16:
-                output =
-                    bilateral<ushort, float, isColor>(in, s_sigma, c_sigma);
-                break;
+            case f64: output = bilateral<double>(in, ssigma, csigma); break;
+            case f32: output = bilateral<float>(in, ssigma, csigma); break;
+            case b8: output = bilateral<char>(in, ssigma, csigma); break;
+            case s32: output = bilateral<int>(in, ssigma, csigma); break;
+            case u32: output = bilateral<uint>(in, ssigma, csigma); break;
+            case u8: output = bilateral<uchar>(in, ssigma, csigma); break;
+            case s16: output = bilateral<short>(in, ssigma, csigma); break;
+            case u16: output = bilateral<ushort>(in, ssigma, csigma); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
@@ -74,14 +61,3 @@ static af_err bilateral(af_array *out, const af_array &in, const float &s_sigma,
 
     return AF_SUCCESS;
 }
-
-af_err af_bilateral(af_array *out, const af_array in, const float spatial_sigma,
-                    const float chromatic_sigma, const bool isColor) {
-    af_err err = AF_ERR_UNKNOWN;
-    if (isColor) {
-        err = bilateral<true>(out, in, spatial_sigma, chromatic_sigma);
-    } else {
-        err = bilateral<false>(out, in, spatial_sigma, chromatic_sigma);
-    }
-    return err;
-}
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 21010de1e8..42aa126929 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -73,8 +73,7 @@ Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy,
 
 Array<float> otsuThreshold(const Array<float>& supEdges,
                            const unsigned NUM_BINS, const float maxVal) {
-    Array<uint> hist =
-        histogram<float, uint, false>(supEdges, NUM_BINS, 0, maxVal);
+    Array<uint> hist = histogram<float>(supEdges, NUM_BINS, 0, maxVal, false);
 
     const dim4& hDims = hist.dims();
 
@@ -208,7 +207,7 @@ af_array cannyHelper(const Array<T>& in, const float t1,
 
     // Run separable convolution to smooth the input image
     Array<float> smt =
-        convolve2<float, float, false>(cast<float, T>(in), cFilter, rFilter);
+        convolve2<float, float>(cast<float, T>(in), cFilter, rFilter, false);
 
     auto g          = sobelDerivatives<float, float>(smt, sw);
     Array<float> gx = g.first;
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 938808a648..4df2f6fe6c 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -38,16 +38,17 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
-inline static af_array convolve(const af_array &s, const af_array &f,
-                                AF_BATCH_KIND kind) {
-    return getHandle(convolve<T, accT, baseDim, expand>(
-        getArray<T>(s), castArray<accT>(f), kind));
+template<typename T, typename accT>
+inline af_array convolve(const af_array &s, const af_array &f,
+                         AF_BATCH_KIND kind, const int rank,
+                         const bool expand) {
+    return getHandle(convolve<T, accT>(getArray<T>(s), castArray<accT>(f), kind,
+                                       rank, expand));
 }
 
-template<typename T, typename accT, bool expand>
-inline static af_array convolve2(const af_array &s, const af_array &c_f,
-                                 const af_array &r_f) {
+template<typename T, typename accT>
+inline af_array convolve2(const af_array &s, const af_array &c_f,
+                          const af_array &r_f, const bool expand) {
     const Array<accT> colFilter = castArray<accT>(c_f);
     const Array<accT> rowFilter = castArray<accT>(r_f);
     const Array<accT> signal    = castArray<accT>(s);
@@ -67,26 +68,21 @@ inline static af_array convolve2(const af_array &s, const af_array &c_f,
     ARG_ASSERT(3, rowFilter.isVector());
 
     return getHandle(
-        convolve2<T, accT, expand>(getArray<T>(s), colFilter, rowFilter));
+        convolve2<T, accT>(getArray<T>(s), colFilter, rowFilter, expand));
 }
 
-template<dim_t baseDim>
-AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
+AF_BATCH_KIND identifyBatchKind(const int rank, const dim4 &sDims,
+                                const dim4 &fDims) {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
-    if (sn == baseDim && fn == baseDim) { return AF_BATCH_NONE; }
-    if (sn == baseDim && (fn > baseDim && fn <= AF_MAX_DIMS)) {
-        return AF_BATCH_RHS;
-    }
-    if ((sn > baseDim && sn <= AF_MAX_DIMS) && fn == baseDim) {
-        return AF_BATCH_LHS;
-    }
-    if ((sn > baseDim && sn <= AF_MAX_DIMS) &&
-        (fn > baseDim && fn <= AF_MAX_DIMS)) {
+    if (sn == rank && fn == rank) { return AF_BATCH_NONE; }
+    if (sn == rank && (fn > rank && fn <= AF_MAX_DIMS)) { return AF_BATCH_RHS; }
+    if ((sn > rank && sn <= AF_MAX_DIMS) && fn == rank) { return AF_BATCH_LHS; }
+    if ((sn > rank && sn <= AF_MAX_DIMS) && (fn > rank && fn <= AF_MAX_DIMS)) {
         bool doesDimensionsMatch = true;
         bool isInterleaved       = true;
-        for (dim_t i = baseDim; i < AF_MAX_DIMS; i++) {
+        for (dim_t i = rank; i < AF_MAX_DIMS; i++) {
             doesDimensionsMatch &= (sDims[i] == fDims[i]);
             isInterleaved &=
                 (sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
@@ -97,8 +93,41 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
     return AF_BATCH_UNSUPPORTED;
 }
 
-template<dim_t baseDim, bool expand>
-af_err convolve(af_array *out, const af_array signal, const af_array filter) {
+bool isFreqDomain(const int rank, const af_array &signal, const af_array filter,
+                  af_conv_domain domain) {
+    if (domain == AF_CONV_FREQ) { return true; }
+    if (domain != AF_CONV_AUTO) { return false; }
+
+    const ArrayInfo &sInfo = getInfo(signal);
+    const ArrayInfo &fInfo = getInfo(filter);
+
+    const dim4 &sdims = sInfo.dims();
+    dim4 fdims        = fInfo.dims();
+
+    if (identifyBatchKind(rank, sdims, fdims) == AF_BATCH_DIFF) { return true; }
+
+    int kbatch = 1;
+    for (int i = 3; i >= rank; i--) { kbatch *= fdims[i]; }
+
+    if (kbatch >= 10) { return true; }
+    if (rank == 1) {
+        if (fdims[0] > 128) { return true; }
+    }
+    if (rank == 2) {
+        // maximum supported size in 2D domain
+        if (fdims[0] > 17 || fdims[1] > 17) { return true; }
+
+        // Maximum supported non square size
+        if (fdims[0] != fdims[1] && fdims[0] > 5) { return true; }
+    }
+    if (rank == 3) {
+        if (fdims[0] > 5 || fdims[1] > 5 || fdims[2] > 5) { return true; }
+    }
+    return false;
+}
+
+af_err convolve(af_array *out, const af_array signal, const af_array filter,
+                const af_conv_mode mode, const int rank) {
     try {
         const ArrayInfo &sInfo = getInfo(signal);
         const ArrayInfo &fInfo = getInfo(filter);
@@ -112,60 +141,62 @@ af_err convolve(af_array *out, const af_array signal, const af_array filter) {
             return af_retain_array(out, signal);
         }
 
-        AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
+        AF_BATCH_KIND convBT = identifyBatchKind(rank, sdims, fdims);
 
         ARG_ASSERT(1,
                    (convBT != AF_BATCH_UNSUPPORTED && convBT != AF_BATCH_DIFF));
 
+        const bool expand = mode == AF_CONV_EXPAND;
+
         af_array output;
         switch (stype) {
             case c32:
-                output = convolve<cfloat, cfloat, baseDim, expand>(
-                    signal, filter, convBT);
+                output = convolve<cfloat, cfloat>(signal, filter, convBT, rank,
+                                                  expand);
                 break;
             case c64:
-                output = convolve<cdouble, cdouble, baseDim, expand>(
-                    signal, filter, convBT);
+                output = convolve<cdouble, cdouble>(signal, filter, convBT,
+                                                    rank, expand);
                 break;
             case f32:
-                output = convolve<float, float, baseDim, expand>(signal, filter,
-                                                                 convBT);
+                output = convolve<float, float>(signal, filter, convBT, rank,
+                                                expand);
                 break;
             case f64:
-                output = convolve<double, double, baseDim, expand>(
-                    signal, filter, convBT);
+                output = convolve<double, double>(signal, filter, convBT, rank,
+                                                  expand);
                 break;
             case u32:
-                output = convolve<uint, float, baseDim, expand>(signal, filter,
-                                                                convBT);
+                output =
+                    convolve<uint, float>(signal, filter, convBT, rank, expand);
                 break;
             case s32:
-                output = convolve<int, float, baseDim, expand>(signal, filter,
-                                                               convBT);
+                output =
+                    convolve<int, float>(signal, filter, convBT, rank, expand);
                 break;
             case u16:
-                output = convolve<ushort, float, baseDim, expand>(
-                    signal, filter, convBT);
+                output = convolve<ushort, float>(signal, filter, convBT, rank,
+                                                 expand);
                 break;
             case s16:
-                output = convolve<short, float, baseDim, expand>(signal, filter,
-                                                                 convBT);
+                output = convolve<short, float>(signal, filter, convBT, rank,
+                                                expand);
                 break;
             case u64:
-                output = convolve<uintl, float, baseDim, expand>(signal, filter,
-                                                                 convBT);
+                output = convolve<uintl, float>(signal, filter, convBT, rank,
+                                                expand);
                 break;
             case s64:
-                output = convolve<intl, float, baseDim, expand>(signal, filter,
-                                                                convBT);
+                output =
+                    convolve<intl, float>(signal, filter, convBT, rank, expand);
                 break;
             case u8:
-                output = convolve<uchar, float, baseDim, expand>(signal, filter,
-                                                                 convBT);
+                output = convolve<uchar, float>(signal, filter, convBT, rank,
+                                                expand);
                 break;
             case b8:
-                output = convolve<char, float, baseDim, expand>(signal, filter,
-                                                                convBT);
+                output =
+                    convolve<char, float>(signal, filter, convBT, rank, expand);
                 break;
             default: TYPE_ERROR(1, stype);
         }
@@ -176,9 +207,50 @@ af_err convolve(af_array *out, const af_array signal, const af_array filter) {
     return AF_SUCCESS;
 }
 
-template<bool expand>
-af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter,
-                     const af_array signal) {
+af_err af_convolve1(af_array *out, const af_array signal, const af_array filter,
+                    const af_conv_mode mode, af_conv_domain domain) {
+    try {
+        if (isFreqDomain(1, signal, filter, domain)) {
+            return af_fft_convolve1(out, signal, filter, mode);
+        }
+        return convolve(out, signal, filter, mode, 1);
+    }
+    CATCHALL;
+}
+
+af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
+                    const af_conv_mode mode, af_conv_domain domain) {
+    try {
+        if (getInfo(signal).dims().ndims() < 2 ||
+            getInfo(filter).dims().ndims() < 2) {
+            return af_convolve1(out, signal, filter, mode, domain);
+        }
+        if (isFreqDomain(2, signal, filter, domain)) {
+            return af_fft_convolve2(out, signal, filter, mode);
+        }
+        return convolve(out, signal, filter, mode, 2);
+    }
+    CATCHALL;
+}
+
+af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
+                    const af_conv_mode mode, af_conv_domain domain) {
+    try {
+        if (getInfo(signal).dims().ndims() < 3 ||
+            getInfo(filter).dims().ndims() < 3) {
+            return af_convolve2(out, signal, filter, mode, domain);
+        }
+        if (isFreqDomain(3, signal, filter, domain)) {
+            return af_fft_convolve3(out, signal, filter, mode);
+        }
+        return convolve(out, signal, filter, mode, 3);
+    }
+    CATCHALL;
+}
+
+af_err af_convolve2_sep(af_array *out, const af_array col_filter,
+                        const af_array row_filter, const af_array signal,
+                        const af_conv_mode mode) {
     try {
         const ArrayInfo &sInfo = getInfo(signal);
 
@@ -190,54 +262,56 @@ af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter,
 
         af_array output = 0;
 
+        const bool expand = mode == AF_CONV_EXPAND;
+
         switch (signalType) {
             case c32:
-                output = convolve2<cfloat, cfloat, expand>(signal, col_filter,
-                                                           row_filter);
+                output = convolve2<cfloat, cfloat>(signal, col_filter,
+                                                   row_filter, expand);
                 break;
             case c64:
-                output = convolve2<cdouble, cdouble, expand>(signal, col_filter,
-                                                             row_filter);
+                output = convolve2<cdouble, cdouble>(signal, col_filter,
+                                                     row_filter, expand);
                 break;
             case f32:
-                output = convolve2<float, float, expand>(signal, col_filter,
-                                                         row_filter);
+                output = convolve2<float, float>(signal, col_filter, row_filter,
+                                                 expand);
                 break;
             case f64:
-                output = convolve2<double, double, expand>(signal, col_filter,
-                                                           row_filter);
+                output = convolve2<double, double>(signal, col_filter,
+                                                   row_filter, expand);
                 break;
             case u32:
-                output = convolve2<uint, float, expand>(signal, col_filter,
-                                                        row_filter);
+                output = convolve2<uint, float>(signal, col_filter, row_filter,
+                                                expand);
                 break;
             case s32:
-                output = convolve2<int, float, expand>(signal, col_filter,
-                                                       row_filter);
+                output = convolve2<int, float>(signal, col_filter, row_filter,
+                                               expand);
                 break;
             case u16:
-                output = convolve2<ushort, float, expand>(signal, col_filter,
-                                                          row_filter);
+                output = convolve2<ushort, float>(signal, col_filter,
+                                                  row_filter, expand);
                 break;
             case s16:
-                output = convolve2<short, float, expand>(signal, col_filter,
-                                                         row_filter);
+                output = convolve2<short, float>(signal, col_filter, row_filter,
+                                                 expand);
                 break;
             case u64:
-                output = convolve2<uintl, float, expand>(signal, col_filter,
-                                                         row_filter);
+                output = convolve2<uintl, float>(signal, col_filter, row_filter,
+                                                 expand);
                 break;
             case s64:
-                output = convolve2<intl, float, expand>(signal, col_filter,
-                                                        row_filter);
+                output = convolve2<intl, float>(signal, col_filter, row_filter,
+                                                expand);
                 break;
             case u8:
-                output = convolve2<uchar, float, expand>(signal, col_filter,
-                                                         row_filter);
+                output = convolve2<uchar, float>(signal, col_filter, row_filter,
+                                                 expand);
                 break;
             case b8:
-                output = convolve2<char, float, expand>(signal, col_filter,
-                                                        row_filter);
+                output = convolve2<char, float>(signal, col_filter, row_filter,
+                                                expand);
                 break;
             default: TYPE_ERROR(1, signalType);
         }
@@ -248,86 +322,10 @@ af_err convolve2_sep(af_array *out, af_array col_filter, af_array row_filter,
     return AF_SUCCESS;
 }
 
-template<int baseDim>
-bool isFreqDomain(const af_array &signal, const af_array filter,
-                  af_conv_domain domain) {
-    if (domain == AF_CONV_FREQ) { return true; }
-    if (domain != AF_CONV_AUTO) { return false; }
-
-    const ArrayInfo &sInfo = getInfo(signal);
-    const ArrayInfo &fInfo = getInfo(filter);
-
-    const dim4 &sdims = sInfo.dims();
-    dim4 fdims        = fInfo.dims();
-
-    if (identifyBatchKind<baseDim>(sdims, fdims) == AF_BATCH_DIFF) {
-        return true;
-    }
-
-    int kbatch = 1;
-    for (int i = 3; i >= baseDim; i--) { kbatch *= fdims[i]; }
-
-    if (kbatch >= 10) { return true; }
-
-    if (baseDim == 1) {
-        if (fdims[0] > 128) { return true; }
-    }
-
-    if (baseDim == 2) {
-        // maximum supported size in 2D domain
-        if (fdims[0] > 17 || fdims[1] > 17) { return true; }
-
-        // Maximum supported non square size
-        if (fdims[0] != fdims[1] && fdims[0] > 5) { return true; }
-    }
-
-    if (baseDim == 3) {
-        if (fdims[0] > 5 || fdims[1] > 5 || fdims[2] > 5) { return true; }
-    }
-
-    return false;
-}
-
-af_err af_convolve1(af_array *out, const af_array signal, const af_array filter,
-                    const af_conv_mode mode, af_conv_domain domain) {
-    try {
-        if (isFreqDomain<1>(signal, filter, domain)) {
-            return af_fft_convolve1(out, signal, filter, mode);
-        }
-
-        if (mode == AF_CONV_EXPAND) {
-            return convolve<1, true>(out, signal, filter);
-        }
-        { return convolve<1, false>(out, signal, filter); }
-    }
-    CATCHALL;
-}
-
-af_err af_convolve2(af_array *out, const af_array signal, const af_array filter,
-                    const af_conv_mode mode, af_conv_domain domain) {
-    try {
-        if (getInfo(signal).dims().ndims() < 2 ||
-            getInfo(filter).dims().ndims() < 2) {
-            return af_convolve1(out, signal, filter, mode, domain);
-        }
-
-        if (isFreqDomain<2>(signal, filter, domain)) {
-            return af_fft_convolve2(out, signal, filter, mode);
-        }
-
-        if (mode == AF_CONV_EXPAND) {
-            return convolve<2, true>(out, signal, filter);
-        } else {
-            return convolve<2, false>(out, signal, filter);
-        }
-    }
-    CATCHALL;
-}
-
 template<typename T>
-inline static af_array convolve2Strided(const af_array &s, const af_array &f,
-                                        const dim4 stride, const dim4 padding,
-                                        const dim4 dilation) {
+inline af_array convolve2Strided(const af_array &s, const af_array &f,
+                                 const dim4 stride, const dim4 padding,
+                                 const dim4 dilation) {
     return getHandle(convolve2<T>(getArray<T>(s), getArray<T>(f), stride,
                                   padding, dilation));
 }
@@ -379,40 +377,6 @@ af_err af_convolve2_nn(af_array *out, const af_array signal,
     return AF_SUCCESS;
 }
 
-af_err af_convolve3(af_array *out, const af_array signal, const af_array filter,
-                    const af_conv_mode mode, af_conv_domain domain) {
-    try {
-        if (getInfo(signal).dims().ndims() < 3 ||
-            getInfo(filter).dims().ndims() < 3) {
-            return af_convolve2(out, signal, filter, mode, domain);
-        }
-
-        if (isFreqDomain<3>(signal, filter, domain)) {
-            return af_fft_convolve3(out, signal, filter, mode);
-        }
-
-        if (mode == AF_CONV_EXPAND) {
-            return convolve<3, true>(out, signal, filter);
-        } else {
-            return convolve<3, false>(out, signal, filter);
-        }
-    }
-    CATCHALL;
-}
-
-af_err af_convolve2_sep(af_array *out, const af_array signal,
-                        const af_array col_filter, const af_array row_filter,
-                        const af_conv_mode mode) {
-    try {
-        if (mode == AF_CONV_EXPAND) {
-            return convolve2_sep<true>(out, signal, col_filter, row_filter);
-        } else {
-            return convolve2_sep<false>(out, signal, col_filter, row_filter);
-        }
-    }
-    CATCHALL;
-}
-
 template<typename T>
 af_array conv2GradCall(const af_array incoming_gradient,
                        const af_array original_signal,
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 79a604173b..6a82d419c5 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -354,13 +354,10 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num) {
     return AF_SUCCESS;
 }
 
-template<typename T, bool is_upper>
-af_array triangle(const af_array in, bool is_unit_diag) {
-    if (is_unit_diag) {
-        return getHandle(triangle<T, is_upper, true>(getArray<T>(in)));
-    } else {
-        return getHandle(triangle<T, is_upper, false>(getArray<T>(in)));
-    }
+template<typename T>
+inline af_array triangle(const af_array in, const bool is_upper,
+                         const bool is_unit_diag) {
+    return getHandle(triangle<T>(getArray<T>(in), is_upper, is_unit_diag));
 }
 
 af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
@@ -372,19 +369,19 @@ af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
 
         af_array res;
         switch (type) {
-            case f32: res = triangle<float, false>(in, is_unit_diag); break;
-            case f64: res = triangle<double, false>(in, is_unit_diag); break;
-            case c32: res = triangle<cfloat, false>(in, is_unit_diag); break;
-            case c64: res = triangle<cdouble, false>(in, is_unit_diag); break;
-            case s32: res = triangle<int, false>(in, is_unit_diag); break;
-            case u32: res = triangle<uint, false>(in, is_unit_diag); break;
-            case s64: res = triangle<intl, false>(in, is_unit_diag); break;
-            case u64: res = triangle<uintl, false>(in, is_unit_diag); break;
-            case s16: res = triangle<short, false>(in, is_unit_diag); break;
-            case u16: res = triangle<ushort, false>(in, is_unit_diag); break;
-            case u8: res = triangle<uchar, false>(in, is_unit_diag); break;
-            case b8: res = triangle<char, false>(in, is_unit_diag); break;
-            case f16: res = triangle<half, false>(in, is_unit_diag); break;
+            case f32: res = triangle<float>(in, false, is_unit_diag); break;
+            case f64: res = triangle<double>(in, false, is_unit_diag); break;
+            case c32: res = triangle<cfloat>(in, false, is_unit_diag); break;
+            case c64: res = triangle<cdouble>(in, false, is_unit_diag); break;
+            case s32: res = triangle<int>(in, false, is_unit_diag); break;
+            case u32: res = triangle<uint>(in, false, is_unit_diag); break;
+            case s64: res = triangle<intl>(in, false, is_unit_diag); break;
+            case u64: res = triangle<uintl>(in, false, is_unit_diag); break;
+            case s16: res = triangle<short>(in, false, is_unit_diag); break;
+            case u16: res = triangle<ushort>(in, false, is_unit_diag); break;
+            case u8: res = triangle<uchar>(in, false, is_unit_diag); break;
+            case b8: res = triangle<char>(in, false, is_unit_diag); break;
+            case f16: res = triangle<half>(in, false, is_unit_diag); break;
         }
         std::swap(*out, res);
     }
@@ -401,19 +398,19 @@ af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
 
         af_array res;
         switch (type) {
-            case f32: res = triangle<float, true>(in, is_unit_diag); break;
-            case f64: res = triangle<double, true>(in, is_unit_diag); break;
-            case c32: res = triangle<cfloat, true>(in, is_unit_diag); break;
-            case c64: res = triangle<cdouble, true>(in, is_unit_diag); break;
-            case s32: res = triangle<int, true>(in, is_unit_diag); break;
-            case u32: res = triangle<uint, true>(in, is_unit_diag); break;
-            case s64: res = triangle<intl, true>(in, is_unit_diag); break;
-            case u64: res = triangle<uintl, true>(in, is_unit_diag); break;
-            case s16: res = triangle<short, true>(in, is_unit_diag); break;
-            case u16: res = triangle<ushort, true>(in, is_unit_diag); break;
-            case u8: res = triangle<uchar, true>(in, is_unit_diag); break;
-            case b8: res = triangle<char, true>(in, is_unit_diag); break;
-            case f16: res = triangle<half, true>(in, is_unit_diag); break;
+            case f32: res = triangle<float>(in, true, is_unit_diag); break;
+            case f64: res = triangle<double>(in, true, is_unit_diag); break;
+            case c32: res = triangle<cfloat>(in, true, is_unit_diag); break;
+            case c64: res = triangle<cdouble>(in, true, is_unit_diag); break;
+            case s32: res = triangle<int>(in, true, is_unit_diag); break;
+            case u32: res = triangle<uint>(in, true, is_unit_diag); break;
+            case s64: res = triangle<intl>(in, true, is_unit_diag); break;
+            case u64: res = triangle<uintl>(in, true, is_unit_diag); break;
+            case s16: res = triangle<short>(in, true, is_unit_diag); break;
+            case u16: res = triangle<ushort>(in, true, is_unit_diag); break;
+            case u8: res = triangle<uchar>(in, true, is_unit_diag); break;
+            case b8: res = triangle<char>(in, true, is_unit_diag); break;
+            case f16: res = triangle<half>(in, true, is_unit_diag); break;
         }
         std::swap(*out, res);
     }
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index 7ce24001b9..d5c67757dc 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -112,13 +112,13 @@ void richardsonLucy(Array<T>& currentEstimate, const Array<T>& in,
                     const unsigned iters, const float normFactor,
                     const dim4 odims) {
     for (unsigned i = 0; i < iters; ++i) {
-        auto fft1  = fft_r2c<CT, T, BASE_DIM>(currentEstimate);
+        auto fft1  = fft_r2c<CT, T>(currentEstimate, BASE_DIM);
         auto cmul1 = arithOp<CT, af_mul_t>(fft1, P, P.dims());
-        auto ifft1 = fft_c2r<CT, T, BASE_DIM>(cmul1, normFactor, odims);
+        auto ifft1 = fft_c2r<CT, T>(cmul1, normFactor, odims, BASE_DIM);
         auto div1  = arithOp<T, af_div_t>(in, ifft1, in.dims());
-        auto fft2  = fft_r2c<CT, T, BASE_DIM>(div1);
+        auto fft2  = fft_r2c<CT, T>(div1, BASE_DIM);
         auto cmul2 = arithOp<CT, af_mul_t>(fft2, Pc, Pc.dims());
-        auto ifft2 = fft_c2r<CT, T, BASE_DIM>(cmul2, normFactor, odims);
+        auto ifft2 = fft_c2r<CT, T>(cmul2, normFactor, odims, BASE_DIM);
 
         currentEstimate =
             arithOp<T, af_mul_t>(currentEstimate, ifft2, ifft2.dims());
@@ -132,7 +132,7 @@ void landweber(Array<T>& currentEstimate, const Array<T>& in,
                const dim4 odims) {
     const dim4& dims = P.dims();
 
-    auto I        = fft_r2c<CT, T, BASE_DIM>(in);
+    auto I        = fft_r2c<CT, T>(in, BASE_DIM);
     auto Pn       = complexNorm<T, CT>(P);
     auto ONE      = createValueArray(dims, scalar<T>(1.0));
     auto alpha    = createValueArray(dims, scalar<T>(relaxFactor));
@@ -148,7 +148,7 @@ void landweber(Array<T>& currentEstimate, const Array<T>& in,
         auto mul = arithOp<CT, af_mul_t>(iterTemp, lhs, dims);
         iterTemp = arithOp<CT, af_add_t>(mul, rhs, dims);
     }
-    currentEstimate = fft_c2r<CT, T, BASE_DIM>(iterTemp, normFactor, odims);
+    currentEstimate = fft_c2r<CT, T>(iterTemp, normFactor, odims, BASE_DIM);
 }
 
 template<typename InputType, typename RealType = float>
@@ -175,7 +175,7 @@ af_array iterDeconv(const af_array in, const af_array ker, const uint iters,
                                           -int(fdims[1] / 2), 0, 0};
     auto shiftedPsf                    = shift(paddedPsf, shiftDims.data());
 
-    auto P  = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
+    auto P  = fft_r2c<CT, T>(shiftedPsf, BASE_DIM);
     auto Pc = conj(P);
 
     Array<T> currentEstimate = paddedIn;
@@ -284,8 +284,8 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
 
     auto shiftedPsf = shift(paddedPsf, shiftDims.data());
 
-    auto I      = fft_r2c<CT, T, BASE_DIM>(paddedIn);
-    auto P      = fft_r2c<CT, T, BASE_DIM>(shiftedPsf);
+    auto I      = fft_r2c<CT, T>(paddedIn, BASE_DIM);
+    auto P      = fft_r2c<CT, T>(shiftedPsf, BASE_DIM);
     auto Pc     = conj(P);
     auto numer  = arithOp<CT, af_mul_t>(I, Pc, I.dims());
     auto denom  = denominator(I, P, gamma, algo);
@@ -297,7 +297,7 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
     select_scalar<CT, false>(val, cond, val, 0);
 
     auto ival =
-        fft_c2r<CT, T, BASE_DIM>(val, 1 / static_cast<double>(nElems), odims);
+        fft_c2r<CT, T>(val, 1 / static_cast<double>(nElems), odims, BASE_DIM);
 
     return getHandle(createSubArray<T>(ival, index));
 }
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index 633f901409..fbbe94d211 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -41,9 +41,9 @@ static af_array dog(const af_array& in, const int radius1, const int radius2) {
     AF_BATCH_KIND bkind = iDims[2] > 1 ? AF_BATCH_LHS : AF_BATCH_NONE;
 
     Array<accT> smth1 =
-        convolve<accT, accT, 2, false>(input, castArray<accT>(g1), bkind);
+        convolve<accT, accT>(input, castArray<accT>(g1), bkind, 2, false);
     Array<accT> smth2 =
-        convolve<accT, accT, 2, false>(input, castArray<accT>(g2), bkind);
+        convolve<accT, accT>(input, castArray<accT>(g2), bkind, 2, false);
     Array<accT> retVal = arithOp<accT, af_sub_t>(smth1, smth2, iDims);
 
     AF_CHECK(af_release_array(g1));
diff --git a/src/api/c/fft.cpp b/src/api/c/fft.cpp
index e68a4a4722..ec3586f839 100644
--- a/src/api/c/fft.cpp
+++ b/src/api/c/fft.cpp
@@ -14,11 +14,15 @@
 #include <af/dim4.hpp>
 #include <af/signal.h>
 
+#include <type_traits>
+
 using af::dim4;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::multiply_inplace;
+using std::conditional;
+using std::is_same;
 
 void computePaddedDims(dim4 &pdims, const dim4 &idims, const dim_t npad,
                        dim_t const *const pad) {
@@ -27,16 +31,19 @@ void computePaddedDims(dim4 &pdims, const dim4 &idims, const dim_t npad,
     }
 }
 
-template<typename inType, typename outType, int rank, bool direction>
-static af_array fft(const af_array in, const double norm_factor,
-                    const dim_t npad, const dim_t *const pad) {
-    return getHandle(fft<inType, outType, rank, direction>(
-        getArray<inType>(in), norm_factor, npad, pad));
+template<typename InType>
+af_array fft(const af_array in, const double norm_factor, const dim_t npad,
+             const dim_t *const pad, int rank, bool direction) {
+    using OutType = typename conditional<is_same<InType, double>::value ||
+                                             is_same<InType, cdouble>::value,
+                                         cdouble, cfloat>::type;
+    return getHandle(fft<InType, OutType>(getArray<InType>(in), norm_factor,
+                                          npad, pad, rank, direction));
 }
 
-template<int rank, bool direction>
-static af_err fft(af_array *out, const af_array in, const double norm_factor,
-                  const dim_t npad, const dim_t *const pad) {
+af_err fft(af_array *out, const af_array in, const double norm_factor,
+           const dim_t npad, const dim_t *const pad, const int rank,
+           const bool direction) {
     try {
         const ArrayInfo &info = getInfo(in);
         af_dtype type         = info.getType();
@@ -49,20 +56,20 @@ static af_err fft(af_array *out, const af_array in, const double norm_factor,
         af_array output;
         switch (type) {
             case c32:
-                output = fft<cfloat, cfloat, rank, direction>(in, norm_factor,
-                                                              npad, pad);
+                output =
+                    fft<cfloat>(in, norm_factor, npad, pad, rank, direction);
                 break;
             case c64:
-                output = fft<cdouble, cdouble, rank, direction>(in, norm_factor,
-                                                                npad, pad);
+                output =
+                    fft<cdouble>(in, norm_factor, npad, pad, rank, direction);
                 break;
             case f32:
-                output = fft<float, cfloat, rank, direction>(in, norm_factor,
-                                                             npad, pad);
+                output =
+                    fft<float>(in, norm_factor, npad, pad, rank, direction);
                 break;
             case f64:
-                output = fft<double, cdouble, rank, direction>(in, norm_factor,
-                                                               npad, pad);
+                output =
+                    fft<double>(in, norm_factor, npad, pad, rank, direction);
                 break;
             default: TYPE_ERROR(1, type);
         }
@@ -76,52 +83,53 @@ static af_err fft(af_array *out, const af_array in, const double norm_factor,
 af_err af_fft(af_array *out, const af_array in, const double norm_factor,
               const dim_t pad0) {
     const dim_t pad[1] = {pad0};
-    return fft<1, true>(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad);
+    return fft(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad, 1, true);
 }
 
 af_err af_fft2(af_array *out, const af_array in, const double norm_factor,
                const dim_t pad0, const dim_t pad1) {
     const dim_t pad[2] = {pad0, pad1};
-    return fft<2, true>(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0),
-                        pad);
+    return fft(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0), pad, 2,
+               true);
 }
 
 af_err af_fft3(af_array *out, const af_array in, const double norm_factor,
                const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     const dim_t pad[3] = {pad0, pad1, pad2};
-    return fft<3, true>(out, in, norm_factor,
-                        (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad);
+    return fft(out, in, norm_factor, (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0),
+               pad, 3, true);
 }
 
 af_err af_ifft(af_array *out, const af_array in, const double norm_factor,
                const dim_t pad0) {
     const dim_t pad[1] = {pad0};
-    return fft<1, false>(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad);
+    return fft(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad, 1, false);
 }
 
 af_err af_ifft2(af_array *out, const af_array in, const double norm_factor,
                 const dim_t pad0, const dim_t pad1) {
     const dim_t pad[2] = {pad0, pad1};
-    return fft<2, false>(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0),
-                         pad);
+    return fft(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0), pad, 2,
+               false);
 }
 
 af_err af_ifft3(af_array *out, const af_array in, const double norm_factor,
                 const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     const dim_t pad[3] = {pad0, pad1, pad2};
-    return fft<3, false>(out, in, norm_factor,
-                         (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad);
+    return fft(out, in, norm_factor, (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0),
+               pad, 3, false);
 }
 
-template<typename T, int rank, bool direction>
-static void fft_inplace(af_array in, const double norm_factor) {
+template<typename T>
+void fft_inplace(af_array in, const double norm_factor, int rank,
+                 bool direction) {
     Array<T> &input = getArray<T>(in);
-    fft_inplace<T, rank, direction>(input);
+    fft_inplace<T>(input, rank, direction);
     if (norm_factor != 1) { multiply_inplace<T>(input, norm_factor); }
 }
 
-template<int rank, bool direction>
-static af_err fft_inplace(af_array in, const double norm_factor) {
+af_err fft_inplace(af_array in, const double norm_factor, int rank,
+                   bool direction) {
     try {
         const ArrayInfo &info = getInfo(in);
         af_dtype type         = info.getType();
@@ -132,10 +140,10 @@ static af_err fft_inplace(af_array in, const double norm_factor) {
 
         switch (type) {
             case c32:
-                fft_inplace<cfloat, rank, direction>(in, norm_factor);
+                fft_inplace<cfloat>(in, norm_factor, rank, direction);
                 break;
             case c64:
-                fft_inplace<cdouble, rank, direction>(in, norm_factor);
+                fft_inplace<cdouble>(in, norm_factor, rank, direction);
                 break;
             default: TYPE_ERROR(1, type);
         }
@@ -146,40 +154,40 @@ static af_err fft_inplace(af_array in, const double norm_factor) {
 }
 
 af_err af_fft_inplace(af_array in, const double norm_factor) {
-    return fft_inplace<1, true>(in, norm_factor);
+    return fft_inplace(in, norm_factor, 1, true);
 }
 
 af_err af_fft2_inplace(af_array in, const double norm_factor) {
-    return fft_inplace<2, true>(in, norm_factor);
+    return fft_inplace(in, norm_factor, 2, true);
 }
 
 af_err af_fft3_inplace(af_array in, const double norm_factor) {
-    return fft_inplace<3, true>(in, norm_factor);
+    return fft_inplace(in, norm_factor, 3, true);
 }
 
 af_err af_ifft_inplace(af_array in, const double norm_factor) {
-    return fft_inplace<1, false>(in, norm_factor);
+    return fft_inplace(in, norm_factor, 1, false);
 }
 
 af_err af_ifft2_inplace(af_array in, const double norm_factor) {
-    return fft_inplace<2, false>(in, norm_factor);
+    return fft_inplace(in, norm_factor, 2, false);
 }
 
 af_err af_ifft3_inplace(af_array in, const double norm_factor) {
-    return fft_inplace<3, false>(in, norm_factor);
+    return fft_inplace(in, norm_factor, 3, false);
 }
 
-template<typename inType, typename outType, int rank>
-static af_array fft_r2c(const af_array in, const double norm_factor,
-                        const dim_t npad, const dim_t *const pad) {
-    return getHandle(fft_r2c<inType, outType, rank>(getArray<inType>(in),
-                                                    norm_factor, npad, pad));
+template<typename InType>
+af_array fft_r2c(const af_array in, const double norm_factor, const dim_t npad,
+                 const dim_t *const pad, const int rank) {
+    using OutType = typename conditional<is_same<InType, double>::value,
+                                         cdouble, cfloat>::type;
+    return getHandle(fft_r2c<InType, OutType>(getArray<InType>(in), norm_factor,
+                                              npad, pad, rank));
 }
 
-template<int rank>
-static af_err fft_r2c(af_array *out, const af_array in,
-                      const double norm_factor, const dim_t npad,
-                      const dim_t *const pad) {
+af_err fft_r2c(af_array *out, const af_array in, const double norm_factor,
+               const dim_t npad, const dim_t *const pad, const int rank) {
     try {
         const ArrayInfo &info = getInfo(in);
         af_dtype type         = info.getType();
@@ -191,12 +199,10 @@ static af_err fft_r2c(af_array *out, const af_array in,
         af_array output;
         switch (type) {
             case f32:
-                output =
-                    fft_r2c<float, cfloat, rank>(in, norm_factor, npad, pad);
+                output = fft_r2c<float>(in, norm_factor, npad, pad, rank);
                 break;
             case f64:
-                output =
-                    fft_r2c<double, cdouble, rank>(in, norm_factor, npad, pad);
+                output = fft_r2c<double>(in, norm_factor, npad, pad, rank);
                 break;
             default: {
                 TYPE_ERROR(1, type);
@@ -212,33 +218,34 @@ static af_err fft_r2c(af_array *out, const af_array in,
 af_err af_fft_r2c(af_array *out, const af_array in, const double norm_factor,
                   const dim_t pad0) {
     const dim_t pad[1] = {pad0};
-    return fft_r2c<1>(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad);
+    return fft_r2c(out, in, norm_factor, (pad0 > 0 ? 1 : 0), pad, 1);
 }
 
 af_err af_fft2_r2c(af_array *out, const af_array in, const double norm_factor,
                    const dim_t pad0, const dim_t pad1) {
     const dim_t pad[2] = {pad0, pad1};
-    return fft_r2c<2>(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0),
-                      pad);
+    return fft_r2c(out, in, norm_factor, (pad0 > 0 && pad1 > 0 ? 2 : 0), pad,
+                   2);
 }
 
 af_err af_fft3_r2c(af_array *out, const af_array in, const double norm_factor,
                    const dim_t pad0, const dim_t pad1, const dim_t pad2) {
     const dim_t pad[3] = {pad0, pad1, pad2};
-    return fft_r2c<3>(out, in, norm_factor,
-                      (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad);
+    return fft_r2c(out, in, norm_factor,
+                   (pad0 > 0 && pad1 > 0 && pad2 > 0 ? 3 : 0), pad, 3);
 }
 
-template<typename inType, typename outType, int rank>
+template<typename InType>
 static af_array fft_c2r(const af_array in, const double norm_factor,
-                        const dim4 &odims) {
-    return getHandle(fft_c2r<inType, outType, rank>(getArray<inType>(in),
-                                                    norm_factor, odims));
+                        const dim4 &odims, const int rank) {
+    using OutType = typename conditional<is_same<InType, cdouble>::value,
+                                         double, float>::type;
+    return getHandle(fft_c2r<InType, OutType>(getArray<InType>(in), norm_factor,
+                                              odims, rank));
 }
 
-template<int rank>
-static af_err fft_c2r(af_array *out, const af_array in,
-                      const double norm_factor, const bool is_odd) {
+af_err fft_c2r(af_array *out, const af_array in, const double norm_factor,
+               const bool is_odd, const int rank) {
     try {
         const ArrayInfo &info = getInfo(in);
         af_dtype type         = info.getType();
@@ -253,10 +260,10 @@ static af_err fft_c2r(af_array *out, const af_array in,
         af_array output;
         switch (type) {
             case c32:
-                output = fft_c2r<cfloat, float, rank>(in, norm_factor, odims);
+                output = fft_c2r<cfloat>(in, norm_factor, odims, rank);
                 break;
             case c64:
-                output = fft_c2r<cdouble, double, rank>(in, norm_factor, odims);
+                output = fft_c2r<cdouble>(in, norm_factor, odims, rank);
                 break;
             default: TYPE_ERROR(1, type);
         }
@@ -269,17 +276,17 @@ static af_err fft_c2r(af_array *out, const af_array in,
 
 af_err af_fft_c2r(af_array *out, const af_array in, const double norm_factor,
                   const bool is_odd) {
-    return fft_c2r<1>(out, in, norm_factor, is_odd);
+    return fft_c2r(out, in, norm_factor, is_odd, 1);
 }
 
 af_err af_fft2_c2r(af_array *out, const af_array in, const double norm_factor,
                    const bool is_odd) {
-    return fft_c2r<2>(out, in, norm_factor, is_odd);
+    return fft_c2r(out, in, norm_factor, is_odd, 2);
 }
 
 af_err af_fft3_c2r(af_array *out, const af_array in, const double norm_factor,
                    const bool is_odd) {
-    return fft_c2r<3>(out, in, norm_factor, is_odd);
+    return fft_c2r(out, in, norm_factor, is_odd, 3);
 }
 
 af_err af_set_fft_plan_cache_size(size_t cache_size) {
diff --git a/src/api/c/fft_common.hpp b/src/api/c/fft_common.hpp
index 992e71ac38..aacc637982 100644
--- a/src/api/c/fft_common.hpp
+++ b/src/api/c/fft_common.hpp
@@ -14,10 +14,11 @@
 void computePaddedDims(af::dim4 &pdims, const af::dim4 &idims, const dim_t npad,
                        dim_t const *const pad);
 
-template<typename inType, typename outType, int rank, bool direction>
+template<typename inType, typename outType>
 detail::Array<outType> fft(const detail::Array<inType> input,
                            const double norm_factor, const dim_t npad,
-                           const dim_t *const pad) {
+                           const dim_t *const pad, const int rank,
+                           const bool direction) {
     using af::dim4;
     using detail::fft_inplace;
     using detail::reshape;
@@ -27,16 +28,16 @@ detail::Array<outType> fft(const detail::Array<inType> input,
     computePaddedDims(pdims, input.dims(), npad, pad);
     auto res = reshape(input, pdims, scalar<outType>(0));
 
-    fft_inplace<outType, rank, direction>(res);
+    fft_inplace<outType>(res, rank, direction);
     if (norm_factor != 1.0) multiply_inplace(res, norm_factor);
 
     return res;
 }
 
-template<typename inType, typename outType, int rank>
+template<typename inType, typename outType>
 detail::Array<outType> fft_r2c(const detail::Array<inType> input,
                                const double norm_factor, const dim_t npad,
-                               const dim_t *const pad) {
+                               const dim_t *const pad, const int rank) {
     using af::dim4;
     using detail::Array;
     using detail::fft_r2c;
@@ -57,21 +58,21 @@ detail::Array<outType> fft_r2c(const detail::Array<inType> input,
         tmp = reshape(input, pdims, scalar<inType>(0));
     }
 
-    auto res = fft_r2c<outType, inType, rank>(tmp);
+    auto res = fft_r2c<outType, inType>(tmp, rank);
     if (norm_factor != 1.0) multiply_inplace(res, norm_factor);
 
     return res;
 }
 
-template<typename inType, typename outType, int rank>
+template<typename inType, typename outType>
 detail::Array<outType> fft_c2r(const detail::Array<inType> input,
-                               const double norm_factor,
-                               const af::dim4 &odims) {
+                               const double norm_factor, const af::dim4 &odims,
+                               const int rank) {
     using detail::Array;
     using detail::fft_c2r;
     using detail::multiply_inplace;
 
-    Array<outType> output = fft_c2r<outType, inType, rank>(input, odims);
+    Array<outType> output = fft_c2r<outType, inType>(input, odims, rank);
 
     if (norm_factor != 1) {
         // Normalize input because tmp was not normalized
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index de756f6ff0..e0aabda55e 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -44,10 +44,9 @@ using std::max;
 using std::swap;
 using std::vector;
 
-template<typename T, int baseDim>
-static inline af_array fftconvolve_fallback(const af_array signal,
-                                            const af_array filter,
-                                            bool expand) {
+template<typename T>
+af_array fftconvolve_fallback(const af_array signal, const af_array filter,
+                              const bool expand, const int baseDim) {
     using convT =
         typename conditional<is_integral<T>::value || is_same<T, float>::value,
                              float, double>::type;
@@ -95,17 +94,17 @@ static inline af_array fftconvolve_fallback(const af_array signal,
     }
 
     // fft(signal)
-    Array<cT> T1 = fft<cT, cT, baseDim, true>(S, 1.0, baseDim, psdims.get());
+    Array<cT> T1 = fft<cT, cT>(S, 1.0, baseDim, psdims.get(), baseDim, true);
 
     // fft(filter)
-    Array<cT> T2 = fft<cT, cT, baseDim, true>(F, 1.0, baseDim, pfdims.get());
+    Array<cT> T2 = fft<cT, cT>(F, 1.0, baseDim, pfdims.get(), baseDim, true);
 
     // fft(signal) * fft(filter)
     T1 = arithOp<cT, af_mul_t>(T1, T2, odims);
 
     // ifft(ffit(signal) * fft(filter))
-    T1 = fft<cT, cT, baseDim, false>(T1, 1.0 / static_cast<double>(count),
-                                     baseDim, odims.get());
+    T1 = fft<cT, cT>(T1, 1.0 / static_cast<double>(count), baseDim, odims.get(),
+                     baseDim, false);
 
     // Index to proper offsets
     T1 = createSubArray<cT>(T1, index);
@@ -117,19 +116,20 @@ static inline af_array fftconvolve_fallback(const af_array signal,
     }
 }
 
-template<typename T, dim_t baseDim>
-inline static af_array fftconvolve(const af_array &s, const af_array &f,
-                                   const bool expand, AF_BATCH_KIND kind) {
+template<typename T>
+inline af_array fftconvolve(const af_array &s, const af_array &f,
+                            const bool expand, AF_BATCH_KIND kind,
+                            const int baseDim) {
     if (kind == AF_BATCH_DIFF) {
-        return fftconvolve_fallback<T, baseDim>(s, f, expand);
+        return fftconvolve_fallback<T>(s, f, expand, baseDim);
     } else {
-        return getHandle(fftconvolve<T, baseDim>(
-            getArray<T>(s), castArray<T>(f), expand, kind));
+        return getHandle(fftconvolve<T>(getArray<T>(s), castArray<T>(f), expand,
+                                        kind, baseDim));
     }
 }
 
-template<dim_t baseDim>
-AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
+AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims,
+                                const int baseDim) {
     dim_t sn = sDims.ndims();
     dim_t fn = fDims.ndims();
 
@@ -155,9 +155,8 @@ AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
     }
 }
 
-template<dim_t baseDim>
 af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
-                    const bool expand) {
+                    const bool expand, const int baseDim) {
     try {
         const ArrayInfo &sInfo = getInfo(signal);
         const ArrayInfo &fInfo = getInfo(filter);
@@ -168,7 +167,7 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
         const dim4 &sdims = sInfo.dims();
         const dim4 &fdims = fInfo.dims();
 
-        AF_BATCH_KIND convBT = identifyBatchKind<baseDim>(sdims, fdims);
+        AF_BATCH_KIND convBT = identifyBatchKind(sdims, fdims, baseDim);
 
         ARG_ASSERT(1, (signalType == filterType));
         ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED));
@@ -176,52 +175,52 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
         af_array output;
         switch (signalType) {
             case f64:
-                output = fftconvolve<double, baseDim>(signal, filter, expand,
-                                                      convBT);
+                output = fftconvolve<double>(signal, filter, expand, convBT,
+                                             baseDim);
                 break;
             case f32:
                 output =
-                    fftconvolve<float, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<float>(signal, filter, expand, convBT, baseDim);
                 break;
             case u32:
                 output =
-                    fftconvolve<uint, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<uint>(signal, filter, expand, convBT, baseDim);
                 break;
             case s32:
                 output =
-                    fftconvolve<int, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<int>(signal, filter, expand, convBT, baseDim);
                 break;
             case u64:
                 output =
-                    fftconvolve<uintl, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<uintl>(signal, filter, expand, convBT, baseDim);
                 break;
             case s64:
                 output =
-                    fftconvolve<intl, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<intl>(signal, filter, expand, convBT, baseDim);
                 break;
             case u16:
-                output = fftconvolve<ushort, baseDim>(signal, filter, expand,
-                                                      convBT);
+                output = fftconvolve<ushort>(signal, filter, expand, convBT,
+                                             baseDim);
                 break;
             case s16:
                 output =
-                    fftconvolve<short, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<short>(signal, filter, expand, convBT, baseDim);
                 break;
             case u8:
                 output =
-                    fftconvolve<uchar, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<uchar>(signal, filter, expand, convBT, baseDim);
                 break;
             case b8:
                 output =
-                    fftconvolve<char, baseDim>(signal, filter, expand, convBT);
+                    fftconvolve<char>(signal, filter, expand, convBT, baseDim);
                 break;
             case c32:
-                output = fftconvolve_fallback<cfloat, baseDim>(signal, filter,
-                                                               expand);
+                output = fftconvolve_fallback<cfloat>(signal, filter, expand,
+                                                      baseDim);
                 break;
             case c64:
-                output = fftconvolve_fallback<cdouble, baseDim>(signal, filter,
-                                                                expand);
+                output = fftconvolve_fallback<cdouble>(signal, filter, expand,
+                                                       baseDim);
                 break;
             default: TYPE_ERROR(1, signalType);
         }
@@ -234,23 +233,23 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
 
 af_err af_fft_convolve1(af_array *out, const af_array signal,
                         const af_array filter, const af_conv_mode mode) {
-    return fft_convolve<1>(out, signal, filter, mode == AF_CONV_EXPAND);
+    return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 1);
 }
 
 af_err af_fft_convolve2(af_array *out, const af_array signal,
                         const af_array filter, const af_conv_mode mode) {
     if (getInfo(signal).dims().ndims() < 2 &&
         getInfo(filter).dims().ndims() < 2) {
-        return fft_convolve<1>(out, signal, filter, mode == AF_CONV_EXPAND);
+        return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 1);
     }
-    return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
+    return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
 }
 
 af_err af_fft_convolve3(af_array *out, const af_array signal,
                         const af_array filter, const af_conv_mode mode) {
     if (getInfo(signal).dims().ndims() < 3 &&
         getInfo(filter).dims().ndims() < 3) {
-        return fft_convolve<2>(out, signal, filter, mode == AF_CONV_EXPAND);
+        return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
     }
-    return fft_convolve<3>(out, signal, filter, mode == AF_CONV_EXPAND);
+    return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 3);
 }
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index c129c01710..dc0067f257 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -30,20 +30,8 @@ af_err af_medfilt(af_array *out, const af_array in, const dim_t wind_length,
 template<typename T>
 static af_array medfilt1(af_array const &in, dim_t w_wid,
                          af_border_type edge_pad) {
-    switch (edge_pad) {
-        case AF_PAD_ZERO:
-            return getHandle<T>(
-                medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid));
-            break;
-        case AF_PAD_SYM:
-            return getHandle<T>(
-                medfilt1<T, AF_PAD_SYM>(getArray<T>(in), w_wid));
-            break;
-        default:
-            return getHandle<T>(
-                medfilt1<T, AF_PAD_ZERO>(getArray<T>(in), w_wid));
-            break;
-    }
+    return getHandle<T>(
+        medfilt1<T>(getArray<T>(in), static_cast<int>(w_wid), edge_pad));
 }
 
 af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
@@ -60,38 +48,26 @@ af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
 
         if (wind_width == 1) {
             *out = retain(in);
-        } else {
-            af_array output;
-            af_dtype type = info.getType();
-            switch (type) {
-                case f32:
-                    output = medfilt1<float>(in, wind_width, edge_pad);
-                    break;
-                case f64:
-                    output = medfilt1<double>(in, wind_width, edge_pad);
-                    break;
-                case b8:
-                    output = medfilt1<char>(in, wind_width, edge_pad);
-                    break;
-                case s32:
-                    output = medfilt1<int>(in, wind_width, edge_pad);
-                    break;
-                case u32:
-                    output = medfilt1<uint>(in, wind_width, edge_pad);
-                    break;
-                case s16:
-                    output = medfilt1<short>(in, wind_width, edge_pad);
-                    break;
-                case u16:
-                    output = medfilt1<ushort>(in, wind_width, edge_pad);
-                    break;
-                case u8:
-                    output = medfilt1<uchar>(in, wind_width, edge_pad);
-                    break;
-                default: TYPE_ERROR(1, type);
-            }
-            std::swap(*out, output);
+            return AF_SUCCESS;
+        }
+        af_array output = nullptr;
+        af_dtype type   = info.getType();
+        switch (type) {
+            case f32: output = medfilt1<float>(in, wind_width, edge_pad); break;
+            case f64:
+                output = medfilt1<double>(in, wind_width, edge_pad);
+                break;
+            case b8: output = medfilt1<char>(in, wind_width, edge_pad); break;
+            case s32: output = medfilt1<int>(in, wind_width, edge_pad); break;
+            case u32: output = medfilt1<uint>(in, wind_width, edge_pad); break;
+            case s16: output = medfilt1<short>(in, wind_width, edge_pad); break;
+            case u16:
+                output = medfilt1<ushort>(in, wind_width, edge_pad);
+                break;
+            case u8: output = medfilt1<uchar>(in, wind_width, edge_pad); break;
+            default: TYPE_ERROR(1, type);
         }
+        std::swap(*out, output);
     }
     CATCHALL;
 
@@ -99,22 +75,10 @@ af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
 }
 
 template<typename T>
-static af_array medfilt2(af_array const &in, dim_t w_len, dim_t w_wid,
+inline af_array medfilt2(af_array const &in, dim_t w_len, dim_t w_wid,
                          af_border_type edge_pad) {
-    switch (edge_pad) {
-        case AF_PAD_ZERO:
-            return getHandle<T>(
-                medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid));
-            break;
-        case AF_PAD_SYM:
-            return getHandle<T>(
-                medfilt2<T, AF_PAD_SYM>(getArray<T>(in), w_len, w_wid));
-            break;
-        default:
-            return getHandle<T>(
-                medfilt2<T, AF_PAD_ZERO>(getArray<T>(in), w_len, w_wid));
-            break;
-    }
+    return getHandle(medfilt2<T>(getArray<T>(in), static_cast<int>(w_len),
+                                 static_cast<int>(w_wid), edge_pad));
 }
 
 af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length,
@@ -137,46 +101,40 @@ af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length,
 
         if (wind_length == 1) {
             *out = retain(in);
-        } else {
-            af_array output;
-            af_dtype type = info.getType();
-            switch (type) {
-                case f32:
-                    output =
-                        medfilt2<float>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case f64:
-                    output =
-                        medfilt2<double>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case b8:
-                    output =
-                        medfilt2<char>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case s32:
-                    output =
-                        medfilt2<int>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case u32:
-                    output =
-                        medfilt2<uint>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case s16:
-                    output =
-                        medfilt2<short>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case u16:
-                    output =
-                        medfilt2<ushort>(in, wind_length, wind_width, edge_pad);
-                    break;
-                case u8:
-                    output =
-                        medfilt2<uchar>(in, wind_length, wind_width, edge_pad);
-                    break;
-                default: TYPE_ERROR(1, type);
-            }
-            std::swap(*out, output);
+            return AF_SUCCESS;
+        }
+        af_array output = nullptr;
+        af_dtype type   = info.getType();
+        switch (type) {
+            case f32:
+                output = medfilt2<float>(in, wind_length, wind_width, edge_pad);
+                break;
+            case f64:
+                output =
+                    medfilt2<double>(in, wind_length, wind_width, edge_pad);
+                break;
+            case b8:
+                output = medfilt2<char>(in, wind_length, wind_width, edge_pad);
+                break;
+            case s32:
+                output = medfilt2<int>(in, wind_length, wind_width, edge_pad);
+                break;
+            case u32:
+                output = medfilt2<uint>(in, wind_length, wind_width, edge_pad);
+                break;
+            case s16:
+                output = medfilt2<short>(in, wind_length, wind_width, edge_pad);
+                break;
+            case u16:
+                output =
+                    medfilt2<ushort>(in, wind_length, wind_width, edge_pad);
+                break;
+            case u8:
+                output = medfilt2<uchar>(in, wind_length, wind_width, edge_pad);
+                break;
+            default: TYPE_ERROR(1, type);
         }
+        std::swap(*out, output);
     }
     CATCHALL;
 
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index f5c5c6497b..ed9472cc83 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -20,19 +20,12 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
-template<typename inType, typename outType>
-static inline af_array histogram(const af_array in, const unsigned &nbins,
-                                 const double &minval, const double &maxval,
-                                 const bool islinear) {
-    af_array out = nullptr;
-    if (islinear) {
-        out = getHandle(histogram<inType, outType, true>(
-            getArray<inType>(in), nbins, minval, maxval));
-    } else {
-        out = getHandle(histogram<inType, outType, false>(
-            getArray<inType>(in), nbins, minval, maxval));
-    }
-    return out;
+template<typename T>
+inline af_array histogram(const af_array in, const unsigned &nbins,
+                          const double &minval, const double &maxval,
+                          const bool islinear) {
+    return getHandle(
+        histogram<T>(getArray<T>(in), nbins, minval, maxval, islinear));
 }
 
 af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
@@ -46,44 +39,44 @@ af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
         af_array output;
         switch (type) {
             case f32:
-                output = histogram<float, uint>(in, nbins, minval, maxval,
-                                                info.isLinear());
+                output = histogram<float>(in, nbins, minval, maxval,
+                                          info.isLinear());
                 break;
             case f64:
-                output = histogram<double, uint>(in, nbins, minval, maxval,
-                                                 info.isLinear());
+                output = histogram<double>(in, nbins, minval, maxval,
+                                           info.isLinear());
                 break;
             case b8:
-                output = histogram<char, uint>(in, nbins, minval, maxval,
-                                               info.isLinear());
+                output =
+                    histogram<char>(in, nbins, minval, maxval, info.isLinear());
                 break;
             case s32:
-                output = histogram<int, uint>(in, nbins, minval, maxval,
-                                              info.isLinear());
+                output =
+                    histogram<int>(in, nbins, minval, maxval, info.isLinear());
                 break;
             case u32:
-                output = histogram<uint, uint>(in, nbins, minval, maxval,
-                                               info.isLinear());
+                output =
+                    histogram<uint>(in, nbins, minval, maxval, info.isLinear());
                 break;
             case s16:
-                output = histogram<short, uint>(in, nbins, minval, maxval,
-                                                info.isLinear());
+                output = histogram<short>(in, nbins, minval, maxval,
+                                          info.isLinear());
                 break;
             case u16:
-                output = histogram<ushort, uint>(in, nbins, minval, maxval,
-                                                 info.isLinear());
+                output = histogram<ushort>(in, nbins, minval, maxval,
+                                           info.isLinear());
                 break;
             case s64:
-                output = histogram<intl, uint>(in, nbins, minval, maxval,
-                                               info.isLinear());
+                output =
+                    histogram<intl>(in, nbins, minval, maxval, info.isLinear());
                 break;
             case u64:
-                output = histogram<uintl, uint>(in, nbins, minval, maxval,
-                                                info.isLinear());
+                output = histogram<uintl>(in, nbins, minval, maxval,
+                                          info.isLinear());
                 break;
             case u8:
-                output = histogram<uchar, uint>(in, nbins, minval, maxval,
-                                                info.isLinear());
+                output = histogram<uchar>(in, nbins, minval, maxval,
+                                          info.isLinear());
                 break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/match_template.cpp b/src/api/c/match_template.cpp
index 7e984b0c86..6882711a7f 100644
--- a/src/api/c/match_template.cpp
+++ b/src/api/c/match_template.cpp
@@ -11,51 +11,28 @@
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <match_template.hpp>
+#include <types.hpp>
 #include <af/defines.h>
 #include <af/vision.h>
 
+#include <type_traits>
+
 using af::dim4;
 using detail::intl;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
 using detail::ushort;
+using std::conditional;
+using std::is_same;
 
-template<typename inType, typename outType>
+template<typename InType>
 static af_array match_template(const af_array& sImg, const af_array tImg,
                                af_match_type mType) {
-    switch (mType) {
-        case AF_SAD:
-            return getHandle(match_template<inType, outType, AF_SAD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_ZSAD:
-            return getHandle(match_template<inType, outType, AF_ZSAD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_LSAD:
-            return getHandle(match_template<inType, outType, AF_LSAD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_SSD:
-            return getHandle(match_template<inType, outType, AF_SSD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_ZSSD:
-            return getHandle(match_template<inType, outType, AF_ZSSD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_LSSD:
-            return getHandle(match_template<inType, outType, AF_LSSD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_NCC:
-            return getHandle(match_template<inType, outType, AF_NCC>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_ZNCC:
-            return getHandle(match_template<inType, outType, AF_ZNCC>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        case AF_SHD:
-            return getHandle(match_template<inType, outType, AF_SHD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-        default:
-            return getHandle(match_template<inType, outType, AF_SAD>(
-                getArray<inType>(sImg), getArray<inType>(tImg)));
-    }
+    using OutType = typename conditional<is_same<InType, double>::value, double,
+                                         float>::type;
+    return getHandle(match_template<InType, OutType>(
+        getArray<InType>(sImg), getArray<InType>(tImg), mType));
 }
 
 af_err af_match_template(af_array* out, const af_array search_img,
@@ -81,36 +58,33 @@ af_err af_match_template(af_array* out, const af_array search_img,
         af_array output = 0;
         switch (sType) {
             case f64:
-                output = match_template<double, double>(search_img,
-                                                        template_img, m_type);
+                output =
+                    match_template<double>(search_img, template_img, m_type);
                 break;
             case f32:
-                output = match_template<float, float>(search_img, template_img,
-                                                      m_type);
+                output =
+                    match_template<float>(search_img, template_img, m_type);
                 break;
             case s32:
-                output = match_template<int, float>(search_img, template_img,
-                                                    m_type);
+                output = match_template<int>(search_img, template_img, m_type);
                 break;
             case u32:
-                output = match_template<uint, float>(search_img, template_img,
-                                                     m_type);
+                output = match_template<uint>(search_img, template_img, m_type);
                 break;
             case s16:
-                output = match_template<short, float>(search_img, template_img,
-                                                      m_type);
+                output =
+                    match_template<short>(search_img, template_img, m_type);
                 break;
             case u16:
-                output = match_template<ushort, float>(search_img, template_img,
-                                                       m_type);
+                output =
+                    match_template<ushort>(search_img, template_img, m_type);
                 break;
             case b8:
-                output = match_template<char, float>(search_img, template_img,
-                                                     m_type);
+                output = match_template<char>(search_img, template_img, m_type);
                 break;
             case u8:
-                output = match_template<uchar, float>(search_img, template_img,
-                                                      m_type);
+                output =
+                    match_template<uchar>(search_img, template_img, m_type);
                 break;
             default: TYPE_ERROR(1, sType);
         }
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 084a26f551..674020c3ec 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -82,12 +82,9 @@ af_array morph<char>(const af_array &input, const af_array &mask,
                         {static_cast<dim_t>(seDims[0] % 2 == 0),
                          static_cast<dim_t>(seDims[1] % 2 == 0), 0, 0},
                         {0, 0, 0, 0}, AF_PAD_ZERO);
-
-    auto fftConv = fftconvolve<float, 2>;
-
     if (isDilation) {
         Array<float> dft =
-            fftConv(cast<float>(in), paddedSe, false, AF_BATCH_LHS);
+            fftconvolve(cast<float>(in), paddedSe, false, AF_BATCH_LHS, 2);
 
         return getHandle(cast<char>(unaryOp<float, af_round_t>(dft)));
     } else {
@@ -96,7 +93,7 @@ af_array morph<char>(const af_array &input, const af_array &mask,
         const Array<char> inv    = arithOp<char, af_sub_t>(ONES, in, inDims);
 
         Array<float> dft =
-            fftConv(cast<float>(inv), paddedSe, false, AF_BATCH_LHS);
+            fftconvolve(cast<float>(inv), paddedSe, false, AF_BATCH_LHS, 2);
 
         Array<float> rounded = unaryOp<float, af_round_t>(dft);
         Array<char> thrshd   = logicOp<float, af_gt_t>(rounded, ZEROS, inDims);
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index b70da95376..995e464302 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -19,21 +19,18 @@ using af::dim4;
 
 namespace cpu {
 
-template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
-                         const float &c_sigma) {
-    const dim4 &dims   = in.dims();
-    Array<outType> out = createEmptyArray<outType>(dims);
-    getQueue().enqueue(kernel::bilateral<outType, inType, isColor>, out, in,
-                       s_sigma, c_sigma);
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &sSigma,
+                         const float &cSigma) {
+    Array<outType> out = createEmptyArray<outType>(in.dims());
+    getQueue().enqueue(kernel::bilateral<outType, inType>, out, in, sSigma,
+                       cSigma);
     return out;
 }
 
-#define INSTANTIATE(inT, outT)                                             \
-    template Array<outT> bilateral<inT, outT, true>(                       \
-        const Array<inT> &in, const float &s_sigma, const float &c_sigma); \
-    template Array<outT> bilateral<inT, outT, false>(                      \
-        const Array<inT> &in, const float &s_sigma, const float &c_sigma);
+#define INSTANTIATE(inT, outT)                                    \
+    template Array<outT> bilateral<inT, outT>(const Array<inT> &, \
+                                              const float &, const float &);
 
 INSTANTIATE(double, double)
 INSTANTIATE(float, float)
diff --git a/src/backend/cpu/bilateral.hpp b/src/backend/cpu/bilateral.hpp
index 57e9d15f13..543f7eeff0 100644
--- a/src/backend/cpu/bilateral.hpp
+++ b/src/backend/cpu/bilateral.hpp
@@ -10,9 +10,7 @@
 #include <Array.hpp>
 
 namespace cpu {
-
-template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
-                         const float &c_sigma);
-
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
+                         const float &chromaticSigma);
 }
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index 90519cda3f..c4588d3b3e 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -50,11 +50,7 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper) {
-        triangle<T, true, false>(out, out);
-    } else {
-        triangle<T, false, false>(out, out);
-    }
+    triangle<T>(out, out, is_upper, false);
 
     return out;
 }
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index efea6e08be..9f647b3367 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -32,39 +32,39 @@ using common::half;
 
 namespace cpu {
 
-template<typename T, typename accT, int baseDim, bool expand>
+template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
-                  AF_BATCH_KIND kind) {
+                  AF_BATCH_KIND kind, const int rank, const bool expand) {
     auto sDims = signal.dims();
     auto fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for (dim_t d = 0; d < 4; ++d) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
             if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
                 oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::convolve_nd<T, accT, baseDim, expand>, out,
-                       signal, filter, kind);
+    getQueue().enqueue(kernel::convolve_nd<T, accT>, out, signal, filter, kind,
+                       rank, expand);
 
     return out;
 }
 
-template<typename T, typename accT, bool expand>
+template<typename T, typename accT>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
-                   Array<accT> const &r_filter) {
+                   Array<accT> const &r_filter, const bool expand) {
     const auto &sDims = signal.dims();
     dim4 tDims        = sDims;
     dim4 oDims        = sDims;
@@ -85,37 +85,18 @@ Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
     Array<T> out  = createEmptyArray<T>(oDims);
     Array<T> temp = createEmptyArray<T>(tDims);
 
-    getQueue().enqueue(kernel::convolve2<T, accT, expand>, out, signal,
-                       c_filter, r_filter, temp);
+    getQueue().enqueue(kernel::convolve2<T, accT>, out, signal, c_filter,
+                       r_filter, temp, expand);
 
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve<T, accT, 1, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 1, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 2, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 2, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 3, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 3, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve2<T, accT, true>(Array<T> const &signal,       \
-                                               Array<accT> const &c_filter,  \
-                                               Array<accT> const &r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const &signal,      \
-                                                Array<accT> const &c_filter, \
-                                                Array<accT> const &r_filter);
+#define INSTANTIATE(T, accT)                                                   \
+    template Array<T> convolve<T, accT>(Array<T> const &, Array<accT> const &, \
+                                        AF_BATCH_KIND, const int, const bool); \
+    template Array<T> convolve2<T, accT>(Array<T> const &,                     \
+                                         Array<accT> const &,                  \
+                                         Array<accT> const &, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index 15f08c616b..e2490e9c96 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -12,13 +12,13 @@
 
 namespace cpu {
 
-template<typename T, typename accT, int baseDim, bool expand>
+template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
-                  AF_BATCH_KIND kind);
+                  AF_BATCH_KIND kind, const int rank, const bool expand);
 
-template<typename T, typename accT, bool expand>
+template<typename T, typename accT>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
-                   Array<accT> const &r_filter);
+                   Array<accT> const &r_filter, const bool expand);
 
 template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index 26b1df7c00..fafc178c29 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -16,9 +16,11 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
+#include <array>
 #include <type_traits>
 
 using af::dim4;
+using std::array;
 
 namespace cpu {
 
@@ -64,23 +66,21 @@ TRANSFORM_REAL(fftw, cdouble, double, r2c)
 TRANSFORM_REAL(fftwf, float, cfloat, c2r)
 TRANSFORM_REAL(fftw, double, cdouble, c2r)
 
-template<int rank>
-void computeDims(int rdims[rank], const af::dim4 &idims) {
-    for (int i = 0; i < rank; i++) { rdims[i] = idims[(rank - 1) - i]; }
+inline array<int, AF_MAX_DIMS> computeDims(const int rank, const dim4 &idims) {
+    array<int, AF_MAX_DIMS> retVal = {};
+    for (int i = 0; i < rank; i++) { retVal[i] = idims[(rank - 1) - i]; }
+    return retVal;
 }
 
 void setFFTPlanCacheSize(size_t numPlans) { UNUSED(numPlans); }
 
-template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in) {
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction) {
     auto func = [=](Param<T> in, const af::dim4 iDataDims) {
-        int t_dims[rank];
-        int in_embed[rank];
-
         const af::dim4 idims = in.dims();
 
-        computeDims<rank>(t_dims, idims);
-        computeDims<rank>(in_embed, iDataDims);
+        auto t_dims   = computeDims(rank, idims);
+        auto in_embed = computeDims(rank, iDataDims);
 
         const af::dim4 istrides = in.strides();
 
@@ -93,10 +93,10 @@ void fft_inplace(Array<T> &in) {
         for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
         plan = transform.create(
-            rank, t_dims, batch, reinterpret_cast<ctype_t *>(in.get()),
-            in_embed, static_cast<int>(istrides[0]),
+            rank, t_dims.data(), batch, reinterpret_cast<ctype_t *>(in.get()),
+            in_embed.data(), static_cast<int>(istrides[0]),
             static_cast<int>(istrides[rank]),
-            reinterpret_cast<ctype_t *>(in.get()), in_embed,
+            reinterpret_cast<ctype_t *>(in.get()), in_embed.data(),
             static_cast<int>(istrides[0]), static_cast<int>(istrides[rank]),
             direction ? FFTW_FORWARD : FFTW_BACKWARD,
             FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
@@ -107,8 +107,8 @@ void fft_inplace(Array<T> &in) {
     getQueue().enqueue(func, in, in.getDataDims());
 }
 
-template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in) {
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
     dim4 odims    = in.dims();
     odims[0]      = odims[0] / 2 + 1;
     Array<Tc> out = createEmptyArray<Tc>(odims);
@@ -117,13 +117,9 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
                     const af::dim4 iDataDims) {
         af::dim4 idims = in.dims();
 
-        int t_dims[rank];
-        int in_embed[rank];
-        int out_embed[rank];
-
-        computeDims<rank>(t_dims, idims);
-        computeDims<rank>(in_embed, iDataDims);
-        computeDims<rank>(out_embed, oDataDims);
+        auto t_dims    = computeDims(rank, idims);
+        auto in_embed  = computeDims(rank, iDataDims);
+        auto out_embed = computeDims(rank, oDataDims);
 
         const af::dim4 istrides = in.strides();
         const af::dim4 ostrides = out.strides();
@@ -138,9 +134,10 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
         for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
         plan = transform.create(
-            rank, t_dims, batch, const_cast<Tr *>(in.get()), in_embed,
-            static_cast<int>(istrides[0]), static_cast<int>(istrides[rank]),
-            reinterpret_cast<ctype_t *>(out.get()), out_embed,
+            rank, t_dims.data(), batch, const_cast<Tr *>(in.get()),
+            in_embed.data(), static_cast<int>(istrides[0]),
+            static_cast<int>(istrides[rank]),
+            reinterpret_cast<ctype_t *>(out.get()), out_embed.data(),
             static_cast<int>(ostrides[0]), static_cast<int>(ostrides[rank]),
             FFTW_ESTIMATE);
 
@@ -153,19 +150,15 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
     return out;
 }
 
-template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
     Array<Tr> out = createEmptyArray<Tr>(odims);
 
     auto func = [=](Param<Tr> out, const af::dim4 oDataDims, CParam<Tc> in,
                     const af::dim4 iDataDims, const af::dim4 odims) {
-        int t_dims[rank];
-        int in_embed[rank];
-        int out_embed[rank];
-
-        computeDims<rank>(t_dims, odims);
-        computeDims<rank>(in_embed, iDataDims);
-        computeDims<rank>(out_embed, oDataDims);
+        auto t_dims    = computeDims(rank, odims);
+        auto in_embed  = computeDims(rank, iDataDims);
+        auto out_embed = computeDims(rank, oDataDims);
 
         const af::dim4 istrides = in.strides();
         const af::dim4 ostrides = out.strides();
@@ -191,11 +184,12 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
         }
 
         plan = transform.create(
-            rank, t_dims, batch,
-            reinterpret_cast<ctype_t *>(const_cast<Tc *>(in.get())), in_embed,
-            static_cast<int>(istrides[0]), static_cast<int>(istrides[rank]),
-            out.get(), out_embed, static_cast<int>(ostrides[0]),
-            static_cast<int>(ostrides[rank]), flags);
+            rank, t_dims.data(), batch,
+            reinterpret_cast<ctype_t *>(const_cast<Tc *>(in.get())),
+            in_embed.data(), static_cast<int>(istrides[0]),
+            static_cast<int>(istrides[rank]), out.get(), out_embed.data(),
+            static_cast<int>(ostrides[0]), static_cast<int>(ostrides[rank]),
+            flags);
 
         transform.execute(plan);
         transform.destroy(plan);
@@ -220,27 +214,16 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     return out;
 }
 
-#define INSTANTIATE(T)                                     \
-    template void fft_inplace<T, 1, true>(Array<T> & in);  \
-    template void fft_inplace<T, 2, true>(Array<T> & in);  \
-    template void fft_inplace<T, 3, true>(Array<T> & in);  \
-    template void fft_inplace<T, 1, false>(Array<T> & in); \
-    template void fft_inplace<T, 2, false>(Array<T> & in); \
-    template void fft_inplace<T, 3, false>(Array<T> & in);
+#define INSTANTIATE(T) \
+    template void fft_inplace<T>(Array<T> &, const int, const bool);
 
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-#define INSTANTIATE_REAL(Tr, Tc)                                \
-    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in); \
-    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in); \
-    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in); \
-    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in,  \
-                                          const dim4 &odims);   \
-    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in,  \
-                                          const dim4 &odims);   \
-    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in,  \
-                                          const dim4 &odims);
+#define INSTANTIATE_REAL(Tr, Tc)                                             \
+    template Array<Tc> fft_r2c<Tc, Tr>(const Array<Tr> &, const int);        \
+    template Array<Tr> fft_c2r<Tr, Tc>(const Array<Tc> &in, const dim4 &odi, \
+                                       const int);
 
 INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
diff --git a/src/backend/cpu/fft.hpp b/src/backend/cpu/fft.hpp
index 84dde77218..fbdf7af339 100644
--- a/src/backend/cpu/fft.hpp
+++ b/src/backend/cpu/fft.hpp
@@ -19,12 +19,12 @@ namespace cpu {
 
 void setFFTPlanCacheSize(size_t numPlans);
 
-template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in);
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction);
 
-template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in);
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank);
 
-template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims);
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 }  // namespace cpu
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 3dd1cae2cc..ee31c5d37c 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -26,9 +26,9 @@ using std::ceil;
 
 namespace cpu {
 
-template<typename T, dim_t baseDim>
+template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind) {
+                     const bool expand, AF_BATCH_KIND kind, const int rank) {
     using convT = typename std::conditional<std::is_integral<T>::value ||
                                                 std::is_same<T, float>::value,
                                             float, double>::type;
@@ -40,36 +40,36 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     dim_t fftScale = 1;
 
     dim4 packedDims(1, 1, 1, 1);
-    array<int, baseDim> fftDims{};
+    array<int, AF_MAX_DIMS> fftDims{};  // AF_MAX_DIMS(4) > rank
 
     // Pack both signal and filter on same memory array, this will ensure
     // better use of batched FFT capabilities
-    fftDims[baseDim - 1] = nextpow2(
+    fftDims[rank - 1] = nextpow2(
         static_cast<unsigned>(static_cast<int>(ceil(sd[0] / 2.f)) + fd[0] - 1));
-    packedDims[0] = 2 * fftDims[baseDim - 1];
-    fftScale *= fftDims[baseDim - 1];
+    packedDims[0] = 2 * fftDims[rank - 1];
+    fftScale *= fftDims[rank - 1];
 
-    for (dim_t k = 1; k < baseDim; k++) {
+    for (int k = 1; k < rank; k++) {
         packedDims[k] = nextpow2(static_cast<unsigned>(sd[k] + fd[k] - 1));
-        fftDims[baseDim - k - 1] = packedDims[k];
-        fftScale *= fftDims[baseDim - k - 1];
+        fftDims[rank - k - 1] = packedDims[k];
+        fftScale *= fftDims[rank - k - 1];
     }
 
     dim_t sbatch = 1, fbatch = 1;
-    for (int k = baseDim; k < AF_MAX_DIMS; k++) {
+    for (int k = rank; k < AF_MAX_DIMS; k++) {
         sbatch *= sd[k];
         fbatch *= fd[k];
     }
-    packedDims[baseDim] = (sbatch + fbatch);
+    packedDims[rank] = (sbatch + fbatch);
 
     Array<convT> packed = createEmptyArray<convT>(packedDims);
 
-    dim4 paddedSigDims(packedDims[0], (1 < baseDim ? packedDims[1] : sd[1]),
-                       (2 < baseDim ? packedDims[2] : sd[2]),
-                       (3 < baseDim ? packedDims[3] : sd[3]));
-    dim4 paddedFilDims(packedDims[0], (1 < baseDim ? packedDims[1] : fd[1]),
-                       (2 < baseDim ? packedDims[2] : fd[2]),
-                       (3 < baseDim ? packedDims[3] : fd[3]));
+    dim4 paddedSigDims(packedDims[0], (1 < rank ? packedDims[1] : sd[1]),
+                       (2 < rank ? packedDims[2] : sd[2]),
+                       (3 < rank ? packedDims[3] : sd[3]));
+    dim4 paddedFilDims(packedDims[0], (1 < rank ? packedDims[1] : fd[1]),
+                       (2 < rank ? packedDims[2] : fd[2]),
+                       (3 < rank ? packedDims[3] : fd[3]));
     dim4 paddedSigStrides = calcStrides(paddedSigDims);
     dim4 paddedFilStrides = calcStrides(paddedFilDims);
 
@@ -88,28 +88,28 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // NOLINTNEXTLINE(performance-unnecessary-value-param)
     auto upstream_dft = [=](Param<convT> packed,
-                            const array<int, baseDim> fftDims) {
+                            const array<int, AF_MAX_DIMS> fftDims) {
         const dim4 packedDims     = packed.dims();
         const dim4 packed_strides = packed.strides();
         // Compute forward FFT
         if (IsTypeDouble) {
             fftw_plan plan = fftw_plan_many_dft(
-                baseDim, fftDims.data(), packedDims[baseDim],
+                rank, fftDims.data(), packedDims[rank],
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2,
+                packed_strides[0], packed_strides[rank] / 2,
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2, FFTW_FORWARD,
+                packed_strides[0], packed_strides[rank] / 2, FFTW_FORWARD,
                 FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftw_execute(plan);
             fftw_destroy_plan(plan);
         } else {
             fftwf_plan plan = fftwf_plan_many_dft(
-                baseDim, fftDims.data(), packedDims[baseDim],
+                rank, fftDims.data(), packedDims[rank],
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2,
+                packed_strides[0], packed_strides[rank] / 2,
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2, FFTW_FORWARD,
+                packed_strides[0], packed_strides[rank] / 2, FFTW_FORWARD,
                 FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftwf_execute(plan);
@@ -125,28 +125,28 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     // NOLINTNEXTLINE(performance-unnecessary-value-param)
     auto upstream_idft = [=](Param<convT> packed,
-                             const array<int, baseDim> fftDims) {
+                             const array<int, AF_MAX_DIMS> fftDims) {
         const dim4 packedDims     = packed.dims();
         const dim4 packed_strides = packed.strides();
         // Compute inverse FFT
         if (IsTypeDouble) {
             fftw_plan plan = fftw_plan_many_dft(
-                baseDim, fftDims.data(), packedDims[baseDim],
+                rank, fftDims.data(), packedDims[rank],
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2,
+                packed_strides[0], packed_strides[rank] / 2,
                 reinterpret_cast<fftw_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2, FFTW_BACKWARD,
+                packed_strides[0], packed_strides[rank] / 2, FFTW_BACKWARD,
                 FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftw_execute(plan);
             fftw_destroy_plan(plan);
         } else {
             fftwf_plan plan = fftwf_plan_many_dft(
-                baseDim, fftDims.data(), packedDims[baseDim],
+                rank, fftDims.data(), packedDims[rank],
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2,
+                packed_strides[0], packed_strides[rank] / 2,
                 reinterpret_cast<fftwf_complex*>(packed.get()), nullptr,
-                packed_strides[0], packed_strides[baseDim] / 2, FFTW_BACKWARD,
+                packed_strides[0], packed_strides[rank] / 2, FFTW_BACKWARD,
                 FFTW_ESTIMATE);  // NOLINT(hicpp-signed-bitwise)
 
             fftwf_execute(plan);
@@ -158,39 +158,32 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
     // Compute output dimensions
     dim4 oDims(1);
     if (expand) {
-        for (dim_t d = 0; d < 4; ++d) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
             if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
                 oDims[d] = sd[d] + fd[d] - 1;
             } else {
-                oDims[d] = (d < baseDim ? sd[d] + fd[d] - 1 : sd[d]);
+                oDims[d] = (d < rank ? sd[d] + fd[d] - 1 : sd[d]);
             }
         }
     } else {
         oDims = sd;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fd[i]; }
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fd[i]; }
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::reorder<T, convT, baseDim>, out, packed, filter,
+    getQueue().enqueue(kernel::reorder<T, convT>, out, packed, filter,
                        sig_half_d0, fftScale, paddedSigDims, paddedSigStrides,
-                       paddedFilDims, paddedFilStrides, expand, kind);
+                       paddedFilDims, paddedFilStrides, expand, kind, rank);
 
     return out;
 }
 
-#define INSTANTIATE(T)                                                     \
-    template Array<T> fftconvolve<T, 1>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, 2>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, 3>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> fftconvolve<T>(Array<T> const&, Array<T> const&, \
+                                     const bool, AF_BATCH_KIND, const int);
 
 INSTANTIATE(double)
 INSTANTIATE(float)
diff --git a/src/backend/cpu/fftconvolve.hpp b/src/backend/cpu/fftconvolve.hpp
index 196dec427a..a2b9845dfd 100644
--- a/src/backend/cpu/fftconvolve.hpp
+++ b/src/backend/cpu/fftconvolve.hpp
@@ -11,7 +11,7 @@
 
 namespace cpu {
 
-template<typename T, dim_t baseDim>
+template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind);
+                     const bool expand, AF_BATCH_KIND kind, const int rank);
 }
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 1bc3a674e2..29fddc5417 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -61,9 +61,9 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
                        in.elements(), ix, iy);
 
     // Convolve second-order derivatives with proper window filter
-    ixx = convolve2<T, convAccT, false>(ixx, filter, filter);
-    ixy = convolve2<T, convAccT, false>(ixy, filter, filter);
-    iyy = convolve2<T, convAccT, false>(iyy, filter, filter);
+    ixx = convolve2<T, convAccT>(ixx, filter, filter, false);
+    ixy = convolve2<T, convAccT>(ixy, filter, filter, false);
+    iyy = convolve2<T, convAccT>(iyy, filter, filter, false);
 
     const unsigned corner_lim = in.elements() * 0.2f;
 
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index a6292d951f..cec6a745d0 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -18,36 +18,34 @@ using af::dim4;
 
 namespace cpu {
 
-template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
-                         const double &minval, const double &maxval) {
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear) {
     const dim4 &inDims = in.dims();
     dim4 outDims       = dim4(nbins, 1, inDims[2], inDims[3]);
-    Array<outType> out = createValueArray<outType>(outDims, outType(0));
+    Array<uint> out    = createValueArray<uint>(outDims, uint(0));
 
-    getQueue().enqueue(kernel::histogram<outType, inType, isLinear>, out, in,
-                       nbins, minval, maxval);
+    getQueue().enqueue(kernel::histogram<T>, out, in, nbins, minval, maxval,
+                       isLinear);
 
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)                                            \
-    template Array<out_t> histogram<in_t, out_t, true>(                     \
-        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
-        const double &maxval);                                              \
-    template Array<out_t> histogram<in_t, out_t, false>(                    \
-        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
-        const double &maxval);
-
-INSTANTIATE(float, uint)
-INSTANTIATE(double, uint)
-INSTANTIATE(char, uint)
-INSTANTIATE(int, uint)
-INSTANTIATE(uint, uint)
-INSTANTIATE(uchar, uint)
-INSTANTIATE(short, uint)
-INSTANTIATE(ushort, uint)
-INSTANTIATE(intl, uint)
-INSTANTIATE(uintl, uint)
+#define INSTANTIATE(T)                                                    \
+    template Array<uint> histogram<T>(const Array<T> &, const unsigned &, \
+                                      const double &, const double &,     \
+                                      const bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/histogram.hpp b/src/backend/cpu/histogram.hpp
index 854c1452e1..650b59d621 100644
--- a/src/backend/cpu/histogram.hpp
+++ b/src/backend/cpu/histogram.hpp
@@ -10,9 +10,8 @@
 #include <Array.hpp>
 
 namespace cpu {
-
-template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
-                         const double &minval, const double &maxval);
-
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear);
 }
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index 801e02a67f..e1f6c0e4e4 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -27,7 +27,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
     }
 
     // Extract the first N elements
-    Array<T> c = convolve<T, T, 1, true>(x, b, type);
+    Array<T> c = convolve<T, T>(x, b, type, 1, true);
     dim4 cdims = c.dims();
     cdims[0]   = x.dims()[0];
     c.resetDims(cdims);
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index d5c0e34473..343b83dd08 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -16,7 +16,7 @@
 namespace cpu {
 namespace kernel {
 
-template<typename OutT, typename InT, bool IsColor>
+template<typename OutT, typename InT>
 void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma,
                float const c_sigma) {
     af::dim4 const dims     = in.dims();
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index a1a5fbdfcd..812236cae9 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -15,12 +15,13 @@
 namespace cpu {
 namespace kernel {
 
-template<typename InT, typename AccT, bool Expand>
+template<typename InT, typename AccT>
 void one2one_1d(InT *optr, InT const *const iptr, AccT const *const fptr,
                 af::dim4 const &oDims, af::dim4 const &sDims,
-                af::dim4 const &fDims, af::dim4 const &sStrides) {
-    dim_t start = (Expand ? 0 : fDims[0] / 2);
-    dim_t end   = (Expand ? oDims[0] : start + sDims[0]);
+                af::dim4 const &fDims, af::dim4 const &sStrides,
+                const bool expand) {
+    dim_t start = (expand ? 0 : fDims[0] / 2);
+    dim_t end   = (expand ? oDims[0] : start + sDims[0]);
     for (dim_t i = start; i < end; ++i) {
         AccT accum = 0.0;
         for (dim_t f = 0; f < fDims[0]; ++f) {
@@ -34,15 +35,16 @@ void one2one_1d(InT *optr, InT const *const iptr, AccT const *const fptr,
     }
 }
 
-template<typename InT, typename AccT, bool Expand>
+template<typename InT, typename AccT>
 void one2one_2d(InT *optr, InT const *const iptr, AccT const *const fptr,
                 af::dim4 const &oDims, af::dim4 const &sDims,
                 af::dim4 const &fDims, af::dim4 const &oStrides,
-                af::dim4 const &sStrides, af::dim4 const &fStrides) {
-    dim_t jStart = (Expand ? 0 : fDims[1] / 2);
-    dim_t jEnd   = (Expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (Expand ? 0 : fDims[0] / 2);
-    dim_t iEnd   = (Expand ? oDims[0] : iStart + sDims[0]);
+                af::dim4 const &sStrides, af::dim4 const &fStrides,
+                const bool expand) {
+    dim_t jStart = (expand ? 0 : fDims[1] / 2);
+    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
+    dim_t iStart = (expand ? 0 : fDims[0] / 2);
+    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
 
     for (dim_t j = jStart; j < jEnd; ++j) {
         dim_t joff = (j - jStart) * oStrides[1];
@@ -71,17 +73,18 @@ void one2one_2d(InT *optr, InT const *const iptr, AccT const *const fptr,
     }
 }
 
-template<typename InT, typename AccT, bool Expand>
+template<typename InT, typename AccT>
 void one2one_3d(InT *optr, InT const *const iptr, AccT const *const fptr,
                 af::dim4 const &oDims, af::dim4 const &sDims,
                 af::dim4 const &fDims, af::dim4 const &oStrides,
-                af::dim4 const &sStrides, af::dim4 const &fStrides) {
-    dim_t kStart = (Expand ? 0 : fDims[2] / 2);
-    dim_t kEnd   = (Expand ? oDims[2] : kStart + sDims[2]);
-    dim_t jStart = (Expand ? 0 : fDims[1] / 2);
-    dim_t jEnd   = (Expand ? oDims[1] : jStart + sDims[1]);
-    dim_t iStart = (Expand ? 0 : fDims[0] / 2);
-    dim_t iEnd   = (Expand ? oDims[0] : iStart + sDims[0]);
+                af::dim4 const &sStrides, af::dim4 const &fStrides,
+                const bool expand) {
+    dim_t kStart = (expand ? 0 : fDims[2] / 2);
+    dim_t kEnd   = (expand ? oDims[2] : kStart + sDims[2]);
+    dim_t jStart = (expand ? 0 : fDims[1] / 2);
+    dim_t jEnd   = (expand ? oDims[1] : jStart + sDims[1]);
+    dim_t iStart = (expand ? 0 : fDims[0] / 2);
+    dim_t iEnd   = (expand ? oDims[0] : iStart + sDims[0]);
 
     for (dim_t k = kStart; k < kEnd; ++k) {
         dim_t koff = (k - kStart) * oStrides[2];
@@ -125,9 +128,9 @@ void one2one_3d(InT *optr, InT const *const iptr, AccT const *const fptr,
     }          // k loop ends here
 }
 
-template<typename InT, typename AccT, dim_t baseDim, bool Expand>
+template<typename InT, typename AccT>
 void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
-                 AF_BATCH_KIND kind) {
+                 AF_BATCH_KIND kind, const int rank, const bool expand) {
     InT *optr              = out.get();
     InT const *const iptr  = signal.get();
     AccT const *const fptr = filter.get();
@@ -140,16 +143,16 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
     af::dim4 const sStrides = signal.strides();
     af::dim4 const fStrides = filter.strides();
 
-    dim_t out_step[4] = {
+    dim_t out_step[AF_MAX_DIMS] = {
         0, 0, 0,
         0}; /* first value is never used, and declared for code simplicity */
-    dim_t in_step[4] = {
+    dim_t in_step[AF_MAX_DIMS] = {
         0, 0, 0,
         0}; /* first value is never used, and declared for code simplicity */
-    dim_t filt_step[4] = {
+    dim_t filt_step[AF_MAX_DIMS] = {
         0, 0, 0,
         0}; /* first value is never used, and declared for code simplicity */
-    dim_t batch[4] = {
+    dim_t batch[AF_MAX_DIMS] = {
         0, 1, 1,
         1}; /* first value is never used, and declared for code simplicity */
 
@@ -158,18 +161,18 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
             case AF_BATCH_LHS:
                 out_step[i] = oStrides[i];
                 in_step[i]  = sStrides[i];
-                if (i >= baseDim) batch[i] = sDims[i];
+                if (i >= rank) batch[i] = sDims[i];
                 break;
             case AF_BATCH_SAME:
                 out_step[i]  = oStrides[i];
                 in_step[i]   = sStrides[i];
                 filt_step[i] = fStrides[i];
-                if (i >= baseDim) batch[i] = sDims[i];
+                if (i >= rank) batch[i] = sDims[i];
                 break;
             case AF_BATCH_RHS:
                 out_step[i]  = oStrides[i];
                 filt_step[i] = fStrides[i];
-                if (i >= baseDim) batch[i] = fDims[i];
+                if (i >= rank) batch[i] = fDims[i];
                 break;
             default: break;
         }
@@ -185,20 +188,20 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
                 AccT const *filt = fptr + b1 * filt_step[1] +
                                    b2 * filt_step[2] + b3 * filt_step[3];
 
-                switch (baseDim) {
+                switch (rank) {
                     case 1:
-                        one2one_1d<InT, AccT, Expand>(out, in, filt, oDims,
-                                                      sDims, fDims, sStrides);
+                        one2one_1d<InT, AccT>(out, in, filt, oDims, sDims,
+                                              fDims, sStrides, expand);
                         break;
                     case 2:
-                        one2one_2d<InT, AccT, Expand>(out, in, filt, oDims,
-                                                      sDims, fDims, oStrides,
-                                                      sStrides, fStrides);
+                        one2one_2d<InT, AccT>(out, in, filt, oDims, sDims,
+                                              fDims, oStrides, sStrides,
+                                              fStrides, expand);
                         break;
                     case 3:
-                        one2one_3d<InT, AccT, Expand>(out, in, filt, oDims,
-                                                      sDims, fDims, oStrides,
-                                                      sStrides, fStrides);
+                        one2one_3d<InT, AccT>(out, in, filt, oDims, sDims,
+                                              fDims, oStrides, sStrides,
+                                              fStrides, expand);
                         break;
                 }
             }
@@ -206,22 +209,23 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
     }
 }
 
-template<typename InT, typename AccT, dim_t conv_dim, bool Expand>
+template<typename InT, typename AccT>
 void convolve2_separable(InT *optr, InT const *const iptr,
                          AccT const *const fptr, af::dim4 const &oDims,
                          af::dim4 const &sDims, af::dim4 const &orgDims,
                          dim_t fDim, af::dim4 const &oStrides,
-                         af::dim4 const &sStrides, dim_t fStride) {
+                         af::dim4 const &sStrides, dim_t fStride,
+                         const bool expand, const int conv_dim) {
     UNUSED(orgDims);
     UNUSED(sStrides);
     UNUSED(fStride);
     for (dim_t j = 0; j < oDims[1]; ++j) {
         dim_t jOff = j * oStrides[1];
-        dim_t cj   = j + (conv_dim == 1) * (Expand ? 0 : fDim >> 1);
+        dim_t cj   = j + (conv_dim == 1) * (expand ? 0 : fDim >> 1);
 
         for (dim_t i = 0; i < oDims[0]; ++i) {
             dim_t iOff = i * oStrides[0];
-            dim_t ci   = i + (conv_dim == 0) * (Expand ? 0 : fDim >> 1);
+            dim_t ci   = i + (conv_dim == 0) * (expand ? 0 : fDim >> 1);
 
             AccT accum = scalar<AccT>(0);
 
@@ -250,9 +254,9 @@ void convolve2_separable(InT *optr, InT const *const iptr,
     }
 }
 
-template<typename InT, typename AccT, bool Expand>
+template<typename InT, typename AccT>
 void convolve2(Param<InT> out, CParam<InT> signal, CParam<AccT> c_filter,
-               CParam<AccT> r_filter, Param<InT> temp) {
+               CParam<AccT> r_filter, Param<InT> temp, const bool expand) {
     dim_t cflen = (dim_t)c_filter.dims().elements();
     dim_t rflen = (dim_t)r_filter.dims().elements();
 
@@ -273,13 +277,13 @@ void convolve2(Param<InT> out, CParam<InT> signal, CParam<AccT> c_filter,
             InT *tptr             = temp.get() + b2 * tStrides[2] + t_b3Off;
             InT *optr             = out.get() + b2 * oStrides[2] + o_b3Off;
 
-            convolve2_separable<InT, AccT, 0, Expand>(
+            convolve2_separable<InT, AccT>(
                 tptr, iptr, c_filter.get(), temp.dims(), sDims, sDims, cflen,
-                tStrides, sStrides, c_filter.strides(0));
+                tStrides, sStrides, c_filter.strides(0), expand, 0);
 
-            convolve2_separable<InT, AccT, 1, Expand>(
+            convolve2_separable<InT, AccT>(
                 optr, tptr, r_filter.get(), oDims, temp.dims(), sDims, rflen,
-                oStrides, tStrides, r_filter.strides(0));
+                oStrides, tStrides, r_filter.strides(0), expand, 1);
         }
     }
 }
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 951ce33641..42b890ed75 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -159,7 +159,7 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims,
 template<typename To, typename Ti>
 void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                    const Ti* in_ptr, const af::dim4& id, const af::dim4& is,
-                   const af::dim4& fd, const int half_di0, const int baseDim,
+                   const af::dim4& fd, const int half_di0, const int rank,
                    const int fftScale, const bool expand) {
     constexpr bool RoundResult = std::is_integral<To>::value;
 
@@ -176,8 +176,8 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                         id3 = d3 * is[3];
                     } else {
                         id0 = d0 + fd[0] / 2;
-                        id1 = (d1 + (baseDim > 1) * (fd[1] / 2)) * is[1];
-                        id2 = (d2 + (baseDim > 2) * (fd[2] / 2)) * is[2];
+                        id1 = (d1 + (rank > 1) * (fd[1] / 2)) * is[1];
+                        id2 = (d2 + (rank > 2) * (fd[2] / 2)) * is[2];
                         id3 = d3 * is[3];
                     }
 
@@ -221,12 +221,12 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
     }
 }
 
-template<typename T, typename convT, int baseDim>
+template<typename T, typename convT>
 void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
              const dim_t sig_half_d0, const dim_t fftScale,
              const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
              const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
-             bool expand, AF_BATCH_KIND kind) {
+             bool expand, AF_BATCH_KIND kind, const int rank) {
     // TODO(pradeep) check if we can avoid convT template parameter also
     // using convT = typename std::conditional<std::is_integral<T>::value,
     // float, double>::type;
@@ -245,12 +245,12 @@ void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
     if (kind == AF_BATCH_RHS) {
         reorderHelper<T, convT>(out_ptr, out_dims, out_strides, filter_tmp_ptr,
                                 filter_tmp_dims, filter_tmp_strides,
-                                filter_dims, sig_half_d0, baseDim, fftScale,
+                                filter_dims, sig_half_d0, rank, fftScale,
                                 expand);
     } else {
         reorderHelper<T, convT>(out_ptr, out_dims, out_strides, sig_tmp_ptr,
                                 sig_tmp_dims, sig_tmp_strides, filter_dims,
-                                sig_half_d0, baseDim, fftScale, expand);
+                                sig_half_d0, rank, fftScale, expand);
     }
 }
 
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 3ec8e12d04..4be4577fbe 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -13,9 +13,9 @@
 namespace cpu {
 namespace kernel {
 
-template<typename OutT, typename InT, bool IsLinear>
-void histogram(Param<OutT> out, CParam<InT> in, unsigned const nbins,
-               double const minval, double const maxval) {
+template<typename T>
+void histogram(Param<uint> out, CParam<T> in, const unsigned nbins,
+               const double minval, const double maxval, const bool IsLinear) {
     dim4 const outDims  = out.dims();
     float const step    = (maxval - minval) / (float)nbins;
     dim4 const inDims   = in.dims();
@@ -24,8 +24,8 @@ void histogram(Param<OutT> out, CParam<InT> in, unsigned const nbins,
     dim_t const nElems  = inDims[0] * inDims[1];
 
     for (dim_t b3 = 0; b3 < outDims[3]; b3++) {
-        OutT* outData     = out.get() + b3 * oStrides[3];
-        const InT* inData = in.get() + b3 * iStrides[3];
+        uint* outData   = out.get() + b3 * oStrides[3];
+        const T* inData = in.get() + b3 * iStrides[3];
         for (dim_t b2 = 0; b2 < outDims[2]; b2++) {
             for (dim_t i = 0; i < nElems; i++) {
                 int idx =
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
index 48df0cbffe..72ac0a0d64 100644
--- a/src/backend/cpu/kernel/match_template.hpp
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -13,8 +13,9 @@
 namespace cpu {
 namespace kernel {
 
-template<typename OutT, typename InT, af_match_type MatchT>
-void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
+template<typename OutT, typename InT>
+void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg,
+                   const af::matchType mType) {
     const af::dim4 sDims    = sImg.dims();
     const af::dim4 tDims    = tImg.dims();
     const af::dim4 sStrides = sImg.strides();
@@ -29,8 +30,8 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
 
     OutT tImgMean        = OutT(0);
     dim_t winNumElements = tImg.dims().elements();
-    bool needMean        = MatchT == AF_ZSAD || MatchT == AF_LSAD ||
-                    MatchT == AF_ZSSD || MatchT == AF_LSSD || MatchT == AF_ZNCC;
+    bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
+                    mType == AF_LSSD || mType == AF_ZNCC;
     const InT* tpl = tImg.get();
 
     if (needMean) {
@@ -57,7 +58,7 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
                     OutT disparity = OutT(0);
 
                     // mean for window
-                    // this variable will be used based on MatchT value
+                    // this variable will be used based on mType value
                     OutT wImgMean = OutT(0);
                     if (needMean) {
                         for (dim_t tj = 0, j = sj; tj < tDim1; tj++, j++) {
@@ -84,7 +85,7 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
                                             : InT(0));
                             InT tVal = tpl[tjStride + ti * tStrides[0]];
                             OutT temp;
-                            switch (MatchT) {
+                            switch (mType) {
                                 case AF_SAD:
                                     disparity += fabs((OutT)sVal - (OutT)tVal);
                                     break;
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index 6f804a0aae..05353aaf35 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -8,15 +8,18 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
+#include <err_cpu.hpp>
 #include <algorithm>
 #include <vector>
 
 namespace cpu {
 namespace kernel {
 
-template<typename T, af_border_type Pad>
-void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid) {
+template<typename T>
+void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid,
+              const af::borderType pad) {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -37,7 +40,7 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid) {
                     for (int wi = 0; wi < (int)w_wid; ++wi) {
                         int im_row = row + wi - w_wid / 2;
                         int im_roff;
-                        switch (Pad) {
+                        switch (pad) {
                             case AF_PAD_ZERO:
                                 im_roff = im_row * istrides[0];
                                 if (im_row < 0 || im_row >= (int)dims[0])
@@ -55,6 +58,9 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid) {
                                 im_roff = im_row * istrides[0];
                                 wind_vals.push_back(in_ptr[im_roff]);
                             } break;
+                            default:
+                                CPU_NOT_SUPPORTED("Unsupported padding type");
+                                break;
                         }
                     }
 
@@ -74,8 +80,9 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid) {
     }
 }
 
-template<typename T, af_border_type Pad>
-void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
+template<typename T>
+void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
+              const af::borderType pad) {
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -97,9 +104,9 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
                     for (int wj = 0; wj < (int)w_wid; ++wj) {
                         bool isColOff = false;
 
-                        int im_col = col + wj - w_wid / 2;
-                        int im_coff;
-                        switch (Pad) {
+                        int im_col  = col + wj - w_wid / 2;
+                        int im_coff = 0;
+                        switch (pad) {
                             case AF_PAD_ZERO:
                                 im_coff = im_col * istrides[1];
                                 if (im_col < 0 || im_col >= (int)dims[1])
@@ -118,14 +125,17 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
 
                                 im_coff = im_col * istrides[1];
                             } break;
+                            default:
+                                CPU_NOT_SUPPORTED("Unsupported padding type");
+                                break;
                         }
 
                         for (int wi = 0; wi < (int)w_len; ++wi) {
                             bool isRowOff = false;
 
-                            int im_row = row + wi - w_len / 2;
-                            int im_roff;
-                            switch (Pad) {
+                            int im_row  = row + wi - w_len / 2;
+                            int im_roff = 0;
+                            switch (pad) {
                                 case AF_PAD_ZERO:
                                     im_roff = im_row * istrides[0];
                                     if (im_row < 0 || im_row >= (int)dims[0])
@@ -145,10 +155,14 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
 
                                     im_roff = im_row * istrides[0];
                                 } break;
+                                default:
+                                    CPU_NOT_SUPPORTED(
+                                        "Unsupported padding type");
+                                    break;
                             }
 
                             if (isRowOff || isColOff) {
-                                switch (Pad) {
+                                switch (pad) {
                                     case AF_PAD_ZERO:
                                         wind_vals.push_back(0);
                                         break;
@@ -156,6 +170,10 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
                                         wind_vals.push_back(
                                             in_ptr[im_coff + im_roff]);
                                         break;
+                                    default:
+                                        CPU_NOT_SUPPORTED(
+                                            "Unsupported padding type");
+                                        break;
                                 }
                             } else
                                 wind_vals.push_back(in_ptr[im_coff + im_roff]);
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift_nonfree.hpp
index 2382ae2e7b..073229c0d4 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift_nonfree.hpp
@@ -820,9 +820,9 @@ Array<T> createInitialImage(const Array<T>& img, const float init_sigma,
     if (double_input) {
         Array<T> double_img =
             resize<T>(img, idims[0] * 2, idims[1] * 2, AF_INTERP_BILINEAR);
-        init_img = convolve2<T, convAccT, false>(double_img, filter, filter);
+        init_img = convolve2<T, convAccT>(double_img, filter, filter, false);
     } else {
-        init_img = convolve2<T, convAccT, false>(img, filter, filter);
+        init_img = convolve2<T, convAccT>(img, filter, filter, false);
     }
 
     return init_img;
@@ -862,8 +862,8 @@ std::vector<Array<T>> buildGaussPyr(const Array<T>& init_img,
             } else {
                 Array<T> filter = gauss_filter<T>(sig_layers[l]);
 
-                gauss_pyr[idx] = convolve2<T, convAccT, false>(
-                    gauss_pyr[src_idx], filter, filter);
+                gauss_pyr[idx] = convolve2<T, convAccT>(gauss_pyr[src_idx],
+                                                        filter, filter, false);
             }
         }
     }
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index 617b74ca0b..c4e240117a 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -14,8 +14,9 @@
 namespace cpu {
 namespace kernel {
 
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Param<T> out, CParam<T> in) {
+template<typename T>
+void triangle(Param<T> out, CParam<T> in, const bool is_upper,
+              const bool is_unit_diag) {
     T *o       = out.get();
     const T *i = in.get();
 
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 9e6dda9431..98c54aa149 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -18,35 +18,20 @@ using af::dim4;
 
 namespace cpu {
 
-template<typename InT, typename OutT, af_match_type MatchT>
-Array<OutT> match_template(const Array<InT> &sImg, const Array<InT> &tImg) {
-    Array<OutT> out = createEmptyArray<OutT>(sImg.dims());
-
-    getQueue().enqueue(kernel::matchTemplate<OutT, InT, MatchT>, out, sImg,
-                       tImg);
+template<typename inType, typename outType>
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg,
+                              const af::matchType mType) {
+    Array<outType> out = createEmptyArray<outType>(sImg.dims());
+    getQueue().enqueue(kernel::matchTemplate<outType, inType>, out, sImg, tImg,
+                       mType);
 
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)                                \
-    template Array<out_t> match_template<in_t, out_t, AF_SAD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_LSAD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_SSD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_LSSD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_NCC>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_SHD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);
+#define INSTANTIATE(in_t, out_t)                       \
+    template Array<out_t> match_template<in_t, out_t>( \
+        const Array<in_t> &, const Array<in_t> &, const af::matchType);
 
 INSTANTIATE(double, double)
 INSTANTIATE(float, float)
diff --git a/src/backend/cpu/match_template.hpp b/src/backend/cpu/match_template.hpp
index ae32d6c839..ebe78e6023 100644
--- a/src/backend/cpu/match_template.hpp
+++ b/src/backend/cpu/match_template.hpp
@@ -10,9 +10,8 @@
 #include <Array.hpp>
 
 namespace cpu {
-
-template<typename inType, typename outType, af_match_type mType>
+template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
-                              const Array<inType> &tImg);
-
+                              const Array<inType> &tImg,
+                              const af::matchType mType);
 }
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 44f611536d..58671c5de3 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -18,33 +18,27 @@ using af::dim4;
 
 namespace cpu {
 
-template<typename T, af_border_type pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType pad) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    getQueue().enqueue(kernel::medfilt1<T, pad>, out, in, w_wid);
-
+    getQueue().enqueue(kernel::medfilt1<T>, out, in, w_wid, pad);
     return out;
 }
 
-template<typename T, af_border_type pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType pad) {
     Array<T> out = createEmptyArray<T>(in.dims());
-
-    getQueue().enqueue(kernel::medfilt2<T, pad>, out, in, w_len, w_wid);
-
+    getQueue().enqueue(kernel::medfilt2<T>, out, in, w_len, w_wid, pad);
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in,             \
-                                               dim_t w_wid);                   \
-    template Array<T> medfilt1<T, AF_PAD_SYM>(const Array<T> &in,              \
-                                              dim_t w_wid);                    \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in,             \
-                                               dim_t w_len, dim_t w_wid);      \
-    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, \
-                                              dim_t w_wid);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> medfilt1<T>(const Array<T> &in, const int w_wid, \
+                                  const af::borderType);               \
+    template Array<T> medfilt2<T>(const Array<T> &in, const int w_len, \
+                                  const int w_wid, const af::borderType);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/medfilt.hpp b/src/backend/cpu/medfilt.hpp
index db177afdbc..25f3ff2fe6 100644
--- a/src/backend/cpu/medfilt.hpp
+++ b/src/backend/cpu/medfilt.hpp
@@ -11,10 +11,12 @@
 
 namespace cpu {
 
-template<typename T, af_border_type edge_pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType edge_pad);
 
-template<typename T, af_border_type edge_pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType edge_pad);
 
 }  // namespace cpu
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 54fd77da4b..0a415c5cee 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -204,8 +204,8 @@ unsigned orb(Array<float>& x, Array<float>& y, Array<float>& score,
 
             // Filter level image with Gaussian kernel to reduce noise
             // sensitivity
-            lvl_filt = convolve2<T, convAccT, false>(lvl_img, gauss_filter,
-                                                     gauss_filter);
+            lvl_filt = convolve2<T, convAccT>(lvl_img, gauss_filter,
+                                              gauss_filter, false);
         }
         lvl_filt.eval();
         getQueue().sync();
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index a9d58303e1..7cf0595eff 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -81,7 +81,7 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     dim4 rdims(M, N);
     r = createEmptyArray<T>(rdims);
 
-    triangle<T, true, false>(r, q);
+    triangle<T>(r, q, true, false);
 
     auto func = [=](Param<T> q, Param<T> t, int M, int N) {
         gqr_func<T>()(AF_LAPACK_COL_MAJOR, M, M, min(M, N), q.get(),
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 7d0cbed448..c8ca71b2a0 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -19,30 +19,24 @@ using common::half;
 
 namespace cpu {
 
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in) {
-    getQueue().enqueue(kernel::triangle<T, is_upper, is_unit_diag>, out, in);
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag) {
+    getQueue().enqueue(kernel::triangle<T>, out, in, is_upper, is_unit_diag);
 }
 
-template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in) {
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag) {
     Array<T> out = createEmptyArray<T>(in.dims());
-    triangle<T, is_upper, is_unit_diag>(out, in);
+    triangle<T>(out, in, is_upper, is_unit_diag);
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template void triangle<T, true, true>(Array<T> & out, const Array<T> &in); \
-    template void triangle<T, false, true>(Array<T> & out,                     \
-                                           const Array<T> &in);                \
-    template void triangle<T, true, false>(Array<T> & out,                     \
-                                           const Array<T> &in);                \
-    template void triangle<T, false, false>(Array<T> & out,                    \
-                                            const Array<T> &in);               \
-    template Array<T> triangle<T, true, true>(const Array<T> &in);             \
-    template Array<T> triangle<T, false, true>(const Array<T> &in);            \
-    template Array<T> triangle<T, true, false>(const Array<T> &in);            \
-    template Array<T> triangle<T, false, false>(const Array<T> &in);
+#define INSTANTIATE(T)                                                  \
+    template void triangle<T>(Array<T> &, const Array<T> &, const bool, \
+                              const bool);                              \
+    template Array<T> triangle<T>(const Array<T> &, const bool, const bool);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/triangle.hpp b/src/backend/cpu/triangle.hpp
index d7bf864d12..8178767b45 100644
--- a/src/backend/cpu/triangle.hpp
+++ b/src/backend/cpu/triangle.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 
 namespace cpu {
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in);
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag);
 
-template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in);
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag);
 }  // namespace cpu
diff --git a/src/backend/cuda/bilateral.cpp b/src/backend/cuda/bilateral.cpp
index 090ca8b65c..12b2907b4f 100644
--- a/src/backend/cuda/bilateral.cpp
+++ b/src/backend/cuda/bilateral.cpp
@@ -16,20 +16,17 @@ using af::dim4;
 
 namespace cuda {
 
-template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
-                         const float &c_sigma) {
-    UNUSED(isColor);
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &sSigma,
+                         const float &cSigma) {
     Array<outType> out = createEmptyArray<outType>(in.dims());
-    kernel::bilateral<inType, outType>(out, in, s_sigma, c_sigma);
+    kernel::bilateral<inType, outType>(out, in, sSigma, cSigma);
     return out;
 }
 
-#define INSTANTIATE(inT, outT)                                             \
-    template Array<outT> bilateral<inT, outT, true>(                       \
-        const Array<inT> &in, const float &s_sigma, const float &c_sigma); \
-    template Array<outT> bilateral<inT, outT, false>(                      \
-        const Array<inT> &in, const float &s_sigma, const float &c_sigma);
+#define INSTANTIATE(inT, outT)                                    \
+    template Array<outT> bilateral<inT, outT>(const Array<inT> &, \
+                                              const float &, const float &);
 
 INSTANTIATE(double, double)
 INSTANTIATE(float, float)
diff --git a/src/backend/cuda/bilateral.hpp b/src/backend/cuda/bilateral.hpp
index bbed9202b9..35fa575500 100644
--- a/src/backend/cuda/bilateral.hpp
+++ b/src/backend/cuda/bilateral.hpp
@@ -10,9 +10,7 @@
 #include <Array.hpp>
 
 namespace cuda {
-
-template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
-                         const float &c_sigma);
-
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
+                         const float &chromaticSigma);
 }
diff --git a/src/backend/cuda/cholesky.cpp b/src/backend/cuda/cholesky.cpp
index 973df87d83..2757d50e26 100644
--- a/src/backend/cuda/cholesky.cpp
+++ b/src/backend/cuda/cholesky.cpp
@@ -85,11 +85,7 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper) {
-        triangle<T, true, false>(out, out);
-    } else {
-        triangle<T, false, false>(out, out);
-    }
+    triangle<T>(out, out, is_upper, false);
 
     return out;
 }
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 90141e2e7a..d471eb0827 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -27,38 +27,38 @@ using std::is_same;
 
 namespace cuda {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
-                  AF_BATCH_KIND kind) {
+                  AF_BATCH_KIND kind, const int rank, const bool expand) {
     const dim4 &sDims = signal.dims();
     const dim4 &fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for (dim_t d = 0; d < 4; ++d) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
             if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
                 oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    kernel::convolve_nd<T, accT>(out, signal, filter, kind, baseDim, expand);
+    kernel::convolve_nd<T, accT>(out, signal, filter, kind, rank, expand);
 
     return out;
 }
 
-template<typename T, typename accT, bool expand>
+template<typename T, typename accT>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
-                   Array<accT> const &r_filter) {
+                   Array<accT> const &r_filter, const bool expand) {
     const dim4 &cfDims = c_filter.dims();
     const dim4 &rfDims = r_filter.dims();
 
@@ -84,31 +84,12 @@ Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve<T, accT, 1, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 1, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 2, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 2, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 3, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 3, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve2<T, accT, true>(Array<T> const &signal,       \
-                                               Array<accT> const &c_filter,  \
-                                               Array<accT> const &r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const &signal,      \
-                                                Array<accT> const &c_filter, \
-                                                Array<accT> const &r_filter);
+#define INSTANTIATE(T, accT)                                                   \
+    template Array<T> convolve<T, accT>(Array<T> const &, Array<accT> const &, \
+                                        AF_BATCH_KIND, const int, const bool); \
+    template Array<T> convolve2<T, accT>(Array<T> const &,                     \
+                                         Array<accT> const &,                  \
+                                         Array<accT> const &, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/cuda/convolve.hpp b/src/backend/cuda/convolve.hpp
index bee4c77ea0..636031b30d 100644
--- a/src/backend/cuda/convolve.hpp
+++ b/src/backend/cuda/convolve.hpp
@@ -11,13 +11,13 @@
 
 namespace cuda {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
-                  AF_BATCH_KIND kind);
+                  AF_BATCH_KIND kind, const int rank, const bool expand);
 
-template<typename T, typename accT, bool expand>
+template<typename T, typename accT>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
-                   Array<accT> const &r_filter);
+                   Array<accT> const &r_filter, const bool expand);
 
 template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
diff --git a/src/backend/cuda/fft.cu b/src/backend/cuda/fft.cu
index 634f22daeb..4254b719bf 100644
--- a/src/backend/cuda/fft.cu
+++ b/src/backend/cuda/fft.cu
@@ -17,7 +17,10 @@
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
+#include <array>
+
 using af::dim4;
+using std::array;
 using std::string;
 
 namespace cuda {
@@ -58,28 +61,26 @@ CUFFT_REAL_FUNC(cdouble, double, D2Z)
 CUFFT_REAL_FUNC(float, cfloat, C2R)
 CUFFT_REAL_FUNC(double, cdouble, Z2D)
 
-template<int rank>
-void computeDims(int rdims[rank], const dim4 &idims) {
-    for (int i = 0; i < rank; i++) { rdims[i] = idims[(rank - 1) - i]; }
+inline array<int, AF_MAX_DIMS> computeDims(const int rank, const dim4 &idims) {
+    array<int, AF_MAX_DIMS> retVal = {};
+    for (int i = 0; i < rank; i++) { retVal[i] = idims[(rank - 1) - i]; }
+    return retVal;
 }
 
-template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in) {
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction) {
     const dim4 idims    = in.dims();
     const dim4 istrides = in.strides();
 
-    int t_dims[rank];
-    int in_embed[rank];
-
-    computeDims<rank>(t_dims, idims);
-    computeDims<rank>(in_embed, in.getDataDims());
+    auto t_dims   = computeDims(rank, idims);
+    auto in_embed = computeDims(rank, in.getDataDims());
 
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 
     SharedPlan plan =
-        findPlan(rank, t_dims, in_embed, istrides[0], istrides[rank], in_embed,
-                 istrides[0], istrides[rank],
+        findPlan(rank, t_dims.data(), in_embed.data(), istrides[0],
+                 istrides[rank], in_embed.data(), istrides[0], istrides[rank],
                  (cufftType)cufft_transform<T>::type, batch);
 
     cufft_transform<T> transform;
@@ -88,8 +89,8 @@ void fft_inplace(Array<T> &in) {
                           direction ? CUFFT_FORWARD : CUFFT_INVERSE));
 }
 
-template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in) {
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
     dim4 idims = in.dims();
     dim4 odims = in.dims();
 
@@ -97,22 +98,19 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
 
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    int t_dims[rank];
-    int in_embed[rank], out_embed[rank];
-
-    computeDims<rank>(t_dims, idims);
-    computeDims<rank>(in_embed, in.getDataDims());
-    computeDims<rank>(out_embed, out.getDataDims());
+    auto t_dims    = computeDims(rank, idims);
+    auto in_embed  = computeDims(rank, in.getDataDims());
+    auto out_embed = computeDims(rank, out.getDataDims());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+    for (int i = rank; i < AF_MAX_DIMS; i++) { batch *= idims[i]; }
 
     dim4 istrides = in.strides();
     dim4 ostrides = out.strides();
 
     SharedPlan plan =
-        findPlan(rank, t_dims, in_embed, istrides[0], istrides[rank], out_embed,
-                 ostrides[0], ostrides[rank],
+        findPlan(rank, t_dims.data(), in_embed.data(), istrides[0],
+                 istrides[rank], out_embed.data(), ostrides[0], ostrides[rank],
                  (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
 
     cufft_real_transform<Tc, Tr> transform;
@@ -121,19 +119,16 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
     return out;
 }
 
-template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
     Array<Tr> out = createEmptyArray<Tr>(odims);
 
-    int t_dims[rank];
-    int in_embed[rank], out_embed[rank];
-
-    computeDims<rank>(t_dims, odims);
-    computeDims<rank>(in_embed, in.getDataDims());
-    computeDims<rank>(out_embed, out.getDataDims());
+    auto t_dims    = computeDims(rank, odims);
+    auto in_embed  = computeDims(rank, in.getDataDims());
+    auto out_embed = computeDims(rank, out.getDataDims());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= odims[i]; }
+    for (int i = rank; i < AF_MAX_DIMS; i++) { batch *= odims[i]; }
 
     dim4 istrides = in.strides();
     dim4 ostrides = out.strides();
@@ -141,8 +136,8 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     cufft_real_transform<Tr, Tc> transform;
 
     SharedPlan plan =
-        findPlan(rank, t_dims, in_embed, istrides[0], istrides[rank], out_embed,
-                 ostrides[0], ostrides[rank],
+        findPlan(rank, t_dims.data(), in_embed.data(), istrides[0],
+                 istrides[rank], out_embed.data(), ostrides[0], ostrides[rank],
                  (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
 
     CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
@@ -150,27 +145,16 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     return out;
 }
 
-#define INSTANTIATE(T)                                     \
-    template void fft_inplace<T, 1, true>(Array<T> & in);  \
-    template void fft_inplace<T, 2, true>(Array<T> & in);  \
-    template void fft_inplace<T, 3, true>(Array<T> & in);  \
-    template void fft_inplace<T, 1, false>(Array<T> & in); \
-    template void fft_inplace<T, 2, false>(Array<T> & in); \
-    template void fft_inplace<T, 3, false>(Array<T> & in);
+#define INSTANTIATE(T) \
+    template void fft_inplace<T>(Array<T> &, const int, const bool);
 
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-#define INSTANTIATE_REAL(Tr, Tc)                                \
-    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in); \
-    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in); \
-    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in); \
-    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in,  \
-                                          const dim4 &odims);   \
-    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in,  \
-                                          const dim4 &odims);   \
-    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in,  \
-                                          const dim4 &odims);
+#define INSTANTIATE_REAL(Tr, Tc)                                               \
+    template Array<Tc> fft_r2c<Tc, Tr>(const Array<Tr> &, const int);          \
+    template Array<Tr> fft_c2r<Tr, Tc>(const Array<Tc> &in, const dim4 &odims, \
+                                       const int);
 
 INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
diff --git a/src/backend/cuda/fft.hpp b/src/backend/cuda/fft.hpp
index b66be18e82..c9ff79877a 100644
--- a/src/backend/cuda/fft.hpp
+++ b/src/backend/cuda/fft.hpp
@@ -13,13 +13,13 @@ namespace cuda {
 
 void setFFTPlanCacheSize(size_t numPlans);
 
-template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &out);
+template<typename T>
+void fft_inplace(Array<T> &out, const int rank, const bool direction);
 
-template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in);
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank);
 
-template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims);
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 
 }  // namespace cuda
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index 8316ab26c3..36a449256a 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -24,20 +24,19 @@ using std::is_same;
 namespace cuda {
 
 template<typename T>
-dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
-                    const dim_t baseDim) {
+dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2, const int rank) {
     const dim4& i1d = i1.dims();
     const dim4& i2d = i2.dims();
 
-    dim_t pd[4] = {1, 1, 1, 1};
+    dim_t pd[AF_MAX_DIMS] = {1, 1, 1, 1};
 
     dim_t max_d0 = (i1d[0] > i2d[0]) ? i1d[0] : i2d[0];
     dim_t min_d0 = (i1d[0] < i2d[0]) ? i1d[0] : i2d[0];
     pd[0]        = nextpow2(static_cast<unsigned>(
         static_cast<int>(ceil(max_d0 / 2.f)) + min_d0 - 1));
 
-    for (dim_t k = 1; k < 4; k++) {
-        if (k < baseDim) {
+    for (int k = 1; k < AF_MAX_DIMS; k++) {
+        if (k < rank) {
             pd[k] = nextpow2(static_cast<unsigned>(i1d[k] + i2d[k] - 1));
         } else {
             pd[k] = i1d[k];
@@ -47,9 +46,9 @@ dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
     return dim4(pd[0], pd[1], pd[2], pd[3]);
 }
 
-template<typename T, dim_t baseDim>
+template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind) {
+                     const bool expand, AF_BATCH_KIND kind, const int rank) {
     using convT =
         typename conditional<is_integral<T>::value || is_same<T, float>::value,
                              float, double>::type;
@@ -61,57 +60,50 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     dim4 oDims(1);
     if (expand) {
-        for (dim_t d = 0; d < 4; ++d) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
             if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
                 oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
-    const dim4 spDims       = calcPackedSize<T>(signal, filter, baseDim);
-    const dim4 fpDims       = calcPackedSize<T>(filter, signal, baseDim);
+    const dim4 spDims       = calcPackedSize<T>(signal, filter, rank);
+    const dim4 fpDims       = calcPackedSize<T>(filter, signal, rank);
     Array<cT> signal_packed = createEmptyArray<cT>(spDims);
     Array<cT> filter_packed = createEmptyArray<cT>(fpDims);
 
     kernel::packDataHelper<cT, T>(signal_packed, filter_packed, signal, filter);
 
-    fft_inplace<cT, baseDim, true>(signal_packed);
-    fft_inplace<cT, baseDim, true>(filter_packed);
+    fft_inplace<cT>(signal_packed, rank, true);
+    fft_inplace<cT>(filter_packed, rank, true);
 
     Array<T> out = createEmptyArray<T>(oDims);
 
     kernel::complexMultiplyHelper<T, cT>(signal_packed, filter_packed, kind);
 
     if (kind == AF_BATCH_RHS) {
-        fft_inplace<cT, baseDim, false>(filter_packed);
+        fft_inplace<cT>(filter_packed, rank, false);
         kernel::reorderOutputHelper<T, cT>(out, filter_packed, signal, filter,
-                                           expand, baseDim);
+                                           expand, rank);
     } else {
-        fft_inplace<cT, baseDim, false>(signal_packed);
+        fft_inplace<cT>(signal_packed, rank, false);
         kernel::reorderOutputHelper<T, cT>(out, signal_packed, signal, filter,
-                                           expand, baseDim);
+                                           expand, rank);
     }
 
     return out;
 }
 
-#define INSTANTIATE(T)                                                     \
-    template Array<T> fftconvolve<T, 1>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, 2>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, 3>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> fftconvolve<T>(Array<T> const&, Array<T> const&, \
+                                     const bool, AF_BATCH_KIND, const int);
 
 INSTANTIATE(double)
 INSTANTIATE(float)
diff --git a/src/backend/cuda/fftconvolve.hpp b/src/backend/cuda/fftconvolve.hpp
index 04df117831..f7cf19a199 100644
--- a/src/backend/cuda/fftconvolve.hpp
+++ b/src/backend/cuda/fftconvolve.hpp
@@ -11,7 +11,7 @@
 
 namespace cuda {
 
-template<typename T, dim_t baseDim>
+template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind);
+                     const bool expand, AF_BATCH_KIND kind, const int rank);
 }
diff --git a/src/backend/cuda/histogram.cpp b/src/backend/cuda/histogram.cpp
index 5b3359e49a..e9f8ce50b5 100644
--- a/src/backend/cuda/histogram.cpp
+++ b/src/backend/cuda/histogram.cpp
@@ -12,42 +12,36 @@
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
 #include <af/dim4.hpp>
-#include <vector>
 
 using af::dim4;
-using std::vector;
 
 namespace cuda {
 
-template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
-                         const double &minval, const double &maxval) {
-    const dim4 &dims   = in.dims();
-    dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
-    Array<outType> out = createValueArray<outType>(outDims, outType(0));
-
-    kernel::histogram<inType, outType>(out, in, nbins, minval, maxval,
-                                       isLinear);
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear) {
+    const dim4 &dims = in.dims();
+    dim4 outDims     = dim4(nbins, 1, dims[2], dims[3]);
+    Array<uint> out  = createValueArray<uint>(outDims, uint(0));
+    kernel::histogram<T>(out, in, nbins, minval, maxval, isLinear);
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)                                            \
-    template Array<out_t> histogram<in_t, out_t, true>(                     \
-        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
-        const double &maxval);                                              \
-    template Array<out_t> histogram<in_t, out_t, false>(                    \
-        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
-        const double &maxval);
-
-INSTANTIATE(float, uint)
-INSTANTIATE(double, uint)
-INSTANTIATE(char, uint)
-INSTANTIATE(int, uint)
-INSTANTIATE(uint, uint)
-INSTANTIATE(uchar, uint)
-INSTANTIATE(short, uint)
-INSTANTIATE(ushort, uint)
-INSTANTIATE(intl, uint)
-INSTANTIATE(uintl, uint)
+#define INSTANTIATE(T)                                                    \
+    template Array<uint> histogram<T>(const Array<T> &, const unsigned &, \
+                                      const double &, const double &,     \
+                                      const bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/histogram.hpp b/src/backend/cuda/histogram.hpp
index c02556df2e..b07453f083 100644
--- a/src/backend/cuda/histogram.hpp
+++ b/src/backend/cuda/histogram.hpp
@@ -10,9 +10,8 @@
 #include <Array.hpp>
 
 namespace cuda {
-
-template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
-                         const double &minval, const double &maxval);
-
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear);
 }
diff --git a/src/backend/cuda/iir.cpp b/src/backend/cuda/iir.cpp
index 9951f4e2da..616411805a 100644
--- a/src/backend/cuda/iir.cpp
+++ b/src/backend/cuda/iir.cpp
@@ -27,7 +27,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
     }
 
     // Extract the first N elements
-    Array<T> c = convolve<T, T, 1, true>(x, b, type);
+    Array<T> c = convolve<T, T>(x, b, type, 1, true);
     dim4 cdims = c.dims();
     cdims[0]   = x.dims()[0];
     c.resetDims(cdims);
diff --git a/src/backend/cuda/kernel/fftconvolve.cuh b/src/backend/cuda/kernel/fftconvolve.cuh
index 814e9b4621..c5df6a1df4 100644
--- a/src/backend/cuda/kernel/fftconvolve.cuh
+++ b/src/backend/cuda/kernel/fftconvolve.cuh
@@ -150,7 +150,7 @@ __global__ void complexMultiply(Param<convT> out, Param<convT> in1,
 
 template<typename To, typename Ti, bool expand, bool roundOut>
 __global__ void reorderOutput(Param<To> out, Param<Ti> in, CParam<To> filter,
-                              const int half_di0, const int baseDim,
+                              const int half_di0, const int rank,
                               const int fftScale) {
     const int t = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -183,8 +183,8 @@ __global__ void reorderOutput(Param<To> out, Param<Ti> in, CParam<To> filter,
         ti3 = to3 * si3;
     } else {
         ti0 = to0 + filter.dims[0] / 2;
-        ti1 = (to1 + (baseDim > 1) * (filter.dims[1] / 2)) * si1;
-        ti2 = (to2 + (baseDim > 2) * (filter.dims[2] / 2)) * si2;
+        ti1 = (to1 + (rank > 1) * (filter.dims[1] / 2)) * si1;
+        ti2 = (to2 + (rank > 2) * (filter.dims[2] / 2)) * si2;
         ti3 = to3 * si3;
     }
 
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 356ebb46bf..01aa7c6fa1 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -104,7 +104,7 @@ void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
 
 template<typename T, typename convT>
 void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
-                         CParam<T> filter, bool expand, int baseDim) {
+                         CParam<T> filter, bool expand, int rank) {
     constexpr bool RoundResult = std::is_integral<T>::value;
 
     auto reorderOut =
@@ -116,7 +116,7 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     int fftScale = 1;
 
     // Calculate the scale by which to divide cuFFT results
-    for (int k = 0; k < baseDim; k++) fftScale *= packed.dims[k];
+    for (int k = 0; k < rank; k++) fftScale *= packed.dims[k];
 
     // Number of packed complex elements in dimension 0
     int sig_half_d0 = divup(sd[0], 2);
@@ -126,7 +126,7 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    reorderOut(qArgs, out, packed, filter, sig_half_d0, baseDim, fftScale);
+    reorderOut(qArgs, out, packed, filter, sig_half_d0, rank, fftScale);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/kernel/histogram.cuh b/src/backend/cuda/kernel/histogram.cuh
index 34666eeb09..8c1ed0c128 100644
--- a/src/backend/cuda/kernel/histogram.cuh
+++ b/src/backend/cuda/kernel/histogram.cuh
@@ -13,18 +13,16 @@
 
 namespace cuda {
 
-template<typename inType, typename outType, bool isLinear>
-__global__
-void histogram(Param<outType> out, CParam<inType> in, int len, int nbins,
-               float minval, float maxval, int nBBS) {
-    SharedMemory<outType> shared;
-    outType *shrdMem = shared.getPointer();
+template<typename T, bool isLinear>
+__global__ void histogram(Param<uint> out, CParam<T> in, int len, int nbins,
+                          float minval, float maxval, int nBBS) {
+    SharedMemory<uint> shared;
+    uint *shrdMem = shared.getPointer();
 
     // offset input and output to account for batch ops
-    unsigned b2 = blockIdx.x / nBBS;
-    const inType *iptr =
-        in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
-    outType *optr = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
+    unsigned b2   = blockIdx.x / nBBS;
+    const T *iptr = in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
+    uint *optr    = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
 
     int start = (blockIdx.x - b2 * nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
     int end   = min((start + THRD_LOAD * blockDim.x), len);
@@ -65,4 +63,4 @@ void histogram(Param<outType> out, CParam<inType> in, int len, int nbins,
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 76efb87597..d04d97cb86 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -22,15 +22,14 @@ constexpr int MAX_BINS  = 4000;
 constexpr int THREADS_X = 256;
 constexpr int THRD_LOAD = 16;
 
-template<typename inType, typename outType>
-void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
+template<typename T>
+void histogram(Param<uint> out, CParam<T> in, int nbins, float minval,
                float maxval, bool isLinear) {
     static const std::string source(histogram_cuh, histogram_cuh_len);
 
     auto histogram =
         common::getKernel("cuda::histogram", {source},
-                          {TemplateTypename<inType>(),
-                           TemplateTypename<outType>(), TemplateArg(isLinear)},
+                          {TemplateTypename<T>(), TemplateArg(isLinear)},
                           {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
     dim3 threads(kernel::THREADS_X, 1);
@@ -41,7 +40,7 @@ void histogram(Param<outType> out, CParam<inType> in, int nbins, float minval,
     dim3 blocks(blk_x * in.dims[2], in.dims[3]);
 
     // If nbins > MAX_BINS, we are using global memory so smem_size can be 0;
-    int smem_size = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 0;
+    int smem_size = nbins <= MAX_BINS ? (nbins * sizeof(uint)) : 0;
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream(), smem_size);
     histogram(qArgs, out, in, nElems, nbins, minval, maxval, blk_x);
diff --git a/src/backend/cuda/match_template.cpp b/src/backend/cuda/match_template.cpp
index 61c2528aca..19043b7cb7 100644
--- a/src/backend/cuda/match_template.cpp
+++ b/src/backend/cuda/match_template.cpp
@@ -17,9 +17,10 @@ using af::dim4;
 
 namespace cuda {
 
-template<typename inType, typename outType, af_match_type mType>
+template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
-                              const Array<inType> &tImg) {
+                              const Array<inType> &tImg,
+                              const af::matchType mType) {
     Array<outType> out = createEmptyArray<outType>(sImg.dims());
     bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
                     mType == AF_LSSD || mType == AF_ZNCC;
@@ -27,25 +28,9 @@ Array<outType> match_template(const Array<inType> &sImg,
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)                                \
-    template Array<out_t> match_template<in_t, out_t, AF_SAD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_LSAD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_SSD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_LSSD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_NCC>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_SHD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);
+#define INSTANTIATE(in_t, out_t)                       \
+    template Array<out_t> match_template<in_t, out_t>( \
+        const Array<in_t> &, const Array<in_t> &, const af::matchType);
 
 INSTANTIATE(double, double)
 INSTANTIATE(float, float)
diff --git a/src/backend/cuda/match_template.hpp b/src/backend/cuda/match_template.hpp
index b6308c91ed..a7f24fc833 100644
--- a/src/backend/cuda/match_template.hpp
+++ b/src/backend/cuda/match_template.hpp
@@ -10,9 +10,8 @@
 #include <Array.hpp>
 
 namespace cuda {
-
-template<typename inType, typename outType, af_match_type mType>
+template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
-                              const Array<inType> &tImg);
-
+                              const Array<inType> &tImg,
+                              const af::matchType mType);
 }
diff --git a/src/backend/cuda/medfilt.cpp b/src/backend/cuda/medfilt.cpp
index fa8435ae80..6561419ddd 100644
--- a/src/backend/cuda/medfilt.cpp
+++ b/src/backend/cuda/medfilt.cpp
@@ -18,8 +18,9 @@ using af::dim4;
 
 namespace cuda {
 
-template<typename T, af_border_type pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType pad) {
     ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
@@ -31,8 +32,9 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
     return out;
 }
 
-template<typename T, af_border_type pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType pad) {
     ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
     ARG_ASSERT(2, (w_len % 2 != 0));
 
@@ -44,15 +46,11 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in,             \
-                                               dim_t w_wid);                   \
-    template Array<T> medfilt1<T, AF_PAD_SYM>(const Array<T> &in,              \
-                                              dim_t w_wid);                    \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in,             \
-                                               dim_t w_len, dim_t w_wid);      \
-    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, \
-                                              dim_t w_wid);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> medfilt1<T>(const Array<T> &in, const int w_wid, \
+                                  const af::borderType);               \
+    template Array<T> medfilt2<T>(const Array<T> &in, const int w_len, \
+                                  const int w_wid, const af::borderType);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/medfilt.hpp b/src/backend/cuda/medfilt.hpp
index b6fa31176a..9fa6868859 100644
--- a/src/backend/cuda/medfilt.hpp
+++ b/src/backend/cuda/medfilt.hpp
@@ -11,10 +11,12 @@
 
 namespace cuda {
 
-template<typename T, af_border_type edge_pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType edge_pad);
 
-template<typename T, af_border_type edge_pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType edge_pad);
 
 }  // namespace cuda
diff --git a/src/backend/cuda/triangle.cpp b/src/backend/cuda/triangle.cpp
index cd0c270df0..8e5f7eec76 100644
--- a/src/backend/cuda/triangle.cpp
+++ b/src/backend/cuda/triangle.cpp
@@ -19,30 +19,24 @@ using common::half;
 
 namespace cuda {
 
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in) {
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag) {
     kernel::triangle<T>(out, in, is_upper, is_unit_diag);
 }
 
-template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in) {
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag) {
     Array<T> out = createEmptyArray<T>(in.dims());
-    triangle<T, is_upper, is_unit_diag>(out, in);
+    triangle<T>(out, in, is_upper, is_unit_diag);
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template void triangle<T, true, true>(Array<T> & out, const Array<T> &in); \
-    template void triangle<T, false, true>(Array<T> & out,                     \
-                                           const Array<T> &in);                \
-    template void triangle<T, true, false>(Array<T> & out,                     \
-                                           const Array<T> &in);                \
-    template void triangle<T, false, false>(Array<T> & out,                    \
-                                            const Array<T> &in);               \
-    template Array<T> triangle<T, true, true>(const Array<T> &in);             \
-    template Array<T> triangle<T, false, true>(const Array<T> &in);            \
-    template Array<T> triangle<T, true, false>(const Array<T> &in);            \
-    template Array<T> triangle<T, false, false>(const Array<T> &in);
+#define INSTANTIATE(T)                                                  \
+    template void triangle<T>(Array<T> &, const Array<T> &, const bool, \
+                              const bool);                              \
+    template Array<T> triangle<T>(const Array<T> &, const bool, const bool);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/triangle.hpp b/src/backend/cuda/triangle.hpp
index ddd7af6aa0..801dfdd900 100644
--- a/src/backend/cuda/triangle.hpp
+++ b/src/backend/cuda/triangle.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 
 namespace cuda {
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in);
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag);
 
-template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in);
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag);
 }  // namespace cuda
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index 77a45a9c11..d75f62d2fc 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -16,19 +16,17 @@ using af::dim4;
 
 namespace opencl {
 
-template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
-                         const float &c_sigma) {
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &sSigma,
+                         const float &cSigma) {
     Array<outType> out = createEmptyArray<outType>(in.dims());
-    kernel::bilateral<inType, outType>(out, in, s_sigma, c_sigma, isColor);
+    kernel::bilateral<inType, outType>(out, in, sSigma, cSigma);
     return out;
 }
 
-#define INSTANTIATE(inT, outT)                                             \
-    template Array<outT> bilateral<inT, outT, true>(                       \
-        const Array<inT> &in, const float &s_sigma, const float &c_sigma); \
-    template Array<outT> bilateral<inT, outT, false>(                      \
-        const Array<inT> &in, const float &s_sigma, const float &c_sigma);
+#define INSTANTIATE(inT, outT)                                    \
+    template Array<outT> bilateral<inT, outT>(const Array<inT> &, \
+                                              const float &, const float &);
 
 INSTANTIATE(double, double)
 INSTANTIATE(float, float)
diff --git a/src/backend/opencl/bilateral.hpp b/src/backend/opencl/bilateral.hpp
index ce587dca17..ab9775f3b2 100644
--- a/src/backend/opencl/bilateral.hpp
+++ b/src/backend/opencl/bilateral.hpp
@@ -10,9 +10,7 @@
 #include <Array.hpp>
 
 namespace opencl {
-
-template<typename inType, typename outType, bool isColor>
-Array<outType> bilateral(const Array<inType> &in, const float &s_sigma,
-                         const float &c_sigma);
-
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
+                         const float &chromaticSigma);
 }
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index e1c0314a33..eac4490baf 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -42,11 +42,7 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
     Array<T> out = copyArray<T>(in);
     *info        = cholesky_inplace(out, is_upper);
 
-    if (is_upper) {
-        triangle<T, true, false>(out, out);
-    } else {
-        triangle<T, false, false>(out, out);
-    }
+    triangle<T>(out, out, is_upper, false);
 
     return out;
 }
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 0382321306..0c294965e7 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -30,25 +30,25 @@ using std::vector;
 
 namespace opencl {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
-                  AF_BATCH_KIND kind) {
+                  AF_BATCH_KIND kind, const int rank, const bool expand) {
     const dim4 &sDims = signal.dims();
     const dim4 &fDims = filter.dims();
 
     dim4 oDims(1);
     if (expand) {
-        for (dim_t d = 0; d < 4; ++d) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
             if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
                 oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
@@ -57,7 +57,7 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
 
     dim_t MCFL2 = kernel::MAX_CONV2_FILTER_LEN;
     dim_t MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
-    switch (baseDim) {
+    switch (rank) {
         case 1:
             if (fDims[0] > kernel::MAX_CONV1_FILTER_LEN) { callKernel = false; }
             break;
@@ -69,7 +69,7 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                 callKernel = false;
             }
             break;
-        default: AF_ERROR("baseDim only supports values 1-3.", AF_ERR_UNKNOWN);
+        default: AF_ERROR("rank only supports values 1-3.", AF_ERR_UNKNOWN);
     }
 
     if (!callKernel) {
@@ -81,30 +81,14 @@ Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
         OPENCL_NOT_SUPPORTED(errMessage);
     }
 
-    kernel::convolve_nd<T, accT, baseDim, expand>(out, signal, filter, kind);
+    kernel::convolve_nd<T, accT>(out, signal, filter, kind, rank, expand);
 
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve<T, accT, 1, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 1, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 2, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 2, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);       \
-    template Array<T> convolve<T, accT, 3, true>(Array<T> const &signal,     \
-                                                 Array<accT> const &filter,  \
-                                                 AF_BATCH_KIND kind);        \
-    template Array<T> convolve<T, accT, 3, false>(Array<T> const &signal,    \
-                                                  Array<accT> const &filter, \
-                                                  AF_BATCH_KIND kind);
+#define INSTANTIATE(T, accT)                                                   \
+    template Array<T> convolve<T, accT>(Array<T> const &, Array<accT> const &, \
+                                        AF_BATCH_KIND, const int, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/opencl/convolve.hpp b/src/backend/opencl/convolve.hpp
index 2ae65e561a..6e52ed6e56 100644
--- a/src/backend/opencl/convolve.hpp
+++ b/src/backend/opencl/convolve.hpp
@@ -11,13 +11,13 @@
 
 namespace opencl {
 
-template<typename T, typename accT, dim_t baseDim, bool expand>
+template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
-                  AF_BATCH_KIND kind);
+                  AF_BATCH_KIND kind, const int rank, const bool expand);
 
-template<typename T, typename accT, bool expand>
+template<typename T, typename accT>
 Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
-                   Array<accT> const &r_filter);
+                   Array<accT> const &r_filter, const bool expand);
 
 template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 045a0a7e37..fc337e718f 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
 #include <convolve.hpp>
+
+#include <Array.hpp>
 #include <err_opencl.hpp>
 #include <kernel/convolve_separable.hpp>
 #include <af/dim4.hpp>
@@ -17,9 +18,9 @@ using af::dim4;
 
 namespace opencl {
 
-template<typename T, typename accT, bool expand>
+template<typename T, typename accT>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
-                   Array<accT> const& r_filter) {
+                   Array<accT> const& r_filter, const bool expand) {
     const auto cflen = c_filter.elements();
     const auto rflen = r_filter.elements();
 
@@ -47,19 +48,15 @@ Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
     Array<T> temp = createEmptyArray<T>(tDims);
     Array<T> out  = createEmptyArray<T>(oDims);
 
-    kernel::convSep<T, accT, 0, expand>(temp, signal, c_filter);
-    kernel::convSep<T, accT, 1, expand>(out, temp, r_filter);
+    kernel::convSep<T, accT>(temp, signal, c_filter, 0, expand);
+    kernel::convSep<T, accT>(out, temp, r_filter, 1, expand);
 
     return out;
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template Array<T> convolve2<T, accT, true>(Array<T> const& signal,       \
-                                               Array<accT> const& c_filter,  \
-                                               Array<accT> const& r_filter); \
-    template Array<T> convolve2<T, accT, false>(Array<T> const& signal,      \
-                                                Array<accT> const& c_filter, \
-                                                Array<accT> const& r_filter);
+#define INSTANTIATE(T, accT)                                                  \
+    template Array<T> convolve2<T, accT>(Array<T> const&, Array<accT> const&, \
+                                         Array<accT> const&, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index 466099dc92..071ef4b9e4 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -7,17 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
+#include <fft.hpp>
+
 #include <clfft.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
-#include <fft.hpp>
 #include <math.hpp>
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
-using std::string;
 
 namespace opencl {
 
@@ -36,8 +35,10 @@ struct Precision<cdouble> {
     enum { type = CLFFT_DOUBLE };
 };
 
-static void computeDims(size_t rdims[4], const dim4 &idims) {
-    for (int i = 0; i < 4; i++) { rdims[i] = static_cast<size_t>(idims[i]); }
+void computeDims(size_t rdims[AF_MAX_DIMS], const dim4 &idims) {
+    for (int i = 0; i < AF_MAX_DIMS; i++) {
+        rdims[i] = static_cast<size_t>(idims[i]);
+    }
 }
 
 //(currently) true is in clFFT if length is a power of 2,3,5
@@ -62,21 +63,20 @@ inline bool isSupLen(dim_t length) {
     return true;
 }
 
-template<int rank>
-void verifySupported(const dim4 &dims) {
+void verifySupported(const int rank, const dim4 &dims) {
     for (int i = 0; i < rank; i++) { ARG_ASSERT(1, isSupLen(dims[i])); }
 }
 
-template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in) {
-    verifySupported<rank>(in.dims());
-    size_t tdims[4], istrides[4];
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction) {
+    verifySupported(rank, in.dims());
+    size_t tdims[AF_MAX_DIMS], istrides[AF_MAX_DIMS];
 
     computeDims(tdims, in.dims());
     computeDims(istrides, in.strides());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
+    for (int i = rank; i < AF_MAX_DIMS; i++) { batch *= tdims[i]; }
 
     SharedPlan plan = findPlan(
         CLFFT_COMPLEX_INTERLEAVED, CLFFT_COMPLEX_INTERLEAVED,
@@ -91,23 +91,23 @@ void fft_inplace(Array<T> &in) {
         NULL, NULL, &imem, &imem, NULL));
 }
 
-template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in) {
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
     dim4 odims = in.dims();
 
     odims[0] = odims[0] / 2 + 1;
 
     Array<Tc> out = createEmptyArray<Tc>(odims);
 
-    verifySupported<rank>(in.dims());
-    size_t tdims[4], istrides[4], ostrides[4];
+    verifySupported(rank, in.dims());
+    size_t tdims[AF_MAX_DIMS], istrides[AF_MAX_DIMS], ostrides[AF_MAX_DIMS];
 
     computeDims(tdims, in.dims());
     computeDims(istrides, in.strides());
     computeDims(ostrides, out.strides());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
+    for (int i = rank; i < AF_MAX_DIMS; i++) { batch *= tdims[i]; }
 
     SharedPlan plan = findPlan(
         CLFFT_REAL, CLFFT_HERMITIAN_INTERLEAVED, static_cast<clfftDim>(rank),
@@ -124,19 +124,19 @@ Array<Tc> fft_r2c(const Array<Tr> &in) {
     return out;
 }
 
-template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
     Array<Tr> out = createEmptyArray<Tr>(odims);
 
-    verifySupported<rank>(odims);
-    size_t tdims[4], istrides[4], ostrides[4];
+    verifySupported(rank, odims);
+    size_t tdims[AF_MAX_DIMS], istrides[AF_MAX_DIMS], ostrides[AF_MAX_DIMS];
 
     computeDims(tdims, odims);
     computeDims(istrides, in.strides());
     computeDims(ostrides, out.strides());
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= tdims[i]; }
+    for (int i = rank; i < AF_MAX_DIMS; i++) { batch *= tdims[i]; }
 
     SharedPlan plan = findPlan(
         CLFFT_HERMITIAN_INTERLEAVED, CLFFT_REAL, static_cast<clfftDim>(rank),
@@ -153,27 +153,16 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims) {
     return out;
 }
 
-#define INSTANTIATE(T)                                     \
-    template void fft_inplace<T, 1, true>(Array<T> & in);  \
-    template void fft_inplace<T, 2, true>(Array<T> & in);  \
-    template void fft_inplace<T, 3, true>(Array<T> & in);  \
-    template void fft_inplace<T, 1, false>(Array<T> & in); \
-    template void fft_inplace<T, 2, false>(Array<T> & in); \
-    template void fft_inplace<T, 3, false>(Array<T> & in);
+#define INSTANTIATE(T) \
+    template void fft_inplace<T>(Array<T> &, const int, const bool);
 
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
-#define INSTANTIATE_REAL(Tr, Tc)                                \
-    template Array<Tc> fft_r2c<Tc, Tr, 1>(const Array<Tr> &in); \
-    template Array<Tc> fft_r2c<Tc, Tr, 2>(const Array<Tr> &in); \
-    template Array<Tc> fft_r2c<Tc, Tr, 3>(const Array<Tr> &in); \
-    template Array<Tr> fft_c2r<Tr, Tc, 1>(const Array<Tc> &in,  \
-                                          const dim4 &odims);   \
-    template Array<Tr> fft_c2r<Tr, Tc, 2>(const Array<Tc> &in,  \
-                                          const dim4 &odims);   \
-    template Array<Tr> fft_c2r<Tr, Tc, 3>(const Array<Tc> &in,  \
-                                          const dim4 &odims);
+#define INSTANTIATE_REAL(Tr, Tc)                                        \
+    template Array<Tc> fft_r2c<Tc, Tr>(const Array<Tr> &, const int);   \
+    template Array<Tr> fft_c2r<Tr, Tc>(const Array<Tc> &, const dim4 &, \
+                                       const int);
 
 INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
diff --git a/src/backend/opencl/fft.hpp b/src/backend/opencl/fft.hpp
index 5c29588602..28adbdfbfa 100644
--- a/src/backend/opencl/fft.hpp
+++ b/src/backend/opencl/fft.hpp
@@ -13,13 +13,13 @@ namespace opencl {
 
 void setFFTPlanCacheSize(size_t numPlans);
 
-template<typename T, int rank, bool direction>
-void fft_inplace(Array<T> &in);
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction);
 
-template<typename Tc, typename Tr, int rank>
-Array<Tc> fft_r2c(const Array<Tr> &in);
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank);
 
-template<typename Tr, typename Tc, int rank>
-Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims);
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 
 }  // namespace opencl
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 2d090a0b0e..10b3015b6b 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -28,8 +28,7 @@ using std::vector;
 namespace opencl {
 
 template<typename T>
-static dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
-                           const dim_t baseDim) {
+dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2, const dim_t rank) {
     const dim4& i1d = i1.dims();
     const dim4& i2d = i2.dims();
 
@@ -40,24 +39,24 @@ static dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2,
     pd[0] = nextpow2(static_cast<unsigned>(
         static_cast<int>(ceil(i1d[0] / 2.f)) + i2d[0] - 1));
 
-    for (dim_t k = 1; k < baseDim; k++) {
+    for (dim_t k = 1; k < rank; k++) {
         pd[k] = nextpow2(static_cast<unsigned>(i1d[k] + i2d[k] - 1));
     }
 
     dim_t i1batch = 1;
     dim_t i2batch = 1;
-    for (int k = baseDim; k < 4; k++) {
+    for (int k = rank; k < 4; k++) {
         i1batch *= i1d[k];
         i2batch *= i2d[k];
     }
-    pd[baseDim] = (i1batch + i2batch);
+    pd[rank] = (i1batch + i2batch);
 
     return dim4(pd[0], pd[1], pd[2], pd[3]);
 }
 
-template<typename T, dim_t baseDim>
+template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind) {
+                     const bool expand, AF_BATCH_KIND kind, const int rank) {
     using convT =
         typename conditional<is_integral<T>::value || is_same<T, float>::value,
                              float, double>::type;
@@ -69,34 +68,34 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     dim4 oDims(1);
     if (expand) {
-        for (dim_t d = 0; d < 4; ++d) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
             if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
                 oDims[d] = sDims[d] + fDims[d] - 1;
             } else {
-                oDims[d] = (d < baseDim ? sDims[d] + fDims[d] - 1 : sDims[d]);
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
             }
         }
     } else {
         oDims = sDims;
         if (kind == AF_BATCH_RHS) {
-            for (dim_t i = baseDim; i < 4; ++i) { oDims[i] = fDims[i]; }
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
         }
     }
 
-    const dim4 pDims = calcPackedSize<T>(signal, filter, baseDim);
+    const dim4 pDims = calcPackedSize<T>(signal, filter, rank);
     Array<cT> packed = createEmptyArray<cT>(pDims);
 
-    kernel::packDataHelper<cT, T>(packed, signal, filter, baseDim, kind);
-    fft_inplace<cT, baseDim, true>(packed);
-    kernel::complexMultiplyHelper<cT, T>(packed, signal, filter, baseDim, kind);
+    kernel::packDataHelper<cT, T>(packed, signal, filter, rank, kind);
+    fft_inplace<cT>(packed, rank, true);
+    kernel::complexMultiplyHelper<cT, T>(packed, signal, filter, rank, kind);
 
     // Compute inverse FFT only on complex-multiplied data
     if (kind == AF_BATCH_RHS) {
         vector<af_seq> seqs;
-        for (dim_t k = 0; k < 4; k++) {
-            if (k < baseDim) {
+        for (int k = 0; k < AF_MAX_DIMS; k++) {
+            if (k < rank) {
                 seqs.push_back({0., static_cast<double>(pDims[k] - 1), 1.});
-            } else if (k == baseDim) {
+            } else if (k == rank) {
                 seqs.push_back({1., static_cast<double>(pDims[k] - 1), 1.});
             } else {
                 seqs.push_back({0., 0., 1.});
@@ -104,13 +103,13 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
-        fft_inplace<cT, baseDim, false>(subPacked);
+        fft_inplace<cT>(subPacked, rank, false);
     } else {
         vector<af_seq> seqs;
-        for (dim_t k = 0; k < 4; k++) {
-            if (k < baseDim) {
+        for (int k = 0; k < AF_MAX_DIMS; k++) {
+            if (k < rank) {
                 seqs.push_back({0., static_cast<double>(pDims[k]) - 1, 1.});
-            } else if (k == baseDim) {
+            } else if (k == rank) {
                 seqs.push_back({0., static_cast<double>(pDims[k] - 2), 1.});
             } else {
                 seqs.push_back({0., 0., 1.});
@@ -118,26 +117,19 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
         }
 
         Array<cT> subPacked = createSubArray<cT>(packed, seqs);
-        fft_inplace<cT, baseDim, false>(subPacked);
+        fft_inplace<cT>(subPacked, rank, false);
     }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    kernel::reorderOutputHelper<T, cT>(out, packed, signal, filter, baseDim,
-                                       kind, expand);
+    kernel::reorderOutputHelper<T, cT>(out, packed, signal, filter, rank, kind,
+                                       expand);
     return out;
 }
 
-#define INSTANTIATE(T)                                                     \
-    template Array<T> fftconvolve<T, 1>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, 2>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);                                               \
-    template Array<T> fftconvolve<T, 3>(                                   \
-        Array<T> const& signal, Array<T> const& filter, const bool expand, \
-        AF_BATCH_KIND kind);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> fftconvolve<T>(Array<T> const&, Array<T> const&, \
+                                     const bool, AF_BATCH_KIND, const int);
 
 INSTANTIATE(double)
 INSTANTIATE(float)
diff --git a/src/backend/opencl/fftconvolve.hpp b/src/backend/opencl/fftconvolve.hpp
index 0267ad6e85..fde659d2b0 100644
--- a/src/backend/opencl/fftconvolve.hpp
+++ b/src/backend/opencl/fftconvolve.hpp
@@ -10,8 +10,7 @@
 #include <Array.hpp>
 
 namespace opencl {
-
-template<typename T, dim_t baseDim>
+template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
-                     const bool expand, AF_BATCH_KIND kind);
+                     const bool expand, AF_BATCH_KIND kind, const int rank);
 }
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index a8eb53506e..929daf67e8 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -12,42 +12,36 @@
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
 #include <af/dim4.hpp>
-#include <vector>
 
 using af::dim4;
-using std::vector;
 
 namespace opencl {
 
-template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
-                         const double &minval, const double &maxval) {
-    const dim4 &dims   = in.dims();
-    dim4 outDims       = dim4(nbins, 1, dims[2], dims[3]);
-    Array<outType> out = createValueArray<outType>(outDims, outType(0));
-
-    kernel::histogram<inType, outType>(out, in, nbins, minval, maxval,
-                                       isLinear);
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear) {
+    const dim4 &dims = in.dims();
+    dim4 outDims     = dim4(nbins, 1, dims[2], dims[3]);
+    Array<uint> out  = createValueArray<uint>(outDims, uint(0));
+    kernel::histogram<T>(out, in, nbins, minval, maxval, isLinear);
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)                                            \
-    template Array<out_t> histogram<in_t, out_t, true>(                     \
-        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
-        const double &maxval);                                              \
-    template Array<out_t> histogram<in_t, out_t, false>(                    \
-        const Array<in_t> &in, const unsigned &nbins, const double &minval, \
-        const double &maxval);
-
-INSTANTIATE(float, uint)
-INSTANTIATE(double, uint)
-INSTANTIATE(char, uint)
-INSTANTIATE(int, uint)
-INSTANTIATE(uint, uint)
-INSTANTIATE(uchar, uint)
-INSTANTIATE(short, uint)
-INSTANTIATE(ushort, uint)
-INSTANTIATE(intl, uint)
-INSTANTIATE(uintl, uint)
+#define INSTANTIATE(T)                                                    \
+    template Array<uint> histogram<T>(const Array<T> &, const unsigned &, \
+                                      const double &, const double &,     \
+                                      const bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/histogram.hpp b/src/backend/opencl/histogram.hpp
index aaa64038a5..583a8150cd 100644
--- a/src/backend/opencl/histogram.hpp
+++ b/src/backend/opencl/histogram.hpp
@@ -10,9 +10,8 @@
 #include <Array.hpp>
 
 namespace opencl {
-
-template<typename inType, typename outType, bool isLinear>
-Array<outType> histogram(const Array<inType> &in, const unsigned &nbins,
-                         const double &minval, const double &maxval);
-
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear);
 }
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 3a70a3aa86..63d34be2bd 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -27,7 +27,7 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
     }
 
     // Extract the first N elements
-    Array<T> c = convolve<T, T, 1, true>(x, b, type);
+    Array<T> c = convolve<T, T>(x, b, type, 1, true);
     dim4 cdims = c.dims();
     cdims[0]   = x.dims()[0];
     c.resetDims(cdims);
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 86f7b74519..3926d85d35 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -26,7 +26,7 @@ namespace kernel {
 
 template<typename inType, typename outType>
 void bilateral(Param out, const Param in, const float s_sigma,
-               const float c_sigma, const bool isColor) {
+               const float c_sigma) {
     constexpr int THREADS_X     = 16;
     constexpr int THREADS_Y     = 16;
     constexpr bool UseNativeExp = !std::is_same<inType, double>::value ||
@@ -37,7 +37,6 @@ void bilateral(Param out, const Param in, const float s_sigma,
     std::vector<TemplateArg> targs = {
         TemplateTypename<inType>(),
         TemplateTypename<outType>(),
-        TemplateArg(isColor),
     };
     std::vector<std::string> options = {
         DefineKeyValue(inType, dtype_traits<inType>::getName()),
diff --git a/src/backend/opencl/kernel/convolve.cl b/src/backend/opencl/kernel/convolve.cl
index 9bb8cd68d3..cf1205dac1 100644
--- a/src/backend/opencl/kernel/convolve.cl
+++ b/src/backend/opencl/kernel/convolve.cl
@@ -11,7 +11,7 @@ int index(int i, int j, int k, int jstride, int kstride) {
     return i + j * jstride + k * kstride;
 }
 
-#if BASE_DIM == 1
+#if RANK == 1
 kernel void convolve(global T *out, KParam oInfo, global T const *signal,
                      KParam sInfo, local T *localMem,
                      constant accType const *impulse, KParam fInfo, int nBBS0,
@@ -67,7 +67,7 @@ kernel void convolve(global T *out, KParam oInfo, global T const *signal,
 }
 #endif
 
-#if BASE_DIM == 2
+#if RANK == 2
 kernel void convolve(global T *out, KParam oInfo, global T const *signal,
                      KParam sInfo, constant accType const *impulse,
                      KParam fInfo, int nBBS0, int nBBS1, int ostep2, int ostep3,
@@ -143,7 +143,7 @@ kernel void convolve(global T *out, KParam oInfo, global T const *signal,
 }
 #endif
 
-#if BASE_DIM == 3
+#if RANK == 3
 kernel void convolve(global T *out, KParam oInfo, global T const *signal,
                      KParam sInfo, local T *localMem,
                      constant accType const *impulse, KParam fInfo, int nBBS0,
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index 06c620de20..6c9e2e5d6d 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -29,9 +29,9 @@ constexpr int MAX_CONV3_FILTER_LEN = 5;
  * file under the folder 'kernel/convovel' with their implementations
  * written in corresponding conv[1|2|3].cpp files under the same folder.
  */
-template<typename T, typename accType, int baseDim, bool expand>
+template<typename T, typename accType>
 void convolve_nd(Param out, const Param signal, const Param filter,
-                 AF_BATCH_KIND kind) {
+                 AF_BATCH_KIND kind, const int rank, const bool expand) {
     conv_kparam_t param;
 
     for (int i = 0; i < 3; ++i) {
@@ -42,12 +42,12 @@ void convolve_nd(Param out, const Param signal, const Param filter,
     param.outHasNoOffset   = kind == AF_BATCH_LHS || kind == AF_BATCH_NONE;
     param.inHasNoOffset    = kind != AF_BATCH_SAME;
 
-    prepareKernelArgs<T>(param, out.info.dims, filter.info.dims, baseDim);
+    prepareKernelArgs<T>(param, out.info.dims, filter.info.dims, rank);
 
-    switch (baseDim) {
-        case 1: conv1<T, accType, expand>(param, out, signal, filter); break;
-        case 2: conv2<T, accType, expand>(param, out, signal, filter); break;
-        case 3: conv3<T, accType, expand>(param, out, signal, filter); break;
+    switch (rank) {
+        case 1: conv1<T, accType>(param, out, signal, filter, expand); break;
+        case 2: conv2<T, accType>(param, out, signal, filter, expand); break;
+        case 3: conv3<T, accType>(param, out, signal, filter, expand); break;
     }
 
     CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/convolve/conv1.cpp b/src/backend/opencl/kernel/convolve/conv1.cpp
index 8992c9d5f5..d870faaf80 100644
--- a/src/backend/opencl/kernel/convolve/conv1.cpp
+++ b/src/backend/opencl/kernel/convolve/conv1.cpp
@@ -12,8 +12,9 @@
 namespace opencl {
 namespace kernel {
 
-template<typename T, typename aT, bool expand>
-void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
+template<typename T, typename aT>
+void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
+           const bool expand) {
     size_t se_size = filt.info.dims[0] * sizeof(aT);
     p.impulse      = bufferAlloc(se_size);
     int f0Off      = filt.info.offset;
@@ -40,17 +41,15 @@ void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
                 p.s[1] = (p.inHasNoOffset ? 0 : b2);
                 p.s[2] = (p.inHasNoOffset ? 0 : b3);
 
-                convNHelper<T, aT, 1, expand>(p, out, sig, filt);
+                convNHelper<T, aT>(p, out, sig, filt, 1, expand);
             }
         }
     }
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template void conv1<T, accT, true>(conv_kparam_t & p, Param & out,       \
-                                       const Param& sig, const Param& filt); \
-    template void conv1<T, accT, false>(conv_kparam_t & p, Param & out,      \
-                                        const Param& sig, const Param& filt);
+#define INSTANTIATE(T, accT)                                           \
+    template void conv1<T, accT>(conv_kparam_t&, Param&, const Param&, \
+                                 const Param&, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index ea9a704701..07cb007a71 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -15,9 +15,9 @@
 namespace opencl {
 namespace kernel {
 
-template<typename T, typename aT, bool expand>
+template<typename T, typename aT>
 void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
-                 const Param filter) {
+                 const Param filter, const bool expand) {
     using cl::EnqueueArgs;
     using cl::NDRange;
     using std::string;
@@ -43,7 +43,7 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
         DefineKeyValue(Ti, dtype_traits<T>::getName()),
         DefineKeyValue(To, dtype_traits<aT>::getName()),
         DefineKeyValue(accType, dtype_traits<aT>::getName()),
-        DefineKeyValue(BASE_DIM, 2),
+        DefineKeyValue(RANK, 2),
         DefineKeyValue(FLEN0, f0),
         DefineKeyValue(FLEN1, f1),
         DefineKeyValue(EXPAND, (expand ? 1 : 0)),
@@ -62,8 +62,9 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
              param.s[2]);
 }
 
-template<typename T, typename aT, bool expand>
-void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
+template<typename T, typename aT>
+void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
+           const bool expand) {
     size_t se_size = filt.info.dims[0] * filt.info.dims[1] * sizeof(aT);
     p.impulse      = bufferAlloc(se_size);
     int f0Off      = filt.info.offset;
@@ -85,16 +86,14 @@ void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
             p.s[1] = (p.inHasNoOffset ? 0 : b2);
             p.s[2] = (p.inHasNoOffset ? 0 : b3);
 
-            conv2Helper<T, aT, expand>(p, out, sig, filt);
+            conv2Helper<T, aT>(p, out, sig, filt, expand);
         }
     }
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template void conv2<T, accT, true>(conv_kparam_t & p, Param & out,       \
-                                       const Param& sig, const Param& filt); \
-    template void conv2<T, accT, false>(conv_kparam_t & p, Param & out,      \
-                                        const Param& sig, const Param& filt);
+#define INSTANTIATE(T, accT)                                           \
+    template void conv2<T, accT>(conv_kparam_t&, Param&, const Param&, \
+                                 const Param&, const bool);
 
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve/conv3.cpp b/src/backend/opencl/kernel/convolve/conv3.cpp
index 9baea7de83..411ff85372 100644
--- a/src/backend/opencl/kernel/convolve/conv3.cpp
+++ b/src/backend/opencl/kernel/convolve/conv3.cpp
@@ -12,8 +12,9 @@
 namespace opencl {
 namespace kernel {
 
-template<typename T, typename aT, bool expand>
-void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
+template<typename T, typename aT>
+void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
+           const bool expand) {
     size_t se_size =
         filt.info.dims[0] * filt.info.dims[1] * filt.info.dims[2] * sizeof(aT);
     p.impulse = bufferAlloc(se_size);
@@ -29,15 +30,13 @@ void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt) {
         p.o[2] = (p.outHasNoOffset ? 0 : b3);
         p.s[2] = (p.inHasNoOffset ? 0 : b3);
 
-        convNHelper<T, aT, 3, expand>(p, out, sig, filt);
+        convNHelper<T, aT>(p, out, sig, filt, 3, expand);
     }
 }
 
-#define INSTANTIATE(T, accT)                                                 \
-    template void conv3<T, accT, true>(conv_kparam_t & p, Param & out,       \
-                                       const Param& sig, const Param& filt); \
-    template void conv3<T, accT, false>(conv_kparam_t & p, Param & out,      \
-                                        const Param& sig, const Param& filt);
+#define INSTANTIATE(T, accT)                                           \
+    template void conv3<T, accT>(conv_kparam_t&, Param&, const Param&, \
+                                 const Param&, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 2d8aa9a5fd..28017415b8 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -50,28 +50,28 @@ struct conv_kparam_t {
 
 template<typename T>
 void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
-                       int baseDim) {
+                       const int rank) {
     using cl::NDRange;
 
     int batchDims[4] = {1, 1, 1, 1};
-    for (int i = baseDim; i < 4; ++i) {
+    for (int i = rank; i < 4; ++i) {
         batchDims[i] = (param.launchMoreBlocks ? 1 : oDims[i]);
     }
 
-    if (baseDim == 1) {
+    if (rank == 1) {
         param.local    = NDRange(THREADS, 1);
         param.nBBS0    = divup(oDims[0], THREADS);
         param.nBBS1    = batchDims[2];
         param.global   = NDRange(param.nBBS0 * THREADS * batchDims[1],
                                param.nBBS1 * batchDims[3]);
         param.loc_size = (THREADS + 2 * (fDims[0] - 1)) * sizeof(T);
-    } else if (baseDim == 2) {
+    } else if (rank == 2) {
         param.local  = NDRange(THREADS_X, THREADS_Y);
         param.nBBS0  = divup(oDims[0], THREADS_X);
         param.nBBS1  = divup(oDims[1], THREADS_Y);
         param.global = NDRange(param.nBBS0 * THREADS_X * batchDims[2],
                                param.nBBS1 * THREADS_Y * batchDims[3]);
-    } else if (baseDim == 3) {
+    } else if (rank == 3) {
         param.local    = NDRange(CUBE_X, CUBE_Y, CUBE_Z);
         param.nBBS0    = divup(oDims[0], CUBE_X);
         param.nBBS1    = divup(oDims[1], CUBE_Y);
@@ -84,9 +84,9 @@ void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
     }
 }
 
-template<typename T, typename aT, int bDim, bool expand>
+template<typename T, typename aT>
 void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
-                 const Param& filter) {
+                 const Param& filter, const int rank, const bool expand) {
     using cl::EnqueueArgs;
     using cl::NDRange;
     using std::string;
@@ -101,7 +101,7 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateTypename<aT>(),
-        TemplateArg(bDim),
+        TemplateArg(rank),
         TemplateArg(expand),
     };
     vector<string> compileOpts = {
@@ -109,7 +109,7 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
         DefineKeyValue(Ti, dtype_traits<T>::getName()),
         DefineKeyValue(To, dtype_traits<aT>::getName()),
         DefineKeyValue(accType, dtype_traits<aT>::getName()),
-        DefineKeyValue(BASE_DIM, bDim),
+        DefineKeyValue(RANK, rank),
         DefineKeyValue(EXPAND, (expand ? 1 : 0)),
         DefineKeyFromStr(binOpName<af_mul_t>()),
         DefineKeyValue(CPLX, (IsComplex ? 1 : 0)),
@@ -125,13 +125,16 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
              param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
 }
 
-template<typename T, typename aT, bool expand>
-void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
+template<typename T, typename aT>
+void conv1(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
+           const bool expand);
 
-template<typename T, typename aT, bool expand>
-void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
+template<typename T, typename aT>
+void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
+           const bool expand);
 
-template<typename T, typename aT, bool expand>
-void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt);
+template<typename T, typename aT>
+void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
+           const bool expand);
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index d348524c13..1d9b95695e 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
+#include <common/err_common.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel/names.hpp>
@@ -24,8 +25,15 @@
 namespace opencl {
 namespace kernel {
 
-template<typename T, typename accType, int conv_dim, bool expand>
-void convSep(Param out, const Param signal, const Param filter) {
+template<typename T, typename accType>
+void convSep(Param out, const Param signal, const Param filter,
+             const int conv_dim, const bool expand) {
+    if (!(conv_dim == 0 || conv_dim == 1)) {
+        AF_ERROR(
+            "Separable convolution accepts only 0 or 1 as convolution "
+            "dimension",
+            AF_ERR_NOT_SUPPORTED);
+    }
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
     constexpr bool IsComplex =
@@ -81,14 +89,8 @@ void convSep(Param out, const Param signal, const Param filter) {
 }
 
 #define INSTANTIATE(T, accT)                                             \
-    template void convSep<T, accT, 0, true>(Param out, const Param sig,  \
-                                            const Param filt);           \
-    template void convSep<T, accT, 1, true>(Param out, const Param sig,  \
-                                            const Param filt);           \
-    template void convSep<T, accT, 0, false>(Param out, const Param sig, \
-                                             const Param filt);          \
-    template void convSep<T, accT, 1, false>(Param out, const Param sig, \
-                                             const Param filt);
+    template void convSep<T, accT>(Param, const Param, const Param filt, \
+                                   const int, const bool);
 
 INSTANTIATE(cdouble, cdouble)
 INSTANTIATE(cfloat, cfloat)
diff --git a/src/backend/opencl/kernel/convolve_separable.hpp b/src/backend/opencl/kernel/convolve_separable.hpp
index aaa23718c0..0d7feddd44 100644
--- a/src/backend/opencl/kernel/convolve_separable.hpp
+++ b/src/backend/opencl/kernel/convolve_separable.hpp
@@ -19,8 +19,9 @@ namespace kernel {
 // considering complex types as well
 constexpr int MAX_SCONV_FILTER_LEN = 31;
 
-template<typename T, typename accT, int cDim, bool expand>
-void convSep(Param out, const Param sig, const Param filt);
+template<typename T, typename accT>
+void convSep(Param out, const Param sig, const Param filt, const int cDim,
+             const bool expand);
 
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 62cf03cbfc..9d70e2f79b 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -28,13 +28,13 @@ namespace kernel {
 constexpr int THREADS = 256;
 
 void calcParamSizes(Param& sig_tmp, Param& filter_tmp, Param& packed,
-                    Param& sig, Param& filter, const int baseDim,
+                    Param& sig, Param& filter, const int rank,
                     AF_BATCH_KIND kind) {
     sig_tmp.info.dims[0] = filter_tmp.info.dims[0] = packed.info.dims[0];
     sig_tmp.info.strides[0] = filter_tmp.info.strides[0] = 1;
 
     for (int k = 1; k < 4; k++) {
-        if (k < baseDim) {
+        if (k < rank) {
             sig_tmp.info.dims[k]    = packed.info.dims[k];
             filter_tmp.info.dims[k] = packed.info.dims[k];
         } else {
@@ -64,7 +64,7 @@ void calcParamSizes(Param& sig_tmp, Param& filter_tmp, Param& packed,
 }
 
 template<typename convT, typename T>
-void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
+void packDataHelper(Param packed, Param sig, Param filter, const int rank,
                     AF_BATCH_KIND kind) {
     constexpr bool IsTypeDouble = std::is_same<T, double>::value;
     constexpr auto ctDType =
@@ -91,7 +91,7 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
     auto padArray = common::getKernel("pad_array", {src}, targs, options);
 
     Param sig_tmp, filter_tmp;
-    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
 
     int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
     int filter_packed_elem =
@@ -124,7 +124,7 @@ void packDataHelper(Param packed, Param sig, Param filter, const int baseDim,
 
 template<typename convT, typename T>
 void complexMultiplyHelper(Param packed, Param sig, Param filter,
-                           const int baseDim, AF_BATCH_KIND kind) {
+                           const int rank, AF_BATCH_KIND kind) {
     constexpr bool IsTypeDouble = std::is_same<T, double>::value;
     constexpr auto ctDType =
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
@@ -153,7 +153,7 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
     auto cplxMul = common::getKernel("complex_multiply", {src}, targs, options);
 
     Param sig_tmp, filter_tmp;
-    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
 
     int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
     int filter_packed_elem =
@@ -174,7 +174,7 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
 
 template<typename T, typename convT>
 void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
-                         const int baseDim, AF_BATCH_KIND kind, bool expand) {
+                         const int rank, AF_BATCH_KIND kind, bool expand) {
     constexpr bool IsTypeDouble = std::is_same<T, double>::value;
     constexpr auto ctDType =
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
@@ -205,10 +205,10 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
     int fftScale = 1;
 
     // Calculate the scale by which to divide clFFT results
-    for (int k = 0; k < baseDim; k++) fftScale *= packed.info.dims[k];
+    for (int k = 0; k < rank; k++) fftScale *= packed.info.dims[k];
 
     Param sig_tmp, filter_tmp;
-    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
 
     // Number of packed complex elements in dimension 0
     int sig_half_d0 = divup(sig.info.dims[0], 2);
@@ -221,10 +221,10 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
     if (kind == AF_BATCH_RHS) {
         reorder(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
                 *filter_tmp.data, filter_tmp.info, filter.info, sig_half_d0,
-                baseDim, fftScale);
+                rank, fftScale);
     } else {
         reorder(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-                *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, baseDim,
+                *sig_tmp.data, sig_tmp.info, filter.info, sig_half_d0, rank,
                 fftScale);
     }
     CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 89b1e8e32d..87312dbd9c 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -52,12 +52,12 @@ void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy,
     Array<convAccT> ixy_tmp = createEmptyArray<convAccT>(ixy.dims());
     Array<convAccT> iyy_tmp = createEmptyArray<convAccT>(iyy.dims());
 
-    convSep<T, convAccT, 0, false>(ixx_tmp, ixx, filter);
-    convSep<T, convAccT, 1, false>(ixx, ixx_tmp, filter);
-    convSep<T, convAccT, 0, false>(ixy_tmp, ixy, filter);
-    convSep<T, convAccT, 1, false>(ixy, ixy_tmp, filter);
-    convSep<T, convAccT, 0, false>(iyy_tmp, iyy, filter);
-    convSep<T, convAccT, 1, false>(iyy, iyy_tmp, filter);
+    convSep<T, convAccT>(ixx_tmp, ixx, filter, 0, false);
+    convSep<T, convAccT>(ixx, ixx_tmp, filter, 1, false);
+    convSep<T, convAccT>(ixy_tmp, ixy, filter, 0, false);
+    convSep<T, convAccT>(ixy, ixy_tmp, filter, 1, false);
+    convSep<T, convAccT>(iyy_tmp, iyy, filter, 0, false);
+    convSep<T, convAccT>(iyy, iyy_tmp, filter, 1, false);
 }
 
 template<typename T>
diff --git a/src/backend/opencl/kernel/histogram.cl b/src/backend/opencl/kernel/histogram.cl
index 8fb30fbb5d..857ead231d 100644
--- a/src/backend/opencl/kernel/histogram.cl
+++ b/src/backend/opencl/kernel/histogram.cl
@@ -7,20 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-kernel void histogram(global outType *d_dst, KParam oInfo,
-                        global const inType *d_src, KParam iInfo,
-                        local outType *localMem, int len, int nbins,
-                        float minval, float maxval, int nBBS) {
+kernel void histogram(global uint *d_dst, KParam oInfo, global const T *d_src,
+                      KParam iInfo, local uint *localMem, int len, int nbins,
+                      float minval, float maxval, int nBBS) {
     unsigned b2 = get_group_id(0) / nBBS;
     int start = (get_group_id(0) - b2 * nBBS) * THRD_LOAD * get_local_size(0) +
                 get_local_id(0);
     int end = min((int)(start + THRD_LOAD * get_local_size(0)), len);
 
     // offset input and output to account for batch ops
-    global const inType *in = d_src + b2 * iInfo.strides[2] +
-                                get_group_id(1) * iInfo.strides[3] +
-                                iInfo.offset;
-    global outType *out =
+    global const T *in = d_src + b2 * iInfo.strides[2] +
+                         get_group_id(1) * iInfo.strides[3] + iInfo.offset;
+    global uint *out =
         d_dst + b2 * oInfo.strides[2] + get_group_id(1) * oInfo.strides[3];
 
     float dx = (maxval - minval) / (float)nbins;
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index bfab05b004..ed1e0125b5 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -22,7 +22,7 @@
 namespace opencl {
 namespace kernel {
 
-template<typename inType, typename outType>
+template<typename T>
 void histogram(Param out, const Param in, int nbins, float minval, float maxval,
                bool isLinear) {
     constexpr int MAX_BINS  = 4000;
@@ -32,24 +32,22 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     static const std::string src(histogram_cl, histogram_cl_len);
 
     std::vector<TemplateArg> targs = {
-        TemplateTypename<inType>(),
-        TemplateTypename<outType>(),
+        TemplateTypename<T>(),
         TemplateArg(isLinear),
     };
     std::vector<std::string> options = {
-        DefineKeyValue(inType, dtype_traits<inType>::getName()),
-        DefineKeyValue(outType, dtype_traits<outType>::getName()),
+        DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(THRD_LOAD),
         DefineValue(MAX_BINS),
     };
-    options.emplace_back(getTypeBuildDefinition<inType>());
+    options.emplace_back(getTypeBuildDefinition<T>());
     if (isLinear) { options.emplace_back(DefineKey(IS_LINEAR)); }
 
     auto histogram = common::getKernel("histogram", {src}, targs, options);
 
     int nElems  = in.info.dims[0] * in.info.dims[1];
     int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
-    int locSize = nbins <= MAX_BINS ? (nbins * sizeof(outType)) : 1;
+    int locSize = nbins <= MAX_BINS ? (nbins * sizeof(uint)) : 1;
 
     cl::NDRange local(THREADS_X, 1);
     cl::NDRange global(blk_x * in.info.dims[2] * THREADS_X, in.info.dims[3]);
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 2f49fb0e41..978f21136f 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -358,8 +358,8 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
             // Filter level image with Gaussian kernel to reduce noise
             // sensitivity
-            convSep<T, convAccT, 0, false>(lvl_tmp, lvl_img, gauss_filter);
-            convSep<T, convAccT, 1, false>(lvl_filt, lvl_tmp, gauss_filter);
+            convSep<T, convAccT>(lvl_tmp, lvl_img, gauss_filter, 0, false);
+            convSep<T, convAccT>(lvl_filt, lvl_tmp, gauss_filter, 1, false);
 
             bufferFree(lvl_tmp.data);
         }
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index aa7388fe1d..63ddcda36b 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -185,8 +185,8 @@ void convSepFull(Param& dst, Param src, Param filter) {
     const dim_t src_el = src.info.dims[3] * src.info.strides[3];
     tmp.data           = bufferAlloc(src_el * sizeof(T));
 
-    convSep<T, convAccT, 0, false>(tmp, src, filter);
-    convSep<T, convAccT, 1, false>(dst, tmp, filter);
+    convSep<T, convAccT>(tmp, src, filter, 0, false);
+    convSep<T, convAccT>(dst, tmp, filter, 1, false);
 
     bufferFree(tmp.data);
 }
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index da5b6f3ef0..8b2d0dd025 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -13,9 +13,10 @@
 
 namespace opencl {
 
-template<typename inType, typename outType, af_match_type mType>
+template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
-                              const Array<inType> &tImg) {
+                              const Array<inType> &tImg,
+                              const af::matchType mType) {
     Array<outType> out = createEmptyArray<outType>(sImg.dims());
 
     bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
@@ -26,25 +27,9 @@ Array<outType> match_template(const Array<inType> &sImg,
     return out;
 }
 
-#define INSTANTIATE(in_t, out_t)                                \
-    template Array<out_t> match_template<in_t, out_t, AF_SAD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_LSAD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSAD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_SSD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_LSSD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZSSD>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_NCC>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_ZNCC>( \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);      \
-    template Array<out_t> match_template<in_t, out_t, AF_SHD>(  \
-        const Array<in_t> &sImg, const Array<in_t> &tImg);
+#define INSTANTIATE(in_t, out_t)                       \
+    template Array<out_t> match_template<in_t, out_t>( \
+        const Array<in_t> &, const Array<in_t> &, const af::matchType);
 
 INSTANTIATE(double, double)
 INSTANTIATE(float, float)
diff --git a/src/backend/opencl/match_template.hpp b/src/backend/opencl/match_template.hpp
index 8a83e1ac92..bf2a76f55d 100644
--- a/src/backend/opencl/match_template.hpp
+++ b/src/backend/opencl/match_template.hpp
@@ -11,9 +11,8 @@
 #include <af/defines.h>
 
 namespace opencl {
-
-template<typename inType, typename outType, af_match_type mType>
+template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
-                              const Array<inType> &tImg);
-
+                              const Array<inType> &tImg,
+                              const af::matchType mType);
 }
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 34860b47ac..0e63834253 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -17,8 +17,9 @@ using af::dim4;
 
 namespace opencl {
 
-template<typename T, af_border_type pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType pad) {
     ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
     ARG_ASSERT(2, (w_wid % 2 != 0));
 
@@ -31,10 +32,9 @@ Array<T> medfilt1(const Array<T> &in, dim_t w_wid) {
     return out;
 }
 
-template<typename T, af_border_type pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
-    UNUSED(w_wid);
-    ARG_ASSERT(2, (w_len == w_wid));
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType pad) {
     ARG_ASSERT(2, (w_len % 2 != 0));
     ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
 
@@ -43,15 +43,11 @@ Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid) {
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template Array<T> medfilt1<T, AF_PAD_ZERO>(const Array<T> &in,             \
-                                               dim_t w_wid);                   \
-    template Array<T> medfilt1<T, AF_PAD_SYM>(const Array<T> &in,              \
-                                              dim_t w_wid);                    \
-    template Array<T> medfilt2<T, AF_PAD_ZERO>(const Array<T> &in,             \
-                                               dim_t w_len, dim_t w_wid);      \
-    template Array<T> medfilt2<T, AF_PAD_SYM>(const Array<T> &in, dim_t w_len, \
-                                              dim_t w_wid);
+#define INSTANTIATE(T)                                                 \
+    template Array<T> medfilt1<T>(const Array<T> &in, const int w_wid, \
+                                  const af::borderType);               \
+    template Array<T> medfilt2<T>(const Array<T> &in, const int w_len, \
+                                  const int w_wid, const af::borderType);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/medfilt.hpp b/src/backend/opencl/medfilt.hpp
index 355dbbcebb..0a010c3154 100644
--- a/src/backend/opencl/medfilt.hpp
+++ b/src/backend/opencl/medfilt.hpp
@@ -11,10 +11,12 @@
 
 namespace opencl {
 
-template<typename T, af_border_type edge_pad>
-Array<T> medfilt1(const Array<T> &in, dim_t w_wid);
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType edge_pad);
 
-template<typename T, af_border_type edge_pad>
-Array<T> medfilt2(const Array<T> &in, dim_t w_len, dim_t w_wid);
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType edge_pad);
 
 }  // namespace opencl
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index cb22d75965..9713c906c8 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -18,30 +18,24 @@ using common::half;
 
 namespace opencl {
 
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in) {
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag) {
     kernel::triangle<T>(out, in, is_upper, is_unit_diag);
 }
 
-template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in) {
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag) {
     Array<T> out = createEmptyArray<T>(in.dims());
-    triangle<T, is_upper, is_unit_diag>(out, in);
+    triangle<T>(out, in, is_upper, is_unit_diag);
     return out;
 }
 
-#define INSTANTIATE(T)                                                         \
-    template void triangle<T, true, true>(Array<T> & out, const Array<T> &in); \
-    template void triangle<T, false, true>(Array<T> & out,                     \
-                                           const Array<T> &in);                \
-    template void triangle<T, true, false>(Array<T> & out,                     \
-                                           const Array<T> &in);                \
-    template void triangle<T, false, false>(Array<T> & out,                    \
-                                            const Array<T> &in);               \
-    template Array<T> triangle<T, true, true>(const Array<T> &in);             \
-    template Array<T> triangle<T, false, true>(const Array<T> &in);            \
-    template Array<T> triangle<T, true, false>(const Array<T> &in);            \
-    template Array<T> triangle<T, false, false>(const Array<T> &in);
+#define INSTANTIATE(T)                                                  \
+    template void triangle<T>(Array<T> &, const Array<T> &, const bool, \
+                              const bool);                              \
+    template Array<T> triangle<T>(const Array<T> &, const bool, const bool);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/triangle.hpp b/src/backend/opencl/triangle.hpp
index f7d59e975f..d616337c7e 100644
--- a/src/backend/opencl/triangle.hpp
+++ b/src/backend/opencl/triangle.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 
 namespace opencl {
-template<typename T, bool is_upper, bool is_unit_diag>
-void triangle(Array<T> &out, const Array<T> &in);
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag);
 
-template<typename T, bool is_upper, bool is_unit_diag>
-Array<T> triangle(const Array<T> &in);
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag);
 }  // namespace opencl

From bdae16fb0450626af23a039de9e0b8439b1f4a0e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 27 May 2020 23:31:47 +0530
Subject: [PATCH 1971/2677] Enable non-type template parameters at cpu/kernel
 level

---
 src/backend/cpu/convolve.cpp              | 10 ++++--
 src/backend/cpu/fftconvolve.cpp           | 21 +++++++++++--
 src/backend/cpu/histogram.cpp             | 11 ++++---
 src/backend/cpu/kernel/convolve.hpp       | 23 +++++++-------
 src/backend/cpu/kernel/fftconvolve.hpp    | 32 ++++++++------------
 src/backend/cpu/kernel/histogram.hpp      |  4 +--
 src/backend/cpu/kernel/match_template.hpp | 17 ++++++-----
 src/backend/cpu/kernel/medfilt.hpp        | 37 +++++++++++++----------
 src/backend/cpu/kernel/triangle.hpp       | 10 +++---
 src/backend/cpu/match_template.cpp        | 22 +++++++++++---
 src/backend/cpu/medfilt.cpp               | 23 ++++++++++++--
 src/backend/cpu/triangle.cpp              | 18 ++++++++---
 12 files changed, 145 insertions(+), 83 deletions(-)

diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 9f647b3367..50beb69860 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -85,9 +85,13 @@ Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
     Array<T> out  = createEmptyArray<T>(oDims);
     Array<T> temp = createEmptyArray<T>(tDims);
 
-    getQueue().enqueue(kernel::convolve2<T, accT>, out, signal, c_filter,
-                       r_filter, temp, expand);
-
+    if (expand) {
+        getQueue().enqueue(kernel::convolve2<T, accT, true>, out, signal,
+                           c_filter, r_filter, temp);
+    } else {
+        getQueue().enqueue(kernel::convolve2<T, accT, false>, out, signal,
+                           c_filter, r_filter, temp);
+    }
     return out;
 }
 
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index ee31c5d37c..20047cf5b9 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -18,6 +18,7 @@
 
 #include <array>
 #include <cmath>
+#include <functional>
 #include <type_traits>
 
 using af::dim4;
@@ -26,6 +27,13 @@ using std::ceil;
 
 namespace cpu {
 
+template<typename T, typename convT>
+using reorderFunc = std::function<void(
+    Param<T> out, Param<convT> packed, CParam<T> filter,
+    const dim_t sig_half_d0, const dim_t fftScale, const dim4 sig_tmp_dims,
+    const dim4 sig_tmp_strides, const dim4 filter_tmp_dims,
+    const dim4 filter_tmp_strides, AF_BATCH_KIND kind)>;
+
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank) {
@@ -174,9 +182,18 @@ Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    getQueue().enqueue(kernel::reorder<T, convT>, out, packed, filter,
+    static const reorderFunc<T, convT> funcs[6] = {
+        kernel::reorder<T, convT, 1, false>,
+        kernel::reorder<T, convT, 2, false>,
+        kernel::reorder<T, convT, 3, false>,
+        kernel::reorder<T, convT, 1, true>,
+        kernel::reorder<T, convT, 2, true>,
+        kernel::reorder<T, convT, 3, true>,
+    };
+
+    getQueue().enqueue(funcs[expand * 3 + (rank - 1)], out, packed, filter,
                        sig_half_d0, fftScale, paddedSigDims, paddedSigStrides,
-                       paddedFilDims, paddedFilStrides, expand, kind, rank);
+                       paddedFilDims, paddedFilStrides, kind);
 
     return out;
 }
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index cec6a745d0..19ef3a9728 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -25,10 +25,13 @@ Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
     const dim4 &inDims = in.dims();
     dim4 outDims       = dim4(nbins, 1, inDims[2], inDims[3]);
     Array<uint> out    = createValueArray<uint>(outDims, uint(0));
-
-    getQueue().enqueue(kernel::histogram<T>, out, in, nbins, minval, maxval,
-                       isLinear);
-
+    if (isLinear) {
+        getQueue().enqueue(kernel::histogram<T, true>, out, in, nbins, minval,
+                           maxval);
+    } else {
+        getQueue().enqueue(kernel::histogram<T, false>, out, in, nbins, minval,
+                           maxval);
+    }
     return out;
 }
 
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 812236cae9..1bb67b569f 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -209,23 +209,22 @@ void convolve_nd(Param<InT> out, CParam<InT> signal, CParam<AccT> filter,
     }
 }
 
-template<typename InT, typename AccT>
+template<typename InT, typename AccT, bool Expand, int ConvDim>
 void convolve2_separable(InT *optr, InT const *const iptr,
                          AccT const *const fptr, af::dim4 const &oDims,
                          af::dim4 const &sDims, af::dim4 const &orgDims,
                          dim_t fDim, af::dim4 const &oStrides,
-                         af::dim4 const &sStrides, dim_t fStride,
-                         const bool expand, const int conv_dim) {
+                         af::dim4 const &sStrides, dim_t fStride) {
     UNUSED(orgDims);
     UNUSED(sStrides);
     UNUSED(fStride);
     for (dim_t j = 0; j < oDims[1]; ++j) {
         dim_t jOff = j * oStrides[1];
-        dim_t cj   = j + (conv_dim == 1) * (expand ? 0 : fDim >> 1);
+        dim_t cj   = j + (ConvDim == 1) * (Expand ? 0 : fDim >> 1);
 
         for (dim_t i = 0; i < oDims[0]; ++i) {
             dim_t iOff = i * oStrides[0];
-            dim_t ci   = i + (conv_dim == 0) * (expand ? 0 : fDim >> 1);
+            dim_t ci   = i + (ConvDim == 0) * (Expand ? 0 : fDim >> 1);
 
             AccT accum = scalar<AccT>(0);
 
@@ -233,7 +232,7 @@ void convolve2_separable(InT *optr, InT const *const iptr,
                 InT f_val = fptr[f];
                 InT s_val;
 
-                if (conv_dim == 0) {
+                if (ConvDim == 0) {
                     dim_t offi     = ci - f;
                     bool isCIValid = offi >= 0 && offi < sDims[0];
                     bool isCJValid = cj >= 0 && cj < sDims[1];
@@ -254,9 +253,9 @@ void convolve2_separable(InT *optr, InT const *const iptr,
     }
 }
 
-template<typename InT, typename AccT>
+template<typename InT, typename AccT, bool Expand>
 void convolve2(Param<InT> out, CParam<InT> signal, CParam<AccT> c_filter,
-               CParam<AccT> r_filter, Param<InT> temp, const bool expand) {
+               CParam<AccT> r_filter, Param<InT> temp) {
     dim_t cflen = (dim_t)c_filter.dims().elements();
     dim_t rflen = (dim_t)r_filter.dims().elements();
 
@@ -277,13 +276,13 @@ void convolve2(Param<InT> out, CParam<InT> signal, CParam<AccT> c_filter,
             InT *tptr             = temp.get() + b2 * tStrides[2] + t_b3Off;
             InT *optr             = out.get() + b2 * oStrides[2] + o_b3Off;
 
-            convolve2_separable<InT, AccT>(
+            convolve2_separable<InT, AccT, Expand, 0>(
                 tptr, iptr, c_filter.get(), temp.dims(), sDims, sDims, cflen,
-                tStrides, sStrides, c_filter.strides(0), expand, 0);
+                tStrides, sStrides, c_filter.strides(0));
 
-            convolve2_separable<InT, AccT>(
+            convolve2_separable<InT, AccT, Expand, 1>(
                 optr, tptr, r_filter.get(), oDims, temp.dims(), sDims, rflen,
-                oStrides, tStrides, r_filter.strides(0), expand, 1);
+                oStrides, tStrides, r_filter.strides(0));
         }
     }
 }
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index 42b890ed75..e85bd4b2f6 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -156,11 +156,10 @@ void complexMultiply(Param<T> packed, const af::dim4 sig_dims,
     }
 }
 
-template<typename To, typename Ti>
+template<typename To, typename Ti, int Rank, bool Expand>
 void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                    const Ti* in_ptr, const af::dim4& id, const af::dim4& is,
-                   const af::dim4& fd, const int half_di0, const int rank,
-                   const int fftScale, const bool expand) {
+                   const af::dim4& fd, const int half_di0, const int fftScale) {
     constexpr bool RoundResult = std::is_integral<To>::value;
 
     UNUSED(id);
@@ -169,15 +168,15 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
             for (int d1 = 0; d1 < (int)od[1]; d1++) {
                 for (int d0 = 0; d0 < (int)od[0]; d0++) {
                     int id0, id1, id2, id3;
-                    if (expand) {
+                    if (Expand) {
                         id0 = d0;
                         id1 = d1 * is[1];
                         id2 = d2 * is[2];
                         id3 = d3 * is[3];
                     } else {
                         id0 = d0 + fd[0] / 2;
-                        id1 = (d1 + (rank > 1) * (fd[1] / 2)) * is[1];
-                        id2 = (d2 + (rank > 2) * (fd[2] / 2)) * is[2];
+                        id1 = (d1 + (Rank > 1) * (fd[1] / 2)) * is[1];
+                        id2 = (d2 + (Rank > 2) * (fd[2] / 2)) * is[2];
                         id3 = d3 * is[3];
                     }
 
@@ -221,16 +220,12 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
     }
 }
 
-template<typename T, typename convT>
+template<typename T, typename convT, int Rank, bool Expand>
 void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
              const dim_t sig_half_d0, const dim_t fftScale,
              const dim4 sig_tmp_dims, const dim4 sig_tmp_strides,
              const dim4 filter_tmp_dims, const dim4 filter_tmp_strides,
-             bool expand, AF_BATCH_KIND kind, const int rank) {
-    // TODO(pradeep) check if we can avoid convT template parameter also
-    // using convT = typename std::conditional<std::is_integral<T>::value,
-    // float, double>::type;
-
+             AF_BATCH_KIND kind) {
     T* out_ptr                 = out.get();
     const af::dim4 out_dims    = out.dims();
     const af::dim4 out_strides = out.strides();
@@ -243,14 +238,13 @@ void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
 
     // Reorder the output
     if (kind == AF_BATCH_RHS) {
-        reorderHelper<T, convT>(out_ptr, out_dims, out_strides, filter_tmp_ptr,
-                                filter_tmp_dims, filter_tmp_strides,
-                                filter_dims, sig_half_d0, rank, fftScale,
-                                expand);
+        reorderHelper<T, convT, Rank, Expand>(
+            out_ptr, out_dims, out_strides, filter_tmp_ptr, filter_tmp_dims,
+            filter_tmp_strides, filter_dims, sig_half_d0, fftScale);
     } else {
-        reorderHelper<T, convT>(out_ptr, out_dims, out_strides, sig_tmp_ptr,
-                                sig_tmp_dims, sig_tmp_strides, filter_dims,
-                                sig_half_d0, rank, fftScale, expand);
+        reorderHelper<T, convT, Rank, Expand>(
+            out_ptr, out_dims, out_strides, sig_tmp_ptr, sig_tmp_dims,
+            sig_tmp_strides, filter_dims, sig_half_d0, fftScale);
     }
 }
 
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 4be4577fbe..903f2d2204 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -13,9 +13,9 @@
 namespace cpu {
 namespace kernel {
 
-template<typename T>
+template<typename T, bool IsLinear>
 void histogram(Param<uint> out, CParam<T> in, const unsigned nbins,
-               const double minval, const double maxval, const bool IsLinear) {
+               const double minval, const double maxval) {
     dim4 const outDims  = out.dims();
     float const step    = (maxval - minval) / (float)nbins;
     dim4 const inDims   = in.dims();
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
index 72ac0a0d64..d2463bf3b0 100644
--- a/src/backend/cpu/kernel/match_template.hpp
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -13,9 +13,12 @@
 namespace cpu {
 namespace kernel {
 
-template<typename OutT, typename InT>
-void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg,
-                   const af::matchType mType) {
+template<typename OutT, typename InT, af::matchType MatchType>
+void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
+    constexpr bool needMean = MatchType == AF_ZSAD || MatchType == AF_LSAD ||
+                              MatchType == AF_ZSSD || MatchType == AF_LSSD ||
+                              MatchType == AF_ZNCC;
+
     const af::dim4 sDims    = sImg.dims();
     const af::dim4 tDims    = tImg.dims();
     const af::dim4 sStrides = sImg.strides();
@@ -30,9 +33,7 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg,
 
     OutT tImgMean        = OutT(0);
     dim_t winNumElements = tImg.dims().elements();
-    bool needMean = mType == AF_ZSAD || mType == AF_LSAD || mType == AF_ZSSD ||
-                    mType == AF_LSSD || mType == AF_ZNCC;
-    const InT* tpl = tImg.get();
+    const InT* tpl       = tImg.get();
 
     if (needMean) {
         for (dim_t tj = 0; tj < tDim1; tj++) {
@@ -58,7 +59,7 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg,
                     OutT disparity = OutT(0);
 
                     // mean for window
-                    // this variable will be used based on mType value
+                    // this variable will be used based on MatchType value
                     OutT wImgMean = OutT(0);
                     if (needMean) {
                         for (dim_t tj = 0, j = sj; tj < tDim1; tj++, j++) {
@@ -85,7 +86,7 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg,
                                             : InT(0));
                             InT tVal = tpl[tjStride + ti * tStrides[0]];
                             OutT temp;
-                            switch (mType) {
+                            switch (MatchType) {
                                 case AF_SAD:
                                     disparity += fabs((OutT)sVal - (OutT)tVal);
                                     break;
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index 05353aaf35..269348cee5 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -10,16 +10,17 @@
 #pragma once
 
 #include <Param.hpp>
-#include <err_cpu.hpp>
+
 #include <algorithm>
 #include <vector>
 
 namespace cpu {
 namespace kernel {
 
-template<typename T>
-void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid,
-              const af::borderType pad) {
+template<typename T, af::borderType Pad>
+void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid) {
+    constexpr bool IsValidPadType = (Pad == AF_PAD_ZERO || Pad == AF_PAD_SYM);
+
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -40,7 +41,7 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid,
                     for (int wi = 0; wi < (int)w_wid; ++wi) {
                         int im_row = row + wi - w_wid / 2;
                         int im_roff;
-                        switch (pad) {
+                        switch (Pad) {
                             case AF_PAD_ZERO:
                                 im_roff = im_row * istrides[0];
                                 if (im_row < 0 || im_row >= (int)dims[0])
@@ -59,7 +60,8 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid,
                                 wind_vals.push_back(in_ptr[im_roff]);
                             } break;
                             default:
-                                CPU_NOT_SUPPORTED("Unsupported padding type");
+                                static_assert(IsValidPadType,
+                                              "Unsupported padding type");
                                 break;
                         }
                     }
@@ -80,9 +82,10 @@ void medfilt1(Param<T> out, CParam<T> in, dim_t w_wid,
     }
 }
 
-template<typename T>
-void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
-              const af::borderType pad) {
+template<typename T, af::borderType Pad>
+void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
+    constexpr bool IsValidPadType = (Pad == AF_PAD_ZERO || Pad == AF_PAD_SYM);
+
     const af::dim4 dims     = in.dims();
     const af::dim4 istrides = in.strides();
     const af::dim4 ostrides = out.strides();
@@ -106,7 +109,7 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
 
                         int im_col  = col + wj - w_wid / 2;
                         int im_coff = 0;
-                        switch (pad) {
+                        switch (Pad) {
                             case AF_PAD_ZERO:
                                 im_coff = im_col * istrides[1];
                                 if (im_col < 0 || im_col >= (int)dims[1])
@@ -126,7 +129,8 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
                                 im_coff = im_col * istrides[1];
                             } break;
                             default:
-                                CPU_NOT_SUPPORTED("Unsupported padding type");
+                                static_assert(IsValidPadType,
+                                              "Unsupported padding type");
                                 break;
                         }
 
@@ -135,7 +139,7 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
 
                             int im_row  = row + wi - w_len / 2;
                             int im_roff = 0;
-                            switch (pad) {
+                            switch (Pad) {
                                 case AF_PAD_ZERO:
                                     im_roff = im_row * istrides[0];
                                     if (im_row < 0 || im_row >= (int)dims[0])
@@ -156,13 +160,13 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
                                     im_roff = im_row * istrides[0];
                                 } break;
                                 default:
-                                    CPU_NOT_SUPPORTED(
-                                        "Unsupported padding type");
+                                    static_assert(IsValidPadType,
+                                                  "Unsupported padding type");
                                     break;
                             }
 
                             if (isRowOff || isColOff) {
-                                switch (pad) {
+                                switch (Pad) {
                                     case AF_PAD_ZERO:
                                         wind_vals.push_back(0);
                                         break;
@@ -171,7 +175,8 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid,
                                             in_ptr[im_coff + im_roff]);
                                         break;
                                     default:
-                                        CPU_NOT_SUPPORTED(
+                                        static_assert(
+                                            IsValidPadType,
                                             "Unsupported padding type");
                                         break;
                                 }
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index c4e240117a..40ba7e4591 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -8,15 +8,15 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <math.hpp>
 
 namespace cpu {
 namespace kernel {
 
-template<typename T>
-void triangle(Param<T> out, CParam<T> in, const bool is_upper,
-              const bool is_unit_diag) {
+template<typename T, bool IsUpper, bool IsUnitDiag>
+void triangle(Param<T> out, CParam<T> in) {
     T *o       = out.get();
     const T *i = in.get();
 
@@ -41,8 +41,8 @@ void triangle(Param<T> out, CParam<T> in, const bool is_upper,
                     const dim_t oMem = oYZW + ox;
                     const dim_t iMem = iYZW + ox;
 
-                    bool cond         = is_upper ? (oy >= ox) : (oy <= ox);
-                    bool do_unit_diag = (is_unit_diag && ox == oy);
+                    bool cond         = IsUpper ? (oy >= ox) : (oy <= ox);
+                    bool do_unit_diag = (IsUnitDiag && ox == oy);
                     if (cond) {
                         o[oMem] = do_unit_diag ? scalar<T>(1) : i[iMem];
                     } else {
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 98c54aa149..5b609ad0a7 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -7,25 +7,37 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <kernel/match_template.hpp>
 #include <match_template.hpp>
+
+#include <kernel/match_template.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
+#include <functional>
+
 using af::dim4;
 
 namespace cpu {
 
+template<typename To, typename Ti>
+using matchFunc = std::function<void(Param<To>, CParam<Ti>, CParam<Ti>)>;
+
 template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
                               const Array<inType> &tImg,
                               const af::matchType mType) {
-    Array<outType> out = createEmptyArray<outType>(sImg.dims());
-    getQueue().enqueue(kernel::matchTemplate<outType, inType>, out, sImg, tImg,
-                       mType);
+    static const matchFunc<outType, inType> funcs[6] = {
+        kernel::matchTemplate<outType, inType, AF_SAD>,
+        kernel::matchTemplate<outType, inType, AF_ZSAD>,
+        kernel::matchTemplate<outType, inType, AF_LSAD>,
+        kernel::matchTemplate<outType, inType, AF_SSD>,
+        kernel::matchTemplate<outType, inType, AF_ZSSD>,
+        kernel::matchTemplate<outType, inType, AF_LSSD>,
+    };
 
+    Array<outType> out = createEmptyArray<outType>(sImg.dims());
+    getQueue().enqueue(funcs[static_cast<int>(mType)], out, sImg, tImg);
     return out;
 }
 
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 58671c5de3..cb24b81c43 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -7,30 +7,47 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <medfilt.hpp>
+
 #include <Array.hpp>
 #include <kernel/medfilt.hpp>
-#include <medfilt.hpp>
 #include <platform.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
+#include <functional>
+
 using af::dim4;
 
 namespace cpu {
 
+template<typename T>
+using medianFilter1 = std::function<void(Param<T>, CParam<T>, dim_t)>;
+
+template<typename T>
+using medianFilter2 = std::function<void(Param<T>, CParam<T>, dim_t, dim_t)>;
+
 template<typename T>
 Array<T> medfilt1(const Array<T> &in, const int w_wid,
                   const af::borderType pad) {
+    static const medianFilter1<T> funcs[2] = {
+        kernel::medfilt1<T, AF_PAD_ZERO>,
+        kernel::medfilt1<T, AF_PAD_SYM>,
+    };
     Array<T> out = createEmptyArray<T>(in.dims());
-    getQueue().enqueue(kernel::medfilt1<T>, out, in, w_wid, pad);
+    getQueue().enqueue(funcs[static_cast<int>(pad)], out, in, w_wid);
     return out;
 }
 
 template<typename T>
 Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
                   const af::borderType pad) {
+    static const medianFilter2<T> funcs[2] = {
+        kernel::medfilt2<T, AF_PAD_ZERO>,
+        kernel::medfilt2<T, AF_PAD_SYM>,
+    };
     Array<T> out = createEmptyArray<T>(in.dims());
-    getQueue().enqueue(kernel::medfilt2<T>, out, in, w_len, w_wid, pad);
+    getQueue().enqueue(funcs[static_cast<int>(pad)], out, in, w_len, w_wid);
     return out;
 }
 
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index c8ca71b2a0..6440a286b4 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -6,23 +6,33 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <kernel/triangle.hpp>
 #include <triangle.hpp>
 
-#include <Array.hpp>
 #include <common/half.hpp>
-#include <math.hpp>
+#include <kernel/triangle.hpp>
 #include <platform.hpp>
 #include <af/dim4.hpp>
 
+#include <functional>
+
 using common::half;
 
 namespace cpu {
 
+template<typename T>
+using triangleFunc = std::function<void(Param<T>, CParam<T>)>;
+
 template<typename T>
 void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
               const bool is_unit_diag) {
-    getQueue().enqueue(kernel::triangle<T>, out, in, is_upper, is_unit_diag);
+    static const triangleFunc<T> funcs[4] = {
+        kernel::triangle<T, false, false>,
+        kernel::triangle<T, false, true>,
+        kernel::triangle<T, true, false>,
+        kernel::triangle<T, true, true>,
+    };
+    const int funcIdx = is_upper * 2 + is_unit_diag;
+    getQueue().enqueue(funcs[funcIdx], out, in);
 }
 
 template<typename T>

From bb76c8901286d2959f53be64242a171a3709f2bd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 28 May 2020 12:51:52 +0530
Subject: [PATCH 1972/2677] enqueueWriteBuffer asynchronously in vision kernels
 (#2910)

* enqueueWriteBuffer asynchronously in vision kernels

There are few locations where initializing the flags or buffers were
earlier using synchronous copy to GPU memory which is not needed since
the kernel execution in-order. Hence, changed them to be asynchronous
copies.

* Fix formatting

* Correct the scope of h_desc_lvl on orb
---
 src/backend/opencl/kernel/fast.hpp         | 6 +++---
 src/backend/opencl/kernel/flood_fill.hpp   | 2 +-
 src/backend/opencl/kernel/orb.hpp          | 6 +++---
 src/backend/opencl/kernel/regions.hpp      | 2 +-
 src/backend/opencl/kernel/sift_nonfree.hpp | 8 ++++----
 src/backend/opencl/kernel/susan.hpp        | 2 +-
 6 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 64eb65f2b2..eeb1cce534 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -60,8 +60,8 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
         bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
     std::vector<float> score_init(in.info.dims[0] * in.info.dims[1], (float)0);
     getQueue().enqueueWriteBuffer(
-        *d_score, CL_TRUE, 0, in.info.dims[0] * in.info.dims[1] * sizeof(float),
-        &score_init[0]);
+        *d_score, CL_FALSE, 0,
+        in.info.dims[0] * in.info.dims[1] * sizeof(float), &score_init[0]);
 
     cl::Buffer *d_flags = d_score;
     if (nonmax) {
@@ -92,7 +92,7 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
 
     unsigned count_init = 0;
     cl::Buffer *d_total = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_total, CL_TRUE, 0, sizeof(unsigned),
+    getQueue().enqueueWriteBuffer(*d_total, CL_FALSE, 0, sizeof(unsigned),
                                   &count_init);
 
     // size_t *global_nonmax_dims = global_nonmax();
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 79310cf7d0..a51af88dff 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -108,7 +108,7 @@ void floodFill(Param out, const Param image, const Param seedsx,
 
     while (notFinished) {
         notFinished = 0;
-        getQueue().enqueueWriteBuffer(*dContinue, CL_TRUE, 0, sizeof(int),
+        getQueue().enqueueWriteBuffer(*dContinue, CL_FALSE, 0, sizeof(int),
                                       &notFinished);
 
         floodStep(cl::EnqueueArgs(getQueue(), global, local), *out.data,
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 978f21136f..179a347f7e 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -210,7 +210,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         unsigned usable_feat  = 0;
         Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
-        getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0,
+        getQueue().enqueueWriteBuffer(*d_usable_feat, CL_FALSE, 0,
                                       sizeof(unsigned), &usable_feat);
 
         Buffer* d_x_harris     = bufferAlloc(lvl_feat * sizeof(float));
@@ -366,9 +366,9 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         // Compute ORB descriptors
         Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
+        vector<unsigned> h_desc_lvl(usable_feat * 8, 0);
         {
-            vector<unsigned> h_desc_lvl(usable_feat * 8);
-            getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0,
+            getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_FALSE, 0,
                                           usable_feat * 8 * sizeof(unsigned),
                                           h_desc_lvl.data());
         }
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index d7fbee0730..1241fed3d6 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -108,7 +108,7 @@ void regions(Param out, Param in, const bool full_conn,
 
     while (h_continue) {
         h_continue = 0;
-        getQueue().enqueueWriteBuffer(*d_continue, CL_TRUE, 0, sizeof(int),
+        getQueue().enqueueWriteBuffer(*d_continue, CL_FALSE, 0, sizeof(int),
                                       &h_continue);
         ueOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *d_continue);
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index 63ddcda36b..117a39b9fa 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -485,7 +485,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
 
         unsigned extrema_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+        getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &extrema_feat);
 
         int dim0 = dog_pyr[o].info.dims[0];
@@ -520,7 +520,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         }
 
         unsigned interp_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+        getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &interp_feat);
 
         Buffer* d_interp_x     = bufferAlloc(extrema_feat * sizeof(float));
@@ -596,7 +596,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         apply_permutation<float>(interp_size_begin, permutation, queue);
 
         unsigned nodup_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+        getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &nodup_feat);
 
         Buffer* d_nodup_x        = bufferAlloc(interp_feat * sizeof(float));
@@ -628,7 +628,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         bufferFree(d_interp_size);
 
         unsigned oriented_feat = 0;
-        getQueue().enqueueWriteBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
+        getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &oriented_feat);
         const unsigned max_oriented_feat = nodup_feat * 3;
 
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 0d4b1576a6..09f1c1c6d5 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -83,7 +83,7 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
 
     unsigned corners_found      = 0;
     cl::Buffer* d_corners_found = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0,
+    getQueue().enqueueWriteBuffer(*d_corners_found, CL_FALSE, 0,
                                   sizeof(unsigned), &corners_found);
 
     cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);

From 1261829e3448fdbe76650a1089946082f04931ad Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 23 May 2020 01:50:20 -0400
Subject: [PATCH 1973/2677] Add out of memory test using custom memory manager

---
 test/memory.cpp | 74 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 72 insertions(+), 2 deletions(-)

diff --git a/test/memory.cpp b/test/memory.cpp
index a661700916..fecfac16b4 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -771,7 +771,9 @@ af_err alloc_fn(af_memory_manager manager, void **ptr,
         af_memory_manager_get_memory_pressure_threshold(manager, &threshold);
         if (pressure >= threshold) { signal_memory_cleanup_fn(manager); }
 
-        af_memory_manager_native_alloc(manager, ptr, size);
+        if (af_err err = af_memory_manager_native_alloc(manager, ptr, size)) {
+            return err;
+        }
 
         auto *payload        = getMemoryManagerPayload<E2ETestPayload>(manager);
         payload->table[*ptr] = size;
@@ -796,7 +798,75 @@ void remove_memory_management_fn(af_memory_manager manager, int id) {}
 
 }  // namespace
 
-TEST(MemoryManagerApi, E2ETest) {
+class MemoryManagerApi : public ::testing::Test {
+   public:
+    af_memory_manager manager;
+    std::unique_ptr<E2ETestPayload> payload{new E2ETestPayload()};
+    void SetUp() override {
+        af_create_memory_manager(&manager);
+
+        // Set payload_fn
+        af_memory_manager_set_payload(manager, payload.get());
+
+        auto initialize_fn = [](af_memory_manager manager) {
+            auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+            payload->initializeCalledTimes++;
+            return AF_SUCCESS;
+        };
+        af_memory_manager_set_initialize_fn(manager, initialize_fn);
+
+        auto shutdown_fn = [](af_memory_manager manager) {
+            auto *payload = getMemoryManagerPayload<E2ETestPayload>(manager);
+            payload->shutdownCalledTimes++;
+            return AF_SUCCESS;
+        };
+        af_memory_manager_set_shutdown_fn(manager, shutdown_fn);
+
+        // alloc
+        af_memory_manager_set_alloc_fn(manager, alloc_fn);
+        af_memory_manager_set_allocated_fn(manager, allocated_fn);
+        af_memory_manager_set_unlock_fn(manager, unlock_fn);
+        // utils
+        af_memory_manager_set_signal_memory_cleanup_fn(
+            manager, signal_memory_cleanup_fn);
+        af_memory_manager_set_print_info_fn(manager, print_info_fn);
+        // user lock/unlock
+        af_memory_manager_set_user_lock_fn(manager, user_lock_fn);
+        af_memory_manager_set_user_unlock_fn(manager, user_unlock_fn);
+        af_memory_manager_set_is_user_locked_fn(manager, is_user_locked_fn);
+        // memory pressure
+        af_memory_manager_set_get_memory_pressure_fn(manager,
+                                                     get_memory_pressure_fn);
+        af_memory_manager_set_jit_tree_exceeds_memory_pressure_fn(
+            manager, jit_tree_exceeds_memory_pressure_fn);
+        // ocl
+        af_memory_manager_set_add_memory_management_fn(
+            manager, add_memory_management_fn);
+        af_memory_manager_set_remove_memory_management_fn(
+            manager, remove_memory_management_fn);
+
+        af_set_memory_manager(manager);
+    }
+
+    void TearDown() override {
+        af_device_gc();
+        af_unset_memory_manager();
+        af_release_memory_manager(manager);
+    }
+};
+
+TEST_F(MemoryManagerApi, OutOfMemory) {
+    af::array a;
+    const unsigned N = 99999;
+    try {
+        a = af::randu({N, N, N}, af::dtype::f32);
+        FAIL();
+    } catch (af::exception &ex) {
+        ASSERT_EQ(ex.err(), AF_ERR_NO_MEM);
+    } catch (...) { FAIL(); }
+}
+
+TEST(MemoryManagerE2E, E2ETest) {
     af_memory_manager manager;
     af_create_memory_manager(&manager);
 

From 1563e772db4bc0298343cc148bb5c03b4b9790d9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 28 May 2020 01:43:58 -0400
Subject: [PATCH 1974/2677] Return cl_mem instead of cl::Buffer from
 nativeAlloc

* Improve documentation of the alloc and free function
* Add tests for memory operations
---
 docs/details/device.dox       |  17 ++++-
 include/af/device.h           |  48 +++++++++-----
 include/af/memory.h           |   2 +-
 src/api/c/memory.cpp          |   4 +-
 src/backend/opencl/Array.cpp  |   2 +-
 src/backend/opencl/memory.cpp | 106 +++++++++++++++++++++++--------
 src/backend/opencl/memory.hpp |   4 +-
 test/CMakeLists.txt           |  43 ++++++++++++-
 test/cuda.cu                  |  35 +++++++++++
 test/manual_memory_test.cpp   |   2 +-
 test/memory.cpp               | 114 ++++++++++++++++++++++++++++++++--
 test/ocl_ext_context.cpp      |  95 +++++++++++++++++-----------
 12 files changed, 381 insertions(+), 91 deletions(-)
 create mode 100644 test/cuda.cu

diff --git a/docs/details/device.dox b/docs/details/device.dox
index 11f02eabef..39741d2c30 100644
--- a/docs/details/device.dox
+++ b/docs/details/device.dox
@@ -84,15 +84,26 @@ have finished.
 
 This function will allocate memory on the device and return a pointer
 to it. The memory is allocated using ArrayFire's memory manager which
-has some different characteristics to standard method of memory
-allocation
+will defer releasing memory to the driver and reuse the same memory
+for later operations.
+
+This function will return different objects based on the type used. The
+interface returns a void pointer that needs to be cast to the backend
+appropriate memory type.
+
+
+| function           | CPU | CUDA | OpenCL      |
+|--------------------|-----|------|-------------|
+| af_alloc_device    | T*  | T*   | cl::Buffer* |
+| af::alloc          | T*  | T*   | cl::Buffer* |
 
 ===============================================================================
 
 \defgroup device_func_free free
 \ingroup device_mat
 
-\brief Free device memory allocated by ArrayFire's memory manager
+\brief Returns memory to ArrayFire's memory manager. The memory will
+       return to the memory pool.
 
 These calls free the device memory. These functions need to be called on
 pointers allocated using alloc function.
diff --git a/include/af/device.h b/include/af/device.h
index b798a6e80d..96ba584df1 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -106,40 +106,44 @@ namespace af
     /// @{
     /// \brief Allocates memory using ArrayFire's memory manager
     ///
-    /// \copydoc device_func_alloc
     /// \param[in] elements the number of elements to allocate
     /// \param[in] type is the type of the elements to allocate
-    /// \returns the pointer to the memory
+    /// \returns Pointer to the device memory on the current device. This is a
+    ///          CUDA device pointer for the CUDA backend. A cl::Buffer pointer
+    ///          from the cl2.hpp header on the OpenCL backend and a C pointer
+    ///          for the CPU backend
     ///
-    /// \note The device memory returned by this function is only freed if af::free() is called explicitly
-
+    /// \note The device memory returned by this function is only freed if
+    ///       af::free() is called explicitly
     AFAPI void *alloc(const size_t elements, const dtype type);
 
     /// \brief Allocates memory using ArrayFire's memory manager
     //
-    /// \copydoc device_func_alloc
     /// \param[in] elements the number of elements to allocate
-    /// \returns the pointer to the memory
+    /// \returns Pointer to the device memory on the current device. This is a
+    ///          CUDA device pointer for the CUDA backend. A cl::Buffer pointer
+    ///          from the cl2.hpp header on the OpenCL backend and a C pointer
+    ///          for the CPU backend
     ///
     /// \note the size of the memory allocated is the number of \p elements *
-    ///         sizeof(type)
-    ///
-    /// \note The device memory returned by this function is only freed if af::free() is called explicitly
-    template<typename T>
-    T* alloc(const size_t elements);
+    ///       sizeof(type)
+    /// \note The device memory returned by this function is only freed if
+    ///       af::free() is called explicitly
+    template <typename T> T *alloc(const size_t elements);
     /// @}
 
     /// \ingroup device_func_free
     ///
     /// \copydoc device_func_free
-    /// \param[in] ptr the memory to free
+    /// \param[in] ptr the memory allocated by the af::alloc function that
+    ///                will be freed
     ///
-    /// This function will free a device pointer even if it has been previously locked.
+    /// \note This function will free a device pointer even if it has been
+    ///       previously locked.
     AFAPI void free(const void *ptr);
 
     /// \ingroup device_func_pinned
     /// @{
-    ///
     /// \copydoc device_func_pinned
     ///
     /// \param[in] elements the number of elements to allocate
@@ -312,18 +316,32 @@ extern "C" {
     AFAPI af_err af_sync(const int device);
 
     /**
+       \brief Allocates memory using ArrayFire's memory manager
        \ingroup device_func_alloc
 
        This device memory returned by this function can only be freed using
        af_free_device
+
+       \param [out] ptr Pointer to the device memory on the current device. This
+                        is a CUDA device pointer for the CUDA backend. A
+                        cl::Buffer pointer on the OpenCL backend and a C pointer
+                        for the CPU backend
+       \param [in] bytes The number of bites to allocate on the device
+
+       \returns AF_SUCCESS if a pointer could be allocated. AF_ERR_NO_MEM if
+                there is no memory
     */
     AFAPI af_err af_alloc_device(void **ptr, const dim_t bytes);
 
     /**
-       \ingroup device_func_free
+       \brief Returns memory to ArrayFire's memory manager.
 
        This function will free a device pointer even if it has been previously
        locked.
+
+       \param[in] ptr The pointer allocated by af_alloc_device to be freed
+
+       \ingroup device_func_free
     */
     AFAPI af_err af_free_device(void *ptr);
 
diff --git a/include/af/memory.h b/include/af/memory.h
index 54e9833adc..c60007a53e 100644
--- a/include/af/memory.h
+++ b/include/af/memory.h
@@ -533,7 +533,7 @@ AFAPI af_err af_memory_manager_get_active_device_id(af_memory_manager handle,
 
    \param[in] handle the \ref af_memory_manager handle
    \param[out] ptr the pointer to the allocated buffer (for the CUDA and CPU
-   backends). For the OpenCL backend, this is a pointer to a cl::Buffer, which
+   backends). For the OpenCL backend, this is a pointer to a cl_mem, which
    can be cast accordingly
    \param[in] size the size of the pointer allocation
 
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index a880b7dbcf..76aefe99d4 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -147,7 +147,7 @@ inline void lockArray(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
-    memLock(static_cast<void *>(getArray<T>(arr).getData().get()));
+    memLock(getArray<T>(arr).getData().get());
 }
 
 af_err af_lock_device_ptr(const af_array arr) { return af_lock_array(arr); }
@@ -217,7 +217,7 @@ inline void unlockArray(const af_array arr) {
     // Ideally we need to use .get(false), i.e. get ptr without offset
     // This is however not supported in opencl
     // Use getData().get() as alternative
-    memUnlock(static_cast<void *>(getArray<T>(arr).getData().get()));
+    memUnlock(getArray<T>(arr).getData().get());
 }
 
 af_err af_unlock_device_ptr(const af_array arr) { return af_unlock_array(arr); }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index b40f999f26..f7bd205aa2 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -378,7 +378,7 @@ template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const cl::Buffer *buf = arr.device();
     if (!buf) { return NULL; }
-    memLock((T *)buf);
+    memLock(buf);
     cl_mem mem = (*buf)();
     return (void *)mem;
 }
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index e50dba24a1..77e8224bbb 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -11,6 +11,7 @@
 #include <common/MemoryManagerBase.hpp>
 #include <common/half.hpp>
 #include <err_opencl.hpp>
+#include <errorcodes.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <spdlog/spdlog.h>
@@ -56,42 +57,69 @@ template<typename T>
 unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
     const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
-    dim4 dims(elements);
-    void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    auto *buf = static_cast<cl::Buffer *>(ptr);
-    return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(buf,
-                                                                bufferFree);
+    if (elements) {
+        dim4 dims(elements);
+        void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+        auto buf  = static_cast<cl_mem>(ptr);
+        cl::Buffer *bptr = new cl::Buffer(buf, true);
+        return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(bptr,
+                                                                    bufferFree);
+    } else {
+        return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(nullptr,
+                                                                    bufferFree);
+    }
 }
 
 void *memAllocUser(const size_t &bytes) {
     dim4 dims(bytes);
     void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
-    return ptr;
+    auto buf  = static_cast<cl_mem>(ptr);
+    return new cl::Buffer(buf, true);
 }
 
 template<typename T>
 void memFree(T *ptr) {
-    return memoryManager().unlock(static_cast<void *>(ptr), false);
+    cl::Buffer *buf = reinterpret_cast<cl::Buffer *>(ptr);
+    cl_mem mem      = static_cast<cl_mem>((*buf)());
+    delete buf;
+    return memoryManager().unlock(static_cast<void *>(mem), false);
 }
 
-void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
+void memFreeUser(void *ptr) {
+    cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
+    cl_mem mem      = (*buf)();
+    delete buf;
+    memoryManager().unlock(mem, true);
+}
 
 cl::Buffer *bufferAlloc(const size_t &bytes) {
     dim4 dims(bytes);
-    void *ptr = memoryManager().alloc(false, 1, dims.get(), 1);
-    return static_cast<cl::Buffer *>(ptr);
+    if (bytes) {
+        void *ptr       = memoryManager().alloc(false, 1, dims.get(), 1);
+        cl_mem mem      = static_cast<cl_mem>(ptr);
+        cl::Buffer *buf = new cl::Buffer(mem, true);
+        return buf;
+    } else {
+        return nullptr;
+    }
 }
 
 void bufferFree(cl::Buffer *buf) {
-    return memoryManager().unlock(static_cast<void *>(buf), false);
+    if (buf) {
+        cl_mem mem = (*buf)();
+        delete buf;
+        memoryManager().unlock(static_cast<void *>(mem), false);
+    }
 }
 
-void memLock(const void *ptr) {
-    memoryManager().userLock(const_cast<void *>(ptr));
+void memLock(const cl::Buffer *ptr) {
+    cl_mem mem = static_cast<cl_mem>((*ptr)());
+    memoryManager().userLock(static_cast<void *>(mem));
 }
 
-void memUnlock(const void *ptr) {
-    memoryManager().userUnlock(const_cast<void *>(ptr));
+void memUnlock(const cl::Buffer *ptr) {
+    cl_mem mem = static_cast<cl_mem>((*ptr)());
+    memoryManager().userUnlock(static_cast<void *>(mem));
 }
 
 bool isLocked(const void *ptr) {
@@ -158,16 +186,28 @@ size_t Allocator::getMaxMemorySize(int id) {
 }
 
 void *Allocator::nativeAlloc(const size_t bytes) {
-    auto ptr = static_cast<void *>(new cl::Buffer(
-        getContext(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
-        bytes));
+    cl_int err = CL_SUCCESS;
+    auto ptr   = static_cast<void *>(clCreateBuffer(
+        getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
+        bytes, nullptr, &err));
+
+    if (err != CL_SUCCESS) {
+        auto str = fmt::format("Failed to allocate device memory of size {}",
+                               bytesToString(bytes));
+        AF_ERROR(str, AF_ERR_NO_MEM);
+    }
+
     AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
     return ptr;
 }
 
 void Allocator::nativeFree(void *ptr) {
+    cl_mem buffer = static_cast<cl_mem>(ptr);
     AF_TRACE("nativeFree:          {}", ptr);
-    delete static_cast<cl::Buffer *>(ptr);
+    cl_int err = clReleaseMemObject(buffer);
+    if (err != CL_SUCCESS) {
+        AF_ERROR("Failed to release device memory.", AF_ERR_RUNTIME);
+    }
 }
 
 AllocatorPinned::AllocatorPinned() : pinnedMaps(opencl::getDeviceCount()) {
@@ -194,23 +234,39 @@ size_t AllocatorPinned::getMaxMemorySize(int id) {
 
 void *AllocatorPinned::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
-    auto *buf = new cl::Buffer(getContext(), CL_MEM_ALLOC_HOST_PTR, bytes);
-    ptr = getQueue().enqueueMapBuffer(*buf, true, CL_MAP_READ | CL_MAP_WRITE, 0,
-                                      bytes);
+
+    cl_int err = CL_SUCCESS;
+    auto buf   = clCreateBuffer(getContext()(), CL_MEM_ALLOC_HOST_PTR, bytes,
+                              nullptr, &err);
+    if (err != CL_SUCCESS) {
+        AF_ERROR("Failed to allocate pinned memory.", AF_ERR_NO_MEM);
+    }
+
+    ptr = clEnqueueMapBuffer(getQueue()(), buf, CL_TRUE,
+                             CL_MAP_READ | CL_MAP_WRITE, 0, bytes, 0, nullptr,
+                             nullptr, &err);
+    if (err != CL_SUCCESS) {
+        AF_ERROR("Failed to map pinned memory", AF_ERR_RUNTIME);
+    }
     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
-    pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, buf);
+    pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, new cl::Buffer(buf));
     return ptr;
 }
 
 void AllocatorPinned::nativeFree(void *ptr) {
     AF_TRACE("Pinned::nativeFree:          {}", ptr);
     int n     = opencl::getActiveDeviceId();
-    auto map  = pinnedMaps[n];
+    auto &map = pinnedMaps[n];
     auto iter = map.find(ptr);
 
     if (iter != map.end()) {
         cl::Buffer *buf = map[ptr];
-        getQueue().enqueueUnmapMemObject(*buf, ptr);
+        if (cl_int err = getQueue().enqueueUnmapMemObject(*buf, ptr)) {
+            getLogger()->warn(
+                "Pinned::nativeFree: Error unmapping pinned memory({}:{}). "
+                "Ignoring",
+                err, getErrorMessage(err));
+        }
         delete buf;
         map.erase(iter);
     }
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 35632a9d12..778c611ad9 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -36,8 +36,8 @@ template<typename T>
 void memFree(T *ptr);
 void memFreeUser(void *ptr);
 
-void memLock(const void *ptr);
-void memUnlock(const void *ptr);
+void memLock(const cl::Buffer *ptr);
+void memUnlock(const cl::Buffer *ptr);
 bool isLocked(const void *ptr);
 
 template<typename T>
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 957800b2bd..890103e442 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -283,9 +283,50 @@ make_test(SRC nodevice.cpp CXX11)
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
             LIBRARIES OpenCL::OpenCL
-            BACKENDS "opencl")
+            BACKENDS "opencl"
+            CXX11)
 endif()
 
+if(CUDA_FOUND)
+  foreach(backend ${enabled_backends})
+    set(cuda_test_backends "cuda" "unified")
+    if(${backend} IN_LIST cuda_test_backends)
+      set(target test_cuda_${backend})
+      cuda_add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
+      target_include_directories(${target} PRIVATE
+        ${ArrayFire_SOURCE_DIR}/extern/half/include
+        ${CMAKE_SOURCE_DIR}
+        ${CMAKE_CURRENT_SOURCE_DIR})
+      if(${backend} STREQUAL "unified")
+        target_link_libraries(${target}
+          ArrayFire::af)
+      else()
+        target_link_libraries(${target}
+          ArrayFire::af${backend})
+      endif()
+      target_link_libraries(${target}
+        mmio
+        gtest)
+
+      # Couldn't get Threads::Threads to work with this cuda binary. The import
+      # target would not add the -pthread flag which is required for this
+      # executable (on Ubuntu 18.04 anyway)
+      check_cxx_compiler_flag(-pthread pthread_flag)
+      if(pthread_flag)
+        target_link_libraries(${target} -pthread)
+      endif()
+
+      set_target_properties(${target}
+        PROPERTIES
+        FOLDER "Tests"
+        OUTPUT_NAME "cuda_${backend}")
+
+      add_test(NAME ${target} COMMAND ${target})
+    endif()
+  endforeach()
+endif()
+
+
 make_test(SRC orb.cpp)
 make_test(SRC pad_borders.cpp CXX11)
 make_test(SRC pinverse.cpp SERIAL)
diff --git a/test/cuda.cu b/test/cuda.cu
new file mode 100644
index 0000000000..ca7f2270df
--- /dev/null
+++ b/test/cuda.cu
@@ -0,0 +1,35 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/array.h>
+#include <af/device.h>
+
+TEST(Memory, AfAllocDeviceCUDA) {
+    void *ptr;
+    ASSERT_SUCCESS(af_alloc_device(&ptr, sizeof(float)));
+
+    /// Tests to see if the pointer returned can be used by cuda functions
+    float gold_val = 5;
+    float *gold    = NULL;
+    ASSERT_EQ(cudaSuccess, cudaMalloc(&gold, sizeof(float)));
+    ASSERT_EQ(cudaSuccess, cudaMemcpy(gold, &gold_val, sizeof(float),
+                                      cudaMemcpyHostToDevice));
+
+    ASSERT_EQ(cudaSuccess,
+              cudaMemcpy(ptr, gold, sizeof(float), cudaMemcpyDeviceToDevice));
+
+    float host;
+    ASSERT_EQ(cudaSuccess,
+              cudaMemcpy(&host, ptr, sizeof(float), cudaMemcpyDeviceToHost));
+    ASSERT_SUCCESS(af_free_device(ptr));
+
+    ASSERT_EQ(5, host);
+}
diff --git a/test/manual_memory_test.cpp b/test/manual_memory_test.cpp
index 408f3af19d..35e66bcde5 100644
--- a/test/manual_memory_test.cpp
+++ b/test/manual_memory_test.cpp
@@ -26,7 +26,7 @@ TEST(Memory, recover) {
             vec[i] = randu(1024, 1024, 256);  // Allocating 1GB
         }
 
-        ASSERT_EQ(true, false);  // Is there a simple assert statement?
+        FAIL();
     } catch (exception &ae) {
         ASSERT_EQ(ae.err(), AF_ERR_NO_MEM);
 
diff --git a/test/memory.cpp b/test/memory.cpp
index fecfac16b4..20f9c3e966 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -855,6 +855,97 @@ class MemoryManagerApi : public ::testing::Test {
     }
 };
 
+TEST_F(MemoryManagerApi, E2ETest1D) {
+    size_t aSize = 8;
+
+    array a = af::array(aSize, af::dtype::f32);
+    ASSERT_EQ(payload->table.size(), 1);
+
+    ASSERT_EQ(payload->table[a.device<float>()], aSize * sizeof(float));
+    ASSERT_EQ(payload->lastNdims, 1);
+    ASSERT_EQ(payload->lastDims, af::dim4(aSize));
+    ASSERT_EQ(payload->lastElementSize, 4);
+}
+
+TEST_F(MemoryManagerApi, E2ETest2D) {
+    size_t aSize = 8;
+
+    af::array a = af::array(aSize, aSize, af::dtype::f32);
+    ASSERT_EQ(payload->table.size(), 1);
+    ASSERT_EQ(payload->table[a.device<float>()], aSize * aSize * sizeof(float));
+    ASSERT_EQ(payload->lastElementSize, 4);
+
+    // Currently this is set to 1 because all allocations request linear memory
+    // This behavior will change in the future
+    ASSERT_EQ(payload->lastNdims, 1);
+    ASSERT_EQ(payload->lastDims, af::dim4(aSize * aSize));
+}
+
+TEST_F(MemoryManagerApi, E2ETest3D) {
+    size_t aSize = 8;
+
+    af::array a = af::array(aSize, aSize, aSize, af::dtype::f32);
+    ASSERT_EQ(payload->table.size(), 1);
+    ASSERT_EQ(payload->table[a.device<float>()],
+              aSize * aSize * aSize * sizeof(float));
+    ASSERT_EQ(payload->lastElementSize, 4);
+
+    // Currently this is set to 1 because all allocations request linear memory
+    // This behavior will change in the future
+    ASSERT_EQ(payload->lastNdims, 1);
+    ASSERT_EQ(payload->lastDims, af::dim4(aSize * aSize * aSize));
+}
+
+TEST_F(MemoryManagerApi, E2ETest4D) {
+    size_t aSize = 8;
+
+    af::array a = af::array(aSize, aSize, aSize, aSize, af::dtype::f32);
+    ASSERT_EQ(payload->table.size(), 1);
+    ASSERT_EQ(payload->table[a.device<float>()],
+              aSize * aSize * aSize * aSize * sizeof(float));
+    ASSERT_EQ(payload->lastElementSize, 4);
+
+    // Currently this is set to 1 because all allocations request linear memory
+    // This behavior will change in the future
+    ASSERT_EQ(payload->lastNdims, 1);
+    ASSERT_EQ(payload->lastDims, af::dim4(aSize * aSize * aSize * aSize));
+    af::sync();
+}
+
+TEST_F(MemoryManagerApi, E2ETest4DComplexDouble) {
+    size_t aSize = 8;
+
+    af::array a = af::array(aSize, aSize, aSize, aSize, af::dtype::c64);
+    ASSERT_EQ(payload->table.size(), 1);
+    ASSERT_EQ(payload->table[a.device<float>()],
+              aSize * aSize * aSize * aSize * sizeof(double) * 2);
+    ASSERT_EQ(payload->lastElementSize, 16);
+
+    // Currently this is set to 1 because all allocations request linear memory
+    // This behavior will change in the future
+    ASSERT_EQ(payload->lastNdims, 1);
+    ASSERT_EQ(payload->lastDims, af::dim4(aSize * aSize * aSize * aSize));
+}
+
+TEST_F(MemoryManagerApi, E2ETestMultipleAllocations) {
+    size_t aSize = 8;
+
+    af::array a = af::array(aSize, af::dtype::c64);
+    ASSERT_EQ(payload->lastElementSize, 16);
+
+    af::array b = af::array(aSize, af::dtype::f64);
+    ASSERT_EQ(payload->lastElementSize, 8);
+
+    ASSERT_EQ(payload->table.size(), 2);
+    ASSERT_EQ(payload->table[a.device<float>()], aSize * sizeof(double) * 2);
+    ASSERT_EQ(payload->table[b.device<float>()], aSize * sizeof(double));
+
+    // Currently this is set to 1 because all allocations request linear memory
+    // This behavior will change in the future
+    ASSERT_EQ(payload->lastNdims, 1);
+    ASSERT_EQ(payload->lastDims, af::dim4(aSize));
+}
+
 TEST_F(MemoryManagerApi, OutOfMemory) {
     af::array a;
     const unsigned N = 99999;
@@ -915,13 +1006,13 @@ TEST(MemoryManagerE2E, E2ETest) {
     {
         size_t aSize = 8;
 
-        void *a = af::alloc(aSize, af::dtype::f32);
+        array a = af::randu(aSize, af::dtype::f32);
         ASSERT_EQ(payload->table.size(), 1);
 
-        ASSERT_EQ(payload->table[a], aSize * sizeof(float));
+        ASSERT_EQ(payload->table[a.device<float>()], aSize * sizeof(float));
         ASSERT_EQ(payload->lastNdims, 1);
-        ASSERT_EQ(payload->lastDims, af::dim4(aSize * sizeof(float)));
-        ASSERT_EQ(payload->lastElementSize, 1);
+        ASSERT_EQ(payload->lastDims, af::dim4(aSize));
+        ASSERT_EQ(payload->lastElementSize, 4);
 
         dim_t bDim = 2;
         auto b     = af::randu({bDim, bDim});
@@ -934,7 +1025,7 @@ TEST(MemoryManagerE2E, E2ETest) {
         ASSERT_EQ(payload->lastDims, af::dim4(bDim * b.numdims()));
         ASSERT_EQ(payload->lastElementSize, sizeof(float));
 
-        af::free(a);
+        a = array();
 
         ASSERT_EQ(payload->totalBytes, aSize * sizeof(float) + b.bytes());
         ASSERT_EQ(payload->totalBuffers, 2);
@@ -963,3 +1054,16 @@ TEST(MemoryManagerE2E, E2ETest) {
     ASSERT_EQ(payload->initializeCalledTimes, 1);
     ASSERT_EQ(payload->shutdownCalledTimes, af::getDeviceCount());
 }
+TEST(Memory, AfAllocDeviceCPUC) {
+    af_backend active_backend;
+    ASSERT_SUCCESS(af_get_active_backend(&active_backend));
+
+    if (active_backend == AF_BACKEND_CPU) {
+        void *ptr;
+        ASSERT_SUCCESS(af_alloc_device(&ptr, sizeof(float)));
+
+        // This is the CPU backend so we can assign to the pointer
+        *static_cast<float *>(ptr) = 5;
+        ASSERT_SUCCESS(af_free_device(ptr));
+    }
+}
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index f64f417092..f9cb8e9c08 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -9,10 +9,26 @@
 
 #include <arrayfire.h>
 #include <gtest/gtest.h>
+#include <testHelpers.hpp>
 #if defined(AF_OPENCL)
 #include <af/opencl.h>
 #include <iostream>
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Wignored-qualifiers"
+#pragma GCC diagnostic ignored "-Wignored-attributes"
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#if __GNUC__ >= 8
+#pragma GCC diagnostic ignored "-Wcatch-value="
+#endif
+#define CL_HPP_MINIMUM_OPENCL_VERSION 120
+#define CL_HPP_TARGET_OPENCL_VERSION 120
+#define CL_HPP_ENABLE_EXCEPTIONS 1
+#include <CL/cl2.hpp>
+#pragma GCC diagnostic pop
+
 using af::array;
 using af::constant;
 using af::getDeviceCount;
@@ -29,14 +45,13 @@ inline void checkErr(cl_int err, const char *name) {
     }
 }
 
-void getExternals(cl_device_id &deviceId, cl_context &context,
-                  cl_command_queue &queue) {
-    static cl_device_id dId     = NULL;
-    static cl_context cId       = NULL;
-    static cl_command_queue qId = NULL;
-    static bool call_once       = true;
+class OCLExtContext : public ::testing::Test {
+   public:
+    cl_device_id deviceId  = NULL;
+    cl_context context     = NULL;
+    cl_command_queue queue = NULL;
 
-    if (call_once) {
+    void SetUp() override {
         cl_platform_id platformId = NULL;
         cl_uint numPlatforms;
         cl_uint numDevices;
@@ -45,64 +60,51 @@ void getExternals(cl_device_id &deviceId, cl_context &context,
         checkErr(clGetPlatformIDs(1, &platformId, &numPlatforms),
                  "Get Platforms failed");
 
-        checkErr(clGetDeviceIDs(platformId, CL_DEVICE_TYPE_DEFAULT, 1, &dId,
-                                &numDevices),
+        checkErr(clGetDeviceIDs(platformId, CL_DEVICE_TYPE_DEFAULT, 1,
+                                &deviceId, &numDevices),
                  "Get cl_device_id failed");
 
-        cId = clCreateContext(NULL, 1, &dId, NULL, NULL, &errorCode);
+        context = clCreateContext(NULL, 1, &deviceId, NULL, NULL, &errorCode);
         checkErr(errorCode, "Context creation failed");
 
 #ifdef CL_VERSION_2_0
-        qId = clCreateCommandQueueWithProperties(cId, dId, 0, &errorCode);
+        queue = clCreateCommandQueueWithProperties(context, deviceId, 0,
+                                                   &errorCode);
 #else
-        qId = clCreateCommandQueue(cId, dId, 0, &errorCode);
+        queue = clCreateCommandQueue(context, deviceId, 0, &errorCode);
 #endif
 
         checkErr(errorCode, "Command queue creation failed");
-        call_once = false;
     }
-    deviceId = dId;
-    context  = cId;
-    queue    = qId;
-}
-
-TEST(OCLExtContext, PushAndPop) {
-    cl_device_id deviceId  = NULL;
-    cl_context context     = NULL;
-    cl_command_queue queue = NULL;
+    void TearDown() override {
+        checkErr(clReleaseCommandQueue(queue), "clReleaseCommandQueue");
+        checkErr(clReleaseContext(context), "clReleaseContext");
+        checkErr(clReleaseDevice(deviceId), "clReleaseDevice");
+    }
+};
 
-    getExternals(deviceId, context, queue);
+TEST_F(OCLExtContext, PushAndPop) {
     int dCount = getDeviceCount();
-    printf("\n%d devices before afcl::addDevice\n\n", dCount);
     info();
 
     afcl::addDevice(deviceId, context, queue);
     ASSERT_EQ(true, dCount + 1 == getDeviceCount());
-    printf("\n%d devices after afcl::addDevice\n", getDeviceCount());
 
     afcl::deleteDevice(deviceId, context);
     ASSERT_EQ(true, dCount == getDeviceCount());
-    printf("\n%d devices after afcl::deleteDevice\n\n", getDeviceCount());
     info();
 }
 
-TEST(OCLExtContext, set) {
-    cl_device_id deviceId  = NULL;
-    cl_context context     = NULL;
-    cl_command_queue queue = NULL;
-
+TEST_F(OCLExtContext, set) {
     int dCount = getDeviceCount();  // Before user device addition
     setDevice(0);
     info();
     array t = randu(5, 5);
     af_print(t);
 
-    getExternals(deviceId, context, queue);
     afcl::addDevice(deviceId, context, queue);
-    printf("\nBefore setting device to newly added one\n\n");
     info();
 
-    printf("\n\nBefore setting device to newly added one\n\n");
     setDevice(
         dCount);  // In 0-based index, dCount is index of newly added device
     info();
@@ -115,7 +117,6 @@ TEST(OCLExtContext, set) {
     a.host((void *)host.data());
     for (int i = 0; i < s; ++i) ASSERT_EQ(host[i], 1.0f);
 
-    printf("\n\nAfter reset to default set of devices\n\n");
     setDevice(0);
     info();
     af_print(t);
@@ -136,3 +137,27 @@ TEST(OCLCheck, DevicePlatform) {
 #else
 TEST(OCLExtContext, NoopCPU) {}
 #endif
+
+TEST(Memory, AfAllocDeviceOpenCL) {
+    /// Tests to see if the pointer returned can be used by opencl functions
+    float gold_val = 5;
+
+    void *alloc_ptr;
+    ASSERT_SUCCESS(af_alloc_device(&alloc_ptr, sizeof(float)));
+    // af_alloc_device returns a cl::Buffer object from alloc unfortunately
+    cl::Buffer *bptr = static_cast<cl::Buffer *>(alloc_ptr);
+    ASSERT_EQ(2, bptr->getInfo<CL_MEM_REFERENCE_COUNT>());
+
+    cl_command_queue queue;
+    afcl_get_queue(&queue, true);
+    cl::CommandQueue cq(queue);
+
+    cl::Buffer gold(cq, &gold_val, &gold_val + 1, false);
+    cq.enqueueCopyBuffer(gold, *bptr, 0, 0, sizeof(float));
+
+    float host;
+    cq.enqueueReadBuffer(*bptr, CL_TRUE, 0, sizeof(float), &host);
+
+    ASSERT_SUCCESS(af_free_device(alloc_ptr));
+    ASSERT_EQ(gold_val, host);
+}

From f620f766881bac818ec8b05c4e204a5bc81a22aa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 28 May 2020 02:44:54 -0400
Subject: [PATCH 1975/2677] Create af::allocV2 and af::freeV2 which return
 cl_mem

* Older alloc functions were returning cl::Buffer objects. This behavior
is deprecated in favor of cl_mem objects on the OpenCL backend
---
 docs/details/device.dox    | 31 ++++++++----
 include/af/device.h        | 79 +++++++++++++++++++++++++++++-
 src/api/c/memory.cpp       | 32 +++++++++++++
 src/api/cpp/device.cpp     | 22 ++++++++-
 src/api/unified/device.cpp | 16 ++++++-
 test/cuda.cu               | 44 +++++++++++++++++
 test/memory.cpp            | 59 +++++++++++++++++++----
 test/ocl_ext_context.cpp   | 98 ++++++++++++++++++++++++++++++++++++++
 8 files changed, 362 insertions(+), 19 deletions(-)

diff --git a/docs/details/device.dox b/docs/details/device.dox
index 39741d2c30..1bc1bbdccc 100644
--- a/docs/details/device.dox
+++ b/docs/details/device.dox
@@ -77,7 +77,7 @@ have finished.
 
 ===============================================================================
 
-\defgroup device_func_alloc alloc
+\defgroup device_func_alloc allocV2
 \ingroup device_mat
 
 \brief Allocate memory using the ArrayFire memory manager
@@ -92,21 +92,36 @@ interface returns a void pointer that needs to be cast to the backend
 appropriate memory type.
 
 
-| function           | CPU | CUDA | OpenCL      |
-|--------------------|-----|------|-------------|
-| af_alloc_device    | T*  | T*   | cl::Buffer* |
-| af::alloc          | T*  | T*   | cl::Buffer* |
+| function                     | CPU | CUDA | OpenCL      |
+|------------------------------|-----|------|-------------|
+| af_alloc_device_v2           | T*  | T*   | cl_mem      |
+| af::allocV2                  | T*  | T*   | cl_mem      |
+| af_alloc_device (deprecated) | T*  | T*   | cl::Buffer* |
+| af::alloc (deprecated)       | T*  | T*   | cl::Buffer* |
+
+CPU Backend
+-----------
+\snippet test/memory.cpp ex_alloc_v2_cpu
+
+CUDA Backend
+------------
+\snippet test/cuda.cu ex_alloc_v2_cuda
+
+OpenCL Backend
+--------------
+\snippet test/ocl_ext_context.cpp ex_alloc_v2_opencl
 
 ===============================================================================
 
-\defgroup device_func_free free
+\defgroup device_func_free freeV2
 \ingroup device_mat
 
 \brief Returns memory to ArrayFire's memory manager. The memory will
        return to the memory pool.
 
-These calls free the device memory. These functions need to be called on
-pointers allocated using alloc function.
+Releases control of the memory allocated by af::allocV2 functions to ArrayFire's
+memory manager. ArrayFire may reuse the memory for subsequent operations. This
+memory should not be used by the client after this point.
 
 ===============================================================================
 
diff --git a/include/af/device.h b/include/af/device.h
index 96ba584df1..94c06d71ba 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -115,8 +115,26 @@ namespace af
     ///
     /// \note The device memory returned by this function is only freed if
     ///       af::free() is called explicitly
+    /// \deprecated Use allocV2 instead. allocV2 accepts number of bytes
+    ///             instead of number of elements and returns a cl_mem object
+    ///             instead of the cl::Buffer object for the OpenCL backend.
+    ///             Otherwise the functionallity is identical to af::alloc.
+    AF_DEPRECATED("Use af::allocV2 instead")
     AFAPI void *alloc(const size_t elements, const dtype type);
 
+#if AF_API_VERSION >= 38
+    /// \brief Allocates memory using ArrayFire's memory manager
+    ///
+    /// \param[in] bytes the number of bytes to allocate
+    /// \returns Pointer to the device memory on the current device. This is a
+    ///          CUDA device pointer for the CUDA backend. A cl_mem pointer
+    ///          on the OpenCL backend and a C pointer for the CPU backend
+    ///
+    /// \note The device memory returned by this function is only freed if
+    ///       af::freeV2() is called explicitly
+    AFAPI void *allocV2(const size_t bytes);
+#endif
+
     /// \brief Allocates memory using ArrayFire's memory manager
     //
     /// \param[in] elements the number of elements to allocate
@@ -129,7 +147,13 @@ namespace af
     ///       sizeof(type)
     /// \note The device memory returned by this function is only freed if
     ///       af::free() is called explicitly
-    template <typename T> T *alloc(const size_t elements);
+    /// \deprecated Use allocV2 instead. allocV2 accepts number of bytes
+    ///             instead of number of elements and returns a cl_mem object
+    ///             instead of the cl::Buffer object for the OpenCL backend.
+    ///             Otherwise the functionallity is identical to af::alloc.
+    template <typename T>
+    AF_DEPRECATED("Use af::allocV2 instead")
+    T *alloc(const size_t elements);
     /// @}
 
     /// \ingroup device_func_free
@@ -140,8 +164,22 @@ namespace af
     ///
     /// \note This function will free a device pointer even if it has been
     ///       previously locked.
+    /// \deprecated Use af::freeV2 instead. af_alloc_device_v2 returns a
+    ///             cl_mem object instead of the cl::Buffer object for the
+    ///             OpenCL backend. Otherwise the functionallity is identical
+    AF_DEPRECATED("Use af::freeV2 instead")
     AFAPI void free(const void *ptr);
 
+#if AF_API_VERSION >= 38
+    /// \ingroup device_func_free
+    /// \copydoc device_func_free
+    /// \param[in] ptr The pointer returned by af::allocV2
+    ///
+    /// This function will free a device pointer even if it has been previously
+    /// locked.
+    AFAPI void freeV2(const void *ptr);
+#endif
+
     /// \ingroup device_func_pinned
     /// @{
     /// \copydoc device_func_pinned
@@ -330,7 +368,11 @@ extern "C" {
 
        \returns AF_SUCCESS if a pointer could be allocated. AF_ERR_NO_MEM if
                 there is no memory
+       \deprecated Use af_alloc_device_v2 instead. af_alloc_device_v2 returns a
+                   cl_mem object instead of the cl::Buffer object for the OpenCL
+                   backend. Otherwise the functionallity is identical
     */
+    AF_DEPRECATED("Use af_alloc_device_v2 instead")
     AFAPI af_err af_alloc_device(void **ptr, const dim_t bytes);
 
     /**
@@ -341,10 +383,45 @@ extern "C" {
 
        \param[in] ptr The pointer allocated by af_alloc_device to be freed
 
+       \deprecated Use af_free_device_v2 instead. The new function handles the
+                   new behavior of the af_alloc_device_v2 function.
        \ingroup device_func_free
     */
+    AF_DEPRECATED("Use af_free_device_v2 instead")
     AFAPI af_err af_free_device(void *ptr);
 
+#if AF_API_VERSION >= 38
+    /**
+       \brief Allocates memory using ArrayFire's memory manager
+
+       This device memory returned by this function can only be freed using
+       af_free_device_v2.
+
+       \param [out] ptr Pointer to the device memory on the current device. This
+                        is a CUDA device pointer for the CUDA backend. A
+                        cl::Buffer pointer on the OpenCL backend and a C pointer
+                        for the CPU backend
+       \param [in] bytes The number of bites to allocate on the device
+
+       \returns AF_SUCCESS if a pointer could be allocated. AF_ERR_NO_MEM if
+                there is no memory
+       \ingroup device_func_alloc
+    */
+    AFAPI af_err af_alloc_device_v2(void **ptr, const dim_t bytes);
+
+    /**
+       \brief Returns memory to ArrayFire's memory manager.
+
+       This function will free a device pointer even if it has been previously
+       locked.
+
+       \param[in] ptr The pointer allocated by af_alloc_device_v2 to be freed
+       \note this function will not work for pointers allocated using the
+             af_alloc_device function for all backends
+       \ingroup device_func_free
+    */
+    AFAPI af_err af_free_device_v2(void *ptr);
+#endif
     /**
        \ingroup device_func_pinned
     */
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 76aefe99d4..2958d6c90c 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -257,6 +257,25 @@ af_err af_alloc_device(void **ptr, const dim_t bytes) {
     return AF_SUCCESS;
 }
 
+af_err af_alloc_device_v2(void **ptr, const dim_t bytes) {
+    try {
+        AF_CHECK(af_init());
+#ifdef AF_OPENCL
+        auto *buf = static_cast<cl::Buffer *>(memAllocUser(bytes));
+        *ptr      = buf->operator()();
+
+        // Calling retain to offset the decrement the reference count by the
+        // destructor of cl::Buffer
+        clRetainMemObject(cl_mem(*ptr));
+        delete buf;
+#else
+        *ptr = static_cast<void *>(memAllocUser(bytes));
+#endif
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
     try {
         AF_CHECK(af_init());
@@ -274,6 +293,19 @@ af_err af_free_device(void *ptr) {
     return AF_SUCCESS;
 }
 
+af_err af_free_device_v2(void *ptr) {
+    try {
+#ifdef AF_OPENCL
+        auto mem = static_cast<cl_mem>(ptr);
+        memFreeUser(new cl::Buffer(mem, false));
+#else
+        memFreeUser(ptr);
+#endif
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
 af_err af_free_pinned(void *ptr) {
     try {
         pinnedFree<char>(static_cast<char *>(ptr));
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index a393fa0d15..0a67d9de19 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -102,11 +102,21 @@ void sync(int device) { AF_THROW(af_sync(device)); }
 // Alloc device memory
 void *alloc(const size_t elements, const af::dtype type) {
     void *ptr;
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
     AF_THROW(af_alloc_device(&ptr, elements * size_of(type)));
+#pragma GCC diagnostic pop
     // FIXME: Add to map
     return ptr;
 }
 
+// Alloc device memory
+void *allocV2(const size_t bytes) {
+    void *ptr;
+    AF_THROW(af_alloc_device_v2(&ptr, bytes));
+    return ptr;
+}
+
 // Alloc pinned memory
 void *pinned(const size_t elements, const af::dtype type) {
     void *ptr;
@@ -117,7 +127,14 @@ void *pinned(const size_t elements, const af::dtype type) {
 
 void free(const void *ptr) {
     // FIXME: look up map and call the right free
-    AF_THROW(af_free_device((void *)ptr));
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_THROW(af_free_device(const_cast<void *>(ptr)));
+#pragma GCC diagnostic pop
+}
+
+void freeV2(const void *ptr) {
+    AF_THROW(af_free_device_v2(const_cast<void *>(ptr)));
 }
 
 void freePinned(const void *ptr) {
@@ -155,6 +172,8 @@ size_t getMemStepSize() {
     return size_bytes;
 }
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 #define INSTANTIATE(T)                                                        \
     template<>                                                                \
     AFAPI T *alloc(const size_t elements) {                                   \
@@ -181,5 +200,6 @@ INSTANTIATE(short)
 INSTANTIATE(unsigned short)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
+#pragma GCC diagnostic pop
 
 }  // namespace af
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index be384d3e11..cf2f906070 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -74,14 +74,28 @@ af_err af_get_device(int *device) { CALL(af_get_device, device); }
 af_err af_sync(const int device) { CALL(af_sync, device); }
 
 af_err af_alloc_device(void **ptr, const dim_t bytes) {
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
     CALL(af_alloc_device, ptr, bytes);
+#pragma GCC diagnostic pop
+}
+
+af_err af_alloc_device_v2(void **ptr, const dim_t bytes) {
+    CALL(af_alloc_device_v2, ptr, bytes);
 }
 
 af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
     CALL(af_alloc_pinned, ptr, bytes);
 }
 
-af_err af_free_device(void *ptr) { CALL(af_free_device, ptr); }
+af_err af_free_device(void *ptr) {
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    CALL(af_free_device, ptr);
+#pragma GCC diagnostic pop
+}
+
+af_err af_free_device_v2(void *ptr) { CALL(af_free_device_v2, ptr); }
 
 af_err af_free_pinned(void *ptr) { CALL(af_free_pinned, ptr); }
 
diff --git a/test/cuda.cu b/test/cuda.cu
index ca7f2270df..d404c514a5 100644
--- a/test/cuda.cu
+++ b/test/cuda.cu
@@ -12,6 +12,11 @@
 #include <af/array.h>
 #include <af/device.h>
 
+using af::allocV2;
+using af::freeV2;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 TEST(Memory, AfAllocDeviceCUDA) {
     void *ptr;
     ASSERT_SUCCESS(af_alloc_device(&ptr, sizeof(float)));
@@ -33,3 +38,42 @@ TEST(Memory, AfAllocDeviceCUDA) {
 
     ASSERT_EQ(5, host);
 }
+#pragma GCC diagnostic pop
+
+TEST(Memory, AfAllocDeviceV2CUDA) {
+    void *ptr;
+    ASSERT_SUCCESS(af_alloc_device_v2(&ptr, sizeof(float)));
+
+    /// Tests to see if the pointer returned can be used by cuda functions
+    float gold_val = 5;
+    float *gold    = NULL;
+    ASSERT_EQ(cudaSuccess, cudaMalloc(&gold, sizeof(float)));
+    ASSERT_EQ(cudaSuccess, cudaMemcpy(gold, &gold_val, sizeof(float),
+                                      cudaMemcpyHostToDevice));
+
+    ASSERT_EQ(cudaSuccess,
+              cudaMemcpy(ptr, gold, sizeof(float), cudaMemcpyDeviceToDevice));
+
+    float host;
+    ASSERT_EQ(cudaSuccess,
+              cudaMemcpy(&host, ptr, sizeof(float), cudaMemcpyDeviceToHost));
+    ASSERT_SUCCESS(af_free_device_v2(ptr));
+
+    ASSERT_EQ(5, host);
+}
+
+TEST(Memory, SNIPPET_AllocCUDA) {
+    //! [ex_alloc_v2_cuda]
+
+    void *ptr = allocV2(sizeof(float));
+
+    float *dptr     = static_cast<float *>(ptr);
+    float host_data = 5.0f;
+
+    cudaError_t error = cudaSuccess;
+    error = cudaMemcpy(dptr, &host_data, sizeof(float), cudaMemcpyHostToDevice);
+    freeV2(ptr);
+
+    //! [ex_alloc_v2_cuda]
+    ASSERT_EQ(cudaSuccess, error);
+}
diff --git a/test/memory.cpp b/test/memory.cpp
index 20f9c3e966..e67a7cfb69 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -22,6 +22,7 @@
 #include <vector>
 
 using af::alloc;
+using af::allocV2;
 using af::array;
 using af::cdouble;
 using af::cfloat;
@@ -30,6 +31,7 @@ using af::deviceMemInfo;
 using af::dim4;
 using af::dtype;
 using af::dtype_traits;
+using af::freeV2;
 using af::randu;
 using af::seq;
 using af::span;
@@ -125,8 +127,9 @@ void memAllocPtrScopeTest(int elements) {
     size_t lock_bytes, lock_buffers;
 
     cleanSlate();  // Clean up everything done so far
-
     {
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
         T *ptr = alloc<T>(elements);
 
         deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
@@ -138,6 +141,7 @@ void memAllocPtrScopeTest(int elements) {
         ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
 
         af::free(ptr);
+#pragma GCC diagnostic pop
     }
 
     deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
@@ -152,7 +156,7 @@ void memAllocPtrScopeTest(int elements) {
     cleanSlate();  // Clean up everything done so far
 
     {
-        void *ptr = alloc(elements, (af_dtype)dtype_traits<T>::af_type);
+        void *ptr = allocV2(elements * sizeof(T));
 
         deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
 
@@ -162,7 +166,7 @@ void memAllocPtrScopeTest(int elements) {
         ASSERT_EQ(alloc_bytes, roundUpToStep(elements * sizeof(T)));
         ASSERT_EQ(lock_bytes, roundUpToStep(elements * sizeof(T)));
 
-        af::free(ptr);
+        af::freeV2(ptr);
     }
 
     deviceMemInfo(&alloc_bytes, &alloc_buffers, &lock_bytes, &lock_buffers);
@@ -1006,13 +1010,13 @@ TEST(MemoryManagerE2E, E2ETest) {
     {
         size_t aSize = 8;
 
-        array a = af::randu(aSize, af::dtype::f32);
+        void *a = af::allocV2(aSize * sizeof(float));
         ASSERT_EQ(payload->table.size(), 1);
 
-        ASSERT_EQ(payload->table[a.device<float>()], aSize * sizeof(float));
+        ASSERT_EQ(payload->table[a], aSize * sizeof(float));
         ASSERT_EQ(payload->lastNdims, 1);
-        ASSERT_EQ(payload->lastDims, af::dim4(aSize));
-        ASSERT_EQ(payload->lastElementSize, 4);
+        ASSERT_EQ(payload->lastDims, af::dim4(aSize) * sizeof(float));
+        ASSERT_EQ(payload->lastElementSize, 1);
 
         dim_t bDim = 2;
         auto b     = af::randu({bDim, bDim});
@@ -1025,7 +1029,7 @@ TEST(MemoryManagerE2E, E2ETest) {
         ASSERT_EQ(payload->lastDims, af::dim4(bDim * b.numdims()));
         ASSERT_EQ(payload->lastElementSize, sizeof(float));
 
-        a = array();
+        af::freeV2(a);
 
         ASSERT_EQ(payload->totalBytes, aSize * sizeof(float) + b.bytes());
         ASSERT_EQ(payload->totalBuffers, 2);
@@ -1054,6 +1058,9 @@ TEST(MemoryManagerE2E, E2ETest) {
     ASSERT_EQ(payload->initializeCalledTimes, 1);
     ASSERT_EQ(payload->shutdownCalledTimes, af::getDeviceCount());
 }
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 TEST(Memory, AfAllocDeviceCPUC) {
     af_backend active_backend;
     ASSERT_SUCCESS(af_get_active_backend(&active_backend));
@@ -1067,3 +1074,39 @@ TEST(Memory, AfAllocDeviceCPUC) {
         ASSERT_SUCCESS(af_free_device(ptr));
     }
 }
+#pragma GCC diagnostic pop
+
+TEST(Memory, AfAllocDeviceV2CPUC) {
+    af_backend active_backend;
+    ASSERT_SUCCESS(af_get_active_backend(&active_backend));
+
+    if (active_backend == AF_BACKEND_CPU) {
+        void *ptr;
+        ASSERT_SUCCESS(af_alloc_device_v2(&ptr, sizeof(float)));
+
+        // This is the CPU backend so we can assign to the pointer
+        *static_cast<float *>(ptr) = 5;
+        ASSERT_SUCCESS(af_free_device_v2(ptr));
+    }
+}
+
+TEST(Memory, SNIPPET_AllocCPU) {
+    af_backend active_backend;
+    ASSERT_SUCCESS(af_get_active_backend(&active_backend));
+
+    if (active_backend == AF_BACKEND_CPU) {
+        //! [ex_alloc_v2_cpu]
+
+        // Allocate one float and cast to float*
+        void *ptr   = af::allocV2(sizeof(float));
+        float *dptr = static_cast<float *>(ptr);
+
+        // This is the CPU backend so we can assign to the pointer
+        dptr[0] = 5.0f;
+        freeV2(ptr);
+
+        //! [ex_alloc_v2_cpu]
+
+        ASSERT_EQ(*dptr, 5.0f);
+    }
+}
diff --git a/test/ocl_ext_context.cpp b/test/ocl_ext_context.cpp
index f9cb8e9c08..2f262bcf5d 100644
--- a/test/ocl_ext_context.cpp
+++ b/test/ocl_ext_context.cpp
@@ -29,8 +29,10 @@
 #include <CL/cl2.hpp>
 #pragma GCC diagnostic pop
 
+using af::allocV2;
 using af::array;
 using af::constant;
+using af::freeV2;
 using af::getDeviceCount;
 using af::info;
 using af::randu;
@@ -138,6 +140,8 @@ TEST(OCLCheck, DevicePlatform) {
 TEST(OCLExtContext, NoopCPU) {}
 #endif
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 TEST(Memory, AfAllocDeviceOpenCL) {
     /// Tests to see if the pointer returned can be used by opencl functions
     float gold_val = 5;
@@ -161,3 +165,97 @@ TEST(Memory, AfAllocDeviceOpenCL) {
     ASSERT_SUCCESS(af_free_device(alloc_ptr));
     ASSERT_EQ(gold_val, host);
 }
+#pragma GCC diagnostic pop
+
+TEST(Memory, AfAllocDeviceV2OpenCLC) {
+    /// Tests to see if the pointer returned can be used by opencl functions
+    float gold_val = 5;
+
+    void *alloc_ptr;
+    ASSERT_SUCCESS(af_alloc_device_v2(&alloc_ptr, sizeof(float)));
+    {
+        cl::Buffer bptr(static_cast<cl_mem>(alloc_ptr), true);
+        ASSERT_EQ(3, bptr.getInfo<CL_MEM_REFERENCE_COUNT>());
+
+        cl_command_queue queue;
+        afcl_get_queue(&queue, true);
+        cl::CommandQueue cq(queue);
+
+        cl::Buffer gold(cq, &gold_val, &gold_val + 1, false);
+        cq.enqueueCopyBuffer(gold, bptr, 0, 0, sizeof(float));
+
+        float host;
+        cq.enqueueReadBuffer(bptr, CL_TRUE, 0, sizeof(float), &host);
+        ASSERT_EQ(gold_val, host);
+    }
+
+    ASSERT_SUCCESS(af_free_device_v2(alloc_ptr));
+}
+
+TEST(Memory, AfAllocDeviceV2OpenCLCPP) {
+    /// Tests to see if the pointer returned can be used by opencl functions
+    float gold_val = 5;
+
+    cl_mem alloc_ptr = static_cast<cl_mem>(allocV2(sizeof(float)));
+    {
+        cl::Buffer bptr(alloc_ptr, true);
+        ASSERT_EQ(3, bptr.getInfo<CL_MEM_REFERENCE_COUNT>());
+
+        cl_command_queue queue;
+        afcl_get_queue(&queue, true);
+        cl::CommandQueue cq(queue);
+
+        cl::Buffer gold(cq, &gold_val, &gold_val + 1, false);
+        cq.enqueueCopyBuffer(gold, bptr, 0, 0, sizeof(float));
+
+        float host;
+        cq.enqueueReadBuffer(bptr, CL_TRUE, 0, sizeof(float), &host);
+        ASSERT_EQ(gold_val, host);
+    }
+
+    freeV2(alloc_ptr);
+}
+
+TEST(Memory, SNIPPET_AllocOpenCL) {
+    // clang-format off
+    //! [ex_alloc_v2_opencl]
+    cl_command_queue queue;
+    afcl_get_queue(&queue, true);
+    cl_context context;
+    afcl_get_context(&context, true);
+
+    void *alloc_ptr = allocV2(sizeof(float));
+    cl_mem mem = static_cast<cl_mem>(alloc_ptr);
+
+    // Map memory from the device to the System memory
+    cl_int map_err_code;
+    void *mapped_ptr = clEnqueueMapBuffer(
+        queue, // command queueu
+        mem, // buffer
+        CL_TRUE, // is blocking
+        CL_MAP_READ | CL_MAP_WRITE, // map type
+        0, // offset
+        sizeof(float), // size
+        0, // num_events_in_wait_list
+        nullptr, // event_wait_list
+        nullptr, // event
+        &map_err_code); // error code
+
+    float *float_ptr = static_cast<float *>(mapped_ptr);
+    float_ptr[0]     = 5.0f;
+
+    // Unmap buffer after we are done using it
+    cl_int unmap_err_code =
+        clEnqueueUnmapMemObject(queue,      // command queue
+                                mem,        // buffer
+                                mapped_ptr, // mapped pointer
+                                0,          // num_events_in_wait_list
+                                nullptr,    // event_wait_list
+                                nullptr);   // event
+    freeV2(alloc_ptr);
+    //! [ex_alloc_v2_opencl]
+    // clang-format on
+
+    ASSERT_EQ(CL_SUCCESS, map_err_code);
+    ASSERT_EQ(CL_SUCCESS, unmap_err_code);
+}

From 3925390611d7057af4aa96d1729cdfd8ce6c3fa9 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Mon, 1 Jun 2020 13:17:34 +0530
Subject: [PATCH 1976/2677] Add missing set stracktrace API in unified source

---
 src/api/unified/error.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index 2e2d51642f..de6fad63e9 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -10,6 +10,7 @@
 #include <af/array.h>
 #include <af/device.h>
 #include <af/exception.h>
+#include <af/util.h>
 #include <algorithm>
 #include "symbol_manager.hpp"
 
@@ -42,3 +43,7 @@ void af_get_last_error(char **str, dim_t *len) {
         func(str, len);
     }
 }
+
+af_err af_set_enable_stacktrace(int is_enabled) {
+    CALL(af_set_enable_stacktrace, is_enabled);
+}

From 68e90dc118ef958905e0c16b8dde5155db02a994 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Mon, 1 Jun 2020 14:34:59 +0530
Subject: [PATCH 1977/2677] Fix undefined set_stacktrace symbol by adding
 missing header in source

---
 src/api/c/error.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index c818414eaa..8ede0ee9c0 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -10,6 +10,7 @@
 #include <common/err_common.hpp>
 #include <af/device.h>
 #include <af/exception.h>
+#include <af/util.h>
 
 #include <algorithm>
 #include <string>

From e975dfd2a0caaf5e5316da9e9b19ec634adbf901 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 14:42:39 -0400
Subject: [PATCH 1978/2677] Fix leak of the cl::Buffer object in makeParam. Not
 leaking cl_mem

---
 src/backend/opencl/Param.cpp                   |  5 +++--
 src/backend/opencl/Param.hpp                   |  3 ++-
 src/backend/opencl/magma/transpose.cpp         | 16 +++++++++++-----
 src/backend/opencl/magma/transpose_inplace.cpp | 13 +++++++++----
 4 files changed, 25 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index 34a01f4a5d..25358310ae 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -16,9 +16,10 @@ namespace opencl {
 Param::Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
 Param::Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_) {}
 
-Param makeParam(cl_mem mem, int off, const int dims[4], const int strides[4]) {
+Param makeParam(cl::Buffer &mem, int off, const int dims[4],
+                const int strides[4]) {
     Param out;
-    out.data        = new cl::Buffer(mem);
+    out.data        = &mem;
     out.info.offset = off;
     for (int i = 0; i < 4; i++) {
         out.info.dims[i]    = dims[i];
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 85f010f2d2..6cf63f356b 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -29,5 +29,6 @@ struct Param {
 };
 
 // AF_DEPRECATED("Use Array<T>")
-Param makeParam(cl_mem mem, int off, const int dims[4], const int strides[4]);
+Param makeParam(cl::Buffer& mem, int off, const int dims[4],
+                const int strides[4]);
 }  // namespace opencl
diff --git a/src/backend/opencl/magma/transpose.cpp b/src/backend/opencl/magma/transpose.cpp
index 7ccb71eb4a..e9ff2243ca 100644
--- a/src/backend/opencl/magma/transpose.cpp
+++ b/src/backend/opencl/magma/transpose.cpp
@@ -54,6 +54,11 @@
 #include "kernel/transpose.hpp"
 #include "magma_data.h"
 
+using cl::Buffer;
+using cl::CommandQueue;
+using opencl::makeParam;
+using opencl::kernel::transpose;
+
 template<typename T>
 void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
                          size_t dA_offset, magma_int_t ldda, cl_mem dAT,
@@ -83,12 +88,13 @@ void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
     int istrides[] = {1, ldda, ldda * n, ldda * n};
     int ostrides[] = {1, lddat, lddat * m, lddat * m};
 
-    using namespace opencl;
+    Buffer dATBuf(dAT, true);
+    Buffer dABuf(dA, true);
 
-    cl::CommandQueue q(queue, true);
-    kernel::transpose<T>(makeParam(dAT, dAT_offset, odims, ostrides),
-                         makeParam(dA, dA_offset, idims, istrides), q, false,
-                         m % 32 == 0 && n % 32 == 0);
+    CommandQueue q(queue, true);
+    transpose<T>(makeParam(dATBuf, dAT_offset, odims, ostrides),
+                 makeParam(dABuf, dA_offset, idims, istrides), q, false,
+                 m % 32 == 0 && n % 32 == 0);
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/opencl/magma/transpose_inplace.cpp b/src/backend/opencl/magma/transpose_inplace.cpp
index 6f649f55bb..21770f98be 100644
--- a/src/backend/opencl/magma/transpose_inplace.cpp
+++ b/src/backend/opencl/magma/transpose_inplace.cpp
@@ -54,6 +54,11 @@
 #include "kernel/transpose_inplace.hpp"
 #include "magma_data.h"
 
+using cl::Buffer;
+using cl::CommandQueue;
+using opencl::makeParam;
+using opencl::kernel::transpose_inplace;
+
 template<typename T>
 void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
                                  magma_int_t ldda, magma_queue_t queue) {
@@ -74,11 +79,11 @@ void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
     int dims[]    = {n, n, 1, 1};
     int strides[] = {1, ldda, ldda * n, ldda * n};
 
-    using namespace opencl;
+    Buffer dABuf(dA, true);
 
-    cl::CommandQueue q(queue, true);
-    kernel::transpose_inplace<T>(makeParam(dA, dA_offset, dims, strides), q,
-                                 false, n % 32 == 0);
+    CommandQueue q(queue, true);
+    transpose_inplace<T>(makeParam(dABuf, dA_offset, dims, strides), q, false,
+                         n % 32 == 0);
 }
 
 #define INSTANTIATE(T)                                                \

From 44f7374f94f4becb5d890b0e1e46ac0fac21e3dc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 15:15:22 -0400
Subject: [PATCH 1979/2677] Refactor Modules and Kernels. Fix leak in getKernel

Refactor Module and fix a leak of the cl::Kernel objects. These objects should
be around for a while so the accumulated leak wasn't significant in most
applications.
---
 CMakeModules/LSANSuppression.txt       |  3 +-
 src/backend/common/ModuleInterface.hpp | 11 +++-
 src/backend/common/kernel_cache.cpp    | 12 ++--
 src/backend/cuda/Module.hpp            |  3 +
 src/backend/opencl/Kernel.hpp          | 14 ++---
 src/backend/opencl/Module.hpp          | 17 ++++--
 src/backend/opencl/compile_module.cpp  | 82 +++++++++++++-------------
 src/backend/opencl/jit.cpp             | 26 ++++----
 8 files changed, 94 insertions(+), 74 deletions(-)

diff --git a/CMakeModules/LSANSuppression.txt b/CMakeModules/LSANSuppression.txt
index dca058df0f..0026fbc27d 100644
--- a/CMakeModules/LSANSuppression.txt
+++ b/CMakeModules/LSANSuppression.txt
@@ -1,8 +1,7 @@
 # This is a known leak.
-leak:getKernel
-#leak:libOpenCL
 leak:libnvidia-ptxjitcompile
 leak:tbb::internal::task_stream
+leak:libnvidia-opencl.so
 
 # Allocated by Intel's OpenMP implementation during inverse_dense_cpu
 # This is not something we can control in ArrayFire
diff --git a/src/backend/common/ModuleInterface.hpp b/src/backend/common/ModuleInterface.hpp
index 052a661916..167c3b2304 100644
--- a/src/backend/common/ModuleInterface.hpp
+++ b/src/backend/common/ModuleInterface.hpp
@@ -18,6 +18,12 @@ class ModuleInterface {
     ModuleType mModuleHandle;
 
    public:
+    /// \brief Creates an uninitialized Module
+    ModuleInterface() = default;
+
+    /// \brief Creates a module given a backend specific ModuleType
+    ///
+    /// \param[in] mod The backend specific module
     ModuleInterface(ModuleType mod) : mModuleHandle(mod) {}
 
     /// \brief Set module
@@ -28,10 +34,13 @@ class ModuleInterface {
     /// \brief Get module
     ///
     /// \returns handle to backend specific module
-    inline ModuleType get() const { return mModuleHandle; }
+    inline const ModuleType& get() const { return mModuleHandle; }
 
     /// \brief Unload module
     virtual void unload() = 0;
+
+    /// \brief Returns true if the module mModuleHandle is initialized
+    virtual operator bool() const = 0;
 };
 
 }  // namespace common
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 0c879070a1..79c6e1c3eb 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -42,7 +42,8 @@ shared_timed_mutex& getCacheMutex(const int device) {
 }
 
 ModuleMap& getCache(const int device) {
-    static ModuleMap caches[detail::DeviceManager::MAX_DEVICES];
+    static ModuleMap* caches =
+        new ModuleMap[detail::DeviceManager::MAX_DEVICES];
     return caches[device];
 }
 
@@ -51,7 +52,7 @@ Module findModule(const int device, const string& key) {
     auto& cache = getCache(device);
     auto iter   = cache.find(key);
     if (iter != cache.end()) { return iter->second; }
-    return Module{nullptr};
+    return Module{};
 }
 
 Kernel getKernel(const string& kernelName, const vector<string>& sources,
@@ -89,9 +90,9 @@ Kernel getKernel(const string& kernelName, const vector<string>& sources,
     const int device       = detail::getActiveDeviceId();
     Module currModule      = findModule(device, moduleKey);
 
-    if (currModule.get() == nullptr) {
+    if (!currModule) {
         currModule = loadModuleFromDisk(device, moduleKey, sourceIsJIT);
-        if (currModule.get() == nullptr) {
+        if (!currModule) {
             currModule = compileModule(moduleKey, sources, options, {tInstance},
                                        sourceIsJIT);
         }
@@ -102,7 +103,8 @@ Kernel getKernel(const string& kernelName, const vector<string>& sources,
         if (iter == cache.end()) {
             // If not found, this thread is the first one to compile this
             // kernel. Keep the generated module.
-            getCache(device).emplace(moduleKey, currModule);
+            Module mod = currModule;
+            getCache(device).emplace(moduleKey, mod);
         } else {
             currModule.unload();  // dump the current threads extra compilation
             currModule = iter->second;
diff --git a/src/backend/cuda/Module.hpp b/src/backend/cuda/Module.hpp
index d910d1f90c..ceefd2f94e 100644
--- a/src/backend/cuda/Module.hpp
+++ b/src/backend/cuda/Module.hpp
@@ -28,10 +28,13 @@ class Module : public common::ModuleInterface<CUmodule> {
     using ModuleType = CUmodule;
     using BaseClass  = common::ModuleInterface<ModuleType>;
 
+    Module() = default;
     Module(ModuleType mod) : BaseClass(mod) {
         mInstanceMangledNames.reserve(1);
     }
 
+    operator bool() const final { return get(); }
+
     void unload() final {
         CU_CHECK(cuModuleUnload(get()));
         set(nullptr);
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index 3284fea367..9953a4d956 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -18,24 +18,24 @@ namespace opencl {
 
 struct Enqueuer {
     template<typename... Args>
-    void operator()(void* ker, const cl::EnqueueArgs& qArgs, Args... args) {
-        auto launchOp =
-            cl::KernelFunctor<Args...>(*static_cast<const cl::Kernel*>(ker));
+    void operator()(cl::Kernel ker, const cl::EnqueueArgs& qArgs,
+                    Args... args) {
+        auto launchOp = cl::KernelFunctor<Args...>(ker);
         launchOp(qArgs, std::forward<Args>(args)...);
     }
 };
 
 class Kernel
-    : public common::KernelInterface<cl::Program*, cl::Kernel*, Enqueuer,
+    : public common::KernelInterface<const cl::Program*, cl::Kernel, Enqueuer,
                                      cl::Buffer*> {
    public:
-    using ModuleType = cl::Program*;
-    using KernelType = cl::Kernel*;
+    using ModuleType = const cl::Program*;
+    using KernelType = cl::Kernel;
     using DevPtrType = cl::Buffer*;
     using BaseClass =
         common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType>;
 
-    Kernel() : BaseClass(nullptr, nullptr) {}
+    Kernel() : BaseClass(nullptr, cl::Kernel{nullptr, false}) {}
     Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
 
     // clang-format off
diff --git a/src/backend/opencl/Module.hpp b/src/backend/opencl/Module.hpp
index c0bafeadec..c918797699 100644
--- a/src/backend/opencl/Module.hpp
+++ b/src/backend/opencl/Module.hpp
@@ -16,17 +16,22 @@
 namespace opencl {
 
 /// OpenCL backend wrapper for cl::Program object
-class Module : public common::ModuleInterface<cl::Program*> {
+class Module : public common::ModuleInterface<cl::Program> {
    public:
-    using ModuleType = cl::Program*;
+    using ModuleType = cl::Program;
     using BaseClass  = common::ModuleInterface<ModuleType>;
 
+    /// \brief Create an uninitialized Module
+    Module() = default;
+
+    /// \brief Create a module given a cl::Program type
     Module(ModuleType mod) : BaseClass(mod) {}
 
-    void unload() final {
-        delete get();
-        set(nullptr);
-    }
+    /// \brief Unload module
+    operator bool() const final { return get()(); }
+
+    /// Unload the module
+    void unload() final { set(cl::Program()); }
 };
 
 }  // namespace opencl
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 69f4414eb6..fab31558b0 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -25,39 +25,48 @@
 #include <string>
 #include <vector>
 
-using detail::Kernel;
-using detail::Module;
-
+using cl::Error;
+using cl::Program;
+using common::loggerFactory;
+using opencl::getActiveDeviceId;
+using opencl::getDevice;
+using opencl::Kernel;
+using opencl::Module;
+using spdlog::logger;
+
+using std::begin;
+using std::end;
 using std::ostringstream;
+using std::shared_ptr;
 using std::string;
 using std::vector;
 using std::chrono::duration_cast;
 using std::chrono::high_resolution_clock;
 using std::chrono::milliseconds;
 
-spdlog::logger *getLogger() {
-    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
+logger *getLogger() {
+    static shared_ptr<logger> logger(loggerFactory("jit"));
     return logger.get();
 }
 
-#define SHOW_DEBUG_BUILD_INFO(PROG)                                        \
-    do {                                                                   \
-        cl_uint numDevices = PROG->getInfo<CL_PROGRAM_NUM_DEVICES>();      \
-        for (unsigned int i = 0; i < numDevices; ++i) {                    \
-            printf("%s\n", PROG->getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
-                                   PROG->getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                  \
-            printf("%s\n", PROG->getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
-                                   PROG->getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                  \
-        }                                                                  \
+#define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
+    do {                                                                  \
+        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();      \
+        for (unsigned int i = 0; i < numDevices; ++i) {                   \
+            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
+                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                 \
+            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
+                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
+                               .c_str());                                 \
+        }                                                                 \
     } while (0)
 
 #if defined(NDEBUG)
 
 #define SHOW_BUILD_INFO(PROG)                                              \
     do {                                                                   \
-        std::string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");         \
+        string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");              \
         if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(PROG); } \
     } while (0)
 
@@ -67,7 +76,7 @@ spdlog::logger *getLogger() {
 
 namespace opencl {
 
-const static std::string DEFAULT_MACROS_STR(
+const static string DEFAULT_MACROS_STR(
     "\n\
                                            #ifdef USE_DOUBLE\n\
                                            #pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
@@ -82,36 +91,32 @@ const static std::string DEFAULT_MACROS_STR(
                                            #endif\n                     \
                                            ");
 
-cl::Program *buildProgram(const std::vector<std::string> &kernelSources,
-                          const std::vector<std::string> &compileOpts) {
-    using std::begin;
-    using std::end;
-
-    cl::Program *retVal = nullptr;
+Program buildProgram(const vector<string> &kernelSources,
+                     const vector<string> &compileOpts) {
+    Program retVal;
     try {
-        static const std::string defaults =
-            std::string(" -D dim_t=") +
-            std::string(dtype_traits<dim_t>::getName());
+        static const string defaults =
+            string(" -D dim_t=") + string(dtype_traits<dim_t>::getName());
 
         auto device = getDevice();
 
-        const std::string cl_std =
-            std::string(" -cl-std=CL") +
+        const string cl_std =
+            string(" -cl-std=CL") +
             device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
 
-        cl::Program::Sources sources;
+        Program::Sources sources;
         sources.emplace_back(DEFAULT_MACROS_STR);
         sources.emplace_back(KParam_hpp, KParam_hpp_len);
         sources.insert(end(sources), begin(kernelSources), end(kernelSources));
 
-        retVal = new cl::Program(getContext(), sources);
+        retVal = Program(getContext(), sources);
 
         ostringstream options;
         for (auto &opt : compileOpts) { options << opt; }
 
-        retVal->build({device}, (cl_std + defaults + options.str()).c_str());
-    } catch (...) {
-        if (retVal) { SHOW_BUILD_INFO(retVal); }
+        retVal.build({device}, (cl_std + defaults + options.str()).c_str());
+    } catch (Error &err) {
+        if (err.err() == CL_BUILD_ERROR) { SHOW_BUILD_INFO(retVal); }
         throw;
     }
     return retVal;
@@ -124,14 +129,11 @@ namespace common {
 Module compileModule(const string &moduleKey, const vector<string> &sources,
                      const vector<string> &options,
                      const vector<string> &kInstances, const bool isJIT) {
-    using opencl::getActiveDeviceId;
-    using opencl::getDevice;
-
     UNUSED(kInstances);
     UNUSED(isJIT);
 
     auto compileBegin = high_resolution_clock::now();
-    auto program      = detail::buildProgram(sources, options);
+    auto program      = opencl::buildProgram(sources, options);
     auto compileEnd   = high_resolution_clock::now();
 
     AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", moduleKey,
@@ -147,13 +149,13 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
     UNUSED(device);
     UNUSED(moduleKey);
     UNUSED(isJIT);
-    return {nullptr};
+    return {};
 }
 
 Kernel getKernel(const Module &mod, const string &nameExpr,
                  const bool sourceWasJIT) {
     UNUSED(sourceWasJIT);
-    return {mod.get(), new cl::Kernel(*mod.get(), nameExpr.c_str())};
+    return {&mod.get(), cl::Kernel(mod.get(), nameExpr.c_str())};
 }
 
 }  // namespace common
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index ac28c3f50f..b49521cffd 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -137,10 +137,10 @@ string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
     return kerStream.str();
 }
 
-cl::Kernel *getKernel(const vector<Node *> &output_nodes,
-                      const vector<int> &output_ids,
-                      const vector<Node *> &full_nodes,
-                      const vector<Node_ids> &full_ids, const bool is_linear) {
+cl::Kernel getKernel(const vector<Node *> &output_nodes,
+                     const vector<int> &output_ids,
+                     const vector<Node *> &full_nodes,
+                     const vector<Node_ids> &full_ids, const bool is_linear) {
     const string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
     const string moduleKey = std::to_string(deterministicHash(funcName));
@@ -150,7 +150,7 @@ cl::Kernel *getKernel(const vector<Node *> &output_nodes,
     // with a way to save jit kernels to disk only once
     auto entry = common::findModule(getActiveDeviceId(), moduleKey);
 
-    if (entry.get() == nullptr) {
+    if (!entry) {
         static const string jit(jit_cl, jit_cl_len);
 
         string jitKer = getKernelString(funcName, full_nodes, full_ids,
@@ -252,25 +252,25 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
     for (const auto &node : full_nodes) {
         nargs = node->setArgs(nargs, is_linear,
                               [&](int id, const void *ptr, size_t arg_size) {
-                                  ker->setArg(id, arg_size, ptr);
+                                  ker.setArg(id, arg_size, ptr);
                               });
     }
 
     // Set output parameters
-    for (auto output : outputs) {
-        ker->setArg(nargs, *(output.data));
+    for (auto &output : outputs) {
+        ker.setArg(nargs, *(output.data));
         ++nargs;
     }
 
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
-    ker->setArg(nargs + 0, out_info);
-    ker->setArg(nargs + 1, groups_0);
-    ker->setArg(nargs + 2, groups_1);
-    ker->setArg(nargs + 3, num_odims);
+    ker.setArg(nargs + 0, out_info);
+    ker.setArg(nargs + 1, groups_0);
+    ker.setArg(nargs + 2, groups_1);
+    ker.setArg(nargs + 3, num_odims);
 
-    getQueue().enqueueNDRangeKernel(*ker, NullRange, global, local);
+    getQueue().enqueueNDRangeKernel(ker, NullRange, global, local);
 
     // Reset the thread local vectors
     nodes.clear();

From 2429dd65ae240fa12fa3c0f7d64fbdb1bee443a5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 15:24:45 -0400
Subject: [PATCH 1980/2677] Fix mismatch new/delete calls in clfft

---
 CMakeModules/LSANSuppression.txt | 1 +
 src/backend/opencl/Array.cpp     | 4 +---
 src/backend/opencl/clfft.cpp     | 3 +--
 test/meanvar.cpp                 | 3 ++-
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/CMakeModules/LSANSuppression.txt b/CMakeModules/LSANSuppression.txt
index 0026fbc27d..43ac584d10 100644
--- a/CMakeModules/LSANSuppression.txt
+++ b/CMakeModules/LSANSuppression.txt
@@ -2,6 +2,7 @@
 leak:libnvidia-ptxjitcompile
 leak:tbb::internal::task_stream
 leak:libnvidia-opencl.so
+leak:FFTRepo::FFTRepoKey::privatizeData
 
 # Allocated by Intel's OpenMP implementation during inverse_dense_cpu
 # This is not something we can control in ArrayFire
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index f7bd205aa2..c47fc56ee0 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -246,9 +246,7 @@ void evalMultiple(vector<Array<T> *> arrays) {
              info.strides()[3]},
             0};
 
-        Param res = {array->data.get(), kInfo};
-
-        outputs.push_back(res);
+        outputs.emplace_back(array->data.get(), kInfo);
         output_arrays.push_back(array);
         nodes.push_back(array->node.get());
     }
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index 1ae27c85cf..21ef1f37d7 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -169,8 +169,7 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
         // thrown. This is related to
         // https://github.com/arrayfire/arrayfire/pull/1899
         CLFFT_CHECK(clfftDestroyPlan(p));
-        // NOLINTNEXTLINE(hicpp-no-malloc)
-        free(p);
+        delete p;
 #endif
     });
     // push the plan into plan cache
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index f7519aed47..059f694842 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -26,6 +26,7 @@ using std::move;
 using std::string;
 using std::vector;
 
+af_err init_err = af_init();
 template<typename T>
 struct elseType {
     typedef typename cond_type<is_same_type<T, uintl>::value ||
@@ -91,7 +92,7 @@ struct meanvar_test {
 
     ~meanvar_test() {
 #ifndef _WIN32
-        af_release_array(in_);
+        if (in_) af_release_array(in_);
         if (weights_) {
             af_release_array(weights_);
             weights_ = 0;

From 4a230024aacf21607f16ce1db1b82a5c339caf33 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 15:46:29 -0400
Subject: [PATCH 1981/2677] Fix leak in susan

---
 src/backend/opencl/kernel/susan.hpp |  6 ++---
 src/backend/opencl/susan.cpp        | 34 ++++++++++++-----------------
 2 files changed, 17 insertions(+), 23 deletions(-)

diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 09f1c1c6d5..f22b8607e1 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -15,6 +15,7 @@
 #include <debug_opencl.hpp>
 #include <kernel/config.hpp>
 #include <kernel_headers/susan.hpp>
+#include <memory.hpp>
 #include <traits.hpp>
 #include <af/defines.h>
 
@@ -81,8 +82,8 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
     auto nonMax =
         common::getKernel("non_maximal", {susanSrc()}, targs, compileOpts);
 
-    unsigned corners_found      = 0;
-    cl::Buffer* d_corners_found = bufferAlloc(sizeof(unsigned));
+    unsigned corners_found = 0;
+    auto d_corners_found   = memAlloc<unsigned>(1);
     getQueue().enqueueWriteBuffer(*d_corners_found, CL_FALSE, 0,
                                   sizeof(unsigned), &corners_found);
 
@@ -95,7 +96,6 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
            max_corners);
     getQueue().enqueueReadBuffer(*d_corners_found, CL_TRUE, 0, sizeof(unsigned),
                                  &corners_found);
-    bufferFree(d_corners_found);
     return corners_found;
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/susan.cpp b/src/backend/opencl/susan.cpp
index 6b5cc5e1f3..35f22a953b 100644
--- a/src/backend/opencl/susan.cpp
+++ b/src/backend/opencl/susan.cpp
@@ -15,6 +15,7 @@
 #include <cmath>
 
 using af::features;
+using std::vector;
 
 namespace opencl {
 
@@ -26,38 +27,31 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     dim4 idims = in.dims();
 
     const unsigned corner_lim = in.elements() * feature_ratio;
-    cl::Buffer *x_corners     = bufferAlloc(corner_lim * sizeof(float));
-    cl::Buffer *y_corners     = bufferAlloc(corner_lim * sizeof(float));
-    cl::Buffer *resp_corners  = bufferAlloc(corner_lim * sizeof(float));
+    Array<float> x_corners    = createEmptyArray<float>({corner_lim});
+    Array<float> y_corners    = createEmptyArray<float>({corner_lim});
+    Array<float> resp_corners = createEmptyArray<float>({corner_lim});
 
-    cl::Buffer *resp = bufferAlloc(in.elements() * sizeof(float));
+    auto resp = memAlloc<float>(in.elements());
 
-    kernel::susan<T>(resp, in.get(), in.getOffset(), idims[0], idims[1],
+    kernel::susan<T>(resp.get(), in.get(), in.getOffset(), idims[0], idims[1],
                      diff_thr, geom_thr, edge, radius);
 
-    unsigned corners_found =
-        kernel::nonMaximal<T>(x_corners, y_corners, resp_corners, idims[0],
-                              idims[1], resp, edge, corner_lim);
-    bufferFree(resp);
+    unsigned corners_found = kernel::nonMaximal<T>(
+        x_corners.get(), y_corners.get(), resp_corners.get(), idims[0],
+        idims[1], resp.get(), edge, corner_lim);
 
     const unsigned corners_out = std::min(corners_found, corner_lim);
     if (corners_out == 0) {
-        bufferFree(x_corners);
-        bufferFree(y_corners);
-        bufferFree(resp_corners);
         x_out    = createEmptyArray<float>(dim4());
         y_out    = createEmptyArray<float>(dim4());
         resp_out = createEmptyArray<float>(dim4());
-        return 0;
     } else {
-        x_out    = createDeviceDataArray<float>(dim4(corners_out),
-                                             (void *)((*x_corners)()));
-        y_out    = createDeviceDataArray<float>(dim4(corners_out),
-                                             (void *)((*y_corners)()));
-        resp_out = createDeviceDataArray<float>(dim4(corners_out),
-                                                (void *)((*resp_corners)()));
-        return corners_out;
+        vector<af_seq> idx{{0., static_cast<double>(corners_out - 1.0), 1.}};
+        x_out    = createSubArray(x_corners, idx);
+        y_out    = createSubArray(y_corners, idx);
+        resp_out = createSubArray(resp_corners, idx);
     }
+    return corners_out;
 }
 
 #define INSTANTIATE(T)                                                        \

From 7669aedbfe04d315567a2cf5a155158f45f24d4e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 15:47:21 -0400
Subject: [PATCH 1982/2677] Fix leak in sparseArith

---
 src/backend/opencl/kernel/sparse_arith.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 8e42e0b96f..78331ed587 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -156,8 +156,8 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
 
-    nnzC            = 0;
-    cl::Buffer *out = bufferAlloc(sizeof(unsigned));
+    nnzC     = 0;
+    auto out = memAlloc<unsigned>(1);
     getQueue().enqueueWriteBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
 
     calcNNZ(cl::EnqueueArgs(getQueue(), global, local), *out, *outRowIdx.data,

From 0d6f630236a241d1aa46d542bcf63f0fa0425579 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 15:47:53 -0400
Subject: [PATCH 1983/2677] Fix leak in OpenCL ireduce

---
 src/backend/opencl/kernel/ireduce.hpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 3fb8a1633b..39e6497d4e 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -183,18 +183,20 @@ void ireduceFirst(Param out, cl::Buffer *oidx, Param in, Param rlen) {
 
 template<typename T, af_op_t op>
 void ireduce(Param out, cl::Buffer *oidx, Param in, int dim, Param rlen) {
+    cl::Buffer buf;
     if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
             rlen.info.dims[3] ==
         0) {
         // empty opencl::Param() does not have nullptr by default
         // set to nullptr explicitly here for consequent kernel calls
         // through cl::Buffer's constructor
-        rlen.data = new cl::Buffer();
+        rlen.data = &buf;
+    }
+    if (dim == 0) {
+        ireduceFirst<T, op>(out, oidx, in, rlen);
+    } else {
+        ireduceDim<T, op>(out, oidx, in, dim, rlen);
     }
-    if (dim == 0)
-        return ireduceFirst<T, op>(out, oidx, in, rlen);
-    else
-        return ireduceDim<T, op>(out, oidx, in, dim, rlen);
 }
 
 #if defined(__GNUC__) || defined(__GNUG__)

From bcdf0bae45bb14d2ab69a4a4f9ee0e7b19f1d252 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 30 May 2020 15:48:52 -0400
Subject: [PATCH 1984/2677] Fix leak in OpenCL Indexing

---
 src/backend/opencl/index.cpp | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 5433401387..a5d00b8373 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -45,6 +45,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
 
     cl::Buffer* bPtrs[4];
 
+    auto buf = cl::Buffer();
     std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
     // look through indexs to read af_array indexs
     for (dim_t x = 0; x < 4; ++x) {
@@ -56,7 +57,7 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
             oDims[x] = idxArrs[x].elements();
         } else {
             // alloc an 1-element buffer to avoid OpenCL from failing
-            bPtrs[x] = bufferAlloc(sizeof(uint));
+            bPtrs[x] = &buf;
         }
     }
 
@@ -65,10 +66,6 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
 
     kernel::index<T>(out, in, p, bPtrs);
 
-    for (dim_t x = 0; x < 4; ++x) {
-        if (p.isSeq[x]) { bufferFree(bPtrs[x]); }
-    }
-
     return out;
 }
 

From 90e6553b4146cf11bf198ad695c7bba78e72978e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 1 Jun 2020 14:46:12 -0400
Subject: [PATCH 1985/2677] Update OpenCL interop page so they discuss deleting
 of memory

* Created snippets for examples in the document
---
 docs/pages/interop_opencl.md                  | 122 +-----------------
 include/af/array.h                            |   2 +
 test/CMakeLists.txt                           |  19 ++-
 test/interop_opencl_custom_kernel_snippet.cpp |  96 ++++++++++++++
 ...nterop_opencl_external_context_snippet.cpp | 104 +++++++++++++++
 5 files changed, 222 insertions(+), 121 deletions(-)
 create mode 100644 test/interop_opencl_custom_kernel_snippet.cpp
 create mode 100644 test/interop_opencl_external_context_snippet.cpp

diff --git a/docs/pages/interop_opencl.md b/docs/pages/interop_opencl.md
index 9b65c8eadf..6c1a7122c6 100644
--- a/docs/pages/interop_opencl.md
+++ b/docs/pages/interop_opencl.md
@@ -64,68 +64,7 @@ synchronization operations.
 
 This process is best illustrated with a fully worked example:
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-#include <arrayfire.h>
-// 1. Add the af/opencl.h include to your project
-#include <af/opencl.h>
-
-int main() {
-    size_t length = 10;
-
-    // Create ArrayFire array objects:
-    af::array A = af::randu(length, f32);
-    af::array B = af::constant(0, length, f32);
-
-    // ... additional ArrayFire operations here
-
-    // 2. Obtain the device, context, and queue used by ArrayFire
-    static cl_context af_context = afcl::getContext();
-    static cl_device_id af_device_id = afcl::getDeviceId();
-    static cl_command_queue af_queue = afcl::getQueue();
-
-    // 3. Obtain cl_mem references to af::array objects
-    cl_mem * d_A = A.device<cl_mem>();
-    cl_mem * d_B = B.device<cl_mem>();
-
-    // 4. Load, build, and use your kernels.
-    //    For the sake of readability, we have omitted error checking.
-    int status = CL_SUCCESS;
-
-    // A simple copy kernel, uses C++11 syntax for multi-line strings.
-    const char * kernel_name = "copy_kernel";
-    const char * source = R"(
-        void __kernel
-        copy_kernel(__global float * gA, __global float * gB)
-        {
-            int id = get_global_id(0);
-            gB[id] = gA[id];
-        }
-    )";
-
-    // Create the program, build the executable, and extract the entry point
-    // for the kernel.
-    cl_program program = clCreateProgramWithSource(af_context, 1, &source, NULL, &status);
-	status = clBuildProgram(program, 1, &af_device_id, NULL, NULL, NULL);
-    cl_kernel kernel = clCreateKernel(program, kernel_name, &status);
-
-    // Set arguments and launch your kernels
-    clSetKernelArg(kernel, 0, sizeof(cl_mem), d_A);
-    clSetKernelArg(kernel, 1, sizeof(cl_mem), d_B);
-	clEnqueueNDRangeKernel(af_queue, kernel, 1, NULL, &length, NULL, 0, NULL, NULL);
-
-    // 5. Return control of af::array memory to ArrayFire
-    A.unlock();
-    B.unlock();
-
-    // ... resume ArrayFire operations
-
-    // Because the device pointers, d_x and d_y, were returned to ArrayFire's
-    // control by the unlock function, there is no need to free them using
-    // clReleaseMemObject()
-
-    return 0;
-}
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+\snippet test/interop_opencl_custom_kernel_snippet.cpp interop_opencl_custom_kernel_snippet
 
 If your kernels needs to operate in their own OpenCL queue, the process is
 essentially identical, except you need to instruct ArrayFire to complete
@@ -187,64 +126,9 @@ so, please be cautious not to call `clReleaseMemObj` on a `cl_mem`  when
 ArrayFire might be using it!
 
 The eight steps above are best illustrated using a fully-worked example. Below we
-use the OpenCL 2.0 C++ API and omit error checking to keep the code readable. 
-
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-#include <CL/cl2.hpp>
-
-// 1. Add arrayfire.h and af/opencl.h to your application
-#include "arrayfire.h"
-#include "af/opencl.h"
-
-#include <cstdio>
-#include <vector>
-
-int main() {
-
-  // Set up the OpenCL context, device, and queues
-  cl::Context context(CL_DEVICE_TYPE_ALL);
-  vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
-  cl::Device device = devices[0];
-  cl::CommandQueue queue(context, device);
-
-  // Create a buffer of size 10 filled with ones, copy it to the device
-  int length = 10;
-  vector<float> h_A(length, 1);
-  cl::Buffer cl_A(context, CL_MEM_READ_WRITE, length * sizeof(float), h_A.data());
+use the OpenCL C++ API and omit error checking to keep the code readable.
 
-  // 2. Instruct OpenCL to complete its operations using clFinish (or similar)
-  queue.finish();
-
-  // 3. Instruct ArrayFire to use the user-created context
-  //    First, create a device from the current OpenCL device + context + queue
-  afcl::addDevice(device(), context(), queue());
-  //    Next switch ArrayFire to the device using the device and context as 
-  //    identifiers:
-  afcl::setDevice(device(), context());
-
-  // 4. Create ArrayFire arrays from OpenCL memory objects
-  af::array af_A = afcl::array(length, cl_A(), f32, true);
-
-  // 5. Perform ArrayFire operations on the Arrays
-  af_A = af_A + af::randu(length);
-    
-  // NOTE: ArrayFire does not perform the above transaction using in-place memory, 
-  // thus the underlying OpenCL buffers containing the memory containing memory to 
-  // probably have changed
-
-  // 6. Instruct ArrayFire to finish operations using af::sync
-  af::sync();
-
-  // 7. Obtain cl_mem references for important memory
-  cl_A = *af_A.device<cl_mem>();
-
-  // 8. Continue your OpenCL application
-
-  // ... 
-  
-  return 0;
-}
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+\snippet test/interop_opencl_external_context_snippet.cpp interop_opencl_external_context_snippet
 
 # Using multiple devices
 
diff --git a/include/af/array.h b/include/af/array.h
index 1b2325f7ac..4f2a3965b8 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -725,6 +725,8 @@ namespace af
 
            The device memory returned by this function is not freed until unlock() is called.
 
+           /note When using the OpenCL backend and using the cl_mem template argument, the
+                 delete function should be called on the pointer returned by this function.
         */
         template<typename T> T* device() const;
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 890103e442..77918ca08e 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -115,7 +115,7 @@ target_compile_definitions(arrayfire_test
 # 'BACKENDS'    Backends to target for this test. If not set then the test will
 #               compiled againat all backends
 function(make_test)
-  set(options CXX11 SERIAL USE_MMIO)
+  set(options CXX11 SERIAL USE_MMIO NO_ARRAYFIRE_TEST)
   set(single_args SRC)
   set(multi_args LIBRARIES DEFINITIONS BACKENDS)
   cmake_parse_arguments(mt_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
@@ -127,7 +127,12 @@ function(make_test)
       continue()
     endif()
     set(target "test_${src_name}_${backend}")
-    add_executable(${target} ${mt_args_SRC} $<TARGET_OBJECTS:arrayfire_test>)
+
+    if (${mt_args_NO_ARRAYFIRE_TEST})
+      add_executable(${target} ${mt_args_SRC})
+    else()
+      add_executable(${target} ${mt_args_SRC} $<TARGET_OBJECTS:arrayfire_test>)
+    endif()
     target_include_directories(${target}
       PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
@@ -285,6 +290,16 @@ if(OpenCL_FOUND)
             LIBRARIES OpenCL::OpenCL
             BACKENDS "opencl"
             CXX11)
+  make_test(SRC interop_opencl_custom_kernel_snippet.cpp
+            LIBRARIES OpenCL::OpenCL
+            BACKENDS "opencl"
+            NO_ARRAYFIRE_TEST
+            CXX11)
+  make_test(SRC interop_opencl_external_context_snippet.cpp
+            LIBRARIES OpenCL::OpenCL
+            BACKENDS "opencl"
+            NO_ARRAYFIRE_TEST
+            CXX11)
 endif()
 
 if(CUDA_FOUND)
diff --git a/test/interop_opencl_custom_kernel_snippet.cpp b/test/interop_opencl_custom_kernel_snippet.cpp
new file mode 100644
index 0000000000..c1864d2e79
--- /dev/null
+++ b/test/interop_opencl_custom_kernel_snippet.cpp
@@ -0,0 +1,96 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+// clang-format off
+// ![interop_opencl_custom_kernel_snippet]
+#include <arrayfire.h>
+// 1. Add the af/opencl.h include to your project
+#include <af/opencl.h>
+
+#include <cassert>
+
+#define OCL_CHECK(call)                                                     \
+    if (cl_int err = (call) != CL_SUCCESS) {                                \
+        fprintf(stderr, __FILE__ "(%d):Returned error code %d\n", __LINE__, \
+                err);                                                       \
+    }
+
+int main() {
+    size_t length = 10;
+
+    // Create ArrayFire array objects:
+    af::array A = af::randu(length, f32);
+    af::array B = af::constant(0, length, f32);
+
+    // ... additional ArrayFire operations here
+
+    // 2. Obtain the device, context, and queue used by ArrayFire
+    static cl_context af_context     = afcl::getContext();
+    static cl_device_id af_device_id = afcl::getDeviceId();
+    static cl_command_queue af_queue = afcl::getQueue();
+
+    // 3. Obtain cl_mem references to af::array objects
+    cl_mem* d_A = A.device<cl_mem>();
+    cl_mem* d_B = B.device<cl_mem>();
+
+    // 4. Load, build, and use your kernels.
+    //    For the sake of readability, we have omitted error checking.
+    int status = CL_SUCCESS;
+
+    // A simple copy kernel, uses C++11 syntax for multi-line strings.
+    const char* kernel_name = "copy_kernel";
+    const char* source      = R"(
+        void __kernel
+        copy_kernel(__global float* gA, __global float* gB) {
+        int id = get_global_id(0);
+        gB[id] = gA[id];
+    }
+    )";
+
+    // Create the program, build the executable, and extract the entry point
+    // for the kernel.
+    cl_program program = clCreateProgramWithSource(af_context, 1, &source, NULL, &status);
+    OCL_CHECK(status);
+    OCL_CHECK(clBuildProgram(program, 1, &af_device_id, NULL, NULL, NULL));
+    cl_kernel kernel = clCreateKernel(program, kernel_name, &status);
+    OCL_CHECK(status);
+
+    // Set arguments and launch your kernels
+    OCL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), d_A));
+    OCL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), d_B));
+    OCL_CHECK(clEnqueueNDRangeKernel(af_queue, kernel, 1, NULL, &length, NULL,
+                                     0, NULL, NULL));
+
+    // 5. Return control of af::array memory to ArrayFire
+    A.unlock();
+    B.unlock();
+
+    /// A and B should not be the same because of the copy_kernel user code
+    assert(af::allTrue<bool>(A == B));
+
+    // Delete the pointers returned by the device function. This does NOT
+    // delete the cl_mem memory and only deletes the pointers
+    delete d_A;
+    delete d_B;
+
+    // ... resume ArrayFire operations
+
+    // Because the device pointers, d_x and d_y, were returned to ArrayFire's
+    // control by the unlock function, there is no need to free them using
+    // clReleaseMemObject()
+
+    // Free the kernel and program objects because they are created in user
+    // code
+    OCL_CHECK(clReleaseKernel(kernel));
+    OCL_CHECK(clReleaseProgram(program));
+
+    return 0;
+}
+// ![interop_opencl_custom_kernel_snippet]
+// clang-format on
diff --git a/test/interop_opencl_external_context_snippet.cpp b/test/interop_opencl_external_context_snippet.cpp
new file mode 100644
index 0000000000..a1259580e6
--- /dev/null
+++ b/test/interop_opencl_external_context_snippet.cpp
@@ -0,0 +1,104 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#pragma GCC diagnostic ignored "-Wignored-qualifiers"
+#pragma GCC diagnostic ignored "-Wignored-attributes"
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#if __GNUC__ >= 8
+#pragma GCC diagnostic ignored "-Wcatch-value="
+#endif
+// ![interop_opencl_external_context_snippet]
+#include <arrayfire.h>
+// 1. Add the af/opencl.h include to your project
+#include <af/opencl.h>
+
+#include <cassert>
+
+// definitions required by cl2.hpp
+#define CL_HPP_ENABLE_EXCEPTIONS
+#define CL_HPP_TARGET_OPENCL_VERSION 120
+#define CL_HPP_MINIMUM_OPENCL_VERSION 120
+#include <CL/cl2.hpp>
+
+// 1. Add arrayfire.h and af/opencl.h to your application
+#include "af/opencl.h"
+#include "arrayfire.h"
+
+#include <cstdio>
+#include <vector>
+
+using std::vector;
+
+int main() {
+    // 1. Set up the OpenCL context, device, and queues
+    cl::Context context;
+    try {
+        context = cl::Context(CL_DEVICE_TYPE_ALL);
+    } catch (const cl::Error& err) {
+        fprintf(stderr, "Exiting creating context");
+        return EXIT_FAILURE;
+    }
+    vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
+    if (devices.empty()) {
+        fprintf(stderr, "Exiting. No devices found");
+        return EXIT_SUCCESS;
+    }
+    cl::Device device = devices[0];
+    cl::CommandQueue queue(context, device);
+
+    // Create a buffer of size 10 filled with ones, copy it to the device
+    int length = 10;
+    vector<float> h_A(length, 1);
+    cl::Buffer cl_A(context, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR,
+                    length * sizeof(float), h_A.data());
+
+    // 2. Instruct OpenCL to complete its operations using clFinish (or similar)
+    queue.finish();
+
+    // 3. Instruct ArrayFire to use the user-created context
+    //    First, create a device from the current OpenCL device + context +
+    //    queue
+    afcl::addDevice(device(), context(), queue());
+    //    Next switch ArrayFire to the device using the device and context as
+    //    identifiers:
+    afcl::setDevice(device(), context());
+
+    // 4. Create ArrayFire arrays from OpenCL memory objects
+    af::array af_A = afcl::array(length, cl_A(), f32, true);
+    clRetainMemObject(cl_A());
+
+    // 5. Perform ArrayFire operations on the Arrays
+    af_A = af_A + af::randu(length);
+
+    // NOTE: ArrayFire does not perform the above transaction using in-place
+    // memory, thus the underlying OpenCL buffers containing the memory
+    // containing memory to probably have changed
+
+    // 6. Instruct ArrayFire to finish operations using af::sync
+    af::sync();
+
+    // 7. Obtain cl_mem references for important memory
+    cl_mem* af_mem = af_A.device<cl_mem>();
+    cl_A           = cl::Buffer(*af_mem, /*retain*/ true);
+
+    /// Delete the af_mem pointer. The buffer returned by the device pointer is
+    /// still valid
+    delete af_mem;
+
+    // 8. Continue your OpenCL application
+
+    // ...
+    return EXIT_SUCCESS;
+}
+// ![interop_opencl_external_context_snippet]
+
+#pragma GCC diagnostic pop

From 1ce244cbf3520869dce39242fff765a49e09827a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 4 Jun 2020 15:26:36 +0530
Subject: [PATCH 1986/2677] Add missing ndims arg check in indexing fns

---
 src/api/c/assign.cpp | 7 ++++---
 src/api/c/index.cpp  | 4 +++-
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 2e357b6ab0..edd769297a 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -129,9 +129,9 @@ static if_real<T> assign(Array<T>& out, const vector<af_seq> iv,
 af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
                      const af_seq* index, const af_array rhs) {
     try {
-        ARG_ASSERT(0, (lhs != 0));
-        ARG_ASSERT(1, (ndims > 0));
-        ARG_ASSERT(3, (rhs != 0));
+        ARG_ASSERT(2, (ndims > 0 && ndims <= AF_MAX_DIMS));
+        ARG_ASSERT(1, (lhs != 0));
+        ARG_ASSERT(4, (rhs != 0));
 
         const ArrayInfo& lInfo = getInfo(lhs);
 
@@ -223,6 +223,7 @@ inline void genAssign(af_array& out, const af_index_t* indexs,
 af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
                      const af_index_t* indexs, const af_array rhs_) {
     try {
+        ARG_ASSERT(2, (ndims > 0 && ndims <= AF_MAX_DIMS));
         ARG_ASSERT(3, (indexs != NULL));
 
         int track = 0;
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 292550a66a..c8e8c6aa05 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -79,6 +79,8 @@ static af_array indexBySeqs(const af_array& src,
 af_err af_index(af_array* result, const af_array in, const unsigned ndims,
                 const af_seq* indices) {
     try {
+        ARG_ASSERT(2, (ndims > 0 && ndims <= AF_MAX_DIMS));
+
         const ArrayInfo& inInfo = getInfo(in);
         af_dtype type           = inInfo.getType();
         const dim4& iDims       = inInfo.dims();
@@ -200,7 +202,7 @@ static inline af_array genIndex(const af_array& in, const af_index_t idxrs[]) {
 af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
                     const af_index_t* indexs) {
     try {
-        ARG_ASSERT(2, (ndims > 0));
+        ARG_ASSERT(2, (ndims > 0 && ndims <= AF_MAX_DIMS));
         ARG_ASSERT(3, (indexs != NULL));
 
         const ArrayInfo& iInfo = getInfo(in);

From 4fa20564841a64750463b95c0ac4189dc34d8da9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Jun 2020 01:13:23 -0400
Subject: [PATCH 1987/2677] Fix libs for the CUDA 9 Toolkit. Remove rdc and
 dlink flags

* The rdc and dlink flags are not required because they are added
  by CMake for separable compilation and static linking respectively
* Add guards around libs that are not included in the CUDA 9.0
  Toolkit
* Only link with OpenMP when linking with cuSOLVER dynamically
* Fix error message when CUDNN is not found
---
 src/backend/cuda/CMakeLists.txt | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a1c6b7a0b0..f24ee82d87 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -7,7 +7,7 @@
 
 dependency_check(CUDA_FOUND "CUDA not found.")
 if(AF_WITH_CUDNN)
-  dependency_check(cuDNN_FOUND "CUDA not found.")
+  dependency_check(cuDNN_FOUND "CUDNN not found.")
 endif()
 
 include(AFcuda_helpers)
@@ -34,7 +34,7 @@ endif()
 
 # Find if CUDA Toolkit is at least 10.0 to use static
 # lapack library. Otherwise, we have to use regular shared library
-if(UNIX AND CUDA_VERSION_MAJOR VERSION_GREATER 10 OR CUDA_VERSION_MAJOR VERSION_EQUAL 10)
+if(UNIX AND (CUDA_VERSION_MAJOR VERSION_GREATER 10 OR CUDA_VERSION_MAJOR VERSION_EQUAL 10))
   set(use_static_cuda_lapack ON)
 else()
   set(use_static_cuda_lapack OFF)
@@ -52,7 +52,6 @@ if(UNIX)
   # FIXME When NVCC resolves this particular issue.
   # NVCC doesn't like -l<full_path_static_lib>, hence we cannot
   # use ${CMAKE_*_LIBRARY} variables in the following flags.
-  set(af_cuda_static_flags "-rdc=true;-dlink")
   set(af_cuda_static_flags "${af_cuda_static_flags};-lculibos")
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcublas_static")
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcublasLt_static")
@@ -71,7 +70,7 @@ if(UNIX)
 
     set(af_cuda_static_flags "${af_cuda_static_flags};-lcusolver_static")
   else()
-    set(cusolver_lib "${CUDA_cusolver_LIBRARY}")
+    set(cusolver_lib "${CUDA_cusolver_LIBRARY}" OpenMP::OpenMP_CXX)
   endif()
 endif()
 
@@ -89,12 +88,6 @@ message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targe
 
 set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
 
-if(${CUDA_SEPARABLE_COMPILATION})
-  # Enable relocatable device code generation for separable
-  # compilation which is in turn required for any device linking done.
-  set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-rdc=true)
-endif()
-
 mark_as_advanced(
     CUDA_LIBRARIES_PATH
     CUDA_architecture_build_targets)
@@ -301,13 +294,19 @@ if(UNIX)
       -Wl,--start-group
       ${CUDA_culibos_LIBRARY} #also a static libary
       ${CUDA_cublas_static_LIBRARY}
-      ${CUDA_cublasLt_static_LIBRARY}
       ${CUDA_cufft_static_LIBRARY}
-      ${CUDA_lapack_static_LIBRARY}
       ${CUDA_cusparse_static_LIBRARY}
       ${cusolver_static_lib}
       -Wl,--end-group
   )
+
+  if(CUDA_VERSION VERSION_GREATER 9.5)
+    target_link_libraries(af_cuda_static_cuda_library
+      PRIVATE
+        ${CUDA_cublasLt_static_LIBRARY}
+        ${CUDA_lapack_static_LIBRARY})
+  endif()
+
   set(CUDA_SEPARABLE_COMPILATION ${pior_val_CUDA_SEPARABLE_COMPILATION})
 else()
   target_link_libraries(af_cuda_static_cuda_library

From 38b0e4626abf810d730aaefff5ad6e2035c80dcc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Jun 2020 01:16:28 -0400
Subject: [PATCH 1988/2677] Fix several error messages when compiling against
 CUDA 9.0

* Address casts from double to __half which are missing in 9.0
* Thrust return_temporary_buffer function can accept void* pointers
  in older versions of Thrust. Use raw_pointer_cast to pass the
  pointer to memFree
* cublasGemmEx doesn't exist in CUDA 9.0. Add ifdefs to guard
  against older builds
* __float2half is not a host function so it needs to be removed
  from mean
* Add template instantiation for memFree to accept void* pointers
---
 src/api/cpp/common.hpp                     | 16 +++++++++++++++-
 src/backend/cuda/ThrustArrayFirePolicy.hpp |  4 ++--
 src/backend/cuda/blas.cu                   | 11 ++++++++++-
 src/backend/cuda/kernel/mean.hpp           |  2 +-
 src/backend/cuda/memory.cpp                |  2 ++
 5 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/src/api/cpp/common.hpp b/src/api/cpp/common.hpp
index 39dec065e4..e1f161bdde 100644
--- a/src/api/cpp/common.hpp
+++ b/src/api/cpp/common.hpp
@@ -15,6 +15,10 @@
 #include "half.hpp"
 #pragma GCC diagnostic pop
 
+#ifdef AF_CUDA
+#include <cuda_fp16.h>
+#endif
+
 #include <cstring>
 
 namespace af {
@@ -36,10 +40,20 @@ static inline dim_t getFNSD(const int dim, af::dim4 dims) {
 namespace {
 // casts from one type to another. Needed for af_half conversions specialization
 template<typename To, typename T>
-To cast(T in) {
+inline To cast(T in) {
     return static_cast<To>(in);
 }
 
+#if defined(AF_CUDA) && CUDA_VERSION < 10000
+template<>
+inline __half cast<__half, double>(double in) {
+    __half_raw out;
+    half_float::half h(in);
+    memcpy(&out, &h, sizeof(__half_raw));
+    return out;
+}
+#endif
+
 template<>
 [[gnu::unused]] af_half cast<af_half, double>(double in) {
     half_float::half tmp = static_cast<half_float::half>(in);
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
index cd9c4e76e5..51b5faa904 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.hpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -34,8 +34,8 @@ get_temporary_buffer(ThrustArrayFirePolicy, std::ptrdiff_t n) {
 }
 
 template<typename Pointer>
-void return_temporary_buffer(ThrustArrayFirePolicy, Pointer p) {
-    memFree(p.get());
+inline void return_temporary_buffer(ThrustArrayFirePolicy, Pointer p) {
+    memFree(thrust::raw_pointer_cast(p));
 }
 
 }  // namespace cuda
diff --git a/src/backend/cuda/blas.cu b/src/backend/cuda/blas.cu
index 3f6dec1fa8..be6cda902d 100644
--- a/src/backend/cuda/blas.cu
+++ b/src/backend/cuda/blas.cu
@@ -216,7 +216,8 @@ cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
                             const Array<T> &rhs, dim_t rStride, const T *beta,
                             Array<T> &out, dim_t oleading) {
     auto prop = getDeviceProp(getActiveDeviceId());
-    if (prop.major > 3) {
+#if __CUDACC_VER_MAJOR__ >= 10
+    if (prop.major > 3 && __CUDACC_VER_MAJOR__ >= 10) {
         return cublasGemmEx(
             blasHandle(), lOpts, rOpts, M, N, K, alpha, lhs.get(), getType<T>(),
             lStride, rhs.get(), getType<T>(), rStride, beta, out.get(),
@@ -233,11 +234,15 @@ cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
             // type is CUDA_R_32F?
             selectGEMMAlgorithm<T>());
     } else {
+#endif
         using Nt = typename common::kernel_type<T>::native;
         return gemm_func<Nt>()(blasHandle(), lOpts, rOpts, M, N, K, (Nt *)alpha,
                                (Nt *)lhs.get(), lStride, (Nt *)rhs.get(),
                                rStride, (Nt *)beta, (Nt *)out.get(), oleading);
+
+#if __CUDACC_VER_MAJOR__ >= 10
     }
+#endif
 }
 
 template<typename T>
@@ -248,6 +253,7 @@ cublasStatus_t gemmBatchedDispatch(BlasHandle handle, cublasOperation_t lOpts,
                                    const T *beta, T **optrs, int oStrides,
                                    int batchSize) {
     auto prop = getDeviceProp(getActiveDeviceId());
+#if __CUDACC_VER_MAJOR__ >= 10
     if (prop.major > 3) {
         return cublasGemmBatchedEx(
             blasHandle(), lOpts, rOpts, M, N, K, alpha, (const void **)lptrs,
@@ -264,12 +270,15 @@ cublasStatus_t gemmBatchedDispatch(BlasHandle handle, cublasOperation_t lOpts,
             // type is CUDA_R_32F?
             selectGEMMAlgorithm<T>());
     } else {
+#endif
         using Nt = typename common::kernel_type<T>::native;
         return gemmBatched_func<Nt>()(
             blasHandle(), lOpts, rOpts, M, N, K, (const Nt *)alpha,
             (const Nt **)lptrs, lStrides, (const Nt **)rptrs, rStrides,
             (const Nt *)beta, (Nt **)optrs, oStrides, batchSize);
+#if __CUDACC_VER_MAJOR__ >= 10
     }
+#endif
 }
 
 template<typename T>
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index d6beffd43e..c981d59656 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -26,7 +26,7 @@
 
 namespace cuda {
 
-__host__ __device__ auto operator*(float lhs, __half rhs) -> __half {
+__device__ auto operator*(float lhs, __half rhs) -> __half {
     return __float2half(lhs * __half2float(rhs));
 }
 
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 9aa2d0c6c8..a914f9f151 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -131,6 +131,8 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 
+template void memFree(void *ptr);
+
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {

From eb81d6b5b3c9855f8d43c60aeb3798f7d96b29a9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Jun 2020 01:22:02 -0400
Subject: [PATCH 1989/2677] Fix CUSOLVER_CHECK error message

CUSOLVER_CHECK error message printed "CUBLAS Error" instead of
CUSOLVER Error
---
 src/backend/cuda/cusolverDn.hpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index 241c89035f..4ec4f4dea3 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -14,16 +14,16 @@ namespace cuda {
 
 const char* errorString(cusolverStatus_t err);
 
-#define CUSOLVER_CHECK(fn)                                                  \
-    do {                                                                    \
-        cusolverStatus_t _error = fn;                                       \
-        if (_error != CUSOLVER_STATUS_SUCCESS) {                            \
-            char _err_msg[1024];                                            \
-            snprintf(_err_msg, sizeof(_err_msg), "CUBLAS Error (%d): %s\n", \
-                     (int)(_error), cuda::errorString(_error));             \
-                                                                            \
-            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
-        }                                                                   \
+#define CUSOLVER_CHECK(fn)                                                    \
+    do {                                                                      \
+        cusolverStatus_t _error = fn;                                         \
+        if (_error != CUSOLVER_STATUS_SUCCESS) {                              \
+            char _err_msg[1024];                                              \
+            snprintf(_err_msg, sizeof(_err_msg), "CUSOLVER Error (%d): %s\n", \
+                     (int)(_error), cuda::errorString(_error));               \
+                                                                              \
+            AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
+        }                                                                     \
     } while (0)
 
 }  // namespace cuda

From d2df83da7251069b64ce52411e971fdbcc135676 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Jun 2020 01:22:53 -0400
Subject: [PATCH 1990/2677] Fix several warnings with older compilers

---
 src/backend/cuda/jit/kernel_generators.hpp |  9 ++---
 src/backend/cuda/types.hpp                 | 40 +++++++++++-----------
 2 files changed, 25 insertions(+), 24 deletions(-)

diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index 3414e439b9..d048c0c7d0 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -71,8 +71,9 @@ void generateBufferRead(std::stringstream& kerStream, int id,
               << "];\n";
 }
 
-void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
-                              bool is_linear, const std::string& type_str) {
+inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& type_str) {
     UNUSED(is_linear);
     std::string idx_str   = std::string("idx") + std::to_string(id);
     std::string info_str  = std::string("in") + std::to_string(id);
@@ -99,8 +100,8 @@ void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
     kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
 }
 
-void generateShiftNodeRead(std::stringstream& kerStream, int id,
-                           const std::string& type_str) {
+inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
+                                  const std::string& type_str) {
     kerStream << type_str << " val" << id << " = in" << id << "_ptr[idx" << id
               << "];\n";
 }
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 5cab8d2edc..5e395ad96e 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -47,69 +47,69 @@ using data_t = typename common::kernel_type<T>::data;
 #ifndef __CUDACC_RTC__
 namespace {
 template<typename T>
-const char *shortname(bool caps = false) {
+inline const char *shortname(bool caps = false) {
     return caps ? "Q" : "q";
 }
 template<>
-const char *shortname<float>(bool caps) {
+inline const char *shortname<float>(bool caps) {
     return caps ? "S" : "s";
 }
 template<>
-const char *shortname<double>(bool caps) {
+inline const char *shortname<double>(bool caps) {
     return caps ? "D" : "d";
 }
 template<>
-const char *shortname<cfloat>(bool caps) {
+inline const char *shortname<cfloat>(bool caps) {
     return caps ? "C" : "c";
 }
 template<>
-const char *shortname<cdouble>(bool caps) {
+inline const char *shortname<cdouble>(bool caps) {
     return caps ? "Z" : "z";
 }
 template<>
-const char *shortname<int>(bool caps) {
+inline const char *shortname<int>(bool caps) {
     return caps ? "I" : "i";
 }
 template<>
-const char *shortname<uint>(bool caps) {
+inline const char *shortname<uint>(bool caps) {
     return caps ? "U" : "u";
 }
 template<>
-const char *shortname<char>(bool caps) {
+inline const char *shortname<char>(bool caps) {
     return caps ? "J" : "j";
 }
 template<>
-const char *shortname<uchar>(bool caps) {
+inline const char *shortname<uchar>(bool caps) {
     return caps ? "V" : "v";
 }
 template<>
-const char *shortname<intl>(bool caps) {
+inline const char *shortname<intl>(bool caps) {
     return caps ? "X" : "x";
 }
 template<>
-const char *shortname<uintl>(bool caps) {
+inline const char *shortname<uintl>(bool caps) {
     return caps ? "Y" : "y";
 }
 template<>
-const char *shortname<short>(bool caps) {
+inline const char *shortname<short>(bool caps) {
     return caps ? "P" : "p";
 }
 template<>
-const char *shortname<ushort>(bool caps) {
+inline const char *shortname<ushort>(bool caps) {
     return caps ? "Q" : "q";
 }
 template<>
-const char *shortname<common::half>(bool caps) {
+inline const char *shortname<common::half>(bool caps) {
     return caps ? "H" : "h";
 }
 
 template<typename T>
-const char *getFullName();
+inline const char *getFullName();
 
-#define SPECIALIZE(T)              \
-    template<>                     \
-    const char *getFullName<T>() { \
-        return #T;                 \
+#define SPECIALIZE(T)                     \
+    template<>                            \
+    inline const char *getFullName<T>() { \
+        return #T;                        \
     }
 
 SPECIALIZE(float)
@@ -126,7 +126,7 @@ SPECIALIZE(unsigned long long)
 SPECIALIZE(long long)
 
 template<>
-const char *getFullName<common::half>() {
+inline const char *getFullName<common::half>() {
     return "half";
 }
 #undef SPECIALIZE

From aaa948e6a92b5dc4d0ec2b3b6582ccee2ae505c0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 12 Jun 2020 13:24:43 +0530
Subject: [PATCH 1991/2677] Fix ccache launch scripts to use sh compatible
 syntax

Earlier to this change, I added bash based syntax which won't work
with /bin/sh or dash shells.

/usr/sh is available on most systems that use init.d scripts. So, it is
safe to assume it's availability on majority of linux distributions.
---
 CMakeModules/launch-c.in   | 2 +-
 CMakeModules/launch-cxx.in | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/launch-c.in b/CMakeModules/launch-c.in
index a033af6cf1..6c6c9180bc 100644
--- a/CMakeModules/launch-c.in
+++ b/CMakeModules/launch-c.in
@@ -2,7 +2,7 @@
 
 # Xcode generator doesn't include the compiler as the
 # first argument, Ninja and Makefiles do. Handle both cases.
-if [[ "$1" = "${CMAKE_C_COMPILER}" ]] ; then
+if [ "$1" = "${CMAKE_C_COMPILER}" ] ; then
     shift
 fi
 
diff --git a/CMakeModules/launch-cxx.in b/CMakeModules/launch-cxx.in
index 457660f5a1..fa541fee0b 100644
--- a/CMakeModules/launch-cxx.in
+++ b/CMakeModules/launch-cxx.in
@@ -2,7 +2,7 @@
 
 # Xcode generator doesn't include the compiler as the
 # first argument, Ninja and Makefiles do. Handle both cases.
-if [[ "$1" = "${CMAKE_CXX_COMPILER}" ]] ; then
+if [ "$1" = "${CMAKE_CXX_COMPILER}" ] ; then
     shift
 fi
 

From bae5527c4300448025b20deaaab9b61bc8dba94e Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 18 Jun 2020 15:27:28 -0400
Subject: [PATCH 1992/2677] Adds PR template (#2929)

* Adds PR template

**Short description of change**
Adds a github PR template for the ArrayFire project. Developers will now face a short suggested checklist when creating a new PR on github.

**Motivation**
Adding a PR template will make it easier to reference old issues when generating reports and link future issue in historical context.

**Future considerations**
Wiki might need to be updated with additional development guidelines. The current guidelines could be more comprehensive.

* Updated pull request template

* Added additional detail.
* Use comments instead of text to communicate with the reader.
* Create a simple checklist

* Grammer + Future changes in the description section

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 .github/pull_request_template.md | 40 ++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 .github/pull_request_template.md

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 0000000000..4482b8c870
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,40 @@
+
+<!-- 
+Short description of change
+
+This should be one or two sentences that describe the overall
+motivation of the PR. Description of what was done for an unfamiliar
+reviewer.
+-->
+
+Description
+-----------
+<!--
+Additional information about the PR answering following questions:
+
+* Is this a new feature or a bug fix?
+* More detail if necessary to describe all commits in pull request.
+* Why these changes are necessary.
+* Potential impact on specific hardware, software or backends.
+* New functions and their functionallity.
+* Can this PR be backported to older versions?
+* Future changes not implemented in this PR.
+-->
+Fixes: #<issue number> ...
+
+Changes to Users
+----------------
+<!--
+* Additional options added to the build.
+* What changes will existing users have to make to their code or build steps?
+Refer to [wiki](https://github.com/arrayfire/arrayfire/wiki) for development guidelines
+-->
+
+Checklist
+---------
+<!-- Check if done or not applicable -->
+- [ ] Rebased on latest master
+- [ ] Code compiles
+- [ ] Tests pass
+- [ ] Functions added to unified API
+- [ ] Functions documented

From 888c7ed6f2d1603acf7e0e6f8caa3a686a2a8fbe Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 18 Jun 2020 17:18:02 -0400
Subject: [PATCH 1993/2677] adds missing WITH_CUDNN guard for cudnn.hpp

---
 src/backend/cuda/convolveNN.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index af192f5c74..5b4878ef04 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -15,7 +15,9 @@
 #include <common/half.hpp>
 #include <common/indexing_helpers.hpp>
 #include <common/unique_handle.hpp>
+#ifdef WITH_CUDNN
 #include <cudnn.hpp>
+#endif
 #include <err_cuda.hpp>
 #include <kernel/convolve.hpp>
 #include <platform.hpp>

From 05d51f84ac2710812fc87dadc0091bcf57010be7 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Wed, 17 Jun 2020 02:46:30 +0530
Subject: [PATCH 1994/2677] Split pack expansion to work around a possible bug
 in VS 2015

---
 src/backend/opencl/types.hpp | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 83a5d624cc..ccf07212e0 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -132,20 +132,23 @@ constexpr const char *getTypeBuildDefinition() {
     using std::begin;
     using std::end;
     using std::is_same;
-    array<bool, sizeof...(ARGS)> is_half = {is_same<ARGS, half>::value...};
-    array<bool, sizeof...(ARGS) * 2> is_double = {
-        is_same<ARGS, double>::value..., is_same<ARGS, cdouble>::value...};
+    array<bool, sizeof...(ARGS)> is_half    = {is_same<ARGS, half>::value...};
+    array<bool, sizeof...(ARGS)> is_double  = {is_same<ARGS, double>::value...};
+    array<bool, sizeof...(ARGS)> is_cdouble = {
+        is_same<ARGS, cdouble>::value...};
 
     bool half_def =
         any_of(begin(is_half), end(is_half), [](bool val) { return val; });
     bool double_def =
         any_of(begin(is_double), end(is_double), [](bool val) { return val; });
+    bool cdouble_def = any_of(begin(is_cdouble), end(is_cdouble),
+                              [](bool val) { return val; });
 
-    if (half_def && double_def) {
+    if (half_def && (double_def || cdouble_def)) {
         return " -D USE_HALF -D USE_DOUBLE";
     } else if (half_def) {
         return " -D USE_HALF";
-    } else if (double_def) {
+    } else if (double_def || cdouble_def) {
         return " -D USE_DOUBLE";
     } else {
         return "";

From 462e13cc749fdcab96fa77edba7a43b175e47201 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 5 Jun 2020 21:48:40 +0530
Subject: [PATCH 1995/2677] Use cxx_relaxed_constexpr check to define
 AF_CONSTEXPR

* AF_CONSTEXPR expands to nothing if constexpr support is not available.

* Replace CONSTEXPR_DH with AF_CONSTEXPR and __DH__ in
  `src/backend/common/half.hpp`

* Removed AF_CONSTEXPR where it is invalid in half.hpp
---
 CMakeModules/InternalUtils.cmake           |  32 ++-
 CMakeModules/compilers.h                   |  47 ++++
 src/backend/common/half.hpp                | 291 +++++++++++----------
 src/backend/common/unique_handle.hpp       |   5 +-
 src/backend/opencl/kernel/sparse_arith.hpp |   2 +-
 src/backend/opencl/types.hpp               |   3 +-
 6 files changed, 226 insertions(+), 154 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 1614f39f08..96bcfc65e7 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -172,18 +172,26 @@ macro(arrayfire_set_cmake_default_variables)
   # This code is used to generate the compilers.h file in CMakeModules. Not all
   # features of this modules are supported in the versions of CMake we wish to
   # support so we are directly including the files here
-  # include(WriteCompilerDetectionHeader)
-  # write_compiler_detection_header(
-  #         FILE ${ArrayFire_BINARY_DIR}/include/af/compilers.h
-  #         PREFIX AF
-  #         COMPILERS AppleClang Clang GNU Intel MSVC
-  #         # NOTE: cxx_attribute_deprecated does not work well with C
-  #         FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas cxx_static_assert cxx_generalized_initializers
-  #         ALLOW_UNKNOWN_COMPILERS
-  #         #[VERSION <version>]
-  #         #[PROLOG <prolog>]
-  #         #[EPILOG <epilog>]
-  #         )
+  #  set(compiler_header_epilogue [=[
+  #  #if defined(AF_COMPILER_CXX_RELAXED_CONSTEXPR) && AF_COMPILER_CXX_RELAXED_CONSTEXPR
+  #  #define AF_CONSTEXPR constexpr
+  #  #else
+  #  #define AF_CONSTEXPR
+  #  #endif
+  #  ]=])
+  #  include(WriteCompilerDetectionHeader)
+  #  write_compiler_detection_header(
+  #          FILE ${ArrayFire_BINARY_DIR}/include/af/compilers.h
+  #          PREFIX AF
+  #          COMPILERS AppleClang Clang GNU Intel MSVC
+  #          # NOTE: cxx_attribute_deprecated does not work well with C
+  #          FEATURES cxx_rvalue_references cxx_noexcept cxx_variadic_templates cxx_alignas
+  #          cxx_static_assert cxx_generalized_initializers cxx_relaxed_constexpr
+  #          ALLOW_UNKNOWN_COMPILERS
+  #          #[VERSION <version>]
+  #          #[PROLOG <prolog>]
+  #          EPILOG ${compiler_header_epilogue}
+  #          )
   configure_file(
     ${CMAKE_MODULE_PATH}/compilers.h
     ${ArrayFire_BINARY_DIR}/include/af/compilers.h)
diff --git a/CMakeModules/compilers.h b/CMakeModules/compilers.h
index cca330d4ca..c247005c80 100644
--- a/CMakeModules/compilers.h
+++ b/CMakeModules/compilers.h
@@ -202,6 +202,13 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
+#if ((__clang_major__ * 100) + __clang_minor__) >= 400 &&                      \
+    __has_feature(cxx_relaxed_constexpr)
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#else
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#endif
+
 #  elif AF_COMPILER_IS_Clang
 
 #    if !(((__clang_major__ * 100) + __clang_minor__) >= 301)
@@ -253,6 +260,13 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
+#if ((__clang_major__ * 100) + __clang_minor__) >= 301 &&                      \
+    __has_feature(cxx_relaxed_constexpr)
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#else
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#endif
+
 #  elif AF_COMPILER_IS_GNU
 
 #    if !((__GNUC__ * 100 + __GNUC_MINOR__) >= 404)
@@ -307,6 +321,12 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 500 && __cplusplus >= 201402L
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#else
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#endif
+
 #  elif AF_COMPILER_IS_Intel
 
 #    if !(__INTEL_COMPILER >= 1210)
@@ -378,6 +398,20 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
+#if __cpp_constexpr >= 201304 ||                                               \
+    (__INTEL_COMPILER >= 1700 &&                                               \
+     ((__cplusplus >= 201300L) ||                                              \
+      ((__cplusplus == 201103L) && !defined(__INTEL_CXX11_MODE__)) ||          \
+      ((((__INTEL_COMPILER == 1500) && (__INTEL_COMPILER_UPDATE == 1))) &&     \
+       defined(__GXX_EXPERIMENTAL_CXX0X__) &&                                  \
+       !defined(__INTEL_CXX11_MODE__)) ||                                      \
+      (defined(__INTEL_CXX11_MODE__) && defined(__cpp_aggregate_nsdmi))) &&    \
+     !defined(_MSC_VER))
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#else
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#endif
+
 #  elif AF_COMPILER_IS_MSVC
 
 #    if !(_MSC_VER >= 1600)
@@ -436,6 +470,12 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
+#if _MSC_VER >= 1911
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#else
+#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#endif
+
 #  endif
 
 #  if defined(AF_COMPILER_CXX_NOEXCEPT) && AF_COMPILER_CXX_NOEXCEPT
@@ -471,4 +511,11 @@ template<> struct AFStaticAssert<true>{};
 
 #endif
 
+#if defined(AF_COMPILER_CXX_RELAXED_CONSTEXPR) &&                              \
+    AF_COMPILER_CXX_RELAXED_CONSTEXPR
+#define AF_CONSTEXPR constexpr
+#else
+#define AF_CONSTEXPR
+#endif
+
 #endif
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 0d378e2871..cb6a9e4385 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -16,6 +16,7 @@
 #include <backend.hpp>
 
 #ifndef __CUDACC_RTC__
+#include <af/compilers.h>
 #include <cstring>
 #include <ostream>
 #include <string>
@@ -26,12 +27,6 @@
 using uint16_t      = unsigned short;
 #endif
 
-#if AF_COMPILER_CXX_RELAXED_CONSTEXPR
-#define CONSTEXPR_DH constexpr __DH__
-#else
-#define CONSTEXPR_DH __DH__
-#endif
-
 namespace common {
 
 #if defined(__CUDA_ARCH__)
@@ -40,7 +35,58 @@ using native_half_t = __half;
 using native_half_t = uint16_t;
 #endif
 
-#ifndef __CUDACC_RTC__
+#ifdef __CUDACC_RTC__
+template<typename T>
+AF_CONSTEXPR __DH__ native_half_t float2half(T value) {
+    return __float2half(value);
+}
+
+AF_CONSTEXPR __DH__ inline float half2float(native_half_t value) noexcept {
+    return __half2float(value);
+}
+
+template<typename T>
+AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept;
+
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(int value) noexcept {
+    return __int2half_rn(value);
+}
+
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(unsigned value) noexcept {
+    return __uint2half_rn(value);
+}
+
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(long long value) noexcept {
+    return __ll2half_rn(value);
+}
+
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(unsigned long long value) noexcept {
+    return __ull2half_rn(value);
+}
+
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(short value) noexcept {
+    return __short2half_rn(value);
+}
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(unsigned short value) noexcept {
+    return __ushort2half_rn(value);
+}
+
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(char value) noexcept {
+    return __ull2half_rn(value);
+}
+template<>
+AF_CONSTEXPR __DH__ native_half_t int2half(unsigned char value) noexcept {
+    return __ull2half_rn(value);
+}
+
+#else
 
 /// Convert integer to half-precision floating point.
 ///
@@ -53,7 +99,7 @@ using native_half_t = uint16_t;
 ///
 /// \return binary representation of half-precision value
 template<std::float_round_style R, bool S, typename T>
-CONSTEXPR_DH native_half_t int2half_impl(T value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
     static_assert(std::is_integral<T>::value,
                   "int to half conversion only supports builtin integer types");
     if (S) value = -value;
@@ -91,17 +137,16 @@ CONSTEXPR_DH native_half_t int2half_impl(T value) noexcept {
 template<typename T, std::float_round_style R = std::round_to_nearest,
          typename std::enable_if_t<std::is_integral<T>::value &&
                                    std::is_signed<T>::value>* = nullptr>
-CONSTEXPR_DH native_half_t int2half(T value) noexcept {
-    uint16_t out;
-    out = (value < 0) ? int2half_impl<R, true, T>(value)
-                      : int2half_impl<R, false, T>(value);
+AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
+    uint16_t out = (value < 0) ? int2half_impl<R, true, T>(value)
+                               : int2half_impl<R, false, T>(value);
     return out;
 }
 
 template<typename T, std::float_round_style R = std::round_to_nearest,
          typename std::enable_if_t<std::is_integral<T>::value &&
                                    std::is_unsigned<T>::value>* = nullptr>
-CONSTEXPR_DH native_half_t int2half(T value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
     return int2half_impl<R, false, T>(value);
 }
 
@@ -114,7 +159,7 @@ CONSTEXPR_DH native_half_t int2half(T value) noexcept {
 /// \param value single-precision value
 /// \return binary representation of half-precision value
 template<std::float_round_style R = std::round_indeterminate>
-CONSTEXPR_DH native_half_t float2half_impl(float value) noexcept {
+__DH__ native_half_t float2half_impl(float value) noexcept {
     uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
                         // //violating strict aliasing!
     std::memcpy(&bits, &value, sizeof(float));
@@ -249,9 +294,9 @@ CONSTEXPR_DH native_half_t float2half_impl(float value) noexcept {
 ///
 /// \return binary representation of half-precision value
 template<std::float_round_style R>
-CONSTEXPR_DH native_half_t float2half_impl(double value) {
-    uint64_t bits;  // = *reinterpret_cast<uint64*>(&value);		//violating
-                    // strict aliasing!
+__DH__ native_half_t float2half_impl(double value) {
+    uint64_t bits{0};  // = *reinterpret_cast<uint64*>(&value);		//violating
+                       // strict aliasing!
     std::memcpy(&bits, &value, sizeof(double));
     uint32_t hi = bits >> 32, lo = bits & 0xFFFFFFFF;
     uint16_t hbits = (hi >> 16) & 0x8000;
@@ -267,7 +312,7 @@ CONSTEXPR_DH native_half_t float2half_impl(double value) {
             return hbits | (0x7BFF + (hbits >> 15));
         return hbits | (0x7BFF + (R != std::round_toward_zero));
     }
-    int g, s = lo != 0;
+    int g = 0, s = lo != 0;
     if (exp > 1008) {
         g = (hi >> 9) & 1;
         s |= (hi & 0x1FF) != 0;
@@ -279,7 +324,6 @@ CONSTEXPR_DH native_half_t float2half_impl(double value) {
         s |= (hi & ((1L << i) - 1)) != 0;
         hbits |= hi >> (i + 1);
     } else {
-        g = 0;
         s |= hi != 0;
     }
     if (R == std::round_to_nearest)
@@ -296,7 +340,11 @@ CONSTEXPR_DH native_half_t float2half_impl(double value) {
 }
 
 template<typename T, std::float_round_style R = std::round_to_nearest>
-CONSTEXPR_DH native_half_t float2half(T val) {
+#ifdef __CUDA_ARCH__
+AF_CONSTEXPR
+#endif
+    __DH__ native_half_t
+    float2half(T val) {
 #ifdef __CUDA_ARCH__
     return __float2half(val);
 #else
@@ -304,12 +352,12 @@ CONSTEXPR_DH native_half_t float2half(T val) {
 #endif
 }
 
-CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
+__DH__ inline float half2float(native_half_t value) noexcept {
 #ifdef __CUDA_ARCH__
     return __half2float(value);
 #else
     // return _cvtsh_ss(data.data_);
-    uint32_t mantissa_table[2048] = {
+    constexpr uint32_t mantissa_table[2048] = {
         0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34A00000,
         0x34C00000, 0x34E00000, 0x35000000, 0x35100000, 0x35200000, 0x35300000,
         0x35400000, 0x35500000, 0x35600000, 0x35700000, 0x35800000, 0x35880000,
@@ -695,7 +743,7 @@ CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
 ///           value
 /// \param value The value to convert to integer
 template<std::float_round_style R, bool E, typename T>
-T half2int(native_half_t value) {
+AF_CONSTEXPR T half2int(native_half_t value) {
     static_assert(std::is_integral<T>::value,
                   "half to int conversion only supports builtin integer types");
     unsigned int e = value & 0x7FFF;
@@ -724,58 +772,6 @@ T half2int(native_half_t value) {
     return (value & 0x8000) ? -static_cast<T>(m) : static_cast<T>(m);
 }
 
-#else
-
-template<typename T>
-CONSTEXPR_DH native_half_t float2half(T value) {
-    return __float2half(value);
-}
-
-CONSTEXPR_DH inline float half2float(native_half_t value) noexcept {
-    return __half2float(value);
-}
-
-template<typename T>
-CONSTEXPR_DH native_half_t int2half(T value) noexcept;
-
-template<>
-CONSTEXPR_DH native_half_t int2half(int value) noexcept {
-    return __int2half_rn(value);
-}
-
-template<>
-CONSTEXPR_DH native_half_t int2half(unsigned value) noexcept {
-    return __uint2half_rn(value);
-}
-
-template<>
-CONSTEXPR_DH native_half_t int2half(long long value) noexcept {
-    return __ll2half_rn(value);
-}
-
-template<>
-CONSTEXPR_DH native_half_t int2half(unsigned long long value) noexcept {
-    return __ull2half_rn(value);
-}
-
-template<>
-CONSTEXPR_DH native_half_t int2half(short value) noexcept {
-    return __short2half_rn(value);
-}
-template<>
-CONSTEXPR_DH native_half_t int2half(unsigned short value) noexcept {
-    return __ushort2half_rn(value);
-}
-
-template<>
-CONSTEXPR_DH native_half_t int2half(char value) noexcept {
-    return __ull2half_rn(value);
-}
-template<>
-CONSTEXPR_DH native_half_t int2half(unsigned char value) noexcept {
-    return __ull2half_rn(value);
-}
-
 #endif  // __CUDACC_RTC__
 
 namespace internal {
@@ -783,28 +779,28 @@ namespace internal {
 struct binary_t {};
 
 /// Tag for binary construction.
-static constexpr binary_t binary;
+static constexpr binary_t binary = binary_t{};
 }  // namespace internal
 
 class half;
 
-CONSTEXPR_DH static inline bool operator==(common::half lhs,
-                                           common::half rhs) noexcept;
-CONSTEXPR_DH static inline bool operator!=(common::half lhs,
-                                           common::half rhs) noexcept;
-CONSTEXPR_DH static inline bool operator<(common::half lhs,
-                                          common::half rhs) noexcept;
-CONSTEXPR_DH static inline bool operator<(common::half lhs, float rhs) noexcept;
-CONSTEXPR_DH static inline bool isinf(half val) noexcept;
+AF_CONSTEXPR __DH__ static inline bool operator==(common::half lhs,
+                                                  common::half rhs) noexcept;
+AF_CONSTEXPR __DH__ static inline bool operator!=(common::half lhs,
+                                                  common::half rhs) noexcept;
+__DH__ static inline bool operator<(common::half lhs,
+                                    common::half rhs) noexcept;
+__DH__ static inline bool operator<(common::half lhs, float rhs) noexcept;
+AF_CONSTEXPR __DH__ static inline bool isinf(half val) noexcept;
 
 /// Classification implementation.
 /// \param arg value to classify
 /// \retval true if not a number
 /// \retval false else
-CONSTEXPR_DH static inline bool isnan(common::half val) noexcept;
+AF_CONSTEXPR __DH__ static inline bool isnan(common::half val) noexcept;
 
 class alignas(2) half {
-    native_half_t data_;
+    native_half_t data_ = 0;
 
 #if !defined(NVCC) && !defined(__CUDACC_RTC__)
     // NVCC on OSX performs a weird transformation where it removes the std::
@@ -814,48 +810,63 @@ class alignas(2) half {
 #endif
 
    public:
-    half() = default;
+    AF_CONSTEXPR half() = default;
 
     /// Constructor.
     /// \param bits binary representation to set half to
-    CONSTEXPR_DH half(internal::binary_t, uint16_t bits) noexcept : data_() {
-        memcpy(&data_, &bits, sizeof(uint16_t));
+    AF_CONSTEXPR __DH__ half(internal::binary_t, uint16_t bits) noexcept
+        :
+#if defined(__CUDA_ARCH__)
+        data_(__ushort_as_half(bits))
+#else
+        data_(bits)
+#endif
+    {
     }
 
-    CONSTEXPR_DH explicit half(double value) noexcept
+#if defined(__CUDA_ARCH__)
+    AF_CONSTEXPR
+#endif
+    __DH__ explicit half(double value) noexcept
         : data_(float2half<double>(value)) {}
 
-    CONSTEXPR_DH explicit half(float value) noexcept
+#if defined(__CUDA_ARCH__)
+    AF_CONSTEXPR
+#endif
+    __DH__ explicit half(float value) noexcept
         : data_(float2half<float>(value)) {}
 
-#ifndef __CUDA_RTC__
     template<typename T>
-    CONSTEXPR_DH explicit half(T value) noexcept : data_(int2half<T>(value)) {}
+    AF_CONSTEXPR __DH__ explicit half(T value) noexcept
+        : data_(int2half<T>(value)) {}
 
-    CONSTEXPR_DH half& operator=(const double& value) noexcept {
+#if defined(__CUDA_ARCH__)
+    AF_CONSTEXPR
+#endif
+    __DH__ half& operator=(const double& value) noexcept {
         data_ = float2half<double>(value);
         return *this;
     }
-#endif
 
 #if defined(__CUDA_ARCH__)
-    CONSTEXPR_DH explicit half(const __half& value) noexcept : data_(value) {}
-    CONSTEXPR_DH half& operator=(__half&& value) noexcept {
+    AF_CONSTEXPR __DH__ explicit half(const __half& value) noexcept
+        : data_(value) {}
+    AF_CONSTEXPR __DH__ half& operator=(__half&& value) noexcept {
         data_ = value;
         return *this;
     }
 #endif
 
-    CONSTEXPR_DH explicit operator float() const noexcept {
+    __DH__ explicit operator float() const noexcept {
         return half2float(data_);
     }
 
-    CONSTEXPR_DH explicit operator double() const noexcept {
+    __DH__ explicit operator double() const noexcept {
         // TODO(umar): convert directly to double
         return half2float(data_);
     }
 
-    CONSTEXPR_DH explicit operator short() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator short() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2short_rn(data_);
 #else
@@ -863,7 +874,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator long long() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator long long() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2ll_rn(data_);
 #else
@@ -871,7 +882,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator int() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator int() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2int_rn(data_);
 #else
@@ -879,7 +890,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator unsigned() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator unsigned() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2uint_rn(data_);
 #else
@@ -887,7 +898,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator unsigned short() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator unsigned short() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2ushort_rn(data_);
 #else
@@ -895,7 +906,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator unsigned long long() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator unsigned long long() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2ull_rn(data_);
 #else
@@ -903,7 +914,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator char() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator char() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2short_rn(data_);
 #else
@@ -911,7 +922,7 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH explicit operator unsigned char() const noexcept {
+    AF_CONSTEXPR __DH__ explicit operator unsigned char() const noexcept {
 #ifdef __CUDA_ARCH__
         return __half2short_rn(data_);
 #else
@@ -920,18 +931,17 @@ class alignas(2) half {
     }
 
 #if defined(__CUDA_ARCH__)
-    CONSTEXPR_DH operator __half() const noexcept { return data_; };
+    AF_CONSTEXPR __DH__ operator __half() const noexcept { return data_; };
 #endif
 
-    friend CONSTEXPR_DH bool operator==(half lhs, half rhs) noexcept;
-    friend CONSTEXPR_DH bool operator!=(half lhs, half rhs) noexcept;
-    friend CONSTEXPR_DH bool operator<(common::half lhs,
-                                       common::half rhs) noexcept;
-    friend CONSTEXPR_DH bool operator<(common::half lhs, float rhs) noexcept;
-    friend CONSTEXPR_DH bool isinf(half val) noexcept;
-    friend CONSTEXPR_DH inline bool isnan(half val) noexcept;
+    friend AF_CONSTEXPR __DH__ bool operator==(half lhs, half rhs) noexcept;
+    friend AF_CONSTEXPR __DH__ bool operator!=(half lhs, half rhs) noexcept;
+    friend __DH__ bool operator<(common::half lhs, common::half rhs) noexcept;
+    friend __DH__ bool operator<(common::half lhs, float rhs) noexcept;
+    friend AF_CONSTEXPR __DH__ bool isinf(half val) noexcept;
+    friend AF_CONSTEXPR __DH__ inline bool isnan(half val) noexcept;
 
-    CONSTEXPR_DH common::half operator-() const {
+    AF_CONSTEXPR __DH__ common::half operator-() const {
 #if __CUDA_ARCH__ >= 530
         return common::half(__hneg(data_));
 #elif defined(__CUDA_ARCH__)
@@ -941,11 +951,17 @@ class alignas(2) half {
 #endif
     }
 
-    CONSTEXPR_DH common::half operator+() const { return *this; }
+    AF_CONSTEXPR __DH__ common::half operator+() const { return *this; }
+
+    AF_CONSTEXPR static half infinity() {
+        half out;
+        out.data_ = 0x7C00;
+        return out;
+    }
 };
 
-CONSTEXPR_DH static inline bool operator==(common::half lhs,
-                                           common::half rhs) noexcept {
+AF_CONSTEXPR __DH__ static inline bool operator==(common::half lhs,
+                                                  common::half rhs) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __heq(lhs.data_, rhs.data_);
 #elif defined(__CUDA_ARCH__)
@@ -956,8 +972,8 @@ CONSTEXPR_DH static inline bool operator==(common::half lhs,
 #endif
 }
 
-CONSTEXPR_DH static inline bool operator!=(common::half lhs,
-                                           common::half rhs) noexcept {
+AF_CONSTEXPR __DH__ static inline bool operator!=(common::half lhs,
+                                                  common::half rhs) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __hne(lhs.data_, rhs.data_);
 #else
@@ -965,8 +981,8 @@ CONSTEXPR_DH static inline bool operator!=(common::half lhs,
 #endif
 }
 
-CONSTEXPR_DH static inline bool operator<(common::half lhs,
-                                          common::half rhs) noexcept {
+__DH__ static inline bool operator<(common::half lhs,
+                                    common::half rhs) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __hlt(lhs.data_, rhs.data_);
 #elif defined(__CUDA_ARCH__)
@@ -979,8 +995,7 @@ CONSTEXPR_DH static inline bool operator<(common::half lhs,
 #endif
 }
 
-CONSTEXPR_DH static inline bool operator<(common::half lhs,
-                                          float rhs) noexcept {
+__DH__ static inline bool operator<(common::half lhs, float rhs) noexcept {
 #if defined(__CUDA_ARCH__)
     return __half2float(lhs.data_) < rhs;
 #else
@@ -1067,49 +1082,49 @@ class numeric_limits<common::half> : public numeric_limits<float> {
     static constexpr int max_exponent10 = 4;
 
     /// Smallest positive normal value.
-    static CONSTEXPR_DH common::half min() noexcept {
+    static AF_CONSTEXPR __DH__ common::half min() noexcept {
         return common::half(common::internal::binary, 0x0400);
     }
 
     /// Smallest finite value.
-    static CONSTEXPR_DH common::half lowest() noexcept {
+    static AF_CONSTEXPR __DH__ common::half lowest() noexcept {
         return common::half(common::internal::binary, 0xFBFF);
     }
 
     /// Largest finite value.
-    static CONSTEXPR_DH common::half max() noexcept {
+    static AF_CONSTEXPR __DH__ common::half max() noexcept {
         return common::half(common::internal::binary, 0x7BFF);
     }
 
     /// Difference between one and next representable value.
-    static CONSTEXPR_DH common::half epsilon() noexcept {
+    static AF_CONSTEXPR __DH__ common::half epsilon() noexcept {
         return common::half(common::internal::binary, 0x1400);
     }
 
     /// Maximum rounding error.
-    static CONSTEXPR_DH common::half round_error() noexcept {
+    static AF_CONSTEXPR __DH__ common::half round_error() noexcept {
         return common::half(
             common::internal::binary,
             (round_style == std::round_to_nearest) ? 0x3800 : 0x3C00);
     }
 
     /// Positive infinity.
-    static CONSTEXPR_DH common::half infinity() noexcept {
+    static AF_CONSTEXPR __DH__ common::half infinity() noexcept {
         return common::half(common::internal::binary, 0x7C00);
     }
 
     /// Quiet NaN.
-    static CONSTEXPR_DH common::half quiet_NaN() noexcept {
+    static AF_CONSTEXPR __DH__ common::half quiet_NaN() noexcept {
         return common::half(common::internal::binary, 0x7FFF);
     }
 
     /// Signalling NaN.
-    static CONSTEXPR_DH common::half signaling_NaN() noexcept {
+    static AF_CONSTEXPR __DH__ common::half signaling_NaN() noexcept {
         return common::half(common::internal::binary, 0x7DFF);
     }
 
     /// Smallest positive subnormal value.
-    static CONSTEXPR_DH common::half denorm_min() noexcept {
+    static AF_CONSTEXPR __DH__ common::half denorm_min() noexcept {
         return common::half(common::internal::binary, 0x0001);
     }
 };
@@ -1139,19 +1154,17 @@ struct hash<common::half>  //: unary_function<common::half,size_t>
 #endif
 
 namespace common {
-CONSTEXPR_DH
-static bool isinf(half val) noexcept {
+AF_CONSTEXPR __DH__ static bool isinf(half val) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __hisinf(val.data_);
 #elif defined(__CUDA_ARCH__)
     return ::isinf(__half2float(val));
 #else
-    return val == std::numeric_limits<half>::infinity() ||
-           val == -std::numeric_limits<half>::infinity();
+    return val == half::infinity() || val == -half::infinity();
 #endif
 }
 
-CONSTEXPR_DH static inline bool isnan(half val) noexcept {
+AF_CONSTEXPR __DH__ static inline bool isnan(half val) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __hisnan(val.data_);
 #elif defined(__CUDA_ARCH__)
diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index f6aa32e57b..f100bd353e 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -8,6 +8,8 @@
  ********************************************************/
 #pragma once
 
+#include <af/compilers.h>
+
 namespace common {
 
 /// Deletes a handle.
@@ -72,7 +74,8 @@ class unique_handle {
     constexpr operator const T &() const noexcept { return handle_; }
 
     unique_handle(const unique_handle &other) noexcept = delete;
-    unique_handle(unique_handle &&other) noexcept : handle_(other.handle_) {
+    AF_CONSTEXPR unique_handle(unique_handle &&other) noexcept
+        : handle_(other.handle_) {
         other.handle_ = 0;
     }
 
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 78331ed587..4f2bef334b 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -33,7 +33,7 @@ constexpr unsigned TY      = 8;
 constexpr unsigned THREADS = TX * TY;
 
 template<af_op_t op>
-constexpr std::string getOpString() {
+AF_CONSTEXPR std::string getOpString() {
     switch (op) {
         case af_add_t: return "ADD";
         case af_sub_t: return "SUB";
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index ccf07212e0..e88086b262 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -12,6 +12,7 @@
 #include <cl2hpp.hpp>
 #include <common/kernel_type.hpp>
 #include <common/traits.hpp>
+#include <af/compilers.h>
 
 #include <algorithm>
 #include <array>
@@ -125,7 +126,7 @@ inline const char *getFullName<cdouble>() {
 }  // namespace
 
 template<typename... ARGS>
-constexpr const char *getTypeBuildDefinition() {
+AF_CONSTEXPR const char *getTypeBuildDefinition() {
     using common::half;
     using std::any_of;
     using std::array;

From 0c53e096f1c404de96494eaa3177d9166971d2cd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 21 Jun 2020 01:03:45 -0400
Subject: [PATCH 1996/2677] Add /Zc:__cplusplus to MSVC CUDA builds. constexpr
 on NVRTC compiles

* Adds the Zc:__cplusplus flag to cuda builds for MSVC if the flag is available.
the cuda_fp16 header does not define the default constructor for __half as
"= default" and that prevents the __half struct to be used in a constexpr
expression

* For older versions of MSVC we define the __cplusplus macro before and
after the inclusion of cuda_fp16.h header.

* Define the AF_CONSTEXPR macro for NVRTC compilation
---
 CMakeModules/InternalUtils.cmake | 20 ++++++++++++++++----
 src/backend/common/half.hpp      | 28 +++++++++++++++++++++++++---
 2 files changed, 41 insertions(+), 7 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 96bcfc65e7..fdb4a1bbe0 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -29,13 +29,25 @@ endif()
 endfunction()
 
 function(arrayfire_get_cuda_cxx_flags cuda_flags)
-  if(NOT MSVC)
-    set(flags -std=c++14 --expt-relaxed-constexpr -Xcompiler -fPIC -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden)
-  else()
-    set(flags -Xcompiler /wd4251 -Xcompiler /wd4068 -Xcompiler /wd4275 -Xcompiler /bigobj -Xcompiler /EHsc)
+  if(MSVC)
+    set(flags -Xcompiler /wd4251
+              -Xcompiler /wd4068
+              -Xcompiler /wd4275
+              -Xcompiler /bigobj
+              -Xcompiler /EHsc
+              --expt-relaxed-constexpr)
     if(CMAKE_GENERATOR MATCHES "Ninja")
       set(flags ${flags} -Xcompiler /FS)
     endif()
+    if(cplusplus_define)
+      list(APPEND flags -Xcompiler /Zc:__cplusplus
+                        -Xcompiler /std:c++14)
+    endif()
+  else()
+    set(flags -std=c++14
+              -Xcompiler -fPIC
+              -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden
+              --expt-relaxed-constexpr)
   endif()
 
   if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index cb6a9e4385..60153786e7 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -10,12 +10,36 @@
 #pragma once
 
 #if defined(NVCC) || defined(__CUDACC_RTC__)
+
+// MSVC sets __cplusplus to 199711L for all versions unless you specify
+// the new \Zc:__cplusplus flag in Visual Studio 2017. This is not possible
+// in older versions of MSVC so we updated it here for the cuda_fp16 header
+// because otherwise it does not define the default constructor for __half
+// as default and that prevents the __half struct to be used in a constexpr
+// expression
+#if defined(_MSC_VER) && __cplusplus == 199711L
+#undef __cplusplus
+#define __cplusplus 201402L
+#define AF_CPLUSPLUS_CHANGED
+#endif
+
 #include <cuda_fp16.h>
+
+#ifdef AF_CPLUSPLUS_CHANGED
+#undef __cplusplus
+#undef AF_CPLUSPLUS_CHANGED
+#define __cplusplus 199711L
+#endif
 #endif
 
 #include <backend.hpp>
 
-#ifndef __CUDACC_RTC__
+#ifdef __CUDACC_RTC__
+using uint16_t = unsigned short;
+// we do not include the af/compilers header in nvrtc compilations so
+// we are defining the AF_CONSTEXPR expression here
+#define AF_CONSTEXPR constexpr
+#else
 #include <af/compilers.h>
 #include <cstring>
 #include <ostream>
@@ -23,8 +47,6 @@
 #include <type_traits>
 
 #include <limits>
-#else
-using uint16_t      = unsigned short;
 #endif
 
 namespace common {

From 3663c0c937d961b64184777ac2fd40be3badfdb3 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Sun, 21 Jun 2020 19:38:33 -0400
Subject: [PATCH 1997/2677] Create issue templates (#2928)

Adds several classes of issues with proposed additional information that would be helpful when debugging.

Co-authored-by: pradeep <pradeep@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 .github/ISSUE_TEMPLATE/bug_report.md        | 76 +++++++++++++++++++++
 .github/ISSUE_TEMPLATE/build_error.md       | 36 ++++++++++
 .github/ISSUE_TEMPLATE/feature_request.md   | 20 ++++++
 .github/ISSUE_TEMPLATE/performance_issue.md | 40 +++++++++++
 .github/ISSUE_TEMPLATE/question.md          | 14 ++++
 5 files changed, 186 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/build_error.md
 create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md
 create mode 100644 .github/ISSUE_TEMPLATE/performance_issue.md
 create mode 100644 .github/ISSUE_TEMPLATE/question.md

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000000..668986c904
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,76 @@
+---
+name: Bug Report
+about: Create a bug report to help us improve ArrayFire
+title: "[BUG]"
+labels: 'bug'
+assignees: ''
+---
+
+<!-- One to two sentences discription of the bug -->
+
+Description
+===========
+<!--
+* Additional details regarding the bug
+* Did you build ArrayFire yourself or did you use the official installers
+* Which backend is experiencing this issue? (CPU, CUDA, OpenCL)
+* Do you have a workaround?
+* Can the bug be reproduced reliably on your system?
+* A clear and concise description of what you expected to happen.
+* Run your executable with AF_TRACE=all and AF_PRINT_ERRORS=1 environment
+  variables set.
+* Screenshot or terminal output of the results
+-->
+
+Reproducible Code and/or Steps
+------------------------------
+<!--
+* Steps or code snippet that can reproduce the bug
+* A full example will allow us to debug and fix the bug faster
+-->
+
+System Information
+------------------
+<!--
+Please provide the following information:
+1. ArrayFire version
+2. Devices installed on the system
+3. (optional) Output from the af::info() function if applicable.
+4. Output from the following scripts:
+
+Run one of the following commands based on your OS
+
+Linux:
+```sh
+lsb_release -a
+if command -v nvidia-smi >/dev/null; then
+  nvidia-smi --query-gpu="name,memory.total,driver_version" --format=csv -i 0
+else
+  echo "nvidia-smi not found"
+fi
+if command -v /opt/rocm/bin/rocm-smi >/dev/null; then
+  /opt/rocm/bin/rocm-smi --showproductname
+else
+  echo "rocm-smi not found."
+fi
+if command -v clinfo > /dev/null; then
+  clinfo
+else
+  echo "clinfo not found."
+fi
+```
+
+Windows:
+Download clinfo from https://github.com/Oblomov/clinfo
+
+If you have NVIDIA GPUs. Run nvidia-smi usually located in
+C:\Program Files\NVIDIA Corporation\NVSMI
+
+Provide driver version for your GPU. (This is vendor specific)
+-->
+
+Checklist
+---------
+
+- [ ] Using the latest available ArrayFire release
+- [ ] GPU drivers are up to date
diff --git a/.github/ISSUE_TEMPLATE/build_error.md b/.github/ISSUE_TEMPLATE/build_error.md
new file mode 100644
index 0000000000..dc457c668e
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/build_error.md
@@ -0,0 +1,36 @@
+---
+name: Build Error
+about: Create a report for errors during the building process
+title: "[Build]"
+labels: 'build'
+assignees: ''
+---
+
+<!--
+A short one or two line description of the error
+-->
+
+Description
+===========
+
+<!--
+* Additional details about the errors during the build
+* What do you suspect is causing the issue?
+* Which steps in the (wiki)[https://github.com/arrayfire/arrayfire/wiki] failed
+* What operating system and/or distro are you using?
+* Versions of the packages related to this bug
+-->
+
+Error Log
+---------
+<!-- Output of the error log. -->
+```
+
+```
+
+Build Environment
+-----------------
+Compiler version: <!-- MSVC v140 or gcc 9.3.2 -->
+Operating system: <!-- Windows 10; Ubuntu 18.04 -->
+Build environment: <!-- Environment variables; Installed software -->
+CMake variables: <!-- Output of  `cmake -L` -->
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000000..662f8e722d
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature Request
+about: Suggest a new idea for ArrayFire
+title: ''
+labels: 'feature'
+assignees: ''
+
+---
+
+<!-- One or two sentences describing the feature. -->
+
+Description
+===========
+<!--
+* Additional information about the feature you would like to add
+* What problem are you trying to solve?
+* (Optional) API of new function
+* (Optional) Algorithms that could be used to implement this feature
+* (Optional)Are there other libraries that implement this feature?
+-->
diff --git a/.github/ISSUE_TEMPLATE/performance_issue.md b/.github/ISSUE_TEMPLATE/performance_issue.md
new file mode 100644
index 0000000000..c563aedee5
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/performance_issue.md
@@ -0,0 +1,40 @@
+---
+name: Performance Issue
+about: For Issues related to lackluster performance
+title: "[Perf]"
+labels: 'perf'
+assignees: ''
+
+---
+
+<!-- One or two line description of the performance issue -->
+
+
+Description
+===========
+
+<!--
+* Additional information about the performance issues
+* Did you build ArrayFire yourself or did you use the official installers
+* Which backend is experiencing this issue? (CPU, CUDA, OpenCL)
+* Do you have a workaround?
+* Can the bug be reproduced reliably on your system?
+-->
+
+Reproducible Code
+-----------------
+<!--
+* Provide a small example that could reproduce the performance issue
+* A full example will allow us to debug and fix this issue faster
+-->
+
+System Information
+------------------
+ArrayFire Version:
+Device:
+Operating System:
+Driver version:
+
+Checklist
+---------
+- [ ] I have read [timing ArrayFire](http://arrayfire.org/docs/timing.htm)
diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md
new file mode 100644
index 0000000000..a37af18d75
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/question.md
@@ -0,0 +1,14 @@
+---
+name: Question
+about: General questions and potential issues
+title: "[Question]"
+labels: ''
+assignees: ''
+
+---
+
+Before asking a question on github, please consider if it is more appropriate for these other platforms:
+
+* [Slack Chat](https://join.slack.com/t/arrayfire-org/shared_invite/MjI4MjIzMDMzMTczLTE1MDI5ODg4NzYtN2QwNGE3ODA5OQ)
+* [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
+* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)

From d3aab54101df3322ea1dd823f4b98e68ac65b38d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 Jun 2020 09:59:25 +0530
Subject: [PATCH 1998/2677] Remove obsolete OSX specific patch in CLBlast
 external project

---
 CMakeModules/build_CLBlast.cmake | 26 --------------------------
 1 file changed, 26 deletions(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 76fd0ae1b0..c5a7567630 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -12,31 +12,6 @@ find_program(GIT git)
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
 set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
-if(APPLE)
-  # We need this patch on macOS until #PR 356 is merged in the CLBlast repo
-  write_file(clblast.patch
-"diff --git a/src/clpp11.hpp b/src/clpp11.hpp
-index 9446499..786f7db 100644
---- a/src/clpp11.hpp
-+++ b/src/clpp11.hpp
-@@ -358,8 +358,10 @@ class Device {
-
-   // Returns if the Nvidia chip is a Volta or later archicture (sm_70 or higher)
-   bool IsPostNVIDIAVolta() const {
--    assert(HasExtension(\"cl_nv_device_attribute_query\"));
--    return GetInfo<cl_uint>(CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV) >= 7;
-+    if(HasExtension(\"cl_nv_device_attribute_query\")) {
-+      return GetInfo<cl_uint>(CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV) >= 7;
-+    }
-+    return false;
-   }
-
-   // Retrieves the above extra information (if present)
-")
-
-  set(CLBLAST_PATCH_COMMAND ${GIT} apply ${ArrayFire_BINARY_DIR}/clblast.patch)
-endif()
-
 if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
 else()
@@ -56,7 +31,6 @@ ExternalProject_Add(
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    PATCH_COMMAND ${CLBLAST_PATCH_COMMAND}
     BUILD_BYPRODUCTS ${CLBlast_location}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
       -Wno-dev <SOURCE_DIR>

From a34ec0cf7fab319e697175da1f87c19f15f50067 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 Jun 2020 10:30:00 +0530
Subject: [PATCH 1999/2677] Workaround for bug in Apple's OpenCL, a missing
 definition

---
 src/backend/opencl/device_manager.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 5286928150..9404614f42 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -181,14 +181,20 @@ DeviceManager::DeviceManager()
     try {
         Platform::get(&platforms);
     } catch (const cl::Error& err) {
+#if !defined(OS_MAC)
+        // CL_PLATFORM_NOT_FOUND_KHR is not defined in Apple's OpenCL
+        // implementation. Thus, it requires this ugly check.
         if (err.err() == CL_PLATFORM_NOT_FOUND_KHR) {
+#endif
             AF_ERROR(
                 "No OpenCL platforms found on this system. Ensure you have "
                 "installed the device driver as well as the OpenCL runtime and "
                 "ICD from your device vendor. You can use the clinfo utility "
                 "to debug OpenCL installation issues.",
                 AF_ERR_RUNTIME);
+#if !defined(OS_MAC)
         }
+#endif
     }
     fgMngr = std::make_unique<graphics::ForgeManager>();
 

From ada7862e67a7ffc4f16da411b604f6923588a747 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 25 Jun 2020 10:00:24 +0530
Subject: [PATCH 2000/2677] Increase minimum required CUDA toolkit version to
 build

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ccf3a755cc..682f416041 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -34,7 +34,7 @@ arrayfire_set_cmake_default_variables()
 #Set Intel OpenMP as default MKL thread layer
 set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
 
-find_package(CUDA 7.0)
+find_package(CUDA 9.0)
 find_package(cuDNN 4.0)
 find_package(OpenCL 1.2)
 find_package(OpenGL)

From a8e86cdbdc1d1ebac2a016a620e17e5517bb1e74 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 25 Jun 2020 11:44:06 -0400
Subject: [PATCH 2001/2677] Fix several errors when compiling on OSX

---
 src/api/unified/CMakeLists.txt             |  1 +
 src/backend/cuda/Array.cpp                 |  3 ---
 src/backend/cuda/Array.hpp                 |  2 +-
 src/backend/cuda/CMakeLists.txt            | 11 +++++++++--
 src/backend/opencl/kernel/sparse_arith.hpp |  2 +-
 5 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index b103c11195..c3e0b8270f 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -76,6 +76,7 @@ target_link_libraries(af
     cpp_api_interface
     spdlog
     Threads::Threads
+    Boost::boost
     ${CMAKE_DL_LIBS}
   )
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 8ade10a592..c937511fda 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -218,9 +218,6 @@ void evalMultiple(std::vector<Array<T> *> arrays) {
     for (Array<T> *array : output_arrays) { array->node = bufferNodePtr<T>(); }
 }
 
-template<typename T>
-Array<T>::~Array() = default;
-
 template<typename T>
 Node_ptr Array<T>::getNode() {
     if (node->isBuffer()) {
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index c528a8306a..9c527ca800 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -179,7 +179,7 @@ class Array {
 
 #undef INFO_IS_FUNC
 
-    ~Array();
+    ~Array() = default;
 
     bool isReady() const { return ready; }
     bool isOwner() const { return owner; }
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f24ee82d87..23d4303168 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -286,18 +286,25 @@ set_target_properties(af_cuda_static_cuda_library
 )
 
 if(UNIX)
+
+  check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
+  if(group_flags)
+    set(START_GROUP -Wl,--start-group)
+    set(END_GROUP -Wl,--end-group)
+  endif()
+
   target_link_libraries(af_cuda_static_cuda_library
     PRIVATE
       Boost::boost
       ${CMAKE_DL_LIBS}
       ${cusolver_lib}
-      -Wl,--start-group
+      ${START_GROUP}
       ${CUDA_culibos_LIBRARY} #also a static libary
       ${CUDA_cublas_static_LIBRARY}
       ${CUDA_cufft_static_LIBRARY}
       ${CUDA_cusparse_static_LIBRARY}
       ${cusolver_static_lib}
-      -Wl,--end-group
+      ${END_GROUP}
   )
 
   if(CUDA_VERSION VERSION_GREATER 9.5)
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 4f2bef334b..87e495bfc7 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -33,7 +33,7 @@ constexpr unsigned TY      = 8;
 constexpr unsigned THREADS = TX * TY;
 
 template<af_op_t op>
-AF_CONSTEXPR std::string getOpString() {
+AF_CONSTEXPR const char *getOpString() {
     switch (op) {
         case af_add_t: return "ADD";
         case af_sub_t: return "SUB";

From f9e33b10359273b259af1141113c0122b099b70f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 25 Jun 2020 12:13:05 -0400
Subject: [PATCH 2002/2677] Add static asserts and move constructors for
 several classes

---
 .github/pull_request_template.md          |  4 +--
 src/backend/common/ArrayInfo.hpp          | 22 +++++++++++++--
 src/backend/common/half.hpp               |  8 ++++++
 src/backend/common/jit/BufferNodeBase.hpp |  4 ++-
 src/backend/common/jit/NaryNode.hpp       | 31 ++++++++++++++++++---
 src/backend/common/jit/Node.hpp           | 34 ++++++++++++++---------
 src/backend/common/jit/ScalarNode.hpp     | 26 ++++++++++++++++-
 src/backend/common/jit/ShiftNodeBase.hpp  | 29 +++++++++++++++++--
 src/backend/common/jit/UnaryNode.hpp      |  7 ++++-
 src/backend/cpu/Array.cpp                 |  4 +++
 src/backend/cpu/Array.hpp                 | 18 ++++++++++++
 src/backend/cuda/Array.cpp                |  6 ++--
 src/backend/opencl/Array.cpp              |  4 +++
 13 files changed, 169 insertions(+), 28 deletions(-)

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 4482b8c870..5669dd9e7f 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,5 +1,5 @@
 
-<!-- 
+<!--
 Short description of change
 
 This should be one or two sentences that describe the overall
@@ -16,7 +16,7 @@ Additional information about the PR answering following questions:
 * More detail if necessary to describe all commits in pull request.
 * Why these changes are necessary.
 * Potential impact on specific hardware, software or backends.
-* New functions and their functionallity.
+* New functions and their functionality.
 * Can this PR be backported to older versions?
 * Future changes not implemented in this PR.
 -->
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index 4dec5c3966..c86d5d3856 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -56,6 +56,10 @@ class ArrayInfo {
         , dim_strides(stride)
         , is_sparse(false) {
         setId(id);
+        static_assert(std::is_move_assignable<ArrayInfo>::value,
+                      "ArrayInfo is not move assignable");
+        static_assert(std::is_move_constructible<ArrayInfo>::value,
+                      "ArrayInfo is not move constructible");
         static_assert(
             offsetof(ArrayInfo, devId) == 0,
             "ArrayInfo::devId must be the first member variable of ArrayInfo. \
@@ -79,10 +83,24 @@ class ArrayInfo {
                    This is then used in the unified backend to check mismatched arrays.");
     }
 
-    // Copy constructors are deprecated if there is a
-    // user-defined destructor in c++11
     ArrayInfo()                       = default;
     ArrayInfo(const ArrayInfo& other) = default;
+    ArrayInfo(ArrayInfo&& other)      = default;
+
+    ArrayInfo& operator=(ArrayInfo other) noexcept {
+        swap(other);
+        return *this;
+    }
+
+    void swap(ArrayInfo& other) noexcept {
+        using std::swap;
+        swap(devId, other.devId);
+        swap(type, other.type);
+        swap(dim_size, other.dim_size);
+        swap(offset, other.offset);
+        swap(dim_strides, other.dim_strides);
+        swap(is_sparse, other.is_sparse);
+    }
 
     const af_dtype& getType() const { return type; }
 
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 60153786e7..50cae18ae7 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -844,6 +844,14 @@ class alignas(2) half {
         data_(bits)
 #endif
     {
+#ifndef __CUDACC_RTC__
+        static_assert(std::is_standard_layout<half>::value,
+                      "half must be a standard layout type");
+        static_assert(std::is_nothrow_move_assignable<half>::value,
+                      "half is not move assignable");
+        static_assert(std::is_nothrow_move_constructible<half>::value,
+                      "half is not move constructible");
+#endif
     }
 
 #if defined(__CUDA_ARCH__)
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index c5a444dbbe..999d9bd078 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -28,7 +28,9 @@ class BufferNodeBase : public common::Node {
     bool m_linear_buffer;
 
    public:
-    BufferNodeBase(af::dtype type) : Node(type, 0, {}) {}
+    BufferNodeBase(af::dtype type) : Node(type, 0, {}) {
+        // This class is not movable because of std::once_flag
+    }
 
     bool isBuffer() const final { return true; }
 
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 0c18a72353..091384114e 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -24,9 +24,9 @@ namespace common {
 
 class NaryNode : public Node {
    private:
-    const int m_num_children;
-    const int m_op;
-    const std::string m_op_str;
+    int m_num_children;
+    int m_op;
+    std::string m_op_str;
 
    public:
     NaryNode(const af::dtype type, const char *op_str, const int num_children,
@@ -39,7 +39,30 @@ class NaryNode : public Node {
                   children))
         , m_num_children(num_children)
         , m_op(op)
-        , m_op_str(op_str) {}
+        , m_op_str(op_str) {
+        static_assert(std::is_nothrow_move_assignable<NaryNode>::value,
+                      "NaryNode is not move assignable");
+        static_assert(std::is_nothrow_move_constructible<NaryNode>::value,
+                      "NaryNode is not move constructible");
+    }
+
+    NaryNode(NaryNode &&other) = default;
+
+    NaryNode(const NaryNode &other) = default;
+
+    /// Default copy assignment operator
+    NaryNode &operator=(const NaryNode &node) = default;
+
+    /// Default move assignment operator
+    NaryNode &operator=(NaryNode &&node) noexcept = default;
+
+    void swap(NaryNode &other) noexcept {
+        using std::swap;
+        Node::swap(other);
+        swap(m_num_children, other.m_num_children);
+        swap(m_op, other.m_op);
+        swap(m_op_str, other.m_op_str);
+    }
 
     void genKerName(std::stringstream &kerStream,
                     const common::Node_ids &ids) const final {
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index b656b92ac4..1c3e94f350 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -20,6 +20,7 @@
 #include <memory>
 #include <string>
 #include <unordered_map>
+#include <utility>
 #include <vector>
 
 enum class kJITHeuristics {
@@ -80,30 +81,37 @@ class Node {
     static const int kMaxChildren = 3;
 
    protected:
-    const std::array<Node_ptr, kMaxChildren> m_children;
-    const af::dtype m_type;
-    const int m_height;
+    std::array<Node_ptr, kMaxChildren> m_children;
+    af::dtype m_type;
+    int m_height;
 
     template<typename T>
     friend class NodeIterator;
 
+    void swap(Node &other) noexcept {
+        using std::swap;
+        for (int i = 0; i < kMaxChildren; i++) {
+            swap(m_children[i], other.m_children[i]);
+        }
+        swap(m_type, other.m_type);
+        swap(m_height, other.m_height);
+    }
+
    public:
+    Node() = default;
     Node(const af::dtype type, const int height,
          const std::array<Node_ptr, kMaxChildren> children)
-        : m_children(children), m_type(type), m_height(height) {}
-
-    /// Default copy constructor
-    Node(Node &node) = default;
+        : m_children(children), m_type(type), m_height(height) {
+        static_assert(std::is_nothrow_move_assignable<Node>::value,
+                      "Node is not move assignable");
+    }
 
-    /// Default move constructor
-    Node(Node &&node) = default;
+    /// Default copy constructor operator
+    Node(const Node &node) = default;
 
     /// Default copy assignment operator
     Node &operator=(const Node &node) = default;
 
-    /// Default move assignment operator
-    Node &operator=(Node &&node) = default;
-
     int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes,
                     std::vector<Node_ids> &full_ids);
 
@@ -213,7 +221,7 @@ class Node {
     virtual std::string getNameStr() const { return getShortName(m_type); }
 
     /// Default destructor
-    virtual ~Node() = default;
+    virtual ~Node() noexcept = default;
 };
 
 struct Node_ids {
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index e4ff5664f0..86e3ad9d98 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -26,7 +26,31 @@ class ScalarNode : public common::Node {
    public:
     ScalarNode(T val)
         : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), 0, {})
-        , m_val(val) {}
+        , m_val(val) {
+        static_assert(std::is_nothrow_move_assignable<ScalarNode>::value,
+                      "ScalarNode is not move assignable");
+        static_assert(std::is_nothrow_move_constructible<ScalarNode>::value,
+                      "ScalarNode is not move constructible");
+    }
+
+    /// Default move copy constructor
+    ScalarNode(const ScalarNode& other) = default;
+
+    /// Default move constructor
+    ScalarNode(ScalarNode&& other) = default;
+
+    /// Default move/copy assignment operator(Rule of 4)
+    ScalarNode& operator=(ScalarNode node) noexcept {
+        swap(node);
+        return *this;
+    }
+
+    // Swap specilization
+    void swap(ScalarNode& other) noexcept {
+        using std::swap;
+        Node::swap(other);
+        swap(m_val, other.m_val);
+    }
 
     void genKerName(std::stringstream& kerStream,
                     const common::Node_ids& ids) const final {
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 68ca54354b..84227ee8df 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -26,12 +26,37 @@ template<typename BufferNode>
 class ShiftNodeBase : public Node {
    private:
     std::shared_ptr<BufferNode> m_buffer_node;
-    const std::array<int, 4> m_shifts;
+    std::array<int, 4> m_shifts;
 
    public:
     ShiftNodeBase(const af::dtype type, std::shared_ptr<BufferNode> buffer_node,
                   const std::array<int, 4> shifts)
-        : Node(type, 0, {}), m_buffer_node(buffer_node), m_shifts(shifts) {}
+        : Node(type, 0, {}), m_buffer_node(buffer_node), m_shifts(shifts) {
+        static_assert(std::is_nothrow_move_assignable<ShiftNodeBase>::value,
+                      "ShiftNode is not move assignable");
+        static_assert(std::is_nothrow_move_constructible<ShiftNodeBase>::value,
+                      "ShiftNode is not move constructible");
+    }
+
+    /// Default move copy constructor
+    ShiftNodeBase(const ShiftNodeBase &other) = default;
+
+    /// Default move constructor
+    ShiftNodeBase(ShiftNodeBase &&other) = default;
+
+    /// Default move/copy assignment operator(Rule of 4)
+    ShiftNodeBase &operator=(ShiftNodeBase node) noexcept {
+        swap(node);
+        return *this;
+    }
+
+    // Swap specilization
+    void swap(ShiftNodeBase &other) noexcept {
+        using std::swap;
+        Node::swap(other);
+        swap(m_buffer_node, other.m_buffer_node);
+        swap(m_shifts, other.m_shifts);
+    }
 
     bool isLinear(dim_t dims[4]) const final {
         UNUSED(dims);
diff --git a/src/backend/common/jit/UnaryNode.hpp b/src/backend/common/jit/UnaryNode.hpp
index c0588f4cee..1ffe9cd25d 100644
--- a/src/backend/common/jit/UnaryNode.hpp
+++ b/src/backend/common/jit/UnaryNode.hpp
@@ -15,6 +15,11 @@ namespace common {
 class UnaryNode : public NaryNode {
    public:
     UnaryNode(const af::dtype type, const char *op_str, Node_ptr child, int op)
-        : NaryNode(type, op_str, 1, {{child}}, op, child->getHeight() + 1) {}
+        : NaryNode(type, op_str, 1, {{child}}, op, child->getHeight() + 1) {
+        static_assert(std::is_nothrow_move_assignable<UnaryNode>::value,
+                      "UnaryNode is not move assignable");
+        static_assert(std::is_nothrow_move_constructible<UnaryNode>::value,
+                      "UnaryNode is not move constructible");
+    }
 };
 }  // namespace common
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index c7b7439295..232948cf19 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -80,6 +80,10 @@ Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
     , owner(true) {
     static_assert(is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
+    static_assert(std::is_move_assignable<Array<T>>::value,
+                  "Array<T> is not move assignable");
+    static_assert(std::is_move_constructible<Array<T>>::value,
+                  "Array<T> is not move constructible");
     static_assert(
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 037db5c58b..39b47d9bda 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -134,6 +134,24 @@ class Array {
           T *const in_data, bool is_device = false);
 
    public:
+    Array<T>(const Array<T> &other) = default;
+    Array<T>(Array<T> &&other)      = default;
+
+    Array<T> &operator=(Array<T> other) noexcept {
+        swap(other);
+        return *this;
+    }
+
+    void swap(Array<T> &other) noexcept {
+        using std::swap;
+        swap(info, other.info);
+        swap(data, other.data);
+        swap(data_dims, other.data_dims);
+        swap(node, other.node);
+        swap(ready, other.ready);
+        swap(owner, other.owner);
+    }
+
     void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
     void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
     void modDims(const af::dim4 &newDims) { info.modDims(newDims); }
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index c937511fda..e3caeba9bc 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -78,13 +78,15 @@ Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
     , node(bufferNodePtr<T>())
     , ready(true)
     , owner(true) {
-#if __cplusplus > 199711L
     static_assert(std::is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
+    static_assert(std::is_move_assignable<Array<T>>::value,
+                  "Array<T> is not move assignable");
+    static_assert(std::is_move_constructible<Array<T>>::value,
+                  "Array<T> is not move constructible");
     static_assert(
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");
-#endif
     if (!is_device) {
         CUDA_CHECK(
             cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index c47fc56ee0..3e837b8279 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -105,6 +105,10 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
     , owner(true) {
     static_assert(is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
+    static_assert(std::is_move_assignable<Array<T>>::value,
+                  "Array<T> is not move assignable");
+    static_assert(std::is_move_constructible<Array<T>>::value,
+                  "Array<T> is not move constructible");
     static_assert(
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");

From 1322fed85d6af47286df202fddbd7a1dd1ad9a2a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 23 Jun 2020 19:15:43 +0530
Subject: [PATCH 2003/2677] Use descriptor based cusparse API for sparse blas
 fns

cusparseSpMv/cusparseSpMM functions use sparse and dense matrix/vector
descriptor objects as arguments. This API is introduced in CUDA 10.1 and
old API has been deprecated. It is also removed in CUDA 11.
---
 src/backend/cuda/CMakeLists.txt               |  14 ++
 src/backend/cuda/blas.cu                      |  51 +------
 src/backend/cuda/cudaDataType.hpp             |  68 ++++++++++
 .../cuda/cusparse_descriptor_helpers.hpp      |  56 ++++++++
 src/backend/cuda/handle.cpp                   |  21 +++
 src/backend/cuda/sparse_blas.cu               | 124 ++++++++++++------
 6 files changed, 245 insertions(+), 89 deletions(-)
 create mode 100644 src/backend/cuda/cudaDataType.hpp
 create mode 100644 src/backend/cuda/cusparse_descriptor_helpers.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 23d4303168..f3e61e3579 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -234,6 +234,12 @@ if(AF_WITH_NONFREE)
   set(cxx_definitions -DAF_WITH_NONFREE_SIFT)
 endif()
 
+if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
+   (UNIX AND
+    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 0))
+  list(APPEND cxx_definitions -DAF_USE_NEW_CUSPARSE_API)
+endif()
+
 # CUDA_NO_HALF prevents the inclusion of the half class in the global namespace
 # which conflicts with the half class in ArrayFire's common namespace. prefer
 # using __half class instead for CUDA
@@ -262,8 +268,10 @@ endif()
 cuda_add_library(af_cuda_static_cuda_library STATIC
     blas.cu
     blas.hpp
+    cudaDataType.hpp
     cufft.cu
     cufft.hpp
+    cusparse_descriptor_helpers.hpp
     fft.cu
     sparse.cu
     sparse.hpp
@@ -285,6 +293,12 @@ set_target_properties(af_cuda_static_cuda_library
   FOLDER "Generated Targets"
 )
 
+if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
+   (UNIX AND
+    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 0))
+  target_compile_definitions(af_cuda_static_cuda_library PRIVATE AF_USE_NEW_CUSPARSE_API)
+endif()
+
 if(UNIX)
 
   check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
diff --git a/src/backend/cuda/blas.cu b/src/backend/cuda/blas.cu
index be6cda902d..dd906b2ecf 100644
--- a/src/backend/cuda/blas.cu
+++ b/src/backend/cuda/blas.cu
@@ -17,6 +17,7 @@
 #include <copy.hpp>
 #include <cublas.hpp>
 #include <cublas_v2.h>
+#include <cudaDataType.hpp>
 #include <cuda_runtime.h>
 #include <err_cuda.hpp>
 #include <math.hpp>
@@ -141,56 +142,6 @@ BLAS_FUNC(dot, cdouble, false, Z, u)
 #undef BLAS_FUNC
 #undef BLAS_FUNC_DEF
 
-template<typename T>
-cudaDataType_t getType();
-
-template<>
-cudaDataType_t getType<float>() {
-    return CUDA_R_32F;
-}
-
-template<>
-cudaDataType_t getType<cfloat>() {
-    return CUDA_C_32F;
-}
-
-template<>
-cudaDataType_t getType<double>() {
-    return CUDA_R_64F;
-}
-
-template<>
-cudaDataType_t getType<cdouble>() {
-    return CUDA_C_64F;
-}
-
-template<>
-cudaDataType_t getType<half>() {
-    return CUDA_R_16F;
-}
-
-template<typename T>
-cudaDataType_t getComputeType() {
-    return getType<T>();
-}
-
-template<>
-cudaDataType_t getComputeType<half>() {
-    cudaDataType_t algo = getType<half>();
-    // There is probbaly a bug in nvidia cuda docs and/or drivers: According to
-    // https://docs.nvidia.com/cuda/cublas/index.html#cublas-GemmEx computeType
-    // could be 32F even if A/B inputs are 16F. But CudaCompute 6.1 GPUs (for
-    // example GTX10X0) dont seem to be capbale to compute at f32 when the
-    // inputs are f16: results are inf if trying to do so and cublasGemmEx even
-    // returns OK. At the moment let's comment out : the drawback is just that
-    // the speed of f16 computation on these GPUs is very slow:
-    //
-    // auto dev            = getDeviceProp(getActiveDeviceId());
-    // if (dev.major == // 6 && dev.minor == 1) { algo = CUDA_R_32F; }
-
-    return algo;
-}
-
 template<typename T>
 cublasGemmAlgo_t selectGEMMAlgorithm() {
     return CUBLAS_GEMM_DEFAULT;
diff --git a/src/backend/cuda/cudaDataType.hpp b/src/backend/cuda/cudaDataType.hpp
new file mode 100644
index 0000000000..4e1d874e97
--- /dev/null
+++ b/src/backend/cuda/cudaDataType.hpp
@@ -0,0 +1,68 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/half.hpp>
+#include <library_types.h>  // cudaDataType enum
+#include <types.hpp>
+
+namespace cuda {
+
+template<typename T>
+inline cudaDataType_t getType();
+
+template<>
+inline cudaDataType_t getType<float>() {
+    return CUDA_R_32F;
+}
+
+template<>
+inline cudaDataType_t getType<cfloat>() {
+    return CUDA_C_32F;
+}
+
+template<>
+inline cudaDataType_t getType<double>() {
+    return CUDA_R_64F;
+}
+
+template<>
+inline cudaDataType_t getType<cdouble>() {
+    return CUDA_C_64F;
+}
+
+template<>
+inline cudaDataType_t getType<common::half>() {
+    return CUDA_R_16F;
+}
+
+template<typename T>
+inline cudaDataType_t getComputeType() {
+    return getType<T>();
+}
+
+template<>
+inline cudaDataType_t getComputeType<common::half>() {
+    cudaDataType_t algo = getType<common::half>();
+    // There is probbaly a bug in nvidia cuda docs and/or drivers: According to
+    // https://docs.nvidia.com/cuda/cublas/index.html#cublas-GemmEx computeType
+    // could be 32F even if A/B inputs are 16F. But CudaCompute 6.1 GPUs (for
+    // example GTX10X0) dont seem to be capbale to compute at f32 when the
+    // inputs are f16: results are inf if trying to do so and cublasGemmEx even
+    // returns OK. At the moment let's comment out : the drawback is just that
+    // the speed of f16 computation on these GPUs is very slow:
+    //
+    // auto dev            = getDeviceProp(getActiveDeviceId());
+    // if (dev.major == // 6 && dev.minor == 1) { algo = CUDA_R_32F; }
+
+    return algo;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/cusparse_descriptor_helpers.hpp b/src/backend/cuda/cusparse_descriptor_helpers.hpp
new file mode 100644
index 0000000000..2a71b3afa0
--- /dev/null
+++ b/src/backend/cuda/cusparse_descriptor_helpers.hpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#if defined(AF_USE_NEW_CUSPARSE_API)
+// CUDA Toolkit 10.0 or later
+
+#include <common/unique_handle.hpp>
+#include <cusparse.hpp>
+
+namespace cuda {
+
+template<typename T>
+common::unique_handle<cusparseSpMatDescr_t> csrMatDescriptor(
+    const common::SparseArray<T> &in) {
+    auto dims                   = in.dims();
+    cusparseSpMatDescr_t resMat = NULL;
+    CUSPARSE_CHECK(cusparseCreateCsr(
+        &resMat, dims[0], dims[1], in.getNNZ(), (void *)(in.getRowIdx().get()),
+        (void *)(in.getColIdx().get()), (void *)(in.getValues().get()),
+        CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO,
+        getType<T>()));
+    return common::unique_handle<cusparseSpMatDescr_t>(resMat);
+}
+
+template<typename T>
+common::unique_handle<cusparseDnVecDescr_t> denVecDescriptor(
+    const Array<T> &in) {
+    auto dims                   = in.dims();
+    cusparseDnVecDescr_t resVec = NULL;
+    CUSPARSE_CHECK(cusparseCreateDnVec(&resVec, dims.elements(),
+                                       (void *)(in.get()), getType<T>()));
+    return common::unique_handle<cusparseDnVecDescr_t>(resVec);
+}
+
+template<typename T>
+common::unique_handle<cusparseDnMatDescr_t> denMatDescriptor(
+    const Array<T> &in) {
+    auto dims                   = in.dims();
+    cusparseDnMatDescr_t resMat = NULL;
+    CUSPARSE_CHECK(cusparseCreateDnMat(&resMat, dims[0], dims[1], dims[0],
+                                       (void *)(in.get()), getType<T>(),
+                                       CUSPARSE_ORDER_COL));
+    return common::unique_handle<cusparseDnMatDescr_t>(resMat);
+}
+
+}  // namespace cuda
+
+#endif
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
index eb1ad7a167..cc336ed292 100644
--- a/src/backend/cuda/handle.cpp
+++ b/src/backend/cuda/handle.cpp
@@ -20,6 +20,27 @@ CREATE_HANDLE(cublasHandle_t, cublasCreate, cublasDestroy);
 CREATE_HANDLE(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
 CREATE_HANDLE(cufftHandle, cufftCreate, cufftDestroy);
 
+#if defined(AF_USE_NEW_CUSPARSE_API)
+namespace common {
+
+template<>
+void handle_deleter<cusparseSpMatDescr_t>(cusparseSpMatDescr_t handle) noexcept {
+    cusparseDestroySpMat(handle);
+}
+
+template<>
+void handle_deleter<cusparseDnVecDescr_t>(cusparseDnVecDescr_t handle) noexcept {
+    cusparseDestroyDnVec(handle);
+}
+
+template<>
+void handle_deleter<cusparseDnMatDescr_t>(cusparseDnMatDescr_t handle) noexcept {
+    cusparseDestroyDnMat(handle);
+}
+
+}  // namespace common
+#endif
+
 #ifdef WITH_CUDNN
 
 #include <cudnn.hpp>
diff --git a/src/backend/cuda/sparse_blas.cu b/src/backend/cuda/sparse_blas.cu
index eb7378776c..179c17615d 100644
--- a/src/backend/cuda/sparse_blas.cu
+++ b/src/backend/cuda/sparse_blas.cu
@@ -11,8 +11,10 @@
 
 #include <common/err_common.hpp>
 #include <complex.hpp>
+#include <cudaDataType.hpp>
 #include <cuda_runtime.h>
 #include <cusparse.hpp>
+#include <cusparse_descriptor_helpers.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 
@@ -32,51 +34,69 @@ cusparseOperation_t toCusparseTranspose(af_mat_prop opt) {
     return out;
 }
 
-// cusparseStatus_t cusparseZcsrmm(  cusparseHandle_t handle,
-//                                  cusparseOperation_t transA,
-//                                  int m, int n, int k, int nnz,
-//                                  const cuDoubleComplex *alpha,
-//                                  const cusparseMatDescr_t descrA,
-//                                  const cuDoubleComplex *csrValA,
-//                                  const int *csrRowPtrA, const int
-//                                  *csrColIndA, const cuDoubleComplex *B, int
-//                                  ldb, const cuDoubleComplex *beta,
-//                                  cuDoubleComplex *C, int ldc);
+#if defined(AF_USE_NEW_CUSPARSE_API)
 
 template<typename T>
-struct csrmm_func_def_t {
-    typedef cusparseStatus_t (*csrmm_func_def)(
-        cusparseHandle_t, cusparseOperation_t, int, int, int, int, const T *,
-        const cusparseMatDescr_t, const T *, const int *, const int *,
-        const T *, int, const T *, T *, int);
-};
+size_t spmvBufferSize(cusparseOperation_t opA, const T *alpha,
+                      const cusparseSpMatDescr_t matA,
+                      const cusparseDnVecDescr_t vecX, const T *beta,
+                      const cusparseDnVecDescr_t vecY) {
+    size_t retVal = 0;
+    CUSPARSE_CHECK(cusparseSpMV_bufferSize(
+        sparseHandle(), opA, alpha, matA, vecX, beta, vecY, getComputeType<T>(),
+        CUSPARSE_CSRMV_ALG1, &retVal));
+    return retVal;
+}
+
+template<typename T>
+void spmv(cusparseOperation_t opA, const T *alpha,
+          const cusparseSpMatDescr_t matA, const cusparseDnVecDescr_t vecX,
+          const T *beta, const cusparseDnVecDescr_t vecY, void *buffer) {
+    CUSPARSE_CHECK(cusparseSpMV(sparseHandle(), opA, alpha, matA, vecX, beta,
+                                vecY, getComputeType<T>(),
+                                CUSPARSE_MV_ALG_DEFAULT, buffer));
+}
 
-// cusparseStatus_t cusparseZcsrmv(  cusparseHandle_t handle,
-//                                  cusparseOperation_t transA,
-//                                  int m, int n, int nnz,
-//                                  const cuDoubleComplex *alpha,
-//                                  const cusparseMatDescr_t descrA,
-//                                  const cuDoubleComplex *csrValA,
-//                                  const int *csrRowPtrA, const int
-//                                  *csrColIndA, const cuDoubleComplex *x, const
-//                                  cuDoubleComplex *beta, cuDoubleComplex *y)
+template<typename T>
+size_t spmmBufferSize(cusparseOperation_t opA, cusparseOperation_t opB,
+                      const T *alpha, const cusparseSpMatDescr_t matA,
+                      const cusparseDnMatDescr_t matB, const T *beta,
+                      const cusparseDnMatDescr_t matC) {
+    size_t retVal = 0;
+    CUSPARSE_CHECK(cusparseSpMM_bufferSize(
+        sparseHandle(), opA, opB, alpha, matA, matB, beta, matC,
+        getComputeType<T>(), CUSPARSE_CSRMM_ALG1, &retVal));
+    return retVal;
+}
+
+template<typename T>
+void spmm(cusparseOperation_t opA, cusparseOperation_t opB, const T *alpha,
+          const cusparseSpMatDescr_t matA, const cusparseDnMatDescr_t matB,
+          const T *beta, const cusparseDnMatDescr_t matC, void *buffer) {
+    CUSPARSE_CHECK(cusparseSpMM(sparseHandle(), opA, opB, alpha, matA, matB,
+                                beta, matC, getComputeType<T>(),
+                                CUSPARSE_CSRMM_ALG1, buffer));
+}
+
+#else
 
 template<typename T>
 struct csrmv_func_def_t {
     typedef cusparseStatus_t (*csrmv_func_def)(
-        cusparseHandle_t, cusparseOperation_t, int, int, int, const T *,
-        const cusparseMatDescr_t, const T *, const int *, const int *,
-        const T *, const T *, T *);
+        cusparseHandle_t handle, cusparseOperation_t transA, int m, int n,
+        int k, const T *alpha, const cusparseMatDescr_t descrA,
+        const T *csrValA, const int *csrRowPtrA, const int *csrColIndA,
+        const T *x, const T *beta, T *y);
 };
 
-// cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
-//                                  int m, int n, int nnz,
-//                                  const cuDoubleComplex *csrSortedVal,
-//                                  const int *csrSortedRowPtr, const int
-//                                  *csrSortedColInd, cuDoubleComplex
-//                                  *cscSortedVal, int *cscSortedRowInd, int
-//                                  *cscSortedColPtr, cusparseAction_t
-//                                  copyValues, cusparseIndexBase_t idxBase);
+template<typename T>
+struct csrmm_func_def_t {
+    typedef cusparseStatus_t (*csrmm_func_def)(
+        cusparseHandle_t handle, cusparseOperation_t transA, int m, int n,
+        int k, int nnz, const T *alpha, const cusparseMatDescr_t descrA,
+        const T *csrValA, const int *csrRowPtrA, const int *csrColIndA,
+        const T *B, int ldb, const T *beta, T *C, int ldc);
+};
 
 #define SPARSE_FUNC_DEF(FUNC) \
     template<typename T>      \
@@ -104,10 +124,11 @@ SPARSE_FUNC(csrmv, cdouble, Z)
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
 
+#endif
+
 template<typename T>
 Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
-    UNUSED(optRhs);
     // Similar Operations to GEMM
     cusparseOperation_t lOpts = toCusparseTranspose(optLhs);
 
@@ -128,6 +149,31 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
 
     dim4 rStrides = rhs.strides();
 
+#if defined(AF_USE_NEW_CUSPARSE_API)
+
+    auto spMat = csrMatDescriptor<T>(lhs);
+
+    if (rDims[rColDim] == 1) {
+        auto dnVec = denVecDescriptor<T>(rhs);
+        auto dnOut = denVecDescriptor<T>(out);
+        size_t bufferSize =
+            spmvBufferSize<T>(lOpts, &alpha, spMat, dnVec, &beta, dnOut);
+        auto tempBuffer = createEmptyArray<char>(dim4(bufferSize));
+        spmv<T>(lOpts, &alpha, spMat, dnVec, &beta, dnOut, tempBuffer.get());
+    } else {
+        cusparseOperation_t rOpts = toCusparseTranspose(optRhs);
+
+        auto dnMat = denMatDescriptor<T>(rhs);
+        auto dnOut = denMatDescriptor<T>(out);
+        size_t bufferSize =
+            spmmBufferSize<T>(lOpts, rOpts, &alpha, spMat, dnMat, &beta, dnOut);
+        auto tempBuffer = createEmptyArray<char>(dim4(bufferSize));
+        spmm<T>(lOpts, rOpts, &alpha, spMat, dnMat, &beta, dnOut,
+                tempBuffer.get());
+    }
+
+#else
+
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
@@ -151,10 +197,10 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
             lhs.getRowIdx().get(), lhs.getColIdx().get(), rhs.get(),
             rStrides[1], &beta, out.get(), out.dims()[0]));
     }
-
-    // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
 
+#endif
+
     return out;
 }
 

From 9bbc2425ba92b22fe708125a3ef54adf3e2a7281 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 23 Jun 2020 23:25:30 +0530
Subject: [PATCH 2004/2677] Changes to support build with CUDA 11

Also, updates CUB version from 1.8.0 to 1.9.10
---
 .gitmodules                      |  4 +-
 extern/cub                       |  1 +
 src/backend/cuda/CMakeLists.txt  |  4 +-
 src/backend/cuda/cub             |  1 -
 src/backend/cuda/sparse.cu       | 24 --------
 src/backend/cuda/sparse_arith.cu | 98 +++++++++++++++++++++++++++-----
 6 files changed, 89 insertions(+), 43 deletions(-)
 create mode 160000 extern/cub
 delete mode 160000 src/backend/cuda/cub

diff --git a/.gitmodules b/.gitmodules
index 40a0000571..ba7e49284c 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -10,8 +10,8 @@
 [submodule "src/backend/cpu/threads"]
 	path = src/backend/cpu/threads
 	url = https://github.com/alltheflops/threads.git
-[submodule "src/backend/cuda/cub"]
-	path = src/backend/cuda/cub
+[submodule "extern/cub"]
+	path = extern/cub
 	url = https://github.com/NVlabs/cub.git
 [submodule "extern/spdlog"]
 	path = extern/spdlog
diff --git a/extern/cub b/extern/cub
new file mode 160000
index 0000000000..d106ddb991
--- /dev/null
+++ b/extern/cub
@@ -0,0 +1 @@
+Subproject commit d106ddb991a56c3df1b6d51b2409e36ba8181ce4
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f3e61e3579..42fada2cee 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -101,11 +101,13 @@ cuda_include_directories(
   ${ArrayFire_BINARY_DIR}/include
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel
   ${CMAKE_CURRENT_SOURCE_DIR}/jit
-  ${CMAKE_CURRENT_SOURCE_DIR}/cub
   ${ArrayFire_SOURCE_DIR}/src/api/c
   ${ArrayFire_SOURCE_DIR}/src/backend
   ${COMMON_INTERFACE_DIRS}
   )
+if(CUDA_VERSION_MAJOR VERSION_LESS 11)
+  cuda_include_directories(${ArrayFire_SOURCE_DIR}/extern/cub)
+endif()
 
 file(GLOB jit_src "kernel/jit.cuh")
 
diff --git a/src/backend/cuda/cub b/src/backend/cuda/cub
deleted file mode 160000
index c3cceac115..0000000000
--- a/src/backend/cuda/cub
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit c3cceac115c072fb63df1836ff46d8c60d9eb304
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index b7186085ba..6511cc4ce6 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -28,24 +28,6 @@ namespace cuda {
 
 using namespace common;
 
-// cusparseStatus_t cusparseZcsr2csc(cusparseHandle_t handle,
-//                                  int m, int n, int nnz,
-//                                  const cuDoubleComplex *csrSortedVal,
-//                                  const int *csrSortedRowPtr, const int
-//                                  *csrSortedColInd, cuDoubleComplex
-//                                  *cscSortedVal, int *cscSortedRowInd, int
-//                                  *cscSortedColPtr, cusparseAction_t
-//                                  copyValues, cusparseIndexBase_t idxBase);
-
-template<typename T>
-struct csr2csc_func_def_t {
-    typedef cusparseStatus_t (*csr2csc_func_def)(cusparseHandle_t, int, int,
-                                                 int, const T *, const int *,
-                                                 const int *, T *, int *, int *,
-                                                 cusparseAction_t,
-                                                 cusparseIndexBase_t);
-};
-
 // cusparseStatus_t cusparseZdense2csr(cusparseHandle_t handle,
 //                                    int m, int n,
 //                                    const cusparseMatDescr_t descrA,
@@ -144,12 +126,6 @@ struct gthr_func_def_t {
                cusparse##PREFIX##FUNC;                                      \
     }
 
-SPARSE_FUNC_DEF(csr2csc)
-SPARSE_FUNC(csr2csc, float, S)
-SPARSE_FUNC(csr2csc, double, D)
-SPARSE_FUNC(csr2csc, cfloat, C)
-SPARSE_FUNC(csr2csc, cdouble, Z)
-
 SPARSE_FUNC_DEF(dense2csr)
 SPARSE_FUNC(dense2csr, float, S)
 SPARSE_FUNC(dense2csr, double, D)
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 66fad0bac2..0107702110 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -111,6 +111,60 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
     return out;
 }
 
+#define SPARSE_ARITH_OP_FUNC_DEF(FUNC) \
+    template<typename T>               \
+    FUNC##_def<T> FUNC##_func();
+
+#define SPARSE_ARITH_OP_FUNC(FUNC, TYPE, INFIX) \
+    template<>                                  \
+    FUNC##_def<TYPE> FUNC##_func<TYPE>() {      \
+        return cusparse##INFIX##FUNC;           \
+    }
+
+#if CUDA_VERSION >= 11000
+
+template<typename T>
+using csrgeam2_buffer_size_def = cusparseStatus_t (*)(
+    cusparseHandle_t, int, int, const T *, const cusparseMatDescr_t, int,
+    const T *, const int *, const int *, const T *, const cusparseMatDescr_t,
+    int, const T *, const int *, const int *, const cusparseMatDescr_t,
+    const T *, const int *, const int *, size_t *);
+
+#define SPARSE_ARITH_OP_BUFFER_SIZE_FUNC_DEF(FUNC) \
+    template<typename T>                           \
+    FUNC##_buffer_size_def<T> FUNC##_buffer_size_func();
+
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC_DEF(csrgeam2);
+
+#define SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(FUNC, TYPE, INFIX)        \
+    template<>                                                     \
+    FUNC##_buffer_size_def<TYPE> FUNC##_buffer_size_func<TYPE>() { \
+        return cusparse##INFIX##FUNC##_bufferSizeExt;              \
+    }
+
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, float, S);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, double, D);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, cfloat, C);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, cdouble, Z);
+
+template<typename T>
+using csrgeam2_def = cusparseStatus_t (*)(cusparseHandle_t, int, int, const T *,
+                                          const cusparseMatDescr_t, int,
+                                          const T *, const int *, const int *,
+                                          const T *, const cusparseMatDescr_t,
+                                          int, const T *, const int *,
+                                          const int *, const cusparseMatDescr_t,
+                                          T *, int *, int *, void *);
+
+SPARSE_ARITH_OP_FUNC_DEF(csrgeam2);
+
+SPARSE_ARITH_OP_FUNC(csrgeam2, float, S);
+SPARSE_ARITH_OP_FUNC(csrgeam2, double, D);
+SPARSE_ARITH_OP_FUNC(csrgeam2, cfloat, C);
+SPARSE_ARITH_OP_FUNC(csrgeam2, cdouble, Z);
+
+#else
+
 template<typename T>
 using csrgeam_def = cusparseStatus_t (*)(cusparseHandle_t, int, int, const T *,
                                          const cusparseMatDescr_t, int,
@@ -120,23 +174,15 @@ using csrgeam_def = cusparseStatus_t (*)(cusparseHandle_t, int, int, const T *,
                                          const int *, const cusparseMatDescr_t,
                                          T *, int *, int *);
 
-#define SPARSE_ARITH_OP_FUNC_DEF(FUNC) \
-    template<typename T>               \
-    FUNC##_def<T> FUNC##_func();
-
 SPARSE_ARITH_OP_FUNC_DEF(csrgeam);
 
-#define SPARSE_ARITH_OP_FUNC(FUNC, TYPE, INFIX) \
-    template<>                                  \
-    FUNC##_def<TYPE> FUNC##_func<TYPE>() {      \
-        return cusparse##INFIX##FUNC;           \
-    }
-
 SPARSE_ARITH_OP_FUNC(csrgeam, float, S);
 SPARSE_ARITH_OP_FUNC(csrgeam, double, D);
 SPARSE_ARITH_OP_FUNC(csrgeam, cfloat, C);
 SPARSE_ARITH_OP_FUNC(csrgeam, cdouble, Z);
 
+#endif
+
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     lhs.eval();
@@ -163,9 +209,28 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     int baseC, nnzC;
     int *nnzcDevHostPtr = &nnzC;
 
+    T alpha = scalar<T>(1);
+    T beta  = op == af_sub_t ? scalar<T>(-1) : alpha;
+
+#if CUDA_VERSION >= 11000
+    size_t pBufferSize = 0;
+
+    csrgeam2_buffer_size_func<T>()(
+        sparseHandle(), M, N, &alpha, desc, nnzA, lhs.getValues().get(),
+        csrRowPtrA, csrColPtrA, &beta, desc, nnzB, rhs.getValues().get(),
+        csrRowPtrB, csrColPtrB, desc, NULL, csrRowPtrC, NULL, &pBufferSize);
+
+    auto tmpBuffer = createEmptyArray<char>(dim4(pBufferSize));
+
+    CUSPARSE_CHECK(cusparseXcsrgeam2Nnz(
+        sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
+        csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr,
+        tmpBuffer.get()));
+#else
     CUSPARSE_CHECK(cusparseXcsrgeamNnz(
         sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
         csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr));
+#endif
     if (NULL != nnzcDevHostPtr) {
         nnzC = *nnzcDevHostPtr;
     } else {
@@ -181,15 +246,18 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
 
     auto outColIdx = createEmptyArray<int>(dim4(nnzC));
     auto outValues = createEmptyArray<T>(dim4(nnzC));
-
-    T alpha = scalar<T>(1);
-    T beta  = op == af_sub_t ? scalar<T>(-1) : alpha;
-
+#if CUDA_VERSION >= 11000
+    csrgeam2_func<T>()(sparseHandle(), M, N, &alpha, desc, nnzA,
+                       lhs.getValues().get(), csrRowPtrA, csrColPtrA, &beta,
+                       desc, nnzB, rhs.getValues().get(), csrRowPtrB,
+                       csrColPtrB, desc, outValues.get(), csrRowPtrC,
+                       outColIdx.get(), tmpBuffer.get());
+#else
     csrgeam_func<T>()(sparseHandle(), M, N, &alpha, desc, nnzA,
                       lhs.getValues().get(), csrRowPtrA, csrColPtrA, &beta,
                       desc, nnzB, rhs.getValues().get(), csrRowPtrB, csrColPtrB,
                       desc, outValues.get(), csrRowPtrC, outColIdx.get());
-
+#endif
     SparseArray<T> retVal = createArrayDataSparseArray(
         ldims, outValues, outRowIdx, outColIdx, sfmt);
     return retVal;

From 71e1a25d960f553565c4cc6e01b93443d27d8bae Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 26 Jun 2020 19:42:46 +0530
Subject: [PATCH 2005/2677] Cautionary notes about default random engine handle
 management

---
 docs/details/random.dox | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/details/random.dox b/docs/details/random.dox
index 4da8fc7ec3..63ca846106 100644
--- a/docs/details/random.dox
+++ b/docs/details/random.dox
@@ -67,6 +67,9 @@ an \ref af::randomEngine object as an argument.
 
 Returns the \ref af::randomEngine that is currently set as default.
 
+Note that there is no need to call \ref af_release_random_engine on the handle
+returned by \ref af_get_default_random_engine.
+
 \ingroup random_mat
 
 ===============================================================================

From f2630727ca8ea176d0234ddcf2370a54a5b40443 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 26 Jun 2020 11:38:40 -0400
Subject: [PATCH 2006/2677] Propagate nvrtc errors up the stack in AFError
 exceptions

* NVRTC errors were only printed in debug builds. The error messages
  were not passed to the exceptions thrown by the lib. This made
  it harder to debug issues
---
 src/backend/cuda/compile_module.cpp | 72 ++++++++++++++---------------
 1 file changed, 34 insertions(+), 38 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index ee4ce27e49..1f54aa8079 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -81,47 +81,44 @@ using std::chrono::duration_cast;
 using std::chrono::high_resolution_clock;
 using std::chrono::milliseconds;
 
-#ifdef NDEBUG
-#define CU_LINK_CHECK(fn)                                                 \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[2048];                                            \
-        const char *cu_err_name;                                          \
-        cuGetErrorName(res, &cu_err_name);                                \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), linkError);                     \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+#define CU_LINK_CHECK(fn)                                               \
+    do {                                                                \
+        CUresult res = (fn);                                            \
+        if (res == CUDA_SUCCESS) break;                                 \
+        array<char, 2048> cu_err_msg;                                   \
+        const char *cu_err_name;                                        \
+        cuGetErrorName(res, &cu_err_name);                              \
+        snprintf(cu_err_msg.data(), cu_err_msg.size(),                  \
+                 "CU Link Error %s(%d): %s\n", cu_err_name, (int)(res), \
+                 linkError);                                            \
+        AF_ERROR(cu_err_msg.data(), AF_ERR_INTERNAL);                   \
     } while (0)
-#else
-#define CU_LINK_CHECK(fn) CU_CHECK(fn)
-#endif
 
-#ifndef NDEBUG
-#define NVRTC_CHECK(fn)                                \
-    do {                                               \
-        nvrtcResult res = fn;                          \
-        if (res == NVRTC_SUCCESS) break;               \
-        size_t logSize;                                \
-        nvrtcGetProgramLogSize(prog, &logSize);        \
-        unique_ptr<char[]> log(new char[logSize + 1]); \
-        char *logptr = log.get();                      \
-        nvrtcGetProgramLog(prog, logptr);              \
-        logptr[logSize] = '\x0';                       \
-        puts(logptr);                                  \
-        AF_ERROR("NVRTC ERROR", AF_ERR_INTERNAL);      \
-    } while (0)
-#else
 #define NVRTC_CHECK(fn)                                                   \
     do {                                                                  \
         nvrtcResult res = (fn);                                           \
         if (res == NVRTC_SUCCESS) break;                                  \
-        char nvrtc_err_msg[2048];                                         \
-        snprintf(nvrtc_err_msg, sizeof(nvrtc_err_msg),                    \
+        array<char, 4096> nvrtc_err_msg;                                  \
+        snprintf(nvrtc_err_msg.data(), nvrtc_err_msg.size(),              \
                  "NVRTC Error(%d): %s\n", res, nvrtcGetErrorString(res)); \
-        AF_ERROR(nvrtc_err_msg, AF_ERR_INTERNAL);                         \
+        AF_ERROR(nvrtc_err_msg.data(), AF_ERR_INTERNAL);                  \
+    } while (0)
+
+#define NVRTC_COMPILE_CHECK(fn)                              \
+    do {                                                     \
+        nvrtcResult res = (fn);                              \
+        if (res == NVRTC_SUCCESS) break;                     \
+        size_t logSize;                                      \
+        nvrtcGetProgramLogSize(prog, &logSize);              \
+        vector<char> log(logSize + 1);                       \
+        nvrtcGetProgramLog(prog, log.data());                \
+        log[logSize] = '\0';                                 \
+        array<char, 4096> nvrtc_err_msg;                     \
+        snprintf(nvrtc_err_msg.data(), nvrtc_err_msg.size(), \
+                 "NVRTC Error(%d): %s\nLog: \n%s\n", res,    \
+                 nvrtcGetErrorString(res), log.data());      \
+        AF_ERROR(nvrtc_err_msg.data(), AF_ERR_INTERNAL);     \
     } while (0)
-#endif
 
 spdlog::logger *getLogger() {
     static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
@@ -264,8 +261,8 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     }
 
     auto compile = high_resolution_clock::now();
-    NVRTC_CHECK(nvrtcCompileProgram(prog, compiler_options.size(),
-                                    compiler_options.data()));
+    NVRTC_COMPILE_CHECK(nvrtcCompileProgram(prog, compiler_options.size(),
+                                            compiler_options.data()));
     auto compile_end = high_resolution_clock::now();
     size_t ptx_size;
     vector<char> ptx;
@@ -273,7 +270,7 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     ptx.resize(ptx_size);
     NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));
 
-    const size_t linkLogSize    = 1024;
+    const size_t linkLogSize    = 4096;
     char linkInfo[linkLogSize]  = {0};
     char linkError[linkLogSize] = {0};
 
@@ -367,8 +364,7 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
                               return lhs + ", " + rhs;
                           });
     };
-    AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}}}",
-             sources[0],
+    AF_TRACE("{{{compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}}",
              duration_cast<milliseconds>(compile_end - compile).count(),
              duration_cast<milliseconds>(link_end - link).count(),
              listOpts(compiler_options), getDeviceProp(device).name);

From 703a1fdc8d0505267c060866ee16d8669ae6fa2b Mon Sep 17 00:00:00 2001
From: Christoph Junghans <junghans@votca.org>
Date: Mon, 29 Jun 2020 09:28:02 -0600
Subject: [PATCH 2007/2677] FindMKL.cmake: allow double include

Only create targets if they are not existing already, this happens
when find_package(MKL) is called twice for some reason.

Also supipress a warning from cmake-3.17 about mismatching package
names.
---
 CMakeModules/FindMKL.cmake | 25 ++++++++++++++++++-------
 1 file changed, 18 insertions(+), 7 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 0f215631c6..718409a186 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -204,6 +204,10 @@ function(find_mkl_library)
 
   cmake_parse_arguments(mkl_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
 
+  if(TARGET MKL::${mkl_args_NAME})
+    return()
+  endif()
+
   add_library(MKL::${mkl_args_NAME}        SHARED IMPORTED)
   add_library(MKL::${mkl_args_NAME}_STATIC STATIC IMPORTED)
 
@@ -315,11 +319,13 @@ elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
   if(MKL_ThreadingLibrary_LINK_LIBRARY)
     mark_as_advanced(MKL_${mkl_args_NAME}_LINK_LIBRARY)
   endif()
-  add_library(MKL::ThreadingLibrary SHARED IMPORTED)
-  set_target_properties(MKL::ThreadingLibrary
-    PROPERTIES
-      IMPORTED_LOCATION "${MKL_ThreadingLibrary_LINK_LIBRARY}"
-      INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_CXX)
+  if(NOT TARGET MKL::ThreadingLibrary)
+    add_library(MKL::ThreadingLibrary SHARED IMPORTED)
+    set_target_properties(MKL::ThreadingLibrary
+      PROPERTIES
+        IMPORTED_LOCATION "${MKL_ThreadingLibrary_LINK_LIBRARY}"
+        INTERFACE_LINK_LIBRARIES OpenMP::OpenMP_CXX)
+  endif()
 elseif(MKL_THREAD_LAYER STREQUAL "TBB")
   find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_tbb_thread SEARCH_STATIC)
   find_mkl_library(NAME ThreadingLibrary LIBRARY_NAME tbb)
@@ -351,6 +357,11 @@ set(MKL_RUNTIME_KERNEL_LIBRARIES "${MKL_RUNTIME_KERNEL_LIBRARIES_TMP}" CACHE STR
     "MKL kernel libraries targeting different CPU architectures")
 mark_as_advanced(MKL_RUNTIME_KERNEL_LIBRARIES)
 
+# Bypass developer warning that the first argument to find_package_handle_standard_args (MKL_...) does not match
+# the name of the calling package (MKL)
+# https://cmake.org/cmake/help/v3.17/module/FindPackageHandleStandardArgs.html
+set(FPHSA_NAME_MISMATCHED TRUE)
+
 find_package_handle_standard_args(MKL_Shared
   FAIL_MESSAGE "Could NOT find MKL: Source the compilervars.sh or mklvars.sh scripts included with your installation of MKL. This script searches for the libraries in MKLROOT, LIBRARY_PATHS(Linux), and LIB(Windows) environment variables"
   VERSION_VAR  MKL_VERSION_STRING
@@ -372,7 +383,7 @@ if(NOT WIN32)
   mark_as_advanced(M_LIB)
 endif()
 
-if(MKL_Shared_FOUND)
+if(MKL_Shared_FOUND AND NOT TARGET MKL::Shared)
   add_library(MKL::Shared SHARED IMPORTED)
   if(MKL_THREAD_LAYER STREQUAL "Sequential")
     set_target_properties(MKL::Shared
@@ -397,7 +408,7 @@ if(MKL_Shared_FOUND)
   endif()
 endif()
 
-if(MKL_Static_FOUND)
+if(MKL_Static_FOUND AND NOT TARGET MKL::Static)
   add_library(MKL::Static STATIC IMPORTED)
 
   if(UNIX AND NOT APPLE)

From a08dbb2eeafe92903530a1be2d2425eb27d9c13e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 29 Jun 2020 16:25:37 -0400
Subject: [PATCH 2008/2677] Store m_op_str as a const char* instead of as a
 std::string

All of the strings that define the operation of an NaryNode are
immutable constant strings. We do not need to create a std::string
object which allocates(unlikely) memory and increases the size
of the NaryNode instances.

Remove std::string also allows for noexcept move constructors and
assignment operators.
---
 src/backend/common/jit/NaryNode.hpp | 4 ++--
 src/backend/common/jit/Node.hpp     | 6 ++++++
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 091384114e..da80d4ea83 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -26,7 +26,7 @@ class NaryNode : public Node {
    private:
     int m_num_children;
     int m_op;
-    std::string m_op_str;
+    const char *m_op_str;
 
    public:
     NaryNode(const af::dtype type, const char *op_str, const int num_children,
@@ -46,7 +46,7 @@ class NaryNode : public Node {
                       "NaryNode is not move constructible");
     }
 
-    NaryNode(NaryNode &&other) = default;
+    NaryNode(NaryNode &&other) noexcept = default;
 
     NaryNode(const NaryNode &other) = default;
 
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 1c3e94f350..39845fa319 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -106,12 +106,18 @@ class Node {
                       "Node is not move assignable");
     }
 
+    /// Default move constructor operator
+    Node(Node &&node) noexcept = default;
+
     /// Default copy constructor operator
     Node(const Node &node) = default;
 
     /// Default copy assignment operator
     Node &operator=(const Node &node) = default;
 
+    /// Default move assignment operator
+    Node &operator=(Node &&node) noexcept = default;
+
     int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes,
                     std::vector<Node_ids> &full_ids);
 

From 0686ecc902b59959d9373b8c39c81fb257d2a5f7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 29 Jun 2020 16:31:24 -0400
Subject: [PATCH 2009/2677] Add cublasLt only when compiling against cuda 10.1
 and later

cublasLt was only added in CUDA 10.1. This commit only adds that
library for versions 10.1 and later
---
 src/backend/cuda/CMakeLists.txt | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 42fada2cee..bd5d8e4f83 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -54,7 +54,10 @@ if(UNIX)
   # use ${CMAKE_*_LIBRARY} variables in the following flags.
   set(af_cuda_static_flags "${af_cuda_static_flags};-lculibos")
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcublas_static")
-  set(af_cuda_static_flags "${af_cuda_static_flags};-lcublasLt_static")
+
+  if(CUDA_VERSION VERSION_GREATER 10.0)
+    set(af_cuda_static_flags "${af_cuda_static_flags};-lcublasLt_static")
+  endif()
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcufft_static")
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcusparse_static")
 
@@ -323,10 +326,14 @@ if(UNIX)
       ${END_GROUP}
   )
 
+  if(CUDA_VERSION VERSION_GREATER 10.0)
+    target_link_libraries(af_cuda_static_cuda_library
+      PRIVATE
+        ${CUDA_cublasLt_static_LIBRARY})
+  endif()
   if(CUDA_VERSION VERSION_GREATER 9.5)
     target_link_libraries(af_cuda_static_cuda_library
       PRIVATE
-        ${CUDA_cublasLt_static_LIBRARY}
         ${CUDA_lapack_static_LIBRARY})
   endif()
 
@@ -798,7 +805,9 @@ if(AF_INSTALL_STANDALONE)
   if(WIN32)
     afcu_collect_libs(cufft)
     afcu_collect_libs(cublas)
-    afcu_collect_libs(cublasLt)
+    if(CUDA_VERSION VERSION_GREATER 10.0)
+      afcu_collect_libs(cublasLt)
+    endif()
     afcu_collect_libs(cusolver)
     afcu_collect_libs(cusparse)
   elseif(NOT ${use_static_cuda_lapack})

From af6992acf13f45e9f0eedcae213dfd83964a6638 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 29 Jun 2020 16:40:16 -0400
Subject: [PATCH 2010/2677] Add 3.7.x release notes to master

---
 docs/pages/release_notes.md | 92 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 91 insertions(+), 1 deletion(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 441f573467..724019a036 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,96 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.8.0
+======
+
+Major Updates
+-------------
+- Ragged max
+- Bitwise not
+- Updated alloc and free
+- Initializer list for af::array
+
+Improvements
+------------
+
+v3.7.2
+======
+
+Improvements
+------------
+- Cache CUDA kernels to disk to improve load times(Thanks to \@cschreib-ibex) /PR{2848}
+- Staticly link against cuda libraries /PR{2785}
+- Make cuDNN an optional build dependency /PR{2836}
+- Improve support for different compilers and OS /PR{2876} /PR{2945} /PR{2925} /PR{2942} /PR{2943} /PR{2945}
+- Improve performance of join and transpose on CPU /PR{2849}
+- Improve documentation /PR{2816} /PR{2821} /PR{2846} /PR{2918} /PR{2928} /PR{2947}
+- Reduce binary size using NVRTC and template reducing instantiations /PR{2849} /PR{2861} /PR{2890}
+- Improve reduceByKey performance on OpenCL by using builtin functions /PR{2851}
+- Improve support for Intel OpenCL GPUs /PR{2855}
+- Allow staticly linking against MKL /PR{2877} (Sponsered by SDL)
+- Better support for older CUDA toolkits /PR{2923}
+- Add support for CUDA 11 /PR{2939}
+- Add support for ccache for faster builds /PR{2931}
+- Add support for the conan package manager on linux /PR{2875}
+
+Fixes
+-----
+- Bug crash when allocating large arrays /PR{2827}
+- Fix various compiler warnings /PR{2827} /PR{2849} /PR{2872} /PR{2876}
+- Fix minor leaks in OpenCL functions /PR{2913}
+- Various continuous integration related fixes /PR{2819}
+- Fix zero padding with convolv2NN /PR{2820}
+- Fix af_get_memory_pressure_threshold return value /PR{2831}
+- Increased the max filter length for morph
+- Handle empty array inputs for LU, QR, and Rank functions /PR{2838}
+- Fix FindMKL.cmake script for sequential threading library /PR{2840}
+- Various internal refactoring /PR{2839} /PR{2861} /PR{2864} /PR{2873} /PR{2890} /PR{2891} /PR{2913}
+- Fix OpenCL 2.0 builtin function name conflict /PR{2851}
+- Fix error caused when releasing memory with multiple devices /PR{2867}
+
+Contributions
+-------------
+Special thanks to our contributors:
+[Corentin Schreiber](https://github.com/cschreib-ibex)
+[Jacob Khan](https://github.com/jacobkahn)
+[Paul Jurczak](https://github.com/pauljurczak)
+
+v3.7.1
+======
+
+Improvements
+------------
+
+- Improve mtx download for test data \PR{2742}
+- Documentation improvements \PR{2754} \PR{2792} \PR{2797}
+- Remove verbose messages in older CMake versions \PR{2773}
+- Reduce binary size with the use of nvrtc  \PR{2790}
+- Use texture memory to load LUT in orb and fast \PR{2791}
+- Add missing print function for f16 \PR{2784}
+- Add checks for f16 support in the CUDA backend \PR{2784}
+- Create a thrust policy to intercept tmp buffer allocations \PR{2806}
+
+Fixes
+-----
+
+- Fix segfault on exit when ArrayFire is not initialized in the main thread
+- Fix support for CMake 3.5.1 \PR{2771} \PR{2772} \PR{2760}
+- Fix evalMultiple if the input array sizes aren't the same \PR{2766}
+- Fix error when AF_BACKEND_DEFAULT is passed directly to backend \PR{2769}
+- Workaround name collision with AMD OpenCL implementation \PR{2802}
+- Fix on-exit errors with the unified backend \PR{2769}
+- Fix check for f16 compatibility in OpenCL \PR{2773}
+- Fix matmul on Intel OpenCL when passing same array as input \PR{2774}
+- Fix CPU OpenCL blas batching \PR{2774}
+- Fix memory pressure in the default memory manager \PR{2801}
+
+Contributions
+-------------
+Special thanks to our contributors:
+[padentomasello](https://github.com/padentomasello)
+[glavaux2](https://github.com/glavaux2)
+
 v3.7.0
 ======
 
@@ -205,7 +295,7 @@ Misc
 Contributions
 -------------
 Special thanks to our contributors: [Jacob Kahn](https://github.com/jacobkahn),
-[Vardan Akopian](https://github.com/vakopian)  
+[Vardan Akopian](https://github.com/vakopian)
 
 v3.6.1
 ======

From a58f492058bed48780f1f1c3c74ea94b77863889 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 29 Jun 2020 18:44:32 -0400
Subject: [PATCH 2011/2677] Fixes for half on cuda 9.0 with constexpr

---
 src/backend/common/half.hpp               |  5 ++++-
 src/backend/cuda/kernel/random_engine.hpp |  3 ++-
 src/backend/cuda/kernel/reduce_by_key.hpp | 14 ++++----------
 3 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 50cae18ae7..edd37ded24 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -832,7 +832,10 @@ class alignas(2) half {
 #endif
 
    public:
-    AF_CONSTEXPR half() = default;
+#if CUDA_VERSION >= 10000
+    AF_CONSTEXPR
+#endif
+    half() = default;
 
     /// Constructor.
     /// \param bits binary representation to set half to
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index ac1bdc4b7b..fc4f84aea4 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <debug_cuda.hpp>
 #include <err_cuda.hpp>
 #include <kernel/random_engine_mersenne.hpp>
@@ -597,7 +598,7 @@ __device__ static void partialWriteOut128Bytes(common::half *out,
 __device__ static void partialBoxMullerWriteOut128Bytes(
     common::half *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
-    common::half n[8];
+    __half n[8];
     boxMullerTransform(n + 0, n + 1, getHalf(r1), getHalf(r1 >> 16));
     boxMullerTransform(n + 2, n + 3, getHalf(r2), getHalf(r2 >> 16));
     boxMullerTransform(n + 4, n + 5, getHalf(r3), getHalf(r3 >> 16));
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index 247bbdd606..ccaf58c942 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -106,9 +106,6 @@ __global__ void compact(int *reduced_block_sizes, Param<Tk> keys_out,
     const int bidz = blockIdx.z % nBlocksZ;
     const int bidw = blockIdx.z / nBlocksZ;
 
-    Tk k;
-    To v;
-
     // reduced_block_sizes should have inclusive sum of block sizes
     int nwrite = (blockIdx.x == 0) ? reduced_block_sizes[0]
                                    : reduced_block_sizes[blockIdx.x] -
@@ -117,8 +114,8 @@ __global__ void compact(int *reduced_block_sizes, Param<Tk> keys_out,
 
     const int bOffset = bidw * vals_in.strides[3] + bidz * vals_in.strides[2] +
                         bidy * vals_in.strides[1];
-    k = keys_in.ptr[tidx];
-    v = vals_in.ptr[bOffset + tidx];
+    Tk k = keys_in.ptr[tidx];
+    To v = vals_in.ptr[bOffset + tidx];
 
     if (threadIdx.x < nwrite) {
         keys_out.ptr[writeloc + threadIdx.x]           = k;
@@ -147,9 +144,6 @@ __global__ void compact_dim(int *reduced_block_sizes, Param<Tk> keys_out,
     const int bidz = blockIdx.z % nBlocksZ;
     const int bidw = blockIdx.z / nBlocksZ;
 
-    Tk k;
-    To v;
-
     // reduced_block_sizes should have inclusive sum of block sizes
     int nwrite = (blockIdx.x == 0) ? reduced_block_sizes[0]
                                    : reduced_block_sizes[blockIdx.x] -
@@ -160,8 +154,8 @@ __global__ void compact_dim(int *reduced_block_sizes, Param<Tk> keys_out,
                     bidz * vals_in.strides[dim_ordering[2]] +
                     bidy * vals_in.strides[dim_ordering[1]] +
                     tidx * vals_in.strides[dim];
-    k = keys_in.ptr[tidx];
-    v = vals_in.ptr[tid];
+    Tk k = keys_in.ptr[tidx];
+    To v = vals_in.ptr[tid];
 
     if (threadIdx.x < nwrite) {
         keys_out.ptr[writeloc + threadIdx.x] = k;

From 04ad81379002669a37abda849a6f1e77c08c7d40 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 30 Jun 2020 08:24:27 +0530
Subject: [PATCH 2012/2677] Refactor unique_handle to support resources created
 from >0 parameters

---
 src/backend/common/unique_handle.hpp          | 104 ++++++++----------
 src/backend/cuda/CMakeLists.txt               |   1 -
 src/backend/cuda/convolve.cpp                 |   3 -
 src/backend/cuda/convolveNN.cpp               |  11 +-
 src/backend/cuda/cublas.hpp                   |   4 +
 src/backend/cuda/cudnn.hpp                    |  11 ++
 src/backend/cuda/cufft.hpp                    |   6 +
 src/backend/cuda/cusolverDn.hpp               |   4 +
 src/backend/cuda/cusparse.hpp                 |  13 +++
 .../cuda/cusparse_descriptor_helpers.hpp      |  38 +++----
 src/backend/cuda/handle.cpp                   |  56 ----------
 src/backend/cuda/sparse_arith.cu              |  17 ++-
 12 files changed, 112 insertions(+), 156 deletions(-)
 delete mode 100644 src/backend/cuda/handle.cpp

diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index f100bd353e..d8da5c7d67 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -10,55 +10,39 @@
 
 #include <af/compilers.h>
 
-namespace common {
+#include <utility>
 
-/// Deletes a handle.
-///
-/// This function deletes a handle. Handle are usually typedefed pointers
-/// which are created by a C API of a library.
-///
-/// \param[in] handle the handle that will deleted by the destroy function
-/// \note This function will need to be specialized for each type of handle
-template<typename T>
-void handle_deleter(T handle) noexcept;
+namespace common {
 
-/// Creates a handle
-/// This function creates a handle. Handle are usually typedefed pointers
-/// which are created by a C API of a library.
-///
-/// \param[in] handle the handle that will be initialzed by the create function
-/// \note This function will need to be specialized for each type of handle
 template<typename T>
-int handle_creator(T *handle) noexcept;
+class ResourceHandler {
+   public:
+    template<typename... Args>
+    static int createHandle(T *handle, Args... args);
+    static int destroyHandle(T handle);
+};
 
 /// \brief A generic class to manage basic RAII lifetimes for C handles
 ///
 /// This class manages the lifetimes of C handles found in many types of
 /// libraries. This class is non-copiable but can be moved.
 ///
-/// You can use this class with a new handle by using the CREATE_HANDLE macro in
-/// the src/backend/*/handle.cpp file. This macro instantiates the
-/// handle_createor and handle_deleter functions used by this class.
+/// You can use this class with a new handle by using the DEFINE_HANDLER
+/// macro to define creatHandle/destroyHandle policy implemention for a
+/// given resource handle type.
 ///
 /// \code{.cpp}
-/// CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
+/// DEFINE_HANDLER(ClassName, HandleName, HandleCreator, HandleDestroyer);
 /// \code{.cpp}
 template<typename T>
 class unique_handle {
+   private:
     T handle_;
 
    public:
     /// Default constructor. Initializes the handle to zero. Does not call the
     /// create function
     constexpr unique_handle() noexcept : handle_(0) {}
-    int create() {
-        if (!handle_) {
-            int error = handle_creator(&handle_);
-            if (error) { handle_ = 0; }
-            return error;
-        }
-        return 0;
-    }
 
     /// \brief Takes ownership of a previously created handle
     ///
@@ -67,24 +51,36 @@ class unique_handle {
 
     /// \brief Deletes the handle if created.
     ~unique_handle() noexcept {
-        if (handle_) handle_deleter(handle_);
+        if (handle_) { ResourceHandler<T>::destroyHandle(handle_); }
     };
 
-    /// \brief Implicit converter for the handle
-    constexpr operator const T &() const noexcept { return handle_; }
-
     unique_handle(const unique_handle &other) noexcept = delete;
+    unique_handle &operator=(unique_handle &other) noexcept = delete;
+
     AF_CONSTEXPR unique_handle(unique_handle &&other) noexcept
         : handle_(other.handle_) {
         other.handle_ = 0;
     }
 
-    unique_handle &operator=(unique_handle &other) noexcept = delete;
     unique_handle &operator=(unique_handle &&other) noexcept {
         handle_       = other.handle_;
         other.handle_ = 0;
     }
 
+    /// \brief Implicit converter for the handle
+    constexpr operator const T &() const noexcept { return handle_; }
+
+    template<typename... Args>
+    int create(Args... args) {
+        if (!handle_) {
+            int error = ResourceHandler<T>::createHandle(
+                &handle_, std::forward<Args>(args)...);
+            if (error) { handle_ = 0; }
+            return error;
+        }
+        return 0;
+    }
+
     // Returns true if the \p other unique_handle is the same as this handle
     constexpr bool operator==(unique_handle &other) const noexcept {
         return handle_ == other.handle_;
@@ -105,32 +101,28 @@ class unique_handle {
 };
 
 /// \brief Returns an initialized handle object. The create function on this
-///        object is already called
-template<typename T>
-unique_handle<T> make_handle() {
+///        object is already called with the parameter pack provided as
+///        function arguments.
+template<typename T, typename... Args>
+unique_handle<T> make_handle(Args... args) {
     unique_handle<T> h;
-    h.create();
+    h.create(std::forward<Args>(args)...);
     return h;
 }
 
 }  // namespace common
 
-/// specializes the handle_creater and handle_deleter functions for a specific
-/// handle
-///
-/// \param[in] HANDLE The type of the handle
-/// \param[in] CREATE The create function for the handle
-/// \param[in] DESTROY The destroy function for the handle
-/// \note Do not add this macro to another namespace, The macro provides a
-///       namespace for the functions.
-#define CREATE_HANDLE(HANDLE, CREATE, DESTROY)             \
-    namespace common {                                     \
-    template<>                                             \
-    void handle_deleter<HANDLE>(HANDLE handle) noexcept {  \
-        DESTROY(handle);                                   \
-    }                                                      \
-    template<>                                             \
-    int handle_creator<HANDLE>(HANDLE * handle) noexcept { \
-        return CREATE(handle);                             \
-    }                                                      \
+#define DEFINE_HANDLER(HANDLE_TYPE, HCREATOR, HDESTROYER)            \
+    namespace common {                                               \
+    template<>                                                       \
+    class ResourceHandler<HANDLE_TYPE> {                             \
+       public:                                                       \
+        template<typename... Args>                                   \
+        static int createHandle(HANDLE_TYPE *handle, Args... args) { \
+            return HCREATOR(handle, std::forward<Args>(args)...);    \
+        }                                                            \
+        static int destroyHandle(HANDLE_TYPE handle) {               \
+            return HDESTROYER(handle);                               \
+        }                                                            \
+    };                                                               \
     }  // namespace common
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index bd5d8e4f83..85bf288402 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -536,7 +536,6 @@ cuda_add_library(afcuda
     GraphicsResourceManager.hpp
     gradient.cpp
     gradient.hpp
-    handle.cpp
     harris.hpp
     hist_graphics.cpp
     hist_graphics.hpp
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index d471eb0827..2fe0b8d653 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -10,7 +10,6 @@
 #include <Array.hpp>
 #include <cast.hpp>
 #include <common/half.hpp>
-#include <common/unique_handle.hpp>
 #include <convolve.hpp>
 #include <err_cuda.hpp>
 #include <kernel/convolve.hpp>
@@ -20,8 +19,6 @@
 
 using af::dim4;
 using common::half;
-using common::make_handle;
-using common::unique_handle;
 using std::conditional;
 using std::is_same;
 
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 5b4878ef04..7e1e2208fa 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -33,7 +33,6 @@ using af::dim4;
 using common::flip;
 using common::half;
 using common::make_handle;
-using common::unique_handle;
 using std::conditional;
 using std::is_same;
 
@@ -42,12 +41,9 @@ namespace cuda {
 #ifdef WITH_CUDNN
 
 template<typename Desc, typename T>
-unique_handle<Desc> toCudnn(Array<T> arr) {
-    const dim4 &dims = arr.dims();
-
-    auto descriptor             = make_handle<Desc>();
-    cudnnDataType_t cudnn_dtype = getCudnnDataType<T>();
-    cudnnSet(descriptor, cudnn_dtype, dims);
+auto toCudnn(Array<T> arr) {
+    auto descriptor = make_handle<Desc>();
+    cudnnSet(descriptor, getCudnnDataType<T>(), arr.dims());
     return descriptor;
 }
 
@@ -378,6 +374,7 @@ Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
 
     // create convolution descriptor
     auto convolution_descriptor = make_handle<cudnnConvolutionDescriptor_t>();
+
     CUDNN_CHECK(cuda::cudnnSetConvolution2dDescriptor(
         convolution_descriptor, padding[1], padding[0], stride[1], stride[0],
         dilation[1], dilation[0], CUDNN_CONVOLUTION, cudnn_dtype));
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index e51454ec32..da93d41791 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -8,9 +8,13 @@
  ********************************************************/
 
 #pragma once
+
 #include <common/defines.hpp>
+#include <common/unique_handle.hpp>
 #include <cublas_v2.h>
 
+DEFINE_HANDLER(cublasHandle_t, cublasCreate, cublasDestroy);
+
 namespace cuda {
 
 const char* errorString(cublasStatus_t err);
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
index 60bd0fe1f1..58eb662611 100644
--- a/src/backend/cuda/cudnn.hpp
+++ b/src/backend/cuda/cudnn.hpp
@@ -11,9 +11,20 @@
 
 #include <common/defines.hpp>
 #include <common/half.hpp>
+#include <common/unique_handle.hpp>
 #include <cudnnModule.hpp>
 #include <af/dim4.hpp>
 
+// clang-format off
+DEFINE_HANDLER(cudnnHandle_t, cuda::getCudnnPlugin().cudnnCreate, cuda::getCudnnPlugin().cudnnDestroy);
+
+DEFINE_HANDLER(cudnnTensorDescriptor_t, cuda::getCudnnPlugin().cudnnCreateTensorDescriptor, cuda::getCudnnPlugin().cudnnDestroyTensorDescriptor);
+
+DEFINE_HANDLER(cudnnFilterDescriptor_t, cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
+
+DEFINE_HANDLER(cudnnConvolutionDescriptor_t, cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
+// clang-format on
+
 namespace cuda {
 
 const char *errorString(cudnnStatus_t err);
diff --git a/src/backend/cuda/cufft.hpp b/src/backend/cuda/cufft.hpp
index bba83ca546..937af94759 100644
--- a/src/backend/cuda/cufft.hpp
+++ b/src/backend/cuda/cufft.hpp
@@ -8,12 +8,17 @@
  ********************************************************/
 
 #pragma once
+
 #include <common/FFTPlanCache.hpp>
 #include <common/err_common.hpp>
+#include <common/unique_handle.hpp>
 #include <cufft.h>
 #include <cstdio>
 
+DEFINE_HANDLER(cufftHandle, cufftCreate, cufftDestroy);
+
 namespace cuda {
+
 typedef cufftHandle PlanType;
 typedef std::shared_ptr<PlanType> SharedPlan;
 
@@ -28,6 +33,7 @@ class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
                                int idist, int *onembed, int ostride, int odist,
                                cufftType type, int batch);
 };
+
 }  // namespace cuda
 
 #define CUFFT_CHECK(fn)                                           \
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index 4ec4f4dea3..e643934930 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -8,8 +8,12 @@
  ********************************************************/
 
 #pragma once
+
+#include <common/unique_handle.hpp>
 #include <cusolverDn.h>
 
+DEFINE_HANDLER(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
+
 namespace cuda {
 
 const char* errorString(cusolverStatus_t err);
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 7a00da9eb6..7eb54900b4 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -8,10 +8,22 @@
  ********************************************************/
 
 #pragma once
+
 #include <common/defines.hpp>
 #include <common/err_common.hpp>
+#include <common/unique_handle.hpp>
 #include <cusparse_v2.h>
 
+// clang-format off
+DEFINE_HANDLER(cusparseHandle_t, cusparseCreate, cusparseDestroy);
+DEFINE_HANDLER(cusparseMatDescr_t, cusparseCreateMatDescr, cusparseDestroyMatDescr);
+#if defined(AF_USE_NEW_CUSPARSE_API)
+DEFINE_HANDLER(cusparseSpMatDescr_t, cusparseCreateCsr, cusparseDestroySpMat);
+DEFINE_HANDLER(cusparseDnVecDescr_t, cusparseCreateDnVec, cusparseDestroyDnVec);
+DEFINE_HANDLER(cusparseDnMatDescr_t, cusparseCreateDnMat, cusparseDestroyDnMat);
+#endif
+// clang-format on
+
 namespace cuda {
 
 const char* errorString(cusparseStatus_t err);
@@ -27,4 +39,5 @@ const char* errorString(cusparseStatus_t err);
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
         }                                                                     \
     } while (0)
+
 }  // namespace cuda
diff --git a/src/backend/cuda/cusparse_descriptor_helpers.hpp b/src/backend/cuda/cusparse_descriptor_helpers.hpp
index 2a71b3afa0..3e94f89f47 100644
--- a/src/backend/cuda/cusparse_descriptor_helpers.hpp
+++ b/src/backend/cuda/cusparse_descriptor_helpers.hpp
@@ -15,40 +15,32 @@
 #include <common/unique_handle.hpp>
 #include <cusparse.hpp>
 
+#include <utility>
+
 namespace cuda {
 
 template<typename T>
-common::unique_handle<cusparseSpMatDescr_t> csrMatDescriptor(
-    const common::SparseArray<T> &in) {
-    auto dims                   = in.dims();
-    cusparseSpMatDescr_t resMat = NULL;
-    CUSPARSE_CHECK(cusparseCreateCsr(
-        &resMat, dims[0], dims[1], in.getNNZ(), (void *)(in.getRowIdx().get()),
+auto csrMatDescriptor(const common::SparseArray<T> &in) {
+    auto dims = in.dims();
+    return common::make_handle<cusparseSpMatDescr_t>(
+        dims[0], dims[1], in.getNNZ(), (void *)(in.getRowIdx().get()),
         (void *)(in.getColIdx().get()), (void *)(in.getValues().get()),
         CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO,
-        getType<T>()));
-    return common::unique_handle<cusparseSpMatDescr_t>(resMat);
+        getType<T>());
 }
 
 template<typename T>
-common::unique_handle<cusparseDnVecDescr_t> denVecDescriptor(
-    const Array<T> &in) {
-    auto dims                   = in.dims();
-    cusparseDnVecDescr_t resVec = NULL;
-    CUSPARSE_CHECK(cusparseCreateDnVec(&resVec, dims.elements(),
-                                       (void *)(in.get()), getType<T>()));
-    return common::unique_handle<cusparseDnVecDescr_t>(resVec);
+auto denVecDescriptor(const Array<T> &in) {
+    return common::make_handle<cusparseDnVecDescr_t>(
+        in.elements(), (void *)(in.get()), getType<T>());
 }
 
 template<typename T>
-common::unique_handle<cusparseDnMatDescr_t> denMatDescriptor(
-    const Array<T> &in) {
-    auto dims                   = in.dims();
-    cusparseDnMatDescr_t resMat = NULL;
-    CUSPARSE_CHECK(cusparseCreateDnMat(&resMat, dims[0], dims[1], dims[0],
-                                       (void *)(in.get()), getType<T>(),
-                                       CUSPARSE_ORDER_COL));
-    return common::unique_handle<cusparseDnMatDescr_t>(resMat);
+auto denMatDescriptor(const Array<T> &in) {
+    auto dims = in.dims();
+    return common::make_handle<cusparseDnMatDescr_t>(
+        dims[0], dims[1], dims[0], (void *)(in.get()), getType<T>(),
+        CUSPARSE_ORDER_COL);
 }
 
 }  // namespace cuda
diff --git a/src/backend/cuda/handle.cpp b/src/backend/cuda/handle.cpp
deleted file mode 100644
index cc336ed292..0000000000
--- a/src/backend/cuda/handle.cpp
+++ /dev/null
@@ -1,56 +0,0 @@
-/*******************************************************
- * Copyright (c) 2019, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <common/unique_handle.hpp>
-#include <cublas.hpp>
-#include <cufft.hpp>
-#include <cusolverDn.hpp>
-#include <cusparse.hpp>
-
-// clang-format off
-CREATE_HANDLE(cusparseMatDescr_t, cusparseCreateMatDescr, cusparseDestroyMatDescr);
-CREATE_HANDLE(cusparseHandle_t, cusparseCreate, cusparseDestroy);
-CREATE_HANDLE(cublasHandle_t, cublasCreate, cublasDestroy);
-CREATE_HANDLE(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
-CREATE_HANDLE(cufftHandle, cufftCreate, cufftDestroy);
-
-#if defined(AF_USE_NEW_CUSPARSE_API)
-namespace common {
-
-template<>
-void handle_deleter<cusparseSpMatDescr_t>(cusparseSpMatDescr_t handle) noexcept {
-    cusparseDestroySpMat(handle);
-}
-
-template<>
-void handle_deleter<cusparseDnVecDescr_t>(cusparseDnVecDescr_t handle) noexcept {
-    cusparseDestroyDnVec(handle);
-}
-
-template<>
-void handle_deleter<cusparseDnMatDescr_t>(cusparseDnMatDescr_t handle) noexcept {
-    cusparseDestroyDnMat(handle);
-}
-
-}  // namespace common
-#endif
-
-#ifdef WITH_CUDNN
-
-#include <cudnn.hpp>
-#include <cudnnModule.hpp>
-
-CREATE_HANDLE(cudnnHandle_t, cuda::getCudnnPlugin().cudnnCreate, cuda::getCudnnPlugin().cudnnDestroy);
-CREATE_HANDLE(cudnnTensorDescriptor_t, cuda::getCudnnPlugin().cudnnCreateTensorDescriptor, cuda::getCudnnPlugin().cudnnDestroyTensorDescriptor);
-CREATE_HANDLE(cudnnFilterDescriptor_t, cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
-CREATE_HANDLE(cudnnConvolutionDescriptor_t, cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
-
-#endif
-
-// clang-format on
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 0107702110..b3fceba7c0 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -187,17 +187,14 @@ template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     lhs.eval();
     rhs.eval();
-    af::storage sfmt = lhs.getStorage();
-
-    auto desc        = make_handle<cusparseMatDescr_t>();
-    const dim4 ldims = lhs.dims();
-
-    const int M = ldims[0];
-    const int N = ldims[1];
-
-    const dim_t nnzA = lhs.getNNZ();
-    const dim_t nnzB = rhs.getNNZ();
 
+    af::storage sfmt      = lhs.getStorage();
+    auto desc             = make_handle<cusparseMatDescr_t>();
+    const dim4 ldims      = lhs.dims();
+    const int M           = ldims[0];
+    const int N           = ldims[1];
+    const dim_t nnzA      = lhs.getNNZ();
+    const dim_t nnzB      = rhs.getNNZ();
     const int *csrRowPtrA = lhs.getRowIdx().get();
     const int *csrColPtrA = lhs.getColIdx().get();
     const int *csrRowPtrB = rhs.getRowIdx().get();

From 64c626b711e660627398605c72dd76bbdff2609d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 1 Jul 2020 23:40:50 +0530
Subject: [PATCH 2013/2677] Work around for bug, in cmake 3.5.1, related to
 NVCC header paths

Include directories included via `target_include_directories` are not being
forwarded to NVCC to correctly in cmake 3.5.1. This work around address that issue.
---
 test/CMakeLists.txt | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 77918ca08e..07b0579d3f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -307,6 +307,15 @@ if(CUDA_FOUND)
     set(cuda_test_backends "cuda" "unified")
     if(${backend} IN_LIST cuda_test_backends)
       set(target test_cuda_${backend})
+      if(${CMAKE_VERSION} VERSION_LESS 3.5.2)
+        cuda_include_directories(
+          ${ArrayFire_SOURCE_DIR}/include
+          ${ArrayFire_BINARY_DIR}/include
+          ${ArrayFire_SOURCE_DIR}/extern/half/include
+          ${CMAKE_CURRENT_SOURCE_DIR}
+          ${CMAKE_CURRENT_SOURCE_DIR}/gtest/googletest/include
+        )
+      endif()
       cuda_add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
       target_include_directories(${target} PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include

From b2533b451df5d3f0794c773adeb1ac250d4d4a97 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 30 Jun 2020 13:40:23 +0530
Subject: [PATCH 2014/2677] Support to load versioned modules by
 DependencyModule class

---
 src/backend/common/DependencyModule.cpp | 99 ++++++++++++++++++++-----
 src/backend/common/DependencyModule.hpp |  8 +-
 2 files changed, 88 insertions(+), 19 deletions(-)

diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index ef99bc501b..bdb5b27e0a 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -10,6 +10,7 @@
 #include <common/DependencyModule.hpp>
 #include <common/Logger.hpp>
 #include <common/module_loading.hpp>
+
 #include <algorithm>
 #include <string>
 
@@ -19,30 +20,73 @@
 #include <dlfcn.h>
 #endif
 
+using common::Version;
+using std::make_tuple;
+using std::string;
+using std::to_string;
+using std::vector;
+
+constexpr Version NullVersion{-1, -1, -1};
+
 #ifdef OS_WIN
 #include <Windows.h>
+
 static const char* librarySuffix = ".dll";
-static const char* libraryPrefix = "";
+
+namespace {
+vector<string> libNames(const std::string& name, const string& suffix,
+                        const Version& ver = NullVersion) {
+    UNUSED(ver);  // Windows DLL files are not version suffixed
+    return {name + suffix + librarySuffix};
+}
+}  // namespace
+
 #elif defined(OS_MAC)
+
 static const char* librarySuffix = ".dylib";
 static const char* libraryPrefix = "lib";
+
+namespace {
+vector<string> libNames(const std::string& name, const string& suffix,
+                        const Version& ver = NullVersion) {
+    UNUSED(suffix);
+    const string noVerName = libraryPrefix + name + librarySuffix;
+    if (ver != NullVersion) {
+        const string infix = "." + to_string(std::get<0>(ver)) + ".";
+        return {libraryPrefix + name + infix + librarySuffix, noVerName};
+    } else {
+        return {noVerName};
+    }
+}
+}  // namespace
+
 #elif defined(OS_LNX)
+
 static const char* librarySuffix = ".so";
 static const char* libraryPrefix = "lib";
-#else
-#error "Unsupported platform"
-#endif
-
-using std::string;
-using std::vector;
 
 namespace {
+vector<string> libNames(const std::string& name, const string& suffix,
+                        const Version& ver = NullVersion) {
+    UNUSED(suffix);
+    const string noVerName = libraryPrefix + name + librarySuffix;
+    if (ver != NullVersion) {
+        const string soname("." + to_string(std::get<0>(ver)));
 
-std::string libName(const std::string& name) {
-    return libraryPrefix + name + librarySuffix;
+        const string vsfx = "." + to_string(std::get<0>(ver)) + "." +
+                            to_string(std::get<1>(ver)) + "." +
+                            to_string(std::get<2>(ver));
+        return {noVerName + vsfx, noVerName + soname, noVerName};
+    } else {
+        return {noVerName};
+    }
 }
 }  // namespace
 
+#else
+#error "Unsupported platform"
+#endif
+
 namespace common {
 
 DependencyModule::DependencyModule(const char* plugin_file_name,
@@ -51,11 +95,11 @@ DependencyModule::DependencyModule(const char* plugin_file_name,
     // TODO(umar): Implement handling of non-standard paths
     UNUSED(paths);
     if (plugin_file_name) {
-        string filename = libName(plugin_file_name);
-        AF_TRACE("Attempting to load: {}", filename);
-        handle = loadLibrary(filename.c_str());
+        auto fileNames = libNames(plugin_file_name, "");
+        AF_TRACE("Attempting to load: {}", fileNames[0]);
+        handle = loadLibrary(fileNames[0].c_str());
         if (handle) {
-            AF_TRACE("Found: {}", filename);
+            AF_TRACE("Found: {}", fileNames[0]);
         } else {
             AF_TRACE("Unable to open {}", plugin_file_name);
         }
@@ -64,17 +108,36 @@ DependencyModule::DependencyModule(const char* plugin_file_name,
 
 DependencyModule::DependencyModule(const vector<string>& plugin_base_file_name,
                                    const vector<string>& suffixes,
-                                   const vector<string>& paths)
+                                   const vector<string>& paths,
+                                   const size_t verListSize,
+                                   const Version* versions)
     : handle(nullptr), logger(common::loggerFactory("platform")) {
     for (const string& base_name : plugin_base_file_name) {
         for (const string& path : paths) {
             UNUSED(path);
             for (const string& suffix : suffixes) {
-                string filename = libName(base_name + suffix);
-                AF_TRACE("Attempting to load: {}", filename);
-                handle = loadLibrary(filename.c_str());
+#if !defined(OS_WIN)
+                // For a non-windows OS, i.e. most likely unix, shared library
+                // names have versions suffix based on the version. Lookup for
+                // libraries for given versions and proceed to a simple name
+                // lookup if versioned library is not found.
+                for (size_t v = 0; v < verListSize; v++) {
+                    auto fileNames = libNames(base_name, suffix, versions[v]);
+                    for (auto& fileName : fileNames) {
+                        AF_TRACE("Attempting to load: {}", fileName);
+                        handle = loadLibrary(fileName.c_str());
+                        if (handle) {
+                            AF_TRACE("Found: {}", fileName);
+                            return;
+                        }
+                    }
+                }
+#endif
+                auto fileNames = libNames(base_name, suffix);
+                AF_TRACE("Attempting to load: {}", fileNames[0]);
+                handle = loadLibrary(fileNames[0].c_str());
                 if (handle) {
-                    AF_TRACE("Found: {}", filename);
+                    AF_TRACE("Found: {}", fileNames[0]);
                     return;
                 }
             }
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 9c2b00b53a..d4f456dbe8 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -8,12 +8,14 @@
  ********************************************************/
 
 #pragma once
+
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/module_loading.hpp>
 
 #include <memory>
 #include <string>
+#include <tuple>
 #include <utility>
 #include <vector>
 
@@ -22,6 +24,8 @@ class logger;
 }
 namespace common {
 
+using Version = std::tuple<int, int, int>;  // major, minor, patch
+
 /// Allows you to create classes which dynamically load dependencies at runtime
 ///
 /// Creates a dependency module which will dynamically load a library
@@ -39,7 +43,9 @@ class DependencyModule {
 
     DependencyModule(const std::vector<std::string>& plugin_base_file_name,
                      const std::vector<std::string>& suffixes,
-                     const std::vector<std::string>& paths);
+                     const std::vector<std::string>& paths,
+                     const size_t verListSize = 0,
+                     const Version* versions  = nullptr);
 
     ~DependencyModule() noexcept;
 

From 4f9ba3b1c39c61e651f861d10900395b48098077 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 30 Jun 2020 13:44:29 +0530
Subject: [PATCH 2015/2677] Add cudnn versions to lookup for dynamic loading

---
 src/backend/cuda/cudnnModule.cpp | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 5a37b6ead3..f98654f8ac 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -14,14 +14,32 @@
 #include <device_manager.hpp>
 #include <utility.hpp>
 
+#include <array>
 #include <string>
 #include <tuple>
 
+using common::Version;
 using std::make_tuple;
 using std::string;
 
 namespace cuda {
 
+// clang-format off
+// Latest version from each minor releases are enlisted below
+constexpr std::array<common::Version, 10> cudnnVersions = {
+    make_tuple(7, 6,  5),
+    make_tuple(7, 5,  1),
+    make_tuple(7, 4,  2),
+    make_tuple(7, 3,  1),
+    make_tuple(7, 2,  1),
+    make_tuple(7, 1,  4),
+    make_tuple(7, 0,  5),
+    make_tuple(6, 0, 21),
+    make_tuple(5, 1, 10),
+    make_tuple(4, 0,  7)
+};
+// clang-format on
+
 spdlog::logger* cudnnModule::getLogger() const noexcept {
     return module.getLogger();
 }
@@ -34,7 +52,8 @@ auto cudnnVersionComponents(size_t version) {
 }
 
 cudnnModule::cudnnModule()
-    : module({"cudnn"}, {"", "64_7", "64_8", "64_6", "64_5", "64_4"}, {""}) {
+    : module({"cudnn"}, {"", "64_7", "64_8", "64_6", "64_5", "64_4"}, {""},
+             cudnnVersions.size(), cudnnVersions.data()) {
     if (!module.isLoaded()) {
         AF_TRACE(
             "WARNING: Unable to load cuDNN: {}"

From 307881b159b69a51b0f7d01308ed49086061e4d3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 30 Jun 2020 13:45:05 +0530
Subject: [PATCH 2016/2677] Fix cudnn cmake install command

---
 src/backend/cuda/CMakeLists.txt | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 85bf288402..e775b135b1 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -797,7 +797,14 @@ endfunction()
 
 if(AF_INSTALL_STANDALONE)
   if(AF_WITH_CUDNN)
-    afcu_collect_libs(cudnn)
+    if(WIN32)
+      set(cudnn_lib "${cuDNN_DLL_LIBRARY}")
+    else()
+      get_filename_component(cudnn_lib "${cuDNN_LINK_LIBRARY}" REALPATH)
+    endif()
+    install(FILES ${cudnn_lib}
+          DESTINATION ${AF_INSTALL_LIB_DIR}
+          COMPONENT   cuda_dependencies)
   endif()
 
   afcu_collect_libs(nvrtc FULL_VERSION)

From ba00aadb4a6469585201f15f21d53d2561d80030 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 30 Jun 2020 17:15:17 -0400
Subject: [PATCH 2017/2677] Fix thrust stream function which can be called from
 device

* ArrayFire couldn't be compiled using device debug symbols because
  the get_stream and synchronize_stream functions with the
  ThrustArrayFirePolicy needed to be device and host compatible.
  These functions were using some host only functions so they
  couldn't be compiled for the device. Although these functions
  aren't really used, they caused missing symbol errors when passing
  the -G flag.
---
 src/backend/cuda/CMakeLists.txt            |  1 -
 src/backend/cuda/ThrustArrayFirePolicy.cpp | 22 ----------------------
 src/backend/cuda/ThrustArrayFirePolicy.hpp | 18 ++++++++++++++++--
 3 files changed, 16 insertions(+), 25 deletions(-)
 delete mode 100644 src/backend/cuda/ThrustArrayFirePolicy.cpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e775b135b1..4488c17873 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -484,7 +484,6 @@ cuda_add_library(afcuda
     Param.hpp
     ThrustAllocator.cuh
     ThrustArrayFirePolicy.hpp
-    ThrustArrayFirePolicy.cpp
     anisotropic_diffusion.hpp
     approx.hpp
     arith.hpp
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.cpp b/src/backend/cuda/ThrustArrayFirePolicy.cpp
deleted file mode 100644
index 6f21b96ed3..0000000000
--- a/src/backend/cuda/ThrustArrayFirePolicy.cpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/*******************************************************
- * Copyright (c) 2020, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <ThrustArrayFirePolicy.hpp>
-
-namespace cuda {
-
-cudaStream_t get_stream(ThrustArrayFirePolicy /*unused*/) {
-    return getActiveStream();
-}
-
-cudaError_t synchronize_stream(ThrustArrayFirePolicy /*unused*/) {
-    return cudaStreamSynchronize(getActiveStream());
-}
-
-}  // namespace cuda
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
index 51b5faa904..4ac230ad94 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.hpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -18,11 +18,25 @@ namespace cuda {
 struct ThrustArrayFirePolicy
     : thrust::device_execution_policy<ThrustArrayFirePolicy> {};
 
+namespace {
 __DH__
-cudaStream_t get_stream(ThrustArrayFirePolicy);
+inline cudaStream_t get_stream(ThrustArrayFirePolicy) {
+#if defined(__CUDA_ARCH__)
+    return 0;
+#else
+    return getActiveStream();
+#endif
+}
 
 __DH__
-cudaError_t synchronize_stream(ThrustArrayFirePolicy);
+inline cudaError_t synchronize_stream(ThrustArrayFirePolicy) {
+#if defined(__CUDA_ARCH__)
+    return cudaDeviceSynchronize();
+#else
+    return cudaStreamSynchronize(getActiveStream());
+#endif
+}
+}  // namespace
 
 template<typename T>
 thrust::pair<thrust::pointer<T, ThrustArrayFirePolicy>, std::ptrdiff_t>

From 9fccfcba543b6468c4c5b76c46967bfcda465011 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 1 Jul 2020 11:24:53 -0400
Subject: [PATCH 2018/2677] Use active stream for CUB operations in ReduceByKey
 functions

---
 src/backend/cuda/reduce_impl.hpp | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 6ff8d71e1f..5ee591e26e 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -71,9 +71,9 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
     auto reduced_block_sizes = memAlloc<int>(numBlocksD0);
 
     size_t temp_storage_bytes = 0;
-    cub::DeviceScan::InclusiveSum(NULL, temp_storage_bytes,
-                                  reduced_block_sizes.get(),
-                                  reduced_block_sizes.get(), numBlocksD0);
+    cub::DeviceScan::InclusiveSum(
+        NULL, temp_storage_bytes, reduced_block_sizes.get(),
+        reduced_block_sizes.get(), numBlocksD0, getActiveStream());
     auto d_temp_storage = memAlloc<char>(temp_storage_bytes);
 
     int n_reduced_host = nelems;
@@ -106,7 +106,8 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
 
         cub::DeviceScan::InclusiveSum(
             (void *)d_temp_storage.get(), temp_storage_bytes,
-            reduced_block_sizes.get(), reduced_block_sizes.get(), numBlocksD0);
+            reduced_block_sizes.get(), reduced_block_sizes.get(), numBlocksD0,
+            getActiveStream());
 
         CUDA_LAUNCH((kernel::compact_dim<Tk, To>), blocks, numThreads,
                     reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
@@ -151,7 +152,7 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
             cub::DeviceScan::InclusiveSum(
                 (void *)d_temp_storage.get(), temp_storage_bytes,
                 reduced_block_sizes.get(), reduced_block_sizes.get(),
-                numBlocksD0);
+                numBlocksD0, getActiveStream());
 
             CUDA_CHECK(cudaMemcpyAsync(
                 &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
@@ -213,9 +214,9 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
     auto reduced_block_sizes = memAlloc<int>(numBlocksD0);
 
     size_t temp_storage_bytes = 0;
-    cub::DeviceScan::InclusiveSum(NULL, temp_storage_bytes,
-                                  reduced_block_sizes.get(),
-                                  reduced_block_sizes.get(), numBlocksD0);
+    cub::DeviceScan::InclusiveSum(
+        NULL, temp_storage_bytes, reduced_block_sizes.get(),
+        reduced_block_sizes.get(), numBlocksD0, getActiveStream());
     auto d_temp_storage = memAlloc<char>(temp_storage_bytes);
 
     int n_reduced_host = nelems;
@@ -246,7 +247,8 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
 
         cub::DeviceScan::InclusiveSum(
             (void *)d_temp_storage.get(), temp_storage_bytes,
-            reduced_block_sizes.get(), reduced_block_sizes.get(), numBlocksD0);
+            reduced_block_sizes.get(), reduced_block_sizes.get(), numBlocksD0,
+            getActiveStream());
 
         CUDA_LAUNCH((kernel::compact<Tk, To>), blocks, numThreads,
                     reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
@@ -291,7 +293,7 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
             cub::DeviceScan::InclusiveSum(
                 (void *)d_temp_storage.get(), temp_storage_bytes,
                 reduced_block_sizes.get(), reduced_block_sizes.get(),
-                numBlocksD0);
+                numBlocksD0, getActiveStream());
 
             CUDA_CHECK(cudaMemcpyAsync(
                 &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),

From d6096e089b5d32e171205e5daed5759f8a9d6261 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 1 Jul 2020 14:23:30 -0400
Subject: [PATCH 2019/2677] fixes incorrect warp continue condition on block
 boundary

---
 src/backend/cuda/kernel/reduce_by_key.hpp |  9 +++++----
 src/backend/cuda/reduce_impl.hpp          |  6 ++++++
 test/reduce.cpp                           | 24 +++++++++++++++++++++++
 3 files changed, 35 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index ccaf58c942..dee09c3e8c 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -77,10 +77,11 @@ __global__ void test_needs_reduction(int *needs_another_reduction,
         atomicOr(needs_another_reduction, remaining_updates);
 
     // check across warp boundaries
-    if ((tid + 1) < n) { k = keys_in.ptr[tid + 1]; }
-
-    update_key = (k == shfl_down_sync(FULL_MASK, k, 1)) &&
-                 ((tid + 1) < (n - 1)) && ((threadIdx.x % 32) < 31);
+    update_key =
+        (((threadIdx.x % 32) == 31)           // last thread in warp
+         && (threadIdx.x < (blockDim.x - 1))  // not last thread in block
+         // next value valid and equal
+         && ((tid + 1) < n) && (k == keys_in.ptr[tid + 1]));
     remaining_updates = any_sync(FULL_MASK, update_key);
 
     // TODO: single per warp? change to assignment rather than atomicOr
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 5ee591e26e..9c706cf95e 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -123,6 +123,7 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                                    sizeof(int), getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
                                    sizeof(int), getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
@@ -139,6 +140,7 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                                    needs_block_boundary_reduction.get(),
                                    sizeof(int), cudaMemcpyDeviceToHost,
                                    getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         if (needs_block_boundary_reduction_host &&
             !needs_another_reduction_host) {
@@ -165,6 +167,7 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
 
             swap(t_reduced_keys, reduced_keys);
             swap(t_reduced_vals, reduced_vals);
+            CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
         }
     } while (needs_another_reduction_host ||
              needs_block_boundary_reduction_host);
@@ -264,6 +267,7 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                                    sizeof(int), getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
                                    sizeof(int), getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
@@ -280,6 +284,7 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                                    needs_block_boundary_reduction.get(),
                                    sizeof(int), cudaMemcpyDeviceToHost,
                                    getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         if (needs_block_boundary_reduction_host &&
             !needs_another_reduction_host) {
@@ -306,6 +311,7 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
 
             swap(t_reduced_keys, reduced_keys);
             swap(t_reduced_vals, reduced_vals);
+            CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
         }
     } while (needs_another_reduction_host ||
              needs_block_boundary_reduction_host);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 8a6efff2be..7ae503928f 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2041,3 +2041,27 @@ TEST_P(RaggedReduceMaxRangeP, rangeMaxTest) {
     ASSERT_ARRAYS_EQ(idxsReducedGold, idx);
 
 }
+
+TEST(ReduceByKey, ISSUE_2955) {
+    int N = 256;
+    af::array val = af::randu(N);
+    af::array key = af::range(af::dim4(N), 0, af::dtype::s32);
+    key(seq(127, af::end)) = 1;
+
+    af::array ok, ov;
+    af::sumByKey(ok, ov, key, val);
+    ASSERT_EQ(ok.dims(0), 128);
+    ASSERT_EQ(ov.dims(0), 128);
+}
+
+TEST(ReduceByKey, ISSUE_2955_dim) {
+    int N = 256;
+    af::array val = af::randu(8, N);
+    af::array key = af::range(af::dim4(N), 0, af::dtype::s32);
+    key(seq(127, af::end)) = 1;
+
+    af::array ok, ov;
+    af::sumByKey(ok, ov, key, val, 1);
+    ASSERT_EQ(ok.dims(0), 128);
+    ASSERT_EQ(ov.dims(1), 128);
+}

From a2d243cfd23d119dfe644b8b20b4c2b9bb8632c2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 1 Jul 2020 17:45:11 -0400
Subject: [PATCH 2020/2677] update reduce by key to use stream events instead
 of synch

---
 .gitignore                       |  4 +++-
 src/backend/common/EventBase.hpp |  4 ++--
 src/backend/cpu/Event.hpp        |  4 +++-
 src/backend/cuda/reduce_impl.hpp | 19 +++++++++++++------
 4 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/.gitignore b/.gitignore
index 5762c63c5a..7840e027a4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,4 +19,6 @@ src/backend/cuda/cub
 conanbuildinfo*
 conaninfo*
 conan.lock
-graph_info.json
\ No newline at end of file
+graph_info.json
+.ccls-cache
+.projectile
diff --git a/src/backend/common/EventBase.hpp b/src/backend/common/EventBase.hpp
index 786fb3aced..46c35e9389 100644
--- a/src/backend/common/EventBase.hpp
+++ b/src/backend/common/EventBase.hpp
@@ -48,7 +48,7 @@ class EventBase {
     ///        is executed, the event is marked complete.
     ///
     /// \returns the error code for the mark call
-    ErrorType mark(QueueType &queue) noexcept {
+    ErrorType mark(QueueType queue) noexcept {
         return NativeEventPolicy::markEvent(&e_, queue);
     }
 
@@ -59,7 +59,7 @@ class EventBase {
     /// \param queue The queue that will wait for the previous tasks to complete
     ///
     /// \returns the error code for the wait call
-    ErrorType enqueueWait(QueueType &queue) noexcept {
+    ErrorType enqueueWait(QueueType queue) noexcept {
         return NativeEventPolicy::waitForEvent(&e_, queue);
     }
 
diff --git a/src/backend/cpu/Event.hpp b/src/backend/cpu/Event.hpp
index bcd2ac31ef..2d15039cfb 100644
--- a/src/backend/cpu/Event.hpp
+++ b/src/backend/cpu/Event.hpp
@@ -12,12 +12,14 @@
 #include <queue.hpp>
 #include <af/event.h>
 
+#include <type_traits>
+
 namespace cpu {
 
 class CPUEventPolicy {
    public:
     using EventType = queue_event;
-    using QueueType = queue;
+    using QueueType = std::add_lvalue_reference<queue>::type;
     using ErrorType = int;
 
     static int createAndMarkEvent(queue_event *e) noexcept {
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 9c706cf95e..67ea8e7b2a 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -9,18 +9,21 @@
 
 #pragma once
 
-#include <Array.hpp>
 #include <af/dim4.hpp>
 
 #undef _GLIBCXX_USE_INT128
+#include <Array.hpp>
+#include <Event.hpp>
 #include <err_cuda.hpp>
 #include <kernel/reduce.hpp>
 #include <kernel/reduce_by_key.hpp>
 #include <reduce.hpp>
 #include <set.hpp>
-#include <complex>
+
 #include <cub/device/device_scan.cuh>
 
+#include <complex>
+
 using af::dim4;
 using std::swap;
 
@@ -117,14 +120,15 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
         CUDA_CHECK(cudaMemcpyAsync(
             &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
             sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+        Event reduce_host_event = makeEvent(getActiveStream());
 
         // reset flags
         CUDA_CHECK(cudaMemsetAsync(needs_another_reduction.get(), 0,
                                    sizeof(int), getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
                                    sizeof(int), getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
+        reduce_host_event.block();
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
         CUDA_LAUNCH((kernel::test_needs_reduction<Tk>), numBlocksD0, numThreads,
@@ -159,6 +163,7 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
             CUDA_CHECK(cudaMemcpyAsync(
                 &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
                 sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+            reduce_host_event.mark(getActiveStream());
 
             CUDA_LAUNCH((kernel::compact_dim<Tk, To>), blocks, numThreads,
                         reduced_block_sizes.get(), reduced_keys, reduced_vals,
@@ -167,7 +172,7 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
 
             swap(t_reduced_keys, reduced_keys);
             swap(t_reduced_vals, reduced_vals);
-            CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
+            reduce_host_event.block();
         }
     } while (needs_another_reduction_host ||
              needs_block_boundary_reduction_host);
@@ -261,14 +266,15 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
         CUDA_CHECK(cudaMemcpyAsync(
             &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
             sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+        Event reduce_host_event = makeEvent(getActiveStream());
 
         // reset flags
         CUDA_CHECK(cudaMemsetAsync(needs_another_reduction.get(), 0,
                                    sizeof(int), getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(needs_block_boundary_reduction.get(), 0,
                                    sizeof(int), getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
+        reduce_host_event.block();
         numBlocksD0 = divup(n_reduced_host, numThreads);
 
         CUDA_LAUNCH((kernel::test_needs_reduction<Tk>), numBlocksD0, numThreads,
@@ -303,6 +309,7 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
             CUDA_CHECK(cudaMemcpyAsync(
                 &n_reduced_host, reduced_block_sizes.get() + (numBlocksD0 - 1),
                 sizeof(int), cudaMemcpyDeviceToHost, getActiveStream()));
+            reduce_host_event.mark(getActiveStream());
 
             CUDA_LAUNCH((kernel::compact<Tk, To>), blocks, numThreads,
                         reduced_block_sizes.get(), reduced_keys, reduced_vals,
@@ -311,7 +318,7 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
 
             swap(t_reduced_keys, reduced_keys);
             swap(t_reduced_vals, reduced_vals);
-            CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
+            reduce_host_event.block();
         }
     } while (needs_another_reduction_host ||
              needs_block_boundary_reduction_host);

From 1c178c5c9ae20e5d9c98c5c372e6b4e594c413e1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 2 Jul 2020 14:25:57 -0400
Subject: [PATCH 2021/2677] Fix compilation error in final_boundary_reduce_dim

the gidx was incorrectly being used as gid
---
 src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl b/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
index 4d97b98390..c8d56ce6be 100644
--- a/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_boundary_dim.cl
@@ -13,9 +13,9 @@ kernel void final_boundary_reduce_dim(global int *reduced_block_sizes,
                                       const int n, const int nBlocksZ) {
     local int dim_ordering[4];
 
-    const uint lid  = get_local_id(0);
-    const uint bid  = get_group_id(0);
-    const uint gidx = get_global_id(0);
+    const uint lid = get_local_id(0);
+    const uint bid = get_group_id(0);
+    const uint gid = get_global_id(0);
 
     const int bidy = get_group_id(1);
     const int bidz = get_group_id(2) % nBlocksZ;

From b146098653dd9c1acb0dca81be0a0ed1c15e7ece Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 2 Jul 2020 14:28:01 -0400
Subject: [PATCH 2022/2677] Correct the build error enum in buildProgram

---
 src/backend/opencl/compile_module.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index fab31558b0..40540e9567 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -116,7 +116,7 @@ Program buildProgram(const vector<string> &kernelSources,
 
         retVal.build({device}, (cl_std + defaults + options.str()).c_str());
     } catch (Error &err) {
-        if (err.err() == CL_BUILD_ERROR) { SHOW_BUILD_INFO(retVal); }
+        if (err.err() == CL_BUILD_PROGRAM_FAILURE) { SHOW_BUILD_INFO(retVal); }
         throw;
     }
     return retVal;

From 930acaa05a02b3df431c7d8ea6966a50803c076f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 2 Jul 2020 14:28:46 -0400
Subject: [PATCH 2023/2677] Propagate build errors to user facing exceptions

---
 src/backend/opencl/compile_module.cpp | 43 ++++++++++++---------------
 1 file changed, 19 insertions(+), 24 deletions(-)

diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 40540e9567..add7f58329 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -28,6 +28,7 @@
 using cl::Error;
 using cl::Program;
 using common::loggerFactory;
+using fmt::format;
 using opencl::getActiveDeviceId;
 using opencl::getDevice;
 using opencl::Kernel;
@@ -49,31 +50,23 @@ logger *getLogger() {
     return logger.get();
 }
 
-#define SHOW_DEBUG_BUILD_INFO(PROG)                                       \
-    do {                                                                  \
-        cl_uint numDevices = PROG.getInfo<CL_PROGRAM_NUM_DEVICES>();      \
-        for (unsigned int i = 0; i < numDevices; ++i) {                   \
-            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(       \
-                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                 \
-            printf("%s\n", PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(   \
-                                   PROG.getInfo<CL_PROGRAM_DEVICES>()[i]) \
-                               .c_str());                                 \
-        }                                                                 \
+#define THROW_BUILD_LOG_EXCEPTION(PROG)                                     \
+    do {                                                                    \
+        string build_error;                                                 \
+        build_error.reserve(4096);                                          \
+        auto devices = PROG.getInfo<CL_PROGRAM_DEVICES>();                  \
+        for (auto &device : PROG.getInfo<CL_PROGRAM_DEVICES>()) {           \
+            build_error +=                                                  \
+                format("OpenCL Device: {}\n\tOptions: {}\n\tLog:\n{}\n",    \
+                       device.getInfo<CL_DEVICE_NAME>(),                    \
+                       PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(device), \
+                       PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(device));    \
+        }                                                                   \
+        string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");               \
+        if (!info.empty() && info != "0") puts(build_error.c_str());        \
+        AF_ERROR(build_error, AF_ERR_INTERNAL);                             \
     } while (0)
 
-#if defined(NDEBUG)
-
-#define SHOW_BUILD_INFO(PROG)                                              \
-    do {                                                                   \
-        string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");              \
-        if (!info.empty() && info != "0") { SHOW_DEBUG_BUILD_INFO(PROG); } \
-    } while (0)
-
-#else
-#define SHOW_BUILD_INFO(PROG) SHOW_DEBUG_BUILD_INFO(PROG)
-#endif
-
 namespace opencl {
 
 const static string DEFAULT_MACROS_STR(
@@ -116,7 +109,9 @@ Program buildProgram(const vector<string> &kernelSources,
 
         retVal.build({device}, (cl_std + defaults + options.str()).c_str());
     } catch (Error &err) {
-        if (err.err() == CL_BUILD_PROGRAM_FAILURE) { SHOW_BUILD_INFO(retVal); }
+        if (err.err() == CL_BUILD_PROGRAM_FAILURE) {
+            THROW_BUILD_LOG_EXCEPTION(retVal);
+        }
         throw;
     }
     return retVal;

From 687533144ef7486480556073df6c63653deb99d9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Jul 2020 20:39:10 +0530
Subject: [PATCH 2024/2677] Change backend Kernel::setScalar to be async by
 default

---
 src/backend/common/KernelInterface.hpp   |  9 +++++++--
 src/backend/cuda/Kernel.cpp              | 11 ++++++-----
 src/backend/cuda/Kernel.hpp              |  3 ++-
 src/backend/cuda/kernel/canny.hpp        |  2 +-
 src/backend/cuda/kernel/flood_fill.hpp   |  2 +-
 src/backend/opencl/Kernel.cpp            |  6 ++++--
 src/backend/opencl/Kernel.hpp            |  9 +++++----
 src/backend/opencl/kernel/canny.hpp      |  2 +-
 src/backend/opencl/kernel/flood_fill.hpp |  8 ++------
 9 files changed, 29 insertions(+), 23 deletions(-)

diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
index 5027255c4a..e7cf005bf0 100644
--- a/src/backend/common/KernelInterface.hpp
+++ b/src/backend/common/KernelInterface.hpp
@@ -65,8 +65,13 @@ class KernelInterface {
     /// to the device memory pointed by `dst`
     ///
     /// \param[in] dst is the device pointer to which data will be copied
-    /// \param[in] value is the integer scalar to set at device pointer
-    virtual void setScalar(DevPtrType dst, int value) = 0;
+    /// \param[in] value is a poiner to the scalar value that is set at device
+    ///            pointer
+    /// \param[in] syncCopy will indicate if the backend call to upload the
+    ///            scalar value to GPU memory has to wait for copy to finish
+    ///            or proceed ahead without wait
+    virtual void setScalar(DevPtrType dst, int* scalarValPtr,
+                           const bool syncCopy = false) = 0;
 
     /// \brief Fetch a scalar from device memory
     ///
diff --git a/src/backend/cuda/Kernel.cpp b/src/backend/cuda/Kernel.cpp
index eb0dc63e4b..0d0a2b5bc3 100644
--- a/src/backend/cuda/Kernel.cpp
+++ b/src/backend/cuda/Kernel.cpp
@@ -13,7 +13,7 @@
 
 namespace cuda {
 
-Kernel::DevPtrType Kernel::getDevPtr(const char *name) {
+Kernel::DevPtrType Kernel::getDevPtr(const char* name) {
     Kernel::DevPtrType out = 0;
     size_t size            = 0;
     CU_CHECK(cuModuleGetGlobal(&out, &size, this->getModuleHandle(), name));
@@ -25,10 +25,11 @@ void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
     CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, cuda::getActiveStream()));
 }
 
-void Kernel::setScalar(Kernel::DevPtrType dst, int value) {
-    CU_CHECK(
-        cuMemcpyHtoDAsync(dst, &value, sizeof(int), cuda::getActiveStream()));
-    CU_CHECK(cuStreamSynchronize(cuda::getActiveStream()));
+void Kernel::setScalar(Kernel::DevPtrType dst, int* scalarValPtr,
+                       const bool syncCopy) {
+    CU_CHECK(cuMemcpyHtoDAsync(dst, scalarValPtr, sizeof(int),
+                               cuda::getActiveStream()));
+    if (syncCopy) { CU_CHECK(cuStreamSynchronize(cuda::getActiveStream())); }
 }
 
 int Kernel::getScalar(Kernel::DevPtrType src) {
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index 180157b069..1e0d25f7ac 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -49,7 +49,8 @@ class Kernel
 
     void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) final;
 
-    void setScalar(DevPtrType dst, int value) final;
+    void setScalar(DevPtrType dst, int* scalarValPtr,
+                   const bool syncCopy = false) final;
 
     int getScalar(DevPtrType src) final;
 };
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index ab3e838314..4b270de3ab 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -84,7 +84,7 @@ void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     int notFinished = 1;
     while (notFinished) {
         notFinished = 0;
-        edgeTrack.setScalar(flagPtr, notFinished);
+        edgeTrack.setScalar(flagPtr, &notFinished);
         edgeTrack(qArgs, output, blk_x, blk_y);
         POST_LAUNCH_CHECK();
         notFinished = edgeTrack.getScalar(flagPtr);
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index 4967d570f4..06c2712738 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -71,7 +71,7 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
 
     for (int doAnotherLaunch = 1; doAnotherLaunch > 0;) {
         doAnotherLaunch = 0;
-        floodStep.setScalar(continueFlagPtr, doAnotherLaunch);
+        floodStep.setScalar(continueFlagPtr, &doAnotherLaunch);
         floodStep(fQArgs, out, image, lowValue, highValue);
         POST_LAUNCH_CHECK();
         doAnotherLaunch = floodStep.getScalar(continueFlagPtr);
diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
index e59366ef13..bb9548fe69 100644
--- a/src/backend/opencl/Kernel.cpp
+++ b/src/backend/opencl/Kernel.cpp
@@ -26,8 +26,10 @@ void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
     getQueue().enqueueCopyBuffer(*src, *dst, 0, 0, bytes);
 }
 
-void Kernel::setScalar(Kernel::DevPtrType dst, int value) {
-    getQueue().enqueueWriteBuffer(*dst, CL_FALSE, 0, sizeof(int), &value);
+void Kernel::setScalar(Kernel::DevPtrType dst, int* scalarValPtr,
+                       const bool syncCopy) {
+    getQueue().enqueueWriteBuffer(*dst, (syncCopy ? CL_TRUE : CL_FALSE), 0,
+                                  sizeof(int), scalarValPtr);
 }
 
 int Kernel::getScalar(Kernel::DevPtrType src) {
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index 9953a4d956..7e5dd89afb 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -40,14 +40,15 @@ class Kernel
 
     // clang-format off
     [[deprecated("OpenCL backend doesn't need Kernel::getDevPtr method")]]
-    DevPtrType getDevPtr(const char* name) override;
+    DevPtrType getDevPtr(const char* name) final;
     // clang-format on
 
-    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) override;
+    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) final;
 
-    void setScalar(DevPtrType dst, int value) override;
+    void setScalar(DevPtrType dst, int* scalarValPtr,
+                   const bool syncCopy = false) final;
 
-    int getScalar(DevPtrType src) override;
+    int getScalar(DevPtrType src) final;
 };
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index de90488303..474a64737b 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -167,7 +167,7 @@ void edgeTrackingHysteresis(Param output, const Param strong,
 
     while (notFinished > 0) {
         notFinished = 0;
-        edgeTraceOp.setScalar(dContinue.get(), notFinished);
+        edgeTraceOp.setScalar(dContinue.get(), &notFinished);
         edgeTraceOp(EnqueueArgs(getQueue(), global, threads), *output.data,
                     output.info, blk_x, blk_y, *dContinue);
         CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index a51af88dff..6972acd2c8 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -108,16 +108,12 @@ void floodFill(Param out, const Param image, const Param seedsx,
 
     while (notFinished) {
         notFinished = 0;
-        getQueue().enqueueWriteBuffer(*dContinue, CL_FALSE, 0, sizeof(int),
-                                      &notFinished);
-
+        floodStep.setScalar(dContinue, &notFinished);
         floodStep(cl::EnqueueArgs(getQueue(), global, local), *out.data,
                   out.info, *image.data, image.info, lowValue, highValue,
                   *dContinue);
         CL_DEBUG_FINISH(getQueue());
-
-        getQueue().enqueueReadBuffer(*dContinue, CL_TRUE, 0, sizeof(int),
-                                     &notFinished);
+        notFinished = floodStep.getScalar(dContinue);
     }
     bufferFree(dContinue);
     finalizeOutput<T>(out, newValue);

From 999a9dc02bc63ca95db3a6d21cccc2ed12af116b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 2 Jul 2020 23:49:33 +0530
Subject: [PATCH 2025/2677] Refactor Kernel::[setScalar|getScalar] to setFlag &
 getFlag respectively

---
 src/backend/common/KernelInterface.hpp   | 6 +++---
 src/backend/cuda/Kernel.cpp              | 6 +++---
 src/backend/cuda/Kernel.hpp              | 6 +++---
 src/backend/cuda/kernel/canny.hpp        | 4 ++--
 src/backend/cuda/kernel/flood_fill.hpp   | 4 ++--
 src/backend/opencl/Kernel.cpp            | 6 +++---
 src/backend/opencl/Kernel.hpp            | 6 +++---
 src/backend/opencl/kernel/canny.hpp      | 4 ++--
 src/backend/opencl/kernel/flood_fill.hpp | 4 ++--
 9 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
index e7cf005bf0..bb9db8b5f1 100644
--- a/src/backend/common/KernelInterface.hpp
+++ b/src/backend/common/KernelInterface.hpp
@@ -70,8 +70,8 @@ class KernelInterface {
     /// \param[in] syncCopy will indicate if the backend call to upload the
     ///            scalar value to GPU memory has to wait for copy to finish
     ///            or proceed ahead without wait
-    virtual void setScalar(DevPtrType dst, int* scalarValPtr,
-                           const bool syncCopy = false) = 0;
+    virtual void setFlag(DevPtrType dst, int* scalarValPtr,
+                         const bool syncCopy = false) = 0;
 
     /// \brief Fetch a scalar from device memory
     ///
@@ -80,7 +80,7 @@ class KernelInterface {
     /// \param[in] src is the device pointer from which data will be copied
     ///
     /// \returns the integer scalar
-    virtual int getScalar(DevPtrType src) = 0;
+    virtual int getFlag(DevPtrType src) = 0;
 
     /// \brief Enqueue Kernel per queueing criteria forwarding other parameters
     ///
diff --git a/src/backend/cuda/Kernel.cpp b/src/backend/cuda/Kernel.cpp
index 0d0a2b5bc3..f2f64bdeb0 100644
--- a/src/backend/cuda/Kernel.cpp
+++ b/src/backend/cuda/Kernel.cpp
@@ -25,14 +25,14 @@ void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
     CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, cuda::getActiveStream()));
 }
 
-void Kernel::setScalar(Kernel::DevPtrType dst, int* scalarValPtr,
-                       const bool syncCopy) {
+void Kernel::setFlag(Kernel::DevPtrType dst, int* scalarValPtr,
+                     const bool syncCopy) {
     CU_CHECK(cuMemcpyHtoDAsync(dst, scalarValPtr, sizeof(int),
                                cuda::getActiveStream()));
     if (syncCopy) { CU_CHECK(cuStreamSynchronize(cuda::getActiveStream())); }
 }
 
-int Kernel::getScalar(Kernel::DevPtrType src) {
+int Kernel::getFlag(Kernel::DevPtrType src) {
     int retVal = 0;
     CU_CHECK(
         cuMemcpyDtoHAsync(&retVal, src, sizeof(int), cuda::getActiveStream()));
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index 1e0d25f7ac..33b53cb1ea 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -49,10 +49,10 @@ class Kernel
 
     void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) final;
 
-    void setScalar(DevPtrType dst, int* scalarValPtr,
-                   const bool syncCopy = false) final;
+    void setFlag(DevPtrType dst, int* scalarValPtr,
+                 const bool syncCopy = false) final;
 
-    int getScalar(DevPtrType src) final;
+    int getFlag(DevPtrType src) final;
 };
 
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 4b270de3ab..f250693a79 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -84,10 +84,10 @@ void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     int notFinished = 1;
     while (notFinished) {
         notFinished = 0;
-        edgeTrack.setScalar(flagPtr, &notFinished);
+        edgeTrack.setFlag(flagPtr, &notFinished);
         edgeTrack(qArgs, output, blk_x, blk_y);
         POST_LAUNCH_CHECK();
-        notFinished = edgeTrack.getScalar(flagPtr);
+        notFinished = edgeTrack.getFlag(flagPtr);
     }
     suppressLeftOver(qArgs, output, blk_x, blk_y);
     POST_LAUNCH_CHECK();
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index 06c2712738..0a0277b0b8 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -71,10 +71,10 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
 
     for (int doAnotherLaunch = 1; doAnotherLaunch > 0;) {
         doAnotherLaunch = 0;
-        floodStep.setScalar(continueFlagPtr, &doAnotherLaunch);
+        floodStep.setFlag(continueFlagPtr, &doAnotherLaunch);
         floodStep(fQArgs, out, image, lowValue, highValue);
         POST_LAUNCH_CHECK();
-        doAnotherLaunch = floodStep.getScalar(continueFlagPtr);
+        doAnotherLaunch = floodStep.getFlag(continueFlagPtr);
     }
     finalizeOutput(fQArgs, out, newValue);
     POST_LAUNCH_CHECK();
diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
index bb9548fe69..6cf893825d 100644
--- a/src/backend/opencl/Kernel.cpp
+++ b/src/backend/opencl/Kernel.cpp
@@ -26,13 +26,13 @@ void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
     getQueue().enqueueCopyBuffer(*src, *dst, 0, 0, bytes);
 }
 
-void Kernel::setScalar(Kernel::DevPtrType dst, int* scalarValPtr,
-                       const bool syncCopy) {
+void Kernel::setFlag(Kernel::DevPtrType dst, int* scalarValPtr,
+                     const bool syncCopy) {
     getQueue().enqueueWriteBuffer(*dst, (syncCopy ? CL_TRUE : CL_FALSE), 0,
                                   sizeof(int), scalarValPtr);
 }
 
-int Kernel::getScalar(Kernel::DevPtrType src) {
+int Kernel::getFlag(Kernel::DevPtrType src) {
     int retVal = 0;
     getQueue().enqueueReadBuffer(*src, CL_TRUE, 0, sizeof(int), &retVal);
     return retVal;
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index 7e5dd89afb..e36d691c4b 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -45,10 +45,10 @@ class Kernel
 
     void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes) final;
 
-    void setScalar(DevPtrType dst, int* scalarValPtr,
-                   const bool syncCopy = false) final;
+    void setFlag(DevPtrType dst, int* scalarValPtr,
+                 const bool syncCopy = false) final;
 
-    int getScalar(DevPtrType src) final;
+    int getFlag(DevPtrType src) final;
 };
 
 }  // namespace opencl
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 474a64737b..ebe2cb5f0c 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -167,11 +167,11 @@ void edgeTrackingHysteresis(Param output, const Param strong,
 
     while (notFinished > 0) {
         notFinished = 0;
-        edgeTraceOp.setScalar(dContinue.get(), &notFinished);
+        edgeTraceOp.setFlag(dContinue.get(), &notFinished);
         edgeTraceOp(EnqueueArgs(getQueue(), global, threads), *output.data,
                     output.info, blk_x, blk_y, *dContinue);
         CL_DEBUG_FINISH(getQueue());
-        notFinished = edgeTraceOp.getScalar(dContinue.get());
+        notFinished = edgeTraceOp.getFlag(dContinue.get());
     }
     suppressLeftOver<T>(output);
 }
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 6972acd2c8..dd2963514c 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -108,12 +108,12 @@ void floodFill(Param out, const Param image, const Param seedsx,
 
     while (notFinished) {
         notFinished = 0;
-        floodStep.setScalar(dContinue, &notFinished);
+        floodStep.setFlag(dContinue, &notFinished);
         floodStep(cl::EnqueueArgs(getQueue(), global, local), *out.data,
                   out.info, *image.data, image.info, lowValue, highValue,
                   *dContinue);
         CL_DEBUG_FINISH(getQueue());
-        notFinished = floodStep.getScalar(dContinue);
+        notFinished = floodStep.getFlag(dContinue);
     }
     bufferFree(dContinue);
     finalizeOutput<T>(out, newValue);

From 316791648b9358cdcd8b36848658a77a5b4542fe Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 Jul 2020 00:58:56 -0400
Subject: [PATCH 2026/2677] Remove cuDNN compute capablity check as its
 inaccurate

---
 src/backend/cuda/cudnnModule.cpp | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index f98654f8ac..86829c4096 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -135,16 +135,6 @@ cudnnModule::cudnnModule()
     MODULE_FUNCTION_INIT(cudnnSetStream);
     MODULE_FUNCTION_INIT(cudnnSetTensor4dDescriptor);
 
-    // Check to see if the cuDNN runtime is compatible with the current device
-    cudaDeviceProp prop = getDeviceProp(getActiveDeviceId());
-    if (!checkDeviceWithRuntime(cudnn_rtversion, {prop.major, prop.minor})) {
-        string error_message = fmt::format(
-            "Error: cuDNN CUDA Runtime({}.{}) does not support the "
-            "current device's compute capability(sm_{}{}).",
-            rtmajor, rtminor, prop.major, prop.minor);
-        AF_ERROR(error_message, AF_ERR_RUNTIME);
-    }
-
     if (!module.symbolsLoaded()) {
         string error_message =
             "Error loading cuDNN symbols. ArrayFire was unable to load some "

From c577fa0b7f3670a898ca8fc163289890126fbc93 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 Jul 2020 01:42:45 -0400
Subject: [PATCH 2027/2677] Fix cuDNN runtime version checks and warnings

---
 src/backend/cuda/cudnnModule.cpp | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 86829c4096..92d7f89e1f 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -51,6 +51,12 @@ auto cudnnVersionComponents(size_t version) {
     return make_tuple(major, minor, patch);
 }
 
+auto cudaRuntimeVersionComponents(size_t version) {
+    auto major = version / 1000;
+    auto minor = (version - (major * 1000)) / 10;
+    return make_tuple(major, minor);
+}
+
 cudnnModule::cudnnModule()
     : module({"cudnn"}, {"", "64_7", "64_8", "64_6", "64_5", "64_4"}, {""},
              cudnnVersions.size(), cudnnVersions.data()) {
@@ -83,8 +89,7 @@ cudnnModule::cudnnModule()
             major, minor);
     }
 
-    std::tie(rtmajor, rtminor, std::ignore) =
-        cudnnVersionComponents(cudnn_rtversion);
+    std::tie(rtmajor, rtminor) = cudaRuntimeVersionComponents(cudnn_rtversion);
 
     AF_TRACE("cuDNN Version: {}.{}.{} cuDNN CUDA Runtime: {}.{}", major, minor,
              patch, rtmajor, rtminor);
@@ -101,15 +106,16 @@ cudnnModule::cudnnModule()
 
     int afcuda_runtime = 0;
     cudaRuntimeGetVersion(&afcuda_runtime);
-    if (afcuda_runtime != static_cast<int>(cudnn_version)) {
+    if (afcuda_runtime != static_cast<int>(cudnn_rtversion)) {
         getLogger()->warn(
             "WARNING: ArrayFire CUDA Runtime({}) and cuDNN CUDA "
-            "Runtime({}.{}) do not match. For maximum compatibility, make sure "
+            "Runtime({}) do not match. For maximum compatibility, make sure "
             "the two versions match.(Ignoring check)",
             // NOTE: the int version formats from CUDA and cuDNN are different
             // so we are using int_version_to_string for the ArrayFire CUDA
             // runtime
-            int_version_to_string(afcuda_runtime), rtmajor, rtminor);
+            int_version_to_string(afcuda_runtime),
+            int_version_to_string(cudnn_rtversion));
     }
 
     MODULE_FUNCTION_INIT(cudnnConvolutionBackwardData);

From b5eb73c2a17928774744fb948dccee2a7fee8f77 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 3 Jul 2020 17:08:02 +0530
Subject: [PATCH 2028/2677] Update 3.7.2 release notes with fixes from the past
 week

---
 docs/pages/release_notes.md | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 724019a036..be7dd1bbc8 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -17,22 +17,27 @@ Improvements
 v3.7.2
 ======
 
+
 Improvements
 ------------
 - Cache CUDA kernels to disk to improve load times(Thanks to \@cschreib-ibex) /PR{2848}
 - Staticly link against cuda libraries /PR{2785}
 - Make cuDNN an optional build dependency /PR{2836}
-- Improve support for different compilers and OS /PR{2876} /PR{2945} /PR{2925} /PR{2942} /PR{2943} /PR{2945}
+- Improve support for different compilers and OS /PR{2876} /PR{2945} /PR{2925} /PR{2942} /PR{2943} /PR{2945} /PR{2958}
 - Improve performance of join and transpose on CPU /PR{2849}
 - Improve documentation /PR{2816} /PR{2821} /PR{2846} /PR{2918} /PR{2928} /PR{2947}
-- Reduce binary size using NVRTC and template reducing instantiations /PR{2849} /PR{2861} /PR{2890}
-- Improve reduceByKey performance on OpenCL by using builtin functions /PR{2851}
+- Reduce binary size using NVRTC and template reducing instantiations /PR{2849} /PR{2861} /PR{2890} /PR{2957}
+- reduceByKey performance improvements /PR{2851} /PR{2957}
 - Improve support for Intel OpenCL GPUs /PR{2855}
 - Allow staticly linking against MKL /PR{2877} (Sponsered by SDL)
 - Better support for older CUDA toolkits /PR{2923}
 - Add support for CUDA 11 /PR{2939}
 - Add support for ccache for faster builds /PR{2931}
 - Add support for the conan package manager on linux /PR{2875}
+- Propagate build errors up the stack in AFError exceptions /PR{2948} /PR{2957}
+- Improve runtime dependency library loading /PR{2954}
+- Improved cuDNN runtime checks and warnings /PR{2960}
+- Document af\_memory\_manager\_* native memory return values /PR{2911}
 
 Fixes
 -----
@@ -44,10 +49,13 @@ Fixes
 - Fix af_get_memory_pressure_threshold return value /PR{2831}
 - Increased the max filter length for morph
 - Handle empty array inputs for LU, QR, and Rank functions /PR{2838}
-- Fix FindMKL.cmake script for sequential threading library /PR{2840}
-- Various internal refactoring /PR{2839} /PR{2861} /PR{2864} /PR{2873} /PR{2890} /PR{2891} /PR{2913}
+- Fix FindMKL.cmake script for sequential threading library /PR{2840} /PR{2952}
+- Various internal refactoring /PR{2839} /PR{2861} /PR{2864} /PR{2873} /PR{2890} /PR{2891} /PR{2913} /PR{2959}
 - Fix OpenCL 2.0 builtin function name conflict /PR{2851}
 - Fix error caused when releasing memory with multiple devices /PR{2867}
+- Fix missing set stacktrace symbol from unified API /PR{2915}
+- Fix zero padding issue in convolve2NN /PR{2820}
+- Fixed bugs in ReduceByKey /PR{2957}
 
 Contributions
 -------------
@@ -55,6 +63,7 @@ Special thanks to our contributors:
 [Corentin Schreiber](https://github.com/cschreib-ibex)
 [Jacob Khan](https://github.com/jacobkahn)
 [Paul Jurczak](https://github.com/pauljurczak)
+[Christoph Junghans](https://github.com/junghans)
 
 v3.7.1
 ======

From 7bea308ec46f5b7bdce0e46d13968c89c42a8d7b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 5 Jul 2020 16:51:52 +0530
Subject: [PATCH 2029/2677] Add support for cuDNN verion 8

Adds runtime support for cuDNN 8
---
 CMakeModules/FindcuDNN.cmake     |  16 ++--
 src/backend/cuda/convolveNN.cpp  | 125 +++++++++++++++++++++++++------
 src/backend/cuda/cudnn.cpp       | 118 +++++++++++++++++++++--------
 src/backend/cuda/cudnn.hpp       |  58 +++++++++-----
 src/backend/cuda/cudnnModule.cpp |  15 +++-
 src/backend/cuda/cudnnModule.hpp |  38 +++++++++-
 6 files changed, 287 insertions(+), 83 deletions(-)

diff --git a/CMakeModules/FindcuDNN.cmake b/CMakeModules/FindcuDNN.cmake
index fd49fbe96b..f6e5d0e592 100644
--- a/CMakeModules/FindcuDNN.cmake
+++ b/CMakeModules/FindcuDNN.cmake
@@ -54,8 +54,8 @@ find_package(CUDA QUIET)
 find_path(cuDNN_INCLUDE_DIRS
   NAMES cudnn.h
   HINTS
-    ${PC_CUDNN_INCLUDE_DIRS}
     ${cuDNN_ROOT_DIR}
+    ${PC_CUDNN_INCLUDE_DIRS}
     ${CUDA_TOOLKIT_INCLUDE}
   PATH_SUFFIXES include
   DOC "cuDNN include directory path." )
@@ -64,6 +64,12 @@ if(cuDNN_INCLUDE_DIRS)
   file(READ ${cuDNN_INCLUDE_DIRS}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
   string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
     CUDNN_MAJOR_VERSION "${CUDNN_VERSION_FILE_CONTENTS}")
+  list(LENGTH CUDNN_MAJOR_VERSION cudnn_ver_matches)
+  if(${cudnn_ver_matches} EQUAL 0)
+    file(READ ${cuDNN_INCLUDE_DIRS}/cudnn_version.h CUDNN_VERSION_FILE_CONTENTS)
+    string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+      CUDNN_MAJOR_VERSION "${CUDNN_VERSION_FILE_CONTENTS}")
+  endif()
   string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
       CUDNN_MAJOR_VERSION "${CUDNN_MAJOR_VERSION}")
   string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
@@ -94,10 +100,10 @@ if(cuDNN_INCLUDE_DIRS)
       libcudnn.${cudnn_ver_suffix}.dylib
       cudnn
     PATHS
-      $ENV{LD_LIBRARY_PATH}
-      ${libpath_cudart}
       ${cuDNN_ROOT_DIR}
       ${PC_CUDNN_LIBRARY_DIRS}
+      $ENV{LD_LIBRARY_PATH}
+      ${libpath_cudart}
       ${CMAKE_INSTALL_PREFIX}
     PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
     DOC "cuDNN link library." )
@@ -106,10 +112,10 @@ if(cuDNN_INCLUDE_DIRS)
     find_file(cuDNN_DLL_LIBRARY
     NAMES cudnn64_${cudnn_ver_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
     PATHS
-      $ENV{PATH}
-      ${libpath_cudart}
       ${cuDNN_ROOT_DIR}
       ${PC_CUDNN_LIBRARY_DIRS}
+      $ENV{PATH}
+      ${libpath_cudart}
       ${CMAKE_INSTALL_PREFIX}
     PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
     DOC "cuDNN Windows DLL." )
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 7e1e2208fa..2a4a57174f 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -28,6 +28,8 @@
 #include <af/dim4.hpp>
 
 #include <type_traits>
+#include <utility>
+#include <vector>
 
 using af::dim4;
 using common::flip;
@@ -35,11 +37,16 @@ using common::half;
 using common::make_handle;
 using std::conditional;
 using std::is_same;
+using std::pair;
+using std::tie;
+using std::vector;
 
 namespace cuda {
 
 #ifdef WITH_CUDNN
 
+auto getLogger() { return getCudnnPlugin().getLogger(); }
+
 template<typename Desc, typename T>
 auto toCudnn(Array<T> arr) {
     auto descriptor = make_handle<Desc>();
@@ -51,6 +58,49 @@ template<typename T>
 using scale_type =
     typename conditional<is_same<T, double>::value, double, float>::type;
 
+pair<cudnnConvolutionFwdAlgo_t, size_t> getForwardAlgorithm(
+    cudnnHandle_t cudnn, cudnnTensorDescriptor_t input_descriptor,
+    cudnnFilterDescriptor_t filter_descriptor,
+    cudnnConvolutionDescriptor_t convolution_descriptor,
+    cudnnTensorDescriptor_t output_descriptor) {
+    cudnnConvolutionFwdAlgo_t convolution_algorithm;
+    size_t workspace_bytes = 0;
+
+    auto version = getCudnnPlugin().getVersion();
+    if (std::get<0>(version) >= 8) {
+        int maxAlgoCount = 0;
+        CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithmMaxCount(
+            cudnn, &maxAlgoCount));
+
+        vector<cudnnConvolutionFwdAlgoPerf_t> perfResults(maxAlgoCount);
+        int returnAlgoCount = 0;
+        CUDNN_CHECK(cuda::cudnnFindConvolutionForwardAlgorithm(
+            cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+            output_descriptor, maxAlgoCount, &returnAlgoCount,
+            perfResults.data()));
+
+        for (int i = 0; i < returnAlgoCount; ++i) {
+            if (perfResults[i].status == CUDNN_STATUS_SUCCESS) {
+                convolution_algorithm = perfResults[i].algo;
+                workspace_bytes       = perfResults[i].memory;
+                break;
+            }
+        }
+    } else {
+        const int memory_limit =
+            0;  // TODO: set to remaining space in memory manager?
+        CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithm(
+            cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+            output_descriptor, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+            memory_limit, &convolution_algorithm));
+        CUDNN_CHECK(cuda::cudnnGetConvolutionForwardWorkspaceSize(
+            cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
+            output_descriptor, convolution_algorithm, &workspace_bytes));
+    }
+
+    return {convolution_algorithm, workspace_bytes};
+}
+
 template<typename T>
 Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
                          const dim4 &stride, const dim4 &padding,
@@ -88,19 +138,12 @@ Array<T> convolve2_cudnn(const Array<T> &signal, const Array<T> &filter,
     auto output_descriptor = toCudnn<cudnnTensorDescriptor_t>(out);
 
     // get convolution algorithm
-    const int memory_limit =
-        0;  // TODO: set to remaining space in memory manager?
     cudnnConvolutionFwdAlgo_t convolution_algorithm;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithm(
-        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
-        output_descriptor, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, memory_limit,
-        &convolution_algorithm));
+    size_t workspace_bytes = 0;
 
-    // figure out scratch space memory requirements
-    size_t workspace_bytes;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionForwardWorkspaceSize(
-        cudnn, input_descriptor, filter_descriptor, convolution_descriptor,
-        output_descriptor, convolution_algorithm, &workspace_bytes));
+    tie(convolution_algorithm, workspace_bytes) =
+        getForwardAlgorithm(cudnn, input_descriptor, filter_descriptor,
+                            convolution_descriptor, output_descriptor);
 
     auto workspace_buffer = memAlloc<char>(workspace_bytes);
 
@@ -355,6 +398,48 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
 }
 
 #ifdef WITH_CUDNN
+
+pair<cudnnConvolutionBwdFilterAlgo_t, size_t> getBackwardFilterAlgorithm(
+    cudnnHandle_t cudnn, cudnnTensorDescriptor_t x_descriptor,
+    cudnnTensorDescriptor_t dy_descriptor,
+    cudnnConvolutionDescriptor_t convolution_descriptor,
+    cudnnFilterDescriptor_t dw_descriptor) {
+    // determine algorithm to use
+    cudnnConvolutionBwdFilterAlgo_t bwd_filt_convolution_algorithm;
+    // figure out scratch space memory requirements
+    size_t workspace_bytes = 0;
+
+    auto version = getCudnnPlugin().getVersion();
+    if (std::get<0>(version) >= 8) {
+        int maxAlgoCount = 0;
+        CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
+            cudnn, &maxAlgoCount));
+
+        vector<cudnnConvolutionBwdFilterAlgoPerf_t> perfResults(maxAlgoCount);
+        int returnAlgoCount = 0;
+        CUDNN_CHECK(cuda::cudnnFindConvolutionBackwardFilterAlgorithm(
+            cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+            dw_descriptor, maxAlgoCount, &returnAlgoCount, perfResults.data()));
+
+        for (int i = 0; i < returnAlgoCount; ++i) {
+            if (perfResults[i].status == CUDNN_STATUS_SUCCESS) {
+                bwd_filt_convolution_algorithm = perfResults[i].algo;
+                workspace_bytes                = perfResults[i].memory;
+                break;
+            }
+        }
+    } else {
+        CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithm(
+            cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+            dw_descriptor, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0,
+            &bwd_filt_convolution_algorithm));
+        CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterWorkspaceSize(
+            cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
+            dw_descriptor, bwd_filt_convolution_algorithm, &workspace_bytes));
+    }
+    return {bwd_filt_convolution_algorithm, workspace_bytes};
+}
+
 template<typename T>
 Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
                                const Array<T> &original_signal,
@@ -384,19 +469,15 @@ Array<T> filter_gradient_cudnn(const Array<T> &incoming_gradient,
 
     // determine algorithm to use
     cudnnConvolutionBwdFilterAlgo_t bwd_filt_convolution_algorithm;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithm(
-        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
-        dw_descriptor, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0,
-        &bwd_filt_convolution_algorithm));
-
     // figure out scratch space memory requirements
-    size_t workspace_bytes;
-    CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterWorkspaceSize(
-        cudnn, x_descriptor, dy_descriptor, convolution_descriptor,
-        dw_descriptor, bwd_filt_convolution_algorithm, &workspace_bytes));
-    // prepare output array and scratch space
-    Array<T> out = createEmptyArray<T>(fDims);
+    size_t workspace_bytes = 0;
 
+    tie(bwd_filt_convolution_algorithm, workspace_bytes) =
+        getBackwardFilterAlgorithm(cudnn, x_descriptor, dy_descriptor,
+                                   convolution_descriptor, dw_descriptor);
+
+    // prepare output array and scratch space
+    Array<T> out          = createEmptyArray<T>(fDims);
     auto workspace_buffer = memAlloc<char>(workspace_bytes);
 
     // perform convolution
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index 5f3c7f982c..f75769d8f6 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -37,6 +37,10 @@ const char *errorString(cudnnStatus_t err) {
             return "CUDNN_STATUS_RUNTIME_IN_PROGRESS";
         case CUDNN_STATUS_RUNTIME_FP_OVERFLOW:
             return "CUDNN_STATUS_RUNTIME_FP_OVERFLOW";
+#if CUDNN_VERSION >= 8000
+        case CUDNN_STATUS_VERSION_MISMATCH:
+            return "CUDNN_STATUS_VERSION_MISMATCH";
+#endif
 #endif
 #endif
         default: return "UNKNOWN";
@@ -171,16 +175,16 @@ cudnnStatus_t cudnnGetConvolutionNdForwardOutputDim(
         convDesc, inputTensorDesc, filterDesc, nbDims, tensorOuputDimA);
 }
 
-cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
-    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
-    const cudnnFilterDescriptor_t wDesc,
-    const cudnnConvolutionDescriptor_t convDesc,
-    const cudnnTensorDescriptor_t yDesc,
-    cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
-    cudnnConvolutionFwdAlgo_t *algo) {
-    return getCudnnPlugin().cudnnGetConvolutionForwardAlgorithm(
-        handle, xDesc, wDesc, convDesc, yDesc, preference, memoryLimitInBytes,
-        algo);
+cudnnStatus_t cudnnGetConvolutionForwardAlgorithmMaxCount(cudnnHandle_t handle,
+                                                          int *count) {
+    return getCudnnPlugin().cudnnGetConvolutionForwardAlgorithmMaxCount(handle,
+                                                                        count);
+}
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
+    cudnnHandle_t handle, int *count) {
+    return getCudnnPlugin().cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
+        handle, count);
 }
 
 cudnnStatus_t cudnnGetConvolutionForwardWorkspaceSize(
@@ -193,16 +197,57 @@ cudnnStatus_t cudnnGetConvolutionForwardWorkspaceSize(
         handle, xDesc, wDesc, convDesc, yDesc, algo, sizeInBytes);
 }
 
-cudnnStatus_t cudnnConvolutionForward(
-    cudnnHandle_t handle, const void *alpha,
-    const cudnnTensorDescriptor_t xDesc, const void *x,
-    const cudnnFilterDescriptor_t wDesc, const void *w,
-    const cudnnConvolutionDescriptor_t convDesc, cudnnConvolutionFwdAlgo_t algo,
-    void *workSpace, size_t workSpaceSizeInBytes, const void *beta,
-    const cudnnTensorDescriptor_t yDesc, void *y) {
-    return getCudnnPlugin().cudnnConvolutionForward(
-        handle, alpha, xDesc, x, wDesc, w, convDesc, algo, workSpace,
-        workSpaceSizeInBytes, beta, yDesc, y);
+cudnnStatus_t cudnnGetConvolutionBackwardFilterWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t gradDesc,
+    cudnnConvolutionBwdFilterAlgo_t algo, size_t *sizeInBytes) {
+    return getCudnnPlugin().cudnnGetConvolutionBackwardFilterWorkspaceSize(
+        handle, xDesc, dyDesc, convDesc, gradDesc, algo, sizeInBytes);
+}
+
+cudnnStatus_t cudnnFindConvolutionForwardAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc, const int requestedAlgoCount,
+    int *returnedAlgoCount, cudnnConvolutionFwdAlgoPerf_t *perfResults) {
+    return getCudnnPlugin().cudnnFindConvolutionForwardAlgorithm(
+        handle, xDesc, wDesc, convDesc, yDesc, requestedAlgoCount,
+        returnedAlgoCount, perfResults);
+}
+
+cudnnStatus_t cudnnFindConvolutionBackwardFilterAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t dwDesc, const int requestedAlgoCount,
+    int *returnedAlgoCount, cudnnConvolutionBwdFilterAlgoPerf_t *perfResults) {
+    return getCudnnPlugin().cudnnFindConvolutionBackwardFilterAlgorithm(
+        handle, xDesc, dyDesc, convDesc, dwDesc, requestedAlgoCount,
+        returnedAlgoCount, perfResults);
+}
+
+cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc,
+    cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionFwdAlgo_t *algo) {
+    auto version = getCudnnPlugin().getVersion();
+    if (std::get<0>(version) < 8) {
+        return getCudnnPlugin().cudnnGetConvolutionForwardAlgorithm(
+            handle, xDesc, wDesc, convDesc, yDesc, preference,
+            memoryLimitInBytes, algo);
+    } else {
+        AF_ERROR(
+            "cudnnGetConvolutionForwardAlgorithm has been removed since cuDNN "
+            "8",
+            AF_ERR_NOT_SUPPORTED);
+        return CUDNN_STATUS_SUCCESS;
+    }
 }
 
 cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
@@ -212,19 +257,30 @@ cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
     const cudnnFilterDescriptor_t dwDesc,
     cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
     cudnnConvolutionBwdFilterAlgo_t *algo) {
-    return getCudnnPlugin().cudnnGetConvolutionBackwardFilterAlgorithm(
-        handle, xDesc, dyDesc, convDesc, dwDesc, preference, memoryLimitInBytes,
-        algo);
+    auto version = getCudnnPlugin().getVersion();
+    if (std::get<0>(version) < 8) {
+        return getCudnnPlugin().cudnnGetConvolutionBackwardFilterAlgorithm(
+            handle, xDesc, dyDesc, convDesc, dwDesc, preference,
+            memoryLimitInBytes, algo);
+    } else {
+        AF_ERROR(
+            "cudnnGetConvolutionBackwardFilterAlgorithm has been removed since "
+            "cuDNN 8",
+            AF_ERR_NOT_SUPPORTED);
+        return CUDNN_STATUS_SUCCESS;
+    }
 }
 
-cudnnStatus_t cudnnGetConvolutionBackwardFilterWorkspaceSize(
-    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
-    const cudnnTensorDescriptor_t dyDesc,
-    const cudnnConvolutionDescriptor_t convDesc,
-    const cudnnFilterDescriptor_t gradDesc,
-    cudnnConvolutionBwdFilterAlgo_t algo, size_t *sizeInBytes) {
-    return getCudnnPlugin().cudnnGetConvolutionBackwardFilterWorkspaceSize(
-        handle, xDesc, dyDesc, convDesc, gradDesc, algo, sizeInBytes);
+cudnnStatus_t cudnnConvolutionForward(
+    cudnnHandle_t handle, const void *alpha,
+    const cudnnTensorDescriptor_t xDesc, const void *x,
+    const cudnnFilterDescriptor_t wDesc, const void *w,
+    const cudnnConvolutionDescriptor_t convDesc, cudnnConvolutionFwdAlgo_t algo,
+    void *workSpace, size_t workSpaceSizeInBytes, const void *beta,
+    const cudnnTensorDescriptor_t yDesc, void *y) {
+    return getCudnnPlugin().cudnnConvolutionForward(
+        handle, alpha, xDesc, x, wDesc, w, convDesc, algo, workSpace,
+        workSpaceSizeInBytes, beta, yDesc, y);
 }
 
 cudnnStatus_t cudnnConvolutionBackwardFilter(
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
index 58eb662611..4fae40692e 100644
--- a/src/backend/cuda/cudnn.hpp
+++ b/src/backend/cuda/cudnn.hpp
@@ -116,6 +116,40 @@ cudnnStatus_t cudnnGetConvolutionNdForwardOutputDim(
     const cudnnFilterDescriptor_t filterDesc, int nbDims,
     int tensorOuputDimA[]);
 
+cudnnStatus_t cudnnGetConvolutionForwardAlgorithmMaxCount(cudnnHandle_t handle,
+                                                          int *count);
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
+    cudnnHandle_t handle, int *count);
+
+cudnnStatus_t cudnnGetConvolutionForwardWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc, cudnnConvolutionFwdAlgo_t algo,
+    size_t *sizeInBytes);
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterWorkspaceSize(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t gradDesc,
+    cudnnConvolutionBwdFilterAlgo_t algo, size_t *sizeInBytes);
+
+cudnnStatus_t cudnnFindConvolutionForwardAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc, const int requestedAlgoCount,
+    int *returnedAlgoCount, cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+cudnnStatus_t cudnnFindConvolutionBackwardFilterAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t dwDesc, const int requestedAlgoCount,
+    int *returnedAlgoCount, cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
 cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
     cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
     const cudnnFilterDescriptor_t wDesc,
@@ -124,12 +158,13 @@ cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
     cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
     cudnnConvolutionFwdAlgo_t *algo);
 
-cudnnStatus_t cudnnGetConvolutionForwardWorkspaceSize(
+cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
     cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
-    const cudnnFilterDescriptor_t wDesc,
+    const cudnnTensorDescriptor_t dyDesc,
     const cudnnConvolutionDescriptor_t convDesc,
-    const cudnnTensorDescriptor_t yDesc, cudnnConvolutionFwdAlgo_t algo,
-    size_t *sizeInBytes);
+    const cudnnFilterDescriptor_t dwDesc,
+    cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionBwdFilterAlgo_t *algo);
 
 cudnnStatus_t cudnnConvolutionForward(
     cudnnHandle_t handle, const void *alpha,
@@ -139,21 +174,6 @@ cudnnStatus_t cudnnConvolutionForward(
     void *workSpace, size_t workSpaceSizeInBytes, const void *beta,
     const cudnnTensorDescriptor_t yDesc, void *y);
 
-cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
-    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
-    const cudnnTensorDescriptor_t dyDesc,
-    const cudnnConvolutionDescriptor_t convDesc,
-    const cudnnFilterDescriptor_t dwDesc,
-    cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
-    cudnnConvolutionBwdFilterAlgo_t *algo);
-
-cudnnStatus_t cudnnGetConvolutionBackwardFilterWorkspaceSize(
-    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
-    const cudnnTensorDescriptor_t dyDesc,
-    const cudnnConvolutionDescriptor_t convDesc,
-    const cudnnFilterDescriptor_t gradDesc,
-    cudnnConvolutionBwdFilterAlgo_t algo, size_t *sizeInBytes);
-
 cudnnStatus_t cudnnConvolutionBackwardFilter(
     cudnnHandle_t handle, const void *alpha,
     const cudnnTensorDescriptor_t xDesc, const void *x,
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 92d7f89e1f..b76b0c65fe 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -26,7 +26,8 @@ namespace cuda {
 
 // clang-format off
 // Latest version from each minor releases are enlisted below
-constexpr std::array<common::Version, 10> cudnnVersions = {
+constexpr std::array<common::Version, 11> cudnnVersions = {
+    make_tuple(8, 0,  1),
     make_tuple(7, 6,  5),
     make_tuple(7, 5,  1),
     make_tuple(7, 4,  2),
@@ -130,10 +131,16 @@ cudnnModule::cudnnModule()
     MODULE_FUNCTION_INIT(cudnnDestroyFilterDescriptor);
     MODULE_FUNCTION_INIT(cudnnDestroyTensorDescriptor);
     MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardDataWorkspaceSize);
-    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterAlgorithm);
-    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterWorkspaceSize);
-    MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardAlgorithm);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardAlgorithmMaxCount);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterAlgorithmMaxCount);
     MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardWorkspaceSize);
+    MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterWorkspaceSize);
+    MODULE_FUNCTION_INIT(cudnnFindConvolutionForwardAlgorithm);
+    MODULE_FUNCTION_INIT(cudnnFindConvolutionBackwardFilterAlgorithm);
+    if (major < 8) {
+        MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardAlgorithm);
+        MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterAlgorithm);
+    }
     MODULE_FUNCTION_INIT(cudnnGetConvolutionNdForwardOutputDim);
     MODULE_FUNCTION_INIT(cudnnSetConvolution2dDescriptor);
     MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor);
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index aa762e25fd..aafefa6b84 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -31,6 +31,36 @@ cudnnStatus_t cudnnSetFilter4dDescriptor_v4(
 size_t cudnnGetCudartVersion(void);
 #endif
 
+#if CUDNN_VERSION >= 8000
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_NO_WORKSPACE            = 0,
+    CUDNN_CONVOLUTION_FWD_PREFER_FASTEST          = 1,
+    CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT = 2,
+} cudnnConvolutionFwdPreference_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE            = 0,
+    CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST          = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT = 2,
+} cudnnConvolutionBwdFilterPreference_t;
+
+cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnFilterDescriptor_t wDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnTensorDescriptor_t yDesc,
+    cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionFwdAlgo_t* algo);
+
+cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
+    cudnnHandle_t handle, const cudnnTensorDescriptor_t xDesc,
+    const cudnnTensorDescriptor_t dyDesc,
+    const cudnnConvolutionDescriptor_t convDesc,
+    const cudnnFilterDescriptor_t dwDesc,
+    cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
+    cudnnConvolutionBwdFilterAlgo_t* algo);
+#endif
+
 namespace cuda {
 
 class cudnnModule {
@@ -51,10 +81,14 @@ class cudnnModule {
     MODULE_MEMBER(cudnnDestroyFilterDescriptor);
     MODULE_MEMBER(cudnnDestroyTensorDescriptor);
     MODULE_MEMBER(cudnnGetConvolutionBackwardDataWorkspaceSize);
-    MODULE_MEMBER(cudnnGetConvolutionBackwardFilterAlgorithm);
+    MODULE_MEMBER(cudnnGetConvolutionForwardAlgorithmMaxCount);
+    MODULE_MEMBER(cudnnGetConvolutionBackwardFilterAlgorithmMaxCount);
+    MODULE_MEMBER(cudnnFindConvolutionForwardAlgorithm);
+    MODULE_MEMBER(cudnnFindConvolutionBackwardFilterAlgorithm);
+    MODULE_MEMBER(cudnnGetConvolutionForwardWorkspaceSize);
     MODULE_MEMBER(cudnnGetConvolutionBackwardFilterWorkspaceSize);
     MODULE_MEMBER(cudnnGetConvolutionForwardAlgorithm);
-    MODULE_MEMBER(cudnnGetConvolutionForwardWorkspaceSize);
+    MODULE_MEMBER(cudnnGetConvolutionBackwardFilterAlgorithm);
     MODULE_MEMBER(cudnnGetConvolutionNdForwardOutputDim);
     MODULE_MEMBER(cudnnSetConvolution2dDescriptor);
     MODULE_MEMBER(cudnnSetFilter4dDescriptor);

From 1682bccceff503520bef866f30cf952e30ca605d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 6 Jul 2020 16:07:14 +0530
Subject: [PATCH 2030/2677] Fix the svd ndims check to limit inputs to 2D
 arrays

---
 src/api/c/svd.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/svd.cpp b/src/api/c/svd.cpp
index 268b68cb26..661831ffc8 100644
--- a/src/api/c/svd.cpp
+++ b/src/api/c/svd.cpp
@@ -75,7 +75,7 @@ af_err af_svd(af_array *u, af_array *s, af_array *vt, const af_array in) {
         const ArrayInfo &info = getInfo(in);
         dim4 dims             = info.dims();
 
-        ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
+        ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 2));
         af_dtype type = info.getType();
 
         if (dims.ndims() == 0) {
@@ -102,7 +102,7 @@ af_err af_svd_inplace(af_array *u, af_array *s, af_array *vt, af_array in) {
         const ArrayInfo &info = getInfo(in);
         dim4 dims             = info.dims();
 
-        ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 3));
+        ARG_ASSERT(3, (dims.ndims() >= 0 && dims.ndims() <= 2));
         af_dtype type = info.getType();
 
         if (dims.ndims() == 0) {

From 2844fa3ce28558edc0573b988c0cbd11286333e7 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 7 Jul 2020 13:51:51 +0530
Subject: [PATCH 2031/2677] Add clblast patch to handle custom context with
 multiple devices (#2967)

* Add clblast patch to handle custom context with multiple devices

* Pass option to fix whitepsace error in clblast patch apply
---
 CMakeModules/build_CLBlast.cmake         |  3 ++
 CMakeModules/clblast_program_getIR.patch | 44 ++++++++++++++++++++++++
 2 files changed, 47 insertions(+)
 create mode 100644 CMakeModules/clblast_program_getIR.patch

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index c5a7567630..82d58c2b7b 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -12,6 +12,8 @@ find_program(GIT git)
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
 set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
+set(CLBLAST_PATCH_COMMAND ${GIT} apply --whitespace=fix ${ArrayFire_SOURCE_DIR}/CMakeModules/clblast_program_getIR.patch)
+
 if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
 else()
@@ -31,6 +33,7 @@ ExternalProject_Add(
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
+    PATCH_COMMAND "${CLBLAST_PATCH_COMMAND}"
     BUILD_BYPRODUCTS ${CLBlast_location}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
       -Wno-dev <SOURCE_DIR>
diff --git a/CMakeModules/clblast_program_getIR.patch b/CMakeModules/clblast_program_getIR.patch
new file mode 100644
index 0000000000..5b3d12e6ad
--- /dev/null
+++ b/CMakeModules/clblast_program_getIR.patch
@@ -0,0 +1,44 @@
+diff --git a/src/clpp11.hpp b/src/clpp11.hpp
+index 4ed157ea..2a25606c 100644
+--- a/src/clpp11.hpp
++++ b/src/clpp11.hpp
+@@ -509,12 +509,35 @@ class Program {
+ 
+   // Retrieves a binary or an intermediate representation of the compiled program
+   std::string GetIR() const {
+-    auto bytes = size_t{0};
+-    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARY_SIZES, sizeof(size_t), &bytes, nullptr));
++    cl_uint num_devices = 0;
++    CheckError(clGetProgramInfo(program_, CL_PROGRAM_NUM_DEVICES,
++                sizeof(cl_uint), &num_devices, nullptr));
++
++    std::vector<size_t> binSizesInBytes(num_devices, 0);
++    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARY_SIZES,
++                num_devices * sizeof(size_t), binSizesInBytes.data(), nullptr));
++
++    auto bytes       = size_t{0};
++    auto binSizeIter = size_t{0};
++    // Loop over the program binary sizes to find a binary whose size is > 0.
++    // The current logic assumes that there ever is only one valid program binary
++    // in a given cl_program. This should be the case unless the cl_program
++    // is built for all or a subset of devices associated to a given cl_program
++    for (; binSizeIter < binSizesInBytes.size(); ++binSizeIter) {
++        if (binSizesInBytes[binSizeIter] > 0) {
++            bytes = binSizesInBytes[binSizeIter];
++            break;
++        }
++    }
+     auto result = std::string{};
+     result.resize(bytes);
+-    auto result_ptr = result.data();
+-    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARIES, sizeof(char*), &result_ptr, nullptr));
++
++    std::vector<char*> out(num_devices, nullptr);
++    out[binSizeIter] = const_cast<char*>(result.data());
++
++    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARIES,
++                                num_devices * sizeof(char*),
++                                out.data(), nullptr));
+     return result;
+   }
+ 

From 0308a5c6994f97db86dae53eec3239690590a68f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 26 Jun 2020 00:36:06 -0400
Subject: [PATCH 2032/2677] Add move constructor and operator= for dim4,
 Array<T> SparseArray<T>

---
 include/af/dim4.hpp                | 42 +++++++++++++++++++++---------
 src/backend/common/ArrayInfo.hpp   |  4 +++
 src/backend/common/SparseArray.cpp |  8 ++++++
 src/backend/common/SparseArray.hpp | 13 +++++++++
 src/backend/common/dim4.cpp        |  5 ++++
 src/backend/cpu/Array.cpp          |  4 +--
 src/backend/cuda/Array.cpp         |  4 +--
 src/backend/cuda/Array.hpp         | 19 ++++++++++++++
 src/backend/opencl/Array.cpp       |  4 +--
 src/backend/opencl/Array.hpp       | 20 +++++++++++++-
 10 files changed, 103 insertions(+), 20 deletions(-)

diff --git a/include/af/dim4.hpp b/include/af/dim4.hpp
index 9a5bad3b33..db78e67228 100644
--- a/include/af/dim4.hpp
+++ b/include/af/dim4.hpp
@@ -40,14 +40,29 @@ class AFAPI dim4
     /// \param[in] other The dim4 that will be copied
     dim4(const dim4& other);
 
+#if AF_API_VERSION >= 38
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
+    /// Default move constructor
+    ///
+    /// \param[in] other The dim4 that will be moved
+    dim4(dim4 &&other) AF_NOEXCEPT = default;
+
+    /// Default move assignment operator
+    ///
+    /// \param[in] other The dim4 that will be moved
+    dim4 &operator=(dim4 other) AF_NOEXCEPT;
+#endif
+#endif
+
     /// Constructs a dim4 object from a C array of dim_t objects
     ///
-    /// Creates a new dim4 from a C array. If the C array is less than 4, all values
-    /// past \p ndims will be assigned the value 1.
+    /// Creates a new dim4 from a C array. If the C array is less than 4, all
+    /// values past \p ndims will be assigned the value 1.
     ///
-    /// \param[in] ndims The number of elements in the C array. Must be less than 4
+    /// \param[in] ndims The number of elements in the C array. Must be less
+    ///                  than 4
     /// \param[in] dims  The values to assign to each element of dim4
-    dim4(const unsigned ndims, const dim_t * const dims);
+    dim4(const unsigned ndims, const dim_t *const dims);
 
     /// Returns the number of elements represented by this dim4
     dim_t elements();
@@ -62,32 +77,33 @@ class AFAPI dim4
     dim_t ndims() const;
 
     /// Returns true if the two dim4 represent the same shape
-    bool operator==(const dim4& other) const;
+    bool operator==(const dim4 &other) const;
 
     /// Returns true if two dim4s store different values
-    bool operator!=(const dim4& other) const;
+    bool operator!=(const dim4 &other) const;
 
     /// Element-wise multiplication of the dim4 objects
-    dim4& operator*=(const dim4& other);
+    dim4 &operator*=(const dim4 &other);
 
     /// Element-wise addition of the dim4 objects
-    dim4& operator+=(const dim4& other);
+    dim4 &operator+=(const dim4 &other);
 
     /// Element-wise subtraction of the dim4 objects
-    dim4& operator-=(const dim4& other);
+    dim4 &operator-=(const dim4 &other);
 
-    /// Returns the reference to the element at a give index. (Must be less than 4)
-    dim_t& operator[](const unsigned dim);
+    /// Returns the reference to the element at a give index. (Must be less than
+    /// 4)
+    dim_t &operator[](const unsigned dim);
 
     /// Returns the reference to the element at a give index. (Must be less than
     /// 4)
-    const dim_t& operator[](const unsigned dim) const;
+    const dim_t &operator[](const unsigned dim) const;
 
     /// Returns the underlying pointer to the dim4 object
     dim_t *get() { return dims; }
 
     /// Returns the underlying pointer to the dim4 object
-    const   dim_t* get() const   { return dims; }
+    const dim_t *get() const { return dims; }
 };
 
 /// Performs an element-wise addition of two dim4 objects
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index c86d5d3856..7f5516e5a4 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -81,6 +81,10 @@ class ArrayInfo {
             "ArrayInfo::devId must be the first member variable of ArrayInfo. \
                    devId is used to encode the backend into the integer. \
                    This is then used in the unified backend to check mismatched arrays.");
+        static_assert(std::is_nothrow_move_assignable<ArrayInfo>::value,
+                      "ArrayInfo is not nothrow move assignable");
+        static_assert(std::is_nothrow_move_constructible<ArrayInfo>::value,
+                      "ArrayInfo is not nothrow move constructible");
     }
 
     ArrayInfo()                       = default;
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index 350bb02789..06156ad3f6 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -52,6 +52,10 @@ SparseArrayBase::SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ,
     static_assert(offsetof(SparseArrayBase, info) == 0,
                   "SparseArrayBase::info must be the first member variable of "
                   "SparseArrayBase.");
+    static_assert(std::is_nothrow_move_assignable<SparseArrayBase>::value,
+                  "SparseArrayBase is not move assignable");
+    static_assert(std::is_nothrow_move_constructible<SparseArrayBase>::value,
+                  "SparseArrayBase is not move constructible");
 }
 
 SparseArrayBase::SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ,
@@ -176,6 +180,10 @@ SparseArray<T>::SparseArray(const dim4 &_dims, dim_t _nNZ, af::storage _storage)
     , values(createValueArray<T>(dim4(_nNZ), scalar<T>(0))) {
     static_assert(std::is_standard_layout<SparseArray<T>>::value,
                   "SparseArray<T> must be a standard layout type");
+    static_assert(std::is_nothrow_move_assignable<SparseArray<T>>::value,
+                  "SparseArray<T> is not move assignable");
+    static_assert(std::is_nothrow_move_constructible<SparseArray<T>>::value,
+                  "SparseArray<T> is not move constructible");
     static_assert(offsetof(SparseArray<T>, base) == 0,
                   "SparseArray<T>::base must be the first member variable of "
                   "SparseArray<T>");
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 2e8c78c99c..2dbcdbd3e0 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -38,6 +38,7 @@ class SparseArrayBase {
     detail::Array<int> colIdx;  ///< Linear array containing col indices
 
    public:
+    SparseArrayBase(SparseArrayBase &&other) noexcept = default;
     SparseArrayBase(const af::dim4 &_dims, dim_t _nNZ, af::storage _storage,
                     af_dtype _type);
 
@@ -51,6 +52,11 @@ class SparseArrayBase {
                     const af::storage _storage, af_dtype _type,
                     bool _copy = false);
 
+    SparseArrayBase &operator=(SparseArrayBase other) noexcept {
+        std::swap(*this, other);
+        return *this;
+    }
+
     /// A copy constructor for SparseArray
     ///
     /// This constructor copies the \p in SparseArray and creates a new object
@@ -151,8 +157,15 @@ class SparseArray {
     SparseArray(const SparseArray<T> &other, bool deep_copy);
 
    public:
+    SparseArray(const SparseArray<T> &other)     = default;
+    SparseArray(SparseArray<T> &&other) noexcept = default;
+
     ~SparseArray() noexcept = default;
 
+    SparseArray<T> &operator=(SparseArray<T> other) noexcept {
+        std::swap(*this, other);
+        return *this;
+    }
 // Functions that call ArrayInfo object's functions
 #define INSTANTIATE_INFO(return_type, func) \
     return_type func() const { return base.func(); }
diff --git a/src/backend/common/dim4.cpp b/src/backend/common/dim4.cpp
index a83ed15457..96d8bc8447 100644
--- a/src/backend/common/dim4.cpp
+++ b/src/backend/common/dim4.cpp
@@ -36,6 +36,11 @@ dim4::dim4(const unsigned ndims_, const dim_t* const dims_) : dims{} {
     for (unsigned i = 0; i < 4; i++) { dims[i] = ndims_ > i ? dims_[i] : 1; }
 }
 
+dim4& dim4::operator=(dim4 other) noexcept {
+    std::swap(dims, other.dims);
+    return *this;
+}
+
 dim_t dim4::elements() const { return dims[0] * dims[1] * dims[2] * dims[3]; }
 
 dim_t dim4::elements() { return static_cast<const dim4&>(*this).elements(); }
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 232948cf19..c40529c2f8 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -80,9 +80,9 @@ Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
     , owner(true) {
     static_assert(is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
-    static_assert(std::is_move_assignable<Array<T>>::value,
+    static_assert(std::is_nothrow_move_assignable<Array<T>>::value,
                   "Array<T> is not move assignable");
-    static_assert(std::is_move_constructible<Array<T>>::value,
+    static_assert(std::is_nothrow_move_constructible<Array<T>>::value,
                   "Array<T> is not move constructible");
     static_assert(
         offsetof(Array<T>, info) == 0,
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index e3caeba9bc..974a36915c 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -80,9 +80,9 @@ Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
     , owner(true) {
     static_assert(std::is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
-    static_assert(std::is_move_assignable<Array<T>>::value,
+    static_assert(std::is_nothrow_move_assignable<Array<T>>::value,
                   "Array<T> is not move assignable");
-    static_assert(std::is_move_constructible<Array<T>>::value,
+    static_assert(std::is_nothrow_move_constructible<Array<T>>::value,
                   "Array<T> is not move constructible");
     static_assert(
         offsetof(Array<T>, info) == 0,
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 9c527ca800..b6b105baf2 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -136,6 +136,25 @@ class Array {
     Array(const af::dim4 &dims, common::Node_ptr n);
 
    public:
+    Array(const Array<T> &other) = default;
+
+    Array(Array<T> &&other) noexcept = default;
+
+    Array<T> &operator=(Array<T> other) noexcept {
+        swap(other);
+        return *this;
+    }
+
+    void swap(Array<T> &other) noexcept {
+        using std::swap;
+        swap(info, other.info);
+        swap(data, other.data);
+        swap(data_dims, other.data_dims);
+        swap(node, other.node);
+        swap(ready, other.ready);
+        swap(owner, other.owner);
+    }
+
     Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
           const T *const in_data, bool is_device = false);
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 3e837b8279..24341b1e16 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -105,9 +105,9 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
     , owner(true) {
     static_assert(is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
-    static_assert(std::is_move_assignable<Array<T>>::value,
+    static_assert(std::is_nothrow_move_assignable<Array<T>>::value,
                   "Array<T> is not move assignable");
-    static_assert(std::is_move_constructible<Array<T>>::value,
+    static_assert(std::is_nothrow_move_constructible<Array<T>>::value,
                   "Array<T> is not move constructible");
     static_assert(
         offsetof(Array<T>, info) == 0,
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 6262ae0048..fded4eca2e 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -134,9 +134,27 @@ class Array {
     explicit Array(const af::dim4 &dims, cl_mem mem, size_t offset, bool copy);
 
    public:
+    Array(const Array<T> &other) = default;
+
+    Array(Array<T> &&other) noexcept = default;
+
+    Array<T> &operator=(Array<T> other) noexcept {
+        swap(other);
+        return *this;
+    }
+
+    void swap(Array<T> &other) noexcept {
+        using std::swap;
+        swap(info, other.info);
+        swap(data, other.data);
+        swap(data_dims, other.data_dims);
+        swap(node, other.node);
+        swap(ready, other.ready);
+        swap(owner, other.owner);
+    }
+
     Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
           const T *const in_data, bool is_device = false);
-
     void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
     void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
     void modDims(const af::dim4 &newDims) { info.modDims(newDims); }

From 1d08272dacc227bd6c87da20bddc6087ab19389b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 9 Jul 2020 12:34:29 +0530
Subject: [PATCH 2033/2677] Update confidence connected components to use brain
 scan image

---
 assets                                        |  2 +-
 .../confidence_connected_components.cpp       | 72 +++++++++++--------
 2 files changed, 43 insertions(+), 31 deletions(-)

diff --git a/assets b/assets
index c53bfab909..cd08d74961 160000
--- a/assets
+++ b/assets
@@ -1 +1 @@
-Subproject commit c53bfab909adfeed626f91ed419555711e20bca5
+Subproject commit cd08d749611b324012555ad6f23fd76c5465bd6c
diff --git a/examples/image_processing/confidence_connected_components.cpp b/examples/image_processing/confidence_connected_components.cpp
index 94617163bd..4671253bc1 100644
--- a/examples/image_processing/confidence_connected_components.cpp
+++ b/examples/image_processing/confidence_connected_components.cpp
@@ -15,40 +15,52 @@
 
 using namespace af;
 
+array normalize01(const array& in) {
+    float min = af::min<float>(in);
+    float max = af::max<float>(in);
+    return (in - min) / (max - min);
+}
+
+void markCrossHair(array& in, const unsigned x, const unsigned y,
+                   const float val) {
+    const int draw_len = 5;
+    for (int i = -1; i < 2; i++) {
+        in(x + i, seq(y - draw_len, y + draw_len), 0) = val;
+        in(x + i, seq(y - draw_len, y + draw_len), 1) = 0.f;
+        in(x + i, seq(y - draw_len, y + draw_len), 2) = 0.f;
+
+        in(seq(x - draw_len, x + draw_len), y + i, 0) = val;
+        in(seq(x - draw_len, x + draw_len), y + i, 1) = 0.f;
+        in(seq(x - draw_len, x + draw_len), y + i, 2) = 0.f;
+    }
+}
+
 int main(int argc, char* argv[]) {
     try {
         unsigned radius     = 3;
-        unsigned multiplier = 3;
-        int iter            = 5;
-
-        array A = loadImage(ASSETS_DIR "/examples/images/donut.png", false);
-
-        unsigned seedx = 132;
-        unsigned seedy = 132;
-        array ring =
-            confidenceCC(A, 1, &seedx, &seedy, radius, multiplier, iter, 255);
-
-        seedx = 152;
-        seedy = 152;
-        array sxArr(dim4(1), &seedx);
-        array syArr(dim4(1), &seedy);
-        array core =
-            confidenceCC(A, sxArr, syArr, radius, multiplier, iter, 255);
-
-        seedx                 = 15;
-        seedy                 = 15;
-        unsigned seedcoords[] = {15, 15};
-        array seeds(dim4(1, 2), seedcoords);
-        array background =
-            confidenceCC(A, seeds, radius, multiplier, iter, 255);
-
-        af::Window wnd("Confidence Connected Components demo");
+        unsigned multiplier = 2;
+        int iter            = 3;
+
+        array input =
+            loadImage(ASSETS_DIR "/examples/images/depression.jpg", false);
+        array normIn = normalize01(input);
+
+        unsigned seedx = 162;
+        unsigned seedy = 126;
+        array blob = confidenceCC(input, 1, &seedx, &seedy, radius, multiplier,
+                                  iter, 255);
+
+        array colorIn  = colorSpace(normIn, AF_RGB, AF_GRAY);
+        array colorOut = colorSpace(blob, AF_RGB, AF_GRAY);
+
+        markCrossHair(colorIn, seedx, seedy, 1);
+        markCrossHair(colorOut, seedx, seedy, 255);
+
+        af::Window wnd("Confidence Connected Components Demo");
         while (!wnd.close()) {
-            wnd.grid(2, 2);
-            wnd(0, 0).image(A, "Input");
-            wnd(0, 1).image(ring, "Ring Component - Seed(132, 132)");
-            wnd(1, 0).image(core, "Center Black Hole - Seed(152, 152)");
-            wnd(1, 1).image(background, "Background - Seed(15, 15)");
+            wnd.grid(1, 2);
+            wnd(0, 0).image(colorIn, "Input Brain Scan");
+            wnd(0, 1).image(colorOut, "Region connected to Seed(162, 126)");
             wnd.show();
         }
     } catch (af::exception& e) {

From 64855cbd9af5ce18dd6f891124526415bb51b051 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 10 Jul 2020 09:00:50 +0530
Subject: [PATCH 2034/2677] Fix barrierOR fn in confidence connected opencl
 kernel (#2969)

* Fix barrierOR fn in confidence connected opencl kernel

Without the extra barrier sync towards end of barrierOR function
after reading the reduction result, the caller's loop if any is
going into infinite loop occasionally which is in turn randoms hangs.

This doesn't seem to be an issue on non-nvidia hardware. Hence, we are
conditionally adding the extra barrier sync conditionally for nvidia
platform.

* Add the hardware check comparison
---
 src/api/c/confidence_connected.cpp       | 7 -------
 src/backend/opencl/kernel/flood_fill.cl  | 9 ++++++++-
 src/backend/opencl/kernel/flood_fill.hpp | 2 ++
 test/confidence_connected.cpp            | 3 ---
 4 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index 74b00cb0ea..012fa89579 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -188,13 +188,6 @@ af_err af_confidence_cc(af_array* out, const af_array in, const af_array seedx,
                         const af_array seedy, const unsigned radius,
                         const unsigned multiplier, const int iter,
                         const double segmented_value) {
-#if defined(AF_OPENCL)
-    // FIXME OpenCL backend keeps running into indefinte loop for
-    // short bit size(16,8) types very often and occasionally
-    // with 32 bit types.
-    AF_ERROR("There is a known issue for OpenCL implementation",
-             AF_ERR_NOT_SUPPORTED);
-#endif
     try {
         const ArrayInfo& inInfo         = getInfo(in);
         const ArrayInfo& seedxInfo      = getInfo(seedx);
diff --git a/src/backend/opencl/kernel/flood_fill.cl b/src/backend/opencl/kernel/flood_fill.cl
index 24e39a15fb..0a7916fd49 100644
--- a/src/backend/opencl/kernel/flood_fill.cl
+++ b/src/backend/opencl/kernel/flood_fill.cl
@@ -41,8 +41,15 @@ int barrierOR(local int *predicates) {
         }
         barrier(CLK_LOCAL_MEM_FENCE);
     }
+    int retVal = predicates[0];
+#if AF_IS_PLATFORM_NVIDIA
+    // Without the extra barrier sync after reading the reduction result,
+    // the caller's loop is going into infinite loop occasionally which is
+    // in turn randoms hangs. This doesn't seem to be an issue on non-nvidia
+    // hardware. Hence, the check.
     barrier(CLK_LOCAL_MEM_FENCE);
-    return predicates[0];
+#endif
+    return retVal;
 }
 
 kernel void flood_step(global T *out, KParam oInfo, global const T *img,
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index dd2963514c..03734b6baa 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -92,6 +92,8 @@ void floodFill(Param out, const Param image, const Param seedsx,
         DefineKeyValue(LMEM_WIDTH, (THREADS_X + 2 * RADIUS)),
         DefineKeyValue(LMEM_HEIGHT, (THREADS_Y + 2 * RADIUS)),
         DefineKeyValue(GROUP_SIZE, (THREADS_Y * THREADS_X)),
+        DefineKeyValue(AF_IS_PLATFORM_NVIDIA,
+                       (int)(AFCL_PLATFORM_NVIDIA == getActivePlatform())),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 5cac824b29..6963edcc1e 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -160,8 +160,6 @@ void testData(CCCTestParams params) {
 class ConfidenceConnectedDataTest
     : public testing::TestWithParam<CCCTestParams> {};
 
-#if !defined(AF_OPENCL)
-
 TYPED_TEST(ConfidenceConnectedImageTest, DonutBackgroundExtraction) {
     const unsigned seedx = 10;
     const unsigned seedy = 10;
@@ -200,4 +198,3 @@ INSTANTIATE_TEST_CASE_P(
            << info.param.iterations << "_replace_" << info.param.replace;
         return ss.str();
     });
-#endif

From 9cd94d3ba7ad3d08719f9649afdf7c1306d9fea6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 11 Jul 2020 23:13:25 +0530
Subject: [PATCH 2035/2677] Enable disk caching support for OpenCL kernel
 binaries

---
 src/backend/opencl/compile_module.cpp | 169 +++++++++++++++++++++++---
 1 file changed, 150 insertions(+), 19 deletions(-)

diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index add7f58329..dcadfffc80 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -20,7 +20,10 @@
 #include <platform.hpp>
 #include <traits.hpp>
 
+#include <algorithm>
+#include <cctype>
 #include <cstdio>
+#include <fstream>
 #include <sstream>
 #include <string>
 #include <vector>
@@ -37,9 +40,12 @@ using spdlog::logger;
 
 using std::begin;
 using std::end;
+using std::ofstream;
 using std::ostringstream;
 using std::shared_ptr;
 using std::string;
+using std::to_string;
+using std::transform;
 using std::vector;
 using std::chrono::duration_cast;
 using std::chrono::high_resolution_clock;
@@ -50,21 +56,30 @@ logger *getLogger() {
     return logger.get();
 }
 
-#define THROW_BUILD_LOG_EXCEPTION(PROG)                                     \
-    do {                                                                    \
-        string build_error;                                                 \
-        build_error.reserve(4096);                                          \
-        auto devices = PROG.getInfo<CL_PROGRAM_DEVICES>();                  \
-        for (auto &device : PROG.getInfo<CL_PROGRAM_DEVICES>()) {           \
-            build_error +=                                                  \
-                format("OpenCL Device: {}\n\tOptions: {}\n\tLog:\n{}\n",    \
-                       device.getInfo<CL_DEVICE_NAME>(),                    \
-                       PROG.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(device), \
-                       PROG.getBuildInfo<CL_PROGRAM_BUILD_LOG>(device));    \
-        }                                                                   \
-        string info = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO");               \
-        if (!info.empty() && info != "0") puts(build_error.c_str());        \
-        AF_ERROR(build_error, AF_ERR_INTERNAL);                             \
+string getProgramBuildLog(const Program &prog) {
+    string build_error("");
+    try {
+        build_error.reserve(4096);
+        auto devices = prog.getInfo<CL_PROGRAM_DEVICES>();
+        for (auto &device : prog.getInfo<CL_PROGRAM_DEVICES>()) {
+            build_error +=
+                format("OpenCL Device: {}\n\tOptions: {}\n\tLog:\n{}\n",
+                       device.getInfo<CL_DEVICE_NAME>(),
+                       prog.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(device),
+                       prog.getBuildInfo<CL_PROGRAM_BUILD_LOG>(device));
+        }
+    } catch (const cl::Error &e) {
+        build_error = format("Failed to fetch build log: {}", e.what());
+    }
+    return build_error;
+}
+
+#define THROW_BUILD_LOG_EXCEPTION(PROG)                              \
+    do {                                                             \
+        string build_error = getProgramBuildLog(PROG);               \
+        string info        = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO"); \
+        if (!info.empty() && info != "0") puts(build_error.c_str()); \
+        AF_ERROR(build_error, AF_ERR_INTERNAL);                      \
     } while (0)
 
 namespace opencl {
@@ -119,6 +134,21 @@ Program buildProgram(const vector<string> &kernelSources,
 
 }  // namespace opencl
 
+string getKernelCacheFilename(const int device, const string &key) {
+    auto &dev = opencl::getDevice(device);
+
+    unsigned vendorId = dev.getInfo<CL_DEVICE_VENDOR_ID>();
+    auto devName      = dev.getInfo<CL_DEVICE_NAME>();
+    string infix      = to_string(vendorId) + "_" + devName;
+
+    transform(infix.begin(), infix.end(), infix.begin(),
+              [](unsigned char c) { return std::toupper(c); });
+    std::replace(infix.begin(), infix.end(), ' ', '_');
+
+    return "KER" + key + "_CL_" + infix + "_AF_" +
+           to_string(AF_API_VERSION_CURRENT) + ".clbin";
+}
+
 namespace common {
 
 Module compileModule(const string &moduleKey, const vector<string> &sources,
@@ -131,6 +161,52 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     auto program      = opencl::buildProgram(sources, options);
     auto compileEnd   = high_resolution_clock::now();
 
+#ifdef AF_CACHE_KERNELS_TO_DISK
+    const int device             = opencl::getActiveDeviceId();
+    const string &cacheDirectory = getCacheDirectory();
+    if (!cacheDirectory.empty()) {
+        const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
+                                 getKernelCacheFilename(device, moduleKey);
+        const string tempFile =
+            cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
+        try {
+            auto binaries = program.getInfo<CL_PROGRAM_BINARIES>();
+
+            // TODO Handle cases where program objects are created from contexts
+            // having multiple devices
+            const size_t clbinSize = binaries[0].size();
+            const char *clbin =
+                reinterpret_cast<const char *>(binaries[0].data());
+            const size_t clbinHash = deterministicHash(clbin, clbinSize);
+
+            // write module hash and binary data to file
+            ofstream out(tempFile, std::ios::binary);
+
+            out.write(reinterpret_cast<const char *>(&clbinHash),
+                      sizeof(clbinHash));
+            out.write(reinterpret_cast<const char *>(&clbinSize),
+                      sizeof(clbinSize));
+            out.write(static_cast<const char *>(clbin), clbinSize);
+            out.close();
+
+            // try to rename temporary file into final cache file, if this fails
+            // this means another thread has finished compiling this kernel
+            // before the current thread.
+            if (!renameFile(tempFile, cacheFile)) { removeFile(tempFile); }
+        } catch (const cl::Error &e) {
+            AF_TRACE("{{{:<30} : Failed to fetch opencl binary for {}, {}}}",
+                     moduleKey,
+                     opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
+                     e.what());
+        } catch (const std::ios_base::failure &e) {
+            AF_TRACE("{{{:<30} : Failed writing binary to {} for {}, {}}}",
+                     moduleKey, cacheFile,
+                     opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
+                     e.what());
+        }
+    }
+#endif
+
     AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", moduleKey,
              duration_cast<milliseconds>(compileEnd - compileBegin).count(),
              fmt::join(options, " "),
@@ -141,10 +217,65 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
 
 Module loadModuleFromDisk(const int device, const string &moduleKey,
                           const bool isJIT) {
-    UNUSED(device);
-    UNUSED(moduleKey);
-    UNUSED(isJIT);
-    return {};
+    const string &cacheDirectory = getCacheDirectory();
+    if (cacheDirectory.empty()) return Module{};
+
+    auto &dev              = opencl::getDevice(device);
+    const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
+                             getKernelCacheFilename(device, moduleKey);
+    Program program;
+    Module retVal{};
+    try {
+        std::ifstream in(cacheFile, std::ios::binary);
+        if (!in.is_open()) {
+            AF_ERROR("Unable to open binary cache file", AF_ERR_INTERNAL);
+        }
+        in.exceptions(std::ios::failbit | std::ios::badbit);
+
+        // TODO Handle cases where program objects are created from contexts
+        // having multiple devices
+        size_t clbinHash = 0;
+        in.read(reinterpret_cast<char *>(&clbinHash), sizeof(clbinHash));
+        size_t clbinSize = 0;
+        in.read(reinterpret_cast<char *>(&clbinSize), sizeof(clbinSize));
+        vector<unsigned char> clbin(clbinSize);
+        in.read(reinterpret_cast<char *>(clbin.data()), clbinSize);
+        in.close();
+
+        const size_t recomputedHash =
+            deterministicHash(clbin.data(), clbinSize);
+        if (recomputedHash != clbinHash) {
+            AF_ERROR("Binary on disk seems to be corrupted", AF_ERR_LOAD_SYM);
+        }
+        program = Program(opencl::getContext(), {dev}, {clbin});
+        program.build();
+
+        AF_TRACE("{{{:<30} : loaded from {} for {} }}", moduleKey, cacheFile,
+                 dev.getInfo<CL_DEVICE_NAME>());
+        retVal.set(program);
+    } catch (const AfError &e) {
+        if (e.getError() == AF_ERR_LOAD_SYM) {
+            AF_TRACE(
+                "{{{:<30} : Corrupt binary({}) found on disk for {}, removed}}",
+                moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>());
+        } else {
+            AF_TRACE("{{{:<30} : Unable to open {} for {}}}", moduleKey,
+                     cacheFile, dev.getInfo<CL_DEVICE_NAME>());
+        }
+        removeFile(cacheFile);
+    } catch (const std::ios_base::failure &e) {
+        AF_TRACE("{{{:<30} : IO failure while loading {} for {}; {}}}",
+                 moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>(), e.what());
+        removeFile(cacheFile);
+    } catch (const cl::Error &e) {
+        AF_TRACE(
+            "{{{:<30} : Loading OpenCL binary({}) failed for {}; {}, Build "
+            "Log: {}}}",
+            moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>(), e.what(),
+            getProgramBuildLog(program));
+        removeFile(cacheFile);
+    }
+    return retVal;
 }
 
 Kernel getKernel(const Module &mod, const string &nameExpr,

From 58dc98ea7836e8805f16465bc6725d99ddcc3b7e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 11 Jul 2020 23:14:50 +0530
Subject: [PATCH 2036/2677] Check exceptions in CUDA compileModule and Log
 binary write/load failures

---
 src/backend/cuda/compile_module.cpp | 97 ++++++++++++++++++-----------
 1 file changed, 61 insertions(+), 36 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 1f54aa8079..8cbab6c3e0 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -316,40 +316,47 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
                                  getKernelCacheFilename(device, moduleKey);
         const string tempFile =
             cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
-
-        // compute CUBIN hash
-        const size_t cubinHash = deterministicHash(cubin, cubinSize);
-
-        // write module hash(everything: names, code & options) and CUBIN data
-        ofstream out(tempFile, std::ios::binary);
-        if (!sourceIsJIT) {
-            size_t mangledNamesListSize = retVal.map().size();
-            out.write(reinterpret_cast<const char *>(&mangledNamesListSize),
-                      sizeof(mangledNamesListSize));
-            for (auto &iter : retVal.map()) {
-                size_t kySize   = iter.first.size();
-                size_t vlSize   = iter.second.size();
-                const char *key = iter.first.c_str();
-                const char *val = iter.second.c_str();
-                out.write(reinterpret_cast<const char *>(&kySize),
-                          sizeof(kySize));
-                out.write(key, iter.first.size());
-                out.write(reinterpret_cast<const char *>(&vlSize),
-                          sizeof(vlSize));
-                out.write(val, iter.second.size());
+        try {
+            // write module hash(everything: names, code & options) and CUBIN
+            // data
+            ofstream out(tempFile, std::ios::binary);
+            if (!sourceIsJIT) {
+                size_t mangledNamesListSize = retVal.map().size();
+                out.write(reinterpret_cast<const char *>(&mangledNamesListSize),
+                          sizeof(mangledNamesListSize));
+                for (auto &iter : retVal.map()) {
+                    size_t kySize   = iter.first.size();
+                    size_t vlSize   = iter.second.size();
+                    const char *key = iter.first.c_str();
+                    const char *val = iter.second.c_str();
+                    out.write(reinterpret_cast<const char *>(&kySize),
+                              sizeof(kySize));
+                    out.write(key, iter.first.size());
+                    out.write(reinterpret_cast<const char *>(&vlSize),
+                              sizeof(vlSize));
+                    out.write(val, iter.second.size());
+                }
             }
+
+            // compute CUBIN hash
+            const size_t cubinHash = deterministicHash(cubin, cubinSize);
+
+            out.write(reinterpret_cast<const char *>(&cubinHash),
+                      sizeof(cubinHash));
+            out.write(reinterpret_cast<const char *>(&cubinSize),
+                      sizeof(cubinSize));
+            out.write(static_cast<const char *>(cubin), cubinSize);
+            out.close();
+
+            // try to rename temporary file into final cache file, if this fails
+            // this means another thread has finished compiling this kernel
+            // before the current thread.
+            if (!renameFile(tempFile, cacheFile)) { removeFile(tempFile); }
+        } catch (const std::ios_base::failure &e) {
+            AF_TRACE("{{{:<30} : failed saving binary to {} for {}, {}}}",
+                     moduleKey, cacheFile, getDeviceProp(device).name,
+                     e.what());
         }
-        out.write(reinterpret_cast<const char *>(&cubinHash),
-                  sizeof(cubinHash));
-        out.write(reinterpret_cast<const char *>(&cubinSize),
-                  sizeof(cubinSize));
-        out.write(static_cast<const char *>(cubin), cubinSize);
-        out.close();
-
-        // try to rename temporary file into final cache file, if this fails
-        // this means another thread has finished compiling this kernel before
-        // the current thread.
-        if (!renameFile(tempFile, cacheFile)) { removeFile(tempFile); }
     }
 #endif
 
@@ -383,8 +390,12 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
     Module retVal{nullptr};
     try {
         std::ifstream in(cacheFile, std::ios::binary);
-        if (!in.is_open()) return Module{nullptr};
-
+        if (!in.is_open()) {
+            AF_TRACE("{{{:<30} : Unable to open {} for {}}}", moduleKey,
+                     cacheFile, getDeviceProp(device).name);
+            removeFile(cacheFile);  // Remove if exists
+            return Module{nullptr};
+        }
         in.exceptions(std::ios::failbit | std::ios::badbit);
 
         if (!isJIT) {
@@ -430,8 +441,22 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
                  getDeviceProp(device).name);
 
         retVal.set(modOut);
-    } catch (...) {
-        if (modOut != nullptr) { CU_CHECK(cuModuleUnload(modOut)); }
+    } catch (const std::ios_base::failure &e) {
+        AF_TRACE("{{{:<30} : Unable to read {} for {}}}", moduleKey, cacheFile,
+                 getDeviceProp(device).name);
+        removeFile(cacheFile);
+    } catch (const AfError &e) {
+        if (e.getError() == AF_ERR_LOAD_SYM) {
+            AF_TRACE(
+                "{{{:<30} : Corrupt binary({}) found on disk for {}, removed}}",
+                moduleKey, cacheFile, getDeviceProp(device).name);
+        } else {
+            if (modOut != nullptr) { CU_CHECK(cuModuleUnload(modOut)); }
+            AF_TRACE(
+                "{{{:<30} : cuModuleLoadData failed with content from {} for "
+                "{}, {}}}",
+                moduleKey, cacheFile, getDeviceProp(device).name, e.what());
+        }
         removeFile(cacheFile);
     }
     return retVal;

From 75e3c6c324b90f7d24c5030aa746f393db8910fe Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 14 Jul 2020 18:42:09 +0530
Subject: [PATCH 2037/2677] Use .bin extension for kernel binaries that are
 saved to disk

---
 src/backend/cuda/compile_module.cpp   | 2 +-
 src/backend/opencl/compile_module.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 8cbab6c3e0..38e2fed991 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -131,7 +131,7 @@ string getKernelCacheFilename(const int device, const string &key) {
         to_string(computeFlag.first) + to_string(computeFlag.second);
 
     return "KER" + key + "_CU_" + computeVersion + "_AF_" +
-           to_string(AF_API_VERSION_CURRENT) + ".cubin";
+           to_string(AF_API_VERSION_CURRENT) + ".bin";
 }
 
 namespace common {
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index dcadfffc80..2f6d374db1 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -146,7 +146,7 @@ string getKernelCacheFilename(const int device, const string &key) {
     std::replace(infix.begin(), infix.end(), ' ', '_');
 
     return "KER" + key + "_CL_" + infix + "_AF_" +
-           to_string(AF_API_VERSION_CURRENT) + ".clbin";
+           to_string(AF_API_VERSION_CURRENT) + ".bin";
 }
 
 namespace common {

From a7f38dc03b42c6c0857be1c6904d25e2d6c583d0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 15 Jul 2020 14:27:37 -0400
Subject: [PATCH 2038/2677] Fix incorrect macro and name in docs. Update links
 to https.

---
 docs/footer.htm             |  8 ++---
 docs/header.htm             |  2 +-
 docs/pages/release_notes.md | 66 ++++++++++++++++++-------------------
 3 files changed, 38 insertions(+), 38 deletions(-)

diff --git a/docs/footer.htm b/docs/footer.htm
index 5a2af817bf..2ca612336a 100644
--- a/docs/footer.htm
+++ b/docs/footer.htm
@@ -7,13 +7,13 @@
 <!--Google Analytics-->
 <script type="text/javascript">
   var _gaq = _gaq || [];
-  _gaq.push(['_setAccount', 'UA-5076919-1']);
+  _gaq.push(['_setAccount', 'UA-130950618-1']);
   _gaq.push(['_setDomainName', '.arrayfire.com']);
   _gaq.push(['_trackPageview']);
 
   (function() {
     var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
-    ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
+    ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'https://www') + '.google-analytics.com/ga.js';
     var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
   })();
 </script>
@@ -26,7 +26,7 @@
   (function() {
     function async_load(){
       var s = document.createElement('script'); s.type = 'text/javascript';
-      s.src = (('https:' == document.location.protocol) ? "https://ssl" : "http://cdn") + ".spectate.com/s.js";
+      s.src = (('https:' == document.location.protocol) ? "https://ssl" : "https://cdn") + ".spectate.com/s.js";
       var c = document.getElementsByTagName('script')[0]; c.parentNode.insertBefore(s, c);
     }
     if(window.attachEvent) { window.attachEvent('onload', async_load); }
@@ -43,7 +43,7 @@
 window.onload = function(){
    __adroll_loaded=true;
    var scr = document.createElement("script");
-   var host = (("https:" == document.location.protocol) ? "https://s.adroll.com" : "http://a.adroll.com");
+   var host = (("https:" == document.location.protocol) ? "https://s.adroll.com" : "https://a.adroll.com");
    scr.setAttribute('async', 'true');
    scr.type = "text/javascript";
    scr.src = host + "/j/roundtrip.js";
diff --git a/docs/header.htm b/docs/header.htm
index f7169bb870..cc7a161d56 100644
--- a/docs/header.htm
+++ b/docs/header.htm
@@ -1,6 +1,6 @@
 <!-- HTML header for doxygen 1.8.17-->
 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml">
+<html xmlns="https://www.w3.org/1999/xhtml">
 <head>
 <meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
 <meta http-equiv="X-UA-Compatible" content="IE=9"/>
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index be7dd1bbc8..15789b0d5b 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -20,48 +20,48 @@ v3.7.2
 
 Improvements
 ------------
-- Cache CUDA kernels to disk to improve load times(Thanks to \@cschreib-ibex) /PR{2848}
-- Staticly link against cuda libraries /PR{2785}
-- Make cuDNN an optional build dependency /PR{2836}
-- Improve support for different compilers and OS /PR{2876} /PR{2945} /PR{2925} /PR{2942} /PR{2943} /PR{2945} /PR{2958}
-- Improve performance of join and transpose on CPU /PR{2849}
-- Improve documentation /PR{2816} /PR{2821} /PR{2846} /PR{2918} /PR{2928} /PR{2947}
-- Reduce binary size using NVRTC and template reducing instantiations /PR{2849} /PR{2861} /PR{2890} /PR{2957}
-- reduceByKey performance improvements /PR{2851} /PR{2957}
-- Improve support for Intel OpenCL GPUs /PR{2855}
-- Allow staticly linking against MKL /PR{2877} (Sponsered by SDL)
-- Better support for older CUDA toolkits /PR{2923}
-- Add support for CUDA 11 /PR{2939}
-- Add support for ccache for faster builds /PR{2931}
-- Add support for the conan package manager on linux /PR{2875}
-- Propagate build errors up the stack in AFError exceptions /PR{2948} /PR{2957}
-- Improve runtime dependency library loading /PR{2954}
-- Improved cuDNN runtime checks and warnings /PR{2960}
-- Document af\_memory\_manager\_* native memory return values /PR{2911}
+- Cache CUDA kernels to disk to improve load times(Thanks to \@cschreib-ibex) \PR{2848}
+- Staticly link against cuda libraries \PR{2785}
+- Make cuDNN an optional build dependency \PR{2836}
+- Improve support for different compilers and OS \PR{2876} \PR{2945} \PR{2925} \PR{2942} \PR{2943} \PR{2945} \PR{2958}
+- Improve performance of join and transpose on CPU \PR{2849}
+- Improve documentation \PR{2816} \PR{2821} \PR{2846} \PR{2918} \PR{2928} \PR{2947}
+- Reduce binary size using NVRTC and template reducing instantiations \PR{2849} \PR{2861} \PR{2890} \PR{2957}
+- reduceByKey performance improvements \PR{2851} \PR{2957}
+- Improve support for Intel OpenCL GPUs \PR{2855}
+- Allow staticly linking against MKL \PR{2877} (Sponsered by SDL)
+- Better support for older CUDA toolkits \PR{2923}
+- Add support for CUDA 11 \PR{2939}
+- Add support for ccache for faster builds \PR{2931}
+- Add support for the conan package manager on linux \PR{2875}
+- Propagate build errors up the stack in AFError exceptions \PR{2948} \PR{2957}
+- Improve runtime dependency library loading \PR{2954}
+- Improved cuDNN runtime checks and warnings \PR{2960}
+- Document af\_memory\_manager\_* native memory return values \PR{2911}
 
 Fixes
 -----
-- Bug crash when allocating large arrays /PR{2827}
-- Fix various compiler warnings /PR{2827} /PR{2849} /PR{2872} /PR{2876}
-- Fix minor leaks in OpenCL functions /PR{2913}
-- Various continuous integration related fixes /PR{2819}
-- Fix zero padding with convolv2NN /PR{2820}
-- Fix af_get_memory_pressure_threshold return value /PR{2831}
+- Bug crash when allocating large arrays \PR{2827}
+- Fix various compiler warnings \PR{2827} \PR{2849} \PR{2872} \PR{2876}
+- Fix minor leaks in OpenCL functions \PR{2913}
+- Various continuous integration related fixes \PR{2819}
+- Fix zero padding with convolv2NN \PR{2820}
+- Fix af_get_memory_pressure_threshold return value \PR{2831}
 - Increased the max filter length for morph
-- Handle empty array inputs for LU, QR, and Rank functions /PR{2838}
-- Fix FindMKL.cmake script for sequential threading library /PR{2840} /PR{2952}
-- Various internal refactoring /PR{2839} /PR{2861} /PR{2864} /PR{2873} /PR{2890} /PR{2891} /PR{2913} /PR{2959}
-- Fix OpenCL 2.0 builtin function name conflict /PR{2851}
-- Fix error caused when releasing memory with multiple devices /PR{2867}
-- Fix missing set stacktrace symbol from unified API /PR{2915}
-- Fix zero padding issue in convolve2NN /PR{2820}
-- Fixed bugs in ReduceByKey /PR{2957}
+- Handle empty array inputs for LU, QR, and Rank functions \PR{2838}
+- Fix FindMKL.cmake script for sequential threading library \PR{2840} \PR{2952}
+- Various internal refactoring \PR{2839} \PR{2861} \PR{2864} \PR{2873} \PR{2890} \PR{2891} \PR{2913} \PR{2959}
+- Fix OpenCL 2.0 builtin function name conflict \PR{2851}
+- Fix error caused when releasing memory with multiple devices \PR{2867}
+- Fix missing set stacktrace symbol from unified API \PR{2915}
+- Fix zero padding issue in convolve2NN \PR{2820}
+- Fixed bugs in ReduceByKey \PR{2957}
 
 Contributions
 -------------
 Special thanks to our contributors:
 [Corentin Schreiber](https://github.com/cschreib-ibex)
-[Jacob Khan](https://github.com/jacobkahn)
+[Jacob Kahn](https://github.com/jacobkahn)
 [Paul Jurczak](https://github.com/pauljurczak)
 [Christoph Junghans](https://github.com/junghans)
 

From a67c346d425a87b3bcfd8bdf796fa3917cdc6c89 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 16 Jul 2020 15:22:18 +0530
Subject: [PATCH 2039/2677] Add missing non-const Array::getNode method in
 CPU/OpenCL backends

The missing methods are causing link issues with Intel compiler only.
Nevertheless, it is an issue that needs to be fixed.
---
 src/backend/cpu/Array.cpp    | 9 ++++++++-
 src/backend/cpu/Array.hpp    | 1 +
 src/backend/cuda/Array.cpp   | 1 +
 src/backend/opencl/Array.cpp | 1 +
 4 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index c40529c2f8..713a752b7c 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -208,7 +208,7 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
 }
 
 template<typename T>
-Node_ptr Array<T>::getNode() const {
+Node_ptr Array<T>::getNode() {
     if (node->isBuffer()) {
         auto *bufNode  = reinterpret_cast<BufferNode<T> *>(node.get());
         unsigned bytes = this->getDataDims().elements() * sizeof(T);
@@ -218,6 +218,12 @@ Node_ptr Array<T>::getNode() const {
     return node;
 }
 
+template<typename T>
+Node_ptr Array<T>::getNode() const {
+    if (node->isBuffer()) { return const_cast<Array<T> *>(this)->getNode(); }
+    return node;
+}
+
 template<typename T>
 Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
     return Array<T>(dims, const_cast<T *>(data), false);
@@ -351,6 +357,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
                              bool is_device, bool copy_device);               \
     template Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides,   \
                              dim_t offset, T *const in_data, bool is_device); \
+    template Node_ptr Array<T>::getNode();                                    \
     template Node_ptr Array<T>::getNode() const;                              \
     template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
                                         const size_t bytes);                  \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 39b47d9bda..8335e325c9 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -246,6 +246,7 @@ class Array {
     }
 
     common::Node_ptr getNode() const;
+    common::Node_ptr getNode();
 
     friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 974a36915c..8aecde7781 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -439,6 +439,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
                              bool is_device);                                 \
     template Array<T>::Array(const af::dim4 &dims, const T *const in_data,    \
                              bool is_device, bool copy_device);               \
+    template Node_ptr Array<T>::getNode();                                    \
     template Node_ptr Array<T>::getNode() const;                              \
     template void Array<T>::eval();                                           \
     template void Array<T>::eval() const;                                     \
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 24341b1e16..23da2f086b 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -520,6 +520,7 @@ size_t Array<T>::getAllocatedBytes() const {
                              bool is_device);                                 \
     template Array<T>::Array(const dim4 &dims, cl_mem mem, size_t src_offset, \
                              bool copy);                                      \
+    template Node_ptr Array<T>::getNode();                                    \
     template Node_ptr Array<T>::getNode() const;                              \
     template void Array<T>::eval();                                           \
     template void Array<T>::eval() const;                                     \

From edb189c267bd9087a2288cf50c55ceb806149862 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 31 Jul 2020 12:40:44 +0530
Subject: [PATCH 2040/2677] Add missing opencl-arrayfire interop fns in unified
 backend

---
 src/api/unified/CMakeLists.txt |  1 +
 src/api/unified/opencl.cpp     | 83 ++++++++++++++++++++++++++++++++++
 2 files changed, 84 insertions(+)
 create mode 100644 src/api/unified/opencl.cpp

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index c3e0b8270f..c44b2680d7 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -23,6 +23,7 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ml.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/opencl.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/signal.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse.cpp
diff --git a/src/api/unified/opencl.cpp b/src/api/unified/opencl.cpp
new file mode 100644
index 0000000000..6ad93ae9ce
--- /dev/null
+++ b/src/api/unified/opencl.cpp
@@ -0,0 +1,83 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/backend.h>
+#include "symbol_manager.hpp"
+
+#include <af/opencl.h>
+
+af_err afcl_get_device_type(afcl_device_type* res) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) { CALL(afcl_get_device_type, res); }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_get_platform(afcl_platform* res) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) { CALL(afcl_get_platform, res); }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_get_context(cl_context* ctx, const bool retain) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) { CALL(afcl_get_context, ctx, retain); }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_get_queue(cl_command_queue* queue, const bool retain) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) { CALL(afcl_get_queue, queue, retain); }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_get_device_id(cl_device_id* id) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) { CALL(afcl_get_device_id, id); }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_set_device_id(cl_device_id id) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) { CALL(afcl_set_device_id, id); }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_add_device_context(cl_device_id dev, cl_context ctx,
+                               cl_command_queue que) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) {
+        CALL(afcl_add_device_context, dev, ctx, que);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_set_device_context(cl_device_id dev, cl_context ctx) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) {
+        CALL(afcl_set_device_context, dev, ctx);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}
+
+af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx) {
+    af_backend backend;
+    af_get_active_backend(&backend);
+    if (backend == AF_BACKEND_OPENCL) {
+        CALL(afcl_delete_device_context, dev, ctx);
+    }
+    return AF_ERR_NOT_SUPPORTED;
+}

From 95919f3b627e62f241d8fdd086c7feaede0ae0ec Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 4 Aug 2020 11:25:10 +0530
Subject: [PATCH 2041/2677] Add min driver versions for CUDA 11

---
 src/backend/cuda/device_manager.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index b2921d7012..1493e5e432 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -345,6 +345,7 @@ struct ToolkitDriverVersions {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11000, 450.51f, 451.48f},
         {10020, 440.33f, 441.22f},
         {10010, 418.39f, 418.96f},
         {10000, 410.48f, 411.31f},

From 8c328cfbd0f8a0fdfb81bbcc60c3290d4dff66fe Mon Sep 17 00:00:00 2001
From: "P. J. Reed" <pjreed@users.noreply.github.com>
Date: Fri, 7 Aug 2020 08:03:52 -0500
Subject: [PATCH 2042/2677] Replace underscores with dashes in package names
 (#2983)

* Replace underscores with dashes in package names

Debian package names may not contain underscores; see
https://www.debian.org/doc/debian-policy/ch-controlfields.html#s-f-source .
Generating packages with underscores in their names will mess up
other Debian utilities that expect that to be used as a version
separator.

Signed-off-by: P. J. Reed <preed@swri.org>

* Remove unnecessary changes
---
 CMakeModules/CPackConfig.cmake | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 059d11c2db..23e30c5637 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -131,15 +131,15 @@ cpack_add_component_group(backends
   DISPLAY_NAME "ArrayFire"
   DESCRIPTION "ArrayFire backend libraries"
   EXPANDED)
-cpack_add_component_group(cpu_backend
+cpack_add_component_group(cpu-backend
   DISPLAY_NAME "CPU backend"
   DESCRIPTION "Libraries and dependencies of the CPU backend."
   PARENT_GROUP backends)
-cpack_add_component_group(cuda_backend
+cpack_add_component_group(cuda-backend
   DISPLAY_NAME "CUDA backend"
   DESCRIPTION "Libraries and dependencies of the CUDA backend."
   PARENT_GROUP backends)
-cpack_add_component_group(opencl_backend
+cpack_add_component_group(opencl-backend
   DISPLAY_NAME "OpenCL backend"
   DESCRIPTION "Libraries and dependencies of the OpenCL backend."
   PARENT_GROUP backends)
@@ -164,13 +164,13 @@ cpack_add_component(common_backend_dependencies
 cpack_add_component(opencl_dependencies
   DISPLAY_NAME "OpenCL Dependencies"
   DESCRIPTION "Libraries required by the OpenCL backend."
-  GROUP opencl_backend
+  GROUP opencl-backend
   INSTALL_TYPES All Development Runtime)
 if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
   cpack_add_component(afopencl_debug_symbols
     DISPLAY_NAME "OpenCL Backend Debug Symbols"
     DESCRIPTION "File containing debug symbols for afopencl dll/so/dylib file"
-    GROUP opencl_backend
+    GROUP opencl-backend
     DISABLED
     INSTALL_TYPES Development)
 endif ()
@@ -178,13 +178,13 @@ endif ()
 cpack_add_component(cuda_dependencies
   DISPLAY_NAME "CUDA Dependencies"
   DESCRIPTION "CUDA runtime and libraries required by the CUDA backend."
-  GROUP cuda_backend
+  GROUP cuda-backend
   INSTALL_TYPES All Development Runtime)
 if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
   cpack_add_component(afcuda_debug_symbols
     DISPLAY_NAME "CUDA Backend Debug Symbols"
     DESCRIPTION "File containing debug symbols for afcuda dll/so/dylib file"
-    GROUP cuda_backend
+    GROUP cuda-backend
     DISABLED
     INSTALL_TYPES Development)
 endif ()
@@ -193,7 +193,7 @@ if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
   cpack_add_component(afcpu_debug_symbols
     DISPLAY_NAME "CPU Backend Debug Symbols"
     DESCRIPTION "File containing debug symbols for afcpu dll/so/dylib file"
-    GROUP cpu_backend
+    GROUP cpu-backend
     DISABLED
     INSTALL_TYPES Development)
 endif ()
@@ -201,7 +201,7 @@ endif ()
 cpack_add_component(cuda
   DISPLAY_NAME "CUDA Backend"
   DESCRIPTION "The CUDA backend allows you to run ArrayFire code on CUDA-enabled GPUs. Verify that you have the CUDA toolkit installed or install the CUDA dependencies component."
-  GROUP cuda_backend
+  GROUP cuda-backend
   DEPENDS common_backend_dependencies cuda_dependencies
   INSTALL_TYPES All Development Runtime)
 
@@ -220,14 +220,14 @@ endif ()
 cpack_add_component(cpu
   DISPLAY_NAME "CPU Backend"
   DESCRIPTION "The CPU backend allows you to run ArrayFire code on your CPU."
-  GROUP cpu_backend
+  GROUP cpu-backend
   DEPENDS ${cpu_deps_comps}
   INSTALL_TYPES All Development Runtime)
 
 cpack_add_component(opencl
   DISPLAY_NAME "OpenCL Backend"
   DESCRIPTION "The OpenCL backend allows you to run ArrayFire code on OpenCL-capable GPUs. Note: ArrayFire does not currently support OpenCL for Intel CPUs on OSX."
-  GROUP opencl_backend
+  GROUP opencl-backend
   DEPENDS ${ocl_deps_comps}
   INSTALL_TYPES All Development Runtime)
 
@@ -301,9 +301,9 @@ get_native_path(bsd3_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/BSD 3-Clause.txt")
 get_native_path(issl_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/ISSL License.txt")
 
 cpack_ifw_configure_component_group(backends)
-cpack_ifw_configure_component_group(cpu_backend)
-cpack_ifw_configure_component_group(cuda_backend)
-cpack_ifw_configure_component_group(opencl_backend)
+cpack_ifw_configure_component_group(cpu-backend)
+cpack_ifw_configure_component_group(cuda-backend)
+cpack_ifw_configure_component_group(opencl-backend)
 if (PACKAGE_MKL_DEPS)
   cpack_ifw_configure_component(mkl_dependencies)
 endif ()

From a1e01fd94b8d95e5246fcd482fbbe0e4af040032 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Aug 2020 13:52:57 -0400
Subject: [PATCH 2043/2677] Don't run sparseTranspose with AF_MAT_CTRANS for
 floats and doubles

---
 test/sparse_common.hpp | 16 ++++++++++++----
 test/testHelpers.hpp   |  6 ++++++
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index 70fb055859..bc95871b68 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -120,21 +120,29 @@ static void sparseTransposeTester(const int m, const int n, const int k,
 
     // Result of GEMM
     af::array dRes2 = matmul(A, B, AF_MAT_TRANS, AF_MAT_NONE);
-    af::array dRes3 = matmul(A, B, AF_MAT_CTRANS, AF_MAT_NONE);
+    af::array dRes3;
+    if (IsComplex<T>::value) {
+        dRes3 = matmul(A, B, AF_MAT_CTRANS, AF_MAT_NONE);
+    }
 
     // Create Sparse Array From Dense
     af::array sA = af::sparse(A, AF_STORAGE_CSR);
 
     // Sparse Matmul
     af::array sRes2 = matmul(sA, B, AF_MAT_TRANS, AF_MAT_NONE);
-    af::array sRes3 = matmul(sA, B, AF_MAT_CTRANS, AF_MAT_NONE);
+    af::array sRes3;
+    if (IsComplex<T>::value) {
+        sRes3 = matmul(sA, B, AF_MAT_CTRANS, AF_MAT_NONE);
+    }
 
     // Verify Results
     ASSERT_NEAR(0, calc_norm(real(dRes2), real(sRes2)), eps);
     ASSERT_NEAR(0, calc_norm(imag(dRes2), imag(sRes2)), eps);
 
-    ASSERT_NEAR(0, calc_norm(real(dRes3), real(sRes3)), eps);
-    ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
+    if (IsComplex<T>::value) {
+        ASSERT_NEAR(0, calc_norm(real(dRes3), real(sRes3)), eps);
+        ASSERT_NEAR(0, calc_norm(imag(dRes3), imag(sRes3)), eps);
+    }
 }
 
 template<typename T>
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index cd7425cbfc..c18b4a2f61 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -187,6 +187,12 @@ inline double imag<af::cfloat>(af::cfloat val) {
     return imag(val);
 }
 
+template<class T>
+struct IsComplex {
+    static const bool value = is_same_type<af::cfloat, T>::value ||
+                              is_same_type<af::cdouble, T>::value;
+};
+
 template<class T>
 struct IsFloatingPoint {
     static const bool value = is_same_type<half_float::half, T>::value ||

From ead53b9c97a9f8599f6c15d4761672c33a0ae367 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Aug 2020 13:28:33 -0400
Subject: [PATCH 2044/2677] Add f16 support for histogram

---
 src/api/c/histogram.cpp               | 4 ++++
 src/backend/cpu/histogram.cpp         | 3 +++
 src/backend/cpu/kernel/histogram.hpp  | 4 +++-
 src/backend/cuda/CMakeLists.txt       | 3 ++-
 src/backend/cuda/histogram.cpp        | 3 +++
 src/backend/cuda/kernel/histogram.cuh | 6 ++++--
 src/backend/opencl/histogram.cpp      | 3 +++
 test/arrayfire_test.cpp               | 1 +
 test/histogram.cpp                    | 6 +++---
 9 files changed, 26 insertions(+), 7 deletions(-)

diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index ed9472cc83..f04f4a23df 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -78,6 +78,10 @@ af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
                 output = histogram<uchar>(in, nbins, minval, maxval,
                                           info.isLinear());
                 break;
+            case f16:
+                output = histogram<common::half>(in, nbins, minval, maxval,
+                                                 info.isLinear());
+                break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*out, output);
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 19ef3a9728..2b044efd02 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
 #include <platform.hpp>
@@ -15,6 +16,7 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cpu {
 
@@ -50,5 +52,6 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(half)
 
 }  // namespace cpu
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 903f2d2204..4b18f94b5b 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+#include <types.hpp>
 
 namespace cpu {
 namespace kernel {
@@ -23,6 +24,7 @@ void histogram(Param<uint> out, CParam<T> in, const unsigned nbins,
     dim4 const oStrides = out.strides();
     dim_t const nElems  = inDims[0] * inDims[1];
 
+    auto minValT = compute_t<T>(minval);
     for (dim_t b3 = 0; b3 < outDims[3]; b3++) {
         uint* outData   = out.get() + b3 * oStrides[3];
         const T* inData = in.get() + b3 * iStrides[3];
@@ -32,7 +34,7 @@ void histogram(Param<uint> out, CParam<T> in, const unsigned nbins,
                     IsLinear
                         ? i
                         : ((i % inDims[0]) + (i / inDims[0]) * iStrides[1]);
-                int bin = (int)((inData[idx] - minval) / step);
+                int bin = (int)((compute_t<T>(inData[idx]) - minValT) / step);
                 bin     = std::max(bin, 0);
                 bin     = std::min(bin, (int)(nbins - 1));
                 outData[bin]++;
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4488c17873..4c320ed6bc 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -289,7 +289,7 @@ cuda_add_library(af_cuda_static_cuda_library STATIC
 
     OPTIONS
     ${platform_flags} ${cuda_cxx_flags} ${af_cuda_static_flags}
-    -Xcudafe \"--diag_suppress=1427\" -DAFDLL
+    -Xcudafe --display_error_number -Xcudafe \"--diag_suppress=1427\" -DAFDLL
 )
 
 set_target_properties(af_cuda_static_cuda_library
@@ -648,6 +648,7 @@ cuda_add_library(afcuda
     OPTIONS
     ${platform_flags}
     ${cuda_cxx_flags}
+    -Xcudafe --display_error_number
     -Xcudafe \"--diag_suppress=1427\"
   )
 
diff --git a/src/backend/cuda/histogram.cpp b/src/backend/cuda/histogram.cpp
index e9f8ce50b5..a2680de686 100644
--- a/src/backend/cuda/histogram.cpp
+++ b/src/backend/cuda/histogram.cpp
@@ -8,12 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace cuda {
 
@@ -43,5 +45,6 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(half)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/histogram.cuh b/src/backend/cuda/kernel/histogram.cuh
index 8c1ed0c128..3cd68a1485 100644
--- a/src/backend/cuda/kernel/histogram.cuh
+++ b/src/backend/cuda/kernel/histogram.cuh
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 #include <shared.hpp>
+#include <types.hpp>
 
 namespace cuda {
 
@@ -21,12 +22,13 @@ __global__ void histogram(Param<uint> out, CParam<T> in, int len, int nbins,
 
     // offset input and output to account for batch ops
     unsigned b2   = blockIdx.x / nBBS;
-    const T *iptr = in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
+    const data_t<T> *iptr = in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
     uint *optr    = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
 
     int start = (blockIdx.x - b2 * nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
     int end   = min((start + THRD_LOAD * blockDim.x), len);
     float step = (maxval - minval) / (float)nbins;
+    compute_t<T> minvalT(minval);
 
     // If nbins > max shared memory allocated, then just use atomicAdd on global
     // memory
@@ -43,7 +45,7 @@ __global__ void histogram(Param<uint> out, CParam<T> in, int len, int nbins,
             isLinear
                 ? row
                 : ((row % in.dims[0]) + (row / in.dims[0]) * in.strides[1]);
-        int bin = (int)((iptr[idx] - minval) / step);
+        int bin = (int)(static_cast<float>(compute_t<T>(iptr[idx]) - minvalT) / step);
         bin     = (bin < 0) ? 0 : bin;
         bin     = (bin >= nbins) ? (nbins - 1) : bin;
 
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 929daf67e8..7963d07d3c 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -8,12 +8,14 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <histogram.hpp>
 #include <kernel/histogram.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
+using common::half;
 
 namespace opencl {
 
@@ -43,5 +45,6 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(half)
 
 }  // namespace opencl
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index cf0d12b0b9..e9dee59789 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -322,6 +322,7 @@ INSTANTIATE(half_float::half, half_float::half, float);
 
 INSTANTIATE(double, af_cdouble, float);
 INSTANTIATE(float, af_cfloat, float);
+INSTANTIATE(half_float::half, uint, uint);
 
 #undef INSTANTIATE
 
diff --git a/test/histogram.cpp b/test/histogram.cpp
index c13c329a43..826eebd506 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -32,8 +32,8 @@ class Histogram : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort,
-                         intl, uintl>
+typedef ::testing::Types<half_float::half, float, double, int, uint, char,
+                         uchar, short, ushort, intl, uintl>
     TestTypes;
 
 // register the type list
@@ -48,7 +48,7 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval) {
 
     vector<vector<inType> > in;
     vector<vector<outType> > tests;
-    readTests<inType, uint, int>(pTestFile, numDims, in, tests);
+    readTests<inType, uint, uint>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
     af_array outArray = 0;

From 8fd7eecd2218dbd725e5d7e217ed382c4638ff34 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Aug 2020 15:50:50 -0400
Subject: [PATCH 2045/2677] Move initializer_list constructor implementation to
 the header

Moves the initialization list constructor to the header so that
we avoid version conflicts between compilers for the implemenation
of the initializer list. This approach will generate the initializer
list constructor for the user's compiler and avoid such conflicts.
---
 include/af/array.h    | 36 +++++++++++++++++++++++++++++++++---
 src/api/cpp/array.cpp | 10 +---------
 test/array.cpp        | 40 ++++++++++++++++++++++++++++++++++++++--
 3 files changed, 72 insertions(+), 14 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 4f2a3965b8..67c25a4824 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -10,6 +10,9 @@
 #pragma once
 #include <af/compilers.h>
 #include <af/defines.h>
+#include <af/device.h>
+#include <af/dim4.hpp>
+#include <af/exception.h>
 #include <af/index.h>
 #include <af/seq.h>
 #include <af/util.h>
@@ -494,10 +497,37 @@ namespace af
 
 #if AF_API_VERSION >= 38
 #if AF_COMPILER_CXX_GENERALIZED_INITIALIZERS
-        template <typename T> array(std::initializer_list<T> list);
-
+        /// \brief Initializer list constructor
+        template <typename T> array(std::initializer_list<T> list)
+        : arr(nullptr) {
+          dim_t size = list.size();
+          if (af_err __aferr = af_create_array(&arr, list.begin(), 1, &size,
+                              static_cast<af_dtype>(af::dtype_traits<T>::af_type))) {
+            char *msg = NULL;
+            af_get_last_error(&msg, NULL);
+            af::exception ex(msg, __PRETTY_FUNCTION__, "include/af/array.h",
+                             __LINE__, __aferr);
+            af_free_host(msg);
+            throw std::move(ex);
+          }
+        }
+
+        /// \brief Initializer list constructor
         template <typename T>
-        array(const af::dim4 &dims, std::initializer_list<T> list);
+        array(const af::dim4 &dims, std::initializer_list<T> list)
+            : arr(nullptr) {
+          const dim_t *size = dims.get();
+          if (af_err __aferr = af_create_array(
+              &arr, list.begin(), AF_MAX_DIMS, size,
+              static_cast<af_dtype>(af::dtype_traits<T>::af_type))) {
+            char *msg = NULL;
+            af_get_last_error(&msg, NULL);
+            af::exception ex(msg, __PRETTY_FUNCTION__, "include/af/array.h",
+                             __LINE__, __aferr);
+            af_free_host(msg);
+            throw std::move(ex);
+          }
+        }
 #endif
 #endif
 
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 95497a0e4d..73bcb90587 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -227,15 +227,7 @@ struct dtype_traits<half_float::half> {
     AFAPI array::array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,         \
                        const T *ptr, af::source src)                           \
         : arr(initDataArray(ptr, dtype_traits<T>::af_type, src, dim0, dim1,    \
-                            dim2, dim3)) {}                                    \
-    template<>                                                                 \
-    AFAPI array::array(std::initializer_list<T> list)                          \
-        : arr(initDataArray(list.begin(), dtype_traits<T>::af_type, afHost,    \
-                            list.size(), 1, 1, 1)) {}                          \
-    template<>                                                                 \
-    AFAPI array::array(const af::dim4 &dims, std::initializer_list<T> list)    \
-        : arr(initDataArray(list.begin(), dtype_traits<T>::af_type, afHost,    \
-                            dims[0], dims[1], dims[2], dims[3])) {}
+                            dim2, dim3)) {}
 
 INSTANTIATE(cdouble)
 INSTANTIATE(cfloat)
diff --git a/test/array.cpp b/test/array.cpp
index 23f7454ccc..ed0f7ac575 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -565,7 +565,7 @@ TEST(ArrayDeathTest, ProxyMoveAssignmentOperator) {
     EXPECT_EXIT(deathTest(), ::testing::ExitedWithCode(0), "");
 }
 
-TEST(Array, InitializerList) {
+TEST(Array, CopyListInitializerList) {
     int h_buffer[] = {23, 34, 18, 99, 34};
 
     array A(5, h_buffer);
@@ -574,7 +574,16 @@ TEST(Array, InitializerList) {
     ASSERT_ARRAYS_EQ(A, B);
 }
 
-TEST(Array, InitializerListAndDim4) {
+TEST(Array, DirectListInitializerList2) {
+    int h_buffer[] = {23, 34, 18, 99, 34};
+
+    array A(5, h_buffer);
+    array B{23, 34, 18, 99, 34};
+
+    ASSERT_ARRAYS_EQ(A, B);
+}
+
+TEST(Array, CopyListInitializerListAndDim4) {
     int h_buffer[] = {23, 34, 18, 99, 34, 44};
 
     array A(2, 3, h_buffer);
@@ -582,3 +591,30 @@ TEST(Array, InitializerListAndDim4) {
 
     ASSERT_ARRAYS_EQ(A, B);
 }
+
+TEST(Array, DirectListInitializerListAndDim4) {
+    int h_buffer[] = {23, 34, 18, 99, 34, 44};
+
+    array A(2, 3, h_buffer);
+    array B{dim4(2, 3), {23, 34, 18, 99, 34, 44}};
+
+    ASSERT_ARRAYS_EQ(A, B);
+}
+
+TEST(Array, CopyListInitializerListAssignment) {
+    int h_buffer[] = {23, 34, 18, 99, 34};
+
+    array A(5, h_buffer);
+    array B = {23, 34, 18, 99, 34};
+
+    ASSERT_ARRAYS_EQ(A, B);
+}
+
+TEST(Array, CopyListInitializerListDim4Assignment) {
+    int h_buffer[] = {23, 34, 18, 99, 34, 44};
+
+    array A(2, 3, h_buffer);
+    array B = {dim4(2, 3), {23, 34, 18, 99, 34, 44}};
+
+    ASSERT_ARRAYS_EQ(A, B);
+}

From e15f587f0bfd31787bdc9f12fd13e1495458d29f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Aug 2020 15:53:25 -0400
Subject: [PATCH 2046/2677] Formatting doxygen comments in the af/array.h
 header

---
 include/af/array.h | 54 +++++++++++++++++++++++++++++++++++-----------
 1 file changed, 42 insertions(+), 12 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index 67c25a4824..b30d5694fc 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -50,7 +50,8 @@ namespace af
         ///
         /// \brief Intermediate data class. Used for assignment and indexing.
         ///
-        /// \note This class is for internal book keeping while indexing. This class is not intended for use in user code.
+        /// \note This class is for internal book keeping while indexing. This
+        ///       class is not intended for use in user code.
         ///
         class AFAPI array_proxy
         {
@@ -374,7 +375,10 @@ namespace af
 
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership of the underlying memory.
+            \note If \p src is \ref afHost, the first \p dim0 elements are
+                  copied. If \p src is \ref afDevice, no copy is done; the
+                  array object wraps the device pointer AND takes ownership
+                  of the underlying memory.
 
         */
         template<typename T>
@@ -398,7 +402,11 @@ namespace af
 
             \image html 2dArray.png
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 elements are copied. If \p src is \ref afDevice, no copy is done; the array object wraps the device pointer AND takes ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 elements
+                  are copied. If \p src is \ref afDevice, no copy is done; the
+                  array object wraps the device pointer AND takes ownership of
+                  the underlying memory. The data is treated as column major
+                  format when performing linear algebra operations.
         */
         template<typename T>
         array(dim_t dim0, dim_t dim1,
@@ -422,7 +430,12 @@ namespace af
             array A(3, 3, 2,  h_buffer);   // copy host data to 3D device array
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 * \p dim2 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer and does not take ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 *
+                  \p dim2 elements are copied. If \p src is \ref afDevice, no
+                  copy is done; the array object just wraps the device pointer
+                  and does not take ownership of the underlying memory. The data
+                  is treated as column major format when performing linear
+                  algebra operations.
 
             \image html 3dArray.png
         */
@@ -451,7 +464,13 @@ namespace af
             array A(2, 2, 2, 2, h_buffer);   // copy host data to 4D device array
             \endcode
 
-            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 * \p dim2 * \p dim3 elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer and does not take ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first \p dim0 * \p dim1 *
+                  \p dim2 * \p dim3 elements are copied. If \p src is
+                  \ref afDevice, no copy is done; the array object just wraps
+                  the device pointer and does not take ownership of the
+                  underlying memory. The data is treated as column major format
+                  when performing linear algebra operations.
+
         */
         template<typename T>
         array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,
@@ -488,7 +507,12 @@ namespace af
                                              // used in ArrayFire
             \endcode
 
-            \note If \p src is \ref afHost, the first dims.elements() elements are copied. If \p src is \ref afDevice, no copy is done; the array object just wraps the device pointer and does not take ownership of the underlying memory. The data is treated as column major format when performing linear algebra operations.
+            \note If \p src is \ref afHost, the first dims.elements() elements
+                  are copied. If \p src is \ref afDevice, no copy is done; the
+                  array object just wraps the device pointer and does not take
+                  ownership of the underlying memory. The data is treated as
+                  column major format when performing linear algebra operations.
+
         */
         template<typename T>
         explicit
@@ -670,17 +694,20 @@ namespace af
         bool isscalar() const;
 
         /**
-           \brief Returns true if only one of the array dimensions has more than one element
+           \brief Returns true if only one of the array dimensions has more
+                  than one element
         */
         bool isvector() const;
 
         /**
-           \brief Returns true if only the second dimension has more than one element
+           \brief Returns true if only the second dimension has more than one
+                  element
         */
         bool isrow() const;
 
         /**
-           \brief Returns true if only the first dimension has more than one element
+           \brief Returns true if only the first dimension has more than one
+                  element
         */
         bool iscolumn() const;
 
@@ -717,12 +744,14 @@ namespace af
         bool isrealfloating() const;
 
         /**
-           \brief Returns true if the array type is \ref f16 \ref f32, \ref f64, \ref c32 or \ref c64
+           \brief Returns true if the array type is \ref f16 \ref f32, \ref f64,
+                  \ref c32 or \ref c64
         */
         bool isfloating() const;
 
         /**
-           \brief Returns true if the array type is \ref u8, \ref b8, \ref s32 \ref u32, \ref s64, \ref u64, \ref s16, \ref u16
+           \brief Returns true if the array type is \ref u8, \ref b8, \ref s32
+                  \ref u32, \ref s64, \ref u64, \ref s16, \ref u16
         */
         bool isinteger() const;
 
@@ -746,7 +775,8 @@ namespace af
         /**
            \brief Get the first element of the array as a scalar
 
-           \note This is recommended for use while debugging. Calling this method constantly reduces performance.
+           \note The scalar function is recommended for use while debugging.
+                 Calling this method often will affect performance.
         */
         template<typename T> T scalar() const;
 

From ea6545391cb45b7ea2cfd69362ebda734aa306ee Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 12 Aug 2020 01:45:04 -0400
Subject: [PATCH 2047/2677] Delete CLBlast patch and update the tag to include
 the changes (#2991)

* Fix failure with git apply after make clean

After the clean target is run, the patch on CLBlast is applied again.
this fails because the patch has already been applied. In order to
fix this we need to reset to the original tag and apply again.

* Delete CLBlast patch and update the tag to include the changes

Windows was failing with the previous commit. I updated the tag to
the changes pradeep pushed upstream and deleted the patch
---
 CMakeModules/build_CLBlast.cmake         |  6 ++--
 CMakeModules/clblast_program_getIR.patch | 44 ------------------------
 2 files changed, 2 insertions(+), 48 deletions(-)
 delete mode 100644 CMakeModules/clblast_program_getIR.patch

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 82d58c2b7b..1d570b6661 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -12,8 +12,6 @@ find_program(GIT git)
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
 set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
-set(CLBLAST_PATCH_COMMAND ${GIT} apply --whitespace=fix ${ArrayFire_SOURCE_DIR}/CMakeModules/clblast_program_getIR.patch)
-
 if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
   set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
 else()
@@ -29,11 +27,11 @@ endif()
 ExternalProject_Add(
     CLBlast-ext
     GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 1.5.1
+    GIT_TAG 41f344d1a6f2d149bba02a6615292e99b50f4856
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
-    PATCH_COMMAND "${CLBLAST_PATCH_COMMAND}"
+    PATCH_COMMAND ""
     BUILD_BYPRODUCTS ${CLBlast_location}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
       -Wno-dev <SOURCE_DIR>
diff --git a/CMakeModules/clblast_program_getIR.patch b/CMakeModules/clblast_program_getIR.patch
deleted file mode 100644
index 5b3d12e6ad..0000000000
--- a/CMakeModules/clblast_program_getIR.patch
+++ /dev/null
@@ -1,44 +0,0 @@
-diff --git a/src/clpp11.hpp b/src/clpp11.hpp
-index 4ed157ea..2a25606c 100644
---- a/src/clpp11.hpp
-+++ b/src/clpp11.hpp
-@@ -509,12 +509,35 @@ class Program {
- 
-   // Retrieves a binary or an intermediate representation of the compiled program
-   std::string GetIR() const {
--    auto bytes = size_t{0};
--    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARY_SIZES, sizeof(size_t), &bytes, nullptr));
-+    cl_uint num_devices = 0;
-+    CheckError(clGetProgramInfo(program_, CL_PROGRAM_NUM_DEVICES,
-+                sizeof(cl_uint), &num_devices, nullptr));
-+
-+    std::vector<size_t> binSizesInBytes(num_devices, 0);
-+    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARY_SIZES,
-+                num_devices * sizeof(size_t), binSizesInBytes.data(), nullptr));
-+
-+    auto bytes       = size_t{0};
-+    auto binSizeIter = size_t{0};
-+    // Loop over the program binary sizes to find a binary whose size is > 0.
-+    // The current logic assumes that there ever is only one valid program binary
-+    // in a given cl_program. This should be the case unless the cl_program
-+    // is built for all or a subset of devices associated to a given cl_program
-+    for (; binSizeIter < binSizesInBytes.size(); ++binSizeIter) {
-+        if (binSizesInBytes[binSizeIter] > 0) {
-+            bytes = binSizesInBytes[binSizeIter];
-+            break;
-+        }
-+    }
-     auto result = std::string{};
-     result.resize(bytes);
--    auto result_ptr = result.data();
--    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARIES, sizeof(char*), &result_ptr, nullptr));
-+
-+    std::vector<char*> out(num_devices, nullptr);
-+    out[binSizeIter] = const_cast<char*>(result.data());
-+
-+    CheckError(clGetProgramInfo(program_, CL_PROGRAM_BINARIES,
-+                                num_devices * sizeof(char*),
-+                                out.data(), nullptr));
-     return result;
-   }
- 

From 706924f5f3403f2475ca2ab57b77f7845738ed8c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Aug 2020 17:20:10 -0400
Subject: [PATCH 2048/2677] Build Unified backend without OpenCL or CUDA
 installed if necessary

The unified backend required the CUDA and OpenCL headers but the end
user may not have either of those backend installed. This causes a
problem with building on CUDA only or OpenCL only systems.

Fixes #2989
---
 src/api/unified/CMakeLists.txt | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index c44b2680d7..4e42fcee52 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -9,7 +9,6 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/arith.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
@@ -23,7 +22,6 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ml.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/opencl.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/signal.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse.cpp
@@ -34,6 +32,28 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/vision.cpp
   )
 
+if(OpenCL_FOUND)
+  target_sources(af
+    PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}/opencl.cpp
+  )
+
+  target_link_libraries(af
+    PRIVATE
+      OpenCL::OpenCL)
+
+endif()
+
+if(CUDA_FOUND)
+  target_sources(af
+    PRIVATE
+      ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp)
+
+  target_include_directories(af
+    PRIVATE
+      ${CUDA_INCLUDE_DIRS})
+endif()
+
 target_sources(af
   PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c/type_util.cpp

From cb06f0e4a45411bda5a167e2da0063a33915e857 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 12 Aug 2020 02:14:39 -0400
Subject: [PATCH 2049/2677] Use sourceforge to download doxygen (#2988)

* Use sourceforge to download doxygen

It seems that the doxygen.nl site removes the older versions of
doxygen once they move to a newer version. This will cause faulures
when a new version is released. This commit uses the source forge
site to download the bin because they keep older versions on their
servers

* Remove ninja from docs workflow
---
 .github/workflows/docs_build.yml | 27 ++++++++-------------------
 1 file changed, 8 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
index 7dec0803dc..c52729d3aa 100644
--- a/.github/workflows/docs_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -13,23 +13,14 @@ jobs:
         name: Documentation
         runs-on: ubuntu-18.04
         env:
-            NINJA_VER: 1.9.0
-            DOXYGEN_VER: 1.8.17
+            DOXYGEN_VER: 1.8.18
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master
 
-            - name: Download Ninja
-              id: ninja
-              run: |
-                  wget --quiet "https://github.com/ninja-build/ninja/releases/download/v${NINJA_VER}/ninja-linux.zip"
-                  unzip ./ninja-linux.zip
-                  chmod +x ninja
-                  ${GITHUB_WORKSPACE}/ninja --version
-
             - name: Install Doxygen
               run: |
-                  wget --quiet http://doxygen.nl/files/doxygen-${DOXYGEN_VER}.linux.bin.tar.gz
+                  wget --quiet https://sourceforge.net/projects/doxygen/files/rel-${DOXYGEN_VER}/doxygen-${DOXYGEN_VER}.linux.bin.tar.gz
                   mkdir doxygen
                   tar -xf doxygen-${DOXYGEN_VER}.linux.bin.tar.gz -C doxygen --strip 1
 
@@ -37,14 +28,12 @@ jobs:
               run: |
                   git submodule update --init --recursive
                   mkdir build && cd build
-                  cmake -G Ninja \
-                      -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
-                      -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
-                      -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
-                      -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
-                      -DBOOST_ROOT:PATH=${BOOST_ROOT_1_72_0} \
-                      -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen \
-                      ..
+                  cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
+                        -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
+                        -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
+                        -DBOOST_ROOT:PATH=${BOOST_ROOT_1_72_0} \
+                        -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen \
+                        ..
 
             - name: Build
               run: |

From c022b3791fe25deed3c384e5e33c7715bf02d949 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Jul 2020 01:31:29 -0400
Subject: [PATCH 2050/2677] Fix randn by passing in correct values to boxMuller

* boxMuller for float randn was being passed incorrect portions
of the counter/seed
---
 src/backend/cuda/kernel/random_engine.hpp        |  4 ++--
 src/backend/opencl/kernel/random_engine_write.cl | 11 ++++-------
 2 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index fc4f84aea4..8e4e26e712 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -253,8 +253,8 @@ __device__ static void boxMullerWriteOut128Bytes(float *out, const uint &index,
     boxMullerTransform(&out[index], &out[index + blockDim.x], getFloat(r1),
                        getFloat(r2));
     boxMullerTransform(&out[index + 2 * blockDim.x],
-                       &out[index + 3 * blockDim.x], getFloat(r1),
-                       getFloat(r2));
+                       &out[index + 3 * blockDim.x], getFloat(r3),
+                       getFloat(r4));
 }
 
 __device__ static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index,
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index e558fe1d16..06834769ea 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -260,8 +260,7 @@ void partialWriteOut128Bytes_short(global short *out, const uint *const index,
     }
 }
 
-void partialWriteOut128Bytes_ushort(global ushort *out,
-                                    const uint *const index,
+void partialWriteOut128Bytes_ushort(global ushort *out, const uint *const index,
                                     const uint *const r1, const uint *const r2,
                                     const uint *const r3, const uint *const r4,
                                     const uint *const elements) {
@@ -357,14 +356,13 @@ void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
 }
 
 // BoxMuller writes without boundary checking
-void boxMullerWriteOut128Bytes_float(global float *out,
-                                     const uint *const index,
+void boxMullerWriteOut128Bytes_float(global float *out, const uint *const index,
                                      const uint *const r1, const uint *const r2,
                                      const uint *const r3,
                                      const uint *const r4) {
     float n1, n2, n3, n4;
     boxMullerTransform((T *)&n1, (T *)&n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform((T *)&n3, (T *)&n4, getFloat(r1), getFloat(r2));
+    boxMullerTransform((T *)&n3, (T *)&n4, getFloat(r3), getFloat(r4));
     out[*index]               = n1;
     out[*index + THREADS]     = n2;
     out[*index + 2 * THREADS] = n3;
@@ -406,8 +404,7 @@ void writeOut128Bytes_double(global double *out, const uint *const index,
     out[*index + THREADS] = 1.0 - getDouble(r3, r4);
 }
 
-void partialWriteOut128Bytes_double(global double *out,
-                                    const uint *const index,
+void partialWriteOut128Bytes_double(global double *out, const uint *const index,
                                     const uint *const r1, const uint *const r2,
                                     const uint *const r3, const uint *const r4,
                                     const uint *const elements) {

From ab11d609dc9ef6e20b209e057a870a2cc033e145 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Jul 2020 01:49:25 -0400
Subject: [PATCH 2051/2677] Enable Uniform Chi2 tests for CUDA and OpenCL

The Chi2 tests measure the quality of the random number generator. This
test is too expensive to run on the CPU
---
 test/random.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/test/random.cpp b/test/random.cpp
index 9c0b416be5..3d8295b174 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -490,14 +490,16 @@ void testRandomEngineUniformChi2(randomEngineType type) {
     }
 }
 
-TYPED_TEST(RandomEngine, DISABLED_philoxRandomEngineUniformChi2) {
+#ifndef AF_CPU
+TYPED_TEST(RandomEngine, philoxRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
 
-TYPED_TEST(RandomEngine, DISABLED_threefryRandomEngineUniformChi2) {
+TYPED_TEST(RandomEngine, threefryRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
 }
 
-TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEngineUniformChi2) {
+TYPED_TEST(RandomEngine, mersenneRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
+#endif

From 4d1c47bcd770e70ece4b7cd232afdcc9466cbc0b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Jul 2020 02:10:52 -0400
Subject: [PATCH 2052/2677] Fix warning in compile module about the length of
 the error message

---
 src/backend/cuda/compile_module.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 38e2fed991..06a96e1f29 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -81,11 +81,13 @@ using std::chrono::duration_cast;
 using std::chrono::high_resolution_clock;
 using std::chrono::milliseconds;
 
+constexpr size_t linkLogSize = 2048;
+
 #define CU_LINK_CHECK(fn)                                               \
     do {                                                                \
         CUresult res = (fn);                                            \
         if (res == CUDA_SUCCESS) break;                                 \
-        array<char, 2048> cu_err_msg;                                   \
+        array<char, linkLogSize + 512> cu_err_msg;                      \
         const char *cu_err_name;                                        \
         cuGetErrorName(res, &cu_err_name);                              \
         snprintf(cu_err_msg.data(), cu_err_msg.size(),                  \
@@ -270,7 +272,6 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     ptx.resize(ptx_size);
     NVRTC_CHECK(nvrtcGetPTX(prog, ptx.data()));
 
-    const size_t linkLogSize    = 4096;
     char linkInfo[linkLogSize]  = {0};
     char linkError[linkLogSize] = {0};
 

From e206e0b9c3c24d827bc69704a122e9016aafa841 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 31 Jul 2020 12:23:50 -0400
Subject: [PATCH 2053/2677] Move RNG(random) quality checks to own file to
 avoid large alloc

Move the RNG quality checks to a separate file to avoid memory allocation
failures because the random tests are running parallely with other
tests. These tests are marked as SERIAL so they are running
serially.
---
 test/CMakeLists.txt  |   1 +
 test/random.cpp      |  91 -----------------------------
 test/rng_quality.cpp | 135 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 136 insertions(+), 91 deletions(-)
 create mode 100644 test/rng_quality.cpp

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 07b0579d3f..0ec99b7944 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -356,6 +356,7 @@ make_test(SRC pad_borders.cpp CXX11)
 make_test(SRC pinverse.cpp SERIAL)
 make_test(SRC qr_dense.cpp SERIAL)
 make_test(SRC random.cpp)
+make_test(SRC rng_quality.cpp BACKENDS "cuda;opencl" SERIAL)
 make_test(SRC range.cpp)
 make_test(SRC rank_dense.cpp SERIAL)
 make_test(SRC reduce.cpp CXX11)
diff --git a/test/random.cpp b/test/random.cpp
index 3d8295b174..ac70aec057 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -412,94 +412,3 @@ TYPED_TEST(RandomEngineSeed, threefrySeedUniform) {
 TYPED_TEST(RandomEngineSeed, mersenneSeedUniform) {
     testRandomEngineSeed<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
-
-template<typename T>
-void testRandomEnginePeriod(randomEngineType type) {
-    SUPPORTED_TYPE_CHECK(T);
-    dtype ty = (dtype)dtype_traits<T>::af_type;
-
-    int elem  = 1024 * 1024;
-    int steps = 4 * 1024;
-    randomEngine r(type, 0);
-
-    array first = randu(elem, ty, r);
-
-    for (int i = 0; i < steps; ++i) {
-        array step     = randu(elem, ty, r);
-        bool different = !allTrue<bool>(first == step);
-        ASSERT_TRUE(different);
-    }
-}
-
-TYPED_TEST(RandomEngine, DISABLED_philoxRandomEnginePeriod) {
-    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
-}
-
-TYPED_TEST(RandomEngine, DISABLED_threefryRandomEnginePeriod) {
-    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
-}
-
-TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEnginePeriod) {
-    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
-}
-
-template<typename T>
-T chi2_statistic(array input, array expected) {
-    expected *= sum<T>(input) / sum<T>(expected);
-    array diff = input - expected;
-    return sum<T>((diff * diff) / expected);
-}
-
-template<typename T>
-void testRandomEngineUniformChi2(randomEngineType type) {
-    SUPPORTED_TYPE_CHECK(T);
-    dtype ty = (dtype)dtype_traits<T>::af_type;
-
-    int elem  = 256 * 1024 * 1024;
-    int steps = 32;
-    int bins  = 100;
-
-    array total_hist = constant(0.0, bins, ty);
-    array expected   = constant(1.0 / bins, bins, ty);
-
-    randomEngine r(type, 0);
-
-    // R> qchisq(c(5e-6, 1 - 5e-6), 99)
-    // [1]  48.68125 173.87456
-    T lower = 48.68125;
-    T upper = 173.87456;
-
-    bool prev_step  = true;
-    bool prev_total = true;
-    for (int i = 0; i < steps; ++i) {
-        array step_hist = histogram(randu(elem, ty, r), bins, 0.0, 1.0);
-        T step_chi2     = chi2_statistic<T>(step_hist, expected);
-        if (!prev_step) {
-            EXPECT_GT(step_chi2, lower) << "at step: " << i;
-            EXPECT_LT(step_chi2, upper) << "at step: " << i;
-        }
-        prev_step = step_chi2 > lower && step_chi2 < upper;
-
-        total_hist += step_hist;
-        T total_chi2 = chi2_statistic<T>(total_hist, expected);
-        if (!prev_total) {
-            EXPECT_GT(total_chi2, lower) << "at step: " << i;
-            EXPECT_LT(total_chi2, upper) << "at step: " << i;
-        }
-        prev_total = total_chi2 > lower && total_chi2 < upper;
-    }
-}
-
-#ifndef AF_CPU
-TYPED_TEST(RandomEngine, philoxRandomEngineUniformChi2) {
-    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
-}
-
-TYPED_TEST(RandomEngine, threefryRandomEngineUniformChi2) {
-    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
-}
-
-TYPED_TEST(RandomEngine, mersenneRandomEngineUniformChi2) {
-    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
-}
-#endif
diff --git a/test/rng_quality.cpp b/test/rng_quality.cpp
new file mode 100644
index 0000000000..915c81f7ee
--- /dev/null
+++ b/test/rng_quality.cpp
@@ -0,0 +1,135 @@
+
+
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+
+using af::allTrue;
+using af::array;
+using af::constant;
+using af::dtype;
+using af::dtype_traits;
+using af::randomEngine;
+using af::randomEngineType;
+using af::sum;
+
+template<typename T>
+class RandomEngine : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
+};
+
+template<typename T>
+class RandomEngineSeed : public ::testing::Test {
+   public:
+    virtual void SetUp() {}
+};
+
+// create a list of types to be tested
+typedef ::testing::Types<float, double> TestTypesEngine;
+// register the type list
+TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
+
+typedef ::testing::Types<unsigned> TestTypesEngineSeed;
+// register the type list
+TYPED_TEST_CASE(RandomEngineSeed, TestTypesEngineSeed);
+
+template<typename T>
+void testRandomEnginePeriod(randomEngineType type) {
+    SUPPORTED_TYPE_CHECK(T);
+    dtype ty = (dtype)dtype_traits<T>::af_type;
+
+    int elem  = 1024 * 1024;
+    int steps = 4 * 1024;
+    randomEngine r(type, 0);
+
+    array first = randu(elem, ty, r);
+
+    for (int i = 0; i < steps; ++i) {
+        array step     = randu(elem, ty, r);
+        bool different = !allTrue<bool>(first == step);
+        ASSERT_TRUE(different);
+    }
+}
+
+TYPED_TEST(RandomEngine, philoxRandomEnginePeriod) {
+    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
+}
+
+TYPED_TEST(RandomEngine, threefryRandomEnginePeriod) {
+    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
+}
+
+TYPED_TEST(RandomEngine, mersenneRandomEnginePeriod) {
+    testRandomEnginePeriod<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
+}
+
+template<typename T>
+double chi2_statistic(array input, array expected) {
+    expected *=
+        convert<float>(sum<T>(input)) / convert<float>(sum<T>(expected));
+    array diff = input - expected;
+    return convert<float>(sum<T>((diff * diff) / expected));
+}
+
+template<>
+double chi2_statistic<half_float::half>(array input, array expected) {
+    expected *= convert<float>(sum<float>(input)) /
+                convert<float>(sum<float>(expected));
+    array diff = input - expected;
+    return convert<float>(sum<float>((diff * diff) / expected));
+}
+
+template<typename T>
+void testRandomEngineUniformChi2(randomEngineType type) {
+    SUPPORTED_TYPE_CHECK(T);
+    dtype ty = (dtype)dtype_traits<T>::af_type;
+
+    int elem  = 256 * 1024 * 1024;
+    int steps = 32;
+    int bins  = 100;
+
+    array total_hist = constant(0.0, bins, ty);
+    array expected   = constant(1.0 / bins, bins, ty);
+
+    randomEngine r(type, 0);
+
+    // R> qchisq(c(5e-6, 1 - 5e-6), 99)
+    // [1]  48.68125 173.87456
+    double lower(48.68125);
+    double upper(173.87456);
+
+    bool prev_step  = true;
+    bool prev_total = true;
+    for (int i = 0; i < steps; ++i) {
+        array step_hist  = histogram(randu(elem, ty, r), bins, 0.0, 1.0);
+        double step_chi2 = chi2_statistic<T>(step_hist, expected);
+        if (!prev_step) {
+            EXPECT_GT(step_chi2, lower) << "at step: " << i;
+            EXPECT_LT(step_chi2, upper) << "at step: " << i;
+        }
+        prev_step = step_chi2 > lower && step_chi2 < upper;
+
+        total_hist += step_hist;
+        double total_chi2 = chi2_statistic<T>(total_hist, expected);
+        if (!prev_total) {
+            EXPECT_GT(total_chi2, lower) << "at step: " << i;
+            EXPECT_LT(total_chi2, upper) << "at step: " << i;
+        }
+        prev_total = total_chi2 > lower && total_chi2 < upper;
+    }
+}
+
+#ifndef AF_CPU
+TYPED_TEST(RandomEngine, philoxRandomEngineUniformChi2) {
+    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
+}
+
+TYPED_TEST(RandomEngine, threefryRandomEngineUniformChi2) {
+    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
+}
+
+TYPED_TEST(RandomEngine, mersenneRandomEngineUniformChi2) {
+    testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
+}
+#endif

From dffa8c55c2ca24ef5ef366c6f0c95b73b6d3c929 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 5 Aug 2020 01:49:57 -0400
Subject: [PATCH 2054/2677] Further improve the box muller function by fixing
 rounding issues

* Fix rounding of some operations by using fused operations like sincospi
  and fma instead of a multiply add.
* Convert half constants to hex values and use __ushort_as_half
  to avoid redundant conversions from float
* Pass integers instead of pointers in the OpenCL backend of
  the rng functions
---
 src/backend/cpu/kernel/random_engine.hpp      | 159 ++--
 src/backend/cuda/kernel/random_engine.hpp     | 332 +++++---
 .../cuda/kernel/random_engine_philox.hpp      |  11 +-
 .../opencl/kernel/random_engine_mersenne.cl   |   5 +-
 .../opencl/kernel/random_engine_philox.cl     |   6 +-
 .../opencl/kernel/random_engine_threefry.cl   |   4 +-
 .../opencl/kernel/random_engine_write.cl      | 799 +++++++++---------
 test/rng_quality.cpp                          | 159 +++-
 8 files changed, 858 insertions(+), 617 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index de70c8fef0..8549bcc01a 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -19,6 +19,7 @@
 #include <types.hpp>
 
 #include <algorithm>
+#include <cmath>
 #include <cstring>
 
 using std::array;
@@ -31,89 +32,146 @@ static const double PI_VAL =
     3.1415926535897932384626433832795028841971693993751058209749445923078164;
 
 // Conversion to half adapted from Random123
-#define USHORTMAX 0xffff
-#define HALF_FACTOR ((1.0f) / (USHORTMAX + (1.0f)))
+#define HALF_FACTOR ((1.0f) / (std::numeric_limits<ushort>::max() + (1.0f)))
 #define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
 
-// Conversion to floats adapted from Random123
-#define UINTMAX 0xffffffff
-#define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
-#define HALF_FLT_FACTOR ((0.5f) * FLT_FACTOR)
+// Conversion to half adapted from Random123
+#define SIGNED_HALF_FACTOR \
+    ((1.0f) / (std::numeric_limits<short>::max() + (1.0f)))
+#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
 
-#define UINTLMAX 0xffffffffffffffff
-#define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
+#define DBL_FACTOR \
+    ((1.0) / (std::numeric_limits<unsigned long long>::max() + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
+// Conversion to floats adapted from Random123
+#define SIGNED_DBL_FACTOR \
+    ((1.0) / (std::numeric_limits<long long>::max() + (1.0)))
+#define SIGNED_HALF_DBL_FACTOR ((0.5) * SIGNED_DBL_FACTOR)
+
 template<typename T>
-T transform(uint *val, int index) {
-    T *oval = (T *)val;
-    return oval[index];
+T transform(uint *val, uint index);
+
+template<>
+uintl transform<uintl>(uint *val, uint index) {
+    uint index2 = index << 1;
+    uintl v     = ((static_cast<uintl>(val[index2]) << 32) |
+               (static_cast<uintl>(val[index2 + 1])));
+    return v;
+}
+
+// Generates rationals in [0, 1)
+float getFloat01(uint *val, uint index) {
+    // Conversion to floats adapted from Random123
+    constexpr float factor =
+        ((1.0f) /
+         (static_cast<float>(std::numeric_limits<unsigned int>::max()) +
+          (1.0f)));
+    constexpr float half_factor = ((0.5f) * factor);
+    return fmaf(val[index], factor, half_factor);
+}
+
+// Generates rationals in (-1, 1]
+static float getFloatNegative11(uint *val, uint index) {
+    // Conversion to floats adapted from Random123
+    constexpr float factor =
+        ((1.0) /
+         (static_cast<double>(std::numeric_limits<int>::max()) + (1.0)));
+    constexpr float half_factor = ((0.5f) * factor);
+
+    return fmaf(static_cast<float>(val[index]), factor, half_factor);
+}
+
+// Generates rationals in [0, 1)
+common::half getHalf01(uint *val, uint index) {
+    float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
+    return static_cast<common::half>(fmaf(v, HALF_FACTOR, HALF_HALF_FACTOR));
+}
+
+// Generates rationals in (-1, 1]
+static common::half getHalfNegative11(uint *val, uint index) {
+    float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
+    return static_cast<common::half>(
+        fmaf(v, SIGNED_HALF_FACTOR, SIGNED_HALF_HALF_FACTOR));
+}
+
+// Generates rationals in [0, 1)
+double getDouble01(uint *val, uint index) {
+    uintl v = transform<uintl>(val, index);
+    constexpr double factor =
+        ((1.0) / (std::numeric_limits<unsigned long long>::max() +
+                  static_cast<long double>(1.0l)));
+    constexpr double half_factor((0.5) * factor);
+    return fma(v, factor, half_factor);
 }
 
 template<>
-char transform<char>(uint *val, int index) {
+char transform<char>(uint *val, uint index) {
     char v = val[index >> 2] >> (8 << (index & 3));
     v      = (v & 0x1) ? 1 : 0;
     return v;
 }
 
 template<>
-uchar transform<uchar>(uint *val, int index) {
+uchar transform<uchar>(uint *val, uint index) {
     uchar v = val[index >> 2] >> (index << 3);
     return v;
 }
 
 template<>
-ushort transform<ushort>(uint *val, int index) {
+ushort transform<ushort>(uint *val, uint index) {
     ushort v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
     return v;
 }
 
 template<>
-short transform<short>(uint *val, int index) {
+short transform<short>(uint *val, uint index) {
     return transform<ushort>(val, index);
 }
 
 template<>
-uint transform<uint>(uint *val, int index) {
+uint transform<uint>(uint *val, uint index) {
     return val[index];
 }
 
 template<>
-int transform<int>(uint *val, int index) {
+int transform<int>(uint *val, uint index) {
     return transform<uint>(val, index);
 }
 
 template<>
-uintl transform<uintl>(uint *val, int index) {
-    uintl v = (((uintl)val[index << 1]) << 32) | ((uintl)val[(index << 1) + 1]);
+intl transform<intl>(uint *val, uint index) {
+    uintl v = transform<uintl>(val, index);
+    intl out;
+    memcpy(&out, &v, sizeof(intl));
     return v;
 }
 
 template<>
-intl transform<intl>(uint *val, int index) {
-    return transform<uintl>(val, index);
+float transform<float>(uint *val, uint index) {
+    return 1.f - getFloat01(val, index);
 }
 
-// Generates rationals in [0, 1)
 template<>
-float transform<float>(uint *val, int index) {
-    return 1.f - (val[index] * FLT_FACTOR + HALF_FLT_FACTOR);
+double transform<double>(uint *val, uint index) {
+    return 1. - getDouble01(val, index);
 }
 
-// Generates rationals in [0, 1)
 template<>
-common::half transform<common::half>(uint *val, int index) {
+common::half transform<common::half>(uint *val, uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
     return static_cast<common::half>(1.f -
-                                     (v * HALF_FACTOR + HALF_HALF_FACTOR));
+                                     fmaf(v, HALF_FACTOR, HALF_HALF_FACTOR));
 }
 
-// Generates rationals in [0, 1)
-template<>
-double transform<double>(uint *val, int index) {
-    uintl v = transform<uintl>(val, index);
-    return 1.0 - (v * DBL_FACTOR + HALF_DBL_FACTOR);
+// Generates rationals in [-1, 1)
+double getDoubleNegative11(uint *val, uint index) {
+    intl v = transform<intl>(val, index);
+    // Conversion to doubles adapted from Random123
+    constexpr double signed_factor =
+        ((1.0l) / (std::numeric_limits<long long>::max() + (1.0l)));
+    constexpr double half_factor = ((0.5) * signed_factor);
+    return fma(v, signed_factor, half_factor);
 }
 
 #define MAX_RESET_CTR_VAL 64
@@ -201,34 +259,35 @@ void boxMullerTransform(data_t<T> *const out1, data_t<T> *const out2,
      * The log of a real value x where 0 < x < 1 is negative.
      */
     using Tc = compute_t<T>;
-    Tc r     = sqrt((Tc)(-2.0) * log((Tc)(1.0) - static_cast<Tc>(r1)));
-    Tc theta = 2 * (Tc)PI_VAL * ((Tc)(1.0) - static_cast<Tc>(r2));
-    *out1    = r * sin(theta);
-    *out2    = r * cos(theta);
+    Tc r     = sqrt((Tc)(-2.0) * log(static_cast<Tc>(r2)));
+    Tc theta = PI_VAL * (static_cast<Tc>(r1));
+
+    *out1 = r * sin(theta);
+    *out2 = r * cos(theta);
 }
 
 void boxMullerTransform(uint val[4], double *temp) {
-    boxMullerTransform<double>(&temp[0], &temp[1], transform<double>(val, 0),
-                               transform<double>(val, 1));
+    boxMullerTransform<double>(&temp[0], &temp[1], getDoubleNegative11(val, 0),
+                               getDouble01(val, 1));
 }
 
 void boxMullerTransform(uint val[4], float *temp) {
-    boxMullerTransform<float>(&temp[0], &temp[1], transform<float>(val, 0),
-                              transform<float>(val, 1));
-    boxMullerTransform<float>(&temp[2], &temp[3], transform<float>(val, 2),
-                              transform<float>(val, 3));
+    boxMullerTransform<float>(&temp[0], &temp[1], getFloatNegative11(val, 0),
+                              getFloat01(val, 1));
+    boxMullerTransform<float>(&temp[2], &temp[3], getFloatNegative11(val, 2),
+                              getFloat01(val, 3));
 }
 
 void boxMullerTransform(uint val[4], common::half *temp) {
     using common::half;
-    boxMullerTransform<half>(&temp[0], &temp[1], transform<half>(val, 0),
-                             transform<half>(val, 1));
-    boxMullerTransform<half>(&temp[2], &temp[3], transform<half>(val, 2),
-                             transform<half>(val, 3));
-    boxMullerTransform<half>(&temp[4], &temp[5], transform<half>(val, 4),
-                             transform<half>(val, 5));
-    boxMullerTransform<half>(&temp[6], &temp[7], transform<half>(val, 6),
-                             transform<half>(val, 7));
+    boxMullerTransform<half>(&temp[0], &temp[1], getHalfNegative11(val, 0),
+                             getHalf01(val, 1));
+    boxMullerTransform<half>(&temp[2], &temp[3], getHalfNegative11(val, 2),
+                             getHalf01(val, 3));
+    boxMullerTransform<half>(&temp[4], &temp[5], getHalfNegative11(val, 4),
+                             getHalf01(val, 5));
+    boxMullerTransform<half>(&temp[6], &temp[7], getHalfNegative11(val, 6),
+                             getHalf01(val, 7));
 }
 
 template<typename T>
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 8e4e26e712..eb343271b9 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -19,6 +19,8 @@
 #include <random_engine.hpp>
 #include <af/defines.h>
 
+#include <limits>
+
 namespace cuda {
 namespace kernel {
 // Utils
@@ -28,34 +30,97 @@ static const int THREADS = 256;
     3.1415926535897932384626433832795028841971693993751058209749445923078164
 
 // Conversion to half adapted from Random123
-#define USHORTMAX 0xffff
-#define HALF_FACTOR ((1.0f) / (USHORTMAX + (1.0f)))
-#define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+// #define HALF_FACTOR (1.0f) / (std::numeric_limits<ushort>::max() + (1.0f))
+// #define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+//
+// NOTE: The following constants for half were calculated using the formulas
+// above. This is done so that we can avoid unnecessary computations because the
+// __half datatype is not a constexprable type. This prevents the compiler from
+// peforming these operations at compile time.
+#define HALF_FACTOR __ushort_as_half(256)
+#define HALF_HALF_FACTOR __ushort_as_half(128)
+
+// Conversion to half adapted from Random123
+//#define SIGNED_HALF_FACTOR                                \
+    //((1.0f) / (std::numeric_limits<short>::max() + (1.0f)))
+//#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
+//
+// NOTE: The following constants for half were calculated using the formulas
+// above. This is done so that we can avoid unnecessary computations because the
+// __half datatype is not a constexprable type. This prevents the compiler from
+// peforming these operations at compile time
+#define SIGNED_HALF_FACTOR __ushort_as_half(512)
+#define SIGNED_HALF_HALF_FACTOR __ushort_as_half(256)
+
+// Conversion to floats adapted from Random123
+constexpr float FLT_FACTOR =
+    ((1.0f) /
+     (static_cast<float>(std::numeric_limits<unsigned int>::max()) + (1.0f)));
+
+constexpr float HALF_FLT_FACTOR = ((0.5f) * FLT_FACTOR);
 
 // Conversion to floats adapted from Random123
-#define UINTMAX 0xffffffff
-#define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
-#define HALF_FLT_FACTOR ((0.5f) * FLT_FACTOR)
+constexpr float SIGNED_FLT_FACTOR =
+    ((1.0) / (std::numeric_limits<int>::max() + (1.0)));
+constexpr float SIGNED_HALF_FLT_FACTOR = ((0.5f) * SIGNED_FLT_FACTOR);
+
+constexpr double DBL_FACTOR =
+    ((1.0) / (std::numeric_limits<unsigned long long>::max() +
+              static_cast<long double>(1.0l)));
+constexpr double HALF_DBL_FACTOR((0.5) * DBL_FACTOR);
 
-#define UINTLMAX 0xffffffffffffffff
-#define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
-#define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
+// Conversion to floats adapted from Random123
+constexpr double SIGNED_DBL_FACTOR =
+    ((1.0l) / (std::numeric_limits<long long>::max() + (1.0l)));
+constexpr double SIGNED_HALF_DBL_FACTOR = ((0.5) * SIGNED_DBL_FACTOR);
 
 // Generates rationals in (0, 1]
-__device__ static compute_t<common::half> getHalf(const uint &num) {
+__device__ static __half oneMinusGetHalf01(uint num) {
     ushort v = num;
-    return (compute_t<common::half>)(v * HALF_FACTOR + HALF_HALF_FACTOR);
+    return __ushort_as_half(0x3c00) -
+           __hfma(static_cast<__half>(v), HALF_FACTOR, HALF_HALF_FACTOR);
 }
 
 // Generates rationals in (0, 1]
-__device__ static float getFloat(const uint &num) {
-    return (num * FLT_FACTOR + HALF_FLT_FACTOR);
+__device__ static __half getHalf01(uint num) {
+    ushort v = num;
+    return __hfma(static_cast<__half>(v), HALF_FACTOR, HALF_HALF_FACTOR);
+}
+
+// Generates rationals in (-1, 1]
+__device__ static __half getHalfNegative11(uint num) {
+    ushort v = num;
+    return __hfma(static_cast<__half>(v), SIGNED_HALF_FACTOR,
+                  SIGNED_HALF_HALF_FACTOR);
 }
 
 // Generates rationals in (0, 1]
-__device__ static double getDouble(const uint &num1, const uint &num2) {
-    uintl num = (((uintl)num1) << 32) | ((uintl)num2);
-    return (num * DBL_FACTOR + HALF_DBL_FACTOR);
+__device__ static float getFloat01(uint num) {
+    return fmaf(static_cast<float>(num), FLT_FACTOR, HALF_FLT_FACTOR);
+}
+
+// Generates rationals in (-1, 1]
+__device__ static float getFloatNegative11(uint num) {
+    return fmaf(static_cast<float>(num), SIGNED_FLT_FACTOR,
+                SIGNED_HALF_FLT_FACTOR);
+}
+
+// Generates rationals in (0, 1]
+__device__ static float getDouble01(uint num1, uint num2) {
+    uint64_t n1 = num1;
+    uint64_t n2 = num2;
+    n1 <<= 32;
+    uint64_t num = n1 | n2;
+    return fma(static_cast<double>(num), DBL_FACTOR, HALF_DBL_FACTOR);
+}
+
+// Generates rationals in (-1, 1]
+__device__ static float getDoubleNegative11(uint num1, uint num2) {
+    uint32_t arr[2] = {num2, num1};
+    uint64_t num;
+    memcpy(&num, arr, sizeof(uint64_t));
+    return fma(static_cast<double>(num), SIGNED_DBL_FACTOR,
+               SIGNED_HALF_DBL_FACTOR);
 }
 
 namespace {
@@ -67,20 +132,64 @@ __device__ __half hsin(const __half a) { return 0; }
 __device__ __half hcos(const __half a) { return 0; }
 #endif
 
-#define MATH_FUNC(OP, HALF_OP)         \
-    template<typename T>               \
-    __device__ T OP(T val) {           \
-        return ::OP(val);              \
-    }                                  \
-    template<>                         \
-    __device__ __half OP(__half val) { \
-        return HALF_OP(val);           \
+#define MATH_FUNC(OP, DOUBLE_OP, FLOAT_OP, HALF_OP) \
+    template<typename T>                            \
+    __device__ T OP(T val);                         \
+    template<>                                      \
+    __device__ double OP(double val) {              \
+        return ::DOUBLE_OP(val);                    \
+    }                                               \
+    template<>                                      \
+    __device__ float OP(float val) {                \
+        return FLOAT_OP(val);                       \
+    }                                               \
+    template<>                                      \
+    __device__ __half OP(__half val) {              \
+        return HALF_OP(val);                        \
     }
 
-MATH_FUNC(log, hlog)
-MATH_FUNC(sqrt, hsqrt)
-MATH_FUNC(sin, hsin)
-MATH_FUNC(cos, hcos)
+MATH_FUNC(log, log, logf, hlog)
+MATH_FUNC(sqrt, sqrt, sqrtf, hsqrt)
+MATH_FUNC(sin, sin, sinf, hsin)
+MATH_FUNC(cos, cos, cosf, hcos)
+
+template<typename T>
+__device__ void sincos(T val, T *sptr, T *cptr);
+
+template<>
+__device__ void sincos(double val, double *sptr, double *cptr) {
+    ::sincos(val, sptr, cptr);
+}
+template<>
+__device__ void sincos(float val, float *sptr, float *cptr) {
+    sincosf(val, sptr, cptr);
+}
+template<>
+__device__ void sincos(__half val, __half *sptr, __half *cptr) {
+    *sptr = hsin(val);
+    *cptr = hcos(val);
+}
+
+template<typename T>
+__device__ void sincospi(T val, T *sptr, T *cptr);
+
+template<>
+__device__ void sincospi(double val, double *sptr, double *cptr) {
+    ::sincospi(val, sptr, cptr);
+}
+template<>
+__device__ void sincospi(float val, float *sptr, float *cptr) {
+    sincospif(val, sptr, cptr);
+}
+template<>
+__device__ void sincospi(__half val, __half *sptr, __half *cptr) {
+    // CUDA cannot make __half into a constexpr as of CUDA 11 so we are
+    // converting this offline
+    const __half pi_val = __ushort_as_half(0x4248);  // 0x4248 == 3.14062h
+    *sptr               = hsin(val) * pi_val;
+    *cptr               = hcos(val) * pi_val;
+}
+
 }  // namespace
 
 template<typename T>
@@ -88,6 +197,11 @@ constexpr __device__ T neg_two() {
     return -2.0;
 }
 
+template<>
+__device__ __half neg_two() {
+    return __ushort_as_half(0xc000);  // 0xc000 == -2.h
+}
+
 template<typename T>
 constexpr __device__ T two_pi() {
     return 2.0 * PI_VAL;
@@ -99,10 +213,15 @@ __device__ static void boxMullerTransform(Td *const out1, Td *const out2,
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
-    Tc r     = sqrt(neg_two<Tc>() * log(r1));
-    Tc theta = two_pi<Tc>() * r2;
-    *out1    = Td(r * sin(theta));
-    *out2    = Td(r * cos(theta));
+    Tc r = sqrt(neg_two<Tc>() * log(r2));
+    Tc s, c;
+
+    // Multiplying by PI instead of 2*PI seems to yeild a better distribution
+    // even though the original boxMuller algorithm calls for 2 * PI
+    // sincos(two_pi<Tc>() * r1, &s, &c);
+    sincospi(r1, &s, &c);
+    *out1 = static_cast<Td>(r * s);
+    *out2 = static_cast<Td>(r * c);
 }
 
 // Writes without boundary checking
@@ -202,46 +321,46 @@ __device__ static void writeOut128Bytes(uintl *out, const uint &index,
 __device__ static void writeOut128Bytes(float *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
-    out[index]                  = 1.f - getFloat(r1);
-    out[index + blockDim.x]     = 1.f - getFloat(r2);
-    out[index + 2 * blockDim.x] = 1.f - getFloat(r3);
-    out[index + 3 * blockDim.x] = 1.f - getFloat(r4);
+    out[index]                  = 1.f - getFloat01(r1);
+    out[index + blockDim.x]     = 1.f - getFloat01(r2);
+    out[index + 2 * blockDim.x] = 1.f - getFloat01(r3);
+    out[index + 3 * blockDim.x] = 1.f - getFloat01(r4);
 }
 
 __device__ static void writeOut128Bytes(cfloat *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
-    out[index].x              = 1.f - getFloat(r1);
-    out[index].y              = 1.f - getFloat(r2);
-    out[index + blockDim.x].x = 1.f - getFloat(r3);
-    out[index + blockDim.x].y = 1.f - getFloat(r4);
+    out[index].x              = 1.f - getFloat01(r1);
+    out[index].y              = 1.f - getFloat01(r2);
+    out[index + blockDim.x].x = 1.f - getFloat01(r3);
+    out[index + blockDim.x].y = 1.f - getFloat01(r4);
 }
 
 __device__ static void writeOut128Bytes(double *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
-    out[index]              = 1.0 - getDouble(r1, r2);
-    out[index + blockDim.x] = 1.0 - getDouble(r3, r4);
+    out[index]              = 1.0 - getDouble01(r1, r2);
+    out[index + blockDim.x] = 1.0 - getDouble01(r3, r4);
 }
 
 __device__ static void writeOut128Bytes(cdouble *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
-    out[index].x = 1.0 - getDouble(r1, r2);
-    out[index].y = 1.0 - getDouble(r3, r4);
+    out[index].x = 1.0 - getDouble01(r1, r2);
+    out[index].y = 1.0 - getDouble01(r3, r4);
 }
 
 __device__ static void writeOut128Bytes(common::half *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
-    out[index]                  = getHalf(r1);
-    out[index + blockDim.x]     = getHalf(r1 >> 16);
-    out[index + 2 * blockDim.x] = getHalf(r2);
-    out[index + 3 * blockDim.x] = getHalf(r2 >> 16);
-    out[index + 4 * blockDim.x] = getHalf(r3);
-    out[index + 5 * blockDim.x] = getHalf(r3 >> 16);
-    out[index + 6 * blockDim.x] = getHalf(r4);
-    out[index + 7 * blockDim.x] = getHalf(r4 >> 16);
+    out[index]                  = oneMinusGetHalf01(r1);
+    out[index + blockDim.x]     = oneMinusGetHalf01(r1 >> 16);
+    out[index + 2 * blockDim.x] = oneMinusGetHalf01(r2);
+    out[index + 3 * blockDim.x] = oneMinusGetHalf01(r2 >> 16);
+    out[index + 4 * blockDim.x] = oneMinusGetHalf01(r3);
+    out[index + 5 * blockDim.x] = oneMinusGetHalf01(r3 >> 16);
+    out[index + 6 * blockDim.x] = oneMinusGetHalf01(r4);
+    out[index + 7 * blockDim.x] = oneMinusGetHalf01(r4 >> 16);
 }
 
 // Normalized writes without boundary checking
@@ -250,29 +369,29 @@ __device__ static void boxMullerWriteOut128Bytes(float *out, const uint &index,
                                                  const uint &r1, const uint &r2,
                                                  const uint &r3,
                                                  const uint &r4) {
-    boxMullerTransform(&out[index], &out[index + blockDim.x], getFloat(r1),
-                       getFloat(r2));
+    boxMullerTransform(&out[index], &out[index + blockDim.x],
+                       getFloatNegative11(r1), getFloat01(r2));
     boxMullerTransform(&out[index + 2 * blockDim.x],
-                       &out[index + 3 * blockDim.x], getFloat(r3),
-                       getFloat(r4));
+                       &out[index + 3 * blockDim.x], getFloatNegative11(r3),
+                       getFloat01(r4));
 }
 
 __device__ static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index,
                                                  const uint &r1, const uint &r2,
                                                  const uint &r3,
                                                  const uint &r4) {
-    boxMullerTransform(&out[index].x, &out[index].y, getFloat(r1),
-                       getFloat(r2));
+    boxMullerTransform(&out[index].x, &out[index].y, getFloatNegative11(r1),
+                       getFloat01(r2));
     boxMullerTransform(&out[index + blockDim.x].x, &out[index + blockDim.x].y,
-                       getFloat(r3), getFloat(r4));
+                       getFloatNegative11(r3), getFloat01(r4));
 }
 
 __device__ static void boxMullerWriteOut128Bytes(double *out, const uint &index,
                                                  const uint &r1, const uint &r2,
                                                  const uint &r3,
                                                  const uint &r4) {
-    boxMullerTransform(&out[index], &out[index + blockDim.x], getDouble(r1, r2),
-                       getDouble(r3, r4));
+    boxMullerTransform(&out[index], &out[index + blockDim.x],
+                       getDoubleNegative11(r1, r2), getDouble01(r3, r4));
 }
 
 __device__ static void boxMullerWriteOut128Bytes(cdouble *out,
@@ -280,8 +399,8 @@ __device__ static void boxMullerWriteOut128Bytes(cdouble *out,
                                                  const uint &r1, const uint &r2,
                                                  const uint &r3,
                                                  const uint &r4) {
-    boxMullerTransform(&out[index].x, &out[index].y, getDouble(r1, r2),
-                       getDouble(r3, r4));
+    boxMullerTransform(&out[index].x, &out[index].y,
+                       getDoubleNegative11(r1, r2), getDouble01(r3, r4));
 }
 
 __device__ static void boxMullerWriteOut128Bytes(common::half *out,
@@ -289,17 +408,17 @@ __device__ static void boxMullerWriteOut128Bytes(common::half *out,
                                                  const uint &r1, const uint &r2,
                                                  const uint &r3,
                                                  const uint &r4) {
-    boxMullerTransform(&out[index], &out[index + blockDim.x], getHalf(r1),
-                       getHalf(r1 >> 16));
+    boxMullerTransform(&out[index], &out[index + blockDim.x],
+                       getHalfNegative11(r1), getHalf01(r1 >> 16));
     boxMullerTransform(&out[index + 2 * blockDim.x],
-                       &out[index + 3 * blockDim.x], getHalf(r2),
-                       getHalf(r2 >> 16));
+                       &out[index + 3 * blockDim.x], getHalfNegative11(r2),
+                       getHalf01(r2 >> 16));
     boxMullerTransform(&out[index + 4 * blockDim.x],
-                       &out[index + 5 * blockDim.x], getHalf(r3),
-                       getHalf(r3 >> 16));
+                       &out[index + 5 * blockDim.x], getHalfNegative11(r3),
+                       getHalf01(r3 >> 16));
     boxMullerTransform(&out[index + 6 * blockDim.x],
-                       &out[index + 7 * blockDim.x], getHalf(r4),
-                       getHalf(r4 >> 16));
+                       &out[index + 7 * blockDim.x], getHalfNegative11(r4),
+                       getHalf01(r4 >> 16));
 }
 
 // Writes with boundary checking
@@ -469,15 +588,15 @@ __device__ static void partialWriteOut128Bytes(float *out, const uint &index,
                                                const uint &r1, const uint &r2,
                                                const uint &r3, const uint &r4,
                                                const uint &elements) {
-    if (index < elements) { out[index] = 1.f - getFloat(r1); }
+    if (index < elements) { out[index] = 1.f - getFloat01(r1); }
     if (index + blockDim.x < elements) {
-        out[index + blockDim.x] = 1.f - getFloat(r2);
+        out[index + blockDim.x] = 1.f - getFloat01(r2);
     }
     if (index + 2 * blockDim.x < elements) {
-        out[index + 2 * blockDim.x] = 1.f - getFloat(r3);
+        out[index + 2 * blockDim.x] = 1.f - getFloat01(r3);
     }
     if (index + 3 * blockDim.x < elements) {
-        out[index + 3 * blockDim.x] = 1.f - getFloat(r4);
+        out[index + 3 * blockDim.x] = 1.f - getFloat01(r4);
     }
 }
 
@@ -486,12 +605,12 @@ __device__ static void partialWriteOut128Bytes(cfloat *out, const uint &index,
                                                const uint &r3, const uint &r4,
                                                const uint &elements) {
     if (index < elements) {
-        out[index].x = 1.f - getFloat(r1);
-        out[index].y = 1.f - getFloat(r2);
+        out[index].x = 1.f - getFloat01(r1);
+        out[index].y = 1.f - getFloat01(r2);
     }
     if (index + blockDim.x < elements) {
-        out[index + blockDim.x].x = 1.f - getFloat(r3);
-        out[index + blockDim.x].y = 1.f - getFloat(r4);
+        out[index + blockDim.x].x = 1.f - getFloat01(r3);
+        out[index + blockDim.x].y = 1.f - getFloat01(r4);
     }
 }
 
@@ -499,9 +618,9 @@ __device__ static void partialWriteOut128Bytes(double *out, const uint &index,
                                                const uint &r1, const uint &r2,
                                                const uint &r3, const uint &r4,
                                                const uint &elements) {
-    if (index < elements) { out[index] = 1.0 - getDouble(r1, r2); }
+    if (index < elements) { out[index] = 1.0 - getDouble01(r1, r2); }
     if (index + blockDim.x < elements) {
-        out[index + blockDim.x] = 1.0 - getDouble(r3, r4);
+        out[index + blockDim.x] = 1.0 - getDouble01(r3, r4);
     }
 }
 
@@ -510,8 +629,8 @@ __device__ static void partialWriteOut128Bytes(cdouble *out, const uint &index,
                                                const uint &r3, const uint &r4,
                                                const uint &elements) {
     if (index < elements) {
-        out[index].x = 1.0 - getDouble(r1, r2);
-        out[index].y = 1.0 - getDouble(r3, r4);
+        out[index].x = 1.0 - getDouble01(r1, r2);
+        out[index].y = 1.0 - getDouble01(r3, r4);
     }
 }
 
@@ -521,8 +640,8 @@ __device__ static void partialBoxMullerWriteOut128Bytes(
     float *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
     float n1, n2, n3, n4;
-    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
+    boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
     if (index < elements) { out[index] = n1; }
     if (index + blockDim.x < elements) { out[index + blockDim.x] = n2; }
     if (index + 2 * blockDim.x < elements) { out[index + 2 * blockDim.x] = n3; }
@@ -533,8 +652,8 @@ __device__ static void partialBoxMullerWriteOut128Bytes(
     cfloat *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
     float n1, n2, n3, n4;
-    boxMullerTransform(&n1, &n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform(&n3, &n4, getFloat(r3), getFloat(r4));
+    boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
     if (index < elements) {
         out[index].x = n1;
         out[index].y = n2;
@@ -549,7 +668,8 @@ __device__ static void partialBoxMullerWriteOut128Bytes(
     double *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
     double n1, n2;
-    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
     if (index < elements) { out[index] = n1; }
     if (index + blockDim.x < elements) { out[index + blockDim.x] = n2; }
 }
@@ -558,7 +678,8 @@ __device__ static void partialBoxMullerWriteOut128Bytes(
     cdouble *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
     double n1, n2;
-    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
+    boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
     if (index < elements) {
         out[index].x = n1;
         out[index].y = n2;
@@ -570,27 +691,27 @@ __device__ static void partialWriteOut128Bytes(common::half *out,
                                                const uint &r1, const uint &r2,
                                                const uint &r3, const uint &r4,
                                                const uint &elements) {
-    if (index < elements) { out[index] = getHalf(r1); }
+    if (index < elements) { out[index] = getHalf01(r1); }
     if (index + blockDim.x < elements) {
-        out[index + blockDim.x] = getHalf(r1 >> 16);
+        out[index + blockDim.x] = getHalf01(r1 >> 16);
     }
     if (index + 2 * blockDim.x < elements) {
-        out[index + 2 * blockDim.x] = getHalf(r2);
+        out[index + 2 * blockDim.x] = getHalf01(r2);
     }
     if (index + 3 * blockDim.x < elements) {
-        out[index + 3 * blockDim.x] = getHalf(r2 >> 16);
+        out[index + 3 * blockDim.x] = getHalf01(r2 >> 16);
     }
     if (index + 4 * blockDim.x < elements) {
-        out[index + 4 * blockDim.x] = getHalf(r3);
+        out[index + 4 * blockDim.x] = getHalf01(r3);
     }
     if (index + 5 * blockDim.x < elements) {
-        out[index + 5 * blockDim.x] = getHalf(r3 >> 16);
+        out[index + 5 * blockDim.x] = getHalf01(r3 >> 16);
     }
     if (index + 6 * blockDim.x < elements) {
-        out[index + 6 * blockDim.x] = getHalf(r4);
+        out[index + 6 * blockDim.x] = getHalf01(r4);
     }
     if (index + 7 * blockDim.x < elements) {
-        out[index + 7 * blockDim.x] = getHalf(r4 >> 16);
+        out[index + 7 * blockDim.x] = getHalf01(r4 >> 16);
     }
 }
 
@@ -599,10 +720,14 @@ __device__ static void partialBoxMullerWriteOut128Bytes(
     common::half *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
     __half n[8];
-    boxMullerTransform(n + 0, n + 1, getHalf(r1), getHalf(r1 >> 16));
-    boxMullerTransform(n + 2, n + 3, getHalf(r2), getHalf(r2 >> 16));
-    boxMullerTransform(n + 4, n + 5, getHalf(r3), getHalf(r3 >> 16));
-    boxMullerTransform(n + 6, n + 7, getHalf(r4), getHalf(r4 >> 16));
+    boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
+                       getHalf01(r1 >> 16));
+    boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
+                       getHalf01(r2 >> 16));
+    boxMullerTransform(n + 4, n + 5, getHalfNegative11(r3),
+                       getHalf01(r3 >> 16));
+    boxMullerTransform(n + 6, n + 7, getHalfNegative11(r4),
+                       getHalf01(r4 >> 16));
     if (index < elements) { out[index] = n[0]; }
     if (index + blockDim.x < elements) { out[index + blockDim.x] = n[1]; }
     if (index + 2 * blockDim.x < elements) {
@@ -733,11 +858,12 @@ __global__ void normalPhilox(T *out, uint hi, uint lo, uint hic, uint loc,
     ctr[0] += index;
     ctr[1] += (ctr[0] < loc);
     ctr[2] += (ctr[1] < hic);
+
+    philox(key, ctr);
+
     if (blockIdx.x != (gridDim.x - 1)) {
-        philox(key, ctr);
         boxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2], ctr[3]);
     } else {
-        philox(key, ctr);
         partialBoxMullerWriteOut128Bytes(out, index, ctr[0], ctr[1], ctr[2],
                                          ctr[3], elements);
     }
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index 6f1764225d..4648617a8a 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -52,13 +52,12 @@ namespace kernel {
 // Source of these constants :
 // github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
 
-static const uint m4x32_0 = 0xD2511F53;
-static const uint m4x32_1 = 0xCD9E8D57;
-static const uint w32_0   = 0x9E3779B9;
-static const uint w32_1   = 0xBB67AE85;
+constexpr uint m4x32_0 = 0xD2511F53;
+constexpr uint m4x32_1 = 0xCD9E8D57;
+constexpr uint w32_0   = 0x9E3779B9;
+constexpr uint w32_1   = 0xBB67AE85;
 
-static inline __device__ void mulhilo(const uint &a, const uint &b, uint &hi,
-                                      uint &lo) {
+static inline __device__ void mulhilo(uint a, uint b, uint &hi, uint &lo) {
     hi = __umulhi(a, b);
     lo = a * b;
 }
diff --git a/src/backend/opencl/kernel/random_engine_mersenne.cl b/src/backend/opencl/kernel/random_engine_mersenne.cl
index ec06ba74f4..ebb5a92120 100644
--- a/src/backend/opencl/kernel/random_engine_mersenne.cl
+++ b/src/backend/opencl/kernel/random_engine_mersenne.cl
@@ -145,10 +145,9 @@ kernel void mersenneGenerator(global T *output, global uint *const state,
         }
         uint writeIndex = index + get_local_id(0);
         if (i == iter - 1) {
-            PARTIAL_WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3],
-                          &elements);
+            PARTIAL_WRITE(output, writeIndex, o[0], o[1], o[2], o[3], elements);
         } else {
-            WRITE(output, &writeIndex, &o[0], &o[1], &o[2], &o[3]);
+            WRITE(output, writeIndex, o[0], o[1], o[2], o[3]);
         }
         index += elementsPerBlockIteration;
     }
diff --git a/src/backend/opencl/kernel/random_engine_philox.cl b/src/backend/opencl/kernel/random_engine_philox.cl
index 76990141f7..ccc6bb455d 100644
--- a/src/backend/opencl/kernel/random_engine_philox.cl
+++ b/src/backend/opencl/kernel/random_engine_philox.cl
@@ -100,7 +100,6 @@ void philox(uint key[2], uint ctr[4]) {
 kernel void philoxGenerator(global T *output, unsigned elements, unsigned hic,
                             unsigned loc, unsigned hi, unsigned lo) {
     unsigned gid   = get_group_id(0);
-    unsigned off   = get_local_size(0);
     unsigned index = gid * ELEMENTS_PER_BLOCK + get_local_id(0);
 
     uint key[2] = {lo, hi};
@@ -112,9 +111,8 @@ kernel void philoxGenerator(global T *output, unsigned elements, unsigned hic,
     philox(key, ctr);
 
     if (gid != get_num_groups(0) - 1) {
-        WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3]);
+        WRITE(output, index, ctr[0], ctr[1], ctr[2], ctr[3]);
     } else {
-        PARTIAL_WRITE(output, &index, &ctr[0], &ctr[1], &ctr[2], &ctr[3],
-                      &elements);
+        PARTIAL_WRITE(output, index, ctr[0], ctr[1], ctr[2], ctr[3], elements);
     }
 }
diff --git a/src/backend/opencl/kernel/random_engine_threefry.cl b/src/backend/opencl/kernel/random_engine_threefry.cl
index ef6aca3ab1..7fdb2bcd07 100644
--- a/src/backend/opencl/kernel/random_engine_threefry.cl
+++ b/src/backend/opencl/kernel/random_engine_threefry.cl
@@ -171,8 +171,8 @@ kernel void threefryGenerator(global T *output, unsigned elements, unsigned hic,
     threefry(key, ctr, o + 2);
 
     if (gid != get_num_groups(0) - 1) {
-        WRITE(output, &index, &o[0], &o[1], &o[2], &o[3]);
+        WRITE(output, index, o[0], o[1], o[2], o[3]);
     } else {
-        PARTIAL_WRITE(output, &index, &o[0], &o[1], &o[2], &o[3], &elements);
+        PARTIAL_WRITE(output, index, o[0], o[1], o[2], o[3], elements);
     }
 }
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 06834769ea..1ccbd1c1a5 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -7,431 +7,381 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#define PI_VAL \
-    3.1415926535897932384626433832795028841971693993751058209749445923078164
-
 // Conversion to floats adapted from Random123
-#define UINTMAX 0xffffffff
-#define FLT_FACTOR ((1.0f) / (UINTMAX + (1.0f)))
+#define FLT_FACTOR ((1.0f) / ((float)UINT_MAX + 1.0f))
 #define HALF_FLT_FACTOR ((0.5f) * FLT_FACTOR)
 
+// Conversion to floats adapted from Random123
+#define SIGNED_FLT_FACTOR ((1.0f) / ((float)INT_MAX + 1.0f))
+#define SIGNED_HALF_FLT_FACTOR (0.5f * SIGNED_FLT_FACTOR)
+
 // Generates rationals in (0, 1]
-float getFloat(const uint *const num) {
-    return ((*num) * FLT_FACTOR + HALF_FLT_FACTOR);
+float getFloat01(uint num) {
+    return fma((float)num, FLT_FACTOR, HALF_FLT_FACTOR);
+}
+
+// Generates rationals in (-1, 1]
+float getFloatNegative11(uint num) {
+    return fma((float)num, SIGNED_FLT_FACTOR, SIGNED_HALF_FLT_FACTOR);
 }
 
 // Writes without boundary checking
 
-void writeOut128Bytes_uchar(global uchar *out, const uint *const index,
-                            const uint *const r1, const uint *const r2,
-                            const uint *const r3, const uint *const r4) {
-    out[*index]                = *r1;
-    out[*index + THREADS]      = *r1 >> 8;
-    out[*index + 2 * THREADS]  = *r1 >> 16;
-    out[*index + 3 * THREADS]  = *r1 >> 24;
-    out[*index + 4 * THREADS]  = *r2;
-    out[*index + 5 * THREADS]  = *r2 >> 8;
-    out[*index + 6 * THREADS]  = *r2 >> 16;
-    out[*index + 7 * THREADS]  = *r2 >> 24;
-    out[*index + 8 * THREADS]  = *r3;
-    out[*index + 9 * THREADS]  = *r3 >> 8;
-    out[*index + 10 * THREADS] = *r3 >> 16;
-    out[*index + 11 * THREADS] = *r3 >> 24;
-    out[*index + 12 * THREADS] = *r4;
-    out[*index + 13 * THREADS] = *r4 >> 8;
-    out[*index + 14 * THREADS] = *r4 >> 16;
-    out[*index + 15 * THREADS] = *r4 >> 24;
-}
-
-void writeOut128Bytes_char(global char *out, const uint *const index,
-                           const uint *const r1, const uint *const r2,
-                           const uint *const r3, const uint *const r4) {
-    out[*index]                = (*r1) & 0x1;
-    out[*index + THREADS]      = (*r1 >> 1) & 0x1;
-    out[*index + 2 * THREADS]  = (*r1 >> 2) & 0x1;
-    out[*index + 3 * THREADS]  = (*r1 >> 3) & 0x1;
-    out[*index + 4 * THREADS]  = (*r2) & 0x1;
-    out[*index + 5 * THREADS]  = (*r2 >> 1) & 0x1;
-    out[*index + 6 * THREADS]  = (*r2 >> 2) & 0x1;
-    out[*index + 7 * THREADS]  = (*r2 >> 3) & 0x1;
-    out[*index + 8 * THREADS]  = (*r3) & 0x1;
-    out[*index + 9 * THREADS]  = (*r3 >> 1) & 0x1;
-    out[*index + 10 * THREADS] = (*r3 >> 2) & 0x1;
-    out[*index + 11 * THREADS] = (*r3 >> 3) & 0x1;
-    out[*index + 12 * THREADS] = (*r4) & 0x1;
-    out[*index + 13 * THREADS] = (*r4 >> 1) & 0x1;
-    out[*index + 14 * THREADS] = (*r4 >> 2) & 0x1;
-    out[*index + 15 * THREADS] = (*r4 >> 3) & 0x1;
-}
-
-void writeOut128Bytes_short(global short *out, const uint *const index,
-                            const uint *const r1, const uint *const r2,
-                            const uint *const r3, const uint *const r4) {
-    out[*index]               = *r1;
-    out[*index + THREADS]     = *r1 >> 16;
-    out[*index + 2 * THREADS] = *r2;
-    out[*index + 3 * THREADS] = *r2 >> 16;
-    out[*index + 4 * THREADS] = *r3;
-    out[*index + 5 * THREADS] = *r3 >> 16;
-    out[*index + 6 * THREADS] = *r4;
-    out[*index + 7 * THREADS] = *r4 >> 16;
-}
-
-void writeOut128Bytes_ushort(global ushort *out, const uint *const index,
-                             const uint *const r1, const uint *const r2,
-                             const uint *const r3, const uint *const r4) {
-    out[*index]               = *r1;
-    out[*index + THREADS]     = *r1 >> 16;
-    out[*index + 2 * THREADS] = *r2;
-    out[*index + 3 * THREADS] = *r2 >> 16;
-    out[*index + 4 * THREADS] = *r3;
-    out[*index + 5 * THREADS] = *r3 >> 16;
-    out[*index + 6 * THREADS] = *r4;
-    out[*index + 7 * THREADS] = *r4 >> 16;
-}
-
-void writeOut128Bytes_int(global int *out, const uint *const index,
-                          const uint *const r1, const uint *const r2,
-                          const uint *const r3, const uint *const r4) {
-    out[*index]               = *r1;
-    out[*index + THREADS]     = *r2;
-    out[*index + 2 * THREADS] = *r3;
-    out[*index + 3 * THREADS] = *r4;
-}
-
-void writeOut128Bytes_uint(global uint *out, const uint *const index,
-                           const uint *const r1, const uint *const r2,
-                           const uint *const r3, const uint *const r4) {
-    out[*index]               = *r1;
-    out[*index + THREADS]     = *r2;
-    out[*index + 2 * THREADS] = *r3;
-    out[*index + 3 * THREADS] = *r4;
-}
-
-void writeOut128Bytes_long(global long *out, const uint *const index,
-                           const uint *const r1, const uint *const r2,
-                           const uint *const r3, const uint *const r4) {
-    long c1               = *r2;
-    c1                    = (c1 << 32) | *r1;
-    long c2               = *r4;
-    c2                    = (c2 << 32) | *r3;
-    out[*index]           = c1;
-    out[*index + THREADS] = c2;
-}
-
-void writeOut128Bytes_ulong(global ulong *out, const uint *const index,
-                            const uint *const r1, const uint *const r2,
-                            const uint *const r3, const uint *const r4) {
-    long c1               = *r2;
-    c1                    = (c1 << 32) | *r1;
-    long c2               = *r4;
-    c2                    = (c2 << 32) | *r3;
-    out[*index]           = c1;
-    out[*index + THREADS] = c2;
-}
-
-void writeOut128Bytes_float(global float *out, const uint *const index,
-                            const uint *const r1, const uint *const r2,
-                            const uint *const r3, const uint *const r4) {
-    out[*index]               = 1.f - getFloat(r1);
-    out[*index + THREADS]     = 1.f - getFloat(r2);
-    out[*index + 2 * THREADS] = 1.f - getFloat(r3);
-    out[*index + 3 * THREADS] = 1.f - getFloat(r4);
+void writeOut128Bytes_uchar(global uchar *out, uint index, uint r1, uint r2,
+                            uint r3, uint r4) {
+    out[index]                = r1;
+    out[index + THREADS]      = r1 >> 8;
+    out[index + 2 * THREADS]  = r1 >> 16;
+    out[index + 3 * THREADS]  = r1 >> 24;
+    out[index + 4 * THREADS]  = r2;
+    out[index + 5 * THREADS]  = r2 >> 8;
+    out[index + 6 * THREADS]  = r2 >> 16;
+    out[index + 7 * THREADS]  = r2 >> 24;
+    out[index + 8 * THREADS]  = r3;
+    out[index + 9 * THREADS]  = r3 >> 8;
+    out[index + 10 * THREADS] = r3 >> 16;
+    out[index + 11 * THREADS] = r3 >> 24;
+    out[index + 12 * THREADS] = r4;
+    out[index + 13 * THREADS] = r4 >> 8;
+    out[index + 14 * THREADS] = r4 >> 16;
+    out[index + 15 * THREADS] = r4 >> 24;
+}
+
+void writeOut128Bytes_char(global char *out, uint index, uint r1, uint r2,
+                           uint r3, uint r4) {
+    out[index]                = (r1)&0x1;
+    out[index + THREADS]      = (r1 >> 1) & 0x1;
+    out[index + 2 * THREADS]  = (r1 >> 2) & 0x1;
+    out[index + 3 * THREADS]  = (r1 >> 3) & 0x1;
+    out[index + 4 * THREADS]  = (r2)&0x1;
+    out[index + 5 * THREADS]  = (r2 >> 1) & 0x1;
+    out[index + 6 * THREADS]  = (r2 >> 2) & 0x1;
+    out[index + 7 * THREADS]  = (r2 >> 3) & 0x1;
+    out[index + 8 * THREADS]  = (r3)&0x1;
+    out[index + 9 * THREADS]  = (r3 >> 1) & 0x1;
+    out[index + 10 * THREADS] = (r3 >> 2) & 0x1;
+    out[index + 11 * THREADS] = (r3 >> 3) & 0x1;
+    out[index + 12 * THREADS] = (r4)&0x1;
+    out[index + 13 * THREADS] = (r4 >> 1) & 0x1;
+    out[index + 14 * THREADS] = (r4 >> 2) & 0x1;
+    out[index + 15 * THREADS] = (r4 >> 3) & 0x1;
+}
+
+void writeOut128Bytes_short(global short *out, uint index, uint r1, uint r2,
+                            uint r3, uint r4) {
+    out[index]               = r1;
+    out[index + THREADS]     = r1 >> 16;
+    out[index + 2 * THREADS] = r2;
+    out[index + 3 * THREADS] = r2 >> 16;
+    out[index + 4 * THREADS] = r3;
+    out[index + 5 * THREADS] = r3 >> 16;
+    out[index + 6 * THREADS] = r4;
+    out[index + 7 * THREADS] = r4 >> 16;
+}
+
+void writeOut128Bytes_ushort(global ushort *out, uint index, uint r1, uint r2,
+                             uint r3, uint r4) {
+    out[index]               = r1;
+    out[index + THREADS]     = r1 >> 16;
+    out[index + 2 * THREADS] = r2;
+    out[index + 3 * THREADS] = r2 >> 16;
+    out[index + 4 * THREADS] = r3;
+    out[index + 5 * THREADS] = r3 >> 16;
+    out[index + 6 * THREADS] = r4;
+    out[index + 7 * THREADS] = r4 >> 16;
+}
+
+void writeOut128Bytes_int(global int *out, uint index, uint r1, uint r2,
+                          uint r3, uint r4) {
+    out[index]               = r1;
+    out[index + THREADS]     = r2;
+    out[index + 2 * THREADS] = r3;
+    out[index + 3 * THREADS] = r4;
+}
+
+void writeOut128Bytes_uint(global uint *out, uint index, uint r1, uint r2,
+                           uint r3, uint r4) {
+    out[index]               = r1;
+    out[index + THREADS]     = r2;
+    out[index + 2 * THREADS] = r3;
+    out[index + 3 * THREADS] = r4;
+}
+
+void writeOut128Bytes_long(global long *out, uint index, uint r1, uint r2,
+                           uint r3, uint r4) {
+    long c1              = r2;
+    c1                   = (c1 << 32) | r1;
+    long c2              = r4;
+    c2                   = (c2 << 32) | r3;
+    out[index]           = c1;
+    out[index + THREADS] = c2;
+}
+
+void writeOut128Bytes_ulong(global ulong *out, uint index, uint r1, uint r2,
+                            uint r3, uint r4) {
+    long c1              = r2;
+    c1                   = (c1 << 32) | r1;
+    long c2              = r4;
+    c2                   = (c2 << 32) | r3;
+    out[index]           = c1;
+    out[index + THREADS] = c2;
+}
+
+void writeOut128Bytes_float(global float *out, uint index, uint r1, uint r2,
+                            uint r3, uint r4) {
+    out[index]               = 1.f - getFloat01(r1);
+    out[index + THREADS]     = 1.f - getFloat01(r2);
+    out[index + 2 * THREADS] = 1.f - getFloat01(r3);
+    out[index + 3 * THREADS] = 1.f - getFloat01(r4);
 }
 
 #if RAND_DIST == 1
+void boxMullerTransform(T *const out1, T *const out2, T r1, T r2) {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
+#if defined(IS_APPLE)  // Because Apple is.. "special"
+    T r = sqrt((T)(-2.0) * log10(r2) * (T)log10_val);
+#else
+    T r = sqrt((T)(-2.0) * log(r2));
+#endif
+    T c   = cospi(r1);
+    T s   = sinpi(r1);
+    *out1 = r * s;
+    *out2 = r * c;
+}
 #endif
 
 // Writes with boundary checking
 
-void partialWriteOut128Bytes_uchar(global uchar *out, const uint *const index,
-                                   const uint *const r1, const uint *const r2,
-                                   const uint *const r3, const uint *const r4,
-                                   const uint *const elements) {
-    if (*index < *elements) { out[*index] = *r1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = *r1 >> 8; }
-    if (*index + 2 * THREADS < *elements) {
-        out[*index + 2 * THREADS] = *r1 >> 16;
-    }
-    if (*index + 3 * THREADS < *elements) {
-        out[*index + 3 * THREADS] = *r1 >> 24;
-    }
-    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = *r2; }
-    if (*index + 5 * THREADS < *elements) {
-        out[*index + 5 * THREADS] = *r2 >> 8;
+void partialWriteOut128Bytes_uchar(global uchar *out, uint index, uint r1,
+                                   uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + THREADS < elements) { out[index + THREADS] = r1 >> 8; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = r1 >> 16; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = r1 >> 24; }
+    if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = r2; }
+    if (index + 5 * THREADS < elements) { out[index + 5 * THREADS] = r2 >> 8; }
+    if (index + 6 * THREADS < elements) { out[index + 6 * THREADS] = r2 >> 16; }
+    if (index + 7 * THREADS < elements) { out[index + 7 * THREADS] = r2 >> 24; }
+    if (index + 8 * THREADS < elements) { out[index + 8 * THREADS] = r3; }
+    if (index + 9 * THREADS < elements) { out[index + 9 * THREADS] = r3 >> 8; }
+    if (index + 10 * THREADS < elements) {
+        out[index + 10 * THREADS] = r3 >> 16;
     }
-    if (*index + 6 * THREADS < *elements) {
-        out[*index + 6 * THREADS] = *r2 >> 16;
+    if (index + 11 * THREADS < elements) {
+        out[index + 11 * THREADS] = r3 >> 24;
     }
-    if (*index + 7 * THREADS < *elements) {
-        out[*index + 7 * THREADS] = *r2 >> 24;
+    if (index + 12 * THREADS < elements) { out[index + 12 * THREADS] = r4; }
+    if (index + 13 * THREADS < elements) {
+        out[index + 13 * THREADS] = r4 >> 8;
     }
-    if (*index + 8 * THREADS < *elements) { out[*index + 8 * THREADS] = *r3; }
-    if (*index + 9 * THREADS < *elements) {
-        out[*index + 9 * THREADS] = *r3 >> 8;
+    if (index + 14 * THREADS < elements) {
+        out[index + 14 * THREADS] = r4 >> 16;
     }
-    if (*index + 10 * THREADS < *elements) {
-        out[*index + 10 * THREADS] = *r3 >> 16;
-    }
-    if (*index + 11 * THREADS < *elements) {
-        out[*index + 11 * THREADS] = *r3 >> 24;
-    }
-    if (*index + 12 * THREADS < *elements) { out[*index + 12 * THREADS] = *r4; }
-    if (*index + 13 * THREADS < *elements) {
-        out[*index + 13 * THREADS] = *r4 >> 8;
-    }
-    if (*index + 14 * THREADS < *elements) {
-        out[*index + 14 * THREADS] = *r4 >> 16;
-    }
-    if (*index + 15 * THREADS < *elements) {
-        out[*index + 15 * THREADS] = *r4 >> 24;
+    if (index + 15 * THREADS < elements) {
+        out[index + 15 * THREADS] = r4 >> 24;
     }
 }
 
-void partialWriteOut128Bytes_char(global char *out, const uint *const index,
-                                  const uint *const r1, const uint *const r2,
-                                  const uint *const r3, const uint *const r4,
-                                  const uint *const elements) {
-    if (*index < *elements) { out[*index] = (*r1) & 0x1; }
-    if (*index + THREADS < *elements) {
-        out[*index + THREADS] = (*r1 >> 1) & 0x1;
-    }
-    if (*index + 2 * THREADS < *elements) {
-        out[*index + 2 * THREADS] = (*r1 >> 2) & 0x1;
-    }
-    if (*index + 3 * THREADS < *elements) {
-        out[*index + 3 * THREADS] = (*r1 >> 3) & 0x1;
-    }
-    if (*index + 4 * THREADS < *elements) {
-        out[*index + 4 * THREADS] = (*r2) & 0x1;
+void partialWriteOut128Bytes_char(global char *out, uint index, uint r1,
+                                  uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = (r1)&0x1; }
+    if (index + THREADS < elements) { out[index + THREADS] = (r1 >> 1) & 0x1; }
+    if (index + 2 * THREADS < elements) {
+        out[index + 2 * THREADS] = (r1 >> 2) & 0x1;
     }
-    if (*index + 5 * THREADS < *elements) {
-        out[*index + 5 * THREADS] = (*r2 >> 1) & 0x1;
+    if (index + 3 * THREADS < elements) {
+        out[index + 3 * THREADS] = (r1 >> 3) & 0x1;
     }
-    if (*index + 6 * THREADS < *elements) {
-        out[*index + 6 * THREADS] = (*r2 >> 2) & 0x1;
+    if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = (r2)&0x1; }
+    if (index + 5 * THREADS < elements) {
+        out[index + 5 * THREADS] = (r2 >> 1) & 0x1;
     }
-    if (*index + 7 * THREADS < *elements) {
-        out[*index + 7 * THREADS] = (*r2 >> 3) & 0x1;
+    if (index + 6 * THREADS < elements) {
+        out[index + 6 * THREADS] = (r2 >> 2) & 0x1;
     }
-    if (*index + 8 * THREADS < *elements) {
-        out[*index + 8 * THREADS] = (*r3) & 0x1;
+    if (index + 7 * THREADS < elements) {
+        out[index + 7 * THREADS] = (r2 >> 3) & 0x1;
     }
-    if (*index + 9 * THREADS < *elements) {
-        out[*index + 9 * THREADS] = (*r3 >> 1) & 0x1;
+    if (index + 8 * THREADS < elements) { out[index + 8 * THREADS] = (r3)&0x1; }
+    if (index + 9 * THREADS < elements) {
+        out[index + 9 * THREADS] = (r3 >> 1) & 0x1;
     }
-    if (*index + 10 * THREADS < *elements) {
-        out[*index + 10 * THREADS] = (*r3 >> 2) & 0x1;
+    if (index + 10 * THREADS < elements) {
+        out[index + 10 * THREADS] = (r3 >> 2) & 0x1;
     }
-    if (*index + 11 * THREADS < *elements) {
-        out[*index + 11 * THREADS] = (*r3 >> 3) & 0x1;
+    if (index + 11 * THREADS < elements) {
+        out[index + 11 * THREADS] = (r3 >> 3) & 0x1;
     }
-    if (*index + 12 * THREADS < *elements) {
-        out[*index + 12 * THREADS] = (*r4) & 0x1;
+    if (index + 12 * THREADS < elements) {
+        out[index + 12 * THREADS] = (r4)&0x1;
     }
-    if (*index + 13 * THREADS < *elements) {
-        out[*index + 13 * THREADS] = (*r4 >> 1) & 0x1;
+    if (index + 13 * THREADS < elements) {
+        out[index + 13 * THREADS] = (r4 >> 1) & 0x1;
     }
-    if (*index + 14 * THREADS < *elements) {
-        out[*index + 14 * THREADS] = (*r4 >> 2) & 0x1;
+    if (index + 14 * THREADS < elements) {
+        out[index + 14 * THREADS] = (r4 >> 2) & 0x1;
     }
-    if (*index + 15 * THREADS < *elements) {
-        out[*index + 15 * THREADS] = (*r4 >> 3) & 0x1;
+    if (index + 15 * THREADS < elements) {
+        out[index + 15 * THREADS] = (r4 >> 3) & 0x1;
     }
 }
 
-void partialWriteOut128Bytes_short(global short *out, const uint *const index,
-                                   const uint *const r1, const uint *const r2,
-                                   const uint *const r3, const uint *const r4,
-                                   const uint *const elements) {
-    if (*index < *elements) { out[*index] = *r1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = *r1 >> 16; }
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r2; }
-    if (*index + 3 * THREADS < *elements) {
-        out[*index + 3 * THREADS] = *r2 >> 16;
-    }
-    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = *r3; }
-    if (*index + 5 * THREADS < *elements) {
-        out[*index + 5 * THREADS] = *r3 >> 16;
-    }
-    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = *r4; }
-    if (*index + 7 * THREADS < *elements) {
-        out[*index + 7 * THREADS] = *r4 >> 16;
-    }
+void partialWriteOut128Bytes_short(global short *out, uint index, uint r1,
+                                   uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + THREADS < elements) { out[index + THREADS] = r1 >> 16; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = r2; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = r2 >> 16; }
+    if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = r3; }
+    if (index + 5 * THREADS < elements) { out[index + 5 * THREADS] = r3 >> 16; }
+    if (index + 6 * THREADS < elements) { out[index + 6 * THREADS] = r4; }
+    if (index + 7 * THREADS < elements) { out[index + 7 * THREADS] = r4 >> 16; }
 }
 
-void partialWriteOut128Bytes_ushort(global ushort *out, const uint *const index,
-                                    const uint *const r1, const uint *const r2,
-                                    const uint *const r3, const uint *const r4,
-                                    const uint *const elements) {
-    if (*index < *elements) { out[*index] = *r1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = *r1 >> 16; }
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r2; }
-    if (*index + 3 * THREADS < *elements) {
-        out[*index + 3 * THREADS] = *r2 >> 16;
-    }
-    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = *r3; }
-    if (*index + 5 * THREADS < *elements) {
-        out[*index + 5 * THREADS] = *r3 >> 16;
-    }
-    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = *r4; }
-    if (*index + 7 * THREADS < *elements) {
-        out[*index + 7 * THREADS] = *r4 >> 16;
-    }
+void partialWriteOut128Bytes_ushort(global ushort *out, uint index, uint r1,
+                                    uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + THREADS < elements) { out[index + THREADS] = r1 >> 16; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = r2; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = r2 >> 16; }
+    if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = r3; }
+    if (index + 5 * THREADS < elements) { out[index + 5 * THREADS] = r3 >> 16; }
+    if (index + 6 * THREADS < elements) { out[index + 6 * THREADS] = r4; }
+    if (index + 7 * THREADS < elements) { out[index + 7 * THREADS] = r4 >> 16; }
 }
 
-void partialWriteOut128Bytes_int(global int *out, const uint *const index,
-                                 const uint *const r1, const uint *const r2,
-                                 const uint *const r3, const uint *const r4,
-                                 const uint *const elements) {
-    if (*index < *elements) { out[*index] = *r1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = *r2; }
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r3; }
-    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = *r4; }
-}
-
-void partialWriteOut128Bytes_uint(global uint *out, const uint *const index,
-                                  const uint *const r1, const uint *const r2,
-                                  const uint *const r3, const uint *const r4,
-                                  const uint *const elements) {
-    if (*index < *elements) { out[*index] = *r1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = *r2; }
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = *r3; }
-    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = *r4; }
-}
-
-void partialWriteOut128Bytes_long(global long *out, const uint *const index,
-                                  const uint *const r1, const uint *const r2,
-                                  const uint *const r3, const uint *const r4,
-                                  const uint *const elements) {
-    long c1 = *r2;
-    c1      = (c1 << 32) | *r1;
-    long c2 = *r4;
-    c2      = (c2 << 32) | *r3;
-    if (*index < *elements) { out[*index] = c1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = c2; }
-}
-
-void partialWriteOut128Bytes_ulong(global ulong *out, const uint *const index,
-                                   const uint *const r1, const uint *const r2,
-                                   const uint *const r3, const uint *const r4,
-                                   const uint *const elements) {
-    long c1 = *r2;
-    c1      = (c1 << 32) | *r1;
-    long c2 = *r4;
-    c2      = (c2 << 32) | *r3;
-    if (*index < *elements) { out[*index] = c1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = c2; }
-}
-
-void partialWriteOut128Bytes_float(global float *out, const uint *const index,
-                                   const uint *const r1, const uint *const r2,
-                                   const uint *const r3, const uint *const r4,
-                                   const uint *const elements) {
-    if (*index < *elements) { out[*index] = 1.f - getFloat(r1); }
-    if (*index + THREADS < *elements) {
-        out[*index + THREADS] = 1.f - getFloat(r2);
+void partialWriteOut128Bytes_int(global int *out, uint index, uint r1, uint r2,
+                                 uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + THREADS < elements) { out[index + THREADS] = r2; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = r3; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = r4; }
+}
+
+void partialWriteOut128Bytes_uint(global uint *out, uint index, uint r1,
+                                  uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + THREADS < elements) { out[index + THREADS] = r2; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = r3; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = r4; }
+}
+
+void partialWriteOut128Bytes_long(global long *out, uint index, uint r1,
+                                  uint r2, uint r3, uint r4, uint elements) {
+    long c1 = r2;
+    c1      = (c1 << 32) | r1;
+    long c2 = r4;
+    c2      = (c2 << 32) | r3;
+    if (index < elements) { out[index] = c1; }
+    if (index + THREADS < elements) { out[index + THREADS] = c2; }
+}
+
+void partialWriteOut128Bytes_ulong(global ulong *out, uint index, uint r1,
+                                   uint r2, uint r3, uint r4, uint elements) {
+    long c1 = r2;
+    c1      = (c1 << 32) | r1;
+    long c2 = r4;
+    c2      = (c2 << 32) | r3;
+    if (index < elements) { out[index] = c1; }
+    if (index + THREADS < elements) { out[index + THREADS] = c2; }
+}
+
+void partialWriteOut128Bytes_float(global float *out, uint index, uint r1,
+                                   uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = 1.f - getFloat01(r1); }
+    if (index + THREADS < elements) {
+        out[index + THREADS] = 1.f - getFloat01(r2);
     }
-    if (*index + 2 * THREADS < *elements) {
-        out[*index + 2 * THREADS] = 1.f - getFloat(r3);
+    if (index + 2 * THREADS < elements) {
+        out[index + 2 * THREADS] = 1.f - getFloat01(r3);
     }
-    if (*index + 3 * THREADS < *elements) {
-        out[*index + 3 * THREADS] = 1.f - getFloat(r4);
+    if (index + 3 * THREADS < elements) {
+        out[index + 3 * THREADS] = 1.f - getFloat01(r4);
     }
 }
 
 #if RAND_DIST == 1
-void boxMullerTransform(T *const out1, T *const out2, const T r1, const T r2) {
-    /*
-     * The log of a real value x where 0 < x < 1 is negative.
-     */
-#if defined(IS_APPLE)  // Because Apple is.. "special"
-    T r = sqrt((T)(-2.0) * log10(r1) * (T)log10_val);
-#else
-    T r = sqrt((T)(-2.0) * log(r1));
-#endif
-    T theta = 2 * (T)PI_VAL * (r2);
-    *out1   = r * sin(theta);
-    *out2   = r * cos(theta);
-}
-
 // BoxMuller writes without boundary checking
-void boxMullerWriteOut128Bytes_float(global float *out, const uint *const index,
-                                     const uint *const r1, const uint *const r2,
-                                     const uint *const r3,
-                                     const uint *const r4) {
+void boxMullerWriteOut128Bytes_float(global float *out, uint index, uint r1,
+                                     uint r2, uint r3, uint r4) {
     float n1, n2, n3, n4;
-    boxMullerTransform((T *)&n1, (T *)&n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform((T *)&n3, (T *)&n4, getFloat(r3), getFloat(r4));
-    out[*index]               = n1;
-    out[*index + THREADS]     = n2;
-    out[*index + 2 * THREADS] = n3;
-    out[*index + 3 * THREADS] = n4;
+    boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
+    out[index]               = n1;
+    out[index + THREADS]     = n2;
+    out[index + 2 * THREADS] = n3;
+    out[index + 3 * THREADS] = n4;
 }
 
 // BoxMuller writes with boundary checking
-void partialBoxMullerWriteOut128Bytes_float(
-    global float *out, const uint *const index, const uint *const r1,
-    const uint *const r2, const uint *const r3, const uint *const r4,
-    const uint *const elements) {
+void partialBoxMullerWriteOut128Bytes_float(global float *out, uint index,
+                                            uint r1, uint r2, uint r3, uint r4,
+                                            uint elements) {
     float n1, n2, n3, n4;
-    boxMullerTransform((T *)&n1, (T *)&n2, getFloat(r1), getFloat(r2));
-    boxMullerTransform((T *)&n3, (T *)&n4, getFloat(r3), getFloat(r4));
-    if (*index < *elements) { out[*index] = n1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = n3; }
-    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = n4; }
+    boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
+    if (index < elements) { out[index] = n1; }
+    if (index + THREADS < elements) { out[index + THREADS] = n2; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = n3; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = n4; }
 }
 #endif
 
 #ifdef USE_DOUBLE
 
 // Conversion to floats adapted from Random123
-#define UINTLMAX 0xffffffffffffffff
-#define DBL_FACTOR ((1.0) / (UINTLMAX + (1.0)))
+#define DBL_FACTOR ((1.0) / (ULONG_MAX + (1.0)))
 #define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
 
+#define SIGNED_DBL_FACTOR ((1.0) / (LONG_MAX + (1.0)))
+#define SIGNED_HALF_DBL_FACTOR ((0.5) * SIGNED_DBL_FACTOR)
+
 // Generates rationals in (0, 1]
-double getDouble(const uint *const num1, const uint *const num2) {
-    ulong num = (((ulong)*num1) << 32) | ((ulong)*num2);
-    return (num * DBL_FACTOR + HALF_DBL_FACTOR);
+double getDouble01(uint num1, uint num2) {
+    ulong num = (((ulong)num1) << 32) | ((ulong)num2);
+    return fma(num, DBL_FACTOR, HALF_DBL_FACTOR);
 }
 
-void writeOut128Bytes_double(global double *out, const uint *const index,
-                             const uint *const r1, const uint *const r2,
-                             const uint *const r3, const uint *const r4) {
-    out[*index]           = 1.0 - getDouble(r1, r2);
-    out[*index + THREADS] = 1.0 - getDouble(r3, r4);
+// Generates rationals in (-1, 1]
+float getDoubleNegative11(uint num1, uint num2) {
+    ulong num = (((ulong)num1) << 32) | ((ulong)num2);
+    return fma(num, SIGNED_DBL_FACTOR, SIGNED_HALF_DBL_FACTOR);
 }
 
-void partialWriteOut128Bytes_double(global double *out, const uint *const index,
-                                    const uint *const r1, const uint *const r2,
-                                    const uint *const r3, const uint *const r4,
-                                    const uint *const elements) {
-    if (*index < *elements) { out[*index] = 1.0 - getDouble(r1, r2); }
-    if (*index + THREADS < *elements) {
-        out[*index + THREADS] = 1.0 - getDouble(r3, r4);
+void writeOut128Bytes_double(global double *out, uint index, uint r1, uint r2,
+                             uint r3, uint r4) {
+    out[index]           = 1.0 - getDouble01(r1, r2);
+    out[index + THREADS] = 1.0 - getDouble01(r3, r4);
+}
+
+void partialWriteOut128Bytes_double(global double *out, uint index, uint r1,
+                                    uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = 1.0 - getDouble01(r1, r2); }
+    if (index + THREADS < elements) {
+        out[index + THREADS] = 1.0 - getDouble01(r3, r4);
     }
 }
 
 #if RAND_DIST == 1
-void boxMullerWriteOut128Bytes_double(
-    global double *out, const uint *const index, const uint *const r1,
-    const uint *const r2, const uint *const r3, const uint *const r4) {
+void boxMullerWriteOut128Bytes_double(global double *out, uint index, uint r1,
+                                      uint r2, uint r3, uint r4) {
     double n1, n2;
-    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
-    out[*index]           = n1;
-    out[*index + THREADS] = n2;
+    boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
+    out[index]           = n1;
+    out[index + THREADS] = n2;
 }
 
-void partialBoxMullerWriteOut128Bytes_double(
-    global double *out, const uint *const index, const uint *const r1,
-    const uint *const r2, const uint *const r3, const uint *const r4,
-    const uint *const elements) {
+void partialBoxMullerWriteOut128Bytes_double(global double *out, uint index,
+                                             uint r1, uint r2, uint r3, uint r4,
+                                             uint elements) {
     double n1, n2;
-    boxMullerTransform(&n1, &n2, getDouble(r1, r2), getDouble(r3, r4));
-    if (*index < *elements) { out[*index] = n1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
+    boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
+    if (index < elements) { out[index] = n1; }
+    if (index + THREADS < elements) { out[index + THREADS] = n2; }
 }
 #endif
 #endif
@@ -439,92 +389,97 @@ void partialBoxMullerWriteOut128Bytes_double(
 #ifdef USE_HALF
 
 // Conversion to floats adapted from Random123
-#define USHORTMAX 0xffff
-#define HALF_FACTOR ((1.0f) / (USHORTMAX + (1.0f)))
-#define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+
+// NOTE HALF_FACTOR is calculated in float to avoid conversion of 65535 to +inf
+// because of the limited range of half.
+#define HALF_FACTOR ((half)((1.f) / ((USHRT_MAX) + (1.f))))
+#define HALF_HALF_FACTOR ((0.5h) * (HALF_FACTOR))
+
+#define SIGNED_HALF_FACTOR ((1.h) / (SHRT_MAX + (1.h)))
+#define SIGNED_HALF_HALF_FACTOR ((0.5h) * SIGNED_HALF_FACTOR)
 
 // Generates rationals in (0, 1]
-half getHalf(const uint *const num, int index) {
-    float v = num[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return 1.0f - (v * HALF_FACTOR + HALF_HALF_FACTOR);
-}
-
-void writeOut128Bytes_half(global half *out, const uint *const index,
-                           const uint *const r1, const uint *const r2,
-                           const uint *const r3, const uint *const r4) {
-    out[*index]               = getHalf(r1, 0);
-    out[*index + THREADS]     = getHalf(r1, 1);
-    out[*index + 2 * THREADS] = getHalf(r2, 0);
-    out[*index + 3 * THREADS] = getHalf(r2, 1);
-    out[*index + 4 * THREADS] = getHalf(r3, 0);
-    out[*index + 5 * THREADS] = getHalf(r3, 1);
-    out[*index + 6 * THREADS] = getHalf(r4, 0);
-    out[*index + 7 * THREADS] = getHalf(r4, 1);
-}
-
-void partialWriteOut128Bytes_half(global half *out, const uint *const index,
-                                  const uint *const r1, const uint *const r2,
-                                  const uint *const r3, const uint *const r4,
-                                  const uint *const elements) {
-    if (*index < *elements) { out[*index] = getHalf(r1, 0); }
-    if (*index + THREADS < *elements) {
-        out[*index + THREADS] = getHalf(r1, 1);
+half getHalf01(uint num, uint index) {
+    half v = num >> (16U * (index & 1U)) & 0x0000ffff;
+    return fma(v, HALF_FACTOR, HALF_HALF_FACTOR);
+}
+
+// Generates rationals in (-1, 1]
+half getHalfNegative11(uint num, uint index) {
+    half v = num >> (16U * (index & 1U)) & 0x0000ffff;
+    return fma(v, SIGNED_HALF_FACTOR, SIGNED_HALF_HALF_FACTOR);
+}
+
+void writeOut128Bytes_half(global half *out, uint index, uint r1, uint r2,
+                           uint r3, uint r4) {
+    out[index]               = 1.h - getHalf01(r1, 0);
+    out[index + THREADS]     = 1.h - getHalf01(r1, 1);
+    out[index + 2 * THREADS] = 1.h - getHalf01(r2, 0);
+    out[index + 3 * THREADS] = 1.h - getHalf01(r2, 1);
+    out[index + 4 * THREADS] = 1.h - getHalf01(r3, 0);
+    out[index + 5 * THREADS] = 1.h - getHalf01(r3, 1);
+    out[index + 6 * THREADS] = 1.h - getHalf01(r4, 0);
+    out[index + 7 * THREADS] = 1.h - getHalf01(r4, 1);
+}
+
+void partialWriteOut128Bytes_half(global half *out, uint index, uint r1,
+                                  uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = 1.h - getHalf01(r1, 0); }
+    if (index + THREADS < elements) {
+        out[index + THREADS] = 1.h - getHalf01(r1, 1);
     }
-    if (*index + 2 * THREADS < *elements) {
-        out[*index + 2 * THREADS] = getHalf(r2, 0);
+    if (index + 2 * THREADS < elements) {
+        out[index + 2 * THREADS] = 1.h - getHalf01(r2, 0);
     }
-    if (*index + 3 * THREADS < *elements) {
-        out[*index + 3 * THREADS] = getHalf(r2, 1);
+    if (index + 3 * THREADS < elements) {
+        out[index + 3 * THREADS] = 1.h - getHalf01(r2, 1);
     }
-    if (*index + 4 * THREADS < *elements) {
-        out[*index + 4 * THREADS] = getHalf(r3, 0);
+    if (index + 4 * THREADS < elements) {
+        out[index + 4 * THREADS] = 1.h - getHalf01(r3, 0);
     }
-    if (*index + 5 * THREADS < *elements) {
-        out[*index + 5 * THREADS] = getHalf(r3, 1);
+    if (index + 5 * THREADS < elements) {
+        out[index + 5 * THREADS] = 1.h - getHalf01(r3, 1);
     }
-    if (*index + 6 * THREADS < *elements) {
-        out[*index + 6 * THREADS] = getHalf(r4, 0);
+    if (index + 6 * THREADS < elements) {
+        out[index + 6 * THREADS] = 1.h - getHalf01(r4, 0);
     }
-    if (*index + 7 * THREADS < *elements) {
-        out[*index + 7 * THREADS] = getHalf(r4, 1);
+    if (index + 7 * THREADS < elements) {
+        out[index + 7 * THREADS] = 1.h - getHalf01(r4, 1);
     }
 }
 
 #if RAND_DIST == 1
-void boxMullerWriteOut128Bytes_half(global half *out, const uint *const index,
-                                    const uint *const r1, const uint *const r2,
-                                    const uint *const r3,
-                                    const uint *const r4) {
-    boxMullerTransform(&out[*index], &out[*index + THREADS], getHalf(r1, 0),
-                       getHalf(r1, 1));
-    boxMullerTransform(&out[*index + 2 * THREADS], &out[*index + 3 * THREADS],
-                       getHalf(r2, 0), getHalf(r2, 1));
-    boxMullerTransform(&out[*index + 4 * THREADS], &out[*index + 5 * THREADS],
-                       getHalf(r3, 0), getHalf(r3, 1));
-    boxMullerTransform(&out[*index + 6 * THREADS], &out[*index + 7 * THREADS],
-                       getHalf(r4, 0), getHalf(r4, 1));
-}
-
-void partialBoxMullerWriteOut128Bytes_half(
-    global half *out, const uint *const index, const uint *const r1,
-    const uint *const r2, const uint *const r3, const uint *const r4,
-    const uint *const elements) {
+void boxMullerWriteOut128Bytes_half(global half *out, uint index, uint r1,
+                                    uint r2, uint r3, uint r4) {
+    boxMullerTransform(&out[index], &out[index + THREADS],
+                       getHalfNegative11(r1, 0), getHalf01(r1, 1));
+    boxMullerTransform(&out[index + 2 * THREADS], &out[index + 3 * THREADS],
+                       getHalfNegative11(r2, 0), getHalf01(r2, 1));
+    boxMullerTransform(&out[index + 4 * THREADS], &out[index + 5 * THREADS],
+                       getHalfNegative11(r3, 0), getHalf01(r3, 1));
+    boxMullerTransform(&out[index + 6 * THREADS], &out[index + 7 * THREADS],
+                       getHalfNegative11(r4, 0), getHalf01(r4, 1));
+}
+
+void partialBoxMullerWriteOut128Bytes_half(global half *out, uint index,
+                                           uint r1, uint r2, uint r3, uint r4,
+                                           uint elements) {
     half n1, n2;
-    boxMullerTransform(&n1, &n2, getHalf(r1, 0), getHalf(r1, 1));
-    if (*index < *elements) { out[*index] = n1; }
-    if (*index + THREADS < *elements) { out[*index + THREADS] = n2; }
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r1, 0), getHalf01(r1, 1));
+    if (index < elements) { out[index] = n1; }
+    if (index + THREADS < elements) { out[index + THREADS] = n2; }
 
-    boxMullerTransform(&n1, &n2, getHalf(r2, 0), getHalf(r2, 1));
-    if (*index + 2 * THREADS < *elements) { out[*index + 2 * THREADS] = n1; }
-    if (*index + 3 * THREADS < *elements) { out[*index + 3 * THREADS] = n2; }
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r2, 0), getHalf01(r2, 1));
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = n1; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = n2; }
 
-    boxMullerTransform(&n1, &n2, getHalf(r3, 0), getHalf(r3, 1));
-    if (*index + 4 * THREADS < *elements) { out[*index + 4 * THREADS] = n1; }
-    if (*index + 5 * THREADS < *elements) { out[*index + 5 * THREADS] = n2; }
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r3, 0), getHalf01(r3, 1));
+    if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = n1; }
+    if (index + 5 * THREADS < elements) { out[index + 5 * THREADS] = n2; }
 
-    boxMullerTransform(&n1, &n2, getHalf(r4, 0), getHalf(r4, 1));
-    if (*index + 6 * THREADS < *elements) { out[*index + 6 * THREADS] = n1; }
-    if (*index + 7 * THREADS < *elements) { out[*index + 7 * THREADS] = n2; }
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r4, 0), getHalf01(r4, 1));
+    if (index + 6 * THREADS < elements) { out[index + 6 * THREADS] = n1; }
+    if (index + 7 * THREADS < elements) { out[index + 7 * THREADS] = n2; }
 }
 #endif
 #endif
diff --git a/test/rng_quality.cpp b/test/rng_quality.cpp
index 915c81f7ee..dab40e656f 100644
--- a/test/rng_quality.cpp
+++ b/test/rng_quality.cpp
@@ -19,21 +19,11 @@ class RandomEngine : public ::testing::Test {
     virtual void SetUp() {}
 };
 
-template<typename T>
-class RandomEngineSeed : public ::testing::Test {
-   public:
-    virtual void SetUp() {}
-};
-
 // create a list of types to be tested
 typedef ::testing::Types<float, double> TestTypesEngine;
 // register the type list
 TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
 
-typedef ::testing::Types<unsigned> TestTypesEngineSeed;
-// register the type list
-TYPED_TEST_CASE(RandomEngineSeed, TestTypesEngineSeed);
-
 template<typename T>
 void testRandomEnginePeriod(randomEngineType type) {
     SUPPORTED_TYPE_CHECK(T);
@@ -65,19 +55,31 @@ TYPED_TEST(RandomEngine, mersenneRandomEnginePeriod) {
 }
 
 template<typename T>
-double chi2_statistic(array input, array expected) {
-    expected *=
-        convert<float>(sum<T>(input)) / convert<float>(sum<T>(expected));
+double chi2_statistic(array input, array expected, bool print = false) {
+    expected *= sum<T>(input) / sum<T>(expected);
     array diff = input - expected;
-    return convert<float>(sum<T>((diff * diff) / expected));
+
+    double chi2 = sum<T>((diff * diff) / expected);
+    if (print && chi2 > 10000) {
+        array legend = af::seq(input.elements());
+        legend -= (input.elements() / 2.);
+        legend *= (14. / input.elements());
+
+        af_print(
+            join(1, legend, expected.as(f32), input.as(f32), diff.as(f32)));
+    }
+
+    return chi2;
 }
 
 template<>
-double chi2_statistic<half_float::half>(array input, array expected) {
+double chi2_statistic<half_float::half>(array input, array expected,
+                                        bool print) {
     expected *= convert<float>(sum<float>(input)) /
                 convert<float>(sum<float>(expected));
-    array diff = input - expected;
-    return convert<float>(sum<float>((diff * diff) / expected));
+    array diff  = input - expected;
+    double chi2 = convert<float>(sum<float>((diff * diff) / expected));
+    return chi2;
 }
 
 template<typename T>
@@ -86,24 +88,26 @@ void testRandomEngineUniformChi2(randomEngineType type) {
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem  = 256 * 1024 * 1024;
-    int steps = 32;
+    int steps = 256;
     int bins  = 100;
 
-    array total_hist = constant(0.0, bins, ty);
-    array expected   = constant(1.0 / bins, bins, ty);
+    array total_hist = constant(0.0, bins, f32);
+    array expected   = constant(1.0 / bins, bins, f32);
 
     randomEngine r(type, 0);
 
     // R> qchisq(c(5e-6, 1 - 5e-6), 99)
     // [1]  48.68125 173.87456
-    double lower(48.68125);
-    double upper(173.87456);
+    float lower(48.68125);
+    float upper(173.87456);
 
     bool prev_step  = true;
     bool prev_total = true;
     for (int i = 0; i < steps; ++i) {
-        array step_hist  = histogram(randu(elem, ty, r), bins, 0.0, 1.0);
-        double step_chi2 = chi2_statistic<T>(step_hist, expected);
+        array rn_numbers = randu(elem, ty, r);
+        array step_hist  = histogram(rn_numbers, bins, 0.0, 1.0);
+        step_hist        = step_hist.as(f32);
+        float step_chi2  = chi2_statistic<float>(step_hist, expected);
         if (!prev_step) {
             EXPECT_GT(step_chi2, lower) << "at step: " << i;
             EXPECT_LT(step_chi2, upper) << "at step: " << i;
@@ -111,7 +115,7 @@ void testRandomEngineUniformChi2(randomEngineType type) {
         prev_step = step_chi2 > lower && step_chi2 < upper;
 
         total_hist += step_hist;
-        double total_chi2 = chi2_statistic<T>(total_hist, expected);
+        float total_chi2 = chi2_statistic<float>(total_hist, expected);
         if (!prev_total) {
             EXPECT_GT(total_chi2, lower) << "at step: " << i;
             EXPECT_LT(total_chi2, upper) << "at step: " << i;
@@ -120,7 +124,6 @@ void testRandomEngineUniformChi2(randomEngineType type) {
     }
 }
 
-#ifndef AF_CPU
 TYPED_TEST(RandomEngine, philoxRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
 }
@@ -132,4 +135,106 @@ TYPED_TEST(RandomEngine, threefryRandomEngineUniformChi2) {
 TYPED_TEST(RandomEngine, mersenneRandomEngineUniformChi2) {
     testRandomEngineUniformChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
 }
-#endif
+
+// should be used only for x <= 5 (roughly)
+
+array cnd(array x) { return 0.5 * erfc(-x * sqrt(0.5)); }
+
+template<typename T>
+bool testRandomEngineNormalChi2(randomEngineType type)
+
+{
+    af::dtype ty = (af::dtype)af::dtype_traits<T>::af_type;
+
+    int elem  = 256 * 1024 * 1024;
+    int steps = 64;  // 256 * 32;
+    int bins  = 100;
+
+    T lower_edge(-7.0);
+    T upper_edge(7.0);
+
+    array total_hist = af::constant(0.0, 2 * bins, f32);
+    array edges      = af::seq(bins + 1) / bins * lower_edge;
+    array expected   = -af::diff1(cnd(edges));
+
+    expected =
+        af::join(0, expected(af::seq(bins - 1, 0, -1)), expected).as(f32);
+    // af_print(expected);
+
+    af::randomEngine r(type, 0);
+
+    // R> qchisq(c(5e-6, 1 - 5e-6), 197)
+
+    // [1] 121.3197 297.2989
+    float lower(121.3197);
+    float upper(297.2989);
+
+    // R> qchisq(c(5e-6, 1 - 5e-6), 199)
+    // [1] 121.3197 297.2989
+    // float lower = 118.1094;
+    // float upper = 308.6010;
+
+    bool prev_step  = true;
+    bool prev_total = true;
+
+    af::setSeed(0x76fa214467690e3c);
+
+    // std::cout << std::setw(4) << "step" << std::setw(7) << "chi2_i"
+    //           << std::setw(7) << "chi2_t" << std::setprecision(2) <<
+    //           std::fixed
+    //           << std::endl;
+
+    for (int i = 0; i < steps; ++i) {
+        array rn_numbers = randn(elem, ty, r);
+        array step_hist =
+            af::histogram(rn_numbers, 2 * bins, lower_edge, upper_edge);
+        step_hist = step_hist.as(f32);
+
+        float step_chi2 = chi2_statistic<float>(step_hist, expected);
+
+        // if (step_chi2 > 10000) af_print(rn_numbers);
+        // std::cout << std::setprecision(2) << std::fixed << std::setw(4) << i
+        //           << std::setw(9) << step_chi2;
+
+        bool step = step_chi2 > lower && step_chi2 < upper;
+
+        if (!prev_step) {
+            EXPECT_GT(step_chi2, lower) << "at step " << i;
+            EXPECT_LT(step_chi2, upper) << "at step: " << i;
+        }
+
+        // if (!(step || prev_step)) break;
+
+        prev_step = step;
+        total_hist += step_hist;
+
+        float total_chi2 = chi2_statistic<float>(total_hist, expected);
+
+        // std::cout << std::setw(9) << total_chi2 << std::endl;
+
+        bool total = total_chi2 > lower && total_chi2 < upper;
+        if (!prev_total) {
+            EXPECT_GT(total_chi2, lower) << "at step " << i;
+            EXPECT_LT(total_chi2, upper) << "at step " << i;
+        }
+
+        // ASSERT_TRUE(total || prev_step);
+        // if (!(total || prev_total)) break;
+
+        prev_total = total;
+    }
+
+    return true;
+}
+
+TYPED_TEST(RandomEngine, philoxRandomEngineNormalChi2) {
+    testRandomEngineNormalChi2<TypeParam>(AF_RANDOM_ENGINE_PHILOX_4X32_10);
+}
+
+TYPED_TEST(RandomEngine, threefryRandomEngineNormalChi2) {
+    testRandomEngineNormalChi2<TypeParam>(AF_RANDOM_ENGINE_THREEFRY_2X32_16);
+}
+
+TYPED_TEST(RandomEngine, DISABLED_mersenneRandomEngineNormalChi2) {
+    testRandomEngineNormalChi2<TypeParam>(AF_RANDOM_ENGINE_MERSENNE_GP11213);
+}

From fe0c8d56a91fa7807d5f9fab2df9237d031d9710 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Aug 2020 19:20:12 -0400
Subject: [PATCH 2055/2677] Fix problems in RNG for older compute architectures
 with fp16

---
 src/backend/common/half.hpp                   |  24 +-
 src/backend/cpu/kernel/random_engine.hpp      |  34 ++-
 src/backend/cuda/kernel/random_engine.hpp     | 207 ++++++++++++------
 src/backend/cuda/math.hpp                     |   4 +-
 .../opencl/kernel/random_engine_write.cl      |  11 +-
 test/convolve.cpp                             |   2 +-
 test/rng_quality.cpp                          |  29 ++-
 7 files changed, 197 insertions(+), 114 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index edd37ded24..885664798e 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -822,7 +822,7 @@ AF_CONSTEXPR __DH__ static inline bool isinf(half val) noexcept;
 AF_CONSTEXPR __DH__ static inline bool isnan(common::half val) noexcept;
 
 class alignas(2) half {
-    native_half_t data_ = 0;
+    native_half_t data_ = native_half_t();
 
 #if !defined(NVCC) && !defined(__CUDACC_RTC__)
     // NVCC on OSX performs a weird transformation where it removes the std::
@@ -881,11 +881,19 @@ class alignas(2) half {
         return *this;
     }
 
-#if defined(__CUDA_ARCH__)
-    AF_CONSTEXPR __DH__ explicit half(const __half& value) noexcept
-        : data_(value) {}
-    AF_CONSTEXPR __DH__ half& operator=(__half&& value) noexcept {
-        data_ = value;
+#if defined(NVCC) || defined(__CUDACC_RTC__)
+    AF_CONSTEXPR __DH__ explicit half(__half value) noexcept
+#ifdef __CUDA_ARCH__
+        : data_(value) {
+    }
+#else
+        : data_(*reinterpret_cast<native_half_t*>(&value)) {
+    }
+#endif
+    AF_CONSTEXPR __DH__ half& operator=(__half value) noexcept {
+        // NOTE Assignment to ushort from __half only works with device code.
+        // using memcpy instead
+        data_ = *reinterpret_cast<native_half_t*>(&value);
         return *this;
     }
 #endif
@@ -988,7 +996,11 @@ class alignas(2) half {
 
     AF_CONSTEXPR static half infinity() {
         half out;
+#ifdef __CUDA_ARCH__
+        out.data_ = __half_raw{0x7C00};
+#else
         out.data_ = 0x7C00;
+#endif
         return out;
     }
 };
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 8549bcc01a..29484e26da 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -32,22 +32,9 @@ static const double PI_VAL =
     3.1415926535897932384626433832795028841971693993751058209749445923078164;
 
 // Conversion to half adapted from Random123
-#define HALF_FACTOR ((1.0f) / (std::numeric_limits<ushort>::max() + (1.0f)))
-#define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
-
-// Conversion to half adapted from Random123
-#define SIGNED_HALF_FACTOR \
-    ((1.0f) / (std::numeric_limits<short>::max() + (1.0f)))
-#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
-
-#define DBL_FACTOR \
-    ((1.0) / (std::numeric_limits<unsigned long long>::max() + (1.0)))
-#define HALF_DBL_FACTOR ((0.5) * DBL_FACTOR)
-
-// Conversion to floats adapted from Random123
-#define SIGNED_DBL_FACTOR \
-    ((1.0) / (std::numeric_limits<long long>::max() + (1.0)))
-#define SIGNED_HALF_DBL_FACTOR ((0.5) * SIGNED_DBL_FACTOR)
+constexpr float unsigned_half_factor =
+    ((1.0f) / (std::numeric_limits<ushort>::max() + (1.0f)));
+constexpr float unsigned_half_half_factor((0.5f) * unsigned_half_factor);
 
 template<typename T>
 T transform(uint *val, uint index);
@@ -85,14 +72,19 @@ static float getFloatNegative11(uint *val, uint index) {
 // Generates rationals in [0, 1)
 common::half getHalf01(uint *val, uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return static_cast<common::half>(fmaf(v, HALF_FACTOR, HALF_HALF_FACTOR));
+    return static_cast<common::half>(
+        fmaf(v, unsigned_half_factor, unsigned_half_half_factor));
 }
 
 // Generates rationals in (-1, 1]
 static common::half getHalfNegative11(uint *val, uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return static_cast<common::half>(
-        fmaf(v, SIGNED_HALF_FACTOR, SIGNED_HALF_HALF_FACTOR));
+    // Conversion to half adapted from Random123
+    constexpr float factor =
+        ((1.0f) / (std::numeric_limits<short>::max() + (1.0f)));
+    constexpr float half_factor = ((0.5f) * factor);
+
+    return static_cast<common::half>(fmaf(v, factor, half_factor));
 }
 
 // Generates rationals in [0, 1)
@@ -160,8 +152,8 @@ double transform<double>(uint *val, uint index) {
 template<>
 common::half transform<common::half>(uint *val, uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return static_cast<common::half>(1.f -
-                                     fmaf(v, HALF_FACTOR, HALF_HALF_FACTOR));
+    return static_cast<common::half>(
+        1.f - fmaf(v, unsigned_half_factor, unsigned_half_half_factor));
 }
 
 // Generates rationals in [-1, 1)
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index eb343271b9..0ef218ad93 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -23,8 +23,26 @@
 
 namespace cuda {
 namespace kernel {
-// Utils
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
+__device__ __half hlog(const __half a) {
+    return __float2half(logf(__half2float(a)));
+}
+__device__ __half hsqrt(const __half a) {
+    return __float2half(sqrtf(__half2float(a)));
+}
+__device__ __half hsin(const __half a) {
+    return __float2half(sinf(__half2float(a)));
+}
+__device__ __half hcos(const __half a) {
+    return __float2half(cosf(__half2float(a)));
+}
+__device__ __half __hfma(const __half a, __half b, __half c) {
+    return __float2half(
+        fmaf(__half2float(a), __half2float(b), __half2float(c)));
+}
+#endif
 
+// Utils
 static const int THREADS = 256;
 #define PI_VAL \
     3.1415926535897932384626433832795028841971693993751058209749445923078164
@@ -37,8 +55,8 @@ static const int THREADS = 256;
 // above. This is done so that we can avoid unnecessary computations because the
 // __half datatype is not a constexprable type. This prevents the compiler from
 // peforming these operations at compile time.
-#define HALF_FACTOR __ushort_as_half(256)
-#define HALF_HALF_FACTOR __ushort_as_half(128)
+#define HALF_FACTOR __ushort_as_half(0x100u)
+#define HALF_HALF_FACTOR __ushort_as_half(0x80)
 
 // Conversion to half adapted from Random123
 //#define SIGNED_HALF_FACTOR                                \
@@ -49,87 +67,112 @@ static const int THREADS = 256;
 // above. This is done so that we can avoid unnecessary computations because the
 // __half datatype is not a constexprable type. This prevents the compiler from
 // peforming these operations at compile time
-#define SIGNED_HALF_FACTOR __ushort_as_half(512)
-#define SIGNED_HALF_HALF_FACTOR __ushort_as_half(256)
+#define SIGNED_HALF_FACTOR __ushort_as_half(0x200u)
+#define SIGNED_HALF_HALF_FACTOR __ushort_as_half(0x100u)
 
-// Conversion to floats adapted from Random123
-constexpr float FLT_FACTOR =
-    ((1.0f) /
-     (static_cast<float>(std::numeric_limits<unsigned int>::max()) + (1.0f)));
-
-constexpr float HALF_FLT_FACTOR = ((0.5f) * FLT_FACTOR);
-
-// Conversion to floats adapted from Random123
-constexpr float SIGNED_FLT_FACTOR =
-    ((1.0) / (std::numeric_limits<int>::max() + (1.0)));
-constexpr float SIGNED_HALF_FLT_FACTOR = ((0.5f) * SIGNED_FLT_FACTOR);
-
-constexpr double DBL_FACTOR =
-    ((1.0) / (std::numeric_limits<unsigned long long>::max() +
-              static_cast<long double>(1.0l)));
-constexpr double HALF_DBL_FACTOR((0.5) * DBL_FACTOR);
-
-// Conversion to floats adapted from Random123
-constexpr double SIGNED_DBL_FACTOR =
-    ((1.0l) / (std::numeric_limits<long long>::max() + (1.0l)));
-constexpr double SIGNED_HALF_DBL_FACTOR = ((0.5) * SIGNED_DBL_FACTOR);
+/// This is the largest integer representable by fp16. We need to
+/// make sure that the value converted from ushort is smaller than this
+/// value to avoid generating infinity
+constexpr ushort max_int_before_infinity = 65504;
 
 // Generates rationals in (0, 1]
 __device__ static __half oneMinusGetHalf01(uint num) {
-    ushort v = num;
-    return __ushort_as_half(0x3c00) -
-           __hfma(static_cast<__half>(v), HALF_FACTOR, HALF_HALF_FACTOR);
+    // convert to ushort before the min operation
+    ushort v = min(max_int_before_infinity, ushort(num));
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
+    return (1.0f - __half2float(__hfma(__ushort2half_rn(v), HALF_FACTOR,
+                                       HALF_HALF_FACTOR)));
+#else
+    __half out = __ushort_as_half(0x3c00u) /*1.0h*/ -
+                 __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
+    if (__hisinf(out)) printf("val: %d ushort: %d\n", num, v);
+    return out;
+#endif
 }
 
 // Generates rationals in (0, 1]
 __device__ static __half getHalf01(uint num) {
-    ushort v = num;
-    return __hfma(static_cast<__half>(v), HALF_FACTOR, HALF_HALF_FACTOR);
+    // convert to ushort before the min operation
+    ushort v = min(max_int_before_infinity, ushort(num));
+    return __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
 }
 
 // Generates rationals in (-1, 1]
 __device__ static __half getHalfNegative11(uint num) {
-    ushort v = num;
-    return __hfma(static_cast<__half>(v), SIGNED_HALF_FACTOR,
+    // convert to ushort before the min operation
+    ushort v = min(max_int_before_infinity, ushort(num));
+    return __hfma(__ushort2half_rn(v), SIGNED_HALF_FACTOR,
                   SIGNED_HALF_HALF_FACTOR);
 }
 
 // Generates rationals in (0, 1]
 __device__ static float getFloat01(uint num) {
-    return fmaf(static_cast<float>(num), FLT_FACTOR, HALF_FLT_FACTOR);
+    // Conversion to floats adapted from Random123
+    constexpr float factor =
+        ((1.0f) /
+         (static_cast<float>(std::numeric_limits<unsigned int>::max()) +
+          (1.0f)));
+    constexpr float half_factor = ((0.5f) * factor);
+
+    return fmaf(static_cast<float>(num), factor, half_factor);
 }
 
 // Generates rationals in (-1, 1]
 __device__ static float getFloatNegative11(uint num) {
-    return fmaf(static_cast<float>(num), SIGNED_FLT_FACTOR,
-                SIGNED_HALF_FLT_FACTOR);
+    // Conversion to floats adapted from Random123
+    constexpr float factor =
+        ((1.0) /
+         (static_cast<double>(std::numeric_limits<int>::max()) + (1.0)));
+    constexpr float half_factor = ((0.5f) * factor);
+
+    return fmaf(static_cast<float>(num), factor, half_factor);
 }
 
 // Generates rationals in (0, 1]
-__device__ static float getDouble01(uint num1, uint num2) {
+__device__ static double getDouble01(uint num1, uint num2) {
     uint64_t n1 = num1;
     uint64_t n2 = num2;
     n1 <<= 32;
     uint64_t num = n1 | n2;
-    return fma(static_cast<double>(num), DBL_FACTOR, HALF_DBL_FACTOR);
+#pragma diag_suppress 3245
+    constexpr double factor =
+        ((1.0) / (std::numeric_limits<unsigned long long>::max() +
+                  static_cast<long double>(1.0l)));
+    constexpr double half_factor((0.5) * factor);
+#pragma diag_default 3245
+
+    return fma(static_cast<double>(num), factor, half_factor);
 }
 
+// Conversion to doubles adapted from Random123
+constexpr double signed_factor =
+    ((1.0l) / (std::numeric_limits<long long>::max() + (1.0l)));
+constexpr double half_factor = ((0.5) * signed_factor);
+
 // Generates rationals in (-1, 1]
-__device__ static float getDoubleNegative11(uint num1, uint num2) {
+__device__ static double getDoubleNegative11(uint num1, uint num2) {
     uint32_t arr[2] = {num2, num1};
     uint64_t num;
+
     memcpy(&num, arr, sizeof(uint64_t));
-    return fma(static_cast<double>(num), SIGNED_DBL_FACTOR,
-               SIGNED_HALF_DBL_FACTOR);
+    return fma(static_cast<double>(num), signed_factor, half_factor);
 }
 
 namespace {
 
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
-__device__ __half hlog(const __half a) { return 0; }
-__device__ __half hsqrt(const __half a) { return 0; }
-__device__ __half hsin(const __half a) { return 0; }
-__device__ __half hcos(const __half a) { return 0; }
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+#define HALF_MATH_FUNC(OP, HALF_OP)    \
+    template<>                         \
+    __device__ __half OP(__half val) { \
+        return ::HALF_OP(val);         \
+    }
+#else
+#define HALF_MATH_FUNC(OP, HALF_OP)     \
+    template<>                          \
+    __device__ __half OP(__half val) {  \
+        float fval = __half2float(val); \
+        return __float2half(OP(fval));  \
+    }
 #endif
 
 #define MATH_FUNC(OP, DOUBLE_OP, FLOAT_OP, HALF_OP) \
@@ -141,12 +184,9 @@ __device__ __half hcos(const __half a) { return 0; }
     }                                               \
     template<>                                      \
     __device__ float OP(float val) {                \
-        return FLOAT_OP(val);                       \
+        return ::FLOAT_OP(val);                     \
     }                                               \
-    template<>                                      \
-    __device__ __half OP(__half val) {              \
-        return HALF_OP(val);                        \
-    }
+    HALF_MATH_FUNC(OP, HALF_OP)
 
 MATH_FUNC(log, log, logf, hlog)
 MATH_FUNC(sqrt, sqrt, sqrtf, hsqrt)
@@ -160,14 +200,24 @@ template<>
 __device__ void sincos(double val, double *sptr, double *cptr) {
     ::sincos(val, sptr, cptr);
 }
+
 template<>
 __device__ void sincos(float val, float *sptr, float *cptr) {
     sincosf(val, sptr, cptr);
 }
+
 template<>
 __device__ void sincos(__half val, __half *sptr, __half *cptr) {
-    *sptr = hsin(val);
-    *cptr = hcos(val);
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+    *sptr = sin(val);
+    *cptr = cos(val);
+#else
+    float s, c;
+    float fval = __half2float(val);
+    sincos(fval, &s, &c);
+    *sptr      = __float2half(s);
+    *cptr      = __float2half(c);
+#endif
 }
 
 template<typename T>
@@ -185,23 +235,27 @@ template<>
 __device__ void sincospi(__half val, __half *sptr, __half *cptr) {
     // CUDA cannot make __half into a constexpr as of CUDA 11 so we are
     // converting this offline
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
     const __half pi_val = __ushort_as_half(0x4248);  // 0x4248 == 3.14062h
-    *sptr               = hsin(val) * pi_val;
-    *cptr               = hcos(val) * pi_val;
+    val *= pi_val;
+    *sptr = sin(val);
+    *cptr = cos(val);
+#else
+    float fval = __half2float(val);
+    float s, c;
+    sincospi(fval, &s, &c);
+    *sptr = __float2half(s);
+    *cptr = __float2half(c);
+#endif
 }
 
 }  // namespace
 
 template<typename T>
-constexpr __device__ T neg_two() {
+constexpr T neg_two() {
     return -2.0;
 }
 
-template<>
-__device__ __half neg_two() {
-    return __ushort_as_half(0xc000);  // 0xc000 == -2.h
-}
-
 template<typename T>
 constexpr __device__ T two_pi() {
     return 2.0 * PI_VAL;
@@ -223,6 +277,19 @@ __device__ static void boxMullerTransform(Td *const out1, Td *const out2,
     *out1 = static_cast<Td>(r * s);
     *out2 = static_cast<Td>(r * c);
 }
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
+template<>
+__device__ void boxMullerTransform<common::half, __half>(
+    common::half *const out1, common::half *const out2, const __half &r1,
+    const __half &r2) {
+    float o1, o2;
+    float fr1 = __half2float(r1);
+    float fr2 = __half2float(r2);
+    boxMullerTransform(&o1, &o2, fr1, fr2);
+    *out1 = o1;
+    *out2 = o2;
+}
+#endif
 
 // Writes without boundary checking
 __device__ static void writeOut128Bytes(uchar *out, const uint &index,
@@ -691,27 +758,27 @@ __device__ static void partialWriteOut128Bytes(common::half *out,
                                                const uint &r1, const uint &r2,
                                                const uint &r3, const uint &r4,
                                                const uint &elements) {
-    if (index < elements) { out[index] = getHalf01(r1); }
+    if (index < elements) { out[index] = oneMinusGetHalf01(r1); }
     if (index + blockDim.x < elements) {
-        out[index + blockDim.x] = getHalf01(r1 >> 16);
+        out[index + blockDim.x] = oneMinusGetHalf01(r1 >> 16);
     }
     if (index + 2 * blockDim.x < elements) {
-        out[index + 2 * blockDim.x] = getHalf01(r2);
+        out[index + 2 * blockDim.x] = oneMinusGetHalf01(r2);
     }
     if (index + 3 * blockDim.x < elements) {
-        out[index + 3 * blockDim.x] = getHalf01(r2 >> 16);
+        out[index + 3 * blockDim.x] = oneMinusGetHalf01(r2 >> 16);
     }
     if (index + 4 * blockDim.x < elements) {
-        out[index + 4 * blockDim.x] = getHalf01(r3);
+        out[index + 4 * blockDim.x] = oneMinusGetHalf01(r3);
     }
     if (index + 5 * blockDim.x < elements) {
-        out[index + 5 * blockDim.x] = getHalf01(r3 >> 16);
+        out[index + 5 * blockDim.x] = oneMinusGetHalf01(r3 >> 16);
     }
     if (index + 6 * blockDim.x < elements) {
-        out[index + 6 * blockDim.x] = getHalf01(r4);
+        out[index + 6 * blockDim.x] = oneMinusGetHalf01(r4);
     }
     if (index + 7 * blockDim.x < elements) {
-        out[index + 7 * blockDim.x] = getHalf01(r4 >> 16);
+        out[index + 7 * blockDim.x] = oneMinusGetHalf01(r4 >> 16);
     }
 }
 
@@ -719,7 +786,7 @@ __device__ static void partialWriteOut128Bytes(common::half *out,
 __device__ static void partialBoxMullerWriteOut128Bytes(
     common::half *out, const uint &index, const uint &r1, const uint &r2,
     const uint &r3, const uint &r4, const uint &elements) {
-    __half n[8];
+    common::half n[8];
     boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
                        getHalf01(r1 >> 16));
     boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 5f01395997..7936ae8d57 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -74,7 +74,7 @@ inline __DH__ __half min<__half>(__half lhs, __half rhs) {
 #if __CUDA_ARCH__ >= 530
     return __hlt(lhs, rhs) ? lhs : rhs;
 #else
-    return (float)lhs < (float)rhs ? lhs : rhs;
+    return __half2float(lhs) < __half2float(rhs) ? lhs : rhs;
 #endif
 }
 
@@ -83,7 +83,7 @@ inline __DH__ __half max<__half>(__half lhs, __half rhs) {
 #if __CUDA_ARCH__ >= 530
     return __hgt(lhs, rhs) ? lhs : rhs;
 #else
-    return (float)lhs > (float)rhs ? lhs : rhs;
+    return __half2float(lhs) > __half2float(rhs) ? lhs : rhs;
 #endif
 }
 
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 1ccbd1c1a5..e61610b24a 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -398,15 +398,22 @@ void partialBoxMullerWriteOut128Bytes_double(global double *out, uint index,
 #define SIGNED_HALF_FACTOR ((1.h) / (SHRT_MAX + (1.h)))
 #define SIGNED_HALF_HALF_FACTOR ((0.5h) * SIGNED_HALF_FACTOR)
 
+/// This is the largest integer representable by fp16. We need to
+/// make sure that the value converted from ushort is smaller than this
+/// value to avoid generating infinity
+#define MAX_INT_BEFORE_INFINITY (ushort)65504u
+
 // Generates rationals in (0, 1]
 half getHalf01(uint num, uint index) {
-    half v = num >> (16U * (index & 1U)) & 0x0000ffff;
+    half v = (half)min(MAX_INT_BEFORE_INFINITY,
+                       (ushort)(num >> (16U * (index & 1U)) & 0x0000ffff));
     return fma(v, HALF_FACTOR, HALF_HALF_FACTOR);
 }
 
 // Generates rationals in (-1, 1]
 half getHalfNegative11(uint num, uint index) {
-    half v = num >> (16U * (index & 1U)) & 0x0000ffff;
+    half v = (half)min(MAX_INT_BEFORE_INFINITY,
+                       (ushort)(num >> (16U * (index & 1U)) & 0x0000ffff));
     return fma(v, SIGNED_HALF_FACTOR, SIGNED_HALF_HALF_FACTOR);
 }
 
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 4a3e193b7a..3e833f4058 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -890,7 +890,7 @@ TEST_P(Conv2ConsistencyTest, RandomConvolutions) {
     array out = convolve2NN(signal, filter, params.stride_, params.padding_,
                             params.dilation_);
 
-    ASSERT_ARRAYS_NEAR(out_native, out, 1e-5);
+    ASSERT_ARRAYS_NEAR(out_native, out, 2e-5);
 }
 
 template<typename T>
diff --git a/test/rng_quality.cpp b/test/rng_quality.cpp
index dab40e656f..8585d552e6 100644
--- a/test/rng_quality.cpp
+++ b/test/rng_quality.cpp
@@ -60,7 +60,7 @@ double chi2_statistic(array input, array expected, bool print = false) {
     array diff = input - expected;
 
     double chi2 = sum<T>((diff * diff) / expected);
-    if (print && chi2 > 10000) {
+    if (print) {
         array legend = af::seq(input.elements());
         legend -= (input.elements() / 2.);
         legend *= (14. / input.elements());
@@ -137,7 +137,6 @@ TYPED_TEST(RandomEngine, mersenneRandomEngineUniformChi2) {
 }
 
 // should be used only for x <= 5 (roughly)
-
 array cnd(array x) { return 0.5 * erfc(-x * sqrt(0.5)); }
 
 template<typename T>
@@ -159,21 +158,22 @@ bool testRandomEngineNormalChi2(randomEngineType type)
 
     expected =
         af::join(0, expected(af::seq(bins - 1, 0, -1)), expected).as(f32);
-    // af_print(expected);
 
     af::randomEngine r(type, 0);
 
+    // NOTE(@rstub): In the chi^2 test one computes the test statistic and
+    // compares the value with the chi^2 distribution with appropriate number of
+    // degrees of freedom. For the uniform distribution one has "number of bins
+    // minus 1" degrees of freedom. For the normal distribution it is "number of
+    // bins minus 3", since there are two parameters mu and sigma. Here I used
+    // the qchisq() function from R to compute "suitable" values from the chi^2
+    // distribution.
+    //
     // R> qchisq(c(5e-6, 1 - 5e-6), 197)
-
     // [1] 121.3197 297.2989
     float lower(121.3197);
     float upper(297.2989);
 
-    // R> qchisq(c(5e-6, 1 - 5e-6), 199)
-    // [1] 121.3197 297.2989
-    // float lower = 118.1094;
-    // float upper = 308.6010;
-
     bool prev_step  = true;
     bool prev_total = true;
 
@@ -201,6 +201,10 @@ bool testRandomEngineNormalChi2(randomEngineType type)
         if (!prev_step) {
             EXPECT_GT(step_chi2, lower) << "at step " << i;
             EXPECT_LT(step_chi2, upper) << "at step: " << i;
+            if (step_chi2 < lower || step_chi2 > upper) {
+                bool print = true;
+                chi2_statistic<float>(step_hist, expected, print);
+            }
         }
 
         // if (!(step || prev_step)) break;
@@ -216,11 +220,12 @@ bool testRandomEngineNormalChi2(randomEngineType type)
         if (!prev_total) {
             EXPECT_GT(total_chi2, lower) << "at step " << i;
             EXPECT_LT(total_chi2, upper) << "at step " << i;
+            if (total_chi2 < lower || total_chi2 > upper) {
+                bool print = true;
+                chi2_statistic<float>(total_hist, expected, print);
+            }
         }
 
-        // ASSERT_TRUE(total || prev_step);
-        // if (!(total || prev_total)) break;
-
         prev_total = total;
     }
 

From 5360328eae28a91d375c2a03fe5b41fa5af99c8e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Jul 2020 19:49:24 +0530
Subject: [PATCH 2056/2677] Address perf regression in approx after dim based
 interop was introduced

---
 src/backend/cuda/kernel/approx.hpp   |  17 ++--
 src/backend/cuda/kernel/approx1.cuh  |  60 +++++++-------
 src/backend/cuda/kernel/approx2.cuh  |  68 +++++++--------
 src/backend/cuda/kernel/interp.hpp   |  43 +++++-----
 src/backend/opencl/kernel/approx.hpp |  20 +++--
 src/backend/opencl/kernel/approx1.cl |  50 ++++++------
 src/backend/opencl/kernel/approx2.cl |  60 +++++++-------
 src/backend/opencl/kernel/interp.cl  | 118 ++++++++++++---------------
 8 files changed, 215 insertions(+), 221 deletions(-)

diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 46057e6d3c..54c1d62503 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -31,9 +31,10 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const af::interpType method, const int order) {
     static const std::string source(approx1_cuh, approx1_cuh_len);
 
-    auto approx1 = common::getKernel(
-        "cuda::approx1", {source},
-        {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
+    auto approx1 =
+        common::getKernel("cuda::approx1", {source},
+                          {TemplateTypename<Ty>(), TemplateTypename<Tp>(),
+                           TemplateArg(xdim), TemplateArg(order)});
 
     dim3 threads(THREADS, 1, 1);
     int blocksPerMat = divup(yo.dims[0], threads.x);
@@ -48,7 +49,7 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    approx1(qArgs, yo, yi, xo, xdim, xi_beg, xi_step, offGrid, blocksPerMat,
+    approx1(qArgs, yo, yi, xo, xi_beg, Tp(1) / xi_step, offGrid, blocksPerMat,
             batch, method);
 
     POST_LAUNCH_CHECK();
@@ -63,7 +64,8 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
 
     auto approx2 = common::getKernel(
         "cuda::approx2", {source},
-        {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(order)});
+        {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(xdim),
+         TemplateArg(ydim), TemplateArg(order)});
 
     dim3 threads(TX, TY, 1);
     int blocksPerMatX = divup(zo.dims[0], threads.x);
@@ -79,8 +81,9 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    approx2(qArgs, zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim, yi_beg, yi_step,
-            offGrid, blocksPerMatX, blocksPerMatY, batch, method);
+    approx2(qArgs, zo, zi, xo, xi_beg, Tp(1) / xi_step, yo, yi_beg,
+            Tp(1) / yi_step, offGrid, blocksPerMatX, blocksPerMatY, batch,
+            method);
 
     POST_LAUNCH_CHECK();
 }
diff --git a/src/backend/cuda/kernel/approx1.cuh b/src/backend/cuda/kernel/approx1.cuh
index e009a990cc..6ef6a837a4 100644
--- a/src/backend/cuda/kernel/approx1.cuh
+++ b/src/backend/cuda/kernel/approx1.cuh
@@ -14,13 +14,11 @@
 
 namespace cuda {
 
-template<typename Ty, typename Tp, int order>
-__global__
-void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
-             const int xdim, const Tp xi_beg,
-             const Tp xi_step, const float offGrid,
-             const int blocksMatX, const bool batch,
-             af::interpType method) {
+template<typename Ty, typename Tp, int xdim, int order>
+__global__ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
+                        const Tp xi_beg, const Tp xi_step_reproc,
+                        const float offGrid, const int blocksMatX,
+                        const bool batch, af::interpType method) {
     const int idy        = blockIdx.x / blocksMatX;
     const int blockIdx_x = blockIdx.x - idy * blocksMatX;
     const int idx        = blockIdx_x * blockDim.x + threadIdx.x;
@@ -32,36 +30,42 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
         idw >= yo.dims[3])
         return;
 
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
-                        xo.dims[3] > 1};
-    bool is_yi_off[] = {true, true, true, true};
-    is_yi_off[xdim]  = false;
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    const bool clamp = order == 3;
+
+    bool is_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                     xo.dims[3] > 1};
+
+    int xo_idx = idx * is_off[0];
+    if (batch) {
+        xo_idx += idw * xo.strides[3] * is_off[3];
+        xo_idx += idz * xo.strides[2] * is_off[2];
+        xo_idx += idy * xo.strides[1] * is_off[1];
+    }
+
+    const Tp x = (xo.ptr[xo_idx] - xi_beg) * xi_step_reproc;
 
     const int yo_idx =
         idw * yo.strides[3] + idz * yo.strides[2] + idy * yo.strides[1] + idx;
-    int xo_idx = idx * is_xo_off[0];
-    xo_idx += idw * xo.strides[3] * is_xo_off[3];
-    xo_idx += idz * xo.strides[2] * is_xo_off[2];
-    xo_idx += idy * xo.strides[1] * is_xo_off[1];
 
-    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
+#pragma unroll
+    for (int flagIdx = 0; flagIdx < 4; ++flagIdx) { is_off[flagIdx] = true; }
+    is_off[xdim] = false;
+
     if (x < 0 || yi.dims[xdim] < x + 1) {
         yo.ptr[yo_idx] = scalar<Ty>(offGrid);
         return;
     }
 
-    int yi_idx = idx * is_yi_off[0];
-    yi_idx += idw * yi.strides[3] * is_yi_off[3];
-    yi_idx += idz * yi.strides[2] * is_yi_off[2];
-    yi_idx += idy * yi.strides[1] * is_yi_off[1];
-
-    // FIXME: Only cubic interpolation is doing clamping
-    // We need to make it consistent across all methods
-    // Not changing the behavior because tests will fail
-    bool clamp = order == 3;
+    int yi_idx = idx * is_off[0];
+    yi_idx += idw * yi.strides[3] * is_off[3];
+    yi_idx += idz * yi.strides[2] * is_off[2];
+    yi_idx += idy * yi.strides[1] * is_off[1];
 
-    Interp1<Ty, Tp, order> interp;
-    interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp, xdim);
+    Interp1<Ty, Tp, xdim, order> interp;
+    interp(yo, yo_idx, yi, yi_idx, x, method, 1, clamp);
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/approx2.cuh b/src/backend/cuda/kernel/approx2.cuh
index aa182e9b60..191a4e8919 100644
--- a/src/backend/cuda/kernel/approx2.cuh
+++ b/src/backend/cuda/kernel/approx2.cuh
@@ -14,15 +14,13 @@
 
 namespace cuda {
 
-template<typename Ty, typename Tp, int order>
-__global__
-void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
-             const int xdim, const Tp xi_beg,
-             const Tp xi_step, CParam<Tp> yo, const int ydim,
-             const Tp yi_beg, const Tp yi_step,
-             const float offGrid, const int blocksMatX,
-             const int blocksMatY, const bool batch,
-             af::interpType method) {
+template<typename Ty, typename Tp, int xdim, int ydim, int order>
+__global__ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
+                        const Tp xi_beg, const Tp xi_step_reproc, CParam<Tp> yo,
+                        const Tp yi_beg, const Tp yi_step_reproc,
+                        const float offGrid, const int blocksMatX,
+                        const int blocksMatY, const bool batch,
+                        af::interpType method) {
     const int idz        = blockIdx.x / blocksMatX;
     const int blockIdx_x = blockIdx.x - idz * blocksMatX;
     const int idx        = threadIdx.x + blockIdx_x * blockDim.x;
@@ -36,39 +34,43 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
         idw >= zo.dims[3])
         return;
 
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
-                        xo.dims[3] > 1};
-    bool is_zi_off[] = {true, true, true, true};
-    is_zi_off[xdim]  = false;
-    is_zi_off[ydim]  = false;
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    const bool clamp = order == 3;
+
+    bool is_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                     xo.dims[3] > 1};
 
     const int zo_idx =
         idw * zo.strides[3] + idz * zo.strides[2] + idy * zo.strides[1] + idx;
-    int xo_idx = idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
-    int yo_idx = idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0];
-    xo_idx +=
-        idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2] * is_xo_off[2];
-    yo_idx +=
-        idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2] * is_xo_off[2];
+    int xo_idx = idy * xo.strides[1] * is_off[1] + idx * is_off[0];
+    int yo_idx = idy * yo.strides[1] * is_off[1] + idx * is_off[0];
+    if (batch) {
+        xo_idx +=
+            idw * xo.strides[3] * is_off[3] + idz * xo.strides[2] * is_off[2];
+        yo_idx +=
+            idw * yo.strides[3] * is_off[3] + idz * yo.strides[2] * is_off[2];
+    }
+
+    const Tp x = (xo.ptr[xo_idx] - xi_beg) * xi_step_reproc;
+    const Tp y = (yo.ptr[yo_idx] - yi_beg) * yi_step_reproc;
+
+#pragma unroll
+    for (int flagIdx = 0; flagIdx < 4; ++flagIdx) { is_off[flagIdx] = true; }
+    is_off[xdim] = false;
+    is_off[ydim] = false;
 
-    const Tp x = (xo.ptr[xo_idx] - xi_beg) / xi_step;
-    const Tp y = (yo.ptr[yo_idx] - yi_beg) / yi_step;
     if (x < 0 || y < 0 || zi.dims[xdim] < x + 1 || zi.dims[ydim] < y + 1) {
         zo.ptr[zo_idx] = scalar<Ty>(offGrid);
         return;
     }
 
-    int zi_idx = idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0];
-    zi_idx +=
-        idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
-
-    // FIXME: Only cubic interpolation is doing clamping
-    // We need to make it consistent across all methods
-    // Not changing the behavior because tests will fail
-    bool clamp = order == 3;
+    int zi_idx = idy * zi.strides[1] * is_off[1] + idx * is_off[0];
+    zi_idx += idw * zi.strides[3] * is_off[3] + idz * zi.strides[2] * is_off[2];
 
-    Interp2<Ty, Tp, order> interp;
-    interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp, xdim, ydim);
+    Interp2<Ty, Tp, xdim, ydim, order> interp;
+    interp(zo, zo_idx, zi, zi_idx, x, y, method, 1, clamp);
 }
 
-}
+}  // namespace cuda
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index ee2fa727aa..48dc6dbe5a 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -85,14 +85,14 @@ __device__ inline static Ty bicubicInterpFunc(Ty val[4][4], Tp xratio,
     return cubicInterpFunc(res, yratio, spline);
 }
 
-template<typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp, int xdim, int order>
 struct Interp1 {};
 
-template<typename Ty, typename Tp>
-struct Interp1<Ty, Tp, 1> {
+template<typename Ty, typename Tp, int xdim>
+struct Interp1<Ty, Tp, xdim, 1> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
                                Tp x, af::interpType method, int batch,
-                               bool clamp, int xdim = 0, int batch_dim = 1) {
+                               bool clamp, int batch_dim = 1) {
         Ty zero = scalar<Ty>(0);
 
         const int x_lim    = in.dims[xdim];
@@ -113,11 +113,11 @@ struct Interp1<Ty, Tp, 1> {
     }
 };
 
-template<typename Ty, typename Tp>
-struct Interp1<Ty, Tp, 2> {
+template<typename Ty, typename Tp, int xdim>
+struct Interp1<Ty, Tp, xdim, 2> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
                                Tp x, af::interpType method, int batch,
-                               bool clamp, int xdim = 0, int batch_dim = 1) {
+                               bool clamp, int batch_dim = 1) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
@@ -149,11 +149,11 @@ struct Interp1<Ty, Tp, 2> {
     }
 };
 
-template<typename Ty, typename Tp>
-struct Interp1<Ty, Tp, 3> {
+template<typename Ty, typename Tp, int xdim>
+struct Interp1<Ty, Tp, xdim, 3> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
                                Tp x, af::interpType method, int batch,
-                               bool clamp, int xdim = 0, int batch_dim = 1) {
+                               bool clamp, int batch_dim = 1) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
@@ -184,15 +184,14 @@ struct Interp1<Ty, Tp, 3> {
     }
 };
 
-template<typename Ty, typename Tp, int order>
+template<typename Ty, typename Tp, int xdim, int ydim, int order>
 struct Interp2 {};
 
-template<typename Ty, typename Tp>
-struct Interp2<Ty, Tp, 1> {
+template<typename Ty, typename Tp, int xdim, int ydim>
+struct Interp2<Ty, Tp, xdim, ydim, 1> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
                                Tp x, Tp y, af::interpType method, int batch,
-                               bool clamp, int xdim = 0, int ydim = 1,
-                               int batch_dim = 2) {
+                               bool clamp, int batch_dim = 2) {
         int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
         int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
 
@@ -222,12 +221,11 @@ struct Interp2<Ty, Tp, 1> {
     }
 };
 
-template<typename Ty, typename Tp>
-struct Interp2<Ty, Tp, 2> {
+template<typename Ty, typename Tp, int xdim, int ydim>
+struct Interp2<Ty, Tp, xdim, ydim, 2> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
                                Tp x, Tp y, af::interpType method, int batch,
-                               bool clamp, int xdim = 0, int ydim = 1,
-                               int batch_dim = 2) {
+                               bool clamp, int batch_dim = 2) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
@@ -275,12 +273,11 @@ struct Interp2<Ty, Tp, 2> {
     }
 };
 
-template<typename Ty, typename Tp>
-struct Interp2<Ty, Tp, 3> {
+template<typename Ty, typename Tp, int xdim, int ydim>
+struct Interp2<Ty, Tp, xdim, ydim, 3> {
     __device__ void operator()(Param<Ty> out, int ooff, CParam<Ty> in, int ioff,
                                Tp x, Tp y, af::interpType method, int batch,
-                               bool clamp, int xdim = 0, int ydim = 1,
-                               int batch_dim = 2) {
+                               bool clamp, int batch_dim = 2) {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 85cfe2310f..782383332f 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -33,7 +33,7 @@ inline std::string interpSrc() {
 }
 
 template<typename Ty, typename Tp>
-auto genCompileOptions(const int order) {
+auto genCompileOptions(const int order, const int xdim, const int ydim = -1) {
     constexpr bool isComplex =
         static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c32 ||
         static_cast<af_dtype>(dtype_traits<Ty>::af_type) == c64;
@@ -47,9 +47,11 @@ auto genCompileOptions(const int order) {
         DefineKeyValue(InterpValTy, dtype_traits<Ty>::getName()),
         DefineKeyValue(InterpPosTy, dtype_traits<Tp>::getName()),
         DefineKeyValue(ZERO, toNumStr(scalar<Ty>(0))),
+        DefineKeyValue(XDIM, xdim),
         DefineKeyValue(INTERP_ORDER, order),
         DefineKeyValue(IS_CPLX, (isComplex ? 1 : 0)),
     };
+    if (ydim != -1) { compileOpts.emplace_back(DefineKeyValue(YDIM, ydim)); }
     compileOpts.emplace_back(getTypeBuildDefinition<Ty>());
     addInterpEnumOptions(compileOpts);
 
@@ -72,9 +74,10 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ty>(),
         TemplateTypename<Tp>(),
+        TemplateArg(xdim),
         TemplateArg(order),
     };
-    auto compileOpts = genCompileOptions<Ty, Tp>(order);
+    auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim);
 
     auto approx1 = common::getKernel("approx1", {interpSrc(), src}, tmpltArgs,
                                      compileOpts);
@@ -89,7 +92,7 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
         !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
     approx1(EnqueueArgs(getQueue(), global, local), *yo.data, yo.info, *yi.data,
-            yi.info, *xo.data, xo.info, xdim, xi_beg, xi_step,
+            yi.info, *xo.data, xo.info, xi_beg, Tp(1) / xi_step,
             scalar<Ty>(offGrid), (int)blocksPerMat, (int)batch, (int)method);
     CL_DEBUG_FINISH(getQueue());
 }
@@ -111,11 +114,10 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     static const string src(approx2_cl, approx2_cl_len);
 
     vector<TemplateArg> tmpltArgs = {
-        TemplateTypename<Ty>(),
-        TemplateTypename<Tp>(),
-        TemplateArg(order),
+        TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(xdim),
+        TemplateArg(ydim),      TemplateArg(order),
     };
-    auto compileOpts = genCompileOptions<Ty, Tp>(order);
+    auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim, ydim);
 
     auto approx2 = common::getKernel("approx2", {interpSrc(), src}, tmpltArgs,
                                      compileOpts);
@@ -130,8 +132,8 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     bool batch = !(xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
     approx2(EnqueueArgs(getQueue(), global, local), *zo.data, zo.info, *zi.data,
-            zi.info, *xo.data, xo.info, xdim, *yo.data, yo.info, ydim, xi_beg,
-            xi_step, yi_beg, yi_step, scalar<Ty>(offGrid),
+            zi.info, *xo.data, xo.info, *yo.data, yo.info, xi_beg,
+            Tp(1) / xi_step, yi_beg, Tp(1) / yi_step, scalar<Ty>(offGrid),
             static_cast<int>(blocksPerMatX), static_cast<int>(blocksPerMatY),
             static_cast<int>(batch), static_cast<int>(method));
     CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/approx1.cl b/src/backend/opencl/kernel/approx1.cl
index 2b22dc7313..60d9ebbae3 100644
--- a/src/backend/opencl/kernel/approx1.cl
+++ b/src/backend/opencl/kernel/approx1.cl
@@ -9,9 +9,8 @@
 
 kernel void approx1(global Ty *d_yo, const KParam yo, global const Ty *d_yi,
                     const KParam yi, global const Tp *d_xo, const KParam xo,
-                    const int xdim, const Tp xi_beg, const Tp xi_step,
-                    const Ty offGrid, const int blocksMatX, const int batch,
-                    const int method) {
+                    const Tp xi_beg, const Tp xi_step_reproc, const Ty offGrid,
+                    const int blocksMatX, const int batch, const int method) {
     const int idw = get_group_id(1) / yo.dims[2];
     const int idz = get_group_id(1) - idw * yo.dims[2];
 
@@ -23,34 +22,39 @@ kernel void approx1(global Ty *d_yo, const KParam yo, global const Ty *d_yi,
         idw >= yo.dims[3])
         return;
 
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
-                        xo.dims[3] > 1};
-    bool is_yi_off[] = {true, true, true, true};
-    is_yi_off[xdim]  = false;
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    const bool doclamp = INTERP_ORDER == 3;
+
+    bool is_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                     xo.dims[3] > 1};
 
     const int yo_idx = idw * yo.strides[3] + idz * yo.strides[2] +
                        idy * yo.strides[1] + idx + yo.offset;
 
-    int xo_idx = idx * is_xo_off[0] + xo.offset;
-    xo_idx += idw * xo.strides[3] * is_xo_off[3];
-    xo_idx += idz * xo.strides[2] * is_xo_off[2];
-    xo_idx += idy * xo.strides[1] * is_xo_off[1];
+    int xo_idx = idx * is_off[0] + xo.offset;
+    if (batch) {
+        xo_idx += idw * xo.strides[3] * is_off[3];
+        xo_idx += idz * xo.strides[2] * is_off[2];
+        xo_idx += idy * xo.strides[1] * is_off[1];
+    }
+
+    const Tp x = (d_xo[xo_idx] - xi_beg) * xi_step_reproc;
 
-    const Tp x = (d_xo[xo_idx] - xi_beg) / xi_step;
-    if (x < 0 || yi.dims[xdim] < x + 1) {
+#pragma unroll
+    for (int flagIdx = 0; flagIdx < 4; ++flagIdx) { is_off[flagIdx] = true; }
+    is_off[XDIM] = false;
+
+    if (x < 0 || yi.dims[XDIM] < x + 1) {
         d_yo[yo_idx] = offGrid;
         return;
     }
 
-    int yi_idx = idx * is_yi_off[0] + yi.offset;
-    yi_idx += idw * yi.strides[3] * is_yi_off[3];
-    yi_idx += idz * yi.strides[2] * is_yi_off[2];
-    yi_idx += idy * yi.strides[1] * is_yi_off[1];
-
-    // FIXME: Only cubic interpolation is doing clamping
-    // We need to make it consistent across all methods
-    // Not changing the behavior because tests will fail
-    bool clamp = INTERP_ORDER == 3;
+    int yi_idx = idx * is_off[0] + yi.offset;
+    yi_idx += idw * yi.strides[3] * is_off[3];
+    yi_idx += idz * yi.strides[2] * is_off[2];
+    yi_idx += idy * yi.strides[1] * is_off[1];
 
-    interp1_dim(d_yo, yo, yo_idx, d_yi, yi, yi_idx, x, method, 1, clamp, xdim);
+    interp1(d_yo, yo, yo_idx, d_yi, yi, yi_idx, x, method, 1, doclamp, 1);
 }
diff --git a/src/backend/opencl/kernel/approx2.cl b/src/backend/opencl/kernel/approx2.cl
index bb544ce807..6df3f0a381 100644
--- a/src/backend/opencl/kernel/approx2.cl
+++ b/src/backend/opencl/kernel/approx2.cl
@@ -9,9 +9,9 @@
 
 kernel void approx2(global Ty *d_zo, const KParam zo, global const Ty *d_zi,
                     const KParam zi, global const Tp *d_xo, const KParam xo,
-                    const int xdim, global const Tp *d_yo, const KParam yo,
-                    const int ydim, const Tp xi_beg, const Tp xi_step,
-                    const Tp yi_beg, const Tp yi_step, const Ty offGrid,
+                    global const Tp *d_yo, const KParam yo, const Tp xi_beg,
+                    const Tp xi_step_reproc, const Tp yi_beg,
+                    const Tp yi_step_reproc, const Ty offGrid,
                     const int blocksMatX, const int blocksMatY, const int batch,
                     int method) {
     const int idz = get_group_id(0) / blocksMatX;
@@ -27,40 +27,40 @@ kernel void approx2(global Ty *d_zo, const KParam zo, global const Ty *d_zi,
         idw >= zo.dims[3])
         return;
 
-    bool is_xo_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
-                        xo.dims[3] > 1};
-    bool is_zi_off[] = {true, true, true, true};
-    is_zi_off[xdim]  = false;
-    is_zi_off[ydim]  = false;
+    // FIXME: Only cubic interpolation is doing clamping
+    // We need to make it consistent across all methods
+    // Not changing the behavior because tests will fail
+    const bool doclamp = INTERP_ORDER == 3;
+
+    bool is_off[] = {xo.dims[0] > 1, xo.dims[1] > 1, xo.dims[2] > 1,
+                     xo.dims[3] > 1};
 
     const int zo_idx = idw * zo.strides[3] + idz * zo.strides[2] +
                        idy * zo.strides[1] + idx + zo.offset;
-    int xo_idx =
-        idy * xo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + xo.offset;
-    int yo_idx =
-        idy * yo.strides[1] * is_xo_off[1] + idx * is_xo_off[0] + yo.offset;
-    xo_idx +=
-        idw * xo.strides[3] * is_xo_off[3] + idz * xo.strides[2] * is_xo_off[2];
-    yo_idx +=
-        idw * yo.strides[3] * is_xo_off[3] + idz * yo.strides[2] * is_xo_off[2];
+    int xo_idx = idy * xo.strides[1] * is_off[1] + idx * is_off[0] + xo.offset;
+    int yo_idx = idy * yo.strides[1] * is_off[1] + idx * is_off[0] + yo.offset;
+    if (batch) {
+        xo_idx +=
+            idw * xo.strides[3] * is_off[3] + idz * xo.strides[2] * is_off[2];
+        yo_idx +=
+            idw * yo.strides[3] * is_off[3] + idz * yo.strides[2] * is_off[2];
+    }
+
+#pragma unroll
+    for (int flagIdx = 0; flagIdx < 4; ++flagIdx) { is_off[flagIdx] = true; }
+    is_off[XDIM] = false;
+    is_off[YDIM] = false;
 
-    const Tp x = (d_xo[xo_idx] - xi_beg) / xi_step;
-    const Tp y = (d_yo[yo_idx] - yi_beg) / yi_step;
-    if (x < 0 || y < 0 || zi.dims[xdim] < x + 1 || zi.dims[ydim] < y + 1) {
+    const Tp x = (d_xo[xo_idx] - xi_beg) * xi_step_reproc;
+    const Tp y = (d_yo[yo_idx] - yi_beg) * yi_step_reproc;
+
+    if (x < 0 || y < 0 || zi.dims[XDIM] < x + 1 || zi.dims[YDIM] < y + 1) {
         d_zo[zo_idx] = offGrid;
         return;
     }
 
-    int zi_idx =
-        idy * zi.strides[1] * is_zi_off[1] + idx * is_zi_off[0] + zi.offset;
-    zi_idx +=
-        idw * zi.strides[3] * is_zi_off[3] + idz * zi.strides[2] * is_zi_off[2];
-
-    // FIXME: Only cubic interpolation is doing clamping
-    // We need to make it consistent across all methods
-    // Not changing the behavior because tests will fail
-    bool clamp = INTERP_ORDER == 3;
+    int zi_idx = idy * zi.strides[1] * is_off[1] + idx * is_off[0] + zi.offset;
+    zi_idx += idw * zi.strides[3] * is_off[3] + idz * zi.strides[2] * is_off[2];
 
-    interp2_dim(d_zo, zo, zo_idx, d_zi, zi, zi_idx, x, y, method, 1, clamp,
-                xdim, ydim);
+    interp2(d_zo, zo, zo_idx, d_zi, zi, zi_idx, x, y, method, 1, doclamp, 2);
 }
diff --git a/src/backend/opencl/kernel/interp.cl b/src/backend/opencl/kernel/interp.cl
index 5313ad8932..8d7b8d8a82 100644
--- a/src/backend/opencl/kernel/interp.cl
+++ b/src/backend/opencl/kernel/interp.cl
@@ -75,37 +75,35 @@ InterpValTy bicubicInterpFunc(InterpValTy val[4][4], InterpPosTy xratio,
 }
 
 #if INTERP_ORDER == 1
-void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
-                     global const InterpInTy *d_in, KParam in, int ioff,
-                     InterpPosTy x, int method, int batch, bool clamp, int xdim,
-                     int batch_dim) {
+void interp1(global InterpInTy *d_out, KParam out, int ooff,
+             global const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x,
+             int method, int batch, bool doclamp, int batch_dim) {
     InterpInTy zero = ZERO;
 
-    const int x_lim    = in.dims[xdim];
-    const int x_stride = in.strides[xdim];
+    const int x_lim    = in.dims[XDIM];
+    const int x_stride = in.strides[XDIM];
 
     int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
     bool cond = xid >= 0 && xid < x_lim;
-    if (clamp) xid = max(0, min(xid, x_lim));
+    if (doclamp) xid = max(0, min(xid, x_lim));
 
     const int idx = ioff + xid * x_stride;
 
     for (int n = 0; n < batch; n++) {
         int idx_n = idx + n * in.strides[batch_dim];
         d_out[ooff + n * out.strides[batch_dim]] =
-            (clamp || cond) ? d_in[idx_n] : zero;
+            (doclamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
-                     global const InterpInTy *d_in, KParam in, int ioff,
-                     InterpPosTy x, int method, int batch, bool clamp, int xdim,
-                     int batch_dim) {
+void interp1(global InterpInTy *d_out, KParam out, int ooff,
+             global const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x,
+             int method, int batch, bool doclamp, int batch_dim) {
     const int grid_x        = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;  // fractional offset
 
-    const int x_lim    = in.dims[xdim];
-    const int x_stride = in.strides[xdim];
+    const int x_lim    = in.dims[XDIM];
+    const int x_stride = in.strides[XDIM];
     const int idx      = ioff + grid_x * x_stride;
 
     InterpValTy zero  = ZERO;
@@ -119,22 +117,21 @@ void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
     for (int n = 0; n < batch; n++) {
         int idx_n          = idx + n * in.strides[batch_dim];
         InterpValTy val[2] = {
-            (clamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
-            (clamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
+            (doclamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
+            (doclamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
 
         d_out[ooff + n * out.strides[batch_dim]] = linearInterpFunc(val, ratio);
     }
 }
 #elif INTERP_ORDER == 3
-void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
-                     global const InterpInTy *d_in, KParam in, int ioff,
-                     InterpPosTy x, int method, int batch, bool clamp, int xdim,
-                     int batch_dim) {
+void interp1(global InterpInTy *d_out, KParam out, int ooff,
+             global const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x,
+             int method, int batch, bool doclamp, int batch_dim) {
     const int grid_x        = floor(x);    // nearest grid
     const InterpPosTy off_x = x - grid_x;  // fractional offset
 
-    const int x_lim    = in.dims[xdim];
-    const int x_stride = in.strides[xdim];
+    const int x_lim    = in.dims[XDIM];
+    const int x_stride = in.strides[XDIM];
     const int idx      = ioff + grid_x * x_stride;
 
     bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
@@ -149,7 +146,7 @@ void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
         int idx_n = idx + n * in.strides[batch_dim];
         for (int i = 0; i < 4; i++) {
             val[i] =
-                (clamp || cond[i]) ? d_in[idx_n + off[i] * x_stride] : zero;
+                (doclamp || cond[i]) ? d_in[idx_n + off[i] * x_stride] : zero;
         }
         bool spline = method == AF_INTERP_CUBIC_SPLINE;
         d_out[ooff + n * out.strides[batch_dim]] =
@@ -159,20 +156,21 @@ void interp1_general(global InterpInTy *d_out, KParam out, int ooff,
 }
 #endif
 
+#if defined(YDIM)  // If 2d interpolation is being used
 #if INTERP_ORDER == 1
-void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
-                     global const InterpInTy *d_in, KParam in, int ioff,
-                     InterpPosTy x, InterpPosTy y, int method, int batch,
-                     bool clamp, int xdim, int ydim, int batch_dim) {
+void interp2(global InterpInTy *d_out, KParam out, int ooff,
+             global const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x,
+             InterpPosTy y, int method, int batch, bool doclamp,
+             int batch_dim) {
     int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
     int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
 
-    const int x_lim    = in.dims[xdim];
-    const int y_lim    = in.dims[ydim];
-    const int x_stride = in.strides[xdim];
-    const int y_stride = in.strides[ydim];
+    const int x_lim    = in.dims[XDIM];
+    const int y_lim    = in.dims[YDIM];
+    const int x_stride = in.strides[XDIM];
+    const int y_stride = in.strides[YDIM];
 
-    if (clamp) {
+    if (doclamp) {
         xid = max(0, min(xid, x_lim));
         yid = max(0, min(yid, y_lim));
     }
@@ -186,24 +184,24 @@ void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
     for (int n = 0; n < batch; n++) {
         int idx_n = idx + n * in.strides[batch_dim];
         d_out[ooff + n * out.strides[batch_dim]] =
-            (clamp || cond) ? d_in[idx_n] : zero;
+            (doclamp || cond) ? d_in[idx_n] : zero;
     }
 }
 #elif INTERP_ORDER == 2
-void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
-                     global const InterpInTy *d_in, KParam in, int ioff,
-                     InterpPosTy x, InterpPosTy y, int method, int batch,
-                     bool clamp, int xdim, int ydim, int batch_dim) {
+void interp2(global InterpInTy *d_out, KParam out, int ooff,
+             global const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x,
+             InterpPosTy y, int method, int batch, bool doclamp,
+             int batch_dim) {
     const int grid_x        = floor(x);
     const InterpPosTy off_x = x - grid_x;
 
     const int grid_y        = floor(y);
     const InterpPosTy off_y = y - grid_y;
 
-    const int x_lim    = in.dims[xdim];
-    const int y_lim    = in.dims[ydim];
-    const int x_stride = in.strides[xdim];
-    const int y_stride = in.strides[ydim];
+    const int x_lim    = in.dims[XDIM];
+    const int y_lim    = in.dims[YDIM];
+    const int x_stride = in.strides[XDIM];
+    const int y_stride = in.strides[YDIM];
     const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
     bool condX[2] = {true, x + 1 < x_lim};
@@ -224,7 +222,7 @@ void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
         for (int j = 0; j < 2; j++) {
             int off_y = idx_n + offy[j] * y_stride;
             for (int i = 0; i < 2; i++) {
-                bool cond = (clamp || (condX[i] && condY[j]));
+                bool cond = (doclamp || (condX[i] && condY[j]));
                 val[j][i] = cond ? d_in[off_y + offx[i] * x_stride] : zero;
             }
         }
@@ -233,20 +231,20 @@ void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
     }
 }
 #elif INTERP_ORDER == 3
-void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
-                     global const InterpInTy *d_in, KParam in, int ioff,
-                     InterpPosTy x, InterpPosTy y, int method, int batch,
-                     bool clamp, int xdim, int ydim, int batch_dim) {
+void interp2(global InterpInTy *d_out, KParam out, int ooff,
+             global const InterpInTy *d_in, KParam in, int ioff, InterpPosTy x,
+             InterpPosTy y, int method, int batch, bool doclamp,
+             int batch_dim) {
     const int grid_x        = floor(x);
     const InterpPosTy off_x = x - grid_x;
 
     const int grid_y        = floor(y);
     const InterpPosTy off_y = y - grid_y;
 
-    const int x_lim    = in.dims[xdim];
-    const int y_lim    = in.dims[ydim];
-    const int x_stride = in.strides[xdim];
-    const int y_stride = in.strides[ydim];
+    const int x_lim    = in.dims[XDIM];
+    const int y_lim    = in.dims[YDIM];
+    const int x_stride = in.strides[XDIM];
+    const int y_stride = in.strides[YDIM];
     const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
 
     // used for setting values at boundaries
@@ -269,7 +267,7 @@ void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
             int ioff_j = idx_n + offY[j] * y_stride;
 #pragma unroll
             for (int i = 0; i < 4; i++) {
-                bool cond = (clamp || (condX[i] && condY[j]));
+                bool cond = (doclamp || (condX[i] && condY[j]));
                 val[j][i] = cond ? d_in[ioff_j + offX[i] * x_stride] : zero;
             }
         }
@@ -280,20 +278,4 @@ void interp2_general(global InterpInTy *d_out, KParam out, int ooff,
     }
 }
 #endif
-
-#define interp1_dim(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp, \
-                    xdim)                                                      \
-    interp1_general(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp, \
-                    xdim, 1)
-
-#define interp1(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp) \
-    interp1_dim(d_out, out, ooff, d_in, in, ioff, x, method, batch, clamp, 0)
-
-#define interp2_dim(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, \
-                    clamp, xdim, ydim)                                     \
-    interp2_general(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, \
-                    clamp, xdim, ydim, 2)
-
-#define interp2(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, clamp) \
-    interp2_dim(d_out, out, ooff, d_in, in, ioff, x, y, method, batch, clamp, \
-                0, 1)\
+#endif

From 8df8e6f5bce0f3d4e176bf99b6fc78eed087f466 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 21 Jul 2020 22:33:59 +0530
Subject: [PATCH 2057/2677] Correct interp helper calls in other kernels that
 use it

---
 src/backend/cuda/kernel/rotate.cuh      | 5 ++---
 src/backend/cuda/kernel/transform.cuh   | 2 +-
 src/backend/opencl/kernel/rotate.cl     | 4 ++--
 src/backend/opencl/kernel/rotate.hpp    | 2 ++
 src/backend/opencl/kernel/transform.cl  | 4 ++--
 src/backend/opencl/kernel/transform.hpp | 2 ++
 6 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/backend/cuda/kernel/rotate.cuh b/src/backend/cuda/kernel/rotate.cuh
index ab4b2ba79f..bd76c490e6 100644
--- a/src/backend/cuda/kernel/rotate.cuh
+++ b/src/backend/cuda/kernel/rotate.cuh
@@ -19,8 +19,7 @@ typedef struct {
 template<typename T, int order>
 __global__ void rotate(Param<T> out, CParam<T> in, const tmat_t t,
                        const int nimages, const int nbatches,
-                       const int blocksXPerImage,
-                       const int blocksYPerImage,
+                       const int blocksXPerImage, const int blocksYPerImage,
                        af::interpType method) {
     // Compute which image set
     const int setId      = blockIdx.x / blocksXPerImage;
@@ -62,7 +61,7 @@ __global__ void rotate(Param<T> out, CParam<T> in, const tmat_t t,
         }
     }
 
-    Interp2<T, WT, order> interp;
+    Interp2<T, WT, 0, 1, order> interp;
     // FIXME: Nearest and lower do not do clamping, but other methods do
     // Make it consistent
     bool clamp = order != 1;
diff --git a/src/backend/cuda/kernel/transform.cuh b/src/backend/cuda/kernel/transform.cuh
index fbb870f8a7..7bece00265 100644
--- a/src/backend/cuda/kernel/transform.cuh
+++ b/src/backend/cuda/kernel/transform.cuh
@@ -164,7 +164,7 @@ void transform(Param<T> out, CParam<T> in,
         return;
     }
 
-    Interp2<T, WT, order> interp;
+    Interp2<T, WT, 0, 1, order> interp;
     // FIXME: Nearest and lower do not do clamping, but other methods do
     // Make it consistent
     bool clamp = order != 1;
diff --git a/src/backend/opencl/kernel/rotate.cl b/src/backend/opencl/kernel/rotate.cl
index 354e2e2d22..da530e66d3 100644
--- a/src/backend/opencl/kernel/rotate.cl
+++ b/src/backend/opencl/kernel/rotate.cl
@@ -62,7 +62,7 @@ kernel void rotateKernel(global T *d_out, const KParam out,
 
     // FIXME: Nearest and lower do not do clamping, but other methods do
     // Make it consistent
-    bool clamp = INTERP_ORDER != 1;
+    const bool doclamp = INTERP_ORDER != 1;
     interp2(d_out, out, loco, d_in, in, inoff, xidi, yidi, method, limages,
-            clamp);
+            doclamp, 2);
 }
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 42733fee85..ac1df0e294 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -70,6 +70,8 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
         DefineKeyValue(InterpInTy, dtype_traits<T>::getName()),
         DefineKeyValue(InterpValTy, dtype_traits<vtype_t<T>>::getName()),
         DefineKeyValue(InterpPosTy, dtype_traits<wtype_t<BT>>::getName()),
+        DefineKeyValue(XDIM, 0),
+        DefineKeyValue(YDIM, 1),
         DefineKeyValue(INTERP_ORDER, order),
         DefineKeyValue(IS_CPLX, (isComplex ? 1 : 0)),
     };
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 7651b35f29..85c6a293ab 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -155,7 +155,7 @@ kernel void transformKernel(global T *d_out, const KParam out,
     const int loco = outoff + (yido * out.strides[1] + xido);
     // FIXME: Nearest and lower do not do clamping, but other methods do
     // Make it consistent
-    bool clamp = INTERP_ORDER != 1;
+    const bool doclamp = INTERP_ORDER != 1;
 
     T zero = ZERO;
     if (xidi < (InterpPosTy)-0.0001 || yidi < (InterpPosTy)-0.0001 ||
@@ -167,5 +167,5 @@ kernel void transformKernel(global T *d_out, const KParam out,
     }
 
     interp2(d_out, out, loco, d_in, in, inoff, xidi, yidi, method, limages,
-            clamp);
+            doclamp, 2);
 }
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index ab9055a703..87e8ba1fc9 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -70,6 +70,8 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
         DefineKeyValue(InterpInTy, dtype_traits<T>::getName()),
         DefineKeyValue(InterpValTy, dtype_traits<vtype_t<T>>::getName()),
         DefineKeyValue(InterpPosTy, dtype_traits<wtype_t<BT>>::getName()),
+        DefineKeyValue(XDIM, 0),
+        DefineKeyValue(YDIM, 1),
         DefineKeyValue(INTERP_ORDER, order),
         DefineKeyValue(IS_CPLX, (isComplex ? 1 : 0)),
     };

From d849785186a0eee6441643f904aa4ba3886535e5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 13 Aug 2020 13:47:00 -0400
Subject: [PATCH 2058/2677] Remove assert that check that signal/filter types
 have to be the same

---
 src/api/c/fftconvolve.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index e0aabda55e..bd10287cb4 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -162,14 +162,12 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
         const ArrayInfo &fInfo = getInfo(filter);
 
         af_dtype signalType = sInfo.getType();
-        af_dtype filterType = fInfo.getType();
 
         const dim4 &sdims = sInfo.dims();
         const dim4 &fdims = fInfo.dims();
 
         AF_BATCH_KIND convBT = identifyBatchKind(sdims, fdims, baseDim);
 
-        ARG_ASSERT(1, (signalType == filterType));
         ARG_ASSERT(1, (convBT != AF_BATCH_UNSUPPORTED));
 
         af_array output;

From 6b314a99c6d52a021b19cfacfe1a4ec14f357a2a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Aug 2020 22:08:48 -0400
Subject: [PATCH 2059/2677] Fix checkAndSetDevMaxCompute when the device cc is
 greater than max

Fixes an issue where the device compute capability is larger than
the supported maximum of the CUDA runtime used to build ArrayFire.
This happens for example when you run the Turing card with a CUDA
runtime of 9.0. The compute capability of Turing is 7.5 and the
maximum supported by the runtime is 7.0/7.2. Before this change
we were only checking the major compute capability and not checking
the minor version to set the max compute capability of the device.
This caused errors like:

In file src/backend/cuda/compile_module.cpp:266
NVRTC Error(5): NVRTC_ERROR_INVALID_OPTION
Log:
nvrtc: error: invalid value for --gpu-architecture (-arch)

This commit also updates the error messages for failure cases.
---
 src/backend/cuda/device_manager.cpp | 123 ++++++++++++++++++----------
 1 file changed, 80 insertions(+), 43 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 1493e5e432..947661c412 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -18,6 +18,7 @@
 #include <common/defines.hpp>
 #include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
+#include <common/util.hpp>
 #include <cublas_v2.h>  // needed for af/cuda.h
 #include <device_manager.hpp>
 #include <driver.h>
@@ -44,10 +45,12 @@
 #include <stdexcept>
 #include <string>
 #include <thread>
+#include <utility>
 #include <vector>
 
 using std::begin;
 using std::end;
+using std::find;
 using std::find_if;
 using std::make_pair;
 using std::pair;
@@ -63,21 +66,39 @@ struct cuNVRTCcompute {
     int major;
     /// Maximum minor compute flag supported by cudaVersion
     int minor;
+    /// Maximum minor compute flag supported on the embedded(Jetson) platforms
+    int embedded_minor;
 };
 
+// clang-format off
+static const int jetsonComputeCapabilities[] = {
+    7020,
+    6020,
+    5030,
+    3020,
+};
+// clang-format on
+
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
-    {10020, 7, 5},
-    {10010, 7, 5},
-    {10000, 7, 2},
-    {9020, 7, 2},
-    {9010, 7, 2},
-    {9000, 7, 2},
-    {8000, 5, 3},
-    {7050, 5, 3},
-    {7000, 5, 3}};
+    {10020, 7, 5, 2},
+    {10010, 7, 5, 2},
+    {10000, 7, 0, 2},
+    { 9020, 7, 0, 2},
+    { 9010, 7, 0, 2},
+    { 9000, 7, 0, 2},
+    { 8000, 5, 2, 3},
+    { 7050, 5, 2, 3},
+    { 7000, 5, 2, 3}};
 // clang-format on
 
+bool isEmbedded(pair<int, int> compute) {
+    int version = compute.first * 1000 + compute.second * 10;
+    return end(jetsonComputeCapabilities) !=
+           find(begin(jetsonComputeCapabilities),
+                end(jetsonComputeCapabilities), version);
+}
+
 bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
     auto rt = find_if(
         begin(Toolkit2MaxCompute), end(Toolkit2MaxCompute),
@@ -88,7 +109,7 @@ bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
                 "CUDA runtime version({}) not recognized. Please "
                 "create an issue or a pull request on the ArrayFire repository "
                 "to update the Toolkit2MaxCompute array with this version of "
-                "the CUDA Runtime. Continuing assuming everything is okay.",
+                "the CUDA Runtime. Continuing.",
                 int_version_to_string(runtime));
         return true;
     }
@@ -105,50 +126,66 @@ bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
 }
 
 /// Check for compatible compute version based on runtime cuda toolkit version
-void checkAndSetDevMaxCompute(pair<int, int> &prop) {
-    auto originalCompute = prop;
-    UNUSED(originalCompute);
-    int rtCudaVer = 0;
+void checkAndSetDevMaxCompute(pair<int, int> &computeCapability) {
+    auto originalCompute = computeCapability;
+    int rtCudaVer        = 0;
     CUDA_CHECK(cudaRuntimeGetVersion(&rtCudaVer));
     auto tkitMaxCompute = find_if(
         begin(Toolkit2MaxCompute), end(Toolkit2MaxCompute),
         [rtCudaVer](cuNVRTCcompute v) { return rtCudaVer == v.cudaVersion; });
 
+    bool embeddedDevice = isEmbedded(computeCapability);
+
     // If runtime cuda version is found in toolkit array
     // check for max possible compute for that cuda version
     if (tkitMaxCompute != end(Toolkit2MaxCompute) &&
-        prop.first > tkitMaxCompute->major) {
-        prop = make_pair(tkitMaxCompute->major, tkitMaxCompute->minor);
-#ifndef NDEBUG
-        char errMsg[] =
-            "Current device compute version (%d.%d) exceeds supported maximum "
-            "cuda runtime compute version (%d.%d). Using %d.%d.";
-        fprintf(stderr, errMsg, originalCompute.first, originalCompute.second,
-                prop.first, prop.second, prop.first, prop.second);
-#endif
-    } else if (prop.first > Toolkit2MaxCompute[0].major) {
+        computeCapability.first >= tkitMaxCompute->major) {
+        int minorVersion = embeddedDevice ? tkitMaxCompute->embedded_minor
+                                          : tkitMaxCompute->minor;
+
+        if (computeCapability.second > minorVersion) {
+            computeCapability = make_pair(tkitMaxCompute->major, minorVersion);
+            spdlog::get("platform")
+                ->warn(
+                    "The compute capability for the current device({}.{}) "
+                    "exceeds maximum supported by ArrayFire's CUDA "
+                    "runtime({}.{}). Download or rebuild the latest version of "
+                    "ArrayFire to avoid this warning. Using {}.{} for JIT "
+                    "compilation kernels.",
+                    originalCompute.first, originalCompute.second,
+                    computeCapability.first, computeCapability.second,
+                    computeCapability.first, computeCapability.second);
+        }
+    } else if (computeCapability.first >= Toolkit2MaxCompute[0].major) {
         // If runtime cuda version is NOT found in toolkit array
         // use the top most toolkit max compute
-        prop =
-            make_pair(Toolkit2MaxCompute[0].major, Toolkit2MaxCompute[0].minor);
-#ifndef NDEBUG
-        char errMsg[] =
-            "Runtime cuda version not found in toolkit info array."
-            "Current device compute version (%d.%d) exceeds supported maximum "
-            "runtime cuda compute version (%d.%d) of latest known cuda toolkit."
-            "Using %d.%d.";
-        fprintf(stderr, errMsg, originalCompute.first, originalCompute.second,
-                prop.first, prop.second, prop.first, prop.second);
-#endif
-    } else if (prop.first < 3) {
+        int minorVersion = embeddedDevice ? tkitMaxCompute->embedded_minor
+                                          : tkitMaxCompute->minor;
+        if (computeCapability.second > minorVersion) {
+            computeCapability =
+                make_pair(Toolkit2MaxCompute[0].major, minorVersion);
+            spdlog::get("platform")
+                ->warn(
+                    "CUDA runtime version({}) not recognized. Targeting "
+                    "compute {}.{} for this device which is the latest compute "
+                    "capability supported by ArrayFire's CUDA runtime({}.{}). "
+                    "Please create an issue or a pull request on the ArrayFire "
+                    "repository to update the Toolkit2MaxCompute array with "
+                    "this version of the CUDA Runtime.",
+                    int_version_to_string(rtCudaVer), originalCompute.first,
+                    originalCompute.second, computeCapability.first,
+                    computeCapability.second, computeCapability.first,
+                    computeCapability.second);
+        }
+    } else if (computeCapability.first < 3) {
         // all compute versions prior to Kepler, we don't support
-        // don't change the prop.
-#ifndef NDEBUG
-        char errMsg[] =
-            "Current device compute version (%d.%d) lower than the"
-            "minimum compute version ArrayFire supports.";
-        fprintf(stderr, errMsg, originalCompute.first, originalCompute.second);
-#endif
+        // don't change the computeCapability.
+        spdlog::get("platform")
+            ->warn(
+                "The compute capability of the current device({}.{}) "
+                "lower than the minimum compute version ArrayFire "
+                "supports.",
+                originalCompute.first, originalCompute.second);
     }
 }
 

From 0e8d5fd58722c371e338b5497c499d590a9799d9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Aug 2020 22:46:10 -0400
Subject: [PATCH 2060/2677] Add utility header included from cuda_fp16.hpp for
 CUDA 9

The utility header in cuda_fp16.hpp is not included automatically
in CUDA 9. Additionally we need to pass the
--device-as-default-execution-space flag to nvrtc for JIT and
non-JIT kernels
---
 src/backend/cuda/compile_module.cpp | 30 +++++++++++++++++++----------
 1 file changed, 20 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 06a96e1f29..8c9e308c76 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -143,20 +143,29 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
                      const vector<string> &kInstances, const bool sourceIsJIT) {
     nvrtcProgram prog;
     if (sourceIsJIT) {
-        array<const char *, 2> headers = {
+        constexpr const char *header_names[] = {
+            "utility",
+            "cuda_fp16.hpp",
+            "cuda_fp16.h",
+        };
+        constexpr size_t numHeaders = extent<decltype(header_names)>::value;
+        array<const char *, numHeaders> headers = {
+            "",
             cuda_fp16_hpp,
             cuda_fp16_h,
         };
-        array<const char *, 2> header_names = {"cuda_fp16.hpp", "cuda_fp16.h"};
+        static_assert(headers.size() == numHeaders,
+                      "headers array contains fewer sources than header_names");
         NVRTC_CHECK(nvrtcCreateProgram(&prog, sources[0].c_str(),
-                                       moduleKey.c_str(), 2, headers.data(),
-                                       header_names.data()));
+                                       moduleKey.c_str(), numHeaders,
+                                       headers.data(), header_names));
     } else {
         constexpr static const char *includeNames[] = {
             "math.h",          // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             "stdbool.h",       // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             "stdlib.h",        // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             "vector_types.h",  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            "utility",         // DUMMY ENTRY TO SATISFY cuda_fp16.hpp inclusion
             "backend.hpp",
             "cuComplex.h",
             "jit.cuh",
@@ -183,12 +192,13 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
             "minmax_op.hpp",
         };
 
-        constexpr size_t NumHeaders = extent<decltype(includeNames)>::value;
-        static const array<string, NumHeaders> sourceStrings = {{
+        constexpr size_t numHeaders = extent<decltype(includeNames)>::value;
+        static const array<string, numHeaders> sourceStrings = {{
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             string(""),  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
+            string(""),  // DUMMY ENTRY TO SATISFY utility inclusion
             string(backend_hpp, backend_hpp_len),
             string(cuComplex_h, cuComplex_h_len),
             string(jit_cuh, jit_cuh_len),
@@ -230,11 +240,11 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
             sourceStrings[22].c_str(), sourceStrings[23].c_str(),
             sourceStrings[24].c_str(), sourceStrings[25].c_str(),
             sourceStrings[26].c_str(), sourceStrings[27].c_str(),
-        };
-        static_assert(extent<decltype(headers)>::value == NumHeaders,
+            sourceStrings[28].c_str()};
+        static_assert(extent<decltype(headers)>::value == numHeaders,
                       "headers array contains fewer sources than includeNames");
         NVRTC_CHECK(nvrtcCreateProgram(&prog, sources[0].c_str(),
-                                       moduleKey.c_str(), NumHeaders, headers,
+                                       moduleKey.c_str(), numHeaders, headers,
                                        includeNames));
     }
 
@@ -246,6 +256,7 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     vector<const char *> compiler_options = {
         arch.data(),
         "--std=c++14",
+        "--device-as-default-execution-space",
 #if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
         "--device-debug",
         "--generate-line-info"
@@ -256,7 +267,6 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
                   back_insert_iterator<vector<const char *>>(compiler_options),
                   [](const string &s) { return s.data(); });
 
-        compiler_options.push_back("--device-as-default-execution-space");
         for (auto &instantiation : kInstances) {
             NVRTC_CHECK(nvrtcAddNameExpression(prog, instantiation.c_str()));
         }

From 3e01de47806afb4d732fa9556039006e54572406 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Aug 2020 22:48:59 -0400
Subject: [PATCH 2061/2677] Formatting and rewording of warning messages

* The moduleKey is an size_t object so the maximum number of digits
  it can have is 20 so the format length for that value is updated
* The runtime check messages are always logged (but not displayed)
  Errors are still only thrown in debug modes
* Display the compute capability of the CUDA device along with
  its name and other stats

  example:
  Found device: Quadro T2000 (sm_75) (3.82 GB | ~3164.06 GFLOPs | 16 SMs)
---
 src/backend/cuda/compile_module.cpp   | 12 ++++----
 src/backend/cuda/device_manager.cpp   | 44 +++++++++++++--------------
 src/backend/opencl/compile_module.cpp | 16 +++++-----
 3 files changed, 35 insertions(+), 37 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 8c9e308c76..c4c3315d0a 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -401,8 +401,8 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
     Module retVal{nullptr};
     try {
         std::ifstream in(cacheFile, std::ios::binary);
-        if (!in.is_open()) {
-            AF_TRACE("{{{:<30} : Unable to open {} for {}}}", moduleKey,
+        if (!in) {
+            AF_TRACE("{{{:<20} : Unable to open {} for {}}}", moduleKey,
                      cacheFile, getDeviceProp(device).name);
             removeFile(cacheFile);  // Remove if exists
             return Module{nullptr};
@@ -448,23 +448,23 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
 
         CU_CHECK(cuModuleLoadData(&modOut, cubin.data()));
 
-        AF_TRACE("{{{:<30} : loaded from {} for {} }}", moduleKey, cacheFile,
+        AF_TRACE("{{{:<20} : loaded from {} for {} }}", moduleKey, cacheFile,
                  getDeviceProp(device).name);
 
         retVal.set(modOut);
     } catch (const std::ios_base::failure &e) {
-        AF_TRACE("{{{:<30} : Unable to read {} for {}}}", moduleKey, cacheFile,
+        AF_TRACE("{{{:<20} : Unable to read {} for {}}}", moduleKey, cacheFile,
                  getDeviceProp(device).name);
         removeFile(cacheFile);
     } catch (const AfError &e) {
         if (e.getError() == AF_ERR_LOAD_SYM) {
             AF_TRACE(
-                "{{{:<30} : Corrupt binary({}) found on disk for {}, removed}}",
+                "{{{:<20} : Corrupt binary({}) found on disk for {}, removed}}",
                 moduleKey, cacheFile, getDeviceProp(device).name);
         } else {
             if (modOut != nullptr) { CU_CHECK(cuModuleUnload(modOut)); }
             AF_TRACE(
-                "{{{:<30} : cuModuleLoadData failed with content from {} for "
+                "{{{:<20} : cuModuleLoadData failed with content from {} for "
                 "{}, {}}}",
                 moduleKey, cacheFile, getDeviceProp(device).name, e.what());
         }
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 947661c412..9ec832fe59 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -407,7 +407,6 @@ static const ToolkitDriverVersions
 /// \note: only works in debug builds
 void debugRuntimeCheck(spdlog::logger *logger, int runtime_version,
                        int driver_version) {
-#ifndef NDEBUG
     auto runtime_it =
         find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
                 [runtime_version](ToolkitDriverVersions ver) {
@@ -425,31 +424,28 @@ void debugRuntimeCheck(spdlog::logger *logger, int runtime_version,
     // display a message in the trace. Do not throw an error unless this is
     // a debug build
     if (runtime_it == end(CudaToDriverVersion)) {
-        char buf[1024];
+        char buf[256];
         char err_msg[] =
-            "CUDA runtime version(%s) not recognized. Please "
-            "create an issue or a pull request on the ArrayFire repository to "
-            "update the CudaToDriverVersion variable with this version of "
-            "the CUDA Toolkit.\n";
-        snprintf(buf, 1024, err_msg,
+            "CUDA runtime version(%s) not recognized. Please create an issue "
+            "or a pull request on the ArrayFire repository to update the "
+            "CudaToDriverVersion variable with this version of the CUDA "
+            "runtime.\n";
+        snprintf(buf, 256, err_msg,
                  int_version_to_string(runtime_version).c_str());
         AF_TRACE("{}", buf);
+#ifndef NDEBUG
         AF_ERROR(buf, AF_ERR_RUNTIME);
+#endif
     }
 
     if (driver_it == end(CudaToDriverVersion)) {
-        char buf[1024];
-        char err_msg[] =
-            "CUDA driver version(%s) not part of the "
-            "CudaToDriverVersion array. Please create an issue or a pull "
-            "request on the ArrayFire repository to update the "
-            "CudaToDriverVersion variable with this version of the CUDA "
-            "Toolkit.\n";
-        snprintf(buf, 1024, err_msg,
-                 int_version_to_string(driver_version).c_str());
-        AF_TRACE("{}", buf);
+        AF_TRACE(
+            "CUDA driver version({}) not part of the CudaToDriverVersion "
+            "array. Please create an issue or a pull request on the ArrayFire "
+            "repository to update the CudaToDriverVersion variable with this "
+            "version of the CUDA runtime.\n",
+            int_version_to_string(driver_version).c_str());
     }
-#endif
 }
 
 // Check if the device driver version is recent enough to run the cuda libs
@@ -552,11 +548,13 @@ DeviceManager::DeviceManager()
                             compute2cores(dev.prop.major, dev.prop.minor) *
                             dev.prop.clockRate;
                 dev.nativeId = i;
-                AF_TRACE("Found device: {} ({:0.3} GB | ~{} GFLOPs | {} SMs)",
-                         dev.prop.name,
-                         dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
-                         dev.flops / 1024. / 1024. * 2,
-                         dev.prop.multiProcessorCount);
+                AF_TRACE(
+                    "Found device: {} (sm_{}{}) ({:0.3} GB | ~{} GFLOPs | {} "
+                    "SMs)",
+                    dev.prop.name, dev.prop.major, dev.prop.minor,
+                    dev.prop.totalGlobalMem / 1024. / 1024. / 1024.,
+                    dev.flops / 1024. / 1024. * 2,
+                    dev.prop.multiProcessorCount);
                 cuDevices.push_back(dev);
             }
         }
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 2f6d374db1..35f992fe02 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -194,12 +194,12 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
             // before the current thread.
             if (!renameFile(tempFile, cacheFile)) { removeFile(tempFile); }
         } catch (const cl::Error &e) {
-            AF_TRACE("{{{:<30} : Failed to fetch opencl binary for {}, {}}}",
+            AF_TRACE("{{{:<20} : Failed to fetch opencl binary for {}, {}}}",
                      moduleKey,
                      opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
                      e.what());
         } catch (const std::ios_base::failure &e) {
-            AF_TRACE("{{{:<30} : Failed writing binary to {} for {}, {}}}",
+            AF_TRACE("{{{:<20} : Failed writing binary to {} for {}, {}}}",
                      moduleKey, cacheFile,
                      opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
                      e.what());
@@ -207,7 +207,7 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     }
 #endif
 
-    AF_TRACE("{{{:<30} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", moduleKey,
+    AF_TRACE("{{{:<20} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", moduleKey,
              duration_cast<milliseconds>(compileEnd - compileBegin).count(),
              fmt::join(options, " "),
              getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_NAME>());
@@ -250,26 +250,26 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
         program = Program(opencl::getContext(), {dev}, {clbin});
         program.build();
 
-        AF_TRACE("{{{:<30} : loaded from {} for {} }}", moduleKey, cacheFile,
+        AF_TRACE("{{{:<20} : loaded from {} for {} }}", moduleKey, cacheFile,
                  dev.getInfo<CL_DEVICE_NAME>());
         retVal.set(program);
     } catch (const AfError &e) {
         if (e.getError() == AF_ERR_LOAD_SYM) {
             AF_TRACE(
-                "{{{:<30} : Corrupt binary({}) found on disk for {}, removed}}",
+                "{{{:<20} : Corrupt binary({}) found on disk for {}, removed}}",
                 moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>());
         } else {
-            AF_TRACE("{{{:<30} : Unable to open {} for {}}}", moduleKey,
+            AF_TRACE("{{{:<20} : Unable to open {} for {}}}", moduleKey,
                      cacheFile, dev.getInfo<CL_DEVICE_NAME>());
         }
         removeFile(cacheFile);
     } catch (const std::ios_base::failure &e) {
-        AF_TRACE("{{{:<30} : IO failure while loading {} for {}; {}}}",
+        AF_TRACE("{{{:<20} : IO failure while loading {} for {}; {}}}",
                  moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>(), e.what());
         removeFile(cacheFile);
     } catch (const cl::Error &e) {
         AF_TRACE(
-            "{{{:<30} : Loading OpenCL binary({}) failed for {}; {}, Build "
+            "{{{:<20} : Loading OpenCL binary({}) failed for {}; {}, Build "
             "Log: {}}}",
             moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>(), e.what(),
             getProgramBuildLog(program));

From e62aab0f1672d60ed0c1f5271124f322d5f5df8d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Aug 2020 13:40:55 -0400
Subject: [PATCH 2062/2677] Fix errors and warnings in RNG for CUDA 9.0

---
 src/backend/common/half.hpp               | 2 --
 src/backend/cuda/kernel/random_engine.hpp | 4 +---
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 885664798e..ce06eedf02 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -832,9 +832,7 @@ class alignas(2) half {
 #endif
 
    public:
-#if CUDA_VERSION >= 10000
     AF_CONSTEXPR
-#endif
     half() = default;
 
     /// Constructor.
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 0ef218ad93..e52e78d354 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -134,12 +134,10 @@ __device__ static double getDouble01(uint num1, uint num2) {
     uint64_t n2 = num2;
     n1 <<= 32;
     uint64_t num = n1 | n2;
-#pragma diag_suppress 3245
     constexpr double factor =
         ((1.0) / (std::numeric_limits<unsigned long long>::max() +
-                  static_cast<long double>(1.0l)));
+                  static_cast<double>(1.0)));
     constexpr double half_factor((0.5) * factor);
-#pragma diag_default 3245
 
     return fma(static_cast<double>(num), factor, half_factor);
 }

From f79d438df25263144b462bb11179e0057436d4ea Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 8 Aug 2020 00:15:20 +0530
Subject: [PATCH 2063/2677] Add new variance APIs with bias enum argument
 instead of bool

Also fixed indentation for af_var_bias enum in defines header
---
 include/af/defines.h           |  6 +--
 include/af/statistics.h        | 96 +++++++++++++++++++++++++++++++---
 src/api/c/var.cpp              | 50 +++++++++++-------
 src/api/cpp/var.cpp            | 40 +++++++++++---
 src/api/unified/statistics.cpp | 12 +++++
 test/var.cpp                   | 66 +++++++++++++++++------
 6 files changed, 218 insertions(+), 52 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index bd58ec1f45..464a3c1d81 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -516,9 +516,9 @@ typedef enum {
 
 #if AF_API_VERSION >= 37
 typedef enum {
-              AF_VARIANCE_DEFAULT    = 0, ///< Default (Population) variance
-              AF_VARIANCE_SAMPLE     = 1, ///< Sample variance
-              AF_VARIANCE_POPULATION = 2  ///< Population variance
+    AF_VARIANCE_DEFAULT    = 0, ///< Default (Population) variance
+    AF_VARIANCE_SAMPLE     = 1, ///< Sample variance
+    AF_VARIANCE_POPULATION = 2  ///< Population variance
 } af_var_bias;
 
 typedef enum {
diff --git a/include/af/statistics.h b/include/af/statistics.h
index 6bd7685233..4b18303749 100644
--- a/include/af/statistics.h
+++ b/include/af/statistics.h
@@ -46,16 +46,37 @@ AFAPI array mean(const array& in, const array& weights, const dim_t dim=-1);
    C++ Interface for variance
 
    \param[in] in is the input array
-   \param[in] isbiased is boolean denoting Population variance (false) or Sample Variance (true)
+   \param[in] isbiased is boolean denoting Population variance (false) or Sample
+              Variance (true)
    \param[in] dim the dimension along which the variance is extracted
-   \return    the variance of the input array along dimension \p dim
+   \return the variance of the input array along dimension \p dim
 
    \ingroup stat_func_var
 
    \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+
+   \deprecated Use \ref af::var that takes \ref af_var_bias instead
 */
+AF_DEPRECATED("Use \ref af::var(const array&, const af_var_bias, const dim_t)")
 AFAPI array var(const array& in, const bool isbiased=false, const dim_t dim=-1);
 
+#if AF_API_VERSION >= 38
+/**
+   C++ Interface for variance
+
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes o
+              value of type \ref af_var_bias.
+   \param[in] dim the dimension along which the variance is extracted
+   \return the variance of the input array along dimension \p dim
+
+   \ingroup stat_func_var
+
+   \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+*/
+AFAPI array var(const array &in, const af_var_bias bias, const dim_t dim = -1);
+#endif
+
 /**
    C++ Interface for variance of weighted inputs
 
@@ -153,13 +174,31 @@ AFAPI T mean(const array& in, const array& weights);
    C++ Interface for variance of all elements
 
    \param[in] in is the input array
-   \param[in] isbiased is boolean denoting Population variance (false) or Sample Variance (true)
-   \return    variance of the entire input array
+   \param[in] isbiased is boolean denoting Population variance (false) or Sample
+              Variance (true)
+   \return variance of the entire input array
 
    \ingroup stat_func_var
+
+   \deprecated Use \ref af::var that takes \ref af_var_bias instead
 */
-template<typename T>
-AFAPI T var(const array& in, const bool isbiased=false);
+template <typename T>
+AF_DEPRECATED("Use af::var(const af::array&, const af_var_bias)")
+AFAPI T var(const array &in, const bool isbiased = false);
+
+#if AF_API_VERSION >= 38
+/**
+   C++ Interface for variance of all elements
+
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias.
+   \return variance of the \p in array
+
+   \ingroup stat_func_var
+*/
+template <typename T> AFAPI T var(const array &in, const af_var_bias bias);
+#endif
 
 /**
    C++ Interface for variance of all elements in weighted input
@@ -278,9 +317,31 @@ AFAPI af_err af_mean_weighted(af_array *out, const af_array in, const af_array w
 
    \ingroup stat_func_var
 
+   \deprecated Use \ref af_var_v2 instead
 */
+AF_DEPRECATED("Use af_var_v2")
 AFAPI af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t dim);
 
+#if AF_API_VERSION >= 38
+/**
+   C Interface for variance
+
+   \param[out] out will contain the variance of the input array along dimension
+               \p dim
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias
+   \param[in] dim the dimension along which the variance is extracted
+   \return \ref AF_SUCCESS if the operation is successful, otherwise an
+           appropriate error code is returned.
+
+   \ingroup stat_func_var
+
+*/
+AFAPI af_err af_var_v2(af_array *out, const af_array in, const af_var_bias bias,
+                       const dim_t dim);
+#endif
+
 /**
    C Interface for variance of weighted input array
 
@@ -393,9 +454,32 @@ AFAPI af_err af_mean_all_weighted(double *real, double *imag, const af_array in,
    otherwise an appropriate error code is returned.
 
    \ingroup stat_func_var
+
+   \deprecated Use \ref af_var_all_v2 instead
 */
+AF_DEPRECATED("Use af_var_all_v2")
 AFAPI af_err af_var_all(double *realVal, double *imagVal, const af_array in, const bool isbiased);
 
+#if AF_API_VERSION >= 38
+/**
+   C Interface for variance of all elements
+
+   \param[out] realVal will contain the real part of variance of the entire
+               input array
+   \param[out] imagVal will contain the imaginary part of variance
+               of the entire input array
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias
+   \return \ref AF_SUCCESS if the operation is successful, otherwise an
+           appropriate error code is returned.
+
+   \ingroup stat_func_var
+*/
+AFAPI af_err af_var_all_v2(double *realVal, double *imagVal, const af_array in,
+                           const af_var_bias bias);
+#endif
+
 /**
    C Interface for variance of all elements in weighted input
 
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index ca68512cd7..6119701560 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -51,7 +51,7 @@ using std::tie;
 using std::tuple;
 
 template<typename inType, typename outType>
-static outType varAll(const af_array& in, const bool isbiased) {
+static outType varAll(const af_array& in, const af_var_bias bias) {
     using weightType          = typename baseOutType<outType>::type;
     const Array<inType> inArr = getArray<inType>(in);
     Array<outType> input      = cast<outType>(inArr);
@@ -64,9 +64,9 @@ static outType varAll(const af_array& in, const bool isbiased) {
 
     Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
 
-    outType result =
-        division(reduce_all<af_add_t, outType, outType>(diffSq),
-                 isbiased ? input.elements() : input.elements() - 1);
+    outType result = division(
+        reduce_all<af_add_t, outType, outType>(diffSq),
+        bias == AF_VARIANCE_SAMPLE ? input.elements() : input.elements() - 1);
 
     return result;
 }
@@ -181,6 +181,13 @@ static af_array var_(const af_array& in, const af_array& weights,
 
 af_err af_var(af_array* out, const af_array in, const bool isbiased,
               const dim_t dim) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return af_var_v2(out, in, bias, dim);
+}
+
+af_err af_var_v2(af_array* out, const af_array in, const af_var_bias bias,
+                 const dim_t dim) {
     try {
         ARG_ASSERT(3, (dim >= 0 && dim <= 3));
 
@@ -189,8 +196,6 @@ af_err af_var(af_array* out, const af_array in, const bool isbiased,
         af_dtype type         = info.getType();
 
         af_array no_weights = 0;
-        af_var_bias bias =
-            (isbiased) ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION;
         switch (type) {
             case f32:
                 output = var_<float, float>(in, no_weights, bias, dim);
@@ -319,28 +324,35 @@ af_err af_var_weighted(af_array* out, const af_array in, const af_array weights,
 
 af_err af_var_all(double* realVal, double* imagVal, const af_array in,
                   const bool isbiased) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return af_var_all_v2(realVal, imagVal, in, bias);
+}
+
+af_err af_var_all_v2(double* realVal, double* imagVal, const af_array in,
+                     const af_var_bias bias) {
     try {
         const ArrayInfo& info = getInfo(in);
         af_dtype type         = info.getType();
         switch (type) {
-            case f64: *realVal = varAll<double, double>(in, isbiased); break;
-            case f32: *realVal = varAll<float, float>(in, isbiased); break;
-            case s32: *realVal = varAll<int, float>(in, isbiased); break;
-            case u32: *realVal = varAll<uint, float>(in, isbiased); break;
-            case s16: *realVal = varAll<short, float>(in, isbiased); break;
-            case u16: *realVal = varAll<ushort, float>(in, isbiased); break;
-            case s64: *realVal = varAll<intl, double>(in, isbiased); break;
-            case u64: *realVal = varAll<uintl, double>(in, isbiased); break;
-            case u8: *realVal = varAll<uchar, float>(in, isbiased); break;
-            case b8: *realVal = varAll<char, float>(in, isbiased); break;
-            case f16: *realVal = varAll<half, float>(in, isbiased); break;
+            case f64: *realVal = varAll<double, double>(in, bias); break;
+            case f32: *realVal = varAll<float, float>(in, bias); break;
+            case s32: *realVal = varAll<int, float>(in, bias); break;
+            case u32: *realVal = varAll<uint, float>(in, bias); break;
+            case s16: *realVal = varAll<short, float>(in, bias); break;
+            case u16: *realVal = varAll<ushort, float>(in, bias); break;
+            case s64: *realVal = varAll<intl, double>(in, bias); break;
+            case u64: *realVal = varAll<uintl, double>(in, bias); break;
+            case u8: *realVal = varAll<uchar, float>(in, bias); break;
+            case b8: *realVal = varAll<char, float>(in, bias); break;
+            case f16: *realVal = varAll<half, float>(in, bias); break;
             case c32: {
-                cfloat tmp = varAll<cfloat, cfloat>(in, isbiased);
+                cfloat tmp = varAll<cfloat, cfloat>(in, bias);
                 *realVal   = real(tmp);
                 *imagVal   = imag(tmp);
             } break;
             case c64: {
-                cdouble tmp = varAll<cdouble, cdouble>(in, isbiased);
+                cdouble tmp = varAll<cdouble, cdouble>(in, bias);
                 *realVal    = real(tmp);
                 *imagVal    = imag(tmp);
             } break;
diff --git a/src/api/cpp/var.cpp b/src/api/cpp/var.cpp
index a5c563420a..80cd6a63c5 100644
--- a/src/api/cpp/var.cpp
+++ b/src/api/cpp/var.cpp
@@ -21,8 +21,14 @@
 namespace af {
 
 array var(const array& in, const bool isbiased, const dim_t dim) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return var(in, bias, dim);
+}
+
+array var(const array& in, const af_var_bias bias, const dim_t dim) {
     af_array temp = 0;
-    AF_THROW(af_var(&temp, in.get(), isbiased, getFNSD(dim, in.dims())));
+    AF_THROW(af_var_v2(&temp, in.get(), bias, getFNSD(dim, in.dims())));
     return array(temp);
 }
 
@@ -35,10 +41,16 @@ array var(const array& in, const array& weights, const dim_t dim) {
 
 #define INSTANTIATE_VAR(T)                                                 \
     template<>                                                             \
-    AFAPI T var(const array& in, const bool isbiased) {                    \
+    AFAPI T var(const array& in, const af_var_bias bias) {                 \
         double ret_val;                                                    \
-        AF_THROW(af_var_all(&ret_val, NULL, in.get(), isbiased));          \
+        AF_THROW(af_var_all_v2(&ret_val, NULL, in.get(), bias));           \
         return cast<T>(ret_val);                                           \
+    }                                                                      \
+    template<>                                                             \
+    AFAPI T var(const array& in, const bool isbiased) {                    \
+        const af_var_bias bias =                                           \
+            (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);      \
+        return var<T>(in, bias);                                           \
     }                                                                      \
                                                                            \
     template<>                                                             \
@@ -50,19 +62,33 @@ array var(const array& in, const array& weights, const dim_t dim) {
     }
 
 template<>
-AFAPI af_cfloat var(const array& in, const bool isbiased) {
+AFAPI af_cfloat var(const array& in, const af_var_bias bias) {
     double real, imag;
-    AF_THROW(af_var_all(&real, &imag, in.get(), isbiased));
+    AF_THROW(af_var_all_v2(&real, &imag, in.get(), bias));
     return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
-AFAPI af_cdouble var(const array& in, const bool isbiased) {
+AFAPI af_cdouble var(const array& in, const af_var_bias bias) {
     double real, imag;
-    AF_THROW(af_var_all(&real, &imag, in.get(), isbiased));
+    AF_THROW(af_var_all_v2(&real, &imag, in.get(), bias));
     return {real, imag};
 }
 
+template<>
+AFAPI af_cfloat var(const array& in, const bool isbiased) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return var<af_cfloat>(in, bias);
+}
+
+template<>
+AFAPI af_cdouble var(const array& in, const bool isbiased) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return var<af_cdouble>(in, bias);
+}
+
 template<>
 AFAPI af_cfloat var(const array& in, const array& weights) {
     double real, imag;
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index fadb506cb0..4dbc36ddb7 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -101,3 +101,15 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
     CHECK_ARRAYS(in);
     CALL(af_topk, values, indices, in, k, dim, order);
 }
+
+af_err af_var_v2(af_array *out, const af_array in, const af_var_bias bias,
+                 const dim_t dim) {
+    CHECK_ARRAYS(in);
+    CALL(af_var_v2, out, in, bias, dim);
+}
+
+af_err af_var_all_v2(double *realVal, double *imagVal, const af_array in,
+                     const af_var_bias bias) {
+    CHECK_ARRAYS(in);
+    CALL(af_var_all_v2, realVal, imagVal, in, bias);
+}
diff --git a/test/var.cpp b/test/var.cpp
index 328a6b6277..5b90428ce8 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -51,7 +51,7 @@ struct varOutType {
 // test var_all interface using cpp api
 
 template<typename T>
-void testCPPVar(T const_value, dim4 dims) {
+void testCPPVar(T const_value, dim4 dims, const bool useDeprecatedAPI = false) {
     typedef typename varOutType<T>::type outType;
     SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
@@ -64,12 +64,18 @@ void testCPPVar(T const_value, dim4 dims) {
     outType gold = outType(0);
 
     array a(dims, &(hundred.front()));
-    outType output = var<outType>(a, false);
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    outType output =
+        (useDeprecatedAPI ? var<outType>(a, false)
+                          : var<outType>(a, AF_VARIANCE_POPULATION));
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 
-    output = var<outType>(a, true);
+    output = (useDeprecatedAPI ? var<outType>(a, true)
+                               : var<outType>(a, AF_VARIANCE_SAMPLE));
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
@@ -78,13 +84,16 @@ void testCPPVar(T const_value, dim4 dims) {
     outType tmp[] = {outType(0), outType(1), outType(2), outType(3),
                      outType(4)};
     array b(5, tmp);
-    output = var<outType>(b, false);
+    output = (useDeprecatedAPI ? var<outType>(b, false)
+                               : var<outType>(b, AF_VARIANCE_POPULATION));
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 
     gold   = outType(2);
-    output = var<outType>(b, true);
+    output = (useDeprecatedAPI ? var<outType>(b, true)
+                               : var<outType>(b, AF_VARIANCE_SAMPLE));
+#pragma GCC diagnostic pop
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
@@ -92,39 +101,51 @@ void testCPPVar(T const_value, dim4 dims) {
 
 TYPED_TEST(Var, AllCPPSmall) {
     testCPPVar<TypeParam>(TypeParam(2), dim4(10, 10, 1, 1));
+    testCPPVar<TypeParam>(TypeParam(2), dim4(10, 10, 1, 1), true);
 }
 
 TYPED_TEST(Var, AllCPPMedium) {
     testCPPVar<TypeParam>(TypeParam(2), dim4(100, 100, 1, 1));
+    testCPPVar<TypeParam>(TypeParam(2), dim4(100, 100, 1, 1), true);
 }
 
 TYPED_TEST(Var, AllCPPLarge) {
     testCPPVar<TypeParam>(TypeParam(2), dim4(1000, 1000, 1, 1));
+    testCPPVar<TypeParam>(TypeParam(2), dim4(1000, 1000, 1, 1), true);
 }
 
-TYPED_TEST(Var, DimCPPSmall) {
-    typedef typename varOutType<TypeParam>::type outType;
+template<typename T>
+void dimCppSmallTest(const string pFileName,
+                     const bool useDeprecatedAPI = false) {
+    typedef typename varOutType<T>::type outType;
     float tol = 0.001f;
-    if ((af_dtype)af::dtype_traits<TypeParam>::af_type == f16) { tol = 0.6f; }
+    if ((af_dtype)af::dtype_traits<T>::af_type == f16) { tol = 0.6f; }
 
-    SUPPORTED_TYPE_CHECK(TypeParam);
+    SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<TypeParam> > in;
+    vector<vector<T> > in;
     vector<vector<outType> > tests;
 
-    readTests<TypeParam, outType, float>(TEST_DIR "/var/var.data", numDims, in,
-                                         tests);
+    readTests<T, outType, float>(pFileName, numDims, in, tests);
 
     for (size_t i = 0; i < in.size(); i++) {
         array input(numDims[i], &in[i].front(), afHost);
 
-        array bout  = var(input, true);
-        array nbout = var(input, false);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+        array bout  = (useDeprecatedAPI ? var(input, true)
+                                       : var(input, AF_VARIANCE_SAMPLE));
+        array nbout = (useDeprecatedAPI ? var(input, false)
+                                        : var(input, AF_VARIANCE_POPULATION));
 
-        array bout1  = var(input, true, 1);
-        array nbout1 = var(input, false, 1);
+        array bout1 = (useDeprecatedAPI ? var(input, true, 1)
+                                        : var(input, AF_VARIANCE_SAMPLE, 1));
+        array nbout1 =
+            (useDeprecatedAPI ? var(input, false, 1)
+                              : var(input, AF_VARIANCE_POPULATION, 1));
+#pragma GCC diagnostic pop
 
         vector<vector<outType> > h_out(4);
 
@@ -145,13 +166,24 @@ TYPED_TEST(Var, DimCPPSmall) {
     }
 }
 
+TYPED_TEST(Var, DimCPPSmall) {
+    dimCppSmallTest<TypeParam>(string(TEST_DIR "/var/var.data"));
+    dimCppSmallTest<TypeParam>(string(TEST_DIR "/var/var.data"), true);
+}
+
 TEST(Var, ISSUE2117) {
     using af::constant;
     using af::sum;
     using af::var;
 
     array myArray = constant(1, 1000, 3000);
-    myArray       = var(myArray, true, 1);
+    myArray       = var(myArray, AF_VARIANCE_SAMPLE, 1);
+    ASSERT_NEAR(0.0f, sum<float>(myArray), 0.000001);
 
+    myArray = constant(1, 1000, 3000);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    myArray = var(myArray, true, 1);
+#pragma GCC diagnostic pop
     ASSERT_NEAR(0.0f, sum<float>(myArray), 0.000001);
 }

From d5789cb12a36adf19109421fafd20dffcd4febc5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 8 Aug 2020 14:15:15 +0530
Subject: [PATCH 2064/2677] Add new covariance APIs with bias enum instead of
 bool

---
 include/af/statistics.h        | 44 +++++++++++++++++++++++++++++++---
 src/api/c/covariance.cpp       | 30 ++++++++++++++---------
 src/api/cpp/covariance.cpp     |  8 ++++++-
 src/api/unified/statistics.cpp |  6 +++++
 test/covariance.cpp            | 20 ++++++++++++----
 5 files changed, 89 insertions(+), 19 deletions(-)

diff --git a/include/af/statistics.h b/include/af/statistics.h
index 4b18303749..584722ce83 100644
--- a/include/af/statistics.h
+++ b/include/af/statistics.h
@@ -121,19 +121,37 @@ AFAPI void meanvar(array& mean, array& var, const array& in, const array& weight
 */
 AFAPI array stdev(const array& in, const dim_t dim=-1);
 
-
 /**
    C++ Interface for covariance
 
    \param[in] X is the first input array
    \param[in] Y is the second input array
-   \param[in] isbiased is boolean specifying if biased estimate should be taken (default: false)
+   \param[in] isbiased is boolean specifying if biased estimate should be
+              taken (default: false)
    \return    the covariance of the input arrays
 
    \ingroup stat_func_cov
+
+   \deprecated Use af::cov(const array&, const array& const af_var_bias)
 */
+AF_DEPRECATED("Use af::cov(const af::array&, const array&, conv af_var_bias)")
 AFAPI array cov(const array& X, const array& Y, const bool isbiased=false);
 
+#if AF_API_VERSION >= 38
+/**
+   C++ Interface for covariance
+
+   \param[in] X is the first input array
+   \param[in] Y is the second input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias.
+   \return the covariance of the input arrays
+
+   \ingroup stat_func_cov
+*/
+AFAPI array cov(const array &X, const array &Y, const af_var_bias bias);
+#endif
+
 /**
    C++ Interface for median
 
@@ -270,7 +288,6 @@ AFAPI T corrcoef(const array& X, const array& Y);
 AFAPI void topk(array &values, array &indices, const array& in, const int k,
                 const int dim = -1, const topkFunction order = AF_TOPK_MAX);
 #endif
-
 }
 #endif
 
@@ -399,9 +416,30 @@ AFAPI af_err af_stdev(af_array *out, const af_array in, const dim_t dim);
    otherwise an appropriate error code is returned.
 
    \ingroup stat_func_cov
+
+   \deprecated Use \ref af_cov_v2 instead
 */
+AF_DEPRECATED("Use af_cov_v2")
 AFAPI af_err af_cov(af_array* out, const af_array X, const af_array Y, const bool isbiased);
 
+#if AF_API_VERSION >= 38
+/**
+   C Interface for covariance
+
+   \param[out] out will the covariance of the input arrays
+   \param[in] X is the first input array
+   \param[in] Y is the second input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias
+   \return \ref AF_SUCCESS if the operation is successful, otherwise an
+           appropriate error code is returned.
+
+   \ingroup stat_func_cov
+*/
+AFAPI af_err af_cov_v2(af_array *out, const af_array X, const af_array Y,
+                       const af_var_bias bias);
+#endif
+
 /**
    C Interface for median
 
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index bbacb71977..c21816b8d1 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -37,7 +37,8 @@ using detail::uintl;
 using detail::ushort;
 
 template<typename T, typename cType>
-static af_array cov(const af_array& X, const af_array& Y, bool isbiased) {
+static af_array cov(const af_array& X, const af_array& Y,
+                    const af_var_bias bias) {
     using weightType  = typename baseOutType<cType>::type;
     const Array<T> _x = getArray<T>(X);
     const Array<T> _y = getArray<T>(Y);
@@ -45,7 +46,7 @@ static af_array cov(const af_array& X, const af_array& Y, bool isbiased) {
     Array<cType> yArr = cast<cType>(_y);
 
     dim4 xDims = xArr.dims();
-    dim_t N    = isbiased ? xDims[0] : xDims[0] - 1;
+    dim_t N    = (bias == AF_VARIANCE_SAMPLE ? xDims[0] : xDims[0] - 1);
 
     Array<cType> xmArr =
         createValueArray<cType>(xDims, mean<T, weightType, cType>(_x));
@@ -65,6 +66,13 @@ static af_array cov(const af_array& X, const af_array& Y, bool isbiased) {
 
 af_err af_cov(af_array* out, const af_array X, const af_array Y,
               const bool isbiased) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return af_cov_v2(out, X, Y, bias);
+}
+
+af_err af_cov_v2(af_array* out, const af_array X, const af_array Y,
+                 const af_var_bias bias) {
     try {
         const ArrayInfo& xInfo = getInfo(X);
         const ArrayInfo& yInfo = getInfo(Y);
@@ -81,15 +89,15 @@ af_err af_cov(af_array* out, const af_array X, const af_array Y,
 
         af_array output = 0;
         switch (xType) {
-            case f64: output = cov<double, double>(X, Y, isbiased); break;
-            case f32: output = cov<float, float>(X, Y, isbiased); break;
-            case s32: output = cov<int, float>(X, Y, isbiased); break;
-            case u32: output = cov<uint, float>(X, Y, isbiased); break;
-            case s64: output = cov<intl, double>(X, Y, isbiased); break;
-            case u64: output = cov<uintl, double>(X, Y, isbiased); break;
-            case s16: output = cov<short, float>(X, Y, isbiased); break;
-            case u16: output = cov<ushort, float>(X, Y, isbiased); break;
-            case u8: output = cov<uchar, float>(X, Y, isbiased); break;
+            case f64: output = cov<double, double>(X, Y, bias); break;
+            case f32: output = cov<float, float>(X, Y, bias); break;
+            case s32: output = cov<int, float>(X, Y, bias); break;
+            case u32: output = cov<uint, float>(X, Y, bias); break;
+            case s64: output = cov<intl, double>(X, Y, bias); break;
+            case u64: output = cov<uintl, double>(X, Y, bias); break;
+            case s16: output = cov<short, float>(X, Y, bias); break;
+            case u16: output = cov<ushort, float>(X, Y, bias); break;
+            case u8: output = cov<uchar, float>(X, Y, bias); break;
             default: TYPE_ERROR(1, xType);
         }
         std::swap(*out, output);
diff --git a/src/api/cpp/covariance.cpp b/src/api/cpp/covariance.cpp
index 44608e4513..8261ea0cd7 100644
--- a/src/api/cpp/covariance.cpp
+++ b/src/api/cpp/covariance.cpp
@@ -14,8 +14,14 @@
 namespace af {
 
 array cov(const array& X, const array& Y, const bool isbiased) {
+    const af_var_bias bias =
+        (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION);
+    return cov(X, Y, bias);
+}
+
+array cov(const array& X, const array& Y, const af_var_bias bias) {
     af_array temp = 0;
-    AF_THROW(af_cov(&temp, X.get(), Y.get(), isbiased));
+    AF_THROW(af_cov_v2(&temp, X.get(), Y.get(), bias));
     return array(temp);
 }
 
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index 4dbc36ddb7..a93a92d459 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -113,3 +113,9 @@ af_err af_var_all_v2(double *realVal, double *imagVal, const af_array in,
     CHECK_ARRAYS(in);
     CALL(af_var_all_v2, realVal, imagVal, in, bias);
 }
+
+af_err af_cov_v2(af_array *out, const af_array X, const af_array Y,
+                 const af_var_bias bias) {
+    CHECK_ARRAYS(X, Y);
+    CALL(af_cov_v2, out, X, Y, bias);
+}
diff --git a/test/covariance.cpp b/test/covariance.cpp
index aadc1a0ebd..9e79d13117 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -72,7 +72,8 @@ struct covOutType {
 };
 
 template<typename T>
-void covTest(string pFileName, bool isbiased = false) {
+void covTest(string pFileName, bool isbiased = false,
+             const bool useDeprecatedAPI = false) {
     typedef typename covOutType<T>::type outType;
     SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
@@ -91,7 +92,14 @@ void covTest(string pFileName, bool isbiased = false) {
     array a(dims1, &(input1.front()));
     array b(dims2, &(input2.front()));
 
-    array c = cov(a, b, isbiased);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    array c =
+        (useDeprecatedAPI
+             ? cov(a, b, isbiased)
+             : cov(a, b,
+                   (isbiased ? AF_VARIANCE_SAMPLE : AF_VARIANCE_POPULATION)));
+#pragma GCC diagnostic pop
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
@@ -112,22 +120,26 @@ void covTest(string pFileName, bool isbiased = false) {
 
 TYPED_TEST(Covariance, Vector) {
     covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"), false);
+    covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"), false,
+                       true);
 }
 
 TYPED_TEST(Covariance, Matrix) {
     covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"),
                        false);
+    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"), false,
+                       true);
 }
 
 TEST(Covariance, c32) {
     array a = constant(cfloat(1.0f, -1.0f), 10, c32);
     array b = constant(cfloat(2.0f, -1.0f), 10, c32);
-    ASSERT_THROW(cov(a, b), exception);
+    ASSERT_THROW(cov(a, b, AF_VARIANCE_POPULATION), exception);
 }
 
 TEST(Covariance, c64) {
     SUPPORTED_TYPE_CHECK(double);
     array a = constant(cdouble(1.0, -1.0), 10, c64);
     array b = constant(cdouble(2.0, -1.0), 10, c64);
-    ASSERT_THROW(cov(a, b), exception);
+    ASSERT_THROW(cov(a, b, AF_VARIANCE_POPULATION), exception);
 }

From 690936f7caf4f0df7437a90ac22bfc6411aed444 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 8 Aug 2020 20:41:48 +0530
Subject: [PATCH 2065/2677] Add new std dev APIs with bias parameter

---
 include/af/statistics.h        | 87 +++++++++++++++++++++++++++++++++-
 src/api/c/stdev.cpp            | 64 ++++++++++++++-----------
 src/api/cpp/stdev.cpp          | 42 +++++++++++-----
 src/api/unified/statistics.cpp | 12 +++++
 test/stdev.cpp                 | 62 ++++++++++++++++++------
 5 files changed, 212 insertions(+), 55 deletions(-)

diff --git a/include/af/statistics.h b/include/af/statistics.h
index 584722ce83..9f7adf455a 100644
--- a/include/af/statistics.h
+++ b/include/af/statistics.h
@@ -118,9 +118,30 @@ AFAPI void meanvar(array& mean, array& var, const array& in, const array& weight
    \ingroup stat_func_stdev
 
    \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+
+   \deprecated Use \ref af::stdev that takes \ref af_var_bias instead
 */
+AF_DEPRECATED("Use af::stdev(const array&, const af_var_bias, const dim_t)")
 AFAPI array stdev(const array& in, const dim_t dim=-1);
 
+#if AF_API_VERSION >= 38
+/**
+   C++ Interface for standard deviation
+
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias.
+   \param[in] dim the dimension along which the standard deviation is extracted
+   \return    the standard deviation of the input array along dimension \p dim
+
+   \ingroup stat_func_stdev
+
+   \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+*/
+AFAPI array stdev(const array &in, const af_var_bias bias,
+                  const dim_t dim = -1);
+#endif
+
 /**
    C++ Interface for covariance
 
@@ -237,9 +258,26 @@ AFAPI T var(const array& in, const array& weights);
    \return    standard deviation of the entire input array
 
    \ingroup stat_func_stdev
+
+   \deprecated Use \ref af::stdev that takes \ref af_var_bias instead
 */
-template<typename T>
-AFAPI T stdev(const array& in);
+template <typename T>
+AF_DEPRECATED("Use af::stdev(const array&, const af_var_bias)")
+AFAPI T stdev(const array &in);
+
+#if AF_API_VERSION >= 38
+/**
+   C++ Interface for standard deviation of all elements
+
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias.
+   \return    standard deviation of the entire input array
+
+   \ingroup stat_func_stdev
+*/
+template <typename T> AFAPI T stdev(const array &in, const af_var_bias bias);
+#endif
 
 /**
    C++ Interface for median of all elements
@@ -402,9 +440,31 @@ AFAPI af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
 
    \ingroup stat_func_stdev
 
+   \deprecated Use \ref af_stdev_v2 instead
 */
+AF_DEPRECATED("Use af_stdev_v2")
 AFAPI af_err af_stdev(af_array *out, const af_array in, const dim_t dim);
 
+#if AF_API_VERSION >= 38
+/**
+   C Interface for standard deviation
+
+   \param[out] out will contain the standard deviation of the input array along
+               dimension \p dim
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias
+   \param[in] dim the dimension along which the standard deviation is extracted
+   \return \ref AF_SUCCESS if the operation is successful, otherwise an
+           appropriate error code is returned.
+
+   \ingroup stat_func_stdev
+
+*/
+AFAPI af_err af_stdev_v2(af_array *out, const af_array in,
+                         const af_var_bias bias, const dim_t dim);
+#endif
+
 /**
    C Interface for covariance
 
@@ -542,9 +602,32 @@ AFAPI af_err af_var_all_weighted(double *realVal, double *imagVal, const af_arra
    otherwise an appropriate error code is returned.
 
    \ingroup stat_func_stdev
+
+   \deprecated Use \ref af_stdev_all_v2 instead
 */
+AF_DEPRECATED("Use af_stdev_all_v2")
 AFAPI af_err af_stdev_all(double *real, double *imag, const af_array in);
 
+#if AF_API_VERSION >= 38
+/**
+   C Interface for standard deviation of all elements
+
+   \param[out] real will contain the real part of standard deviation of the
+               entire input array
+   \param[out] imag will contain the imaginary part of standard deviation
+               of the entire input array
+   \param[in] in is the input array
+   \param[in] bias The type of bias used for variance calculation. Takes of
+              value of type \ref af_var_bias
+   \return     \ref AF_SUCCESS if the operation is successful,
+   otherwise an appropriate error code is returned.
+
+   \ingroup stat_func_stdev
+*/
+AFAPI af_err af_stdev_all_v2(double *real, double *imag, const af_array in,
+                             const af_var_bias bias);
+#endif
+
 /**
    C Interface for median
 
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 8620f00bd4..4123a4f315 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -42,7 +42,7 @@ using detail::uintl;
 using detail::ushort;
 
 template<typename inType, typename outType>
-static outType stdev(const af_array& in) {
+static outType stdev(const af_array& in, const af_var_bias bias) {
     using weightType        = typename baseOutType<outType>::type;
     const Array<inType> _in = getArray<inType>(in);
     Array<outType> input    = cast<outType>(_in);
@@ -52,14 +52,14 @@ static outType stdev(const af_array& in) {
         detail::arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
     Array<outType> diffSq =
         detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
-    outType result = division(reduce_all<af_add_t, outType, outType>(diffSq),
-                              input.elements());
-
+    outType result =
+        division(reduce_all<af_add_t, outType, outType>(diffSq),
+                 (input.elements() - (bias == AF_VARIANCE_SAMPLE)));
     return sqrt(result);
 }
 
 template<typename inType, typename outType>
-static af_array stdev(const af_array& in, int dim) {
+static af_array stdev(const af_array& in, int dim, const af_var_bias bias) {
     using weightType        = typename baseOutType<outType>::type;
     const Array<inType> _in = getArray<inType>(in);
     Array<outType> input    = cast<outType>(_in);
@@ -80,8 +80,8 @@ static af_array stdev(const af_array& in, int dim) {
     Array<outType> redDiff = reduce<af_add_t, outType, outType>(diffSq, dim);
     const dim4& oDims      = redDiff.dims();
 
-    Array<outType> divArr =
-        createValueArray<outType>(oDims, scalar<outType>(iDims[dim]));
+    Array<outType> divArr = createValueArray<outType>(
+        oDims, scalar<outType>((iDims[dim] - (bias == AF_VARIANCE_SAMPLE))));
     Array<outType> varArr =
         detail::arithOp<outType, af_div_t>(redDiff, divArr, redDiff.dims());
     Array<outType> result = detail::unaryOp<outType, af_sqrt_t>(varArr);
@@ -91,21 +91,26 @@ static af_array stdev(const af_array& in, int dim) {
 
 // NOLINTNEXTLINE(readability-non-const-parameter)
 af_err af_stdev_all(double* realVal, double* imagVal, const af_array in) {
+    return af_stdev_all_v2(realVal, imagVal, in, AF_VARIANCE_POPULATION);
+}
+
+af_err af_stdev_all_v2(double* realVal, double* imagVal, const af_array in,
+                       const af_var_bias bias) {
     UNUSED(imagVal);  // TODO implement for complex values
     try {
         const ArrayInfo& info = getInfo(in);
         af_dtype type         = info.getType();
         switch (type) {
-            case f64: *realVal = stdev<double, double>(in); break;
-            case f32: *realVal = stdev<float, float>(in); break;
-            case s32: *realVal = stdev<int, float>(in); break;
-            case u32: *realVal = stdev<uint, float>(in); break;
-            case s16: *realVal = stdev<short, float>(in); break;
-            case u16: *realVal = stdev<ushort, float>(in); break;
-            case s64: *realVal = stdev<intl, double>(in); break;
-            case u64: *realVal = stdev<uintl, double>(in); break;
-            case u8: *realVal = stdev<uchar, float>(in); break;
-            case b8: *realVal = stdev<char, float>(in); break;
+            case f64: *realVal = stdev<double, double>(in, bias); break;
+            case f32: *realVal = stdev<float, float>(in, bias); break;
+            case s32: *realVal = stdev<int, float>(in, bias); break;
+            case u32: *realVal = stdev<uint, float>(in, bias); break;
+            case s16: *realVal = stdev<short, float>(in, bias); break;
+            case u16: *realVal = stdev<ushort, float>(in, bias); break;
+            case s64: *realVal = stdev<intl, double>(in, bias); break;
+            case u64: *realVal = stdev<uintl, double>(in, bias); break;
+            case u8: *realVal = stdev<uchar, float>(in, bias); break;
+            case b8: *realVal = stdev<char, float>(in, bias); break;
             // TODO(umar): FIXME: sqrt(complex) is not present in cuda/opencl
             // backend case c32: {
             //    cfloat tmp = stdev<cfloat,cfloat>(in);
@@ -125,6 +130,11 @@ af_err af_stdev_all(double* realVal, double* imagVal, const af_array in) {
 }
 
 af_err af_stdev(af_array* out, const af_array in, const dim_t dim) {
+    return af_stdev_v2(out, in, AF_VARIANCE_POPULATION, dim);
+}
+
+af_err af_stdev_v2(af_array* out, const af_array in, const af_var_bias bias,
+                   const dim_t dim) {
     try {
         ARG_ASSERT(2, (dim >= 0 && dim <= 3));
 
@@ -132,16 +142,16 @@ af_err af_stdev(af_array* out, const af_array in, const dim_t dim) {
         const ArrayInfo& info = getInfo(in);
         af_dtype type         = info.getType();
         switch (type) {
-            case f64: output = stdev<double, double>(in, dim); break;
-            case f32: output = stdev<float, float>(in, dim); break;
-            case s32: output = stdev<int, float>(in, dim); break;
-            case u32: output = stdev<uint, float>(in, dim); break;
-            case s16: output = stdev<short, float>(in, dim); break;
-            case u16: output = stdev<ushort, float>(in, dim); break;
-            case s64: output = stdev<intl, double>(in, dim); break;
-            case u64: output = stdev<uintl, double>(in, dim); break;
-            case u8: output = stdev<uchar, float>(in, dim); break;
-            case b8: output = stdev<char, float>(in, dim); break;
+            case f64: output = stdev<double, double>(in, dim, bias); break;
+            case f32: output = stdev<float, float>(in, dim, bias); break;
+            case s32: output = stdev<int, float>(in, dim, bias); break;
+            case u32: output = stdev<uint, float>(in, dim, bias); break;
+            case s16: output = stdev<short, float>(in, dim, bias); break;
+            case u16: output = stdev<ushort, float>(in, dim, bias); break;
+            case s64: output = stdev<intl, double>(in, dim, bias); break;
+            case u64: output = stdev<uintl, double>(in, dim, bias); break;
+            case u8: output = stdev<uchar, float>(in, dim, bias); break;
+            case b8: output = stdev<char, float>(in, dim, bias); break;
             // TODO(umar): FIXME: sqrt(complex) is not present in cuda/opencl
             // backend case c32: output = stdev<cfloat,  cfloat>(in, dim);
             // break; case c64: output = stdev<cdouble,cdouble>(in, dim); break;
diff --git a/src/api/cpp/stdev.cpp b/src/api/cpp/stdev.cpp
index 4031e53ba9..a9e22d58f6 100644
--- a/src/api/cpp/stdev.cpp
+++ b/src/api/cpp/stdev.cpp
@@ -15,28 +15,42 @@
 
 namespace af {
 
-#define INSTANTIATE_STDEV(T)                              \
-    template<>                                            \
-    AFAPI T stdev(const array& in) {                      \
-        double ret_val;                                   \
-        AF_THROW(af_stdev_all(&ret_val, NULL, in.get())); \
-        return (T)ret_val;                                \
+#define INSTANTIATE_STDEV(T)                                       \
+    template<>                                                     \
+    AFAPI T stdev(const array& in, const af_var_bias bias) {       \
+        double ret_val;                                            \
+        AF_THROW(af_stdev_all_v2(&ret_val, NULL, in.get(), bias)); \
+        return (T)ret_val;                                         \
+    }                                                              \
+    template<>                                                     \
+    AFAPI T stdev(const array& in) {                               \
+        return stdev<T>(in, AF_VARIANCE_POPULATION);               \
     }
 
 template<>
-AFAPI af_cfloat stdev(const array& in) {
+AFAPI af_cfloat stdev(const array& in, const af_var_bias bias) {
     double real, imag;
-    AF_THROW(af_stdev_all(&real, &imag, in.get()));
+    AF_THROW(af_stdev_all_v2(&real, &imag, in.get(), bias));
     return {static_cast<float>(real), static_cast<float>(imag)};
 }
 
 template<>
-AFAPI af_cdouble stdev(const array& in) {
+AFAPI af_cdouble stdev(const array& in, const af_var_bias bias) {
     double real, imag;
-    AF_THROW(af_stdev_all(&real, &imag, in.get()));
+    AF_THROW(af_stdev_all_v2(&real, &imag, in.get(), bias));
     return {real, imag};
 }
 
+template<>
+AFAPI af_cfloat stdev(const array& in) {
+    return stdev<af_cfloat>(in, AF_VARIANCE_POPULATION);
+}
+
+template<>
+AFAPI af_cdouble stdev(const array& in) {
+    return stdev<af_cdouble>(in, AF_VARIANCE_POPULATION);
+}
+
 INSTANTIATE_STDEV(float);
 INSTANTIATE_STDEV(double);
 INSTANTIATE_STDEV(int);
@@ -50,10 +64,14 @@ INSTANTIATE_STDEV(unsigned char);
 
 #undef INSTANTIATE_STDEV
 
-array stdev(const array& in, const dim_t dim) {
+array stdev(const array& in, const af_var_bias bias, const dim_t dim) {
     af_array temp = 0;
-    AF_THROW(af_stdev(&temp, in.get(), getFNSD(dim, in.dims())));
+    AF_THROW(af_stdev_v2(&temp, in.get(), bias, getFNSD(dim, in.dims())));
     return array(temp);
 }
 
+array stdev(const array& in, const dim_t dim) {
+    return stdev(in, AF_VARIANCE_POPULATION, dim);
+}
+
 }  // namespace af
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index a93a92d459..efd6959dbb 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -119,3 +119,15 @@ af_err af_cov_v2(af_array *out, const af_array X, const af_array Y,
     CHECK_ARRAYS(X, Y);
     CALL(af_cov_v2, out, X, Y, bias);
 }
+
+af_err af_stdev_v2(af_array *out, const af_array in, const af_var_bias bias,
+                   const dim_t dim) {
+    CHECK_ARRAYS(in);
+    CALL(af_stdev_v2, out, in, bias, dim);
+}
+
+af_err af_stdev_all_v2(double *real, double *imag, const af_array in,
+                       const af_var_bias bias) {
+    CHECK_ARRAYS(in);
+    CALL(af_stdev_all_v2, real, imag, in, bias);
+}
diff --git a/test/stdev.cpp b/test/stdev.cpp
index aef4099886..20187f8655 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -74,7 +74,8 @@ struct sdOutType {
 };
 
 template<typename T>
-void stdevDimTest(string pFileName, dim_t dim = -1) {
+void stdevDimTest(string pFileName, dim_t dim,
+                  const bool useDeprecatedAPI = false) {
     typedef typename sdOutType<T>::type outType;
     SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
@@ -90,7 +91,11 @@ void stdevDimTest(string pFileName, dim_t dim = -1) {
 
     array a(dims, &(input.front()));
 
-    array b = stdev(a, dim);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    array b = (useDeprecatedAPI ? stdev(a, dim)
+                                : stdev(a, AF_VARIANCE_POPULATION, dim));
+#pragma GCC diagnostic pop
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
@@ -111,30 +116,42 @@ void stdevDimTest(string pFileName, dim_t dim = -1) {
 
 TYPED_TEST(StandardDev, Dim0) {
     stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_dim0.test"), 0);
+    stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_dim0.test"), 0,
+                            true);
 }
 
 TYPED_TEST(StandardDev, Dim1) {
     stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_dim1.test"), 1);
+    stdevDimTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_dim1.test"), 1,
+                            true);
 }
 
 TYPED_TEST(StandardDev, Dim2) {
     stdevDimTest<TypeParam>(
         string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim2.test"), 2);
+    stdevDimTest<TypeParam>(
+        string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim2.test"), 2, true);
 }
 
 TYPED_TEST(StandardDev, Dim3) {
     stdevDimTest<TypeParam>(
         string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim3.test"), 3);
+    stdevDimTest<TypeParam>(
+        string(TEST_DIR "/stdev/hypercube_10x10x5x5_dim3.test"), 3, true);
 }
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
 TEST(StandardDev, InvalidDim) { ASSERT_THROW(stdev(array(), 5), exception); }
 
 TEST(StandardDev, InvalidType) {
     ASSERT_THROW(stdev(constant(cdouble(1.0, -1.0), 10)), exception);
 }
+#pragma GCC diagnostic pop
 
 template<typename T>
-void stdevDimIndexTest(string pFileName, dim_t dim = -1) {
+void stdevDimIndexTest(string pFileName, dim_t dim,
+                       const bool useDeprecatedAPI = false) {
     typedef typename sdOutType<T>::type outType;
     SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
@@ -151,7 +168,11 @@ void stdevDimIndexTest(string pFileName, dim_t dim = -1) {
     array a(dims, &(input.front()));
     array b = a(seq(2, 6), seq(1, 7));
 
-    array c = stdev(b, dim);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    array c = (useDeprecatedAPI ? stdev(b, dim)
+                                : stdev(b, AF_VARIANCE_POPULATION, dim));
+#pragma GCC diagnostic pop
 
     vector<outType> currGoldBar(tests[0].begin(), tests[0].end());
 
@@ -173,32 +194,39 @@ void stdevDimIndexTest(string pFileName, dim_t dim = -1) {
 TYPED_TEST(StandardDev, IndexedArrayDim0) {
     stdevDimIndexTest<TypeParam>(
         string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim0.test"), 0);
+    stdevDimIndexTest<TypeParam>(
+        string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim0.test"), 0);
 }
 
 TYPED_TEST(StandardDev, IndexedArrayDim1) {
     stdevDimIndexTest<TypeParam>(
-        string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim1.test"), 1);
+        string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim1.test"), 1, true);
+    stdevDimIndexTest<TypeParam>(
+        string(TEST_DIR "/stdev/mat_10x10_seq2_6x1_7_dim1.test"), 1, true);
 }
 
-TYPED_TEST(StandardDev, All) {
-    typedef typename sdOutType<TypeParam>::type outType;
-    SUPPORTED_TYPE_CHECK(TypeParam);
+template<typename T>
+void stdevAllTest(string pFileName, const bool useDeprecatedAPI = false) {
+    typedef typename sdOutType<T>::type outType;
+    SUPPORTED_TYPE_CHECK(T);
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
     vector<vector<int> > in;
     vector<vector<float> > tests;
 
-    readTestsFromFile<int, float>(
-        string(TEST_DIR "/stdev/mat_10x10_scalar.test"), numDims, in, tests);
+    readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
     dim4 dims = numDims[0];
-    vector<TypeParam> input(in[0].size());
-    transform(in[0].begin(), in[0].end(), input.begin(),
-              convert_to<TypeParam, int>);
+    vector<T> input(in[0].size());
+    transform(in[0].begin(), in[0].end(), input.begin(), convert_to<T, int>);
 
     array a(dims, &(input.front()));
-    outType b = stdev<outType>(a);
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    outType b = (useDeprecatedAPI ? stdev<outType>(a)
+                                  : stdev<outType>(a, AF_VARIANCE_POPULATION));
+#pragma GCC diagnostic pop
 
     vector<outType> currGoldBar(tests[0].size());
     transform(tests[0].begin(), tests[0].end(), currGoldBar.begin(),
@@ -207,3 +235,9 @@ TYPED_TEST(StandardDev, All) {
     ASSERT_NEAR(::real(currGoldBar[0]), ::real(b), 1.0e-3);
     ASSERT_NEAR(::imag(currGoldBar[0]), ::imag(b), 1.0e-3);
 }
+
+TYPED_TEST(StandardDev, All) {
+    stdevAllTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_scalar.test"));
+    stdevAllTest<TypeParam>(string(TEST_DIR "/stdev/mat_10x10_scalar.test"),
+                            true);
+}

From 13e1904ade7b06dc96cce2484198682d5850c990 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 14 Aug 2020 12:11:46 +0530
Subject: [PATCH 2066/2677] Fix bias factor of variance in var_all and cov
 functions

---
 src/api/c/covariance.cpp |  2 +-
 src/api/c/var.cpp        |  6 +++---
 test/covariance.cpp      | 13 +++++--------
 test/var.cpp             |  5 +++--
 4 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index c21816b8d1..be86a36e17 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -46,7 +46,7 @@ static af_array cov(const af_array& X, const af_array& Y,
     Array<cType> yArr = cast<cType>(_y);
 
     dim4 xDims = xArr.dims();
-    dim_t N    = (bias == AF_VARIANCE_SAMPLE ? xDims[0] : xDims[0] - 1);
+    dim_t N    = (bias == AF_VARIANCE_SAMPLE ? xDims[0] - 1 : xDims[0]);
 
     Array<cType> xmArr =
         createValueArray<cType>(xDims, mean<T, weightType, cType>(_x));
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 6119701560..2b9ea45c6a 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -64,9 +64,9 @@ static outType varAll(const af_array& in, const af_var_bias bias) {
 
     Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
 
-    outType result = division(
-        reduce_all<af_add_t, outType, outType>(diffSq),
-        bias == AF_VARIANCE_SAMPLE ? input.elements() : input.elements() - 1);
+    outType result =
+        division(reduce_all<af_add_t, outType, outType>(diffSq),
+                 (input.elements() - (bias == AF_VARIANCE_SAMPLE)));
 
     return result;
 }
diff --git a/test/covariance.cpp b/test/covariance.cpp
index 9e79d13117..6eea33e224 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -72,7 +72,7 @@ struct covOutType {
 };
 
 template<typename T>
-void covTest(string pFileName, bool isbiased = false,
+void covTest(string pFileName, bool isbiased = true,
              const bool useDeprecatedAPI = false) {
     typedef typename covOutType<T>::type outType;
     SUPPORTED_TYPE_CHECK(T);
@@ -119,16 +119,13 @@ void covTest(string pFileName, bool isbiased = false,
 }
 
 TYPED_TEST(Covariance, Vector) {
-    covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"), false);
-    covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"), false,
-                       true);
+    covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"));
+    covTest<TypeParam>(string(TEST_DIR "/covariance/vec_size60.test"), true);
 }
 
 TYPED_TEST(Covariance, Matrix) {
-    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"),
-                       false);
-    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"), false,
-                       true);
+    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"));
+    covTest<TypeParam>(string(TEST_DIR "/covariance/matrix_65x121.test"), true);
 }
 
 TEST(Covariance, c32) {
diff --git a/test/var.cpp b/test/var.cpp
index 5b90428ce8..b88fbaebbd 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -80,17 +80,18 @@ void testCPPVar(T const_value, dim4 dims, const bool useDeprecatedAPI = false) {
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 
-    gold          = outType(2.5);
+    gold          = outType(2);
     outType tmp[] = {outType(0), outType(1), outType(2), outType(3),
                      outType(4)};
     array b(5, tmp);
+    af_print(b);
     output = (useDeprecatedAPI ? var<outType>(b, false)
                                : var<outType>(b, AF_VARIANCE_POPULATION));
 
     ASSERT_NEAR(::real(output), ::real(gold), 1.0e-3);
     ASSERT_NEAR(::imag(output), ::imag(gold), 1.0e-3);
 
-    gold   = outType(2);
+    gold   = outType(2.5);
     output = (useDeprecatedAPI ? var<outType>(b, true)
                                : var<outType>(b, AF_VARIANCE_SAMPLE));
 #pragma GCC diagnostic pop

From 87b94ad6819bcc68b605f6b36b924e6d2df33290 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 17 Aug 2020 02:31:51 -0400
Subject: [PATCH 2067/2677] Create a more portable macro that disables
 deprecated warnings (#2997)

* Create a more portable macro that disables deprecated warnings

* Fix deprecated warnings in unified/statistics.cpp and test/random.cpp
---
 src/api/cpp/device.cpp                        | 16 +++++------
 src/api/unified/CMakeLists.txt                |  1 +
 src/api/unified/device.cpp                    | 21 +++++++--------
 src/api/unified/graphics.cpp                  | 21 +++++++--------
 src/api/unified/statistics.cpp                |  9 +++++++
 src/backend/common/deprecated.hpp             | 27 +++++++++++++++++++
 src/backend/opencl/cl2hpp.hpp                 |  5 +++-
 src/backend/opencl/kernel/regions.hpp         |  8 +++---
 src/backend/opencl/kernel/sift_nonfree.hpp    |  8 +++---
 src/backend/opencl/kernel/sort.hpp            |  7 ++---
 .../opencl/kernel/sort_by_key_impl.hpp        |  7 ++---
 src/backend/opencl/set.cpp                    |  7 ++---
 test/random.cpp                               |  6 ++---
 13 files changed, 81 insertions(+), 62 deletions(-)
 create mode 100644 src/backend/common/deprecated.hpp

diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 0a67d9de19..89aab84754 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/deprecated.hpp>
 #include <af/array.h>
 #include <af/backend.h>
 #include <af/compatible.h>
@@ -102,10 +103,9 @@ void sync(int device) { AF_THROW(af_sync(device)); }
 // Alloc device memory
 void *alloc(const size_t elements, const af::dtype type) {
     void *ptr;
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     AF_THROW(af_alloc_device(&ptr, elements * size_of(type)));
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
     // FIXME: Add to map
     return ptr;
 }
@@ -127,10 +127,9 @@ void *pinned(const size_t elements, const af::dtype type) {
 
 void free(const void *ptr) {
     // FIXME: look up map and call the right free
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     AF_THROW(af_free_device(const_cast<void *>(ptr)));
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 void freeV2(const void *ptr) {
@@ -172,8 +171,7 @@ size_t getMemStepSize() {
     return size_bytes;
 }
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+AF_DEPRECATED_WARNINGS_OFF
 #define INSTANTIATE(T)                                                        \
     template<>                                                                \
     AFAPI T *alloc(const size_t elements) {                                   \
@@ -200,6 +198,6 @@ INSTANTIATE(short)
 INSTANTIATE(unsigned short)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
-#pragma GCC diagnostic pop
+AF_DEPRECATED_WARNINGS_ON
 
 }  // namespace af
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 4e42fcee52..967eaa631c 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -65,6 +65,7 @@ target_sources(af
     ${ArrayFire_SOURCE_DIR}/src/backend/common/err_common.cpp
     ${ArrayFire_SOURCE_DIR}/src/backend/common/util.cpp
     ${ArrayFire_SOURCE_DIR}/src/backend/common/util.hpp
+    ${ArrayFire_SOURCE_DIR}/src/backend/common/deprecated.hpp
   )
 
 arrayfire_set_default_cxx_flags(af)
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index cf2f906070..3b97a29fbc 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/deprecated.hpp>
 #include <af/array.h>
 #include <af/backend.h>
 #include <af/device.h>
@@ -74,10 +75,9 @@ af_err af_get_device(int *device) { CALL(af_get_device, device); }
 af_err af_sync(const int device) { CALL(af_sync, device); }
 
 af_err af_alloc_device(void **ptr, const dim_t bytes) {
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_alloc_device, ptr, bytes);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_alloc_device_v2(void **ptr, const dim_t bytes) {
@@ -89,10 +89,9 @@ af_err af_alloc_pinned(void **ptr, const dim_t bytes) {
 }
 
 af_err af_free_device(void *ptr) {
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_free_device, ptr);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_free_device_v2(void *ptr) { CALL(af_free_device_v2, ptr); }
@@ -136,18 +135,16 @@ af_err af_get_mem_step_size(size_t *step_bytes) {
 
 af_err af_lock_device_ptr(const af_array arr) {
     CHECK_ARRAYS(arr);
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_lock_device_ptr, arr);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_unlock_device_ptr(const af_array arr) {
     CHECK_ARRAYS(arr);
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_unlock_device_ptr, arr);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_lock_array(const af_array arr) {
diff --git a/src/api/unified/graphics.cpp b/src/api/unified/graphics.cpp
index f3808091ed..49fb036457 100644
--- a/src/api/unified/graphics.cpp
+++ b/src/api/unified/graphics.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/deprecated.hpp>
 #include <af/array.h>
 #include <af/graphics.h>
 #include "symbol_manager.hpp"
@@ -38,19 +39,17 @@ af_err af_draw_image(const af_window wind, const af_array in,
 af_err af_draw_plot(const af_window wind, const af_array X, const af_array Y,
                     const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_draw_plot, wind, X, Y, props);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_draw_plot3(const af_window wind, const af_array P,
                      const af_cell* const props) {
     CHECK_ARRAYS(P);
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_draw_plot3, wind, P, props);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_draw_plot_nd(const af_window wind, const af_array in,
@@ -75,20 +74,18 @@ af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
                        const af_marker_type marker,
                        const af_cell* const props) {
     CHECK_ARRAYS(X, Y);
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_draw_scatter, wind, X, Y, marker, props);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_draw_scatter3(const af_window wind, const af_array P,
                         const af_marker_type marker,
                         const af_cell* const props) {
     CHECK_ARRAYS(P);
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+    AF_DEPRECATED_WARNINGS_OFF
     CALL(af_draw_scatter3, wind, P, marker, props);
-#pragma GCC diagnostic pop
+    AF_DEPRECATED_WARNINGS_ON
 }
 
 af_err af_draw_scatter_nd(const af_window wind, const af_array in,
diff --git a/src/api/unified/statistics.cpp b/src/api/unified/statistics.cpp
index efd6959dbb..d97bd33237 100644
--- a/src/api/unified/statistics.cpp
+++ b/src/api/unified/statistics.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/deprecated.hpp>
 #include <af/array.h>
 #include <af/statistics.h>
 #include "symbol_manager.hpp"
@@ -22,11 +23,13 @@ af_err af_mean_weighted(af_array *out, const af_array in,
     CALL(af_mean_weighted, out, in, weights, dim);
 }
 
+AF_DEPRECATED_WARNINGS_OFF
 af_err af_var(af_array *out, const af_array in, const bool isbiased,
               const dim_t dim) {
     CHECK_ARRAYS(in);
     CALL(af_var, out, in, isbiased, dim);
 }
+AF_DEPRECATED_WARNINGS_ON
 
 af_err af_var_weighted(af_array *out, const af_array in, const af_array weights,
                        const dim_t dim) {
@@ -41,6 +44,7 @@ af_err af_meanvar(af_array *mean, af_array *var, const af_array in,
     CALL(af_meanvar, mean, var, in, weights, bias, dim);
 }
 
+AF_DEPRECATED_WARNINGS_OFF
 af_err af_stdev(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
     CALL(af_stdev, out, in, dim);
@@ -51,6 +55,7 @@ af_err af_cov(af_array *out, const af_array X, const af_array Y,
     CHECK_ARRAYS(X, Y);
     CALL(af_cov, out, X, Y, isbiased);
 }
+AF_DEPRECATED_WARNINGS_ON
 
 af_err af_median(af_array *out, const af_array in, const dim_t dim) {
     CHECK_ARRAYS(in);
@@ -68,11 +73,13 @@ af_err af_mean_all_weighted(double *real, double *imag, const af_array in,
     CALL(af_mean_all_weighted, real, imag, in, weights);
 }
 
+AF_DEPRECATED_WARNINGS_OFF
 af_err af_var_all(double *realVal, double *imagVal, const af_array in,
                   const bool isbiased) {
     CHECK_ARRAYS(in);
     CALL(af_var_all, realVal, imagVal, in, isbiased);
 }
+AF_DEPRECATED_WARNINGS_ON
 
 af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in,
                            const af_array weights) {
@@ -80,10 +87,12 @@ af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in,
     CALL(af_var_all_weighted, realVal, imagVal, in, weights);
 }
 
+AF_DEPRECATED_WARNINGS_OFF
 af_err af_stdev_all(double *real, double *imag, const af_array in) {
     CHECK_ARRAYS(in);
     CALL(af_stdev_all, real, imag, in);
 }
+AF_DEPRECATED_WARNINGS_ON
 
 af_err af_median_all(double *realVal, double *imagVal, const af_array in) {
     CHECK_ARRAYS(in);
diff --git a/src/backend/common/deprecated.hpp b/src/backend/common/deprecated.hpp
new file mode 100644
index 0000000000..4a7aca99a5
--- /dev/null
+++ b/src/backend/common/deprecated.hpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <af/compilers.h>
+
+// clang-format off
+#if AF_COMPILER_IS_MSVC
+#define AF_DEPRECATED_WARNINGS_OFF  \
+    __pragma(warning(push))         \
+    __pragma(warning(disable:4996))
+
+#define AF_DEPRECATED_WARNINGS_ON \
+    __pragma(warning(pop))
+#else
+#define AF_DEPRECATED_WARNINGS_OFF                                  \
+  _Pragma("GCC diagnostic push")                                 \
+  _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"")
+
+#define AF_DEPRECATED_WARNINGS_ON                                   \
+  _Pragma("GCC diagnostic pop")
+#endif
+// clang-format on
diff --git a/src/backend/opencl/cl2hpp.hpp b/src/backend/opencl/cl2hpp.hpp
index f7a94d5391..ef6f80037b 100644
--- a/src/backend/opencl/cl2hpp.hpp
+++ b/src/backend/opencl/cl2hpp.hpp
@@ -9,13 +9,16 @@
 
 #pragma once
 
+#include <common/deprecated.hpp>
+
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 #pragma GCC diagnostic ignored "-Wignored-qualifiers"
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+AF_DEPRECATED_WARNINGS_OFF
 #if __GNUC__ >= 8
 #pragma GCC diagnostic ignored "-Wcatch-value="
 #endif
 #include <CL/cl2.hpp>
+AF_DEPRECATED_WARNINGS_ON
 #pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 1241fed3d6..f8b54b3070 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <Param.hpp>
+#include <common/deprecated.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
@@ -19,9 +20,7 @@
 #include <traits.hpp>
 #include <af/defines.h>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
+AF_DEPRECATED_WARNINGS_OFF
 #include <boost/compute/algorithm/adjacent_difference.hpp>
 #include <boost/compute/algorithm/exclusive_scan.hpp>
 #include <boost/compute/algorithm/sort.hpp>
@@ -30,8 +29,7 @@
 #include <boost/compute/iterator/counting_iterator.hpp>
 #include <boost/compute/lambda.hpp>
 #include <boost/compute/lambda/placeholders.hpp>
-
-#pragma GCC diagnostic pop
+AF_DEPRECATED_WARNINGS_ON
 
 #include <array>
 #include <string>
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift_nonfree.hpp
index 117a39b9fa..96fdc0f26e 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift_nonfree.hpp
@@ -70,6 +70,7 @@
 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+#include <common/deprecated.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
@@ -80,16 +81,13 @@
 #include <memory.hpp>
 #include <af/defines.h>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
+AF_DEPRECATED_WARNINGS_OFF
 #include <boost/compute/algorithm/gather.hpp>
 #include <boost/compute/algorithm/iota.hpp>
 #include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/core.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
-
-#pragma GCC diagnostic pop
+AF_DEPRECATED_WARNINGS_ON
 
 #include <vector>
 
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index 6250ef454a..a55eb2b966 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -16,14 +16,13 @@
 #include <kernel/sort_helper.hpp>
 #include <traits.hpp>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
+AF_DEPRECATED_WARNINGS_OFF
 #include <boost/compute/algorithm/sort.hpp>
 #include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/core.hpp>
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
+AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
@@ -129,5 +128,3 @@ void sort0(Param val, bool isAscending) {
 }
 }  // namespace kernel
 }  // namespace opencl
-
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 2c7f9b9822..02f23cfa67 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -21,9 +21,7 @@
 #include <memory.hpp>
 #include <traits.hpp>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
+AF_DEPRECATED_WARNINGS_OFF
 #include <boost/compute/algorithm/copy.hpp>
 #include <boost/compute/algorithm/sort_by_key.hpp>
 #include <boost/compute/algorithm/transform.hpp>
@@ -34,6 +32,7 @@
 #include <boost/compute/functional/operator.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
 #include <boost/compute/types/pair.hpp>
+AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
@@ -254,5 +253,3 @@ void sort0ByKey(Param pKey, Param pVal, bool isAscending) {
 
 }  // namespace kernel
 }  // namespace opencl
-
-#pragma GCC diagnostic pop
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index cb83765be2..30aa475a01 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -14,14 +14,13 @@
 #include <sort.hpp>
 #include <af/dim4.hpp>
 
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-
+AF_DEPRECATED_WARNINGS_OFF
 #include <boost/compute/algorithm/set_intersection.hpp>
 #include <boost/compute/algorithm/set_union.hpp>
 #include <boost/compute/algorithm/sort.hpp>
 #include <boost/compute/algorithm/unique.hpp>
 #include <boost/compute/iterator/buffer_iterator.hpp>
+AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
@@ -153,5 +152,3 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace opencl
-
-#pragma GCC diagnostic pop
diff --git a/test/random.cpp b/test/random.cpp
index ac70aec057..4669b7515e 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -317,12 +317,12 @@ void testRandomEngineUniform(randomEngineType type) {
     if (af::isDoubleAvailable(af::getDevice())) {
         array Ad = A.as(f64);
         double m = mean<double>(Ad);
-        double s = stdev<double>(Ad);
+        double s = stdev<double>(Ad, AF_VARIANCE_POPULATION);
         ASSERT_NEAR(m, 0.5, 1e-3);
         ASSERT_NEAR(s, 0.2887, 1e-2);
     } else {
         T m = mean<T>(A);
-        T s = stdev<T>(A);
+        T s = stdev<T>(A, AF_VARIANCE_POPULATION);
         ASSERT_NEAR(m, 0.5, 1e-3);
         ASSERT_NEAR(s, 0.2887, 1e-2);
     }
@@ -337,7 +337,7 @@ void testRandomEngineNormal(randomEngineType type) {
     randomEngine r(type, 0);
     array A = randn(elem, ty, r);
     T m     = mean<T>(A);
-    T s     = stdev<T>(A);
+    T s     = stdev<T>(A, AF_VARIANCE_POPULATION);
     ASSERT_NEAR(m, 0, 1e-1);
     ASSERT_NEAR(s, 1, 1e-1);
 }

From fe97af769aa8c1a5d5a7fe82f34b001bac643597 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Aug 2020 15:43:46 -0400
Subject: [PATCH 2068/2677] Add functions to set and get the cache directory
 for runtime kernels

* Adds an enviornment variable AF_JIT_KERNEL_CACHE_DIRECTORY which
  can change the path where ArrayFire will store the runtime generated
  kernels. If the path is not writeable ArrayFire will fallback to defaults
* Add functions af_get_kernel_cache_directory and af_set_kernel_cache_directory
  which can also set the directory in the code.
* The set function is capablie of overriding the environment variable values
  based on the override_env variable
---
 .../configuring_arrayfire_environment.md      | 21 ++++++++-
 include/af/device.h                           | 43 +++++++++++++++++++
 src/api/c/device.cpp                          | 37 ++++++++++++++++
 src/api/unified/device.cpp                    |  8 ++++
 src/backend/common/util.cpp                   | 27 +++++++++---
 src/backend/common/util.hpp                   |  7 ++-
 test/jit.cpp                                  | 35 +++++++++++++++
 7 files changed, 170 insertions(+), 8 deletions(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index 3ea0ecaca6..a4641e1529 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -223,7 +223,7 @@ AF_BUILD_LIB_CUSTOM_PATH {#af_build_lib_custom_path}
 -------------------------------------------------------------------------------
 
 When set, this environment variable specifies a custom path along which the
-symbol manager will search for dynamic (shared library) backends to load. This 
+symbol manager will search for dynamic (shared library) backends to load. This
 is useful for specialized build configurations that use the unified backend and
 build shared libraries separately.
 
@@ -243,3 +243,22 @@ three values:
 CUDA backend kernels are stored in files with cu file extension.
 
 OpenCL backend kernels are stored in files with cl file extension.
+
+AF_JIT_KERNEL_CACHE_DIRECTORY {#af_jit_kernel_cache_directory}
+-------------------------------------------------------------------------------
+
+This variable sets the path to the ArrayFire cache on the filesystem. If set
+ArrayFire will write the kernels that are compiled at runtime to this directory.
+If the path is not writeable, the default path is used.
+
+This path is different from AF_JIT_KERNEL_TRACE which stores strings. These
+kernels will store binaries and the content will be dependent on the
+backend and platforms used.
+
+The default path is determined in the following order:
+  Unix:
+      1. $HOME/.arrayfire
+      2. /tmp/arrayfire
+  Windows:
+      1. ArrayFire application Temp folder(Usually
+          C:\Users\<user_name>\AppData\Local\Temp\ArrayFire)
diff --git a/include/af/device.h b/include/af/device.h
index 94c06d71ba..f081394d65 100644
--- a/include/af/device.h
+++ b/include/af/device.h
@@ -575,6 +575,49 @@ extern "C" {
     */
     AFAPI af_err af_get_device_ptr(void **ptr, const af_array arr);
 
+#if AF_API_VERSION >= 38
+    /**
+       Sets the path where the kernels generated at runtime will be cached
+
+       Sets the path where the kernels generated at runtime will be stored to
+       cache for later use. The files in this directory can be safely deleted.
+       The default location for these kernels is in $HOME/.arrayfire on Unix
+       systems and in the ArrayFire temp directory on Windows.
+
+       \param[in] path The location where the kernels will be stored
+       \param[in] override_env if true this path will take precedence over the
+                               AF_JIT_KERNEL_CACHE_DIRECTORY environment variable.
+                               If false, the environment variable takes precedence
+                               over this path.
+
+       \returns AF_SUCCESS if the variable is set. AF_ERR_ARG if path is NULL.
+       \ingroup device_func_mem
+    */
+    AFAPI af_err af_set_kernel_cache_directory(const char* path,
+                                               int override_env);
+
+    /**
+       Gets the path where the kernels generated at runtime will be cached
+
+       Gets the path where the kernels generated at runtime will be stored to
+       cache for later use. The files in this directory can be safely deleted.
+       The default location for these kernels is in $HOME/.arrayfire on Unix
+       systems and in the ArrayFire temp directory on Windows.
+
+       \param[out] length The length of the path array. If \p path is NULL, the
+                          length of the current path is assigned to this pointer
+       \param[out] path The path of the runtime generated kernel cache
+                         variable. If NULL, the current path length is assigned
+                         to \p length
+       \returns AF_SUCCESS if the variable is set.
+                AF_ERR_ARG if path and length are null at the same time.
+                AF_ERR_SIZE if \p length not sufficient enought to store the
+                            path
+       \ingroup device_func_mem
+    */
+    AFAPI af_err af_get_kernel_cache_directory(size_t *length, char *path);
+
+#endif
 
 #ifdef __cplusplus
 }
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index b82319d030..c9ae999390 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <common/util.hpp>
 #include <handle.hpp>
 #include <platform.hpp>
 #include <sparse_handle.hpp>
@@ -326,3 +327,39 @@ af_err af_get_manual_eval_flag(bool* flag) {
     CATCHALL;
     return AF_SUCCESS;
 }
+
+af_err af_get_kernel_cache_directory(size_t* length, char* path) {
+    try {
+        std::string& cache_path = getCacheDirectory();
+        if (path == nullptr) {
+            ARG_ASSERT(length != nullptr, 1);
+            *length = cache_path.size();
+        } else {
+            size_t min_len = cache_path.size();
+            if (length) {
+                if (*length < cache_path.size()) {
+                    AF_ERROR("Length not sufficient to store the path",
+                             AF_ERR_SIZE);
+                }
+                min_len = std::min(*length, cache_path.size());
+            }
+            memcpy(path, cache_path.c_str(), min_len);
+        }
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
+
+af_err af_set_kernel_cache_directory(const char* path, int override_env) {
+    try {
+        ARG_ASSERT(path != nullptr, 1);
+        if (override_env) {
+            getCacheDirectory() = std::string(path);
+        } else {
+            auto env_path = getEnvVar(JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME);
+            if (env_path.empty()) { getCacheDirectory() = std::string(path); }
+        }
+    }
+    CATCHALL
+    return AF_SUCCESS;
+}
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 3b97a29fbc..826d44a83d 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -179,3 +179,11 @@ af_err af_set_manual_eval_flag(bool flag) {
 af_err af_get_manual_eval_flag(bool *flag) {
     CALL(af_get_manual_eval_flag, flag);
 }
+
+af_err af_set_kernel_cache_directory(const char *path, int override_eval) {
+    CALL(af_set_kernel_cache_directory, path, override_eval);
+}
+
+af_err af_get_kernel_cache_directory(size_t *length, char *path) {
+    CALL(af_get_kernel_cache_directory, length, path);
+}
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index 125ff535ef..ce207be5d0 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -15,6 +15,7 @@
 #include <unistd.h>
 #endif
 
+#include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/util.hpp>
 #include <af/defines.h>
@@ -166,12 +167,12 @@ bool isDirectoryWritable(const string& path) {
     return true;
 }
 
-const string& getCacheDirectory() {
+string& getCacheDirectory() {
     static std::once_flag flag;
     static string cacheDirectory;
 
     std::call_once(flag, []() {
-        const vector<string> pathList = {
+        std::string pathList[] = {
 #if defined(OS_WIN)
             getTemporaryDirectory() + "\\ArrayFire"
 #else
@@ -180,10 +181,24 @@ const string& getCacheDirectory() {
 #endif
         };
 
-        auto iterDir =
-            std::find_if(pathList.begin(), pathList.end(), isDirectoryWritable);
-
-        cacheDirectory = iterDir != pathList.end() ? *iterDir : "";
+        auto env_path = getEnvVar(JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME);
+        if (!env_path.empty() && !isDirectoryWritable(env_path)) {
+            spdlog::get("platform")
+                ->warn(
+                    "The environment variable {}({}) is "
+                    "not writeable. Falling back to default.",
+                    JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME, env_path);
+            env_path.clear();
+        }
+
+        if (env_path.empty()) {
+            auto iterDir = std::find_if(begin(pathList), end(pathList),
+                                        isDirectoryWritable);
+
+            cacheDirectory = iterDir != end(pathList) ? *iterDir : "";
+        } else {
+            cacheDirectory = env_path;
+        }
     });
 
     return cacheDirectory;
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 9d49f8524f..efa3ce2501 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -14,6 +14,11 @@
 #include <string>
 #include <vector>
 
+/// The environment variable that determines where the runtime kernels
+/// will be stored on the file system
+constexpr const char* JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME =
+    "AF_JIT_KERNEL_CACHE_DIRECTORY";
+
 std::string getEnvVar(const std::string& key);
 
 // Dump the kernel sources only if the environment variable is defined
@@ -22,7 +27,7 @@ void saveKernel(const std::string& funcName, const std::string& jit_ker,
 
 std::string int_version_to_string(int version);
 
-const std::string& getCacheDirectory();
+std::string& getCacheDirectory();
 
 bool directoryExists(const std::string& path);
 
diff --git a/test/jit.cpp b/test/jit.cpp
index 3fb73764b2..c9e93b0254 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -793,3 +793,38 @@ TEST(JIT, DISABLED_ManyConstants) {
     eval(res2, res4, res6);//, res8);
     af::sync();
 }
+
+TEST(JIT, getKernelCacheDirectory) {
+  size_t length = 0;
+  ASSERT_SUCCESS(af_get_kernel_cache_directory(&length, NULL));
+
+  std::string path;
+  path.resize(length);
+  ASSERT_SUCCESS(af_get_kernel_cache_directory(&length, &path.at(0)));
+}
+
+TEST(JIT, setKernelCacheDirectory) {
+  std::string path = ".";
+
+  // Get the old path so we can reset it after the test
+  size_t length = 0;
+  ASSERT_SUCCESS(af_get_kernel_cache_directory(&length, NULL));
+  std::string old_path;
+  old_path.resize(length);
+  ASSERT_SUCCESS(af_get_kernel_cache_directory(&length, &old_path.at(0)));
+
+  // Set cache directory to the new path
+  ASSERT_SUCCESS(af_set_kernel_cache_directory(path.c_str(), false));
+
+  // Get the new path for verification
+  size_t new_length = path.size();
+  std::string new_path;
+  new_path.resize(new_length);
+  ASSERT_SUCCESS(af_get_kernel_cache_directory(&new_length, &new_path.at(0)));
+
+  ASSERT_EQ(path, new_path);
+  ASSERT_EQ(path.size(), new_path.size());
+
+  // Reset to the old path
+  ASSERT_SUCCESS(af_set_kernel_cache_directory(old_path.c_str(), false));
+}

From 2fb662a779db131b19946d37f62cd1cf281b2297 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Aug 2020 16:02:19 -0400
Subject: [PATCH 2069/2677] Add CUDA 11 to Toolkit2MaxCompute. Add Ampere sm to
 compute2cores

---
 src/backend/cuda/device_manager.cpp | 71 +++++++++++++++--------------
 1 file changed, 36 insertions(+), 35 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 9ec832fe59..4ddee634a9 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -70,6 +70,22 @@ struct cuNVRTCcompute {
     int embedded_minor;
 };
 
+/// Struct represents the cuda toolkit version and its associated minimum
+/// required driver versions.
+struct ToolkitDriverVersions {
+    /// The CUDA Toolkit version returned by cudaDriverGetVersion or
+    /// cudaRuntimeGetVersion
+    int version;
+
+    /// The minimum GPU driver version required for the \p version toolkit on
+    /// Linux or macOS
+    float unix_min_version;
+
+    /// The minimum GPU driver version required for the \p version toolkit on
+    /// Windows
+    float windows_min_version;
+};
+
 // clang-format off
 static const int jetsonComputeCapabilities[] = {
     7020,
@@ -81,6 +97,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11000, 8, 0, 0},
     {10020, 7, 5, 2},
     {10010, 7, 5, 2},
     {10000, 7, 0, 2},
@@ -92,6 +109,24 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
     { 7000, 5, 2, 3}};
 // clang-format on
 
+/// Map giving the minimum device driver needed in order to run a given version
+/// of CUDA for both Linux/Mac and Windows from:
+/// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
+// clang-format off
+static const ToolkitDriverVersions
+    CudaToDriverVersion[] = {
+        {11000, 450.51f, 451.48f},
+        {10020, 440.33f, 441.22f},
+        {10010, 418.39f, 418.96f},
+        {10000, 410.48f, 411.31f},
+        {9020,  396.37f, 398.26f},
+        {9010,  390.46f, 391.29f},
+        {9000,  384.81f, 385.54f},
+        {8000,  375.26f, 376.51f},
+        {7050,  352.31f, 353.66f},
+        {7000,  346.46f, 347.62f}};
+// clang-format on
+
 bool isEmbedded(pair<int, int> compute) {
     int version = compute.first * 1000 + compute.second * 10;
     return end(jetsonComputeCapabilities) !=
@@ -202,7 +237,7 @@ static inline int compute2cores(unsigned major, unsigned minor) {
         {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
         {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
         {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
-        {0x62, 128}, {0x70, 64},  {0x75, 64},  {-1, -1},
+        {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {-1, -1},
     };
 
     for (int i = 0; gpus[i].compute != -1; ++i) {
@@ -360,40 +395,6 @@ void DeviceManager::resetMemoryManagerPinned() {
     setMemoryManagerPinned(std::move(mgr));
 }
 
-/// Struct represents the cuda toolkit version and its associated minimum
-/// required driver versions.
-struct ToolkitDriverVersions {
-    /// The CUDA Toolkit version returned by cudaDriverGetVersion or
-    /// cudaRuntimeGetVersion
-    int version;
-
-    /// The minimum GPU driver version required for the \p version toolkit on
-    /// Linux or macOS
-    float unix_min_version;
-
-    /// The minimum GPU driver version required for the \p version toolkit on
-    /// Windows
-    float windows_min_version;
-};
-
-/// Map giving the minimum device driver needed in order to run a given version
-/// of CUDA for both Linux/Mac and Windows from:
-/// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
-// clang-format off
-static const ToolkitDriverVersions
-    CudaToDriverVersion[] = {
-        {11000, 450.51f, 451.48f},
-        {10020, 440.33f, 441.22f},
-        {10010, 418.39f, 418.96f},
-        {10000, 410.48f, 411.31f},
-        {9020,  396.37f, 398.26f},
-        {9010,  390.46f, 391.29f},
-        {9000,  384.81f, 385.54f},
-        {8000,  375.26f, 376.51f},
-        {7050,  352.31f, 353.66f},
-        {7000,  346.46f, 347.62f}};
-// clang-format on
-
 /// A debug only function that checks to see if the driver or runtime
 /// function is part of the CudaToDriverVersion array. If the runtime
 /// version is not part of the array then an error is thrown in debug

From 4c158e1b8b25109072816c883e78b8b342e8f9f2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Aug 2020 16:04:05 -0400
Subject: [PATCH 2070/2677] Fix variance warning in naive_bayes example

---
 examples/machine_learning/naive_bayes.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/machine_learning/naive_bayes.cpp b/examples/machine_learning/naive_bayes.cpp
index 1ea0d45afa..9fe6456f0e 100644
--- a/examples/machine_learning/naive_bayes.cpp
+++ b/examples/machine_learning/naive_bayes.cpp
@@ -39,7 +39,7 @@ void naive_bayes_train(float *priors, array &mu, array &sig2,
         mu(span, ii) = mean(train_feats_ii, 1);
 
         // Some pixels are always 0. Add a small variance.
-        sig2(span, ii) = var(train_feats_ii, 0, 1) + 0.01;
+        sig2(span, ii) = var(train_feats_ii, AF_VARIANCE_SAMPLE, 1) + 0.01;
 
         // Calculate priors
         priors[ii] = (float)idx.elements() / (float)num_samples;

From 1405a9448f79ca56cc875f57db36cc6c33806272 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Aug 2020 15:52:09 -0400
Subject: [PATCH 2071/2677] Update select_compute_arch to support CUDA 11 and
 Ampere

* Remove Kepler from CUDA 11 builds. Keeps 3.5 and 3.7
* Adds support for 7.2
* Fix All and Common options for CUDA_architecture_build_targets
* All targets now include Tegra builds
---
 CMakeModules/select_compute_arch.cmake | 159 +++++++++++++++++++++----
 src/backend/cuda/CMakeLists.txt        |  15 +--
 2 files changed, 143 insertions(+), 31 deletions(-)

diff --git a/CMakeModules/select_compute_arch.cmake b/CMakeModules/select_compute_arch.cmake
index d0ace2aab6..dd107551ed 100644
--- a/CMakeModules/select_compute_arch.cmake
+++ b/CMakeModules/select_compute_arch.cmake
@@ -5,9 +5,9 @@
 #       - "Auto" detects local machine GPU compute arch at runtime.
 #       - "Common" and "All" cover common and entire subsets of architectures
 #      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
-#      NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal
+#      NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing Ampere
 #      NUM: Any number. Only those pairs are currently accepted by NVCC though:
-#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2
+#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0
 #      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
 #      Additionally, sets ${out_variable}_readable to the resulting numeric list
 #      Example:
@@ -16,31 +16,95 @@
 #
 #      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
 #
+if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+  if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
+      AND CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
+    set(CUDA_VERSION "${CMAKE_MATCH_1}")
+  endif()
+endif()
+
+# See: https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list
 
 # This list will be used for CUDA_ARCH_NAME = All option
-set(CUDA_KNOWN_GPU_ARCHITECTURES  "Fermi" "Kepler" "Maxwell")
+set(CUDA_KNOWN_GPU_ARCHITECTURES "Fermi" "Kepler" )
 
 # This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
 set(CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.5" "5.0")
 
-if (CUDA_VERSION VERSION_GREATER "6.5")
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell+Tegra")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2")
-endif ()
+if(CUDA_VERSION VERSION_LESS "7.0")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "5.2")
+endif()
+
+# This list is used to filter CUDA archs when autodetecting
+set(CUDA_ALL_GPU_ARCHITECTURES "3.0" "3.2" "3.5" "5.0")
 
-if (CUDA_VERSION VERSION_GREATER "7.5")
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal")
+if(CUDA_VERSION VERSION_GREATER "7.0" OR CUDA_VERSION VERSION_EQUAL "7.0" )
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell" "Maxwell+Tegra")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.0" "5.2")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "5.0" "5.2" "5.3")
+
+  if(CUDA_VERSION VERSION_LESS "8.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "6.0")
+  endif()
+endif()
+
+if(CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0" )
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal" "Pascal+Tegra")
   list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1")
-else()
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "6.0" "6.1" "6.2")
+
+  if(CUDA_VERSION VERSION_LESS "9.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.2+PTX")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "7.0")
+  endif()
 endif ()
 
-if (CUDA_VERSION VERSION_GREATER "8.5")
+if(CUDA_VERSION VERSION_GREATER "9.0" OR CUDA_VERSION VERSION_EQUAL "9.0")
   list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.0")
+
+  if(CUDA_VERSION VERSION_GREATER "9.1" OR CUDA_VERSION VERSION_EQUAL "9.1")
+    list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta+Tegra")
+    list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.2")
+  endif()
+
   list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Fermi")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0" "7.0+PTX")
-else()
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.1+PTX")
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "2.0")
+
+  if(CUDA_VERSION VERSION_GREATER "9.1" OR CUDA_VERSION VERSION_EQUAL "9.1"
+     AND CUDA_VERSION VERSION_LESS "10.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0+PTX")
+  endif()
+
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
+
+endif()
+
+if(CUDA_VERSION VERSION_GREATER "10.0" OR CUDA_VERSION VERSION_EQUAL "10.0")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Turing")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.5")
+
+  if(CUDA_VERSION VERSION_LESS "11.0")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5+PTX")
+  endif()
+endif()
+
+if(CUDA_VERSION VERSION_GREATER "11.0" OR CUDA_VERSION VERSION_EQUAL "11.0")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ampere")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0" "8.0+PTX")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.0")
+
+  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra")
+  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Kepler")
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.2")
+
+  if(CUDA_VERSION VERSION_LESS "12.0")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
+  endif()
 endif()
 
 ################################################################################################
@@ -50,7 +114,11 @@ endif()
 #
 function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
   if(NOT CUDA_GPU_DETECT_OUTPUT)
-    set(file ${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cpp)
+    if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+      set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cu")
+    else()
+      set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cpp")
+    endif()
 
     file(WRITE ${file} ""
       "#include <cuda_runtime.h>\n"
@@ -69,10 +137,18 @@ function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
       "  return 0;\n"
       "}\n")
 
-    try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
-            CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
-            LINK_LIBRARIES ${CUDA_LIBRARIES}
-            RUN_OUTPUT_VARIABLE compute_capabilities)
+    if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+      try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+              RUN_OUTPUT_VARIABLE compute_capabilities)
+    else()
+      try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+              CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
+              LINK_LIBRARIES ${CUDA_LIBRARIES}
+              RUN_OUTPUT_VARIABLE compute_capabilities)
+    endif()
+
+    # Filter unrelated content out of the output.
+    string(REGEX MATCHALL "[0-9]+\\.[0-9]+" compute_capabilities "${compute_capabilities}")
 
     if(run_result EQUAL 0)
       string(REPLACE "2.1" "2.1(2.0)" compute_capabilities "${compute_capabilities}")
@@ -85,7 +161,20 @@ function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
     message(STATUS "Automatic GPU detection failed. Building for common architectures.")
     set(${OUT_VARIABLE} ${CUDA_COMMON_GPU_ARCHITECTURES} PARENT_SCOPE)
   else()
-    set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT} PARENT_SCOPE)
+    # Filter based on CUDA version supported archs
+    set(CUDA_GPU_DETECT_OUTPUT_FILTERED "")
+    separate_arguments(CUDA_GPU_DETECT_OUTPUT)
+    foreach(ITEM IN ITEMS ${CUDA_GPU_DETECT_OUTPUT})
+      if(CUDA_LIMIT_GPU_ARCHITECTURE AND (ITEM VERSION_GREATER CUDA_LIMIT_GPU_ARCHITECTURE OR
+                                          ITEM VERSION_EQUAL CUDA_LIMIT_GPU_ARCHITECTURE))
+        list(GET CUDA_COMMON_GPU_ARCHITECTURES -1 NEWITEM)
+        string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${NEWITEM}")
+      else()
+        string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${ITEM}")
+      endif()
+    endforeach()
+
+    set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT_FILTERED} PARENT_SCOPE)
   endif()
 endfunction()
 
@@ -103,9 +192,11 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
 
   set(cuda_arch_bin)
   set(cuda_arch_ptx)
+  set(cuda_arch_with_ptx false)
 
   if("${CUDA_ARCH_LIST}" STREQUAL "All")
     set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
+    set(cuda_arch_with_ptx true)
   elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
     set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
   elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto")
@@ -116,10 +207,18 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
   # Now process the list and look for names
   string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
   list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
+
+  list(GET CUDA_ARCH_LIST -1 latest_arch)
+
   foreach(arch_name ${CUDA_ARCH_LIST})
     set(arch_bin)
     set(arch_ptx)
     set(add_ptx FALSE)
+
+    if(${arch_name} STREQUAL ${latest_arch} AND cuda_arch_with_ptx)
+      set(add_ptx TRUE)
+    endif()
+
     # Check to see if we are compiling PTX
     if(arch_name MATCHES "(.*)\\+PTX$")
       set(add_ptx TRUE)
@@ -134,10 +233,11 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
         set(arch_bin 2.0 "2.1(2.0)")
       elseif(${arch_name} STREQUAL "Kepler+Tegra")
         set(arch_bin 3.2)
-      elseif(${arch_name} STREQUAL "Kepler+Tesla")
-        set(arch_bin 3.7)
       elseif(${arch_name} STREQUAL "Kepler")
-        set(arch_bin 3.0 3.5)
+        set(arch_bin 3.0)
+        set(arch_ptx 3.0)
+      elseif(${arch_name} STREQUAL "Kepler+Tesla")
+        set(arch_bin 3.5 3.7)
         set(arch_ptx 3.5)
       elseif(${arch_name} STREQUAL "Maxwell+Tegra")
         set(arch_bin 5.3)
@@ -147,9 +247,20 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
       elseif(${arch_name} STREQUAL "Pascal")
         set(arch_bin 6.0 6.1)
         set(arch_ptx 6.1)
+      elseif(${arch_name} STREQUAL "Pascal+Tegra")
+        set(arch_bin 6.2)
+        set(arch_ptx 6.2)
       elseif(${arch_name} STREQUAL "Volta")
         set(arch_bin 7.0 7.0)
         set(arch_ptx 7.0)
+      elseif(${arch_name} STREQUAL "Volta+Tegra")
+        set(arch_bin 7.2)
+      elseif(${arch_name} STREQUAL "Turing")
+        set(arch_bin 7.5)
+        set(arch_ptx 7.5)
+      elseif(${arch_name} STREQUAL "Ampere")
+        set(arch_bin 8.0)
+        set(arch_ptx 8.0)
       else()
         message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
       endif()
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4c320ed6bc..7e3e4089ee 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -79,15 +79,16 @@ endif()
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
-if(NOT CUDA_architecture_build_targets)
-  cuda_detect_installed_gpus(detected_gpus)
-endif()
-
-set(CUDA_architecture_build_targets ${detected_gpus} CACHE
-  STRING "The compute architectures targeted by this build. (Options: 3.0;Maxwell;All;Common)")
+set(CUDA_architecture_build_targets "Auto" CACHE
+    STRING "The compute architectures targeted by this build. (Options: Auto;3.0;Maxwell;All;Common)")
 
 cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
-message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets}")
+
+string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=sm_([0-9]+)" "\\1|" cuda_build_targets ${cuda_architecture_flags})
+string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=compute_([0-9]+)" "\\1+PTX|" cuda_build_targets ${cuda_build_targets})
+string(REGEX REPLACE "([0-9]+)([0-9])\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
+string(REGEX REPLACE "([0-9]+)([0-9]\\+PTX)\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
+message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets} ( ${cuda_build_targets} )")
 
 set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
 

From e0ed6ceb9f9c0d28f055ce775e2d71c2b6836b8f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 15 Sep 2020 18:20:54 -0400
Subject: [PATCH 2072/2677] Fix comment in CUDA interop code example

The code example had a comment that incorrectly stated that 10 blocks
were being launched instead of the actual one. This PR fixes that comment
---
 docs/pages/interop_cuda.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index c3cfed3b9c..dae46ae027 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -84,7 +84,7 @@ int main() {
     cudaStream_t af_cuda_stream = afcu::getStream(cuda_id);
 
     // 6. Set arguments and run your kernel in ArrayFire's stream
-    //    Here launch with 10 blocks of 10 threads
+    //    Here launch with 1 block of 10 threads
     increment<<<1, num, 0, af_cuda_stream>>>(d_x);
 
     // 7. Return control of af::array memory to ArrayFire using

From 6b2d7177d68210d1f1721855b48c9c4cbe436711 Mon Sep 17 00:00:00 2001
From: Wes Bouaziz <5843554+wesbz@users.noreply.github.com>
Date: Sun, 13 Sep 2020 18:20:54 +0200
Subject: [PATCH 2073/2677] minor error in doc

f64 is 64-bit floating point values, nots complex floating point values.
---
 include/af/defines.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index 464a3c1d81..a346a14e24 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -210,7 +210,7 @@ typedef enum {
 typedef enum {
     f32,    ///< 32-bit floating point values
     c32,    ///< 32-bit complex floating point values
-    f64,    ///< 64-bit complex floating point values
+    f64,    ///< 64-bit floating point values
     c64,    ///< 64-bit complex floating point values
     b8 ,    ///< 8-bit boolean values
     s32,    ///< 32-bit signed integral values

From 465013c0d5f4d4e69be50560d2ff4b65b2bdb076 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 5 Oct 2020 15:54:52 +0530
Subject: [PATCH 2074/2677] Fix features copy constructor for multithreaded
 usage

Features class now uses rule of five guideline.
---
 include/af/features.h    | 13 +++++++++++++
 src/api/cpp/features.cpp | 14 ++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/include/af/features.h b/include/af/features.h
index aa5e049a91..0f3a146883 100644
--- a/include/af/features.h
+++ b/include/af/features.h
@@ -40,6 +40,19 @@ namespace af
         /// Copy assignment operator
         features& operator= (const features& other);
 
+#if AF_API_VERSION >= 38
+        /// Copy constructor
+        features(const features &other);
+
+#if AF_COMPILER_CXX_RVALUE_REFERENCES
+        /// Move constructor
+        features(features &&other);
+
+        /// Move assignment operator
+        features &operator=(features &&other);
+#endif
+#endif
+
         /// Returns  the number of features represented by this object
         size_t getNumFeatures() const;
 
diff --git a/src/api/cpp/features.cpp b/src/api/cpp/features.cpp
index 96a669b5ab..9422c487e4 100644
--- a/src/api/cpp/features.cpp
+++ b/src/api/cpp/features.cpp
@@ -11,6 +11,8 @@
 #include <af/features.h>
 #include "error.hpp"
 
+#include <utility>
+
 namespace af {
 
 features::features() : feat{} { AF_THROW(af_create_features(&feat, 0)); }
@@ -21,6 +23,10 @@ features::features(const size_t n) : feat{} {
 
 features::features(af_features f) : feat(f) {}
 
+features::features(const features& other) {
+    if (this != &other) { AF_THROW(af_retain_features(&feat, other.get())); }
+}
+
 features& features::operator=(const features& other) {
     if (this != &other) {
         AF_THROW(af_release_features(feat));
@@ -29,6 +35,14 @@ features& features::operator=(const features& other) {
     return *this;
 }
 
+features::features(features&& other)
+    : feat(std::exchange(other.feat, nullptr)) {}
+
+features& features::operator=(features&& other) {
+    std::swap(feat, other.feat);
+    return *this;
+}
+
 features::~features() {
     // THOU SHALL NOT THROW IN DESTRUCTORS
     if (feat) { af_release_features(feat); }

From c903e574235ae003edce7ad264c35319b974a0c0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 5 Oct 2020 08:08:17 -0400
Subject: [PATCH 2075/2677] Fix index copy constructor by retaining array based
 index

Array based indexing caused segfaults sometimes when the index object was not
immidiately consumed by the indexing operations. This PR fixes this by
retaining the index array on the copy constructor
---
 src/api/cpp/index.cpp | 10 ++++++++--
 test/index.cpp        | 13 +++++++++++++
 2 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/src/api/cpp/index.cpp b/src/api/cpp/index.cpp
index 134c58f0cb..c2664432ef 100644
--- a/src/api/cpp/index.cpp
+++ b/src/api/cpp/index.cpp
@@ -66,7 +66,13 @@ index::index(const af::array &idx0) : impl{} {
     impl.isBatch = false;
 }
 
-index::index(const af::index &idx0) : impl{idx0.impl} {}  // NOLINT
+index::index(const af::index &idx0) : impl{idx0.impl} {
+    if (!impl.isSeq && impl.idx.arr) {
+        // increment reference count to avoid double free
+        // when/if idx0 is destroyed
+        AF_THROW(af_retain_array(&impl.idx.arr, impl.idx.arr));
+    }
+}
 
 // NOLINTNEXTLINE(hicpp-noexcept-move, performance-noexcept-move-constructor)
 index::index(index &&idx0) : impl{idx0.impl} { idx0.impl.idx.arr = nullptr; }
@@ -79,7 +85,7 @@ index &index::operator=(const index &idx0) {
     if (this == &idx0) { return *this; }
 
     impl = idx0.get();
-    if (!impl.isSeq) {
+    if (!impl.isSeq && impl.idx.arr) {
         // increment reference count to avoid double free
         // when/if idx0 is destroyed
         AF_THROW(af_retain_array(&impl.idx.arr, impl.idx.arr));
diff --git a/test/index.cpp b/test/index.cpp
index a2901ed830..9c60bc3dde 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1764,6 +1764,19 @@ TEST(Index, ISSUE_2273_Flipped) {
     ASSERT_ARRAYS_EQ(input_slice_gold, input_slice);
 }
 
+TEST(Index, CopiedIndexDestroyed) {
+    array in = randu(10, 10);
+    array a  = constant(1, 10);
+
+    af::index index1(a);
+    af::index index2(seq(10));
+
+    af::index index3(index1);
+    { af::index index4(index1); }
+
+    af_print(in(index1, index2));
+}
+
 // clang-format off
 class IndexDocs : public ::testing::Test {
 public:

From db3c333504f0d494d8edf8a828f0cb2e832efc83 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 5 Oct 2020 08:26:15 -0400
Subject: [PATCH 2076/2677] Add support for cuda 11.1 and compute 8.6

---
 CMakeModules/select_compute_arch.cmake | 11 ++++++++++-
 src/backend/cuda/device_manager.cpp    |  5 ++++-
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/select_compute_arch.cmake b/CMakeModules/select_compute_arch.cmake
index dd107551ed..38180edeff 100644
--- a/CMakeModules/select_compute_arch.cmake
+++ b/CMakeModules/select_compute_arch.cmake
@@ -95,13 +95,22 @@ endif()
 
 if(CUDA_VERSION VERSION_GREATER "11.0" OR CUDA_VERSION VERSION_EQUAL "11.0")
   list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ampere")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0" "8.0+PTX")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0")
   list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.0")
 
   list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra")
   list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Kepler")
   list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.2")
 
+  if(CUDA_VERSION VERSION_GREATER "11.1" OR CUDA_VERSION VERSION_EQUAL "11.1")
+    list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.6")
+  endif()
+
+  if(CUDA_VERSION VERSION_GREATER "11.1" OR CUDA_VERSION VERSION_EQUAL "11.1"
+      AND CUDA_VERSION VERSION_LESS "12.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0+PTX")
+  endif()
+
   if(CUDA_VERSION VERSION_LESS "12.0")
     set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
   endif()
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 4ddee634a9..d1b483878f 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -97,6 +97,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11010, 8, 0, 0},
     {11000, 8, 0, 0},
     {10020, 7, 5, 2},
     {10010, 7, 5, 2},
@@ -115,6 +116,7 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11010, 455.23f, 456.38f},
         {11000, 450.51f, 451.48f},
         {10020, 440.33f, 441.22f},
         {10010, 418.39f, 418.96f},
@@ -237,7 +239,8 @@ static inline int compute2cores(unsigned major, unsigned minor) {
         {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
         {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
         {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
-        {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {-1, -1},
+        {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {0x86, 128},
+        {-1, -1},
     };
 
     for (int i = 0; gpus[i].compute != -1; ++i) {

From 096221d6e0040f092a259094d623e03856389ad5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 5 Oct 2020 21:33:22 +0530
Subject: [PATCH 2077/2677] Fix input ndims check for regions function

---
 src/api/c/regions.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/regions.cpp b/src/api/c/regions.cpp
index 8009527f90..a76391de5a 100644
--- a/src/api/c/regions.cpp
+++ b/src/api/c/regions.cpp
@@ -35,7 +35,7 @@ af_err af_regions(af_array *out, const af_array in,
         af::dim4 dims         = info.dims();
 
         dim_t in_ndims = dims.ndims();
-        DIM_ASSERT(1, (in_ndims <= 3 && in_ndims >= 2));
+        DIM_ASSERT(1, (in_ndims == 2));
 
         af_dtype in_type = info.getType();
         if (in_type != b8) { TYPE_ERROR(1, in_type); }

From 01326aaab69b38c2d9c107bef8d51ab2f4afa2d0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 22 Oct 2020 18:32:34 -0400
Subject: [PATCH 2078/2677] Update GitHub workflows away from set-env

---
 .github/workflows/cpu_build.yml            | 8 ++++----
 .github/workflows/release_src_artifact.yml | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index ed74a7194a..5f3b9c2544 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -59,13 +59,13 @@ jobs:
                   cmake_lnx_dir=$(echo "${cmake_install_dir}/bin")
                   cmake_osx_dir=$(echo "${cmake_install_dir}/CMake.app/Contents/bin")
                   cmake_dir=$(if [ $OS_NAME == 'macos-latest' ]; then echo "${cmake_osx_dir}"; else echo "${cmake_lnx_dir}"; fi)
-                  echo "::set-env name=CMAKE_PROGRAM::$(pwd)/${cmake_dir}/cmake"
+                  echo "CMAKE_PROGRAM=$(pwd)/${cmake_dir}/cmake" >> $GITHUB_ENV
 
             - name: Install Dependencies for Macos
               if: matrix.os == 'macos-latest'
               run: |
                   brew install boost fontconfig glfw freeimage fftw lapack openblas
-                  echo "::set-env name=CMAKE_PROGRAM::cmake"
+                  echo "CMAKE_PROGRAM=cmake" >> $GITHUB_ENV
 
             - name: Install Common Dependencies for Ubuntu
               if: matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04'
@@ -114,7 +114,7 @@ jobs:
                       -DUSE_CPU_MKL:BOOL=$USE_MKL \
                       -DBUILDNAME:STRING=${buildname} \
                       ..
-                  echo "::set-env name=CTEST_DASHBOARD::${dashboard}"
+                  echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 
             - name: Build and Test
               run: |
@@ -176,7 +176,7 @@ jobs:
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
                       -DBUILDNAME:STRING="$buildname"
-                  echo "::set-env name=CTEST_DASHBOARD::${dashboard}"
+                  echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 
             - name: Build and Test
               run: |
diff --git a/.github/workflows/release_src_artifact.yml b/.github/workflows/release_src_artifact.yml
index 0dee8ffea4..da25ff3522 100644
--- a/.github/workflows/release_src_artifact.yml
+++ b/.github/workflows/release_src_artifact.yml
@@ -19,9 +19,9 @@ jobs:
                   id_line=$(echo "${response}" | grep -m 1 "id.:")
                   rel_id=$(echo "${id_line}" | awk '{split($0, a, ":"); split(a[2], b, ","); print b[1]}')
                   trimmed_rel_id=$(echo "${rel_id}" | awk '{gsub(/^[ \t]+/,""); print $0 }')
-                  echo "::set-env name=RELEASE_ID::${trimmed_rel_id}"
-                  echo "::set-env name=AF_TAG::${tag}"
-                  echo "::set-env name=AF_VER::${ver}"
+                  echo "RELEASE_ID=${trimmed_rel_id}" >> $GITHUB_ENV
+                  echo "AF_TAG=${tag}" >> $GITHUB_ENV
+                  echo "AF_VER=${ver}" >> $GITHUB_ENV
 
             - name: Checkout with Submodules
               run: |
@@ -37,7 +37,7 @@ jobs:
                   rm -rf arrayfire-full-${AF_VER}/.github
                   rm arrayfire-full-${AF_VER}/.gitmodules
                   tar -cjf arrayfire-full-${AF_VER}.tar.bz2 arrayfire-full-${AF_VER}/
-                  echo "::set-env name=UPLOAD_FILE::arrayfire-full-${AF_VER}.tar.bz2"
+                  echo "UPLOAD_FILE=arrayfire-full-${AF_VER}.tar.bz2" >> $GITHUB_ENV
 
             - name: Upload source tarball
               uses: actions/upload-release-asset@v1

From c0822aba0f0189192587662f6cd04989ddddc42f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 18 Aug 2020 17:54:15 +0530
Subject: [PATCH 2079/2677] Update release notes docs for v3.7.3 release

(cherry picked from commit b9fc2199c00ae582b904e5644dfff258371b5cc6)
---
 docs/pages/release_notes.md | 32 +++++++++++++++++++++++++++++++-
 1 file changed, 31 insertions(+), 1 deletion(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 15789b0d5b..d2c9252f9f 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -14,9 +14,39 @@ Major Updates
 Improvements
 ------------
 
-v3.7.2
+v3.7.3
 ======
 
+Improvements
+------------
+- Add f16 support for histogram - \PR{2984}
+- Update confidence connected components example for better illustration - \PR{2968}
+- Enable disk caching of OpenCL kernel binaries - \PR{2970}
+- Refactor extension of kernel binaries stored to disk `.bin` - \PR{2970}
+- Add minimum driver versions for CUDA toolkit 11 in internal map - \PR{2982}
+- Improve warnings messages from run-time kernel compilation functions - \PR{2996}
+
+Fixes
+-----
+- Fix bias factor of variance in var_all and cov functions - \PR{2986}
+- Fix a race condition in confidence connected components function for OpenCL backend - \PR{2969}
+- Safely ignore disk cache failures in CUDA backend for compiled kernel binaries - \PR{2970}
+- Fix randn by passing in correct values to Box-Muller - \PR{2980}
+- Fix rounding issues in Box-Muller function used for RNG - \PR{2980}
+- Fix problems in RNG for older compute architectures with fp16 - \PR{2980}  \PR{2996}
+- Fix performance regression of approx functions - \PR{2977}
+- Remove assert that check that signal/filter types have to be the same - \PR{2993}
+- Fix `checkAndSetDevMaxCompute` when the device cc is greater than max - \PR{2996}
+- Fix documentation errors and warnings - \PR{2973} , \PR{2987}
+- Add missing opencl-arrayfire interoperability functions in unified backend  - \PR{2981}
+
+Contributions
+-------------
+Special thanks to our contributors:
+[P. J. Reed](https://github.com/pjreed)
+
+v3.7.2
+======
 
 Improvements
 ------------

From fe937b87d2d43e461eca388fd6b42ee5dec8118e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 18 Aug 2020 18:07:19 +0530
Subject: [PATCH 2080/2677] Update v3.8 release notes

---
 docs/pages/release_notes.md | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index d2c9252f9f..571f37801f 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -5,14 +5,18 @@ v3.8.0
 ======
 
 Major Updates
--------------
-- Ragged max
-- Bitwise not
-- Updated alloc and free
-- Initializer list for af::array
+--------
+- Non-uniform(ragged) reductions \PR{2786}
+- Bit-wise not operator support for array and C API (af\_bitnot) \PR{2865}
+- Initialization list constructor for array class \PR{2829} \PR{2987}
 
 Improvements
 ------------
+- New API for following statistics function: cov, var and stdev - \PR{2986}
+- allocV2 and freeV2 which return cl\_mem on OpenCL backend \PR{2911}
+- Move constructor and move assignment operator for Dim4 class \PR{2946}
+- Support for CUDA 11.1 and Compute 8.6 \PR{3023}
+- Fix af::feature copy constructor for multi-threaded sceanarios \PR{3022}
 
 v3.7.3
 ======
@@ -20,7 +24,7 @@ v3.7.3
 Improvements
 ------------
 - Add f16 support for histogram - \PR{2984}
-- Update confidence connected components example for better illustration - \PR{2968}
+- Update confidence connected components example with better illustration - \PR{2968}
 - Enable disk caching of OpenCL kernel binaries - \PR{2970}
 - Refactor extension of kernel binaries stored to disk `.bin` - \PR{2970}
 - Add minimum driver versions for CUDA toolkit 11 in internal map - \PR{2982}

From f84141eeb5c187898b9133736830a30c8490196d Mon Sep 17 00:00:00 2001
From: HO-COOH <42881734+HO-COOH@users.noreply.github.com>
Date: Wed, 28 Oct 2020 08:31:41 -0500
Subject: [PATCH 2081/2677] Fix the tutorial link in README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e6103c8aeb..73ebdd77dd 100644
--- a/README.md
+++ b/README.md
@@ -99,7 +99,7 @@ You can find our complete documentation [here](http://www.arrayfire.com/docs/ind
 Quick links:
 
 * [List of functions](http://www.arrayfire.org/docs/group__arrayfire__func.htm)
-* [Tutorials](http://www.arrayfire.org/docs/usergroup0.htm)
+* [Tutorials](http://arrayfire.org/docs/tutorials.htm)
 * [Examples](http://www.arrayfire.org/docs/examples.htm)
 * [Blog](http://arrayfire.com/blog/)
 

From e9dcb696a675903b5a5177ce4e1725e2ccc5709a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 6 Oct 2020 19:59:12 +0530
Subject: [PATCH 2082/2677] Add version info resource file for Window build

---
 CMakeLists.txt                              |  6 +++
 CMakeModules/generate_product_version.cmake | 45 +++++++++++++++++++
 CMakeModules/version_info.rc.in             | 50 +++++++++++++++++++++
 src/api/unified/CMakeLists.txt              |  6 ++-
 src/backend/cpu/CMakeLists.txt              |  6 +++
 src/backend/cuda/CMakeLists.txt             |  6 +++
 src/backend/opencl/CMakeLists.txt           |  6 +++
 7 files changed, 124 insertions(+), 1 deletion(-)
 create mode 100644 CMakeModules/generate_product_version.cmake
 create mode 100644 CMakeModules/version_info.rc.in

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 682f416041..9df1f808a6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -23,6 +23,12 @@ include(GetPrerequisites)
 include(CheckCXXCompilerFlag)
 include(SplitDebugInfo)
 
+# Use the function generate_product_version on Windows
+# to attach version info in dll file attributes.
+# Make sure to pass appropriate arguments for each backend
+# to generate the correct resource file
+include(generate_product_version)
+
 set_policies(
   TYPE NEW
   POLICIES CMP0073
diff --git a/CMakeModules/generate_product_version.cmake b/CMakeModules/generate_product_version.cmake
new file mode 100644
index 0000000000..6f4aae1da0
--- /dev/null
+++ b/CMakeModules/generate_product_version.cmake
@@ -0,0 +1,45 @@
+function(generate_product_version outfile)
+  set(options)
+  set(oneValueArgs
+    COMPANY_NAME
+    FILE_DESCRIPTION
+    FILE_NAME
+    ORIGINAL_FILE_NAME
+    COMPANY_COPYRIGHT
+  )
+  set(multiValueArgs)
+  cmake_parse_arguments(PRODUCT "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  if(NOT PRODUCT_COMPANY_NAME OR "${PRODUCT_COMPANY_NAME}" STREQUAL "")
+      set(PRODUCT_COMPANY_NAME "ArrayFire")
+  endif()
+  if(NOT PRODUCT_FILE_DESCRIPTION OR "${PRODUCT_FILE_DESCRIPTION}" STREQUAL "")
+    set(PRODUCT_FILE_DESCRIPTION "ArrayFire Library")
+  endif()
+  if(NOT PRODUCT_FILE_NAME OR "${PRODUCT_FILE_NAME}" STREQUAL "")
+    set(PRODUCT_FILE_NAME "${PROJECT_NAME}")
+  endif()
+  if(NOT PRODUCT_ORIGINAL_FILE_NAME OR "${PRODUCT_ORIGINAL_FILE_NAME}" STREQUAL "")
+    set(PRODUCT_ORIGINAL_FILE_NAME "${PRODUCT_FILE_NAME}")
+  endif()
+  if(NOT PRODUCT_FILE_DESCRIPTION OR "${PRODUCT_FILE_DESCRIPTION}" STREQUAL "")
+      set(PRODUCT_FILE_DESCRIPTION "${PRODUCT_FILE_NAME}")
+  endif()
+  if(NOT PRODUCT_COMPANY_COPYRIGHT OR "${PRODUCT_COMPANY_COPYRIGHT}" STREQUAL "")
+    string(TIMESTAMP PRODUCT_CURRENT_YEAR "%Y")
+    set(PRODUCT_COMPANY_COPYRIGHT "${PRODUCT_COMPANY_NAME} (C) Copyright ${PRODUCT_CURRENT_YEAR}")
+  endif()
+
+  set(PRODUCT_VERSION ${PROJECT_VERSION})
+  set(PRODUCT_VERSION_MAJOR ${PROJECT_VERSION_MAJOR})
+  set(PRODUCT_VERSION_MINOR ${PROJECT_VERSION_MINOR})
+  set(PRODUCT_VERSION_PATCH ${PROJECT_VERSION_PATCH})
+  set(PRODUCT_INTERNAL_FILE_NAME ${PRODUCT_ORIGINAL_FILE_NAME})
+
+  set(ver_res_file "${PROJECT_BINARY_DIR}/${PRODUCT_FILE_NAME}_version_info.rc")
+  configure_file(
+    ${PROJECT_SOURCE_DIR}/CMakeModules/version_info.rc.in
+    ${ver_res_file}
+  )
+  set(${outfile} ${ver_res_file} PARENT_SCOPE)
+endfunction()
diff --git a/CMakeModules/version_info.rc.in b/CMakeModules/version_info.rc.in
new file mode 100644
index 0000000000..d738ce20d0
--- /dev/null
+++ b/CMakeModules/version_info.rc.in
@@ -0,0 +1,50 @@
+#include <winresrc.h>
+
+#define VER_FILEVERSION             @PRODUCT_VERSION_MAJOR@,@PRODUCT_VERSION_MINOR@,@PRODUCT_VERSION_PATCH@
+#define VER_FILEVERSION_STR         "@PRODUCT_VERSION@\0"
+
+
+#define VER_PRODUCTVERSION          @PRODUCT_VERSION_MAJOR@,@PRODUCT_VERSION_MINOR@,@PRODUCT_VERSION_PATCH@
+#define VER_PRODUCTVERSION_STR      "@PRODUCT_VERSION@\0"
+
+#ifndef NDEBUG
+#define VER_DEBUG 0
+#else
+#define VER_DEBUG VS_FF_DEBUG
+#endif
+
+VS_VERSION_INFO VERSIONINFO
+FILEVERSION     VER_FILEVERSION
+PRODUCTVERSION  VER_PRODUCTVERSION
+FILEFLAGSMASK   VS_FFI_FILEFLAGSMASK
+FILEFLAGS       VER_DEBUG
+FILEOS          VOS__WINDOWS32
+FILETYPE        VFT_DLL
+FILESUBTYPE     VFT2_UNKNOWN
+BEGIN
+    BLOCK "StringFileInfo"
+    BEGIN
+        BLOCK "040904E4"
+        BEGIN
+            VALUE "CompanyName",      "@PRODUCT_COMPANY_NAME@\0"
+            VALUE "FileDescription",  "@PRODUCT_FILE_DESCRIPTION@\0"
+            VALUE "FileVersion",      "@PRODUCT_VERSION@\0"
+            VALUE "InternalName",     "@PRODUCT_INTERNAL_FILE_NAME@\0"
+            VALUE "LegalCopyright",   "@PRODUCT_COMPANY_COPYRIGHT@\0"
+            VALUE "OriginalFilename", "@PRODUCT_ORIGINAL_FILE_NAME@\0"
+            VALUE "ProductName",      "@PRODUCT_FILE_NAME@\0"
+            VALUE "ProductVersion",   "@PRODUCT_VERSION@\0"
+        END
+    END
+
+    BLOCK "VarFileInfo"
+    BEGIN
+        /* The following line should only be modified for localized versions.     */
+        /* It consists of any number of WORD,WORD pairs, with each pair           */
+        /* describing a language,codepage combination supported by the file.      */
+        /*                                                                        */
+        /* For example, a file might have values "0x409,1252" indicating that it  */
+        /* supports English language (0x409) in the Windows ANSI codepage (1252). */
+        VALUE "Translation", 0x409, 1252
+    END
+END
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 967eaa631c..026418a39b 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -1,10 +1,14 @@
-
+generate_product_version(af_unified_ver_res_file
+  FILE_NAME "af"
+  FILE_DESCRIPTION "Unified Backend Dynamic-link library"
+)
 
 add_library(af "")
 add_library(ArrayFire::af ALIAS af)
 
 target_sources(af
   PRIVATE
+    ${af_unified_ver_res_file}
     ${CMAKE_CURRENT_SOURCE_DIR}/algorithm.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/arith.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 170bb0f3be..deddd9df33 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -7,12 +7,18 @@
 
 include(InternalUtils)
 
+generate_product_version(af_cpu_ver_res_file
+  FILE_NAME "afcpu"
+  FILE_DESCRIPTION "CPU Backend Dynamic-link library"
+)
+
 add_library(afcpu "")
 add_library(ArrayFire::afcpu ALIAS afcpu)
 
 # CPU backend source files
 target_sources(afcpu
   PRIVATE
+    $<$<PLATFORM_ID:Windows>:${af_cpu_ver_res_file}>
     Array.cpp
     Array.hpp
     anisotropic_diffusion.cpp
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 7e3e4089ee..bc05593b1b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -5,6 +5,11 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
+generate_product_version(af_cuda_ver_res_file
+  FILE_NAME "afcuda"
+  FILE_DESCRIPTION "CUDA Backend Dynamic-link library"
+)
+
 dependency_check(CUDA_FOUND "CUDA not found.")
 if(AF_WITH_CUDNN)
   dependency_check(cuDNN_FOUND "CUDNN not found.")
@@ -351,6 +356,7 @@ else()
 endif()
 
 cuda_add_library(afcuda
+    $<$<PLATFORM_ID:Windows>:${af_cuda_ver_res_file}>
     ${thrust_sort_sources}
 
     EnqueueArgs.hpp
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index f970da06b4..e30bc4a084 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -7,6 +7,11 @@
 
 include(InternalUtils)
 
+generate_product_version(af_opencl_ver_res_file
+  FILE_NAME "afopencl"
+  FILE_DESCRIPTION "OpenCL Backend Dynamic-link library"
+)
+
 set(AF_OPENCL_BLAS_LIBRARY CLBlast CACHE STRING "Select OpenCL BLAS back-end")
 set_property(CACHE AF_OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
 
@@ -45,6 +50,7 @@ add_library(ArrayFire::afopencl ALIAS afopencl)
 
 target_sources(afopencl
   PRIVATE
+    $<$<PLATFORM_ID:Windows>:${af_opencl_ver_res_file}>
     Array.cpp
     Array.hpp
     Kernel.cpp

From 56be9286367491df9a1679455d8e5629c7900c12 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 15 Oct 2020 16:30:56 +0530
Subject: [PATCH 2083/2677] Fix lapack support check in CPU/OpenCL backend
 CMakeLists

---
 src/backend/cpu/CMakeLists.txt    | 2 +-
 src/backend/opencl/CMakeLists.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index deddd9df33..f7fd76e0cf 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -342,7 +342,7 @@ else()
   endif()
 endif()
 
-if(LAPACK_FOUND OR MKL_Shared_FOUND)
+if(LAPACK_FOUND OR (USE_CPU_MKL AND MKL_Shared_FOUND))
   target_compile_definitions(afcpu
     PRIVATE
       WITH_LINEAR_ALGEBRA)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index e30bc4a084..b27de32f6e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -455,7 +455,7 @@ if(APPLE)
   target_link_libraries(afopencl PRIVATE OpenGL::GL)
 endif()
 
-if(LAPACK_FOUND OR MKL_Shared_FOUND)
+if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
   target_sources(afopencl
     PRIVATE
       magma/gebrd.cpp

From 69d55f75d61ae28e7a30168b01f4d9b609a00e95 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 19 Oct 2020 17:22:53 +0530
Subject: [PATCH 2084/2677] Fix function name typo in timing tutorial

---
 docs/pages/timing.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/timing.md b/docs/pages/timing.md
index 4949c4e97f..fc9b1a725f 100644
--- a/docs/pages/timing.md
+++ b/docs/pages/timing.md
@@ -6,7 +6,7 @@ timer() : A platform-independent timer with microsecond accuracy:
 
 * [timer::start()](\ref af::timer::stop) seconds since last \ref af::timer::start "start"
 
-* \ref af::timer::stop(af::timer start) "timer::start(timer start)" seconds since 'start'
+* \ref af::timer::stop(af::timer start) "timer::stop(timer start)" seconds since 'start'
 
 Example: single timer
 

From ec49f1a2971de44b72919bfd5f70e2dc30bc7fcf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 22 Oct 2020 01:19:58 -0400
Subject: [PATCH 2085/2677] Fix stream assigned to Thrust functions

---
 src/backend/cuda/ThrustArrayFirePolicy.hpp | 48 ++++++++++++----------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
index 4ac230ad94..d58b508453 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.hpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -12,31 +12,11 @@
 #include <backend.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
-#include <thrust/execution_policy.h>
+#include <thrust/system/cuda/execution_policy.h>
 
 namespace cuda {
 struct ThrustArrayFirePolicy
-    : thrust::device_execution_policy<ThrustArrayFirePolicy> {};
-
-namespace {
-__DH__
-inline cudaStream_t get_stream(ThrustArrayFirePolicy) {
-#if defined(__CUDA_ARCH__)
-    return 0;
-#else
-    return getActiveStream();
-#endif
-}
-
-__DH__
-inline cudaError_t synchronize_stream(ThrustArrayFirePolicy) {
-#if defined(__CUDA_ARCH__)
-    return cudaDeviceSynchronize();
-#else
-    return cudaStreamSynchronize(getActiveStream());
-#endif
-}
-}  // namespace
+    : thrust::cuda::execution_policy<ThrustArrayFirePolicy> {};
 
 template<typename T>
 thrust::pair<thrust::pointer<T, ThrustArrayFirePolicy>, std::ptrdiff_t>
@@ -53,3 +33,27 @@ inline void return_temporary_buffer(ThrustArrayFirePolicy, Pointer p) {
 }
 
 }  // namespace cuda
+
+namespace thrust {
+namespace cuda_cub {
+template<>
+__DH__ inline cudaStream_t get_stream<::cuda::ThrustArrayFirePolicy>(
+    execution_policy<::cuda::ThrustArrayFirePolicy> &) {
+#if defined(__CUDA_ARCH__)
+    return 0;
+#else
+    return ::cuda::getActiveStream();
+#endif
+}
+
+__DH__
+inline cudaError_t synchronize_stream(const ::cuda::ThrustArrayFirePolicy &) {
+#if defined(__CUDA_ARCH__)
+    return cudaDeviceSynchronize();
+#else
+    return cudaStreamSynchronize(::cuda::getActiveStream());
+#endif
+}
+
+}  // namespace cuda_cub
+}  // namespace thrust

From 0493478fe5ea3eabb54d4d598f10117db61c86ea Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 4 Nov 2020 04:03:15 +0100
Subject: [PATCH 2086/2677] Max parameter length fetched from device (#3032)

* Max parameter length is now fetched from device.

Values for opencl parameter maximum length were hardcoded.  The maximum is now requested at the device, so that the correct value for all devices is used.

* Removed isAmd & isNvidia, since they are no longer used.
---
 src/backend/opencl/Array.cpp | 32 +++++++-------------------------
 1 file changed, 7 insertions(+), 25 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 23da2f086b..9d8f2f99ea 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -300,10 +300,6 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
     auto platform      = getActivePlatform();
 
     // The Apple platform can have the nvidia card or the AMD card
-    bool isNvidia =
-        platform == AFCL_PLATFORM_NVIDIA || platform == AFCL_PLATFORM_APPLE;
-    bool isAmd =
-        platform == AFCL_PLATFORM_AMD || platform == AFCL_PLATFORM_APPLE;
     bool isIntel = platform == AFCL_PLATFORM_INTEL;
 
     /// Intels param_size limit is much smaller than the other platforms
@@ -320,27 +316,13 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
         constexpr size_t base_param_size =
             sizeof(T *) + sizeof(KParam) + (3 * sizeof(uint));
 
-        // This is the maximum size of the params that can be allowed by the
-        // CUDA platform.
-        constexpr size_t max_nvidia_param_size = (4096 - base_param_size);
-        constexpr size_t max_amd_param_size    = (3520 - base_param_size);
-
-        // This value is really for the Intel HD Graphics platform. The CPU
-        // platform seems like it can handle unlimited parameters but the
-        // compile times become very large.
-        constexpr size_t max_intel_igpu_param_size =
-            (1024 - 256 - base_param_size);
-
-        size_t max_param_size = 0;
-        if (isNvidia) {
-            max_param_size = max_nvidia_param_size;
-        } else if (isAmd) {
-            max_param_size = max_amd_param_size;
-        } else if (isIntel && getDeviceType() == CL_DEVICE_TYPE_GPU) {
-            max_param_size = max_intel_igpu_param_size;
-        } else {
-            max_param_size = 8192;
-        }
+        const cl::Device &device = getDevice();
+        size_t max_param_size = device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
+        // typical values:
+        //   NVIDIA     = 4096
+        //   AMD        = 3520  (AMD A10 iGPU = 1024)
+        //   Intel iGPU = 1024
+        max_param_size -= base_param_size;
 
         struct tree_info {
             size_t total_buffer_size;

From d0645fe1d6c148bf241a4058651386bc593edb1d Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 4 Nov 2020 16:14:57 +0100
Subject: [PATCH 2087/2677] JIT optimization: Faster generation of an unique
 funcName (#3040)

Use strings instead of stringstream to generate funcNames for JIT kernels.
* JIT optimization: Faster generation of an unique funcName
* Extra separator between returned names and IDs, to be certain that they never concatenate.
* Added separator for output nodes
* For improved performance: Use the operation ID iso operation string.
Add a separator between names of multiple output nodes.
---
 src/backend/common/jit/BufferNodeBase.hpp |  9 +++++----
 src/backend/common/jit/NaryNode.hpp       | 14 +++++++-------
 src/backend/common/jit/Node.cpp           | 18 ++++++------------
 src/backend/common/jit/Node.hpp           |  2 +-
 src/backend/common/jit/ScalarNode.hpp     |  9 +++++----
 src/backend/common/jit/ShiftNodeBase.hpp  |  9 +++++----
 src/backend/cpu/jit/BinaryNode.hpp        |  4 ++--
 src/backend/cpu/jit/BufferNode.hpp        |  4 ++--
 src/backend/cpu/jit/ScalarNode.hpp        |  4 ++--
 src/backend/cpu/jit/UnaryNode.hpp         |  4 ++--
 10 files changed, 37 insertions(+), 40 deletions(-)

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 999d9bd078..3402f9a50d 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -53,11 +53,12 @@ class BufferNodeBase : public common::Node {
         return m_linear_buffer && same_dims;
     }
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
-        kerStream << "_" << getNameStr();
-        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
-                  << std::dec;
+        kerString += '_';
+        kerString += getNameStr();
+        kerString += ',';
+        kerString += std::to_string(ids.id);
     }
 
     void genParams(std::stringstream &kerStream, int id,
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index da80d4ea83..75d9a5a38a 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -64,17 +64,17 @@ class NaryNode : public Node {
         swap(m_op_str, other.m_op_str);
     }
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
         // Make the dec representation of enum part of the Kernel name
-        kerStream << "_" << std::setw(3) << std::setfill('0') << std::dec
-                  << m_op;
+        kerString += '_';
+        kerString += std::to_string(m_op);
+        kerString += ',';
         for (int i = 0; i < m_num_children; i++) {
-            kerStream << std::setw(3) << std::setfill('0') << std::dec
-                      << ids.child_ids[i];
+            kerString += std::to_string(ids.child_ids[i]);
+            kerString += ',';
         }
-        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
-                  << std::dec;
+        kerString += std::to_string(ids.id);
     }
 
     void genFuncs(std::stringstream &kerStream,
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 8b1b8736b8..3ed3bc4b89 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -41,26 +41,20 @@ int Node::getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes,
 std::string getFuncName(const vector<Node *> &output_nodes,
                         const vector<Node *> &full_nodes,
                         const vector<Node_ids> &full_ids, bool is_linear) {
-    std::stringstream funcName;
-    std::stringstream hashName;
-
-    if (is_linear) {
-        funcName << "L_";  // Kernel Linear
-    } else {
-        funcName << "G_";  // Kernel General
-    }
+    std::string funcName;
+    funcName.reserve(512);
+    funcName = (is_linear ? 'L' : 'G');
 
     for (const auto &node : output_nodes) {
-        funcName << node->getNameStr() << "_";
+        funcName += '_';
+        funcName += node->getNameStr();
     }
 
     for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
 
-    hashName << "KER";
-    hashName << deterministicHash(funcName.str());
-    return hashName.str();
+    return "KER" + std::to_string(deterministicHash(funcName));
 }
 
 }  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 39845fa319..d4b3a23d51 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -122,7 +122,7 @@ class Node {
                     std::vector<Node_ids> &full_ids);
 
     /// Generates the string that will be used to hash the kernel
-    virtual void genKerName(std::stringstream &kerStream,
+    virtual void genKerName(std::string &kerString,
                             const Node_ids &ids) const = 0;
 
     /// Generates the function parameters for the node.
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 86e3ad9d98..3528675d19 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -52,11 +52,12 @@ class ScalarNode : public common::Node {
         swap(m_val, other.m_val);
     }
 
-    void genKerName(std::stringstream& kerStream,
+    void genKerName(std::string& kerString,
                     const common::Node_ids& ids) const final {
-        kerStream << "_" << getTypeStr();
-        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
-                  << std::dec;
+        kerString += '_';
+        kerString += getTypeStr();
+        kerString += ',';
+        kerString += std::to_string(ids.id);
     }
 
     void genParams(std::stringstream& kerStream, int id,
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 84227ee8df..5049b6d71f 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -63,11 +63,12 @@ class ShiftNodeBase : public Node {
         return false;
     }
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
-        kerStream << "_" << getNameStr();
-        kerStream << std::setw(3) << std::setfill('0') << std::dec << ids.id
-                  << std::dec;
+        kerString += '_';
+        kerString += getNameStr();
+        kerString += ',';
+        kerString += std::to_string(ids.id);
     }
 
     void genParams(std::stringstream &kerStream, int id,
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index f82172c97a..0967e381b4 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -49,9 +49,9 @@ class BinaryNode : public TNode<compute_t<To>> {
         m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
     }
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
-        UNUSED(kerStream);
+        UNUSED(kerString);
         UNUSED(ids);
     }
 
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index d4360393cb..e26b0aa4a4 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -85,9 +85,9 @@ class BufferNode : public TNode<T> {
 
     size_t getBytes() const final { return m_bytes; }
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
-        UNUSED(kerStream);
+        UNUSED(kerString);
         UNUSED(ids);
     }
 
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index 196ce6a08c..ab91a92aac 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -21,9 +21,9 @@ class ScalarNode : public TNode<T> {
    public:
     ScalarNode(T val) : TNode<T>(val, 0, {}) {}
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
-        UNUSED(kerStream);
+        UNUSED(kerString);
         UNUSED(ids);
     }
 
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 87dd911ba8..3532b24abd 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -48,9 +48,9 @@ class UnaryNode : public TNode<To> {
         m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
     }
 
-    void genKerName(std::stringstream &kerStream,
+    void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
-        UNUSED(kerStream);
+        UNUSED(kerString);
         UNUSED(ids);
     }
 

From 0541fd4d193322449520fcec6c8a5b6004b63bc7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 15 Nov 2020 21:56:35 -0500
Subject: [PATCH 2088/2677] Fix constexpr error with vs2019 with half

---
 src/backend/common/half.hpp | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index ce06eedf02..fb25d0336d 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -879,15 +879,9 @@ class alignas(2) half {
         return *this;
     }
 
-#if defined(NVCC) || defined(__CUDACC_RTC__)
-    AF_CONSTEXPR __DH__ explicit half(__half value) noexcept
 #ifdef __CUDA_ARCH__
-        : data_(value) {
-    }
-#else
-        : data_(*reinterpret_cast<native_half_t*>(&value)) {
-    }
-#endif
+    AF_CONSTEXPR __DH__ explicit half(__half value) noexcept : data_(value) {}
+
     AF_CONSTEXPR __DH__ half& operator=(__half value) noexcept {
         // NOTE Assignment to ushort from __half only works with device code.
         // using memcpy instead

From 375ef6cc4d59870fe6f40909063f457c9814acd1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 18 Nov 2020 21:18:09 +0530
Subject: [PATCH 2089/2677] Fix the extra braces in cuda compile log message

Formatted opencl compile log message braces for a slightly better
readability.
---
 src/backend/cuda/compile_module.cpp   | 3 ++-
 src/backend/opencl/compile_module.cpp | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index c4c3315d0a..4f3a5c90ca 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -382,7 +382,8 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
                               return lhs + ", " + rhs;
                           });
     };
-    AF_TRACE("{{{compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}}",
+    AF_TRACE("{{ {:<20} : compile:{:>5} ms, link:{:>4} ms, {{ {} }}, {} }}",
+             moduleKey,
              duration_cast<milliseconds>(compile_end - compile).count(),
              duration_cast<milliseconds>(link_end - link).count(),
              listOpts(compiler_options), getDeviceProp(device).name);
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 35f992fe02..15a94a7e75 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -207,7 +207,7 @@ Module compileModule(const string &moduleKey, const vector<string> &sources,
     }
 #endif
 
-    AF_TRACE("{{{:<20} : {{ compile:{:>5} ms, {{ {} }}, {} }}}}", moduleKey,
+    AF_TRACE("{{ {:<20} : {{ compile:{:>5} ms, {{ {} }}, {} }} }}", moduleKey,
              duration_cast<milliseconds>(compileEnd - compileBegin).count(),
              fmt::join(options, " "),
              getDevice(getActiveDeviceId()).getInfo<CL_DEVICE_NAME>());

From 82a8c77d5f11202e26e5c31adb6d7c57b40f0c3e Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Fri, 30 Oct 2020 16:24:24 +0530
Subject: [PATCH 2090/2677] Fix cmake warning for mismatched cond in if else
 arms

---
 src/backend/opencl/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index b27de32f6e..7fd29d1f3a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -520,7 +520,7 @@ if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
     afopencl
     PRIVATE
       WITH_LINEAR_ALGEBRA)
-endif(LAPACK_FOUND OR MKL_Shared_FOUND)
+endif()
 
 af_split_debug_info(afopencl ${AF_INSTALL_LIB_DIR})
 

From 28f286ba5d73c47a941744401fc038aa0cee2992 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 2 Dec 2020 13:45:09 +0530
Subject: [PATCH 2091/2677] Workaround for new cuSparse API introduced in CUDA
 patch release

New API of cuSparse was introduced in 10.1.168 for Linux and the older
10.1.105 version doesn't it.

Unfortunately, when the new API was introduced in ArrayFire's code base,
I was testing against versions 10.1.168 or newer and hence didn't realize
that this new API was introduced in a patch/fix release - unconventional.

This change enables the new API only from 10.2.* on Linux since CUDA toolkit
version variable set by CMake doesn't provide patch number.
---
 src/backend/cuda/CMakeLists.txt | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index bc05593b1b..52925f6ebc 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -245,9 +245,14 @@ if(AF_WITH_NONFREE)
   set(cxx_definitions -DAF_WITH_NONFREE_SIFT)
 endif()
 
+# New API of cuSparse was introduced in 10.1.168 for Linux and the older
+# 10.1.105 fix version doesn't it. Unfortunately, the new API was introduced in
+# in a fix release of CUDA - unconventionally. As CMake's FindCUDA module
+# doesn't provide patch/fix version number, we use 10.2 as the minimum
+# CUDA version to enable this new cuSparse API.
 if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
    (UNIX AND
-    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 0))
+    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 1))
   list(APPEND cxx_definitions -DAF_USE_NEW_CUSPARSE_API)
 endif()
 
@@ -306,7 +311,7 @@ set_target_properties(af_cuda_static_cuda_library
 
 if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
    (UNIX AND
-    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 0))
+    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 1))
   target_compile_definitions(af_cuda_static_cuda_library PRIVATE AF_USE_NEW_CUSPARSE_API)
 endif()
 

From a004f5352e71d5b4b540684e0b3f6149e548079e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 17 Dec 2020 18:01:16 +0530
Subject: [PATCH 2092/2677] Update CUDA maps for newer version 11.2

---
 src/backend/cuda/device_manager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index d1b483878f..54a558ed01 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -97,6 +97,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11020, 8, 0, 0},
     {11010, 8, 0, 0},
     {11000, 8, 0, 0},
     {10020, 7, 5, 2},
@@ -116,6 +117,7 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11020, 460.27f, 460.89f},
         {11010, 455.23f, 456.38f},
         {11000, 450.51f, 451.48f},
         {10020, 440.33f, 441.22f},

From 0efcbc070113c3eda79ce384ec950483a93277ba Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 8 Dec 2020 17:13:10 +0530
Subject: [PATCH 2093/2677] Use short function name in non-debug builds in
 error messages

Prior to this, error message from an exception would look like below

    In function af_err af_transpose_inplace(af_array, bool)
    In file src/api/c/transpose.cpp:97

Earlier approach was hindering any useful log messages, especially from
runtime(like nvrtc) compilation phase, to be properly captured by
by the string returned by af_get_last_error function call.

Now it would look the same in debug builds but for release builds it
shall look like as following

    In function af_transpose_inplace
    In file src/api/c/transpose.cpp:97
---
 src/api/cpp/error.hpp             | 11 +++---
 src/backend/common/defines.hpp    | 10 ++++--
 src/backend/common/err_common.hpp | 57 +++++++++++++++----------------
 src/backend/cpu/err_cpu.hpp       |  8 ++---
 src/backend/cuda/err_cuda.hpp     |  8 ++---
 src/backend/opencl/err_opencl.hpp |  8 ++---
 6 files changed, 52 insertions(+), 50 deletions(-)

diff --git a/src/api/cpp/error.hpp b/src/api/cpp/error.hpp
index 37e03fc0e5..188f25b40b 100644
--- a/src/api/cpp/error.hpp
+++ b/src/api/cpp/error.hpp
@@ -17,14 +17,13 @@
         if (__err == AF_SUCCESS) break;                                       \
         char *msg = NULL;                                                     \
         af_get_last_error(&msg, NULL);                                        \
-        af::exception ex(msg, __PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                         __err);                                              \
+        af::exception ex(msg, __AF_FUNC__, __AF_FILENAME__, __LINE__, __err); \
         af_free_host(msg);                                                    \
         throw std::move(ex);                                                  \
     } while (0)
 
-#define AF_THROW_ERR(__msg, __err)                                       \
-    do {                                                                 \
-        throw af::exception(__msg, __PRETTY_FUNCTION__, __AF_FILENAME__, \
-                            __LINE__, __err);                            \
+#define AF_THROW_ERR(__msg, __err)                                         \
+    do {                                                                   \
+        throw af::exception(__msg, __AF_FUNC__, __AF_FILENAME__, __LINE__, \
+                            __err);                                        \
     } while (0)
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 658be6819a..79f39c5061 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -36,13 +36,17 @@ inline std::string clipFilePath(std::string path, std::string str) {
 #define STATIC_ static
 #define __AF_FILENAME__ (clipFilePath(__FILE__, "src\\").c_str())
 #else
-//#ifndef __PRETTY_FUNCTION__
-//    #define __PRETTY_FUNCTION__ __func__ // __PRETTY_FUNCTION__ Fallback
-//#endif
 #define STATIC_ inline
 #define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
 #endif
 
+#if defined(NDEBUG)
+#define __AF_FUNC__ __FUNCTION__
+#else
+// Debug
+#define __AF_FUNC__ __PRETTY_FUNCTION__
+#endif
+
 #ifdef OS_WIN
 #include <Windows.h>
 using LibHandle = HMODULE;
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 8da138d3a7..65e25bb0c8 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -146,40 +146,39 @@ af_err processException();
 af_err set_global_error_string(const std::string& msg,
                                af_err err = AF_ERR_UNKNOWN);
 
-#define DIM_ASSERT(INDEX, COND)                                        \
-    do {                                                               \
-        if ((COND) == false) {                                         \
-            throw DimensionError(__PRETTY_FUNCTION__, __AF_FILENAME__, \
-                                 __LINE__, INDEX, #COND,               \
-                                 boost::stacktrace::stacktrace());     \
-        }                                                              \
+#define DIM_ASSERT(INDEX, COND)                                          \
+    do {                                                                 \
+        if ((COND) == false) {                                           \
+            throw DimensionError(__AF_FUNC__, __AF_FILENAME__, __LINE__, \
+                                 INDEX, #COND,                           \
+                                 boost::stacktrace::stacktrace());       \
+        }                                                                \
     } while (0)
 
-#define ARG_ASSERT(INDEX, COND)                                       \
-    do {                                                              \
-        if ((COND) == false) {                                        \
-            throw ArgumentError(__PRETTY_FUNCTION__, __AF_FILENAME__, \
-                                __LINE__, INDEX, #COND,               \
-                                boost::stacktrace::stacktrace());     \
-        }                                                             \
+#define ARG_ASSERT(INDEX, COND)                                                \
+    do {                                                                       \
+        if ((COND) == false) {                                                 \
+            throw ArgumentError(__AF_FUNC__, __AF_FILENAME__, __LINE__, INDEX, \
+                                #COND, boost::stacktrace::stacktrace());       \
+        }                                                                      \
     } while (0)
 
-#define TYPE_ERROR(INDEX, type)                                                \
-    do {                                                                       \
-        throw TypeError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, INDEX, \
-                        type, boost::stacktrace::stacktrace());                \
+#define TYPE_ERROR(INDEX, type)                                              \
+    do {                                                                     \
+        throw TypeError(__AF_FUNC__, __AF_FILENAME__, __LINE__, INDEX, type, \
+                        boost::stacktrace::stacktrace());                    \
     } while (0)
 
-#define AF_ERROR(MSG, ERR_TYPE)                                            \
-    do {                                                                   \
-        throw AfError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, MSG, \
-                      ERR_TYPE, boost::stacktrace::stacktrace());          \
+#define AF_ERROR(MSG, ERR_TYPE)                                              \
+    do {                                                                     \
+        throw AfError(__AF_FUNC__, __AF_FILENAME__, __LINE__, MSG, ERR_TYPE, \
+                      boost::stacktrace::stacktrace());                      \
     } while (0)
 
 #define AF_RETURN_ERROR(MSG, ERR_TYPE)                                       \
     do {                                                                     \
         std::stringstream s;                                                 \
-        s << "Error in " << __PRETTY_FUNCTION__ << "\n"                      \
+        s << "Error in " << __AF_FUNC__ << "\n"                              \
           << "In file " << __AF_FILENAME__ << ":" << __LINE__ << ": " << MSG \
           << "\n"                                                            \
           << boost::stacktrace::stacktrace();                                \
@@ -200,12 +199,12 @@ af_err set_global_error_string(const std::string& msg,
         return processException(); \
     }
 
-#define AF_CHECK(fn)                                                        \
-    do {                                                                    \
-        af_err __err = fn;                                                  \
-        if (__err == AF_SUCCESS) break;                                     \
-        throw AfError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, "\n", \
-                      __err, boost::stacktrace::stacktrace());              \
+#define AF_CHECK(fn)                                                       \
+    do {                                                                   \
+        af_err __err = fn;                                                 \
+        if (__err == AF_SUCCESS) break;                                    \
+        throw AfError(__AF_FUNC__, __AF_FILENAME__, __LINE__, "\n", __err, \
+                      boost::stacktrace::stacktrace());                    \
     } while (0)
 
 static const int MAX_ERR_SIZE = 1024;
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index 3715c94988..d618cecb1e 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -9,8 +9,8 @@
 
 #include <common/err_common.hpp>
 
-#define CPU_NOT_SUPPORTED(message)                                         \
-    do {                                                                   \
-        throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                           message, boost::stacktrace::stacktrace());      \
+#define CPU_NOT_SUPPORTED(message)                                          \
+    do {                                                                    \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
+                           boost::stacktrace::stacktrace());                \
     } while (0)
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 061522aa4e..091b848283 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -12,10 +12,10 @@
 #include <common/err_common.hpp>
 #include <stdio.h>
 
-#define CUDA_NOT_SUPPORTED(message)                                        \
-    do {                                                                   \
-        throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                           message, boost::stacktrace::stacktrace());      \
+#define CUDA_NOT_SUPPORTED(message)                                         \
+    do {                                                                    \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
+                           boost::stacktrace::stacktrace());                \
     } while (0)
 
 #define CUDA_CHECK(fn)                                               \
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 7e715bbd77..845db9ee02 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -11,8 +11,8 @@
 
 #include <common/err_common.hpp>
 
-#define OPENCL_NOT_SUPPORTED(message)                                      \
-    do {                                                                   \
-        throw SupportError(__PRETTY_FUNCTION__, __AF_FILENAME__, __LINE__, \
-                           message, boost::stacktrace::stacktrace());      \
+#define OPENCL_NOT_SUPPORTED(message)                                       \
+    do {                                                                    \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
+                           boost::stacktrace::stacktrace());                \
     } while (0)

From 7d9fe0880338226fd5b627359321d4b5dfd78724 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 9 Jan 2021 04:31:44 +0530
Subject: [PATCH 2094/2677] Fix bitnot documentation

---
 docs/details/arith.dox | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 2ad28273e2..79e8cce0d0 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -147,6 +147,14 @@ Logical not of an input
 Negative of an input
 
 
+\defgroup arith_func_bitnot bitnot
+
+\ingroup logic_mat
+
+Bitwise not on the input
+
+\copydoc arith_int_only
+
 
 \defgroup arith_func_bitand bitand
 

From 98719a429a556cba9a0ec61337d71864799f56d2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 9 Jan 2021 04:36:30 +0530
Subject: [PATCH 2095/2677] Escape \ and < characters for doxygen in a path

---
 docs/pages/configuring_arrayfire_environment.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index a4641e1529..fd11628105 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -261,4 +261,4 @@ The default path is determined in the following order:
       2. /tmp/arrayfire
   Windows:
       1. ArrayFire application Temp folder(Usually
-          C:\Users\<user_name>\AppData\Local\Temp\ArrayFire)
+          C:\\Users\\\<user_name\>\\AppData\\Local\\Temp\\ArrayFire)

From 95abf36fdcd29e3319874fd158355d070002c19c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 9 Jan 2021 15:03:09 +0530
Subject: [PATCH 2096/2677] Update documentation install page with package
 manager instructions

---
 docs/pages/install.md | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index 5485c3a257..2cbabab9b9 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -43,9 +43,17 @@ For more information on using ArrayFire on Windows, visit the following
 
 ## <a name="Linux"></a> Linux
 
-Once you have downloaded the ArrayFire installer, execute the installer from the
-terminal as shown below. Set the `--prefix` argument to the directory you would
-like to install ArrayFire to - we recommend `/opt`.
+There are two ways to install ArrayFire on Linux.
+1. Package Manager
+2. Using ArrayFire Linux Installer
+
+As of today, approach (1) is only supported for Ubuntu 18.04 and 20.04. Please go
+through [our GitHub wiki page](https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers)
+for the detailed instructions.
+
+For approach (2), once you have downloaded the ArrayFire installer, execute the
+installer from the terminal as shown below. Set the `--prefix` argument to the
+directory you would like to install ArrayFire to - we recommend `/opt`.
 
     ./Arrayfire_*_Linux_x86_64.sh --include-subdir --prefix=/opt
 

From f9ffb863cd27ade5ce301f62dfacb883fd965146 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 9 Jan 2021 15:10:59 +0530
Subject: [PATCH 2097/2677] Update README with package manager install
 instructions

---
 README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/README.md b/README.md
index 73ebdd77dd..a9d37f7731 100644
--- a/README.md
+++ b/README.md
@@ -29,6 +29,11 @@ on Windows, Mac, and Linux.
 
 You can install the ArrayFire library from one of the following ways:
 
+### Package Managers
+
+This approach is currently only supported for Ubuntu 18.04 and 20.04. Please
+go through [our GitHub wiki page][1] for the detailed instructions.
+
 #### Official installers
 
 Execute one of our [official binary installers](https://arrayfire.com/download)
@@ -163,3 +168,5 @@ The literal mark “ArrayFire” and ArrayFire logos are trademarks of
 AccelerEyes LLC DBA ArrayFire.
 If you wish to use either of these marks in your own project, please consult
 [ArrayFire's Trademark Policy](http://arrayfire.com/trademark-policy/)
+
+[1]: https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers

From 0a0b1d4eb20e77c0464a714db2e61be46e436c7f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 5 Jan 2021 18:42:29 +0530
Subject: [PATCH 2098/2677] Fix dot product documentation

---
 docs/details/blas.dox | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index ccbe6649e7..7ec09af9c3 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -10,12 +10,6 @@
 Scalar dot product between two vectors.  Also referred to as the inner
 product.
 
-This function returns the scalar product of two equal sized vectors or
-between a matrix and a vector. The second operand needs to be a vector
-in either case.
-
-\image html matrix_vector_dot_product.png
-
 =======================================================================
 
 \defgroup blas_func_matmul matmul

From d13a65650e77b022a71d29d77b7663bcb28560c3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 29 Dec 2020 22:06:47 +0530
Subject: [PATCH 2099/2677] Remove non-free guards for SIFT/GLOH algorithms

SIFT patent expired recently and these algorithms can be
provided as part of open source binaries that are distributed from our
website.
---
 CMakeLists.txt                                | 11 ---
 docs/details/vision.dox                       |  8 --
 src/api/c/sift.cpp                            | 32 --------
 src/backend/cpu/CMakeLists.txt                |  6 +-
 .../cpu/kernel/{sift_nonfree.hpp => sift.hpp} | 78 ++++--------------
 src/backend/cpu/sift.cpp                      | 42 +---------
 src/backend/cuda/CMakeLists.txt               | 10 +--
 .../kernel/{sift_nonfree.hpp => sift.hpp}     | 68 +---------------
 src/backend/cuda/sift.cu                      | 34 +-------
 src/backend/opencl/CMakeLists.txt             |  7 +-
 .../kernel/{sift_nonfree.hpp => sift.hpp}     | 79 +++----------------
 src/backend/opencl/sift.cpp                   | 37 +--------
 test/CMakeLists.txt                           |  8 +-
 test/{gloh_nonfree.cpp => gloh.cpp}           |  6 --
 test/{sift_nonfree.cpp => sift.cpp}           |  7 +-
 15 files changed, 45 insertions(+), 388 deletions(-)
 rename src/backend/cpu/kernel/{sift_nonfree.hpp => sift.hpp} (91%)
 rename src/backend/cuda/kernel/{sift_nonfree.hpp => sift.hpp} (93%)
 rename src/backend/opencl/kernel/{sift_nonfree.hpp => sift.hpp} (88%)
 rename test/{gloh_nonfree.cpp => gloh.cpp} (99%)
 rename test/{sift_nonfree.cpp => sift.cpp} (99%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9df1f808a6..0852624e08 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -127,17 +127,6 @@ configure_file(
     ${ArrayFire_BINARY_DIR}/version.hpp
 )
 
-if(AF_WITH_NONFREE)
-  message("Building with NONFREE requires the following patents")
-  message("Method and apparatus for identifying scale invariant features\n"
-    "in an image and use of same for locating an object in an image, David\n"
-    "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application\n"
-    "filed March 8, 1999. Asignee: The University of British Columbia. For\n"
-    "further details, contact David Lowe (lowe@cs.ubc.ca) or the\n"
-    "University-Industry Liaison Office of the University of British\n"
-    "Columbia.")
-endif()
-
 # when crosscompiling use the bin2cpp file from the native bin directory
 if(CMAKE_CROSSCOMPILING)
   set(NATIVE_BIN_DIR "NATIVE_BIN_DIR-NOTFOUND"
diff --git a/docs/details/vision.dox b/docs/details/vision.dox
index d5d1c5fc06..c870f18c07 100644
--- a/docs/details/vision.dox
+++ b/docs/details/vision.dox
@@ -85,9 +85,6 @@ Transform (SIFT), by David Lowe.
 Lowe, D. G., "Distinctive Image Features from Scale-Invariant Keypoints",
 International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.
 
-WARNING: The SIFT algorithm is patented by the University of British Columbia,
-before using it, make sure you have the appropriate permission to do so.
-
 =======================================================================
 
 \defgroup cv_func_gloh gloh
@@ -106,11 +103,6 @@ Mikolajczyk, K., and Schmid, C., "A performance evaluation of local
 descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence,
 10, 27, pp. 1615-1630, 2005.
 
-WARNING: Although GLOH is free of patents, the SIFT algorithm, used to detect
-features that will later be used by GLOH descriptors, is patented by the
-University of British Columbia, before using it, make sure you have the
-appropriate permission to do so.
-
 =======================================================================
 
 \defgroup cv_func_hamming_matcher hammingMatcher
diff --git a/src/api/c/sift.cpp b/src/api/c/sift.cpp
index 7ce4028897..b615025f80 100644
--- a/src/api/c/sift.cpp
+++ b/src/api/c/sift.cpp
@@ -57,7 +57,6 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in,
                const bool double_input, const float img_scale,
                const float feature_ratio) {
     try {
-#ifdef AF_WITH_NONFREE_SIFT
         const ArrayInfo& info = getInfo(in);
         af::dim4 dims         = info.dims();
 
@@ -89,21 +88,6 @@ af_err af_sift(af_features* feat, af_array* desc, const af_array in,
             default: TYPE_ERROR(1, type);
         }
         std::swap(*desc, tmp_desc);
-#else
-        UNUSED(feat);
-        UNUSED(desc);
-        UNUSED(in);
-        UNUSED(n_layers);
-        UNUSED(contrast_thr);
-        UNUSED(edge_thr);
-        UNUSED(init_sigma);
-        UNUSED(double_input);
-        UNUSED(img_scale);
-        UNUSED(feature_ratio);
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, SIFT disabled\n",
-            AF_ERR_NONFREE);
-#endif
     }
     CATCHALL;
 
@@ -116,7 +100,6 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in,
                const bool double_input, const float img_scale,
                const float feature_ratio) {
     try {
-#ifdef AF_WITH_NONFREE_SIFT
         const ArrayInfo& info = getInfo(in);
         af::dim4 dims         = info.dims();
 
@@ -148,21 +131,6 @@ af_err af_gloh(af_features* feat, af_array* desc, const af_array in,
             default: TYPE_ERROR(1, type);
         }
         std::swap(*desc, tmp_desc);
-#else
-        UNUSED(feat);
-        UNUSED(desc);
-        UNUSED(in);
-        UNUSED(n_layers);
-        UNUSED(contrast_thr);
-        UNUSED(edge_thr);
-        UNUSED(init_sigma);
-        UNUSED(double_input);
-        UNUSED(img_scale);
-        UNUSED(feature_ratio);
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, GLOH disabled\n",
-            AF_ERR_NONFREE);
-#endif
     }
     CATCHALL;
 
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index f7fd76e0cf..a71ede7a47 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -251,6 +251,7 @@ target_sources(afcpu
     kernel/scan_by_key.hpp
     kernel/select.hpp
     kernel/shift.hpp
+    kernel/sift.hpp
     kernel/sobel.hpp
     kernel/sort.hpp
     kernel/sort_by_key.hpp
@@ -280,11 +281,6 @@ arrayfire_set_default_cxx_flags(afcpu)
 
 include("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
-if(AF_WITH_NONFREE)
-  target_sources(afcpu PRIVATE kernel/sift_nonfree.hpp)
-  target_compile_definitions(afcpu PRIVATE AF_WITH_NONFREE_SIFT)
-endif()
-
 target_include_directories(afcpu
   PUBLIC
     $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
diff --git a/src/backend/cpu/kernel/sift_nonfree.hpp b/src/backend/cpu/kernel/sift.hpp
similarity index 91%
rename from src/backend/cpu/kernel/sift_nonfree.hpp
rename to src/backend/cpu/kernel/sift.hpp
index 073229c0d4..e8698a97c5 100644
--- a/src/backend/cpu/kernel/sift_nonfree.hpp
+++ b/src/backend/cpu/kernel/sift.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2021, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -9,66 +9,20 @@
 
 // The source code contained in this file is based on the original code by
 // Rob Hess. Please note that SIFT is an algorithm patented and protected
-// by US law, before using this code or any binary forms generated from it,
-// verify that you have permission to do so. The original license by Rob Hess
-// can be read below:
-//
-// Copyright (c) 2006-2012, Rob Hess <rob@iqengines.com>
-// All rights reserved.
-//
-// The following patent has been issued for methods embodied in this
-// software: "Method and apparatus for identifying scale invariant features
-// in an image and use of same for locating an object in an image," David
-// G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application
-// filed March 8, 1999. Asignee: The University of British Columbia. For
-// further details, contact David Lowe (lowe@cs.ubc.ca) or the
-// University-Industry Liaison Office of the University of British
-// Columbia.
-//
-// Note that restrictions imposed by this patent (and possibly others)
-// exist independently of and may be in conflict with the freedoms granted
-// in this license, which refers to copyright of the program, not patents
-// for any methods that it implements.  Both copyright and patent law must
-// be obeyed to legally use and redistribute this program and it is not the
-// purpose of this license to induce you to infringe any patents or other
-// property right claims or to contest validity of any such claims.  If you
-// redistribute or use the program, then this license merely protects you
-// from committing copyright infringement.  It does not protect you from
-// committing patent infringement.  So, before you do anything with this
-// program, make sure that you have permission to do so not merely in terms
-// of copyright, but also in terms of patent law.
-//
-// Please note that this license is not to be understood as a guarantee
-// either.  If you use the program according to this license, but in
-// conflict with patent law, it does not mean that the licensor will refund
-// you for any losses that you incur if you are sued for your patent
-// infringement.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//     * Redistributions of source code must retain the above copyright and
-//       patent notices, this list of conditions and the following
-//       disclaimer.
-//     * Redistributions in binary form must reproduce the above copyright
-//       notice, this list of conditions and the following disclaimer in
-//       the documentation and/or other materials provided with the
-//       distribution.
-//     * Neither the name of Oregon State University nor the names of its
-//       contributors may be used to endorse or promote products derived
-//       from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
-// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
-// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// HOLDER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+// by US law. As of 29-Dec-2020, the patent stands expired. It can be looked
+// up here - https://patents.google.com/patent/US6711293B1/en
+
+#pragma once
+
+#include <convolve.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <resize.hpp>
+#include <sort_index.hpp>
+
+#include <cfloat>
+#include <cstring>
+#include <vector>
 
 using af::dim4;
 
@@ -851,7 +805,7 @@ std::vector<Array<T>> buildGaussPyr(const Array<T>& init_img,
         for (unsigned l = 0; l < n_layers + 3; l++) {
             unsigned src_idx = (l == 0) ? (o - 1) * (n_layers + 3) + n_layers
                                         : o * (n_layers + 3) + l - 1;
-            unsigned idx = o * (n_layers + 3) + l;
+            unsigned idx     = o * (n_layers + 3) + l;
 
             if (o == 0 && l == 0) {
                 gauss_pyr[idx] = init_img;
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 455f22c608..3b7e6b554c 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -7,21 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <convolve.hpp>
-#include <err_cpu.hpp>
-#include <math.hpp>
-#include <memory.hpp>
-#include <resize.hpp>
-#include <sort_index.hpp>
-#include <af/dim4.hpp>
-#include <cfloat>
-#include <cstring>
-#include <vector>
+#include <sift.hpp>
 
-#ifdef AF_WITH_NONFREE_SIFT
-#include <kernel/sift_nonfree.hpp>
-#endif
+#include <kernel/sift.hpp>
 
 using af::dim4;
 
@@ -35,35 +23,9 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float init_sigma, const bool double_input,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH) {
-#ifdef AF_WITH_NONFREE_SIFT
     return sift_impl<T, convAccT>(
         x, y, score, ori, size, desc, in, n_layers, contrast_thr, edge_thr,
         init_sigma, double_input, img_scale, feature_ratio, compute_GLOH);
-#else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(score);
-    UNUSED(ori);
-    UNUSED(size);
-    UNUSED(desc);
-    UNUSED(in);
-    UNUSED(n_layers);
-    UNUSED(contrast_thr);
-    UNUSED(edge_thr);
-    UNUSED(init_sigma);
-    UNUSED(double_input);
-    UNUSED(img_scale);
-    UNUSED(feature_ratio);
-    if (compute_GLOH) {
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, GLOH disabled\n",
-            AF_ERR_NONFREE);
-    } else {
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, SIFT disabled\n",
-            AF_ERR_NONFREE);
-    }
-#endif
 }
 
 #define INSTANTIATE(T, convAccT)                                               \
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 52925f6ebc..5edfc82e19 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -237,14 +237,6 @@ string(REPLACE ";" ";-D" boost_definitions "-D${boost_definitions}")
 set(cuda_cxx_flags "${cuda_cxx_flags};${boost_includes}")
 set(cuda_cxx_flags "${cuda_cxx_flags};${boost_definitions}")
 
-# This definition is required in addition to the definition below because in
-# an older verion of cmake definitions added using target_compile_definitions
-# were not added to the nvcc flags. This manually adds these definitions and
-# pass them to the options parameter in cuda_add_library
-if(AF_WITH_NONFREE)
-  set(cxx_definitions -DAF_WITH_NONFREE_SIFT)
-endif()
-
 # New API of cuSparse was introduced in 10.1.168 for Linux and the older
 # 10.1.105 fix version doesn't it. Unfortunately, the new API was introduced in
 # in a fix release of CUDA - unconventionally. As CMake's FindCUDA module
@@ -468,7 +460,7 @@ cuda_add_library(afcuda
     kernel/select.hpp
     kernel/shared.hpp
     kernel/shfl_intrinsics.hpp
-    kernel/sift_nonfree.hpp
+    kernel/sift.hpp
     kernel/sobel.hpp
     kernel/sort.hpp
     kernel/sort_by_key.hpp
diff --git a/src/backend/cuda/kernel/sift_nonfree.hpp b/src/backend/cuda/kernel/sift.hpp
similarity index 93%
rename from src/backend/cuda/kernel/sift_nonfree.hpp
rename to src/backend/cuda/kernel/sift.hpp
index 8ede0fe412..509267402b 100644
--- a/src/backend/cuda/kernel/sift_nonfree.hpp
+++ b/src/backend/cuda/kernel/sift.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2021, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -9,66 +9,8 @@
 
 // The source code contained in this file is based on the original code by
 // Rob Hess. Please note that SIFT is an algorithm patented and protected
-// by US law, before using this code or any binary forms generated from it,
-// verify that you have permission to do so. The original license by Rob Hess
-// can be read below:
-//
-// Copyright (c) 2006-2012, Rob Hess <rob@iqengines.com>
-// All rights reserved.
-//
-// The following patent has been issued for methods embodied in this
-// software: "Method and apparatus for identifying scale invariant features
-// in an image and use of same for locating an object in an image," David
-// G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application
-// filed March 8, 1999. Asignee: The University of British Columbia. For
-// further details, contact David Lowe (lowe@cs.ubc.ca) or the
-// University-Industry Liaison Office of the University of British
-// Columbia.
-//
-// Note that restrictions imposed by this patent (and possibly others)
-// exist independently of and may be in conflict with the freedoms granted
-// in this license, which refers to copyright of the program, not patents
-// for any methods that it implements.  Both copyright and patent law must
-// be obeyed to legally use and redistribute this program and it is not the
-// purpose of this license to induce you to infringe any patents or other
-// property right claims or to contest validity of any such claims.  If you
-// redistribute or use the program, then this license merely protects you
-// from committing copyright infringement.  It does not protect you from
-// committing patent infringement.  So, before you do anything with this
-// program, make sure that you have permission to do so not merely in terms
-// of copyright, but also in terms of patent law.
-//
-// Please note that this license is not to be understood as a guarantee
-// either.  If you use the program according to this license, but in
-// conflict with patent law, it does not mean that the licensor will refund
-// you for any losses that you incur if you are sued for your patent
-// infringement.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//     * Redistributions of source code must retain the above copyright and
-//       patent notices, this list of conditions and the following
-//       disclaimer.
-//     * Redistributions in binary form must reproduce the above copyright
-//       notice, this list of conditions and the following disclaimer in
-//       the documentation and/or other materials provided with the
-//       distribution.
-//     * Neither the name of Oregon State University nor the names of its
-//       contributors may be used to endorse or promote products derived
-//       from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
-// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
-// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// HOLDER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+// by US law. As of 29-Dec-2020, the patent stands expired. It can be looked
+// up here - https://patents.google.com/patent/US6711293B1/en
 
 #pragma once
 
@@ -94,7 +36,6 @@
 #include <cfloat>
 
 namespace cuda {
-
 namespace kernel {
 
 static const dim_t SIFT_THREADS   = 256;
@@ -1101,7 +1042,7 @@ std::vector<Array<T>> buildGaussPyr(Param<T> init_img, const unsigned n_octaves,
         for (unsigned l = 0; l < n_layers + 3; l++) {
             unsigned src_idx = (l == 0) ? (o - 1) * (n_layers + 3) + n_layers
                                         : o * (n_layers + 3) + l - 1;
-            unsigned idx = o * (n_layers + 3) + l;
+            unsigned idx     = o * (n_layers + 3) + l;
 
             if (o == 0 && l == 0) {
                 tmp_pyr.push_back(createParamArray(init_img, false));
@@ -1465,5 +1406,4 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
 }
 
 }  // namespace kernel
-
 }  // namespace cuda
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index 9df00c9e03..78314981cd 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -7,14 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_cuda.hpp>
-#include <af/dim4.hpp>
-#include <af/features.h>
+#include <sift.hpp>
 
-#ifdef AF_WITH_NONFREE_SIFT
-#include <kernel/sift_nonfree.hpp>
-#endif
+#include <kernel/sift.hpp>
 
 using af::dim4;
 using af::features;
@@ -29,7 +24,6 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float init_sigma, const bool double_input,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH) {
-#ifdef AF_WITH_NONFREE_SIFT
     unsigned nfeat_out;
     unsigned desc_len;
     float* x_out;
@@ -62,30 +56,6 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
     }
 
     return nfeat_out;
-#else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(score);
-    UNUSED(ori);
-    UNUSED(size);
-    UNUSED(desc);
-    UNUSED(in);
-    UNUSED(n_layers);
-    UNUSED(contrast_thr);
-    UNUSED(edge_thr);
-    UNUSED(init_sigma);
-    UNUSED(double_input);
-    UNUSED(img_scale);
-    UNUSED(feature_ratio);
-    if (compute_GLOH)
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, GLOH disabled\n",
-            AF_ERR_NONFREE);
-    else
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, SIFT disabled\n",
-            AF_ERR_NONFREE);
-#endif
 }
 
 #define INSTANTIATE(T, convAccT)                                               \
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 7fd29d1f3a..06f6d6347a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -321,6 +321,7 @@ target_sources(afopencl
     kernel/scan_first_by_key.hpp
     kernel/scan_first_by_key_impl.hpp
     kernel/select.hpp
+    kernel/sift.hpp
     kernel/sobel.hpp
     kernel/sort.hpp
     kernel/sort_by_key.hpp
@@ -445,12 +446,6 @@ elseif(AF_OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
     add_dependencies(afopencl CLBlast-ext)
 endif()
 
-
-if(AF_WITH_NONFREE)
-  target_sources(afopencl PRIVATE kernel/sift_nonfree.hpp)
-  target_compile_definitions(afopencl PRIVATE AF_WITH_NONFREE_SIFT)
-endif()
-
 if(APPLE)
   target_link_libraries(afopencl PRIVATE OpenGL::GL)
 endif()
diff --git a/src/backend/opencl/kernel/sift_nonfree.hpp b/src/backend/opencl/kernel/sift.hpp
similarity index 88%
rename from src/backend/opencl/kernel/sift_nonfree.hpp
rename to src/backend/opencl/kernel/sift.hpp
index 96fdc0f26e..4fbe88ac9d 100644
--- a/src/backend/opencl/kernel/sift_nonfree.hpp
+++ b/src/backend/opencl/kernel/sift.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2015, ArrayFire
+ * Copyright (c) 2021, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -9,66 +9,10 @@
 
 // The source code contained in this file is based on the original code by
 // Rob Hess. Please note that SIFT is an algorithm patented and protected
-// by US law, before using this code or any binary forms generated from it,
-// verify that you have permission to do so. The original license by Rob Hess
-// can be read below:
-//
-// Copyright (c) 2006-2012, Rob Hess <rob@iqengines.com>
-// All rights reserved.
-//
-// The following patent has been issued for methods embodied in this
-// software: "Method and apparatus for identifying scale invariant features
-// in an image and use of same for locating an object in an image," David
-// G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application
-// filed March 8, 1999. Asignee: The University of British Columbia. For
-// further details, contact David Lowe (lowe@cs.ubc.ca) or the
-// University-Industry Liaison Office of the University of British
-// Columbia.
-//
-// Note that restrictions imposed by this patent (and possibly others)
-// exist independently of and may be in conflict with the freedoms granted
-// in this license, which refers to copyright of the program, not patents
-// for any methods that it implements.  Both copyright and patent law must
-// be obeyed to legally use and redistribute this program and it is not the
-// purpose of this license to induce you to infringe any patents or other
-// property right claims or to contest validity of any such claims.  If you
-// redistribute or use the program, then this license merely protects you
-// from committing copyright infringement.  It does not protect you from
-// committing patent infringement.  So, before you do anything with this
-// program, make sure that you have permission to do so not merely in terms
-// of copyright, but also in terms of patent law.
-//
-// Please note that this license is not to be understood as a guarantee
-// either.  If you use the program according to this license, but in
-// conflict with patent law, it does not mean that the licensor will refund
-// you for any losses that you incur if you are sued for your patent
-// infringement.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//     * Redistributions of source code must retain the above copyright and
-//       patent notices, this list of conditions and the following
-//       disclaimer.
-//     * Redistributions in binary form must reproduce the above copyright
-//       notice, this list of conditions and the following disclaimer in
-//       the documentation and/or other materials provided with the
-//       distribution.
-//     * Neither the name of Oregon State University nor the names of its
-//       contributors may be used to endorse or promote products derived
-//       from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
-// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
-// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// HOLDER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+// by US law. As of 29-Dec-2020, the patent stands expired. It can be looked
+// up here - https://patents.google.com/patent/US6711293B1/en
+
+#pragma once
 
 #include <common/deprecated.hpp>
 #include <common/dispatch.hpp>
@@ -89,6 +33,7 @@ AF_DEPRECATED_WARNINGS_OFF
 #include <boost/compute/iterator/buffer_iterator.hpp>
 AF_DEPRECATED_WARNINGS_ON
 
+#include <cmath>
 #include <vector>
 
 namespace compute = boost::compute;
@@ -273,7 +218,7 @@ std::vector<Param> buildGaussPyr(Param init_img, const unsigned n_octaves,
         for (unsigned l = 0; l < n_layers + 3; l++) {
             unsigned src_idx = (l == 0) ? (o - 1) * (n_layers + 3) + n_layers
                                         : o * (n_layers + 3) + l - 1;
-            unsigned idx = o * (n_layers + 3) + l;
+            unsigned idx     = o * (n_layers + 3) + l;
 
             tmp_pyr[o].info.offset = 0;
             if (o == 0 && l == 0) {
@@ -437,7 +382,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
     auto kernels = getSiftKernels<T>();
 
-    unsigned min_dim = min(img.info.dims[0], img.info.dims[1]);
+    unsigned min_dim = std::min(img.info.dims[0], img.info.dims[1]);
     if (double_input) min_dim *= 2;
 
     const unsigned n_octaves = floor(log(min_dim) / log(2)) - 2;
@@ -507,7 +452,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                      &extrema_feat);
-        extrema_feat = min(extrema_feat, max_feat);
+        extrema_feat = std::min(extrema_feat, max_feat);
 
         if (extrema_feat == 0) {
             bufferFree(d_extrema_x);
@@ -546,7 +491,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                      &interp_feat);
-        interp_feat = min(interp_feat, extrema_feat);
+        interp_feat = std::min(interp_feat, extrema_feat);
 
         if (interp_feat == 0) {
             bufferFree(d_interp_x);
@@ -617,7 +562,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                      &nodup_feat);
-        nodup_feat = min(nodup_feat, interp_feat);
+        nodup_feat = std::min(nodup_feat, interp_feat);
 
         bufferFree(d_interp_x);
         bufferFree(d_interp_y);
@@ -663,7 +608,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                      &oriented_feat);
-        oriented_feat = min(oriented_feat, max_oriented_feat);
+        oriented_feat = std::min(oriented_feat, max_oriented_feat);
 
         if (oriented_feat == 0) {
             bufferFree(d_oriented_x);
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index 626654c053..aa4dea46e5 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -7,15 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <Array.hpp>
-#include <err_opencl.hpp>
-#include <math.hpp>
-#include <af/dim4.hpp>
-#include <af/features.h>
+#include <sift.hpp>
 
-#ifdef AF_WITH_NONFREE_SIFT
-#include <kernel/sift_nonfree.hpp>
-#endif
+#include <kernel/sift.hpp>
+#include <math.hpp>
 
 using af::dim4;
 using af::features;
@@ -30,7 +25,6 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
               const float edge_thr, const float init_sigma,
               const bool double_input, const float img_scale,
               const float feature_ratio, const bool compute_GLOH) {
-#ifdef AF_WITH_NONFREE_SIFT
     unsigned nfeat_out;
     unsigned desc_len;
 
@@ -59,31 +53,6 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
     }
 
     return nfeat_out;
-#else
-    UNUSED(x_out);
-    UNUSED(y_out);
-    UNUSED(score_out);
-    UNUSED(ori_out);
-    UNUSED(size_out);
-    UNUSED(desc_out);
-    UNUSED(in);
-    UNUSED(n_layers);
-    UNUSED(contrast_thr);
-    UNUSED(edge_thr);
-    UNUSED(init_sigma);
-    UNUSED(double_input);
-    UNUSED(img_scale);
-    UNUSED(feature_ratio);
-    if (compute_GLOH) {
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, GLOH disabled\n",
-            AF_ERR_NONFREE);
-    } else {
-        AF_ERROR(
-            "ArrayFire was not built with nonfree support, SIFT disabled\n",
-            AF_ERR_NONFREE);
-    }
-#endif
 }
 
 #define INSTANTIATE(T, convAccT)                                              \
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 0ec99b7944..90c8f232cf 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -373,12 +373,8 @@ make_test(SRC scan_by_key.cpp)
 make_test(SRC select.cpp)
 make_test(SRC set.cpp CXX11)
 make_test(SRC shift.cpp)
-
-if(AF_WITH_NONFREE)
-  make_test(SRC gloh_nonfree.cpp DEFINITIONS AF_WITH_NONFREE_SIFT)
-  make_test(SRC sift_nonfree.cpp DEFINITIONS AF_WITH_NONFREE_SIFT)
-endif()
-
+make_test(SRC gloh.cpp)
+make_test(SRC sift.cpp)
 make_test(SRC sobel.cpp)
 make_test(SRC solve_dense.cpp       CXX11 SERIAL)
 make_test(SRC sort.cpp)
diff --git a/test/gloh_nonfree.cpp b/test/gloh.cpp
similarity index 99%
rename from test/gloh_nonfree.cpp
rename to test/gloh.cpp
index f9f02cc679..4777728789 100644
--- a/test/gloh_nonfree.cpp
+++ b/test/gloh.cpp
@@ -41,7 +41,6 @@ typedef struct {
     float d[272];
 } desc_t;
 
-#ifdef AF_WITH_NONFREE_SIFT
 static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
     for (int k = 0; k < 5; k++)
         if (round(i.f[k] * 1e1f) != round(j.f[k] * 1e1f))
@@ -124,7 +123,6 @@ static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float* cpu,
 
     return ret;
 }
-#endif
 
 template<typename T>
 class GLOH : public ::testing::Test {
@@ -138,7 +136,6 @@ TYPED_TEST_CASE(GLOH, TestTypes);
 
 template<typename T>
 void glohTest(string pTestFile) {
-#ifdef AF_WITH_NONFREE_SIFT
     SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
@@ -252,7 +249,6 @@ void glohTest(string pTestFile) {
         delete[] outSize;
         delete[] outDesc;
     }
-#endif
 }
 
 #define GLOH_INIT(desc, image)                                         \
@@ -265,7 +261,6 @@ GLOH_INIT(man, man);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(GLOH, CPP) {
-#ifdef AF_WITH_NONFREE_SIFT
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -341,5 +336,4 @@ TEST(GLOH, CPP) {
     delete[] outOrientation;
     delete[] outSize;
     delete[] outDesc;
-#endif
 }
diff --git a/test/sift_nonfree.cpp b/test/sift.cpp
similarity index 99%
rename from test/sift_nonfree.cpp
rename to test/sift.cpp
index db61436bca..3d68a02766 100644
--- a/test/sift_nonfree.cpp
+++ b/test/sift.cpp
@@ -40,7 +40,7 @@ typedef struct {
 typedef struct {
     float d[128];
 } desc_t;
-#ifdef AF_WITH_NONFREE_SIFT
+
 static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
     for (int k = 0; k < 5; k++)
         if (round(i.f[k] * 1e1f) != round(j.f[k] * 1e1f))
@@ -123,7 +123,6 @@ static bool compareEuclidean(dim_t desc_len, dim_t ndesc, float* cpu,
 
     return ret;
 }
-#endif
 
 template<typename T>
 class SIFT : public ::testing::Test {
@@ -138,7 +137,6 @@ TYPED_TEST_CASE(SIFT, TestTypes);
 template<typename T>
 void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
               float edgeThr, float initSigma, bool doubleInput) {
-#ifdef AF_WITH_NONFREE_SIFT
     SUPPORTED_TYPE_CHECK(T);
     if (noImageIOTests()) return;
 
@@ -253,7 +251,6 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
         delete[] outSize;
         delete[] outDesc;
     }
-#endif
 }
 
 #define SIFT_INIT(desc, image, nLayers, contrastThr, edgeThr, initSigma,  \
@@ -275,7 +272,6 @@ SIFT_INIT(Man_NoDoubleInput, man_nodoubleinput, 3, 0.04f, 10.0f, 1.6f, false);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(SIFT, CPP) {
-#ifdef AF_WITH_NONFREE_SIFT
     if (noImageIOTests()) return;
 
     vector<dim4> inDims;
@@ -351,5 +347,4 @@ TEST(SIFT, CPP) {
     delete[] outOrientation;
     delete[] outSize;
     delete[] outDesc;
-#endif
 }

From 43b34a9f5e27dca98356dc2d6c5399e33b34b1f0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 29 Dec 2020 22:48:31 +0530
Subject: [PATCH 2100/2677] Update clang format version to 11 on github action

---
 .github/workflows/clang-format-lint.yml     | 12 ++++++------
 src/backend/common/host_memory.cpp          | 12 ++++++------
 src/backend/cpu/queue.hpp                   |  2 +-
 src/backend/cuda/kernel/fftconvolve.hpp     |  2 +-
 src/backend/cuda/kernel/interp.hpp          |  6 +++---
 src/backend/cuda/kernel/random_engine.hpp   |  4 ++--
 src/backend/cuda/kernel/reduce_by_key.hpp   |  8 ++++----
 src/backend/cuda/kernel/shfl_intrinsics.hpp |  4 ++--
 src/backend/cuda/types.hpp                  |  2 +-
 src/backend/opencl/kernel/fftconvolve.hpp   |  2 +-
 src/backend/opencl/kernel/homography.hpp    |  6 +++---
 src/backend/opencl/magma/magma_types.h      |  2 +-
 test/var.cpp                                |  2 +-
 13 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/.github/workflows/clang-format-lint.yml b/.github/workflows/clang-format-lint.yml
index 93a2957856..9b1037d4ab 100644
--- a/.github/workflows/clang-format-lint.yml
+++ b/.github/workflows/clang-format-lint.yml
@@ -17,22 +17,22 @@ jobs:
             uses: actions/checkout@master
 
           - name: Check Sources
-            uses: DoozyX/clang-format-lint-action@v0.5
+            uses: DoozyX/clang-format-lint-action@v0.11
             with:
               source: './src'
               extensions: 'h,cpp,hpp'
-              clangFormatVersion: 9
+              clangFormatVersion: 11
 
           - name: Check Tests
-            uses: DoozyX/clang-format-lint-action@v0.5
+            uses: DoozyX/clang-format-lint-action@v0.11
             with:
               source: './test'
               extensions: 'h,cpp,hpp'
-              clangFormatVersion: 9
+              clangFormatVersion: 11
 
           - name: Check Examples
-            uses: DoozyX/clang-format-lint-action@v0.5
+            uses: DoozyX/clang-format-lint-action@v0.11
             with:
               source: './examples'
               extensions: 'h,cpp,hpp'
-              clangFormatVersion: 9
+              clangFormatVersion: 11
diff --git a/src/backend/common/host_memory.cpp b/src/backend/common/host_memory.cpp
index 51a01e2164..a44a920db3 100644
--- a/src/backend/common/host_memory.cpp
+++ b/src/backend/common/host_memory.cpp
@@ -63,13 +63,13 @@ size_t getHostMemorySize() {
 
 #if defined(CTL_HW) && (defined(HW_MEMSIZE) || defined(HW_PHYSMEM64))
     int mib[2];
-    mib[0]       = CTL_HW;
+    mib[0] = CTL_HW;
 #if defined(HW_MEMSIZE)
-    mib[1]       = HW_MEMSIZE; /* OSX. --------------------- */
+    mib[1] = HW_MEMSIZE; /* OSX. --------------------- */
 #elif defined(HW_PHYSMEM64)
     mib[1] = HW_PHYSMEM64; /* NetBSD, OpenBSD. --------- */
 #endif
-    int64_t size = 0;          /* 64-bit */
+    int64_t size = 0; /* 64-bit */
     size_t len   = sizeof(size);
     if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
     return 0L; /* Failed? */
@@ -90,13 +90,13 @@ size_t getHostMemorySize() {
 #elif defined(CTL_HW) && (defined(HW_PHYSMEM) || defined(HW_REALMEM))
     /* DragonFly BSD, FreeBSD, NetBSD, OpenBSD, and OSX. -------- */
     int mib[2];
-    mib[0]            = CTL_HW;
+    mib[0] = CTL_HW;
 #if defined(HW_REALMEM)
-    mib[1]            = HW_REALMEM; /* FreeBSD. ----------------- */
+    mib[1] = HW_REALMEM; /* FreeBSD. ----------------- */
 #elif defined(HW_PYSMEM)
     mib[1] = HW_PHYSMEM; /* Others. ------------------ */
 #endif
-    unsigned int size = 0;          /* 32-bit */
+    unsigned int size = 0; /* 32-bit */
     size_t len        = sizeof(size);
     if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
     return 0L; /* Failed? */
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 213ccda892..2a0db9d638 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -59,7 +59,7 @@ class queue {
                      getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
 
     template<typename F, typename... Args>
-    void enqueue(const F func, Args &&... args) {
+    void enqueue(const F func, Args &&...args) {
         count++;
         if (sync_calls) {
             func(toParam(std::forward<Args>(args))...);
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 01aa7c6fa1..c4faecd2ed 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -91,7 +91,7 @@ void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
 
     int mul_elem = (sig_packed_elem < filter_packed_elem) ? filter_packed_elem
                                                           : sig_packed_elem;
-    blocks = dim3(divup(mul_elem, threads.x));
+    blocks       = dim3(divup(mul_elem, threads.x));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
     if (kind == AF_BATCH_RHS) {
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index 48dc6dbe5a..8101fba41e 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -105,9 +105,9 @@ struct Interp1<Ty, Tp, xdim, 1> {
         const int idx = ioff + xid * x_stride;
 
         for (int n = 0; n < batch; n++) {
-            Ty outval = (cond || clamp)
-                            ? in.ptr[idx + n * in.strides[batch_dim]]
-                            : zero;
+            Ty outval                                  = (cond || clamp)
+                                                             ? in.ptr[idx + n * in.strides[batch_dim]]
+                                                             : zero;
             out.ptr[ooff + n * out.strides[batch_dim]] = outval;
         }
     }
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index e52e78d354..1f983a08eb 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -213,8 +213,8 @@ __device__ void sincos(__half val, __half *sptr, __half *cptr) {
     float s, c;
     float fval = __half2float(val);
     sincos(fval, &s, &c);
-    *sptr      = __float2half(s);
-    *cptr      = __float2half(c);
+    *sptr = __float2half(s);
+    *cptr = __float2half(c);
 #endif
 }
 
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index dee09c3e8c..72b5c7b146 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -108,8 +108,8 @@ __global__ void compact(int *reduced_block_sizes, Param<Tk> keys_out,
     const int bidw = blockIdx.z / nBlocksZ;
 
     // reduced_block_sizes should have inclusive sum of block sizes
-    int nwrite = (blockIdx.x == 0) ? reduced_block_sizes[0]
-                                   : reduced_block_sizes[blockIdx.x] -
+    int nwrite   = (blockIdx.x == 0) ? reduced_block_sizes[0]
+                                     : reduced_block_sizes[blockIdx.x] -
                                          reduced_block_sizes[blockIdx.x - 1];
     int writeloc = (blockIdx.x == 0) ? 0 : reduced_block_sizes[blockIdx.x - 1];
 
@@ -146,8 +146,8 @@ __global__ void compact_dim(int *reduced_block_sizes, Param<Tk> keys_out,
     const int bidw = blockIdx.z / nBlocksZ;
 
     // reduced_block_sizes should have inclusive sum of block sizes
-    int nwrite = (blockIdx.x == 0) ? reduced_block_sizes[0]
-                                   : reduced_block_sizes[blockIdx.x] -
+    int nwrite   = (blockIdx.x == 0) ? reduced_block_sizes[0]
+                                     : reduced_block_sizes[blockIdx.x] -
                                          reduced_block_sizes[blockIdx.x - 1];
     int writeloc = (blockIdx.x == 0) ? 0 : reduced_block_sizes[blockIdx.x - 1];
 
diff --git a/src/backend/cuda/kernel/shfl_intrinsics.hpp b/src/backend/cuda/kernel/shfl_intrinsics.hpp
index ef12aafe29..9a3f3cf2f3 100644
--- a/src/backend/cuda/kernel/shfl_intrinsics.hpp
+++ b/src/backend/cuda/kernel/shfl_intrinsics.hpp
@@ -57,7 +57,7 @@ inline __device__ cuda::cfloat shfl_down_sync(unsigned mask, cuda::cfloat var,
     cuda::cfloat res = {__shfl_down_sync(mask, var.x, delta),
                         __shfl_down_sync(mask, var.y, delta)};
 #else
-    cuda::cfloat res  = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
+    cuda::cfloat res = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
 #endif
     return res;
 }
@@ -91,7 +91,7 @@ inline __device__ cuda::cfloat shfl_up_sync(unsigned mask, cuda::cfloat var,
     cuda::cfloat res = {__shfl_up_sync(mask, var.x, delta),
                         __shfl_up_sync(mask, var.y, delta)};
 #else
-    cuda::cfloat res  = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
+    cuda::cfloat res = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
 #endif
     return res;
 }
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 5e395ad96e..de98d2b24f 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -162,7 +162,7 @@ struct kernel_type<common::half> {
     using compute = float;
 
 #if defined(NVCC) || defined(__CUDACC_RTC__)
-    using native  = __half;
+    using native = __half;
 #else
     using native = common::half;
 #endif
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 9d70e2f79b..7e6bcaf8a8 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -160,7 +160,7 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
         filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
     int mul_elem = (sig_packed_elem < filter_packed_elem) ? filter_packed_elem
                                                           : sig_packed_elem;
-    int blocks = divup(mul_elem, THREADS);
+    int blocks   = divup(mul_elem, THREADS);
 
     cl::NDRange local(THREADS);
     cl::NDRange global(blocks * THREADS);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index b84e599fa1..2aee301d3b 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -92,9 +92,9 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
     // Allocate some temporary buffers
     Param inliers, idx, median;
     inliers.info.offset = idx.info.offset = median.info.offset = 0;
-    inliers.info.dims[0] = (htype == AF_HOMOGRAPHY_RANSAC)
-                               ? blk_x_eh
-                               : divup(nsamples, HG_THREADS);
+    inliers.info.dims[0]    = (htype == AF_HOMOGRAPHY_RANSAC)
+                                  ? blk_x_eh
+                                  : divup(nsamples, HG_THREADS);
     inliers.info.strides[0] = 1;
     idx.info.dims[0] = median.info.dims[0] = blk_x_eh;
     idx.info.strides[0] = median.info.strides[0] = 1;
diff --git a/src/backend/opencl/magma/magma_types.h b/src/backend/opencl/magma/magma_types.h
index 90dcc6ab8d..fe844e78d4 100644
--- a/src/backend/opencl/magma/magma_types.h
+++ b/src/backend/opencl/magma/magma_types.h
@@ -388,7 +388,7 @@ typedef enum {
 // 2b) update min & max here, which are used to check bounds for
 // magma2lapack_constants[] 2c) add lapack_xxxx_const() converter below and in
 // control/constants.cpp
-#define Magma2lapack_Min MagmaFalse    // 0
+#define Magma2lapack_Min MagmaFalse  // 0
 #define Magma2lapack_Max MagmaRowwise  // 402
 
 // ----------------------------------------
diff --git a/test/var.cpp b/test/var.cpp
index b88fbaebbd..b02442dba1 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -137,7 +137,7 @@ void dimCppSmallTest(const string pFileName,
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
         array bout  = (useDeprecatedAPI ? var(input, true)
-                                       : var(input, AF_VARIANCE_SAMPLE));
+                                        : var(input, AF_VARIANCE_SAMPLE));
         array nbout = (useDeprecatedAPI ? var(input, false)
                                         : var(input, AF_VARIANCE_POPULATION));
 

From 6ce9d9a3489f67fec344a30c29a9a3aacd6e1ce2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 13 Jan 2021 06:06:17 +0530
Subject: [PATCH 2101/2677] Fix const array indexing inside gfor

---
 src/api/cpp/array.cpp | 20 ++++----------------
 test/gfor.cpp         | 21 +++++++++++++++++++++
 2 files changed, 25 insertions(+), 16 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 73bcb90587..3600f60e83 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -732,22 +732,6 @@ array::array_proxy::operator array() const {
     AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
     if (impl->is_linear_) { AF_THROW(af_release_array(arr)); }
 
-    return array(tmp);
-}
-
-array::array_proxy::operator array() {
-    af_array tmp = nullptr;
-    af_array arr = nullptr;
-
-    if (impl->is_linear_) {
-        AF_THROW(af_flat(&arr, impl->parent_->get()));
-    } else {
-        arr = impl->parent_->get();
-    }
-
-    AF_THROW(af_index_gen(&tmp, arr, AF_MAX_DIMS, impl->indices_));
-    if (impl->is_linear_) { AF_THROW(af_release_array(arr)); }
-
     int dim = gforDim(impl->indices_);
     if (tmp && dim >= 0) {
         arr = gforReorder(tmp, dim);
@@ -759,6 +743,10 @@ array::array_proxy::operator array() {
     return array(arr);
 }
 
+array::array_proxy::operator array() {
+    return const_cast<const array::array_proxy *>(this)->operator array();
+}
+
 #define MEM_INDEX(FUNC_SIG, USAGE)                                \
     array::array_proxy array::array_proxy::FUNC_SIG {             \
         array *out               = new array(*this);              \
diff --git a/test/gfor.cpp b/test/gfor.cpp
index b73d29fe5c..42fc12723b 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -20,8 +20,10 @@ using af::array;
 using af::cdouble;
 using af::cfloat;
 using af::constant;
+using af::dim4;
 using af::freeHost;
 using af::gforSet;
+using af::iota;
 using af::randu;
 using af::seq;
 using af::span;
@@ -543,3 +545,22 @@ TEST(GFOR, MatmulLoopWithNonUnitIncrSeq) {
     }
     ASSERT_ARRAYS_NEAR(C, G, 1E-03);
 }
+
+TEST(GFOR, ConstArrayIndexing) {
+    const std::size_t dim = 4;
+
+    array m        = iota(dim4(1, dim), dim4(dim));
+    const array cm = iota(dim4(1, dim), dim4(dim));
+
+    array out_cm(dim), out_m(dim);
+
+    EXPECT_NO_THROW({
+        gfor(seq i, static_cast<double>(dim)) {
+            out_cm(i) = af::sum(cm(span,i) * cm(span,i));
+}
+});
+gfor(seq i, static_cast<double>(dim)) {
+    out_m(i) = af::sum(m(span, i) * m(span, i));
+}
+ASSERT_ARRAYS_EQ(out_cm, out_m);
+}

From 083de755d97d98a434f4efd5c5fa638d437fe5d0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 8 Dec 2020 12:45:19 +0530
Subject: [PATCH 2102/2677] Fix backend copyData to host for zero elements
 scenario

Earlier to this change, CPU and CUDA backends are working fine although
doing unncessary work. OpenCL on the other hand is seg-faulting due to
cl::Buffer being nullptr doing the following:

cl::Buffer buf = *A.get(); // Calls retain/release on invalid object
---
 src/backend/cpu/copy.cpp    |  2 ++
 src/backend/cuda/copy.cpp   |  2 ++
 src/backend/opencl/copy.cpp |  2 ++
 test/array.cpp              | 11 +++++++++++
 4 files changed, 17 insertions(+)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 359db199cc..6bc7b0d840 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -30,6 +30,8 @@ namespace cpu {
 
 template<typename T>
 void copyData(T *to, const Array<T> &from) {
+    if (from.elements() == 0) { return; }
+
     from.eval();
     // Ensure all operations on 'from' are complete before copying data to host.
     getQueue().sync();
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index 17118b9058..a2cc5b9495 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -23,6 +23,8 @@ namespace cuda {
 
 template<typename T>
 void copyData(T *dst, const Array<T> &src) {
+    if (src.elements() == 0) { return; }
+
     // FIXME: Merge this with copyArray
     src.eval();
 
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index e6692541ae..dbcd001927 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -22,6 +22,8 @@ namespace opencl {
 
 template<typename T>
 void copyData(T *data, const Array<T> &A) {
+    if (A.elements() == 0) { return; }
+
     // FIXME: Merge this with copyArray
     A.eval();
 
diff --git a/test/array.cpp b/test/array.cpp
index ed0f7ac575..fca8830589 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -618,3 +618,14 @@ TEST(Array, CopyListInitializerListDim4Assignment) {
 
     ASSERT_ARRAYS_EQ(A, B);
 }
+
+TEST(Array, EmptyArrayHostCopy) {
+    EXPECT_EXIT(
+        {
+            af::array empty;
+            std::vector<float> hdata(100);
+            empty.host(hdata.data());
+            exit(0);
+        },
+        ::testing::ExitedWithCode(0), ".*");
+}

From d86edd1842f083fa51ebc3ef30a42026069c631c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 8 Dec 2020 13:34:58 +0530
Subject: [PATCH 2103/2677] Add shortcut check for zero elements in
 detail::copyArray

---
 src/backend/cpu/copy.cpp    | 2 +-
 src/backend/cuda/copy.cpp   | 1 +
 src/backend/opencl/copy.cpp | 3 ++-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 6bc7b0d840..0790454957 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -48,7 +48,7 @@ void copyData(T *to, const Array<T> &from) {
 template<typename T>
 Array<T> copyArray(const Array<T> &A) {
     Array<T> out = createEmptyArray<T>(A.dims());
-    getQueue().enqueue(kernel::copy<T, T>, out, A);
+    if (A.elements() > 0) { getQueue().enqueue(kernel::copy<T, T>, out, A); }
     return out;
 }
 
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index a2cc5b9495..12ec5e93e0 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -51,6 +51,7 @@ void copyData(T *dst, const Array<T> &src) {
 template<typename T>
 Array<T> copyArray(const Array<T> &src) {
     Array<T> out = createEmptyArray<T>(src.dims());
+    if (src.elements() == 0) { return out; }
 
     if (src.isLinear()) {
         CUDA_CHECK(
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index dbcd001927..44eac01444 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -51,8 +51,9 @@ void copyData(T *data, const Array<T> &A) {
 template<typename T>
 Array<T> copyArray(const Array<T> &A) {
     Array<T> out = createEmptyArray<T>(A.dims());
-    dim_t offset = A.getOffset();
+    if (A.elements() == 0) { return out; }
 
+    dim_t offset = A.getOffset();
     if (A.isLinear()) {
         // FIXME: Add checks
         getQueue().enqueueCopyBuffer(*A.get(), *out.get(), sizeof(T) * offset,

From 422f1bdb1096e005ac753e39275980802caedeb5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 12 Feb 2021 08:20:47 +0530
Subject: [PATCH 2104/2677] Fix cmake arguments for external projects for msvc
 (#3088)

* Fix cmake arguments for external projects for msvc

Without the additional toolset argument being forwarded to msvc
toolchain, cmake/msvc is free to choose a toolset as per their
respective logic. This causes build issues.

Also fixed some conditions that are based on CMakeBuildType variable -
not recommended to use checks based on that variable and often resulted
in issues when used with untested multi-config generators.
---
 CMakeModules/AFBuildConfigurations.cmake |  8 ++---
 CMakeModules/build_CLBlast.cmake         | 23 ++++++++------
 CMakeModules/build_clFFT.cmake           | 38 ++++++++++++------------
 3 files changed, 37 insertions(+), 32 deletions(-)

diff --git a/CMakeModules/AFBuildConfigurations.cmake b/CMakeModules/AFBuildConfigurations.cmake
index 68d75fd34d..48dd07001b 100644
--- a/CMakeModules/AFBuildConfigurations.cmake
+++ b/CMakeModules/AFBuildConfigurations.cmake
@@ -2,15 +2,15 @@
 # or single-config generator. Before 3.9, the defintion of CMAKE_CONFIGURATION_TYPES
 # variable indicated multi-config, but developers might modify.
 if(NOT CMAKE_VERSION VERSION_LESS 3.9)
-  get_property(_isMultiConfig GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
+  get_property(isMultiConfig GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
 elseif(CMAKE_CONFIGURATION_TYPES)
   # CMAKE_CONFIGURATION_TYPES is set by project() call for multi-config generators
-  set(_isMultiConfig True)
+  set(isMultiConfig True)
 else()
-  set(_isMultiConfig False)
+  set(isMultiConfig False)
 endif()
 
-if(_isMultiConfig)
+if(isMultiConfig)
   set(CMAKE_CONFIGURATION_TYPES
     "Coverage;Debug;MinSizeRel;Release;RelWithDebInfo"
     CACHE STRING "Configurations for Multi-Config CMake Generator" FORCE)
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 1d570b6661..3e07cec311 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -6,22 +6,27 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 include(ExternalProject)
-
 find_program(GIT git)
 
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
 set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
 
+set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
 if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
-  set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
-else()
-  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
+  list(APPEND extproj_gen_opts "-A${CMAKE_GENERATOR_PLATFORM}")
+  if(CMAKE_GENERATOR_TOOLSET)
+    list(APPEND extproj_gen_opts "-T${CMAKE_GENERATOR_TOOLSET}")
+  endif()
 endif()
 
-if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
-  set(extproj_build_type "Release")
-else()
-  set(extproj_build_type ${CMAKE_BUILD_TYPE})
+set(extproj_build_type_option "")
+if(NOT isMultiConfig)
+  if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
+    set(extproj_build_type "Release")
+  else()
+    set(extproj_build_type ${CMAKE_BUILD_TYPE})
+  endif()
+  set(extproj_build_type_option "-DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}")
 endif()
 
 ExternalProject_Add(
@@ -40,7 +45,7 @@ ExternalProject_Add(
       -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
       -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
       "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-      -DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}
+      ${extproj_build_type_option}
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
       -DCMAKE_INSTALL_LIBDIR:PATH=lib
       -DBUILD_SHARED_LIBS:BOOL=OFF
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index e0b7716553..18609e1e56 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -5,29 +5,28 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-INCLUDE(ExternalProject)
+include(ExternalProject)
+find_program(GIT git)
 
-SET(prefix "${PROJECT_BINARY_DIR}/third_party/clFFT")
-SET(clFFT_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clFFT${CMAKE_STATIC_LIBRARY_SUFFIX})
-IF(CMAKE_VERSION VERSION_LESS 3.2)
-    IF(CMAKE_GENERATOR MATCHES "Ninja")
-        MESSAGE(WARNING "Building clFFT with Ninja has known issues with CMake older than 3.2")
-    endif()
-    SET(byproducts)
-ELSE()
-    SET(byproducts BUILD_BYPRODUCTS ${clFFT_location})
-ENDIF()
+set(prefix "${PROJECT_BINARY_DIR}/third_party/clFFT")
+set(clFFT_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clFFT${CMAKE_STATIC_LIBRARY_SUFFIX})
 
+set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
 if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
-  set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
-else()
-  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
+  list(APPEND extproj_gen_opts "-A${CMAKE_GENERATOR_PLATFORM}")
+  if(CMAKE_GENERATOR_TOOLSET)
+    list(APPEND extproj_gen_opts "-T${CMAKE_GENERATOR_TOOLSET}")
+  endif()
 endif()
 
-if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
-  set(extproj_build_type "Release")
-else()
-  set(extproj_build_type ${CMAKE_BUILD_TYPE})
+set(extproj_build_type_option "")
+if(NOT isMultiConfig)
+  if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
+    set(extproj_build_type "Release")
+  else()
+    set(extproj_build_type ${CMAKE_BUILD_TYPE})
+  endif()
+  set(extproj_build_type_option "-DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}")
 endif()
 
 ExternalProject_Add(
@@ -37,13 +36,14 @@ ExternalProject_Add(
     PREFIX "${prefix}"
     INSTALL_DIR "${prefix}"
     UPDATE_COMMAND ""
+    BUILD_BYPRODUCTS ${clFFT_location}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
       -Wno-dev <SOURCE_DIR>/src
       -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
       "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
       -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
       "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-      -DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}
+	  ${extproj_build_type_option}
       -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
       -DBUILD_SHARED_LIBS:BOOL=OFF
       -DBUILD_EXAMPLES:BOOL=OFF

From 938910332ed4cd533c16f31a69d829a0ddaf3c2c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 11 Feb 2021 19:32:13 +0530
Subject: [PATCH 2105/2677] Update cuDNN find module to reflect cuDNN 8 changes

---
 CMakeModules/FindcuDNN.cmake    | 141 +++++++++++++++++++++++++-------
 src/backend/cuda/CMakeLists.txt |  34 ++++++--
 2 files changed, 137 insertions(+), 38 deletions(-)

diff --git a/CMakeModules/FindcuDNN.cmake b/CMakeModules/FindcuDNN.cmake
index f6e5d0e592..717daed105 100644
--- a/CMakeModules/FindcuDNN.cmake
+++ b/CMakeModules/FindcuDNN.cmake
@@ -5,7 +5,7 @@
 # Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
 # file Copyright.txt or https://cmake.org/licensing for details.
 #
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2021, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -37,14 +37,50 @@
 #
 # ``cuDNN_INCLUDE_DIRS``
 #   where to find cudnn.h.
+#
 # ``cuDNN_LINK_LIBRARY``
-#   the libraries to link against to use cuDNN.
-# ``cuDNN_DLL_LIBRARY``
-#   Windows DLL of cuDNN
+#   the libraries to link against to use cuDNN. Priot to cuDNN 8, this is a huge monolithic
+#   library. However, since cuDNN 8 it has been split into multiple shared libraries. If
+#   cuDNN version 8 if found, this variable contains the shared library that dlopens the
+#   other libraries: cuDNN_*_INFER_LINK_LIBRARY and cuDNN_*_TRAIN_LINK_LIBRARY as needed.
+#   For versions of cuDNN 7 or lower, cuDNN_*_INFER_LINK_LIBRARY and cuDNN_*_TRAIN_LINK_LIBRARY
+#   are not defined.
+#
+# ``cuDNN_ADV_INFER_LINK_LIBRARY``
+#   the libraries to link directly to use advanced inference API from cuDNN.
+# ``cuDNN_ADV_INFER_DLL_LIBRARY``
+#   Corresponding advanced inference API Windows DLL. This is not set on non-Windows platforms.
+# ``cuDNN_ADV_TRAIN_LINK_LIBRARY``
+#   the libraries to link directly to use advanced training API from cuDNN.
+# ``cuDNN_ADV_TRAIN_DLL_LIBRARY``
+#   Corresponding advanced training API Windows DLL. This is not set on non-Windows platforms.
+#
+# ``cuDNN_CNN_INFER_LINK_LIBRARY``
+#   the libraries to link directly to use convolutional nueral networks inference API from cuDNN.
+# ``cuDNN_CNN_INFER_DLL_LIBRARY``
+#   Corresponding CNN inference API Windows DLL. This is not set on non-Windows platforms.
+# ``cuDNN_CNN_TRAIN_LINK_LIBRARY``
+#   the libraries to link directly to use convolutional nueral networks training API from cuDNN.
+# ``cuDNN_CNN_TRAIN_DLL_LIBRARY``
+#   Corresponding CNN training API Windows DLL. This is not set on non-Windows platforms.
+#
+# ``cuDNN_OPS_INFER_LINK_LIBRARY``
+#   the libraries to link directly to use starndard ML operations API from cuDNN.
+# ``cuDNN_OPS_INFER_DLL_LIBRARY``
+#   Corresponding OPS inference API Windows DLL. This is not set on non-Windows platforms.
+# ``cuDNN_OPS_TRAIN_LINK_LIBRARY``
+#   the libraries to link directly to use starndard ML operations API from cuDNN.
+# ``cuDNN_OPS_TRAIN_DLL_LIBRARY``
+#   Corresponding OPS inference API Windows DLL. This is not set on non-Windows platforms.
+#
 # ``cuDNN_FOUND``
 #   If false, do not try to use cuDNN.
 # ``cuDNN_VERSION``
-#   Version of the cuDNN library we looked for
+#   Version of the cuDNN library found
+# ``cuDNN_VERSION_MAJOR``
+#   Major Version of the cuDNN library found
+# ``cuDNN_VERSION_MINOR``
+#   Minor Version of the cuDNN library found
 
 find_package(PkgConfig)
 pkg_check_modules(PC_CUDNN QUIET cuDNN)
@@ -80,6 +116,8 @@ if(cuDNN_INCLUDE_DIRS)
     CUDNN_PATCH_VERSION "${CUDNN_VERSION_FILE_CONTENTS}")
   string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
       CUDNN_PATCH_VERSION "${CUDNN_PATCH_VERSION}")
+  set(cuDNN_VERSION_MAJOR ${CUDNN_MAJOR_VERSION})
+  set(cuDNN_VERSION_MINOR ${CUDNN_MINOR_VERSION})
   set(cuDNN_VERSION ${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION})
 endif()
 
@@ -94,31 +132,48 @@ endif()
 if(cuDNN_INCLUDE_DIRS)
   get_filename_component(libpath_cudart "${CUDA_CUDART_LIBRARY}" PATH)
 
-  find_library(cuDNN_LINK_LIBRARY
-    NAMES
-      libcudnn.so.${cudnn_ver_suffix}
-      libcudnn.${cudnn_ver_suffix}.dylib
-      cudnn
-    PATHS
-      ${cuDNN_ROOT_DIR}
-      ${PC_CUDNN_LIBRARY_DIRS}
-      $ENV{LD_LIBRARY_PATH}
-      ${libpath_cudart}
-      ${CMAKE_INSTALL_PREFIX}
-    PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
-    DOC "cuDNN link library." )
+  macro(af_find_cudnn_libs cudnn_lib_name_infix)
+    if("${cudnn_lib_name_infix}" STREQUAL "")
+	  set(LIB_INFIX "")
+	else()
+	  string(TOUPPER ${cudnn_lib_name_infix} LIB_INFIX)
+	endif()
+    find_library(cuDNN${LIB_INFIX}_LINK_LIBRARY
+      NAMES
+        libcudnn${cudnn_lib_name_infix}.so.${cudnn_ver_suffix}
+        libcudnn${cudnn_lib_name_infix}.${cudnn_ver_suffix}.dylib
+        cudnn${cudnn_lib_name_infix}
+      PATHS
+        ${cuDNN_ROOT_DIR}
+        ${PC_CUDNN_LIBRARY_DIRS}
+        $ENV{LD_LIBRARY_PATH}
+        ${libpath_cudart}
+        ${CMAKE_INSTALL_PREFIX}
+      PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
+      DOC "cudnn${cudnn_lib_name_infix} link library." )
+
+    if(WIN32 AND cuDNN_LINK_LIBRARY)
+      find_file(cuDNN${LIB_INFIX}_DLL_LIBRARY
+      NAMES cudnn${cudnn_lib_name_infix}64_${cudnn_ver_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+      PATHS
+        ${cuDNN_ROOT_DIR}
+        ${PC_CUDNN_LIBRARY_DIRS}
+        $ENV{PATH}
+        ${libpath_cudart}
+        ${CMAKE_INSTALL_PREFIX}
+      PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
+      DOC "cudnn${cudnn_lib_name_infix} Windows DLL." )
+    endif()
+  endmacro()
 
-  if(WIN32 AND cuDNN_LINK_LIBRARY)
-    find_file(cuDNN_DLL_LIBRARY
-    NAMES cudnn64_${cudnn_ver_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
-    PATHS
-      ${cuDNN_ROOT_DIR}
-      ${PC_CUDNN_LIBRARY_DIRS}
-      $ENV{PATH}
-      ${libpath_cudart}
-      ${CMAKE_INSTALL_PREFIX}
-    PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
-    DOC "cuDNN Windows DLL." )
+  af_find_cudnn_libs("") # gets base cudnn shared library
+  if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
+    af_find_cudnn_libs("_adv_infer")
+    af_find_cudnn_libs("_adv_train")
+    af_find_cudnn_libs("_cnn_infer")
+    af_find_cudnn_libs("_cnn_train")
+    af_find_cudnn_libs("_ops_infer")
+    af_find_cudnn_libs("_ops_train")
   endif()
 endif()
 
@@ -146,4 +201,32 @@ if(cuDNN_FOUND)
       IMPORTED_LOCATION "${cuDNN_LINK_LIBRARY}"
     )
   endif(WIN32)
+  if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
+    macro(create_cudnn_target cudnn_target_name)
+	  string(TOUPPER ${cudnn_target_name} target_infix)
+	  add_library(cuDNN::${cudnn_target_name} SHARED IMPORTED)
+	  if(WIN32)
+        set_target_properties(cuDNN::${cudnn_target_name}
+          PROPERTIES
+          IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+          INTERFACE_INCLUDE_DIRECTORIES "${cuDNN_INCLUDE_DIRS}"
+          IMPORTED_LOCATION "${cuDNN_${target_infix}_DLL_LIBRARY}"
+          IMPORTED_IMPLIB "${cuDNN_${target_infix}_LINK_LIBRARY}"
+        )
+      else(WIN32)
+          set_target_properties(cuDNN::${cudnn_target_name}
+            PROPERTIES
+            IMPORTED_LINK_INTERFACE_LANGUAGE "C"
+            INTERFACE_INCLUDE_DIRECTORIES "${cuDNN_INCLUDE_DIRS}"
+            IMPORTED_LOCATION "${cuDNN_${target_infix}_LINK_LIBRARY}"
+          )
+      endif(WIN32)
+	endmacro()
+	create_cudnn_target(adv_infer)
+	create_cudnn_target(adv_train)
+	create_cudnn_target(cnn_infer)
+	create_cudnn_target(cnn_train)
+	create_cudnn_target(ops_infer)
+	create_cudnn_target(ops_train)
+  endif()
 endif(cuDNN_FOUND)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 5edfc82e19..35cc1cecd6 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -799,18 +799,34 @@ function(afcu_collect_libs libname)
   endif ()
 endfunction()
 
+function(afcu_collect_cudnn_libs cudnn_infix)
+  set(internal_infix "_")
+  if(NOT "${cudnn_infix}" STREQUAL "")
+    set(internal_infix "_${cudnn_infix}_")
+    string(TOUPPER ${internal_infix} internal_infix)
+  endif()
+  if(WIN32)
+    set(cudnn_lib "${cuDNN${internal_infix}DLL_LIBRARY}")
+  else()
+    get_filename_component(cudnn_lib "${cuDNN${internal_infix}LINK_LIBRARY}" REALPATH)
+  endif()
+  install(FILES ${cudnn_lib} DESTINATION ${AF_INSTALL_LIB_DIR} COMPONENT cuda_dependencies)
+endfunction()
+
 if(AF_INSTALL_STANDALONE)
   if(AF_WITH_CUDNN)
-    if(WIN32)
-      set(cudnn_lib "${cuDNN_DLL_LIBRARY}")
-    else()
-      get_filename_component(cudnn_lib "${cuDNN_LINK_LIBRARY}" REALPATH)
-    endif()
-    install(FILES ${cudnn_lib}
-          DESTINATION ${AF_INSTALL_LIB_DIR}
-          COMPONENT   cuda_dependencies)
+    afcu_collect_cudnn_libs("")
+	if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
+	  # cudnn changed how dlls are shipped starting major version 8
+      # except the main dll a lot of the other DLLs are loaded upon demand
+	  afcu_collect_cudnn_libs(adv_infer)
+	  afcu_collect_cudnn_libs(adv_train)
+	  afcu_collect_cudnn_libs(cnn_infer)
+	  afcu_collect_cudnn_libs(cnn_train)
+	  afcu_collect_cudnn_libs(ops_infer)
+	  afcu_collect_cudnn_libs(ops_train)
+	endif()
   endif()
-
   afcu_collect_libs(nvrtc FULL_VERSION)
   if(WIN32)
     afcu_collect_libs(cufft)

From 1c215c8f10003c14681b87d268a2a246891e8c46 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 11 Feb 2021 19:38:20 +0530
Subject: [PATCH 2106/2677] Refactor cuda deps collection to reflect CUDA
 versioning

---
 src/backend/cuda/CMakeLists.txt | 32 +++++++++++++++++++++++---------
 1 file changed, 23 insertions(+), 9 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 35cc1cecd6..beda8b769c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -758,16 +758,26 @@ endif ()
 
 function(afcu_collect_libs libname)
   set(options "FULL_VERSION")
-  set(single_args "")
+  set(single_args "LIB_MAJOR;LIB_MINOR")
   set(multi_args "")
 
   cmake_parse_arguments(cuda_args "${options}" "${single_args}" "${multi_args}" ${ARGN})
+
+  if(cuda_args_LIB_MAJOR AND cuda_args_LIB_MINOR)
+    set(lib_major ${cuda_args_LIB_MAJOR})
+	set(lib_minor ${cuda_args_LIB_MINOR})
+  else()
+    set(lib_major ${CUDA_VERSION_MAJOR})
+	set(lib_minor ${CUDA_VERSION_MINOR})
+  endif()
+  set(lib_version "${lib_major}.${lib_minor}")
+
   if (WIN32)
     find_file(CUDA_${libname}_LIBRARY_DLL
       NAMES
-        "${PX}${libname}64_${CUDA_VERSION_MAJOR}${SX}"
-        "${PX}${libname}64_${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}${SX}"
-        "${PX}${libname}64_${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}_0${SX}"
+        "${PX}${libname}64_${lib_major}${SX}"
+        "${PX}${libname}64_${lib_major}${lib_minor}${SX}"
+        "${PX}${libname}64_${lib_major}${lib_minor}_0${SX}"
       PATHS ${dlib_path_prefix}
     )
     mark_as_advanced(CUDA_${libname}_LIBRARY_DLL)
@@ -775,10 +785,10 @@ function(afcu_collect_libs libname)
       DESTINATION ${AF_INSTALL_BIN_DIR}
       COMPONENT cuda_dependencies)
   elseif (APPLE)
-    get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
+    get_filename_component(outpath "${dlib_path_prefix}/${PX}${libname}.${lib_major}.${lib_minor}${SX}" REALPATH)
     install(FILES       "${outpath}"
             DESTINATION ${AF_INSTALL_BIN_DIR}
-            RENAME      "${PX}${libname}.${CUDA_VERSION}${SX}"
+            RENAME      "${PX}${libname}.${lib_version}${SX}"
             COMPONENT   cuda_dependencies)
   else () #UNIX
     find_library(CUDA_${libname}_LIBRARY
@@ -788,9 +798,9 @@ function(afcu_collect_libs libname)
 
     get_filename_component(outpath "${CUDA_${libname}_LIBRARY}" REALPATH)
     if(cuda_args_FULL_VERSION)
-      set(library_install_name "${PX}${libname}${SX}.${CUDA_VERSION}")
+      set(library_install_name "${PX}${libname}${SX}.${lib_version}")
     else()
-      set(library_install_name "${PX}${libname}${SX}.${CUDA_VERSION_MAJOR}")
+      set(library_install_name "${PX}${libname}${SX}.${lib_major}")
     endif()
     install(FILES       ${outpath}
             DESTINATION ${AF_INSTALL_LIB_DIR}
@@ -829,7 +839,11 @@ if(AF_INSTALL_STANDALONE)
   endif()
   afcu_collect_libs(nvrtc FULL_VERSION)
   if(WIN32)
-    afcu_collect_libs(cufft)
+	if(CUDA_VERSION_MAJOR VERSION_EQUAL 11)
+      afcu_collect_libs(cufft LIB_MAJOR 10 LIB_MINOR 4)
+	else()
+      afcu_collect_libs(cufft)
+	endif()
     afcu_collect_libs(cublas)
     if(CUDA_VERSION VERSION_GREATER 10.0)
       afcu_collect_libs(cublasLt)

From 8a907d4a132da134ef0975cd21c11d45ba689170 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 16 Feb 2021 18:59:01 +0530
Subject: [PATCH 2107/2677] Move opencl::Kernel::Enqueuer Args instead of
 copying

---
 src/backend/opencl/Kernel.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index e36d691c4b..b27ef43a84 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -19,7 +19,7 @@ namespace opencl {
 struct Enqueuer {
     template<typename... Args>
     void operator()(cl::Kernel ker, const cl::EnqueueArgs& qArgs,
-                    Args... args) {
+                    Args&&... args) {
         auto launchOp = cl::KernelFunctor<Args...>(ker);
         launchOp(qArgs, std::forward<Args>(args)...);
     }

From c6d1341c69e597f7d9b4060cd67e985d4c9b601a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 17 Feb 2021 01:03:18 +0530
Subject: [PATCH 2108/2677] Fix double free regression by retaining cl_mem
 input

---
 src/backend/opencl/Array.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 9d8f2f99ea..1553438c6c 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -120,8 +120,9 @@ template<typename T>
 Array<T>::Array(const dim4 &dims, cl_mem mem, size_t src_offset, bool copy)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(copy ? memAlloc<T>(info.elements()).release() : new Buffer(mem),
-           bufferFree)
+    , data(
+          copy ? memAlloc<T>(info.elements()).release() : new Buffer(mem, true),
+          bufferFree)
     , data_dims(dims)
     , node(bufferNodePtr<T>())
     , ready(true)

From 5263b9331058596706aed17d4788e12fc7eb65c2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 17 Feb 2021 01:15:01 +0530
Subject: [PATCH 2109/2677] Add compute 8.6 to Toolkit2MaxCompute internal map

---
 src/backend/cuda/device_manager.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 54a558ed01..18aedbec11 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -97,7 +97,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
-    {11020, 8, 0, 0},
+    {11020, 8, 6, 0},
     {11010, 8, 0, 0},
     {11000, 8, 0, 0},
     {10020, 7, 5, 2},
@@ -117,7 +117,7 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
-        {11020, 460.27f, 460.89f},
+        {11020, 460.27f, 460.82f},
         {11010, 455.23f, 456.38f},
         {11000, 450.51f, 451.48f},
         {10020, 440.33f, 441.22f},

From 20ae16650efb894d03a2703cd7b3b380b8746c57 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep.garigipati@gmail.com>
Date: Wed, 17 Feb 2021 01:39:25 +0530
Subject: [PATCH 2110/2677] Fix max cuda compute version for CUDA 11.1

---
 src/backend/cuda/device_manager.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 18aedbec11..bbd8b9183c 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -98,7 +98,7 @@ static const int jetsonComputeCapabilities[] = {
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
     {11020, 8, 6, 0},
-    {11010, 8, 0, 0},
+    {11010, 8, 6, 0},
     {11000, 8, 0, 0},
     {10020, 7, 5, 2},
     {10010, 7, 5, 2},

From a36e42643b24e73781412a6acd37e4779b9d0548 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 30 Oct 2020 20:46:31 +0530
Subject: [PATCH 2111/2677] Fetch assets & test/data during cmake build
 configuation

Removed assets and test/data as submodules
---
 .gitmodules                                   |   6 -
 CMakeLists.txt                                |  11 +-
 CMakeModules/AFfetch_content.cmake            | 916 ++++++++++++++++++
 CMakeModules/FetchContent/CMakeLists.cmake.in |  21 +
 assets                                        |   1 -
 docs/CMakeLists.txt                           |   1 -
 test/CMakeLists.txt                           |  28 +-
 test/data                                     |   1 -
 8 files changed, 965 insertions(+), 20 deletions(-)
 create mode 100644 CMakeModules/AFfetch_content.cmake
 create mode 100644 CMakeModules/FetchContent/CMakeLists.cmake.in
 delete mode 160000 assets
 delete mode 160000 test/data

diff --git a/.gitmodules b/.gitmodules
index ba7e49284c..c88fd43e8b 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,9 +1,3 @@
-[submodule "test/data"]
-	path = test/data
-	url = https://github.com/arrayfire/arrayfire_data
-[submodule "assets"]
-	path = assets
-	url = https://github.com/arrayfire/assets
 [submodule "test/gtest"]
 	path = test/gtest
 	url = https://github.com/google/googletest.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0852624e08..3efe9b4297 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2020, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -11,6 +11,7 @@ project(ArrayFire VERSION 3.8.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 
+include(AFfetch_content)
 include(config_ccache)
 include(AFBuildConfigurations)
 include(AFInstallDirs)
@@ -375,7 +376,13 @@ endif()
 
 conditional_directory(BUILD_TESTING test)
 
-set(ASSETS_DIR "${ArrayFire_SOURCE_DIR}/assets")
+FetchContent_Declare(
+  af_assets
+  GIT_REPOSITORY https://github.com/arrayfire/assets.git
+  GIT_TAG        master
+)
+FetchContent_Populate(af_assets)
+set(ASSETS_DIR ${af_assets_SOURCE_DIR})
 conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
 
diff --git a/CMakeModules/AFfetch_content.cmake b/CMakeModules/AFfetch_content.cmake
new file mode 100644
index 0000000000..98cdf6cb96
--- /dev/null
+++ b/CMakeModules/AFfetch_content.cmake
@@ -0,0 +1,916 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#[=======================================================================[.rst:
+FetchContent
+------------------
+
+.. only:: html
+
+  .. contents::
+
+Overview
+^^^^^^^^
+
+This module enables populating content at configure time via any method
+supported by the :module:`ExternalProject` module.  Whereas
+:command:`ExternalProject_Add` downloads at build time, the
+``FetchContent`` module makes content available immediately, allowing the
+configure step to use the content in commands like :command:`add_subdirectory`,
+:command:`include` or :command:`file` operations.
+
+Content population details would normally be defined separately from the
+command that performs the actual population.  Projects should also
+check whether the content has already been populated somewhere else in the
+project hierarchy.  Typical usage would look something like this:
+
+.. code-block:: cmake
+
+  FetchContent_Declare(
+    googletest
+    GIT_REPOSITORY https://github.com/google/googletest.git
+    GIT_TAG        release-1.8.0
+  )
+
+  FetchContent_GetProperties(googletest)
+  if(NOT googletest_POPULATED)
+    FetchContent_Populate(googletest)
+    add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR})
+  endif()
+
+When using the above pattern with a hierarchical project arrangement,
+projects at higher levels in the hierarchy are able to define or override
+the population details of content specified anywhere lower in the project
+hierarchy.  The ability to detect whether content has already been
+populated ensures that even if multiple child projects want certain content
+to be available, the first one to populate it wins.  The other child project
+can simply make use of the already available content instead of repeating
+the population for itself.  See the
+:ref:`Examples <fetch-content-examples>` section which demonstrates
+this scenario.
+
+The ``FetchContent`` module also supports defining and populating
+content in a single call, with no check for whether the content has been
+populated elsewhere in the project already.  This is a more low level
+operation and would not normally be the way the module is used, but it is
+sometimes useful as part of implementing some higher level feature or to
+populate some content in CMake's script mode.
+
+
+Declaring Content Details
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. command:: FetchContent_Declare
+
+  .. code-block:: cmake
+
+    FetchContent_Declare(<name> <contentOptions>...)
+
+  The ``FetchContent_Declare()`` function records the options that describe
+  how to populate the specified content, but if such details have already
+  been recorded earlier in this project (regardless of where in the project
+  hierarchy), this and all later calls for the same content ``<name>`` are
+  ignored.  This "first to record, wins" approach is what allows hierarchical
+  projects to have parent projects override content details of child projects.
+
+  The content ``<name>`` can be any string without spaces, but good practice
+  would be to use only letters, numbers and underscores.  The name will be
+  treated case-insensitively and it should be obvious for the content it
+  represents, often being the name of the child project or the value given
+  to its top level :command:`project` command (if it is a CMake project).
+  For well-known public projects, the name should generally be the official
+  name of the project.  Choosing an unusual name makes it unlikely that other
+  projects needing that same content will use the same name, leading to
+  the content being populated multiple times.
+
+  The ``<contentOptions>`` can be any of the download or update/patch options
+  that the :command:`ExternalProject_Add` command understands.  The configure,
+  build, install and test steps are explicitly disabled and therefore options
+  related to them will be ignored.  In most cases, ``<contentOptions>`` will
+  just be a couple of options defining the download method and method-specific
+  details like a commit tag or archive hash.  For example:
+
+  .. code-block:: cmake
+
+    FetchContent_Declare(
+      googletest
+      GIT_REPOSITORY https://github.com/google/googletest.git
+      GIT_TAG        release-1.8.0
+    )
+
+    FetchContent_Declare(
+      myCompanyIcons
+      URL      https://intranet.mycompany.com/assets/iconset_1.12.tar.gz
+      URL_HASH 5588a7b18261c20068beabfb4f530b87
+    )
+
+    FetchContent_Declare(
+      myCompanyCertificates
+      SVN_REPOSITORY svn+ssh://svn.mycompany.com/srv/svn/trunk/certs
+      SVN_REVISION   -r12345
+    )
+
+Populating The Content
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. command:: FetchContent_Populate
+
+  .. code-block:: cmake
+
+    FetchContent_Populate( <name> )
+
+  In most cases, the only argument given to ``FetchContent_Populate()`` is the
+  ``<name>``.  When used this way, the command assumes the content details have
+  been recorded by an earlier call to :command:`FetchContent_Declare`.  The
+  details are stored in a global property, so they are unaffected by things
+  like variable or directory scope.  Therefore, it doesn't matter where in the
+  project the details were previously declared, as long as they have been
+  declared before the call to ``FetchContent_Populate()``.  Those saved details
+  are then used to construct a call to :command:`ExternalProject_Add` in a
+  private sub-build to perform the content population immediately.  The
+  implementation of ``ExternalProject_Add()`` ensures that if the content has
+  already been populated in a previous CMake run, that content will be reused
+  rather than repopulating them again.  For the common case where population
+  involves downloading content, the cost of the download is only paid once.
+
+  An internal global property records when a particular content population
+  request has been processed.  If ``FetchContent_Populate()`` is called more
+  than once for the same content name within a configure run, the second call
+  will halt with an error.  Projects can and should check whether content
+  population has already been processed with the
+  :command:`FetchContent_GetProperties` command before calling
+  ``FetchContent_Populate()``.
+
+  ``FetchContent_Populate()`` will set three variables in the scope of the
+  caller; ``<lcName>_POPULATED``, ``<lcName>_SOURCE_DIR`` and
+  ``<lcName>_BINARY_DIR``, where ``<lcName>`` is the lowercased ``<name>``.
+  ``<lcName>_POPULATED`` will always be set to ``True`` by the call.
+  ``<lcName>_SOURCE_DIR`` is the location where the
+  content can be found upon return (it will have already been populated), while
+  ``<lcName>_BINARY_DIR`` is a directory intended for use as a corresponding
+  build directory.  The main use case for the two directory variables is to
+  call :command:`add_subdirectory` immediately after population, i.e.:
+
+  .. code-block:: cmake
+
+    FetchContent_Populate(FooBar ...)
+    add_subdirectory(${foobar_SOURCE_DIR} ${foobar_BINARY_DIR})
+
+  The values of the three variables can also be retrieved from anywhere in the
+  project hierarchy using the :command:`FetchContent_GetProperties` command.
+
+  A number of cache variables influence the behavior of all content population
+  performed using details saved from a :command:`FetchContent_Declare` call:
+
+  ``FETCHCONTENT_BASE_DIR``
+    In most cases, the saved details do not specify any options relating to the
+    directories to use for the internal sub-build, final source and build areas.
+    It is generally best to leave these decisions up to the ``FetchContent``
+    module to handle on the project's behalf.  The ``FETCHCONTENT_BASE_DIR``
+    cache variable controls the point under which all content population
+    directories are collected, but in most cases developers would not need to
+    change this.  The default location is ``${CMAKE_BINARY_DIR}/_deps``, but if
+    developers change this value, they should aim to keep the path short and
+    just below the top level of the build tree to avoid running into path
+    length problems on Windows.
+
+  ``FETCHCONTENT_QUIET``
+    The logging output during population can be quite verbose, making the
+    configure stage quite noisy.  This cache option (``ON`` by default) hides
+    all population output unless an error is encountered.  If experiencing
+    problems with hung downloads, temporarily switching this option off may
+    help diagnose which content population is causing the issue.
+
+  ``FETCHCONTENT_FULLY_DISCONNECTED``
+    When this option is enabled, no attempt is made to download or update
+    any content.  It is assumed that all content has already been populated in
+    a previous run or the source directories have been pointed at existing
+    contents the developer has provided manually (using options described
+    further below).  When the developer knows that no changes have been made to
+    any content details, turning this option ``ON`` can significantly speed up
+    the configure stage.  It is ``OFF`` by default.
+
+  ``FETCHCONTENT_UPDATES_DISCONNECTED``
+    This is a less severe download/update control compared to
+    ``FETCHCONTENT_FULLY_DISCONNECTED``.  Instead of bypassing all download and
+    update logic, the ``FETCHCONTENT_UPDATES_DISCONNECTED`` only disables the
+    update stage.  Therefore, if content has not been downloaded previously,
+    it will still be downloaded when this option is enabled.  This can speed up
+    the configure stage, but not as much as
+    ``FETCHCONTENT_FULLY_DISCONNECTED``.  It is ``OFF`` by default.
+
+  In addition to the above cache variables, the following cache variables are
+  also defined for each content name (``<ucName>`` is the uppercased value of
+  ``<name>``):
+
+  ``FETCHCONTENT_SOURCE_DIR_<ucName>``
+    If this is set, no download or update steps are performed for the specified
+    content and the ``<lcName>_SOURCE_DIR`` variable returned to the caller is
+    pointed at this location.  This gives developers a way to have a separate
+    checkout of the content that they can modify freely without interference
+    from the build.  The build simply uses that existing source, but it still
+    defines ``<lcName>_BINARY_DIR`` to point inside its own build area.
+    Developers are strongly encouraged to use this mechanism rather than
+    editing the sources populated in the default location, as changes to
+    sources in the default location can be lost when content population details
+    are changed by the project.
+
+  ``FETCHCONTENT_UPDATES_DISCONNECTED_<ucName>``
+    This is the per-content equivalent of
+    ``FETCHCONTENT_UPDATES_DISCONNECTED``. If the global option or this option
+    is ``ON``, then updates will be disabled for the named content.
+    Disabling updates for individual content can be useful for content whose
+    details rarely change, while still leaving other frequently changing
+    content with updates enabled.
+
+
+  The ``FetchContent_Populate()`` command also supports a syntax allowing the
+  content details to be specified directly rather than using any saved
+  details.  This is more low-level and use of this form is generally to be
+  avoided in favour of using saved content details as outlined above.
+  Nevertheless, in certain situations it can be useful to invoke the content
+  population as an isolated operation (typically as part of implementing some
+  other higher level feature or when using CMake in script mode):
+
+  .. code-block:: cmake
+
+    FetchContent_Populate( <name>
+      [QUIET]
+      [SUBBUILD_DIR <subBuildDir>]
+      [SOURCE_DIR <srcDir>]
+      [BINARY_DIR <binDir>]
+      ...
+    )
+
+  This form has a number of key differences to that where only ``<name>`` is
+  provided:
+
+  - All required population details are assumed to have been provided directly
+    in the call to ``FetchContent_Populate()``. Any saved details for
+    ``<name>`` are ignored.
+  - No check is made for whether content for ``<name>`` has already been
+    populated.
+  - No global property is set to record that the population has occurred.
+  - No global properties record the source or binary directories used for the
+    populated content.
+  - The ``FETCHCONTENT_FULLY_DISCONNECTED`` and
+    ``FETCHCONTENT_UPDATES_DISCONNECTED`` cache variables are ignored.
+
+  The ``<lcName>_SOURCE_DIR`` and ``<lcName>_BINARY_DIR`` variables are still
+  returned to the caller, but since these locations are not stored as global
+  properties when this form is used, they are only available to the calling
+  scope and below rather than the entire project hierarchy.  No
+  ``<lcName>_POPULATED`` variable is set in the caller's scope with this form.
+
+  The supported options for ``FetchContent_Populate()`` are the same as those
+  for :command:`FetchContent_Declare()`.  Those few options shown just
+  above are either specific to ``FetchContent_Populate()`` or their behavior is
+  slightly modified from how :command:`ExternalProject_Add` treats them.
+
+  ``QUIET``
+    The ``QUIET`` option can be given to hide the output associated with
+    populating the specified content.  If the population fails, the output will
+    be shown regardless of whether this option was given or not so that the
+    cause of the failure can be diagnosed.  The global ``FETCHCONTENT_QUIET``
+    cache variable has no effect on ``FetchContent_Populate()`` calls where the
+    content details are provided directly.
+
+  ``SUBBUILD_DIR``
+    The ``SUBBUILD_DIR`` argument can be provided to change the location of the
+    sub-build created to perform the population.  The default value is
+    ``${CMAKE_CURRENT_BINARY_DIR}/<lcName>-subbuild`` and it would be unusual
+    to need to override this default.  If a relative path is specified, it will
+    be interpreted as relative to :variable:`CMAKE_CURRENT_BINARY_DIR`.
+
+  ``SOURCE_DIR``, ``BINARY_DIR``
+    The ``SOURCE_DIR`` and ``BINARY_DIR`` arguments are supported by
+    :command:`ExternalProject_Add`, but different default values are used by
+    ``FetchContent_Populate()``.  ``SOURCE_DIR`` defaults to
+    ``${CMAKE_CURRENT_BINARY_DIR}/<lcName>-src`` and ``BINARY_DIR`` defaults to
+    ``${CMAKE_CURRENT_BINARY_DIR}/<lcName>-build``.  If a relative path is
+    specified, it will be interpreted as relative to
+    :variable:`CMAKE_CURRENT_BINARY_DIR`.
+
+  In addition to the above explicit options, any other unrecognized options are
+  passed through unmodified to :command:`ExternalProject_Add` to perform the
+  download, patch and update steps.  The following options are explicitly
+  prohibited (they are disabled by the ``FetchContent_Populate()`` command):
+
+  - ``CONFIGURE_COMMAND``
+  - ``BUILD_COMMAND``
+  - ``INSTALL_COMMAND``
+  - ``TEST_COMMAND``
+
+  If using ``FetchContent_Populate()`` within CMake's script mode, be aware
+  that the implementation sets up a sub-build which therefore requires a CMake
+  generator and build tool to be available. If these cannot be found by
+  default, then the :variable:`CMAKE_GENERATOR` and/or
+  :variable:`CMAKE_MAKE_PROGRAM` variables will need to be set appropriately
+  on the command line invoking the script.
+
+
+Retrieve Population Properties
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. command:: FetchContent_GetProperties
+
+  When using saved content details, a call to :command:`FetchContent_Populate`
+  records information in global properties which can be queried at any time.
+  This information includes the source and binary directories associated with
+  the content and also whether or not the content population has been processed
+  during the current configure run.
+
+  .. code-block:: cmake
+
+    FetchContent_GetProperties( <name>
+      [SOURCE_DIR <srcDirVar>]
+      [BINARY_DIR <binDirVar>]
+      [POPULATED <doneVar>]
+    )
+
+  The ``SOURCE_DIR``, ``BINARY_DIR`` and ``POPULATED`` options can be used to
+  specify which properties should be retrieved.  Each option accepts a value
+  which is the name of the variable in which to store that property.  Most of
+  the time though, only ``<name>`` is given, in which case the call will then
+  set the same variables as a call to
+  :command:`FetchContent_Populate(name) <FetchContent_Populate>`.  This allows
+  the following canonical pattern to be used, which ensures that the relevant
+  variables will always be defined regardless of whether or not the population
+  has been performed elsewhere in the project already:
+
+  .. code-block:: cmake
+
+    FetchContent_GetProperties(foobar)
+    if(NOT foobar_POPULATED)
+      FetchContent_Populate(foobar)
+
+      # Set any custom variables, etc. here, then
+      # populate the content as part of this build
+
+      add_subdirectory(${foobar_SOURCE_DIR} ${foobar_BINARY_DIR})
+    endif()
+
+  The above pattern allows other parts of the overall project hierarchy to
+  re-use the same content and ensure that it is only populated once.
+
+
+.. _`fetch-content-examples`:
+
+Examples
+^^^^^^^^
+
+Consider a project hierarchy where ``projA`` is the top level project and it
+depends on projects ``projB`` and ``projC``. Both ``projB`` and ``projC``
+can be built standalone and they also both depend on another project
+``projD``.  For simplicity, this example will assume that all four projects
+are available on a company git server.  The ``CMakeLists.txt`` of each project
+might have sections like the following:
+
+*projA*:
+
+.. code-block:: cmake
+
+  include(FetchContent)
+  FetchContent_Declare(
+    projB
+    GIT_REPOSITORY git@mycompany.com/git/projB.git
+    GIT_TAG        4a89dc7e24ff212a7b5167bef7ab079d
+  )
+  FetchContent_Declare(
+    projC
+    GIT_REPOSITORY git@mycompany.com/git/projC.git
+    GIT_TAG        4ad4016bd1d8d5412d135cf8ceea1bb9
+  )
+  FetchContent_Declare(
+    projD
+    GIT_REPOSITORY git@mycompany.com/git/projD.git
+    GIT_TAG        origin/integrationBranch
+  )
+
+  FetchContent_GetProperties(projB)
+  if(NOT projb_POPULATED)
+    FetchContent_Populate(projB)
+    add_subdirectory(${projb_SOURCE_DIR} ${projb_BINARY_DIR})
+  endif()
+
+  FetchContent_GetProperties(projC)
+  if(NOT projc_POPULATED)
+    FetchContent_Populate(projC)
+    add_subdirectory(${projc_SOURCE_DIR} ${projc_BINARY_DIR})
+  endif()
+
+*projB*:
+
+.. code-block:: cmake
+
+  include(FetchContent)
+  FetchContent_Declare(
+    projD
+    GIT_REPOSITORY git@mycompany.com/git/projD.git
+    GIT_TAG        20b415f9034bbd2a2e8216e9a5c9e632
+  )
+
+  FetchContent_GetProperties(projD)
+  if(NOT projd_POPULATED)
+    FetchContent_Populate(projD)
+    add_subdirectory(${projd_SOURCE_DIR} ${projd_BINARY_DIR})
+  endif()
+
+
+*projC*:
+
+.. code-block:: cmake
+
+  include(FetchContent)
+  FetchContent_Declare(
+    projD
+    GIT_REPOSITORY git@mycompany.com/git/projD.git
+    GIT_TAG        7d9a17ad2c962aa13e2fbb8043fb6b8a
+  )
+
+  FetchContent_GetProperties(projD)
+  if(NOT projd_POPULATED)
+    FetchContent_Populate(projD)
+    add_subdirectory(${projd_SOURCE_DIR} ${projd_BINARY_DIR})
+  endif()
+
+A few key points should be noted in the above:
+
+- ``projB`` and ``projC`` define different content details for ``projD``,
+  but ``projA`` also defines a set of content details for ``projD`` and
+  because ``projA`` will define them first, the details from ``projB`` and
+  ``projC`` will not be used.  The override details defined by ``projA``
+  are not required to match either of those from ``projB`` or ``projC``, but
+  it is up to the higher level project to ensure that the details it does
+  define still make sense for the child projects.
+- While ``projA`` defined content details for ``projD``, it did not need
+  to explicitly call ``FetchContent_Populate(projD)`` itself.  Instead, it
+  leaves that to a child project to do (in this case it will be ``projB``
+  since it is added to the build ahead of ``projC``).  If ``projA`` needed to
+  customize how the ``projD`` content was brought into the build as well
+  (e.g. define some CMake variables before calling
+  :command:`add_subdirectory` after populating), it would do the call to
+  ``FetchContent_Populate()``, etc. just as it did for the ``projB`` and
+  ``projC`` content.  For higher level projects, it is usually enough to
+  just define the override content details and leave the actual population
+  to the child projects.  This saves repeating the same thing at each level
+  of the project hierarchy unnecessarily.
+- Even though ``projA`` is the top level project in this example, it still
+  checks whether ``projB`` and ``projC`` have already been populated before
+  going ahead to do those populations.  This makes ``projA`` able to be more
+  easily incorporated as a child of some other higher level project in the
+  future if required.  Always protect a call to
+  :command:`FetchContent_Populate` with a check to
+  :command:`FetchContent_GetProperties`, even in what may be considered a top
+  level project at the time.
+
+
+The following example demonstrates how one might download and unpack a
+firmware tarball using CMake's :manual:`script mode <cmake(1)>`.  The call to
+:command:`FetchContent_Populate` specifies all the content details and the
+unpacked firmware will be placed in a ``firmware`` directory below the
+current working directory.
+
+*getFirmware.cmake*:
+
+.. code-block:: cmake
+
+  # NOTE: Intended to be run in script mode with cmake -P
+  include(FetchContent)
+  FetchContent_Populate(
+    firmware
+    URL        https://mycompany.com/assets/firmware-1.23-arm.tar.gz
+    URL_HASH   MD5=68247684da89b608d466253762b0ff11
+    SOURCE_DIR firmware
+  )
+
+#]=======================================================================]
+
+
+set(__FetchContent_privateDir "${CMAKE_CURRENT_LIST_DIR}/FetchContent")
+
+#=======================================================================
+# Recording and retrieving content details for later population
+#=======================================================================
+
+# Internal use, projects must not call this directly. It is
+# intended for use by FetchContent_Declare() only.
+#
+# Sets a content-specific global property (not meant for use
+# outside of functions defined here in this file) which can later
+# be retrieved using __FetchContent_getSavedDetails() with just the
+# same content name. If there is already a value stored in the
+# property, it is left unchanged and this call has no effect.
+# This allows parent projects to define the content details,
+# overriding anything a child project may try to set (properties
+# are not cached between runs, so the first thing to set it in a
+# build will be in control).
+function(__FetchContent_declareDetails contentName)
+
+  string(TOLOWER ${contentName} contentNameLower)
+  set(propertyName "_FetchContent_${contentNameLower}_savedDetails")
+  get_property(alreadyDefined GLOBAL PROPERTY ${propertyName} DEFINED)
+  if(NOT alreadyDefined)
+    define_property(GLOBAL PROPERTY ${propertyName}
+      BRIEF_DOCS "Internal implementation detail of FetchContent_Populate()"
+      FULL_DOCS  "Details used by FetchContent_Populate() for ${contentName}"
+    )
+    set_property(GLOBAL PROPERTY ${propertyName} ${ARGN})
+  endif()
+
+endfunction()
+
+
+# Internal use, projects must not call this directly. It is
+# intended for use by the FetchContent_Declare() function.
+#
+# Retrieves details saved for the specified content in an
+# earlier call to __FetchContent_declareDetails().
+function(__FetchContent_getSavedDetails contentName outVar)
+
+  string(TOLOWER ${contentName} contentNameLower)
+  set(propertyName "_FetchContent_${contentNameLower}_savedDetails")
+  get_property(alreadyDefined GLOBAL PROPERTY ${propertyName} DEFINED)
+  if(NOT alreadyDefined)
+    message(FATAL_ERROR "No content details recorded for ${contentName}")
+  endif()
+  get_property(propertyValue GLOBAL PROPERTY ${propertyName})
+  set(${outVar} "${propertyValue}" PARENT_SCOPE)
+
+endfunction()
+
+
+# Saves population details of the content, sets defaults for the
+# SOURCE_DIR and BUILD_DIR.
+function(FetchContent_Declare contentName)
+
+  set(options "")
+  set(oneValueArgs SVN_REPOSITORY)
+  set(multiValueArgs "")
+
+  cmake_parse_arguments(ARG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  unset(srcDirSuffix)
+  unset(svnRepoArgs)
+  if(ARG_SVN_REPOSITORY)
+    # Add a hash of the svn repository URL to the source dir. This works
+    # around the problem where if the URL changes, the download would
+    # fail because it tries to checkout/update rather than switch the
+    # old URL to the new one. We limit the hash to the first 7 characters
+    # so that the source path doesn't get overly long (which can be a
+    # problem on windows due to path length limits).
+    string(SHA1 urlSHA ${ARG_SVN_REPOSITORY})
+    string(SUBSTRING ${urlSHA} 0 7 urlSHA)
+    set(srcDirSuffix "-${urlSHA}")
+    set(svnRepoArgs  SVN_REPOSITORY ${ARG_SVN_REPOSITORY})
+  endif()
+
+  string(TOLOWER ${contentName} contentNameLower)
+  __FetchContent_declareDetails(
+    ${contentNameLower}
+    SOURCE_DIR "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-src${srcDirSuffix}"
+    BINARY_DIR "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-build"
+    ${svnRepoArgs}
+    # List these last so they can override things we set above
+    ${ARG_UNPARSED_ARGUMENTS}
+  )
+
+endfunction()
+
+
+#=======================================================================
+# Set/get whether the specified content has been populated yet.
+# The setter also records the source and binary dirs used.
+#=======================================================================
+
+# Internal use, projects must not call this directly. It is
+# intended for use by the FetchContent_Populate() function to
+# record when FetchContent_Populate() is called for a particular
+# content name.
+function(__FetchContent_setPopulated contentName sourceDir binaryDir)
+
+  string(TOLOWER ${contentName} contentNameLower)
+  set(prefix "_FetchContent_${contentNameLower}")
+
+  set(propertyName "${prefix}_sourceDir")
+  define_property(GLOBAL PROPERTY ${propertyName}
+    BRIEF_DOCS "Internal implementation detail of FetchContent_Populate()"
+    FULL_DOCS  "Details used by FetchContent_Populate() for ${contentName}"
+  )
+  set_property(GLOBAL PROPERTY ${propertyName} ${sourceDir})
+
+  set(propertyName "${prefix}_binaryDir")
+  define_property(GLOBAL PROPERTY ${propertyName}
+    BRIEF_DOCS "Internal implementation detail of FetchContent_Populate()"
+    FULL_DOCS  "Details used by FetchContent_Populate() for ${contentName}"
+  )
+  set_property(GLOBAL PROPERTY ${propertyName} ${binaryDir})
+
+  set(propertyName "${prefix}_populated")
+  define_property(GLOBAL PROPERTY ${propertyName}
+    BRIEF_DOCS "Internal implementation detail of FetchContent_Populate()"
+    FULL_DOCS  "Details used by FetchContent_Populate() for ${contentName}"
+  )
+  set_property(GLOBAL PROPERTY ${propertyName} True)
+
+endfunction()
+
+
+# Set variables in the calling scope for any of the retrievable
+# properties. If no specific properties are requested, variables
+# will be set for all retrievable properties.
+#
+# This function is intended to also be used by projects as the canonical
+# way to detect whether they should call FetchContent_Populate()
+# and pull the populated source into the build with add_subdirectory(),
+# if they are using the populated content in that way.
+function(FetchContent_GetProperties contentName)
+
+  string(TOLOWER ${contentName} contentNameLower)
+
+  set(options "")
+  set(oneValueArgs SOURCE_DIR BINARY_DIR POPULATED)
+  set(multiValueArgs "")
+
+  cmake_parse_arguments(ARG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  if(NOT ARG_SOURCE_DIR AND
+     NOT ARG_BINARY_DIR AND
+     NOT ARG_POPULATED)
+    # No specific properties requested, provide them all
+    set(ARG_SOURCE_DIR ${contentNameLower}_SOURCE_DIR)
+    set(ARG_BINARY_DIR ${contentNameLower}_BINARY_DIR)
+    set(ARG_POPULATED  ${contentNameLower}_POPULATED)
+  endif()
+
+  set(prefix "_FetchContent_${contentNameLower}")
+
+  if(ARG_SOURCE_DIR)
+    set(propertyName "${prefix}_sourceDir")
+    get_property(value GLOBAL PROPERTY ${propertyName})
+    if(value)
+      set(${ARG_SOURCE_DIR} ${value} PARENT_SCOPE)
+    endif()
+  endif()
+
+  if(ARG_BINARY_DIR)
+    set(propertyName "${prefix}_binaryDir")
+    get_property(value GLOBAL PROPERTY ${propertyName})
+    if(value)
+      set(${ARG_BINARY_DIR} ${value} PARENT_SCOPE)
+    endif()
+  endif()
+
+  if(ARG_POPULATED)
+    set(propertyName "${prefix}_populated")
+    get_property(value GLOBAL PROPERTY ${propertyName} DEFINED)
+    set(${ARG_POPULATED} ${value} PARENT_SCOPE)
+  endif()
+
+endfunction()
+
+
+#=======================================================================
+# Performing the population
+#=======================================================================
+
+# The value of contentName will always have been lowercased by the caller.
+# All other arguments are assumed to be options that are understood by
+# ExternalProject_Add(), except for QUIET and SUBBUILD_DIR.
+function(__FetchContent_directPopulate contentName)
+
+  set(options
+      QUIET
+  )
+  set(oneValueArgs
+      SUBBUILD_DIR
+      SOURCE_DIR
+      BINARY_DIR
+      # Prevent the following from being passed through
+      CONFIGURE_COMMAND
+      BUILD_COMMAND
+      INSTALL_COMMAND
+      TEST_COMMAND
+  )
+  set(multiValueArgs "")
+
+  cmake_parse_arguments(ARG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  if(NOT ARG_SUBBUILD_DIR)
+    message(FATAL_ERROR "Internal error: SUBBUILD_DIR not set")
+  elseif(NOT IS_ABSOLUTE "${ARG_SUBBUILD_DIR}")
+    set(ARG_SUBBUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/${ARG_SUBBUILD_DIR}")
+  endif()
+
+  if(NOT ARG_SOURCE_DIR)
+    message(FATAL_ERROR "Internal error: SOURCE_DIR not set")
+  elseif(NOT IS_ABSOLUTE "${ARG_SOURCE_DIR}")
+    set(ARG_SOURCE_DIR "${CMAKE_CURRENT_BINARY_DIR}/${ARG_SOURCE_DIR}")
+  endif()
+
+  if(NOT ARG_BINARY_DIR)
+    message(FATAL_ERROR "Internal error: BINARY_DIR not set")
+  elseif(NOT IS_ABSOLUTE "${ARG_BINARY_DIR}")
+    set(ARG_BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}/${ARG_BINARY_DIR}")
+  endif()
+
+  # Ensure the caller can know where to find the source and build directories
+  # with some convenient variables. Doing this here ensures the caller sees
+  # the correct result in the case where the default values are overridden by
+  # the content details set by the project.
+  set(${contentName}_SOURCE_DIR "${ARG_SOURCE_DIR}" PARENT_SCOPE)
+  set(${contentName}_BINARY_DIR "${ARG_BINARY_DIR}" PARENT_SCOPE)
+
+  # The unparsed arguments may contain spaces, so build up ARG_EXTRA
+  # in such a way that it correctly substitutes into the generated
+  # CMakeLists.txt file with each argument quoted.
+  unset(ARG_EXTRA)
+  foreach(arg IN LISTS ARG_UNPARSED_ARGUMENTS)
+    set(ARG_EXTRA "${ARG_EXTRA} \"${arg}\"")
+  endforeach()
+
+  # Hide output if requested, but save it to a variable in case there's an
+  # error so we can show the output upon failure. When not quiet, don't
+  # capture the output to a variable because the user may want to see the
+  # output as it happens (e.g. progress during long downloads). Combine both
+  # stdout and stderr in the one capture variable so the output stays in order.
+  if (ARG_QUIET)
+    set(outputOptions
+        OUTPUT_VARIABLE capturedOutput
+        ERROR_VARIABLE  capturedOutput
+    )
+  else()
+    set(capturedOutput)
+    set(outputOptions)
+    message(STATUS "Populating ${contentName}")
+  endif()
+
+  if(CMAKE_GENERATOR)
+    set(generatorOpts "-G${CMAKE_GENERATOR}")
+    if(CMAKE_GENERATOR_PLATFORM)
+      list(APPEND generatorOpts "-A${CMAKE_GENERATOR_PLATFORM}")
+    endif()
+    if(CMAKE_GENERATOR_TOOLSET)
+      list(APPEND generatorOpts "-T${CMAKE_GENERATOR_TOOLSET}")
+    endif()
+
+    if(CMAKE_MAKE_PROGRAM)
+      list(APPEND generatorOpts "-DCMAKE_MAKE_PROGRAM:FILEPATH=${CMAKE_MAKE_PROGRAM}")
+    endif()
+
+  else()
+    # Likely we've been invoked via CMake's script mode where no
+    # generator is set (and hence CMAKE_MAKE_PROGRAM could not be
+    # trusted even if provided). We will have to rely on being
+    # able to find the default generator and build tool.
+    unset(generatorOpts)
+  endif()
+
+  # Create and build a separate CMake project to carry out the population.
+  # If we've already previously done these steps, they will not cause
+  # anything to be updated, so extra rebuilds of the project won't occur.
+  # Make sure to pass through CMAKE_MAKE_PROGRAM in case the main project
+  # has this set to something not findable on the PATH.
+  configure_file("${__FetchContent_privateDir}/CMakeLists.cmake.in"
+                 "${ARG_SUBBUILD_DIR}/CMakeLists.txt")
+  execute_process(
+    COMMAND ${CMAKE_COMMAND} ${generatorOpts} .
+    RESULT_VARIABLE result
+    ${outputOptions}
+    WORKING_DIRECTORY "${ARG_SUBBUILD_DIR}"
+  )
+  if(result)
+    if(capturedOutput)
+      message("${capturedOutput}")
+    endif()
+    message(FATAL_ERROR "CMake step for ${contentName} failed: ${result}")
+  endif()
+  execute_process(
+    COMMAND ${CMAKE_COMMAND} --build .
+    RESULT_VARIABLE result
+    ${outputOptions}
+    WORKING_DIRECTORY "${ARG_SUBBUILD_DIR}"
+  )
+  if(result)
+    if(capturedOutput)
+      message("${capturedOutput}")
+    endif()
+    message(FATAL_ERROR "Build step for ${contentName} failed: ${result}")
+  endif()
+
+endfunction()
+
+
+option(FETCHCONTENT_FULLY_DISCONNECTED   "Disables all attempts to download or update content and assumes source dirs already exist")
+option(FETCHCONTENT_UPDATES_DISCONNECTED "Enables UPDATE_DISCONNECTED behavior for all content population")
+option(FETCHCONTENT_QUIET                "Enables QUIET option for all content population" ON)
+set(FETCHCONTENT_BASE_DIR "${CMAKE_BINARY_DIR}/_deps" CACHE PATH "Directory under which to collect all populated content")
+
+# Populate the specified content using details stored from
+# an earlier call to FetchContent_Declare().
+function(FetchContent_Populate contentName)
+
+  if(NOT contentName)
+    message(FATAL_ERROR "Empty contentName not allowed for FetchContent_Populate()")
+  endif()
+
+  string(TOLOWER ${contentName} contentNameLower)
+
+  if(ARGN)
+    # This is the direct population form with details fully specified
+    # as part of the call, so we already have everything we need
+    __FetchContent_directPopulate(
+      ${contentNameLower}
+      SUBBUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/${contentNameLower}-subbuild"
+      SOURCE_DIR   "${CMAKE_CURRENT_BINARY_DIR}/${contentNameLower}-src"
+      BINARY_DIR   "${CMAKE_CURRENT_BINARY_DIR}/${contentNameLower}-build"
+      ${ARGN}  # Could override any of the above ..._DIR variables
+    )
+
+    # Pass source and binary dir variables back to the caller
+    set(${contentNameLower}_SOURCE_DIR "${${contentNameLower}_SOURCE_DIR}" PARENT_SCOPE)
+    set(${contentNameLower}_BINARY_DIR "${${contentNameLower}_BINARY_DIR}" PARENT_SCOPE)
+
+    # Don't set global properties, or record that we did this population, since
+    # this was a direct call outside of the normal declared details form.
+    # We only want to save values in the global properties for content that
+    # honours the hierarchical details mechanism so that projects are not
+    # robbed of the ability to override details set in nested projects.
+    return()
+  endif()
+
+  # No details provided, so assume they were saved from an earlier call
+  # to FetchContent_Declare(). Do a check that we haven't already
+  # populated this content before in case the caller forgot to check.
+  FetchContent_GetProperties(${contentName})
+  if(${contentNameLower}_POPULATED)
+    message(FATAL_ERROR "Content ${contentName} already populated in ${${contentNameLower}_SOURCE_DIR}")
+  endif()
+
+  string(TOUPPER ${contentName} contentNameUpper)
+  set(FETCHCONTENT_SOURCE_DIR_${contentNameUpper}
+      "${FETCHCONTENT_SOURCE_DIR_${contentNameUpper}}"
+      CACHE PATH "When not empty, overrides where to find pre-populated content for ${contentName}")
+
+  if(FETCHCONTENT_SOURCE_DIR_${contentNameUpper})
+    # The source directory has been explicitly provided in the cache,
+    # so no population is required
+    set(${contentNameLower}_SOURCE_DIR "${FETCHCONTENT_SOURCE_DIR_${contentNameUpper}}")
+    set(${contentNameLower}_BINARY_DIR "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-build")
+
+  elseif(FETCHCONTENT_FULLY_DISCONNECTED)
+    # Bypass population and assume source is already there from a previous run
+    set(${contentNameLower}_SOURCE_DIR "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-src")
+    set(${contentNameLower}_BINARY_DIR "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-build")
+
+  else()
+    # Support both a global "disconnect all updates" and a per-content
+    # update test (either one being set disables updates for this content).
+    option(FETCHCONTENT_UPDATES_DISCONNECTED_${contentNameUpper}
+           "Enables UPDATE_DISCONNECTED behavior just for population of ${contentName}")
+    if(FETCHCONTENT_UPDATES_DISCONNECTED OR
+       FETCHCONTENT_UPDATES_DISCONNECTED_${contentNameUpper})
+      set(disconnectUpdates True)
+    else()
+      set(disconnectUpdates False)
+    endif()
+
+    if(FETCHCONTENT_QUIET)
+      set(quietFlag QUIET)
+    else()
+      unset(quietFlag)
+    endif()
+
+    __FetchContent_getSavedDetails(${contentName} contentDetails)
+    if("${contentDetails}" STREQUAL "")
+      message(FATAL_ERROR "No details have been set for content: ${contentName}")
+    endif()
+
+    __FetchContent_directPopulate(
+      ${contentNameLower}
+      ${quietFlag}
+      UPDATE_DISCONNECTED ${disconnectUpdates}
+      SUBBUILD_DIR "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-subbuild"
+      SOURCE_DIR   "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-src"
+      BINARY_DIR   "${FETCHCONTENT_BASE_DIR}/${contentNameLower}-build"
+      # Put the saved details last so they can override any of the
+      # the options we set above (this can include SOURCE_DIR or
+      # BUILD_DIR)
+      ${contentDetails}
+    )
+  endif()
+
+  __FetchContent_setPopulated(
+    ${contentName}
+    ${${contentNameLower}_SOURCE_DIR}
+    ${${contentNameLower}_BINARY_DIR}
+  )
+
+  # Pass variables back to the caller. The variables passed back here
+  # must match what FetchContent_GetProperties() sets when it is called
+  # with just the content name.
+  set(${contentNameLower}_SOURCE_DIR "${${contentNameLower}_SOURCE_DIR}" PARENT_SCOPE)
+  set(${contentNameLower}_BINARY_DIR "${${contentNameLower}_BINARY_DIR}" PARENT_SCOPE)
+  set(${contentNameLower}_POPULATED  True PARENT_SCOPE)
+
+endfunction()
diff --git a/CMakeModules/FetchContent/CMakeLists.cmake.in b/CMakeModules/FetchContent/CMakeLists.cmake.in
new file mode 100644
index 0000000000..9a7a7715ab
--- /dev/null
+++ b/CMakeModules/FetchContent/CMakeLists.cmake.in
@@ -0,0 +1,21 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+cmake_minimum_required(VERSION ${CMAKE_VERSION})
+
+# We name the project and the target for the ExternalProject_Add() call
+# to something that will highlight to the user what we are working on if
+# something goes wrong and an error message is produced.
+
+project(${contentName}-populate NONE)
+
+include(ExternalProject)
+ExternalProject_Add(${contentName}-populate
+                    ${ARG_EXTRA}
+                    SOURCE_DIR          "${ARG_SOURCE_DIR}"
+                    BINARY_DIR          "${ARG_BINARY_DIR}"
+                    CONFIGURE_COMMAND   ""
+                    BUILD_COMMAND       ""
+                    INSTALL_COMMAND     ""
+                    TEST_COMMAND        ""
+)
diff --git a/assets b/assets
deleted file mode 160000
index cd08d74961..0000000000
--- a/assets
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit cd08d749611b324012555ad6f23fd76c5465bd6c
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 37938b3746..1310b3c87b 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -7,7 +7,6 @@ set(AF_DOCS_LAYOUT "${CMAKE_CURRENT_SOURCE_DIR}/layout.xml")
 set(AF_DOCS_LAYOUT_OUT "${CMAKE_CURRENT_BINARY_DIR}/layout.xml.out")
 
 set(DOCS_DIR ${CMAKE_CURRENT_SOURCE_DIR})
-set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../assets")
 set(INCLUDE_DIR "${PROJECT_SOURCE_DIR}/include")
 set(EXAMPLES_DIR "${PROJECT_SOURCE_DIR}/examples")
 set(SNIPPETS_DIR "${PROJECT_SOURCE_DIR}/test")
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 90c8f232cf..ca4c673d2a 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -1,4 +1,4 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2020, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -10,9 +10,10 @@ set(AF_TEST_WITH_MTX_FILES
     "Download and run tests on large matrices form sparse.tamu.edu")
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
-if (AF_TEST_WITH_MTX_FILES)
+
+if(AF_TEST_WITH_MTX_FILES)
   include(download_sparse_datasets)
-endif ()
+endif()
 
 if(NOT TARGET gtest)
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
@@ -45,14 +46,23 @@ endif()
 
 # Reset the CXX flags for tests
 set(CMAKE_CXX_STANDARD 98)
-set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
 
+# TODO(pradeep) perhaps rename AF_USE_RELATIVE_TEST_DIR to AF_WITH_TEST_DATA_DIR
+#               with empty default value
 if(${AF_USE_RELATIVE_TEST_DIR})
-    # RELATIVE_TEST_DATA_DIR is a User-visible option with default value of test/data directory
-    set(RELATIVE_TEST_DATA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data" CACHE STRING "Relative Test Data Directory")
-    set(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
-else(${AF_USE_RELATIVE_TEST_DIR})  # Not using relative test data directory
-    set(TESTDATA_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data")
+  # RELATIVE_TEST_DATA_DIR is a User-visible option with default value of test/data directory
+  # This code arm assumes user is responsible for providing the test data path
+  set(RELATIVE_TEST_DATA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/data" CACHE
+      STRING "Relative Test Data Directory")
+  set(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
+else(${AF_USE_RELATIVE_TEST_DIR})
+  FetchContent_Declare(
+    af_test_data
+    GIT_REPOSITORY https://github.com/arrayfire/arrayfire-data.git
+    GIT_TAG        master
+  )
+  FetchContent_Populate(af_test_data)
+  set(TESTDATA_SOURCE_DIR "${af_test_data_SOURCE_DIR}")
 endif(${AF_USE_RELATIVE_TEST_DIR})
 
 if(AF_BUILD_CPU)
diff --git a/test/data b/test/data
deleted file mode 160000
index 408f440590..0000000000
--- a/test/data
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 408f44059015c57a66e13b4c98df86ebcb427950

From b9b78d127bdee39aff670111bbaf8010b8322722 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 31 Oct 2020 18:09:41 +0530
Subject: [PATCH 2112/2677] Move header only deps to be fetch using cmake
 FetchContent

- spdlog
- cub
- threads
---
 .gitmodules                     |  9 ---------
 CMakeLists.txt                  |  9 ++++++++-
 extern/cub                      |  1 -
 extern/spdlog                   |  1 -
 src/backend/cpu/CMakeLists.txt  | 13 ++++++++++---
 src/backend/cpu/threads         |  1 -
 src/backend/cuda/CMakeLists.txt |  8 +++++++-
 7 files changed, 25 insertions(+), 17 deletions(-)
 delete mode 160000 extern/cub
 delete mode 160000 extern/spdlog
 delete mode 160000 src/backend/cpu/threads

diff --git a/.gitmodules b/.gitmodules
index c88fd43e8b..99184e946e 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,15 +1,6 @@
 [submodule "test/gtest"]
 	path = test/gtest
 	url = https://github.com/google/googletest.git
-[submodule "src/backend/cpu/threads"]
-	path = src/backend/cpu/threads
-	url = https://github.com/alltheflops/threads.git
-[submodule "extern/cub"]
-	path = extern/cub
-	url = https://github.com/NVlabs/cub.git
-[submodule "extern/spdlog"]
-	path = extern/spdlog
-	url = https://github.com/gabime/spdlog.git
 [submodule "extern/forge"]
 	path = extern/forge
 	url = https://github.com/arrayfire/forge.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3efe9b4297..c30f1a1f98 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -156,7 +156,14 @@ if(NOT LAPACK_FOUND)
 endif()
 
 set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
-add_subdirectory(extern/spdlog EXCLUDE_FROM_ALL)
+FetchContent_Declare(
+  af_spdlog
+  GIT_REPOSITORY https://github.com/gabime/spdlog.git
+  GIT_TAG        v1.0.0
+)
+FetchContent_Populate(af_spdlog)
+add_subdirectory(${af_spdlog_SOURCE_DIR} ${af_spdlog_BINARY_DIR} EXCLUDE_FROM_ALL)
+
 add_subdirectory(extern/glad)
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
diff --git a/extern/cub b/extern/cub
deleted file mode 160000
index d106ddb991..0000000000
--- a/extern/cub
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit d106ddb991a56c3df1b6d51b2409e36ba8181ce4
diff --git a/extern/spdlog b/extern/spdlog
deleted file mode 160000
index caff7296b1..0000000000
--- a/extern/spdlog
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit caff7296b162d97e44d6a1cc039adf689cfc02b3
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index a71ede7a47..cd02510dc4 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -271,10 +271,17 @@ if (AF_WITH_CPUID)
   target_compile_definitions(afcpu PRIVATE -DAF_WITH_CPUID)
 endif(AF_WITH_CPUID)
 
+FetchContent_Declare(
+  af_threads
+  GIT_REPOSITORY https://github.com/arrayfire/threads.git
+  GIT_TAG        b666773940269179f19ef11c8f1eb77005e85d9a
+)
+FetchContent_Populate(af_threads)
+
 target_sources(afcpu
   PRIVATE
-    ${CMAKE_CURRENT_SOURCE_DIR}/threads/async_queue.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/threads/event.hpp
+    ${af_threads_SOURCE_DIR}/include/threads/async_queue.hpp
+    ${af_threads_SOURCE_DIR}/include/threads/event.hpp
   )
 
 arrayfire_set_default_cxx_flags(afcpu)
@@ -288,7 +295,7 @@ target_include_directories(afcpu
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${CMAKE_CURRENT_SOURCE_DIR}
-    threads
+    ${af_threads_SOURCE_DIR}/include
     ${CBLAS_INCLUDE_DIR}
   )
 
diff --git a/src/backend/cpu/threads b/src/backend/cpu/threads
deleted file mode 160000
index c483ad32b6..0000000000
--- a/src/backend/cpu/threads
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit c483ad32b68c0301d91ff5d2bfc88d02589e9a43
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index beda8b769c..05ecaa87e6 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -115,7 +115,13 @@ cuda_include_directories(
   ${COMMON_INTERFACE_DIRS}
   )
 if(CUDA_VERSION_MAJOR VERSION_LESS 11)
-  cuda_include_directories(${ArrayFire_SOURCE_DIR}/extern/cub)
+  FetchContent_Declare(
+    nv_cub
+    GIT_REPOSITORY https://github.com/NVIDIA/cub.git
+    GIT_TAG        1.10.0
+  )
+  FetchContent_Populate(nv_cub)
+  cuda_include_directories(${nv_cub_SOURCE_DIR})
 endif()
 
 file(GLOB jit_src "kernel/jit.cuh")

From fe1bdb0a34eb1df5dcf90b545a1154f67f3accd6 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 31 Oct 2020 19:29:40 +0530
Subject: [PATCH 2113/2677] Get graphics dependencies using cmake FetchContent

---
 .gitmodules                                   |  6 -----
 CMakeLists.txt                                | 11 ++++++--
 ...dule.cmake => AFconfigure_forge_dep.cmake} | 25 ++++++++++++-------
 extern/forge                                  |  1 -
 extern/glad                                   |  1 -
 src/backend/common/CMakeLists.txt             |  6 ++---
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  6 ++---
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  6 ++---
 8 files changed, 34 insertions(+), 28 deletions(-)
 rename CMakeModules/{AFconfigure_forge_submodule.cmake => AFconfigure_forge_dep.cmake} (68%)
 delete mode 160000 extern/forge
 delete mode 160000 extern/glad

diff --git a/.gitmodules b/.gitmodules
index 99184e946e..3c25e3e2c6 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,9 +1,3 @@
 [submodule "test/gtest"]
 	path = test/gtest
 	url = https://github.com/google/googletest.git
-[submodule "extern/forge"]
-	path = extern/forge
-	url = https://github.com/arrayfire/forge.git
-[submodule "extern/glad"]
-	path = extern/glad
-	url = https://github.com/arrayfire/glad.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c30f1a1f98..f6cd4914d5 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -121,7 +121,7 @@ mark_as_advanced(
 #Configure forge submodule
 #forge is included in ALL target if AF_BUILD_FORGE is ON
 #otherwise, forge is not built at all
-include(AFconfigure_forge_submodule)
+include(AFconfigure_forge_dep)
 
 configure_file(
     ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
@@ -164,7 +164,14 @@ FetchContent_Declare(
 FetchContent_Populate(af_spdlog)
 add_subdirectory(${af_spdlog_SOURCE_DIR} ${af_spdlog_BINARY_DIR} EXCLUDE_FROM_ALL)
 
-add_subdirectory(extern/glad)
+FetchContent_Declare(
+  af_glad
+  GIT_REPOSITORY https://github.com/arrayfire/glad.git
+  GIT_TAG        master
+)
+FetchContent_Populate(af_glad)
+add_subdirectory(${af_glad_SOURCE_DIR})
+
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
 add_subdirectory(src/api/cpp)
diff --git a/CMakeModules/AFconfigure_forge_submodule.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
similarity index 68%
rename from CMakeModules/AFconfigure_forge_submodule.cmake
rename to CMakeModules/AFconfigure_forge_dep.cmake
index d16849f050..e8f680bf0f 100644
--- a/CMakeModules/AFconfigure_forge_submodule.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -5,16 +5,28 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
+set(FG_VERSION_MAJOR 1)
+set(FG_VERSION_MINOR 0)
+set(FG_VERSION_PATCH 5)
+set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
+set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
+
+FetchContent_Declare(
+  af_forge
+  GIT_REPOSITORY https://github.com/arrayfire/forge.git
+  GIT_TAG        "v${FG_VERSION}"
+)
+FetchContent_Populate(af_forge)
 if(AF_BUILD_FORGE)
   set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
   set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
-  set(CMAKE_INSTALL_PREFIX ${ArrayFire_BINARY_DIR}/extern/forge/package)
+  set(CMAKE_INSTALL_PREFIX ${af_forge_BINARY_DIR}/extern/forge/package)
   set(CMAKE_BUILD_TYPE Release)
   set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
   set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
   set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
-  add_subdirectory(extern/forge EXCLUDE_FROM_ALL)
+  add_subdirectory(${af_forge_SOURCE_DIR} ${af_forge_BINARY_DIR} EXCLUDE_FROM_ALL)
 
   mark_as_advanced(
       FG_BUILD_EXAMPLES
@@ -39,13 +51,8 @@ if(AF_BUILD_FORGE)
       COMPONENT common_backend_dependencies)
   set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
 else(AF_BUILD_FORGE)
-  set(FG_VERSION "1.0.0")
-  set(FG_VERSION_MAJOR 1)
-  set(FG_VERSION_MINOR 0)
-  set(FG_VERSION_PATCH 0)
-  set(FG_API_VERSION_CURRENT 10)
   configure_file(
-    ${PROJECT_SOURCE_DIR}/extern/forge/CMakeModules/version.h.in
-    ${PROJECT_BINARY_DIR}/extern/forge/include/fg/version.h
+    ${af_forge_SOURCE_DIR}/CMakeModules/version.h.in
+    ${af_forge_BINARY_DIR}/include/fg/version.h
     )
 endif(AF_BUILD_FORGE)
diff --git a/extern/forge b/extern/forge
deleted file mode 160000
index 1a0f0cb637..0000000000
--- a/extern/forge
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 1a0f0cb6371a8c8053ab5eb7cbe3039c95132389
diff --git a/extern/glad b/extern/glad
deleted file mode 160000
index 6e58ccdfa8..0000000000
--- a/extern/glad
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 6e58ccdfa8e65e1dc5d04a0b9c752c6508ef80b5
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index c9fe0889c5..caa3ea056c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -81,7 +81,7 @@ target_link_libraries(afcommon_interface
   INTERFACE
     spdlog
     Boost::boost
-    af_glad_interface
+    glad_interface
     ${CMAKE_DL_LIBS}
 )
 
@@ -95,8 +95,8 @@ target_include_directories(afcommon_interface
     ${ArrayFire_BINARY_DIR}
   SYSTEM INTERFACE
     $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
-    ${ArrayFire_SOURCE_DIR}/extern/forge/include
-    ${ArrayFire_BINARY_DIR}/extern/forge/include
+    ${af_forge_SOURCE_DIR}/include
+    ${af_forge_BINARY_DIR}/include
   )
 
 if(APPLE AND NOT USE_MKL)
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 9a796c9e77..9ed829d8eb 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -39,9 +39,9 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:af_glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
-        ${ArrayFire_SOURCE_DIR}/extern/forge/include
-        ${ArrayFire_BINARY_DIR}/extern/forge/include
+        $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
+        ${af_forge_SOURCE_DIR}/include
+        ${af_forge_BINARY_DIR}/include
       )
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index d618ff2f47..974b9a3a7c 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -37,9 +37,9 @@ foreach(SBK_TYPE ${SBK_TYPES})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:af_glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
-        ${ArrayFire_SOURCE_DIR}/extern/forge/include
-        ${ArrayFire_BINARY_DIR}/extern/forge/include
+        $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
+        ${af_forge_SOURCE_DIR}/include
+        ${af_forge_BINARY_DIR}/include
       )
 
     set_target_properties(opencl_sort_by_key_${SBK_TYPE}

From 5ad1930bb7c455da99eb69070be02f320ac998be Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 31 Oct 2020 20:26:43 +0530
Subject: [PATCH 2114/2677] Get googltest using cmake FetchContent instead of
 submodule

---
 .gitmodules         |  3 ---
 test/CMakeLists.txt | 17 +++++++++++++----
 test/gtest          |  1 -
 3 files changed, 13 insertions(+), 8 deletions(-)
 delete mode 160000 test/gtest

diff --git a/.gitmodules b/.gitmodules
index 3c25e3e2c6..e69de29bb2 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +0,0 @@
-[submodule "test/gtest"]
-	path = test/gtest
-	url = https://github.com/google/googletest.git
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index ca4c673d2a..2bbb312d99 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -15,7 +15,14 @@ if(AF_TEST_WITH_MTX_FILES)
   include(download_sparse_datasets)
 endif()
 
+FetchContent_Declare(
+  googletest
+  GIT_REPOSITORY https://github.com/google/googletest.git
+  GIT_TAG        release-1.8.1
+)
 if(NOT TARGET gtest)
+  FetchContent_Populate(googletest)
+
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
   # newer cmakes. This sets the default global setting for that policy.
   set(CMAKE_POLICY_DEFAULT_CMP0042 NEW)
@@ -25,7 +32,7 @@ if(NOT TARGET gtest)
     set(BUILD_SHARED_LIBS OFF)
   endif()
 
-  add_subdirectory(gtest EXCLUDE_FROM_ALL)
+  add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
   set_target_properties(gtest gtest_main
     PROPERTIES
       FOLDER "ExternalProjectTargets/gtest")
@@ -33,11 +40,13 @@ if(NOT TARGET gtest)
   # Hide gtest project variables
   mark_as_advanced(
     BUILD_SHARED_LIBS
+    gmock_build_tests
     gtest_build_samples
     gtest_build_tests
     gtest_disable_pthreads
     gtest_force_shared_crt
-    gtest_hide_internal_symbols)
+    gtest_hide_internal_symbols
+  )
 endif()
 
 if(NOT TARGET mmio)
@@ -93,7 +102,7 @@ target_include_directories(arrayfire_test
     ${ArrayFire_BINARY_DIR}/include
     ${ArrayFire_SOURCE_DIR}/extern/half/include
     mmio
-    gtest/googletest/include)
+    ${googletest_SOURCE_DIR}/googletest/include)
 
 if(WIN32)
   target_compile_options(arrayfire_test
@@ -323,7 +332,7 @@ if(CUDA_FOUND)
           ${ArrayFire_BINARY_DIR}/include
           ${ArrayFire_SOURCE_DIR}/extern/half/include
           ${CMAKE_CURRENT_SOURCE_DIR}
-          ${CMAKE_CURRENT_SOURCE_DIR}/gtest/googletest/include
+          ${googletest_SOURCE_DIR}/googletest/include
         )
       endif()
       cuda_add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
diff --git a/test/gtest b/test/gtest
deleted file mode 160000
index 2fe3bd994b..0000000000
--- a/test/gtest
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 2fe3bd994b3189899d93f1d5a881e725e046fdc2

From 8aa39399b721f45732551bfd0a60b7fb4969791b Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 2 Nov 2020 22:50:56 +0530
Subject: [PATCH 2115/2677] Add offline build cmake option AF_BUILD_OFFLINE

When the above cmake option is turned ON, via the below command
```cmake
ccmake .. -DAF_BUILD_OFFLINE:BOOL=ON
```
FetchContent will look for dependencies under build tree's extern
folder and will not attempt to download any of them.

By default this option is turned OFF
---
 CMakeLists.txt                                | 21 +++----
 CMakeModules/AFconfigure_deps_vars.cmake      | 57 +++++++++++++++++++
 CMakeModules/AFconfigure_forge_dep.cmake      | 12 ++--
 src/backend/common/CMakeLists.txt             |  4 +-
 src/backend/cpu/CMakeLists.txt                | 10 ++--
 src/backend/cuda/CMakeLists.txt               | 11 +++-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  4 +-
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  4 +-
 test/CMakeLists.txt                           | 16 +++---
 9 files changed, 101 insertions(+), 38 deletions(-)
 create mode 100644 CMakeModules/AFconfigure_deps_vars.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f6cd4914d5..21753aca12 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -11,7 +11,7 @@ project(ArrayFire VERSION 3.8.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 
-include(AFfetch_content)
+include(AFconfigure_deps_vars)
 include(config_ccache)
 include(AFBuildConfigurations)
 include(AFInstallDirs)
@@ -157,20 +157,20 @@ endif()
 
 set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
 FetchContent_Declare(
-  af_spdlog
+  ${spdlog_prefix}
   GIT_REPOSITORY https://github.com/gabime/spdlog.git
   GIT_TAG        v1.0.0
 )
-FetchContent_Populate(af_spdlog)
-add_subdirectory(${af_spdlog_SOURCE_DIR} ${af_spdlog_BINARY_DIR} EXCLUDE_FROM_ALL)
+FetchContent_Populate(${spdlog_prefix})
+add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
 FetchContent_Declare(
-  af_glad
+  ${glad_prefix}
   GIT_REPOSITORY https://github.com/arrayfire/glad.git
   GIT_TAG        master
 )
-FetchContent_Populate(af_glad)
-add_subdirectory(${af_glad_SOURCE_DIR})
+FetchContent_Populate(${glad_prefix})
+add_subdirectory(${${glad_prefix}_SOURCE_DIR})
 
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
@@ -391,12 +391,13 @@ endif()
 conditional_directory(BUILD_TESTING test)
 
 FetchContent_Declare(
-  af_assets
+  ${assets_prefix}
   GIT_REPOSITORY https://github.com/arrayfire/assets.git
   GIT_TAG        master
 )
-FetchContent_Populate(af_assets)
-set(ASSETS_DIR ${af_assets_SOURCE_DIR})
+FetchContent_Populate(${assets_prefix})
+
+set(ASSETS_DIR ${${assets_prefix}_SOURCE_DIR})
 conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
 
diff --git a/CMakeModules/AFconfigure_deps_vars.cmake b/CMakeModules/AFconfigure_deps_vars.cmake
new file mode 100644
index 0000000000..aa11b40bcc
--- /dev/null
+++ b/CMakeModules/AFconfigure_deps_vars.cmake
@@ -0,0 +1,57 @@
+# Copyright (c) 2021, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+option(AF_BUILD_OFFLINE "Build ArrayFire assuming there is no network" OFF)
+
+# Override fetch content base dir before including AFfetch_content
+set(FETCHCONTENT_BASE_DIR "${ArrayFire_BINARY_DIR}/extern" CACHE PATH
+    "Base directory where ArrayFire dependencies are downloaded and/or built" FORCE)
+
+include(AFfetch_content)
+
+macro(set_and_mark_depname var name)
+  string(TOLOWER ${name} ${var})
+  string(TOUPPER ${name} ${var}_ucname)
+  mark_as_advanced(
+      FETCHCONTENT_SOURCE_DIR_${${var}_ucname}
+      FETCHCONTENT_UPDATES_DISCONNECTED_${${var}_ucname}
+  )
+endmacro()
+
+mark_as_advanced(
+  FETCHCONTENT_BASE_DIR
+  FETCHCONTENT_QUIET
+  FETCHCONTENT_FULLY_DISCONNECTED
+  FETCHCONTENT_UPDATES_DISCONNECTED
+)
+
+set_and_mark_depname(assets_prefix "af_assets")
+set_and_mark_depname(testdata_prefix "af_test_data")
+set_and_mark_depname(gtest_prefix "googletest")
+set_and_mark_depname(glad_prefix "af_glad")
+set_and_mark_depname(forge_prefix "af_forge")
+set_and_mark_depname(spdlog_prefix "spdlog")
+set_and_mark_depname(threads_prefix "af_threads")
+set_and_mark_depname(cub_prefix "nv_cub")
+
+if(AF_BUILD_OFFLINE)
+  macro(set_fetchcontent_src_dir prefix_var dep_name)
+    set(FETCHCONTENT_SOURCE_DIR_${${prefix_var}_ucname}
+        "${FETCHCONTENT_BASE_DIR}/${${prefix_var}}-src" CACHE PATH
+        "Source directory for ${dep_name} dependency")
+    mark_as_advanced(FETCHCONTENT_SOURCE_DIR_${${prefix_var}_ucname})
+  endmacro()
+
+  set_fetchcontent_src_dir(assets_prefix "Assets")
+  set_fetchcontent_src_dir(testdata_prefix "Test Data")
+  set_fetchcontent_src_dir(gtest_prefix "googletest")
+  set_fetchcontent_src_dir(glad_prefix "glad")
+  set_fetchcontent_src_dir(forge_prefix "forge")
+  set_fetchcontent_src_dir(spdlog_prefix "spdlog")
+  set_fetchcontent_src_dir(threads_prefix "threads")
+  set_fetchcontent_src_dir(cub_prefix "NVIDIA CUB")
+endif()
diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index e8f680bf0f..3dee59bf1d 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -12,21 +12,21 @@ set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
 set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
 
 FetchContent_Declare(
-  af_forge
+  ${forge_prefix}
   GIT_REPOSITORY https://github.com/arrayfire/forge.git
   GIT_TAG        "v${FG_VERSION}"
 )
-FetchContent_Populate(af_forge)
+FetchContent_Populate(${forge_prefix})
 if(AF_BUILD_FORGE)
   set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
   set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
-  set(CMAKE_INSTALL_PREFIX ${af_forge_BINARY_DIR}/extern/forge/package)
+  set(CMAKE_INSTALL_PREFIX ${${forge_prefix}_BINARY_DIR}/extern/forge/package)
   set(CMAKE_BUILD_TYPE Release)
   set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
   set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
   set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
 
-  add_subdirectory(${af_forge_SOURCE_DIR} ${af_forge_BINARY_DIR} EXCLUDE_FROM_ALL)
+  add_subdirectory(${${forge_prefix}_SOURCE_DIR} ${${forge_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
   mark_as_advanced(
       FG_BUILD_EXAMPLES
@@ -52,7 +52,7 @@ if(AF_BUILD_FORGE)
   set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
 else(AF_BUILD_FORGE)
   configure_file(
-    ${af_forge_SOURCE_DIR}/CMakeModules/version.h.in
-    ${af_forge_BINARY_DIR}/include/fg/version.h
+    ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
+    ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
     )
 endif(AF_BUILD_FORGE)
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index caa3ea056c..15718b37b9 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -95,8 +95,8 @@ target_include_directories(afcommon_interface
     ${ArrayFire_BINARY_DIR}
   SYSTEM INTERFACE
     $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
-    ${af_forge_SOURCE_DIR}/include
-    ${af_forge_BINARY_DIR}/include
+    ${${forge_prefix}_SOURCE_DIR}/include
+    ${${forge_prefix}_BINARY_DIR}/include
   )
 
 if(APPLE AND NOT USE_MKL)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index cd02510dc4..86c4350523 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -272,16 +272,16 @@ if (AF_WITH_CPUID)
 endif(AF_WITH_CPUID)
 
 FetchContent_Declare(
-  af_threads
+  ${threads_prefix}
   GIT_REPOSITORY https://github.com/arrayfire/threads.git
   GIT_TAG        b666773940269179f19ef11c8f1eb77005e85d9a
 )
-FetchContent_Populate(af_threads)
+FetchContent_Populate(${threads_prefix})
 
 target_sources(afcpu
   PRIVATE
-    ${af_threads_SOURCE_DIR}/include/threads/async_queue.hpp
-    ${af_threads_SOURCE_DIR}/include/threads/event.hpp
+    ${${threads_prefix}_SOURCE_DIR}/include/threads/async_queue.hpp
+    ${${threads_prefix}_SOURCE_DIR}/include/threads/event.hpp
   )
 
 arrayfire_set_default_cxx_flags(afcpu)
@@ -295,7 +295,7 @@ target_include_directories(afcpu
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${CMAKE_CURRENT_SOURCE_DIR}
-    ${af_threads_SOURCE_DIR}/include
+    ${${threads_prefix}_SOURCE_DIR}/include
     ${CBLAS_INCLUDE_DIR}
   )
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 05ecaa87e6..a6632f43e7 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -116,12 +116,12 @@ cuda_include_directories(
   )
 if(CUDA_VERSION_MAJOR VERSION_LESS 11)
   FetchContent_Declare(
-    nv_cub
+    ${cub_prefix}
     GIT_REPOSITORY https://github.com/NVIDIA/cub.git
     GIT_TAG        1.10.0
   )
-  FetchContent_Populate(nv_cub)
-  cuda_include_directories(${nv_cub_SOURCE_DIR})
+  FetchContent_Populate(${cub_prefix})
+  cuda_include_directories(${${cub_prefix}_SOURCE_DIR})
 endif()
 
 file(GLOB jit_src "kernel/jit.cuh")
@@ -888,3 +888,8 @@ source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/ker
 source_group("generated files"  FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h
                                 REGULAR_EXPRESSION ${CMAKE_CURRENT_BINARY_DIR}/${kernel_headers_dir}/*)
 source_group("" FILES CMakeLists.txt)
+
+mark_as_advanced(
+  FETCHCONTENT_SOURCE_DIR_NV_CUB
+  FETCHCONTENT_UPDATES_DISCONNECTED_NV_CUB
+)
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 9ed829d8eb..f7911698b6 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -40,8 +40,8 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
-        ${af_forge_SOURCE_DIR}/include
-        ${af_forge_BINARY_DIR}/include
+        ${${forge_prefix}_SOURCE_DIR}/include
+        ${${forge_prefix}_BINARY_DIR}/include
       )
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 974b9a3a7c..5490a96001 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -38,8 +38,8 @@ foreach(SBK_TYPE ${SBK_TYPES})
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
-        ${af_forge_SOURCE_DIR}/include
-        ${af_forge_BINARY_DIR}/include
+        ${${forge_prefix}_SOURCE_DIR}/include
+        ${${forge_prefix}_BINARY_DIR}/include
       )
 
     set_target_properties(opencl_sort_by_key_${SBK_TYPE}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 2bbb312d99..454546d7d0 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -16,12 +16,12 @@ if(AF_TEST_WITH_MTX_FILES)
 endif()
 
 FetchContent_Declare(
-  googletest
+  ${gtest_prefix}
   GIT_REPOSITORY https://github.com/google/googletest.git
   GIT_TAG        release-1.8.1
 )
 if(NOT TARGET gtest)
-  FetchContent_Populate(googletest)
+  FetchContent_Populate(${gtest_prefix})
 
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
   # newer cmakes. This sets the default global setting for that policy.
@@ -32,7 +32,7 @@ if(NOT TARGET gtest)
     set(BUILD_SHARED_LIBS OFF)
   endif()
 
-  add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
+  add_subdirectory(${${gtest_prefix}_SOURCE_DIR} ${${gtest_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
   set_target_properties(gtest gtest_main
     PROPERTIES
       FOLDER "ExternalProjectTargets/gtest")
@@ -66,12 +66,12 @@ if(${AF_USE_RELATIVE_TEST_DIR})
   set(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
 else(${AF_USE_RELATIVE_TEST_DIR})
   FetchContent_Declare(
-    af_test_data
+    ${testdata_prefix}
     GIT_REPOSITORY https://github.com/arrayfire/arrayfire-data.git
     GIT_TAG        master
   )
-  FetchContent_Populate(af_test_data)
-  set(TESTDATA_SOURCE_DIR "${af_test_data_SOURCE_DIR}")
+  FetchContent_Populate(${testdata_prefix})
+  set(TESTDATA_SOURCE_DIR "${${testdata_prefix}_SOURCE_DIR}")
 endif(${AF_USE_RELATIVE_TEST_DIR})
 
 if(AF_BUILD_CPU)
@@ -102,7 +102,7 @@ target_include_directories(arrayfire_test
     ${ArrayFire_BINARY_DIR}/include
     ${ArrayFire_SOURCE_DIR}/extern/half/include
     mmio
-    ${googletest_SOURCE_DIR}/googletest/include)
+    ${${gtest_prefix}_SOURCE_DIR}/googletest/include)
 
 if(WIN32)
   target_compile_options(arrayfire_test
@@ -332,7 +332,7 @@ if(CUDA_FOUND)
           ${ArrayFire_BINARY_DIR}/include
           ${ArrayFire_SOURCE_DIR}/extern/half/include
           ${CMAKE_CURRENT_SOURCE_DIR}
-          ${googletest_SOURCE_DIR}/googletest/include
+          ${${gtest_prefix}_SOURCE_DIR}/googletest/include
         )
       endif()
       cuda_add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)

From ea01252393477bae0a51681443588ed62f92b555 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 3 Nov 2020 21:47:22 +0530
Subject: [PATCH 2116/2677] Change OpenCL dependencies to use FetchContent
 workflow

- cl2.hpp header download
- clBLAS build
- clFFT build
- CLBlast build

Use clBLAS and clFFT via add_subdir instead of external project
---
 CMakeLists.txt                                |   1 -
 CMakeModules/AFconfigure_deps_vars.cmake      |   8 ++
 CMakeModules/build_CLBlast.cmake              |  23 ++--
 CMakeModules/build_cl2hpp.cmake               |  30 ++---
 CMakeModules/build_clBLAS.cmake               | 112 +++++++++---------
 CMakeModules/build_clFFT.cmake                |  89 ++++----------
 src/backend/opencl/CMakeLists.txt             |   7 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |   1 +
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |   1 +
 test/CMakeLists.txt                           |   4 +-
 10 files changed, 124 insertions(+), 152 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 21753aca12..5b25607dd1 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,7 +18,6 @@ include(AFInstallDirs)
 include(CMakeDependentOption)
 include(InternalUtils)
 include(Version)
-include(build_cl2hpp)
 include(platform)
 include(GetPrerequisites)
 include(CheckCXXCompilerFlag)
diff --git a/CMakeModules/AFconfigure_deps_vars.cmake b/CMakeModules/AFconfigure_deps_vars.cmake
index aa11b40bcc..45b78cde90 100644
--- a/CMakeModules/AFconfigure_deps_vars.cmake
+++ b/CMakeModules/AFconfigure_deps_vars.cmake
@@ -37,6 +37,10 @@ set_and_mark_depname(forge_prefix "af_forge")
 set_and_mark_depname(spdlog_prefix "spdlog")
 set_and_mark_depname(threads_prefix "af_threads")
 set_and_mark_depname(cub_prefix "nv_cub")
+set_and_mark_depname(cl2hpp_prefix "ocl_cl2hpp")
+set_and_mark_depname(clblast_prefix "ocl_clblast")
+set_and_mark_depname(clfft_prefix "ocl_clfft")
+set_and_mark_depname(clblas_prefix "ocl_clblas")
 
 if(AF_BUILD_OFFLINE)
   macro(set_fetchcontent_src_dir prefix_var dep_name)
@@ -54,4 +58,8 @@ if(AF_BUILD_OFFLINE)
   set_fetchcontent_src_dir(spdlog_prefix "spdlog")
   set_fetchcontent_src_dir(threads_prefix "threads")
   set_fetchcontent_src_dir(cub_prefix "NVIDIA CUB")
+  set_fetchcontent_src_dir(cl2hpp_prefix "OpenCL cl2 hpp header")
+  set_fetchcontent_src_dir(clblast_prefix "CLBlast library")
+  set_fetchcontent_src_dir(clfft_prefix "clFFT library")
+  set_fetchcontent_src_dir(clblas_prefix "clBLAS library")
 endif()
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 3e07cec311..b4a1d4bb6c 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -5,11 +5,19 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
+FetchContent_Declare(
+  ${clblast_prefix}
+  GIT_REPOSITORY    https://github.com/cnugteren/CLBlast.git
+  GIT_TAG           41f344d1a6f2d149bba02a6615292e99b50f4856
+)
+FetchContent_Populate(${clblast_prefix})
+
 include(ExternalProject)
 find_program(GIT git)
 
 set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
-set(CLBlast_location ${prefix}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
+set(CLBlast_libname ${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
+set(CLBlast_location ${${clblast_prefix}_BINARY_DIR}/pkg/lib/${CLBlast_libname})
 
 set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
 if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
@@ -31,12 +39,13 @@ endif()
 
 ExternalProject_Add(
     CLBlast-ext
-    GIT_REPOSITORY https://github.com/cnugteren/CLBlast.git
-    GIT_TAG 41f344d1a6f2d149bba02a6615292e99b50f4856
-    PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}"
+    DOWNLOAD_COMMAND ""
     UPDATE_COMMAND ""
     PATCH_COMMAND ""
+    SOURCE_DIR "${${clblast_prefix}_SOURCE_DIR}"
+    BINARY_DIR "${${clblast_prefix}_BINARY_DIR}"
+    PREFIX "${prefix}"
+    INSTALL_DIR "${${clblast_prefix}_BINARY_DIR}/pkg"
     BUILD_BYPRODUCTS ${CLBlast_location}
     CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
       -Wno-dev <SOURCE_DIR>
@@ -56,8 +65,7 @@ ExternalProject_Add(
       -DNETLIB:BOOL=OFF
     )
 
-ExternalProject_Get_Property(CLBlast-ext install_dir)
-set(CLBLAST_INCLUDE_DIRS ${install_dir}/include)
+set(CLBLAST_INCLUDE_DIRS "${${clblast_prefix}_BINARY_DIR}/pkg/include")
 set(CLBLAST_LIBRARIES CLBlast)
 set(CLBLAST_FOUND ON)
 
@@ -67,4 +75,5 @@ add_library(CLBlast UNKNOWN IMPORTED)
 set_target_properties(CLBlast PROPERTIES
   IMPORTED_LOCATION "${CLBlast_location}"
   INTERFACE_INCLUDE_DIRECTORIES "${CLBLAST_INCLUDE_DIRS}")
+
 add_dependencies(CLBlast CLBlast-ext)
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 70a94c56b3..9e67afc6d1 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -1,4 +1,4 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2021, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -13,23 +13,17 @@
 
 find_package(OpenCL)
 
-set(cl2hpp_file_url "https://github.com/KhronosGroup/OpenCL-CLHPP/releases/download/v2.0.10/cl2.hpp")
-set(cl2hpp_file "${ArrayFire_BINARY_DIR}/include/CL/cl2.hpp")
+FetchContent_Declare(
+  ${cl2hpp_prefix}
+  GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
+  GIT_TAG v2.0.12
+)
+FetchContent_Populate(${cl2hpp_prefix})
 
-if(OpenCL_FOUND)
-  if (NOT EXISTS ${cl2hpp_file})
-      message(STATUS "Downloading ${cl2hpp_file_url}")
-      file(DOWNLOAD ${cl2hpp_file_url} ${cl2hpp_file}
-        EXPECTED_HASH MD5=c38d1b78cd98cc809fa2a49dbd1734a5)
-  endif()
-  get_filename_component(download_dir ${cl2hpp_file} DIRECTORY)
+if (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
+  add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
+  add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
 
-  if (NOT TARGET OpenCL::cl2hpp OR
-      NOT TARGET cl2hpp)
-    add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
-    add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
-
-    set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
-      INTERFACE_INCLUDE_DIRECTORIES ${download_dir}/..)
-  endif()
+  set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES ${${cl2hpp_prefix}_SOURCE_DIR}/include)
 endif()
diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
index c30f015f1c..5bf7c29350 100644
--- a/CMakeModules/build_clBLAS.cmake
+++ b/CMakeModules/build_clBLAS.cmake
@@ -1,63 +1,61 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2021, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-include(ExternalProject)
-
-set(prefix ${PROJECT_BINARY_DIR}/third_party/clBLAS)
-set(clBLAS_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clBLAS${CMAKE_STATIC_LIBRARY_SUFFIX})
-
-find_package(OpenCL)
-
-if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
-  set(extproj_gen_opts "-G${CMAKE_GENERATOR}" "-A${CMAKE_GENERATOR_PLATFORM}")
-else()
-  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
-endif()
-
-if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
-  set(extproj_build_type "Release")
-else()
-  set(extproj_build_type ${CMAKE_BUILD_TYPE})
-endif()
-
-ExternalProject_Add(
-    clBLAS-ext
-    GIT_REPOSITORY https://github.com/arrayfire/clBLAS.git
-    GIT_TAG arrayfire-release
-    BUILD_BYPRODUCTS ${clBLAS_location}
-    PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}"
-    UPDATE_COMMAND ""
-    DOWNLOAD_NO_PROGRESS 1
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
-      -Wno-dev <SOURCE_DIR>/src
-      -DCMAKE_CXX_FLAGS:STRING="-fPIC"
-      -DCMAKE_C_FLAGS:STRING="-fPIC"
-      -DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}
-      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-      -DBUILD_SHARED_LIBS:BOOL=OFF
-      -DBUILD_CLIENT:BOOL=OFF
-      -DBUILD_TEST:BOOL=OFF
-      -DBUILD_KTEST:BOOL=OFF
-      -DSUFFIX_LIB:STRING=
-
-      # clBLAS uses a custom FindOpenCL that doesn't work well on Ubuntu
-      -DOPENCL_LIBRARIES:FILEPATH=${OpenCL_LIBRARIES}
-    )
-
-ExternalProject_Get_Property(clBLAS-ext install_dir)
-
-set(CLBLAS_INCLUDE_DIRS ${install_dir}/include)
-set(CLBLAS_LIBRARIES clBLAS::clBLAS)
-set(CLBLAS_FOUND ON)
-make_directory("${CLBLAS_INCLUDE_DIRS}")
-
-add_library(clBLAS::clBLAS UNKNOWN IMPORTED)
-set_target_properties(clBLAS::clBLAS PROPERTIES
-  IMPORTED_LOCATION "${clBLAS_location}"
-  INTERFACE_INCLUDE_DIRECTORIES "${CLBLAS_INCLUDE_DIRS}")
-add_dependencies(clBLAS::clBLAS clBLAS-ext)
+FetchContent_Declare(
+  ${clblas_prefix}
+  GIT_REPOSITORY    https://github.com/arrayfire/clBLAS.git
+  GIT_TAG           cmake_fixes
+)
+FetchContent_Populate(${clblas_prefix})
+
+set(current_build_type ${BUILD_SHARED_LIBS})
+set(BUILD_SHARED_LIBS OFF)
+add_subdirectory(${${clblas_prefix}_SOURCE_DIR}/src ${${clblas_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+set(BUILD_SHARED_LIBS ${current_build_type})
+
+mark_as_advanced(
+  INSTALL_SRC
+  AUTOGEMM_ARCHITECTURE
+  Boost_PROGRAM_OPTIONS_LIBRARY_RELEASE
+  CLBLAS_BUILD64
+  CLBLAS_BUILD_CALLBACK_CLIENT
+  CLBLAS_BUILD_CLIENT
+  CLBLAS_BUILD_EXAMPLES
+  CLBLAS_BUILD_LOADLIBRARIES
+  CLBLAS_BUILD_RUNTIME
+  CLBLAS_BUILD_TEST
+  CLBLAS_CODE_COVERAGE
+  CLBLAS_SUFFIX_BIN
+  CLBLAS_SUFFIX_LIB
+  BLAS_DEBUG_TOOLS
+  BLAS_DUMP_CLBLAS_KERNELS
+  BLAS_KEEP_KERNEL_SOURCES
+  BLAS_PRINT_BUILD_ERRORS
+  BLAS_TRACE_MALLOC
+  CLBLAS_BUILD_KTEST
+  CLBLAS_BUILD_PERFORMANCE
+  CLBLAS_BUILD_SAMPLE
+  CORR_TEST_WITH_ACML
+  OPENCL_COMPILER_DIR
+  OPENCL_VERSION
+  PRECOMPILE_GEMM_PRECISION_CGEMM
+  PRECOMPILE_GEMM_PRECISION_DGEMM
+  PRECOMPILE_GEMM_PRECISION_SGEMM
+  PRECOMPILE_GEMM_PRECISION_ZGEMM
+  PRECOMPILE_GEMM_TRANS_CC
+  PRECOMPILE_GEMM_TRANS_CN
+  PRECOMPILE_GEMM_TRANS_CT
+  PRECOMPILE_GEMM_TRANS_NC
+  PRECOMPILE_GEMM_TRANS_NN
+  PRECOMPILE_GEMM_TRANS_NT
+  PRECOMPILE_GEMM_TRANS_TC
+  PRECOMPILE_GEMM_TRANS_TN
+  PRECOMPILE_GEMM_TRANS_TT
+  PRECOMPILE_TRSM_DTRSM
+  PRECOMPILE_TRSM_STRSM
+  TARGET_PLATFORM
+)
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 18609e1e56..fdc72b3173 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -1,69 +1,32 @@
-# Copyright (c) 2017, ArrayFire
+# Copyright (c) 2021, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-include(ExternalProject)
-find_program(GIT git)
-
-set(prefix "${PROJECT_BINARY_DIR}/third_party/clFFT")
-set(clFFT_location ${prefix}/lib/import/${CMAKE_STATIC_LIBRARY_PREFIX}clFFT${CMAKE_STATIC_LIBRARY_SUFFIX})
-
-set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
-if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
-  list(APPEND extproj_gen_opts "-A${CMAKE_GENERATOR_PLATFORM}")
-  if(CMAKE_GENERATOR_TOOLSET)
-    list(APPEND extproj_gen_opts "-T${CMAKE_GENERATOR_TOOLSET}")
-  endif()
-endif()
-
-set(extproj_build_type_option "")
-if(NOT isMultiConfig)
-  if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
-    set(extproj_build_type "Release")
-  else()
-    set(extproj_build_type ${CMAKE_BUILD_TYPE})
-  endif()
-  set(extproj_build_type_option "-DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}")
-endif()
-
-ExternalProject_Add(
-    clFFT-ext
-    GIT_REPOSITORY https://github.com/arrayfire/clFFT.git
-    GIT_TAG arrayfire-release
-    PREFIX "${prefix}"
-    INSTALL_DIR "${prefix}"
-    UPDATE_COMMAND ""
-    BUILD_BYPRODUCTS ${clFFT_location}
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
-      -Wno-dev <SOURCE_DIR>/src
-      -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-      "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
-      -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-      "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-	  ${extproj_build_type_option}
-      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-      -DBUILD_SHARED_LIBS:BOOL=OFF
-      -DBUILD_EXAMPLES:BOOL=OFF
-      -DBUILD_CLIENT:BOOL=OFF
-      -DBUILD_TEST:BOOL=OFF
-      -DSUFFIX_LIB:STRING=
-    ${byproducts}
-    )
-
-ExternalProject_Get_Property(clFFT-ext install_dir)
-
-set(CLFFT_INCLUDE_DIRS ${install_dir}/include)
-make_directory(${install_dir}/include)
-
-add_library(clFFT::clFFT IMPORTED STATIC)
-set_target_properties(clFFT::clFFT PROPERTIES
-  IMPORTED_LOCATION ${clFFT_location}
-  INTERFACE_INCLUDE_DIRECTORIES ${install_dir}/include
-  )
-add_dependencies(clFFT::clFFT clFFT-ext)
-
-set(CLFFT_LIBRARIES clFFT)
-set(CLFFT_FOUND ON)
+FetchContent_Declare(
+  ${clfft_prefix}
+  GIT_REPOSITORY    https://github.com/arrayfire/clFFT.git
+  GIT_TAG           cmake_fixes
+)
+FetchContent_Populate(${clfft_prefix})
+
+set(current_build_type ${BUILD_SHARED_LIBS})
+set(BUILD_SHARED_LIBS OFF)
+add_subdirectory(${${clfft_prefix}_SOURCE_DIR}/src ${${clfft_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+set(BUILD_SHARED_LIBS ${current_build_type})
+
+mark_as_advanced(
+  Boost_PROGRAM_OPTIONS_LIBRARY_RELEASE
+  CLFFT_BUILD64
+  CLFFT_BUILD_CALLBACK_CLIENT
+  CLFFT_BUILD_CLIENT
+  CLFFT_BUILD_EXAMPLES
+  CLFFT_BUILD_LOADLIBRARIES
+  CLFFT_BUILD_RUNTIME
+  CLFFT_BUILD_TEST
+  CLFFT_CODE_COVERAGE
+  CLFFT_SUFFIX_BIN
+  CLFFT_SUFFIX_LIB
+)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 06f6d6347a..d0ab7351be 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -6,6 +6,7 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 include(InternalUtils)
+include(build_cl2hpp)
 
 generate_product_version(af_opencl_ver_res_file
   FILE_NAME "afopencl"
@@ -425,7 +426,7 @@ target_link_libraries(afopencl
     OpenCL::OpenCL
     OpenCL::cl2hpp
     afcommon_interface
-    clFFT::clFFT
+    clFFT
     opencl_scan_by_key
     opencl_sort_by_key
     Threads::Threads
@@ -434,9 +435,7 @@ target_link_libraries(afopencl
 if(AF_OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
   include(build_clBLAS)
   target_compile_definitions(afopencl PRIVATE USE_CLBLAS)
-  target_link_libraries(afopencl
-    PRIVATE
-      clBLAS::clBLAS)
+  target_link_libraries(afopencl PRIVATE clBLAS)
 elseif(AF_OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
   include(build_CLBlast)
   target_compile_definitions(afopencl PRIVATE USE_CLBLAST)
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index f7911698b6..d92b214e44 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -42,6 +42,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
         ${${forge_prefix}_SOURCE_DIR}/include
         ${${forge_prefix}_BINARY_DIR}/include
+        ${ArrayFire_BINARY_DIR}/include
       )
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 5490a96001..280a5d22c6 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -40,6 +40,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
         $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
         ${${forge_prefix}_SOURCE_DIR}/include
         ${${forge_prefix}_BINARY_DIR}/include
+        ${ArrayFire_BINARY_DIR}/include
       )
 
     set_target_properties(opencl_sort_by_key_${SBK_TYPE}
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 454546d7d0..2a6e34dc3b 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -306,7 +306,7 @@ make_test(SRC nodevice.cpp CXX11)
 
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
-            LIBRARIES OpenCL::OpenCL
+            LIBRARIES OpenCL::OpenCL OpenCL::cl2hpp
             BACKENDS "opencl"
             CXX11)
   make_test(SRC interop_opencl_custom_kernel_snippet.cpp
@@ -315,7 +315,7 @@ if(OpenCL_FOUND)
             NO_ARRAYFIRE_TEST
             CXX11)
   make_test(SRC interop_opencl_external_context_snippet.cpp
-            LIBRARIES OpenCL::OpenCL
+            LIBRARIES OpenCL::OpenCL OpenCL::cl2hpp
             BACKENDS "opencl"
             NO_ARRAYFIRE_TEST
             CXX11)

From f1e64bf0077c98cf4e2223f507e0a5737f287162 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 6 Nov 2020 23:10:14 +0530
Subject: [PATCH 2117/2677] Remove clBLAS support as it is no longer maintained
 by AMD

---
 CMakeModules/build_clBLAS.cmake              | 61 --------------
 src/backend/opencl/CMakeLists.txt            | 32 ++-----
 src/backend/opencl/magma/magma_blas.h        |  6 --
 src/backend/opencl/magma/magma_blas_clblas.h | 89 --------------------
 4 files changed, 6 insertions(+), 182 deletions(-)
 delete mode 100644 CMakeModules/build_clBLAS.cmake
 delete mode 100644 src/backend/opencl/magma/magma_blas_clblas.h

diff --git a/CMakeModules/build_clBLAS.cmake b/CMakeModules/build_clBLAS.cmake
deleted file mode 100644
index 5bf7c29350..0000000000
--- a/CMakeModules/build_clBLAS.cmake
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright (c) 2021, ArrayFire
-# All rights reserved.
-#
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
-
-FetchContent_Declare(
-  ${clblas_prefix}
-  GIT_REPOSITORY    https://github.com/arrayfire/clBLAS.git
-  GIT_TAG           cmake_fixes
-)
-FetchContent_Populate(${clblas_prefix})
-
-set(current_build_type ${BUILD_SHARED_LIBS})
-set(BUILD_SHARED_LIBS OFF)
-add_subdirectory(${${clblas_prefix}_SOURCE_DIR}/src ${${clblas_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
-set(BUILD_SHARED_LIBS ${current_build_type})
-
-mark_as_advanced(
-  INSTALL_SRC
-  AUTOGEMM_ARCHITECTURE
-  Boost_PROGRAM_OPTIONS_LIBRARY_RELEASE
-  CLBLAS_BUILD64
-  CLBLAS_BUILD_CALLBACK_CLIENT
-  CLBLAS_BUILD_CLIENT
-  CLBLAS_BUILD_EXAMPLES
-  CLBLAS_BUILD_LOADLIBRARIES
-  CLBLAS_BUILD_RUNTIME
-  CLBLAS_BUILD_TEST
-  CLBLAS_CODE_COVERAGE
-  CLBLAS_SUFFIX_BIN
-  CLBLAS_SUFFIX_LIB
-  BLAS_DEBUG_TOOLS
-  BLAS_DUMP_CLBLAS_KERNELS
-  BLAS_KEEP_KERNEL_SOURCES
-  BLAS_PRINT_BUILD_ERRORS
-  BLAS_TRACE_MALLOC
-  CLBLAS_BUILD_KTEST
-  CLBLAS_BUILD_PERFORMANCE
-  CLBLAS_BUILD_SAMPLE
-  CORR_TEST_WITH_ACML
-  OPENCL_COMPILER_DIR
-  OPENCL_VERSION
-  PRECOMPILE_GEMM_PRECISION_CGEMM
-  PRECOMPILE_GEMM_PRECISION_DGEMM
-  PRECOMPILE_GEMM_PRECISION_SGEMM
-  PRECOMPILE_GEMM_PRECISION_ZGEMM
-  PRECOMPILE_GEMM_TRANS_CC
-  PRECOMPILE_GEMM_TRANS_CN
-  PRECOMPILE_GEMM_TRANS_CT
-  PRECOMPILE_GEMM_TRANS_NC
-  PRECOMPILE_GEMM_TRANS_NN
-  PRECOMPILE_GEMM_TRANS_NT
-  PRECOMPILE_GEMM_TRANS_TC
-  PRECOMPILE_GEMM_TRANS_TN
-  PRECOMPILE_GEMM_TRANS_TT
-  PRECOMPILE_TRSM_DTRSM
-  PRECOMPILE_TRSM_STRSM
-  TARGET_PLATFORM
-)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index d0ab7351be..2c20ad2d0d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -7,25 +7,18 @@
 
 include(InternalUtils)
 include(build_cl2hpp)
+include(build_CLBlast)
+include(build_clFFT)
+include(FileToString)
 
 generate_product_version(af_opencl_ver_res_file
   FILE_NAME "afopencl"
   FILE_DESCRIPTION "OpenCL Backend Dynamic-link library"
 )
 
-set(AF_OPENCL_BLAS_LIBRARY CLBlast CACHE STRING "Select OpenCL BLAS back-end")
-set_property(CACHE AF_OPENCL_BLAS_LIBRARY PROPERTY STRINGS "clBLAS" "CLBlast")
-
-af_deprecate(OPENCL_BLAS_LIBRARY AF_OPENCL_BLAS_LIBRARY)
-
-include(build_clFFT)
-
 file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
 
-set( kernel_headers_dir
-    "kernel_headers")
-
-include(FileToString)
+set( kernel_headers_dir "kernel_headers")
 
 file_to_string(
     SOURCES ${kernel_src}
@@ -407,7 +400,7 @@ target_include_directories(afopencl
 
 arrayfire_set_default_cxx_flags(afopencl)
 
-add_dependencies(afopencl ${cl_kernel_targets})
+add_dependencies(afopencl ${cl_kernel_targets} CLBlast-ext)
 add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 
@@ -427,24 +420,12 @@ target_link_libraries(afopencl
     OpenCL::cl2hpp
     afcommon_interface
     clFFT
+    CLBlast
     opencl_scan_by_key
     opencl_sort_by_key
     Threads::Threads
     )
 
-if(AF_OPENCL_BLAS_LIBRARY STREQUAL "clBLAS")
-  include(build_clBLAS)
-  target_compile_definitions(afopencl PRIVATE USE_CLBLAS)
-  target_link_libraries(afopencl PRIVATE clBLAS)
-elseif(AF_OPENCL_BLAS_LIBRARY STREQUAL "CLBlast")
-  include(build_CLBlast)
-  target_compile_definitions(afopencl PRIVATE USE_CLBLAST)
-  target_link_libraries(afopencl
-    PRIVATE
-      CLBlast)
-    add_dependencies(afopencl CLBlast-ext)
-endif()
-
 if(APPLE)
   target_link_libraries(afopencl PRIVATE OpenGL::GL)
 endif()
@@ -464,7 +445,6 @@ if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
       magma/laswp.cpp
       magma/magma.h
       magma/magma_blas.h
-      magma/magma_blas_clblas.h
       magma/magma_blas_clblast.h
       magma/magma_common.h
       magma/magma_cpu_blas.h
diff --git a/src/backend/opencl/magma/magma_blas.h b/src/backend/opencl/magma/magma_blas.h
index 7a1f341680..d34d04c29a 100644
--- a/src/backend/opencl/magma/magma_blas.h
+++ b/src/backend/opencl/magma/magma_blas.h
@@ -33,12 +33,6 @@ struct gpu_blas_trsv_func;
 template<typename T>
 struct gpu_blas_herk_func;
 
-#if defined(USE_CLBLAST)
 #include "magma_blas_clblast.h"
-#endif
-
-#if defined(USE_CLBLAS)
-#include "magma_blas_clblas.h"
-#endif
 
 #endif  // __MAGMA_BLAS_H
diff --git a/src/backend/opencl/magma/magma_blas_clblas.h b/src/backend/opencl/magma/magma_blas_clblas.h
deleted file mode 100644
index b2e1680bc2..0000000000
--- a/src/backend/opencl/magma/magma_blas_clblas.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <common/defines.hpp>
-
-#include <clBLAS.h>
-#include <err_clblas.hpp>
-#include <mutex>  // for std::once_flag
-
-// Convert MAGMA constants to clBLAS constants
-clblasOrder clblas_order_const(magma_order_t order);
-clblasTranspose clblas_trans_const(magma_trans_t trans);
-clblasUplo clblas_uplo_const(magma_uplo_t uplo);
-clblasDiag clblas_diag_const(magma_diag_t diag);
-clblasSide clblas_side_const(magma_side_t side);
-
-// Error checking
-#define OPENCL_BLAS_CHECK CLBLAS_CHECK
-
-// Transposing
-#define OPENCL_BLAS_TRANS_T clblasTranspose  // the type
-#define OPENCL_BLAS_NO_TRANS clblasNoTrans
-#define OPENCL_BLAS_TRANS clblasTrans
-#define OPENCL_BLAS_CONJ_TRANS clblasConjTrans
-
-// Triangles
-#define OPENCL_BLAS_TRIANGLE_T clblasUplo  // the type
-#define OPENCL_BLAS_TRIANGLE_UPPER clblasUpper
-#define OPENCL_BLAS_TRIANGLE_LOWER clblasLower
-
-// Sides
-#define OPENCL_BLAS_SIDE_RIGHT clblasRight
-#define OPENCL_BLAS_SIDE_LEFT clblasLeft
-
-// Unit or non-unit diagonal
-#define OPENCL_BLAS_UNIT_DIAGONAL clblasUnit
-#define OPENCL_BLAS_NON_UNIT_DIAGONAL clblasNonUnit
-
-// Initialization of the OpenCL BLAS library
-// Only meant to be once and from constructor
-// of DeviceManager singleton
-// DONT'T CALL FROM ANY OTHER LOCATION
-inline void gpu_blas_init() { clblasSetup(); }
-
-// tear down of the OpenCL BLAS library
-// Only meant to be called from destructor
-// of DeviceManager singleton
-// DONT'T CALL FROM ANY OTHER LOCATION
-inline void gpu_blas_deinit() {
-#ifndef OS_WIN
-    // FIXME:
-    // clblasTeardown() causes a "Pure Virtual Function Called" crash on
-    // Windows for Intel devices. This causes tests to fail.
-    clblasTeardown();
-#endif
-}
-
-#define clblasSherk(...) clblasSsyrk(__VA_ARGS__)
-#define clblasDherk(...) clblasDsyrk(__VA_ARGS__)
-
-#define BLAS_FUNC(NAME, TYPE, PREFIX)                                \
-    template<>                                                       \
-    struct gpu_blas_##NAME##_func<TYPE> {                            \
-        template<typename... Args>                                   \
-        clblasStatus operator()(Args... args) {                      \
-            return clblas##PREFIX##NAME(clblasColumnMajor, args...); \
-        }                                                            \
-    };
-
-#define BLAS_FUNC_DECL(NAME)   \
-    BLAS_FUNC(NAME, float, S)  \
-    BLAS_FUNC(NAME, double, D) \
-    BLAS_FUNC(NAME, cfloat, C) \
-    BLAS_FUNC(NAME, cdouble, Z)
-
-BLAS_FUNC_DECL(gemm)
-BLAS_FUNC_DECL(gemv)
-BLAS_FUNC_DECL(trmm)
-BLAS_FUNC_DECL(trsm)
-BLAS_FUNC_DECL(trsv)
-BLAS_FUNC_DECL(herk)

From 3cde757face979cd9f51a4c01bd26107e69e4605 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Thu, 18 Feb 2021 18:25:27 +0100
Subject: [PATCH 2118/2677] Speedup of kernel caching mechanism by hashing
 sources at compile time (#3043)

* Reduced overhead of kernel caching for OpenCL & CUDA.

The program source files memory footprint is reduced (-30%) by eliminating
comments in the generated kernel headers.  Hash calculation of each source
file  is performed at compile time and incrementally extended at runtime
with the options & tInstance vectors.  Overall performance increased up to
21%, up to the point that the GPU becomes the bottleneck, and the overhead
to launch the same (small) kernel was improved by 63%.

* Fix couple of minor cmake changes

* Move spdlog fetch to use it in bin2cpp link command

Co-authored-by: pradeep <pradeep@arrayfire.com>
---
 CMakeLists.txt                                |  35 ++-
 CMakeModules/bin2cpp.cpp                      | 292 +++++++++++++-----
 src/backend/common/kernel_cache.cpp           |  82 +++--
 src/backend/common/kernel_cache.hpp           |  10 +-
 src/backend/common/kernel_type.hpp            |   2 +
 src/backend/common/util.cpp                   |  34 +-
 src/backend/common/util.hpp                   |  23 +-
 src/backend/cuda/jit.cpp                      |   7 +-
 .../cuda/kernel/anisotropic_diffusion.hpp     |   6 +-
 src/backend/cuda/kernel/approx.hpp            |  10 +-
 src/backend/cuda/kernel/assign.hpp            |   8 +-
 src/backend/cuda/kernel/bilateral.hpp         |   6 +-
 src/backend/cuda/kernel/canny.hpp             |  14 +-
 src/backend/cuda/kernel/convolve.hpp          |  20 +-
 src/backend/cuda/kernel/diagonal.hpp          |  14 +-
 src/backend/cuda/kernel/diff.hpp              |   6 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |  13 +-
 src/backend/cuda/kernel/fftconvolve.hpp       |  15 +-
 src/backend/cuda/kernel/flood_fill.hpp        |  18 +-
 src/backend/cuda/kernel/gradient.hpp          |  10 +-
 src/backend/cuda/kernel/histogram.hpp         |   6 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |   6 +-
 src/backend/cuda/kernel/identity.hpp          |   8 +-
 src/backend/cuda/kernel/iir.hpp               |   6 +-
 src/backend/cuda/kernel/index.hpp             |   8 +-
 src/backend/cuda/kernel/iota.hpp              |   8 +-
 src/backend/cuda/kernel/ireduce.hpp           |  10 +-
 src/backend/cuda/kernel/join.hpp              |   8 +-
 src/backend/cuda/kernel/lookup.hpp            |   8 +-
 src/backend/cuda/kernel/lu_split.hpp          |   9 +-
 src/backend/cuda/kernel/match_template.hpp    |   6 +-
 src/backend/cuda/kernel/meanshift.hpp         |   5 +-
 src/backend/cuda/kernel/medfilt.hpp           |  10 +-
 src/backend/cuda/kernel/memcopy.hpp           |  11 +-
 src/backend/cuda/kernel/moments.hpp           |   8 +-
 src/backend/cuda/kernel/morph.hpp             |   9 +-
 src/backend/cuda/kernel/pad_array_borders.hpp |   6 +-
 src/backend/cuda/kernel/range.hpp             |   8 +-
 src/backend/cuda/kernel/reorder.hpp           |   8 +-
 src/backend/cuda/kernel/resize.hpp            |   9 +-
 src/backend/cuda/kernel/rotate.hpp            |   9 +-
 src/backend/cuda/kernel/scan_dim.hpp          |   6 +-
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |  16 +-
 src/backend/cuda/kernel/scan_first.hpp        |   6 +-
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  17 +-
 src/backend/cuda/kernel/select.hpp            |  11 +-
 src/backend/cuda/kernel/sobel.hpp             |   5 +-
 src/backend/cuda/kernel/sparse.hpp            |   8 +-
 src/backend/cuda/kernel/sparse_arith.hpp      |  15 +-
 src/backend/cuda/kernel/susan.hpp             |  11 +-
 src/backend/cuda/kernel/tile.hpp              |   6 +-
 src/backend/cuda/kernel/transform.hpp         |   5 +-
 src/backend/cuda/kernel/transpose.hpp         |   6 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |   6 +-
 src/backend/cuda/kernel/triangle.hpp          |   6 +-
 src/backend/cuda/kernel/unwrap.hpp            |   6 +-
 src/backend/cuda/kernel/where.hpp             |   5 +-
 src/backend/cuda/kernel/wrap.hpp              |  10 +-
 src/backend/opencl/jit.cpp                    |  13 +-
 .../opencl/kernel/anisotropic_diffusion.hpp   |   6 +-
 src/backend/opencl/kernel/approx.hpp          |  17 +-
 src/backend/opencl/kernel/assign.hpp          |   5 +-
 src/backend/opencl/kernel/bilateral.hpp       |   5 +-
 src/backend/opencl/kernel/canny.hpp           |  19 +-
 .../opencl/kernel/convolve/conv2_impl.hpp     |   7 +-
 .../opencl/kernel/convolve/conv_common.hpp    |   7 +-
 .../opencl/kernel/convolve_separable.cpp      |   7 +-
 src/backend/opencl/kernel/cscmm.hpp           |   5 +-
 src/backend/opencl/kernel/cscmv.hpp           |   5 +-
 src/backend/opencl/kernel/csrmm.hpp           |   5 +-
 src/backend/opencl/kernel/csrmv.hpp           |   8 +-
 src/backend/opencl/kernel/diagonal.hpp        |  12 +-
 src/backend/opencl/kernel/diff.hpp            |   5 +-
 src/backend/opencl/kernel/exampleFunction.hpp |   4 +-
 src/backend/opencl/kernel/fast.hpp            |  11 +-
 src/backend/opencl/kernel/fftconvolve.hpp     |  19 +-
 src/backend/opencl/kernel/flood_fill.hpp      |  11 +-
 src/backend/opencl/kernel/gradient.hpp        |   5 +-
 src/backend/opencl/kernel/harris.hpp          |  11 +-
 src/backend/opencl/kernel/histogram.hpp       |   5 +-
 src/backend/opencl/kernel/homography.hpp      |  17 +-
 src/backend/opencl/kernel/hsv_rgb.hpp         |   5 +-
 src/backend/opencl/kernel/identity.hpp        |   4 +-
 src/backend/opencl/kernel/iir.hpp             |   4 +-
 src/backend/opencl/kernel/index.hpp           |   4 +-
 src/backend/opencl/kernel/iota.hpp            |   6 +-
 src/backend/opencl/kernel/ireduce.hpp         |  12 +-
 src/backend/opencl/kernel/join.hpp            |   4 +-
 src/backend/opencl/kernel/laset.hpp           |   5 +-
 src/backend/opencl/kernel/laswp.hpp           |   4 +-
 src/backend/opencl/kernel/lookup.hpp          |   5 +-
 src/backend/opencl/kernel/lu_split.hpp        |   5 +-
 src/backend/opencl/kernel/match_template.hpp  |   5 +-
 src/backend/opencl/kernel/mean.hpp            |  13 +-
 src/backend/opencl/kernel/meanshift.hpp       |   5 +-
 src/backend/opencl/kernel/medfilt.hpp         |  10 +-
 src/backend/opencl/kernel/memcopy.hpp         |   9 +-
 src/backend/opencl/kernel/moments.hpp         |   5 +-
 src/backend/opencl/kernel/morph.hpp           |  10 +-
 .../opencl/kernel/nearest_neighbour.hpp       |   6 +-
 src/backend/opencl/kernel/orb.hpp             |  10 +-
 .../opencl/kernel/pad_array_borders.hpp       |   5 +-
 src/backend/opencl/kernel/random_engine.hpp   |  21 +-
 src/backend/opencl/kernel/range.hpp           |   5 +-
 src/backend/opencl/kernel/reduce.hpp          |  13 +-
 src/backend/opencl/kernel/reduce_by_key.hpp   |  60 ++--
 src/backend/opencl/kernel/regions.hpp         |   8 +-
 src/backend/opencl/kernel/reorder.hpp         |   4 +-
 src/backend/opencl/kernel/resize.hpp          |   5 +-
 src/backend/opencl/kernel/rotate.hpp          |   7 +-
 src/backend/opencl/kernel/scan_dim.hpp        |   6 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   6 +-
 src/backend/opencl/kernel/scan_first.hpp      |   6 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   6 +-
 src/backend/opencl/kernel/select.hpp          |   9 +-
 src/backend/opencl/kernel/sift.hpp            |  22 +-
 src/backend/opencl/kernel/sobel.hpp           |   5 +-
 src/backend/opencl/kernel/sparse.hpp          |  35 +--
 src/backend/opencl/kernel/sparse_arith.hpp    |  38 +--
 src/backend/opencl/kernel/susan.hpp           |  11 +-
 src/backend/opencl/kernel/swapdblk.hpp        |   5 +-
 src/backend/opencl/kernel/tile.hpp            |   4 +-
 src/backend/opencl/kernel/transform.hpp       |   8 +-
 src/backend/opencl/kernel/transpose.hpp       |   6 +-
 .../opencl/kernel/transpose_inplace.hpp       |   5 +-
 src/backend/opencl/kernel/triangle.hpp        |   6 +-
 src/backend/opencl/kernel/unwrap.hpp          |   5 +-
 src/backend/opencl/kernel/where.hpp           |   6 +-
 src/backend/opencl/kernel/wrap.hpp            |  11 +-
 129 files changed, 702 insertions(+), 902 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5b25607dd1..4c6dcc4b49 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -127,6 +127,15 @@ configure_file(
     ${ArrayFire_BINARY_DIR}/version.hpp
 )
 
+set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
+FetchContent_Declare(
+  ${spdlog_prefix}
+  GIT_REPOSITORY https://github.com/gabime/spdlog.git
+  GIT_TAG        v1.0.0
+)
+FetchContent_Populate(${spdlog_prefix})
+add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+
 # when crosscompiling use the bin2cpp file from the native bin directory
 if(CMAKE_CROSSCOMPILING)
   set(NATIVE_BIN_DIR "NATIVE_BIN_DIR-NOTFOUND"
@@ -138,11 +147,24 @@ if(CMAKE_CROSSCOMPILING)
                        "directory and build the bin2cpp target.")
   endif()
 else()
-  add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp)
-  target_link_libraries(bin2cpp)
+  add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp
+                         ${ArrayFire_SOURCE_DIR}/src/backend/common/util.cpp)
+  if(WIN32)
+    target_compile_definitions(bin2cpp PRIVATE OS_WIN)
+  elseif(APPLE)
+    target_compile_definitions(bin2cpp PRIVATE OS_MAC)
+  elseif(UNIX)
+    target_compile_definitions(bin2cpp PRIVATE OS_LNX)
+  endif()
+  target_include_directories(bin2cpp PRIVATE
+                             ${ArrayFire_SOURCE_DIR}/include
+                             ${ArrayFire_BINARY_DIR}/include
+                             ${ArrayFire_SOURCE_DIR}/src/backend)
+  target_link_libraries(bin2cpp PRIVATE spdlog)
   export(TARGETS bin2cpp FILE ${CMAKE_BINARY_DIR}/ImportExecutables.cmake)
 endif()
 
+
 if(NOT LAPACK_FOUND)
     if(APPLE)
         # UNSET THE VARIABLES FROM LAPACKE
@@ -154,15 +176,6 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
-set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
-FetchContent_Declare(
-  ${spdlog_prefix}
-  GIT_REPOSITORY https://github.com/gabime/spdlog.git
-  GIT_TAG        v1.0.0
-)
-FetchContent_Populate(${spdlog_prefix})
-add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
-
 FetchContent_Declare(
   ${glad_prefix}
   GIT_REPOSITORY https://github.com/arrayfire/glad.git
diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index 95286cc232..b72a02e636 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -1,18 +1,36 @@
 // Umar Arshad
 // Copyright 2014
 
+// this enables template overloads of standard CRT functions that call the
+// more secure variants automatically,
+#define _CRT_SECURE_CPP_OVERLOAD_SECURE_NAMES 1
+
+#include <cstring>
+// strtok symbol name that keeps context is not on windows and linux
+// so, the above overload define won't help with that function
+#if defined(OS_WIN)
+#define STRTOK_CALL(...) strtok_s(__VA_ARGS__)
+#else
+#define STRTOK_CALL(...) strtok_r(__VA_ARGS__)
+#endif
+
+#include <assert.h>
+#include <common/util.hpp>
+#include <algorithm>
 #include <cstdlib>
+#include <cstring>
 #include <fstream>
 #include <functional>
 #include <iostream>
 #include <map>
 #include <memory>
-#include <sstream> // IWYU pragma: keep
+#include <sstream>  // IWYU pragma: keep
 #include <string>
 #include <utility>
 #include <vector>
 
 using namespace std;
+using std::cout;
 typedef map<string, string> opt_t;
 
 void print_usage() {
@@ -37,111 +55,230 @@ Example
 ./bin2cpp --file blah.txt --namespace blah detail --formatted --name blah_var
 
 Will produce:
+#pragma once
+#include <common/util.hpp>
 #include <cstddef>
 namespace blah {
 	namespace detail {
-		static const char blah_var[] = {
+		static const unsigned char blah_var_uchar [] = {
 			0x2f,	0x2f,	0x20,	0x62,	0x6c,	0x61,	0x68,	0x2e,	0x74,	0x78,
 			0x74,	0xa,	0x62,	0x6c,	0x61,	0x68,	0x20,	0x62,	0x6c,	0x61,
 			0x68,	0x20,	0x62,	0x6c,	0x61,	0x68,	0xa,	};
-		static const size_t blah_var_len = 27;
+		static const char *blah_var = (const char*)blah_var_uchar;
+		static const size_t blah_var_len  = 27;
+		static const size_t blah_var_hash = 12345678901234567890ULL;
+		static const common::Source blah_var_src = {
+			blah_var,
+			blah_var_len,
+			blah_var_hash
+		};
 	}
 })delimiter";
-        exit(0);
+    exit(0);
 }
 
 static bool formatted;
-static bool binary = false;
+static bool binary   = false;
 static bool nullterm = false;
 
-void add_tabs(const int level ){
-    if(formatted) {
-        for(int i =0; i < level; i++) {
-            cout << "\t";
-        }
+void add_tabs(const int level) {
+    if (formatted) {
+        for (int i = 0; i < level; i++) { cout << "\t"; }
     }
 }
 
-opt_t
-parse_options(const vector<string>& args) {
+opt_t parse_options(const vector<string> &args) {
     opt_t options;
 
-    options["--name"]       = "";
-    options["--type"]       = "";
-    options["--file"]       = "";
-    options["--output"]     = "";
-    options["--namespace"]  = "";
+    options["--name"]      = "";
+    options["--type"]      = "";
+    options["--file"]      = "";
+    options["--output"]    = "";
+    options["--namespace"] = "";
 
-    //Parse Arguments
+    // Parse Arguments
     string curr_opt;
     bool verbose = false;
-    for(auto arg : args) {
-        if(arg == "--verbose") {
+    for (auto arg : args) {
+        if (arg == "--verbose") {
             verbose = true;
-        }
-        else if(arg == "--binary") {
+        } else if (arg == "--binary") {
             binary = true;
-        }
-        else if(arg == "--nullterm") {
+        } else if (arg == "--nullterm") {
             nullterm = true;
-        }
-        else if(arg == "--formatted") {
+        } else if (arg == "--formatted") {
             formatted = true;
-        }
-        else if(arg == "--version") {
+        } else if (arg == "--version") {
             cout << args[0] << " By Umar Arshad" << endl;
-        }
-        else if(arg == "--help") {
+        } else if (arg == "--help") {
             print_usage();
-        }
-        else if(options.find(arg) != options.end()) {
+        } else if (options.find(arg) != options.end()) {
             curr_opt = arg;
-        }
-        else if(curr_opt.empty()) {
-            //cerr << "Invalid Argument: " << arg << endl;
-        }
-        else {
-            if(options[curr_opt] != "") {
+        } else if (curr_opt.empty()) {
+            // cerr << "Invalid Argument: " << arg << endl;
+        } else {
+            if (options[curr_opt] != "") {
                 options[curr_opt] += " " + arg;
-            }
-            else {
+            } else {
                 options[curr_opt] += arg;
             }
         }
     }
 
-    if(verbose) {
-        for(auto opts : options) {
+    if (verbose) {
+        for (auto opts : options) {
             cout << get<0>(opts) << " " << get<1>(opts) << endl;
         }
     }
     return options;
 }
 
-int main(int argc, const char * const * const argv)
-{
-    vector<string> args(argv, argv+argc);
+stringstream removeComments(ifstream &input, string &filename) {
+    stringstream ss;
+    char line[256]{
+        '\0'};  // Maximum length of lines in OpenCL code is limited to 256
+    const char *tokenCommentsStart = "/*";
+    const char *tokenCommentsEnd   = "*/";
+    const char *tokenCommentsLine  = "//";
+    const char *tokenString        = "\"";
+    const char *delimitors         = " \t;";  // Only the subset we need
+    enum { NO, STRING, ENDOFLINE, MULTILINE } commentsLevel{NO};
+
+    while (input.getline(line, sizeof(line) - 1)) {
+        char local[sizeof(line)];
+        struct segment {
+            char *start;
+            char *end;
+        } del{commentsLevel == MULTILINE ? line : nullptr, nullptr};
+        vector<segment> dels;
+        memcpy(local, line, sizeof(line));   // will be overwritten by strtok
+        local[sizeof(local) - 1] = '\0';     // string is always terminated
+        char *context            = nullptr;
+        char *token              = STRTOK_CALL(local, delimitors, &context);
+        do {
+            char *subtoken = nullptr;
+            while (token) {
+                switch (commentsLevel) {
+                    case MULTILINE:
+                        subtoken = strstr(token, tokenCommentsEnd);
+                        if (subtoken != nullptr) {
+                            if (del.start == nullptr) del.start = line;
+                            del.end = subtoken + strlen(tokenCommentsEnd) -
+                                      local + line;
+                            dels.push_back(del);
+                            del           = {nullptr, nullptr};
+                            token         = subtoken + strlen(tokenCommentsEnd);
+                            commentsLevel = NO;
+                        } else {
+                            token = nullptr;
+                        }
+                        break;
+                    case STRING:
+                        subtoken = strstr(token, tokenString);
+                        if (subtoken != nullptr) {
+                            token         = subtoken + strlen(tokenString);
+                            commentsLevel = NO;
+                        } else {
+                            token = nullptr;
+                        }
+                        break;
+                    case NO: {
+                        // select first subtoken inside this token
+                        subtoken = strstr(token, tokenCommentsStart);
+                        if (subtoken != nullptr) { commentsLevel = MULTILINE; }
+                        char *ptr = strstr(token, tokenCommentsLine);
+                        if ((ptr != nullptr) &&
+                            ((subtoken == nullptr) || (ptr < subtoken))) {
+                            commentsLevel = ENDOFLINE;
+                            subtoken      = ptr;
+                        }
+                        ptr = strstr(token, tokenString);
+                        if ((ptr != nullptr) &&
+                            ((subtoken == nullptr) || ptr < subtoken)) {
+                            commentsLevel = STRING;
+                            subtoken      = ptr;
+                        }
+                        switch (commentsLevel) {
+                            case MULTILINE:
+                                del.start = subtoken - local + line;
+                                token = subtoken + strlen(tokenCommentsStart);
+                                break;
+                            case ENDOFLINE:
+                                del.start = subtoken - local + line;
+                                token = subtoken + strlen(tokenCommentsLine);
+                                break;
+                            case STRING:
+                                token = subtoken + strlen(tokenString);
+                                break;
+                            case NO:
+                            default: token = nullptr;
+                        }
+                    } break;
+                    case ENDOFLINE:
+                    default: token = nullptr;
+                }
+            }
+            token = STRTOK_CALL(nullptr, delimitors, &context);
+        } while (token != nullptr);
+        if (del.start != nullptr) {
+            if (commentsLevel == ENDOFLINE) commentsLevel = NO;
+            del.end = line + strlen(line);
+            dels.push_back(del);
+            del = {nullptr, nullptr};
+        }
+        // Delete all segments starting from the end!!!
+        for (auto d = dels.crbegin(); d != dels.crend(); d++) {
+            char *ptr1 = d->start;
+            char *ptr2 = d->end;
+            // Do not use strncpy, it has problems with overlapping because the
+            // order isn't defined in the standard
+            while ((*ptr2 != '\0') && (ptr2 != line + sizeof(line))) { *ptr1++ = *ptr2++; }
+            *ptr1 = '\0';
+        }
+        // Remove trailing blanks
+        for (long i = static_cast<long>(std::min(sizeof(line),strlen(line))) - 1;
+             (i >= 0) && (line[i] == ' '); --i) {
+            line[i] = '\0';
+        }
+        // Remove leading blanks
+        char *linePtr = line;
+        for (size_t i = 0, len = std::min(sizeof(line),strlen(line));
+            (i < len) && (line[i] == ' ');
+             ++i, ++linePtr) {}
+        // Useful text is terminated by '\n';
+        if (linePtr[0] != '\0') { ss << linePtr << "\n"; }
+    }
+    return (ss);
+}
+
+int main(int argc, const char *const *const argv) {
+    vector<string> args(argv, argv + argc);
 
-    opt_t&& options = parse_options(args);
+    if (argc == 1) {
+        print_usage();
+        return 0;
+    }
+    opt_t &&options = parse_options(args);
 
-    //Save default cout buffer. Need this to prevent crash.
+    // Save default cout buffer. Need this to prevent crash.
     auto bak = cout.rdbuf();
     unique_ptr<ofstream> outfile;
 
     // Set defaults
-    if(options["--name"] == "")     { options["--name"]     = "var"; }
-    if(options["--output"] != "")   {
-        //redirect stream if output file is specified
+    if (options["--name"] == "") { options["--name"] = "var"; }
+    if (options["--output"] != "") {
+        // redirect stream if output file is specified
         outfile.reset(new ofstream(options["--output"]));
         cout.rdbuf(outfile->rdbuf());
     }
 
     cout << "#pragma once\n";
-    cout << "#include <cstddef>\n"; // defines size_t
+    cout << "#include <cstddef>\n";          // defines size_t
+    cout << "#include <common/util.hpp>\n";  // defines common::Source
 
     int ns_cnt = 0;
-    int level = 0;
-    if(options["--namespace"] != "") {
+    int level  = 0;
+    if (options["--namespace"] != "") {
         stringstream namespaces(options["--namespace"]);
         string name;
         namespaces >> name;
@@ -150,24 +287,26 @@ int main(int argc, const char * const * const argv)
             cout << "namespace " << name << " { \n";
             ns_cnt++;
             namespaces >> name;
-        } while(!namespaces.fail());
+        } while (!namespaces.fail());
     }
 
-    if(options["--type"] == "") {
-        options["--type"]     = "char";
-    }
+    if (options["--type"] == "") { options["--type"] = "char"; }
     add_tabs(level);
 
     // Always create unsigned char to avoid narrowing
-    cout << "static const " << "unsigned char" << " " << options["--name"] << "_uchar [] = {\n";
+    cout << "static const "
+         << "unsigned char"
+         << " " << options["--name"] << "_uchar [] = {\n";
 
-    ifstream input(options["--file"], (binary ? std::ios::binary : std::ios::in));
+    ifstream input(options["--file"],
+                   (binary ? std::ios::binary : std::ios::in));
     size_t char_cnt = 0;
+    stringstream ss = removeComments(input, options["--file"]);
     add_tabs(++level);
-    for(char i; input.get(i);) {
+    for (char i; ss.get(i);) {
         cout << "0x" << std::hex << static_cast<int>(i & 0xff) << ",\t";
         char_cnt++;
-        if(!(char_cnt % 10)) {
+        if (!(char_cnt % 10)) {
             cout << endl;
             add_tabs(level);
         }
@@ -183,17 +322,32 @@ int main(int argc, const char * const * const argv)
     add_tabs(--level);
 
     // Cast to proper output type
-    cout << "static const "
-         << options["--type"] << " *"
-         << options["--name"] << " = (const "
-         << options["--type"] << " *)"
-         << options["--name"] << "_uchar;\n";
-
-    cout << "static const size_t " << options["--name"] << "_len" << " = " << std::dec << char_cnt << ";\n";
+    cout << "static const " << options["--type"] << " *" << options["--name"]
+         << " = (const " << options["--type"] << " *)" << options["--name"]
+         << "_uchar;\n";
+    add_tabs(level);
+    cout << "static const size_t " << options["--name"] << "_len"
+         << "  = " << std::dec << char_cnt << ";\n";
+    add_tabs(level);
+    cout << "static const size_t " << options["--name"] << "_hash"
+         << " = " << deterministicHash(ss.str()) << "ULL;\n";
+    add_tabs(level);
+    cout << "static const common::Source " << options["--name"] << "_src{\n";
+    add_tabs(++level);
+    cout << options["--name"] << ",\n";
+    add_tabs(level);
+    cout << options["--name"] << "_len,\n";
+    add_tabs(level);
+    cout << options["--name"] << "_hash\n";
+    add_tabs(--level);
+    cout << "};\n";
 
-    while(ns_cnt--) {
+    while (ns_cnt--) {
         add_tabs(--level);
         cout << "}\n";
     }
+
     cout.rdbuf(bak);
+
+    return 0;
 }
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 79c6e1c3eb..5031d6b75a 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -9,9 +9,8 @@
 
 #if !defined(AF_CPU)
 
-#include <common/kernel_cache.hpp>
-
 #include <common/compile_module.hpp>
+#include <common/kernel_cache.hpp>
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <platform.hpp>
@@ -28,13 +27,14 @@ using detail::Module;
 using std::back_inserter;
 using std::shared_timed_mutex;
 using std::string;
+using std::to_string;
 using std::transform;
 using std::unordered_map;
 using std::vector;
 
 namespace common {
 
-using ModuleMap = unordered_map<string, Module>;
+using ModuleMap = unordered_map<size_t, Module>;
 
 shared_timed_mutex& getCacheMutex(const int device) {
     static shared_timed_mutex mutexes[detail::DeviceManager::MAX_DEVICES];
@@ -47,7 +47,7 @@ ModuleMap& getCache(const int device) {
     return caches[device];
 }
 
-Module findModule(const int device, const string& key) {
+Module findModule(const int device, const size_t& key) {
     std::shared_lock<shared_timed_mutex> readLock(getCacheMutex(device));
     auto& cache = getCache(device);
     auto iter   = cache.find(key);
@@ -55,66 +55,64 @@ Module findModule(const int device, const string& key) {
     return Module{};
 }
 
-Kernel getKernel(const string& kernelName, const vector<string>& sources,
+Kernel getKernel(const string& kernelName,
+                 const vector<common::Source>& sources,
                  const vector<TemplateArg>& targs,
                  const vector<string>& options, const bool sourceIsJIT) {
-    vector<string> args;
-    args.reserve(targs.size());
-
-    transform(targs.begin(), targs.end(), back_inserter(args),
-              [](const TemplateArg& arg) -> string { return arg._tparam; });
-
     string tInstance = kernelName;
-    if (args.size() > 0) {
-        tInstance = kernelName + "<" + args[0];
-        for (size_t i = 1; i < args.size(); ++i) {
-            tInstance += ("," + args[i]);
-        }
-        tInstance += ">";
-    }
 
-    const bool notJIT = !sourceIsJIT;
-
-    vector<string> hashingVals;
-    hashingVals.reserve(1 + (notJIT * (sources.size() + options.size())));
-    hashingVals.push_back(tInstance);
-    if (notJIT) {
-        // This code path is only used for regular kernel compilation
-        // since, jit funcName(kernelName) is unique to use it's hash
-        // for caching the relevant compiled/linked module
-        hashingVals.insert(hashingVals.end(), sources.begin(), sources.end());
-        hashingVals.insert(hashingVals.end(), options.begin(), options.end());
+#if defined(AF_CUDA)
+    auto targsIt  = targs.begin();
+    auto targsEnd = targs.end();
+    if (targsIt != targsEnd) {
+        tInstance += '<' + targsIt->_tparam;
+        while (++targsIt != targsEnd) { tInstance += ',' + targsIt->_tparam; }
+        tInstance += '>';
     }
+#else
+    UNUSED(targs);
+#endif
 
-    const string moduleKey = std::to_string(deterministicHash(hashingVals));
-    const int device       = detail::getActiveDeviceId();
-    Module currModule      = findModule(device, moduleKey);
+    size_t moduleKey = 0;
+    if (sourceIsJIT) {
+        moduleKey = deterministicHash(tInstance);
+    } else {
+        moduleKey = (sources.size() == 1 && sources[0].hash)
+                        ? sources[0].hash
+                        : deterministicHash(sources);
+        moduleKey = deterministicHash(options, moduleKey);
+#if defined(AF_CUDA)
+        moduleKey = deterministicHash(tInstance, moduleKey);
+#endif
+    }
+    const int device  = detail::getActiveDeviceId();
+    Module currModule = findModule(device, moduleKey);
 
     if (!currModule) {
-        currModule = loadModuleFromDisk(device, moduleKey, sourceIsJIT);
+        currModule =
+            loadModuleFromDisk(device, to_string(moduleKey), sourceIsJIT);
         if (!currModule) {
-            currModule = compileModule(moduleKey, sources, options, {tInstance},
-                                       sourceIsJIT);
+            vector<string> sources_str;
+            for (auto s : sources) { sources_str.push_back({s.ptr, s.length}); }
+            currModule = compileModule(to_string(moduleKey), sources_str,
+                                       options, {tInstance}, sourceIsJIT);
         }
 
         std::unique_lock<shared_timed_mutex> writeLock(getCacheMutex(device));
         auto& cache = getCache(device);
         auto iter   = cache.find(moduleKey);
         if (iter == cache.end()) {
-            // If not found, this thread is the first one to compile this
-            // kernel. Keep the generated module.
+            // If not found, this thread is the first one to compile
+            // this kernel. Keep the generated module.
             Module mod = currModule;
             getCache(device).emplace(moduleKey, mod);
         } else {
-            currModule.unload();  // dump the current threads extra compilation
+            currModule.unload();  // dump the current threads extra
+                                  // compilation
             currModule = iter->second;
         }
     }
-#if defined(AF_CUDA)
     return getKernel(currModule, tInstance, sourceIsJIT);
-#elif defined(AF_OPENCL)
-    return getKernel(currModule, kernelName, sourceIsJIT);
-#endif
 }
 
 }  // namespace common
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index 3ac04081a1..c63c4278a4 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -15,6 +15,7 @@
 #include <Module.hpp>
 #include <backend.hpp>
 #include <common/TemplateTypename.hpp>
+#include <common/util.hpp>
 
 #include <string>
 #include <vector>
@@ -45,8 +46,7 @@ namespace common {
 /// Example Usage: transpose
 ///
 /// \code
-/// static const std::string src(transpose_cuh, transpose_cuh_len);
-/// auto transpose = getKernel("cuda::transpose", {src},
+/// auto transpose = getKernel("cuda::transpose", {transpase_cuh_src},
 ///         {
 ///           TemplateTypename<T>(),
 ///           TemplateArg(conjugate),
@@ -62,7 +62,7 @@ namespace common {
 /// \endcode
 ///
 /// \param[in] kernelName is the name of the kernel qualified as kernel in code
-/// \param[in] sources is the list of source strings to be compiled if required
+/// \param[in] sources is the list of common::Source to be compiled if required
 /// \param[in] templateArgs is a vector of strings containing stringified names
 ///            of the template arguments of kernel to be compiled.
 /// \param[in] options is a vector of strings that enables the user to
@@ -70,7 +70,7 @@ namespace common {
 ///            the kernel compilation.
 ///
 detail::Kernel getKernel(const std::string& kernelName,
-                         const std::vector<std::string>& sources,
+                         const std::vector<common::Source>& sources,
                          const std::vector<TemplateArg>& templateArgs,
                          const std::vector<std::string>& options = {},
                          const bool sourceIsJIT                  = false);
@@ -86,7 +86,7 @@ detail::Kernel getKernel(const std::string& kernelName,
 ///            the module look up has to be done
 /// \param[in] key is hash generated from code + options + kernel_name
 ///            at caller scope
-detail::Module findModule(const int device, const std::string& key);
+detail::Module findModule(const int device, const std::size_t& key);
 
 /// \brief Get Kernel object for given name from given Module
 ///
diff --git a/src/backend/common/kernel_type.hpp b/src/backend/common/kernel_type.hpp
index f38e481fca..d61f796f67 100644
--- a/src/backend/common/kernel_type.hpp
+++ b/src/backend/common/kernel_type.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 namespace common {
 
 /// \brief Maps a type between its data representation and the type used
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index ce207be5d0..c0d1d30cc9 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -215,23 +215,35 @@ string makeTempFilename() {
                                               std::to_string(fileCount)));
 }
 
-std::size_t deterministicHash(const void* data, std::size_t byteSize) {
+std::size_t deterministicHash(const void* data, std::size_t byteSize,
+                              std::size_t prevHash) {
     // Fowler-Noll-Vo "1a" 32 bit hash
     // https://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function
-    constexpr std::size_t seed  = 0x811C9DC5;
-    constexpr std::size_t prime = 0x01000193;
-    const auto* byteData        = static_cast<const std::uint8_t*>(data);
-    return std::accumulate(byteData, byteData + byteSize, seed,
+    const auto* byteData = static_cast<const std::uint8_t*>(data);
+    return std::accumulate(byteData, byteData + byteSize, prevHash,
                            [&](std::size_t hash, std::uint8_t data) {
-                               return (hash ^ data) * prime;
+                               return (hash ^ data) * FNV1A_PRIME;
                            });
 }
 
-std::size_t deterministicHash(const std::string& data) {
-    return deterministicHash(data.data(), data.size());
+std::size_t deterministicHash(const std::string& data,
+                              const std::size_t prevHash) {
+    return deterministicHash(data.data(), data.size(), prevHash);
 }
 
-std::size_t deterministicHash(const vector<string>& list) {
-    string accumStr = accumulate(list.begin(), list.end(), string(""));
-    return deterministicHash(accumStr.data(), accumStr.size());
+std::size_t deterministicHash(const vector<std::string>& list,
+                              const std::size_t prevHash) {
+    std::size_t hash = prevHash;
+    for (auto s : list) { hash = deterministicHash(s.data(), s.size(), hash); }
+    return hash;
+}
+
+std::size_t deterministicHash(const std::vector<common::Source>& list) {
+    // Combine the different source codes, via their hashes
+    std::size_t hash = FNV1A_BASE_OFFSET;
+    for (auto s : list) {
+        size_t h = s.hash ? s.hash : deterministicHash(s.ptr, s.length);
+        hash     = deterministicHash(&h, sizeof(size_t), hash);
+    }
+    return hash;
 }
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index efa3ce2501..4968fa3568 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -14,6 +14,14 @@
 #include <string>
 #include <vector>
 
+namespace common {
+struct Source {
+    const char* ptr;           // Pointer to the kernel source
+    const std::size_t length;  // Length of the kernel source
+    const std::size_t hash;    // hash value for the source *ptr;
+};
+}  // namespace common
+
 /// The environment variable that determines where the runtime kernels
 /// will be stored on the file system
 constexpr const char* JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME =
@@ -51,12 +59,21 @@ std::string makeTempFilename();
 ///
 /// \param[in] data Binary data to hash
 /// \param[in] byteSize Size of the data in bytes
+/// \param[in] optional prevHash Hash of previous parts when string is split
 ///
 /// \returns An unsigned integer representing the hash of the data
-std::size_t deterministicHash(const void* data, std::size_t byteSize);
+constexpr std::size_t FNV1A_BASE_OFFSET = 0x811C9DC5;
+constexpr std::size_t FNV1A_PRIME       = 0x01000193;
+std::size_t deterministicHash(const void* data, std::size_t byteSize,
+                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
 
 // This is just a wrapper around the above function.
-std::size_t deterministicHash(const std::string& data);
+std::size_t deterministicHash(const std::string& data,
+                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
 
 // This concatenates strings in the vector and computes hash
-std::size_t deterministicHash(const std::vector<std::string>& list);
+std::size_t deterministicHash(const std::vector<std::string>& list,
+                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
+
+// This concatenates hashes of multiple sources
+std::size_t deterministicHash(const std::vector<common::Source>& list);
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 0298e6fdfa..d2b25c2d78 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -182,7 +182,7 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                             const bool is_linear) {
     const string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    const string moduleKey = to_string(deterministicHash(funcName));
+    const size_t moduleKey = deterministicHash(funcName);
 
     // A forward lookup in module cache helps avoid recompiling the jit
     // source generated from identical jit-trees. It also enables us
@@ -194,7 +194,10 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
                                               output_ids, is_linear);
         saveKernel(funcName, jitKer, ".cu");
 
-        return common::getKernel(funcName, {jitKer}, {}, {}, true).get();
+        common::Source jit_src{jitKer.c_str(), jitKer.size(),
+                               deterministicHash(jitKer)};
+
+        return common::getKernel(funcName, {jit_src}, {}, {}, true).get();
     }
     return common::getKernel(entry, funcName, true).get();
 }
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index c8b7e06bbb..32e10b9942 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -16,8 +16,6 @@
 #include <nvrtc_kernel_headers/anisotropic_diffusion_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -28,10 +26,8 @@ constexpr int YDIM_LOAD = 2 * THREADS_X / THREADS_Y;
 template<typename T>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
-    static const std::string source(anisotropic_diffusion_cuh,
-                                    anisotropic_diffusion_cuh_len);
     auto diffUpdate = common::getKernel(
-        "cuda::diffUpdate", {source},
+        "cuda::diffUpdate", {anisotropic_diffusion_cuh_src},
         {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
         {DefineValue(THREADS_X), DefineValue(THREADS_Y),
          DefineValue(YDIM_LOAD)});
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 54c1d62503..47473a4f03 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -15,8 +15,6 @@
 #include <nvrtc_kernel_headers/approx2_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -29,10 +27,8 @@ template<typename Ty, typename Tp>
 void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, const float offGrid,
              const af::interpType method, const int order) {
-    static const std::string source(approx1_cuh, approx1_cuh_len);
-
     auto approx1 =
-        common::getKernel("cuda::approx1", {source},
+        common::getKernel("cuda::approx1", {approx1_cuh_src},
                           {TemplateTypename<Ty>(), TemplateTypename<Tp>(),
                            TemplateArg(xdim), TemplateArg(order)});
 
@@ -60,10 +56,8 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, CParam<Tp> yo, const int ydim,
              const Tp &yi_beg, const Tp &yi_step, const float offGrid,
              const af::interpType method, const int order) {
-    static const std::string source(approx2_cuh, approx2_cuh_len);
-
     auto approx2 = common::getKernel(
-        "cuda::approx2", {source},
+        "cuda::approx2", {approx2_cuh_src},
         {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(xdim),
          TemplateArg(ydim), TemplateArg(order)});
 
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 9de3cdbfe2..9632892cc4 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -14,8 +14,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/assign_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -24,10 +22,8 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    static const std::string src(assign_cuh, assign_cuh_len);
-
-    auto assignKer =
-        common::getKernel("cuda::assign", {src}, {TemplateTypename<T>()});
+    auto assignKer = common::getKernel("cuda::assign", {assign_cuh_src},
+                                       {TemplateTypename<T>()});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 0f1995c87c..a7788a5deb 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -13,8 +13,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/bilateral_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -24,10 +22,8 @@ static const int THREADS_Y = 16;
 template<typename inType, typename outType>
 void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
-    static const std::string source(bilateral_cuh, bilateral_cuh_len);
-
     auto bilateral = common::getKernel(
-        "cuda::bilateral", {source},
+        "cuda::bilateral", {bilateral_cuh_src},
         {TemplateTypename<inType>(), TemplateTypename<outType>()},
         {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index f250693a79..4dd6ce739c 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -13,8 +13,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/canny_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -28,10 +26,8 @@ static const int THREADS_Y = 16;
 template<typename T>
 void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
-    static const std::string source(canny_cuh, canny_cuh_len);
-
     auto nonMaxSuppress = common::getKernel(
-        "cuda::nonMaxSuppression", {source}, {TemplateTypename<T>()},
+        "cuda::nonMaxSuppression", {canny_cuh_src}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
@@ -51,18 +47,16 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
 
 template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
-    static const std::string source(canny_cuh, canny_cuh_len);
-
     auto initEdgeOut = common::getKernel(
-        "cuda::initEdgeOut", {source}, {TemplateTypename<T>()},
+        "cuda::initEdgeOut", {canny_cuh_src}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto edgeTrack = common::getKernel(
-        "cuda::edgeTrack", {source}, {TemplateTypename<T>()},
+        "cuda::edgeTrack", {canny_cuh_src}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto suppressLeftOver = common::getKernel(
-        "cuda::suppressLeftOver", {source}, {TemplateTypename<T>()},
+        "cuda::suppressLeftOver", {canny_cuh_src}, {TemplateTypename<T>()},
         {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
          DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index b2829b3af8..40485d0148 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -20,10 +20,6 @@
 #include <nvrtc_kernel_headers/convolve_separable_cuh.hpp>
 #include <traits.hpp>
 
-#include <string>
-
-using std::string;
-
 namespace cuda {
 namespace kernel {
 
@@ -104,10 +100,8 @@ void prepareKernelArgs(conv_kparam_t& params, dim_t oDims[], dim_t fDims[],
 template<typename T, typename aT>
 void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
-    static const std::string src(convolve1_cuh, convolve1_cuh_len);
-
     auto convolve1 = common::getKernel(
-        "cuda::convolve1", {src},
+        "cuda::convolve1", {convolve1_cuh_src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS)});
 
@@ -161,10 +155,8 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    static const std::string src(convolve2_cuh, convolve2_cuh_len);
-
     auto convolve2 = common::getKernel(
-        "cuda::convolve2", {src},
+        "cuda::convolve2", {convolve2_cuh_src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand),
          TemplateArg(f0), TemplateArg(f1)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
@@ -208,10 +200,8 @@ void convolve_2d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
 template<typename T, typename aT>
 void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
-    static const std::string src(convolve3_cuh, convolve3_cuh_len);
-
     auto convolve3 = common::getKernel(
-        "cuda::convolve3", {src},
+        "cuda::convolve3", {convolve3_cuh_src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
         {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
          DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
@@ -314,10 +304,8 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    static const std::string src(convolve_separable_cuh,
-                                 convolve_separable_cuh_len);
     auto convolve2_separable = common::getKernel(
-        "cuda::convolve2_separable", {src},
+        "cuda::convolve2_separable", {convolve_separable_cuh_src},
         {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(conv_dim),
          TemplateArg(expand), TemplateArg(fLen)},
         {DefineValue(MAX_SCONV_FILTER_LEN), DefineValue(SCONV_THREADS_X),
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index d356b5d1bb..93b974420e 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -15,17 +15,13 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/diagonal_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 void diagCreate(Param<T> out, CParam<T> in, int num) {
-    static const std::string src(diagonal_cuh, diagonal_cuh_len);
-
-    auto genDiagMat = common::getKernel("cuda::createDiagonalMat", {src},
-                                        {TemplateTypename<T>()});
+    auto genDiagMat = common::getKernel(
+        "cuda::createDiagonalMat", {diagonal_cuh_src}, {TemplateTypename<T>()});
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
@@ -49,10 +45,8 @@ void diagCreate(Param<T> out, CParam<T> in, int num) {
 
 template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int num) {
-    static const std::string src(diagonal_cuh, diagonal_cuh_len);
-
-    auto extractDiag = common::getKernel("cuda::extractDiagonal", {src},
-                                         {TemplateTypename<T>()});
+    auto extractDiag = common::getKernel(
+        "cuda::extractDiagonal", {diagonal_cuh_src}, {TemplateTypename<T>()});
 
     dim3 threads(256, 1);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index d8450a3085..1d3d4c5278 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/diff_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -26,10 +24,8 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
     constexpr unsigned TX = 16;
     constexpr unsigned TY = 16;
 
-    static const std::string src(diff_cuh, diff_cuh_len);
-
     auto diff = common::getKernel(
-        "cuda::diff", {src},
+        "cuda::diff", {diff_cuh_src},
         {TemplateTypename<T>(), TemplateArg(dim), TemplateArg(isDiff2)});
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 9f6825f206..64229c88d7 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -18,8 +18,6 @@
 
 #include <nvrtc_kernel_headers/exampleFunction_cuh.hpp>  //kernel generated by nvrtc
 
-#include <string>
-
 namespace cuda {
 
 namespace kernel {
@@ -29,12 +27,11 @@ static const unsigned TY = 16;  // Kernel Launch Config Values
 
 template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
-    static const std::string source(exampleFunction_cuh,
-                                    exampleFunction_cuh_len);
-    auto exampleFunc = common::getKernel("cuda::exampleFunc", {source},
-                                         {
-                                             TemplateTypename<T>(),
-                                         });
+    auto exampleFunc =
+        common::getKernel("cuda::exampleFunc", {exampleFunction_cuh_src},
+                          {
+                              TemplateTypename<T>(),
+                          });
 
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index c4faecd2ed..df6836c8af 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -15,26 +15,19 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/fftconvolve_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
 static const int THREADS = 256;
 
-static inline std::string fftConvSource() {
-    static const std::string src(fftconvolve_cuh, fftconvolve_cuh_len);
-    return src;
-}
-
 template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
     auto packData =
-        common::getKernel("cuda::packData", {fftConvSource()},
+        common::getKernel("cuda::packData", {fftconvolve_cuh_src},
                           {TemplateTypename<convT>(), TemplateTypename<T>()});
     auto padArray =
-        common::getKernel("cuda::padArray", {fftConvSource()},
+        common::getKernel("cuda::padArray", {fftconvolve_cuh_src},
                           {TemplateTypename<convT>(), TemplateTypename<T>()});
 
     dim_t *sd = sig.dims;
@@ -75,7 +68,7 @@ template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
     auto cplxMul =
-        common::getKernel("cuda::complexMultiply", {fftConvSource()},
+        common::getKernel("cuda::complexMultiply", {fftconvolve_cuh_src},
                           {TemplateTypename<convT>(), TemplateArg(kind)});
 
     int sig_packed_elem    = 1;
@@ -108,7 +101,7 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     constexpr bool RoundResult = std::is_integral<T>::value;
 
     auto reorderOut =
-        common::getKernel("cuda::reorderOutput", {fftConvSource()},
+        common::getKernel("cuda::reorderOutput", {fftconvolve_cuh_src},
                           {TemplateTypename<T>(), TemplateTypename<convT>(),
                            TemplateArg(expand), TemplateArg(RoundResult)});
 
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index 0a0277b0b8..b6f9615a6c 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -16,8 +16,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/flood_fill_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -38,8 +36,6 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
                CParam<uint> seedsy, const T newValue, const T lowValue,
                const T highValue, const af::connectivity nlookup) {
     UNUSED(nlookup);
-    static const std::string source(flood_fill_cuh, flood_fill_cuh_len);
-
     if (sharedMemRequiredByFloodFill<T>() >
         cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock) {
         char errMessage[256];
@@ -49,13 +45,13 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds =
-        common::getKernel("cuda::initSeeds", {source}, {TemplateTypename<T>()});
-    auto floodStep =
-        common::getKernel("cuda::floodStep", {source}, {TemplateTypename<T>()},
-                          {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
-    auto finalizeOutput = common::getKernel("cuda::finalizeOutput", {source},
-                                            {TemplateTypename<T>()});
+    auto initSeeds = common::getKernel("cuda::initSeeds", {flood_fill_cuh_src},
+                                       {TemplateTypename<T>()});
+    auto floodStep = common::getKernel(
+        "cuda::floodStep", {flood_fill_cuh_src}, {TemplateTypename<T>()},
+        {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto finalizeOutput = common::getKernel(
+        "cuda::finalizeOutput", {flood_fill_cuh_src}, {TemplateTypename<T>()});
 
     EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
                       getActiveStream());
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index 59bd37b6dd..f413faec2d 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/gradient_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,11 +23,9 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     constexpr unsigned TX = 32;
     constexpr unsigned TY = 8;
 
-    static const std::string source(gradient_cuh, gradient_cuh_len);
-
-    auto gradient =
-        common::getKernel("cuda::gradient", {source}, {TemplateTypename<T>()},
-                          {DefineValue(TX), DefineValue(TY)});
+    auto gradient = common::getKernel("cuda::gradient", {gradient_cuh_src},
+                                      {TemplateTypename<T>()},
+                                      {DefineValue(TX), DefineValue(TY)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index d04d97cb86..bdf7d2283e 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -13,8 +13,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/histogram_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,10 +23,8 @@ constexpr int THRD_LOAD = 16;
 template<typename T>
 void histogram(Param<uint> out, CParam<T> in, int nbins, float minval,
                float maxval, bool isLinear) {
-    static const std::string source(histogram_cuh, histogram_cuh_len);
-
     auto histogram =
-        common::getKernel("cuda::histogram", {source},
+        common::getKernel("cuda::histogram", {histogram_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(isLinear)},
                           {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index a959853e6f..ec3f0098eb 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -13,8 +13,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/hsv_rgb_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -23,10 +21,8 @@ static const int THREADS_Y = 16;
 
 template<typename T>
 void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
-    static const std::string source(hsv_rgb_cuh, hsv_rgb_cuh_len);
-
     auto hsvrgbConverter =
-        common::getKernel("cuda::hsvrgbConverter", {source},
+        common::getKernel("cuda::hsvrgbConverter", {hsv_rgb_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 2bcac932b1..ae92d7535c 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -15,17 +15,13 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/identity_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 void identity(Param<T> out) {
-    static const std::string source(identity_cuh, identity_cuh_len);
-
-    auto identity =
-        common::getKernel("cuda::identity", {source}, {TemplateTypename<T>()});
+    auto identity = common::getKernel("cuda::identity", {identity_cuh_src},
+                                      {TemplateTypename<T>()});
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index bfce16993a..985e623249 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/iir_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -24,9 +22,7 @@ template<typename T, bool batch_a>
 void iir(Param<T> y, CParam<T> c, CParam<T> a) {
     constexpr int MAX_A_SIZE = 1024;
 
-    static const std::string source(iir_cuh, iir_cuh_len);
-
-    auto iir = common::getKernel("cuda::iir", {source},
+    auto iir = common::getKernel("cuda::iir", {iir_cuh_src},
                                  {TemplateTypename<T>(), TemplateArg(batch_a)},
                                  {DefineValue(MAX_A_SIZE)});
 
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 590ef87acd..a11f5a996e 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -16,8 +16,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/index_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -26,10 +24,8 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    static const std::string source(index_cuh, index_cuh_len);
-
-    auto index =
-        common::getKernel("cuda::index", {source}, {TemplateTypename<T>()});
+    auto index = common::getKernel("cuda::index", {index_cuh_src},
+                                   {TemplateTypename<T>()});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 18dc0716fc..0b5cd61b78 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -16,8 +16,6 @@
 #include <nvrtc_kernel_headers/iota_cuh.hpp>
 #include <af/dim4.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -28,10 +26,8 @@ void iota(Param<T> out, const af::dim4 &sdims) {
     constexpr unsigned TILEX   = 512;
     constexpr unsigned TILEY   = 32;
 
-    static const std::string source(iota_cuh, iota_cuh_len);
-
-    auto iota =
-        common::getKernel("cuda::iota", {source}, {TemplateTypename<T>()});
+    auto iota = common::getKernel("cuda::iota", {iota_cuh_src},
+                                  {TemplateTypename<T>()});
 
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 091081170a..f1fd13d054 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -19,16 +19,10 @@
 #include "config.hpp"
 
 #include <memory>
-#include <string>
 
 namespace cuda {
 namespace kernel {
 
-static inline std::string ireduceSource() {
-    static const std::string src(ireduce_cuh, ireduce_cuh_len);
-    return src;
-}
-
 template<typename T, af_op_t op, int dim, bool is_first>
 void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
                           const uint *ilptr, const uint threads_y,
@@ -43,7 +37,7 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     blocks.y = divup(blocks.y, blocks.z);
 
     auto ireduceDim = common::getKernel(
-        "cuda::ireduceDim", {ireduceSource()},
+        "cuda::ireduceDim", {ireduce_cuh_src},
         {TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
          TemplateArg(is_first), TemplateArg(threads_y)},
         {DefineValue(THREADS_X)});
@@ -111,7 +105,7 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     // threads_x can take values 32, 64, 128, 256
     auto ireduceFirst =
-        common::getKernel("cuda::ireduceFirst", {ireduceSource()},
+        common::getKernel("cuda::ireduceFirst", {ireduce_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(op),
                            TemplateArg(is_first), TemplateArg(threads_x)},
                           {DefineValue(THREADS_PER_BLOCK)});
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
index e65cc95b20..f404f7b8bf 100644
--- a/src/backend/cuda/kernel/join.hpp
+++ b/src/backend/cuda/kernel/join.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/join_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ void join(Param<T> out, CParam<T> X, const af::dim4 &offset, int dim) {
     constexpr unsigned TILEX = 256;
     constexpr unsigned TILEY = 32;
 
-    static const std::string source(join_cuh, join_cuh_len);
-
-    auto join =
-        common::getKernel("cuda::join", {source}, {TemplateTypename<T>()});
+    auto join = common::getKernel("cuda::join", {join_cuh_src},
+                                  {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index afa7df98cb..4f4758dca3 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/lookup_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -28,8 +26,6 @@ constexpr int THRD_LOAD = THREADS_X / THREADS_Y;
 template<typename in_t, typename idx_t>
 void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
             unsigned dim) {
-    static const std::string src(lookup_cuh, lookup_cuh_len);
-
     /* find which dimension has non-zero # of elements */
     unsigned vDim = 0;
     for (int i = 0; i < 4; i++) {
@@ -47,7 +43,7 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         dim3 blocks(blks, 1);
 
         auto lookup1d = common::getKernel(
-            "cuda::lookup1D", {src},
+            "cuda::lookup1D", {lookup_cuh_src},
             {TemplateTypename<in_t>(), TemplateTypename<idx_t>()},
             {DefineValue(THREADS), DefineValue(THRD_LOAD)});
 
@@ -68,7 +64,7 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         blocks.y = divup(blocks.y, blocks.z);
 
         auto lookupnd =
-            common::getKernel("cuda::lookupND", {src},
+            common::getKernel("cuda::lookupND", {lookup_cuh_src},
                               {TemplateTypename<in_t>(),
                                TemplateTypename<idx_t>(), TemplateArg(dim)});
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 84fabaf18e..72def543e3 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/lu_split_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,13 +25,12 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
     constexpr unsigned TILEX = 128;
     constexpr unsigned TILEY = 32;
 
-    static const std::string src(lu_split_cuh, lu_split_cuh_len);
-
     const bool sameDims =
         lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
-    auto luSplit = common::getKernel(
-        "cuda::luSplit", {src}, {TemplateTypename<T>(), TemplateArg(sameDims)});
+    auto luSplit =
+        common::getKernel("cuda::luSplit", {lu_split_cuh_src},
+                          {TemplateTypename<T>(), TemplateArg(sameDims)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 58cc99d118..31d75e1bd6 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -14,8 +14,6 @@
 #include <nvrtc_kernel_headers/match_template_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -26,10 +24,8 @@ template<typename inType, typename outType>
 void matchTemplate(Param<outType> out, CParam<inType> srch,
                    CParam<inType> tmplt, const af::matchType mType,
                    bool needMean) {
-    static const std::string source(match_template_cuh, match_template_cuh_len);
-
     auto matchTemplate = common::getKernel(
-        "cuda::matchTemplate", {source},
+        "cuda::matchTemplate", {match_template_cuh_src},
         {TemplateTypename<inType>(), TemplateTypename<outType>(),
          TemplateArg(mType), TemplateArg(needMean)});
 
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index a082f0a5d3..ffa3cba76b 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -13,7 +13,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/meanshift_cuh.hpp>
 
-#include <string>
 #include <type_traits>
 
 namespace cuda {
@@ -27,10 +26,8 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
                const float chromaticSigma, const uint numIters, bool IsColor) {
     typedef typename std::conditional<std::is_same<T, double>::value, double,
                                       float>::type AccType;
-    static const std::string source(meanshift_cuh, meanshift_cuh_len);
-
     auto meanshift = common::getKernel(
-        "cuda::meanshift", {source},
+        "cuda::meanshift", {meanshift_cuh_src},
         {
             TemplateTypename<AccType>(), TemplateTypename<T>(),
             TemplateArg((IsColor ? 3 : 1))  // channels
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index c1ab6d50d3..3095db1a46 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -14,8 +14,6 @@
 #include <nvrtc_kernel_headers/medfilt_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -28,10 +26,8 @@ template<typename T>
 void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
               int w_wid) {
     UNUSED(w_wid);
-    static const std::string source(medfilt_cuh, medfilt_cuh_len);
-
     auto medfilt2 =
-        common::getKernel("cuda::medfilt2", {source},
+        common::getKernel("cuda::medfilt2", {medfilt_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(pad),
                            TemplateArg(w_len), TemplateArg(w_wid)},
                           {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
@@ -50,10 +46,8 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
 
 template<typename T>
 void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
-    static const std::string source(medfilt_cuh, medfilt_cuh_len);
-
     auto medfilt1 = common::getKernel(
-        "cuda::medfilt1", {source},
+        "cuda::medfilt1", {medfilt_cuh_src},
         {TemplateTypename<T>(), TemplateArg(pad), TemplateArg(w_wid)});
 
     const dim3 threads(THREADS_X);
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index e966d69490..49d18f7fa3 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -19,7 +19,6 @@
 #include <nvrtc_kernel_headers/memcopy_cuh.hpp>
 
 #include <algorithm>
-#include <string>
 
 namespace cuda {
 namespace kernel {
@@ -29,10 +28,8 @@ constexpr uint DIMY = 8;
 
 template<typename T>
 void memcopy(Param<T> out, CParam<T> in, const dim_t ndims) {
-    static const std::string src(memcopy_cuh, memcopy_cuh_len);
-
-    auto memCopy =
-        common::getKernel("cuda::memcopy", {src}, {TemplateTypename<T>()});
+    auto memCopy = common::getKernel("cuda::memcopy", {memcopy_cuh_src},
+                                     {TemplateTypename<T>()});
 
     dim3 threads(DIMX, DIMY);
 
@@ -62,8 +59,6 @@ void memcopy(Param<T> out, CParam<T> in, const dim_t ndims) {
 template<typename inType, typename outType>
 void copy(Param<outType> dst, CParam<inType> src, int ndims,
           outType default_value, double factor) {
-    static const std::string source(copy_cuh, copy_cuh_len);
-
     dim3 threads(DIMX, DIMY);
     size_t local_size[] = {DIMX, DIMY};
 
@@ -92,7 +87,7 @@ void copy(Param<outType> dst, CParam<inType> src, int ndims,
          (src.dims[2] == dst.dims[2]) && (src.dims[3] == dst.dims[3]));
 
     auto copy = common::getKernel(
-        "cuda::copy", {source},
+        "cuda::copy", {copy_cuh_src},
         {TemplateTypename<inType>(), TemplateTypename<outType>(),
          TemplateArg(same_dims)});
 
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index f1d7909942..03f536eaeb 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -14,8 +14,6 @@
 #include <nvrtc_kernel_headers/moments_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -23,10 +21,8 @@ static const int THREADS = 128;
 
 template<typename T>
 void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
-    static const std::string source(moments_cuh, moments_cuh_len);
-
-    auto moments =
-        common::getKernel("cuda::moments", {source}, {TemplateTypename<T>()});
+    auto moments = common::getKernel("cuda::moments", {moments_cuh_src},
+                                     {TemplateTypename<T>()});
 
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 3853a020ad..d9ae0ea37f 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -14,7 +14,6 @@
 #include <nvrtc_kernel_headers/morph_cuh.hpp>
 
 #include <limits>
-#include <string>
 
 namespace cuda {
 namespace kernel {
@@ -28,13 +27,11 @@ static const int CUBE_Z               = 8;
 
 template<typename T>
 void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
-    static const std::string source(morph_cuh, morph_cuh_len);
-
     const int windLen  = mask.dims[0];
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
     auto morph = common::getKernel(
-        "cuda::morph", {source},
+        "cuda::morph", {morph_cuh_src},
         {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(SeLength)},
         {
             DefineValue(MAX_MORPH_FILTER_LEN),
@@ -64,8 +61,6 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
 
 template<typename T>
 void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
-    static const std::string source(morph_cuh, morph_cuh_len);
-
     const int windLen = mask.dims[0];
 
     if (windLen > 7) {
@@ -73,7 +68,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     }
 
     auto morph3D = common::getKernel(
-        "cuda::morph3D", {source},
+        "cuda::morph3D", {morph_cuh_src},
         {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(windLen)},
         {
             DefineValue(MAX_MORPH_FILTER_LEN),
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index daf6fc9c53..decc7a5ae2 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -16,8 +16,6 @@
 #include <nvrtc_kernel_headers/pad_array_borders_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ static const int PADB_THREADS_Y = 8;
 template<typename T>
 void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
                 const af::borderType btype) {
-    static const std::string source(pad_array_borders_cuh,
-                                    pad_array_borders_cuh_len);
     auto padBorders =
-        common::getKernel("cuda::padBorders", {source},
+        common::getKernel("cuda::padBorders", {pad_array_borders_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(btype)});
 
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 1bd88ccd70..4364d3e6a6 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/range_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ void range(Param<T> out, const int dim) {
     constexpr unsigned RANGE_TILEX = 512;
     constexpr unsigned RANGE_TILEY = 32;
 
-    static const std::string source(range_cuh, range_cuh_len);
-
-    auto range =
-        common::getKernel("cuda::range", {source}, {TemplateTypename<T>()});
+    auto range = common::getKernel("cuda::range", {range_cuh_src},
+                                   {TemplateTypename<T>()});
 
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index 2cac3be7d5..fc6920ab7f 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/reorder_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    static const std::string source(reorder_cuh, reorder_cuh_len);
-
-    auto reorder =
-        common::getKernel("cuda::reorder", {source}, {TemplateTypename<T>()});
+    auto reorder = common::getKernel("cuda::reorder", {reorder_cuh_src},
+                                     {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index 5964bcf11b..7c5504c75b 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -14,8 +14,6 @@
 #include <nvrtc_kernel_headers/resize_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,10 +23,9 @@ static const unsigned TY = 16;
 
 template<typename T>
 void resize(Param<T> out, CParam<T> in, af_interp_type method) {
-    static const std::string source(resize_cuh, resize_cuh_len);
-
-    auto resize = common::getKernel(
-        "cuda::resize", {source}, {TemplateTypename<T>(), TemplateArg(method)});
+    auto resize =
+        common::getKernel("cuda::resize", {resize_cuh_src},
+                          {TemplateTypename<T>(), TemplateArg(method)});
 
     dim3 threads(TX, TY, 1);
     dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 1af65b67be..648e126230 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -16,8 +16,6 @@
 #include <nvrtc_kernel_headers/rotate_cuh.hpp>
 #include <af/defines.h>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -34,10 +32,9 @@ typedef struct {
 template<typename T>
 void rotate(Param<T> out, CParam<T> in, const float theta,
             const af::interpType method, const int order) {
-    static const std::string source(rotate_cuh, rotate_cuh_len);
-
-    auto rotate = common::getKernel(
-        "cuda::rotate", {source}, {TemplateTypename<T>(), TemplateArg(order)});
+    auto rotate =
+        common::getKernel("cuda::rotate", {rotate_cuh_src},
+                          {TemplateTypename<T>(), TemplateArg(order)});
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 1282ad415b..dafa280267 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -20,14 +20,12 @@
 namespace cuda {
 namespace kernel {
 
-static const std::string ScanDimSource(scan_dim_cuh, scan_dim_cuh_len);
-
 template<typename Ti, typename To, af_op_t op>
 static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                               const uint threads_y, const dim_t blocks_all[4],
                               int dim, bool isFinalPass, bool inclusive_scan) {
     auto scan_dim = common::getKernel(
-        "cuda::scan_dim", {ScanDimSource},
+        "cuda::scan_dim", {scan_dim_cuh_src},
         {TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(op),
          TemplateArg(dim), TemplateArg(isFinalPass), TemplateArg(threads_y),
          TemplateArg(inclusive_scan)},
@@ -55,7 +53,7 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
                                const uint threads_y, const dim_t blocks_all[4],
                                int dim, bool inclusive_scan) {
     auto scan_dim_bcast = common::getKernel(
-        "cuda::scan_dim_bcast", {ScanDimSource},
+        "cuda::scan_dim_bcast", {scan_dim_cuh_src},
         {TemplateTypename<To>(), TemplateArg(op), TemplateArg(dim)});
 
     dim3 threads(THREADS_X, threads_y);
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 04c4bd8925..e3a618d125 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -20,16 +20,10 @@
 #include <traits.hpp>
 
 #include <algorithm>
-#include <string>
 
 namespace cuda {
 namespace kernel {
 
-static inline std::string sbkDimSource() {
-    static const std::string src(scan_dim_by_key_cuh, scan_dim_by_key_cuh_len);
-    return src;
-}
-
 template<typename Ti, typename Tk, typename To, af_op_t op>
 static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        Param<char> tflg, Param<int> tlid,
@@ -38,7 +32,7 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
     auto scanbykey_dim_nonfinal = common::getKernel(
-        "cuda::scanbykey_dim_nonfinal", {sbkDimSource()},
+        "cuda::scanbykey_dim_nonfinal", {scan_dim_by_key_cuh_src},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
@@ -62,7 +56,7 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
     auto scanbykey_dim_final = common::getKernel(
-        "cuda::scanbykey_dim_final", {sbkDimSource()},
+        "cuda::scanbykey_dim_final", {scan_dim_by_key_cuh_src},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
@@ -83,9 +77,9 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
-    auto scanbykey_dim_bcast =
-        common::getKernel("cuda::scanbykey_dim_bcast", {sbkDimSource()},
-                          {TemplateTypename<To>(), TemplateArg(op)});
+    auto scanbykey_dim_bcast = common::getKernel(
+        "cuda::scanbykey_dim_bcast", {scan_dim_by_key_cuh_src},
+        {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 14ff57df61..f400f4b5d3 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -20,15 +20,13 @@
 namespace cuda {
 namespace kernel {
 
-static const std::string ScanFirstSource(scan_first_cuh, scan_first_cuh_len);
-
 template<typename Ti, typename To, af_op_t op>
 static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
     auto scan_first =
-        common::getKernel("cuda::scan_first", {ScanFirstSource},
+        common::getKernel("cuda::scan_first", {scan_first_cuh_src},
                           {TemplateTypename<Ti>(), TemplateTypename<To>(),
                            TemplateArg(op), TemplateArg(isFinalPass),
                            TemplateArg(threads_x), TemplateArg(inclusive_scan)},
@@ -54,7 +52,7 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
     auto scan_first_bcast =
-        common::getKernel("cuda::scan_first_bcast", {ScanFirstSource},
+        common::getKernel("cuda::scan_first_bcast", {scan_first_cuh_src},
                           {TemplateTypename<To>(), TemplateArg(op)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 89bda149d0..b5e2d070e1 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -19,17 +19,10 @@
 #include <optypes.hpp>
 
 #include <algorithm>
-#include <string>
 
 namespace cuda {
 namespace kernel {
 
-static inline std::string sbkFirstSource() {
-    static const std::string src(scan_first_by_key_cuh,
-                                 scan_first_by_key_cuh_len);
-    return src;
-}
-
 template<typename Ti, typename Tk, typename To, af_op_t op>
 static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    Param<char> tflg, Param<int> tlid,
@@ -37,7 +30,7 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
     auto scanbykey_first_nonfinal = common::getKernel(
-        "cuda::scanbykey_first_nonfinal", {sbkFirstSource()},
+        "cuda::scanbykey_first_nonfinal", {scan_first_by_key_cuh_src},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
@@ -58,7 +51,7 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
     auto scanbykey_first_final = common::getKernel(
-        "cuda::scanbykey_first_final", {sbkFirstSource()},
+        "cuda::scanbykey_first_final", {scan_first_by_key_cuh_src},
         {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
          TemplateArg(op)},
         {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
@@ -77,9 +70,9 @@ template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
-    auto scanbykey_first_bcast =
-        common::getKernel("cuda::scanbykey_first_bcast", {sbkFirstSource()},
-                          {TemplateTypename<To>(), TemplateArg(op)});
+    auto scanbykey_first_bcast = common::getKernel(
+        "cuda::scanbykey_first_bcast", {scan_first_by_key_cuh_src},
+        {TemplateTypename<To>(), TemplateArg(op)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 547c2adf05..433875c009 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -16,8 +16,6 @@
 #include <math.hpp>
 #include <nvrtc_kernel_headers/select_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,11 +23,6 @@ constexpr uint DIMX  = 32;
 constexpr uint DIMY  = 8;
 constexpr int REPEAT = 64;
 
-static inline std::string selectSource() {
-    static const std::string src(select_cuh, select_cuh_len);
-    return src;
-}
-
 template<typename T>
 void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
             int ndims) {
@@ -37,7 +30,7 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
     auto select =
-        common::getKernel("cuda::select", {selectSource()},
+        common::getKernel("cuda::select", {select_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(is_same)});
 
     dim3 threads(DIMX, DIMY);
@@ -67,7 +60,7 @@ template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
                    int ndims, bool flip) {
     auto selectScalar =
-        common::getKernel("cuda::selectScalar", {selectSource()},
+        common::getKernel("cuda::selectScalar", {select_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(flip)});
 
     dim3 threads(DIMX, DIMY);
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index d00649598c..0c2f5a5324 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/sobel_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,9 @@ template<typename Ti, typename To>
 void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
            const unsigned& ker_size) {
     UNUSED(ker_size);
-    static const std::string source(sobel_cuh, sobel_cuh_len);
 
     auto sobel3x3 =
-        common::getKernel("cuda::sobel3x3", {source},
+        common::getKernel("cuda::sobel3x3", {sobel_cuh_src},
                           {
                               TemplateTypename<Ti>(),
                               TemplateTypename<To>(),
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 0147bc165e..797b7fec5f 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/sparse_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,11 +23,9 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
                CParam<int> colIdx) {
     constexpr int reps = 4;
 
-    static const std::string source(sparse_cuh, sparse_cuh_len);
-
     auto coo2Dense =
-        common::getKernel("cuda::coo2Dense", {source}, {TemplateTypename<T>()},
-                          {DefineValue(reps)});
+        common::getKernel("cuda::coo2Dense", {sparse_cuh_src},
+                          {TemplateTypename<T>()}, {DefineValue(reps)});
 
     dim3 threads(256, 1, 1);
 
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 7544c2ab04..0f2f4ac70d 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -16,8 +16,6 @@
 #include <nvrtc_kernel_headers/sparse_arith_cuh.hpp>
 #include <optypes.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,16 +23,11 @@ constexpr unsigned TX      = 32;
 constexpr unsigned TY      = 8;
 constexpr unsigned THREADS = TX * TY;
 
-static inline std::string sparseArithSrc() {
-    static const std::string src(sparse_arith_cuh, sparse_arith_cuh_len);
-    return src;
-}
-
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     auto csrArithDSD =
-        common::getKernel("cuda::csrArithDSD", {sparseArithSrc()},
+        common::getKernel("cuda::csrArithDSD", {sparse_arith_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(op)},
                           {DefineValue(TX), DefineValue(TY)});
 
@@ -54,7 +47,7 @@ template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     auto cooArithDSD = common::getKernel(
-        "cuda::cooArithDSD", {sparseArithSrc()},
+        "cuda::cooArithDSD", {sparse_arith_cuh_src},
         {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
@@ -73,7 +66,7 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
     auto csrArithSSD =
-        common::getKernel("cuda::csrArithSSD", {sparseArithSrc()},
+        common::getKernel("cuda::csrArithSSD", {sparse_arith_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(op)},
                           {DefineValue(TX), DefineValue(TY)});
 
@@ -93,7 +86,7 @@ template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
     auto cooArithSSD = common::getKernel(
-        "cuda::cooArithSSD", {sparseArithSrc()},
+        "cuda::cooArithSSD", {sparse_arith_cuh_src},
         {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index ab767e67d3..6d45a41058 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -15,25 +15,18 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/susan_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
 constexpr unsigned BLOCK_X = 16;
 constexpr unsigned BLOCK_Y = 16;
 
-static inline std::string susanSource() {
-    static const std::string src(susan_cuh, susan_cuh_len);
-    return src;
-}
-
 template<typename T>
 void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
     auto susan = common::getKernel(
-        "cuda::susan", {susanSource()}, {TemplateTypename<T>()},
+        "cuda::susan", {susan_cuh_src}, {TemplateTypename<T>()},
         {DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
@@ -52,7 +45,7 @@ template<typename T>
 void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
-    auto nonMax = common::getKernel("cuda::nonMax", {susanSource()},
+    auto nonMax = common::getKernel("cuda::nonMax", {susan_cuh_src},
                                     {TemplateTypename<T>()});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index e6f34d616a..8edebf3991 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -25,10 +25,8 @@ void tile(Param<T> out, CParam<T> in) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    static const std::string source(tile_cuh, tile_cuh_len);
-
-    auto tile =
-        common::getKernel("cuda::tile", {source}, {TemplateTypename<T>()});
+    auto tile = common::getKernel("cuda::tile", {tile_cuh_src},
+                                  {TemplateTypename<T>()});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 78182d18ab..df9bf32c8b 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -17,7 +17,6 @@
 #include <af/defines.h>
 
 #include <algorithm>
-#include <string>
 
 namespace cuda {
 namespace kernel {
@@ -31,10 +30,8 @@ static const unsigned TI = 4;
 template<typename T>
 void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
                const bool perspective, const af::interpType method, int order) {
-    static const std::string src(transform_cuh, transform_cuh_len);
-
     auto transform = common::getKernel(
-        "cuda::transform", {src},
+        "cuda::transform", {transform_cuh_src},
         {TemplateTypename<T>(), TemplateArg(inverse), TemplateArg(order)});
 
     const unsigned int nImg2  = in.dims[2];
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 518ecb77da..3a5101a37d 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/transpose_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T>
 void transpose(Param<T> out, CParam<T> in, const bool conjugate,
                const bool is32multiple) {
-    static const std::string source(transpose_cuh, transpose_cuh_len);
-
     auto transpose =
-        common::getKernel("cuda::transpose", {source},
+        common::getKernel("cuda::transpose", {transpose_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(conjugate),
                            TemplateArg(is32multiple)},
                           {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index 5452a7c19c..0ba76f19da 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/transpose_inplace_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T>
 void transpose_inplace(Param<T> in, const bool conjugate,
                        const bool is32multiple) {
-    static const std::string source(transpose_inplace_cuh,
-                                    transpose_inplace_cuh_len);
     auto transposeIP =
-        common::getKernel("cuda::transposeIP", {source},
+        common::getKernel("cuda::transposeIP", {transpose_inplace_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(conjugate),
                            TemplateArg(is32multiple)},
                           {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 00451e1ec7..b49601ce51 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -15,8 +15,6 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/triangle_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -27,10 +25,8 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
     constexpr unsigned TILEX = 128;
     constexpr unsigned TILEY = 32;
 
-    static const std::string source(triangle_cuh, triangle_cuh_len);
-
     auto triangle =
-        common::getKernel("cuda::triangle", {source},
+        common::getKernel("cuda::triangle", {triangle_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(is_upper),
                            TemplateArg(is_unit_diag)});
 
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 5cb267a7f2..d1d83efa60 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -16,8 +16,6 @@
 #include <kernel/config.hpp>
 #include <nvrtc_kernel_headers/unwrap_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
@@ -25,10 +23,8 @@ template<typename T>
 void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
             const int sx, const int sy, const int px, const int py,
             const int dx, const int dy, const int nx, const bool is_column) {
-    static const std::string source(unwrap_cuh, unwrap_cuh_len);
-
     auto unwrap =
-        common::getKernel("cuda::unwrap", {source},
+        common::getKernel("cuda::unwrap", {unwrap_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(is_column)});
 
     dim3 threads, blocks;
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 380f05786a..66555253c0 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -23,9 +23,8 @@ namespace kernel {
 
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
-    static const std::string src(where_cuh, where_cuh_len);
-    auto where =
-        common::getKernel("cuda::where", {src}, {TemplateTypename<T>()});
+    auto where = common::getKernel("cuda::where", {where_cuh_src},
+                                   {TemplateTypename<T>()});
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index be0cacef19..33a32a6ef3 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -16,18 +16,14 @@
 #include <kernel/config.hpp>
 #include <nvrtc_kernel_headers/wrap_cuh.hpp>
 
-#include <string>
-
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
           const int sy, const int px, const int py, const bool is_column) {
-    static const std::string source(wrap_cuh, wrap_cuh_len);
-
     auto wrap =
-        common::getKernel("cuda::wrap", {source},
+        common::getKernel("cuda::wrap", {wrap_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(is_column)});
 
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
@@ -55,10 +51,8 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                   const dim_t sx, const dim_t sy, const dim_t px,
                   const dim_t py, const dim_t dx, const dim_t dy,
                   const bool is_column) {
-    static const std::string source(wrap_cuh, wrap_cuh_len);
-
     auto wrap =
-        common::getKernel("cuda::wrap_dilated", {source},
+        common::getKernel("cuda::wrap_dilated", {wrap_cuh_src},
                           {TemplateTypename<T>(), TemplateArg(is_column)});
 
     int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index b49521cffd..5478f6e315 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -36,6 +36,7 @@ using cl::NullRange;
 
 using std::string;
 using std::stringstream;
+using std::to_string;
 using std::vector;
 
 namespace opencl {
@@ -143,7 +144,7 @@ cl::Kernel getKernel(const vector<Node *> &output_nodes,
                      const vector<Node_ids> &full_ids, const bool is_linear) {
     const string funcName =
         getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    const string moduleKey = std::to_string(deterministicHash(funcName));
+    const size_t moduleKey = deterministicHash(funcName);
 
     // A forward lookup in module cache helps avoid recompiling the jit
     // source generated from identical jit-trees. It also enables us
@@ -151,11 +152,12 @@ cl::Kernel getKernel(const vector<Node *> &output_nodes,
     auto entry = common::findModule(getActiveDeviceId(), moduleKey);
 
     if (!entry) {
-        static const string jit(jit_cl, jit_cl_len);
-
         string jitKer = getKernelString(funcName, full_nodes, full_ids,
                                         output_ids, is_linear);
-        int device    = getActiveDeviceId();
+        common::Source jitKer_cl_src{
+            jitKer.data(), jitKer.size(),
+            deterministicHash(jitKer.data(), jitKer.size())};
+        int device = getActiveDeviceId();
         vector<string> options;
         if (isDoubleSupported(device)) {
             options.emplace_back(DefineKey(USE_DOUBLE));
@@ -166,7 +168,8 @@ cl::Kernel getKernel(const vector<Node *> &output_nodes,
 
         saveKernel(funcName, jitKer, ".cl");
 
-        return common::getKernel(funcName, {jit, jitKer}, {}, options, true)
+        return common::getKernel(funcName, {jit_cl_src, jitKer_cl_src}, {},
+                                 options, true)
             .get();
     }
     return common::getKernel(entry, funcName, true).get();
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index 61fdde34b3..e7d18136dd 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -34,9 +34,6 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     constexpr int THREADS_Y = 8;
     constexpr int YDIM_LOAD = 2 * THREADS_X / THREADS_Y;
 
-    static const string src(anisotropic_diffusion_cl,
-                            anisotropic_diffusion_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(isMCDE),
@@ -53,7 +50,8 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto diffUpdate =
-        common::getKernel("aisoDiffUpdate", {src}, tmpltArgs, compileOpts);
+        common::getKernel("aisoDiffUpdate", {anisotropic_diffusion_cl_src},
+                          tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 782383332f..be569fbf61 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -27,11 +27,6 @@
 namespace opencl {
 namespace kernel {
 
-inline std::string interpSrc() {
-    static const std::string src(interp_cl, interp_cl_len);
-    return src;
-}
-
 template<typename Ty, typename Tp>
 auto genCompileOptions(const int order, const int xdim, const int ydim = -1) {
     constexpr bool isComplex =
@@ -69,8 +64,6 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
 
     constexpr int THREADS = 256;
 
-    static const string src(approx1_cl, approx1_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ty>(),
         TemplateTypename<Tp>(),
@@ -79,8 +72,8 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim);
 
-    auto approx1 = common::getKernel("approx1", {interpSrc(), src}, tmpltArgs,
-                                     compileOpts);
+    auto approx1 = common::getKernel("approx1", {interp_cl_src, approx1_cl_src},
+                                     tmpltArgs, compileOpts);
 
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
@@ -111,16 +104,14 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     constexpr int TX = 16;
     constexpr int TY = 16;
 
-    static const string src(approx2_cl, approx2_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(xdim),
         TemplateArg(ydim),      TemplateArg(order),
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim, ydim);
 
-    auto approx2 = common::getKernel("approx2", {interpSrc(), src}, tmpltArgs,
-                                     compileOpts);
+    auto approx2 = common::getKernel("approx2", {interp_cl_src, approx2_cl_src},
+                                     tmpltArgs, compileOpts);
 
     NDRange local(TX, TY, 1);
     dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 83943d5b7d..568ec9b185 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -34,8 +34,6 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    static const std::string src(assign_cl, assign_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -44,7 +42,8 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto assign = common::getKernel("assignKernel", {src}, targs, options);
+    auto assign =
+        common::getKernel("assignKernel", {assign_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 3926d85d35..168fbcea6d 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -32,8 +32,6 @@ void bilateral(Param out, const Param in, const float s_sigma,
     constexpr bool UseNativeExp = !std::is_same<inType, double>::value ||
                                   std::is_same<inType, cdouble>::value;
 
-    static const std::string src(bilateral_cl, bilateral_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<inType>(),
         TemplateTypename<outType>(),
@@ -45,7 +43,8 @@ void bilateral(Param out, const Param in, const float s_sigma,
     if (UseNativeExp) { options.emplace_back(DefineKey(USE_NATIVE_EXP)); }
     options.emplace_back(getTypeBuildDefinition<inType>());
 
-    auto bilateralOp = common::getKernel("bilateral", {src}, targs, options);
+    auto bilateralOp =
+        common::getKernel("bilateral", {bilateral_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index ebe2cb5f0c..3c82b9df4f 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -34,7 +34,6 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     using std::string;
     using std::vector;
 
-    static const string src(nonmax_suppression_cl, nonmax_suppression_cl_len);
     vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(SHRD_MEM_HEIGHT, THREADS_X + 2),
@@ -42,7 +41,8 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto nonMaxOp = common::getKernel("nonMaxSuppressionKernel", {src},
+    auto nonMaxOp = common::getKernel("nonMaxSuppressionKernel",
+                                      {nonmax_suppression_cl_src},
                                       {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
@@ -68,15 +68,13 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
     using std::string;
     using std::vector;
 
-    static const string src(trace_edge_cl, trace_edge_cl_len);
-
     vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKey(INIT_EDGE_OUT),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto initOp = common::getKernel("initEdgeOutKernel", {src},
+    auto initOp = common::getKernel("initEdgeOutKernel", {trace_edge_cl_src},
                                     {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
@@ -102,16 +100,15 @@ void suppressLeftOver(Param output) {
     using std::string;
     using std::vector;
 
-    static const string src(trace_edge_cl, trace_edge_cl_len);
-
     vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKey(SUPPRESS_LEFT_OVER),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalOp = common::getKernel("suppressLeftOverKernel", {src},
-                                     {TemplateTypename<T>()}, options);
+    auto finalOp =
+        common::getKernel("suppressLeftOverKernel", {trace_edge_cl_src},
+                          {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -136,8 +133,6 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     using std::string;
     using std::vector;
 
-    static const string src(trace_edge_cl, trace_edge_cl_len);
-
     vector<string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKey(EDGE_TRACER),
@@ -147,7 +142,7 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto edgeTraceOp = common::getKernel("edgeTrackKernel", {src},
+    auto edgeTraceOp = common::getKernel("edgeTrackKernel", {trace_edge_cl_src},
                                          {TemplateTypename<T>()}, options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 07cb007a71..abe95ae896 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -26,9 +26,6 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    static const string src1(ops_cl, ops_cl_len);
-    static const string src2(convolve_cl, convolve_cl_len);
-
     const int f0 = filter.info.dims[0];
     const int f1 = filter.info.dims[1];
     const size_t LOC_SIZE =
@@ -53,8 +50,8 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto convolve =
-        common::getKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+    auto convolve = common::getKernel("convolve", {ops_cl_src, convolve_cl_src},
+                                      tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, *param.impulse, filter.info,
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 28017415b8..9f160703ef 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -95,9 +95,6 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    static const string src1(ops_cl, ops_cl_len);
-    static const string src2(convolve_cl, convolve_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateTypename<aT>(),
@@ -116,8 +113,8 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto convolve =
-        common::getKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+    auto convolve = common::getKernel("convolve", {ops_cl_src, convolve_cl_src},
+                                      tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, cl::Local(param.loc_size),
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 1d9b95695e..85b9bfadb9 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -39,10 +39,6 @@ void convSep(Param out, const Param signal, const Param filter,
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(convolve_separable_cl,
-                                  convolve_separable_cl_len);
-
     const int fLen       = filter.info.dims[0] * filter.info.dims[1];
     const size_t C0_SIZE = (THREADS_X + 2 * (fLen - 1)) * THREADS_Y;
     const size_t C1_SIZE = (THREADS_Y + 2 * (fLen - 1)) * THREADS_X;
@@ -68,7 +64,8 @@ void convSep(Param out, const Param signal, const Param filter,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto conv =
-        common::getKernel("convolve", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("convolve", {ops_cl_src, convolve_separable_cl_src},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 54c52d35fe..7047af13aa 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -35,8 +35,6 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
     constexpr int rows_per_group = 8;
     constexpr int cols_per_group = 8;
 
-    static const std::string src(cscmm_cl, cscmm_cl_len);
-
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
@@ -58,7 +56,8 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto cscmmNN = common::getKernel("cscmm_nn", {src}, targs, options);
+    auto cscmmNN =
+        common::getKernel("cscmm_nn", {cscmm_cl_src}, targs, options);
 
     cl::NDRange local(threads, 1);
     int M = out.info.dims[0];
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 9d91fafb19..bc741a3051 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -34,8 +34,6 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
     // handle this.
     constexpr int rows_per_group = 64;
 
-    static const std::string src(cscmv_cl, cscmv_cl_len);
-
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
@@ -55,7 +53,8 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto cscmvBlock = common::getKernel("cscmv_block", {src}, targs, options);
+    auto cscmvBlock =
+        common::getKernel("cscmv_block", {cscmv_cl_src}, targs, options);
 
     cl::NDRange local(threads);
     int K        = colIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index c5e742daa5..00100ba389 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -35,8 +35,6 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
     // FIXME: Figure out why
     constexpr bool use_greedy = false;
 
-    static const std::string src(csrmm_cl, csrmm_cl_len);
-
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
@@ -57,7 +55,8 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     // FIXME: Switch to perf (thread vs block) baesd kernel
-    auto csrmm_nt_func = common::getKernel("csrmm_nt", {src}, targs, options);
+    auto csrmm_nt_func =
+        common::getKernel("csrmm_nt", {csrmm_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 56af2d05f6..92ab380a7d 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -36,8 +36,6 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
     // FIXME: Find a better number based on average non zeros per row
     constexpr int threads = 64;
 
-    static const std::string src(csrmv_cl, csrmv_cl_len);
-
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
@@ -55,8 +53,10 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csrmvThread = common::getKernel("csrmv_thread", {src}, targs, options);
-    auto csrmvBlock  = common::getKernel("csrmv_block", {src}, targs, options);
+    auto csrmvThread =
+        common::getKernel("csrmv_thread", {csrmv_cl_src}, targs, options);
+    auto csrmvBlock =
+        common::getKernel("csrmv_block", {csrmv_cl_src}, targs, options);
 
     int count           = 0;
     cl::Buffer *counter = bufferAlloc(sizeof(int));
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 3de60858e7..4ed94e2ba6 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -27,8 +27,6 @@ namespace kernel {
 
 template<typename T>
 static void diagCreate(Param out, Param in, int num) {
-    static const std::string src(diag_create_cl, diag_create_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -38,8 +36,8 @@ static void diagCreate(Param out, Param in, int num) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto diagCreate =
-        common::getKernel("diagCreateKernel", {src}, targs, options);
+    auto diagCreate = common::getKernel("diagCreateKernel",
+                                        {diag_create_cl_src}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
@@ -54,8 +52,6 @@ static void diagCreate(Param out, Param in, int num) {
 
 template<typename T>
 static void diagExtract(Param out, Param in, int num) {
-    static const std::string src(diag_extract_cl, diag_extract_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -65,8 +61,8 @@ static void diagExtract(Param out, Param in, int num) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto diagExtract =
-        common::getKernel("diagExtractKernel", {src}, targs, options);
+    auto diagExtract = common::getKernel("diagExtractKernel",
+                                         {diag_extract_cl_src}, targs, options);
 
     cl::NDRange local(256, 1);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index bc04be7dc8..02251f6d41 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -28,8 +28,6 @@ void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
     constexpr int TX = 16;
     constexpr int TY = 16;
 
-    static const std::string src(diff_cl, diff_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(dim),
@@ -42,7 +40,8 @@ void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto diffOp = common::getKernel("diff_kernel", {src}, targs, options);
+    auto diffOp =
+        common::getKernel("diff_kernel", {diff_cl_src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
     if (dim == 0 && indims == 1) { local = cl::NDRange(TX * TY, 1, 1); }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 3473145aa8..98ff024060 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -41,8 +41,6 @@ constexpr int THREADS_Y = 16;
 
 template<typename T>
 void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
-    static const std::string src(example_cl, example_cl_len);
-
     // Compilation options for compiling OpenCL kernel.
     // Go to common/kernel_cache.hpp to find details on this.
     std::vector<TemplateArg> targs = {
@@ -63,7 +61,7 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
 
     // Fetch the Kernel functor, go to common/kernel_cache.hpp
     // to find details of this function
-    auto exOp = common::getKernel("example", {src}, targs, options);
+    auto exOp = common::getKernel("example", {example_cl_src}, targs, options);
 
     // configure work group parameters
     cl::NDRange local(THREADS_X, THREADS_Y);
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index eeb1cce534..82cb2bd51d 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -33,8 +33,6 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     constexpr int FAST_THREADS_NONMAX_X = 32;
     constexpr int FAST_THREADS_NONMAX_Y = 8;
 
-    static const std::string src(fast_cl, fast_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(arc_length),
@@ -47,9 +45,12 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto locate  = common::getKernel("locate_features", {src}, targs, options);
-    auto nonMax  = common::getKernel("non_max_counts", {src}, targs, options);
-    auto getFeat = common::getKernel("get_features", {src}, targs, options);
+    auto locate =
+        common::getKernel("locate_features", {fast_cl_src}, targs, options);
+    auto nonMax =
+        common::getKernel("non_max_counts", {fast_cl_src}, targs, options);
+    auto getFeat =
+        common::getKernel("get_features", {fast_cl_src}, targs, options);
 
     const unsigned max_feat =
         ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 7e6bcaf8a8..157c779936 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -70,8 +70,6 @@ void packDataHelper(Param packed, Param sig, Param filter, const int rank,
     constexpr auto ctDType =
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
 
-    static const std::string src(fftconvolve_pack_cl, fftconvolve_pack_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateTypename<convT>(),
@@ -87,8 +85,10 @@ void packDataHelper(Param packed, Param sig, Param filter, const int rank,
     }
     options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto packData = common::getKernel("pack_data", {src}, targs, options);
-    auto padArray = common::getKernel("pad_array", {src}, targs, options);
+    auto packData = common::getKernel("pack_data", {fftconvolve_pack_cl_src},
+                                      targs, options);
+    auto padArray = common::getKernel("pad_array", {fftconvolve_pack_cl_src},
+                                      targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
@@ -129,8 +129,6 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
     constexpr auto ctDType =
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
 
-    static const std::string src(fftconvolve_multiply_cl,
-                                 fftconvolve_multiply_cl_len);
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateTypename<convT>(),
@@ -150,7 +148,8 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
     }
     options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto cplxMul = common::getKernel("complex_multiply", {src}, targs, options);
+    auto cplxMul = common::getKernel(
+        "complex_multiply", {fftconvolve_multiply_cl_src}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
@@ -180,9 +179,6 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
     constexpr bool RoundResult = std::is_integral<T>::value;
 
-    static const std::string src(fftconvolve_reorder_cl,
-                                 fftconvolve_reorder_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),     TemplateTypename<convT>(),
         TemplateArg(IsTypeDouble), TemplateArg(RoundResult),
@@ -200,7 +196,8 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
     }
     options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto reorder = common::getKernel("reorder_output", {src}, targs, options);
+    auto reorder = common::getKernel(
+        "reorder_output", {fftconvolve_reorder_cl_src}, targs, options);
 
     int fftScale = 1;
 
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 03734b6baa..4061db1472 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -31,11 +31,6 @@ constexpr int VALID     = 2;
 constexpr int INVALID   = 1;
 constexpr int ZERO      = 0;
 
-static inline std::string floodfillSrc() {
-    static const std::string src(flood_fill_cl, flood_fill_cl_len);
-    return src;
-}
-
 template<typename T>
 void initSeeds(Param out, const Param seedsx, const Param seedsy) {
     std::vector<std::string> options = {
@@ -45,7 +40,7 @@ void initSeeds(Param out, const Param seedsx, const Param seedsy) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto initSeeds = common::getKernel("init_seeds", {floodfillSrc()},
+    auto initSeeds = common::getKernel("init_seeds", {flood_fill_cl_src},
                                        {TemplateTypename<T>()}, options);
     cl::NDRange local(kernel::THREADS, 1, 1);
     cl::NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
@@ -65,7 +60,7 @@ void finalizeOutput(Param out, const T newValue) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalizeOut = common::getKernel("finalize_output", {floodfillSrc()},
+    auto finalizeOut = common::getKernel("finalize_output", {flood_fill_cl_src},
                                          {TemplateTypename<T>()}, options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
@@ -97,7 +92,7 @@ void floodFill(Param out, const Param image, const Param seedsx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto floodStep = common::getKernel("flood_step", {floodfillSrc()},
+    auto floodStep = common::getKernel("flood_step", {flood_fill_cl_src},
                                        {TemplateTypename<T>()}, options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index 0f9239d457..f18e2a965f 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -29,8 +29,6 @@ void gradient(Param grad0, Param grad1, const Param in) {
     constexpr int TX = 32;
     constexpr int TY = 8;
 
-    static const std::string src(gradient_cl, gradient_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -43,7 +41,8 @@ void gradient(Param grad0, Param grad1, const Param in) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto gradOp = common::getKernel("gradient", {src}, targs, options);
+    auto gradOp =
+        common::getKernel("gradient", {gradient_cl_src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 87312dbd9c..2fc4bbae82 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -62,8 +62,6 @@ void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy,
 
 template<typename T>
 std::array<Kernel, 4> getHarrisKernels() {
-    static const std::string src(harris_cl, harris_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -73,10 +71,11 @@ std::array<Kernel, 4> getHarrisKernels() {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("second_order_deriv", {src}, targs, options),
-        common::getKernel("keep_corners", {src}, targs, options),
-        common::getKernel("harris_responses", {src}, targs, options),
-        common::getKernel("non_maximal", {src}, targs, options),
+        common::getKernel("second_order_deriv", {harris_cl_src}, targs,
+                          options),
+        common::getKernel("keep_corners", {harris_cl_src}, targs, options),
+        common::getKernel("harris_responses", {harris_cl_src}, targs, options),
+        common::getKernel("non_maximal", {harris_cl_src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index ed1e0125b5..b14fe5c0b3 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -29,8 +29,6 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     constexpr int THREADS_X = 256;
     constexpr int THRD_LOAD = 16;
 
-    static const std::string src(histogram_cl, histogram_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(isLinear),
@@ -43,7 +41,8 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     options.emplace_back(getTypeBuildDefinition<T>());
     if (isLinear) { options.emplace_back(DefineKey(IS_LINEAR)); }
 
-    auto histogram = common::getKernel("histogram", {src}, targs, options);
+    auto histogram =
+        common::getKernel("histogram", {histogram_cl_src}, targs, options);
 
     int nElems  = in.info.dims[0] * in.info.dims[1];
     int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 2aee301d3b..854d858103 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -30,8 +30,6 @@ constexpr int HG_THREADS   = 256;
 
 template<typename T>
 std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
-    static const std::string src(homography_cl, homography_cl_len);
-
     std::vector<TemplateArg> targs   = {TemplateTypename<T>(),
                                       TemplateArg(htype)};
     std::vector<std::string> options = {
@@ -50,11 +48,16 @@ std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
         options.emplace_back(DefineKey(IS_CPU));
     }
     return {
-        common::getKernel("compute_homography", {src}, targs, options),
-        common::getKernel("eval_homography", {src}, targs, options),
-        common::getKernel("compute_median", {src}, targs, options),
-        common::getKernel("find_min_median", {src}, targs, options),
-        common::getKernel("compute_lmeds_inliers", {src}, targs, options),
+        common::getKernel("compute_homography", {homography_cl_src}, targs,
+                          options),
+        common::getKernel("eval_homography", {homography_cl_src}, targs,
+                          options),
+        common::getKernel("compute_median", {homography_cl_src}, targs,
+                          options),
+        common::getKernel("find_min_median", {homography_cl_src}, targs,
+                          options),
+        common::getKernel("compute_lmeds_inliers", {homography_cl_src}, targs,
+                          options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index a00d33ed10..e0afe9f14e 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -27,8 +27,6 @@ void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    static const std::string src(hsv_rgb_cl, hsv_rgb_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(isHSV2RGB),
@@ -39,7 +37,8 @@ void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
     options.emplace_back(getTypeBuildDefinition<T>());
     if (isHSV2RGB) { options.emplace_back(DefineKey(isHSV2RGB)); }
 
-    auto convert = common::getKernel("hsvrgbConvert", {src}, targs, options);
+    auto convert =
+        common::getKernel("hsvrgbConvert", {hsv_rgb_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index e570f482eb..6ae1aa2eb0 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -27,8 +27,6 @@ namespace kernel {
 
 template<typename T>
 static void identity(Param out) {
-    static const std::string src(identity_cl, identity_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -40,7 +38,7 @@ static void identity(Param out) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto identityOp =
-        common::getKernel("identity_kernel", {src}, targs, options);
+        common::getKernel("identity_kernel", {identity_cl_src}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 42996a80e0..2a85b5d447 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -28,8 +28,6 @@ void iir(Param y, Param c, Param a) {
     // allocted outside
     constexpr int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
 
-    static const std::string src(iir_cl, iir_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(batch_a),
@@ -42,7 +40,7 @@ void iir(Param y, Param c, Param a) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto iir = common::getKernel("iir_kernel", {src}, targs, options);
+    auto iir = common::getKernel("iir_kernel", {iir_cl_src}, targs, options);
 
     const int groups_y = y.info.dims[1];
     const int groups_x = y.info.dims[2];
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 481be5a9df..b009497a7c 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -34,14 +34,12 @@ void index(Param out, const Param in, const IndexKernelParam_t& p,
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    static const std::string src(index_cl, index_cl_len);
-
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto index = common::getKernel("indexKernel", {src},
+    auto index = common::getKernel("indexKernel", {index_cl_src},
                                    {TemplateTypename<T>()}, options);
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 8650bfff0b..b0aced9524 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -31,15 +31,13 @@ void iota(Param out, const af::dim4& sdims) {
     constexpr int TILEX   = 512;
     constexpr int TILEY   = 32;
 
-    static const std::string src(iota_cl, iota_cl_len);
-
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto iota = common::getKernel("iota_kernel", {src}, {TemplateTypename<T>()},
-                                  options);
+    auto iota = common::getKernel("iota_kernel", {iota_cl_src},
+                                  {TemplateTypename<T>()}, options);
     cl::NDRange local(IOTA_TX, IOTA_TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], TILEX);
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 39e6497d4e..d6a89f03d5 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -32,9 +32,6 @@ template<typename T, af_op_t op>
 void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
                         const int dim, const int threads_y, const bool is_first,
                         const uint groups_all[4], Param rlen) {
-    static const std::string src1(iops_cl, iops_cl_len);
-    static const std::string src2(ireduce_dim_cl, ireduce_dim_cl_len);
-
     ToNumStr<T> toNumStr;
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(), TemplateArg(dim),       TemplateArg(op),
@@ -53,7 +50,8 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto ireduceDim =
-        common::getKernel("ireduce_dim_kernel", {src1, src2}, targs, options);
+        common::getKernel("ireduce_dim_kernel",
+                          {iops_cl_src, ireduce_dim_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -110,9 +108,6 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
                           cl::Buffer *iidx, const int threads_x,
                           const bool is_first, const uint groups_x,
                           const uint groups_y, Param rlen) {
-    static const std::string src1(iops_cl, iops_cl_len);
-    static const std::string src2(ireduce_first_cl, ireduce_first_cl_len);
-
     ToNumStr<T> toNumStr;
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
@@ -132,7 +127,8 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto ireduceFirst =
-        common::getKernel("ireduce_first_kernel", {src1, src2}, targs, options);
+        common::getKernel("ireduce_first_kernel",
+                          {iops_cl_src, ireduce_first_cl_src}, targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
index 0a7b4c8d8a..5a4016eee6 100644
--- a/src/backend/opencl/kernel/join.hpp
+++ b/src/backend/opencl/kernel/join.hpp
@@ -29,15 +29,13 @@ void join(Param out, const Param in, dim_t dim, const af::dim4 offset) {
     constexpr int TILEX = 256;
     constexpr int TILEY = 32;
 
-    static const std::string src(join_cl, join_cl_len);
-
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto join =
-        common::getKernel("join_kernel", {src},
+        common::getKernel("join_kernel", {join_cl_src},
                           {TemplateTypename<T>(), TemplateArg(dim)}, options);
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 95af3ba329..07399511e6 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -46,8 +46,6 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
     constexpr int BLK_X = 64;
     constexpr int BLK_Y = 32;
 
-    static const std::string src(laset_cl, laset_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(uplo),
@@ -60,7 +58,8 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto lasetOp = common::getKernel(laset_name<uplo>(), {src}, targs, options);
+    auto lasetOp =
+        common::getKernel(laset_name<uplo>(), {laset_cl_src}, targs, options);
 
     int groups_x = (m - 1) / BLK_X + 1;
     int groups_y = (n - 1) / BLK_Y + 1;
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 49c192babd..ace55aacfe 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -34,8 +34,6 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
            const int *ipiv, int inci, cl::CommandQueue &queue) {
     constexpr int NTHREADS = 256;
 
-    static const std::string src(laswp_cl, laswp_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -45,7 +43,7 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto laswpOp = common::getKernel("laswp", {src}, targs, options);
+    auto laswpOp = common::getKernel("laswp", {laswp_cl_src}, targs, options);
 
     int groups = divup(n, NTHREADS);
     cl::NDRange local(NTHREADS);
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index ecbacc3f42..f00ef8a8bb 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -29,8 +29,6 @@ void lookup(Param out, const Param in, const Param indices,
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    static const std::string src(lookup_cl, lookup_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<in_t>(),
         TemplateTypename<idx_t>(),
@@ -51,7 +49,8 @@ void lookup(Param out, const Param in, const Param indices,
     cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
                        blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp = common::getKernel("lookupND", {src}, targs, options);
+    auto arrIdxOp =
+        common::getKernel("lookupND", {lookup_cl_src}, targs, options);
 
     arrIdxOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 5f34afed4e..f2ac2d983d 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -30,8 +30,6 @@ void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
     constexpr unsigned TILEX = 128;
     constexpr unsigned TILEY = 32;
 
-    static const std::string src(lu_split_cl, lu_split_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(same_dims),
@@ -44,7 +42,8 @@ void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto luSplit = common::getKernel("luSplit", {src}, targs, options);
+    auto luSplit =
+        common::getKernel("luSplit", {lu_split_cl_src}, targs, options);
 
     cl::NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index b109bcf16a..f32fd722ef 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -28,8 +28,6 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    static const std::string src(matchTemplate_cl, matchTemplate_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<inType>(),
         TemplateTypename<outType>(),
@@ -53,7 +51,8 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
     };
     options.emplace_back(getTypeBuildDefinition<outType>());
 
-    auto matchImgOp = common::getKernel("matchTemplate", {src}, targs, options);
+    auto matchImgOp = common::getKernel("matchTemplate", {matchTemplate_cl_src},
+                                        targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 649f427b8f..35bcee0fef 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -104,9 +104,6 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
     bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                            owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-    static const std::string src1(mean_ops_cl, mean_ops_cl_len);
-    static const std::string src2(mean_dim_cl, mean_dim_cl_len);
-
     ToNumStr<To> toNumStr;
     ToNumStr<Tw> twNumStr;
     common::Transform<uint, Tw, af_add_t> transform_weight;
@@ -132,7 +129,8 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
     if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
-    auto meanOp = common::getKernel("meanDim", {src1, src2}, targs, options);
+    auto meanOp = common::getKernel(
+        "meanDim", {mean_ops_cl_src, mean_dim_cl_src}, targs, options);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -200,10 +198,6 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
 
     bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                            owt.info.dims[2] * owt.info.dims[3]) != 0);
-
-    static const std::string src1(mean_ops_cl, mean_ops_cl_len);
-    static const std::string src2(mean_first_cl, mean_first_cl_len);
-
     ToNumStr<To> toNumStr;
     ToNumStr<Tw> twNumStr;
     common::Transform<uint, Tw, af_add_t> transform_weight;
@@ -227,7 +221,8 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
     if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
-    auto meanOp = common::getKernel("meanFirst", {src1, src2}, targs, options);
+    auto meanOp = common::getKernel(
+        "meanFirst", {mean_ops_cl_src, mean_first_cl_src}, targs, options);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index c39b58daf8..a616f6abc0 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -32,8 +32,6 @@ void meanshift(Param out, const Param in, const float spatialSigma,
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    static const std::string src(meanshift_cl, meanshift_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(is_color),
@@ -45,7 +43,8 @@ void meanshift(Param out, const Param in, const float spatialSigma,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto meanshiftOp = common::getKernel("meanshift", {src}, targs, options);
+    auto meanshiftOp =
+        common::getKernel("meanshift", {meanshift_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index 2b3237dd93..af1d4f3615 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -32,8 +32,6 @@ constexpr int THREADS_Y = 16;
 template<typename T>
 void medfilt1(Param out, const Param in, const unsigned w_wid,
               const af_border_type pad) {
-    static const std::string src(medfilt1_cl, medfilt1_cl_len);
-
     const int ARR_SIZE = (w_wid - w_wid / 2) + 1;
     size_t loc_size    = (THREADS_X + w_wid - 1) * sizeof(T);
 
@@ -51,7 +49,8 @@ void medfilt1(Param out, const Param in, const unsigned w_wid,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto medfiltOp = common::getKernel("medfilt1", {src}, targs, options);
+    auto medfiltOp =
+        common::getKernel("medfilt1", {medfilt1_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, 1, 1);
 
@@ -68,8 +67,6 @@ void medfilt1(Param out, const Param in, const unsigned w_wid,
 template<typename T>
 void medfilt2(Param out, const Param in, const af_border_type pad,
               const unsigned w_len, const unsigned w_wid) {
-    static const std::string src(medfilt2_cl, medfilt2_cl_len);
-
     const int ARR_SIZE = w_len * (w_wid - w_wid / 2);
     const size_t loc_size =
         (THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1) * sizeof(T);
@@ -91,7 +88,8 @@ void medfilt2(Param out, const Param in, const af_border_type pad,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto medfiltOp = common::getKernel("medfilt2", {src}, targs, options);
+    auto medfiltOp =
+        common::getKernel("medfilt2", {medfilt2_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 94abc8ffe6..115bc5178b 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -35,8 +35,6 @@ template<typename T>
 void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
              const dim_t *idims, const dim_t *istrides, int offset,
              uint ndims) {
-    static const std::string source(memcopy_cl, memcopy_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -45,7 +43,8 @@ void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto memCopy = common::getKernel("memCopy", {source}, targs, options);
+    auto memCopy =
+        common::getKernel("memCopy", {memcopy_cl_src}, targs, options);
 
     dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
     dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
@@ -75,8 +74,6 @@ void copy(Param dst, const Param src, const int ndims,
           const bool same_dims) {
     using std::string;
 
-    static const string source(copy_cl, copy_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<inType>(),
         TemplateTypename<outType>(),
@@ -91,7 +88,7 @@ void copy(Param dst, const Param src, const int ndims,
     };
     options.emplace_back(getTypeBuildDefinition<inType, outType>());
 
-    auto copy = common::getKernel("reshapeCopy", {source}, targs, options);
+    auto copy = common::getKernel("reshapeCopy", {copy_cl_src}, targs, options);
 
     cl::NDRange local(DIM0, DIM1);
     size_t local_size[] = {DIM0, DIM1};
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index cbe787f2e0..facabba3ff 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -28,8 +28,6 @@ template<typename T>
 void moments(Param out, const Param in, af_moment_type moment) {
     constexpr int THREADS = 128;
 
-    static const std::string src(moments_cl, moments_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(out.info.dims[0]),
@@ -40,7 +38,8 @@ void moments(Param out, const Param in, af_moment_type moment) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto momentsOp = common::getKernel("moments", {src}, targs, options);
+    auto momentsOp =
+        common::getKernel("moments", {moments_cl_src}, targs, options);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(in.info.dims[1] * local[0],
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index fc401f87cb..a89b729613 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -39,9 +39,6 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     ToNumStr<T> toNumStr;
     const T DefaultVal = isDilation ? common::Binary<T, af_max_t>::init()
                                     : common::Binary<T, af_min_t>::init();
-
-    static const string src(morph_cl, morph_cl_len);
-
     const int windLen  = mask.info.dims[0];
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
@@ -58,7 +55,7 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::getKernel("morph", {src}, targs, options);
+    auto morphOp = common::getKernel("morph", {morph_cl_src}, targs, options);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -102,9 +99,6 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     ToNumStr<T> toNumStr;
     const T DefaultVal = isDilation ? common::Binary<T, af_max_t>::init()
                                     : common::Binary<T, af_min_t>::init();
-
-    static const string src(morph_cl, morph_cl_len);
-
     const int SeLength = mask.info.dims[0];
 
     std::vector<TemplateArg> targs = {
@@ -120,7 +114,7 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::getKernel("morph3d", {src}, targs, options);
+    auto morphOp = common::getKernel("morph3d", {morph_cl_src}, targs, options);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index bc4343a1c6..f8e523f03c 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -45,9 +45,6 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
     unsigned unroll_len = nextpow2(feat_len);
     if (unroll_len != feat_len) unroll_len = 0;
 
-    static const std::string src(nearest_neighbour_cl,
-                                 nearest_neighbour_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(dist_type),
@@ -73,7 +70,8 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
         options.emplace_back(DefineKeyValue(DISTOP, "_shd_"));
         options.emplace_back(DefineKey(__SHD__));
     }
-    auto hmOp = common::getKernel("knnAllDistances", {src}, targs, options);
+    auto hmOp = common::getKernel("knnAllDistances", {nearest_neighbour_cl_src},
+                                  targs, options);
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 179a347f7e..7a3bafe20c 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -77,8 +77,6 @@ void gaussian1D(T* out, const int dim, double sigma = 0.0) {
 
 template<typename T>
 std::array<Kernel, 4> getOrbKernels() {
-    static const std::string src(orb_cl, orb_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -89,10 +87,10 @@ std::array<Kernel, 4> getOrbKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("harris_response", {src}, targs, compileOpts),
-        common::getKernel("keep_features", {src}, targs, compileOpts),
-        common::getKernel("centroid_angle", {src}, targs, compileOpts),
-        common::getKernel("extract_orb", {src}, targs, compileOpts),
+        common::getKernel("harris_response", {orb_cl_src}, targs, compileOpts),
+        common::getKernel("keep_features", {orb_cl_src}, targs, compileOpts),
+        common::getKernel("centroid_angle", {orb_cl_src}, targs, compileOpts),
+        common::getKernel("extract_orb", {orb_cl_src}, targs, compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index 87b7a23049..567f2d33b4 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -32,8 +32,6 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding,
     using std::string;
     using std::vector;
 
-    static const string src(pad_array_borders_cl, pad_array_borders_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(borderType),
@@ -47,7 +45,8 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto pad = common::getKernel("padBorders", {src}, tmpltArgs, compileOpts);
+    auto pad = common::getKernel("padBorders", {pad_array_borders_cl_src},
+                                 tmpltArgs, compileOpts);
 
     NDRange local(PADB_THREADS_X, PADB_THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 44a1903347..21f932ba28 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -39,23 +39,19 @@ static Kernel getRandomEngineKernel(const af_random_engine_type type,
                                     const int kerIdx,
                                     const uint elementsPerBlock) {
     std::string key;
-    std::vector<std::string> sources = {
-        std::string(random_engine_write_cl, random_engine_write_cl_len)};
+    std::vector<common::Source> sources{random_engine_write_cl_src};
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
             key = "philoxGenerator";
-            sources.emplace_back(random_engine_philox_cl,
-                                 random_engine_philox_cl_len);
+            sources.emplace_back(random_engine_philox_cl_src);
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
             key = "threefryGenerator";
-            sources.emplace_back(random_engine_threefry_cl,
-                                 random_engine_threefry_cl_len);
+            sources.emplace_back(random_engine_threefry_cl_src);
             break;
         case AF_RANDOM_ENGINE_MERSENNE_GP11213:
             key = "mersenneGenerator";
-            sources.emplace_back(random_engine_mersenne_cl,
-                                 random_engine_mersenne_cl_len);
+            sources.emplace_back(random_engine_mersenne_cl_src);
             break;
         default:
             AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
@@ -82,12 +78,6 @@ static Kernel getRandomEngineKernel(const af_random_engine_type type,
     return common::getKernel(key, sources, targs, options);
 }
 
-static Kernel getMersenneInitKernel(void) {
-    static const std::string src(random_engine_mersenne_init_cl,
-                                 random_engine_mersenne_init_cl_len);
-    return common::getKernel("mersenneInitState", {src}, {});
-}
-
 template<typename T>
 static void randomDistribution(cl::Buffer out, const size_t elements,
                                const af_random_engine_type type,
@@ -172,7 +162,8 @@ void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
     cl::NDRange local(THREADS_PER_GROUP, 1);
     cl::NDRange global(local[0] * MAX_BLOCKS, 1);
 
-    auto initOp = getMersenneInitKernel();
+    auto initOp = common::getKernel("mersenneInitState",
+                                    {random_engine_mersenne_init_cl_src}, {});
     initOp(cl::EnqueueArgs(getQueue(), global, local), state, table, seed);
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index 82087a390b..b8eb75dfe6 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -30,15 +30,14 @@ void range(Param out, const int dim) {
     constexpr int RANGE_TILEX = 512;
     constexpr int RANGE_TILEY = 32;
 
-    static const std::string src(range_cl, range_cl_len);
-
     std::vector<TemplateArg> targs   = {TemplateTypename<T>()};
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto rangeOp = common::getKernel("range_kernel", {src}, targs, options);
+    auto rangeOp =
+        common::getKernel("range_kernel", {range_cl_src}, targs, options);
 
     cl::NDRange local(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index c5a0347ad8..0b803ba794 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -36,9 +36,6 @@ template<typename Ti, typename To, af_op_t op>
 void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
                        const uint groups_all[4], int change_nan,
                        double nanval) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_dim_cl, reduce_dim_cl_len);
-
     ToNumStr<To> toNumStr;
     std::vector<TemplateArg> targs = {
         TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(dim),
@@ -57,8 +54,8 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
     };
     options.emplace_back(getTypeBuildDefinition<Ti, To>());
 
-    auto reduceDim =
-        common::getKernel("reduce_dim_kernel", {src1, src2}, targs, options);
+    auto reduceDim = common::getKernel(
+        "reduce_dim_kernel", {ops_cl_src, reduce_dim_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -116,9 +113,6 @@ template<typename Ti, typename To, af_op_t op>
 void reduceFirstLauncher(Param out, Param in, const uint groups_x,
                          const uint groups_y, const uint threads_x,
                          int change_nan, double nanval) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_first_cl, reduce_first_cl_len);
-
     ToNumStr<To> toNumStr;
     std::vector<TemplateArg> targs = {
         TemplateTypename<Ti>(),
@@ -139,7 +133,8 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
     options.emplace_back(getTypeBuildDefinition<Ti, To>());
 
     auto reduceFirst =
-        common::getKernel("reduce_first_kernel", {src1, src2}, targs, options);
+        common::getKernel("reduce_first_kernel",
+                          {ops_cl_src, reduce_first_cl_src}, targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 429081b976..50bf22b706 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -45,10 +45,6 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
                           int change_nan, double nanval, const int n,
                           const uint threads_x, const int dim,
                           std::vector<int> dim_ordering) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_blocks_by_key_dim_cl,
-                                  reduce_blocks_by_key_dim_cl_len);
-
     ToNumStr<To> toNumStr;
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateTypename<Tk>(),
@@ -68,7 +64,8 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
     auto reduceBlocksByKeyDim = common::getKernel(
-        "reduce_blocks_by_key_dim", {src1, src2}, tmpltArgs, compileOpts);
+        "reduce_blocks_by_key_dim",
+        {ops_cl_src, reduce_blocks_by_key_dim_cl_src}, tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
     cl::NDRange local(threads_x);
@@ -91,10 +88,6 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
                        Param vals_out, const Param keys, const Param vals,
                        int change_nan, double nanval, const int n,
                        const uint threads_x) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_blocks_by_key_first_cl,
-                                  reduce_blocks_by_key_first_cl_len);
-
     ToNumStr<To> toNumStr;
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateTypename<Tk>(),
@@ -112,8 +105,10 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto reduceBlocksByKeyFirst = common::getKernel(
-        "reduce_blocks_by_key_first", {src1, src2}, tmpltArgs, compileOpts);
+    auto reduceBlocksByKeyFirst =
+        common::getKernel("reduce_blocks_by_key_first",
+                          {ops_cl_src, reduce_blocks_by_key_first_cl_src},
+                          tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
     cl::NDRange local(threads_x);
@@ -132,10 +127,6 @@ template<typename Tk, typename To, af_op_t op>
 void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
                          Param vals_out, const int n, const int numBlocks,
                          const int threads_x) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_by_key_boundary_cl,
-                                  reduce_by_key_boundary_cl_len);
-
     ToNumStr<To> toNumStr;
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<To>(),
@@ -156,7 +147,8 @@ void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto finalBoundaryReduce = common::getKernel(
-        "final_boundary_reduce", {src1, src2}, tmpltArgs, compileOpts);
+        "final_boundary_reduce", {ops_cl_src, reduce_by_key_boundary_cl_src},
+        tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks);
@@ -172,10 +164,6 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
                             Param vals_out, const int n, const int numBlocks,
                             const int threads_x, const int dim,
                             std::vector<int> dim_ordering) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_by_key_boundary_dim_cl,
-                                  reduce_by_key_boundary_dim_cl_len);
-
     ToNumStr<To> toNumStr;
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<To>(),
@@ -196,8 +184,10 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduceDim = common::getKernel(
-        "final_boundary_reduce_dim", {src1, src2}, tmpltArgs, compileOpts);
+    auto finalBoundaryReduceDim =
+        common::getKernel("final_boundary_reduce_dim",
+                          {ops_cl_src, reduce_by_key_boundary_dim_cl_src},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks,
@@ -216,10 +206,6 @@ template<typename Tk, typename To>
 void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
              const Param keys, const Param vals, const int numBlocks,
              const int threads_x) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_by_key_compact_cl,
-                                  reduce_by_key_compact_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<To>(),
         TemplateTypename<Tk>(),
@@ -235,7 +221,8 @@ void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto compact =
-        common::getKernel("compact", {src1, src2}, tmpltArgs, compileOpts);
+        common::getKernel("compact", {ops_cl_src, reduce_by_key_compact_cl_src},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
@@ -253,10 +240,6 @@ void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
                 const Param keys, const Param vals, const int numBlocks,
                 const int threads_x, const int dim,
                 std::vector<int> dim_ordering) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_by_key_compact_dim_cl,
-                                  reduce_by_key_compact_dim_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<To>(),
         TemplateTypename<Tk>(),
@@ -272,8 +255,9 @@ void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto compactDim =
-        common::getKernel("compact_dim", {src1, src2}, tmpltArgs, compileOpts);
+    auto compactDim = common::getKernel(
+        "compact_dim", {ops_cl_src, reduce_by_key_compact_dim_cl_src},
+        tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks,
@@ -292,10 +276,6 @@ template<typename Tk>
 void testNeedsReduction(cl::Buffer needs_reduction, cl::Buffer needs_boundary,
                         const Param keys, const int n, const int numBlocks,
                         const int threads_x) {
-    static const std::string src1(ops_cl, ops_cl_len);
-    static const std::string src2(reduce_by_key_needs_reduction_cl,
-                                  reduce_by_key_needs_reduction_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Tk>(),
         TemplateArg(threads_x),
@@ -305,8 +285,10 @@ void testNeedsReduction(cl::Buffer needs_reduction, cl::Buffer needs_boundary,
         DefineKeyValue(DIMX, threads_x),
     };
 
-    auto testIfNeedsReduction = common::getKernel(
-        "test_needs_reduction", {src1, src2}, tmpltArgs, compileOpts);
+    auto testIfNeedsReduction =
+        common::getKernel("test_needs_reduction",
+                          {ops_cl_src, reduce_by_key_needs_reduction_cl_src},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks);
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index f8b54b3070..27a2949b41 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -49,8 +49,6 @@ std::array<Kernel, 3> getRegionsKernels(const bool full_conn,
     constexpr int block_dim = 16;
     constexpr int num_warps = 8;
 
-    static const std::string src(regions_cl, regions_cl_len);
-
     ToNumStr<T> toNumStr;
     vector<TemplateArg> targs = {
         TemplateTypename<T>(),
@@ -68,9 +66,9 @@ std::array<Kernel, 3> getRegionsKernels(const bool full_conn,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("initial_label", {src}, targs, options),
-        common::getKernel("final_relabel", {src}, targs, options),
-        common::getKernel("update_equiv", {src}, targs, options),
+        common::getKernel("initial_label", {regions_cl_src}, targs, options),
+        common::getKernel("final_relabel", {regions_cl_src}, targs, options),
+        common::getKernel("update_equiv", {regions_cl_src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index a164d64e7f..550ff127cc 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -28,7 +28,6 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
     constexpr int TILEX = 512;
     constexpr int TILEY = 32;
 
-    static const std::string src(reorder_cl, reorder_cl_len);
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -37,7 +36,8 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto reorderOp = common::getKernel("reorder_kernel", {src}, targs, options);
+    auto reorderOp =
+        common::getKernel("reorder_kernel", {reorder_cl_src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 598737009b..0e55caa4e7 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -40,8 +40,6 @@ void resize(Param out, const Param in, const af_interp_type method) {
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    static const std::string src(resize_cl, resize_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(method),
@@ -70,7 +68,8 @@ void resize(Param out, const Param in, const af_interp_type method) {
         default: break;
     }
 
-    auto resizeOp = common::getKernel("resize_kernel", {src}, targs, options);
+    auto resizeOp =
+        common::getKernel("resize_kernel", {resize_cl_src}, targs, options);
 
     cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index ac1df0e294..2edf47cf91 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -56,9 +56,6 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
         static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
         static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
 
-    static const std::string src1(interp_cl, interp_cl_len);
-    static const std::string src2(rotate_cl, rotate_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(order),
@@ -82,8 +79,8 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto rotate =
-        common::getKernel("rotateKernel", {src1, src2}, tmpltArgs, compileOpts);
+    auto rotate = common::getKernel(
+        "rotateKernel", {interp_cl_src, rotate_cl_src}, tmpltArgs, compileOpts);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 76efa76131..c246711c47 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -32,9 +32,6 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     using std::string;
     using std::vector;
 
-    static const string src1(ops_cl, ops_cl_len);
-    static const string src2(scan_dim_cl, scan_dim_cl_len);
-
     ToNumStr<To> toNumStr;
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ti>(),
@@ -60,7 +57,8 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {ops_cl_src, scan_dim_cl_src}, tmpltArgs,
+                             compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 8a7e931e85..b73c30ec07 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -34,9 +34,6 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     using std::string;
     using std::vector;
 
-    static const string src1(ops_cl, ops_cl_len);
-    static const string src2(scan_dim_by_key_cl, scan_dim_by_key_cl_len);
-
     ToNumStr<To> toNumStr;
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<Ti>(),      TemplateTypename<To>(),
@@ -60,7 +57,8 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {ops_cl_src, scan_dim_by_key_cl_src},
+                             tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index 3cf29ae8c2..d4c03d041c 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -34,9 +34,6 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     using std::string;
     using std::vector;
 
-    static const string src1(ops_cl, ops_cl_len);
-    static const string src2(scan_first_cl, scan_first_cl_len);
-
     const uint threads_y       = THREADS_PER_GROUP / threads_x;
     const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
     ToNumStr<To> toNumStr;
@@ -61,7 +58,8 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {ops_cl_src, scan_first_cl_src}, tmpltArgs,
+                             compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index a4f1f3ac6b..3deee884b3 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -33,9 +33,6 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     using std::string;
     using std::vector;
 
-    static const string src1(ops_cl, ops_cl_len);
-    static const string src2(scan_first_by_key_cl, scan_first_by_key_cl_len);
-
     const uint threads_y       = THREADS_PER_GROUP / threads_x;
     const uint SHARED_MEM_SIZE = THREADS_PER_GROUP;
     ToNumStr<To> toNumStr;
@@ -64,7 +61,8 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {src1, src2}, tmpltArgs, compileOpts);
+    return common::getKernel(key, {ops_cl_src, scan_first_by_key_cl_src},
+                             tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename Tk, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 38f378b795..cd98ac5662 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -26,11 +26,6 @@ constexpr uint DIMX  = 32;
 constexpr uint DIMY  = 8;
 constexpr int REPEAT = 64;
 
-static inline auto selectSrc() {
-    static const std::string src(select_cl, select_cl_len);
-    return src;
-};
-
 template<typename T>
 void selectLauncher(Param out, Param cond, Param a, Param b, const int ndims,
                     const bool is_same) {
@@ -45,7 +40,7 @@ void selectLauncher(Param out, Param cond, Param a, Param b, const int ndims,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto selectOp =
-        common::getKernel("select_kernel", {selectSrc()}, targs, options);
+        common::getKernel("select_kernel", {select_cl_src}, targs, options);
 
     int threads[] = {DIMX, DIMY};
 
@@ -89,7 +84,7 @@ void select_scalar(Param out, Param cond, Param a, const double b,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto selectOp = common::getKernel("select_scalar_kernel", {selectSrc()},
+    auto selectOp = common::getKernel("select_scalar_kernel", {select_cl_src},
                                       targs, options);
 
     int threads[] = {DIMX, DIMY};
diff --git a/src/backend/opencl/kernel/sift.hpp b/src/backend/opencl/kernel/sift.hpp
index 4fbe88ac9d..bd10faa1ce 100644
--- a/src/backend/opencl/kernel/sift.hpp
+++ b/src/backend/opencl/kernel/sift.hpp
@@ -346,8 +346,6 @@ void apply_permutation(compute::buffer_iterator<T>& keys,
 
 template<typename T>
 std::array<Kernel, 7> getSiftKernels() {
-    static const std::string src(sift_nonfree_cl, sift_nonfree_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -357,13 +355,19 @@ std::array<Kernel, 7> getSiftKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("sub", {src}, targs, compileOpts),
-        common::getKernel("detectExtrema", {src}, targs, compileOpts),
-        common::getKernel("interpolateExtrema", {src}, targs, compileOpts),
-        common::getKernel("calcOrientation", {src}, targs, compileOpts),
-        common::getKernel("removeDuplicates", {src}, targs, compileOpts),
-        common::getKernel("computeDescriptor", {src}, targs, compileOpts),
-        common::getKernel("computeGLOHDescriptor", {src}, targs, compileOpts),
+        common::getKernel("sub", {sift_nonfree_cl_src}, targs, compileOpts),
+        common::getKernel("detectExtrema", {sift_nonfree_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("interpolateExtrema", {sift_nonfree_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("calcOrientation", {sift_nonfree_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("removeDuplicates", {sift_nonfree_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("computeDescriptor", {sift_nonfree_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("computeGLOHDescriptor", {sift_nonfree_cl_src}, targs,
+                          compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index eb13187e2a..d68b2dc933 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -26,8 +26,6 @@ void sobel(Param dx, Param dy, const Param in) {
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    static const std::string src(sobel_cl, sobel_cl_len);
-
     std::vector<TemplateArg> targs = {
         TemplateTypename<Ti>(),
         TemplateTypename<To>(),
@@ -40,7 +38,8 @@ void sobel(Param dx, Param dy, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto sobel = common::getKernel("sobel3x3", {src}, targs, compileOpts);
+    auto sobel =
+        common::getKernel("sobel3x3", {sobel_cl_src}, targs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 6ef8e0973c..36dc719180 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -32,8 +32,6 @@ namespace kernel {
 template<typename T>
 void coo2dense(Param out, const Param values, const Param rowIdx,
                const Param colIdx) {
-    static const std::string src(coo2dense_cl, coo2dense_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(REPEAT),
@@ -44,8 +42,8 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto coo2dense =
-        common::getKernel("coo2Dense", {src}, tmpltArgs, compileOpts);
+    auto coo2dense = common::getKernel("coo2Dense", {coo2dense_cl_src},
+                                       tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_PER_GROUP, 1, 1);
 
@@ -65,8 +63,6 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
     // FIXME: This needs to be based non nonzeros per row
     constexpr int threads = 64;
 
-    static const std::string src(csr2dense_cl, csr2dense_cl_len);
-
     const int M = rowIdx.info.dims[0] - 1;
 
     std::vector<TemplateArg> tmpltArgs = {
@@ -79,8 +75,8 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2dense =
-        common::getKernel("csr2Dense", {src}, tmpltArgs, compileOpts);
+    auto csr2dense = common::getKernel("csr2Dense", {csr2dense_cl_src},
+                                       tmpltArgs, compileOpts);
 
     cl::NDRange local(threads, 1);
     int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
@@ -96,8 +92,6 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    static const std::string src(dense2csr_cl, dense2csr_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
     };
@@ -107,8 +101,8 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto dense2Csr =
-        common::getKernel("dense2Csr", {src}, tmpltArgs, compileOpts);
+    auto dense2Csr = common::getKernel("dense2Csr", {dense2csr_cl_src},
+                                       tmpltArgs, compileOpts);
 
     int num_rows = dense.info.dims[0];
     int num_cols = dense.info.dims[1];
@@ -144,8 +138,6 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
 template<typename T>
 void swapIndex(Param ovalues, Param oindex, const Param ivalues,
                const cl::Buffer *iindex, const Param swapIdx) {
-    static const std::string src(csr2coo_cl, csr2coo_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
     };
@@ -154,8 +146,8 @@ void swapIndex(Param ovalues, Param oindex, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapIndex =
-        common::getKernel("swapIndex", {src}, tmpltArgs, compileOpts);
+    auto swapIndex = common::getKernel("swapIndex", {csr2coo_cl_src}, tmpltArgs,
+                                       compileOpts);
 
     cl::NDRange global(ovalues.info.dims[0], 1, 1);
 
@@ -168,8 +160,6 @@ void swapIndex(Param ovalues, Param oindex, const Param ivalues,
 template<typename T>
 void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
              const Param irowIdx, const Param icolIdx, Param index) {
-    static const std::string src(csr2coo_cl, csr2coo_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
     };
@@ -178,7 +168,8 @@ void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2coo = common::getKernel("csr2Coo", {src}, tmpltArgs, compileOpts);
+    auto csr2coo =
+        common::getKernel("csr2Coo", {csr2coo_cl_src}, tmpltArgs, compileOpts);
 
     const int MAX_GROUPS = 4096;
     int M                = irowIdx.info.dims[0] - 1;
@@ -209,8 +200,6 @@ template<typename T>
 void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
              const Param irowIdx, const Param icolIdx, Param index,
              Param rowCopy, const int M) {
-    static const std::string src(csr2coo_cl, csr2coo_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
     };
@@ -219,8 +208,8 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csrReduce =
-        common::getKernel("csrReduce", {src}, tmpltArgs, compileOpts);
+    auto csrReduce = common::getKernel("csrReduce", {csr2coo_cl_src}, tmpltArgs,
+                                       compileOpts);
 
     // Now we need to sort this into column major
     kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 87e495bfc7..3506978433 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -45,14 +45,11 @@ AF_CONSTEXPR const char *getOpString() {
 }
 
 template<typename T, af_op_t op>
-auto fetchKernel(const std::string key, const std::string &additionalSrc,
+auto fetchKernel(const std::string key, const common::Source &additionalSrc,
                  const std::vector<std::string> additionalOptions = {}) {
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    static const std::string src(sparse_arith_common_cl,
-                                 sparse_arith_common_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(op),
@@ -65,15 +62,15 @@ auto fetchKernel(const std::string key, const std::string &additionalSrc,
     options.emplace_back(getTypeBuildDefinition<T>());
     options.insert(std::end(options), std::begin(additionalOptions),
                    std::end(additionalOptions));
-    return common::getKernel(key, {src, additionalSrc}, tmpltArgs, options);
+    return common::getKernel(key, {sparse_arith_common_cl_src, additionalSrc},
+                             tmpltArgs, options);
 }
 
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param out, const Param values, const Param rowIdx,
                       const Param colIdx, const Param rhs, const bool reverse) {
-    static const std::string src(sparse_arith_csr_cl, sparse_arith_csr_cl_len);
-
-    auto sparseArithCSR = fetchKernel<T, op>("sparseArithCSR", src);
+    auto sparseArithCSR =
+        fetchKernel<T, op>("sparseArithCSR", sparse_arith_csr_cl_src);
 
     cl::NDRange local(TX, TY, 1);
     cl::NDRange global(divup(out.info.dims[0], TY) * TX, TY, 1);
@@ -88,9 +85,8 @@ void sparseArithOpCSR(Param out, const Param values, const Param rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param out, const Param values, const Param rowIdx,
                       const Param colIdx, const Param rhs, const bool reverse) {
-    static const std::string src(sparse_arith_coo_cl, sparse_arith_coo_cl_len);
-
-    auto sparseArithCOO = fetchKernel<T, op>("sparseArithCOO", src);
+    auto sparseArithCOO =
+        fetchKernel<T, op>("sparseArithCOO", sparse_arith_coo_cl_src);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
@@ -105,9 +101,8 @@ void sparseArithOpCOO(Param out, const Param values, const Param rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx, const Param rhs,
                       const bool reverse) {
-    static const std::string src(sparse_arith_csr_cl, sparse_arith_csr_cl_len);
-
-    auto sparseArithCSR = fetchKernel<T, op>("sparseArithCSR2", src);
+    auto sparseArithCSR =
+        fetchKernel<T, op>("sparseArithCSR2", sparse_arith_csr_cl_src);
 
     cl::NDRange local(TX, TY, 1);
     cl::NDRange global(divup(rhs.info.dims[0], TY) * TX, TY, 1);
@@ -122,9 +117,8 @@ void sparseArithOpCSR(Param values, Param rowIdx, Param colIdx, const Param rhs,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param values, Param rowIdx, Param colIdx, const Param rhs,
                       const bool reverse) {
-    static const std::string src(sparse_arith_coo_cl, sparse_arith_coo_cl_len);
-
-    auto sparseArithCOO = fetchKernel<T, op>("sparseArithCOO2", src);
+    auto sparseArithCOO =
+        fetchKernel<T, op>("sparseArithCOO2", sparse_arith_coo_cl_src);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(divup(values.info.dims[0], THREADS) * THREADS, 1, 1);
@@ -144,14 +138,12 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
     UNUSED(nnzA);
     UNUSED(nnzB);
 
-    static const std::string src(ssarith_calc_out_nnz_cl,
-                                 ssarith_calc_out_nnz_cl_len);
-
     std::vector<TemplateArg> tmpltArgs = {
         TemplateTypename<uint>(),
     };
 
-    auto calcNNZ = common::getKernel("csr_calc_out_nnz", {src}, tmpltArgs, {});
+    auto calcNNZ = common::getKernel(
+        "csr_calc_out_nnz", {ssarith_calc_out_nnz_cl_src}, tmpltArgs, {});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
@@ -172,13 +164,11 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
                 const uint N, unsigned nnzA, const Param lVals,
                 const Param lRowIdx, const Param lColIdx, unsigned nnzB,
                 const Param rVals, const Param rRowIdx, const Param rColIdx) {
-    static const std::string src(sp_sp_arith_csr_cl, sp_sp_arith_csr_cl_len);
-
     const T iden_val =
         (op == af_mul_t || op == af_div_t ? scalar<T>(1) : scalar<T>(0));
 
     auto arithOp = fetchKernel<T, op>(
-        "ssarith_csr", src,
+        "ssarith_csr", sp_sp_arith_csr_cl_src,
         {DefineKeyValue(IDENTITY_VALUE, af::scalar_to_option(iden_val))});
 
     cl::NDRange local(256, 1);
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index f22b8607e1..5429e96a07 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -27,11 +27,6 @@ namespace kernel {
 constexpr unsigned SUSAN_THREADS_X = 16;
 constexpr unsigned SUSAN_THREADS_Y = 16;
 
-static inline std::string susanSrc() {
-    static const std::string src(susan_cl, susan_cl_len);
-    return src;
-}
-
 template<typename T>
 void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
            const unsigned idim0, const unsigned idim1, const float t,
@@ -53,8 +48,8 @@ void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto susan =
-        common::getKernel("susan_responses", {susanSrc()}, targs, compileOpts);
+    auto susan = common::getKernel("susan_responses", {susan_cl_src}, targs,
+                                   compileOpts);
 
     cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
     cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
@@ -80,7 +75,7 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto nonMax =
-        common::getKernel("non_maximal", {susanSrc()}, targs, compileOpts);
+        common::getKernel("non_maximal", {susan_cl_src}, targs, compileOpts);
 
     unsigned corners_found = 0;
     auto d_corners_found   = memAlloc<unsigned>(1);
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index ab5a4db4be..106db3c4d2 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -33,8 +33,6 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     using std::string;
     using std::vector;
 
-    static const string src(swapdblk_cl, swapdblk_cl_len);
-
     vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -43,7 +41,8 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapdblk = common::getKernel("swapdblk", {src}, targs, compileOpts);
+    auto swapdblk =
+        common::getKernel("swapdblk", {swapdblk_cl_src}, targs, compileOpts);
 
     int nblocks = n / nb;
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index 287550e0db..e0b268e594 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -33,8 +33,6 @@ void tile(Param out, const Param in) {
     constexpr int TILEX = 512;
     constexpr int TILEY = 32;
 
-    static const string src(tile_cl, tile_cl_len);
-
     vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -43,7 +41,7 @@ void tile(Param out, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto tile = common::getKernel("tile", {src}, targs, compileOpts);
+    auto tile = common::getKernel("tile", {tile_cl_src}, targs, compileOpts);
 
     NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index 87e8ba1fc9..c107361771 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -52,9 +52,6 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
         static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
         static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
 
-    static const std::string src1(interp_cl, interp_cl_len);
-    static const std::string src2(transform_cl, transform_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(isInverse),
@@ -82,8 +79,9 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto transform = common::getKernel("transformKernel", {src1, src2},
-                                       tmpltArgs, compileOpts);
+    auto transform =
+        common::getKernel("transformKernel", {interp_cl_src, transform_cl_src},
+                          tmpltArgs, compileOpts);
 
     const int nImg2 = in.info.dims[2];
     const int nImg3 = in.info.dims[3];
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index ec5c8c9eb1..39b775d0cc 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -34,8 +34,6 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
     using std::string;
     using std::vector;
 
-    static const string src(transpose_cl, transpose_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(conjugate),
@@ -50,8 +48,8 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto transpose =
-        common::getKernel("transpose", {src}, tmpltArgs, compileOpts);
+    auto transpose = common::getKernel("transpose", {transpose_cl_src},
+                                       tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 73ecf2b8a5..f53340fd26 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -34,8 +34,6 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
     using std::string;
     using std::vector;
 
-    static const string src(transpose_inplace_cl, transpose_inplace_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(conjugate),
@@ -51,7 +49,8 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     auto transpose =
-        common::getKernel("transpose_inplace", {src}, tmpltArgs, compileOpts);
+        common::getKernel("transpose_inplace", {transpose_inplace_cl_src},
+                          tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 031ce1e744..0421b09e8d 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -37,8 +37,6 @@ void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
     constexpr unsigned TILEX = 128;
     constexpr unsigned TILEY = 32;
 
-    static const string src(triangle_cl, triangle_cl_len);
-
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(is_upper),
@@ -53,8 +51,8 @@ void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto triangle =
-        common::getKernel("triangle", {src}, tmpltArgs, compileOpts);
+    auto triangle = common::getKernel("triangle", {triangle_cl_src}, tmpltArgs,
+                                      compileOpts);
 
     NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 64205178e4..d525015772 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -34,8 +34,6 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     using std::string;
     using std::vector;
 
-    static const string src(unwrap_cl, unwrap_cl_len);
-
     ToNumStr<T> toNumStr;
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
@@ -48,7 +46,8 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto unwrap = common::getKernel("unwrap", {src}, tmpltArgs, compileOpts);
+    auto unwrap =
+        common::getKernel("unwrap", {unwrap_cl_src}, tmpltArgs, compileOpts);
 
     dim_t TX = 1, TY = 1;
     dim_t BX       = 1;
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 1fbceb1fa7..3cc9601e4d 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -34,8 +34,6 @@ static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
     using std::string;
     using std::vector;
 
-    static const string src(where_cl, where_cl_len);
-
     ToNumStr<T> toNumStr;
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
@@ -47,8 +45,8 @@ static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto getIdx =
-        common::getKernel("get_out_idx", {src}, tmpltArgs, compileOpts);
+    auto getIdx = common::getKernel("get_out_idx", {where_cl_src}, tmpltArgs,
+                                    compileOpts);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(local[0] * groups_x * in.info.dims[2],
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 32c4695c78..ba202a48c3 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -34,8 +34,6 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     using std::string;
     using std::vector;
 
-    static const string src(wrap_cl, wrap_cl_len);
-
     ToNumStr<T> toNumStr;
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
@@ -48,7 +46,8 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto wrap = common::getKernel("wrap", {src}, tmpltArgs, compileOpts);
+    auto wrap =
+        common::getKernel("wrap", {wrap_cl_src}, tmpltArgs, compileOpts);
 
     dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
     dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
@@ -80,8 +79,6 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
     using std::string;
     using std::vector;
 
-    static const string src(wrap_dilated_cl, wrap_dilated_cl_len);
-
     ToNumStr<T> toNumStr;
     vector<TemplateArg> tmpltArgs = {
         TemplateTypename<T>(),
@@ -94,8 +91,8 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto dilatedWrap =
-        common::getKernel("wrap_dilated", {src}, tmpltArgs, compileOpts);
+    auto dilatedWrap = common::getKernel("wrap_dilated", {wrap_dilated_cl_src},
+                                         tmpltArgs, compileOpts);
 
     dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;

From 0d0826f4f94b70e62ba9335db6bc08a1cca651d7 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 17 Dec 2020 22:31:11 +0100
Subject: [PATCH 2119/2677] CL_DEVICE_HALF_FP_CONFIG returns CL_INVALID_VALUE.

16fp and 64fp are optional extensions to OpenCL.  The CONFIG's only exists when the extension is available.  It is therefore better to check the availability of the extension, so that no errors are thrown (and have to treated).

+ Cleanup of compiler warnings.
---
 src/backend/opencl/platform.cpp | 38 +++++++++++++--------------------
 1 file changed, 15 insertions(+), 23 deletions(-)

diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d8af15f2fd..56032ad125 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -184,6 +184,7 @@ string getDeviceInfo() noexcept {
             nDevices++;
         }
     } catch (const AfError& err) {
+        UNUSED(err);
         info << "No platforms found.\n";
         // Don't throw an exception here. Info should pass even if the system
         // doesn't have the correct drivers installed.
@@ -215,8 +216,9 @@ int getDeviceCount() noexcept try {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
-    return devMngr.mQueues.size();
+    return static_cast<int>(devMngr.mQueues.size());
 } catch (const AfError& err) {
+    UNUSED(err);
     // If device manager threw an error then return 0 because no platforms
     // were found
     return 0;
@@ -233,7 +235,7 @@ int getDeviceIdFromNativeId(cl_device_id id) {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    int nDevices = devMngr.mDevices.size();
+    int nDevices = static_cast<int>(devMngr.mDevices.size());
     int devId    = 0;
     for (devId = 0; devId < nDevices; ++devId) {
         if (id == devMngr.mDevices[devId]->operator()()) { break; }
@@ -359,8 +361,9 @@ bool isDoubleSupported(unsigned device) {
         common::lock_guard_t lock(devMngr.deviceMutex);
         dev = *devMngr.mDevices[device];
     }
-
-    return (dev.getInfo<CL_DEVICE_DOUBLE_FP_CONFIG>() > 0);
+    // 64bit fp is an optional extension
+    return (dev.getInfo<CL_DEVICE_EXTENSIONS>().find("cl_khr_fp64") !=
+            string::npos);
 }
 
 bool isHalfSupported(unsigned device) {
@@ -371,21 +374,9 @@ bool isHalfSupported(unsigned device) {
         common::lock_guard_t lock(devMngr.deviceMutex);
         dev = *devMngr.mDevices[device];
     }
-    cl_device_fp_config config = 0;
-    size_t ret_size            = 0;
-    // NVIDIA OpenCL seems to return error codes for CL_DEVICE_HALF_FP_CONFIG.
-    // It seems to be a bug in their implementation. Assuming if this function
-    // fails that the implemenation does not support f16 type. Using the C API
-    // to avoid exceptions
-    cl_int err =
-        clGetDeviceInfo(dev(), CL_DEVICE_HALF_FP_CONFIG,
-                        sizeof(cl_device_fp_config), &config, &ret_size);
-
-    if (err) {
-        return false;
-    } else {
-        return config > 0;
-    }
+    // 16bit fp is an option extension
+    return (dev.getInfo<CL_DEVICE_EXTENSIONS>().find("cl_khr_fp16") !=
+            string::npos);
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
@@ -481,12 +472,13 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
         devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
-        devMngr.mDeviceTypes.push_back(tDevice->getInfo<CL_DEVICE_TYPE>());
+        devMngr.mDeviceTypes.push_back(
+            static_cast<int>(tDevice->getInfo<CL_DEVICE_TYPE>()));
 
         devMngr.mDevices.push_back(move(tDevice));
         devMngr.mContexts.push_back(move(tContext));
         devMngr.mQueues.push_back(move(tQueue));
-        nDevices = devMngr.mDevices.size() - 1;
+        nDevices = static_cast<int>(devMngr.mDevices.size()) - 1;
 
         // cache the boost program_cache object, clean up done on program exit
         // not during removeDeviceContext
@@ -507,7 +499,7 @@ void setDeviceContext(cl_device_id dev, cl_context ctx) {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    const int dCount = devMngr.mDevices.size();
+    const int dCount = static_cast<int>(devMngr.mDevices.size());
     for (int i = 0; i < dCount; ++i) {
         if (devMngr.mDevices[i]->operator()() == dev &&
             devMngr.mContexts[i]->operator()() == ctx) {
@@ -529,7 +521,7 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
 
-        const int dCount = devMngr.mDevices.size();
+        const int dCount = static_cast<int>(devMngr.mDevices.size());
         for (int i = 0; i < dCount; ++i) {
             if (devMngr.mDevices[i]->operator()() == dev &&
                 devMngr.mContexts[i]->operator()() == ctx) {

From 911e1720b8874563bbcb35d1e9ec15c2f1e69403 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 1 Jan 2021 13:51:46 +0530
Subject: [PATCH 2120/2677] Bump up project version to next feature version:
 3.9

---
 CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4c6dcc4b49..af019dea61 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-# Copyright (c) 2020, ArrayFire
+# Copyright (c) 2021, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -7,7 +7,7 @@
 
 cmake_minimum_required(VERSION 3.5)
 
-project(ArrayFire VERSION 3.8.0 LANGUAGES C CXX)
+project(ArrayFire VERSION 3.9.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 

From 6bd2099ecffdf8cdbee7d4cb0ca3327d90f3ba93 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 30 Dec 2020 10:49:02 +0530
Subject: [PATCH 2121/2677] Fix a infinite recursion bug in NaryNode JIT Node

When the maximum JIT tree height is one, createNaryNode goes into
infinite recursion. This effects CUDA and OpenCL backends
---
 src/backend/common/jit/NaryNode.hpp |  4 +--
 src/backend/cpu/Array.cpp           |  2 +-
 src/backend/cuda/Array.cpp          |  2 +-
 src/backend/cuda/select.cpp         | 49 +++++++++++++++--------------
 src/backend/opencl/Array.cpp        |  2 +-
 src/backend/opencl/select.cpp       | 49 +++++++++++++++--------------
 6 files changed, 56 insertions(+), 52 deletions(-)

diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 75d9a5a38a..6001c25b51 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -98,8 +98,7 @@ common::Node_ptr createNaryNode(
 
     common::Node_ptr ptr = createNode(childNodes);
 
-    switch (static_cast<kJITHeuristics>(
-        detail::passesJitHeuristics<Ti>(ptr.get()))) {
+    switch (detail::passesJitHeuristics<Ti>(ptr.get())) {
         case kJITHeuristics::Pass: {
             return ptr;
         }
@@ -113,7 +112,6 @@ common::Node_ptr createNaryNode(
                     max_height       = childNodes[i]->getHeight();
                 }
             }
-
             children[max_height_index]->eval();
             return createNaryNode<Ti, N>(odims, createNode, move(children));
         }
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 713a752b7c..c5a4cce329 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -248,7 +248,7 @@ Array<T> createEmptyArray(const dim4 &dims) {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= static_cast<int>(getMaxJitSize())) {
+    if (root_node->getHeight() > static_cast<int>(getMaxJitSize())) {
         return kJITHeuristics::TreeHeight;
     }
 
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 8aecde7781..e2b2b3dbf0 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -254,7 +254,7 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= static_cast<int>(getMaxJitSize())) {
+    if (root_node->getHeight() > static_cast<int>(getMaxJitSize())) {
         return kJITHeuristics::TreeHeight;
     }
 
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index 47123f1156..666bf1b5de 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -41,56 +41,59 @@ void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const Array<T> &b, const af::dim4 &odims) {
-    auto cond_node = cond.getNode();
-    auto a_node    = a.getNode();
-    auto b_node    = b.getNode();
-    int height     = max(a_node->getHeight(), b_node->getHeight());
-    height         = max(height, cond_node->getHeight()) + 1;
-    auto node      = make_shared<NaryNode>(NaryNode(
+    auto cond_node   = cond.getNode();
+    auto a_node      = a.getNode();
+    auto b_node      = b.getNode();
+    auto a_height    = a_node->getHeight();
+    auto b_height    = b_node->getHeight();
+    auto cond_height = cond_node->getHeight();
+    const int height = max(max(a_height, b_height), cond_height) + 1;
+
+    auto node = make_shared<NaryNode>(NaryNode(
         static_cast<af::dtype>(dtype_traits<T>::af_type), "__select", 3,
         {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
-        return createNodeArray<T>(odims, node);
-    } else {
-        if (a_node->getHeight() >
-            max(b_node->getHeight(), cond_node->getHeight())) {
+    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+        if (a_height > max(b_height, cond_height)) {
             a.eval();
-        } else if (b_node->getHeight() > cond_node->getHeight()) {
+        } else if (b_height > cond_height) {
             b.eval();
         } else {
             cond.eval();
         }
         return createSelectNode<T>(cond, a, b, odims);
     }
+    return createNodeArray<T>(odims, node);
 }
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const double &b_val, const af::dim4 &odims) {
-    auto cond_node = cond.getNode();
-    auto a_node    = a.getNode();
-    Array<T> b     = createScalarNode<T>(odims, scalar<T>(b_val));
-    auto b_node    = b.getNode();
-    int height     = max(a_node->getHeight(), b_node->getHeight());
-    height         = max(height, cond_node->getHeight()) + 1;
+    auto cond_node   = cond.getNode();
+    auto a_node      = a.getNode();
+    Array<T> b       = createScalarNode<T>(odims, scalar<T>(b_val));
+    auto b_node      = b.getNode();
+    auto a_height    = a_node->getHeight();
+    auto b_height    = b_node->getHeight();
+    auto cond_height = cond_node->getHeight();
+    const int height = max(max(a_height, b_height), cond_height) + 1;
 
     auto node = make_shared<NaryNode>(NaryNode(
         static_cast<af::dtype>(dtype_traits<T>::af_type),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         static_cast<int>(flip ? af_not_select_t : af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
-        return createNodeArray<T>(odims, node);
-    } else {
-        if (a_node->getHeight() >
-            max(b_node->getHeight(), cond_node->getHeight())) {
+    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+        if (a_height > max(b_height, cond_height)) {
             a.eval();
+        } else if (b_height > cond_height) {
+            b.eval();
         } else {
             cond.eval();
         }
         return createSelectNode<T, flip>(cond, a, b_val, odims);
     }
+    return createNodeArray<T>(odims, node);
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 1553438c6c..5935d51ec9 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -293,7 +293,7 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(Node *root_node) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() >= static_cast<int>(getMaxJitSize())) {
+    if (root_node->getHeight() > static_cast<int>(getMaxJitSize())) {
         return kJITHeuristics::TreeHeight;
     }
 
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 2721a04bab..fe1e50351a 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -29,56 +29,59 @@ namespace opencl {
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const Array<T> &b, const dim4 &odims) {
-    auto cond_node = cond.getNode();
-    auto a_node    = a.getNode();
-    auto b_node    = b.getNode();
-    int height     = max(a_node->getHeight(), b_node->getHeight());
-    height         = max(height, cond_node->getHeight()) + 1;
-    auto node      = make_shared<NaryNode>(NaryNode(
+    auto cond_node   = cond.getNode();
+    auto a_node      = a.getNode();
+    auto b_node      = b.getNode();
+    auto a_height    = a_node->getHeight();
+    auto b_height    = b_node->getHeight();
+    auto cond_height = cond_node->getHeight();
+    const int height = max(max(a_height, b_height), cond_height) + 1;
+
+    auto node = make_shared<NaryNode>(NaryNode(
         static_cast<af::dtype>(dtype_traits<T>::af_type), "__select", 3,
         {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
-        return createNodeArray<T>(odims, node);
-    } else {
-        if (a_node->getHeight() >
-            max(b_node->getHeight(), cond_node->getHeight())) {
+    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+        if (a_height > max(b_height, cond_height)) {
             a.eval();
-        } else if (b_node->getHeight() > cond_node->getHeight()) {
+        } else if (b_height > cond_height) {
             b.eval();
         } else {
             cond.eval();
         }
         return createSelectNode<T>(cond, a, b, odims);
     }
+    return createNodeArray<T>(odims, node);
 }
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const double &b_val, const dim4 &odims) {
-    auto cond_node = cond.getNode();
-    auto a_node    = a.getNode();
-    Array<T> b     = createScalarNode<T>(odims, scalar<T>(b_val));
-    auto b_node    = b.getNode();
-    int height     = max(a_node->getHeight(), b_node->getHeight());
-    height         = max(height, cond_node->getHeight()) + 1;
+    auto cond_node   = cond.getNode();
+    auto a_node      = a.getNode();
+    Array<T> b       = createScalarNode<T>(odims, scalar<T>(b_val));
+    auto b_node      = b.getNode();
+    auto a_height    = a_node->getHeight();
+    auto b_height    = b_node->getHeight();
+    auto cond_height = cond_node->getHeight();
+    const int height = max(max(a_height, b_height), cond_height) + 1;
 
     auto node = make_shared<NaryNode>(NaryNode(
         static_cast<af::dtype>(dtype_traits<T>::af_type),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         static_cast<int>(flip ? af_not_select_t : af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) == kJITHeuristics::Pass) {
-        return createNodeArray<T>(odims, node);
-    } else {
-        if (a_node->getHeight() >
-            max(b_node->getHeight(), cond_node->getHeight())) {
+    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+        if (a_height > max(b_height, cond_height)) {
             a.eval();
+        } else if (b_height > cond_height) {
+            b.eval();
         } else {
             cond.eval();
         }
         return createSelectNode<T, flip>(cond, a, b_val, odims);
     }
+    return createNodeArray<T>(odims, node);
 }
 
 template<typename T>

From 01f34e8b46cde32cce30f4f0d6d898645a30c1cb Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 4 Jan 2021 22:40:58 +0530
Subject: [PATCH 2122/2677] Check for empty Arrays in JIT evalNodes

---
 src/backend/cuda/jit.cpp   | 26 +++++++++++++-------------
 src/backend/opencl/jit.cpp | 21 +++++++++++----------
 2 files changed, 24 insertions(+), 23 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index d2b25c2d78..756aaf15dd 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -205,10 +205,13 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
 template<typename T>
 void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
     size_t num_outputs = outputs.size();
-    int device         = getActiveDeviceId();
-
     if (num_outputs == 0) { return; }
 
+    int device         = getActiveDeviceId();
+    dim_t *outDims     = outputs[0].dims;
+    size_t numOutElems = outDims[0] * outDims[1] * outDims[2] * outDims[3];
+    if (numOutElems == 0) { return; }
+
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
     thread_local vector<Node *> full_nodes;
@@ -229,9 +232,7 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
     }
 
     bool is_linear = true;
-    for (auto node : full_nodes) {
-        is_linear &= node->isLinear(outputs[0].dims);
-    }
+    for (auto node : full_nodes) { is_linear &= node->isLinear(outDims); }
 
     CUfunction ker =
         getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
@@ -246,7 +247,7 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
 
     int num_odims = 4;
     while (num_odims >= 1) {
-        if (outputs[0].dims[num_odims - 1] == 1) {
+        if (outDims[num_odims - 1] == 1) {
             num_odims--;
         } else {
             break;
@@ -257,9 +258,8 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
         threads_x = 256;
         threads_y = 1;
 
-        blocks_x_total = divup((outputs[0].dims[0] * outputs[0].dims[1] *
-                                outputs[0].dims[2] * outputs[0].dims[3]),
-                               threads_x);
+        blocks_x_total = divup(
+            (outDims[0] * outDims[1] * outDims[2] * outDims[3]), threads_x);
 
         int repeat_x = divup(blocks_x_total, max_blocks_x);
         blocks_x     = divup(blocks_x_total, repeat_x);
@@ -267,11 +267,11 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
         threads_x = 32;
         threads_y = 8;
 
-        blocks_x_ = divup(outputs[0].dims[0], threads_x);
-        blocks_y_ = divup(outputs[0].dims[1], threads_y);
+        blocks_x_ = divup(outDims[0], threads_x);
+        blocks_y_ = divup(outDims[1], threads_y);
 
-        blocks_x = blocks_x_ * outputs[0].dims[2];
-        blocks_y = blocks_y_ * outputs[0].dims[3];
+        blocks_x = blocks_x_ * outDims[2];
+        blocks_y = blocks_y_ * outDims[3];
 
         blocks_z = divup(blocks_y, max_blocks_y);
         blocks_y = divup(blocks_y, blocks_z);
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 5478f6e315..02471d53e3 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -180,7 +180,10 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
 
     // Assume all ouputs are of same size
     // FIXME: Add assert to check if all outputs are same size?
-    KParam out_info = outputs[0].info;
+    KParam out_info    = outputs[0].info;
+    dim_t *outDims     = out_info.dims;
+    size_t numOutElems = outDims[0] * outDims[1] * outDims[2] * outDims[3];
+    if (numOutElems == 0) { return; }
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
@@ -202,9 +205,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
     }
 
     bool is_linear = true;
-    for (auto node : full_nodes) {
-        is_linear &= node->isLinear(outputs[0].info.dims);
-    }
+    for (auto node : full_nodes) { is_linear &= node->isLinear(outDims); }
 
     auto ker =
         getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
@@ -222,7 +223,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
         (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
 
     while (num_odims >= 1) {
-        if (out_info.dims[num_odims - 1] == 1) {
+        if (outDims[num_odims - 1] == 1) {
             num_odims--;
         } else {
             break;
@@ -231,7 +232,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
 
     if (is_linear) {
         local_0           = work_group_size;
-        uint out_elements = out_info.dims[3] * out_info.strides[3];
+        uint out_elements = outDims[3] * out_info.strides[3];
         uint groups       = divup(out_elements, local_0);
 
         global_1 = divup(groups, 1000) * local_1;
@@ -241,11 +242,11 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
         local_1 = 4;
         local_0 = work_group_size / local_1;
 
-        groups_0 = divup(out_info.dims[0], local_0);
-        groups_1 = divup(out_info.dims[1], local_1);
+        groups_0 = divup(outDims[0], local_0);
+        groups_1 = divup(outDims[1], local_1);
 
-        global_0 = groups_0 * local_0 * out_info.dims[2];
-        global_1 = groups_1 * local_1 * out_info.dims[3];
+        global_0 = groups_0 * local_0 * outDims[2];
+        global_1 = groups_1 * local_1 * outDims[3];
     }
 
     NDRange local(local_0, local_1);

From 40de5183b116b02a96431b1f5ab68df119b31059 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 1 Jan 2021 13:50:51 +0530
Subject: [PATCH 2123/2677] Add hidden functions to get/set max jit length for
 tests

These functions are not exposed to users. They are not included when
generating installers. These functions are purely for testing certain
internal behavior given a certain combination of environment variables.

Test for unit max JIT height infinite recursion bug
---
 src/api/c/CMakeLists.txt         |  2 ++
 src/api/c/jit_test_api.cpp       | 28 ++++++++++++++++++
 src/api/c/jit_test_api.h         | 51 ++++++++++++++++++++++++++++++++
 src/api/cpp/CMakeLists.txt       |  1 +
 src/api/cpp/jit_test_api.cpp     | 21 +++++++++++++
 src/api/unified/CMakeLists.txt   |  1 +
 src/api/unified/jit_test_api.cpp | 18 +++++++++++
 src/backend/cpu/platform.cpp     | 12 ++++----
 src/backend/cpu/platform.hpp     |  2 +-
 src/backend/cuda/platform.cpp    | 12 ++++----
 src/backend/cuda/platform.hpp    |  2 +-
 src/backend/opencl/platform.cpp  | 12 ++++----
 src/backend/opencl/platform.hpp  |  2 +-
 test/CMakeLists.txt              |  2 ++
 test/jit_test_api.cpp            | 34 +++++++++++++++++++++
 15 files changed, 179 insertions(+), 21 deletions(-)
 create mode 100644 src/api/c/jit_test_api.cpp
 create mode 100644 src/api/c/jit_test_api.h
 create mode 100644 src/api/cpp/jit_test_api.cpp
 create mode 100644 src/api/unified/jit_test_api.cpp
 create mode 100644 test/jit_test_api.cpp

diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index e76dd02d80..2220990b76 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -105,6 +105,8 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/inverse.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit_test_api.h
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit_test_api.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/join.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/lu.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/match_template.cpp
diff --git a/src/api/c/jit_test_api.cpp b/src/api/c/jit_test_api.cpp
new file mode 100644
index 0000000000..784994f267
--- /dev/null
+++ b/src/api/c/jit_test_api.cpp
@@ -0,0 +1,28 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <jit_test_api.h>
+
+#include <backend.hpp>
+#include <common/err_common.hpp>
+#include <platform.hpp>
+
+af_err af_get_max_jit_len(int *jitLen) {
+    *jitLen = detail::getMaxJitSize();
+    return AF_SUCCESS;
+}
+
+af_err af_set_max_jit_len(const int maxJitLen) {
+    try {
+        ARG_ASSERT(1, maxJitLen > 0);
+        detail::getMaxJitSize() = maxJitLen;
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
diff --git a/src/api/c/jit_test_api.h b/src/api/c/jit_test_api.h
new file mode 100644
index 0000000000..d99bc3b077
--- /dev/null
+++ b/src/api/c/jit_test_api.h
@@ -0,0 +1,51 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <af/defines.h>
+
+#ifdef __cplusplus
+namespace af {
+/// Get the maximum jit tree length for active backend
+///
+/// \returns the maximum length of jit tree from root to any leaf
+AFAPI int getMaxJitLen(void);
+
+/// Set the maximum jit tree length for active backend
+///
+/// \param[in] jit_len is the maximum length of jit tree from root to any
+/// leaf
+AFAPI void setMaxJitLen(const int jitLen);
+}  // namespace af
+#endif  //__cplusplus
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/// Get the maximum jit tree length for active backend
+///
+/// \param[out] jit_len is the maximum length of jit tree from root to any
+/// leaf
+///
+/// \returns Always returns AF_SUCCESS
+AFAPI af_err af_get_max_jit_len(int *jit_len);
+
+/// Set the maximum jit tree length for active backend
+///
+/// \param[in] jit_len is the maximum length of jit tree from root to any
+/// leaf
+///
+/// \returns Always returns AF_SUCCESS
+AFAPI af_err af_set_max_jit_len(const int jit_len);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index a714eeae4f..1df8c7ff77 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -45,6 +45,7 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/imageio.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit_test_api.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/matchTemplate.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/mean.cpp
diff --git a/src/api/cpp/jit_test_api.cpp b/src/api/cpp/jit_test_api.cpp
new file mode 100644
index 0000000000..bc6930dc04
--- /dev/null
+++ b/src/api/cpp/jit_test_api.cpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <jit_test_api.h>
+#include "error.hpp"
+
+namespace af {
+int getMaxJitLen(void) {
+    int retVal = 0;
+    AF_THROW(af_get_max_jit_len(&retVal));
+    return retVal;
+}
+
+void setMaxJitLen(const int jitLen) { AF_THROW(af_set_max_jit_len(jitLen)); }
+}  // namespace af
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 026418a39b..4140e13ca8 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -22,6 +22,7 @@ target_sources(af
     ${CMAKE_CURRENT_SOURCE_DIR}/image.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit_test_api.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ml.cpp
diff --git a/src/api/unified/jit_test_api.cpp b/src/api/unified/jit_test_api.cpp
new file mode 100644
index 0000000000..de60ac1eb1
--- /dev/null
+++ b/src/api/unified/jit_test_api.cpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <jit_test_api.h>
+
+#include "symbol_manager.hpp"
+
+af_err af_get_max_jit_len(int *jitLen) { CALL(af_get_max_jit_len, jitLen); }
+
+af_err af_set_max_jit_len(const int jitLen) {
+    CALL(af_set_max_jit_len, jitLen);
+}
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index da634b0d82..2b5b91a718 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -104,14 +104,14 @@ void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
     snprintf(d_compute, 10, "%s", "0.0");
 }
 
-unsigned getMaxJitSize() {
-    const int MAX_JIT_LEN = 100;
-
-    thread_local int length = 0;
-    if (length == 0) {
+int& getMaxJitSize() {
+    constexpr int MAX_JIT_LEN = 100;
+    thread_local int length   = 0;
+    if (length <= 0) {
         string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            length = stoi(env_var);
+            int input_len = std::stoi(env_var);
+            length        = input_len > 0 ? input_len : MAX_JIT_LEN;
         } else {
             length = MAX_JIT_LEN;
         }
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index f51691f741..a37f12351f 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -36,7 +36,7 @@ bool isHalfSupported(int device);
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
-unsigned getMaxJitSize();
+int& getMaxJitSize();
 
 int getDeviceCount();
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 33b2fe5a81..ee5776d057 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -325,14 +325,14 @@ string getCUDARuntimeVersion() noexcept {
     }
 }
 
-unsigned getMaxJitSize() {
-    const int MAX_JIT_LEN = 100;
-
-    thread_local int length = 0;
-    if (length == 0) {
+int &getMaxJitSize() {
+    constexpr int MAX_JIT_LEN = 100;
+    thread_local int length   = 0;
+    if (length <= 0) {
         std::string env_var = getEnvVar("AF_CUDA_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            length = std::stoi(env_var);
+            int input_len = std::stoi(env_var);
+            length        = input_len > 0 ? input_len : MAX_JIT_LEN;
         } else {
             length = MAX_JIT_LEN;
         }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index ff73c5fcc3..b4e9dd2360 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -76,7 +76,7 @@ bool isHalfSupported(int device);
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
-unsigned getMaxJitSize();
+int& getMaxJitSize();
 
 int getDeviceCount();
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 56032ad125..f06f446004 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -571,18 +571,18 @@ bool synchronize_calls() {
     return sync;
 }
 
-unsigned getMaxJitSize() {
+int& getMaxJitSize() {
 #if defined(OS_MAC)
-    const int MAX_JIT_LEN = 50;
+    constexpr int MAX_JIT_LEN = 50;
 #else
-    const int MAX_JIT_LEN = 100;
+    constexpr int MAX_JIT_LEN = 100;
 #endif
-
     thread_local int length = 0;
-    if (length == 0) {
+    if (length <= 0) {
         string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            length = stoi(env_var);
+            int input_len = std::stoi(env_var);
+            length        = input_len > 0 ? input_len : MAX_JIT_LEN;
         } else {
             length = MAX_JIT_LEN;
         }
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 94d5d37120..6292c1331d 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -57,7 +57,7 @@ int getDeviceCount() noexcept;
 
 unsigned getActiveDeviceId();
 
-unsigned getMaxJitSize();
+int& getMaxJitSize();
 
 const cl::Context& getContext();
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 2a6e34dc3b..0f9564afeb 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -450,3 +450,5 @@ elseif(AF_BUILD_CUDA)
 elseif(AF_BUILD_CPU)
   target_link_libraries(print_info ArrayFire::afcpu)
 endif()
+
+make_test(SRC jit_test_api.cpp)
diff --git a/test/jit_test_api.cpp b/test/jit_test_api.cpp
new file mode 100644
index 0000000000..79430ab874
--- /dev/null
+++ b/test/jit_test_api.cpp
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <af/data.h>
+
+namespace af {
+int getMaxJitLen(void);
+
+void setMaxJitLen(const int jitLen);
+}  // namespace af
+
+TEST(JIT, UnitMaxHeight) {
+    const int oldMaxJitLen = af::getMaxJitLen();
+    af::setMaxJitLen(1);
+    af::array a = af::constant(1, 10);
+    af::array b = af::constant(2, 10);
+    af::array c = a * b;
+    af::array d = b * c;
+    c.eval();
+    d.eval();
+    af::setMaxJitLen(oldMaxJitLen);
+}
+
+TEST(JIT, ZeroMaxHeight) {
+    EXPECT_THROW({ af::setMaxJitLen(0); }, af::exception);
+}

From e50c3a87768c8eae71036d42bae6120e75f61383 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 18 Feb 2021 23:26:06 +0530
Subject: [PATCH 2124/2677] Mark result variables of cmake cmds as advanced

unmarked CUDA_VERSION as advanced so that users may see what CUDA
toolkit is picked up
---
 CMakeLists.txt               | 4 ++++
 CMakeModules/FindMKL.cmake   | 2 +-
 CMakeModules/FindcuDNN.cmake | 1 +
 test/CMakeLists.txt          | 2 ++
 4 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index af019dea61..29f147862a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -107,7 +107,9 @@ mark_as_advanced(
   AF_BUILD_FRAMEWORK
   AF_INSTALL_STANDALONE
   AF_WITH_CPUID
+  Boost_INCLUDE_DIR
   CUDA_HOST_COMPILER
+  CUDA_SDK_ROOT_DIR
   CUDA_USE_STATIC_CUDA_RUNTIME
   CUDA_rt_LIBRARY
   SPDLOG_BUILD_EXAMPLES
@@ -115,7 +117,9 @@ mark_as_advanced(
   ADDR2LINE_PROGRAM
   Backtrace_LIBRARY
   AF_WITH_STATIC_MKL
+  GIT
   )
+mark_as_advanced(CLEAR CUDA_VERSION)
 
 #Configure forge submodule
 #forge is included in ALL target if AF_BUILD_FORGE is ON
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 718409a186..12ab882dff 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -265,8 +265,8 @@ function(find_mkl_library)
         if (CMAKE_VERSION VERSION_GREATER 3.14)
           message(VERBOSE "MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}")
         endif()
-        mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
       endif()
+      mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
     endif()
 
     set_target_properties(MKL::${mkl_args_NAME}
diff --git a/CMakeModules/FindcuDNN.cmake b/CMakeModules/FindcuDNN.cmake
index 717daed105..bf113afd5d 100644
--- a/CMakeModules/FindcuDNN.cmake
+++ b/CMakeModules/FindcuDNN.cmake
@@ -151,6 +151,7 @@ if(cuDNN_INCLUDE_DIRS)
         ${CMAKE_INSTALL_PREFIX}
       PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
       DOC "cudnn${cudnn_lib_name_infix} link library." )
+    mark_as_advanced(cuDNN${LIB_INFIX}_LINK_LIBRARY)
 
     if(WIN32 AND cuDNN_LINK_LIBRARY)
       find_file(cuDNN${LIB_INFIX}_DLL_LIBRARY
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 0f9564afeb..4128538113 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -40,6 +40,8 @@ if(NOT TARGET gtest)
   # Hide gtest project variables
   mark_as_advanced(
     BUILD_SHARED_LIBS
+    BUILD_GMOCK
+    INSTALL_GTEST
     gmock_build_tests
     gtest_build_samples
     gtest_build_tests

From 017f78d207d2f29b05b8dc7a5975934d189f1d6d Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 20 Feb 2021 01:59:52 +0530
Subject: [PATCH 2125/2677] Add populated checks for fetchcontent dependencies

This results in faster re-runs of cmake command after the first run.

Also removed obsolete clblas prefix and associated variables.
---
 CMakeLists.txt                           | 6 +++---
 CMakeModules/AFconfigure_deps_vars.cmake | 9 +++++++--
 CMakeModules/AFconfigure_forge_dep.cmake | 3 ++-
 CMakeModules/build_CLBlast.cmake         | 2 +-
 CMakeModules/build_cl2hpp.cmake          | 2 +-
 CMakeModules/build_clFFT.cmake           | 2 +-
 src/backend/cpu/CMakeLists.txt           | 2 +-
 src/backend/cuda/CMakeLists.txt          | 2 +-
 test/CMakeLists.txt                      | 4 ++--
 9 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 29f147862a..ca35ad44f6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -137,7 +137,7 @@ FetchContent_Declare(
   GIT_REPOSITORY https://github.com/gabime/spdlog.git
   GIT_TAG        v1.0.0
 )
-FetchContent_Populate(${spdlog_prefix})
+af_dep_check_and_populate(${spdlog_prefix})
 add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
 # when crosscompiling use the bin2cpp file from the native bin directory
@@ -185,7 +185,7 @@ FetchContent_Declare(
   GIT_REPOSITORY https://github.com/arrayfire/glad.git
   GIT_TAG        master
 )
-FetchContent_Populate(${glad_prefix})
+af_dep_check_and_populate(${glad_prefix})
 add_subdirectory(${${glad_prefix}_SOURCE_DIR})
 
 add_subdirectory(src/backend/common)
@@ -411,7 +411,7 @@ FetchContent_Declare(
   GIT_REPOSITORY https://github.com/arrayfire/assets.git
   GIT_TAG        master
 )
-FetchContent_Populate(${assets_prefix})
+af_dep_check_and_populate(${assets_prefix})
 
 set(ASSETS_DIR ${${assets_prefix}_SOURCE_DIR})
 conditional_directory(AF_BUILD_EXAMPLES examples)
diff --git a/CMakeModules/AFconfigure_deps_vars.cmake b/CMakeModules/AFconfigure_deps_vars.cmake
index 45b78cde90..4e030db432 100644
--- a/CMakeModules/AFconfigure_deps_vars.cmake
+++ b/CMakeModules/AFconfigure_deps_vars.cmake
@@ -40,7 +40,6 @@ set_and_mark_depname(cub_prefix "nv_cub")
 set_and_mark_depname(cl2hpp_prefix "ocl_cl2hpp")
 set_and_mark_depname(clblast_prefix "ocl_clblast")
 set_and_mark_depname(clfft_prefix "ocl_clfft")
-set_and_mark_depname(clblas_prefix "ocl_clblas")
 
 if(AF_BUILD_OFFLINE)
   macro(set_fetchcontent_src_dir prefix_var dep_name)
@@ -61,5 +60,11 @@ if(AF_BUILD_OFFLINE)
   set_fetchcontent_src_dir(cl2hpp_prefix "OpenCL cl2 hpp header")
   set_fetchcontent_src_dir(clblast_prefix "CLBlast library")
   set_fetchcontent_src_dir(clfft_prefix "clFFT library")
-  set_fetchcontent_src_dir(clblas_prefix "clBLAS library")
 endif()
+
+macro(af_dep_check_and_populate prefix)
+  FetchContent_GetProperties(${prefix})
+  if(NOT ${prefix}_POPULATED)
+    FetchContent_Populate(${prefix})
+  endif()
+endmacro()
diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index 3dee59bf1d..72d9591908 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -16,7 +16,8 @@ FetchContent_Declare(
   GIT_REPOSITORY https://github.com/arrayfire/forge.git
   GIT_TAG        "v${FG_VERSION}"
 )
-FetchContent_Populate(${forge_prefix})
+af_dep_check_and_populate(${forge_prefix})
+
 if(AF_BUILD_FORGE)
   set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
   set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index b4a1d4bb6c..5b21289e54 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -10,7 +10,7 @@ FetchContent_Declare(
   GIT_REPOSITORY    https://github.com/cnugteren/CLBlast.git
   GIT_TAG           41f344d1a6f2d149bba02a6615292e99b50f4856
 )
-FetchContent_Populate(${clblast_prefix})
+af_dep_check_and_populate(${clblast_prefix})
 
 include(ExternalProject)
 find_program(GIT git)
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 9e67afc6d1..f34fc216be 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -18,7 +18,7 @@ FetchContent_Declare(
   GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
   GIT_TAG v2.0.12
 )
-FetchContent_Populate(${cl2hpp_prefix})
+af_dep_check_and_populate(${cl2hpp_prefix})
 
 if (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
   add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index fdc72b3173..dda658f569 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -10,7 +10,7 @@ FetchContent_Declare(
   GIT_REPOSITORY    https://github.com/arrayfire/clFFT.git
   GIT_TAG           cmake_fixes
 )
-FetchContent_Populate(${clfft_prefix})
+af_dep_check_and_populate(${clfft_prefix})
 
 set(current_build_type ${BUILD_SHARED_LIBS})
 set(BUILD_SHARED_LIBS OFF)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 86c4350523..282f411e38 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -276,7 +276,7 @@ FetchContent_Declare(
   GIT_REPOSITORY https://github.com/arrayfire/threads.git
   GIT_TAG        b666773940269179f19ef11c8f1eb77005e85d9a
 )
-FetchContent_Populate(${threads_prefix})
+af_dep_check_and_populate(${threads_prefix})
 
 target_sources(afcpu
   PRIVATE
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a6632f43e7..2808c80ba9 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -120,7 +120,7 @@ if(CUDA_VERSION_MAJOR VERSION_LESS 11)
     GIT_REPOSITORY https://github.com/NVIDIA/cub.git
     GIT_TAG        1.10.0
   )
-  FetchContent_Populate(${cub_prefix})
+  af_dep_check_and_populate(${cub_prefix})
   cuda_include_directories(${${cub_prefix}_SOURCE_DIR})
 endif()
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 4128538113..fa38f8fa82 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -21,7 +21,7 @@ FetchContent_Declare(
   GIT_TAG        release-1.8.1
 )
 if(NOT TARGET gtest)
-  FetchContent_Populate(${gtest_prefix})
+  af_dep_check_and_populate(${gtest_prefix})
 
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
   # newer cmakes. This sets the default global setting for that policy.
@@ -72,7 +72,7 @@ else(${AF_USE_RELATIVE_TEST_DIR})
     GIT_REPOSITORY https://github.com/arrayfire/arrayfire-data.git
     GIT_TAG        master
   )
-  FetchContent_Populate(${testdata_prefix})
+  af_dep_check_and_populate(${testdata_prefix})
   set(TESTDATA_SOURCE_DIR "${${testdata_prefix}_SOURCE_DIR}")
 endif(${AF_USE_RELATIVE_TEST_DIR})
 

From c13302eb1b42909087c1dd25bbe8f2f1ceba4fdd Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 20 Feb 2021 02:46:43 +0530
Subject: [PATCH 2126/2677] Refactor boost dependency to use fetch content
 module

---
 CMakeModules/AFconfigure_deps_vars.cmake |  2 ++
 CMakeModules/boost_package.cmake         | 36 ++++++++----------------
 2 files changed, 13 insertions(+), 25 deletions(-)

diff --git a/CMakeModules/AFconfigure_deps_vars.cmake b/CMakeModules/AFconfigure_deps_vars.cmake
index 4e030db432..748e911473 100644
--- a/CMakeModules/AFconfigure_deps_vars.cmake
+++ b/CMakeModules/AFconfigure_deps_vars.cmake
@@ -40,6 +40,7 @@ set_and_mark_depname(cub_prefix "nv_cub")
 set_and_mark_depname(cl2hpp_prefix "ocl_cl2hpp")
 set_and_mark_depname(clblast_prefix "ocl_clblast")
 set_and_mark_depname(clfft_prefix "ocl_clfft")
+set_and_mark_depname(boost_prefix "boost_compute")
 
 if(AF_BUILD_OFFLINE)
   macro(set_fetchcontent_src_dir prefix_var dep_name)
@@ -60,6 +61,7 @@ if(AF_BUILD_OFFLINE)
   set_fetchcontent_src_dir(cl2hpp_prefix "OpenCL cl2 hpp header")
   set_fetchcontent_src_dir(clblast_prefix "CLBlast library")
   set_fetchcontent_src_dir(clfft_prefix "clFFT library")
+  set_fetchcontent_src_dir(boost_prefix "boost-compute headers")
 endif()
 
 macro(af_dep_check_and_populate prefix)
diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 9f40409251..9736dab753 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -18,35 +18,21 @@ if(NOT
    (Boost_VERSION_MACRO VERSION_GREATER Boost_MIN_VER OR
     Boost_VERSION_MACRO VERSION_EQUAL Boost_MIN_VER)))
   set(VER 1.70.0)
-  set(MD5 e160ec0ff825fc2850ea4614323b1fb5)
-  include(ExternalProject)
-
-  ExternalProject_Add(
-    boost_compute
-    URL       https://github.com/boostorg/compute/archive/boost-${VER}.tar.gz
-    URL_MD5   ${MD5}
-    INSTALL_COMMAND ""
-    CONFIGURE_COMMAND ""
-    BUILD_COMMAND ""
-    )
-
-  ExternalProject_Get_Property(boost_compute source_dir)
-
-  if(NOT EXISTS ${source_dir}/include)
-      message(WARNING "WARN: Found Boost v${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION}."
-                      " Required ${VER}. Build will download Boost Compute.")
-  endif()
-  make_directory(${source_dir}/include)
-
+  message(WARNING
+      "WARN: Found Boost v${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION}."
+      "Minimum required ${VER}. Build will download Boost Compute.")
+  FetchContent_Declare(
+    ${boost_prefix}
+    URL https://github.com/boostorg/compute/archive/boost-${VER}.tar.gz
+    URL_HASH MD5=e160ec0ff825fc2850ea4614323b1fb5
+  )
+  af_dep_check_and_populate(${boost_prefix})
   if(NOT TARGET Boost::boost)
     add_library(Boost::boost IMPORTED INTERFACE GLOBAL)
   endif()
-
-  add_dependencies(Boost::boost boost_compute)
-
   set_target_properties(Boost::boost PROPERTIES
-    INTERFACE_INCLUDE_DIRECTORIES "${source_dir}/include;${Boost_INCLUDE_DIR}"
-    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${source_dir}/include;${Boost_INCLUDE_DIR}"
+    INTERFACE_INCLUDE_DIRECTORIES "${${boost_prefix}_SOURCE_DIR}/include;${Boost_INCLUDE_DIR}"
+    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${${boost_prefix}_SOURCE_DIR}/include;${Boost_INCLUDE_DIR}"
     )
 else()
   if(NOT TARGET Boost::boost)

From 92392db7d1b474717d32ad98b35106267ede19f2 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 22 Feb 2021 13:59:29 +0530
Subject: [PATCH 2127/2677] Refactor mtx test data sets to fetchcontent
 workflow

---
 test/CMakeLists.txt                           |  3 +-
 .../download_sparse_datasets.cmake            | 37 +++++++++----------
 2 files changed, 19 insertions(+), 21 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index fa38f8fa82..4ba67af7c0 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -196,10 +196,9 @@ function(make_test)
       )
     target_link_libraries(${target} PRIVATE mmio)
     if(AF_TEST_WITH_MTX_FILES AND ${mt_args_USE_MMIO})
-      add_dependencies(${target} mtxDownloads)
       target_compile_definitions(${target}
         PRIVATE
-          MTX_TEST_DIR="${CMAKE_CURRENT_BINARY_DIR}/matrixmarket/"
+        MTX_TEST_DIR="${ArrayFire_BINARY_DIR}/extern/matrixmarket/"
         )
     endif()
     if(WIN32)
diff --git a/test/CMakeModules/download_sparse_datasets.cmake b/test/CMakeModules/download_sparse_datasets.cmake
index 8d94b828d9..283dad53ac 100644
--- a/test/CMakeModules/download_sparse_datasets.cmake
+++ b/test/CMakeModules/download_sparse_datasets.cmake
@@ -1,31 +1,30 @@
-# Copyright (c) 2020, ArrayFire
+# Copyright (c) 2021, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-include(ExternalProject)
-
-add_custom_target(mtxDownloads)
-
 set(URL "https://sparse.tamu.edu")
-set(mtx_data_dir "${CMAKE_CURRENT_BINARY_DIR}/matrixmarket")
-file(MAKE_DIRECTORY ${mtx_data_dir})
 
 function(mtxDownload name group)
-  set(extproj_name mtxDownload-${group}-${name})
-  set(path_prefix "${ArrayFire_BINARY_DIR}/mtx_datasets/${group}")
-  ExternalProject_Add(
-      ${extproj_name}
-      PREFIX "${path_prefix}"
-      URL "${URL}/MM/${group}/${name}.tar.gz"
-      SOURCE_DIR "${mtx_data_dir}/${group}/${name}"
-      CONFIGURE_COMMAND ""
-      BUILD_COMMAND ""
-      INSTALL_COMMAND ""
-    )
-  add_dependencies(mtxDownloads mtxDownload-${group}-${name})
+  set(root_dir ${ArrayFire_BINARY_DIR}/extern/matrixmarket)
+  set(target_dir ${root_dir}/${group}/${name})
+  set(mtx_name mtxDownload_${group}_${name})
+  string(TOLOWER ${mtx_name} mtx_name)
+  FetchContent_Declare(
+    ${mtx_name}
+    URL ${URL}/MM/${group}/${name}.tar.gz
+  )
+  af_dep_check_and_populate(${mtx_name})
+  set_and_mark_depname(mtx_prefix ${mtx_name})
+  if(AF_BUILD_OFFLINE)
+    set_fetchcontent_src_dir(mtx_prefix "{name}.mtx file from {group} group")
+  endif()
+  if(NOT EXISTS "${target_dir}/${name}.mtx")
+    file(MAKE_DIRECTORY ${target_dir})
+    file(COPY ${${mtx_name}_SOURCE_DIR}/${name}.mtx DESTINATION ${target_dir})
+  endif()
 endfunction()
 
 # Following files are used for testing mtx read fn

From 43009dcbe057ad88ccf6cb91d6a0a17ddb7ee716 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 19 Feb 2021 13:44:42 +0530
Subject: [PATCH 2128/2677] Remove submodule commands from github action
 workflows

These are not needed since the move to getting dependencies using
fetch content module of cmake.

Refactored release source tar ball action to relfect the same as well
---
 .github/workflows/cpu_build.yml            |  8 -----
 .github/workflows/docs_build.yml           |  1 -
 .github/workflows/release_src_artifact.yml | 39 ++++++++++++++++++++--
 3 files changed, 37 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/cpu_build.yml
index 5f3b9c2544..88a83cd15c 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/cpu_build.yml
@@ -31,10 +31,6 @@ jobs:
             - name: Checkout Repository
               uses: actions/checkout@master
 
-            - name: Checkout Submodules
-              shell: bash
-              run: git submodule update --init --recursive
-
             - name: Download Ninja
               env:
                   OS_NAME: ${{ matrix.os }}
@@ -131,10 +127,6 @@ jobs:
             - name: Checkout Repository
               uses: actions/checkout@master
 
-            - name: Checkout Submodules
-              shell: bash
-              run: git submodule update --init --recursive
-
             - name: VCPKG Cache
               uses: actions/cache@v1
               id: vcpkg-cache
diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
index c52729d3aa..2f93f0a690 100644
--- a/.github/workflows/docs_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -26,7 +26,6 @@ jobs:
 
             - name: Configure
               run: |
-                  git submodule update --init --recursive
                   mkdir build && cd build
                   cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
                         -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
diff --git a/.github/workflows/release_src_artifact.yml b/.github/workflows/release_src_artifact.yml
index da25ff3522..8dc6e2cd62 100644
--- a/.github/workflows/release_src_artifact.yml
+++ b/.github/workflows/release_src_artifact.yml
@@ -23,11 +23,30 @@ jobs:
                   echo "AF_TAG=${tag}" >> $GITHUB_ENV
                   echo "AF_VER=${ver}" >> $GITHUB_ENV
 
-            - name: Checkout with Submodules
+            - name: Checkout Repo
               run: |
                   cd ${GITHUB_WORKSPACE}
                   clone_url="https://github.com/${GITHUB_REPOSITORY}"
-                  git clone --depth 1 --recursive -b ${AF_TAG} ${clone_url} arrayfire-full-${AF_VER}
+                  git clone --depth 1 -b ${AF_TAG} ${clone_url} arrayfire-full-${AF_VER}
+
+            - name: Install Dependencies
+              run: |
+                  sudo add-apt-repository ppa:mhier/libboost-latest
+                  sudo apt-get -qq update
+                  sudo apt-get install -y libfontconfig1-dev \
+                                          libglfw3-dev \
+                                          libfftw3-dev \
+                                          liblapacke-dev \
+                                          libopenblas-dev \
+                                          ocl-icd-opencl-dev \
+                                          nvidia-cuda-toolkit \
+                                          libboost1.68-dev
+
+            - name: CMake Configure
+              run: |
+                  cd ${GITHUB_WORKSPACE}/arrayfire-full-${AF_VER}
+                  mkdir build && cd build
+                  cmake .. -DAF_BUILD_FORGE:BOOL=ON
 
             - name: Create source tarball
               id: create-src-tarball
@@ -36,6 +55,22 @@ jobs:
                   rm -rf arrayfire-full-${AF_VER}/.git
                   rm -rf arrayfire-full-${AF_VER}/.github
                   rm arrayfire-full-${AF_VER}/.gitmodules
+                  cd arrayfire-full-${AF_VER}/build/
+                  shopt -s extglob
+                  rm -r !(extern)
+                  cd ./extern
+                  rm -rf ./*-build
+                  rm -rf ./*-subbuild
+                  declare -a deps
+                  deps=($(ls))
+                  for dep in ${deps[@]}; do
+                    rm -rf ./${dep}/.git
+                    rm -rf ./${dep}/.gitattributes
+                    rm -rf ./${dep}/.gitmodules
+                  done
+                  shopt -u extglob
+                  rm -rf matrixmarket
+                  cd ../../..
                   tar -cjf arrayfire-full-${AF_VER}.tar.bz2 arrayfire-full-${AF_VER}/
                   echo "UPLOAD_FILE=arrayfire-full-${AF_VER}.tar.bz2" >> $GITHUB_ENV
 

From f6ed89cb19e93966320bd3ad1a6bf598cdb1b0d3 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 23 Feb 2021 17:07:01 +0530
Subject: [PATCH 2129/2677] Fix examples install directory post fetchcontent
 changes

---
 CMakeLists.txt | 41 ++++++++++++++++++++---------------------
 1 file changed, 20 insertions(+), 21 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ca35ad44f6..266636a643 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -125,6 +125,25 @@ mark_as_advanced(CLEAR CUDA_VERSION)
 #forge is included in ALL target if AF_BUILD_FORGE is ON
 #otherwise, forge is not built at all
 include(AFconfigure_forge_dep)
+FetchContent_Declare(
+  ${spdlog_prefix}
+  GIT_REPOSITORY https://github.com/gabime/spdlog.git
+  GIT_TAG        v1.0.0
+)
+af_dep_check_and_populate(${spdlog_prefix})
+FetchContent_Declare(
+  ${glad_prefix}
+  GIT_REPOSITORY https://github.com/arrayfire/glad.git
+  GIT_TAG        master
+)
+af_dep_check_and_populate(${glad_prefix})
+FetchContent_Declare(
+  ${assets_prefix}
+  GIT_REPOSITORY https://github.com/arrayfire/assets.git
+  GIT_TAG        master
+)
+af_dep_check_and_populate(${assets_prefix})
+set(ASSETS_DIR ${${assets_prefix}_SOURCE_DIR})
 
 configure_file(
     ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
@@ -132,12 +151,6 @@ configure_file(
 )
 
 set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
-FetchContent_Declare(
-  ${spdlog_prefix}
-  GIT_REPOSITORY https://github.com/gabime/spdlog.git
-  GIT_TAG        v1.0.0
-)
-af_dep_check_and_populate(${spdlog_prefix})
 add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
 # when crosscompiling use the bin2cpp file from the native bin directory
@@ -180,12 +193,6 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
-FetchContent_Declare(
-  ${glad_prefix}
-  GIT_REPOSITORY https://github.com/arrayfire/glad.git
-  GIT_TAG        master
-)
-af_dep_check_and_populate(${glad_prefix})
 add_subdirectory(${${glad_prefix}_SOURCE_DIR})
 
 add_subdirectory(src/backend/common)
@@ -295,7 +302,7 @@ install(DIRECTORY examples/ #NOTE The slash at the end is important
     DESTINATION ${AF_INSTALL_EXAMPLE_DIR}
     COMPONENT examples)
 
-install(DIRECTORY assets/examples/ #NOTE The slash at the end is important
+install(DIRECTORY ${ASSETS_DIR}/examples/ #NOTE The slash at the end is important
     DESTINATION ${AF_INSTALL_EXAMPLE_DIR}
     COMPONENT examples)
 
@@ -406,14 +413,6 @@ endif()
 
 conditional_directory(BUILD_TESTING test)
 
-FetchContent_Declare(
-  ${assets_prefix}
-  GIT_REPOSITORY https://github.com/arrayfire/assets.git
-  GIT_TAG        master
-)
-af_dep_check_and_populate(${assets_prefix})
-
-set(ASSETS_DIR ${${assets_prefix}_SOURCE_DIR})
 conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
 

From 58573eda4ded71fe4e0be6305a6f71386d175d12 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 26 Feb 2021 12:59:29 +0530
Subject: [PATCH 2130/2677] Separate Windows ci(gh-action) workflow and some
 improvs

Splitting the windows ci job into a separate workflow enables the
ci to re-run windows specific jobs independent of unix jobs.

Updated Ninja dependency to 1.10.2 fix release in all ci(gh-actions)

Refactored boost dependency to be installed via packages managers as
GitHub Actions is removing pre-installed versions from March 8, 2021

Update VCPKG hash to newer version to enable fast and better ports.
---
 .../{cpu_build.yml => unix_cpu_build.yml}     | 67 ++----------------
 .github/workflows/win_cpu_build.yml           | 69 +++++++++++++++++++
 2 files changed, 73 insertions(+), 63 deletions(-)
 rename .github/workflows/{cpu_build.yml => unix_cpu_build.yml} (62%)
 create mode 100644 .github/workflows/win_cpu_build.yml

diff --git a/.github/workflows/cpu_build.yml b/.github/workflows/unix_cpu_build.yml
similarity index 62%
rename from .github/workflows/cpu_build.yml
rename to .github/workflows/unix_cpu_build.yml
index 88a83cd15c..3a70a093a4 100644
--- a/.github/workflows/cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -13,7 +13,7 @@ jobs:
         name: CPU
         runs-on: ${{ matrix.os }}
         env:
-          NINJA_VER: 1.10.0
+          NINJA_VER: 1.10.2
           CMAKE_VER: 3.5.1
         strategy:
             fail-fast: false
@@ -66,8 +66,10 @@ jobs:
             - name: Install Common Dependencies for Ubuntu
               if: matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04'
               run: |
+                  sudo add-apt-repository ppa:mhier/libboost-latest
                   sudo apt-get -qq update
-                  sudo apt-get install -y libfreeimage-dev \
+                  sudo apt-get install -y libboost1.74-dev \
+                                          libfreeimage-dev \
                                           libglfw3-dev \
                                           libfftw3-dev \
                                           liblapacke-dev
@@ -103,7 +105,6 @@ jobs:
                   mkdir build && cd build
                   ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
-                      -DBOOST_ROOT:PATH=${BOOST_ROOT_1_72_0} \
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \
@@ -116,63 +117,3 @@ jobs:
               run: |
                   cd ${GITHUB_WORKSPACE}/build
                   ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2
-
-    window_build_cpu:
-        name: CPU (OpenBLAS, windows-latest)
-        runs-on: windows-latest
-        env:
-          VCPKG_HASH: b79f7675aaa82eb6c5a96ae764fb1ce379a9d5d6 # March 29, 2020 - [hdf5] add tools and fortran feature
-          NINJA_VER: 1.10.0
-        steps:
-            - name: Checkout Repository
-              uses: actions/checkout@master
-
-            - name: VCPKG Cache
-              uses: actions/cache@v1
-              id: vcpkg-cache
-              with:
-                path: vcpkg
-                key: vcpkg-deps-${{ env.VCPKG_HASH }}
-
-            - name: Install VCPKG Common Deps
-              if: steps.vcpkg-cache.outputs.cache-hit != 'true'
-              run: |
-                  git clone --recursive https://github.com/microsoft/vcpkg
-                  Set-Location -Path .\vcpkg
-                  git reset --hard $env:VCPKG_HASH
-                  .\bootstrap-vcpkg.bat
-                  .\vcpkg.exe install --triplet x64-windows fftw3 freeimage freetype glfw3 openblas
-                  Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
-
-            - name: Download Ninja
-              run: |
-                  Invoke-WebRequest -Uri "https://github.com/ninja-build/ninja/releases/download/v$env:NINJA_VER/ninja-win.zip" -OutFile ninja.zip
-                  Expand-Archive -Path ninja.zip -DestinationPath .
-
-            - name: CMake Configure
-              run: |
-                  $cwd = (Get-Item -Path ".\").FullName
-                  $ref = $env:GITHUB_REF | %{ if ($_ -match "refs/pull/[0-9]+/merge") { $_;} }
-                  $prnum = $ref | %{$_.Split("/")[2]}
-                  $branch = git branch --show-current
-                  $buildname = if($prnum -eq $null) { $branch } else { "PR-$prnum" }
-                  $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
-                  $buildname = "$buildname-cpu-openblas"
-                  mkdir build && cd build
-                  cmake .. -G "Visual Studio 16 2019" -A x64 `
-                      -DCMAKE_TOOLCHAIN_FILE:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\scripts\buildsystems\vcpkg.cmake" `
-                      -DFFTW_INCLUDE_DIR:PATH="$env:GITHUB_WORKSPACE\vcpkg\installed/x64-windows\include" `
-                      -DFFTW_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3.lib" `
-                      -DFFTWF_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3f.lib" `
-                      -DBOOST_ROOT:PATH="$env:BOOST_ROOT_1_72_0" `
-                      -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
-                      -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
-                      -DBUILDNAME:STRING="$buildname"
-                  echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
-
-            - name: Build and Test
-              run: |
-                  $cwd = (Get-Item -Path ".\").FullName
-                  $Env:PATH += ";$cwd/vcpkg/installed/x64-windows/bin"
-                  Set-Location -Path $cwd/build
-                  ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C Release -R cpu -E pinverse -j2
diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
new file mode 100644
index 0000000000..ef4492f6d6
--- /dev/null
+++ b/.github/workflows/win_cpu_build.yml
@@ -0,0 +1,69 @@
+on:
+  push:
+    branches:
+    - master
+  pull_request:
+    branches:
+    - master
+
+name: ci
+
+jobs:
+    window_build_cpu:
+        name: CPU (OpenBLAS, windows-latest)
+        runs-on: windows-latest
+        env:
+          VCPKG_HASH: 0cbc579e1ee21fa4ad0974a9ed926f60c6ed1a4a # FEB 25, 2021 - [rsasynccpp] Add new port (Rstein.AsyncCpp) (#16380)
+          NINJA_VER: 1.10.2
+        steps:
+            - name: Checkout Repository
+              uses: actions/checkout@master
+
+            - name: VCPKG Cache
+              uses: actions/cache@v1
+              id: vcpkg-cache
+              with:
+                path: vcpkg
+                key: vcpkg-deps-${{ env.VCPKG_HASH }}
+
+            - name: Install VCPKG Common Deps
+              if: steps.vcpkg-cache.outputs.cache-hit != 'true'
+              run: |
+                  git clone --recursive https://github.com/microsoft/vcpkg
+                  Set-Location -Path .\vcpkg
+                  git reset --hard $env:VCPKG_HASH
+                  .\bootstrap-vcpkg.bat
+                  .\vcpkg.exe install --triplet x64-windows boost fftw3 freeimage freetype glfw3 openblas
+                  Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
+
+            - name: Download Ninja
+              run: |
+                  Invoke-WebRequest -Uri "https://github.com/ninja-build/ninja/releases/download/v$env:NINJA_VER/ninja-win.zip" -OutFile ninja.zip
+                  Expand-Archive -Path ninja.zip -DestinationPath .
+
+            - name: CMake Configure
+              run: |
+                  $cwd = (Get-Item -Path ".\").FullName
+                  $ref = $env:GITHUB_REF | %{ if ($_ -match "refs/pull/[0-9]+/merge") { $_;} }
+                  $prnum = $ref | %{$_.Split("/")[2]}
+                  $branch = git branch --show-current
+                  $buildname = if($prnum -eq $null) { $branch } else { "PR-$prnum" }
+                  $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
+                  $buildname = "$buildname-cpu-openblas"
+                  mkdir build && cd build
+                  cmake .. -G "Visual Studio 16 2019" -A x64 `
+                      -DCMAKE_TOOLCHAIN_FILE:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\scripts\buildsystems\vcpkg.cmake" `
+                      -DFFTW_INCLUDE_DIR:PATH="$env:GITHUB_WORKSPACE\vcpkg\installed/x64-windows\include" `
+                      -DFFTW_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3.lib" `
+                      -DFFTWF_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3f.lib" `
+                      -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
+                      -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
+                      -DBUILDNAME:STRING="$buildname"
+                  echo "CTEST_DASHBOARD=${dashboard}" >> $env:GITHUB_ENV
+
+            - name: Build and Test
+              run: |
+                  $cwd = (Get-Item -Path ".\").FullName
+                  $Env:PATH += ";$cwd/vcpkg/installed/x64-windows/bin"
+                  Set-Location -Path $cwd/build
+                  ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C Release -R cpu -E pinverse -j2

From 52f349be07e88a74561ff09208c082c58f04686e Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Fri, 26 Feb 2021 15:26:30 +0530
Subject: [PATCH 2131/2677] Mark couple of cmake variables as advanced that I
 missed earlier

---
 CMakeModules/AFconfigure_forge_dep.cmake | 2 ++
 CMakeModules/FindcuDNN.cmake             | 1 +
 2 files changed, 3 insertions(+)

diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index 72d9591908..364bd8375f 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -36,6 +36,8 @@ if(AF_BUILD_FORGE)
       FG_USE_WINDOW_TOOLKIT
       FG_USE_SYSTEM_CL2HPP
       FG_ENABLE_HUNTER
+      FG_RENDERING_BACKEND
+      SPHINX_EXECUTABLE
       glfw3_DIR
       glm_DIR
       )
diff --git a/CMakeModules/FindcuDNN.cmake b/CMakeModules/FindcuDNN.cmake
index bf113afd5d..4c28d3c854 100644
--- a/CMakeModules/FindcuDNN.cmake
+++ b/CMakeModules/FindcuDNN.cmake
@@ -164,6 +164,7 @@ if(cuDNN_INCLUDE_DIRS)
         ${CMAKE_INSTALL_PREFIX}
       PATH_SUFFIXES lib lib64 bin lib/x64 bin/x64
       DOC "cudnn${cudnn_lib_name_infix} Windows DLL." )
+      mark_as_advanced(cuDNN${LIB_INFIX}_DLL_LIBRARY)
     endif()
   endmacro()
 

From 29dc6721357516394aa299cf12742221debc855e Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 8 Mar 2021 11:40:41 +0530
Subject: [PATCH 2132/2677] Remove leftover clblas references from licenses &
 codebase

---
 CMakeModules/CPackConfig.cmake    |  2 +-
 src/backend/opencl/CMakeLists.txt |  1 -
 src/backend/opencl/err_clblas.hpp | 73 -------------------------------
 3 files changed, 1 insertion(+), 75 deletions(-)
 delete mode 100644 src/backend/opencl/err_clblas.hpp

diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 23e30c5637..07d1d46962 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -322,7 +322,7 @@ cpack_ifw_configure_component(documentation)
 cpack_ifw_configure_component(examples)
 cpack_ifw_configure_component(licenses FORCED_INSTALLATION
   LICENSES "GLFW" ${zlib_lic_path} "FreeImage" ${fimg_lic_path}
-  "Boost" ${boost_lic_path} "clBLAS, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
+  "Boost" ${boost_lic_path} "CLBlast, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
   "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
 )
 if (AF_INSTALL_FORGE_DEV)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 2c20ad2d0d..d8daa3c0a2 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -90,7 +90,6 @@ target_sources(afopencl
     diagonal.hpp
     diff.cpp
     diff.hpp
-    err_clblas.hpp
     err_clblast.hpp
     err_opencl.hpp
     errorcodes.cpp
diff --git a/src/backend/opencl/err_clblas.hpp b/src/backend/opencl/err_clblas.hpp
deleted file mode 100644
index f01d272adb..0000000000
--- a/src/backend/opencl/err_clblas.hpp
+++ /dev/null
@@ -1,73 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-#include <clBLAS.h>
-#include <err_opencl.hpp>
-#include <stdio.h>
-#include <mutex>
-
-static const char* _clblasGetResultString(clblasStatus st) {
-    switch (st) {
-        case clblasSuccess: return "Success";
-        case clblasInvalidValue: return "Invalid value";
-        case clblasInvalidCommandQueue: return "Invalid queue";
-        case clblasInvalidContext: return "Invalid context";
-        case clblasInvalidMemObject: return "Invalid memory object";
-        case clblasInvalidDevice: return "Invalid device";
-        case clblasInvalidEventWaitList: return "Invalid event list";
-        case clblasOutOfResources: return "Out of resources";
-        case clblasOutOfHostMemory: return "Out of host memory";
-        case clblasInvalidOperation: return "Invalid operation";
-        case clblasCompilerNotAvailable: return "Compiler not available";
-        case clblasBuildProgramFailure: return "Build program failure";
-        case clblasNotImplemented: return "Not implemented";
-        case clblasNotInitialized: return "CLBLAS Not initialized";
-        case clblasInvalidMatA: return "Invalid matrix A";
-        case clblasInvalidMatB: return "Invalid matrix B";
-        case clblasInvalidMatC: return "Invalid matrix C";
-        case clblasInvalidVecX: return "Invalid vector X";
-        case clblasInvalidVecY: return "Invalid vector Y";
-        case clblasInvalidDim: return "Invalid dimension";
-        case clblasInvalidLeadDimA: return "Invalid lda";
-        case clblasInvalidLeadDimB: return "Invalid ldb";
-        case clblasInvalidLeadDimC: return "Invalid ldc";
-        case clblasInvalidIncX: return "Invalid incx";
-        case clblasInvalidIncY: return "Invalid incy";
-        case clblasInsufficientMemMatA:
-            return "Insufficient Memory for Matrix A";
-        case clblasInsufficientMemMatB:
-            return "Insufficient Memory for Matrix B";
-        case clblasInsufficientMemMatC:
-            return "Insufficient Memory for Matrix C";
-        case clblasInsufficientMemVecX:
-            return "Insufficient Memory for Vector X";
-        case clblasInsufficientMemVecY:
-            return "Insufficient Memory for Vector Y";
-    }
-
-    return "Unknown error";
-}
-
-static std::recursive_mutex gCLBlasMutex;
-
-#define CLBLAS_CHECK(fn)                                           \
-    do {                                                           \
-        gCLBlasMutex.lock();                                       \
-        clblasStatus _clblas_st = fn;                              \
-        gCLBlasMutex.unlock();                                     \
-        if (_clblas_st != clblasSuccess) {                         \
-            char clblas_st_msg[1024];                              \
-            snprintf(clblas_st_msg, sizeof(clblas_st_msg),         \
-                     "clblas Error (%d): %s\n", (int)(_clblas_st), \
-                     _clblasGetResultString(_clblas_st));          \
-                                                                   \
-            AF_ERROR(clblas_st_msg, AF_ERR_INTERNAL);              \
-        }                                                          \
-    } while (0)

From 799cba74eaeecd1a5dc6f6b7b450c8322f8e1bb3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 Mar 2021 13:26:55 -0500
Subject: [PATCH 2133/2677] Fix glad add_subdirectory to fix out of tree builds

This was a problem on the arrayfire-benchmark repo where the repository
is built as a subproject
---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 266636a643..79df0ec19b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -193,7 +193,7 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
-add_subdirectory(${${glad_prefix}_SOURCE_DIR})
+add_subdirectory(${${glad_prefix}_SOURCE_DIR} ${${glad_prefix}_BINARY_DIR})
 
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)

From d85675f03961f2230a88b62c64eeaefa23abccd9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 9 Mar 2021 10:25:43 +0530
Subject: [PATCH 2134/2677] Fix for CUDA 11 nvrtc-builtins shared lib packaging

---
 src/backend/cuda/CMakeLists.txt | 37 +++++++++++++++++++--------------
 1 file changed, 21 insertions(+), 16 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 2808c80ba9..7e65278db9 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -843,7 +843,7 @@ if(AF_INSTALL_STANDALONE)
 	  afcu_collect_cudnn_libs(ops_train)
 	endif()
   endif()
-  afcu_collect_libs(nvrtc FULL_VERSION)
+
   if(WIN32)
 	if(CUDA_VERSION_MAJOR VERSION_EQUAL 11)
       afcu_collect_libs(cufft LIB_MAJOR 10 LIB_MINOR 4)
@@ -860,22 +860,27 @@ if(AF_INSTALL_STANDALONE)
     afcu_collect_libs(cusolver)
   endif()
 
-  if(APPLE)
-    afcu_collect_libs(cudart)
-
-    get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
-    install(FILES       ${nvrtc_outpath}
-            DESTINATION ${AF_INSTALL_BIN_DIR}
-            RENAME      "${PX}nvrtc-builtins${SX}"
-            COMPONENT   cuda_dependencies)
-  elseif(UNIX)
-    get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
-    install(FILES       ${nvrtc_outpath}
-            DESTINATION ${AF_INSTALL_LIB_DIR}
-            RENAME      "${PX}nvrtc-builtins${SX}"
-            COMPONENT   cuda_dependencies)
+  afcu_collect_libs(nvrtc FULL_VERSION)
+  if(CUDA_VERSION VERSION_GREATER 10.0)
+    afcu_collect_libs(nvrtc-builtins FULL_VERSION)
   else()
-    afcu_collect_libs(nvrtc-builtins)
+    if(APPLE)
+      afcu_collect_libs(cudart)
+
+      get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
+      install(FILES       ${nvrtc_outpath}
+              DESTINATION ${AF_INSTALL_BIN_DIR}
+              RENAME      "${PX}nvrtc-builtins${SX}"
+              COMPONENT   cuda_dependencies)
+    elseif(UNIX)
+      get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
+      install(FILES       ${nvrtc_outpath}
+              DESTINATION ${AF_INSTALL_LIB_DIR}
+              RENAME      "${PX}nvrtc-builtins${SX}"
+              COMPONENT   cuda_dependencies)
+    else()
+      afcu_collect_libs(nvrtc-builtins)
+    endif()
   endif()
 endif()
 

From 67b0e1f611467e37ce824c8f7b311f18d3128e96 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 12 Mar 2021 21:59:09 +0530
Subject: [PATCH 2135/2677] Change to reflect BOOST removal from gh action
 images (#3108)

---
 .github/workflows/docs_build.yml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
index 2f93f0a690..9cdab11385 100644
--- a/.github/workflows/docs_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -24,13 +24,18 @@ jobs:
                   mkdir doxygen
                   tar -xf doxygen-${DOXYGEN_VER}.linux.bin.tar.gz -C doxygen --strip 1
 
+            - name: Install Boost
+              run: |
+                  sudo add-apt-repository ppa:mhier/libboost-latest
+                  sudo apt-get -qq update
+                  sudo apt-get install -y libboost1.74-dev
+
             - name: Configure
               run: |
                   mkdir build && cd build
                   cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
                         -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
                         -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
-                        -DBOOST_ROOT:PATH=${BOOST_ROOT_1_72_0} \
                         -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen \
                         ..
 

From d56c3bc366a593211c64318fd1151ec1dfec8059 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 18 Mar 2021 13:41:07 -0400
Subject: [PATCH 2136/2677] OPT: Optimize indexing using dynamic thread block
 sizes.

This optimization dynamically sets the block size based on the output array
dimension. Originally we had a block size of 32x8 threads per block. This
configuration was not ideal when indexing into a long array where you
had few columns and many rows. The current approach creates blocks of
256x1, 128x2, 64x4 and 32x8 to better accommodate smaller dimensions.
---
 src/backend/cuda/kernel/index.hpp   | 14 +++++++++-----
 src/backend/opencl/kernel/index.hpp | 25 ++++++++++++++++---------
 2 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index a11f5a996e..589245213f 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -21,13 +21,17 @@ namespace kernel {
 
 template<typename T>
 void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
-    constexpr int THREADS_X = 32;
-    constexpr int THREADS_Y = 8;
-
     auto index = common::getKernel("cuda::index", {index_cuh_src},
                                    {TemplateTypename<T>()});
-
-    const dim3 threads(THREADS_X, THREADS_Y);
+    dim3 threads;
+    switch (out.dims[1]) {
+        case 1: threads.y = 1; break;
+        case 2: threads.y = 2; break;
+        case 3:
+        case 4: threads.y = 4; break;
+        default: threads.y = 8; break;
+    }
+    threads.x = static_cast<unsigned>(256.f / threads.y);
 
     int blks_x = divup(out.dims[0], threads.x);
     int blks_y = divup(out.dims[1], threads.y);
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index b009497a7c..abcd89715c 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -31,23 +31,30 @@ typedef struct {
 template<typename T>
 void index(Param out, const Param in, const IndexKernelParam_t& p,
            cl::Buffer* bPtr[4]) {
-    constexpr int THREADS_X = 32;
-    constexpr int THREADS_Y = 8;
-
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto index = common::getKernel("indexKernel", {index_cl_src},
+    auto index    = common::getKernel("indexKernel", {index_cl_src},
                                    {TemplateTypename<T>()}, options);
-    cl::NDRange local(THREADS_X, THREADS_Y);
+    int threads_x = 256;
+    int threads_y = 1;
+    cl::NDRange local(threads_x, threads_y);
+    switch (out.info.dims[1]) {
+        case 1: threads_y = 1; break;
+        case 2: threads_y = 2; break;
+        case 3:
+        case 4: threads_y = 4; break;
+        default: threads_y = 8; break;
+    }
+    threads_x = static_cast<unsigned>(256.f / threads_y);
 
-    int blk_x = divup(out.info.dims[0], THREADS_X);
-    int blk_y = divup(out.info.dims[1], THREADS_Y);
+    int blk_x = divup(out.info.dims[0], local[0]);
+    int blk_y = divup(out.info.dims[1], local[1]);
 
-    cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
-                       blk_y * out.info.dims[3] * THREADS_Y);
+    cl::NDRange global(blk_x * out.info.dims[2] * local[0],
+                       blk_y * out.info.dims[3] * local[1]);
 
     index(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
           *in.data, in.info, p, *bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x,

From e21691d38a3ddd589baac4adcf848fe91176b4a1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 8 Apr 2021 16:39:25 +0530
Subject: [PATCH 2137/2677] Fix indentation in FindMKL cmake module

---
 CMakeModules/FindMKL.cmake | 68 +++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 34 deletions(-)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 12ab882dff..8de3ea0449 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -261,47 +261,47 @@ function(find_mkl_library)
         IntelSWTools/compilers_and_libraries/windows/compiler/lib/intel64
         IntelSWTools/compilers_and_libraries/windows/tbb/lib/intel64/${msvc_dir}
         )
-      if(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
-        if (CMAKE_VERSION VERSION_GREATER 3.14)
-          message(VERBOSE "MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}")
-        endif()
+    if(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+      if(CMAKE_VERSION VERSION_GREATER 3.14)
+        message(VERBOSE "MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}")
       endif()
-      mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
     endif()
+    mark_as_advanced(MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY)
+  endif()
 
-    set_target_properties(MKL::${mkl_args_NAME}
+  set_target_properties(MKL::${mkl_args_NAME}
+    PROPERTIES
+      INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
+      IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_LINK_LIBRARY}"
+      IMPORTED_NO_SONAME TRUE)
+
+  set_target_properties(MKL::${mkl_args_NAME}_STATIC
       PROPERTIES
-        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
-        IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_LINK_LIBRARY}"
-        IMPORTED_NO_SONAME TRUE)
+      INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
+      IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}"
+      IMPORTED_NO_SONAME TRUE)
 
-    set_target_properties(MKL::${mkl_args_NAME}_STATIC
-        PROPERTIES
-        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
-        IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_STATIC_LINK_LIBRARY}"
-        IMPORTED_NO_SONAME TRUE)
+  if(WIN32)
+    find_file(MKL_${mkl_args_NAME}_DLL_LIBRARY
+      NAMES
+        ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX}
+        ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+        lib${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+        $ENV{LIB}
+        $ENV{LIBRARY_PATH}
+      PATH_SUFFIXES
+        IntelSWTools/compilers_and_libraries/windows/redist/intel64/mkl
+        IntelSWTools/compilers_and_libraries/windows/redist/intel64/compiler
+        IntelSWTools/compilers_and_libraries/windows/redist/intel64/tbb/${msvc_dir}
+      NO_SYSTEM_ENVIRONMENT_PATH)
 
-    if(WIN32)
-      find_file(MKL_${mkl_args_NAME}_DLL_LIBRARY
-        NAMES
-          ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX}
-          ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
-          lib${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
-          $ENV{LIB}
-          $ENV{LIBRARY_PATH}
-        PATH_SUFFIXES
-          IntelSWTools/compilers_and_libraries/windows/redist/intel64/mkl
-          IntelSWTools/compilers_and_libraries/windows/redist/intel64/compiler
-          IntelSWTools/compilers_and_libraries/windows/redist/intel64/tbb/${msvc_dir}
-        NO_SYSTEM_ENVIRONMENT_PATH)
-
-      set_target_properties(MKL::${mkl_args_NAME}
-        PROPERTIES
-          IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_DLL_LIBRARY}"
-          IMPORTED_IMPLIB "${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
+    set_target_properties(MKL::${mkl_args_NAME}
+      PROPERTIES
+        IMPORTED_LOCATION "${MKL_${mkl_args_NAME}_DLL_LIBRARY}"
+        IMPORTED_IMPLIB "${MKL_${mkl_args_NAME}_LINK_LIBRARY}")
 
-      mark_as_advanced(MKL_${mkl_args_NAME}_DLL_LIBRARY)
-    endif()
+    mark_as_advanced(MKL_${mkl_args_NAME}_DLL_LIBRARY)
+  endif()
 endfunction()
 
 

From fe123bc347e3f757e6bc4ef941c451a1bf8f9e39 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 9 Apr 2021 11:08:59 +0530
Subject: [PATCH 2138/2677] Check new find_library suffix for oneMKL in FindMKL
 module

---
 CMakeModules/FindMKL.cmake | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 8de3ea0449..6ff862c905 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -212,6 +212,7 @@ function(find_mkl_library)
   add_library(MKL::${mkl_args_NAME}_STATIC STATIC IMPORTED)
 
   if(NOT (WIN32 AND mkl_args_DLL_ONLY))
+    list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES ".so.1")
     find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
       NAMES
         ${mkl_args_LIBRARY_NAME}${shared_suffix}
@@ -232,6 +233,7 @@ function(find_mkl_library)
         ""
         intel64
         intel64/gcc4.7)
+    list(REMOVE_ITEM CMAKE_FIND_LIBRARY_SUFFIXES ".so.1")
     if(MKL_${mkl_args_NAME}_LINK_LIBRARY)
       if (CMAKE_VERSION VERSION_GREATER 3.14)
         message(VERBOSE "MKL_${mkl_args_NAME}_LINK_LIBRARY: ${MKL_${mkl_args_NAME}_LINK_LIBRARY}")

From 56f7b1faa0c9984b9c6fed0a0317b0309888b20a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sat, 10 Apr 2021 16:25:01 +0530
Subject: [PATCH 2139/2677] Use Intel MKL single dynamic library

Using single dynamic library instead of the tuple of interface,
threading-layer & core libraries removes the linking issues in unified
backend library. This further removes issues from wrappers that use
unified backend when loading Intel MKL libraries at runtime.

With this change, we also package mkl_rt single dynamic library along
with all other required libraries.
---
 CMakeLists.txt                    |  1 +
 CMakeModules/FindMKL.cmake        |  8 ++++++
 src/api/c/CMakeLists.txt          | 17 ++++++++++++
 src/api/c/device.cpp              | 45 ++++++++++++++++++++++++++++++-
 src/api/unified/CMakeLists.txt    | 14 ----------
 src/backend/cpu/CMakeLists.txt    |  2 +-
 src/backend/opencl/CMakeLists.txt |  2 +-
 7 files changed, 72 insertions(+), 17 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 79df0ec19b..cd109e57e3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -364,6 +364,7 @@ if((USE_CPU_MKL OR USE_OPENCL_MKL) AND AF_INSTALL_STANDALONE)
     endif()
 
     install(FILES
+      $<TARGET_FILE:MKL::RT>
       $<TARGET_FILE:MKL::Shared>
       $<TARGET_FILE:MKL::ThreadLayer>
       ${MKL_RUNTIME_KERNEL_LIBRARIES}
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 6ff862c905..0cad3b970c 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -61,6 +61,12 @@
 #
 # ``MKL::{mkl_def;mkl_mc;mkl_mc3;mkl_avx;mkl_avx2;mkl_avx512}{_STATIC}``
 #   Targets for MKL kernel libraries.
+#
+# This module has the following result variables:
+#
+# ``MKL_INTERFACE_INTEGER_SIZE``
+#   This variable is set integer size in bytes on the platform where this module
+#   runs. This is usually 4/8, and set of values this is dependent on MKL library.
 
 include(CheckTypeSize)
 include(FindPackageHandleStandardArgs)
@@ -336,8 +342,10 @@ elseif(MKL_THREAD_LAYER STREQUAL "Sequential")
 endif()
 
 if("${INT_SIZE}" EQUAL 4)
+  set(MKL_INTERFACE_INTEGER_SIZE 4)
   find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_lp64 SEARCH_STATIC)
 else()
+  set(MKL_INTERFACE_INTEGER_SIZE 8)
   find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_ilp64 SEARCH_STATIC)
 endif()
 
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 2220990b76..a626ce6ea8 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -184,6 +184,23 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
   endif ()
 endif()
 
+if(USE_CPU_MKL OR USE_OPENCL_MKL)
+  target_compile_definitions(c_api_interface
+    INTERFACE
+      AF_MKL_INTERFACE_SIZE=${MKL_INTERFACE_INTEGER_SIZE}
+    )
+  # Create mkl thread layer compile option based on cmake cache variable
+  if(MKL_THREAD_LAYER STREQUAL "Sequential")
+    target_compile_definitions(c_api_interface INTERFACE AF_MKL_THREAD_LAYER=0)
+  elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
+    target_compile_definitions(c_api_interface INTERFACE AF_MKL_THREAD_LAYER=1)
+  elseif(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
+    target_compile_definitions(c_api_interface INTERFACE AF_MKL_THREAD_LAYER=2)
+  else() #default Intel Thread Layer for ArrayFire
+    target_compile_definitions(c_api_interface INTERFACE AF_MKL_THREAD_LAYER=3)
+  endif()
+endif()
+
 target_include_directories(c_api_interface
   INTERFACE
     ${CMAKE_CURRENT_SOURCE_DIR}
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index c9ae999390..d77969aeb1 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -20,6 +20,10 @@
 #include <af/dim4.hpp>
 #include <af/version.h>
 
+#if defined(USE_MKL)
+#include <mkl_service.h>
+#endif
+
 #include <cstring>
 #include <string>
 
@@ -102,7 +106,46 @@ af_err af_get_active_backend(af_backend* result) {
 af_err af_init() {
     try {
         thread_local std::once_flag flag;
-        std::call_once(flag, []() { getDeviceInfo(); });
+        std::call_once(flag, []() {
+            getDeviceInfo();
+#if defined(USE_MKL)
+            int errCode = -1;
+            // Have used the AF_MKL_INTERFACE_SIZE as regular if's so that
+            // we will know if these are not defined when using MKL when a
+            // compilation error is generated.
+            if (AF_MKL_INTERFACE_SIZE == 4) {
+                errCode = mkl_set_interface_layer(MKL_INTERFACE_LP64);
+            } else if (AF_MKL_INTERFACE_SIZE == 8) {
+                errCode = mkl_set_interface_layer(MKL_INTERFACE_ILP64);
+            }
+            if (errCode == -1) {
+                AF_ERROR(
+                    "Intel MKL Interface layer was not specified prior to the "
+                    "call and the input parameter is incorrect.",
+                    AF_ERR_RUNTIME);
+            }
+            switch (AF_MKL_THREAD_LAYER) {
+                case 0:
+                    errCode = mkl_set_threading_layer(MKL_THREADING_SEQUENTIAL);
+                    break;
+                case 1:
+                    errCode = mkl_set_threading_layer(MKL_THREADING_GNU);
+                    break;
+                case 2:
+                    errCode = mkl_set_threading_layer(MKL_THREADING_INTEL);
+                    break;
+                case 3:
+                    errCode = mkl_set_threading_layer(MKL_THREADING_TBB);
+                    break;
+            }
+            if (errCode == -1) {
+                AF_ERROR(
+                    "Intel MKL Thread layer was not specified prior to the "
+                    "call and the input parameter is incorrect.",
+                    AF_ERR_RUNTIME);
+            }
+#endif
+        });
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 4140e13ca8..b4204928b8 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -107,20 +107,6 @@ target_link_libraries(af
     ${CMAKE_DL_LIBS}
   )
 
-
-# NOTE: When loading libraries we only use the RTLD_LAZY flag for the unified
-# backend. This will only load the symbols but will not make those symbols
-# available to libraries loaded in the future. Because we link against MKL
-# and since MKL also dynamically loads libraries at runtime, the linker
-# is not able to load those symbols that are needed by those files. You could
-# pass the RTLD_GLOBAL flag to dlload, but that causes issues with the ArrayFire
-# libraries. To get around this we are also linking the unified backend with
-# the MKL library
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared AND NOT AF_WITH_STATIC_MKL)
-  target_link_libraries(af PRIVATE MKL::Shared)
-endif()
-
-
 install(TARGETS af
   EXPORT ArrayFireUnifiedTargets
   COMPONENT unified
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 282f411e38..cd60809ecb 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -318,7 +318,7 @@ if(USE_CPU_MKL)
   if(AF_WITH_STATIC_MKL)
       target_link_libraries(afcpu PRIVATE MKL::Static)
   else()
-      target_link_libraries(afcpu PRIVATE MKL::Shared)
+      target_link_libraries(afcpu PRIVATE MKL::RT)
   endif()
 else()
   dependency_check(FFTW_FOUND "FFTW not found")
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index d8daa3c0a2..c23edac82a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -469,7 +469,7 @@ if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
     if(AF_WITH_STATIC_MKL)
         target_link_libraries(afopencl PRIVATE MKL::Static)
     else()
-        target_link_libraries(afopencl PRIVATE MKL::Shared)
+        target_link_libraries(afopencl PRIVATE MKL::RT)
     endif()
   else()
     dependency_check(OpenCL_FOUND "OpenCL not found.")

From 290974f13f22477a52105f5ddc1a1008f40be519 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Sun, 2 May 2021 18:11:07 +0530
Subject: [PATCH 2140/2677] Add CUDA 11.3 max toolkit compute and driver
 versions

---
 src/backend/cuda/device_manager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index bbd8b9183c..37e4dd7f67 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -97,6 +97,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11030, 8, 6, 0},
     {11020, 8, 6, 0},
     {11010, 8, 6, 0},
     {11000, 8, 0, 0},
@@ -117,6 +118,7 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11030, 465.19f, 465.89f},
         {11020, 460.27f, 460.82f},
         {11010, 455.23f, 456.38f},
         {11000, 450.51f, 451.48f},

From 25178df1190346a8cee98c73fdceb3a77717cfbe Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 4 May 2021 15:33:43 +0530
Subject: [PATCH 2141/2677] Use CL fill buffer instead of host allocation in
 csrmm kernel

---
 src/backend/opencl/kernel/csrmm.hpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 00100ba389..a9b7b8fb95 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -67,10 +67,8 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
     groups_y     = std::min(groups_y, MAX_CSRMM_GROUPS);
     cl::NDRange global(local[0] * groups_x, local[1] * groups_y);
 
-    std::vector<int> count(groups_x);
-    cl::Buffer *counter = bufferAlloc(count.size() * sizeof(int));
-    getQueue().enqueueWriteBuffer(
-        *counter, CL_TRUE, 0, count.size() * sizeof(int), (void *)count.data());
+    cl::Buffer *counter = bufferAlloc(groups_x * sizeof(int));
+    getQueue().enqueueFillBuffer(*counter, 0, 0, groups_x * sizeof(int));
 
     csrmm_nt_func(cl::EnqueueArgs(getQueue(), global, local), *out.data,
                   *values.data, *rowIdx.data, *colIdx.data, M, N, *rhs.data,

From ecce06498fcaef8b3a3358c2daf814f1ab39b709 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 5 May 2021 19:26:47 +0530
Subject: [PATCH 2142/2677] Add missing batch support check in sparse-dense
 arith ops (#3129)

* Add missing batch support check in sparse-dense arith ops

* Fix formatting
---
 src/api/c/binary.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 1a2890f85b..f2263bf579 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -164,7 +164,13 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs,
                                     const bool reverse = false) {
     try {
         const common::SparseArrayBase linfo = getSparseArrayBase(lhs);
-        const ArrayInfo &rinfo              = getInfo(rhs);
+        if (linfo.ndims() > 2) {
+            AF_ERROR(
+                "Sparse-Dense arithmetic operations cannot be used in batch "
+                "mode",
+                AF_ERR_BATCH);
+        }
+        const ArrayInfo &rinfo = getInfo(rhs);
 
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;

From 9f60aca430b21551a5b98e57b2554716bc732001 Mon Sep 17 00:00:00 2001
From: Gilad Avidov <avidov@fb.com>
Date: Mon, 14 Dec 2020 00:15:19 -0800
Subject: [PATCH 2143/2677] Add shortcut check for zero elements in
 af_write_array

---
 src/api/c/array.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index d2bca69180..206073f252 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -343,6 +343,7 @@ void write_array(af_array arr, const T *const data, const size_t bytes,
 
 af_err af_write_array(af_array arr, const void *data, const size_t bytes,
                       af_source src) {
+    if (bytes == 0) { return AF_SUCCESS; }
     try {
         af_dtype type = getInfo(arr).getType();
         // DIM_ASSERT(2, bytes <= getInfo(arr).bytes());

From 5f53724e7e14b32db950caf918e4c3ce96773db4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 9 Apr 2021 11:33:08 +0530
Subject: [PATCH 2144/2677] Add missing input checks in af_write_array

---
 src/api/c/array.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 206073f252..8cb79bfae8 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -346,6 +346,9 @@ af_err af_write_array(af_array arr, const void *data, const size_t bytes,
     if (bytes == 0) { return AF_SUCCESS; }
     try {
         af_dtype type = getInfo(arr).getType();
+        ARG_ASSERT(1, (data != nullptr));
+        ARG_ASSERT(3, (src == afHost || src == afDevice));
+        // FIXME ArrayInfo class no bytes method, hence commented
         // DIM_ASSERT(2, bytes <= getInfo(arr).bytes());
 
         switch (type) {

From eb9e9af21af0c3fedeef7b72d32e969f74b7088f Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 19 Oct 2020 19:17:25 +0530
Subject: [PATCH 2145/2677] Minor variable cleanup in cpu sparse blas helper
 functions

---
 src/backend/cpu/sparse_blas.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index bac8bba6ac..dcb8158d9a 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -293,7 +293,6 @@ cdouble getConjugate(const cdouble &in) {
 template<typename T, bool conjugate>
 void mv(Param<T> output, CParam<T> values, CParam<int> rowIdx,
         CParam<int> colIdx, CParam<T> right, int M) {
-    UNUSED(M);
     const T *valPtr   = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
@@ -301,8 +300,9 @@ void mv(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 
     T *outPtr = output.get();
 
-    for (int i = 0; i < rowIdx.dims(0) - 1; ++i) {
-        outPtr[i] = scalar<T>(0);
+    // Output Array Created is a zero value Array
+    // Hence, no need to initialize to zero here
+    for (int i = 0; i < M; ++i) {
         for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
             // If stride[0] of right is not 1 then rightPtr[colPtr[j]*stride]
             if (conjugate) {
@@ -317,14 +317,16 @@ void mv(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 template<typename T, bool conjugate>
 void mtv(Param<T> output, CParam<T> values, CParam<int> rowIdx,
          CParam<int> colIdx, CParam<T> right, int M) {
+    UNUSED(M);
+
     const T *valPtr   = values.get();
     const int *rowPtr = rowIdx.get();
     const int *colPtr = colIdx.get();
     const T *rightPtr = right.get();
     T *outPtr         = output.get();
 
-    for (int i = 0; i < M; ++i) { outPtr[i] = scalar<T>(0); }
-
+    // Output Array Created is a zero value Array
+    // Hence, no need to initialize to zero here
     for (int i = 0; i < rowIdx.dims(0) - 1; ++i) {
         for (int j = rowPtr[i]; j < rowPtr[i + 1]; ++j) {
             // If stride[0] of right is not 1 then rightPtr[i*stride]

From 3f080baaee98f1e6aa6ae2d4c636831e78a1f854 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 6 May 2021 09:57:24 +0530
Subject: [PATCH 2146/2677] Fix OpenCL csrmv launch config & cleanup kernel
 wrapper

---
 src/backend/opencl/kernel/cscmv.hpp |  8 ++---
 src/backend/opencl/kernel/csrmv.cl  | 12 +++++--
 src/backend/opencl/kernel/csrmv.hpp | 51 +++++++++++++++--------------
 3 files changed, 41 insertions(+), 30 deletions(-)

diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index bc741a3051..5d948783fb 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -29,7 +29,6 @@ template<typename T>
 void cscmv(Param out, const Param &values, const Param &colIdx,
            const Param &rowIdx, const Param &rhs, const T alpha, const T beta,
            bool is_conj) {
-    constexpr int threads = 256;
     // TODO: rows_per_group limited by register pressure. Find better way to
     // handle this.
     constexpr int rows_per_group = 64;
@@ -37,17 +36,19 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
+    cl::NDRange local(THREADS_PER_GROUP);
+
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),       TemplateArg(use_alpha),
         TemplateArg(use_beta),       TemplateArg(is_conj),
-        TemplateArg(rows_per_group), TemplateArg(threads),
+        TemplateArg(rows_per_group), TemplateArg(local[0]),
     };
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
         DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(IS_CONJ, is_conj),
-        DefineKeyValue(THREADS, threads),
+        DefineKeyValue(THREADS, local[0]),
         DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
         DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
     };
@@ -56,7 +57,6 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
     auto cscmvBlock =
         common::getKernel("cscmv_block", {cscmv_cl_src}, targs, options);
 
-    cl::NDRange local(threads);
     int K        = colIdx.info.dims[0] - 1;
     int M        = out.info.dims[0];
     int groups_x = divup(M, rows_per_group);
diff --git a/src/backend/opencl/kernel/csrmv.cl b/src/backend/opencl/kernel/csrmv.cl
index b9655fc67a..4ac7e04881 100644
--- a/src/backend/opencl/kernel/csrmv.cl
+++ b/src/backend/opencl/kernel/csrmv.cl
@@ -43,7 +43,11 @@ kernel void csrmv_thread(global T *output, __global const T *values,
                            global const int *rowidx,
                            global const int *colidx, const int M,
                            global const T *rhs, const KParam rinfo,
-                           const T alpha, const T beta, global int *counter) {
+                           const T alpha, const T beta
+#if USE_GREEDY
+                           , global int *counter
+#endif
+                           ) {
     rhs += rinfo.offset;
     int rowNext = get_global_id(0);
 
@@ -95,7 +99,11 @@ kernel void csrmv_block(global T *output, __global const T *values,
                           global const int *rowidx,
                           global const int *colidx, const int M,
                           global const T *rhs, const KParam rinfo,
-                          const T alpha, const T beta, global int *counter) {
+                          const T alpha, const T beta
+#if USE_GREEDY
+                          , global int *counter
+#endif
+                          ) {
     rhs += rinfo.offset;
     int lid     = get_local_id(0);
     int rowNext = get_group_id(0);
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 92ab380a7d..d6b52ff6b4 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -33,42 +33,36 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
     // Using greedy indexing is causing performance issues on many platforms
     // FIXME: Figure out why
     constexpr bool use_greedy = false;
-    // FIXME: Find a better number based on average non zeros per row
-    constexpr int threads = 64;
+
+    // TODO: Figure out the proper way to choose either csrmv_thread or
+    // csrmv_block
+    bool is_csrmv_block = true;
 
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
+    cl::NDRange local(THREADS_PER_GROUP);
+
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),   TemplateArg(use_alpha), TemplateArg(use_beta),
-        TemplateArg(use_greedy), TemplateArg(threads),
+        TemplateArg(use_greedy), TemplateArg(local[0]),
     };
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
         DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(USE_GREEDY, use_greedy),
-        DefineKeyValue(THREADS, threads),
+        DefineKeyValue(THREADS, local[0]),
         DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csrmvThread =
-        common::getKernel("csrmv_thread", {csrmv_cl_src}, targs, options);
-    auto csrmvBlock =
-        common::getKernel("csrmv_block", {csrmv_cl_src}, targs, options);
-
-    int count           = 0;
-    cl::Buffer *counter = bufferAlloc(sizeof(int));
-    getQueue().enqueueWriteBuffer(*counter, CL_TRUE, 0, sizeof(int),
-                                  (void *)&count);
-
-    // TODO: Figure out the proper way to choose either csrmv_thread or
-    // csrmv_block
-    bool is_csrmv_block = true;
-    auto csrmv          = is_csrmv_block ? csrmvBlock : csrmvThread;
+    auto csrmv =
+        (is_csrmv_block
+             ? common::getKernel("csrmv_thread", {csrmv_cl_src}, targs, options)
+             : common::getKernel("csrmv_block", {csrmv_cl_src}, targs,
+                                 options));
 
-    cl::NDRange local(is_csrmv_block ? threads : THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
 
     int groups_x =
@@ -76,11 +70,20 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
     groups_x = std::min(groups_x, MAX_CSRMV_GROUPS);
     cl::NDRange global(local[0] * groups_x, 1);
 
-    csrmv(cl::EnqueueArgs(getQueue(), global, local), *out.data, *values.data,
-          *rowIdx.data, *colIdx.data, M, *rhs.data, rhs.info, alpha, beta,
-          *counter);
-    CL_DEBUG_FINISH(getQueue());
-    bufferFree(counter);
+    if (use_greedy) {
+        cl::Buffer *counter = bufferAlloc(sizeof(int));
+        getQueue().enqueueFillBuffer(*counter, 0, 0, sizeof(int));
+        csrmv(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+              *values.data, *rowIdx.data, *colIdx.data, M, *rhs.data, rhs.info,
+              alpha, beta, *counter);
+        CL_DEBUG_FINISH(getQueue());
+        bufferFree(counter);
+    } else {
+        csrmv(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+              *values.data, *rowIdx.data, *colIdx.data, M, *rhs.data, rhs.info,
+              alpha, beta);
+        CL_DEBUG_FINISH(getQueue());
+    }
 }
 }  // namespace kernel
 }  // namespace opencl

From 62d0aea29d19412550425769c9c36261d6ca5508 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 10 May 2021 09:34:22 +0530
Subject: [PATCH 2147/2677] Mark advanced build options reflecting the same in
 cmake

---
 CMakeLists.txt | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cd109e57e3..54e67a9f1c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -105,8 +105,17 @@ af_deprecate(USE_CPUID             AF_WITH_CPUID)
 
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
+  AF_BUILD_OFFLINE
+  AF_CACHE_KERNELS_TO_DISK
   AF_INSTALL_STANDALONE
   AF_WITH_CPUID
+  AF_WITH_LOGGING
+  AF_WITH_STACKTRACE
+  AF_WITH_STATIC_FREEIMAGE
+  AF_WITH_NONFREE
+  AF_WITH_IMAGEIO
+  AF_TEST_WITH_MTX_FILES
+  ArrayFire_DIR
   Boost_INCLUDE_DIR
   CUDA_HOST_COMPILER
   CUDA_SDK_ROOT_DIR

From 4ed555a403dfa62a55bea719b88c197e3a3c998a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 30 Apr 2021 00:33:29 +0530
Subject: [PATCH 2148/2677] Fix missing fftw include dir to MKL::RT imported
 target

---
 CMakeModules/FindMKL.cmake | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 0cad3b970c..47e5dfaa2a 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -393,6 +393,12 @@ if(NOT WIN32)
   mark_as_advanced(M_LIB)
 endif()
 
+if(TARGET MKL::RT)
+  set_target_properties(MKL::RT
+  PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR};${MKL_FFTW_INCLUDE_DIR}")
+endif()
+
 if(MKL_Shared_FOUND AND NOT TARGET MKL::Shared)
   add_library(MKL::Shared SHARED IMPORTED)
   if(MKL_THREAD_LAYER STREQUAL "Sequential")

From 007d00576fd7af76259782a716b33925a4b8d564 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 30 Apr 2021 11:38:32 +0530
Subject: [PATCH 2149/2677] Bump up CLBlast dependency version to 1.5.2

---
 CMakeModules/build_CLBlast.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 5b21289e54..7582967dcb 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -8,7 +8,7 @@
 FetchContent_Declare(
   ${clblast_prefix}
   GIT_REPOSITORY    https://github.com/cnugteren/CLBlast.git
-  GIT_TAG           41f344d1a6f2d149bba02a6615292e99b50f4856
+  GIT_TAG           1.5.2
 )
 af_dep_check_and_populate(${clblast_prefix})
 

From 0fe333217a2dd956c96f8af26a191484ea0287c9 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 5 May 2021 02:07:57 +0530
Subject: [PATCH 2150/2677] Fix MKL dependencies install for oneMKL(oneAPI)

Intel MKL(not oneAPI oneMKL) didn't have soname files at all. All files
were simple so files. However, oneAPI introduced soname files and this
change takes into account that while collecting mkl dependencies for
arrayfire packaging.

When using old intel MKL, the resolution to REALPATH results in same
file and cmake doesn't complain if same file is copied twice. Not an
ideal scenario but that is fine for now.
---
 CMakeLists.txt | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 54e67a9f1c..ee20b03b75 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -358,21 +358,31 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
 
 if((USE_CPU_MKL OR USE_OPENCL_MKL) AND AF_INSTALL_STANDALONE)
   if(TARGET MKL::ThreadingLibrary)
+    get_filename_component(mkl_tl ${MKL_ThreadingLibrary_LINK_LIBRARY} REALPATH)
     install(FILES
       $<TARGET_FILE:MKL::ThreadingLibrary>
+      ${mkl_tl}
       DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT mkl_dependencies)
   endif()
 
   if(NOT AF_WITH_STATIC_MKL AND TARGET MKL::Shared)
     if(NOT WIN32)
+      get_filename_component(mkl_int ${MKL_Interface_LINK_LIBRARY} REALPATH)
       install(FILES
         $<TARGET_FILE:MKL::Interface>
+        ${mkl_int}
         DESTINATION ${AF_INSTALL_LIB_DIR}
         COMPONENT mkl_dependencies)
     endif()
 
+    get_filename_component(mkl_rnt ${MKL_RT_LINK_LIBRARY} REALPATH)
+    get_filename_component(mkl_shd ${MKL_Core_LINK_LIBRARY} REALPATH)
+    get_filename_component(mkl_tly ${MKL_ThreadLayer_LINK_LIBRARY} REALPATH)
     install(FILES
+      ${mkl_rnt}
+      ${mkl_shd}
+      ${mkl_tly}
       $<TARGET_FILE:MKL::RT>
       $<TARGET_FILE:MKL::Shared>
       $<TARGET_FILE:MKL::ThreadLayer>

From c5cd3fd15ca3a30faebb2486df4a622289c7dcdc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 19 May 2021 14:24:46 +0530
Subject: [PATCH 2151/2677] CMake presets to enable faster development cmake
 setup (#3137)

Run `cmake .. --list-presets` to see the list of presets available.

Run `cmake .. --preset <name_of_preset>` to setup build folder using the
options in the particular preset.
---
 CMakePresets.json | 219 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 219 insertions(+)
 create mode 100644 CMakePresets.json

diff --git a/CMakePresets.json b/CMakePresets.json
new file mode 100644
index 0000000000..7f95210c7f
--- /dev/null
+++ b/CMakePresets.json
@@ -0,0 +1,219 @@
+{
+  "version": 2,
+  "cmakeMinimumRequired": {
+    "major": 3,
+    "minor": 20,
+    "patch": 0
+  },
+  "configurePresets": [
+    {
+      "name": "ninja-all-off-debug",
+      "hidden": true,
+      "description": "Base preset with all backends off with Debug build configuration",
+      "binaryDir": "${sourceDir}/build/${presetName}",
+      "generator": "Ninja",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": {
+          "type": "String",
+          "value": "Debug"
+        },
+        "AF_BUILD_CPU": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "AF_BUILD_CUDA": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "AF_BUILD_OPENCL": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "AF_BUILD_UNIFIED": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "AF_BUILD_FORGE": {
+          "type": "BOOL",
+          "value": "ON"
+        },
+        "AF_BUILD_DOCS": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "AF_BUILD_EXAMPLES": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "AF_TEST_WITH_MTX_FILES": {
+          "type": "BOOL",
+          "value": "OFF"
+        },
+        "CMAKE_INSTALL_PREFIX": {
+          "type": "PATH",
+          "value": "${sourceDir}/build/${presetName}/pkg"
+        }
+      }
+    },
+    {
+      "name": "ninja-cpu-debug",
+      "description": "Build CPU Backend with FFTW and a BLAS library using Ninja Generator in Debug Configuration",
+      "inherits": "ninja-all-off-debug",
+      "cacheVariables": {
+        "AF_BUILD_CPU": "ON"
+      }
+    },
+    {
+      "name": "ninja-cpu-relwithdebinfo",
+      "description": "Build CPU Backend with FFTW and a BLAS library using Ninja Generator in RelWithDebInfo Configuration",
+      "inherits": "ninja-cpu-debug",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+      }
+    },
+    {
+      "name": "ninja-cpu-mkl-debug",
+      "description": "Build CPU Backend using Intel MKL in Debug Configuration with Ninja Generator",
+      "inherits": "ninja-cpu-debug",
+      "cacheVariables": {
+        "USE_CPU_MKL": "ON"
+      }
+    },
+    {
+      "name": "ninja-cpu-mkl-relwithdebinfo",
+      "description": "Build CPU Backend using Intel MKL in RelWithDebInfo Configuration with Ninja Generator",
+      "inherits": "ninja-cpu-mkl-debug",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+      }
+    },
+    {
+      "name": "ninja-cuda-debug",
+      "description": "Build CUDA Backend in debug configuration using Ninja Generator",
+      "inherits": "ninja-all-off-debug",
+      "cacheVariables": {
+        "AF_BUILD_CUDA": "ON"
+      }
+    },
+    {
+      "name": "ninja-cuda-relwithdebinfo",
+      "description": "Build CUDA Backend in RelWithDebInfo configuration using Ninja Generator",
+      "inherits": "ninja-cuda-debug",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+      }
+    },
+    {
+      "name": "ninja-opencl-debug",
+      "description": "Build OpenCL Backend in debug configuration using Ninja Generator",
+      "inherits": "ninja-all-off-debug",
+      "cacheVariables": {
+        "AF_BUILD_OPENCL": "ON"
+      }
+    },
+    {
+      "name": "ninja-opencl-mkl-debug",
+      "description": "Build OpenCL Backend in debug configuration using Ninja Generator",
+      "inherits": "ninja-opencl-debug",
+      "cacheVariables": {
+        "USE_OPENCL_MKL": "ON"
+      }
+    },
+    {
+      "name": "ninja-opencl-relwithdebinfo",
+      "description": "Build OpenCL Backend in RelWithDebInfo configuration using Ninja Generator",
+      "inherits": "ninja-opencl-debug",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+      }
+    },
+    {
+      "name": "ninja-opencl-mkl-relwithdebinfo",
+      "description": "Build OpenCL Backend in RelWithDebInfo configuration using Ninja Generator. This preset uses Intel MKL for CPU fallback code.",
+      "inherits": "ninja-opencl-mkl-debug",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+      }
+    },
+    {
+        "name": "ninja-all-debug",
+        "description": "Build all feasible backends using Ninja Generator in Debug Configuraiton",
+        "inherits": "ninja-all-off-debug",
+        "cacheVariables": {
+            "AF_BUILD_CPU": "ON",
+            "AF_BUILD_CUDA": "ON",
+            "AF_BUILD_OPENCL": "ON",
+            "AF_BUILD_UNIFIED": "ON"
+        }
+    },
+    {
+        "name": "ninja-all-mkl-debug",
+        "description": "Build all feasible backends using Ninja Generator in Debug Configuraiton",
+        "inherits": "ninja-all-debug",
+        "cacheVariables": {
+            "USE_CPU_MKL": "ON",
+            "USE_OPENCL_MKL": "ON"
+        }
+    },
+    {
+        "name": "ninja-all-relwithdebinfo",
+        "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
+        "inherits": "ninja-all-debug",
+        "cacheVariables": {
+            "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+        }
+    },
+    {
+        "name": "ninja-all-mkl-relwithdebinfo",
+        "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
+        "inherits": "ninja-all-mkl-debug",
+        "cacheVariables": {
+            "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+        }
+    },
+    {
+        "name": "ninja-all-mkl-local-install",
+        "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
+        "inherits": "ninja-all-mkl-relwithdebinfo",
+        "cacheVariables": {
+            "BUILD_TESTING": "OFF"
+        }
+    },
+    {
+        "name": "ninja-all-mkl-standalone-install",
+        "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
+        "inherits": "ninja-all-mkl-local-install",
+        "cacheVariables": {
+            "AF_INSTALL_STANDALONE": "ON"
+        }
+    },
+    {
+      "name": "ninja-docs",
+      "description": "Build ArrayFire Documentation, needs doxygen installed",
+      "inherits": "ninja-all-off-debug",
+      "cacheVariables": {
+          "BUILD_TESTING": "OFF",
+          "AF_BUILD_FORGE": "OFF",
+          "AF_BUILD_DOCS": "ON"
+      }
+    },
+    {
+        "name": "ninja-any-debug",
+        "description": "Build available backends in Debug configuration using Ninja Generator",
+        "binaryDir": "${sourceDir}/build/${presetName}",
+        "generator": "Ninja",
+        "cacheVariables": {
+            "CMAKE_BUILD_TYPE": "Debug",
+            "CMAKE_INSTALL_PREFIX": "${sourceDir}/build/${presetName}/pkg"
+        }
+    },
+    {
+        "name": "ninja-any-relwithdebinfo",
+        "description": "Build available backends in RelWithDebInfo configuration using Ninja Generator",
+        "inherits": "ninja-any-debug",
+        "cacheVariables": {
+            "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+        }
+    }
+  ]
+}

From 34833d19e4e7f9cfba806f3a11449fee3a4c3747 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 12 May 2021 09:01:41 +0530
Subject: [PATCH 2152/2677] Increase half type error tolerance to 0.07 for
 convolve tests

---
 test/convolve.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/convolve.cpp b/test/convolve.cpp
index 3e833f4058..efe1c63f40 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -908,7 +908,7 @@ float tolerance<double>() {
 
 template<>
 float tolerance<half_float::half>() {
-    return 4e-2;
+    return 7e-2;
 }
 
 template<typename T>

From 26604b79201bab30de38043f8b1d0dda5e34dad5 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 20 May 2021 08:35:37 +0200
Subject: [PATCH 2153/2677] OPT: Eliminates synchronised initialisation of
 OpenCL Buffers

enqueueWriteBuffer is replaced by enqueueFillBuffer calls, which always operates asynchronisly because the pattern is copied during the call and not during the execution as for the enqueueWriteBuffer.
Optimizes: susan, sparse, regions, orb, harris and fast.
---
 src/backend/opencl/Kernel.cpp              |  4 ++--
 src/backend/opencl/kernel/fast.hpp         | 10 +++-------
 src/backend/opencl/kernel/harris.hpp       |  4 ++--
 src/backend/opencl/kernel/orb.hpp          | 12 ++++--------
 src/backend/opencl/kernel/regions.hpp      |  3 +--
 src/backend/opencl/kernel/sparse.hpp       |  6 +++---
 src/backend/opencl/kernel/sparse_arith.hpp |  2 +-
 src/backend/opencl/kernel/susan.hpp        |  4 ++--
 8 files changed, 18 insertions(+), 27 deletions(-)

diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
index 6cf893825d..a096979f9a 100644
--- a/src/backend/opencl/Kernel.cpp
+++ b/src/backend/opencl/Kernel.cpp
@@ -28,8 +28,8 @@ void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
 
 void Kernel::setFlag(Kernel::DevPtrType dst, int* scalarValPtr,
                      const bool syncCopy) {
-    getQueue().enqueueWriteBuffer(*dst, (syncCopy ? CL_TRUE : CL_FALSE), 0,
-                                  sizeof(int), scalarValPtr);
+    UNUSED(syncCopy);
+    getQueue().enqueueFillBuffer(*dst, *scalarValPtr, 0, sizeof(int));
 }
 
 int Kernel::getFlag(Kernel::DevPtrType src) {
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 82cb2bd51d..1ef1ca46ff 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -59,10 +59,8 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     // same coordinates as features, dimensions should be equal to in.
     cl::Buffer *d_score =
         bufferAlloc(in.info.dims[0] * in.info.dims[1] * sizeof(float));
-    std::vector<float> score_init(in.info.dims[0] * in.info.dims[1], (float)0);
-    getQueue().enqueueWriteBuffer(
-        *d_score, CL_FALSE, 0,
-        in.info.dims[0] * in.info.dims[1] * sizeof(float), &score_init[0]);
+    getQueue().enqueueFillBuffer(
+        *d_score, 0.0F, 0, in.info.dims[0] * in.info.dims[1] * sizeof(float));
 
     cl::Buffer *d_flags = d_score;
     if (nonmax) {
@@ -91,10 +89,8 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     const cl::NDRange global_nonmax(blk_nonmax_x * FAST_THREADS_NONMAX_X,
                                     blk_nonmax_y * FAST_THREADS_NONMAX_Y);
 
-    unsigned count_init = 0;
     cl::Buffer *d_total = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_total, CL_FALSE, 0, sizeof(unsigned),
-                                  &count_init);
+    getQueue().enqueueFillBuffer(*d_total, 0U, 0, sizeof(unsigned));
 
     // size_t *global_nonmax_dims = global_nonmax();
     size_t blocks_sz = blk_nonmax_x * FAST_THREADS_NONMAX_X * blk_nonmax_y *
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 2fc4bbae82..3b3bedb3a9 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -162,8 +162,8 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
 
     unsigned corners_found      = 0;
     cl::Buffer *d_corners_found = bufferAlloc(sizeof(unsigned));
-    getQueue().enqueueWriteBuffer(*d_corners_found, CL_TRUE, 0,
-                                  sizeof(unsigned), &corners_found);
+    getQueue().enqueueFillBuffer(*d_corners_found, corners_found, 0,
+                                 sizeof(unsigned));
 
     cl::Buffer *d_x_corners    = bufferAlloc(corner_lim * sizeof(float));
     cl::Buffer *d_y_corners    = bufferAlloc(corner_lim * sizeof(float));
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 7a3bafe20c..14f28e6fe5 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -208,8 +208,8 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         unsigned usable_feat  = 0;
         Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
-        getQueue().enqueueWriteBuffer(*d_usable_feat, CL_FALSE, 0,
-                                      sizeof(unsigned), &usable_feat);
+        getQueue().enqueueFillBuffer(*d_usable_feat, usable_feat, 0,
+                                     sizeof(unsigned));
 
         Buffer* d_x_harris     = bufferAlloc(lvl_feat * sizeof(float));
         Buffer* d_y_harris     = bufferAlloc(lvl_feat * sizeof(float));
@@ -364,12 +364,8 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
         // Compute ORB descriptors
         Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
-        vector<unsigned> h_desc_lvl(usable_feat * 8, 0);
-        {
-            getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_FALSE, 0,
-                                          usable_feat * 8 * sizeof(unsigned),
-                                          h_desc_lvl.data());
-        }
+        getQueue().enqueueFillBuffer(*d_desc_lvl, 0U, 0,
+                                     usable_feat * 8 * sizeof(unsigned));
         auto eoOp = kernels[3];
         if (blur_img) {
             eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 27a2949b41..0baa0abfaf 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -104,8 +104,7 @@ void regions(Param out, Param in, const bool full_conn,
 
     while (h_continue) {
         h_continue = 0;
-        getQueue().enqueueWriteBuffer(*d_continue, CL_FALSE, 0, sizeof(int),
-                                      &h_continue);
+        getQueue().enqueueFillBuffer(*d_continue, h_continue, 0, sizeof(int));
         ueOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *d_continue);
         CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 36dc719180..e938ed2f46 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -117,10 +117,10 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     scanFirst<int, int, af_add_t>(rowIdx, rd1, false);
 
     int nnz = values.info.dims[0];
-    getQueue().enqueueWriteBuffer(
-        *rowIdx.data, CL_TRUE,
+    getQueue().enqueueFillBuffer(
+        *rowIdx.data, nnz,
         rowIdx.info.offset + (rowIdx.info.dims[0] - 1) * sizeof(int),
-        sizeof(int), (void *)&nnz);
+        sizeof(int));
 
     cl::NDRange local(THREADS_X, THREADS_Y);
     int groups_x = divup(dense.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 3506978433..25ae4e3db5 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -150,7 +150,7 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
 
     nnzC     = 0;
     auto out = memAlloc<unsigned>(1);
-    getQueue().enqueueWriteBuffer(*out, CL_TRUE, 0, sizeof(unsigned), &nnzC);
+    getQueue().enqueueFillBuffer(*out, nnzC, 0, sizeof(unsigned));
 
     calcNNZ(cl::EnqueueArgs(getQueue(), global, local), *out, *outRowIdx.data,
             M, *lrowIdx.data, *lcolIdx.data, *rrowIdx.data, *rcolIdx.data,
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 5429e96a07..7ebb1a20ec 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -79,8 +79,8 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
 
     unsigned corners_found = 0;
     auto d_corners_found   = memAlloc<unsigned>(1);
-    getQueue().enqueueWriteBuffer(*d_corners_found, CL_FALSE, 0,
-                                  sizeof(unsigned), &corners_found);
+    getQueue().enqueueFillBuffer(*d_corners_found, corners_found, 0,
+                                 sizeof(unsigned));
 
     cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
     cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],

From 9738a3164faf2eecd3703d70003eac65c09b8213 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Thu, 20 May 2021 16:10:55 +0530
Subject: [PATCH 2154/2677] vcpkg manifest file for ease of development

Developers can now invoke cmake as shown below to install dependencies automatically when using vcpkg and cmake.

```cmake
cmake .. -DVCPKG_ROOT:PATH=<path to vcpkg repository tool>
```
or
```cmake
export VCPKG_ROOT=<path to vcpkg repository tool>
cmake ..
```

One may add `-DAF_BUILD_CUDA:BOOL=ON` command line argument to enable CUDA dependency check.
Even if not provided, ArrayFire will silently check for CUDA and enable the backend if available.

There are couple of caveats though for the following dependencies
- cuda
- cudnn
- intel-mkl

As these libraries have complex installation mechanisms, their respective vcpkg dependency is merely a check
for user. They have to be installed using respective vendor provided installers.

A few important notes regarding using vcpg manifest file:

1. For linux developers, currently full support for only Intel MKL compute backend is availalbe.
2. As x64-linux triplet creates static builds only as of now, forge cannot be part of vcpkg dependency list
   on non windows platforms. Nevertheless, the user doesn't need to do anything as fetchcontent workflow is
   the fallback.
3. vcpkg manifest is for development puporses only and isn't intended to be production ready dependency
   management for arrayfire as there are dependencies that don't get built with vcpkg at all.
---
 .github/workflows/docs_build.yml              |   5 +-
 .github/workflows/unix_cpu_build.yml          |  15 +--
 .github/workflows/win_cpu_build.yml           |  50 +++-----
 CMakeLists.txt                                |  54 +++++++--
 CMakeModules/AF_vcpkg_options.cmake           |  22 ++++
 CMakeModules/AFconfigure_forge_dep.cmake      | 112 +++++++++++-------
 CMakeModules/build_CLBlast.cmake              |   3 +
 src/backend/common/CMakeLists.txt             |  18 ++-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  24 +++-
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  24 +++-
 vcpkg.json                                    |  41 +++++++
 11 files changed, 265 insertions(+), 103 deletions(-)
 create mode 100644 CMakeModules/AF_vcpkg_options.cmake
 create mode 100644 vcpkg.json

diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
index 9cdab11385..bf81164cdd 100644
--- a/.github/workflows/docs_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -13,7 +13,7 @@ jobs:
         name: Documentation
         runs-on: ubuntu-18.04
         env:
-            DOXYGEN_VER: 1.8.18
+          DOXYGEN_VER: 1.8.18
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master
@@ -36,8 +36,7 @@ jobs:
                   cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
                         -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
                         -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
-                        -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen \
-                        ..
+                        -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen ..
 
             - name: Build
               run: |
diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 3a70a093a4..40211fb06f 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -19,10 +19,8 @@ jobs:
             fail-fast: false
             matrix:
                 blas_backend: [Atlas, MKL, OpenBLAS]
-                os: [ubuntu-16.04, ubuntu-18.04, macos-latest]
+                os: [ubuntu-18.04, ubuntu-20.04, macos-latest]
                 exclude:
-                    - os: ubuntu-16.04
-                      blas_backend: Atlas
                     - os: macos-latest
                       blas_backend: Atlas
                     - os: macos-latest
@@ -64,7 +62,7 @@ jobs:
                   echo "CMAKE_PROGRAM=cmake" >> $GITHUB_ENV
 
             - name: Install Common Dependencies for Ubuntu
-              if: matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04'
+              if: matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-18.04'
               run: |
                   sudo add-apt-repository ppa:mhier/libboost-latest
                   sudo apt-get -qq update
@@ -75,11 +73,11 @@ jobs:
                                           liblapacke-dev
 
             - name: Install Atlas for Ubuntu
-              if: matrix.os == 'ubuntu-18.04' && matrix.blas_backend == 'Atlas'
+              if: matrix.os != 'macos-latest' && matrix.blas_backend == 'Atlas'
               run: sudo apt-get install -y libatlas-base-dev
 
             - name: Install MKL for Ubuntu
-              if: (matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04') && matrix.blas_backend == 'MKL'
+              if: matrix.os != 'macos-latest' && matrix.blas_backend == 'MKL'
               run: |
                   wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
                   sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
@@ -88,7 +86,7 @@ jobs:
                   sudo apt-get install -y intel-mkl-64bit-2020.0-088
 
             - name: Install OpenBLAS for Ubuntu
-              if: (matrix.os == 'ubuntu-16.04' || matrix.os == 'ubuntu-18.04') && matrix.blas_backend == 'OpenBLAS'
+              if: matrix.os != 'macos-latest' && matrix.blas_backend == 'OpenBLAS'
               run: sudo apt-get install -y libopenblas-dev
 
             - name: CMake Configure
@@ -109,8 +107,7 @@ jobs:
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \
                       -DUSE_CPU_MKL:BOOL=$USE_MKL \
-                      -DBUILDNAME:STRING=${buildname} \
-                      ..
+                      -DBUILDNAME:STRING=${buildname} ..
                   echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 
             - name: Build and Test
diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index ef4492f6d6..df98161545 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -10,40 +10,29 @@ name: ci
 
 jobs:
     window_build_cpu:
-        name: CPU (OpenBLAS, windows-latest)
+        name: CPU (fftw, OpenBLAS, windows-latest)
         runs-on: windows-latest
         env:
-          VCPKG_HASH: 0cbc579e1ee21fa4ad0974a9ed926f60c6ed1a4a # FEB 25, 2021 - [rsasynccpp] Add new port (Rstein.AsyncCpp) (#16380)
-          NINJA_VER: 1.10.2
+          VCPKG_HASH: 5568f110b509a9fd90711978a7cb76bae75bb092 # vcpkg release tag 2021.05.12 with Forge v1.0.7 update
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master
 
-            - name: VCPKG Cache
-              uses: actions/cache@v1
-              id: vcpkg-cache
+            - name: VCPKG Binary Cache
+              uses: actions/cache@v2
+              id: vcpkg-bin-cache
               with:
-                path: vcpkg
-                key: vcpkg-deps-${{ env.VCPKG_HASH }}
-
-            - name: Install VCPKG Common Deps
-              if: steps.vcpkg-cache.outputs.cache-hit != 'true'
-              run: |
-                  git clone --recursive https://github.com/microsoft/vcpkg
-                  Set-Location -Path .\vcpkg
-                  git reset --hard $env:VCPKG_HASH
-                  .\bootstrap-vcpkg.bat
-                  .\vcpkg.exe install --triplet x64-windows boost fftw3 freeimage freetype glfw3 openblas
-                  Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
-
-            - name: Download Ninja
-              run: |
-                  Invoke-WebRequest -Uri "https://github.com/ninja-build/ninja/releases/download/v$env:NINJA_VER/ninja-win.zip" -OutFile ninja.zip
-                  Expand-Archive -Path ninja.zip -DestinationPath .
+                path: vcpkg_cache
+                key: vcpkg_bin_cache_${{ env.VCPKG_HASH }} # vcpkg manifest baseline
 
             - name: CMake Configure
               run: |
                   $cwd = (Get-Item -Path ".\").FullName
+                  Set-Location -Path ${env:VCPKG_INSTALLATION_ROOT}
+                  git pull
+                  .\bootstrap-vcpkg.bat
+                  .\vcpkg.exe install --triplet x64-windows boost-compute boost-functional boost-stacktrace fftw3 forge freeimage freetype glfw3 openblas
+                  Set-Location -Path $cwd
                   $ref = $env:GITHUB_REF | %{ if ($_ -match "refs/pull/[0-9]+/merge") { $_;} }
                   $prnum = $ref | %{$_.Split("/")[2]}
                   $branch = git branch --show-current
@@ -51,19 +40,18 @@ jobs:
                   $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
                   $buildname = "$buildname-cpu-openblas"
                   mkdir build && cd build
+                  New-Item -Path "${cwd}/vcpkg_cache" -ItemType "directory" -Force
+                  $env:VCPKG_DEFAULT_BINARY_CACHE="${cwd}/vcpkg_cache"
                   cmake .. -G "Visual Studio 16 2019" -A x64 `
-                      -DCMAKE_TOOLCHAIN_FILE:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\scripts\buildsystems\vcpkg.cmake" `
-                      -DFFTW_INCLUDE_DIR:PATH="$env:GITHUB_WORKSPACE\vcpkg\installed/x64-windows\include" `
-                      -DFFTW_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3.lib" `
-                      -DFFTWF_LIBRARY:FILEPATH="$env:GITHUB_WORKSPACE\vcpkg\installed\x64-windows\lib\fftw3f.lib" `
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
-                      -DBUILDNAME:STRING="$buildname"
+                      -DBUILDNAME:STRING="$buildname" `
+                      -DVCPKG_ROOT:PATH="${env:VCPKG_INSTALLATION_ROOT}" `
+                      -DVCPKG_MANIFEST_MODE:BOOL=OFF
                   echo "CTEST_DASHBOARD=${dashboard}" >> $env:GITHUB_ENV
 
             - name: Build and Test
               run: |
-                  $cwd = (Get-Item -Path ".\").FullName
-                  $Env:PATH += ";$cwd/vcpkg/installed/x64-windows/bin"
-                  Set-Location -Path $cwd/build
+                  Set-Location -Path .\build
+                  $Env:PATH += ";${env:VCPKG_INSTALLATION_ROOT}/installed/x64-windows/bin"
                   ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C Release -R cpu -E pinverse -j2
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ee20b03b75..f45b5fff8e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -7,6 +7,8 @@
 
 cmake_minimum_required(VERSION 3.5)
 
+include(CMakeModules/AF_vcpkg_options.cmake)
+
 project(ArrayFire VERSION 3.9.0 LANGUAGES C CXX)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
@@ -44,6 +46,7 @@ find_package(CUDA 9.0)
 find_package(cuDNN 4.0)
 find_package(OpenCL 1.2)
 find_package(OpenGL)
+find_package(glad CONFIG QUIET)
 find_package(FreeImage)
 find_package(Threads)
 find_package(FFTW)
@@ -127,6 +130,9 @@ mark_as_advanced(
   Backtrace_LIBRARY
   AF_WITH_STATIC_MKL
   GIT
+  Forge_DIR
+  glad_DIR
+  FG_BUILD_OFFLINE
   )
 mark_as_advanced(CLEAR CUDA_VERSION)
 
@@ -140,12 +146,25 @@ FetchContent_Declare(
   GIT_TAG        v1.0.0
 )
 af_dep_check_and_populate(${spdlog_prefix})
-FetchContent_Declare(
-  ${glad_prefix}
-  GIT_REPOSITORY https://github.com/arrayfire/glad.git
-  GIT_TAG        master
-)
-af_dep_check_and_populate(${glad_prefix})
+
+
+if(NOT TARGET glad::glad)
+  FetchContent_Declare(
+    ${glad_prefix}
+    GIT_REPOSITORY https://github.com/arrayfire/glad.git
+    GIT_TAG main
+    )
+  af_dep_check_and_populate(${glad_prefix})
+  add_subdirectory(${${glad_prefix}_SOURCE_DIR} ${${glad_prefix}_BINARY_DIR})
+
+  add_library(af_glad STATIC $<TARGET_OBJECTS:af_glad_obj_lib>)
+  target_link_libraries(af_glad PUBLIC ${CMAKE_DL_LIBS})
+  target_include_directories(af_glad
+    PUBLIC
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:af_glad_obj_lib,INTERFACE_INCLUDE_DIRECTORIES>>
+    )
+endif()
+
 FetchContent_Declare(
   ${assets_prefix}
   GIT_REPOSITORY https://github.com/arrayfire/assets.git
@@ -202,8 +221,6 @@ if(NOT LAPACK_FOUND)
     endif()
 endif()
 
-add_subdirectory(${${glad_prefix}_SOURCE_DIR} ${${glad_prefix}_BINARY_DIR})
-
 add_subdirectory(src/backend/common)
 add_subdirectory(src/api/c)
 add_subdirectory(src/api/cpp)
@@ -437,3 +454,24 @@ conditional_directory(AF_BUILD_EXAMPLES examples)
 conditional_directory(AF_BUILD_DOCS docs)
 
 include(CPackConfig)
+
+# VCPKG variables that aren't necessarily important
+# for ArrayFire Development. They are marked hidden.
+# If VCPKG is not used, marking them is not harmful
+mark_as_advanced(
+  VCPKG_APPLOCAL_DEPS
+  VCPKG_BOOTSTRAP_OPTIONS
+  VCPKG_INSTALL_OPTIONS
+  VCPKG_MANIFEST_DIR
+  VCPKG_MANIFEST_INSTALL
+  VCPKG_MANIFEST_MODE
+  VCPKG_OVERLAY_PORTS
+  VCPKG_OVERLAY_TRIPLETS
+  VCPKG_TARGET_TRIPLET
+  X_VCPKG_APPLOCAL_DEPS_INSTALL
+  X_VCPKG_APPLOCAL_DEPS_SERIALIZED
+  Z_VCPKG_BUILTIN_POWERSHELL_PATH
+  Z_VCPKG_PWSH_PATH
+  Z_VCPKG_CL
+  _VCPKG_INSTALLED_DIR
+  )
diff --git a/CMakeModules/AF_vcpkg_options.cmake b/CMakeModules/AF_vcpkg_options.cmake
new file mode 100644
index 0000000000..0639c377a4
--- /dev/null
+++ b/CMakeModules/AF_vcpkg_options.cmake
@@ -0,0 +1,22 @@
+# Copyright (c) 2021, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+set(ENV{VCPKG_FEATURE_FLAGS} "versions")
+set(ENV{VCPKG_KEEP_ENV_VARS} "MKLROOT")
+
+if(AF_BUILD_CUDA)
+  list(APPEND VCPKG_MANIFEST_FEATURES "cuda")
+endif()
+if(AF_BUILD_OPENCL)
+  list(APPEND VCPKG_MANIFEST_FEATURES "opencl")
+endif()
+
+if(DEFINED VCPKG_ROOT AND NOT DEFINED CMAKE_TOOLCHAIN_FILE)
+  set(CMAKE_TOOLCHAIN_FILE "${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "")
+elseif(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE)
+  set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "")
+endif()
diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index 364bd8375f..c2bc2f42f7 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -7,55 +7,75 @@
 
 set(FG_VERSION_MAJOR 1)
 set(FG_VERSION_MINOR 0)
-set(FG_VERSION_PATCH 5)
-set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
-set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
+set(FG_VERSION_PATCH 7)
 
-FetchContent_Declare(
-  ${forge_prefix}
-  GIT_REPOSITORY https://github.com/arrayfire/forge.git
-  GIT_TAG        "v${FG_VERSION}"
+find_package(Forge
+  ${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}
+  QUIET
 )
-af_dep_check_and_populate(${forge_prefix})
 
-if(AF_BUILD_FORGE)
-  set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
-  set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
-  set(CMAKE_INSTALL_PREFIX ${${forge_prefix}_BINARY_DIR}/extern/forge/package)
-  set(CMAKE_BUILD_TYPE Release)
-  set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
-  set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
-  set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
+if(TARGET Forge::forge)
+  get_target_property(fg_lib_type Forge::forge TYPE)
+  if(NOT ${fg_lib_type} STREQUAL "STATIC_LIBRARY")
+      install(FILES
+          $<TARGET_FILE:Forge::forge>
+          $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:Forge::forge>>
+          $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:Forge::forge>>
+          $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:Forge::forge>>
+          $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:Forge::forge>>
+          DESTINATION "${AF_INSTALL_LIB_DIR}"
+          COMPONENT common_backend_dependencies)
+  endif()
+else()
+  set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
+  set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
 
-  add_subdirectory(${${forge_prefix}_SOURCE_DIR} ${${forge_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+  FetchContent_Declare(
+    ${forge_prefix}
+    GIT_REPOSITORY https://github.com/arrayfire/forge.git
+    GIT_TAG        "v${FG_VERSION}"
+  )
+  af_dep_check_and_populate(${forge_prefix})
 
-  mark_as_advanced(
-      FG_BUILD_EXAMPLES
-      FG_BUILD_DOCS
-      FG_WITH_FREEIMAGE
-      FG_USE_WINDOW_TOOLKIT
-      FG_USE_SYSTEM_CL2HPP
-      FG_ENABLE_HUNTER
-      FG_RENDERING_BACKEND
-      SPHINX_EXECUTABLE
-      glfw3_DIR
-      glm_DIR
-      )
-  set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
-  set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
+  if(AF_BUILD_FORGE)
+    set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
+    set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
+    set(CMAKE_INSTALL_PREFIX ${${forge_prefix}_BINARY_DIR}/extern/forge/package)
+    set(CMAKE_BUILD_TYPE Release)
+    set(FG_BUILD_EXAMPLES OFF CACHE BOOL "Used to build Forge examples")
+    set(FG_BUILD_DOCS OFF CACHE BOOL "Used to build Forge documentation")
+    set(FG_WITH_FREEIMAGE OFF CACHE BOOL "Turn on usage of freeimage dependency")
+
+    add_subdirectory(
+        ${${forge_prefix}_SOURCE_DIR} ${${forge_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+    mark_as_advanced(
+        FG_BUILD_EXAMPLES
+        FG_BUILD_DOCS
+        FG_WITH_FREEIMAGE
+        FG_USE_WINDOW_TOOLKIT
+        FG_USE_SYSTEM_CL2HPP
+        FG_ENABLE_HUNTER
+        FG_RENDERING_BACKEND
+        SPHINX_EXECUTABLE
+        glfw3_DIR
+        glm_DIR
+        )
+    set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
+    set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
 
-  install(FILES
-      $<TARGET_FILE:forge>
-      $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
-      $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
-      $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:forge>>
-      $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:forge>>
-      DESTINATION "${AF_INSTALL_LIB_DIR}"
-      COMPONENT common_backend_dependencies)
-  set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
-else(AF_BUILD_FORGE)
-  configure_file(
-    ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
-    ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
-    )
-endif(AF_BUILD_FORGE)
+    install(FILES
+        $<TARGET_FILE:forge>
+        $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
+        $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
+        $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:forge>>
+        $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:forge>>
+        DESTINATION "${AF_INSTALL_LIB_DIR}"
+        COMPONENT common_backend_dependencies)
+    set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
+  else(AF_BUILD_FORGE)
+    configure_file(
+      ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
+      ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
+      )
+  endif(AF_BUILD_FORGE)
+endif()
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 7582967dcb..0e32b38d6f 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -26,6 +26,9 @@ if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
     list(APPEND extproj_gen_opts "-T${CMAKE_GENERATOR_TOOLSET}")
   endif()
 endif()
+if(VCPKG_TARGET_TRIPLET)
+  list(APPEND extproj_gen_opts "-DOPENCL_ROOT:PATH=${_VCPKG_INSTALLED_DIR}/${VCPKG_TARGET_TRIPLET}")
+endif()
 
 set(extproj_build_type_option "")
 if(NOT isMultiConfig)
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 15718b37b9..41b4196474 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -81,11 +81,15 @@ target_link_libraries(afcommon_interface
   INTERFACE
     spdlog
     Boost::boost
-    glad_interface
     ${CMAKE_DL_LIBS}
 )
+if(TARGET glad::glad)
+  target_link_libraries(afcommon_interface INTERFACE glad::glad)
+else()
+  target_link_libraries(afcommon_interface INTERFACE af_glad)
+endif()
 
-if(AF_BUILD_FORGE)
+if(AF_BUILD_FORGE AND NOT Forge_FOUND)
   add_dependencies(afcommon_interface forge)
 endif()
 
@@ -95,9 +99,19 @@ target_include_directories(afcommon_interface
     ${ArrayFire_BINARY_DIR}
   SYSTEM INTERFACE
     $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
+  )
+if(TARGET Forge::forge)
+  target_include_directories(afcommon_interface
+    SYSTEM INTERFACE
+    $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
+  )
+else()
+  target_include_directories(afcommon_interface
+    SYSTEM INTERFACE
     ${${forge_prefix}_SOURCE_DIR}/include
     ${${forge_prefix}_BINARY_DIR}/include
   )
+endif()
 
 if(APPLE AND NOT USE_MKL)
   target_sources(afcommon_interface
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index d92b214e44..f017b37e73 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -39,11 +39,31 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
+        ${ArrayFire_BINARY_DIR}/include
+      )
+    if(TARGET Forge::forge)
+      target_include_directories(opencl_scan_by_key_${SBK_BINARY_OP}
+        SYSTEM INTERFACE
+        $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
+      )
+    else()
+      target_include_directories(opencl_scan_by_key_${SBK_BINARY_OP}
+        SYSTEM INTERFACE
         ${${forge_prefix}_SOURCE_DIR}/include
         ${${forge_prefix}_BINARY_DIR}/include
-        ${ArrayFire_BINARY_DIR}/include
       )
+    endif()
+    if(TARGET glad::glad)
+      target_include_directories(opencl_scan_by_key_${SBK_BINARY_OP}
+        SYSTEM INTERFACE
+        $<TARGET_PROPERTY:glad::glad,INTERFACE_INCLUDE_DIRECTORIES>
+      )
+    else()
+      target_include_directories(opencl_scan_by_key_${SBK_BINARY_OP}
+        SYSTEM INTERFACE
+        $<TARGET_PROPERTY:af_glad,INTERFACE_INCLUDE_DIRECTORIES>
+      )
+    endif()
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
       PROPERTIES
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 280a5d22c6..32d078faa2 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -37,11 +37,31 @@ foreach(SBK_TYPE ${SBK_TYPES})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:glad_interface,INTERFACE_INCLUDE_DIRECTORIES>
+        ${ArrayFire_BINARY_DIR}/include
+      )
+    if(TARGET Forge::forge)
+      target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+        SYSTEM INTERFACE
+        $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
+      )
+    else()
+      target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+        SYSTEM INTERFACE
         ${${forge_prefix}_SOURCE_DIR}/include
         ${${forge_prefix}_BINARY_DIR}/include
-        ${ArrayFire_BINARY_DIR}/include
       )
+    endif()
+    if(TARGET glad::glad)
+      target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+        SYSTEM INTERFACE
+        $<TARGET_PROPERTY:glad::glad,INTERFACE_INCLUDE_DIRECTORIES>
+      )
+    else()
+      target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+        SYSTEM INTERFACE
+        $<TARGET_PROPERTY:af_glad,INTERFACE_INCLUDE_DIRECTORIES>
+      )
+    endif()
 
     set_target_properties(opencl_sort_by_key_${SBK_TYPE}
       PROPERTIES
diff --git a/vcpkg.json b/vcpkg.json
new file mode 100644
index 0000000000..1104d55800
--- /dev/null
+++ b/vcpkg.json
@@ -0,0 +1,41 @@
+{
+    "name": "arrayfire",
+    "version": "3.9.0",
+    "homepage": "https://github.com/arrayfire/arrayfire",
+    "description": "ArrayFire is a HPC general-purpose library targeting parallel and massively-parallel architectures such as CPUs, GPUs, etc.",
+    "supports": "x64",
+    "dependencies": [
+        "boost-compute",
+        "boost-functional",
+        "boost-stacktrace",
+        {
+            "name": "forge",
+            "version>=": "1.0.7",
+            "platform": "windows"
+        },
+        "freeimage",
+        {
+            "name": "fontconfig",
+            "platform": "!windows"
+        },
+        "glad",
+        "intel-mkl"
+    ],
+    "features": {
+        "cuda": {
+            "description": "Build CUDA backend",
+            "dependencies": [
+                "cuda",
+                "cudnn"
+            ]
+        },
+        "opencl": {
+            "description": "Build OpenCL backend",
+            "dependencies": [
+                "boost-program-options",
+                "opencl"
+            ]
+        }
+    },
+    "builtin-baseline": "5568f110b509a9fd90711978a7cb76bae75bb092"
+}

From 57082c969d8118f0f1bf4ac6e1b54ae7ab15d459 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Tue, 1 Jun 2021 22:59:14 +0200
Subject: [PATCH 2155/2677] Perf: elimination of temp buffer in cascading
 joins.

It is faster to join multiple array's directly into the final buffer, iso using temp buffers.
Previous flow:
- join (array A & array B) into temp buffer
- join (temp & array C) into final buffer
New flow:
- join (array A, array B & array C) into final buffer
---
 src/api/c/rgb_gray.cpp  | 3 +--
 src/api/c/ycbcr_rgb.cpp | 6 ++----
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 73717cdd46..250958124d 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -96,8 +96,7 @@ static af_array gray2rgb(const af_array& in, const float r, const float g,
     AF_CHECK(af_release_array(mod_input));
 
     // join channels
-    Array<cType> expr4 = join<cType>(2, expr1, expr2);
-    return getHandle(join<cType>(2, expr3, expr4));
+    return getHandle(join<cType>(2, {expr3, expr1, expr2}));
 }
 
 template<typename T, typename cType, bool isRGB2GRAY>
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index 3e4238d28e..b5beee4fae 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -108,8 +108,7 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
                    INV_112 * (kb - 1) * kb * invKl);
         Array<T> B = mix<T>(Y_, Cb_, INV_219, INV_112 * (1 - kb));
         // join channels
-        Array<T> RG = join<T>(2, R, G);
-        return getHandle(join<T>(2, RG, B));
+        return getHandle(join<T>(2, {R, G, B}));
     }
     Array<T> Ey = mix<T>(X, Y, Z, kr, kl, kb);
     Array<T> Ecr =
@@ -120,8 +119,7 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
     Array<T> Cr = digitize<T>(Ecr, 224.0, 128.0);
     Array<T> Cb = digitize<T>(Ecb, 224.0, 128.0);
     // join channels
-    Array<T> YCb = join<T>(2, Y_, Cb);
-    return getHandle(join<T>(2, YCb, Cr));
+    return getHandle(join<T>(2, {Y_, Cb, Cr}));
 }
 
 template<bool isYCbCr2RGB>

From 04393d27a11cdfcc0187cac4eaf7e4d8c8030aa8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 8 Jun 2021 19:02:14 -0400
Subject: [PATCH 2156/2677] Add kernel launch traces with block and grid sizes
 for CUDA/OpenCL

---
 src/backend/common/KernelInterface.hpp        |  9 ++---
 src/backend/cuda/CMakeLists.txt               |  1 +
 src/backend/cuda/Kernel.hpp                   | 22 ++++++++++--
 src/backend/cuda/compile_module.cpp           |  8 +++--
 src/backend/cuda/debug_cuda.hpp               | 35 +++++++++++++++++--
 src/backend/cuda/jit.cpp                      |  9 ++++-
 src/backend/opencl/Kernel.hpp                 | 19 +++++++---
 src/backend/opencl/compile_module.cpp         |  2 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  1 +
 9 files changed, 88 insertions(+), 18 deletions(-)

diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
index bb9db8b5f1..537c2a7a86 100644
--- a/src/backend/common/KernelInterface.hpp
+++ b/src/backend/common/KernelInterface.hpp
@@ -10,7 +10,7 @@
 #pragma once
 
 #include <cstddef>
-#include <utility>
+#include <string>
 
 namespace common {
 
@@ -21,10 +21,11 @@ class KernelInterface {
    private:
     ModuleType mModuleHandle;
     KernelType mKernelHandle;
+    std::string mName;
 
    public:
-    KernelInterface(ModuleType mod, KernelType ker)
-        : mModuleHandle(mod), mKernelHandle(ker) {}
+    KernelInterface(std::string name, ModuleType mod, KernelType ker)
+        : mModuleHandle(mod), mKernelHandle(ker), mName(name) {}
 
     /// \brief Set kernel
     ///
@@ -95,7 +96,7 @@ class KernelInterface {
     template<typename EnqueueArgsType, typename... Args>
     void operator()(const EnqueueArgsType& qArgs, Args... args) {
         EnqueuerType launch;
-        launch(mKernelHandle, qArgs, std::forward<Args>(args)...);
+        launch(mName, mKernelHandle, qArgs, std::forward<Args>(args)...);
     }
 };
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 7e65278db9..f454fa532e 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -338,6 +338,7 @@ if(UNIX)
   if(CUDA_VERSION VERSION_GREATER 10.0)
     target_link_libraries(af_cuda_static_cuda_library
       PRIVATE
+        spdlog
         ${CUDA_cublasLt_static_LIBRARY})
   endif()
   if(CUDA_VERSION VERSION_GREATER 9.5)
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index 33b53cb1ea..1e2459bc73 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -10,20 +10,35 @@
 #pragma once
 
 #include <common/KernelInterface.hpp>
+#include <common/Logger.hpp>
 
 #include <EnqueueArgs.hpp>
 #include <backend.hpp>
 #include <cu_check_macro.hpp>
+#include <cstdlib>
+#include <string>
 
 namespace cuda {
 
 struct Enqueuer {
+    static auto getLogger() {
+        static auto logger = common::loggerFactory("kernel");
+        return logger.get();
+    };
+
     template<typename... Args>
-    void operator()(void* ker, const EnqueueArgs& qArgs, Args... args) {
+    void operator()(std::string name, void* ker, const EnqueueArgs& qArgs,
+                    Args... args) {
         void* params[] = {reinterpret_cast<void*>(&args)...};
         for (auto& event : qArgs.mEvents) {
             CU_CHECK(cuStreamWaitEvent(qArgs.mStream, event, 0));
         }
+        AF_TRACE(
+            "Launching {}: Blocks: [{}, {}, {}] Threads: [{}, {}, {}] Shared "
+            "Memory: {}",
+            name, qArgs.mBlocks.x, qArgs.mBlocks.y, qArgs.mBlocks.z,
+            qArgs.mThreads.x, qArgs.mThreads.y, qArgs.mThreads.z,
+            qArgs.mSharedMemSize);
         CU_CHECK(cuLaunchKernel(static_cast<CUfunction>(ker), qArgs.mBlocks.x,
                                 qArgs.mBlocks.y, qArgs.mBlocks.z,
                                 qArgs.mThreads.x, qArgs.mThreads.y,
@@ -42,8 +57,9 @@ class Kernel
     using BaseClass =
         common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType>;
 
-    Kernel() : BaseClass(nullptr, nullptr) {}
-    Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
+    Kernel() : BaseClass("", nullptr, nullptr) {}
+    Kernel(std::string name, ModuleType mod, KernelType ker)
+        : BaseClass(name, mod, ker) {}
 
     DevPtrType getDevPtr(const char* name) final;
 
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 4f3a5c90ca..cbc7d98517 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -49,14 +49,17 @@
 #include <algorithm>
 #include <array>
 #include <chrono>
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
 #include <fstream>
 #include <iterator>
-#include <map>
 #include <memory>
 #include <numeric>
 #include <string>
 #include <type_traits>
 #include <utility>
+#include <vector>
 
 using namespace cuda;
 
@@ -69,7 +72,6 @@ using std::end;
 using std::extent;
 using std::find_if;
 using std::make_pair;
-using std::map;
 using std::ofstream;
 using std::pair;
 using std::string;
@@ -479,7 +481,7 @@ Kernel getKernel(const Module &mod, const string &nameExpr,
     std::string name  = (sourceWasJIT ? nameExpr : mod.mangledName(nameExpr));
     CUfunction kernel = nullptr;
     CU_CHECK(cuModuleGetFunction(&kernel, mod.get(), name.c_str()));
-    return {mod.get(), kernel};
+    return {nameExpr, mod.get(), kernel};
 }
 
 }  // namespace common
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index f9482b9521..25f266c268 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -8,11 +8,42 @@
  ********************************************************/
 
 #pragma once
+#include <common/Logger.hpp>
 #include <err_cuda.hpp>
 #include <platform.hpp>
+#include <string>
 
-#define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...) \
-    fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__)
+namespace cuda {
+namespace kernel_logger {
+
+inline auto getLogger() {
+    static auto logger = common::loggerFactory("kernel");
+    return logger;
+}
+}  // namespace kernel_logger
+}  // namespace cuda
+
+template<>
+struct fmt::formatter<dim3> : fmt::formatter<std::string> {
+    // parse is inherited from formatter<string_view>.
+    template<typename FormatContext>
+    auto format(dim3 c, FormatContext& ctx) {
+        std::string name = fmt::format("{} {} {}", c.x, c.y, c.z);
+        return formatter<std::string>::format(name, ctx);
+    }
+};
+
+#define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...)                     \
+    do {                                                                      \
+        {                                                                     \
+            using namespace cuda::kernel_logger;                              \
+            AF_TRACE(                                                         \
+                "Launching {}: Blocks: [{}] Threads: [{}] "                   \
+                "Shared Memory: {}",                                          \
+                #fn, blks, thrds, smem_size);                                 \
+        }                                                                     \
+        fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__); \
+    } while (false)
 
 #define CUDA_LAUNCH(fn, blks, thrds, ...) \
     CUDA_LAUNCH_SMEM(fn, blks, thrds, 0, __VA_ARGS__)
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 756aaf15dd..26345591e1 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -23,7 +23,8 @@
 #include <platform.hpp>
 #include <af/dim4.hpp>
 
-#include <cstdio>
+#include <cstdlib>
+#include <sstream>
 #include <stdexcept>
 #include <string>
 #include <thread>
@@ -299,6 +300,12 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
     args.push_back(static_cast<void *>(&blocks_x_total));
     args.push_back(static_cast<void *>(&num_odims));
 
+    {
+        using namespace cuda::kernel_logger;
+        AF_TRACE("Launching : Blocks: [{}] Threads: [{}] ",
+                 dim3(blocks_x, blocks_y, blocks_z),
+                 dim3(threads_x, threads_y));
+    }
     CU_CHECK(cuLaunchKernel(ker, blocks_x, blocks_y, blocks_z, threads_x,
                             threads_y, 1, 0, getActiveStream(), args.data(),
                             NULL));
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index b27ef43a84..92eb28be1e 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -10,17 +10,27 @@
 #pragma once
 
 #include <common/KernelInterface.hpp>
+#include <common/Logger.hpp>
 
 #include <backend.hpp>
 #include <cl2hpp.hpp>
+#include <string>
 
 namespace opencl {
+namespace kernel_logger {
+inline auto getLogger() -> spdlog::logger* {
+    static auto logger = common::loggerFactory("kernel");
+    return logger.get();
+}
+}  // namespace kernel_logger
 
 struct Enqueuer {
     template<typename... Args>
-    void operator()(cl::Kernel ker, const cl::EnqueueArgs& qArgs,
-                    Args&&... args) {
+    void operator()(std::string name, cl::Kernel ker,
+                    const cl::EnqueueArgs& qArgs, Args&&... args) {
         auto launchOp = cl::KernelFunctor<Args...>(ker);
+        using namespace kernel_logger;
+        AF_TRACE("Launching {}", name);
         launchOp(qArgs, std::forward<Args>(args)...);
     }
 };
@@ -35,8 +45,9 @@ class Kernel
     using BaseClass =
         common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType>;
 
-    Kernel() : BaseClass(nullptr, cl::Kernel{nullptr, false}) {}
-    Kernel(ModuleType mod, KernelType ker) : BaseClass(mod, ker) {}
+    Kernel() : BaseClass("", nullptr, cl::Kernel{nullptr, false}) {}
+    Kernel(std::string name, ModuleType mod, KernelType ker)
+        : BaseClass(name, mod, ker) {}
 
     // clang-format off
     [[deprecated("OpenCL backend doesn't need Kernel::getDevPtr method")]]
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 15a94a7e75..999632d55a 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -281,7 +281,7 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
 Kernel getKernel(const Module &mod, const string &nameExpr,
                  const bool sourceWasJIT) {
     UNUSED(sourceWasJIT);
-    return {&mod.get(), cl::Kernel(mod.get(), nameExpr.c_str())};
+    return {nameExpr, &mod.get(), cl::Kernel(mod.get(), nameExpr.c_str())};
 }
 
 }  // namespace common
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index f017b37e73..cb06a2ce84 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -36,6 +36,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         ../common
         ../../../include
         ${CMAKE_CURRENT_BINARY_DIR}
+        $<TARGET_PROPERTY:spdlog,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>

From 9267ee79f2ec009af301e629914136668a8f278f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 8 Jun 2021 19:03:04 -0400
Subject: [PATCH 2157/2677] Fix doxygen warnings in memory manager and inplace
 FFT

---
 include/af/memory.h | 15 +++++++--------
 include/af/signal.h |  4 ----
 2 files changed, 7 insertions(+), 12 deletions(-)

diff --git a/include/af/memory.h b/include/af/memory.h
index c60007a53e..6c53837a6c 100644
--- a/include/af/memory.h
+++ b/include/af/memory.h
@@ -50,7 +50,6 @@ typedef af_err (*af_memory_manager_shutdown_fn)(af_memory_manager handle);
 
    \param[in] handle a pointer to the active \ref af_memory_manager handle
    \param[out] ptr pointer to the allocated buffer
-   \param[in] bytes number of bytes to allocate
    \param[in] user_lock a truthy value corresponding to whether or not the
    memory should have a user lock associated with it
    \param[in] ndims the number of dimensions associated with the allocated
@@ -118,9 +117,9 @@ typedef af_err (*af_memory_manager_signal_memory_cleanup_fn)(
    enforced and can include any information that could be useful to the user.
    This function is only called by \ref af_print_mem_info.
 
-   \param[in] handle a pointer to the active \ref af_memory_manager handle
-   \param[out] a buffer to which a message will be populated
-   \param[in] the device id for which to print memory
+   \param[in]  handle a pointer to the active \ref af_memory_manager handle
+   \param[out] buffer a buffer to which a message will be populated
+   \param[in]  id     the device id for which to print memory
    \returns AF_SUCCESS
 
    \ingroup memory_manager_api
@@ -174,8 +173,8 @@ typedef af_err (*af_memory_manager_is_user_locked_fn)(af_memory_manager handle,
 
    \ingroup memory_manager_api
 */
-typedef af_err (*af_memory_manager_get_memory_pressure_fn)(af_memory_manager,
-                                                           float* pressure);
+typedef af_err (*af_memory_manager_get_memory_pressure_fn)(
+    af_memory_manager handle, float* pressure);
 
 /**
    \brief Called to query if additions to the JIT tree would exert too much
@@ -225,8 +224,8 @@ typedef void (*af_memory_manager_add_memory_management_fn)(
 
     \ingroup memory_manager_api
 */
-typedef void (*af_memory_manager_remove_memory_management_fn)(af_memory_manager,
-                                                              int id);
+typedef void (*af_memory_manager_remove_memory_management_fn)(
+    af_memory_manager handle, int id);
 
 /**
    \brief Creates an \ref af_memory_manager handle
diff --git a/include/af/signal.h b/include/af/signal.h
index 6b6720201d..5e131706b8 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -184,7 +184,6 @@ AFAPI void fftInPlace(array& in, const double norm_factor = 1);
 
    \param[inout]  in is the input array on entry and the output of 2D forward fourier transform on exit
    \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
-   \return     the transformed array
 
    \note The input \p in must be complex
 
@@ -199,7 +198,6 @@ AFAPI void fft2InPlace(array& in, const double norm_factor = 1);
 
    \param[inout]  in is the input array on entry and the output of 3D forward fourier transform on exit
    \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
-   \return     the transformed array
 
    \note The input \p in must be complex
 
@@ -351,7 +349,6 @@ AFAPI void ifftInPlace(array& in, const double norm_factor = 1);
 
    \param[inout]  in is the input array on entry and the output of 2D inverse fourier transform on exit
    \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
-   \return     the transformed array
 
    \note The input \p in must be complex
 
@@ -366,7 +363,6 @@ AFAPI void ifft2InPlace(array& in, const double norm_factor = 1);
 
    \param[inout]  in is the input array on entry and the output of 3D inverse fourier transform on exit
    \param[in]  norm_factor is the normalization factor with which the input is scaled after the transformation is applied
-   \return     the transformed array
 
    \note The input \p in must be complex
 

From bde5bd2d12f74caa2c8f7c6d9eb8e317893c486c Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 22 Jun 2021 20:40:15 +0530
Subject: [PATCH 2158/2677] Free unlocked buffers before tests run in rng
 quality tests (#3151)

* Free unlocked buffers before tests run in rng quality tests

This is needed when running rng quality tests on lesser memory cards
where higher memory usage is causing out of memory issues.

* Fix formatting
---
 test/rng_quality.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/test/rng_quality.cpp b/test/rng_quality.cpp
index 8585d552e6..0c2ec5667e 100644
--- a/test/rng_quality.cpp
+++ b/test/rng_quality.cpp
@@ -7,6 +7,7 @@
 using af::allTrue;
 using af::array;
 using af::constant;
+using af::deviceGC;
 using af::dtype;
 using af::dtype_traits;
 using af::randomEngine;
@@ -16,7 +17,10 @@ using af::sum;
 template<typename T>
 class RandomEngine : public ::testing::Test {
    public:
-    virtual void SetUp() {}
+    virtual void SetUp() {
+        // Ensure all unlocked buffers are freed
+        deviceGC();
+    }
 };
 
 // create a list of types to be tested

From a7c695065bd871d6db9c6b65dcee148f2ab3d229 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Tue, 22 Jun 2021 12:32:48 +0530
Subject: [PATCH 2159/2677] Use ONEAPI_ROOT env variable also for looking up
 MKL Installation

---
 CMakeModules/FindMKL.cmake | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index 47e5dfaa2a..a350a6f499 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -12,6 +12,9 @@
 # script is located in the bin folder of your mkl installation. This will set the
 # MKLROOT environment variable which will be used to find the libraries on your system.
 #
+# In case you have oneAPI base toolkit installed, having ONEAPI_ROOT environment variable available
+# also will enable picking Intel oneMKL automatically.
+#
 # Example:
 # set(MKL_THREAD_LAYER "TBB")
 # find_package(MKL)
@@ -101,6 +104,7 @@ find_path(MKL_INCLUDE_DIR
     /opt/intel
     /opt/intel/mkl
     $ENV{MKLROOT}
+    $ENV{ONEAPI_ROOT}/mkl/latest
     /opt/intel/compilers_and_libraries/linux/mkl
   PATH_SUFFIXES
     include
@@ -230,6 +234,7 @@ function(find_mkl_library)
         /opt/intel/tbb/lib
         /opt/intel/lib
         $ENV{MKLROOT}/lib
+        $ENV{ONEAPI_ROOT}/mkl/latest/lib
         ${ENV_LIBRARY_PATHS}
         /opt/intel/compilers_and_libraries/linux/mkl/lib
       PATH_SUFFIXES
@@ -259,6 +264,7 @@ function(find_mkl_library)
         /opt/intel/tbb/lib
         /opt/intel/lib
         $ENV{MKLROOT}/lib
+        $ENV{ONEAPI_ROOT}/mkl/latest/lib
         ${ENV_LIBRARY_PATHS}
         /opt/intel/compilers_and_libraries/linux/mkl/lib
       PATH_SUFFIXES

From 3bd788320d87219ec694e01a33d3d40ce85be219 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 21 Jun 2021 15:14:51 +0530
Subject: [PATCH 2160/2677] Use cpp numericlimits helper fns instead of C
 macros

---
 src/backend/cpu/homography.cpp           | 16 +++++++++-------
 src/backend/cpu/kernel/sift.hpp          |  9 +++++----
 src/backend/cuda/homography.cu           |  5 +++--
 src/backend/opencl/homography.cpp        |  5 ++++-
 src/backend/opencl/kernel/homography.hpp |  7 +++++--
 5 files changed, 26 insertions(+), 16 deletions(-)

diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 98e93f0f08..9fbdf9fead 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -16,9 +16,9 @@
 #include <af/dim4.hpp>
 
 #include <array>
-#include <cfloat>
 #include <cmath>
 #include <cstring>
+#include <limits>
 #include <vector>
 
 using af::dim4;
@@ -27,6 +27,7 @@ using std::array;
 using std::log;
 using std::max;
 using std::min;
+using std::numeric_limits;
 using std::pow;
 using std::round;
 using std::sqrt;
@@ -47,17 +48,17 @@ static const float LMEDSOutlierRatio = 0.4f;
 
 template<typename T>
 struct EPS {
-    T eps() { return FLT_EPSILON; }
+    T eps() { return numeric_limits<float>::epsilon(); }
 };
 
 template<>
 struct EPS<float> {
-    static float eps() { return FLT_EPSILON; }
+    static float eps() { return numeric_limits<float>::epsilon(); }
 };
 
 template<>
 struct EPS<double> {
-    static double eps() { return DBL_EPSILON; }
+    static double eps() { return numeric_limits<double>::epsilon(); }
 };
 
 template<typename T, int M, int N>
@@ -138,7 +139,7 @@ unsigned updateIterations(float inlier_ratio, unsigned iter) {
     float wn = pow(1 - w, 4.f);
 
     float d = 1.f - wn;
-    if (d < FLT_MIN) { return 0; }
+    if (d < numeric_limits<float>::min()) { return 0; }
 
     d = log(d);
 
@@ -284,7 +285,7 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
     unsigned iter    = iterations;
     unsigned bestIdx = 0;
     int bestInliers  = 0;
-    float minMedian  = FLT_MAX;
+    float minMedian  = numeric_limits<float>::max();
 
     for (unsigned i = 0; i < iter; i++) {
         const unsigned Hidx = Hdims[0] * i;
@@ -344,7 +345,8 @@ int findBestHomography(Array<T>& bestH, const Array<float>& x_src,
                 median = (median + err[nsamples / 2 - 1]) * 0.5f;
             }
 
-            if (median < minMedian && median > FLT_EPSILON) {
+            if (median < minMedian &&
+                median > numeric_limits<float>::epsilon()) {
                 minMedian = median;
                 bestIdx   = i;
             }
diff --git a/src/backend/cpu/kernel/sift.hpp b/src/backend/cpu/kernel/sift.hpp
index e8698a97c5..49b5ae5c34 100644
--- a/src/backend/cpu/kernel/sift.hpp
+++ b/src/backend/cpu/kernel/sift.hpp
@@ -20,8 +20,8 @@
 #include <resize.hpp>
 #include <sort_index.hpp>
 
-#include <cfloat>
 #include <cstring>
+#include <limits>
 #include <vector>
 
 using af::dim4;
@@ -330,8 +330,9 @@ void interpolateExtrema(float* x_out, float* y_out, unsigned* layer_out,
         float det = dxx * dyy - dxy * dxy;
 
         // add FLT_EPSILON for double-precision compatibility
-        if (det <= 0 || tr * tr * edge_thr >=
-                            (edge_thr + 1) * (edge_thr + 1) * det + FLT_EPSILON)
+        if (det <= 0 ||
+            tr * tr * edge_thr >= (edge_thr + 1) * (edge_thr + 1) * det +
+                                      std::numeric_limits<float>::epsilon())
             continue;
 
         if (*counter < max_feat) {
@@ -692,7 +693,7 @@ void computeGLOHDescriptor(float* desc_out, const unsigned desc_len,
                                      (float)(GLOHRadii[1] - GLOHRadii[0])
                            : min(2 + (r - GLOHRadii[1]) /
                                          (float)(GLOHRadii[2] - GLOHRadii[1]),
-                                 3.f - FLT_EPSILON));
+                                 3.f - std::numeric_limits<float>::epsilon()));
 
             if (r <= GLOHRadii[rb - 1] && y > 0 && y < idims[0] - 1 && x > 0 &&
                 x < idims[1] - 1) {
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index 102bf35f18..b8525dee8e 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -14,7 +14,7 @@
 #include <af/dim4.hpp>
 #include <algorithm>
 
-#include <cfloat>
+#include <limits>
 
 using af::dim4;
 
@@ -39,7 +39,8 @@ int homography(Array<T> &bestH, const Array<float> &x_src,
         iter = ::std::min(
             iter, (unsigned)(log(1.f - LMEDSConfidence) /
                              log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
-        err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
+        err = createValueArray<float>(af::dim4(nsamples, iter),
+                                      std::numeric_limits<float>::max());
     }
 
     af::dim4 rdims(4, iter);
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 3b598b0275..9153336471 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -14,8 +14,10 @@
 #include <af/dim4.hpp>
 
 #include <algorithm>
+#include <limits>
 
 using af::dim4;
+using std::numeric_limits;
 
 namespace opencl {
 
@@ -39,7 +41,8 @@ int homography(Array<T> &bestH, const Array<float> &x_src,
             ::std::min(iter, static_cast<unsigned>(
                                  log(1.f - LMEDSConfidence) /
                                  log(1.f - pow(1.f - LMEDSOutlierRatio, 4.f))));
-        err = createValueArray<float>(af::dim4(nsamples, iter), FLT_MAX);
+        err = createValueArray<float>(af::dim4(nsamples, iter),
+                                      numeric_limits<float>::max());
     } else {
         // Avoid passing "null" cl_mem object to kernels
         err = createEmptyArray<float>(af::dim4(1));
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 854d858103..3293c06ea0 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -19,6 +19,7 @@
 #include <memory.hpp>
 #include <af/defines.h>
 
+#include <limits>
 #include <string>
 #include <vector>
 
@@ -36,8 +37,10 @@ std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     options.emplace_back(getTypeBuildDefinition<T>());
-    options.emplace_back(DefineKeyValue(
-        EPS, (std::is_same<T, double>::value ? DBL_EPSILON : FLT_EPSILON)));
+    options.emplace_back(
+        DefineKeyValue(EPS, (std::is_same<T, double>::value
+                                 ? std::numeric_limits<double>::epsilon()
+                                 : std::numeric_limits<float>::epsilon())));
     if (htype == AF_HOMOGRAPHY_RANSAC) {
         options.emplace_back(DefineKey(RANSAC));
     }

From 80d8ef683b1028526164e22a1e590fbfd555572a Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 22 Jun 2021 20:08:15 +0530
Subject: [PATCH 2161/2677] Build option AF_COMPUTE_LIBRARY to select CPU
 compute dependency

This new cmake option can take the following values
- `Intel-MKL` - Intel MKL is used for blas, fft and sparse related routines
- `FFTW/LAPACK/BLAS` - OpenBLAS for blas routines; fftw for fft routines; netlib compatible
  lapack library for lapack routines
- `Intel-MKL` is the default value of this option.

We intend to add AMD-AOCL as the third option.

To preserve the behavior provided by the old flags, USE_CPU_MKL &
USE_OPENCL_MKL, if provided(command-line/cmake-gui) will take
precedence even if `AF_COMPUTE_LIBRARY` has `FFTW/LAPACK/BLAS`.

Add back vcpkg caching mechanism. The work around we tried so far has
increased the build time too much on windows github action

Putting vcpkg under arrayfire source root or build folder is making
vcpkg think it is in manifest mode and any `vcpkg install` commands are
not doing expected standalone dependency installations.

Cannot use af_deprecate calls of USE_*_MKL flags, it cannot handle different type cmake variables
---
 .github/workflows/unix_cpu_build.yml |  3 +-
 .github/workflows/win_cpu_build.yml  | 40 ++++++++-------
 CMakeLists.txt                       | 44 +++++++++++++++-
 CMakeModules/FindMKL.cmake           |  5 ++
 CMakePresets.json                    | 75 +++++++++++++++-------------
 src/api/c/CMakeLists.txt             |  2 +-
 src/backend/cpu/CMakeLists.txt       | 41 +++++----------
 src/backend/opencl/CMakeLists.txt    | 15 ++----
 8 files changed, 131 insertions(+), 94 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 40211fb06f..36649284bf 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -99,6 +99,7 @@ jobs:
                   branch=$(git rev-parse --abbrev-ref HEAD)
                   buildname=$(if [ -z "$prnum" ]; then echo "$branch"; else echo "PR-$prnum"; fi)
                   dashboard=$(if [ -z "$prnum" ]; then echo "Continuous"; else echo "Experimental"; fi)
+                  backend=$(if [ "$USE_MKL" == 1 ]; then echo "Intel-MKL"; else echo "FFTW/LAPACK/BLAS"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
                   mkdir build && cd build
                   ${CMAKE_PROGRAM} -G Ninja \
@@ -106,7 +107,7 @@ jobs:
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \
-                      -DUSE_CPU_MKL:BOOL=$USE_MKL \
+                      -DAF_COMPUTE_LIBRARY:STRING=$backend \
                       -DBUILDNAME:STRING=${buildname} ..
                   echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 
diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index df98161545..ed47fd8676 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -14,25 +14,31 @@ jobs:
         runs-on: windows-latest
         env:
           VCPKG_HASH: 5568f110b509a9fd90711978a7cb76bae75bb092 # vcpkg release tag 2021.05.12 with Forge v1.0.7 update
+          VCPKG_DEFAULT_TRIPLET: x64-windows
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master
 
-            - name: VCPKG Binary Cache
+            - name: VCPKG Cache
               uses: actions/cache@v2
-              id: vcpkg-bin-cache
+              id: vcpkg-cache
               with:
-                path: vcpkg_cache
-                key: vcpkg_bin_cache_${{ env.VCPKG_HASH }} # vcpkg manifest baseline
+                path: ~/vcpkg
+                key: vcpkg-deps-${{ env.VCPKG_HASH }}
+
+            - name: Install VCPKG Dependencies
+              if: steps.vcpkg-cache.outputs.cache-hit != 'true'
+              run: |
+                cd ~
+                git clone --quiet --recursive https://github.com/microsoft/vcpkg.git
+                cd vcpkg
+                git checkout $env:VCPKG_HASH
+                .\bootstrap-vcpkg.bat
+                .\vcpkg.exe install boost-compute boost-functional boost-stacktrace fftw3 forge freeimage freetype glfw3 openblas
+                Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
 
             - name: CMake Configure
               run: |
-                  $cwd = (Get-Item -Path ".\").FullName
-                  Set-Location -Path ${env:VCPKG_INSTALLATION_ROOT}
-                  git pull
-                  .\bootstrap-vcpkg.bat
-                  .\vcpkg.exe install --triplet x64-windows boost-compute boost-functional boost-stacktrace fftw3 forge freeimage freetype glfw3 openblas
-                  Set-Location -Path $cwd
                   $ref = $env:GITHUB_REF | %{ if ($_ -match "refs/pull/[0-9]+/merge") { $_;} }
                   $prnum = $ref | %{$_.Split("/")[2]}
                   $branch = git branch --show-current
@@ -40,18 +46,18 @@ jobs:
                   $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
                   $buildname = "$buildname-cpu-openblas"
                   mkdir build && cd build
-                  New-Item -Path "${cwd}/vcpkg_cache" -ItemType "directory" -Force
-                  $env:VCPKG_DEFAULT_BINARY_CACHE="${cwd}/vcpkg_cache"
                   cmake .. -G "Visual Studio 16 2019" -A x64 `
+                      -DVCPKG_ROOT:PATH="~/vcpkg" `
+                      -DVCPKG_MANIFEST_MODE:BOOL=OFF `
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
                       -DBUILDNAME:STRING="$buildname" `
-                      -DVCPKG_ROOT:PATH="${env:VCPKG_INSTALLATION_ROOT}" `
-                      -DVCPKG_MANIFEST_MODE:BOOL=OFF
+                      -DAF_COMPUTE_LIBRARY:STRING="FFTW/LAPACK/BLAS"
                   echo "CTEST_DASHBOARD=${dashboard}" >> $env:GITHUB_ENV
 
             - name: Build and Test
               run: |
-                  Set-Location -Path .\build
-                  $Env:PATH += ";${env:VCPKG_INSTALLATION_ROOT}/installed/x64-windows/bin"
-                  ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C Release -R cpu -E pinverse -j2
+                  cd build
+                  $vcpkg_path = (Resolve-Path ~).Path
+                  $Env:PATH += ";${vcpkg_path}/vcpkg/installed/x64-windows/bin"
+                  ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C RelWithDebInfo -R cpu -E pinverse -j2
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f45b5fff8e..ea7c87ad70 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -73,6 +73,11 @@ option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
 option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
 option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
 
+set(AF_COMPUTE_LIBRARY "Intel-MKL"
+    CACHE STRING "Compute library for signal processing and linear algebra routines")
+set_property(CACHE AF_COMPUTE_LIBRARY
+    PROPERTY STRINGS "Intel-MKL" "FFTW/LAPACK/BLAS")
+
 if(WIN32)
   set(AF_STACKTRACE_TYPE "Windbg" CACHE STRING "The type of backtrace features. Windbg(simple), None")
   set_property(CACHE AF_STACKTRACE_TYPE PROPERTY STRINGS "Windbg" "None")
@@ -105,6 +110,21 @@ af_deprecate(BUILD_EXAMPLES        AF_BUILD_EXAMPLES)
 af_deprecate(USE_RELATIVE_TEST_DIR AF_WITH_RELATIVE_TEST_DIR)
 af_deprecate(USE_FREEIMAGE_STATIC  AF_WITH_STATIC_FREEIMAGE)
 af_deprecate(USE_CPUID             AF_WITH_CPUID)
+if(DEFINED USE_CPU_MKL OR DEFINED USE_OPENCL_MKL)
+  # Cannot use af_deprecated as it expects the new and old variables to store values of
+  # same type. In this case, USE_*_MKL variables are BOOLs and AF_COMPUTE_LIBRARY is a STRING
+  message(DEPRECATION
+    "Variables USE_CPU_MKL/USE_OPENCL_MKL are deprecated. Use AF_COMPUTE_LIBRARY instead.")
+  message(WARNING
+    "USE_CPU_MKL/USE_OPENCL_MKL defined. These values take precendence over the value of
+    AF_COMPUTE_LIBRARY until they are removed to preserve existing build behavior.")
+  # Until USE_CPU_MKL and USE_OPENCL_MKL are removed, if they are defined, they take
+  # precendence and cmake will check and report error if Intel-MKL is not found
+  if(USE_CPU_MKL OR USE_OPENCL_MKL)
+    get_property(doc CACHE AF_COMPUTE_LIBRARY PROPERTY HELPSTRING)
+    set(AF_COMPUTE_LIBRARY "Intel-MKL" CACHE STRING "${doc}" FORCE)
+  endif()
+endif()
 
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
@@ -117,6 +137,7 @@ mark_as_advanced(
   AF_WITH_STATIC_FREEIMAGE
   AF_WITH_NONFREE
   AF_WITH_IMAGEIO
+  AF_WITH_RELATIVE_TEST_DIR
   AF_TEST_WITH_MTX_FILES
   ArrayFire_DIR
   Boost_INCLUDE_DIR
@@ -136,6 +157,27 @@ mark_as_advanced(
   )
 mark_as_advanced(CLEAR CUDA_VERSION)
 
+# IF: the old USE_CPU_MKL/USE_OPENCL_MKL flags are present,
+# THEN Irrespective of AF_COMPUTE_LIBRARY value, continue with MKL to preserve old
+#      behavior. Once the deprecated USE_CPU_MKL/USE_OPENCL_MKL are removed in later
+#      versions AF_COMPUTE_LIBRARY will take over total control of selecting CPU
+#      compute backend.
+#
+# Note that the default value of AF_COMPUTE_LIBRARY is Intel-MKL.
+# Also, cmake doesn't have short-circuit of OR/AND conditions in if
+if(${AF_BUILD_CPU} OR ${AF_BUILD_OPENCL})
+  if("${AF_COMPUTE_LIBRARY}" STREQUAL "Intel-MKL")
+    dependency_check(MKL_FOUND "Please ensure Intel-MKL / oneAPI-oneMKL is installed")
+    set(BUILD_WITH_MKL ON)
+  elseif("${AF_COMPUTE_LIBRARY}" STREQUAL "FFTW/LAPACK/BLAS")
+    dependency_check(FFTW_FOUND "FFTW not found")
+    dependency_check(CBLAS_FOUND "CBLAS not found")
+    if(UNIX AND NOT APPLE)
+      dependency_check(LAPACK_FOUND "LAPACK not found")
+    endif()
+  endif()
+endif()
+
 #Configure forge submodule
 #forge is included in ALL target if AF_BUILD_FORGE is ON
 #otherwise, forge is not built at all
@@ -373,7 +415,7 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
-if((USE_CPU_MKL OR USE_OPENCL_MKL) AND AF_INSTALL_STANDALONE)
+if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
   if(TARGET MKL::ThreadingLibrary)
     get_filename_component(mkl_tl ${MKL_ThreadingLibrary_LINK_LIBRARY} REALPATH)
     install(FILES
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindMKL.cmake
index a350a6f499..7c9baefecb 100644
--- a/CMakeModules/FindMKL.cmake
+++ b/CMakeModules/FindMKL.cmake
@@ -467,3 +467,8 @@ if(MKL_Static_FOUND AND NOT TARGET MKL::Static)
     endif()
   endif()
 endif()
+
+set(MKL_FOUND OFF)
+if(MKL_Shared_FOUND OR MKL_Static_FOUND)
+  set(MKL_FOUND ON)
+endif()
diff --git a/CMakePresets.json b/CMakePresets.json
index 7f95210c7f..340d4b62b9 100644
--- a/CMakePresets.json
+++ b/CMakePresets.json
@@ -17,6 +17,10 @@
           "type": "String",
           "value": "Debug"
         },
+        "AF_COMPUTE_LIBRARY": {
+          "type": "String",
+          "value": "Intel-MKL"
+        },
         "AF_BUILD_CPU": {
           "type": "BOOL",
           "value": "OFF"
@@ -56,33 +60,33 @@
       }
     },
     {
-      "name": "ninja-cpu-debug",
-      "description": "Build CPU Backend with FFTW and a BLAS library using Ninja Generator in Debug Configuration",
+      "name": "ninja-cpu-mkl-debug",
+      "description": "Build CPU Backend using Intel MKL in Debug Configuration with Ninja Generator",
       "inherits": "ninja-all-off-debug",
       "cacheVariables": {
         "AF_BUILD_CPU": "ON"
       }
     },
     {
-      "name": "ninja-cpu-relwithdebinfo",
-      "description": "Build CPU Backend with FFTW and a BLAS library using Ninja Generator in RelWithDebInfo Configuration",
-      "inherits": "ninja-cpu-debug",
+      "name": "ninja-cpu-mkl-relwithdebinfo",
+      "description": "Build CPU Backend using Intel MKL in RelWithDebInfo Configuration with Ninja Generator",
+      "inherits": "ninja-cpu-mkl-debug",
       "cacheVariables": {
         "CMAKE_BUILD_TYPE": "RelWithDebInfo"
       }
     },
     {
-      "name": "ninja-cpu-mkl-debug",
-      "description": "Build CPU Backend using Intel MKL in Debug Configuration with Ninja Generator",
-      "inherits": "ninja-cpu-debug",
+      "name": "ninja-cpu-debug",
+      "description": "Build CPU Backend with FFTW and a BLAS library using Ninja Generator in Debug Configuration",
+      "inherits": "ninja-cpu-mkl-debug",
       "cacheVariables": {
-        "USE_CPU_MKL": "ON"
+        "AF_COMPUTE_LIBRARY": "FFTW/LAPCK/BLAS"
       }
     },
     {
-      "name": "ninja-cpu-mkl-relwithdebinfo",
-      "description": "Build CPU Backend using Intel MKL in RelWithDebInfo Configuration with Ninja Generator",
-      "inherits": "ninja-cpu-mkl-debug",
+      "name": "ninja-cpu-relwithdebinfo",
+      "description": "Build CPU Backend with FFTW and a BLAS library using Ninja Generator in RelWithDebInfo Configuration",
+      "inherits": "ninja-cpu-debug",
       "cacheVariables": {
         "CMAKE_BUILD_TYPE": "RelWithDebInfo"
       }
@@ -104,7 +108,7 @@
       }
     },
     {
-      "name": "ninja-opencl-debug",
+      "name": "ninja-opencl-mkl-debug",
       "description": "Build OpenCL Backend in debug configuration using Ninja Generator",
       "inherits": "ninja-all-off-debug",
       "cacheVariables": {
@@ -112,31 +116,31 @@
       }
     },
     {
-      "name": "ninja-opencl-mkl-debug",
-      "description": "Build OpenCL Backend in debug configuration using Ninja Generator",
-      "inherits": "ninja-opencl-debug",
+      "name": "ninja-opencl-mkl-relwithdebinfo",
+      "description": "Build OpenCL Backend in RelWithDebInfo configuration using Ninja Generator. This preset uses Intel MKL for CPU fallback code.",
+      "inherits": "ninja-opencl-mkl-debug",
       "cacheVariables": {
-        "USE_OPENCL_MKL": "ON"
+        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
       }
     },
     {
-      "name": "ninja-opencl-relwithdebinfo",
-      "description": "Build OpenCL Backend in RelWithDebInfo configuration using Ninja Generator",
-      "inherits": "ninja-opencl-debug",
+      "name": "ninja-opencl-debug",
+      "description": "Build OpenCL Backend in debug configuration using Ninja Generator",
+      "inherits": "ninja-opencl-mkl-debug",
       "cacheVariables": {
-        "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+        "AF_COMPUTE_LIBRARY": "FFTW/LAPCK/BLAS"
       }
     },
     {
-      "name": "ninja-opencl-mkl-relwithdebinfo",
-      "description": "Build OpenCL Backend in RelWithDebInfo configuration using Ninja Generator. This preset uses Intel MKL for CPU fallback code.",
-      "inherits": "ninja-opencl-mkl-debug",
+      "name": "ninja-opencl-relwithdebinfo",
+      "description": "Build OpenCL Backend in RelWithDebInfo configuration using Ninja Generator",
+      "inherits": "ninja-opencl-debug",
       "cacheVariables": {
         "CMAKE_BUILD_TYPE": "RelWithDebInfo"
       }
     },
     {
-        "name": "ninja-all-debug",
+        "name": "ninja-all-mkl-debug",
         "description": "Build all feasible backends using Ninja Generator in Debug Configuraiton",
         "inherits": "ninja-all-off-debug",
         "cacheVariables": {
@@ -147,26 +151,25 @@
         }
     },
     {
-        "name": "ninja-all-mkl-debug",
-        "description": "Build all feasible backends using Ninja Generator in Debug Configuraiton",
-        "inherits": "ninja-all-debug",
+        "name": "ninja-all-mkl-relwithdebinfo",
+        "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
+        "inherits": "ninja-all-mkl-debug",
         "cacheVariables": {
-            "USE_CPU_MKL": "ON",
-            "USE_OPENCL_MKL": "ON"
+            "CMAKE_BUILD_TYPE": "RelWithDebInfo"
         }
     },
     {
-        "name": "ninja-all-relwithdebinfo",
-        "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
-        "inherits": "ninja-all-debug",
+        "name": "ninja-all-debug",
+        "description": "Build all feasible backends using Ninja Generator in Debug Configuraiton",
+        "inherits": "ninja-all-mkl-debug",
         "cacheVariables": {
-            "CMAKE_BUILD_TYPE": "RelWithDebInfo"
+            "AF_COMPUTE_LIBRARY": "FFTW/LAPCK/BLAS"
         }
     },
     {
-        "name": "ninja-all-mkl-relwithdebinfo",
+        "name": "ninja-all-relwithdebinfo",
         "description": "Build all feasible backends using Ninja Generator in RelWithDebInfo Configuraiton",
-        "inherits": "ninja-all-mkl-debug",
+        "inherits": "ninja-all-debug",
         "cacheVariables": {
             "CMAKE_BUILD_TYPE": "RelWithDebInfo"
         }
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index a626ce6ea8..0830402a1f 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -184,7 +184,7 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
   endif ()
 endif()
 
-if(USE_CPU_MKL OR USE_OPENCL_MKL)
+if(BUILD_WITH_MKL)
   target_compile_definitions(c_api_interface
     INTERFACE
       AF_MKL_INTERFACE_SIZE=${MKL_INTERFACE_INTEGER_SIZE}
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index cd60809ecb..b899d6f887 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -304,51 +304,36 @@ target_compile_definitions(afcpu
     AF_CPU
   )
 
-if(USE_CPU_MKL)
-  dependency_check(MKL_Shared_FOUND "MKL not found")
+target_link_libraries(afcpu
+  PRIVATE
+    c_api_interface
+    cpp_api_interface
+    afcommon_interface
+    cpu_sort_by_key
+    Threads::Threads
+  )
+if(BUILD_WITH_MKL)
   target_compile_definitions(afcpu PRIVATE USE_MKL)
-  target_link_libraries(afcpu
-    PRIVATE
-      c_api_interface
-      cpp_api_interface
-      afcommon_interface
-      cpu_sort_by_key
-      Threads::Threads
-    )
   if(AF_WITH_STATIC_MKL)
       target_link_libraries(afcpu PRIVATE MKL::Static)
   else()
       target_link_libraries(afcpu PRIVATE MKL::RT)
   endif()
 else()
-  dependency_check(FFTW_FOUND "FFTW not found")
-  dependency_check(CBLAS_FOUND "CBLAS not found")
-
   target_link_libraries(afcpu
     PRIVATE
-      c_api_interface
-      cpp_api_interface
-      afcommon_interface
-      cpu_sort_by_key
       ${CBLAS_LIBRARIES}
       FFTW::FFTW
       FFTW::FFTWF
-      Threads::Threads
     )
   if(LAPACK_FOUND)
-    target_link_libraries(afcpu
-      PRIVATE
-        ${LAPACK_LIBRARIES})
-    target_include_directories(afcpu
-      PRIVATE
-        ${LAPACK_INCLUDE_DIR})
+    target_link_libraries(afcpu PRIVATE ${LAPACK_LIBRARIES})
+    target_include_directories(afcpu PRIVATE ${LAPACK_INCLUDE_DIR})
   endif()
 endif()
 
-if(LAPACK_FOUND OR (USE_CPU_MKL AND MKL_Shared_FOUND))
-  target_compile_definitions(afcpu
-    PRIVATE
-      WITH_LINEAR_ALGEBRA)
+if(LAPACK_FOUND OR BUILD_WITH_MKL)
+  target_compile_definitions(afcpu PRIVATE WITH_LINEAR_ALGEBRA)
 endif()
 
 af_split_debug_info(afcpu ${AF_INSTALL_LIB_DIR})
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index c23edac82a..b04572f2f3 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -5,6 +5,8 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
+dependency_check(OpenCL_FOUND "OpenCL not found.")
+
 include(InternalUtils)
 include(build_cl2hpp)
 include(build_CLBlast)
@@ -429,7 +431,7 @@ if(APPLE)
   target_link_libraries(afopencl PRIVATE OpenGL::GL)
 endif()
 
-if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
+if(LAPACK_FOUND OR BUILD_WITH_MKL)
   target_sources(afopencl
     PRIVATE
       magma/gebrd.cpp
@@ -462,8 +464,7 @@ if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
       #magma/unmqr2.cpp
       )
 
-  if(USE_OPENCL_MKL)
-    dependency_check(MKL_Shared_FOUND "MKL not found")
+  if(BUILD_WITH_MKL)
     target_compile_definitions(afopencl PRIVATE USE_MKL)
 
     if(AF_WITH_STATIC_MKL)
@@ -472,13 +473,10 @@ if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
         target_link_libraries(afopencl PRIVATE MKL::RT)
     endif()
   else()
-    dependency_check(OpenCL_FOUND "OpenCL not found.")
-
     if(USE_CPU_F77_BLAS)
       target_compile_definitions(afopencl PRIVATE USE_F77_BLAS)
     endif()
 
-    dependency_check(CBLAS_LIBRARIES "CBLAS not found.")
     target_include_directories(afopencl
       PRIVATE
         ${CBLAS_INCLUDE_DIR}
@@ -489,10 +487,7 @@ if(LAPACK_FOUND OR (USE_OPENCL_MKL AND MKL_Shared_FOUND))
         ${LAPACK_LIBRARIES})
   endif()
 
-  target_compile_definitions(
-    afopencl
-    PRIVATE
-      WITH_LINEAR_ALGEBRA)
+  target_compile_definitions(afopencl PRIVATE WITH_LINEAR_ALGEBRA)
 endif()
 
 af_split_debug_info(afopencl ${AF_INSTALL_LIB_DIR})

From 77181f1d9c860144554cd61e4de69b9dd82ccad9 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 23 Jun 2021 05:41:20 +0200
Subject: [PATCH 2162/2677] The compare function should return false, for equal
 elements. (#3141)

* The compare function should return false, for equal elements.

When compiling in debug mode, the MSVC compiler returns an non-compliance error.

* compare functions should always return false when equal
---
 src/backend/cpu/kernel/sift.hpp | 2 +-
 test/gloh.cpp                   | 2 +-
 test/orb.cpp                    | 2 +-
 test/sift.cpp                   | 2 +-
 test/topk.cpp                   | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/kernel/sift.hpp b/src/backend/cpu/kernel/sift.hpp
index 49b5ae5c34..e7d4821e37 100644
--- a/src/backend/cpu/kernel/sift.hpp
+++ b/src/backend/cpu/kernel/sift.hpp
@@ -91,7 +91,7 @@ bool feat_cmp(feat_t i, feat_t j) {
         if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
     if (i.l != j.l) return (i.l < j.l);
 
-    return true;
+    return false;
 }
 
 void array_to_feat(std::vector<feat_t>& feat, float* x, float* y,
diff --git a/test/gloh.cpp b/test/gloh.cpp
index 4777728789..004f00b7be 100644
--- a/test/gloh.cpp
+++ b/test/gloh.cpp
@@ -46,7 +46,7 @@ static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
         if (round(i.f[k] * 1e1f) != round(j.f[k] * 1e1f))
             return (round(i.f[k] * 1e1f) < round(j.f[k] * 1e1f));
 
-    return true;
+    return false;
 }
 
 static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
diff --git a/test/orb.cpp b/test/orb.cpp
index 862b942555..846bb2146b 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -45,7 +45,7 @@ static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
     for (int k = 0; k < 5; k++)
         if (i.f[k] != j.f[k]) return (i.f[k] < j.f[k]);
 
-    return true;
+    return false;
 }
 
 static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
diff --git a/test/sift.cpp b/test/sift.cpp
index 3d68a02766..616557f93a 100644
--- a/test/sift.cpp
+++ b/test/sift.cpp
@@ -46,7 +46,7 @@ static bool feat_cmp(feat_desc_t i, feat_desc_t j) {
         if (round(i.f[k] * 1e1f) != round(j.f[k] * 1e1f))
             return (round(i.f[k] * 1e1f) < round(j.f[k] * 1e1f));
 
-    return true;
+    return false;
 }
 
 static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
diff --git a/test/topk.cpp b/test/topk.cpp
index 8841303db1..241380d4f8 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -121,7 +121,7 @@ void topkTest(const int ndims, const dim_t* dims, const unsigned k,
         } else {
             stable_sort(kvPairs.begin(), kvPairs.end(),
                         [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
-                            return lhs.first >= rhs.first;
+                            return lhs.first > rhs.first;
                         });
         }
 

From 3abc38d691565801327705aa5d246187719aa0b4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 23 Jun 2021 13:32:26 +0530
Subject: [PATCH 2163/2677] Fix gtest project warning/error with GCC greater
 than 10.3

---
 test/CMakeLists.txt | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 4ba67af7c0..7c86a4cbe4 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -36,6 +36,13 @@ if(NOT TARGET gtest)
   set_target_properties(gtest gtest_main
     PROPERTIES
       FOLDER "ExternalProjectTargets/gtest")
+  if(UNIX)
+    if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
+      CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "10.3.0")
+      target_compile_options(gtest PRIVATE -Wno-maybe-uninitialized)
+      target_compile_options(gtest_main PRIVATE -Wno-maybe-uninitialized)
+    endif()
+  endif()
 
   # Hide gtest project variables
   mark_as_advanced(

From 2a2b677431992a8e73b6724bb61e5e3af0c572e0 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 23 Jun 2021 15:11:57 +0530
Subject: [PATCH 2164/2677] Use normalized data for Large* tests of pinverse

Float type has accuracy issues with large input values for pinverse
computations. This change updates the data sets for Large & LargeTall
tests that has this accuracy issue.
---
 test/CMakeLists.txt | 4 +++-
 test/pinverse.cpp   | 4 ++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 7c86a4cbe4..cb9dde8e76 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -77,7 +77,9 @@ else(${AF_USE_RELATIVE_TEST_DIR})
   FetchContent_Declare(
     ${testdata_prefix}
     GIT_REPOSITORY https://github.com/arrayfire/arrayfire-data.git
-    GIT_TAG        master
+
+    #pinv large data set update change
+    GIT_TAG        0144a599f913cc67c76c9227031b4100156abc25
   )
   af_dep_check_and_populate(${testdata_prefix})
   set(TESTDATA_SOURCE_DIR "${${testdata_prefix}_SOURCE_DIR}")
diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index d6e27b20ee..0e8575feca 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -159,7 +159,7 @@ TYPED_TEST(Pinverse, ApinvA_IsHermitian) {
 
 TYPED_TEST(Pinverse, Large) {
     array in = readTestInput<TypeParam>(
-        string(TEST_DIR "/pinverse/pinverse640x480.test"));
+        string(TEST_DIR "/pinverse/pinv_640x480_inputs.test"));
     array inpinv = pinverse(in);
     array out    = matmul(in, inpinv, in);
     ASSERT_ARRAYS_NEAR(in, out, relEps<TypeParam>(in));
@@ -167,7 +167,7 @@ TYPED_TEST(Pinverse, Large) {
 
 TYPED_TEST(Pinverse, LargeTall) {
     array in = readTestInput<TypeParam>(
-                   string(TEST_DIR "/pinverse/pinverse640x480.test"))
+                   string(TEST_DIR "/pinverse/pinv_640x480_inputs.test"))
                    .T();
     array inpinv = pinverse(in);
     array out    = matmul(in, inpinv, in);

From 4740ba8bbf14e341c83a0796075043bca967b359 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Mon, 5 Jul 2021 15:43:47 +0530
Subject: [PATCH 2165/2677] Add MSVC generator based cmake presets for ease of
 development on Windows

---
 CMakePresets.json | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/CMakePresets.json b/CMakePresets.json
index 340d4b62b9..ba1520ddf5 100644
--- a/CMakePresets.json
+++ b/CMakePresets.json
@@ -217,6 +217,43 @@
         "cacheVariables": {
             "CMAKE_BUILD_TYPE": "RelWithDebInfo"
         }
+    },
+    {
+      "name": "msvc2019",
+      "hidden": true,
+      "description": "Base preset for Visual Studio 16 2019 generator.",
+      "generator": "Visual Studio 16 2019",
+      "architecture": "x64"
+    },
+    {
+      "name": "msvc2019-cpu-mkl",
+      "description": "Build CPU Backend using Intel MKL with MSVC 2019 Generator",
+      "inherits": [ "msvc2019", "ninja-cpu-mkl-debug" ]
+    },
+    {
+      "name": "msvc2019-cuda",
+      "description": "Build CUDA Backend with MSVC 2019 Generator",
+      "inherits": [ "msvc2019", "ninja-cuda-debug" ]
+    },
+    {
+      "name": "msvc2019-opencl-mkl",
+      "description": "Build OpenCL Backend with MSVC 2019 Generator. Uses MKL for CPU fallback.",
+      "inherits": [ "msvc2019", "ninja-opencl-mkl-debug" ]
+    },
+    {
+      "name": "msvc2019-all-mkl",
+      "description": "Build all feasible Backends with MSVC 2019 Generator. Uses MKL for CPU fallback.",
+      "inherits": [ "msvc2019", "ninja-all-mkl-debug" ]
+    },
+    {
+      "name": "msvc2019-all-mkl-local-install",
+      "description": "Build all feasible Backends with MSVC 2019 Generator. Installs to specified path prefix.",
+      "inherits": [ "msvc2019", "ninja-all-mkl-local-install" ]
+    },
+    {
+      "name": "msvc2019-all-mkl-standalone-install",
+      "description": "Build all feasible Backends with MSVC 2019 Generator. Also packages dependencies while installing to specified path prefix.",
+      "inherits": [ "msvc2019", "ninja-all-mkl-standalone-install" ]
     }
   ]
 }

From 7a4dbbe7cce47022b94082f69c49853065abc2fc Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 6 Jul 2021 09:26:21 +0530
Subject: [PATCH 2166/2677] Correct extern arrayfire deps download location

Although the build isn't broken, since forge project setup runs before
arrayfire fetch content variables are set, fetch-content-variables that
doesn't have suffixes are set by forge project specific settings. This
change fixes that.
---
 CMakeModules/AFconfigure_forge_dep.cmake | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index c2bc2f42f7..a49b44d71d 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -38,6 +38,11 @@ else()
   af_dep_check_and_populate(${forge_prefix})
 
   if(AF_BUILD_FORGE)
+    set(af_FETCHCONTENT_BASE_DIR ${FETCHCONTENT_BASE_DIR})
+    set(af_FETCHCONTENT_QUIET ${FETCHCONTENT_QUIET})
+    set(af_FETCHCONTENT_FULLY_DISCONNECTED ${FETCHCONTENT_FULLY_DISCONNECTED})
+    set(af_FETCHCONTENT_UPDATES_DISCONNECTED ${FETCHCONTENT_UPDATES_DISCONNECTED})
+
     set(ArrayFireInstallPrefix ${CMAKE_INSTALL_PREFIX})
     set(ArrayFireBuildType ${CMAKE_BUILD_TYPE})
     set(CMAKE_INSTALL_PREFIX ${${forge_prefix}_BINARY_DIR}/extern/forge/package)
@@ -62,6 +67,10 @@ else()
         )
     set(CMAKE_BUILD_TYPE ${ArrayFireBuildType})
     set(CMAKE_INSTALL_PREFIX ${ArrayFireInstallPrefix})
+    set(FETCHCONTENT_BASE_DIR ${af_FETCHCONTENT_BASE_DIR})
+    set(FETCHCONTENT_QUIET ${af_FETCHCONTENT_QUIET})
+    set(FETCHCONTENT_FULLY_DISCONNECTED ${af_FETCHCONTENT_FULLY_DISCONNECTED})
+    set(FETCHCONTENT_UPDATES_DISCONNECTED ${af_FETCHCONTENT_UPDATES_DISCONNECTED})
 
     install(FILES
         $<TARGET_FILE:forge>

From 955152b6570c608ae74ebd9e6b31d48351cb8a16 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 6 Jul 2021 09:27:26 +0530
Subject: [PATCH 2167/2677] Use system/vcpkg spdlog if not fallback to
 fetchcontent

---
 CMakeLists.txt                                | 33 ++++++++++++-------
 src/api/unified/CMakeLists.txt                |  2 +-
 src/backend/common/CMakeLists.txt             |  2 +-
 src/backend/cuda/CMakeLists.txt               |  3 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  2 +-
 vcpkg.json                                    | 13 +++++++-
 6 files changed, 39 insertions(+), 16 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea7c87ad70..0515e9f74f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -54,6 +54,7 @@ find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
 find_package(MKL)
+find_package(spdlog 1.8.5 QUIET)
 
 include(boost_package)
 
@@ -153,6 +154,7 @@ mark_as_advanced(
   GIT
   Forge_DIR
   glad_DIR
+  spdlog_DIR
   FG_BUILD_OFFLINE
   )
 mark_as_advanced(CLEAR CUDA_VERSION)
@@ -182,13 +184,21 @@ endif()
 #forge is included in ALL target if AF_BUILD_FORGE is ON
 #otherwise, forge is not built at all
 include(AFconfigure_forge_dep)
-FetchContent_Declare(
-  ${spdlog_prefix}
-  GIT_REPOSITORY https://github.com/gabime/spdlog.git
-  GIT_TAG        v1.0.0
-)
-af_dep_check_and_populate(${spdlog_prefix})
-
+add_library(af_spdlog INTERFACE)
+if(TARGET spdlog::spdlog_header_only)
+  target_include_directories(af_spdlog
+    SYSTEM INTERFACE
+    $<TARGET_PROPERTY:spdlog::spdlog_header_only,INTERFACE_INCLUDE_DIRECTORIES>
+    )
+else()
+  FetchContent_Declare(
+    ${spdlog_prefix}
+    GIT_REPOSITORY https://github.com/gabime/spdlog.git
+    GIT_TAG        v1.8.5
+  )
+  af_dep_check_and_populate(${spdlog_prefix})
+  target_include_directories(af_spdlog INTERFACE "${${spdlog_prefix}_SOURCE_DIR}/include")
+endif()
 
 if(NOT TARGET glad::glad)
   FetchContent_Declare(
@@ -220,9 +230,6 @@ configure_file(
     ${ArrayFire_BINARY_DIR}/version.hpp
 )
 
-set(SPDLOG_BUILD_TESTING OFF CACHE INTERNAL "Disable testing in spdlog")
-add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
-
 # when crosscompiling use the bin2cpp file from the native bin directory
 if(CMAKE_CROSSCOMPILING)
   set(NATIVE_BIN_DIR "NATIVE_BIN_DIR-NOTFOUND"
@@ -247,7 +254,11 @@ else()
                              ${ArrayFire_SOURCE_DIR}/include
                              ${ArrayFire_BINARY_DIR}/include
                              ${ArrayFire_SOURCE_DIR}/src/backend)
-  target_link_libraries(bin2cpp PRIVATE spdlog)
+  if(TARGET spdlog::spdlog_header_only)
+    target_link_libraries(bin2cpp PRIVATE spdlog::spdlog_header_only)
+  else()
+    target_link_libraries(bin2cpp PRIVATE af_spdlog)
+  endif()
   export(TARGETS bin2cpp FILE ${CMAKE_BINARY_DIR}/ImportExecutables.cmake)
 endif()
 
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index b4204928b8..cc08659976 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -100,8 +100,8 @@ target_include_directories(af
 
 target_link_libraries(af
   PRIVATE
+    af_spdlog
     cpp_api_interface
-    spdlog
     Threads::Threads
     Boost::boost
     ${CMAKE_DL_LIBS}
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 41b4196474..61c2290f29 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -79,7 +79,7 @@ endif()
 
 target_link_libraries(afcommon_interface
   INTERFACE
-    spdlog
+    af_spdlog
     Boost::boost
     ${CMAKE_DL_LIBS}
 )
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f454fa532e..f874fd1ec3 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -113,6 +113,7 @@ cuda_include_directories(
   ${ArrayFire_SOURCE_DIR}/src/api/c
   ${ArrayFire_SOURCE_DIR}/src/backend
   ${COMMON_INTERFACE_DIRS}
+  $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>
   )
 if(CUDA_VERSION_MAJOR VERSION_LESS 11)
   FetchContent_Declare(
@@ -323,6 +324,7 @@ if(UNIX)
 
   target_link_libraries(af_cuda_static_cuda_library
     PRIVATE
+      af_spdlog
       Boost::boost
       ${CMAKE_DL_LIBS}
       ${cusolver_lib}
@@ -338,7 +340,6 @@ if(UNIX)
   if(CUDA_VERSION VERSION_GREATER 10.0)
     target_link_libraries(af_cuda_static_cuda_library
       PRIVATE
-        spdlog
         ${CUDA_cublasLt_static_LIBRARY})
   endif()
   if(CUDA_VERSION VERSION_GREATER 9.5)
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index cb06a2ce84..6add18a881 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -36,7 +36,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         ../common
         ../../../include
         ${CMAKE_CURRENT_BINARY_DIR}
-        $<TARGET_PROPERTY:spdlog,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
diff --git a/vcpkg.json b/vcpkg.json
index 1104d55800..020c25131f 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -19,7 +19,18 @@
             "platform": "!windows"
         },
         "glad",
-        "intel-mkl"
+        "intel-mkl",
+        "spdlog"
+    ],
+    "overrides": [
+       {
+           "name": "fmt",
+           "version": "6.2.1"
+       },
+        {
+            "name": "spdlog",
+            "version": "1.6.1"
+        }
     ],
     "features": {
         "cuda": {

From a9338f8422c4a558031024b4f61758fb807d8896 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 14:12:28 -0400
Subject: [PATCH 2168/2677] Fix bug in getMappedPtr in OpenCL due to invalid
 lambda capture

This commit fixes a bug that was caused by an invalid capture of
the Array class in the destructor of the mapped_ptr function. This
caused intermittent errors when using the getMappedPtr function.
---
 src/backend/opencl/Array.hpp | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index fded4eca2e..1c1cc0dd99 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -21,7 +21,10 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
+#include <algorithm>
+#include <cstdlib>
 #include <memory>
+#include <vector>
 
 namespace opencl {
 typedef std::shared_ptr<cl::Buffer> Buffer_ptr;
@@ -258,7 +261,7 @@ class Array {
    public:
     mapped_ptr<T> getMappedPtr(cl_map_flags map_flags = CL_MAP_READ |
                                                         CL_MAP_WRITE) const {
-        auto func = [this](void *ptr) {
+        auto func = [data = data](void *ptr) {
             if (ptr != nullptr) {
                 cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);
                 UNUSED(err);
@@ -266,14 +269,10 @@ class Array {
             }
         };
 
-        T *ptr = nullptr;
-        if (ptr == nullptr) {
-            cl_int err;
-            ptr = (T *)getQueue().enqueueMapBuffer(
-                *const_cast<cl::Buffer *>(get()), CL_TRUE, map_flags,
-                getOffset() * sizeof(T), elements() * sizeof(T), nullptr,
-                nullptr, &err);
-        }
+        T *ptr = (T *)getQueue().enqueueMapBuffer(
+            *static_cast<const cl::Buffer *>(get()), CL_TRUE, map_flags,
+            getOffset() * sizeof(T), elements() * sizeof(T), nullptr, nullptr,
+            nullptr);
 
         return mapped_ptr<T>(ptr, func);
     }

From 3ff9b242d6f48f088f756b242a364c378cb353e7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 15:15:07 -0400
Subject: [PATCH 2169/2677] Fix bug in getMappedPtr on Arrays that are not
 ready

Fixes a bug in getMappedPtr where the Array object was not ready
and needed to be evaluated when the map function was called. This
appeared when the LHS or the RHS of the matmul function were
JIT nodes and were sparse Arrays.
---
 src/backend/opencl/Array.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 1c1cc0dd99..2ea9d85a53 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -261,6 +261,7 @@ class Array {
    public:
     mapped_ptr<T> getMappedPtr(cl_map_flags map_flags = CL_MAP_READ |
                                                         CL_MAP_WRITE) const {
+        if (!isReady()) eval();
         auto func = [data = data](void *ptr) {
             if (ptr != nullptr) {
                 cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);

From bd2b137d5f2eaa50abd96574ff61e3196b656fe5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Jul 2021 11:14:44 -0400
Subject: [PATCH 2170/2677] cleanup namespaces in platform

---
 src/backend/cpu/platform.cpp    | 11 ++---
 src/backend/cuda/platform.cpp   | 79 ++++++++++++++-------------------
 src/backend/opencl/platform.cpp | 53 +++++++++++-----------
 3 files changed, 67 insertions(+), 76 deletions(-)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 2b5b91a718..179ff7a659 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -15,10 +15,11 @@
 #include <version.hpp>
 #include <af/version.h>
 
-#include <algorithm>
 #include <cctype>
+#include <cstdio>
 #include <memory>
 #include <sstream>
+#include <string>
 
 using common::memory::MemoryManagerBase;
 using std::endl;
@@ -110,7 +111,7 @@ int& getMaxJitSize() {
     if (length <= 0) {
         string env_var = getEnvVar("AF_CPU_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            int input_len = std::stoi(env_var);
+            int input_len = stoi(env_var);
             length        = input_len > 0 ? input_len : MAX_JIT_LEN;
         } else {
             length = MAX_JIT_LEN;
@@ -161,15 +162,15 @@ MemoryManagerBase& memoryManager() {
 }
 
 void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
-    return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
+    return DeviceManager::getInstance().setMemoryManager(move(mgr));
 }
 
 void resetMemoryManager() {
     return DeviceManager::getInstance().resetMemoryManager();
 }
 
-void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr) {
-    return DeviceManager::getInstance().setMemoryManagerPinned(std::move(mgr));
+void setMemoryManagerPinned(unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManagerPinned(move(mgr));
 }
 
 void resetMemoryManagerPinned() {
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index ee5776d057..dd715e4691 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -40,18 +40,17 @@
 #include <af/device.h>
 #include <af/version.h>
 
-#include <algorithm>
 #include <array>
-#include <cstdio>
+#include <cstdlib>
 #include <memory>
 #include <mutex>
 #include <sstream>
 #include <stdexcept>
 #include <string>
 #include <thread>
-#include <vector>
 
 using std::call_once;
+using std::make_unique;
 using std::once_flag;
 using std::ostringstream;
 using std::runtime_error;
@@ -61,11 +60,13 @@ using std::unique_ptr;
 
 using common::unique_handle;
 using common::memory::MemoryManagerBase;
+using cuda::Allocator;
+using cuda::AllocatorPinned;
 
 namespace cuda {
 
-static std::string get_system() {
-    std::string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
+static string get_system() {
+    string arch = (sizeof(void *) == 4) ? "32-bit " : "64-bit ";
 
     return arch +
 #if defined(OS_LNX)
@@ -77,17 +78,6 @@ static std::string get_system() {
 #endif
 }
 
-static inline int getMinSupportedCompute(int cudaMajorVer) {
-    // Vector of minimum supported compute versions
-    // for CUDA toolkit (i+1).* where i is the index
-    // of the vector
-    static const std::array<int, 10> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3}};
-
-    int CVSize = static_cast<int>(minSV.size());
-    return (cudaMajorVer > CVSize ? minSV[CVSize - 1]
-                                  : minSV[cudaMajorVer - 1]);
-}
-
 unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
     thread_local unique_handle<cublasHandle_t>
         handles[DeviceManager::MAX_DEVICES];
@@ -109,11 +99,11 @@ unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
 unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
     thread_local unique_handle<cudnnHandle_t>
         cudnnHandles[DeviceManager::MAX_DEVICES];
-    thread_local std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     auto *handle        = &cudnnHandles[deviceId];
     cudnnStatus_t error = CUDNN_STATUS_SUCCESS;
-    std::call_once(initFlags[deviceId], [deviceId, handle, &error] {
+    call_once(initFlags[deviceId], [handle, &error] {
         auto getLogger = [&] { return spdlog::get("platform"); };
         AF_TRACE("Initializing cuDNN");
         error = static_cast<cudnnStatus_t>(handle->create());
@@ -138,7 +128,7 @@ unique_ptr<PlanCache> &cufftManager(const int deviceId) {
     thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId],
-              [&] { caches[deviceId] = std::make_unique<PlanCache>(); });
+              [&] { caches[deviceId] = make_unique<PlanCache>(); });
     return caches[deviceId];
 }
 
@@ -234,7 +224,7 @@ string getDeviceInfo(int device) noexcept {
 string getDeviceInfo() noexcept {
     ostringstream info;
     info << "ArrayFire v" << AF_VERSION << " (CUDA, " << get_system()
-         << ", build " << AF_REVISION << ")" << std::endl;
+         << ", build " << AF_REVISION << ")\n";
     info << getPlatformInfo();
     for (int i = 0; i < getDeviceCount(); ++i) { info << getDeviceInfo(i); }
     return info.str();
@@ -280,7 +270,7 @@ void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
     snprintf(d_name, 256, "%s", dev.name);
 
     // Platform
-    std::string cudaRuntime = getCUDARuntimeVersion();
+    string cudaRuntime = getCUDARuntimeVersion();
     snprintf(d_platform, 10, "CUDA");
     snprintf(d_toolkit, 64, "v%s", cudaRuntime.c_str());
 
@@ -329,9 +319,9 @@ int &getMaxJitSize() {
     constexpr int MAX_JIT_LEN = 100;
     thread_local int length   = 0;
     if (length <= 0) {
-        std::string env_var = getEnvVar("AF_CUDA_MAX_JIT_LEN");
+        string env_var = getEnvVar("AF_CUDA_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            int input_len = std::stoi(env_var);
+            int input_len = stoi(env_var);
             length        = input_len > 0 ? input_len : MAX_JIT_LEN;
         } else {
             length = MAX_JIT_LEN;
@@ -377,9 +367,9 @@ int getDeviceIdFromNativeId(int nativeId) {
 }
 
 cudaStream_t getStream(int device) {
-    static std::once_flag streamInitFlags[DeviceManager::MAX_DEVICES];
+    static once_flag streamInitFlags[DeviceManager::MAX_DEVICES];
 
-    std::call_once(streamInitFlags[device], [device]() {
+    call_once(streamInitFlags[device], [device]() {
         DeviceManager &inst = DeviceManager::getInstance();
         CUDA_CHECK(cudaStreamCreate(&(inst.streams[device])));
     });
@@ -408,19 +398,18 @@ cudaDeviceProp getDeviceProp(int device) {
 }
 
 MemoryManagerBase &memoryManager() {
-    static std::once_flag flag;
+    static once_flag flag;
 
     DeviceManager &inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
+    call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.memManager = std::make_unique<common::DefaultMemoryManager>(
+        inst.memManager = make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
             AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG);
         // Set the memory manager's device memory manager
-        std::unique_ptr<cuda::Allocator> deviceMemoryManager(
-            new cuda::Allocator());
-        inst.memManager->setAllocator(std::move(deviceMemoryManager));
+        unique_ptr<Allocator> deviceMemoryManager(new Allocator());
+        inst.memManager->setAllocator(move(deviceMemoryManager));
         inst.memManager->initialize();
     });
 
@@ -428,35 +417,33 @@ MemoryManagerBase &memoryManager() {
 }
 
 MemoryManagerBase &pinnedMemoryManager() {
-    static std::once_flag flag;
+    static once_flag flag;
 
     DeviceManager &inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
+    call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.pinnedMemManager = std::make_unique<common::DefaultMemoryManager>(
-            getDeviceCount(), common::MAX_BUFFERS,
-            AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG);
+        inst.pinnedMemManager = make_unique<common::DefaultMemoryManager>(
+            1, common::MAX_BUFFERS, AF_MEM_DEBUG || AF_CUDA_MEM_DEBUG);
         // Set the memory manager's device memory manager
-        std::unique_ptr<cuda::AllocatorPinned> deviceMemoryManager(
-            new cuda::AllocatorPinned());
-        inst.pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+        unique_ptr<AllocatorPinned> deviceMemoryManager(new AllocatorPinned());
+        inst.pinnedMemManager->setAllocator(move(deviceMemoryManager));
         inst.pinnedMemManager->initialize();
     });
 
     return *(inst.pinnedMemManager.get());
 }
 
-void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr) {
-    return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
+void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManager(move(mgr));
 }
 
 void resetMemoryManager() {
     return DeviceManager::getInstance().resetMemoryManager();
 }
 
-void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr) {
-    return DeviceManager::getInstance().setMemoryManagerPinned(std::move(mgr));
+void setMemoryManagerPinned(unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManagerPinned(move(mgr));
 }
 
 void resetMemoryManagerPinned() {
@@ -468,14 +455,14 @@ graphics::ForgeManager &forgeManager() {
 }
 
 GraphicsResourceManager &interopManager() {
-    static std::once_flag initFlags[DeviceManager::MAX_DEVICES];
+    static once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     int id = getActiveDeviceId();
 
     DeviceManager &inst = DeviceManager::getInstance();
 
-    std::call_once(initFlags[id], [&] {
-        inst.gfxManagers[id] = std::make_unique<GraphicsResourceManager>();
+    call_once(initFlags[id], [&] {
+        inst.gfxManagers[id] = make_unique<GraphicsResourceManager>();
     });
 
     return *(inst.gfxManagers[id].get());
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index f06f446004..94706135ea 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -32,9 +32,8 @@
 #include <boost/compute/context.hpp>
 #include <boost/compute/utility/program_cache.hpp>
 
-#include <algorithm>
 #include <cctype>
-#include <cstring>
+#include <cstdlib>
 #include <functional>
 #include <map>
 #include <mutex>
@@ -57,15 +56,19 @@ using std::get;
 using std::make_pair;
 using std::make_unique;
 using std::map;
+using std::move;
 using std::once_flag;
 using std::ostringstream;
 using std::pair;
 using std::ptr_fun;
 using std::string;
 using std::to_string;
+using std::unique_ptr;
 using std::vector;
 
 using common::memory::MemoryManagerBase;
+using opencl::Allocator;
+using opencl::AllocatorPinned;
 
 namespace opencl {
 
@@ -92,12 +95,12 @@ static inline string& ltrim(string& s) {
     return s;
 }
 
-bool verify_present(const std::string& pname, const std::string ref) {
-    auto iter = std::search(
-        begin(pname), end(pname), std::begin(ref), std::end(ref),
-        [](const std::string::value_type& l, const std::string::value_type& r) {
-            return tolower(l) == tolower(r);
-        });
+bool verify_present(const string& pname, const string ref) {
+    auto iter =
+        search(begin(pname), end(pname), begin(ref), end(ref),
+               [](const string::value_type& l, const string::value_type& r) {
+                   return tolower(l) == tolower(r);
+               });
 
     return iter != end(pname);
 }
@@ -124,7 +127,7 @@ static string platformMap(string& platStr) {
 }
 
 afcl::platform getPlatformEnum(cl::Device dev) {
-    std::string pname = getPlatformName(dev);
+    string pname = getPlatformName(dev);
     if (verify_present(pname, "AMD"))
         return AFCL_PLATFORM_AMD;
     else if (verify_present(pname, "NVIDIA"))
@@ -581,7 +584,7 @@ int& getMaxJitSize() {
     if (length <= 0) {
         string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
         if (!env_var.empty()) {
-            int input_len = std::stoi(env_var);
+            int input_len = stoi(env_var);
             length        = input_len > 0 ? input_len : MAX_JIT_LEN;
         } else {
             length = MAX_JIT_LEN;
@@ -600,15 +603,15 @@ MemoryManagerBase& memoryManager() {
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
+    call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.memManager = std::make_unique<common::DefaultMemoryManager>(
+        inst.memManager = make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
             AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG);
         // Set the memory manager's device memory manager
-        std::unique_ptr<opencl::Allocator> deviceMemoryManager;
-        deviceMemoryManager = std::make_unique<opencl::Allocator>();
-        inst.memManager->setAllocator(std::move(deviceMemoryManager));
+        unique_ptr<Allocator> deviceMemoryManager;
+        deviceMemoryManager = make_unique<Allocator>();
+        inst.memManager->setAllocator(move(deviceMemoryManager));
         inst.memManager->initialize();
     });
 
@@ -620,31 +623,31 @@ MemoryManagerBase& pinnedMemoryManager() {
 
     DeviceManager& inst = DeviceManager::getInstance();
 
-    std::call_once(flag, [&]() {
+    call_once(flag, [&]() {
         // By default, create an instance of the default memory manager
-        inst.pinnedMemManager = std::make_unique<common::DefaultMemoryManager>(
+        inst.pinnedMemManager = make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
             AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG);
         // Set the memory manager's device memory manager
-        std::unique_ptr<opencl::AllocatorPinned> deviceMemoryManager;
-        deviceMemoryManager = std::make_unique<opencl::AllocatorPinned>();
-        inst.pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+        unique_ptr<AllocatorPinned> deviceMemoryManager;
+        deviceMemoryManager = make_unique<AllocatorPinned>();
+        inst.pinnedMemManager->setAllocator(move(deviceMemoryManager));
         inst.pinnedMemManager->initialize();
     });
 
     return *(inst.pinnedMemManager.get());
 }
 
-void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr) {
-    return DeviceManager::getInstance().setMemoryManager(std::move(mgr));
+void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManager(move(mgr));
 }
 
 void resetMemoryManager() {
     return DeviceManager::getInstance().resetMemoryManager();
 }
 
-void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr) {
-    return DeviceManager::getInstance().setMemoryManagerPinned(std::move(mgr));
+void setMemoryManagerPinned(unique_ptr<MemoryManagerBase> mgr) {
+    return DeviceManager::getInstance().setMemoryManagerPinned(move(mgr));
 }
 
 void resetMemoryManagerPinned() {
@@ -663,7 +666,7 @@ GraphicsResourceManager& interopManager() {
     DeviceManager& inst = DeviceManager::getInstance();
 
     call_once(initFlags[id], [&] {
-        inst.gfxManagers[id] = std::make_unique<GraphicsResourceManager>();
+        inst.gfxManagers[id] = make_unique<GraphicsResourceManager>();
     });
 
     return *(inst.gfxManagers[id].get());

From 17b1f363e2f141d5447011ab6443e082dce7e2f5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Jul 2021 11:15:39 -0400
Subject: [PATCH 2171/2677] Add additional logging in memory manager

---
 src/backend/common/DefaultMemoryManager.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 65ed9dc191..3ac5ab7324 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -16,6 +16,8 @@
 #include <af/event.h>
 #include <af/memory.h>
 
+#include <algorithm>
+#include <cstdio>
 #include <memory>
 #include <string>
 #include <vector>
@@ -121,6 +123,8 @@ void DefaultMemoryManager::setMaxMemorySize() {
             memsize == 0
                 ? ONE_GB
                 : max(memsize * 0.75, static_cast<double>(memsize - ONE_GB));
+        AF_TRACE("memory[{}].max_bytes: {}", n,
+                 bytesToString(memory[n].max_bytes));
     }
 }
 
@@ -161,6 +165,13 @@ void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,
             // Perhaps look at total memory available as a metric
             if (current.lock_bytes >= current.max_bytes ||
                 current.total_buffers >= this->max_buffers) {
+                AF_TRACE(
+                    "Running GC: current.lock_bytes({}) >= "
+                    "current.max_bytes({}) || current.total_buffers({}) >= "
+                    "this->max_buffers({})\n",
+                    current.lock_bytes, current.max_bytes,
+                    current.total_buffers, this->max_buffers);
+
                 this->signalMemoryCleanup();
             }
 

From 974f83dc56aea92f682d2378587788e350d7a8f9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Jul 2021 11:15:58 -0400
Subject: [PATCH 2172/2677] Fix doxygen warning by remove COLS_IN_ALPHA_INDEX

---
 docs/doxygen.mk | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 7994a8a315..b9bfa4158e 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1087,13 +1087,6 @@ VERBATIM_HEADERS       = YES
 
 ALPHABETICAL_INDEX     = YES
 
-# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
-# which the alphabetical index list will be split.
-# Minimum value: 1, maximum value: 20, default value: 5.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-COLS_IN_ALPHA_INDEX    = 5
-
 # In case all classes in a project start with a common prefix, all classes will
 # be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
 # can be used to specify a prefix (or a list of prefixes) that should be ignored

From 40bcd5a16b89d08aadb3045b21c2bbff38cd960c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Jul 2021 10:34:37 -0400
Subject: [PATCH 2173/2677] Update CUDA driver checks for 11.4

---
 src/backend/cuda/device_manager.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 37e4dd7f67..c718bc72af 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -97,6 +97,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11040, 8, 6, 0},
     {11030, 8, 6, 0},
     {11020, 8, 6, 0},
     {11010, 8, 6, 0},
@@ -118,6 +119,7 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11040, 470.42f, 471.11f},
         {11030, 465.19f, 465.89f},
         {11020, 460.27f, 460.82f},
         {11010, 455.23f, 456.38f},
@@ -313,10 +315,9 @@ static inline bool card_compare_num(const cudaDevice_t &l,
 }
 
 static inline int getMinSupportedCompute(int cudaMajorVer) {
-    // Vector of minimum supported compute versions
-    // for CUDA toolkit (i+1).* where i is the index
-    // of the vector
-    static const std::array<int, 10> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3}};
+    // Vector of minimum supported compute versions for CUDA toolkit (i+1).*
+    // where i is the index of the vector
+    static const std::array<int, 11> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3}};
 
     int CVSize = static_cast<int>(minSV.size());
     return (cudaMajorVer > CVSize ? minSV[CVSize - 1]

From 13959d41e451279514dacbf1bf191f8b9a0f9556 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Jul 2021 11:08:50 -0400
Subject: [PATCH 2174/2677] Move CUDA check structs and functions closer
 together

---
 src/backend/cuda/device_manager.cpp | 72 ++++++++++++++++-------------
 1 file changed, 39 insertions(+), 33 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index c718bc72af..1a994424e6 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -38,7 +38,6 @@
 
 #include <algorithm>
 #include <array>
-#include <cstdio>
 #include <memory>
 #include <mutex>
 #include <sstream>
@@ -46,7 +45,6 @@
 #include <string>
 #include <thread>
 #include <utility>
-#include <vector>
 
 using std::begin;
 using std::end;
@@ -113,6 +111,16 @@ static const cuNVRTCcompute Toolkit2MaxCompute[] = {
     { 7000, 5, 2, 3}};
 // clang-format on
 
+// A tuple of Compute Capability and the associated number of cores in each
+// streaming multiprocessors for that architecture
+struct ComputeCapabilityToStreamingProcessors {
+    // The compute capability in hex
+    // 0xMm (hex), M = major version, m = minor version
+    int compute_capability;
+    // Number of CUDA cores per SM
+    int cores_per_sm;
+};
+
 /// Map giving the minimum device driver needed in order to run a given version
 /// of CUDA for both Linux/Mac and Windows from:
 /// https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
@@ -135,6 +143,35 @@ static const ToolkitDriverVersions
         {7000,  346.46f, 347.62f}};
 // clang-format on
 
+// Vector of minimum supported compute versions for CUDA toolkit (i+1).*
+// where i is the index of the vector
+static const std::array<int, 11> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3}};
+
+static ComputeCapabilityToStreamingProcessors gpus[] = {
+    {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
+    {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
+    {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
+    {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {0x86, 128},
+    {-1, -1},
+};
+
+// pulled from CUTIL from CUDA SDK
+static inline int compute2cores(unsigned major, unsigned minor) {
+    for (int i = 0; gpus[i].compute_capability != -1; ++i) {
+        if (static_cast<unsigned>(gpus[i].compute_capability) ==
+            (major << 4U) + minor) {
+            return gpus[i].cores_per_sm;
+        }
+    }
+    return 0;
+}
+
+static inline int getMinSupportedCompute(int cudaMajorVer) {
+    int CVSize = static_cast<int>(minSV.size());
+    return (cudaMajorVer > CVSize ? minSV[CVSize - 1]
+                                  : minSV[cudaMajorVer - 1]);
+}
+
 bool isEmbedded(pair<int, int> compute) {
     int version = compute.first * 1000 + compute.second * 10;
     return end(jetsonComputeCapabilities) !=
@@ -236,27 +273,6 @@ pair<int, int> getComputeCapability(const int device) {
     return DeviceManager::getInstance().devJitComputes[device];
 }
 
-// pulled from CUTIL from CUDA SDK
-static inline int compute2cores(unsigned major, unsigned minor) {
-    struct {
-        int compute;  // 0xMm (hex), M = major version, m = minor version
-        int cores;
-    } gpus[] = {
-        {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
-        {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
-        {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
-        {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {0x86, 128},
-        {-1, -1},
-    };
-
-    for (int i = 0; gpus[i].compute != -1; ++i) {
-        if (static_cast<unsigned>(gpus[i].compute) == (major << 4U) + minor) {
-            return gpus[i].cores;
-        }
-    }
-    return 0;
-}
-
 // Return true if greater, false if lesser.
 // if equal, it continues to next comparison
 #define COMPARE(a, b, f)                   \
@@ -314,16 +330,6 @@ static inline bool card_compare_num(const cudaDevice_t &l,
     return false;
 }
 
-static inline int getMinSupportedCompute(int cudaMajorVer) {
-    // Vector of minimum supported compute versions for CUDA toolkit (i+1).*
-    // where i is the index of the vector
-    static const std::array<int, 11> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3}};
-
-    int CVSize = static_cast<int>(minSV.size());
-    return (cudaMajorVer > CVSize ? minSV[CVSize - 1]
-                                  : minSV[cudaMajorVer - 1]);
-}
-
 bool DeviceManager::checkGraphicsInteropCapability() {
     static std::once_flag checkInteropFlag;
     thread_local bool capable = true;

From 8c635962bb9609a831cdbca9caf618f143c923c9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 19:15:45 -0400
Subject: [PATCH 2175/2677] Fix the edgeTraceKernel for CPU devices

The barrier in the while loop is necessary for Intel CPUs and maybe other
platforms to work correctly. I am not sure why it is required because we seem to
be performing sufficient synchronization otherwise.
---
 src/backend/opencl/kernel/trace_edge.cl | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
index d92e95a117..40eda6cf29 100644
--- a/src/backend/opencl/kernel/trace_edge.cl
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -7,9 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-__constant int STRONG = 1;
-__constant int WEAK   = 2;
-__constant int NOEDGE = 0;
+#define STRONG 1
+#define WEAK 2
+#define NOEDGE 0
 
 #if defined(INIT_EDGE_OUT)
 kernel void initEdgeOutKernel(global T* output, KParam oInfo,
@@ -154,7 +154,10 @@ kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
         }
 
         continueIter = predicates[0];
-    };
+
+        // Needed for Intel OpenCL implementation targeting CPUs
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
 
     // Check if any 1-pixel border ring
     // has weak pixels with strong candidates

From 57a3247a78ddf229c1c6cab62e2c28b65f9647bb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 19:17:56 -0400
Subject: [PATCH 2176/2677] Formatting changes accompanying the edgeTraceKernel
 changes

---
 src/backend/opencl/kernel/trace_edge.cl | 35 ++++++++++++-------------
 1 file changed, 17 insertions(+), 18 deletions(-)

diff --git a/src/backend/opencl/kernel/trace_edge.cl b/src/backend/opencl/kernel/trace_edge.cl
index 40eda6cf29..5291b0158c 100644
--- a/src/backend/opencl/kernel/trace_edge.cl
+++ b/src/backend/opencl/kernel/trace_edge.cl
@@ -13,9 +13,9 @@
 
 #if defined(INIT_EDGE_OUT)
 kernel void initEdgeOutKernel(global T* output, KParam oInfo,
-                                global const T* strong, KParam sInfo,
-                                global const T* weak, KParam wInfo,
-                                unsigned nBBS0, unsigned nBBS1) {
+                              global const T* strong, KParam sInfo,
+                              global const T* weak, KParam wInfo,
+                              unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = get_group_id(0) / nBBS0;
     const unsigned b3 = get_group_id(1) / nBBS1;
@@ -55,8 +55,7 @@ kernel void initEdgeOutKernel(global T* output, KParam oInfo,
 
 #if defined(EDGE_TRACER)
 kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
-                              unsigned nBBS1,
-                              global volatile int* hasChanged) {
+                            unsigned nBBS1, global volatile int* hasChanged) {
     // shared memory with 1 pixel border
     // strong and weak images are binary(char) images thus,
     // occupying only (16+2)*(16+2) = 324 bytes per shared memory tile
@@ -102,13 +101,11 @@ kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
 
     int tid = lx + get_local_size(0) * ly;
 
-    bool continueIter = 1;
+    bool continueIter = true;
 
-    int mycounter = 0;
     while (continueIter) {
-        int nw, no, ne, we, ea, sw, so, se;
-
         if (outMem[j][i] == WEAK) {
+            int nw, no, ne, we, ea, sw, so, se;
             nw = outMem[j - 1][i - 1];
             no = outMem[j - 1][i];
             ne = outMem[j - 1][i + 1];
@@ -129,14 +126,17 @@ kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
 
         predicates[tid] = false;
         if (outMem[j][i] == STRONG) {
+            bool nw, no, ne, we, ea, sw, so, se;
+            // clang-format off
             nw = outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
-            no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+            no = outMem[j - 1][i]     == WEAK && VALID_BLOCK_IDX(j - 1, i);
             ne = outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
-            we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
-            ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+            we = outMem[j][i - 1]     == WEAK && VALID_BLOCK_IDX(j, i - 1);
+            ea = outMem[j][i + 1]     == WEAK && VALID_BLOCK_IDX(j, i + 1);
             sw = outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
-            so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+            so = outMem[j + 1][i]     == WEAK && VALID_BLOCK_IDX(j + 1, i);
             se = outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
+            // clang-format on
 
             bool hasWeakNeighbour =
                 nw || no || ne || ea || se || so || sw || we;
@@ -146,7 +146,7 @@ kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
         barrier(CLK_LOCAL_MEM_FENCE);
 
         // Following Block is equivalent of __syncthreads_or in CUDA
-        for (int nt = TOTAL_NUM_THREADS / 2; nt > 0; nt >>= 1) {
+        for (int nt = TOTAL_NUM_THREADS >> 1; nt > 0; nt >>= 1) {
             if (tid < nt) {
                 predicates[tid] = predicates[tid] || predicates[tid + nt];
             }
@@ -198,7 +198,7 @@ kernel void edgeTrackKernel(global T* output, KParam oInfo, unsigned nBBS0,
 
 #if defined(SUPPRESS_LEFT_OVER)
 kernel void suppressLeftOverKernel(global T* output, KParam oInfo,
-                                     unsigned nBBS0, unsigned nBBS1) {
+                                   unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = get_group_id(0) / nBBS0;
     const unsigned b3 = get_group_id(1) / nBBS1;
@@ -211,9 +211,8 @@ kernel void suppressLeftOverKernel(global T* output, KParam oInfo,
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    global T* oPtr = output +
-                       (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
-                       oInfo.strides[1] + 1;
+    global T* oPtr = output + (b2 * oInfo.strides[2] + b3 * oInfo.strides[3]) +
+                     oInfo.strides[1] + 1;
 
     if (gx < (oInfo.dims[0] - 2) && gy < (oInfo.dims[1] - 2)) {
         int idx = gx * oInfo.strides[0] + gy * oInfo.strides[1];

From 6633e108fc4b2f201d032f092e1d5845951c2ad5 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Fri, 30 Jul 2021 09:15:45 +0530
Subject: [PATCH 2177/2677] Move array death test into a separate serially
 executed test

Death tests are known to have issues when threads are involved. A better
explanation is provided as part of google-test documentation at the
below link.

https://github.com/google/googletest/blob/master/docs/advanced.md#death-tests-and-threads
---
 test/CMakeLists.txt        |  1 +
 test/array.cpp             | 37 ----------------------
 test/array_death_tests.cpp | 63 ++++++++++++++++++++++++++++++++++++++
 3 files changed, 64 insertions(+), 37 deletions(-)
 create mode 100644 test/array_death_tests.cpp

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index cb9dde8e76..5aec753c08 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -238,6 +238,7 @@ make_test(SRC anisotropic_diffusion.cpp)
 make_test(SRC approx1.cpp)
 make_test(SRC approx2.cpp)
 make_test(SRC array.cpp CXX11)
+make_test(SRC array_death_tests.cpp CXX11 SERIAL)
 make_test(SRC arrayio.cpp)
 make_test(SRC assign.cpp CXX11)
 make_test(SRC backend.cpp CXX11)
diff --git a/test/array.cpp b/test/array.cpp
index fca8830589..526ca40224 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -20,9 +20,6 @@ using std::vector;
 template<typename T>
 class Array : public ::testing::Test {};
 
-template<typename T>
-using ArrayDeathTest = Array<T>;
-
 typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
                          int, uint, intl, uintl, short, ushort,
                          half_float::half>
@@ -531,40 +528,6 @@ TEST(Array, ScalarTypeMismatch) {
     EXPECT_THROW(a.scalar<int>(), exception);
 }
 
-void deathTest() {
-    info();
-    setDevice(0);
-
-    array A = randu(5, 3, f32);
-
-    array B = sin(A) + 1.5;
-
-    B(seq(0, 2), 1) = B(seq(0, 2), 1) * -1;
-
-    array C = fft(B);
-
-    array c = C.row(end);
-
-    dim4 dims(16, 4, 1, 1);
-    array r = constant(2, dims);
-
-    array S = scan(r, 0, AF_BINARY_MUL);
-
-    float d[] = {1, 2, 3, 4, 5, 6};
-    array D(2, 3, d, afHost);
-
-    D.col(0) = D.col(end);
-
-    array vals, inds;
-    sort(vals, inds, A);
-
-    _exit(0);
-}
-
-TEST(ArrayDeathTest, ProxyMoveAssignmentOperator) {
-    EXPECT_EXIT(deathTest(), ::testing::ExitedWithCode(0), "");
-}
-
 TEST(Array, CopyListInitializerList) {
     int h_buffer[] = {23, 34, 18, 99, 34};
 
diff --git a/test/array_death_tests.cpp b/test/array_death_tests.cpp
new file mode 100644
index 0000000000..9c2868da4a
--- /dev/null
+++ b/test/array_death_tests.cpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+
+#include <cstdlib>
+
+using af::array;
+using af::constant;
+using af::dim4;
+using af::end;
+using af::fft;
+using af::info;
+using af::randu;
+using af::scan;
+using af::seq;
+using af::setDevice;
+using af::sin;
+using af::sort;
+
+template<typename T>
+class ArrayDeathTest : public ::testing::Test {};
+
+void deathTest() {
+    info();
+    setDevice(0);
+
+    array A = randu(5, 3, f32);
+
+    array B = sin(A) + 1.5;
+
+    B(seq(0, 2), 1) = B(seq(0, 2), 1) * -1;
+
+    array C = fft(B);
+
+    array c = C.row(end);
+
+    dim4 dims(16, 4, 1, 1);
+    array r = constant(2, dims);
+
+    array S = scan(r, 0, AF_BINARY_MUL);
+
+    float d[] = {1, 2, 3, 4, 5, 6};
+    array D(2, 3, d, afHost);
+
+    D.col(0) = D.col(end);
+
+    array vals, inds;
+    sort(vals, inds, A);
+
+    _exit(0);
+}
+
+TEST(ArrayDeathTest, ProxyMoveAssignmentOperator) {
+    EXPECT_EXIT(deathTest(), ::testing::ExitedWithCode(0), "");
+}

From 0d9ffc2a7e82763c713ff589b546e1d962bbbc99 Mon Sep 17 00:00:00 2001
From: Pavan Yalamanchili <pavan@arrayfire.com>
Date: Sun, 8 Jan 2017 22:18:58 -0800
Subject: [PATCH 2178/2677] Adding support for batched solve in CUDA backend

---
 src/api/c/solve.cpp       |   4 --
 src/backend/cuda/solve.cu | 136 +++++++++++++++++++++++++++++++++++++-
 2 files changed, 135 insertions(+), 5 deletions(-)

diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index 6328e90f01..ec17aafaba 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -34,10 +34,6 @@ af_err af_solve(af_array* out, const af_array a, const af_array b,
         const ArrayInfo& a_info = getInfo(a);
         const ArrayInfo& b_info = getInfo(b);
 
-        if (a_info.ndims() > 2 || b_info.ndims() > 2) {
-            AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
-        }
-
         af_dtype a_type = a_info.getType();
         af_dtype b_type = b_info.getType();
 
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 92cdb64b2e..988061ba12 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -12,8 +12,9 @@
 #include <blas.hpp>
 #include <common/err_common.hpp>
 #include <copy.hpp>
-#include <cublas_v2.h>
+#include <cublas.hpp>
 #include <cusolverDn.hpp>
+#include <err_cuda.hpp>
 #include <identity.hpp>
 #include <lu.hpp>
 #include <math.hpp>
@@ -24,6 +25,64 @@
 
 namespace cuda {
 
+// cublasStatus_t cublas<>getrsBatched( cublasHandle_t handle,
+//                                      cublasOperation_t trans,
+//                                      int n,
+//                                      int nrhs,
+//                                      const <> *Aarray[],
+//                                      int lda,
+//                                      const int *devIpiv,
+//                                      <> *Barray[],
+//                                      int ldb,
+//                                      int *info,
+//                                      int batchSize);
+
+template<typename T>
+struct getrsBatched_func_def_t {
+    typedef cublasStatus_t (*getrsBatched_func_def)(cublasHandle_t,
+                                                    cublasOperation_t, int, int,
+                                                    const T **, int,
+                                                    const int *, T **, int,
+                                                    int *, int);
+};
+
+// cublasStatus_t cublas<>getrfBatched(cublasHandle_t handle,
+//                                     int n,
+//                                     float *A[],
+//                                     int lda,
+//                                     int *P,
+//                                     int *info,
+//                                     int batchSize);
+
+template<typename T>
+struct getrfBatched_func_def_t {
+    typedef cublasStatus_t (*getrfBatched_func_def)(cublasHandle_t, int, T **,
+                                                    int, int *, int *, int);
+};
+
+#define SOLVE_BATCH_FUNC_DEF(FUNC) \
+    template<typename T>           \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
+
+#define SOLVE_BATCH_FUNC(FUNC, TYPE, PREFIX)                                \
+    template<>                                                              \
+    typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
+               cublas##PREFIX##FUNC;                                        \
+    }
+
+SOLVE_BATCH_FUNC_DEF(getrfBatched)
+SOLVE_BATCH_FUNC(getrfBatched, float, S)
+SOLVE_BATCH_FUNC(getrfBatched, double, D)
+SOLVE_BATCH_FUNC(getrfBatched, cfloat, C)
+SOLVE_BATCH_FUNC(getrfBatched, cdouble, Z)
+
+SOLVE_BATCH_FUNC_DEF(getrsBatched)
+SOLVE_BATCH_FUNC(getrsBatched, float, S)
+SOLVE_BATCH_FUNC(getrsBatched, double, D)
+SOLVE_BATCH_FUNC(getrsBatched, cfloat, C)
+SOLVE_BATCH_FUNC(getrsBatched, cdouble, Z)
+
 // cusolverStatus_t cusolverDn<>getrs(
 //    cusolverDnHandle_t handle,
 //    cublasOperation_t trans,
@@ -172,8 +231,83 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
     return B;
 }
 
+template<typename T>
+Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
+    Array<T> A = copyArray<T>(a);
+    Array<T> B = copyArray<T>(b);
+
+    dim4 aDims = a.dims();
+    int M      = aDims[0];
+    int N      = aDims[1];
+    int NRHS   = b.dims()[1];
+
+    if (M != N) {
+        AF_ERROR("Batched solve requires square matrices", AF_ERR_ARG);
+    }
+
+    int batchz = aDims[2];
+    int batchw = aDims[3];
+    int batch  = batchz * batchw;
+
+    size_t bytes         = batch * sizeof(T *);
+    using unique_mem_ptr = std::unique_ptr<char, void (*)(char *)>;
+
+    unique_mem_ptr aBatched_host_mem(pinnedAlloc<char>(bytes),
+                                     pinnedFree<char>);
+    unique_mem_ptr bBatched_host_mem(pinnedAlloc<char>(bytes),
+                                     pinnedFree<char>);
+
+    T *a_ptr               = A.get();
+    T *b_ptr               = B.get();
+    T **aBatched_host_ptrs = (T **)aBatched_host_mem.get();
+    T **bBatched_host_ptrs = (T **)bBatched_host_mem.get();
+
+    for (int i = 0; i < batchw; i++) {
+        for (int j = 0; j < batchz; j++) {
+            aBatched_host_ptrs[i * batchz + j] =
+                a_ptr + j * A.strides()[2] + i * A.strides()[3];
+            bBatched_host_ptrs[i * batchz + j] =
+                b_ptr + j * B.strides()[2] + i * B.strides()[3];
+        }
+    }
+
+    auto aBatched_device_mem = memAlloc<char>(bytes);
+    auto bBatched_device_mem = memAlloc<char>(bytes);
+
+    T **aBatched_device_ptrs = (T **)aBatched_device_mem.get();
+    T **bBatched_device_ptrs = (T **)bBatched_device_mem.get();
+
+    CUDA_CHECK(cudaMemcpyAsync(aBatched_device_ptrs, aBatched_host_ptrs, bytes,
+                               cudaMemcpyHostToDevice,
+                               getStream(getActiveDeviceId())));
+
+    // Perform batched LU
+    // getrf requires pivot and info to be device pointers
+    Array<int> pivots = createEmptyArray<int>(af::dim4(N, batch, 1, 1));
+    Array<int> info   = createEmptyArray<int>(af::dim4(batch, 1, 1, 1));
+
+    CUBLAS_CHECK(getrfBatched_func<T>()(blasHandle(), N, aBatched_device_ptrs,
+                                        A.strides()[1], pivots.get(),
+                                        info.get(), batch));
+
+    CUDA_CHECK(cudaMemcpyAsync(bBatched_device_ptrs, bBatched_host_ptrs, bytes,
+                               cudaMemcpyHostToDevice,
+                               getStream(getActiveDeviceId())));
+
+    // getrs requires info to be host pointer
+    unique_mem_ptr info_host_mem(pinnedAlloc<char>(batch * sizeof(int)),
+                                 pinnedFree<char>);
+    CUBLAS_CHECK(getrsBatched_func<T>()(
+        blasHandle(), CUBLAS_OP_N, N, NRHS, (const T **)aBatched_device_ptrs,
+        A.strides()[1], pivots.get(), bBatched_device_ptrs, B.strides()[1],
+        (int *)info_host_mem.get(), batch));
+    return B;
+}
+
 template<typename T>
 Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
+    if (a.dims()[2] > 1 || a.dims()[3] > 1) return generalSolveBatched(a, b);
+
     int M = a.dims()[0];
     int N = a.dims()[1];
     int K = b.dims()[1];

From c826ddf190c9199de1a8068ae669772ec1241f6d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 14 Jul 2020 15:55:53 -0400
Subject: [PATCH 2179/2677] Add batch support for CPU and OpenCL solve. Add
 solver batch tests

Add support for batching to the CPU and OpenCL backends. Uses
the MKL batching functions when MKL is enabled otherwise it
iterates over all of the slices if using LAPACK.
---
 src/backend/cpu/Array.hpp             |   4 +
 src/backend/cpu/lapack_helper.hpp     |   1 +
 src/backend/cpu/solve.cpp             | 198 ++++++++++++++++++++++---
 src/backend/opencl/cpu/cpu_helper.hpp |   1 +
 src/backend/opencl/cpu/cpu_solve.cpp  | 169 +++++++++++++++++++--
 src/backend/opencl/solve.cpp          |  95 +++++++-----
 test/solve_dense.cpp                  | 204 +++++++++++++++++++++++---
 7 files changed, 585 insertions(+), 87 deletions(-)

diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 8335e325c9..fd8ca3dce3 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -22,6 +22,7 @@
 #include <af/dim4.hpp>
 #include <af/seq.h>
 
+#include <algorithm>
 #include <cstddef>
 #include <memory>
 #include <vector>
@@ -153,6 +154,9 @@ class Array {
     }
 
     void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
+
+    // Modifies the dimensions of the array without modifing the underlying
+    // data
     void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
     void modDims(const af::dim4 &newDims) { info.modDims(newDims); }
     void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides); }
diff --git a/src/backend/cpu/lapack_helper.hpp b/src/backend/cpu/lapack_helper.hpp
index a7bc77aaf3..e9b509f921 100644
--- a/src/backend/cpu/lapack_helper.hpp
+++ b/src/backend/cpu/lapack_helper.hpp
@@ -18,6 +18,7 @@
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
 #ifdef USE_MKL
+#include <mkl_lapack.h>
 #include <mkl_lapacke.h>
 #else
 #ifdef __APPLE__
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index d9fb586782..feac9737d5 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -17,6 +17,9 @@
 #include <math.hpp>
 #include <queue.hpp>
 #include <af/dim4.hpp>
+#include <algorithm>
+#include <complex>
+#include <vector>
 
 using af::dim4;
 
@@ -29,6 +32,21 @@ template<typename T>
 using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int, T *, int, T *,
                               int);
 
+#ifdef USE_MKL
+template<typename T>
+using getrf_batch_strided_func_def =
+    void (*)(const MKL_INT *m, const MKL_INT *n, T *a, const MKL_INT *lda,
+             const MKL_INT *stride_a, MKL_INT *ipiv, const MKL_INT *stride_ipiv,
+             const MKL_INT *batch_size, MKL_INT *info);
+
+template<typename T>
+using getrs_batch_strided_func_def =
+    void (*)(const char *trans, const MKL_INT *n, const MKL_INT *nrhs, T *a,
+             const MKL_INT *lda, const MKL_INT *stride_a, MKL_INT *ipiv,
+             const MKL_INT *stride_ipiv, T *b, const MKL_INT *ldb,
+             const MKL_INT *stride_b, const MKL_INT *batch_size, MKL_INT *info);
+#endif
+
 template<typename T>
 using getrs_func_def = int (*)(ORDER_TYPE, char, int, int, const T *, int,
                                const int *, T *, int);
@@ -59,6 +77,70 @@ SOLVE_FUNC(gels, double, d)
 SOLVE_FUNC(gels, cfloat, c)
 SOLVE_FUNC(gels, cdouble, z)
 
+#ifdef USE_MKL
+
+template<typename T>
+struct mkl_type {
+    using type = T;
+};
+template<>
+struct mkl_type<std::complex<float>> {
+    using type = MKL_Complex8;
+};
+template<>
+struct mkl_type<std::complex<double>> {
+    using type = MKL_Complex16;
+};
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wnoexcept-type"
+template<typename T>
+getrf_batch_strided_func_def<T> getrf_batch_strided_func();
+
+template<>
+getrf_batch_strided_func_def<float> getrf_batch_strided_func<float>() {
+    return &sgetrf_batch_strided;
+}
+template<>
+getrf_batch_strided_func_def<double> getrf_batch_strided_func<double>() {
+    return &dgetrf_batch_strided;
+}
+template<>
+getrf_batch_strided_func_def<MKL_Complex8>
+getrf_batch_strided_func<MKL_Complex8>() {
+    return &cgetrf_batch_strided;
+}
+template<>
+getrf_batch_strided_func_def<MKL_Complex16>
+getrf_batch_strided_func<MKL_Complex16>() {
+    return &zgetrf_batch_strided;
+}
+
+template<typename T>
+getrs_batch_strided_func_def<T> getrs_batch_strided_func();
+
+template<>
+getrs_batch_strided_func_def<float> getrs_batch_strided_func<float>() {
+    return &sgetrs_batch_strided;
+}
+template<>
+getrs_batch_strided_func_def<double> getrs_batch_strided_func<double>() {
+    return &dgetrs_batch_strided;
+}
+template<>
+getrs_batch_strided_func_def<MKL_Complex8>
+getrs_batch_strided_func<MKL_Complex8>() {
+    return &cgetrs_batch_strided;
+}
+template<>
+getrs_batch_strided_func_def<MKL_Complex16>
+getrs_batch_strided_func<MKL_Complex16>() {
+    return &zgetrs_batch_strided;
+}
+
+#pragma GCC diagnostic pop
+#endif
+
 SOLVE_FUNC_DEF(getrs)
 SOLVE_FUNC(getrs, float, s)
 SOLVE_FUNC(getrs, double, d)
@@ -109,6 +191,60 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     return B;
 }
 
+#ifdef USE_MKL
+
+template<typename T>
+Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b,
+                             const af_mat_prop options) {
+    using std::vector;
+    int batches = a.dims()[2] * a.dims()[3];
+
+    dim4 aDims = a.dims();
+    dim4 bDims = b.dims();
+    int M      = aDims[0];
+    int N      = aDims[1];
+    int K      = bDims[1];
+    int MN     = std::min(M, N);
+
+    int lda     = a.strides()[1];
+    int astride = a.strides()[2];
+
+    vector<int> ipiv(MN * batches);
+    int ipivstride = MN;
+
+    int ldb     = b.strides()[1];
+    int bstride = b.strides()[2];
+
+    vector<int> info(batches, 0);
+
+    char trans = 'N';
+
+    Array<T> A = copyArray<T>(a);
+    Array<T> B = copyArray<T>(b);
+
+    auto getrf_rs = [](char TRANS, int M, int N, int K, Param<T> a, int LDA,
+                       int ASTRIDE, vector<int> IPIV, int IPIVSTRIDE,
+                       Param<T> b, int LDB, int BSTRIDE, int BATCH_SIZE,
+                       vector<int> INFO) {
+        getrf_batch_strided_func<typename mkl_type<T>::type>()(
+            &M, &N, reinterpret_cast<typename mkl_type<T>::type *>(a.get()),
+            &LDA, &ASTRIDE, IPIV.data(), &IPIVSTRIDE, &BATCH_SIZE, INFO.data());
+
+        getrs_batch_strided_func<typename mkl_type<T>::type>()(
+            &TRANS, &M, &K,
+            reinterpret_cast<typename mkl_type<T>::type *>(a.get()), &LDA,
+            &ASTRIDE, IPIV.data(), &IPIVSTRIDE,
+            reinterpret_cast<typename mkl_type<T>::type *>(b.get()), &LDB,
+            &BSTRIDE, &BATCH_SIZE, INFO.data());
+    };
+
+    getQueue().enqueue(getrf_rs, trans, M, N, K, A, lda, astride, ipiv,
+                       ipivstride, B, ldb, bstride, batches, info);
+
+    return B;
+}
+#endif
+
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options) {
@@ -116,10 +252,20 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
         return triangleSolve<T>(a, b, options);
     }
 
+#ifdef USE_MKL
+    if (a.dims()[2] > 1 || a.dims()[3] > 1) {
+        return generalSolveBatched(a, b, options);
+    }
+#endif
+
     const dim4 NullShape(0, 0, 0, 0);
 
-    int M = a.dims()[0];
-    int N = a.dims()[1];
+    dim4 aDims = a.dims();
+    int batchz = aDims[2];
+    int batchw = aDims[3];
+
+    int M = aDims[0];
+    int N = aDims[1];
     int K = b.dims()[1];
 
     Array<T> A = copyArray<T>(a);
@@ -129,27 +275,37 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
                       ? copyArray(b)
                       : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
 
-    if (M == N) {
-        Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
-
-        auto func = [=](Param<T> A, Param<T> B, Param<int> pivot, int N,
-                        int K) {
-            gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides(1),
-                           pivot.get(), B.get(), B.strides(1));
-        };
-        getQueue().enqueue(func, A, B, pivot, N, K);
-    } else {
-        auto func = [=](Param<T> A, Param<T> B, int M, int N, int K) {
-            int sM = A.strides(1);
-            int sN = A.strides(2) / sM;
-
-            gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N', M, N, K, A.get(),
-                           A.strides(1), B.get(), max(sM, sN));
-        };
-        B.resetDims(dim4(N, K));
-        getQueue().enqueue(func, A, B, M, N, K);
+    for (int i = 0; i < batchw; i++) {
+        for (int j = 0; j < batchz; j++) {
+            Param<T> pA(A.get() + A.strides()[2] * j + A.strides()[3] * i,
+                        A.dims(), A.strides());
+            Param<T> pB(B.get() + B.strides()[2] * j + B.strides()[3] * i,
+                        B.dims(), B.strides());
+            if (M == N) {
+                Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
+
+                auto func = [](Param<T> A, Param<T> B, Param<int> pivot, int N,
+                               int K) {
+                    gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(),
+                                   A.strides(1), pivot.get(), B.get(),
+                                   B.strides(1));
+                };
+                getQueue().enqueue(func, pA, pB, pivot, N, K);
+            } else {
+                auto func = [=](Param<T> A, Param<T> B, int M, int N, int K) {
+                    int sM = A.dims(0);
+                    int sN = A.dims(1);
+
+                    gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N', M, N, K, A.get(),
+                                   A.strides(1), B.get(), max(sM, sN));
+                };
+                getQueue().enqueue(func, pA, pB, M, N, K);
+            }
+        }
     }
 
+    if (M != N) { B.resetDims(dim4(N, K, B.dims()[2], B.dims()[3])); }
+
     return B;
 }
 
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index 8ca6a4928c..b614e53be1 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -28,6 +28,7 @@
 #define LAPACK_NAME(fn) LAPACKE_##fn
 
 #ifdef USE_MKL
+#include <mkl_lapack.h>
 #include <mkl_lapacke.h>
 #else
 #ifdef __APPLE__
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index b9f2fc9933..31fbaddc62 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -12,6 +12,8 @@
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
 #include <math.hpp>
+#include <algorithm>
+#include <vector>
 
 namespace opencl {
 namespace cpu {
@@ -23,6 +25,21 @@ template<typename T>
 using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int, T *, int, T *,
                               int);
 
+#ifdef USE_MKL
+template<typename T>
+using getrf_batch_strided_func_def =
+    void (*)(const MKL_INT *m, const MKL_INT *n, T *a, const MKL_INT *lda,
+             const MKL_INT *stride_a, MKL_INT *ipiv, const MKL_INT *stride_ipiv,
+             const MKL_INT *batch_size, MKL_INT *info);
+
+template<typename T>
+using getrs_batch_strided_func_def =
+    void (*)(const char *trans, const MKL_INT *n, const MKL_INT *nrhs, T *a,
+             const MKL_INT *lda, const MKL_INT *stride_a, MKL_INT *ipiv,
+             const MKL_INT *stride_ipiv, T *b, const MKL_INT *ldb,
+             const MKL_INT *stride_b, const MKL_INT *batch_size, MKL_INT *info);
+#endif
+
 template<typename T>
 using getrs_func_def = int (*)(ORDER_TYPE, char, int, int, const T *, int,
                                const int *, T *, int);
@@ -53,6 +70,70 @@ SOLVE_FUNC(gels, double, d)
 SOLVE_FUNC(gels, cfloat, c)
 SOLVE_FUNC(gels, cdouble, z)
 
+#ifdef USE_MKL
+
+template<typename T>
+struct mkl_type {
+    using type = T;
+};
+template<>
+struct mkl_type<cl_float2> {
+    using type = MKL_Complex8;
+};
+template<>
+struct mkl_type<cl_double2> {
+    using type = MKL_Complex16;
+};
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wnoexcept-type"
+template<typename T>
+getrf_batch_strided_func_def<T> getrf_batch_strided_func();
+
+template<>
+getrf_batch_strided_func_def<float> getrf_batch_strided_func<float>() {
+    return &sgetrf_batch_strided;
+}
+template<>
+getrf_batch_strided_func_def<double> getrf_batch_strided_func<double>() {
+    return &dgetrf_batch_strided;
+}
+template<>
+getrf_batch_strided_func_def<MKL_Complex8>
+getrf_batch_strided_func<MKL_Complex8>() {
+    return &cgetrf_batch_strided;
+}
+template<>
+getrf_batch_strided_func_def<MKL_Complex16>
+getrf_batch_strided_func<MKL_Complex16>() {
+    return &zgetrf_batch_strided;
+}
+
+template<typename T>
+getrs_batch_strided_func_def<T> getrs_batch_strided_func();
+
+template<>
+getrs_batch_strided_func_def<float> getrs_batch_strided_func<float>() {
+    return &sgetrs_batch_strided;
+}
+template<>
+getrs_batch_strided_func_def<double> getrs_batch_strided_func<double>() {
+    return &dgetrs_batch_strided;
+}
+template<>
+getrs_batch_strided_func_def<MKL_Complex8>
+getrs_batch_strided_func<MKL_Complex8>() {
+    return &cgetrs_batch_strided;
+}
+template<>
+getrs_batch_strided_func_def<MKL_Complex16>
+getrs_batch_strided_func<MKL_Complex16>() {
+    return &zgetrs_batch_strided;
+}
+
+#pragma GCC diagnostic pop
+#endif
+
 SOLVE_FUNC_DEF(getrs)
 SOLVE_FUNC(getrs, float, s)
 SOLVE_FUNC(getrs, double, d)
@@ -102,6 +183,55 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     return B;
 }
 
+#ifdef USE_MKL
+
+template<typename T>
+Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b,
+                             const af_mat_prop options) {
+    using std::vector;
+    int batches = a.dims()[2] * a.dims()[3];
+
+    dim4 aDims = a.dims();
+    dim4 bDims = b.dims();
+    int M      = aDims[0];
+    int N      = aDims[1];
+    int K      = bDims[1];
+    int MN     = std::min(M, N);
+
+    int lda     = a.strides()[1];
+    int astride = a.strides()[2];
+
+    vector<int> ipiv(MN * batches);
+    int ipivstride = MN;
+
+    int ldb     = b.strides()[1];
+    int bstride = b.strides()[2];
+
+    vector<int> info(batches, 0);
+
+    char trans = 'N';
+
+    Array<T> A = copyArray<T>(a);
+    Array<T> B = copyArray<T>(b);
+
+    mapped_ptr<T> aPtr = A.getMappedPtr();
+    mapped_ptr<T> bPtr = B.getMappedPtr();
+
+    getrf_batch_strided_func<typename mkl_type<T>::type>()(
+        &M, &N, reinterpret_cast<typename mkl_type<T>::type *>(aPtr.get()),
+        &lda, &astride, ipiv.data(), &ipivstride, &batches, info.data());
+
+    getrs_batch_strided_func<typename mkl_type<T>::type>()(
+        &trans, &M, &K,
+        reinterpret_cast<typename mkl_type<T>::type *>(aPtr.get()), &lda,
+        &astride, ipiv.data(), &ipivstride,
+        reinterpret_cast<typename mkl_type<T>::type *>(bPtr.get()), &ldb,
+        &bstride, &batches, info.data());
+
+    return B;
+}
+#endif
+
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options) {
@@ -109,8 +239,18 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
         return triangleSolve<T>(a, b, options);
     }
 
+#ifdef USE_MKL
+    if (a.dims()[2] > 1 || a.dims()[3] > 1) {
+        return generalSolveBatched(a, b, options);
+    }
+#endif
+
     const dim4 NullShape(0, 0, 0, 0);
 
+    dim4 aDims = a.dims();
+    int batchz = aDims[2];
+    int batchw = aDims[3];
+
     int M = a.dims()[0];
     int N = a.dims()[1];
     int K = b.dims()[1];
@@ -124,18 +264,25 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
     mapped_ptr<T> aPtr = A.getMappedPtr();
     mapped_ptr<T> bPtr = B.getMappedPtr();
 
-    if (M == N) {
-        std::vector<int> pivot(N);
-        gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, aPtr.get(), A.strides()[1],
-                       &pivot.front(), bPtr.get(), B.strides()[1]);
-    } else {
-        int sM = a.strides()[1];
-        int sN = a.strides()[2] / sM;
-
-        gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N', M, N, K, aPtr.get(),
-                       A.strides()[1], bPtr.get(), max(sM, sN));
-        B.resetDims(dim4(N, K));
+    for (int i = 0; i < batchw; i++) {
+        for (int j = 0; j < batchz; j++) {
+            auto pA = aPtr.get() + A.strides()[2] * j + A.strides()[3] * i;
+            auto pB = bPtr.get() + B.strides()[2] * j + B.strides()[3] * i;
+
+            if (M == N) {
+                std::vector<int> pivot(N);
+                gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, pA, A.strides()[1],
+                               &pivot.front(), pB, B.strides()[1]);
+            } else {
+                int sM = a.strides()[1];
+                int sN = a.strides()[2] / sM;
+
+                gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N', M, N, K, pA,
+                               A.strides()[1], pB, max(sM, sN));
+            }
+        }
     }
+    if (M != N) { B.resetDims(dim4(N, K, B.dims()[2], B.dims()[3])); }
 
     return B;
 }
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index bedd987287..ad73e21d27 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -25,6 +25,13 @@
 #include <transpose.hpp>
 #include <af/opencl.h>
 
+#include <algorithm>
+#include <vector>
+
+using cl::Buffer;
+using std::min;
+using std::vector;
+
 namespace opencl {
 
 template<typename T>
@@ -35,13 +42,13 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
     int N    = A.dims()[0];
     int NRHS = b.dims()[1];
 
-    std::vector<int> ipiv(N);
+    vector<int> ipiv(N);
     copyData(&ipiv[0], pivot);
 
     Array<T> B = copyArray<T>(b);
 
-    const cl::Buffer *A_buf = A.get();
-    cl::Buffer *B_buf       = B.get();
+    const Buffer *A_buf = A.get();
+    Buffer *B_buf       = B.get();
 
     int info = 0;
     magma_getrs_gpu<T>(MagmaNoTrans, N, NRHS, (*A_buf)(), A.getOffset(),
@@ -52,26 +59,38 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
 
 template<typename T>
 Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
-    dim4 iDims = a.dims();
-    int M      = iDims[0];
-    int N      = iDims[1];
-    int MN     = std::min(M, N);
-    std::vector<int> ipiv(MN);
+    dim4 aDims = a.dims();
+    int batchz = aDims[2];
+    int batchw = aDims[3];
 
     Array<T> A = copyArray<T>(a);
     Array<T> B = copyArray<T>(b);
 
-    cl::Buffer *A_buf  = A.get();
-    int info           = 0;
-    cl_command_queue q = getQueue()();
-    magma_getrf_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
-                       &ipiv[0], q, &info);
-
-    cl::Buffer *B_buf = B.get();
-    int K             = B.dims()[1];
-    magma_getrs_gpu<T>(MagmaNoTrans, M, K, (*A_buf)(), A.getOffset(),
-                       A.strides()[1], &ipiv[0], (*B_buf)(), B.getOffset(),
-                       B.strides()[1], q, &info);
+    for (int i = 0; i < batchw; i++) {
+        for (int j = 0; j < batchz; j++) {
+            int M  = aDims[0];
+            int N  = aDims[1];
+            int MN = min(M, N);
+            vector<int> ipiv(MN);
+
+            Buffer *A_buf      = A.get();
+            int info           = 0;
+            cl_command_queue q = getQueue()();
+            auto aoffset =
+                A.getOffset() + j * A.strides()[2] + i * A.strides()[3];
+            magma_getrf_gpu<T>(M, N, (*A_buf)(), aoffset, A.strides()[1],
+                               &ipiv[0], q, &info);
+
+            Buffer *B_buf = B.get();
+            int K         = B.dims()[1];
+
+            auto boffset =
+                B.getOffset() + j * B.strides()[2] + i * B.strides()[3];
+            magma_getrs_gpu<T>(MagmaNoTrans, M, K, (*A_buf)(), aoffset,
+                               A.strides()[1], &ipiv[0], (*B_buf)(), boffset,
+                               B.strides()[1], q, &info);
+        }
+    }
     return B;
 }
 
@@ -80,7 +99,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
     int M  = a.dims()[0];
     int N  = a.dims()[1];
     int K  = b.dims()[1];
-    int MN = std::min(M, N);
+    int MN = min(M, N);
 
     Array<T> B = createEmptyArray<T>(dim4());
     gpu_blas_trsm_func<T> gpu_blas_trsm;
@@ -117,12 +136,12 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         int NUM      = (2 * MN + ((M + 31) / 32) * 32) * NB;
         Array<T> tmp = createEmptyArray<T>(dim4(NUM));
 
-        std::vector<T> h_tau(MN);
+        vector<T> h_tau(MN);
 
-        int info       = 0;
-        cl::Buffer *dA = A.get();
-        cl::Buffer *dT = tmp.get();
-        cl::Buffer *dB = B.get();
+        int info   = 0;
+        Buffer *dA = A.get();
+        Buffer *dT = tmp.get();
+        Buffer *dB = B.get();
 
         magma_geqrf3_gpu<T>(A.dims()[0], A.dims()[1], (*dA)(), A.getOffset(),
                             A.strides()[1], &h_tau[0], (*dT)(), tmp.getOffset(),
@@ -147,7 +166,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
 
 #if UNMQR
         int lwork = (B.dims()[0] - A.dims()[0] + NB) * (B.dims()[1] + 2 * NB);
-        std::vector<T> h_work(lwork);
+        vector<T> h_work(lwork);
         B.resetDims(dim4(N, K));
         magma_unmqr_gpu<T>(MagmaLeft, MagmaNoTrans, B.dims()[0], B.dims()[1],
                            A.dims()[0], (*dA)(), A.getOffset(), A.strides()[1],
@@ -156,7 +175,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
                            queue, &info);
 #else
         A.resetDims(dim4(N, M));
-        magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], std::min(M, N), (*dA)(),
+        magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], min(M, N), (*dA)(),
                            A.getOffset(), A.strides()[1], &h_tau[0], (*dT)(),
                            tmp.getOffset(), NB, queue, &info);
 
@@ -178,18 +197,18 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         Array<T> A = copyArray<T>(a);
         B          = copyArray(b);
 
-        int MN = std::min(M, N);
+        int MN = min(M, N);
         int NB = magma_get_geqrf_nb<T>(M);
 
         int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
         Array<T> tmp = createEmptyArray<T>(dim4(NUM));
 
-        std::vector<T> h_tau(NUM);
+        vector<T> h_tau(NUM);
 
-        int info          = 0;
-        cl::Buffer *A_buf = A.get();
-        cl::Buffer *B_buf = B.get();
-        cl::Buffer *dT    = tmp.get();
+        int info      = 0;
+        Buffer *A_buf = A.get();
+        Buffer *B_buf = B.get();
+        Buffer *dT    = tmp.get();
 
         magma_geqrf3_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
                             &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
@@ -198,7 +217,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         int NRHS   = B.dims()[1];
         int lhwork = (M - N + NB) * (NRHS + NB) + NRHS * NB;
 
-        std::vector<T> h_work(lhwork);
+        vector<T> h_work(lhwork);
         h_work[0] = scalar<T>(lhwork);
 
         magma_unmqr_gpu<T>(MagmaLeft, MagmaConjTrans, M, NRHS, N, (*A_buf)(),
@@ -211,8 +230,8 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
                               tmp.getOffset() + NB * MN, NB, 0, queue);
 
         if (getActivePlatform() == AFCL_PLATFORM_NVIDIA) {
-            Array<T> AT        = transpose<T>(A, true);
-            cl::Buffer *AT_buf = AT.get();
+            Array<T> AT    = transpose<T>(A, true);
+            Buffer *AT_buf = AT.get();
             OPENCL_BLAS_CHECK(gpu_blas_trsm(
                 OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
                 OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
@@ -243,8 +262,8 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     int N    = B.dims()[0];
     int NRHS = B.dims()[1];
 
-    const cl::Buffer *A_buf = A.get();
-    cl::Buffer *B_buf       = B.get();
+    const Buffer *A_buf = A.get();
+    Buffer *B_buf       = B.get();
 
     cl_event event         = 0;
     cl_command_queue queue = getQueue()();
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 5014357566..0820ed51da 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -12,9 +12,163 @@
 // issue https://github.com/arrayfire/arrayfire/issues/1617
 
 #include <gtest/gtest.h>
+
 #include <testHelpers.hpp>
+#include <af/algorithm.h>
+#include <af/arith.h>
+#include <af/blas.h>
+#include <af/defines.h>
+#include <af/device.h>
+#include <af/dim4.hpp>
+#include <af/lapack.h>
+#include <af/traits.hpp>
+
+#include <iostream>
+#include <string>
 #include <thread>
-#include "solve_common.hpp"
+#include <vector>
+#include <cstdlib>
+
+using af::array;
+using af::cdouble;
+using af::cfloat;
+using af::deviceGC;
+using af::dim4;
+using af::dtype_traits;
+using af::setDevice;
+using af::sum;
+using std::abs;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
+
+template<typename T>
+void solveTester(const int m, const int n, const int k, const int b, double eps,
+                 int targetDevice = -1) {
+    if (targetDevice >= 0) setDevice(targetDevice);
+
+    deviceGC();
+
+    SUPPORTED_TYPE_CHECK(T);
+    if (noLAPACKTests()) return;
+
+#if 1
+    array A  = cpu_randu<T>(dim4(m, n, b));
+    array X0 = cpu_randu<T>(dim4(n, k, b));
+#else
+    array A  = randu(m, n, (dtype)dtype_traits<T>::af_type);
+    array X0 = randu(n, k, (dtype)dtype_traits<T>::af_type);
+#endif
+    array B0 = matmul(A, X0);
+
+    //! [ex_solve]
+    array X1 = solve(A, B0);
+    //! [ex_solve]
+
+    //! [ex_solve_recon]
+    array B1 = matmul(A, X1);
+    //! [ex_solve_recon]
+
+    ASSERT_NEAR(
+        0,
+        sum<typename dtype_traits<T>::base_type>(abs(real(B0 - B1))) / (m * k),
+        eps);
+    ASSERT_NEAR(
+        0,
+        sum<typename dtype_traits<T>::base_type>(abs(imag(B0 - B1))) / (m * k),
+        eps);
+}
+
+template<typename T>
+void solveLUTester(const int n, const int k, double eps,
+                   int targetDevice = -1) {
+    if (targetDevice >= 0) setDevice(targetDevice);
+
+    deviceGC();
+
+    SUPPORTED_TYPE_CHECK(T);
+    if (noLAPACKTests()) return;
+
+#if 1
+    array A  = cpu_randu<T>(dim4(n, n));
+    array X0 = cpu_randu<T>(dim4(n, k));
+#else
+    array A  = randu(n, n, (dtype)dtype_traits<T>::af_type);
+    array X0 = randu(n, k, (dtype)dtype_traits<T>::af_type);
+#endif
+    array B0 = matmul(A, X0);
+
+    //! [ex_solve_lu]
+    array A_lu, pivot;
+    lu(A_lu, pivot, A);
+    array X1 = solveLU(A_lu, pivot, B0);
+    //! [ex_solve_lu]
+
+    array B1 = matmul(A, X1);
+
+    ASSERT_NEAR(
+        0,
+        sum<typename dtype_traits<T>::base_type>(abs(real(B0 - B1))) / (n * k),
+        eps);
+    ASSERT_NEAR(
+        0,
+        sum<typename dtype_traits<T>::base_type>(abs(imag(B0 - B1))) / (n * k),
+        eps);
+}
+
+template<typename T>
+void solveTriangleTester(const int n, const int k, bool is_upper, double eps,
+                         int targetDevice = -1) {
+    if (targetDevice >= 0) setDevice(targetDevice);
+
+    deviceGC();
+
+    SUPPORTED_TYPE_CHECK(T);
+    if (noLAPACKTests()) return;
+
+#if 1
+    array A  = cpu_randu<T>(dim4(n, n));
+    array X0 = cpu_randu<T>(dim4(n, k));
+#else
+    array A  = randu(n, n, (dtype)dtype_traits<T>::af_type);
+    array X0 = randu(n, k, (dtype)dtype_traits<T>::af_type);
+#endif
+
+    array L, U, pivot;
+    lu(L, U, pivot, A);
+
+    array AT = is_upper ? U : L;
+    array B0 = matmul(AT, X0);
+    array X1;
+
+    if (is_upper) {
+        //! [ex_solve_upper]
+        array X = solve(AT, B0, AF_MAT_UPPER);
+        //! [ex_solve_upper]
+
+        X1 = X;
+    } else {
+        //! [ex_solve_lower]
+        array X = solve(AT, B0, AF_MAT_LOWER);
+        //! [ex_solve_lower]
+
+        X1 = X;
+    }
+
+    array B1 = matmul(AT, X1);
+
+    ASSERT_NEAR(
+        0,
+        sum<typename dtype_traits<T>::base_type>(af::abs(real(B0 - B1))) /
+            (n * k),
+        eps);
+    ASSERT_NEAR(
+        0,
+        sum<typename dtype_traits<T>::base_type>(af::abs(imag(B0 - B1))) /
+            (n * k),
+        eps);
+}
 
 template<typename T>
 class Solve : public ::testing::Test {};
@@ -37,7 +191,7 @@ double eps<double>() {
 
 template<>
 double eps<cfloat>() {
-    return 0.01f;
+    return 0.015f;
 }
 
 template<>
@@ -46,51 +200,67 @@ double eps<cdouble>() {
 }
 
 TYPED_TEST(Solve, Square) {
-    solveTester<TypeParam>(100, 100, 10, eps<TypeParam>());
+    solveTester<TypeParam>(100, 100, 10, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, SquareMultipleOfTwo) {
-    solveTester<TypeParam>(96, 96, 16, eps<TypeParam>());
+    solveTester<TypeParam>(96, 96, 16, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, SquareLarge) {
-    solveTester<TypeParam>(1000, 1000, 10, eps<TypeParam>());
+    solveTester<TypeParam>(1000, 1000, 10, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, SquareMultipleOfTwoLarge) {
-    solveTester<TypeParam>(2048, 2048, 32, eps<TypeParam>());
+    solveTester<TypeParam>(2048, 2048, 32, 1, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareBatch) {
+    solveTester<TypeParam>(100, 100, 10, 10, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareMultipleOfTwoBatch) {
+    solveTester<TypeParam>(96, 96, 16, 10, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareLargeBatch) {
+    solveTester<TypeParam>(1000, 1000, 10, 10, eps<TypeParam>());
+}
+
+TYPED_TEST(Solve, SquareMultipleOfTwoLargeBatch) {
+    solveTester<TypeParam>(2048, 2048, 32, 10, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresUnderDetermined) {
-    solveTester<TypeParam>(80, 100, 20, eps<TypeParam>());
+    solveTester<TypeParam>(80, 100, 20, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresUnderDeterminedMultipleOfTwo) {
-    solveTester<TypeParam>(96, 128, 40, eps<TypeParam>());
+    solveTester<TypeParam>(96, 128, 40, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresUnderDeterminedLarge) {
-    solveTester<TypeParam>(800, 1000, 200, eps<TypeParam>());
+    solveTester<TypeParam>(800, 1000, 200, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresUnderDeterminedMultipleOfTwoLarge) {
-    solveTester<TypeParam>(1536, 2048, 400, eps<TypeParam>());
+    solveTester<TypeParam>(1536, 2048, 400, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresOverDetermined) {
-    solveTester<TypeParam>(80, 60, 20, eps<TypeParam>());
+    solveTester<TypeParam>(80, 60, 20, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresOverDeterminedMultipleOfTwo) {
-    solveTester<TypeParam>(96, 64, 1, eps<TypeParam>());
+    solveTester<TypeParam>(96, 64, 1, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresOverDeterminedLarge) {
-    solveTester<TypeParam>(800, 600, 64, eps<TypeParam>());
+    solveTester<TypeParam>(800, 600, 64, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LeastSquaresOverDeterminedMultipleOfTwoLarge) {
-    solveTester<TypeParam>(1536, 1024, 1, eps<TypeParam>());
+    solveTester<TypeParam>(1536, 1024, 1, 1, eps<TypeParam>());
 }
 
 TYPED_TEST(Solve, LU) { solveLUTester<TypeParam>(100, 10, eps<TypeParam>()); }
@@ -152,11 +322,11 @@ int nextTargetDeviceId() {
                        nextTargetDeviceId() % numDevices);            \
     tests.emplace_back(solveTriangleTester<T>, 1000, 100, false, eps, \
                        nextTargetDeviceId() % numDevices);            \
-    tests.emplace_back(solveTester<T>, 1000, 1000, 100, eps,          \
+    tests.emplace_back(solveTester<T>, 1000, 1000, 100, 1, eps,       \
                        nextTargetDeviceId() % numDevices);            \
-    tests.emplace_back(solveTester<T>, 800, 1000, 200, eps,           \
+    tests.emplace_back(solveTester<T>, 800, 1000, 200, 1, eps,        \
                        nextTargetDeviceId() % numDevices);            \
-    tests.emplace_back(solveTester<T>, 800, 600, 64, eps,             \
+    tests.emplace_back(solveTester<T>, 800, 600, 64, 1, eps,          \
                        nextTargetDeviceId() % numDevices);
 
 TEST(Solve, Threading) {

From 854e5f378b236874e3a6482f205d0b0052c8b3de Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 22:47:32 -0400
Subject: [PATCH 2180/2677] Use pinned memory to copy device pointers in CUDA
 solve

---
 src/backend/cuda/solve.cu | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 988061ba12..f9e80efdf0 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -271,8 +271,8 @@ Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
         }
     }
 
-    auto aBatched_device_mem = memAlloc<char>(bytes);
-    auto bBatched_device_mem = memAlloc<char>(bytes);
+    unique_mem_ptr aBatched_device_mem(pinnedAlloc<char>(bytes), pinnedFree<char>);
+    unique_mem_ptr bBatched_device_mem(pinnedAlloc<char>(bytes), pinnedFree<char>);
 
     T **aBatched_device_ptrs = (T **)aBatched_device_mem.get();
     T **bBatched_device_ptrs = (T **)bBatched_device_mem.get();
@@ -306,7 +306,9 @@ Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
 
 template<typename T>
 Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
-    if (a.dims()[2] > 1 || a.dims()[3] > 1) return generalSolveBatched(a, b);
+    if (a.dims()[2] > 1 || a.dims()[3] > 1) {
+        return generalSolveBatched(a, b);
+    }
 
     int M = a.dims()[0];
     int N = a.dims()[1];

From 2e54562180307993be041c9788df632d5a693c3e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 23:05:21 -0400
Subject: [PATCH 2181/2677] Allow MKL as a valid entry for AF_COMPUTE_LIBRARY

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0515e9f74f..5f1685d72e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -168,7 +168,8 @@ mark_as_advanced(CLEAR CUDA_VERSION)
 # Note that the default value of AF_COMPUTE_LIBRARY is Intel-MKL.
 # Also, cmake doesn't have short-circuit of OR/AND conditions in if
 if(${AF_BUILD_CPU} OR ${AF_BUILD_OPENCL})
-  if("${AF_COMPUTE_LIBRARY}" STREQUAL "Intel-MKL")
+  if("${AF_COMPUTE_LIBRARY}" STREQUAL "Intel-MKL"
+      OR "${AF_COMPUTE_LIBRARY}" STREQUAL "MKL")
     dependency_check(MKL_FOUND "Please ensure Intel-MKL / oneAPI-oneMKL is installed")
     set(BUILD_WITH_MKL ON)
   elseif("${AF_COMPUTE_LIBRARY}" STREQUAL "FFTW/LAPACK/BLAS")

From d14977fb0987346bc50c185c51aed744a93a6fda Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Jul 2021 23:06:30 -0400
Subject: [PATCH 2182/2677] Improve disabled linear algebra error message.
 minor header updates

---
 src/backend/cpu/solve.cpp    | 8 ++++++--
 src/backend/cuda/memory.cpp  | 2 +-
 src/backend/opencl/Array.cpp | 3 ++-
 test/solve_dense.cpp         | 2 +-
 4 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index feac9737d5..0113a8ec7d 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -318,13 +318,17 @@ namespace cpu {
 template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options) {
-    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR(
+        "This version of ArrayFire was built without linear algebra routines",
+        AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options) {
-    AF_ERROR("Linear Algebra is disabled on CPU", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR(
+        "This version of ArrayFire was built without linear algebra routines",
+        AF_ERR_NOT_CONFIGURED);
 }
 
 }  // namespace cpu
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index a914f9f151..969574a1c4 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -24,8 +24,8 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
+#include <cstdlib>
 #include <mutex>
-#include <utility>
 
 using af::dim4;
 using common::bytesToString;
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 5935d51ec9..d47a0e7bec 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -23,8 +23,9 @@
 #include <af/opencl.h>
 
 #include <cstddef>
+#include <cstdlib>
 #include <numeric>
-#include <utility>
+#include <vector>
 
 using af::dim4;
 using af::dtype_traits;
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index 0820ed51da..a63a8eede1 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -23,11 +23,11 @@
 #include <af/lapack.h>
 #include <af/traits.hpp>
 
+#include <cstdlib>
 #include <iostream>
 #include <string>
 #include <thread>
 #include <vector>
-#include <cstdlib>
 
 using af::array;
 using af::cdouble;

From dc2e02394e3bcb9114c39337d5f7071af74e7290 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 7 Jul 2021 14:59:10 +0530
Subject: [PATCH 2183/2677] Use correct assert macros in solve_dense tests

---
 test/solve_common.hpp | 38 +++++---------------------------------
 1 file changed, 5 insertions(+), 33 deletions(-)

diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index 341d0afc49..c464bfdc47 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -8,10 +8,12 @@
  ********************************************************/
 
 #pragma once
+
 #include <arrayfire.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
 #include <complex>
 #include <iostream>
 #include <string>
@@ -25,9 +27,6 @@ using std::endl;
 using std::string;
 using std::vector;
 
-///////////////////////////////// CPP ////////////////////////////////////
-//
-
 template<typename T>
 void solveTester(const int m, const int n, const int k, double eps,
                  int targetDevice = -1) {
@@ -55,16 +54,7 @@ void solveTester(const int m, const int n, const int k, double eps,
     af::array B1 = af::matmul(A, X1);
     //! [ex_solve_recon]
 
-    ASSERT_NEAR(0,
-                af::sum<typename af::dtype_traits<T>::base_type>(
-                    af::abs(real(B0 - B1))) /
-                    (m * k),
-                eps);
-    ASSERT_NEAR(0,
-                af::sum<typename af::dtype_traits<T>::base_type>(
-                    af::abs(imag(B0 - B1))) /
-                    (m * k),
-                eps);
+    ASSERT_ARRAYS_NEAR(B0, B1, eps);
 }
 
 template<typename T>
@@ -94,16 +84,7 @@ void solveLUTester(const int n, const int k, double eps,
 
     af::array B1 = af::matmul(A, X1);
 
-    ASSERT_NEAR(0,
-                af::sum<typename af::dtype_traits<T>::base_type>(
-                    af::abs(real(B0 - B1))) /
-                    (n * k),
-                eps);
-    ASSERT_NEAR(0,
-                af::sum<typename af::dtype_traits<T>::base_type>(
-                    af::abs(imag(B0 - B1))) /
-                    (n * k),
-                eps);
+    ASSERT_ARRAYS_NEAR(B0, B1, eps);
 }
 
 template<typename T>
@@ -147,14 +128,5 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps,
 
     af::array B1 = af::matmul(AT, X1);
 
-    ASSERT_NEAR(0,
-                af::sum<typename af::dtype_traits<T>::base_type>(
-                    af::abs(real(B0 - B1))) /
-                    (n * k),
-                eps);
-    ASSERT_NEAR(0,
-                af::sum<typename af::dtype_traits<T>::base_type>(
-                    af::abs(imag(B0 - B1))) /
-                    (n * k),
-                eps);
+    ASSERT_ARRAYS_NEAR(B0, B1, eps);
 }

From b6680d531ec7ba26e3f3844a05a4654895217488 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Jul 2021 16:32:10 -0400
Subject: [PATCH 2184/2677] Check symbols in MKL to enable solve batch
 functionality

We will first make sure that the getrf_batch_strided function is
available in MKL to determine if the batch functionality can be
used in ArrayFire. If it is available we will define the
AF_USE_MKL_BATCH function to enable the batching functions.
---
 CMakeLists.txt                       | 2 ++
 CMakeModules/InternalUtils.cmake     | 5 +++++
 src/backend/cpu/CMakeLists.txt       | 5 +++++
 src/backend/cpu/solve.cpp            | 8 ++++----
 src/backend/opencl/CMakeLists.txt    | 3 +++
 src/backend/opencl/cpu/cpu_solve.cpp | 8 ++++----
 6 files changed, 23 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5f1685d72e..06bfcdd995 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -23,6 +23,7 @@ include(Version)
 include(platform)
 include(GetPrerequisites)
 include(CheckCXXCompilerFlag)
+include(CheckSymbolExists)
 include(SplitDebugInfo)
 
 # Use the function generate_product_version on Windows
@@ -170,6 +171,7 @@ mark_as_advanced(CLEAR CUDA_VERSION)
 if(${AF_BUILD_CPU} OR ${AF_BUILD_OPENCL})
   if("${AF_COMPUTE_LIBRARY}" STREQUAL "Intel-MKL"
       OR "${AF_COMPUTE_LIBRARY}" STREQUAL "MKL")
+    af_mkl_batch_check()
     dependency_check(MKL_FOUND "Please ensure Intel-MKL / oneAPI-oneMKL is installed")
     set(BUILD_WITH_MKL ON)
   elseif("${AF_COMPUTE_LIBRARY}" STREQUAL "FFTW/LAPACK/BLAS")
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index fdb4a1bbe0..1c1a8e5f5f 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -218,6 +218,11 @@ macro(set_policies)
   endforeach()
 endmacro()
 
+macro(af_mkl_batch_check)
+  set(CMAKE_REQUIRED_LIBRARIES "MKL::RT")
+  check_symbol_exists(sgetrf_batch_strided "mkl_lapack.h" MKL_BATCH)
+endmacro()
+
 mark_as_advanced(
     pkgcfg_lib_PC_CBLAS_cblas
     pkgcfg_lib_PC_LAPACKE_lapacke
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index b899d6f887..7282d611ac 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -314,6 +314,11 @@ target_link_libraries(afcpu
   )
 if(BUILD_WITH_MKL)
   target_compile_definitions(afcpu PRIVATE USE_MKL)
+
+  if(MKL_BATCH)
+    target_compile_definitions(afcpu PRIVATE AF_USE_MKL_BATCH)
+  endif()
+
   if(AF_WITH_STATIC_MKL)
       target_link_libraries(afcpu PRIVATE MKL::Static)
   else()
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 0113a8ec7d..4d43405d55 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -32,7 +32,7 @@ template<typename T>
 using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int, T *, int, T *,
                               int);
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
 template<typename T>
 using getrf_batch_strided_func_def =
     void (*)(const MKL_INT *m, const MKL_INT *n, T *a, const MKL_INT *lda,
@@ -77,7 +77,7 @@ SOLVE_FUNC(gels, double, d)
 SOLVE_FUNC(gels, cfloat, c)
 SOLVE_FUNC(gels, cdouble, z)
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
 
 template<typename T>
 struct mkl_type {
@@ -191,7 +191,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     return B;
 }
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
 
 template<typename T>
 Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b,
@@ -252,7 +252,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
         return triangleSolve<T>(a, b, options);
     }
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
     if (a.dims()[2] > 1 || a.dims()[3] > 1) {
         return generalSolveBatched(a, b, options);
     }
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index b04572f2f3..5385f4fa1f 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -466,6 +466,9 @@ if(LAPACK_FOUND OR BUILD_WITH_MKL)
 
   if(BUILD_WITH_MKL)
     target_compile_definitions(afopencl PRIVATE USE_MKL)
+    if(MKL_BATCH)
+      target_compile_definitions(afopencl PRIVATE AF_USE_MKL_BATCH)
+    endif()
 
     if(AF_WITH_STATIC_MKL)
         target_link_libraries(afopencl PRIVATE MKL::Static)
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 31fbaddc62..f5f2510597 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -25,7 +25,7 @@ template<typename T>
 using gels_func_def = int (*)(ORDER_TYPE, char, int, int, int, T *, int, T *,
                               int);
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
 template<typename T>
 using getrf_batch_strided_func_def =
     void (*)(const MKL_INT *m, const MKL_INT *n, T *a, const MKL_INT *lda,
@@ -70,7 +70,7 @@ SOLVE_FUNC(gels, double, d)
 SOLVE_FUNC(gels, cfloat, c)
 SOLVE_FUNC(gels, cdouble, z)
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
 
 template<typename T>
 struct mkl_type {
@@ -183,7 +183,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     return B;
 }
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
 
 template<typename T>
 Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b,
@@ -239,7 +239,7 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
         return triangleSolve<T>(a, b, options);
     }
 
-#ifdef USE_MKL
+#ifdef AF_USE_MKL_BATCH
     if (a.dims()[2] > 1 || a.dims()[3] > 1) {
         return generalSolveBatched(a, b, options);
     }

From 1b9536668d27c25929d5da52feaaa3907f8fba10 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Aug 2021 21:02:23 -0400
Subject: [PATCH 2185/2677] Create ASSERT_IMAGE_NEAR which compares two images
 for equality

Add an image comparison assertion to the tests that compares two images
and if there is an error, uploads the result and the gold image to CDash
for comparison. Useful for when image tests fail
---
 test/CMakeLists.txt            |   1 +
 test/anisotropic_diffusion.cpp |   9 +-
 test/arrayfire_test.cpp        | 160 ++++++++++++++++++++++++++++++++-
 test/bilateral.cpp             |   9 +-
 test/canny.cpp                 |   9 +-
 test/inverse_deconv.cpp        |   9 +-
 test/iterative_deconv.cpp      |   9 +-
 test/meanshift.cpp             |  18 +---
 test/medfilt.cpp               |   9 +-
 test/morph.cpp                 |  15 ++--
 test/testHelpers.hpp           |  28 ++++++
 test/threading.cpp             |  10 +--
 12 files changed, 201 insertions(+), 85 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5aec753c08..06484c274a 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -128,6 +128,7 @@ endif()
 
 target_compile_definitions(arrayfire_test
   PRIVATE
+    TEST_RESULT_IMAGE_DIR="${CMAKE_BINARY_DIR}/test/"
     USE_MTX)
 
 # Creates tests for all backends
diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 3957e6aa7c..f20f1f009c 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -125,14 +125,7 @@ void imageTest(string pTestFile, const float dt, const float K,
         ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
         ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
-        vector<OutType> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
-
-        vector<OutType> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void *)goldData.data(), goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.025f));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.025);
 
         ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(_outArray));
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index e9dee59789..26dbdbcc71 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -19,7 +19,13 @@
 
 #include <algorithm>
 #include <cfloat>
+#include <cmath>
+#include <complex>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
 #include <fstream>
+#include <iomanip>
 #include <iterator>
 #include <limits>
 #include <numeric>
@@ -164,6 +170,83 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
     return ::testing::AssertionSuccess();
 }
 
+template<typename T>
+::testing::AssertionResult imageEq(std::string aName, std::string bName,
+                                   const af::array &a, const af::array &b,
+                                   float maxAbsDiff) {
+    std::vector<T> avec(a.elements());
+    a.host(avec.data());
+    std::vector<T> bvec(b.elements());
+    b.host(bvec.data());
+    double NRMSD = computeArraysRMSD(a.elements(), avec.data(), bvec.data());
+
+    if (NRMSD < maxAbsDiff) {
+        return ::testing::AssertionSuccess();
+    } else {
+        std::string test_name =
+            ::testing::UnitTest::GetInstance()->current_test_info()->name();
+
+        std::string valid_path =
+            std::string(TEST_RESULT_IMAGE_DIR) + test_name + "ValidImage.png";
+        std::string result_path =
+            std::string(TEST_RESULT_IMAGE_DIR) + test_name + "ResultImage.png";
+        std::string diff_path =
+            std::string(TEST_RESULT_IMAGE_DIR) + test_name + "DiffImage.png";
+
+        // af::array img = af::join(1, a, b);
+        // af::Window win;
+        // while (!win.close()) { win.image(img); }
+        af::saveImage(valid_path.c_str(), a.as(f32));
+        af::saveImage(result_path.c_str(), b.as(f32));
+        af::saveImage(diff_path.c_str(), abs(a.as(f32) - b.as(f32)));
+
+        std::cout
+            << "<DartMeasurementFile type=\"image/png\" name=\"ValidImage\">"
+            << valid_path << "</DartMeasurementFile>\n";
+        std::cout
+            << "<DartMeasurementFile type=\"image/png\" name=\"TestImage\">"
+            << result_path << "</DartMeasurementFile>\n";
+
+        std::cout << "<DartMeasurementFile "
+                  << "type=\"image/png\" name=\"DifferenceImage2\">"
+                  << diff_path << "</DartMeasurementFile>\n";
+
+        return ::testing::AssertionFailure()
+               << "RMSD Error(" << NRMSD << ") exceeds threshold(" << maxAbsDiff
+               << "): " << bName << "(" << b.type() << ") and " << aName << "("
+               << a.type() << ")";
+    }
+}
+
+// Called by ASSERT_ARRAYS_EQ
+::testing::AssertionResult assertImageEq(std::string aName, std::string bName,
+                                         const af::array &a, const af::array &b,
+                                         float maxAbsDiff) {
+    af::dtype aType = a.type();
+    af::dtype bType = b.type();
+    if (aType != bType)
+        return ::testing::AssertionFailure()
+               << "TYPE MISMATCH: \n"
+               << "  Actual: " << bName << "(" << b.type() << ")\n"
+               << "Expected: " << aName << "(" << a.type() << ")";
+
+    af::dtype arrDtype = aType;
+    if (a.dims() != b.dims())
+        return ::testing::AssertionFailure()
+               << "SIZE MISMATCH: \n"
+               << "  Actual: " << bName << "([" << b.dims() << "])\n"
+               << "Expected: " << aName << "([" << a.dims() << "])";
+
+    switch (arrDtype) {
+        case u8: return imageEq<unsigned char>(aName, bName, a, b, maxAbsDiff);
+        case b8: return imageEq<char>(aName, bName, a, b, maxAbsDiff);
+        case f32: return imageEq<float>(aName, bName, a, b, maxAbsDiff);
+        case f64: return imageEq<double>(aName, bName, a, b, maxAbsDiff);
+        default: throw(AF_ERR_NOT_SUPPORTED);
+    }
+    return ::testing::AssertionSuccess();
+}
+
 template<>
 float convert(af::half in) {
     return static_cast<float>(half_float::half(in.data_));
@@ -641,6 +724,30 @@ ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
     return assertArrayEq(aName, bName, a, b, maxAbsDiff);
 }
 
+// Called by ASSERT_IMAGES_NEAR
+::testing::AssertionResult assertImageNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           const af_array &a, const af_array &b,
+                                           float maxAbsDiff) {
+    UNUSED(maxAbsDiffName);
+    af_array aa = 0, bb = 0;
+    af_retain_array(&aa, a);
+    af_retain_array(&bb, b);
+    af::array aaa(aa);
+    af::array bbb(bb);
+    return assertImageEq(aName, bName, aaa, bbb, maxAbsDiff);
+}
+
+// Called by ASSERT_IMAGES_NEAR
+::testing::AssertionResult assertImageNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           const af::array &a,
+                                           const af::array &b,
+                                           float maxAbsDiff) {
+    UNUSED(maxAbsDiffName);
+    return assertImageEq(aName, bName, a, b, maxAbsDiff);
+}
+
 // To support C API
 ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            std::string maxAbsDiffName,
@@ -908,6 +1015,53 @@ INSTANTIATE(double);
 INSTANTIATE(unsigned int);
 #undef INSTANTIATE
 
+template<typename T>
+double computeArraysRMSD(dim_t data_size, T *gold, T *data) {
+    double accum  = 0.0;
+    double maxion = -FLT_MAX;  //(double)std::numeric_limits<T>::lowest();
+    double minion = FLT_MAX;   //(double)std::numeric_limits<T>::max();
+
+    for (dim_t i = 0; i < data_size; i++) {
+        double dTemp = (double)data[i];
+        double gTemp = (double)gold[i];
+        double diff  = gTemp - dTemp;
+        if (diff > 1.e-4) {
+            // printf("%d: diff: %f %f %f\n", i, diff, data[i], gold[i]);
+        }
+        double err =
+            (std::isfinite(diff) && (std::abs(diff) > 1.0e-4)) ? diff : 0.0f;
+        accum += std::pow(err, 2.0);
+        maxion = std::max(maxion, dTemp);
+        minion = std::min(minion, dTemp);
+    }
+    accum /= data_size;
+    double NRMSD = std::sqrt(accum) / (maxion - minion);
+
+    return NRMSD;
+}
+
+template<>
+double computeArraysRMSD<unsigned char>(dim_t data_size, unsigned char *gold,
+                                        unsigned char *data) {
+    double accum = 0.0;
+    int maxion   = 0;    //(double)std::numeric_limits<T>::lowest();
+    int minion   = 255;  //(double)std::numeric_limits<T>::max();
+
+    for (dim_t i = 0; i < data_size; i++) {
+        int dTemp  = data[i];
+        int gTemp  = gold[i];
+        int diff   = abs(gTemp - dTemp);
+        double err = (diff > 1) ? diff : 0.0f;
+        accum += std::pow(err, 2.0);
+        maxion = std::max(maxion, dTemp);
+        minion = std::min(minion, dTemp);
+    }
+    accum /= data_size;
+    double NRMSD = std::sqrt(accum) / (maxion - minion);
+
+    return NRMSD;
+}
+
 template<typename T>
 bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance) {
     double accum  = 0.0;
@@ -937,8 +1091,10 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance) {
     return true;
 }
 
-#define INSTANTIATE(TYPE)                                               \
-    template bool compareArraysRMSD<TYPE>(dim_t data_size, TYPE * gold, \
+#define INSTANTIATE(TYPE)                                                 \
+    template double computeArraysRMSD<TYPE>(dim_t data_size, TYPE * gold, \
+                                            TYPE * data);                 \
+    template bool compareArraysRMSD<TYPE>(dim_t data_size, TYPE * gold,   \
                                           TYPE * data, double tolerance)
 
 INSTANTIATE(float);
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 3db5c2c12c..07d95debba 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -54,14 +54,7 @@ void bilateralTest(string pTestFile) {
         ASSERT_SUCCESS(
             af_bilateral(&outArray, inArray, 2.25f, 25.56f, isColor));
 
-        vector<T> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        vector<T> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.02f));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.02f);
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(outArray));
diff --git a/test/canny.cpp b/test/canny.cpp
index 38df71e5f3..36b50f673f 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -147,14 +147,7 @@ void cannyImageOtsuTest(string pTestFile, bool isColor) {
         ASSERT_SUCCESS(af_mul(&mulArray, cstArray, _outArray, false));
         ASSERT_SUCCESS(af_cast(&outArray, mulArray, u8));
 
-        vector<unsigned char> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        vector<unsigned char> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 1.0e-3));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 1.0e-3);
 
         ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(inArray));
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index 986cae421f..e811fe3f8b 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -102,11 +102,7 @@ void invDeconvImageTest(string pTestFile, const float gamma,
         ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
         ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
-        std::vector<OutType> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        std::vector<OutType> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.03);
 
         ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(inArray));
@@ -120,9 +116,6 @@ void invDeconvImageTest(string pTestFile, const float gamma,
         ASSERT_SUCCESS(af_release_array(outArray));
         ASSERT_SUCCESS(af_release_array(_goldArray));
         ASSERT_SUCCESS(af_release_array(goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.03));
     }
 }
 
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 77f4eaaf2b..80403786d5 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -102,11 +102,7 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
         ASSERT_SUCCESS(af_div(&divArray, numArray, denArray, false));
         ASSERT_SUCCESS(af_mul(&outArray, divArray, cstArray, false));
 
-        std::vector<OutType> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        std::vector<OutType> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.03);
 
         ASSERT_SUCCESS(af_release_array(_inArray));
         ASSERT_SUCCESS(af_release_array(inArray));
@@ -120,9 +116,6 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
         ASSERT_SUCCESS(af_release_array(outArray));
         ASSERT_SUCCESS(af_release_array(_goldArray));
         ASSERT_SUCCESS(af_release_array(goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.03));
     }
 }
 
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index d6585f5979..92d2408ef6 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -89,14 +89,7 @@ void meanshiftTest(string pTestFile, const float ss) {
 
         ASSERT_SUCCESS(af_mean_shift(&outArray, inArray, ss, 30.f, 5, isColor));
 
-        vector<T> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        vector<T> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.02f));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.02f);
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(inArray_f32));
@@ -159,14 +152,7 @@ TEST(Meanshift, Color_CPP) {
         dim_t nElems = gold.elements();
         array output = meanShift(img, 3.5f, 30.f, 5, true);
 
-        vector<float> outData(nElems);
-        output.host((void*)outData.data());
-
-        vector<float> goldData(nElems);
-        gold.host((void*)goldData.data());
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.02f));
+        ASSERT_IMAGES_NEAR(gold, output, 0.02f);
     }
 }
 
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 1fadf73afb..1e330d3702 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -195,14 +195,7 @@ void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid) {
         ASSERT_SUCCESS(
             af_medfilt2(&outArray, inArray, w_len, w_wid, AF_PAD_ZERO));
 
-        vector<T> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        vector<T> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.018f));
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.018f);
 
         ASSERT_SUCCESS(af_release_array(inArray));
         ASSERT_SUCCESS(af_release_array(outArray));
diff --git a/test/morph.cpp b/test/morph.cpp
index 4558a50f42..ecce0738f8 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -183,20 +183,15 @@ void morphImageTest(string pTestFile, dim_t seLen) {
         }
 
 #if defined(AF_CPU)
-        ASSERT_EQ(error_code, AF_SUCCESS);
-
-        vector<T> outData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)outData.data(), outArray));
-
-        vector<T> goldData(nElems);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)goldData.data(), goldArray));
-
-        ASSERT_EQ(true, compareArraysRMSD(nElems, goldData.data(),
-                                          outData.data(), 0.018f));
+        ASSERT_SUCCESS(error_code);
+        ASSERT_IMAGES_NEAR(goldArray, outArray, 0.018f);
 #else
         ASSERT_EQ(error_code,
                   (targetType != b8 && seLen > 19 ? AF_ERR_NOT_SUPPORTED
                                                   : AF_SUCCESS));
+        if (!(targetType != b8 && seLen > 19)) {
+            ASSERT_IMAGES_NEAR(goldArray, outArray, 0.018f);
+        }
 #endif
 
         ASSERT_SUCCESS(af_release_array(_inArray));
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index c18b4a2f61..cdbb811700 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -27,6 +27,8 @@
 
 #if defined(USE_MTX)
 #include <mmio.h>
+#include <cstdlib>
+
 #endif
 
 bool operator==(const af_half &lhs, const af_half &rhs);
@@ -130,6 +132,9 @@ void readImageFeaturesDescriptors(
 template<typename T>
 bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance);
 
+template<typename T>
+double computeArraysRMSD(dim_t data_size, T *gold, T *data);
+
 template<typename T, typename Other>
 struct is_same_type {
     static const bool value = false;
@@ -324,6 +329,17 @@ ::testing::AssertionResult assertArrayNear(std::string aName, std::string bName,
                                            const af::array &b,
                                            float maxAbsDiff);
 
+::testing::AssertionResult assertImageNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           const af_array &a, const af_array &b,
+                                           float maxAbsDiff);
+
+::testing::AssertionResult assertImageNear(std::string aName, std::string bName,
+                                           std::string maxAbsDiffName,
+                                           const af::array &a,
+                                           const af::array &b,
+                                           float maxAbsDiff);
+
 // Called by ASSERT_VEC_ARRAY_NEAR
 template<typename T>
 ::testing::AssertionResult assertArrayNear(
@@ -389,6 +405,18 @@ ::testing::AssertionResult assertArrayNear(
 #define ASSERT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
     ASSERT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
 
+/// Compares two af::array or af_arrays for their type, dims, and values (with a
+/// given tolerance).
+///
+/// \param[in] EXPECTED Expected value of the assertion
+/// \param[in] ACTUAL Actual value of the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+///
+/// \NOTE: This macro will deallocate the af_arrays after the call
+#define ASSERT_IMAGES_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
+    ASSERT_PRED_FORMAT3(assertImageNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
+
 /// Compares a std::vector with an af::array for their dims and values (with a
 /// given tolerance).
 ///
diff --git a/test/threading.cpp b/test/threading.cpp
index 99a789df49..daf613070e 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -132,20 +132,12 @@ void morphTest(const array input, const array mask, const bool isDilation,
                const array gold, int targetDevice) {
     setDevice(targetDevice);
 
-    vector<float> goldData(gold.elements());
-    vector<float> outData(gold.elements());
-
-    gold.host((void*)goldData.data());
-
     array out;
 
     for (unsigned i = 0; i < ITERATION_COUNT; ++i)
         out = isDilation ? dilate(input, mask) : erode(input, mask);
 
-    out.host((void*)outData.data());
-
-    ASSERT_EQ(true, compareArraysRMSD(gold.elements(), goldData.data(),
-                                      outData.data(), 0.018f));
+    ASSERT_IMAGES_NEAR(gold, out, 0.018f);
 }
 
 TEST(Threading, SetPerThreadActiveDevice) {

From 7ddf462fd8ac3e80ac665d490602d9e8cec4c9be Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Aug 2021 09:47:22 +0530
Subject: [PATCH 2186/2677] Improve Readme (#3168)

* Update README's: Prelude, Acknowledgement, Citations & Copyright Sections

Increase image size

Co-authored-by: John Melonakos <john@arrayfire.com>
Co-authored-by: syurkevi <stefan@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 README.md | 256 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 140 insertions(+), 116 deletions(-)

diff --git a/README.md b/README.md
index a9d37f7731..c56f29623f 100644
--- a/README.md
+++ b/README.md
@@ -1,105 +1,105 @@
-<a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="300"></a>
 
-ArrayFire is a general-purpose library that simplifies the process of developing
-software that targets parallel and massively-parallel architectures including
-CPUs, GPUs, and other hardware acceleration devices.
+<p align="center"><a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="800"></a></p>
+
+ArrayFire is a general-purpose tensor library that simplifies the process of
+software development for the parallel architectures found in CPUs, GPUs, and
+other hardware acceleration devices. The library serves users in every technical
+computing market.
 
 Several of ArrayFire's benefits include:
 
+* Hundreds of accelerated [tensor computing functions](https://arrayfire.org/docs/group__arrayfire__func.htm), in the following areas:
+    * Array handling
+    * Computer vision
+    * Image processing
+    * Linear algebra
+    * Machine learning
+    * Standard math
+    * Signal Processing
+    * Statistics
+    * Vector algorithms
 * [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable,
   [well-documented](http://arrayfire.org/docs) API
-* Rigorously tested for performance and accuracy
+* Rigorous benchmarks and tests ensuring top performance and numerical accuracy
+* Cross-platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux
+* Built-in visualization functions through [Forge](https://github.com/arrayfire/forge)
 * Commercially friendly open-source licensing
-* Commercial support from [ArrayFire](http://arrayfire.com)
-* [Read about more benefits on arrayfire.com](http://arrayfire.com/the-arrayfire-library/)
-
-ArrayFire provides software developers with a high-level
-abstraction of data which resides on the accelerator, the `af::array` object.
-Developers write code which performs operations on ArrayFire arrays which, in turn,
-are automatically translated into near-optimal kernels that execute on the computational
-device.
-
-ArrayFire is successfully used on devices ranging from low-power mobile phones
-to high-power GPU-enabled supercomputers. ArrayFire runs on CPUs from all
-major vendors (Intel, AMD, ARM), GPUs from the prominent manufacturers
-(NVIDIA, AMD, and Qualcomm), as well as a variety of other accelerator devices
-on Windows, Mac, and Linux.
-
-## Installation
-
-You can install the ArrayFire library from one of the following ways:
-
-### Package Managers
+* Enterprise support from [ArrayFire](http://arrayfire.com)
 
-This approach is currently only supported for Ubuntu 18.04 and 20.04. Please
-go through [our GitHub wiki page][1] for the detailed instructions.
+ArrayFire provides software developers with a high-level abstraction of data
+that resides on the accelerator, the `af::array` object. Developers write code
+that performs operations on ArrayFire arrays, which, in turn, are automatically
+translated into near-optimal kernels that execute on the computational device.
 
-#### Official installers
+ArrayFire runs on devices ranging from low-power mobile phones to high-power
+GPU-enabled supercomputers. ArrayFire runs on CPUs from all major vendors
+(Intel, AMD, ARM), GPUs from the prominent manufacturers (NVIDIA, AMD, and
+Qualcomm), as well as a variety of other accelerator devices on Windows, Mac,
+and Linux.
 
-Execute one of our [official binary installers](https://arrayfire.com/download)
-for Linux, OSX, and Windows platforms.
+# Getting ArrayFire
 
-#### Build from source
+Instructions to [install][32] or to build ArrayFire from source can be found on the [wiki][1].
 
-Build from source by following instructions on our
-[wiki](https://github.com/arrayfire/arrayfire/wiki).
+### Conway's Game of Life Using ArrayFire
 
-## Examples
+Visit the [Wikipedia page][2] for a description of Conway's Game of Life.
 
-The following examples are simplified versions of
-[`helloworld.cpp`](https://github.com/arrayfire/arrayfire/blob/master/examples/helloworld/helloworld.cpp)
-and
-[`conway_pretty.cpp`](https://github.com/arrayfire/arrayfire/blob/master/examples/graphics/conway_pretty.cpp),
-respectively. For more code examples, visit the
-[`examples/`](https://github.com/arrayfire/arrayfire/blob/master/examples/)
-directory.
-
-#### Hello, world!
+<img align="left" src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif" alt="Conway's Game of Life" height="256" width="256">
 
 ```cpp
-array A = randu(5, 3, f32); // Create 5x3 matrix of random floats on the GPU
-array B = sin(A) + 1.5;     // Element-wise arithmetic
-array C = fft(B);           // Fourier transform the result
-
-float d[] = { 1, 2, 3, 4, 5, 6 };
-array D(2, 3, d, afHost);   // Create 2x3 matrix from host data
-D.col(0) = D.col(end);      // Copy last column onto first
-
-array vals, inds;
-sort(vals, inds, A);        // Sort A and print sorted array and corresponding indices
-af_print(vals);
-af_print(inds);
+static const float h_kernel[] = { 1, 1, 1, 1, 0, 1, 1, 1, 1 };
+static const array kernel(3, 3, h_kernel, afHost);
+
+array state = (randu(128, 128, f32) > 0.5).as(f32); // Init state
+Window myWindow(256, 256);
+while(!myWindow.close()) {
+    array nHood = convolve(state, kernel); // Obtain neighbors
+    array C0 = (nHood == 2);  // Generate conditions for life
+    array C1 = (nHood == 3);
+    state = state * C0 + C1;  // Update state
+    myWindow.image(state);    // Display
+}
 ```
+The complete source code can be found [here][3].
 
-#### Conway's Game of Life
+### Perceptron
 
-Visit the
-[Wikipedia page](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) for a
-description of Conway's Game of Life.
+<img align="left" src="https://github.com/arrayfire/assets/blob/imgs_readme_improv/gifs/perceptron.gif" alt="Perceptron" height="400" width="300">
 
 ```cpp
-static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
-static const array kernel(3, 3, h_kernel, afHost);
+array predict(const array &X, const array &W) {
+    return sigmoid(matmul(X, W));
+}
 
-array state = (randu(128, 128, f32) > 0.5).as(f32); // Generate starting state
-Window myWindow(256, 256);
-while(!myWindow.close()) {
-  array nHood = convolve(state, kernel); // Obtain neighbors
-  array C0 = (nHood == 2);               // Generate conditions for life
-  array C1 = (nHood == 3);
-  state = state * C0 + C1;               // Update state
-  myWindow.image(state);                 // Display
+array train(const array &X, const array &Y,
+        double alpha = 0.1, double maxerr = 0.05,
+        int maxiter = 1000, bool verbose = false) {
+    array Weights = constant(0, X.dims(1), Y.dims(1));
+
+    for (int i = 0; i < maxiter; i++) {
+        array P   = predict(X, Weights);
+        array err = Y - P;
+        if (mean<float>(abs(err) < maxerr) break;
+        Weights += alpha * matmulTN(X, err);
+    }
+    return Weights;
 }
+...
 
+array Weights = train(train_feats, train_targets);
+array test_outputs  = predict(test_feats, Weights);
+display_results<true>(test_images, test_outputs,
+                      test_targets, 20);
 ```
 
-<p align="center">
-<img src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif" alt="Conway's Game of Life" height="256" width="256">
-</p>
+The complete source code can be found [here][31].
 
-## Documentation
+For more code examples, visit the [`examples/`][4] directory.
 
-You can find our complete documentation [here](http://www.arrayfire.com/docs/index.htm).
+# Documentation
+
+You can find the complete documentation [here](http://www.arrayfire.com/docs/index.htm).
 
 Quick links:
 
@@ -108,65 +108,89 @@ Quick links:
 * [Examples](http://www.arrayfire.org/docs/examples.htm)
 * [Blog](http://arrayfire.com/blog/)
 
-## Language support
-
-ArrayFire has several official and third-party language API`s:
-
-__Native__
-
-* [C++](http://arrayfire.org/docs/gettingstarted.htm#gettingstarted_api_usage)
-
-__Official wrappers__
+# Language support
 
-We currently support the following language wrappers for ArrayFire:
+ArrayFire has several official and community maintained language API's:
 
-* [`arrayfire-python`](https://github.com/arrayfire/arrayfire-python)
-* [`arrayfire-rust`](https://github.com/arrayfire/arrayfire-rust)
+[![C++][5]][6] [![Python][7]][8] [![Rust][9]][10] [![Julia][27]][28]<sub><span>&#8224;</span></sub>
+[![Nim][29]][30]<sub><span>&#8224;</span></sub>
 
-Wrappers for other languages are a work-in-progress:
-  [.NET](https://github.com/arrayfire/arrayfire-dotnet),
-  [Fortran](https://github.com/arrayfire/arrayfire-fortran),
-  [Go](https://github.com/arrayfire/arrayfire-go),
-  [Java](https://github.com/arrayfire/arrayfire-java),
-  [Lua](https://github.com/arrayfire/arrayfire-lua),
-  [NodeJS](https://github.com/arrayfire/arrayfire-js),
-  [R](https://github.com/arrayfire/arrayfire-r),
-  [Ruby](https://github.com/arrayfire/arrayfire-rb)
+<sup><span>&#8224;</span></sup>&nbsp; Community maintained wrappers
 
-__Third-party wrappers__
+__In-Progress Wrappers__
 
-The following wrappers are being maintained and supported by third parties:
+[![.NET][11]][12] [![Fortran][13]][14] [![Go][15]][16]
+[![Java][17]][18] [![Lua][19]][20] [![NodeJS][21]][22] [![R][23]][24] [![Ruby][25]][26]
 
-* [`ArrayFire.jl`](https://github.com/JuliaComputing/ArrayFire.jl)
-* [`ArrayFire-Nim`](https://github.com/bitstormGER/ArrayFire-Nim)
+# Contributing
 
-## Contributing
+The community of ArrayFire developers invites you to build with us if you are
+interested and able to write top-performing tensor functions. Together we can
+fulfill [The ArrayFire
+Mission](https://github.com/arrayfire/arrayfire/wiki/The-ArrayFire-Mission-Statement)
+for fast scientific computing for all.
 
-Contributions of any kind are welcome! Please refer to
-[CONTRIBUTING.md](https://github.com/arrayfire/arrayfire/blob/master/CONTRIBUTING.md)
-to learn more about how you can get involved with ArrayFire.
+Contributions of any kind are welcome! Please refer to [the
+wiki](https://github.com/arrayfire/arrayfire/wiki) and our [Code of Conduct](33)
+to learn more about how you can get involved with the ArrayFire Community
+through [Sponsorship](https://github.com/arrayfire/arrayfire/wiki/Sponsorship),
+[Developer
+Commits](https://github.com/arrayfire/arrayfire/wiki/Contributing-Code-to-ArrayFire),
+or [Governance](https://github.com/arrayfire/arrayfire/wiki/Governance).
 
-## Citations and Acknowledgements
+# Citations and Acknowledgements
 
-If you redistribute ArrayFire, please follow the terms established in
-[the license](LICENSE). If you wish to cite ArrayFire in an academic
-publication, please use the following [citation document](.github/CITATION.md).
+If you redistribute ArrayFire, please follow the terms established in [the
+license](LICENSE). If you wish to cite ArrayFire in an academic publication,
+please use the following [citation document](.github/CITATION.md).
 
-ArrayFire development is funded by ArrayFire LLC and several third parties,
-please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for further
-details.
+ArrayFire development is funded by AccelerEyes LLC and several third parties,
+please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for an expression
+of our gratitude.
 
-## Support and Contact Info
+# Support and Contact Info
 
 * [Slack Chat](https://join.slack.com/t/arrayfire-org/shared_invite/MjI4MjIzMDMzMTczLTE1MDI5ODg4NzYtN2QwNGE3ODA5OQ)
 * [Google Groups](https://groups.google.com/forum/#!forum/arrayfire-users)
-* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting/)  |  [Support](http://arrayfire.com/support/)   |  [Training](http://arrayfire.com/training/)
+* ArrayFire Services:  [Consulting](http://arrayfire.com/consulting)  |  [Support](http://arrayfire.com/download)   |  [Training](http://arrayfire.com/training)
 
-## Trademark Policy
+# Trademark Policy
 
-The literal mark “ArrayFire” and ArrayFire logos are trademarks of
-AccelerEyes LLC DBA ArrayFire.
+The literal mark "ArrayFire" and ArrayFire logos are trademarks of
+AccelerEyes LLC (dba ArrayFire).
 If you wish to use either of these marks in your own project, please consult
 [ArrayFire's Trademark Policy](http://arrayfire.com/trademark-policy/)
 
-[1]: https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers
+[1]: https://github.com/arrayfire/arrayfire/wiki
+[2]: https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
+[3]: https://github.com/arrayfire/arrayfire/blob/master/examples/graphics/conway_pretty.cpp
+[4]: https://github.com/arrayfire/arrayfire/blob/master/examples/
+[5]: https://img.shields.io/badge/c++-%2300599C.svg?style=for-the-badge&logo=c%2B%2B&logoColor=white
+[6]: http://arrayfire.org/docs/gettingstarted.htm#gettingstarted_api_usage
+[7]: https://img.shields.io/badge/python-%2314354C.svg?style=for-the-badge&logo=python&logoColor=white
+[8]: https://github.com/arrayfire/arrayfire-python
+[9]: https://img.shields.io/badge/rust-%23000000.svg?style=for-the-badge&logo=rust&logoColor=white
+[10]: https://github.com/arrayfire/arrayfire-rust
+[11]: https://img.shields.io/badge/.NET-5C2D91?style=for-the-badge&logo=.net&logoColor=white
+[12]: https://github.com/arrayfire/arrayfire-dotnet
+[13]: https://img.shields.io/badge/F-Fortran-734f96?style=for-the-badge
+[14]: https://github.com/arrayfire/arrayfire-fortran
+[15]: https://img.shields.io/badge/go-%2300ADD8.svg?style=for-the-badge&logo=go&logoColor=white
+[16]: https://github.com/arrayfire/arrayfire-go
+[17]: https://img.shields.io/badge/java-%23ED8B00.svg?style=for-the-badge&logo=java&logoColor=white
+[18]: https://github.com/arrayfire/arrayfire-java
+[19]: https://img.shields.io/badge/lua-%232C2D72.svg?style=for-the-badge&logo=lua&logoColor=white
+[20]: https://github.com/arrayfire/arrayfire-lua
+[21]: https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E
+[22]: https://github.com/arrayfire/arrayfire-js
+[23]: https://img.shields.io/badge/r-%23276DC3.svg?style=for-the-badge&logo=r&logoColor=white
+[24]: https://github.com/arrayfire/arrayfire-r
+[25]: https://img.shields.io/badge/ruby-%23CC342D.svg?style=for-the-badge&logo=ruby&logoColor=white
+[26]: https://github.com/arrayfire/arrayfire-rb
+[27]: https://img.shields.io/badge/j-Julia-cb3c33?style=for-the-badge&labelColor=4063d8
+[28]: https://github.com/JuliaComputing/ArrayFire.jl
+[29]: https://img.shields.io/badge/n-Nim-000000?style=for-the-badge&labelColor=efc743
+[30]: https://github.com/bitstormGER/ArrayFire-Nim
+[31]: https://github.com/arrayfire/arrayfire/blob/master/examples/machine_learning/perceptron.cpp
+[32]: https://github.com/arrayfire/arrayfire/wiki/Getting-ArrayFire
+[33]: https://github.com/arrayfire/arrayfire/wiki/Code-Of-Conduct

From f6b06b72c53162a3863d5dc54637c793b3616eec Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 16 Aug 2021 11:17:49 -0400
Subject: [PATCH 2187/2677] Fix canny by resizing the sigma array to the
 correct size

The otsuThreshold function was creating an empty Array for the sigmas variable
and this sometimes failed because the last value was not always written to. This
commit adjusts the size of the sigmas array to better match the values that are
assigned to it
---
 src/api/c/canny.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 42aa126929..84a8763483 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -95,8 +95,8 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
 
     const dim4& iDims = supEdges.dims();
 
-    Array<float> sigmas = createEmptyArray<float>(hDims);
-
+    dim4 sigmaDims(NUM_BINS - 1, hDims[1], hDims[2], hDims[3]);
+    Array<float> sigmas = createEmptyArray<float>(sigmaDims);
     for (unsigned b = 0; b < (NUM_BINS - 1); ++b) {
         seqBegin[0].end  = static_cast<double>(b);
         seqRest[0].begin = static_cast<double>(b + 1);

From e7f000d9bde36c27a3b7f540f164a9e7687d55f1 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Aug 2021 14:28:24 +0530
Subject: [PATCH 2188/2677] Fix edgeTracking CPU kernel to handle batch support

Prior to this change, edge tracking CPU backend kernel wasn't processing
the batch input sets. Thus, the output of corresponding input sets was
missing in the array returned by canny API. This is fixed now.

Added a batch test for this scenario.
---
 src/api/c/canny.cpp              |  6 ++--
 src/backend/cpu/kernel/canny.hpp | 42 +++++++++++++++------------
 test/canny.cpp                   | 50 +++++++++++++++++++++++++++++---
 3 files changed, 72 insertions(+), 26 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 84a8763483..e87eef712c 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -89,6 +89,7 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
 
     vector<af_seq> seqBegin(4, af_span);
     vector<af_seq> seqRest(4, af_span);
+    vector<af_seq> sliceIndex(4, af_span);
 
     seqBegin[0] = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
     seqRest[0]  = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
@@ -129,11 +130,8 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
         auto op2   = arithOp<float, af_mul_t>(qL, qH, tdims);
         auto sigma = arithOp<float, af_mul_t>(sqrd, op2, tdims);
 
-        vector<af_seq> sliceIndex(4, af_span);
         sliceIndex[0] = {double(b), double(b), 1};
-
-        auto binRes = createSubArray<float>(sigmas, sliceIndex, false);
-
+        auto binRes   = createSubArray<float>(sigmas, sliceIndex, false);
         copyArray(binRes, sigma);
     }
 
diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
index 55ff282db7..ebf3474cf8 100644
--- a/src/backend/cpu/kernel/canny.hpp
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -114,7 +114,7 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dxParam,
 }
 
 template<typename T>
-void traceEdge(T* out, const T* strong, const T* weak, int t, int width) {
+void traceEdge(T* out, const T* strong, const T* weak, int t, int stride1) {
     if (!out || !strong || !weak) return;
 
     const T EDGE = 1;
@@ -129,12 +129,12 @@ void traceEdge(T* out, const T* strong, const T* weak, int t, int width) {
         // get indices of 8 neighbours
         std::array<dim_t, 8> potentials;
 
-        potentials[0] = t - width - 1;      // north-west
+        potentials[0] = t - stride1 - 1;    // north-west
         potentials[1] = potentials[0] + 1;  // north
         potentials[2] = potentials[1] + 1;  // north-east
         potentials[3] = t - 1;              // west
         potentials[4] = t + 1;              // east
-        potentials[5] = t + width - 1;      // south-west
+        potentials[5] = t + stride1 - 1;    // south-west
         potentials[6] = potentials[5] + 1;  // south
         potentials[7] = potentials[6] + 1;  // south-east
 
@@ -151,27 +151,33 @@ void traceEdge(T* out, const T* strong, const T* weak, int t, int width) {
 
 template<typename T>
 void edgeTrackingHysteresis(Param<T> out, CParam<T> strong, CParam<T> weak) {
-    const af::dim4 dims = strong.dims();
+    const af::dim4 dims    = strong.dims();
+    const dim_t batchCount = dims[2] * dims[3];
+    const dim_t jMax       = dims[1] - 1;
+    const dim_t iMax       = dims[0] - 1;
 
-    dim_t t = dims[0] +
-              1;  // skip the first coloumn and first element of second coloumn
-    dim_t jMax = dims[1] - 1;  // max Y value to traverse, ignore right coloumn
-    dim_t iMax = dims[0] - 1;  // max X value to traverse, ignore bottom border
-
-    T* optr       = out.get();
     const T* sptr = strong.get();
     const T* wptr = weak.get();
+    T* optr       = out.get();
 
-    for (dim_t j = 1; j <= jMax; ++j) {
-        for (dim_t i = 1; i <= iMax; ++i, ++t) {
-            // if current pixel(sptr) is part of a edge
-            // and output doesn't have it marked already,
-            // mark it and trace the pixels from here.
-            if (sptr[t] > 0 && optr[t] != 1) {
-                optr[t] = 1;
-                traceEdge(optr, sptr, wptr, t, dims[0]);
+    for (dim_t batchId = 0; batchId < batchCount; ++batchId) {
+        // Skip processing borders
+        dim_t t = dims[0] + 1;
+
+        for (dim_t j = 1; j <= jMax; ++j) {
+            for (dim_t i = 1; i <= iMax; ++i, ++t) {
+                // if current pixel(sptr) is part of a edge
+                // and output doesn't have it marked already,
+                // mark it and trace the pixels from here.
+                if (sptr[t] > 0 && optr[t] != 1) {
+                    optr[t] = 1;
+                    traceEdge(optr, sptr, wptr, t, dims[0]);
+                }
             }
         }
+        optr += out.strides(2);
+        sptr += strong.strides(2);
+        wptr += weak.strides(2);
     }
 }
 }  // namespace kernel
diff --git a/test/canny.cpp b/test/canny.cpp
index 36b50f673f..e00e9b0c30 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -114,7 +114,6 @@ void cannyImageOtsuTest(string pTestFile, bool isColor) {
         af_array mulArray  = 0;
         af_array outArray  = 0;
         af_array goldArray = 0;
-        dim_t nElems       = 0;
 
         inFiles[testId].insert(0, string(TEST_DIR "/CannyEdgeDetector/"));
         outFiles[testId].insert(0, string(TEST_DIR "/CannyEdgeDetector/"));
@@ -129,12 +128,9 @@ void cannyImageOtsuTest(string pTestFile, bool isColor) {
         ASSERT_SUCCESS(
             af_load_image_native(&goldArray, outFiles[testId].c_str()));
 
-        ASSERT_SUCCESS(af_get_elements(&nElems, goldArray));
-
         ASSERT_SUCCESS(af_canny(&_outArray, inArray,
                                 AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3,
                                 false));
-
         unsigned ndims = 0;
         dim_t dims[4];
 
@@ -220,3 +216,49 @@ TEST(CannyEdgeDetector, Sobel5x5_Invalid) {
 
     ASSERT_SUCCESS(af_release_array(inArray));
 }
+
+template<typename T>
+void cannyImageOtsuBatchTest(string pTestFile, const dim_t targetBatchCount) {
+    SUPPORTED_TYPE_CHECK(T);
+    if (noImageIOTests()) return;
+
+    using af::array;
+    using af::canny;
+    using af::loadImage;
+    using af::loadImageNative;
+    using af::tile;
+
+    vector<dim4> inDims;
+    vector<string> inFiles;
+    vector<dim_t> outSizes;
+    vector<string> outFiles;
+
+    readImageTests(pTestFile, inDims, inFiles, outSizes, outFiles);
+
+    size_t testCount = inDims.size();
+
+    for (size_t testId = 0; testId < testCount; ++testId) {
+        inFiles[testId].insert(0, string(TEST_DIR "/CannyEdgeDetector/"));
+        outFiles[testId].insert(0, string(TEST_DIR "/CannyEdgeDetector/"));
+
+        af_dtype type  = (af_dtype)dtype_traits<T>::af_type;
+        array readGold = loadImageNative(outFiles[testId].c_str());
+        array goldIm   = tile(readGold, 1, 1, targetBatchCount);
+        array readImg  = loadImage(inFiles[testId].c_str(), false).as(type);
+        array inputIm  = tile(readImg, 1, 1, targetBatchCount);
+
+        array outIm =
+            canny(inputIm, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false);
+        outIm *= 255.0;
+
+        ASSERT_IMAGES_NEAR(outIm.as(u8), goldIm, 1.0e-3);
+    }
+}
+
+TEST(CannyEdgeDetector, BatchofImagesUsingCPPAPI) {
+    // DO NOT INCREASE BATCH COUNT BEYOND 4
+    // This is a limitation on the test assert macro that is saving
+    // images to disk which can't handle a batch of images.
+    cannyImageOtsuBatchTest<float>(
+        string(TEST_DIR "/CannyEdgeDetector/gray.test"), 3);
+}

From 4ea695f9f4a0bcdeddfc9ee0b72b24b30f6c29a8 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Tue, 17 Aug 2021 19:23:28 +0530
Subject: [PATCH 2189/2677] Improve canny's otsu helper by precomputing some
 arrays

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/api/c/canny.cpp | 92 +++++++++++++++++++++------------------------
 1 file changed, 42 insertions(+), 50 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index e87eef712c..d625360d3b 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -21,6 +21,7 @@
 #include <ireduce.hpp>
 #include <logic.hpp>
 #include <reduce.hpp>
+#include <scan.hpp>
 #include <sobel.hpp>
 #include <tile.hpp>
 #include <transpose.hpp>
@@ -47,6 +48,7 @@ using detail::ireduce;
 using detail::logicOp;
 using detail::reduce;
 using detail::reduce_all;
+using detail::scan;
 using detail::sobelDerivatives;
 using detail::uchar;
 using detail::uint;
@@ -71,22 +73,14 @@ Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy,
     }
 }
 
-Array<float> otsuThreshold(const Array<float>& supEdges,
-                           const unsigned NUM_BINS, const float maxVal) {
-    Array<uint> hist = histogram<float>(supEdges, NUM_BINS, 0, maxVal, false);
+Array<float> otsuThreshold(const Array<float>& in, const unsigned NUM_BINS,
+                           const float maxVal) {
+    Array<uint> hist = histogram<float>(in, NUM_BINS, 0, maxVal, false);
 
-    const dim4& hDims = hist.dims();
-
-    // reduce along histogram dimension i.e. 0th dimension
-    auto totals = reduce<af_add_t, uint, float>(hist, 0);
-
-    // tile histogram total along 0th dimension
-    auto ttotals = tile(totals, dim4(hDims[0]));
-
-    // pixel frequency probabilities
-    auto probability =
-        arithOp<float, af_div_t>(cast<float, uint>(hist), ttotals, hDims);
+    const dim4& inDims = in.dims();
+    const dim4& hDims  = hist.dims();
 
+    const dim4 oDims(1, hDims[1], hDims[2], hDims[3]);
     vector<af_seq> seqBegin(4, af_span);
     vector<af_seq> seqRest(4, af_span);
     vector<af_seq> sliceIndex(4, af_span);
@@ -94,55 +88,53 @@ Array<float> otsuThreshold(const Array<float>& supEdges,
     seqBegin[0] = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
     seqRest[0]  = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
 
-    const dim4& iDims = supEdges.dims();
+    Array<float> TWOS   = createValueArray<float>(oDims, 2.0f);
+    Array<float> UnitP  = createValueArray<float>(oDims, 1.0f);
+    Array<float> histf  = cast<float, uint>(hist);
+    Array<float> totals = createValueArray<float>(hDims, inDims[0] * inDims[1]);
+    Array<float> weights =
+        iota<float>(dim4(NUM_BINS), oDims);  // a.k.a histogram shape
+
+    // pixel frequency probabilities
+    auto freqs        = arithOp<float, af_div_t>(histf, totals, hDims);
+    auto cumFreqs     = scan<af_add_t, float, float>(freqs, 0);
+    auto oneMCumFreqs = arithOp<float, af_sub_t>(UnitP, cumFreqs, hDims);
+    auto qLqH         = arithOp<float, af_mul_t>(cumFreqs, oneMCumFreqs, hDims);
+    auto product      = arithOp<float, af_mul_t>(weights, freqs, hDims);
+    auto cumProduct   = scan<af_add_t, float, float>(product, 0);
+    auto weightedSum  = reduce<af_add_t, float, float>(product, 0);
 
     dim4 sigmaDims(NUM_BINS - 1, hDims[1], hDims[2], hDims[3]);
     Array<float> sigmas = createEmptyArray<float>(sigmaDims);
     for (unsigned b = 0; b < (NUM_BINS - 1); ++b) {
+        const dim4 fDims(b + 1, hDims[1], hDims[2], hDims[3]);
+        const dim4 eDims(NUM_BINS - 1 - b, hDims[1], hDims[2], hDims[3]);
+
+        sliceIndex[0]    = {double(b), double(b), 1};
         seqBegin[0].end  = static_cast<double>(b);
         seqRest[0].begin = static_cast<double>(b + 1);
 
-        auto frontPartition = createSubArray(probability, seqBegin, false);
-        auto endPartition   = createSubArray(probability, seqRest, false);
-
-        auto qL = reduce<af_add_t, float, float>(frontPartition, 0);
-        auto qH = reduce<af_add_t, float, float>(endPartition, 0);
-
-        const dim4 fdims(b + 1, hDims[1], hDims[2], hDims[3]);
-        const dim4 edims(NUM_BINS - 1 - b, hDims[1], hDims[2], hDims[3]);
-
-        const dim4 tdims(1, hDims[1], hDims[2], hDims[3]);
-        auto frontWeights = iota<float>(dim4(b + 1), tdims);
-        auto endWeights   = iota<float>(dim4(NUM_BINS - 1 - b), tdims);
-        auto offsetValues = createValueArray<float>(edims, b + 1);
-
-        endWeights = arithOp<float, af_add_t>(endWeights, offsetValues, edims);
-        auto __muL =
-            arithOp<float, af_mul_t>(frontPartition, frontWeights, fdims);
-        auto __muH = arithOp<float, af_mul_t>(endPartition, endWeights, edims);
-        auto _muL  = reduce<af_add_t, float, float>(__muL, 0);
-        auto _muH  = reduce<af_add_t, float, float>(__muH, 0);
-        auto muL   = arithOp<float, af_div_t>(_muL, qL, tdims);
-        auto muH   = arithOp<float, af_div_t>(_muH, qH, tdims);
-        auto TWOS  = createValueArray<float>(tdims, 2.0f);
-        auto diff  = arithOp<float, af_sub_t>(muL, muH, tdims);
-        auto sqrd  = arithOp<float, af_pow_t>(diff, TWOS, tdims);
-        auto op2   = arithOp<float, af_mul_t>(qL, qH, tdims);
-        auto sigma = arithOp<float, af_mul_t>(sqrd, op2, tdims);
-
-        sliceIndex[0] = {double(b), double(b), 1};
-        auto binRes   = createSubArray<float>(sigmas, sliceIndex, false);
+        auto qL    = createSubArray(cumFreqs, sliceIndex, false);
+        auto qH    = arithOp<float, af_sub_t>(UnitP, qL, oDims);
+        auto _muL  = createSubArray(cumProduct, sliceIndex, false);
+        auto _muH  = arithOp<float, af_sub_t>(weightedSum, _muL, oDims);
+        auto muL   = arithOp<float, af_div_t>(_muL, qL, oDims);
+        auto muH   = arithOp<float, af_div_t>(_muH, qH, oDims);
+        auto diff  = arithOp<float, af_sub_t>(muL, muH, oDims);
+        auto sqrd  = arithOp<float, af_pow_t>(diff, TWOS, oDims);
+        auto op2   = createSubArray(qLqH, sliceIndex, false);
+        auto sigma = arithOp<float, af_mul_t>(sqrd, op2, oDims);
+
+        auto binRes = createSubArray<float>(sigmas, sliceIndex, false);
         copyArray(binRes, sigma);
     }
 
-    dim4 odims          = sigmas.dims();
-    odims[0]            = 1;
-    Array<float> thresh = createEmptyArray<float>(odims);
-    Array<uint> locs    = createEmptyArray<uint>(odims);
+    Array<float> thresh = createEmptyArray<float>(oDims);
+    Array<uint> locs    = createEmptyArray<uint>(oDims);
 
     ireduce<af_max_t, float>(thresh, locs, sigmas, 0);
 
-    return cast<float, uint>(tile(locs, dim4(iDims[0], iDims[1], 1, 1)));
+    return cast<float, uint>(tile(locs, dim4(inDims[0], inDims[1])));
 }
 
 Array<float> normalize(const Array<float>& supEdges, const float minVal,

From 1ce9429965a74009aa8c3d0cf1b4a975972d4744 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 6 Aug 2021 21:16:57 -0400
Subject: [PATCH 2190/2677] Add ASSERT_REF to check for reference counts of
 af::arrays

---
 test/arrayfire_test.cpp | 17 +++++++++++++++++
 test/testHelpers.hpp    |  7 +++++++
 2 files changed, 24 insertions(+)

diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 26dbdbcc71..de9b423fe5 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -1634,6 +1634,23 @@ ::testing::AssertionResult assertArrayNear(
                            bbb, maxAbsDiff);
 }
 
+::testing::AssertionResult assertRefEq(std::string hA_name,
+                                       std::string expected_name,
+                                       const af::array &a, int expected) {
+    int count = 0;
+    af_get_data_ref_count(&count, a.get());
+    if (count != expected) {
+        std::stringstream ss;
+        ss << "Incorrect reference count:\nExpected: " << expected << "\n"
+           << std::setw(8) << hA_name << ": " << count;
+
+        return ::testing::AssertionFailure() << ss.str();
+
+    } else {
+        return ::testing::AssertionSuccess();
+    }
+}
+
 #define INSTANTIATE(To)                                                        \
     template std::string printContext(                                         \
         const std::vector<To> &hGold, std::string goldName,                    \
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index cdbb811700..33b03db93b 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -360,6 +360,10 @@ ::testing::AssertionResult assertArrayNear(
     std::string maxAbsDiffName, const std::vector<T> &hA, af::dim4 aDims,
     const af_array b, float maxAbsDiff);
 
+::testing::AssertionResult assertRefEq(std::string hA_name,
+                                       std::string expected_name,
+                                       const af::array &a, int expected);
+
 /// Checks if the C-API arrayfire function returns successfully
 ///
 /// \param[in] CALL This is the arrayfire C function
@@ -430,6 +434,9 @@ ::testing::AssertionResult assertArrayNear(
     ASSERT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
                         ACTUAL_ARR, MAX_ABSDIFF)
 
+#define ASSERT_REF(arr, expected) \
+    ASSERT_PRED_FORMAT2(assertRefEq, arr, expected)
+
 #if defined(USE_MTX)
 ::testing::AssertionResult mtxReadSparseMatrix(af::array &out,
                                                const char *fileName);

From 92dd704efac7d1990bb9c0aa8b179aba803c788a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Aug 2021 17:41:34 -0400
Subject: [PATCH 2191/2677] Move createBinaryNode to common

---
 src/backend/common/CMakeLists.txt     |   1 +
 src/backend/common/jit/BinaryNode.cpp | 149 +++++++++++++++++++++++++
 src/backend/common/jit/BinaryNode.hpp |   8 ++
 src/backend/cpu/CMakeLists.txt        |   1 +
 src/backend/cpu/arith.hpp             |  76 +------------
 src/backend/cpu/binary.hpp            | 152 ++++++++++++++++++++++++++
 src/backend/cpu/jit/BinaryNode.hpp    |   7 +-
 src/backend/cpu/logic.hpp             |  85 +-------------
 src/backend/cuda/arith.hpp            |   5 +-
 src/backend/cuda/binary.hpp           |  22 ----
 src/backend/cuda/complex.hpp          |   3 +-
 src/backend/cuda/logic.hpp            |   9 +-
 src/backend/opencl/arith.hpp          |   4 +-
 src/backend/opencl/binary.hpp         |  22 ----
 src/backend/opencl/complex.hpp        |   3 +-
 src/backend/opencl/kernel/iir.hpp     |   1 +
 src/backend/opencl/logic.hpp          |   5 +-
 17 files changed, 334 insertions(+), 219 deletions(-)
 create mode 100644 src/backend/common/jit/BinaryNode.cpp
 create mode 100644 src/backend/cpu/binary.hpp

diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 61c2290f29..3175f2b4cd 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -9,6 +9,7 @@ add_library(afcommon_interface INTERFACE)
 
 target_sources(afcommon_interface
   INTERFACE
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/BinaryNode.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/BinaryNode.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/NaryNode.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.cpp
diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
new file mode 100644
index 0000000000..b5e2cfb312
--- /dev/null
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -0,0 +1,149 @@
+
+#include <Array.hpp>
+#include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
+#include <complex.hpp>
+#include <types.hpp>
+
+using af::dim4;
+using af::dtype_traits;
+using detail::Array;
+using detail::BinOp;
+using detail::cdouble;
+using detail::cfloat;
+using detail::createNodeArray;
+
+namespace common {
+#ifdef AF_CPU
+template<typename To, typename Ti, af_op_t op>
+Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
+                           const af::dim4 &odims) {
+    common::Node_ptr lhs_node = lhs.getNode();
+    common::Node_ptr rhs_node = rhs.getNode();
+
+    detail::jit::BinaryNode<To, Ti, op> *node =
+        new detail::jit::BinaryNode<To, Ti, op>(lhs_node, rhs_node);
+
+    return createNodeArray<To>(odims, common::Node_ptr(node));
+}
+
+#else
+
+template<typename To, typename Ti, af_op_t op>
+Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
+                           const af::dim4 &odims) {
+    auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
+        BinOp<To, Ti, op> bop;
+        return Node_ptr(
+            new BinaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
+                           bop.name(), operands[0], operands[1], (int)(op)));
+    };
+
+    Node_ptr out =
+        common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
+    return createNodeArray<To>(odims, out);
+}
+
+#endif
+
+#define INSTANTIATE(To, Ti, op)                      \
+    template Array<To> createBinaryNode<To, Ti, op>( \
+        const Array<Ti> &lhs, const Array<Ti> &rhs, const dim4 &odims)
+
+INSTANTIATE(cfloat, float, af_cplx2_t);
+INSTANTIATE(cdouble, double, af_cplx2_t);
+
+#define INSTANTIATE_ARITH(op)                                \
+    INSTANTIATE(float, float, op);                           \
+    INSTANTIATE(cfloat, cfloat, op);                         \
+    INSTANTIATE(double, double, op);                         \
+    INSTANTIATE(cdouble, cdouble, op);                       \
+    INSTANTIATE(unsigned, unsigned, op);                     \
+    INSTANTIATE(short, short, op);                           \
+    INSTANTIATE(unsigned short, unsigned short, op);         \
+    INSTANTIATE(unsigned long long, unsigned long long, op); \
+    INSTANTIATE(long long, long long, op);                   \
+    INSTANTIATE(unsigned char, unsigned char, op);           \
+    INSTANTIATE(char, char, op);                             \
+    INSTANTIATE(common::half, common::half, op);             \
+    INSTANTIATE(int, int, op)
+
+INSTANTIATE_ARITH(af_add_t);
+INSTANTIATE_ARITH(af_sub_t);
+INSTANTIATE_ARITH(af_mul_t);
+INSTANTIATE_ARITH(af_div_t);
+INSTANTIATE_ARITH(af_min_t);
+INSTANTIATE_ARITH(af_max_t);
+
+#undef INSTANTIATE_ARITH
+
+#define INSTANTIATE_ARITH_REAL(op)                           \
+    INSTANTIATE(float, float, op);                           \
+    INSTANTIATE(double, double, op);                         \
+    INSTANTIATE(unsigned, unsigned, op);                     \
+    INSTANTIATE(short, short, op);                           \
+    INSTANTIATE(unsigned short, unsigned short, op);         \
+    INSTANTIATE(unsigned long long, unsigned long long, op); \
+    INSTANTIATE(long long, long long, op);                   \
+    INSTANTIATE(unsigned char, unsigned char, op);           \
+    INSTANTIATE(char, char, op);                             \
+    INSTANTIATE(common::half, common::half, op);             \
+    INSTANTIATE(int, int, op)
+
+INSTANTIATE_ARITH_REAL(af_rem_t);
+INSTANTIATE_ARITH_REAL(af_pow_t);
+INSTANTIATE_ARITH_REAL(af_mod_t);
+
+#define INSTANTIATE_FLOATOPS(op)     \
+    INSTANTIATE(float, float, op);   \
+    INSTANTIATE(double, double, op); \
+    INSTANTIATE(common::half, common::half, op)
+
+INSTANTIATE_FLOATOPS(af_hypot_t);
+INSTANTIATE_FLOATOPS(af_atan2_t);
+
+#define INSTANTIATE_BITOP(op)                                \
+    INSTANTIATE(unsigned, unsigned, op);                     \
+    INSTANTIATE(short, short, op);                           \
+    INSTANTIATE(unsigned short, unsigned short, op);         \
+    INSTANTIATE(unsigned long long, unsigned long long, op); \
+    INSTANTIATE(long long, long long, op);                   \
+    INSTANTIATE(unsigned char, unsigned char, op);           \
+    INSTANTIATE(char, char, op);                             \
+    INSTANTIATE(int, int, op)
+
+INSTANTIATE_BITOP(af_bitshiftl_t);
+INSTANTIATE_BITOP(af_bitshiftr_t);
+INSTANTIATE_BITOP(af_bitor_t);
+INSTANTIATE_BITOP(af_bitand_t);
+INSTANTIATE_BITOP(af_bitxor_t);
+#undef INSTANTIATE_BITOP
+
+#define INSTANTIATE_LOGIC(op)                  \
+    INSTANTIATE(char, float, op);              \
+    INSTANTIATE(char, double, op);             \
+    INSTANTIATE(char, cfloat, op);             \
+    INSTANTIATE(char, cdouble, op);            \
+    INSTANTIATE(char, common::half, op);       \
+    INSTANTIATE(char, unsigned, op);           \
+    INSTANTIATE(char, short, op);              \
+    INSTANTIATE(char, unsigned short, op);     \
+    INSTANTIATE(char, unsigned long long, op); \
+    INSTANTIATE(char, long long, op);          \
+    INSTANTIATE(char, unsigned char, op);      \
+    INSTANTIATE(char, char, op);               \
+    INSTANTIATE(char, int, op)
+
+INSTANTIATE_LOGIC(af_and_t);
+INSTANTIATE_LOGIC(af_or_t);
+INSTANTIATE_LOGIC(af_eq_t);
+INSTANTIATE_LOGIC(af_neq_t);
+INSTANTIATE_LOGIC(af_lt_t);
+INSTANTIATE_LOGIC(af_le_t);
+INSTANTIATE_LOGIC(af_gt_t);
+INSTANTIATE_LOGIC(af_ge_t);
+
+#undef INSTANTIATE_LOGIC
+#undef INSTANTIATE
+
+}  // namespace common
diff --git a/src/backend/common/jit/BinaryNode.hpp b/src/backend/common/jit/BinaryNode.hpp
index 636deda7ad..e1aa7ac74f 100644
--- a/src/backend/common/jit/BinaryNode.hpp
+++ b/src/backend/common/jit/BinaryNode.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <common/jit/NaryNode.hpp>
 
 #include <cmath>
@@ -19,4 +21,10 @@ class BinaryNode : public NaryNode {
         : NaryNode(type, op_str, 2, {{lhs, rhs}}, op,
                    std::max(lhs->getHeight(), rhs->getHeight()) + 1) {}
 };
+
+template<typename To, typename Ti, af_op_t op>
+detail::Array<To> createBinaryNode(const detail::Array<Ti> &lhs,
+                                   const detail::Array<Ti> &rhs,
+                                   const af::dim4 &odims);
+
 }  // namespace common
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 7282d611ac..c3b77996ec 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -29,6 +29,7 @@ target_sources(afcpu
     assign.cpp
     assign.hpp
     backend.hpp
+    binary.hpp
     bilateral.cpp
     bilateral.hpp
     blas.cpp
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index cf0a94e40b..edce28eddf 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -10,87 +10,15 @@
 #pragma once
 
 #include <Array.hpp>
-#include <err_cpu.hpp>
-#include <jit/BinaryNode.hpp>
-#include <optypes.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <af/dim4.hpp>
-#include <cmath>
 
 namespace cpu {
 
-#define ARITH_FN(OP, op)                                                 \
-    template<typename T>                                                 \
-    struct BinOp<T, T, OP> {                                             \
-        void eval(jit::array<compute_t<T>> &out,                         \
-                  const jit::array<compute_t<T>> &lhs,                   \
-                  const jit::array<compute_t<T>> &rhs, int lim) const {  \
-            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
-        }                                                                \
-    };
-
-ARITH_FN(af_add_t, +)
-ARITH_FN(af_sub_t, -)
-ARITH_FN(af_mul_t, *)
-ARITH_FN(af_div_t, /)
-
-#undef ARITH_FN
-
-template<typename T>
-static T __mod(T lhs, T rhs) {
-    T res = lhs % rhs;
-    return (res < 0) ? abs(rhs - res) : res;
-}
-
-template<typename T>
-static T __rem(T lhs, T rhs) {
-    return lhs % rhs;
-}
-
-template<>
-STATIC_ float __mod<float>(float lhs, float rhs) {
-    return fmod(lhs, rhs);
-}
-template<>
-STATIC_ double __mod<double>(double lhs, double rhs) {
-    return fmod(lhs, rhs);
-}
-template<>
-STATIC_ float __rem<float>(float lhs, float rhs) {
-    return remainder(lhs, rhs);
-}
-template<>
-STATIC_ double __rem<double>(double lhs, double rhs) {
-    return remainder(lhs, rhs);
-}
-
-#define NUMERIC_FN(OP, FN)                                                 \
-    template<typename T>                                                   \
-    struct BinOp<T, T, OP> {                                               \
-        void eval(jit::array<compute_t<T>> &out,                           \
-                  const jit::array<compute_t<T>> &lhs,                     \
-                  const jit::array<compute_t<T>> &rhs, int lim) {          \
-            for (int i = 0; i < lim; i++) { out[i] = FN(lhs[i], rhs[i]); } \
-        }                                                                  \
-    };
-
-NUMERIC_FN(af_max_t, max)
-NUMERIC_FN(af_min_t, min)
-NUMERIC_FN(af_mod_t, __mod)
-NUMERIC_FN(af_pow_t, pow)
-NUMERIC_FN(af_rem_t, __rem)
-NUMERIC_FN(af_atan2_t, atan2)
-NUMERIC_FN(af_hypot_t, hypot)
-
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
-    common::Node_ptr lhs_node = lhs.getNode();
-    common::Node_ptr rhs_node = rhs.getNode();
-
-    jit::BinaryNode<T, T, op> *node =
-        new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
-
-    return createNodeArray<T>(odims, common::Node_ptr(node));
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 
 }  // namespace cpu
diff --git a/src/backend/cpu/binary.hpp b/src/backend/cpu/binary.hpp
new file mode 100644
index 0000000000..1d7c1583a3
--- /dev/null
+++ b/src/backend/cpu/binary.hpp
@@ -0,0 +1,152 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <math.hpp>
+#include <optypes.hpp>
+#include <types.hpp>
+#include <cmath>
+
+namespace cpu {
+
+template<typename To, typename Ti, af_op_t op>
+struct BinOp;
+
+#define ARITH_FN(OP, op)                                                 \
+    template<typename T>                                                 \
+    struct BinOp<T, T, OP> {                                             \
+        void eval(jit::array<compute_t<T>> &out,                         \
+                  const jit::array<compute_t<T>> &lhs,                   \
+                  const jit::array<compute_t<T>> &rhs, int lim) const {  \
+            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
+        }                                                                \
+    };
+
+ARITH_FN(af_add_t, +)
+ARITH_FN(af_sub_t, -)
+ARITH_FN(af_mul_t, *)
+ARITH_FN(af_div_t, /)
+
+#undef ARITH_FN
+
+#define LOGIC_FN(OP, op)                                                      \
+    template<typename T>                                                      \
+    struct BinOp<char, T, OP> {                                               \
+        void eval(jit::array<char> &out, const jit::array<compute_t<T>> &lhs, \
+                  const jit::array<compute_t<T>> &rhs, int lim) {             \
+            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; }      \
+        }                                                                     \
+    };
+
+LOGIC_FN(af_eq_t, ==)
+LOGIC_FN(af_neq_t, !=)
+LOGIC_FN(af_lt_t, <)
+LOGIC_FN(af_gt_t, >)
+LOGIC_FN(af_le_t, <=)
+LOGIC_FN(af_ge_t, >=)
+LOGIC_FN(af_and_t, &&)
+LOGIC_FN(af_or_t, ||)
+
+#undef LOGIC_FN
+
+#define LOGIC_CPLX_FN(T, OP, op)                                               \
+    template<>                                                                 \
+    struct BinOp<char, std::complex<T>, OP> {                                  \
+        typedef std::complex<T> Ti;                                            \
+        void eval(jit::array<char> &out, const jit::array<compute_t<Ti>> &lhs, \
+                  const jit::array<compute_t<Ti>> &rhs, int lim) {             \
+            for (int i = 0; i < lim; i++) {                                    \
+                T lhs_mag = std::abs(lhs[i]);                                  \
+                T rhs_mag = std::abs(rhs[i]);                                  \
+                out[i]    = lhs_mag op rhs_mag;                                \
+            }                                                                  \
+        }                                                                      \
+    };
+
+LOGIC_CPLX_FN(float, af_lt_t, <)
+LOGIC_CPLX_FN(float, af_le_t, <=)
+LOGIC_CPLX_FN(float, af_gt_t, >)
+LOGIC_CPLX_FN(float, af_ge_t, >=)
+LOGIC_CPLX_FN(float, af_and_t, &&)
+LOGIC_CPLX_FN(float, af_or_t, ||)
+
+LOGIC_CPLX_FN(double, af_lt_t, <)
+LOGIC_CPLX_FN(double, af_le_t, <=)
+LOGIC_CPLX_FN(double, af_gt_t, >)
+LOGIC_CPLX_FN(double, af_ge_t, >=)
+LOGIC_CPLX_FN(double, af_and_t, &&)
+LOGIC_CPLX_FN(double, af_or_t, ||)
+
+#undef LOGIC_CPLX_FN
+
+template<typename T>
+static T __mod(T lhs, T rhs) {
+    T res = lhs % rhs;
+    return (res < 0) ? abs(rhs - res) : res;
+}
+
+template<typename T>
+static T __rem(T lhs, T rhs) {
+    return lhs % rhs;
+}
+
+template<>
+STATIC_ float __mod<float>(float lhs, float rhs) {
+    return fmod(lhs, rhs);
+}
+template<>
+STATIC_ double __mod<double>(double lhs, double rhs) {
+    return fmod(lhs, rhs);
+}
+template<>
+STATIC_ float __rem<float>(float lhs, float rhs) {
+    return remainder(lhs, rhs);
+}
+template<>
+STATIC_ double __rem<double>(double lhs, double rhs) {
+    return remainder(lhs, rhs);
+}
+
+#define BITWISE_FN(OP, op)                                               \
+    template<typename T>                                                 \
+    struct BinOp<T, T, OP> {                                             \
+        void eval(jit::array<compute_t<T>> &out,                         \
+                  const jit::array<compute_t<T>> &lhs,                   \
+                  const jit::array<compute_t<T>> &rhs, int lim) {        \
+            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
+        }                                                                \
+    };
+
+BITWISE_FN(af_bitor_t, |)
+BITWISE_FN(af_bitand_t, &)
+BITWISE_FN(af_bitxor_t, ^)
+BITWISE_FN(af_bitshiftl_t, <<)
+BITWISE_FN(af_bitshiftr_t, >>)
+
+#undef BITWISE_FN
+
+#define NUMERIC_FN(OP, FN)                                                 \
+    template<typename T>                                                   \
+    struct BinOp<T, T, OP> {                                               \
+        void eval(jit::array<compute_t<T>> &out,                           \
+                  const jit::array<compute_t<T>> &lhs,                     \
+                  const jit::array<compute_t<T>> &rhs, int lim) {          \
+            for (int i = 0; i < lim; i++) { out[i] = FN(lhs[i], rhs[i]); } \
+        }                                                                  \
+    };
+
+NUMERIC_FN(af_max_t, max)
+NUMERIC_FN(af_min_t, min)
+NUMERIC_FN(af_mod_t, __mod)
+NUMERIC_FN(af_pow_t, pow)
+NUMERIC_FN(af_rem_t, __rem)
+NUMERIC_FN(af_atan2_t, atan2)
+NUMERIC_FN(af_hypot_t, hypot)
+
+}  // namespace cpu
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 0967e381b4..138a80a7ee 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -9,17 +9,16 @@
 
 #pragma once
 
+#include <binary.hpp>
+#include <common/jit/Node.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
+
 #include <array>
 #include <vector>
-#include "Node.hpp"
 
 namespace cpu {
 
-template<typename To, typename Ti, af_op_t op>
-struct BinOp;
-
 namespace jit {
 
 template<typename To, typename Ti, af_op_t op>
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index 0ea4222d81..b5ed91f615 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -8,102 +8,23 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <err_cpu.hpp>
-#include <jit/BinaryNode.hpp>
 #include <optypes.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
 
 namespace cpu {
 
-#define LOGIC_FN(OP, op)                                                 \
-    template<typename T>                                                 \
-    struct BinOp<char, T, OP> {                                          \
-        void eval(jit::array<char> &out, const jit::array<T> &lhs,       \
-                  const jit::array<T> &rhs, int lim) {                   \
-            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
-        }                                                                \
-    };
-
-LOGIC_FN(af_eq_t, ==)
-LOGIC_FN(af_neq_t, !=)
-LOGIC_FN(af_lt_t, <)
-LOGIC_FN(af_gt_t, >)
-LOGIC_FN(af_le_t, <=)
-LOGIC_FN(af_ge_t, >=)
-LOGIC_FN(af_and_t, &&)
-LOGIC_FN(af_or_t, ||)
-
-#undef LOGIC_FN
-
-#define LOGIC_CPLX_FN(T, OP, op)                                    \
-    template<>                                                      \
-    struct BinOp<char, std::complex<T>, OP> {                       \
-        typedef std::complex<T> Ti;                                 \
-        void eval(jit::array<char> &out, const jit::array<Ti> &lhs, \
-                  const jit::array<Ti> &rhs, int lim) {             \
-            for (int i = 0; i < lim; i++) {                         \
-                T lhs_mag = std::abs(lhs[i]);                       \
-                T rhs_mag = std::abs(rhs[i]);                       \
-                out[i]    = lhs_mag op rhs_mag;                     \
-            }                                                       \
-        }                                                           \
-    };
-
-LOGIC_CPLX_FN(float, af_lt_t, <)
-LOGIC_CPLX_FN(float, af_le_t, <=)
-LOGIC_CPLX_FN(float, af_gt_t, >)
-LOGIC_CPLX_FN(float, af_ge_t, >=)
-LOGIC_CPLX_FN(float, af_and_t, &&)
-LOGIC_CPLX_FN(float, af_or_t, ||)
-
-LOGIC_CPLX_FN(double, af_lt_t, <)
-LOGIC_CPLX_FN(double, af_le_t, <=)
-LOGIC_CPLX_FN(double, af_gt_t, >)
-LOGIC_CPLX_FN(double, af_ge_t, >=)
-LOGIC_CPLX_FN(double, af_and_t, &&)
-LOGIC_CPLX_FN(double, af_or_t, ||)
-
-#undef LOGIC_CPLX_FN
-
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
                     const af::dim4 &odims) {
-    common::Node_ptr lhs_node = lhs.getNode();
-    common::Node_ptr rhs_node = rhs.getNode();
-
-    jit::BinaryNode<char, T, op> *node =
-        new jit::BinaryNode<char, T, op>(lhs_node, rhs_node);
-
-    return createNodeArray<char>(odims, common::Node_ptr(node));
+    return common::createBinaryNode<char, T, op>(lhs, rhs, odims);
 }
 
-#define BITWISE_FN(OP, op)                                               \
-    template<typename T>                                                 \
-    struct BinOp<T, T, OP> {                                             \
-        void eval(jit::array<T> &out, const jit::array<T> &lhs,          \
-                  const jit::array<T> &rhs, int lim) {                   \
-            for (int i = 0; i < lim; i++) { out[i] = lhs[i] op rhs[i]; } \
-        }                                                                \
-    };
-
-BITWISE_FN(af_bitor_t, |)
-BITWISE_FN(af_bitand_t, &)
-BITWISE_FN(af_bitxor_t, ^)
-BITWISE_FN(af_bitshiftl_t, <<)
-BITWISE_FN(af_bitshiftr_t, >>)
-
-#undef BITWISE_FN
-
 template<typename T, af_op_t op>
 Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
                const af::dim4 &odims) {
-    common::Node_ptr lhs_node = lhs.getNode();
-    common::Node_ptr rhs_node = rhs.getNode();
-
-    jit::BinaryNode<T, T, op> *node =
-        new jit::BinaryNode<T, T, op>(lhs_node, rhs_node);
-
-    return createNodeArray<T>(odims, common::Node_ptr(node));
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace cpu
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index b245d2df71..500845c15b 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -10,14 +10,13 @@
 #pragma once
 
 #include <Array.hpp>
-#include <binary.hpp>
-#include <optypes.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <af/dim4.hpp>
 
 namespace cuda {
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
-    return createBinaryNode<T, T, op>(lhs, rhs, odims);
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace cuda
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index 61e4bceefb..ad3b95bb89 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -8,12 +8,8 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
-#include <common/jit/BinaryNode.hpp>
-#include <common/jit/NaryNode.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
-#include <af/dim4.hpp>
 
 namespace cuda {
 
@@ -128,22 +124,4 @@ struct BinOp<To, Ti, af_hypot_t> {
     const char *name() { return "hypot"; }
 };
 
-template<typename To, typename Ti, af_op_t op>
-Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
-                           const af::dim4 &odims) {
-    using common::Node;
-    using common::Node_ptr;
-
-    auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
-        BinOp<To, Ti, op> bop;
-        return Node_ptr(new common::BinaryNode(
-            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(),
-            operands[0], operands[1], (int)(op)));
-    };
-
-    Node_ptr out =
-        common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
-    return createNodeArray<To>(odims, out);
-}
-
 }  // namespace cuda
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index f86a6fb027..605ac51ccd 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <common/jit/UnaryNode.hpp>
 #include <optypes.hpp>
 #include <af/dim4.hpp>
@@ -17,7 +18,7 @@ namespace cuda {
 template<typename To, typename Ti>
 Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
                const af::dim4 &odims) {
-    return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
+    return common::createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
 }
 
 template<typename To, typename Ti>
diff --git a/src/backend/cuda/logic.hpp b/src/backend/cuda/logic.hpp
index 1f044e8ee4..e32a15548f 100644
--- a/src/backend/cuda/logic.hpp
+++ b/src/backend/cuda/logic.hpp
@@ -8,22 +8,19 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <binary.hpp>
-#include <err_cuda.hpp>
-#include <optypes.hpp>
-#include <af/defines.h>
+#include <common/jit/BinaryNode.hpp>
 #include <af/dim4.hpp>
 
 namespace cuda {
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
                     const af::dim4 &odims) {
-    return createBinaryNode<char, T, op>(lhs, rhs, odims);
+    return common::createBinaryNode<char, T, op>(lhs, rhs, odims);
 }
 
 template<typename T, af_op_t op>
 Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
                const af::dim4 &odims) {
-    return createBinaryNode<T, T, op>(lhs, rhs, odims);
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace cuda
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index edc4749e35..3e6e9aa226 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -10,7 +10,7 @@
 #pragma once
 
 #include <Array.hpp>
-#include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 
@@ -18,6 +18,6 @@ namespace opencl {
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
-    return createBinaryNode<T, T, op>(lhs, rhs, odims);
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace opencl
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 8623fcce7a..700a1b3c49 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -8,11 +8,7 @@
  ********************************************************/
 
 #pragma once
-#include <Array.hpp>
-#include <common/jit/BinaryNode.hpp>
-#include <math.hpp>
 #include <optypes.hpp>
-#include <af/dim4.hpp>
 
 namespace opencl {
 
@@ -128,22 +124,4 @@ struct BinOp<To, Ti, af_hypot_t> {
     const char *name() { return "hypot"; }
 };
 
-template<typename To, typename Ti, af_op_t op>
-Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
-                           const af::dim4 &odims) {
-    using common::Node;
-    using common::Node_ptr;
-
-    auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
-        BinOp<To, Ti, op> bop;
-        return Node_ptr(new common::BinaryNode(
-            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(),
-            operands[0], operands[1], (int)(op)));
-    };
-
-    Node_ptr out =
-        common::createNaryNode<Ti, 2>(odims, createBinary, {&lhs, &rhs});
-    return createNodeArray<To>(odims, out);
-}
-
 }  // namespace opencl
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index d927005ef2..3facc57090 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <common/jit/UnaryNode.hpp>
 #include <optypes.hpp>
 #include <traits.hpp>
@@ -18,7 +19,7 @@ namespace opencl {
 template<typename To, typename Ti>
 Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
                const af::dim4 &odims) {
-    return createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
+    return common::createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
 }
 
 template<typename To, typename Ti>
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 2a85b5d447..a2b3942b81 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -14,6 +14,7 @@
 #include <common/kernel_cache.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/iir.hpp>
+#include <math.hpp>
 #include <traits.hpp>
 
 #include <string>
diff --git a/src/backend/opencl/logic.hpp b/src/backend/opencl/logic.hpp
index 61f10e038f..b7132ac01c 100644
--- a/src/backend/opencl/logic.hpp
+++ b/src/backend/opencl/logic.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
 #include <err_opencl.hpp>
 #include <optypes.hpp>
 #include <af/defines.h>
@@ -18,12 +19,12 @@ namespace opencl {
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
                     const af::dim4 &odims) {
-    return createBinaryNode<char, T, op>(lhs, rhs, odims);
+    return common::createBinaryNode<char, T, op>(lhs, rhs, odims);
 }
 
 template<typename T, af_op_t op>
 Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
                const af::dim4 &odims) {
-    return createBinaryNode<T, T, op>(lhs, rhs, odims);
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace opencl

From ea52651a56d166627874272747547b5271002057 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Aug 2021 19:11:10 -0400
Subject: [PATCH 2192/2677] Move cast and castArray to the common directory

---
 src/api/c/anisotropic_diffusion.cpp |  4 +-
 src/api/c/canny.cpp                 |  6 ++-
 src/api/c/cast.cpp                  |  2 +-
 src/api/c/confidence_connected.cpp  |  4 +-
 src/api/c/convolve.cpp              |  4 +-
 src/api/c/corrcoef.cpp              |  4 +-
 src/api/c/covariance.cpp            |  4 +-
 src/api/c/deconvolution.cpp         |  4 +-
 src/api/c/fftconvolve.cpp           |  6 ++-
 src/api/c/handle.hpp                | 34 ++------------
 src/api/c/hist.cpp                  |  2 +-
 src/api/c/histeq.cpp                |  4 +-
 src/api/c/image.cpp                 |  4 +-
 src/api/c/imgproc_common.hpp        |  8 ++--
 src/api/c/implicit.hpp              |  2 +-
 src/api/c/mean.cpp                  |  2 +-
 src/api/c/median.cpp                |  4 +-
 src/api/c/moments.cpp               |  2 +-
 src/api/c/morph.cpp                 |  4 +-
 src/api/c/pinverse.cpp              |  4 +-
 src/api/c/rgb_gray.cpp              |  4 +-
 src/api/c/sparse_handle.hpp         |  4 +-
 src/api/c/stdev.cpp                 |  4 +-
 src/api/c/unary.cpp                 |  2 +-
 src/api/c/var.cpp                   |  4 +-
 src/backend/common/CMakeLists.txt   |  2 +
 src/backend/common/cast.cpp         | 62 +++++++++++++++++++++++++
 src/backend/common/cast.hpp         | 72 +++++++++++++++++++++++++++++
 src/backend/cpu/blas.cpp            |  3 +-
 src/backend/cpu/cast.hpp            | 23 ---------
 src/backend/cpu/sparse.cpp          |  3 +-
 src/backend/cuda/blas.cu            |  2 +-
 src/backend/cuda/cast.hpp           | 23 ---------
 src/backend/cuda/convolveNN.cpp     |  2 +-
 src/backend/cuda/sparse.cu          |  2 +-
 src/backend/cuda/sparse_arith.cu    |  2 +-
 src/backend/opencl/cast.hpp         | 23 ---------
 src/backend/opencl/sparse.cpp       |  2 +-
 src/backend/opencl/sparse_arith.cpp |  2 +-
 39 files changed, 197 insertions(+), 152 deletions(-)
 create mode 100644 src/backend/common/cast.cpp
 create mode 100644 src/backend/common/cast.hpp

diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index ceed210548..24335a406e 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -11,7 +11,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <copy.hpp>
 #include <gradient.hpp>
@@ -24,9 +24,9 @@
 #include <type_traits>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::createEmptyArray;
 using detail::gradient;
 using detail::reduce_all;
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index d625360d3b..0c67ddb03d 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -7,10 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <canny.hpp>
+
 #include <Array.hpp>
 #include <arith.hpp>
 #include <backend.hpp>
-#include <canny.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <convolve.hpp>
@@ -34,9 +36,9 @@
 #include <vector>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::convolve2;
 using detail::createEmptyArray;
 using detail::createHostDataArray;
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 43ee4e9dad..c4f66cdf34 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index 012fa89579..174ed3c688 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -10,7 +10,7 @@
 #include <af/image.h>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <flood_fill.hpp>
 #include <handle.hpp>
@@ -24,10 +24,10 @@
 #include <type_traits>
 
 using af::dim4;
+using common::cast;
 using common::createSpanIndex;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::createValueArray;
 using detail::reduce_all;
 using detail::uchar;
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 4df2f6fe6c..b7581dd484 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -10,7 +10,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <fftconvolve.hpp>
@@ -25,10 +25,10 @@
 #include <cstdio>
 
 using af::dim4;
+using common::cast;
 using common::half;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::convolve;
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index 462d8897ce..2ee5e45d6a 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <math.hpp>
@@ -23,9 +23,9 @@
 #include <cmath>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::intl;
 using detail::reduce_all;
 using detail::uchar;
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index be86a36e17..80108c4b0b 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <mean.hpp>
@@ -23,9 +23,9 @@
 #include "stats.h"
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::createValueArray;
 using detail::intl;
 using detail::mean;
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index d5c67757dc..43c83965e3 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/dispatch.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
@@ -32,9 +32,9 @@
 #include <vector>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createSubArray;
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index bd10287cb4..58cbc9e2c4 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -7,13 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <fftconvolve.hpp>
+
 #include <arith.hpp>
 #include <backend.hpp>
+#include <common/cast.hpp>
 #include <common/dispatch.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <fft_common.hpp>
-#include <fftconvolve.hpp>
 #include <handle.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -24,9 +26,9 @@
 #include <vector>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createSubArray;
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index de91cbfdc2..6332d1d162 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -10,7 +10,6 @@
 #pragma once
 #include <Array.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <common/traits.hpp>
@@ -33,6 +32,9 @@ af_array createHandle(const af::dim4 &d, af_dtype dtype);
 
 af_array createHandleFromValue(const af::dim4 &d, double val, af_dtype dtype);
 
+template<typename To>
+detail::Array<To> castArray(const af_array &in);
+
 namespace {
 
 template<typename T>
@@ -68,36 +70,6 @@ detail::Array<T> &getArray(af_array &arr) {
     return *A;
 }
 
-template<typename To>
-detail::Array<To> castArray(const af_array &in) {
-    using detail::cdouble;
-    using detail::cfloat;
-    using detail::intl;
-    using detail::uchar;
-    using detail::uint;
-    using detail::uintl;
-    using detail::ushort;
-
-    const ArrayInfo &info = getInfo(in);
-    switch (info.getType()) {
-        case f32: return detail::cast<To, float>(getArray<float>(in));
-        case f64: return detail::cast<To, double>(getArray<double>(in));
-        case c32: return detail::cast<To, cfloat>(getArray<cfloat>(in));
-        case c64: return detail::cast<To, cdouble>(getArray<cdouble>(in));
-        case s32: return detail::cast<To, int>(getArray<int>(in));
-        case u32: return detail::cast<To, uint>(getArray<uint>(in));
-        case u8: return detail::cast<To, uchar>(getArray<uchar>(in));
-        case b8: return detail::cast<To, char>(getArray<char>(in));
-        case s64: return detail::cast<To, intl>(getArray<intl>(in));
-        case u64: return detail::cast<To, uintl>(getArray<uintl>(in));
-        case s16: return detail::cast<To, short>(getArray<short>(in));
-        case u16: return detail::cast<To, ushort>(getArray<ushort>(in));
-        case f16:
-            return detail::cast<To, common::half>(getArray<common::half>(in));
-        default: TYPE_ERROR(1, info.getType());
-    }
-}
-
 template<typename T>
 af_array getHandle(const detail::Array<T> &A) {
     detail::Array<T> *ret = new detail::Array<T>(A);
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index ae93108e79..0fad162819 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 6b1e57cf49..a542d97a73 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <lookup.hpp>
@@ -21,9 +21,9 @@
 #include <af/index.h>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::createValueArray;
 using detail::intl;
 using detail::lookup;
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 8f172a6762..4b93727d01 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -14,8 +14,8 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <handle.hpp>
@@ -27,9 +27,9 @@
 #include <limits>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::copy_image;
 using detail::createValueArray;
 using detail::forgeManager;
diff --git a/src/api/c/imgproc_common.hpp b/src/api/c/imgproc_common.hpp
index 818d11c763..bf16be980a 100644
--- a/src/api/c/imgproc_common.hpp
+++ b/src/api/c/imgproc_common.hpp
@@ -11,7 +11,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <logic.hpp>
 #include <reduce.hpp>
 #include <scan.hpp>
@@ -22,7 +22,7 @@ namespace common {
 
 template<typename To, typename Ti = To>
 detail::Array<To> integralImage(const detail::Array<Ti>& in) {
-    auto input                       = detail::cast<To, Ti>(in);
+    auto input                       = common::cast<To, Ti>(in);
     detail::Array<To> horizontalScan = detail::scan<af_add_t, To, To>(input, 0);
     return detail::scan<af_add_t, To, To>(horizontalScan, 1);
 }
@@ -37,7 +37,7 @@ detail::Array<T> threshold(const detail::Array<T>& in, T min, T max) {
     auto above = detail::logicOp<T, af_ge_t>(in, MN, inDims);
     auto valid = detail::logicOp<char, af_and_t>(below, above, inDims);
 
-    return detail::arithOp<T, af_mul_t>(in, detail::cast<T, char>(valid),
+    return detail::arithOp<T, af_mul_t>(in, common::cast<T, char>(valid),
                                         inDims);
 }
 
@@ -45,7 +45,7 @@ template<typename To, typename Ti>
 detail::Array<To> convRange(const detail::Array<Ti>& in,
                             const To newLow = To(0), const To newHigh = To(1)) {
     auto dims  = in.dims();
-    auto input = detail::cast<To, Ti>(in);
+    auto input = common::cast<To, Ti>(in);
     To high    = detail::reduce_all<af_max_t, To, To>(input);
     To low     = detail::reduce_all<af_min_t, To, To>(input);
     To range   = high - low;
diff --git a/src/api/c/implicit.hpp b/src/api/c/implicit.hpp
index 704e90a4f5..d70240e33a 100644
--- a/src/api/c/implicit.hpp
+++ b/src/api/c/implicit.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <handle.hpp>
 #include <optypes.hpp>
 #include <types.hpp>
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 28c41eb334..2dfb7bdbf2 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 07652b121c..5e22c1c36a 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -8,7 +8,7 @@
  ********************************************************/
 
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <handle.hpp>
 #include <math.hpp>
@@ -36,7 +36,7 @@ static double median(const af_array& in) {
 
     af_array temp = 0;
     AF_CHECK(af_moddims(&temp, in, 1, dims.get()));
-    const Array<T> input = getArray<T>(temp);
+    const Array<T>& input = getArray<T>(temp);
 
     // Shortcut cases for 1 or 2 elements
     if (nElems == 1) {
diff --git a/src/api/c/moments.cpp b/src/api/c/moments.cpp
index 985c1e6e60..ecef793a50 100644
--- a/src/api/c/moments.cpp
+++ b/src/api/c/moments.cpp
@@ -13,8 +13,8 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <handle.hpp>
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 674020c3ec..e95ee06b25 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/indexing_helpers.hpp>
 #include <copy.hpp>
@@ -24,10 +24,10 @@
 #include <af/image.h>
 
 using af::dim4;
+using common::cast;
 using common::flip;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 0d0c8496af..0aff145194 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -12,8 +12,8 @@
 
 #include <arith.hpp>
 #include <blas.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <diagonal.hpp>
 #include <handle.hpp>
@@ -31,9 +31,9 @@
 
 using af::dim4;
 using af::dtype_traits;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 250958124d..e801881447 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -15,17 +15,17 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <math.hpp>
 #include <tile.hpp>
 
 using af::dim4;
+using common::cast;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::createValueArray;
 using detail::join;
 using detail::scalar;
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 3356be24cb..72b251473b 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -10,7 +10,7 @@
 #pragma once
 #include <Array.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
@@ -66,7 +66,7 @@ common::SparseArray<To> castSparse(const af_array &in) {
 #define CAST_SPARSE(Ti)                                                          \
     do {                                                                         \
         const SparseArray<Ti> sparse = getSparseArray<Ti>(in);                   \
-        detail::Array<To> values     = detail::cast<To, Ti>(sparse.getValues()); \
+        detail::Array<To> values     = common::cast<To, Ti>(sparse.getValues()); \
         return createArrayDataSparseArray(                                       \
             sparse.dims(), values, sparse.getRowIdx(), sparse.getColIdx(),       \
             sparse.getStorage());                                                \
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 4123a4f315..4f66328782 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <mean.hpp>
@@ -25,8 +25,8 @@
 #include "stats.h"
 
 using af::dim4;
+using common::cast;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createValueArray;
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 8ea0abe3c5..95e48d75bc 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -15,8 +15,8 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <complex.hpp>
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index 2b9ea45c6a..fe111de5f5 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -9,7 +9,7 @@
 
 #include <arith.hpp>
 #include <backend.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <handle.hpp>
@@ -25,10 +25,10 @@
 #include <tuple>
 
 using af::dim4;
+using common::cast;
 using common::half;
 using detail::arithOp;
 using detail::Array;
-using detail::cast;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 3175f2b4cd..204b27f927 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -43,6 +43,8 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateTypename.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/cast.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/cast.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cblas.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/compile_module.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/complex.hpp
diff --git a/src/backend/common/cast.cpp b/src/backend/common/cast.cpp
new file mode 100644
index 0000000000..f02267ecd0
--- /dev/null
+++ b/src/backend/common/cast.cpp
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/cast.hpp>
+#include <handle.hpp>
+
+using common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+
+template<typename To>
+detail::Array<To> castArray(const af_array &in) {
+    const ArrayInfo &info = getInfo(in);
+
+    if (static_cast<af::dtype>(af::dtype_traits<To>::af_type) ==
+        info.getType()) {
+        return getArray<To>(in);
+    }
+
+    switch (info.getType()) {
+        case f32: return common::cast<To, float>(getArray<float>(in));
+        case f64: return common::cast<To, double>(getArray<double>(in));
+        case c32: return common::cast<To, cfloat>(getArray<cfloat>(in));
+        case c64: return common::cast<To, cdouble>(getArray<cdouble>(in));
+        case s32: return common::cast<To, int>(getArray<int>(in));
+        case u32: return common::cast<To, uint>(getArray<uint>(in));
+        case u8: return common::cast<To, uchar>(getArray<uchar>(in));
+        case b8: return common::cast<To, char>(getArray<char>(in));
+        case s64: return common::cast<To, intl>(getArray<intl>(in));
+        case u64: return common::cast<To, uintl>(getArray<uintl>(in));
+        case s16: return common::cast<To, short>(getArray<short>(in));
+        case u16: return common::cast<To, ushort>(getArray<ushort>(in));
+        case f16:
+            return common::cast<To, common::half>(getArray<common::half>(in));
+        default: TYPE_ERROR(1, info.getType());
+    }
+}
+
+template detail::Array<float> castArray(const af_array &in);
+template detail::Array<double> castArray(const af_array &in);
+template detail::Array<cfloat> castArray(const af_array &in);
+template detail::Array<cdouble> castArray(const af_array &in);
+template detail::Array<int> castArray(const af_array &in);
+template detail::Array<uint> castArray(const af_array &in);
+template detail::Array<uchar> castArray(const af_array &in);
+template detail::Array<char> castArray(const af_array &in);
+template detail::Array<intl> castArray(const af_array &in);
+template detail::Array<uintl> castArray(const af_array &in);
+template detail::Array<short> castArray(const af_array &in);
+template detail::Array<ushort> castArray(const af_array &in);
+template detail::Array<half> castArray(const af_array &in);
diff --git a/src/backend/common/cast.hpp b/src/backend/common/cast.hpp
new file mode 100644
index 0000000000..c8579a2596
--- /dev/null
+++ b/src/backend/common/cast.hpp
@@ -0,0 +1,72 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <cast.hpp>
+
+#ifdef AF_CPU
+#include <jit/UnaryNode.hpp>
+#endif
+
+namespace common {
+
+#ifdef AF_CPU
+template<typename To, typename Ti>
+struct CastWrapper {
+    detail::Array<To> operator()(const detail::Array<Ti> &in) {
+        using cpu::jit::UnaryNode;
+        Node_ptr in_node = in.getNode();
+        UnaryNode<To, Ti, af_cast_t> *node =
+            new UnaryNode<To, Ti, af_cast_t>(in_node);
+        return detail::createNodeArray<To>(
+            in.dims(),
+            common::Node_ptr(reinterpret_cast<common::Node *>(node)));
+    }
+};
+#else
+template<typename To, typename Ti>
+struct CastWrapper {
+    detail::Array<To> operator()(const detail::Array<Ti> &in) {
+        detail::CastOp<To, Ti> cop;
+        common::Node_ptr in_node = in.getNode();
+        common::UnaryNode *node  = new common::UnaryNode(
+            static_cast<af::dtype>(dtype_traits<To>::af_type), cop.name(),
+            in_node, af_cast_t);
+        return detail::createNodeArray<To>(in.dims(), common::Node_ptr(node));
+    }
+};
+#endif
+
+template<typename T>
+struct CastWrapper<T, T> {
+    detail::Array<T> operator()(const detail::Array<T> &in);
+};
+
+template<typename To, typename Ti>
+auto cast(detail::Array<Ti> &&in)
+    -> std::enable_if_t<std::is_same<Ti, To>::value, detail::Array<To>> {
+    return std::move(in);
+}
+
+template<typename To, typename Ti>
+auto cast(const detail::Array<Ti> &in)
+    -> std::enable_if_t<std::is_same<Ti, To>::value, detail::Array<To>> {
+    return in;
+}
+
+template<typename To, typename Ti>
+auto cast(const detail::Array<Ti> &in)
+    -> std::enable_if_t<std::is_same<Ti, To>::value == false,
+                        detail::Array<To>> {
+    CastWrapper<To, Ti> cast_op;
+    return cast_op(in);
+}
+
+}  // namespace common
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 6f59974a80..463c3e8fe1 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -15,8 +15,8 @@
 
 #include <Array.hpp>
 #include <Param.hpp>
-#include <cast.hpp>
 #include <common/blas_headers.hpp>
+#include <common/cast.hpp>
 #include <common/complex.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
@@ -34,6 +34,7 @@
 #include <vector>
 
 using af::dtype_traits;
+using common::cast;
 using common::half;
 using common::is_complex;
 using std::conditional;
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 5098d8b109..992030407a 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -152,27 +152,4 @@ CAST_B8(int)
 CAST_B8(uchar)
 CAST_B8(char)
 
-template<typename To, typename Ti>
-struct CastWrapper {
-    Array<To> operator()(const Array<Ti> &in) {
-        common::Node_ptr in_node = in.getNode();
-        jit::UnaryNode<To, Ti, af_cast_t> *node =
-            new jit::UnaryNode<To, Ti, af_cast_t>(in_node);
-        return createNodeArray<To>(
-            in.dims(),
-            common::Node_ptr(reinterpret_cast<common::Node *>(node)));
-    }
-};
-
-template<typename T>
-struct CastWrapper<T, T> {
-    Array<T> operator()(const Array<T> &in) { return in; }
-};
-
-template<typename To, typename Ti>
-Array<To> cast(const Array<Ti> &in) {
-    CastWrapper<To, Ti> cast_op;
-    return cast_op(in);
-}
-
 }  // namespace cpu
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 7e490d0983..bf2565883e 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -14,7 +14,7 @@
 #include <string>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/complex.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
@@ -28,6 +28,7 @@
 
 #include <functional>
 
+using common::cast;
 using std::function;
 
 namespace cpu {
diff --git a/src/backend/cuda/blas.cu b/src/backend/cuda/blas.cu
index dd906b2ecf..bb88c60feb 100644
--- a/src/backend/cuda/blas.cu
+++ b/src/backend/cuda/blas.cu
@@ -10,7 +10,7 @@
 #include <blas.hpp>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <complex.hpp>
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 1dc8c3ae06..bae9b3cbb6 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -84,27 +84,4 @@ struct CastOp<unsigned char, common::half> {
 #undef CAST_FN
 #undef CAST_CFN
 
-template<typename To, typename Ti>
-struct CastWrapper {
-    Array<To> operator()(const Array<Ti> &in) {
-        CastOp<To, Ti> cop;
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node  = new common::UnaryNode(
-            static_cast<af::dtype>(dtype_traits<To>::af_type), cop.name(),
-            in_node, af_cast_t);
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
-};
-
-template<typename T>
-struct CastWrapper<T, T> {
-    Array<T> operator()(const Array<T> &in) { return in; }
-};
-
-template<typename To, typename Ti>
-Array<To> cast(const Array<Ti> &in) {
-    CastWrapper<To, Ti> cast_op;
-    return cast_op(in);
-}
-
 }  // namespace cuda
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 2a4a57174f..8e8d7194d7 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -11,7 +11,7 @@
 
 #include <Array.hpp>
 #include <blas.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/half.hpp>
 #include <common/indexing_helpers.hpp>
 #include <common/unique_handle.hpp>
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 6511cc4ce6..47dad93e07 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -10,7 +10,7 @@
 #include <sparse.hpp>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index b3fceba7c0..11a38c58e1 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -10,7 +10,7 @@
 #include <sparse_arith.hpp>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/unique_handle.hpp>
 #include <complex.hpp>
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index 2ce6f5fc7b..3f3a0c1001 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -70,27 +70,4 @@ struct CastOp<cdouble, cdouble> {
 #undef CAST_FN
 #undef CAST_CFN
 
-template<typename To, typename Ti>
-struct CastWrapper {
-    Array<To> operator()(const Array<Ti> &in) {
-        CastOp<To, Ti> cop;
-        common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node  = new common::UnaryNode(
-            static_cast<af::dtype>(dtype_traits<To>::af_type), cop.name(),
-            in_node, af_cast_t);
-        return createNodeArray<To>(in.dims(), common::Node_ptr(node));
-    }
-};
-
-template<typename T>
-struct CastWrapper<T, T> {
-    Array<T> operator()(const Array<T> &in) { return in; }
-};
-
-template<typename To, typename Ti>
-Array<To> cast(const Array<Ti> &in) {
-    CastWrapper<To, Ti> cast_op;
-    return cast_op(in);
-}
-
 }  // namespace opencl
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 2e79d558c2..ceba3469cc 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -14,7 +14,7 @@
 #include <string>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index 9e7545503d..5de05b873a 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -14,7 +14,7 @@
 #include <string>
 
 #include <arith.hpp>
-#include <cast.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>

From fb23fc94c8d9849fa479b38033becef8f077eabf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Aug 2021 20:10:19 -0400
Subject: [PATCH 2193/2677] Use getArray instead of castArray if types are the
 same in arithOp

---
 src/api/c/binary.cpp                | 15 ++++++++++++---
 src/backend/common/jit/NaryNode.hpp |  4 +++-
 src/backend/cpu/Array.cpp           |  2 +-
 src/backend/cpu/arith.hpp           |  6 ++++++
 src/backend/cuda/arith.hpp          |  7 +++++++
 src/backend/opencl/arith.hpp        |  7 +++++++
 6 files changed, 36 insertions(+), 5 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index f2263bf579..ffe21e2591 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -27,6 +27,7 @@
 #include <common/half.hpp>
 
 using af::dim4;
+using af::dtype;
 using common::half;
 using detail::arithOp;
 using detail::arithOpD;
@@ -41,9 +42,17 @@ using detail::ushort;
 template<typename T, af_op_t op>
 static inline af_array arithOp(const af_array lhs, const af_array rhs,
                                const dim4 &odims) {
-    af_array res =
-        getHandle(arithOp<T, op>(castArray<T>(lhs), castArray<T>(rhs), odims));
-    return res;
+    const ArrayInfo &linfo = getInfo(lhs);
+    const ArrayInfo &rinfo = getInfo(rhs);
+
+    dtype type = static_cast<af::dtype>(af::dtype_traits<T>::af_type);
+
+    const detail::Array<T> &l =
+        linfo.getType() == type ? getArray<T>(lhs) : castArray<T>(lhs);
+    const detail::Array<T> &r =
+        rinfo.getType() == type ? getArray<T>(rhs) : castArray<T>(rhs);
+
+    return getHandle(arithOp<T, op>(l, r, odims));
 }
 
 template<typename T, af_op_t op>
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 6001c25b51..5c37b0da82 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -94,7 +94,9 @@ common::Node_ptr createNaryNode(
     const af::dim4 &odims, FUNC createNode,
     std::array<const detail::Array<Ti> *, N> &&children) {
     std::array<common::Node_ptr, N> childNodes;
-    for (int i = 0; i < N; i++) { childNodes[i] = children[i]->getNode(); }
+    for (int i = 0; i < N; i++) {
+        childNodes[i] = move(children[i]->getNode());
+    }
 
     common::Node_ptr ptr = createNode(childNodes);
 
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index c5a4cce329..0d0438621f 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -273,7 +273,7 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
 
 template<typename T>
 Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
-    Array<T> out = Array<T>(dims, node);
+    Array<T> out(dims, node);
     return out;
 }
 
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index edce28eddf..7a8e5a2402 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -15,6 +15,12 @@
 
 namespace cpu {
 
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &&lhs, const Array<T> &&rhs,
+                 const af::dim4 &odims) {
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
+}
+
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index 500845c15b..f478ecf6c0 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -14,6 +14,13 @@
 #include <af/dim4.hpp>
 
 namespace cuda {
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &&lhs, const Array<T> &&rhs,
+                 const af::dim4 &odims) {
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
+}
+
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index 3e6e9aa226..48bab53038 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -15,6 +15,13 @@
 #include <af/dim4.hpp>
 
 namespace opencl {
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &&lhs, const Array<T> &&rhs,
+                 const af::dim4 &odims) {
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
+}
+
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {

From fad0bce65e2994ddf0f256cdd4d3a964bd127ff7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 10 Aug 2021 02:02:20 -0400
Subject: [PATCH 2194/2677] Create a hash function for Node objects for NodeMap

The Node_map_t unordered_map object uses the pointer of the nodes for the key.
This worked because you could previously because the node buffer objects tracked
the buffer object's shared pointer. This required holding an additional
reference to the buffer object when an Array was used in a JIT operation. This
did not leak memory because both the buffer and the node were deleted when the
Array object was destroyed.

This commit creates a new hash function for the node pointers which dereferences
the Node pointers and if they are buffers, it checks the buffer's pointer and
its offset to determine if its unique. This approach allows us to remove the
call_once construct from the setData member function of the buffer node.
You can now create node objects for each invocation getNode function.
---
 src/backend/common/jit/BinaryNode.cpp     |  6 ++--
 src/backend/common/jit/BufferNodeBase.hpp | 23 +++++++++++++
 src/backend/common/jit/Node.cpp           | 10 ++++++
 src/backend/common/jit/Node.hpp           | 42 +++++++++++++++++++++--
 src/backend/cpu/jit/BufferNode.hpp        | 39 +++++++++++++++++++--
 src/backend/cpu/jit/Node.hpp              |  1 -
 src/backend/cuda/jit/BufferNode.hpp       | 15 ++++++++
 src/backend/opencl/jit/BufferNode.hpp     | 14 ++++++++
 8 files changed, 141 insertions(+), 9 deletions(-)

diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
index b5e2cfb312..05e855ca3c 100644
--- a/src/backend/common/jit/BinaryNode.cpp
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -34,9 +34,9 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
                            const af::dim4 &odims) {
     auto createBinary = [](std::array<Node_ptr, 2> &operands) -> Node_ptr {
         BinOp<To, Ti, op> bop;
-        return Node_ptr(
-            new BinaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
-                           bop.name(), operands[0], operands[1], (int)(op)));
+        return std::make_shared<BinaryNode>(
+            static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(),
+            operands[0], operands[1], (int)(op));
     };
 
     Node_ptr out =
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 3402f9a50d..9fea280504 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -92,6 +92,29 @@ class BufferNodeBase : public common::Node {
     }
 
     size_t getBytes() const final { return m_bytes; }
+
+    size_t getHash() const noexcept {
+        size_t out = 0;
+        auto ptr   = m_data.get();
+        memcpy(&out, &ptr, std::max(sizeof(Node *), sizeof(size_t)));
+        return out;
+    }
+
+    /// Compares two BufferNodeBase objects for equality
+    bool operator==(
+        const BufferNodeBase<DataType, ParamType> &other) const noexcept;
+
+    /// Overloads the equality operator to call comparisons between Buffer
+    /// objects. Calls the BufferNodeBase equality operator if the other
+    /// object is also a Buffer Node
+    bool operator==(const common::Node &other) const noexcept final {
+        if (other.isBuffer()) {
+            return *this ==
+                   static_cast<const BufferNodeBase<DataType, ParamType> &>(
+                       other);
+        }
+        return false;
+    }
 };
 
 }  // namespace common
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 3ed3bc4b89..096164a16b 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -57,4 +57,14 @@ std::string getFuncName(const vector<Node *> &output_nodes,
     return "KER" + std::to_string(deterministicHash(funcName));
 }
 
+bool NodePtr_equalto::operator()(const Node *l, const Node *r) const noexcept {
+    return *l == *r;
+}
+
 }  // namespace common
+
+size_t std::hash<common::Node *>::operator()(
+    common::Node *const node) const noexcept {
+    common::Node *const node_ptr = static_cast<common::Node *const>(node);
+    return node_ptr->getHash();
+}
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index d4b3a23d51..81daca577d 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -30,14 +30,33 @@ enum class kJITHeuristics {
     MemoryPressure      = 3  /* eval due to memory pressure */
 };
 
+namespace common {
+class Node;
+}
+
+namespace std {
+template<>
+struct hash<common::Node *> {
+    /// Calls the getHash function of the Node pointer
+    size_t operator()(common::Node *const n) const noexcept;
+};
+}  // namespace std
+
 namespace common {
 class Node;
 struct Node_ids;
 
-using Node_ptr      = std::shared_ptr<Node>;
-using Node_map_t    = std::unordered_map<Node *, int>;
+/// A equal_to class that calls the dereference nodes equality operator
+struct NodePtr_equalto {
+    bool operator()(const Node *l, const Node *r) const noexcept;
+};
+
+using Node_map_t =
+    std::unordered_map<Node *, int, std::hash<Node *>, NodePtr_equalto>;
 using Node_map_iter = Node_map_t::iterator;
 
+using Node_ptr = std::shared_ptr<Node>;
+
 static const char *getFullName(af::dtype type) {
     switch (type) {
         case f32: return detail::getFullName<float>();
@@ -215,6 +234,8 @@ class Node {
         return true;
     }
 
+    af::dtype getType() const { return m_type; }
+
     /// Returns the string representation of the type
     std::string getTypeStr() const { return getFullName(m_type); }
 
@@ -228,6 +249,23 @@ class Node {
 
     /// Default destructor
     virtual ~Node() noexcept = default;
+
+    /// Returns the hash of the node. For all Nodes other than the Buffer node,
+    /// this is the pointer of the object
+    virtual size_t getHash() const noexcept {
+        std::hash<const void *> ptr_hash;
+        std::hash<af::dtype> aftype_hash;
+        std::hash<int> int_hash;
+        const void *ptr = this;
+        size_t h =
+            ptr_hash(ptr) ^ (aftype_hash(m_type) << 1) ^ (int_hash(m_height));
+        return h;
+    }
+
+    /// A very bad equality operator used only for the hash function.
+    virtual bool operator==(const Node &other) const noexcept {
+        return this == &other;
+    }
 };
 
 struct Node_ids {
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index e26b0aa4a4..d32060cf60 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -11,10 +11,13 @@
 
 #include <optypes.hpp>
 #include <af/defines.h>
-
-#include <mutex>
-#include <vector>
 #include "Node.hpp"
+
+#include <functional>
+#include <memory>
+#include <sstream>
+#include <string>
+
 namespace cpu {
 
 namespace jit {
@@ -126,6 +129,36 @@ class BufferNode : public TNode<T> {
     }
 
     bool isBuffer() const final { return true; }
+
+    size_t getHash() const noexcept final {
+        std::hash<const void *> ptr_hash;
+        std::hash<af::dtype> aftype_hash;
+        return ptr_hash(static_cast<const void *>(m_ptr)) ^
+               (aftype_hash(
+                    static_cast<af::dtype>(af::dtype_traits<T>::af_type))
+                << 1);
+    }
+
+    /// Compares two BufferNodeBase objects for equality
+    bool operator==(const BufferNode<T> &other) const noexcept {
+        using std::begin;
+        using std::end;
+        using std::equal;
+        return m_ptr == other.m_ptr && m_bytes == other.m_bytes &&
+               m_linear_buffer == other.m_linear_buffer &&
+               equal(begin(m_dims), end(m_dims), begin(other.m_dims)) &&
+               equal(begin(m_strides), end(m_strides), begin(other.m_strides));
+    };
+
+    /// Overloads the equality operator to call comparisons between Buffer
+    /// objects. Calls the BufferNodeBase equality operator if the other
+    /// object is also a Buffer Node
+    bool operator==(const common::Node &other) const noexcept final {
+        if (other.isBuffer() && this->getType() == other.getType()) {
+            return *this == static_cast<const BufferNode<T> &>(other);
+        }
+        return false;
+    }
 };
 
 }  // namespace jit
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index 174489274c..c7e7f3a708 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -18,7 +18,6 @@
 #include <array>
 #include <memory>
 #include <unordered_map>
-#include <vector>
 
 namespace common {
 template<typename T>
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
index 371a263245..21601f2a03 100644
--- a/src/backend/cuda/jit/BufferNode.hpp
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -16,4 +16,19 @@ namespace jit {
 template<typename T>
 using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
 }
+
 }  // namespace cuda
+
+namespace common {
+
+template<typename DataType, typename ParamType>
+bool BufferNodeBase<DataType, ParamType>::operator==(
+    const BufferNodeBase<DataType, ParamType> &other) const noexcept {
+    // clang-format off
+    return m_data.get() == other.m_data.get() &&
+           m_bytes == other.m_bytes &&
+           m_param.ptr == other.m_param.ptr;
+    // clang-format on
+}
+
+}  // namespace common
diff --git a/src/backend/opencl/jit/BufferNode.hpp b/src/backend/opencl/jit/BufferNode.hpp
index 84ca574965..1aa2e00f2b 100644
--- a/src/backend/opencl/jit/BufferNode.hpp
+++ b/src/backend/opencl/jit/BufferNode.hpp
@@ -20,3 +20,17 @@ namespace jit {
 using BufferNode = common::BufferNodeBase<std::shared_ptr<cl::Buffer>, KParam>;
 }
 }  // namespace opencl
+
+namespace common {
+
+template<typename DataType, typename ParamType>
+bool BufferNodeBase<DataType, ParamType>::operator==(
+    const BufferNodeBase<DataType, ParamType> &other) const noexcept {
+    // clang-format off
+    return m_data.get() == other.m_data.get() &&
+           m_bytes == other.m_bytes &&
+           m_param.offset == other.m_param.offset;
+    // clang-format on
+}
+
+}  // namespace common

From a57b29194608b421fb962d5ce114bf6502a8a5dc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 5 Aug 2021 03:14:52 -0400
Subject: [PATCH 2195/2677] Fix reference count if array used in JIT
 operations.

Previously when an af::array was used in a jit operation and it was backed by a
buffer, a buffer node was created and the internal shared_ptr was stored in the
Array for future use and returned when getNode was called. This increased the
reference count of the internal buffer. This reference count never decreased
because of the internal reference to the shared_ptr.

This commit changes this behavior by createing new buffer nodes for each
call the getNode. We use the new hash function to ensure the equality of
the buffer node when the jit code is generated. This avoids holding the
call_once flag in the buffer object and simplifies the management of
the buffer node objects. Additionally when a jit node goes out of scope
the reference count decrements as expected.
---
 src/backend/common/cast.hpp               |  9 ++-
 src/backend/common/jit/BinaryNode.cpp     | 10 ++-
 src/backend/common/jit/BufferNodeBase.hpp | 19 ++----
 src/backend/common/jit/Node.hpp           | 48 +++++++++++---
 src/backend/cpu/Array.cpp                 | 76 +++++++++--------------
 src/backend/cpu/Array.hpp                 | 26 +++++---
 src/backend/cpu/binary.hpp                |  1 +
 src/backend/cpu/complex.hpp               | 24 +++----
 src/backend/cpu/jit/BinaryNode.hpp        | 15 ++++-
 src/backend/cpu/jit/BufferNode.hpp        | 34 +++++-----
 src/backend/cpu/jit/Node.hpp              |  6 +-
 src/backend/cpu/jit/UnaryNode.hpp         |  8 ++-
 src/backend/cpu/kernel/Array.hpp          | 28 ++++++---
 src/backend/cpu/unary.hpp                 |  9 ++-
 src/backend/cuda/Array.cpp                | 74 ++++++++++------------
 src/backend/cuda/Array.hpp                | 18 +++---
 src/backend/opencl/Array.cpp              | 62 ++++++++----------
 src/backend/opencl/Array.hpp              | 23 ++++---
 src/backend/opencl/jit/BufferNode.hpp     |  6 +-
 test/array.cpp                            | 48 ++++++++++++++
 test/convolve.cpp                         |  8 +--
 test/jit.cpp                              |  7 ++-
 22 files changed, 319 insertions(+), 240 deletions(-)

diff --git a/src/backend/common/cast.hpp b/src/backend/common/cast.hpp
index c8579a2596..b266d8517a 100644
--- a/src/backend/common/cast.hpp
+++ b/src/backend/common/cast.hpp
@@ -22,12 +22,11 @@ template<typename To, typename Ti>
 struct CastWrapper {
     detail::Array<To> operator()(const detail::Array<Ti> &in) {
         using cpu::jit::UnaryNode;
+
         Node_ptr in_node = in.getNode();
-        UnaryNode<To, Ti, af_cast_t> *node =
-            new UnaryNode<To, Ti, af_cast_t>(in_node);
-        return detail::createNodeArray<To>(
-            in.dims(),
-            common::Node_ptr(reinterpret_cast<common::Node *>(node)));
+        auto node = std::make_shared<UnaryNode<To, Ti, af_cast_t>>(in_node);
+
+        return detail::createNodeArray<To>(in.dims(), move(node));
     }
 };
 #else
diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
index 05e855ca3c..00af405ecf 100644
--- a/src/backend/common/jit/BinaryNode.cpp
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -5,6 +5,8 @@
 #include <complex.hpp>
 #include <types.hpp>
 
+#include <memory>
+
 using af::dim4;
 using af::dtype_traits;
 using detail::Array;
@@ -13,6 +15,8 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createNodeArray;
 
+using std::make_shared;
+
 namespace common {
 #ifdef AF_CPU
 template<typename To, typename Ti, af_op_t op>
@@ -21,10 +25,10 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
     common::Node_ptr lhs_node = lhs.getNode();
     common::Node_ptr rhs_node = rhs.getNode();
 
-    detail::jit::BinaryNode<To, Ti, op> *node =
-        new detail::jit::BinaryNode<To, Ti, op>(lhs_node, rhs_node);
+    auto node =
+        make_shared<detail::jit::BinaryNode<To, Ti, op>>(lhs_node, rhs_node);
 
-    return createNodeArray<To>(odims, common::Node_ptr(node));
+    return createNodeArray<To>(odims, move(node));
 }
 
 #else
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 9fea280504..026fbd4ce7 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -12,8 +12,6 @@
 #include <common/jit/Node.hpp>
 #include <jit/kernel_generators.hpp>
 
-#include <iomanip>
-#include <mutex>
 #include <sstream>
 
 namespace common {
@@ -24,25 +22,20 @@ class BufferNodeBase : public common::Node {
     DataType m_data;
     ParamType m_param;
     unsigned m_bytes;
-    std::once_flag m_set_data_flag;
     bool m_linear_buffer;
 
    public:
-    BufferNodeBase(af::dtype type) : Node(type, 0, {}) {
-        // This class is not movable because of std::once_flag
-    }
+    BufferNodeBase(af::dtype type)
+        : Node(type, 0, {}), m_bytes(0), m_linear_buffer(true) {}
 
     bool isBuffer() const final { return true; }
 
     void setData(ParamType param, DataType data, const unsigned bytes,
                  bool is_linear) {
-        std::call_once(m_set_data_flag,
-                       [this, param, data, bytes, is_linear]() {
-                           m_param         = param;
-                           m_data          = data;
-                           m_bytes         = bytes;
-                           m_linear_buffer = is_linear;
-                       });
+        m_param         = param;
+        m_data          = data;
+        m_bytes         = bytes;
+        m_linear_buffer = is_linear;
     }
 
     bool isLinear(dim_t dims[4]) const final {
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 81daca577d..25eb4a3d43 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -15,12 +15,13 @@
 #include <types.hpp>
 #include <af/defines.h>
 
+#include <algorithm>
 #include <array>
 #include <functional>
 #include <memory>
+#include <sstream>
 #include <string>
 #include <unordered_map>
-#include <utility>
 #include <vector>
 
 enum class kJITHeuristics {
@@ -34,6 +35,17 @@ namespace common {
 class Node;
 }
 
+#ifdef AF_CPU
+namespace cpu {
+namespace kernel {
+
+template<typename T>
+void evalMultiple(std::vector<Param<T>> arrays,
+                  std::vector<std::shared_ptr<common::Node>> output_nodes_);
+}
+}  // namespace cpu
+#endif
+
 namespace std {
 template<>
 struct hash<common::Node *> {
@@ -107,15 +119,6 @@ class Node {
     template<typename T>
     friend class NodeIterator;
 
-    void swap(Node &other) noexcept {
-        using std::swap;
-        for (int i = 0; i < kMaxChildren; i++) {
-            swap(m_children[i], other.m_children[i]);
-        }
-        swap(m_type, other.m_type);
-        swap(m_height, other.m_height);
-    }
-
    public:
     Node() = default;
     Node(const af::dtype type, const int height,
@@ -125,6 +128,15 @@ class Node {
                       "Node is not move assignable");
     }
 
+    void swap(Node &other) noexcept {
+        using std::swap;
+        for (int i = 0; i < kMaxChildren; i++) {
+            swap(m_children[i], other.m_children[i]);
+        }
+        swap(m_type, other.m_type);
+        swap(m_height, other.m_height);
+    }
+
     /// Default move constructor operator
     Node(Node &&node) noexcept = default;
 
@@ -266,6 +278,22 @@ class Node {
     virtual bool operator==(const Node &other) const noexcept {
         return this == &other;
     }
+
+#ifdef AF_CPU
+    /// Replaces a child node pointer in the cpu::jit::BinaryNode<T> or the
+    /// cpu::jit::UnaryNode classes at \p id with *ptr. Used only in the CPU
+    /// backend and does not modify the m_children pointers in the
+    /// common::Node_ptr class.
+    virtual void replaceChild(int id, void *ptr) noexcept {
+        UNUSED(id);
+        UNUSED(ptr);
+    }
+
+    template<typename U>
+    friend void cpu::kernel::evalMultiple(
+        std::vector<cpu::Param<U>> arrays,
+        std::vector<common::Node_ptr> output_nodes_);
+#endif
 };
 
 struct Node_ids {
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 0d0438621f..40480566ee 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -43,17 +43,19 @@ using common::Node_map_t;
 using common::Node_ptr;
 using common::NodeIterator;
 using cpu::jit::BufferNode;
+
 using std::adjacent_find;
 using std::copy;
 using std::is_standard_layout;
+using std::make_shared;
 using std::move;
 using std::vector;
 
 namespace cpu {
 
 template<typename T>
-Node_ptr bufferNodePtr() {
-    return Node_ptr(reinterpret_cast<Node *>(new BufferNode<T>()));
+shared_ptr<BufferNode<T>> bufferNodePtr() {
+    return std::make_shared<BufferNode<T>>();
 }
 
 template<typename T>
@@ -62,8 +64,7 @@ Array<T>::Array(dim4 dims)
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(memAlloc<T>(dims.elements()).release(), memFree<T>)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {}
 
 template<typename T>
@@ -75,8 +76,7 @@ Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
                                       : memAlloc<T>(dims.elements()).release(),
            memFree<T>)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     static_assert(is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
@@ -101,7 +101,6 @@ Array<T>::Array(const af::dim4 &dims, Node_ptr n)
     , data()
     , data_dims(dims)
     , node(move(n))
-    , ready(false)
     , owner(true) {}
 
 template<typename T>
@@ -111,8 +110,7 @@ Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(parent.getData())
     , data_dims(parent.getDataDims())
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(false) {}
 
 template<typename T>
@@ -123,8 +121,7 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
     , data(is_device ? in_data : memAlloc<T>(info.total()).release(),
            memFree<T>)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     if (!is_device) {
         // Ensure the memory being written to isnt used anywhere else.
@@ -135,40 +132,27 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
 
 template<typename T>
 void Array<T>::eval() {
-    if (isReady()) { return; }
-    if (getQueue().is_worker()) {
-        AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
-    }
-
-    this->setId(getActiveDeviceId());
-
-    data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
-
-    getQueue().enqueue(kernel::evalArray<T>, *this, this->node);
-    // Reset shared_ptr
-    this->node = bufferNodePtr<T>();
-    ready      = true;
+    evalMultiple<T>({this});
 }
 
 template<typename T>
 void Array<T>::eval() const {
-    if (isReady()) { return; }
     const_cast<Array<T> *>(this)->eval();
 }
 
 template<typename T>
 T *Array<T>::device() {
-    getQueue().sync();
     if (!isOwner() || getOffset() || data.use_count() > 1) {
         *this = copyArray<T>(*this);
     }
+    getQueue().sync();
     return this->get();
 }
 
 template<typename T>
 void evalMultiple(vector<Array<T> *> array_ptrs) {
     vector<Array<T> *> outputs;
-    vector<Node_ptr> nodes;
+    vector<common::Node_ptr> nodes;
     vector<Param<T>> params;
     if (getQueue().is_worker()) {
         AF_ERROR("Array not evaluated", AF_ERR_INTERNAL);
@@ -187,41 +171,39 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
     }
 
     for (Array<T> *array : array_ptrs) {
-        if (array->ready) { continue; }
+        if (array->isReady()) { continue; }
 
         array->setId(getActiveDeviceId());
         array->data =
             shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
         outputs.push_back(array);
-        params.push_back(*array);
+        params.emplace_back(array->getData().get(), array->dims(),
+                            array->strides());
         nodes.push_back(array->node);
     }
 
-    if (!outputs.empty()) {
-        getQueue().enqueue(kernel::evalMultiple<T>, params, nodes);
-        for (Array<T> *array : outputs) {
-            array->ready = true;
-            array->node  = bufferNodePtr<T>();
-        }
-    }
+    if (params.empty()) return;
+
+    getQueue().enqueue(cpu::kernel::evalMultiple<T>, params, nodes);
+
+    for (Array<T> *array : outputs) { array->node.reset(); }
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() {
-    if (node->isBuffer()) {
-        auto *bufNode  = reinterpret_cast<BufferNode<T> *>(node.get());
-        unsigned bytes = this->getDataDims().elements() * sizeof(T);
-        bufNode->setData(data, bytes, getOffset(), dims().get(),
-                         strides().get(), isLinear());
-    }
-    return node;
+    if (node) { return node; }
+
+    std::shared_ptr<BufferNode<T>> out = bufferNodePtr<T>();
+    unsigned bytes = this->getDataDims().elements() * sizeof(T);
+    out->setData(data, bytes, getOffset(), dims().get(), strides().get(),
+                 isLinear());
+    return out;
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() const {
-    if (node->isBuffer()) { return const_cast<Array<T> *>(this)->getNode(); }
-    return node;
+    return const_cast<Array<T> *>(this)->getNode();
 }
 
 template<typename T>
@@ -236,8 +218,7 @@ Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
 
 template<typename T>
 Array<T> createValueArray(const dim4 &dims, const T &value) {
-    auto *node = new jit::ScalarNode<T>(value);
-    return createNodeArray<T>(dims, Node_ptr(node));
+    return createNodeArray<T>(dims, make_shared<jit::ScalarNode<T>>(value));
 }
 
 template<typename T>
@@ -337,7 +318,6 @@ template<typename T>
 void Array<T>::setDataDims(const dim4 &new_dims) {
     modDims(new_dims);
     data_dims = new_dims;
-    if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index fd8ca3dce3..792b582de2 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -28,6 +28,12 @@
 #include <vector>
 
 namespace cpu {
+
+namespace jit {
+template<typename T>
+class BufferNode;
+}
+
 namespace kernel {
 template<typename T>
 void evalArray(Param<T> in, common::Node_ptr node);
@@ -115,15 +121,23 @@ template<typename T>
 class Array {
     ArrayInfo info;  // Must be the first element of Array<T>
 
-    // data if parent. empty if child
+    /// Pointer to the data
     std::shared_ptr<T> data;
+
+    /// The shape of the underlying parent data.
     af::dim4 data_dims;
+
+    /// Null if this a buffer node. Otherwise this points to a JIT node
     common::Node_ptr node;
 
-    bool ready;
+    /// If true, the Array object is the parent. If false the data object points
+    /// to another array's data
     bool owner;
 
+    /// Default constructor
     Array() = default;
+
+    /// Creates an uninitialized array of a specific shape
     Array(dim4 dims);
 
     explicit Array(const af::dim4 &dims, T *const in_data, bool is_device,
@@ -149,7 +163,6 @@ class Array {
         swap(data, other.data);
         swap(data_dims, other.data_dims);
         swap(node, other.node);
-        swap(ready, other.ready);
         swap(owner, other.owner);
     }
 
@@ -198,7 +211,7 @@ class Array {
 
     ~Array() = default;
 
-    bool isReady() const { return ready; }
+    bool isReady() const { return static_cast<bool>(node) == false; }
 
     bool isOwner() const { return owner; }
 
@@ -236,10 +249,7 @@ class Array {
         return data.get() + (withOffset ? getOffset() : 0);
     }
 
-    int useCount() const {
-        if (!data.get()) eval();
-        return static_cast<int>(data.use_count());
-    }
+    int useCount() const { return static_cast<int>(data.use_count()); }
 
     operator Param<T>() {
         return Param<T>(this->get(), this->dims(), this->strides());
diff --git a/src/backend/cpu/binary.hpp b/src/backend/cpu/binary.hpp
index 1d7c1583a3..635b082d99 100644
--- a/src/backend/cpu/binary.hpp
+++ b/src/backend/cpu/binary.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #pragma once
 
+#include <jit/Node.hpp>
 #include <math.hpp>
 #include <optypes.hpp>
 #include <types.hpp>
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index 61b10f49e1..4d262f7565 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -54,40 +54,32 @@ CPLX_UNARY_FN(abs)
 template<typename To, typename Ti>
 Array<To> real(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
-    jit::UnaryNode<To, Ti, af_real_t> *node =
-        new jit::UnaryNode<To, Ti, af_real_t>(in_node);
+    auto node = std::make_shared<jit::UnaryNode<To, Ti, af_real_t>>(in_node);
 
-    return createNodeArray<To>(
-        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
+    return createNodeArray<To>(in.dims(), move(node));
 }
 
 template<typename To, typename Ti>
 Array<To> imag(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
-    jit::UnaryNode<To, Ti, af_imag_t> *node =
-        new jit::UnaryNode<To, Ti, af_imag_t>(in_node);
+    auto node = std::make_shared<jit::UnaryNode<To, Ti, af_imag_t>>(in_node);
 
-    return createNodeArray<To>(
-        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
+    return createNodeArray<To>(in.dims(), move(node));
 }
 
 template<typename To, typename Ti>
 Array<To> abs(const Array<Ti> &in) {
     common::Node_ptr in_node = in.getNode();
-    jit::UnaryNode<To, Ti, af_abs_t> *node =
-        new jit::UnaryNode<To, Ti, af_abs_t>(in_node);
+    auto node = std::make_shared<jit::UnaryNode<To, Ti, af_abs_t>>(in_node);
 
-    return createNodeArray<To>(
-        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
+    return createNodeArray<To>(in.dims(), move(node));
 }
 
 template<typename T>
 Array<T> conj(const Array<T> &in) {
     common::Node_ptr in_node = in.getNode();
-    jit::UnaryNode<T, T, af_conj_t> *node =
-        new jit::UnaryNode<T, T, af_conj_t>(in_node);
+    auto node = std::make_shared<jit::UnaryNode<T, T, af_conj_t>>(in_node);
 
-    return createNodeArray<T>(
-        in.dims(), common::Node_ptr(static_cast<common::Node *>(node)));
+    return createNodeArray<T>(in.dims(), move(node));
 }
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 138a80a7ee..b83092d6d4 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -32,8 +32,8 @@ class BinaryNode : public TNode<compute_t<To>> {
         : TNode<compute_t<To>>(compute_t<To>(0),
                                std::max(lhs->getHeight(), rhs->getHeight()) + 1,
                                {{lhs, rhs}})
-        , m_lhs(reinterpret_cast<TNode<compute_t<Ti>> *>(lhs.get()))
-        , m_rhs(reinterpret_cast<TNode<compute_t<Ti>> *>(rhs.get())) {}
+        , m_lhs(static_cast<TNode<compute_t<Ti>> *>(lhs.get()))
+        , m_rhs(static_cast<TNode<compute_t<Ti>> *>(rhs.get())) {}
 
     void calc(int x, int y, int z, int w, int lim) final {
         UNUSED(x);
@@ -43,6 +43,17 @@ class BinaryNode : public TNode<compute_t<To>> {
         m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
     }
 
+    /// Replaces a child node pointer in the cpu::jit::BinaryNode<T> class at \p
+    /// id with *ptr. Used only in the CPU backend and does not modify the
+    /// m_children pointers in the common::Node_ptr class.
+    void replaceChild(int id, void *ptr) noexcept final {
+        auto nnode = static_cast<TNode<compute_t<Ti>> *>(ptr);
+        if (nnode->isBuffer()) {
+            if (id == 0 && m_lhs != ptr) { m_lhs = nnode; }
+            if (id == 1 && m_rhs != ptr) { m_rhs = nnode; }
+        }
+    }
+
     void calc(int idx, int lim) final {
         UNUSED(idx);
         m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index d32060cf60..2793966dcc 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -22,35 +22,35 @@ namespace cpu {
 
 namespace jit {
 
-using std::shared_ptr;
 template<typename T>
 class BufferNode : public TNode<T> {
    protected:
-    shared_ptr<T> m_sptr;
+    std::shared_ptr<T> m_data;
     T *m_ptr;
     unsigned m_bytes;
     dim_t m_strides[4];
     dim_t m_dims[4];
-    std::once_flag m_set_data_flag;
     bool m_linear_buffer;
 
    public:
-    BufferNode() : TNode<T>(T(0), 0, {}) {}
-
-    void setData(shared_ptr<T> data, unsigned bytes, dim_t data_off,
+    BufferNode()
+        : TNode<T>(T(0), 0, {})
+        , m_bytes(0)
+        , m_strides{0, 0, 0, 0}
+        , m_dims{0, 0, 0, 0}
+        , m_linear_buffer(true) {}
+
+    void setData(std::shared_ptr<T> data, unsigned bytes, dim_t data_off,
                  const dim_t *dims, const dim_t *strides,
                  const bool is_linear) {
-        std::call_once(m_set_data_flag, [this, data, bytes, data_off, dims,
-                                         strides, is_linear]() {
-            m_sptr          = data;
-            m_ptr           = data.get() + data_off;
-            m_bytes         = bytes;
-            m_linear_buffer = is_linear;
-            for (int i = 0; i < 4; i++) {
-                m_strides[i] = strides[i];
-                m_dims[i]    = dims[i];
-            }
-        });
+        m_data          = data;
+        m_ptr           = data.get() + data_off;
+        m_bytes         = bytes;
+        m_linear_buffer = is_linear;
+        for (int i = 0; i < 4; i++) {
+            m_strides[i] = strides[i];
+            m_dims[i]    = dims[i];
+        }
     }
 
     void calc(int x, int y, int z, int w, int lim) final {
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index c7e7f3a708..51ec0646ae 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -38,15 +38,17 @@ template<typename T>
 class TNode : public common::Node {
    public:
     alignas(16) jit::array<compute_t<T>> m_val;
+    using common::Node::m_children;
 
    public:
     TNode(T val, const int height,
-          const std::array<common::Node_ptr, kMaxChildren> children)
+          const std::array<common::Node_ptr, kMaxChildren> &&children)
         : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), height,
-               children) {
+               move(children)) {
         using namespace common;
         m_val.fill(static_cast<compute_t<T>>(val));
     }
+
     virtual ~TNode() = default;
 };
 
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 3532b24abd..0481455793 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -13,6 +13,7 @@
 #include <types.hpp>
 #include "Node.hpp"
 
+#include <jit/BufferNode.hpp>
 #include <vector>
 
 namespace cpu {
@@ -33,7 +34,12 @@ class UnaryNode : public TNode<To> {
    public:
     UnaryNode(common::Node_ptr child)
         : TNode<To>(To(0), child->getHeight() + 1, {{child}})
-        , m_child(reinterpret_cast<TNode<Ti> *>(child.get())) {}
+        , m_child(static_cast<TNode<Ti> *>(child.get())) {}
+
+    void replaceChild(int id, void *ptr) noexcept final {
+        auto nnode = static_cast<TNode<Ti> *>(ptr);
+        if (id == 0 && nnode->isBuffer() && m_child != ptr) { m_child = nnode; }
+    }
 
     void calc(int x, int y, int z, int w, int lim) final {
         UNUSED(x);
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index bc320f6285..30dd989777 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -9,7 +9,10 @@
 
 #pragma once
 #include <Param.hpp>
+#include <common/jit/Node.hpp>
+#include <jit/BufferNode.hpp>
 #include <jit/Node.hpp>
+#include <jit/UnaryNode.hpp>
 #include <platform.hpp>
 #include <vector>
 
@@ -31,11 +34,27 @@ void evalMultiple(std::vector<Param<T>> arrays,
     int narrays = static_cast<int>(arrays.size());
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
-        output_nodes.push_back(
-            reinterpret_cast<TNode<T> *>(output_nodes_[i].get()));
+        output_nodes.push_back(static_cast<TNode<T> *>(output_nodes_[i].get()));
         output_nodes_[i]->getNodesMap(nodes, full_nodes, ids);
     }
 
+    /// Replace all nodes in the tree with the nodes in the node map. This
+    /// removes duplicate BufferNode objects that have different pointers
+    /// but have duplicate pointer and dimenstions
+    for (auto fn : full_nodes) {
+        common::Node *tnode = static_cast<common::Node *>(fn);
+
+        if (tnode->isBuffer() == false) {
+            // Go though all the children. Replace them with nodes in map
+            for (int i = 0;
+                 i < common::Node::kMaxChildren && tnode->m_children[i]; i++) {
+                tnode->replaceChild(
+                    i, static_cast<void *>(
+                           full_nodes[nodes[tnode->m_children[i].get()]]));
+            }
+        }
+    }
+
     bool is_linear = true;
     for (auto node : full_nodes) { is_linear &= node->isLinear(odims.get()); }
 
@@ -85,10 +104,5 @@ void evalMultiple(std::vector<Param<T>> arrays,
     }
 }
 
-template<typename T>
-void evalArray(Param<T> arr, common::Node_ptr node) {
-    evalMultiple<T>({arr}, {node});
-}
-
 }  // namespace kernel
 }  // namespace cpu
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 46bbb23e2d..3a1c7677dd 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -88,10 +88,10 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     using UnaryNode = jit::UnaryNode<T, T, op>;
 
     common::Node_ptr in_node = in.getNode();
-    UnaryNode *node          = new UnaryNode(in_node);
+    auto node                = std::make_shared<UnaryNode>(in_node);
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
-    return createNodeArray<T>(outDim, common::Node_ptr(node));
+    return createNodeArray<T>(outDim, move(node));
 }
 
 #define iszero(a) ((a) == 0)
@@ -113,11 +113,10 @@ CHECK_FN(iszero, iszero)
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
     common::Node_ptr in_node = in.getNode();
-    jit::UnaryNode<char, T, op> *node =
-        new jit::UnaryNode<char, T, op>(in_node);
+    auto node = std::make_shared<jit::UnaryNode<char, T, op>>(in_node);
 
     if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
-    return createNodeArray<char>(outDim, common::Node_ptr(node));
+    return createNodeArray<char>(outDim, move(node));
 }
 
 }  // namespace cpu
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index e2b2b3dbf0..0712d9862f 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -49,9 +49,9 @@ void verifyTypeSupport() {
 }
 
 template<typename T>
-Node_ptr bufferNodePtr() {
-    return Node_ptr(
-        new BufferNode<T>(static_cast<af::dtype>(dtype_traits<T>::af_type)));
+std::shared_ptr<BufferNode<T>> bufferNodePtr() {
+    return std::make_shared<BufferNode<T>>(
+        static_cast<af::dtype>(dtype_traits<T>::af_type));
 }
 
 template<typename T>
@@ -61,8 +61,7 @@ Array<T>::Array(const af::dim4 &dims)
     , data((dims.elements() ? memAlloc<T>(dims.elements()).release() : nullptr),
            memFree<T>)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {}
 
 template<typename T>
@@ -75,8 +74,7 @@ Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
                                       : memAlloc<T>(dims.elements()).release()),
           memFree<T>)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     static_assert(std::is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
@@ -107,8 +105,7 @@ Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(parent.getData())
     , data_dims(parent.getDataDims())
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(false) {}
 
 template<typename T>
@@ -121,8 +118,7 @@ Array<T>::Array(Param<T> &tmp, bool owner_)
     , data(tmp.ptr, owner_ ? std::function<void(T *)>(memFree<T>)
                            : std::function<void(T *)>([](T * /*unused*/) {}))
     , data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]))
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(owner_) {}
 
 template<typename T>
@@ -132,7 +128,6 @@ Array<T>::Array(const af::dim4 &dims, common::Node_ptr n)
     , data()
     , data_dims(dims)
     , node(move(n))
-    , ready(false)
     , owner(true) {}
 
 template<typename T>
@@ -144,8 +139,7 @@ Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset_,
                      : memAlloc<T>(info.total()).release(),
            memFree<T>)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     if (!is_device) {
         cudaStream_t stream = getActiveStream();
@@ -163,11 +157,14 @@ void Array<T>::eval() {
     this->setId(getActiveDeviceId());
     this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
 
-    ready = true;
-    evalNodes<T>(*this, this->getNode().get());
-    // FIXME: Replace the current node in any JIT possible trees with the new
-    // BufferNode
-    node = bufferNodePtr<T>();
+    Param<T> p(data.get(), dims().get(), strides().get());
+    evalNodes<T>(p, node.get());
+    node.reset();
+}
+
+template<typename T>
+void Array<T>::eval() const {
+    const_cast<Array<T> *>(this)->eval();
 }
 
 template<typename T>
@@ -178,15 +175,9 @@ T *Array<T>::device() {
     return this->get();
 }
 
-template<typename T>
-void Array<T>::eval() const {
-    if (isReady()) { return; }
-    const_cast<Array<T> *>(this)->eval();
-}
-
 template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays) {
-    vector<Param<T>> outputs;
+    vector<Param<T>> output_params;
     vector<Array<T> *> output_arrays;
     vector<Node *> nodes;
 
@@ -205,36 +196,38 @@ void evalMultiple(std::vector<Array<T> *> arrays) {
     for (Array<T> *array : arrays) {
         if (array->isReady()) { continue; }
 
-        array->ready = true;
         array->setId(getActiveDeviceId());
         array->data =
             shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
 
-        outputs.push_back(*array);
+        output_params.emplace_back(array->getData().get(), array->dims().get(),
+                                   array->strides().get());
         output_arrays.push_back(array);
-        nodes.push_back(array->node.get());
+        nodes.push_back(array->getNode().get());
     }
 
-    evalNodes(outputs, nodes);
+    if (output_params.empty()) return;
+
+    evalNodes(output_params, nodes);
 
-    for (Array<T> *array : output_arrays) { array->node = bufferNodePtr<T>(); }
+    for (Array<T> *array : output_arrays) { array->node.reset(); }
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() {
-    if (node->isBuffer()) {
-        unsigned bytes = this->getDataDims().elements() * sizeof(T);
-        auto *bufNode  = reinterpret_cast<BufferNode<T> *>(node.get());
-        Param<T> param = *this;
-        bufNode->setData(param, data, bytes, isLinear());
-    }
-    return node;
+    if (node) { return node; }
+
+    Param<T> kinfo = *this;
+    unsigned bytes = this->dims().elements() * sizeof(T);
+    auto nn        = bufferNodePtr<T>();
+    nn->setData(kinfo, data, bytes, isLinear());
+
+    return nn;
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() const {
-    if (node->isBuffer()) { return const_cast<Array<T> *>(this)->getNode(); }
-    return node;
+    return const_cast<Array<T> *>(this)->getNode();
 }
 
 /// This function should be called after a new JIT node is created. It will
@@ -419,7 +412,6 @@ template<typename T>
 void Array<T>::setDataDims(const dim4 &new_dims) {
     modDims(new_dims);
     data_dims = new_dims;
-    if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index b6b105baf2..b279ffcab4 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -15,6 +15,7 @@
 #include <common/jit/Node.hpp>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
+#include <jit/BufferNode.hpp>
 #include <memory.hpp>
 #include <traits.hpp>
 #include <types.hpp>
@@ -119,11 +120,18 @@ void *getRawPtr(const Array<T> &arr) {
 template<typename T>
 class Array {
     ArrayInfo info;  // This must be the first element of Array<T>
+
+    /// Pointer to the data
     std::shared_ptr<T> data;
+
+    /// The shape of the underlying parent data.
     af::dim4 data_dims;
 
+    /// Null if this a buffer node. Otherwise this points to a JIT node
     common::Node_ptr node;
-    bool ready;
+
+    /// If true, the Array object is the parent. If false the data object points
+    /// to another array's data
     bool owner;
 
     Array(const af::dim4 &dims);
@@ -151,7 +159,6 @@ class Array {
         swap(data, other.data);
         swap(data_dims, other.data_dims);
         swap(node, other.node);
-        swap(ready, other.ready);
         swap(owner, other.owner);
     }
 
@@ -200,7 +207,7 @@ class Array {
 
     ~Array() = default;
 
-    bool isReady() const { return ready; }
+    bool isReady() const { return static_cast<bool>(node) == false; }
     bool isOwner() const { return owner; }
 
     void eval();
@@ -239,10 +246,7 @@ class Array {
         return data.get() + (withOffset ? getOffset() : 0);
     }
 
-    int useCount() const {
-        if (!isReady()) eval();
-        return data.use_count();
-    }
+    int useCount() const { return data.use_count(); }
 
     operator Param<data_t<T>>() {
         return Param<data_t<T>>(this->get(), this->dims().get(),
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index d47a0e7bec..3627a1115d 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -45,7 +45,7 @@ using std::vector;
 
 namespace opencl {
 template<typename T>
-Node_ptr bufferNodePtr() {
+std::shared_ptr<BufferNode> bufferNodePtr() {
     return make_shared<BufferNode>(
         static_cast<af::dtype>(dtype_traits<T>::af_type));
 }
@@ -82,8 +82,7 @@ Array<T>::Array(const dim4 &dims)
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(memAlloc<T>(info.elements()).release(), bufferFree)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {}
 
 template<typename T>
@@ -91,8 +90,7 @@ Array<T>::Array(const dim4 &dims, Node_ptr n)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data_dims(dims)
-    , node(std::move(std::move(n)))
-    , ready(false)
+    , node(std::move(n))
     , owner(true) {}
 
 template<typename T>
@@ -101,8 +99,7 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(memAlloc<T>(info.elements()).release(), bufferFree)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     static_assert(is_standard_layout<Array<T>>::value,
                   "Array<T> must be a standard layout type");
@@ -125,8 +122,7 @@ Array<T>::Array(const dim4 &dims, cl_mem mem, size_t src_offset, bool copy)
           copy ? memAlloc<T>(info.elements()).release() : new Buffer(mem, true),
           bufferFree)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     if (copy) {
         clRetainMemObject(mem);
@@ -143,8 +139,7 @@ Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(parent.getData())
     , data_dims(parent.getDataDims())
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(false) {}
 
 template<typename T>
@@ -160,8 +155,7 @@ Array<T>::Array(Param &tmp, bool owner_)
           tmp.data, owner_ ? bufferFree : [](Buffer * /*unused*/) {})
     , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
                      tmp.info.dims[3]))
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(owner_) {}
 
 template<typename T>
@@ -175,8 +169,7 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
               : (memAlloc<T>(info.elements()).release()),
           bufferFree)
     , data_dims(dims)
-    , node(bufferNodePtr<T>())
-    , ready(true)
+    , node()
     , owner(true) {
     if (!is_device) {
         getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
@@ -189,7 +182,8 @@ void Array<T>::eval() {
     if (isReady()) { return; }
 
     this->setId(getActiveDeviceId());
-    data = Buffer_ptr(memAlloc<T>(info.elements()).release(), bufferFree);
+    data = std::shared_ptr<cl::Buffer>(memAlloc<T>(info.elements()).release(),
+                                       bufferFree);
 
     // Do not replace this with cast operator
     KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
@@ -198,14 +192,12 @@ void Array<T>::eval() {
 
     Param res = {data.get(), info};
 
-    evalNodes(res, node.get());
-    ready = true;
-    node  = bufferNodePtr<T>();
+    evalNodes(res, getNode().get());
+    node.reset();
 }
 
 template<typename T>
 void Array<T>::eval() const {
-    if (isReady()) { return; }
     const_cast<Array<T> *>(this)->eval();
 }
 
@@ -240,10 +232,9 @@ void evalMultiple(vector<Array<T> *> arrays) {
 
         const ArrayInfo info = array->info;
 
-        array->ready = true;
         array->setId(getActiveDeviceId());
-        array->data =
-            Buffer_ptr(memAlloc<T>(info.elements()).release(), bufferFree);
+        array->data = std::shared_ptr<cl::Buffer>(
+            memAlloc<T>(info.elements()).release(), bufferFree);
 
         // Do not replace this with cast operator
         KParam kInfo = {
@@ -254,27 +245,29 @@ void evalMultiple(vector<Array<T> *> arrays) {
 
         outputs.emplace_back(array->data.get(), kInfo);
         output_arrays.push_back(array);
-        nodes.push_back(array->node.get());
+        nodes.push_back(array->getNode().get());
     }
+
     evalNodes(outputs, nodes);
-    for (Array<T> *array : output_arrays) { array->node = bufferNodePtr<T>(); }
+
+    for (Array<T> *array : output_arrays) { array->node.reset(); }
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() {
-    if (node->isBuffer()) {
-        KParam kinfo   = *this;
-        auto *bufNode  = reinterpret_cast<BufferNode *>(node.get());
-        unsigned bytes = this->getDataDims().elements() * sizeof(T);
-        bufNode->setData(kinfo, data, bytes, isLinear());
-    }
-    return node;
+    if (node) { return node; }
+
+    KParam kinfo   = *this;
+    unsigned bytes = this->dims().elements() * sizeof(T);
+    auto nn        = bufferNodePtr<T>();
+    nn->setData(kinfo, data, bytes, isLinear());
+
+    return nn;
 }
 
 template<typename T>
 Node_ptr Array<T>::getNode() const {
-    if (node->isBuffer()) { return const_cast<Array<T> *>(this)->getNode(); }
-    return node;
+    return const_cast<Array<T> *>(this)->getNode();
 }
 
 /// This function should be called after a new JIT node is created. It will
@@ -476,7 +469,6 @@ template<typename T>
 void Array<T>::setDataDims(const dim4 &new_dims) {
     modDims(new_dims);
     data_dims = new_dims;
-    if (node->isBuffer()) { node = bufferNodePtr<T>(); }
 }
 
 template<typename T>
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 2ea9d85a53..df976b45e3 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -15,10 +15,12 @@
 #include <common/MemoryManagerBase.hpp>
 #include <common/jit/Node.hpp>
 #include <err_opencl.hpp>
+#include <jit/BufferNode.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+
 #include <af/dim4.hpp>
 
 #include <algorithm>
@@ -120,11 +122,18 @@ using mapped_ptr = std::unique_ptr<T, std::function<void(void *)>>;
 template<typename T>
 class Array {
     ArrayInfo info;  // This must be the first element of Array<T>
-    Buffer_ptr data;
+
+    /// Pointer to the data
+    std::shared_ptr<cl::Buffer> data;
+
+    /// The shape of the underlying parent data.
     af::dim4 data_dims;
 
+    /// Null if this a buffer node. Otherwise this points to a JIT node
     common::Node_ptr node;
-    bool ready;
+
+    /// If true, the Array object is the parent. If false the data object points
+    /// to another array's data
     bool owner;
 
     Array(const af::dim4 &dims);
@@ -152,7 +161,6 @@ class Array {
         swap(data, other.data);
         swap(data_dims, other.data_dims);
         swap(node, other.node);
-        swap(ready, other.ready);
         swap(owner, other.owner);
     }
 
@@ -199,7 +207,7 @@ class Array {
 #undef INFO_IS_FUNC
     ~Array() = default;
 
-    bool isReady() const { return ready; }
+    bool isReady() const { return static_cast<bool>(node) == false; }
     bool isOwner() const { return owner; }
 
     void eval();
@@ -222,14 +230,11 @@ class Array {
         return data.get();
     }
 
-    int useCount() const {
-        if (!isReady()) eval();
-        return data.use_count();
-    }
+    int useCount() const { return data.use_count(); }
 
     dim_t getOffset() const { return info.getOffset(); }
 
-    Buffer_ptr getData() const { return data; }
+    std::shared_ptr<cl::Buffer> getData() const { return data; }
 
     dim4 getDataDims() const { return data_dims; }
 
diff --git a/src/backend/opencl/jit/BufferNode.hpp b/src/backend/opencl/jit/BufferNode.hpp
index 1aa2e00f2b..0746c0538e 100644
--- a/src/backend/opencl/jit/BufferNode.hpp
+++ b/src/backend/opencl/jit/BufferNode.hpp
@@ -9,12 +9,10 @@
 
 #pragma once
 #include <common/jit/BufferNodeBase.hpp>
-#include <common/jit/Node.hpp>
-#include <af/defines.h>
-#include <iomanip>
-#include <mutex>
 #include "../kernel/KParam.hpp"
 
+#include <memory>
+
 namespace opencl {
 namespace jit {
 using BufferNode = common::BufferNodeBase<std::shared_ptr<cl::Buffer>, KParam>;
diff --git a/test/array.cpp b/test/array.cpp
index 526ca40224..9770549d2d 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -13,6 +13,7 @@
 #include <cstddef>
 #include <cstdlib>
 #include <initializer_list>
+#include <iomanip>
 
 using namespace af;
 using std::vector;
@@ -592,3 +593,50 @@ TEST(Array, EmptyArrayHostCopy) {
         },
         ::testing::ExitedWithCode(0), ".*");
 }
+
+TEST(Array, ReferenceCount1) {
+    int counta = 0, countb = 0, countc = 0;
+    array a = af::randu(10, 10);
+    a.eval();
+    af::sync();
+    {
+        ASSERT_REF(a, 1) << "After a = randu(10, 10);";
+
+        array b = af::randu(10, 10);  //(af::seq(100));
+        ASSERT_REF(b, 1) << "After b = randu(10, 10);";
+
+        array c = a + b;
+        ASSERT_REF(a, 2) << "After c = a + b;";
+        ASSERT_REF(b, 2) << "After c = a + b;";
+        ASSERT_REF(c, 0) << "After c = a + b;";
+
+        c.eval();
+        af::sync();
+        ASSERT_REF(a, 1) << "After c.eval();";
+        ASSERT_REF(b, 1) << "After c.eval();";
+        ASSERT_REF(c, 1) << "After c.eval();";
+    }
+}
+
+TEST(Array, ReferenceCount2) {
+    int counta = 0, countb = 0, countc = 0;
+    array a = af::randu(10, 10);
+    array b = af::randu(10, 10);
+    {
+        ASSERT_REF(a, 1) << "After a = randu(10, 10);";
+        ASSERT_REF(b, 1) << "After a = randu(10, 10);";
+
+        array c = a + b;
+
+        ASSERT_REF(a, 2) << "After c = a + b;";
+        ASSERT_REF(b, 2) << "After c = a + b;";
+        ASSERT_REF(c, 0) << "After c = a + b;";
+
+        array d = c;
+
+        ASSERT_REF(a, 2) << "After d = c;";
+        ASSERT_REF(b, 2) << "After d = c;";
+        ASSERT_REF(c, 0) << "After d = c;";
+        ASSERT_REF(d, 0) << "After d = c;";
+    }
+}
diff --git a/test/convolve.cpp b/test/convolve.cpp
index efe1c63f40..c3abe056cd 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -672,8 +672,8 @@ TEST(Convolve, 1D_C32) {
 
     cfloat acc = sum<cfloat>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
+    EXPECT_LT(std::abs(real(acc)), 1E-3);
+    EXPECT_LT(std::abs(imag(acc)), 1E-3);
 }
 
 TEST(Convolve, 2D_C32) {
@@ -685,8 +685,8 @@ TEST(Convolve, 2D_C32) {
 
     cfloat acc = sum<cfloat>(out - gld);
 
-    EXPECT_EQ(std::abs(real(acc)) < 1E-3, true);
-    EXPECT_EQ(std::abs(imag(acc)) < 1E-3, true);
+    EXPECT_LT(std::abs(real(acc)), 1E-3);
+    EXPECT_LT(std::abs(imag(acc)), 1E-3);
 }
 
 TEST(Convolve, 3D_C32) {
diff --git a/test/jit.cpp b/test/jit.cpp
index c9e93b0254..b2d690a7ca 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -53,9 +53,10 @@ TEST(JIT, CPP_JIT_HASH) {
 
     // Creating a kernel
     {
-        array d    = a + b;
-        array e    = a + c;
-        array f1   = d * e - e;
+        array d  = a + b;
+        array e  = a + c;
+        array f1 = d * e - e;
+
         float* hF1 = f1.host<float>();
 
         for (int i = 0; i < num; i++) { ASSERT_EQ(hF1[i], valF1); }

From 7995750bdf640d60cb9bfea5f737371946b7a455 Mon Sep 17 00:00:00 2001
From: pradeep <3270458+9prady9@users.noreply.github.com>
Date: Sat, 25 Sep 2021 05:11:16 +0530
Subject: [PATCH 2196/2677] Improve offline build experience for developers
 (#3162)

* Improve offline build experience for developers

The following common scenarios(majority we think) are covered with this
change.
- Developer has cloud connection always.
- Developer has cloud connection for initial cmake run, but not later.
- Developer has lost cloud connection for a while after the initial
  successful cmake run but regained the connection later.
- Developer is doing an completely disconnected build using the source
  tarball we generate and attach to our release assets.

When the developer wants to do an offline build for a specific commit
other than release tags, they would have to generate the relevant source
tarball themselves. The commands required to do the same can be found
from the following ci workflow file in our repository.

.github/workflows/release_src_artifact.yml

The source tarball generation CI job has also been changed to reflect
the change in external dependencies location.

* Update vcpkg commit in windows github action to required
---
 .github/workflows/release_src_artifact.yml    |   9 +-
 .github/workflows/win_cpu_build.yml           |   4 +-
 CMakeLists.txt                                |  27 ++-
 CMakeModules/AFconfigure_deps_vars.cmake      | 164 +++++++++++++-----
 CMakeModules/AFconfigure_forge_dep.cmake      |  12 +-
 CMakeModules/boost_package.cmake              |   9 +-
 CMakeModules/build_CLBlast.cmake              |   8 +-
 CMakeModules/build_cl2hpp.cmake               |   8 +-
 CMakeModules/build_clFFT.cmake                |   8 +-
 src/backend/cpu/CMakeLists.txt                |   8 +-
 src/backend/cuda/CMakeLists.txt               |   8 +-
 test/CMakeLists.txt                           |  19 +-
 .../download_sparse_datasets.cmake            |  13 +-
 vcpkg.json                                    |   2 +-
 14 files changed, 177 insertions(+), 122 deletions(-)

diff --git a/.github/workflows/release_src_artifact.yml b/.github/workflows/release_src_artifact.yml
index 8dc6e2cd62..273c7a9249 100644
--- a/.github/workflows/release_src_artifact.yml
+++ b/.github/workflows/release_src_artifact.yml
@@ -70,7 +70,14 @@ jobs:
                   done
                   shopt -u extglob
                   rm -rf matrixmarket
-                  cd ../../..
+                  cp -r ./* ../../extern/
+                  cd ..
+                  wget https://github.com/arrayfire/forge/releases/download/v1.0.8/forge-full-1.0.8.tar.bz2
+                  tar -xf forge-full-1.0.8.tar.bz2
+                  mv forge-full-1.0.8 ../extern/af_forge-src
+                  cd ..
+                  rm -rf build
+                  cd ..
                   tar -cjf arrayfire-full-${AF_VER}.tar.bz2 arrayfire-full-${AF_VER}/
                   echo "UPLOAD_FILE=arrayfire-full-${AF_VER}.tar.bz2" >> $GITHUB_ENV
 
diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index ed47fd8676..e265f6f877 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -13,7 +13,9 @@ jobs:
         name: CPU (fftw, OpenBLAS, windows-latest)
         runs-on: windows-latest
         env:
-          VCPKG_HASH: 5568f110b509a9fd90711978a7cb76bae75bb092 # vcpkg release tag 2021.05.12 with Forge v1.0.7 update
+
+          VCPKG_HASH: 4428702c1c56fdb7cb779584efdcba254d7b57ca #[neon2sse] create a new port; Has forge v1.0.8 and other cmake/vcpkg fixes
+
           VCPKG_DEFAULT_TRIPLET: x64-windows
         steps:
             - name: Checkout Repository
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 06bfcdd995..129927c0d2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -130,7 +130,6 @@ endif()
 
 mark_as_advanced(
   AF_BUILD_FRAMEWORK
-  AF_BUILD_OFFLINE
   AF_CACHE_KERNELS_TO_DISK
   AF_INSTALL_STANDALONE
   AF_WITH_CPUID
@@ -194,22 +193,18 @@ if(TARGET spdlog::spdlog_header_only)
     $<TARGET_PROPERTY:spdlog::spdlog_header_only,INTERFACE_INCLUDE_DIRECTORIES>
     )
 else()
-  FetchContent_Declare(
-    ${spdlog_prefix}
-    GIT_REPOSITORY https://github.com/gabime/spdlog.git
-    GIT_TAG        v1.8.5
+  af_dep_check_and_populate(${spdlog_prefix}
+    URI https://github.com/gabime/spdlog.git
+    REF v1.8.5
   )
-  af_dep_check_and_populate(${spdlog_prefix})
   target_include_directories(af_spdlog INTERFACE "${${spdlog_prefix}_SOURCE_DIR}/include")
 endif()
 
 if(NOT TARGET glad::glad)
-  FetchContent_Declare(
-    ${glad_prefix}
-    GIT_REPOSITORY https://github.com/arrayfire/glad.git
-    GIT_TAG main
-    )
-  af_dep_check_and_populate(${glad_prefix})
+  af_dep_check_and_populate(${glad_prefix}
+    URI https://github.com/arrayfire/glad.git
+    REF main
+  )
   add_subdirectory(${${glad_prefix}_SOURCE_DIR} ${${glad_prefix}_BINARY_DIR})
 
   add_library(af_glad STATIC $<TARGET_OBJECTS:af_glad_obj_lib>)
@@ -220,12 +215,10 @@ if(NOT TARGET glad::glad)
     )
 endif()
 
-FetchContent_Declare(
-  ${assets_prefix}
-  GIT_REPOSITORY https://github.com/arrayfire/assets.git
-  GIT_TAG        master
+af_dep_check_and_populate(${assets_prefix}
+  URI https://github.com/arrayfire/assets.git
+  REF master
 )
-af_dep_check_and_populate(${assets_prefix})
 set(ASSETS_DIR ${${assets_prefix}_SOURCE_DIR})
 
 configure_file(
diff --git a/CMakeModules/AFconfigure_deps_vars.cmake b/CMakeModules/AFconfigure_deps_vars.cmake
index 748e911473..aac332f5ab 100644
--- a/CMakeModules/AFconfigure_deps_vars.cmake
+++ b/CMakeModules/AFconfigure_deps_vars.cmake
@@ -5,7 +5,37 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-option(AF_BUILD_OFFLINE "Build ArrayFire assuming there is no network" OFF)
+file(DOWNLOAD
+  "https://github.com/arrayfire/arrayfire/blob/v3.0.0/CMakeLists.txt"
+  "${ArrayFire_BINARY_DIR}/download_copy_cmakelists.stamp"
+  STATUS af_check_result
+  TIMEOUT 4
+)
+list(GET af_check_result 0 af_is_connected)
+if(${af_is_connected})
+  set(BUILD_OFFLINE ON)
+  # Turn ON disconnected flag when connected to cloud
+  set(FETCHCONTENT_FULLY_DISCONNECTED ON CACHE BOOL
+      "Disable Download/Update stages of FetchContent workflow" FORCE)
+
+  message(STATUS "No cloud connection. Attempting offline build if dependencies are available")
+else()
+  set(BUILD_OFFLINE OFF)
+  # Turn OFF disconnected flag when connected to cloud
+  # This is required especially in the following scenario:
+  # - cmake run successfully first
+  # - lost connection, but development can still be done
+  # - Now, connection regained. Hence updates should be allowed
+  set(FETCHCONTENT_FULLY_DISCONNECTED OFF CACHE BOOL
+      "Disable Download/Update stages of FetchContent workflow" FORCE)
+endif()
+
+# Track dependencies download persistently across multiple
+# cmake configure runs. *_POPULATED variables are reset for each
+# cmake run to 0. Hence, this internal cache value is needed to
+# check for already (from previous cmake run's) populated data
+# during the current cmake run if it looses network connection.
+set(AF_INTERNAL_DOWNLOAD_FLAG OFF CACHE BOOL "Deps Download Flag")
 
 # Override fetch content base dir before including AFfetch_content
 set(FETCHCONTENT_BASE_DIR "${ArrayFire_BINARY_DIR}/extern" CACHE PATH
@@ -13,7 +43,15 @@ set(FETCHCONTENT_BASE_DIR "${ArrayFire_BINARY_DIR}/extern" CACHE PATH
 
 include(AFfetch_content)
 
-macro(set_and_mark_depname var name)
+mark_as_advanced(
+  AF_INTERNAL_DOWNLOAD_FLAG
+  FETCHCONTENT_BASE_DIR
+  FETCHCONTENT_QUIET
+  FETCHCONTENT_FULLY_DISCONNECTED
+  FETCHCONTENT_UPDATES_DISCONNECTED
+)
+
+macro(set_and_mark_depnames_advncd var name)
   string(TOLOWER ${name} ${var})
   string(TOUPPER ${name} ${var}_ucname)
   mark_as_advanced(
@@ -22,51 +60,89 @@ macro(set_and_mark_depname var name)
   )
 endmacro()
 
-mark_as_advanced(
-  FETCHCONTENT_BASE_DIR
-  FETCHCONTENT_QUIET
-  FETCHCONTENT_FULLY_DISCONNECTED
-  FETCHCONTENT_UPDATES_DISCONNECTED
-)
+set_and_mark_depnames_advncd(assets_prefix "af_assets")
+set_and_mark_depnames_advncd(testdata_prefix "af_test_data")
+set_and_mark_depnames_advncd(gtest_prefix "googletest")
+set_and_mark_depnames_advncd(glad_prefix "af_glad")
+set_and_mark_depnames_advncd(forge_prefix "af_forge")
+set_and_mark_depnames_advncd(spdlog_prefix "spdlog")
+set_and_mark_depnames_advncd(threads_prefix "af_threads")
+set_and_mark_depnames_advncd(cub_prefix "nv_cub")
+set_and_mark_depnames_advncd(cl2hpp_prefix "ocl_cl2hpp")
+set_and_mark_depnames_advncd(clblast_prefix "ocl_clblast")
+set_and_mark_depnames_advncd(clfft_prefix "ocl_clfft")
+set_and_mark_depnames_advncd(boost_prefix "boost_compute")
 
-set_and_mark_depname(assets_prefix "af_assets")
-set_and_mark_depname(testdata_prefix "af_test_data")
-set_and_mark_depname(gtest_prefix "googletest")
-set_and_mark_depname(glad_prefix "af_glad")
-set_and_mark_depname(forge_prefix "af_forge")
-set_and_mark_depname(spdlog_prefix "spdlog")
-set_and_mark_depname(threads_prefix "af_threads")
-set_and_mark_depname(cub_prefix "nv_cub")
-set_and_mark_depname(cl2hpp_prefix "ocl_cl2hpp")
-set_and_mark_depname(clblast_prefix "ocl_clblast")
-set_and_mark_depname(clfft_prefix "ocl_clfft")
-set_and_mark_depname(boost_prefix "boost_compute")
+macro(af_dep_check_and_populate dep_prefix)
+  set(single_args URI REF)
+  cmake_parse_arguments(adcp_args "" "${single_args}" "" ${ARGN})
 
-if(AF_BUILD_OFFLINE)
-  macro(set_fetchcontent_src_dir prefix_var dep_name)
-    set(FETCHCONTENT_SOURCE_DIR_${${prefix_var}_ucname}
-        "${FETCHCONTENT_BASE_DIR}/${${prefix_var}}-src" CACHE PATH
-        "Source directory for ${dep_name} dependency")
-    mark_as_advanced(FETCHCONTENT_SOURCE_DIR_${${prefix_var}_ucname})
-  endmacro()
+  if("${adcp_args_URI}" STREQUAL "")
+    message(FATAL_ERROR [=[
+        Cannot check requested dependency source's availability.
+        Please provide a valid URI(almost always a URL to a github repo).
+        Note that the above error message if for developers of ArrayFire.
+        ]=])
+  endif()
 
-  set_fetchcontent_src_dir(assets_prefix "Assets")
-  set_fetchcontent_src_dir(testdata_prefix "Test Data")
-  set_fetchcontent_src_dir(gtest_prefix "googletest")
-  set_fetchcontent_src_dir(glad_prefix "glad")
-  set_fetchcontent_src_dir(forge_prefix "forge")
-  set_fetchcontent_src_dir(spdlog_prefix "spdlog")
-  set_fetchcontent_src_dir(threads_prefix "threads")
-  set_fetchcontent_src_dir(cub_prefix "NVIDIA CUB")
-  set_fetchcontent_src_dir(cl2hpp_prefix "OpenCL cl2 hpp header")
-  set_fetchcontent_src_dir(clblast_prefix "CLBlast library")
-  set_fetchcontent_src_dir(clfft_prefix "clFFT library")
-  set_fetchcontent_src_dir(boost_prefix "boost-compute headers")
-endif()
+  string(FIND "${adcp_args_REF}" "=" adcp_has_algo_id)
 
-macro(af_dep_check_and_populate prefix)
-  FetchContent_GetProperties(${prefix})
-  if(NOT ${prefix}_POPULATED)
-    FetchContent_Populate(${prefix})
+  if(${BUILD_OFFLINE} AND NOT ${AF_INTERNAL_DOWNLOAD_FLAG})
+    if(NOT ${adcp_has_algo_id} EQUAL -1)
+      FetchContent_Populate(${dep_prefix}
+        QUIET
+        URL            ${adcp_args_URI}
+        URL_HASH       ${adcp_args_REF}
+        DOWNLOAD_COMMAND \"\"
+        UPDATE_DISCONNECTED ON
+        SOURCE_DIR     "${ArrayFire_SOURCE_DIR}/extern/${dep_prefix}-src"
+        BINARY_DIR     "${ArrayFire_BINARY_DIR}/extern/${dep_prefix}-build"
+        SUBBUILD_DIR   "${ArrayFire_BINARY_DIR}/extern/${dep_prefix}-subbuild"
+      )
+    elseif("${adcp_args_REF}" STREQUAL "")
+      FetchContent_Populate(${dep_prefix}
+        QUIET
+        URL            ${adcp_args_URI}
+        DOWNLOAD_COMMAND \"\"
+        UPDATE_DISCONNECTED ON
+        SOURCE_DIR     "${ArrayFire_SOURCE_DIR}/extern/${dep_prefix}-src"
+        BINARY_DIR     "${ArrayFire_BINARY_DIR}/extern/${dep_prefix}-build"
+        SUBBUILD_DIR   "${ArrayFire_BINARY_DIR}/extern/${dep_prefix}-subbuild"
+      )
+    else()
+      # The left over alternative is assumed to be a cloud hosted git repository
+      FetchContent_Populate(${dep_prefix}
+        QUIET
+        GIT_REPOSITORY ${adcp_args_URI}
+        GIT_TAG        ${adcp_args_REF}
+        DOWNLOAD_COMMAND \"\"
+        UPDATE_DISCONNECTED ON
+        SOURCE_DIR     "${ArrayFire_SOURCE_DIR}/extern/${dep_prefix}-src"
+        BINARY_DIR     "${ArrayFire_BINARY_DIR}/extern/${dep_prefix}-build"
+        SUBBUILD_DIR   "${ArrayFire_BINARY_DIR}/extern/${dep_prefix}-subbuild"
+      )
+    endif()
+  else()
+    if(NOT ${adcp_has_algo_id} EQUAL -1)
+      FetchContent_Declare(${dep_prefix}
+        URL            ${adcp_args_URI}
+        URL_HASH       ${adcp_args_REF}
+      )
+    elseif("${adcp_args_REF}" STREQUAL "")
+      FetchContent_Declare(${dep_prefix}
+        URL            ${adcp_args_URI}
+      )
+    else()
+      # The left over alternative is assumed to be a cloud hosted git repository
+      FetchContent_Declare(${dep_prefix}
+        GIT_REPOSITORY ${adcp_args_URI}
+        GIT_TAG        ${adcp_args_REF}
+      )
+    endif()
+    FetchContent_GetProperties(${dep_prefix})
+    if(NOT ${dep_prefix}_POPULATED)
+      FetchContent_Populate(${dep_prefix})
+    endif()
+    set(AF_INTERNAL_DOWNLOAD_FLAG ON CACHE BOOL "Deps Download Flag" FORCE)
   endif()
 endmacro()
diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index a49b44d71d..162e26c3ee 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -7,7 +7,7 @@
 
 set(FG_VERSION_MAJOR 1)
 set(FG_VERSION_MINOR 0)
-set(FG_VERSION_PATCH 7)
+set(FG_VERSION_PATCH 8)
 
 find_package(Forge
   ${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}
@@ -30,12 +30,10 @@ else()
   set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
   set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
 
-  FetchContent_Declare(
-    ${forge_prefix}
-    GIT_REPOSITORY https://github.com/arrayfire/forge.git
-    GIT_TAG        "v${FG_VERSION}"
+  af_dep_check_and_populate(${forge_prefix}
+    URI https://github.com/arrayfire/forge.git
+    REF "v${FG_VERSION}"
   )
-  af_dep_check_and_populate(${forge_prefix})
 
   if(AF_BUILD_FORGE)
     set(af_FETCHCONTENT_BASE_DIR ${FETCHCONTENT_BASE_DIR})
@@ -58,8 +56,6 @@ else()
         FG_BUILD_DOCS
         FG_WITH_FREEIMAGE
         FG_USE_WINDOW_TOOLKIT
-        FG_USE_SYSTEM_CL2HPP
-        FG_ENABLE_HUNTER
         FG_RENDERING_BACKEND
         SPHINX_EXECUTABLE
         glfw3_DIR
diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index 9736dab753..a0b1c84329 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -21,12 +21,11 @@ if(NOT
   message(WARNING
       "WARN: Found Boost v${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION}."
       "Minimum required ${VER}. Build will download Boost Compute.")
-  FetchContent_Declare(
-    ${boost_prefix}
-    URL https://github.com/boostorg/compute/archive/boost-${VER}.tar.gz
-    URL_HASH MD5=e160ec0ff825fc2850ea4614323b1fb5
+  af_dep_check_and_populate(${boost_prefix}
+    URL_AND_HASH
+    URI https://github.com/boostorg/compute/archive/boost-${VER}.tar.gz
+    REF MD5=e160ec0ff825fc2850ea4614323b1fb5
   )
-  af_dep_check_and_populate(${boost_prefix})
   if(NOT TARGET Boost::boost)
     add_library(Boost::boost IMPORTED INTERFACE GLOBAL)
   endif()
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 0e32b38d6f..64263df928 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -5,12 +5,10 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-FetchContent_Declare(
-  ${clblast_prefix}
-  GIT_REPOSITORY    https://github.com/cnugteren/CLBlast.git
-  GIT_TAG           1.5.2
+af_dep_check_and_populate(${clblast_prefix}
+  URI https://github.com/cnugteren/CLBlast.git
+  REF 1.5.2
 )
-af_dep_check_and_populate(${clblast_prefix})
 
 include(ExternalProject)
 find_program(GIT git)
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index f34fc216be..fd8709fb02 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -13,12 +13,10 @@
 
 find_package(OpenCL)
 
-FetchContent_Declare(
-  ${cl2hpp_prefix}
-  GIT_REPOSITORY https://github.com/KhronosGroup/OpenCL-CLHPP.git
-  GIT_TAG v2.0.12
+af_dep_check_and_populate(${cl2hpp_prefix}
+  URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
+  REF v2.0.12
 )
-af_dep_check_and_populate(${cl2hpp_prefix})
 
 if (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
   add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index dda658f569..380357e02e 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -5,12 +5,10 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-FetchContent_Declare(
-  ${clfft_prefix}
-  GIT_REPOSITORY    https://github.com/arrayfire/clFFT.git
-  GIT_TAG           cmake_fixes
+af_dep_check_and_populate(${clfft_prefix}
+  URI https://github.com/arrayfire/clFFT.git
+  REF cmake_fixes
 )
-af_dep_check_and_populate(${clfft_prefix})
 
 set(current_build_type ${BUILD_SHARED_LIBS})
 set(BUILD_SHARED_LIBS OFF)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index c3b77996ec..9707ef5f23 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -272,12 +272,10 @@ if (AF_WITH_CPUID)
   target_compile_definitions(afcpu PRIVATE -DAF_WITH_CPUID)
 endif(AF_WITH_CPUID)
 
-FetchContent_Declare(
-  ${threads_prefix}
-  GIT_REPOSITORY https://github.com/arrayfire/threads.git
-  GIT_TAG        b666773940269179f19ef11c8f1eb77005e85d9a
+af_dep_check_and_populate(${threads_prefix}
+  URI https://github.com/arrayfire/threads.git
+  REF b666773940269179f19ef11c8f1eb77005e85d9a
 )
-af_dep_check_and_populate(${threads_prefix})
 
 target_sources(afcpu
   PRIVATE
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f874fd1ec3..218878e163 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -116,12 +116,10 @@ cuda_include_directories(
   $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>
   )
 if(CUDA_VERSION_MAJOR VERSION_LESS 11)
-  FetchContent_Declare(
-    ${cub_prefix}
-    GIT_REPOSITORY https://github.com/NVIDIA/cub.git
-    GIT_TAG        1.10.0
+  af_dep_check_and_populate(${cub_prefix}
+    URI https://github.com/NVIDIA/cub.git
+    REF 1.10.0
   )
-  af_dep_check_and_populate(${cub_prefix})
   cuda_include_directories(${${cub_prefix}_SOURCE_DIR})
 endif()
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 06484c274a..57e0a307a8 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -15,13 +15,11 @@ if(AF_TEST_WITH_MTX_FILES)
   include(download_sparse_datasets)
 endif()
 
-FetchContent_Declare(
-  ${gtest_prefix}
-  GIT_REPOSITORY https://github.com/google/googletest.git
-  GIT_TAG        release-1.8.1
-)
 if(NOT TARGET gtest)
-  af_dep_check_and_populate(${gtest_prefix})
+  af_dep_check_and_populate(${gtest_prefix}
+    URI https://github.com/google/googletest.git
+    REF release-1.8.1
+  )
 
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
   # newer cmakes. This sets the default global setting for that policy.
@@ -74,14 +72,11 @@ if(${AF_USE_RELATIVE_TEST_DIR})
       STRING "Relative Test Data Directory")
   set(TESTDATA_SOURCE_DIR ${RELATIVE_TEST_DATA_DIR})
 else(${AF_USE_RELATIVE_TEST_DIR})
-  FetchContent_Declare(
-    ${testdata_prefix}
-    GIT_REPOSITORY https://github.com/arrayfire/arrayfire-data.git
-
+  af_dep_check_and_populate(${testdata_prefix}
+    URI https://github.com/arrayfire/arrayfire-data.git
     #pinv large data set update change
-    GIT_TAG        0144a599f913cc67c76c9227031b4100156abc25
+    REF 0144a599f913cc67c76c9227031b4100156abc25
   )
-  af_dep_check_and_populate(${testdata_prefix})
   set(TESTDATA_SOURCE_DIR "${${testdata_prefix}_SOURCE_DIR}")
 endif(${AF_USE_RELATIVE_TEST_DIR})
 
diff --git a/test/CMakeModules/download_sparse_datasets.cmake b/test/CMakeModules/download_sparse_datasets.cmake
index 283dad53ac..74b2e8a69a 100644
--- a/test/CMakeModules/download_sparse_datasets.cmake
+++ b/test/CMakeModules/download_sparse_datasets.cmake
@@ -12,15 +12,12 @@ function(mtxDownload name group)
   set(target_dir ${root_dir}/${group}/${name})
   set(mtx_name mtxDownload_${group}_${name})
   string(TOLOWER ${mtx_name} mtx_name)
-  FetchContent_Declare(
-    ${mtx_name}
-    URL ${URL}/MM/${group}/${name}.tar.gz
+
+  set_and_mark_depnames_advncd(mtx_prefix ${mtx_name})
+  af_dep_check_and_populate(${mtx_name}
+    URI ${URL}/MM/${group}/${name}.tar.gz
   )
-  af_dep_check_and_populate(${mtx_name})
-  set_and_mark_depname(mtx_prefix ${mtx_name})
-  if(AF_BUILD_OFFLINE)
-    set_fetchcontent_src_dir(mtx_prefix "{name}.mtx file from {group} group")
-  endif()
+
   if(NOT EXISTS "${target_dir}/${name}.mtx")
     file(MAKE_DIRECTORY ${target_dir})
     file(COPY ${${mtx_name}_SOURCE_DIR}/${name}.mtx DESTINATION ${target_dir})
diff --git a/vcpkg.json b/vcpkg.json
index 020c25131f..a3fafdecf2 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -10,7 +10,7 @@
         "boost-stacktrace",
         {
             "name": "forge",
-            "version>=": "1.0.7",
+            "version>=": "1.0.8",
             "platform": "windows"
         },
         "freeimage",

From 6fbf5fb676059a4b9040cdc63e85067203d6b595 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Sep 2021 15:05:28 -0400
Subject: [PATCH 2197/2677] Add assert that check if topk is called with a
 negative value for k

---
 src/api/c/topk.cpp |  3 ++-
 test/topk.cpp      | 32 ++++++++++++++++++++++++++++++--
 2 files changed, 32 insertions(+), 3 deletions(-)

diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index 93445883f4..9375d857c0 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -66,7 +66,8 @@ af_err af_topk(af_array *values, af_array *indices, const af_array in,
         }
 
         ARG_ASSERT(2, (inInfo.dims()[rdim] >= k));
-        ARG_ASSERT(4, (k <= 256));  // TODO(umar): Remove this limitation
+        ARG_ASSERT(
+            4, (k > 0) && (k <= 256));  // TODO(umar): Remove this limitation
 
         if (rdim != 0) {
             AF_ERROR("topk is supported along dimenion 0 only.",
diff --git a/test/topk.cpp b/test/topk.cpp
index 241380d4f8..46eba3f159 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -333,9 +333,37 @@ TEST_P(TopKParams, CPP) {
                 float gold  = static_cast<float>(ii * d0 + j);
                 int goldidx = j;
                 ASSERT_FLOAT_EQ(gold, hval[i])
-                    << print_context(i, 0, hval, hidx);
-                ASSERT_EQ(goldidx, hidx[i]) << print_context(i, 0, hval, hidx);
+                    << print_context(i, j, hval, hidx);
+                ASSERT_EQ(goldidx, hidx[i]) << print_context(i, j, hval, hidx);
             }
         }
     }
 }
+
+TEST(TopK, KGreaterThan256) {
+    af::array a = af::randu(500);
+    af::array vals, idx;
+
+    int k = 257;
+    EXPECT_THROW(topk(vals, idx, a, k), af::exception)
+        << "The current limitation of the K value as increased. Please check "
+           "or remove this test";
+}
+
+TEST(TopK, KEquals0) {
+    af::array a = af::randu(500);
+    af::array vals, idx;
+
+    int k = 0;
+    EXPECT_THROW(topk(vals, idx, a, k), af::exception)
+        << "K cannot be less than 1";
+}
+
+TEST(TopK, KLessThan0) {
+    af::array a = af::randu(500);
+    af::array vals, idx;
+
+    int k = -1;
+    EXPECT_THROW(topk(vals, idx, a, k), af::exception)
+        << "K cannot be less than 0";
+}

From 2444ef5083584da453d7774900a8a7347a0a2d17 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 1 Oct 2021 15:35:40 -0400
Subject: [PATCH 2198/2677] fix transform operator for countByKey (#3175)

* fix transform operator for countByKey
---
 src/backend/cuda/reduce_impl.hpp            |  6 +++--
 src/backend/opencl/kernel/reduce_by_key.hpp |  6 +++--
 test/reduce.cpp                             | 26 +++++++++++++++++++++
 3 files changed, 34 insertions(+), 4 deletions(-)

diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 67ea8e7b2a..73b0d47761 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -99,8 +99,9 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
             POST_LAUNCH_CHECK();
             first_pass = false;
         } else {
+            constexpr af_op_t op2 = op == af_notzero_t ? af_add_t : op;
             CUDA_LAUNCH(
-                (kernel::reduce_blocks_dim_by_key<To, Tk, To, op, numThreads>),
+                (kernel::reduce_blocks_dim_by_key<To, Tk, To, op2, numThreads>),
                 blocks, numThreads, reduced_block_sizes.get(), reduced_keys,
                 reduced_vals, t_reduced_keys, t_reduced_vals, n_reduced_host,
                 change_nan, scalar<To>(nanval), dim, folded_dim_sz);
@@ -245,8 +246,9 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
             POST_LAUNCH_CHECK();
             first_pass = false;
         } else {
+            constexpr af_op_t op2 = op == af_notzero_t ? af_add_t : op;
             CUDA_LAUNCH(
-                (kernel::reduce_blocks_by_key<To, Tk, To, op, numThreads>),
+                (kernel::reduce_blocks_by_key<To, Tk, To, op2, numThreads>),
                 blocks, numThreads, reduced_block_sizes.get(), reduced_keys,
                 reduced_vals, t_reduced_keys, t_reduced_vals, n_reduced_host,
                 change_nan, scalar<To>(nanval), odims[2]);
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 50bf22b706..ec841dafc4 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -338,7 +338,8 @@ int reduceByKeyFirst(Array<Tk> &keys_out, Array<To> &vals_out, const Param keys,
                 vals, change_nan, nanval, n_reduced_host, numThreads);
             first_pass = false;
         } else {
-            reduceBlocksByKey<To, Tk, To, op>(
+            constexpr af_op_t op2 = op == af_notzero_t ? af_add_t : op;
+            reduceBlocksByKey<To, Tk, To, op2>(
                 reduced_block_sizes.get(), reduced_keys, reduced_vals,
                 t_reduced_keys, t_reduced_vals, change_nan, nanval,
                 n_reduced_host, numThreads);
@@ -458,7 +459,8 @@ int reduceByKeyDim(Array<Tk> &keys_out, Array<To> &vals_out, const Param keys,
                 dim_ordering);
             first_pass = false;
         } else {
-            reduceBlocksByKeyDim<To, Tk, To, op>(
+            constexpr af_op_t op2 = op == af_notzero_t ? af_add_t : op;
+            reduceBlocksByKeyDim<To, Tk, To, op2>(
                 reduced_block_sizes.get(), reduced_keys, reduced_vals,
                 t_reduced_keys, t_reduced_vals, change_nan, nanval,
                 n_reduced_host, numThreads, dim, dim_ordering);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 7ae503928f..3cb1c33a55 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2065,3 +2065,29 @@ TEST(ReduceByKey, ISSUE_2955_dim) {
     ASSERT_EQ(ok.dims(0), 128);
     ASSERT_EQ(ov.dims(1), 128);
 }
+
+TEST(ReduceByKey, ISSUE_3062) {
+    size_t N = 129;
+
+    af::array ones = af::constant(1, N, u32);
+    af::array zeros = af::constant(0, N, u32);
+
+    af::array okeys;
+    af::array ovalues;
+
+    af::sumByKey(okeys, ovalues, zeros, ones);
+    ASSERT_EQ(ovalues.scalar<unsigned>(), 129);
+
+    af::countByKey(okeys, ovalues, zeros, ones);
+    ASSERT_EQ(ovalues.scalar<unsigned>(), 129);
+
+    // test reduction on non-zero dimension as well
+    ones = af::constant(1, 2, N, u32);
+    zeros = af::constant(0, N, u32);
+
+    af::sumByKey(okeys, ovalues, zeros, ones, 1);
+    ASSERT_EQ(ovalues.scalar<unsigned>(), 129);
+
+    af::countByKey(okeys, ovalues, zeros, ones, 1);
+    ASSERT_EQ(ovalues.scalar<unsigned>(), 129);
+}

From 451331de5b3efd762db3ce700462ae2c4cb7f128 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Oct 2021 14:19:48 -0400
Subject: [PATCH 2199/2677] Fix default parameters for fftR2C and fftC2R from 0
 to 1.0

---
 include/af/signal.h | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/include/af/signal.h b/include/af/signal.h
index 5e131706b8..f24e4df3df 100644
--- a/include/af/signal.h
+++ b/include/af/signal.h
@@ -175,7 +175,7 @@ AFAPI array fft3Norm(const array& in, const double norm_factor, const dim_t odim
 
    \ingroup signal_func_fft
  */
-AFAPI void fftInPlace(array& in, const double norm_factor = 1);
+AFAPI void fftInPlace(array& in, const double norm_factor = 1.0);
 #endif
 
 #if AF_API_VERSION >= 31
@@ -189,7 +189,7 @@ AFAPI void fftInPlace(array& in, const double norm_factor = 1);
 
    \ingroup signal_func_fft2
  */
-AFAPI void fft2InPlace(array& in, const double norm_factor = 1);
+AFAPI void fft2InPlace(array& in, const double norm_factor = 1.0);
 #endif
 
 #if AF_API_VERSION >= 31
@@ -203,7 +203,7 @@ AFAPI void fft2InPlace(array& in, const double norm_factor = 1);
 
    \ingroup signal_func_fft3
  */
-AFAPI void fft3InPlace(array& in, const double norm_factor = 1);
+AFAPI void fft3InPlace(array& in, const double norm_factor = 1.0);
 #endif
 
 /**
@@ -340,7 +340,7 @@ AFAPI array ifft3Norm(const array& in, const double norm_factor, const dim_t odi
 
    \ingroup signal_func_ifft
  */
-AFAPI void ifftInPlace(array& in, const double norm_factor = 1);
+AFAPI void ifftInPlace(array& in, const double norm_factor = 1.0);
 #endif
 
 #if AF_API_VERSION >= 31
@@ -354,7 +354,7 @@ AFAPI void ifftInPlace(array& in, const double norm_factor = 1);
 
    \ingroup signal_func_ifft2
  */
-AFAPI void ifft2InPlace(array& in, const double norm_factor = 1);
+AFAPI void ifft2InPlace(array& in, const double norm_factor = 1.0);
 #endif
 
 #if AF_API_VERSION >= 31
@@ -368,7 +368,7 @@ AFAPI void ifft2InPlace(array& in, const double norm_factor = 1);
 
    \ingroup signal_func_ifft3
  */
-AFAPI void ifft3InPlace(array& in, const double norm_factor = 1);
+AFAPI void ifft3InPlace(array& in, const double norm_factor = 1.0);
 #endif
 
 /**
@@ -471,7 +471,7 @@ AFAPI array idft(const array& in);
 template<int rank>
 array fftR2C(const array &in,
              const dim4& dims,
-             const double norm_factor = 0);
+             const double norm_factor = 1.0);
 #endif
 
 #if AF_API_VERSION >= 31
@@ -488,7 +488,7 @@ array fftR2C(const array &in,
 */
 template<int rank>
 array fftR2C(const array &in,
-             const double norm_factor = 0);
+             const double norm_factor = 1.0);
 #endif
 
 #if AF_API_VERSION >= 31
@@ -506,7 +506,7 @@ array fftR2C(const array &in,
 
 template<int rank>
 array fftC2R(const array &in, bool is_odd = false,
-                 const double norm_factor = 0);
+                 const double norm_factor = 1.0);
 #endif
 
 /**

From 1ad0400cae9ec449b5a4b476e952a532a79d362e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Oct 2021 14:32:01 -0400
Subject: [PATCH 2200/2677] Update CLBlast to fix some errors on Turing cards

---
 CMakeModules/build_CLBlast.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 64263df928..eaa0908ca8 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -7,7 +7,7 @@
 
 af_dep_check_and_populate(${clblast_prefix}
   URI https://github.com/cnugteren/CLBlast.git
-  REF 1.5.2
+  REF 4500a03440e2cc54998c0edab366babf5e504d67
 )
 
 include(ExternalProject)

From a800d9f2ffee28aaebb90ea569063e572822d020 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 13 Oct 2021 01:53:16 -0400
Subject: [PATCH 2201/2677] Allow moddims operations to be part of the JIT tree
 if possible (#3177)

* Implement JIT moddims for CUDA and OpenCL

* Create a moddims node instead of modifying setDataDims

* Cleanup headers after moddims change

* Address feedback
---
 .gitignore                                |  12 +--
 src/api/c/assign.cpp                      |   2 +
 src/api/c/handle.hpp                      |  18 ----
 src/api/c/histeq.cpp                      |   2 +
 src/api/c/index.cpp                       |   4 +-
 src/api/c/moddims.cpp                     |   5 +-
 src/api/c/optypes.hpp                     |   7 +-
 src/api/c/pinverse.cpp                    |   2 +
 src/api/c/surface.cpp                     |   2 +
 src/backend/common/CMakeLists.txt         |   3 +
 src/backend/common/TemplateArg.cpp        |   4 +
 src/backend/common/TemplateArg.hpp        |   4 +
 src/backend/common/jit/BinaryNode.cpp     |   2 +-
 src/backend/common/jit/BinaryNode.hpp     |   2 +-
 src/backend/common/jit/BufferNodeBase.hpp |   8 +-
 src/backend/common/jit/ModdimNode.hpp     |  32 ++++++
 src/backend/common/jit/NaryNode.hpp       |  10 +-
 src/backend/common/jit/Node.cpp           |   5 +
 src/backend/common/jit/Node.hpp           |  25 ++---
 src/backend/common/jit/NodeIterator.hpp   |   2 -
 src/backend/common/jit/ScalarNode.hpp     |   4 +
 src/backend/common/jit/ShiftNodeBase.hpp  |   6 +-
 src/backend/common/jit/UnaryNode.hpp      |   3 +-
 src/backend/common/moddims.cpp            | 102 ++++++++++++++++++
 src/backend/common/moddims.hpp            |  41 ++++++++
 src/backend/common/util.hpp               |   4 +
 src/backend/cpu/Array.cpp                 |   6 +-
 src/backend/cpu/convolve.cpp              |  30 ++++--
 src/backend/cpu/jit/BinaryNode.hpp        |  31 +++---
 src/backend/cpu/jit/BufferNode.hpp        |  18 +++-
 src/backend/cpu/jit/ScalarNode.hpp        |   4 +
 src/backend/cpu/jit/UnaryNode.hpp         |  18 ++--
 src/backend/cpu/kernel/Array.hpp          | 123 ++++++++++++++++++----
 src/backend/cuda/Array.cpp                |   5 +-
 src/backend/cuda/convolveNN.cpp           |  30 ++++--
 src/backend/cuda/jit.cpp                  |  51 ++++++++-
 src/backend/cuda/select.cpp               |   8 +-
 src/backend/cuda/unary.hpp                |   1 +
 src/backend/opencl/Array.cpp              |  15 ++-
 src/backend/opencl/Array.hpp              |   5 +
 src/backend/opencl/convolve.cpp           |  35 +++---
 src/backend/opencl/jit.cpp                |  63 ++++++++++-
 src/backend/opencl/select.cpp             |   8 +-
 src/backend/opencl/sparse.cpp             |  11 +-
 src/backend/opencl/unary.hpp              |   1 +
 test/gfor.cpp                             |   2 +-
 test/index.cpp                            |   8 +-
 test/jit.cpp                              |   2 -
 test/moddims.cpp                          |  26 ++++-
 49 files changed, 637 insertions(+), 175 deletions(-)
 create mode 100644 src/backend/common/jit/ModdimNode.hpp
 create mode 100644 src/backend/common/moddims.cpp
 create mode 100644 src/backend/common/moddims.hpp

diff --git a/.gitignore b/.gitignore
index 7840e027a4..d56dd8ccf0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,21 +1,21 @@
-CMakeCache.txt
-CMakeFiles/
+#CMakeCache.txt
+#./CMakeFiles/
 build*/
 Release/
-Makefile
-cmake_install.cmake
+#Makefile
+#cmake_install.cmake
 GTAGS
 GRTAGS
 GPATH
 .dir-locals.el
-docs/details/examples.dox
+#docs/details/examples.dox
 /TAGS
 external/
 extern/
 compile_commands.json
 venv
 test/gtest
-src/backend/cuda/cub
+#src/backend/cuda/cub
 conanbuildinfo*
 conaninfo*
 conan.lock
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index edd769297a..ef7bacd821 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -14,6 +14,7 @@
 #include <common/complex.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <common/moddims.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <indexing_common.hpp>
@@ -34,6 +35,7 @@ using common::createSpanIndex;
 using common::half;
 using common::if_complex;
 using common::if_real;
+using common::modDims;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 6332d1d162..2499c9781a 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -11,7 +11,6 @@
 #include <Array.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
-#include <common/half.hpp>
 #include <common/traits.hpp>
 #include <copy.hpp>
 #include <math.hpp>
@@ -37,23 +36,6 @@ detail::Array<To> castArray(const af_array &in);
 
 namespace {
 
-template<typename T>
-detail::Array<T> modDims(const detail::Array<T> &in, const af::dim4 &newDims) {
-    in.eval();  // FIXME: Figure out a better way
-
-    detail::Array<T> Out = in;
-    if (!in.isLinear()) Out = detail::copyArray<T>(in);
-    Out.setDataDims(newDims);
-
-    return Out;
-}
-
-template<typename T>
-detail::Array<T> flat(const detail::Array<T> &in) {
-    const af::dim4 newDims(in.elements());
-    return modDims<T>(in, newDims);
-}
-
 template<typename T>
 const detail::Array<T> &getArray(const af_array &arr) {
     const detail::Array<T> *A = static_cast<const detail::Array<T> *>(arr);
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index a542d97a73..0c2ce6f8ca 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
+#include <common/moddims.hpp>
 #include <handle.hpp>
 #include <lookup.hpp>
 #include <reduce.hpp>
@@ -22,6 +23,7 @@
 
 using af::dim4;
 using common::cast;
+using common::modDims;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index c8e8c6aa05..0f36e0b463 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -14,6 +14,7 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/moddims.hpp>
 #include <handle.hpp>
 #include <lookup.hpp>
 #include <af/arith.h>
@@ -33,6 +34,7 @@ using std::vector;
 using af::dim4;
 using common::convert2Canonical;
 using common::createSpanIndex;
+using common::flat;
 using common::half;
 using detail::cdouble;
 using detail::cfloat;
@@ -70,7 +72,7 @@ static af_array indexBySeqs(const af_array& src,
     const auto& input = getArray<T>(src);
 
     if (ndims == 1U && ndims != input.ndims()) {
-        return getHandle(createSubArray(::flat(input), indicesV));
+        return getHandle(createSubArray(flat(input), indicesV));
     } else {
         return getHandle(createSubArray(input, indicesV));
     }
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 07471692ca..5f07c6bf8b 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -10,6 +10,7 @@
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <common/moddims.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <af/data.h>
@@ -29,11 +30,11 @@ using detail::ushort;
 namespace {
 template<typename T>
 af_array modDims(const af_array in, const dim4& newDims) {
-    return getHandle(::modDims(getArray<T>(in), newDims));
+    return getHandle(common::modDims(getArray<T>(in), newDims));
 }
 template<typename T>
 af_array flat(const af_array in) {
-    return getHandle(::flat(getArray<T>(in)));
+    return getHandle(common::flat(getArray<T>(in)));
 }
 }  // namespace
 
diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index c1ce3c0784..aeb90e1dcd 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -10,7 +10,8 @@
 #pragma once
 
 typedef enum {
-    af_add_t = 0,
+    af_none_t = -1,
+    af_add_t  = 0,
     af_sub_t,
     af_mul_t,
     af_div_t,
@@ -96,5 +97,7 @@ typedef enum {
 
     af_select_t,
     af_not_select_t,
-    af_rsqrt_t
+    af_rsqrt_t,
+
+    af_moddims_t
 } af_op_t;
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 0aff145194..49086043af 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -15,6 +15,7 @@
 #include <common/ArrayInfo.hpp>
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
+#include <common/moddims.hpp>
 #include <diagonal.hpp>
 #include <handle.hpp>
 #include <logic.hpp>
@@ -32,6 +33,7 @@
 using af::dim4;
 using af::dtype_traits;
 using common::cast;
+using common::modDims;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index e8361c8c49..92e916e2f4 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -14,6 +14,7 @@
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
+#include <common/moddims.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
@@ -22,6 +23,7 @@
 #include <tile.hpp>
 
 using af::dim4;
+using common::modDims;
 using detail::Array;
 using detail::copy_surface;
 using detail::forgeManager;
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 204b27f927..9805b42ae4 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -11,6 +11,7 @@ target_sources(afcommon_interface
   INTERFACE
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/BinaryNode.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/BinaryNode.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/ModdimNode.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/NaryNode.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.hpp
@@ -65,6 +66,8 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel_cache.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel_cache.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel_type.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/moddims.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/moddims.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/module_loading.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/sparse_helpers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/traits.hpp
diff --git a/src/backend/common/TemplateArg.cpp b/src/backend/common/TemplateArg.cpp
index 436099412b..740138b337 100644
--- a/src/backend/common/TemplateArg.cpp
+++ b/src/backend/common/TemplateArg.cpp
@@ -13,6 +13,7 @@
 #include <optypes.hpp>
 #include <af/defines.h>
 
+#include <cstdlib>
 #include <string>
 
 using std::string;
@@ -159,6 +160,9 @@ string getOpEnumStr(af_op_t val) {
         CASE_STMT(af_select_t);
         CASE_STMT(af_not_select_t);
         CASE_STMT(af_rsqrt_t);
+        CASE_STMT(af_moddims_t);
+
+        CASE_STMT(af_none_t);
     }
 #undef CASE_STMT
     return retVal;
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
index 8239a5033f..d82d30e12a 100644
--- a/src/backend/common/TemplateArg.hpp
+++ b/src/backend/common/TemplateArg.hpp
@@ -12,9 +12,13 @@
 #include <string>
 #include <utility>
 
+#include <optypes.hpp>
+
 template<typename T>
 std::string toString(T value);
 
+std::string getOpEnumStr(af_op_t val);
+
 struct TemplateArg {
     std::string _tparam;
 
diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
index 00af405ecf..f67015b9fa 100644
--- a/src/backend/common/jit/BinaryNode.cpp
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -40,7 +40,7 @@ Array<To> createBinaryNode(const Array<Ti> &lhs, const Array<Ti> &rhs,
         BinOp<To, Ti, op> bop;
         return std::make_shared<BinaryNode>(
             static_cast<af::dtype>(dtype_traits<To>::af_type), bop.name(),
-            operands[0], operands[1], (int)(op));
+            operands[0], operands[1], op);
     };
 
     Node_ptr out =
diff --git a/src/backend/common/jit/BinaryNode.hpp b/src/backend/common/jit/BinaryNode.hpp
index e1aa7ac74f..bfc68bd8ea 100644
--- a/src/backend/common/jit/BinaryNode.hpp
+++ b/src/backend/common/jit/BinaryNode.hpp
@@ -17,7 +17,7 @@ namespace common {
 class BinaryNode : public NaryNode {
    public:
     BinaryNode(const af::dtype type, const char *op_str, common::Node_ptr lhs,
-               common::Node_ptr rhs, int op)
+               common::Node_ptr rhs, af_op_t op)
         : NaryNode(type, op_str, 2, {{lhs, rhs}}, op,
                    std::max(lhs->getHeight(), rhs->getHeight()) + 1) {}
 };
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 026fbd4ce7..5027cd5671 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -20,16 +20,20 @@ template<typename DataType, typename ParamType>
 class BufferNodeBase : public common::Node {
    private:
     DataType m_data;
-    ParamType m_param;
     unsigned m_bytes;
     bool m_linear_buffer;
 
    public:
+    ParamType m_param;
     BufferNodeBase(af::dtype type)
         : Node(type, 0, {}), m_bytes(0), m_linear_buffer(true) {}
 
     bool isBuffer() const final { return true; }
 
+    std::unique_ptr<Node> clone() final {
+        return std::make_unique<BufferNodeBase>(*this);
+    }
+
     void setData(ParamType param, DataType data, const unsigned bytes,
                  bool is_linear) {
         m_param         = param;
@@ -38,7 +42,7 @@ class BufferNodeBase : public common::Node {
         m_linear_buffer = is_linear;
     }
 
-    bool isLinear(dim_t dims[4]) const final {
+    bool isLinear(const dim_t dims[4]) const final {
         bool same_dims = true;
         for (int i = 0; same_dims && i < 4; i++) {
             same_dims &= (dims[i] == m_param.dims[i]);
diff --git a/src/backend/common/jit/ModdimNode.hpp b/src/backend/common/jit/ModdimNode.hpp
new file mode 100644
index 0000000000..209593df5c
--- /dev/null
+++ b/src/backend/common/jit/ModdimNode.hpp
@@ -0,0 +1,32 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <common/jit/NaryNode.hpp>
+
+namespace common {
+
+class ModdimNode : public NaryNode {
+   public:
+    af::dim4 m_new_shape;
+    ModdimNode(const af::dim4& new_shape, const af::dtype type, Node_ptr child)
+        : NaryNode(type, "__noop", 1, {{child}}, af_moddims_t,
+                   child->getHeight() + 1)
+        , m_new_shape(new_shape) {
+        static_assert(std::is_nothrow_move_assignable<ModdimNode>::value,
+                      "ModdimNode is not move assignable");
+        static_assert(std::is_nothrow_move_constructible<ModdimNode>::value,
+                      "ModdimNode is not move constructible");
+    }
+
+    virtual std::unique_ptr<Node> clone() noexcept final {
+        return std::make_unique<ModdimNode>(*this);
+    }
+};
+}  // namespace common
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 5c37b0da82..885edb277d 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -25,13 +25,13 @@ namespace common {
 class NaryNode : public Node {
    private:
     int m_num_children;
-    int m_op;
+    af_op_t m_op;
     const char *m_op_str;
 
    public:
     NaryNode(const af::dtype type, const char *op_str, const int num_children,
              const std::array<common::Node_ptr, Node::kMaxChildren> &&children,
-             const int op, const int height)
+             const af_op_t op, const int height)
         : common::Node(
               type, height,
               std::forward<
@@ -64,6 +64,12 @@ class NaryNode : public Node {
         swap(m_op_str, other.m_op_str);
     }
 
+    af_op_t getOp() const noexcept final { return m_op; }
+
+    virtual std::unique_ptr<Node> clone() override {
+        return std::make_unique<NaryNode>(*this);
+    }
+
     void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
         // Make the dec representation of enum part of the Kernel name
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 096164a16b..b59222de86 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -61,6 +61,11 @@ bool NodePtr_equalto::operator()(const Node *l, const Node *r) const noexcept {
     return *l == *r;
 }
 
+auto isBuffer(const Node &ptr) -> bool { return ptr.isBuffer(); }
+
+/// Returns true if the buffer is linear
+bool Node::isLinear(const dim_t dims[4]) const { return true; }
+
 }  // namespace common
 
 size_t std::hash<common::Node *>::operator()(
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 25eb4a3d43..3cad47f03e 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -112,14 +112,13 @@ class Node {
     static const int kMaxChildren = 3;
 
    protected:
+   public:
     std::array<Node_ptr, kMaxChildren> m_children;
     af::dtype m_type;
     int m_height;
 
     template<typename T>
     friend class NodeIterator;
-
-   public:
     Node() = default;
     Node(const af::dtype type, const int height,
          const std::array<Node_ptr, kMaxChildren> children)
@@ -149,6 +148,8 @@ class Node {
     /// Default move assignment operator
     Node &operator=(Node &&node) noexcept = default;
 
+    virtual af_op_t getOp() const noexcept { return af_none_t; }
+
     int getNodesMap(Node_map_t &node_map, std::vector<Node *> &full_nodes,
                     std::vector<Node_ids> &full_ids);
 
@@ -241,10 +242,7 @@ class Node {
     virtual bool isBuffer() const { return false; }
 
     /// Returns true if the buffer is linear
-    virtual bool isLinear(dim_t dims[4]) const {
-        UNUSED(dims);
-        return true;
-    }
+    virtual bool isLinear(const dim_t dims[4]) const;
 
     af::dtype getType() const { return m_type; }
 
@@ -278,21 +276,16 @@ class Node {
     virtual bool operator==(const Node &other) const noexcept {
         return this == &other;
     }
+    virtual std::unique_ptr<Node> clone() = 0;
 
 #ifdef AF_CPU
-    /// Replaces a child node pointer in the cpu::jit::BinaryNode<T> or the
-    /// cpu::jit::UnaryNode classes at \p id with *ptr. Used only in the CPU
-    /// backend and does not modify the m_children pointers in the
-    /// common::Node_ptr class.
-    virtual void replaceChild(int id, void *ptr) noexcept {
-        UNUSED(id);
-        UNUSED(ptr);
-    }
-
     template<typename U>
     friend void cpu::kernel::evalMultiple(
         std::vector<cpu::Param<U>> arrays,
         std::vector<common::Node_ptr> output_nodes_);
+
+    virtual void setShape(af::dim4 new_shape) { UNUSED(new_shape); }
+
 #endif
 };
 
@@ -305,4 +298,6 @@ std::string getFuncName(const std::vector<Node *> &output_nodes,
                         const std::vector<Node *> &full_nodes,
                         const std::vector<Node_ids> &full_ids, bool is_linear);
 
+auto isBuffer(const Node &ptr) -> bool;
+
 }  // namespace common
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index 9b3671cee0..da01c0b5bb 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -15,8 +15,6 @@
 #include <vector>
 
 namespace common {
-class Node;  // TODO(umar): Remove when CPU backend Node class is moved from JIT
-             // to common
 
 /// A node iterator that performs a breadth first traversal of the node tree
 template<typename Node = common::Node>
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 3528675d19..bf0978359f 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -45,6 +45,10 @@ class ScalarNode : public common::Node {
         return *this;
     }
 
+    std::unique_ptr<Node> clone() final {
+        return std::make_unique<ScalarNode>(*this);
+    }
+
     // Swap specilization
     void swap(ScalarNode& other) noexcept {
         using std::swap;
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 5049b6d71f..df42002576 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -50,6 +50,10 @@ class ShiftNodeBase : public Node {
         return *this;
     }
 
+    std::unique_ptr<Node> clone() final {
+        return std::make_unique<ShiftNodeBase>(*this);
+    }
+
     // Swap specilization
     void swap(ShiftNodeBase &other) noexcept {
         using std::swap;
@@ -58,7 +62,7 @@ class ShiftNodeBase : public Node {
         swap(m_shifts, other.m_shifts);
     }
 
-    bool isLinear(dim_t dims[4]) const final {
+    bool isLinear(const dim_t dims[4]) const final {
         UNUSED(dims);
         return false;
     }
diff --git a/src/backend/common/jit/UnaryNode.hpp b/src/backend/common/jit/UnaryNode.hpp
index 1ffe9cd25d..d7470a3378 100644
--- a/src/backend/common/jit/UnaryNode.hpp
+++ b/src/backend/common/jit/UnaryNode.hpp
@@ -14,7 +14,8 @@ namespace common {
 
 class UnaryNode : public NaryNode {
    public:
-    UnaryNode(const af::dtype type, const char *op_str, Node_ptr child, int op)
+    UnaryNode(const af::dtype type, const char *op_str, Node_ptr child,
+              af_op_t op)
         : NaryNode(type, op_str, 1, {{child}}, op, child->getHeight() + 1) {
         static_assert(std::is_nothrow_move_assignable<UnaryNode>::value,
                       "UnaryNode is not move assignable");
diff --git a/src/backend/common/moddims.cpp b/src/backend/common/moddims.cpp
new file mode 100644
index 0000000000..50f9fc6846
--- /dev/null
+++ b/src/backend/common/moddims.cpp
@@ -0,0 +1,102 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/moddims.hpp>
+
+#include <common/jit/ModdimNode.hpp>
+#include <common/jit/NodeIterator.hpp>
+#include <copy.hpp>
+
+using af::dim4;
+using detail::Array;
+using detail::copyArray;
+using detail::createNodeArray;
+
+using std::make_shared;
+using std::shared_ptr;
+using std::vector;
+
+namespace common {
+template<typename T>
+Array<T> moddimOp(const Array<T> &in, af::dim4 outDim) {
+    using common::Node;
+    using common::Node_ptr;
+    using std::array;
+
+    auto createModdim = [outDim](array<Node_ptr, 1> &operands) {
+        return make_shared<ModdimNode>(
+            outDim, static_cast<af::dtype>(af::dtype_traits<T>::af_type),
+            operands[0]);
+    };
+
+    const auto &node = in.getNode();
+
+    NodeIterator<> it(node.get());
+
+    dim4 olddims_t = in.dims();
+
+    bool all_linear = true;
+    while (all_linear && it != NodeIterator<>()) {
+        all_linear &= it->isLinear(olddims_t.get());
+        ++it;
+    }
+    if (all_linear == false) in.eval();
+
+    Node_ptr out = createNaryNode<T, 1>(outDim, createModdim, {&in});
+    return createNodeArray<T>(outDim, out);
+}
+
+template<typename T>
+Array<T> modDims(const Array<T> &in, const af::dim4 &newDims) {
+    if (in.isLinear() == false) {
+        // Nonlinear array's shape cannot be modified. Copy the data and modify
+        // the shape of the array
+        Array<T> out = copyArray<T>(in);
+        out.setDataDims(newDims);
+        return out;
+    } else if (in.isReady()) {
+        /// If the array is a buffer, modify the dimension and return
+        auto out = in;
+        out.setDataDims(newDims);
+        return out;
+    } else {
+        /// If the array is a node and not linear and not a buffer, then create
+        /// a moddims node
+        auto out = moddimOp<T>(in, newDims);
+        return out;
+    }
+}
+
+template<typename T>
+detail::Array<T> flat(const detail::Array<T> &in) {
+    const af::dim4 newDims(in.elements());
+    return common::modDims<T>(in, newDims);
+}
+
+}  // namespace common
+
+#define INSTANTIATE(TYPE)                                        \
+    template detail::Array<TYPE> common::modDims<TYPE>(          \
+        const detail::Array<TYPE> &in, const af::dim4 &newDims); \
+    template detail::Array<TYPE> common::flat<TYPE>(             \
+        const detail::Array<TYPE> &in)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(detail::cfloat);
+INSTANTIATE(detail::cdouble);
+INSTANTIATE(common::half);
+INSTANTIATE(unsigned char);
+INSTANTIATE(char);
+INSTANTIATE(unsigned short);
+INSTANTIATE(short);
+INSTANTIATE(unsigned);
+INSTANTIATE(int);
+INSTANTIATE(long long);
+INSTANTIATE(unsigned long long);
diff --git a/src/backend/common/moddims.hpp b/src/backend/common/moddims.hpp
new file mode 100644
index 0000000000..a132db018c
--- /dev/null
+++ b/src/backend/common/moddims.hpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/dim4.hpp>
+
+namespace common {
+
+/// Modifies the shape of the Array<T> object to \p newDims
+///
+/// Modifies the shape of the Array<T> object to \p newDims. Depending on the
+/// in Array, different operations will be performed.
+///
+/// * If the object is a linear array and it is an unevaluated JIT node, this
+///   function will createa a JIT Node.
+/// * If the object is not a JIT node but it is still linear, It will create a
+///   reference to the internal array with the new shape.
+/// * If the array is non-linear a moddims operation will be performed
+///
+/// \param in       The input array that who's shape will be modified
+/// \param newDims  The new shape of the input Array<T>
+///
+/// \returns        a new Array<T> with the specified shape.
+template<typename T>
+detail::Array<T> modDims(const detail::Array<T> &in, const af::dim4 &newDims);
+
+/// Calls moddims where all elements are in the first dimension of the array
+///
+/// \param in  The input Array to be flattened
+///
+/// \returns A new array where all elements are in the first dimension.
+template<typename T>
+detail::Array<T> flat(const detail::Array<T> &in);
+
+}  // namespace common
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 4968fa3568..bb197e2af3 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -10,6 +10,8 @@
 /// This file contains platform independent utility functions
 #pragma once
 
+#include <af/defines.h>
+
 #include <iosfwd>
 #include <string>
 #include <vector>
@@ -55,6 +57,8 @@ bool isDirectoryWritable(const std::string& path);
 /// no extension.
 std::string makeTempFilename();
 
+const char* getName(af_dtype type);
+
 /// Return the FNV-1a hash of the provided bata.
 ///
 /// \param[in] data Binary data to hash
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 40480566ee..5b2385866c 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -264,10 +264,9 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
     parent.eval();
 
     dim4 dDims          = parent.getDataDims();
-    dim4 dStrides       = calcStrides(dDims);
     dim4 parent_strides = parent.strides();
 
-    if (dStrides != parent_strides) {
+    if (parent.isLinear() == false) {
         const Array<T> parentCopy = copyArray(parent);
         return createSubArray(parentCopy, index, copy);
     }
@@ -316,8 +315,8 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
 template<typename T>
 void Array<T>::setDataDims(const dim4 &new_dims) {
-    modDims(new_dims);
     data_dims = new_dims;
+    modDims(new_dims);
 }
 
 #define INSTANTIATE(T)                                                        \
@@ -344,6 +343,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
+    template kJITHeuristics passesJitHeuristics<T>(Node * n);                 \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 50beb69860..dc780c450e 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -13,6 +13,7 @@
 #include <common/defines.hpp>
 #include <common/half.hpp>
 #include <common/indexing_helpers.hpp>
+#include <common/moddims.hpp>
 #include <convolve.hpp>
 #include <handle.hpp>
 #include <kernel/convolve.hpp>
@@ -29,6 +30,7 @@
 using af::dim4;
 using common::flip;
 using common::half;
+using common::modDims;
 
 namespace cpu {
 
@@ -137,15 +139,17 @@ Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
 
     unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
     dim4 uDims = unwrapped.dims();
-    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
 
     Array<T> collapsedFilter = flip(filter, {1, 1, 0, 0});
-    collapsedFilter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+    collapsedFilter          = modDims(collapsedFilter,
+                              dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> res =
         matmul(unwrapped, collapsedFilter, AF_MAT_TRANS, AF_MAT_NONE);
-    res.modDims(dim4(outputWidth, outputHeight, signal.dims()[3],
-                     collapsedFilter.dims()[1]));
+    res = modDims(res, dim4(outputWidth, outputHeight, signal.dims()[3],
+                            collapsedFilter.dims()[1]));
     Array<T> out = reorder(res, dim4(0, 1, 3, 2));
 
     return out;
@@ -182,16 +186,18 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
     const dim4 &fDims = original_filter.dims();
 
     Array<T> collapsed_filter = flip(original_filter, {1, 1, 0, 0});
-    collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+    collapsed_filter          = modDims(collapsed_filter,
+                               dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
-    res.modDims(dim4(res.dims()[0] / sDims[3], sDims[3], fDims[0] * fDims[1],
-                     sDims[2]));
+    res = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
+                            fDims[0] * fDims[1], sDims[2]));
     res = reorder(res, dim4(0, 2, 3, 1));
 
     const bool retCols = false;
@@ -219,15 +225,17 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
 
     unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
     dim4 uDims = unwrapped.dims();
-    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
-    res.modDims(dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+    res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
 
     return flip(res, {1, 1, 0, 0});
 }
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index b83092d6d4..2342bb30cb 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -25,38 +25,35 @@ template<typename To, typename Ti, af_op_t op>
 class BinaryNode : public TNode<compute_t<To>> {
    protected:
     BinOp<compute_t<To>, compute_t<Ti>, op> m_op;
-    TNode<compute_t<Ti>> *m_lhs, *m_rhs;
+    using common::Node::m_children;
 
    public:
     BinaryNode(common::Node_ptr lhs, common::Node_ptr rhs)
         : TNode<compute_t<To>>(compute_t<To>(0),
                                std::max(lhs->getHeight(), rhs->getHeight()) + 1,
-                               {{lhs, rhs}})
-        , m_lhs(static_cast<TNode<compute_t<Ti>> *>(lhs.get()))
-        , m_rhs(static_cast<TNode<compute_t<Ti>> *>(rhs.get())) {}
+                               {{lhs, rhs}}) {}
+
+    std::unique_ptr<common::Node> clone() final {
+        return std::make_unique<BinaryNode>(*this);
+    }
+
+    af_op_t getOp() const noexcept final { return op; }
 
     void calc(int x, int y, int z, int w, int lim) final {
         UNUSED(x);
         UNUSED(y);
         UNUSED(z);
         UNUSED(w);
-        m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
-    }
-
-    /// Replaces a child node pointer in the cpu::jit::BinaryNode<T> class at \p
-    /// id with *ptr. Used only in the CPU backend and does not modify the
-    /// m_children pointers in the common::Node_ptr class.
-    void replaceChild(int id, void *ptr) noexcept final {
-        auto nnode = static_cast<TNode<compute_t<Ti>> *>(ptr);
-        if (nnode->isBuffer()) {
-            if (id == 0 && m_lhs != ptr) { m_lhs = nnode; }
-            if (id == 1 && m_rhs != ptr) { m_rhs = nnode; }
-        }
+        auto lhs = static_cast<TNode<compute_t<Ti>> *>(m_children[0].get());
+        auto rhs = static_cast<TNode<compute_t<Ti>> *>(m_children[1].get());
+        m_op.eval(this->m_val, lhs->m_val, rhs->m_val, lim);
     }
 
     void calc(int idx, int lim) final {
         UNUSED(idx);
-        m_op.eval(this->m_val, m_lhs->m_val, m_rhs->m_val, lim);
+        auto lhs = static_cast<TNode<compute_t<Ti>> *>(m_children[0].get());
+        auto rhs = static_cast<TNode<compute_t<Ti>> *>(m_children[1].get());
+        m_op.eval(this->m_val, lhs->m_val, rhs->m_val, lim);
     }
 
     void genKerName(std::string &kerString,
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index 2793966dcc..ac789dc2ee 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -40,6 +40,10 @@ class BufferNode : public TNode<T> {
         , m_dims{0, 0, 0, 0}
         , m_linear_buffer(true) {}
 
+    std::unique_ptr<common::Node> clone() final {
+        return std::make_unique<BufferNode>(*this);
+    }
+
     void setData(std::shared_ptr<T> data, unsigned bytes, dim_t data_off,
                  const dim_t *dims, const dim_t *strides,
                  const bool is_linear) {
@@ -53,6 +57,18 @@ class BufferNode : public TNode<T> {
         }
     }
 
+    void setShape(af::dim4 new_shape) final {
+        auto new_strides = calcStrides(new_shape);
+        m_dims[0]        = new_shape[0];
+        m_dims[1]        = new_shape[1];
+        m_dims[2]        = new_shape[2];
+        m_dims[3]        = new_shape[3];
+        m_strides[0]     = new_strides[0];
+        m_strides[1]     = new_strides[1];
+        m_strides[2]     = new_strides[2];
+        m_strides[3]     = new_strides[3];
+    }
+
     void calc(int x, int y, int z, int w, int lim) final {
         using Tc = compute_t<T>;
 
@@ -122,7 +138,7 @@ class BufferNode : public TNode<T> {
         UNUSED(ids);
     }
 
-    bool isLinear(dim_t *dims) const final {
+    bool isLinear(const dim_t *dims) const final {
         return m_linear_buffer && dims[0] == m_dims[0] &&
                dims[1] == m_dims[1] && dims[2] == m_dims[2] &&
                dims[3] == m_dims[3];
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index ab91a92aac..657cbbf355 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -21,6 +21,10 @@ class ScalarNode : public TNode<T> {
    public:
     ScalarNode(T val) : TNode<T>(val, 0, {}) {}
 
+    std::unique_ptr<common::Node> clone() final {
+        return std::make_unique<ScalarNode>(*this);
+    }
+
     void genKerName(std::string &kerString,
                     const common::Node_ids &ids) const final {
         UNUSED(kerString);
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 0481455793..527d078dcc 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -28,30 +28,32 @@ namespace jit {
 template<typename To, typename Ti, af_op_t op>
 class UnaryNode : public TNode<To> {
    protected:
+    using common::Node::m_children;
     UnOp<To, Ti, op> m_op;
-    TNode<Ti> *m_child;
 
    public:
     UnaryNode(common::Node_ptr child)
-        : TNode<To>(To(0), child->getHeight() + 1, {{child}})
-        , m_child(static_cast<TNode<Ti> *>(child.get())) {}
+        : TNode<To>(To(0), child->getHeight() + 1, {{child}}) {}
 
-    void replaceChild(int id, void *ptr) noexcept final {
-        auto nnode = static_cast<TNode<Ti> *>(ptr);
-        if (id == 0 && nnode->isBuffer() && m_child != ptr) { m_child = nnode; }
+    std::unique_ptr<common::Node> clone() final {
+        return std::make_unique<UnaryNode>(*this);
     }
 
+    af_op_t getOp() const noexcept final { return op; }
+
     void calc(int x, int y, int z, int w, int lim) final {
         UNUSED(x);
         UNUSED(y);
         UNUSED(z);
         UNUSED(w);
-        m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
+        auto child = static_cast<TNode<Ti> *>(m_children[0].get());
+        m_op.eval(TNode<To>::m_val, child->m_val, lim);
     }
 
     void calc(int idx, int lim) final {
         UNUSED(idx);
-        m_op.eval(TNode<To>::m_val, m_child->m_val, lim);
+        auto child = static_cast<TNode<Ti> *>(m_children[0].get());
+        m_op.eval(TNode<To>::m_val, child->m_val, lim);
     }
 
     void genKerName(std::string &kerString,
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 30dd989777..32ef5f6634 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -9,7 +9,9 @@
 
 #pragma once
 #include <Param.hpp>
+#include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <jit/BufferNode.hpp>
 #include <jit/Node.hpp>
 #include <jit/UnaryNode.hpp>
@@ -19,13 +21,86 @@
 namespace cpu {
 namespace kernel {
 
+/// Clones nodes and update the child pointers
+std::vector<std::shared_ptr<common::Node>> cloneNodes(
+    const std::vector<common::Node *> &nodes,
+    const std::vector<common::Node_ids> &ids) {
+    using common::Node;
+    // find all moddims in the tree
+    std::vector<std::shared_ptr<Node>> node_clones;
+    node_clones.reserve(nodes.size());
+    transform(begin(nodes), end(nodes), back_inserter(node_clones),
+              [](Node *n) { return n->clone(); });
+
+    for (common::Node_ids id : ids) {
+        auto &children = node_clones[id.id]->m_children;
+        for (int i = 0; i < Node::kMaxChildren && children[i] != nullptr; i++) {
+            children[i] = node_clones[id.child_ids[i]];
+        }
+    }
+    return node_clones;
+}
+
+/// Sets the shape of the buffer nodes under the moddims node to the new shape
+void propagateModdimsShape(
+    std::vector<std::shared_ptr<common::Node>> &node_clones) {
+    using common::NodeIterator;
+    for (auto &node : node_clones) {
+        if (node->getOp() == af_moddims_t) {
+            common::ModdimNode *mn =
+                static_cast<common::ModdimNode *>(node.get());
+
+            NodeIterator<> it(node.get());
+            while (it != NodeIterator<>()) {
+                it = find_if(it, NodeIterator<>(), common::isBuffer);
+                if (it == NodeIterator<>()) { break; }
+
+                it->setShape(mn->m_new_shape);
+
+                ++it;
+            }
+        }
+    }
+}
+
+/// Removes nodes whos operation matchs a unary operation \p op.
+void removeNodeOfOperation(std::vector<std::shared_ptr<common::Node>> &nodes,
+                           std::vector<common::Node_ids> &ids, af_op_t op) {
+    using common::Node;
+
+    std::vector<std::vector<std::shared_ptr<Node>>::iterator> moddims_loc;
+    for (size_t nid = 0; nid < nodes.size(); nid++) {
+        auto &node = nodes[nid];
+
+        for (int i = 0;
+             i < Node::kMaxChildren && node->m_children[i] != nullptr; i++) {
+            if (node->m_children[i]->getOp() == op) {
+                // replace moddims
+                auto moddim_node    = node->m_children[i];
+                node->m_children[i] = moddim_node->m_children[0];
+
+                int parent_id = ids[nid].id;
+                int moddim_id = ids[parent_id].child_ids[i];
+                moddims_loc.emplace_back(begin(nodes) + moddim_id);
+            }
+        }
+    }
+
+    for (auto &loc : moddims_loc) { nodes.erase(loc); }
+}
+
 template<typename T>
 void evalMultiple(std::vector<Param<T>> arrays,
                   std::vector<common::Node_ptr> output_nodes_) {
+    using common::ModdimNode;
+    using common::Node;
+    using common::Node_map_t;
+    using common::NodeIterator;
+
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
-    common::Node_map_t nodes;
+    Node_map_t nodes;
     std::vector<T *> ptrs;
     std::vector<TNode<T> *> output_nodes;
     std::vector<common::Node *> full_nodes;
@@ -34,40 +109,42 @@ void evalMultiple(std::vector<Param<T>> arrays,
     int narrays = static_cast<int>(arrays.size());
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
-        output_nodes.push_back(static_cast<TNode<T> *>(output_nodes_[i].get()));
         output_nodes_[i]->getNodesMap(nodes, full_nodes, ids);
     }
 
-    /// Replace all nodes in the tree with the nodes in the node map. This
-    /// removes duplicate BufferNode objects that have different pointers
-    /// but have duplicate pointer and dimenstions
-    for (auto fn : full_nodes) {
-        common::Node *tnode = static_cast<common::Node *>(fn);
-
-        if (tnode->isBuffer() == false) {
-            // Go though all the children. Replace them with nodes in map
-            for (int i = 0;
-                 i < common::Node::kMaxChildren && tnode->m_children[i]; i++) {
-                tnode->replaceChild(
-                    i, static_cast<void *>(
-                           full_nodes[nodes[tnode->m_children[i].get()]]));
-            }
+    auto node_clones = cloneNodes(full_nodes, ids);
+
+    for (auto &n : output_nodes_) {
+        if (n->getOp() == af_moddims_t) {
+            // if the output node is a moddims node, then set the output node to
+            // be the child of the moddims node. This is necessary because we
+            // remove the moddim nodes from the tree later
+            output_nodes.push_back(static_cast<TNode<T> *>(
+                node_clones[nodes[n->m_children[0].get()]].get()));
+        } else {
+            output_nodes.push_back(
+                static_cast<TNode<T> *>(node_clones[nodes[n.get()]].get()));
         }
     }
 
+    propagateModdimsShape(node_clones);
+    removeNodeOfOperation(node_clones, ids, af_moddims_t);
+
     bool is_linear = true;
-    for (auto node : full_nodes) { is_linear &= node->isLinear(odims.get()); }
+    for (auto &node : node_clones) { is_linear &= node->isLinear(odims.get()); }
 
+    int num_nodes        = node_clones.size();
+    int num_output_nodes = output_nodes.size();
     if (is_linear) {
         int num = arrays[0].dims().elements();
         int cnum =
             jit::VECTOR_LENGTH * std::ceil(double(num) / jit::VECTOR_LENGTH);
         for (int i = 0; i < cnum; i += jit::VECTOR_LENGTH) {
             int lim = std::min(jit::VECTOR_LENGTH, num - i);
-            for (int n = 0; n < (int)full_nodes.size(); n++) {
-                full_nodes[n]->calc(i, lim);
+            for (int n = 0; n < num_nodes; n++) {
+                node_clones[n]->calc(i, lim);
             }
-            for (int n = 0; n < (int)output_nodes.size(); n++) {
+            for (int n = 0; n < num_output_nodes; n++) {
                 std::copy(output_nodes[n]->m_val.begin(),
                           output_nodes[n]->m_val.begin() + lim, ptrs[n] + i);
             }
@@ -89,10 +166,10 @@ void evalMultiple(std::vector<Param<T>> arrays,
                         int lim  = std::min(jit::VECTOR_LENGTH, dim0 - x);
                         dim_t id = x + offy;
 
-                        for (int n = 0; n < (int)full_nodes.size(); n++) {
-                            full_nodes[n]->calc(x, y, z, w, lim);
+                        for (int n = 0; n < num_nodes; n++) {
+                            node_clones[n]->calc(x, y, z, w, lim);
                         }
-                        for (int n = 0; n < (int)output_nodes.size(); n++) {
+                        for (int n = 0; n < num_output_nodes; n++) {
                             std::copy(output_nodes[n]->m_val.begin(),
                                       output_nodes[n]->m_val.begin() + lim,
                                       ptrs[n] + id);
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 0712d9862f..44169eccbd 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -347,10 +347,9 @@ Array<T> createSubArray(const Array<T> &parent,
     parent.eval();
 
     dim4 dDims          = parent.getDataDims();
-    dim4 dStrides       = calcStrides(dDims);
     dim4 parent_strides = parent.strides();
 
-    if (dStrides != parent_strides) {
+    if (parent.isLinear() == false) {
         const Array<T> parentCopy = copyArray(parent);
         return createSubArray(parentCopy, index, copy);
     }
@@ -410,8 +409,8 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
 template<typename T>
 void Array<T>::setDataDims(const dim4 &new_dims) {
-    modDims(new_dims);
     data_dims = new_dims;
+    modDims(new_dims);
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 8e8d7194d7..0a95a7c9ae 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -14,6 +14,7 @@
 #include <common/cast.hpp>
 #include <common/half.hpp>
 #include <common/indexing_helpers.hpp>
+#include <common/moddims.hpp>
 #include <common/unique_handle.hpp>
 #ifdef WITH_CUDNN
 #include <cudnn.hpp>
@@ -35,6 +36,7 @@ using af::dim4;
 using common::flip;
 using common::half;
 using common::make_handle;
+using common::modDims;
 using std::conditional;
 using std::is_same;
 using std::pair;
@@ -190,12 +192,14 @@ Array<T> convolve2_base(const Array<T> &signal, const Array<T> &filter,
 
     unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
     dim4 uDims = unwrapped.dims();
-    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
 
     Array<T> collapsedFilter = filter;
 
     collapsedFilter = flip(collapsedFilter, {1, 1, 0, 0});
-    collapsedFilter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+    collapsedFilter = modDims(collapsedFilter,
+                              dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
@@ -206,8 +210,8 @@ Array<T> convolve2_base(const Array<T> &signal, const Array<T> &filter,
              unwrapped.dims()[2], unwrapped.dims()[3]));
     gemm(res, AF_MAT_TRANS, AF_MAT_NONE, &alpha, unwrapped, collapsedFilter,
          &beta);
-    res.modDims(dim4(outputWidth, outputHeight, signal.dims()[3],
-                     collapsedFilter.dims()[1]));
+    res = modDims(res, dim4(outputWidth, outputHeight, signal.dims()[3],
+                            collapsedFilter.dims()[1]));
     Array<T> out = reorder(res, dim4(0, 1, 3, 2));
 
     return out;
@@ -249,11 +253,13 @@ Array<T> data_gradient_base(const Array<T> &incoming_gradient,
     Array<T> collapsed_filter = original_filter;
 
     collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
-    collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+    collapsed_filter = modDims(collapsed_filter,
+                               dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
@@ -264,8 +270,8 @@ Array<T> data_gradient_base(const Array<T> &incoming_gradient,
              collapsed_gradient.dims()[3], collapsed_gradient.dims()[3]));
     gemm(res, AF_MAT_NONE, AF_MAT_TRANS, &alpha, collapsed_gradient,
          collapsed_filter, &beta);
-    res.modDims(dim4(res.dims()[0] / sDims[3], sDims[3], fDims[0] * fDims[1],
-                     sDims[2]));
+    res = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
+                            fDims[0] * fDims[1], sDims[2]));
     res = reorder(res, dim4(0, 2, 3, 1));
 
     const bool retCols = false;
@@ -377,11 +383,13 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
 
     unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
     dim4 uDims = unwrapped.dims();
-    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
@@ -392,7 +400,7 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
              unwrapped.dims()[2], unwrapped.dims()[3]));
     gemm(res, AF_MAT_NONE, AF_MAT_NONE, &alpha, unwrapped, collapsed_gradient,
          &beta);
-    res.modDims(dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+    res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
 
     return flip(res, {1, 1, 0, 0});
 }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 26345591e1..c8612f1c19 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -11,7 +11,9 @@
 #include <Kernel.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
+#include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <common/kernel_cache.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
@@ -232,8 +234,55 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
         output_ids.push_back(id);
     }
 
+    using common::ModdimNode;
+    using common::NodeIterator;
+    using jit::BufferNode;
+
+    // find all moddims in the tree
+    vector<std::shared_ptr<Node>> node_clones;
+    for (auto *node : full_nodes) { node_clones.emplace_back(node->clone()); }
+
+    for (common::Node_ids ids : full_ids) {
+        auto &children = node_clones[ids.id]->m_children;
+        for (int i = 0; i < Node::kMaxChildren && children[i] != nullptr; i++) {
+            children[i] = node_clones[ids.child_ids[i]];
+        }
+    }
+
+    for (auto &node : node_clones) {
+        if (node->getOp() == af_moddims_t) {
+            ModdimNode *mn = static_cast<ModdimNode *>(node.get());
+            auto isBuffer  = [](const Node &ptr) { return ptr.isBuffer(); };
+
+            NodeIterator<> it(node.get());
+            auto new_strides = calcStrides(mn->m_new_shape);
+            while (it != NodeIterator<>()) {
+                it = find_if(it, NodeIterator<>(), isBuffer);
+                if (it == NodeIterator<>()) { break; }
+
+                BufferNode<T> *buf = static_cast<BufferNode<T> *>(&(*it));
+
+                buf->m_param.dims[0]    = mn->m_new_shape[0];
+                buf->m_param.dims[1]    = mn->m_new_shape[1];
+                buf->m_param.dims[2]    = mn->m_new_shape[2];
+                buf->m_param.dims[3]    = mn->m_new_shape[3];
+                buf->m_param.strides[0] = new_strides[0];
+                buf->m_param.strides[1] = new_strides[1];
+                buf->m_param.strides[2] = new_strides[2];
+                buf->m_param.strides[3] = new_strides[3];
+
+                ++it;
+            }
+        }
+    }
+
+    full_nodes.clear();
+    for (auto &node : node_clones) { full_nodes.push_back(node.get()); }
+
     bool is_linear = true;
-    for (auto node : full_nodes) { is_linear &= node->isLinear(outDims); }
+    for (auto *node : full_nodes) {
+        is_linear &= node->isLinear(outputs[0].dims);
+    }
 
     CUfunction ker =
         getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index 666bf1b5de..0b554d1dbf 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -49,9 +49,9 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto cond_height = cond_node->getHeight();
     const int height = max(max(a_height, b_height), cond_height) + 1;
 
-    auto node = make_shared<NaryNode>(NaryNode(
-        static_cast<af::dtype>(dtype_traits<T>::af_type), "__select", 3,
-        {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
+    auto node = make_shared<NaryNode>(
+        NaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type), "__select",
+                 3, {{cond_node, a_node, b_node}}, af_select_t, height));
 
     if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
@@ -81,7 +81,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto node = make_shared<NaryNode>(NaryNode(
         static_cast<af::dtype>(dtype_traits<T>::af_type),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
-        static_cast<int>(flip ? af_not_select_t : af_select_t), height));
+        flip ? af_not_select_t : af_select_t, height));
 
     if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index 4c87932cf7..f060fd8190 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #include <Array.hpp>
 #include <common/jit/NaryNode.hpp>
 #include <common/jit/UnaryNode.hpp>
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 3627a1115d..3aa63b40d4 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -11,6 +11,7 @@
 
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
+#include <common/jit/ScalarNode.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
@@ -24,7 +25,13 @@
 
 #include <cstddef>
 #include <cstdlib>
+#include <memory>
 #include <numeric>
+
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+
 #include <vector>
 
 using af::dim4;
@@ -41,11 +48,12 @@ using opencl::jit::BufferNode;
 using std::accumulate;
 using std::is_standard_layout;
 using std::make_shared;
+using std::shared_ptr;
 using std::vector;
 
 namespace opencl {
 template<typename T>
-std::shared_ptr<BufferNode> bufferNodePtr() {
+shared_ptr<BufferNode> bufferNodePtr() {
     return make_shared<BufferNode>(
         static_cast<af::dtype>(dtype_traits<T>::af_type));
 }
@@ -375,10 +383,9 @@ Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
     parent.eval();
 
     dim4 dDims          = parent.getDataDims();
-    dim4 dStrides       = calcStrides(dDims);
     dim4 parent_strides = parent.strides();
 
-    if (dStrides != parent_strides) {
+    if (parent.isLinear() == false) {
         const Array<T> parentCopy = copyArray(parent);
         return createSubArray(parentCopy, index, copy);
     }
@@ -467,8 +474,8 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
 template<typename T>
 void Array<T>::setDataDims(const dim4 &new_dims) {
-    modDims(new_dims);
     data_dims = new_dims;
+    modDims(new_dims);
 }
 
 template<typename T>
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index df976b45e3..67290207df 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -28,6 +28,11 @@
 #include <memory>
 #include <vector>
 
+namespace common {
+template<typename T>
+class SparseArray;
+}
+
 namespace opencl {
 typedef std::shared_ptr<cl::Buffer> Buffer_ptr;
 using af::dim4;
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index 0c294965e7..dd05838760 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -11,9 +11,9 @@
 #include <blas.hpp>
 #include <common/half.hpp>
 #include <common/indexing_helpers.hpp>
+#include <common/moddims.hpp>
 #include <convolve.hpp>
 #include <err_opencl.hpp>
-#include <handle.hpp>
 #include <kernel/convolve.hpp>
 #include <reorder.hpp>
 #include <transpose.hpp>
@@ -26,6 +26,7 @@
 using af::dim4;
 using common::flip;
 using common::half;
+using common::modDims;
 using std::vector;
 
 namespace opencl {
@@ -125,17 +126,20 @@ Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
 
     unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
     dim4 uDims = unwrapped.dims();
-    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
 
     Array<T> collapsedFilter = filter;
 
     collapsedFilter = flip(collapsedFilter, {1, 1, 0, 0});
-    collapsedFilter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+    collapsedFilter = modDims(collapsedFilter,
+                              dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> res =
         matmul(unwrapped, collapsedFilter, AF_MAT_TRANS, AF_MAT_NONE);
-    res.modDims(dim4(outputWidth, outputHeight, signal.dims()[3],
-                     collapsedFilter.dims()[1]));
+    res = modDims(res, dim4(outputWidth, outputHeight, signal.dims()[3],
+                            collapsedFilter.dims()[1]));
     Array<T> out = reorder(res, dim4(0, 1, 3, 2));
 
     return out;
@@ -174,16 +178,18 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_filter = original_filter;
 
     collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
-    collapsed_filter.modDims(dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+    collapsed_filter = modDims(collapsed_filter,
+                               dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
-    res.modDims(dim4(res.dims()[0] / sDims[3], sDims[3], fDims[0] * fDims[1],
-                     sDims[2]));
+    res = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
+                            fDims[0] * fDims[1], sDims[2]));
     res = reorder(res, dim4(0, 2, 3, 1));
 
     const bool retCols = false;
@@ -211,17 +217,20 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
 
     unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
     dim4 uDims = unwrapped.dims();
-    unwrapped.modDims(dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-    collapsed_gradient.modDims(dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
-    res.modDims(dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+    res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
 
-    return flip(res, {1, 1, 0, 0});
+    auto out = flip(res, {1, 1, 0, 0});
+    return out;
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 02471d53e3..b8b486cae0 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -10,7 +10,9 @@
 #include <Array.hpp>
 #include <common/compile_module.hpp>
 #include <common/dispatch.hpp>
+#include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
+#include <common/jit/NodeIterator.hpp>
 #include <common/kernel_cache.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
@@ -20,7 +22,11 @@
 #include <af/dim4.hpp>
 #include <af/opencl.h>
 
+#include <jit/BufferNode.hpp>
+
+#include <cstdio>
 #include <functional>
+#include <sstream>
 #include <stdexcept>
 #include <string>
 #include <vector>
@@ -50,8 +56,8 @@ string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
     static const char *kernelVoid = "__kernel void\n";
     static const char *dimParams =
         "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
-    static const char *blockStart = "{\n\n";
-    static const char *blockEnd   = "\n\n}";
+    static const char *blockStart = "{\n";
+    static const char *blockEnd   = "\n}\n";
 
     static const char *linearIndex = R"JIT(
         uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
@@ -199,13 +205,60 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
         full_ids.reserve(1024);
     }
 
-    for (auto &node : output_nodes) {
+    for (auto *node : output_nodes) {
         int id = node->getNodesMap(nodes, full_nodes, full_ids);
         output_ids.push_back(id);
     }
 
+    using common::ModdimNode;
+    using common::NodeIterator;
+    using jit::BufferNode;
+
+    // find all moddims in the tree
+    vector<std::shared_ptr<Node>> node_clones;
+    for (auto *node : full_nodes) { node_clones.emplace_back(node->clone()); }
+
+    for (common::Node_ids ids : full_ids) {
+        auto &children = node_clones[ids.id]->m_children;
+        for (int i = 0; i < Node::kMaxChildren && children[i] != nullptr; i++) {
+            children[i] = node_clones[ids.child_ids[i]];
+        }
+    }
+
+    for (auto &node : node_clones) {
+        if (node->getOp() == af_moddims_t) {
+            ModdimNode *mn = static_cast<ModdimNode *>(node.get());
+            auto isBuffer  = [](const Node &ptr) { return ptr.isBuffer(); };
+
+            NodeIterator<> it(node.get());
+            auto new_strides = calcStrides(mn->m_new_shape);
+            while (it != NodeIterator<>()) {
+                it = find_if(it, NodeIterator<>(), isBuffer);
+                if (it == NodeIterator<>()) { break; }
+
+                BufferNode *buf = static_cast<BufferNode *>(&(*it));
+
+                buf->m_param.dims[0]    = mn->m_new_shape[0];
+                buf->m_param.dims[1]    = mn->m_new_shape[1];
+                buf->m_param.dims[2]    = mn->m_new_shape[2];
+                buf->m_param.dims[3]    = mn->m_new_shape[3];
+                buf->m_param.strides[0] = new_strides[0];
+                buf->m_param.strides[1] = new_strides[1];
+                buf->m_param.strides[2] = new_strides[2];
+                buf->m_param.strides[3] = new_strides[3];
+
+                ++it;
+            }
+        }
+    }
+
+    full_nodes.clear();
+    for (auto &node : node_clones) { full_nodes.push_back(node.get()); }
+
     bool is_linear = true;
-    for (auto node : full_nodes) { is_linear &= node->isLinear(outDims); }
+    for (auto *node : full_nodes) {
+        is_linear &= node->isLinear(outputs[0].info.dims);
+    }
 
     auto ker =
         getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
@@ -255,7 +308,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
     int nargs = 0;
     for (const auto &node : full_nodes) {
         nargs = node->setArgs(nargs, is_linear,
-                              [&](int id, const void *ptr, size_t arg_size) {
+                              [&ker](int id, const void *ptr, size_t arg_size) {
                                   ker.setArg(id, arg_size, ptr);
                               });
     }
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index fe1e50351a..9821e7ee89 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -37,9 +37,9 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto cond_height = cond_node->getHeight();
     const int height = max(max(a_height, b_height), cond_height) + 1;
 
-    auto node = make_shared<NaryNode>(NaryNode(
-        static_cast<af::dtype>(dtype_traits<T>::af_type), "__select", 3,
-        {{cond_node, a_node, b_node}}, static_cast<int>(af_select_t), height));
+    auto node = make_shared<NaryNode>(
+        NaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type), "__select",
+                 3, {{cond_node, a_node, b_node}}, af_select_t, height));
 
     if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
@@ -69,7 +69,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto node = make_shared<NaryNode>(NaryNode(
         static_cast<af::dtype>(dtype_traits<T>::af_type),
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
-        static_cast<int>(flip ? af_not_select_t : af_select_t), height));
+        (flip ? af_not_select_t : af_select_t), height));
 
     if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index ceba3469cc..d579761a72 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -10,11 +10,9 @@
 #include <kernel/sparse.hpp>
 #include <sparse.hpp>
 
-#include <stdexcept>
-#include <string>
-
 #include <arith.hpp>
 #include <common/cast.hpp>
+#include <common/moddims.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
 #include <err_opencl.hpp>
@@ -25,6 +23,9 @@
 #include <reduce.hpp>
 #include <where.hpp>
 
+#include <stdexcept>
+#include <string>
+
 namespace opencl {
 
 using namespace common;
@@ -49,8 +50,8 @@ SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in) {
         arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
     Array<T> values = copyArray<T>(in);
-    values.modDims(dim4(values.elements()));
-    values = lookup<T, int>(values, nonZeroIdx, 0);
+    values          = modDims(values, dim4(values.elements()));
+    values          = lookup<T, int>(values, nonZeroIdx, 0);
 
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
                                          AF_STORAGE_COO);
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index a07cc5b0a2..f4a81ab29f 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
 #include <Array.hpp>
 #include <common/jit/UnaryNode.hpp>
 #include <math.hpp>
diff --git a/test/gfor.cpp b/test/gfor.cpp
index 42fc12723b..3e3d95e51d 100644
--- a/test/gfor.cpp
+++ b/test/gfor.cpp
@@ -120,7 +120,7 @@ TEST(GFOR, Assign_Array_Span) {
     float *hA = A.host<float>();
     float val = B.scalar<float>();
 
-    for (int i = 0; i < nx; i++) { ASSERT_EQ(hA[i], val); }
+    ASSERT_ARRAYS_EQ(A, constant(val, nx));
 
     freeHost(hA);
 }
diff --git a/test/index.cpp b/test/index.cpp
index 9c60bc3dde..aaac6f74f7 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -1673,10 +1673,10 @@ TEST(Index, ISSUE_1101_MODDIMS) {
     size_t aby1, abu1, lby1, lbu1;
     deviceMemInfo(&aby1, &abu1, &lby1, &lbu1);
 
-    ASSERT_EQ(aby, aby1);
-    ASSERT_EQ(abu, abu1);
-    ASSERT_EQ(lby, lby1);
-    ASSERT_EQ(lbu, lbu1);
+    EXPECT_EQ(aby, aby1) << "Number of bytes different";
+    EXPECT_EQ(abu, abu1) << "Number of buffers different";
+    EXPECT_EQ(lby, lby1) << "Number of bytes different";
+    EXPECT_EQ(lbu, lbu1) << "Number of buffers different";
 
     vector<float> hb(b.elements());
     b.host(&hb[0]);
diff --git a/test/jit.cpp b/test/jit.cpp
index b2d690a7ca..c1f0fbd2fa 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -238,8 +238,6 @@ TEST(JIT, CPP_common_node) {
 
     array x = tile(r, 1, r.dims(0));
     array y = tile(r.T(), r.dims(0), 1);
-    x.eval();
-    y.eval();
 
     vector<float> hx(x.elements());
     vector<float> hy(y.elements());
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 52c7596472..6794e4c90e 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -12,7 +12,7 @@
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
-#include <iostream>
+#include <cstdlib>
 #include <string>
 #include <vector>
 
@@ -255,3 +255,27 @@ TEST(Moddims, Subref_CPP) {
     cppModdimsTest<float>(string(TEST_DIR "/moddims/subref.test"), true,
                           &subMat);
 }
+
+TEST(Moddims, jit) {
+    using namespace af;
+    array c1 = constant(1, 10, 5);
+    c1.eval();
+    array c2 = randu(10, 10);
+
+    vector<float> hc2(100);
+    c2.host(hc2.data());
+
+    array c3 = c2(span, seq(5));
+    c3.eval();
+
+    array a = c1;
+    a       = a + c3;
+    a       = moddims(a, 5, 10);
+    a       = a + constant(2, 5, 10);
+
+    for (int i = 0; i < hc2.size(); i++) { hc2[i] += 3; }
+
+    array gold(10, 5, hc2.data());
+    gold = moddims(gold, 5, 10);
+    ASSERT_ARRAYS_EQ(gold, a);
+}

From 72b73ff2f77e4d3efef45a653b2d1c3d1d332b41 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 13 Oct 2021 16:16:48 +0530
Subject: [PATCH 2202/2677] Use appropriate MKL getrs_batch_strided API based
 on MKL Versions

---
 src/backend/cpu/solve.cpp            | 12 ++++++++++++
 src/backend/opencl/cpu/cpu_solve.cpp | 12 ++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 4d43405d55..c5126275cb 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -15,6 +15,9 @@
 #include <copy.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
+#if INTEL_MKL_VERSION >= 20210004
+#include <mkl_version.h>
+#endif
 #include <queue.hpp>
 #include <af/dim4.hpp>
 #include <algorithm>
@@ -39,6 +42,14 @@ using getrf_batch_strided_func_def =
              const MKL_INT *stride_a, MKL_INT *ipiv, const MKL_INT *stride_ipiv,
              const MKL_INT *batch_size, MKL_INT *info);
 
+#if INTEL_MKL_VERSION >= 20210004
+template<typename T>
+using getrs_batch_strided_func_def = void (*)(
+    const char *trans, const MKL_INT *n, const MKL_INT *nrhs, const T *a,
+    const MKL_INT *lda, const MKL_INT *stride_a, const MKL_INT *ipiv,
+    const MKL_INT *stride_ipiv, T *b, const MKL_INT *ldb,
+    const MKL_INT *stride_b, const MKL_INT *batch_size, MKL_INT *info);
+#else
 template<typename T>
 using getrs_batch_strided_func_def =
     void (*)(const char *trans, const MKL_INT *n, const MKL_INT *nrhs, T *a,
@@ -46,6 +57,7 @@ using getrs_batch_strided_func_def =
              const MKL_INT *stride_ipiv, T *b, const MKL_INT *ldb,
              const MKL_INT *stride_b, const MKL_INT *batch_size, MKL_INT *info);
 #endif
+#endif
 
 template<typename T>
 using getrs_func_def = int (*)(ORDER_TYPE, char, int, int, const T *, int,
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index f5f2510597..3afdeca804 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -12,6 +12,9 @@
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
 #include <math.hpp>
+#if INTEL_MKL_VERSION >= 20210004
+#include <mkl_version.h>
+#endif
 #include <algorithm>
 #include <vector>
 
@@ -32,6 +35,14 @@ using getrf_batch_strided_func_def =
              const MKL_INT *stride_a, MKL_INT *ipiv, const MKL_INT *stride_ipiv,
              const MKL_INT *batch_size, MKL_INT *info);
 
+#if INTEL_MKL_VERSION >= 20210004
+template<typename T>
+using getrs_batch_strided_func_def = void (*)(
+    const char *trans, const MKL_INT *n, const MKL_INT *nrhs, const T *a,
+    const MKL_INT *lda, const MKL_INT *stride_a, const MKL_INT *ipiv,
+    const MKL_INT *stride_ipiv, T *b, const MKL_INT *ldb,
+    const MKL_INT *stride_b, const MKL_INT *batch_size, MKL_INT *info);
+#else
 template<typename T>
 using getrs_batch_strided_func_def =
     void (*)(const char *trans, const MKL_INT *n, const MKL_INT *nrhs, T *a,
@@ -39,6 +50,7 @@ using getrs_batch_strided_func_def =
              const MKL_INT *stride_ipiv, T *b, const MKL_INT *ldb,
              const MKL_INT *stride_b, const MKL_INT *batch_size, MKL_INT *info);
 #endif
+#endif
 
 template<typename T>
 using getrs_func_def = int (*)(ORDER_TYPE, char, int, int, const T *, int,

From 1ff07ca29469d3765fed0b780711b402fcd848e4 Mon Sep 17 00:00:00 2001
From: pradeep <pradeep@arrayfire.com>
Date: Wed, 13 Oct 2021 18:25:02 +0530
Subject: [PATCH 2203/2677] Update Intel MKL to oneMKL on github ci jobs

---
 .github/workflows/unix_cpu_build.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 36649284bf..9fcb37b87e 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -79,11 +79,11 @@ jobs:
             - name: Install MKL for Ubuntu
               if: matrix.os != 'macos-latest' && matrix.blas_backend == 'MKL'
               run: |
-                  wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
-                  sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
-                  sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
+                  wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+                  sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+                  sudo sh -c 'echo deb https://apt.repos.intel.com/oneapi all main > /etc/apt/sources.list.d/oneAPI.list'
                   sudo apt-get -qq update
-                  sudo apt-get install -y intel-mkl-64bit-2020.0-088
+                  sudo apt-get install -y intel-basekit
 
             - name: Install OpenBLAS for Ubuntu
               if: matrix.os != 'macos-latest' && matrix.blas_backend == 'OpenBLAS'

From 1d03d07e705315540567dbaa57a5febdcb24218e Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Tue, 12 Oct 2021 19:02:08 +0200
Subject: [PATCH 2204/2677] Disk hash is now based on the full code + options,
 also for JIT code.

---
 src/backend/common/kernel_cache.cpp | 47 +++++++++++++++++++++--------
 1 file changed, 34 insertions(+), 13 deletions(-)

diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 5031d6b75a..981d544511 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -73,39 +73,60 @@ Kernel getKernel(const string& kernelName,
     UNUSED(targs);
 #endif
 
-    size_t moduleKey = 0;
+    // The JIT kernel uses the hashing of the kernelName (tInstance) only to
+    // speed up to search for its cached kernel.  All the other kernels have the
+    // full source code linked in, and will hash the full code + options
+    // instead.
+    size_t moduleKeyCache = 0;
     if (sourceIsJIT) {
-        moduleKey = deterministicHash(tInstance);
+        moduleKeyCache = deterministicHash(tInstance);
     } else {
-        moduleKey = (sources.size() == 1 && sources[0].hash)
-                        ? sources[0].hash
-                        : deterministicHash(sources);
-        moduleKey = deterministicHash(options, moduleKey);
+        moduleKeyCache = (sources.size() == 1 && sources[0].hash)
+                             ? sources[0].hash
+                             : deterministicHash(sources);
+        moduleKeyCache = deterministicHash(options, moduleKeyCache);
 #if defined(AF_CUDA)
-        moduleKey = deterministicHash(tInstance, moduleKey);
+        moduleKeyCache = deterministicHash(tInstance, moduleKeyCache);
 #endif
     }
     const int device  = detail::getActiveDeviceId();
-    Module currModule = findModule(device, moduleKey);
+    Module currModule = findModule(device, moduleKeyCache);
 
     if (!currModule) {
+        // When saving on disk, the moduleKeyDisk has to correspond with the
+        // full code + optinos (in all circumstances). A recalculation for JIT
+        // is necessary, while for the others we can reuse the moduleKeyCache.
+        size_t moduleKeyDisk = 0;
+        if (sourceIsJIT) {
+            moduleKeyDisk = (sources.size() == 1 && sources[0].hash)
+                                ? sources[0].hash
+                                : deterministicHash(sources);
+            moduleKeyDisk = deterministicHash(options, moduleKeyDisk);
+#if defined(AF_CUDA)
+            moduleKeyDisk = deterministicHash(tInstance, moduleKeyDisk);
+#endif
+        } else {
+            moduleKeyDisk = moduleKeyCache;
+        }
         currModule =
-            loadModuleFromDisk(device, to_string(moduleKey), sourceIsJIT);
+            loadModuleFromDisk(device, to_string(moduleKeyDisk), sourceIsJIT);
         if (!currModule) {
             vector<string> sources_str;
-            for (auto s : sources) { sources_str.push_back({s.ptr, s.length}); }
-            currModule = compileModule(to_string(moduleKey), sources_str,
+            for (const auto& s : sources) {
+                sources_str.push_back({s.ptr, s.length});
+            }
+            currModule = compileModule(to_string(moduleKeyDisk), sources_str,
                                        options, {tInstance}, sourceIsJIT);
         }
 
         std::unique_lock<shared_timed_mutex> writeLock(getCacheMutex(device));
         auto& cache = getCache(device);
-        auto iter   = cache.find(moduleKey);
+        auto iter   = cache.find(moduleKeyCache);
         if (iter == cache.end()) {
             // If not found, this thread is the first one to compile
             // this kernel. Keep the generated module.
             Module mod = currModule;
-            getCache(device).emplace(moduleKey, mod);
+            getCache(device).emplace(moduleKeyCache, mod);
         } else {
             currModule.unload();  // dump the current threads extra
                                   // compilation

From 970f32a2313fee96b986184a4399405f9c20ff82 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 7 Feb 2022 15:20:56 -0500
Subject: [PATCH 2205/2677] Add CUDA 11.5 to max compute and compute capability
 arrays (#3203)

* Add CUDA 11.5 to max compute and compute capability arrays

* Add CUDA 11.6 to max compute and compute capability arrays

Signed-off-by: Pradeep Garigipati <pradeep.garigipati@gmail.com>

Co-authored-by: Pradeep Garigipati <pradeep.garigipati@gmail.com>
---
 src/backend/cuda/device_manager.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 1a994424e6..ca46388484 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -95,6 +95,8 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11060, 8, 6, 0},
+    {11050, 8, 6, 0},
     {11040, 8, 6, 0},
     {11030, 8, 6, 0},
     {11020, 8, 6, 0},
@@ -127,6 +129,8 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11060, 510.39f, 511.23f},
+        {11050, 495.29f, 496.13f},
         {11040, 470.42f, 471.11f},
         {11030, 465.19f, 465.89f},
         {11020, 460.27f, 460.82f},

From d2b09a6881054d22f586e440eef9e7c8252f2de0 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Sat, 12 Feb 2022 12:43:09 +0530
Subject: [PATCH 2206/2677] Fix mkl_version inclusion guard in CPU and OpenCL
 backends

Signed-off-by: Pradeep Garigipati <pradeep.garigipati@gmail.com>
---
 src/backend/cpu/solve.cpp            | 2 +-
 src/backend/opencl/cpu/cpu_solve.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index c5126275cb..52843d2fae 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -15,7 +15,7 @@
 #include <copy.hpp>
 #include <lapack_helper.hpp>
 #include <math.hpp>
-#if INTEL_MKL_VERSION >= 20210004
+#if USE_MKL
 #include <mkl_version.h>
 #endif
 #include <queue.hpp>
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 3afdeca804..8b2cd79f64 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -12,7 +12,7 @@
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_solve.hpp>
 #include <math.hpp>
-#if INTEL_MKL_VERSION >= 20210004
+#if USE_MKL
 #include <mkl_version.h>
 #endif
 #include <algorithm>

From f58b849ca1d993030a807eccb1c653d325646857 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Sat, 12 Feb 2022 13:56:15 +0530
Subject: [PATCH 2207/2677] Update windows GA job to use new VS toolchain for
 respective GH image

Signed-off-by: Pradeep Garigipati <pradeep.garigipati@gmail.com>
---
 .github/workflows/win_cpu_build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index e265f6f877..72c6955238 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -48,7 +48,7 @@ jobs:
                   $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
                   $buildname = "$buildname-cpu-openblas"
                   mkdir build && cd build
-                  cmake .. -G "Visual Studio 16 2019" -A x64 `
+                  cmake .. -G "Visual Studio 17 2022" -A x64 `
                       -DVCPKG_ROOT:PATH="~/vcpkg" `
                       -DVCPKG_MANIFEST_MODE:BOOL=OFF `
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `

From 13cbff8a0a3cf0703f6223903d670f269293ead3 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Sat, 12 Feb 2022 23:10:47 +0530
Subject: [PATCH 2208/2677] intl/uintl versions of select/replace to fix
 accuracy issues

Without specific intl/uintl versions of these functions, when a
succifiently large 64 bit integer value is passed to select/replace
the output is incorrect or getting transformed to zero.

Signed-off-by: Pradeep Garigipati <pradeep.garigipati@gmail.com>
---
 include/af/data.h                    | 161 ++++++++++++++++++++++++++-
 src/api/c/deconvolution.cpp          |   3 +-
 src/api/c/replace.cpp                |  26 ++++-
 src/api/c/select.cpp                 | 148 ++++++++++++------------
 src/api/cpp/data.cpp                 |  32 ++++++
 src/api/unified/data.cpp             |  36 ++++++
 src/backend/cpu/kernel/select.hpp    |   7 +-
 src/backend/cpu/select.cpp           |  20 ++--
 src/backend/cpu/select.hpp           |   4 +-
 src/backend/cuda/kernel/select.hpp   |   4 +-
 src/backend/cuda/select.cpp          |  42 +++----
 src/backend/cuda/select.hpp          |   4 +-
 src/backend/opencl/kernel/select.hpp |   7 +-
 src/backend/opencl/select.cpp        |  42 +++----
 src/backend/opencl/select.hpp        |   4 +-
 test/CMakeLists.txt                  |   4 +-
 test/replace.cpp                     |  11 +-
 test/select.cpp                      |   9 +-
 18 files changed, 406 insertions(+), 158 deletions(-)

diff --git a/include/af/data.h b/include/af/data.h
index 05ef5f9f35..52ebb78ed7 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -409,7 +409,7 @@ namespace af
        \param[in]  cond is the conditional array.
        \param[in]  b is the replacement value.
 
-       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
+       \note Values of \p a are replaced with value \p b, when \p cond is false.
 
        \ingroup data_func_replace
     */
@@ -432,6 +432,81 @@ namespace af
     AFAPI array pad(const array &in, const dim4 &beginPadding,
                     const dim4 &endPadding, const borderType padFillType);
 #endif
+
+#if AF_API_VERSION >= 39
+    /**
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement scalar value.
+
+       \note Values of \p a are replaced with value \p b, when \p cond is false.
+
+       \ingroup data_func_replace
+    */
+    AFAPI void replace(array &a, const array &cond, const long long b);
+
+    /**
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement scalar value.
+
+       \note Values of \p a are replaced with value \p b, when \p cond is false.
+
+       \ingroup data_func_replace
+    */
+    AFAPI void replace(array &a, const array &cond,
+                       const unsigned long long b);
+
+    /**
+       \param[in]  cond is the conditional array
+       \param[in]  a is the array containing elements from the true part of the
+                   condition
+       \param[in]  b is a scalar assigned to \p out when \p cond is false
+       \return  the output containing elements of \p a when \p cond is true
+                else the value \p b
+
+       \ingroup data_func_select
+    */
+    AFAPI array select(const array &cond, const array &a, const long long b);
+
+    /**
+       \param[in]  cond is the conditional array
+       \param[in]  a is the array containing elements from the true part of the
+                   condition
+       \param[in]  b is a scalar assigned to \p out when \p cond is false
+       \return  the output containing elements of \p a when \p cond is true
+                else the value \p b
+
+       \ingroup data_func_select
+    */
+    AFAPI array select(const array &cond, const array &a,
+                       const unsigned long long b);
+
+    /**
+       \param[in]  cond is the conditional array
+       \param[in]  a is a scalar assigned to \p out when \p cond is true
+       \param[in]  b is the array containing elements from the false part of the
+                   condition
+       \return  the output containing the value \p a when \p cond is true else
+                elements from \p b
+
+       \ingroup data_func_select
+    */
+    AFAPI array select(const array &cond, const long long a, const array &b);
+
+    /**
+       \param[in]  cond is the conditional array
+       \param[in]  a is a scalar assigned to \p out when \p cond is true
+       \param[in]  b is the array containing elements from the false part of the
+                   condition
+       \return  the output containing the value \p a when \p cond is true else
+                elements from \p b
+
+       \ingroup data_func_select
+    */
+    AFAPI array select(const array &cond, const unsigned long long a,
+                       const array &b);
+#endif
 }
 #endif
 
@@ -735,6 +810,90 @@ extern "C" {
                         const af_border_type pad_fill_type);
 #endif
 
+#if AF_API_VERSION >= 39
+    /**
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when
+       \p cond is false.
+
+       \ingroup data_func_replace
+    */
+    AFAPI af_err af_replace_scalar_long(af_array a, const af_array cond,
+                                        const long long b);
+
+    /**
+       \param[inout]  a is the input array
+       \param[in]  cond is the conditional array.
+       \param[in]  b is the replacement array.
+
+       \note Values of \p a are replaced with corresponding values of \p b, when
+       \p cond is false.
+
+       \ingroup data_func_replace
+    */
+    AFAPI af_err af_replace_scalar_ulong(af_array a, const af_array cond,
+                                         const unsigned long long b);
+
+    /**
+       \param[out] out is the output containing elements of \p a when \p cond is
+       true else elements from \p b
+       \param[in]  cond is the conditional array
+       \param[in]  a is the array containing elements from the true part of the
+       condition
+       \param[in]  b is a scalar assigned to \p out when \p cond is
+       false
+
+       \ingroup data_func_select
+    */
+    AFAPI af_err af_select_scalar_r_long(af_array *out, const af_array cond,
+                                         const af_array a, const long long b);
+
+    /**
+       \param[out] out is the output containing elements of \p a when \p cond is
+       true else elements from \p b
+       \param[in]  cond is the conditional array
+       \param[in]  a is the array containing elements from the true part of the
+       condition
+       \param[in]  b is a scalar assigned to \p out when \p cond is
+       false
+
+       \ingroup data_func_select
+    */
+    AFAPI af_err af_select_scalar_r_ulong(af_array *out, const af_array cond,
+                                          const af_array a,
+                                          const unsigned long long b);
+
+    /**
+       \param[out] out is the output containing elements of \p a when \p cond is
+       true else elements from \p b
+       \param[in]  cond is the conditional array
+       \param[in]  a is a scalar assigned to \p out when \p cond is true
+       \param[in]  b is the array containing elements from the false part of the
+       condition
+
+       \ingroup data_func_select
+    */
+    AFAPI af_err af_select_scalar_l_long(af_array *out, const af_array cond,
+                                         const long long a, const af_array b);
+
+    /**
+       \param[out] out is the output containing elements of \p a when \p cond is
+       true else elements from \p b
+       \param[in]  cond is the conditional array
+       \param[in]  a is a scalar assigned to \p out when \p cond is true
+       \param[in]  b is the array containing elements from the false part of the
+       condition
+
+       \ingroup data_func_select
+    */
+    AFAPI af_err af_select_scalar_l_ulong(af_array *out, const af_array cond,
+                                          const unsigned long long a,
+                                          const af_array b);
+#endif
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index 43c83965e3..21180b2d8b 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -19,6 +19,7 @@
 #include <fftconvolve.hpp>
 #include <handle.hpp>
 #include <logic.hpp>
+#include <math.hpp>
 #include <reduce.hpp>
 #include <select.hpp>
 #include <shift.hpp>
@@ -294,7 +295,7 @@ af_array invDeconv(const af_array in, const af_array ker, const float gamma,
     auto cond   = logicOp<T, af_ge_t>(absVal, THRESH, absVal.dims());
     auto val    = arithOp<CT, af_div_t>(numer, denom, numer.dims());
 
-    select_scalar<CT, false>(val, cond, val, 0);
+    select_scalar<CT, false>(val, cond, val, scalar<CT>(0.0));
 
     auto ival =
         fft_c2r<CT, T>(val, 1 / static_cast<double>(nElems), odims, BASE_DIM);
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index 27455982e9..bd4814157a 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -82,13 +82,15 @@ af_err af_replace(af_array a, const af_array cond, const af_array b) {
     return AF_SUCCESS;
 }
 
-template<typename T>
-void replace_scalar(af_array a, const af_array cond, const double b) {
-    select_scalar<T, false>(getCopyOnWriteArray<T>(a), getArray<char>(cond),
-                            getArray<T>(a), b);
+template<typename ArrayType, typename ScalarType>
+void replace_scalar(af_array a, const af_array cond, const ScalarType& b) {
+    select_scalar<ArrayType, false>(
+        getCopyOnWriteArray<ArrayType>(a), getArray<char>(cond),
+        getArray<ArrayType>(a), detail::scalar<ArrayType>(b));
 }
 
-af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
+template<typename ScalarType>
+af_err replaceScalar(af_array a, const af_array cond, const ScalarType b) {
     try {
         const ArrayInfo& ainfo = getInfo(a);
         const ArrayInfo& cinfo = getInfo(cond);
@@ -121,3 +123,17 @@ af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
     CATCHALL;
     return AF_SUCCESS;
 }
+
+af_err af_replace_scalar(af_array a, const af_array cond, const double b) {
+    return replaceScalar(a, cond, b);
+}
+
+af_err af_replace_scalar_long(af_array a, const af_array cond,
+                              const long long b) {
+    return replaceScalar(a, cond, b);
+}
+
+af_err af_replace_scalar_ulong(af_array a, const af_array cond,
+                               const unsigned long long b) {
+    return replaceScalar(a, cond, b);
+}
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 952a8568fa..31d7facbcd 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -88,71 +88,90 @@ af_err af_select(af_array* out, const af_array cond, const af_array a,
     return AF_SUCCESS;
 }
 
-template<typename T, bool flip>
-af_array select_scalar(const af_array cond, const af_array a, const double b,
-                       const dim4& odims) {
-    Array<T> out = createSelectNode<T, flip>(getArray<char>(cond),
-                                             getArray<T>(a), b, odims);
-    return getHandle<T>(out);
+template<typename ArrayType, typename ScalarType, bool flip>
+af_array select_scalar(const af_array cond, const af_array a,
+                       const ScalarType b, const dim4& odims) {
+    auto scalar = detail::scalar<ArrayType>(b);
+    auto out    = createSelectNode<ArrayType, flip>(
+        getArray<char>(cond), getArray<ArrayType>(a), scalar, odims);
+    return getHandle(out);
 }
 
-af_err af_select_scalar_r(af_array* out, const af_array cond, const af_array a,
-                          const double b) {
+template<typename ScalarType, bool IsScalarTrueOutput>
+af_err selectScalar(af_array* out, const af_array cond, const af_array e,
+                    const ScalarType c) {
     try {
-        const ArrayInfo& ainfo = getInfo(a);
+        const ArrayInfo& einfo = getInfo(e);
         const ArrayInfo& cinfo = getInfo(cond);
 
         ARG_ASSERT(1, cinfo.getType() == b8);
 
-        dim4 adims     = ainfo.dims();
+        dim4 edims     = einfo.dims();
         dim4 cond_dims = cinfo.dims();
         dim4 odims(1);
 
         for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cond_dims[i] == adims[i] || cond_dims[i] == 1 ||
-                              adims[i] == 1);
-            odims[i] = std::max(cond_dims[i], adims[i]);
+            DIM_ASSERT(1, cond_dims[i] == edims[i] || cond_dims[i] == 1 ||
+                              edims[i] == 1);
+            odims[i] = std::max(cond_dims[i], edims[i]);
         }
 
         af_array res;
 
-        switch (ainfo.getType()) {
+        switch (einfo.getType()) {
             case f16:
-                res = select_scalar<half, false>(cond, a, b, odims);
+                res = select_scalar<half, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case f32:
-                res = select_scalar<float, false>(cond, a, b, odims);
+                res = select_scalar<float, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case f64:
-                res = select_scalar<double, false>(cond, a, b, odims);
+                res = select_scalar<double, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case c32:
-                res = select_scalar<cfloat, false>(cond, a, b, odims);
+                res = select_scalar<cfloat, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case c64:
-                res = select_scalar<cdouble, false>(cond, a, b, odims);
+                res = select_scalar<cdouble, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
+                break;
+            case s32:
+                res = select_scalar<int, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
-            case s32: res = select_scalar<int, false>(cond, a, b, odims); break;
             case u32:
-                res = select_scalar<uint, false>(cond, a, b, odims);
+                res = select_scalar<uint, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case s16:
-                res = select_scalar<short, false>(cond, a, b, odims);
+                res = select_scalar<short, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case u16:
-                res = select_scalar<ushort, false>(cond, a, b, odims);
+                res = select_scalar<ushort, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case s64:
-                res = select_scalar<intl, false>(cond, a, b, odims);
+                res = select_scalar<intl, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case u64:
-                res = select_scalar<uintl, false>(cond, a, b, odims);
+                res = select_scalar<uintl, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
             case u8:
-                res = select_scalar<uchar, false>(cond, a, b, odims);
+                res = select_scalar<uchar, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
                 break;
-            case b8: res = select_scalar<char, false>(cond, a, b, odims); break;
-            default: TYPE_ERROR(2, ainfo.getType());
+            case b8:
+                res = select_scalar<char, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
+                break;
+            default: TYPE_ERROR((IsScalarTrueOutput ? 3 : 2), einfo.getType());
         }
 
         std::swap(*out, res);
@@ -161,59 +180,32 @@ af_err af_select_scalar_r(af_array* out, const af_array cond, const af_array a,
     return AF_SUCCESS;
 }
 
-af_err af_select_scalar_l(af_array* out, const af_array cond, const double a,
-                          const af_array b) {
-    try {
-        const ArrayInfo& binfo = getInfo(b);
-        const ArrayInfo& cinfo = getInfo(cond);
-
-        ARG_ASSERT(1, cinfo.getType() == b8);
+af_err af_select_scalar_r(af_array* out, const af_array cond, const af_array a,
+                          const double b) {
+    return selectScalar<double, false>(out, cond, a, b);
+}
 
-        dim4 bdims     = binfo.dims();
-        dim4 cond_dims = cinfo.dims();
-        dim4 odims(1);
+af_err af_select_scalar_r_long(af_array* out, const af_array cond,
+                               const af_array a, const long long b) {
+    return selectScalar<long long, false>(out, cond, a, b);
+}
 
-        for (int i = 0; i < 4; i++) {
-            DIM_ASSERT(1, cond_dims[i] == bdims[i] || cond_dims[i] == 1 ||
-                              bdims[i] == 1);
-            odims[i] = std::max(cond_dims[i], bdims[i]);
-        }
+af_err af_select_scalar_r_ulong(af_array* out, const af_array cond,
+                                const af_array a, const unsigned long long b) {
+    return selectScalar<unsigned long long, false>(out, cond, a, b);
+}
 
-        af_array res;
+af_err af_select_scalar_l(af_array* out, const af_array cond, const double a,
+                          const af_array b) {
+    return selectScalar<double, true>(out, cond, b, a);
+}
 
-        switch (binfo.getType()) {
-            case f16: res = select_scalar<half, true>(cond, b, a, odims); break;
-            case f32:
-                res = select_scalar<float, true>(cond, b, a, odims);
-                break;
-            case f64:
-                res = select_scalar<double, true>(cond, b, a, odims);
-                break;
-            case c32:
-                res = select_scalar<cfloat, true>(cond, b, a, odims);
-                break;
-            case c64:
-                res = select_scalar<cdouble, true>(cond, b, a, odims);
-                break;
-            case s32: res = select_scalar<int, true>(cond, b, a, odims); break;
-            case u32: res = select_scalar<uint, true>(cond, b, a, odims); break;
-            case s16:
-                res = select_scalar<short, true>(cond, b, a, odims);
-                break;
-            case u16:
-                res = select_scalar<ushort, true>(cond, b, a, odims);
-                break;
-            case s64: res = select_scalar<intl, true>(cond, b, a, odims); break;
-            case u64:
-                res = select_scalar<uintl, true>(cond, b, a, odims);
-                break;
-            case u8: res = select_scalar<uchar, true>(cond, b, a, odims); break;
-            case b8: res = select_scalar<char, true>(cond, b, a, odims); break;
-            default: TYPE_ERROR(2, binfo.getType());
-        }
+af_err af_select_scalar_l_long(af_array* out, const af_array cond,
+                               const long long a, const af_array b) {
+    return selectScalar<long long, true>(out, cond, b, a);
+}
 
-        std::swap(*out, res);
-    }
-    CATCHALL;
-    return AF_SUCCESS;
+af_err af_select_scalar_l_ulong(af_array* out, const af_array cond,
+                                const unsigned long long a, const af_array b) {
+    return selectScalar<unsigned long long, true>(out, cond, b, a);
 }
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 5ca5077b91..3f86520bd0 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -313,6 +313,38 @@ void replace(array &a, const array &cond, const double &b) {
     AF_THROW(af_replace_scalar(a.get(), cond.get(), b));
 }
 
+void replace(array &a, const array &cond, const long long b) {
+    AF_THROW(af_replace_scalar_long(a.get(), cond.get(), b));
+}
+
+void replace(array &a, const array &cond, const unsigned long long b) {
+    AF_THROW(af_replace_scalar_ulong(a.get(), cond.get(), b));
+}
+
+array select(const array &cond, const array &a, const long long b) {
+    af_array res;
+    AF_THROW(af_select_scalar_r_long(&res, cond.get(), a.get(), b));
+    return array(res);
+}
+
+array select(const array &cond, const array &a, const unsigned long long b) {
+    af_array res;
+    AF_THROW(af_select_scalar_r_ulong(&res, cond.get(), a.get(), b));
+    return array(res);
+}
+
+array select(const array &cond, const long long a, const array &b) {
+    af_array res;
+    AF_THROW(af_select_scalar_l_long(&res, cond.get(), a, b.get()));
+    return array(res);
+}
+
+array select(const array &cond, const unsigned long long a, const array &b) {
+    af_array res;
+    AF_THROW(af_select_scalar_l_ulong(&res, cond.get(), a, b.get()));
+    return array(res);
+}
+
 array pad(const array &in, const dim4 &beginPadding, const dim4 &endPadding,
           const borderType padFillType) {
     af_array out = 0;
diff --git a/src/api/unified/data.cpp b/src/api/unified/data.cpp
index b67868d181..3fb7312fdd 100644
--- a/src/api/unified/data.cpp
+++ b/src/api/unified/data.cpp
@@ -148,3 +148,39 @@ af_err af_pad(af_array *out, const af_array in, const unsigned b_ndims,
     CHECK_ARRAYS(in);
     CALL(af_pad, out, in, b_ndims, b_dims, e_ndims, e_dims, ptype);
 }
+
+af_err af_replace_scalar_long(af_array a, const af_array cond,
+                              const long long b) {
+    CHECK_ARRAYS(a, cond);
+    CALL(af_replace_scalar_long, a, cond, b);
+}
+
+af_err af_replace_scalar_ulong(af_array a, const af_array cond,
+                               const unsigned long long b) {
+    CHECK_ARRAYS(a, cond);
+    CALL(af_replace_scalar_ulong, a, cond, b);
+}
+
+af_err af_select_scalar_r_long(af_array *out, const af_array cond,
+                               const af_array a, const long long b) {
+    CHECK_ARRAYS(cond, a);
+    CALL(af_select_scalar_r_long, out, cond, a, b);
+}
+
+af_err af_select_scalar_r_ulong(af_array *out, const af_array cond,
+                                const af_array a, const unsigned long long b) {
+    CHECK_ARRAYS(cond, a);
+    CALL(af_select_scalar_r_ulong, out, cond, a, b);
+}
+
+af_err af_select_scalar_l_long(af_array *out, const af_array cond,
+                               const long long a, const af_array b) {
+    CHECK_ARRAYS(cond, b);
+    CALL(af_select_scalar_l_long, out, cond, a, b);
+}
+
+af_err af_select_scalar_l_ulong(af_array *out, const af_array cond,
+                                const unsigned long long a, const af_array b) {
+    CHECK_ARRAYS(cond, b);
+    CALL(af_select_scalar_l_ulong, out, cond, a, b);
+}
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index 6ab9e9ec5b..88a95fd5bc 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -71,8 +71,7 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b) {
 }
 
 template<typename T, bool flip>
-void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a,
-                   const double b) {
+void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b) {
     af::dim4 astrides = a.strides();
     af::dim4 adims    = a.dims();
     af::dim4 cstrides = cond.strides();
@@ -85,6 +84,8 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a,
     data_t<T> *optr       = out.get();
     const char *cptr      = cond.get();
 
+    const compute_t<T> scalar = static_cast<compute_t<T>>(b);
+
     bool is_a_same[] = {adims[0] == odims[0], adims[1] == odims[1],
                         adims[2] == odims[2], adims[3] == odims[3]};
 
@@ -110,7 +111,7 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a,
                     bool cval = is_c_same[0] ? cptr[c_off1 + i] : cptr[c_off1];
                     compute_t<T> aval = static_cast<compute_t<T>>(
                         is_a_same[0] ? aptr[a_off1 + i] : aptr[a_off1]);
-                    optr[o_off1 + i] = (flip ^ cval) ? aval : b;
+                    optr[o_off1 + i] = (flip ^ cval) ? aval : scalar;
                 }
             }
         }
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 31812949de..a801bb5e86 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -27,19 +27,19 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
-                   const double &b) {
+                   const T &b) {
     getQueue().enqueue(kernel::select_scalar<T, flip>, out, cond, a, b);
 }
 
-#define INSTANTIATE(T)                                                        \
-    template void select<T>(Array<T> & out, const Array<char> &cond,          \
-                            const Array<T> &a, const Array<T> &b);            \
-    template void select_scalar<T, true>(Array<T> & out,                      \
-                                         const Array<char> &cond,             \
-                                         const Array<T> &a, const double &b); \
-    template void select_scalar<T, false>(Array<T> & out,                     \
-                                          const Array<char> &cond,            \
-                                          const Array<T> &a, const double &b);
+#define INSTANTIATE(T)                                                   \
+    template void select<T>(Array<T> & out, const Array<char> &cond,     \
+                            const Array<T> &a, const Array<T> &b);       \
+    template void select_scalar<T, true>(Array<T> & out,                 \
+                                         const Array<char> &cond,        \
+                                         const Array<T> &a, const T &b); \
+    template void select_scalar<T, false>(Array<T> & out,                \
+                                          const Array<char> &cond,       \
+                                          const Array<T> &a, const T &b);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/select.hpp b/src/backend/cpu/select.hpp
index dfe13ae9ea..b92a8d36c5 100644
--- a/src/backend/cpu/select.hpp
+++ b/src/backend/cpu/select.hpp
@@ -16,7 +16,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
-                   const double &b);
+                   const T &b);
 
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
@@ -28,7 +28,7 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
-                          const double &b, const af::dim4 &odims) {
+                          const T &b, const af::dim4 &odims) {
     Array<T> out = createEmptyArray<T>(odims);
     select_scalar<T, flip>(out, cond, a, b);
     return out;
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 433875c009..6f8972e04f 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -57,7 +57,7 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
 }
 
 template<typename T>
-void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
+void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b,
                    int ndims, bool flip) {
     auto selectScalar =
         common::getKernel("cuda::selectScalar", {select_cuh_src},
@@ -77,7 +77,7 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const double b,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    selectScalar(qArgs, out, cond, a, scalar<T>(b), blk_x, blk_y);
+    selectScalar(qArgs, out, cond, a, b, blk_x, blk_y);
     POST_LAUNCH_CHECK();
 }
 
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index 0b554d1dbf..6f6f399960 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -34,7 +34,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
-                   const double &b) {
+                   const T &b) {
     kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
 }
 
@@ -68,10 +68,10 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
-                          const double &b_val, const af::dim4 &odims) {
+                          const T &b_val, const af::dim4 &odims) {
     auto cond_node   = cond.getNode();
     auto a_node      = a.getNode();
-    Array<T> b       = createScalarNode<T>(odims, scalar<T>(b_val));
+    Array<T> b       = createScalarNode<T>(odims, b_val);
     auto b_node      = b.getNode();
     auto a_height    = a_node->getHeight();
     auto b_height    = b_node->getHeight();
@@ -96,24 +96,24 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     return createNodeArray<T>(odims, node);
 }
 
-#define INSTANTIATE(T)                                                        \
-    template Array<T> createSelectNode<T>(                                    \
-        const Array<char> &cond, const Array<T> &a, const Array<T> &b,        \
-        const af::dim4 &odims);                                               \
-    template Array<T> createSelectNode<T, true>(                              \
-        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
-        const af::dim4 &odims);                                               \
-    template Array<T> createSelectNode<T, false>(                             \
-        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
-        const af::dim4 &odims);                                               \
-    template void select<T>(Array<T> & out, const Array<char> &cond,          \
-                            const Array<T> &a, const Array<T> &b);            \
-    template void select_scalar<T, true>(Array<T> & out,                      \
-                                         const Array<char> &cond,             \
-                                         const Array<T> &a, const double &b); \
-    template void select_scalar<T, false>(Array<T> & out,                     \
-                                          const Array<char> &cond,            \
-                                          const Array<T> &a, const double &b)
+#define INSTANTIATE(T)                                                   \
+    template Array<T> createSelectNode<T>(                               \
+        const Array<char> &cond, const Array<T> &a, const Array<T> &b,   \
+        const af::dim4 &odims);                                          \
+    template Array<T> createSelectNode<T, true>(                         \
+        const Array<char> &cond, const Array<T> &a, const T &b_val,      \
+        const af::dim4 &odims);                                          \
+    template Array<T> createSelectNode<T, false>(                        \
+        const Array<char> &cond, const Array<T> &a, const T &b_val,      \
+        const af::dim4 &odims);                                          \
+    template void select<T>(Array<T> & out, const Array<char> &cond,     \
+                            const Array<T> &a, const Array<T> &b);       \
+    template void select_scalar<T, true>(Array<T> & out,                 \
+                                         const Array<char> &cond,        \
+                                         const Array<T> &a, const T &b); \
+    template void select_scalar<T, false>(Array<T> & out,                \
+                                          const Array<char> &cond,       \
+                                          const Array<T> &a, const T &b)
 
 INSTANTIATE(float);
 INSTANTIATE(double);
diff --git a/src/backend/cuda/select.hpp b/src/backend/cuda/select.hpp
index edd51a93bb..6552ca3ccd 100644
--- a/src/backend/cuda/select.hpp
+++ b/src/backend/cuda/select.hpp
@@ -17,7 +17,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
-                   const double &b);
+                   const T &b);
 
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
@@ -25,5 +25,5 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
-                          const double &b_val, const af::dim4 &odims);
+                          const T &b_val, const af::dim4 &odims);
 }  // namespace cuda
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index cd98ac5662..743f200d5c 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -72,8 +72,8 @@ void select(Param out, Param cond, Param a, Param b, int ndims) {
 }
 
 template<typename T>
-void select_scalar(Param out, Param cond, Param a, const double b,
-                   const int ndims, const bool flip) {
+void select_scalar(Param out, Param cond, Param a, const T b, const int ndims,
+                   const bool flip) {
     std::vector<TemplateArg> targs = {
         TemplateTypename<T>(),
         TemplateArg(flip),
@@ -103,8 +103,7 @@ void select_scalar(Param out, Param cond, Param a, const double b,
                        groups_1 * out.info.dims[3] * local[1]);
 
     selectOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-             *cond.data, cond.info, *a.data, a.info, scalar<T>(b), groups_0,
-             groups_1);
+             *cond.data, cond.info, *a.data, a.info, b, groups_0, groups_1);
 }
 }  // namespace kernel
 }  // namespace opencl
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 9821e7ee89..32c2734f75 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -56,10 +56,10 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
-                          const double &b_val, const dim4 &odims) {
+                          const T &b_val, const dim4 &odims) {
     auto cond_node   = cond.getNode();
     auto a_node      = a.getNode();
-    Array<T> b       = createScalarNode<T>(odims, scalar<T>(b_val));
+    Array<T> b       = createScalarNode<T>(odims, b_val);
     auto b_node      = b.getNode();
     auto a_height    = a_node->getHeight();
     auto b_height    = b_node->getHeight();
@@ -92,28 +92,28 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
-                   const double &b) {
+                   const T &b) {
     kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
 }
 
-#define INSTANTIATE(T)                                                        \
-    template Array<T> createSelectNode<T>(                                    \
-        const Array<char> &cond, const Array<T> &a, const Array<T> &b,        \
-        const af::dim4 &odims);                                               \
-    template Array<T> createSelectNode<T, true>(                              \
-        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
-        const af::dim4 &odims);                                               \
-    template Array<T> createSelectNode<T, false>(                             \
-        const Array<char> &cond, const Array<T> &a, const double &b_val,      \
-        const af::dim4 &odims);                                               \
-    template void select<T>(Array<T> & out, const Array<char> &cond,          \
-                            const Array<T> &a, const Array<T> &b);            \
-    template void select_scalar<T, true>(Array<T> & out,                      \
-                                         const Array<char> &cond,             \
-                                         const Array<T> &a, const double &b); \
-    template void select_scalar<T, false>(Array<T> & out,                     \
-                                          const Array<char> &cond,            \
-                                          const Array<T> &a, const double &b)
+#define INSTANTIATE(T)                                                   \
+    template Array<T> createSelectNode<T>(                               \
+        const Array<char> &cond, const Array<T> &a, const Array<T> &b,   \
+        const af::dim4 &odims);                                          \
+    template Array<T> createSelectNode<T, true>(                         \
+        const Array<char> &cond, const Array<T> &a, const T &b_val,      \
+        const af::dim4 &odims);                                          \
+    template Array<T> createSelectNode<T, false>(                        \
+        const Array<char> &cond, const Array<T> &a, const T &b_val,      \
+        const af::dim4 &odims);                                          \
+    template void select<T>(Array<T> & out, const Array<char> &cond,     \
+                            const Array<T> &a, const Array<T> &b);       \
+    template void select_scalar<T, true>(Array<T> & out,                 \
+                                         const Array<char> &cond,        \
+                                         const Array<T> &a, const T &b); \
+    template void select_scalar<T, false>(Array<T> & out,                \
+                                          const Array<char> &cond,       \
+                                          const Array<T> &a, const T &b)
 
 INSTANTIATE(float);
 INSTANTIATE(double);
diff --git a/src/backend/opencl/select.hpp b/src/backend/opencl/select.hpp
index 01b99ae554..4dbd0635da 100644
--- a/src/backend/opencl/select.hpp
+++ b/src/backend/opencl/select.hpp
@@ -17,7 +17,7 @@ void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
-                   const double &b);
+                   const T &b);
 
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
@@ -25,5 +25,5 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
-                          const double &b_val, const af::dim4 &odims);
+                          const T &b_val, const af::dim4 &odims);
 }  // namespace opencl
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 57e0a307a8..a43cfb51d3 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -388,7 +388,7 @@ make_test(SRC rank_dense.cpp SERIAL)
 make_test(SRC reduce.cpp CXX11)
 make_test(SRC regions.cpp)
 make_test(SRC reorder.cpp)
-make_test(SRC replace.cpp)
+make_test(SRC replace.cpp CXX11)
 make_test(SRC resize.cpp)
 make_test(SRC rng_match.cpp CXX11 BACKENDS "unified")
 make_test(SRC rotate.cpp)
@@ -396,7 +396,7 @@ make_test(SRC rotate_linear.cpp)
 make_test(SRC sat.cpp)
 make_test(SRC scan.cpp)
 make_test(SRC scan_by_key.cpp)
-make_test(SRC select.cpp)
+make_test(SRC select.cpp CXX11)
 make_test(SRC set.cpp CXX11)
 make_test(SRC shift.cpp)
 make_test(SRC gloh.cpp)
diff --git a/test/replace.cpp b/test/replace.cpp
index c8787dc5ee..26baf63a9d 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -13,8 +13,10 @@
 #include <testHelpers.hpp>
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
+
 #include <iostream>
 #include <string>
+#include <type_traits>
 #include <vector>
 
 using af::array;
@@ -77,6 +79,11 @@ void replaceTest(const dim4 &dims) {
 template<typename T>
 void replaceScalarTest(const dim4 &dims) {
     SUPPORTED_TYPE_CHECK(T);
+    using scalar_t =
+        typename std::conditional<std::is_same<T, intl>::value ||
+                                      std::is_same<T, uintl>::value,
+                                  T, double>::type;
+
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a = randu(dims, ty);
@@ -85,7 +92,7 @@ void replaceScalarTest(const dim4 &dims) {
 
     array c    = a.copy();
     array cond = randu(dims, ty) > a;
-    double b   = 3;
+    scalar_t b = static_cast<scalar_t>(3);
 
     replace(c, cond, b);
     int num = (int)a.elements();
@@ -170,7 +177,7 @@ TEST(Replace, ISSUE_1683) {
     A.host(ha1.data());
 
     array B = A(0, span);
-    replace(B, A(0, span) > 0.5, 0);
+    replace(B, A(0, span) > 0.5, 0.0);
 
     vector<float> ha2(A.elements());
     A.host(ha2.data());
diff --git a/test/select.cpp b/test/select.cpp
index 9ee331dff2..7df6b6a862 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -11,13 +11,13 @@
 #include <gtest/gtest.h>
 #include <half.hpp>
 #include <testHelpers.hpp>
-
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 
 #include <cstdio>
 #include <iostream>
 #include <string>
+#include <type_traits>
 #include <vector>
 
 using af::array;
@@ -83,11 +83,16 @@ void selectTest(const dim4& dims) {
 template<typename T, bool is_right>
 void selectScalarTest(const dim4& dims) {
     SUPPORTED_TYPE_CHECK(T);
+    using scalar_t =
+        typename std::conditional<std::is_same<T, intl>::value ||
+                                      std::is_same<T, uintl>::value,
+                                  T, double>::type;
+
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     array a    = randu(dims, ty);
     array cond = randu(dims, ty) > a;
-    double b   = 3;
+    scalar_t b = static_cast<scalar_t>(3);
 
     if (a.isinteger()) { a = (a % (1 << 30)).as(ty); }
 

From bb892342c1e7bef1f0e70fac16a4e2f005de2f69 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 7 Feb 2022 16:42:35 -0500
Subject: [PATCH 2209/2677] Use c++11 when building tests

---
 test/CMakeLists.txt |  2 +-
 test/dot.cpp        | 12 ++++++++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index a43cfb51d3..66e87d9a67 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -61,7 +61,7 @@ if(NOT TARGET mmio)
 endif()
 
 # Reset the CXX flags for tests
-set(CMAKE_CXX_STANDARD 98)
+set(CMAKE_CXX_STANDARD 11)
 
 # TODO(pradeep) perhaps rename AF_USE_RELATIVE_TEST_DIR to AF_WITH_TEST_DATA_DIR
 #               with empty default value
diff --git a/test/dot.cpp b/test/dot.cpp
index 8a1905397c..37b84d2818 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -47,8 +47,14 @@ typedef ::testing::Types<cfloat, cdouble> TestTypesC;
 TYPED_TEST_CASE(DotF, TestTypesF);
 TYPED_TEST_CASE(DotC, TestTypesC);
 
-bool isinf(af::af_cfloat val) { return isinf(val.real) || isinf(val.imag); }
-bool isinf(af::af_cdouble val) { return isinf(val.real) || isinf(val.imag); }
+bool isinf(af::af_cfloat val) {
+    using std::isinf;
+    return isinf(val.real) || isinf(val.imag);
+}
+bool isinf(af::af_cdouble val) {
+    using std::isinf;
+    return isinf(val.real) || isinf(val.imag);
+}
 
 template<typename T>
 void dotTest(string pTestFile, const int resultIdx,
@@ -135,6 +141,8 @@ void dotAllTest(string pTestFile, const int resultIdx,
 
     vector<T> goldData = tests[resultIdx];
 
+    using ::isinf;
+    using std::isinf;
     if (false == (isinf(rval) && isinf(goldData[0]))) {
         compare<T>(rval, ival, goldData[0]);
     }

From 333009069be3ef6e8545b419c6a36bcb22cf25e7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 7 Feb 2022 16:46:39 -0500
Subject: [PATCH 2210/2677] Use boost's epsilon difference when comparing
 floating point values

This commit changes the way we compare floating point values in the
tests to use the boost math's epsilon difference to compare two floating
point values for equality. This is a more accurate form of equality and
handles differences in half float values when the values reach a
certain threshold.
---
 test/CMakeLists.txt          |   1 +
 test/arrayfire_test.cpp      |  44 +++++++++++-
 test/join.cpp                |   2 +-
 test/relative_difference.hpp | 135 +++++++++++++++++++++++++++++++++++
 test/testHelpers.hpp         |  13 ----
 5 files changed, 179 insertions(+), 16 deletions(-)
 create mode 100644 test/relative_difference.hpp

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 66e87d9a67..af9afe4991 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -108,6 +108,7 @@ target_include_directories(arrayfire_test
     ${ArrayFire_BINARY_DIR}/include
     ${ArrayFire_SOURCE_DIR}/extern/half/include
     mmio
+    $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
     ${${gtest_prefix}_SOURCE_DIR}/googletest/include)
 
 if(WIN32)
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index de9b423fe5..63896a791a 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -16,6 +16,8 @@
 #include <af/internal.h>
 
 #include <gtest/gtest.h>
+#include <half.hpp>
+#include <relative_difference.hpp>
 
 #include <algorithm>
 #include <cfloat>
@@ -159,7 +161,7 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
             return elemWiseEq<unsigned short>(aName, bName, a, b, maxAbsDiff);
             break;
         case f16:
-            return elemWiseEq<af::half>(aName, bName, a, b, maxAbsDiff);
+            return elemWiseEq<half_float::half>(aName, bName, a, b, maxAbsDiff);
             break;
         default:
             return ::testing::AssertionFailure()
@@ -1501,6 +1503,45 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     }
 }
 
+struct absMatch {
+    float diff_;
+    absMatch(float diff) : diff_(diff) {}
+
+    template<typename T>
+    bool operator()(T lhs, T rhs) {
+        if (diff_ > 0) {
+            using half_float::abs;
+            using std::abs;
+            return abs(rhs - lhs) <= diff_;
+        } else {
+            return boost::math::epsilon_difference(lhs, rhs) < T(1.f);
+        }
+    }
+};
+
+template<>
+bool absMatch::operator()<af::af_cfloat>(af::af_cfloat lhs, af::af_cfloat rhs) {
+    return af::abs(rhs - lhs) <= diff_;
+}
+
+template<>
+bool absMatch::operator()<af::af_cdouble>(af::af_cdouble lhs,
+                                          af::af_cdouble rhs) {
+    return af::abs(rhs - lhs) <= diff_;
+}
+
+template<>
+bool absMatch::operator()<std::complex<float> >(std::complex<float> lhs,
+                                                std::complex<float> rhs) {
+    return std::abs(rhs - lhs) <= diff_;
+}
+
+template<>
+bool absMatch::operator()<std::complex<double> >(std::complex<double> lhs,
+                                                 std::complex<double> rhs) {
+    return std::abs(rhs - lhs) <= diff_;
+}
+
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T> &a, af::dim4 aDims,
@@ -1687,7 +1728,6 @@ INSTANTIATE(long long);
 INSTANTIATE(unsigned long long);
 INSTANTIATE(std::complex<float>);
 INSTANTIATE(std::complex<double>);
-INSTANTIATE(af_half);
 #undef INSTANTIATE
 
 int main(int argc, char **argv) {
diff --git a/test/join.cpp b/test/join.cpp
index 24120c2b3f..0024fe5542 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -48,7 +48,7 @@ class Join : public ::testing::Test {
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
                          intl, uintl, char, unsigned char, short, ushort,
-                         af_half>
+                         half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/relative_difference.hpp b/test/relative_difference.hpp
new file mode 100644
index 0000000000..3fdfb28dc3
--- /dev/null
+++ b/test/relative_difference.hpp
@@ -0,0 +1,135 @@
+//  (C) Copyright John Maddock 2006, 2015
+//  Use, modification and distribution are subject to the
+//  Boost Software License, Version 1.0. (See accompanying file
+//  LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+
+#ifndef BOOST_MATH_RELATIVE_ERROR
+#define BOOST_MATH_RELATIVE_ERROR
+
+#include <boost/math/special_functions/fpclassify.hpp>
+#include <boost/math/tools/precision.hpp>
+#include <boost/math/tools/promotion.hpp>
+
+namespace boost {
+namespace math {
+
+template<class T, class U>
+typename boost::math::tools::promote_args<T, U>::type relative_difference(
+    const T& arg_a, const U& arg_b) {
+    typedef typename boost::math::tools::promote_args<T, U>::type result_type;
+    result_type a = arg_a;
+    result_type b = arg_b;
+    BOOST_MATH_STD_USING
+#ifdef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS
+    //
+    // If math.h has no long double support we can't rely
+    // on the math functions generating exponents outside
+    // the range of a double:
+    //
+    result_type min_val = (std::max)(
+        tools::min_value<result_type>(),
+        static_cast<result_type>((std::numeric_limits<double>::min)()));
+    result_type max_val = (std::min)(
+        tools::max_value<result_type>(),
+        static_cast<result_type>((std::numeric_limits<double>::max)()));
+#else
+    result_type min_val = tools::min_value<result_type>();
+    result_type max_val = tools::max_value<result_type>();
+#endif
+    // Screen out NaN's first, if either value is a NaN then the distance is
+    // "infinite":
+    if ((boost::math::isnan)(a) || (boost::math::isnan)(b)) return max_val;
+    // Screen out infinities:
+    if (fabs(b) > max_val) {
+        if (fabs(a) > max_val)
+            return (a < 0) == (b < 0)
+                       ? result_type(0)
+                       : max_val;  // one infinity is as good as another!
+        else
+            return max_val;  // one infinity and one finite value implies
+                             // infinite difference
+    } else if (fabs(a) > max_val)
+        return max_val;  // one infinity and one finite value implies infinite
+                         // difference
+
+    //
+    // If the values have different signs, treat as infinite difference:
+    //
+    if (((a < 0) != (b < 0)) && (a != 0) && (b != 0)) return max_val;
+    a = fabs(a);
+    b = fabs(b);
+    //
+    // Now deal with zero's, if one value is zero (or denorm) then treat it the
+    // same as min_val for the purposes of the calculation that follows:
+    //
+    if (a < min_val) a = min_val;
+    if (b < min_val) b = min_val;
+
+    return (std::max)(fabs((a - b) / a), fabs((a - b) / b));
+}
+
+#if (defined(macintosh) || defined(__APPLE__) || defined(__APPLE_CC__)) && \
+    (LDBL_MAX_EXP <= DBL_MAX_EXP)
+template<>
+inline boost::math::tools::promote_args<double, double>::type
+relative_difference(const double& arg_a, const double& arg_b) {
+    BOOST_MATH_STD_USING
+    double a = arg_a;
+    double b = arg_b;
+    //
+    // On Mac OS X we evaluate "double" functions at "long double" precision,
+    // but "long double" actually has a very slightly narrower range than
+    // "double"! Therefore use the range of "long double" as our limits since
+    // results outside that range may have been truncated to 0 or INF:
+    //
+    double min_val = (std::max)((double)tools::min_value<long double>(),
+                                tools::min_value<double>());
+    double max_val = (std::min)((double)tools::max_value<long double>(),
+                                tools::max_value<double>());
+
+    // Screen out NaN's first, if either value is a NaN then the distance is
+    // "infinite":
+    if ((boost::math::isnan)(a) || (boost::math::isnan)(b)) return max_val;
+    // Screen out infinities:
+    if (fabs(b) > max_val) {
+        if (fabs(a) > max_val)
+            return 0;  // one infinity is as good as another!
+        else
+            return max_val;  // one infinity and one finite value implies
+                             // infinite difference
+    } else if (fabs(a) > max_val)
+        return max_val;  // one infinity and one finite value implies infinite
+                         // difference
+
+    //
+    // If the values have different signs, treat as infinite difference:
+    //
+    if (((a < 0) != (b < 0)) && (a != 0) && (b != 0)) return max_val;
+    a = fabs(a);
+    b = fabs(b);
+    //
+    // Now deal with zero's, if one value is zero (or denorm) then treat it the
+    // same as min_val for the purposes of the calculation that follows:
+    //
+    if (a < min_val) a = min_val;
+    if (b < min_val) b = min_val;
+
+    return (std::max)(fabs((a - b) / a), fabs((a - b) / b));
+}
+#endif
+
+template<class T, class U>
+inline typename boost::math::tools::promote_args<T, U>::type epsilon_difference(
+    const T& arg_a, const U& arg_b) {
+    typedef typename boost::math::tools::promote_args<T, U>::type result_type;
+    result_type r = relative_difference(arg_a, arg_b);
+    if (tools::max_value<result_type>() *
+            boost::math::tools::epsilon<result_type>() <
+        r)
+        return tools::max_value<result_type>();
+    return r / boost::math::tools::epsilon<result_type>();
+}
+}  // namespace math
+}  // namespace boost
+
+#endif
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 33b03db93b..024b46657f 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -273,19 +273,6 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T> &b, af::dim4 bDims,
                                       float maxAbsDiff, IntegerTag);
 
-struct absMatch {
-    float diff_;
-    absMatch(float diff) : diff_(diff) {}
-
-    template<typename T>
-    bool operator()(T lhs, T rhs) {
-        using af::abs;
-        using half_float::abs;
-        using std::abs;
-        return abs(rhs - lhs) <= diff_;
-    }
-};
-
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T> &a, af::dim4 aDims,

From 24665c7afb2d2f72cce34494c2ae2acce7ee6205 Mon Sep 17 00:00:00 2001
From: Pradeep Garigipati <pradeep.garigipati@gmail.com>
Date: Sat, 19 Feb 2022 11:23:55 +0530
Subject: [PATCH 2211/2677] Remove double underscore from identifiers

---
 src/backend/common/graphics_common.cpp | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index e8e24834b9..fc8256f999 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -182,10 +182,10 @@ void makeContextCurrent(fg_window window) {
 double step_round(const double in, const bool dir) {
     if (in == 0) { return 0; }
 
-    static const double __log2 = log10(2);
-    static const double __log4 = log10(4);
-    static const double __log6 = log10(6);
-    static const double __log8 = log10(8);
+    static const double LOG2 = log10(2);
+    static const double LOG4 = log10(4);
+    static const double LOG6 = log10(6);
+    static const double LOG8 = log10(8);
 
     // log_in is of the form "s abc.xyz", where
     // s is either + or -; + indicates abs(in) >= 1 and - indicates 0 < abs(in)
@@ -206,25 +206,25 @@ double step_round(const double in, const bool dir) {
 
     // Round up
     if (op_dir) {
-        if (dec <= __log2) {
+        if (dec <= LOG2) {
             mult = 2;
-        } else if (dec <= __log4) {
+        } else if (dec <= LOG4) {
             mult = 4;
-        } else if (dec <= __log6) {
+        } else if (dec <= LOG6) {
             mult = 6;
-        } else if (dec <= __log8) {
+        } else if (dec <= LOG8) {
             mult = 8;
         } else {
             mult = 10;
         }
     } else {  // Round down
-        if (dec < __log2) {
+        if (dec < LOG2) {
             mult = 1;
-        } else if (dec < __log4) {
+        } else if (dec < LOG4) {
             mult = 2;
-        } else if (dec < __log6) {
+        } else if (dec < LOG6) {
             mult = 4;
-        } else if (dec < __log8) {
+        } else if (dec < LOG8) {
             mult = 6;
         } else {
             mult = 8;

From 394466f234f61d0e2a7664236c822ba4c12bb08c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 24 Feb 2022 13:44:58 -0500
Subject: [PATCH 2212/2677] Update docs to new doxygen version. Fix warnings

---
 docs/doxygen.mk                | 586 +++++++++++++++++++++------------
 docs/footer.htm                |  68 +---
 docs/header.htm                |  66 ++--
 docs/pages/install.md          |   8 +-
 docs/pages/using_on_linux.md   |   2 +-
 docs/pages/using_on_osx.md     |   4 +-
 docs/pages/using_on_windows.md |  33 +-
 7 files changed, 452 insertions(+), 315 deletions(-)

diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index b9bfa4158e..b7eded0238 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1,4 +1,4 @@
-# Doxyfile 1.8.14
+# Doxyfile 1.9.3
 
 # This file describes the settings to be used by the documentation system
 # doxygen (www.doxygen.org) for a project.
@@ -17,10 +17,10 @@
 # Project related configuration options
 #---------------------------------------------------------------------------
 
-# This tag specifies the encoding used for all characters in the config file
-# that follow. The default is UTF-8 which is also the encoding used for all text
-# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
-# built into libc) for the transcoding. See
+# This tag specifies the encoding used for all characters in the configuration
+# file that follow. The default is UTF-8 which is also the encoding used for all
+# text before the first occurrence of this tag. Doxygen uses libiconv (or the
+# iconv built into libc) for the transcoding. See
 # https://www.gnu.org/software/libiconv/ for the list of possible encodings.
 # The default value is: UTF-8.
 
@@ -32,13 +32,13 @@ DOXYFILE_ENCODING      = UTF-8
 # title of most generated pages and in a few other places.
 # The default value is: My Project.
 
-PROJECT_NAME           = "${PROJECT_NAME}"
+PROJECT_NAME           = ${PROJECT_NAME}
 
 # The PROJECT_NUMBER tag can be used to enter a project or revision number. This
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = "${AF_VERSION}"
+PROJECT_NUMBER         = ${AF_VERSION}
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
@@ -180,6 +180,16 @@ SHORT_NAMES            = NO
 
 JAVADOC_AUTOBRIEF      = YES
 
+# If the JAVADOC_BANNER tag is set to YES then doxygen will interpret a line
+# such as
+# /***************
+# as being the beginning of a Javadoc-style comment "banner". If set to NO, the
+# Javadoc-style will behave just like regular comments and it will not be
+# interpreted by doxygen.
+# The default value is: NO.
+
+JAVADOC_BANNER         = NO
+
 # If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
 # line (until the first dot) of a Qt-style comment as the brief description. If
 # set to NO, the Qt-style will behave just like regular Qt-style comments (thus
@@ -200,6 +210,14 @@ QT_AUTOBRIEF           = NO
 
 MULTILINE_CPP_IS_BRIEF = NO
 
+# By default Python docstrings are displayed as preformatted text and doxygen's
+# special commands cannot be used. By setting PYTHON_DOCSTRING to NO the
+# doxygen's special commands can be used and the contents of the docstring
+# documentation blocks is shown as doxygen documentation.
+# The default value is: YES.
+
+PYTHON_DOCSTRING       = YES
+
 # If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
 # documentation from any documented member that it re-implements.
 # The default value is: YES.
@@ -223,12 +241,16 @@ TAB_SIZE               = 4
 # the documentation. An alias has the form:
 # name=value
 # For example adding
-# "sideeffect=@par Side Effects:\n"
+# "sideeffect=@par Side Effects:^^"
 # will allow you to put the command \sideeffect (or @sideeffect) in the
 # documentation, which will result in a user-defined paragraph with heading
-# "Side Effects:". You can put \n's in the value part of an alias to insert
-# newlines (in the resulting output). You can put ^^ in the value part of an
-# alias to insert a newline as if a physical newline was in the original file.
+# "Side Effects:". Note that you cannot put \n's in the value part of an alias
+# to insert newlines (in the resulting output). You can put ^^ in the value part
+# of an alias to insert a newline as if a physical newline was in the original
+# file. When you need a literal { or } or , in the value part of an alias you
+# have to escape them by means of a backslash (\), this can lead to conflicts
+# with the commands \{ and \} for these it is advised to use the version @{ and
+# @} or use a double escape (\\{ and \\})
 
 ALIASES                = "support{1}=<DIV class=\"support\">\1</DIV>" \
                          "opencl=<IMG src=\"OpenCL.png\" alt=\"OpenCL Support\" />" \
@@ -246,17 +268,14 @@ ALIASES                = "support{1}=<DIV class=\"support\">\1</DIV>" \
                          "funcgroups{5}=\ingroup \3 \4 \5 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "funcgroups{6}=\ingroup \3 \4 \5 \6 \n @{ \n \defgroup \1 \2 \n @{ \n" \
                          "endfuncgroups=@} \n @}" \
-                         "PR{1}=[[#\1](https://github.com/arrayfire/arrayfire/pull/\1)]"
-
-# Now add special commands for math equations. All of the following commands
-# are only expected to be used inside math mode
-ALIASES += "dims{4}=\f$ [\1 \ \2 \ \3 \ \4] \f$"
-ALIASES += "shape_eq{5}=\f$ \underset{[\2 \ \3 \ \4 \ \5]}{\1} \f$"
-ALIASES += "shape_t{5}=\underset{[\2 \ \3 \ \4 \ \5]}{\1}"
-ALIASES += "convolve_eq{2}=\f$ \1 \ast \2 \f$"
-ALIASES += "convolve_t{2}=\1 \ast \2"
-ALIASES += "set_eq{2}=\f$ \left\\{ \1 \ \Bigg\vert \ \2 \right\\} \f$"
-ALIASES += "set_t{2}=\left\\\{ \1 \ \Bigg\vert \ \2 \right\\\}"
+                         "PR{1}=[[#\1](https://github.com/arrayfire/arrayfire/pull/\1)]" \
+                         "dims{4}=\f$ [\1 \ \2 \ \3 \ \4] \f$" \
+                         "shape_eq{5}=\f$ \underset{[\2 \ \3 \ \4 \ \5]}{\1} \f$" \
+                         "shape_t{5}=\underset{[\2 \ \3 \ \4 \ \5]}{\1}" \
+                         "convolve_eq{2}=\f$ \1 \ast \2 \f$" \
+                         "convolve_t{2}=\1 \ast \2" \
+                         "set_eq{2}=\f$ \left\\{ \1 \ \Bigg\vert \ \2 \right\\} \f$" \
+                         "set_t{2}=\left\\\{ \1 \ \Bigg\vert \ \2 \right\\\}"
 
 # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
 # only. Doxygen will then generate output that is more tailored for C. For
@@ -286,28 +305,40 @@ OPTIMIZE_FOR_FORTRAN   = NO
 
 OPTIMIZE_OUTPUT_VHDL   = NO
 
+# Set the OPTIMIZE_OUTPUT_SLICE tag to YES if your project consists of Slice
+# sources only. Doxygen will then generate output that is more tailored for that
+# language. For instance, namespaces will be presented as modules, types will be
+# separated into more groups, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_SLICE  = NO
+
 # Doxygen selects the parser to use depending on the extension of the files it
 # parses. With this tag you can assign which parser to use for a given
 # extension. Doxygen has a built-in mapping, but you can override or extend it
 # using this tag. The format is ext=language, where ext is a file extension, and
-# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
-# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
-# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
-# Fortran. In the later case the parser tries to guess whether the code is fixed
-# or free formatted code, this is the default for Fortran type files), VHDL. For
-# instance to make doxygen treat .inc files as Fortran files (default is PHP),
-# and .f files as C (default is Fortran), use: inc=Fortran f=C.
+# language is one of the parsers supported by doxygen: IDL, Java, JavaScript,
+# Csharp (C#), C, C++, Lex, D, PHP, md (Markdown), Objective-C, Python, Slice,
+# VHDL, Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:
+# FortranFree, unknown formatted Fortran: Fortran. In the later case the parser
+# tries to guess whether the code is fixed or free formatted code, this is the
+# default for Fortran type files). For instance to make doxygen treat .inc files
+# as Fortran files (default is PHP), and .f files as C (default is Fortran),
+# use: inc=Fortran f=C.
 #
 # Note: For files without extension you can use no_extension as a placeholder.
 #
 # Note that for custom extensions you also need to set FILE_PATTERNS otherwise
-# the files are not read by doxygen.
+# the files are not read by doxygen. When specifying no_extension you should add
+# * to the FILE_PATTERNS.
+#
+# Note see also the list of default file extension mappings.
 
 EXTENSION_MAPPING      =
 
 # If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
 # according to the Markdown format, which allows for more readable
-# documentation. See http://daringfireball.net/projects/markdown/ for details.
+# documentation. See https://daringfireball.net/projects/markdown/ for details.
 # The output of markdown processing is further processed by doxygen, so you can
 # mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
 # case of backward compatibilities issues.
@@ -319,7 +350,7 @@ MARKDOWN_SUPPORT       = YES
 # to that level are automatically included in the table of contents, even if
 # they do not have an id attribute.
 # Note: This feature currently applies only to Markdown headings.
-# Minimum value: 0, maximum value: 99, default value: 0.
+# Minimum value: 0, maximum value: 99, default value: 5.
 # This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
 
 TOC_INCLUDE_HEADINGS   = 0
@@ -435,6 +466,19 @@ TYPEDEF_HIDES_STRUCT   = NO
 
 LOOKUP_CACHE_SIZE      = 0
 
+# The NUM_PROC_THREADS specifies the number threads doxygen is allowed to use
+# during processing. When set to 0 doxygen will based this on the number of
+# cores available in the system. You can set it explicitly to a value larger
+# than 0 to get more control over the balance between CPU load and processing
+# speed. At this moment only the input processing can be done using multiple
+# threads. Since this is still an experimental feature the default is set to 1,
+# which effectively disables parallel processing. Please report any issues you
+# encounter. Generating dot graphs in parallel is controlled by the
+# DOT_NUM_THREADS setting.
+# Minimum value: 0, maximum value: 32, default value: 1.
+
+NUM_PROC_THREADS       = 0
+
 #---------------------------------------------------------------------------
 # Build related configuration options
 #---------------------------------------------------------------------------
@@ -455,6 +499,12 @@ EXTRACT_ALL            = YES
 
 EXTRACT_PRIVATE        = NO
 
+# If the EXTRACT_PRIV_VIRTUAL tag is set to YES, documented private virtual
+# methods of a class will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIV_VIRTUAL   = NO
+
 # If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
 # scope will be included in the documentation.
 # The default value is: NO.
@@ -492,6 +542,13 @@ EXTRACT_LOCAL_METHODS  = NO
 
 EXTRACT_ANON_NSPACES   = NO
 
+# If this flag is set to YES, the name of an unnamed parameter in a declaration
+# will be determined by the corresponding definition. By default unnamed
+# parameters remain unnamed in the output.
+# The default value is: YES.
+
+RESOLVE_UNNAMED_PARAMS = YES
+
 # If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
 # undocumented members inside documented classes or files. If set to NO these
 # members will be included in the various overviews, but no documentation
@@ -509,8 +566,8 @@ HIDE_UNDOC_MEMBERS     = NO
 HIDE_UNDOC_CLASSES     = NO
 
 # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
-# (class|struct|union) declarations. If set to NO, these declarations will be
-# included in the documentation.
+# declarations. If set to NO, these declarations will be included in the
+# documentation.
 # The default value is: NO.
 
 HIDE_FRIEND_COMPOUNDS  = NO
@@ -529,11 +586,18 @@ HIDE_IN_BODY_DOCS      = NO
 
 INTERNAL_DOCS          = NO
 
-# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
-# names in lower-case letters. If set to YES, upper-case letters are also
-# allowed. This is useful if you have classes or files whose names only differ
-# in case and if your file system supports case sensitive file names. Windows
-# and Mac users are advised to set this option to NO.
+# With the correct setting of option CASE_SENSE_NAMES doxygen will better be
+# able to match the capabilities of the underlying filesystem. In case the
+# filesystem is case sensitive (i.e. it supports files in the same directory
+# whose names only differ in casing), the option must be set to YES to properly
+# deal with such files in case they appear in the input. For filesystems that
+# are not case sensitive the option should be be set to NO to properly deal with
+# output files written for symbols that only differ in casing, such as for two
+# classes, one named CLASS and the other named Class, and to also support
+# references to files without having to specify the exact matching casing. On
+# Windows (including Cygwin) and MacOS, users should typically set this option
+# to NO, whereas on Linux or other Unix flavors it should typically be set to
+# YES.
 # The default value is: system dependent.
 
 CASE_SENSE_NAMES       = YES
@@ -552,6 +616,12 @@ HIDE_SCOPE_NAMES       = YES
 
 HIDE_COMPOUND_REFERENCE= NO
 
+# If the SHOW_HEADERFILE tag is set to YES then the documentation for a class
+# will show which file needs to be included to use the class.
+# The default value is: YES.
+
+SHOW_HEADERFILE        = YES
+
 # If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
 # the files that are included by a file in the documentation of that file.
 # The default value is: YES.
@@ -709,7 +779,8 @@ FILE_VERSION_FILTER    = "/bin/sh -c 'git log --pretty=\"format:%ci, (build %h)\
 # output files in an output format independent way. To create the layout file
 # that represents doxygen's defaults, run doxygen with the -l option. You can
 # optionally specify a file name after the option, if omitted DoxygenLayout.xml
-# will be used as the name of the layout file.
+# will be used as the name of the layout file. See also section "Changing the
+# layout of pages" for information.
 #
 # Note that if you run doxygen from a directory containing a file called
 # DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
@@ -755,23 +826,35 @@ WARNINGS               = YES
 WARN_IF_UNDOCUMENTED   = YES
 
 # If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as not documenting some parameters
-# in a documented function, or documenting parameters that don't exist or using
-# markup commands wrongly.
+# potential errors in the documentation, such as documenting some parameters in
+# a documented function twice, or documenting parameters that don't exist or
+# using markup commands wrongly.
 # The default value is: YES.
 
 WARN_IF_DOC_ERROR      = YES
 
+# If WARN_IF_INCOMPLETE_DOC is set to YES, doxygen will warn about incomplete
+# function parameter documentation. If set to NO, doxygen will accept that some
+# parameters have no documentation without warning.
+# The default value is: YES.
+
+WARN_IF_INCOMPLETE_DOC = YES
+
 # This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
 # are documented, but have no documentation for their parameters or return
-# value. If set to NO, doxygen will only warn about wrong or incomplete
-# parameter documentation, but not about the absence of documentation.
+# value. If set to NO, doxygen will only warn about wrong parameter
+# documentation, but not about the absence of documentation. If EXTRACT_ALL is
+# set to YES then this flag will automatically be disabled. See also
+# WARN_IF_INCOMPLETE_DOC
 # The default value is: NO.
 
 WARN_NO_PARAMDOC       = YES
 
 # If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
-# a warning is encountered.
+# a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS
+# then doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but
+# at the end of the doxygen process doxygen will return with a non-zero status.
+# Possible values are: NO, YES and FAIL_ON_WARNINGS.
 # The default value is: NO.
 
 WARN_AS_ERROR          = NO
@@ -788,7 +871,10 @@ WARN_FORMAT            = "$file:$line: $text"
 
 # The WARN_LOGFILE tag can be used to specify a file to which warning and error
 # messages should be written. If left blank the output is written to standard
-# error (stderr).
+# error (stderr). In case the file specified cannot be opened for writing the
+# warning and error messages are written to standard error. When as file - is
+# specified the warning and error messages are written to standard output
+# (stdout).
 
 WARN_LOGFILE           =
 
@@ -810,8 +896,8 @@ INPUT                  = ${DOCS_DIR}/pages \
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
 # libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see: https://www.gnu.org/software/libiconv/) for the list of
-# possible encodings.
+# documentation (see:
+# https://www.gnu.org/software/libiconv/) for the list of possible encodings.
 # The default value is: UTF-8.
 
 INPUT_ENCODING         = UTF-8
@@ -824,11 +910,15 @@ INPUT_ENCODING         = UTF-8
 # need to set EXTENSION_MAPPING for the extension otherwise the files are not
 # read by doxygen.
 #
+# Note the list of default checked file patterns might differ from the list of
+# default file extension mappings.
+#
 # If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
 # *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
-# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
-# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,
-# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf.
+# *.hh, *.hxx, *.hpp, *.h++, *.l, *.cs, *.d, *.php, *.php4, *.php5, *.phtml,
+# *.inc, *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C
+# comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f18, *.f, *.for, *.vhd,
+# *.vhdl, *.ucf, *.qsf and *.ice.
 
 FILE_PATTERNS          =
 
@@ -867,7 +957,7 @@ EXCLUDE_PATTERNS       = *.cpp
 # (namespaces, classes, functions, etc.) that should be excluded from the
 # output. The symbol name can be a fully qualified name, a word, or if the
 # wildcard * is used, a substring. Examples: ANamespace, AClass,
-# AClass::ANamespace, ANamespace::*Test
+# ANamespace::AClass, ANamespace::*Test
 #
 # Note that the wildcards are matched against the file with absolute path, so to
 # exclude all test directories use the pattern */test/*
@@ -987,7 +1077,7 @@ INLINE_SOURCES         = YES
 STRIP_CODE_COMMENTS    = YES
 
 # If the REFERENCED_BY_RELATION tag is set to YES then for each documented
-# function all documented functions referencing it will be listed.
+# entity all documented functions referencing it will be listed.
 # The default value is: NO.
 
 REFERENCED_BY_RELATION = NO
@@ -1024,7 +1114,7 @@ SOURCE_TOOLTIPS        = YES
 #
 # To use it do the following:
 # - Install the latest version of global
-# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
+# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file
 # - Make sure the INPUT points to the root of the source tree
 # - Run doxygen as normal
 #
@@ -1046,36 +1136,6 @@ USE_HTAGS              = NO
 
 VERBATIM_HEADERS       = YES
 
-# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
-# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
-# cost of reduced performance. This can be particularly helpful with template
-# rich C++ code for which doxygen's built-in parser lacks the necessary type
-# information.
-# Note: The availability of this option depends on whether or not doxygen was
-# generated with the -Duse-libclang=ON option for CMake.
-# The default value is: NO.
-
-#CLANG_ASSISTED_PARSING = NO
-
-# If clang assisted parsing is enabled you can provide the compiler with command
-# line options that you would normally use when invoking the compiler. Note that
-# the include paths will already be set by doxygen for the files and directories
-# specified with INPUT and INCLUDE_PATH.
-# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
-
-#CLANG_OPTIONS          = -Wno-pragma-once-outside-header
-
-# If clang assisted parsing is enabled you can provide the clang parser with the
-# path to the compilation database (see:
-# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) used when the files
-# were built. This is equivalent to specifying the "-p" option to a clang tool,
-# such as clang-check. These options will then be passed to the parser.
-# Note: The availability of this option depends on whether or not doxygen was
-# generated with the -Duse-libclang=ON option for CMake.
-# The default value is: 0.
-
-#CLANG_COMPILATION_DATABASE_PATH  = ${ArrayFire_BINARY_DIR}
-
 #---------------------------------------------------------------------------
 # Configuration options related to the alphabetical class index
 #---------------------------------------------------------------------------
@@ -1186,7 +1246,7 @@ HTML_EXTRA_FILES       =
 
 # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
 # will adjust the colors in the style sheet and background images according to
-# this color. Hue is specified as an angle on a colorwheel, see
+# this color. Hue is specified as an angle on a color-wheel, see
 # https://en.wikipedia.org/wiki/Hue for more information. For instance the value
 # 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
 # purple, and 360 is red again.
@@ -1196,7 +1256,7 @@ HTML_EXTRA_FILES       =
 HTML_COLORSTYLE_HUE    = 19
 
 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
-# in the HTML output. For a value of 0 the output will use grayscales only. A
+# in the HTML output. For a value of 0 the output will use gray-scales only. A
 # value of 255 will produce the most vivid colors.
 # Minimum value: 0, maximum value: 255, default value: 100.
 # This tag requires that the tag GENERATE_HTML is set to YES.
@@ -1225,9 +1285,9 @@ HTML_TIMESTAMP         = YES
 
 # If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
 # documentation will contain a main index with vertical navigation menus that
-# are dynamically created via Javascript. If disabled, the navigation index will
+# are dynamically created via JavaScript. If disabled, the navigation index will
 # consists of multiple levels of tabs that are statically embedded in every HTML
-# page. Disable this option to support browsers that do not have Javascript,
+# page. Disable this option to support browsers that do not have JavaScript,
 # like the Qt help browser.
 # The default value is: YES.
 # This tag requires that the tag GENERATE_HTML is set to YES.
@@ -1257,13 +1317,14 @@ HTML_INDEX_NUM_ENTRIES = 100
 
 # If the GENERATE_DOCSET tag is set to YES, additional index files will be
 # generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see: https://developer.apple.com/tools/xcode/), introduced with
-# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
-# Makefile in the HTML output directory. Running make will produce the docset in
-# that directory and running make install will install the docset in
+# environment (see:
+# https://developer.apple.com/xcode/), introduced with OSX 10.5 (Leopard). To
+# create a documentation set, doxygen will generate a Makefile in the HTML
+# output directory. Running make will produce the docset in that directory and
+# running make install will install the docset in
 # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
-# startup. See https://developer.apple.com/tools/creatingdocsetswithdoxygen.html
-# for more information.
+# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
+# genXcode/_index.html for more information.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
@@ -1277,6 +1338,13 @@ GENERATE_DOCSET        = NO
 
 DOCSET_FEEDNAME        = "Doxygen generated docs"
 
+# This tag determines the URL of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDURL         =
+
 # This tag specifies a string that should uniquely identify the documentation
 # set bundle. This should be a reverse domain-name style string, e.g.
 # com.mycompany.MyDocSet. Doxygen will append .docset to the name.
@@ -1302,8 +1370,12 @@ DOCSET_PUBLISHER_NAME  = Publisher
 # If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
 # additional HTML index files: index.hhp, index.hhc, and index.hhk. The
 # index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
-# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
-# Windows.
+# on Windows. In the beginning of 2021 Microsoft took the original page, with
+# a.o. the download links, offline the HTML help workshop was already many years
+# in maintenance mode). You can download the HTML help workshop from the web
+# archives at Installation executable (see:
+# http://web.archive.org/web/20160201063255/http://download.microsoft.com/downlo
+# ad/0/A/9/0A939EF6-E31C-430F-A3DF-DFAE7960D564/htmlhelp.exe).
 #
 # The HTML Help Workshop contains a compiler that can convert all HTML output
 # generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
@@ -1333,7 +1405,7 @@ CHM_FILE               =
 HHC_LOCATION           =
 
 # The GENERATE_CHI flag controls if a separate .chi index file is generated
-# (YES) or that it should be included in the master .chm file (NO).
+# (YES) or that it should be included in the main .chm file (NO).
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
@@ -1378,7 +1450,8 @@ QCH_FILE               =
 
 # The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
 # Project output. For more information please see Qt Help Project / Namespace
-# (see: http://doc.qt.io/qt-4.8/qthelpproject.html#namespace).
+# (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
 # The default value is: org.doxygen.Project.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
@@ -1386,7 +1459,8 @@ QHP_NAMESPACE          = org.doxygen.Project
 
 # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
 # Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see: http://doc.qt.io/qt-4.8/qthelpproject.html#virtual-folders).
+# Folders (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-folders).
 # The default value is: doc.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
@@ -1394,28 +1468,30 @@ QHP_VIRTUAL_FOLDER     = doc
 
 # If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
 # filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters).
+# Filters (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_NAME   =
 
 # The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
 # custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters).
+# Filters (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_ATTRS  =
 
 # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
 # project's filter section matches. Qt Help Project / Filter Attributes (see:
-# http://doc.qt.io/qt-4.8/qthelpproject.html#filter-attributes).
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_SECT_FILTER_ATTRS  =
 
-# The QHG_LOCATION tag can be used to specify the location of Qt's
-# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
-# generated .qhp file.
+# The QHG_LOCATION tag can be used to specify the location (absolute path
+# including file name) of Qt's qhelpgenerator. If non-empty doxygen will try to
+# run qhelpgenerator on the generated .qhp file.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHG_LOCATION           =
@@ -1458,16 +1534,28 @@ DISABLE_INDEX          = NO
 # to work a browser that supports JavaScript, DHTML, CSS and frames is required
 # (i.e. any modern browser). Windows users are probably better off using the
 # HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
-# further fine-tune the look of the index. As an example, the default style
-# sheet generated by doxygen has an example that shows how to put an image at
-# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
-# the same information as the tab index, you could consider setting
-# DISABLE_INDEX to YES when enabling this option.
+# further fine tune the look of the index (see "Fine-tuning the output"). As an
+# example, the default style sheet generated by doxygen has an example that
+# shows how to put an image at the root of the tree instead of the PROJECT_NAME.
+# Since the tree basically has the same information as the tab index, you could
+# consider setting DISABLE_INDEX to YES when enabling this option.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_TREEVIEW      = YES
 
+# When both GENERATE_TREEVIEW and DISABLE_INDEX are set to YES, then the
+# FULL_SIDEBAR option determines if the side bar is limited to only the treeview
+# area (value NO) or if it should extend to the full height of the window (value
+# YES). Setting this to YES gives a layout similar to
+# https://docs.readthedocs.io with more room for contents, but less room for the
+# project logo, title, and description. If either GENERATE_TREEVIEW or
+# DISABLE_INDEX is set to NO, this option has no effect.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FULL_SIDEBAR           = NO
+
 # The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
 # doxygen will group on one line in the generated HTML documentation.
 #
@@ -1492,6 +1580,24 @@ TREEVIEW_WIDTH         = 250
 
 EXT_LINKS_IN_WINDOW    = NO
 
+# If the OBFUSCATE_EMAILS tag is set to YES, doxygen will obfuscate email
+# addresses.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+OBFUSCATE_EMAILS       = YES
+
+# If the HTML_FORMULA_FORMAT option is set to svg, doxygen will use the pdf2svg
+# tool (see https://github.com/dawbarton/pdf2svg) or inkscape (see
+# https://inkscape.org) to generate formulas as SVG images instead of PNGs for
+# the HTML output. These images will generally look nicer at scaled resolutions.
+# Possible values are: png (the default) and svg (looks nicer but requires the
+# pdf2svg or inkscape tool).
+# The default value is: png.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FORMULA_FORMAT    = png
+
 # Use this tag to change the font size of LaTeX formulas included as images in
 # the HTML documentation. When you change the font size after a successful
 # doxygen run you need to manually remove any form_*.png images from the HTML
@@ -1512,8 +1618,14 @@ FORMULA_FONTSIZE       = 12
 
 FORMULA_TRANSPARENT    = YES
 
+# The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
+# to create new LaTeX commands to be used in formulas as building blocks. See
+# the section "Including formulas" for details.
+
+FORMULA_MACROFILE      =
+
 # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
-# https://www.mathjax.org) which uses client side Javascript for the rendering
+# https://www.mathjax.org) which uses client side JavaScript for the rendering
 # instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
 # installed or if you want to formulas look prettier in the HTML output. When
 # enabled you may also need to install MathJax separately and configure the path
@@ -1523,11 +1635,29 @@ FORMULA_TRANSPARENT    = YES
 
 USE_MATHJAX            = YES
 
+# With MATHJAX_VERSION it is possible to specify the MathJax version to be used.
+# Note that the different versions of MathJax have different requirements with
+# regards to the different settings, so it is possible that also other MathJax
+# settings have to be changed when switching between the different MathJax
+# versions.
+# Possible values are: MathJax_2 and MathJax_3.
+# The default value is: MathJax_2.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_VERSION        = MathJax_2
+
 # When MathJax is enabled you can set the default output format to be used for
-# the MathJax output. See the MathJax site (see:
-# http://docs.mathjax.org/en/latest/output.html) for more details.
+# the MathJax output. For more details about the output format see MathJax
+# version 2 (see:
+# http://docs.mathjax.org/en/v2.7-latest/output.html) and MathJax version 3
+# (see:
+# http://docs.mathjax.org/en/latest/web/components/output.html).
 # Possible values are: HTML-CSS (which is slower, but has the best
-# compatibility), NativeMML (i.e. MathML) and SVG.
+# compatibility. This is the name for Mathjax version 2, for MathJax version 3
+# this will be translated into chtml), NativeMML (i.e. MathML. Only supported
+# for NathJax 2. For MathJax version 3 chtml will be used instead.), chtml (This
+# is the name for Mathjax version 3, for MathJax version 2 this will be
+# translated into HTML-CSS) and SVG.
 # The default value is: HTML-CSS.
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
@@ -1540,22 +1670,29 @@ MATHJAX_FORMAT         = HTML-CSS
 # MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
 # Content Delivery Network so you can quickly see the result without installing
 # MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from https://www.mathjax.org before deployment.
-# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/.
+# MathJax from https://www.mathjax.org before deployment. The default value is:
+# - in case of MathJax version 2: https://cdn.jsdelivr.net/npm/mathjax@2
+# - in case of MathJax version 3: https://cdn.jsdelivr.net/npm/mathjax@3
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
-MATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest
+MATHJAX_RELPATH        = https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1
 
 # The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
 # extension names that should be enabled during MathJax rendering. For example
+# for MathJax version 2 (see
+# https://docs.mathjax.org/en/v2.7-latest/tex.html#tex-and-latex-extensions):
 # MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# For example for MathJax version 3 (see
+# http://docs.mathjax.org/en/latest/input/tex/extensions/index.html):
+# MATHJAX_EXTENSIONS = ams
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_EXTENSIONS     =
 
 # The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
 # of code that will be used on startup of the MathJax code. See the MathJax site
-# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# (see:
+# http://docs.mathjax.org/en/v2.7-latest/output.html) for more details. For an
 # example see the documentation.
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
@@ -1583,7 +1720,7 @@ MATHJAX_CODEFILE       =
 SEARCHENGINE           = NO
 
 # When the SERVER_BASED_SEARCH tag is enabled the search engine will be
-# implemented using a web server instead of a web client using Javascript. There
+# implemented using a web server instead of a web client using JavaScript. There
 # are two flavors of web server based searching depending on the EXTERNAL_SEARCH
 # setting. When disabled, doxygen will generate a PHP script for searching and
 # an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
@@ -1602,7 +1739,8 @@ SERVER_BASED_SEARCH    = NO
 #
 # Doxygen ships with an example indexer (doxyindexer) and search engine
 # (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: https://xapian.org/).
+# Xapian (see:
+# https://xapian.org/).
 #
 # See the section "External Indexing and Searching" for details.
 # The default value is: NO.
@@ -1615,8 +1753,9 @@ EXTERNAL_SEARCH        = NO
 #
 # Doxygen ships with an example indexer (doxyindexer) and search engine
 # (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: https://xapian.org/). See the section "External Indexing and
-# Searching" for details.
+# Xapian (see:
+# https://xapian.org/). See the section "External Indexing and Searching" for
+# details.
 # This tag requires that the tag SEARCHENGINE is set to YES.
 
 SEARCHENGINE_URL       =
@@ -1667,21 +1806,35 @@ LATEX_OUTPUT           = latex
 # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
 # invoked.
 #
-# Note that when enabling USE_PDFLATEX this option is only used for generating
-# bitmaps for formulas in the HTML output, but not in the Makefile that is
-# written to the output directory.
-# The default file is: latex.
+# Note that when not enabling USE_PDFLATEX the default is latex when enabling
+# USE_PDFLATEX the default is pdflatex and when in the later case latex is
+# chosen this is overwritten by pdflatex. For specific output languages the
+# default can have been set differently, this depends on the implementation of
+# the output language.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_CMD_NAME         = latex
 
 # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
 # index for LaTeX.
+# Note: This tag is used in the Makefile / make.bat.
+# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file
+# (.tex).
 # The default file is: makeindex.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 MAKEINDEX_CMD_NAME     = makeindex
 
+# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to
+# generate index for LaTeX. In case there is no backslash (\) as first character
+# it will be automatically added in the LaTeX code.
+# Note: This tag is used in the generated output file (.tex).
+# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.
+# The default value is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_MAKEINDEX_CMD    = makeindex
+
 # If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
 # documents. This may be useful for small projects and may help to save some
 # trees in general.
@@ -1711,29 +1864,31 @@ PAPER_TYPE             = a4
 
 EXTRA_PACKAGES         =
 
-# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
-# generated LaTeX document. The header should contain everything until the first
-# chapter. If it is left blank doxygen will generate a standard header. See
-# section "Doxygen usage" for information on how to let doxygen write the
-# default header to a separate file.
+# The LATEX_HEADER tag can be used to specify a user-defined LaTeX header for
+# the generated LaTeX document. The header should contain everything until the
+# first chapter. If it is left blank doxygen will generate a standard header. It
+# is highly recommended to start with a default header using
+# doxygen -w latex new_header.tex new_footer.tex new_stylesheet.sty
+# and then modify the file new_header.tex. See also section "Doxygen usage" for
+# information on how to generate the default header that doxygen normally uses.
 #
-# Note: Only use a user-defined header if you know what you are doing! The
-# following commands have a special meaning inside the header: $title,
-# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
-# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
-# string, for the replacement values of the other commands the user is referred
-# to HTML_HEADER.
+# Note: Only use a user-defined header if you know what you are doing!
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. The following
+# commands have a special meaning inside the header (and footer): For a
+# description of the possible markers and block names see the documentation.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_HEADER           =
 
-# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
-# generated LaTeX document. The footer should contain everything after the last
-# chapter. If it is left blank doxygen will generate a standard footer. See
+# The LATEX_FOOTER tag can be used to specify a user-defined LaTeX footer for
+# the generated LaTeX document. The footer should contain everything after the
+# last chapter. If it is left blank doxygen will generate a standard footer. See
 # LATEX_HEADER for more information on how to generate a default footer and what
-# special commands can be used inside the footer.
-#
-# Note: Only use a user-defined footer if you know what you are doing!
+# special commands can be used inside the footer. See also section "Doxygen
+# usage" for information on how to generate the default footer that doxygen
+# normally uses. Note: Only use a user-defined footer if you know what you are
+# doing!
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_FOOTER           =
@@ -1766,9 +1921,11 @@ LATEX_EXTRA_FILES      =
 
 PDF_HYPERLINKS         = YES
 
-# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
-# the PDF file directly from the LaTeX files. Set this option to YES, to get a
-# higher quality PDF documentation.
+# If the USE_PDFLATEX tag is set to YES, doxygen will use the engine as
+# specified with LATEX_CMD_NAME to generate the PDF file directly from the LaTeX
+# files. Set this option to YES, to get a higher quality PDF documentation.
+#
+# See also section LATEX_CMD_NAME for selecting the engine.
 # The default value is: YES.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
@@ -1776,8 +1933,7 @@ USE_PDFLATEX           = YES
 
 # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
 # command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help. This option is also used
-# when generating formulas in HTML.
+# if errors occur, instead of asking the user for help.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
@@ -1790,16 +1946,6 @@ LATEX_BATCHMODE        = NO
 
 LATEX_HIDE_INDICES     = NO
 
-# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
-# code with syntax highlighting in the LaTeX output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_SOURCE_CODE      = NO
-
 # The LATEX_BIB_STYLE tag can be used to specify the style to use for the
 # bibliography, e.g. plainnat, or ieeetr. See
 # https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
@@ -1816,6 +1962,14 @@ LATEX_BIB_STYLE        = plain
 
 LATEX_TIMESTAMP        = NO
 
+# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
+# path from which the emoji images will be read. If a relative path is entered,
+# it will be relative to the LATEX_OUTPUT directory. If left blank the
+# LATEX_OUTPUT directory will be used.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EMOJI_DIRECTORY  =
+
 #---------------------------------------------------------------------------
 # Configuration options related to the RTF output
 #---------------------------------------------------------------------------
@@ -1855,9 +2009,9 @@ COMPACT_RTF            = NO
 
 RTF_HYPERLINKS         = NO
 
-# Load stylesheet definitions from file. Syntax is similar to doxygen's config
-# file, i.e. a series of assignments. You only have to provide replacements,
-# missing definitions are set to their default value.
+# Load stylesheet definitions from file. Syntax is similar to doxygen's
+# configuration file, i.e. a series of assignments. You only have to provide
+# replacements, missing definitions are set to their default value.
 #
 # See also section "Doxygen usage" for information on how to generate the
 # default style sheet that doxygen normally uses.
@@ -1866,22 +2020,12 @@ RTF_HYPERLINKS         = NO
 RTF_STYLESHEET_FILE    =
 
 # Set optional variables used in the generation of an RTF document. Syntax is
-# similar to doxygen's config file. A template extensions file can be generated
-# using doxygen -e rtf extensionFile.
+# similar to doxygen's configuration file. A template extensions file can be
+# generated using doxygen -e rtf extensionFile.
 # This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_EXTENSIONS_FILE    =
 
-# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
-# with syntax highlighting in the RTF output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_RTF is set to YES.
-
-RTF_SOURCE_CODE        = NO
-
 #---------------------------------------------------------------------------
 # Configuration options related to the man page output
 #---------------------------------------------------------------------------
@@ -1953,6 +2097,13 @@ XML_OUTPUT             = xml
 
 XML_PROGRAMLISTING     = YES
 
+# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
+# namespace members in file scope as well, matching the HTML output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_NS_MEMB_FILE_SCOPE = NO
+
 #---------------------------------------------------------------------------
 # Configuration options related to the DOCBOOK output
 #---------------------------------------------------------------------------
@@ -1971,15 +2122,6 @@ GENERATE_DOCBOOK       = NO
 
 DOCBOOK_OUTPUT         = docbook
 
-# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
-# program listings (including syntax highlighting and cross-referencing
-# information) to the DOCBOOK output. Note that enabling this will significantly
-# increase the size of the DOCBOOK output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-DOCBOOK_PROGRAMLISTING = NO
-
 #---------------------------------------------------------------------------
 # Configuration options for the AutoGen Definitions output
 #---------------------------------------------------------------------------
@@ -2158,30 +2300,10 @@ EXTERNAL_GROUPS        = YES
 
 EXTERNAL_PAGES         = YES
 
-# The PERL_PATH should be the absolute path and name of the perl script
-# interpreter (i.e. the result of 'which perl').
-# The default file (with absolute path) is: /usr/bin/perl.
-
 #---------------------------------------------------------------------------
 # Configuration options related to the dot tool
 #---------------------------------------------------------------------------
 
-# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
-# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
-# NO turns the diagrams off. Note that this option also works with HAVE_DOT
-# disabled, but it is recommended to install and use dot, since it yields more
-# powerful graphs.
-# The default value is: YES.
-
-CLASS_DIAGRAMS         = YES
-
-# You can define message sequence charts within doxygen comments using the \msc
-# command. Doxygen will then run the mscgen tool (see:
-# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
-# documentation. The MSCGEN_PATH tag allows you to specify the directory where
-# the mscgen tool resides. If left empty the tool is assumed to be found in the
-# default search path.
-
 # You can include diagrams made with dia in doxygen documentation. Doxygen will
 # then run dia to produce the diagram and insert it in the documentation. The
 # DIA_PATH tag allows you to specify the directory where the dia binary resides.
@@ -2238,11 +2360,14 @@ DOT_FONTSIZE           = 10
 
 DOT_FONTPATH           =
 
-# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
-# each documented class showing the direct and indirect inheritance relations.
-# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# If the CLASS_GRAPH tag is set to YES (or GRAPH) then doxygen will generate a
+# graph for each documented class showing the direct and indirect inheritance
+# relations. In case HAVE_DOT is set as well dot will be used to draw the graph,
+# otherwise the built-in generator will be used. If the CLASS_GRAPH tag is set
+# to TEXT the direct and indirect inheritance relations will be shown as texts /
+# links.
+# Possible values are: NO, YES, TEXT and GRAPH.
 # The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
 
 CLASS_GRAPH            = YES
 
@@ -2279,10 +2404,32 @@ UML_LOOK               = NO
 # but if the number exceeds 15, the total amount of fields shown is limited to
 # 10.
 # Minimum value: 0, maximum value: 100, default value: 10.
-# This tag requires that the tag HAVE_DOT is set to YES.
+# This tag requires that the tag UML_LOOK is set to YES.
 
 UML_LIMIT_NUM_FIELDS   = 10
 
+# If the DOT_UML_DETAILS tag is set to NO, doxygen will show attributes and
+# methods without types and arguments in the UML graphs. If the DOT_UML_DETAILS
+# tag is set to YES, doxygen will add type and arguments for attributes and
+# methods in the UML graphs. If the DOT_UML_DETAILS tag is set to NONE, doxygen
+# will not generate fields with class member information in the UML graphs. The
+# class diagrams will look similar to the default class diagrams but using UML
+# notation for the relationships.
+# Possible values are: NO, YES and NONE.
+# The default value is: NO.
+# This tag requires that the tag UML_LOOK is set to YES.
+
+DOT_UML_DETAILS        = NO
+
+# The DOT_WRAP_THRESHOLD tag can be used to set the maximum number of characters
+# to display on a single line. If the actual line length exceeds this threshold
+# significantly it will wrapped across multiple lines. Some heuristics are apply
+# to avoid ugly line breaks.
+# Minimum value: 0, maximum value: 1000, default value: 17.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_WRAP_THRESHOLD     = 17
+
 # If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
 # collaboration graphs will show the relations between templates and their
 # instances.
@@ -2349,6 +2496,13 @@ GRAPHICAL_HIERARCHY    = YES
 
 DIRECTORY_GRAPH        = YES
 
+# The DIR_GRAPH_MAX_DEPTH tag can be used to limit the maximum number of levels
+# of child directories generated in directory dependency graphs by dot.
+# Minimum value: 1, maximum value: 25, default value: 1.
+# This tag requires that the tag DIRECTORY_GRAPH is set to YES.
+
+DIR_GRAPH_MAX_DEPTH    = 1
+
 # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
 # generated by dot. For an explanation of the image formats see the section
 # output formats in the documentation of the dot tool (Graphviz (see:
@@ -2402,10 +2556,10 @@ MSCFILE_DIRS           =
 DIAFILE_DIRS           =
 
 # When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
-# path where java can find the plantuml.jar file. If left blank, it is assumed
-# PlantUML is not used or called during a preprocessing step. Doxygen will
-# generate a warning when it encounters a \startuml command in this case and
-# will not generate output for the diagram.
+# path where java can find the plantuml.jar file or to the filename of jar file
+# to be used. If left blank, it is assumed PlantUML is not used or called during
+# a preprocessing step. Doxygen will generate a warning when it encounters a
+# \startuml command in this case and will not generate output for the diagram.
 
 PLANTUML_JAR_PATH      =
 
@@ -2467,14 +2621,18 @@ DOT_MULTI_TARGETS      = NO
 # If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
 # explaining the meaning of the various boxes and arrows in the dot generated
 # graphs.
+# Note: This tag requires that UML_LOOK isn't set, i.e. the doxygen internal
+# graphical representation for inheritance and collaboration diagrams is used.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 GENERATE_LEGEND        = YES
 
-# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot
+# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate
 # files that are used to generate the various graphs.
+#
+# Note: This setting is not only used for dot files but also for msc temporary
+# files.
 # The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_CLEANUP            = YES
diff --git a/docs/footer.htm b/docs/footer.htm
index 2ca612336a..ca355c3af8 100644
--- a/docs/footer.htm
+++ b/docs/footer.htm
@@ -1,57 +1,17 @@
+<!-- HTML footer for doxygen 1.9.3-->
+<!-- start footer part -->
+<!--BEGIN GENERATE_TREEVIEW-->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+    <ul>
+        $navpath
+        <li class="footer">$generatedby <a href="https://www.doxygen.org/index.html"><img class="footer" src="$relpath^doxygen.svg" width="104" height="31" alt="doxygen"/></a> $doxygenversion </li>
+    </ul>
 </div>
-</div>
-</div>
-</div>
-</div>
-
-<!--Google Analytics-->
-<script type="text/javascript">
-  var _gaq = _gaq || [];
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-
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-  })();
-</script>
-
-<!--Adroll-->
-<script type="text/javascript">
-adroll_adv_id = "ZRWI4W4RTRHENOWGXZY5JQ";
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-   __adroll_loaded=true;
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-   ((document.getElementsByTagName('head') || [null])[0] ||
-    document.getElementsByTagName('script')[0].parentNode).appendChild(scr);
-   if(oldonload){oldonload()}};
-}());
-</script>
-
+<!--END GENERATE_TREEVIEW-->
+<!--BEGIN !GENERATE_TREEVIEW-->
+<hr class="footer"/><address class="footer"><small>
+    $generatedby&#160;<a href="https://www.doxygen.org/index.html"><img class="footer" src="$relpath^doxygen.svg" width="104" height="31" alt="doxygen"/></a> $doxygenversion
+</small></address>
+<!--END !GENERATE_TREEVIEW-->
 </body>
 </html>
diff --git a/docs/header.htm b/docs/header.htm
index cc7a161d56..5704d89dfb 100644
--- a/docs/header.htm
+++ b/docs/header.htm
@@ -1,14 +1,28 @@
-<!-- HTML header for doxygen 1.8.17-->
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
-<html xmlns="https://www.w3.org/1999/xhtml">
+<!-- HTML header for doxygen 1.9.3-->
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml">
 <head>
+<!-- Global site tag (gtag.js) - Google Analytics -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=UA-130950618-1"></script>
+<script>
+    window.dataLayer = window.dataLayer || [];
+    function gtag(){dataLayer.push(arguments);}
+    gtag('js', new Date());
+
+    gtag('config', 'UA-130950618-1');
+</script>
 <meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
-<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta http-equiv="X-UA-Compatible" content="IE=11"/>
 <meta name="generator" content="Doxygen $doxygenversion"/>
 <meta name="viewport" content="width=device-width, initial-scale=1"/>
 <!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME-->
 <!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME-->
 <link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/>
+<!--BEGIN DISABLE_INDEX-->
+  <!--BEGIN FULL_SIDEBAR-->
+<script type="text/javascript">var page_layout=1;</script>
+  <!--END FULL_SIDEBAR-->
+<!--END DISABLE_INDEX-->
 <script type="text/javascript" src="$relpath^jquery.js"></script>
 <script type="text/javascript" src="$relpath^dynsections.js"></script>
 $treeview
@@ -18,47 +32,53 @@
 $extrastylesheet
 </head>
 <body>
+<!--BEGIN DISABLE_INDEX-->
+  <!--BEGIN FULL_SIDEBAR-->
+<div id="side-nav" class="ui-resizable side-nav-resizable"><!-- do not remove this div, it is closed by doxygen! -->
+  <!--END FULL_SIDEBAR-->
+<!--END DISABLE_INDEX-->
+
 <div id="top"><!-- do not remove this div, it is closed by doxygen! -->
 
 <!--BEGIN TITLEAREA-->
 <div id="titlearea">
 <table cellspacing="0" cellpadding="0" width="100%">
  <tbody>
- <tr style="height: 56px;">
+ <tr id="projectrow">
   <!--BEGIN PROJECT_LOGO-->
   <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/></td>
   <!--END PROJECT_LOGO-->
   <!--BEGIN PROJECT_NAME-->
+  <!--<td id="projectalign">
+   <div id="projectname">$projectname<!--BEGIN PROJECT_NUMBER--><span id="projectnumber">&#160;$projectnumber</span><!--END PROJECT_NUMBER-->
+   </div>
+   <!--BEGIN PROJECT_BRIEF--><div id="projectbrief">$projectbrief</div><!--END PROJECT_BRIEF-->
+  </td>-->
   <!--END PROJECT_NAME-->
   <!--BEGIN !PROJECT_NAME-->
    <!--BEGIN PROJECT_BRIEF-->
-    <td style="padding-left: 0.5em;">
+    <td>
     <div id="projectbrief">$projectbrief</div>
     </td>
    <!--END PROJECT_BRIEF-->
   <!--END !PROJECT_NAME-->
   <!--BEGIN DISABLE_INDEX-->
    <!--BEGIN SEARCHENGINE-->
-   <td>$searchbox</td>
+     <!--BEGIN !FULL_SIDEBAR-->
+    <td>$searchbox</td>
+     <!--END !FULL_SIDEBAR-->
    <!--END SEARCHENGINE-->
   <!--END DISABLE_INDEX-->
-   <td id="gsearch">
-       <div><script>
-             (function() {
-                 var cx = '004356362924927882526:zup3ehe-7bs';
-                 var gcse = document.createElement('script');
-                 gcse.type = 'text/javascript';
-                 gcse.async = true;
-                 gcse.src = (document.location.protocol == 'https:' ? 'https:' : 'http:') +
-                            '//www.google.com/cse/cse.js?cx=' + cx;
-                 var s = document.getElementsByTagName('script')[0];
-                 s.parentNode.insertBefore(gcse, s);
-             })();
-       </script>
-       <gcse:search></gcse:search>
-       <div>
-   </td>
+    <td id="gsearch">
+        <script async src="https://cse.google.com/cse.js?cx=004356362924927882526:zup3ehe-7bs"></script>
+        <div class="gcse-search"></div>
+    </td>
  </tr>
+  <!--BEGIN SEARCHENGINE-->
+   <!--BEGIN FULL_SIDEBAR-->
+ <tr><td colspan="2">$searchbox</td></tr>
+   <!--END FULL_SIDEBAR-->
+  <!--END SEARCHENGINE-->
  </tbody>
 </table>
 </div>
diff --git a/docs/pages/install.md b/docs/pages/install.md
index 2cbabab9b9..7a78b95f71 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -20,13 +20,13 @@ OpenCL backend, you will need to have the OpenCL **runtime** installed on your
 system. Drivers and runtimes should be downloaded and installed from your device
 vendor’s website.
 
-# Install Instructions
+# Install Instructions {#InstallInstructions}
 
 * [Windows](#Windows)
 * [Linux](#Linux)
 * [macOS](#macOS)
 
-## <a name="Windows"></a> Windows
+## Windows {#Windows}
 
 Prior to installing ArrayFire on Windows,
 [download](https://www.microsoft.com/en-in/download/details.aspx?id=48145)
@@ -41,7 +41,7 @@ can find ArrayFire DLLs.
 For more information on using ArrayFire on Windows, visit the following
 [page](http://arrayfire.org/docs/using_on_windows.htm).
 
-## <a name="Linux"></a> Linux
+## Linux {#Linux}
 
 There are two ways to install ArrayFire on Linux.
 1. Package Manager
@@ -90,7 +90,7 @@ __Fedora, Redhat, CentOS__
     yum install freeimage fontconfig mesa-libGLU
 
 
-## <a name="macOS"></a> macOS
+## macOS {#macOS}
 
 Once you have downloaded the ArrayFire installer, execute the installer by
 either double clicking on the ArrayFire `pkg` file or running the following
diff --git a/docs/pages/using_on_linux.md b/docs/pages/using_on_linux.md
index 87cab953bc..4948763d77 100644
--- a/docs/pages/using_on_linux.md
+++ b/docs/pages/using_on_linux.md
@@ -8,7 +8,7 @@ requirements are that you include the ArrayFire header directories and link with
 the ArrayFire library you intend to use i.e. CUDA, OpenCL, CPU, or Unified
 backends.
 
-## <a name="big-picture"/> The big picture
+## The big picture  {#big-picture}
 
 On Linux, we recommend installing ArrayFire to `/opt/arrayfire` directory. The
 installer will populate files in the following sub-directories:
diff --git a/docs/pages/using_on_osx.md b/docs/pages/using_on_osx.md
index f5643e3f93..272898ec5e 100644
--- a/docs/pages/using_on_osx.md
+++ b/docs/pages/using_on_osx.md
@@ -30,7 +30,7 @@ CMake or Makefiles with CMake being our preferred build system.
 * [CMake](#CMake)
 * [Makefiles](#Makefiles)
 
-## <a name="CMake"></a>CMake
+## CMake {#CMake}
 
 The CMake build system can be used to create ArrayFire projects. As [discussed
 above](#big-picture), ArrayFire ships with a series of CMake scripts to make
@@ -80,7 +80,7 @@ you would modify the `cmake` command above to contain the following definition:
 
 You can also specify this information in the `ccmake` command-line interface.
 
-## <a name="Makefiles"></a> Makefiles
+## Makefiles {#Makefiles}
 
 Building ArrayFire projects with Makefiles is fairly similar to CMake except you
 must specify all paths and libraries manually.
diff --git a/docs/pages/using_on_windows.md b/docs/pages/using_on_windows.md
index 99d321b886..924fca2794 100644
--- a/docs/pages/using_on_windows.md
+++ b/docs/pages/using_on_windows.md
@@ -2,10 +2,9 @@ Using ArrayFire with Microsoft Windows and Visual Studio {#using_on_windows}
 ============================================================================
 
 If you have not already done so, please make sure you have installed,
-configured, and tested ArrayFire following the [installation instructions](\ref
-installing).
+configured, and tested ArrayFire following the [installation instructions](#installing).
 
-## The big picture
+# The big picture
 
 The ArrayFire Windows installer creates the following:
 1. **AF_PATH** environment variable to point to the installation location. The
@@ -26,12 +25,12 @@ If you chose not to modify PATH during installation please make sure to do so
 manually so that all applications using ArrayFire libraries will be able to find
 the required DLLs.
 
-## <a name="section1" />Build and Run Helloworld
+# Build and Run Helloworld {#section1}
 
 This can be done in two ways either by using CMake build tool or using Visual
 Studio directly.
 
-### <a name="section1part1"/> Using CMake
+##  Using CMake {#section1part1}
 1. Download and install [CMake](https://cmake.org/download/), preferrably the
    latest version.
 2. Open CMake-GUI and set the field __Where is the source code__ to the root
@@ -59,7 +58,7 @@ Studio directly.
 10. Once the helloworld example builds, you will see a console window with the
     output from helloworld program.
 
-### <a name="section1part2"/> Using Visual Studio
+## Using Visual Studio {#section1part2}
 
 1. Open Visual Studio of your choice and create an empty C++ project.
 2. Right click the project and add an existing source file
@@ -76,16 +75,16 @@ Studio directly.
 7. Build and run the project. You will see a console window with the output from
    helloworld program.
 
-## <a name="section2" />Using ArrayFire within Existing Visual Studio Projects
+# Using ArrayFire within Existing Visual Studio Projects {#section2}
 This is divided into three parts:
-* [Part A: Adding ArrayFire to an existing solution (Single
-  Backend)](#section3partA)
-* [Part B: Adding ArrayFire CUDA to a new/existing CUDA project](#section3partB)
-* [Part C: Project with all ArrayFire backends](#section3partC)
+* [Part A: Adding ArrayFire to an existing solution (Single Backend)](#section2partA)
+* [Part B: Adding ArrayFire CUDA to a new/existing CUDA project](#section2partB)
+* [Part C: Project with all ArrayFire backends](#section2partC)
+
+## Part A: Adding ArrayFire to an existing solution (Single Backend) {#section2partA}
 
-### <a name="section3partA" />Part A: Adding ArrayFire to an existing solution (Single Backend)
 Note: If you plan on using Native CUDA code in the project, use the steps under
-[Part B](#section3partB).
+[Part B](#section2partB).
 
 Adding a single backend to an existing project is quite simple.
 
@@ -97,7 +96,7 @@ Adding a single backend to an existing project is quite simple.
    Properties -> Linker -> Input -> Additional Dependencies_. based on your
    preferred backend.
 
-### <a name="section3partB" />Part B: Adding ArrayFire CUDA to a new/existing CUDA project
+## Part B: Adding ArrayFire CUDA to a new/existing CUDA project {#section2partB}
 Lastly, if your project contains custom CUDA code, the instructions are slightly
 different as it requires using a CUDA NVCC Project:
 
@@ -109,15 +108,15 @@ different as it requires using a CUDA NVCC Project:
 4. Add `afcpu.lib`, `afcuda.lib`, `afopencl.lib`, or `af.lib` to _Project Properties ->
    Linker -> Input -> Additional Dependencies_. based on your preferred backend.
 
-### <a name="section3partC" />Part C: Project with all ArrayFire backends
+### Part C: Project with all ArrayFire backends {#section2partC}
 If you wish to create a project that allows you to use all the ArrayFire
 backends with ease, you should use `af.lib` in step 3 from [Part
-A](#section3partA).
+A](#section2partA).
 
 You can alternately download the template project from [ArrayFire Template
 Projects](https://github.com/arrayfire/arrayfire-project-templates)
 
-## <a name="section4" />Using ArrayFire with CMake
+# <a name="section3" />Using ArrayFire with CMake
 ArrayFire ships with a series of CMake scripts to make finding and using our
 library easy.
 

From bcda3cdbc245b4c24e78efdb5285c8241f6516c5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 24 Feb 2022 13:56:52 -0500
Subject: [PATCH 2213/2677] Move 3.8.1 release notes to master branch.

This should have been included in the master before being backported
---
 docs/pages/release_notes.md | 64 +++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 571f37801f..259b927772 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,70 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.8.1
+======
+
+## Improvements
+
+- moddims now uses JIT approach for certain special cases - \PR{3177}
+- Embed Version Info in Windows DLLs - \PR{3025} 
+- OpenCL device max parameter is now queries from device properties - \PR{3032} 
+- JIT Performance Optimization: Unique funcName generation sped up - \PR{3040} 
+- Improved readability of log traces  - \PR{3050} 
+- Use short function name in non-debug build error messages - \PR{3060} 
+- SIFT/GLOH are now available as part of website binaries - \PR{3071} 
+- Short-circuit zero elements case in detail::copyArray backend function - \PR{3059} 
+- Speedup of kernel caching mechanism - \PR{3043} 
+- Add short-circuit check for empty Arrays in JIT evalNodes - \PR{3072} 
+- Performance optimization of indexing using dynamic thread block sizes - \PR{3111} 
+- ArrayFire starting with this release will use Intel MKL single dynamic library which resolves lot of linking issues unified library had when user applications used MKL themselves - \PR{3120} 
+- Add shortcut check for zero elements in af_write_array - \PR{3130} 
+- Speedup join by eliminating temp buffers for cascading joins - \PR{3145} 
+- Added batch support for solve - \PR{1705} 
+- Use pinned memory to copy device pointers in CUDA solve - \PR{1705} 
+- Added package manager instructions to docs - \PR{3076} 
+- CMake Build Improvements - \PR{3027} , \PR{3089} , \PR{3037} , \PR{3072} , \PR{3095} , \PR{3096} , \PR{3097} , \PR{3102} , \PR{3106} , \PR{3105} , \PR{3120} , \PR{3136} , \PR{3135} , \PR{3137} , \PR{3119} , \PR{3150} , \PR{3138} , \PR{3156} , \PR{3139} , \PR{1705} , \PR{3162} 
+- CPU backend improvements - \PR{3010} , \PR{3138} , \PR{3161} 
+- CUDA backend improvements - \PR{3066} , \PR{3091} , \PR{3093} , \PR{3125} , \PR{3143} , \PR{3161} 
+- OpenCL backend improvements - \PR{3091} , \PR{3068} , \PR{3127} , \PR{3010} , \PR{3039} , \PR{3138} , \PR{3161} 
+- General(including JIT) performance improvements across backends - \PR{3167} 
+- Testing improvements - \PR{3072} , \PR{3131} , \PR{3151} , \PR{3141} , \PR{3153} , \PR{3152} , \PR{3157} , \PR{1705} , \PR{3170} , \PR{3167} 
+- Update CLBlast to latest version - \PR{3135} , \PR{3179} 
+- Improved Otsu threshold computation helper in canny algorithm - \PR{3169} 
+- Modified default parameters for fftR2C and fftC2R C++ API from 0 to 1.0 - \PR{3178} 
+- Use appropriate MKL getrs_batch_strided API based on MKL Versions - \PR{3181} 
+
+## Fixes
+
+- Fixed a bug JIT kernel disk caching - \PR{3182} 
+- Fixed stream used by thrust(CUDA backend) functions - \PR{3029}  
+- Added workaround for new cuSparse API that was added by CUDA amid fix releases - \PR{3057} 
+- Fixed `const` array indexing inside `gfor` - \PR{3078} 
+- Handle zero elements in copyData to host - \PR{3059} 
+- Fixed double free regression in OpenCL backend - \PR{3091} 
+- Fixed an infinite recursion bug in NaryNode JIT Node - \PR{3072} 
+- Added missing input validation check in sparse-dense arithmetic operations - \PR{3129} 
+- Fixed bug in `getMappedPtr` in OpenCL due to invalid lambda capture - \PR{3163} 
+- Fixed bug in `getMappedPtr` on Arrays that are not ready - \PR{3163} 
+- Fixed edgeTraceKernel for CPU devices on OpenCL backend - \PR{3164} 
+- Fixed windows build issue(s) with VS2019 - \PR{3048}
+- API documentation fixes - \PR{3075} , \PR{3076} , \PR{3143} , \PR{3161} 
+- CMake Build Fixes - \PR{3088} 
+- Fixed the tutorial link in README - \PR{3033} 
+- Fixed function name typo in timing tutorial - \PR{3028} 
+- Fixed couple of bugs in CPU backend canny implementation - \PR{3169} 
+- Fixed reference count of array(s) used in JIT operations. It is related to arrayfire's internal memory book keeping. The behavior/accuracy of arrayfire code wasn't broken earlier. It corrected the reference count to be of optimal value in the said scenarios. This may potentially reduce memory usage in some narrow cases - \PR{3167} 
+- Added assert that checks if topk is called with a negative value for k - \PR{3176} 
+- Fixed an Issue where countByKey would give incorrect results for any n > 128 - \PR{3175} 
+
+## Contributions
+
+Special thanks to our contributors:
+[HO-COOH][https://github.com/HO-COOH]
+[Willy Born][https://github.com/willyborn]
+[Gilad Avidov][https://github.com/avidov]
+[Pavan Yalamanchili][https://github.com/pavanky]
+
 v3.8.0
 ======
 

From 259c2ffcc58684b47dda3d93d5be2eda11e00394 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 3 Feb 2022 19:20:28 -0500
Subject: [PATCH 2214/2677] handles empty arrays in join_many

---
 include/af/data.h           | 10 +++++++
 src/api/c/join.cpp          | 56 ++++++++++++++++++++++++++++++++-----
 src/api/c/rgb_gray.cpp      |  6 +++-
 src/api/c/surface.cpp       |  7 ++++-
 src/api/c/vector_field.cpp  | 10 +++++--
 src/api/c/ycbcr_rgb.cpp     | 11 ++++++--
 src/backend/cpu/join.cpp    | 29 +++----------------
 src/backend/cpu/join.hpp    |  2 +-
 src/backend/cuda/join.cpp   | 30 +++-----------------
 src/backend/cuda/join.hpp   |  2 +-
 src/backend/opencl/join.cpp | 29 +++----------------
 src/backend/opencl/join.hpp |  2 +-
 test/join.cpp               | 20 +++++++++++++
 13 files changed, 122 insertions(+), 92 deletions(-)

diff --git a/include/af/data.h b/include/af/data.h
index 52ebb78ed7..6da90fe801 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -200,6 +200,8 @@ namespace af
         \param[in] second is the second input array
         \return the array that joins input arrays along the given dimension
 
+        \note empty arrays will be ignored
+
         \ingroup manip_func_join
     */
     AFAPI array join(const int dim, const array &first, const array &second);
@@ -213,6 +215,8 @@ namespace af
         \param[in] third is the third input array
         \return the array that joins input arrays along the given dimension
 
+        \note empty arrays will be ignored
+
         \ingroup manip_func_join
     */
     AFAPI array join(const int dim, const array &first, const array &second, const array &third);
@@ -227,6 +231,8 @@ namespace af
         \param[in] fourth is the fourth input array
         \return the array that joins input arrays along the given dimension
 
+        \note empty arrays will be ignored
+
         \ingroup manip_func_join
     */
     AFAPI array join(const int dim, const array &first, const array &second,
@@ -622,6 +628,8 @@ extern "C" {
         \param[in] first is the first input array
         \param[in] second is the second input array
 
+        \note empty arrays will be ignored
+
         \ingroup manip_func_join
     */
     AFAPI af_err af_join(af_array *out, const int dim, const af_array first, const af_array second);
@@ -636,6 +644,8 @@ extern "C" {
         \param[in] n_arrays number of arrays to join
         \param[in] inputs is an array of af_arrays containing handles to the arrays to be joined
 
+        \note empty arrays will be ignored
+
         \ingroup manip_func_join
     */
     AFAPI af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs);
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 79e45d3f9f..dad2bc1ffd 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -14,6 +14,7 @@
 #include <handle.hpp>
 #include <join.hpp>
 #include <af/data.h>
+#include <algorithm>
 #include <vector>
 
 using af::dim4;
@@ -21,6 +22,7 @@ using common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
+using detail::createEmptyArray;
 using detail::intl;
 using detail::uchar;
 using detail::uint;
@@ -43,8 +45,30 @@ static inline af_array join_many(const int dim, const unsigned n_arrays,
 
     for (unsigned i = 0; i < n_arrays; i++) {
         inputs_.push_back(getArray<T>(inputs[i]));
+        if (inputs_.back().isEmpty()) { inputs_.pop_back(); }
     }
-    return getHandle(join<T>(dim, inputs_));
+
+    // All dimensions except join dimension must be equal
+    // calculate odims size
+    std::vector<af::dim4> idims(inputs_.size());
+    dim_t dim_size = 0;
+    for (unsigned i = 0; i < idims.size(); i++) {
+        idims[i] = inputs_[i].dims();
+        dim_size += idims[i][dim];
+    }
+
+    af::dim4 odims;
+    for (int i = 0; i < 4; i++) {
+        if (i == dim) {
+            odims[i] = dim_size;
+        } else {
+            odims[i] = idims[0][i];
+        }
+    }
+
+    Array<T> out = createEmptyArray<T>(odims);
+    join<T>(out, dim, inputs_);
+    return getHandle(out);
 }
 
 af_err af_join(af_array *out, const int dim, const af_array first,
@@ -117,24 +141,42 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
 
+        bool allEmpty = std::all_of(
+            info.begin(), info.end(),
+            [](const ArrayInfo &i) -> bool { return i.elements() <= 0; });
+        if (allEmpty) {
+            af_array ret = nullptr;
+            AF_CHECK(af_retain_array(&ret, inputs[0]));
+            std::swap(*out, ret);
+            return AF_SUCCESS;
+        }
+
+        auto first_valid_afinfo = std::find_if(
+            info.begin(), info.end(),
+            [](const ArrayInfo &i) -> bool { return i.elements() > 0; });
+
+        af_dtype assertType = first_valid_afinfo->getType();
         for (unsigned i = 1; i < n_arrays; i++) {
-            ARG_ASSERT(3, info[0].getType() == info[i].getType());
-            DIM_ASSERT(3, info[i].elements() > 0);
+            if (info[i].elements() > 0) {
+                ARG_ASSERT(3, assertType == info[i].getType());
+            }
         }
 
         // All dimensions except join dimension must be equal
-        // Compute output dims
+        af::dim4 assertDims = first_valid_afinfo->dims();
         for (int i = 0; i < 4; i++) {
             if (i != dim) {
-                for (unsigned j = 1; j < n_arrays; j++) {
-                    DIM_ASSERT(3, dims[0][i] == dims[j][i]);
+                for (unsigned j = 0; j < n_arrays; j++) {
+                    if (info[j].elements() > 0) {
+                        DIM_ASSERT(3, assertDims[i] == dims[j][i]);
+                    }
                 }
             }
         }
 
         af_array output;
 
-        switch (info[0].getType()) {
+        switch (assertType) {
             case f32: output = join_many<float>(dim, n_arrays, inputs); break;
             case c32: output = join_many<cfloat>(dim, n_arrays, inputs); break;
             case f64: output = join_many<double>(dim, n_arrays, inputs); break;
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index e801881447..635474e846 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -26,6 +26,7 @@ using af::dim4;
 using common::cast;
 using detail::arithOp;
 using detail::Array;
+using detail::createEmptyArray;
 using detail::createValueArray;
 using detail::join;
 using detail::scalar;
@@ -96,7 +97,10 @@ static af_array gray2rgb(const af_array& in, const float r, const float g,
     AF_CHECK(af_release_array(mod_input));
 
     // join channels
-    return getHandle(join<cType>(2, {expr3, expr1, expr2}));
+    dim4 odims(expr1.dims()[0], expr1.dims()[1], 3);
+    Array<cType> out = createEmptyArray<cType>(odims);
+    join<cType>(out, 2, {expr3, expr1, expr2});
+    return getHandle(out);
 }
 
 template<typename T, typename cType, bool isRGB2GRAY>
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 92e916e2f4..986cedae09 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -26,6 +26,7 @@ using af::dim4;
 using common::modDims;
 using detail::Array;
 using detail::copy_surface;
+using detail::createEmptyArray;
 using detail::forgeManager;
 using detail::reduce_all;
 using detail::uchar;
@@ -72,7 +73,11 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
 
     // Now join along first dimension, skip reorder
     std::vector<Array<T>> inputs{xIn, yIn, zIn};
-    Array<T> Z = join(0, inputs);
+
+    dim4 odims(3, rowDims[1]);
+    Array<T> out = createEmptyArray<T>(odims);
+    join(out, 0, inputs);
+    Array<T> Z = out;
 
     ForgeManager& fgMngr = forgeManager();
 
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index c2f764c5c7..fa48328462 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -25,6 +25,7 @@
 using af::dim4;
 using detail::Array;
 using detail::copy_vector_field;
+using detail::createEmptyArray;
 using detail::forgeManager;
 using detail::reduce;
 using detail::transpose;
@@ -50,8 +51,13 @@ fg_chart setup_vector_field(fg_window window, const vector<af_array>& points,
     }
 
     // Join for set up vector
-    Array<T> pIn = detail::join(1, pnts);
-    Array<T> dIn = detail::join(1, dirs);
+    dim4 odims(3, points.size());
+    Array<T> out_pnts = createEmptyArray<T>(odims);
+    Array<T> out_dirs = createEmptyArray<T>(odims);
+    detail::join(out_pnts, 1, pnts);
+    detail::join(out_dirs, 1, dirs);
+    Array<T> pIn = out_pnts;
+    Array<T> dIn = out_dirs;
 
     // do transpose if required
     if (transpose_) {
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index b5beee4fae..d3c56a7117 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -20,6 +20,7 @@
 using af::dim4;
 using detail::arithOp;
 using detail::Array;
+using detail::createEmptyArray;
 using detail::createValueArray;
 using detail::join;
 using detail::scalar;
@@ -108,7 +109,10 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
                    INV_112 * (kb - 1) * kb * invKl);
         Array<T> B = mix<T>(Y_, Cb_, INV_219, INV_112 * (1 - kb));
         // join channels
-        return getHandle(join<T>(2, {R, G, B}));
+        dim4 odims(R.dims()[0], R.dims()[1], 3);
+        Array<T> rgbout = createEmptyArray<T>(odims);
+        join<T>(rgbout, 2, {R, G, B});
+        return getHandle(rgbout);
     }
     Array<T> Ey = mix<T>(X, Y, Z, kr, kl, kb);
     Array<T> Ecr =
@@ -119,7 +123,10 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
     Array<T> Cr = digitize<T>(Ecr, 224.0, 128.0);
     Array<T> Cb = digitize<T>(Ecb, 224.0, 128.0);
     // join channels
-    return getHandle(join<T>(2, {Y_, Cb, Cr}));
+    dim4 odims(Y_.dims()[0], Y_.dims()[1], 3);
+    Array<T> ycbcrout = createEmptyArray<T>(odims);
+    join<T>(ycbcrout, 2, {Y_, Cb, Cr});
+    return getHandle(ycbcrout);
 }
 
 template<bool isYCbCr2RGB>
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 5b9382ee25..52f73747e2 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -44,26 +44,8 @@ Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
 }
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
-    // All dimensions except join dimension must be equal
-    // Compute output dims
-    af::dim4 odims;
+void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs) {
     const dim_t n_arrays = inputs.size();
-    std::vector<af::dim4> idims(n_arrays);
-
-    dim_t dim_size = 0;
-    for (unsigned i = 0; i < idims.size(); i++) {
-        idims[i] = inputs[i].dims();
-        dim_size += idims[i][dim];
-    }
-
-    for (int i = 0; i < 4; i++) {
-        if (i == dim) {
-            odims[i] = dim_size;
-        } else {
-            odims[i] = idims[0][i];
-        }
-    }
 
     std::vector<Array<T> *> input_ptrs(inputs.size());
     std::transform(
@@ -71,11 +53,8 @@ Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
         [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
     evalMultiple(input_ptrs);
     std::vector<CParam<T>> inputParams(inputs.begin(), inputs.end());
-    Array<T> out = createEmptyArray<T>(odims);
 
     getQueue().enqueue(kernel::join<T>, dim, out, inputParams, n_arrays);
-
-    return out;
 }
 
 #define INSTANTIATE(T)                                              \
@@ -98,9 +77,9 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                       \
-    template Array<T> join<T>(const int dim, \
-                              const std::vector<Array<T>> &inputs);
+#define INSTANTIATE(T)                                   \
+    template void join<T>(Array<T> & out, const int dim, \
+                          const std::vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/join.hpp b/src/backend/cpu/join.hpp
index 622e70c742..efabe9c8a5 100644
--- a/src/backend/cpu/join.hpp
+++ b/src/backend/cpu/join.hpp
@@ -15,5 +15,5 @@ template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
+void join(Array<T> &output, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace cpu
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 47f5a56205..880716e22b 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -69,36 +69,14 @@ void join_wrapper(const int dim, Array<T> &out,
 }
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs) {
-    // All dimensions except join dimension must be equal
-    // Compute output dims
-    af::dim4 odims;
-    const dim_t n_arrays = inputs.size();
-    std::vector<af::dim4> idims(n_arrays);
-
-    dim_t dim_size = 0;
-    for (size_t i = 0; i < idims.size(); i++) {
-        idims[i] = inputs[i].dims();
-        dim_size += idims[i][dim];
-    }
-
-    for (int i = 0; i < 4; i++) {
-        if (i == dim) {
-            odims[i] = dim_size;
-        } else {
-            odims[i] = idims[0][i];
-        }
-    }
-
+void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs) {
     std::vector<Array<T> *> input_ptrs(inputs.size());
     std::transform(
         begin(inputs), end(inputs), begin(input_ptrs),
         [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
     evalMultiple(input_ptrs);
-    Array<T> out = createEmptyArray<T>(odims);
 
     join_wrapper<T>(dim, out, inputs);
-    return out;
 }
 
 #define INSTANTIATE(T)                                              \
@@ -121,9 +99,9 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                       \
-    template Array<T> join<T>(const int dim, \
-                              const std::vector<Array<T>> &inputs);
+#define INSTANTIATE(T)                                   \
+    template void join<T>(Array<T> & out, const int dim, \
+                          const std::vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/join.hpp b/src/backend/cuda/join.hpp
index 7f88e5cad1..cf74076b8a 100644
--- a/src/backend/cuda/join.hpp
+++ b/src/backend/cuda/join.hpp
@@ -14,5 +14,5 @@ template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
+void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace cuda
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 162229af7f..0c7109a895 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -72,37 +72,15 @@ void join_wrapper(const int dim, Array<T> &out,
 }
 
 template<typename T>
-Array<T> join(const int dim, const vector<Array<T>> &inputs) {
-    // All dimensions except join dimension must be equal
-    // Compute output dims
-    dim4 odims;
-    const dim_t n_arrays = inputs.size();
-    vector<dim4> idims(n_arrays);
-
-    dim_t dim_size = 0;
-    for (size_t i = 0; i < idims.size(); i++) {
-        idims[i] = inputs[i].dims();
-        dim_size += idims[i][dim];
-    }
-
-    for (int i = 0; i < 4; i++) {
-        if (i == dim) {
-            odims[i] = dim_size;
-        } else {
-            odims[i] = idims[0][i];
-        }
-    }
-
+void join(Array<T> &out, const int dim, const vector<Array<T>> &inputs) {
     vector<Array<T> *> input_ptrs(inputs.size());
     transform(
         begin(inputs), end(inputs), begin(input_ptrs),
         [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
     evalMultiple(input_ptrs);
     vector<Param> inputParams(inputs.begin(), inputs.end());
-    Array<T> out = createEmptyArray<T>(odims);
 
     join_wrapper<T>(dim, out, inputs);
-    return out;
 }
 
 #define INSTANTIATE(T)                                              \
@@ -125,8 +103,9 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T) \
-    template Array<T> join<T>(const int dim, const vector<Array<T>> &inputs);
+#define INSTANTIATE(T)                                   \
+    template void join<T>(Array<T> & out, const int dim, \
+                          const vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/join.hpp b/src/backend/opencl/join.hpp
index 2f05a4fcf9..ea101d03f2 100644
--- a/src/backend/opencl/join.hpp
+++ b/src/backend/opencl/join.hpp
@@ -14,5 +14,5 @@ template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 
 template<typename T>
-Array<T> join(const int dim, const std::vector<Array<T>> &inputs);
+void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace opencl
diff --git a/test/join.cpp b/test/join.cpp
index 0024fe5542..4a98763b9b 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -246,3 +246,23 @@ TEST(Join, SameSize) {
 
     ASSERT_VEC_ARRAY_EQ(hgold, dim4(10 + 10 + 10), d);
 }
+
+TEST(Join, ManyEmpty) {
+    array gold = af::constant(0, 15, 5);
+    array a    = af::randn(5, 5);
+    array e;
+    array c  = af::randn(10, 5);
+    array ee = af::join(0, e, e);
+    ASSERT_EQ(ee.elements(), 0);
+    array eee = af::join(0, e, e, e);
+    ASSERT_EQ(eee.elements(), 0);
+
+    array eeac                     = af::join(0, e, e, a, c);
+    array eace                     = af::join(0, e, a, c, e);
+    array acee                     = af::join(0, a, c, e, e);
+    gold(af::seq(0, 4), af::span)  = a;
+    gold(af::seq(5, 14), af::span) = c;
+    ASSERT_ARRAYS_EQ(gold, eeac);
+    ASSERT_ARRAYS_EQ(gold, eace);
+    ASSERT_ARRAYS_EQ(gold, acee);
+}

From 60277cf173881e3a7aa8530db5b68649078bdce7 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 9 Feb 2022 22:25:53 -0500
Subject: [PATCH 2215/2677] fixes missing glfw with AF_BUILD_FORGE

---
 CMakeModules/AFconfigure_forge_dep.cmake | 66 ++++++++++++------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index 162e26c3ee..f15014e72b 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -8,34 +8,16 @@
 set(FG_VERSION_MAJOR 1)
 set(FG_VERSION_MINOR 0)
 set(FG_VERSION_PATCH 8)
+set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
+set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
 
-find_package(Forge
-  ${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}
-  QUIET
-)
 
-if(TARGET Forge::forge)
-  get_target_property(fg_lib_type Forge::forge TYPE)
-  if(NOT ${fg_lib_type} STREQUAL "STATIC_LIBRARY")
-      install(FILES
-          $<TARGET_FILE:Forge::forge>
-          $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:Forge::forge>>
-          $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:Forge::forge>>
-          $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:Forge::forge>>
-          $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:Forge::forge>>
-          DESTINATION "${AF_INSTALL_LIB_DIR}"
-          COMPONENT common_backend_dependencies)
-  endif()
-else()
-  set(FG_VERSION "${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}")
-  set(FG_API_VERSION_CURRENT ${FG_VERSION_MAJOR}${FG_VERSION_MINOR})
+if(AF_BUILD_FORGE)
+    af_dep_check_and_populate(${forge_prefix}
+        URI https://github.com/arrayfire/forge.git
+        REF "v${FG_VERSION}"
+    )
 
-  af_dep_check_and_populate(${forge_prefix}
-    URI https://github.com/arrayfire/forge.git
-    REF "v${FG_VERSION}"
-  )
-
-  if(AF_BUILD_FORGE)
     set(af_FETCHCONTENT_BASE_DIR ${FETCHCONTENT_BASE_DIR})
     set(af_FETCHCONTENT_QUIET ${FETCHCONTENT_QUIET})
     set(af_FETCHCONTENT_FULLY_DISCONNECTED ${FETCHCONTENT_FULLY_DISCONNECTED})
@@ -67,9 +49,9 @@ else()
     set(FETCHCONTENT_QUIET ${af_FETCHCONTENT_QUIET})
     set(FETCHCONTENT_FULLY_DISCONNECTED ${af_FETCHCONTENT_FULLY_DISCONNECTED})
     set(FETCHCONTENT_UPDATES_DISCONNECTED ${af_FETCHCONTENT_UPDATES_DISCONNECTED})
-
     install(FILES
         $<TARGET_FILE:forge>
+        $<TARGET_RUNTIME_DLLS:forge>
         $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
         $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
         $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:forge>>
@@ -77,10 +59,28 @@ else()
         DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT common_backend_dependencies)
     set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
-  else(AF_BUILD_FORGE)
-    configure_file(
-      ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
-      ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
-      )
-  endif(AF_BUILD_FORGE)
-endif()
+else(AF_BUILD_FORGE)
+    find_package(Forge
+	    ${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}
+	    QUIET
+    )
+
+    if(TARGET Forge::forge)
+        get_target_property(fg_lib_type Forge::forge TYPE)
+        if(NOT ${fg_lib_type} STREQUAL "STATIC_LIBRARY")
+            install(FILES
+                    $<TARGET_FILE:Forge::forge>
+                    $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:Forge::forge>>
+                    $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:Forge::forge>>
+                    $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:Forge::forge>>
+                    $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:Forge::forge>>
+                    DESTINATION "${AF_INSTALL_LIB_DIR}"
+                    COMPONENT common_backend_dependencies)
+        endif()
+    else()
+        configure_file(
+		  ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
+		  ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
+		  )
+    endif()
+endif(AF_BUILD_FORGE)

From d596bf79cdb26246f5ed36f3d34c5a895674c464 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 11 Feb 2022 16:31:11 -0500
Subject: [PATCH 2216/2677] fix intel defaults in ci workflows, fix
 configure_file for non-building forge

---
 .github/workflows/release_src_artifact.yml |  2 +-
 CMakeModules/AFconfigure_forge_dep.cmake   | 15 ++++++++++-----
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/release_src_artifact.yml b/.github/workflows/release_src_artifact.yml
index 273c7a9249..c616c8db5b 100644
--- a/.github/workflows/release_src_artifact.yml
+++ b/.github/workflows/release_src_artifact.yml
@@ -46,7 +46,7 @@ jobs:
               run: |
                   cd ${GITHUB_WORKSPACE}/arrayfire-full-${AF_VER}
                   mkdir build && cd build
-                  cmake .. -DAF_BUILD_FORGE:BOOL=ON
+                  cmake .. -DAF_BUILD_FORGE:BOOL=ON -DAF_COMPUTE_LIBRARY="FFTW/LAPACK/BLAS"
 
             - name: Create source tarball
               id: create-src-tarball
diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index f15014e72b..0b3352cf12 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -61,8 +61,8 @@ if(AF_BUILD_FORGE)
     set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
 else(AF_BUILD_FORGE)
     find_package(Forge
-	    ${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}
-	    QUIET
+        ${FG_VERSION_MAJOR}.${FG_VERSION_MINOR}.${FG_VERSION_PATCH}
+        QUIET
     )
 
     if(TARGET Forge::forge)
@@ -78,9 +78,14 @@ else(AF_BUILD_FORGE)
                     COMPONENT common_backend_dependencies)
         endif()
     else()
+        af_dep_check_and_populate(${forge_prefix}
+            URI https://github.com/arrayfire/forge.git
+            REF "v${FG_VERSION}"
+        )
+
         configure_file(
-		  ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
-		  ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
-		  )
+            ${${forge_prefix}_SOURCE_DIR}/CMakeModules/version.h.in
+            ${${forge_prefix}_BINARY_DIR}/include/fg/version.h
+        )
     endif()
 endif(AF_BUILD_FORGE)

From f4dc55c18b092209d05acfe5ba24537a2b0c4095 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 3 Mar 2022 15:18:48 -0500
Subject: [PATCH 2217/2677] check cmake version for TARGET_RUNETIME_DLLS
 generator

---
 CMakeModules/AFconfigure_forge_dep.cmake | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index 0b3352cf12..6944d9e9f1 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -51,13 +51,21 @@ if(AF_BUILD_FORGE)
     set(FETCHCONTENT_UPDATES_DISCONNECTED ${af_FETCHCONTENT_UPDATES_DISCONNECTED})
     install(FILES
         $<TARGET_FILE:forge>
-        $<TARGET_RUNTIME_DLLS:forge>
         $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:forge>>
         $<$<PLATFORM_ID:Darwin>:$<TARGET_SONAME_FILE:forge>>
         $<$<PLATFORM_ID:Linux>:$<TARGET_LINKER_FILE:forge>>
         $<$<PLATFORM_ID:Darwin>:$<TARGET_LINKER_FILE:forge>>
         DESTINATION "${AF_INSTALL_LIB_DIR}"
         COMPONENT common_backend_dependencies)
+
+    if(AF_INSTALL_STANDALONE)
+        cmake_minimum_required(VERSION 3.21)
+        install(FILES
+            $<TARGET_RUNTIME_DLLS:forge>
+            DESTINATION "${AF_INSTALL_LIB_DIR}"
+            COMPONENT common_backend_dependencies)
+    endif(AF_INSTALL_STANDALONE)
+
     set_property(TARGET forge APPEND_STRING PROPERTY COMPILE_FLAGS " -w")
 else(AF_BUILD_FORGE)
     find_package(Forge

From fcaa40caa9b98b49cf71f7b678ca0dc0914568fe Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 8 Mar 2022 17:51:58 -0500
Subject: [PATCH 2218/2677] Set AF_COMPUTE_LIBRARY to MKL only if found.

---
 CMakeLists.txt | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 129927c0d2..db3b8978d8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -75,7 +75,12 @@ option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
 option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
 option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
 
-set(AF_COMPUTE_LIBRARY "Intel-MKL"
+set(default_compute_library "FFTW/LAPACK/BLAS")
+if(MKL_FOUND)
+  set(default_compute_library "Intel-MKL")
+endif()
+
+set(AF_COMPUTE_LIBRARY ${default_compute_library}
     CACHE STRING "Compute library for signal processing and linear algebra routines")
 set_property(CACHE AF_COMPUTE_LIBRARY
     PROPERTY STRINGS "Intel-MKL" "FFTW/LAPACK/BLAS")

From 27d424532bd2d06e1f10656f5826a3a9c55bc452 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 10 Mar 2022 10:59:21 -0500
Subject: [PATCH 2219/2677] fix multiprocess filename collisions in imageio
 tests (#3204)

* fix multiprocess filename collisions in imageio
* change imageio names to include backend to avoid collisions
---
 test/arrayfire_test.cpp | 16 ++++++++++++++++
 test/imageio.cpp        | 27 +++++++++++++++++++--------
 test/testHelpers.hpp    |  3 +++
 3 files changed, 38 insertions(+), 8 deletions(-)

diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 63896a791a..a7d823e040 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -100,6 +100,22 @@ std::string readNextNonEmptyLine(std::ifstream &file) {
     return result;
 }
 
+std::string getBackendName() {
+    af::Backend backend = af::getActiveBackend();
+    if (backend == AF_BACKEND_OPENCL)
+        return std::string("opencl");
+    else if (backend == AF_BACKEND_CUDA)
+        return std::string("cuda");
+
+    return std::string("cpu");
+}
+
+std::string getTestName() {
+    std::string testname =
+        ::testing::UnitTest::GetInstance()->current_test_info()->name();
+    return testname;
+}
+
 namespace half_float {
 std::ostream &operator<<(std::ostream &os, half_float::half val) {
     os << (float)val;
diff --git a/test/imageio.cpp b/test/imageio.cpp
index cd66348b9f..9dc85a5865 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -160,8 +160,11 @@ TEST(ImageIO, SavePNGCPP) {
     input(9, 0, 2)          = 255;
     input(9, 9, span)       = 255;
 
-    saveImage("SaveCPP.png", input);
-    array out = loadImage("SaveCPP.png", true);
+    std::string testname  = getTestName() + "_" + getBackendName();
+    std::string imagename = "SaveCPP_" + testname + ".png";
+
+    saveImage(imagename.c_str(), input);
+    array out = loadImage(imagename.c_str(), true);
 
     ASSERT_FALSE(anyTrue<bool>(out - input));
 }
@@ -177,8 +180,11 @@ TEST(ImageIO, SaveBMPCPP) {
     input(9, 0, 2)          = 255;
     input(9, 9, span)       = 255;
 
-    saveImage("SaveCPP.bmp", input);
-    array out = loadImage("SaveCPP.bmp", true);
+    std::string testname  = getTestName() + "_" + getBackendName();
+    std::string imagename = "SaveCPP_" + testname + ".bmp";
+
+    saveImage(imagename.c_str(), input);
+    array out = loadImage(imagename.c_str(), true);
 
     ASSERT_FALSE(anyTrue<bool>(out - input));
 }
@@ -285,9 +291,12 @@ TEST(ImageIO, SaveImage16CPP) {
     array input     = randu(dims, u16);
     array input_255 = (input / 257).as(u16);
 
-    saveImage("saveImage16CPP.png", input);
+    std::string testname  = getTestName() + "_" + getBackendName();
+    std::string imagename = "saveImage16CPP_" + testname + ".png";
 
-    array img = loadImage("saveImage16CPP.png", true);
+    saveImage(imagename.c_str(), input);
+
+    array img = loadImage(imagename.c_str(), true);
     ASSERT_EQ(img.type(), f32);  // loadImage should always return float
 
     ASSERT_FALSE(anyTrue<bool>(abs(img - input_255)));
@@ -357,9 +366,11 @@ void saveLoadImageNativeCPPTest(dim4 dims) {
 
     array input = randu(dims, (af_dtype)dtype_traits<T>::af_type);
 
-    saveImageNative("saveImageNative.png", input);
+    std::string imagename = getTestName() + "_" + getBackendName() + ".png";
+
+    saveImageNative(imagename.c_str(), input);
 
-    array loaded = loadImageNative("saveImageNative.png");
+    array loaded = loadImageNative(imagename.c_str());
     ASSERT_EQ(loaded.type(), input.type());
 
     ASSERT_FALSE(anyTrue<bool>(input - loaded));
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 024b46657f..2e13ff9bbf 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -69,6 +69,9 @@ typedef unsigned char uchar;
 typedef unsigned int uint;
 typedef unsigned short ushort;
 
+std::string getBackendName();
+std::string getTestName();
+
 std::string readNextNonEmptyLine(std::ifstream &file);
 
 namespace half_float {

From e7625d1eee9a7f916dba9716937c00c5a4576d0d Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Fri, 5 Nov 2021 19:20:39 +0100
Subject: [PATCH 2220/2677] Improved precision of timeit

---
 src/api/cpp/timing.cpp | 66 ++++++++++++++++++++----------------------
 1 file changed, 31 insertions(+), 35 deletions(-)

diff --git a/src/api/cpp/timing.cpp b/src/api/cpp/timing.cpp
index 847c8d7873..285cb0cdb9 100644
--- a/src/api/cpp/timing.cpp
+++ b/src/api/cpp/timing.cpp
@@ -10,6 +10,7 @@
 #include <af/device.h>
 #include <af/timing.h>
 #include <algorithm>
+#include <array>
 #include <cmath>
 #include <vector>
 
@@ -71,43 +72,38 @@ double timer::stop(timer start) { return time_seconds(start, time_now()); }
 double timer::stop() { return time_seconds(_timer_, time_now()); }
 
 double timeit(void (*fn)()) {
-    // parameters
-    static const int trials      = 10;  // trial runs
-    static const int s_trials    = 5;   // trial runs
-    static const double min_time = 1;   // seconds
+    // Minimum target duration to limit impact of clock precision
+    constexpr double targetDurationPerTest = 0.050;
+    // samples during which the nr of cycles are determined to obtain target
+    // duration
+    constexpr int testSamples = 2;
+    // cycles needed to include CPU-GPU overlapping (if present)
+    constexpr int minCycles = 3;
+    // initial cycles used for the test samples
+    int cycles = minCycles;
+    // total number of real samples taken, of which the median is returned
+    constexpr int nrSamples = 10;
 
-    std::vector<double> sample_times(s_trials);
-
-    // estimate time for a few samples
-    for (int i = 0; i < s_trials; ++i) {
-        sync();
-        timer start = timer::start();
-        fn();
-        sync();
-        sample_times[i] = timer::stop(start);
-    }
-
-    // Sort sample times and select the median time
-    std::sort(sample_times.begin(), sample_times.end());
-
-    double median_time = sample_times[s_trials / 2];
-
-    // Run a bunch of batches of fn
-    // Each batch runs trial runs before sync
-    // If trials * median_time < min time,
-    //   then run (min time / (trials * median_time)) batches
-    // else
-    //   run 1 batch
-    int batches = static_cast<int>(ceilf(min_time / (trials * median_time)));
-    double run_time = 0;
-
-    for (int b = 0; b < batches; b++) {
-        timer start = timer::start();
-        for (int i = 0; i < trials; ++i) { fn(); }
-        sync();
-        run_time += timer::stop(start) / trials;
+    std::array<double, nrSamples> X;
+    for (int s = -testSamples; s < nrSamples; ++s) {
+        af::sync();
+        af::timer start = af::timer::start();
+        for (int i = cycles; i > 0; --i) { fn(); }
+        af::sync();
+        const double time = af::timer::stop(start);
+        if (s >= 0) {
+            // real sample, so store it for later processing
+            X[s] = time;
+        } else {
+            // test sample, so improve nr cycles
+            cycles = std::max(
+                minCycles,
+                static_cast<int>(trunc(targetDurationPerTest / time * cycles)));
+        };
     }
-    return run_time / batches;
+    std::sort(X.begin(), X.end());
+    // returns the median (iso of mean), to limit impact of outliers
+    return X[nrSamples / 2] / cycles;
 }
 
 }  // namespace af

From cb507b1d386f3db0e6af2f9af4c4c55fd032f5ba Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 10 Mar 2022 15:08:25 -0500
Subject: [PATCH 2221/2677] Add span-lite span header to the project

---
 CMakeLists.txt                                       | 5 +++++
 src/backend/common/CMakeLists.txt                    | 1 +
 src/backend/opencl/kernel/sort_by_key/CMakeLists.txt | 1 +
 3 files changed, 7 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index db3b8978d8..5ccfff22bb 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -220,6 +220,11 @@ if(NOT TARGET glad::glad)
     )
 endif()
 
+af_dep_check_and_populate(span-lite
+  URI https://github.com/martinmoene/span-lite
+  REF "ccf2351"
+  )
+
 af_dep_check_and_populate(${assets_prefix}
   URI https://github.com/arrayfire/assets.git
   REF master
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 9805b42ae4..9ac53b8454 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -102,6 +102,7 @@ endif()
 target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
+    ${span-lite_SOURCE_DIR}/include
     ${ArrayFire_BINARY_DIR}
   SYSTEM INTERFACE
     $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 32d078faa2..e7a7ca27f3 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -30,6 +30,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
         ../../api/c
         ../common
         ../../../include
+        ${span-lite_SOURCE_DIR}/include
         ${CMAKE_CURRENT_BINARY_DIR})
 
     target_include_directories(opencl_sort_by_key_${SBK_TYPE}

From 9f04bd4fbaf004703d0481606bbea1962b46ee43 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 10 Mar 2022 15:13:48 -0500
Subject: [PATCH 2222/2677] Allow passesJitHeuristics to accept multiple nodes

This commit will change passesJitHeuristics function to accept
multiple root nodes to determine if the resulting kernel is passing
in too many parameters. This change will allow us to use this function
in eval multiple functions.
---
 src/backend/common/jit/NaryNode.hpp |  5 ++-
 src/backend/cpu/Array.cpp           | 43 +++++++++++---------
 src/backend/cpu/Array.hpp           |  3 +-
 src/backend/cuda/Array.cpp          | 63 +++++++++++++++++------------
 src/backend/cuda/Array.hpp          |  3 +-
 src/backend/cuda/select.cpp         |  6 ++-
 src/backend/opencl/Array.cpp        | 62 ++++++++++++++++------------
 src/backend/opencl/Array.hpp        |  3 +-
 src/backend/opencl/select.cpp       |  8 ++--
 9 files changed, 117 insertions(+), 79 deletions(-)

diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 885edb277d..c03af9c2a5 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -14,6 +14,7 @@
 #include <common/defines.hpp>
 #include <common/jit/Node.hpp>
 
+#include <nonstd/span.hpp>
 #include <array>
 #include <iomanip>
 #include <sstream>
@@ -100,13 +101,15 @@ common::Node_ptr createNaryNode(
     const af::dim4 &odims, FUNC createNode,
     std::array<const detail::Array<Ti> *, N> &&children) {
     std::array<common::Node_ptr, N> childNodes;
+    std::array<common::Node *, N> nodes;
     for (int i = 0; i < N; i++) {
         childNodes[i] = move(children[i]->getNode());
+        nodes[i]      = childNodes[i].get();
     }
 
     common::Node_ptr ptr = createNode(childNodes);
 
-    switch (detail::passesJitHeuristics<Ti>(ptr.get())) {
+    switch (detail::passesJitHeuristics<Ti>(nodes)) {
         case kJITHeuristics::Pass: {
             return ptr;
         }
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 5b2385866c..dcd79dd9ed 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -30,6 +30,7 @@
 #include <af/seq.h>
 #include <af/traits.hpp>
 
+#include <nonstd/span.hpp>
 #include <algorithm>  // IWYU pragma: keep
 #include <cstddef>
 #include <cstring>
@@ -44,6 +45,7 @@ using common::Node_ptr;
 using common::NodeIterator;
 using cpu::jit::BufferNode;
 
+using nonstd::span;
 using std::adjacent_find;
 using std::copy;
 using std::is_standard_layout;
@@ -227,28 +229,31 @@ Array<T> createEmptyArray(const dim4 &dims) {
 }
 
 template<typename T>
-kJITHeuristics passesJitHeuristics(Node *root_node) {
+kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() > static_cast<int>(getMaxJitSize())) {
-        return kJITHeuristics::TreeHeight;
+    size_t bytes = 0;
+    for (Node *n : root_nodes) {
+        if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            return kJITHeuristics::TreeHeight;
+        }
+        // Check if approaching the memory limit
+        if (getMemoryPressure() >= getMemoryPressureThreshold()) {
+            NodeIterator<Node> it(n);
+            NodeIterator<Node> end_node;
+            bytes = accumulate(it, end_node, bytes,
+                               [=](const size_t prev, const Node &n) {
+                                   // getBytes returns the size of the data
+                                   // Array. Sub arrays will be represented
+                                   // by their parent size.
+                                   return prev + n.getBytes();
+                               });
+        }
     }
 
-    // Check if approaching the memory limit
-    if (getMemoryPressure() >= getMemoryPressureThreshold()) {
-        NodeIterator<Node> it(root_node);
-        NodeIterator<Node> end_node;
-        size_t bytes = accumulate(it, end_node, size_t(0),
-                                  [=](const size_t prev, const Node &n) {
-                                      // getBytes returns the size of the data
-                                      // Array. Sub arrays will be represented
-                                      // by their parent size.
-                                      return prev + n.getBytes();
-                                  });
-
-        if (jitTreeExceedsMemoryPressure(bytes)) {
-            return kJITHeuristics::MemoryPressure;
-        }
+    if (jitTreeExceedsMemoryPressure(bytes)) {
+        return kJITHeuristics::MemoryPressure;
     }
+
     return kJITHeuristics::Pass;
 }
 
@@ -343,7 +348,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
-    template kJITHeuristics passesJitHeuristics<T>(Node * n);                 \
+    template kJITHeuristics passesJitHeuristics<T>(span<Node *> n);           \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 792b582de2..8db2ee7e44 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -22,6 +22,7 @@
 #include <af/dim4.hpp>
 #include <af/seq.h>
 
+#include <nonstd/span.hpp>
 #include <algorithm>
 #include <cstddef>
 #include <memory>
@@ -100,7 +101,7 @@ template<typename T>
 void destroyArray(Array<T> *A);
 
 template<typename T>
-kJITHeuristics passesJitHeuristics(common::Node *node);
+kJITHeuristics passesJitHeuristics(nonstd::span<common::Node *> node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 44169eccbd..134645f496 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -30,6 +30,7 @@ using common::Node_ptr;
 using common::NodeIterator;
 using cuda::jit::BufferNode;
 
+using nonstd::span;
 using std::accumulate;
 using std::move;
 using std::shared_ptr;
@@ -245,27 +246,33 @@ Node_ptr Array<T>::getNode() const {
 /// 2. The number of parameters we are passing into the kernel exceeds the
 ///    limitation on the platform. For NVIDIA this is 4096 bytes. The
 template<typename T>
-kJITHeuristics passesJitHeuristics(Node *root_node) {
+kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() > static_cast<int>(getMaxJitSize())) {
-        return kJITHeuristics::TreeHeight;
+    for (Node *n : root_nodes) {
+        if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            return kJITHeuristics::TreeHeight;
+        }
     }
 
     // A lightweight check based on the height of the node. This is an
     // inexpensive operation and does not traverse the JIT tree.
-    if (root_node->getHeight() > 6 ||
-        getMemoryPressure() >= getMemoryPressureThreshold()) {
+    int heightCheckLimit = 6;
+    bool atHeightLimit =
+        std::any_of(std::begin(root_nodes), std::end(root_nodes),
+                    [heightCheckLimit](Node *n) {
+                        return (n->getHeight() + 1 >= heightCheckLimit);
+                    });
+    if (atHeightLimit || getMemoryPressure() >= getMemoryPressureThreshold()) {
         // The size of the parameters without any extra arguments from the
         // JIT tree. This includes one output Param object and 4 integers.
-        constexpr size_t base_param_size =
-            sizeof(Param<T>) + (4 * sizeof(uint));
+        size_t base_param_size =
+            sizeof(Param<T>) * root_nodes.size() + (4 * sizeof(uint));
 
         // extra padding for safety to avoid failure during compilation
         constexpr size_t jit_padding_size = 256;  //@umar dontfix!
         // This is the maximum size of the params that can be allowed by the
         // CUDA platform.
-        constexpr size_t max_param_size =
-            4096 - base_param_size - jit_padding_size;
+        size_t max_param_size = 4096 - base_param_size - jit_padding_size;
 
         struct tree_info {
             size_t total_buffer_size;
@@ -273,22 +280,26 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
             size_t param_scalar_size;
         };
         NodeIterator<> end_node;
-        tree_info info =
-            accumulate(NodeIterator<>(root_node), end_node, tree_info{0, 0, 0},
-                       [](tree_info &prev, const Node &node) {
-                           if (node.isBuffer()) {
-                               const auto &buf_node =
-                                   static_cast<const BufferNode<T> &>(node);
-                               // getBytes returns the size of the data Array.
-                               // Sub arrays will be represented by their parent
-                               // size.
-                               prev.total_buffer_size += buf_node.getBytes();
-                               prev.num_buffers++;
-                           } else {
-                               prev.param_scalar_size += node.getParamBytes();
-                           }
-                           return prev;
-                       });
+        tree_info info = tree_info{0, 0, 0};
+
+        for (Node *n : root_nodes) {
+            info = accumulate(
+                NodeIterator<>(n), end_node, info,
+                [](tree_info &prev, const Node &node) {
+                    if (node.isBuffer()) {
+                        const auto &buf_node =
+                            static_cast<const BufferNode<T> &>(node);
+                        // getBytes returns the size of the data Array.
+                        // Sub arrays will be represented by their
+                        // parent size.
+                        prev.total_buffer_size += buf_node.getBytes();
+                        prev.num_buffers++;
+                    } else {
+                        prev.param_scalar_size += node.getParamBytes();
+                    }
+                    return prev;
+                });
+        }
         size_t param_size =
             info.num_buffers * sizeof(Param<T>) + info.param_scalar_size;
 
@@ -440,7 +451,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(std::vector<Array<T> *> arrays);            \
-    template kJITHeuristics passesJitHeuristics<T>(Node * n);                 \
+    template kJITHeuristics passesJitHeuristics<T>(span<Node *> n);           \
     template void Array<T>::setDataDims(const dim4 &new_dims);
 
 INSTANTIATE(float)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index b279ffcab4..52dbed7aeb 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -22,6 +22,7 @@
 #include <af/dim4.hpp>
 #include "traits.hpp"
 
+#include <nonstd/span.hpp>
 #include <vector>
 
 namespace cuda {
@@ -103,7 +104,7 @@ void destroyArray(Array<T> *A);
 /// \returns false if the kernel generated by this node will fail to compile
 ///          or its nodes are consuming too much memory.
 template<typename T>
-kJITHeuristics passesJitHeuristics(common::Node *node);
+kJITHeuristics passesJitHeuristics(nonstd::span<common::Node *> node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index 6f6f399960..739e150c05 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -53,7 +53,8 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         NaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type), "__select",
                  3, {{cond_node, a_node, b_node}}, af_select_t, height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+    std::array<common::Node *, 1> nodes{node.get()};
+    if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
             a.eval();
         } else if (b_height > cond_height) {
@@ -83,7 +84,8 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         flip ? af_not_select_t : af_select_t, height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+    std::array<common::Node *, 1> nodes{node.get()};
+    if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
             a.eval();
         } else if (b_height > cond_height) {
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 3aa63b40d4..6e490f82a8 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -45,6 +45,7 @@ using common::Node_ptr;
 using common::NodeIterator;
 using opencl::jit::BufferNode;
 
+using nonstd::span;
 using std::accumulate;
 using std::is_standard_layout;
 using std::make_shared;
@@ -293,10 +294,12 @@ Node_ptr Array<T>::getNode() const {
 /// 2. The number of parameters we are passing into the kernel exceeds the
 ///    limitation on the platform. For NVIDIA this is 4096 bytes. The
 template<typename T>
-kJITHeuristics passesJitHeuristics(Node *root_node) {
+kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
-    if (root_node->getHeight() > static_cast<int>(getMaxJitSize())) {
-        return kJITHeuristics::TreeHeight;
+    for (const Node *n : root_nodes) {
+        if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            return kJITHeuristics::TreeHeight;
+        }
     }
 
     bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
@@ -312,12 +315,18 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
 
     // A lightweight check based on the height of the node. This is
     // an inexpensive operation and does not traverse the JIT tree.
-    bool isParamLimit = (root_node->getHeight() >= heightCheckLimit);
-    if (isParamLimit || isBufferLimit) {
+    bool atHeightLimit =
+        std::any_of(std::begin(root_nodes), std::end(root_nodes),
+                    [heightCheckLimit](Node *n) {
+                        return (n->getHeight() + 1 >= heightCheckLimit);
+                    });
+
+    if (atHeightLimit || isBufferLimit) {
         // This is the base parameter size if the kernel had no
         // arguments
-        constexpr size_t base_param_size =
-            sizeof(T *) + sizeof(KParam) + (3 * sizeof(uint));
+        size_t base_param_size =
+            (sizeof(T *) + sizeof(KParam)) * root_nodes.size() +
+            (3 * sizeof(uint));
 
         const cl::Device &device = getDevice();
         size_t max_param_size = device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
@@ -332,28 +341,31 @@ kJITHeuristics passesJitHeuristics(Node *root_node) {
             size_t num_buffers;
             size_t param_scalar_size;
         };
-        NodeIterator<> it(root_node);
-        tree_info info =
-            accumulate(it, NodeIterator<>(), tree_info{0, 0, 0},
-                       [](tree_info &prev, Node &n) {
-                           if (n.isBuffer()) {
-                               auto &buf_node = static_cast<BufferNode &>(n);
-                               // getBytes returns the size of the data Array.
-                               // Sub arrays will be represented by their parent
-                               // size.
-                               prev.total_buffer_size += buf_node.getBytes();
-                               prev.num_buffers++;
-                           } else {
-                               prev.param_scalar_size += n.getParamBytes();
-                           }
-                           return prev;
-                       });
+
+        tree_info info{0, 0, 0};
+        for (Node *n : root_nodes) {
+            NodeIterator<> it(n);
+            info = accumulate(
+                it, NodeIterator<>(), info, [](tree_info &prev, Node &n) {
+                    if (n.isBuffer()) {
+                        auto &buf_node = static_cast<BufferNode &>(n);
+                        // getBytes returns the size of the data Array.
+                        // Sub arrays will be represented by their parent
+                        // size.
+                        prev.total_buffer_size += buf_node.getBytes();
+                        prev.num_buffers++;
+                    } else {
+                        prev.param_scalar_size += n.getParamBytes();
+                    }
+                    return prev;
+                });
+        }
         isBufferLimit = jitTreeExceedsMemoryPressure(info.total_buffer_size);
 
         size_t param_size = (info.num_buffers * (sizeof(KParam) + sizeof(T *)) +
                              info.param_scalar_size);
 
-        isParamLimit = param_size >= max_param_size;
+        bool isParamLimit = param_size >= max_param_size;
 
         if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
         if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
@@ -513,7 +525,7 @@ size_t Array<T>::getAllocatedBytes() const {
     template void writeDeviceDataArray<T>(                                    \
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
-    template kJITHeuristics passesJitHeuristics<T>(Node * node);              \
+    template kJITHeuristics passesJitHeuristics<T>(span<Node *> node);        \
     template void *getDevicePtr<T>(const Array<T> &arr);                      \
     template void Array<T>::setDataDims(const dim4 &new_dims);                \
     template size_t Array<T>::getAllocatedBytes() const;
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 67290207df..d3362cfa9a 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -23,6 +23,7 @@
 
 #include <af/dim4.hpp>
 
+#include <nonstd/span.hpp>
 #include <algorithm>
 #include <cstdlib>
 #include <memory>
@@ -108,7 +109,7 @@ void destroyArray(Array<T> *A);
 /// \returns false if the kernel generated by this node will fail to compile
 ///          or its nodes are consuming too much memory.
 template<typename T>
-kJITHeuristics passesJitHeuristics(common::Node *node);
+kJITHeuristics passesJitHeuristics(nonstd::span<common::Node *> node);
 
 template<typename T>
 void *getDevicePtr(const Array<T> &arr);
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index 32c2734f75..d652df25c6 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -15,6 +15,7 @@
 #include <err_opencl.hpp>
 #include <scalar.hpp>
 
+#include <nonstd/span.hpp>
 #include <memory>
 
 using af::dim4;
@@ -40,8 +41,8 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto node = make_shared<NaryNode>(
         NaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type), "__select",
                  3, {{cond_node, a_node, b_node}}, af_select_t, height));
-
-    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+    std::array<common::Node *, 1> nodes{node.get()};
+    if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
             a.eval();
         } else if (b_height > cond_height) {
@@ -71,7 +72,8 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
         (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
         (flip ? af_not_select_t : af_select_t), height));
 
-    if (detail::passesJitHeuristics<T>(node.get()) != kJITHeuristics::Pass) {
+    std::array<common::Node *, 1> nodes{node.get()};
+    if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
             a.eval();
         } else if (b_height > cond_height) {

From ddb55c40c095252311cbb7d23707eba295595b0a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 11 Mar 2022 18:34:45 -0500
Subject: [PATCH 2223/2677] Add some debugging macros

---
 src/backend/common/ArrayFireTypesIO.hpp | 37 ++++++++++
 src/backend/common/CMakeLists.txt       |  2 +
 src/backend/common/debug.hpp            | 62 +++++++++++++++++
 src/backend/common/jit/NodeIO.hpp       | 93 +++++++++++++++++++++++++
 4 files changed, 194 insertions(+)
 create mode 100644 src/backend/common/ArrayFireTypesIO.hpp
 create mode 100644 src/backend/common/debug.hpp
 create mode 100644 src/backend/common/jit/NodeIO.hpp

diff --git a/src/backend/common/ArrayFireTypesIO.hpp b/src/backend/common/ArrayFireTypesIO.hpp
new file mode 100644
index 0000000000..234df93b43
--- /dev/null
+++ b/src/backend/common/ArrayFireTypesIO.hpp
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <spdlog/fmt/bundled/ranges.h>
+#include <spdlog/fmt/ostr.h>
+#include <af/seq.h>
+
+template<>
+struct fmt::formatter<af_seq> {
+    // Parses format specifications of the form ['f' | 'e'].
+    constexpr auto parse(format_parse_context& ctx) -> decltype(ctx.begin()) {
+        return ctx.begin();
+    }
+
+    // Formats the point p using the parsed format specification (presentation)
+    // stored in this formatter.
+    template<typename FormatContext>
+    auto format(const af_seq& p, FormatContext& ctx) -> decltype(ctx.out()) {
+        // ctx.out() is an output iterator to write to.
+        if (p.begin == af_span.begin && p.end == af_span.end &&
+            p.step == af_span.step) {
+            return format_to(ctx.out(), "span");
+        }
+        if (p.begin == p.end) { return format_to(ctx.out(), "{}", p.begin); }
+        if (p.step == 1) {
+            return format_to(ctx.out(), "({} -> {})", p.begin, p.end);
+        }
+        return format_to(ctx.out(), "({} -({})-> {})", p.begin, p.step, p.end);
+    }
+};
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 9ac53b8454..125c620754 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -15,6 +15,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/NaryNode.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/Node.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/jit/NodeIO.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/NodeIterator.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/ScalarNode.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/jit/UnaryNode.hpp
@@ -25,6 +26,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/AllocatorInterface.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ArrayInfo.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/ArrayFireTypesIO.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DefaultMemoryManager.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DefaultMemoryManager.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/DependencyModule.cpp
diff --git a/src/backend/common/debug.hpp b/src/backend/common/debug.hpp
new file mode 100644
index 0000000000..6c2c6cbfb8
--- /dev/null
+++ b/src/backend/common/debug.hpp
@@ -0,0 +1,62 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#define FMT_HEADER_ONLY
+#include <boost/stacktrace.hpp>
+#include <common/ArrayFireTypesIO.hpp>
+#include <common/jit/NodeIO.hpp>
+#include <spdlog/fmt/bundled/format.h>
+#include <iostream>
+
+#define DBGTRACE(msg)                                              \
+    fmt::print(std::cout, __FILE__ ":{}:{}\n{}\n", __LINE__, #msg, \
+               boost::stacktrace::stacktrace())
+
+namespace debugging {
+
+template<typename first>
+void print(const char *F, const first &FF) {
+    fmt::print(std::cout, "{} = {}", F, FF);
+}
+
+template<typename first, typename... ARGS>
+void print(const char *F, const first &FF, ARGS... args) {
+    fmt::print(std::cout, "{} = {} | ", F, FF);
+    print(args...);
+}
+}  // namespace debugging
+
+#define SHOW1(val1) debugging::print(#val1, val1)
+#define SHOW2(val1, val2) debugging::print(#val1, val1, #val2, val2)
+#define SHOW3(val1, val2, val3) \
+    debugging::print(#val1, val1, #val2, val2, #val3, val3)
+
+#define SHOW4(val1, val2, val3, val4) \
+    debugging::print(#val1, val1, #val2, val2, #val3, val3, #val4, val4)
+#define SHOW5(val1, val2, val3, val4, val5)                              \
+    debugging::print(#val1, val1, #val2, val2, #val3, val3, #val4, val4, \
+                     #val5, val5)
+
+#define GET_MACRO(_1, _2, _3, _4, _5, NAME, ...) NAME
+
+#define SHOW(...)                                                 \
+    do {                                                          \
+        fmt::print(std::cout, "{}:({}): ", __FILE__, __LINE__);   \
+        GET_MACRO(__VA_ARGS__, SHOW5, SHOW4, SHOW3, SHOW2, SHOW1) \
+        (__VA_ARGS__);                                            \
+        fmt::print(std::cout, "\n");                              \
+    } while (0)
+
+#define PRINTVEC(val)                                                        \
+    do {                                                                     \
+        fmt::print(std::cout, "{}:({}):{} [{}]\n", __FILE__, __LINE__, #val, \
+                   fmt::join(val, ", "));                                    \
+    } while (0)
diff --git a/src/backend/common/jit/NodeIO.hpp b/src/backend/common/jit/NodeIO.hpp
new file mode 100644
index 0000000000..55d40c2b2d
--- /dev/null
+++ b/src/backend/common/jit/NodeIO.hpp
@@ -0,0 +1,93 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <common/jit/Node.hpp>
+#include <common/util.hpp>
+#include <spdlog/fmt/bundled/format.h>
+
+#include <common/TemplateArg.hpp>
+
+template<>
+struct fmt::formatter<af::dtype> : fmt::formatter<char> {
+    template<typename FormatContext>
+    auto format(const af::dtype& p, FormatContext& ctx) -> decltype(ctx.out()) {
+        format_to(ctx.out(), "{}", getName(p));
+        return ctx.out();
+    }
+};
+
+template<>
+struct fmt::formatter<common::Node> {
+    // Presentation format: 'p' - pointer, 't' - type.
+    // char presentation;
+    bool pointer;
+    bool type;
+    bool children;
+    bool op;
+
+    // Parses format specifications of the form ['f' | 'e'].
+    constexpr auto parse(format_parse_context& ctx) -> decltype(ctx.begin()) {
+        auto it = ctx.begin(), end = ctx.end();
+
+        if (it == end || *it == '}') {
+            pointer = type = children = op = true;
+            return it;
+        }
+
+        while (it != end && *it != '}') {
+            switch (*it) {
+                case 'p': pointer = true; break;
+                case 't': type = true; break;
+                case 'c': children = true; break;
+                case 'o': op = true; break;
+                default: throw format_error("invalid format");
+            }
+            ++it;
+        }
+
+        // Return an iterator past the end of the parsed range:
+        return it;
+    }
+
+    // Formats the point p using the parsed format specification (presentation)
+    // stored in this formatter.
+    template<typename FormatContext>
+    auto format(const common::Node& node, FormatContext& ctx)
+        -> decltype(ctx.out()) {
+        // ctx.out() is an output iterator to write to.
+
+        format_to(ctx.out(), "{{");
+        if (pointer) format_to(ctx.out(), "{} ", (void*)&node);
+        if (op) {
+            if (node.isBuffer()) {
+                format_to(ctx.out(), "buffer ");
+            } else {
+                format_to(ctx.out(), "{} ", getOpEnumStr(node.getOp()));
+            }
+        }
+        if (type) format_to(ctx.out(), "{} ", node.getType());
+        if (children) {
+            int count;
+            for (count = 0; count < common::Node::kMaxChildren &&
+                            node.m_children[count].get() != nullptr;
+                 count++) {}
+            if (count > 0) {
+                format_to(ctx.out(), "children: {{ ");
+                for (int i = 0; i < count; i++) {
+                    format_to(ctx.out(), "{} ", *(node.m_children[i].get()));
+                }
+                format_to(ctx.out(), "\b}} ");
+            }
+        }
+        format_to(ctx.out(), "\b}}");
+
+        return ctx.out();
+    }
+};

From c5af6ef031096f6f1227ed3d831c95aaffeb5906 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Mar 2022 13:39:34 -0400
Subject: [PATCH 2224/2677] Add a function to check if Node is a scalar object

---
 src/backend/common/jit/Node.cpp       | 2 ++
 src/backend/common/jit/Node.hpp       | 5 +++++
 src/backend/common/jit/NodeIO.hpp     | 4 +++-
 src/backend/common/jit/ScalarNode.hpp | 3 +++
 src/backend/cpu/jit/ScalarNode.hpp    | 2 ++
 5 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index b59222de86..83767f502f 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -63,6 +63,8 @@ bool NodePtr_equalto::operator()(const Node *l, const Node *r) const noexcept {
 
 auto isBuffer(const Node &ptr) -> bool { return ptr.isBuffer(); }
 
+auto isScalar(const Node &ptr) -> bool { return ptr.isScalar(); }
+
 /// Returns true if the buffer is linear
 bool Node::isLinear(const dim_t dims[4]) const { return true; }
 
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 3cad47f03e..0b284c072e 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -241,6 +241,9 @@ class Node {
     // Returns true if this node is a Buffer
     virtual bool isBuffer() const { return false; }
 
+    // Returns true if this node is a Buffer
+    virtual bool isScalar() const { return false; }
+
     /// Returns true if the buffer is linear
     virtual bool isLinear(const dim_t dims[4]) const;
 
@@ -300,4 +303,6 @@ std::string getFuncName(const std::vector<Node *> &output_nodes,
 
 auto isBuffer(const Node &ptr) -> bool;
 
+auto isScalar(const Node &ptr) -> bool;
+
 }  // namespace common
diff --git a/src/backend/common/jit/NodeIO.hpp b/src/backend/common/jit/NodeIO.hpp
index 55d40c2b2d..050c8e3a7c 100644
--- a/src/backend/common/jit/NodeIO.hpp
+++ b/src/backend/common/jit/NodeIO.hpp
@@ -66,8 +66,10 @@ struct fmt::formatter<common::Node> {
         format_to(ctx.out(), "{{");
         if (pointer) format_to(ctx.out(), "{} ", (void*)&node);
         if (op) {
-            if (node.isBuffer()) {
+            if (isBuffer(node)) {
                 format_to(ctx.out(), "buffer ");
+            } else if (isScalar(node)) {
+                format_to(ctx.out(), "scalar ", getOpEnumStr(node.getOp()));
             } else {
                 format_to(ctx.out(), "{} ", getOpEnumStr(node.getOp()));
             }
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index bf0978359f..126e8860f7 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -84,6 +84,9 @@ class ScalarNode : public common::Node {
                   << ";\n";
     }
 
+    // Returns true if this node is a Buffer
+    virtual bool isScalar() const { return false; }
+
     std::string getNameStr() const final { return detail::shortname<T>(false); }
 
     // Return the info for the params and the size of the buffers
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index 657cbbf355..79a9f40f22 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -58,6 +58,8 @@ class ScalarNode : public TNode<T> {
         UNUSED(kerStream);
         UNUSED(ids);
     }
+
+    bool isScalar() const final { return true; }
 };
 }  // namespace jit
 

From 6543bee5dbc74931c986b753fc77649837cd2745 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Mar 2022 16:00:56 -0400
Subject: [PATCH 2225/2677] Download only mkl instead of basekit when building
 the CI env

---
 .github/workflows/unix_cpu_build.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 9fcb37b87e..47dff97a42 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -83,7 +83,8 @@ jobs:
                   sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
                   sudo sh -c 'echo deb https://apt.repos.intel.com/oneapi all main > /etc/apt/sources.list.d/oneAPI.list'
                   sudo apt-get -qq update
-                  sudo apt-get install -y intel-basekit
+                  sudo apt-get install -y intel-oneapi-mkl-devel
+                  echo "MKLROOT=/opt/intel/oneapi/mkl/latest" >> ${GITHUB_ENV}
 
             - name: Install OpenBLAS for Ubuntu
               if: matrix.os != 'macos-latest' && matrix.blas_backend == 'OpenBLAS'
@@ -107,7 +108,7 @@ jobs:
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \
-                      -DAF_COMPUTE_LIBRARY:STRING=$backend \
+                      -DAF_COMPUTE_LIBRARY:STRING=${backend} \
                       -DBUILDNAME:STRING=${buildname} ..
                   echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 

From 86cdffd219bdb4ab13d5810ab950176f55506234 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 19 Mar 2022 00:51:16 -0400
Subject: [PATCH 2226/2677] Fix nested and duplicate moddims jit issue with the
 CPU backend

Fix an issue that caused errors with nested moddims caused errors with the CPU
backend. this was caused when the moddims function was called back to back on
the same array.

Another issue that this fixes is when you have the same node which are composed
of moddims arrays in the same jit tree
---
 src/backend/cpu/kernel/Array.hpp | 104 +++++++++++++++++++------------
 test/moddims.cpp                 |  67 ++++++++++++++++++++
 2 files changed, 130 insertions(+), 41 deletions(-)

diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 32ef5f6634..48987a5d4d 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -21,16 +21,16 @@
 namespace cpu {
 namespace kernel {
 
-/// Clones nodes and update the child pointers
+/// Clones node_index_map and update the child pointers
 std::vector<std::shared_ptr<common::Node>> cloneNodes(
-    const std::vector<common::Node *> &nodes,
+    const std::vector<common::Node *> &node_index_map,
     const std::vector<common::Node_ids> &ids) {
     using common::Node;
     // find all moddims in the tree
     std::vector<std::shared_ptr<Node>> node_clones;
-    node_clones.reserve(nodes.size());
-    transform(begin(nodes), end(nodes), back_inserter(node_clones),
-              [](Node *n) { return n->clone(); });
+    node_clones.reserve(node_index_map.size());
+    transform(begin(node_index_map), end(node_index_map),
+              back_inserter(node_clones), [](Node *n) { return n->clone(); });
 
     for (common::Node_ids id : ids) {
         auto &children = node_clones[id.id]->m_children;
@@ -41,7 +41,8 @@ std::vector<std::shared_ptr<common::Node>> cloneNodes(
     return node_clones;
 }
 
-/// Sets the shape of the buffer nodes under the moddims node to the new shape
+/// Sets the shape of the buffer node_index_map under the moddims node to the
+/// new shape
 void propagateModdimsShape(
     std::vector<std::shared_ptr<common::Node>> &node_clones) {
     using common::NodeIterator;
@@ -63,14 +64,13 @@ void propagateModdimsShape(
     }
 }
 
-/// Removes nodes whos operation matchs a unary operation \p op.
-void removeNodeOfOperation(std::vector<std::shared_ptr<common::Node>> &nodes,
-                           std::vector<common::Node_ids> &ids, af_op_t op) {
+/// Removes node_index_map whos operation matchs a unary operation \p op.
+void removeNodeOfOperation(
+    std::vector<std::shared_ptr<common::Node>> &node_index_map, af_op_t op) {
     using common::Node;
 
-    std::vector<std::vector<std::shared_ptr<Node>>::iterator> moddims_loc;
-    for (size_t nid = 0; nid < nodes.size(); nid++) {
-        auto &node = nodes[nid];
+    for (size_t nid = 0; nid < node_index_map.size(); nid++) {
+        auto &node = node_index_map[nid];
 
         for (int i = 0;
              i < Node::kMaxChildren && node->m_children[i] != nullptr; i++) {
@@ -78,15 +78,47 @@ void removeNodeOfOperation(std::vector<std::shared_ptr<common::Node>> &nodes,
                 // replace moddims
                 auto moddim_node    = node->m_children[i];
                 node->m_children[i] = moddim_node->m_children[0];
-
-                int parent_id = ids[nid].id;
-                int moddim_id = ids[parent_id].child_ids[i];
-                moddims_loc.emplace_back(begin(nodes) + moddim_id);
             }
         }
     }
 
-    for (auto &loc : moddims_loc) { nodes.erase(loc); }
+    node_index_map.erase(remove_if(begin(node_index_map), end(node_index_map),
+                                   [op](std::shared_ptr<Node> &node) {
+                                       return node->getOp() == op;
+                                   }),
+                         end(node_index_map));
+}
+
+/// Returns the cloned output_nodes located in the node_clones array
+///
+/// This function returns the new cloned version of the output_nodes_ from
+/// the node_clones array. If the output node is a moddim node, then it will
+/// set the output node to be its first non-moddim node child
+template<typename T>
+std::vector<TNode<T> *> getClonedOutputNodes(
+    common::Node_map_t &node_index_map,
+    const std::vector<std::shared_ptr<common::Node>> &node_clones,
+    const std::vector<common::Node_ptr> &output_nodes_) {
+    std::vector<TNode<T> *> cloned_output_nodes;
+    cloned_output_nodes.reserve(output_nodes_.size());
+    for (auto &n : output_nodes_) {
+        TNode<T> *ptr;
+        if (n->getOp() == af_moddims_t) {
+            // if the output node is a moddims node, then set the output node
+            // to be the child of the moddims node. This is necessary because
+            // we remove the moddim node_index_map from the tree later
+            int child_index = node_index_map[n->m_children[0].get()];
+            ptr = static_cast<TNode<T> *>(node_clones[child_index].get());
+            while (ptr->getOp() == af_moddims_t) {
+                ptr = static_cast<TNode<T> *>(ptr->m_children[0].get());
+            }
+        } else {
+            int node_index = node_index_map[n.get()];
+            ptr = static_cast<TNode<T> *>(node_clones[node_index].get());
+        }
+        cloned_output_nodes.push_back(ptr);
+    }
+    return cloned_output_nodes;
 }
 
 template<typename T>
@@ -100,41 +132,29 @@ void evalMultiple(std::vector<Param<T>> arrays,
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
 
-    Node_map_t nodes;
+    Node_map_t node_index_map;
     std::vector<T *> ptrs;
-    std::vector<TNode<T> *> output_nodes;
     std::vector<common::Node *> full_nodes;
     std::vector<common::Node_ids> ids;
 
     int narrays = static_cast<int>(arrays.size());
+    ptrs.reserve(narrays);
     for (int i = 0; i < narrays; i++) {
         ptrs.push_back(arrays[i].get());
-        output_nodes_[i]->getNodesMap(nodes, full_nodes, ids);
+        output_nodes_[i]->getNodesMap(node_index_map, full_nodes, ids);
     }
-
     auto node_clones = cloneNodes(full_nodes, ids);
 
-    for (auto &n : output_nodes_) {
-        if (n->getOp() == af_moddims_t) {
-            // if the output node is a moddims node, then set the output node to
-            // be the child of the moddims node. This is necessary because we
-            // remove the moddim nodes from the tree later
-            output_nodes.push_back(static_cast<TNode<T> *>(
-                node_clones[nodes[n->m_children[0].get()]].get()));
-        } else {
-            output_nodes.push_back(
-                static_cast<TNode<T> *>(node_clones[nodes[n.get()]].get()));
-        }
-    }
-
+    std::vector<TNode<T> *> cloned_output_nodes =
+        getClonedOutputNodes<T>(node_index_map, node_clones, output_nodes_);
     propagateModdimsShape(node_clones);
-    removeNodeOfOperation(node_clones, ids, af_moddims_t);
+    removeNodeOfOperation(node_clones, af_moddims_t);
 
     bool is_linear = true;
     for (auto &node : node_clones) { is_linear &= node->isLinear(odims.get()); }
 
     int num_nodes        = node_clones.size();
-    int num_output_nodes = output_nodes.size();
+    int num_output_nodes = cloned_output_nodes.size();
     if (is_linear) {
         int num = arrays[0].dims().elements();
         int cnum =
@@ -145,8 +165,9 @@ void evalMultiple(std::vector<Param<T>> arrays,
                 node_clones[n]->calc(i, lim);
             }
             for (int n = 0; n < num_output_nodes; n++) {
-                std::copy(output_nodes[n]->m_val.begin(),
-                          output_nodes[n]->m_val.begin() + lim, ptrs[n] + i);
+                std::copy(cloned_output_nodes[n]->m_val.begin(),
+                          cloned_output_nodes[n]->m_val.begin() + lim,
+                          ptrs[n] + i);
             }
         }
     } else {
@@ -170,9 +191,10 @@ void evalMultiple(std::vector<Param<T>> arrays,
                             node_clones[n]->calc(x, y, z, w, lim);
                         }
                         for (int n = 0; n < num_output_nodes; n++) {
-                            std::copy(output_nodes[n]->m_val.begin(),
-                                      output_nodes[n]->m_val.begin() + lim,
-                                      ptrs[n] + id);
+                            std::copy(
+                                cloned_output_nodes[n]->m_val.begin(),
+                                cloned_output_nodes[n]->m_val.begin() + lim,
+                                ptrs[n] + id);
                         }
                     }
                 }
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 6794e4c90e..630e4e6783 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -279,3 +279,70 @@ TEST(Moddims, jit) {
     gold = moddims(gold, 5, 10);
     ASSERT_ARRAYS_EQ(gold, a);
 }
+
+TEST(Moddims, JitNested) {
+    array a    = af::constant(1, 5, 5);
+    array b    = moddims(moddims(moddims(a, 25), 1, 5, 5), 5, 5);
+    array gold = af::constant(1, 5, 5);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, b);
+}
+
+TEST(Moddims, JitDuplicate) {
+    array a = af::constant(1, 5, 5);
+    array b = af::moddims(a, 25);
+    array c = b + b;
+
+    array gold = af::constant(2, 25);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, c);
+}
+
+TEST(Moddims, JitNestedAndDuplicate) {
+    array a = af::constant(1, 10, 10);
+    array b = af::constant(1, 10, 10);
+    array c = af::constant(2, 100) + moddims(a + b, 100);
+    array d = moddims(
+        moddims(af::constant(2, 1, 10, 10) + moddims(c, 1, 10, 10), 100), 10,
+        10);
+    array e    = d + d;
+    array gold = af::constant(12, 10, 10);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, e);
+}
+
+TEST(Moddims, JitTileThenModdims) {
+    array a    = af::constant(1, 10);
+    array b    = tile(a, 1, 10);
+    array c    = moddims(b, 100);
+    array gold = af::constant(1, 100);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, c);
+}
+
+TEST(Moddims, JitModdimsThenTiled) {
+    array a    = af::constant(1, 10);
+    array b    = moddims(a, 1, 10);
+    array c    = tile(b, 10);
+    array gold = af::constant(1, 10, 10);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, c);
+}
+
+TEST(Moddims, JitTileThenMultipleModdims) {
+    array a    = af::constant(1, 10);
+    array b    = tile(a, 1, 10);
+    array c    = moddims(moddims(b, 100), 10, 10);
+    array gold = af::constant(1, 10, 10);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, c);
+}
+
+TEST(Moddims, JitMultipleModdimsThenTiled) {
+    array a    = af::constant(1, 10);
+    array b    = moddims(moddims(a, 1, 10), 1, 1, 10);
+    array c    = tile(b, 10);
+    array gold = af::constant(1, 10, 1, 10);
+    gold.eval();
+    ASSERT_ARRAYS_EQ(gold, c);
+}

From 699df329363072375273f69a2ff3ed19a92f7b7c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 13 Jan 2022 13:40:34 -0500
Subject: [PATCH 2227/2677] Link afcuda with static numeric libs by default

ArrayFire's CUDA backend linked against the CUDA numeric libraries
staticly before this change. This caused the libafcuda library to be
in the 1.1GB range for CUDA 11.5 even if you were targeting one compute
capability. This is partially due to the fact that the linker does not
remove the compute capabilities of older architectures when linking.

One way around this would be to use nvprune to remove the architectures
that are not being used by the compute cability when building. This
approach is not yet implemented.

This commit will revert back to dynamically linking the CUDA numeric
libraries by default. You can still select the old behavior by setting
the AF_WITH_STATIC_CUDA_NUMERIC_LIBS option in CMake
---
 CMakeLists.txt                  |  1 +
 src/backend/cuda/CMakeLists.txt | 41 ++++++++++++++++-----------------
 2 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5ccfff22bb..dce9076c8c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -74,6 +74,7 @@ option(AF_WITH_LOGGING  "Build ArrayFire with logging support" ON)
 option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
 option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
 option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
+option(AF_WITH_STATIC_CUDA_NUMERIC_LIBS "Link libafcuda with static numeric libraries(cublas, cufft, etc.)" OFF)
 
 set(default_compute_library "FFTW/LAPACK/BLAS")
 if(MKL_FOUND)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 218878e163..f10ae0dc0c 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -47,7 +47,7 @@ endif()
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
-if(UNIX)
+if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   af_find_static_cuda_libs(culibos)
   af_find_static_cuda_libs(cublas_static)
   af_find_static_cuda_libs(cublasLt_static)
@@ -312,8 +312,7 @@ if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
   target_compile_definitions(af_cuda_static_cuda_library PRIVATE AF_USE_NEW_CUSPARSE_API)
 endif()
 
-if(UNIX)
-
+if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
   if(group_flags)
     set(START_GROUP -Wl,--start-group)
@@ -349,7 +348,7 @@ if(UNIX)
   set(CUDA_SEPARABLE_COMPILATION ${pior_val_CUDA_SEPARABLE_COMPILATION})
 else()
   target_link_libraries(af_cuda_static_cuda_library
-    PRIVATE
+    PUBLIC
       Boost::boost
       ${CUDA_CUBLAS_LIBRARIES}
       ${CUDA_CUFFT_LIBRARIES}
@@ -771,10 +770,10 @@ function(afcu_collect_libs libname)
 
   if(cuda_args_LIB_MAJOR AND cuda_args_LIB_MINOR)
     set(lib_major ${cuda_args_LIB_MAJOR})
-	set(lib_minor ${cuda_args_LIB_MINOR})
+	  set(lib_minor ${cuda_args_LIB_MINOR})
   else()
     set(lib_major ${CUDA_VERSION_MAJOR})
-	set(lib_minor ${CUDA_VERSION_MINOR})
+	  set(lib_minor ${CUDA_VERSION_MINOR})
   endif()
   set(lib_version "${lib_major}.${lib_minor}")
 
@@ -832,24 +831,24 @@ endfunction()
 if(AF_INSTALL_STANDALONE)
   if(AF_WITH_CUDNN)
     afcu_collect_cudnn_libs("")
-	if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
-	  # cudnn changed how dlls are shipped starting major version 8
+    if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
+      # cudnn changed how dlls are shipped starting major version 8
       # except the main dll a lot of the other DLLs are loaded upon demand
-	  afcu_collect_cudnn_libs(adv_infer)
-	  afcu_collect_cudnn_libs(adv_train)
-	  afcu_collect_cudnn_libs(cnn_infer)
-	  afcu_collect_cudnn_libs(cnn_train)
-	  afcu_collect_cudnn_libs(ops_infer)
-	  afcu_collect_cudnn_libs(ops_train)
-	endif()
+      afcu_collect_cudnn_libs(adv_infer)
+      afcu_collect_cudnn_libs(adv_train)
+      afcu_collect_cudnn_libs(cnn_infer)
+      afcu_collect_cudnn_libs(cnn_train)
+      afcu_collect_cudnn_libs(ops_infer)
+      afcu_collect_cudnn_libs(ops_train)
+    endif()
   endif()
 
-  if(WIN32)
-	if(CUDA_VERSION_MAJOR VERSION_EQUAL 11)
-      afcu_collect_libs(cufft LIB_MAJOR 10 LIB_MINOR 4)
-	else()
-      afcu_collect_libs(cufft)
-	endif()
+  if(WIN32 OR NOT AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+    if(CUDA_VERSION_MAJOR VERSION_EQUAL 11)
+        afcu_collect_libs(cufft LIB_MAJOR 10 LIB_MINOR 4)
+    else()
+        afcu_collect_libs(cufft)
+    endif()
     afcu_collect_libs(cublas)
     if(CUDA_VERSION VERSION_GREATER 10.0)
       afcu_collect_libs(cublasLt)

From e3f9559375bb3ac42ba0202bc77f45a4dd0a40a4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 13 Jan 2022 17:04:35 -0500
Subject: [PATCH 2228/2677] Fix find_library call when searching for CUDA
 libraries

---
 src/backend/cuda/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index f10ae0dc0c..fd81ebd3eb 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -797,8 +797,8 @@ function(afcu_collect_libs libname)
             COMPONENT   cuda_dependencies)
   else () #UNIX
     find_library(CUDA_${libname}_LIBRARY
-      NAME ${libname}
-      PATH
+      NAMES ${libname}
+      PATHS
         ${dlib_path_prefix})
 
     get_filename_component(outpath "${CUDA_${libname}_LIBRARY}" REALPATH)

From 453cdc3f520a7e4f179ef344d261847c18f77e34 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 22 Mar 2022 17:26:17 -0400
Subject: [PATCH 2229/2677] Prune CUDA static numerical libraries for specifice
 compute capability

Prune CUDA static libraries so that the binary size of the final executable
is smaller. This commit will run the nvprune utility on some static libraries
(cublasLt, cublas, cusolver, and cusparse) to remove unused architectures
from the binary. The resulting binary is significantly smaller when targeting
a single compute capability.
---
 CMakeModules/AFcuda_helpers.cmake | 25 +++++++++++++++-
 src/backend/cuda/CMakeLists.txt   | 49 ++++++++++++++++---------------
 2 files changed, 50 insertions(+), 24 deletions(-)

diff --git a/CMakeModules/AFcuda_helpers.cmake b/CMakeModules/AFcuda_helpers.cmake
index 4fde494df8..578c49956b 100644
--- a/CMakeModules/AFcuda_helpers.cmake
+++ b/CMakeModules/AFcuda_helpers.cmake
@@ -5,14 +5,37 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-
+find_program(NVPRUNE NAMES nvprune)
 # The following macro uses a macro defined by
 # FindCUDA module from cmake.
 function(af_find_static_cuda_libs libname)
+  cmake_parse_arguments(fscl "PRUNE" "" "" ${ARGN})
+
   set(search_name
     "${CMAKE_STATIC_LIBRARY_PREFIX}${libname}${CMAKE_STATIC_LIBRARY_SUFFIX}")
   cuda_find_library_local_first(CUDA_${libname}_LIBRARY
     ${search_name} "${libname} static library")
+
+  if(fscl_PRUNE)
+    get_filename_component(af_${libname} ${CUDA_${libname}_LIBRARY} NAME)
+
+    set(liboutput ${CMAKE_CURRENT_BINARY_DIR}/${af_${libname}})
+    add_custom_command(OUTPUT ${liboutput}.depend
+      COMMAND ${NVPRUNE} ${cuda_architecture_flags} ${CUDA_${libname}_LIBRARY} -o ${liboutput}
+      COMMAND ${CMAKE_COMMAND} -E touch ${liboutput}.depend
+      BYPRODUCTS ${liboutput}
+      MAIN_DEPENDENCY ${CUDA_${libname}_LIBRARY}
+      COMMENT "Pruning ${CUDA_${libname}_LIBRARY} for ${cuda_build_targets}"
+      VERBATIM)
+    add_custom_target(AF_CUDA_${libname}_LIBRARY_TARGET
+      DEPENDS ${liboutput}.depend)
+    list(APPEND cuda_pruned_libraries AF_CUDA_${libname}_LIBRARY_TARGET PARENT_SCOPE)
+
+    set(AF_CUDA_${libname}_LIBRARY ${liboutput} PARENT_SCOPE)
+    mark_as_advanced(AF_CUDA_${libname}_LIBRARY)
+  else()
+    set(AF_CUDA_${libname}_LIBRARY ${CUDA_${libname}_LIBRARY} PARENT_SCOPE)
+  endif()
   mark_as_advanced(CUDA_${libname}_LIBRARY)
 endfunction()
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index fd81ebd3eb..7694170aa5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -45,14 +45,27 @@ else()
   set(use_static_cuda_lapack OFF)
 endif()
 
+set(CUDA_architecture_build_targets "Auto" CACHE
+  STRING "The compute architectures targeted by this build. (Options: Auto;3.0;Maxwell;All;Common)")
+
+cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
+
+string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=sm_([0-9]+)" "\\1|" cuda_build_targets ${cuda_architecture_flags})
+string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=compute_([0-9]+)" "\\1+PTX|" cuda_build_targets ${cuda_build_targets})
+string(REGEX REPLACE "([0-9]+)([0-9])\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
+string(REGEX REPLACE "([0-9]+)([0-9]\\+PTX)\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
+message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets} ( ${cuda_build_targets} )")
+
+set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
+
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   af_find_static_cuda_libs(culibos)
-  af_find_static_cuda_libs(cublas_static)
-  af_find_static_cuda_libs(cublasLt_static)
+  af_find_static_cuda_libs(cublas_static PRUNE)
+  af_find_static_cuda_libs(cublasLt_static PRUNE)
   af_find_static_cuda_libs(cufft_static)
-  af_find_static_cuda_libs(cusparse_static)
+  af_find_static_cuda_libs(cusparse_static PRUNE)
 
   # FIXME When NVCC resolves this particular issue.
   # NVCC doesn't like -l<full_path_static_lib>, hence we cannot
@@ -67,8 +80,8 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcusparse_static")
 
   if(${use_static_cuda_lapack})
-    af_find_static_cuda_libs(cusolver_static)
-    set(cusolver_static_lib "${CUDA_cusolver_static_LIBRARY}")
+    af_find_static_cuda_libs(cusolver_static PRUNE)
+    set(cusolver_static_lib "${AF_CUDA_cusolver_static_LIBRARY}")
 
     # NVIDIA LAPACK library liblapack_static.a is a subset of LAPACK and only
     # contains GPU accelerated stedc and bdsqr. The user has to link
@@ -84,19 +97,6 @@ endif()
 
 get_filename_component(CUDA_LIBRARIES_PATH ${CUDA_cudart_static_LIBRARY} DIRECTORY CACHE)
 
-set(CUDA_architecture_build_targets "Auto" CACHE
-    STRING "The compute architectures targeted by this build. (Options: Auto;3.0;Maxwell;All;Common)")
-
-cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
-
-string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=sm_([0-9]+)" "\\1|" cuda_build_targets ${cuda_architecture_flags})
-string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=compute_([0-9]+)" "\\1+PTX|" cuda_build_targets ${cuda_build_targets})
-string(REGEX REPLACE "([0-9]+)([0-9])\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
-string(REGEX REPLACE "([0-9]+)([0-9]\\+PTX)\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
-message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets} ( ${cuda_build_targets} )")
-
-set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
-
 mark_as_advanced(
     CUDA_LIBRARIES_PATH
     CUDA_architecture_build_targets)
@@ -327,9 +327,10 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
       ${cusolver_lib}
       ${START_GROUP}
       ${CUDA_culibos_LIBRARY} #also a static libary
-      ${CUDA_cublas_static_LIBRARY}
-      ${CUDA_cufft_static_LIBRARY}
-      ${CUDA_cusparse_static_LIBRARY}
+      ${AF_CUDA_cublas_static_LIBRARY}
+      ${AF_CUDA_cufft_static_LIBRARY}
+      ${AF_CUDA_cusparse_static_LIBRARY}
+      ${AF_CUDA_cublasLt_static_LIBRARY}
       ${cusolver_static_lib}
       ${END_GROUP}
   )
@@ -337,7 +338,7 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   if(CUDA_VERSION VERSION_GREATER 10.0)
     target_link_libraries(af_cuda_static_cuda_library
       PRIVATE
-        ${CUDA_cublasLt_static_LIBRARY})
+        ${AF_CUDA_cublasLt_static_LIBRARY})
   endif()
   if(CUDA_VERSION VERSION_GREATER 9.5)
     target_link_libraries(af_cuda_static_cuda_library
@@ -687,7 +688,9 @@ add_library(ArrayFire::afcuda ALIAS afcuda)
 
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
 add_dependencies(af_cuda_static_cuda_library ${nvrtc_kernel_targets})
-add_dependencies(afcuda af_cuda_static_cuda_library)
+if(cuda_pruned_libraries)
+  add_dependencies(afcuda ${cuda_pruned_libraries})
+endif()
 
 target_include_directories (afcuda
   PUBLIC

From 83aad432c50732f0645d911bbec2ed62c7459ddb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 22 Mar 2022 18:31:32 -0400
Subject: [PATCH 2230/2677] Remove adv_infer and adv_train cudnn libs from
 install step

---
 src/backend/cuda/CMakeLists.txt | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 7694170aa5..d75b96296a 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -837,8 +837,6 @@ if(AF_INSTALL_STANDALONE)
     if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
       # cudnn changed how dlls are shipped starting major version 8
       # except the main dll a lot of the other DLLs are loaded upon demand
-      afcu_collect_cudnn_libs(adv_infer)
-      afcu_collect_cudnn_libs(adv_train)
       afcu_collect_cudnn_libs(cnn_infer)
       afcu_collect_cudnn_libs(cnn_train)
       afcu_collect_cudnn_libs(ops_infer)

From b76b12711f7296bc544cd25b6f72111addb290cb Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 23 Mar 2022 20:16:57 -0400
Subject: [PATCH 2231/2677] Deterministic topK (#3210)

Add the ability to perform topk and maintain a stable order of the indices
---
 include/af/defines.h                          |   7 +-
 include/af/statistics.h                       |   6 +-
 src/backend/cpu/topk.cpp                      |  54 ++++++--
 src/backend/cuda/kernel/topk.hpp              |  56 ++++++--
 .../opencl/kernel/sort_by_key_impl.hpp        |   5 +-
 src/backend/opencl/topk.cpp                   |  56 ++++++--
 test/topk.cpp                                 | 128 +++++++++++++++++-
 7 files changed, 268 insertions(+), 44 deletions(-)

diff --git a/include/af/defines.h b/include/af/defines.h
index a346a14e24..611a025375 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -508,8 +508,11 @@ typedef enum {
 } af_diffusion_eq;
 
 typedef enum {
-    AF_TOPK_MIN     = 1,  ///< Top k min values
-    AF_TOPK_MAX     = 2,  ///< Top k max values
+    AF_TOPK_MIN         = 1,  ///< Top k min values
+    AF_TOPK_MAX         = 2,  ///< Top k max values
+    AF_TOPK_STABLE      = 4,  ///< Preserve order of indices for equal values
+    AF_TOPK_STABLE_MIN  = AF_TOPK_STABLE | AF_TOPK_MIN, ///< Top k min with stable indices
+    AF_TOPK_STABLE_MAX  = AF_TOPK_STABLE | AF_TOPK_MAX, ///< Top k max with stable indices
     AF_TOPK_DEFAULT = 0   ///< Default option (max)
 } af_topk_function;
 #endif
diff --git a/include/af/statistics.h b/include/af/statistics.h
index 9f7adf455a..86851a3a7b 100644
--- a/include/af/statistics.h
+++ b/include/af/statistics.h
@@ -320,7 +320,8 @@ AFAPI T corrcoef(const array& X, const array& Y);
 
    \note{This function is optimized for small values of k.}
    \note{The order of the returned keys may not be in the same order as the
-   appear in the input array}
+   appear in the input array, for a stable topk, set the AF_TOPK_STABLE flag
+   in the order param. These are equivalent to AF_TOPK_STABLE_MAX and AF_TOPK_STABLE_MIN}
    \ingroup stat_func_topk
 */
 AFAPI void topk(array &values, array &indices, const array& in, const int k,
@@ -673,7 +674,8 @@ AFAPI af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, con
 
    \note{This function is optimized for small values of k.}
    \note{The order of the returned keys may not be in the same order as the
-         appear in the input array}
+   appear in the input array, for a stable topk, set the AF_TOPK_STABLE flag
+   in the order param. These are equivalent to AF_TOPK_STABLE_MAX and AF_TOPK_STABLE_MIN}
    \ingroup stat_func_topk
 */
 AFAPI af_err af_topk(af_array *values, af_array *indices, const af_array in,
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index 645e48d2e2..a87d257a8c 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -57,19 +57,49 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
             auto idx_itr = begin(idx) + i * in.strides()[1];
             auto* kiptr  = iptr + k * i;
 
-            if (order == AF_TOPK_MIN) {
-                // Sort the top k values in each column
-                partial_sort_copy(
-                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-                    [ptr](const uint lhs, const uint rhs) -> bool {
-                        return compute_t<T>(ptr[lhs]) < compute_t<T>(ptr[rhs]);
-                    });
+            if (order & AF_TOPK_MIN) {
+                if (order & AF_TOPK_STABLE) {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return compute_t<T>(ptr[lhs]) <
+                                           compute_t<T>(ptr[rhs])
+                                       ? true
+                                   : compute_t<T>(ptr[lhs]) ==
+                                           compute_t<T>(ptr[rhs])
+                                       ? (lhs < rhs)
+                                       : false;
+                        });
+                } else {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return compute_t<T>(ptr[lhs]) <
+                                   compute_t<T>(ptr[rhs]);
+                        });
+                    // Sort the top k values in each column
+                }
             } else {
-                partial_sort_copy(
-                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-                    [ptr](const uint lhs, const uint rhs) -> bool {
-                        return compute_t<T>(ptr[lhs]) >= compute_t<T>(ptr[rhs]);
-                    });
+                if (order & AF_TOPK_STABLE) {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return compute_t<T>(ptr[lhs]) >
+                                           compute_t<T>(ptr[rhs])
+                                       ? true
+                                   : compute_t<T>(ptr[lhs]) ==
+                                           compute_t<T>(ptr[rhs])
+                                       ? (lhs < rhs)
+                                       : false;
+                        });
+                } else {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return compute_t<T>(ptr[lhs]) >
+                                   compute_t<T>(ptr[rhs]);
+                        });
+                }
             }
 
             auto* kvptr = vptr + k * i;
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 4552ab0b97..0d71d4949c 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -36,14 +36,26 @@ static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
     using BlockRadixSortT = BlockRadixSort<compute_t<T>, TOPK_THRDS_PER_BLK,
                                            TOPK_IDX_THRD_LOAD, ValueType>;
 
-    __shared__ typename BlockRadixSortT::TempStorage smem;
+    struct keyValBlocks {
+        // used for rearranging each granule's data items
+        // we want each thread(granule) to own TOPK_IDX_THRD_LOAD=4 consecutive
+        // datum for both coalesced memory reads and this blocked layout we need
+        // this SMEM to rearrange
+        compute_t<T> keys[TOPK_IDX_THRD_LOAD * TOPK_THRDS_PER_BLK];
+        ValueType vals[TOPK_IDX_THRD_LOAD * TOPK_THRDS_PER_BLK];
+    };
+
+    union smemUnion {
+        // used for cub radix sort
+        typename BlockRadixSortT::TempStorage sortmem;
+        // used for rearranging
+        keyValBlocks blkt;
+    } __shared__ smem;
 
     const int bw = blockIdx.y / numLaunchBlocksY;
     const int bz = blockIdx.z;
     const int by = (blockIdx.y - bw * numLaunchBlocksY);
 
-    const uint gx       = blockIdx.x * blockDim.x + threadIdx.x;
-    const uint gxStride = blockDim.x * gridDim.x;
     const uint elements = ivals.dims[0];
 
     const data_t<T>* kdata = ivals.ptr + by * ivals.strides[1] +
@@ -60,21 +72,41 @@ static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
     compute_t<T> keys[TOPK_IDX_THRD_LOAD];
     ValueType vals[TOPK_IDX_THRD_LOAD];
 
-    for (uint li = 0, i = gx; li < TOPK_IDX_THRD_LOAD; i += gxStride, li++) {
+    const int blockOffset =
+        blockDim.x * blockIdx.x * TOPK_IDX_THRD_LOAD + threadIdx.x;
+// each block will load consecutive data items while iterating a block-width at
+// a time [B0][][]...[][B1][][]...[] ... [BN][][]...[]
+#pragma unroll
+    for (uint li = 0, i = blockOffset; li < TOPK_IDX_THRD_LOAD;
+         i += blockDim.x, li++) {
         if (i < elements) {
-            keys[li] = static_cast<compute_t<T>>(kdata[i]);
-            vals[li] = (READ_INDEX) ? idata[i] : i;
+            smem.blkt.keys[li * TOPK_THRDS_PER_BLK + threadIdx.x] =
+                static_cast<compute_t<T>>(kdata[i]);
+            smem.blkt.vals[li * TOPK_THRDS_PER_BLK + threadIdx.x] =
+                (READ_INDEX) ? idata[i] : i;
         } else {
-            keys[li] = (order == AF_TOPK_MAX) ? minval<compute_t<T>>()
-                                              : maxval<compute_t<T>>();
-            vals[li] = maxval<ValueType>();
+            smem.blkt.keys[li * TOPK_THRDS_PER_BLK + threadIdx.x] =
+                (order & AF_TOPK_MAX) ? minval<compute_t<T>>()
+                                      : maxval<compute_t<T>>();
+            smem.blkt.vals[li * TOPK_THRDS_PER_BLK + threadIdx.x] =
+                maxval<ValueType>();
         }
     }
+    __syncthreads();
 
-    if (order == AF_TOPK_MAX) {
-        BlockRadixSortT(smem).SortDescendingBlockedToStriped(keys, vals);
+#pragma unroll
+    for (uint li = 0; li < TOPK_IDX_THRD_LOAD; li++) {
+        // transposed read into registers for cub radix sort
+        keys[li] = smem.blkt.keys[li + (threadIdx.x * TOPK_IDX_THRD_LOAD)];
+        vals[li] = smem.blkt.vals[li + (threadIdx.x * TOPK_IDX_THRD_LOAD)];
+    }
+    __syncthreads();
+
+    if (order & AF_TOPK_MAX) {
+        BlockRadixSortT(smem.sortmem)
+            .SortDescendingBlockedToStriped(keys, vals);
     } else {
-        BlockRadixSortT(smem).SortBlockedToStriped(keys, vals);
+        BlockRadixSortT(smem.sortmem).SortBlockedToStriped(keys, vals);
     }
 
     if (threadIdx.x < k) {
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 02f23cfa67..2d6f84493b 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -222,10 +222,11 @@ void sort0ByKey(Param pKey, Param pVal, bool isAscending) {
     // But this is only useful before GPU is saturated
     // The GPU is saturated at around 1000,000 integers
     // Call batched sort only if both conditions are met
-    if (higherDims > 4 && pKey.info.dims[0] < 1000000)
+    if (higherDims > 4 && pKey.info.dims[0] < 1000000) {
         kernel::sortByKeyBatched<Tk, Tv>(pKey, pVal, 0, isAscending);
-    else
+    } else {
         kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
+    }
 }
 
 #define INSTANTIATE(Tk, Tv)                                           \
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 5795ddd380..08155b9d8a 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -94,19 +94,49 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
             auto idx_itr = begin(idx) + i * in.strides()[1];
             auto kiptr   = iptr + k * i;
 
-            if (order == AF_TOPK_MIN) {
-                // Sort the top k values in each column
-                partial_sort_copy(
-                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-                    [ptr](const uint lhs, const uint rhs) -> bool {
-                        return compute_t<T>(ptr[lhs]) < compute_t<T>(ptr[rhs]);
-                    });
+            if (order & AF_TOPK_MIN) {
+                if (order & AF_TOPK_STABLE) {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return (compute_t<T>(ptr[lhs]) <
+                                    compute_t<T>(ptr[rhs]))
+                                       ? true
+                                   : compute_t<T>(ptr[lhs]) ==
+                                           compute_t<T>(ptr[rhs])
+                                       ? (lhs < rhs)
+                                       : false;
+                        });
+                } else {
+                    // Sort the top k values in each column
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return compute_t<T>(ptr[lhs]) <
+                                   compute_t<T>(ptr[rhs]);
+                        });
+                }
             } else {
-                partial_sort_copy(
-                    idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-                    [ptr](const uint lhs, const uint rhs) -> bool {
-                        return compute_t<T>(ptr[lhs]) >= compute_t<T>(ptr[rhs]);
-                    });
+                if (order & AF_TOPK_STABLE) {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return (compute_t<T>(ptr[lhs]) >
+                                    compute_t<T>(ptr[rhs]))
+                                       ? true
+                                   : compute_t<T>(ptr[lhs]) ==
+                                           compute_t<T>(ptr[rhs])
+                                       ? (lhs < rhs)
+                                       : false;
+                        });
+                } else {
+                    partial_sort_copy(
+                        idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+                        [ptr](const uint lhs, const uint rhs) -> bool {
+                            return compute_t<T>(ptr[lhs]) >
+                                   compute_t<T>(ptr[rhs]);
+                        });
+                }
             }
             ev_val.wait();
 
@@ -128,7 +158,7 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
     } else {
         auto values  = createEmptyArray<T>(in.dims());
         auto indices = createEmptyArray<unsigned>(in.dims());
-        sort_index(values, indices, in, dim, order == AF_TOPK_MIN);
+        sort_index(values, indices, in, dim, order & AF_TOPK_MIN);
         auto indVec = indexForTopK(k);
         vals        = index<T>(values, indVec.data());
         idxs        = index<unsigned>(indices, indVec.data());
diff --git a/test/topk.cpp b/test/topk.cpp
index 46eba3f159..46c4355d6a 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -113,7 +113,7 @@ void topkTest(const int ndims, const dim_t* dims, const unsigned k,
         for (size_t i = b * bSize; i < ((b + 1) * bSize); ++i)
             kvPairs.push_back(make_pair(inData[i], (i - b * bSize)));
 
-        if (order == AF_TOPK_MIN) {
+        if (order & AF_TOPK_MIN) {
             stable_sort(kvPairs.begin(), kvPairs.end(),
                         [](const KeyValuePair& lhs, const KeyValuePair& rhs) {
                             return lhs.first < rhs.first;
@@ -233,6 +233,74 @@ TEST(TopK, ValidationCheck_DefaultDim) {
     ASSERT_SUCCESS(af_release_array(idx));
 }
 
+// stable variants
+TYPED_TEST(TopK, Max1D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t), 1, 1, 1};
+    topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_STABLE_MAX);
+}
+
+TYPED_TEST(TopK, Max2D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 10, 10, 1, 1};
+    topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_STABLE_MAX);
+}
+
+TYPED_TEST(TopK, Max3D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 100, 10, 10, 1};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_STABLE_MAX);
+}
+
+TYPED_TEST(TopK, Max4D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 1000, 10, 10, 10};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_STABLE_MAX);
+}
+
+TYPED_TEST(TopK, Min1D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t), 1, 1, 1};
+    topkTest<TypeParam>(1, dims, 5, 0, AF_TOPK_STABLE_MIN);
+}
+
+TYPED_TEST(TopK, Min2D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 10, 10, 1, 1};
+    topkTest<TypeParam>(2, dims, 3, 0, AF_TOPK_STABLE_MIN);
+}
+
+TYPED_TEST(TopK, Min3D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 100, 10, 10, 1};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_STABLE_MIN);
+}
+
+TYPED_TEST(TopK, Min4D0_Stable) {
+    af_dtype t    = (af_dtype)dtype_traits<TypeParam>::af_type;
+    dim_t dims[4] = {type_max(t) / 1000, 10, 10, 10};
+    topkTest<TypeParam>(2, dims, 5, 0, AF_TOPK_STABLE_MIN);
+}
+
+TEST(TopK, ValidationCheck_DimN_Stable) {
+    dim_t dims[4] = {10, 10, 1, 1};
+    af_array out, idx, in;
+    ASSERT_SUCCESS(af_randu(&in, 2, dims, f32));
+    ASSERT_EQ(AF_ERR_NOT_SUPPORTED,
+              af_topk(&out, &idx, in, 10, 1, AF_TOPK_STABLE_MAX));
+    ASSERT_SUCCESS(af_release_array(in));
+}
+
+TEST(TopK, ValidationCheck_DefaultDim_Stable) {
+    dim_t dims[4] = {10, 10, 1, 1};
+    af_array out, idx, in;
+    ASSERT_SUCCESS(af_randu(&in, 4, dims, f32));
+    ASSERT_SUCCESS(af_topk(&out, &idx, in, 10, -1, AF_TOPK_STABLE_MAX));
+    ASSERT_SUCCESS(af_release_array(in));
+    ASSERT_SUCCESS(af_release_array(out));
+    ASSERT_SUCCESS(af_release_array(idx));
+}
+
 struct topk_params {
     int d0;
     int d1;
@@ -367,3 +435,61 @@ TEST(TopK, KLessThan0) {
     EXPECT_THROW(topk(vals, idx, a, k), af::exception)
         << "K cannot be less than 0";
 }
+
+TEST(TopK, DeterministicTiesMin) {
+    af::array a           = af::constant(1, 500);
+    a(af::seq(0, 499, 2)) = 7;
+    af::array vals_min, idx_min;
+
+    int k = 6;
+    topk(vals_min, idx_min, a, k, 0, AF_TOPK_STABLE_MIN);
+
+    af::array expected_idx_min   = af::seq(1, 499, 2);
+    af::array k_expected_idx_min = expected_idx_min(af::seq(0, k - 1));
+    ASSERT_ARRAYS_EQ(idx_min, k_expected_idx_min.as(u32));
+}
+
+TEST(TopK, DeterministicTiesMax) {
+    af::array a           = af::constant(1, 500);
+    a(af::seq(0, 499, 2)) = 7;
+    af::array vals_max, idx_max;
+
+    int k = 6;
+    topk(vals_max, idx_max, a, k, 0, AF_TOPK_STABLE_MAX);
+
+    af::array expected_idx_max   = af::seq(0, 499, 2);
+    af::array k_expected_idx_max = expected_idx_max(af::seq(0, k - 1));
+    ASSERT_ARRAYS_EQ(idx_max, k_expected_idx_max.as(u32));
+}
+
+TEST(TopK, DeterministicTiesBatchedMin) {
+    const int nbatch = 10;
+    af::array a      = af::constant(1, 500, nbatch, nbatch, nbatch);
+    a(af::seq(0, 499, 2), af::span, af::span, af::span) = 7;
+    af::array vals_min, idx_min;
+
+    int k = 6;
+    topk(vals_min, idx_min, a, k, 0, AF_TOPK_STABLE_MIN);
+
+    af::array expected_idx_min = af::seq(1, 499, 2);
+    af::array k_expected_idx_min =
+        af::tile(expected_idx_min(af::seq(0, k - 1)),
+                 af::dim4(1, nbatch, nbatch, nbatch));
+    ASSERT_ARRAYS_EQ(idx_min, k_expected_idx_min.as(u32));
+}
+
+TEST(TopK, DeterministicTiesBatchedMax) {
+    const int nbatch = 10;
+    af::array a      = af::constant(1, 500, nbatch, nbatch, nbatch);
+    a(af::seq(0, 499, 2), af::span, af::span, af::span) = 7;
+    af::array vals_max, idx_max;
+
+    int k = 6;
+    topk(vals_max, idx_max, a, k, 0, AF_TOPK_STABLE_MAX);
+
+    af::array expected_idx_max = af::seq(0, 499, 2);
+    af::array k_expected_idx_max =
+        af::tile(expected_idx_max(af::seq(0, k - 1)),
+                 af::dim4(1, nbatch, nbatch, nbatch));
+    ASSERT_ARRAYS_EQ(idx_max, k_expected_idx_max.as(u32));
+}

From 35bd3f88c8cde9ca99b2ff0626e22f54feb8fc0e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 25 Mar 2022 22:07:14 -0400
Subject: [PATCH 2232/2677] Add support for staticly linking nvrtc starting
 CUDA 11.5

---
 src/backend/cuda/CMakeLists.txt | 57 +++++++++++++++++++++------------
 1 file changed, 36 insertions(+), 21 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index d75b96296a..8bd6a18391 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -60,6 +60,8 @@ set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
+list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY})
+
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   af_find_static_cuda_libs(culibos)
   af_find_static_cuda_libs(cublas_static PRUNE)
@@ -67,6 +69,15 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   af_find_static_cuda_libs(cufft_static)
   af_find_static_cuda_libs(cusparse_static PRUNE)
 
+  if(CUDA_VERSION VERSION_GREATER 11.4)
+    af_find_static_cuda_libs(nvrtc_static)
+    af_find_static_cuda_libs(nvrtc-builtins_static)
+    af_find_static_cuda_libs(nvptxcompiler_static)
+    set(nvrtc_libs ${AF_CUDA_nvrtc_static_LIBRARY}
+                   ${AF_CUDA_nvrtc-builtins_static_LIBRARY}
+                   ${AF_CUDA_nvptxcompiler_static_LIBRARY})
+  endif()
+
   # FIXME When NVCC resolves this particular issue.
   # NVCC doesn't like -l<full_path_static_lib>, hence we cannot
   # use ${CMAKE_*_LIBRARY} variables in the following flags.
@@ -328,9 +339,10 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
       ${START_GROUP}
       ${CUDA_culibos_LIBRARY} #also a static libary
       ${AF_CUDA_cublas_static_LIBRARY}
+      ${AF_CUDA_cublasLt_static_LIBRARY}
       ${AF_CUDA_cufft_static_LIBRARY}
       ${AF_CUDA_cusparse_static_LIBRARY}
-      ${AF_CUDA_cublasLt_static_LIBRARY}
+      ${nvrtc_libs}
       ${cusolver_static_lib}
       ${END_GROUP}
   )
@@ -340,6 +352,7 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
       PRIVATE
         ${AF_CUDA_cublasLt_static_LIBRARY})
   endif()
+
   if(CUDA_VERSION VERSION_GREATER 9.5)
     target_link_libraries(af_cuda_static_cuda_library
       PRIVATE
@@ -355,6 +368,7 @@ else()
       ${CUDA_CUFFT_LIBRARIES}
       ${CUDA_cusolver_LIBRARY}
       ${CUDA_cusparse_LIBRARY}
+      ${nvrtc_libs}
   )
 endif()
 
@@ -712,7 +726,6 @@ target_link_libraries(afcuda
     cpp_api_interface
     afcommon_interface
     ${CMAKE_DL_LIBS}
-    ${CUDA_nvrtc_LIBRARY}
     af_cuda_static_cuda_library
   )
 
@@ -860,26 +873,28 @@ if(AF_INSTALL_STANDALONE)
     afcu_collect_libs(cusolver)
   endif()
 
-  afcu_collect_libs(nvrtc FULL_VERSION)
-  if(CUDA_VERSION VERSION_GREATER 10.0)
-    afcu_collect_libs(nvrtc-builtins FULL_VERSION)
-  else()
-    if(APPLE)
-      afcu_collect_libs(cudart)
-
-      get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
-      install(FILES       ${nvrtc_outpath}
-              DESTINATION ${AF_INSTALL_BIN_DIR}
-              RENAME      "${PX}nvrtc-builtins${SX}"
-              COMPONENT   cuda_dependencies)
-    elseif(UNIX)
-      get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
-      install(FILES       ${nvrtc_outpath}
-              DESTINATION ${AF_INSTALL_LIB_DIR}
-              RENAME      "${PX}nvrtc-builtins${SX}"
-              COMPONENT   cuda_dependencies)
+  if(WIN32 OR CUDA_VERSION VERSION_LESS 11.5 OR NOT AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+    afcu_collect_libs(nvrtc FULL_VERSION)
+    if(CUDA_VERSION VERSION_GREATER 10.0)
+      afcu_collect_libs(nvrtc-builtins FULL_VERSION)
     else()
-      afcu_collect_libs(nvrtc-builtins)
+      if(APPLE)
+        afcu_collect_libs(cudart)
+
+        get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins.${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}${SX}" REALPATH)
+        install(FILES       ${nvrtc_outpath}
+                DESTINATION ${AF_INSTALL_BIN_DIR}
+                RENAME      "${PX}nvrtc-builtins${SX}"
+                COMPONENT   cuda_dependencies)
+      elseif(UNIX)
+        get_filename_component(nvrtc_outpath "${dlib_path_prefix}/${PX}nvrtc-builtins${SX}" REALPATH)
+        install(FILES       ${nvrtc_outpath}
+                DESTINATION ${AF_INSTALL_LIB_DIR}
+                RENAME      "${PX}nvrtc-builtins${SX}"
+                COMPONENT   cuda_dependencies)
+      else()
+        afcu_collect_libs(nvrtc-builtins)
+      endif()
     endif()
   endif()
 endif()

From ff774fe48b0e639307218b5116cae8a9985f0986 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 27 Mar 2022 11:52:17 -0400
Subject: [PATCH 2233/2677] Fix prune by making the cuda_prune_library_targets
 a set with parent_scope

The cuda_prune_library_targets was not being exposed in the parent
scope because it was used in a list. This commit changes the list
to a set to append the targets to that CMake variable which allows
us to use PARENT_SCOPE in the command.
---
 CMakeModules/AFcuda_helpers.cmake | 6 +++---
 src/backend/cuda/CMakeLists.txt   | 5 +++--
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/CMakeModules/AFcuda_helpers.cmake b/CMakeModules/AFcuda_helpers.cmake
index 578c49956b..598c6cd233 100644
--- a/CMakeModules/AFcuda_helpers.cmake
+++ b/CMakeModules/AFcuda_helpers.cmake
@@ -27,11 +27,11 @@ function(af_find_static_cuda_libs libname)
       MAIN_DEPENDENCY ${CUDA_${libname}_LIBRARY}
       COMMENT "Pruning ${CUDA_${libname}_LIBRARY} for ${cuda_build_targets}"
       VERBATIM)
-    add_custom_target(AF_CUDA_${libname}_LIBRARY_TARGET
+    add_custom_target(prune_${libname}
       DEPENDS ${liboutput}.depend)
-    list(APPEND cuda_pruned_libraries AF_CUDA_${libname}_LIBRARY_TARGET PARENT_SCOPE)
+    set(cuda_pruned_library_targets ${cuda_pruned_library_targets};prune_${libname} PARENT_SCOPE)
 
-    set(AF_CUDA_${libname}_LIBRARY ${liboutput} PARENT_SCOPE)
+    set(AF_CUDA_${libname}_LIBRARY "${liboutput}" PARENT_SCOPE)
     mark_as_advanced(AF_CUDA_${libname}_LIBRARY)
   else()
     set(AF_CUDA_${libname}_LIBRARY ${CUDA_${libname}_LIBRARY} PARENT_SCOPE)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8bd6a18391..fe794bfb61 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -702,8 +702,9 @@ add_library(ArrayFire::afcuda ALIAS afcuda)
 
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
 add_dependencies(af_cuda_static_cuda_library ${nvrtc_kernel_targets})
-if(cuda_pruned_libraries)
-  add_dependencies(afcuda ${cuda_pruned_libraries})
+
+if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+  add_dependencies(afcuda ${cuda_pruned_library_targets})
 endif()
 
 target_include_directories (afcuda

From c696425aaadb605a873d4933ef90a0ef2e87cf63 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 27 Mar 2022 15:28:19 -0400
Subject: [PATCH 2234/2677] Make pruning static CUDA libs optional with flag

Make pruning CUDA static libraries optional for static CUDA libraries
because nvprune seems to fail for some combination of CUDA toolkits
and compute capabilities.
---
 CMakeLists.txt                    | 4 ++++
 CMakeModules/AFcuda_helpers.cmake | 4 ++--
 src/backend/cuda/CMakeLists.txt   | 2 +-
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index dce9076c8c..1ef063ac52 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -76,6 +76,10 @@ option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
 option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
 option(AF_WITH_STATIC_CUDA_NUMERIC_LIBS "Link libafcuda with static numeric libraries(cublas, cufft, etc.)" OFF)
 
+if(AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+  option(AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS "Prune CUDA static libraries to reduce binary size.(WARNING: May break some libs on older CUDA toolkits for some compute arch)" OFF)
+endif()
+
 set(default_compute_library "FFTW/LAPACK/BLAS")
 if(MKL_FOUND)
   set(default_compute_library "Intel-MKL")
diff --git a/CMakeModules/AFcuda_helpers.cmake b/CMakeModules/AFcuda_helpers.cmake
index 598c6cd233..59cfb2002a 100644
--- a/CMakeModules/AFcuda_helpers.cmake
+++ b/CMakeModules/AFcuda_helpers.cmake
@@ -6,6 +6,7 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 find_program(NVPRUNE NAMES nvprune)
+
 # The following macro uses a macro defined by
 # FindCUDA module from cmake.
 function(af_find_static_cuda_libs libname)
@@ -16,7 +17,7 @@ function(af_find_static_cuda_libs libname)
   cuda_find_library_local_first(CUDA_${libname}_LIBRARY
     ${search_name} "${libname} static library")
 
-  if(fscl_PRUNE)
+  if(fscl_PRUNE AND AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS)
     get_filename_component(af_${libname} ${CUDA_${libname}_LIBRARY} NAME)
 
     set(liboutput ${CMAKE_CURRENT_BINARY_DIR}/${af_${libname}})
@@ -32,7 +33,6 @@ function(af_find_static_cuda_libs libname)
     set(cuda_pruned_library_targets ${cuda_pruned_library_targets};prune_${libname} PARENT_SCOPE)
 
     set(AF_CUDA_${libname}_LIBRARY "${liboutput}" PARENT_SCOPE)
-    mark_as_advanced(AF_CUDA_${libname}_LIBRARY)
   else()
     set(AF_CUDA_${libname}_LIBRARY ${CUDA_${libname}_LIBRARY} PARENT_SCOPE)
   endif()
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index fe794bfb61..ee20e453ac 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -703,7 +703,7 @@ add_library(ArrayFire::afcuda ALIAS afcuda)
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
 add_dependencies(af_cuda_static_cuda_library ${nvrtc_kernel_targets})
 
-if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+if(UNIX AND AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS)
   add_dependencies(afcuda ${cuda_pruned_library_targets})
 endif()
 

From 35861bf4cc42444158f7a210eda5ac7e46082e76 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 27 Mar 2022 15:48:35 -0400
Subject: [PATCH 2235/2677] Add support for ccache to the CUDA backend

---
 CMakeModules/config_ccache.cmake |  4 ++++
 CMakeModules/launch-nvcc.in      | 10 ++++++++++
 2 files changed, 14 insertions(+)
 create mode 100644 CMakeModules/launch-nvcc.in

diff --git a/CMakeModules/config_ccache.cmake b/CMakeModules/config_ccache.cmake
index b112787d76..1bf3adaef6 100644
--- a/CMakeModules/config_ccache.cmake
+++ b/CMakeModules/config_ccache.cmake
@@ -14,11 +14,14 @@ if (UNIX)
     # Set up wrapper scripts
     set(C_LAUNCHER   "${CCACHE_PROGRAM}")
     set(CXX_LAUNCHER "${CCACHE_PROGRAM}")
+    set(NVCC_LAUNCHER "${CCACHE_PROGRAM}")
     configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-c.in   launch-c)
     configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-cxx.in launch-cxx)
+    configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-nvcc.in launch-nvcc)
     execute_process(COMMAND chmod a+rx
         "${ArrayFire_BINARY_DIR}/launch-c"
         "${ArrayFire_BINARY_DIR}/launch-cxx"
+        "${ArrayFire_BINARY_DIR}/launch-nvcc"
       )
     if(CMAKE_GENERATOR STREQUAL "Xcode")
       # Set Xcode project attributes to route compilation and linking
@@ -31,6 +34,7 @@ if (UNIX)
       # Support Unix Makefiles and Ninja
       set(CMAKE_C_COMPILER_LAUNCHER   "${ArrayFire_BINARY_DIR}/launch-c")
       set(CMAKE_CXX_COMPILER_LAUNCHER "${ArrayFire_BINARY_DIR}/launch-cxx")
+      set(CUDA_NVCC_EXECUTABLE "${ArrayFire_BINARY_DIR}/launch-nvcc")
     endif()
   endif()
   mark_as_advanced(CCACHE_PROGRAM)
diff --git a/CMakeModules/launch-nvcc.in b/CMakeModules/launch-nvcc.in
new file mode 100644
index 0000000000..47a4591850
--- /dev/null
+++ b/CMakeModules/launch-nvcc.in
@@ -0,0 +1,10 @@
+#!/bin/sh
+
+# Xcode generator doesn't include the compiler as the
+# first argument, Ninja and Makefiles do. Handle both cases.
+if [ "$1" = "${CUDA_NVCC_EXECUTABLE}" ] ; then
+    shift
+fi
+
+export CCACHE_CPP2=true
+exec "${NVCC_LAUNCHER}" "${CUDA_NVCC_EXECUTABLE}" "$@"

From 5b2e8ea34ff6d7f35bd68768be886d50463dd6e4 Mon Sep 17 00:00:00 2001
From: Jacob Kahn <jacobkahn1@gmail.com>
Date: Wed, 6 Apr 2022 12:00:42 -0500
Subject: [PATCH 2236/2677] JIT optimization for sequential casts that are
 idempotent (#3031)

Adds a JIT optimization which removes sequential casts in cases that don't
result in a differently-typed result.

This commit removes the following casts:

* Casts for conversions between any floating point types.
* Casts from smaller integer types to larger integer type and back

Following casts are NOT removed

* Floating point to integer types and back
* Integer types from larger types to smaller types and back

Casts can be forced by calling eval on the casted intermediate array
---
 include/af/arith.h                        |  29 ++++++
 include/af/array.h                        |  29 +++++-
 src/backend/common/ArrayInfo.cpp          |  24 ++---
 src/backend/common/cast.hpp               | 121 +++++++++++++++++++++-
 src/backend/common/jit/BufferNodeBase.hpp |   2 +
 src/backend/common/jit/NaryNode.hpp       |  10 +-
 src/backend/common/jit/Node.hpp           |   5 +
 src/backend/common/traits.hpp             |  53 +++++++++-
 src/backend/cuda/Array.cpp                |   8 +-
 src/backend/cuda/cast.hpp                 |   1 -
 src/backend/opencl/Array.cpp              |   6 +-
 test/cast.cpp                             |  91 ++++++++++++++++
 12 files changed, 350 insertions(+), 29 deletions(-)

diff --git a/include/af/arith.h b/include/af/arith.h
index 6b0c08dea5..83240ffc6d 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -822,6 +822,35 @@ extern "C" {
     /**
        C Interface for casting an array from one type to another
 
+       This function casts an af_array object from one type to another. If the
+       type of the original array is the same as \p type then the same array is
+       returned.
+
+       \note Consecitive casting operations may be may be optimized out if the
+       original type of the af_array is the same as the final type. For example
+       if the original type is f64 which is then cast to f32 and then back to
+       f64, then the cast to f32 will be skipped and that operation will *NOT*
+       be performed by ArrayFire. The following table shows which casts will
+       be optimized out. outer -> inner -> outer
+       | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
+       |---------|-----|-----|-----|-----|-----|-----|----|----|-----|-----|-----|-----|-----|
+       | f32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+       | f64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+       | c32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+       | c64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+       | s32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
+       | u32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
+       | u8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
+       | b8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
+       | s64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
+       | u64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
+       | s16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
+       | u16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
+       | f16     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+       If you want to avoid this behavior use af_eval after the first cast
+       operation. This will ensure that the cast operation is performed on the
+       af_array
+
        \param[out] out will contain the values in the specified type
        \param[in] in is the input
        \param[in] type is the target data type \ref af_dtype
diff --git a/include/af/array.h b/include/af/array.h
index b30d5694fc..bdd9ac4e9c 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -933,9 +933,34 @@ namespace af
         const array::array_proxy slices(int first, int last) const; ///< \copydoc slices
         /// @}
 
-        /// \brief Converts the array into another type
+        /// \brief Casts the array into another data type
         ///
-        ///  \param[in] type is the desired type(f32, s64, etc.)
+        /// \note Consecitive casting operations may be may be optimized out if
+        /// the original type of the af::array is the same as the final type.
+        /// For example if the original type is f64 which is then cast to f32
+        /// and then back to f64, then the cast to f32 will be skipped and that
+        /// operation will *NOT* be performed by ArrayFire. The following table
+        /// shows which casts will be optimized out. outer -> inner -> outer
+        /// | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
+        /// |---------|-----|-----|-----|-----|-----|-----|----|----|-----|-----|-----|-----|-----|
+        /// | f32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+        /// | f64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+        /// | c32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+        /// | c64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+        /// | s32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
+        /// | u32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
+        /// | u8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
+        /// | b8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
+        /// | s64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
+        /// | u64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
+        /// | s16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
+        /// | u16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
+        /// | f16     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+        /// If you want to avoid this behavior use af_eval after the first cast
+        /// operation. This will ensure that the cast operation is performed on
+        /// the af::array
+        ///
+        /// \param[in] type is the desired type(f32, s64, etc.)
         /// \returns an array with the type specified by \p type
         const array as(dtype type) const;
 
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index 6cf55d20ea..585b48d403 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -9,6 +9,7 @@
 
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/traits.hpp>
 #include <algorithm>
 #include <functional>
 #include <numeric>
@@ -93,28 +94,23 @@ bool ArrayInfo::isVector() const {
     return singular_dims == AF_MAX_DIMS - 1 && non_singular_dims == 1;
 }
 
-bool ArrayInfo::isComplex() const { return ((type == c32) || (type == c64)); }
+bool ArrayInfo::isComplex() const { return common::isComplex(type); }
 
-bool ArrayInfo::isReal() const { return !isComplex(); }
+bool ArrayInfo::isReal() const { return common::isReal(type); }
 
-bool ArrayInfo::isDouble() const { return (type == f64 || type == c64); }
+bool ArrayInfo::isDouble() const { return common::isDouble(type); }
 
-bool ArrayInfo::isSingle() const { return (type == f32 || type == c32); }
+bool ArrayInfo::isSingle() const { return common::isSingle(type); }
 
-bool ArrayInfo::isHalf() const { return (type == f16); }
+bool ArrayInfo::isHalf() const { return common::isHalf(type); }
 
-bool ArrayInfo::isRealFloating() const {
-    return (type == f64 || type == f32 || type == f16);
-}
+bool ArrayInfo::isRealFloating() const { return common::isRealFloating(type); }
 
-bool ArrayInfo::isFloating() const { return (!isInteger() && !isBool()); }
+bool ArrayInfo::isFloating() const { return common::isFloating(type); }
 
-bool ArrayInfo::isInteger() const {
-    return (type == s32 || type == u32 || type == s64 || type == u64 ||
-            type == s16 || type == u16 || type == u8);
-}
+bool ArrayInfo::isInteger() const { return common::isInteger(type); }
 
-bool ArrayInfo::isBool() const { return (type == b8); }
+bool ArrayInfo::isBool() const { return common::isBool(type); }
 
 bool ArrayInfo::isLinear() const {
     if (ndims() == 1) { return dim_strides[0] == 1; }
diff --git a/src/backend/common/cast.hpp b/src/backend/common/cast.hpp
index b266d8517a..d80caacfe6 100644
--- a/src/backend/common/cast.hpp
+++ b/src/backend/common/cast.hpp
@@ -10,37 +10,150 @@
 #pragma once
 #include <Array.hpp>
 #include <cast.hpp>
+#include <common/Logger.hpp>
+#include <memory>
 
 #ifdef AF_CPU
 #include <jit/UnaryNode.hpp>
 #endif
 
 namespace common {
+/// This function determines if consecutive cast operations should be
+/// removed from a JIT AST.
+///
+/// This function returns true if consecutive cast operations in the JIT AST
+/// should be removed. Multiple cast operations are removed when going from
+/// a smaller type to a larger type and back again OR if the conversion is
+/// between two floating point types including complex types.
+///
+///                  Cast operations that will be removed
+///                        outer -> inner -> outer
+///
+///                                inner cast
+///           f32  f64  c32  c64  s32  u32   u8   b8  s64  u64  s16  u16  f16
+///     f32    x    x    x    x                                            x
+///     f64    x    x    x    x                                            x
+///  o  c32    x    x    x    x                                            x
+///  u  c64    x    x    x    x                                            x
+///  t  s32    x    x    x    x    x    x              x    x              x
+///  e  u32    x    x    x    x    x    x              x    x              x
+///  r   u8    x    x    x    x    x    x    x    x    x    x    x    x    x
+///      b8    x    x    x    x    x    x    x    x    x    x    x    x    x
+///  c  s64    x    x    x    x                        x    x              x
+///  a  u64    x    x    x    x                        x    x              x
+///  s  s16    x    x    x    x    x    x              x    x    x    x    x
+///  t  u16    x    x    x    x    x    x              x    x    x    x    x
+///     f16    x    x    x    x                                            x
+///
+/// \param[in] outer The type of the second cast and the child of the
+///            previous cast
+/// \param[in] inner  The type of the first cast
+///
+/// \returns True if the inner cast operation should be removed
+constexpr bool canOptimizeCast(af::dtype outer, af::dtype inner) {
+    if (isFloating(outer)) {
+        if (isFloating(inner)) { return true; }
+    } else {
+        if (isFloating(inner)) { return true; }
+        if (dtypeSize(inner) >= dtypeSize(outer)) { return true; }
+    }
+
+    return false;
+}
 
 #ifdef AF_CPU
 template<typename To, typename Ti>
 struct CastWrapper {
+    static spdlog::logger *getLogger() noexcept {
+        static std::shared_ptr<spdlog::logger> logger =
+            common::loggerFactory("ast");
+        return logger.get();
+    }
+
     detail::Array<To> operator()(const detail::Array<Ti> &in) {
         using cpu::jit::UnaryNode;
 
-        Node_ptr in_node = in.getNode();
+        common::Node_ptr in_node = in.getNode();
+        constexpr af::dtype to_dtype =
+            static_cast<af::dtype>(af::dtype_traits<To>::af_type);
+        constexpr af::dtype in_dtype =
+            static_cast<af::dtype>(af::dtype_traits<Ti>::af_type);
+
+        if (canOptimizeCast(to_dtype, in_dtype)) {
+            // JIT optimization in the cast of multiple sequential casts that
+            // become idempotent - check to see if the previous operation was
+            // also a cast
+            // TODO: handle arbitrarily long chains of casts
+            auto in_node_unary =
+                std::dynamic_pointer_cast<UnaryNode<To, Ti, af_cast_t>>(
+                    in_node);
+
+            if (in_node_unary && in_node_unary->getOp() == af_cast_t) {
+                // child child's output type is the input type of the child
+                AF_TRACE("Cast optimiztion performed by removing cast to {}",
+                         af::dtype_traits<Ti>::getName());
+                auto in_child_node = in_node_unary->getChildren()[0];
+                if (in_child_node->getType() == to_dtype) {
+                    // ignore the input node and simply connect a noop node from
+                    // the child's child to produce this op's output
+                    return detail::createNodeArray<To>(in.dims(),
+                                                       in_child_node);
+                }
+            }
+        }
+
         auto node = std::make_shared<UnaryNode<To, Ti, af_cast_t>>(in_node);
 
         return detail::createNodeArray<To>(in.dims(), move(node));
     }
 };
 #else
+
 template<typename To, typename Ti>
 struct CastWrapper {
+    static spdlog::logger *getLogger() noexcept {
+        static std::shared_ptr<spdlog::logger> logger =
+            common::loggerFactory("ast");
+        return logger.get();
+    }
+
     detail::Array<To> operator()(const detail::Array<Ti> &in) {
+        using common::UnaryNode;
         detail::CastOp<To, Ti> cop;
         common::Node_ptr in_node = in.getNode();
-        common::UnaryNode *node  = new common::UnaryNode(
-            static_cast<af::dtype>(dtype_traits<To>::af_type), cop.name(),
-            in_node, af_cast_t);
+        constexpr af::dtype to_dtype =
+            static_cast<af::dtype>(dtype_traits<To>::af_type);
+        constexpr af::dtype in_dtype =
+            static_cast<af::dtype>(af::dtype_traits<Ti>::af_type);
+
+        if (canOptimizeCast(to_dtype, in_dtype)) {
+            // JIT optimization in the cast of multiple sequential casts that
+            // become idempotent - check to see if the previous operation was
+            // also a cast
+            // TODO: handle arbitrarily long chains of casts
+            auto in_node_unary =
+                std::dynamic_pointer_cast<common::UnaryNode>(in_node);
+
+            if (in_node_unary && in_node_unary->getOp() == af_cast_t) {
+                // child child's output type is the input type of the child
+                AF_TRACE("Cast optimiztion performed by removing cast to {}",
+                         dtype_traits<Ti>::getName());
+                auto in_child_node = in_node_unary->getChildren()[0];
+                if (in_child_node->getType() == to_dtype) {
+                    // ignore the input node and simply connect a noop node from
+                    // the child's child to produce this op's output
+                    return detail::createNodeArray<To>(in.dims(),
+                                                       in_child_node);
+                }
+            }
+        }
+
+        common::UnaryNode *node =
+            new common::UnaryNode(to_dtype, cop.name(), in_node, af_cast_t);
         return detail::createNodeArray<To>(in.dims(), common::Node_ptr(node));
     }
 };
+
 #endif
 
 template<typename T>
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 5027cd5671..8bb8185378 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -34,6 +34,8 @@ class BufferNodeBase : public common::Node {
         return std::make_unique<BufferNodeBase>(*this);
     }
 
+    DataType getDataPointer() const { return m_data; }
+
     void setData(ParamType param, DataType data, const unsigned bytes,
                  bool is_linear) {
         m_param         = param;
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index c03af9c2a5..5e97e249dd 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -26,9 +26,11 @@ namespace common {
 class NaryNode : public Node {
    private:
     int m_num_children;
-    af_op_t m_op;
     const char *m_op_str;
 
+   protected:
+    af_op_t m_op;
+
    public:
     NaryNode(const af::dtype type, const char *op_str, const int num_children,
              const std::array<common::Node_ptr, Node::kMaxChildren> &&children,
@@ -39,8 +41,8 @@ class NaryNode : public Node {
                   const std::array<common::Node_ptr, Node::kMaxChildren>>(
                   children))
         , m_num_children(num_children)
-        , m_op(op)
-        , m_op_str(op_str) {
+        , m_op_str(op_str)
+        , m_op(op) {
         static_assert(std::is_nothrow_move_assignable<NaryNode>::value,
                       "NaryNode is not move assignable");
         static_assert(std::is_nothrow_move_constructible<NaryNode>::value,
@@ -61,8 +63,8 @@ class NaryNode : public Node {
         using std::swap;
         Node::swap(other);
         swap(m_num_children, other.m_num_children);
-        swap(m_op, other.m_op);
         swap(m_op_str, other.m_op_str);
+        swap(m_op, other.m_op);
     }
 
     af_op_t getOp() const noexcept final { return m_op; }
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 0b284c072e..ca557a50d6 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -181,6 +181,10 @@ class Node {
         UNUSED(lim);
     }
 
+    const std::array<Node_ptr, kMaxChildren> &getChildren() const {
+        return m_children;
+    }
+
     /// Generates the variable that stores the thread's/work-item's offset into
     /// the memory.
     ///
@@ -247,6 +251,7 @@ class Node {
     /// Returns true if the buffer is linear
     virtual bool isLinear(const dim_t dims[4]) const;
 
+    /// Returns the type
     af::dtype getType() const { return m_type; }
 
     /// Returns the string representation of the type
diff --git a/src/backend/common/traits.hpp b/src/backend/common/traits.hpp
index 8f27ce952f..cfd07b8a0e 100644
--- a/src/backend/common/traits.hpp
+++ b/src/backend/common/traits.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #pragma once
 
+#include <common/err_common.hpp>
 #include <af/defines.h>
 
 namespace af {
@@ -17,13 +18,63 @@ struct dtype_traits;
 
 namespace common {
 class half;
+
+namespace {
+
+inline size_t dtypeSize(af::dtype type) {
+    switch (type) {
+        case u8:
+        case b8: return 1;
+        case s16:
+        case u16:
+        case f16: return 2;
+        case s32:
+        case u32:
+        case f32: return 4;
+        case u64:
+        case s64:
+        case c32:
+        case f64: return 8;
+        case c64: return 16;
+        default: AF_RETURN_ERROR("Unsupported type", AF_ERR_INTERNAL);
+    }
+}
+
+constexpr bool isComplex(af::dtype type) {
+    return ((type == c32) || (type == c64));
+}
+
+constexpr bool isReal(af::dtype type) { return !isComplex(type); }
+
+constexpr bool isDouble(af::dtype type) { return (type == f64 || type == c64); }
+
+constexpr bool isSingle(af::dtype type) { return (type == f32 || type == c32); }
+
+constexpr bool isHalf(af::dtype type) { return (type == f16); }
+
+constexpr bool isRealFloating(af::dtype type) {
+    return (type == f64 || type == f32 || type == f16);
+}
+
+constexpr bool isInteger(af::dtype type) {
+    return (type == s32 || type == u32 || type == s64 || type == u64 ||
+            type == s16 || type == u16 || type == u8);
 }
 
+constexpr bool isBool(af::dtype type) { return (type == b8); }
+
+constexpr bool isFloating(af::dtype type) {
+    return (!isInteger(type) && !isBool(type));
+}
+
+}  // namespace
+}  // namespace common
+
 namespace af {
 template<>
 struct dtype_traits<common::half> {
     enum { af_type = f16, ctype = f16 };
     typedef common::half base_type;
-    static const char* getName() { return "half"; }
+    static const char *getName() { return "half"; }
 };
 }  // namespace af
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 134645f496..c6347d1bbe 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -21,7 +21,7 @@
 #include <cstddef>
 #include <memory>
 #include <numeric>
-#include <utility>
+#include <vector>
 
 using af::dim4;
 using common::half;
@@ -129,7 +129,11 @@ Array<T>::Array(const af::dim4 &dims, common::Node_ptr n)
     , data()
     , data_dims(dims)
     , node(move(n))
-    , owner(true) {}
+    , owner(true) {
+    if (node->isBuffer()) {
+        data = std::static_pointer_cast<BufferNode<T>>(node)->getDataPointer();
+    }
+}
 
 template<typename T>
 Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset_,
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index bae9b3cbb6..cfcc9a8042 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -16,7 +16,6 @@
 #include <optypes.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
-#include <complex>
 
 namespace cuda {
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 6e490f82a8..f3dd8d97ed 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -100,7 +100,11 @@ Array<T>::Array(const dim4 &dims, Node_ptr n)
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data_dims(dims)
     , node(std::move(n))
-    , owner(true) {}
+    , owner(true) {
+    if (node->isBuffer()) {
+        data = std::static_pointer_cast<BufferNode>(node)->getDataPointer();
+    }
+}
 
 template<typename T>
 Array<T>::Array(const dim4 &dims, const T *const in_data)
diff --git a/test/cast.cpp b/test/cast.cpp
index 75ff9aca42..96178a470c 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -14,6 +14,9 @@
 #include <af/array.h>
 #include <af/data.h>
 #include <af/random.h>
+#include <algorithm>
+#include <cstdlib>
+#include <vector>
 
 using af::cdouble;
 using af::cfloat;
@@ -99,3 +102,91 @@ COMPLEX_REAL_TESTS(cfloat, float)
 COMPLEX_REAL_TESTS(cfloat, double)
 COMPLEX_REAL_TESTS(cdouble, float)
 COMPLEX_REAL_TESTS(cdouble, double)
+
+TEST(CAST_TEST, Test_JIT_DuplicateCastNoop) {
+    // Does a trivial cast - check JIT kernel trace to ensure a __noop is
+    // generated since we don't have a way to test it directly
+    af_dtype ta = (af_dtype)dtype_traits<float>::af_type;
+    af_dtype tb = (af_dtype)dtype_traits<double>::af_type;
+    dim4 dims(num, 1, 1, 1);
+    af_array a, b, c;
+    af_randu(&a, dims.ndims(), dims.get(), ta);
+
+    af_cast(&b, a, tb);
+    af_cast(&c, b, ta);
+
+    std::vector<float> a_vals(num);
+    std::vector<float> c_vals(num);
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&a_vals[0], a));
+    ASSERT_SUCCESS(af_get_data_ptr((void **)&c_vals[0], c));
+
+    for (size_t i = 0; i < num; ++i) { ASSERT_FLOAT_EQ(a_vals[i], c_vals[i]); }
+
+    af_release_array(a);
+    af_release_array(b);
+    af_release_array(c);
+}
+
+TEST(Cast, ImplicitCast) {
+    using namespace af;
+    array a = randu(100, 100, f64);
+    array b = a.as(f32);
+
+    array c = max(abs(a - b));
+    ASSERT_ARRAYS_NEAR(constant(0, 1, 100, f64), c, 1e-7);
+}
+
+TEST(Cast, ConstantCast) {
+    using namespace af;
+    array a = constant(1, 100, f64);
+    array b = a.as(f32);
+
+    array c = max(abs(a - b));
+    ASSERT_ARRAYS_NEAR(c, constant(0, 1, f64), 1e-7);
+}
+
+TEST(Cast, OpCast) {
+    using namespace af;
+    array a = constant(1, 100, f64);
+    a       = a + a;
+    array b = a.as(f32);
+
+    array c = max(abs(a - b));
+    ASSERT_ARRAYS_NEAR(c, constant(0, 1, f64), 1e-7);
+}
+TEST(Cast, ImplicitCastIndexed) {
+    using namespace af;
+    array a = randu(100, 100, f64);
+    array b = a(span, 1).as(f32);
+    array c = max(abs(a(span, 1) - b));
+    ASSERT_ARRAYS_NEAR(constant(0, 1, 1, f64), c, 1e-7);
+}
+
+TEST(Cast, ImplicitCastIndexedNonLinear) {
+    using namespace af;
+    array a = randu(100, 100, f64);
+    array b = a(seq(10, 20, 2), 1).as(f32);
+    array c = max(abs(a(seq(10, 20, 2), 1) - b));
+    ASSERT_ARRAYS_NEAR(constant(0, 1, 1, f64), c, 1e-7);
+}
+
+TEST(Cast, ImplicitCastIndexedNonLinearArray) {
+    using namespace af;
+    array a   = randu(100, 100, f64);
+    array idx = seq(10, 20, 2);
+    array b   = a(idx, 1).as(f32);
+    array c   = max(abs(a(idx, 1) - b));
+    ASSERT_ARRAYS_NEAR(constant(0, 1, 1, f64), c, 1e-7);
+}
+
+TEST(Cast, ImplicitCastIndexedAndScoped) {
+    using namespace af;
+    array c;
+    {
+        array a = randu(100, 100, f64);
+        array b = a(span, 1).as(f32);
+        c       = abs(a(span, 1) - b);
+    }
+    c = max(c);
+    ASSERT_ARRAYS_NEAR(constant(0, 1, 1, f64), c, 1e-7);
+}

From 8c232900aa0e448b3e226ab00d2656b05b3d8edf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 Mar 2022 13:21:49 -0400
Subject: [PATCH 2237/2677] Create the af_multiple_option CMake macro

This commit adds the af_multiple_option macro which allows you to
create a CMake variable that has limited set of optional string
values assigned to it.
---
 CMakeLists.txt                   | 20 ++++++++++++--------
 CMakeModules/InternalUtils.cmake | 19 +++++++++++++++++++
 2 files changed, 31 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1ef063ac52..784ed20144 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -85,17 +85,21 @@ if(MKL_FOUND)
   set(default_compute_library "Intel-MKL")
 endif()
 
-set(AF_COMPUTE_LIBRARY ${default_compute_library}
-    CACHE STRING "Compute library for signal processing and linear algebra routines")
-set_property(CACHE AF_COMPUTE_LIBRARY
-    PROPERTY STRINGS "Intel-MKL" "FFTW/LAPACK/BLAS")
+af_multiple_option(NAME        AF_COMPUTE_LIBRARY
+                   DEFAULT     ${default_compute_library}
+                   DESCRIPTION "Compute library for signal processing and linear algebra routines"
+                   OPTIONS     "Intel-MKL" "FFTW/LAPACK/BLAS")
 
 if(WIN32)
-  set(AF_STACKTRACE_TYPE "Windbg" CACHE STRING "The type of backtrace features. Windbg(simple), None")
-  set_property(CACHE AF_STACKTRACE_TYPE PROPERTY STRINGS "Windbg" "None")
+  af_multiple_option(NAME         AF_STACKTRACE_TYPE
+                     DEFAULT      "Windbg"
+                     DESCRIPTION  "The type of backtrace features. Windbg(simple), None"
+                     OPTIONS       "Windbg" "None")
 else()
-  set(AF_STACKTRACE_TYPE "Basic" CACHE STRING "The type of backtrace features. Basic(simple), libbacktrace(fancy), addr2line(fancy), None")
-  set_property(CACHE AF_STACKTRACE_TYPE PROPERTY STRINGS "Basic" "libbacktrace" "addr2line" "None")
+  af_multiple_option(NAME         AF_STACKTRACE_TYPE
+                     DEFAULT      "Basic"
+                     DESCRIPTION  "The type of backtrace features. Basic(simple), libbacktrace(fancy), addr2line(fancy), None"
+                     OPTIONS       "Basic" "libbacktrace" "addr2line" "None")
 endif()
 
 option(AF_INSTALL_STANDALONE "Build installers that include all dependencies" OFF)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 1c1a8e5f5f..8fd21e7447 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -223,6 +223,25 @@ macro(af_mkl_batch_check)
   check_symbol_exists(sgetrf_batch_strided "mkl_lapack.h" MKL_BATCH)
 endmacro()
 
+# Creates a CACHEd CMake variable which has limited set of possible string values
+# Argumehts:
+#   NAME: The name of the variable
+#   DEFAULT: The default value of the variable
+#   DESCRIPTION: The description of the variable
+#   OPTIONS: The possible set of values for the option
+#
+# Example:
+#
+# af_multiple_option(NAME        AF_COMPUTE_LIBRARY
+#                    DEFAULT     "Intel-MKL"
+#                    DESCRIPTION "Compute library for signal processing and linear algebra routines"
+#                    OPTIONS     "Intel-MKL" "FFTW/LAPACK/BLAS")
+macro(af_multiple_option)
+  cmake_parse_arguments(opt "" "NAME;DEFAULT;DESCRIPTION" "OPTIONS" ${ARGN})
+  set(${opt_NAME} ${opt_DEFAULT} CACHE STRING ${opt_DESCRIPTION})
+  set_property(CACHE ${opt_NAME} PROPERTY STRINGS ${opt_OPTIONS})
+endmacro()
+
 mark_as_advanced(
     pkgcfg_lib_PC_CBLAS_cblas
     pkgcfg_lib_PC_LAPACKE_lapacke

From d7905f7299ed4cfbfaba7a77ed049372635d007d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Mar 2022 18:13:07 -0400
Subject: [PATCH 2238/2677] Make cuSparse a runtime dependency. Optionally
 allow static linking

This PR adds the ability to load cuSparse at runtime and not at link
time. This allows us to not have cuSparse on the system at startup if
you don't need to use the sparse functionallity in CUDA. Optionally
it also allows you to staticly link against the cuSparse library
if you want to package the library with ArrayFire.
---
 src/backend/common/DependencyModule.hpp |   5 +
 src/backend/cuda/CMakeLists.txt         |  30 ++++--
 src/backend/cuda/cusparse.hpp           |  11 +-
 src/backend/cuda/cusparseModule.cpp     | 135 ++++++++++++++++++++++++
 src/backend/cuda/cusparseModule.hpp     |  96 +++++++++++++++++
 src/backend/cuda/platform.cpp           |   6 +-
 src/backend/cuda/sparse.cu              |  43 ++++----
 src/backend/cuda/sparse_arith.cu        |  21 ++--
 src/backend/cuda/sparse_blas.cu         |  37 ++++---
 9 files changed, 328 insertions(+), 56 deletions(-)
 create mode 100644 src/backend/cuda/cusparseModule.cpp
 create mode 100644 src/backend/cuda/cusparseModule.hpp

diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index d4f456dbe8..923ba96a47 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -38,6 +38,11 @@ class DependencyModule {
     std::vector<void*> functions;
 
    public:
+    /// Loads the library \p plugin_file_name from the \p paths locations
+    /// \param plugin_file_name  The name of the library without any prefix or
+    ///                          extensions
+    /// \param paths             The locations to search for the libraries if
+    ///                          not found in standard locations
     DependencyModule(const char* plugin_file_name,
                      const char** paths = nullptr);
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ee20e453ac..8f25f1bea1 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -63,11 +63,23 @@ find_cuda_helper_libs(nvrtc-builtins)
 list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY})
 
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+  # The libraries that may be staticly linked or may be loaded at runtime
+  set(AF_CUDA_optionally_static_libraries)
+
+  af_multiple_option(NAME        AF_cusparse_LINK_LOADING
+    DEFAULT     "Module"
+    DESCRIPTION "The approach to load the cusparse library. Static linking(Static) or Dynamic runtime loading(Module) of the module"
+    OPTIONS     "Module" "Static")
+
+  if(AF_cusparse_LINK_LOADING STREQUAL "Static")
+    af_find_static_cuda_libs(cusparse_static PRUNE)
+    list(APPEND AF_CUDA_optionally_static_libraries ${AF_CUDA_cusparse_static_LIBRARY})
+  endif()
+
   af_find_static_cuda_libs(culibos)
   af_find_static_cuda_libs(cublas_static PRUNE)
   af_find_static_cuda_libs(cublasLt_static PRUNE)
   af_find_static_cuda_libs(cufft_static)
-  af_find_static_cuda_libs(cusparse_static PRUNE)
 
   if(CUDA_VERSION VERSION_GREATER 11.4)
     af_find_static_cuda_libs(nvrtc_static)
@@ -88,7 +100,6 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
     set(af_cuda_static_flags "${af_cuda_static_flags};-lcublasLt_static")
   endif()
   set(af_cuda_static_flags "${af_cuda_static_flags};-lcufft_static")
-  set(af_cuda_static_flags "${af_cuda_static_flags};-lcusparse_static")
 
   if(${use_static_cuda_lapack})
     af_find_static_cuda_libs(cusolver_static PRUNE)
@@ -341,11 +352,10 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
       ${AF_CUDA_cublas_static_LIBRARY}
       ${AF_CUDA_cublasLt_static_LIBRARY}
       ${AF_CUDA_cufft_static_LIBRARY}
-      ${AF_CUDA_cusparse_static_LIBRARY}
+      ${AF_CUDA_optionally_static_libraries}
       ${nvrtc_libs}
       ${cusolver_static_lib}
-      ${END_GROUP}
-  )
+      ${END_GROUP})
 
   if(CUDA_VERSION VERSION_GREATER 10.0)
     target_link_libraries(af_cuda_static_cuda_library
@@ -367,7 +377,6 @@ else()
       ${CUDA_CUBLAS_LIBRARIES}
       ${CUDA_CUFFT_LIBRARIES}
       ${CUDA_cusolver_LIBRARY}
-      ${CUDA_cusparse_LIBRARY}
       ${nvrtc_libs}
   )
 endif()
@@ -536,6 +545,8 @@ cuda_add_library(afcuda
     cusolverDn.hpp
     cusparse.cpp
     cusparse.hpp
+    cusparseModule.cpp
+    cusparseModule.hpp
     device_manager.cpp
     device_manager.hpp
     debug_cuda.hpp
@@ -690,6 +701,13 @@ if(AF_WITH_CUDNN)
     )
 endif()
 
+if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS AND AF_cusparse_LINK_LOADING STREQUAL "Static")
+  target_compile_definitions(afcuda
+    PRIVATE
+      AF_cusparse_STATIC_LINKING)
+endif()
+
+
 arrayfire_set_default_cxx_flags(afcuda)
 
 # NOTE: Do not add additional CUDA specific definitions here. Add it to the
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 7eb54900b4..b7a332a856 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -12,15 +12,16 @@
 #include <common/defines.hpp>
 #include <common/err_common.hpp>
 #include <common/unique_handle.hpp>
+#include <cusparseModule.hpp>
 #include <cusparse_v2.h>
 
 // clang-format off
-DEFINE_HANDLER(cusparseHandle_t, cusparseCreate, cusparseDestroy);
-DEFINE_HANDLER(cusparseMatDescr_t, cusparseCreateMatDescr, cusparseDestroyMatDescr);
+DEFINE_HANDLER(cusparseHandle_t, cuda::getCusparsePlugin().cusparseCreate, cuda::getCusparsePlugin().cusparseDestroy);
+DEFINE_HANDLER(cusparseMatDescr_t, cuda::getCusparsePlugin().cusparseCreateMatDescr, cuda::getCusparsePlugin().cusparseDestroyMatDescr);
 #if defined(AF_USE_NEW_CUSPARSE_API)
-DEFINE_HANDLER(cusparseSpMatDescr_t, cusparseCreateCsr, cusparseDestroySpMat);
-DEFINE_HANDLER(cusparseDnVecDescr_t, cusparseCreateDnVec, cusparseDestroyDnVec);
-DEFINE_HANDLER(cusparseDnMatDescr_t, cusparseCreateDnMat, cusparseDestroyDnMat);
+DEFINE_HANDLER(cusparseSpMatDescr_t, cuda::getCusparsePlugin().cusparseCreateCsr, cuda::getCusparsePlugin().cusparseDestroySpMat);
+DEFINE_HANDLER(cusparseDnVecDescr_t, cuda::getCusparsePlugin().cusparseCreateDnVec, cuda::getCusparsePlugin().cusparseDestroyDnVec);
+DEFINE_HANDLER(cusparseDnMatDescr_t, cuda::getCusparsePlugin().cusparseCreateDnMat, cuda::getCusparsePlugin().cusparseDestroyDnMat);
 #endif
 // clang-format on
 
diff --git a/src/backend/cuda/cusparseModule.cpp b/src/backend/cuda/cusparseModule.cpp
new file mode 100644
index 0000000000..f229372b43
--- /dev/null
+++ b/src/backend/cuda/cusparseModule.cpp
@@ -0,0 +1,135 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <cusparseModule.hpp>
+
+#include <common/err_common.hpp>
+#include <af/defines.h>
+
+#include <cuda.h>
+#include <string>
+
+namespace cuda {
+
+cusparseModule::cusparseModule()
+    :
+#ifdef AF_cusparse_STATIC_LINKING
+    module(nullptr, nullptr)
+#else
+    module("cusparse", nullptr)
+#endif
+{
+#ifdef AF_cusparse_STATIC_LINKING
+    AF_TRACE("CuSparse linked staticly.");
+#undef MODULE_FUNCTION_INIT
+#define MODULE_FUNCTION_INIT(NAME) NAME = &::NAME
+#else
+    if (!module.isLoaded()) {
+        AF_TRACE(
+            "WARNING: Unable to load cuSparse: {}\n"
+            "cuSparse failed to load. Try installing cuSparse or check if\n"
+            "cuSparse is in the search path. On Linux, you can set the\n"
+            "LD_DEBUG=libs environment variable to debug loading issues.\n"
+            "Falling back to matmul based implementation",
+            module.getErrorMessage());
+
+        return;
+    }
+#endif
+
+    MODULE_FUNCTION_INIT(cusparseCcsc2dense);
+    MODULE_FUNCTION_INIT(cusparseCcsr2dense);
+    MODULE_FUNCTION_INIT(cusparseCdense2csc);
+    MODULE_FUNCTION_INIT(cusparseCdense2csr);
+    MODULE_FUNCTION_INIT(cusparseCgthr);
+    MODULE_FUNCTION_INIT(cusparseCnnz);
+    MODULE_FUNCTION_INIT(cusparseCreateCsr);
+    MODULE_FUNCTION_INIT(cusparseCreateDnMat);
+    MODULE_FUNCTION_INIT(cusparseCreateDnVec);
+    MODULE_FUNCTION_INIT(cusparseCreateIdentityPermutation);
+    MODULE_FUNCTION_INIT(cusparseCreate);
+    MODULE_FUNCTION_INIT(cusparseCreateMatDescr);
+    MODULE_FUNCTION_INIT(cusparseDcsc2dense);
+    MODULE_FUNCTION_INIT(cusparseDcsr2dense);
+    MODULE_FUNCTION_INIT(cusparseDdense2csc);
+    MODULE_FUNCTION_INIT(cusparseDdense2csr);
+    MODULE_FUNCTION_INIT(cusparseDestroyDnMat);
+    MODULE_FUNCTION_INIT(cusparseDestroyDnVec);
+    MODULE_FUNCTION_INIT(cusparseDestroy);
+    MODULE_FUNCTION_INIT(cusparseDestroyMatDescr);
+    MODULE_FUNCTION_INIT(cusparseDestroySpMat);
+    MODULE_FUNCTION_INIT(cusparseDgthr);
+    MODULE_FUNCTION_INIT(cusparseDnnz);
+    MODULE_FUNCTION_INIT(cusparseScsc2dense);
+    MODULE_FUNCTION_INIT(cusparseScsr2dense);
+    MODULE_FUNCTION_INIT(cusparseSdense2csc);
+    MODULE_FUNCTION_INIT(cusparseSdense2csr);
+    MODULE_FUNCTION_INIT(cusparseSetMatIndexBase);
+    MODULE_FUNCTION_INIT(cusparseSetMatType);
+    MODULE_FUNCTION_INIT(cusparseSetStream);
+    MODULE_FUNCTION_INIT(cusparseSgthr);
+    MODULE_FUNCTION_INIT(cusparseSnnz);
+    MODULE_FUNCTION_INIT(cusparseSpMM_bufferSize);
+    MODULE_FUNCTION_INIT(cusparseSpMM);
+    MODULE_FUNCTION_INIT(cusparseSpMV_bufferSize);
+    MODULE_FUNCTION_INIT(cusparseSpMV);
+    MODULE_FUNCTION_INIT(cusparseXcoo2csr);
+    MODULE_FUNCTION_INIT(cusparseXcoosort_bufferSizeExt);
+    MODULE_FUNCTION_INIT(cusparseXcoosortByColumn);
+    MODULE_FUNCTION_INIT(cusparseXcoosortByRow);
+    MODULE_FUNCTION_INIT(cusparseXcsr2coo);
+#if CUDA_VERSION >= 11000
+    MODULE_FUNCTION_INIT(cusparseXcsrgeam2Nnz);
+#else
+    MODULE_FUNCTION_INIT(cusparseXcsrgeamNnz);
+#endif
+    MODULE_FUNCTION_INIT(cusparseZcsc2dense);
+    MODULE_FUNCTION_INIT(cusparseZcsr2dense);
+#if CUDA_VERSION >= 11000
+    MODULE_FUNCTION_INIT(cusparseScsrgeam2_bufferSizeExt);
+    MODULE_FUNCTION_INIT(cusparseScsrgeam2);
+    MODULE_FUNCTION_INIT(cusparseDcsrgeam2_bufferSizeExt);
+    MODULE_FUNCTION_INIT(cusparseDcsrgeam2);
+    MODULE_FUNCTION_INIT(cusparseCcsrgeam2_bufferSizeExt);
+    MODULE_FUNCTION_INIT(cusparseCcsrgeam2);
+    MODULE_FUNCTION_INIT(cusparseZcsrgeam2_bufferSizeExt);
+    MODULE_FUNCTION_INIT(cusparseZcsrgeam2);
+#else
+    MODULE_FUNCTION_INIT(cusparseScsrgeam);
+    MODULE_FUNCTION_INIT(cusparseDcsrgeam);
+    MODULE_FUNCTION_INIT(cusparseCcsrgeam);
+    MODULE_FUNCTION_INIT(cusparseZcsrgeam);
+#endif
+    MODULE_FUNCTION_INIT(cusparseZdense2csc);
+    MODULE_FUNCTION_INIT(cusparseZdense2csr);
+    MODULE_FUNCTION_INIT(cusparseZgthr);
+    MODULE_FUNCTION_INIT(cusparseZnnz);
+
+#ifndef AF_cusparse_STATIC_LINKING
+    if (!module.symbolsLoaded()) {
+        std::string error_message =
+            "Error loading cuSparse symbols. ArrayFire was unable to load some "
+            "symbols from the cuSparse library. Please create an issue on the "
+            "ArrayFire repository with information about the installed "
+            "cuSparse and ArrayFire on your system.";
+        AF_ERROR(error_message, AF_ERR_LOAD_LIB);
+    }
+#endif
+}
+
+spdlog::logger* cusparseModule::getLogger() const noexcept {
+    return module.getLogger();
+}
+
+cusparseModule& getCusparsePlugin() noexcept {
+    static auto* plugin = new cusparseModule();
+    return *plugin;
+}
+
+}  // namespace cuda
diff --git a/src/backend/cuda/cusparseModule.hpp b/src/backend/cuda/cusparseModule.hpp
new file mode 100644
index 0000000000..57878c2cf8
--- /dev/null
+++ b/src/backend/cuda/cusparseModule.hpp
@@ -0,0 +1,96 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/DependencyModule.hpp>
+#include <cuda.h>
+#include <cusparse_v2.h>
+
+namespace cuda {
+class cusparseModule {
+    common::DependencyModule module;
+
+   public:
+    cusparseModule();
+    ~cusparseModule() = default;
+
+    MODULE_MEMBER(cusparseCcsc2dense);
+    MODULE_MEMBER(cusparseCcsr2dense);
+    MODULE_MEMBER(cusparseCdense2csc);
+    MODULE_MEMBER(cusparseCdense2csr);
+    MODULE_MEMBER(cusparseCgthr);
+    MODULE_MEMBER(cusparseCnnz);
+    MODULE_MEMBER(cusparseCreateCsr);
+    MODULE_MEMBER(cusparseCreateDnMat);
+    MODULE_MEMBER(cusparseCreateDnVec);
+    MODULE_MEMBER(cusparseCreateIdentityPermutation);
+    MODULE_MEMBER(cusparseCreate);
+    MODULE_MEMBER(cusparseCreateMatDescr);
+    MODULE_MEMBER(cusparseDcsc2dense);
+    MODULE_MEMBER(cusparseDcsr2dense);
+    MODULE_MEMBER(cusparseDdense2csc);
+    MODULE_MEMBER(cusparseDdense2csr);
+    MODULE_MEMBER(cusparseDestroyDnMat);
+    MODULE_MEMBER(cusparseDestroyDnVec);
+    MODULE_MEMBER(cusparseDestroy);
+    MODULE_MEMBER(cusparseDestroyMatDescr);
+    MODULE_MEMBER(cusparseDestroySpMat);
+    MODULE_MEMBER(cusparseDgthr);
+    MODULE_MEMBER(cusparseDnnz);
+    MODULE_MEMBER(cusparseScsc2dense);
+    MODULE_MEMBER(cusparseScsr2dense);
+    MODULE_MEMBER(cusparseSdense2csc);
+    MODULE_MEMBER(cusparseSdense2csr);
+    MODULE_MEMBER(cusparseSetMatIndexBase);
+    MODULE_MEMBER(cusparseSetMatType);
+    MODULE_MEMBER(cusparseSetStream);
+    MODULE_MEMBER(cusparseSgthr);
+    MODULE_MEMBER(cusparseSnnz);
+    MODULE_MEMBER(cusparseSpMM_bufferSize);
+    MODULE_MEMBER(cusparseSpMM);
+    MODULE_MEMBER(cusparseSpMV_bufferSize);
+    MODULE_MEMBER(cusparseSpMV);
+    MODULE_MEMBER(cusparseXcoo2csr);
+    MODULE_MEMBER(cusparseXcoosort_bufferSizeExt);
+    MODULE_MEMBER(cusparseXcoosortByColumn);
+    MODULE_MEMBER(cusparseXcoosortByRow);
+    MODULE_MEMBER(cusparseXcsr2coo);
+    MODULE_MEMBER(cusparseZcsc2dense);
+    MODULE_MEMBER(cusparseZcsr2dense);
+
+#if CUDA_VERSION >= 11000
+    MODULE_MEMBER(cusparseXcsrgeam2Nnz);
+    MODULE_MEMBER(cusparseCcsrgeam2_bufferSizeExt);
+    MODULE_MEMBER(cusparseCcsrgeam2);
+    MODULE_MEMBER(cusparseDcsrgeam2_bufferSizeExt);
+    MODULE_MEMBER(cusparseDcsrgeam2);
+    MODULE_MEMBER(cusparseScsrgeam2_bufferSizeExt);
+    MODULE_MEMBER(cusparseScsrgeam2);
+    MODULE_MEMBER(cusparseZcsrgeam2_bufferSizeExt);
+    MODULE_MEMBER(cusparseZcsrgeam2);
+#else
+    MODULE_MEMBER(cusparseXcsrgeamNnz);
+    MODULE_MEMBER(cusparseCcsrgeam);
+    MODULE_MEMBER(cusparseDcsrgeam);
+    MODULE_MEMBER(cusparseScsrgeam);
+    MODULE_MEMBER(cusparseZcsrgeam);
+#endif
+
+    MODULE_MEMBER(cusparseZdense2csc);
+    MODULE_MEMBER(cusparseZdense2csr);
+    MODULE_MEMBER(cusparseZgthr);
+    MODULE_MEMBER(cusparseZnnz);
+
+    spdlog::logger* getLogger() const noexcept;
+};
+
+cusparseModule& getCusparsePlugin() noexcept;
+
+}  // namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index dd715e4691..ab94cf298f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -29,6 +29,7 @@
 #include <cufft.hpp>
 #include <cusolverDn.hpp>
 #include <cusparse.hpp>
+#include <cusparseModule.hpp>
 #include <device_manager.hpp>
 #include <driver.h>
 #include <err_cuda.hpp>
@@ -84,7 +85,7 @@ unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
 
     call_once(initFlags[deviceId], [&] {
-        handles[deviceId].create();
+        CUBLAS_CHECK((cublasStatus_t)handles[deviceId].create());
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
@@ -159,12 +160,13 @@ unique_handle<cusparseHandle_t> *cusparseManager(const int deviceId) {
         handles[DeviceManager::MAX_DEVICES];
     thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
     call_once(initFlags[deviceId], [&] {
+        auto &_ = getCusparsePlugin();
         handles[deviceId].create();
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
         CUSPARSE_CHECK(
-            cusparseSetStream(handles[deviceId], cuda::getStream(deviceId)));
+            _.cusparseSetStream(handles[deviceId], cuda::getStream(deviceId)));
     });
     return &handles[deviceId];
 }
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 47dad93e07..27b805e9ea 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -15,6 +15,7 @@
 #include <complex.hpp>
 #include <copy.hpp>
 #include <cusparse.hpp>
+#include <cusparseModule.hpp>
 #include <kernel/sparse.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
@@ -122,8 +123,9 @@ struct gthr_func_def_t {
 #define SPARSE_FUNC(FUNC, TYPE, PREFIX)                                     \
     template<>                                                              \
     typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
-        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
-               cusparse##PREFIX##FUNC;                                      \
+        cusparseModule &_ = getCusparsePlugin();                            \
+        return (FUNC##_func_def_t<TYPE>::FUNC##_func_def)(                  \
+            _.cusparse##PREFIX##FUNC);                                      \
     }
 
 SPARSE_FUNC_DEF(dense2csr)
@@ -194,11 +196,12 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     const int M = in.dims()[0];
     const int N = in.dims()[1];
 
+    cusparseModule &_ = getCusparsePlugin();
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
-    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
-    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
-    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+    CUSPARSE_CHECK(_.cusparseCreateMatDescr(&descr));
+    _.cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+    _.cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
 
     int d                   = -1;
     cusparseDirection_t dir = CUSPARSE_DIRECTION_ROW;
@@ -238,7 +241,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
             nnzPerDir.get(), values.get(), rowIdx.get(), colIdx.get()));
 
     // Destory Sparse Matrix Descriptor
-    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+    CUSPARSE_CHECK(_.cusparseDestroyMatDescr(descr));
 
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
                                          stype);
@@ -262,10 +265,11 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
     // Create Sparse Matrix Descriptor
+    cusparseModule &_        = getCusparsePlugin();
     cusparseMatDescr_t descr = 0;
-    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
-    cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
-    cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
+    CUSPARSE_CHECK(_.cusparseCreateMatDescr(&descr));
+    _.cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
+    _.cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
 
     int M          = in.dims()[0];
     int N          = in.dims()[1];
@@ -284,7 +288,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
                                 in.getColIdx().get(), dense.get(), d_strides1));
 
     // Destory Sparse Matrix Descriptor
-    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+    CUSPARSE_CHECK(_.cusparseDestroyMatDescr(descr));
 
     return dense;
 }
@@ -297,6 +301,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     int nNZ                  = in.getNNZ();
     SparseArray<T> converted = createEmptySparseArray<T>(in.dims(), nNZ, dest);
 
+    cusparseModule &_ = getCusparsePlugin();
     if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
         // Copy colIdx as is
         CUDA_CHECK(
@@ -305,13 +310,13 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
                             cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         // cusparse function to expand compressed row into coordinate
-        CUSPARSE_CHECK(cusparseXcsr2coo(
+        CUSPARSE_CHECK(_.cusparseXcsr2coo(
             sparseHandle(), in.getRowIdx().get(), nNZ, in.dims()[0],
             converted.getRowIdx().get(), CUSPARSE_INDEX_BASE_ZERO));
 
         // Call sort
         size_t pBufferSizeInBytes = 0;
-        CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
+        CUSPARSE_CHECK(_.cusparseXcoosort_bufferSizeExt(
             sparseHandle(), in.dims()[0], in.dims()[1], nNZ,
             converted.getRowIdx().get(), converted.getColIdx().get(),
             &pBufferSizeInBytes));
@@ -320,9 +325,9 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
 
         shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
         CUSPARSE_CHECK(
-            cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
+            _.cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
-        CUSPARSE_CHECK(cusparseXcoosortByColumn(
+        CUSPARSE_CHECK(_.cusparseXcoosortByColumn(
             sparseHandle(), in.dims()[0], in.dims()[1], nNZ,
             converted.getRowIdx().get(), converted.getColIdx().get(), P.get(),
             (void *)pBuffer.get()));
@@ -344,7 +349,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
         // Call sort to convert column major to row major
         {
             size_t pBufferSizeInBytes = 0;
-            CUSPARSE_CHECK(cusparseXcoosort_bufferSizeExt(
+            CUSPARSE_CHECK(_.cusparseXcoosort_bufferSizeExt(
                 sparseHandle(), cooT.dims()[0], cooT.dims()[1], nNZ,
                 cooT.getRowIdx().get(), cooT.getColIdx().get(),
                 &pBufferSizeInBytes));
@@ -352,10 +357,10 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
                 memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
 
             shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
-            CUSPARSE_CHECK(cusparseCreateIdentityPermutation(sparseHandle(),
-                                                             nNZ, P.get()));
+            CUSPARSE_CHECK(_.cusparseCreateIdentityPermutation(sparseHandle(),
+                                                               nNZ, P.get()));
 
-            CUSPARSE_CHECK(cusparseXcoosortByRow(
+            CUSPARSE_CHECK(_.cusparseXcoosortByRow(
                 sparseHandle(), cooT.dims()[0], cooT.dims()[1], nNZ,
                 cooT.getRowIdx().get(), cooT.getColIdx().get(), P.get(),
                 (void *)pBuffer.get()));
@@ -376,7 +381,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
                             cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
 
         // cusparse function to compress row from coordinate
-        CUSPARSE_CHECK(cusparseXcoo2csr(
+        CUSPARSE_CHECK(_.cusparseXcoo2csr(
             sparseHandle(), cooT.getRowIdx().get(), nNZ, cooT.dims()[0],
             converted.getRowIdx().get(), CUSPARSE_INDEX_BASE_ZERO));
 
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 11a38c58e1..a41c356397 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -115,10 +115,11 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
     template<typename T>               \
     FUNC##_def<T> FUNC##_func();
 
-#define SPARSE_ARITH_OP_FUNC(FUNC, TYPE, INFIX) \
-    template<>                                  \
-    FUNC##_def<TYPE> FUNC##_func<TYPE>() {      \
-        return cusparse##INFIX##FUNC;           \
+#define SPARSE_ARITH_OP_FUNC(FUNC, TYPE, INFIX)  \
+    template<>                                   \
+    FUNC##_def<TYPE> FUNC##_func<TYPE>() {       \
+        cusparseModule &_ = getCusparsePlugin(); \
+        return _.cusparse##INFIX##FUNC;          \
     }
 
 #if CUDA_VERSION >= 11000
@@ -139,7 +140,8 @@ SPARSE_ARITH_OP_BUFFER_SIZE_FUNC_DEF(csrgeam2);
 #define SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(FUNC, TYPE, INFIX)        \
     template<>                                                     \
     FUNC##_buffer_size_def<TYPE> FUNC##_buffer_size_func<TYPE>() { \
-        return cusparse##INFIX##FUNC##_bufferSizeExt;              \
+        cusparseModule &_ = getCusparsePlugin();                   \
+        return _.cusparse##INFIX##FUNC##_bufferSizeExt;            \
     }
 
 SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, float, S);
@@ -206,8 +208,9 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     int baseC, nnzC;
     int *nnzcDevHostPtr = &nnzC;
 
-    T alpha = scalar<T>(1);
-    T beta  = op == af_sub_t ? scalar<T>(-1) : alpha;
+    T alpha           = scalar<T>(1);
+    T beta            = op == af_sub_t ? scalar<T>(-1) : alpha;
+    cusparseModule &_ = getCusparsePlugin();
 
 #if CUDA_VERSION >= 11000
     size_t pBufferSize = 0;
@@ -219,12 +222,12 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
 
     auto tmpBuffer = createEmptyArray<char>(dim4(pBufferSize));
 
-    CUSPARSE_CHECK(cusparseXcsrgeam2Nnz(
+    CUSPARSE_CHECK(_.cusparseXcsrgeam2Nnz(
         sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
         csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr,
         tmpBuffer.get()));
 #else
-    CUSPARSE_CHECK(cusparseXcsrgeamNnz(
+    CUSPARSE_CHECK(_.cusparseXcsrgeamNnz(
         sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
         csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr));
 #endif
diff --git a/src/backend/cuda/sparse_blas.cu b/src/backend/cuda/sparse_blas.cu
index 179c17615d..33a2957a62 100644
--- a/src/backend/cuda/sparse_blas.cu
+++ b/src/backend/cuda/sparse_blas.cu
@@ -14,6 +14,7 @@
 #include <cudaDataType.hpp>
 #include <cuda_runtime.h>
 #include <cusparse.hpp>
+#include <cusparseModule.hpp>
 #include <cusparse_descriptor_helpers.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -41,8 +42,9 @@ size_t spmvBufferSize(cusparseOperation_t opA, const T *alpha,
                       const cusparseSpMatDescr_t matA,
                       const cusparseDnVecDescr_t vecX, const T *beta,
                       const cusparseDnVecDescr_t vecY) {
-    size_t retVal = 0;
-    CUSPARSE_CHECK(cusparseSpMV_bufferSize(
+    size_t retVal     = 0;
+    cusparseModule &_ = getCusparsePlugin();
+    CUSPARSE_CHECK(_.cusparseSpMV_bufferSize(
         sparseHandle(), opA, alpha, matA, vecX, beta, vecY, getComputeType<T>(),
         CUSPARSE_CSRMV_ALG1, &retVal));
     return retVal;
@@ -52,9 +54,10 @@ template<typename T>
 void spmv(cusparseOperation_t opA, const T *alpha,
           const cusparseSpMatDescr_t matA, const cusparseDnVecDescr_t vecX,
           const T *beta, const cusparseDnVecDescr_t vecY, void *buffer) {
-    CUSPARSE_CHECK(cusparseSpMV(sparseHandle(), opA, alpha, matA, vecX, beta,
-                                vecY, getComputeType<T>(),
-                                CUSPARSE_MV_ALG_DEFAULT, buffer));
+    cusparseModule &_ = getCusparsePlugin();
+    CUSPARSE_CHECK(_.cusparseSpMV(sparseHandle(), opA, alpha, matA, vecX, beta,
+                                  vecY, getComputeType<T>(),
+                                  CUSPARSE_MV_ALG_DEFAULT, buffer));
 }
 
 template<typename T>
@@ -62,8 +65,9 @@ size_t spmmBufferSize(cusparseOperation_t opA, cusparseOperation_t opB,
                       const T *alpha, const cusparseSpMatDescr_t matA,
                       const cusparseDnMatDescr_t matB, const T *beta,
                       const cusparseDnMatDescr_t matC) {
-    size_t retVal = 0;
-    CUSPARSE_CHECK(cusparseSpMM_bufferSize(
+    size_t retVal     = 0;
+    cusparseModule &_ = getCusparsePlugin();
+    CUSPARSE_CHECK(_.cusparseSpMM_bufferSize(
         sparseHandle(), opA, opB, alpha, matA, matB, beta, matC,
         getComputeType<T>(), CUSPARSE_CSRMM_ALG1, &retVal));
     return retVal;
@@ -73,9 +77,10 @@ template<typename T>
 void spmm(cusparseOperation_t opA, cusparseOperation_t opB, const T *alpha,
           const cusparseSpMatDescr_t matA, const cusparseDnMatDescr_t matB,
           const T *beta, const cusparseDnMatDescr_t matC, void *buffer) {
-    CUSPARSE_CHECK(cusparseSpMM(sparseHandle(), opA, opB, alpha, matA, matB,
-                                beta, matC, getComputeType<T>(),
-                                CUSPARSE_CSRMM_ALG1, buffer));
+    cusparseModule &_ = getCusparsePlugin();
+    CUSPARSE_CHECK(_.cusparseSpMM(sparseHandle(), opA, opB, alpha, matA, matB,
+                                  beta, matC, getComputeType<T>(),
+                                  CUSPARSE_CSRMM_ALG1, buffer));
 }
 
 #else
@@ -105,8 +110,9 @@ struct csrmm_func_def_t {
 #define SPARSE_FUNC(FUNC, TYPE, PREFIX)                                     \
     template<>                                                              \
     typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
+        cusparseModule &_ = getCusparsePlugin();                            \
         return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
-               cusparse##PREFIX##FUNC;                                      \
+               _.cusparse##PREFIX##FUNC;                                    \
     }
 
 SPARSE_FUNC_DEF(csrmm)
@@ -174,11 +180,12 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
 
 #else
 
+    cusparseModule &_ = getCusparsePlugin();
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
-    CUSPARSE_CHECK(cusparseCreateMatDescr(&descr));
-    CUSPARSE_CHECK(cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL));
-    CUSPARSE_CHECK(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
+    CUSPARSE_CHECK(_.cusparseCreateMatDescr(&descr));
+    CUSPARSE_CHECK(_.cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL));
+    CUSPARSE_CHECK(_.cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO));
 
     // Call Matrix-Vector or Matrix-Matrix
     // Note:
@@ -197,7 +204,7 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
             lhs.getRowIdx().get(), lhs.getColIdx().get(), rhs.get(),
             rStrides[1], &beta, out.get(), out.dims()[0]));
     }
-    CUSPARSE_CHECK(cusparseDestroyMatDescr(descr));
+    CUSPARSE_CHECK(_.cusparseDestroyMatDescr(descr));
 
 #endif
 

From 8bdcc77e1b648d36b686667bebc2139b6d186341 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 11 Apr 2022 15:12:24 -0400
Subject: [PATCH 2239/2677] reduce all -> array (#3199)

* change return type for reduce_all
adds single cuda kernel for reduce_all_array
adds cpu reduce_all
adds opencl reduce_all functions, kernel: todo
remove old versions of reduce_all
update missing reduction functions
adds missing reduce tests, other reduce functions
update test precision, fix kernel shared ptrs
fixes failing tests, clang format, fix init assignment
update api, minor unified error handling

* Fix reduce_all on Intel's OpenCL. Removed unused variables

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 include/af/algorithm.h                  | 130 +++++++++++++
 src/api/c/anisotropic_diffusion.cpp     |   4 +-
 src/api/c/canny.cpp                     |  14 +-
 src/api/c/confidence_connected.cpp      |  19 +-
 src/api/c/corrcoef.cpp                  |  12 +-
 src/api/c/gaussian_kernel.cpp           |   4 +-
 src/api/c/hist.cpp                      |   5 +-
 src/api/c/histeq.cpp                    |   6 +-
 src/api/c/imgproc_common.hpp            |   8 +-
 src/api/c/norm.cpp                      |  19 +-
 src/api/c/rank.cpp                      |   4 +-
 src/api/c/reduce.cpp                    | 189 +++++++++++++++++-
 src/api/c/stdev.cpp                     |   8 +-
 src/api/c/surface.cpp                   |  14 +-
 src/api/c/var.cpp                       |  16 +-
 src/api/cpp/reduce.cpp                  |  28 +++
 src/api/unified/algorithm.cpp           |  27 +++
 src/api/unified/symbol_manager.hpp      |   5 +
 src/backend/cpu/kernel/reduce.hpp       |  41 ++++
 src/backend/cpu/reduce.cpp              |  52 ++---
 src/backend/cpu/reduce.hpp              |   3 +-
 src/backend/cpu/sparse.cpp              |   2 +-
 src/backend/cuda/kernel/reduce.hpp      | 249 ++++++++++++++++++------
 src/backend/cuda/reduce.hpp             |   3 +-
 src/backend/cuda/reduce_impl.hpp        |  11 +-
 src/backend/opencl/kernel/reduce.hpp    | 114 ++++++-----
 src/backend/opencl/kernel/reduce_all.cl | 160 +++++++++++++++
 src/backend/opencl/reduce.hpp           |   3 +-
 src/backend/opencl/reduce_impl.hpp      |  11 +-
 src/backend/opencl/sparse.cpp           |   2 +-
 src/backend/opencl/svd.cpp              |   2 +-
 test/mean.cpp                           |   5 +-
 test/reduce.cpp                         | 204 ++++++++++++++++++-
 33 files changed, 1150 insertions(+), 224 deletions(-)
 create mode 100644 src/backend/opencl/kernel/reduce_all.cl

diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 7c8cfdd393..801792a32a 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -674,6 +674,19 @@ extern "C" {
     */
     AFAPI af_err af_sum(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for sum of all elements in an array, resulting in an array
+
+       \param[out] out will contain the sum of all values in \p in
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_sum
+    */
+    AFAPI af_err af_sum_all_array(af_array *out, const af_array in);
+#endif
+
 #if AF_API_VERSION >= 31
     /**
        C Interface for sum of elements in an array while replacing nans
@@ -690,6 +703,21 @@ extern "C" {
                             const int dim, const double nanval);
 #endif
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for sum of all elements in an array, resulting in an array with
+       nan substitution
+
+       \param[out] out will contain the sum of all values in \p in
+       \param[in] in is the input array
+       \param[in] nanval  The value that will replace the NaNs in \p in
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_sum
+    */
+    AFAPI af_err af_sum_nan_all_array(af_array *out, const af_array in, const double nanval);
+#endif
+
 #if AF_API_VERSION >= 37
     /**
        C Interface for sum of elements in an array according to key
@@ -741,6 +769,19 @@ extern "C" {
     */
     AFAPI af_err af_product(af_array *out, const af_array in, const int dim);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for product of elements in an array, resulting in an array
+
+       \param[out] out will contain the product of all values in \p in
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_product
+    */
+    AFAPI af_err af_product_all_array(af_array *out, const af_array in);
+#endif
+
 #if AF_API_VERSION >= 31
     /**
        C Interface for product of elements in an array while replacing nans
@@ -757,6 +798,21 @@ extern "C" {
     AFAPI af_err af_product_nan(af_array *out, const af_array in, const int dim, const double nanval);
 #endif
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for product of elements in an array, resulting in an array
+       while replacing nans
+
+       \param[out] out will contain the product of all values in \p in
+       \param[in] in   is the input array
+       \param[in] nanval  The value that will replace the NaNs in \p in
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_product
+    */
+    AFAPI af_err af_product_nan_all_array(af_array *out, const af_array in, const double nanval);
+#endif
+
 #if AF_API_VERSION >= 37
     /**
        C Interface for product of elements in an array according to key
@@ -1052,6 +1108,19 @@ extern "C" {
     */
     AFAPI af_err af_min_all(double *real, double *imag, const af_array in);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for minimum values in an array, returning an array
+
+       \param[out] out will contain the minimum of all values in \p in
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_min
+    */
+    AFAPI af_err af_min_all_array(af_array *out, const af_array in);
+#endif
+
     /**
        C Interface for getting maximum value of an array
 
@@ -1066,6 +1135,21 @@ extern "C" {
     */
     AFAPI af_err af_max_all(double *real, double *imag, const af_array in);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for getting maximum value of an array, returning an array
+
+       \param[out] out will contain the maximum of all values in \p in
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \note \p imag is always set to 0 when \p in is real.
+
+       \ingroup reduce_func_max
+    */
+    AFAPI af_err af_max_all_array(af_array *out, const af_array in);
+#endif
+
     /**
        C Interface for checking if all values in an array are true
 
@@ -1080,6 +1164,22 @@ extern "C" {
     */
     AFAPI af_err af_all_true_all(double *real, double *imag, const af_array in);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for checking if all values in an array are true,
+       while returning an af_array
+
+       \param[out] out will contain 1 if all values of input \p in are true, 0 otherwise
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \note \p imag is always set to 0.
+
+       \ingroup reduce_func_all_true
+    */
+    AFAPI af_err af_all_true_all_array(af_array *out, const af_array in);
+#endif
+
     /**
        C Interface for checking if any values in an array are true
 
@@ -1094,6 +1194,22 @@ extern "C" {
     */
     AFAPI af_err af_any_true_all(double *real, double *imag, const af_array in);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for checking if any values in an array are true,
+       while returning an af_array
+
+       \param[out] out will contain 1 if any value of input \p in is true, 0 otherwise
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \note \p imag is always set to 0.
+
+       \ingroup reduce_func_any_true
+    */
+    AFAPI af_err af_any_true_all_array(af_array *out, const af_array in);
+#endif
+
     /**
        C Interface for counting total number of non-zero values in an array
 
@@ -1108,6 +1224,20 @@ extern "C" {
     */
     AFAPI af_err af_count_all(double *real, double *imag, const af_array in);
 
+#if AF_API_VERSION >= 39
+    /**
+       C Interface for counting total number of non-zero values in an array,
+       while returning an af_array
+
+       \param[out] out contain the number of non-zero values in \p in.
+       \param[in] in is the input array
+       \return \ref AF_SUCCESS if the execution completes properly
+
+       \ingroup reduce_func_count
+    */
+    AFAPI af_err af_count_all_array(af_array *out, const af_array in);
+#endif
+
     /**
        C Interface for getting minimum values and their locations in an array
 
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index 24335a406e..fd2f83c5c1 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -28,6 +28,7 @@ using common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::createEmptyArray;
+using detail::getScalar;
 using detail::gradient;
 using detail::reduce_all;
 
@@ -48,7 +49,8 @@ af_array diffusion(const Array<float>& in, const float dt, const float K,
         auto g0Sqr = arithOp<float, af_mul_t>(g0, g0, dims);
         auto g1Sqr = arithOp<float, af_mul_t>(g1, g1, dims);
         auto sumd  = arithOp<float, af_add_t>(g0Sqr, g1Sqr, dims);
-        float avg  = reduce_all<af_add_t, float, float>(sumd, true, 0);
+        float avg =
+            getScalar<float>(reduce_all<af_add_t, float, float>(sumd, true, 0));
 
         anisotropicDiffusion(out, dt, 1.0f / (cnst * avg), fftype, eq);
     }
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 0c67ddb03d..d9d74da7d9 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -44,6 +44,7 @@ using detail::createEmptyArray;
 using detail::createHostDataArray;
 using detail::createSubArray;
 using detail::createValueArray;
+using detail::getScalar;
 using detail::histogram;
 using detail::iota;
 using detail::ireduce;
@@ -151,7 +152,9 @@ pair<Array<char>, Array<char>> computeCandidates(const Array<float>& supEdges,
                                                  const float t1,
                                                  const af_canny_threshold ct,
                                                  const float t2) {
-    float maxVal  = reduce_all<af_max_t, float, float>(supEdges);
+    float maxVal =
+        getScalar<float>(reduce_all<af_max_t, float, float>(supEdges));
+    ;
     auto NUM_BINS = static_cast<unsigned>(maxVal);
 
     auto lowRatio = createValueArray<float>(supEdges.dims(), t1);
@@ -171,10 +174,11 @@ pair<Array<char>, Array<char>> computeCandidates(const Array<float>& supEdges,
             return make_pair(strong, weak);
         };
         default: {
-            float minVal = reduce_all<af_min_t, float, float>(supEdges);
-            auto normG   = normalize(supEdges, minVal, maxVal);
-            auto T2      = createValueArray<float>(supEdges.dims(), t2);
-            auto T1      = createValueArray<float>(supEdges.dims(), t1);
+            float minVal =
+                getScalar<float>(reduce_all<af_min_t, float, float>(supEdges));
+            auto normG = normalize(supEdges, minVal, maxVal);
+            auto T2    = createValueArray<float>(supEdges.dims(), t2);
+            auto T1    = createValueArray<float>(supEdges.dims(), t1);
             Array<char> weak1 =
                 logicOp<float, af_ge_t>(normG, T1, normG.dims());
             Array<char> weak2 =
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index 174ed3c688..b42decc227 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -29,6 +29,7 @@ using common::createSpanIndex;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
+using detail::getScalar;
 using detail::reduce_all;
 using detail::uchar;
 using detail::uint;
@@ -127,8 +128,8 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
     Array<CT> I2       = common::integralImage<CT>(in_2);
     Array<CT> S1       = sum(I1, _x, x_, _y, y_);
     Array<CT> S2       = sum(I2, _x, x_, _y, y_);
-    CT totSum          = reduce_all<af_add_t, CT, CT>(S1);
-    CT totSumSq        = reduce_all<af_add_t, CT, CT>(S2);
+    CT totSum          = getScalar<CT>(reduce_all<af_add_t, CT, CT>(S1));
+    CT totSumSq        = getScalar<CT>(reduce_all<af_add_t, CT, CT>(S2));
     CT totalNum        = numSeeds * nhoodSize;
     CT s1mean          = totSum / totalNum;
     CT s1var           = calcVar(totSumSq, totSum, totalNum);
@@ -137,8 +138,10 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
     CT upper           = s1mean + mult * s1stddev;
 
     Array<CT> seedIntensities = pointList(in, seedx, seedy);
-    CT maxSeedIntensity       = reduce_all<af_max_t, CT, CT>(seedIntensities);
-    CT minSeedIntensity       = reduce_all<af_min_t, CT, CT>(seedIntensities);
+    CT maxSeedIntensity =
+        getScalar<CT>(reduce_all<af_max_t, CT, CT>(seedIntensities));
+    CT minSeedIntensity =
+        getScalar<CT>(reduce_all<af_min_t, CT, CT>(seedIntensities));
 
     if (lower > minSeedIntensity) { lower = minSeedIntensity; }
     if (upper < maxSeedIntensity) { upper = maxSeedIntensity; }
@@ -155,7 +158,8 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
         // Segmented images are set with 1's and 0's thus essentially
         // making them into mask arrays for each iteration's input image
 
-        uint sampleCount = reduce_all<af_notzero_t, CT, uint>(segmented, true);
+        uint sampleCount = getScalar<uint>(
+            reduce_all<af_notzero_t, CT, uint>(segmented, true));
         if (sampleCount == 0) {
             // If no valid pixels are found, skip iterations
             break;
@@ -163,8 +167,9 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
         Array<CT> valids = arithOp<CT, af_mul_t>(segmented, in, inDims);
         Array<CT> vsqrd  = arithOp<CT, af_mul_t>(valids, valids, inDims);
 
-        CT validsSum  = reduce_all<af_add_t, CT, CT>(valids, true);
-        CT sumOfSqs   = reduce_all<af_add_t, CT, CT>(vsqrd, true);
+        CT validsSum =
+            getScalar<CT>(reduce_all<af_add_t, CT, CT>(valids, true));
+        CT sumOfSqs = getScalar<CT>(reduce_all<af_add_t, CT, CT>(vsqrd, true));
         CT validsMean = validsSum / sampleCount;
         CT validsVar  = calcVar(sumOfSqs, validsSum, CT(sampleCount));
         CT stddev     = sqrt(validsVar);
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index 2ee5e45d6a..0efc503cd4 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <reduce.hpp>
@@ -26,6 +27,7 @@ using af::dim4;
 using common::cast;
 using detail::arithOp;
 using detail::Array;
+using detail::getScalar;
 using detail::intl;
 using detail::reduce_all;
 using detail::uchar;
@@ -41,16 +43,16 @@ static To corrcoef(const af_array& X, const af_array& Y) {
     const dim4& dims = xIn.dims();
     dim_t n          = xIn.elements();
 
-    To xSum = reduce_all<af_add_t, To, To>(xIn);
-    To ySum = reduce_all<af_add_t, To, To>(yIn);
+    To xSum = getScalar<To>(reduce_all<af_add_t, To, To>(xIn));
+    To ySum = getScalar<To>(reduce_all<af_add_t, To, To>(yIn));
 
     Array<To> xSq = arithOp<To, af_mul_t>(xIn, xIn, dims);
     Array<To> ySq = arithOp<To, af_mul_t>(yIn, yIn, dims);
     Array<To> xy  = arithOp<To, af_mul_t>(xIn, yIn, dims);
 
-    To xSqSum = reduce_all<af_add_t, To, To>(xSq);
-    To ySqSum = reduce_all<af_add_t, To, To>(ySq);
-    To xySum  = reduce_all<af_add_t, To, To>(xy);
+    To xSqSum = getScalar<To>(reduce_all<af_add_t, To, To>(xSq));
+    To ySqSum = getScalar<To>(reduce_all<af_add_t, To, To>(ySq));
+    To xySum  = getScalar<To>(reduce_all<af_add_t, To, To>(xy));
 
     To result =
         (n * xySum - xSum * ySum) / (std::sqrt(n * xSqSum - xSum * xSum) *
diff --git a/src/api/c/gaussian_kernel.cpp b/src/api/c/gaussian_kernel.cpp
index 79492f87ea..529aa378e9 100644
--- a/src/api/c/gaussian_kernel.cpp
+++ b/src/api/c/gaussian_kernel.cpp
@@ -10,6 +10,7 @@
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/err_common.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <range.hpp>
@@ -24,6 +25,7 @@ using af::dim4;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
+using detail::getScalar;
 using detail::range;
 using detail::reduce_all;
 using detail::scalar;
@@ -77,7 +79,7 @@ Array<T> gaussianKernel(const int rows, const int cols, const double sigma_r,
 
     // Use this instead of (2 * pi * sig^2);
     // This ensures the window adds up to 1
-    T norm_factor = reduce_all<af_add_t, T, T>(tmp);
+    T norm_factor = getScalar<T>(reduce_all<af_add_t, T, T>(tmp));
 
     Array<T> norm = createValueArray(odims, norm_factor);
     Array<T> res  = arithOp<T, af_div_t>(tmp, norm, odims);
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 0fad162819..4b74e33cdf 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -12,6 +12,7 @@
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <hist_graphics.hpp>
 #include <reduce.hpp>
@@ -20,6 +21,7 @@
 using detail::Array;
 using detail::copy_histogram;
 using detail::forgeManager;
+using detail::getScalar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -57,7 +59,8 @@ fg_chart setup_histogram(fg_window const window, const af_array in,
         float xMin, xMax, yMin, yMax, zMin, zMax;
         FG_CHECK(_.fg_get_chart_axes_limits(&xMin, &xMax, &yMin, &yMax, &zMin,
                                             &zMax, chart));
-        T freqMax = detail::reduce_all<af_max_t, T, T>(histogramInput);
+        T freqMax =
+            getScalar<T>(detail::reduce_all<af_max_t, T, T>(histogramInput));
 
         if (xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
             // No previous limits. Set without checking
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 0c2ce6f8ca..8fef8a2684 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -12,6 +12,7 @@
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/moddims.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <lookup.hpp>
 #include <reduce.hpp>
@@ -27,6 +28,7 @@ using common::modDims;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
+using detail::getScalar;
 using detail::intl;
 using detail::lookup;
 using detail::reduce_all;
@@ -50,8 +52,8 @@ static af_array hist_equal(const af_array& in, const af_array& hist) {
 
     Array<float> cdf = scan<af_add_t, float, float>(fHist, 0);
 
-    float minCdf = reduce_all<af_min_t, float, float>(cdf);
-    float maxCdf = reduce_all<af_max_t, float, float>(cdf);
+    float minCdf = getScalar<float>(reduce_all<af_min_t, float, float>(cdf));
+    float maxCdf = getScalar<float>(reduce_all<af_max_t, float, float>(cdf));
     float factor = static_cast<float>(grayLevels - 1) / (maxCdf - minCdf);
 
     // constant array of min value from cdf
diff --git a/src/api/c/imgproc_common.hpp b/src/api/c/imgproc_common.hpp
index bf16be980a..214fbe6c7a 100644
--- a/src/api/c/imgproc_common.hpp
+++ b/src/api/c/imgproc_common.hpp
@@ -12,6 +12,7 @@
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/cast.hpp>
+#include <copy.hpp>
 #include <logic.hpp>
 #include <reduce.hpp>
 #include <scan.hpp>
@@ -46,9 +47,10 @@ detail::Array<To> convRange(const detail::Array<Ti>& in,
                             const To newLow = To(0), const To newHigh = To(1)) {
     auto dims  = in.dims();
     auto input = common::cast<To, Ti>(in);
-    To high    = detail::reduce_all<af_max_t, To, To>(input);
-    To low     = detail::reduce_all<af_min_t, To, To>(input);
-    To range   = high - low;
+    To high =
+        detail::getScalar<To>(detail::reduce_all<af_max_t, To, To>(input));
+    To low = detail::getScalar<To>(detail::reduce_all<af_min_t, To, To>(input));
+    To range = high - low;
 
     if (std::abs(range) < 1.0e-6) {
         if (low == To(0) && newLow == To(0)) {
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index 79f064ebb7..84444eed58 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -12,6 +12,7 @@
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <lu.hpp>
 #include <math.hpp>
@@ -29,6 +30,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
 using detail::createValueArray;
+using detail::getScalar;
 using detail::reduce;
 using detail::reduce_all;
 using detail::scalar;
@@ -37,11 +39,11 @@ template<typename T>
 double matrixNorm(const Array<T> &A, double p) {
     if (p == 1) {
         Array<T> colSum = reduce<af_add_t, T, T>(A, 0);
-        return reduce_all<af_max_t, T, T>(colSum);
+        return getScalar<T>(reduce_all<af_max_t, T, T>(colSum));
     }
     if (p == af::Inf) {
         Array<T> rowSum = reduce<af_add_t, T, T>(A, 1);
-        return reduce_all<af_max_t, T, T>(rowSum);
+        return getScalar<T>(reduce_all<af_max_t, T, T>(rowSum));
     }
 
     AF_ERROR("This type of norm is not supported in ArrayFire\n",
@@ -50,17 +52,17 @@ double matrixNorm(const Array<T> &A, double p) {
 
 template<typename T>
 double vectorNorm(const Array<T> &A, double p) {
-    if (p == 1) { return reduce_all<af_add_t, T, T>(A); }
+    if (p == 1) { return getScalar<T>(reduce_all<af_add_t, T, T>(A)); }
     if (p == af::Inf) {
-        return reduce_all<af_max_t, T, T>(A);
+        return getScalar<T>(reduce_all<af_max_t, T, T>(A));
     } else if (p == 2) {
         Array<T> A_sq = arithOp<T, af_mul_t>(A, A, A.dims());
-        return std::sqrt(reduce_all<af_add_t, T, T>(A_sq));
+        return std::sqrt(getScalar<T>(reduce_all<af_add_t, T, T>(A_sq)));
     }
 
     Array<T> P   = createValueArray<T>(A.dims(), scalar<T>(p));
     Array<T> A_p = arithOp<T, af_pow_t>(A, P, A.dims());
-    return std::pow(reduce_all<af_add_t, T, T>(A_p), T(1.0 / p));
+    return std::pow(getScalar<T>(reduce_all<af_add_t, T, T>(A_p)), T(1.0 / p));
 }
 
 template<typename T>
@@ -78,12 +80,13 @@ double LPQNorm(const Array<T> &A, double p, double q) {
         A_p_norm         = arithOp<T, af_pow_t>(A_p_sum, invP, invP.dims());
     }
 
-    if (q == 1) { return reduce_all<af_add_t, T, T>(A_p_norm); }
+    if (q == 1) { return getScalar<T>(reduce_all<af_add_t, T, T>(A_p_norm)); }
 
     Array<T> Q          = createValueArray<T>(A_p_norm.dims(), scalar<T>(q));
     Array<T> A_p_norm_q = arithOp<T, af_pow_t>(A_p_norm, Q, Q.dims());
 
-    return std::pow(reduce_all<af_add_t, T, T>(A_p_norm_q), T(1.0 / q));
+    return std::pow(getScalar<T>(reduce_all<af_add_t, T, T>(A_p_norm_q)),
+                    T(1.0 / q));
 }
 
 template<typename T>
diff --git a/src/api/c/rank.cpp b/src/api/c/rank.cpp
index 8880814a82..770c331a7a 100644
--- a/src/api/c/rank.cpp
+++ b/src/api/c/rank.cpp
@@ -11,6 +11,7 @@
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <logic.hpp>
 #include <qr.hpp>
@@ -25,6 +26,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
 using detail::createValueArray;
+using detail::getScalar;
 using detail::logicOp;
 using detail::reduce;
 using detail::reduce_all;
@@ -52,7 +54,7 @@ static inline uint rank(const af_array in, double tol) {
     Array<BT> val  = createValueArray<BT>(R.dims(), scalar<BT>(tol));
     Array<char> gt = logicOp<BT, af_gt_t>(R, val, val.dims());
     Array<char> at = reduce<af_or_t, char, char>(gt, 1);
-    return reduce_all<af_notzero_t, char, uint>(at);
+    return getScalar<uint>(reduce_all<af_notzero_t, char, uint>(at));
 }
 
 af_err af_rank(uint* out, const af_array in, const double tol) {
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 544ced2368..1849255257 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -10,6 +10,7 @@
 #include <backend.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <ireduce.hpp>
 #include <math.hpp>
@@ -25,6 +26,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
+using detail::getScalar;
 using detail::imag;
 using detail::intl;
 using detail::real;
@@ -533,11 +535,19 @@ af_err af_any_true_by_key(af_array *keys_out, af_array *vals_out,
                                              dim);
 }
 
+template<af_op_t op, typename Ti, typename To>
+static inline af_array reduce_all_array(const af_array in,
+                                        bool change_nan = false,
+                                        double nanval   = 0) {
+    return getHandle(
+        detail::reduce_all<op, Ti, To>(getArray<Ti>(in), change_nan, nanval));
+}
+
 template<af_op_t op, typename Ti, typename Tacc, typename Tret = double>
 static inline Tret reduce_all(const af_array in, bool change_nan = false,
                               double nanval = 0) {
-    return static_cast<Tret>(
-        reduce_all<op, Ti, Tacc>(getArray<Ti>(in), change_nan, nanval));
+    return static_cast<Tret>(getScalar<Tacc>(
+        reduce_all<op, Ti, Tacc>(getArray<Ti>(in), change_nan, nanval)));
 }
 
 template<af_op_t op, typename To>
@@ -574,6 +584,38 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in) {
     return AF_SUCCESS;
 }
 
+template<af_op_t op, typename To>
+static af_err reduce_all_type_array(af_array *out, const af_array in) {
+    try {
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
+
+        af_array res;
+        switch (type) {
+            // clang-format off
+            case f32: res = reduce_all_array<op, float,   To>(in); break;
+            case f64: res = reduce_all_array<op, double,  To>(in); break;
+            case c32: res = reduce_all_array<op, cfloat,  To>(in); break;
+            case c64: res = reduce_all_array<op, cdouble, To>(in); break;
+            case u32: res = reduce_all_array<op, uint,    To>(in); break;
+            case s32: res = reduce_all_array<op, int,     To>(in); break;
+            case u64: res = reduce_all_array<op, uintl,   To>(in); break;
+            case s64: res = reduce_all_array<op, intl,    To>(in); break;
+            case u16: res = reduce_all_array<op, ushort,  To>(in); break;
+            case s16: res = reduce_all_array<op, short,   To>(in); break;
+            case b8:  res = reduce_all_array<op, char,    To>(in); break;
+            case u8:  res = reduce_all_array<op, uchar,   To>(in); break;
+            case f16: res = reduce_all_array<op, half,    To>(in); break;
+            // clang-format on
+            default: TYPE_ERROR(1, type);
+        }
+        std::swap(*out, res);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 template<af_op_t op>
 static af_err reduce_all_common(double *real_val, double *imag_val,
                                 const af_array in) {
@@ -625,6 +667,40 @@ static af_err reduce_all_common(double *real_val, double *imag_val,
     return AF_SUCCESS;
 }
 
+template<af_op_t op>
+static af_err reduce_all_common_array(af_array *out, const af_array in) {
+    try {
+        const ArrayInfo &in_info = getInfo(in);
+        af_dtype type            = in_info.getType();
+
+        ARG_ASSERT(2, in_info.ndims() > 0);
+        af_array res;
+
+        switch (type) {
+            // clang-format off
+            case f32: res = reduce_all_array<op, float,  float>(in); break;
+            case f64: res = reduce_all_array<op, double, double>(in); break;
+            case u32: res = reduce_all_array<op, uint,   uint>(in); break;
+            case s32: res = reduce_all_array<op, int,    int>(in); break;
+            case u64: res = reduce_all_array<op, uintl,  uintl>(in); break;
+            case s64: res = reduce_all_array<op, intl,   intl>(in); break;
+            case u16: res = reduce_all_array<op, ushort, ushort>(in); break;
+            case s16: res = reduce_all_array<op, short,  short>(in); break;
+            case b8:  res = reduce_all_array<op, char,   char>(in); break;
+            case u8:  res = reduce_all_array<op, uchar,  uchar>(in); break;
+            case f16: res = reduce_all_array<op, half,   half>(in); break;
+            // clang-format on
+            case c32: res = reduce_all_array<op, cfloat, cfloat>(in); break;
+            case c64: res = reduce_all_array<op, cdouble, cdouble>(in); break;
+            default: TYPE_ERROR(1, type);
+        }
+        std::swap(*out, res);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 template<af_op_t op>
 static af_err reduce_all_promote(double *real_val, double *imag_val,
                                  const af_array in, bool change_nan = false,
@@ -686,34 +762,133 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
     return AF_SUCCESS;
 }
 
+template<af_op_t op>
+static af_err reduce_all_promote_array(af_array *out, const af_array in,
+                                       bool change_nan = false,
+                                       double nanval   = 0.0) {
+    try {
+        const ArrayInfo &in_info = getInfo(in);
+
+        af_dtype type = in_info.getType();
+        af_array res;
+
+        switch (type) {
+            case f32:
+                res =
+                    reduce_all_array<op, float, float>(in, change_nan, nanval);
+                break;
+            case f64:
+                res = reduce_all_array<op, double, double>(in, change_nan,
+                                                           nanval);
+                break;
+            case c32:
+                res = reduce_all_array<op, cfloat, cfloat>(in, change_nan,
+                                                           nanval);
+                break;
+            case c64:
+                res = reduce_all_array<op, cdouble, cdouble>(in, change_nan,
+                                                             nanval);
+                break;
+            case u32:
+                res = reduce_all_array<op, uint, uint>(in, change_nan, nanval);
+                break;
+            case s32:
+                res = reduce_all_array<op, int, int>(in, change_nan, nanval);
+                break;
+            case u64:
+                res =
+                    reduce_all_array<op, uintl, uintl>(in, change_nan, nanval);
+                break;
+            case s64:
+                res = reduce_all_array<op, intl, intl>(in, change_nan, nanval);
+                break;
+            case u16:
+                res =
+                    reduce_all_array<op, ushort, uint>(in, change_nan, nanval);
+                break;
+            case s16:
+                res = reduce_all_array<op, short, int>(in, change_nan, nanval);
+                break;
+            case u8:
+                res = reduce_all_array<op, uchar, uint>(in, change_nan, nanval);
+                break;
+            case b8: {
+                if (op == af_mul_t) {
+                    res = reduce_all_array<af_and_t, char, char>(in, change_nan,
+                                                                 nanval);
+                } else {
+                    res = reduce_all_array<af_notzero_t, char, uint>(
+                        in, change_nan, nanval);
+                }
+            } break;
+            case f16:
+                res = reduce_all_array<op, half, float>(in, change_nan, nanval);
+                break;
+            default: TYPE_ERROR(1, type);
+        }
+        std::swap(*out, res);
+    }
+    CATCHALL;
+
+    return AF_SUCCESS;
+}
+
 af_err af_min_all(double *real, double *imag, const af_array in) {
     return reduce_all_common<af_min_t>(real, imag, in);
 }
 
+af_err af_min_all_array(af_array *out, const af_array in) {
+    return reduce_all_common_array<af_min_t>(out, in);
+}
+
 af_err af_max_all(double *real, double *imag, const af_array in) {
     return reduce_all_common<af_max_t>(real, imag, in);
 }
 
+af_err af_max_all_array(af_array *out, const af_array in) {
+    return reduce_all_common_array<af_max_t>(out, in);
+}
+
 af_err af_sum_all(double *real, double *imag, const af_array in) {
     return reduce_all_promote<af_add_t>(real, imag, in);
 }
 
+af_err af_sum_all_array(af_array *out, const af_array in) {
+    return reduce_all_promote_array<af_add_t>(out, in);
+}
+
 af_err af_product_all(double *real, double *imag, const af_array in) {
     return reduce_all_promote<af_mul_t>(real, imag, in);
 }
 
+af_err af_product_all_array(af_array *out, const af_array in) {
+    return reduce_all_promote_array<af_mul_t>(out, in);
+}
+
 af_err af_count_all(double *real, double *imag, const af_array in) {
     return reduce_all_type<af_notzero_t, uint>(real, imag, in);
 }
 
+af_err af_count_all_array(af_array *out, const af_array in) {
+    return reduce_all_type_array<af_notzero_t, uint>(out, in);
+}
+
 af_err af_all_true_all(double *real, double *imag, const af_array in) {
     return reduce_all_type<af_and_t, char>(real, imag, in);
 }
 
+af_err af_all_true_all_array(af_array *out, const af_array in) {
+    return reduce_all_type_array<af_and_t, char>(out, in);
+}
+
 af_err af_any_true_all(double *real, double *imag, const af_array in) {
     return reduce_all_type<af_or_t, char>(real, imag, in);
 }
 
+af_err af_any_true_all_array(af_array *out, const af_array in) {
+    return reduce_all_type_array<af_or_t, char>(out, in);
+}
+
 template<af_op_t op, typename T>
 static inline void ireduce(af_array *res, af_array *loc, const af_array in,
                            const int dim) {
@@ -948,7 +1123,17 @@ af_err af_sum_nan_all(double *real, double *imag, const af_array in,
     return reduce_all_promote<af_add_t>(real, imag, in, true, nanval);
 }
 
+af_err af_sum_nan_all_array(af_array *out, const af_array in,
+                            const double nanval) {
+    return reduce_all_promote_array<af_add_t>(out, in, true, nanval);
+}
+
 af_err af_product_nan_all(double *real, double *imag, const af_array in,
                           const double nanval) {
     return reduce_all_promote<af_mul_t>(real, imag, in, true, nanval);
 }
+
+af_err af_product_nan_all_array(af_array *out, const af_array in,
+                                const double nanval) {
+    return reduce_all_promote_array<af_mul_t>(out, in, true, nanval);
+}
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 4f66328782..3be779e544 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -10,6 +10,7 @@
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/cast.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <mean.hpp>
@@ -31,6 +32,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createValueArray;
 using detail::division;
+using detail::getScalar;
 using detail::intl;
 using detail::mean;
 using detail::reduce;
@@ -52,9 +54,9 @@ static outType stdev(const af_array& in, const af_var_bias bias) {
         detail::arithOp<outType, af_sub_t>(input, meanCnst, input.dims());
     Array<outType> diffSq =
         detail::arithOp<outType, af_mul_t>(diff, diff, diff.dims());
-    outType result =
-        division(reduce_all<af_add_t, outType, outType>(diffSq),
-                 (input.elements() - (bias == AF_VARIANCE_SAMPLE)));
+    outType result = division(
+        getScalar<outType>(reduce_all<af_add_t, outType, outType>(diffSq)),
+        (input.elements() - (bias == AF_VARIANCE_SAMPLE)));
     return sqrt(result);
 }
 
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 986cedae09..2f6a3eda7b 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -15,6 +15,7 @@
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <common/moddims.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
@@ -28,6 +29,7 @@ using detail::Array;
 using detail::copy_surface;
 using detail::createEmptyArray;
 using detail::forgeManager;
+using detail::getScalar;
 using detail::reduce_all;
 using detail::uchar;
 using detail::uint;
@@ -101,12 +103,12 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
         T dmin[3], dmax[3];
         FG_CHECK(_.fg_get_chart_axes_limits(
             &cmin[0], &cmax[0], &cmin[1], &cmax[1], &cmin[2], &cmax[2], chart));
-        dmin[0] = reduce_all<af_min_t, T, T>(xIn);
-        dmax[0] = reduce_all<af_max_t, T, T>(xIn);
-        dmin[1] = reduce_all<af_min_t, T, T>(yIn);
-        dmax[1] = reduce_all<af_max_t, T, T>(yIn);
-        dmin[2] = reduce_all<af_min_t, T, T>(zIn);
-        dmax[2] = reduce_all<af_max_t, T, T>(zIn);
+        dmin[0] = getScalar<T>(reduce_all<af_min_t, T, T>(xIn));
+        dmax[0] = getScalar<T>(reduce_all<af_max_t, T, T>(xIn));
+        dmin[1] = getScalar<T>(reduce_all<af_min_t, T, T>(yIn));
+        dmax[1] = getScalar<T>(reduce_all<af_max_t, T, T>(yIn));
+        dmin[2] = getScalar<T>(reduce_all<af_min_t, T, T>(zIn));
+        dmax[2] = getScalar<T>(reduce_all<af_max_t, T, T>(zIn));
 
         if (cmin[0] == 0 && cmax[0] == 0 && cmin[1] == 0 && cmax[1] == 0 &&
             cmin[2] == 0 && cmax[2] == 0) {
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index fe111de5f5..efbbfc8a70 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -12,6 +12,7 @@
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <copy.hpp>
 #include <handle.hpp>
 #include <math.hpp>
 #include <mean.hpp>
@@ -34,6 +35,7 @@ using detail::cfloat;
 using detail::createEmptyArray;
 using detail::createValueArray;
 using detail::division;
+using detail::getScalar;
 using detail::imag;
 using detail::intl;
 using detail::mean;
@@ -64,9 +66,9 @@ static outType varAll(const af_array& in, const af_var_bias bias) {
 
     Array<outType> diffSq = arithOp<outType, af_mul_t>(diff, diff, diff.dims());
 
-    outType result =
-        division(reduce_all<af_add_t, outType, outType>(diffSq),
-                 (input.elements() - (bias == AF_VARIANCE_SAMPLE)));
+    outType result = division(
+        getScalar<outType>(reduce_all<af_add_t, outType, outType>(diffSq)),
+        (input.elements() - (bias == AF_VARIANCE_SAMPLE)));
 
     return result;
 }
@@ -78,7 +80,8 @@ static outType varAll(const af_array& in, const af_array weights) {
     Array<outType> input = cast<outType>(getArray<inType>(in));
     Array<outType> wts   = cast<outType>(getArray<bType>(weights));
 
-    bType wtsSum = reduce_all<af_add_t, bType, bType>(getArray<bType>(weights));
+    bType wtsSum = getScalar<bType>(
+        reduce_all<af_add_t, bType, bType>(getArray<bType>(weights)));
     auto wtdMean = mean<outType, bType>(input, getArray<bType>(weights));
 
     Array<outType> meanArr = createValueArray<outType>(input.dims(), wtdMean);
@@ -89,8 +92,9 @@ static outType varAll(const af_array& in, const af_array weights) {
     Array<outType> accDiffSq =
         arithOp<outType, af_mul_t>(diffSq, wts, diffSq.dims());
 
-    outType result =
-        division(reduce_all<af_add_t, outType, outType>(accDiffSq), wtsSum);
+    outType result = division(
+        getScalar<outType>(reduce_all<af_add_t, outType, outType>(accDiffSq)),
+        wtsSum);
 
     return result;
 }
diff --git a/src/api/cpp/reduce.cpp b/src/api/cpp/reduce.cpp
index 44f981982d..cfdadf85ae 100644
--- a/src/api/cpp/reduce.cpp
+++ b/src/api/cpp/reduce.cpp
@@ -212,6 +212,14 @@ void max(array &val, array &idx, const array &in, const int dim) {
         return out;                                       \
     }
 
+#define INSTANTIATE_ARRAY(fnC, fnCPP)                   \
+    template<>                                          \
+    AFAPI af::array fnCPP(const array &in) {            \
+        af_array out = 0;                               \
+        AF_THROW(af_##fnC##_all_array(&out, in.get())); \
+        return array(out);                              \
+    }
+
 INSTANTIATE(sum, sum)
 INSTANTIATE(product, product)
 INSTANTIATE(min, min)
@@ -223,8 +231,17 @@ INSTANTIATE(count, count)
 INSTANTIATE_REAL(all_true, allTrue, bool);
 INSTANTIATE_REAL(any_true, anyTrue, bool);
 
+INSTANTIATE_ARRAY(sum, sum)
+INSTANTIATE_ARRAY(product, product)
+INSTANTIATE_ARRAY(min, min)
+INSTANTIATE_ARRAY(max, max)
+INSTANTIATE_ARRAY(all_true, allTrue)
+INSTANTIATE_ARRAY(any_true, anyTrue)
+INSTANTIATE_ARRAY(count, count)
+
 #undef INSTANTIATE_REAL
 #undef INSTANTIATE_CPLX
+#undef INSTANTIATE_ARRAY
 
 #define INSTANTIATE_REAL(fnC, fnCPP, T)                           \
     template<>                                                    \
@@ -243,12 +260,23 @@ INSTANTIATE_REAL(any_true, anyTrue, bool);
         return out;                                               \
     }
 
+#define INSTANTIATE_ARRAY(fnC, fnCPP)                             \
+    template<>                                                    \
+    AFAPI af::array fnCPP(const array &in, const double nanval) { \
+        af_array out = 0;                                         \
+        AF_THROW(af_##fnC##_all_array(&out, in.get(), nanval));   \
+        return array(out);                                        \
+    }
+INSTANTIATE_ARRAY(sum_nan, sum)
+INSTANTIATE_ARRAY(product_nan, product)
+
 INSTANTIATE(sum_nan, sum)
 INSTANTIATE(product_nan, product)
 
 #undef INSTANTIATE_REAL
 #undef INSTANTIATE_CPLX
 #undef INSTANTIATE
+#undef INSTANTIATE_ARRAY
 
 #define INSTANTIATE_COMPAT(fnCPP, fnCompat, T) \
     template<>                                 \
diff --git a/src/api/unified/algorithm.cpp b/src/api/unified/algorithm.cpp
index 87f03a053a..8f990fb535 100644
--- a/src/api/unified/algorithm.cpp
+++ b/src/api/unified/algorithm.cpp
@@ -124,6 +124,33 @@ ALGO_HAPI_DEF(af_imax_all)
 
 #undef ALGO_HAPI_DEF
 
+#define ALGO_HAPI_DEF(af_func)                         \
+    af_err af_func(af_array *out, const af_array in) { \
+        CHECK_ARRAYS(in);                              \
+        CALL(af_func, out, in);                        \
+    }
+
+ALGO_HAPI_DEF(af_sum_all_array)
+ALGO_HAPI_DEF(af_product_all_array)
+ALGO_HAPI_DEF(af_min_all_array)
+ALGO_HAPI_DEF(af_max_all_array)
+ALGO_HAPI_DEF(af_count_all_array)
+ALGO_HAPI_DEF(af_any_true_all_array)
+ALGO_HAPI_DEF(af_all_true_all_array)
+
+#undef ALGO_HAPI_DEF
+
+#define ALGO_HAPI_DEF(af_func)                                              \
+    af_err af_func(af_array *out, const af_array in, const double nanval) { \
+        CHECK_ARRAYS(in);                                                   \
+        CALL(af_func, out, in, nanval);                                     \
+    }
+
+ALGO_HAPI_DEF(af_sum_nan_all_array)
+ALGO_HAPI_DEF(af_product_nan_all_array)
+
+#undef ALGO_HAPI_DEF
+
 af_err af_where(af_array *idx, const af_array in) {
     CHECK_ARRAYS(in);
     CALL(af_where, idx, in);
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index aeed23a415..cbf6e76861 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -144,6 +144,11 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
     if (unified::getActiveHandle()) {                                            \
         thread_local af_func func = (af_func)common::getFunctionPointer(         \
             unified::getActiveHandle(), __func__);                               \
+        if (!func) {                                                             \
+            AF_RETURN_ERROR(                                                     \
+                "requested symbol name could not be found in loaded library.",   \
+                AF_ERR_LOAD_LIB);                                                \
+        }                                                                        \
         if (index_ != unified::getActiveBackend()) {                             \
             index_ = unified::getActiveBackend();                                \
             func   = (af_func)common::getFunctionPointer(                        \
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index cd8678edda..374816102e 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -156,5 +156,46 @@ struct reduce_dim_by_key<op, Ti, Tk, To, 0> {
         }
     }
 };
+
+template<af_op_t op, typename Ti, typename To>
+struct reduce_all {
+    common::Transform<data_t<Ti>, compute_t<To>, op> transform;
+    common::Binary<compute_t<To>, op> reduce;
+    void operator()(Param<To> out, CParam<Ti> in, bool change_nan,
+                    double nanval) {
+        // Decrement dimension of select dimension
+        af::dim4 dims            = in.dims();
+        af::dim4 strides         = in.strides();
+        const data_t<Ti> *inPtr  = in.get();
+        data_t<To> *const outPtr = out.get();
+
+        compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
+
+        for (dim_t l = 0; l < dims[3]; l++) {
+            dim_t off3 = l * strides[3];
+
+            for (dim_t k = 0; k < dims[2]; k++) {
+                dim_t off2 = k * strides[2];
+
+                for (dim_t j = 0; j < dims[1]; j++) {
+                    dim_t off1 = j * strides[1];
+
+                    for (dim_t i = 0; i < dims[0]; i++) {
+                        dim_t idx = i + off1 + off2 + off3;
+
+                        compute_t<To> in_val = transform(inPtr[idx]);
+                        if (change_nan) {
+                            in_val = IS_NAN(in_val) ? nanval : in_val;
+                        }
+                        out_val = reduce(in_val, out_val);
+                    }
+                }
+            }
+        }
+
+        *outPtr = data_t<To>(out_val);
+    }
+};
+
 }  // namespace kernel
 }  // namespace cpu
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 795390a04e..e1baf5daea 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -107,51 +107,27 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
     vals_out = ovals;
 }
 
-template<af_op_t op, typename Ti, typename Taccumulate>
-Taccumulate reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
-    in.eval();
-    getQueue().sync();
-
-    Transform<Ti, compute_t<Taccumulate>, op> transform;
-    Binary<compute_t<Taccumulate>, op> reduce;
-
-    compute_t<Taccumulate> out = Binary<compute_t<Taccumulate>, op>::init();
-
-    // Decrement dimension of select dimension
-    af::dim4 dims           = in.dims();
-    af::dim4 strides        = in.strides();
-    const data_t<Ti> *inPtr = in.get();
-
-    for (dim_t l = 0; l < dims[3]; l++) {
-        dim_t off3 = l * strides[3];
-
-        for (dim_t k = 0; k < dims[2]; k++) {
-            dim_t off2 = k * strides[2];
-
-            for (dim_t j = 0; j < dims[1]; j++) {
-                dim_t off1 = j * strides[1];
-
-                for (dim_t i = 0; i < dims[0]; i++) {
-                    dim_t idx = i + off1 + off2 + off3;
+template<af_op_t op, typename Ti, typename To>
+using reduce_all_func =
+    std::function<void(Param<To>, CParam<Ti>, bool, double)>;
 
-                    compute_t<Taccumulate> in_val = transform(inPtr[idx]);
-                    if (change_nan) {
-                        in_val = IS_NAN(in_val) ? nanval : in_val;
-                    }
-                    out = reduce(in_val, out);
-                }
-            }
-        }
-    }
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+    in.eval();
 
-    return data_t<Taccumulate>(out);
+    Array<To> out = createEmptyArray<To>(1);
+    static const reduce_all_func<op, Ti, To> reduce_all_kernel =
+        kernel::reduce_all<op, Ti, To>();
+    getQueue().enqueue(reduce_all_kernel, out, in, change_nan, nanval);
+    getQueue().sync();
+    return out;
 }
 
 #define INSTANTIATE(ROp, Ti, To)                                               \
     template Array<To> reduce<ROp, Ti, To>(const Array<Ti> &in, const int dim, \
                                            bool change_nan, double nanval);    \
-    template To reduce_all<ROp, Ti, To>(const Array<Ti> &in, bool change_nan,  \
-                                        double nanval);                        \
+    template Array<To> reduce_all<ROp, Ti, To>(                                \
+        const Array<Ti> &in, bool change_nan, double nanval);                  \
     template void reduce_by_key<ROp, Ti, int, To>(                             \
         Array<int> & keys_out, Array<To> & vals_out, const Array<int> &keys,   \
         const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
diff --git a/src/backend/cpu/reduce.hpp b/src/backend/cpu/reduce.hpp
index 9923d2aef3..3db9b0cc8a 100644
--- a/src/backend/cpu/reduce.hpp
+++ b/src/backend/cpu/reduce.hpp
@@ -21,5 +21,6 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    bool change_nan = false, double nanval = 0);
 
 template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
+                     double nanval = 0);
 }  // namespace cpu
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index bf2565883e..30c7475292 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -40,7 +40,7 @@ using common::SparseArray;
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     if (stype == AF_STORAGE_CSR) {
-        uint nNZ = reduce_all<af_notzero_t, T, uint>(in);
+        uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in));
 
         auto sparse = createEmptySparseArray<T>(in.dims(), nNZ, stype);
         sparse.eval();
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index 02eedb4237..fb51a72851 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -21,6 +21,7 @@
 
 #include <cub/warp/warp_reduce.cuh>
 
+#include <climits>
 #include <vector>
 
 using std::unique_ptr;
@@ -258,6 +259,176 @@ __global__ static void reduce_first_kernel(Param<To> out, CParam<Ti> in,
     if (tidx == 0) optr[blockIdx_x] = data_t<To>(out_val);
 }
 
+template<typename Ti, typename To, af_op_t op, uint DIMX>
+__global__ static void reduce_all_kernel(Param<To> out,
+                                         Param<unsigned> retirementCount,
+                                         Param<To> tmp, CParam<Ti> in,
+                                         uint blocks_x, uint blocks_y,
+                                         uint repeat, bool change_nan,
+                                         To nanval) {
+    const uint tidx = threadIdx.x;
+    const uint tidy = threadIdx.y;
+    const uint tid  = tidy * DIMX + tidx;
+
+    const uint zid        = blockIdx.x / blocks_x;
+    const uint blockIdx_x = blockIdx.x - (blocks_x)*zid;
+    const uint xid        = blockIdx_x * blockDim.x * repeat + tidx;
+
+    const uint wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
+    const uint blockIdx_y =
+        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
+    const uint yid = blockIdx_y * blockDim.y + tidy;
+
+    common::Binary<compute_t<To>, op> reduce;
+    common::Transform<Ti, compute_t<To>, op> transform;
+
+    const int nwarps = THREADS_PER_BLOCK / 32;
+    __shared__ compute_t<To> s_val[nwarps];
+
+    const data_t<Ti> *const iptr =
+        in.ptr +
+        (wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1]);
+
+    bool cond = yid < in.dims[1] && zid < in.dims[2] && wid < in.dims[3];
+
+    int lim = min((int)(xid + repeat * DIMX), in.dims[0]);
+
+    compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
+    for (int id = xid; cond && id < lim; id += DIMX) {
+        compute_t<To> in_val = transform(iptr[id]);
+        if (change_nan)
+            in_val =
+                !IS_NAN(in_val) ? in_val : static_cast<compute_t<To>>(nanval);
+        out_val = reduce(in_val, out_val);
+    }
+
+    const int warpid = tid / 32;
+    const int lid    = tid % 32;
+
+    typedef cub::WarpReduce<compute_t<To>> WarpReduce;
+    __shared__ typename WarpReduce::TempStorage temp_storage[nwarps];
+
+    out_val = WarpReduce(temp_storage[warpid]).Reduce(out_val, reduce);
+
+    if (cond && lid == 0) {
+        s_val[warpid] = out_val;
+    } else if (!cond) {
+        s_val[warpid] = common::Binary<compute_t<To>, op>::init();
+    }
+    __syncthreads();
+
+    if (tid < 32) {
+        out_val = tid < nwarps ? s_val[tid]
+                               : common::Binary<compute_t<To>, op>::init();
+        out_val = WarpReduce(temp_storage[0]).Reduce(out_val, reduce);
+    }
+
+    const unsigned total_blocks = (gridDim.x * gridDim.y * gridDim.z);
+    const int uubidx            = (gridDim.x * gridDim.y) * blockIdx.z +
+                       (gridDim.x * blockIdx.y) + blockIdx.x;
+    if (cond && tid == 0) {
+        if (total_blocks != 1) {
+            tmp.ptr[uubidx] = data_t<To>(out_val);
+        } else {
+            out.ptr[0] = data_t<To>(out_val);
+        }
+    }
+
+    // Last block to perform final reduction
+    if (total_blocks > 1) {
+        __shared__ bool amLast;
+
+        // wait until all outstanding memory instructions in this thread are
+        // finished
+        __threadfence();
+
+        // Thread 0 takes a ticket
+        if (tid == 0) {
+            unsigned int ticket = atomicInc(retirementCount.ptr, total_blocks);
+            // If the ticket ID == number of blocks, we are the last block
+            amLast = (ticket == (total_blocks - 1));
+        }
+        __syncthreads();  // for amlast
+
+        if (amLast) {
+            int i   = tid;
+            out_val = common::Binary<compute_t<To>, op>::init();
+
+            while (i < total_blocks) {
+                compute_t<To> in_val = compute_t<To>(tmp.ptr[i]);
+                out_val              = reduce(in_val, out_val);
+                i += THREADS_PER_BLOCK;
+            }
+
+            out_val = WarpReduce(temp_storage[warpid]).Reduce(out_val, reduce);
+            if (lid == 0) { s_val[warpid] = out_val; }
+            __syncthreads();
+
+            if (tid < 32) {
+                out_val = tid < nwarps
+                              ? s_val[tid]
+                              : common::Binary<compute_t<To>, op>::init();
+                out_val = WarpReduce(temp_storage[0]).Reduce(out_val, reduce);
+            }
+
+            if (tid == 0) {
+                out.ptr[0] = out_val;
+
+                // reset retirement count so that next run succeeds
+                retirementCount.ptr[0] = 0;
+            }
+        }
+    }
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_all_launcher(Param<To> out, CParam<Ti> in, const uint blocks_x,
+                         const uint blocks_y, const uint threads_x,
+                         bool change_nan, double nanval) {
+    dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
+    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
+
+    uint repeat = divup(in.dims[0], (blocks_x * threads_x));
+
+    const int maxBlocksY =
+        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    blocks.z = divup(blocks.y, maxBlocksY);
+    blocks.y = divup(blocks.y, blocks.z);
+
+    long tmp_elements = blocks.x * blocks.y * blocks.z;
+    if (tmp_elements > UINT_MAX) {
+        AF_ERROR("Too many blocks requested (retirementCount == unsigned)",
+                 AF_ERR_RUNTIME);
+    }
+    Array<To> tmp                   = createEmptyArray<To>(tmp_elements);
+    Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
+
+    switch (threads_x) {
+        case 32:
+            CUDA_LAUNCH((reduce_all_kernel<Ti, To, op, 32>), blocks, threads,
+                        out, retirementCount, tmp, in, blocks_x, blocks_y,
+                        repeat, change_nan, scalar<To>(nanval));
+            break;
+        case 64:
+            CUDA_LAUNCH((reduce_all_kernel<Ti, To, op, 64>), blocks, threads,
+                        out, retirementCount, tmp, in, blocks_x, blocks_y,
+                        repeat, change_nan, scalar<To>(nanval));
+            break;
+        case 128:
+            CUDA_LAUNCH((reduce_all_kernel<Ti, To, op, 128>), blocks, threads,
+                        out, retirementCount, tmp, in, blocks_x, blocks_y,
+                        repeat, change_nan, scalar<To>(nanval));
+            break;
+        case 256:
+            CUDA_LAUNCH((reduce_all_kernel<Ti, To, op, 256>), blocks, threads,
+                        out, retirementCount, tmp, in, blocks_x, blocks_y,
+                        repeat, change_nan, scalar<To>(nanval));
+            break;
+    }
+
+    POST_LAUNCH_CHECK();
+}
+
 template<typename Ti, typename To, af_op_t op>
 void reduce_first_launcher(Param<To> out, CParam<Ti> in, const uint blocks_x,
                            const uint blocks_y, const uint threads_x,
@@ -344,81 +515,33 @@ void reduce(Param<To> out, CParam<Ti> in, int dim, bool change_nan,
         case 3: return reduce_dim<Ti, To, op, 3>(out, in, change_nan, nanval);
     }
 }
-
 template<typename Ti, typename To, af_op_t op>
-To reduce_all(CParam<Ti> in, bool change_nan, double nanval) {
+void reduce_all(Param<To> out, CParam<Ti> in, bool change_nan, double nanval) {
     int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
     bool is_linear  = (in.strides[0] == 1);
     for (int k = 1; k < 4; k++) {
         is_linear &= (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
     }
 
-    // FIXME: Use better heuristics to get to the optimum number
-    if (in_elements > 4096 || !is_linear) {
-        if (is_linear) {
-            in.dims[0] = in_elements;
-            for (int k = 1; k < 4; k++) {
-                in.dims[k]    = 1;
-                in.strides[k] = in_elements;
-            }
-        }
-        uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
-        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
-        uint threads_y = THREADS_PER_BLOCK / threads_x;
-
-        Param<To> tmp;
-
-        uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
-        uint blocks_y = divup(in.dims[1], threads_y);
-
-        tmp.dims[0]    = blocks_x;
-        tmp.strides[0] = 1;
-
+    if (is_linear) {
+        in.dims[0] = in_elements;
         for (int k = 1; k < 4; k++) {
-            tmp.dims[k]    = in.dims[k];
-            tmp.strides[k] = tmp.dims[k - 1] * tmp.strides[k - 1];
+            in.dims[k]    = 1;
+            in.strides[k] = in_elements;
         }
+    }
 
-        int tmp_elements = tmp.strides[3] * tmp.dims[3];
-
-        auto tmp_alloc = memAlloc<To>(tmp_elements);
-        tmp.ptr        = tmp_alloc.get();
-        reduce_first_launcher<Ti, To, op>(tmp, in, blocks_x, blocks_y,
-                                          threads_x, change_nan, nanval);
-
-        std::vector<To> h_data(tmp_elements);
-        CUDA_CHECK(
-            cudaMemcpyAsync(h_data.data(), tmp.ptr, tmp_elements * sizeof(To),
-                            cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-        common::Binary<compute_t<To>, op> reduce;
-        compute_t<To> out = common::Binary<compute_t<To>, op>::init();
-        for (int i = 0; i < tmp_elements; i++) {
-            out = reduce(out, compute_t<To>(h_data[i]));
-        }
+    uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
 
-        return data_t<To>(out);
-    } else {
-        std::vector<Ti> h_data(in_elements);
-        CUDA_CHECK(
-            cudaMemcpyAsync(h_data.data(), in.ptr, in_elements * sizeof(Ti),
-                            cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
-
-        common::Transform<Ti, compute_t<To>, op> transform;
-        common::Binary<compute_t<To>, op> reduce;
-        compute_t<To> out       = common::Binary<compute_t<To>, op>::init();
-        compute_t<To> nanval_to = scalar<compute_t<To>>(nanval);
-
-        for (int i = 0; i < in_elements; i++) {
-            compute_t<To> in_val = transform(h_data[i]);
-            if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval_to;
-            out = reduce(out, in_val);
-        }
+    // TODO: perf REPEAT, consider removing or runtime eval
+    // max problem size < SM resident threads, don't use REPEAT
+    uint blocks_x = divup(in.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.dims[1], threads_y);
 
-        return data_t<To>(out);
-    }
+    reduce_all_launcher<Ti, To, op>(out, in, blocks_x, blocks_y, threads_x,
+                                    change_nan, nanval);
 }
 
 }  // namespace kernel
diff --git a/src/backend/cuda/reduce.hpp b/src/backend/cuda/reduce.hpp
index 8f3ad82898..d606153650 100644
--- a/src/backend/cuda/reduce.hpp
+++ b/src/backend/cuda/reduce.hpp
@@ -21,5 +21,6 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    bool change_nan = false, double nanval = 0);
 
 template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
+                     double nanval = 0);
 }  // namespace cuda
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 73b0d47761..0c4e2e3e87 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -353,9 +353,12 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
 }
 
 template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
-    return kernel::reduce_all<Ti, To, op>(in, change_nan, nanval);
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+    Array<To> out = createEmptyArray<To>(1);
+    kernel::reduce_all<Ti, To, op>(out, in, change_nan, nanval);
+    return out;
 }
+
 }  // namespace cuda
 
 #define INSTANTIATE(Op, Ti, To)                                                \
@@ -367,5 +370,5 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     template void reduce_by_key<Op, Ti, uint, To>(                             \
         Array<uint> & keys_out, Array<To> & vals_out, const Array<uint> &keys, \
         const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
-    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,   \
-                                       double nanval);
+    template Array<To> reduce_all<Op, Ti, To>(const Array<Ti> &in,             \
+                                              bool change_nan, double nanval);
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 0b803ba794..f3c8022b71 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -20,6 +20,7 @@
 #include <kernel/config.hpp>
 #include <kernel/names.hpp>
 #include <kernel_headers/ops.hpp>
+#include <kernel_headers/reduce_all.hpp>
 #include <kernel_headers/reduce_dim.hpp>
 #include <kernel_headers/reduce_first.hpp>
 #include <math.hpp>
@@ -109,6 +110,54 @@ void reduceDim(Param out, Param in, int change_nan, double nanval, int dim) {
     }
 }
 
+template<typename Ti, typename To, af_op_t op>
+void reduceAllLauncher(Param out, Param in, const uint groups_x,
+                       const uint groups_y, const uint threads_x,
+                       int change_nan, double nanval) {
+    ToNumStr<To> toNumStr;
+    std::vector<TemplateArg> targs = {
+        TemplateTypename<Ti>(),
+        TemplateTypename<To>(),
+        TemplateArg(op),
+        TemplateArg(threads_x),
+    };
+    std::vector<std::string> options = {
+        DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
+        DefineKeyValue(To, dtype_traits<To>::getName()),
+        DefineKeyValue(T, "To"),
+        DefineKeyValue(DIMX, threads_x),
+        DefineValue(THREADS_PER_GROUP),
+        DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
+        DefineKeyFromStr(binOpName<op>()),
+        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+    };
+    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+
+    auto reduceAll = common::getKernel(
+        "reduce_all_kernel", {ops_cl_src, reduce_all_cl_src}, targs, options);
+
+    cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
+    cl::NDRange global(groups_x * in.info.dims[2] * local[0],
+                       groups_y * in.info.dims[3] * local[1]);
+
+    uint repeat = divup(in.info.dims[0], (local[0] * groups_x));
+
+    long tmp_elements = groups_x * in.info.dims[2] * groups_y * in.info.dims[3];
+    if (tmp_elements > UINT_MAX) {
+        AF_ERROR("Too many blocks requested (retirementCount == unsigned)",
+                 AF_ERR_RUNTIME);
+    }
+    Array<To> tmp                   = createEmptyArray<To>(tmp_elements);
+    Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
+    Param p_tmp(tmp);
+    Param p_Count(retirementCount);
+
+    reduceAll(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
+              *p_Count.data, *p_tmp.data, p_tmp.info, *in.data, in.info,
+              groups_x, groups_y, repeat, change_nan, scalar<To>(nanval));
+    CL_DEBUG_FINISH(getQueue());
+}
+
 template<typename Ti, typename To, af_op_t op>
 void reduceFirstLauncher(Param out, Param in, const uint groups_x,
                          const uint groups_y, const uint threads_x,
@@ -192,7 +241,7 @@ void reduce(Param out, Param in, int dim, int change_nan, double nanval) {
 }
 
 template<typename Ti, typename To, af_op_t op>
-To reduceAll(Param in, int change_nan, double nanval) {
+void reduceAll(Param out, Param in, int change_nan, double nanval) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
@@ -202,59 +251,22 @@ To reduceAll(Param in, int change_nan, double nanval) {
                       (in.info.strides[k - 1] * in.info.dims[k - 1]));
     }
 
-    // FIXME: Use better heuristics to get to the optimum number
-    if (in_elements > 4096 || !is_linear) {
-        if (is_linear) {
-            in.info.dims[0] = in_elements;
-            for (int k = 1; k < 4; k++) {
-                in.info.dims[k]    = 1;
-                in.info.strides[k] = in_elements;
-            }
+    if (is_linear) {
+        in.info.dims[0] = in_elements;
+        for (int k = 1; k < 4; k++) {
+            in.info.dims[k]    = 1;
+            in.info.strides[k] = in_elements;
         }
+    }
 
-        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-        threads_x      = std::min(threads_x, THREADS_PER_GROUP);
-        uint threads_y = THREADS_PER_GROUP / threads_x;
-
-        uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
-        uint groups_y = divup(in.info.dims[1], threads_y);
-        Array<To> tmp = createEmptyArray<To>(
-            {groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
-
-        int tmp_elements = tmp.elements();
-
-        reduceFirstLauncher<Ti, To, op>(tmp, in, groups_x, groups_y, threads_x,
-                                        change_nan, nanval);
-
-        std::vector<To> h_ptr(tmp_elements);
-        getQueue().enqueueReadBuffer(*tmp.get(), CL_TRUE, 0,
-                                     sizeof(To) * tmp_elements, h_ptr.data());
-
-        common::Binary<compute_t<To>, op> reduce;
-        compute_t<To> out = common::Binary<compute_t<To>, op>::init();
-        for (int i = 0; i < (int)tmp_elements; i++) {
-            out = reduce(out, compute_t<To>(h_ptr[i]));
-        }
-        return data_t<To>(out);
-    } else {
-        std::vector<Ti> h_ptr(in_elements);
-        getQueue().enqueueReadBuffer(*in.data, CL_TRUE,
-                                     sizeof(Ti) * in.info.offset,
-                                     sizeof(Ti) * in_elements, h_ptr.data());
-
-        common::Transform<Ti, compute_t<To>, op> transform;
-        common::Binary<compute_t<To>, op> reduce;
-        compute_t<To> out       = common::Binary<compute_t<To>, op>::init();
-        compute_t<To> nanval_to = scalar<compute_t<To>>(nanval);
-
-        for (int i = 0; i < (int)in_elements; i++) {
-            compute_t<To> in_val = transform(h_ptr[i]);
-            if (change_nan) in_val = IS_NAN(in_val) ? nanval_to : in_val;
-            out = reduce(out, compute_t<To>(in_val));
-        }
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_GROUP);
+    uint threads_y = THREADS_PER_GROUP / threads_x;
 
-        return data_t<To>(out);
-    }
+    uint groups_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(in.info.dims[1], threads_y);
+    reduceAllLauncher<Ti, To, op>(out, in, groups_x, groups_y, threads_x,
+                                  change_nan, nanval);
 }
 
 }  // namespace kernel
diff --git a/src/backend/opencl/kernel/reduce_all.cl b/src/backend/opencl/kernel/reduce_all.cl
new file mode 100644
index 0000000000..dccb0f1c69
--- /dev/null
+++ b/src/backend/opencl/kernel/reduce_all.cl
@@ -0,0 +1,160 @@
+/*******************************************************
+ * Copyright (c) 2021, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+// careful w/__threadfence substitution!
+// http://www.whatmannerofburgeristhis.com/blog/opencl-vs-cuda-gpu-memory-fences/
+
+kernel void reduce_all_kernel(global To *oData, KParam oInfo,
+                              global int* retirementCount, global To *tmp, KParam tmpInfo,
+                              const global Ti *iData, KParam iInfo,
+                              uint groups_x, uint groups_y, uint repeat,
+                              int change_nan, To nanval) {
+
+    const uint tidx = get_local_id(0);
+    const uint tidy = get_local_id(1);
+    const uint tid  = tidy * DIMX + tidx;
+
+    const uint zid       = get_group_id(0) / groups_x;
+    const uint groupId_x = get_group_id(0) - (groups_x)*zid;
+    const uint xid       = groupId_x * get_local_size(0) * repeat + tidx;
+
+    const uint wid       = get_group_id(1) / groups_y;
+    const uint groupId_y = get_group_id(1) - (groups_y)*wid;
+    const uint yid       = groupId_y * get_local_size(1) + tidy;
+
+    local To s_val[THREADS_PER_GROUP];
+    local bool amLast;
+
+    iData += wid * iInfo.strides[3] + zid * iInfo.strides[2] +
+             yid * iInfo.strides[1] + iInfo.offset;
+
+    bool cond =
+        (yid < iInfo.dims[1]) && (zid < iInfo.dims[2]) && (wid < iInfo.dims[3]);
+
+
+    int last   = (xid + repeat * DIMX);
+    int lim    = last > iInfo.dims[0] ? iInfo.dims[0] : last;
+
+    To out_val = init;
+    for (int id = xid; cond && id < lim; id += DIMX) {
+        To in_val = transform(iData[id]);
+        if (change_nan) in_val = !IS_NAN(in_val) ? in_val : nanval;
+        out_val = binOp(in_val, out_val);
+    }
+
+    s_val[tid] = out_val;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (THREADS_PER_GROUP == 256) {
+        if (tid < 128) s_val[tid] = binOp(s_val[tid], s_val[tid + 128]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (THREADS_PER_GROUP >= 128) {
+        if (tid < 64) s_val[tid] = binOp(s_val[tid], s_val[tid + 64]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (THREADS_PER_GROUP >= 64) {
+        if (tid < 32) s_val[tid] = binOp(s_val[tid], s_val[tid + 32]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    if (tid < 16) s_val[tid] = binOp(s_val[tid], s_val[tid + 16]);
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (tid < 8) s_val[tid] = binOp(s_val[tid], s_val[tid + 8]);
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (tid < 4) s_val[tid] = binOp(s_val[tid], s_val[tid + 4]);
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (tid < 2) s_val[tid] = binOp(s_val[tid], s_val[tid + 2]);
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (tid < 1) s_val[tid] = binOp(s_val[tid], s_val[tid + 1]);
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+
+    const unsigned total_blocks = (get_num_groups(0) * get_num_groups(1) * get_num_groups(2));
+    const int uubidx = (get_num_groups(0) * get_num_groups(1)) * get_group_id(2)
+                       + (get_num_groups(0) * get_group_id(1)) + get_group_id(0);
+    if (cond && tid == 0) {
+        if(total_blocks != 1) {
+            tmp[uubidx] = s_val[0];
+        } else {
+            oData[0] = s_val[0];
+        }
+    }
+
+    // Last block to perform final reduction
+    if (total_blocks > 1) {
+
+        mem_fence(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE);
+
+        // Thread 0 takes a ticket
+        if (tid == 0) {
+            unsigned int ticket = atomic_inc(retirementCount);
+            // If the ticket ID == number of blocks, we are the last block
+            amLast = (ticket == (total_blocks - 1));
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (amLast) {
+            int i = tid;
+            To fout_val = init;
+
+            while (i < total_blocks) {
+                To in_val = tmp[i];
+                fout_val = binOp(in_val, fout_val);
+                i += THREADS_PER_GROUP;
+            }
+
+            s_val[tid] = fout_val;
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            // reduce final block
+            if (THREADS_PER_GROUP == 256) {
+                if (tid < 128) s_val[tid] = binOp(s_val[tid], s_val[tid + 128]);
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+
+            if (THREADS_PER_GROUP >= 128) {
+                if (tid < 64) s_val[tid] = binOp(s_val[tid], s_val[tid + 64]);
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+
+            if (THREADS_PER_GROUP >= 64) {
+                if (tid < 32) s_val[tid] = binOp(s_val[tid], s_val[tid + 32]);
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+
+            if (tid < 16) s_val[tid] = binOp(s_val[tid], s_val[tid + 16]);
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if (tid < 8) s_val[tid] = binOp(s_val[tid], s_val[tid + 8]);
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if (tid < 4) s_val[tid] = binOp(s_val[tid], s_val[tid + 4]);
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if (tid < 2) s_val[tid] = binOp(s_val[tid], s_val[tid + 2]);
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if (tid < 1) s_val[tid] = binOp(s_val[tid], s_val[tid + 1]);
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            if (tid == 0) {
+                oData[0] = s_val[0];
+
+                // reset retirement count so that next run succeeds
+                retirementCount[0] = 0;
+            }
+        }
+    }
+}
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 4da84d10df..4c9581c761 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -22,5 +22,6 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    bool change_nan = false, double nanval = 0);
 
 template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan = false, double nanval = 0);
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
+                     double nanval = 0);
 }  // namespace opencl
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index f7c8c675b6..4211dc9050 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -37,9 +37,12 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
 }
 
 template<af_op_t op, typename Ti, typename To>
-To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
-    return kernel::reduceAll<Ti, To, op>(in, change_nan, nanval);
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+    Array<To> out = createEmptyArray<To>(1);
+    kernel::reduceAll<Ti, To, op>(out, in, change_nan, nanval);
+    return out;
 }
+
 }  // namespace opencl
 
 #define INSTANTIATE(Op, Ti, To)                                                \
@@ -51,5 +54,5 @@ To reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
     template void reduce_by_key<Op, Ti, uint, To>(                             \
         Array<uint> & keys_out, Array<To> & vals_out, const Array<uint> &keys, \
         const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
-    template To reduce_all<Op, Ti, To>(const Array<Ti> &in, bool change_nan,   \
-                                       double nanval);
+    template Array<To> reduce_all<Op, Ti, To>(const Array<Ti> &in,             \
+                                              bool change_nan, double nanval);
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index d579761a72..580822d5d1 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -61,7 +61,7 @@ template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_) {
     in_.eval();
 
-    uint nNZ = reduce_all<af_notzero_t, T, uint>(in_);
+    uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in_));
 
     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
     sparse_.eval();
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 2d76c46961..5aa6c0e1ed 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -87,7 +87,7 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
     static const double smlnum = std::sqrt(cpu_lapack_lamch('S')) / eps;
     static const double bignum = 1. / smlnum;
 
-    Tr anrm = abs(reduce_all<af_max_t, T, T>(arrA));
+    Tr anrm = abs(getScalar<T>(reduce_all<af_max_t, T, T>(arrA)));
 
     T scale                = scalar<T>(1);
     static const int ione  = 1;
diff --git a/test/mean.cpp b/test/mean.cpp
index 22b622c868..9c4c8f7fb4 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -362,8 +362,9 @@ TEST(MeanAll, SubArray) {
     array in  = randu(inDims);
     array sub = in(0, span, span, span);
 
-    size_t nElems = sub.elements();
-    ASSERT_FLOAT_EQ(mean<float>(sub), sum<float>(sub) / nElems);
+    size_t nElems   = sub.elements();
+    float max_error = std::numeric_limits<float>::epsilon() * nElems;
+    ASSERT_NEAR(mean<float>(sub), sum<float>(sub) / nElems, max_error);
 }
 
 TEST(MeanHalf, dim0) {
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 3cb1c33a55..0633bd0536 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -13,7 +13,9 @@
 #include <af/dim4.hpp>
 #include <af/traits.hpp>
 
+#include <math.h>
 #include <algorithm>
+#include <cmath>
 #include <functional>
 #include <iostream>
 #include <string>
@@ -96,7 +98,6 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef = false,
                 EXPECT_EQ(currGoldBar[elIter], outData[elIter])
                     << "at: " << elIter << " for dim " << d + off << endl;
             }
-            af_print_array(outArray);
             for (int i = 0; i < (int)nElems; i++) {
                 cout << currGoldBar[i] << ", ";
             }
@@ -1263,10 +1264,14 @@ TEST(Reduce, KernelName) {
 }
 
 TEST(Reduce, AllSmallIndexed) {
-    const int len = 1000;
-    array a       = af::range(dim4(len, 2));
-    array b       = a(seq(len / 2), span);
-    ASSERT_EQ(max<float>(b), len / 2 - 1);
+    const int len = 512;
+    for (int i = 0; i < 1000; ++i) {
+        // const int len = 10000;
+        array a = af::range(dim4(len, 2));
+        array b = a(seq(len / 2), span);
+        // af::sync();
+        ASSERT_EQ(max<float>(b), len / 2 - 1);
+    }
 }
 
 TEST(ProductAll, BoolIn_ISSUE2543_All_Ones) {
@@ -2091,3 +2096,192 @@ TEST(ReduceByKey, ISSUE_3062) {
     af::countByKey(okeys, ovalues, zeros, ones, 1);
     ASSERT_EQ(ovalues.scalar<unsigned>(), 129);
 }
+
+TEST(Reduce, Test_Sum_Global_Array) {
+    const int num = 513;
+    array a       = af::randn(num, 2, 33, 4);
+
+    float res          = af::sum<float>(a);
+    array full_reduce  = af::sum<af::array>(a);
+
+    float *h_a = a.host<float>();
+    float gold = 0.f;
+
+    for (int i = 0; i < a.elements(); i++) { gold += h_a[i]; }
+
+    float max_error = std::numeric_limits<float>::epsilon() * (float)a.elements();
+    ASSERT_NEAR(gold, res, max_error);
+    ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
+    freeHost(h_a);
+}
+
+TEST(Reduce, Test_Product_Global_Array) {
+    const int num = 512;
+    array a       = 1 + (0.005 * af::randn(num, 2, 3, 4));
+
+    float res          = af::product<float>(a);
+    array full_reduce  = af::product<af::array>(a);
+
+    float *h_a = a.host<float>();
+    float gold = 1.f;
+
+    for (int i = 0; i < a.elements(); i++) { gold *= h_a[i]; }
+
+    float max_error = std::numeric_limits<float>::epsilon() * (float)a.elements();
+    ASSERT_NEAR(gold, res, max_error);
+    ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
+    freeHost(h_a);
+}
+
+TEST(Reduce, Test_Count_Global_Array) {
+    const int num = 10000;
+    array a       = round(2 * randu(num, 2, 3, 4));
+    array b       = a.as(b8);
+
+    int res       = count<int>(b);
+    array res_arr = count<af::array>(b);
+    char *h_b     = b.host<char>();
+    unsigned gold      = 0;
+
+    for (int i = 0; i < a.elements(); i++) { gold += h_b[i]; }
+
+    ASSERT_EQ(gold, res);
+    ASSERT_EQ(gold, res_arr.scalar<unsigned>());
+    freeHost(h_b);
+}
+
+TEST(Reduce, Test_min_Global_Array) {
+    SUPPORTED_TYPE_CHECK(double);
+
+    const int num = 10000;
+    array a       = af::randn(num, 2, 3, 4, f64);
+    double res    = min<double>(a);
+    array res_arr = min<af::array>(a);
+    double *h_a   = a.host<double>();
+    double gold   = std::numeric_limits<double>::max();
+
+    SUPPORTED_TYPE_CHECK(double);
+
+    for (int i = 0; i < a.elements(); i++) { gold = std::min(gold, h_a[i]); }
+
+    ASSERT_EQ(gold, res);
+    ASSERT_EQ(gold, res_arr.scalar<double>());
+    freeHost(h_a);
+}
+
+TEST(Reduce, Test_max_Global_Array) {
+    const int num = 10000;
+    array a       = af::randn(num, 2, 3, 4);
+    float res     = max<float>(a);
+    array res_arr = max<af::array>(a);
+    float *h_a    = a.host<float>();
+    float gold    = -std::numeric_limits<float>::max();
+
+    for (int i = 0; i < a.elements(); i++) { gold = std::max(gold, h_a[i]); }
+
+    ASSERT_EQ(gold, res);
+    ASSERT_EQ(gold, res_arr.scalar<float>());
+    freeHost(h_a);
+}
+
+TYPED_TEST(Reduce, Test_All_Global_Array) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+
+    // Input size test
+    for (int i = 1; i < 1000; i += 100) {
+        int num = 10 * i;
+        vector<TypeParam> h_vals(num, (TypeParam) true);
+        array a(2, num / 2, &h_vals.front());
+
+        TypeParam res = allTrue<TypeParam>(a);
+        array res_arr = allTrue<array>(a);
+        typed_assert_eq((TypeParam) true, res, false);
+        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(), false);
+
+        h_vals[3] = false;
+        a         = array(2, num / 2, &h_vals.front());
+
+        res = allTrue<TypeParam>(a);
+        res_arr = allTrue<array>(a);
+        typed_assert_eq((TypeParam) false, res, false);
+        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(), false);
+    }
+
+    // false value location test
+    const int num = 10000;
+    vector<TypeParam> h_vals(num, (TypeParam) true);
+    for (int i = 1; i < 10000; i += 100) {
+        h_vals[i] = false;
+        array a(2, num / 2, &h_vals.front());
+
+        TypeParam res = allTrue<TypeParam>(a);
+        array res_arr = allTrue<array>(a);
+        typed_assert_eq((TypeParam) false, res, false);
+        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(), false);
+
+        h_vals[i] = true;
+    }
+}
+
+TYPED_TEST(Reduce, Test_Any_Global_Array) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+
+    // Input size test
+    for (int i = 1; i < 1000; i += 100) {
+        int num = 10 * i;
+        vector<TypeParam> h_vals(num, (TypeParam) false);
+        array a(2, num / 2, &h_vals.front());
+
+        TypeParam res = anyTrue<TypeParam>(a);
+        array res_arr = anyTrue<array>(a);
+        typed_assert_eq((TypeParam) false, res, false);
+        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(), false);
+
+        h_vals[3] = true;
+        a         = array(2, num / 2, &h_vals.front());
+
+        res = anyTrue<TypeParam>(a);
+        res_arr = anyTrue<array>(a);
+        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(), false);
+    }
+
+    // true value location test
+    const int num = 10000;
+    vector<TypeParam> h_vals(num, (TypeParam) false);
+    for (int i = 1; i < 10000; i += 100) {
+        h_vals[i] = true;
+        array a(2, num / 2, &h_vals.front());
+
+        TypeParam res = anyTrue<TypeParam>(a);
+        array res_arr = anyTrue<array>(a);
+        typed_assert_eq((TypeParam) true, res, false);
+        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(), false);
+
+        h_vals[i] = false;
+    }
+}
+
+
+TEST(Reduce, Test_Sum_Global_Array_nanval) {
+    const int num = 100000;
+    array a = af::randn(num, 2, 34, 4);
+    a(1, 0, 0, 0) = NAN;
+    a(0, 1, 0, 0) = NAN;
+    a(0, 0, 1, 0) = NAN;
+    a(0, 0, 0, 1) = NAN;
+
+    double nanval = 0.2;
+    float res          = af::sum<float>(a, nanval);
+    array full_reduce  = af::sum<af::array>(a, nanval);
+
+    float *h_a = a.host<float>();
+    float gold = 0.f;
+
+    for (int i = 0; i < a.elements(); i++) {
+        gold += (isnan(h_a[i])) ? nanval : h_a[i];
+    }
+    float max_error = std::numeric_limits<float>::epsilon() * (float)a.elements();
+    ASSERT_NEAR(gold, res, max_error);
+    ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
+    freeHost(h_a);
+}

From 4868a37947672a09e138744646bbfb6b3afd87b3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 9 Apr 2022 14:09:18 -0400
Subject: [PATCH 2240/2677] Fix ccache configuration issue because it was
 configured before CUDA

Ccache was configured before CUDA was setup. This caused the launch-nvcc
script to include an empty CUDA_NVCC_EXECUTABLE variable.
---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 784ed20144..cb88845889 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -14,7 +14,6 @@ project(ArrayFire VERSION 3.9.0 LANGUAGES C CXX)
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 
 include(AFconfigure_deps_vars)
-include(config_ccache)
 include(AFBuildConfigurations)
 include(AFInstallDirs)
 include(CMakeDependentOption)
@@ -58,6 +57,7 @@ find_package(MKL)
 find_package(spdlog 1.8.5 QUIET)
 
 include(boost_package)
+include(config_ccache)
 
 option(AF_BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
 option(AF_BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})

From 096e0cae2c14d879a6ef600b1a35df4eef2d86f0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 9 Apr 2022 14:13:28 -0400
Subject: [PATCH 2241/2677] Fix issue with CMAKE_MODULE_PATH when it has
 multiple values

The path to some configuration files were relative to CMAKE_MODULE_PATH.
This variable can be a list of strings which causes errors when
CMAKE_MODULE_PATH was modified to include additional values.
---
 CMakeLists.txt                   | 6 +++---
 CMakeModules/InternalUtils.cmake | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cb88845889..adfe1d59bf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -430,7 +430,7 @@ write_basic_package_version_file(
 set(INCLUDE_DIRS include)
 set(CMAKE_DIR ${AF_INSTALL_CMAKE_DIR})
 configure_package_config_file(
-  ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+  ${ArrayFire_SOURCE_DIR}/CMakeModules/ArrayFireConfig.cmake.in
   cmake/install/ArrayFireConfig.cmake
   INSTALL_DESTINATION "${AF_INSTALL_CMAKE_DIR}"
   PATH_VARS INCLUDE_DIRS CMAKE_DIR
@@ -488,7 +488,7 @@ endif()
 set(INCLUDE_DIRS "${ArrayFire_SOURCE_DIR}/include" "${ArrayFire_BINARY_DIR}/include")
 set(CMAKE_DIR "${ArrayFire_BINARY_DIR}/cmake")
 configure_package_config_file(
-  ${CMAKE_MODULE_PATH}/ArrayFireConfig.cmake.in
+  ${ArrayFire_SOURCE_DIR}/CMakeModules/ArrayFireConfig.cmake.in
   ArrayFireConfig.cmake
   INSTALL_DESTINATION "${ArrayFire_BINARY_DIR}"
   PATH_VARS INCLUDE_DIRS CMAKE_DIR
@@ -506,7 +506,7 @@ configure_package_config_file(
 unset(CMAKE_CXX_VISIBILITY_PRESET)
 
 configure_file(
-  ${CMAKE_MODULE_PATH}/CTestCustom.cmake
+  ${ArrayFire_SOURCE_DIR}/CMakeModules/CTestCustom.cmake
   ${ArrayFire_BINARY_DIR}/CTestCustom.cmake)
 
 include(CTest)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 8fd21e7447..3b19485d6f 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -205,7 +205,7 @@ macro(arrayfire_set_cmake_default_variables)
   #          EPILOG ${compiler_header_epilogue}
   #          )
   configure_file(
-    ${CMAKE_MODULE_PATH}/compilers.h
+    ${ArrayFire_SOURCE_DIR}/CMakeModules/compilers.h
     ${ArrayFire_BINARY_DIR}/include/af/compilers.h)
 endmacro()
 

From 590267d2f719a0ea62901ec0fdc0d7b7a426d531 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 11 Apr 2022 13:20:07 -0400
Subject: [PATCH 2242/2677] Do not add cuda_unified test.

---
 test/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index af9afe4991..1c7bc8792e 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -372,7 +372,9 @@ if(CUDA_FOUND)
         FOLDER "Tests"
         OUTPUT_NAME "cuda_${backend}")
 
-      add_test(NAME ${target} COMMAND ${target})
+      if(NOT ${backend} STREQUAL "unified")
+        add_test(NAME ${target} COMMAND ${target})
+      endif()
     endif()
   endforeach()
 endif()

From a7f422dc7056528321a96d910e2395ca5266d2e5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 Apr 2022 17:44:24 -0400
Subject: [PATCH 2243/2677] Fix static MKL. Avoid calling interface/threading
 layer functions

We only need to call the mkl_set_threading_layer and mkl_set_interface_layer
functions for shared library builds of MKL. Static builds do not need those
functions.
---
 src/api/c/device.cpp              | 2 +-
 src/backend/cpu/CMakeLists.txt    | 1 +
 src/backend/opencl/CMakeLists.txt | 1 +
 3 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index d77969aeb1..3ed23a0c3e 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -108,7 +108,7 @@ af_err af_init() {
         thread_local std::once_flag flag;
         std::call_once(flag, []() {
             getDeviceInfo();
-#if defined(USE_MKL)
+#if defined(USE_MKL) && !defined(USE_STATIC_MKL)
             int errCode = -1;
             // Have used the AF_MKL_INTERFACE_SIZE as regular if's so that
             // we will know if these are not defined when using MKL when a
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 9707ef5f23..e3c862d169 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -320,6 +320,7 @@ if(BUILD_WITH_MKL)
 
   if(AF_WITH_STATIC_MKL)
       target_link_libraries(afcpu PRIVATE MKL::Static)
+      target_compile_definitions(afcpu PRIVATE USE_STATIC_MKL)
   else()
       target_link_libraries(afcpu PRIVATE MKL::RT)
   endif()
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 5385f4fa1f..dd557ede47 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -472,6 +472,7 @@ if(LAPACK_FOUND OR BUILD_WITH_MKL)
 
     if(AF_WITH_STATIC_MKL)
         target_link_libraries(afopencl PRIVATE MKL::Static)
+        target_compile_definitions(afopencl PRIVATE USE_STATIC_MKL)
     else()
         target_link_libraries(afopencl PRIVATE MKL::RT)
     endif()

From 18d8131537f337d9dedfbc8b2065e3bc436e40bc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 Apr 2022 17:48:27 -0400
Subject: [PATCH 2244/2677] Remove link to OpenCL library with unified backend

The unified backend was linking to the OpenCL library. This was done
to include the header but the library was also linking. Fixed this issue
by using the INTERFACE_INCLUDE_DIRECTORIES generator expression to include
the OpenCL header
---
 src/api/unified/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index cc08659976..5c0cec9d6f 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -43,9 +43,9 @@ if(OpenCL_FOUND)
     ${CMAKE_CURRENT_SOURCE_DIR}/opencl.cpp
   )
 
-  target_link_libraries(af
+  target_include_directories(af
     PRIVATE
-      OpenCL::OpenCL)
+      $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>)
 
 endif()
 

From bc4919fad6ea7ae640d3acc8d3510bf0da03de62 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 19 Apr 2022 18:36:19 -0400
Subject: [PATCH 2245/2677] Remove complex not supported note on some trig
 function

---
 docs/details/arith.dox | 38 --------------------------------------
 include/af/arith.h     |  4 ++--
 2 files changed, 2 insertions(+), 40 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 79e8cce0d0..f53de09a87 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -295,8 +295,6 @@ Hypotenuse of the two inputs
 
 sin of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_cos cos
 
@@ -304,8 +302,6 @@ sin of input
 
 cos of input
 
-\copydoc arith_real_only
-
 
 
 \defgroup arith_func_tan tan/tan2
@@ -314,8 +310,6 @@ cos of input
 
 tan of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_asin asin
 
@@ -323,8 +317,6 @@ tan of input
 
 arc sin of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_acos acos
 \brief Inverse cosine.
@@ -333,8 +325,6 @@ arc sin of input
 
 arc cos of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_atan atan/atan2
 
@@ -342,8 +332,6 @@ arc cos of input
 
 arc tan of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_sinh sinh
 
@@ -351,8 +339,6 @@ arc tan of input
 
 sinh of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_cosh cosh
 
@@ -360,8 +346,6 @@ sinh of input
 
 cosh of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_tanh tanh
 
@@ -369,8 +353,6 @@ cosh of input
 
 tanh of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_asinh asinh
 
@@ -378,8 +360,6 @@ tanh of input
 
 asinh of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_acosh acosh
 \brief Inverse hyperbolic cosine
@@ -388,8 +368,6 @@ asinh of input
 
 acosh of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_atanh atanh
 
@@ -397,8 +375,6 @@ acosh of input
 
 atanh of input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_cplx complex
 
@@ -439,8 +415,6 @@ Get complex conjugate
 
 Find root of an input
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_pow pow
 
@@ -464,8 +438,6 @@ point types used to compute power is given below.
 
 The output array will be of the same type as input.
 
-\copydoc arith_real_only
-
 
 
 \defgroup arith_func_exp exp
@@ -509,8 +481,6 @@ Complementary Error function value
 
 Natural logarithm
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_log1p log1p
 
@@ -536,8 +506,6 @@ logarithm base 10
 
 Square root of input arrays
 
-\copydoc arith_real_only
-
 \defgroup arith_func_rsqrt rsqrt
 
 \ingroup explog_mat
@@ -590,8 +558,6 @@ Logarithm of absolute values of Gamma function
 
 Check if values are zero
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_isinf isinf
 
@@ -599,8 +565,6 @@ Check if values are zero
 
 Check if values are infinite
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_isnan isNan
 
@@ -608,8 +572,6 @@ Check if values are infinite
 
 Check if values are Nan
 
-\copydoc arith_real_only
-
 
 \defgroup arith_func_cast cast
 
diff --git a/include/af/arith.h b/include/af/arith.h
index 83240ffc6d..319bda674b 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -473,7 +473,7 @@ namespace af
     /// \param[in] in is input
     /// \return the natural logarithm of (1 + input)
     ///
-    /// \note This function is useful when \p is small
+    /// \note This function is useful when \p in is small
     /// \ingroup arith_func_log1p
     AFAPI array log1p  (const array &in);
 
@@ -488,7 +488,7 @@ namespace af
     /// C++ Interface for logarithm base 2
     ///
     /// \param[in] in is input
-    /// \return the logarithm of input in base 2
+    /// \return the logarithm of input \p in base 2
     ///
     /// \ingroup explog_func_log2
     AFAPI array log2   (const array &in);

From df57c5679829d1e4e5122c3acc0630b651a4a642 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 21 Apr 2022 19:15:11 -0400
Subject: [PATCH 2246/2677] Release notes for v3.8.2

---
 docs/pages/release_notes.md | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 259b927772..fe893c564c 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,37 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.8.2
+======
+
+## Improvements
+
+- Optimize JIT by removing some consecutive cast operations \PR{3031}
+- Add driver checks checks for CUDA 11.5 and 11.6 \PR{3203}
+- Improve the timing algorithm used for timeit \PR{3185}
+- Dynamically link against CUDA numeric libraries by default \PR{3205}
+- Add support for pruning CUDA binaries to reduce static binary sizes \PR{3234} \PR{3237}
+- Remove unused cuDNN libraries from installations \PR{3235}
+- Add support to staticly link NVRTC libraries after CUDA 11.5 \PR{3236}
+- Add support for compiling with ccache when building the CUDA backend \PR{3241}
+- Make cuSparse an optional runtime dependency \PR{3240}
+
+## Fixes
+
+- Fix issue with consecutive moddims operations in the CPU backend \PR{3232}
+- Better floating point comparisons for tests \PR{3212}
+- Fix several warnings and inconsistencies with doxygen and documentation \PR{3226}
+- Fix issue when passing empty arrays into join \PR{3211}
+- Fix default value for the `AF_COMPUTE_LIBRARY` when not set \PR{3228}
+- Fix missing symbol issue when MKL is staticly linked \PR{3244}
+- Remove linking of OpenCL's library to the unified backend \PR{3244}
+
+## Contributions
+
+Special thanks to our contributors:
+[Jacob Kahn](https://github.com/jacobkahn)
+[Willy Born](https://github.com/willyborn)
+
 v3.8.1
 ======
 

From 2e36e8ce848fcb6c1e3e9fa569d1c0574461d917 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 00:45:03 -0400
Subject: [PATCH 2247/2677] Improve vcpkg support

* Improves vcpkg support with new packages
* Fix spdlog dependency version number
* Add features for MKL and forge
* Remove unused packages
---
 CMakeLists.txt                                |  6 ++-
 CMakeModules/AF_vcpkg_options.cmake           | 12 +++++
 CMakeModules/vcpkg-triplets/x64-windows.cmake |  9 ++++
 src/backend/common/CMakeLists.txt             |  5 +-
 src/backend/common/debug.hpp                  |  1 -
 vcpkg.json                                    | 53 +++++++++++--------
 6 files changed, 61 insertions(+), 25 deletions(-)
 create mode 100644 CMakeModules/vcpkg-triplets/x64-windows.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index adfe1d59bf..8dfee21544 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -201,10 +201,14 @@ endif()
 #otherwise, forge is not built at all
 include(AFconfigure_forge_dep)
 add_library(af_spdlog INTERFACE)
+set_target_properties(af_spdlog
+  PROPERTIES
+    INTERFACE_COMPILE_DEFINITIONS FMT_HEADER_ONLY)
+
 if(TARGET spdlog::spdlog_header_only)
   target_include_directories(af_spdlog
     SYSTEM INTERFACE
-    $<TARGET_PROPERTY:spdlog::spdlog_header_only,INTERFACE_INCLUDE_DIRECTORIES>
+      $<TARGET_PROPERTY:spdlog::spdlog_header_only,INTERFACE_INCLUDE_DIRECTORIES>
     )
 else()
   af_dep_check_and_populate(${spdlog_prefix}
diff --git a/CMakeModules/AF_vcpkg_options.cmake b/CMakeModules/AF_vcpkg_options.cmake
index 0639c377a4..75297a02b6 100644
--- a/CMakeModules/AF_vcpkg_options.cmake
+++ b/CMakeModules/AF_vcpkg_options.cmake
@@ -7,14 +7,26 @@
 
 set(ENV{VCPKG_FEATURE_FLAGS} "versions")
 set(ENV{VCPKG_KEEP_ENV_VARS} "MKLROOT")
+set(VCPKG_MANIFEST_NO_DEFAULT_FEATURES ON)
+
+set(VCPKG_OVERLAY_TRIPLETS ${ArrayFire_SOURCE_DIR}/CMakeModules/vcpkg-triplets)
 
 if(AF_BUILD_CUDA)
   list(APPEND VCPKG_MANIFEST_FEATURES "cuda")
 endif()
+
 if(AF_BUILD_OPENCL)
   list(APPEND VCPKG_MANIFEST_FEATURES "opencl")
 endif()
 
+if(AF_BUILD_FORGE)
+  list(APPEND VCPKG_MANIFEST_FEATURES "forge")
+endif()
+
+if(AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
+  list(APPEND VCPKG_MANIFEST_FEATURES "mkl")
+endif()
+
 if(DEFINED VCPKG_ROOT AND NOT DEFINED CMAKE_TOOLCHAIN_FILE)
   set(CMAKE_TOOLCHAIN_FILE "${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "")
 elseif(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE)
diff --git a/CMakeModules/vcpkg-triplets/x64-windows.cmake b/CMakeModules/vcpkg-triplets/x64-windows.cmake
new file mode 100644
index 0000000000..67dfc468eb
--- /dev/null
+++ b/CMakeModules/vcpkg-triplets/x64-windows.cmake
@@ -0,0 +1,9 @@
+set(VCPKG_TARGET_ARCHITECTURE x64)
+
+if(PORT MATCHES "freetype")
+  set(VCPKG_CRT_LINKAGE static)
+  set(VCPKG_LIBRARY_LINKAGE static)
+else()
+  set(VCPKG_CRT_LINKAGE dynamic)
+  set(VCPKG_LIBRARY_LINKAGE dynamic)
+endif()
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 125c620754..d12823c6a3 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -91,6 +91,7 @@ target_link_libraries(afcommon_interface
     Boost::boost
     ${CMAKE_DL_LIBS}
 )
+
 if(TARGET glad::glad)
   target_link_libraries(afcommon_interface INTERFACE glad::glad)
 else()
@@ -105,7 +106,9 @@ target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
     ${span-lite_SOURCE_DIR}/include
-    ${ArrayFire_BINARY_DIR}
+    ${ArrayFire_BINARY_DIR})
+
+target_include_directories(afcommon_interface
   SYSTEM INTERFACE
     $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
   )
diff --git a/src/backend/common/debug.hpp b/src/backend/common/debug.hpp
index 6c2c6cbfb8..e91c903d53 100644
--- a/src/backend/common/debug.hpp
+++ b/src/backend/common/debug.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#define FMT_HEADER_ONLY
 #include <boost/stacktrace.hpp>
 #include <common/ArrayFireTypesIO.hpp>
 #include <common/jit/NodeIO.hpp>
diff --git a/vcpkg.json b/vcpkg.json
index a3fafdecf2..654d9ad8b6 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -5,34 +5,37 @@
     "description": "ArrayFire is a HPC general-purpose library targeting parallel and massively-parallel architectures such as CPUs, GPUs, etc.",
     "supports": "x64",
     "dependencies": [
-        "boost-compute",
-        "boost-functional",
+        "boost-math",
         "boost-stacktrace",
-        {
-            "name": "forge",
-            "version>=": "1.0.8",
-            "platform": "windows"
-        },
-        "freeimage",
-        {
-            "name": "fontconfig",
-            "platform": "!windows"
-        },
-        "glad",
-        "intel-mkl",
-        "spdlog"
+        "spdlog",
+        "freeimage"
     ],
     "overrides": [
-       {
-           "name": "fmt",
-           "version": "6.2.1"
-       },
+        {
+            "name": "fmt",
+            "version": "7.1.3"
+        },
         {
             "name": "spdlog",
-            "version": "1.6.1"
+            "version": "1.8.5"
         }
     ],
     "features": {
+        "forge": {
+            "description": "Build Forge",
+            "dependencies": [
+                {
+                    "name": "freetype",
+                    "default-features": false
+                },
+                {
+                    "name": "fontconfig",
+                    "platform": "!windows"
+                },
+                "glfw3",
+                "glad"
+            ]
+        },
         "cuda": {
             "description": "Build CUDA backend",
             "dependencies": [
@@ -43,10 +46,16 @@
         "opencl": {
             "description": "Build OpenCL backend",
             "dependencies": [
-                "boost-program-options",
+                "boost-compute",
                 "opencl"
             ]
+        },
+        "mkl": {
+            "description": "Build with MKL",
+            "dependencies": [
+                "intel-mkl"
+            ]
         }
     },
-    "builtin-baseline": "5568f110b509a9fd90711978a7cb76bae75bb092"
+    "builtin-baseline": "14e7bb4ae24616ec54ff6b2f6ef4e8659434ea44"
 }

From a8c5dea2058c587f09e5ee0a28e2f7c36622a8a7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 11:40:31 -0400
Subject: [PATCH 2248/2677] Fix trivial warnings in gcc 12

---
 src/api/c/data.cpp                           |  6 +++---
 src/backend/common/util.cpp                  |  8 ++++++++
 src/backend/common/util.hpp                  |  2 ++
 src/backend/cpu/platform.cpp                 | 10 ----------
 src/backend/opencl/jit/kernel_generators.hpp |  2 +-
 src/backend/opencl/platform.cpp              |  9 ---------
 6 files changed, 14 insertions(+), 23 deletions(-)

diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 6a82d419c5..f231c7b300 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -325,7 +325,7 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num) {
 
         DIM_ASSERT(1, in_info.ndims() >= 2);
 
-        af_array result;
+        af_array result = nullptr;
         switch (type) {
             case f32: result = diagExtract<float>(in, num); break;
             case c32: result = diagExtract<cfloat>(in, num); break;
@@ -367,7 +367,7 @@ af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
 
         if (info.ndims() == 0) { return af_retain_array(out, in); }
 
-        af_array res;
+        af_array res = nullptr;
         switch (type) {
             case f32: res = triangle<float>(in, false, is_unit_diag); break;
             case f64: res = triangle<double>(in, false, is_unit_diag); break;
@@ -396,7 +396,7 @@ af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
 
         if (info.ndims() == 0) { return af_retain_array(out, in); }
 
-        af_array res;
+        af_array res = nullptr;
         switch (type) {
             case f32: res = triangle<float>(in, true, is_unit_diag); break;
             case f64: res = triangle<double>(in, true, is_unit_diag); break;
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index c0d1d30cc9..ee579d67ac 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -35,6 +35,14 @@ using std::accumulate;
 using std::string;
 using std::vector;
 
+// http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
+// trim from start
+string& ltrim(string& s) {
+    s.erase(s.begin(),
+            find_if(s.begin(), s.end(), [](char c) { return !isspace(c); }));
+    return s;
+}
+
 string getEnvVar(const std::string& key) {
 #if defined(OS_WIN)
     DWORD bufSize =
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index bb197e2af3..c0f712ec0e 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -31,6 +31,8 @@ constexpr const char* JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME =
 
 std::string getEnvVar(const std::string& key);
 
+std::string& ltrim(std::string& s);
+
 // Dump the kernel sources only if the environment variable is defined
 void saveKernel(const std::string& funcName, const std::string& jit_ker,
                 const std::string& ext);
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 179ff7a659..523737b07a 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -23,9 +23,7 @@
 
 using common::memory::MemoryManagerBase;
 using std::endl;
-using std::not1;
 using std::ostringstream;
-using std::ptr_fun;
 using std::stoi;
 using std::string;
 using std::unique_ptr;
@@ -45,14 +43,6 @@ static string get_system() {
 #endif
 }
 
-// http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
-// trim from start
-static inline string& ltrim(string& s) {
-    s.erase(s.begin(),
-            find_if(s.begin(), s.end(), not1(ptr_fun<int, int>(isspace))));
-    return s;
-}
-
 int getBackend() { return AF_BACKEND_CPU; }
 
 string getDeviceInfo() noexcept {
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 54ebc69720..c2eb711c1b 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -28,7 +28,7 @@ void generateParamDeclaration(std::stringstream& kerStream, int id,
 }
 
 /// Calls the setArg function to set the arguments for a kernel call
-int setKernelArguments(
+inline int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
     const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 94706135ea..e2c4571995 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -60,7 +60,6 @@ using std::move;
 using std::once_flag;
 using std::ostringstream;
 using std::pair;
-using std::ptr_fun;
 using std::string;
 using std::to_string;
 using std::unique_ptr;
@@ -87,14 +86,6 @@ static string get_system() {
 
 int getBackend() { return AF_BACKEND_OPENCL; }
 
-// http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
-// trim from start
-static inline string& ltrim(string& s) {
-    s.erase(s.begin(),
-            find_if(s.begin(), s.end(), not1(ptr_fun<int, int>(isspace))));
-    return s;
-}
-
 bool verify_present(const string& pname, const string ref) {
     auto iter =
         search(begin(pname), end(pname), begin(ref), end(ref),

From 077a52a7e04fce8d9946f43c365ef6cc82a1e248 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 11:44:10 -0400
Subject: [PATCH 2249/2677] Add reset function to unique_handle

---
 src/backend/common/unique_handle.hpp | 12 +++++++++---
 src/backend/cuda/platform.cpp        |  8 ++++----
 2 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index d8da5c7d67..52d0acfeda 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -50,9 +50,15 @@ class unique_handle {
     explicit constexpr unique_handle(T handle) noexcept : handle_(handle){};
 
     /// \brief Deletes the handle if created.
-    ~unique_handle() noexcept {
-        if (handle_) { ResourceHandler<T>::destroyHandle(handle_); }
-    };
+    ~unique_handle() noexcept { reset(); }
+
+    /// \brief Deletes the handle if created.
+    void reset() noexcept {
+        if (handle_) {
+            ResourceHandler<T>::destroyHandle(handle_);
+            handle_ = 0;
+        }
+    }
 
     unique_handle(const unique_handle &other) noexcept = delete;
     unique_handle &operator=(unique_handle &other) noexcept = delete;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index ab94cf298f..0e639ec62d 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -177,12 +177,12 @@ DeviceManager::~DeviceManager() {
         // handles of all devices
         for (int i = 0; i < nDevices; ++i) {
             setDevice(i);
-            delete cusolverManager(i);
-            delete cusparseManager(i);
+            cusolverManager(i)->reset();
+            cusparseManager(i)->reset();
             cufftManager(i).reset();
-            delete cublasManager(i);
+            cublasManager(i)->reset();
 #ifdef WITH_CUDNN
-            delete nnManager(i);
+            nnManager(i)->reset();
 #endif
         }
     } catch (const AfError &err) {

From 92badad9e35a9bbc460caac4643607cb3a9fbd28 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 11:46:58 -0400
Subject: [PATCH 2250/2677] Update license date

---
 LICENSE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LICENSE b/LICENSE
index f7b9cfdcf7..8f4c645ca1 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,4 +1,4 @@
-Copyright (c) 2014-2018, ArrayFire
+Copyright (c) 2014-2022, ArrayFire
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

From a84fa2ea7aa466ec4f2f9ddf8e9195b5ed27c362 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 11:48:36 -0400
Subject: [PATCH 2251/2677] Fix NSIS template, MaybeSelectionChanged should be
 in quotes

---
 CMakeModules/nsis/NSIS.template.in | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
index f45b01127a..bc3a44f233 100644
--- a/CMakeModules/nsis/NSIS.template.in
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -815,7 +815,7 @@ SectionEnd
 ;--------------------------------
 ; Component dependencies
 Function .onSelChange
-  !insertmacro SectionList MaybeSelectionChanged
+  !insertmacro SectionList "MaybeSelectionChanged"
 FunctionEnd
 
 ;--------------------------------

From 252767fd2b5316bce3c34d466394f91e27a1a59d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 12:49:25 -0400
Subject: [PATCH 2252/2677] Several CPack changes to improve NSIS and DEB
 installers

---
 CMakeLists.txt                        |   2 +-
 CMakeModules/CPackConfig.cmake        | 310 ++------------
 CMakeModules/CPackProjectConfig.cmake | 560 ++++++++++++++++++++++++++
 CMakeModules/debian/postinst          |   9 +
 4 files changed, 607 insertions(+), 274 deletions(-)
 create mode 100644 CMakeModules/CPackProjectConfig.cmake
 create mode 100644 CMakeModules/debian/postinst

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8dfee21544..537ae9a736 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -415,7 +415,7 @@ foreach(backend CPU CUDA OpenCL Unified)
     install(EXPORT ArrayFire${backend}Targets
             NAMESPACE ArrayFire::
             DESTINATION ${AF_INSTALL_CMAKE_DIR}
-            COMPONENT ${lower_backend})
+            COMPONENT ${lower_backend}_dev)
 
     export( EXPORT ArrayFire${backend}Targets
             NAMESPACE ArrayFire::
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 07d1d46962..d073527089 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -10,10 +10,10 @@ cmake_minimum_required(VERSION 3.5)
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PROJECT_SOURCE_DIR}/CMakeModules/nsis")
 
 include(Version)
-include(CPackIFW)
+
+set(CPACK_THREADS 8)
 
 set(CPACK_GENERATOR "STGZ;TGZ" CACHE STRING "STGZ;TGZ;DEB;RPM;productbuild")
-set_property(CACHE CPACK_GENERATOR PROPERTY STRINGS STGZ DEB RPM productbuild)
 mark_as_advanced(CPACK_GENERATOR)
 
 set(VENDOR_NAME "ArrayFire")
@@ -42,7 +42,7 @@ set(CPACK_PREFIX_DIR ${CMAKE_INSTALL_PREFIX})
 set(CPACK_PACKAGE_NAME "${LIBRARY_NAME}")
 set(CPACK_PACKAGE_VENDOR "${VENDOR_NAME}")
 set(CPACK_PACKAGE_INSTALL_REGISTRY_KEY ${LIBRARY_NAME})
-set(CPACK_PACKAGE_CONTACT "ArrayFire Development Group <technical@arrayfire.com>")
+set(CPACK_PACKAGE_CONTACT "ArrayFire <technical@arrayfire.com>")
 set(MY_CPACK_PACKAGE_ICON "${CMAKE_SOURCE_DIR}/assets/${APP_LOW_NAME}.ico")
 
 file(TO_NATIVE_PATH "${CMAKE_SOURCE_DIR}/assets/" NATIVE_ASSETS_PATH)
@@ -55,14 +55,38 @@ set(CPACK_PACKAGE_VERSION_PATCH "${ArrayFire_VERSION_PATCH}")
 
 set(CPACK_PACKAGE_INSTALL_DIRECTORY "${LIBRARY_NAME}")
 
-set(inst_pkg_name ${APP_LOW_NAME})
-set(inst_pkg_hash "")
-if (WIN32)
-  set(inst_pkg_name ${CPACK_PACKAGE_NAME})
-  set(inst_pkg_hash "-${GIT_COMMIT_HASH}")
-endif ()
-
-set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}${inst_pkg_hash}")
+set(CPACK_DEBIAN_FILE_NAME DEB-DEFAULT)
+set(CPACK_DEB_COMPONENT_INSTALL ON)
+set(CPACK_DEBIAN_DEBUGINFO_PACKAGE OFF)
+set(CPACK_DEBIAN_PACKAGE_DEBUG ON)
+set(CPACK_DEBIAN_PACKAGE_GENERATE_SHLIBS ON)
+set(CPACK_DEBIAN_PACKAGE_GENERATE_SHLIBS_POLICY ">=")
+set(CPACK_DEBIAN_PACKAGE_HOMEPAGE http://www.arrayfire.com)
+set(CPACK_DEBIAN_PACKAGE_CONTROL_STRICT_PERMISSION TRUE)
+set(CPACK_DEBIAN_COMPRESSION_TYPE xz)
+set(CPACK_DEBIAN_DEBUGINFO_PACKAGE ON)
+
+# Creates a variable from a ArrayFire variable so that it can be passed
+# into cpack project file. This is done by prepending CPACK_ before the
+# variable name
+macro(to_cpack_variable variable)
+  set(CPACK_${variable} ${${variable}})
+endmacro()
+
+to_cpack_variable(AF_COMPUTE_LIBRARY)
+to_cpack_variable(ArrayFire_SOURCE_DIR)
+to_cpack_variable(ArrayFire_BINARY_DIR)
+to_cpack_variable(CUDA_VERSION_MAJOR)
+to_cpack_variable(CUDA_VERSION_MINOR)
+
+# Create a arrayfire component so that Debian package has a top level
+# package that installs all the backends. This package needs to have
+# some files associated with it so that it doesn't get deleted by
+# APT after its installed.
+file(WRITE ${ArrayFire_BINARY_DIR}/arrayfire_version.txt ${ArrayFire_VERSION})
+install(FILES ${ArrayFire_BINARY_DIR}/arrayfire_version.txt
+	DESTINATION ${CMAKE_INSTALL_SYSCONFDIR}
+  COMPONENT arrayfire)
 
 # Platform specific settings for CPACK generators
 # - OSX specific
@@ -107,6 +131,7 @@ elseif(WIN32)
   set(CPACK_NSIS_HELP_LINK "${SITE_URL}")
   set(CPACK_NSIS_URL_INFO_ABOUT "${SITE_URL}")
   set(CPACK_NSIS_INSTALLED_ICON_NAME "${MY_CPACK_PACKAGE_ICON}")
+  set(CPACK_NSIS_COMPRESSOR "lzma")
   if (CMAKE_CL_64)
     set(CPACK_NSIS_INSTALL_ROOT "$PROGRAMFILES64")
   else (CMAKE_CL_64)
@@ -117,267 +142,6 @@ else()
   set(CPACK_RESOURCE_FILE_README "${ArrayFire_SOURCE_DIR}/README.md")
 endif()
 
-# Set the default components installed in the package
-get_cmake_property(CPACK_COMPONENTS_ALL COMPONENTS)
-
-include(CPackComponent)
-
-cpack_add_install_type(All DISPLAY_NAME "All Components")
-cpack_add_install_type(Development DISPLAY_NAME "Development")
-cpack_add_install_type(Extra DISPLAY_NAME "Extra")
-cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
-
-cpack_add_component_group(backends
-  DISPLAY_NAME "ArrayFire"
-  DESCRIPTION "ArrayFire backend libraries"
-  EXPANDED)
-cpack_add_component_group(cpu-backend
-  DISPLAY_NAME "CPU backend"
-  DESCRIPTION "Libraries and dependencies of the CPU backend."
-  PARENT_GROUP backends)
-cpack_add_component_group(cuda-backend
-  DISPLAY_NAME "CUDA backend"
-  DESCRIPTION "Libraries and dependencies of the CUDA backend."
-  PARENT_GROUP backends)
-cpack_add_component_group(opencl-backend
-  DISPLAY_NAME "OpenCL backend"
-  DESCRIPTION "Libraries and dependencies of the OpenCL backend."
-  PARENT_GROUP backends)
-
-set(PACKAGE_MKL_DEPS OFF)
-
-if ((USE_CPU_MKL OR USE_OPENCL_MKL) AND TARGET MKL::Shared)
-  set(PACKAGE_MKL_DEPS ON)
-  cpack_add_component(mkl_dependencies
-    DISPLAY_NAME "Intel MKL"
-	DESCRIPTION "Intel Math Kernel Libraries for FFTW, BLAS, and LAPACK routines."
-	GROUP backends
-    INSTALL_TYPES All Development Runtime)
-endif ()
-
-cpack_add_component(common_backend_dependencies
-  DISPLAY_NAME "Dependencies"
-  DESCRIPTION "Libraries commonly required by all ArrayFire backends."
-  GROUP backends
-  INSTALL_TYPES All Development Runtime)
-
-cpack_add_component(opencl_dependencies
-  DISPLAY_NAME "OpenCL Dependencies"
-  DESCRIPTION "Libraries required by the OpenCL backend."
-  GROUP opencl-backend
-  INSTALL_TYPES All Development Runtime)
-if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
-  cpack_add_component(afopencl_debug_symbols
-    DISPLAY_NAME "OpenCL Backend Debug Symbols"
-    DESCRIPTION "File containing debug symbols for afopencl dll/so/dylib file"
-    GROUP opencl-backend
-    DISABLED
-    INSTALL_TYPES Development)
-endif ()
-
-cpack_add_component(cuda_dependencies
-  DISPLAY_NAME "CUDA Dependencies"
-  DESCRIPTION "CUDA runtime and libraries required by the CUDA backend."
-  GROUP cuda-backend
-  INSTALL_TYPES All Development Runtime)
-if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
-  cpack_add_component(afcuda_debug_symbols
-    DISPLAY_NAME "CUDA Backend Debug Symbols"
-    DESCRIPTION "File containing debug symbols for afcuda dll/so/dylib file"
-    GROUP cuda-backend
-    DISABLED
-    INSTALL_TYPES Development)
-endif ()
-
-if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
-  cpack_add_component(afcpu_debug_symbols
-    DISPLAY_NAME "CPU Backend Debug Symbols"
-    DESCRIPTION "File containing debug symbols for afcpu dll/so/dylib file"
-    GROUP cpu-backend
-    DISABLED
-    INSTALL_TYPES Development)
-endif ()
-
-cpack_add_component(cuda
-  DISPLAY_NAME "CUDA Backend"
-  DESCRIPTION "The CUDA backend allows you to run ArrayFire code on CUDA-enabled GPUs. Verify that you have the CUDA toolkit installed or install the CUDA dependencies component."
-  GROUP cuda-backend
-  DEPENDS common_backend_dependencies cuda_dependencies
-  INSTALL_TYPES All Development Runtime)
-
-list(APPEND cpu_deps_comps common_backend_dependencies)
-list(APPEND ocl_deps_comps common_backend_dependencies)
-
-if (NOT APPLE)
-  list(APPEND ocl_deps_comps opencl_dependencies)
-endif ()
-
-if (PACKAGE_MKL_DEPS)
-  list(APPEND cpu_deps_comps mkl_dependencies)
-  list(APPEND ocl_deps_comps mkl_dependencies)
-endif ()
-
-cpack_add_component(cpu
-  DISPLAY_NAME "CPU Backend"
-  DESCRIPTION "The CPU backend allows you to run ArrayFire code on your CPU."
-  GROUP cpu-backend
-  DEPENDS ${cpu_deps_comps}
-  INSTALL_TYPES All Development Runtime)
-
-cpack_add_component(opencl
-  DISPLAY_NAME "OpenCL Backend"
-  DESCRIPTION "The OpenCL backend allows you to run ArrayFire code on OpenCL-capable GPUs. Note: ArrayFire does not currently support OpenCL for Intel CPUs on OSX."
-  GROUP opencl-backend
-  DEPENDS ${ocl_deps_comps}
-  INSTALL_TYPES All Development Runtime)
-
-if (NOT APPLE) #TODO(pradeep) Remove check after OSX support addition
-  cpack_add_component(af_debug_symbols
-    DISPLAY_NAME "Unified Backend Debug Symbols"
-    DESCRIPTION "File containing debug symbols for af dll/so/dylib file"
-    GROUP backends
-    DISABLED
-    INSTALL_TYPES Development)
-endif ()
-cpack_add_component(unified
-  DISPLAY_NAME "Unified Backend"
-  DESCRIPTION "The Unified backend allows you to choose between any of the installed backends (CUDA, OpenCL, or CPU) at runtime."
-  GROUP backends
-  INSTALL_TYPES All Development Runtime)
-
-cpack_add_component(headers
-  DISPLAY_NAME "C/C++ Headers"
-  DESCRIPTION "Headers for the ArrayFire libraries."
-  GROUP backends
-  INSTALL_TYPES All Development)
-cpack_add_component(cmake
-  DISPLAY_NAME "CMake Support"
-  DESCRIPTION "Configuration files to use ArrayFire using CMake."
-  INSTALL_TYPES All Development)
-cpack_add_component(documentation
-  DISPLAY_NAME "Documentation"
-  DESCRIPTION "ArrayFire html documentation"
-  INSTALL_TYPES All Extra)
-cpack_add_component(examples
-  DISPLAY_NAME "ArrayFire Examples"
-  DESCRIPTION "Various examples using ArrayFire."
-  INSTALL_TYPES All Extra)
-cpack_add_component(licenses
-  DISPLAY_NAME "Licenses"
-  DESCRIPTION "License files for ArrayFire and its upstream libraries."
-  REQUIRED)
-
-if (AF_INSTALL_FORGE_DEV)
-  cpack_add_component(forge
-    DISPLAY_NAME "Forge"
-    DESCRIPTION "High Performance Visualization Library"
-    INSTALL_TYPES Extra)
-endif ()
-
-##
-# IFW CPACK generator
-# Uses Qt installer framework, cross platform installer generator.
-# Uniform installer GUI on all major desktop platforms: Windows, OSX & Linux.
-##
-set(CPACK_IFW_PACKAGE_TITLE "${CPACK_PACKAGE_NAME}")
-set(CPACK_IFW_PACKAGE_PUBLISHER "${CPACK_PACKAGE_VENDOR}")
-set(CPACK_IFW_PRODUCT_URL "${SITE_URL}")
-set(CPACK_IFW_PACKAGE_ICON "${MY_CPACK_PACKAGE_ICON}")
-set(CPACK_IFW_PACKAGE_WINDOW_ICON "${CMAKE_SOURCE_DIR}/assets/${APP_LOW_NAME}_icon.png")
-set(CPACK_IFW_PACKAGE_WIZARD_DEFAULT_WIDTH 640)
-set(CPACK_IFW_PACKAGE_WIZARD_DEFAULT_HEIGHT 480)
-if (WIN32)
-    set(CPACK_IFW_ADMIN_TARGET_DIRECTORY "@ApplicationsDirX64@/${CPACK_PACKAGE_INSTALL_DIRECTORY}")
-else ()
-    set(CPACK_IFW_ADMIN_TARGET_DIRECTORY "/opt/${CPACK_PACKAGE_INSTALL_DIRECTORY}")
-endif ()
-
-get_native_path(zlib_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/zlib-libpng License.txt")
-get_native_path(boost_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/Boost Software License.txt")
-get_native_path(fimg_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/FreeImage Public License.txt")
-get_native_path(apache_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/Apache-2.0.txt")
-get_native_path(sift_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/OpenSIFT License.txt")
-get_native_path(bsd3_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/BSD 3-Clause.txt")
-get_native_path(issl_lic_path "${CMAKE_SOURCE_DIR}/LICENSES/ISSL License.txt")
-
-cpack_ifw_configure_component_group(backends)
-cpack_ifw_configure_component_group(cpu-backend)
-cpack_ifw_configure_component_group(cuda-backend)
-cpack_ifw_configure_component_group(opencl-backend)
-if (PACKAGE_MKL_DEPS)
-  cpack_ifw_configure_component(mkl_dependencies)
-endif ()
-if (NOT APPLE)
-  cpack_ifw_configure_component(opencl_dependencies)
-endif ()
-cpack_ifw_configure_component(common_backend_dependencies)
-cpack_ifw_configure_component(cuda_dependencies)
-cpack_ifw_configure_component(cpu)
-cpack_ifw_configure_component(cuda)
-cpack_ifw_configure_component(opencl)
-cpack_ifw_configure_component(unified)
-cpack_ifw_configure_component(headers)
-cpack_ifw_configure_component(cmake)
-cpack_ifw_configure_component(documentation)
-cpack_ifw_configure_component(examples)
-cpack_ifw_configure_component(licenses FORCED_INSTALLATION
-  LICENSES "GLFW" ${zlib_lic_path} "FreeImage" ${fimg_lic_path}
-  "Boost" ${boost_lic_path} "CLBlast, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
-  "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
-)
-if (AF_INSTALL_FORGE_DEV)
-  cpack_ifw_configure_component(forge)
-endif ()
-
-##
-# Debian package
-##
-set(CPACK_DEBIAN_FILE_NAME DEB-DEFAULT)
-set(CPACK_DEB_COMPONENT_INSTALL ON)
-#set(CMAKE_INSTALL_RPATH /usr/lib;${ArrayFire_BUILD_DIR}/third_party/forge/lib)
-#set(CPACK_DEBIAN_PACKAGE_SHLIBDEPS ON)
-set(CPACK_DEBIAN_PACKAGE_HOMEPAGE http://www.arrayfire.com)
-
-##
-# RPM package
-##
-set(CPACK_RPM_PACKAGE_ARCHITECTURE "x86_64")
-set(CPACK_RPM_PACKAGE_AUTOREQPROV " no")
-set(CPACK_RPM_PACKAGE_GROUP "Development/Libraries")
-set(CPACK_RPM_PACKAGE_LICENSE "BSD")
-set(CPACK_RPM_PACKAGE_URL "${SITE_URL}")
-if(AF_BUILD_FORGE)
-    set(CPACK_RPM_PACKAGE_SUGGESTS "fontconfig-devel, libX11, libXrandr, libXinerama, libXxf86vm, libXcursor, mesa-libGL-devel")
-endif()
-
-##
-# Source package
-##
-set(CPACK_SOURCE_GENERATOR "TGZ")
-set(CPACK_SOURCE_PACKAGE_FILE_NAME
-    ${CPACK_PACKAGE_NAME}_src_${GIT_COMMIT_HASH}_${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR})
-set(CPACK_SOURCE_IGNORE_FILES
-    "/build"
-    "CMakeFiles"
-    "/\\\\.dir"
-    "/\\\\.git"
-    "/\\\\.gitignore$"
-    ".*~$"
-    "\\\\.bak$"
-    "\\\\.swp$"
-    "\\\\.orig$"
-    "/\\\\.DS_Store$"
-    "/Thumbs\\\\.db"
-    "/CMakeLists.txt.user$"
-    ${CPACK_SOURCE_IGNORE_FILES})
-# Ignore build directories that may be in the source tree
-file(GLOB_RECURSE CACHES "${CMAKE_SOURCE_DIR}/CMakeCache.txt")
-
-if (WIN32)
-  # Configure file with custom definitions for NSIS.
-  configure_file(
-    ${PROJECT_SOURCE_DIR}/CMakeModules/nsis/NSIS.definitions.nsh.in
-    ${CMAKE_CURRENT_BINARY_DIR}/NSIS.definitions.nsh)
-endif ()
+set(CPACK_PROJECT_CONFIG_FILE "${CMAKE_SOURCE_DIR}/CMakeModules/CPackProjectConfig.cmake")
 
 include(CPack)
diff --git a/CMakeModules/CPackProjectConfig.cmake b/CMakeModules/CPackProjectConfig.cmake
new file mode 100644
index 0000000000..6cd6e20088
--- /dev/null
+++ b/CMakeModules/CPackProjectConfig.cmake
@@ -0,0 +1,560 @@
+
+include(CPackIFW)
+include(CPackComponent)
+
+# Only install the components created using the af_component macro
+set(CPACK_COMPONENTS_ALL "")
+
+# This is necessary if you don't have a cuda driver installed on your system
+# but you are still building the cuda package. You need the libcuda.so library
+# which is installed by the driver. This tell the dpkg-shlibs to ignore
+# this library because it is a private library
+set (CPACK_DEBIAN_PACKAGE_SHLIBDEPS_PRIVATE_DIRS
+  "/usr/local/cuda-${CPACK_CUDA_VERSION_MAJOR}.${CPACK_CUDA_VERSION_MINOR}/lib64/stubs")
+
+
+# Create an ArrayFire component with a set of properties for each package manager
+# This function sets all the variables for each component in ArrayFire.
+#
+# ``COMPONENT``
+# The name of the ArrayFire component used in the install(XXX) commands
+#
+# ``DISPLAY_NAME``
+# The name that will appear in the GUI installers for this component
+#
+# ``SUMMARY``
+# A short one line summary of the package
+#
+# ``DESCRIPTION``
+# A longer description of the package
+#
+# ``GROUP``
+# Used to combine packages in GUI installers. Ignored in DEB and RPM installers
+#
+# ``DEB_PACKAGE_NAME``
+# Name of the package for the DEB installers. This is the first component of the
+# file name.
+#
+# ``DEB_PROVIDES``
+# The virtual packages provided by the deb package. This is a higher level name
+# of the file that can be used across version numbers. also includes the version
+# information about the package
+#
+# ``DEB_REPLACES``
+# The packages and virtual packages this will replace. Used if there is a package
+# that is installed as part of the base debian installation
+#
+# ``REQUIRES``
+# The components required for the GUI installers
+#
+# ``OPTIONAL``
+# Optional packages that this component can use.
+#
+# ``INSTALL_TYPE``
+# A group of components that will be selected in GUI installers from a drop down
+#
+# ``DEB_REQUIRES``
+# Set of packages required by the debian package. This is slighly different from
+# REQUIRES because it also takes into account external dependencies that can be
+# installed by apt
+#
+# ``DEB_OPTIONAL``
+# Same as OPTIONAL but for debian packages
+#
+# ``DEB_RECOMMENDS``
+# Packages that should be installed but are not required. These packages will
+# be installed by default but if removed will not also delete this package
+#
+# ``HIDDEN``
+# If set, the package will not appear in the GUI installers like NSIS. Usually
+# components that install dependencies
+macro(af_component)
+  cmake_parse_arguments(RC
+    "HIDDEN;DISABLED;DEB_USE_SHLIBDEPS;DEB_ADD_POSTINST"
+    "COMPONENT;DISPLAY_NAME;SUMMARY;DESCRIPTION;GROUP;DEB_PACKAGE_NAME;DEB_PROVIDES;DEB_REPLACES"
+    "REQUIRES;OPTIONAL;INSTALL_TYPES;DEB_REQUIRES;DEB_OPTIONAL;DEB_RECOMMENDS" ${ARGN})
+
+  list(APPEND CPACK_COMPONENTS_ALL ${RC_COMPONENT})
+
+  string(TOUPPER ${RC_COMPONENT} COMPONENT_UPPER)
+  string(REPLACE ";" ", " DEB_REQ "${RC_DEB_REQUIRES}")
+  string(REPLACE ";" ", " DEB_REC "${RC_DEB_RECOMMENDS}")
+  string(REPLACE ";" ", " DEB_OPT "${RC_DEB_OPTIONAL}")
+  string(REPLACE ";" ", " DEB_PROVIDES "${RC_DEB_PROVIDES}")
+
+  if(CPACK_GENERATOR MATCHES "DEB")
+    cpack_add_component(${RC_COMPONENT}
+      DISPLAY_NAME "${RC_DISPLAY_NAME}"
+      INSTALL_TYPES ${RC_INSTALL_TYPES}
+      DESCRIPTION ${RC_DESCRIPTION})
+
+    if(RC_DEB_RECOMMENDS)
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_RECOMMENDS ${DEB_REC})
+    endif()
+
+    if(RC_DEB_PACKAGE_NAME)
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_NAME "${RC_DEB_PACKAGE_NAME}")
+    endif()
+
+    set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_SUGGESTS ${DEB_OPT})
+
+    if(RC_DEB_REQUIRES)
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_DEPENDS "${DEB_REQ}")
+    endif()
+
+    if(RC_DEB_USE_SHLIBDEPS)
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_SHLIBDEPS ON)
+    else()
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_SHLIBDEPS OFF)
+    endif()
+
+    if(RC_DEB_PROVIDES)
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_PROVIDES ${DEB_PROVIDES})
+    endif()
+
+    if(RC_DEB_REPLACES)
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_REPLACES ${RC_DEB_REPLACES})
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_CONFLICTS ${RC_DEB_REPLACES})
+    endif()
+
+    if(RC_DEB_ADD_POSTINST)
+      configure_file(
+        "${CPACK_ArrayFire_SOURCE_DIR}/CMakeModules/debian/postinst"
+        "${CPACK_ArrayFire_BINARY_DIR}/cpack/${COMPONENT_UPPER}/postinst")
+
+      set(CPACK_DEBIAN_${COMPONENT_UPPER}_PACKAGE_CONTROL_EXTRA
+        "${CPACK_ArrayFire_BINARY_DIR}/cpack/${COMPONENT_UPPER}/postinst")
+    endif()
+  else()
+    cpack_add_component(${RC_COMPONENT}
+      DISPLAY_NAME "${RC_DISPLAY_NAME}"
+      DEPENDS ${RC_REQUIRES}
+      GROUP ${RC_GROUP}
+      INSTALL_TYPES ${RC_INSTALL_TYPES}
+      DESCRIPTION ${RC_DESCRIPTION})
+  endif()
+
+  set(CPACK_COMPONENT_${RC_COMPONENT}_DESCRIPTION_SUMMARY ${RC_SUMMARY})
+  set(CPACK_COMPONENT_${COMPONENT_UPPER}_DESCRIPTION ${RC_DESCRIPTION})
+
+  set(CPACK_COMPONENT_${COMPONENT_UPPER}_HIDDEN ${RC_HIDDEN})
+  set(CPACK_COMPONENT_${COMPONENT_UPPER}_DISABLED ${RC_DISABLED})
+
+  # Does not work with RPM for some reason using
+  # CPACK_RPM_${COMPONENT_UPPER}_PACKAGE_REQUIRES  instead
+
+endmacro()
+
+cpack_add_install_type(All DISPLAY_NAME "All Components")
+cpack_add_install_type(Development DISPLAY_NAME "Development")
+cpack_add_install_type(Runtime DISPLAY_NAME "Runtime")
+
+# Groups on debian packages will combine all the packages into one
+# debian component
+if(NOT CPACK_GENERATOR MATCHES "DEB")
+  cpack_add_component_group(afruntime
+    DISPLAY_NAME "ArrayFire Runtime"
+    DESCRIPTION "ArrayFire runtime libraries")
+
+  cpack_add_component_group(afdevelopment
+    DISPLAY_NAME "ArrayFire Development"
+    DESCRIPTION "ArrayFire development files including headers and configuration files"
+    EXPANDED)
+
+  cpack_add_component_group(debug
+    DISPLAY_NAME "ArrayFire Debug Symbols"
+    DESCRIPTION "ArrayFire Debug symbols")
+endif()
+
+set(arrayfire_cuda_runtime_name "CUDA Runtime(${CPACK_CUDA_VERSION_MAJOR}.${CPACK_CUDA_VERSION_MINOR})")
+set(arrayfire_cuda_dev_name "CUDA Dev")
+
+if(CPACK_GENERATOR MATCHES "DEB")
+  af_component(
+    COMPONENT arrayfire
+    REQUIRES cpu_dev cuda_dev opencl_dev examples documentation
+    SUMMARY  "ArrayFire high performance library"
+    DESCRIPTION  "ArrayFire
+ArrayFire is a general-purpose library that simplifies software
+development that targets parallel and massively-parallel architectures
+including CPUs, GPUs, and other hardware acceleration devices."
+
+    DEB_PACKAGE_NAME arrayfire
+    DEB_REQUIRES arrayfire-cpu3-dev
+                 arrayfire-headers
+
+    DEB_RECOMMENDS arrayfire-cuda3-dev
+                   arrayfire-opencl3-dev
+                   arrayfire-unified3-dev
+                   arrayfire-examples
+                   arrayfire-cmake
+                   arrayfire-doc
+  )
+endif()
+
+
+list(APPEND cpu_deps_comps common_backend_dependencies)
+list(APPEND ocl_deps_comps common_backend_dependencies)
+
+if (NOT APPLE)
+  list(APPEND ocl_deps_comps opencl_dependencies)
+endif ()
+
+set(PACKAGE_MKL_DEPS OFF)
+
+if(CPACK_CUDA_VERSION_MAJOR STREQUAL "10" AND CPACK_GENERATOR MATCHES "DEB")
+  set(deb_cuda_runtime_requirements "libcublas${CPACK_CUDA_VERSION_MAJOR}")
+elseif(CPACK_CUDA_VERSION_MAJOR STREQUAL "11" AND CPACK_GENERATOR MATCHES "DEB")
+  set(deb_cuda_runtime_requirements "libcublas-${CPACK_CUDA_VERSION_MAJOR}-${CPACK_CUDA_VERSION_MINOR}")
+elseif(CPACK_GENERATOR MATCHES "DEB")
+  message(FATAL_ERROR "THIS CUDA VERSION NOT ADDRESSED FOR DEBIN PACKAGES")
+endif()
+
+if (CPACK_AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
+  set(PACKAGE_MKL_DEPS ON)
+  if(NOT CPACK_GENERATOR STREQUAL "DEB")
+    af_component(
+      COMPONENT mkl_dependencies
+      DISPLAY_NAME "Intel MKL Libraries"
+            DESCRIPTION "Intel Math Kernel Libraries for FFTW, BLAS, and LAPACK routines."
+      HIDDEN
+      INSTALL_TYPES All Runtime)
+    list(APPEND cpu_deps_comps mkl_dependencies)
+    list(APPEND ocl_deps_comps mkl_dependencies)
+  endif()
+  set(deb_opencl_runtime_package_name arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR}-mkl)
+  set(deb_opencl_runtime_requirements "intel-mkl-core-rt-2020.0-166, intel-mkl-gnu-rt-2020.0-166")
+  set(deb_cpu_runtime_package_name arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-mkl)
+  set(deb_cpu_runtime_requirements "intel-mkl-core-rt-2020.0-166, intel-mkl-gnu-rt-2020.0-166")
+else()
+  # OpenCL and CPU runtime dependencies are detected using
+  # SHLIBDEPS
+  set(deb_opencl_runtime_package_name arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR}-openblas)
+  set(deb_opencl_runtime_requirements "")
+  set(deb_cpu_runtime_package_name arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-openblas)
+  set(deb_cpu_runtime_requirements "")
+endif ()
+
+af_component(
+  COMPONENT cpu
+  DISPLAY_NAME "CPU Runtime"
+  SUMMARY "ArrayFire CPU backend shared libraries"
+  DESCRIPTION "ArrayFire CPU backend shared libraries"
+  OPTIONAL forge
+  GROUP afruntime
+  REQUIRES ${cpu_deps_comps} licenses
+  INSTALL_TYPES All Runtime
+
+  DEB_PACKAGE_NAME ${deb_cpu_runtime_package_name}
+  DEB_REQUIRES ${deb_cpu_runtime_requirements}
+  DEB_PROVIDES "arrayfire-cpu (= ${CPACK_PACKAGE_VERSION}), arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION}), libarrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-cpu, arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_USE_SHLIBDEPS
+  DEB_ADD_POSTINST
+  DEB_OPTIONAL forge libfreeimage3
+)
+
+af_component(
+  COMPONENT cpu_dev
+  DISPLAY_NAME "CPU Dev"
+  SUMMARY  "ArrayFire CPU backend development files"
+  DESCRIPTION  "ArrayFire CPU backend development files"
+  REQUIRES cpu headers cmake
+  GROUP afdevelopment
+  INSTALL_TYPES All Development
+
+  DEB_PACKAGE_NAME arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-dev
+  DEB_PROVIDES "arrayfire-cpu-dev (= ${CPACK_PACKAGE_VERSION}), arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-dev (= ${CPACK_PACKAGE_VERSION}), libarrayfire-cpu-dev (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-cpu-dev (<< ${CPACK_PACKAGE_VERSION}), arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-dev (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-cpu3-dev (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES "arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-openblas (>= ${CPACK_PACKAGE_VERSION}) | arrayfire-cpu${CPACK_PACKAGE_VERSION_MAJOR}-mkl (>= ${CPACK_PACKAGE_VERSION}), arrayfire-headers (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_RECOMMENDS "arrayfire-cmake (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_OPTIONAL "cmake (>= 3.0)"
+)
+
+af_component(
+  COMPONENT cuda
+  DISPLAY_NAME "${arrayfire_cuda_runtime_name}"
+  SUMMARY "ArrayFire CUDA backend shared libraries"
+  DESCRIPTION "ArrayFire CUDA backend shared libraries"
+  OPTIONAL forge
+  REQUIRES common_backend_dependencies cuda_dependencies licenses
+  GROUP afruntime
+  INSTALL_TYPES All Runtime
+
+  DEB_PACKAGE_NAME arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR}-cuda-${CPACK_CUDA_VERSION_MAJOR}-${CPACK_CUDA_VERSION_MINOR}
+  DEB_REQUIRES ${deb_cuda_runtime_requirements}
+  DEB_ADD_POSTINST
+  DEB_USE_SHLIBDEPS
+  DEB_PROVIDES "arrayfire-cuda (= ${CPACK_PACKAGE_VERSION}), arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION}), libarrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-cuda (<< ${CPACK_PACKAGE_VERSION}), arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_OPTIONAL libcudnn8 forge libfreeimage3
+)
+
+af_component(
+  COMPONENT cuda_dev
+  DISPLAY_NAME "${arrayfire_cuda_dev_name}"
+  SUMMARY  "ArrayFire CUDA backend development files"
+  DESCRIPTION  "ArrayFire CUDA backend development files"
+  REQUIRES cuda headers cmake
+  GROUP afdevelopment
+  INSTALL_TYPES All Development
+
+  DEB_PACKAGE_NAME arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR}-dev
+  DEB_PROVIDES "arrayfire-cuda-dev (= ${CPACK_PACKAGE_VERSION}), arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR}-dev (= ${CPACK_PACKAGE_VERSION}), libarrayfire-cuda-dev (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-cuda-dev (<< ${CPACK_PACKAGE_VERSION}), arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR}-dev (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES "arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (>= ${CPACK_PACKAGE_VERSION}), arrayfire-headers (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_RECOMMENDS "arrayfire-cmake (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_OPTIONAL "cmake (>= 3.0)"
+)
+
+af_component(
+  COMPONENT opencl
+  DISPLAY_NAME "OpenCL Runtime"
+  SUMMARY "ArrayFire OpenCL backend shared libraries"
+  DESCRIPTION "ArrayFire OpenCL backend shared libraries"
+  REQUIRES ${opencl_deps_comps} licenses
+  OPTIONAL forge
+  GROUP afruntime
+  INSTALL_TYPES All Runtime
+
+  DEB_PACKAGE_NAME ${deb_opencl_runtime_package_name}
+  DEB_PROVIDES "arrayfire-opencl (= ${CPACK_PACKAGE_VERSION}), arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION}), libarrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-opencl (<< ${CPACK_PACKAGE_VERSION}), arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES ${deb_opencl_runtime_requirements}
+  DEB_USE_SHLIBDEPS
+  DEB_ADD_POSTINST
+  DEB_OPTIONAL forge libfreeimage3
+)
+
+af_component(
+  COMPONENT opencl_dev
+  DISPLAY_NAME "OpenCL Dev"
+  SUMMARY  "ArrayFire OpenCL backend development files"
+  DESCRIPTION  "ArrayFire OpenCL backend development files"
+  REQUIRES opencl headers cmake
+  GROUP afdevelopment
+  INSTALL_TYPES All Development
+
+  DEB_PACKAGE_NAME arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR}-dev
+  DEB_PROVIDES "arrayfire-opencl-dev (= ${CPACK_PACKAGE_VERSION}), arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR}-dev (= ${CPACK_PACKAGE_VERSION}), libarrayfire-opencl-dev (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-opencl-dev (<< ${CPACK_PACKAGE_VERSION}), arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR}-dev (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-opencl-dev (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES "arrayfire-opencl${CPACK_PACKAGE_VERSION_MAJOR} (>= ${CPACK_PACKAGE_VERSION}), arrayfire-headers (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_RECOMMENDS "arrayfire-cmake (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_OPTIONAL "cmake (>= 3.0)"
+)
+
+af_component(
+  COMPONENT unified
+  DISPLAY_NAME "Unified Runtime"
+  SUMMARY "ArrayFire Unified backend shared libraries."
+  DESCRIPTION "ArrayFire Unified backend shared libraries. Requires other backends to function."
+  OPTIONAL forge
+  REQUIRES licenses
+  GROUP afruntime
+  INSTALL_TYPES All Runtime
+
+  DEB_PACKAGE_NAME arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR}
+  DEB_PROVIDES "arrayfire-unified (= ${CPACK_PACKAGE_VERSION}), arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION}), libarrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-unified (<< ${CPACK_PACKAGE_VERSION}), arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES "arrayfire-cpu (>= ${CPACK_PACKAGE_VERSION}) | arrayfire-cuda (>= ${CPACK_PACKAGE_VERSION}) | arrayfire-opencl (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_USE_SHLIBDEPS
+)
+
+af_component(
+  COMPONENT unified_dev
+  DISPLAY_NAME "Unified Dev"
+  SUMMARY  "ArrayFire Unified backend development files"
+  DESCRIPTION  "ArrayFire Unified backend development files"
+  REQUIRES unified headers cmake
+  OPTIONAL forge
+  GROUP afdevelopment
+  INSTALL_TYPES All Development
+
+  DEB_PACKAGE_NAME arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR}-dev
+  DEB_PROVIDES "arrayfire-unified-dev (= ${CPACK_PACKAGE_VERSION}), arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR}-dev (= ${CPACK_PACKAGE_VERSION}), libarrayfire-unified-dev (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-unified-dev (<< ${CPACK_PACKAGE_VERSION}), arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR}-dev (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-unified-dev (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES "arrayfire-unified${CPACK_PACKAGE_VERSION_MAJOR} (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_RECOMMENDS "arrayfire-cmake (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_OPTIONAL "cmake (>= 3.0)"
+)
+
+af_component(
+  COMPONENT documentation
+  DISPLAY_NAME "Documentation"
+  SUMMARY  "ArrayFire Documentation"
+  INSTALL_TYPES All
+  DESCRIPTION  "ArrayFire Doxygen Documentation"
+
+  DEB_PACKAGE_NAME arrayfire-doc
+  DEB_REPLACES "arrayfire-doc (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-doc (<< ${CPACK_PACKAGE_VERSION})"
+)
+
+af_component(
+  COMPONENT headers
+  DISPLAY_NAME "C/C++ Headers"
+  HIDDEN
+  INSTALL_TYPES All Development
+  DESCRIPTION "Headers for the ArrayFire libraries.")
+
+af_component(
+  COMPONENT examples
+  DISPLAY_NAME "ArrayFire Examples"
+  INSTALL_TYPES All
+  DESCRIPTION "Various examples using ArrayFire.")
+
+af_component(
+  COMPONENT cmake
+  DISPLAY_NAME "CMake Files"
+  HIDDEN
+  INSTALL_TYPES All Development
+  DESCRIPTION "Configuration files to use ArrayFire using CMake.")
+
+af_component(
+  COMPONENT licenses
+  DISPLAY_NAME "Licenses"
+  DESCRIPTION "License files for ArrayFire and its upstream libraries."
+  HIDDEN
+  REQUIRED)
+
+if(NOT CPACK_GENERATOR MATCHES "DEB")
+  af_component(
+    COMPONENT common_backend_dependencies
+    DISPLAY_NAME "Common Dependencies"
+    DESCRIPTION "Libraries commonly required by all ArrayFire backends."
+    HIDDEN
+    INSTALL_TYPES All Development Runtime)
+
+  af_component(
+    COMPONENT cuda_dependencies
+    DISPLAY_NAME "CUDA Dependencies"
+    DESCRIPTION "Shared libraries required for the CUDA backend."
+    HIDDEN
+    INSTALL_TYPES All Development Runtime)
+
+endif()
+
+#TODO(pradeep) Remove check after OSX support addition
+# Debug symbols in debian installers are created using the DEBINFO property
+if(NOT APPLE AND
+   NOT CPACK_GENERATOR MATCHES "DEB")
+  af_component(
+    COMPONENT afopencl_debug_symbols
+    DISPLAY_NAME "OpenCL Debug Symbols"
+    DESCRIPTION "Debug symbols for the OpenCL backend."
+    GROUP debug
+    DISABLED
+    INSTALL_TYPES Development)
+
+  af_component(
+    COMPONENT afcuda_debug_symbols
+    DISPLAY_NAME "CUDA Debug Symbols"
+    DESCRIPTION "Debug symbols for CUDA backend backend."
+    GROUP debug
+    DISABLED
+    INSTALL_TYPES Development)
+
+  af_component(
+    COMPONENT afcpu_debug_symbols
+    DISPLAY_NAME "CPU Debug Symbols"
+    DESCRIPTION "Debug symbols for CPU backend backend."
+    GROUP debug
+    DISABLED
+    INSTALL_TYPES Development)
+
+  af_component(
+    COMPONENT af_debug_symbols
+    DISPLAY_NAME "Unified Debug Symbols"
+    DESCRIPTION "Debug symbols for the Unified backend."
+    GROUP debug
+    DISABLED
+    INSTALL_TYPES Development)
+endif()
+
+# if (AF_INSTALL_FORGE_DEV)
+#   list(APPEND CPACK_COMPONENTS_ALL forge)
+#   af_component(
+#     COMPONENT forge
+#     DISPLAY_NAME "Forge Vizualiation"
+#     DESCRIPTION "Visualization Library"
+#     INSTALL_TYPES Extra)
+# endif ()
+#
+#set(LIBRARY_NAME ${PROJECT_NAME})
+#string(TOLOWER "${LIBRARY_NAME}" APP_LOW_NAME)
+#set(SITE_URL "https://arrayfire.com")
+#
+# set(inst_pkg_name ${APP_LOW_NAME})
+# set(inst_pkg_hash "")
+# if (WIN32)
+#   set(inst_pkg_name ${CPACK_PACKAGE_NAME})
+#   set(inst_pkg_hash "-${GIT_COMMIT_HASH}")
+# endif ()
+#
+#set(CPACK_PACKAGE_FILE_NAME "${inst_pkg_name}${inst_pkg_hash}")
+
+# ##
+# # IFW CPACK generator
+# # Uses Qt installer framework, cross platform installer generator.
+# # Uniform installer GUI on all major desktop platforms: Windows, OSX & Linux.
+# ##
+# set(CPACK_IFW_PACKAGE_TITLE "${CPACK_PACKAGE_NAME}")
+# set(CPACK_IFW_PACKAGE_PUBLISHER "${CPACK_PACKAGE_VENDOR}")
+# set(CPACK_IFW_PRODUCT_URL "${SITE_URL}")
+# set(CPACK_IFW_PACKAGE_ICON "${MY_CPACK_PACKAGE_ICON}")
+# set(CPACK_IFW_PACKAGE_WINDOW_ICON "${CMAKE_SOURCE_DIR}/assets/${APP_LOW_NAME}_icon.png")
+# set(CPACK_IFW_PACKAGE_WIZARD_DEFAULT_WIDTH 640)
+# set(CPACK_IFW_PACKAGE_WIZARD_DEFAULT_HEIGHT 480)
+# if (WIN32)
+#     set(CPACK_IFW_ADMIN_TARGET_DIRECTORY "@ApplicationsDirX64@/${CPACK_PACKAGE_INSTALL_DIRECTORY}")
+# else ()
+#     set(CPACK_IFW_ADMIN_TARGET_DIRECTORY "/opt/${CPACK_PACKAGE_INSTALL_DIRECTORY}")
+# endif ()
+#
+# function(get_native_path out_path path)
+#   file(TO_NATIVE_PATH ${path} native_path)
+#   if (WIN32)
+#     string(REPLACE "\\" "\\\\" native_path  ${native_path})
+#     set(${out_path} ${native_path} PARENT_SCOPE)
+#   else ()
+#     set(${out_path} ${path} PARENT_SCOPE)
+#   endif ()
+# endfunction()
+#
+# get_native_path(zlib_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/zlib-libpng License.txt")
+# get_native_path(boost_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/Boost Software License.txt")
+# get_native_path(fimg_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/FreeImage Public License.txt")
+# get_native_path(apache_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/Apache-2.0.txt")
+# get_native_path(sift_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/OpenSIFT License.txt")
+# get_native_path(bsd3_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/BSD 3-Clause.txt")
+# get_native_path(issl_lic_path "${CPACK_ArrayFire_SOURCE_DIR}/LICENSES/ISSL License.txt")
+
+#cpack_ifw_configure_component_group(backends)
+#cpack_ifw_configure_component_group(cpu-backend)
+#cpack_ifw_configure_component_group(cuda-backend)
+#cpack_ifw_configure_component_group(opencl-backend)
+#if (PACKAGE_MKL_DEPS)
+#  cpack_ifw_configure_component(mkl_dependencies)
+#endif ()
+#if (NOT APPLE)
+#  cpack_ifw_configure_component(opencl_dependencies)
+#endif ()
+#cpack_ifw_configure_component(common_backend_dependencies)
+#cpack_ifw_configure_component(cuda_dependencies)
+#cpack_ifw_configure_component(cpu)
+#cpack_ifw_configure_component(cuda)
+#cpack_ifw_configure_component(opencl)
+#cpack_ifw_configure_component(unified)
+#cpack_ifw_configure_component(headers)
+#cpack_ifw_configure_component(cmake)
+#cpack_ifw_configure_component(documentation)
+#cpack_ifw_configure_component(examples)
+#cpack_ifw_configure_component(licenses FORCED_INSTALLATION
+#  LICENSES "GLFW" ${zlib_lic_path} "FreeImage" ${fimg_lic_path}
+#  "Boost" ${boost_lic_path} "CLBlast, clFFT" ${apache_lic_path} "SIFT" ${sift_lic_path}
+#  "BSD3" ${bsd3_lic_path} "Intel MKL" ${issl_lic_path}
+#)
+#if (AF_INSTALL_FORGE_DEV)
+#  cpack_ifw_configure_component(forge)
+#endif ()
+
+
diff --git a/CMakeModules/debian/postinst b/CMakeModules/debian/postinst
new file mode 100644
index 0000000000..093371bd32
--- /dev/null
+++ b/CMakeModules/debian/postinst
@@ -0,0 +1,9 @@
+#!/bin/sh
+
+set -e
+
+if [ "$1" = "configure" ]; then
+    echo "/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64_lin" >> /etc/ld.so.conf.d/99_arrayfire_${RC_COMPONENT}.conf
+    echo "/usr/local/cuda-${CPACK_CUDA_VERSION_MAJOR}.${CPACK_CUDA_VERSION_MINOR}/lib64" >> /etc/ld.so.conf.d/99_arrayfire_${RC_COMPONENT}.conf
+    ldconfig
+fi

From b0a322a9d3c0af4cbdfe4dc7ae6ba0067955988a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 May 2022 17:27:04 -0400
Subject: [PATCH 2253/2677] Update GitHub workflow with updated hash and
 freetype features

---
 .github/workflows/win_cpu_build.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index 72c6955238..067f951fff 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -14,7 +14,7 @@ jobs:
         runs-on: windows-latest
         env:
 
-          VCPKG_HASH: 4428702c1c56fdb7cb779584efdcba254d7b57ca #[neon2sse] create a new port; Has forge v1.0.8 and other cmake/vcpkg fixes
+          VCPKG_HASH: 14e7bb4ae24616ec54ff6b2f6ef4e8659434ea44
 
           VCPKG_DEFAULT_TRIPLET: x64-windows
         steps:
@@ -36,7 +36,7 @@ jobs:
                 cd vcpkg
                 git checkout $env:VCPKG_HASH
                 .\bootstrap-vcpkg.bat
-                .\vcpkg.exe install boost-compute boost-functional boost-stacktrace fftw3 forge freeimage freetype glfw3 openblas
+                .\vcpkg.exe install boost-compute boost-math boost-stacktrace fftw3 freeimage freetype[core] forge glfw3 openblas
                 Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
 
             - name: CMake Configure

From 20aaff0f490143953243ce789d3ffc44d6c4e63d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar456@gmail.com>
Date: Sat, 28 May 2022 23:12:50 -0400
Subject: [PATCH 2254/2677] Add driver information for CUDA 11.7

---
 src/backend/cuda/device_manager.cpp | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index ca46388484..354a216741 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -86,6 +86,7 @@ struct ToolkitDriverVersions {
 
 // clang-format off
 static const int jetsonComputeCapabilities[] = {
+    8070,
     7020,
     6020,
     5030,
@@ -95,6 +96,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11070, 8, 7, 0},
     {11060, 8, 6, 0},
     {11050, 8, 6, 0},
     {11040, 8, 6, 0},
@@ -129,13 +131,14 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
-        {11060, 510.39f, 511.23f},
-        {11050, 495.29f, 496.13f},
-        {11040, 470.42f, 471.11f},
-        {11030, 465.19f, 465.89f},
-        {11020, 460.27f, 460.82f},
-        {11010, 455.23f, 456.38f},
-        {11000, 450.51f, 451.48f},
+        {11070, 450.80f, 452.39f},
+        {11060, 450.80f, 452.39f},
+        {11050, 450.80f, 452.39f},
+        {11040, 450.80f, 452.39f},
+        {11030, 450.80f, 452.39f},
+        {11020, 450.80f, 452.39f},
+        {11010, 450.80f, 452.39f},
+        {11000, 450.36f, 451.22f},
         {10020, 440.33f, 441.22f},
         {10010, 418.39f, 418.96f},
         {10000, 410.48f, 411.31f},
@@ -156,7 +159,7 @@ static ComputeCapabilityToStreamingProcessors gpus[] = {
     {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
     {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
     {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {0x86, 128},
-    {-1, -1},
+    {0x87, 128}, {-1, -1},
 };
 
 // pulled from CUTIL from CUDA SDK

From c2f24a8bfc6ae1268553221cda43c80066d98dde Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar456@gmail.com>
Date: Tue, 31 May 2022 11:16:46 -0400
Subject: [PATCH 2255/2677] Fix search for cuSparse libraries on Windows

cuSparse libraries on windows encode the cuda version in the
DLL names. This commit adds the suffixes to the cuSparse module
class
---
 src/backend/cuda/cusparseModule.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/cusparseModule.cpp b/src/backend/cuda/cusparseModule.cpp
index f229372b43..e7b8105221 100644
--- a/src/backend/cuda/cusparseModule.cpp
+++ b/src/backend/cuda/cusparseModule.cpp
@@ -22,7 +22,7 @@ cusparseModule::cusparseModule()
 #ifdef AF_cusparse_STATIC_LINKING
     module(nullptr, nullptr)
 #else
-    module("cusparse", nullptr)
+    module({"cusparse"}, {"64_11", "64_10", "64_9", "64_8"}, {""})
 #endif
 {
 #ifdef AF_cusparse_STATIC_LINKING

From f2f68edebdceb561be27082adae3ad40e4f71950 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar456@gmail.com>
Date: Tue, 31 May 2022 11:19:32 -0400
Subject: [PATCH 2256/2677] Add support for ccache on Windows

Ccache has support for windows. This seems to work with the Windows
binaries of the ccache program with the Ninja generator. I don't
think this is working in Visual Studio.
---
 CMakeModules/config_ccache.cmake | 69 ++++++++++++++++----------------
 1 file changed, 34 insertions(+), 35 deletions(-)

diff --git a/CMakeModules/config_ccache.cmake b/CMakeModules/config_ccache.cmake
index 1bf3adaef6..80783b06c1 100644
--- a/CMakeModules/config_ccache.cmake
+++ b/CMakeModules/config_ccache.cmake
@@ -1,42 +1,41 @@
 # picked up original content from https://crascit.com/2016/04/09/using-ccache-with-cmake/
 
-if (UNIX)
-  find_program(CCACHE_PROGRAM ccache)
+find_program(CCACHE_PROGRAM ccache)
 
-  set(CCACHE_FOUND OFF)
-  if(CCACHE_PROGRAM)
-    set(CCACHE_FOUND ON)
-  endif()
+set(CCACHE_FOUND OFF)
+if(CCACHE_PROGRAM)
+  set(CCACHE_FOUND ON)
+endif()
 
-  option(AF_USE_CCACHE "Use ccache when compiling" ${CCACHE_FOUND})
+option(AF_USE_CCACHE "Use ccache when compiling" ${CCACHE_FOUND})
 
-  if(${AF_USE_CCACHE})
-    # Set up wrapper scripts
-    set(C_LAUNCHER   "${CCACHE_PROGRAM}")
-    set(CXX_LAUNCHER "${CCACHE_PROGRAM}")
-    set(NVCC_LAUNCHER "${CCACHE_PROGRAM}")
-    configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-c.in   launch-c)
-    configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-cxx.in launch-cxx)
-    configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-nvcc.in launch-nvcc)
-    execute_process(COMMAND chmod a+rx
-        "${ArrayFire_BINARY_DIR}/launch-c"
-        "${ArrayFire_BINARY_DIR}/launch-cxx"
-        "${ArrayFire_BINARY_DIR}/launch-nvcc"
-      )
-    if(CMAKE_GENERATOR STREQUAL "Xcode")
-      # Set Xcode project attributes to route compilation and linking
-      # through our scripts
-      set(CMAKE_XCODE_ATTRIBUTE_CC         "${ArrayFire_BINARY_DIR}/launch-c")
-      set(CMAKE_XCODE_ATTRIBUTE_CXX        "${ArrayFire_BINARY_DIR}/launch-cxx")
-      set(CMAKE_XCODE_ATTRIBUTE_LD         "${ArrayFire_BINARY_DIR}/launch-c")
-      set(CMAKE_XCODE_ATTRIBUTE_LDPLUSPLUS "${ArrayFire_BINARY_DIR}/launch-cxx")
-    else()
-      # Support Unix Makefiles and Ninja
-      set(CMAKE_C_COMPILER_LAUNCHER   "${ArrayFire_BINARY_DIR}/launch-c")
-      set(CMAKE_CXX_COMPILER_LAUNCHER "${ArrayFire_BINARY_DIR}/launch-cxx")
-      set(CUDA_NVCC_EXECUTABLE "${ArrayFire_BINARY_DIR}/launch-nvcc")
-    endif()
+if(${AF_USE_CCACHE})
+  message(STATUS "ccache FOUND: ${CCACHE_PROGRAM}")
+  # Set up wrapper scripts
+  set(C_LAUNCHER   "${CCACHE_PROGRAM}")
+  set(CXX_LAUNCHER "${CCACHE_PROGRAM}")
+  set(NVCC_LAUNCHER "${CCACHE_PROGRAM}")
+  configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-c.in   launch-c)
+  configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-cxx.in launch-cxx)
+  configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/launch-nvcc.in launch-nvcc)
+  execute_process(COMMAND chmod a+rx
+      "${ArrayFire_BINARY_DIR}/launch-c"
+      "${ArrayFire_BINARY_DIR}/launch-cxx"
+      "${ArrayFire_BINARY_DIR}/launch-nvcc"
+    )
+  if(CMAKE_GENERATOR STREQUAL "Xcode")
+    # Set Xcode project attributes to route compilation and linking
+    # through our scripts
+    set(CMAKE_XCODE_ATTRIBUTE_CC         "${ArrayFire_BINARY_DIR}/launch-c")
+    set(CMAKE_XCODE_ATTRIBUTE_CXX        "${ArrayFire_BINARY_DIR}/launch-cxx")
+    set(CMAKE_XCODE_ATTRIBUTE_LD         "${ArrayFire_BINARY_DIR}/launch-c")
+    set(CMAKE_XCODE_ATTRIBUTE_LDPLUSPLUS "${ArrayFire_BINARY_DIR}/launch-cxx")
+  else()
+    # Support Unix Makefiles and Ninja
+    set(CMAKE_C_COMPILER_LAUNCHER   "${CCACHE_PROGRAM}")
+    set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
+    set(CUDA_NVCC_EXECUTABLE ${CCACHE_PROGRAM} "${CUDA_NVCC_EXECUTABLE}")
   endif()
-  mark_as_advanced(CCACHE_PROGRAM)
-  mark_as_advanced(AF_USE_CCACHE)
 endif()
+mark_as_advanced(CCACHE_PROGRAM)
+mark_as_advanced(AF_USE_CCACHE)

From 338a1adb13b9ce7291f6ec7b8c6dba6c7ad09275 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar456@gmail.com>
Date: Tue, 31 May 2022 12:00:33 -0400
Subject: [PATCH 2257/2677] Catch errors when creating OCL contexts from device

Catch OpenCL errors when creating Contexts from OpenCL devices.
This change is necessary because some platforms(Intel FPGA) were
crashing if certain environment variables were not set when
crating contexts even though the platform returned the device
from the platform. We catch errors for particular devices and
then we remove them from the device list.
---
 src/backend/opencl/device_manager.cpp | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 9404614f42..6452ee590e 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -244,20 +244,29 @@ DeviceManager::DeviceManager()
     // Sort OpenCL devices based on default criteria
     stable_sort(mDevices.begin(), mDevices.end(), compare_default);
 
+    auto devices = move(mDevices);
+    mDevices.clear();
+
     // Create contexts and queues once the sort is done
     for (int i = 0; i < nDevices; i++) {
         cl_platform_id device_platform =
-            mDevices[i]->getInfo<CL_DEVICE_PLATFORM>();
+            devices[i]->getInfo<CL_DEVICE_PLATFORM>();
         cl_context_properties cps[3] = {
             CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
-
-        mContexts.push_back(make_unique<Context>(*mDevices[i], cps));
-        mQueues.push_back(make_unique<CommandQueue>(
-            *mContexts.back(), *mDevices[i], cl::QueueProperties::None));
-        mIsGLSharingOn.push_back(false);
-        mDeviceTypes.push_back(getDeviceTypeEnum(*mDevices[i]));
-        mPlatforms.push_back(getPlatformEnum(*mDevices[i]));
+        try {
+            mContexts.push_back(make_unique<Context>(*devices[i], cps));
+            mQueues.push_back(make_unique<CommandQueue>(
+                *mContexts.back(), *devices[i], cl::QueueProperties::None));
+            mIsGLSharingOn.push_back(false);
+            mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
+            mPlatforms.push_back(getPlatformEnum(*devices[i]));
+            mDevices.emplace_back(std::move(devices[i]));
+        } catch (const cl::Error& err) {
+            AF_TRACE("Error creating context for device {} with error {}\n",
+                     devices[i]->getInfo<CL_DEVICE_NAME>(), err.what());
+        }
     }
+    nDevices = mDevices.size();
 
     bool default_device_set = false;
     deviceENV               = getEnvVar("AF_OPENCL_DEFAULT_DEVICE");

From 6228a4d43439d14cec3d59e51f2c342d94704621 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar456@gmail.com>
Date: Tue, 31 May 2022 12:14:18 -0400
Subject: [PATCH 2258/2677] Make cuDNN an optional feature in vcpkg

---
 vcpkg.json | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/vcpkg.json b/vcpkg.json
index 654d9ad8b6..8986d52dbe 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -39,8 +39,7 @@
         "cuda": {
             "description": "Build CUDA backend",
             "dependencies": [
-                "cuda",
-                "cudnn"
+                "cuda"
             ]
         },
         "opencl": {
@@ -55,6 +54,12 @@
             "dependencies": [
                 "intel-mkl"
             ]
+        },
+        "cudnn": {
+            "description": "Build CUDA with support for cuDNN",
+            "dependencies": [
+                "cudnn"
+            ]
         }
     },
     "builtin-baseline": "14e7bb4ae24616ec54ff6b2f6ef4e8659434ea44"

From 20982dfd448cb75c8787754eaca111c84d5d718b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar456@gmail.com>
Date: Tue, 31 May 2022 12:22:09 -0400
Subject: [PATCH 2259/2677] Fix linear jit workgroup calculations for CPU
 devices

---
 src/backend/opencl/jit.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index b8b486cae0..06d2b41b08 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -288,7 +288,7 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
         uint out_elements = outDims[3] * out_info.strides[3];
         uint groups       = divup(out_elements, local_0);
 
-        global_1 = divup(groups, 1000) * local_1;
+        global_1 = divup(groups, work_group_size) * local_1;
         global_0 = divup(groups, global_1) * local_0;
 
     } else {

From bd0b86448ccb590a650a2b16ce9f205c988deff8 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 15 Jun 2022 13:44:37 -0400
Subject: [PATCH 2260/2677] fixes nanval substitution on new keys

---
 src/backend/cpu/kernel/reduce.hpp |  1 +
 test/reduce.cpp                   | 25 +++++++++++++++++++++++++
 2 files changed, 26 insertions(+)

diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index 374816102e..db39dbc8b8 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -147,6 +147,7 @@ struct reduce_dim_by_key<op, Ti, Tk, To, 0> {
 
                 current_key = keyval;
                 out_val     = transform(inValsPtr[vOffset + (i * istride)]);
+                if (change_nan) out_val = IS_NAN(out_val) ? nanval : out_val;
                 ++keyidx;
             }
 
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 0633bd0536..c9e09f53fd 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2285,3 +2285,28 @@ TEST(Reduce, Test_Sum_Global_Array_nanval) {
     ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
     freeHost(h_a);
 }
+
+TEST(Reduce, nanval_issue_3255) {
+    char *info_str;
+    af_array  ikeys, ivals, okeys, ovals;
+    dim_t dims[1] = {8};
+
+    int ikeys_src[8] = {0, 0,  1, 1, 1,  2, 2,  0};
+    af_create_array(&ikeys, ikeys_src, 1, dims, u32);
+
+    int i;
+    for (i=0; i<8; i++) {
+        double ivals_src[8] = {1, 2,  3, 4, 5,  6, 7,  8};
+        ivals_src[i] = NAN;
+        af_create_array(&ivals, ivals_src, 1, dims, f64);
+
+        af_product_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);
+        af::array ovals_cpp(ovals);
+        ASSERT_FALSE(af::anyTrue<bool>(af::isNaN(ovals_cpp)));
+
+        af_sum_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);
+        ovals_cpp = af::array(ovals);
+
+        ASSERT_FALSE(af::anyTrue<bool>(af::isNaN(ovals_cpp)));
+    }
+}

From 2688275d2de79ad114a4b115e3594fb6d28c8033 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 21 Jun 2022 16:26:23 -0400
Subject: [PATCH 2261/2677] Restrict initializer list to fundamental types

This commit limits the types that can be used in the initializer
list to fundamental types. This change is necessary because when
we use the uniform initialization syntax and pass in an array, the
compiler incorrectly uses the initialization list constructor instead
of the other array constructor.
---
 include/af/array.h |  7 +++++--
 test/array.cpp     | 21 +++++++++++++++++++++
 2 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index bdd9ac4e9c..b1405c903c 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -522,7 +522,9 @@ namespace af
 #if AF_API_VERSION >= 38
 #if AF_COMPILER_CXX_GENERALIZED_INITIALIZERS
         /// \brief Initializer list constructor
-        template <typename T> array(std::initializer_list<T> list)
+        template <typename T, typename = typename std::enable_if<
+                                  std::is_fundamental<T>::value, void>::type>
+        array(std::initializer_list<T> list)
         : arr(nullptr) {
           dim_t size = list.size();
           if (af_err __aferr = af_create_array(&arr, list.begin(), 1, &size,
@@ -537,7 +539,8 @@ namespace af
         }
 
         /// \brief Initializer list constructor
-        template <typename T>
+        template <typename T, typename = typename std::enable_if<
+                                  std::is_fundamental<T>::value, void>::type>
         array(const af::dim4 &dims, std::initializer_list<T> list)
             : arr(nullptr) {
           const dim_t *size = dims.get();
diff --git a/test/array.cpp b/test/array.cpp
index 9770549d2d..7d45cf1ea7 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -640,3 +640,24 @@ TEST(Array, ReferenceCount2) {
         ASSERT_REF(d, 0) << "After d = c;";
     }
 }
+
+// This tests situations where the compiler incorrectly assumes the initializer
+// list constructor instead of the regular constructor when using the uniform
+// initilization syntax
+TEST(Array, InitializerListFixAFArray) {
+    array a = randu(1);
+    array b{a};
+
+    ASSERT_ARRAYS_EQ(a, b);
+}
+
+// This tests situations where the compiler incorrectly assumes the initializer
+// list constructor instead of the regular constructor when using the uniform
+// initilization syntax
+TEST(Array, InitializerListFixDim4) {
+    array a            = randu(1);
+    vector<float> data = {3.14f, 3.14f, 3.14f, 3.14f, 3.14f,
+                          3.14f, 3.14f, 3.14f, 3.14f};
+    array b{dim4(3, 3), data.data()};
+    ASSERT_ARRAYS_EQ(constant(3.14, 3, 3), b);
+}

From ef69c518a7bef74859b88e8c929955450978ff24 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 4 Jul 2022 19:58:44 -0400
Subject: [PATCH 2262/2677] Move tile function to common namespace. Avoid
 calling from detail

This commit moves the implementation of the tile funciton to the common
namespace. This is done because the tile funciton in detail does not perform JIT
optimization. It instead calls the tile kernel directly. This is undesirable
because there are some instances where tile funciton can be performed by
indexing. This commit also updates several calls to tile in the codebase
to use this new version.

It is still fairly easy to call the detail::tile function and we need to address
this at some point. Perhaps it should be deprecated and only called by the
common::tile function. This commit does not address this issue.
---
 src/api/c/assign.cpp        |  4 ++--
 src/api/c/canny.cpp         |  5 ++--
 src/api/c/convolve.cpp      |  6 ++---
 src/api/c/rgb_gray.cpp      |  4 ++--
 src/api/c/surface.cpp       |  6 ++---
 src/api/c/tile.cpp          | 27 ++-------------------
 src/backend/common/tile.hpp | 48 +++++++++++++++++++++++++++++++++++++
 7 files changed, 63 insertions(+), 37 deletions(-)
 create mode 100644 src/backend/common/tile.hpp

diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index ef7bacd821..20aa69e629 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -15,11 +15,11 @@
 #include <common/err_common.hpp>
 #include <common/half.hpp>
 #include <common/moddims.hpp>
+#include <common/tile.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <indexing_common.hpp>
 #include <math.hpp>
-#include <tile.hpp>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -78,7 +78,7 @@ static void assign(Array<Tout>& out, const vector<af_seq> seqs,
         // If both out and in are vectors of equal elements,
         // reshape in to out dims
         Array<Tin> in_ =
-            in.elements() == 1 ? tile(in, oDims) : modDims(in, oDims);
+            in.elements() == 1 ? common::tile(in, oDims) : modDims(in, oDims);
         auto dst = createSubArray<Tout>(out, seqs, false);
 
         copyArray<Tin, Tout>(dst, in_);
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index d9d74da7d9..625ce748fa 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -14,6 +14,7 @@
 #include <backend.hpp>
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
+#include <common/tile.hpp>
 #include <complex.hpp>
 #include <convolve.hpp>
 #include <copy.hpp>
@@ -25,7 +26,6 @@
 #include <reduce.hpp>
 #include <scan.hpp>
 #include <sobel.hpp>
-#include <tile.hpp>
 #include <transpose.hpp>
 #include <unary.hpp>
 #include <af/defines.h>
@@ -37,6 +37,7 @@
 
 using af::dim4;
 using common::cast;
+using common::tile;
 using detail::arithOp;
 using detail::Array;
 using detail::convolve2;
@@ -137,7 +138,7 @@ Array<float> otsuThreshold(const Array<float>& in, const unsigned NUM_BINS,
 
     ireduce<af_max_t, float>(thresh, locs, sigmas, 0);
 
-    return cast<float, uint>(tile(locs, dim4(inDims[0], inDims[1])));
+    return cast<float, uint>(common::tile(locs, dim4(inDims[0], inDims[1])));
 }
 
 Array<float> normalize(const Array<float>& supEdges, const float minVal,
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index b7581dd484..ddcd916ae6 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -13,9 +13,9 @@
 #include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <common/half.hpp>
+#include <common/tile.hpp>
 #include <fftconvolve.hpp>
 #include <handle.hpp>
-#include <tile.hpp>
 #include <af/data.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -54,8 +54,8 @@ inline af_array convolve2(const af_array &s, const af_array &c_f,
     const Array<accT> signal    = castArray<accT>(s);
 
     if (colFilter.isScalar() && rowFilter.isScalar()) {
-        Array<accT> colArray = detail::tile(colFilter, signal.dims());
-        Array<accT> rowArray = detail::tile(rowFilter, signal.dims());
+        Array<accT> colArray = common::tile(colFilter, signal.dims());
+        Array<accT> rowArray = common::tile(rowFilter, signal.dims());
 
         Array<accT> filter =
             arithOp<accT, af_mul_t>(colArray, rowArray, signal.dims());
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 635474e846..3c189af5df 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -17,10 +17,10 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/cast.hpp>
+#include <common/tile.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <math.hpp>
-#include <tile.hpp>
 
 using af::dim4;
 using common::cast;
@@ -75,7 +75,7 @@ static af_array gray2rgb(const af_array& in, const float r, const float g,
                          const float b) {
     if (r == 1.0 && g == 1.0 && b == 1.0) {
         dim4 tileDims(1, 1, 3, 1);
-        return getHandle(tile(getArray<T>(in), tileDims));
+        return getHandle(common::tile(getArray<T>(in), tileDims));
     }
 
     af_array mod_input = 0;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 2f6a3eda7b..58cc9476aa 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -15,13 +15,13 @@
 #include <common/err_common.hpp>
 #include <common/graphics_common.hpp>
 #include <common/moddims.hpp>
+#include <common/tile.hpp>
 #include <copy.hpp>
 #include <handle.hpp>
 #include <join.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
 #include <surface.hpp>
-#include <tile.hpp>
 
 using af::dim4;
 using common::modDims;
@@ -58,13 +58,13 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
         xIn = modDims(xIn, xIn.elements());
         // Now tile along second dimension
         dim4 x_tdims(1, Y_dims[0], 1, 1);
-        xIn = tile(xIn, x_tdims);
+        xIn = common::tile(xIn, x_tdims);
 
         // Convert yIn to a row vector
         yIn = modDims(yIn, dim4(1, yIn.elements()));
         // Now tile along first dimension
         dim4 y_tdims(X_dims[0], 1, 1, 1);
-        yIn = tile(yIn, y_tdims);
+        yIn = common::tile(yIn, y_tdims);
     }
 
     // Flatten xIn, yIn and zIn into row vectors
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index db3d456691..443419b540 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <tile.hpp>
+#include <common/tile.hpp>
 
 #include <arith.hpp>
 #include <backend.hpp>
@@ -33,30 +33,7 @@ using detail::ushort;
 
 template<typename T>
 static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
-    const Array<T> inArray = getArray<T>(in);
-    const dim4 &inDims     = inArray.dims();
-
-    // FIXME: Always use JIT instead of checking for the condition.
-    // The current limitation exists for performance reasons. it should change
-    // in the future.
-
-    bool take_jit_path = true;
-    dim4 outDims(1, 1, 1, 1);
-
-    // Check if JIT path can be taken. JIT path can only be taken if tiling a
-    // singleton dimension.
-    for (int i = 0; i < 4; i++) {
-        take_jit_path &= (inDims[i] == 1 || tileDims[i] == 1);
-        outDims[i] = inDims[i] * tileDims[i];
-    }
-
-    af_array out = nullptr;
-    if (take_jit_path) {
-        out = getHandle(unaryOp<T, af_noop_t>(inArray, outDims));
-    } else {
-        out = getHandle(tile<T>(inArray, tileDims));
-    }
-    return out;
+    return getHandle(common::tile<T>(getArray<T>(in), tileDims));
 }
 
 af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims) {
diff --git a/src/backend/common/tile.hpp b/src/backend/common/tile.hpp
new file mode 100644
index 0000000000..512d14b62b
--- /dev/null
+++ b/src/backend/common/tile.hpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <tile.hpp>
+
+#include <Array.hpp>
+#include <arith.hpp>
+#include <backend.hpp>
+#include <optypes.hpp>
+#include <unary.hpp>
+
+#include <af/dim4.hpp>
+
+namespace common {
+
+/// duplicates the elements of an Array<T> array.
+template<typename T>
+detail::Array<T> tile(const detail::Array<T> &in, const af::dim4 tileDims) {
+    const af::dim4 &inDims = in.dims();
+
+    // FIXME: Always use JIT instead of checking for the condition.
+    // The current limitation exists for performance reasons. it should change
+    // in the future.
+
+    bool take_jit_path = true;
+    af::dim4 outDims(1, 1, 1, 1);
+
+    // Check if JIT path can be taken. JIT path can only be taken if tiling a
+    // singleton dimension.
+    for (int i = 0; i < 4; i++) {
+        take_jit_path &= (inDims[i] == 1 || tileDims[i] == 1);
+        outDims[i] = inDims[i] * tileDims[i];
+    }
+
+    if (take_jit_path) {
+        return detail::unaryOp<T, af_noop_t>(in, outDims);
+    } else {
+        return detail::tile<T>(in, tileDims);
+    }
+}
+
+}  // namespace common

From c115cbcb2d1532939d2ede27cf678a8cfca1644c Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 5 Jul 2022 18:55:05 -0400
Subject: [PATCH 2263/2677] broadcasting in af_arith for binary operations
 (#2871)

This commit adds broadcasting capabilities to the arithmetic functions without
the need of tile. Automatic broadcasting is performed for binary operations when
one of the operands has one element across one dimension and another is greater
than one. In this case ArrayFire will automatically perform a tiling operation.
Multiple broadcasts can be performed at one time.

Here are a couple of examples:

```
array c(10)         = randu(10) + randu(1);
array c(10, 10)     = randu(10, 10) + randu(1);
array c(10, 10, 10) = randu(10, 10, 10) + randu(1);
array c(10, 10, 10) = randu(10, 10, 10) + randu(10);
array c(10, 10, 10) = randu(1 , 10, 10) + randu(10);

array c(10, 1, 10) = randu(1, 1, 10) + randu(10);
```

Co-authored-by: pradeep <pradeep@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/api/c/binary.cpp |  89 +++++++++---
 test/array.cpp       |  20 +--
 test/binary.cpp      | 325 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 407 insertions(+), 27 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index ffe21e2591..fc24fd64eb 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -10,6 +10,8 @@
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <common/err_common.hpp>
+#include <common/moddims.hpp>
+#include <common/tile.hpp>
 #include <handle.hpp>
 #include <implicit.hpp>
 #include <optypes.hpp>
@@ -29,8 +31,11 @@
 using af::dim4;
 using af::dtype;
 using common::half;
+using common::modDims;
+using common::tile;
 using detail::arithOp;
 using detail::arithOpD;
+using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
@@ -55,6 +60,36 @@ static inline af_array arithOp(const af_array lhs, const af_array rhs,
     return getHandle(arithOp<T, op>(l, r, odims));
 }
 
+template<typename T, af_op_t op>
+static inline af_array arithOpBroadcast(const af_array lhs,
+                                        const af_array rhs) {
+    const ArrayInfo &linfo = getInfo(lhs);
+    const ArrayInfo &rinfo = getInfo(rhs);
+
+    dim4 odims(1), ltile(1), rtile(1);
+    dim4 lshape = linfo.dims();
+    dim4 rshape = rinfo.dims();
+
+    for (int d = 0; d < AF_MAX_DIMS; ++d) {
+        DIM_ASSERT(
+            1, ((lshape[d] == rshape[d]) || (lshape[d] == 1 && rshape[d] > 1) ||
+                (lshape[d] > 1 && rshape[d] == 1)));
+        odims[d] = std::max(lshape[d], rshape[d]);
+        if (lshape[d] == rshape[d]) {
+            ltile[d] = rtile[d] = 1;
+        } else if (lshape[d] == 1 && rshape[d] > 1) {
+            ltile[d] = odims[d];
+        } else if (lshape[d] > 1 && rshape[d] == 1) {
+            rtile[d] = odims[d];
+        }
+    }
+
+    Array<T> lhst = common::tile<T>(modDims(getArray<T>(lhs), lshape), ltile);
+    Array<T> rhst = common::tile<T>(modDims(getArray<T>(rhs), rshape), rtile);
+
+    return getHandle(arithOp<T, op>(lhst, rhst, odims));
+}
+
 template<typename T, af_op_t op>
 static inline af_array sparseArithOp(const af_array lhs, const af_array rhs) {
     auto res = arithOp<T, op>(getSparseArray<T>(lhs), getSparseArray<T>(rhs));
@@ -82,25 +117,45 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
         const ArrayInfo &linfo = getInfo(lhs);
         const ArrayInfo &rinfo = getInfo(rhs);
 
-        dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
-
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
         af_array res;
-        switch (otype) {
-            case f32: res = arithOp<float, op>(lhs, rhs, odims); break;
-            case f64: res = arithOp<double, op>(lhs, rhs, odims); break;
-            case c32: res = arithOp<cfloat, op>(lhs, rhs, odims); break;
-            case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
-            case s32: res = arithOp<int, op>(lhs, rhs, odims); break;
-            case u32: res = arithOp<uint, op>(lhs, rhs, odims); break;
-            case u8: res = arithOp<uchar, op>(lhs, rhs, odims); break;
-            case b8: res = arithOp<char, op>(lhs, rhs, odims); break;
-            case s64: res = arithOp<intl, op>(lhs, rhs, odims); break;
-            case u64: res = arithOp<uintl, op>(lhs, rhs, odims); break;
-            case s16: res = arithOp<short, op>(lhs, rhs, odims); break;
-            case u16: res = arithOp<ushort, op>(lhs, rhs, odims); break;
-            case f16: res = arithOp<half, op>(lhs, rhs, odims); break;
-            default: TYPE_ERROR(0, otype);
+
+        if (batchMode || linfo.dims() == rinfo.dims()) {
+            dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
+
+            switch (otype) {
+                case f32: res = arithOp<float, op>(lhs, rhs, odims); break;
+                case f64: res = arithOp<double, op>(lhs, rhs, odims); break;
+                case c32: res = arithOp<cfloat, op>(lhs, rhs, odims); break;
+                case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
+                case s32: res = arithOp<int, op>(lhs, rhs, odims); break;
+                case u32: res = arithOp<uint, op>(lhs, rhs, odims); break;
+                case u8: res = arithOp<uchar, op>(lhs, rhs, odims); break;
+                case b8: res = arithOp<char, op>(lhs, rhs, odims); break;
+                case s64: res = arithOp<intl, op>(lhs, rhs, odims); break;
+                case u64: res = arithOp<uintl, op>(lhs, rhs, odims); break;
+                case s16: res = arithOp<short, op>(lhs, rhs, odims); break;
+                case u16: res = arithOp<ushort, op>(lhs, rhs, odims); break;
+                case f16: res = arithOp<half, op>(lhs, rhs, odims); break;
+                default: TYPE_ERROR(0, otype);
+            }
+        } else {
+            switch (otype) {
+                case f32: res = arithOpBroadcast<float, op>(lhs, rhs); break;
+                case f64: res = arithOpBroadcast<double, op>(lhs, rhs); break;
+                case c32: res = arithOpBroadcast<cfloat, op>(lhs, rhs); break;
+                case c64: res = arithOpBroadcast<cdouble, op>(lhs, rhs); break;
+                case s32: res = arithOpBroadcast<int, op>(lhs, rhs); break;
+                case u32: res = arithOpBroadcast<uint, op>(lhs, rhs); break;
+                case u8: res = arithOpBroadcast<uchar, op>(lhs, rhs); break;
+                case b8: res = arithOpBroadcast<char, op>(lhs, rhs); break;
+                case s64: res = arithOpBroadcast<intl, op>(lhs, rhs); break;
+                case u64: res = arithOpBroadcast<uintl, op>(lhs, rhs); break;
+                case s16: res = arithOpBroadcast<short, op>(lhs, rhs); break;
+                case u16: res = arithOpBroadcast<ushort, op>(lhs, rhs); break;
+                case f16: res = arithOpBroadcast<half, op>(lhs, rhs); break;
+                default: TYPE_ERROR(0, otype);
+            }
         }
 
         std::swap(*out, res);
diff --git a/test/array.cpp b/test/array.cpp
index 7d45cf1ea7..deb85e2e22 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -641,23 +641,23 @@ TEST(Array, ReferenceCount2) {
     }
 }
 
-// This tests situations where the compiler incorrectly assumes the initializer
-// list constructor instead of the regular constructor when using the uniform
-// initilization syntax
+// This tests situations where the compiler incorrectly assumes the
+// initializer list constructor instead of the regular constructor when
+// using the uniform initilization syntax
 TEST(Array, InitializerListFixAFArray) {
-    array a = randu(1);
-    array b{a};
+    af::array a = randu(1);
+    af::array b{a};
 
     ASSERT_ARRAYS_EQ(a, b);
 }
 
-// This tests situations where the compiler incorrectly assumes the initializer
-// list constructor instead of the regular constructor when using the uniform
-// initilization syntax
+// This tests situations where the compiler incorrectly assumes the
+// initializer list constructor instead of the regular constructor when
+// using the uniform initilization syntax
 TEST(Array, InitializerListFixDim4) {
-    array a            = randu(1);
+    af::array a        = randu(1);
     vector<float> data = {3.14f, 3.14f, 3.14f, 3.14f, 3.14f,
                           3.14f, 3.14f, 3.14f, 3.14f};
-    array b{dim4(3, 3), data.data()};
+    af::array b{dim4(3, 3), data.data()};
     ASSERT_ARRAYS_EQ(constant(3.14, 3, 3), b);
 }
diff --git a/test/binary.cpp b/test/binary.cpp
index 2bc2a1a62a..06e720ed8e 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -577,3 +577,328 @@ TYPED_TEST(ResultTypeScalar, FloatMultiplication) {
 TYPED_TEST(ResultTypeScalar, FloatDivision) {
     ASSERT_EQ(f32, (af::array(10, f32) / this->scalar).type());
 }
+
+class Broadcast : public ::testing::TestWithParam<std::tuple<dim4, dim4> > {
+    void SetUp() override {}
+};
+/// clang-format off
+
+INSTANTIATE_TEST_CASE_P(
+    CorrectCases, Broadcast,
+    ::testing::Combine(
+        ::testing::Values(dim4(1), dim4(10), dim4(1, 10), dim4(1, 1, 10),
+                          dim4(1, 1, 1, 10), dim4(10, 10), dim4(1, 10, 10),
+                          dim4(1, 1, 10, 10), dim4(10, 1, 10),
+                          dim4(1, 10, 1, 10), dim4(10, 1, 1, 10),
+                          dim4(10, 10, 10), dim4(1, 10, 10, 10),
+                          dim4(10, 1, 10, 10), dim4(10, 10, 1, 10),
+                          dim4(10, 10, 10, 10)),
+        ::testing::Values(dim4(1), dim4(10), dim4(1, 10), dim4(1, 1, 10),
+                          dim4(1, 1, 1, 10), dim4(10, 10), dim4(1, 10, 10),
+                          dim4(1, 1, 10, 10), dim4(10, 1, 10),
+                          dim4(1, 10, 1, 10), dim4(10, 1, 1, 10),
+                          dim4(10, 10, 10), dim4(1, 10, 10, 10),
+                          dim4(10, 1, 10, 10), dim4(10, 10, 1, 10),
+                          dim4(10, 10, 10, 10))),
+    [](const ::testing::TestParamInfo<Broadcast::ParamType> info) {
+        stringstream ss;
+        ss << "lhs_" << get<0>(info.param) << "_rhs_" << get<1>(info.param);
+        string s = ss.str();
+        std::replace(begin(s), std::end(s), ' ', '_');
+        return s;
+    });
+/// clang-format on
+
+af::dim4 broadcastOut(dim4 lhs, dim4 rhs) {
+    dim4 out(1);
+    for (int i = 0; i < AF_MAX_DIMS; i++) {
+        if (lhs[i] == rhs[i])
+            out[i] = lhs[i];
+        else if (lhs[i] == 1 && rhs[i] > 1)
+            out[i] = rhs[i];
+        else if (lhs[i] > 1 && rhs[i] == 1)
+            out[i] = lhs[i];
+        else {
+            std::cout << "incorrect dimension" << lhs << " op " << rhs;
+            return dim4(0);
+        }
+    }
+    return out;
+}
+
+af::dim4 tileRepeations(dim4 in, dim4 other) {
+    af::dim4 out;
+    for (int i = 0; i < AF_MAX_DIMS; i++) {
+        out[i] = std::max(dim_t(1), other[i] / in[i]);
+    }
+    return out;
+}
+
+TEST_P(Broadcast, Addition) {
+    auto params   = GetParam();
+    af::array lhs = iota(get<0>(params));
+    af::array rhs = constant(1, get<1>(params));
+
+    af::array out = lhs + rhs;
+
+    af::dim4 outdims       = broadcastOut(lhs.dims(), rhs.dims());
+    af::dim4 tilerepetions = tileRepeations(lhs.dims(), rhs.dims());
+    af::array tiledlhs     = tile(lhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out += 1; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST_P(Broadcast, Subtraction) {
+    auto params   = GetParam();
+    af::array lhs = range(get<0>(params));
+    af::array rhs = constant(1, get<1>(params));
+
+    af::array out          = lhs - rhs;
+    af::dim4 outdims       = broadcastOut(lhs.dims(), rhs.dims());
+    af::dim4 tilerepetions = tileRepeations(lhs.dims(), rhs.dims());
+    af::array tiledlhs     = tile(lhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out -= 1; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST_P(Broadcast, Multiplication) {
+    auto params   = GetParam();
+    af::array lhs = range(get<0>(params));
+    af::array rhs = constant(2, get<1>(params));
+
+    af::array out          = lhs * rhs;
+    af::dim4 outdims       = broadcastOut(lhs.dims(), rhs.dims());
+    af::dim4 tilerepetions = tileRepeations(lhs.dims(), rhs.dims());
+    af::array tiledlhs     = tile(lhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out *= 2; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST_P(Broadcast, Division) {
+    auto params   = GetParam();
+    af::array lhs = range(get<0>(params));
+    af::array rhs = constant(2, get<1>(params));
+
+    af::array out          = lhs / rhs;
+    af::dim4 outdims       = broadcastOut(lhs.dims(), rhs.dims());
+    af::dim4 tilerepetions = tileRepeations(lhs.dims(), rhs.dims());
+    af::array tiledlhs     = tile(lhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out /= 2; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST_P(Broadcast, AdditionLHSIndexed) {
+    auto params   = GetParam();
+    af::array lhs = iota(get<0>(params) * 2);
+    af::array rhs = constant(1, get<1>(params));
+
+    dim4 lhs_dims = get<0>(params);
+    af::array out = lhs(seq(lhs_dims[0]), seq(lhs_dims[1]), seq(lhs_dims[2]),
+                        seq(lhs_dims[3])) +
+                    rhs;
+
+    af::dim4 outdims       = broadcastOut(get<0>(params), rhs.dims());
+    af::array indexedlhs   = lhs(seq(lhs_dims[0]), seq(lhs_dims[1]),
+                               seq(lhs_dims[2]), seq(lhs_dims[3]));
+    af::dim4 tilerepetions = tileRepeations(get<0>(params), rhs.dims());
+    af::array tiledlhs     = tile(indexedlhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out += 1; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST_P(Broadcast, AdditionRHSIndexed) {
+    auto params   = GetParam();
+    af::array lhs = iota(get<0>(params));
+    af::array rhs = constant(1, get<1>(params) * 2);
+
+    dim4 rhs_dims = get<1>(params);
+    af::array out = lhs + rhs(seq(rhs_dims[0]), seq(rhs_dims[1]),
+                              seq(rhs_dims[2]), seq(rhs_dims[3]));
+
+    af::dim4 outdims       = broadcastOut(get<0>(params), get<1>(params));
+    af::dim4 tilerepetions = tileRepeations(get<0>(params), get<1>(params));
+    af::array tiledlhs     = tile(lhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out += 1; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST_P(Broadcast, AdditionBothIndexed) {
+    auto params   = GetParam();
+    af::array lhs = iota(get<0>(params) * 2);
+    af::array rhs = constant(1, get<1>(params) * 2);
+
+    dim4 lhs_dims = get<0>(params);
+    dim4 rhs_dims = get<1>(params);
+    af::array out = lhs(seq(lhs_dims[0]), seq(lhs_dims[1]), seq(lhs_dims[2]),
+                        seq(lhs_dims[3])) +
+                    rhs(seq(rhs_dims[0]), seq(rhs_dims[1]), seq(rhs_dims[2]),
+                        seq(rhs_dims[3]));
+
+    af::dim4 outdims       = broadcastOut(lhs_dims, rhs_dims);
+    af::array indexedlhs   = lhs(seq(lhs_dims[0]), seq(lhs_dims[1]),
+                               seq(lhs_dims[2]), seq(lhs_dims[3]));
+    af::dim4 tilerepetions = tileRepeations(get<0>(params), get<1>(params));
+    af::array tiledlhs     = tile(indexedlhs, tilerepetions);
+
+    vector<float> outvec(outdims.elements());
+    tiledlhs.host(outvec.data());
+    for (auto &out : outvec) { out += 1; }
+
+    ASSERT_VEC_ARRAY_EQ(outvec, outdims, out);
+}
+
+TEST(Broadcast, VectorMatrix2d) {
+    dim_t s     = 10;
+    af::array A = range(dim4(s, 3), 1);
+    af::array B = -range(dim4(3));
+
+    try {
+        A + B;
+        FAIL();
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+    try {
+        B + A;
+        FAIL();
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+}
+
+TEST(Broadcast, VectorMatrix3d) {
+    dim_t s     = 10;
+    af::array A = range(dim4(s, s, 3), 2);
+    af::array B = -range(dim4(3));
+
+    try {
+        A + B;
+        FAIL();
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+    try {
+        B + A;
+        FAIL();
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+}
+
+TEST(Broadcast, VectorMatrix4d) {
+    dim_t s     = 10;
+    af::array A = range(dim4(s, s, s, 3), 3);
+    af::array B = -range(dim4(3));
+
+    try {
+        A + B;
+        FAIL();
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+    try {
+        B + A;
+        FAIL();
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+}
+
+void testAllBroadcast(dim4 dims) {
+    af::array A = constant(1, dims);
+    for (int k = 0; k < dims.ndims(); ++k) {
+        dim4 rdims  = dims;
+        rdims[k]    = 1;
+        af::array B = constant(-1, rdims);
+        af::array C = A + B;
+        ASSERT_ARRAYS_EQ(C, constant(0, dims));
+
+        C = B + A;
+        ASSERT_ARRAYS_EQ(C, constant(0, dims));
+    }
+}
+
+TEST(Broadcast, MatrixMatrix2d) { testAllBroadcast(dim4(10, 15)); }
+
+TEST(Broadcast, MatrixMatrix3d) { testAllBroadcast(dim4(10, 15, 20)); }
+
+TEST(Broadcast, MatrixMatrix4d) { testAllBroadcast(dim4(10, 15, 20, 25)); }
+
+TEST(Broadcast, MismatchingDim0) {
+    af::array A = range(dim4(2, 3, 5), 1);
+    af::array B = -range(dim4(3, 5), 0);
+
+    try {
+        A + B;
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+}
+
+TEST(Broadcast, TestFirstMatchingDim) {
+    af::array A = range(dim4(3, 2, 2, 4), 1);
+    af::array B = -range(dim4(2));
+
+    try {
+        A + B;
+    } catch (af::exception &e) { ASSERT_EQ(e.err(), AF_ERR_SIZE); }
+}
+
+TEST(Broadcast, ManySlicesVsOneSlice) {
+    af::array A = constant(1, dim4(3, 3, 2));
+    af::array B = constant(2, dim4(3, 3));
+    af::array C = A + B;
+
+    ASSERT_ARRAYS_EQ(C, constant(3, dim4(3, 3, 2)));
+
+    C = B + A;
+    ASSERT_ARRAYS_EQ(C, constant(3, dim4(3, 3, 2)));
+}
+
+TEST(Broadcast, SubArray) {
+    dim_t subdim = 5;
+    af::array A  = constant(1, dim4(10, 10, 2));
+    af::array B  = constant(2, dim4(5, 5));
+    af::array C  = A(seq(subdim), seq(subdim), span) + B;
+
+    ASSERT_ARRAYS_EQ(C, constant(3, dim4(subdim, subdim, 2)));
+
+    C = B + A(seq(subdim), seq(subdim), span);
+    ASSERT_ARRAYS_EQ(C, constant(3, dim4(subdim, subdim, 2)));
+}
+
+TEST(Broadcast, SubArrays) {
+    dim_t subdim = 5;
+    af::array A  = constant(1, dim4(10, 10, 2));
+    af::array B  = constant(2, dim4(15, 15));
+
+    af::array C =
+        A(seq(subdim), seq(subdim), span) + B(seq(subdim), seq(subdim));
+    ASSERT_ARRAYS_EQ(C, constant(3, dim4(subdim, subdim, 2)));
+
+    C = B(seq(subdim), seq(subdim)) + A(seq(subdim), seq(subdim), span);
+    ASSERT_ARRAYS_EQ(C, constant(3, dim4(subdim, subdim, 2)));
+}
+
+TEST(Broadcast, IndexedArray) {
+    af::array A = constant(1, dim4(2, 2, 2, 2));
+    af::array B = constant(-1, dim4(1, 5));
+
+    af::array idx = range(dim4(2, 2, 2, 2), 0);
+
+    af::array C = A(idx % 2 == 0) + B;
+    ASSERT_ARRAYS_EQ(C, constant(0, dim4(8, 5)));
+
+    C = B + A(idx % 2 == 0);
+    ASSERT_ARRAYS_EQ(C, constant(0, dim4(8, 5)));
+}

From f199a5d1d9074e0c77d140a9ef3aee66bf871422 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 1 Jul 2022 20:58:58 -0400
Subject: [PATCH 2264/2677] Call setDevice on each thread at entry point.

CUDA requires that cudaSetDevice be called in each thread before
any other calls are made to the CUDA API. This is done by default
on the main thread but it is not done on new threads created. This
commit changes the behavior or the af_init function so that it call
the cudaSetDevice when creating a new object in ArrayFire.

This commit also refactors the af_init function so that it calls a lower
overhead init function which initializes the device manager.
---
 src/api/c/device.cpp            | 3 ++-
 src/backend/cpu/platform.cpp    | 5 +++++
 src/backend/cpu/platform.hpp    | 2 ++
 src/backend/cuda/platform.cpp   | 6 ++++++
 src/backend/cuda/platform.hpp   | 2 ++
 src/backend/opencl/platform.cpp | 5 +++++
 src/backend/opencl/platform.hpp | 2 ++
 7 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 3ed23a0c3e..cf65bfd81c 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -39,6 +39,7 @@ using detail::getActiveDeviceId;
 using detail::getBackend;
 using detail::getDeviceCount;
 using detail::getDeviceInfo;
+using detail::init;
 using detail::intl;
 using detail::isDoubleSupported;
 using detail::isHalfSupported;
@@ -107,7 +108,7 @@ af_err af_init() {
     try {
         thread_local std::once_flag flag;
         std::call_once(flag, []() {
-            getDeviceInfo();
+            init();
 #if defined(USE_MKL) && !defined(USE_STATIC_MKL)
             int errCode = -1;
             // Have used the AF_MKL_INTERFACE_SIZE as regular if's so that
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 523737b07a..3f83956b91 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -112,6 +112,11 @@ int& getMaxJitSize() {
 
 int getDeviceCount() { return DeviceManager::NUM_DEVICES; }
 
+void init() {
+    thread_local const auto& instance = DeviceManager::getInstance();
+    UNUSED(instance);
+}
+
 // Get the currently active device id
 unsigned getActiveDeviceId() { return DeviceManager::ACTIVE_DEVICE_ID; }
 
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index a37f12351f..f50e16461b 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -40,6 +40,8 @@ int& getMaxJitSize();
 
 int getDeviceCount();
 
+void init();
+
 unsigned getActiveDeviceId();
 
 size_t getDeviceMemorySize(int device);
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 0e639ec62d..647566eb2a 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -348,6 +348,12 @@ int getDeviceCount() {
     }
 }
 
+void init() {
+    thread_local auto err =
+        cudaSetDevice(getDeviceNativeId(getActiveDeviceId()));
+    UNUSED(err);
+}
+
 unsigned getActiveDeviceId() { return tlocalActiveDeviceId(); }
 
 int getDeviceNativeId(int device) {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index b4e9dd2360..6d1778b3ab 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -80,6 +80,8 @@ int& getMaxJitSize();
 
 int getDeviceCount();
 
+void init();
+
 unsigned getActiveDeviceId();
 
 int getDeviceNativeId(int device);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index e2c4571995..b159758b37 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -218,6 +218,11 @@ int getDeviceCount() noexcept try {
     return 0;
 }
 
+void init() {
+    thread_local const DeviceManager& devMngr = DeviceManager::getInstance();
+    UNUSED(devMngr);
+}
+
 unsigned getActiveDeviceId() {
     // Second element is the queue id, which is
     // what we mean by active device id in opencl backend
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 6292c1331d..8ea6ca2540 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -55,6 +55,8 @@ std::string getDeviceInfo() noexcept;
 
 int getDeviceCount() noexcept;
 
+void init();
+
 unsigned getActiveDeviceId();
 
 int& getMaxJitSize();

From ae348f5e931919cf77f8dafdfce0de3a6e26a8b0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 15:47:08 -0400
Subject: [PATCH 2265/2677] Fix missing release_array calls in the reduce tests

---
 test/reduce.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/test/reduce.cpp b/test/reduce.cpp
index c9e09f53fd..69e6573d3c 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2303,10 +2303,14 @@ TEST(Reduce, nanval_issue_3255) {
         af_product_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);
         af::array ovals_cpp(ovals);
         ASSERT_FALSE(af::anyTrue<bool>(af::isNaN(ovals_cpp)));
+        ASSERT_SUCCESS(af_release_array(okeys));
 
         af_sum_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);
         ovals_cpp = af::array(ovals);
 
         ASSERT_FALSE(af::anyTrue<bool>(af::isNaN(ovals_cpp)));
+        ASSERT_SUCCESS(af_release_array(ivals));
+        ASSERT_SUCCESS(af_release_array(okeys));
     }
+    ASSERT_SUCCESS(af_release_array(ikeys));
 }

From 4d04cd3ed87cb6ad0857b50c50b78f2d7c305f90 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 15:47:42 -0400
Subject: [PATCH 2266/2677] Remove unnecessary death test in test/array.cpp

---
 test/array.cpp | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/test/array.cpp b/test/array.cpp
index deb85e2e22..08b5a568d7 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -584,14 +584,10 @@ TEST(Array, CopyListInitializerListDim4Assignment) {
 }
 
 TEST(Array, EmptyArrayHostCopy) {
-    EXPECT_EXIT(
-        {
-            af::array empty;
-            std::vector<float> hdata(100);
-            empty.host(hdata.data());
-            exit(0);
-        },
-        ::testing::ExitedWithCode(0), ".*");
+    af::array empty;
+    std::vector<float> hdata(100);
+    empty.host(hdata.data());
+    SUCCEED();
 }
 
 TEST(Array, ReferenceCount1) {

From aefe79addd7c54ade1788417ce9b37f509512049 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 15:48:28 -0400
Subject: [PATCH 2267/2677] Refactor SIFT for memory usage and fix memory leak
 in GLOH and SIFT tests

---
 src/backend/opencl/kernel/sift.hpp | 129 ++++++++++-------------------
 src/backend/opencl/memory.hpp      |   5 +-
 2 files changed, 46 insertions(+), 88 deletions(-)

diff --git a/src/backend/opencl/kernel/sift.hpp b/src/backend/opencl/kernel/sift.hpp
index bd10faa1ce..4b1609514e 100644
--- a/src/backend/opencl/kernel/sift.hpp
+++ b/src/backend/opencl/kernel/sift.hpp
@@ -400,13 +400,20 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     vector<Param> dog_pyr =
         buildDoGPyr<T>(gauss_pyr, n_octaves, n_layers, kernels[0]);
 
-    vector<Buffer*> d_x_pyr(n_octaves, NULL);
-    vector<Buffer*> d_y_pyr(n_octaves, NULL);
-    vector<Buffer*> d_response_pyr(n_octaves, NULL);
-    vector<Buffer*> d_size_pyr(n_octaves, NULL);
-    vector<Buffer*> d_ori_pyr(n_octaves, NULL);
-    vector<Buffer*> d_desc_pyr(n_octaves, NULL);
+    vector<bufptr> d_x_pyr;
+    vector<bufptr> d_y_pyr;
+    vector<bufptr> d_response_pyr;
+    vector<bufptr> d_size_pyr;
+    vector<bufptr> d_ori_pyr;
+    vector<bufptr> d_desc_pyr;
     vector<unsigned> feat_pyr(n_octaves, 0);
+
+    d_x_pyr.reserve(n_octaves);
+    d_y_pyr.reserve(n_octaves);
+    d_response_pyr.reserve(n_octaves);
+    d_size_pyr.reserve(n_octaves);
+    d_ori_pyr.reserve(n_octaves);
+    d_desc_pyr.reserve(n_octaves);
     unsigned total_feat = 0;
 
     const unsigned d  = DescrWidth;
@@ -417,7 +424,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
     const unsigned desc_len =
         (compute_GLOH) ? (1 + (rb - 1) * ab) * hb : d * d * n;
 
-    Buffer* d_count = bufferAlloc(sizeof(unsigned));
+    auto d_count = memAlloc<unsigned>(1);
 
     for (unsigned o = 0; o < n_octaves; o++) {
         if (dog_pyr[o].info.dims[0] - 2 * ImgBorder < 1 ||
@@ -427,9 +434,9 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         const unsigned imel = dog_pyr[o].info.dims[0] * dog_pyr[o].info.dims[1];
         const unsigned max_feat = ceil(imel * feature_ratio);
 
-        Buffer* d_extrema_x     = bufferAlloc(max_feat * sizeof(float));
-        Buffer* d_extrema_y     = bufferAlloc(max_feat * sizeof(float));
-        Buffer* d_extrema_layer = bufferAlloc(max_feat * sizeof(unsigned));
+        auto d_extrema_x     = memAlloc<float>(max_feat);
+        auto d_extrema_y     = memAlloc<float>(max_feat);
+        auto d_extrema_layer = memAlloc<unsigned>(max_feat);
 
         unsigned extrema_feat = 0;
         getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
@@ -458,23 +465,17 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
                                      &extrema_feat);
         extrema_feat = std::min(extrema_feat, max_feat);
 
-        if (extrema_feat == 0) {
-            bufferFree(d_extrema_x);
-            bufferFree(d_extrema_y);
-            bufferFree(d_extrema_layer);
-
-            continue;
-        }
+        if (extrema_feat == 0) { continue; }
 
         unsigned interp_feat = 0;
         getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &interp_feat);
 
-        Buffer* d_interp_x     = bufferAlloc(extrema_feat * sizeof(float));
-        Buffer* d_interp_y     = bufferAlloc(extrema_feat * sizeof(float));
-        Buffer* d_interp_layer = bufferAlloc(extrema_feat * sizeof(unsigned));
-        Buffer* d_interp_response = bufferAlloc(extrema_feat * sizeof(float));
-        Buffer* d_interp_size     = bufferAlloc(extrema_feat * sizeof(float));
+        auto d_interp_x        = memAlloc<float>(extrema_feat);
+        auto d_interp_y        = memAlloc<float>(extrema_feat);
+        auto d_interp_layer    = memAlloc<unsigned>(extrema_feat);
+        auto d_interp_response = memAlloc<float>(extrema_feat);
+        auto d_interp_size     = memAlloc<float>(extrema_feat);
 
         const int blk_x_interp = divup(extrema_feat, SIFT_THREADS);
         const NDRange local_interp(SIFT_THREADS, 1);
@@ -489,23 +490,11 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
              n_layers, contrast_thr, edge_thr, init_sigma, img_scale);
         CL_DEBUG_FINISH(getQueue());
 
-        bufferFree(d_extrema_x);
-        bufferFree(d_extrema_y);
-        bufferFree(d_extrema_layer);
-
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                      &interp_feat);
         interp_feat = std::min(interp_feat, extrema_feat);
 
-        if (interp_feat == 0) {
-            bufferFree(d_interp_x);
-            bufferFree(d_interp_y);
-            bufferFree(d_interp_layer);
-            bufferFree(d_interp_response);
-            bufferFree(d_interp_size);
-
-            continue;
-        }
+        if (interp_feat == 0) { continue; }
 
         compute::command_queue queue(getQueue()());
         compute::context context(getContext()());
@@ -546,11 +535,11 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &nodup_feat);
 
-        Buffer* d_nodup_x        = bufferAlloc(interp_feat * sizeof(float));
-        Buffer* d_nodup_y        = bufferAlloc(interp_feat * sizeof(float));
-        Buffer* d_nodup_layer    = bufferAlloc(interp_feat * sizeof(unsigned));
-        Buffer* d_nodup_response = bufferAlloc(interp_feat * sizeof(float));
-        Buffer* d_nodup_size     = bufferAlloc(interp_feat * sizeof(float));
+        auto d_nodup_x        = memAlloc<float>(interp_feat);
+        auto d_nodup_y        = memAlloc<float>(interp_feat);
+        auto d_nodup_layer    = memAlloc<unsigned>(interp_feat);
+        auto d_nodup_response = memAlloc<float>(interp_feat);
+        auto d_nodup_size     = memAlloc<float>(interp_feat);
 
         const int blk_x_nodup = divup(extrema_feat, SIFT_THREADS);
         const NDRange local_nodup(SIFT_THREADS, 1);
@@ -568,26 +557,17 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
                                      &nodup_feat);
         nodup_feat = std::min(nodup_feat, interp_feat);
 
-        bufferFree(d_interp_x);
-        bufferFree(d_interp_y);
-        bufferFree(d_interp_layer);
-        bufferFree(d_interp_response);
-        bufferFree(d_interp_size);
-
         unsigned oriented_feat = 0;
         getQueue().enqueueWriteBuffer(*d_count, CL_FALSE, 0, sizeof(unsigned),
                                       &oriented_feat);
         const unsigned max_oriented_feat = nodup_feat * 3;
 
-        Buffer* d_oriented_x = bufferAlloc(max_oriented_feat * sizeof(float));
-        Buffer* d_oriented_y = bufferAlloc(max_oriented_feat * sizeof(float));
-        Buffer* d_oriented_layer =
-            bufferAlloc(max_oriented_feat * sizeof(unsigned));
-        Buffer* d_oriented_response =
-            bufferAlloc(max_oriented_feat * sizeof(float));
-        Buffer* d_oriented_size =
-            bufferAlloc(max_oriented_feat * sizeof(float));
-        Buffer* d_oriented_ori = bufferAlloc(max_oriented_feat * sizeof(float));
+        auto d_oriented_x        = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_y        = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_layer    = memAlloc<unsigned>(max_oriented_feat);
+        auto d_oriented_response = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_size     = memAlloc<float>(max_oriented_feat);
+        auto d_oriented_ori      = memAlloc<float>(max_oriented_feat);
 
         const int blk_x_ori = divup(nodup_feat, SIFT_THREADS_Y);
         const NDRange local_ori(SIFT_THREADS_X, SIFT_THREADS_Y);
@@ -604,27 +584,13 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
              Local(OriHistBins * SIFT_THREADS_Y * 2 * sizeof(float)));
         CL_DEBUG_FINISH(getQueue());
 
-        bufferFree(d_nodup_x);
-        bufferFree(d_nodup_y);
-        bufferFree(d_nodup_layer);
-        bufferFree(d_nodup_response);
-        bufferFree(d_nodup_size);
-
         getQueue().enqueueReadBuffer(*d_count, CL_TRUE, 0, sizeof(unsigned),
                                      &oriented_feat);
         oriented_feat = std::min(oriented_feat, max_oriented_feat);
 
-        if (oriented_feat == 0) {
-            bufferFree(d_oriented_x);
-            bufferFree(d_oriented_y);
-            bufferFree(d_oriented_layer);
-            bufferFree(d_oriented_response);
-            bufferFree(d_oriented_size);
+        if (oriented_feat == 0) { continue; }
 
-            continue;
-        }
-
-        Buffer* d_desc = bufferAlloc(oriented_feat * desc_len * sizeof(float));
+        auto d_desc = memAlloc<float>(oriented_feat * desc_len);
 
         float scale = 1.f / (1 << o);
         if (double_input) scale *= 2.f;
@@ -660,17 +626,15 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
         feat_pyr[o] = oriented_feat;
 
         if (oriented_feat > 0) {
-            d_x_pyr[o]        = d_oriented_x;
-            d_y_pyr[o]        = d_oriented_y;
-            d_response_pyr[o] = d_oriented_response;
-            d_ori_pyr[o]      = d_oriented_ori;
-            d_size_pyr[o]     = d_oriented_size;
-            d_desc_pyr[o]     = d_desc;
+            d_x_pyr.emplace_back(std::move(d_oriented_x));
+            d_y_pyr.emplace_back(std::move(d_oriented_y));
+            d_response_pyr.emplace_back(std::move(d_oriented_response));
+            d_ori_pyr.emplace_back(std::move(d_oriented_ori));
+            d_size_pyr.emplace_back(std::move(d_oriented_size));
+            d_desc_pyr.emplace_back(std::move(d_desc));
         }
     }
 
-    bufferFree(d_count);
-
     for (size_t i = 0; i < gauss_pyr.size(); i++) bufferFree(gauss_pyr[i].data);
     for (size_t i = 0; i < dog_pyr.size(); i++) bufferFree(dog_pyr[i].data);
 
@@ -755,13 +719,6 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
                                      offset * desc_len * sizeof(unsigned),
                                      feat_pyr[i] * desc_len * sizeof(unsigned));
 
-        bufferFree(d_x_pyr[i]);
-        bufferFree(d_y_pyr[i]);
-        bufferFree(d_response_pyr[i]);
-        bufferFree(d_ori_pyr[i]);
-        bufferFree(d_size_pyr[i]);
-        bufferFree(d_desc_pyr[i]);
-
         offset += feat_pyr[i];
     }
 
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 778c611ad9..ba7e340d32 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -24,9 +24,10 @@ namespace opencl {
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
 
+using bufptr = std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>>;
+
 template<typename T>
-std::unique_ptr<cl::Buffer, std::function<void(cl::Buffer *)>> memAlloc(
-    const size_t &elements);
+bufptr memAlloc(const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument

From 9ca49de3db6271866315adff917e57255aea019e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 15:49:57 -0400
Subject: [PATCH 2268/2677] Rename the name for the basic_c.cpp tests

---
 test/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 1c7bc8792e..09a794c63b 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -432,7 +432,7 @@ make_test(SRC write.cpp)
 make_test(SRC ycbcr_rgb.cpp)
 
 foreach(backend ${enabled_backends})
-  set(target "test_basic_c_${backend}")
+  set(target "basic_c_${backend}")
   add_executable(${target} basic_c.c)
   if(${backend} STREQUAL "unified")
     target_link_libraries(${target}
@@ -443,7 +443,7 @@ foreach(backend ${enabled_backends})
       PRIVATE
       ArrayFire::af${backend})
   endif()
-  add_test(NAME ${target} COMMAND ${target})
+  add_test(NAME test_${target} COMMAND ${target})
 endforeach()
 
 if(AF_TEST_WITH_MTX_FILES)

From b05da694a3f789579af25887108a214f1a978326 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 17:41:46 -0400
Subject: [PATCH 2269/2677] Fix leaks in clFFT and update reference. Update
 LSANSuppressions

---
 CMakeModules/LSANSuppression.txt | 2 +-
 CMakeModules/build_clFFT.cmake   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/LSANSuppression.txt b/CMakeModules/LSANSuppression.txt
index 43ac584d10..b305e805f3 100644
--- a/CMakeModules/LSANSuppression.txt
+++ b/CMakeModules/LSANSuppression.txt
@@ -2,11 +2,11 @@
 leak:libnvidia-ptxjitcompile
 leak:tbb::internal::task_stream
 leak:libnvidia-opencl.so
-leak:FFTRepo::FFTRepoKey::privatizeData
 
 # Allocated by Intel's OpenMP implementation during inverse_dense_cpu
 # This is not something we can control in ArrayFire
 leak:kmp_alloc_cpp*::bget
+leak:kmp_b_alloc
 
 # ArrayFire leaks the default random engine on each thread. This is to avoid
 # errors on exit on Windows.
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index 380357e02e..dc29e22ced 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -7,7 +7,7 @@
 
 af_dep_check_and_populate(${clfft_prefix}
   URI https://github.com/arrayfire/clFFT.git
-  REF cmake_fixes
+  REF arrayfire-release
 )
 
 set(current_build_type ${BUILD_SHARED_LIBS})

From 5a512056921929d2dbce1a1449a32115bd123588 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 23 Jul 2022 16:56:06 -0400
Subject: [PATCH 2270/2677] Fix issue where ndims was incorrectly used to
 calculate shape of input

---
 src/api/c/convolve.cpp | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index ddcd916ae6..9a496633b0 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -344,14 +344,17 @@ af_err af_convolve2_nn(af_array *out, const af_array signal,
 
         const af_dtype signalType = sInfo.getType();
 
-        ARG_ASSERT(3, stride_dims > 0 && stride_dims <= 2);
-        ARG_ASSERT(5, padding_dims > 0 && padding_dims <= 2);
-        ARG_ASSERT(7, dilation_dims > 0 && dilation_dims <= 2);
-
         dim4 stride(stride_dims, strides);
         dim4 padding(padding_dims, paddings);
         dim4 dilation(dilation_dims, dilations);
 
+        size_t stride_ndims   = stride.ndims();
+        size_t padding_ndims  = padding.ndims();
+        size_t dilation_ndims = dilation.ndims();
+        ARG_ASSERT(3, stride_ndims > 0 && stride_ndims <= 2);
+        ARG_ASSERT(5, padding_ndims >= 0 && padding_ndims <= 2);
+        ARG_ASSERT(7, dilation_ndims > 0 && dilation_ndims <= 2);
+
         // assert number of features matches between signal and filter
         DIM_ASSERT(1, sDims[2] == fDims[2]);
 
@@ -424,14 +427,17 @@ af_err af_convolve2_gradient_nn(
 
         af_array output;
 
-        ARG_ASSERT(3, stride_dims > 0 && stride_dims <= 2);
-        ARG_ASSERT(5, padding_dims > 0 && padding_dims <= 2);
-        ARG_ASSERT(7, dilation_dims > 0 && dilation_dims <= 2);
-
         af::dim4 stride(stride_dims, strides);
         af::dim4 padding(padding_dims, paddings);
         af::dim4 dilation(dilation_dims, dilations);
 
+        size_t stride_ndims   = stride.ndims();
+        size_t padding_ndims  = padding.ndims();
+        size_t dilation_ndims = dilation.ndims();
+        ARG_ASSERT(3, stride_ndims > 0 && stride_ndims <= 2);
+        ARG_ASSERT(5, padding_ndims > 0 && padding_ndims <= 2);
+        ARG_ASSERT(7, dilation_ndims > 0 && dilation_ndims <= 2);
+
         af_dtype type = oinfo.getType();
         switch (type) {
             case f32:

From be7f2d93de3796050e56037cc0c340a2ef34e813 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 10 Jun 2022 18:43:52 -0400
Subject: [PATCH 2271/2677] Make constructors that accept simple types explicit

Some of the ArrayFire constructors that accept the dim_t type are not
marked explicit. this allows the initialization of the ArrayFire's array
using integer types. For example

```
af::array a = 5
```

will create an af::array with 5 elements. This is not intended behavior.

I have looked into the ABI for this change and it doesn't seem to
be affected on GCC. I have to still test this on MSVC. This CAN
break some existing code because it does change the API but ArrayFire
was never designed with this code in mind.
---
 .../machine_learning/geneticalgorithm.cpp     | 21 ++++++++++---------
 include/af/array.h                            | 16 ++++++++++----
 2 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/examples/machine_learning/geneticalgorithm.cpp b/examples/machine_learning/geneticalgorithm.cpp
index d930a9cd44..184bc9914e 100644
--- a/examples/machine_learning/geneticalgorithm.cpp
+++ b/examples/machine_learning/geneticalgorithm.cpp
@@ -123,8 +123,8 @@ void reproducePrint(float& currentMax, array& searchSpace, array& sampleX,
 }
 
 void geneticSearch(bool console, const int nSamples, const int n) {
-    array searchSpaceXDisplay = 0;
-    array searchSpaceYDisplay = 0;
+    array searchSpaceXDisplay;
+    array searchSpaceYDisplay;
     array searchSpace;
     array sampleX;
     array sampleY;
@@ -170,17 +170,18 @@ int main(int argc, char** argv) {
 
     try {
         af::info();
-        printf("** ArrayFire Genetic Algorithm Search Demo **\n\n");
         printf(
-            "Search for trueMax in a search space where the objective function "
-            "is defined as :\n\n");
-        printf("SS(x ,y) = min(x, n - (x + 1)) + min(y, n - (y + 1))\n\n");
-        printf("(x, y) belongs to RxR; R = [0, n); n = %d\n\n", n);
+            "** ArrayFire Genetic Algorithm Search Demo **\n\n"
+            "Search for trueMax in a search space where the objective "
+            "function is defined as :\n\n"
+            "SS(x ,y) = min(x, n - (x + 1)) + min(y, n - (y + 1))\n\n"
+            "(x, y) belongs to RxR; R = [0, n); n = %d\n\n",
+            n);
         if (!console) {
-            printf("The left figure shows the objective function.\n");
             printf(
-                "The figure on the right shows current generation's parameters "
-                "and function values.\n\n");
+                "The left figure shows the objective function.\n"
+                "The right figure shows current generation's "
+                "parameters and function values.\n\n");
         }
         geneticSearch(console, nSamples, n);
     } catch (af::exception& e) { fprintf(stderr, "%s\n", e.what()); }
diff --git a/include/af/array.h b/include/af/array.h
index b1405c903c..0edb9558e1 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -246,6 +246,7 @@ namespace af
                        (default is f32)
 
         */
+        explicit
         array(dim_t dim0, dtype ty = f32);
 
         /**
@@ -271,6 +272,7 @@ namespace af
                        (default is f32)
 
         */
+        explicit
         array(dim_t dim0, dim_t dim1, dtype ty = f32);
 
         /**
@@ -297,6 +299,7 @@ namespace af
                        (default is f32)
 
         */
+        explicit
         array(dim_t dim0, dim_t dim1, dim_t dim2, dtype ty = f32);
 
         /**
@@ -324,6 +327,7 @@ namespace af
                        (default is f32)
 
         */
+        explicit
         array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3, dtype ty = f32);
 
         /**
@@ -368,10 +372,10 @@ namespace af
 
             array A(4, h_buffer);   // copy host data to device
                                     //
-                                    // A = 23
-                                    //   = 34
-                                    //   = 18
-                                    //   = 99
+                                    // A = [23]
+                                    //     [34]
+                                    //     [18]
+                                    //     [99]
 
             \endcode
 
@@ -382,6 +386,7 @@ namespace af
 
         */
         template<typename T>
+        explicit
         array(dim_t dim0,
               const T *pointer, af::source src=afHost);
 
@@ -409,6 +414,7 @@ namespace af
                   format when performing linear algebra operations.
         */
         template<typename T>
+        explicit
         array(dim_t dim0, dim_t dim1,
               const T *pointer, af::source src=afHost);
 
@@ -440,6 +446,7 @@ namespace af
             \image html 3dArray.png
         */
         template<typename T>
+        explicit
         array(dim_t dim0, dim_t dim1, dim_t dim2,
               const T *pointer, af::source src=afHost);
 
@@ -473,6 +480,7 @@ namespace af
 
         */
         template<typename T>
+        explicit
         array(dim_t dim0, dim_t dim1, dim_t dim2, dim_t dim3,
               const T *pointer, af::source src=afHost);
 

From 04bcd18aa4851e0fd933b5164249634f3243cebd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 2 Sep 2022 13:22:41 -0400
Subject: [PATCH 2272/2677] Update cmake minimum version to 3.10.2

---
 .github/workflows/unix_cpu_build.yml | 4 ++--
 CMakeLists.txt                       | 2 +-
 CMakeModules/CPackConfig.cmake       | 2 +-
 test/mmio/CMakeLists.txt             | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 47dff97a42..ad616ddd3d 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -14,7 +14,7 @@ jobs:
         runs-on: ${{ matrix.os }}
         env:
           NINJA_VER: 1.10.2
-          CMAKE_VER: 3.5.1
+          CMAKE_VER: 3.10.2
         strategy:
             fail-fast: false
             matrix:
@@ -39,7 +39,7 @@ jobs:
                   chmod +x ninja
                   ${GITHUB_WORKSPACE}/ninja --version
 
-            - name: Download CMake 3.5.1 for Linux
+            - name: Download CMake 3.10.2 for Linux
               if: matrix.os != 'macos-latest'
               env:
                   OS_NAME: ${{ matrix.os }}
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 537ae9a736..721b9136fc 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.10.2)
 
 include(CMakeModules/AF_vcpkg_options.cmake)
 
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index d073527089..6cd13a1d71 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # https://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.10.2)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${PROJECT_SOURCE_DIR}/CMakeModules/nsis")
 
diff --git a/test/mmio/CMakeLists.txt b/test/mmio/CMakeLists.txt
index 5ef52292ad..5f4bd419f0 100644
--- a/test/mmio/CMakeLists.txt
+++ b/test/mmio/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.10.2)
 
 project(MatrixMarketIO LANGUAGES C)
 

From d3c02906a6f93a98bf71c799a190037e3f4180db Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 2 Sep 2022 11:44:04 -0400
Subject: [PATCH 2273/2677] Fix LAPACKE warnings and Update OpenCL library
 directory

---
 CMakeLists.txt                    | 11 ++++++-
 CMakeModules/FindLAPACKE.cmake    | 53 ++++---------------------------
 CMakeModules/FindOpenCL.cmake     |  3 +-
 src/backend/cpu/CMakeLists.txt    |  5 ++-
 src/backend/opencl/CMakeLists.txt |  6 ++--
 5 files changed, 23 insertions(+), 55 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 721b9136fc..973508e280 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -156,6 +156,7 @@ mark_as_advanced(
   AF_TEST_WITH_MTX_FILES
   ArrayFire_DIR
   Boost_INCLUDE_DIR
+  CLEAR CUDA_VERSION
   CUDA_HOST_COMPILER
   CUDA_SDK_ROOT_DIR
   CUDA_USE_STATIC_CUDA_RUNTIME
@@ -171,7 +172,15 @@ mark_as_advanced(
   spdlog_DIR
   FG_BUILD_OFFLINE
   )
-mark_as_advanced(CLEAR CUDA_VERSION)
+
+if(MKL_FOUND)
+  set(BLA_VENDOR "Intel10_64lp")
+  if(MKL_THREAD_LAYER STREQUAL "Sequential")
+    set(BLA_VENDOR "${BLA_VENDOR}_seq")
+  endif()
+endif()
+find_package(BLAS)
+find_package(LAPACK)
 
 # IF: the old USE_CPU_MKL/USE_OPENCL_MKL flags are present,
 # THEN Irrespective of AF_COMPUTE_LIBRARY value, continue with MKL to preserve old
diff --git a/CMakeModules/FindLAPACKE.cmake b/CMakeModules/FindLAPACKE.cmake
index 84e20fe7e9..65c513abb2 100644
--- a/CMakeModules/FindLAPACKE.cmake
+++ b/CMakeModules/FindLAPACKE.cmake
@@ -3,12 +3,8 @@
 # Usage:
 #   FIND_PACKAGE(LAPACKE [REQUIRED] [QUIET] )
 #
-# It sets the following variables:
-#   LAPACK_FOUND               ... true if LAPACKE is found on the system
-#   LAPACK_LIBRARIES           ... full path to LAPACKE library
-#   LAPACK_INCLUDES            ... LAPACKE include directory
-#
 
+INCLUDE(FindPackageHandleStandardArgs)
 SET(LAPACKE_ROOT_DIR CACHE STRING
   "Root directory for custom LAPACK implementation")
 
@@ -77,14 +73,6 @@ ELSE(PC_LAPACKE_FOUND)
             DOC "LAPACKE Library"
             NO_DEFAULT_PATH
             )
-        FIND_LIBRARY(
-            LAPACK_LIB
-            NAMES "lapack" "LAPACK" "liblapack" "mkl_rt"
-            PATHS ${LAPACKE_ROOT_DIR}
-            PATH_SUFFIXES "lib" "lib64" "lib/${MKL_LIB_DIR_SUFFIX}"
-            DOC "LAPACK Library"
-            NO_DEFAULT_PATH
-            )
         FIND_PATH(
             LAPACKE_INCLUDES
             NAMES "lapacke.h" "mkl_lapacke.h"
@@ -109,21 +97,6 @@ ELSE(PC_LAPACKE_FOUND)
             /opt/local/lib
             DOC "LAPACKE Library"
             )
-        FIND_LIBRARY(
-           LAPACK_LIB
-            NAMES "lapack" "liblapack" "openblas" "mkl_rt"
-            PATHS
-            ${PC_LAPACKE_LIBRARY_DIRS}
-            ${LIB_INSTALL_DIR}
-            /opt/intel/mkl/lib/${MKL_LIB_DIR_SUFFIX}
-            /usr/lib64
-            /usr/lib
-            /usr/local/lib64
-            /usr/local/lib
-            /sw/lib
-            /opt/local/lib
-            DOC "LAPACK Library"
-            )
         FIND_PATH(
             LAPACKE_INCLUDES
             NAMES "lapacke.h" "mkl_lapacke.h"
@@ -140,34 +113,20 @@ ELSE(PC_LAPACKE_FOUND)
             lapacke
             )
     ENDIF(LAPACKE_ROOT_DIR)
+    find_package_handle_standard_args(LAPACKE DEFAULT_MSG LAPACKE_LIB LAPACKE_INCLUDES)
 ENDIF(PC_LAPACKE_FOUND)
 
-IF(PC_LAPACKE_FOUND OR (LAPACKE_LIB AND LAPACK_LIB))
-    SET(LAPACK_LIBRARIES ${LAPACKE_LIB} ${LAPACK_LIB})
-ENDIF()
-IF(LAPACKE_INCLUDES)
-    SET(LAPACK_INCLUDE_DIR ${LAPACKE_INCLUDES})
-ENDIF()
-
-INCLUDE(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(LAPACK DEFAULT_MSG
-  LAPACK_INCLUDE_DIR LAPACK_LIBRARIES)
-
 MARK_AS_ADVANCED(
   LAPACKE_ROOT_DIR
-  LAPACK_INCLUDES
-  LAPACK_LIBRARIES
-  LAPACK_LIB
   LAPACKE_INCLUDES
   LAPACKE_LIB
-  lapack_LIBRARY
   lapacke_LIBRARY)
 
-if(LAPACK_FOUND)
+if(PC_LAPACKE_FOUND OR (LAPACKE_LIB AND LAPACKE_INCLUDES))
   add_library(LAPACKE::LAPACKE UNKNOWN IMPORTED)
   set_target_properties(LAPACKE::LAPACKE PROPERTIES
       IMPORTED_LINK_INTERFACE_LANGUAGE "C"
-      IMPORTED_LOCATION "${LAPACK_LIBRARIES}"
-      INTERFACE_INCLUDE_DIRECTORIES "${LAPACK_INCLUDE_DIR}"
+      IMPORTED_LOCATION "${LAPACKE_LIB}"
+      INTERFACE_INCLUDE_DIRECTORIES "${LAPACKE_INCLUDES}"
     )
-endif(LAPACK_FOUND)
+endif()
diff --git a/CMakeModules/FindOpenCL.cmake b/CMakeModules/FindOpenCL.cmake
index 54c26e5c84..cdaeba20cc 100644
--- a/CMakeModules/FindOpenCL.cmake
+++ b/CMakeModules/FindOpenCL.cmake
@@ -117,7 +117,8 @@ if(WIN32)
   endif()
 else()
   find_library(OpenCL_LIBRARY
-    NAMES OpenCL)
+    NAMES OpenCL
+    PATH_SUFFIXES lib64/)
 endif()
 
 set(OpenCL_LIBRARIES ${OpenCL_LIBRARY})
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index e3c862d169..7aa10bc529 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -331,9 +331,8 @@ else()
       FFTW::FFTW
       FFTW::FFTWF
     )
-  if(LAPACK_FOUND)
-    target_link_libraries(afcpu PRIVATE ${LAPACK_LIBRARIES})
-    target_include_directories(afcpu PRIVATE ${LAPACK_INCLUDE_DIR})
+  if(LAPACK_FOUND AND LAPACKE_FOUND)
+    target_link_libraries(afcpu PRIVATE LAPACKE::LAPACKE ${LAPACK_LIBRARIES})
   endif()
 endif()
 
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index dd557ede47..4660b99754 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -483,12 +483,12 @@ if(LAPACK_FOUND OR BUILD_WITH_MKL)
 
     target_include_directories(afopencl
       PRIVATE
-        ${CBLAS_INCLUDE_DIR}
-        ${LAPACK_INCLUDE_DIR})
+        ${CBLAS_INCLUDE_DIR})
     target_link_libraries(afopencl
       PRIVATE
         ${CBLAS_LIBRARIES}
-        ${LAPACK_LIBRARIES})
+        ${LAPACK_LIBRARIES}
+        LAPACKE::LAPACKE)
   endif()
 
   target_compile_definitions(afopencl PRIVATE WITH_LINEAR_ALGEBRA)

From 293ce5c220acde98f6bafb9259d6f09e37a30d33 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 1 Sep 2022 14:28:53 -0400
Subject: [PATCH 2274/2677] Add option to use external dependencies instead of
 fetchcontent

This commit adds the ability to search for already installed software on the
system instead of downloading the required libraries using fetchcontent. This
allows package managers to select dependencies that are more compatible with the
system than the one targeted by the ArrayFire build system. One disadvantage of
this approach is the increase build failures and version incompatibilities
---
 CMakeLists.txt                                |  82 +++++++----
 CMakeModules/bin2cpp.cpp                      |   5 +-
 CMakeModules/boost_package.cmake              |   5 +-
 CMakeModules/build_CLBlast.cmake              | 137 ++++++++++--------
 CMakeModules/build_cl2hpp.cmake               |  15 +-
 examples/CMakeLists.txt                       |   2 +-
 src/api/unified/CMakeLists.txt                |   7 +
 src/backend/common/CMakeLists.txt             |   9 +-
 src/backend/common/util.cpp                   |  80 +++++-----
 src/backend/cuda/CMakeLists.txt               |   9 +-
 src/backend/opencl/CMakeLists.txt             |   4 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |   1 +
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |   1 +
 test/CMakeLists.txt                           |  12 +-
 14 files changed, 221 insertions(+), 148 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 973508e280..c79cc691e5 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -39,6 +39,11 @@ set_policies(
            CMP0079)
 arrayfire_set_cmake_default_variables()
 
+option(AF_WITH_EXTERNAL_PACKAGES_ONLY "Build ArrayFire with External packages only" OFF)
+if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
+  set(AF_REQUIRED REQUIRED)
+endif()
+
 #Set Intel OpenMP as default MKL thread layer
 set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
 
@@ -54,7 +59,15 @@ find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
 find_package(MKL)
-find_package(spdlog 1.8.5 QUIET)
+find_package(spdlog QUIET ${AF_REQUIRED})
+find_package(fmt QUIET ${AF_REQUIRED})
+find_package(span-lite QUIET)
+find_package(GTest)
+find_package(CLBlast QUIET)
+find_package(Boost 1.70 ${AF_REQUIRED})
+
+# CLFFT used in ArrayFire requires a specific fork
+#find_package(clFFT QUIET)
 
 include(boost_package)
 include(config_ccache)
@@ -75,6 +88,8 @@ option(AF_WITH_STACKTRACE  "Add stacktraces to the error messages." ON)
 option(AF_CACHE_KERNELS_TO_DISK "Enable caching kernels to disk" ON)
 option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
 option(AF_WITH_STATIC_CUDA_NUMERIC_LIBS "Link libafcuda with static numeric libraries(cublas, cufft, etc.)" OFF)
+option(AF_WITH_SPDLOG_HEADER_ONLY "Build ArrayFire with header only version of spdlog" OFF)
+option(AF_WITH_FMT_HEADER_ONLY "Build ArrayFire with header only version of fmt" OFF)
 
 if(AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   option(AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS "Prune CUDA static libraries to reduce binary size.(WARNING: May break some libs on older CUDA toolkits for some compute arch)" OFF)
@@ -173,7 +188,7 @@ mark_as_advanced(
   FG_BUILD_OFFLINE
   )
 
-if(MKL_FOUND)
+if(AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
   set(BLA_VENDOR "Intel10_64lp")
   if(MKL_THREAD_LAYER STREQUAL "Sequential")
     set(BLA_VENDOR "${BLA_VENDOR}_seq")
@@ -209,22 +224,38 @@ endif()
 #forge is included in ALL target if AF_BUILD_FORGE is ON
 #otherwise, forge is not built at all
 include(AFconfigure_forge_dep)
-add_library(af_spdlog INTERFACE)
-set_target_properties(af_spdlog
-  PROPERTIES
-    INTERFACE_COMPILE_DEFINITIONS FMT_HEADER_ONLY)
-
-if(TARGET spdlog::spdlog_header_only)
-  target_include_directories(af_spdlog
-    SYSTEM INTERFACE
-      $<TARGET_PROPERTY:spdlog::spdlog_header_only,INTERFACE_INCLUDE_DIRECTORIES>
-    )
+
+if(TARGET fmt::fmt AND AF_WITH_FMT_HEADER_ONLY)
+  set_target_properties(fmt::fmt
+    PROPERTIES
+      INTERFACE_COMPILE_DEFINITIONS "FMT_HEADER_ONLY=1")
+endif()
+
+if(TARGET spdlog::spdlog OR AF_WITH_EXTERNAL_PACKAGES_ONLY)
+  if(AF_WITH_SPDLOG_HEADER_ONLY)
+    add_library(af_spdlog ALIAS spdlog::spdlog_header_only)
+  else()
+    add_library(af_spdlog ALIAS spdlog::spdlog)
+  endif()
 else()
+  add_library(af_spdlog INTERFACE)
   af_dep_check_and_populate(${spdlog_prefix}
     URI https://github.com/gabime/spdlog.git
-    REF v1.8.5
+    REF v1.9.2
   )
+  add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+
   target_include_directories(af_spdlog INTERFACE "${${spdlog_prefix}_SOURCE_DIR}/include")
+  if(TARGET fmt::fmt)
+    set_target_properties(af_spdlog
+      PROPERTIES
+        INTERFACE_COMPILE_DEFINITIONS "SPDLOG_FMT_EXTERNAL")
+  endif()
+  if(AF_WITH_SPDLOG_HEADER_ONLY)
+    set_target_properties(af_spdlog
+      PROPERTIES
+        INTERFACE_COMPILE_DEFINITIONS "$<TARGET_PROPERTY:af_spdlog,INTERFACE_COMPILE_DEFINITIONS>;SPDLOG_HEADER_ONLY")
+  endif()
 endif()
 
 if(NOT TARGET glad::glad)
@@ -237,15 +268,17 @@ if(NOT TARGET glad::glad)
   add_library(af_glad STATIC $<TARGET_OBJECTS:af_glad_obj_lib>)
   target_link_libraries(af_glad PUBLIC ${CMAKE_DL_LIBS})
   target_include_directories(af_glad
-    PUBLIC
-    $<BUILD_INTERFACE:$<TARGET_PROPERTY:af_glad_obj_lib,INTERFACE_INCLUDE_DIRECTORIES>>
-    )
+    SYSTEM PUBLIC
+      $<BUILD_INTERFACE:$<TARGET_PROPERTY:af_glad_obj_lib,INTERFACE_INCLUDE_DIRECTORIES>>)
 endif()
 
-af_dep_check_and_populate(span-lite
-  URI https://github.com/martinmoene/span-lite
-  REF "ccf2351"
-  )
+if(NOT TARGET nonstd::span-lite)
+  af_dep_check_and_populate(span-lite
+    URI https://github.com/martinmoene/span-lite
+    REF "ccf2351"
+    )
+  add_subdirectory(${span-lite_SOURCE_DIR} EXCLUDE_FROM_ALL)
+endif()
 
 af_dep_check_and_populate(${assets_prefix}
   URI https://github.com/arrayfire/assets.git
@@ -271,6 +304,9 @@ if(CMAKE_CROSSCOMPILING)
 else()
   add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp
                          ${ArrayFire_SOURCE_DIR}/src/backend/common/util.cpp)
+
+  # NOSPDLOG is used to remove the spdlog dependency from bin2cpp
+  target_compile_definitions(bin2cpp PRIVATE NOSPDLOG)
   if(WIN32)
     target_compile_definitions(bin2cpp PRIVATE OS_WIN)
   elseif(APPLE)
@@ -282,11 +318,6 @@ else()
                              ${ArrayFire_SOURCE_DIR}/include
                              ${ArrayFire_BINARY_DIR}/include
                              ${ArrayFire_SOURCE_DIR}/src/backend)
-  if(TARGET spdlog::spdlog_header_only)
-    target_link_libraries(bin2cpp PRIVATE spdlog::spdlog_header_only)
-  else()
-    target_link_libraries(bin2cpp PRIVATE af_spdlog)
-  endif()
   export(TARGETS bin2cpp FILE ${CMAKE_BINARY_DIR}/ImportExecutables.cmake)
 endif()
 
@@ -298,7 +329,6 @@ if(NOT LAPACK_FOUND)
         unset(LAPACK_LIB CACHE)
         unset(LAPACKE_INCLUDES CACHE)
         unset(LAPACKE_ROOT_DIR CACHE)
-        find_package(LAPACK)
     endif()
 endif()
 
diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index b72a02e636..217b3efe14 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -14,9 +14,8 @@
 #define STRTOK_CALL(...) strtok_r(__VA_ARGS__)
 #endif
 
-#include <assert.h>
-#include <common/util.hpp>
 #include <algorithm>
+#include <cassert>
 #include <cstdlib>
 #include <cstring>
 #include <fstream>
@@ -29,6 +28,8 @@
 #include <utility>
 #include <vector>
 
+#include <common/util.hpp>
+
 using namespace std;
 using std::cout;
 typedef map<string, string> opt_t;
diff --git a/CMakeModules/boost_package.cmake b/CMakeModules/boost_package.cmake
index a0b1c84329..f6fa995c7f 100644
--- a/CMakeModules/boost_package.cmake
+++ b/CMakeModules/boost_package.cmake
@@ -5,8 +5,6 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-find_package(Boost 1.66 REQUIRED)
-
 set(Boost_MIN_VER 107000)
 set(Boost_MIN_VER_STR "1.70")
 
@@ -16,7 +14,8 @@ if(NOT
    (Boost_VERSION_STRING VERSION_GREATER Boost_MIN_VER_STR OR
     Boost_VERSION_STRING VERSION_EQUAL Boost_MIN_VER_STR) OR
    (Boost_VERSION_MACRO VERSION_GREATER Boost_MIN_VER OR
-    Boost_VERSION_MACRO VERSION_EQUAL Boost_MIN_VER)))
+    Boost_VERSION_MACRO VERSION_EQUAL Boost_MIN_VER))
+  AND NOT AF_WITH_EXTERNAL_PACKAGES_ONLY)
   set(VER 1.70.0)
   message(WARNING
       "WARN: Found Boost v${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION}."
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index eaa0908ca8..780cddbaaf 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -5,76 +5,89 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-af_dep_check_and_populate(${clblast_prefix}
-  URI https://github.com/cnugteren/CLBlast.git
-  REF 4500a03440e2cc54998c0edab366babf5e504d67
-)
+if(TARGET clblast OR AF_WITH_EXTERNAL_PACKAGES_ONLY)
+  if(TARGET clblast)
+    # CLBlast has a broken imported link interface where it lists
+    # the full path to the OpenCL library. OpenCL is imported by
+    # another package so we dont need this property to link against
+    # CLBlast.
+    set_target_properties(clblast PROPERTIES
+      IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "")
+  else()
+    message(ERROR "CLBlast now found")
+  endif()
+else()
+  af_dep_check_and_populate(${clblast_prefix}
+    URI https://github.com/cnugteren/CLBlast.git
+    REF 4500a03440e2cc54998c0edab366babf5e504d67
+  )
 
-include(ExternalProject)
-find_program(GIT git)
+  include(ExternalProject)
+  find_program(GIT git)
 
-set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
-set(CLBlast_libname ${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
-set(CLBlast_location ${${clblast_prefix}_BINARY_DIR}/pkg/lib/${CLBlast_libname})
+  set(prefix ${PROJECT_BINARY_DIR}/third_party/CLBlast)
+  set(CLBlast_libname ${CMAKE_STATIC_LIBRARY_PREFIX}clblast${CMAKE_STATIC_LIBRARY_SUFFIX})
+  set(CLBlast_location ${${clblast_prefix}_BINARY_DIR}/pkg/lib/${CLBlast_libname})
 
-set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
-if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
-  list(APPEND extproj_gen_opts "-A${CMAKE_GENERATOR_PLATFORM}")
-  if(CMAKE_GENERATOR_TOOLSET)
-    list(APPEND extproj_gen_opts "-T${CMAKE_GENERATOR_TOOLSET}")
+  set(extproj_gen_opts "-G${CMAKE_GENERATOR}")
+  if(WIN32 AND CMAKE_GENERATOR_PLATFORM AND NOT CMAKE_GENERATOR MATCHES "Ninja")
+    list(APPEND extproj_gen_opts "-A${CMAKE_GENERATOR_PLATFORM}")
+    if(CMAKE_GENERATOR_TOOLSET)
+      list(APPEND extproj_gen_opts "-T${CMAKE_GENERATOR_TOOLSET}")
+    endif()
+  endif()
+  if(VCPKG_TARGET_TRIPLET)
+    list(APPEND extproj_gen_opts "-DOPENCL_ROOT:PATH=${_VCPKG_INSTALLED_DIR}/${VCPKG_TARGET_TRIPLET}")
   endif()
-endif()
-if(VCPKG_TARGET_TRIPLET)
-  list(APPEND extproj_gen_opts "-DOPENCL_ROOT:PATH=${_VCPKG_INSTALLED_DIR}/${VCPKG_TARGET_TRIPLET}")
-endif()
 
-set(extproj_build_type_option "")
-if(NOT isMultiConfig)
-  if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
-    set(extproj_build_type "Release")
-  else()
-    set(extproj_build_type ${CMAKE_BUILD_TYPE})
+  set(extproj_build_type_option "")
+  if(NOT isMultiConfig)
+    if("${CMAKE_BUILD_TYPE}" MATCHES "Release|RelWithDebInfo")
+      set(extproj_build_type "Release")
+    else()
+      set(extproj_build_type ${CMAKE_BUILD_TYPE})
+    endif()
+    set(extproj_build_type_option "-DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}")
   endif()
-  set(extproj_build_type_option "-DCMAKE_BUILD_TYPE:STRING=${extproj_build_type}")
-endif()
 
-ExternalProject_Add(
-    CLBlast-ext
-    DOWNLOAD_COMMAND ""
-    UPDATE_COMMAND ""
-    PATCH_COMMAND ""
-    SOURCE_DIR "${${clblast_prefix}_SOURCE_DIR}"
-    BINARY_DIR "${${clblast_prefix}_BINARY_DIR}"
-    PREFIX "${prefix}"
-    INSTALL_DIR "${${clblast_prefix}_BINARY_DIR}/pkg"
-    BUILD_BYPRODUCTS ${CLBlast_location}
-    CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
-      -Wno-dev <SOURCE_DIR>
-      -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-      "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
-      -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
-      -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-      "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
-      ${extproj_build_type_option}
-      -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
-      -DCMAKE_INSTALL_LIBDIR:PATH=lib
-      -DBUILD_SHARED_LIBS:BOOL=OFF
-      -DSAMPLES:BOOL=OFF
-      -DTUNERS:BOOL=OFF
-      -DCLIENTS:BOOL=OFF
-      -DTESTS:BOOL=OFF
-      -DNETLIB:BOOL=OFF
-    )
+  ExternalProject_Add(
+      CLBlast-ext
+      DOWNLOAD_COMMAND ""
+      UPDATE_COMMAND ""
+      PATCH_COMMAND ""
+      SOURCE_DIR "${${clblast_prefix}_SOURCE_DIR}"
+      BINARY_DIR "${${clblast_prefix}_BINARY_DIR}"
+      PREFIX "${prefix}"
+      INSTALL_DIR "${${clblast_prefix}_BINARY_DIR}/pkg"
+      BUILD_BYPRODUCTS ${CLBlast_location}
+      CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
+        -Wno-dev <SOURCE_DIR>
+        -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
+        "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+        -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
+        -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
+        "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
+        ${extproj_build_type_option}
+        -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
+        -DCMAKE_INSTALL_LIBDIR:PATH=lib
+        -DBUILD_SHARED_LIBS:BOOL=OFF
+        -DSAMPLES:BOOL=OFF
+        -DTUNERS:BOOL=OFF
+        -DCLIENTS:BOOL=OFF
+        -DTESTS:BOOL=OFF
+        -DNETLIB:BOOL=OFF
+      )
 
-set(CLBLAST_INCLUDE_DIRS "${${clblast_prefix}_BINARY_DIR}/pkg/include")
-set(CLBLAST_LIBRARIES CLBlast)
-set(CLBLAST_FOUND ON)
+  set(CLBLAST_INCLUDE_DIRS "${${clblast_prefix}_BINARY_DIR}/pkg/include")
+  set(CLBLAST_LIBRARIES CLBlast)
+  set(CLBLAST_FOUND ON)
 
-make_directory("${CLBLAST_INCLUDE_DIRS}")
+  make_directory("${CLBLAST_INCLUDE_DIRS}")
 
-add_library(CLBlast UNKNOWN IMPORTED)
-set_target_properties(CLBlast PROPERTIES
-  IMPORTED_LOCATION "${CLBlast_location}"
-  INTERFACE_INCLUDE_DIRECTORIES "${CLBLAST_INCLUDE_DIRS}")
+  add_library(clblast UNKNOWN IMPORTED)
+  set_target_properties(clblast PROPERTIES
+    IMPORTED_LOCATION "${CLBlast_location}"
+    INTERFACE_INCLUDE_DIRECTORIES "${CLBLAST_INCLUDE_DIRS}")
 
-add_dependencies(CLBlast CLBlast-ext)
+  add_dependencies(clblast CLBlast-ext)
+endif()
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index fd8709fb02..e090dd0800 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -13,15 +13,18 @@
 
 find_package(OpenCL)
 
-af_dep_check_and_populate(${cl2hpp_prefix}
-  URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
-  REF v2.0.12
-)
-
 if (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
+  af_dep_check_and_populate(${cl2hpp_prefix}
+    URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
+    REF v2.0.12)
+
+  find_path(cl2hpp_var
+    NAMES CL/cl2.hpp
+    PATHS ${ArrayFire_BINARY_DIR}/extern/${cl2hpp_prefix}-src/include)
+
   add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
   add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
 
   set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
-    INTERFACE_INCLUDE_DIRECTORIES ${${cl2hpp_prefix}_SOURCE_DIR}/include)
+    INTERFACE_INCLUDE_DIRECTORIES ${cl2hpp_var})
 endif()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index e6bf747554..f69eff6e1f 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -11,7 +11,7 @@ project(ArrayFire-Examples
   VERSION 3.7.0
   LANGUAGES CXX)
 
-set(CMAKE_CXX_STANDARD 98)
+set(CMAKE_CXX_STANDARD 14)
 if(NOT EXISTS "${ArrayFire_SOURCE_DIR}/CMakeLists.txt")
   set(ASSETS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/..")
 endif()
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 5c0cec9d6f..522a19ba2a 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -107,6 +107,13 @@ target_link_libraries(af
     ${CMAKE_DL_LIBS}
   )
 
+if(TARGET fmt::fmt)
+  target_link_libraries(af
+    PRIVATE
+      fmt::fmt
+  )
+endif()
+
 install(TARGETS af
   EXPORT ArrayFireUnifiedTargets
   COMPONENT unified
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index d12823c6a3..8f553814e7 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -89,9 +89,17 @@ target_link_libraries(afcommon_interface
   INTERFACE
     af_spdlog
     Boost::boost
+    nonstd::span-lite
     ${CMAKE_DL_LIBS}
 )
 
+if(TARGET fmt::fmt)
+  target_link_libraries(afcommon_interface
+    INTERFACE
+      fmt::fmt
+  )
+endif()
+
 if(TARGET glad::glad)
   target_link_libraries(afcommon_interface INTERFACE glad::glad)
 else()
@@ -105,7 +113,6 @@ endif()
 target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
-    ${span-lite_SOURCE_DIR}/include
     ${ArrayFire_BINARY_DIR})
 
 target_include_directories(afcommon_interface
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index ee579d67ac..a5af7f80e6 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -15,7 +15,10 @@
 #include <unistd.h>
 #endif
 
+#ifndef NOSPDLOG
 #include <common/Logger.hpp>
+#endif
+
 #include <common/defines.hpp>
 #include <common/util.hpp>
 #include <af/defines.h>
@@ -32,7 +35,15 @@
 #include <vector>
 
 using std::accumulate;
+using std::hash;
+using std::ofstream;
+using std::once_flag;
+using std::rename;
+using std::size_t;
 using std::string;
+using std::thread;
+using std::to_string;
+using std::uint8_t;
 using std::vector;
 
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
@@ -43,7 +54,7 @@ string& ltrim(string& s) {
     return s;
 }
 
-string getEnvVar(const std::string& key) {
+string getEnvVar(const string& key) {
 #if defined(OS_WIN)
     DWORD bufSize =
         32767;  // limit according to GetEnvironment Variable documentation
@@ -80,23 +91,23 @@ const char* getName(af_dtype type) {
     }
 }
 
-void saveKernel(const std::string& funcName, const std::string& jit_ker,
-                const std::string& ext) {
+void saveKernel(const string& funcName, const string& jit_ker,
+                const string& ext) {
     static constexpr const char* saveJitKernelsEnvVarName =
         "AF_JIT_KERNEL_TRACE";
     static const char* jitKernelsOutput = getenv(saveJitKernelsEnvVarName);
     if (!jitKernelsOutput) { return; }
-    if (std::strcmp(jitKernelsOutput, "stdout") == 0) {
+    if (strcmp(jitKernelsOutput, "stdout") == 0) {
         fputs(jit_ker.c_str(), stdout);
         return;
     }
-    if (std::strcmp(jitKernelsOutput, "stderr") == 0) {
+    if (strcmp(jitKernelsOutput, "stderr") == 0) {
         fputs(jit_ker.c_str(), stderr);
         return;
     }
     // Path to a folder
-    const std::string ffp =
-        std::string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
+    const string ffp =
+        string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
     FILE* f = fopen(ffp.c_str(), "we");
     if (!f) {
         fprintf(stderr, "Cannot open file %s\n", ffp.c_str());
@@ -108,9 +119,9 @@ void saveKernel(const std::string& funcName, const std::string& jit_ker,
     fclose(f);
 }
 
-std::string int_version_to_string(int version) {
-    return std::to_string(version / 1000) + "." +
-           std::to_string(static_cast<int>((version % 1000) / 10.));
+string int_version_to_string(int version) {
+    return to_string(version / 1000) + "." +
+           to_string(static_cast<int>((version % 1000) / 10.));
 }
 
 #if defined(OS_WIN)
@@ -162,25 +173,26 @@ bool removeFile(const string& path) {
 }
 
 bool renameFile(const string& sourcePath, const string& destPath) {
-    return std::rename(sourcePath.c_str(), destPath.c_str()) == 0;
+    return rename(sourcePath.c_str(), destPath.c_str()) == 0;
 }
 
 bool isDirectoryWritable(const string& path) {
     if (!directoryExists(path) && !createDirectory(path)) { return false; }
 
     const string testPath = path + AF_PATH_SEPARATOR + "test";
-    if (!std::ofstream(testPath).is_open()) { return false; }
+    if (!ofstream(testPath).is_open()) { return false; }
     removeFile(testPath);
 
     return true;
 }
 
+#ifndef NOSPDLOG
 string& getCacheDirectory() {
-    static std::once_flag flag;
+    static once_flag flag;
     static string cacheDirectory;
 
-    std::call_once(flag, []() {
-        std::string pathList[] = {
+    call_once(flag, []() {
+        string pathList[] = {
 #if defined(OS_WIN)
             getTemporaryDirectory() + "\\ArrayFire"
 #else
@@ -200,8 +212,8 @@ string& getCacheDirectory() {
         }
 
         if (env_path.empty()) {
-            auto iterDir = std::find_if(begin(pathList), end(pathList),
-                                        isDirectoryWritable);
+            auto iterDir =
+                find_if(begin(pathList), end(pathList), isDirectoryWritable);
 
             cacheDirectory = iterDir != end(pathList) ? *iterDir : "";
         } else {
@@ -211,44 +223,40 @@ string& getCacheDirectory() {
 
     return cacheDirectory;
 }
+#endif
 
 string makeTempFilename() {
-    thread_local std::size_t fileCount = 0u;
+    thread_local size_t fileCount = 0u;
 
     ++fileCount;
-    const std::size_t threadID =
-        std::hash<std::thread::id>{}(std::this_thread::get_id());
+    const size_t threadID = hash<thread::id>{}(std::this_thread::get_id());
 
-    return std::to_string(std::hash<string>{}(std::to_string(threadID) + "_" +
-                                              std::to_string(fileCount)));
+    return to_string(
+        hash<string>{}(to_string(threadID) + "_" + to_string(fileCount)));
 }
 
-std::size_t deterministicHash(const void* data, std::size_t byteSize,
-                              std::size_t prevHash) {
+size_t deterministicHash(const void* data, size_t byteSize, size_t prevHash) {
     // Fowler-Noll-Vo "1a" 32 bit hash
     // https://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function
-    const auto* byteData = static_cast<const std::uint8_t*>(data);
-    return std::accumulate(byteData, byteData + byteSize, prevHash,
-                           [&](std::size_t hash, std::uint8_t data) {
-                               return (hash ^ data) * FNV1A_PRIME;
-                           });
+    const auto* byteData = static_cast<const uint8_t*>(data);
+    return accumulate(
+        byteData, byteData + byteSize, prevHash,
+        [&](size_t hash, uint8_t data) { return (hash ^ data) * FNV1A_PRIME; });
 }
 
-std::size_t deterministicHash(const std::string& data,
-                              const std::size_t prevHash) {
+size_t deterministicHash(const string& data, const size_t prevHash) {
     return deterministicHash(data.data(), data.size(), prevHash);
 }
 
-std::size_t deterministicHash(const vector<std::string>& list,
-                              const std::size_t prevHash) {
-    std::size_t hash = prevHash;
+size_t deterministicHash(const vector<string>& list, const size_t prevHash) {
+    size_t hash = prevHash;
     for (auto s : list) { hash = deterministicHash(s.data(), s.size(), hash); }
     return hash;
 }
 
-std::size_t deterministicHash(const std::vector<common::Source>& list) {
+size_t deterministicHash(const vector<common::Source>& list) {
     // Combine the different source codes, via their hashes
-    std::size_t hash = FNV1A_BASE_OFFSET;
+    size_t hash = FNV1A_BASE_OFFSET;
     for (auto s : list) {
         size_t h = s.hash ? s.hash : deterministicHash(s.ptr, s.length);
         hash     = deterministicHash(&h, sizeof(size_t), hash);
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8f25f1bea1..3fcf1d2259 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -334,6 +334,12 @@ if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
   target_compile_definitions(af_cuda_static_cuda_library PRIVATE AF_USE_NEW_CUSPARSE_API)
 endif()
 
+target_link_libraries(af_cuda_static_cuda_library
+  PRIVATE
+    Boost::boost
+    af_spdlog
+    nonstd::span-lite)
+
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
   if(group_flags)
@@ -343,8 +349,6 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
 
   target_link_libraries(af_cuda_static_cuda_library
     PRIVATE
-      af_spdlog
-      Boost::boost
       ${CMAKE_DL_LIBS}
       ${cusolver_lib}
       ${START_GROUP}
@@ -373,7 +377,6 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
 else()
   target_link_libraries(af_cuda_static_cuda_library
     PUBLIC
-      Boost::boost
       ${CUDA_CUBLAS_LIBRARIES}
       ${CUDA_CUFFT_LIBRARIES}
       ${CUDA_cusolver_LIBRARY}
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 4660b99754..506b9b3f55 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -402,8 +402,6 @@ target_include_directories(afopencl
 arrayfire_set_default_cxx_flags(afopencl)
 
 add_dependencies(afopencl ${cl_kernel_targets} CLBlast-ext)
-add_dependencies(opencl_scan_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
-add_dependencies(opencl_sort_by_key ${cl_kernel_targets} cl2hpp Boost::boost)
 
 set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
@@ -421,7 +419,7 @@ target_link_libraries(afopencl
     OpenCL::cl2hpp
     afcommon_interface
     clFFT
-    CLBlast
+    clblast
     opencl_scan_by_key
     opencl_sort_by_key
     Threads::Threads
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 6add18a881..91f1cc9ffc 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -76,6 +76,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
       PRIVATE
         ${opencl_compile_definitions}
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
+        $<TARGET_PROPERTY:af_spdlog,INTERFACE_COMPILE_DEFINITIONS>
         TYPE=${SBK_BINARY_OP} AFDLL)
     target_sources(opencl_scan_by_key
       INTERFACE $<TARGET_OBJECTS:opencl_scan_by_key_${SBK_BINARY_OP}>)
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index e7a7ca27f3..0d55ffce4e 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -38,6 +38,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>
         ${ArrayFire_BINARY_DIR}/include
       )
     if(TARGET Forge::forge)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 09a794c63b..c7add80ca3 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -15,7 +15,9 @@ if(AF_TEST_WITH_MTX_FILES)
   include(download_sparse_datasets)
 endif()
 
-if(NOT TARGET gtest)
+if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
+    dependency_check(GTest_FOUND)
+else()
   af_dep_check_and_populate(${gtest_prefix}
     URI https://github.com/google/googletest.git
     REF release-1.8.1
@@ -34,6 +36,7 @@ if(NOT TARGET gtest)
   set_target_properties(gtest gtest_main
     PROPERTIES
       FOLDER "ExternalProjectTargets/gtest")
+  add_library(GTest::gtest ALIAS gtest)
   if(UNIX)
     if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
       CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "10.3.0")
@@ -109,7 +112,7 @@ target_include_directories(arrayfire_test
     ${ArrayFire_SOURCE_DIR}/extern/half/include
     mmio
     $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-    ${${gtest_prefix}_SOURCE_DIR}/googletest/include)
+    $<TARGET_PROPERTY:GTest::gtest,INTERFACE_INCLUDE_DIRECTORIES>)
 
 if(WIN32)
   target_compile_options(arrayfire_test
@@ -169,7 +172,7 @@ function(make_test)
     target_link_libraries(${target}
       PRIVATE
         ${mt_args_LIBRARIES}
-        gtest
+	      GTest::gtest
       )
 
     if(${backend} STREQUAL "unified")
@@ -340,7 +343,6 @@ if(CUDA_FOUND)
           ${ArrayFire_BINARY_DIR}/include
           ${ArrayFire_SOURCE_DIR}/extern/half/include
           ${CMAKE_CURRENT_SOURCE_DIR}
-          ${${gtest_prefix}_SOURCE_DIR}/googletest/include
         )
       endif()
       cuda_add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
@@ -357,7 +359,7 @@ if(CUDA_FOUND)
       endif()
       target_link_libraries(${target}
         mmio
-        gtest)
+        GTest::gtest)
 
       # Couldn't get Threads::Threads to work with this cuda binary. The import
       # target would not add the -pthread flag which is required for this

From 2dff454176565900621787ebec8f52c8df426266 Mon Sep 17 00:00:00 2001
From: Carlo Cabrera <30379873+carlocab@users.noreply.github.com>
Date: Tue, 13 Sep 2022 01:37:41 +0800
Subject: [PATCH 2275/2677] Avoid overriding `CMAKE_INSTALL_RPATH` on macOS.
 (#3283)

* Avoid overriding `CMAKE_INSTALL_RPATH` on macOS.

Currently, `InternalUtils.cmake` sets `CMAKE_INSTALL_RPATH` on macOS to
`/opt/arrayfire/lib`. This is not always the install location (e.g. if a
user sets `CMAKE_INSTALL_PREFIX`), nor does it always make sense to only
have a single `LC_RPATH` command inside the libraries on macOS.

In particular, if a user passes `CMAKE_INSTALL_RPATH` from the
command-line on macOS, it would be good to avoid overriding that, since
the user is more likely to supply the correct paths for their system
than keeping a fixed value of `/opt/arrayfire/lib`.

This PR emits a warning if `CMAKE_INSTALL_RPATH` is not set on macOS to
warn the user to set it through the command line.
---
 .github/workflows/unix_cpu_build.yml | 2 ++
 CMakeModules/InternalUtils.cmake     | 4 ++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index ad616ddd3d..1962db4891 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -102,6 +102,7 @@ jobs:
                   dashboard=$(if [ -z "$prnum" ]; then echo "Continuous"; else echo "Experimental"; fi)
                   backend=$(if [ "$USE_MKL" == 1 ]; then echo "Intel-MKL"; else echo "FFTW/LAPACK/BLAS"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
+                  cmake_rpath=$(if [ $OS_NAME == 'macos-latest' ]; then echo "-DCMAKE_INSTALL_RPATH=/opt/arrayfire/lib"; fi)
                   mkdir build && cd build
                   ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
@@ -109,6 +110,7 @@ jobs:
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_EXAMPLES:BOOL=ON \
                       -DAF_BUILD_FORGE:BOOL=ON \
                       -DAF_COMPUTE_LIBRARY:STRING=${backend} \
+                      "$cmake_rpath" \
                       -DBUILDNAME:STRING=${buildname} ..
                   echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 3b19485d6f..f212c50750 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -177,8 +177,8 @@ macro(arrayfire_set_cmake_default_variables)
     set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${ArrayFire_BINARY_DIR}/bin)
   endif()
 
-  if(APPLE)
-    set(CMAKE_INSTALL_RPATH "/opt/arrayfire/lib")
+  if(APPLE AND (NOT DEFINED CMAKE_INSTALL_RPATH))
+      message(WARNING "CMAKE_INSTALL_RPATH is required when installing ArrayFire to the local system. Set it to /opt/arrayfire/lib if making the installer or your own custom install path.")
   endif()
 
   # This code is used to generate the compilers.h file in CMakeModules. Not all

From 5216b7a40acf53fa7d9113803cf31d83d1565cf2 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 4 Aug 2022 01:09:21 +0200
Subject: [PATCH 2276/2677] Threads management General Threads/Blocks
 (Local/Global) calculations when all available dimensions are used.,
 including optimized number of active parallel GPU threads.

---
 src/backend/common/dispatch.hpp     | 151 ++++++++++++-
 src/backend/cuda/device_manager.hpp |   2 +-
 src/backend/cuda/platform.cpp       |  48 ++--
 src/backend/cuda/platform.hpp       |  19 +-
 src/backend/cuda/threadsMgt.hpp     | 327 +++++++++++++++++++++++++++
 src/backend/opencl/platform.cpp     |  17 +-
 src/backend/opencl/platform.hpp     |  51 ++++-
 src/backend/opencl/threadsMgt.hpp   | 328 ++++++++++++++++++++++++++++
 8 files changed, 908 insertions(+), 35 deletions(-)
 create mode 100644 src/backend/cuda/threadsMgt.hpp
 create mode 100644 src/backend/opencl/threadsMgt.hpp

diff --git a/src/backend/common/dispatch.hpp b/src/backend/common/dispatch.hpp
index 099b0aa6a5..e248a22a97 100644
--- a/src/backend/common/dispatch.hpp
+++ b/src/backend/common/dispatch.hpp
@@ -9,6 +9,10 @@
 
 #pragma once
 
+#include <assert.h>
+#include <platform.hpp>
+#include <af/defines.h>
+#include <algorithm>
 #include <cmath>
 
 #define divup(a, b) (((a) + (b)-1) / (b))
@@ -21,8 +25,8 @@ template<typename T>
 inline bool isPrime(T n) {
     if (n <= 1) return false;
 
-    const T last = (T)std::sqrt((double)n);
-    for (T x = 2; x <= last; ++x) {
+    const T last{(T)std::sqrt((double)n)};
+    for (T x{2}; x <= last; ++x) {
         if (n % x == 0) return false;
     }
 
@@ -31,7 +35,7 @@ inline bool isPrime(T n) {
 
 template<typename T>
 inline T greatestPrimeFactor(T n) {
-    T v = 2;
+    T v{2};
 
     while (v <= n) {
         if (n % v == 0 && isPrime(v))
@@ -42,3 +46,144 @@ inline T greatestPrimeFactor(T n) {
 
     return v;
 }
+// Empty columns (dim==1) in refDims are removed from dims & strides.
+// INPUT: refDims, refNdims
+// UPDATE: dims, strides
+// RETURN: ndims
+template<typename T>
+T removeEmptyColumns(const T refDims[AF_MAX_DIMS], const T refNdims,
+                     T dims[AF_MAX_DIMS], T strides[AF_MAX_DIMS]) {
+    T ndims{0};
+    const T* refPtr{refDims};
+    const T* refPtr_end{refDims + refNdims};
+    // Search for first dimension == 1
+    while (refPtr != refPtr_end && *refPtr != 1) {
+        ++refPtr;
+        ++ndims;
+    }
+    if (ndims != refNdims) {
+        T* dPtr_out{dims + ndims};
+        const T* dPtr_in{dPtr_out};
+        T* sPtr_out{strides + ndims};
+        const T* sPtr_in{sPtr_out};
+        // Compress all remaining dimensions
+        while (refPtr != refPtr_end) {
+            if (*refPtr != 1) {
+                *(dPtr_out++) = *dPtr_in;
+                *(sPtr_out++) = *sPtr_in;
+                ++ndims;
+            }
+            ++refPtr;
+            ++dPtr_in;
+            ++sPtr_in;
+        }
+        // Fill remaining dimensions with 1 and calculate corresponding strides
+        // lastStride = last written dim * last written stride
+        const T lastStride{*(dPtr_out - 1) * *(sPtr_out - 1)};
+        const T lastDim{1};
+        for (const T* dPtr_end{dims + AF_MAX_DIMS}; dPtr_out != dPtr_end;
+             ++dPtr_out, ++sPtr_out) {
+            *dPtr_out = lastDim;
+            *sPtr_out = lastStride;
+        }
+    }
+    return ndims;
+}
+
+// Empty columns (dim==1) in refDims are removed from strides
+// ASSUMPTION: dims are equal to refDims, so are not provided
+// INPUT: refDims, refNdims
+// UPDATE: strides
+// RETURN: ndims
+template<typename T>
+T removeEmptyColumns(const T refDims[AF_MAX_DIMS], const T refNdims,
+                     T strides[AF_MAX_DIMS]) {
+    T ndims{0};
+    const T* refPtr{refDims};
+    const T* refPtr_end{refDims + refNdims};
+    // Search for first dimension == 1
+    while (refPtr != refPtr_end && *refPtr != 1) {
+        ++refPtr;
+        ++ndims;
+    }
+    if (ndims != refNdims) {
+        T* sPtr_out{strides + ndims};
+        const T* sPtr_in{sPtr_out};
+        // Compress all remaining dimensions
+        while (refPtr != refPtr_end) {
+            if (*refPtr != 1) {
+                *(sPtr_out++) = *sPtr_in;
+                ++ndims;
+            };
+            ++refPtr;
+            ++sPtr_in;
+        }
+        // Calculate remaining strides
+        // lastStride = last written dim * last written stride
+        const T lastStride{*(refPtr - 1) * *(sPtr_out - 1)};
+        for (const T* sPtr_end{strides + AF_MAX_DIMS}; sPtr_out != sPtr_end;
+             ++sPtr_out) {
+            *sPtr_out = lastStride;
+        }
+    }
+    return ndims;
+}
+
+// Columns with the same stride in both arrays are combined.  Both arrays will
+// remain in sync and will return the same ndims.
+// ASSUMPTION: both arrays have the same ndims
+// UPDATE: dims1, strides1, UPDATE: dims2, strides2, ndims
+// RETURN: ndims
+template<typename T>
+T combineColumns(T dims1[AF_MAX_DIMS], T strides1[AF_MAX_DIMS], T& ndims,
+                 T dims2[AF_MAX_DIMS], T strides2[AF_MAX_DIMS]) {
+    for (T c{0}; c < ndims - 1; ++c) {
+        if (dims1[c] == dims2[c] && dims1[c] * strides1[c] == strides1[c + 1] &&
+            dims1[c] * strides2[c] == strides2[c + 1]) {
+            // Combine columns, since they are linear
+            // This will increase the dimension of the resulting column,
+            // given more opportunities for kernel optimization
+            dims1[c] *= dims1[c + 1];
+            dims2[c] *= dims2[c + 1];
+            --ndims;
+            for (T i{c + 1}; i < ndims; ++i) {
+                dims1[i]    = dims1[i + 1];
+                dims2[i]    = dims2[i + 1];
+                strides1[i] = strides1[i + 1];
+                strides2[i] = strides2[i + 1];
+            }
+            dims1[ndims] = 1;
+            dims2[ndims] = 1;
+            --c;  // Redo this colum, since it is removed now
+        }
+    }
+    return ndims;
+}
+// Columns with the same stride in both arrays are combined.  Both arrays will
+// remain in sync and will return the same ndims.
+// ASSUMPTION: both arrays have the same dims
+// UPDATE: dims1, strides1,
+// UPDATE: strides2, ndims
+// RETURN: ndims
+template<typename T>
+T combineColumns(T dims1[AF_MAX_DIMS], T strides1[AF_MAX_DIMS], T& ndims,
+                 T strides2[AF_MAX_DIMS]) {
+    for (T c{0}; c < ndims - 1; ++c) {
+        if (dims1[c] * strides1[c] == strides1[c + 1] &&
+            dims1[c] * strides2[c] == strides2[c + 1]) {
+            // Combine columns, since they are linear
+            // This will increase the dimension of the resulting column,
+            // given more opportunities for kernel optimization
+            dims1[c] *= dims1[c + 1];
+            --ndims;
+            for (T i{c + 1}; i < ndims; ++i) {
+                dims1[i]    = dims1[i + 1];
+                strides1[i] = strides1[i + 1];
+                strides2[i] = strides2[i + 1];
+            }
+            dims1[ndims] = 1;
+            --c;  // Redo this colum, since it is removed now
+        }
+    }
+    return ndims;
+}
\ No newline at end of file
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index c6009337d2..5ea6d3a2f6 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -90,7 +90,7 @@ class DeviceManager {
 
     friend int setDevice(int device);
 
-    friend cudaDeviceProp getDeviceProp(int device);
+    friend const cudaDeviceProp& getDeviceProp(int device);
 
     friend std::pair<int, int> getComputeCapability(const int device);
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 647566eb2a..520d4f90f5 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -58,6 +58,7 @@ using std::runtime_error;
 using std::string;
 using std::to_string;
 using std::unique_ptr;
+using std::vector;
 
 using common::unique_handle;
 using common::memory::MemoryManagerBase;
@@ -202,7 +203,7 @@ DeviceManager::~DeviceManager() {
 int getBackend() { return AF_BACKEND_CUDA; }
 
 string getDeviceInfo(int device) noexcept {
-    cudaDeviceProp dev = getDeviceProp(device);
+    const cudaDeviceProp &dev = getDeviceProp(device);
 
     size_t mem_gpu_total = dev.totalGlobalMem;
     // double cc = double(dev.major) + double(dev.minor) / 10;
@@ -244,19 +245,19 @@ string getPlatformInfo() noexcept {
     return platform;
 }
 
-bool isDoubleSupported(int device) {
+bool isDoubleSupported(int device) noexcept {
     UNUSED(device);
     return true;
 }
 
 bool isHalfSupported(int device) {
-    std::array<bool, DeviceManager::MAX_DEVICES> half_supported = []() {
+    static std::array<bool, DeviceManager::MAX_DEVICES> half_supported = []() {
         std::array<bool, DeviceManager::MAX_DEVICES> out{};
         int count = getDeviceCount();
         for (int i = 0; i < count; i++) {
-            auto prop   = getDeviceProp(i);
-            int compute = prop.major * 1000 + prop.minor * 10;
-            out[i]      = compute >= 5030;
+            const auto &prop = getDeviceProp(i);
+            int compute      = prop.major * 1000 + prop.minor * 10;
+            out[i]           = compute >= 5030;
         }
         return out;
     }();
@@ -266,7 +267,7 @@ bool isHalfSupported(int device) {
 void devprop(char *d_name, char *d_platform, char *d_toolkit, char *d_compute) {
     if (getDeviceCount() <= 0) { return; }
 
-    cudaDeviceProp dev = getDeviceProp(getActiveDeviceId());
+    const cudaDeviceProp &dev = getDeviceProp(getActiveDeviceId());
 
     // Name
     snprintf(d_name, 256, "%s", dev.name);
@@ -354,7 +355,7 @@ void init() {
     UNUSED(err);
 }
 
-unsigned getActiveDeviceId() { return tlocalActiveDeviceId(); }
+int getActiveDeviceId() { return tlocalActiveDeviceId(); }
 
 int getDeviceNativeId(int device) {
     if (device <
@@ -397,12 +398,31 @@ int setDevice(int device) {
     return DeviceManager::getInstance().setActiveDevice(device);
 }
 
-cudaDeviceProp getDeviceProp(int device) {
-    if (device <
-        static_cast<int>(DeviceManager::getInstance().cuDevices.size())) {
-        return DeviceManager::getInstance().cuDevices[device].prop;
-    }
-    return DeviceManager::getInstance().cuDevices[0].prop;
+size_t getL2CacheSize(const int device) {
+    return getDeviceProp(device).l2CacheSize;
+}
+
+const int *getMaxGridSize(const int device) {
+    return getDeviceProp(device).maxGridSize;
+}
+
+unsigned getMemoryBusWidth(const int device) {
+    return getDeviceProp(device).memoryBusWidth;
+}
+
+unsigned getMultiProcessorCount(const int device) {
+    return getDeviceProp(device).multiProcessorCount;
+}
+
+unsigned getMaxParallelThreads(const int device) {
+    const cudaDeviceProp &prop{getDeviceProp(device)};
+    return prop.multiProcessorCount * prop.maxThreadsPerMultiProcessor;
+}
+
+const cudaDeviceProp &getDeviceProp(const int device) {
+    const vector<cudaDevice_t> &devs = DeviceManager::getInstance().cuDevices;
+    if (device < static_cast<int>(devs.size())) { return devs[device].prop; }
+    return devs[0].prop;
 }
 
 MemoryManagerBase &memoryManager() {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 6d1778b3ab..bbdf5a8d6d 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -69,7 +69,7 @@ std::string getDriverVersion() noexcept;
 std::string getCUDARuntimeVersion() noexcept;
 
 // Returns true if double is supported by the device
-bool isDoubleSupported(int device);
+bool isDoubleSupported(int device) noexcept;
 
 // Returns true if half is supported by the device
 bool isHalfSupported(int device);
@@ -82,7 +82,7 @@ int getDeviceCount();
 
 void init();
 
-unsigned getActiveDeviceId();
+int getActiveDeviceId();
 
 int getDeviceNativeId(int device);
 
@@ -94,6 +94,19 @@ size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
 
+size_t getL2CacheSize(const int device);
+
+// Returns int[3] of maxGridSize
+const int* getMaxGridSize(const int device);
+
+unsigned getMemoryBusWidth(const int device);
+
+// maximum nr of threads the device really can run in parallel, without
+// scheduling
+unsigned getMaxParallelThreads(const int device);
+
+unsigned getMultiProcessorCount(const int device);
+
 int setDevice(int device);
 
 void sync(int device);
@@ -101,7 +114,7 @@ void sync(int device);
 // Returns true if the AF_SYNCHRONIZE_CALLS environment variable is set to 1
 bool synchronize_calls();
 
-cudaDeviceProp getDeviceProp(int device);
+const cudaDeviceProp& getDeviceProp(const int device);
 
 std::pair<int, int> getComputeCapability(const int device);
 
diff --git a/src/backend/cuda/threadsMgt.hpp b/src/backend/cuda/threadsMgt.hpp
new file mode 100644
index 0000000000..06fccdb0a3
--- /dev/null
+++ b/src/backend/cuda/threadsMgt.hpp
@@ -0,0 +1,327 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <common/dispatch.hpp>
+#include <platform.hpp>
+
+namespace cuda {
+// OVERALL USAGE (With looping):
+// ...                                                      // OWN CODE
+// threadsMgt<T> th(...);                                   // backend.hpp
+// const dim3 threads{th.genThreads()};                     // backend.hpp
+// const dim3 blocks{th.genBlocks(threads,..)};             // backend.hpp
+// cuda::Kernel KER{GETKERNEL(..., th.loop0, th.loop1, th.loop2,
+//                               th.loop3)};                // OWN CODE
+// KER(threads,blocks,...);                                 // OWN CODE
+// ...                                                      // OWN CODE
+//
+// OVERALL USAGE (without looping):
+// ...                                                      // OWN CODE
+// threadsMgt<T> th(...);                                   // backend.hpp
+// const dim3 threads{th.genThreads()};                     // backend.hpp
+// const dim3 blocks{th.genBlocksFull(threads,...)};        // backend.hpp
+// cuda::Kernel KER{GETKERNEL(...)};                        // OWN CODE
+// KER(threads,blocks,...);                                 // OWN CODE
+// ...                                                      // OWN CODE
+template<typename T>
+class threadsMgt {
+   public:
+    bool loop0, loop1, loop2, loop3;
+
+   private:
+    const unsigned d0, d1, d2, d3;
+    const T ndims;
+    const unsigned maxParallelThreads;
+
+   public:
+    // INPUT: dims of the output array
+    // INPUT: ndims of previous dims
+    threadsMgt(const T dims[4], const T ndims);
+
+    // Generate optimal thread values
+    inline const dim3 genThreads() const;
+
+    // INPUT threads, generated by genThreads()
+    // OUTPUT blocks, supposing that each element results in 1 thread
+    inline dim3 genBlocksFull(const dim3& threads) const;
+
+    // Generate the optimal block values
+    // INPUT threads, generated by genThreads()
+    // INPUT nrInputs = number of input buffers read by kernel in parallel
+    // INPUT nrOutputs = number of output buffers written by kernel in parallel
+    // INPUT totalSize = size of all input arrays and all output arrays together
+    // INPUT sizeofT = size of 1 element TO BE WRITTEN
+    // OUTPUT blocks, assuming that the previously calculated loopings will be
+    // executed in the kernel
+    inline dim3 genBlocks(const dim3& threads, const unsigned nrInputs,
+                          const unsigned nrOutputs, const size_t totalSize,
+                          const size_t sizeofT);
+};
+
+// INPUT: dims of the output array
+// INPUT: ndims of previous dims
+template<typename T>
+threadsMgt<T>::threadsMgt(const T dims[4], const T ndims)
+    : loop0(false)
+    , loop1(false)
+    , loop2(false)
+    , loop3(false)
+    , d0(static_cast<unsigned>(dims[0]))
+    , d1(static_cast<unsigned>(dims[1]))
+    , d2(static_cast<unsigned>(dims[2]))
+    , d3(static_cast<unsigned>(dims[3]))
+    , ndims(ndims)
+    , maxParallelThreads(getMaxParallelThreads(getActiveDeviceId())){};
+
+// Generate optimal thread values
+template<typename T>
+const dim3 threadsMgt<T>::genThreads() const {
+    // Performance is mainly dependend on:
+    //    - reducing memory latency, by preferring a sequential read of
+    //    cachelines (principally dim0)
+    //    - more parallel threads --> higher occupation of available
+    //    threads
+    //    - more I/O operations per thread --> dims[3] indicates the #
+    //    of I/Os handled by the kernel inside each thread, and outside
+    //    the scope of the block scheduler
+    // High performance is achievable with occupation rates as low as
+    // 30%. Here we aim at 50%, to also cover older hardware with slower
+    // cores.
+    // https://stackoverflow.com/questions/7737772/improving-kernel-performance-by-increasing-occupancy
+    // http://www.nvidia.com/content/gtc-2010/pdfs/2238_gtc2010.pdf
+    // https://www.cvg.ethz.ch/teaching/2011spring/gpgpu/GPU-Optimization.pdf
+    // https://en.wikipedia.org/wiki/Graphics_Core_Next#SIMD_Vector_Unit
+
+    // The performance for vectors is independent from array sizes.
+    if ((d1 == 1) & (d2 == 1)) return dim3(128U);
+
+    // TOTAL OCCUPATION = occup(dim0) * occup(dim1) * occup(dim2).
+    // For linearized arrays, each linear block is allocated to a dim,
+    // resulting in large numbers for dim0 & dim1.
+    // - For dim2, we only return exact dividers of the array dim[3], so
+    // occup(dim2)=100%
+    // - For dim0 & dim1, we aim somewhere between 30% and 50%
+    //      * Having 2 blocks filled + 1 thread in block 3 --> occup >
+    //      2/3=66%
+    //      * Having 3 blocks filled + 1 thread in block 4 --> occup >
+    //      3/4=75%
+    //      * Having 4 blocks filled + 1 thread in block 5 --> occup >
+    //      4/5=80%
+    constexpr unsigned OCCUPANCY_FACTOR{2U};  // at least 2 blocks filled
+
+    // NVIDIA:
+    //  warp             = 32
+    //  possible blocks  = [32, 64, 96, 128, 160, 192, 224, 256, ..
+    //  1024] best performance = [32, 64, 96, 128] optimal perf     =
+    //  128; any combination
+    //   NIVIDA always processes full wavefronts.  Allocating partial
+    //   warps
+    //   (<32) reduces throughput.  Performance reaches a plateau from
+    //   128 with a slightly slowing for very large sizes.
+    // For algorithm below:
+    //  parallelThreads  = [32, 64, 96, 128]
+    constexpr unsigned minThreads{32};
+    const unsigned relevantElements{d0 * d1 * d2};
+    constexpr unsigned warp{32};
+
+    // For small array's, we reduce the maximum threads in 1 block to
+    // improve parallelisme.  In worst case the scheduler can have 1
+    // block per CU, even when only partly loaded. Range for block is:
+    // [minThreads ... 4 * warp multiple]
+    //   * NVIDIA: [4*32=128 threads]
+    // At 4 * warp multiple, full wavefronts (queue of 4 partial
+    // wavefronts) are all occupied.
+
+    // We need at least maxParallelThreads to occupy all the CU's.
+    const unsigned parallelThreads{
+        relevantElements <= maxParallelThreads
+            ? minThreads
+            : std::min(4U, relevantElements / maxParallelThreads) * warp};
+
+    // Priority 1: keep cachelines filled.  Aparrantly sharing
+    // cachelines between CU's has a heavy cost. Testing confirmed that
+    // the occupation is mostly > 50%
+    const unsigned threads0{d0 == 1 ? 1
+                            : d0 <= minThreads
+                                ? minThreads  // better distribution
+                                : std::min(128U, (divup(d0, warp) * warp))};
+
+    // Priority 2: Fill the block, while respecting the occupation limit
+    // (>66%) (through parallelThreads limit)
+    const unsigned threads1{
+        (threads0 * 64U <= parallelThreads) &&
+                (!(d1 & (64U - 1U)) || (d1 > OCCUPANCY_FACTOR * 64U))
+            ? 64U
+        : (threads0 * 32U <= parallelThreads) &&
+                (!(d1 & (32U - 1U)) || (d1 > OCCUPANCY_FACTOR * 32U))
+            ? 32U
+        : (threads0 * 16U <= parallelThreads) &&
+                (!(d1 & (16U - 1U)) || (d1 > OCCUPANCY_FACTOR * 16U))
+            ? 16U
+        : (threads0 * 8U <= parallelThreads) &&
+                (!(d1 & (8U - 1U)) || (d1 > OCCUPANCY_FACTOR * 8U))
+            ? 8U
+        : (threads0 * 4U <= parallelThreads) &&
+                (!(d1 & (4U - 1U)) || (d1 > OCCUPANCY_FACTOR * 4U))
+            ? 4U
+        : (threads0 * 2U <= parallelThreads) &&
+                (!(d1 & (2U - 1U)) || (d1 > OCCUPANCY_FACTOR * 2U))
+            ? 2U
+            : 1U};
+
+    const unsigned threads01{threads0 * threads1};
+    if ((d2 == 1) | (threads01 * 2 > parallelThreads))
+        return dim3(threads0, threads1);
+
+    // Priority 3: Only exact dividers are used, so that
+    //  - overflow checking is not needed in the kernel.
+    //  - occupation rate never is reduced
+    // Chances are low that threads2 will be different from 1.
+    const unsigned threads2{
+        (threads01 * 8 <= parallelThreads) && !(d2 & (8U - 1U))   ? 8U
+        : (threads01 * 4 <= parallelThreads) && !(d2 & (4U - 1U)) ? 4U
+        : (threads01 * 2 <= parallelThreads) && !(d2 & (2U - 1U)) ? 2U
+                                                                  : 1U};
+    return dim3(threads0, threads1, threads2);
+};
+
+// INPUT threads, generated by genThreads()
+// OUTPUT blocks, supposing that each element results in 1 thread
+template<typename T>
+inline dim3 threadsMgt<T>::genBlocksFull(const dim3& threads) const {
+    const dim3 blocks{divup(d0, threads.x), divup(d1, threads.y),
+                      divup(d2, threads.z)};
+    return dim3(divup(d0, threads.x), divup(d1, threads.y),
+                divup(d2, threads.z));
+};
+
+// Generate the optimal block values
+// INPUT threads, generated by genThreads()
+// INPUT nrInputs = number of input buffers read by kernel in parallel
+// INPUT nrOutputs = number of output buffers written by kernel in parallel
+// INPUT totalSize = size of all input arrays and all output arrays together
+// INPUT sizeofT = size of 1 element TO BE WRITTEN
+// OUTPUT blocks, assuming that the previously calculated loopings will be
+// executed in the kernel
+template<typename T>
+inline dim3 threadsMgt<T>::genBlocks(const dim3& threads,
+                                     const unsigned nrInputs,
+                                     const unsigned nrOutputs,
+                                     const size_t totalSize,
+                                     const size_t sizeofT) {
+    // The bottleneck of anykernel is dependent on the type of memory
+    // used.
+    // a) For very small arrays (elements < maxParallelThreads), each
+    //  element receives it individual thread.
+    // b) For arrays (in+out) smaller than 3/2 L2cache, memory access no
+    //  longer is the bottleneck, because enough L2cache is available at any
+    //  time. Threads are limited to reduce scheduling overhead.
+    // c) For very large arrays and type sizes (<long double), 1 thread will
+    //  not generate enough data to keep the memory sync mechanism
+    //  saturated, so we start loooping inside each thread.
+    dim3 blocks{1};
+    const int activeDeviceId{getActiveDeviceId()};
+    const unsigned* maxGridSize{
+        reinterpret_cast<const unsigned*>(getMaxGridSize(activeDeviceId))};
+    const size_t L2CacheSize{getL2CacheSize(activeDeviceId)};
+    const unsigned cacheLine{getMemoryBusWidth(activeDeviceId)};
+    const unsigned multiProcessorCount{getMultiProcessorCount(activeDeviceId)};
+    const unsigned maxThreads{maxParallelThreads *
+                              (sizeofT * nrInputs * nrInputs > 8 ? 1 : 2)};
+
+    if (ndims == 1) {
+        if (d0 > maxThreads) {
+            if (totalSize * 2 > L2CacheSize * 3) {
+                // General formula to calculate best #loops
+                // Dedicated GPUs:
+                //  32/sizeof(T)**2/#outBuffers*(3/4)**(#inBuffers-1)
+                // Integrated GPUs:
+                //  4/sizeof(T)/#outBuffers*(3/4)**(#inBuffers-1)
+                unsigned largeVolDivider{cacheLine == 64
+                                             ? sizeofT == 1   ? 4
+                                               : sizeofT == 2 ? 2
+                                                              : 1
+                                             : (sizeofT == 1   ? 32
+                                                : sizeofT == 2 ? 8
+                                                               : 1) /
+                                                   nrOutputs};
+                for (unsigned i{1}; i < nrInputs; ++i)
+                    largeVolDivider = largeVolDivider * 3 / 4;
+                if (largeVolDivider > 1) {
+                    blocks.x = d0 / (largeVolDivider * threads.x);
+                    if (blocks.x == 0) blocks.x = 1;
+                    loop0 = true;
+                }
+            } else {
+                // A reduction to (1|2*)maxParallelThreads will be
+                // performed
+                blocks.x = maxThreads / threads.x;
+                if (blocks.x == 0) blocks.x = 1;
+                loop0 = true;
+            }
+        }
+        if (!loop0) { blocks.x = divup(d0, threads.x); }
+    } else {
+        loop3    = d3 != 1;
+        blocks.x = divup(d0, threads.x);
+        blocks.z = divup(d2, threads.z);
+        // contains the mandatory loops introduced by dim3 and dim2
+        // gridSize overflow
+        unsigned dim2and3Multiplier{d3};
+        if (blocks.z > maxGridSize[2]) {
+            dim2and3Multiplier = dim2and3Multiplier * blocks.z / maxGridSize[2];
+            blocks.z           = maxGridSize[2];
+            loop2              = true;
+        }
+        if ((d1 > threads.y) &
+            (threads.x * blocks.x * d1 * threads.z * blocks.z > maxThreads)) {
+            if ((d0 * sizeofT * 8 > cacheLine * multiProcessorCount) &
+                (totalSize * 2 > L2CacheSize * 3)) {
+                // General formula to calculate best #loops
+                // Dedicated GPUs:
+                //  32/sizeof(T)**2/#outBuffers*(3/4)**(#inBuffers-1)
+                // Integrated GPUs:
+                //  4/sizeof(T)/#outBuffers*(3/4)**(#inBuffers-1)
+                unsigned largeVolDivider{
+                    cacheLine == 64 ? sizeofT == 1   ? 4
+                                      : sizeofT == 2 ? 2
+                                                     : 1
+                                    : (sizeofT == 1   ? 32
+                                       : sizeofT == 2 ? 8
+                                       : sizeofT == 4 ? 2
+                                                      : 1) /
+                                          (dim2and3Multiplier * nrOutputs)};
+                for (unsigned i{1}; i < nrInputs; ++i)
+                    largeVolDivider = largeVolDivider * 3 / 4;
+                if (largeVolDivider > 1) {
+                    blocks.y = d1 / (largeVolDivider * threads.y);
+                    if (blocks.y == 0) blocks.y = 1;
+                    loop1 = true;
+                }
+            } else {
+                // A reduction to (1|2*)maxParallelThreads will be
+                // performed
+                blocks.y = maxThreads / (threads.x * blocks.x * threads.z *
+                                         blocks.z * threads.y);
+                if (blocks.y == 0) blocks.y = 1;
+                loop1 = true;
+            }
+        }
+        if (!loop1) { blocks.y = divup(d1, threads.y); }
+        // Check on new overflows
+        if (blocks.y > maxGridSize[1]) {
+            blocks.y = maxGridSize[1];
+            loop1    = true;
+        }
+    }
+
+    return blocks;
+};
+}  // namespace cuda
\ No newline at end of file
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index b159758b37..0f0f19764b 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -21,9 +21,9 @@
 #include <device_manager.hpp>
 #include <err_opencl.hpp>
 #include <errorcodes.hpp>
+#include <platform.hpp>
 #include <version.hpp>
 #include <af/version.h>
-#include <memory>
 
 #ifdef OS_MAC
 #include <OpenGL/CGLCurrent.h>
@@ -36,6 +36,7 @@
 #include <cstdlib>
 #include <functional>
 #include <map>
+#include <memory>
 #include <mutex>
 #include <sstream>
 #include <string>
@@ -223,7 +224,7 @@ void init() {
     UNUSED(devMngr);
 }
 
-unsigned getActiveDeviceId() {
+int getActiveDeviceId() {
     // Second element is the queue id, which is
     // what we mean by active device id in opencl backend
     return get<1>(tlocalActiveDeviceId());
@@ -314,10 +315,6 @@ cl_device_type getDeviceType() {
     return type;
 }
 
-bool isHostUnifiedMemory(const cl::Device& device) {
-    return device.getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-}
-
 bool OpenCLCPUOffload(bool forceOffloadOSX) {
     static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") != "0";
     bool offload                 = false;
@@ -360,9 +357,7 @@ bool isDoubleSupported(unsigned device) {
         common::lock_guard_t lock(devMngr.deviceMutex);
         dev = *devMngr.mDevices[device];
     }
-    // 64bit fp is an optional extension
-    return (dev.getInfo<CL_DEVICE_EXTENSIONS>().find("cl_khr_fp64") !=
-            string::npos);
+    return isDoubleSupported(dev);
 }
 
 bool isHalfSupported(unsigned device) {
@@ -373,9 +368,7 @@ bool isHalfSupported(unsigned device) {
         common::lock_guard_t lock(devMngr.deviceMutex);
         dev = *devMngr.mDevices[device];
     }
-    // 16bit fp is an option extension
-    return (dev.getInfo<CL_DEVICE_EXTENSIONS>().find("cl_khr_fp16") !=
-            string::npos);
+    return isHalfSupported(dev);
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 8ea6ca2540..fa937b0e0f 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -57,7 +57,7 @@ int getDeviceCount() noexcept;
 
 void init();
 
-unsigned getActiveDeviceId();
+int getActiveDeviceId();
 
 int& getMaxJitSize();
 
@@ -71,18 +71,65 @@ size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
 
+inline unsigned getMemoryBusWidth(const cl::Device& device) {
+    return device.getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
+}
+
+// OCL only reports on L1 cache, so we have to estimate the L2 Cache
+// size. From studying many GPU cards, it is noticed that their is a
+// direct correlation between Cache line and L2 Cache size:
+//      - 16KB L2 Cache for each bit in Cache line.
+//        Example: RTX3070 (4096KB of L2 Cache, 256Bit of Cache
+//        line)
+//                   --> 256*16KB = 4096KB
+//      - This is also valid for all AMD GPU's
+//      - Exceptions
+//          * GTX10XX series have 8KB per bit of cache line
+//          * iGPU (64bit cacheline) have 5KB per bit of cache line
+inline size_t getL2CacheSize(const cl::Device& device) {
+    const unsigned cacheLine{getMemoryBusWidth(device)};
+    return cacheLine * 1024ULL *
+           (cacheLine == 64 ? 5
+            : device.getInfo<CL_DEVICE_NAME>().find("GTX 10") ==
+                    std::string::npos
+                ? 16
+                : 8);
+}
+
+inline unsigned getComputeUnits(const cl::Device& device) {
+    return device.getInfo<CL_DEVICE_MAX_COMPUTE_UNITS>();
+}
+
+// maximum nr of threads the device really can run in parallel, without
+// scheduling
+inline unsigned getMaxParallelThreads(const cl::Device& device) {
+    return getComputeUnits(device) * 2048;
+}
+
 cl_device_type getDeviceType();
 
-bool isHostUnifiedMemory(const cl::Device& device);
+inline bool isHostUnifiedMemory(const cl::Device& device) {
+    return device.getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
+}
 
 bool OpenCLCPUOffload(bool forceOffloadOSX = true);
 
 bool isGLSharingSupported();
 
 bool isDoubleSupported(unsigned device);
+inline bool isDoubleSupported(const cl::Device& device) {
+    // 64bit fp is an optional extension
+    return (device.getInfo<CL_DEVICE_EXTENSIONS>().find("cl_khr_fp64") !=
+            std::string::npos);
+}
 
 // Returns true if 16-bit precision floats are supported by the device
 bool isHalfSupported(unsigned device);
+inline bool isHalfSupported(const cl::Device& device) {
+    // 16bit fp is an option extension
+    return (device.getInfo<CL_DEVICE_EXTENSIONS>().find("cl_khr_fp16") !=
+            std::string::npos);
+}
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
 
diff --git a/src/backend/opencl/threadsMgt.hpp b/src/backend/opencl/threadsMgt.hpp
new file mode 100644
index 0000000000..4fb3838e5b
--- /dev/null
+++ b/src/backend/opencl/threadsMgt.hpp
@@ -0,0 +1,328 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/dispatch.hpp>
+#include <platform.hpp>
+#include <af/defines.h>
+
+namespace opencl {
+// OVERALL USAGE (With looping):
+// ...                                                      // OWN CODE
+// threadsMgt<T> th(...);                                   // backend.hpp
+// cl::Kernel KER{GETKERNEL(..., th.loop0, th.loop1,
+//                               th.loop3)};                // OWN CODE
+// const cl::NDRange local{th.genLocal(KER)};               // backend.hpp
+// const cl::NDRange global{th.genGlobal(local)};           // backend.hpp
+// KER(local,global,...);                                   // OWN CODE
+// ...                                                      // OWN CODE
+//
+// OVERALL USAGE (without looping):
+// ...                                                      // OWN CODE
+// threadsMgt<T> th(...);                                   // backend.hpp
+// cl::Kernel KER{GETKERNEL(...)};                          // OWN CODE
+// const cl::NDRange local{th.genLocal(KER)};               // backend.hpp
+// const cl::NDRange global{th.genGlobalFull(local)};       // backend.hpp
+// KER(local,global,...);                                   // OWN CODE
+// ...                                                      // OWN CODE
+template<typename T>
+class threadsMgt {
+   public:
+    bool loop0, loop1, loop3;
+
+   private:
+    const unsigned d0, d1, d2, d3;
+    const T ndims;
+    const size_t totalSize;
+    const cl::Device dev;
+    const unsigned maxParallelThreads;
+    const unsigned maxThreads;
+    unsigned largeVolDivider;
+
+   public:
+    // INPUT dims = dims of output array
+    // INPUT ndims = ndims of output array
+    // INPUT nrInputs = number of buffers read by kernel in parallel
+    // INPUT nrOutputs = number of buffer written by kernel in parallel
+    // INPUT totalSize = size of all input & output arrays
+    // INPUT sizeofT = size of 1 element to be written
+    // OUTPUT this.loop0, this.loop1, this.loop3 are ready to create the kernel
+    threadsMgt(const T dims[4], const T ndims, const unsigned nrInputs,
+               const unsigned nrOutputs, const size_t totalSize,
+               const size_t sizeofT);
+
+    // The generated local is only best for independent element operations,
+    //  as are: copying, scaling, math on independent elements,
+    // ... Since vector dimensions can be returned, it is NOT USABLE FOR
+    // BLOCK OPERATIONS, as are: matmul, etc.
+    inline cl::NDRange genLocal(const cl::Kernel& ker) const;
+
+    // INPUT local generated by genLocal()
+    // OUTPUT global, supposing that each element results in 1 thread
+    inline cl::NDRange genGlobalFull(const cl::NDRange& local) const;
+
+    // INPUT local generated by genLocal()
+    // OUTPUT global, assuming the the previous calculated looping will be
+    // executed in the kernel
+    inline cl::NDRange genGlobal(const cl::NDRange& local) const;
+};
+
+// INPUT dims = dims of output array
+// INPUT ndims = ndims of output array
+// INPUT nrInputs = number of buffers read by kernel in parallel
+// INPUT nrOutputs = number of buffer written by kernel in parallel
+// INPUT totalSize = size of all input & output arrays
+// INPUT sizeofT = size of 1 element to be written
+// OUTPUT this.loop0, this.loop1, this.loop3 are ready to create the kernel
+template<typename T>
+threadsMgt<T>::threadsMgt(const T dims[4], const T ndims,
+                          const unsigned nrInputs, const unsigned nrOutputs,
+                          const size_t totalSize, const size_t sizeofT)
+    : loop0(false)
+    , loop1(false)
+    , loop3(false)
+    , d0(static_cast<unsigned>(dims[0]))
+    , d1(static_cast<unsigned>(dims[1]))
+    , d2(static_cast<unsigned>(dims[2]))
+    , d3(static_cast<unsigned>(dims[3]))
+    , ndims(ndims)
+    , totalSize(totalSize)
+    , dev(opencl::getDevice())
+    , maxParallelThreads(getMaxParallelThreads(dev))
+    , maxThreads(maxParallelThreads *
+                 (sizeofT * nrInputs * nrInputs > 8 ? 1 : 2))
+    , largeVolDivider(1) {
+    const unsigned cacheLine{getMemoryBusWidth(dev)};
+    const size_t L2CacheSize{getL2CacheSize(dev)};
+    // The bottleneck of anykernel is dependent on the type of memory
+    // used.
+    // a) For very small arrays (elements < maxParallelThreads), each
+    //  element receives it individual thread
+    // b) For arrays (in+out) smaller
+    //  than 3/2 L2cache, memory access no longer is the bottleneck,
+    //  because enough L2cache is available at any time. Threads are
+    //  limited to reduce scheduling overhead.
+    // c) For very large arrays and type sizes
+    //  (<long double), 1 thread will not generate enough data to keep
+    //  the memory sync mechanism saturated, so we start loooping inside
+    //  each thread.
+    //
+    if (ndims == 1) {
+        if (d0 > maxThreads) {
+            loop0 = true;
+            if (totalSize * 2 > L2CacheSize * 3) {
+                // General formula to calculate best #loops
+                // Dedicated GPUs:
+                //  32/sizeof(T)**2/#outBuffers*(3/4)**(#inBuffers-1)
+                // Integrated GPUs:
+                //  4/sizeof(T)/#outBuffers*(3/4)**(#inBuffers-1)
+                largeVolDivider = cacheLine == 64 ? sizeofT == 1   ? 4
+                                                    : sizeofT == 2 ? 2
+                                                                   : 1
+                                                  : (sizeofT == 1   ? 32
+                                                     : sizeofT == 2 ? 8
+                                                                    : 1) /
+                                                        nrOutputs;
+                for (unsigned i = 1; i < nrInputs; ++i)
+                    largeVolDivider = largeVolDivider * 3 / 4;
+                loop0 = largeVolDivider > 1;
+            }
+        }
+    } else {
+        loop3 = d3 != 1;
+        if ((d1 > 1) & (d0 * d1 * d2 > maxThreads)) {
+            loop1 = true;
+            if ((d0 * sizeofT * 8 > cacheLine * getComputeUnits(dev)) &
+                (totalSize * 2 > L2CacheSize * 3)) {
+                // General formula to calculate best #loops
+                // Dedicated GPUs:
+                //  32/sizeof(T)**2/#outBuffers*(3/4)**(#inBuffers-1)
+                // Integrated GPUs:
+                //  4/sizeof(T)/#outBuffers*(3/4)**(#inBuffers-1)
+                //
+                // dims[3] already loops, so the remaining #loops needs
+                // to be divided
+                largeVolDivider = cacheLine == 64 ? sizeofT == 1   ? 4
+                                                    : sizeofT == 2 ? 2
+                                                                   : 1
+                                                  : (sizeofT == 1   ? 32
+                                                     : sizeofT == 2 ? 8
+                                                     : sizeofT == 4 ? 2
+                                                                    : 1) /
+                                                        (d3 * nrOutputs);
+                for (unsigned i{1}; i < nrInputs; ++i)
+                    largeVolDivider = largeVolDivider * 3 / 4;
+                loop1 = largeVolDivider > 1;
+            }
+        }
+    }
+};
+
+// The generated local is only best for independent element operations,
+//  as are: copying, scaling, math on independent elements,
+// ... Since vector dimensions can be returned, it is NOT USABLE FOR
+// BLOCK OPERATIONS, as are: matmul, etc.
+template<typename T>
+inline cl::NDRange threadsMgt<T>::genLocal(const cl::Kernel& ker) const {
+    // Performance is mainly dependend on:
+    //    - reducing memory latency, by preferring a sequential read of
+    //    cachelines (principally dim0)
+    //    - more parallel threads --> higher occupation of available
+    //    threads
+    //    - more I/O operations per thread --> dims[3] indicates the #
+    //    of I/Os handled by the kernel inside each thread, and outside
+    //    the scope of the block scheduler
+    // High performance is achievable with occupation rates as low as
+    // 30%. Here we aim at 50%, to also cover older hardware with slower
+    // cores.
+    // https://stackoverflow.com/questions/7737772/improving-kernel-performance-by-increasing-occupancy
+    // http://www.nvidia.com/content/gtc-2010/pdfs/2238_gtc2010.pdf
+    // https://www.cvg.ethz.ch/teaching/2011spring/gpgpu/GPU-Optimization.pdf
+    // https://en.wikipedia.org/wiki/Graphics_Core_Next#SIMD_Vector_Unit
+
+    // The performance for vectors is independent from array sizes.
+    if ((d1 == 1) & (d2 == 1)) return cl::NDRange{128ULL};
+
+    // TOTAL OCCUPATION = occup(dim0) * occup(dim1) * occup(dim2).
+    // For linearized arrays, each linear block is allocated to a dim,
+    // resulting in large numbers for dim0 & dim1.
+    // - For dim2, we only return exact dividers of the array dim[3], so
+    // occup(dim2)=100%
+    // - For dim0 & dim1, we aim somewhere between 30% and 50%
+    //      * Having 2 blocks filled + 1 thread in block 3 --> occup >
+    //      2/3=66%
+    //      * Having 3 blocks filled + 1 thread in block 4 --> occup >
+    //      3/4=75%
+    //      * Having 4 blocks filled + 1 thread in block 5 --> occup >
+    //      4/5=80%
+    constexpr unsigned OCCUPANCY_FACTOR{2U};  // at least 2 blocks filled
+
+    // NVIDIA:
+    //  WG multiple      = 32
+    //  possible blocks  = [32, 64, 96, 128, 160, 192, 224, 256, .. 1024]
+    //  best performance = [32, 64, 96, 128]
+    //  optimal perf     = 128; any combination
+    //   NIVIDA always processes full wavefronts.  Allocating partial WG
+    //   (<32) reduces throughput.  Performance reaches a plateau from
+    //   128 with a slightly slowing for very large sizes.
+    // AMD:
+    //  WG multiple      = 64
+    //  possible block   = [16, 32, 48, 64, 128, 192, 256]
+    //  best performance = [(32, low #threads) 64, 128, 256]
+    //  optimal perf     = (128,2,1); max 128 for 1 dimension
+    //   AMD can process partial wavefronts (multiple of 16), although
+    //   all threads of a full WG are allocated, only the active ones
+    //   are executed, so the same number of WGs will fit a CU. When we
+    //   have insufficent threads to occupy all the CU's, partial
+    //   wavefronts (<64) are usefull to distribute all threads over the
+    //   available CU's iso all concentrating on the 1st CU.
+    // For algorithm below:
+    //  parallelThreads  = [32, 64, (96 for NIVIDA), 128, (256 for AMD)]
+    constexpr unsigned minThreads{32};
+    const unsigned relevantElements{d0 * d1 * d2};
+    const unsigned WG{static_cast<unsigned>(
+        ker.getWorkGroupInfo<CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE>(
+            dev))};
+
+    // For small array's, we reduce the maximum threads in 1 block to
+    // improve parallelisme.  In worst case the scheduler can have 1
+    // block per CU, even when only partly loaded. Range for block is:
+    //   [minThreads ... 4 * WG multiple]
+    //   * NVIDIA: [4*32=128 threads]
+    //   * AMD:    [4*64=256 threads]
+    // At 4 * WG multiple, full wavefronts (queue of 4 partial
+    // wavefronts) are all occupied.
+
+    // We need at least maxParallelThreads to occupy all the CU's.
+    const unsigned parallelThreads{
+        relevantElements <= maxParallelThreads
+            ? minThreads
+            : std::min(4U, relevantElements / maxParallelThreads) * WG};
+
+    // Priority 1: keep cachelines filled.  Aparrantly sharing
+    // cachelines between CU's has a cost. Testing confirmed that the
+    // occupation is mostly > 50%
+    const unsigned threads0{d0 == 1 ? 1
+                            : d0 <= minThreads
+                                ? minThreads  // better distribution
+                                : std::min(128U, (divup(d0, WG) * WG))};
+
+    // Priority 2: Fill the block, while respecting the occupation limit
+    // (>66%) (through parallelThreads limit)
+    const unsigned threads1{
+        (threads0 * 64U <= parallelThreads) &&
+                (!(d1 & (64U - 1U)) || (d1 > OCCUPANCY_FACTOR * 64U))
+            ? 64U
+        : (threads0 * 32U <= parallelThreads) &&
+                (!(d1 & (32U - 1U)) || (d1 > OCCUPANCY_FACTOR * 32U))
+            ? 32U
+        : (threads0 * 16U <= parallelThreads) &&
+                (!(d1 & (16U - 1U)) || (d1 > OCCUPANCY_FACTOR * 16U))
+            ? 16U
+        : (threads0 * 8U <= parallelThreads) &&
+                (!(d1 & (8U - 1U)) || (d1 > OCCUPANCY_FACTOR * 8U))
+            ? 8U
+        : (threads0 * 4U <= parallelThreads) &&
+                (!(d1 & (4U - 1U)) || (d1 > OCCUPANCY_FACTOR * 4U))
+            ? 4U
+        : (threads0 * 2U <= parallelThreads) &&
+                (!(d1 & (2U - 1U)) || (d1 > OCCUPANCY_FACTOR * 2U))
+            ? 2U
+            : 1U};
+
+    const unsigned threads01{threads0 * threads1};
+    if ((d2 == 1) | (threads01 * 2 > parallelThreads))
+        return cl::NDRange(threads0, threads1);
+
+    // Priority 3: Only exact dividers are used, so that
+    //  - overflow checking is not needed in the kernel.
+    //  - occupation rate never is reduced
+    // Chances are low that threads2 will be different from 1.
+    const unsigned threads2{
+        (threads01 * 8 <= parallelThreads) && !(d2 & (8U - 1U))   ? 8U
+        : (threads01 * 4 <= parallelThreads) && !(d2 & (4U - 1U)) ? 4U
+        : (threads01 * 2 <= parallelThreads) && !(d2 & (2U - 1U)) ? 2U
+                                                                  : 1U};
+    return cl::NDRange(threads0, threads1, threads2);
+};
+
+// INPUT local generated by genLocal()
+// OUTPUT global, supposing that each element results in 1 thread
+template<typename T>
+inline cl::NDRange threadsMgt<T>::genGlobalFull(
+    const cl::NDRange& local) const {
+    return cl::NDRange(divup(d0, local[0]) * local[0],
+                       divup(d1, local[1]) * local[1],
+                       divup(d2, local[2]) * local[2]);
+};
+
+// INPUT local generated by genLocal()
+// OUTPUT global, assuming the the previous calculated looping will be
+// executed in the kernel
+template<typename T>
+inline cl::NDRange threadsMgt<T>::genGlobal(const cl::NDRange& local) const {
+    if (loop0) {
+        const size_t blocks0{largeVolDivider > 1
+                                 ? d0 / (largeVolDivider * local[0])
+                                 : maxThreads / local[0]};
+        return cl::NDRange(blocks0 == 0 ? local[0] : blocks0 * local[0]);
+    } else if (loop1) {
+        const size_t global0{divup(d0, local[0]) * local[0]};
+        const size_t global2{divup(d2, local[2]) * local[2]};
+        const size_t blocks1{largeVolDivider > 1
+                                 ? d1 / (largeVolDivider * local[1])
+                                 : maxThreads / (global0 * local[1] * global2)};
+        return cl::NDRange(
+            global0, blocks1 == 0 ? local[1] : blocks1 * local[1], global2);
+    } else {
+        return genGlobalFull(local);
+    }
+};
+}  // namespace opencl
\ No newline at end of file

From 5fdf4283f204fb4a14ede36d33d89b206035ab8e Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 4 Aug 2022 01:10:12 +0200
Subject: [PATCH 2277/2677] OPT: memcopy

---
 src/backend/cuda/copy.cpp             | 108 +++++-----
 src/backend/cuda/kernel/copy.cuh      | 222 +++++++++++++++++---
 src/backend/cuda/kernel/memcopy.cuh   | 228 ++++++++++++++++++---
 src/backend/cuda/kernel/memcopy.hpp   | 219 +++++++++++++++-----
 src/backend/cuda/reshape.cpp          |   4 +-
 src/backend/opencl/copy.cpp           | 136 +++++++------
 src/backend/opencl/kernel/copy.cl     | 225 ++++++++++++++++----
 src/backend/opencl/kernel/memcopy.cl  | 186 ++++++++++++++---
 src/backend/opencl/kernel/memcopy.hpp | 283 +++++++++++++++++++-------
 src/backend/opencl/reshape.cpp        |   5 +-
 10 files changed, 1243 insertions(+), 373 deletions(-)

diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index 12ec5e93e0..dbcf1284fe 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -22,87 +22,89 @@ using common::is_complex;
 namespace cuda {
 
 template<typename T>
-void copyData(T *dst, const Array<T> &src) {
-    if (src.elements() == 0) { return; }
-
-    // FIXME: Merge this with copyArray
-    src.eval();
-
-    Array<T> out = src;
-    const T *ptr = NULL;
-
-    if (src.isLinear() ||  // No offsets, No strides
-        src.ndims() == 1   // Simple offset, no strides.
-    ) {
-        // A.get() gets data with offsets
-        ptr = src.get();
-    } else {
-        // FIXME: Think about implementing eval
-        out = copyArray(src);
-        ptr = out.get();
+void copyData(T *data, const Array<T> &src) {
+    if (src.elements() > 0) {
+        Array<T> lin = src.isReady() && src.isLinear() ? src : copyArray(src);
+        // out is now guaranteed linear
+        auto stream = cuda::getActiveStream();
+        CUDA_CHECK(cudaMemcpyAsync(data, lin.get(), lin.elements() * sizeof(T),
+                                   cudaMemcpyDeviceToHost, stream));
+        CUDA_CHECK(cudaStreamSynchronize(stream));
     }
-
-    auto stream = cuda::getActiveStream();
-    CUDA_CHECK(cudaMemcpyAsync(dst, ptr, src.elements() * sizeof(T),
-                               cudaMemcpyDeviceToHost, stream));
-    CUDA_CHECK(cudaStreamSynchronize(stream));
 }
 
 template<typename T>
 Array<T> copyArray(const Array<T> &src) {
     Array<T> out = createEmptyArray<T>(src.dims());
-    if (src.elements() == 0) { return out; }
-
-    if (src.isLinear()) {
-        CUDA_CHECK(
-            cudaMemcpyAsync(out.get(), src.get(), src.elements() * sizeof(T),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-    } else {
-        kernel::memcopy<T>(out, src, src.ndims());
+    if (src.elements() > 0) {
+        if (src.isReady()) {
+            if (src.isLinear()) {
+                CUDA_CHECK(cudaMemcpyAsync(
+                    out.get(), src.get(), src.elements() * sizeof(T),
+                    cudaMemcpyDeviceToDevice, getActiveStream()));
+            } else {
+                kernel::memcopy<T>(out, src, src.ndims());
+            }
+        } else {
+            evalNodes<T>(out, src.getNode().get());
+        }
     }
     return out;
 }
 
 template<typename T>
-void multiply_inplace(Array<T> &in, double val) {
-    kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val);
+void multiply_inplace(Array<T> &src, double norm) {
+    if (src.elements() > 0) {
+        kernel::copy<T, T>(src, src, src.ndims(), scalar<T>(0), norm);
+    }
 }
 
 template<typename inType, typename outType>
 struct copyWrapper {
-    void operator()(Array<outType> &out, Array<inType> const &in) {
-        kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
-                                      1);
+    void operator()(Array<outType> &dst, Array<inType> const &src) {
+        kernel::copy<inType, outType>(dst, src, dst.ndims(), scalar<outType>(0),
+                                      1.0);
     }
 };
 
 template<typename T>
 struct copyWrapper<T, T> {
-    void operator()(Array<T> &out, Array<T> const &in) {
-        if (out.isLinear() && in.isLinear() &&
-            out.elements() == in.elements()) {
-            CUDA_CHECK(cudaMemcpyAsync(
-                out.get(), in.get(), in.elements() * sizeof(T),
-                cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
-        } else {
-            kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1);
+    void operator()(Array<T> &dst, Array<T> const &src) {
+        if (src.elements() > 0) {
+            if (dst.dims() == src.dims()) {
+                if (src.isReady()) {
+                    if (dst.isLinear() && src.isLinear()) {
+                        CUDA_CHECK(cudaMemcpyAsync(
+                            dst.get(), src.get(), src.elements() * sizeof(T),
+                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                    } else {
+                        kernel::memcopy<T>(dst, src, src.ndims());
+                    }
+                } else {
+                    Param<T> info(dst.get(), src.dims().dims,
+                                  dst.strides().dims);
+                    evalNodes(info, src.getNode().get());
+                }
+            } else {
+                // dst has more elements than src, so default has to be applied
+                kernel::copy<T, T>(dst, src, dst.ndims(), scalar<T>(0), 1.0);
+            }
         }
     }
 };
 
 template<typename inType, typename outType>
-void copyArray(Array<outType> &out, Array<inType> const &in) {
+void copyArray(Array<outType> &dst, Array<inType> const &src) {
     static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
                   "Cannot copy from complex value to a non complex value");
-    ARG_ASSERT(1, (in.ndims() == out.dims().ndims()));
     copyWrapper<inType, outType> copyFn;
-    copyFn(out, in);
+    copyFn(dst, src);
 }
 
-#define INSTANTIATE(T)                                       \
-    template void copyData<T>(T * dst, const Array<T> &src); \
-    template Array<T> copyArray<T>(const Array<T> &src);     \
-    template void multiply_inplace<T>(Array<T> & in, double norm);
+#define INSTANTIATE(T)                                        \
+    template void copyData<T>(T * data, const Array<T> &src); \
+    template Array<T> copyArray<T>(const Array<T> &src);      \
+    template void multiply_inplace<T>(Array<T> & src, double norm);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -168,9 +170,9 @@ INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat)
 INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
-T getScalar(const Array<T> &in) {
+T getScalar(const Array<T> &src) {
     T retVal{};
-    CUDA_CHECK(cudaMemcpyAsync(&retVal, in.get(), sizeof(T),
+    CUDA_CHECK(cudaMemcpyAsync(&retVal, src.get(), sizeof(T),
                                cudaMemcpyDeviceToHost,
                                cuda::getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
diff --git a/src/backend/cuda/kernel/copy.cuh b/src/backend/cuda/kernel/copy.cuh
index 628a898904..5c6b6e485a 100644
--- a/src/backend/cuda/kernel/copy.cuh
+++ b/src/backend/cuda/kernel/copy.cuh
@@ -94,41 +94,199 @@ OTHER_SPECIALIZATIONS(uchar)
 OTHER_SPECIALIZATIONS(char)
 OTHER_SPECIALIZATIONS(common::half)
 
-template<typename inType, typename outType, bool same_dims>
-__global__ void copy(Param<outType> dst, CParam<inType> src,
-                     outType default_value, double factor, const dims_t trgt,
-                     uint blk_x, uint blk_y) {
-    const uint lx = threadIdx.x;
-    const uint ly = threadIdx.y;
-
-    const uint gz         = blockIdx.x / blk_x;
-    const uint gw         = (blockIdx.y + (blockIdx.z * gridDim.y)) / blk_y;
-    const uint blockIdx_x = blockIdx.x - (blk_x)*gz;
-    const uint blockIdx_y =
-        (blockIdx.y + (blockIdx.z * gridDim.y)) - (blk_y)*gw;
-    const uint gx = blockIdx_x * blockDim.x + lx;
-    const uint gy = blockIdx_y * blockDim.y + ly;
-
-    const inType *in = src.ptr + (gw * src.strides[3] + gz * src.strides[2] +
-                                  gy * src.strides[1]);
-    outType *out     = dst.ptr + (gw * dst.strides[3] + gz * dst.strides[2] +
-                              gy * dst.strides[1]);
-
-    int istride0 = src.strides[0];
-    int ostride0 = dst.strides[0];
-
-    if (gy < dst.dims[1] && gz < dst.dims[2] && gw < dst.dims[3]) {
-        int loop_offset = blockDim.x * blk_x;
-        bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
-        for (int rep = gx; rep < dst.dims[0]; rep += loop_offset) {
-            outType temp = default_value;
-            if (same_dims || (rep < trgt.dim[0] && cond)) {
-                temp = convertType<inType, outType>(
-                    scale<inType>(in[rep * istride0], factor));
+// scaledCopy without looping, so dim3 has to be 1.
+// conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] >= dims[1]
+//      global dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+template<typename inType, typename outType, bool SAME_DIMS, bool FACTOR>
+__global__ void scaledCopy(Param<outType> dst, CParam<inType> src,
+                           const outType default_value, const double factor) {
+    const int id0 = blockIdx.x * blockDim.x + threadIdx.x;
+    const int id1 = blockIdx.y * blockDim.y + threadIdx.y;
+    if ((id0 < (int)dst.dims[0]) & (id1 < (int)dst.dims[1])) {
+        const int id2 = blockIdx.z * blockDim.z + threadIdx.z;
+
+        const int idx_in =
+            id0 * src.strides[0] + id1 * src.strides[1] + id2 * src.strides[2];
+        const int idx_out =
+            id0 * dst.strides[0] + id1 * dst.strides[1] + id2 * dst.strides[2];
+
+        if (SAME_DIMS | ((id0 < (int)src.dims[0]) & (id1 < (int)src.dims[1]) &
+                         (id2 < (int)src.dims[2]))) {
+            dst.ptr[idx_out] = convertType<inType, outType>(
+                FACTOR ? scale<inType>(src.ptr[idx_in], factor)
+                       : src.ptr[idx_in]);
+        } else {
+            dst.ptr[idx_out] = default_value;
+        }
+    }
+}
+
+// scaledCopy with looping over dims[0] -- VECTOR ONLY
+// Conditions:
+//      global dims[0] has no restrictions
+//      only dims[1] == 1 will be processed!!
+//      only dims[2] == 1 will be processed!!
+//      only dims[3] == 1 will be processed!!
+template<typename inType, typename outType, bool SAME_DIMS, bool FACTOR>
+__global__ void scaledCopyLoop0(Param<outType> dst, CParam<inType> src,
+                                const outType default_value,
+                                const double factor) {
+    int id0              = blockIdx.x * blockDim.x + threadIdx.x;
+    const int id0End_out = dst.dims[0];
+    if (id0 < id0End_out) {
+        const int id0End_in     = src.dims[0];
+        const int istrides0     = src.strides[0];
+        const int ostrides0     = dst.strides[0];
+        const int id0Inc        = gridDim.x * blockDim.x;
+        int idx_in              = id0 * istrides0;
+        const int idxID0Inc_in  = id0Inc * istrides0;
+        int idx_out             = id0 * ostrides0;
+        const int idxID0Inc_out = id0Inc * ostrides0;
+
+        while (id0 < id0End_in) {
+            // inside input array, so convert
+            dst.ptr[idx_out] = convertType<inType, outType>(
+                FACTOR ? scale<inType>(src.ptr[idx_in], factor)
+                       : src.ptr[idx_in]);
+            id0 += id0Inc;
+            idx_in += idxID0Inc_in;
+            idx_out += idxID0Inc_out;
+        }
+        if (!SAME_DIMS) {
+            while (id0 < id0End_out) {
+                // outside the input array, so copy default value
+                dst.ptr[idx_out] = default_value;
+                id0 += id0Inc;
+                idx_out += idxID0Inc_out;
             }
-            out[rep * ostride0] = temp;
         }
     }
 }
 
+// scaledCopy with looping over dims[1]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] has no restrictions
+//      global dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+template<typename inType, typename outType, bool SAME_DIMS, bool FACTOR>
+__global__ void scaledCopyLoop1(Param<outType> dst, CParam<inType> src,
+                                const outType default_value,
+                                const double factor) {
+    const int id0        = blockIdx.x * blockDim.x + threadIdx.x;
+    int id1              = blockIdx.y * blockDim.y + threadIdx.y;
+    const int id1End_out = dst.dims[1];
+    if ((id0 < (int)dst.dims[0]) & (id1 < id1End_out)) {
+        const int id2       = blockIdx.z * blockDim.z + threadIdx.z;
+        const int ostrides1 = dst.strides[1];
+        const int id1Inc    = gridDim.y * blockDim.y;
+        int idx_out         = id0 * (int)dst.strides[0] + id1 * ostrides1 +
+                      id2 * (int)dst.strides[2];
+        const int idxID1Inc_out = id1Inc * ostrides1;
+        const int id1End_in     = src.dims[1];
+        const int istrides1     = src.strides[1];
+        int idx_in              = id0 * (int)src.strides[0] + id1 * istrides1 +
+                     id2 * (int)src.strides[2];
+        const int idxID1Inc_in = id1Inc * istrides1;
+
+        if (SAME_DIMS | ((id0 < (int)src.dims[0]) & (id2 < src.dims[2]))) {
+            while (id1 < id1End_in) {
+                // inside input array, so convert
+                dst.ptr[idx_out] = convertType<inType, outType>(
+                    FACTOR ? scale<inType>(src.ptr[idx_in], factor)
+                           : src.ptr[idx_in]);
+                id1 += id1Inc;
+                idx_in += idxID1Inc_in;
+                idx_out += idxID1Inc_out;
+            }
+        }
+        if (!SAME_DIMS) {
+            while (id1 < id1End_out) {
+                // outside the input array, so copy default value
+                dst.ptr[idx_out] = default_value;
+                id1 += id1Inc;
+                idx_out += idxID1Inc_out;
+            }
+        }
+    }
+}
+
+// scaledCopy with looping over dims[1], dims[2] and dims[3]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] has no restrictions
+//      global dims[2] <= dims[2]
+template<typename inType, typename outType, bool SAME_DIMS, bool FACTOR>
+__global__ void scaledCopyLoop123(Param<outType> out, CParam<inType> in,
+                                  outType default_value, double factor) {
+    const int id0    = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    int id1          = blockIdx.y * blockDim.y + threadIdx.y;  // Limit 64K
+    const int odims0 = out.dims[0];
+    const int odims1 = out.dims[1];
+    if ((id0 < odims0) & (id1 < odims1)) {
+        int id2 = blockIdx.z * blockDim.z + threadIdx.z;  // Limit 64K
+        int idxBaseBase_out = id0 * (int)out.strides[0] +
+                              id1 * (int)out.strides[1] +
+                              id2 * (int)out.strides[2];
+        const int idxIncID3_out     = out.strides[3];
+        const int odims2            = out.dims[2];
+        const int idxEndIncID3_out  = out.dims[3] * idxIncID3_out;
+        const int incID1            = gridDim.y * blockDim.y;
+        const int idxBaseIncID1_out = incID1 * (int)out.strides[1];
+        const int incID2            = gridDim.z * blockDim.z;
+        const int idxBaseIncID2_out = incID2 * (int)out.strides[2];
+
+        int idxBaseBase_in = id0 * (int)in.strides[0] +
+                             id1 * (int)in.strides[1] +
+                             id2 * (int)in.strides[2];
+        const int idxIncID3_in     = in.strides[3];
+        const int idims0           = in.dims[0];
+        const int idims1           = in.dims[1];
+        const int idims2           = in.dims[2];
+        const int idxEndIncID3_in  = in.dims[3] * idxIncID3_in;
+        const int idxBaseIncID1_in = incID1 * (int)in.strides[1];
+        const int idxBaseIncID2_in = incID2 * (int)in.strides[2];
+
+        do {
+            int idxBase_in  = idxBaseBase_in;
+            int idxBase_out = idxBaseBase_out;
+            do {
+                int idxEndID3_in  = idxEndIncID3_in + idxBase_in;
+                int idxEndID3_out = idxEndIncID3_out + idxBase_out;
+                int idx_in        = idxBase_in;
+                int idx_out       = idxBase_out;
+                if (SAME_DIMS |
+                    ((id0 < idims0) & (id1 < idims1) & (id2 < idims2))) {
+                    // inside input array, so convert
+                    do {
+                        out.ptr[idx_out] = convertType<inType, outType>(
+                            FACTOR ? scale<inType>(in.ptr[idx_in], factor)
+                                   : in.ptr[idx_in]);
+                        idx_in += idxIncID3_in;
+                        idx_out += idxIncID3_out;
+                    } while (idx_in != idxEndID3_in);
+                }
+                if (!SAME_DIMS) {
+                    while (idx_out != idxEndID3_out) {
+                        // outside the input array, so copy default value
+                        out.ptr[idx_out] = default_value;
+                        idx_out += idxIncID3_out;
+                    }
+                }
+                id1 += incID1;
+                if (id1 >= odims1) break;
+                idxBase_in += idxBaseIncID1_in;
+                idxBase_out += idxBaseIncID1_out;
+            } while (true);
+            id2 += incID2;
+            if (id2 >= odims2) break;
+            idxBaseBase_in += idxBaseIncID2_in;
+            idxBaseBase_out += idxBaseIncID2_out;
+        } while (true);
+    }
+}
+
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/memcopy.cuh b/src/backend/cuda/kernel/memcopy.cuh
index f22a013279..ecef444cce 100644
--- a/src/backend/cuda/kernel/memcopy.cuh
+++ b/src/backend/cuda/kernel/memcopy.cuh
@@ -13,31 +13,213 @@
 
 namespace cuda {
 
+// memCopy without looping, so dim3 has to be 1.
+// conditions:
+//      kernel dims[0] >= dims[0]
+//      kernel dims[1] >= dims[1]
+//      kernel dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
 template<typename T>
-__global__ void memcopy(Param<T> out, CParam<T> in, uint blocks_x,
-                        uint blocks_y) {
-    const int tidx = threadIdx.x;
-    const int tidy = threadIdx.y;
-
-    const int zid        = blockIdx.x / blocks_x;
-    const int blockIdx_x = blockIdx.x - (blocks_x)*zid;
-    const int xid        = blockIdx_x * blockDim.x + tidx;
-
-    const int wid = (blockIdx.y + blockIdx.z * gridDim.y) / blocks_y;
-    const int blockIdx_y =
-        (blockIdx.y + blockIdx.z * gridDim.y) - (blocks_y)*wid;
-    const int yid = blockIdx_y * blockDim.y + tidy;
-    // FIXME: Do more work per block
-    T *const optr = out.ptr + wid * out.strides[3] + zid * out.strides[2] +
-                    yid * out.strides[1];
-    const T *iptr = in.ptr + wid * in.strides[3] + zid * in.strides[2] +
-                    yid * in.strides[1];
-
-    int istride0 = in.strides[0];
-    if (xid < in.dims[0] && yid < in.dims[1] && zid < in.dims[2] &&
-        wid < in.dims[3]) {
-        optr[xid] = iptr[xid * istride0];
+__global__ void memCopy(Param<T> out, CParam<T> in) {
+    const int id0 = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    const int id1 = blockIdx.y * blockDim.y + threadIdx.y;  // Limit 64K
+    if ((id0 < (int)in.dims[0]) & (id1 < (int)in.dims[1])) {
+        const int id2 = blockIdx.z * blockDim.z + threadIdx.z;  // Limit 64K
+
+        out.ptr[id0 * (int)out.strides[0] + id1 * (int)out.strides[1] +
+                id2 * (int)out.strides[2]] =
+            in.ptr[id0 * (int)in.strides[0] + id1 * (int)in.strides[1] +
+                   id2 * (int)in.strides[2]];
+    }
+}
+
+// memCopy with looping over dims[0] -- VECTOR ONLY
+// Conditions:
+//      kernel dims[0] has no restrictions
+//      only dims[1] == 1 will be processed!!
+//      only dims[2] == 1 will be procesed!!
+//      only dims[3] == 1 will be processed!!
+template<typename T>
+__global__ void memCopyLoop0(Param<T> out, CParam<T> in) {
+    int id0          = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    const int idims0 = in.dims[0];
+    if (id0 < idims0) {
+        const int incID0        = gridDim.x * blockDim.x;
+        const int istrides0     = in.strides[0];
+        int idx_in              = id0 * istrides0;
+        const int idxIncID0_in  = incID0 * istrides0;
+        const int ostrides0     = out.strides[0];
+        int idx_out             = id0 * ostrides0;
+        const int idxIncID0_out = incID0 * ostrides0;
+
+        do {
+            out.ptr[idx_out] = in.ptr[idx_in];
+            id0 += incID0;
+            if (id0 >= idims0) break;
+            idx_in += idxIncID0_in;
+            idx_out += idxIncID0_out;
+        } while (true);
+    }
+}
+
+// memCopy with looping over dims[1]
+// Conditions:
+//      kernel dims[0] >= dims[0]
+//      kernel dims[1] has no restrictions
+//      kernel dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+template<typename T>
+__global__ void memCopyLoop1(Param<T> out, CParam<T> in) {
+    const int id0    = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    int id1          = blockIdx.y * blockDim.y + threadIdx.y;  // Limit 64K
+    const int idims1 = in.dims[1];
+    if ((id0 < (int)in.dims[0]) & (id1 < idims1)) {
+        const int id2 = blockIdx.z * blockDim.z + threadIdx.z;  // Limit 64K
+        const int istrides1 = in.strides[1];
+        int idx_in          = id0 * (int)in.strides[0] + id1 * istrides1 +
+                     id2 * (int)in.strides[2];
+        const int incID1       = gridDim.y * blockDim.y;
+        const int idxIncID1_in = incID1 * istrides1;
+        const int ostrides1    = out.strides[1];
+        int idx_out            = id0 * (int)out.strides[0] + id1 * ostrides1 +
+                      id2 * (int)out.strides[2];
+        const int idxIncID1_out = incID1 * ostrides1;
+
+        do {
+            out.ptr[idx_out] = in.ptr[idx_in];
+            id1 += incID1;
+            if (id1 >= idims1) break;
+            idx_in += idxIncID1_in;
+            idx_out += idxIncID1_out;
+        } while (true);
+    }
+}
+
+// memCopy with looping over dims[3]
+// Conditions:
+//      kernel dims[0] >= dims[0]
+//      kernel dims[1] >= dims[1]
+//      kernel dims[2] == dims[2]
+template<typename T>
+__global__ void memCopyLoop3(Param<T> out, CParam<T> in) {
+    const int id0 = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    const int id1 = blockIdx.y * blockDim.y + threadIdx.y;  // Limit 64K
+    if ((id0 < (int)in.dims[0]) & (id1 < (int)in.dims[1])) {
+        const int id2 = blockIdx.z * blockDim.z + threadIdx.z;  // Limit 64K
+        int idx_in    = id0 * (int)in.strides[0] + id1 * (int)in.strides[1] +
+                     id2 * (int)in.strides[2];
+        const int idxIncID3_in = in.strides[3];
+        const int idxEnd_in    = (int)in.dims[3] * idxIncID3_in + idx_in;
+        int idx_out = id0 * (int)out.strides[0] + id1 * (int)out.strides[1] +
+                      id2 * (int)out.strides[2];
+        const int idxIncID3_out = out.strides[3];
+
+        do {
+            out.ptr[idx_out] = in.ptr[idx_in];
+            idx_in += idxIncID3_in;
+            if (idx_in == idxEnd_in) break;
+            idx_out += idxIncID3_out;
+        } while (true);
     }
 }
 
+// memCopy with looping over dims[1] and dims[3]
+// Conditions:
+//      kernel dims[0] >= dims[0]
+//      kernel dims[1] has no restrictions
+//      kernel dims[2] == dims[2]
+template<typename T>
+__global__ void memCopyLoop13(Param<T> out, CParam<T> in) {
+    const int id0    = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    int id1          = blockIdx.y * blockDim.y + threadIdx.y;  // Limit 64K
+    const int idims1 = in.dims[1];
+    if ((id0 < (int)in.dims[0]) & (g1 < idims1)) {
+        const int id2 = blockIdx.z * blockDim.z + threadIdx.z;  // Limit 64K
+        const int istrides1 = in.strides[1];
+        int idxBase_in      = id0 * (int)in.strides[0] + id1 * istrides1 +
+                         id2 * (int)in.strides[2];
+        const int incID1           = gridDim.y * blockDim.y;
+        const int idxBaseIncID1_in = incID1 * istrides1;
+        const int idxIncID3_in     = (int)in.strides[3];
+        int idxEndID3_in = (int)in.dims[3] * idxIncID3_in + idxBase_in;
+        int idxBase_out  = id0 * (int)out.strides[0] +
+                          id1 * (int)out.strides[1] + id2 * (int)out.strides[2];
+        const int idxBaseIncID1_out = incID1 * (int)out.strides[1];
+        const int idxIncID3_out     = (int)out.strides[3];
+
+        do {
+            int idx_in  = idxBase_in;
+            int idx_out = idxBase_out;
+            while (true) {
+                out.ptr[idx_out] = in.ptr[idx_in];
+                idx_in += idxIncID3_in;
+                if (idx_in == idxEndID3_in) break;
+                idx_out += idxIncID3_out;
+            }
+            id1 += incID1;
+            if (id1 >= idims1) break;
+            idxBase_in += idxBaseIncID1_in;
+            idxEndID3_in += idxBaseIncID1_in;
+            idxBase_out += idxBaseIncID1_out;
+        } while (true);
+    }
+}
+
+// memCopy with looping over dims[1],dims[2] and dims[3]
+// Conditions:
+//      kernel dims[0] >= dims[0]
+//      kernel dims[1] has no restrictions
+//      kernel dims[2] <= dims[2]
+template<typename T>
+__global__ void memCopyLoop123(Param<T> out, CParam<T> in) {
+    const int id0    = blockIdx.x * blockDim.x + threadIdx.x;  // Limit 2G
+    int id1          = blockIdx.y * blockDim.y + threadIdx.y;  // Limit 64K
+    const int idims1 = in.dims[1];
+    if ((id0 < (int)in.dims[0]) & (id1 < idims1)) {
+        int id2 = blockIdx.z * blockDim.z + threadIdx.z;  // Limit 64K
+        const int istrides1 = in.strides[1];
+        const int istrides2 = in.strides[2];
+        int idxBaseBase_in =
+            id0 * (int)in.strides[0] + id1 * istrides1 + id2 * istrides2;
+        const int incID1           = gridDim.y * blockDim.y;
+        const int idxBaseIncID1_in = incID1 * istrides1;
+        const int incID2           = gridDim.z * blockDim.z;
+        const int idxBaseIncID2_in = incID2 * istrides2;
+        const int idxIncID3_in     = in.strides[3];
+        const int idxEndIncID3_in  = (int)in.dims[3] * idxIncID3_in;
+
+        const int ostrides1 = out.strides[1];
+        const int ostrides2 = out.strides[2];
+        int idxBaseBase_out =
+            id0 * (int)out.strides[0] + id1 * ostrides1 + id2 * ostrides2;
+        const int idxBaseIncID1_out = incID1 * ostrides1;
+        const int idxBaseIncID2_out = incID2 * ostrides2;
+        const int idxIncID3_out     = out.strides[3];
+        const int idims2            = in.dims[2];
+
+        do {
+            int idxBase_in  = idxBaseBase_in;
+            int idxBase_out = idxBaseBase_out;
+            do {
+                int idxEndID3_in = idxEndIncID3_in + idxBase_in;
+                int idx_in       = idxBase_in;
+                int idx_out      = idxBase_out;
+                do {
+                    out.ptr[idx_out] = in.ptr[idx_in];
+                    idx_in += idxIncID3_in;
+                    if (idx_in == idxEndID3_in) break;
+                    idx_out += idxIncID3_out;
+                } while (true);
+                id1 += incID1;
+                if (id1 >= idims1) break;
+                idxBase_in += idxBaseIncID1_in;
+                idxBase_out += idxBaseIncID1_out;
+            } while (true);
+            id2 += incID2;
+            if (id2 >= idims2) break;
+            idxBaseBase_in += idxBaseIncID2_in;
+            idxBaseBase_out += idxBaseIncID2_out;
+        } while (true);
+    }
+}
 }  // namespace cuda
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 49d18f7fa3..f37252c633 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -11,92 +11,199 @@
 
 #include <Param.hpp>
 #include <backend.hpp>
-#include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_cuda.hpp>
 #include <dims_param.hpp>
 #include <nvrtc_kernel_headers/copy_cuh.hpp>
 #include <nvrtc_kernel_headers/memcopy_cuh.hpp>
+#include <threadsMgt.hpp>
 
 #include <algorithm>
 
 namespace cuda {
 namespace kernel {
 
-constexpr uint DIMX = 32;
-constexpr uint DIMY = 8;
-
+// Increase vectorization by increasing the used type up to maxVectorWidth.
+// Example:
+//  input array<int> with return value = 4, means that the array became
+//  array<int4>.
+//
+// Parameters
+//  - IN     maxVectorWidth: maximum vectorisation desired
+//  - IN/OUT dims[4]: dimensions of the array
+//  - IN/OUT istrides[4]: strides of the input array
+//  - IN/OUT indims: ndims of the input array.  Updates when dim[0] becomes 1
+//  - IN/OUT ioffset: offset of the input array
+//  - IN/OUT ostrides[4]: strides of the output array
+//  - IN/OUT ooffset: offset of the output array
+//
+// Returns
+//  - maximum obtained vectorization.
+//  - All the parameters are updated accordingly
+//
 template<typename T>
-void memcopy(Param<T> out, CParam<T> in, const dim_t ndims) {
-    auto memCopy = common::getKernel("cuda::memcopy", {memcopy_cuh_src},
-                                     {TemplateTypename<T>()});
-
-    dim3 threads(DIMX, DIMY);
-
-    if (ndims == 1) {
-        threads.x *= threads.y;
-        threads.y = 1;
+dim_t vectorizeShape(const dim_t maxVectorWidth, Param<T> &out, dim_t &indims,
+                     CParam<T> &in) {
+    dim_t vectorWidth{1};
+    if ((maxVectorWidth != 1) & (in.strides[0] == 1) & (out.strides[0] == 1)) {
+        // Only adjacent items can be grouped into a base vector type
+        void *in_ptr{(void *)in.ptr};
+        void *out_ptr{(void *)out.ptr};
+        // - global is the OR of the values to be checked.  When global is
+        // divisable by 2, than all source values are also
+        dim_t global{in.dims[0]};
+        for (int i{1}; i < indims; ++i) {
+            global |= in.strides[i] | out.strides[i];
+        }
+        // - The buffers are always aligned at 128 Bytes.  The pointers in the
+        // Param<T> structure are however, direct pointers (including the
+        // offset), so the final pointer has to be chedked on alignment
+        size_t filler{64};  // give enough space for the align to move
+        unsigned count{0};
+        while (((global & 1) == 0) & (vectorWidth < maxVectorWidth) &&
+               (in.ptr ==
+                std::align(alignof(T) * vectorWidth * 2, 1, in_ptr, filler)) &&
+               (out.ptr ==
+                std::align(alignof(T) * vectorWidth * 2, 1, out_ptr, filler))) {
+            ++count;
+            vectorWidth <<= 1;
+            global >>= 1;
+        }
+        if (count != 0) {
+            // update the dimensions, to compensate for the vector base
+            // type change
+            in.dims[0] >>= count;
+            for (int i{1}; i < indims; ++i) {
+                in.strides[i] >>= count;
+                out.strides[i] >>= count;
+            }
+            if (in.dims[0] == 1) {
+                // Vectorization has absorbed the full dim0, so eliminate
+                // this dimension
+                --indims;
+                for (int i{0}; i < indims; ++i) {
+                    in.dims[i]     = in.dims[i + 1];
+                    in.strides[i]  = in.strides[i + 1];
+                    out.strides[i] = out.strides[i + 1];
+                }
+                in.dims[indims] = 1;
+            }
+        }
     }
+    return vectorWidth;
+}
 
-    // FIXME: DO more work per block
-    uint blocks_x = divup(in.dims[0], threads.x);
-    uint blocks_y = divup(in.dims[1], threads.y);
+template<typename T>
+void memcopy(Param<T> out, CParam<T> in, dim_t indims) {
+    const size_t totalSize{in.elements() * sizeof(T) * 2};
+    removeEmptyColumns(in.dims, indims, out.strides);
+    indims = removeEmptyColumns(in.dims, indims, in.dims, in.strides);
+    indims = combineColumns(in.dims, in.strides, indims, out.strides);
 
-    dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
+    // Optimization memory access and caching.
+    // Best performance is achieved with the highest vectorization
+    // (<int> --> <int2>,<int4>, ...), since more data is processed per IO.
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    // 16 Bytes gives best performance (=cdouble)
+    const dim_t maxVectorWidth{sizeof(T) > 8 ? 1 : 16 / sizeof(T)};
+    const dim_t vectorWidth{vectorizeShape(maxVectorWidth, out, indims, in)};
+    const size_t sizeofNewT{sizeof(T) * vectorWidth};
 
-    EnqueueArgs qArgs(blocks, threads, getActiveStream());
+    threadsMgt<dim_t> th(in.dims, indims);
+    const dim3 threads{th.genThreads()};
+    const dim3 blocks{th.genBlocks(threads, 1, 1, totalSize, sizeofNewT)};
 
-    memCopy(qArgs, out, in, blocks_x, blocks_y);
+    EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
+    // select the kernel with the necessary loopings
+    const char *kernelName{th.loop0   ? "cuda::memCopyLoop0"
+                           : th.loop2 ? "cuda::memCopyLoop123"
+                           : th.loop1 ? th.loop3 ? "cuda::memCopyLoop13"
+                                                 : "cuda::memCopyLoop1"
+                           : th.loop3 ? "cuda::memCopyLoop3"
+                                      : "cuda::memCopy"};
+
+    // Conversion to cuda base vector types.
+    switch (sizeofNewT) {
+        case 1: {
+            auto memCopy{
+                common::getKernel(kernelName, {memcopy_cuh_src}, {"char"})};
+            memCopy(qArgs, Param<char>((char *)out.ptr, out.dims, out.strides),
+                    CParam<char>((const char *)in.ptr, in.dims, in.strides));
+        } break;
+        case 2: {
+            auto memCopy{
+                common::getKernel(kernelName, {memcopy_cuh_src}, {"short"})};
+            memCopy(qArgs,
+                    Param<short>((short *)out.ptr, out.dims, out.strides),
+                    CParam<short>((const short *)in.ptr, in.dims, in.strides));
+        } break;
+        case 4: {
+            auto memCopy{
+                common::getKernel(kernelName, {memcopy_cuh_src}, {"float"})};
+            memCopy(qArgs,
+                    Param<float>((float *)out.ptr, out.dims, out.strides),
+                    CParam<float>((const float *)in.ptr, in.dims, in.strides));
+        } break;
+        case 8: {
+            auto memCopy{
+                common::getKernel(kernelName, {memcopy_cuh_src}, {"float2"})};
+            memCopy(
+                qArgs, Param<float2>((float2 *)out.ptr, out.dims, out.strides),
+                CParam<float2>((const float2 *)in.ptr, in.dims, in.strides));
+        } break;
+        case 16: {
+            auto memCopy{
+                common::getKernel(kernelName, {memcopy_cuh_src}, {"float4"})};
+            memCopy(
+                qArgs, Param<float4>((float4 *)out.ptr, out.dims, out.strides),
+                CParam<float4>((const float4 *)in.ptr, in.dims, in.strides));
+        } break;
+        default: assert("type is larger than 16 bytes, which is unsupported");
+    }
     POST_LAUNCH_CHECK();
 }
 
 template<typename inType, typename outType>
-void copy(Param<outType> dst, CParam<inType> src, int ndims,
+void copy(Param<outType> dst, CParam<inType> src, dim_t ondims,
           outType default_value, double factor) {
-    dim3 threads(DIMX, DIMY);
-    size_t local_size[] = {DIMX, DIMY};
-
-    // FIXME: Why isn't threads being updated??
-    local_size[0] *= local_size[1];
-    if (ndims == 1) { local_size[1] = 1; }
-
-    uint blk_x = divup(dst.dims[0], local_size[0]);
-    uint blk_y = divup(dst.dims[1], local_size[1]);
-
-    dim3 blocks(blk_x * dst.dims[2], blk_y * dst.dims[3]);
-
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
-
-    int trgt_l       = std::min(dst.dims[3], src.dims[3]);
-    int trgt_k       = std::min(dst.dims[2], src.dims[2]);
-    int trgt_j       = std::min(dst.dims[1], src.dims[1]);
-    int trgt_i       = std::min(dst.dims[0], src.dims[0]);
-    dims_t trgt_dims = {{trgt_i, trgt_j, trgt_k, trgt_l}};
-
-    bool same_dims =
-        ((src.dims[0] == dst.dims[0]) && (src.dims[1] == dst.dims[1]) &&
-         (src.dims[2] == dst.dims[2]) && (src.dims[3] == dst.dims[3]));
+    const size_t totalSize{dst.elements() * sizeof(outType) +
+                           src.elements() * sizeof(inType)};
+    bool same_dims{true};
+    for (dim_t i{0}; i < ondims; ++i) {
+        if (src.dims[i] > dst.dims[i]) {
+            src.dims[i] = dst.dims[i];
+        } else if (src.dims[i] != dst.dims[i]) {
+            same_dims = false;
+        }
+    }
+    removeEmptyColumns(dst.dims, ondims, src.dims, src.strides);
+    ondims = removeEmptyColumns(dst.dims, ondims, dst.dims, dst.strides);
+    ondims =
+        combineColumns(dst.dims, dst.strides, ondims, src.dims, src.strides);
 
-    auto copy = common::getKernel(
-        "cuda::copy", {copy_cuh_src},
-        {TemplateTypename<inType>(), TemplateTypename<outType>(),
-         TemplateArg(same_dims)});
+    threadsMgt<dim_t> th(dst.dims, ondims);
+    const dim3 threads{th.genThreads()};
+    const dim3 blocks{th.genBlocks(threads, 1, 1, totalSize, sizeof(outType))};
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    copy(qArgs, dst, src, default_value, factor, trgt_dims, blk_x, blk_y);
+    auto copy{common::getKernel(th.loop0 ? "cuda::scaledCopyLoop0"
+                                : th.loop2 | th.loop3
+                                    ? "cuda::scaledCopyLoop123"
+                                : th.loop1 ? "cuda::scaledCopyLoop1"
+                                           : "cuda::scaledCopy",
+                                {copy_cuh_src},
+                                {
+                                    TemplateTypename<inType>(),
+                                    TemplateTypename<outType>(),
+                                    TemplateArg(same_dims),
+                                    TemplateArg(factor != 1.0),
+                                })};
+
+    copy(qArgs, dst, src, default_value, factor);
 
     POST_LAUNCH_CHECK();
 }
-
 }  // namespace kernel
 }  // namespace cuda
diff --git a/src/backend/cuda/reshape.cpp b/src/backend/cuda/reshape.cpp
index 6e4c541adc..8d48000457 100644
--- a/src/backend/cuda/reshape.cpp
+++ b/src/backend/cuda/reshape.cpp
@@ -21,7 +21,9 @@ template<typename inType, typename outType>
 Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                        outType defaultValue, double scale) {
     Array<outType> out = createEmptyArray<outType>(outDims);
-    kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale);
+    if (out.elements() > 0) {
+        kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale);
+    }
     return out;
 }
 
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 44eac01444..cfb5e5b61d 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -21,93 +21,105 @@ using common::is_complex;
 namespace opencl {
 
 template<typename T>
-void copyData(T *data, const Array<T> &A) {
-    if (A.elements() == 0) { return; }
-
-    // FIXME: Merge this with copyArray
-    A.eval();
-
-    dim_t offset = 0;
-    cl::Buffer buf;
-    Array<T> out = A;
-
-    if (A.isLinear() ||  // No offsets, No strides
-        A.ndims() == 1   // Simple offset, no strides.
-    ) {
-        buf    = *A.get();
-        offset = A.getOffset();
-    } else {
-        // FIXME: Think about implementing eval
-        out    = copyArray(A);
-        buf    = *out.get();
-        offset = 0;
+void copyData(T *data, const Array<T> &src) {
+    if (src.elements() > 0) {
+        Array<T> out = src.isReady() && src.isLinear() ? src : copyArray(src);
+        // out is now guaranteed linear
+        getQueue().enqueueReadBuffer(*out.get(), CL_TRUE,
+                                     sizeof(T) * out.getOffset(),
+                                     sizeof(T) * out.elements(), data);
     }
-
-    // FIXME: Add checks
-    getQueue().enqueueReadBuffer(buf, CL_TRUE, sizeof(T) * offset,
-                                 sizeof(T) * A.elements(), data);
 }
 
 template<typename T>
-Array<T> copyArray(const Array<T> &A) {
-    Array<T> out = createEmptyArray<T>(A.dims());
-    if (A.elements() == 0) { return out; }
-
-    dim_t offset = A.getOffset();
-    if (A.isLinear()) {
-        // FIXME: Add checks
-        getQueue().enqueueCopyBuffer(*A.get(), *out.get(), sizeof(T) * offset,
-                                     0, A.elements() * sizeof(T));
-    } else {
-        kernel::memcopy<T>(*out.get(), out.strides().get(), *A.get(),
-                           A.dims().get(), A.strides().get(), offset,
-                           (uint)A.ndims());
+Array<T> copyArray(const Array<T> &src) {
+    Array<T> out = createEmptyArray<T>(src.dims());
+    if (src.elements() > 0) {
+        if (src.isReady()) {
+            if (src.isLinear()) {
+                getQueue().enqueueCopyBuffer(
+                    *src.get(), *out.get(), src.getOffset() * sizeof(T), 0,
+                    src.elements() * sizeof(T), nullptr, nullptr);
+            } else {
+                kernel::memcopy<T>(*out.get(), out.strides(), *src.get(),
+                                   src.dims(), src.strides(), src.getOffset(),
+                                   src.ndims());
+            }
+        } else {
+            Param info = {out.get(),
+                          {{src.dims().dims[0], src.dims().dims[1],
+                            src.dims().dims[2], src.dims().dims[3]},
+                           {out.strides().dims[0], out.strides().dims[1],
+                            out.strides().dims[2], out.strides().dims[3]},
+                           0}};
+            evalNodes(info, src.getNode().get());
+        }
     }
     return out;
 }
 
 template<typename T>
-void multiply_inplace(Array<T> &in, double val) {
-    kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val, true);
+void multiply_inplace(Array<T> &src, double norm) {
+    if (src.elements() > 0) {
+        kernel::copy<T, T>(src, src, src.ndims(), scalar<T>(0), norm);
+    }
 }
 
 template<typename inType, typename outType>
 struct copyWrapper {
-    void operator()(Array<outType> &out, Array<inType> const &in) {
-        kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
-                                      1, in.dims() == out.dims());
+    void operator()(Array<outType> &dst, Array<inType> const &src) {
+        kernel::copy<inType, outType>(dst, src, dst.ndims(), scalar<outType>(0),
+                                      1.0);
     }
 };
 
 template<typename T>
 struct copyWrapper<T, T> {
-    void operator()(Array<T> &out, Array<T> const &in) {
-        if (out.isLinear() && in.isLinear() &&
-            out.elements() == in.elements()) {
-            dim_t in_offset  = in.getOffset() * sizeof(T);
-            dim_t out_offset = out.getOffset() * sizeof(T);
-
-            getQueue().enqueueCopyBuffer(*in.get(), *out.get(), in_offset,
-                                         out_offset, in.elements() * sizeof(T));
-        } else {
-            kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1,
-                               in.dims() == out.dims());
+    void operator()(Array<T> &dst, Array<T> const &src) {
+        if (src.elements() > 0) {
+            if (dst.dims() == src.dims()) {
+                if (src.isReady()) {
+                    if (dst.isLinear() && src.isLinear()) {
+                        getQueue().enqueueCopyBuffer(
+                            *src.get(), *dst.get(), src.getOffset() * sizeof(T),
+                            dst.getOffset() * sizeof(T),
+                            src.elements() * sizeof(T), nullptr, nullptr);
+                    } else {
+                        kernel::memcopy<T>(*dst.get(), dst.strides(),
+                                           *src.get(), src.dims(),
+                                           src.strides(), src.getOffset(),
+                                           src.ndims(), dst.getOffset());
+                    }
+                } else {
+                    Param info = {
+                        dst.get(),
+                        {{src.dims().dims[0], src.dims().dims[1],
+                          src.dims().dims[2], src.dims().dims[3]},
+                         {dst.strides().dims[0], dst.strides().dims[1],
+                          dst.strides().dims[2], dst.strides().dims[3]},
+                         dst.getOffset()}};
+                    evalNodes(info, src.getNode().get());
+                }
+            } else {
+                // dst has more elements than src, so default has to be applied
+                kernel::copy<T, T>(dst, src, dst.ndims(), scalar<T>(0), 1.0);
+            }
         }
     }
 };
 
 template<typename inType, typename outType>
-void copyArray(Array<outType> &out, Array<inType> const &in) {
+void copyArray(Array<outType> &dst, Array<inType> const &src) {
     static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
                   "Cannot copy from complex value to a non complex value");
     copyWrapper<inType, outType> copyFn;
-    copyFn(out, in);
+    copyFn(dst, src);
 }
 
-#define INSTANTIATE(T)                                         \
-    template void copyData<T>(T * data, const Array<T> &from); \
-    template Array<T> copyArray<T>(const Array<T> &A);         \
-    template void multiply_inplace<T>(Array<T> & in, double norm);
+#define INSTANTIATE(T)                                        \
+    template void copyData<T>(T * data, const Array<T> &src); \
+    template Array<T> copyArray<T>(const Array<T> &src);      \
+    template void multiply_inplace<T>(Array<T> & src, double norm);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
@@ -173,10 +185,10 @@ INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat)
 INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
-T getScalar(const Array<T> &in) {
+T getScalar(const Array<T> &src) {
     T retVal{};
-    getQueue().enqueueReadBuffer(*in.get(), CL_TRUE, sizeof(T) * in.getOffset(),
-                                 sizeof(T), &retVal);
+    getQueue().enqueueReadBuffer(
+        *src.get(), CL_TRUE, sizeof(T) * src.getOffset(), sizeof(T), &retVal);
     return retVal;
 }
 
diff --git a/src/backend/opencl/kernel/copy.cl b/src/backend/opencl/kernel/copy.cl
index 308f177d94..8cbe2cbf93 100644
--- a/src/backend/opencl/kernel/copy.cl
+++ b/src/backend/opencl/kernel/copy.cl
@@ -8,16 +8,14 @@
  ********************************************************/
 
 typedef struct {
-    dim_t dim[4];
-} dims_t;
+    int dims[4];
+} dims_type;
 
-inType scale(inType value, float factor) {
-#ifdef inType_float2
-    return (inType)(value.s0 * factor, value.s1 * factor);
+#ifdef FACTOR
+#define SCALE(value, factor) (value * factor)
 #else
-    return (inType)(value * factor);
+#define SCALE(value, factor) (value)
 #endif
-}
 
 #if defined(outType_double2)
 
@@ -47,42 +45,185 @@ inType scale(inType value, float factor) {
 
 #endif
 
-kernel void reshapeCopy(global outType *dst, KParam oInfo,
-                        global const inType *src, KParam iInfo,
-                        outType default_value, float factor, dims_t trgt,
-                        int blk_x, int blk_y) {
-    uint lx = get_local_id(0);
-    uint ly = get_local_id(1);
-
-    uint gz         = get_group_id(0) / blk_x;
-    uint gw         = get_group_id(1) / blk_y;
-    uint blockIdx_x = get_group_id(0) - (blk_x)*gz;
-    uint blockIdx_y = get_group_id(1) - (blk_y)*gw;
-    uint gx         = blockIdx_x * get_local_size(0) + lx;
-    uint gy         = blockIdx_y * get_local_size(1) + ly;
-
-    global const inType *in =
-        src + (gw * iInfo.strides[3] + gz * iInfo.strides[2] +
-               gy * iInfo.strides[1] + iInfo.offset);
-    global outType *out = dst + (gw * oInfo.strides[3] + gz * oInfo.strides[2] +
-                                 gy * oInfo.strides[1] + oInfo.offset);
-
-    uint istride0 = iInfo.strides[0];
-    uint ostride0 = oInfo.strides[0];
-
-    if (gy < oInfo.dims[1] && gz < oInfo.dims[2] && gw < oInfo.dims[3]) {
-        int loop_offset = get_local_size(0) * blk_x;
-        bool cond = gy < trgt.dim[1] && gz < trgt.dim[2] && gw < trgt.dim[3];
-        for (int rep = gx; rep < oInfo.dims[0]; rep += loop_offset) {
-            outType temp = default_value;
-#if SAME_DIMS
-            temp = CONVERT(scale(in[rep * istride0], factor));
-#else
-            if (rep < trgt.dim[0] && cond) {
-                temp = CONVERT(scale(in[rep * istride0], factor));
+// scaledCopy without looping, so dim3 has to be 1.
+// conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] >= dims[1]
+//      global dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+kernel void scaledCopy(global outType *out, const dims_type odims,
+                       const dims_type ostrides, const int ooffset,
+                       global const inType *in, const dims_type idims,
+                       const dims_type istrides, const int ioffset,
+                       const outType default_value, const factorType factor) {
+    const int g0 = get_global_id(0);
+    const int g1 = get_global_id(1);
+    if ((g0 < (int)odims.dims[0]) & (g1 < (int)odims.dims[1])) {
+        const int g2 = get_global_id(2);
+
+        int idx_in = g0 * (int)istrides.dims[0] + g1 * (int)istrides.dims[1] +
+                     g2 * (int)istrides.dims[2] + ioffset;
+        int idx_out = g0 * (int)ostrides.dims[0] + g1 * (int)ostrides.dims[1] +
+                      g2 * (int)ostrides.dims[2] + ooffset;
+
+        if (SAME_DIMS | ((g0 < (int)idims.dims[0]) & (g1 < (int)idims.dims[1]) &
+                         (g2 < (int)idims.dims[2]))) {
+            out[idx_out] = CONVERT(SCALE(in[idx_in], factor));
+        } else {
+            out[idx_out] = default_value;
+        }
+    }
+}
+
+// scaledCopy with looping over dims[0] -- VECTOR ONLY
+// Conditions:
+//      global dims[0] has no restrictions
+//      only dims[1] == 1 will be processed!!
+//      only dims[2] == 1 will be processed!!
+//      only dims[3] == 1 will be processed!!
+kernel void scaledCopyLoop0(global outType *out, const dims_type odims,
+                            const dims_type ostrides, const int ooffset,
+                            global const inType *in, const dims_type idims,
+                            const dims_type istrides, const int ioffset,
+                            const outType default_value,
+                            const factorType factor) {
+    int id0              = get_global_id(0);
+    const int id0End_out = odims.dims[0];
+    if (id0 < id0End_out) {
+        const int ostrides0     = ostrides.dims[0];
+        const int id0Inc        = get_global_size(0);
+        int idx_out             = id0 * ostrides0 + ooffset;
+        const int idxID0Inc_out = id0Inc * ostrides0;
+        const int id0End_in     = idims.dims[0];
+        const int istrides0     = istrides.dims[0];
+        int idx_in              = id0 * istrides0 + ioffset;
+        const int idxID0Inc_in  = id0Inc * istrides0;
+
+        while (id0 < id0End_in) {
+            // inside input array, so convert
+            out[idx_out] = CONVERT(SCALE(in[idx_in], factor));
+            id0 += id0Inc;
+            idx_in += idxID0Inc_in;
+            idx_out += idxID0Inc_out;
+        }
+        if (!SAME_DIMS) {
+            while (id0 < id0End_out) {
+                // outside the input array, so copy default value
+                out[idx_out] = default_value;
+                id0 += id0Inc;
+                idx_out += idxID0Inc_out;
             }
-#endif
-            out[rep * ostride0] = temp;
         }
     }
 }
+
+// scaledCopy with looping over dims[1]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] has no restrictions
+//      global dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+kernel void scaledCopyLoop1(global outType *out, const dims_type odims,
+                            const dims_type ostrides, const int ooffset,
+                            global const inType *in, const dims_type idims,
+                            const dims_type istrides, const int ioffset,
+                            const outType default_value,
+                            const factorType factor) {
+    const int id0        = get_global_id(0);
+    int id1              = get_global_id(1);
+    const int id1End_out = odims.dims[1];
+    if ((id0 < (int)odims.dims[0]) & (id1 < id1End_out)) {
+        const int id2       = get_global_id(2);
+        const int ostrides1 = ostrides.dims[1];
+        const int id1Inc    = get_global_size(1);
+        int idx_out         = id0 * (int)ostrides.dims[0] + id1 * ostrides1 +
+                      id2 * (int)ostrides.dims[2] + ooffset;
+        const int idxID1Inc_out = id1Inc * ostrides1;
+        const int id1End_in     = idims.dims[1];
+        const int istrides1     = istrides.dims[1];
+        int idx_in = id0 * (int)istrides.dims[0] + id1 * istrides1 +
+                     id2 * (int)istrides.dims[2] + ioffset;
+        const int idxID1Inc_in = id1Inc * istrides1;
+
+        if (SAME_DIMS | ((id0 < idims.dims[0]) & (id2 < idims.dims[2]))) {
+            while (id1 < id1End_in) {
+                // inside input array, so convert
+                out[idx_out] = CONVERT(SCALE(in[idx_in], factor));
+                id1 += id1Inc;
+                idx_in += idxID1Inc_in;
+                idx_out += idxID1Inc_out;
+            }
+        }
+        if (!SAME_DIMS) {
+            while (id1 < id1End_out) {
+                // outside the input array, so copy default value
+                out[idx_out] = default_value;
+                id1 += id1Inc;
+                idx_out += idxID1Inc_out;
+            }
+        }
+    }
+}
+
+// scaledCopy with looping over dims[1] and dims[3]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] has no restrictions
+//      global dims[2] == dims[2]
+kernel void scaledCopyLoop13(global outType *out, const dims_type odims,
+                             const dims_type ostrides, const int ooffset,
+                             global const inType *in, const dims_type idims,
+                             const dims_type istrides, const int ioffset,
+                             const outType default_value,
+                             const factorType factor) {
+    const int id0        = get_global_id(0);
+    int id1              = get_global_id(1);
+    const int id1End_out = odims.dims[1];
+    if ((id0 < (int)odims.dims[0]) & (id1 < id1End_out)) {
+        const int id2               = get_global_id(2);
+        const int id1Inc            = get_global_size(1);
+        const int ostrides1         = ostrides.dims[1];
+        const int idxIncID3_out     = ostrides.dims[3];
+        const int idxBaseIncID1_out = id1Inc * ostrides1;
+        int idxBase_out             = id0 * ostrides.dims[0] + id1 * ostrides1 +
+                          id2 * ostrides.dims[2] + ooffset;
+        int idxEndID3_out = odims.dims[3] * idxIncID3_out + idxBase_out;
+
+        const int id0End_in        = idims.dims[0];
+        const int id1End_in        = idims.dims[1];
+        const int id2End_in        = idims.dims[2];
+        const int istrides1        = istrides.dims[1];
+        const int idxIncID3_in     = istrides.dims[3];
+        const int idxBaseIncID1_in = id1Inc * istrides1;
+        int idxBase_in             = id0 * istrides.dims[0] + id1 * istrides1 +
+                         id2 * istrides.dims[2] + ioffset;
+        int idxEndID3_in = idims.dims[3] * idxIncID3_in + idxBase_in;
+
+        do {
+            int idx_in  = idxBase_in;
+            int idx_out = idxBase_out;
+            if (SAME_DIMS |
+                ((id0 < id0End_in) & (id1 < id1End_in) & (id2 < id2End_in))) {
+                // inside input array, so convert
+                do {
+                    out[idx_out] = CONVERT(SCALE(in[idx_in], factor));
+                    idx_in += idxIncID3_in;
+                    idx_out += idxIncID3_out;
+                } while (idx_in != idxEndID3_in);
+            }
+            if (!SAME_DIMS) {
+                while (idx_out != idxEndID3_out) {
+                    // outside the input array, so copy default value
+                    out[idx_out] = default_value;
+                    idx_out += idxIncID3_out;
+                }
+            }
+            id1 += id1Inc;
+            if (id1 >= id1End_out) break;
+            idxBase_in += idxBaseIncID1_in;
+            idxEndID3_in += idxBaseIncID1_in;
+            idxBase_out += idxBaseIncID1_out;
+            idxEndID3_out += idxBaseIncID1_out;
+        } while (true);
+    }
+}
\ No newline at end of file
diff --git a/src/backend/opencl/kernel/memcopy.cl b/src/backend/opencl/kernel/memcopy.cl
index 912b5b028c..984ecf25f0 100644
--- a/src/backend/opencl/kernel/memcopy.cl
+++ b/src/backend/opencl/kernel/memcopy.cl
@@ -8,32 +8,168 @@
  ********************************************************/
 
 typedef struct {
-    dim_t dim[4];
+    int dims[4];
 } dims_t;
 
-kernel void memCopy(global T *out, dims_t ostrides, global const T *in,
-                    dims_t idims, dims_t istrides, int offset, int groups_0,
-                    int groups_1) {
-    const int lid0 = get_local_id(0);
-    const int lid1 = get_local_id(1);
-
-    const int id2        = get_group_id(0) / groups_0;
-    const int id3        = get_group_id(1) / groups_1;
-    const int group_id_0 = get_group_id(0) - groups_0 * id2;
-    const int group_id_1 = get_group_id(1) - groups_1 * id3;
-    const int id0        = group_id_0 * get_local_size(0) + lid0;
-    const int id1        = group_id_1 * get_local_size(1) + lid1;
-
-    in += offset;
-
-    // FIXME: Do more work per work group
-    out +=
-        id3 * ostrides.dim[3] + id2 * ostrides.dim[2] + id1 * ostrides.dim[1];
-    in += id3 * istrides.dim[3] + id2 * istrides.dim[2] + id1 * istrides.dim[1];
-
-    int istride0 = istrides.dim[0];
-    if (id0 < idims.dim[0] && id1 < idims.dim[1] && id2 < idims.dim[2] &&
-        id3 < idims.dim[3]) {
-        out[id0] = in[id0 * istride0];
+// memcopy without looping, so dim3 has to be 1.
+// conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] >= dims[1]
+//      global dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+kernel void memCopy(global T *d_out, const dims_t ostrides, const int ooffset,
+                    global const T *d_in, const dims_t idims,
+                    const dims_t istrides, const int ioffset) {
+    const int id0 = get_global_id(0);  // dim[0]
+    const int id1 = get_global_id(1);  // dim[1]
+    if ((id0 < idims.dims[0]) & (id1 < idims.dims[1])) {
+        const int id2 = get_global_id(2);  // dim[2] never overflows
+                                           // dim[3] is no processed
+        d_out[id0 * ostrides.dims[0] + id1 * ostrides.dims[1] +
+              id2 * ostrides.dims[2] + ooffset] =
+            d_in[id0 * istrides.dims[0] + id1 * istrides.dims[1] +
+                 id2 * istrides.dims[2] + ioffset];
+    }
+}
+
+// memcopy with looping over dims[0] -- VECTOR ONLY
+// Conditions:
+//      global dims[0] has no restrictions
+//      only dims[1] == 1 will be processed!!
+//      only dims[2] == 1 will be processed!!
+//      only dims[3] == 1 will be processed!!
+kernel void memCopyLoop0(global T *d_out, const dims_t ostrides,
+                         const int ooffset, global const T *d_in,
+                         const dims_t idims, const dims_t istrides,
+                         const int ioffset) {
+    int id0          = get_global_id(0);  // dim[0]
+    const int idims0 = idims.dims[0];
+    if (id0 < idims0) {
+        const int incID0        = get_global_size(0);
+        const int istrides0     = istrides.dims[0];
+        int idx_in              = id0 * istrides0 + ioffset;
+        const int idxIncID0_in  = incID0 * istrides0;
+        const int ostrides0     = ostrides.dims[0];
+        int idx_out             = id0 * ostrides0 + ooffset;
+        const int idxIncID0_out = incID0 * ostrides0;
+
+        do {
+            d_out[idx_out] = d_in[idx_in];
+            id0 += incID0;
+            if (id0 >= idims0) break;
+            idx_in += idxIncID0_in;
+            idx_out += idxIncID0_out;
+        } while (true);
+    }
+}
+
+// memcopy with looping over dims[1]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] has no restrictions
+//      global dims[2] == dims[2]
+//      only dims[3] == 1 will be processed!!
+kernel void memCopyLoop1(global T *d_out, const dims_t ostrides,
+                         const int ooffset, global const T *d_in,
+                         const dims_t idims, const dims_t istrides,
+                         const int ioffset) {
+    const int id0    = get_global_id(0);  // dim[0]
+    int id1          = get_global_id(1);  // dim[1]
+    const int idims1 = idims.dims[1];
+    if ((id0 < idims.dims[0]) & (id1 < idims1)) {
+        const int id2 = get_global_id(2);  // dim[2] never overflows
+                                           // dim[3] is no processed
+        const int istrides1 = istrides.dims[1];
+        int idx_in          = id0 * istrides.dims[0] + id1 * istrides1 +
+                     id2 * istrides.dims[2] + ioffset;
+        const int incID1       = get_global_size(1);
+        const int idxIncID1_in = incID1 * istrides1;
+        const int ostrides1    = ostrides.dims[1];
+        int idx_out            = id0 * ostrides.dims[0] + id1 * ostrides1 +
+                      id2 * ostrides.dims[2] + ooffset;
+        const int idxIncID1_out = incID1 * ostrides1;
+
+        do {
+            d_out[idx_out] = d_in[idx_in];
+            id1 += incID1;
+            if (id1 >= idims1) break;
+            idx_in += idxIncID1_in;
+            idx_out += idxIncID1_out;
+        } while (true);
+    }
+}
+
+// memcopy with looping over dims[3]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] >= dims[1]
+//      global dims[2] == dims[2]
+kernel void memCopyLoop3(global T *d_out, const dims_t ostrides,
+                         const int ooffset, global const T *d_in,
+                         const dims_t idims, const dims_t istrides,
+                         const int ioffset) {
+    const int id0 = get_global_id(0);  // dim[0]
+    const int id1 = get_global_id(1);  // dim[1]
+    if ((id0 < idims.dims[0]) & (id1 < idims.dims[1])) {
+        const int id2 = get_global_id(2);  // dim[2] never overflows
+                                           // dim[3] is no processed
+        int idx_in = id0 * istrides.dims[0] + id1 * istrides.dims[1] +
+                     id2 * istrides.dims[2] + ioffset;
+        const int idxIncID3_in = istrides.dims[3];
+        const int idxEnd_in    = idims.dims[3] * idxIncID3_in + idx_in;
+        int idx_out = id0 * ostrides.dims[0] + id1 * ostrides.dims[1] +
+                      id2 * ostrides.dims[2] + ooffset;
+        const int idxIncID3_out = ostrides.dims[3];
+
+        do {
+            d_out[idx_out] = d_in[idx_in];
+            idx_in += idxIncID3_in;
+            if (idx_in == idxEnd_in) break;
+            idx_out += idxIncID3_out;
+        } while (true);
+    }
+}
+
+// memcopy with looping over dims[1] and dims[3]
+// Conditions:
+//      global dims[0] >= dims[0]
+//      global dims[1] has no restrictions
+//      global dims[2] == dims[2]
+kernel void memCopyLoop13(global T *d_out, const dims_t ostrides,
+                          const int ooffset, global const T *d_in,
+                          const dims_t idims, const dims_t istrides,
+                          const int ioffset) {
+    const int id0    = get_global_id(0);  // dim[0]
+    int id1          = get_global_id(1);  // dim[1]
+    const int idims1 = idims.dims[1];
+    if ((id0 < idims.dims[0]) & (id1 < idims1)) {
+        const int id2       = get_global_id(2);  // dim[2] never overflows
+        const int istrides1 = istrides.dims[1];
+        int idxBase_in      = id0 * istrides.dims[0] + id1 * istrides1 +
+                         id2 * istrides.dims[2] + ioffset;
+        const int incID1           = get_global_size(1);
+        const int idxBaseIncID1_in = incID1 * istrides1;
+        const int idxIncID3_in     = istrides.dims[3];
+        int idxEndID3_in           = idims.dims[3] * idxIncID3_in + idxBase_in;
+        int idxBase_out = id0 * ostrides.dims[0] + id1 * ostrides.dims[1] +
+                          id2 * ostrides.dims[2] + ooffset;
+        const int idxBaseIncID1_out = incID1 * ostrides.dims[1];
+        const int idxIncID3_out     = ostrides.dims[3];
+
+        do {
+            int idx_in  = idxBase_in;
+            int idx_out = idxBase_out;
+            while (true) {
+                d_out[idx_out] = d_in[idx_in];
+                idx_in += idxIncID3_in;
+                if (idx_in == idxEndID3_in) break;
+                idx_out += idxIncID3_out;
+            }
+            id1 += incID1;
+            if (id1 >= idims1) break;
+            idxBase_in += idxBaseIncID1_in;
+            idxEndID3_in += idxBaseIncID1_in;
+            idxBase_out += idxBaseIncID1_out;
+        } while (true);
     }
 }
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 115bc5178b..9358315cd5 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -10,113 +10,242 @@
 #pragma once
 
 #include <Param.hpp>
-#include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <common/traits.hpp>
 #include <debug_opencl.hpp>
 #include <kernel_headers/copy.hpp>
 #include <kernel_headers/memcopy.hpp>
+#include <threadsMgt.hpp>
 #include <traits.hpp>
 
 #include <algorithm>
+#include <iostream>
 #include <string>
 #include <vector>
 
+using std::string;
+using std::vector;
+
 namespace opencl {
 namespace kernel {
 typedef struct {
-    dim_t dim[4];
-} dims_t;
-
-constexpr uint DIM0 = 32;
-constexpr uint DIM1 = 8;
+    int dims[4];
+} dims_type;
+
+// Increase vectorization by increasing the used type up to maxVectorWidth.
+// Example:
+//  input array<int> with return value = 4, means that the array became
+//  array<int4>.
+//
+// Parameters
+//  - IN     maxVectorWidth: maximum vectorisation desired
+//  - IN/OUT dims[4]: dimensions of the array
+//  - IN/OUT istrides[4]: strides of the input array
+//  - IN/OUT indims: ndims of the input array.  Updates when dim[0] becomes 1
+//  - IN/OUT ioffset: offset of the input array
+//  - IN/OUT ostrides[4]: strides of the output array
+//  - IN/OUT ooffset: offset of the output array
+//
+// Returns
+//  - maximum obtained vectorization.
+//  - All the parameters are updated accordingly
+//
+static unsigned vectorizeShape(const unsigned maxVectorWidth, int dims[4],
+                               int istrides[4], int& indims, dim_t& ioffset,
+                               int ostrides[4], dim_t& ooffset) {
+    unsigned vectorWidth{1};
+    if ((maxVectorWidth != 1) & (istrides[0] == 1) & (ostrides[0] == 1)) {
+        // - Only adjacent items can be vectorized into a base vector type
+        // - global is the OR of the values to be checked.  When global is
+        // divisable by 2, than all source values are also
+        // - The buffers are always aligned at 128 Bytes, so the alignment is
+        // only dependable on the offsets
+        dim_t global{dims[0] | ioffset | ooffset};
+        for (int i{1}; i < indims; ++i) { global |= istrides[i] | ostrides[i]; }
+
+        // Determine the maximum vectorization possible
+        unsigned count{0};
+        while (((global & 1) == 0) & (vectorWidth < maxVectorWidth)) {
+            ++count;
+            vectorWidth <<= 1;
+            global >>= 1;
+        }
+        if (count != 0) {
+            // update the dimensions, to correspond with the new vectorization
+            dims[0] >>= count;
+            ioffset >>= count;
+            ooffset >>= count;
+            for (int i{1}; i < indims; ++i) {
+                istrides[i] >>= count;
+                ostrides[i] >>= count;
+            }
+            if (dims[0] == 1) {
+                // Vectorization has absorbed the full dim0, so eliminate
+                // the 1st dimension
+                --indims;
+                for (int i{0}; i < indims; ++i) {
+                    dims[i]     = dims[i + 1];
+                    istrides[i] = istrides[i + 1];
+                    ostrides[i] = ostrides[i + 1];
+                }
+                dims[indims] = 1;
+            }
+        }
+    }
+    return vectorWidth;
+}
 
 template<typename T>
-void memcopy(cl::Buffer out, const dim_t *ostrides, const cl::Buffer in,
-             const dim_t *idims, const dim_t *istrides, int offset,
-             uint ndims) {
-    std::vector<TemplateArg> targs = {
-        TemplateTypename<T>(),
-    };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
-
-    auto memCopy =
-        common::getKernel("memCopy", {memcopy_cl_src}, targs, options);
-
-    dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
-    dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
-    dims_t _idims    = {{idims[0], idims[1], idims[2], idims[3]}};
+void memcopy(const cl::Buffer& b_out, const dim4& ostrides,
+             const cl::Buffer& b_in, const dim4& idims, const dim4& istrides,
+             dim_t ioffset, const dim_t indims, dim_t ooffset = 0) {
+    dims_type idims_{
+        static_cast<int>(idims.dims[0]), static_cast<int>(idims.dims[1]),
+        static_cast<int>(idims.dims[2]), static_cast<int>(idims.dims[3])};
+    dims_type istrides_{
+        static_cast<int>(istrides.dims[0]), static_cast<int>(istrides.dims[1]),
+        static_cast<int>(istrides.dims[2]), static_cast<int>(istrides.dims[3])};
+    dims_type ostrides_{
+        static_cast<int>(ostrides.dims[0]), static_cast<int>(ostrides.dims[1]),
+        static_cast<int>(ostrides.dims[2]), static_cast<int>(ostrides.dims[3])};
+    int indims_{static_cast<int>(indims)};
+
+    const size_t totalSize{idims.elements() * sizeof(T) * 2};
+    removeEmptyColumns(idims_.dims, indims_, ostrides_.dims);
+    indims_ =
+        removeEmptyColumns(idims_.dims, indims_, idims_.dims, istrides_.dims);
+    indims_ =
+        combineColumns(idims_.dims, istrides_.dims, indims_, ostrides_.dims);
+
+    // Optimization memory access and caching.
+    // Best performance is achieved with the highest vectorization
+    // (<int> --> <int2>,<int4>, ...), since more data is processed per IO.
+    const cl::Device dev{opencl::getDevice()};
+    const unsigned DevicePreferredVectorWidthChar{
+        dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR>()};
+    // When the architecture prefers some width's, it is certainly
+    // on char.  No preference means vector width 1 returned.
+    const bool DevicePreferredVectorWidth{DevicePreferredVectorWidthChar != 1};
+    unsigned maxVectorWidth{
+        DevicePreferredVectorWidth
+            ? sizeof(T) == 1 ? DevicePreferredVectorWidthChar
+              : sizeof(T) == 2
+                  ? dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT>()
+              : sizeof(T) == 4
+                  ? dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT>()
+              : sizeof(T) == 8
+                  ? dev.getInfo<CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE>()
+                  : 1
+        : sizeof(T) > 8 ? 1
+                        : 16 / sizeof(T)};
+    const unsigned vectorWidth{vectorizeShape(maxVectorWidth, idims_.dims,
+                                              istrides_.dims, indims_, ioffset,
+                                              ostrides_.dims, ooffset)};
+    const dim_t sizeofNewT{sizeof(T) * vectorWidth};
+
+    threadsMgt<int> th(idims_.dims, indims_, 1, 1, totalSize, sizeofNewT);
+    const char* kernelName{
+        th.loop0   ? "memCopyLoop0"
+        : th.loop1 ? th.loop3 ? "memCopyLoop13" : "memCopyLoop1"
+        : th.loop3 ? "memCopyLoop3"
+                   : "memCopy"};  // Conversion to  base vector types.
+    const char* tArg{
+        sizeofNewT == 1   ? "char"
+        : sizeofNewT == 2 ? "short"
+        : sizeofNewT == 4 ? "float"
+        : sizeofNewT == 8 ? "float2"
+        : sizeofNewT == 16
+            ? "float4"
+            : "type is larger than 16 bytes, which is unsupported"};
+    auto memCopy{common::getKernel(kernelName, {memcopy_cl_src}, {tArg},
+                                   {DefineKeyValue(T, tArg)})};
+    const cl::NDRange local{th.genLocal(memCopy.get())};
+    const cl::NDRange global{th.genGlobal(local)};
+
+    memCopy(cl::EnqueueArgs(getQueue(), global, local), b_out, ostrides_,
+            static_cast<int>(ooffset), b_in, idims_, istrides_,
+            static_cast<int>(ioffset));
+    CL_DEBUG_FINISH(getQueue());
+}
 
-    size_t local_size[2] = {DIM0, DIM1};
-    if (ndims == 1) {
-        local_size[0] *= local_size[1];
-        local_size[1] = 1;
+template<typename inType, typename outType>
+void copy(const Param out, const Param in, dim_t ondims,
+          const outType default_value, const double factor) {
+    dims_type idims_{
+        static_cast<int>(in.info.dims[0]), static_cast<int>(in.info.dims[1]),
+        static_cast<int>(in.info.dims[2]), static_cast<int>(in.info.dims[3])};
+    dims_type istrides_{static_cast<int>(in.info.strides[0]),
+                        static_cast<int>(in.info.strides[1]),
+                        static_cast<int>(in.info.strides[2]),
+                        static_cast<int>(in.info.strides[3])};
+    dims_type odims_{
+        static_cast<int>(out.info.dims[0]), static_cast<int>(out.info.dims[1]),
+        static_cast<int>(out.info.dims[2]), static_cast<int>(out.info.dims[3])};
+    dims_type ostrides_{static_cast<int>(out.info.strides[0]),
+                        static_cast<int>(out.info.strides[1]),
+                        static_cast<int>(out.info.strides[2]),
+                        static_cast<int>(out.info.strides[3])};
+    int ondims_{static_cast<int>(ondims)};
+    const size_t totalSize{odims_.dims[0] * odims_.dims[1] * odims_.dims[2] *
+                               odims_.dims[3] * sizeof(outType) +
+                           idims_.dims[0] * idims_.dims[1] * idims_.dims[2] *
+                               idims_.dims[3] * sizeof(inType)};
+    bool same_dims{true};
+    for (int i{0}; i < ondims_; ++i) {
+        if (idims_.dims[i] > odims_.dims[i]) {
+            idims_.dims[i] = odims_.dims[i];
+        } else if (idims_.dims[i] != odims_.dims[i]) {
+            same_dims = false;
+        }
     }
 
-    int groups_0 = divup(idims[0], local_size[0]);
-    int groups_1 = divup(idims[1], local_size[1]);
+    removeEmptyColumns(odims_.dims, ondims_, idims_.dims, istrides_.dims);
+    ondims_ =
+        removeEmptyColumns(odims_.dims, ondims_, odims_.dims, ostrides_.dims);
+    ondims_ = combineColumns(odims_.dims, ostrides_.dims, ondims_, idims_.dims,
+                             istrides_.dims);
 
-    cl::NDRange local(local_size[0], local_size[1]);
-    cl::NDRange global(groups_0 * idims[2] * local_size[0],
-                       groups_1 * idims[3] * local_size[1]);
+    constexpr int factorTypeIdx{std::is_same<inType, double>::value ||
+                                std::is_same<inType, cdouble>::value};
+    const char* factorType[]{"float", "double"};
 
-    memCopy(cl::EnqueueArgs(getQueue(), global, local), out, _ostrides, in,
-            _idims, _istrides, offset, groups_0, groups_1);
-    CL_DEBUG_FINISH(getQueue());
-}
-
-template<typename inType, typename outType>
-void copy(Param dst, const Param src, const int ndims,
-          const outType default_value, const double factor,
-          const bool same_dims) {
-    using std::string;
-
-    std::vector<TemplateArg> targs = {
-        TemplateTypename<inType>(),
-        TemplateTypename<outType>(),
-        TemplateArg(same_dims),
+    const std::vector<TemplateArg> targs{
+        TemplateTypename<inType>(), TemplateTypename<outType>(),
+        TemplateArg(same_dims),     TemplateArg(factorType[factorTypeIdx]),
+        TemplateArg(factor != 1.0),
     };
-    std::vector<string> options = {
+    const std::vector<std::string> options{
         DefineKeyValue(inType, dtype_traits<inType>::getName()),
         DefineKeyValue(outType, dtype_traits<outType>::getName()),
-        string(" -D inType_" + string(dtype_traits<inType>::getName())),
-        string(" -D outType_" + string(dtype_traits<outType>::getName())),
+        std::string(" -D inType_") + dtype_traits<inType>::getName(),
+        std::string(" -D outType_") + dtype_traits<outType>::getName(),
         DefineKeyValue(SAME_DIMS, static_cast<int>(same_dims)),
+        std::string(" -D factorType=") + factorType[factorTypeIdx],
+        std::string((factor != 1.0) ? " -D FACTOR" : " -D NOFACTOR"),
+        {getTypeBuildDefinition<inType, outType>()},
     };
-    options.emplace_back(getTypeBuildDefinition<inType, outType>());
-
-    auto copy = common::getKernel("reshapeCopy", {copy_cl_src}, targs, options);
-
-    cl::NDRange local(DIM0, DIM1);
-    size_t local_size[] = {DIM0, DIM1};
-
-    local_size[0] *= local_size[1];
-    if (ndims == 1) { local_size[1] = 1; }
-
-    int blk_x = divup(dst.info.dims[0], local_size[0]);
-    int blk_y = divup(dst.info.dims[1], local_size[1]);
-
-    cl::NDRange global(blk_x * dst.info.dims[2] * DIM0,
-                       blk_y * dst.info.dims[3] * DIM1);
 
-    dims_t trgt_dims;
-    if (same_dims) {
-        trgt_dims = {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2],
-                      dst.info.dims[3]}};
+    threadsMgt<int> th(odims_.dims, ondims_, 1, 1, totalSize, sizeof(outType));
+    auto copy = common::getKernel(th.loop0   ? "scaledCopyLoop0"
+                                  : th.loop3 ? "scaledCopyLoop13"
+                                  : th.loop1 ? "scaledCopyLoop1"
+                                             : "scaledCopy",
+                                  {copy_cl_src}, targs, options);
+    const cl::NDRange local{th.genLocal(copy.get())};
+    const cl::NDRange global{th.genGlobal(local)};
+
+    if (factorTypeIdx == 0) {
+        copy(cl::EnqueueArgs(getQueue(), global, local), *out.data, odims_,
+             ostrides_, static_cast<uint>(out.info.offset), *in.data, idims_,
+             istrides_, static_cast<uint>(in.info.offset), default_value,
+             static_cast<float>(factor));
     } else {
-        dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
-        dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
-        dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
-        dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
-        trgt_dims    = {{trgt_i, trgt_j, trgt_k, trgt_l}};
+        copy(cl::EnqueueArgs(getQueue(), global, local), *out.data, odims_,
+             ostrides_, static_cast<uint>(out.info.offset), *in.data, idims_,
+             istrides_, static_cast<uint>(in.info.offset), default_value,
+             static_cast<double>(factor));
     }
 
-    copy(cl::EnqueueArgs(getQueue(), global, local), *dst.data, dst.info,
-         *src.data, src.info, default_value, (float)factor, trgt_dims, blk_x,
-         blk_y);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/reshape.cpp b/src/backend/opencl/reshape.cpp
index 6eb8862e28..0ec77e27bc 100644
--- a/src/backend/opencl/reshape.cpp
+++ b/src/backend/opencl/reshape.cpp
@@ -21,8 +21,9 @@ template<typename inType, typename outType>
 Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                        outType defaultValue, double scale) {
     Array<outType> out = createEmptyArray<outType>(outDims);
-    kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale,
-                                  in.dims() == outDims);
+    if (out.elements() > 0) {
+        kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale);
+    }
     return out;
 }
 

From 1dfeb9761287070ce282ed1dc9dfcf3d859ed0f1 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 4 Aug 2022 01:10:52 +0200
Subject: [PATCH 2278/2677] OPT: JIT

---
 src/backend/common/jit/Node.cpp              |  12 +-
 src/backend/common/jit/Node.hpp              |   6 +-
 src/backend/cuda/jit.cpp                     | 718 +++++++++++--------
 src/backend/cuda/jit/kernel_generators.hpp   |  46 +-
 src/backend/opencl/jit.cpp                   | 569 +++++++++------
 src/backend/opencl/jit/kernel_generators.hpp |  50 +-
 6 files changed, 843 insertions(+), 558 deletions(-)

diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 83767f502f..c637926d79 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -11,6 +11,7 @@
 #include <common/jit/Node.hpp>
 #include <common/util.hpp>
 
+#include <version.hpp>
 #include <sstream>
 #include <string>
 #include <vector>
@@ -29,7 +30,7 @@ int Node::getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes,
             ids.child_ids[i] =
                 m_children[i]->getNodesMap(node_map, full_nodes, full_ids);
         }
-        ids.id         = node_map.size();
+        ids.id         = static_cast<int>(node_map.size());
         node_map[this] = ids.id;
         full_nodes.push_back(this);
         full_ids.push_back(ids);
@@ -40,10 +41,16 @@ int Node::getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes,
 
 std::string getFuncName(const vector<Node *> &output_nodes,
                         const vector<Node *> &full_nodes,
-                        const vector<Node_ids> &full_ids, bool is_linear) {
+                        const vector<Node_ids> &full_ids, const bool is_linear,
+                        const bool loop0, const bool loop1, const bool loop2,
+                        const bool loop3) {
     std::string funcName;
     funcName.reserve(512);
     funcName = (is_linear ? 'L' : 'G');
+    funcName += (loop0 ? '0' : 'X');
+    funcName += (loop1 ? '1' : 'X');
+    funcName += (loop2 ? '2' : 'X');
+    funcName += (loop3 ? '3' : 'X');
 
     for (const auto &node : output_nodes) {
         funcName += '_';
@@ -65,7 +72,6 @@ auto isBuffer(const Node &ptr) -> bool { return ptr.isBuffer(); }
 
 auto isScalar(const Node &ptr) -> bool { return ptr.isScalar(); }
 
-/// Returns true if the buffer is linear
 bool Node::isLinear(const dim_t dims[4]) const { return true; }
 
 }  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index ca557a50d6..bbe3fcb859 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -245,7 +245,7 @@ class Node {
     // Returns true if this node is a Buffer
     virtual bool isBuffer() const { return false; }
 
-    // Returns true if this node is a Buffer
+    // Returns true if this node is a Scalar
     virtual bool isScalar() const { return false; }
 
     /// Returns true if the buffer is linear
@@ -304,7 +304,9 @@ struct Node_ids {
 
 std::string getFuncName(const std::vector<Node *> &output_nodes,
                         const std::vector<Node *> &full_nodes,
-                        const std::vector<Node_ids> &full_ids, bool is_linear);
+                        const std::vector<Node_ids> &full_ids,
+                        const bool is_linear, const bool loop0,
+                        const bool loop1, const bool loop2, const bool loop3);
 
 auto isBuffer(const Node &ptr) -> bool;
 
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index c8612f1c19..262d5c8c45 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -9,7 +9,6 @@
 
 #include <Array.hpp>
 #include <Kernel.hpp>
-#include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
@@ -23,33 +22,46 @@
 #include <kernel_headers/jit_cuh.hpp>
 #include <math.hpp>
 #include <platform.hpp>
+#include <threadsMgt.hpp>
+#include <type_util.hpp>
 #include <af/dim4.hpp>
 
+#include <algorithm>
 #include <cstdlib>
 #include <sstream>
 #include <stdexcept>
 #include <string>
-#include <thread>
 #include <vector>
 
 using common::findModule;
 using common::getFuncName;
 using common::half;
+using common::ModdimNode;
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
+using common::Node_ptr;
+using common::NodeIterator;
 
+using std::array;
+using std::equal;
+using std::for_each;
+using std::shared_ptr;
 using std::string;
 using std::stringstream;
 using std::to_string;
 using std::vector;
 
 namespace cuda {
-
-static string getKernelString(const string &funcName,
-                              const vector<Node *> &full_nodes,
-                              const vector<Node_ids> &full_ids,
-                              const vector<int> &output_ids, bool is_linear) {
+using jit::BufferNode;
+
+static string getKernelString(const string& funcName,
+                              const vector<Node*>& full_nodes,
+                              const vector<Node_ids>& full_ids,
+                              const vector<int>& output_ids,
+                              const bool is_linear, const bool loop0,
+                              const bool loop1, const bool loop2,
+                              const bool loop3) {
     const std::string includeFileStr(jit_cuh, jit_cuh_len);
 
     const std::string paramTStr = R"JIT(
@@ -61,144 +73,249 @@ struct Param {
 };
 )JIT";
 
-    std::string typedefStr = "typedef unsigned int uint;\n";
-    typedefStr += "typedef ";
+    std::string typedefStr{"typedef unsigned int uint;\ntypedef "};
     typedefStr += getFullName<dim_t>();
     typedefStr += " dim_t;\n";
 
     // Common CUDA code
     // This part of the code does not change with the kernel.
 
-    static const char *kernelVoid = "extern \"C\" __global__ void\n";
-    static const char *dimParams =
-        "uint blocks_x, uint blocks_y, uint blocks_x_total, uint num_odims";
-
-    static const char *loopStart = R"JIT(
-    for (int blockIdx_x = blockIdx.x; blockIdx_x < blocks_x_total; blockIdx_x += gridDim.x) {
-    )JIT";
-    static const char *loopEnd   = "}\n\n";
-
-    static const char *blockStart = "{\n\n";
-    static const char *blockEnd   = "\n\n}";
-
-    static const char *linearIndex = R"JIT(
-        uint threadId = threadIdx.x;
-        long long idx = blockIdx_x * blockDim.x * blockDim.y + threadId;
-        if (idx >= outref.dims[3] * outref.strides[3]) return;
-        )JIT";
-
-    static const char *generalIndex = R"JIT(
-        long long id0 = 0, id1 = 0, id2 = 0, id3 = 0;
-        long blockIdx_y = blockIdx.z * gridDim.y + blockIdx.y;
-        if (num_odims > 2) {
-            id2 = blockIdx_x / blocks_x;
-            id0 = blockIdx_x - id2 * blocks_x;
-            id0 = threadIdx.x + id0 * blockDim.x;
-            if (num_odims > 3) {
-                id3 = blockIdx_y / blocks_y;
-                id1 = blockIdx_y - id3 * blocks_y;
-                id1 = threadIdx.y + id1 * blockDim.y;
-            } else {
-                id1 = threadIdx.y + blockDim.y * blockIdx_y;
-            }
-        } else {
-            id3 = 0;
-            id2 = 0;
-            id1 = threadIdx.y + blockDim.y * blockIdx_y;
-            id0 = threadIdx.x + blockDim.x * blockIdx_x;
-        }
-
-        bool cond = id0 < outref.dims[0] &&
-                    id1 < outref.dims[1] &&
-                    id2 < outref.dims[2] &&
-                    id3 < outref.dims[3];
-
-        if (!cond) { continue; }
-
-        long long idx = outref.strides[3] * id3 +
-                        outref.strides[2] * id2 +
-                        outref.strides[1] * id1 + id0;
-        )JIT";
-
-    stringstream inParamStream;
-    stringstream outParamStream;
-    stringstream outWriteStream;
-    stringstream offsetsStream;
-    stringstream opsStream;
-    stringstream outrefstream;
-
-    for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
-        const auto &node     = full_nodes[i];
-        const auto &ids_curr = full_ids[i];
+    static const char* kernelVoid = "extern \"C\" __global__ void\n";
+    static const char* dimParams  = "";
+
+    static const char* blockStart = "{";
+    static const char* blockEnd   = "\n}\n";
+
+    static const char* linearInit = R"JIT(
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int idxEnd = outref.dims[0];
+    if (idx < idxEnd) {)JIT";
+    static const char* linearEnd  = R"JIT(
+    })JIT";
+
+    static const char* linearLoop0Start = R"JIT(
+        const int idxID0Inc = gridDim.x*blockDim.x;
+        do {)JIT";
+    static const char* linearLoop0End   = R"JIT(
+            idx += idxID0Inc;
+            if (idx >= idxEnd) break;
+        } while (true);)JIT";
+
+    // ///////////////////////////////////////////////
+    // oInfo = output optimized information (dims, strides, offset).
+    //         oInfo has removed dimensions, to optimized block scheduling
+    // iInfo = input internal information (dims, strides, offset)
+    //         iInfo has the original dimensions, auto generated code
+    //
+    // Loop3 is fastest and becomes inside loop, since
+    //      - #of loops is known upfront
+    // Loop1 is used for extra dynamic looping (writing into cache)
+    // Loop0 is used for extra dynamic looping (writing into cache),
+    //       VECTORS ONLY!!
+    // All loops are conditional and idependent Format Loop1 & Loop3
+    // ////////////////////////////
+    //  *stridedLoopNInit               // Always
+    //  *stridedLoop1Init               // Conditional
+    //  *stridedLoop2Init               // Conditional
+    //  *stridedLoop3Init               // Conditional
+    //  *stridedLoop1Start              // Conditional
+    //      *stridedLoop2Start          // Conditional
+    //          *stridedLoop3Start      // Conditional
+    //              auto generated code // Always
+    //          *stridedLoop3End        // Conditional
+    //      *stridedLoop2End            // Conditional
+    //  *stridedLoop1End                // Conditional
+    //  *stridedEnd                     // Always
+    //
+    // Format loop0 (Vector only)
+    // //////////////////////////
+    // *stridedLoop0Init                // Always
+    // *stridedLoop0Start               // Always
+    //      auto generated code         // Always
+    // *stridedLoop0End                 // Always
+    // *stridedEnd                      // Always
+
+    // -----
+    static const char* stridedLoop0Init  = R"JIT(
+    int id0 = blockIdx.x * blockDim.x + threadIdx.x;
+    const int id0End = outref.dims[0];
+    if (id0 < id0End) {
+#define id1 0
+#define id2 0
+#define id3 0
+        const int ostrides0 = outref.strides[0];
+        int idx = ostrides0*id0;)JIT";
+    static const char* stridedLoop0Start = R"JIT(
+        const int id0Inc = gridDim.x*blockDim.x;
+        const int idxID0Inc = ostrides0*id0Inc;
+        do {)JIT";
+    static const char* stridedLoop0End   = R"JIT(
+            id0 += id0Inc;
+            if (id0 >= id0End) break;
+            idx += idxID0Inc;
+        } while (true);)JIT";
+
+    static const char* stridedLoopNInit = R"JIT(
+    int id0 = blockIdx.x * blockDim.x + threadIdx.x;
+    int id1 = blockIdx.y * blockDim.y + threadIdx.y;
+    const int id0End = outref.dims[0];
+    const int id1End = outref.dims[1];
+    if ((id0 < id0End) & (id1 < id1End)) {
+        int id2 = blockIdx.z * blockDim.z + threadIdx.z;
+#define id3 0
+        const int ostrides1 = outref.strides[1];
+        int idx = (int)outref.strides[0]*id0 + ostrides1*id1 + (int)outref.strides[2]*id2;)JIT";
+    static const char* stridedEnd       = R"JIT(
+    })JIT";
+
+    static const char* stridedLoop3Init  = R"JIT(
+#undef id3
+        int id3 = 0;
+        const int id3End = outref.dims[3];
+        const int idxID3Inc = outref.strides[3];)JIT";
+    static const char* stridedLoop3Start = R"JIT(
+                    const int idxBaseID3 = idx;
+                    do {)JIT";
+    // Looping over outside dim3 means that all dimensions are present,
+    // so the internal id3 can be used directly
+    static const char* stridedLoop3End = R"JIT(
+                       ++id3;
+                       if (id3 == id3End) break;
+                       idx += idxID3Inc;
+                    } while (true);
+                    id3 = 0;
+                    idx = idxBaseID3;)JIT";
+
+    static const char* stridedLoop2Init  = R"JIT(
+        const int id2End = outref.dims[2];
+        const int id2Inc = gridDim.z*blockDim.z;
+        const int idxID2Inc = (int)outref.strides[2]*id2Inc;)JIT";
+    static const char* stridedLoop2Start = R"JIT(
+                const int idxBaseID2 = idx;
+                const int baseID2 = id2;
+                do {)JIT";
+    static const char* stridedLoop2End   = R"JIT(
+                    id2 += id2Inc;
+                    if (id2 >= id2End) break;
+                    idx += idxID2Inc;
+                } while (true);
+                id2 = baseID2;
+                idx = idxBaseID2;)JIT";
+
+    // No reset of od1/id[decode.dim1] is necessary since this is the overall
+    // loop
+    static const char* stridedLoop1Init  = R"JIT(
+        const int id1Inc = gridDim.y*blockDim.y;
+        const int idxID1Inc = ostrides1*id1Inc;)JIT";
+    static const char* stridedLoop1Start = R"JIT(
+            do {)JIT";
+    static const char* stridedLoop1End   = R"JIT(
+                id1 += id1Inc;
+                if (id1 >= id1End) break;
+                idx += idxID1Inc;
+            } while (true);)JIT";
+
+    // Reuse stringstreams, because they are very costly during initialization
+    thread_local stringstream inParamStream;
+    thread_local stringstream outParamStream;
+    thread_local stringstream inOffsetsStream;
+    thread_local stringstream opsStream;
+    thread_local stringstream outrefStream;
+
+    int oid{0};
+    for (size_t i{0}; i < full_nodes.size(); i++) {
+        const auto& node{full_nodes[i]};
+        const auto& ids_curr{full_ids[i]};
         // Generate input parameters, only needs current id
         node->genParams(inParamStream, ids_curr.id, is_linear);
         // Generate input offsets, only needs current id
-        node->genOffsets(offsetsStream, ids_curr.id, is_linear);
+        node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
         // Generate the core function body, needs children ids as well
         node->genFuncs(opsStream, ids_curr);
+        for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
+             (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
+            // Generate also output parameters
+            outParamStream << (oid == 0 ? "" : ",\n") << "Param<"
+                           << full_nodes[ids_curr.id]->getTypeStr() << "> out"
+                           << oid;
+            // Generate code to write the output (offset already in ptr)
+            opsStream << "out" << oid << ".ptr[idx] = val" << ids_curr.id
+                      << ";\n";
+            ++oid;
+        }
     }
 
-    outrefstream << "const Param<" << full_nodes[output_ids[0]]->getTypeStr()
-                 << "> &outref = out" << output_ids[0] << ";\n";
-
-    for (int id : output_ids) {
-        // Generate output parameters
-        outParamStream << "Param<" << full_nodes[id]->getTypeStr() << "> out"
-                       << id << ", \n";
-        // Generate code to write the output
-        outWriteStream << "out" << id << ".ptr[idx] = val" << id << ";\n";
-    }
+    outrefStream << "\n    const Param<"
+                 << full_nodes[output_ids[0]]->getTypeStr()
+                 << "> &outref = out0;";
 
     // Put various blocks into a single stream
-    stringstream kerStream;
-    kerStream << typedefStr;
-    kerStream << includeFileStr << "\n\n";
-    kerStream << paramTStr << "\n";
-    kerStream << kernelVoid;
-    kerStream << funcName;
-    kerStream << "(\n";
-    kerStream << inParamStream.str();
-    kerStream << outParamStream.str();
-    kerStream << dimParams;
-    kerStream << ")\n";
-    kerStream << blockStart;
-    kerStream << outrefstream.str();
-    kerStream << loopStart;
+    thread_local stringstream kerStream;
+    kerStream << typedefStr << includeFileStr << "\n\n"
+              << paramTStr << '\n'
+              << kernelVoid << funcName << "(\n"
+              << inParamStream.str() << outParamStream.str() << dimParams << ')'
+              << blockStart << outrefStream.str();
     if (is_linear) {
-        kerStream << linearIndex;
+        kerStream << linearInit;
+        if (loop0) kerStream << linearLoop0Start;
+        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+        if (loop0) kerStream << linearLoop0End;
+        kerStream << linearEnd;
     } else {
-        kerStream << generalIndex;
+        if (loop0) {
+            kerStream << stridedLoop0Init << stridedLoop0Start;
+        } else {
+            kerStream << stridedLoopNInit;
+            if (loop3) kerStream << stridedLoop3Init;
+            if (loop2) kerStream << stridedLoop2Init;
+            if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
+            if (loop2) kerStream << stridedLoop2Start;
+            if (loop3) kerStream << stridedLoop3Start;
+        }
+        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+        if (loop3) kerStream << stridedLoop3End;
+        if (loop2) kerStream << stridedLoop2End;
+        if (loop1) kerStream << stridedLoop1End;
+        if (loop0) kerStream << stridedLoop0End;
+        kerStream << stridedEnd;
     }
-    kerStream << offsetsStream.str();
-    kerStream << opsStream.str();
-    kerStream << outWriteStream.str();
-    kerStream << loopEnd;
     kerStream << blockEnd;
+    const string ret{kerStream.str()};
+
+    // Prepare for next round
+    inParamStream.str("");
+    outParamStream.str("");
+    inOffsetsStream.str("");
+    opsStream.str("");
+    outrefStream.str("");
+    kerStream.str("");
 
-    return kerStream.str();
+    return ret;
 }
 
-static CUfunction getKernel(const vector<Node *> &output_nodes,
-                            const vector<int> &output_ids,
-                            const vector<Node *> &full_nodes,
-                            const vector<Node_ids> &full_ids,
-                            const bool is_linear) {
-    const string funcName =
-        getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    const size_t moduleKey = deterministicHash(funcName);
-
-    // A forward lookup in module cache helps avoid recompiling the jit
-    // source generated from identical jit-trees. It also enables us
-    // with a way to save jit kernels to disk only once
-    auto entry = findModule(getActiveDeviceId(), moduleKey);
-
-    if (entry.get() == nullptr) {
-        const string jitKer = getKernelString(funcName, full_nodes, full_ids,
-                                              output_ids, is_linear);
+static CUfunction getKernel(const vector<Node*>& output_nodes,
+                            const vector<int>& output_ids,
+                            const vector<Node*>& full_nodes,
+                            const vector<Node_ids>& full_ids,
+                            const bool is_linear, const bool loop0,
+                            const bool loop1, const bool loop2,
+                            const bool loop3) {
+    const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
+                                      is_linear, loop0, loop1, loop2, loop3)};
+    // A forward lookup in module cache helps avoid recompiling
+    // the JIT source generated from identical JIT-trees.
+    const auto entry{
+        findModule(getActiveDeviceId(), deterministicHash(funcName))};
+
+    if (!entry) {
+        const string jitKer{getKernelString(funcName, full_nodes, full_ids,
+                                            output_ids, is_linear, loop0, loop1,
+                                            loop2, loop3)};
         saveKernel(funcName, jitKer, ".cu");
 
-        common::Source jit_src{jitKer.c_str(), jitKer.size(),
-                               deterministicHash(jitKer)};
+        const common::Source jit_src{jitKer.c_str(), jitKer.size(),
+                                     deterministicHash(jitKer)};
 
         return common::getKernel(funcName, {jit_src}, {}, {}, true).get();
     }
@@ -206,158 +323,184 @@ static CUfunction getKernel(const vector<Node *> &output_nodes,
 }
 
 template<typename T>
-void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
-    size_t num_outputs = outputs.size();
-    if (num_outputs == 0) { return; }
-
-    int device         = getActiveDeviceId();
-    dim_t *outDims     = outputs[0].dims;
-    size_t numOutElems = outDims[0] * outDims[1] * outDims[2] * outDims[3];
+void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
+    const unsigned nrOutputs{static_cast<unsigned>(output_nodes.size())};
+    if (nrOutputs == 0) { return; }
+    assert(outputs.size() == output_nodes.size());
+    dim_t* outDims{outputs[0].dims};
+    dim_t* outStrides{outputs[0].strides};
+    for_each(
+        begin(outputs)++, end(outputs),
+        [outDims, outStrides](Param<T>& output) {
+            assert(equal(output.dims, output.dims + AF_MAX_DIMS, outDims) &&
+                   equal(output.strides, output.strides + AF_MAX_DIMS,
+                         outStrides));
+        });
+
+    dim_t ndims{outDims[3] > 1   ? 4
+                : outDims[2] > 1 ? 3
+                : outDims[1] > 1 ? 2
+                : outDims[0] > 0 ? 1
+                                 : 0};
+    bool is_linear{true};
+    dim_t numOutElems{1};
+    for (dim_t dim{0}; dim < ndims; ++dim) {
+        is_linear &= (numOutElems == outStrides[dim]);
+        numOutElems *= outDims[dim];
+    }
     if (numOutElems == 0) { return; }
 
-    // Use thread local to reuse the memory every time you are here.
+    // Use thread local to reuse the memory every time you are
+    // here.
     thread_local Node_map_t nodes;
-    thread_local vector<Node *> full_nodes;
+    thread_local vector<Node*> full_nodes;
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 
-    // Reserve some space to improve performance at smaller sizes
-    if (nodes.empty()) {
-        nodes.reserve(1024);
-        output_ids.reserve(output_nodes.size());
-        full_nodes.reserve(1024);
-        full_ids.reserve(1024);
+    // Reserve some space to improve performance at smaller
+    // sizes
+    constexpr size_t CAP{1024};
+    if (full_nodes.capacity() < CAP) {
+        nodes.reserve(CAP);
+        output_ids.reserve(10);
+        full_nodes.reserve(CAP);
+        full_ids.reserve(CAP);
     }
 
-    for (auto &node : output_nodes) {
-        int id = node->getNodesMap(nodes, full_nodes, full_ids);
+    const af::dtype outputType{output_nodes[0]->getType()};
+    const size_t outputSizeofType{size_of(outputType)};
+    for (Node* node : output_nodes) {
+        assert(node->getType() == outputType);
+        const int id = node->getNodesMap(nodes, full_nodes, full_ids);
         output_ids.push_back(id);
     }
 
-    using common::ModdimNode;
-    using common::NodeIterator;
-    using jit::BufferNode;
-
-    // find all moddims in the tree
-    vector<std::shared_ptr<Node>> node_clones;
-    for (auto *node : full_nodes) { node_clones.emplace_back(node->clone()); }
-
-    for (common::Node_ids ids : full_ids) {
-        auto &children = node_clones[ids.id]->m_children;
-        for (int i = 0; i < Node::kMaxChildren && children[i] != nullptr; i++) {
-            children[i] = node_clones[ids.child_ids[i]];
-        }
-    }
-
-    for (auto &node : node_clones) {
-        if (node->getOp() == af_moddims_t) {
-            ModdimNode *mn = static_cast<ModdimNode *>(node.get());
-            auto isBuffer  = [](const Node &ptr) { return ptr.isBuffer(); };
-
-            NodeIterator<> it(node.get());
-            auto new_strides = calcStrides(mn->m_new_shape);
-            while (it != NodeIterator<>()) {
-                it = find_if(it, NodeIterator<>(), isBuffer);
-                if (it == NodeIterator<>()) { break; }
-
-                BufferNode<T> *buf = static_cast<BufferNode<T> *>(&(*it));
-
-                buf->m_param.dims[0]    = mn->m_new_shape[0];
-                buf->m_param.dims[1]    = mn->m_new_shape[1];
-                buf->m_param.dims[2]    = mn->m_new_shape[2];
-                buf->m_param.dims[3]    = mn->m_new_shape[3];
-                buf->m_param.strides[0] = new_strides[0];
-                buf->m_param.strides[1] = new_strides[1];
-                buf->m_param.strides[2] = new_strides[2];
-                buf->m_param.strides[3] = new_strides[3];
-
-                ++it;
-            }
-        }
-    }
-
-    full_nodes.clear();
-    for (auto &node : node_clones) { full_nodes.push_back(node.get()); }
-
-    bool is_linear = true;
-    for (auto *node : full_nodes) {
-        is_linear &= node->isLinear(outputs[0].dims);
-    }
-
-    CUfunction ker =
-        getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
-
-    int threads_x = 1, threads_y = 1;
-    int blocks_x_ = 1, blocks_y_ = 1;
-    int blocks_x = 1, blocks_y = 1, blocks_z = 1, blocks_x_total;
-
-    cudaDeviceProp properties    = getDeviceProp(device);
-    const long long max_blocks_x = properties.maxGridSize[0];
-    const long long max_blocks_y = properties.maxGridSize[1];
-
-    int num_odims = 4;
-    while (num_odims >= 1) {
-        if (outDims[num_odims - 1] == 1) {
-            num_odims--;
-        } else {
-            break;
+    size_t inputSize{0};
+    unsigned nrInputs{0};
+    bool moddimsFound{false};
+    for (const Node* node : full_nodes) {
+        is_linear &= node->isLinear(outDims);
+        moddimsFound |= (node->getOp() == af_moddims_t);
+        if (node->isBuffer()) {
+            ++nrInputs;
+            inputSize += node->getBytes();
         }
     }
+    const size_t outputSize{numOutElems * outputSizeofType * nrOutputs};
+    const size_t totalSize{inputSize + outputSize};
 
+    bool emptyColumnsFound{false};
     if (is_linear) {
-        threads_x = 256;
-        threads_y = 1;
-
-        blocks_x_total = divup(
-            (outDims[0] * outDims[1] * outDims[2] * outDims[3]), threads_x);
-
-        int repeat_x = divup(blocks_x_total, max_blocks_x);
-        blocks_x     = divup(blocks_x_total, repeat_x);
+        outDims[0]    = numOutElems;
+        outDims[1]    = 1;
+        outDims[2]    = 1;
+        outDims[3]    = 1;
+        outStrides[0] = 1;
+        outStrides[1] = numOutElems;
+        outStrides[2] = numOutElems;
+        outStrides[3] = numOutElems;
+        ndims         = 1;
     } else {
-        threads_x = 32;
-        threads_y = 8;
+        emptyColumnsFound = ndims > (outDims[0] == 1   ? 1
+                                     : outDims[1] == 1 ? 2
+                                     : outDims[2] == 1 ? 3
+                                                       : 4);
+    }
 
-        blocks_x_ = divup(outDims[0], threads_x);
-        blocks_y_ = divup(outDims[1], threads_y);
+    // Keep node_clones in scope, so that the nodes remain active for later
+    // referral in case moddims or Column elimination operations have to take
+    // place
+    vector<Node_ptr> node_clones;
+    if (moddimsFound | emptyColumnsFound) {
+        node_clones.reserve(full_nodes.size());
+        for (Node* node : full_nodes) {
+            node_clones.emplace_back(node->clone());
+        }
 
-        blocks_x = blocks_x_ * outDims[2];
-        blocks_y = blocks_y_ * outDims[3];
+        for (const Node_ids& ids : full_ids) {
+            auto& children{node_clones[ids.id]->m_children};
+            for (int i{0}; i < Node::kMaxChildren && children[i] != nullptr;
+                 i++) {
+                children[i] = node_clones[ids.child_ids[i]];
+            }
+        }
 
-        blocks_z = divup(blocks_y, max_blocks_y);
-        blocks_y = divup(blocks_y, blocks_z);
+        if (moddimsFound) {
+            const auto isModdim{[](const Node_ptr& node) {
+                return node->getOp() == af_moddims_t;
+            }};
+            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                 (nodeIt = find_if(nodeIt, endIt, isModdim)) != endIt;
+                 ++nodeIt) {
+                const ModdimNode* mn{static_cast<ModdimNode*>(nodeIt->get())};
+
+                const auto new_strides{calcStrides(mn->m_new_shape)};
+                const auto isBuffer{
+                    [](const Node& ptr) { return ptr.isBuffer(); }};
+                for (NodeIterator<> it{nodeIt->get()}, end{NodeIterator<>()};
+                     (it = find_if(it, end, isBuffer)) != end; ++it) {
+                    BufferNode<T>* buf{static_cast<BufferNode<T>*>(&(*it))};
+                    buf->m_param.dims[0]    = mn->m_new_shape[0];
+                    buf->m_param.dims[1]    = mn->m_new_shape[1];
+                    buf->m_param.dims[2]    = mn->m_new_shape[2];
+                    buf->m_param.dims[3]    = mn->m_new_shape[3];
+                    buf->m_param.strides[0] = new_strides[0];
+                    buf->m_param.strides[1] = new_strides[1];
+                    buf->m_param.strides[2] = new_strides[2];
+                    buf->m_param.strides[3] = new_strides[3];
+                }
+            }
+        }
+        if (emptyColumnsFound) {
+            const auto isBuffer{
+                [](const Node_ptr& node) { return node->isBuffer(); }};
+            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                 (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
+                 ++nodeIt) {
+                BufferNode<T>* buf{static_cast<BufferNode<T>*>(nodeIt->get())};
+                removeEmptyColumns(outDims, ndims, buf->m_param.dims,
+                                   buf->m_param.strides);
+            }
+            for_each(++begin(outputs), end(outputs),
+                     [outDims, ndims](Param<T>& output) {
+                         removeEmptyColumns(outDims, ndims, output.dims,
+                                            output.strides);
+                     });
+            ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
+        }
 
-        blocks_x_total = blocks_x;
-        int repeat_x   = divup(blocks_x_total, max_blocks_x);
-        blocks_x       = divup(blocks_x_total, repeat_x);
+        full_nodes.clear();
+        for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
     }
 
-    vector<void *> args;
+    threadsMgt<dim_t> th(outDims, ndims);
+    const dim3 threads{th.genThreads()};
+    const dim3 blocks{th.genBlocks(threads, nrInputs, nrOutputs, totalSize,
+                                   outputSizeofType)};
+    auto ker = getKernel(output_nodes, output_ids, full_nodes, full_ids,
+                         is_linear, th.loop0, th.loop1, th.loop2, th.loop3);
 
-    for (const auto &node : full_nodes) {
+    vector<void*> args;
+    for (const Node* node : full_nodes) {
         node->setArgs(0, is_linear,
-                      [&](int /*id*/, const void *ptr, size_t /*size*/) {
-                          args.push_back(const_cast<void *>(ptr));
+                      [&](int /*id*/, const void* ptr, size_t /*size*/) {
+                          args.push_back(const_cast<void*>(ptr));
                       });
     }
 
-    for (size_t i = 0; i < num_outputs; i++) {
-        args.push_back(static_cast<void *>(&outputs[i]));
-    }
-
-    args.push_back(static_cast<void *>(&blocks_x_));
-    args.push_back(static_cast<void *>(&blocks_y_));
-    args.push_back(static_cast<void *>(&blocks_x_total));
-    args.push_back(static_cast<void *>(&num_odims));
+    for (auto& out : outputs) { args.push_back(static_cast<void*>(&out)); }
 
     {
         using namespace cuda::kernel_logger;
-        AF_TRACE("Launching : Blocks: [{}] Threads: [{}] ",
-                 dim3(blocks_x, blocks_y, blocks_z),
-                 dim3(threads_x, threads_y));
+        AF_TRACE(
+            "Launching : Dims: [{},{},{},{}] Blocks: [{}] "
+            "Threads: [{}] threads: {}",
+            outDims[0], outDims[1], outDims[2], outDims[3], blocks, threads,
+            blocks.x * threads.x * blocks.y * threads.y * blocks.z * threads.z);
     }
-    CU_CHECK(cuLaunchKernel(ker, blocks_x, blocks_y, blocks_z, threads_x,
-                            threads_y, 1, 0, getActiveStream(), args.data(),
-                            NULL));
+    CU_CHECK(cuLaunchKernel(ker, blocks.x, blocks.y, blocks.z, threads.x,
+                            threads.y, threads.z, 0, getActiveStream(),
+                            args.data(), NULL));
 
     // Reset the thread local vectors
     nodes.clear();
@@ -367,53 +510,50 @@ void evalNodes(vector<Param<T>> &outputs, const vector<Node *> &output_nodes) {
 }
 
 template<typename T>
-void evalNodes(Param<T> out, Node *node) {
-    vector<Param<T>> outputs;
-    vector<Node *> output_nodes;
-
-    outputs.push_back(out);
-    output_nodes.push_back(node);
-    evalNodes(outputs, output_nodes);
+void evalNodes(Param<T> out, Node* node) {
+    vector<Param<T>> outputs{out};
+    vector<Node*> nodes{node};
+    evalNodes(outputs, nodes);
 }
 
-template void evalNodes<float>(Param<float> out, Node *node);
-template void evalNodes<double>(Param<double> out, Node *node);
-template void evalNodes<cfloat>(Param<cfloat> out, Node *node);
-template void evalNodes<cdouble>(Param<cdouble> out, Node *node);
-template void evalNodes<int>(Param<int> out, Node *node);
-template void evalNodes<uint>(Param<uint> out, Node *node);
-template void evalNodes<char>(Param<char> out, Node *node);
-template void evalNodes<uchar>(Param<uchar> out, Node *node);
-template void evalNodes<intl>(Param<intl> out, Node *node);
-template void evalNodes<uintl>(Param<uintl> out, Node *node);
-template void evalNodes<short>(Param<short> out, Node *node);
-template void evalNodes<ushort>(Param<ushort> out, Node *node);
-template void evalNodes<half>(Param<half> out, Node *node);
-
-template void evalNodes<float>(vector<Param<float>> &out,
-                               const vector<Node *> &node);
-template void evalNodes<double>(vector<Param<double>> &out,
-                                const vector<Node *> &node);
-template void evalNodes<cfloat>(vector<Param<cfloat>> &out,
-                                const vector<Node *> &node);
-template void evalNodes<cdouble>(vector<Param<cdouble>> &out,
-                                 const vector<Node *> &node);
-template void evalNodes<int>(vector<Param<int>> &out,
-                             const vector<Node *> &node);
-template void evalNodes<uint>(vector<Param<uint>> &out,
-                              const vector<Node *> &node);
-template void evalNodes<char>(vector<Param<char>> &out,
-                              const vector<Node *> &node);
-template void evalNodes<uchar>(vector<Param<uchar>> &out,
-                               const vector<Node *> &node);
-template void evalNodes<intl>(vector<Param<intl>> &out,
-                              const vector<Node *> &node);
-template void evalNodes<uintl>(vector<Param<uintl>> &out,
-                               const vector<Node *> &node);
-template void evalNodes<short>(vector<Param<short>> &out,
-                               const vector<Node *> &node);
-template void evalNodes<ushort>(vector<Param<ushort>> &out,
-                                const vector<Node *> &node);
-template void evalNodes<half>(vector<Param<half>> &out,
-                              const vector<Node *> &node);
+template void evalNodes<float>(Param<float> out, Node* node);
+template void evalNodes<double>(Param<double> out, Node* node);
+template void evalNodes<cfloat>(Param<cfloat> out, Node* node);
+template void evalNodes<cdouble>(Param<cdouble> out, Node* node);
+template void evalNodes<int>(Param<int> out, Node* node);
+template void evalNodes<uint>(Param<uint> out, Node* node);
+template void evalNodes<char>(Param<char> out, Node* node);
+template void evalNodes<uchar>(Param<uchar> out, Node* node);
+template void evalNodes<intl>(Param<intl> out, Node* node);
+template void evalNodes<uintl>(Param<uintl> out, Node* node);
+template void evalNodes<short>(Param<short> out, Node* node);
+template void evalNodes<ushort>(Param<ushort> out, Node* node);
+template void evalNodes<half>(Param<half> out, Node* node);
+
+template void evalNodes<float>(vector<Param<float>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<double>(vector<Param<double>>& out,
+                                const vector<Node*>& node);
+template void evalNodes<cfloat>(vector<Param<cfloat>>& out,
+                                const vector<Node*>& node);
+template void evalNodes<cdouble>(vector<Param<cdouble>>& out,
+                                 const vector<Node*>& node);
+template void evalNodes<int>(vector<Param<int>>& out,
+                             const vector<Node*>& node);
+template void evalNodes<uint>(vector<Param<uint>>& out,
+                              const vector<Node*>& node);
+template void evalNodes<char>(vector<Param<char>>& out,
+                              const vector<Node*>& node);
+template void evalNodes<uchar>(vector<Param<uchar>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<intl>(vector<Param<intl>>& out,
+                              const vector<Node*>& node);
+template void evalNodes<uintl>(vector<Param<uintl>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<short>(vector<Param<short>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<ushort>(vector<Param<ushort>>& out,
+                                const vector<Node*>& node);
+template void evalNodes<half>(vector<Param<half>>& out,
+                              const vector<Node*>& node);
 }  // namespace cuda
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index d048c0c7d0..cc67ac6996 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -48,18 +48,18 @@ int setKernelArguments(
 /// Generates the code to calculate the offsets for a buffer
 void generateBufferOffsets(std::stringstream& kerStream, int id, bool is_linear,
                            const std::string& type_str) {
-    std::string idx_str = std::string("int idx") + std::to_string(id);
+    const std::string idx_str  = std::string("idx") + std::to_string(id);
+    const std::string info_str = std::string("in") + std::to_string(id);
 
     if (is_linear) {
-        kerStream << idx_str << " = idx;\n";
+        kerStream << "#define " << idx_str << " idx\n";
     } else {
-        std::string info_str = std::string("in") + std::to_string(id);
-        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
-                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
-                  << info_str << ".dims[1]) * " << info_str
-                  << ".strides[1] * id1 + (id0 < " << info_str
-                  << ".dims[0]) * id0;\n";
+        kerStream << "int " << idx_str << " = id0*(id0<" << info_str
+                  << ".dims[0])*" << info_str << ".strides[0] + id1*(id1<"
+                  << info_str << ".dims[1])*" << info_str
+                  << ".strides[1] + id2*(id2<" << info_str << ".dims[2])*"
+                  << info_str << ".strides[2] + id3*(id3<" << info_str
+                  << ".dims[3])*" << info_str << ".strides[3];\n";
         kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
     }
 }
@@ -75,28 +75,24 @@ inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
                                      bool is_linear,
                                      const std::string& type_str) {
     UNUSED(is_linear);
-    std::string idx_str   = std::string("idx") + std::to_string(id);
-    std::string info_str  = std::string("in") + std::to_string(id);
-    std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
-    std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+    const std::string idx_str  = std::string("idx") + std::to_string(id);
+    const std::string info_str = std::string("in") + std::to_string(id);
+    const std::string id_str = std::string("sh_id_") + std::to_string(id) + '_';
+    const std::string shift_str =
+        std::string("shift") + std::to_string(id) + '_';
 
     for (int i = 0; i < 4; i++) {
         kerStream << "int " << id_str << i << " = __circular_mod(id" << i
                   << " + " << shift_str << i << ", " << info_str << ".dims["
                   << i << "]);\n";
     }
-
-    kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str
-              << ".dims[3]) * " << info_str << ".strides[3] * " << id_str
-              << "3;\n";
-    kerStream << idx_str << " += (" << id_str << "2 < " << info_str
-              << ".dims[2]) * " << info_str << ".strides[2] * " << id_str
-              << "2;\n";
-    kerStream << idx_str << " += (" << id_str << "1 < " << info_str
-              << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
-              << "1;\n";
-    kerStream << idx_str << " += (" << id_str << "0 < " << info_str
-              << ".dims[0]) * " << id_str << "0;\n";
+    kerStream << "int " << idx_str << " = " << id_str << "0*(" << id_str << "0<"
+              << info_str << ".dims[0])*" << info_str << ".strides[0] + "
+              << id_str << "1*(" << id_str << "1<" << info_str << ".dims[1])*"
+              << info_str << ".strides[1] + " << id_str << "2*(" << id_str
+              << "2<" << info_str << ".dims[2])*" << info_str
+              << ".strides[2] + " << id_str << "3*(" << id_str << "3<"
+              << info_str << ".dims[3])*" << info_str << ".strides[3];\n";
     kerStream << type_str << " *in" << id << "_ptr = in" << id << ".ptr;\n";
 }
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 06d2b41b08..8d717680d6 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -9,7 +9,6 @@
 
 #include <Array.hpp>
 #include <common/compile_module.hpp>
-#include <common/dispatch.hpp>
 #include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
 #include <common/jit/NodeIterator.hpp>
@@ -18,12 +17,14 @@
 #include <copy.hpp>
 #include <device_manager.hpp>
 #include <err_opencl.hpp>
+#include <jit/BufferNode.hpp>
 #include <kernel_headers/jit.hpp>
+#include <threadsMgt.hpp>
+#include <type_util.hpp>
 #include <af/dim4.hpp>
 #include <af/opencl.h>
 
-#include <jit/BufferNode.hpp>
-
+#include <algorithm>
 #include <cstdio>
 #include <functional>
 #include <sstream>
@@ -31,139 +32,244 @@
 #include <string>
 #include <vector>
 
+using common::findModule;
 using common::getFuncName;
+using common::ModdimNode;
 using common::Node;
 using common::Node_ids;
 using common::Node_map_t;
+using common::Node_ptr;
+using common::NodeIterator;
 
 using cl::Kernel;
 using cl::NDRange;
 using cl::NullRange;
 
+using std::equal;
+using std::for_each;
+using std::shared_ptr;
 using std::string;
 using std::stringstream;
 using std::to_string;
 using std::vector;
 
 namespace opencl {
+using jit::BufferNode;
 
-string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
-                       const vector<Node_ids> &full_ids,
-                       const vector<int> &output_ids, bool is_linear) {
+string getKernelString(const string& funcName, const vector<Node*>& full_nodes,
+                       const vector<Node_ids>& full_ids,
+                       const vector<int>& output_ids, const bool is_linear,
+                       const bool loop0, const bool loop1, const bool loop3) {
     // Common OpenCL code
     // This part of the code does not change with the kernel.
 
-    static const char *kernelVoid = "__kernel void\n";
-    static const char *dimParams =
-        "KParam oInfo, uint groups_0, uint groups_1, uint num_odims";
-    static const char *blockStart = "{\n";
-    static const char *blockEnd   = "\n}\n";
-
-    static const char *linearIndex = R"JIT(
-        uint groupId  = get_group_id(1) * get_num_groups(0) + get_group_id(0);
-        uint threadId = get_local_id(0);
-        int idx = groupId * get_local_size(0) * get_local_size(1) + threadId;
-        if (idx >= oInfo.dims[3] * oInfo.strides[3]) return;
-        )JIT";
-
-    static const char *generalIndex = R"JIT(
-        uint id0 = 0, id1 = 0, id2 = 0, id3 = 0;
-        if (num_odims > 2) {
-            id2 = get_group_id(0) / groups_0;
-            id0 = get_group_id(0) - id2 * groups_0;
-            id0 = get_local_id(0) + id0 * get_local_size(0);
-            if (num_odims > 3) {
-                id3 = get_group_id(1) / groups_1;
-                id1 = get_group_id(1) - id3 * groups_1;
-                id1 = get_local_id(1) + id1 * get_local_size(1);
-            } else {
-                id1 = get_global_id(1);
-            }
-        } else {
-            id3 = 0;
-            id2 = 0;
-            id1 = get_global_id(1);
-            id0 = get_global_id(0);
-        }
-        bool cond = id0 < oInfo.dims[0] &&
-                    id1 < oInfo.dims[1] &&
-                    id2 < oInfo.dims[2] &&
-                    id3 < oInfo.dims[3];
-        if (!cond) return;
-        int idx = oInfo.strides[3] * id3 +
-                  oInfo.strides[2] * id2 +
-                  oInfo.strides[1] * id1 +
-                  id0 + oInfo.offset;
-        )JIT";
-
-    stringstream inParamStream;
-    stringstream outParamStream;
-    stringstream outWriteStream;
-    stringstream offsetsStream;
-    stringstream opsStream;
-
-    for (size_t i = 0; i < full_nodes.size(); i++) {
-        const auto &node     = full_nodes[i];
-        const auto &ids_curr = full_ids[i];
+    static const char* kernelVoid = R"JIT(
+__kernel void )JIT";
+    static const char* dimParams  = "KParam oInfo";
+    static const char* blockStart = "{";
+    static const char* blockEnd   = "\n}\n";
+
+    static const char* linearInit = R"JIT(
+   int idx = get_global_id(0);
+   const int idxEnd = oInfo.dims[0];
+   if (idx < idxEnd) {
+)JIT";
+    static const char* linearEnd  = R"JIT(
+   })JIT";
+
+    static const char* linearLoop0Start = R"JIT(
+        const int idxID0Inc = get_global_size(0);
+        do {)JIT";
+    static const char* linearLoop0End   = R"JIT(
+            idx += idxID0Inc;
+            if (idx >= idxEnd) break;
+        } while (true);)JIT";
+
+    // ///////////////////////////////////////////////
+    // oInfo = output optimized information (dims, strides, offset).
+    //         oInfo has removed dimensions, to optimized block scheduling
+    // iInfo = input internal information (dims, strides, offset)
+    //         iInfo has the original dimensions, auto generated code
+    //
+    // Loop3 is fastest and becomes inside loop, since
+    //      - #of loops is known upfront
+    // Loop1 is used for extra dynamic looping (writing into cache)
+    // All loops are conditional and idependent
+    // Format Loop1 & Loop3
+    // ////////////////////////////
+    //  *stridedLoopNInit               // Always
+    //  *stridedLoop1Init               // Conditional
+    //  *stridedLoop2Init               // Conditional
+    //  *stridedLoop3Init               // Conditional
+    //  *stridedLoop1Start              // Conditional
+    //      *stridedLoop3Start          // Conditional
+    //          auto generated code     // Always
+    //      *stridedLoop3End            // Conditional
+    //  *stridedLoop1End                // Conditional
+    //  *StridedEnd                     // Always
+    //
+    // format loop0 (Vector only)
+    // //////////////////////////
+    // *stridedLoop0Init                // Always
+    // *stridedLoop0Start               // Always
+    //      auto generated code         // Always
+    // *stridedLoop0End                 // Always
+    // *stridedEnd                      // Always
+
+    static const char* stridedLoop0Init  = R"JIT(
+    int id0 = get_global_id(0);
+    const int id0End = oInfo.dims[0];
+    if (id0 < id0End) {
+#define id1 0
+#define id2 0
+#define id3 0
+        const int ostrides0 = oInfo.strides[0];
+        int idx = ostrides0*id0;)JIT";
+    static const char* stridedLoop0Start = R"JIT(
+        const int id0Inc = get_global_size(0);
+        const int idxID0Inc = ostrides0*id0Inc;
+        do {)JIT";
+    static const char* stridedLoop0End   = R"JIT(
+            id0 += id0Inc;
+            if (id0 >= id0End) break;
+            idx += idxID0Inc;
+        } while (true);)JIT";
+
+    // -------------
+    static const char* stridedLoopNInit = R"JIT(
+    int id0 = get_global_id(0);
+    int id1 = get_global_id(1);
+    const int id0End = oInfo.dims[0];
+    const int id1End = oInfo.dims[1];
+    if ((id0 < id0End) & (id1 < id1End)) {
+        const int id2 = get_global_id(2);
+#define id3 0
+        const int ostrides1 = oInfo.strides[1];
+        int idx = (int)oInfo.strides[0]*id0 + ostrides1*id1 + (int)oInfo.strides[2]*id2;)JIT";
+    static const char* stridedEnd       = R"JIT(
+    })JIT";
+
+    static const char* stridedLoop3Init  = R"JIT(
+#undef id3
+        int id3 = 0;
+        const int id3End = oInfo.dims[3];
+        const int idxID3Inc = oInfo.strides[3];)JIT";
+    static const char* stridedLoop3Start = R"JIT(
+                const int idxBaseID3 = idx;
+                do {)JIT";
+    static const char* stridedLoop3End   = R"JIT(
+                    ++id3;
+                    if (id3 == id3End) break;
+                    idx += idxID3Inc;
+                } while (true);
+                id3 = 0;
+                idx = idxBaseID3;)JIT";
+
+    static const char* stridedLoop1Init  = R"JIT(
+        const int id1Inc = get_global_size(1);
+        const int idxID1Inc = id1Inc * ostrides1;)JIT";
+    static const char* stridedLoop1Start = R"JIT(
+        do {)JIT";
+    static const char* stridedLoop1End   = R"JIT(
+            id1 += id1Inc;
+            if (id1 >= id1End) break;
+            idx += idxID1Inc;
+        } while (true);)JIT";
+
+    // Reuse stringstreams, because they are very costly during initilization
+    thread_local stringstream inParamStream;
+    thread_local stringstream outParamStream;
+    thread_local stringstream outOffsetStream;
+    thread_local stringstream inOffsetsStream;
+    thread_local stringstream opsStream;
+
+    int oid{0};
+    for (size_t i{0}; i < full_nodes.size(); i++) {
+        const auto& node{full_nodes[i]};
+        const auto& ids_curr{full_ids[i]};
         // Generate input parameters, only needs current id
         node->genParams(inParamStream, ids_curr.id, is_linear);
         // Generate input offsets, only needs current id
-        node->genOffsets(offsetsStream, ids_curr.id, is_linear);
+        node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
         // Generate the core function body, needs children ids as well
         node->genFuncs(opsStream, ids_curr);
+        for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
+             (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
+            // Generate also output parameters
+            outParamStream << "__global "
+                           << full_nodes[ids_curr.id]->getTypeStr() << " *out"
+                           << oid << ", int offset" << oid << ",\n";
+            // Apply output offset
+            outOffsetStream << "\nout" << oid << " += offset" << oid << ';';
+            // Generate code to write the output
+            opsStream << "out" << oid << "[idx] = val" << ids_curr.id << ";\n";
+            ++oid;
+        }
     }
 
-    for (int id : output_ids) {
-        // Generate output parameters
-        outParamStream << "__global " << full_nodes[id]->getTypeStr() << " *out"
-                       << id << ", \n";
-        // Generate code to write the output
-        outWriteStream << "out" << id << "[idx] = val" << id << ";\n";
-    }
-
-    // Put various blocks into a single stream
-    stringstream kerStream;
-    kerStream << kernelVoid;
-    kerStream << funcName;
-    kerStream << "(\n";
-    kerStream << inParamStream.str();
-    kerStream << outParamStream.str();
-    kerStream << dimParams;
-    kerStream << ")\n";
-    kerStream << blockStart;
+    thread_local stringstream kerStream;
+    kerStream << kernelVoid << funcName << "(\n"
+              << inParamStream.str() << outParamStream.str() << dimParams << ")"
+              << blockStart;
     if (is_linear) {
-        kerStream << linearIndex;
+        kerStream << linearInit << inOffsetsStream.str()
+                  << outOffsetStream.str() << '\n';
+        if (loop0) kerStream << linearLoop0Start;
+        kerStream << "\n\n" << opsStream.str();
+        if (loop0) kerStream << linearLoop0End;
+        kerStream << linearEnd;
     } else {
-        kerStream << generalIndex;
+        if (loop0) {
+            kerStream << stridedLoop0Init << outOffsetStream.str() << '\n'
+                      << stridedLoop0Start;
+        } else {
+            kerStream << stridedLoopNInit << outOffsetStream.str() << '\n';
+            if (loop3) kerStream << stridedLoop3Init;
+            if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
+            if (loop3) kerStream << stridedLoop3Start;
+        }
+        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+        if (loop3) kerStream << stridedLoop3End;
+        if (loop1) kerStream << stridedLoop1End;
+        if (loop0) kerStream << stridedLoop0End;
+        kerStream << stridedEnd;
     }
-    kerStream << offsetsStream.str();
-    kerStream << opsStream.str();
-    kerStream << outWriteStream.str();
     kerStream << blockEnd;
+    const string ret{kerStream.str()};
 
-    return kerStream.str();
-}
+    // Prepare for next round, limit memory
+    inParamStream.str("");
+    outParamStream.str("");
+    inOffsetsStream.str("");
+    outOffsetStream.str("");
+    opsStream.str("");
+    kerStream.str("");
 
-cl::Kernel getKernel(const vector<Node *> &output_nodes,
-                     const vector<int> &output_ids,
-                     const vector<Node *> &full_nodes,
-                     const vector<Node_ids> &full_ids, const bool is_linear) {
-    const string funcName =
-        getFuncName(output_nodes, full_nodes, full_ids, is_linear);
-    const size_t moduleKey = deterministicHash(funcName);
+    return ret;
+}
 
-    // A forward lookup in module cache helps avoid recompiling the jit
-    // source generated from identical jit-trees. It also enables us
-    // with a way to save jit kernels to disk only once
-    auto entry = common::findModule(getActiveDeviceId(), moduleKey);
+cl::Kernel getKernel(const vector<Node*>& output_nodes,
+                     const vector<int>& output_ids,
+                     const vector<Node*>& full_nodes,
+                     const vector<Node_ids>& full_ids, const bool is_linear,
+                     const bool loop0, const bool loop1, const bool loop3) {
+    const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
+                                      is_linear, loop0, loop1, false, loop3)};
+    // A forward lookup in module cache helps avoid recompiling the JIT
+    // source generated from identical JIT-trees.
+    const auto entry{
+        findModule(getActiveDeviceId(), deterministicHash(funcName))};
 
     if (!entry) {
-        string jitKer = getKernelString(funcName, full_nodes, full_ids,
-                                        output_ids, is_linear);
-        common::Source jitKer_cl_src{
+        const string jitKer{getKernelString(funcName, full_nodes, full_ids,
+                                            output_ids, is_linear, loop0, loop1,
+                                            loop3)};
+        saveKernel(funcName, jitKer, ".cl");
+
+        const common::Source jitKer_cl_src{
             jitKer.data(), jitKer.size(),
             deterministicHash(jitKer.data(), jitKer.size())};
-        int device = getActiveDeviceId();
+        const cl::Device device{getDevice()};
         vector<string> options;
         if (isDoubleSupported(device)) {
             options.emplace_back(DefineKey(USE_DOUBLE));
@@ -171,9 +277,6 @@ cl::Kernel getKernel(const vector<Node *> &output_nodes,
         if (isHalfSupported(device)) {
             options.emplace_back(DefineKey(USE_HALF));
         }
-
-        saveKernel(funcName, jitKer, ".cl");
-
         return common::getKernel(funcName, {jit_cl_src, jitKer_cl_src}, {},
                                  options, true)
             .get();
@@ -181,152 +284,190 @@ cl::Kernel getKernel(const vector<Node *> &output_nodes,
     return common::getKernel(entry, funcName, true).get();
 }
 
-void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
-    if (outputs.empty()) { return; }
-
-    // Assume all ouputs are of same size
-    // FIXME: Add assert to check if all outputs are same size?
-    KParam out_info    = outputs[0].info;
-    dim_t *outDims     = out_info.dims;
-    size_t numOutElems = outDims[0] * outDims[1] * outDims[2] * outDims[3];
+void evalNodes(vector<Param>& outputs, const vector<Node*>& output_nodes) {
+    const unsigned nrOutputs{static_cast<unsigned>(outputs.size())};
+    if (nrOutputs == 0) { return; }
+    assert(outputs.size() == output_nodes.size());
+    KParam& out_info{outputs[0].info};
+    dim_t* outDims{out_info.dims};
+    dim_t* outStrides{out_info.strides};
+    for_each(begin(outputs)++, end(outputs),
+             [outDims, outStrides](Param& output) {
+                 assert(equal(output.info.dims, output.info.dims + AF_MAX_DIMS,
+                              outDims) &&
+                        equal(output.info.strides,
+                              output.info.strides + AF_MAX_DIMS, outStrides));
+             });
+
+    dim_t ndims{outDims[3] > 1   ? 4
+                : outDims[2] > 1 ? 3
+                : outDims[1] > 1 ? 2
+                : outDims[0] > 0 ? 1
+                                 : 0};
+    bool is_linear{true};
+    dim_t numOutElems{1};
+    for (dim_t dim{0}; dim < ndims; ++dim) {
+        is_linear &= (numOutElems == outStrides[dim]);
+        numOutElems *= outDims[dim];
+    }
     if (numOutElems == 0) { return; }
 
     // Use thread local to reuse the memory every time you are here.
     thread_local Node_map_t nodes;
-    thread_local vector<Node *> full_nodes;
+    thread_local vector<Node*> full_nodes;
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 
     // Reserve some space to improve performance at smaller sizes
-    if (nodes.empty()) {
-        nodes.reserve(1024);
-        output_ids.reserve(output_nodes.size());
-        full_nodes.reserve(1024);
-        full_ids.reserve(1024);
+    constexpr size_t CAP{1024};
+    if (full_nodes.capacity() < CAP) {
+        nodes.reserve(CAP);
+        output_ids.reserve(10);
+        full_nodes.reserve(CAP);
+        full_ids.reserve(CAP);
     }
 
-    for (auto *node : output_nodes) {
-        int id = node->getNodesMap(nodes, full_nodes, full_ids);
+    const af::dtype outputType{output_nodes[0]->getType()};
+    const size_t outputSizeofType{size_of(outputType)};
+    for (Node* node : output_nodes) {
+        assert(node->getType() == outputType);
+        const int id{node->getNodesMap(nodes, full_nodes, full_ids)};
         output_ids.push_back(id);
     }
 
-    using common::ModdimNode;
-    using common::NodeIterator;
-    using jit::BufferNode;
-
-    // find all moddims in the tree
-    vector<std::shared_ptr<Node>> node_clones;
-    for (auto *node : full_nodes) { node_clones.emplace_back(node->clone()); }
-
-    for (common::Node_ids ids : full_ids) {
-        auto &children = node_clones[ids.id]->m_children;
-        for (int i = 0; i < Node::kMaxChildren && children[i] != nullptr; i++) {
-            children[i] = node_clones[ids.child_ids[i]];
+    const size_t outputSize{numOutElems * outputSizeofType * nrOutputs};
+    size_t inputSize{0};
+    unsigned nrInputs{0};
+    bool moddimsFound{false};
+    for (const Node* node : full_nodes) {
+        is_linear &= node->isLinear(outDims);
+        moddimsFound |= (node->getOp() == af_moddims_t);
+        if (node->isBuffer()) {
+            ++nrInputs;
+            inputSize += node->getBytes();
         }
     }
+    const size_t totalSize{inputSize + outputSize};
 
-    for (auto &node : node_clones) {
-        if (node->getOp() == af_moddims_t) {
-            ModdimNode *mn = static_cast<ModdimNode *>(node.get());
-            auto isBuffer  = [](const Node &ptr) { return ptr.isBuffer(); };
-
-            NodeIterator<> it(node.get());
-            auto new_strides = calcStrides(mn->m_new_shape);
-            while (it != NodeIterator<>()) {
-                it = find_if(it, NodeIterator<>(), isBuffer);
-                if (it == NodeIterator<>()) { break; }
-
-                BufferNode *buf = static_cast<BufferNode *>(&(*it));
-
-                buf->m_param.dims[0]    = mn->m_new_shape[0];
-                buf->m_param.dims[1]    = mn->m_new_shape[1];
-                buf->m_param.dims[2]    = mn->m_new_shape[2];
-                buf->m_param.dims[3]    = mn->m_new_shape[3];
-                buf->m_param.strides[0] = new_strides[0];
-                buf->m_param.strides[1] = new_strides[1];
-                buf->m_param.strides[2] = new_strides[2];
-                buf->m_param.strides[3] = new_strides[3];
-
-                ++it;
-            }
-        }
-    }
-
-    full_nodes.clear();
-    for (auto &node : node_clones) { full_nodes.push_back(node.get()); }
-
-    bool is_linear = true;
-    for (auto *node : full_nodes) {
-        is_linear &= node->isLinear(outputs[0].info.dims);
+    bool emptyColumnsFound{false};
+    if (is_linear) {
+        outDims[0]    = numOutElems;
+        outDims[1]    = 1;
+        outDims[2]    = 1;
+        outDims[3]    = 1;
+        outStrides[0] = 1;
+        outStrides[1] = numOutElems;
+        outStrides[2] = numOutElems;
+        outStrides[3] = numOutElems;
+        ndims         = 1;
+    } else {
+        emptyColumnsFound = ndims > (outDims[0] == 1   ? 1
+                                     : outDims[1] == 1 ? 2
+                                     : outDims[2] == 1 ? 3
+                                                       : 4);
     }
 
-    auto ker =
-        getKernel(output_nodes, output_ids, full_nodes, full_ids, is_linear);
-
-    uint local_0   = 1;
-    uint local_1   = 1;
-    uint global_0  = 1;
-    uint global_1  = 1;
-    uint groups_0  = 1;
-    uint groups_1  = 1;
-    uint num_odims = 4;
-
-    // CPUs seem to perform better with work group size 1024
-    const int work_group_size =
-        (getActiveDeviceType() == AFCL_DEVICE_TYPE_CPU) ? 1024 : 256;
-
-    while (num_odims >= 1) {
-        if (outDims[num_odims - 1] == 1) {
-            num_odims--;
-        } else {
-            break;
+    // Keep in global scope, so that the nodes remain active for later referral
+    // in case moddims operations or column elimination have to take place
+    vector<Node_ptr> node_clones;
+    // Avoid all cloning/copying when no moddims node is present (high chance)
+    if (moddimsFound | emptyColumnsFound) {
+        node_clones.reserve(full_nodes.size());
+        for (Node* node : full_nodes) {
+            node_clones.emplace_back(node->clone());
         }
-    }
 
-    if (is_linear) {
-        local_0           = work_group_size;
-        uint out_elements = outDims[3] * out_info.strides[3];
-        uint groups       = divup(out_elements, local_0);
-
-        global_1 = divup(groups, work_group_size) * local_1;
-        global_0 = divup(groups, global_1) * local_0;
-
-    } else {
-        local_1 = 4;
-        local_0 = work_group_size / local_1;
+        for (const Node_ids& ids : full_ids) {
+            auto& children{node_clones[ids.id]->m_children};
+            for (int i{0}; i < Node::kMaxChildren && children[i] != nullptr;
+                 i++) {
+                children[i] = node_clones[ids.child_ids[i]];
+            }
+        }
 
-        groups_0 = divup(outDims[0], local_0);
-        groups_1 = divup(outDims[1], local_1);
+        if (moddimsFound) {
+            const auto isModdim{[](const Node_ptr& ptr) {
+                return ptr->getOp() == af_moddims_t;
+            }};
+            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                 (nodeIt = find_if(nodeIt, endIt, isModdim)) != endIt;
+                 ++nodeIt) {
+                const ModdimNode* mn{static_cast<ModdimNode*>(nodeIt->get())};
+
+                const auto new_strides{calcStrides(mn->m_new_shape)};
+                const auto isBuffer{
+                    [](const Node& node) { return node.isBuffer(); }};
+                for (NodeIterator<> it{nodeIt->get()}, end{NodeIterator<>()};
+                     (it = find_if(it, end, isBuffer)) != end; ++it) {
+                    BufferNode* buf{static_cast<BufferNode*>(&(*it))};
+                    buf->m_param.dims[0]    = mn->m_new_shape[0];
+                    buf->m_param.dims[1]    = mn->m_new_shape[1];
+                    buf->m_param.dims[2]    = mn->m_new_shape[2];
+                    buf->m_param.dims[3]    = mn->m_new_shape[3];
+                    buf->m_param.strides[0] = new_strides[0];
+                    buf->m_param.strides[1] = new_strides[1];
+                    buf->m_param.strides[2] = new_strides[2];
+                    buf->m_param.strides[3] = new_strides[3];
+                }
+            }
+        }
+        if (emptyColumnsFound) {
+            const auto isBuffer{
+                [](const Node_ptr& ptr) { return ptr->isBuffer(); }};
+            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                 (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
+                 ++nodeIt) {
+                BufferNode* buf{static_cast<BufferNode*>(nodeIt->get())};
+                removeEmptyColumns(outDims, ndims, buf->m_param.dims,
+                                   buf->m_param.strides);
+            }
+            for_each(++begin(outputs), end(outputs),
+                     [outDims, ndims](Param& output) {
+                         removeEmptyColumns(outDims, ndims, output.info.dims,
+                                            output.info.strides);
+                     });
+            ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
+        }
 
-        global_0 = groups_0 * local_0 * outDims[2];
-        global_1 = groups_1 * local_1 * outDims[3];
+        full_nodes.clear();
+        for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
     }
 
-    NDRange local(local_0, local_1);
-    NDRange global(global_0, global_1);
+    threadsMgt<dim_t> th(outDims, ndims, nrInputs, nrOutputs, totalSize,
+                         outputSizeofType);
+    auto ker = getKernel(output_nodes, output_ids, full_nodes, full_ids,
+                         is_linear, th.loop0, th.loop1, th.loop3);
+    const cl::NDRange local{th.genLocal(ker)};
+    const cl::NDRange global{th.genGlobal(local)};
 
-    int nargs = 0;
-    for (const auto &node : full_nodes) {
+    int nargs{0};
+    for (const Node* node : full_nodes) {
         nargs = node->setArgs(nargs, is_linear,
-                              [&ker](int id, const void *ptr, size_t arg_size) {
+                              [&ker](int id, const void* ptr, size_t arg_size) {
                                   ker.setArg(id, arg_size, ptr);
                               });
     }
 
     // Set output parameters
-    for (auto &output : outputs) {
-        ker.setArg(nargs, *(output.data));
-        ++nargs;
+    for (const auto& output : outputs) {
+        ker.setArg(nargs++, *(output.data));
+        ker.setArg(nargs++, static_cast<int>(output.info.offset));
     }
 
     // Set dimensions
     // All outputs are asserted to be of same size
     // Just use the size from the first output
-    ker.setArg(nargs + 0, out_info);
-    ker.setArg(nargs + 1, groups_0);
-    ker.setArg(nargs + 2, groups_1);
-    ker.setArg(nargs + 3, num_odims);
-
+    ker.setArg(nargs++, out_info);
+
+    {
+        using namespace opencl::kernel_logger;
+        AF_TRACE(
+            "Launching : Dims: [{},{},{},{}] Global: [{},{},{}] Local: "
+            "[{},{},{}] threads: {}",
+            outDims[0], outDims[1], outDims[2], outDims[3], global[0],
+            global[1], global[2], local[0], local[1], local[2],
+            global[0] * global[1] * global[2]);
+    }
     getQueue().enqueueNDRangeKernel(ker, NullRange, global, local);
 
     // Reset the thread local vectors
@@ -336,9 +477,9 @@ void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
     full_ids.clear();
 }
 
-void evalNodes(Param &out, Node *node) {
+void evalNodes(Param& out, Node* node) {
     vector<Param> outputs{out};
-    vector<Node *> nodes{node};
+    vector<Node*> nodes{node};
     return evalNodes(outputs, nodes);
 }
 
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index c2eb711c1b..fe87ebc21b 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -47,18 +47,21 @@ inline int setKernelArguments(
 inline void generateBufferOffsets(std::stringstream& kerStream, int id,
                                   bool is_linear, const std::string& type_str) {
     UNUSED(type_str);
-    std::string idx_str  = std::string("int idx") + std::to_string(id);
-    std::string info_str = std::string("iInfo") + std::to_string(id);
+    const std::string idx_str  = std::string("idx") + std::to_string(id);
+    const std::string info_str = std::string("iInfo") + std::to_string(id);
+    const std::string in_str   = std::string("in") + std::to_string(id);
 
     if (is_linear) {
-        kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
+        kerStream << in_str << " += " << info_str << "_offset;\n"
+                  << "#define " << idx_str << " idx\n";
     } else {
-        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
-                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
-                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
-                  << info_str << ".dims[1]) * " << info_str
-                  << ".strides[1] * id1 + (id0 < " << info_str
-                  << ".dims[0]) * id0 + " << info_str << ".offset;\n";
+        kerStream << "int " << idx_str << " = id0*(id0<" << info_str
+                  << ".dims[0])*" << info_str << ".strides[0] + id1*(id1<"
+                  << info_str << ".dims[1])*" << info_str
+                  << ".strides[1] + id2*(id2<" << info_str << ".dims[2])*"
+                  << info_str << ".strides[2] + id3*(id3<" << info_str
+                  << ".dims[3])*" << info_str << ".strides[3] + " << info_str
+                  << ".offset;\n";
     }
 }
 
@@ -74,28 +77,25 @@ inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
                                      const std::string& type_str) {
     UNUSED(is_linear);
     UNUSED(type_str);
-    std::string idx_str   = std::string("idx") + std::to_string(id);
-    std::string info_str  = std::string("iInfo") + std::to_string(id);
-    std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
-    std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+    const std::string idx_str  = std::string("idx") + std::to_string(id);
+    const std::string info_str = std::string("iInfo") + std::to_string(id);
+    const std::string id_str = std::string("sh_id_") + std::to_string(id) + '_';
+    const std::string shift_str =
+        std::string("shift") + std::to_string(id) + '_';
 
     for (int i = 0; i < 4; i++) {
         kerStream << "int " << id_str << i << " = __circular_mod(id" << i
                   << " + " << shift_str << i << ", " << info_str << ".dims["
                   << i << "]);\n";
     }
-
-    kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str
-              << ".dims[3]) * " << info_str << ".strides[3] * " << id_str
-              << "3;\n";
-    kerStream << idx_str << " += (" << id_str << "2 < " << info_str
-              << ".dims[2]) * " << info_str << ".strides[2] * " << id_str
-              << "2;\n";
-    kerStream << idx_str << " += (" << id_str << "1 < " << info_str
-              << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
-              << "1;\n";
-    kerStream << idx_str << " += (" << id_str << "0 < " << info_str
-              << ".dims[0]) * " << id_str << "0 + " << info_str << ".offset;\n";
+    kerStream << "int " << idx_str << " = " << id_str << "0*(" << id_str << "0<"
+              << info_str << ".dims[0])*" << info_str << ".strides[0] + "
+              << id_str << "1*(" << id_str << "1<" << info_str << ".dims[1])*"
+              << info_str << ".strides[1] + " << id_str << "2*(" << id_str
+              << "2<" << info_str << ".dims[2])*" << info_str
+              << ".strides[2] + " << id_str << "3*(" << id_str << "3<"
+              << info_str << ".dims[3])*" << info_str << ".strides[3] + "
+              << info_str << ".offset;\n";
 }
 
 inline void generateShiftNodeRead(std::stringstream& kerStream, int id,

From bef4f10a9e31bb780bb89df675171fdee3207986 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 4 Aug 2022 01:11:18 +0200
Subject: [PATCH 2279/2677] OPT: join

---
 src/api/c/join.cpp                    | 113 ++++++-------
 src/backend/cuda/CMakeLists.txt       |   3 +-
 src/backend/cuda/join.cpp             | 213 ++++++++++++++++++------
 src/backend/cuda/kernel/join.cuh      |  50 ------
 src/backend/cuda/kernel/join.hpp      |  51 ------
 src/backend/cuda/platform.cpp         |   2 +-
 src/backend/opencl/CMakeLists.txt     |   2 +-
 src/backend/opencl/join.cpp           | 227 ++++++++++++++++++++------
 src/backend/opencl/kernel/join.cl     |  41 -----
 src/backend/opencl/kernel/join.hpp    |  55 -------
 src/backend/opencl/kernel/memcopy.hpp |  10 +-
 11 files changed, 397 insertions(+), 370 deletions(-)
 delete mode 100644 src/backend/cuda/kernel/join.cuh
 delete mode 100644 src/backend/cuda/kernel/join.hpp
 delete mode 100644 src/backend/opencl/kernel/join.cl
 delete mode 100644 src/backend/opencl/kernel/join.hpp

diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index dad2bc1ffd..a31a728874 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -14,7 +14,9 @@
 #include <handle.hpp>
 #include <join.hpp>
 #include <af/data.h>
+
 #include <algorithm>
+#include <climits>
 #include <vector>
 
 using af::dim4;
@@ -43,30 +45,21 @@ static inline af_array join_many(const int dim, const unsigned n_arrays,
     vector<Array<T>> inputs_;
     inputs_.reserve(n_arrays);
 
-    for (unsigned i = 0; i < n_arrays; i++) {
-        inputs_.push_back(getArray<T>(inputs[i]));
-        if (inputs_.back().isEmpty()) { inputs_.pop_back(); }
+    dim_t dim_size{0};
+    for (unsigned i{0}; i < n_arrays; ++i) {
+        const Array<T> &iArray = getArray<T>(inputs[i]);
+        if (!iArray.isEmpty()) {
+            inputs_.push_back(iArray);
+            dim_size += iArray.dims().dims[dim];
+        }
     }
 
     // All dimensions except join dimension must be equal
     // calculate odims size
-    std::vector<af::dim4> idims(inputs_.size());
-    dim_t dim_size = 0;
-    for (unsigned i = 0; i < idims.size(); i++) {
-        idims[i] = inputs_[i].dims();
-        dim_size += idims[i][dim];
-    }
-
-    af::dim4 odims;
-    for (int i = 0; i < 4; i++) {
-        if (i == dim) {
-            odims[i] = dim_size;
-        } else {
-            odims[i] = idims[0][i];
-        }
-    }
+    af::dim4 odims{inputs_[0].dims()};
+    odims.dims[dim] = dim_size;
 
-    Array<T> out = createEmptyArray<T>(odims);
+    Array<T> out{createEmptyArray<T>(odims)};
     join<T>(out, dim, inputs_);
     return getHandle(out);
 }
@@ -74,24 +67,21 @@ static inline af_array join_many(const int dim, const unsigned n_arrays,
 af_err af_join(af_array *out, const int dim, const af_array first,
                const af_array second) {
     try {
-        const ArrayInfo &finfo = getInfo(first);
-        const ArrayInfo &sinfo = getInfo(second);
-        dim4 fdims             = finfo.dims();
-        dim4 sdims             = sinfo.dims();
+        const ArrayInfo &finfo{getInfo(first)};
+        const ArrayInfo &sinfo{getInfo(second)};
+        const dim4 &fdims{finfo.dims()};
+        const dim4 &sdims{sinfo.dims()};
 
         ARG_ASSERT(1, dim >= 0 && dim < 4);
         ARG_ASSERT(2, finfo.getType() == sinfo.getType());
         if (sinfo.elements() == 0) { return af_retain_array(out, first); }
-
         if (finfo.elements() == 0) { return af_retain_array(out, second); }
-
-        DIM_ASSERT(2, sinfo.elements() > 0);
-        DIM_ASSERT(3, finfo.elements() > 0);
+        DIM_ASSERT(2, finfo.elements() > 0);
+        DIM_ASSERT(3, sinfo.elements() > 0);
 
         // All dimensions except join dimension must be equal
-        // Compute output dims
-        for (int i = 0; i < 4; i++) {
-            if (i != dim) { DIM_ASSERT(2, fdims[i] == sdims[i]); }
+        for (int i{0}; i < AF_MAX_DIMS; i++) {
+            if (i != dim) { DIM_ASSERT(2, fdims.dims[i] == sdims.dims[i]); }
         }
 
         af_array output;
@@ -125,55 +115,46 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
         ARG_ASSERT(3, inputs != nullptr);
 
         if (n_arrays == 1) {
-            af_array ret = nullptr;
-            AF_CHECK(af_retain_array(&ret, inputs[0]));
+            af_array ret{nullptr};
+            AF_CHECK(af_retain_array(&ret, *inputs));
             std::swap(*out, ret);
             return AF_SUCCESS;
         }
 
-        vector<ArrayInfo> info;
-        info.reserve(n_arrays);
-        vector<af::dim4> dims(n_arrays);
-        for (unsigned i = 0; i < n_arrays; i++) {
-            info.push_back(getInfo(inputs[i]));
-            dims[i] = info[i].dims();
-        }
+        ARG_ASSERT(1, dim >= 0 && dim < AF_MAX_DIMS);
+        ARG_ASSERT(2, n_arrays > 0);
 
-        ARG_ASSERT(1, dim >= 0 && dim < 4);
-
-        bool allEmpty = std::all_of(
-            info.begin(), info.end(),
-            [](const ArrayInfo &i) -> bool { return i.elements() <= 0; });
-        if (allEmpty) {
+        const af_array *inputIt{inputs};
+        const af_array *inputEnd{inputs + n_arrays};
+        while ((inputIt != inputEnd) && (getInfo(*inputIt).elements() == 0)) {
+            ++inputIt;
+        }
+        if (inputIt == inputEnd) {
+            // All arrays have 0 elements
             af_array ret = nullptr;
-            AF_CHECK(af_retain_array(&ret, inputs[0]));
+            AF_CHECK(af_retain_array(&ret, *inputs));
             std::swap(*out, ret);
             return AF_SUCCESS;
         }
 
-        auto first_valid_afinfo = std::find_if(
-            info.begin(), info.end(),
-            [](const ArrayInfo &i) -> bool { return i.elements() > 0; });
-
-        af_dtype assertType = first_valid_afinfo->getType();
-        for (unsigned i = 1; i < n_arrays; i++) {
-            if (info[i].elements() > 0) {
-                ARG_ASSERT(3, assertType == info[i].getType());
-            }
-        }
-
-        // All dimensions except join dimension must be equal
-        af::dim4 assertDims = first_valid_afinfo->dims();
-        for (int i = 0; i < 4; i++) {
-            if (i != dim) {
-                for (unsigned j = 0; j < n_arrays; j++) {
-                    if (info[j].elements() > 0) {
-                        DIM_ASSERT(3, assertDims[i] == dims[j][i]);
+        // inputIt points to first non empty array
+        const af_dtype assertType{getInfo(*inputIt).getType()};
+        const dim4 &assertDims{getInfo(*inputIt).dims()};
+
+        // Check all remaining arrays on assertType and assertDims
+        while (++inputIt != inputEnd) {
+            const ArrayInfo &info = getInfo(*inputIt);
+            if (info.elements() > 0) {
+                ARG_ASSERT(3, assertType == info.getType());
+                const dim4 &infoDims{getInfo(*inputIt).dims()};
+                // All dimensions except join dimension must be equal
+                for (int i{0}; i < AF_MAX_DIMS; i++) {
+                    if (i != dim) {
+                        DIM_ASSERT(3, assertDims.dims[i] == infoDims.dims[i]);
                     }
                 }
             }
         }
-
         af_array output;
 
         switch (assertType) {
@@ -190,7 +171,7 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
             case u16: output = join_many<ushort>(dim, n_arrays, inputs); break;
             case u8: output = join_many<uchar>(dim, n_arrays, inputs); break;
             case f16: output = join_many<half>(dim, n_arrays, inputs); break;
-            default: TYPE_ERROR(1, info[0].getType());
+            default: TYPE_ERROR(1, assertType);
         }
         swap(*out, output);
     }
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 3fcf1d2259..a6a750f83f 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -208,7 +208,6 @@ set(nvrtc_src
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/index.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/iota.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/ireduce.cuh
-  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/join.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/lookup.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/lu_split.cuh
   ${CMAKE_CURRENT_SOURCE_DIR}/kernel/match_template.cuh
@@ -458,7 +457,6 @@ cuda_add_library(afcuda
     kernel/interp.hpp
     kernel/iota.hpp
     kernel/ireduce.hpp
-    kernel/join.hpp
     kernel/lookup.hpp
     kernel/lu_split.hpp
     kernel/match_template.hpp
@@ -659,6 +657,7 @@ cuda_add_library(afcuda
     svd.hpp
     tile.cpp
     tile.hpp
+    threadsMgt.hpp
     topk.hpp
     traits.hpp
     transform.hpp
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 880716e22b..a605867863 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -11,76 +11,191 @@
 #include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <join.hpp>
-#include <kernel/join.hpp>
+#include <kernel/memcopy.hpp>
 
 #include <algorithm>
+#include <map>
 #include <stdexcept>
+#include <vector>
 
+using af::dim4;
 using common::half;
+using common::Node;
+using common::Node_ptr;
+using std::vector;
 
 namespace cuda {
 
-af::dim4 calcOffset(const af::dim4 &dims, const int dim) {
-    af::dim4 offset;
-    offset[0] = (dim == 0) * dims[0];
-    offset[1] = (dim == 1) * dims[1];
-    offset[2] = (dim == 2) * dims[2];
-    offset[3] = (dim == 3) * dims[3];
-    return offset;
-}
-
 template<typename T>
-Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
+Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     // All dimensions except join dimension must be equal
+    const dim4 &fdims{first.dims()};
+    const dim4 &sdims{second.dims()};
     // Compute output dims
-    af::dim4 odims;
-    af::dim4 fdims = first.dims();
-    af::dim4 sdims = second.dims();
+    dim4 odims(fdims);
+    odims.dims[jdim] += sdims.dims[jdim];
+    Array<T> out{createEmptyArray<T>(odims)};
+    const cudaStream_t activeStream{getActiveStream()};
+
+    // topspeed is achieved when byte size(in+out) ~= L2CacheSize
+    //
+    // 1 array: memcpy always copies 1 array.  topspeed
+    //      --> size(in) < L2CacheSize/2
+    // 2 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/2
+    //          --> JIT can copy 2 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - size(in) < L2CacheSize/2
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called twice
+    //      - size(in) >= L2CacheSize/2
+    //          --> memcpy will achieve veryLargeArray speed.  The kernel
+    //              will be called twice
+    if (fdims.dims[jdim] == sdims.dims[jdim]) {
+        const size_t L2CacheSize{getL2CacheSize(getActiveDeviceId())};
+        if (!(first.isReady() | second.isReady()) ||
+            (fdims.elements() * sizeof(T) * 2 * 2 < L2CacheSize)) {
+            // Both arrays have same size & everything fits into the cache,
+            // so treat in 1 JIT kernel, iso individual copies which is
+            // always slower
+            const dim_t *outStrides{out.strides().dims};
+            vector<Param<T>> outputs{
+                {out.get(), fdims.dims, outStrides},
+                {out.get() + fdims.dims[jdim] * outStrides[jdim], sdims.dims,
+                 outStrides}};
+            // Extend the life of the returned node, by saving the
+            // corresponding shared_ptr
+            const Node_ptr fNode{first.getNode()};
+            const Node_ptr sNode{second.getNode()};
+            vector<Node *> nodes{fNode.get(), sNode.get()};
+            evalNodes(outputs, nodes);
+            return out;
+        }
+        // continue because individually processing is faster
+    }
 
-    for (int i = 0; i < 4; i++) {
-        if (i == dim) {
-            odims[i] = fdims[i] + sdims[i];
+    // Handle each array individually
+    if (first.isReady()) {
+        if (1LL + jdim >= first.ndims() && first.isLinear()) {
+            // first & out are linear
+            CUDA_CHECK(cudaMemcpyAsync(out.get(), first.get(),
+                                       first.elements() * sizeof(T),
+                                       cudaMemcpyDeviceToDevice, activeStream));
         } else {
-            odims[i] = fdims[i];
+            kernel::memcopy<T>(out, first, first.ndims());
         }
+    } else {
+        // Write the result directly in the out array
+        const Param<T> output(out.get(), fdims.dims, out.strides().dims);
+        evalNodes(output, first.getNode().get());
     }
 
-    Array<T> out = createEmptyArray<T>(odims);
-
-    af::dim4 zero(0, 0, 0, 0);
-
-    kernel::join<T>(out, first, zero, dim);
-    kernel::join<T>(out, second, calcOffset(fdims, dim), dim);
+    if (second.isReady()) {
+        if (1LL + jdim >= second.ndims() && second.isLinear()) {
+            // second & out are linear
+            CUDA_CHECK(cudaMemcpyAsync(
+                out.get() + fdims.dims[jdim] * out.strides().dims[jdim],
+                second.get(), second.elements() * sizeof(T),
+                cudaMemcpyDeviceToDevice, activeStream));
+        } else {
+            Param<T> output(
+                out.get() + fdims.dims[jdim] * out.strides().dims[jdim],
+                sdims.dims, out.strides().dims);
+            kernel::memcopy<T>(output, second, second.ndims());
+        }
+    } else {
+        // Write the result directly in the out array
+        const Param<T> output(
+            out.get() + fdims.dims[jdim] * out.strides().dims[jdim], sdims.dims,
+            out.strides().dims);
+        evalNodes(output, second.getNode().get());
+    }
 
-    return out;
+    return (out);
 }
 
 template<typename T>
-void join_wrapper(const int dim, Array<T> &out,
-                  const std::vector<Array<T>> &inputs) {
-    af::dim4 zero(0, 0, 0, 0);
-    af::dim4 d = zero;
-
-    kernel::join<T>(out, inputs[0], zero, dim);
-    for (size_t i = 1; i < inputs.size(); i++) {
-        d += inputs[i - 1].dims();
-        kernel::join<T>(out, inputs[i], calcOffset(d, dim), dim);
+void join(Array<T> &out, const int jdim, const vector<Array<T>> &inputs) {
+    class eval {
+       public:
+        vector<Param<T>> outputs;
+        vector<Node_ptr> nodePtrs;
+        vector<Node *> nodes;
+        vector<const Array<T> *> ins;
+    };
+    std::map<dim_t, eval> evals;
+    const cudaStream_t activeStream{getActiveStream()};
+    const size_t L2CacheSize{getL2CacheSize(getActiveDeviceId())};
+
+    // topspeed is achieved when byte size(in+out) ~= L2CacheSize
+    //
+    // 1 array: memcpy always copies 1 array.  topspeed
+    //      --> size(in) <= L2CacheSize/2
+    // 2 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/2
+    //          --> JIT can copy 2 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - else
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called twice
+    // 3 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/3
+    //          --> JIT can copy 3 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - else
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called multiple times
+
+    // Group all arrays according to size
+    dim_t outOffset{0};
+    for (const Array<T> &iArray : inputs) {
+        const dim_t *idims{iArray.dims().dims};
+        eval &e{evals[idims[jdim]]};
+        e.outputs.emplace_back(out.get() + outOffset, idims,
+                               out.strides().dims);
+        // Extend life of the returned node by saving the corresponding
+        // shared_ptr
+        e.nodePtrs.emplace_back(iArray.getNode());
+        e.nodes.push_back(e.nodePtrs.back().get());
+        e.ins.push_back(&iArray);
+        outOffset += idims[jdim] * out.strides().dims[jdim];
     }
-}
 
-template<typename T>
-void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs) {
-    std::vector<Array<T> *> input_ptrs(inputs.size());
-    std::transform(
-        begin(inputs), end(inputs), begin(input_ptrs),
-        [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
-    evalMultiple(input_ptrs);
-
-    join_wrapper<T>(dim, out, inputs);
+    for (auto &eval : evals) {
+        auto &s{eval.second};
+        if (s.ins.size() == 1 ||
+            s.ins[0]->elements() * sizeof(T) * 2 * 2 > L2CacheSize) {
+            // Process (evaluated arrays) individually for
+            //  - single small array
+            //  - very large arrays
+            auto nodeIt{begin(s.nodes)};
+            auto outputIt{begin(s.outputs)};
+            for (const Array<T> *in : s.ins) {
+                if (in->isReady()) {
+                    if (1LL + jdim >= in->ndims() && in->isLinear()) {
+                        CUDA_CHECK(cudaMemcpyAsync(outputIt->ptr, in->get(),
+                                                   in->elements() * sizeof(T),
+                                                   cudaMemcpyHostToDevice,
+                                                   activeStream));
+                    } else {
+                        kernel::memcopy<T>(*outputIt, *in, in->ndims());
+                    }
+                    // eliminate this array from the list, so that it will
+                    // not be processed as bulk via JIT
+                    outputIt = s.outputs.erase(outputIt);
+                    nodeIt   = s.nodes.erase(nodeIt);
+                } else {
+                    ++outputIt;
+                    ++nodeIt;
+                }
+            }
+        }
+        evalNodes(s.outputs, s.nodes);
+    }
 }
 
-#define INSTANTIATE(T)                                              \
-    template Array<T> join<T>(const int dim, const Array<T> &first, \
+#define INSTANTIATE(T)                                               \
+    template Array<T> join<T>(const int jdim, const Array<T> &first, \
                               const Array<T> &second);
 
 INSTANTIATE(float)
@@ -99,9 +214,9 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                                   \
-    template void join<T>(Array<T> & out, const int dim, \
-                          const std::vector<Array<T>> &inputs);
+#define INSTANTIATE(T)                                    \
+    template void join<T>(Array<T> & out, const int jdim, \
+                          const vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/kernel/join.cuh b/src/backend/cuda/kernel/join.cuh
deleted file mode 100644
index 666114e07b..0000000000
--- a/src/backend/cuda/kernel/join.cuh
+++ /dev/null
@@ -1,50 +0,0 @@
-/*******************************************************
- * Copyright (c) 2020, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <Param.hpp>
-
-namespace cuda {
-
-template<typename T>
-__global__ void join(Param<T> out, CParam<T> in, const int o0, const int o1,
-                     const int o2, const int o3, const int blocksPerMatX,
-                     const int blocksPerMatY) {
-    const int incy = blocksPerMatY * blockDim.y;
-    const int incx = blocksPerMatX * blockDim.x;
-
-    const int iz         = blockIdx.x / blocksPerMatX;
-    const int blockIdx_x = blockIdx.x - iz * blocksPerMatX;
-    const int xx         = threadIdx.x + blockIdx_x * blockDim.x;
-
-    T *d_out      = out.ptr;
-    T const *d_in = in.ptr;
-
-    const int iw = (blockIdx.y + (blockIdx.z * gridDim.y)) / blocksPerMatY;
-    const int blockIdx_y =
-        (blockIdx.y + (blockIdx.z * gridDim.y)) - iw * blocksPerMatY;
-    const int yy = threadIdx.y + blockIdx_y * blockDim.y;
-
-    if (iz < in.dims[2] && iw < in.dims[3]) {
-        d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
-        d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
-
-        for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            T const *d_in_ = d_in + iy * in.strides[1];
-            T *d_out_      = d_out + (iy + o1) * out.strides[1];
-
-            for (int ix = xx; ix < in.dims[0]; ix += incx) {
-                d_out_[ix + o0] = d_in_[ix];
-            }
-        }
-    }
-}
-
-}  // namespace cuda
diff --git a/src/backend/cuda/kernel/join.hpp b/src/backend/cuda/kernel/join.hpp
deleted file mode 100644
index f404f7b8bf..0000000000
--- a/src/backend/cuda/kernel/join.hpp
+++ /dev/null
@@ -1,51 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <Param.hpp>
-#include <common/dispatch.hpp>
-#include <common/kernel_cache.hpp>
-#include <debug_cuda.hpp>
-#include <nvrtc_kernel_headers/join_cuh.hpp>
-
-namespace cuda {
-namespace kernel {
-
-template<typename T>
-void join(Param<T> out, CParam<T> X, const af::dim4 &offset, int dim) {
-    constexpr unsigned TX    = 32;
-    constexpr unsigned TY    = 8;
-    constexpr unsigned TILEX = 256;
-    constexpr unsigned TILEY = 32;
-
-    auto join = common::getKernel("cuda::join", {join_cuh_src},
-                                  {TemplateTypename<T>()});
-
-    dim3 threads(TX, TY, 1);
-
-    int blocksPerMatX = divup(X.dims[0], TILEX);
-    int blocksPerMatY = divup(X.dims[1], TILEY);
-
-    dim3 blocks(blocksPerMatX * X.dims[2], blocksPerMatY * X.dims[3], 1);
-
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
-
-    EnqueueArgs qArgs(blocks, threads, getActiveStream());
-
-    join(qArgs, out, X, offset[0], offset[1], offset[2], offset[3],
-         blocksPerMatX, blocksPerMatY);
-    POST_LAUNCH_CHECK();
-}
-
-}  // namespace kernel
-}  // namespace cuda
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 520d4f90f5..fa412101f0 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -208,7 +208,7 @@ string getDeviceInfo(int device) noexcept {
     size_t mem_gpu_total = dev.totalGlobalMem;
     // double cc = double(dev.major) + double(dev.minor) / 10;
 
-    bool show_braces = getActiveDeviceId() == static_cast<unsigned>(device);
+    bool show_braces = getActiveDeviceId() == device;
 
     string id = (show_braces ? string("[") : "-") + to_string(device) +
                 (show_braces ? string("]") : "-");
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 506b9b3f55..024c92551a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -227,6 +227,7 @@ target_sources(afopencl
     svd.hpp
     tile.cpp
     tile.hpp
+    threadsMgt.hpp
     topk.cpp
     topk.hpp
     traits.hpp
@@ -285,7 +286,6 @@ target_sources(afopencl
     kernel/interp.hpp
     kernel/iota.hpp
     kernel/ireduce.hpp
-    kernel/join.hpp
     kernel/laset.hpp
     #kernel/laset_band.hpp
     kernel/laswp.hpp
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 0c7109a895..2d166b693e 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -11,80 +11,209 @@
 #include <common/half.hpp>
 #include <err_opencl.hpp>
 #include <join.hpp>
-#include <kernel/join.hpp>
+#include <kernel/memcopy.hpp>
 
 #include <algorithm>
+#include <map>
 #include <stdexcept>
 #include <vector>
 
 using af::dim4;
 using common::half;
-using std::transform;
+using common::Node;
+using common::Node_ptr;
 using std::vector;
 
 namespace opencl {
-dim4 calcOffset(const dim4 &dims, int dim) {
-    dim4 offset;
-    offset[0] = (dim == 0) ? dims[0] : 0;
-    offset[1] = (dim == 1) ? dims[1] : 0;
-    offset[2] = (dim == 2) ? dims[2] : 0;
-    offset[3] = (dim == 3) ? dims[3] : 0;
-    return offset;
-}
-
 template<typename T>
-Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
+Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     // All dimensions except join dimension must be equal
+    const dim4 &fdims{first.dims()};
+    const dim4 &sdims{second.dims()};
     // Compute output dims
-    dim4 odims;
-    dim4 fdims = first.dims();
-    dim4 sdims = second.dims();
+    dim4 odims(fdims);
+    odims.dims[jdim] += sdims.dims[jdim];
+    Array<T> out = createEmptyArray<T>(odims);
 
-    for (int i = 0; i < 4; i++) {
-        if (i == dim) {
-            odims[i] = fdims[i] + sdims[i];
-        } else {
-            odims[i] = fdims[i];
+    // topspeed is achieved when byte size(in+out) ~= L2CacheSize
+    //
+    // 1 array: memcpy always copies 1 array.  topspeed
+    //      --> size(in) <= L2CacheSize/2
+    // 2 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/2
+    //          --> JIT can copy 2 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - size(in) < L2CacheSize/2
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called twice
+    //      - size(in) >= L2CacheSize/2
+    //          --> memcpy will achieve veryLargeArray speed.  The kernel
+    //              will be called twice
+    if (fdims.dims[jdim] == sdims.dims[jdim]) {
+        const size_t L2CacheSize{getL2CacheSize(opencl::getDevice())};
+        if (!(first.isReady() | second.isReady()) ||
+            (fdims.elements() * sizeof(T) * 2 * 2 < L2CacheSize)) {
+            // Both arrays have same size & everything fits into the cache,
+            // so thread in 1 JIT kernel, iso individual copies which is
+            // always slower
+            const dim_t *outStrides{out.strides().dims};
+            vector<Param> outputs{
+                {out.get(),
+                 {{fdims.dims[0], fdims.dims[1], fdims.dims[2], fdims.dims[3]},
+                  {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+                  0}},
+                {out.get(),
+                 {{sdims.dims[0], sdims.dims[1], sdims.dims[2], sdims.dims[3]},
+                  {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+                  fdims.dims[jdim] * outStrides[jdim]}}};
+            // Extend the life of the returned node, bij saving the
+            // corresponding shared_ptr
+            const Node_ptr fNode{first.getNode()};
+            const Node_ptr sNode{second.getNode()};
+            vector<Node *> nodes{fNode.get(), sNode.get()};
+            evalNodes(outputs, nodes);
+            return out;
         }
+        // continue because individually processing is faster
     }
 
-    Array<T> out = createEmptyArray<T>(odims);
-
-    dim4 zero(0, 0, 0, 0);
+    // Handle each array individually
+    if (first.isReady()) {
+        if (1LL + jdim >= first.ndims() && first.isLinear()) {
+            // first & out are linear
+            getQueue().enqueueCopyBuffer(
+                *first.get(), *out.get(), first.getOffset() * sizeof(T), 0,
+                first.elements() * sizeof(T), nullptr, nullptr);
+        } else {
+            kernel::memcopy<T>(*out.get(), out.strides(), *first.get(), fdims,
+                               first.strides(), first.getOffset(),
+                               first.ndims(), 0);
+        }
+    } else {
+        // Write the result directly in the out array
+        const dim_t *outStrides{out.strides().dims};
+        Param output{
+            out.get(),
+            {{fdims.dims[0], fdims.dims[1], fdims.dims[2], fdims.dims[3]},
+             {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+             0}};
+        evalNodes(output, first.getNode().get());
+    }
 
-    kernel::join<T>(out, first, dim, zero);
-    kernel::join<T>(out, second, dim, calcOffset(fdims, dim));
+    if (second.isReady()) {
+        if (1LL + jdim >= second.ndims() && second.isLinear()) {
+            // second & out are linear
+            getQueue().enqueueCopyBuffer(
+                *second.get(), *out.get(), second.getOffset() * sizeof(T),
+                (fdims.dims[jdim] * out.strides().dims[jdim]) * sizeof(T),
+                second.elements() * sizeof(T), nullptr, nullptr);
+        } else {
+            kernel::memcopy<T>(*out.get(), out.strides(), *second.get(), sdims,
+                               second.strides(), second.getOffset(),
+                               second.ndims(),
+                               fdims.dims[jdim] * out.strides().dims[jdim]);
+        }
+    } else {
+        // Write the result directly in the out array
+        const dim_t *outStrides{out.strides().dims};
+        Param output{
+            out.get(),
+            {{sdims.dims[0], sdims.dims[1], sdims.dims[2], sdims.dims[3]},
+             {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+             fdims.dims[jdim] * outStrides[jdim]}};
+        evalNodes(output, second.getNode().get());
+    }
 
     return out;
 }
 
 template<typename T>
-void join_wrapper(const int dim, Array<T> &out,
-                  const vector<Array<T>> &inputs) {
-    dim4 zero(0, 0, 0, 0);
-    dim4 d = zero;
-
-    kernel::join<T>(out, inputs[0], dim, zero);
-    for (size_t i = 1; i < inputs.size(); i++) {
-        d += inputs[i - 1].dims();
-        kernel::join<T>(out, inputs[i], dim, calcOffset(d, dim));
+void join(Array<T> &out, const int jdim, const vector<Array<T>> &inputs) {
+    class eval {
+       public:
+        vector<Param> outputs;
+        vector<Node_ptr> nodePtrs;
+        vector<Node *> nodes;
+        vector<const Array<T> *> ins;
+    };
+    std::map<dim_t, eval> evals;
+    const dim_t *ostrides{out.strides().dims};
+    const size_t L2CacheSize{getL2CacheSize(opencl::getDevice())};
+
+    // topspeed is achieved when byte size(in+out) ~= L2CacheSize
+    //
+    // 1 array: memcpy always copies 1 array.  topspeed
+    //      --> size(in) <= L2CacheSize/2
+    // 2 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/2
+    //          --> JIT can copy 2 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - size(in) < L2CacheSize/2
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called twice
+    //      - size(in) >= L2CacheSize/2
+    //          --> memcpy will achieve veryLargeArray speed.  The kernel
+    //              will be called twice
+
+    // Group all arrays according to size
+    dim_t outOffset{0};
+    for (const Array<T> &iArray : inputs) {
+        const dim_t *idims{iArray.dims().dims};
+        eval &e{evals[idims[jdim]]};
+        const Param output{
+            out.get(),
+            {{idims[0], idims[1], idims[2], idims[3]},
+             {ostrides[0], ostrides[1], ostrides[2], ostrides[3]},
+             outOffset}};
+        e.outputs.push_back(output);
+        // Extend life of the returned node by saving the corresponding
+        // shared_ptr
+        e.nodePtrs.emplace_back(iArray.getNode());
+        e.nodes.push_back(e.nodePtrs.back().get());
+        e.ins.push_back(&iArray);
+        outOffset += idims[jdim] * ostrides[jdim];
     }
-}
 
-template<typename T>
-void join(Array<T> &out, const int dim, const vector<Array<T>> &inputs) {
-    vector<Array<T> *> input_ptrs(inputs.size());
-    transform(
-        begin(inputs), end(inputs), begin(input_ptrs),
-        [](const Array<T> &input) { return const_cast<Array<T> *>(&input); });
-    evalMultiple(input_ptrs);
-    vector<Param> inputParams(inputs.begin(), inputs.end());
-
-    join_wrapper<T>(dim, out, inputs);
+    for (auto &eval : evals) {
+        auto &s{eval.second};
+        if (s.ins.size() == 1 ||
+            s.ins[0]->elements() * sizeof(T) * 2 * 2 > L2CacheSize) {
+            // Process (evaluate arrays) individually for
+            //  - single small array
+            //  - very large arrays
+            auto nodeIt{begin(s.nodes)};
+            auto outputIt{begin(s.outputs)};
+            for (const Array<T> *in : s.ins) {
+                if (in->isReady()) {
+                    if (1LL + jdim >= in->ndims() && in->isLinear()) {
+                        getQueue().enqueueCopyBuffer(
+                            *in->get(), *outputIt->data,
+                            in->getOffset() * sizeof(T),
+                            outputIt->info.offset * sizeof(T),
+                            in->elements() * sizeof(T), nullptr, nullptr);
+                    } else {
+                        kernel::memcopy<T>(*outputIt->data,
+                                           af::dim4(4, outputIt->info.strides),
+                                           *in->get(), in->dims(),
+                                           in->strides(), in->getOffset(),
+                                           in->ndims(), outputIt->info.offset);
+                    }
+                    // eliminate this array from the list, so that it will
+                    // not be processed in bulk via JIT
+                    outputIt = s.outputs.erase(outputIt);
+                    nodeIt   = s.nodes.erase(nodeIt);
+                } else {
+                    ++outputIt;
+                    ++nodeIt;
+                }
+            }
+        }
+        evalNodes(s.outputs, s.nodes);
+    }
 }
 
-#define INSTANTIATE(T)                                              \
-    template Array<T> join<T>(const int dim, const Array<T> &first, \
+#define INSTANTIATE(T)                                               \
+    template Array<T> join<T>(const int jdim, const Array<T> &first, \
                               const Array<T> &second);
 
 INSTANTIATE(float)
@@ -103,8 +232,8 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 
-#define INSTANTIATE(T)                                   \
-    template void join<T>(Array<T> & out, const int dim, \
+#define INSTANTIATE(T)                                    \
+    template void join<T>(Array<T> & out, const int jdim, \
                           const vector<Array<T>> &inputs);
 
 INSTANTIATE(float)
diff --git a/src/backend/opencl/kernel/join.cl b/src/backend/opencl/kernel/join.cl
deleted file mode 100644
index 884ec56d62..0000000000
--- a/src/backend/opencl/kernel/join.cl
+++ /dev/null
@@ -1,41 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-kernel void join_kernel(global T *d_out, const KParam out, global const T *d_in,
-                        const KParam in, const int o0, const int o1,
-                        const int o2, const int o3, const int blocksPerMatX,
-                        const int blocksPerMatY) {
-    const int iz = get_group_id(0) / blocksPerMatX;
-    const int iw = get_group_id(1) / blocksPerMatY;
-
-    const int blockIdx_x = get_group_id(0) - iz * blocksPerMatX;
-    const int blockIdx_y = get_group_id(1) - iw * blocksPerMatY;
-
-    const int xx = get_local_id(0) + blockIdx_x * get_local_size(0);
-    const int yy = get_local_id(1) + blockIdx_y * get_local_size(1);
-
-    const int incy = blocksPerMatY * get_local_size(1);
-    const int incx = blocksPerMatX * get_local_size(0);
-
-    d_in = d_in + in.offset;
-
-    if (iz < in.dims[2] && iw < in.dims[3]) {
-        d_out = d_out + (iz + o2) * out.strides[2] + (iw + o3) * out.strides[3];
-        d_in  = d_in + iz * in.strides[2] + iw * in.strides[3];
-
-        for (int iy = yy; iy < in.dims[1]; iy += incy) {
-            global T *d_in_  = d_in + iy * in.strides[1];
-            global T *d_out_ = d_out + (iy + o1) * out.strides[1];
-
-            for (int ix = xx; ix < in.dims[0]; ix += incx) {
-                d_out_[ix + o0] = d_in_[ix];
-            }
-        }
-    }
-}
diff --git a/src/backend/opencl/kernel/join.hpp b/src/backend/opencl/kernel/join.hpp
deleted file mode 100644
index 5a4016eee6..0000000000
--- a/src/backend/opencl/kernel/join.hpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*******************************************************
- * Copyright (c) 2014, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <Param.hpp>
-#include <common/dispatch.hpp>
-#include <common/kernel_cache.hpp>
-#include <debug_opencl.hpp>
-#include <kernel_headers/join.hpp>
-#include <traits.hpp>
-
-#include <string>
-#include <vector>
-
-namespace opencl {
-namespace kernel {
-
-template<typename T>
-void join(Param out, const Param in, dim_t dim, const af::dim4 offset) {
-    constexpr int TX    = 32;
-    constexpr int TY    = 8;
-    constexpr int TILEX = 256;
-    constexpr int TILEY = 32;
-
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
-
-    auto join =
-        common::getKernel("join_kernel", {join_cl_src},
-                          {TemplateTypename<T>(), TemplateArg(dim)}, options);
-    cl::NDRange local(TX, TY, 1);
-
-    int blocksPerMatX = divup(in.info.dims[0], TILEX);
-    int blocksPerMatY = divup(in.info.dims[1], TILEY);
-    cl::NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
-                       local[1] * blocksPerMatY * in.info.dims[3], 1);
-
-    join(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
-         *in.data, in.info, static_cast<int>(offset[0]),
-         static_cast<int>(offset[1]), static_cast<int>(offset[2]),
-         static_cast<int>(offset[3]), blocksPerMatX, blocksPerMatY);
-    CL_DEBUG_FINISH(getQueue());
-}
-
-}  // namespace kernel
-}  // namespace opencl
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 9358315cd5..159fe4d35a 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -126,7 +126,7 @@ void memcopy(const cl::Buffer& b_out, const dim4& ostrides,
     // When the architecture prefers some width's, it is certainly
     // on char.  No preference means vector width 1 returned.
     const bool DevicePreferredVectorWidth{DevicePreferredVectorWidthChar != 1};
-    unsigned maxVectorWidth{
+    size_t maxVectorWidth{
         DevicePreferredVectorWidth
             ? sizeof(T) == 1 ? DevicePreferredVectorWidthChar
               : sizeof(T) == 2
@@ -138,10 +138,10 @@ void memcopy(const cl::Buffer& b_out, const dim4& ostrides,
                   : 1
         : sizeof(T) > 8 ? 1
                         : 16 / sizeof(T)};
-    const unsigned vectorWidth{vectorizeShape(maxVectorWidth, idims_.dims,
-                                              istrides_.dims, indims_, ioffset,
-                                              ostrides_.dims, ooffset)};
-    const dim_t sizeofNewT{sizeof(T) * vectorWidth};
+    const size_t vectorWidth{vectorizeShape(maxVectorWidth, idims_.dims,
+                                            istrides_.dims, indims_, ioffset,
+                                            ostrides_.dims, ooffset)};
+    const size_t sizeofNewT{sizeof(T) * vectorWidth};
 
     threadsMgt<int> th(idims_.dims, indims_, 1, 1, totalSize, sizeofNewT);
     const char* kernelName{

From 0842e286684dcca6785f0ae6f24d051fc5c7e6cb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 26 Sep 2022 13:23:15 -0400
Subject: [PATCH 2280/2677] Update standard to C++17

---
 CMakeLists.txt                                |  6 +++
 CMakeModules/InternalUtils.cmake              | 13 +------
 CMakeModules/build_clFFT.cmake                |  8 ++++
 src/backend/common/ArrayInfo.cpp              |  3 +-
 src/backend/common/half.hpp                   | 38 +++++++++----------
 src/backend/common/jit/NodeIterator.hpp       |  9 +++--
 src/backend/cpu/Array.cpp                     |  2 +
 src/backend/cpu/jit/BinaryNode.hpp            |  2 +-
 src/backend/cpu/kernel/Array.hpp              |  2 +-
 src/backend/cpu/kernel/bilateral.hpp          | 10 +++--
 src/backend/cpu/kernel/fast.hpp               |  1 +
 .../cpu/kernel/sort_by_key/CMakeLists.txt     |  3 ++
 src/backend/cpu/math.hpp                      |  7 ----
 src/backend/cuda/jit.cpp                      |  1 +
 src/backend/cuda/math.hpp                     | 10 ++++-
 src/backend/opencl/jit.cpp                    |  1 +
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  3 ++
 .../opencl/kernel/sort_by_key/CMakeLists.txt  |  3 ++
 18 files changed, 74 insertions(+), 48 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c79cc691e5..73d8cbe9aa 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -304,6 +304,9 @@ if(CMAKE_CROSSCOMPILING)
 else()
   add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp
                          ${ArrayFire_SOURCE_DIR}/src/backend/common/util.cpp)
+  set_target_properties(bin2cpp
+    PROPERTIES
+      CXX_STANDARD 17)
 
   # NOSPDLOG is used to remove the spdlog dependency from bin2cpp
   target_compile_definitions(bin2cpp PRIVATE NOSPDLOG)
@@ -358,6 +361,9 @@ if(TARGET afopencl)
 endif()
 
 set_target_properties(${built_backends} PROPERTIES
+                      CXX_STANDARD 17
+                      CXX_EXTENSIONS OFF
+                      CXX_VISIBILITY_PRESET hidden
                       VERSION "${ArrayFire_VERSION}"
                       SOVERSION "${ArrayFire_VERSION_MAJOR}")
 
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index f212c50750..3f0828ef3e 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -41,20 +41,15 @@ function(arrayfire_get_cuda_cxx_flags cuda_flags)
     endif()
     if(cplusplus_define)
       list(APPEND flags -Xcompiler /Zc:__cplusplus
-                        -Xcompiler /std:c++14)
+                        -Xcompiler /std:c++17)
     endif()
   else()
-    set(flags -std=c++14
+    set(flags -std=c++17
               -Xcompiler -fPIC
               -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden
               --expt-relaxed-constexpr)
   endif()
 
-  if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
-      CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.3.0" AND
-      ${CUDA_VERSION_MAJOR} LESS 8)
-    set(flags ${flags} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED)
-  endif()
   set(${cuda_flags} ${flags} PARENT_SCOPE)
 endfunction()
 
@@ -122,10 +117,6 @@ macro(arrayfire_set_cmake_default_variables)
   set(CMAKE_PREFIX_PATH "${ArrayFire_BINARY_DIR};${CMAKE_PREFIX_PATH}")
   set(BUILD_SHARED_LIBS ON)
 
-  set(CMAKE_CXX_STANDARD 14)
-  set(CMAKE_CXX_EXTENSIONS OFF)
-  set(CMAKE_CXX_VISIBILITY_PRESET hidden)
-
   set(CMAKE_CXX_FLAGS_COVERAGE
       "-g -O0"
       CACHE STRING "Flags used by the C++ compiler during coverage builds.")
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index dc29e22ced..d4f3081e63 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -13,6 +13,14 @@ af_dep_check_and_populate(${clfft_prefix}
 set(current_build_type ${BUILD_SHARED_LIBS})
 set(BUILD_SHARED_LIBS OFF)
 add_subdirectory(${${clfft_prefix}_SOURCE_DIR}/src ${${clfft_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+# OpenCL targets need this flag to avoid ignored attribute warnings in the
+# OpenCL headers
+check_cxx_compiler_flag(-Wno-ignored-attributes has_ignored_attributes_flag)
+if(has_ignored_attributes_flag)
+  target_compile_options(clFFT
+    PRIVATE -Wno-ignored-attributes)
+endif()
 set(BUILD_SHARED_LIBS ${current_build_type})
 
 mark_as_advanced(
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index 585b48d403..6a0ca86086 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -188,7 +188,8 @@ const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
     // are accepted Otherwise only regular Array<T> is accepted
     if (sparse_check) { ARG_ASSERT(0, info->isSparse() == false); }
 
-    if (device_check && info->getDevId() != detail::getActiveDeviceId()) {
+    if (device_check && info->getDevId() != static_cast<unsigned>(
+                                                detail::getActiveDeviceId())) {
         AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
     }
 
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index fb25d0336d..a8737862f2 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -127,9 +127,9 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
     if (S) value = -value;
     uint16_t bits = S << 15;
     if (value > 0xFFFF) {
-        if (R == std::round_toward_infinity)
+        if constexpr (R == std::round_toward_infinity)
             bits |= (0x7C00 - S);
-        else if (R == std::round_toward_neg_infinity)
+        else if constexpr (R == std::round_toward_neg_infinity)
             bits |= (0x7BFF + S);
         else
             bits |= (0x7BFF + (R != std::round_toward_zero));
@@ -141,15 +141,15 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
             ;
         bits |= (exp << 10) + m;
         if (exp > 24) {
-            if (R == std::round_to_nearest)
+            if constexpr (R == std::round_to_nearest)
                 bits += (value >> (exp - 25)) & 1
 #if HALF_ROUND_TIES_TO_EVEN
                         & (((((1 << (exp - 25)) - 1) & value) != 0) | bits)
 #endif
                     ;
-            else if (R == std::round_toward_infinity)
+            else if constexpr (R == std::round_toward_infinity)
                 bits += ((value & ((1 << (exp - 24)) - 1)) != 0) & !S;
-            else if (R == std::round_toward_neg_infinity)
+            else if constexpr (R == std::round_toward_neg_infinity)
                 bits += ((value & ((1 << (exp - 24)) - 1)) != 0) & S;
         }
     }
@@ -277,7 +277,7 @@ __DH__ native_half_t float2half_impl(float value) noexcept {
     uint16_t hbits =
         base_table[bits >> 23] +
         static_cast<uint16_t>((bits & 0x7FFFFF) >> shift_table[bits >> 23]);
-    if (R == std::round_to_nearest)
+    if constexpr (R == std::round_to_nearest)
         hbits +=
             (((bits & 0x7FFFFF) >> (shift_table[bits >> 23] - 1)) |
              (((bits >> 23) & 0xFF) == 102)) &
@@ -289,16 +289,16 @@ __DH__ native_half_t float2half_impl(float value) noexcept {
              hbits)
 #endif
             ;
-    else if (R == std::round_toward_zero)
+    else if constexpr (R == std::round_toward_zero)
         hbits -= ((hbits & 0x7FFF) == 0x7C00) & ~shift_table[bits >> 23];
-    else if (R == std::round_toward_infinity)
+    else if constexpr (R == std::round_toward_infinity)
         hbits += ((((bits & 0x7FFFFF &
                      ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) -
                       1)) != 0) |
                    (((bits >> 23) <= 102) & ((bits >> 23) != 0))) &
                   (hbits < 0x7C00)) -
                  ((hbits == 0xFC00) & ((bits >> 23) != 511));
-    else if (R == std::round_toward_neg_infinity)
+    else if constexpr (R == std::round_toward_neg_infinity)
         hbits += ((((bits & 0x7FFFFF &
                      ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) -
                       1)) != 0) |
@@ -328,9 +328,9 @@ __DH__ native_half_t float2half_impl(double value) {
         return hbits | 0x7C00 |
                (0x3FF & -static_cast<unsigned>((bits & 0xFFFFFFFFFFFFF) != 0));
     if (exp > 1038) {
-        if (R == std::round_toward_infinity)
+        if constexpr (R == std::round_toward_infinity)
             return hbits | (0x7C00 - (hbits >> 15));
-        if (R == std::round_toward_neg_infinity)
+        if constexpr (R == std::round_toward_neg_infinity)
             return hbits | (0x7BFF + (hbits >> 15));
         return hbits | (0x7BFF + (R != std::round_toward_zero));
     }
@@ -348,15 +348,15 @@ __DH__ native_half_t float2half_impl(double value) {
     } else {
         s |= hi != 0;
     }
-    if (R == std::round_to_nearest)
+    if constexpr (R == std::round_to_nearest)
 #if HALF_ROUND_TIES_TO_EVEN
         hbits += g & (s | hbits);
 #else
         hbits += g;
 #endif
-    else if (R == std::round_toward_infinity)
+    else if constexpr (R == std::round_toward_infinity)
         hbits += ~(hbits >> 15) & (s | g);
-    else if (R == std::round_toward_neg_infinity)
+    else if constexpr (R == std::round_toward_neg_infinity)
         hbits += (hbits >> 15) & (g | s);
     return hbits;
 }
@@ -773,20 +773,20 @@ AF_CONSTEXPR T half2int(native_half_t value) {
         return (value & 0x8000) ? std::numeric_limits<T>::min()
                                 : std::numeric_limits<T>::max();
     if (e < 0x3800) {
-        if (R == std::round_toward_infinity)
+        if constexpr (R == std::round_toward_infinity)
             return T(~(value >> 15) & (e != 0));
-        else if (R == std::round_toward_neg_infinity)
+        else if constexpr (R == std::round_toward_neg_infinity)
             return -T(value > 0x8000);
         return T();
     }
     unsigned int m = (value & 0x3FF) | 0x400;
     e >>= 10;
     if (e < 25) {
-        if (R == std::round_to_nearest)
+        if constexpr (R == std::round_to_nearest)
             m += (1 << (24 - e)) - (~(m >> (25 - e)) & E);
-        else if (R == std::round_toward_infinity)
+        else if constexpr (R == std::round_toward_infinity)
             m += ((value >> 15) - 1) & ((1 << (25 - e)) - 1U);
-        else if (R == std::round_toward_neg_infinity)
+        else if constexpr (R == std::round_toward_neg_infinity)
             m += -(value >> 15) & ((1 << (25 - e)) - 1U);
         m >>= 25 - e;
     } else
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index da01c0b5bb..e286f6359d 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -18,10 +18,13 @@ namespace common {
 
 /// A node iterator that performs a breadth first traversal of the node tree
 template<typename Node = common::Node>
-class NodeIterator : public std::iterator<std::input_iterator_tag, Node> {
+class NodeIterator {
    public:
-    using pointer   = Node*;
-    using reference = Node&;
+    using iterator_category = std::input_iterator_tag;
+    using value_type        = Node;
+    using difference_type   = std::ptrdiff_t;
+    using pointer           = Node*;
+    using reference         = Node&;
 
    private:
     std::vector<pointer> tree;
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index dcd79dd9ed..159fd2aa7c 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -46,8 +46,10 @@ using common::NodeIterator;
 using cpu::jit::BufferNode;
 
 using nonstd::span;
+using std::accumulate;
 using std::adjacent_find;
 using std::copy;
+using std::find_if;
 using std::is_standard_layout;
 using std::make_shared;
 using std::move;
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 2342bb30cb..0ce7e348f4 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -25,7 +25,7 @@ template<typename To, typename Ti, af_op_t op>
 class BinaryNode : public TNode<compute_t<To>> {
    protected:
     BinOp<compute_t<To>, compute_t<Ti>, op> m_op;
-    using common::Node::m_children;
+    using TNode<compute_t<To>>::m_children;
 
    public:
     BinaryNode(common::Node_ptr lhs, common::Node_ptr rhs)
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index 48987a5d4d..e13548aa60 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -53,7 +53,7 @@ void propagateModdimsShape(
 
             NodeIterator<> it(node.get());
             while (it != NodeIterator<>()) {
-                it = find_if(it, NodeIterator<>(), common::isBuffer);
+                it = std::find_if(it, NodeIterator<>(), common::isBuffer);
                 if (it == NodeIterator<>()) { break; }
 
                 it->setShape(mn->m_new_shape);
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index 343b83dd08..a2f316d15f 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -19,14 +19,18 @@ namespace kernel {
 template<typename OutT, typename InT>
 void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma,
                float const c_sigma) {
+    using std::clamp;
+    using std::max;
+    using std::min;
+
     af::dim4 const dims     = in.dims();
     af::dim4 const istrides = in.strides();
     af::dim4 const ostrides = out.strides();
 
     // clamp spatical and chromatic sigma's
-    float space_       = std::min(11.5f, std::max(s_sigma, 0.f));
-    float color_       = std::max(c_sigma, 0.f);
-    dim_t const radius = std::max((dim_t)(space_ * 1.5f), (dim_t)1);
+    float space_       = min(11.5f, max(s_sigma, 0.f));
+    float color_       = max(c_sigma, 0.f);
+    dim_t const radius = max((dim_t)(space_ * 1.5f), (dim_t)1);
     float const svar   = space_ * space_;
     float const cvar   = color_ * color_;
 
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index f2a3d148ee..b168021903 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -15,6 +15,7 @@ namespace cpu {
 namespace kernel {
 
 inline int idx_y(int i) {
+    using std::clamp;
     if (i >= 8) return clamp(-(i - 8 - 4), -3, 3);
 
     return clamp(i - 4, -3, 3);
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
index 9abd9b3f84..3c894b37f5 100644
--- a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -23,6 +23,9 @@ foreach(SBK_TYPE ${SBK_TYPES})
   set_target_properties(cpu_sort_by_key_${SBK_TYPE}
     PROPERTIES
       COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL;$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>"
+      CXX_STANDARD 17
+      CXX_EXTENSIONS OFF
+      CXX_VISIBILITY_PRESET hidden
       FOLDER "Generated Targets")
 
   arrayfire_set_default_cxx_flags(cpu_sort_by_key_${SBK_TYPE})
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 2142604095..b01a11bb04 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -107,13 +107,6 @@ cfloat scalar(float val);
 
 cdouble scalar(double val);
 
-#if __cplusplus < 201703L
-template<typename T>
-static inline T clamp(const T value, const T lo, const T hi) {
-    return (value < lo ? lo : (value > hi ? hi : value));
-}
-#endif
-
 inline double real(cdouble in) noexcept { return std::real(in); }
 inline float real(cfloat in) noexcept { return std::real(in); }
 inline double imag(cdouble in) noexcept { return std::imag(in); }
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 262d5c8c45..6904d0673d 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -45,6 +45,7 @@ using common::NodeIterator;
 
 using std::array;
 using std::equal;
+using std::find_if;
 using std::for_each;
 using std::shared_ptr;
 using std::string;
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 7936ae8d57..a0b77265f4 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -388,9 +388,15 @@ static inline cdouble division(cdouble lhs, double rhs) {
     return retVal;
 }
 
+template<typename T, typename Compare>
+constexpr const __DH__ T clamp(const T value, const T lo, const T hi,
+                               Compare comp) {
+    return comp(value, lo) ? lo : comp(hi, value) ? hi : value;
+}
+
 template<typename T>
-static inline __DH__ T clamp(const T value, const T lo, const T hi) {
-    return max(lo, min(value, hi));
+constexpr const __DH__ T clamp(const T value, const T lo, const T hi) {
+    return clamp(value, lo, hi, [](auto lhs, auto rhs) { return lhs < rhs; });
 }
 
 }  // namespace cuda
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 8d717680d6..18a89e00a7 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -46,6 +46,7 @@ using cl::NDRange;
 using cl::NullRange;
 
 using std::equal;
+using std::find_if;
 using std::for_each;
 using std::shared_ptr;
 using std::string;
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index 91f1cc9ffc..a59904cfe7 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -68,6 +68,9 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
 
     set_target_properties(opencl_scan_by_key_${SBK_BINARY_OP}
       PROPERTIES
+        CXX_STANDARD 17
+        CXX_EXTENSIONS False
+        CXX_VISIBILITY_PRESET hidden
         POSITION_INDEPENDENT_CODE ON
         FOLDER "Generated Targets")
 
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 0d55ffce4e..2853d75cd9 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -67,6 +67,9 @@ foreach(SBK_TYPE ${SBK_TYPES})
 
     set_target_properties(opencl_sort_by_key_${SBK_TYPE}
       PROPERTIES
+        CXX_STANDARD 17
+        CXX_EXTENSIONS False
+        CXX_VISIBILITY_PRESET hidden
         POSITION_INDEPENDENT_CODE ON
         FOLDER "Generated Targets")
 

From 0c391cc08cbc335a1a47b26ef16be407adac854e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Sep 2022 01:55:58 -0400
Subject: [PATCH 2281/2677] Update select_compute_arch.cmake from version in
 3.24

---
 CMakeModules/select_compute_arch.cmake | 134 ++++++++++---------------
 1 file changed, 55 insertions(+), 79 deletions(-)

diff --git a/CMakeModules/select_compute_arch.cmake b/CMakeModules/select_compute_arch.cmake
index 38180edeff..16abb8e6cd 100644
--- a/CMakeModules/select_compute_arch.cmake
+++ b/CMakeModules/select_compute_arch.cmake
@@ -7,7 +7,7 @@
 #      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
 #      NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing Ampere
 #      NUM: Any number. Only those pairs are currently accepted by NVCC though:
-#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0
+#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0 8.6
 #      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
 #      Additionally, sets ${out_variable}_readable to the resulting numeric list
 #      Example:
@@ -16,6 +16,7 @@
 #
 #      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
 #
+
 if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
   if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
       AND CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
@@ -24,98 +25,85 @@ if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
 endif()
 
 # See: https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list
+# Additions, deprecations, and removals can be found in the release notes:
+# https://developer.nvidia.com/cuda-toolkit-archive
 
-# This list will be used for CUDA_ARCH_NAME = All option
-set(CUDA_KNOWN_GPU_ARCHITECTURES "Fermi" "Kepler" )
-
-# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
-set(CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.5" "5.0")
-
-if(CUDA_VERSION VERSION_LESS "7.0")
-  set(CUDA_LIMIT_GPU_ARCHITECTURE "5.2")
-endif()
-
-# This list is used to filter CUDA archs when autodetecting
-set(CUDA_ALL_GPU_ARCHITECTURES "3.0" "3.2" "3.5" "5.0")
+# The initial status here is for CUDA 7.0
+set(CUDA_KNOWN_GPU_ARCHITECTURES  "Fermi" "Kepler" "Maxwell" "Kepler+Tegra" "Kepler+Tesla" "Maxwell+Tegra")
+set(CUDA_COMMON_GPU_ARCHITECTURES "2.0" "2.1" "3.0" "3.5" "5.0" "5.3")
+set(CUDA_LIMIT_GPU_ARCHITECTURE "6.0")
+set(CUDA_ALL_GPU_ARCHITECTURES "2.0" "2.1" "3.0" "3.2" "3.5" "3.7" "5.0" "5.2" "5.3")
+set(_CUDA_MAX_COMMON_ARCHITECTURE "5.2+PTX")
 
-if(CUDA_VERSION VERSION_GREATER "7.0" OR CUDA_VERSION VERSION_EQUAL "7.0" )
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell" "Maxwell+Tegra")
-  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.0" "5.2")
-  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "5.0" "5.2" "5.3")
 
-  if(CUDA_VERSION VERSION_LESS "8.0")
-    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX")
-    set(CUDA_LIMIT_GPU_ARCHITECTURE "6.0")
-  endif()
-endif()
-
-if(CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0" )
-  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal" "Pascal+Tegra")
+if(CUDA_VERSION VERSION_GREATER_EQUAL "8.0")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal")
   list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1")
   list(APPEND CUDA_ALL_GPU_ARCHITECTURES "6.0" "6.1" "6.2")
 
-  if(CUDA_VERSION VERSION_LESS "9.0")
-    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.2+PTX")
-    set(CUDA_LIMIT_GPU_ARCHITECTURE "7.0")
-  endif()
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "6.2+PTX")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "7.0")
+
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "2.0" "2.1")
 endif ()
 
-if(CUDA_VERSION VERSION_GREATER "9.0" OR CUDA_VERSION VERSION_EQUAL "9.0")
+if(CUDA_VERSION VERSION_GREATER_EQUAL "9.0")
   list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta")
   list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0")
-  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.0")
-
-  if(CUDA_VERSION VERSION_GREATER "9.1" OR CUDA_VERSION VERSION_EQUAL "9.1")
-    list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Volta+Tegra")
-    list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.2")
-  endif()
-
-  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Fermi")
-  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "2.0")
-
-  if(CUDA_VERSION VERSION_GREATER "9.1" OR CUDA_VERSION VERSION_EQUAL "9.1"
-     AND CUDA_VERSION VERSION_LESS "10.0")
-    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.0+PTX")
-  endif()
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.0" "7.2")
 
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "7.2+PTX")
   set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
 
+  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Fermi")
+  list(REMOVE_ITEM CUDA_ALL_GPU_ARCHITECTURES "2.0" "2.1")
 endif()
 
-if(CUDA_VERSION VERSION_GREATER "10.0" OR CUDA_VERSION VERSION_EQUAL "10.0")
+if(CUDA_VERSION VERSION_GREATER_EQUAL "10.0")
   list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Turing")
   list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5")
   list(APPEND CUDA_ALL_GPU_ARCHITECTURES "7.5")
 
-  if(CUDA_VERSION VERSION_LESS "11.0")
-    set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
-    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "7.5+PTX")
-  endif()
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "7.5+PTX")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
+
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.0")
 endif()
 
-if(CUDA_VERSION VERSION_GREATER "11.0" OR CUDA_VERSION VERSION_EQUAL "11.0")
+# https://docs.nvidia.com/cuda/archive/11.0/cuda-toolkit-release-notes/index.html#cuda-general-new-features
+# https://docs.nvidia.com/cuda/archive/11.0/cuda-toolkit-release-notes/index.html#deprecated-features
+if(CUDA_VERSION VERSION_GREATER_EQUAL "11.0")
   list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ampere")
   list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0")
   list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.0")
 
-  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra")
-  list(REMOVE_ITEM CUDA_KNOWN_GPU_ARCHITECTURES "Kepler")
-  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.2")
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "8.0+PTX")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "8.6")
 
-  if(CUDA_VERSION VERSION_GREATER "11.1" OR CUDA_VERSION VERSION_EQUAL "11.1")
-    list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.6")
-  endif()
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.5" "5.0")
+  list(REMOVE_ITEM CUDA_ALL_GPU_ARCHITECTURES "3.0" "3.2")
+endif()
 
-  if(CUDA_VERSION VERSION_GREATER "11.1" OR CUDA_VERSION VERSION_EQUAL "11.1"
-      AND CUDA_VERSION VERSION_LESS "12.0")
-    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0+PTX")
-  endif()
+if(CUDA_VERSION VERSION_GREATER_EQUAL "11.1")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.6")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.6")
 
-  if(CUDA_VERSION VERSION_LESS "12.0")
-    set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
-  endif()
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "8.6+PTX")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
+endif()
+
+list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "${_CUDA_MAX_COMMON_ARCHITECTURE}")
+
+# Check with: cmake -DCUDA_VERSION=7.0 -P select_compute_arch.cmake
+if(DEFINED CMAKE_SCRIPT_MODE_FILE)
+  include(CMakePrintHelpers)
+  cmake_print_variables(CUDA_KNOWN_GPU_ARCHITECTURES)
+  cmake_print_variables(CUDA_COMMON_GPU_ARCHITECTURES)
+  cmake_print_variables(CUDA_LIMIT_GPU_ARCHITECTURE)
+  cmake_print_variables(CUDA_ALL_GPU_ARCHITECTURES)
 endif()
 
+
 ################################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
 # Usage:
@@ -174,8 +162,7 @@ function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
     set(CUDA_GPU_DETECT_OUTPUT_FILTERED "")
     separate_arguments(CUDA_GPU_DETECT_OUTPUT)
     foreach(ITEM IN ITEMS ${CUDA_GPU_DETECT_OUTPUT})
-      if(CUDA_LIMIT_GPU_ARCHITECTURE AND (ITEM VERSION_GREATER CUDA_LIMIT_GPU_ARCHITECTURE OR
-                                          ITEM VERSION_EQUAL CUDA_LIMIT_GPU_ARCHITECTURE))
+        if(CUDA_LIMIT_GPU_ARCHITECTURE AND ITEM VERSION_GREATER_EQUAL CUDA_LIMIT_GPU_ARCHITECTURE)
         list(GET CUDA_COMMON_GPU_ARCHITECTURES -1 NEWITEM)
         string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${NEWITEM}")
       else()
@@ -201,11 +188,9 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
 
   set(cuda_arch_bin)
   set(cuda_arch_ptx)
-  set(cuda_arch_with_ptx false)
 
   if("${CUDA_ARCH_LIST}" STREQUAL "All")
     set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
-    set(cuda_arch_with_ptx true)
   elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
     set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
   elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto")
@@ -216,18 +201,10 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
   # Now process the list and look for names
   string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
   list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
-
-  list(GET CUDA_ARCH_LIST -1 latest_arch)
-
   foreach(arch_name ${CUDA_ARCH_LIST})
     set(arch_bin)
     set(arch_ptx)
     set(add_ptx FALSE)
-
-    if(${arch_name} STREQUAL ${latest_arch} AND cuda_arch_with_ptx)
-      set(add_ptx TRUE)
-    endif()
-
     # Check to see if we are compiling PTX
     if(arch_name MATCHES "(.*)\\+PTX$")
       set(add_ptx TRUE)
@@ -242,11 +219,10 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
         set(arch_bin 2.0 "2.1(2.0)")
       elseif(${arch_name} STREQUAL "Kepler+Tegra")
         set(arch_bin 3.2)
-      elseif(${arch_name} STREQUAL "Kepler")
-        set(arch_bin 3.0)
-        set(arch_ptx 3.0)
       elseif(${arch_name} STREQUAL "Kepler+Tesla")
-        set(arch_bin 3.5 3.7)
+        set(arch_bin 3.7)
+      elseif(${arch_name} STREQUAL "Kepler")
+        set(arch_bin 3.0 3.5)
         set(arch_ptx 3.5)
       elseif(${arch_name} STREQUAL "Maxwell+Tegra")
         set(arch_bin 5.3)

From 51b0b36e1576c1609f8c9cf368b78e18f43ffa56 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Sep 2022 01:59:40 -0400
Subject: [PATCH 2282/2677] use __NVCC__ definition instead of the NVCC macro

Looks like the NVCC macro is only used when compiling cuda with cmake.
this does not seem to be a standard definition
---
 src/backend/common/half.hpp | 6 +++---
 src/backend/cuda/types.hpp  | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index a8737862f2..7904598eb8 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#if defined(NVCC) || defined(__CUDACC_RTC__)
+#if defined(__NVCC__) || defined(__CUDACC_RTC__)
 
 // MSVC sets __cplusplus to 199711L for all versions unless you specify
 // the new \Zc:__cplusplus flag in Visual Studio 2017. This is not possible
@@ -824,7 +824,7 @@ AF_CONSTEXPR __DH__ static inline bool isnan(common::half val) noexcept;
 class alignas(2) half {
     native_half_t data_ = native_half_t();
 
-#if !defined(NVCC) && !defined(__CUDACC_RTC__)
+#if !defined(__NVCC__) && !defined(__CUDACC_RTC__)
     // NVCC on OSX performs a weird transformation where it removes the std::
     // namespace and complains that the std:: namespace is not there
     friend class std::numeric_limits<half>;
@@ -1054,7 +1054,7 @@ static inline std::string to_string(const half&& val) {
 
 }  // namespace common
 
-#if !defined(NVCC) && !defined(__CUDACC_RTC__)
+#if !defined(__NVCC__) && !defined(__CUDACC_RTC__)
 //#endif
 /// Extensions to the C++ standard library.
 namespace std {
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index de98d2b24f..c3897a3397 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -161,7 +161,7 @@ struct kernel_type<common::half> {
     // outside of a cuda kernel use float
     using compute = float;
 
-#if defined(NVCC) || defined(__CUDACC_RTC__)
+#if defined(__NVCC__) || defined(__CUDACC_RTC__)
     using native = __half;
 #else
     using native = common::half;

From c7555d2797170873333b85c42c23055bfccfaf84 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Sep 2022 02:02:22 -0400
Subject: [PATCH 2283/2677] Use updated CUDA language support in CMake

This commit moves the CUDA code to use the new CUDA language support
in cmake. This allows us to remove the cuda_add_* functions in favor
of the normal CMake versions.
---
 CMakeLists.txt                    |  14 ++++
 CMakeModules/AFcuda_helpers.cmake |  70 +++++++---------
 CMakeModules/InternalUtils.cmake  |   5 --
 CMakeModules/config_ccache.cmake  |   2 +-
 src/backend/cuda/CMakeLists.txt   | 131 ++++++++++++++----------------
 test/CMakeLists.txt               |   9 +-
 6 files changed, 108 insertions(+), 123 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 73d8cbe9aa..091980b6e3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,6 +6,7 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 cmake_minimum_required(VERSION 3.10.2)
+include(CheckLanguage)
 
 include(CMakeModules/AF_vcpkg_options.cmake)
 
@@ -131,6 +132,19 @@ option(AF_WITH_STATIC_FREEIMAGE "Use Static FreeImage Lib" OFF)
 
 set(AF_WITH_CPUID ON CACHE BOOL "Build with CPUID integration")
 
+if(AF_BUILD_CUDA)
+  check_language(CUDA)
+  if(CMAKE_CUDA_COMPILER)
+    enable_language(CUDA)
+  elseif(CUDA_NVCC_EXECUTABLE)
+    message(STATUS "Using the FindCUDA script to search for the CUDA compiler")
+    set(CMAKE_CUDA_COMPILER ${CUDA_NVCC_EXECUTABLE} CACHE INTERNAL "CUDA compiler executable")
+    enable_language(CUDA)
+  else()
+    message(WARNING "No CUDA support")
+  endif()
+endif()
+
 af_deprecate(BUILD_CPU             AF_BUILD_CPU)
 af_deprecate(BUILD_CUDA            AF_BUILD_CUDA)
 af_deprecate(BUILD_OPENCL          AF_BUILD_OPENCL)
diff --git a/CMakeModules/AFcuda_helpers.cmake b/CMakeModules/AFcuda_helpers.cmake
index 59cfb2002a..a5d20c4a62 100644
--- a/CMakeModules/AFcuda_helpers.cmake
+++ b/CMakeModules/AFcuda_helpers.cmake
@@ -6,6 +6,34 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 find_program(NVPRUNE NAMES nvprune)
+cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
+set(cuda_architecture_flags ${cuda_architecture_flags} CACHE INTERNAL "CUDA compute flags" FORCE)
+set(cuda_architecture_flags_readable ${cuda_architecture_flags_readable} CACHE INTERNAL "Readable CUDA compute flags" FORCE)
+
+function(af_detect_and_set_cuda_architectures target)
+  if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
+    string(REGEX REPLACE "sm_([0-9]+)[ ]*" "\\1-real|" cuda_build_targets ${cuda_architecture_flags_readable})
+    string(REGEX REPLACE "compute_([0-9]+)[ ]*" "\\1-virtual|" cuda_build_targets ${cuda_build_targets})
+    string(REPLACE "|" ";" cuda_build_targets ${cuda_build_targets})
+
+    set_target_properties(${target}
+      PROPERTIES
+        CUDA_ARCHITECTURES "${cuda_build_targets}")
+  else()
+    # CMake 3.12 adds deduplication of compile options. This breaks the way the
+    # gencode flags are passed into the compiler. these replace instructions add
+    # the SHELL: prefix to each of the gencode options so that it is not removed
+    # from the command
+    if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.12")
+      string(REPLACE ";" "|" cuda_architecture_flags "${cuda_architecture_flags}")
+      string(REGEX REPLACE "(-gencode)\\|" "SHELL:\\1 " cuda_architecture_flags2 "${cuda_architecture_flags}")
+      string(REPLACE "|" ";" cuda_architecture_flags ${cuda_architecture_flags2})
+    endif()
+    target_compile_options(${target}
+      PRIVATE
+        $<$<COMPILE_LANGUAGE:CUDA>:${cuda_architecture_flags}>)
+  endif()
+endfunction()
 
 # The following macro uses a macro defined by
 # FindCUDA module from cmake.
@@ -39,45 +67,3 @@ function(af_find_static_cuda_libs libname)
   mark_as_advanced(CUDA_${libname}_LIBRARY)
 endfunction()
 
-## Copied from FindCUDA.cmake
-## The target_link_library needs to link with the cuda libraries using
-## PRIVATE
-function(cuda_add_library cuda_target)
-  cuda_add_cuda_include_once()
-
-  # Separate the sources from the options
-  cuda_get_sources_and_options(_sources _cmake_options _options ${ARGN})
-  cuda_build_shared_library(_cuda_shared_flag ${ARGN})
-  # Create custom commands and targets for each file.
-  cuda_wrap_srcs( ${cuda_target} OBJ _generated_files ${_sources}
-    ${_cmake_options} ${_cuda_shared_flag}
-    OPTIONS ${_options} )
-
-  # Compute the file name of the intermedate link file used for separable
-  # compilation.
-  cuda_compute_separable_compilation_object_file_name(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
-
-  # Add the library.
-  add_library(${cuda_target} ${_cmake_options}
-    ${_generated_files}
-    ${_sources}
-    ${link_file}
-    )
-
-  # Add a link phase for the separable compilation if it has been enabled.  If
-  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
-  # variable will have been defined.
-  cuda_link_separable_compilation_objects("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
-
-  target_link_libraries(${cuda_target}
-      PRIVATE ${CUDA_LIBRARIES}
-    )
-
-  # We need to set the linker language based on what the expected generated file
-  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
-  set_target_properties(${cuda_target}
-    PROPERTIES
-    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
-    POSITION_INDEPENDENT_CODE ON
-  )
-endfunction()
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 3f0828ef3e..5d02277b61 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -43,11 +43,6 @@ function(arrayfire_get_cuda_cxx_flags cuda_flags)
       list(APPEND flags -Xcompiler /Zc:__cplusplus
                         -Xcompiler /std:c++17)
     endif()
-  else()
-    set(flags -std=c++17
-              -Xcompiler -fPIC
-              -Xcompiler ${CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY}hidden
-              --expt-relaxed-constexpr)
   endif()
 
   set(${cuda_flags} ${flags} PARENT_SCOPE)
diff --git a/CMakeModules/config_ccache.cmake b/CMakeModules/config_ccache.cmake
index 80783b06c1..04b3a97901 100644
--- a/CMakeModules/config_ccache.cmake
+++ b/CMakeModules/config_ccache.cmake
@@ -34,7 +34,7 @@ if(${AF_USE_CCACHE})
     # Support Unix Makefiles and Ninja
     set(CMAKE_C_COMPILER_LAUNCHER   "${CCACHE_PROGRAM}")
     set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CUDA_NVCC_EXECUTABLE ${CCACHE_PROGRAM} "${CUDA_NVCC_EXECUTABLE}")
+    set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
   endif()
 endif()
 mark_as_advanced(CCACHE_PROGRAM)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a6a750f83f..67061740a0 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -48,16 +48,6 @@ endif()
 set(CUDA_architecture_build_targets "Auto" CACHE
   STRING "The compute architectures targeted by this build. (Options: Auto;3.0;Maxwell;All;Common)")
 
-cuda_select_nvcc_arch_flags(cuda_architecture_flags ${CUDA_architecture_build_targets})
-
-string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=sm_([0-9]+)" "\\1|" cuda_build_targets ${cuda_architecture_flags})
-string(REGEX REPLACE "-gencodearch=compute_[0-9]+,code=compute_([0-9]+)" "\\1+PTX|" cuda_build_targets ${cuda_build_targets})
-string(REGEX REPLACE "([0-9]+)([0-9])\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
-string(REGEX REPLACE "([0-9]+)([0-9]\\+PTX)\\|" "\\1.\\2 " cuda_build_targets ${cuda_build_targets})
-message(STATUS "CUDA_architecture_build_targets: ${CUDA_architecture_build_targets} ( ${cuda_build_targets} )")
-
-set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};${cuda_architecture_flags})
-
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
 list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY})
@@ -123,20 +113,6 @@ mark_as_advanced(
     CUDA_LIBRARIES_PATH
     CUDA_architecture_build_targets)
 
-get_target_property(COMMON_INTERFACE_DIRS afcommon_interface INTERFACE_INCLUDE_DIRECTORIES)
-
-cuda_include_directories(
-  ${CMAKE_CURRENT_BINARY_DIR}
-  ${CMAKE_CURRENT_SOURCE_DIR}
-  ${ArrayFire_SOURCE_DIR}/include
-  ${ArrayFire_BINARY_DIR}/include
-  ${CMAKE_CURRENT_SOURCE_DIR}/kernel
-  ${CMAKE_CURRENT_SOURCE_DIR}/jit
-  ${ArrayFire_SOURCE_DIR}/src/api/c
-  ${ArrayFire_SOURCE_DIR}/src/backend
-  ${COMMON_INTERFACE_DIRS}
-  $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>
-  )
 if(CUDA_VERSION_MAJOR VERSION_LESS 11)
   af_dep_check_and_populate(${cub_prefix}
     URI https://github.com/NVIDIA/cub.git
@@ -254,15 +230,6 @@ file_to_string(
 arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
 arrayfire_get_platform_definitions(platform_flags)
 
-get_property(boost_includes TARGET Boost::boost PROPERTY INTERFACE_INCLUDE_DIRECTORIES)
-get_property(boost_definitions TARGET Boost::boost PROPERTY INTERFACE_COMPILE_DEFINITIONS)
-
-string(REPLACE ";" ";-I" boost_includes "-I${boost_includes}")
-string(REPLACE ";" ";-D" boost_definitions "-D${boost_definitions}")
-
-set(cuda_cxx_flags "${cuda_cxx_flags};${boost_includes}")
-set(cuda_cxx_flags "${cuda_cxx_flags};${boost_definitions}")
-
 # New API of cuSparse was introduced in 10.1.168 for Linux and the older
 # 10.1.105 fix version doesn't it. Unfortunately, the new API was introduced in
 # in a fix release of CUDA - unconventionally. As CMake's FindCUDA module
@@ -283,23 +250,8 @@ list(APPEND cuda_cxx_flags ${cxx_definitions})
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
-# CUDA static libraries require device linking to successfully link
-# against afcuda target. Device linking requires CUDA_SEPARABLE_COMPILATION
-# to be ON. Therefore, we turn on separable compilation for a subset of
-# source files while compiling af_cuda_static_cuda_library target. Once
-# this subset is compiled, separable compilation is reset to it's original
-# value.
-if(UNIX)
-  # Static linking cuda libs require device linking, which in turn
-  # requires separable compilation.
-  set(pior_val_CUDA_SEPARABLE_COMPILATION OFF)
-  if(DEFINED CUDA_SEPARABLE_COMPILATION)
-    set(pior_val_CUDA_SEPARABLE_COMPILATION ${CUDA_SEPARABLE_COMPILATION})
-  endif()
-  set(CUDA_SEPARABLE_COMPILATION ON)
-endif()
-
-cuda_add_library(af_cuda_static_cuda_library STATIC
+add_library(af_cuda_static_cuda_library
+  STATIC
     blas.cu
     blas.hpp
     cudaDataType.hpp
@@ -315,18 +267,46 @@ cuda_add_library(af_cuda_static_cuda_library STATIC
     sparse_blas.hpp
     solve.cu
     solve.hpp
-
-    OPTIONS
-    ${platform_flags} ${cuda_cxx_flags} ${af_cuda_static_flags}
-    -Xcudafe --display_error_number -Xcudafe \"--diag_suppress=1427\" -DAFDLL
 )
 
+af_detect_and_set_cuda_architectures(af_cuda_static_cuda_library)
+if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
+  set_target_properties(af_cuda_static_cuda_library
+    PROPERTIES
+      CUDA_STANDARD 17)
+else()
+  target_compile_options(af_cuda_static_cuda_library
+    PRIVATE
+      $<$<COMPILE_LANGUAGE:CUDA>:--std=c++17>)
+endif()
+
 set_target_properties(af_cuda_static_cuda_library
   PROPERTIES
-  LINKER_LANGUAGE CXX
-  FOLDER "Generated Targets"
+    POSITION_INDEPENDENT_CODE ON
+    LINKER_LANGUAGE CUDA
+    FOLDER "Generated Targets")
+
+target_include_directories(af_cuda_static_cuda_library
+  PRIVATE
+    ${CMAKE_CURRENT_BINARY_DIR}
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${ArrayFire_SOURCE_DIR}/include
+    ${ArrayFire_BINARY_DIR}/include
+    ${ArrayFire_SOURCE_DIR}/src/api/c
+    ${ArrayFire_SOURCE_DIR}/src/backend
 )
 
+target_compile_definitions(af_cuda_static_cuda_library
+  PRIVATE
+    ${platform_flags}
+    AFDLL)
+
+target_compile_options(af_cuda_static_cuda_library
+  PRIVATE
+    $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe --diag_suppress=unrecognized_gcc_pragma>
+    $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
+    ${cuda_cxx_flags})
+
 if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
    (UNIX AND
     CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 1))
@@ -372,7 +352,6 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
         ${CUDA_lapack_static_LIBRARY})
   endif()
 
-  set(CUDA_SEPARABLE_COMPILATION ${pior_val_CUDA_SEPARABLE_COMPILATION})
 else()
   target_link_libraries(af_cuda_static_cuda_library
     PUBLIC
@@ -383,7 +362,7 @@ else()
   )
 endif()
 
-cuda_add_library(afcuda
+add_library(afcuda
     $<$<PLATFORM_ID:Windows>:${af_cuda_ver_res_file}>
     ${thrust_sort_sources}
 
@@ -681,13 +660,30 @@ cuda_add_library(afcuda
     jit/kernel_generators.hpp
 
     ${scan_by_key_sources}
+  )
 
-    OPTIONS
-    ${platform_flags}
+af_detect_and_set_cuda_architectures(afcuda)
+if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
+  set_target_properties(afcuda
+    PROPERTIES
+      CUDA_STANDARD 17)
+else()
+  target_compile_options(afcuda
+    PRIVATE
+      $<$<COMPILE_LANGUAGE:CUDA>:--std=c++17>)
+endif()
+
+target_compile_definitions(afcuda
+  PRIVATE
+    ${platform_flags})
+
+target_compile_options(afcuda
+  PRIVATE
     ${cuda_cxx_flags}
-    -Xcudafe --display_error_number
-    -Xcudafe \"--diag_suppress=1427\"
-  )
+    $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
+    $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe --diag_suppress=unrecognized_gcc_pragma>
+)
+
 
 if(AF_WITH_CUDNN)
   target_sources(afcuda PRIVATE
@@ -709,15 +705,6 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS AND AF_cusparse_LINK_LOADING STREQU
       AF_cusparse_STATIC_LINKING)
 endif()
 
-
-arrayfire_set_default_cxx_flags(afcuda)
-
-# NOTE: Do not add additional CUDA specific definitions here. Add it to the
-# cxx_definitions variable above. cxx_definitions is used to propigate
-# definitions to the scan_by_key and thrust_sort_by_key targets as well as the
-# cuda library above.
-target_compile_options(afcuda PRIVATE ${cxx_definitions})
-
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index c7add80ca3..e6468848d6 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -333,6 +333,7 @@ if(OpenCL_FOUND)
 endif()
 
 if(CUDA_FOUND)
+  include(AFcuda_helpers)
   foreach(backend ${enabled_backends})
     set(cuda_test_backends "cuda" "unified")
     if(${backend} IN_LIST cuda_test_backends)
@@ -345,7 +346,7 @@ if(CUDA_FOUND)
           ${CMAKE_CURRENT_SOURCE_DIR}
         )
       endif()
-      cuda_add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
+      add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
       target_include_directories(${target} PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
         ${CMAKE_SOURCE_DIR}
@@ -369,10 +370,12 @@ if(CUDA_FOUND)
         target_link_libraries(${target} -pthread)
       endif()
 
+      af_detect_and_set_cuda_architectures(${target})
+
       set_target_properties(${target}
         PROPERTIES
-        FOLDER "Tests"
-        OUTPUT_NAME "cuda_${backend}")
+          FOLDER "Tests"
+          OUTPUT_NAME "cuda_${backend}")
 
       if(NOT ${backend} STREQUAL "unified")
         add_test(NAME ${target} COMMAND ${target})

From 1731fff184426112f6c7086ce083ff8401bd1448 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 27 Sep 2022 02:03:42 -0400
Subject: [PATCH 2284/2677] Remove cudaDeviceSynchronize from the
 ThrustArrayFirePolicy

---
 src/backend/cuda/ThrustArrayFirePolicy.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
index d58b508453..6787d405de 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.hpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -49,7 +49,7 @@ __DH__ inline cudaStream_t get_stream<::cuda::ThrustArrayFirePolicy>(
 __DH__
 inline cudaError_t synchronize_stream(const ::cuda::ThrustArrayFirePolicy &) {
 #if defined(__CUDA_ARCH__)
-    return cudaDeviceSynchronize();
+    return cudaSuccess;
 #else
     return cudaStreamSynchronize(::cuda::getActiveStream());
 #endif

From 274da93f474ffd0016ad31aa81648b9ee9bcb4f7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 28 Sep 2022 13:20:24 -0400
Subject: [PATCH 2285/2677] Fix several CI issues due to changes in GitHub
 actions' environment

VCPKG_ROOT is now defined as an environment variable in GitHub actions. This
change causes some of our jobs to fail because our scripts detect the
environment variable to trigger some work. In this commit I remove
the VCPKG_ROOT environment variable from the ubuntu jobs and remove
the setting of the VCPKG_ROOT CMake variable on the windows job

Use clean-after-build flag instead of Remove-Item to clean vcpkg builds

Fix missing expat package in new macOS GitHub workflow
---
 .github/workflows/docs_build.yml     | 2 +-
 .github/workflows/unix_cpu_build.yml | 4 ++--
 .github/workflows/win_cpu_build.yml  | 4 +---
 3 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
index bf81164cdd..38091d113a 100644
--- a/.github/workflows/docs_build.yml
+++ b/.github/workflows/docs_build.yml
@@ -32,7 +32,7 @@ jobs:
 
             - name: Configure
               run: |
-                  mkdir build && cd build
+                  mkdir build && cd build && unset VCPKG_ROOT
                   cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
                         -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
                         -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 1962db4891..114799bbca 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -58,7 +58,7 @@ jobs:
             - name: Install Dependencies for Macos
               if: matrix.os == 'macos-latest'
               run: |
-                  brew install boost fontconfig glfw freeimage fftw lapack openblas
+                  brew install boost fontconfig glfw freeimage fftw lapack openblas expat
                   echo "CMAKE_PROGRAM=cmake" >> $GITHUB_ENV
 
             - name: Install Common Dependencies for Ubuntu
@@ -103,7 +103,7 @@ jobs:
                   backend=$(if [ "$USE_MKL" == 1 ]; then echo "Intel-MKL"; else echo "FFTW/LAPACK/BLAS"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
                   cmake_rpath=$(if [ $OS_NAME == 'macos-latest' ]; then echo "-DCMAKE_INSTALL_RPATH=/opt/arrayfire/lib"; fi)
-                  mkdir build && cd build
+                  mkdir build && cd build && unset VCPKG_ROOT
                   ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF \
diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index 067f951fff..9d5419f7dd 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -36,8 +36,7 @@ jobs:
                 cd vcpkg
                 git checkout $env:VCPKG_HASH
                 .\bootstrap-vcpkg.bat
-                .\vcpkg.exe install boost-compute boost-math boost-stacktrace fftw3 freeimage freetype[core] forge glfw3 openblas
-                Remove-Item .\downloads,.\buildtrees,.\packages -Recurse -Force
+                .\vcpkg.exe install --clean-after-build boost-compute boost-math boost-stacktrace fftw3 freeimage freetype[core] forge glfw3 openblas
 
             - name: CMake Configure
               run: |
@@ -49,7 +48,6 @@ jobs:
                   $buildname = "$buildname-cpu-openblas"
                   mkdir build && cd build
                   cmake .. -G "Visual Studio 17 2022" -A x64 `
-                      -DVCPKG_ROOT:PATH="~/vcpkg" `
                       -DVCPKG_MANIFEST_MODE:BOOL=OFF `
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `

From cdb6797f39c9efc50400152229331cedbc97d826 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 29 Sep 2022 15:56:11 -0400
Subject: [PATCH 2286/2677] CMake compiler flag refactor

---
 CMakeLists.txt                    |  13 +-
 CMakeModules/InternalUtils.cmake  | 100 ++++++-------
 CMakeModules/build_CLBlast.cmake  |   5 +-
 CMakeModules/platform.cmake       |  21 ---
 src/api/unified/CMakeLists.txt    |  92 ++++++------
 src/backend/cpu/CMakeLists.txt    |   2 -
 src/backend/cuda/CMakeLists.txt   | 226 ++++++++++++------------------
 src/backend/opencl/CMakeLists.txt |   2 -
 8 files changed, 176 insertions(+), 285 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 091980b6e3..08445a986f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -411,16 +411,11 @@ find_library(Backtrace_LIBRARY backtrace
 find_program(ADDR2LINE_PROGRAM addr2line
   DOC "The path to the addr2line program for informative stacktraces")
 
+check_cxx_compiler_flag(-Wno-ignored-attributes has_ignored_attributes_flag)
+check_cxx_compiler_flag(-Wall has_all_warnings_flag)
+
 foreach(backend ${built_backends})
-  target_compile_definitions(${backend} PRIVATE AFDLL)
-  if(AF_WITH_LOGGING)
-    target_compile_definitions(${backend}
-      PRIVATE AF_WITH_LOGGING)
-  endif()
-  if(AF_CACHE_KERNELS_TO_DISK)
-    target_compile_definitions(${backend}
-      PRIVATE AF_CACHE_KERNELS_TO_DISK)
-  endif()
+  arrayfire_set_default_cxx_flags(${backend})
 endforeach()
 
 if(AF_BUILD_FRAMEWORK)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 5d02277b61..dde0756aaa 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -18,68 +18,54 @@ function(conditional_directory variable directory)
   endif()
 endfunction()
 
-function(arrayfire_get_platform_definitions variable)
+include(CheckCXXCompilerFlag)
+
 if(WIN32)
-  set(${variable} -DOS_WIN -DWIN32_LEAN_AND_MEAN -DNOMINMAX PARENT_SCOPE)
-elseif(APPLE)
-  set(${variable} -DOS_MAC PARENT_SCOPE)
-elseif(UNIX)
-  set(${variable} -DOS_LNX PARENT_SCOPE)
+  check_cxx_compiler_flag(/Zc:__cplusplus cplusplus_define)
+  check_cxx_compiler_flag(/permissive- cxx_compliance)
 endif()
-endfunction()
-
-function(arrayfire_get_cuda_cxx_flags cuda_flags)
-  if(MSVC)
-    set(flags -Xcompiler /wd4251
-              -Xcompiler /wd4068
-              -Xcompiler /wd4275
-              -Xcompiler /bigobj
-              -Xcompiler /EHsc
-              --expt-relaxed-constexpr)
-    if(CMAKE_GENERATOR MATCHES "Ninja")
-      set(flags ${flags} -Xcompiler /FS)
-    endif()
-    if(cplusplus_define)
-      list(APPEND flags -Xcompiler /Zc:__cplusplus
-                        -Xcompiler /std:c++17)
-    endif()
-  endif()
-
-  set(${cuda_flags} ${flags} PARENT_SCOPE)
-endfunction()
-
-include(CheckCXXCompilerFlag)
 
 function(arrayfire_set_default_cxx_flags target)
-  arrayfire_get_platform_definitions(defs)
-  target_compile_definitions(${target} PRIVATE ${defs})
-
-  if(MSVC)
-    target_compile_options(${target}
-      PRIVATE
-        /wd4251 /wd4068 /wd4275 /bigobj /EHsc)
-
-    if(CMAKE_GENERATOR MATCHES "Ninja")
-      target_compile_options(${target}
-        PRIVATE
-          /FS)
-    endif()
-  else()
-    check_cxx_compiler_flag(-Wno-ignored-attributes has_ignored_attributes_flag)
-
-    # OpenCL targets need this flag to avoid ignored attribute warnings in the
-    # OpenCL headers
-    if(has_ignored_attributes_flag)
-        target_compile_options(${target}
-          PRIVATE -Wno-ignored-attributes)
-    endif()
+  target_compile_options(${target}
+    PRIVATE
+      $<$<COMPILE_LANGUAGE:CXX>:
+              # C4068: Warnings about unknown pragmas
+              # C4668: Warnings about unknown defintions
+              # C4275: Warnings about using non-exported classes as base class of an
+              #        exported class
+              $<$<CXX_COMPILER_ID:MSVC>:  /wd4251
+                                          /wd4068
+                                          /wd4275
+                                          /wd4668
+                                          /wd4710
+                                          /wd4505
+                                          /bigobj
+                                          /EHsc
+                                          # MSVC incorrectly sets the cplusplus to 199711L even if the compiler supports
+                                          # c++11 features. This flag sets it to the correct standard supported by the
+                                          # compiler
+                                          $<$<BOOL:${cplusplus_define}>:/Zc:__cplusplus>
+                                          $<$<BOOL:${cxx_compliance}>:/permissive-> >
+
+              # OpenCL targets need this flag to avoid
+              # ignored attribute warnings in the OpenCL
+              # headers
+              $<$<BOOL:${has_ignored_attributes_flag}>:-Wno-ignored-attributes>
+              $<$<BOOL:${has_all_warnings_flag}>:-Wall>>
+    )
 
-    check_cxx_compiler_flag(-Wall has_all_warnings_flag)
-    if(has_all_warnings_flag)
-      target_compile_options(${target}
-        PRIVATE -Wall)
-    endif()
-  endif()
+  target_compile_definitions(${target}
+    PRIVATE
+      AFDLL
+      $<$<PLATFORM_ID:Windows>:             OS_WIN
+                                            WIN32_LEAN_AND_MEAN
+                                            NOMINMAX>
+      $<$<PLATFORM_ID:Darwin>:              OS_MAC>
+      $<$<PLATFORM_ID:Linux>:               OS_LNX>
+
+      $<$<BOOL:${AF_WITH_LOGGING}>:           AF_WITH_LOGGING>
+      $<$<BOOL:${AF_CACHE_KERNELS_TO_DISK}>:  AF_CACHE_KERNELS_TO_DISK>
+  )
 endfunction()
 
 function(__af_deprecate_var var access value)
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 780cddbaaf..446ceb7e00 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -63,10 +63,11 @@ else()
       CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
         -Wno-dev <SOURCE_DIR>
         -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
-        "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} -w -fPIC"
+        "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS}"
         -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
         -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
-        "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS} -w -fPIC"
+        "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS}"
+        -DCMAKE_POSITION_INDEPENDENT_CODE=ON
         ${extproj_build_type_option}
         -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
         -DCMAKE_INSTALL_LIBDIR:PATH=lib
diff --git a/CMakeModules/platform.cmake b/CMakeModules/platform.cmake
index cfaf92dd5d..cf0f72f8ed 100644
--- a/CMakeModules/platform.cmake
+++ b/CMakeModules/platform.cmake
@@ -19,24 +19,3 @@ if(UNIX AND NOT APPLE)
   set(CMAKE_PREFIX_PATH "${CMAKE_PREFIX_PATH};/opt/intel/mkl/lib/intel64")
 endif()
 
-if(WIN32)
-  # C4068: Warnings about unknown pragmas
-  # C4275: Warnings about using non-exported classes as base class of an
-  #        exported class
-  add_compile_options(/wd4068 /wd4275)
-
-  # MSVC incorrectly sets the cplusplus to 199711L even if the compiler supports
-  # c++11 features. This flag sets it to the correct standard supported by the
-  # compiler
-  check_cxx_compiler_flag(/Zc:__cplusplus cplusplus_define)
-  if(cplusplus_define)
-    add_compile_options(/Zc:__cplusplus)
-  endif()
-
-  # The "permissive-" option enforces strict(er?) standards compliance by
-  # MSVC
-  check_cxx_compiler_flag(/permissive- cxx_compliance)
-  if(cxx_compliance)
-    add_compile_options(/permissive-)
-  endif()
-endif()
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 522a19ba2a..67b6b80dd2 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -1,3 +1,10 @@
+# Copyright (c) 2022, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
 generate_product_version(af_unified_ver_res_file
   FILE_NAME "af"
   FILE_DESCRIPTION "Unified Backend Dynamic-link library"
@@ -9,58 +16,36 @@ add_library(ArrayFire::af ALIAS af)
 target_sources(af
   PRIVATE
     ${af_unified_ver_res_file}
-    ${CMAKE_CURRENT_SOURCE_DIR}/algorithm.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/arith.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/blas.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/data.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/error.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/event.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/features.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/graphics.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/image.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/index.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/internal.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/jit_test_api.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/lapack.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/memory.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/ml.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/moments.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/signal.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/sparse.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/statistics.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/symbol_manager.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/symbol_manager.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/vision.cpp
-  )
-
-if(OpenCL_FOUND)
-  target_sources(af
-    PRIVATE
-    ${CMAKE_CURRENT_SOURCE_DIR}/opencl.cpp
-  )
+    algorithm.cpp
+    arith.cpp
+    array.cpp
+    blas.cpp
+    data.cpp
+    device.cpp
+    error.cpp
+    event.cpp
+    features.cpp
+    graphics.cpp
+    image.cpp
+    index.cpp
+    internal.cpp
+    jit_test_api.cpp
+    lapack.cpp
+    memory.cpp
+    ml.cpp
+    moments.cpp
+    random.cpp
+    signal.cpp
+    sparse.cpp
+    statistics.cpp
+    symbol_manager.cpp
+    symbol_manager.hpp
+    util.cpp
+    vision.cpp
+
+    $<$<BOOL:${OpenCL_FOUND}>: ${CMAKE_CURRENT_SOURCE_DIR}/opencl.cpp>
+    $<$<BOOL:${CUDA_FOUND}>: ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp>
 
-  target_include_directories(af
-    PRIVATE
-      $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>)
-
-endif()
-
-if(CUDA_FOUND)
-  target_sources(af
-    PRIVATE
-      ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp)
-
-  target_include_directories(af
-    PRIVATE
-      ${CUDA_INCLUDE_DIRS})
-endif()
-
-target_sources(af
-  PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c/type_util.cpp
     ${ArrayFire_SOURCE_DIR}/src/api/c/version.cpp
     ${ArrayFire_SOURCE_DIR}/src/backend/common/Logger.cpp
@@ -73,7 +58,6 @@ target_sources(af
     ${ArrayFire_SOURCE_DIR}/src/backend/common/deprecated.hpp
   )
 
-arrayfire_set_default_cxx_flags(af)
 if(WIN32)
   target_sources(af
     PRIVATE
@@ -94,8 +78,10 @@ target_include_directories(af
   PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c
     ${ArrayFire_SOURCE_DIR}/src/api/unified
+    ${ArrayFire_BINARY_DIR}
     $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
-    ${CMAKE_BINARY_DIR}
+    $<$<BOOL:${OpenCL_FOUND}>: $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>>
+    $<$<BOOL:${CUDA_FOUND}>:  ${CUDA_INCLUDE_DIRS}>
   )
 
 target_link_libraries(af
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 7aa10bc529..04d0d3390b 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -283,8 +283,6 @@ target_sources(afcpu
     ${${threads_prefix}_SOURCE_DIR}/include/threads/event.hpp
   )
 
-arrayfire_set_default_cxx_flags(afcpu)
-
 include("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
 
 target_include_directories(afcpu
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 67061740a0..e1f47b2947 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -227,31 +227,14 @@ file_to_string(
     NULLTERM
     )
 
-arrayfire_get_cuda_cxx_flags(cuda_cxx_flags)
-arrayfire_get_platform_definitions(platform_flags)
-
-# New API of cuSparse was introduced in 10.1.168 for Linux and the older
-# 10.1.105 fix version doesn't it. Unfortunately, the new API was introduced in
-# in a fix release of CUDA - unconventionally. As CMake's FindCUDA module
-# doesn't provide patch/fix version number, we use 10.2 as the minimum
-# CUDA version to enable this new cuSparse API.
-if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
-   (UNIX AND
-    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 1))
-  list(APPEND cxx_definitions -DAF_USE_NEW_CUSPARSE_API)
-endif()
-
-# CUDA_NO_HALF prevents the inclusion of the half class in the global namespace
-# which conflicts with the half class in ArrayFire's common namespace. prefer
-# using __half class instead for CUDA
-list(APPEND cxx_definitions -DAF_CUDA;-DCUDA_NO_HALF)
-list(APPEND cuda_cxx_flags ${cxx_definitions})
-
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/thrust_sort_by_key/CMakeLists.txt)
 
-add_library(af_cuda_static_cuda_library
-  STATIC
+
+add_library(afcuda
+    $<$<PLATFORM_ID:Windows>:${af_cuda_ver_res_file}>
+    ${thrust_sort_sources}
+
     blas.cu
     blas.hpp
     cudaDataType.hpp
@@ -267,104 +250,6 @@ add_library(af_cuda_static_cuda_library
     sparse_blas.hpp
     solve.cu
     solve.hpp
-)
-
-af_detect_and_set_cuda_architectures(af_cuda_static_cuda_library)
-if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
-  set_target_properties(af_cuda_static_cuda_library
-    PROPERTIES
-      CUDA_STANDARD 17)
-else()
-  target_compile_options(af_cuda_static_cuda_library
-    PRIVATE
-      $<$<COMPILE_LANGUAGE:CUDA>:--std=c++17>)
-endif()
-
-set_target_properties(af_cuda_static_cuda_library
-  PROPERTIES
-    POSITION_INDEPENDENT_CODE ON
-    LINKER_LANGUAGE CUDA
-    FOLDER "Generated Targets")
-
-target_include_directories(af_cuda_static_cuda_library
-  PRIVATE
-    ${CMAKE_CURRENT_BINARY_DIR}
-    ${CMAKE_CURRENT_SOURCE_DIR}
-    ${ArrayFire_SOURCE_DIR}/include
-    ${ArrayFire_BINARY_DIR}/include
-    ${ArrayFire_SOURCE_DIR}/src/api/c
-    ${ArrayFire_SOURCE_DIR}/src/backend
-)
-
-target_compile_definitions(af_cuda_static_cuda_library
-  PRIVATE
-    ${platform_flags}
-    AFDLL)
-
-target_compile_options(af_cuda_static_cuda_library
-  PRIVATE
-    $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe --diag_suppress=unrecognized_gcc_pragma>
-    $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
-    ${cuda_cxx_flags})
-
-if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
-   (UNIX AND
-    CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 1))
-  target_compile_definitions(af_cuda_static_cuda_library PRIVATE AF_USE_NEW_CUSPARSE_API)
-endif()
-
-target_link_libraries(af_cuda_static_cuda_library
-  PRIVATE
-    Boost::boost
-    af_spdlog
-    nonstd::span-lite)
-
-if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
-  check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
-  if(group_flags)
-    set(START_GROUP -Wl,--start-group)
-    set(END_GROUP -Wl,--end-group)
-  endif()
-
-  target_link_libraries(af_cuda_static_cuda_library
-    PRIVATE
-      ${CMAKE_DL_LIBS}
-      ${cusolver_lib}
-      ${START_GROUP}
-      ${CUDA_culibos_LIBRARY} #also a static libary
-      ${AF_CUDA_cublas_static_LIBRARY}
-      ${AF_CUDA_cublasLt_static_LIBRARY}
-      ${AF_CUDA_cufft_static_LIBRARY}
-      ${AF_CUDA_optionally_static_libraries}
-      ${nvrtc_libs}
-      ${cusolver_static_lib}
-      ${END_GROUP})
-
-  if(CUDA_VERSION VERSION_GREATER 10.0)
-    target_link_libraries(af_cuda_static_cuda_library
-      PRIVATE
-        ${AF_CUDA_cublasLt_static_LIBRARY})
-  endif()
-
-  if(CUDA_VERSION VERSION_GREATER 9.5)
-    target_link_libraries(af_cuda_static_cuda_library
-      PRIVATE
-        ${CUDA_lapack_static_LIBRARY})
-  endif()
-
-else()
-  target_link_libraries(af_cuda_static_cuda_library
-    PUBLIC
-      ${CUDA_CUBLAS_LIBRARIES}
-      ${CUDA_CUFFT_LIBRARIES}
-      ${CUDA_cusolver_LIBRARY}
-      ${nvrtc_libs}
-  )
-endif()
-
-add_library(afcuda
-    $<$<PLATFORM_ID:Windows>:${af_cuda_ver_res_file}>
-    ${thrust_sort_sources}
 
     EnqueueArgs.hpp
     all.cu
@@ -520,6 +405,12 @@ add_library(afcuda
     cu_check_macro.hpp
     cublas.cpp
     cublas.hpp
+
+    $<$<BOOL:${AF_WITH_CUDNN}>: cudnn.cpp
+                             cudnn.hpp
+                             cudnnModule.cpp
+                             cudnnModule.hpp>
+
     cufft.hpp
     cusolverDn.cpp
     cusolverDn.hpp
@@ -662,11 +553,56 @@ add_library(afcuda
     ${scan_by_key_sources}
   )
 
+
+if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
+  check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
+  if(group_flags)
+    set(START_GROUP -Wl,--start-group)
+    set(END_GROUP -Wl,--end-group)
+  endif()
+
+  target_link_libraries(afcuda
+    PRIVATE
+      ${cusolver_lib}
+      ${START_GROUP}
+      ${CUDA_culibos_LIBRARY} #also a static libary
+      ${AF_CUDA_cublas_static_LIBRARY}
+      ${AF_CUDA_cublasLt_static_LIBRARY}
+      ${AF_CUDA_cufft_static_LIBRARY}
+      ${AF_CUDA_optionally_static_libraries}
+      ${nvrtc_libs}
+      ${cusolver_static_lib}
+      ${END_GROUP})
+
+  if(CUDA_VERSION VERSION_GREATER 10.0)
+    target_link_libraries(afcuda
+      PRIVATE
+        ${AF_CUDA_cublasLt_static_LIBRARY})
+  endif()
+
+  if(CUDA_VERSION VERSION_GREATER 9.5)
+    target_link_libraries(afcuda
+      PRIVATE
+        ${CUDA_lapack_static_LIBRARY})
+  endif()
+
+else()
+  target_link_libraries(afcuda
+    PUBLIC
+      ${CUDA_CUBLAS_LIBRARIES}
+      ${CUDA_CUFFT_LIBRARIES}
+      ${CUDA_cusolver_LIBRARY}
+      ${nvrtc_libs}
+  )
+endif()
+
+
 af_detect_and_set_cuda_architectures(afcuda)
 if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
   set_target_properties(afcuda
     PROPERTIES
-      CUDA_STANDARD 17)
+      CUDA_STANDARD 17
+      CUDA_STANDARD_REQUIRED ON)
 else()
   target_compile_options(afcuda
     PRIVATE
@@ -675,30 +611,43 @@ endif()
 
 target_compile_definitions(afcuda
   PRIVATE
-    ${platform_flags})
+    AF_CUDA
+
+    # CUDA_NO_HALF prevents the inclusion of the half class in the global namespace
+    # which conflicts with the half class in ArrayFire's common namespace. prefer
+    # using __half class instead for CUDA
+    CUDA_NO_HALF
+
+    $<$<BOOL:${AF_WITH_CUDNN}>:WITH_CUDNN>
+)
+
+# New API of cuSparse was introduced in 10.1.168 for Linux and the older
+# 10.1.105 fix version doesn't it. Unfortunately, the new API was introduced in
+# in a fix release of CUDA - unconventionally. As CMake's FindCUDA module
+# doesn't provide patch/fix version number, we use 10.2 as the minimum
+# CUDA version to enable this new cuSparse API.
+if(CUDA_VERSION_MAJOR VERSION_GREATER 10 OR
+    (UNIX AND
+      CUDA_VERSION_MAJOR VERSION_EQUAL 10 AND CUDA_VERSION_MINOR VERSION_GREATER 1))
+  target_compile_definitions(afcuda
+    PRIVATE
+      AF_USE_NEW_CUSPARSE_API)
+endif()
 
 target_compile_options(afcuda
   PRIVATE
-    ${cuda_cxx_flags}
     $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
     $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe --diag_suppress=unrecognized_gcc_pragma>
+    $<$<COMPILE_LANGUAGE:CUDA>: $<$<CXX_COMPILER_ID:MSVC>:  -Xcompiler=/wd4251
+                                                            -Xcompiler=/wd4068
+                                                            -Xcompiler=/wd4275
+                                                            -Xcompiler=/wd4668
+                                                            -Xcompiler=/wd4710
+                                                            -Xcompiler=/wd4505
+                                                            -Xcompiler=/bigobj>>
 )
 
 
-if(AF_WITH_CUDNN)
-  target_sources(afcuda PRIVATE
-    cudnn.cpp
-    cudnn.hpp
-    cudnnModule.cpp
-    cudnnModule.hpp)
-  target_compile_definitions(afcuda PRIVATE WITH_CUDNN)
-
-  target_include_directories (afcuda
-    PRIVATE
-      ${cuDNN_INCLUDE_DIRS}
-    )
-endif()
-
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS AND AF_cusparse_LINK_LOADING STREQUAL "Static")
   target_compile_definitions(afcuda
     PRIVATE
@@ -708,7 +657,6 @@ endif()
 add_library(ArrayFire::afcuda ALIAS afcuda)
 
 add_dependencies(afcuda ${jit_kernel_targets} ${nvrtc_kernel_targets})
-add_dependencies(af_cuda_static_cuda_library ${nvrtc_kernel_targets})
 
 if(UNIX AND AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS)
   add_dependencies(afcuda ${cuda_pruned_library_targets})
@@ -720,6 +668,7 @@ target_include_directories (afcuda
     $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
+    $<$<BOOL:${AF_WITH_CUDNN}>:${cuDNN_INCLUDE_DIRS}>
     ${CUDA_INCLUDE_DIRS}
     ${ArrayFire_SOURCE_DIR}/src/api/c
     ${CMAKE_CURRENT_SOURCE_DIR}
@@ -734,7 +683,6 @@ target_link_libraries(afcuda
     cpp_api_interface
     afcommon_interface
     ${CMAKE_DL_LIBS}
-    af_cuda_static_cuda_library
   )
 
 # If the driver is not found the cuda driver api need to be linked against the
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 024c92551a..32d3172a1a 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -399,8 +399,6 @@ target_include_directories(afopencl
     ../../../include
   )
 
-arrayfire_set_default_cxx_flags(afopencl)
-
 add_dependencies(afopencl ${cl_kernel_targets} CLBlast-ext)
 
 set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)

From 83babaf91a42b442d2b2c77b0782fdee6e6b136f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Sep 2022 12:16:34 -0400
Subject: [PATCH 2287/2677] Fix vcpkg support and improve external detection of
 packages

---
 CMakeModules/AF_vcpkg_options.cmake |  4 ++++
 CMakeModules/build_CLBlast.cmake    |  9 ++++++++-
 CMakeModules/build_cl2hpp.cmake     | 12 +++++++++++-
 src/backend/opencl/CMakeLists.txt   |  4 +++-
 test/CMakeLists.txt                 |  2 +-
 vcpkg.json                          | 13 ++++++++++---
 6 files changed, 37 insertions(+), 7 deletions(-)

diff --git a/CMakeModules/AF_vcpkg_options.cmake b/CMakeModules/AF_vcpkg_options.cmake
index 75297a02b6..00745f846c 100644
--- a/CMakeModules/AF_vcpkg_options.cmake
+++ b/CMakeModules/AF_vcpkg_options.cmake
@@ -23,6 +23,10 @@ if(AF_BUILD_FORGE)
   list(APPEND VCPKG_MANIFEST_FEATURES "forge")
 endif()
 
+if(BUILD_TESTING)
+  list(APPEND VCPKG_MANIFEST_FEATURES "tests")
+endif()
+
 if(AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
   list(APPEND VCPKG_MANIFEST_FEATURES "mkl")
 endif()
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 446ceb7e00..0f67d3fdee 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -12,7 +12,14 @@ if(TARGET clblast OR AF_WITH_EXTERNAL_PACKAGES_ONLY)
     # another package so we dont need this property to link against
     # CLBlast.
     set_target_properties(clblast PROPERTIES
-      IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "")
+      IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE ""
+      IMPORTED_LINK_INTERFACE_LIBRARIES_DEBUG "")
+
+    if(WIN32 AND VCPKG_ROOT)
+      set_target_properties(clblast PROPERTIES
+        IMPORTED_LOCATION_RELEASE ""
+        IMPORTED_LOCATION_DEBUG "")
+    endif()
   else()
     message(ERROR "CLBlast now found")
   endif()
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index e090dd0800..14c2646c2e 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -13,7 +13,17 @@
 
 find_package(OpenCL)
 
-if (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
+find_path(cl2hpp_header_file_path
+  NAMES CL/cl2.hpp
+  PATHS ${OpenCL_INCLUDE_PATHS})
+
+if(cl2hpp_header_file_path)
+  add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
+  add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
+
+  set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES ${cl2hpp_header_file_path})
+elseif (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
   af_dep_check_and_populate(${cl2hpp_prefix}
     URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
     REF v2.0.12)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 32d3172a1a..a827c55193 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -399,7 +399,9 @@ target_include_directories(afopencl
     ../../../include
   )
 
-add_dependencies(afopencl ${cl_kernel_targets} CLBlast-ext)
+if(NOT TARGET clblast)
+  add_dependencies(afopencl ${cl_kernel_targets} CLBlast-ext)
+endif()
 
 set_target_properties(afopencl PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index e6468848d6..2a66ea8291 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -17,7 +17,7 @@ endif()
 
 if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
     dependency_check(GTest_FOUND)
-else()
+elseif(NOT TARGET GTest::gtest)
   af_dep_check_and_populate(${gtest_prefix}
     URI https://github.com/google/googletest.git
     REF release-1.8.1
diff --git a/vcpkg.json b/vcpkg.json
index 8986d52dbe..70aab906ed 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -8,19 +8,26 @@
         "boost-math",
         "boost-stacktrace",
         "spdlog",
-        "freeimage"
+        "freeimage",
+        "span-lite"
     ],
     "overrides": [
         {
             "name": "fmt",
-            "version": "7.1.3"
+            "version": "8.1.1"
         },
         {
             "name": "spdlog",
-            "version": "1.8.5"
+            "version": "1.9.2"
         }
     ],
     "features": {
+        "tests": {
+            "description": "Build with tests",
+            "dependencies": [
+                "gtest"
+            ]
+        },
         "forge": {
             "description": "Build Forge",
             "dependencies": [

From 83edd0983824549cfab33789a2039f8a205cc27e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Sep 2022 14:15:59 -0400
Subject: [PATCH 2288/2677] Update deprecated macro from GTest. Add workaround
 for older versions

---
 test/anisotropic_diffusion.cpp |  2 +-
 test/approx1.cpp               |  6 ++--
 test/approx2.cpp               |  6 ++--
 test/array.cpp                 |  2 +-
 test/arrayio.cpp               |  2 +-
 test/assign.cpp                |  2 +-
 test/bilateral.cpp             |  2 +-
 test/binary.cpp                | 56 +++++++++++++++++-----------------
 test/blas.cpp                  | 14 ++++-----
 test/canny.cpp                 |  2 +-
 test/cholesky_dense.cpp        |  2 +-
 test/clamp.cpp                 |  2 +-
 test/compare.cpp               |  2 +-
 test/confidence_connected.cpp  |  4 +--
 test/constant.cpp              |  2 +-
 test/convolve.cpp              | 10 +++---
 test/corrcoef.cpp              |  2 +-
 test/covariance.cpp            |  2 +-
 test/diagonal.cpp              |  2 +-
 test/diff1.cpp                 |  2 +-
 test/diff2.cpp                 |  2 +-
 test/dog.cpp                   |  2 +-
 test/dot.cpp                   | 14 ++++-----
 test/fast.cpp                  |  4 +--
 test/fft.cpp                   | 24 +++++++--------
 test/fft_real.cpp              |  2 +-
 test/fftconvolve.cpp           |  4 +--
 test/gaussiankernel.cpp        |  2 +-
 test/gen_index.cpp             |  2 +-
 test/gloh.cpp                  |  2 +-
 test/gradient.cpp              |  2 +-
 test/half.cpp                  | 46 ++++++++++++++--------------
 test/hamming.cpp               |  4 +--
 test/harris.cpp                |  2 +-
 test/histogram.cpp             |  2 +-
 test/homography.cpp            |  2 +-
 test/iir.cpp                   |  2 +-
 test/imageio.cpp               |  2 +-
 test/index.cpp                 | 10 +++---
 test/inverse_deconv.cpp        |  2 +-
 test/inverse_dense.cpp         |  2 +-
 test/iota.cpp                  |  2 +-
 test/iterative_deconv.cpp      |  2 +-
 test/jit.cpp                   |  4 +--
 test/join.cpp                  |  2 +-
 test/lu_dense.cpp              |  2 +-
 test/match_template.cpp        |  2 +-
 test/mean.cpp                  |  4 +--
 test/meanshift.cpp             |  2 +-
 test/meanvar.cpp               | 14 ++++-----
 test/medfilt.cpp               |  4 +--
 test/memory.cpp                |  2 +-
 test/moddims.cpp               |  2 +-
 test/moments.cpp               |  2 +-
 test/morph.cpp                 |  2 +-
 test/nearest_neighbour.cpp     | 14 ++++-----
 test/orb.cpp                   |  2 +-
 test/pad_borders.cpp           |  2 +-
 test/pinverse.cpp              |  2 +-
 test/qr_dense.cpp              |  2 +-
 test/random.cpp                | 10 +++---
 test/range.cpp                 |  4 +--
 test/rank_dense.cpp            |  4 +--
 test/reduce.cpp                | 14 ++++-----
 test/regions.cpp               |  2 +-
 test/reorder.cpp               |  2 +-
 test/replace.cpp               |  2 +-
 test/resize.cpp                |  4 +--
 test/rng_match.cpp             |  2 +-
 test/rng_quality.cpp           |  2 +-
 test/rotate.cpp                |  2 +-
 test/rotate_linear.cpp         |  2 +-
 test/sat.cpp                   |  2 +-
 test/select.cpp                | 38 +++++++++++------------
 test/shift.cpp                 |  2 +-
 test/sift.cpp                  |  2 +-
 test/sobel.cpp                 |  4 +--
 test/solve_dense.cpp           |  2 +-
 test/sort.cpp                  |  2 +-
 test/sort_by_key.cpp           |  2 +-
 test/sort_index.cpp            |  2 +-
 test/sparse.cpp                |  2 +-
 test/stdev.cpp                 |  2 +-
 test/susan.cpp                 |  2 +-
 test/svd_dense.cpp             |  2 +-
 test/testHelpers.hpp           | 10 ++++++
 test/tile.cpp                  |  2 +-
 test/topk.cpp                  |  4 +--
 test/transform.cpp             |  8 ++---
 test/transform_coordinates.cpp |  2 +-
 test/translate.cpp             |  4 +--
 test/transpose.cpp             |  2 +-
 test/transpose_inplace.cpp     |  2 +-
 test/triangle.cpp              |  2 +-
 test/unwrap.cpp                |  2 +-
 test/var.cpp                   |  2 +-
 test/where.cpp                 |  2 +-
 test/wrap.cpp                  | 10 +++---
 test/write.cpp                 |  2 +-
 99 files changed, 252 insertions(+), 242 deletions(-)

diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index f20f1f009c..f4d78382f3 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -32,7 +32,7 @@ class AnisotropicDiffusion : public ::testing::Test {};
 typedef ::testing::Types<float, double, int, uint, uchar, short, ushort>
     TestTypes;
 
-TYPED_TEST_CASE(AnisotropicDiffusion, TestTypes);
+TYPED_TEST_SUITE(AnisotropicDiffusion, TestTypes);
 
 template<typename T>
 array normalize(const array &p_in) {
diff --git a/test/approx1.cpp b/test/approx1.cpp
index a13c51c173..17d7579cec 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -63,7 +63,7 @@ class Approx1 : public ::testing::Test {
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 
 // Register the type list
-TYPED_TEST_CASE(Approx1, TestTypes);
+TYPED_TEST_SUITE(Approx1, TestTypes);
 
 template<typename T>
 void approx1Test(string pTestFile, const unsigned resultIdx,
@@ -926,7 +926,7 @@ class Approx1V2 : public ::testing::Test {
     }
 };
 
-TYPED_TEST_CASE(Approx1V2, TestTypes);
+TYPED_TEST_SUITE(Approx1V2, TestTypes);
 
 class SimpleTestData {
    public:
@@ -969,7 +969,7 @@ class Approx1V2Simple : public Approx1V2<T> {
     }
 };
 
-TYPED_TEST_CASE(Approx1V2Simple, TestTypes);
+TYPED_TEST_SUITE(Approx1V2Simple, TestTypes);
 
 TYPED_TEST(Approx1V2Simple, UseNullOutputArray) {
     this->testSpclOutArray(NULL_ARRAY);
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 8ea4f5b8a4..796c639fd0 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -56,7 +56,7 @@ class Approx2 : public ::testing::Test {
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Approx2, TestTypes);
+TYPED_TEST_SUITE(Approx2, TestTypes);
 
 template<typename T>
 void approx2Test(string pTestFile, const unsigned resultIdx,
@@ -862,7 +862,7 @@ class Approx2V2 : public ::testing::Test {
     }
 };
 
-TYPED_TEST_CASE(Approx2V2, TestTypes);
+TYPED_TEST_SUITE(Approx2V2, TestTypes);
 
 class SimpleTestData {
    public:
@@ -911,7 +911,7 @@ class Approx2V2Simple : public Approx2V2<T> {
     }
 };
 
-TYPED_TEST_CASE(Approx2V2Simple, TestTypes);
+TYPED_TEST_SUITE(Approx2V2Simple, TestTypes);
 
 TYPED_TEST(Approx2V2Simple, UseNullOutputArray) {
     this->testSpclOutArray(NULL_ARRAY);
diff --git a/test/array.cpp b/test/array.cpp
index 08b5a568d7..eeb7f2952b 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -26,7 +26,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
                          half_float::half>
     TestTypes;
 
-TYPED_TEST_CASE(Array, TestTypes);
+TYPED_TEST_SUITE(Array, TestTypes);
 
 TEST(Array, ConstructorDefault) {
     array a;
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
index fbbb9c5030..7a578b612a 100644
--- a/test/arrayio.cpp
+++ b/test/arrayio.cpp
@@ -42,7 +42,7 @@ string getTypeName(
     return info.param.name;
 }
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Types, ArrayIOType,
     ::testing::Values(type_params("f32", f32, 3.14f, 0),
                       type_params("f64", f64, 3.14, 0),
diff --git a/test/assign.cpp b/test/assign.cpp
index 0e2aea05d7..7c32a2cc33 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -99,7 +99,7 @@ typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, uchar,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(ArrayAssign, TestTypes);
+TYPED_TEST_SUITE(ArrayAssign, TestTypes);
 
 template<typename inType, typename outType>
 void assignTest(string pTestFile, const vector<af_seq> *seqv) {
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 07d95debba..d4da723ddb 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -77,7 +77,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
     DataTestTypes;
 
 // register the type list
-TYPED_TEST_CASE(BilateralOnData, DataTestTypes);
+TYPED_TEST_SUITE(BilateralOnData, DataTestTypes);
 
 template<typename inType>
 void bilateralDataTest(string pTestFile) {
diff --git a/test/binary.cpp b/test/binary.cpp
index 06e720ed8e..f5fd0610e8 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -386,27 +386,27 @@ DEF_TEST(UChar, unsigned char)
 
 #undef DEF_TEST
 
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestULong,
-                        testing::Range<unsigned long long>(1, 1e7, 1e6));
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestLong,
-                        testing::Range<long long>(1, 1e7, 1e6));
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUInt,
-                        testing::Range<unsigned int>(1, 65000, 15e3));
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestInt,
-                        testing::Range<int>(1, 46340, 10e3));
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUShort,
-                        testing::Range<unsigned short>(1, 255, 100));
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestShort,
-                        testing::Range<short>(1, 180, 50));
-INSTANTIATE_TEST_CASE_P(PositiveValues, PowPrecisionTestUChar,
-                        testing::Range<unsigned char>(1, 12, 5));
-
-INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestLong,
-                        testing::Range<long long>(-1e7, 0, 1e6));
-INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestInt,
-                        testing::Range<int>(-46340, 0, 10e3));
-INSTANTIATE_TEST_CASE_P(NegativeValues, PowPrecisionTestShort,
-                        testing::Range<short>(-180, 0, 50));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestULong,
+                         testing::Range<unsigned long long>(1, 1e7, 1e6));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestLong,
+                         testing::Range<long long>(1, 1e7, 1e6));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestUInt,
+                         testing::Range<unsigned int>(1, 65000, 15e3));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestInt,
+                         testing::Range<int>(1, 46340, 10e3));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestUShort,
+                         testing::Range<unsigned short>(1, 255, 100));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestShort,
+                         testing::Range<short>(1, 180, 50));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestUChar,
+                         testing::Range<unsigned char>(1, 12, 5));
+
+INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestLong,
+                         testing::Range<long long>(-1e7, 0, 1e6));
+INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestInt,
+                         testing::Range<int>(-46340, 0, 10e3));
+INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestShort,
+                         testing::Range<short>(-180, 0, 50));
 
 struct result_type_param {
     af_dtype result_;
@@ -453,7 +453,7 @@ std::string print_types(
     return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     SameTypes, ResultType,
     // clang-format off
     ::testing::Values(result_type_param(f32),
@@ -472,7 +472,7 @@ INSTANTIATE_TEST_CASE_P(
     // clang-format on
     print_types);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Float, ResultType,
     // clang-format off
     ::testing::Values(result_type_param(f32),
@@ -491,7 +491,7 @@ INSTANTIATE_TEST_CASE_P(
     // clang-format on
     print_types);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Double, ResultType,
     ::testing::Values(
         // clang-format off
@@ -540,7 +540,7 @@ class ResultTypeScalar : public ::testing::Test {
 typedef ::testing::Types<float, double, unsigned int, int, short,
                          unsigned short, char, unsigned char, half_float::half>
     TestTypes;
-TYPED_TEST_CASE(ResultTypeScalar, TestTypes);
+TYPED_TEST_SUITE(ResultTypeScalar, TestTypes);
 
 TYPED_TEST(ResultTypeScalar, HalfAddition) {
     SUPPORTED_TYPE_CHECK(half_float::half);
@@ -583,7 +583,7 @@ class Broadcast : public ::testing::TestWithParam<std::tuple<dim4, dim4> > {
 };
 /// clang-format off
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     CorrectCases, Broadcast,
     ::testing::Combine(
         ::testing::Values(dim4(1), dim4(10), dim4(1, 10), dim4(1, 1, 10),
@@ -715,7 +715,7 @@ TEST_P(Broadcast, AdditionLHSIndexed) {
 
     af::dim4 outdims       = broadcastOut(get<0>(params), rhs.dims());
     af::array indexedlhs   = lhs(seq(lhs_dims[0]), seq(lhs_dims[1]),
-                               seq(lhs_dims[2]), seq(lhs_dims[3]));
+                                 seq(lhs_dims[2]), seq(lhs_dims[3]));
     af::dim4 tilerepetions = tileRepeations(get<0>(params), rhs.dims());
     af::array tiledlhs     = tile(indexedlhs, tilerepetions);
 
@@ -760,7 +760,7 @@ TEST_P(Broadcast, AdditionBothIndexed) {
 
     af::dim4 outdims       = broadcastOut(lhs_dims, rhs_dims);
     af::array indexedlhs   = lhs(seq(lhs_dims[0]), seq(lhs_dims[1]),
-                               seq(lhs_dims[2]), seq(lhs_dims[3]));
+                                 seq(lhs_dims[2]), seq(lhs_dims[3]));
     af::dim4 tilerepetions = tileRepeations(get<0>(params), get<1>(params));
     af::array tiledlhs     = tile(indexedlhs, tilerepetions);
 
diff --git a/test/blas.cpp b/test/blas.cpp
index 612f6dd97f..62491a366f 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -45,7 +45,7 @@ template<typename T>
 class MatrixMultiply : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cdouble, cfloat> TestTypes;
-TYPED_TEST_CASE(MatrixMultiply, TestTypes);
+TYPED_TEST_SUITE(MatrixMultiply, TestTypes);
 
 template<typename T, bool isBVector>
 void MatMulCheck(string TestFile) {
@@ -339,7 +339,7 @@ std::string print_blas_params(
     return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     LHSBroadcast, MatrixMultiplyBatch,
     ::testing::Values(
 
@@ -365,7 +365,7 @@ INSTANTIATE_TEST_CASE_P(
         ),
     print_blas_params);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     RHSBroadcast, MatrixMultiplyBatch,
     ::testing::Values(
         // clang-format off
@@ -389,7 +389,7 @@ INSTANTIATE_TEST_CASE_P(
         ),
     print_blas_params);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     SameBatch, MatrixMultiplyBatch,
     ::testing::Values(
         // clang-format off
@@ -609,7 +609,7 @@ string out_info(const ::testing::TestParamInfo<Gemm::ParamType> info) {
 }
 
 // clang-format off
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Square, Gemm,
     ::testing::Values(
         //          lhs_opts     rhs_opts     alpha  lhs    rhs    gold    lhs_dims    rhs_dims    out_dims    beta  out_array_type
@@ -623,7 +623,7 @@ INSTANTIATE_TEST_CASE_P(
 // clang-format on
 
 // clang-format off
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Batched, Gemm,
     ::testing::Values(
         //          lhs_opts     rhs_opts     alpha  lhs          rhs    gold          lhs_dims       rhs_dims    out_dims       beta  out_array_type
@@ -637,7 +637,7 @@ INSTANTIATE_TEST_CASE_P(
 // clang-format on
 
 // clang-format off
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     NonSquare, Gemm,
     ::testing::Values(
         //          lhs_opts      rhs_opts      alpha  lhs         rhs         gold       lhs_dims    rhs_dims    out_dims    beta  out_array_type
diff --git a/test/canny.cpp b/test/canny.cpp
index e00e9b0c30..8e1cb9c2b6 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -32,7 +32,7 @@ typedef ::testing::Types<float, int, uint, short, ushort, uchar, double>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(CannyEdgeDetector, TestTypes);
+TYPED_TEST_SUITE(CannyEdgeDetector, TestTypes);
 
 template<typename T>
 void cannyTest(string pTestFile) {
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index 3800d0c0e1..0631ec2bad 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -78,7 +78,7 @@ template<typename T>
 class Cholesky : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
-TYPED_TEST_CASE(Cholesky, TestTypes);
+TYPED_TEST_SUITE(Cholesky, TestTypes);
 
 template<typename T>
 double eps();
diff --git a/test/clamp.cpp b/test/clamp.cpp
index eb0b46a187..7f888a56ac 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -104,7 +104,7 @@ string testNameGenerator(const ::testing::TestParamInfo<clamp_params> info) {
 typedef Clamp<double> ClampFloatingPoint;
 
 // clang-format off
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     SmallDims, ClampFloatingPoint,
     ::testing::Values(
                       clamp_params(dim4(10), f32, f32, f32, f32),
diff --git a/test/compare.cpp b/test/compare.cpp
index 576186d164..66d9778039 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -26,7 +26,7 @@ class Compare : public ::testing::Test {};
 typedef ::testing::Types<float, double, uint, int, intl, uintl, uchar, short,
                          ushort, half_float::half>
     TestTypes;
-TYPED_TEST_CASE(Compare, TestTypes);
+TYPED_TEST_SUITE(Compare, TestTypes);
 
 #define COMPARE(OP, Name)                                   \
     TYPED_TEST(Compare, Test_##Name) {                      \
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 6963edcc1e..8ef707aca7 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -31,7 +31,7 @@ class ConfidenceConnectedImageTest : public testing::Test {
 
 typedef ::testing::Types<float, uint, ushort, uchar> TestTypes;
 
-TYPED_TEST_CASE(ConfidenceConnectedImageTest, TestTypes);
+TYPED_TEST_SUITE(ConfidenceConnectedImageTest, TestTypes);
 
 struct CCCTestParams {
     const char *prefix;
@@ -185,7 +185,7 @@ TEST_P(ConfidenceConnectedDataTest, SegmentARegion) {
     testData<unsigned char>(GetParam());
 }
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     SingleSeed, ConfidenceConnectedDataTest,
     testing::Values(CCCTestParams{"core", 0u, 1u, 5u, 255.0},
                     CCCTestParams{"background", 0u, 1u, 5u, 255.0},
diff --git a/test/constant.cpp b/test/constant.cpp
index e54a3d01f7..0a75e3d974 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -33,7 +33,7 @@ class Constant : public ::testing::Test {};
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
                          uchar, uintl, intl, short, ushort, half_float::half>
     TestTypes;
-TYPED_TEST_CASE(Constant, TestTypes);
+TYPED_TEST_SUITE(Constant, TestTypes);
 
 template<typename T>
 void ConstantCPPCheck(T value) {
diff --git a/test/convolve.cpp b/test/convolve.cpp
index c3abe056cd..7b31e532a3 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -38,7 +38,7 @@ typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, char, uchar,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Convolve, TestTypes);
+TYPED_TEST_SUITE(Convolve, TestTypes);
 
 template<typename T>
 void convolveTest(string pTestFile, int baseDim, bool expand) {
@@ -877,9 +877,9 @@ vector<conv2_strided_params> genConsistencyTests() {
             conv2_consistency_data(dim4(257, 257), dim4(3, 3))};
 }
 
-INSTANTIATE_TEST_CASE_P(Conv2Consistency, Conv2ConsistencyTest,
-                        ::testing::ValuesIn(genConsistencyTests()),
-                        testNameGenerator<Conv2ConsistencyTest>);
+INSTANTIATE_TEST_SUITE_P(Conv2Consistency, Conv2ConsistencyTest,
+                         ::testing::ValuesIn(genConsistencyTests()),
+                         testNameGenerator<Conv2ConsistencyTest>);
 
 TEST_P(Conv2ConsistencyTest, RandomConvolutions) {
     conv2_strided_params params = GetParam();
@@ -1039,7 +1039,7 @@ typedef ::testing::Types<float, double, half_float::half>
     TestTypesStrided;  // TODO: integral types??
 
 // register the type list
-TYPED_TEST_CASE(ConvolveStrided, TestTypesStrided);
+TYPED_TEST_SUITE(ConvolveStrided, TestTypesStrided);
 
 TYPED_TEST(ConvolveStrided, Strided_sig1010_filt33_s11_p11_d11) {
     convolve2stridedTest<TypeParam>(
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 7fa6e57ffa..1c7f378961 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -35,7 +35,7 @@ typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(CorrelationCoefficient, TestTypes);
+TYPED_TEST_SUITE(CorrelationCoefficient, TestTypes);
 
 template<typename T>
 struct f32HelperType {
diff --git a/test/covariance.cpp b/test/covariance.cpp
index 6eea33e224..aa06c58a10 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -39,7 +39,7 @@ typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Covariance, TestTypes);
+TYPED_TEST_SUITE(Covariance, TestTypes);
 
 template<typename T>
 struct f32HelperType {
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index a73a2096ff..1eecb883ae 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -34,7 +34,7 @@ class Diagonal : public ::testing::Test {};
 typedef ::testing::Types<float, double, int, uint, char, unsigned char,
                          half_float::half>
     TestTypes;
-TYPED_TEST_CASE(Diagonal, TestTypes);
+TYPED_TEST_SUITE(Diagonal, TestTypes);
 
 TYPED_TEST(Diagonal, Create) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 510d9ce61b..605cd75fa9 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -50,7 +50,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Diff1, TestTypes);
+TYPED_TEST_SUITE(Diff1, TestTypes);
 
 template<typename T>
 void diff1Test(string pTestFile, unsigned dim, bool isSubRef = false,
diff --git a/test/diff2.cpp b/test/diff2.cpp
index c5ff4ce9f3..4a68627d7b 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -55,7 +55,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Diff2, TestTypes);
+TYPED_TEST_SUITE(Diff2, TestTypes);
 
 template<typename T>
 void diff2Test(string pTestFile, unsigned dim, bool isSubRef = false,
diff --git a/test/dog.cpp b/test/dog.cpp
index 9b8e952567..0b764f2c06 100644
--- a/test/dog.cpp
+++ b/test/dog.cpp
@@ -37,7 +37,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(DOG, TestTypes);
+TYPED_TEST_SUITE(DOG, TestTypes);
 
 TYPED_TEST(DOG, Basic) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/dot.cpp b/test/dot.cpp
index 37b84d2818..357e0784d4 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -44,8 +44,8 @@ typedef ::testing::Types<float, double> TestTypesF;
 typedef ::testing::Types<cfloat, cdouble> TestTypesC;
 
 // register the type list
-TYPED_TEST_CASE(DotF, TestTypesF);
-TYPED_TEST_CASE(DotC, TestTypesC);
+TYPED_TEST_SUITE(DotF, TestTypesF);
+TYPED_TEST_SUITE(DotC, TestTypesC);
 
 bool isinf(af::af_cfloat val) {
     using std::isinf;
@@ -301,11 +301,11 @@ std::string print_dot(const ::testing::TestParamInfo<Dot::ParamType> info) {
     return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(Small, Dot,
-                        ::testing::Values(2, 4, 5, 10, 31, 32, 33, 100, 127,
-                                          128, 129, 200, 500, 511, 512, 513,
-                                          1000),
-                        print_dot);
+INSTANTIATE_TEST_SUITE_P(Small, Dot,
+                         ::testing::Values(2, 4, 5, 10, 31, 32, 33, 100, 127,
+                                           128, 129, 200, 500, 511, 512, 513,
+                                           1000),
+                         print_dot);
 
 TEST_P(Dot, Half) {
     SUPPORTED_TYPE_CHECK(half_float::half);
diff --git a/test/fast.cpp b/test/fast.cpp
index 4dc0c8896f..77281955a5 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -63,8 +63,8 @@ class FixedFAST : public ::testing::Test {
 typedef ::testing::Types<float, double> FloatTestTypes;
 typedef ::testing::Types<int, unsigned, short, ushort> FixedTestTypes;
 
-TYPED_TEST_CASE(FloatFAST, FloatTestTypes);
-TYPED_TEST_CASE(FixedFAST, FixedTestTypes);
+TYPED_TEST_SUITE(FloatFAST, FloatTestTypes);
+TYPED_TEST_SUITE(FixedFAST, FixedTestTypes);
 
 template<typename T>
 void fastTest(string pTestFile, bool nonmax) {
diff --git a/test/fft.cpp b/test/fft.cpp
index ce654d3c05..acd0ad7521 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -742,34 +742,34 @@ string to_test_params(const ::testing::TestParamInfo<FFTBase::ParamType> info) {
     return out.replace(out.find("."), 1, "_");
 }
 
-INSTANTIATE_TEST_CASE_P(
-    Inputs2D, FFTC2R2D,
-    ::testing::Values(fft_params(dim4(513, 512), false, 0.5),
-                      fft_params(dim4(1025, 1024), false, 0.5),
-                      fft_params(dim4(2049, 2048), false, 0.5)),
-    to_test_params);
-
-INSTANTIATE_TEST_CASE_P(
+// INSTANTIATE_TEST_SUITE_P(
+//     Inputs2D, FFTC2R2D,
+//     ::testing::Values(fft_params(dim4(513, 512), false, 0.5),
+//                       fft_params(dim4(1025, 1024), false, 0.5),
+//                       fft_params(dim4(2049, 2048), false, 0.5)),
+//     to_test_params);
+
+INSTANTIATE_TEST_SUITE_P(
     Inputs2D, FFT2D,
     ::testing::Values(fft_params(dim4(512, 512), false, 0.5),
                       fft_params(dim4(1024, 1024), false, 0.5),
                       fft_params(dim4(2048, 2048), false, 0.5)),
     to_test_params);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Inputs3D, FFTC2R3D,
     ::testing::Values(fft_params(dim4(512, 512, 3), false, 0.5),
                       fft_params(dim4(1024, 1024, 3), false, 0.5),
                       fft_params(dim4(2048, 2048, 3), false, 0.5)),
     to_test_params);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Inputs3D, FFT3D,
     ::testing::Values(fft_params(dim4(1024, 1024, 3), true, 0.5),
                       fft_params(dim4(1024, 1024, 3), false, 0.5)),
     to_test_params);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     InputsND, FFTND,
     ::testing::Values(fft_params(dim4(512), false, 0.5),
                       fft_params(dim4(1024), false, 0.5),
@@ -777,7 +777,7 @@ INSTANTIATE_TEST_CASE_P(
                       fft_params(dim4(1024, 1024, 3), false, 0.5)),
     to_test_params);
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     InputsND, FFTC2R,
     ::testing::Values(fft_params(dim4(513), false, 0.5),
                       fft_params(dim4(1025), false, 0.5),
diff --git a/test/fft_real.cpp b/test/fft_real.cpp
index d0816d976c..863f66d74c 100644
--- a/test/fft_real.cpp
+++ b/test/fft_real.cpp
@@ -37,7 +37,7 @@ template<typename T>
 class FFT_REAL : public ::testing::Test {};
 
 typedef ::testing::Types<cfloat, cdouble> TestTypes;
-TYPED_TEST_CASE(FFT_REAL, TestTypes);
+TYPED_TEST_SUITE(FFT_REAL, TestTypes);
 
 template<int rank>
 array fft(const array &in, double norm) {
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index 98fa9c315c..7465891bde 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -45,8 +45,8 @@ typedef ::testing::Types<cfloat, cdouble, float, double, int, uint, char, uchar,
 typedef ::testing::Types<float, double> TestTypesLarge;
 
 // register the type list
-TYPED_TEST_CASE(FFTConvolve, TestTypes);
-TYPED_TEST_CASE(FFTConvolveLarge, TestTypesLarge);
+TYPED_TEST_SUITE(FFTConvolve, TestTypes);
+TYPED_TEST_SUITE(FFTConvolveLarge, TestTypesLarge);
 
 template<typename T, int baseDim>
 void fftconvolveTest(string pTestFile, bool expand) {
diff --git a/test/gaussiankernel.cpp b/test/gaussiankernel.cpp
index a6675720ef..3c4db5386f 100644
--- a/test/gaussiankernel.cpp
+++ b/test/gaussiankernel.cpp
@@ -30,7 +30,7 @@ class GaussianKernel : public ::testing::Test {
 typedef ::testing::Types<float> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(GaussianKernel, TestTypes);
+TYPED_TEST_SUITE(GaussianKernel, TestTypes);
 
 template<typename T>
 void gaussianKernelTest(string pFileName, double sigma) {
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index b8f041d47b..b491a9ac4c 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -103,7 +103,7 @@ string testNameGenerator(
     return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Legacy, IndexGeneralizedLegacy,
     ::testing::Combine(
         ::testing::Values(index_test(
diff --git a/test/gloh.cpp b/test/gloh.cpp
index 004f00b7be..eb193e7ec4 100644
--- a/test/gloh.cpp
+++ b/test/gloh.cpp
@@ -132,7 +132,7 @@ class GLOH : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestTypes;
 
-TYPED_TEST_CASE(GLOH, TestTypes);
+TYPED_TEST_SUITE(GLOH, TestTypes);
 
 template<typename T>
 void glohTest(string pTestFile) {
diff --git a/test/gradient.cpp b/test/gradient.cpp
index 98df0830c5..b30e9bb649 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -41,7 +41,7 @@ class Grad : public ::testing::Test {
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Grad, TestTypes);
+TYPED_TEST_SUITE(Grad, TestTypes);
 
 template<typename T>
 void gradTest(string pTestFile, const unsigned resultIdx0,
diff --git a/test/half.cpp b/test/half.cpp
index 541af826a9..18fcdb4077 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -36,29 +36,29 @@ struct convert_params {
 
 class HalfConvert : public ::testing::TestWithParam<convert_params> {};
 
-INSTANTIATE_TEST_CASE_P(ToF16, HalfConvert,
-                        ::testing::Values(convert_params(f32, f16, 10),
-                                          convert_params(f64, f16, 10),
-                                          convert_params(s32, f16, 10),
-                                          convert_params(u32, f16, 10),
-                                          convert_params(u8, f16, 10),
-                                          convert_params(s64, f16, 10),
-                                          convert_params(u64, f16, 10),
-                                          convert_params(s16, f16, 10),
-                                          convert_params(u16, f16, 10),
-                                          convert_params(f16, f16, 10)));
-
-INSTANTIATE_TEST_CASE_P(FromF16, HalfConvert,
-                        ::testing::Values(convert_params(f16, f32, 10),
-                                          convert_params(f16, f64, 10),
-                                          convert_params(f16, s32, 10),
-                                          convert_params(f16, u32, 10),
-                                          convert_params(f16, u8, 10),
-                                          convert_params(f16, s64, 10),
-                                          convert_params(f16, u64, 10),
-                                          convert_params(f16, s16, 10),
-                                          convert_params(f16, u16, 10),
-                                          convert_params(f16, f16, 10)));
+INSTANTIATE_TEST_SUITE_P(ToF16, HalfConvert,
+                         ::testing::Values(convert_params(f32, f16, 10),
+                                           convert_params(f64, f16, 10),
+                                           convert_params(s32, f16, 10),
+                                           convert_params(u32, f16, 10),
+                                           convert_params(u8, f16, 10),
+                                           convert_params(s64, f16, 10),
+                                           convert_params(u64, f16, 10),
+                                           convert_params(s16, f16, 10),
+                                           convert_params(u16, f16, 10),
+                                           convert_params(f16, f16, 10)));
+
+INSTANTIATE_TEST_SUITE_P(FromF16, HalfConvert,
+                         ::testing::Values(convert_params(f16, f32, 10),
+                                           convert_params(f16, f64, 10),
+                                           convert_params(f16, s32, 10),
+                                           convert_params(f16, u32, 10),
+                                           convert_params(f16, u8, 10),
+                                           convert_params(f16, s64, 10),
+                                           convert_params(f16, u64, 10),
+                                           convert_params(f16, s16, 10),
+                                           convert_params(f16, u16, 10),
+                                           convert_params(f16, f16, 10)));
 
 TEST_P(HalfConvert, convert) {
     SUPPORTED_TYPE_CHECK(af_half);
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 6c0edd0618..8b3d9f85f7 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -39,8 +39,8 @@ typedef ::testing::Types<uchar, ushort> TestTypes8;
 typedef ::testing::Types<uint, uintl> TestTypes32;
 
 // register the type list
-TYPED_TEST_CASE(HammingMatcher8, TestTypes8);
-TYPED_TEST_CASE(HammingMatcher32, TestTypes32);
+TYPED_TEST_SUITE(HammingMatcher8, TestTypes8);
+TYPED_TEST_SUITE(HammingMatcher32, TestTypes32);
 
 template<typename T>
 void hammingMatcherTest(string pTestFile, int feat_dim) {
diff --git a/test/harris.cpp b/test/harris.cpp
index e4e832fc05..955c676251 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -56,7 +56,7 @@ class Harris : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestTypes;
 
-TYPED_TEST_CASE(Harris, TestTypes);
+TYPED_TEST_SUITE(Harris, TestTypes);
 
 template<typename T>
 void harrisTest(string pTestFile, float sigma, unsigned block_size) {
diff --git a/test/histogram.cpp b/test/histogram.cpp
index 826eebd506..ff2049b390 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -37,7 +37,7 @@ typedef ::testing::Types<half_float::half, float, double, int, uint, char,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Histogram, TestTypes);
+TYPED_TEST_SUITE(Histogram, TestTypes);
 
 template<typename inType, typename outType>
 void histTest(string pTestFile, unsigned nbins, double minval, double maxval) {
diff --git a/test/homography.cpp b/test/homography.cpp
index f305933396..6b0e620869 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -33,7 +33,7 @@ class Homography : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestTypes;
 
-TYPED_TEST_CASE(Homography, TestTypes);
+TYPED_TEST_SUITE(Homography, TestTypes);
 
 template<typename T>
 array perspectiveTransform(dim4 inDims, array H) {
diff --git a/test/iir.cpp b/test/iir.cpp
index dba2369061..fd03e7ccc6 100644
--- a/test/iir.cpp
+++ b/test/iir.cpp
@@ -37,7 +37,7 @@ class filter : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
-TYPED_TEST_CASE(filter, TestTypes);
+TYPED_TEST_SUITE(filter, TestTypes);
 
 template<typename T>
 void firTest(const int xrows, const int xcols, const int brows,
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 9dc85a5865..a4e12e834e 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -33,7 +33,7 @@ class ImageIO : public ::testing::Test {
 typedef ::testing::Types<float> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(ImageIO, TestTypes);
+TYPED_TEST_SUITE(ImageIO, TestTypes);
 
 void loadImageTest(string pTestFile, string pImageFile, const bool isColor) {
     if (noImageIOTests()) return;
diff --git a/test/index.cpp b/test/index.cpp
index aaac6f74f7..2f61d40adb 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -141,7 +141,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned,
                          unsigned char, intl, uintl, short, ushort,
                          half_float::half>
     AllTypes;
-TYPED_TEST_CASE(Indexing1D, AllTypes);
+TYPED_TEST_SUITE(Indexing1D, AllTypes);
 
 TYPED_TEST(Indexing1D, Continious) {
     DimCheck<TypeParam>(this->continuous_seqs);
@@ -373,7 +373,7 @@ void DimCheck2D(const vector<vector<af_seq> > &seqs, string TestFile,
     }
 }
 
-TYPED_TEST_CASE(Indexing2D, AllTypes);
+TYPED_TEST_SUITE(Indexing2D, AllTypes);
 
 TYPED_TEST(Indexing2D, ColumnContinious) {
     DimCheck2D<TypeParam>(this->column_continuous_seq,
@@ -548,7 +548,7 @@ void DimCheckND(const vector<vector<af_seq> > &seqs, string TestFile,
     DimCheck2D<T>(seqs, TestFile, NDims);
 }
 
-TYPED_TEST_CASE(Indexing, AllTypes);
+TYPED_TEST_SUITE(Indexing, AllTypes);
 
 TYPED_TEST(Indexing, 4D_to_4D) {
     DimCheckND<TypeParam>(this->continuous4d_to_4d,
@@ -710,7 +710,7 @@ class lookup : public ::testing::Test {
 typedef ::testing::Types<float, double, int, unsigned, unsigned char, short,
                          ushort, intl, uintl, half_float::half>
     ArrIdxTestTypes;
-TYPED_TEST_CASE(lookup, ArrIdxTestTypes);
+TYPED_TEST_SUITE(lookup, ArrIdxTestTypes);
 
 template<typename T>
 void arrayIndexTest(string pTestFile, int dim) {
@@ -1249,7 +1249,7 @@ class IndexedMembers : public ::testing::Test {
     virtual void SetUp() {}
 };
 
-TYPED_TEST_CASE(IndexedMembers, AllTypes);
+TYPED_TEST_SUITE(IndexedMembers, AllTypes);
 
 TYPED_TEST(IndexedMembers, MemFuncs) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index e811fe3f8b..9cce59ea62 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -28,7 +28,7 @@ class InverseDeconvolution : public ::testing::Test {};
 typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(InverseDeconvolution, TestTypes);
+TYPED_TEST_SUITE(InverseDeconvolution, TestTypes);
 
 template<typename T, bool isColor>
 void invDeconvImageTest(string pTestFile, const float gamma,
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index cd39d0239e..a0bb6145d9 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -81,7 +81,7 @@ double eps<cdouble>() {
 }
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
-TYPED_TEST_CASE(Inverse, TestTypes);
+TYPED_TEST_SUITE(Inverse, TestTypes);
 
 TYPED_TEST(Inverse, Square) {
     inverseTester<TypeParam>(1000, 1000, eps<TypeParam>());
diff --git a/test/iota.cpp b/test/iota.cpp
index 09cba79a94..c776d7628e 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -43,7 +43,7 @@ typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Iota, TestTypes);
+TYPED_TEST_SUITE(Iota, TestTypes);
 
 template<typename T>
 void iotaTest(const dim4 idims, const dim4 tdims) {
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 80403786d5..59e6b4598b 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -28,7 +28,7 @@ class IterativeDeconvolution : public ::testing::Test {};
 typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(IterativeDeconvolution, TestTypes);
+TYPED_TEST_SUITE(IterativeDeconvolution, TestTypes);
 
 template<typename T, bool isColor>
 void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
diff --git a/test/jit.cpp b/test/jit.cpp
index c1f0fbd2fa..64d72d25b7 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -534,7 +534,7 @@ std::string tile_info(const ::testing::TestParamInfo<JIT::ParamType> info) {
 }
 
 // clang-format off
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
                         JitTile, JIT,
                                                    //  input_dim            tile_dim             output_dim
                         ::testing::Values(
@@ -677,7 +677,7 @@ class JITSelect : public ::testing::TestWithParam<std::tuple<int, int, int> > {
 };
 
 // clang-format off
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
                         JitSelect, JITSelect,
                         testing::Combine(
                                          testing::Range(10, 22),
diff --git a/test/join.cpp b/test/join.cpp
index 4a98763b9b..de61bdf91e 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -52,7 +52,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Join, TestTypes);
+TYPED_TEST_SUITE(Join, TestTypes);
 
 template<typename T>
 void joinTest(string pTestFile, const unsigned dim, const unsigned in0,
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index 88ed274112..e5b4b8ac97 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -212,7 +212,7 @@ template<typename T>
 class LU : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
-TYPED_TEST_CASE(LU, TestTypes);
+TYPED_TEST_SUITE(LU, TestTypes);
 
 TYPED_TEST(LU, SquareLarge) { luTester<TypeParam>(500, 500, eps<TypeParam>()); }
 
diff --git a/test/match_template.cpp b/test/match_template.cpp
index a94ab94f15..90c199bd0a 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -35,7 +35,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(MatchTemplate, TestTypes);
+TYPED_TEST_SUITE(MatchTemplate, TestTypes);
 
 template<typename T>
 void matchTemplateTest(string pTestFile, af_match_type pMatchType) {
diff --git a/test/mean.cpp b/test/mean.cpp
index 9c4c8f7fb4..89a89efeb9 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -44,7 +44,7 @@ typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, intl, uintl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Mean, TestTypes);
+TYPED_TEST_SUITE(Mean, TestTypes);
 
 template<typename T>
 struct f32HelperType {
@@ -270,7 +270,7 @@ class WeightedMean : public ::testing::Test {
 };
 
 // register the type list
-TYPED_TEST_CASE(WeightedMean, TestTypes);
+TYPED_TEST_SUITE(WeightedMean, TestTypes);
 
 template<typename T, typename wtsType>
 void weightedMeanAllTest(dim4 dims) {
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 92d2408ef6..59f6bd2ee7 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -32,7 +32,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort,
                          intl, uintl>
     TestTypes;
 
-TYPED_TEST_CASE(Meanshift, TestTypes);
+TYPED_TEST_SUITE(Meanshift, TestTypes);
 
 TYPED_TEST(Meanshift, InvalidArgs) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 059f694842..e9286027a2 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -73,10 +73,10 @@ struct meanvar_test {
         for (auto &v : mean) mean_.push_back((outType<T>)v);
         for (auto &v : variance) variance_.push_back((outType<T>)v);
     }
-    meanvar_test()                        = default;
-    meanvar_test(meanvar_test<T> &&other) = default;
+    meanvar_test()                                   = default;
+    meanvar_test(meanvar_test<T> &&other)            = default;
     meanvar_test &operator=(meanvar_test<T> &&other) = default;
-    meanvar_test &operator=(meanvar_test<T> &other) = delete;
+    meanvar_test &operator=(meanvar_test<T> &other)  = delete;
 
     meanvar_test(const meanvar_test<T> &other)
         : test_description_(other.test_description_)
@@ -279,12 +279,12 @@ vector<meanvar_test<T> > large_test_values() {
 
 #define MEANVAR_TEST(NAME, TYPE)                                              \
     using MeanVar##NAME = MeanVarTyped<TYPE>;                                 \
-    INSTANTIATE_TEST_CASE_P(                                                  \
+    INSTANTIATE_TEST_SUITE_P(                                                 \
         Small, MeanVar##NAME, ::testing::ValuesIn(small_test_values<TYPE>()), \
         [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) {   \
             return info.param.test_description_;                              \
         });                                                                   \
-    INSTANTIATE_TEST_CASE_P(                                                  \
+    INSTANTIATE_TEST_SUITE_P(                                                 \
         Large, MeanVar##NAME, ::testing::ValuesIn(large_test_values<TYPE>()), \
         [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> info) {   \
             return info.param.test_description_;                              \
@@ -313,7 +313,7 @@ MEANVAR_TEST(ComplexDouble, af::af_cdouble)
 #undef MEANVAR_TEST
 
 using MeanVarHalf = MeanVarTyped<half_float::half>;
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     Small, MeanVarHalf,
     ::testing::ValuesIn(small_test_values<half_float::half>()),
     [](const ::testing::TestParamInfo<MeanVarHalf::ParamType> info) {
@@ -330,7 +330,7 @@ TEST_P(MeanVarHalf, TestingCPP) {
 
 #define MEANVAR_TEST(NAME, TYPE)                                              \
     using MeanVar##NAME = MeanVarTyped<TYPE>;                                 \
-    INSTANTIATE_TEST_CASE_P(                                                  \
+    INSTANTIATE_TEST_SUITE_P(                                                 \
         Small, MeanVar##NAME, ::testing::ValuesIn(small_test_values<TYPE>()), \
         [](const ::testing::TestParamInfo<MeanVar##NAME::ParamType> &info) {  \
             return info.param.test_description_;                              \
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 1e330d3702..4bc7e69924 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -39,8 +39,8 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(MedianFilter, TestTypes);
-TYPED_TEST_CASE(MedianFilter1d, TestTypes);
+TYPED_TEST_SUITE(MedianFilter, TestTypes);
+TYPED_TEST_SUITE(MedianFilter1d, TestTypes);
 
 template<typename T>
 void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid,
diff --git a/test/memory.cpp b/test/memory.cpp
index e67a7cfb69..37a1de87b1 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -78,7 +78,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(MemAlloc, TestTypes);
+TYPED_TEST_SUITE(MemAlloc, TestTypes);
 
 size_t roundUpToStep(size_t bytes) {
     if (step_bytes == 0) return bytes;
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 630e4e6783..69af67860e 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -41,7 +41,7 @@ typedef ::testing::Types<float, double, int, unsigned, char, unsigned char,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Moddims, TestTypes);
+TYPED_TEST_SUITE(Moddims, TestTypes);
 
 template<typename T>
 void moddimsTest(string pTestFile, bool isSubRef = false,
diff --git a/test/moments.cpp b/test/moments.cpp
index f0ea3072de..5656a17ec5 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -39,7 +39,7 @@ class Image : public ::testing::Test {
 typedef ::testing::Types<float, double, int> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Image, TestTypes);
+TYPED_TEST_SUITE(Image, TestTypes);
 
 template<typename T>
 void momentsTest(string pTestFile) {
diff --git a/test/morph.cpp b/test/morph.cpp
index ecce0738f8..220253c8c4 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -34,7 +34,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Morph, TestTypes);
+TYPED_TEST_SUITE(Morph, TestTypes);
 
 template<typename inType, bool isDilation, bool isVolume>
 void morphTest(string pTestFile) {
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index e2a09dc20d..5286923dd8 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -59,7 +59,7 @@ struct otype_t<uchar> {
 };
 
 // register the type list
-TYPED_TEST_CASE(NearestNeighbour, TestTypes);
+TYPED_TEST_SUITE(NearestNeighbour, TestTypes);
 
 template<typename T>
 void nearestNeighbourTest(string pTestFile, int feat_dim,
@@ -426,13 +426,13 @@ vector<nearest_neighbors_params> genKNNTests() {
             knn_data("1q1000t256k", 1, 1000, 1, 256, 0)};
 }
 
-INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD, NearestNeighborsTest,
-                        ::testing::ValuesIn(genNNTests()),
-                        testNameGenerator<NearestNeighborsTest>);
+INSTANTIATE_TEST_SUITE_P(KNearestNeighborsSSD, NearestNeighborsTest,
+                         ::testing::ValuesIn(genNNTests()),
+                         testNameGenerator<NearestNeighborsTest>);
 
-INSTANTIATE_TEST_CASE_P(KNearestNeighborsSSD, KNearestNeighborsTest,
-                        ::testing::ValuesIn(genKNNTests()),
-                        testNameGenerator<KNearestNeighborsTest>);
+INSTANTIATE_TEST_SUITE_P(KNearestNeighborsSSD, KNearestNeighborsTest,
+                         ::testing::ValuesIn(genKNNTests()),
+                         testNameGenerator<KNearestNeighborsTest>);
 
 TEST_P(NearestNeighborsTest, SingleQTests) {
     nearest_neighbors_params params = GetParam();
diff --git a/test/orb.cpp b/test/orb.cpp
index 846bb2146b..42df3ea2f5 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -125,7 +125,7 @@ class ORB : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestTypes;
 
-TYPED_TEST_CASE(ORB, TestTypes);
+TYPED_TEST_SUITE(ORB, TestTypes);
 
 template<typename T>
 void orbTest(string pTestFile) {
diff --git a/test/pad_borders.cpp b/test/pad_borders.cpp
index 33a977e03d..028c946719 100644
--- a/test/pad_borders.cpp
+++ b/test/pad_borders.cpp
@@ -29,7 +29,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
                          ushort /*, half_float::half*/>
     TestTypes;
 
-TYPED_TEST_CASE(PadBorders, TestTypes);
+TYPED_TEST_SUITE(PadBorders, TestTypes);
 
 template<typename T>
 void testPad(const vector<T>& input, const dim4& inDims, const dim4& lbPadding,
diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 0e8575feca..44a0f884b0 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -119,7 +119,7 @@ double relEps(array in) {
 }
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
-TYPED_TEST_CASE(Pinverse, TestTypes);
+TYPED_TEST_SUITE(Pinverse, TestTypes);
 
 // Test Moore-Penrose conditions in the following first 4 tests
 // See https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Definition
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 640171a754..09477dcbf5 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -162,7 +162,7 @@ template<typename T>
 class QR : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
-TYPED_TEST_CASE(QR, TestTypes);
+TYPED_TEST_SUITE(QR, TestTypes);
 
 TYPED_TEST(QR, RectangularLarge0) {
     qrTester<TypeParam>(1000, 500, eps<TypeParam>());
diff --git a/test/random.cpp b/test/random.cpp
index 4669b7515e..df65ac8006 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -40,7 +40,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Random, TestTypes);
+TYPED_TEST_SUITE(Random, TestTypes);
 
 template<typename T>
 class Random_norm : public ::testing::Test {
@@ -69,21 +69,21 @@ class RandomSeed : public ::testing::Test {
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, af_half> TestTypesNorm;
 // register the type list
-TYPED_TEST_CASE(Random_norm, TestTypesNorm);
+TYPED_TEST_SUITE(Random_norm, TestTypesNorm);
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double> TestTypesEngine;
 // register the type list
-TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
+TYPED_TEST_SUITE(RandomEngine, TestTypesEngine);
 
 typedef ::testing::Types<unsigned> TestTypesEngineSeed;
 // register the type list
-TYPED_TEST_CASE(RandomEngineSeed, TestTypesEngineSeed);
+TYPED_TEST_SUITE(RandomEngineSeed, TestTypesEngineSeed);
 
 // create a list of types to be tested
 typedef ::testing::Types<unsigned> TestTypesSeed;
 // register the type list
-TYPED_TEST_CASE(RandomSeed, TestTypesSeed);
+TYPED_TEST_SUITE(RandomSeed, TestTypesSeed);
 
 template<typename T>
 void randuTest(dim4 &dims) {
diff --git a/test/range.cpp b/test/range.cpp
index 78e7782379..4d90b8a42f 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -55,8 +55,8 @@ typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
     RegularTypes;
 
 // register the type list
-TYPED_TEST_CASE(Range, AllTypes);
-TYPED_TEST_CASE(RangeMax, RegularTypes);
+TYPED_TEST_SUITE(Range, AllTypes);
+TYPED_TEST_SUITE(RangeMax, RegularTypes);
 
 template<typename T>
 void rangeTest(const uint x, const uint y, const uint z, const uint w,
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index 003979ad62..30c7ade1ca 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -40,8 +40,8 @@ template<typename T>
 class Det : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
-TYPED_TEST_CASE(Rank, TestTypes);
-TYPED_TEST_CASE(Det, TestTypes);
+TYPED_TEST_SUITE(Rank, TestTypes);
+TYPED_TEST_SUITE(Det, TestTypes);
 
 template<typename T>
 void rankSmall() {
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 69e6573d3c..bfff42959f 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -39,7 +39,7 @@ class Reduce : public ::testing::Test {};
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
                          uchar, short, ushort>
     TestTypes;
-TYPED_TEST_CASE(Reduce, TestTypes);
+TYPED_TEST_SUITE(Reduce, TestTypes);
 
 typedef af_err (*reduceFunc)(af_array *, const af_array, const int);
 
@@ -546,9 +546,9 @@ string testNameGenerator(
     return s.str();
 }
 
-INSTANTIATE_TEST_CASE_P(UniqueKeyTests, ReduceByKeyP,
-                        ::testing::ValuesIn(generateAllTypes()),
-                        testNameGenerator<ReduceByKeyP>);
+INSTANTIATE_TEST_SUITE_P(UniqueKeyTests, ReduceByKeyP,
+                         ::testing::ValuesIn(generateAllTypes()),
+                         testNameGenerator<ReduceByKeyP>);
 
 TEST_P(ReduceByKeyP, SumDim0) {
     if (noHalfTests(GetParam()->vType_)) { return; }
@@ -1307,7 +1307,7 @@ struct reduce_params {
 
 class ReduceHalf : public ::testing::TestWithParam<reduce_params> {};
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     SumFirstNonZeroDim, ReduceHalf,
     ::testing::Values(
         reduce_params(1, dim4(10), dim4(1), -1),
@@ -1330,7 +1330,7 @@ INSTANTIATE_TEST_CASE_P(
         reduce_params(1, dim4(8192, 10, 10), dim4(1, 10, 10), -1),
         reduce_params(1, dim4(8192, 10, 10, 10), dim4(1, 10, 10, 10), -1)));
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     SumNonZeroDim, ReduceHalf,
     ::testing::Values(
         reduce_params(1.25, dim4(10, 10), dim4(10), 1),
@@ -2031,7 +2031,7 @@ string testNameGeneratorRagged(
     return s.str();
 }
 
-INSTANTIATE_TEST_CASE_P(RaggedReduceTests, RaggedReduceMaxRangeP,
+INSTANTIATE_TEST_SUITE_P(RaggedReduceTests, RaggedReduceMaxRangeP,
                         ::testing::ValuesIn(generateAllTypesRagged()),
                         testNameGeneratorRagged<RaggedReduceMaxRangeP>);
 
diff --git a/test/regions.cpp b/test/regions.cpp
index 7deae9f5a5..4df7b90793 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -39,7 +39,7 @@ class Regions : public ::testing::Test {
 typedef ::testing::Types<float, double, int, unsigned, short, ushort> TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Regions, TestTypes);
+TYPED_TEST_SUITE(Regions, TestTypes);
 
 template<typename T>
 void regionsTest(string pTestFile, af_connectivity connectivity,
diff --git a/test/reorder.cpp b/test/reorder.cpp
index f835de8fea..6652f75210 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -48,7 +48,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Reorder, TestTypes);
+TYPED_TEST_SUITE(Reorder, TestTypes);
 
 template<typename T>
 void reorderTest(string pTestFile, const unsigned resultIdx, const uint x,
diff --git a/test/replace.cpp b/test/replace.cpp
index 26baf63a9d..1d0a758489 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -38,7 +38,7 @@ typedef ::testing::Types<half_float::half, float, double, cfloat, cdouble, uint,
                          int, intl, uintl, uchar, char, short, ushort>
     TestTypes;
 
-TYPED_TEST_CASE(Replace, TestTypes);
+TYPED_TEST_SUITE(Replace, TestTypes);
 
 template<typename T>
 void replaceTest(const dim4 &dims) {
diff --git a/test/resize.cpp b/test/resize.cpp
index ab53631fd4..816dd7cf9e 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -60,8 +60,8 @@ typedef ::testing::Types<int, unsigned, intl, uintl, unsigned char, char, short,
     TestTypesI;
 
 // register the type list
-TYPED_TEST_CASE(Resize, TestTypesF);
-TYPED_TEST_CASE(ResizeI, TestTypesI);
+TYPED_TEST_SUITE(Resize, TestTypesF);
+TYPED_TEST_SUITE(ResizeI, TestTypesI);
 
 TYPED_TEST(Resize, InvalidDims) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/rng_match.cpp b/test/rng_match.cpp
index 4e64ddf121..f13872889e 100644
--- a/test/rng_match.cpp
+++ b/test/rng_match.cpp
@@ -94,7 +94,7 @@ std::string rngmatch_info(
     return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     PhiloxCPU_CUDA, RNGMatch,
     ::testing::Combine(
         ::testing::Values(AF_RANDOM_ENGINE_PHILOX),
diff --git a/test/rng_quality.cpp b/test/rng_quality.cpp
index 0c2ec5667e..8274b1dfa9 100644
--- a/test/rng_quality.cpp
+++ b/test/rng_quality.cpp
@@ -26,7 +26,7 @@ class RandomEngine : public ::testing::Test {
 // create a list of types to be tested
 typedef ::testing::Types<float, double> TestTypesEngine;
 // register the type list
-TYPED_TEST_CASE(RandomEngine, TestTypesEngine);
+TYPED_TEST_SUITE(RandomEngine, TestTypesEngine);
 
 template<typename T>
 void testRandomEnginePeriod(randomEngineType type) {
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 7a576804ae..31019db269 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -38,7 +38,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Rotate, TestTypes);
+TYPED_TEST_SUITE(Rotate, TestTypes);
 
 #define PI 3.1415926535897931f
 
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 807859e91d..7d0dc8d5b7 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -43,7 +43,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(RotateLinear, TestTypes);
+TYPED_TEST_SUITE(RotateLinear, TestTypes);
 
 #define PI 3.1415926535897931f
 
diff --git a/test/sat.cpp b/test/sat.cpp
index b4811bb8e5..892e2f8f4e 100644
--- a/test/sat.cpp
+++ b/test/sat.cpp
@@ -36,7 +36,7 @@ typedef ::testing::Types<float, double, int, uint, char, uchar, uintl, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(SAT, TestTypes);
+TYPED_TEST_SUITE(SAT, TestTypes);
 
 TYPED_TEST(SAT, IntegralImage) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/select.cpp b/test/select.cpp
index 7df6b6a862..a147bb3039 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -44,7 +44,7 @@ class Select : public ::testing::Test {};
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
                          uchar, char, short, ushort, half_float::half>
     TestTypes;
-TYPED_TEST_CASE(Select, TestTypes);
+TYPED_TEST_SUITE(Select, TestTypes);
 
 template<typename T>
 void selectTest(const dim4& dims) {
@@ -337,17 +337,17 @@ vector<select_params> getSelectTestParams(int M, int N) {
     return vector<select_params>(_, _ + sizeof(_) / sizeof(_[0]));
 }
 
-INSTANTIATE_TEST_CASE_P(SmallDims, Select_,
-                        ::testing::ValuesIn(getSelectTestParams(10, 5)),
-                        testNameGenerator);
+INSTANTIATE_TEST_SUITE_P(SmallDims, Select_,
+                         ::testing::ValuesIn(getSelectTestParams(10, 5)),
+                         testNameGenerator);
 
-INSTANTIATE_TEST_CASE_P(Dims33_9, Select_,
-                        ::testing::ValuesIn(getSelectTestParams(33, 9)),
-                        testNameGenerator);
+INSTANTIATE_TEST_SUITE_P(Dims33_9, Select_,
+                         ::testing::ValuesIn(getSelectTestParams(33, 9)),
+                         testNameGenerator);
 
-INSTANTIATE_TEST_CASE_P(DimsLg, Select_,
-                        ::testing::ValuesIn(getSelectTestParams(512, 32)),
-                        testNameGenerator);
+INSTANTIATE_TEST_SUITE_P(DimsLg, Select_,
+                         ::testing::ValuesIn(getSelectTestParams(512, 32)),
+                         testNameGenerator);
 
 TEST_P(Select_, Batch) {
     select_params params = GetParam();
@@ -404,17 +404,17 @@ string testNameGeneratorLR(
     return ss.str();
 }
 
-INSTANTIATE_TEST_CASE_P(SmallDims, SelectLR_,
-                        ::testing::ValuesIn(getSelectLRTestParams(10, 5)),
-                        testNameGeneratorLR);
+INSTANTIATE_TEST_SUITE_P(SmallDims, SelectLR_,
+                         ::testing::ValuesIn(getSelectLRTestParams(10, 5)),
+                         testNameGeneratorLR);
 
-INSTANTIATE_TEST_CASE_P(Dims33_9, SelectLR_,
-                        ::testing::ValuesIn(getSelectLRTestParams(33, 9)),
-                        testNameGeneratorLR);
+INSTANTIATE_TEST_SUITE_P(Dims33_9, SelectLR_,
+                         ::testing::ValuesIn(getSelectLRTestParams(33, 9)),
+                         testNameGeneratorLR);
 
-INSTANTIATE_TEST_CASE_P(DimsLg, SelectLR_,
-                        ::testing::ValuesIn(getSelectLRTestParams(512, 32)),
-                        testNameGeneratorLR);
+INSTANTIATE_TEST_SUITE_P(DimsLg, SelectLR_,
+                         ::testing::ValuesIn(getSelectLRTestParams(512, 32)),
+                         testNameGeneratorLR);
 
 TEST_P(SelectLR_, BatchL) {
     selectlr_params params = GetParam();
diff --git a/test/shift.cpp b/test/shift.cpp
index 394a9cd8c2..91df07c39c 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -45,7 +45,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
                          intl, uintl, char, unsigned char, short, ushort>
     TestTypes;
 // register the type list
-TYPED_TEST_CASE(Shift, TestTypes);
+TYPED_TEST_SUITE(Shift, TestTypes);
 
 template<typename T>
 void shiftTest(string pTestFile, const unsigned resultIdx, const int x,
diff --git a/test/sift.cpp b/test/sift.cpp
index 616557f93a..90d3b40cdc 100644
--- a/test/sift.cpp
+++ b/test/sift.cpp
@@ -132,7 +132,7 @@ class SIFT : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestTypes;
 
-TYPED_TEST_CASE(SIFT, TestTypes);
+TYPED_TEST_SUITE(SIFT, TestTypes);
 
 template<typename T>
 void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
diff --git a/test/sobel.cpp b/test/sobel.cpp
index c1e7306b48..449722af38 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -39,8 +39,8 @@ typedef ::testing::Types<int, unsigned, char, unsigned char, short, ushort>
     TestTypesInt;
 
 // register the type list
-TYPED_TEST_CASE(Sobel, TestTypes);
-TYPED_TEST_CASE(Sobel_Integer, TestTypesInt);
+TYPED_TEST_SUITE(Sobel, TestTypes);
+TYPED_TEST_SUITE(Sobel_Integer, TestTypesInt);
 
 template<typename Ti, typename To>
 void testSobelDerivatives(string pTestFile) {
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index a63a8eede1..b09c77645c 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -174,7 +174,7 @@ template<typename T>
 class Solve : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> TestTypes;
-TYPED_TEST_CASE(Solve, TestTypes);
+TYPED_TEST_SUITE(Solve, TestTypes);
 
 template<typename T>
 double eps();
diff --git a/test/sort.cpp b/test/sort.cpp
index 86b03eb8b2..307573d7a0 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -45,7 +45,7 @@ typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Sort, TestTypes);
+TYPED_TEST_SUITE(Sort, TestTypes);
 
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index dc7382e159..b76e31ffbf 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -45,7 +45,7 @@ typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(SortByKey, TestTypes);
+TYPED_TEST_SUITE(SortByKey, TestTypes);
 
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index 9eee997b29..bfec5b429b 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -45,7 +45,7 @@ typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(SortIndex, TestTypes);
+TYPED_TEST_SUITE(SortIndex, TestTypes);
 
 template<typename T>
 void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 75a577de56..a130a6bb58 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -185,7 +185,7 @@ template<typename T>
 class Sparse : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble> SparseTypes;
-TYPED_TEST_CASE(Sparse, SparseTypes);
+TYPED_TEST_SUITE(Sparse, SparseTypes);
 
 TYPED_TEST(Sparse, DeepCopy) {
     SUPPORTED_TYPE_CHECK(TypeParam);
diff --git a/test/stdev.cpp b/test/stdev.cpp
index 20187f8655..85f3bf079d 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -41,7 +41,7 @@ typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar>
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(StandardDev, TestTypes);
+TYPED_TEST_SUITE(StandardDev, TestTypes);
 
 template<typename T>
 struct f32HelperType {
diff --git a/test/susan.cpp b/test/susan.cpp
index 223704bb26..6d40177132 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -62,7 +62,7 @@ class Susan : public ::testing::Test {
 typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
     TestTypes;
 
-TYPED_TEST_CASE(Susan, TestTypes);
+TYPED_TEST_SUITE(Susan, TestTypes);
 
 template<typename T>
 void susanTest(string pTestFile, float t, float g) {
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index 18b0173957..e31603a84b 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -38,7 +38,7 @@ template<typename T>
 class svd : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
-TYPED_TEST_CASE(svd, TestTypes);
+TYPED_TEST_SUITE(svd, TestTypes);
 
 template<typename T>
 inline double get_val(T val) {
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 2e13ff9bbf..035c76991b 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -28,7 +28,17 @@
 #if defined(USE_MTX)
 #include <mmio.h>
 #include <cstdlib>
+#endif
 
+/// GTest deprecated the INSTANTIATED_TEST_CASE_P macro in favor of the
+/// INSTANTIATE_TEST_SUITE_P macro which has the same syntax but the older
+/// versions of gtest do not support this new macro adds the
+/// INSTANTIATE_TEST_SUITE_P macro and maps it to the old macro
+#ifndef INSTANTIATE_TEST_SUITE_P
+#define INSTANTIATE_TEST_SUITE_P INSTANTIATE_TEST_CASE_P
+#endif
+#ifndef TYPED_TEST_SUITE
+#define TYPED_TEST_SUITE TYPED_TEST_CASE
 #endif
 
 bool operator==(const af_half &lhs, const af_half &rhs);
diff --git a/test/tile.cpp b/test/tile.cpp
index 8127379e78..0a649d00ac 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -52,7 +52,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Tile, TestTypes);
+TYPED_TEST_SUITE(Tile, TestTypes);
 
 template<typename T>
 void tileTest(string pTestFile, const unsigned resultIdx, const uint x,
diff --git a/test/topk.cpp b/test/topk.cpp
index 46c4355d6a..86cf1287f9 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -49,7 +49,7 @@ class TopK : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, int, uint, half_float::half> TestTypes;
 
-TYPED_TEST_CASE(TopK, TestTypes);
+TYPED_TEST_SUITE(TopK, TestTypes);
 
 template<typename T>
 void increment_next(T& val,
@@ -318,7 +318,7 @@ ostream& operator<<(ostream& os, const topk_params& param) {
 
 class TopKParams : public ::testing::TestWithParam<topk_params> {};
 
-INSTANTIATE_TEST_CASE_P(
+INSTANTIATE_TEST_SUITE_P(
     InstantiationName, TopKParams,
     ::testing::Values(topk_params{100, 10, 32, 0, AF_TOPK_MIN},
                       topk_params{100, 10, 64, 0, AF_TOPK_MIN},
diff --git a/test/transform.cpp b/test/transform.cpp
index b5bf76f2ec..77cdcfc881 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -41,8 +41,8 @@ typedef ::testing::Types<float, double> TestTypes;
 typedef ::testing::Types<int, intl, uint, uintl, short, ushort, uchar>
     TestTypesInt;
 
-TYPED_TEST_CASE(Transform, TestTypes);
-TYPED_TEST_CASE(TransformInt, TestTypesInt);
+TYPED_TEST_SUITE(Transform, TestTypes);
+TYPED_TEST_SUITE(TransformInt, TestTypesInt);
 
 template<typename T>
 void genTestData(af_array *gold, af_array *in, af_array *transform,
@@ -403,7 +403,7 @@ class TransformV2 : public Transform<T> {
     }
 };
 
-TYPED_TEST_CASE(TransformV2, TestTypes);
+TYPED_TEST_SUITE(TransformV2, TestTypes);
 
 template<typename T>
 class TransformV2TuxNearest : public TransformV2<T> {
@@ -416,7 +416,7 @@ class TransformV2TuxNearest : public TransformV2<T> {
     }
 };
 
-TYPED_TEST_CASE(TransformV2TuxNearest, TestTypes);
+TYPED_TEST_SUITE(TransformV2TuxNearest, TestTypes);
 
 TYPED_TEST(TransformV2TuxNearest, UseNullOutputArray) {
     this->testSpclOutArray(NULL_ARRAY);
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 7d8805d043..01ab960e93 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -31,7 +31,7 @@ class TransformCoordinates : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestTypes;
 
-TYPED_TEST_CASE(TransformCoordinates, TestTypes);
+TYPED_TEST_SUITE(TransformCoordinates, TestTypes);
 
 template<typename T>
 void transformCoordinatesTest(string pTestFile) {
diff --git a/test/translate.cpp b/test/translate.cpp
index dcdb06953a..4c84b19009 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -42,8 +42,8 @@ typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
 typedef ::testing::Types<int, intl, char, short> TestTypesInt;
 
 // register the type list
-TYPED_TEST_CASE(Translate, TestTypes);
-TYPED_TEST_CASE(TranslateInt, TestTypesInt);
+TYPED_TEST_SUITE(Translate, TestTypes);
+TYPED_TEST_SUITE(TranslateInt, TestTypesInt);
 
 template<typename T>
 void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims,
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 72543d2e7a..cb36640885 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -49,7 +49,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Transpose, TestTypes);
+TYPED_TEST_SUITE(Transpose, TestTypes);
 
 template<typename T>
 void trsTest(string pTestFile, bool isSubRef = false,
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index 88d61cad16..82b071488a 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -35,7 +35,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Transpose, TestTypes);
+TYPED_TEST_SUITE(Transpose, TestTypes);
 
 template<typename T>
 void transposeip_test(dim4 dims) {
diff --git a/test/triangle.cpp b/test/triangle.cpp
index c7b9c7b029..90b50bb6dc 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -37,7 +37,7 @@ class Triangle : public ::testing::Test {};
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
                          uchar, uintl, intl, short, ushort, half_float::half>
     TestTypes;
-TYPED_TEST_CASE(Triangle, TestTypes);
+TYPED_TEST_SUITE(Triangle, TestTypes);
 
 template<typename T>
 void triangleTester(const dim4 dims, bool is_upper, bool is_unit_diag = false) {
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index 9224e90d8f..b33dc8c7d5 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -41,7 +41,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Unwrap, TestTypes);
+TYPED_TEST_SUITE(Unwrap, TestTypes);
 
 template<typename T>
 void unwrapTest(string pTestFile, const unsigned resultIdx, const dim_t wx,
diff --git a/test/var.cpp b/test/var.cpp
index b02442dba1..45c7b6847f 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -28,7 +28,7 @@ class Var : public ::testing::Test {};
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, uintl, intl,
                          char, uchar, short, ushort, half_float::half>
     TestTypes;
-TYPED_TEST_CASE(Var, TestTypes);
+TYPED_TEST_SUITE(Var, TestTypes);
 
 template<typename T>
 struct elseType {
diff --git a/test/where.cpp b/test/where.cpp
index 20913845a3..746a9aa5b4 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -36,7 +36,7 @@ class Where : public ::testing::Test {};
 typedef ::testing::Types<float, double, cfloat, cdouble, int, uint, intl, uintl,
                          char, uchar, short, ushort>
     TestTypes;
-TYPED_TEST_CASE(Where, TestTypes);
+TYPED_TEST_SUITE(Where, TestTypes);
 
 template<typename T>
 void whereTest(string pTestFile, bool isSubRef = false,
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 92193bc88d..91b57c4bc0 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -46,7 +46,7 @@ typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Wrap, TestTypes);
+TYPED_TEST_SUITE(Wrap, TestTypes);
 
 template<typename T>
 inline double get_val(T val) {
@@ -354,7 +354,7 @@ class WrapV2 : public WrapCommon {
     }
 };
 
-TYPED_TEST_CASE(WrapV2, TestTypes);
+TYPED_TEST_SUITE(WrapV2, TestTypes);
 
 template<typename T>
 class WrapV2Simple : public WrapV2<T> {
@@ -379,7 +379,7 @@ class WrapV2Simple : public WrapV2<T> {
     }
 };
 
-TYPED_TEST_CASE(WrapV2Simple, TestTypes);
+TYPED_TEST_SUITE(WrapV2Simple, TestTypes);
 
 TYPED_TEST(WrapV2Simple, UseNullOutputArray) {
     this->testSpclOutArray(NULL_ARRAY);
@@ -510,7 +510,7 @@ TEST_P(WrapAPITest, CheckDifferentWrapArgs) {
 
     af_array out_ = 0;
     af_err err    = af_wrap(&out_, in_, in_dims[0], in_dims[1], win_d0, win_d1,
-                         str_d0, str_d1, pad_d0, pad_d1, input.is_column);
+                            str_d0, str_d1, pad_d0, pad_d1, input.is_column);
 
     ASSERT_EQ(err, input.err);
     if (out_ != 0) af_release_array(out_);
@@ -537,4 +537,4 @@ WrapArgs args[] = {
     // clang-format on
 };
 
-INSTANTIATE_TEST_CASE_P(BulkTest, WrapAPITest, ::testing::ValuesIn(args));
+INSTANTIATE_TEST_SUITE_P(BulkTest, WrapAPITest, ::testing::ValuesIn(args));
diff --git a/test/write.cpp b/test/write.cpp
index 5a6d14c021..8f18f6e954 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -38,7 +38,7 @@ typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
     TestTypes;
 
 // register the type list
-TYPED_TEST_CASE(Write, TestTypes);
+TYPED_TEST_SUITE(Write, TestTypes);
 
 template<typename T>
 void writeTest(dim4 dims) {

From e401fce849cdde11120847c7be475e0e99063af4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Sep 2022 14:24:04 -0400
Subject: [PATCH 2289/2677] Update clang-format version on github workflow

---
 .github/workflows/clang-format-lint.yml       | 12 +--
 examples/getting_started/convolve.cpp         |  2 +-
 examples/image_processing/morphing.cpp        |  2 +-
 examples/pde/swe.cpp                          |  2 +-
 src/api/c/pinverse.cpp                        |  4 +-
 src/api/c/ycbcr_rgb.cpp                       |  2 +-
 src/api/cpp/array.cpp                         |  6 +-
 src/api/unified/symbol_manager.hpp            |  2 +-
 src/backend/common/DefaultMemoryManager.hpp   |  6 +-
 src/backend/common/HandleBase.hpp             |  4 +-
 src/backend/common/MemoryManagerBase.hpp      |  2 +-
 src/backend/common/graphics_common.cpp        |  2 +-
 src/backend/common/graphics_common.hpp        |  4 +-
 src/backend/common/host_memory.cpp            | 12 +--
 src/backend/common/jit/NodeIterator.hpp       | 10 +--
 src/backend/common/unique_handle.hpp          |  2 +-
 src/backend/cpu/Param.hpp                     | 16 ++--
 src/backend/cpu/convolve.cpp                  |  8 +-
 src/backend/cpu/device_manager.hpp            |  2 +-
 src/backend/cpu/kernel/diff.hpp               |  2 +-
 src/backend/cpu/kernel/fftconvolve.hpp        |  5 +-
 src/backend/cpu/kernel/orb.hpp                |  4 +-
 src/backend/cuda/LookupTable1D.hpp            |  8 +-
 src/backend/cuda/Param.hpp                    | 12 +--
 src/backend/cuda/convolveNN.cpp               | 10 +--
 src/backend/cuda/kernel/fast.hpp              |  2 +-
 src/backend/cuda/kernel/harris.hpp            |  2 +-
 src/backend/cuda/kernel/homography.hpp        |  2 +-
 src/backend/cuda/kernel/orb.hpp               |  4 +-
 src/backend/cuda/kernel/random_engine.hpp     |  4 +-
 src/backend/cuda/kernel/shfl_intrinsics.hpp   |  4 +-
 src/backend/cuda/kernel/unwrap.hpp            |  2 +-
 src/backend/cuda/types.hpp                    |  2 +-
 src/backend/opencl/convolve.cpp               |  4 +-
 .../opencl/kernel/convolve/conv_common.hpp    |  4 +-
 src/backend/opencl/kernel/homography.hpp      |  2 +-
 src/backend/opencl/kernel/index.hpp           |  2 +-
 src/backend/opencl/kernel/orb.hpp             |  4 +-
 src/backend/opencl/magma/geqrf2.cpp           |  4 +-
 src/backend/opencl/magma/magma_data.h         | 12 +--
 src/backend/opencl/magma/magma_types.h        |  2 +-
 src/backend/opencl/memory.cpp                 |  6 +-
 src/backend/opencl/svd.cpp                    |  4 +-
 src/backend/opencl/topk.cpp                   |  6 +-
 test/.clang-format                            |  2 +-
 test/approx1.cpp                              | 20 ++---
 test/approx2.cpp                              | 20 ++---
 test/arrayfire_test.cpp                       | 36 ++++----
 test/assign.cpp                               | 12 +--
 test/bilateral.cpp                            |  8 +-
 test/binary.cpp                               |  2 +-
 test/blas.cpp                                 |  8 +-
 test/canny.cpp                                |  4 +-
 test/confidence_connected.cpp                 |  4 +-
 test/convolve.cpp                             | 36 ++++----
 test/corrcoef.cpp                             |  4 +-
 test/covariance.cpp                           |  4 +-
 test/diff1.cpp                                | 12 +--
 test/diff2.cpp                                | 12 +--
 test/dot.cpp                                  | 24 +++---
 test/fast.cpp                                 |  4 +-
 test/fft.cpp                                  | 16 ++--
 test/fftconvolve.cpp                          | 16 ++--
 test/gaussiankernel.cpp                       |  8 +-
 test/gen_assign.cpp                           | 12 +--
 test/gen_index.cpp                            | 12 +--
 test/gloh.cpp                                 | 10 +--
 test/gradient.cpp                             |  8 +-
 test/hamming.cpp                              | 10 +--
 test/harris.cpp                               |  4 +-
 test/histogram.cpp                            |  8 +-
 test/homography.cpp                           |  4 +-
 test/hsv_rgb.cpp                              | 16 ++--
 test/iir.cpp                                  |  4 +-
 test/imageio.cpp                              | 16 ++--
 test/index.cpp                                | 86 +++++++++----------
 test/internal.cpp                             |  2 +-
 test/ireduce.cpp                              |  8 +-
 test/jit.cpp                                  |  4 +-
 test/join.cpp                                 |  8 +-
 test/lu_dense.cpp                             |  8 +-
 test/match_template.cpp                       |  4 +-
 test/mean.cpp                                 |  4 +-
 test/meanvar.cpp                              | 36 ++++----
 test/medfilt.cpp                              | 16 ++--
 test/moddims.cpp                              | 16 ++--
 test/moments.cpp                              |  8 +-
 test/morph.cpp                                | 14 +--
 test/nearest_neighbour.cpp                    | 12 +--
 test/orb.cpp                                  | 11 ++-
 test/pinverse.cpp                             |  8 +-
 test/qr_dense.cpp                             |  4 +-
 test/rank_dense.cpp                           |  4 +-
 test/reduce.cpp                               | 18 ++--
 test/regions.cpp                              |  8 +-
 test/reorder.cpp                              |  8 +-
 test/resize.cpp                               | 20 ++---
 test/rotate.cpp                               |  8 +-
 test/rotate_linear.cpp                        |  8 +-
 test/scan.cpp                                 |  8 +-
 test/set.cpp                                  |  8 +-
 test/shift.cpp                                |  8 +-
 test/sift.cpp                                 | 10 +--
 test/sobel.cpp                                |  4 +-
 test/sort.cpp                                 | 16 ++--
 test/sort_by_key.cpp                          | 16 ++--
 test/sort_index.cpp                           | 16 ++--
 test/stdev.cpp                                | 12 +--
 test/susan.cpp                                |  2 +-
 test/testHelpers.hpp                          | 14 +--
 test/threading.cpp                            |  8 +-
 test/tile.cpp                                 |  8 +-
 test/transform.cpp                            | 12 +--
 test/transform_coordinates.cpp                |  8 +-
 test/translate.cpp                            |  4 +-
 test/transpose.cpp                            |  8 +-
 test/unwrap.cpp                               |  8 +-
 test/var.cpp                                  |  6 +-
 test/where.cpp                                |  8 +-
 test/ycbcr_rgb.cpp                            | 16 ++--
 120 files changed, 546 insertions(+), 546 deletions(-)

diff --git a/.github/workflows/clang-format-lint.yml b/.github/workflows/clang-format-lint.yml
index 9b1037d4ab..25e79545ac 100644
--- a/.github/workflows/clang-format-lint.yml
+++ b/.github/workflows/clang-format-lint.yml
@@ -17,22 +17,22 @@ jobs:
             uses: actions/checkout@master
 
           - name: Check Sources
-            uses: DoozyX/clang-format-lint-action@v0.11
+            uses: DoozyX/clang-format-lint-action@v0.14
             with:
               source: './src'
               extensions: 'h,cpp,hpp'
-              clangFormatVersion: 11
+              clangFormatVersion: 14
 
           - name: Check Tests
-            uses: DoozyX/clang-format-lint-action@v0.11
+            uses: DoozyX/clang-format-lint-action@v0.14
             with:
               source: './test'
               extensions: 'h,cpp,hpp'
-              clangFormatVersion: 11
+              clangFormatVersion: 14
 
           - name: Check Examples
-            uses: DoozyX/clang-format-lint-action@v0.11
+            uses: DoozyX/clang-format-lint-action@v0.14
             with:
               source: './examples'
               extensions: 'h,cpp,hpp'
-              clangFormatVersion: 11
+              clangFormatVersion: 14
diff --git a/examples/getting_started/convolve.cpp b/examples/getting_started/convolve.cpp
index c07cedfc3c..7c2d0626ca 100644
--- a/examples/getting_started/convolve.cpp
+++ b/examples/getting_started/convolve.cpp
@@ -20,7 +20,7 @@ static array img;
 
 // 5x5 derivative with separable kernels
 static float h_dx[]     = {1.f / 12, -8.f / 12, 0, 8.f / 12,
-                       -1.f / 12};  // five point stencil
+                           -1.f / 12};  // five point stencil
 static float h_spread[] = {1.f / 5, 1.f / 5, 1.f / 5, 1.f / 5, 1.f / 5};
 static array dx, spread, kernel;  // device kernels
 
diff --git a/examples/image_processing/morphing.cpp b/examples/image_processing/morphing.cpp
index 51108490c2..ad66b7ea2a 100644
--- a/examples/image_processing/morphing.cpp
+++ b/examples/image_processing/morphing.cpp
@@ -45,7 +45,7 @@ array border(const array& img, const int left, const int right, const int top,
     array ret    = constant(value, imgDims);
     ret(seq(top, imgDims[0] - bottom), seq(left, imgDims[1] - right), span,
         span)    = img(seq(top, imgDims[0] - bottom),
-                    seq(left, imgDims[1] - right), span, span);
+                       seq(left, imgDims[1] - right), span, span);
 
     return ret;
 }
diff --git a/examples/pde/swe.cpp b/examples/pde/swe.cpp
index c7f9d6ebda..7e5a9af017 100644
--- a/examples/pde/swe.cpp
+++ b/examples/pde/swe.cpp
@@ -54,7 +54,7 @@ static void swe(bool console) {
         if (iter > 2000) {
             // Initial condition
             etam  = 0.01f * exp((-((x - io) * (x - io) + (y - jo) * (y - jo))) /
-                               (k * k));
+                                (k * k));
             m_eta = max<float>(etam);
             eta   = etam;
             iter  = 0;
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 49086043af..05d2d92fba 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -92,7 +92,7 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol) {
             Array<Tr> sVecSlice = getSubArray(
                 sVec, false, 0, sVec.dims()[0] - 1, 0, 0, i, i, j, j);
             Array<T> uSlice  = getSubArray(u, false, 0, u.dims()[0] - 1, 0,
-                                          u.dims()[1] - 1, i, i, j, j);
+                                           u.dims()[1] - 1, i, i, j, j);
             Array<T> vTSlice = getSubArray(vT, false, 0, vT.dims()[0] - 1, 0,
                                            vT.dims()[1] - 1, i, i, j, j);
             svd<T, Tr>(sVecSlice, uSlice, vTSlice, inSlice);
@@ -131,7 +131,7 @@ Array<T> pinverseSvd(const Array<T> &in, const double tol) {
         dim4(sVecRecip.dims()[0], (sVecRecip.dims()[2] * sVecRecip.dims()[3])));
     Array<T> sPinv = diagCreate<T>(sVecRecipMod, 0);
     sPinv          = modDims<T>(sPinv, dim4(sPinv.dims()[0], sPinv.dims()[1],
-                                   sVecRecip.dims()[2], sVecRecip.dims()[3]));
+                                            sVecRecip.dims()[2], sVecRecip.dims()[3]));
 
     Array<T> uT = transpose(u, true);
 
diff --git a/src/api/c/ycbcr_rgb.cpp b/src/api/c/ycbcr_rgb.cpp
index d3c56a7117..a871618d28 100644
--- a/src/api/c/ycbcr_rgb.cpp
+++ b/src/api/c/ycbcr_rgb.cpp
@@ -69,7 +69,7 @@ static af_array convert(const af_array& in, const af_ycc_std standard) {
     static const float INV_219 = 0.004566210;
     static const float INV_112 = 0.008928571;
     const static float k[6]    = {0.1140f, 0.2990f, 0.0722f,
-                               0.2126f, 0.0593f, 0.2627f};
+                                  0.2126f, 0.0593f, 0.2627f};
     unsigned stdIdx            = 0;  // Default standard is AF_YCC_601
     switch (standard) {
         case AF_YCC_709: stdIdx = 2; break;
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 3600f60e83..5889c0d99c 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -166,9 +166,9 @@ struct array::array_proxy::array_proxy_impl {
         if (delete_on_destruction_) { delete parent_; }
     }
 
-    array_proxy_impl(const array_proxy_impl &)  = delete;
-    array_proxy_impl(const array_proxy_impl &&) = delete;
-    array_proxy_impl operator=(const array_proxy_impl &) = delete;
+    array_proxy_impl(const array_proxy_impl &)            = delete;
+    array_proxy_impl(const array_proxy_impl &&)           = delete;
+    array_proxy_impl operator=(const array_proxy_impl &)  = delete;
     array_proxy_impl operator=(const array_proxy_impl &&) = delete;
 };
 
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index cbf6e76861..b77f7e9bbe 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -152,7 +152,7 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
         if (index_ != unified::getActiveBackend()) {                             \
             index_ = unified::getActiveBackend();                                \
             func   = (af_func)common::getFunctionPointer(                        \
-                unified::getActiveHandle(), __func__);                         \
+                  unified::getActiveHandle(), __func__);                         \
         }                                                                        \
         return func(__VA_ARGS__);                                                \
     } else {                                                                     \
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index 25eb4bd06a..0881f318a1 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -57,9 +57,9 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
             , lock_bytes(0)
             , lock_buffers(0) {}
 
-        memory_info(memory_info &other)  = delete;
-        memory_info(memory_info &&other) = default;
-        memory_info &operator=(memory_info &other) = delete;
+        memory_info(memory_info &other)             = delete;
+        memory_info(memory_info &&other)            = default;
+        memory_info &operator=(memory_info &other)  = delete;
         memory_info &operator=(memory_info &&other) = default;
     };
 
diff --git a/src/backend/common/HandleBase.hpp b/src/backend/common/HandleBase.hpp
index bf7df20a20..4ffaf4dca1 100644
--- a/src/backend/common/HandleBase.hpp
+++ b/src/backend/common/HandleBase.hpp
@@ -21,10 +21,10 @@ class HandleBase {
     operator H() { return handle_; }
     H* get() { return &handle_; }
 
-    HandleBase(HandleBase const&) = delete;
+    HandleBase(HandleBase const&)     = delete;
     void operator=(HandleBase const&) = delete;
 
-    HandleBase(HandleBase&& h) = default;
+    HandleBase(HandleBase&& h)            = default;
     HandleBase& operator=(HandleBase&& h) = default;
 };
 }  // namespace common
diff --git a/src/backend/common/MemoryManagerBase.hpp b/src/backend/common/MemoryManagerBase.hpp
index 5ba3281294..c338db1020 100644
--- a/src/backend/common/MemoryManagerBase.hpp
+++ b/src/backend/common/MemoryManagerBase.hpp
@@ -29,7 +29,7 @@ namespace memory {
  */
 class MemoryManagerBase {
    public:
-    MemoryManagerBase()        = default;
+    MemoryManagerBase()                                     = default;
     MemoryManagerBase &operator=(const MemoryManagerBase &) = delete;
     MemoryManagerBase(const MemoryManagerBase &)            = delete;
     virtual ~MemoryManagerBase() {}
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index fc8256f999..d1a572a153 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -258,7 +258,7 @@ fg_window ForgeManager::getMainWindow() {
             }
             fg_window w = nullptr;
             forgeError  = this->mPlugin->fg_create_window(
-                &w, WIDTH, HEIGHT, "ArrayFire", NULL, true);
+                 &w, WIDTH, HEIGHT, "ArrayFire", NULL, true);
             if (forgeError != FG_ERR_NONE) { return; }
             this->setWindowChartGrid(w, 1, 1);
             this->mPlugin->fg_make_window_current(w);
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 1f2b9f60b1..6db366f323 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -53,10 +53,10 @@ class ForgeManager {
     using WindowGridDims = std::pair<int, int>;
 
     ForgeManager();
-    ForgeManager(ForgeManager const&) = delete;
+    ForgeManager(ForgeManager const&)            = delete;
     ForgeManager& operator=(ForgeManager const&) = delete;
     ForgeManager(ForgeManager&&)                 = delete;
-    ForgeManager& operator=(ForgeManager&&) = delete;
+    ForgeManager& operator=(ForgeManager&&)      = delete;
 
     /// \brief Module used to invoke forge API calls
     ForgeModule& plugin();
diff --git a/src/backend/common/host_memory.cpp b/src/backend/common/host_memory.cpp
index a44a920db3..51a01e2164 100644
--- a/src/backend/common/host_memory.cpp
+++ b/src/backend/common/host_memory.cpp
@@ -63,13 +63,13 @@ size_t getHostMemorySize() {
 
 #if defined(CTL_HW) && (defined(HW_MEMSIZE) || defined(HW_PHYSMEM64))
     int mib[2];
-    mib[0] = CTL_HW;
+    mib[0]       = CTL_HW;
 #if defined(HW_MEMSIZE)
-    mib[1] = HW_MEMSIZE; /* OSX. --------------------- */
+    mib[1]       = HW_MEMSIZE; /* OSX. --------------------- */
 #elif defined(HW_PHYSMEM64)
     mib[1] = HW_PHYSMEM64; /* NetBSD, OpenBSD. --------- */
 #endif
-    int64_t size = 0; /* 64-bit */
+    int64_t size = 0;          /* 64-bit */
     size_t len   = sizeof(size);
     if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
     return 0L; /* Failed? */
@@ -90,13 +90,13 @@ size_t getHostMemorySize() {
 #elif defined(CTL_HW) && (defined(HW_PHYSMEM) || defined(HW_REALMEM))
     /* DragonFly BSD, FreeBSD, NetBSD, OpenBSD, and OSX. -------- */
     int mib[2];
-    mib[0] = CTL_HW;
+    mib[0]            = CTL_HW;
 #if defined(HW_REALMEM)
-    mib[1] = HW_REALMEM; /* FreeBSD. ----------------- */
+    mib[1]            = HW_REALMEM; /* FreeBSD. ----------------- */
 #elif defined(HW_PYSMEM)
     mib[1] = HW_PHYSMEM; /* Others. ------------------ */
 #endif
-    unsigned int size = 0; /* 32-bit */
+    unsigned int size = 0;          /* 32-bit */
     size_t len        = sizeof(size);
     if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
     return 0L; /* Failed? */
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index e286f6359d..e2883079a1 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -92,11 +92,11 @@ class NodeIterator {
     pointer operator->() const noexcept { return tree[index]; }
 
     /// Creates a sentinel iterator. This is equivalent to the end iterator
-    NodeIterator()                              = default;
-    NodeIterator(const NodeIterator& other)     = default;
-    NodeIterator(NodeIterator&& other) noexcept = default;
-    ~NodeIterator() noexcept                    = default;
-    NodeIterator& operator=(const NodeIterator& other) = default;
+    NodeIterator()                                         = default;
+    NodeIterator(const NodeIterator& other)                = default;
+    NodeIterator(NodeIterator&& other) noexcept            = default;
+    ~NodeIterator() noexcept                               = default;
+    NodeIterator& operator=(const NodeIterator& other)     = default;
     NodeIterator& operator=(NodeIterator&& other) noexcept = default;
 };
 
diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index 52d0acfeda..0c3fe8fe6f 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -60,7 +60,7 @@ class unique_handle {
         }
     }
 
-    unique_handle(const unique_handle &other) noexcept = delete;
+    unique_handle(const unique_handle &other) noexcept      = delete;
     unique_handle &operator=(unique_handle &other) noexcept = delete;
 
     AF_CONSTEXPR unique_handle(unique_handle &&other) noexcept
diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
index ec3613e21f..20686c4430 100644
--- a/src/backend/cpu/Param.hpp
+++ b/src/backend/cpu/Param.hpp
@@ -53,10 +53,10 @@ class CParam {
     /// \param[in] i The dimension
     constexpr dim_t strides(int i) const noexcept { return m_strides[i]; }
 
-    constexpr CParam()                    = delete;
-    constexpr CParam(const CParam &other) = default;
-    constexpr CParam(CParam &&other)      = default;
-    CParam<T> &operator=(CParam &&other) noexcept = default;
+    constexpr CParam()                                 = delete;
+    constexpr CParam(const CParam &other)              = default;
+    constexpr CParam(CParam &&other)                   = default;
+    CParam<T> &operator=(CParam &&other) noexcept      = default;
     CParam<T> &operator=(const CParam &other) noexcept = default;
     ~CParam()                                          = default;
 };
@@ -108,10 +108,10 @@ class Param {
     /// \param[in] i The dimension
     constexpr dim_t strides(int i) const noexcept { return m_strides[i]; }
 
-    ~Param()                            = default;
-    constexpr Param(const Param &other) = default;
-    constexpr Param(Param &&other)      = default;
-    Param<T> &operator=(Param &&other) noexcept = default;
+    ~Param()                                         = default;
+    constexpr Param(const Param &other)              = default;
+    constexpr Param(Param &&other)                   = default;
+    Param<T> &operator=(Param &&other) noexcept      = default;
     Param<T> &operator=(const Param &other) noexcept = default;
 };
 
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index dc780c450e..d760b724b9 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -144,7 +144,7 @@ Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
 
     Array<T> collapsedFilter = flip(filter, {1, 1, 0, 0});
     collapsedFilter          = modDims(collapsedFilter,
-                              dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+                                       dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> res =
         matmul(unwrapped, collapsedFilter, AF_MAT_TRANS, AF_MAT_NONE);
@@ -187,12 +187,12 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
 
     Array<T> collapsed_filter = flip(original_filter, {1, 1, 0, 0});
     collapsed_filter          = modDims(collapsed_filter,
-                               dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+                                        dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
 
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
@@ -231,7 +231,7 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index 170f61df4b..3015ae05f6 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -131,7 +131,7 @@ class DeviceManager {
     // avoid copying accidental copy/assignment
     // of instance returned by getInstance to other
     // variables
-    DeviceManager(DeviceManager const&) = delete;
+    DeviceManager(DeviceManager const&)  = delete;
     void operator=(DeviceManager const&) = delete;
 
     // Attributes
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
index 72283e7a7e..9e2e8a4e21 100644
--- a/src/backend/cpu/kernel/diff.hpp
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -35,7 +35,7 @@ void diff1(Param<T> out, CParam<T> in, int const dim) {
                     // in[index]
                     int idx     = getIdx(in.strides(), i, j, k, l);
                     int jdx     = getIdx(in.strides(), i + is_dim0, j + is_dim1,
-                                     k + is_dim2, l + is_dim3);
+                                         k + is_dim2, l + is_dim3);
                     int odx     = getIdx(out.strides(), i, j, k, l);
                     outPtr[odx] = inPtr[jdx] - inPtr[idx];
                 }
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index e85bd4b2f6..d6c6f8493e 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -202,8 +202,9 @@ void reorderHelper(To* out_ptr, const af::dim4& od, const af::dim4& os,
                                 (float)((in_ptr[iidx1] + in_ptr[iidx2]) /
                                         fftScale));
                         else
-                            out_ptr[oidx] = (To)(
-                                (in_ptr[iidx1] + in_ptr[iidx2]) / fftScale);
+                            out_ptr[oidx] =
+                                (To)((in_ptr[iidx1] + in_ptr[iidx2]) /
+                                     fftScale);
                     } else {
                         // Copy bottom elements
                         const int iidx =
diff --git a/src/backend/cpu/kernel/orb.hpp b/src/backend/cpu/kernel/orb.hpp
index 33c642cd8d..df36f3655b 100644
--- a/src/backend/cpu/kernel/orb.hpp
+++ b/src/backend/cpu/kernel/orb.hpp
@@ -257,12 +257,12 @@ void extract_orb(unsigned* desc_out, const unsigned n_feat, float* x_in_out,
                 int dist_x = ref_pat[i * 32 * 4 + j * 4];
                 int dist_y = ref_pat[i * 32 * 4 + j * 4 + 1];
                 T p1       = get_pixel(x, y, ori, size, dist_x, dist_y, image,
-                                 patch_size);
+                                       patch_size);
 
                 dist_x = ref_pat[i * 32 * 4 + j * 4 + 2];
                 dist_y = ref_pat[i * 32 * 4 + j * 4 + 3];
                 T p2   = get_pixel(x, y, ori, size, dist_x, dist_y, image,
-                                 patch_size);
+                                   patch_size);
 
                 // Calculate bit based on p1 and p2 and shifts it to correct
                 // position
diff --git a/src/backend/cuda/LookupTable1D.hpp b/src/backend/cuda/LookupTable1D.hpp
index 746607d5d5..ffbfb0f4c8 100644
--- a/src/backend/cuda/LookupTable1D.hpp
+++ b/src/backend/cuda/LookupTable1D.hpp
@@ -19,10 +19,10 @@ namespace cuda {
 template<typename T>
 class LookupTable1D {
    public:
-    LookupTable1D()                          = delete;
-    LookupTable1D(const LookupTable1D& arg)  = delete;
-    LookupTable1D(const LookupTable1D&& arg) = delete;
-    LookupTable1D& operator=(const LookupTable1D& arg) = delete;
+    LookupTable1D()                                     = delete;
+    LookupTable1D(const LookupTable1D& arg)             = delete;
+    LookupTable1D(const LookupTable1D&& arg)            = delete;
+    LookupTable1D& operator=(const LookupTable1D& arg)  = delete;
     LookupTable1D& operator=(const LookupTable1D&& arg) = delete;
 
     LookupTable1D(const Array<T>& lutArray) : mTexture(0), mData(lutArray) {
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 3b7476f7a5..cd1651cae5 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -34,10 +34,10 @@ class Param {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
 
-    Param(const Param<T> &other) noexcept = default;
-    Param(Param<T> &&other) noexcept      = default;
+    Param(const Param<T> &other) noexcept               = default;
+    Param(Param<T> &&other) noexcept                    = default;
     Param<T> &operator=(const Param<T> &other) noexcept = default;
-    Param<T> &operator=(Param<T> &&other) noexcept = default;
+    Param<T> &operator=(Param<T> &&other) noexcept      = default;
 };
 
 template<typename T>
@@ -70,10 +70,10 @@ class CParam {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
 
-    CParam(const CParam<T> &other) noexcept = default;
-    CParam(CParam<T> &&other) noexcept      = default;
+    CParam(const CParam<T> &other) noexcept               = default;
+    CParam(CParam<T> &&other) noexcept                    = default;
     CParam<T> &operator=(const CParam<T> &other) noexcept = default;
-    CParam<T> &operator=(CParam<T> &&other) noexcept = default;
+    CParam<T> &operator=(CParam<T> &&other) noexcept      = default;
 };
 
 }  // namespace cuda
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 0a95a7c9ae..075817925e 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -207,7 +207,7 @@ Array<T> convolve2_base(const Array<T> &signal, const Array<T> &filter,
     const int Ndim = 1;
     Array<T> res   = createEmptyArray<T>(
         dim4(unwrapped.dims()[Mdim], collapsedFilter.dims()[Ndim],
-             unwrapped.dims()[2], unwrapped.dims()[3]));
+               unwrapped.dims()[2], unwrapped.dims()[3]));
     gemm(res, AF_MAT_TRANS, AF_MAT_NONE, &alpha, unwrapped, collapsedFilter,
          &beta);
     res = modDims(res, dim4(outputWidth, outputHeight, signal.dims()[3],
@@ -259,7 +259,7 @@ Array<T> data_gradient_base(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
@@ -267,7 +267,7 @@ Array<T> data_gradient_base(const Array<T> &incoming_gradient,
     const int Ndim = 0;
     Array<T> res   = createEmptyArray<T>(
         dim4(collapsed_gradient.dims()[Mdim], collapsed_filter.dims()[Ndim],
-             collapsed_gradient.dims()[3], collapsed_gradient.dims()[3]));
+               collapsed_gradient.dims()[3], collapsed_gradient.dims()[3]));
     gemm(res, AF_MAT_NONE, AF_MAT_TRANS, &alpha, collapsed_gradient,
          collapsed_filter, &beta);
     res = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
@@ -389,7 +389,7 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
@@ -397,7 +397,7 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
     const int Ndim = 1;
     Array<T> res   = createEmptyArray<T>(
         dim4(unwrapped.dims()[Mdim], collapsed_gradient.dims()[Ndim],
-             unwrapped.dims()[2], unwrapped.dims()[3]));
+               unwrapped.dims()[2], unwrapped.dims()[3]));
     gemm(res, AF_MAT_NONE, AF_MAT_NONE, &alpha, unwrapped, collapsed_gradient,
          &beta);
     res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index e88722c7bc..3521f8cfcb 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -246,7 +246,7 @@ __global__ void non_max_counts(unsigned *d_counts, unsigned *d_offsets,
             if (nonmax) {
                 float max_v = v;
                 max_v       = max_val(score[x - 1 + idim0 * (y - 1)],
-                                score[x - 1 + idim0 * y]);
+                                      score[x - 1 + idim0 * y]);
                 max_v       = max_val(max_v, score[x - 1 + idim0 * (y + 1)]);
                 max_v       = max_val(max_v, score[x + idim0 * (y - 1)]);
                 max_v       = max_val(max_v, score[x + idim0 * (y + 1)]);
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index 7db3a1fc57..e8fe490b52 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -249,7 +249,7 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
     // Calculate Harris responses for all pixels
     threads = dim3(BLOCK_SIZE, BLOCK_SIZE);
     blocks  = dim3(divup(in.dims[1] - border_len * 2, threads.x),
-                  divup(in.dims[0] - border_len * 2, threads.y));
+                   divup(in.dims[0] - border_len * 2, threads.y));
     CUDA_LAUNCH((harris_responses<T>), blocks, threads, d_responses.get(),
                 in.dims[0], in.dims[1], ixx.ptr, ixy.ptr, iyy.ptr, k_thr,
                 border_len);
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index 7d3033f647..aaad7af358 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -157,7 +157,7 @@ __device__ bool computeMeanScale(
     CParam<float> x_dst, CParam<float> y_dst, CParam<float> rnd, int i) {
     const unsigned ridx = rnd.dims[0] * i;
     unsigned r[4]       = {(unsigned)rnd.ptr[ridx], (unsigned)rnd.ptr[ridx + 1],
-                     (unsigned)rnd.ptr[ridx + 2], (unsigned)rnd.ptr[ridx + 3]};
+                           (unsigned)rnd.ptr[ridx + 2], (unsigned)rnd.ptr[ridx + 3]};
 
     // If one of the points is repeated, it's a bad samples, will still
     // compute homography to ensure all threads pass __syncthreads()
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 15ef584bb0..672da31fc3 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -246,12 +246,12 @@ __global__ void extract_orb(unsigned* desc_out, const unsigned n_feat,
                 int dist_x = lookup(i * 16 * 4 + j * 4, luTable);
                 int dist_y = lookup(i * 16 * 4 + j * 4 + 1, luTable);
                 T p1       = get_pixel(x, y, ori, size, dist_x, dist_y, image,
-                                 patch_size);
+                                       patch_size);
 
                 dist_x = lookup(i * 16 * 4 + j * 4 + 2, luTable);
                 dist_y = lookup(i * 16 * 4 + j * 4 + 3, luTable);
                 T p2   = get_pixel(x, y, ori, size, dist_x, dist_y, image,
-                                 patch_size);
+                                   patch_size);
 
                 // Calculate bit based on p1 and p2 and shifts it to correct
                 // position
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 1f983a08eb..e52e78d354 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -213,8 +213,8 @@ __device__ void sincos(__half val, __half *sptr, __half *cptr) {
     float s, c;
     float fval = __half2float(val);
     sincos(fval, &s, &c);
-    *sptr = __float2half(s);
-    *cptr = __float2half(c);
+    *sptr      = __float2half(s);
+    *cptr      = __float2half(c);
 #endif
 }
 
diff --git a/src/backend/cuda/kernel/shfl_intrinsics.hpp b/src/backend/cuda/kernel/shfl_intrinsics.hpp
index 9a3f3cf2f3..ef12aafe29 100644
--- a/src/backend/cuda/kernel/shfl_intrinsics.hpp
+++ b/src/backend/cuda/kernel/shfl_intrinsics.hpp
@@ -57,7 +57,7 @@ inline __device__ cuda::cfloat shfl_down_sync(unsigned mask, cuda::cfloat var,
     cuda::cfloat res = {__shfl_down_sync(mask, var.x, delta),
                         __shfl_down_sync(mask, var.y, delta)};
 #else
-    cuda::cfloat res = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
+    cuda::cfloat res  = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
 #endif
     return res;
 }
@@ -91,7 +91,7 @@ inline __device__ cuda::cfloat shfl_up_sync(unsigned mask, cuda::cfloat var,
     cuda::cfloat res = {__shfl_up_sync(mask, var.x, delta),
                         __shfl_up_sync(mask, var.y, delta)};
 #else
-    cuda::cfloat res = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
+    cuda::cfloat res  = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
 #endif
     return res;
 }
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index d1d83efa60..8e171ac816 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -36,7 +36,7 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
         threads = dim3(TX, THREADS_PER_BLOCK / TX);
         blocks = dim3(divup(out.dims[1], threads.y), out.dims[2] * out.dims[3]);
         reps   = divup((wx * wy),
-                     threads.x);  // is > 1 only when TX == 256 && wx * wy > 256
+                       threads.x);  // is > 1 only when TX == 256 && wx * wy > 256
     } else {
         threads = dim3(THREADS_X, THREADS_Y);
         blocks = dim3(divup(out.dims[0], threads.x), out.dims[2] * out.dims[3]);
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index c3897a3397..91bcdbbda7 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -162,7 +162,7 @@ struct kernel_type<common::half> {
     using compute = float;
 
 #if defined(__NVCC__) || defined(__CUDACC_RTC__)
-    using native = __half;
+    using native  = __half;
 #else
     using native = common::half;
 #endif
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index dd05838760..a4924303f3 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -184,7 +184,7 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
@@ -223,7 +223,7 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 9f160703ef..92cf5858e7 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -63,7 +63,7 @@ void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
         param.nBBS0    = divup(oDims[0], THREADS);
         param.nBBS1    = batchDims[2];
         param.global   = NDRange(param.nBBS0 * THREADS * batchDims[1],
-                               param.nBBS1 * batchDims[3]);
+                                 param.nBBS1 * batchDims[3]);
         param.loc_size = (THREADS + 2 * (fDims[0] - 1)) * sizeof(T);
     } else if (rank == 2) {
         param.local  = NDRange(THREADS_X, THREADS_Y);
@@ -77,7 +77,7 @@ void prepareKernelArgs(conv_kparam_t& param, dim_t* oDims, const dim_t* fDims,
         param.nBBS1    = divup(oDims[1], CUBE_Y);
         int blk_z      = divup(oDims[2], CUBE_Z);
         param.global   = NDRange(param.nBBS0 * CUBE_X * batchDims[3],
-                               param.nBBS1 * CUBE_Y, blk_z * CUBE_Z);
+                                 param.nBBS1 * CUBE_Y, blk_z * CUBE_Z);
         param.loc_size = (CUBE_X + 2 * (fDims[0] - 1)) *
                          (CUBE_Y + 2 * (fDims[1] - 1)) *
                          (CUBE_Z + 2 * (fDims[2] - 1)) * sizeof(T);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 3293c06ea0..4585d7636e 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -32,7 +32,7 @@ constexpr int HG_THREADS   = 256;
 template<typename T>
 std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
     std::vector<TemplateArg> targs   = {TemplateTypename<T>(),
-                                      TemplateArg(htype)};
+                                        TemplateArg(htype)};
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index abcd89715c..3215ee22b5 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -37,7 +37,7 @@ void index(Param out, const Param in, const IndexKernelParam_t& p,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto index    = common::getKernel("indexKernel", {index_cl_src},
-                                   {TemplateTypename<T>()}, options);
+                                      {TemplateTypename<T>()}, options);
     int threads_x = 256;
     int threads_y = 1;
     cl::NDRange local(threads_x, threads_y);
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index 14f28e6fe5..b755644e37 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -174,7 +174,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 
             lvl_img.info.offset = 0;
             lvl_img.data        = bufferAlloc(lvl_img.info.dims[3] *
-                                       lvl_img.info.strides[3] * sizeof(T));
+                                              lvl_img.info.strides[3] * sizeof(T));
 
             resize<T>(lvl_img, prev_img, AF_INTERP_BILINEAR);
 
@@ -331,7 +331,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
             lvl_filt.data = bufferAlloc(lvl_filt.info.dims[0] *
                                         lvl_filt.info.dims[1] * sizeof(T));
             lvl_tmp.data  = bufferAlloc(lvl_tmp.info.dims[0] *
-                                       lvl_tmp.info.dims[1] * sizeof(T));
+                                        lvl_tmp.info.dims[1] * sizeof(T));
 
             // Calculate a separable Gaussian kernel
             if (h_gauss == nullptr) {
diff --git a/src/backend/opencl/magma/geqrf2.cpp b/src/backend/opencl/magma/geqrf2.cpp
index 2d09f0ba60..bcb71ad51f 100644
--- a/src/backend/opencl/magma/geqrf2.cpp
+++ b/src/backend/opencl/magma/geqrf2.cpp
@@ -234,8 +234,8 @@ magma_int_t magma_geqrf2_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
                                    CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR,
                                    sizeof(Ty) * lwork, NULL, NULL);
     work          = (Ty *)clEnqueueMapBuffer(queue[0], buffer, CL_TRUE,
-                                    CL_MAP_READ | CL_MAP_WRITE, 0,
-                                    lwork * sizeof(Ty), 0, NULL, NULL, NULL);
+                                             CL_MAP_READ | CL_MAP_WRITE, 0,
+                                             lwork * sizeof(Ty), 0, NULL, NULL, NULL);
 
     cpu_lapack_geqrf_work_func<Ty> cpu_lapack_geqrf;
     cpu_lapack_larft_func<Ty> cpu_lapack_larft;
diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 38470a5f76..4d6834b42e 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -321,9 +321,9 @@ static void magma_setmatrix_async(magma_int_t m, magma_int_t n, T const* hA_src,
     size_t host_orig[3]     = {0, 0, 0};
     size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
     cl_int err              = clEnqueueWriteBufferRect(
-        queue, dB_dst, CL_FALSE,  // non-blocking
-        buffer_origin, host_orig, region, lddb * sizeof(T), 0, ldha * sizeof(T),
-        0, hA_src, 0, NULL, event);
+                     queue, dB_dst, CL_FALSE,  // non-blocking
+                     buffer_origin, host_orig, region, lddb * sizeof(T), 0, ldha * sizeof(T),
+                     0, hA_src, 0, NULL, event);
     clFlush(queue);
     check_error(err);
 }
@@ -357,9 +357,9 @@ static void magma_getmatrix_async(magma_int_t m, magma_int_t n, cl_mem dA_src,
     size_t host_orig[3]     = {0, 0, 0};
     size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
     cl_int err              = clEnqueueReadBufferRect(
-        queue, dA_src, CL_FALSE,  // non-blocking
-        buffer_origin, host_orig, region, ldda * sizeof(T), 0, ldhb * sizeof(T),
-        0, hB_dst, 0, NULL, event);
+                     queue, dA_src, CL_FALSE,  // non-blocking
+                     buffer_origin, host_orig, region, ldda * sizeof(T), 0, ldhb * sizeof(T),
+                     0, hB_dst, 0, NULL, event);
     clFlush(queue);
     check_error(err);
 }
diff --git a/src/backend/opencl/magma/magma_types.h b/src/backend/opencl/magma/magma_types.h
index fe844e78d4..90dcc6ab8d 100644
--- a/src/backend/opencl/magma/magma_types.h
+++ b/src/backend/opencl/magma/magma_types.h
@@ -388,7 +388,7 @@ typedef enum {
 // 2b) update min & max here, which are used to check bounds for
 // magma2lapack_constants[] 2c) add lapack_xxxx_const() converter below and in
 // control/constants.cpp
-#define Magma2lapack_Min MagmaFalse  // 0
+#define Magma2lapack_Min MagmaFalse    // 0
 #define Magma2lapack_Max MagmaRowwise  // 402
 
 // ----------------------------------------
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 77e8224bbb..8dab1f428b 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -188,8 +188,8 @@ size_t Allocator::getMaxMemorySize(int id) {
 void *Allocator::nativeAlloc(const size_t bytes) {
     cl_int err = CL_SUCCESS;
     auto ptr   = static_cast<void *>(clCreateBuffer(
-        getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
-        bytes, nullptr, &err));
+          getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
+          bytes, nullptr, &err));
 
     if (err != CL_SUCCESS) {
         auto str = fmt::format("Failed to allocate device memory of size {}",
@@ -237,7 +237,7 @@ void *AllocatorPinned::nativeAlloc(const size_t bytes) {
 
     cl_int err = CL_SUCCESS;
     auto buf   = clCreateBuffer(getContext()(), CL_MEM_ALLOC_HOST_PTR, bytes,
-                              nullptr, &err);
+                                nullptr, &err);
     if (err != CL_SUCCESS) {
         AF_ERROR("Failed to allocate pinned memory.", AF_ERR_NO_MEM);
     }
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 5aa6c0e1ed..5c7aed92c4 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -136,8 +136,8 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
 
     if (want_vectors) {
         mappedU  = static_cast<T *>(getQueue().enqueueMapBuffer(
-            *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
-            sizeof(T) * arrU.elements()));
+             *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
+             sizeof(T) * arrU.elements()));
         mappedVT = static_cast<T *>(getQueue().enqueueMapBuffer(
             *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
             sizeof(T) * arrVT.elements()));
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 08155b9d8a..5fcf157946 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -75,13 +75,13 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         cl::Event ev_in, ev_val, ev_ind;
 
         T* ptr     = static_cast<T*>(getQueue().enqueueMapBuffer(
-            *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
-            nullptr, &ev_in));
+                *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
+                nullptr, &ev_in));
         uint* iptr = static_cast<uint*>(getQueue().enqueueMapBuffer(
             *ibuf, CL_FALSE, CL_MAP_READ | CL_MAP_WRITE, 0, k * sizeof(uint),
             nullptr, &ev_ind));
         T* vptr    = static_cast<T*>(getQueue().enqueueMapBuffer(
-            *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
+               *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
 
         vector<uint> idx(in.elements());
 
diff --git a/test/.clang-format b/test/.clang-format
index 692cbc2f40..47afdf3208 100644
--- a/test/.clang-format
+++ b/test/.clang-format
@@ -138,7 +138,7 @@ SpacesInContainerLiterals: true
 SpacesInCStyleCastParentheses: false
 SpacesInParentheses: false
 SpacesInSquareBrackets: false
-Standard:        Cpp03
+Standard:        Cpp11
 TabWidth:        4
 UseTab:          Never
 
diff --git a/test/approx1.cpp b/test/approx1.cpp
index 17d7579cec..ed7bf83066 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -73,8 +73,8 @@ void approx1Test(string pTestFile, const unsigned resultIdx,
 
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<T> > tests;
+    vector<vector<BT>> in;
+    vector<vector<T>> tests;
     readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -146,8 +146,8 @@ void approx1CubicTest(string pTestFile, const unsigned resultIdx,
 
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<T> > tests;
+    vector<vector<BT>> in;
+    vector<vector<T>> tests;
     readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -233,8 +233,8 @@ void approx1ArgsTest(string pTestFile, const af_interp_type method,
     SUPPORTED_TYPE_CHECK(T);
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<T> > tests;
+    vector<vector<BT>> in;
+    vector<vector<T>> tests;
     readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -279,8 +279,8 @@ void approx1ArgsTestPrecision(string pTestFile, const unsigned,
                               const af_interp_type method) {
     SUPPORTED_TYPE_CHECK(T);
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -335,8 +335,8 @@ TEST(Approx1, CPP) {
     const unsigned resultIdx = 1;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<float> > tests;
+    vector<vector<BT>> in;
+    vector<vector<float>> tests;
     readTests<BT, float, float>(string(TEST_DIR "/approx/approx1.test"),
                                 numDims, in, tests);
 
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 796c639fd0..1b7901bf8d 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -65,8 +65,8 @@ void approx2Test(string pTestFile, const unsigned resultIdx,
     SUPPORTED_TYPE_CHECK(T);
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<T> > tests;
+    vector<vector<BT>> in;
+    vector<vector<T>> tests;
     readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -152,8 +152,8 @@ void approx2ArgsTest(string pTestFile, const af_interp_type method,
     SUPPORTED_TYPE_CHECK(T);
     typedef typename dtype_traits<T>::base_type BT;
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<T> > tests;
+    vector<vector<BT>> in;
+    vector<vector<T>> tests;
     readTests<BT, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -208,8 +208,8 @@ void approx2ArgsTestPrecision(string pTestFile, const unsigned resultIdx,
     UNUSED(resultIdx);
     SUPPORTED_TYPE_CHECK(T);
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -264,8 +264,8 @@ TEST(Approx2, CPP) {
     const unsigned resultIdx = 1;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<float> > tests;
+    vector<vector<BT>> in;
+    vector<vector<float>> tests;
     readTests<BT, float, float>(string(TEST_DIR "/approx/approx2.test"),
                                 numDims, in, tests);
 
@@ -301,8 +301,8 @@ TEST(Approx2Cubic, CPP) {
     const unsigned resultIdx = 0;
 #define BT dtype_traits<float>::base_type
     vector<dim4> numDims;
-    vector<vector<BT> > in;
-    vector<vector<float> > tests;
+    vector<vector<BT>> in;
+    vector<vector<float>> tests;
     readTests<BT, float, float>(string(TEST_DIR "/approx/approx2_cubic.test"),
                                 numDims, in, tests);
 
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index a7d823e040..6a7f6e7000 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -280,8 +280,8 @@ af_half convert(int in) {
 
 template<typename inType, typename outType, typename FileElementType>
 void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
-               std::vector<std::vector<inType> > &testInputs,
-               std::vector<std::vector<outType> > &testOutputs) {
+               std::vector<std::vector<inType>> &testInputs,
+               std::vector<std::vector<outType>> &testOutputs) {
     using std::vector;
 
     std::ifstream testFile(FileName.c_str());
@@ -326,8 +326,8 @@ void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
 #define INSTANTIATE(Tin, Tout, Tfile)                                  \
     template void readTests<Tin, Tout, Tfile>(                         \
         const std::string &FileName, std::vector<af::dim4> &inputDims, \
-        std::vector<std::vector<Tin> > &testInputs,                    \
-        std::vector<std::vector<Tout> > &testOutputs)
+        std::vector<std::vector<Tin>> &testInputs,                     \
+        std::vector<std::vector<Tout>> &testOutputs)
 
 INSTANTIATE(float, float, int);
 INSTANTIATE(double, float, int);
@@ -814,8 +814,8 @@ bool noLAPACKTests() {
 template<typename inType, typename outType>
 void readTestsFromFile(const std::string &FileName,
                        std::vector<af::dim4> &inputDims,
-                       std::vector<std::vector<inType> > &testInputs,
-                       std::vector<std::vector<outType> > &testOutputs) {
+                       std::vector<std::vector<inType>> &testInputs,
+                       std::vector<std::vector<outType>> &testOutputs) {
     using std::vector;
 
     std::ifstream testFile(FileName.c_str());
@@ -863,8 +863,8 @@ void readTestsFromFile(const std::string &FileName,
 #define INSTANTIATE(Ti, To)                                            \
     template void readTestsFromFile<Ti, To>(                           \
         const std::string &FileName, std::vector<af::dim4> &inputDims, \
-        std::vector<std::vector<Ti> > &testInputs,                     \
-        std::vector<std::vector<To> > &testOutputs)
+        std::vector<std::vector<Ti>> &testInputs,                      \
+        std::vector<std::vector<To>> &testOutputs)
 
 INSTANTIATE(float, float);
 INSTANTIATE(float, af_cfloat);
@@ -880,7 +880,7 @@ template<typename outType>
 void readImageTests(const std::string &pFileName,
                     std::vector<af::dim4> &pInputDims,
                     std::vector<std::string> &pTestInputs,
-                    std::vector<std::vector<outType> > &pTestOutputs) {
+                    std::vector<std::vector<outType>> &pTestOutputs) {
     using std::vector;
 
     std::ifstream testFile(pFileName.c_str());
@@ -923,7 +923,7 @@ void readImageTests(const std::string &pFileName,
     template void readImageTests<To>(                                    \
         const std::string &pFileName, std::vector<af::dim4> &pInputDims, \
         std::vector<std::string> &pTestInputs,                           \
-        std::vector<std::vector<To> > &pTestOutputs)
+        std::vector<std::vector<To>> &pTestOutputs)
 
 INSTANTIATE(float);
 #undef INSTANTIATE
@@ -972,8 +972,8 @@ template<typename descType>
 void readImageFeaturesDescriptors(
     const std::string &pFileName, std::vector<af::dim4> &pInputDims,
     std::vector<std::string> &pTestInputs,
-    std::vector<std::vector<float> > &pTestFeats,
-    std::vector<std::vector<descType> > &pTestDescs) {
+    std::vector<std::vector<float>> &pTestFeats,
+    std::vector<std::vector<descType>> &pTestDescs) {
     using std::vector;
 
     std::ifstream testFile(pFileName.c_str());
@@ -1025,8 +1025,8 @@ void readImageFeaturesDescriptors(
     template void readImageFeaturesDescriptors<TYPE>(                    \
         const std::string &pFileName, std::vector<af::dim4> &pInputDims, \
         std::vector<std::string> &pTestInputs,                           \
-        std::vector<std::vector<float> > &pTestFeats,                    \
-        std::vector<std::vector<TYPE> > &pTestDescs)
+        std::vector<std::vector<float>> &pTestFeats,                     \
+        std::vector<std::vector<TYPE>> &pTestDescs)
 
 INSTANTIATE(float);
 INSTANTIATE(double);
@@ -1547,14 +1547,14 @@ bool absMatch::operator()<af::af_cdouble>(af::af_cdouble lhs,
 }
 
 template<>
-bool absMatch::operator()<std::complex<float> >(std::complex<float> lhs,
-                                                std::complex<float> rhs) {
+bool absMatch::operator()<std::complex<float>>(std::complex<float> lhs,
+                                               std::complex<float> rhs) {
     return std::abs(rhs - lhs) <= diff_;
 }
 
 template<>
-bool absMatch::operator()<std::complex<double> >(std::complex<double> lhs,
-                                                 std::complex<double> rhs) {
+bool absMatch::operator()<std::complex<double>>(std::complex<double> lhs,
+                                                std::complex<double> rhs) {
     return std::abs(rhs - lhs) <= diff_;
 }
 
diff --git a/test/assign.cpp b/test/assign.cpp
index 7c32a2cc33..cbfe6359b1 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -107,8 +107,8 @@ void assignTest(string pTestFile, const vector<af_seq> *seqv) {
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTests<inType, outType, int>(pTestFile, numDims, in, tests);
 
@@ -151,8 +151,8 @@ void assignTestCPP(string pTestFile, const vector<af_seq> &seqv) {
     SUPPORTED_TYPE_CHECK(T);
     try {
         vector<dim4> numDims;
-        vector<vector<T> > in;
-        vector<vector<T> > tests;
+        vector<vector<T>> in;
+        vector<vector<T>> tests;
 
         readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -290,8 +290,8 @@ void assignScalarCPP(string pTestFile, const vector<af_seq> &seqv) {
     SUPPORTED_TYPE_CHECK(T);
     try {
         vector<dim4> numDims;
-        vector<vector<T> > in;
-        vector<vector<T> > tests;
+        vector<vector<T>> in;
+        vector<vector<T>> tests;
 
         readTests<T, T, int>(pTestFile, numDims, in, tests);
 
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index d4da723ddb..8d83d2798b 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -87,8 +87,8 @@ void bilateralDataTest(string pTestFile) {
                                float>::type outType;
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTests<inType, outType, float>(pTestFile, numDims, in, tests);
 
@@ -152,8 +152,8 @@ using af::bilateral;
 
 TEST(Bilateral, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, float>(string(TEST_DIR "/bilateral/rectangle.test"),
                                    numDims, in, tests);
diff --git a/test/binary.cpp b/test/binary.cpp
index f5fd0610e8..b0c04a4c30 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -578,7 +578,7 @@ TYPED_TEST(ResultTypeScalar, FloatDivision) {
     ASSERT_EQ(f32, (af::array(10, f32) / this->scalar).type());
 }
 
-class Broadcast : public ::testing::TestWithParam<std::tuple<dim4, dim4> > {
+class Broadcast : public ::testing::TestWithParam<std::tuple<dim4, dim4>> {
     void SetUp() override {}
 };
 /// clang-format off
diff --git a/test/blas.cpp b/test/blas.cpp
index 62491a366f..6b0590d73b 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -53,8 +53,8 @@ void MatMulCheck(string TestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > hData;
-    vector<vector<T> > tests;
+    vector<vector<T>> hData;
+    vector<vector<T>> tests;
     readTests<T, T, int>(TestFile, numDims, hData, tests);
 
     af_array a, aT, b, bT;
@@ -132,8 +132,8 @@ void cppMatMulCheck(string TestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > hData;
-    vector<vector<T> > tests;
+    vector<vector<T>> hData;
+    vector<vector<T>> tests;
     readTests<T, T, int>(TestFile, numDims, hData, tests);
 
     array a(numDims[0], &hData[0].front());
diff --git a/test/canny.cpp b/test/canny.cpp
index 8e1cb9c2b6..7e72d4e356 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -39,8 +39,8 @@ void cannyTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<char> > tests;
+    vector<vector<T>> in;
+    vector<vector<char>> tests;
 
     readTests<T, char, int>(pTestFile, numDims, in, tests);
 
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 8ef707aca7..9d081f068d 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -122,8 +122,8 @@ void testData(CCCTestParams params) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     string file = string(TEST_DIR) + "/confidence_cc/" + string(params.prefix) +
                   "_" + to_string(params.radius) + "_" +
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 7b31e532a3..5fb61e7ee0 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -45,8 +45,8 @@ void convolveTest(string pTestFile, int baseDim, bool expand) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -218,8 +218,8 @@ void sepConvolveTest(string pTestFile, bool expand) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -378,8 +378,8 @@ using af::sum;
 
 TEST(Convolve1, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(string(TEST_DIR "/convolve/vector_same.test"),
                                  numDims, in, tests);
@@ -411,8 +411,8 @@ TEST(Convolve1, CPP) {
 
 TEST(Convolve2, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/convolve/rectangle_same_one2many.test"), numDims, in,
@@ -447,8 +447,8 @@ TEST(Convolve2, CPP) {
 
 TEST(Convolve3, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/convolve/cuboid_same_many2many.test"), numDims, in,
@@ -482,8 +482,8 @@ TEST(Convolve3, CPP) {
 
 TEST(Convolve, separable_CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/convolve/separable_conv2d_same_rectangle_batch.test"),
@@ -809,8 +809,8 @@ TEST(Convolve, CuboidBatchLaunchBugFix) {
     std::string testFile(TEST_DIR "/convolve/conv3d_launch_bug.test");
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, float>(testFile, numDims, in, tests);
 
@@ -917,8 +917,8 @@ void convolve2stridedTest(string pTestFile, dim4 stride, dim4 padding,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
@@ -962,8 +962,8 @@ void convolve2GradientTest(string pTestFile, dim4 stride, dim4 padding,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 1c7f378961..213a8de092 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -73,8 +73,8 @@ TYPED_TEST(CorrelationCoefficient, All) {
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(
         string(TEST_DIR "/corrcoef/mat_10x10_scalar.test"), numDims, in, tests);
diff --git a/test/covariance.cpp b/test/covariance.cpp
index aa06c58a10..4d4e4877f1 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -79,8 +79,8 @@ void covTest(string pFileName, bool isbiased = true,
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
diff --git a/test/diff1.cpp b/test/diff1.cpp
index 605cd75fa9..a7456fd0a2 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -59,8 +59,8 @@ void diff1Test(string pTestFile, unsigned dim, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -151,8 +151,8 @@ void diff1ArgsTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -214,8 +214,8 @@ TEST(Diff1, CPP) {
     const unsigned dim = 0;
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/diff1/matrix0.test"),
                                  numDims, in, tests);
     dim4 dims = numDims[0];
diff --git a/test/diff2.cpp b/test/diff2.cpp
index 4a68627d7b..c7c17f333f 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -64,8 +64,8 @@ void diff2Test(string pTestFile, unsigned dim, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -153,8 +153,8 @@ void diff2ArgsTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -209,8 +209,8 @@ TEST(Diff2, CPP) {
     const unsigned dim = 1;
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/diff2/matrix1.test"),
                                  numDims, in, tests);
     dim4 dims = numDims[0];
diff --git a/test/dot.cpp b/test/dot.cpp
index 357e0784d4..834260af44 100644
--- a/test/dot.cpp
+++ b/test/dot.cpp
@@ -63,8 +63,8 @@ void dotTest(string pTestFile, const int resultIdx,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, T>(pTestFile, numDims, in, tests);
 
@@ -118,8 +118,8 @@ void dotAllTest(string pTestFile, const int resultIdx,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, T>(pTestFile, numDims, in, tests);
 
@@ -194,8 +194,8 @@ INSTANTIATEC(25600, dot_c_25600);
 //
 TEST(DotF, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, float>(TEST_DIR "/blas/dot_f_1000.test", numDims,
                                    in, tests);
@@ -215,8 +215,8 @@ TEST(DotF, CPP) {
 
 TEST(DotCCU, CPP) {
     vector<dim4> numDims;
-    vector<vector<cfloat> > in;
-    vector<vector<cfloat> > tests;
+    vector<vector<cfloat>> in;
+    vector<vector<cfloat>> tests;
 
     readTests<cfloat, cfloat, cfloat>(TEST_DIR "/blas/dot_c_1000.test", numDims,
                                       in, tests);
@@ -236,8 +236,8 @@ TEST(DotCCU, CPP) {
 
 TEST(DotAllF, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, float>(TEST_DIR "/blas/dot_f_1000.test", numDims,
                                    in, tests);
@@ -257,8 +257,8 @@ TEST(DotAllF, CPP) {
 
 TEST(DotAllCCU, CPP) {
     vector<dim4> numDims;
-    vector<vector<cfloat> > in;
-    vector<vector<cfloat> > tests;
+    vector<vector<cfloat>> in;
+    vector<vector<cfloat>> tests;
 
     readTests<cfloat, cfloat, cfloat>(TEST_DIR "/blas/dot_c_1000.test", numDims,
                                       in, tests);
diff --git a/test/fast.cpp b/test/fast.cpp
index 77281955a5..316fe57ad6 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -73,7 +73,7 @@ void fastTest(string pTestFile, bool nonmax) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
@@ -184,7 +184,7 @@ TEST(FloatFAST, CPP) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(string(TEST_DIR "/fast/square_nonmax_float.test"), inDims,
                    inFiles, gold);
diff --git a/test/fft.cpp b/test/fft.cpp
index acd0ad7521..49176ca522 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -127,8 +127,8 @@ void fftTest(string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0, dim_t pad2 = 0) {
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
@@ -293,8 +293,8 @@ void fftBatchTest(string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0,
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
@@ -430,8 +430,8 @@ void cppFFTTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
@@ -476,8 +476,8 @@ void cppDFTTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index 7465891bde..57d9398a04 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -53,8 +53,8 @@ void fftconvolveTest(string pTestFile, bool expand) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -345,8 +345,8 @@ TYPED_TEST(FFTConvolve, Same_Cuboid_One2Many) {
 
 TEST(FFTConvolve1, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(string(TEST_DIR "/convolve/vector.test"),
                                  numDims, in, tests);
@@ -378,8 +378,8 @@ TEST(FFTConvolve1, CPP) {
 
 TEST(FFTConvolve2, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/convolve/rectangle_one2many.test"), numDims, in,
@@ -414,8 +414,8 @@ TEST(FFTConvolve2, CPP) {
 
 TEST(FFTConvolve3, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/convolve/cuboid_many2many.test"), numDims, in, tests);
diff --git a/test/gaussiankernel.cpp b/test/gaussiankernel.cpp
index 3c4db5386f..3fc8de1c23 100644
--- a/test/gaussiankernel.cpp
+++ b/test/gaussiankernel.cpp
@@ -37,8 +37,8 @@ void gaussianKernelTest(string pFileName, double sigma) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<T> > tests;
+    vector<vector<int>> in;
+    vector<vector<T>> tests;
 
     readTestsFromFile<int, T>(pFileName, numDims, in, tests);
 
@@ -114,8 +114,8 @@ using af::gaussianKernel;
 
 void gaussianKernelTestCPP(string pFileName, double sigma) {
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 716735740a..7cfd78ae62 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -38,8 +38,8 @@ using std::vector;
 void testGeneralAssignOneArray(string pTestFile, const dim_t ndims,
                                af_index_t *indexs, int arrayDim) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(pTestFile, numDims, in, tests);
 
@@ -105,8 +105,8 @@ TEST(GeneralAssign, SASS) {
 
 TEST(GeneralAssign, SSSS) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(
         string(TEST_DIR "/gen_assign/s10_14s0_9s0_ns0_n.test"), numDims, in,
@@ -152,8 +152,8 @@ TEST(GeneralAssign, SSSS) {
 
 TEST(GeneralAssign, AAAA) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR "/gen_assign/aaaa.test"),
                                     numDims, in, tests);
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index b491a9ac4c..e65d4e48e5 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -47,8 +47,8 @@ class IndexGeneralizedLegacy : public ::testing::TestWithParam<index_params> {
     void SetUp() {
         index_params params = GetParam();
         vector<dim4> numDims;
-        vector<vector<float> > in;
-        vector<vector<float> > tests;
+        vector<vector<float>> in;
+        vector<vector<float>> tests;
 
         if (noDoubleTests(get<1>(params))) return;
         if (noHalfTests(get<1>(params))) return;
@@ -138,8 +138,8 @@ TEST_P(IndexGeneralizedLegacy, SSSA) {
 void testGeneralIndexOneArray(string pTestFile, const dim_t ndims,
                               af_index_t *indexs, int arrayDim) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(pTestFile, numDims, in, tests);
 
@@ -202,8 +202,8 @@ TEST(GeneralIndex, SASS) {
 
 TEST(GeneralIndex, AASS) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(
         string(TEST_DIR "/gen_index/aas0_ns0_n.test"), numDims, in, tests);
diff --git a/test/gloh.cpp b/test/gloh.cpp
index eb193e7ec4..e370984fbf 100644
--- a/test/gloh.cpp
+++ b/test/gloh.cpp
@@ -65,7 +65,7 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
 
 static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
                                float* score, float* ori, float* size,
-                               vector<vector<float> >& desc, unsigned nfeat) {
+                               vector<vector<float>>& desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -141,8 +141,8 @@ void glohTest(string pTestFile) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > goldFeat;
-    vector<vector<float> > goldDesc;
+    vector<vector<float>> goldFeat;
+    vector<vector<float>> goldDesc;
 
     readImageFeaturesDescriptors<float>(pTestFile, inDims, inFiles, goldFeat,
                                         goldDesc);
@@ -265,8 +265,8 @@ TEST(GLOH, CPP) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > goldFeat;
-    vector<vector<float> > goldDesc;
+    vector<vector<float>> goldFeat;
+    vector<vector<float>> goldDesc;
 
     readImageFeaturesDescriptors<float>(string(TEST_DIR "/gloh/man.test"),
                                         inDims, inFiles, goldFeat, goldDesc);
diff --git a/test/gradient.cpp b/test/gradient.cpp
index b30e9bb649..5d04d3dd98 100644
--- a/test/gradient.cpp
+++ b/test/gradient.cpp
@@ -50,8 +50,8 @@ void gradTest(string pTestFile, const unsigned resultIdx0,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -128,8 +128,8 @@ TEST(Grad, CPP) {
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/grad/grad3D.test"),
                                    numDims, in, tests);
 
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 8b3d9f85f7..763e0f7774 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -47,12 +47,12 @@ void hammingMatcherTest(string pTestFile, int feat_dim) {
     using af::dim4;
 
     vector<dim4> numDims;
-    vector<vector<uint> > in32;
-    vector<vector<uint> > tests;
+    vector<vector<uint>> in32;
+    vector<vector<uint>> tests;
 
     readTests<uint, uint, int>(pTestFile, numDims, in32, tests);
 
-    vector<vector<T> > in(in32.size());
+    vector<vector<T>> in(in32.size());
     for (size_t i = 0; i < in32[0].size(); i++) in[0].push_back((T)in32[0][i]);
     for (size_t i = 0; i < in32[1].size(); i++) in[1].push_back((T)in32[1][i]);
 
@@ -121,8 +121,8 @@ TEST(HammingMatcher, CPP) {
     using af::dim4;
 
     vector<dim4> numDims;
-    vector<vector<uint> > in;
-    vector<vector<uint> > tests;
+    vector<vector<uint>> in;
+    vector<vector<uint>> tests;
 
     readTests<uint, uint, int>(
         TEST_DIR "/hamming/hamming_500_5000_dim0_u32.test", numDims, in, tests);
diff --git a/test/harris.cpp b/test/harris.cpp
index 955c676251..ec6a1fa626 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -65,7 +65,7 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
@@ -171,7 +171,7 @@ TEST(FloatHarris, CPP) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(string(TEST_DIR "/harris/square_0_3.test"), inDims, inFiles,
                    gold);
diff --git a/test/histogram.cpp b/test/histogram.cpp
index ff2049b390..ca3df72f74 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -46,8 +46,8 @@ void histTest(string pTestFile, unsigned nbins, double minval, double maxval) {
 
     vector<dim4> numDims;
 
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
     readTests<inType, uint, uint>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -120,8 +120,8 @@ TEST(Histogram, CPP) {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<uint> > tests;
+    vector<vector<float>> in;
+    vector<vector<uint>> tests;
     readTests<float, uint, int>(
         string(TEST_DIR "/histogram/100bin0min99max.test"), numDims, in, tests);
 
diff --git a/test/homography.cpp b/test/homography.cpp
index 6b0e620869..c6a6e43450 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -53,7 +53,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
@@ -224,7 +224,7 @@ TEST(Homography, CPP) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(string(TEST_DIR "/homography/tux.test"), inDims, inFiles,
                    gold);
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index f00f5ab7f1..423fc5fad5 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -39,8 +39,8 @@ TEST(hsv_rgb, InvalidArray) {
 
 TEST(hsv2rgb, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/hsv2rgb.test"),
                                     numDims, in, tests);
@@ -55,8 +55,8 @@ TEST(hsv2rgb, CPP) {
 
 TEST(rgb2hsv, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/rgb2hsv.test"),
                                     numDims, in, tests);
@@ -71,8 +71,8 @@ TEST(rgb2hsv, CPP) {
 
 TEST(rgb2hsv, MaxDim) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/rgb2hsv.test"),
                                     numDims, in, tests);
@@ -109,8 +109,8 @@ TEST(rgb2hsv, MaxDim) {
 
 TEST(hsv2rgb, MaxDim) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(string(TEST_DIR "/hsv_rgb/hsv2rgb.test"),
                                     numDims, in, tests);
diff --git a/test/iir.cpp b/test/iir.cpp
index fd03e7ccc6..85fda2a959 100644
--- a/test/iir.cpp
+++ b/test/iir.cpp
@@ -124,8 +124,8 @@ void iirTest(const char *testFile) {
     SUPPORTED_TYPE_CHECK(T);
     vector<dim4> inDims;
 
-    vector<vector<T> > inputs;
-    vector<vector<T> > outputs;
+    vector<vector<T>> inputs;
+    vector<vector<T>> outputs;
     readTests<T, T, float>(testFile, inDims, inputs, outputs);
 
     try {
diff --git a/test/imageio.cpp b/test/imageio.cpp
index a4e12e834e..6d3de9f45b 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -40,8 +40,8 @@ void loadImageTest(string pTestFile, string pImageFile, const bool isColor) {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -126,8 +126,8 @@ TEST(ImageIO, CPP) {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/imageio/color_small.test"),
                                    numDims, in, tests);
 
@@ -258,8 +258,8 @@ TEST(ImageIO, LoadImage16CPP) {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(
         string(TEST_DIR "/imageio/color_seq_16.test"), numDims, in, tests);
 
@@ -316,8 +316,8 @@ void loadImageNativeCPPTest(string pTestFile, string pImageFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
diff --git a/test/index.cpp b/test/index.cpp
index 2f61d40adb..a593348773 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -300,39 +300,39 @@ class Indexing2D : public ::testing::Test {
             make_vec(af_make_seq(3, 6, 4), af_make_seq(1, 9, 4)));
     }
 
-    vector<vector<af_seq> > column_continuous_seq;
-    vector<vector<af_seq> > column_continuous_reverse_seq;
-    vector<vector<af_seq> > column_strided_seq;
-    vector<vector<af_seq> > column_strided_reverse_seq;
-
-    vector<vector<af_seq> > row_continuous_seq;
-    vector<vector<af_seq> > row_continuous_reverse_seq;
-    vector<vector<af_seq> > row_strided_seq;
-    vector<vector<af_seq> > row_strided_reverse_seq;
-
-    vector<vector<af_seq> > continuous_continuous_seq;
-    vector<vector<af_seq> > continuous_strided_seq;
-    vector<vector<af_seq> > continuous_reverse_seq;
-    vector<vector<af_seq> > continuous_strided_reverse_seq;
-
-    vector<vector<af_seq> > reverse_continuous_seq;
-    vector<vector<af_seq> > reverse_reverse_seq;
-    vector<vector<af_seq> > reverse_strided_seq;
-    vector<vector<af_seq> > reverse_strided_reverse_seq;
-
-    vector<vector<af_seq> > strided_continuous_seq;
-    vector<vector<af_seq> > strided_strided_seq;
+    vector<vector<af_seq>> column_continuous_seq;
+    vector<vector<af_seq>> column_continuous_reverse_seq;
+    vector<vector<af_seq>> column_strided_seq;
+    vector<vector<af_seq>> column_strided_reverse_seq;
+
+    vector<vector<af_seq>> row_continuous_seq;
+    vector<vector<af_seq>> row_continuous_reverse_seq;
+    vector<vector<af_seq>> row_strided_seq;
+    vector<vector<af_seq>> row_strided_reverse_seq;
+
+    vector<vector<af_seq>> continuous_continuous_seq;
+    vector<vector<af_seq>> continuous_strided_seq;
+    vector<vector<af_seq>> continuous_reverse_seq;
+    vector<vector<af_seq>> continuous_strided_reverse_seq;
+
+    vector<vector<af_seq>> reverse_continuous_seq;
+    vector<vector<af_seq>> reverse_reverse_seq;
+    vector<vector<af_seq>> reverse_strided_seq;
+    vector<vector<af_seq>> reverse_strided_reverse_seq;
+
+    vector<vector<af_seq>> strided_continuous_seq;
+    vector<vector<af_seq>> strided_strided_seq;
 };
 
 template<typename T>
-void DimCheck2D(const vector<vector<af_seq> > &seqs, string TestFile,
+void DimCheck2D(const vector<vector<af_seq>> &seqs, string TestFile,
                 size_t NDims) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
 
-    vector<vector<T> > hData;
-    vector<vector<T> > tests;
+    vector<vector<T>> hData;
+    vector<vector<T>> tests;
     readTests<T, T, int>(TestFile, numDims, hData, tests);
     dim4 dimensions = numDims[0];
 
@@ -528,18 +528,18 @@ class Indexing : public ::testing::Test {
                       af_make_seq(0, 0, 1), af_make_seq(0, 0, 1)));
     }
 
-    vector<vector<af_seq> > continuous3d_to_3d;
-    vector<vector<af_seq> > continuous3d_to_2d;
-    vector<vector<af_seq> > continuous3d_to_1d;
+    vector<vector<af_seq>> continuous3d_to_3d;
+    vector<vector<af_seq>> continuous3d_to_2d;
+    vector<vector<af_seq>> continuous3d_to_1d;
 
-    vector<vector<af_seq> > continuous4d_to_4d;
-    vector<vector<af_seq> > continuous4d_to_3d;
-    vector<vector<af_seq> > continuous4d_to_2d;
-    vector<vector<af_seq> > continuous4d_to_1d;
+    vector<vector<af_seq>> continuous4d_to_4d;
+    vector<vector<af_seq>> continuous4d_to_3d;
+    vector<vector<af_seq>> continuous4d_to_2d;
+    vector<vector<af_seq>> continuous4d_to_1d;
 };
 
 template<typename T>
-void DimCheckND(const vector<vector<af_seq> > &seqs, string TestFile,
+void DimCheckND(const vector<vector<af_seq>> &seqs, string TestFile,
                 size_t NDims) {
     SUPPORTED_TYPE_CHECK(T);
 
@@ -589,7 +589,7 @@ TEST(Index, Docs_Util_C_API) {
     //![ex_index_util_0]
     af_index_t *indexers = 0;
     af_err err           = af_create_indexers(
-        &indexers);  // Memory is allocated on heap by the callee
+                  &indexers);  // Memory is allocated on heap by the callee
     // by default all the indexers span all the elements along the given
     // dimension
 
@@ -658,7 +658,7 @@ using af::span;
 using af::where;
 
 TEST(Indexing2D, ColumnContiniousCPP) {
-    vector<vector<af_seq> > seqs;
+    vector<vector<af_seq>> seqs;
 
     seqs.push_back(make_vec(af_span, af_make_seq(0, 6, 1)));
     // seqs.push_back(make_vec(span, af_make_seq(  4,  9,  1)));
@@ -666,8 +666,8 @@ TEST(Indexing2D, ColumnContiniousCPP) {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > hData;
-    vector<vector<float> > tests;
+    vector<vector<float>> hData;
+    vector<vector<float>> tests;
     readTests<float, float, int>(TEST_DIR "/index/ColumnContinious.test",
                                  numDims, hData, tests);
     dim4 dimensions = numDims[0];
@@ -717,8 +717,8 @@ void arrayIndexTest(string pTestFile, int dim) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -767,8 +767,8 @@ TYPED_TEST(lookup, Dim3) {
 
 TEST(lookup, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(string(TEST_DIR "/arrayindex/dim0.test"),
                                  numDims, in, tests);
@@ -978,8 +978,8 @@ TEST(SeqIndex, CPP_SCOPE_ARR) {
 
 TEST(SeqIndex, CPPLarge) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(string(TEST_DIR "/arrayindex/dim0Large.test"),
                                  numDims, in, tests);
diff --git a/test/internal.cpp b/test/internal.cpp
index 3540ff0ee0..ede8e697a7 100644
--- a/test/internal.cpp
+++ b/test/internal.cpp
@@ -36,7 +36,7 @@ TEST(Internal, CreateStrided) {
     dim_t dims[]    = {3, 3, 2};
     dim_t strides[] = {1, 5, 20};
     array a         = createStridedArray((void *)ha, offset, dim4(ndims, dims),
-                                 dim4(ndims, strides), f32, afHost);
+                                         dim4(ndims, strides), f32, afHost);
 
     dim4 astrides = getStrides(a);
     dim4 adims    = a.dims();
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 5c49e8c3e8..92596528d4 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -261,7 +261,7 @@ TEST(IndexedReduce, MinCplxNaN) {
     array min_idx;
     af::min(min_val, min_idx, a);
 
-    vector<complex<float> > h_min_val(cols);
+    vector<complex<float>> h_min_val(cols);
     min_val.host(&h_min_val[0]);
 
     vector<int> h_min_idx(cols);
@@ -296,7 +296,7 @@ TEST(IndexedReduce, MaxCplxNaN) {
     array max_idx;
     af::max(max_val, max_idx, a);
 
-    vector<complex<float> > h_max_val(cols);
+    vector<complex<float>> h_max_val(cols);
     max_val.host(&h_max_val[0]);
 
     vector<int> h_max_idx(cols);
@@ -371,7 +371,7 @@ TEST(IndexedReduce, MinCplxPreferLargerIdxIfEqual) {
     array min_idx;
     min(min_val, min_idx, a);
 
-    vector<complex<float> > h_min_val(1);
+    vector<complex<float>> h_min_val(1);
     min_val.host(&h_min_val[0]);
 
     vector<int> h_min_idx(1);
@@ -400,7 +400,7 @@ TEST(IndexedReduce, MaxCplxPreferSmallerIdxIfEqual) {
     array max_idx;
     max(max_val, max_idx, a);
 
-    vector<complex<float> > h_max_val(1);
+    vector<complex<float>> h_max_val(1);
     max_val.host(&h_max_val[0]);
 
     vector<int> h_max_idx(1);
diff --git a/test/jit.cpp b/test/jit.cpp
index 64d72d25b7..101580a488 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -665,13 +665,13 @@ TEST(JIT, TwoLargeNonLinearHalf) {
 }
 
 std::string select_info(
-    const ::testing::TestParamInfo<std::tuple<int, int, int> > info) {
+    const ::testing::TestParamInfo<std::tuple<int, int, int>> info) {
     return "a_" + to_string(get<0>(info.param)) + "_b_" +
            to_string(get<1>(info.param)) + "_cond_" +
            to_string(get<2>(info.param));
 }
 
-class JITSelect : public ::testing::TestWithParam<std::tuple<int, int, int> > {
+class JITSelect : public ::testing::TestWithParam<std::tuple<int, int, int>> {
    protected:
     void SetUp() {}
 };
diff --git a/test/join.cpp b/test/join.cpp
index de61bdf91e..cf33fccb67 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -61,8 +61,8 @@ void joinTest(string pTestFile, const unsigned dim, const unsigned in0,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 i0dims = numDims[in0];
@@ -161,8 +161,8 @@ TEST(Join, CPP) {
     const unsigned dim       = 2;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/join/join_big.test"),
                                  numDims, in, tests);
 
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index e5b4b8ac97..ec69e1ccd9 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -42,8 +42,8 @@ TEST(LU, InPlaceSmall) {
     int resultIdx = 0;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/lapack/lu.test"), numDims,
                                    in, tests);
 
@@ -80,8 +80,8 @@ TEST(LU, SplitSmall) {
     int resultIdx = 0;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/lapack/lufactorized.test"),
                                    numDims, in, tests);
 
diff --git a/test/match_template.cpp b/test/match_template.cpp
index 90c199bd0a..33b6096815 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -45,8 +45,8 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<outType> > tests;
+    vector<vector<T>> in;
+    vector<vector<outType>> tests;
 
     readTests<T, outType, float>(pTestFile, numDims, in, tests);
 
diff --git a/test/mean.cpp b/test/mean.cpp
index 89a89efeb9..c9c6eb567b 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -85,8 +85,8 @@ void meanDimTest(string pFileName, dim_t dim, bool isWeighted = false) {
     double tol = 1.0e-3;
     if ((af_dtype)af::dtype_traits<T>::af_type == f16) tol = 4.e-3;
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index e9286027a2..81f3fb8099 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -55,8 +55,8 @@ struct meanvar_test {
     af_array weights_;
     af_var_bias bias_;
     int dim_;
-    vector<outType<T> > mean_;
-    vector<outType<T> > variance_;
+    vector<outType<T>> mean_;
+    vector<outType<T>> variance_;
 
     meanvar_test(string description, af_array in, af_array weights,
                  af_var_bias bias, int dim, vector<double> &&mean,
@@ -105,7 +105,7 @@ template<typename T>
 af_dtype meanvar_test<T>::af_type = dtype_traits<T>::af_type;
 
 template<typename T>
-class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
+class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T>> {
    public:
     void meanvar_test_function(const meanvar_test<T> &test) {
         SUPPORTED_TYPE_CHECK(T);
@@ -119,18 +119,18 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
         EXPECT_EQ(AF_SUCCESS, af_meanvar(&mean, &var, in, test.weights_,
                                          test.bias_, test.dim_));
 
-        vector<outType<T> > h_mean(test.mean_.size()),
+        vector<outType<T>> h_mean(test.mean_.size()),
             h_var(test.variance_.size());
 
         dim4 outDim(1);
         af_get_dims(&outDim[0], &outDim[1], &outDim[2], &outDim[3], in);
         outDim[test.dim_] = 1;
 
-        if (is_same_type<half_float::half, outType<T> >::value) {
+        if (is_same_type<half_float::half, outType<T>>::value) {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 1.f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.5f);
-        } else if (is_same_type<float, outType<T> >::value ||
-                   is_same_type<cfloat, outType<T> >::value) {
+        } else if (is_same_type<float, outType<T>>::value ||
+                   is_same_type<cfloat, outType<T>>::value) {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
         } else {
@@ -160,17 +160,17 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T> > {
         array weights(weights_tmp);
         meanvar(mean, var, in, weights, test.bias_, test.dim_);
 
-        vector<outType<T> > h_mean(test.mean_.size()),
+        vector<outType<T>> h_mean(test.mean_.size()),
             h_var(test.variance_.size());
 
         dim4 outDim       = in.dims();
         outDim[test.dim_] = 1;
 
-        if (is_same_type<half_float::half, outType<T> >::value) {
+        if (is_same_type<half_float::half, outType<T>>::value) {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 1.f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.5f);
-        } else if (is_same_type<float, outType<T> >::value ||
-                   is_same_type<cfloat, outType<T> >::value) {
+        } else if (is_same_type<float, outType<T>>::value ||
+                   is_same_type<cfloat, outType<T>>::value) {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
         } else {
@@ -189,11 +189,11 @@ meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
                                  af_var_bias bias, int dim, int mean_index,
                                  int var_index, test_size size) {
     vector<af_array> inputs;
-    vector<vector<double> > outputs;
+    vector<vector<double>> outputs;
     if (size == MEANVAR_SMALL) {
         vector<af::dim4> numDims_;
-        vector<vector<double> > in_;
-        vector<vector<double> > tests_;
+        vector<vector<double>> in_;
+        vector<vector<double>> tests_;
         readTests<double, typename varOutType<double>::type, double>(
             TEST_DIR "/meanvar/meanvar.data", numDims_, in_, tests_);
 
@@ -208,8 +208,8 @@ meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
             copy(tests_[i].begin(), tests_[i].end(), back_inserter(outputs[i]));
         }
     } else {
-        dim_t full_array_size             = 2000;
-        vector<vector<dim_t> > dimensions = {
+        dim_t full_array_size            = 2000;
+        vector<vector<dim_t>> dimensions = {
             {2000, 1, 1, 1},  // 0
             {1, 2000, 1, 1},  // 1
             {1, 1, 2000, 1},  // 2
@@ -245,7 +245,7 @@ meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
 }
 
 template<typename T>
-vector<meanvar_test<T> > small_test_values() {
+vector<meanvar_test<T>> small_test_values() {
     // clang-format off
     return {
         //                  |           Name |   in_index | weight_index |                  bias |  dim | mean_index | var_index |
@@ -262,7 +262,7 @@ vector<meanvar_test<T> > small_test_values() {
 }
 
 template<typename T>
-vector<meanvar_test<T> > large_test_values() {
+vector<meanvar_test<T>> large_test_values() {
     return {
         // clang-format off
         //                  |       Name |      in_index | weight_index |                  bias |  dim | mean_index | var_index |
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 4bc7e69924..2120da8e4c 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -48,8 +48,8 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -108,8 +108,8 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
 
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
@@ -342,8 +342,8 @@ TEST(MedianFilter, CPP) {
     const dim_t w_wid = 3;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x3_window.test"),
@@ -368,8 +368,8 @@ TEST(MedianFilter1d, CPP) {
     const dim_t w_wid = 3;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTests<float, float, int>(
         string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x1_window.test"),
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 69af67860e..9674c5a4f1 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -50,8 +50,8 @@ void moddimsTest(string pTestFile, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -131,8 +131,8 @@ void moddimsArgsTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -164,8 +164,8 @@ void moddimsMismatchTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -200,8 +200,8 @@ void cppModdimsTest(string pTestFile, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
diff --git a/test/moments.cpp b/test/moments.cpp
index 5656a17ec5..d7a396ea95 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -47,8 +47,8 @@ void momentsTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<float> > tests;
+    vector<vector<T>> in;
+    vector<vector<float>> tests;
     readTests<T, float, float>(pTestFile, numDims, in, tests);
 
     array imgArray(numDims.front(), &in.front()[0]);
@@ -101,8 +101,8 @@ void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor) {
     if (noImageIOTests()) return;
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(pTestFile, numDims, in, tests);
 
     array imgArray = loadImage(pImageFile.c_str(), isColor);
diff --git a/test/morph.cpp b/test/morph.cpp
index 220253c8c4..b24106b88b 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -41,8 +41,8 @@ void morphTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(inType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<inType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<inType>> tests;
 
     readTests<inType, inType, int>(pTestFile, numDims, in, tests);
 
@@ -458,11 +458,11 @@ TEST(Morph, EdgeIssue1564) {
                               0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
                               0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
     int goldData[10 * 10]  = {0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,
-                             0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                             0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
-                             0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
-                             1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1,
-                             1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+                              0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                              0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
+                              0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
+                              1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1,
+                              1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
     array input(10, 10, inputData);
     int maskData[3 * 3] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
     array mask(3, 3, maskData);
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 5286923dd8..01847aea65 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -69,8 +69,8 @@ void nearestNeighbourTest(string pTestFile, int feat_dim,
     typedef typename otype_t<T>::otype To;
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<uint> > tests;
+    vector<vector<T>> in;
+    vector<vector<uint>> tests;
 
     readTests<T, uint, uint>(pTestFile, numDims, in, tests);
 
@@ -171,8 +171,8 @@ TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim1) {
 //
 TEST(NearestNeighbourSSD, CPP) {
     vector<dim4> numDims;
-    vector<vector<uint> > in;
-    vector<vector<uint> > tests;
+    vector<vector<uint>> in;
+    vector<vector<uint>> tests;
 
     readTests<uint, uint, uint>(TEST_DIR
                                 "/nearest_neighbour/ssd_500_5000_dim0.test",
@@ -207,8 +207,8 @@ TEST(NearestNeighbourSSD, CPP) {
 
 TEST(NearestNeighbourSAD, CPP) {
     vector<dim4> numDims;
-    vector<vector<uint> > in;
-    vector<vector<uint> > tests;
+    vector<vector<uint>> in;
+    vector<vector<uint>> tests;
 
     readTests<uint, uint, uint>(TEST_DIR
                                 "/nearest_neighbour/sad_100_1000_dim1.test",
diff --git a/test/orb.cpp b/test/orb.cpp
index 42df3ea2f5..b29c7021ba 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -64,8 +64,7 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
 
 static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
                                float* score, float* ori, float* size,
-                               vector<vector<unsigned> >& desc,
-                               unsigned nfeat) {
+                               vector<vector<unsigned>>& desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -134,8 +133,8 @@ void orbTest(string pTestFile) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > goldFeat;
-    vector<vector<unsigned> > goldDesc;
+    vector<vector<float>> goldFeat;
+    vector<vector<unsigned>> goldDesc;
 
     readImageFeaturesDescriptors<unsigned>(pTestFile, inDims, inFiles, goldFeat,
                                            goldDesc);
@@ -251,8 +250,8 @@ TEST(ORB, CPP) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > goldFeat;
-    vector<vector<unsigned> > goldDesc;
+    vector<vector<float>> goldFeat;
+    vector<vector<unsigned>> goldDesc;
 
     readImageFeaturesDescriptors<unsigned>(string(TEST_DIR "/orb/square.test"),
                                            inDims, inFiles, goldFeat, goldDesc);
diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 44a0f884b0..7258558bc2 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -48,8 +48,8 @@ array readTestInput(string testFilePath) {
     dtype outAfType = (dtype)dtype_traits<T>::af_type;
 
     vector<dim4> dimsVec;
-    vector<vector<InBaseType> > inVec;
-    vector<vector<InBaseType> > goldVec;
+    vector<vector<InBaseType>> inVec;
+    vector<vector<InBaseType>> goldVec;
     readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec,
                                               goldVec);
     dim4 inDims = dimsVec[0];
@@ -67,8 +67,8 @@ array readTestGold(string testFilePath) {
     dtype outAfType = (dtype)dtype_traits<T>::af_type;
 
     vector<dim4> dimsVec;
-    vector<vector<InBaseType> > inVec;
-    vector<vector<InBaseType> > goldVec;
+    vector<vector<InBaseType>> inVec;
+    vector<vector<InBaseType>> goldVec;
     readTestsFromFile<InBaseType, InBaseType>(testFilePath, dimsVec, inVec,
                                               goldVec);
     dim4 goldDims(dimsVec[0][1], dimsVec[0][0]);
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 09477dcbf5..9d5f3f1c78 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -39,8 +39,8 @@ TEST(QRFactorized, CPP) {
     int resultIdx = 0;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/lapack/qrfactorized.test"),
                                    numDims, in, tests);
 
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index 30c7ade1ca..bb838686f5 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -99,8 +99,8 @@ void detTest() {
 
     vector<dim4> numDims;
 
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/lapack/detSmall.test"),
                                    numDims, in, tests);
     dim4 dims = numDims[0];
diff --git a/test/reduce.cpp b/test/reduce.cpp
index bfff42959f..31845b8d0c 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -51,8 +51,8 @@ void reduceTest(string pTestFile, int off = 0, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
@@ -217,8 +217,8 @@ void cppReduceTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
@@ -507,7 +507,7 @@ vector<reduce_by_key_params*> genSingleKeyTests() {
 
 vector<reduce_by_key_params *> generateAllTypes() {
     vector<reduce_by_key_params *> out;
-    vector<vector<reduce_by_key_params *> > tmp{
+    vector<vector<reduce_by_key_params *>> tmp{
         genUniqueKeyTests<int, float, float>(),
         genSingleKeyTests<int, float, float>(),
         genUniqueKeyTests<unsigned, float, float>(),
@@ -593,8 +593,8 @@ TEST(ReduceByKey, MultiBlockReduceSingleval) {
 
 void reduce_by_key_test(std::string test_fn) {
     vector<dim4> numDims;
-    vector<vector<float> > data;
-    vector<vector<float> > tests;
+    vector<vector<float>> data;
+    vector<vector<float>> tests;
     readTests<float, float, float>(test_fn, numDims, data, tests);
 
     for (size_t t = 0; t < numDims.size() / 2; ++t) {
@@ -1112,7 +1112,7 @@ TEST(MinMax, MinCplxNaN) {
 
     array min_val = af::min(a);
 
-    vector<complex<float> > h_min_val(cols);
+    vector<complex<float>> h_min_val(cols);
     min_val.host(&h_min_val[0]);
 
     for (int i = 0; i < cols; i++) {
@@ -1148,7 +1148,7 @@ TEST(MinMax, MaxCplxNaN) {
 
     array max_val = af::max(a);
 
-    vector<complex<float> > h_max_val(cols);
+    vector<complex<float>> h_max_val(cols);
     max_val.host(&h_max_val[0]);
 
     for (int i = 0; i < cols; i++) {
diff --git a/test/regions.cpp b/test/regions.cpp
index 4df7b90793..182a22e9b5 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -47,8 +47,8 @@ void regionsTest(string pTestFile, af_connectivity connectivity,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<uchar> > in;
-    vector<vector<T> > tests;
+    vector<vector<uchar>> in;
+    vector<vector<T>> tests;
     readTests<uchar, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -110,8 +110,8 @@ REGIONS_INIT(Regions3, regions_128x128, 8, AF_CONNECTIVITY_8);
 //
 TEST(Regions, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/regions/regions_8x8_4.test"),
                                  numDims, in, tests);
 
diff --git a/test/reorder.cpp b/test/reorder.cpp
index 6652f75210..b06f72cdda 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -57,8 +57,8 @@ void reorderTest(string pTestFile, const unsigned resultIdx, const uint x,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -141,8 +141,8 @@ TEST(Reorder, CPP) {
     const unsigned w         = 3;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/reorder/reorder4d.test"),
                                  numDims, in, tests);
 
diff --git a/test/resize.cpp b/test/resize.cpp
index 816dd7cf9e..423bb55416 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -119,8 +119,8 @@ void resizeTest(string pTestFile, const unsigned resultIdx, const dim_t odim0,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
@@ -320,8 +320,8 @@ void resizeArgsTest(af_err err, string pTestFile, const dim4 odims,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
@@ -363,8 +363,8 @@ using af::span;
 
 TEST(Resize, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/resize/square.test"),
                                    numDims, in, tests);
 
@@ -378,8 +378,8 @@ TEST(Resize, CPP) {
 
 TEST(ResizeScale1, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/resize/square.test"),
                                    numDims, in, tests);
 
@@ -393,8 +393,8 @@ TEST(ResizeScale1, CPP) {
 
 TEST(ResizeScale2, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/resize/square.test"),
                                    numDims, in, tests);
 
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 31019db269..01675fa1d7 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -48,8 +48,8 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
@@ -164,8 +164,8 @@ TEST(Rotate, CPP) {
     const bool crop          = false;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(string(TEST_DIR "/rotate/rotate1.test"),
                                    numDims, in, tests);
 
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 7d0dc8d5b7..ea19f217e7 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -54,8 +54,8 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, float>(pTestFile, numDims, in, tests);
 
     dim4 dims = numDims[0];
@@ -182,8 +182,8 @@ TEST(RotateLinear, CPP) {
     const bool crop          = false;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, float>(
         string(TEST_DIR "/rotate/rotatelinear1.test"), numDims, in, tests);
 
diff --git a/test/scan.cpp b/test/scan.cpp
index cc42624ba9..a29c6e0e52 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -48,8 +48,8 @@ void scanTest(string pTestFile, int off = 0, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
@@ -129,8 +129,8 @@ TEST(Scan, Test_Scan_Big1) {
 TEST(Accum, CPP) {
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(string(TEST_DIR "/scan/accum.test"), numDims, data,
                              tests);
     dim4 dims = numDims[0];
diff --git a/test/set.cpp b/test/set.cpp
index f085da33b3..97e05d484b 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -32,8 +32,8 @@ void uniqueTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(pTestFile, numDims, data, tests);
 
     // Compare result
@@ -92,8 +92,8 @@ void setTest(string pTestFile) {
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(pTestFile, numDims, data, tests);
 
     // Compare result
diff --git a/test/shift.cpp b/test/shift.cpp
index 91df07c39c..b37385a6f8 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -54,8 +54,8 @@ void shiftTest(string pTestFile, const unsigned resultIdx, const int x,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -118,8 +118,8 @@ TEST(Shift, CPP) {
     const unsigned w         = 0;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/shift/shift4d.test"),
                                  numDims, in, tests);
 
diff --git a/test/sift.cpp b/test/sift.cpp
index 90d3b40cdc..2410472b53 100644
--- a/test/sift.cpp
+++ b/test/sift.cpp
@@ -65,7 +65,7 @@ static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
 
 static void array_to_feat_desc(vector<feat_desc_t>& feat, float* x, float* y,
                                float* score, float* ori, float* size,
-                               vector<vector<float> >& desc, unsigned nfeat) {
+                               vector<vector<float>>& desc, unsigned nfeat) {
     feat.resize(nfeat);
     for (size_t i = 0; i < feat.size(); i++) {
         feat[i].f[0] = x[i];
@@ -142,8 +142,8 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > goldFeat;
-    vector<vector<float> > goldDesc;
+    vector<vector<float>> goldFeat;
+    vector<vector<float>> goldDesc;
 
     readImageFeaturesDescriptors<float>(pTestFile, inDims, inFiles, goldFeat,
                                         goldDesc);
@@ -276,8 +276,8 @@ TEST(SIFT, CPP) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > goldFeat;
-    vector<vector<float> > goldDesc;
+    vector<vector<float>> goldFeat;
+    vector<vector<float>> goldDesc;
 
     readImageFeaturesDescriptors<float>(string(TEST_DIR "/sift/man.test"),
                                         inDims, inFiles, goldFeat, goldDesc);
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 449722af38..298d36d299 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -47,8 +47,8 @@ void testSobelDerivatives(string pTestFile) {
     SUPPORTED_TYPE_CHECK(Ti);
 
     vector<dim4> numDims;
-    vector<vector<Ti> > in;
-    vector<vector<To> > tests;
+    vector<vector<Ti>> in;
+    vector<vector<To>> tests;
 
     readTests<Ti, To, int>(pTestFile, numDims, in, tests);
 
diff --git a/test/sort.cpp b/test/sort.cpp
index 307573d7a0..c9da609f93 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -53,8 +53,8 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<float> > tests;
+    vector<vector<T>> in;
+    vector<vector<float>> tests;
     readTests<T, float, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -129,8 +129,8 @@ TEST(Sort, CPPDim0) {
     const unsigned resultIdx0 = 0;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
                                  numDims, in, tests);
 
@@ -160,8 +160,8 @@ TEST(Sort, CPPDim1) {
     const unsigned resultIdx0 = 0;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
                                  numDims, in, tests);
 
@@ -196,8 +196,8 @@ TEST(Sort, CPPDim2) {
     const unsigned resultIdx0 = 2;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_med.test"),
                                  numDims, in, tests);
 
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index b76e31ffbf..afd7908660 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -53,8 +53,8 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -126,8 +126,8 @@ TEST(SortByKey, CPPDim0) {
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_by_key_tiny.test"),
                                  numDims, in, tests);
 
@@ -147,8 +147,8 @@ TEST(SortByKey, CPPDim1) {
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(
         string(TEST_DIR "/sort/sort_by_key_large.test"), numDims, in, tests);
 
@@ -175,8 +175,8 @@ TEST(SortByKey, CPPDim2) {
     const unsigned resultIdx1 = 3;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(
         string(TEST_DIR "/sort/sort_by_key_large.test"), numDims, in, tests);
 
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index bfec5b429b..f3a10b9084 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -54,8 +54,8 @@ void sortTest(string pTestFile, const bool dir, const unsigned resultIdx0,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<float> > tests;
+    vector<vector<T>> in;
+    vector<vector<float>> tests;
     readTests<T, float, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -130,8 +130,8 @@ TEST(SortIndex, CPPDim0) {
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
                                  numDims, in, tests);
 
@@ -155,8 +155,8 @@ TEST(SortIndex, CPPDim1) {
     const unsigned resultIdx1 = 1;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_10x10.test"),
                                  numDims, in, tests);
 
@@ -182,8 +182,8 @@ TEST(SortIndex, CPPDim2) {
     const unsigned resultIdx1 = 3;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/sort/sort_med.test"),
                                  numDims, in, tests);
 
diff --git a/test/stdev.cpp b/test/stdev.cpp
index 85f3bf079d..4b93f5b220 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -81,8 +81,8 @@ void stdevDimTest(string pFileName, dim_t dim,
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
@@ -157,8 +157,8 @@ void stdevDimIndexTest(string pFileName, dim_t dim,
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
@@ -212,8 +212,8 @@ void stdevAllTest(string pFileName, const bool useDeprecatedAPI = false) {
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<int> > in;
-    vector<vector<float> > tests;
+    vector<vector<int>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<int, float>(pFileName, numDims, in, tests);
 
diff --git a/test/susan.cpp b/test/susan.cpp
index 6d40177132..9bdc16d3d9 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -71,7 +71,7 @@ void susanTest(string pTestFile, float t, float g) {
 
     vector<dim4> inDims;
     vector<string> inFiles;
-    vector<vector<float> > gold;
+    vector<vector<float>> gold;
 
     readImageTests(pTestFile, inDims, inFiles, gold);
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 035c76991b..faf7162a3b 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -100,14 +100,14 @@ extern template af_half convert(int in);
 
 template<typename inType, typename outType, typename FileElementType>
 void readTests(const std::string &FileName, std::vector<af::dim4> &inputDims,
-               std::vector<std::vector<inType> > &testInputs,
-               std::vector<std::vector<outType> > &testOutputs);
+               std::vector<std::vector<inType>> &testInputs,
+               std::vector<std::vector<outType>> &testOutputs);
 
 template<typename inType, typename outType>
 void readTestsFromFile(const std::string &FileName,
                        std::vector<af::dim4> &inputDims,
-                       std::vector<std::vector<inType> > &testInputs,
-                       std::vector<std::vector<outType> > &testOutputs);
+                       std::vector<std::vector<inType>> &testInputs,
+                       std::vector<std::vector<outType>> &testOutputs);
 
 void readImageTests(const std::string &pFileName,
                     std::vector<af::dim4> &pInputDims,
@@ -119,14 +119,14 @@ template<typename outType>
 void readImageTests(const std::string &pFileName,
                     std::vector<af::dim4> &pInputDims,
                     std::vector<std::string> &pTestInputs,
-                    std::vector<std::vector<outType> > &pTestOutputs);
+                    std::vector<std::vector<outType>> &pTestOutputs);
 
 template<typename descType>
 void readImageFeaturesDescriptors(
     const std::string &pFileName, std::vector<af::dim4> &pInputDims,
     std::vector<std::string> &pTestInputs,
-    std::vector<std::vector<float> > &pTestFeats,
-    std::vector<std::vector<descType> > &pTestDescs);
+    std::vector<std::vector<float>> &pTestFeats,
+    std::vector<std::vector<descType>> &pTestDescs);
 
 /**
  * Below is not a pair wise comparition method, rather
diff --git a/test/threading.cpp b/test/threading.cpp
index daf613070e..f26047ce95 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -257,8 +257,8 @@ void fftTest(int targetDevice, string pTestFile, dim_t pad0 = 0, dim_t pad1 = 0,
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<inType> > in;
-    vector<vector<outType> > tests;
+    vector<vector<inType>> in;
+    vector<vector<outType>> tests;
 
     readTestsFromFile<inType, outType>(pTestFile, numDims, in, tests);
 
@@ -580,8 +580,8 @@ void cppMatMulCheck(int targetDevice, string TestFile) {
     using std::vector;
     vector<dim4> numDims;
 
-    vector<vector<T> > hData;
-    vector<vector<T> > tests;
+    vector<vector<T>> hData;
+    vector<vector<T>> tests;
     readTests<T, T, int>(TestFile, numDims, hData, tests);
 
     setDevice(targetDevice);
diff --git a/test/tile.cpp b/test/tile.cpp
index 0a649d00ac..bc0cdddba7 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -61,8 +61,8 @@ void tileTest(string pTestFile, const unsigned resultIdx, const uint x,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -128,8 +128,8 @@ TEST(Tile, CPP) {
     const unsigned w         = 1;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/tile/tile_large3D.test"),
                                  numDims, in, tests);
 
diff --git a/test/transform.cpp b/test/transform.cpp
index 77cdcfc881..b7719d46fc 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -62,8 +62,8 @@ void genTestData(af_array *gold, af_array *in, af_array *transform,
     dim4 objDims = inNumDims[0];
 
     vector<dim4> HNumDims;
-    vector<vector<float> > HIn;
-    vector<vector<float> > HTests;
+    vector<vector<float>> HIn;
+    vector<vector<float>> HTests;
     readTests<float, float, float>(pHomographyFile, HNumDims, HIn, HTests);
 
     dim4 HDims = HNumDims[0];
@@ -489,8 +489,8 @@ TEST(Transform, CPP) {
     vector<string> goldFiles;
 
     vector<dim4> HDims;
-    vector<vector<float> > HIn;
-    vector<vector<float> > HTests;
+    vector<vector<float>> HIn;
+    vector<vector<float>> HTests;
     readTests<float, float, float>(TEST_DIR "/transform/tux_tmat.test", HDims,
                                    HIn, HTests);
 
@@ -543,8 +543,8 @@ TEST(Transform, CPP) {
 // This test simply makes sure the batching is working correctly
 TEST(TransformBatching, CPP) {
     vector<dim4> vDims;
-    vector<vector<float> > in;
-    vector<vector<float> > gold;
+    vector<vector<float>> in;
+    vector<vector<float>> gold;
 
     readTests<float, float, int>(
         string(TEST_DIR "/transform/transform_batching.test"), vDims, in, gold);
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 01ab960e93..2875f18c1a 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -38,8 +38,8 @@ void transformCoordinatesTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> inDims;
-    vector<vector<T> > in;
-    vector<vector<float> > gold;
+    vector<vector<T>> in;
+    vector<vector<float>> gold;
 
     readTests<T, float, float>(pTestFile, inDims, in, gold);
 
@@ -89,8 +89,8 @@ TYPED_TEST(TransformCoordinates, 3DMatrix) {
 //
 TEST(TransformCoordinates, CPP) {
     vector<dim4> inDims;
-    vector<vector<float> > in;
-    vector<vector<float> > gold;
+    vector<vector<float>> in;
+    vector<vector<float>> gold;
 
     readTests<float, float, float>(
         TEST_DIR "/transformCoordinates/3d_matrix.test", inDims, in, gold);
diff --git a/test/translate.cpp b/test/translate.cpp
index 4c84b19009..55fd570ffb 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -52,8 +52,8 @@ void translateTest(string pTestFile, const unsigned resultIdx, dim4 odims,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<float> > tests;
+    vector<vector<T>> in;
+    vector<vector<float>> tests;
     readTests<T, float, float>(pTestFile, numDims, in, tests);
 
     af_array inArray  = 0;
diff --git a/test/transpose.cpp b/test/transpose.cpp
index cb36640885..8bc0c1c6e9 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -58,8 +58,8 @@ void trsTest(string pTestFile, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
     dim4 dims = numDims[0];
 
@@ -157,8 +157,8 @@ template<typename T>
 void trsCPPTest(string pFileName) {
     vector<dim4> numDims;
 
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pFileName, numDims, in, tests);
     dim4 dims = numDims[0];
 
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index b33dc8c7d5..f43b73e7f4 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -50,8 +50,8 @@ void unwrapTest(string pTestFile, const unsigned resultIdx, const dim_t wx,
     SUPPORTED_TYPE_CHECK(T);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<T> > tests;
+    vector<vector<T>> in;
+    vector<vector<T>> tests;
     readTests<T, T, int>(pTestFile, numDims, in, tests);
 
     dim4 idims = numDims[0];
@@ -161,8 +161,8 @@ TEST(Unwrap, CPP) {
     const unsigned py        = 3;
 
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
     readTests<float, float, int>(string(TEST_DIR "/unwrap/unwrap_small.test"),
                                  numDims, in, tests);
 
diff --git a/test/var.cpp b/test/var.cpp
index 45c7b6847f..db846f5d57 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -126,8 +126,8 @@ void dimCppSmallTest(const string pFileName,
     SUPPORTED_TYPE_CHECK(outType);
 
     vector<dim4> numDims;
-    vector<vector<T> > in;
-    vector<vector<outType> > tests;
+    vector<vector<T>> in;
+    vector<vector<outType>> tests;
 
     readTests<T, outType, float>(pFileName, numDims, in, tests);
 
@@ -148,7 +148,7 @@ void dimCppSmallTest(const string pFileName,
                               : var(input, AF_VARIANCE_POPULATION, 1));
 #pragma GCC diagnostic pop
 
-        vector<vector<outType> > h_out(4);
+        vector<vector<outType>> h_out(4);
 
         h_out[0].resize(bout.elements());
         h_out[1].resize(nbout.elements());
diff --git a/test/where.cpp b/test/where.cpp
index 746a9aa5b4..bb5375822c 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -45,8 +45,8 @@ void whereTest(string pTestFile, bool isSubRef = false,
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(pTestFile, numDims, data, tests);
     dim4 dims = numDims[0];
 
@@ -99,8 +99,8 @@ TYPED_TEST(Where, CPP) {
 
     vector<dim4> numDims;
 
-    vector<vector<int> > data;
-    vector<vector<int> > tests;
+    vector<vector<int>> data;
+    vector<vector<int>> tests;
     readTests<int, int, int>(string(TEST_DIR "/where/where.test"), numDims,
                              data, tests);
     dim4 dims = numDims[0];
diff --git a/test/ycbcr_rgb.cpp b/test/ycbcr_rgb.cpp
index e137e1ede0..ec365db9a4 100644
--- a/test/ycbcr_rgb.cpp
+++ b/test/ycbcr_rgb.cpp
@@ -37,8 +37,8 @@ TEST(ycbcr_rgb, InvalidArray) {
 
 TEST(ycbcr2rgb, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(
         string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
@@ -60,8 +60,8 @@ TEST(ycbcr2rgb, CPP) {
 
 TEST(ycbcr2rgb, MaxDim) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(
         string(TEST_DIR "/ycbcr_rgb/ycbcr2rgb.test"), numDims, in, tests);
@@ -98,8 +98,8 @@ TEST(ycbcr2rgb, MaxDim) {
 
 TEST(rgb2ycbcr, CPP) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(
         string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);
@@ -121,8 +121,8 @@ TEST(rgb2ycbcr, CPP) {
 
 TEST(rgb2ycbcr, MaxDim) {
     vector<dim4> numDims;
-    vector<vector<float> > in;
-    vector<vector<float> > tests;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;
 
     readTestsFromFile<float, float>(
         string(TEST_DIR "/ycbcr_rgb/rgb2ycbcr.test"), numDims, in, tests);

From 4a96346298ba6ad136ba306cfc984f6114ed7a8b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Sep 2022 18:06:06 -0400
Subject: [PATCH 2290/2677] Fix issue with multiple definition of symbols in
 tests on Windows

---
 test/CMakeLists.txt | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 2a66ea8291..d1bbebbdeb 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -100,7 +100,7 @@ if(AF_BUILD_UNIFIED)
 endif(AF_BUILD_UNIFIED)
 
 
-add_library(arrayfire_test OBJECT
+add_library(arrayfire_test STATIC
   testHelpers.hpp
   arrayfire_test.cpp)
 
@@ -110,9 +110,7 @@ target_include_directories(arrayfire_test
     ${ArrayFire_SOURCE_DIR}/include
     ${ArrayFire_BINARY_DIR}/include
     ${ArrayFire_SOURCE_DIR}/extern/half/include
-    mmio
-    $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-    $<TARGET_PROPERTY:GTest::gtest,INTERFACE_INCLUDE_DIRECTORIES>)
+  )
 
 if(WIN32)
   target_compile_options(arrayfire_test
@@ -130,6 +128,14 @@ target_compile_definitions(arrayfire_test
     TEST_RESULT_IMAGE_DIR="${CMAKE_BINARY_DIR}/test/"
     USE_MTX)
 
+target_link_libraries(arrayfire_test
+  PRIVATE
+    mmio
+  PUBLIC
+    GTest::gtest
+    Boost::boost
+  )
+
 # Creates tests for all backends
 #
 # Creates a standard test for all backends. Most of the time you only need to
@@ -158,11 +164,7 @@ function(make_test)
     endif()
     set(target "test_${src_name}_${backend}")
 
-    if (${mt_args_NO_ARRAYFIRE_TEST})
-      add_executable(${target} ${mt_args_SRC})
-    else()
-      add_executable(${target} ${mt_args_SRC} $<TARGET_OBJECTS:arrayfire_test>)
-    endif()
+    add_executable(${target} ${mt_args_SRC})
     target_include_directories(${target}
       PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
@@ -172,7 +174,7 @@ function(make_test)
     target_link_libraries(${target}
       PRIVATE
         ${mt_args_LIBRARIES}
-	      GTest::gtest
+        arrayfire_test
       )
 
     if(${backend} STREQUAL "unified")
@@ -346,7 +348,7 @@ if(CUDA_FOUND)
           ${CMAKE_CURRENT_SOURCE_DIR}
         )
       endif()
-      add_executable(${target} cuda.cu  $<TARGET_OBJECTS:arrayfire_test>)
+      add_executable(${target} cuda.cu)
       target_include_directories(${target} PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
         ${CMAKE_SOURCE_DIR}
@@ -360,7 +362,7 @@ if(CUDA_FOUND)
       endif()
       target_link_libraries(${target}
         mmio
-        GTest::gtest)
+        arrayfire_test)
 
       # Couldn't get Threads::Threads to work with this cuda binary. The import
       # target would not add the -pthread flag which is required for this

From 93017c6f3a1fc269ee7860e529cf24e50fc50e36 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 1 Oct 2022 15:23:15 -0400
Subject: [PATCH 2291/2677] Reorder Error classes' members to reduce padding

---
 src/backend/common/err_common.cpp | 20 ++++++++++----------
 src/backend/common/err_common.hpp | 12 ++++++------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 21e7b7212b..7a19bcb941 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -38,9 +38,9 @@ AfError::AfError(const char *const func, const char *const file, const int line,
     : logic_error(message)
     , functionName(func)
     , fileName(file)
+    , st_(move(st))
     , lineNumber(line)
-    , error(err)
-    , st_(move(st)) {}
+    , error(err) {}
 
 AfError::AfError(string func, string file, const int line,
                  const string &message, af_err err,
@@ -48,9 +48,9 @@ AfError::AfError(string func, string file, const int line,
     : logic_error(message)
     , functionName(move(func))
     , fileName(move(file))
+    , st_(move(st))
     , lineNumber(line)
-    , error(err)
-    , st_(move(st)) {}
+    , error(err) {}
 
 const string &AfError::getFunctionName() const noexcept { return functionName; }
 
@@ -66,8 +66,8 @@ TypeError::TypeError(const char *const func, const char *const file,
                      const int line, const int index, const af_dtype type,
                      boost::stacktrace::stacktrace st)
     : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE, move(st))
-    , argIndex(index)
-    , errTypeName(getName(type)) {}
+    , errTypeName(getName(type))
+    , argIndex(index) {}
 
 const string &TypeError::getTypeName() const noexcept { return errTypeName; }
 
@@ -78,8 +78,8 @@ ArgumentError::ArgumentError(const char *const func, const char *const file,
                              const char *const expectString,
                              boost::stacktrace::stacktrace st)
     : AfError(func, file, line, "Invalid argument", AF_ERR_ARG, move(st))
-    , argIndex(index)
-    , expected(expectString) {}
+    , expected(expectString)
+    , argIndex(index) {}
 
 const string &ArgumentError::getExpectedCondition() const noexcept {
     return expected;
@@ -101,8 +101,8 @@ DimensionError::DimensionError(const char *const func, const char *const file,
                                const char *const expectString,
                                const boost::stacktrace::stacktrace &st)
     : AfError(func, file, line, "Invalid size", AF_ERR_SIZE, st)
-    , argIndex(index)
-    , expected(expectString) {}
+    , expected(expectString)
+    , argIndex(index) {}
 
 const string &DimensionError::getExpectedCondition() const noexcept {
     return expected;
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 65e25bb0c8..6adf600cf6 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -26,9 +26,9 @@
 class AfError : public std::logic_error {
     std::string functionName;
     std::string fileName;
+    boost::stacktrace::stacktrace st_;
     int lineNumber;
     af_err error;
-    boost::stacktrace::stacktrace st_;
     AfError();
 
    public:
@@ -49,9 +49,9 @@ class AfError : public std::logic_error {
         : std::logic_error(std::forward<std::logic_error>(other))
         , functionName(std::forward<std::string>(other.functionName))
         , fileName(std::forward<std::string>(other.fileName))
+        , st_(std::forward<boost::stacktrace::stacktrace>(other.st_))
         , lineNumber(std::forward<int>(other.lineNumber))
-        , error(std::forward<af_err>(other.error))
-        , st_(std::forward<boost::stacktrace::stacktrace>(other.st_)) {}
+        , error(std::forward<af_err>(other.error)) {}
 
     const std::string& getFunctionName() const noexcept;
 
@@ -70,8 +70,8 @@ class AfError : public std::logic_error {
 
 // TODO: Perhaps add a way to return supported types
 class TypeError : public AfError {
-    int argIndex;
     std::string errTypeName;
+    int argIndex;
     TypeError();
 
    public:
@@ -89,8 +89,8 @@ class TypeError : public AfError {
 };
 
 class ArgumentError : public AfError {
-    int argIndex;
     std::string expected;
+    int argIndex;
     ArgumentError();
 
    public:
@@ -123,8 +123,8 @@ class SupportError : public AfError {
 };
 
 class DimensionError : public AfError {
-    int argIndex;
     std::string expected;
+    int argIndex;
     DimensionError();
 
    public:

From 9848c9348bd4622fdcf2211ed875174564b451e6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 1 Oct 2022 15:24:08 -0400
Subject: [PATCH 2292/2677] Update CTestCustom to show more error contexts

---
 CMakeModules/CTestCustom.cmake | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/CTestCustom.cmake b/CMakeModules/CTestCustom.cmake
index 514a5ee4d8..604f697465 100644
--- a/CMakeModules/CTestCustom.cmake
+++ b/CMakeModules/CTestCustom.cmake
@@ -5,8 +5,11 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-set(CTEST_CUSTOM_ERROR_POST_CONTEXT 50)
-set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 50)
+set(CTEST_CUSTOM_ERROR_POST_CONTEXT 200)
+set(CTEST_CUSTOM_ERROR_PRE_CONTEXT 200)
+set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS 300)
+set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS 300)
+
 if(WIN32)
   if(CMAKE_GENERATOR MATCHES "Ninja")
     set(CTEST_CUSTOM_POST_TEST ./bin/print_info.exe)

From 3996a4a6ad08f0d895f8bc57d813e51f46826968 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 1 Oct 2022 16:14:34 -0400
Subject: [PATCH 2293/2677] Remove STATIC_ definition with the inline keyword

---
 src/backend/common/defines.hpp      |  2 -
 src/backend/cpu/binary.hpp          |  8 ++--
 src/backend/cpu/math.hpp            | 20 ++++-----
 src/backend/cuda/complex.hpp        |  8 ++--
 src/backend/cuda/kernel/regions.hpp |  6 +--
 src/backend/cuda/math.hpp           | 70 ++++++++++++++---------------
 src/backend/cuda/unary.hpp          |  8 ++--
 src/backend/opencl/complex.hpp      |  8 ++--
 src/backend/opencl/kernel/names.hpp | 14 +++---
 src/backend/opencl/math.hpp         | 28 ++++++------
 src/backend/opencl/traits.hpp       | 10 ++---
 src/backend/opencl/unary.hpp        |  8 ++--
 12 files changed, 92 insertions(+), 98 deletions(-)

diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index 79f39c5061..c72c7b1b32 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -33,10 +33,8 @@ inline std::string clipFilePath(std::string path, std::string str) {
 #if _MSC_VER < 1900
 #define snprintf sprintf_s
 #endif
-#define STATIC_ static
 #define __AF_FILENAME__ (clipFilePath(__FILE__, "src\\").c_str())
 #else
-#define STATIC_ inline
 #define __AF_FILENAME__ (clipFilePath(__FILE__, "src/").c_str())
 #endif
 
diff --git a/src/backend/cpu/binary.hpp b/src/backend/cpu/binary.hpp
index 635b082d99..1af89bd3a6 100644
--- a/src/backend/cpu/binary.hpp
+++ b/src/backend/cpu/binary.hpp
@@ -98,19 +98,19 @@ static T __rem(T lhs, T rhs) {
 }
 
 template<>
-STATIC_ float __mod<float>(float lhs, float rhs) {
+inline float __mod<float>(float lhs, float rhs) {
     return fmod(lhs, rhs);
 }
 template<>
-STATIC_ double __mod<double>(double lhs, double rhs) {
+inline double __mod<double>(double lhs, double rhs) {
     return fmod(lhs, rhs);
 }
 template<>
-STATIC_ float __rem<float>(float lhs, float rhs) {
+inline float __rem<float>(float lhs, float rhs) {
     return remainder(lhs, rhs);
 }
 template<>
-STATIC_ double __rem<double>(double lhs, double rhs) {
+inline double __rem<double>(double lhs, double rhs) {
     return remainder(lhs, rhs);
 }
 
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index b01a11bb04..f55530f531 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -47,48 +47,48 @@ static inline T division(T lhs, double rhs) {
 }
 
 template<>
-STATIC_ cfloat division<cfloat>(cfloat lhs, double rhs) {
+inline cfloat division<cfloat>(cfloat lhs, double rhs) {
     cfloat retVal(real(lhs) / static_cast<float>(rhs),
                   imag(lhs) / static_cast<float>(rhs));
     return retVal;
 }
 
 template<>
-STATIC_ cdouble division<cdouble>(cdouble lhs, double rhs) {
+inline cdouble division<cdouble>(cdouble lhs, double rhs) {
     cdouble retVal(real(lhs) / rhs, imag(lhs) / rhs);
     return retVal;
 }
 
 template<typename T>
-STATIC_ T maxval() {
+inline T maxval() {
     return std::numeric_limits<T>::max();
 }
 template<typename T>
-STATIC_ T minval() {
+inline T minval() {
     return std::numeric_limits<T>::lowest();
 }
 template<>
-STATIC_ float maxval() {
+inline float maxval() {
     return std::numeric_limits<float>::infinity();
 }
 template<>
-STATIC_ double maxval() {
+inline double maxval() {
     return std::numeric_limits<double>::infinity();
 }
 template<>
-STATIC_ common::half maxval() {
+inline common::half maxval() {
     return std::numeric_limits<common::half>::infinity();
 }
 template<>
-STATIC_ float minval() {
+inline float minval() {
     return -std::numeric_limits<float>::infinity();
 }
 template<>
-STATIC_ double minval() {
+inline double minval() {
     return -std::numeric_limits<double>::infinity();
 }
 template<>
-STATIC_ common::half minval() {
+inline common::half minval() {
     return -std::numeric_limits<common::half>::infinity();
 }
 
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 605ac51ccd..68b5313150 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -46,11 +46,11 @@ static const char *abs_name() {
     return "fabs";
 }
 template<>
-STATIC_ const char *abs_name<cfloat>() {
+inline const char *abs_name<cfloat>() {
     return "__cabsf";
 }
 template<>
-STATIC_ const char *abs_name<cdouble>() {
+inline const char *abs_name<cdouble>() {
     return "__cabs";
 }
 
@@ -69,11 +69,11 @@ static const char *conj_name() {
     return "__noop";
 }
 template<>
-STATIC_ const char *conj_name<cfloat>() {
+inline const char *conj_name<cfloat>() {
     return "__cconjf";
 }
 template<>
-STATIC_ const char *conj_name<cdouble>() {
+inline const char *conj_name<cdouble>() {
     return "__cconj";
 }
 
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 4a9547ef35..7a459a6fb9 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -40,9 +40,9 @@ static inline __device__ T fetch(const int n, cuda::Param<T> equiv_map,
 }
 
 template<>
-__device__ STATIC_ double fetch<double>(const int n,
-                                        cuda::Param<double> equiv_map,
-                                        cudaTextureObject_t tex) {
+__device__ inline double fetch<double>(const int n,
+                                       cuda::Param<double> equiv_map,
+                                       cudaTextureObject_t tex) {
     return equiv_map.ptr[n];
 }
 
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index a0b77265f4..5987017fa7 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -9,11 +9,7 @@
 
 #pragma once
 
-#ifdef __CUDACC_RTC__
-
-#define STATIC_ inline
-
-#else  //__CUDACC_RTC__
+#ifndef __CUDACC_RTC__
 
 #include <common/defines.hpp>
 
@@ -99,22 +95,22 @@ static inline __DH__ T max(T lhs, T rhs) {
 #endif
 
 template<>
-__DH__ STATIC_ cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
+__DH__ inline cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
     return abs(lhs) > abs(rhs) ? lhs : rhs;
 }
 
 template<>
-__DH__ STATIC_ cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
+__DH__ inline cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
     return abs(lhs) > abs(rhs) ? lhs : rhs;
 }
 
 template<>
-__DH__ STATIC_ cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
+__DH__ inline cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
     return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
 template<>
-__DH__ STATIC_ cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
+__DH__ inline cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
     return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
@@ -124,13 +120,13 @@ __DH__ static T scalar(double val) {
 }
 
 template<>
-__DH__ STATIC_ cfloat scalar<cfloat>(double val) {
+__DH__ inline cfloat scalar<cfloat>(double val) {
     cfloat cval = {(float)val, 0};
     return cval;
 }
 
 template<>
-__DH__ STATIC_ cdouble scalar<cdouble>(double val) {
+__DH__ inline cdouble scalar<cdouble>(double val) {
     cdouble cval = {val, 0};
     return cval;
 }
@@ -143,109 +139,109 @@ __DH__ static To scalar(Ti real, Ti imag) {
 
 #ifndef __CUDA_ARCH__
 template<typename T>
-STATIC_ T maxval() {
+inline T maxval() {
     return std::numeric_limits<T>::max();
 }
 template<typename T>
-STATIC_ T minval() {
+inline T minval() {
     return std::numeric_limits<T>::min();
 }
 template<>
-STATIC_ float maxval() {
+inline float maxval() {
     return std::numeric_limits<float>::infinity();
 }
 template<>
-STATIC_ double maxval() {
+inline double maxval() {
     return std::numeric_limits<double>::infinity();
 }
 template<>
-STATIC_ float minval() {
+inline float minval() {
     return -std::numeric_limits<float>::infinity();
 }
 template<>
-STATIC_ double minval() {
+inline double minval() {
     return -std::numeric_limits<double>::infinity();
 }
 #else
 template<typename T>
-STATIC_ __device__ T maxval() {
+inline __device__ T maxval() {
     return 1u << (8 * sizeof(T) - 1);
 }
 template<typename T>
-STATIC_ __device__ T minval() {
+inline __device__ T minval() {
     return scalar<T>(0);
 }
 
 template<>
-STATIC_ __device__ int maxval<int>() {
+inline __device__ int maxval<int>() {
     return 0x7fffffff;
 }
 template<>
-STATIC_ __device__ int minval<int>() {
+inline __device__ int minval<int>() {
     return 0x80000000;
 }
 template<>
-STATIC_ __device__ intl maxval<intl>() {
+inline __device__ intl maxval<intl>() {
     return 0x7fffffffffffffff;
 }
 template<>
-STATIC_ __device__ intl minval<intl>() {
+inline __device__ intl minval<intl>() {
     return 0x8000000000000000;
 }
 template<>
-STATIC_ __device__ uintl maxval<uintl>() {
+inline __device__ uintl maxval<uintl>() {
     return 1ULL << (8 * sizeof(uintl) - 1);
 }
 template<>
-STATIC_ __device__ char maxval<char>() {
+inline __device__ char maxval<char>() {
     return 0x7f;
 }
 template<>
-STATIC_ __device__ char minval<char>() {
+inline __device__ char minval<char>() {
     return 0x80;
 }
 template<>
-STATIC_ __device__ float maxval<float>() {
+inline __device__ float maxval<float>() {
     return CUDART_INF_F;
 }
 template<>
-STATIC_ __device__ float minval<float>() {
+inline __device__ float minval<float>() {
     return -CUDART_INF_F;
 }
 template<>
-STATIC_ __device__ double maxval<double>() {
+inline __device__ double maxval<double>() {
     return CUDART_INF;
 }
 template<>
-STATIC_ __device__ double minval<double>() {
+inline __device__ double minval<double>() {
     return -CUDART_INF;
 }
 template<>
-STATIC_ __device__ short maxval<short>() {
+inline __device__ short maxval<short>() {
     return 0x7fff;
 }
 template<>
-STATIC_ __device__ short minval<short>() {
+inline __device__ short minval<short>() {
     return 0x8000;
 }
 template<>
-STATIC_ __device__ ushort maxval<ushort>() {
+inline __device__ ushort maxval<ushort>() {
     return ((ushort)1) << (8 * sizeof(ushort) - 1);
 }
 template<>
-STATIC_ __device__ common::half maxval<common::half>() {
+inline __device__ common::half maxval<common::half>() {
     return common::half(65537.f);
 }
 template<>
-STATIC_ __device__ common::half minval<common::half>() {
+inline __device__ common::half minval<common::half>() {
     return common::half(-65537.f);
 }
 template<>
-STATIC_ __device__ __half maxval<__half>() {
+inline __device__ __half maxval<__half>() {
     return __float2half(CUDART_INF);
 }
 template<>
-STATIC_ __device__ __half minval<__half>() {
+inline __device__ __half minval<__half>() {
     return __float2half(-CUDART_INF);
 }
 #endif
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index f060fd8190..a94c84dfa2 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -19,10 +19,10 @@ namespace cuda {
 template<af_op_t op>
 static const char *unaryName();
 
-#define UNARY_DECL(OP, FNAME)                      \
-    template<>                                     \
-    STATIC_ const char *unaryName<af_##OP##_t>() { \
-        return FNAME;                              \
+#define UNARY_DECL(OP, FNAME)                     \
+    template<>                                    \
+    inline const char *unaryName<af_##OP##_t>() { \
+        return FNAME;                             \
     }
 
 #define UNARY_FN(OP) UNARY_DECL(OP, #OP)
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index 3facc57090..124d3b49ca 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -47,11 +47,11 @@ static const char *abs_name() {
     return "fabs";
 }
 template<>
-STATIC_ const char *abs_name<cfloat>() {
+inline const char *abs_name<cfloat>() {
     return "__cabsf";
 }
 template<>
-STATIC_ const char *abs_name<cdouble>() {
+inline const char *abs_name<cdouble>() {
     return "__cabs";
 }
 
@@ -70,11 +70,11 @@ static const char *conj_name() {
     return "__noop";
 }
 template<>
-STATIC_ const char *conj_name<cfloat>() {
+inline const char *conj_name<cfloat>() {
     return "__cconjf";
 }
 template<>
-STATIC_ const char *conj_name<cdouble>() {
+inline const char *conj_name<cdouble>() {
     return "__cconj";
 }
 
diff --git a/src/backend/opencl/kernel/names.hpp b/src/backend/opencl/kernel/names.hpp
index 73489b1e10..2dc4e63254 100644
--- a/src/backend/opencl/kernel/names.hpp
+++ b/src/backend/opencl/kernel/names.hpp
@@ -17,30 +17,30 @@ static const char *binOpName() {
 }
 
 template<>
-STATIC_ const char *binOpName<af_add_t>() {
+inline const char *binOpName<af_add_t>() {
     return "ADD_OP";
 }
 template<>
-STATIC_ const char *binOpName<af_mul_t>() {
+inline const char *binOpName<af_mul_t>() {
     return "MUL_OP";
 }
 template<>
-STATIC_ const char *binOpName<af_and_t>() {
+inline const char *binOpName<af_and_t>() {
     return "AND_OP";
 }
 template<>
-STATIC_ const char *binOpName<af_or_t>() {
+inline const char *binOpName<af_or_t>() {
     return "OR_OP";
 }
 template<>
-STATIC_ const char *binOpName<af_min_t>() {
+inline const char *binOpName<af_min_t>() {
     return "MIN_OP";
 }
 template<>
-STATIC_ const char *binOpName<af_max_t>() {
+inline const char *binOpName<af_max_t>() {
     return "MAX_OP";
 }
 template<>
-STATIC_ const char *binOpName<af_notzero_t>() {
+inline const char *binOpName<af_notzero_t>() {
     return "NOTZERO_OP";
 }
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index 86ee50556d..e1e9c28f12 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -58,22 +58,22 @@ cfloat division(cfloat lhs, double rhs);
 cdouble division(cdouble lhs, double rhs);
 
 template<>
-STATIC_ cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
+inline cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
     return abs(lhs) > abs(rhs) ? lhs : rhs;
 }
 
 template<>
-STATIC_ cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
+inline cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
     return abs(lhs) > abs(rhs) ? lhs : rhs;
 }
 
 template<>
-STATIC_ cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
+inline cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
     return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
 template<>
-STATIC_ cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
+inline cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
     return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
@@ -83,7 +83,7 @@ static T scalar(double val) {
 }
 
 template<>
-STATIC_ cfloat scalar<cfloat>(double val) {
+inline cfloat scalar<cfloat>(double val) {
     cfloat cval;
     cval.s[0] = (float)val;
     cval.s[1] = 0;
@@ -91,7 +91,7 @@ STATIC_ cfloat scalar<cfloat>(double val) {
 }
 
 template<>
-STATIC_ cdouble scalar<cdouble>(double val) {
+inline cdouble scalar<cdouble>(double val) {
     cdouble cval;
     cval.s[0] = val;
     cval.s[1] = 0;
@@ -107,38 +107,38 @@ static To scalar(Ti real, Ti imag) {
 }
 
 template<typename T>
-STATIC_ T maxval() {
+inline T maxval() {
     return std::numeric_limits<T>::max();
 }
 template<typename T>
-STATIC_ T minval() {
+inline T minval() {
     return std::numeric_limits<T>::min();
 }
 template<>
-STATIC_ float maxval() {
+inline float maxval() {
     return std::numeric_limits<float>::infinity();
 }
 template<>
-STATIC_ double maxval() {
+inline double maxval() {
     return std::numeric_limits<double>::infinity();
 }
 
 template<>
-STATIC_ common::half maxval() {
+inline common::half maxval() {
     return std::numeric_limits<common::half>::infinity();
 }
 
 template<>
-STATIC_ float minval() {
+inline float minval() {
     return -std::numeric_limits<float>::infinity();
 }
 
 template<>
-STATIC_ double minval() {
+inline double minval() {
     return -std::numeric_limits<double>::infinity();
 }
 template<>
-STATIC_ common::half minval() {
+inline common::half minval() {
     return -std::numeric_limits<common::half>::infinity();
 }
 
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 60a08831e7..6610c7aee1 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -37,30 +37,30 @@ static bool iscplx() {
     return false;
 }
 template<>
-STATIC_ bool iscplx<opencl::cfloat>() {
+inline bool iscplx<opencl::cfloat>() {
     return true;
 }
 template<>
-STATIC_ bool iscplx<opencl::cdouble>() {
+inline bool iscplx<opencl::cdouble>() {
     return true;
 }
 
 template<typename T>
-STATIC_ std::string scalar_to_option(const T &val) {
+inline std::string scalar_to_option(const T &val) {
     using namespace common;
     using namespace std;
     return to_string(+val);
 }
 
 template<>
-STATIC_ std::string scalar_to_option<cl_float2>(const cl_float2 &val) {
+inline std::string scalar_to_option<cl_float2>(const cl_float2 &val) {
     std::ostringstream ss;
     ss << val.s[0] << "," << val.s[1];
     return ss.str();
 }
 
 template<>
-STATIC_ std::string scalar_to_option<cl_double2>(const cl_double2 &val) {
+inline std::string scalar_to_option<cl_double2>(const cl_double2 &val) {
     std::ostringstream ss;
     ss << val.s[0] << "," << val.s[1];
     return ss.str();
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index f4a81ab29f..65da1b690b 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -18,10 +18,10 @@ namespace opencl {
 template<af_op_t op>
 static const char *unaryName();
 
-#define UNARY_DECL(OP, FNAME)                      \
-    template<>                                     \
-    STATIC_ const char *unaryName<af_##OP##_t>() { \
-        return FNAME;                              \
+#define UNARY_DECL(OP, FNAME)                     \
+    template<>                                    \
+    inline const char *unaryName<af_##OP##_t>() { \
+        return FNAME;                             \
     }
 
 #define UNARY_FN(OP) UNARY_DECL(OP, #OP)

From cf314568c6b18a489a93cf6f48a345dfdd7c3930 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 3 Oct 2022 12:35:32 -0400
Subject: [PATCH 2294/2677] Update vcpkg baseline hash and update vcpkg caching
 in GitHub actions

---
 .github/workflows/win_cpu_build.yml | 27 ++++++++++++++++++---------
 CMakeModules/AF_vcpkg_options.cmake |  2 ++
 vcpkg.json                          | 10 +++++++++-
 3 files changed, 29 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index 9d5419f7dd..dc73cf7c28 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -13,16 +13,14 @@ jobs:
         name: CPU (fftw, OpenBLAS, windows-latest)
         runs-on: windows-latest
         env:
-
-          VCPKG_HASH: 14e7bb4ae24616ec54ff6b2f6ef4e8659434ea44
-
+          VCPKG_HASH: 6ca56aeb457f033d344a7106cb3f9f1abf8f4e98
           VCPKG_DEFAULT_TRIPLET: x64-windows
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master
 
             - name: VCPKG Cache
-              uses: actions/cache@v2
+              uses: actions/cache@v3
               id: vcpkg-cache
               with:
                 path: ~/vcpkg
@@ -31,12 +29,20 @@ jobs:
             - name: Install VCPKG Dependencies
               if: steps.vcpkg-cache.outputs.cache-hit != 'true'
               run: |
+                pushd .
                 cd ~
                 git clone --quiet --recursive https://github.com/microsoft/vcpkg.git
                 cd vcpkg
                 git checkout $env:VCPKG_HASH
                 .\bootstrap-vcpkg.bat
-                .\vcpkg.exe install --clean-after-build boost-compute boost-math boost-stacktrace fftw3 freeimage freetype[core] forge glfw3 openblas
+                popd
+                mkdir build && cd build && set VCPKG_ROOT=
+                cmake .. -G "Visual Studio 17 2022" -A x64 `
+                      -DVCPKG_ROOT:PATH=~/vcpkg `
+                      -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
+                      -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
+                      -DBUILDNAME:STRING="$buildname" `
+                      -DAF_COMPUTE_LIBRARY:STRING="FFTW/LAPACK/BLAS"
 
             - name: CMake Configure
               run: |
@@ -46,9 +52,12 @@ jobs:
                   $buildname = if($prnum -eq $null) { $branch } else { "PR-$prnum" }
                   $dashboard = if($prnum -eq $null) { "Continuous" } else { "Experimental" }
                   $buildname = "$buildname-cpu-openblas"
-                  mkdir build && cd build
+                  if((Test-Path build) -eq 0) {
+                      mkdir build
+                  }
+                  cd build && set VCPKG_ROOT=
                   cmake .. -G "Visual Studio 17 2022" -A x64 `
-                      -DVCPKG_MANIFEST_MODE:BOOL=OFF `
+                      -DVCPKG_ROOT:PATH=~/vcpkg `
                       -DAF_BUILD_CUDA:BOOL=OFF -DAF_BUILD_OPENCL:BOOL=OFF `
                       -DAF_BUILD_UNIFIED:BOOL=OFF -DAF_BUILD_FORGE:BOOL=ON `
                       -DBUILDNAME:STRING="$buildname" `
@@ -58,6 +67,6 @@ jobs:
             - name: Build and Test
               run: |
                   cd build
-                  $vcpkg_path = (Resolve-Path ~).Path
-                  $Env:PATH += ";${vcpkg_path}/vcpkg/installed/x64-windows/bin"
+                  $build_path = (pwd).Path
+                  $Env:PATH += ";$build_path/vcpkg_installed/x64-windows/bin"
                   ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -C RelWithDebInfo -R cpu -E pinverse -j2
diff --git a/CMakeModules/AF_vcpkg_options.cmake b/CMakeModules/AF_vcpkg_options.cmake
index 00745f846c..59cdeb8fbf 100644
--- a/CMakeModules/AF_vcpkg_options.cmake
+++ b/CMakeModules/AF_vcpkg_options.cmake
@@ -29,6 +29,8 @@ endif()
 
 if(AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
   list(APPEND VCPKG_MANIFEST_FEATURES "mkl")
+else()
+  list(APPEND VCPKG_MANIFEST_FEATURES "openblasfftw")
 endif()
 
 if(DEFINED VCPKG_ROOT AND NOT DEFINED CMAKE_TOOLCHAIN_FILE)
diff --git a/vcpkg.json b/vcpkg.json
index 70aab906ed..4562e14f80 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -43,6 +43,14 @@
                 "glad"
             ]
         },
+        "openblasfftw": {
+            "description": "Build with OpenBLAS/FFTW",
+            "dependencies": [
+                "fftw3",
+                "openblas",
+                "lapack"
+            ]
+        },
         "cuda": {
             "description": "Build CUDA backend",
             "dependencies": [
@@ -69,5 +77,5 @@
             ]
         }
     },
-    "builtin-baseline": "14e7bb4ae24616ec54ff6b2f6ef4e8659434ea44"
+    "builtin-baseline": "6ca56aeb457f033d344a7106cb3f9f1abf8f4e98"
 }

From 7e02b8cebd82ed9afecc1d5a1bea03a3b5390b6c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 4 Oct 2022 03:17:12 -0400
Subject: [PATCH 2295/2677] Make a lapack overlay to build lapacke library and
 headers

---
 CMakeModules/AF_vcpkg_options.cmake           |   3 +-
 .../ports/lapack-reference/FindLAPACK.cmake   | 559 ++++++++++++++++++
 .../ports/lapack-reference/lapacke.patch      |  16 +
 .../ports/lapack-reference/portfile.cmake     | 164 +++++
 .../vcpkg-cmake-wrapper.cmake                 |  11 +
 .../vcpkg/ports/lapack-reference/vcpkg.json   |  48 ++
 .../vcpkg-triplets/x64-windows.cmake          |   0
 7 files changed, 800 insertions(+), 1 deletion(-)
 create mode 100644 CMakeModules/vcpkg/ports/lapack-reference/FindLAPACK.cmake
 create mode 100644 CMakeModules/vcpkg/ports/lapack-reference/lapacke.patch
 create mode 100644 CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake
 create mode 100644 CMakeModules/vcpkg/ports/lapack-reference/vcpkg-cmake-wrapper.cmake
 create mode 100644 CMakeModules/vcpkg/ports/lapack-reference/vcpkg.json
 rename CMakeModules/{ => vcpkg}/vcpkg-triplets/x64-windows.cmake (100%)

diff --git a/CMakeModules/AF_vcpkg_options.cmake b/CMakeModules/AF_vcpkg_options.cmake
index 59cdeb8fbf..09701af274 100644
--- a/CMakeModules/AF_vcpkg_options.cmake
+++ b/CMakeModules/AF_vcpkg_options.cmake
@@ -9,7 +9,8 @@ set(ENV{VCPKG_FEATURE_FLAGS} "versions")
 set(ENV{VCPKG_KEEP_ENV_VARS} "MKLROOT")
 set(VCPKG_MANIFEST_NO_DEFAULT_FEATURES ON)
 
-set(VCPKG_OVERLAY_TRIPLETS ${ArrayFire_SOURCE_DIR}/CMakeModules/vcpkg-triplets)
+set(VCPKG_OVERLAY_TRIPLETS ${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules/vcpkg/vcpkg-triplets)
+set(VCPKG_OVERLAY_PORTS ${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules/vcpkg/ports)
 
 if(AF_BUILD_CUDA)
   list(APPEND VCPKG_MANIFEST_FEATURES "cuda")
diff --git a/CMakeModules/vcpkg/ports/lapack-reference/FindLAPACK.cmake b/CMakeModules/vcpkg/ports/lapack-reference/FindLAPACK.cmake
new file mode 100644
index 0000000000..f4d25477d8
--- /dev/null
+++ b/CMakeModules/vcpkg/ports/lapack-reference/FindLAPACK.cmake
@@ -0,0 +1,559 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#[=======================================================================[.rst:
+FindLAPACK
+----------
+
+Find Linear Algebra PACKage (LAPACK) library
+
+This module finds an installed Fortran library that implements the
+LAPACK linear-algebra interface (see http://www.netlib.org/lapack/).
+
+The approach follows that taken for the ``autoconf`` macro file,
+``acx_lapack.m4`` (distributed at
+http://ac-archive.sourceforge.net/ac-archive/acx_lapack.html).
+
+Input Variables
+^^^^^^^^^^^^^^^
+
+The following variables may be set to influence this module's behavior:
+
+``BLA_STATIC``
+  if ``ON`` use static linkage
+
+``BLA_VENDOR``
+  If set, checks only the specified vendor, if not set checks all the
+  possibilities.  List of vendors valid in this module:
+
+  * ``OpenBLAS``
+  * ``FLAME``
+  * ``Intel10_32`` (intel mkl v10 32 bit)
+  * ``Intel10_64lp`` (intel mkl v10+ 64 bit, threaded code, lp64 model)
+  * ``Intel10_64lp_seq`` (intel mkl v10+ 64 bit, sequential code, lp64 model)
+  * ``Intel10_64ilp`` (intel mkl v10+ 64 bit, threaded code, ilp64 model)
+  * ``Intel10_64ilp_seq`` (intel mkl v10+ 64 bit, sequential code, ilp64 model)
+  * ``Intel10_64_dyn`` (intel mkl v10+ 64 bit, single dynamic library)
+  * ``Intel`` (obsolete versions of mkl 32 and 64 bit)
+  * ``ACML``
+  * ``Apple``
+  * ``NAS``
+  * ``Arm``
+  * ``Arm_mp``
+  * ``Arm_ilp64``
+  * ``Arm_ilp64_mp``
+  * ``Generic``
+
+``BLA_F95``
+  if ``ON`` tries to find the BLAS95/LAPACK95 interfaces
+
+Imported targets
+^^^^^^^^^^^^^^^^
+
+This module defines the following :prop_tgt:`IMPORTED` target:
+
+``LAPACK::LAPACK``
+  The libraries to use for LAPACK, if found.
+
+Result Variables
+^^^^^^^^^^^^^^^^
+
+This module defines the following variables:
+
+``LAPACK_FOUND``
+  library implementing the LAPACK interface is found
+``LAPACK_LINKER_FLAGS``
+  uncached list of required linker flags (excluding ``-l`` and ``-L``).
+``LAPACK_LIBRARIES``
+  uncached list of libraries (using full path name) to link against
+  to use LAPACK
+``LAPACK95_LIBRARIES``
+  uncached list of libraries (using full path name) to link against
+  to use LAPACK95
+``LAPACK95_FOUND``
+  library implementing the LAPACK95 interface is found
+
+.. note::
+
+  C, CXX or Fortran must be enabled to detect a BLAS/LAPACK library.
+  C or CXX must be enabled to use Intel Math Kernel Library (MKL).
+
+  For example, to use Intel MKL libraries and/or Intel compiler:
+
+  .. code-block:: cmake
+
+    set(BLA_VENDOR Intel10_64lp)
+    find_package(LAPACK)
+#]=======================================================================]
+
+enable_language(C)
+# Check the language being used
+if(NOT (CMAKE_C_COMPILER_LOADED OR CMAKE_CXX_COMPILER_LOADED OR CMAKE_Fortran_COMPILER_LOADED))
+  if(LAPACK_FIND_REQUIRED)
+    message(FATAL_ERROR "FindLAPACK requires Fortran, C, or C++ to be enabled.")
+  else()
+    message(STATUS "Looking for LAPACK... - NOT found (Unsupported languages)")
+    return()
+  endif()
+endif()
+
+if(CMAKE_Fortran_COMPILER_LOADED)
+  include(${CMAKE_ROOT}/Modules/CheckFortranFunctionExists.cmake)
+else()
+  include(${CMAKE_ROOT}/Modules/CheckFunctionExists.cmake)
+endif()
+include(${CMAKE_ROOT}/Modules/CMakePushCheckState.cmake)
+
+cmake_push_check_state()
+set(CMAKE_REQUIRED_QUIET ${LAPACK_FIND_QUIETLY})
+
+set(LAPACK_FOUND FALSE)
+set(LAPACK95_FOUND FALSE)
+
+# store original values for CMAKE_FIND_LIBRARY_SUFFIXES
+set(_lapack_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
+if (CMAKE_SYSTEM_NAME STREQUAL "Linux")
+    list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.3gfs .so.3 .so.4 .so.5)
+endif()
+
+# TODO: move this stuff to a separate module
+
+macro(CHECK_LAPACK_LIBRARIES LIBRARIES _prefix _name _flags _list _threadlibs _addlibdir _subdirs _blas)
+  # This macro checks for the existence of the combination of fortran libraries
+  # given by _list.  If the combination is found, this macro checks (using the
+  # Check_Fortran_Function_Exists macro) whether can link against that library
+  # combination using the name of a routine given by _name using the linker
+  # flags given by _flags.  If the combination of libraries is found and passes
+  # the link test, LIBRARIES is set to the list of complete library paths that
+  # have been found.  Otherwise, LIBRARIES is set to FALSE.
+
+  # N.B. _prefix is the prefix applied to the names of all cached variables that
+  # are generated internally and marked advanced by this macro.
+  # _addlibdir is a list of additional search paths. _subdirs is a list of path
+  # suffixes to be used by find_library().
+
+  set(_libraries_work TRUE)
+  set(${LIBRARIES})
+  set(_combined_name)
+
+  set(_extaddlibdir "${_addlibdir}")
+  if(WIN32)
+    list(APPEND _extaddlibdir ENV LIB)
+  elseif(APPLE)
+    list(APPEND _extaddlibdir ENV DYLD_LIBRARY_PATH)
+  else()
+    list(APPEND _extaddlibdir ENV LD_LIBRARY_PATH)
+  endif()
+  list(APPEND _extaddlibdir "${CMAKE_C_IMPLICIT_LINK_DIRECTORIES}")
+
+  foreach(_library ${_list})
+    if(_library MATCHES "^-Wl,--(start|end)-group$")
+      # Respect linker flags like --start/end-group (required by MKL)
+      set(${LIBRARIES} ${${LIBRARIES}} "${_library}")
+    else()
+      set(_combined_name ${_combined_name}_${_library})
+      if(_libraries_work)
+        find_library(${_prefix}_${_library}_LIBRARY
+          NAMES ${_library}
+          PATHS ${_extaddlibdir}
+          PATH_SUFFIXES ${_subdirs}
+        )
+        #message("DEBUG: find_library(${_library}) got ${${_prefix}_${_library}_LIBRARY}")
+        mark_as_advanced(${_prefix}_${_library}_LIBRARY)
+        set(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
+        set(_libraries_work ${${_prefix}_${_library}_LIBRARY})
+      endif()
+    endif()
+  endforeach()
+
+  if(_libraries_work)
+    # Test this combination of libraries.
+    set(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}} ${_blas} ${_threadlibs})
+    #message("DEBUG: CMAKE_REQUIRED_LIBRARIES = ${CMAKE_REQUIRED_LIBRARIES}")
+    if(CMAKE_Fortran_COMPILER_LOADED)
+      check_fortran_function_exists("${_name}" ${_prefix}${_combined_name}_WORKS)
+    else()
+      check_function_exists("${_name}_" ${_prefix}${_combined_name}_WORKS)
+    endif()
+    set(CMAKE_REQUIRED_LIBRARIES)
+    set(_libraries_work ${${_prefix}${_combined_name}_WORKS})
+  endif()
+
+  if(_libraries_work)
+    if("${_list}${_blas}" STREQUAL "")
+      set(${LIBRARIES} "${LIBRARIES}-PLACEHOLDER-FOR-EMPTY-LIBRARIES")
+    else()
+      set(${LIBRARIES} ${${LIBRARIES}} ${_blas} ${_threadlibs})
+    endif()
+  else()
+    set(${LIBRARIES} FALSE)
+  endif()
+  #message("DEBUG: ${LIBRARIES} = ${${LIBRARIES}}")
+endmacro()
+
+set(LAPACK_LINKER_FLAGS)
+set(LAPACK_LIBRARIES)
+set(LAPACK95_LIBRARIES)
+
+include(CMakeFindDependencyMacro)
+find_dependency(BLAS)
+
+if(BLAS_FOUND)
+  set(LAPACK_LINKER_FLAGS ${BLAS_LINKER_FLAGS})
+  if(NOT $ENV{BLA_VENDOR} STREQUAL "")
+    set(BLA_VENDOR $ENV{BLA_VENDOR})
+  else()
+    if(NOT BLA_VENDOR)
+      set(BLA_VENDOR "All")
+    endif()
+  endif()
+
+  # LAPACK in the Intel MKL 10+ library?
+  if(BLA_VENDOR MATCHES "Intel" OR BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      if(CMAKE_C_COMPILER_LOADED OR CMAKE_CXX_COMPILER_LOADED)
+        # System-specific settings
+        if(NOT WIN32)
+          set(LAPACK_mkl_LM "-lm")
+          set(LAPACK_mkl_LDL "-ldl")
+        endif()
+
+        if(LAPACK_FIND_QUIETLY OR NOT LAPACK_FIND_REQUIRED)
+          find_package(Threads)
+        else()
+          find_package(Threads REQUIRED)
+        endif()
+
+        if(BLA_VENDOR MATCHES "_64ilp")
+          set(LAPACK_mkl_ILP_MODE "ilp64")
+        else()
+          set(LAPACK_mkl_ILP_MODE "lp64")
+        endif()
+
+        set(LAPACK_SEARCH_LIBS "")
+
+        if(BLA_F95)
+          set(LAPACK_mkl_SEARCH_SYMBOL "cheev_f95")
+          set(_LIBRARIES LAPACK95_LIBRARIES)
+          set(_BLAS_LIBRARIES ${BLAS95_LIBRARIES})
+
+          # old
+          list(APPEND LAPACK_SEARCH_LIBS
+            "mkl_lapack95")
+          # new >= 10.3
+          list(APPEND LAPACK_SEARCH_LIBS
+            "mkl_intel_c")
+          list(APPEND LAPACK_SEARCH_LIBS
+            "mkl_lapack95_${LAPACK_mkl_ILP_MODE}")
+        else()
+          set(LAPACK_mkl_SEARCH_SYMBOL "cheev")
+          set(_LIBRARIES LAPACK_LIBRARIES)
+          set(_BLAS_LIBRARIES ${BLAS_LIBRARIES})
+
+          # old and new >= 10.3
+          list(APPEND LAPACK_SEARCH_LIBS
+            "mkl_lapack")
+        endif()
+
+        # MKL uses a multitude of partially platform-specific subdirectories:
+        if(BLA_VENDOR STREQUAL "Intel10_32")
+          set(LAPACK_mkl_ARCH_NAME "ia32")
+        else()
+          set(LAPACK_mkl_ARCH_NAME "intel64")
+        endif()
+        if(WIN32)
+          set(LAPACK_mkl_OS_NAME "win")
+        elseif(APPLE)
+          set(LAPACK_mkl_OS_NAME "mac")
+        else()
+          set(LAPACK_mkl_OS_NAME "lin")
+        endif()
+        if(DEFINED ENV{MKLROOT})
+          file(TO_CMAKE_PATH "$ENV{MKLROOT}" LAPACK_mkl_MKLROOT)
+          # If MKLROOT points to the subdirectory 'mkl', use the parent directory instead
+          # so we can better detect other relevant libraries in 'compiler' or 'tbb':
+          get_filename_component(LAPACK_mkl_MKLROOT_LAST_DIR "${LAPACK_mkl_MKLROOT}" NAME)
+          if(LAPACK_mkl_MKLROOT_LAST_DIR STREQUAL "mkl")
+              get_filename_component(LAPACK_mkl_MKLROOT "${LAPACK_mkl_MKLROOT}" DIRECTORY)
+          endif()
+        endif()
+        set(LAPACK_mkl_LIB_PATH_SUFFIXES
+            "compiler/lib" "compiler/lib/${LAPACK_mkl_ARCH_NAME}_${LAPACK_mkl_OS_NAME}"
+            "mkl/lib" "mkl/lib/${LAPACK_mkl_ARCH_NAME}_${LAPACK_mkl_OS_NAME}"
+            "lib/${LAPACK_mkl_ARCH_NAME}_${LAPACK_mkl_OS_NAME}")
+
+        # First try empty lapack libs
+        if(NOT ${_LIBRARIES})
+          check_lapack_libraries(
+            ${_LIBRARIES}
+            LAPACK
+            ${LAPACK_mkl_SEARCH_SYMBOL}
+            ""
+            ""
+            "${CMAKE_THREAD_LIBS_INIT};${LAPACK_mkl_LM};${LAPACK_mkl_LDL}"
+            "${LAPACK_mkl_MKLROOT}"
+            "${LAPACK_mkl_LIB_PATH_SUFFIXES}"
+            "${_BLAS_LIBRARIES}"
+          )
+        endif()
+
+        # Then try the search libs
+        foreach(IT ${LAPACK_SEARCH_LIBS})
+          string(REPLACE " " ";" SEARCH_LIBS ${IT})
+          if(NOT ${_LIBRARIES})
+            check_lapack_libraries(
+              ${_LIBRARIES}
+              LAPACK
+              ${LAPACK_mkl_SEARCH_SYMBOL}
+              ""
+              "${SEARCH_LIBS}"
+              "${CMAKE_THREAD_LIBS_INIT};${LAPACK_mkl_LM};${LAPACK_mkl_LDL}"
+              "${LAPACK_mkl_MKLROOT}"
+              "${LAPACK_mkl_LIB_PATH_SUFFIXES}"
+              "${_BLAS_LIBRARIES}"
+            )
+          endif()
+        endforeach()
+
+        unset(LAPACK_mkl_ILP_MODE)
+        unset(LAPACK_mkl_SEARCH_SYMBOL)
+        unset(LAPACK_mkl_LM)
+        unset(LAPACK_mkl_LDL)
+        unset(LAPACK_mkl_MKLROOT)
+        unset(LAPACK_mkl_ARCH_NAME)
+        unset(LAPACK_mkl_OS_NAME)
+        unset(LAPACK_mkl_LIB_PATH_SUFFIXES)
+      endif()
+    endif()
+  endif()
+
+  # gotoblas? (http://www.tacc.utexas.edu/tacc-projects/gotoblas2)
+  if(BLA_VENDOR STREQUAL "Goto" OR BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "goto2"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+
+  # OpenBLAS? (http://www.openblas.net)
+  if(BLA_VENDOR STREQUAL "OpenBLAS" OR BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "openblas"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+
+  # ArmPL? (https://developer.arm.com/tools-and-software/server-and-hpc/compile/arm-compiler-for-linux/arm-performance-libraries)
+  if(BLA_VENDOR MATCHES "Arm" OR BLA_VENDOR STREQUAL "All")
+
+    # Check for 64bit Integer support
+    if(BLA_VENDOR MATCHES "_ilp64")
+      set(LAPACK_armpl_LIB "armpl_ilp64")
+    else()
+      set(LAPACK_armpl_LIB "armpl_lp64")
+    endif()
+
+    # Check for OpenMP support, VIA BLA_VENDOR of Arm_mp or Arm_ipl64_mp
+    if(BLA_VENDOR MATCHES "_mp")
+     set(LAPACK_armpl_LIB "${LAPACK_armpl_LIB}_mp")
+    endif()
+
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "${LAPACK_armpl_LIB}"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+
+  # FLAME's blis library? (https://github.com/flame/blis)
+  if(BLA_VENDOR STREQUAL "FLAME" OR BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "flame"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+
+  # BLAS in acml library?
+  if(BLA_VENDOR MATCHES "ACML" OR BLA_VENDOR STREQUAL "All")
+    if(BLAS_LIBRARIES MATCHES ".+acml.+")
+      set(LAPACK_LIBRARIES ${BLAS_LIBRARIES})
+    endif()
+  endif()
+
+  # Apple LAPACK library?
+  if(BLA_VENDOR STREQUAL "Apple" OR BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "Accelerate"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+
+  # Apple NAS (vecLib) library?
+  if(BLA_VENDOR STREQUAL "NAS" OR BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "vecLib"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+
+  # Generic LAPACK library?
+  if(BLA_VENDOR STREQUAL "Generic" OR
+      BLA_VENDOR STREQUAL "ATLAS" OR
+      BLA_VENDOR STREQUAL "All")
+    if(NOT LAPACK_LIBRARIES)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "lapack"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+    if(NOT LAPACK_LIBRARIES AND NOT WIN32)
+      check_lapack_libraries(
+        LAPACK_LIBRARIES
+        LAPACK
+        cheev
+        ""
+        "lapack;m;gfortran"
+        ""
+        ""
+        ""
+        "${BLAS_LIBRARIES}"
+      )
+    endif()
+  endif()
+else()
+  message(STATUS "LAPACK requires BLAS")
+endif()
+
+if(BLA_F95)
+  if(LAPACK95_LIBRARIES)
+    set(LAPACK95_FOUND TRUE)
+  else()
+    set(LAPACK95_FOUND FALSE)
+  endif()
+  if(NOT LAPACK_FIND_QUIETLY)
+    if(LAPACK95_FOUND)
+      message(STATUS "A library with LAPACK95 API found.")
+    else()
+      if(LAPACK_FIND_REQUIRED)
+        message(FATAL_ERROR
+          "A required library with LAPACK95 API not found. Please specify library location."
+        )
+      else()
+        message(STATUS
+          "A library with LAPACK95 API not found. Please specify library location."
+        )
+      endif()
+    endif()
+  endif()
+  set(LAPACK_FOUND "${LAPACK95_FOUND}")
+  set(LAPACK_LIBRARIES "${LAPACK95_LIBRARIES}")
+else()
+  if(LAPACK_LIBRARIES)
+    set(LAPACK_FOUND TRUE)
+  else()
+    set(LAPACK_FOUND FALSE)
+  endif()
+
+  if(NOT LAPACK_FIND_QUIETLY)
+    if(LAPACK_FOUND)
+      message(STATUS "A library with LAPACK API found.")
+    else()
+      if(LAPACK_FIND_REQUIRED)
+        message(FATAL_ERROR
+          "A required library with LAPACK API not found. Please specify library location."
+        )
+      else()
+        message(STATUS
+          "A library with LAPACK API not found. Please specify library location."
+        )
+      endif()
+    endif()
+  endif()
+endif()
+
+# On compilers that implicitly link LAPACK (such as ftn, cc, and CC on Cray HPC machines)
+# we used a placeholder for empty LAPACK_LIBRARIES to get through our logic above.
+if(LAPACK_LIBRARIES STREQUAL "LAPACK_LIBRARIES-PLACEHOLDER-FOR-EMPTY-LIBRARIES")
+  set(LAPACK_LIBRARIES "")
+endif()
+
+if(NOT TARGET LAPACK::LAPACK)
+  add_library(LAPACK::LAPACK INTERFACE IMPORTED)
+  set(_lapack_libs "${LAPACK_LIBRARIES}")
+  if(_lapack_libs AND TARGET BLAS::BLAS)
+    # remove the ${BLAS_LIBRARIES} from the interface and replace it
+    # with the BLAS::BLAS target
+    list(REMOVE_ITEM _lapack_libs "${BLAS_LIBRARIES}")
+  endif()
+
+  if(_lapack_libs)
+    set_target_properties(LAPACK::LAPACK PROPERTIES
+      INTERFACE_LINK_LIBRARIES "${_lapack_libs}"
+    )
+  endif()
+  unset(_lapack_libs)
+endif()
+
+cmake_pop_check_state()
+# restore original values for CMAKE_FIND_LIBRARY_SUFFIXES
+set(CMAKE_FIND_LIBRARY_SUFFIXES ${_lapack_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
diff --git a/CMakeModules/vcpkg/ports/lapack-reference/lapacke.patch b/CMakeModules/vcpkg/ports/lapack-reference/lapacke.patch
new file mode 100644
index 0000000000..964f0e3192
--- /dev/null
+++ b/CMakeModules/vcpkg/ports/lapack-reference/lapacke.patch
@@ -0,0 +1,16 @@
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 1ee66f1..7cec7ca 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -392,8 +392,9 @@ endif()
+ set(LAPACK_INSTALL_EXPORT_NAME ${LAPACK_INSTALL_EXPORT_NAME_CACHE})
+ unset(LAPACK_INSTALL_EXPORT_NAME_CACHE)
+ 
+-add_subdirectory(LAPACKE)
+-
++if(LAPACKE)
++    add_subdirectory(LAPACKE)
++endif()
+ 
+ #-------------------------------------
+ # BLAS++ / LAPACK++
diff --git a/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake b/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake
new file mode 100644
index 0000000000..ba8999d36e
--- /dev/null
+++ b/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake
@@ -0,0 +1,164 @@
+#TODO: Features to add:
+# USE_XBLAS??? extended precision blas. needs xblas
+# LAPACKE should be its own PORT
+# USE_OPTIMIZED_LAPACK (Probably not what we want. Does a find_package(LAPACK): probably for LAPACKE only builds _> own port?)
+# LAPACKE Builds LAPACKE
+# LAPACKE_WITH_TMG Build LAPACKE with tmglib routines
+if(EXISTS "${CURRENT_INSTALLED_DIR}/share/clapack/copyright")
+    message(FATAL_ERROR "Can't build ${PORT} if clapack is installed. Please remove clapack:${TARGET_TRIPLET}, and try to install ${PORT}:${TARGET_TRIPLET} again.")
+endif()
+
+include(vcpkg_find_fortran)
+SET(VCPKG_POLICY_EMPTY_INCLUDE_FOLDER enabled)
+
+set(lapack_ver 3.10.1)
+
+vcpkg_from_github(
+    OUT_SOURCE_PATH SOURCE_PATH
+    REPO  "Reference-LAPACK/lapack"
+    REF "v${lapack_ver}"
+    SHA512 0500bbbb48483208c0a35b74972ff0059c389da6032824a2079637266a99fa980882eedf7f1fc490219ee4ff27812ac8c6afe118e25f40a9c2387e7b997762fb
+    HEAD_REF master
+    PATCHES
+        lapacke.patch
+)
+
+if(NOT VCPKG_TARGET_IS_WINDOWS)
+    set(ENV{FFLAGS} "$ENV{FFLAGS} -fPIC")
+endif()
+
+set(CBLAS OFF)
+if("cblas" IN_LIST FEATURES)
+    set(CBLAS ON)
+    if("noblas" IN_LIST FEATURES)
+        message(FATAL_ERROR "Cannot built feature 'cblas' together with feature 'noblas'. cblas requires blas!")
+    endif()
+endif()
+
+set(USE_OPTIMIZED_BLAS OFF) 
+if("noblas" IN_LIST FEATURES)
+    set(USE_OPTIMIZED_BLAS ON)
+    set(pcfile "${CURRENT_INSTALLED_DIR}/lib/pkgconfig/openblas.pc")
+    if(EXISTS "${pcfile}")
+        file(CREATE_LINK "${pcfile}" "${CURRENT_PACKAGES_DIR}/lib/pkgconfig/blas.pc" COPY_ON_ERROR)
+    endif()
+    set(pcfile "${CURRENT_INSTALLED_DIR}/debug/lib/pkgconfig/openblas.pc")
+    if(EXISTS "${pcfile}")
+        file(CREATE_LINK "${pcfile}" "${CURRENT_PACKAGES_DIR}/debug/lib/pkgconfig/blas.pc" COPY_ON_ERROR)
+    endif()
+endif()
+
+set(VCPKG_CRT_LINKAGE_BACKUP ${VCPKG_CRT_LINKAGE})
+vcpkg_find_fortran(FORTRAN_CMAKE)
+if(VCPKG_USE_INTERNAL_Fortran)
+    if(VCPKG_CRT_LINKAGE_BACKUP STREQUAL static) 
+    # If openblas has been built with static crt linkage we cannot use it with gfortran!
+        set(USE_OPTIMIZED_BLAS OFF) 
+        #Cannot use openblas from vcpkg if we are building with gfortran here. 
+        if("noblas" IN_LIST FEATURES)
+            message(FATAL_ERROR "Feature 'noblas' cannot be used without supplying an external fortran compiler")
+        endif()
+    endif()
+else()
+    set(USE_OPTIMIZED_BLAS ON)
+endif()
+
+vcpkg_cmake_configure(
+    SOURCE_PATH "${SOURCE_PATH}"
+    OPTIONS
+        "-DUSE_OPTIMIZED_BLAS=${USE_OPTIMIZED_BLAS}"
+        "-DCBLAS=${CBLAS}"
+	"-DLAPACKE=ON"
+        ${FORTRAN_CMAKE}
+)
+
+vcpkg_cmake_install()
+
+vcpkg_cmake_config_fixup(PACKAGE_NAME lapack-${lapack_ver} CONFIG_PATH lib/cmake/lapack-${lapack_ver}) #Should the target path be lapack and not lapack-reference?
+
+message("CURRENT_PACKAGES_DIR: ${CURRENT_PACKAGES_DIR}")
+set(pcfile "${CURRENT_PACKAGES_DIR}/lib/pkgconfig/lapack.pc")
+if(EXISTS "${pcfile}")
+    file(READ "${pcfile}" _contents)
+    set(_contents "prefix=${CURRENT_INSTALLED_DIR}\n${_contents}")
+    file(WRITE "${pcfile}" "${_contents}")
+endif()
+set(pcfile "${CURRENT_PACKAGES_DIR}/debug/lib/pkgconfig/lapack.pc")
+if(EXISTS "${pcfile}")
+    file(READ "${pcfile}" _contents)
+    set(_contents "prefix=${CURRENT_INSTALLED_DIR}/debug\n${_contents}")
+    file(WRITE "${pcfile}" "${_contents}")
+endif()
+set(pcfile "${CURRENT_PACKAGES_DIR}/lib/pkgconfig/lapacke.pc")
+if(EXISTS "${pcfile}")
+    file(READ "${pcfile}" _contents)
+    set(_contents "prefix=${CURRENT_INSTALLED_DIR}\n${_contents}")
+    file(WRITE "${pcfile}" "${_contents}")
+endif()
+set(pcfile "${CURRENT_PACKAGES_DIR}/debug/lib/pkgconfig/lapacke.pc")
+if(EXISTS "${pcfile}")
+    file(READ "${pcfile}" _contents)
+    set(_contents "prefix=${CURRENT_INSTALLED_DIR}/debug\n${_contents}")
+    file(WRITE "${pcfile}" "${_contents}")
+endif()
+if(NOT USE_OPTIMIZED_BLAS AND NOT (VCPKG_TARGET_IS_WINDOWS AND VCPKG_LIBRARY_LINKAGE STREQUAL "static"))
+    set(pcfile "${CURRENT_PACKAGES_DIR}/lib/pkgconfig/blas.pc")
+    if(EXISTS "${pcfile}")
+        file(READ "${pcfile}" _contents)
+        set(_contents "prefix=${CURRENT_INSTALLED_DIR}\n${_contents}")
+        file(WRITE "${pcfile}" "${_contents}")
+    endif()
+    set(pcfile "${CURRENT_PACKAGES_DIR}/debug/lib/pkgconfig/blas.pc")
+    if(EXISTS "${pcfile}")
+        file(READ "${pcfile}" _contents)
+        set(_contents "prefix=${CURRENT_INSTALLED_DIR}/debug\n${_contents}")
+        file(WRITE "${pcfile}" "${_contents}")
+    endif()
+endif()
+if("cblas" IN_LIST FEATURES)
+    set(pcfile "${CURRENT_PACKAGES_DIR}/lib/pkgconfig/cblas.pc")
+    if(EXISTS "${pcfile}")
+        file(READ "${pcfile}" _contents)
+        set(_contents "prefix=${CURRENT_INSTALLED_DIR}\n${_contents}")
+        file(WRITE "${pcfile}" "${_contents}")
+    endif()
+    set(pcfile "${CURRENT_PACKAGES_DIR}/debug/lib/pkgconfig/cblas.pc")
+    if(EXISTS "${pcfile}")
+        file(READ "${pcfile}" _contents)
+        set(_contents "prefix=${CURRENT_INSTALLED_DIR}/debug\n${_contents}")
+        file(WRITE "${pcfile}" "${_contents}")
+    endif()
+endif()
+#vcpkg_fixup_pkgconfig()
+
+# Handle copyright
+file(INSTALL "${SOURCE_PATH}/LICENSE" DESTINATION "${CURRENT_PACKAGES_DIR}/share/${PORT}" RENAME copyright)
+
+# remove debug includes
+file(REMOVE_RECURSE ${CURRENT_PACKAGES_DIR}/debug/include)
+
+if(VCPKG_TARGET_IS_WINDOWS)
+    if(EXISTS "${CURRENT_PACKAGES_DIR}/lib/liblapack.lib")
+        file(RENAME "${CURRENT_PACKAGES_DIR}/lib/liblapack.lib" "${CURRENT_PACKAGES_DIR}/lib/lapack.lib")
+    endif()
+    if(EXISTS "${CURRENT_PACKAGES_DIR}/debug/lib/liblapack.lib")
+        file(RENAME "${CURRENT_PACKAGES_DIR}/debug/lib/liblapack.lib" "${CURRENT_PACKAGES_DIR}/debug/lib/lapack.lib")
+    endif()
+    if(EXISTS "${CURRENT_PACKAGES_DIR}/lib/liblapacke.lib")
+        file(RENAME "${CURRENT_PACKAGES_DIR}/lib/liblapacke.lib" "${CURRENT_PACKAGES_DIR}/lib/lapacke.lib")
+    endif()
+    if(EXISTS "${CURRENT_PACKAGES_DIR}/debug/lib/liblapacke.lib")
+        file(RENAME "${CURRENT_PACKAGES_DIR}/debug/lib/liblapacke.lib" "${CURRENT_PACKAGES_DIR}/debug/lib/lapacke.lib")
+    endif()
+    if(NOT USE_OPTIMIZED_BLAS)
+        if(EXISTS "${CURRENT_PACKAGES_DIR}/lib/libblas.lib")
+            file(RENAME "${CURRENT_PACKAGES_DIR}/lib/libblas.lib" "${CURRENT_PACKAGES_DIR}/lib/blas.lib")
+        endif()
+        if(EXISTS "${CURRENT_PACKAGES_DIR}/debug/lib/libblas.lib")
+            file(RENAME "${CURRENT_PACKAGES_DIR}/debug/lib/libblas.lib" "${CURRENT_PACKAGES_DIR}/debug/lib/blas.lib")
+        endif()
+    endif()
+endif()
+
+file(COPY ${CMAKE_CURRENT_LIST_DIR}/vcpkg-cmake-wrapper.cmake DESTINATION ${CURRENT_PACKAGES_DIR}/share/lapack)
+file(COPY ${CMAKE_CURRENT_LIST_DIR}/FindLAPACK.cmake DESTINATION ${CURRENT_PACKAGES_DIR}/share/lapack)
diff --git a/CMakeModules/vcpkg/ports/lapack-reference/vcpkg-cmake-wrapper.cmake b/CMakeModules/vcpkg/ports/lapack-reference/vcpkg-cmake-wrapper.cmake
new file mode 100644
index 0000000000..b3a7128fff
--- /dev/null
+++ b/CMakeModules/vcpkg/ports/lapack-reference/vcpkg-cmake-wrapper.cmake
@@ -0,0 +1,11 @@
+message(STATUS "Using VCPKG FindLAPACK from package 'lapack-reference'")
+set(LAPACK_PREV_MODULE_PATH ${CMAKE_MODULE_PATH})
+list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_LIST_DIR})
+
+list(REMOVE_ITEM ARGS "NO_MODULE")
+list(REMOVE_ITEM ARGS "CONFIG")
+list(REMOVE_ITEM ARGS "MODULE")
+
+_find_package(${ARGS})
+
+set(CMAKE_MODULE_PATH ${LAPACK_PREV_MODULE_PATH})
diff --git a/CMakeModules/vcpkg/ports/lapack-reference/vcpkg.json b/CMakeModules/vcpkg/ports/lapack-reference/vcpkg.json
new file mode 100644
index 0000000000..b2fe5d6998
--- /dev/null
+++ b/CMakeModules/vcpkg/ports/lapack-reference/vcpkg.json
@@ -0,0 +1,48 @@
+{
+  "name": "lapack-reference",
+  "version": "3.10.1",
+  "description": "LAPACK - Linear Algebra PACKage",
+  "homepage": "http://www.netlib.org/lapack/",
+  "license": "BSD-3-Clause-Open-MPI",
+  "dependencies": [
+    {
+      "name": "vcpkg-cmake",
+      "host": true
+    },
+    {
+      "name": "vcpkg-cmake-config",
+      "host": true
+    },
+    {
+      "name": "vcpkg-gfortran",
+      "platform": "windows"
+    }
+  ],
+  "default-features": [
+    "blas-select"
+  ],
+  "features": {
+    "blas-select": {
+      "description": "Use external optimized BLAS",
+      "dependencies": [
+        {
+          "name": "lapack-reference",
+          "default-features": false,
+          "features": [
+            "noblas"
+          ],
+          "platform": "!windows | !static"
+        }
+      ]
+    },
+    "cblas": {
+      "description": "Builds CBLAS"
+    },
+    "noblas": {
+      "description": "Use external optimized BLAS",
+      "dependencies": [
+        "blas"
+      ]
+    }
+  }
+}
diff --git a/CMakeModules/vcpkg-triplets/x64-windows.cmake b/CMakeModules/vcpkg/vcpkg-triplets/x64-windows.cmake
similarity index 100%
rename from CMakeModules/vcpkg-triplets/x64-windows.cmake
rename to CMakeModules/vcpkg/vcpkg-triplets/x64-windows.cmake

From 60ee8506e717b7c5123a16167877454716b2befb Mon Sep 17 00:00:00 2001
From: Yurkevitch <stephen.yurkevitch@intel.com>
Date: Thu, 1 Sep 2022 18:16:25 -0400
Subject: [PATCH 2296/2677] Initial oneAPI backend code

* afoneapi compiles and links

* Files that compile:
                      Array.cpp
                      Event.cpp
                      wrap.cpp
                      where.cpp
                      unwrap.cpp
                      triangle.cpp
                      transpose.cpp
                      transform.cpp
                      topk.cpp
                      tile.cpp
                      svd.cpp
                      susan.cpp
                      surface.cpp
                      sum.cpp
                      sparse_blas.cpp
                      sparse_arith.cpp
                      sparse.cpp
                      sort_index.cpp
                      sort.cpp
                      solve.cpp
                      sobel.cpp
                      sift.cpp
                      set.cpp
                      select.cpp
                      scan_by_key.cpp
                      scan.cpp
                      rotate.cpp
                      resize.cpp
                      reshape.cpp
                      reorder.cpp
                      regions.cpp
                      range.cpp
                      random_engine.cpp
                      qr.cpp
                      product.cpp
                      plot.cpp
                      orb.cpp
                      nearest_neighbor.cpp
                      morph.cpp
                      moments.cpp
                      min.cpp
                      memory.cpp
                      medfilt.cpp
                      meanshift.cpp
                      mean.cpp
                      max.cpp
                      platform.cpp
---
 CMakeLists.txt                                |   6 +
 src/api/c/det.cpp                             |   2 +-
 src/api/c/morph.cpp                           |   2 +
 src/backend/common/EventBase.hpp              |   3 +-
 src/backend/common/Logger.hpp                 |  32 +
 src/backend/common/forge_loader.hpp           |  31 +-
 src/backend/common/jit/BinaryNode.cpp         |   1 +
 src/backend/common/jit/BufferNodeBase.hpp     |   3 +-
 src/backend/common/jit/Node.hpp               |  48 +-
 src/backend/common/kernel_cache.cpp           |   2 +-
 src/backend/oneapi/Array.cpp                  | 563 ++++++++++++++++++
 src/backend/oneapi/Array.hpp                  | 327 ++++++++++
 src/backend/oneapi/CMakeLists.txt             | 240 ++++++++
 src/backend/oneapi/Event.cpp                  |  78 +++
 src/backend/oneapi/Event.hpp                  |  64 ++
 .../oneapi/GraphicsResourceManager.cpp        |  20 +
 .../oneapi/GraphicsResourceManager.hpp        |  33 +
 src/backend/oneapi/Kernel.hpp                 |  90 +++
 src/backend/oneapi/Module.hpp                 |  40 ++
 src/backend/oneapi/Param.hpp                  |  36 ++
 src/backend/oneapi/all.cpp                    |  30 +
 src/backend/oneapi/anisotropic_diffusion.cpp  |  31 +
 src/backend/oneapi/anisotropic_diffusion.hpp  |  17 +
 src/backend/oneapi/any.cpp                    |  30 +
 src/backend/oneapi/approx.cpp                 |  89 +++
 src/backend/oneapi/approx.hpp                 |  24 +
 src/backend/oneapi/arith.hpp                  |  30 +
 src/backend/oneapi/assign.cpp                 |  48 ++
 src/backend/oneapi/assign.hpp                 |  18 +
 src/backend/oneapi/backend.hpp                |  22 +
 src/backend/oneapi/bilateral.cpp              |  41 ++
 src/backend/oneapi/bilateral.hpp              |  16 +
 src/backend/oneapi/binary.hpp                 | 127 ++++
 src/backend/oneapi/blas.cpp                   |  84 +++
 src/backend/oneapi/blas.hpp                   |  41 ++
 src/backend/oneapi/canny.cpp                  |  28 +
 src/backend/oneapi/canny.hpp                  |  19 +
 src/backend/oneapi/cast.hpp                   |  73 +++
 src/backend/oneapi/cholesky.cpp               |  70 +++
 src/backend/oneapi/cholesky.hpp               |  18 +
 src/backend/oneapi/compile_module.cpp         | 131 ++++
 src/backend/oneapi/complex.hpp                |  90 +++
 src/backend/oneapi/convolve.cpp               | 125 ++++
 src/backend/oneapi/convolve.hpp               |  39 ++
 src/backend/oneapi/convolve_separable.cpp     |  45 ++
 src/backend/oneapi/copy.cpp                   | 150 +++++
 src/backend/oneapi/copy.hpp                   |  67 +++
 src/backend/oneapi/count.cpp                  |  30 +
 src/backend/oneapi/device_manager.cpp         |  95 +++
 src/backend/oneapi/device_manager.hpp         | 163 +++++
 src/backend/oneapi/diagonal.cpp               |  58 ++
 src/backend/oneapi/diagonal.hpp               |  18 +
 src/backend/oneapi/diff.cpp                   |  61 ++
 src/backend/oneapi/diff.hpp                   |  18 +
 src/backend/oneapi/err_oneapi.hpp             |  18 +
 src/backend/oneapi/errorcodes.cpp             |  18 +
 src/backend/oneapi/errorcodes.hpp             |  14 +
 src/backend/oneapi/exampleFunction.cpp        |  65 ++
 src/backend/oneapi/exampleFunction.hpp        |  16 +
 src/backend/oneapi/fast.cpp                   |  44 ++
 src/backend/oneapi/fast.hpp                   |  23 +
 src/backend/oneapi/fft.cpp                    | 106 ++++
 src/backend/oneapi/fft.hpp                    |  25 +
 src/backend/oneapi/fftconvolve.cpp            |  82 +++
 src/backend/oneapi/fftconvolve.hpp            |  16 +
 src/backend/oneapi/flood_fill.cpp             |  36 ++
 src/backend/oneapi/flood_fill.hpp             |  21 +
 src/backend/oneapi/gradient.cpp               |  31 +
 src/backend/oneapi/gradient.hpp               |  15 +
 src/backend/oneapi/harris.cpp                 |  40 ++
 src/backend/oneapi/harris.hpp                 |  24 +
 src/backend/oneapi/hist_graphics.cpp          |  32 +
 src/backend/oneapi/hist_graphics.hpp          |  18 +
 src/backend/oneapi/histogram.cpp              |  49 ++
 src/backend/oneapi/histogram.hpp              |  17 +
 src/backend/oneapi/homography.cpp             |  44 ++
 src/backend/oneapi/homography.hpp             |  21 +
 src/backend/oneapi/hsv_rgb.cpp                |  37 ++
 src/backend/oneapi/hsv_rgb.hpp                |  20 +
 src/backend/oneapi/identity.cpp               |  43 ++
 src/backend/oneapi/identity.hpp               |  15 +
 src/backend/oneapi/iir.cpp                    |  37 ++
 src/backend/oneapi/iir.hpp                    |  16 +
 src/backend/oneapi/image.cpp                  |  36 ++
 src/backend/oneapi/image.hpp                  |  18 +
 src/backend/oneapi/index.cpp                  |  46 ++
 src/backend/oneapi/index.hpp                  |  18 +
 src/backend/oneapi/inverse.cpp                |  54 ++
 src/backend/oneapi/inverse.hpp                |  15 +
 src/backend/oneapi/iota.cpp                   |  43 ++
 src/backend/oneapi/iota.hpp                   |  16 +
 src/backend/oneapi/ireduce.cpp                |  78 +++
 src/backend/oneapi/ireduce.hpp                |  24 +
 src/backend/oneapi/jit.cpp                    |  71 +++
 src/backend/oneapi/jit/BufferNode.hpp         |  34 ++
 src/backend/oneapi/jit/kernel_generators.hpp  | 112 ++++
 src/backend/oneapi/join.cpp                   |  91 +++
 src/backend/oneapi/join.hpp                   |  18 +
 src/backend/oneapi/kernel/KParam.hpp          |  26 +
 src/backend/oneapi/logic.hpp                  |  30 +
 src/backend/oneapi/lookup.cpp                 |  63 ++
 src/backend/oneapi/lookup.hpp                 |  16 +
 src/backend/oneapi/lu.cpp                     |  86 +++
 src/backend/oneapi/lu.hpp                     |  21 +
 src/backend/oneapi/match_template.cpp         |  38 ++
 src/backend/oneapi/match_template.hpp         |  18 +
 src/backend/oneapi/math.cpp                   |  53 ++
 src/backend/oneapi/math.hpp                   | 155 +++++
 src/backend/oneapi/max.cpp                    |  30 +
 src/backend/oneapi/mean.cpp                   |  94 +++
 src/backend/oneapi/mean.hpp                   |  26 +
 src/backend/oneapi/meanshift.cpp              |  48 ++
 src/backend/oneapi/meanshift.hpp              |  17 +
 src/backend/oneapi/medfilt.cpp                |  67 +++
 src/backend/oneapi/medfilt.hpp                |  22 +
 src/backend/oneapi/memory.cpp                 | 351 +++++++++++
 src/backend/oneapi/memory.hpp                 |  94 +++
 src/backend/oneapi/min.cpp                    |  30 +
 src/backend/oneapi/moments.cpp                |  57 ++
 src/backend/oneapi/moments.hpp                |  15 +
 src/backend/oneapi/morph.cpp                  |  70 +++
 src/backend/oneapi/morph.hpp                  |  18 +
 src/backend/oneapi/nearest_neighbour.cpp      |  89 +++
 src/backend/oneapi/nearest_neighbour.hpp      |  23 +
 src/backend/oneapi/orb.cpp                    |  69 +++
 src/backend/oneapi/orb.hpp                    |  24 +
 src/backend/oneapi/platform.cpp               | 462 ++++++++++++++
 src/backend/oneapi/platform.hpp               | 121 ++++
 src/backend/oneapi/plot.cpp                   |  79 +++
 src/backend/oneapi/plot.hpp                   |  18 +
 src/backend/oneapi/print.hpp                  |  24 +
 src/backend/oneapi/product.cpp                |  30 +
 src/backend/oneapi/qr.cpp                     | 142 +++++
 src/backend/oneapi/qr.hpp                     |  18 +
 src/backend/oneapi/random_engine.cpp          | 160 +++++
 src/backend/oneapi/random_engine.hpp          |  41 ++
 src/backend/oneapi/range.cpp                  |  57 ++
 src/backend/oneapi/range.hpp                  |  16 +
 src/backend/oneapi/reduce.hpp                 |  27 +
 src/backend/oneapi/reduce_impl.hpp            |  56 ++
 src/backend/oneapi/regions.cpp                |  42 ++
 src/backend/oneapi/regions.hpp                |  17 +
 src/backend/oneapi/reorder.cpp                |  52 ++
 src/backend/oneapi/reorder.hpp                |  15 +
 src/backend/oneapi/reshape.cpp                |  82 +++
 src/backend/oneapi/resize.cpp                 |  48 ++
 src/backend/oneapi/resize.hpp                 |  16 +
 src/backend/oneapi/rotate.cpp                 |  59 ++
 src/backend/oneapi/rotate.hpp                 |  16 +
 src/backend/oneapi/scalar.hpp                 |  23 +
 src/backend/oneapi/scan.cpp                   |  58 ++
 src/backend/oneapi/scan.hpp                   |  16 +
 src/backend/oneapi/scan_by_key.cpp            |  65 ++
 src/backend/oneapi/scan_by_key.hpp            |  17 +
 src/backend/oneapi/select.cpp                 | 145 +++++
 src/backend/oneapi/select.hpp                 |  29 +
 src/backend/oneapi/set.cpp                    | 157 +++++
 src/backend/oneapi/set.hpp                    |  23 +
 src/backend/oneapi/shift.cpp                  |  73 +++
 src/backend/oneapi/shift.hpp                  |  15 +
 src/backend/oneapi/sift.cpp                   |  76 +++
 src/backend/oneapi/sift.hpp                   |  26 +
 src/backend/oneapi/sobel.cpp                  |  49 ++
 src/backend/oneapi/sobel.hpp                  |  19 +
 src/backend/oneapi/solve.cpp                  | 368 ++++++++++++
 src/backend/oneapi/solve.hpp                  |  20 +
 src/backend/oneapi/sort.cpp                   |  67 +++
 src/backend/oneapi/sort.hpp                   |  15 +
 src/backend/oneapi/sort_by_key.cpp            |  55 ++
 src/backend/oneapi/sort_by_key.hpp            |  16 +
 src/backend/oneapi/sort_index.cpp             |  82 +++
 src/backend/oneapi/sort_index.hpp             |  16 +
 src/backend/oneapi/sparse.cpp                 | 225 +++++++
 src/backend/oneapi/sparse.hpp                 |  27 +
 src/backend/oneapi/sparse_arith.cpp           | 180 ++++++
 src/backend/oneapi/sparse_arith.hpp           |  30 +
 src/backend/oneapi/sparse_blas.cpp            |  99 +++
 src/backend/oneapi/sparse_blas.hpp            |  20 +
 src/backend/oneapi/sum.cpp                    |  39 ++
 src/backend/oneapi/surface.cpp                |  81 +++
 src/backend/oneapi/surface.hpp                |  18 +
 src/backend/oneapi/susan.cpp                  |  75 +++
 src/backend/oneapi/susan.hpp                  |  24 +
 src/backend/oneapi/svd.cpp                    | 268 +++++++++
 src/backend/oneapi/svd.hpp                    |  18 +
 src/backend/oneapi/tile.cpp                   |  51 ++
 src/backend/oneapi/tile.hpp                   |  15 +
 src/backend/oneapi/topk.cpp                   | 182 ++++++
 src/backend/oneapi/topk.hpp                   |  14 +
 src/backend/oneapi/traits.hpp                 |  56 ++
 src/backend/oneapi/transform.cpp              |  58 ++
 src/backend/oneapi/transform.hpp              |  17 +
 src/backend/oneapi/transpose.cpp              |  54 ++
 src/backend/oneapi/transpose.hpp              |  20 +
 src/backend/oneapi/transpose_inplace.cpp      |  44 ++
 src/backend/oneapi/triangle.cpp               |  56 ++
 src/backend/oneapi/triangle.hpp               |  20 +
 src/backend/oneapi/types.hpp                  | 163 +++++
 src/backend/oneapi/unary.hpp                  | 111 ++++
 src/backend/oneapi/unwrap.cpp                 |  63 ++
 src/backend/oneapi/unwrap.hpp                 |  17 +
 src/backend/oneapi/vector_field.cpp           |  36 ++
 src/backend/oneapi/vector_field.hpp           |  18 +
 src/backend/oneapi/where.cpp                  |  44 ++
 src/backend/oneapi/where.hpp                  |  15 +
 src/backend/oneapi/wrap.cpp                   |  76 +++
 src/backend/oneapi/wrap.hpp                   |  24 +
 test/CMakeLists.txt                           |   4 +
 208 files changed, 12095 insertions(+), 29 deletions(-)
 create mode 100644 src/backend/oneapi/Array.cpp
 create mode 100644 src/backend/oneapi/Array.hpp
 create mode 100644 src/backend/oneapi/CMakeLists.txt
 create mode 100644 src/backend/oneapi/Event.cpp
 create mode 100644 src/backend/oneapi/Event.hpp
 create mode 100644 src/backend/oneapi/GraphicsResourceManager.cpp
 create mode 100644 src/backend/oneapi/GraphicsResourceManager.hpp
 create mode 100644 src/backend/oneapi/Kernel.hpp
 create mode 100644 src/backend/oneapi/Module.hpp
 create mode 100644 src/backend/oneapi/Param.hpp
 create mode 100644 src/backend/oneapi/all.cpp
 create mode 100644 src/backend/oneapi/anisotropic_diffusion.cpp
 create mode 100644 src/backend/oneapi/anisotropic_diffusion.hpp
 create mode 100644 src/backend/oneapi/any.cpp
 create mode 100644 src/backend/oneapi/approx.cpp
 create mode 100644 src/backend/oneapi/approx.hpp
 create mode 100644 src/backend/oneapi/arith.hpp
 create mode 100644 src/backend/oneapi/assign.cpp
 create mode 100644 src/backend/oneapi/assign.hpp
 create mode 100644 src/backend/oneapi/backend.hpp
 create mode 100644 src/backend/oneapi/bilateral.cpp
 create mode 100644 src/backend/oneapi/bilateral.hpp
 create mode 100644 src/backend/oneapi/binary.hpp
 create mode 100644 src/backend/oneapi/blas.cpp
 create mode 100644 src/backend/oneapi/blas.hpp
 create mode 100644 src/backend/oneapi/canny.cpp
 create mode 100644 src/backend/oneapi/canny.hpp
 create mode 100644 src/backend/oneapi/cast.hpp
 create mode 100644 src/backend/oneapi/cholesky.cpp
 create mode 100644 src/backend/oneapi/cholesky.hpp
 create mode 100644 src/backend/oneapi/compile_module.cpp
 create mode 100644 src/backend/oneapi/complex.hpp
 create mode 100644 src/backend/oneapi/convolve.cpp
 create mode 100644 src/backend/oneapi/convolve.hpp
 create mode 100644 src/backend/oneapi/convolve_separable.cpp
 create mode 100644 src/backend/oneapi/copy.cpp
 create mode 100644 src/backend/oneapi/copy.hpp
 create mode 100644 src/backend/oneapi/count.cpp
 create mode 100644 src/backend/oneapi/device_manager.cpp
 create mode 100644 src/backend/oneapi/device_manager.hpp
 create mode 100644 src/backend/oneapi/diagonal.cpp
 create mode 100644 src/backend/oneapi/diagonal.hpp
 create mode 100644 src/backend/oneapi/diff.cpp
 create mode 100644 src/backend/oneapi/diff.hpp
 create mode 100644 src/backend/oneapi/err_oneapi.hpp
 create mode 100644 src/backend/oneapi/errorcodes.cpp
 create mode 100644 src/backend/oneapi/errorcodes.hpp
 create mode 100644 src/backend/oneapi/exampleFunction.cpp
 create mode 100644 src/backend/oneapi/exampleFunction.hpp
 create mode 100644 src/backend/oneapi/fast.cpp
 create mode 100644 src/backend/oneapi/fast.hpp
 create mode 100644 src/backend/oneapi/fft.cpp
 create mode 100644 src/backend/oneapi/fft.hpp
 create mode 100644 src/backend/oneapi/fftconvolve.cpp
 create mode 100644 src/backend/oneapi/fftconvolve.hpp
 create mode 100644 src/backend/oneapi/flood_fill.cpp
 create mode 100644 src/backend/oneapi/flood_fill.hpp
 create mode 100644 src/backend/oneapi/gradient.cpp
 create mode 100644 src/backend/oneapi/gradient.hpp
 create mode 100644 src/backend/oneapi/harris.cpp
 create mode 100644 src/backend/oneapi/harris.hpp
 create mode 100644 src/backend/oneapi/hist_graphics.cpp
 create mode 100644 src/backend/oneapi/hist_graphics.hpp
 create mode 100644 src/backend/oneapi/histogram.cpp
 create mode 100644 src/backend/oneapi/histogram.hpp
 create mode 100644 src/backend/oneapi/homography.cpp
 create mode 100644 src/backend/oneapi/homography.hpp
 create mode 100644 src/backend/oneapi/hsv_rgb.cpp
 create mode 100644 src/backend/oneapi/hsv_rgb.hpp
 create mode 100644 src/backend/oneapi/identity.cpp
 create mode 100644 src/backend/oneapi/identity.hpp
 create mode 100644 src/backend/oneapi/iir.cpp
 create mode 100644 src/backend/oneapi/iir.hpp
 create mode 100644 src/backend/oneapi/image.cpp
 create mode 100644 src/backend/oneapi/image.hpp
 create mode 100644 src/backend/oneapi/index.cpp
 create mode 100644 src/backend/oneapi/index.hpp
 create mode 100644 src/backend/oneapi/inverse.cpp
 create mode 100644 src/backend/oneapi/inverse.hpp
 create mode 100644 src/backend/oneapi/iota.cpp
 create mode 100644 src/backend/oneapi/iota.hpp
 create mode 100644 src/backend/oneapi/ireduce.cpp
 create mode 100644 src/backend/oneapi/ireduce.hpp
 create mode 100644 src/backend/oneapi/jit.cpp
 create mode 100644 src/backend/oneapi/jit/BufferNode.hpp
 create mode 100644 src/backend/oneapi/jit/kernel_generators.hpp
 create mode 100644 src/backend/oneapi/join.cpp
 create mode 100644 src/backend/oneapi/join.hpp
 create mode 100644 src/backend/oneapi/kernel/KParam.hpp
 create mode 100644 src/backend/oneapi/logic.hpp
 create mode 100644 src/backend/oneapi/lookup.cpp
 create mode 100644 src/backend/oneapi/lookup.hpp
 create mode 100644 src/backend/oneapi/lu.cpp
 create mode 100644 src/backend/oneapi/lu.hpp
 create mode 100644 src/backend/oneapi/match_template.cpp
 create mode 100644 src/backend/oneapi/match_template.hpp
 create mode 100644 src/backend/oneapi/math.cpp
 create mode 100644 src/backend/oneapi/math.hpp
 create mode 100644 src/backend/oneapi/max.cpp
 create mode 100644 src/backend/oneapi/mean.cpp
 create mode 100644 src/backend/oneapi/mean.hpp
 create mode 100644 src/backend/oneapi/meanshift.cpp
 create mode 100644 src/backend/oneapi/meanshift.hpp
 create mode 100644 src/backend/oneapi/medfilt.cpp
 create mode 100644 src/backend/oneapi/medfilt.hpp
 create mode 100644 src/backend/oneapi/memory.cpp
 create mode 100644 src/backend/oneapi/memory.hpp
 create mode 100644 src/backend/oneapi/min.cpp
 create mode 100644 src/backend/oneapi/moments.cpp
 create mode 100644 src/backend/oneapi/moments.hpp
 create mode 100644 src/backend/oneapi/morph.cpp
 create mode 100644 src/backend/oneapi/morph.hpp
 create mode 100644 src/backend/oneapi/nearest_neighbour.cpp
 create mode 100644 src/backend/oneapi/nearest_neighbour.hpp
 create mode 100644 src/backend/oneapi/orb.cpp
 create mode 100644 src/backend/oneapi/orb.hpp
 create mode 100644 src/backend/oneapi/platform.cpp
 create mode 100644 src/backend/oneapi/platform.hpp
 create mode 100644 src/backend/oneapi/plot.cpp
 create mode 100644 src/backend/oneapi/plot.hpp
 create mode 100644 src/backend/oneapi/print.hpp
 create mode 100644 src/backend/oneapi/product.cpp
 create mode 100644 src/backend/oneapi/qr.cpp
 create mode 100644 src/backend/oneapi/qr.hpp
 create mode 100644 src/backend/oneapi/random_engine.cpp
 create mode 100644 src/backend/oneapi/random_engine.hpp
 create mode 100644 src/backend/oneapi/range.cpp
 create mode 100644 src/backend/oneapi/range.hpp
 create mode 100644 src/backend/oneapi/reduce.hpp
 create mode 100644 src/backend/oneapi/reduce_impl.hpp
 create mode 100644 src/backend/oneapi/regions.cpp
 create mode 100644 src/backend/oneapi/regions.hpp
 create mode 100644 src/backend/oneapi/reorder.cpp
 create mode 100644 src/backend/oneapi/reorder.hpp
 create mode 100644 src/backend/oneapi/reshape.cpp
 create mode 100644 src/backend/oneapi/resize.cpp
 create mode 100644 src/backend/oneapi/resize.hpp
 create mode 100644 src/backend/oneapi/rotate.cpp
 create mode 100644 src/backend/oneapi/rotate.hpp
 create mode 100644 src/backend/oneapi/scalar.hpp
 create mode 100644 src/backend/oneapi/scan.cpp
 create mode 100644 src/backend/oneapi/scan.hpp
 create mode 100644 src/backend/oneapi/scan_by_key.cpp
 create mode 100644 src/backend/oneapi/scan_by_key.hpp
 create mode 100644 src/backend/oneapi/select.cpp
 create mode 100644 src/backend/oneapi/select.hpp
 create mode 100644 src/backend/oneapi/set.cpp
 create mode 100644 src/backend/oneapi/set.hpp
 create mode 100644 src/backend/oneapi/shift.cpp
 create mode 100644 src/backend/oneapi/shift.hpp
 create mode 100644 src/backend/oneapi/sift.cpp
 create mode 100644 src/backend/oneapi/sift.hpp
 create mode 100644 src/backend/oneapi/sobel.cpp
 create mode 100644 src/backend/oneapi/sobel.hpp
 create mode 100644 src/backend/oneapi/solve.cpp
 create mode 100644 src/backend/oneapi/solve.hpp
 create mode 100644 src/backend/oneapi/sort.cpp
 create mode 100644 src/backend/oneapi/sort.hpp
 create mode 100644 src/backend/oneapi/sort_by_key.cpp
 create mode 100644 src/backend/oneapi/sort_by_key.hpp
 create mode 100644 src/backend/oneapi/sort_index.cpp
 create mode 100644 src/backend/oneapi/sort_index.hpp
 create mode 100644 src/backend/oneapi/sparse.cpp
 create mode 100644 src/backend/oneapi/sparse.hpp
 create mode 100644 src/backend/oneapi/sparse_arith.cpp
 create mode 100644 src/backend/oneapi/sparse_arith.hpp
 create mode 100644 src/backend/oneapi/sparse_blas.cpp
 create mode 100644 src/backend/oneapi/sparse_blas.hpp
 create mode 100644 src/backend/oneapi/sum.cpp
 create mode 100644 src/backend/oneapi/surface.cpp
 create mode 100644 src/backend/oneapi/surface.hpp
 create mode 100644 src/backend/oneapi/susan.cpp
 create mode 100644 src/backend/oneapi/susan.hpp
 create mode 100644 src/backend/oneapi/svd.cpp
 create mode 100644 src/backend/oneapi/svd.hpp
 create mode 100644 src/backend/oneapi/tile.cpp
 create mode 100644 src/backend/oneapi/tile.hpp
 create mode 100644 src/backend/oneapi/topk.cpp
 create mode 100644 src/backend/oneapi/topk.hpp
 create mode 100644 src/backend/oneapi/traits.hpp
 create mode 100644 src/backend/oneapi/transform.cpp
 create mode 100644 src/backend/oneapi/transform.hpp
 create mode 100644 src/backend/oneapi/transpose.cpp
 create mode 100644 src/backend/oneapi/transpose.hpp
 create mode 100644 src/backend/oneapi/transpose_inplace.cpp
 create mode 100644 src/backend/oneapi/triangle.cpp
 create mode 100644 src/backend/oneapi/triangle.hpp
 create mode 100644 src/backend/oneapi/types.hpp
 create mode 100644 src/backend/oneapi/unary.hpp
 create mode 100644 src/backend/oneapi/unwrap.cpp
 create mode 100644 src/backend/oneapi/unwrap.hpp
 create mode 100644 src/backend/oneapi/vector_field.cpp
 create mode 100644 src/backend/oneapi/vector_field.hpp
 create mode 100644 src/backend/oneapi/where.cpp
 create mode 100644 src/backend/oneapi/where.hpp
 create mode 100644 src/backend/oneapi/wrap.cpp
 create mode 100644 src/backend/oneapi/wrap.hpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 08445a986f..60df46c5a3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -76,6 +76,7 @@ include(config_ccache)
 option(AF_BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
 option(AF_BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})
 option(AF_BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FOUND})
+option(AF_BUILD_ONEAPI   "Build ArrayFire with a oneAPI backend"     ${IntelDPCPP_FOUND})
 option(AF_BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
 option(AF_BUILD_DOCS     "Create ArrayFire Documentation"            ${DOXYGEN_FOUND})
 option(AF_BUILD_EXAMPLES "Build Examples"                            ON)
@@ -355,6 +356,7 @@ add_subdirectory(src/api/cpp)
 
 conditional_directory(AF_BUILD_CPU     src/backend/cpu)
 conditional_directory(AF_BUILD_CUDA    src/backend/cuda)
+conditional_directory(AF_BUILD_ONEAPI  src/backend/oneapi)
 conditional_directory(AF_BUILD_OPENCL  src/backend/opencl)
 conditional_directory(AF_BUILD_UNIFIED src/api/unified)
 
@@ -370,6 +372,10 @@ if(TARGET afcuda)
   list(APPEND built_backends afcuda)
 endif()
 
+if(TARGET afoneapi)
+  list(APPEND built_backends afoneapi)
+endif()
+
 if(TARGET afopencl)
   list(APPEND built_backends afopencl)
 endif()
diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index 8507675b85..0d0e5cc1d7 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -24,9 +24,9 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
+using detail::scalar;
 using detail::imag;
 using detail::real;
-using detail::scalar;
 
 template<typename T>
 T det(const af_array a) {
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index e95ee06b25..948effd652 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -62,6 +62,8 @@ af_array morph<char>(const af_array &input, const af_array &mask,
     constexpr unsigned fftMethodThreshold = 17;
 #elif defined(AF_OPENCL)
     constexpr unsigned fftMethodThreshold = 19;
+#elif defined(AF_ONEAPI)
+    constexpr unsigned fftMethodThreshold = 19;
 #endif  // defined(AF_CPU)
 
     const Array<float> se = castArray<float>(mask);
diff --git a/src/backend/common/EventBase.hpp b/src/backend/common/EventBase.hpp
index 46c35e9389..874ec5b6c6 100644
--- a/src/backend/common/EventBase.hpp
+++ b/src/backend/common/EventBase.hpp
@@ -36,7 +36,8 @@ class EventBase {
 
     /// \brief Event destructor. Calls the destroy event call on the native API
     ~EventBase() noexcept {
-        if (e_) NativeEventPolicy::destroyEvent(&e_);
+      //if (e_)
+          NativeEventPolicy::destroyEvent(&e_);
     }
 
     /// \brief Creates the event object by calling the native create API
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index aa56fc4ed0..50e74ae03b 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -13,8 +13,40 @@
 #include <string>
 #include <type_traits>
 
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wignored-attributes"
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+/* Intel ICC/ICPC */
+// Fix the warning code here, if any
+#elif defined(__GNUC__) || defined(__GNUG__)
+/* GNU GCC/G++ */
+#elif defined(_MSC_VER)
+/* Microsoft Visual Studio */
+#else
+/* Other */
+#endif
+
 #include <spdlog/spdlog.h>
 
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic pop
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+/* Intel ICC/ICPC */
+// Fix the warning code here, if any
+#elif defined(__GNUC__) || defined(__GNUG__)
+/* GNU GCC/G++ */
+#pragma GCC diagnostic pop
+#elif defined(_MSC_VER)
+/* Microsoft Visual Studio */
+#pragma warning(pop)
+#else
+/* Other */
+#endif
+
+
 namespace common {
 std::shared_ptr<spdlog::logger> loggerFactory(const std::string& name);
 std::string bytesToString(size_t bytes);
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index bf1cce8c5d..1e3edc7125 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -10,10 +10,39 @@
 #pragma once
 
 #include <common/DependencyModule.hpp>
+#include <forge.h>
+
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wignored-attributes"
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+/* Intel ICC/ICPC */
+// Fix the warning code here, if any
+#elif defined(_MSC_VER)
+/* Microsoft Visual Studio */
+#else
+/* Other */
+#endif
 
 #include <glad/glad.h>
 
-#include <forge.h>
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic pop
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+/* Intel ICC/ICPC */
+// Fix the warning code here, if any
+#elif defined(__GNUC__) || defined(__GNUG__)
+/* GNU GCC/G++ */
+#pragma GCC diagnostic pop
+#elif defined(_MSC_VER)
+/* Microsoft Visual Studio */
+#pragma warning(pop)
+#else
+/* Other */
+#endif
+
 
 class ForgeModule : public common::DependencyModule {
    public:
diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
index f67015b9fa..1277aa10be 100644
--- a/src/backend/common/jit/BinaryNode.cpp
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -4,6 +4,7 @@
 #include <common/jit/BinaryNode.hpp>
 #include <complex.hpp>
 #include <types.hpp>
+#include <af/traits.hpp>
 
 #include <memory>
 
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 8bb8185378..6b3d56162b 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -13,6 +13,7 @@
 #include <jit/kernel_generators.hpp>
 
 #include <sstream>
+#include <cstring>
 
 namespace common {
 
@@ -95,7 +96,7 @@ class BufferNodeBase : public common::Node {
     size_t getHash() const noexcept {
         size_t out = 0;
         auto ptr   = m_data.get();
-        memcpy(&out, &ptr, std::max(sizeof(Node *), sizeof(size_t)));
+        std::memcpy(&out, &ptr, std::max(sizeof(Node *), sizeof(size_t)));
         return out;
     }
 
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index bbe3fcb859..3062935909 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -71,18 +71,18 @@ using Node_ptr = std::shared_ptr<Node>;
 
 static const char *getFullName(af::dtype type) {
     switch (type) {
-        case f32: return detail::getFullName<float>();
-        case f64: return detail::getFullName<double>();
-        case c32: return detail::getFullName<detail::cfloat>();
-        case c64: return detail::getFullName<detail::cdouble>();
-        case u32: return detail::getFullName<unsigned>();
-        case s32: return detail::getFullName<int>();
-        case u64: return detail::getFullName<unsigned long long>();
-        case s64: return detail::getFullName<long long>();
-        case u16: return detail::getFullName<unsigned short>();
-        case s16: return detail::getFullName<short>();
-        case b8: return detail::getFullName<char>();
-        case u8: return detail::getFullName<unsigned char>();
+    case f32: return detail::getFullName<float>();
+    case f64: return detail::getFullName<double>();
+    case c32: return detail::getFullName<detail::cfloat>();
+    case c64: return detail::getFullName<detail::cdouble>();
+    case u32: return detail::getFullName<unsigned>();
+    case s32: return detail::getFullName<int>();
+    case u64: return detail::getFullName<unsigned long long>();
+    case s64: return detail::getFullName<long long>();
+    case u16: return detail::getFullName<unsigned short>();
+    case s16: return detail::getFullName<short>();
+    case b8: return  detail::getFullName<char>();
+    case u8: return  detail::getFullName<unsigned char>();
         case f16: return "half";
     }
     return "";
@@ -90,18 +90,18 @@ static const char *getFullName(af::dtype type) {
 
 static const char *getShortName(af::dtype type) {
     switch (type) {
-        case f32: return detail::shortname<float>();
-        case f64: return detail::shortname<double>();
-        case c32: return detail::shortname<detail::cfloat>();
-        case c64: return detail::shortname<detail::cdouble>();
-        case u32: return detail::shortname<unsigned>();
-        case s32: return detail::shortname<int>();
-        case u64: return detail::shortname<unsigned long long>();
-        case s64: return detail::shortname<long long>();
-        case u16: return detail::shortname<unsigned short>();
-        case s16: return detail::shortname<short>();
-        case b8: return detail::shortname<char>();
-        case u8: return detail::shortname<unsigned char>();
+    case f32: return detail::shortname<float>();
+    case f64: return detail::shortname<double>();
+    case c32: return detail::shortname<detail::cfloat>();
+    case c64: return detail::shortname<detail::cdouble>();
+    case u32: return detail::shortname<unsigned>();
+    case s32: return detail::shortname<int>();
+    case u64: return detail::shortname<unsigned long long>();
+    case s64: return detail::shortname<long long>();
+    case u16: return detail::shortname<unsigned short>();
+    case s16: return detail::shortname<short>();
+    case b8: return  detail::shortname<char>();
+    case u8: return  detail::shortname<unsigned char>();
         case f16: return "h";
     }
     return "";
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 981d544511..869ea8d5e9 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#if !defined(AF_CPU)
+#if !defined(AF_CPU) && !defined(AF_ONEAPI)
 
 #include <common/compile_module.hpp>
 #include <common/kernel_cache.hpp>
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
new file mode 100644
index 0000000000..5f53e37052
--- /dev/null
+++ b/src/backend/oneapi/Array.cpp
@@ -0,0 +1,563 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+#include <common/half.hpp>
+#include <common/jit/NodeIterator.hpp>
+#include <common/jit/ScalarNode.hpp>
+#include <jit/BufferNode.hpp>
+#include <common/util.hpp>
+#include <err_oneapi.hpp>
+#include <copy.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include <scalar.hpp>
+#include <traits.hpp>
+#include <af/dim4.hpp>
+
+#include <cstddef>
+#include <cstdlib>
+#include <memory>
+#include <numeric>
+
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+
+#include <vector>
+
+using af::dim4;
+using af::dtype_traits;
+
+using oneapi::jit::BufferNode;
+using common::half;
+using common::Node;
+using common::Node_ptr;
+using common::NodeIterator;
+
+using nonstd::span;
+using std::accumulate;
+using std::is_standard_layout;
+using std::make_shared;
+using std::shared_ptr;
+using std::vector;
+
+using sycl::buffer;
+
+namespace oneapi {
+namespace {
+template<typename T>
+shared_ptr<BufferNode<T>> bufferNodePtr() {
+    return make_shared<BufferNode<T>>(
+        static_cast<af::dtype>(dtype_traits<T>::af_type));
+}
+
+template<typename T>
+void verifyTypeSupport() {}
+
+template<>
+void verifyTypeSupport<double>() {
+    if (!isDoubleSupported(getActiveDeviceId())) {
+        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
+    }
+}
+
+template<>
+void verifyTypeSupport<cdouble>() {
+    if (!isDoubleSupported(getActiveDeviceId())) {
+        AF_ERROR("Double precision not supported", AF_ERR_NO_DBL);
+    }
+}
+
+template<>
+void verifyTypeSupport<common::half>() {
+    if (!isHalfSupported(getActiveDeviceId())) {
+        AF_ERROR("Half precision not supported", AF_ERR_NO_HALF);
+    }
+}
+}  // namespace
+
+template<typename T>
+Array<T>::Array(const dim4 &dims)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(memAlloc<T>(info.elements()).release(), bufferFree<T>)
+    , data_dims(dims)
+    , node()
+    , owner(true) {}
+
+template<typename T>
+Array<T>::Array(const dim4 &dims, Node_ptr n)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data_dims(dims)
+    , node(std::move(n))
+    , owner(true) {
+    if (node->isBuffer()) {
+        data = std::static_pointer_cast<BufferNode<T>>(node)->getDataPointer();
+    }
+}
+
+template<typename T>
+Array<T>::Array(const dim4 &dims, const T *const in_data)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(memAlloc<T>(info.elements()).release(), bufferFree<T>)
+    , data_dims(dims)
+    , node()
+    , owner(true) {
+    static_assert(is_standard_layout<Array<T>>::value,
+                  "Array<T> must be a standard layout type");
+    static_assert(std::is_nothrow_move_assignable<Array<T>>::value,
+                  "Array<T> is not move assignable");
+    static_assert(std::is_nothrow_move_constructible<Array<T>>::value,
+                  "Array<T> is not move constructible");
+    static_assert(
+        offsetof(Array<T>, info) == 0,
+        "Array<T>::info must be the first member variable of Array<T>");
+    // TODO(oneapi): Copy to buffer
+    //getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
+    //sizeof(T) * info.elements(), in_data);
+}
+
+template<typename T>
+Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
+                bool copy)
+    : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(
+           copy ? memAlloc<T>(info.elements()).release() : new buffer<T>(*mem),
+          bufferFree<T>)
+    , data_dims(dims)
+    , node()
+    , owner(true) {
+    if (copy) {
+      //clRetainMemObject(mem);
+      //buffer src_buf = buffer(mem);
+        // TODO(oneapi): copy buffer
+        ONEAPI_NOT_SUPPORTED("Buffer constructor not implamented");
+        //getQueue().enqueueCopyBuffer(src_buf, *data.get(), src_offset, 0,
+        //sizeof(T) * info.elements());
+    }
+}
+
+template<typename T>
+Array<T>::Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset_,
+                const dim4 &stride)
+    : info(parent.getDevId(), dims, offset_, stride,
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(parent.getData())
+    , data_dims(parent.getDataDims())
+    , node()
+    , owner(false) {}
+
+template<typename T>
+Array<T>::Array(Param<T> &tmp, bool owner_)
+    : info(getActiveDeviceId(),
+           dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
+                tmp.info.dims[3]),
+           0,
+           dim4(tmp.info.strides[0], tmp.info.strides[1], tmp.info.strides[2],
+                tmp.info.strides[3]),
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(
+           tmp.data, owner_ ? bufferFree<T> : [](buffer<T> * /*unused*/) {})
+    , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
+                     tmp.info.dims[3]))
+    , node()
+    , owner(owner_) {}
+
+template<typename T>
+Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
+                const T *const in_data, bool is_device)
+    : info(getActiveDeviceId(), dims, offset_, strides,
+           static_cast<af_dtype>(dtype_traits<T>::af_type))
+    , data(is_device ? (new buffer<T>(*reinterpret_cast<buffer<T>*>(
+                                                                    const_cast<T *>(in_data))))
+                     : (memAlloc<T>(info.elements()).release()),
+           bufferFree<T>)
+    , data_dims(dims)
+    , node()
+    , owner(true) {
+    if (!is_device) {
+      ONEAPI_NOT_SUPPORTED("Write to buffer from Host");
+          //getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
+          //sizeof(T) * info.total(), in_data);
+    }
+}
+
+template<typename T>
+void Array<T>::eval() {
+    if (isReady()) { return; }
+
+    this->setId(getActiveDeviceId());
+    data = std::shared_ptr<buffer<T>>(memAlloc<T>(info.elements()).release(),
+                                       bufferFree<T>);
+
+    ONEAPI_NOT_SUPPORTED("JIT Not supported");
+    // Do not replace this with cast operator
+    Param<T> info; //= {{dims()[0], dims()[1], dims()[2], dims()[3]},
+                  // {strides()[0], strides()[1], strides()[2], strides()[3]},
+                  // 0};
+
+    Param<T> res;// = {data.get(), info};
+
+    evalNodes(res, getNode().get());
+    node.reset();
+}
+
+template<typename T>
+void Array<T>::eval() const {
+    const_cast<Array<T> *>(this)->eval();
+}
+
+template<typename T>
+buffer<T> *Array<T>::device() {
+    if (!isOwner() || getOffset() || data.use_count() > 1) {
+        *this = copyArray<T>(*this);
+    }
+    return this->get();
+}
+
+template<typename T>
+void evalMultiple(vector<Array<T> *> arrays) {
+    vector<Param<T>> outputs;
+    vector<Array<T> *> output_arrays;
+    vector<Node *> nodes;
+
+    ONEAPI_NOT_SUPPORTED("JIT Not supported");
+    // // Check if all the arrays have the same dimension
+    // auto it = std::adjacent_find(begin(arrays), end(arrays),
+    //                              [](const Array<T> *l, const Array<T> *r) {
+    //                                  return l->dims() != r->dims();
+    //                              });
+
+    // // If they are not the same. eval individually
+    // if (it != end(arrays)) {
+    //     for (auto ptr : arrays) { ptr->eval(); }
+    //     return;
+    // }
+
+    // for (Array<T> *array : arrays) {
+    //     if (array->isReady()) { continue; }
+
+    //     const ArrayInfo info = array->info;
+
+    //     array->setId(getActiveDeviceId());
+    //     array->data = std::shared_ptr<buffer<T>>(
+    //         memAlloc<T>(info.elements()).release(), bufferFree<T>);
+
+    //     // Do not replace this with cast operator
+    //     Param<T> kInfo = {
+    //         {info.dims()[0], info.dims()[1], info.dims()[2], info.dims()[3]},
+    //         {info.strides()[0], info.strides()[1], info.strides()[2],
+    //          info.strides()[3]},
+    //         0};
+
+    //     outputs.emplace_back(array->data.get(), kInfo);
+    //     output_arrays.push_back(array);
+    //     nodes.push_back(array->getNode().get());
+    // }
+
+    // evalNodes(outputs, nodes);
+
+    // for (Array<T> *array : output_arrays) { array->node.reset(); }
+}
+
+template<typename T>
+Node_ptr Array<T>::getNode() {
+    if (node) { return node; }
+
+    KParam kinfo   = *this;
+    unsigned bytes = this->dims().elements() * sizeof(T);
+    auto nn        = bufferNodePtr<T>();
+    nn->setData(kinfo, data, bytes, isLinear());
+
+    return nn;
+}
+
+template<typename T>
+Node_ptr Array<T>::getNode() const {
+    return const_cast<Array<T> *>(this)->getNode();
+}
+
+/// This function should be called after a new JIT node is created. It will
+/// return true if the newly created node will generate a valid kernel. If
+/// false the node will fail to compile or the node and its referenced buffers
+/// are consuming too many resources. If false, the node's child nodes should
+/// be evaluated before continuing.
+///
+/// We eval in the following cases:
+///
+/// 1. Too many bytes are locked up by JIT causing memory
+///    pressure. Too many bytes is assumed to be half of all bytes
+///    allocated so far.
+///
+/// 2. The number of parameters we are passing into the kernel exceeds the
+///    limitation on the platform. For NVIDIA this is 4096 bytes. The
+template<typename T>
+kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
+    if (!evalFlag()) { return kJITHeuristics::Pass; }
+    for (const Node *n : root_nodes) {
+        if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            return kJITHeuristics::TreeHeight;
+        }
+    }
+
+    bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
+    auto platform      = getActivePlatform();
+
+    // The Apple platform can have the nvidia card or the AMD card
+    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
+    // bool isIntel = platform == AFCL_PLATFORM_INTEL;
+
+    // /// Intels param_size limit is much smaller than the other platforms
+    // /// so we need to start checking earlier with smaller trees
+    // int heightCheckLimit =
+    //     isIntel && getDeviceType() == CL_DEVICE_TYPE_GPU ? 3 : 6;
+
+    // // A lightweight check based on the height of the node. This is
+    // // an inexpensive operation and does not traverse the JIT tree.
+    // bool atHeightLimit =
+    //     std::any_of(std::begin(root_nodes), std::end(root_nodes),
+    //                 [heightCheckLimit](Node *n) {
+    //                     return (n->getHeight() + 1 >= heightCheckLimit);
+    //                 });
+
+    // if (atHeightLimit || isBufferLimit) {
+    //     // This is the base parameter size if the kernel had no
+    //     // arguments
+    //     size_t base_param_size =
+    //         (sizeof(T *) + sizeof(Param<T>)) * root_nodes.size() +
+    //         (3 * sizeof(uint));
+
+    //     const cl::Device &device = getDevice();
+    //     size_t max_param_size = device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
+    //     // typical values:
+    //     //   NVIDIA     = 4096
+    //     //   AMD        = 3520  (AMD A10 iGPU = 1024)
+    //     //   Intel iGPU = 1024
+    //     max_param_size -= base_param_size;
+
+    //     struct tree_info {
+    //         size_t total_buffer_size;
+    //         size_t num_buffers;
+    //         size_t param_scalar_size;
+    //     };
+
+    //     tree_info info{0, 0, 0};
+    //     for (Node *n : root_nodes) {
+    //         NodeIterator<> it(n);
+    //         info = accumulate(
+    //             it, NodeIterator<>(), info, [](tree_info &prev, Node &n) {
+    //                 if (n.isBuffer()) {
+    //                     auto &buf_node = static_cast<BufferNode &>(n);
+    //                     // getBytes returns the size of the data Array.
+    //                     // Sub arrays will be represented by their parent
+    //                     // size.
+    //                     prev.total_buffer_size += buf_node.getBytes();
+    //                     prev.num_buffers++;
+    //                 } else {
+    //                     prev.param_scalar_size += n.getParamBytes();
+    //                 }
+    //                 return prev;
+    //             });
+    //     }
+    //     isBufferLimit = jitTreeExceedsMemoryPressure(info.total_buffer_size);
+
+    //     size_t param_size = (info.num_buffers * (sizeof(Param<T>) + sizeof(T *)) +
+    //                          info.param_scalar_size);
+
+    //     bool isParamLimit = param_size >= max_param_size;
+
+    //     if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
+    //     if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
+    // }
+    return kJITHeuristics::Pass;
+}
+
+template<typename T>
+void *getDevicePtr(const Array<T> &arr) {
+    const buffer<T> *buf = arr.device();
+    //if (!buf) { return NULL; }
+    //memLock(buf);
+    //cl_mem mem = (*buf)();
+    return (void *)buf;
+}
+
+template<typename T>
+Array<T> createNodeArray(const dim4 &dims, Node_ptr node) {
+    verifyTypeSupport<T>();
+    Array<T> out = Array<T>(dims, node);
+    return out;
+}
+
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent, const vector<af_seq> &index,
+                        bool copy) {
+    parent.eval();
+
+    dim4 dDims          = parent.getDataDims();
+    dim4 parent_strides = parent.strides();
+
+    if (parent.isLinear() == false) {
+        const Array<T> parentCopy = copyArray(parent);
+        return createSubArray(parentCopy, index, copy);
+    }
+
+    const dim4 &pDims = parent.dims();
+
+    dim4 dims    = toDims(index, pDims);
+    dim4 strides = toStride(index, dDims);
+
+    // Find total offsets after indexing
+    dim4 offsets = toOffset(index, pDims);
+    dim_t offset = parent.getOffset();
+    for (int i = 0; i < 4; i++) { offset += offsets[i] * parent_strides[i]; }
+
+    Array<T> out = Array<T>(parent, dims, offset, strides);
+
+    if (!copy) { return out; }
+
+    if (strides[0] != 1 || strides[1] < 0 || strides[2] < 0 || strides[3] < 0) {
+        out = copyArray(out);
+    }
+
+    return out;
+}
+
+template<typename T>
+Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
+    verifyTypeSupport<T>();
+    return Array<T>(dims, data);
+}
+
+template<typename T>
+Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+    verifyTypeSupport<T>();
+
+    bool copy_device = false;
+    return Array<T>(dims, static_cast<buffer<T>*>(data), 0, copy_device);
+}
+
+template<typename T>
+Array<T> createValueArray(const dim4 &dims, const T &value) {
+    verifyTypeSupport<T>();
+    return createScalarNode<T>(dims, value);
+}
+
+template<typename T>
+Array<T> createEmptyArray(const dim4 &dims) {
+    verifyTypeSupport<T>();
+    return Array<T>(dims);
+}
+
+template<typename T>
+Array<T> createParamArray(Param<T> &tmp, bool owner) {
+    verifyTypeSupport<T>();
+    return Array<T>(tmp, owner);
+}
+
+template<typename T>
+void destroyArray(Array<T> *A) {
+    delete A;
+}
+
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data,
+                        const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
+
+    ONEAPI_NOT_SUPPORTED("writeHostDataArray Not supported");
+    //getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(), bytes,
+    //data);
+}
+
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes) {
+    if (!arr.isOwner()) { arr = copyArray<T>(arr); }
+
+    buffer<T> &buf = *arr.get();
+
+    //clRetainMemObject(
+    //    reinterpret_cast<buffer<T> *>(const_cast<void *>(data)));
+    //buffer<T> data_buf =
+    //  buffer<T>(reinterpret_cast<buffer<T>*>(const_cast<void *>(data)));
+
+    ONEAPI_NOT_SUPPORTED("writeDeviceDataArray not supported");
+    //getQueue().enqueueCopyBuffer(data_buf, buf, 0,
+    //static_cast<size_t>(arr.getOffset()), bytes);
+}
+
+template<typename T>
+void Array<T>::setDataDims(const dim4 &new_dims) {
+    data_dims = new_dims;
+    modDims(new_dims);
+}
+
+template<typename T>
+size_t Array<T>::getAllocatedBytes() const {
+    return 0;
+    /*
+    if (!isReady()) { return 0; }
+    size_t bytes = memoryManager().allocated(data.get());
+    // External device pointer
+    if (bytes == 0 && data.get()) { return data_dims.elements() * sizeof(T); }
+    return bytes;
+    */
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> createHostDataArray<T>(const dim4 &dims,                \
+                                             const T *const data);            \
+    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
+    template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
+    template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
+    template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);           \
+    template Array<T> createSubArray<T>(                                      \
+        const Array<T> &parent, const vector<af_seq> &index, bool copy);      \
+    template void destroyArray<T>(Array<T> * A);                              \
+    template Array<T> createNodeArray<T>(const dim4 &dims, Node_ptr node);    \
+    template Array<T>::Array(const dim4 &dims, const dim4 &strides,           \
+                             dim_t offset, const T *const in_data,            \
+                             bool is_device);                                 \
+    template Array<T>::Array(const dim4 &dims, buffer<T>* mem, size_t src_offset, \
+                             bool copy);                                      \
+    template Node_ptr Array<T>::getNode();                                    \
+    template Node_ptr Array<T>::getNode() const;                              \
+    template void Array<T>::eval();                                           \
+    template void Array<T>::eval() const;                                     \
+    template buffer<T> *Array<T>::device();                             \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
+                                        const size_t bytes);                  \
+    template void writeDeviceDataArray<T>(                                    \
+        Array<T> & arr, const void *const data, const size_t bytes);          \
+    template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
+    template kJITHeuristics passesJitHeuristics<T>(span<Node *> node);        \
+    template void *getDevicePtr<T>(const Array<T> &arr);                      \
+    template void Array<T>::setDataDims(const dim4 &new_dims);                \
+    template size_t Array<T>::getAllocatedBytes() const;
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
new file mode 100644
index 0000000000..eb010385d4
--- /dev/null
+++ b/src/backend/oneapi/Array.hpp
@@ -0,0 +1,327 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/ArrayInfo.hpp>
+#include <common/MemoryManagerBase.hpp>
+#include <common/jit/Node.hpp>
+//#include <err_opencl.hpp>
+//#include <jit/BufferNode.hpp>
+//#include <memory.hpp>
+//#include <platform.hpp>
+//#include <traits.hpp>
+//#include <types.hpp>
+
+//#include <af/dim4.hpp>
+
+#include <nonstd/span.hpp>
+#include <algorithm>
+#include <cstdlib>
+#include <memory>
+#include <vector>
+
+namespace common {
+template<typename T>
+class SparseArray;
+}
+
+namespace oneapi {
+
+template<typename T>
+using Buffer_ptr = std::shared_ptr<sycl::buffer<T>>;
+using af::dim4;
+template<typename T>
+class Array;
+
+template<typename T>
+void evalMultiple(std::vector<Array<T> *> arrays);
+
+  template<typename T>
+void evalNodes(Param<T> &out, common::Node *node);
+
+  template<typename T>
+  void evalNodes(std::vector<Param<T>> &outputs,
+                 const std::vector<common::Node *> &nodes);
+
+  /// Creates a new Array object on the heap and returns a reference to it.
+  template<typename T>
+  Array<T> createNodeArray(const af::dim4 &dims, common::Node_ptr node);
+
+  /// Creates a new Array object on the heap and returns a reference to it.
+  template<typename T>
+  Array<T> createValueArray(const af::dim4 &dims, const T &value);
+
+  /// Creates a new Array object on the heap and returns a reference to it.
+  template<typename T>
+  Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
+
+  template<typename T>
+  Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
+
+  template<typename T>
+  Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
+                              dim_t offset, const T *const in_data,
+                              bool is_device) {
+      return Array<T>(dims, strides, offset, in_data, is_device);
+}
+
+/// Copies data to an existing Array object from a host pointer
+template<typename T>
+void writeHostDataArray(Array<T> &arr, const T *const data, const size_t bytes);
+
+/// Copies data to an existing Array object from a device pointer
+template<typename T>
+void writeDeviceDataArray(Array<T> &arr, const void *const data,
+                          const size_t bytes);
+
+/// Creates an empty array of a given size. No data is initialized
+///
+/// \param[in] size The dimension of the output array
+template<typename T>
+Array<T> createEmptyArray(const af::dim4 &dims);
+
+/// Create an Array object from Param object.
+///
+/// \param[in] in    The Param array that is created.
+/// \param[in] owner If true, the new Array<T> object is the owner of the data.
+/// If false
+///                  the Array<T> will not delete the object on destruction
+template<typename T>
+Array<T> createParamArray(Param<T> &tmp, bool owner);
+
+template<typename T>
+Array<T> createSubArray(const Array<T> &parent,
+                        const std::vector<af_seq> &index, bool copy = true);
+
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+void destroyArray(Array<T> *A);
+
+/// \brief Checks if the Node can be compiled successfully and the buffers
+///        references are not consuming most of the allocated memory
+///
+/// \param [in] node The root node which needs to be checked
+///
+/// \returns false if the kernel generated by this node will fail to compile
+///          or its nodes are consuming too much memory.
+template<typename T>
+kJITHeuristics passesJitHeuristics(nonstd::span<common::Node *> node);
+
+template<typename T>
+void *getDevicePtr(const Array<T> &arr);
+
+template<typename T>
+void *getRawPtr(const Array<T> &arr) {
+  //const sycl::buffer<T> *buf = arr.get();
+  //if (!buf) return NULL;
+  //cl_mem mem = (*buf)();
+  //return (void *)mem;
+
+  // TODO:
+  return nullptr;
+}
+
+template<typename T>
+using mapped_ptr = std::unique_ptr<T, std::function<void(void *)>>;
+
+template<typename T>
+class Array {
+    ArrayInfo info;  // This must be the first element of Array<T>
+
+    /// Pointer to the data
+    std::shared_ptr<sycl::buffer<T>> data;
+
+    /// The shape of the underlying parent data.
+    af::dim4 data_dims;
+
+    /// Null if this a buffer node. Otherwise this points to a JIT node
+    common::Node_ptr node;
+
+    /// If true, the Array object is the parent. If false the data object points
+    /// to another array's data
+    bool owner;
+
+    Array(const af::dim4 &dims);
+
+    Array(const Array<T> &parent, const dim4 &dims, const dim_t &offset,
+          const dim4 &stride);
+    Array(Param<T> &tmp, bool owner);
+    explicit Array(const af::dim4 &dims, common::Node_ptr n);
+    explicit Array(const af::dim4 &dims, const T *const in_data);
+
+  explicit Array(const af::dim4 &dims, sycl::buffer<T>* const mem, size_t offset, bool copy);
+
+   public:
+    Array(const Array<T> &other) = default;
+
+    Array(Array<T> &&other) noexcept = default;
+
+    Array<T> &operator=(Array<T> other) noexcept {
+        swap(other);
+        return *this;
+    }
+
+    void swap(Array<T> &other) noexcept {
+        using std::swap;
+        swap(info, other.info);
+        swap(data, other.data);
+        swap(data_dims, other.data_dims);
+        swap(node, other.node);
+        swap(owner, other.owner);
+    }
+
+    Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset,
+          const T *const in_data, bool is_device = false);
+    void resetInfo(const af::dim4 &dims) { info.resetInfo(dims); }
+    void resetDims(const af::dim4 &dims) { info.resetDims(dims); }
+    void modDims(const af::dim4 &newDims) { info.modDims(newDims); }
+    void modStrides(const af::dim4 &newStrides) { info.modStrides(newStrides); }
+    void setId(int id) { info.setId(id); }
+
+#define INFO_FUNC(RET_TYPE, NAME) \
+    RET_TYPE NAME() const { return info.NAME(); }
+
+    INFO_FUNC(const af_dtype &, getType)
+    INFO_FUNC(const af::dim4 &, strides)
+    INFO_FUNC(dim_t, elements)
+    INFO_FUNC(dim_t, ndims)
+    INFO_FUNC(const af::dim4 &, dims)
+    INFO_FUNC(int, getDevId)
+
+#undef INFO_FUNC
+
+#define INFO_IS_FUNC(NAME) \
+    bool NAME() const { return info.NAME(); }
+
+    INFO_IS_FUNC(isEmpty);
+    INFO_IS_FUNC(isScalar);
+    INFO_IS_FUNC(isRow);
+    INFO_IS_FUNC(isColumn);
+    INFO_IS_FUNC(isVector);
+    INFO_IS_FUNC(isComplex);
+    INFO_IS_FUNC(isReal);
+    INFO_IS_FUNC(isDouble);
+    INFO_IS_FUNC(isSingle);
+    INFO_IS_FUNC(isHalf);
+    INFO_IS_FUNC(isRealFloating);
+    INFO_IS_FUNC(isFloating);
+    INFO_IS_FUNC(isInteger);
+    INFO_IS_FUNC(isBool);
+    INFO_IS_FUNC(isLinear);
+    INFO_IS_FUNC(isSparse);
+
+#undef INFO_IS_FUNC
+    ~Array() = default;
+
+    bool isReady() const { return static_cast<bool>(node) == false; }
+    bool isOwner() const { return owner; }
+
+    void eval();
+    void eval() const;
+
+    sycl::buffer<T> *device();
+    sycl::buffer<T> *device() const {
+        return const_cast<Array<T> *>(this)->device();
+    }
+
+    // FIXME: This should do a copy if it is not owner. You do not want to
+    // overwrite parents data
+    sycl::buffer<T> *get() {
+        if (!isReady()) eval();
+        return data.get();
+    }
+
+    const sycl::buffer<T> *get() const {
+        if (!isReady()) eval();
+        return data.get();
+    }
+
+    int useCount() const { return data.use_count(); }
+
+    dim_t getOffset() const { return info.getOffset(); }
+
+    std::shared_ptr<sycl::buffer<T>> getData() const { return data; }
+
+    dim4 getDataDims() const { return data_dims; }
+
+    void setDataDims(const dim4 &new_dims);
+
+    size_t getAllocatedBytes() const;
+
+    operator Param<T>() const {
+        KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
+                       {strides()[0], strides()[1], strides()[2], strides()[3]},
+                       getOffset()};
+
+        Param<T> out{(sycl::buffer<T> *)this->get(), info};
+        return out;
+    }
+
+  operator KParam() const {
+      KParam kinfo = {
+          {dims()[0], dims()[1], dims()[2], dims()[3]},
+          {strides()[0], strides()[1], strides()[2], strides()[3]},
+          getOffset()};
+
+      return kinfo;
+  }
+
+    common::Node_ptr getNode() const;
+    common::Node_ptr getNode();
+
+   public:
+    mapped_ptr<T> getMappedPtr(cl_map_flags map_flags = CL_MAP_READ |
+                                                        CL_MAP_WRITE) const {
+        if (!isReady()) eval();
+        auto func = [data = data](void *ptr) {
+            if (ptr != nullptr) {
+              //cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);
+              //UNUSED(err);
+                ptr = nullptr;
+            }
+        };
+
+        //T *ptr = (T *)getQueue().enqueueMapBuffer(
+            //*static_cast<const sycl::buffer<T> *>(get()), CL_TRUE, map_flags,
+            //getOffset() * sizeof(T), elements() * sizeof(T), nullptr, nullptr,
+            //nullptr);
+
+        return mapped_ptr<T>(nullptr, func);
+    }
+
+    friend void evalMultiple<T>(std::vector<Array<T> *> arrays);
+
+    friend Array<T> createValueArray<T>(const af::dim4 &dims, const T &value);
+    friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
+                                           const T *const data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
+    friend Array<T> createStridedArray<T>(const af::dim4 &dims,
+                                          const af::dim4 &strides, dim_t offset,
+                                          const T *const in_data,
+                                          bool is_device);
+
+    friend Array<T> createEmptyArray<T>(const af::dim4 &dims);
+    friend Array<T> createParamArray<T>(Param<T> &tmp, bool owner);
+    friend Array<T> createNodeArray<T>(const af::dim4 &dims,
+                                       common::Node_ptr node);
+
+    friend Array<T> createSubArray<T>(const Array<T> &parent,
+                                      const std::vector<af_seq> &index,
+                                      bool copy);
+
+    friend void destroyArray<T>(Array<T> *arr);
+    friend void *getDevicePtr<T>(const Array<T> &arr);
+    friend void *getRawPtr<T>(const Array<T> &arr);
+};
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
new file mode 100644
index 0000000000..61ce0f1eae
--- /dev/null
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -0,0 +1,240 @@
+# Copyright (c) 2022, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+include(InternalUtils)
+include(build_cl2hpp)
+
+add_library(afoneapi
+  Array.cpp
+  Array.hpp
+  Event.cpp
+  Event.hpp
+  GraphicsResourceManager.cpp
+  GraphicsResourceManager.hpp
+  Module.hpp
+  Param.hpp
+  all.cpp
+  anisotropic_diffusion.cpp
+  anisotropic_diffusion.hpp
+  any.cpp
+  approx.cpp
+  approx.hpp
+  arith.hpp
+  assign.cpp
+  assign.hpp
+  backend.hpp
+  bilateral.cpp
+  bilateral.hpp
+  binary.hpp
+  blas.cpp
+  blas.hpp
+  canny.cpp
+  canny.hpp
+  cast.hpp
+  cholesky.cpp
+  cholesky.hpp
+  compile_module.cpp
+  complex.hpp
+  convolve.cpp
+  convolve.hpp
+  convolve_separable.cpp
+  copy.cpp
+  copy.hpp
+  count.cpp
+  device_manager.cpp
+  device_manager.hpp
+  diagonal.cpp
+  diagonal.hpp
+  diff.cpp
+  diff.hpp
+  err_oneapi.hpp
+  errorcodes.cpp
+  errorcodes.hpp
+  exampleFunction.cpp
+  exampleFunction.hpp
+  fast.cpp
+  fast.hpp
+  fft.cpp
+  fft.hpp
+  fftconvolve.cpp
+  fftconvolve.hpp
+  flood_fill.cpp
+  flood_fill.hpp
+  gradient.cpp
+  gradient.hpp
+  harris.cpp
+  harris.hpp
+  hist_graphics.cpp
+  hist_graphics.hpp
+  histogram.cpp
+  histogram.hpp
+  homography.cpp
+  homography.hpp
+  hsv_rgb.cpp
+  hsv_rgb.hpp
+  identity.cpp
+  identity.hpp
+  iir.cpp
+  iir.hpp
+  image.cpp
+  image.hpp
+  index.cpp
+  index.hpp
+  inverse.cpp
+  inverse.hpp
+  iota.cpp
+  iota.hpp
+  ireduce.cpp
+  ireduce.hpp
+  jit.cpp
+  join.cpp
+  join.hpp
+  logic.hpp
+  lookup.cpp
+  lookup.hpp
+  lu.cpp
+  lu.hpp
+  match_template.cpp
+  match_template.hpp
+  math.cpp
+  math.hpp
+  max.cpp
+  mean.cpp
+  mean.hpp
+  meanshift.cpp
+  meanshift.hpp
+  medfilt.cpp
+  medfilt.hpp
+  memory.cpp
+  memory.hpp
+  min.cpp
+  moments.cpp
+  moments.hpp
+  morph.cpp
+  morph.hpp
+  nearest_neighbour.cpp
+  nearest_neighbour.hpp
+  orb.cpp
+  orb.hpp
+  platform.cpp
+  platform.hpp
+  plot.cpp
+  plot.hpp
+  print.hpp
+  product.cpp
+  qr.cpp
+  qr.hpp
+  random_engine.cpp
+  random_engine.hpp
+  range.cpp
+  range.hpp
+  reduce.hpp
+  reduce_impl.hpp
+  regions.cpp
+  regions.hpp
+  reorder.cpp
+  reorder.hpp
+  reshape.cpp
+  resize.cpp
+  resize.hpp
+  rotate.cpp
+  rotate.hpp
+  scalar.hpp
+  scan.cpp
+  scan.hpp
+  scan_by_key.cpp
+  scan_by_key.hpp
+  select.cpp
+  select.hpp
+  set.cpp
+  set.hpp
+  shift.cpp
+  shift.hpp
+  sift.cpp
+  sift.hpp
+  sobel.cpp
+  sobel.hpp
+  solve.cpp
+  solve.hpp
+  sort.cpp
+  sort.hpp
+  sort_by_key.cpp
+  sort_by_key.hpp
+  sort_index.cpp
+  sort_index.hpp
+  sparse.cpp
+  sparse.hpp
+  sparse_arith.cpp
+  sparse_arith.hpp
+  sparse_blas.cpp
+  sparse_blas.hpp
+  sum.cpp
+  surface.cpp
+  surface.hpp
+  susan.cpp
+  susan.hpp
+  svd.cpp
+  svd.hpp
+  tile.cpp
+  tile.hpp
+  topk.cpp
+  topk.hpp
+  transform.cpp
+  transform.hpp
+  transpose.cpp
+  transpose_inplace.cpp
+  transpose.hpp
+  triangle.cpp
+  triangle.hpp
+  types.hpp
+  unwrap.cpp
+  unwrap.hpp
+  vector_field.cpp
+  vector_field.hpp
+  where.cpp
+  where.hpp
+  wrap.cpp
+  wrap.hpp
+  )
+
+add_library(ArrayFire::afoneapi ALIAS afoneapi)
+
+arrayfire_set_default_cxx_flags(afoneapi)
+
+target_include_directories(afoneapi
+  PUBLIC
+    $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
+    $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
+  PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${SYCL_INCLUDE_DIR}
+  )
+
+target_compile_options(afoneapi
+  PRIVATE -fsycl)
+
+target_compile_definitions(afoneapi
+  PRIVATE
+    AF_ONEAPI
+  )
+
+target_link_libraries(afoneapi
+  PRIVATE
+    c_api_interface
+    cpp_api_interface
+    afcommon_interface
+    -fsycl
+  )
+
+source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
+source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
+source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
+source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
+source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*)
+source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
+source_group("" FILES CMakeLists.txt)
diff --git a/src/backend/oneapi/Event.cpp b/src/backend/oneapi/Event.cpp
new file mode 100644
index 0000000000..7e08c2fd44
--- /dev/null
+++ b/src/backend/oneapi/Event.cpp
@@ -0,0 +1,78 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Event.hpp>
+
+#include <events.hpp>
+#include <platform.hpp>
+#include <af/event.h>
+#include <memory>
+#include <err_oneapi.hpp>
+
+#include <memory>
+
+using std::make_unique;
+using std::unique_ptr;
+
+namespace oneapi {
+/// \brief Creates a new event and marks it in the queue
+Event makeEvent(sycl::queue& queue) {
+    ONEAPI_NOT_SUPPORTED("makeEvent");
+    return Event();
+}
+
+af_event createEvent() {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+    // auto e = make_unique<Event>();
+    // // Ensure the default CL command queue is initialized
+    // getQueue();
+    // if (e->create() != CL_SUCCESS) {
+    //     AF_ERROR("Could not create event", AF_ERR_RUNTIME);
+    // }
+    // Event& ref = *e.release();
+    // return getHandle(ref);
+}
+
+void markEventOnActiveQueue(af_event eventHandle) {
+    ONEAPI_NOT_SUPPORTED("");
+    //Event& event = getEvent(eventHandle);
+    //// Use the currently-active stream
+    //if (event.mark(getQueue()()) != CL_SUCCESS) {
+    //    AF_ERROR("Could not mark event on active queue", AF_ERR_RUNTIME);
+    //}
+}
+
+void enqueueWaitOnActiveQueue(af_event eventHandle) {
+    ONEAPI_NOT_SUPPORTED("");
+    //Event& event = getEvent(eventHandle);
+    //// Use the currently-active stream
+    //if (event.enqueueWait(getQueue()()) != CL_SUCCESS) {
+    //    AF_ERROR("Could not enqueue wait on active queue for event",
+    //             AF_ERR_RUNTIME);
+    //}
+}
+
+void block(af_event eventHandle) {
+    ONEAPI_NOT_SUPPORTED("");
+    //Event& event = getEvent(eventHandle);
+    //if (event.block() != CL_SUCCESS) {
+    //    AF_ERROR("Could not block on active queue for event", AF_ERR_RUNTIME);
+    //}
+}
+
+af_event createAndMarkEvent() {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+    //af_event handle = createEvent();
+    //markEventOnActiveQueue(handle);
+    //return handle;
+}
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/Event.hpp b/src/backend/oneapi/Event.hpp
new file mode 100644
index 0000000000..bc143283d0
--- /dev/null
+++ b/src/backend/oneapi/Event.hpp
@@ -0,0 +1,64 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <common/EventBase.hpp>
+#include <af/event.h>
+
+namespace oneapi {
+class OneAPIEventPolicy {
+   public:
+    using EventType = sycl::event;
+    using QueueType = sycl::queue;
+    //using ErrorType = sycl::exception; //does this make sense
+    using ErrorType = int;
+
+    static ErrorType createAndMarkEvent(EventType *e) noexcept {
+        // Events are created when you mark them
+        return 0;
+    }
+
+    static ErrorType markEvent(EventType *e, QueueType stream) noexcept {
+        //return clEnqueueMarkerWithWaitList(stream, 0, nullptr, e);
+      return 0;
+    }
+
+    static ErrorType waitForEvent(EventType *e, QueueType stream) noexcept {
+        //return clEnqueueMarkerWithWaitList(stream, 1, e, nullptr);
+      return 0;
+    }
+
+    static ErrorType syncForEvent(EventType *e) noexcept {
+        //return clWaitForEvents(1, e);
+      return 0;
+    }
+
+    static ErrorType destroyEvent(EventType *e) noexcept {
+        //return clReleaseEvent(*e);
+      return 0;
+    }
+};
+
+using Event = common::EventBase<OneAPIEventPolicy>;
+
+/// \brief Creates a new event and marks it in the queue
+Event makeEvent(sycl::queue &queue);
+
+af_event createEvent();
+
+void markEventOnActiveQueue(af_event eventHandle);
+
+void enqueueWaitOnActiveQueue(af_event eventHandle);
+
+void block(af_event eventHandle);
+
+af_event createAndMarkEvent();
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/GraphicsResourceManager.cpp b/src/backend/oneapi/GraphicsResourceManager.cpp
new file mode 100644
index 0000000000..8cf078e8be
--- /dev/null
+++ b/src/backend/oneapi/GraphicsResourceManager.cpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <GraphicsResourceManager.hpp>
+#include <platform.hpp>
+
+namespace oneapi {
+GraphicsResourceManager::ShrdResVector
+GraphicsResourceManager::registerResources(
+    const std::vector<uint32_t>& resources) {
+    ShrdResVector output;
+    return output;
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/GraphicsResourceManager.hpp b/src/backend/oneapi/GraphicsResourceManager.hpp
new file mode 100644
index 0000000000..bdc889708a
--- /dev/null
+++ b/src/backend/oneapi/GraphicsResourceManager.hpp
@@ -0,0 +1,33 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/InteropManager.hpp>
+
+#include <map>
+#include <memory>
+#include <vector>
+
+
+namespace oneapi {
+class GraphicsResourceManager
+    : public common::InteropManager<GraphicsResourceManager, std::byte> {
+   public:
+    using ShrdResVector = std::vector<std::shared_ptr<std::byte>>;
+
+    GraphicsResourceManager() {}
+    static ShrdResVector registerResources(
+        const std::vector<uint32_t>& resources);
+
+   protected:
+    GraphicsResourceManager(GraphicsResourceManager const&);
+    void operator=(GraphicsResourceManager const&);
+};
+}  // namespace oneapi
diff --git a/src/backend/oneapi/Kernel.hpp b/src/backend/oneapi/Kernel.hpp
new file mode 100644
index 0000000000..823fc511ef
--- /dev/null
+++ b/src/backend/oneapi/Kernel.hpp
@@ -0,0 +1,90 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/KernelInterface.hpp>
+#include <common/Logger.hpp>
+
+#include <backend.hpp>
+#include <CL/sycl.hpp>
+#include <string>
+
+namespace oneapi {
+namespace kernel_logger {
+inline auto getLogger() -> spdlog::logger* {
+    static auto logger = common::loggerFactory("kernel");
+    return logger.get();
+}
+}  // namespace kernel_logger
+
+/*
+struct Enqueuer {
+    template<typename... Args>
+    void operator()(std::string name, sycl::kernel ker,
+                    const cl::EnqueueArgs& qArgs, Args&&... args) {
+        auto launchOp = cl::KernelFunctor<Args...>(ker);
+        using namespace kernel_logger;
+        AF_TRACE("Launching {}", name);
+        launchOp(qArgs, std::forward<Args>(args)...);
+    }
+};
+
+class Kernel
+    : public common::KernelInterface<const cl::Program*, cl::Kernel, Enqueuer,
+                                     cl::Buffer*> {
+   public:
+    using ModuleType = const sycl::program*;
+    using KernelType = sycl::kernel;
+    using DevPtrType<T> = sycl::buffer<T>*;
+    using BaseClass =
+        common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType<T>>;
+
+    Kernel() : BaseClass("", nullptr, cl::Kernel{nullptr, false}) {}
+    Kernel(std::string name, ModuleType mod, KernelType ker)
+        : BaseClass(name, mod, ker) {}
+
+    // clang-format off
+    [[deprecated("OpenCL backend doesn't need Kernel::getDevPtr method")]]
+    DevPtrType<T> getDevPtr(const char* name) final;
+    // clang-format on
+
+    void copyToReadOnly(DevPtrType<T> dst, DevPtrType<T> src, size_t bytes) final;
+
+    void setFlag(DevPtrType<T> dst, int* scalarValPtr,
+                 const bool syncCopy = false) final;
+
+    int getFlag(DevPtrType<T> src) final;
+};
+*/
+
+class Kernel {
+   public:
+    using ModuleType = const sycl::kernel_bundle<sycl::bundle_state::executable> *;
+    using KernelType = sycl::kernel;
+  template<typename T>
+    using DevPtrType = sycl::buffer<T>*;
+    //using BaseClass =
+        //common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType<T>>;
+
+    Kernel() {}
+    Kernel(std::string name, ModuleType mod, KernelType ker) {}
+
+    template<typename T>
+    void copyToReadOnly(DevPtrType<T> dst, DevPtrType<T> src, size_t bytes);
+
+    template<typename T>
+    void setFlag(DevPtrType<T> dst, int* scalarValPtr,
+                 const bool syncCopy = false);
+
+    template<typename T>
+    int getFlag(DevPtrType<T> src);
+};
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
new file mode 100644
index 0000000000..1c34306d68
--- /dev/null
+++ b/src/backend/oneapi/Module.hpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/ModuleInterface.hpp>
+#include <CL/sycl.hpp>
+
+
+namespace oneapi {
+
+/// oneapi backend wrapper for cl::Program object
+  class Module : public common::ModuleInterface<sycl::kernel_bundle<sycl::bundle_state::executable>> {
+   public:
+    using ModuleType = sycl::kernel_bundle<sycl::bundle_state::executable>;
+    using BaseClass  = common::ModuleInterface<ModuleType>;
+
+    /// \brief Create an uninitialized Module
+    Module() = default;
+
+    /// \brief Create a module given a sycl::program type
+    Module(ModuleType mod) : BaseClass(mod) {}
+
+    /// \brief Unload module
+    operator bool() const final { return get().empty(); }
+
+    /// Unload the module
+    void unload() final {
+      // TODO(oneapi): Unload kernel/program
+      ;
+    }
+};
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
new file mode 100644
index 0000000000..0536d3dc0c
--- /dev/null
+++ b/src/backend/oneapi/Param.hpp
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <kernel/KParam.hpp>
+
+namespace oneapi {
+
+template<typename T>
+struct Param {
+    sycl::buffer<T>* data;
+    KParam info;
+    Param& operator=(const Param& other) = default;
+    Param(const Param& other)            = default;
+    Param(Param&& other)                 = default;
+
+    // AF_DEPRECATED("Use Array<T>")
+    Param();
+    // AF_DEPRECATED("Use Array<T>")
+    Param(sycl::buffer<T>* data_, KParam info_);
+    ~Param() = default;
+};
+
+// AF_DEPRECATED("Use Array<T>")
+template<typename T>
+Param<T> makeParam(sycl::buffer<T>& mem, int off, const int dims[4],
+                   const int strides[4]);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/all.cpp b/src/backend/oneapi/all.cpp
new file mode 100644
index 0000000000..e74df9806c
--- /dev/null
+++ b/src/backend/oneapi/all.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// alltrue
+INSTANTIATE(af_and_t, float, char)
+INSTANTIATE(af_and_t, double, char)
+INSTANTIATE(af_and_t, cfloat, char)
+INSTANTIATE(af_and_t, cdouble, char)
+INSTANTIATE(af_and_t, int, char)
+INSTANTIATE(af_and_t, uint, char)
+INSTANTIATE(af_and_t, intl, char)
+INSTANTIATE(af_and_t, uintl, char)
+INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, uchar, char)
+INSTANTIATE(af_and_t, short, char)
+INSTANTIATE(af_and_t, ushort, char)
+INSTANTIATE(af_and_t, half, char)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/anisotropic_diffusion.cpp b/src/backend/oneapi/anisotropic_diffusion.cpp
new file mode 100644
index 0000000000..c063736c21
--- /dev/null
+++ b/src/backend/oneapi/anisotropic_diffusion.cpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <anisotropic_diffusion.hpp>
+#include <err_oneapi.hpp>
+#include <copy.hpp>
+#include <af/dim4.hpp>
+
+namespace oneapi {
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
+                          const af::diffusionEq eq) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T)                                     \
+    template void anisotropicDiffusion<T>(                 \
+        Array<T> & inout, const float dt, const float mct, \
+        const af::fluxFunction fftype, const af::diffusionEq eq);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/anisotropic_diffusion.hpp b/src/backend/oneapi/anisotropic_diffusion.hpp
new file mode 100644
index 0000000000..e71d8928ef
--- /dev/null
+++ b/src/backend/oneapi/anisotropic_diffusion.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
+                          const af::fluxFunction fftype,
+                          const af::diffusionEq eq);
+}
diff --git a/src/backend/oneapi/any.cpp b/src/backend/oneapi/any.cpp
new file mode 100644
index 0000000000..3a3e62431f
--- /dev/null
+++ b/src/backend/oneapi/any.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// anytrue
+INSTANTIATE(af_or_t, float, char)
+INSTANTIATE(af_or_t, double, char)
+INSTANTIATE(af_or_t, cfloat, char)
+INSTANTIATE(af_or_t, cdouble, char)
+INSTANTIATE(af_or_t, int, char)
+INSTANTIATE(af_or_t, uint, char)
+INSTANTIATE(af_or_t, intl, char)
+INSTANTIATE(af_or_t, uintl, char)
+INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, uchar, char)
+INSTANTIATE(af_or_t, short, char)
+INSTANTIATE(af_or_t, ushort, char)
+INSTANTIATE(af_or_t, half, char)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
new file mode 100644
index 0000000000..df22448704
--- /dev/null
+++ b/src/backend/oneapi/approx.cpp
@@ -0,0 +1,89 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <approx.hpp>
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid) {
+
+    ONEAPI_NOT_SUPPORTED("");
+    return;
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            //kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    //method, 1);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+            //kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    //method, 2);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+            //kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    //method, 3);
+            break;
+        default: break;
+    }
+}
+
+template<typename Ty, typename Tp>
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid) {
+    ONEAPI_NOT_SUPPORTED("");
+    return;
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            //kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    //yi_beg, yi_step, offGrid, method, 1);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+        case AF_INTERP_BILINEAR_COSINE:
+            //kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    //yi_beg, yi_step, offGrid, method, 2);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+        case AF_INTERP_BICUBIC_SPLINE:
+            //kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    //yi_beg, yi_step, offGrid, method, 3);
+            break;
+        default: break;
+    }
+}
+
+#define INSTANTIATE(Ty, Tp)                                       \
+    template void approx1<Ty, Tp>(                                \
+        Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const af_interp_type method, const float offGrid);        \
+    template void approx2<Ty, Tp>(                                \
+        Array<Ty> & zo, const Array<Ty> &zi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const Array<Tp> &yo, const int ydim, const Tp &yi_beg,    \
+        const Tp &yi_step, const af_interp_type method, const float offGrid);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/approx.hpp b/src/backend/oneapi/approx.hpp
new file mode 100644
index 0000000000..68d06967eb
--- /dev/null
+++ b/src/backend/oneapi/approx.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid);
+
+template<typename Ty, typename Tp>
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/arith.hpp b/src/backend/oneapi/arith.hpp
new file mode 100644
index 0000000000..2a004b5766
--- /dev/null
+++ b/src/backend/oneapi/arith.hpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <common/jit/BinaryNode.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
+
+namespace oneapi {
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &&lhs, const Array<T> &&rhs,
+                 const af::dim4 &odims) {
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
+}
+
+template<typename T, af_op_t op>
+Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
+                 const af::dim4 &odims) {
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/assign.cpp b/src/backend/oneapi/assign.cpp
new file mode 100644
index 0000000000..06e0f63abf
--- /dev/null
+++ b/src/backend/oneapi/assign.cpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <assign.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <handle.hpp>
+#include <memory.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
+    ONEAPI_NOT_SUPPORTED("");
+    return;
+}
+
+#define INSTANTIATE(T)                                                \
+    template void assign<T>(Array<T> & out, const af_index_t idxrs[], \
+                            const Array<T>& rhs);
+
+INSTANTIATE(cdouble)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/assign.hpp b/src/backend/oneapi/assign.hpp
new file mode 100644
index 0000000000..7cb69fb9f4
--- /dev/null
+++ b/src/backend/oneapi/assign.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/index.h>
+
+namespace oneapi {
+
+template<typename T>
+void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
+
+}
diff --git a/src/backend/oneapi/backend.hpp b/src/backend/oneapi/backend.hpp
new file mode 100644
index 0000000000..5c805903c5
--- /dev/null
+++ b/src/backend/oneapi/backend.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include "types.hpp"
+#ifdef __DH__
+#undef __DH__
+#endif
+
+#ifdef __CUDACC__
+#include <opencl_runtime.h>
+#define __DH__ __device__ __host__
+#else
+#define __DH__
+#endif
+
+namespace detail = oneapi;
diff --git a/src/backend/oneapi/bilateral.cpp b/src/backend/oneapi/bilateral.cpp
new file mode 100644
index 0000000000..4fef2afd5e
--- /dev/null
+++ b/src/backend/oneapi/bilateral.cpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <bilateral.hpp>
+#include <af/dim4.hpp>
+#include <err_oneapi.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &sSigma,
+                         const float &cSigma) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<outType> out = createEmptyArray<outType>(in.dims());
+    return out;
+
+}
+
+#define INSTANTIATE(inT, outT)                                    \
+    template Array<outT> bilateral<inT, outT>(const Array<inT> &, \
+                                              const float &, const float &);
+
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/bilateral.hpp b/src/backend/oneapi/bilateral.hpp
new file mode 100644
index 0000000000..14a221f48f
--- /dev/null
+++ b/src/backend/oneapi/bilateral.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename inType, typename outType>
+Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
+                         const float &chromaticSigma);
+}
diff --git a/src/backend/oneapi/binary.hpp b/src/backend/oneapi/binary.hpp
new file mode 100644
index 0000000000..b0d02195b6
--- /dev/null
+++ b/src/backend/oneapi/binary.hpp
@@ -0,0 +1,127 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <optypes.hpp>
+
+namespace oneapi {
+
+template<typename To, typename Ti, af_op_t op>
+struct BinOp;
+
+#define BINARY_TYPE_1(fn)                            \
+    template<typename To, typename Ti>               \
+    struct BinOp<To, Ti, af_##fn##_t> {              \
+        const char *name() { return "__" #fn; }      \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cfloat, af_##fn##_t> {          \
+        const char *name() { return "__c" #fn "f"; } \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cdouble, af_##fn##_t> {         \
+        const char *name() { return "__c" #fn; }     \
+    };
+
+BINARY_TYPE_1(eq)
+BINARY_TYPE_1(neq)
+BINARY_TYPE_1(lt)
+BINARY_TYPE_1(le)
+BINARY_TYPE_1(gt)
+BINARY_TYPE_1(ge)
+BINARY_TYPE_1(add)
+BINARY_TYPE_1(sub)
+BINARY_TYPE_1(mul)
+BINARY_TYPE_1(div)
+BINARY_TYPE_1(and)
+BINARY_TYPE_1(or)
+BINARY_TYPE_1(bitand)
+BINARY_TYPE_1(bitor)
+BINARY_TYPE_1(bitxor)
+BINARY_TYPE_1(bitshiftl)
+BINARY_TYPE_1(bitshiftr)
+
+#undef BINARY_TYPE_1
+
+#define BINARY_TYPE_2(fn)                            \
+    template<typename To, typename Ti>               \
+    struct BinOp<To, Ti, af_##fn##_t> {              \
+        const char *name() { return "__" #fn; }      \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, float, af_##fn##_t> {           \
+        const char *name() { return "f" #fn; }       \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, double, af_##fn##_t> {          \
+        const char *name() { return "f" #fn; }       \
+    };                                               \
+    template<typename To>                            \
+    struct BinOp<To, cfloat, af_##fn##_t> {          \
+        const char *name() { return "__c" #fn "f"; } \
+    };                                               \
+                                                     \
+    template<typename To>                            \
+    struct BinOp<To, cdouble, af_##fn##_t> {         \
+        const char *name() { return "__c" #fn; }     \
+    };
+
+BINARY_TYPE_2(min)
+BINARY_TYPE_2(max)
+BINARY_TYPE_2(rem)
+BINARY_TYPE_2(mod)
+
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_pow_t> {
+    const char *name() { return "__pow"; }
+};
+
+#define POW_BINARY_OP(INTYPE, OPNAME)         \
+    template<typename To>                     \
+    struct BinOp<To, INTYPE, af_pow_t> {      \
+        const char *name() { return OPNAME; } \
+    };
+
+POW_BINARY_OP(double, "pow")
+POW_BINARY_OP(float, "pow")
+POW_BINARY_OP(intl, "__powll")
+POW_BINARY_OP(uintl, "__powul")
+POW_BINARY_OP(uint, "__powui")
+POW_BINARY_OP(int, "__powsi")
+
+#undef POW_BINARY_OP
+
+template<typename Ti>
+struct BinOp<cfloat, Ti, af_cplx2_t> {
+    const char *name() { return "__cplx2f"; }
+};
+
+template<typename Ti>
+struct BinOp<cdouble, Ti, af_cplx2_t> {
+    const char *name() { return "__cplx2"; }
+};
+
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_cplx2_t> {
+    const char *name() { return "noop"; }
+};
+
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_atan2_t> {
+    const char *name() { return "atan2"; }
+};
+
+template<typename To, typename Ti>
+struct BinOp<To, Ti, af_hypot_t> {
+    const char *name() { return "hypot"; }
+};
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
new file mode 100644
index 0000000000..852b277870
--- /dev/null
+++ b/src/backend/oneapi/blas.cpp
@@ -0,0 +1,84 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <blas.hpp>
+
+#include <Array.hpp>
+#include <arith.hpp>
+#include <common/half.hpp>
+#include <common/traits.hpp>
+#include <complex.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+#include <reduce.hpp>
+#include <transpose.hpp>
+
+#include <complex>
+#include <vector>
+
+using common::half;
+
+namespace oneapi {
+
+void initBlas() { /*gpu_blas_init();*/ }
+
+void deInitBlas() { /*gpu_blas_deinit();*/ }
+
+template<typename T>
+void gemm_fallback(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+                   const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
+                   const T *beta) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<>
+void gemm_fallback<half>(Array<half> & /*out*/, af_mat_prop /*optLhs*/,
+                         af_mat_prop /*optRhs*/, const half * /*alpha*/,
+                         const Array<half> & /*lhs*/,
+                         const Array<half> & /*rhs*/, const half * /*beta*/) {
+    ONEAPI_NOT_SUPPORTED("");
+    assert(false && "CPU fallback not implemented for f16");
+}
+
+template<typename T>
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<typename T>
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE_GEMM(TYPE)                                               \
+    template void gemm<TYPE>(Array<TYPE> & out, af_mat_prop optLhs,          \
+                             af_mat_prop optRhs, const TYPE *alpha,          \
+                             const Array<TYPE> &lhs, const Array<TYPE> &rhs, \
+                             const TYPE *beta);
+
+INSTANTIATE_GEMM(float)
+INSTANTIATE_GEMM(cfloat)
+INSTANTIATE_GEMM(double)
+INSTANTIATE_GEMM(cdouble)
+INSTANTIATE_GEMM(half)
+
+#define INSTANTIATE_DOT(TYPE)                                                  \
+    template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
+                                   const Array<TYPE> &rhs, af_mat_prop optLhs, \
+                                   af_mat_prop optRhs);
+
+INSTANTIATE_DOT(float)
+INSTANTIATE_DOT(double)
+INSTANTIATE_DOT(cfloat)
+INSTANTIATE_DOT(cdouble)
+INSTANTIATE_DOT(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/blas.hpp b/src/backend/oneapi/blas.hpp
new file mode 100644
index 0000000000..7371d4884f
--- /dev/null
+++ b/src/backend/oneapi/blas.hpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+
+// This file contains the common interface for OneAPI BLAS
+// functions
+
+namespace oneapi {
+
+void initBlas();
+void deInitBlas();
+
+template<typename T>
+void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
+          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+
+template<typename T>
+Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+                af_mat_prop optRhs) {
+    int Mdim     = optLhs == AF_MAT_NONE ? 0 : 1;
+    int Ndim     = optRhs == AF_MAT_NONE ? 1 : 0;
+    Array<T> res = createEmptyArray<T>(
+        dim4(lhs.dims()[Mdim], rhs.dims()[Ndim], lhs.dims()[2], lhs.dims()[3]));
+    static const T alpha = T(1.0);
+    static const T beta  = T(0.0);
+    gemm(res, optLhs, optRhs, &alpha, lhs, rhs, &beta);
+    return res;
+}
+
+template<typename T>
+Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
+             af_mat_prop optRhs);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/canny.cpp b/src/backend/oneapi/canny.cpp
new file mode 100644
index 0000000000..ac85af2e1b
--- /dev/null
+++ b/src/backend/oneapi/canny.cpp
@@ -0,0 +1,28 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <canny.hpp>
+#include <err_oneapi.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx,
+                                   const Array<float>& gy) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/canny.hpp b/src/backend/oneapi/canny.hpp
new file mode 100644
index 0000000000..25f7f5458b
--- /dev/null
+++ b/src/backend/oneapi/canny.hpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+Array<float> nonMaximumSuppression(const Array<float>& mag,
+                                   const Array<float>& gx,
+                                   const Array<float>& gy);
+
+Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
+                                     const Array<char>& weak);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/cast.hpp b/src/backend/oneapi/cast.hpp
new file mode 100644
index 0000000000..aef3711589
--- /dev/null
+++ b/src/backend/oneapi/cast.hpp
@@ -0,0 +1,73 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <common/jit/UnaryNode.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+
+namespace oneapi {
+
+template<typename To, typename Ti>
+struct CastOp {
+    const char *name() { return ""; }
+};
+
+#define CAST_FN(TYPE)                                   \
+    template<typename Ti>                               \
+    struct CastOp<TYPE, Ti> {                           \
+        const char *name() { return "convert_" #TYPE; } \
+    };
+
+CAST_FN(int)
+CAST_FN(uint)
+CAST_FN(uchar)
+CAST_FN(float)
+CAST_FN(double)
+
+#define CAST_CFN(TYPE)                                    \
+    template<typename Ti>                                 \
+    struct CastOp<TYPE, Ti> {                             \
+        const char *name() { return "__convert_" #TYPE; } \
+    };
+
+CAST_CFN(cfloat)
+CAST_CFN(cdouble)
+CAST_CFN(char)
+
+template<>
+struct CastOp<cfloat, cdouble> {
+    const char *name() { return "__convert_z2c"; }
+};
+
+template<>
+struct CastOp<cdouble, cfloat> {
+    const char *name() { return "__convert_c2z"; }
+};
+
+template<>
+struct CastOp<cfloat, cfloat> {
+    const char *name() { return "__convert_c2c"; }
+};
+
+template<>
+struct CastOp<cdouble, cdouble> {
+    const char *name() { return "__convert_z2z"; }
+};
+
+#undef CAST_FN
+#undef CAST_CFN
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/cholesky.cpp b/src/backend/oneapi/cholesky.cpp
new file mode 100644
index 0000000000..bd6b286654
--- /dev/null
+++ b/src/backend/oneapi/cholesky.cpp
@@ -0,0 +1,70 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <blas.hpp>
+#include <cholesky.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+//#include <triangle.hpp>
+
+namespace oneapi {
+
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
+
+INSTANTIATE_CH(float)
+INSTANTIATE_CH(cfloat)
+INSTANTIATE_CH(double)
+INSTANTIATE_CH(cdouble)
+
+}  // namespace oneapi
+
+#else  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
+    AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
+}
+
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper) {
+    AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
+}
+
+#define INSTANTIATE_CH(T)                                                 \
+    template int cholesky_inplace<T>(Array<T> & in, const bool is_upper); \
+    template Array<T> cholesky<T>(int *info, const Array<T> &in,          \
+                                  const bool is_upper);
+
+INSTANTIATE_CH(float)
+INSTANTIATE_CH(cfloat)
+INSTANTIATE_CH(double)
+INSTANTIATE_CH(cdouble)
+
+}  // namespace oneapi
+
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/cholesky.hpp b/src/backend/oneapi/cholesky.hpp
new file mode 100644
index 0000000000..d934beb566
--- /dev/null
+++ b/src/backend/oneapi/cholesky.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
+
+template<typename T>
+int cholesky_inplace(Array<T> &in, const bool is_upper);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
new file mode 100644
index 0000000000..a682ac7bfd
--- /dev/null
+++ b/src/backend/oneapi/compile_module.cpp
@@ -0,0 +1,131 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/compile_module.hpp>  //compileModule & loadModuleFromDisk
+#include <common/kernel_cache.hpp>    //getKernel(Module&, ...)
+
+#include <CL/sycl.hpp>
+#include <common/Logger.hpp>
+#include <common/defines.hpp>
+#include <common/util.hpp>
+//#include <debug_opencl.hpp> TODO: remove?
+#include <err_oneapi.hpp>
+//#include <kernel_headers/KParam.hpp>
+#include <platform.hpp>
+#include <traits.hpp>
+
+#include <algorithm>
+#include <cctype>
+#include <cstdio>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+using common::loggerFactory;
+using fmt::format;
+//using oneapi::getActiveDeviceId;
+//using oneapi::getDevice;
+using sycl::kernel_bundle;
+using sycl::bundle_state;
+using oneapi::Kernel;
+using oneapi::Module;
+using spdlog::logger;
+
+using std::begin;
+using std::end;
+using std::ofstream;
+using std::ostringstream;
+using std::shared_ptr;
+using std::string;
+using std::to_string;
+using std::transform;
+using std::vector;
+using std::chrono::duration_cast;
+using std::chrono::high_resolution_clock;
+using std::chrono::milliseconds;
+
+logger *getLogger() {
+    static shared_ptr<logger> logger(loggerFactory("jit"));
+    return logger.get();
+}
+
+string getProgramBuildLog(const kernel_bundle<bundle_state::executable> &prog) {
+    ONEAPI_NOT_SUPPORTED("");
+    return "";
+}
+
+//#define THROW_BUILD_LOG_EXCEPTION(PROG)                              \
+//    do {                                                             \
+//        string build_error = getProgramBuildLog(PROG);               \
+//        string info        = getEnvVar("AF_OPENCL_SHOW_BUILD_INFO"); \
+//        if (!info.empty() && info != "0") puts(build_error.c_str()); \
+//        AF_ERROR(build_error, AF_ERR_INTERNAL);                      \
+//    } while (0)
+
+namespace oneapi {
+
+//const static string DEFAULT_MACROS_STR(
+    //"\n\
+                                           //#ifdef USE_DOUBLE\n\
+                                           //#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
+                                           //#endif\n                     \
+                                           //#ifdef USE_HALF\n\
+                                           //#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
+                                           //#else\n                     \
+                                           //#define half short\n          \
+                                           //#endif\n                      \
+                                           //#ifndef M_PI\n               \
+                                           //#define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
+                                           //#endif\n                     \
+                                           //");
+
+/*
+get_kernel_bundle<>() needs sycl::context
+kernel_bundle<bundle_state::executable> buildProgram(const vector<string> &kernelSources,
+                           const vector<string> &compileOpts) {
+    ONEAPI_NOT_SUPPORTED("");
+    kernel_bundle<bundle_state::executable> bb;
+    return bb;
+}
+*/
+
+}  // namespace oneapi
+
+string getKernelCacheFilename(const int device, const string &key) {
+    ONEAPI_NOT_SUPPORTED("");
+    return "";
+}
+
+namespace common {
+
+/*
+Module compileModule(const string &moduleKey, const vector<string> &sources,
+                     const vector<string> &options,
+                     const vector<string> &kInstances, const bool isJIT) {
+    ONEAPI_NOT_SUPPORTED("");
+    Module m{}
+    return m;
+}
+
+Module loadModuleFromDisk(const int device, const string &moduleKey,
+                          const bool isJIT) {
+    ONEAPI_NOT_SUPPORTED("");
+    Module m{}
+    return m;
+}
+
+Kernel getKernel(const Module &mod, const string &nameExpr,
+                 const bool sourceWasJIT) {
+    ONEAPI_NOT_SUPPORTED("");
+    return {nameExpr, &mod.get(), sycl::Kernel()};
+}
+*/
+
+}  // namespace common
diff --git a/src/backend/oneapi/complex.hpp b/src/backend/oneapi/complex.hpp
new file mode 100644
index 0000000000..c087959b42
--- /dev/null
+++ b/src/backend/oneapi/complex.hpp
@@ -0,0 +1,90 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
+#include <common/jit/UnaryNode.hpp>
+#include <optypes.hpp>
+#include <traits.hpp>
+#include <af/dim4.hpp>
+
+namespace oneapi {
+template<typename To, typename Ti>
+Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
+               const af::dim4 &odims) {
+    return common::createBinaryNode<To, Ti, af_cplx2_t>(lhs, rhs, odims);
+}
+
+template<typename To, typename Ti>
+Array<To> real(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
+                              "__creal", in_node, af_real_t);
+
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
+
+template<typename To, typename Ti>
+Array<To> imag(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
+                              "__cimag", in_node, af_imag_t);
+
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
+
+template<typename T>
+static const char *abs_name() {
+    return "fabs";
+}
+template<>
+inline const char *abs_name<cfloat>() {
+    return "__cabsf";
+}
+template<>
+inline const char *abs_name<cdouble>() {
+    return "__cabs";
+}
+
+template<typename To, typename Ti>
+Array<To> abs(const Array<Ti> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<To>::af_type),
+                              abs_name<Ti>(), in_node, af_abs_t);
+
+    return createNodeArray<To>(in.dims(), common::Node_ptr(node));
+}
+
+template<typename T>
+static const char *conj_name() {
+    return "__noop";
+}
+template<>
+inline const char *conj_name<cfloat>() {
+    return "__cconjf";
+}
+template<>
+inline const char *conj_name<cdouble>() {
+    return "__cconj";
+}
+
+template<typename T>
+Array<T> conj(const Array<T> &in) {
+    common::Node_ptr in_node = in.getNode();
+    common::UnaryNode *node =
+        new common::UnaryNode(static_cast<af::dtype>(dtype_traits<T>::af_type),
+                              conj_name<T>(), in_node, af_conj_t);
+
+    return createNodeArray<T>(in.dims(), common::Node_ptr(node));
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/convolve.cpp b/src/backend/oneapi/convolve.cpp
new file mode 100644
index 0000000000..94e6d48d09
--- /dev/null
+++ b/src/backend/oneapi/convolve.cpp
@@ -0,0 +1,125 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <blas.hpp>
+#include <common/half.hpp>
+#include <common/indexing_helpers.hpp>
+#include <common/moddims.hpp>
+#include <convolve.hpp>
+#include <err_oneapi.hpp>
+#include <reorder.hpp>
+#include <transpose.hpp>
+#include <unwrap.hpp>
+#include <wrap.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+#include <vector>
+
+using af::dim4;
+using common::flip;
+using common::half;
+using common::modDims;
+using std::vector;
+
+namespace oneapi {
+
+template<typename T, typename accT>
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
+                  AF_BATCH_KIND kind, const int rank, const bool expand) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+#define INSTANTIATE(T, accT)                                                   \
+    template Array<T> convolve<T, accT>(Array<T> const &, Array<accT> const &, \
+                                        AF_BATCH_KIND, const int, const bool);
+
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
+                          const dim4 &stride, const dim4 &padding,
+                          const dim4 &dilation) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+template<typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> convolve2<T>(Array<T> const &signal,                    \
+                                   Array<T> const &filter, const dim4 stride, \
+                                   const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> & /*convolved_output*/,
+                           af::dim4 stride, af::dim4 padding,
+                           af::dim4 dilation) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+template<typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> & /*convolved_output*/,
+                             af::dim4 stride, af::dim4 padding,
+                             af::dim4 dilation) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> conv2DataGradient<T>(                                 \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);        \
+    template Array<T> conv2FilterGradient<T>(                               \
+        Array<T> const &incoming_gradient, Array<T> const &original_signal, \
+        Array<T> const &original_filter, Array<T> const &convolved_output,  \
+        const dim4 stride, const dim4 padding, const dim4 dilation);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/convolve.hpp b/src/backend/oneapi/convolve.hpp
new file mode 100644
index 0000000000..7fbf2e86a1
--- /dev/null
+++ b/src/backend/oneapi/convolve.hpp
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T, typename accT>
+Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
+                  AF_BATCH_KIND kind, const int rank, const bool expand);
+
+template<typename T, typename accT>
+Array<T> convolve2(Array<T> const &signal, Array<accT> const &c_filter,
+                   Array<accT> const &r_filter, const bool expand);
+
+template<typename T>
+Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
+                   const dim4 stride, const dim4 padding, const dim4 dilation);
+
+template<typename T>
+Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
+                           const Array<T> &original_signal,
+                           const Array<T> &original_filter,
+                           const Array<T> &convolved_output, af::dim4 stride,
+                           af::dim4 padding, af::dim4 dilation);
+
+template<typename T>
+Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
+                             const Array<T> &original_signal,
+                             const Array<T> &original_filter,
+                             const Array<T> &convolved_output, af::dim4 stride,
+                             af::dim4 padding, af::dim4 dilation);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/convolve_separable.cpp b/src/backend/oneapi/convolve_separable.cpp
new file mode 100644
index 0000000000..d9b1e1f64a
--- /dev/null
+++ b/src/backend/oneapi/convolve_separable.cpp
@@ -0,0 +1,45 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <convolve.hpp>
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T, typename accT>
+Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
+                   Array<accT> const& r_filter, const bool expand) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+#define INSTANTIATE(T, accT)                                                  \
+    template Array<T> convolve2<T, accT>(Array<T> const&, Array<accT> const&, \
+                                         Array<accT> const&, const bool);
+
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(intl, float)
+INSTANTIATE(uintl, float)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
new file mode 100644
index 0000000000..5e708bb593
--- /dev/null
+++ b/src/backend/oneapi/copy.cpp
@@ -0,0 +1,150 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <copy.hpp>
+
+#include <Array.hpp>
+#include <common/complex.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+
+using common::half;
+using common::is_complex;
+
+namespace oneapi {
+
+template<typename T>
+void copyData(T *data, const Array<T> &A) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<typename T>
+Array<T> copyArray(const Array<T> &A) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dim4(1));
+    return out;
+}
+
+template<typename T>
+void multiply_inplace(Array<T> &in, double val) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<typename inType, typename outType>
+struct copyWrapper {
+    void operator()(Array<outType> &out, Array<inType> const &in) {
+        ONEAPI_NOT_SUPPORTED("");
+    }
+};
+
+template<typename T>
+struct copyWrapper<T, T> {
+    void operator()(Array<T> &out, Array<T> const &in) {
+        ONEAPI_NOT_SUPPORTED("");
+    }
+};
+
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, Array<inType> const &in) {
+    static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
+                  "Cannot copy from complex value to a non complex value");
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T)                                         \
+    template void copyData<T>(T * data, const Array<T> &from); \
+    template Array<T> copyArray<T>(const Array<T> &A);         \
+    template void multiply_inplace<T>(Array<T> & in, double norm);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+#define INSTANTIATE_COPY_ARRAY(SRC_T)                                 \
+    template void copyArray<SRC_T, float>(Array<float> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, double>(Array<double> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,     \
+                                            Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, int>(Array<int> & dst,             \
+                                        Array<SRC_T> const &src);     \
+    template void copyArray<SRC_T, uint>(Array<uint> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, intl>(Array<intl> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, uintl>(Array<uintl> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, short>(Array<short> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
+                                           Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
+                                          Array<SRC_T> const &src);   \
+    template void copyArray<SRC_T, char>(Array<char> & dst,           \
+                                         Array<SRC_T> const &src);    \
+    template void copyArray<SRC_T, half>(Array<half> & dst,           \
+                                         Array<SRC_T> const &src);
+
+INSTANTIATE_COPY_ARRAY(float)
+INSTANTIATE_COPY_ARRAY(double)
+INSTANTIATE_COPY_ARRAY(int)
+INSTANTIATE_COPY_ARRAY(uint)
+INSTANTIATE_COPY_ARRAY(intl)
+INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(uchar)
+INSTANTIATE_COPY_ARRAY(char)
+INSTANTIATE_COPY_ARRAY(short)
+INSTANTIATE_COPY_ARRAY(ushort)
+INSTANTIATE_COPY_ARRAY(half)
+
+#define INSTANTIATE_COPY_ARRAY_COMPLEX(SRC_T)                        \
+    template void copyArray<SRC_T, cfloat>(Array<cfloat> & dst,      \
+                                           Array<SRC_T> const &src); \
+    template void copyArray<SRC_T, cdouble>(Array<cdouble> & dst,    \
+                                            Array<SRC_T> const &src);
+
+INSTANTIATE_COPY_ARRAY_COMPLEX(cfloat)
+INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
+
+template<typename T>
+T getScalar(const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    return (T)0;
+}
+
+#define INSTANTIATE_GETSCALAR(T) template T getScalar(const Array<T> &in);
+
+INSTANTIATE_GETSCALAR(float)
+INSTANTIATE_GETSCALAR(double)
+INSTANTIATE_GETSCALAR(cfloat)
+INSTANTIATE_GETSCALAR(cdouble)
+INSTANTIATE_GETSCALAR(int)
+INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(uchar)
+INSTANTIATE_GETSCALAR(char)
+INSTANTIATE_GETSCALAR(intl)
+INSTANTIATE_GETSCALAR(uintl)
+INSTANTIATE_GETSCALAR(short)
+INSTANTIATE_GETSCALAR(ushort)
+INSTANTIATE_GETSCALAR(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/copy.hpp b/src/backend/oneapi/copy.hpp
new file mode 100644
index 0000000000..00f01a8ac4
--- /dev/null
+++ b/src/backend/oneapi/copy.hpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <Array.hpp>
+//#include <kernel/pad_array_borders.hpp>
+
+namespace oneapi {
+template<typename T>
+void copyData(T *data, const Array<T> &A);
+
+template<typename T>
+Array<T> copyArray(const Array<T> &A);
+
+template<typename inType, typename outType>
+void copyArray(Array<outType> &out, const Array<inType> &in);
+
+// Resize Array to target dimensions and convert type
+//
+// Depending on the \p outDims, the output Array can be either truncated
+// or padded (towards end of respective dimensions).
+//
+// While resizing copying, if output dimensions are larger than input, then
+// elements beyond the input dimensions are set to the \p defaultValue.
+//
+// \param[in] in is input Array
+// \param[in] outDims is the target output dimensions
+// \param[in] defaultValue is the value to which padded locations are set.
+// \param[in] scale is the value by which all output elements are scaled.
+//
+// \returns Array<outType>
+template<typename inType, typename outType>
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue = outType(0), double scale = 1.0);
+
+template<typename T>
+Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
+                         dim4 const &upperBoundPadding,
+                         const af::borderType btype) {
+    auto iDims = in.dims();
+
+    dim4 oDims(lowerBoundPadding[0] + iDims[0] + upperBoundPadding[0],
+               lowerBoundPadding[1] + iDims[1] + upperBoundPadding[1],
+               lowerBoundPadding[2] + iDims[2] + upperBoundPadding[2],
+               lowerBoundPadding[3] + iDims[3] + upperBoundPadding[3]);
+
+    if (oDims == iDims) { return in; }
+
+    auto ret = createEmptyArray<T>(oDims);
+
+    //kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
+
+    return ret;
+}
+
+template<typename T>
+void multiply_inplace(Array<T> &in, double val);
+
+template<typename T>
+T getScalar(const Array<T> &in);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/count.cpp b/src/backend/oneapi/count.cpp
new file mode 100644
index 0000000000..d50f35b694
--- /dev/null
+++ b/src/backend/oneapi/count.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// count
+INSTANTIATE(af_notzero_t, float, uint)
+INSTANTIATE(af_notzero_t, double, uint)
+INSTANTIATE(af_notzero_t, cfloat, uint)
+INSTANTIATE(af_notzero_t, cdouble, uint)
+INSTANTIATE(af_notzero_t, int, uint)
+INSTANTIATE(af_notzero_t, uint, uint)
+INSTANTIATE(af_notzero_t, intl, uint)
+INSTANTIATE(af_notzero_t, uintl, uint)
+INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, uchar, uint)
+INSTANTIATE(af_notzero_t, short, uint)
+INSTANTIATE(af_notzero_t, ushort, uint)
+INSTANTIATE(af_notzero_t, half, uint)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
new file mode 100644
index 0000000000..5ef59d2682
--- /dev/null
+++ b/src/backend/oneapi/device_manager.cpp
@@ -0,0 +1,95 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/graphics_common.hpp>
+
+#include <GraphicsResourceManager.hpp>
+#include <common/DefaultMemoryManager.hpp>
+#include <common/MemoryManagerBase.hpp>
+#include <common/Logger.hpp>
+#include <common/defines.hpp>
+#include <common/host_memory.hpp>
+#include <common/util.hpp>
+#include <device_manager.hpp>
+#include <err_oneapi.hpp>
+//#include <errorcodes.hpp>
+#include <version.hpp>
+//#include <af/oneapi.h>
+#include <af/version.h>
+#include <memory>
+
+#ifdef OS_MAC
+#include <OpenGL/CGLCurrent.h>
+#endif
+
+#include <algorithm>
+#include <iterator>
+#include <sstream>
+#include <string>
+#include <vector>
+
+using std::begin;
+using std::end;
+using std::find;
+using std::make_unique;
+using std::string;
+using std::stringstream;
+using std::unique_ptr;
+using std::vector;
+using sycl::device;
+
+namespace oneapi {
+
+bool checkExtnAvailability(const device& pDevice, const string& pName) {
+    ONEAPI_NOT_SUPPORTED("");
+    return false;
+}
+
+DeviceManager::DeviceManager()
+    : logger(common::loggerFactory("platform"))
+    , mUserDeviceOffset(0)
+    , fgMngr(nullptr) {
+}
+
+spdlog::logger* DeviceManager::getLogger() { return logger.get(); }
+
+DeviceManager& DeviceManager::getInstance() {
+    ONEAPI_NOT_SUPPORTED("");
+    static auto* my_instance = new DeviceManager();
+    return *my_instance;
+}
+
+void DeviceManager::setMemoryManager(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void DeviceManager::resetMemoryManager() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void DeviceManager::setMemoryManagerPinned(
+    std::unique_ptr<MemoryManagerBase> newMgr) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void DeviceManager::resetMemoryManagerPinned() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+DeviceManager::~DeviceManager() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void DeviceManager::markDeviceForInterop(const int device,
+                                         const void* wHandle) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
new file mode 100644
index 0000000000..b4f291afc2
--- /dev/null
+++ b/src/backend/oneapi/device_manager.hpp
@@ -0,0 +1,163 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <memory>
+#include <mutex>
+#include <string>
+#include <vector>
+
+#ifndef AF_OPENCL_MEM_DEBUG
+#define AF_OPENCL_MEM_DEBUG 0
+#endif
+
+namespace boost {
+template<typename T>
+class shared_ptr;
+}  // namespace boost
+
+namespace spdlog {
+class logger;
+}
+
+namespace graphics {
+class ForgeManager;
+}
+
+namespace common {
+namespace memory {
+class MemoryManagerBase;
+}
+}  // namespace common
+
+using common::memory::MemoryManagerBase;
+
+namespace oneapi {
+
+// opencl namespace forward declarations
+class GraphicsResourceManager;
+struct kc_entry_t;  // kernel cache entry
+
+class DeviceManager {
+    friend MemoryManagerBase& memoryManager();
+
+    friend void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManager();
+
+    void resetMemoryManager();
+
+    friend MemoryManagerBase& pinnedMemoryManager();
+
+    friend void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+    friend void resetMemoryManagerPinned();
+
+    void resetMemoryManagerPinned();
+
+    friend graphics::ForgeManager& forgeManager();
+
+    friend GraphicsResourceManager& interopManager();
+
+    //friend PlanCache& fftManager();
+
+    friend void addKernelToCache(int device, const std::string& key,
+                                 const kc_entry_t entry);
+
+    friend void removeKernelFromCache(int device, const std::string& key);
+
+    friend kc_entry_t kernelCache(int device, const std::string& key);
+
+    friend std::string getDeviceInfo() noexcept;
+
+    friend int getDeviceCount() noexcept;
+
+    //friend int getDeviceIdFromNativeId(cl_device_id id);
+
+    friend const sycl::context& getContext();
+
+    friend sycl::queue& getQueue();
+
+    friend const sycl::device& getDevice(int id);
+
+    friend size_t getDeviceMemorySize(int device);
+
+    friend bool isGLSharingSupported();
+
+    friend bool isDoubleSupported(unsigned device);
+
+    friend bool isHalfSupported(unsigned device);
+
+    friend void devprop(char* d_name, char* d_platform, char* d_toolkit,
+                        char* d_compute);
+
+    friend int setDevice(int device);
+
+/*
+    friend void addDeviceContext(cl_device_id dev, cl_context ctx,
+                                 cl_command_queue que);
+
+    friend void setDeviceContext(cl_device_id dev, cl_context ctx);
+
+    friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
+*/
+
+    friend int getActiveDeviceType();
+
+    friend int getActivePlatform();
+
+   public:
+    static const int MAX_DEVICES = 32;
+
+    static DeviceManager& getInstance();
+
+    ~DeviceManager();
+
+    spdlog::logger* getLogger();
+
+   protected:
+    DeviceManager();
+
+    // Following two declarations are required to
+    // avoid copying accidental copy/assignment
+    // of instance returned by getInstance to other
+    // variables
+    DeviceManager(DeviceManager const&);
+    void operator=(DeviceManager const&);
+    void markDeviceForInterop(const int device, const void* wHandle);
+
+   private:
+    // Attributes
+    std::shared_ptr<spdlog::logger> logger;
+    std::mutex deviceMutex;
+    std::vector<std::unique_ptr<sycl::device>> mDevices;
+    std::vector<std::unique_ptr<sycl::context>> mContexts;
+    std::vector<std::unique_ptr<sycl::queue>> mQueues;
+    std::vector<bool> mIsGLSharingOn;
+    std::vector<int> mDeviceTypes;
+    std::vector<int> mPlatforms;
+    unsigned mUserDeviceOffset;
+
+    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<MemoryManagerBase> memManager;
+    std::unique_ptr<MemoryManagerBase> pinnedMemManager;
+    std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
+    std::mutex mutex;
+
+  //using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
+  //std::vector<BoostProgCache*> mBoostProgCacheVector;
+};
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/diagonal.cpp b/src/backend/oneapi/diagonal.cpp
new file mode 100644
index 0000000000..f22b2440c2
--- /dev/null
+++ b/src/backend/oneapi/diagonal.cpp
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <diagonal.hpp>
+#include <err_oneapi.hpp>
+//#include <kernel/diagonal.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num) {
+    ONEAPI_NOT_SUPPORTED("");
+    int size     = in.dims()[0] + std::abs(num);
+    int batch    = in.dims()[1];
+    Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
+    return out;
+}
+
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num) {
+    ONEAPI_NOT_SUPPORTED("");
+    const dim_t *idims = in.dims().get();
+    dim_t size         = std::min(idims[0], idims[1]) - std::abs(num);
+    Array<T> out       = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
+
+    return out;
+}
+
+#define INSTANTIATE_DIAGONAL(T)                                          \
+    template Array<T> diagExtract<T>(const Array<T> &in, const int num); \
+    template Array<T> diagCreate<T>(const Array<T> &in, const int num);
+
+INSTANTIATE_DIAGONAL(float)
+INSTANTIATE_DIAGONAL(double)
+INSTANTIATE_DIAGONAL(cfloat)
+INSTANTIATE_DIAGONAL(cdouble)
+INSTANTIATE_DIAGONAL(int)
+INSTANTIATE_DIAGONAL(uint)
+INSTANTIATE_DIAGONAL(intl)
+INSTANTIATE_DIAGONAL(uintl)
+INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(uchar)
+INSTANTIATE_DIAGONAL(short)
+INSTANTIATE_DIAGONAL(ushort)
+INSTANTIATE_DIAGONAL(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/diagonal.hpp b/src/backend/oneapi/diagonal.hpp
new file mode 100644
index 0000000000..28b4f46df6
--- /dev/null
+++ b/src/backend/oneapi/diagonal.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> diagCreate(const Array<T> &in, const int num);
+
+template<typename T>
+Array<T> diagExtract(const Array<T> &in, const int num);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/diff.cpp b/src/backend/oneapi/diff.cpp
new file mode 100644
index 0000000000..7dfffc1881
--- /dev/null
+++ b/src/backend/oneapi/diff.cpp
@@ -0,0 +1,61 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <diff.hpp>
+//#include <kernel/diff.hpp>
+#include <af/dim4.hpp>
+#include <stdexcept>
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> diff(const Array<T> &in, const int dim, const bool isDiff2) {
+    ONEAPI_NOT_SUPPORTED("");
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
+    oDims[dim] -= (isDiff2 + 1);
+
+    if (iDims.elements() == 0 || oDims.elements() == 0) {
+        throw std::runtime_error("Elements are 0");
+    }
+    Array<T> out = createEmptyArray<T>(oDims);
+    return out;
+}
+
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim) {
+    ONEAPI_NOT_SUPPORTED("");
+    return diff<T>(in, dim, false);
+}
+
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim) {
+    ONEAPI_NOT_SUPPORTED("");
+    return diff<T>(in, dim, true);
+}
+
+#define INSTANTIATE(T)                                             \
+    template Array<T> diff1<T>(const Array<T> &in, const int dim); \
+    template Array<T> diff2<T>(const Array<T> &in, const int dim);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(char)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/diff.hpp b/src/backend/oneapi/diff.hpp
new file mode 100644
index 0000000000..d7f5aaf477
--- /dev/null
+++ b/src/backend/oneapi/diff.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> diff1(const Array<T> &in, const int dim);
+
+template<typename T>
+Array<T> diff2(const Array<T> &in, const int dim);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/err_oneapi.hpp b/src/backend/oneapi/err_oneapi.hpp
new file mode 100644
index 0000000000..ff6c83d6ca
--- /dev/null
+++ b/src/backend/oneapi/err_oneapi.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/err_common.hpp>
+
+#define ONEAPI_NOT_SUPPORTED(message)                                       \
+    do {                                                                    \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
+                           boost::stacktrace::stacktrace());                \
+    } while (0)
diff --git a/src/backend/oneapi/errorcodes.cpp b/src/backend/oneapi/errorcodes.cpp
new file mode 100644
index 0000000000..615bbb94e7
--- /dev/null
+++ b/src/backend/oneapi/errorcodes.cpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <errorcodes.hpp>
+#include <err_oneapi.hpp>
+
+
+std::string getErrorMessage(int error_code) {
+    ONEAPI_NOT_SUPPORTED("");
+    //return boost::compute::opencl_error::to_string(error_code);
+    return "";
+}
diff --git a/src/backend/oneapi/errorcodes.hpp b/src/backend/oneapi/errorcodes.hpp
new file mode 100644
index 0000000000..ff30326ae9
--- /dev/null
+++ b/src/backend/oneapi/errorcodes.hpp
@@ -0,0 +1,14 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <string>
+
+std::string getErrorMessage(int error_code);
diff --git a/src/backend/oneapi/exampleFunction.cpp b/src/backend/oneapi/exampleFunction.cpp
new file mode 100644
index 0000000000..dc5c6a8680
--- /dev/null
+++ b/src/backend/oneapi/exampleFunction.cpp
@@ -0,0 +1,65 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>  // header with oneapi backend specific
+                      // Array class implementation that inherits
+                      // ArrayInfo base class
+
+#include <exampleFunction.hpp>  // oneapi backend function header
+
+#include <err_oneapi.hpp>  // error check functions and Macros
+                           // specific to oneapi backend
+
+//#include <kernel/exampleFunction.hpp>  // this header under the folder src/oneapi/kernel
+                                       // defines the OneAPI kernel wrapper
+// function to which the main computation of your
+// algorithm should be relayed to
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method) {
+    ONEAPI_NOT_SUPPORTED("");
+    dim4 outputDims;  // this should be '= in.dims();' in most cases
+                      // but would definitely depend on the type of
+                      // algorithm you are implementing.
+
+    Array<T> out = createEmptyArray<T>(outputDims);
+    // Please use the create***Array<T> helper
+    // functions defined in Array.hpp to create
+    // different types of Arrays. Please check the
+    // file to know what are the different types you
+    // can create.
+
+    // Relay the actual computation to OneAPI kernel wrapper
+    //kernel::exampleFunc<T>(out, a, b, method);
+
+    return out;  // return the result
+}
+
+#define INSTANTIATE(T)                                                         \
+    template Array<T> exampleFunction<T>(const Array<T> &a, const Array<T> &b, \
+                                         const af_someenum_t method);
+
+// INSTANTIATIONS for all the types which
+// are present in the switch case statement
+// in src/api/c/exampleFunction.cpp should be available
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/exampleFunction.hpp b/src/backend/oneapi/exampleFunction.hpp
new file mode 100644
index 0000000000..7f51018f83
--- /dev/null
+++ b/src/backend/oneapi/exampleFunction.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
+                         const af_someenum_t method);
+}
diff --git a/src/backend/oneapi/fast.cpp b/src/backend/oneapi/fast.cpp
new file mode 100644
index 0000000000..25f8c47e6a
--- /dev/null
+++ b/src/backend/oneapi/fast.cpp
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
+
+using af::dim4;
+using af::features;
+
+namespace oneapi {
+
+template<typename T>
+unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
+              const Array<T> &in, const float thr, const unsigned arc_length,
+              const bool non_max, const float feature_ratio,
+              const unsigned edge) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+#define INSTANTIATE(T)                                                        \
+    template unsigned fast<T>(                                                \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const float thr, const unsigned arc_length,       \
+        const bool nonmax, const float feature_ratio, const unsigned edge);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/fast.hpp b/src/backend/oneapi/fast.hpp
new file mode 100644
index 0000000000..19667cf49e
--- /dev/null
+++ b/src/backend/oneapi/fast.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/features.h>
+
+using af::features;
+
+namespace oneapi {
+
+template<typename T>
+unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
+              const Array<T> &in, const float thr, const unsigned arc_length,
+              const bool non_max, const float feature_ratio,
+              const unsigned edge);
+
+}
diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
new file mode 100644
index 0000000000..684cc860b7
--- /dev/null
+++ b/src/backend/oneapi/fft.cpp
@@ -0,0 +1,106 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <fft.hpp>
+
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+void setFFTPlanCacheSize(size_t numPlans) {
+}
+
+/*
+template<typename T>
+struct Precision;
+template<>
+struct Precision<cfloat> {
+    enum { type = CLFFT_SINGLE };
+};
+template<>
+struct Precision<cdouble> {
+    enum { type = CLFFT_DOUBLE };
+};
+*/
+
+void computeDims(size_t rdims[AF_MAX_DIMS], const dim4 &idims) {
+    for (int i = 0; i < AF_MAX_DIMS; i++) {
+        rdims[i] = static_cast<size_t>(idims[i]);
+    }
+}
+
+//(currently) true is in clFFT if length is a power of 2,3,5
+inline bool isSupLen(dim_t length) {
+    while (length > 1) {
+        if (length % 2 == 0) {
+            length /= 2;
+        } else if (length % 3 == 0) {
+            length /= 3;
+        } else if (length % 5 == 0) {
+            length /= 5;
+        } else if (length % 7 == 0) {
+            length /= 7;
+        } else if (length % 11 == 0) {
+            length /= 11;
+        } else if (length % 13 == 0) {
+            length /= 13;
+        } else {
+            return false;
+        }
+    }
+    return true;
+}
+
+void verifySupported(const int rank, const dim4 &dims) {
+    for (int i = 0; i < rank; i++) { ARG_ASSERT(1, isSupLen(dims[i])); }
+}
+
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
+    ONEAPI_NOT_SUPPORTED("");
+    dim4 odims = in.dims();
+
+    odims[0] = odims[0] / 2 + 1;
+
+    Array<Tc> out = createEmptyArray<Tc>(odims);
+    return out;
+}
+
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<Tr> out = createEmptyArray<Tr>(odims);
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template void fft_inplace<T>(Array<T> &, const int, const bool);
+
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+
+#define INSTANTIATE_REAL(Tr, Tc)                                        \
+    template Array<Tc> fft_r2c<Tc, Tr>(const Array<Tr> &, const int);   \
+    template Array<Tr> fft_c2r<Tr, Tc>(const Array<Tc> &, const dim4 &, \
+                                       const int);
+
+INSTANTIATE_REAL(float, cfloat)
+INSTANTIATE_REAL(double, cdouble)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/fft.hpp b/src/backend/oneapi/fft.hpp
new file mode 100644
index 0000000000..57de589db2
--- /dev/null
+++ b/src/backend/oneapi/fft.hpp
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+void setFFTPlanCacheSize(size_t numPlans);
+
+template<typename T>
+void fft_inplace(Array<T> &in, const int rank, const bool direction);
+
+template<typename Tc, typename Tr>
+Array<Tc> fft_r2c(const Array<Tr> &in, const int rank);
+
+template<typename Tr, typename Tc>
+Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/fftconvolve.cpp b/src/backend/oneapi/fftconvolve.cpp
new file mode 100644
index 0000000000..5a2a64d869
--- /dev/null
+++ b/src/backend/oneapi/fftconvolve.cpp
@@ -0,0 +1,82 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <fftconvolve.hpp>
+
+#include <Array.hpp>
+#include <common/dispatch.hpp>
+#include <fft.hpp>
+#include <af/dim4.hpp>
+#include <err_oneapi.hpp>
+
+#include <cmath>
+#include <type_traits>
+#include <vector>
+
+using af::dim4;
+using std::ceil;
+using std::conditional;
+using std::is_integral;
+using std::is_same;
+using std::vector;
+
+namespace oneapi {
+
+template<typename T>
+dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2, const dim_t rank) {
+    const dim4& i1d = i1.dims();
+    const dim4& i2d = i2.dims();
+
+    dim_t pd[4] = {1, 1, 1, 1};
+
+    // Pack both signal and filter on same memory array, this will ensure
+    // better use of batched cuFFT capabilities
+    pd[0] = nextpow2(static_cast<unsigned>(
+        static_cast<int>(ceil(i1d[0] / 2.f)) + i2d[0] - 1));
+
+    for (dim_t k = 1; k < rank; k++) {
+        pd[k] = nextpow2(static_cast<unsigned>(i1d[k] + i2d[k] - 1));
+    }
+
+    dim_t i1batch = 1;
+    dim_t i2batch = 1;
+    for (int k = rank; k < 4; k++) {
+        i1batch *= i1d[k];
+        i2batch *= i2d[k];
+    }
+    pd[rank] = (i1batch + i2batch);
+
+    return dim4(pd[0], pd[1], pd[2], pd[3]);
+}
+
+template<typename T>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind, const int rank) {
+    ONEAPI_NOT_SUPPORTED("");
+    dim4 oDims(1);
+    Array<T> out = createEmptyArray<T>(oDims);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                 \
+    template Array<T> fftconvolve<T>(Array<T> const&, Array<T> const&, \
+                                     const bool, AF_BATCH_KIND, const int);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(int)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(uintl)
+INSTANTIATE(intl)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/fftconvolve.hpp b/src/backend/oneapi/fftconvolve.hpp
new file mode 100644
index 0000000000..7eac7750aa
--- /dev/null
+++ b/src/backend/oneapi/fftconvolve.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
+                     const bool expand, AF_BATCH_KIND kind, const int rank);
+}
diff --git a/src/backend/oneapi/flood_fill.cpp b/src/backend/oneapi/flood_fill.cpp
new file mode 100644
index 0000000000..a336a441ec
--- /dev/null
+++ b/src/backend/oneapi/flood_fill.cpp
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <flood_fill.hpp>
+
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup) {
+    ONEAPI_NOT_SUPPORTED("");
+    auto out = createValueArray(image.dims(), T(0));
+    return out;
+}
+
+#define INSTANTIATE(T)                                                         \
+    template Array<T> floodFill(const Array<T>&, const Array<uint>&,           \
+                                const Array<uint>&, const T, const T, const T, \
+                                const af::connectivity);
+
+INSTANTIATE(float)
+INSTANTIATE(uint)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/flood_fill.hpp b/src/backend/oneapi/flood_fill.hpp
new file mode 100644
index 0000000000..6590f33e59
--- /dev/null
+++ b/src/backend/oneapi/flood_fill.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <af/defines.h>
+
+namespace oneapi {
+template<typename T>
+Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
+                   const Array<uint>& seedsY, const T newValue,
+                   const T lowValue, const T highValue,
+                   const af::connectivity nlookup = AF_CONNECTIVITY_8);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/gradient.cpp b/src/backend/oneapi/gradient.cpp
new file mode 100644
index 0000000000..0755b7a691
--- /dev/null
+++ b/src/backend/oneapi/gradient.cpp
@@ -0,0 +1,31 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <gradient.hpp>
+#include <err_oneapi.hpp>
+//#include <kernel/gradient.hpp>
+#include <math.hpp>
+#include <stdexcept>
+
+namespace oneapi {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T)                                            \
+    template void gradient<T>(Array<T> & grad0, Array<T> & grad1, \
+                              const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/gradient.hpp b/src/backend/oneapi/gradient.hpp
new file mode 100644
index 0000000000..e5ebff012c
--- /dev/null
+++ b/src/backend/oneapi/gradient.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
+}
diff --git a/src/backend/oneapi/harris.cpp b/src/backend/oneapi/harris.cpp
new file mode 100644
index 0000000000..ef6b844fd4
--- /dev/null
+++ b/src/backend/oneapi/harris.cpp
@@ -0,0 +1,40 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
+
+using af::dim4;
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename convAccT>
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &score_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned harris<T, convAccT>(                                    \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned max_corners,                       \
+        const float min_response, const float sigma,                          \
+        const unsigned filter_len, const float k_thr);
+
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/harris.hpp b/src/backend/oneapi/harris.hpp
new file mode 100644
index 0000000000..8eeef1dcc3
--- /dev/null
+++ b/src/backend/oneapi/harris.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/features.h>
+
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename convAccT>
+unsigned harris(Array<float> &x_out, Array<float> &y_out,
+                Array<float> &score_out, const Array<T> &in,
+                const unsigned max_corners, const float min_response,
+                const float sigma, const unsigned filter_len,
+                const float k_thr);
+
+}
diff --git a/src/backend/oneapi/hist_graphics.cpp b/src/backend/oneapi/hist_graphics.cpp
new file mode 100644
index 0000000000..12d9bb2b33
--- /dev/null
+++ b/src/backend/oneapi/hist_graphics.cpp
@@ -0,0 +1,32 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
+#include <err_oneapi.hpp>
+#include <hist_graphics.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void copy_histogram(const Array<T> &data, fg_histogram hist) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T) \
+    template void copy_histogram<T>(const Array<T> &, fg_histogram);
+
+INSTANTIATE(float)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/hist_graphics.hpp b/src/backend/oneapi/hist_graphics.hpp
new file mode 100644
index 0000000000..4be3935750
--- /dev/null
+++ b/src/backend/oneapi/hist_graphics.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/graphics_common.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void copy_histogram(const Array<T> &data, fg_histogram hist);
+
+}
diff --git a/src/backend/oneapi/histogram.cpp b/src/backend/oneapi/histogram.cpp
new file mode 100644
index 0000000000..cf85c4e844
--- /dev/null
+++ b/src/backend/oneapi/histogram.cpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <histogram.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear) {
+    ONEAPI_NOT_SUPPORTED("");
+    const dim4 &dims = in.dims();
+    dim4 outDims     = dim4(nbins, 1, dims[2], dims[3]);
+    Array<uint> out  = createValueArray<uint>(outDims, uint(0));
+    return out;
+}
+
+#define INSTANTIATE(T)                                                    \
+    template Array<uint> histogram<T>(const Array<T> &, const unsigned &, \
+                                      const double &, const double &,     \
+                                      const bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/histogram.hpp b/src/backend/oneapi/histogram.hpp
new file mode 100644
index 0000000000..f899faffbe
--- /dev/null
+++ b/src/backend/oneapi/histogram.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
+                      const double &minval, const double &maxval,
+                      const bool isLinear);
+}
diff --git a/src/backend/oneapi/homography.cpp b/src/backend/oneapi/homography.cpp
new file mode 100644
index 0000000000..e9b08cc475
--- /dev/null
+++ b/src/backend/oneapi/homography.cpp
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <homography.hpp>
+
+#include <arith.hpp>
+#include <af/dim4.hpp>
+#include <err_oneapi.hpp>
+
+#include <algorithm>
+#include <limits>
+
+using af::dim4;
+using std::numeric_limits;
+
+namespace oneapi {
+
+template<typename T>
+int homography(Array<T> &bestH, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
+               const af_homography_type htype, const float inlier_thr,
+               const unsigned iterations) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+#define INSTANTIATE(T)                                                     \
+    template int homography(                                               \
+        Array<T> &H, const Array<float> &x_src, const Array<float> &y_src, \
+        const Array<float> &x_dst, const Array<float> &y_dst,              \
+        const Array<float> &initial, const af_homography_type htype,       \
+        const float inlier_thr, const unsigned iterations);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/homography.hpp b/src/backend/oneapi/homography.hpp
new file mode 100644
index 0000000000..6c4e54be66
--- /dev/null
+++ b/src/backend/oneapi/homography.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+int homography(Array<T> &H, const Array<float> &x_src,
+               const Array<float> &y_src, const Array<float> &x_dst,
+               const Array<float> &y_dst, const Array<float> &initial,
+               const af_homography_type htype, const float inlier_thr,
+               const unsigned iterations);
+
+}
diff --git a/src/backend/oneapi/hsv_rgb.cpp b/src/backend/oneapi/hsv_rgb.cpp
new file mode 100644
index 0000000000..6902f0f6c2
--- /dev/null
+++ b/src/backend/oneapi/hsv_rgb.cpp
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <hsv_rgb.hpp>
+
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> hsv2rgb(const Array<T>& in) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(in.dims());
+    return out;
+}
+
+template<typename T>
+Array<T> rgb2hsv(const Array<T>& in) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(in.dims());
+    return out;
+}
+
+#define INSTANTIATE(T)                                \
+    template Array<T> hsv2rgb<T>(const Array<T>& in); \
+    template Array<T> rgb2hsv<T>(const Array<T>& in);
+
+INSTANTIATE(double)
+INSTANTIATE(float)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/hsv_rgb.hpp b/src/backend/oneapi/hsv_rgb.hpp
new file mode 100644
index 0000000000..e46da55a80
--- /dev/null
+++ b/src/backend/oneapi/hsv_rgb.hpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> hsv2rgb(const Array<T>& in);
+
+template<typename T>
+Array<T> rgb2hsv(const Array<T>& in);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/identity.cpp b/src/backend/oneapi/identity.cpp
new file mode 100644
index 0000000000..ccb633aef2
--- /dev/null
+++ b/src/backend/oneapi/identity.cpp
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <identity.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <af/dim4.hpp>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+Array<T> identity(const dim4& dims) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(dims);
+    return out;
+}
+
+#define INSTANTIATE_IDENTITY(T) \
+    template Array<T> identity<T>(const af::dim4& dims);
+
+INSTANTIATE_IDENTITY(float)
+INSTANTIATE_IDENTITY(double)
+INSTANTIATE_IDENTITY(cfloat)
+INSTANTIATE_IDENTITY(cdouble)
+INSTANTIATE_IDENTITY(int)
+INSTANTIATE_IDENTITY(uint)
+INSTANTIATE_IDENTITY(intl)
+INSTANTIATE_IDENTITY(uintl)
+INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(uchar)
+INSTANTIATE_IDENTITY(short)
+INSTANTIATE_IDENTITY(ushort)
+INSTANTIATE_IDENTITY(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/identity.hpp b/src/backend/oneapi/identity.hpp
new file mode 100644
index 0000000000..b9fed4aa03
--- /dev/null
+++ b/src/backend/oneapi/identity.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> identity(const dim4& dim);
+}
diff --git a/src/backend/oneapi/iir.cpp b/src/backend/oneapi/iir.cpp
new file mode 100644
index 0000000000..9051e34b5f
--- /dev/null
+++ b/src/backend/oneapi/iir.cpp
@@ -0,0 +1,37 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <arith.hpp>
+#include <convolve.hpp>
+#include <err_oneapi.hpp>
+#include <iir.hpp>
+//#include <kernel/iir.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+template<typename T>
+Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> y = createEmptyArray<T>(dim4(1));
+    return y;
+}
+
+#define INSTANTIATE(T)                                          \
+    template Array<T> iir(const Array<T> &b, const Array<T> &a, \
+                          const Array<T> &x);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/iir.hpp b/src/backend/oneapi/iir.hpp
new file mode 100644
index 0000000000..6f7d052119
--- /dev/null
+++ b/src/backend/oneapi/iir.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
+}
diff --git a/src/backend/oneapi/image.cpp b/src/backend/oneapi/image.cpp
new file mode 100644
index 0000000000..8406294a44
--- /dev/null
+++ b/src/backend/oneapi/image.cpp
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
+#include <err_oneapi.hpp>
+#include <image.hpp>
+
+#include <stdexcept>
+#include <vector>
+
+namespace oneapi {
+
+template<typename T>
+void copy_image(const Array<T> &in, fg_image image) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T) template void copy_image<T>(const Array<T> &, fg_image);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/image.hpp b/src/backend/oneapi/image.hpp
new file mode 100644
index 0000000000..5647efea36
--- /dev/null
+++ b/src/backend/oneapi/image.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/graphics_common.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void copy_image(const Array<T> &in, fg_image image);
+
+}
diff --git a/src/backend/oneapi/index.cpp b/src/backend/oneapi/index.cpp
new file mode 100644
index 0000000000..481da0f9ec
--- /dev/null
+++ b/src/backend/oneapi/index.cpp
@@ -0,0 +1,46 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <index.hpp>
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+#include <handle.hpp>
+#include <memory.hpp>
+#include <af/dim4.hpp>
+
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(af::dim4(1));
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> index<T>(const Array<T>& in, const af_index_t idxrs[]);
+
+INSTANTIATE(cdouble)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(float)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/index.hpp b/src/backend/oneapi/index.hpp
new file mode 100644
index 0000000000..d8fdb674b5
--- /dev/null
+++ b/src/backend/oneapi/index.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/index.h>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
+
+}
diff --git a/src/backend/oneapi/inverse.cpp b/src/backend/oneapi/inverse.cpp
new file mode 100644
index 0000000000..60026719db
--- /dev/null
+++ b/src/backend/oneapi/inverse.cpp
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <err_oneapi.hpp>
+#include <identity.hpp>
+#include <solve.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+#include <platform.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> inverse(const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> I = identity<T>(in.dims());
+    return I;
+}
+
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+
+}  // namespace oneapi
+
+#else  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+template<typename T>
+Array<T> inverse(const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend", AF_ERR_NOT_CONFIGURED);
+}
+
+#define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+
+}  // namespace oneapi
+
+#endif
diff --git a/src/backend/oneapi/inverse.hpp b/src/backend/oneapi/inverse.hpp
new file mode 100644
index 0000000000..2011950ed1
--- /dev/null
+++ b/src/backend/oneapi/inverse.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> inverse(const Array<T> &in);
+}
diff --git a/src/backend/oneapi/iota.cpp b/src/backend/oneapi/iota.cpp
new file mode 100644
index 0000000000..92fbbd2ede
--- /dev/null
+++ b/src/backend/oneapi/iota.cpp
@@ -0,0 +1,43 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <iota.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
+    ONEAPI_NOT_SUPPORTED("");
+    dim4 outdims = dims * tile_dims;
+
+    Array<T> out = createEmptyArray<T>(outdims);
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/iota.hpp b/src/backend/oneapi/iota.hpp
new file mode 100644
index 0000000000..fe9b1cdf8c
--- /dev/null
+++ b/src/backend/oneapi/iota.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
+}
diff --git a/src/backend/oneapi/ireduce.cpp b/src/backend/oneapi/ireduce.cpp
new file mode 100644
index 0000000000..cf97ad3a4a
--- /dev/null
+++ b/src/backend/oneapi/ireduce.cpp
@@ -0,0 +1,78 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <ireduce.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <optypes.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+
+using af::dim4;
+using common::half;
+
+namespace oneapi {
+
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    return T(0);
+}
+
+#define INSTANTIATE(ROp, T)                                           \
+    template void ireduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim); \
+    template void rreduce<ROp, T>(Array<T> & out, Array<uint> & loc,  \
+                                  const Array<T> &in, const int dim,  \
+                                  const Array<uint> &rlen);           \
+    template T ireduce_all<ROp, T>(unsigned *loc, const Array<T> &in);
+
+// min
+INSTANTIATE(af_min_t, float)
+INSTANTIATE(af_min_t, double)
+INSTANTIATE(af_min_t, cfloat)
+INSTANTIATE(af_min_t, cdouble)
+INSTANTIATE(af_min_t, int)
+INSTANTIATE(af_min_t, uint)
+INSTANTIATE(af_min_t, intl)
+INSTANTIATE(af_min_t, uintl)
+INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, uchar)
+INSTANTIATE(af_min_t, short)
+INSTANTIATE(af_min_t, ushort)
+INSTANTIATE(af_min_t, half)
+
+// max
+INSTANTIATE(af_max_t, float)
+INSTANTIATE(af_max_t, double)
+INSTANTIATE(af_max_t, cfloat)
+INSTANTIATE(af_max_t, cdouble)
+INSTANTIATE(af_max_t, int)
+INSTANTIATE(af_max_t, uint)
+INSTANTIATE(af_max_t, intl)
+INSTANTIATE(af_max_t, uintl)
+INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, uchar)
+INSTANTIATE(af_max_t, short)
+INSTANTIATE(af_max_t, ushort)
+INSTANTIATE(af_max_t, half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/ireduce.hpp b/src/backend/oneapi/ireduce.hpp
new file mode 100644
index 0000000000..3ae1b6c476
--- /dev/null
+++ b/src/backend/oneapi/ireduce.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <optypes.hpp>
+
+namespace oneapi {
+template<af_op_t op, typename T>
+void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
+             const int dim);
+
+template<af_op_t op, typename T>
+void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
+             const Array<uint> &rlen);
+
+template<af_op_t op, typename T>
+T ireduce_all(unsigned *loc, const Array<T> &in);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
new file mode 100644
index 0000000000..c957c86c1d
--- /dev/null
+++ b/src/backend/oneapi/jit.cpp
@@ -0,0 +1,71 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/compile_module.hpp>
+#include <common/dispatch.hpp>
+#include <common/jit/ModdimNode.hpp>
+#include <common/jit/Node.hpp>
+#include <common/jit/NodeIterator.hpp>
+#include <common/kernel_cache.hpp>
+#include <common/util.hpp>
+#include <copy.hpp>
+#include <device_manager.hpp>
+#include <err_oneapi.hpp>
+#include <af/dim4.hpp>
+
+//#include <jit/BufferNode.hpp>
+
+#include <cstdio>
+#include <functional>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+using common::getFuncName;
+using common::Node;
+using common::Node_ids;
+using common::Node_map_t;
+
+using std::string;
+using std::stringstream;
+using std::to_string;
+using std::vector;
+
+namespace oneapi {
+
+string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
+                       const vector<Node_ids> &full_ids,
+                       const vector<int> &output_ids, bool is_linear) {
+    ONEAPI_NOT_SUPPORTED("");
+    return "";
+}
+
+/*
+cl::Kernel getKernel(const vector<Node *> &output_nodes,
+                     const vector<int> &output_ids,
+                     const vector<Node *> &full_nodes,
+                     const vector<Node_ids> &full_ids, const bool is_linear) {
+    ONEAPI_NOT_SUPPORTED("");
+    return common::getKernel("", "", true).get();
+}
+*/
+
+/*
+void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void evalNodes(Param &out, Node *node) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+*/
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
new file mode 100644
index 0000000000..2e6ef7fe34
--- /dev/null
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -0,0 +1,34 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <common/jit/BufferNodeBase.hpp>
+
+#include <memory>
+
+namespace oneapi {
+namespace jit {
+  template<typename T>
+  using BufferNode = common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, KParam>;
+}
+}  // namespace opencl
+
+namespace common {
+
+template<typename DataType, typename ParamType>
+bool BufferNodeBase<DataType, ParamType>::operator==(
+    const BufferNodeBase<DataType, ParamType> &other) const noexcept {
+    // clang-format off
+    return m_data.get() == other.m_data.get() &&
+           m_bytes == other.m_bytes &&
+           m_param.offset == other.m_param.offset;
+    // clang-format on
+}
+
+}  // namespace common
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
new file mode 100644
index 0000000000..607d85ce98
--- /dev/null
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -0,0 +1,112 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <sstream>
+#include <string>
+
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+
+namespace {
+
+/// Creates a string that will be used to declare the parameter of kernel
+void generateParamDeclaration(std::stringstream& kerStream, int id,
+                              bool is_linear, const std::string& m_type_str) {
+    if (is_linear) {
+        kerStream << "__global " << m_type_str << " *in" << id
+                  << ", dim_t iInfo" << id << "_offset, \n";
+    } else {
+        kerStream << "__global " << m_type_str << " *in" << id
+                  << ", Param iInfo" << id << ", \n";
+    }
+}
+
+/// Calls the setArg function to set the arguments for a kernel call
+template<typename T>
+inline int setKernelArguments(
+    int start_id, bool is_linear,
+    std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+    const std::shared_ptr<sycl::buffer<T>>& ptr, const KParam& info) {
+  // TODO(oneapi)
+  ONEAPI_NOT_SUPPORTED("ERROR");
+  //setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
+  //sizeof(cl_mem));
+    if (is_linear) {
+      //setArg(start_id + 1, static_cast<const void*>(&info.offset),
+      //sizeof(dim_t));
+    } else {
+      //setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam));
+    }
+    return start_id + 2;
+}
+
+/// Generates the code to calculate the offsets for a buffer
+inline void generateBufferOffsets(std::stringstream& kerStream, int id,
+                                  bool is_linear, const std::string& type_str) {
+    UNUSED(type_str);
+    std::string idx_str  = std::string("int idx") + std::to_string(id);
+    std::string info_str = std::string("iInfo") + std::to_string(id);
+
+    if (is_linear) {
+        kerStream << idx_str << " = idx + " << info_str << "_offset;\n";
+    } else {
+        kerStream << idx_str << " = (id3 < " << info_str << ".dims[3]) * "
+                  << info_str << ".strides[3] * id3 + (id2 < " << info_str
+                  << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
+                  << info_str << ".dims[1]) * " << info_str
+                  << ".strides[1] * id1 + (id0 < " << info_str
+                  << ".dims[0]) * id0 + " << info_str << ".offset;\n";
+    }
+}
+
+/// Generates the code to read a buffer and store it in a local variable
+inline void generateBufferRead(std::stringstream& kerStream, int id,
+                               const std::string& type_str) {
+    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
+              << "];\n";
+}
+
+inline void generateShiftNodeOffsets(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& type_str) {
+    UNUSED(is_linear);
+    UNUSED(type_str);
+    std::string idx_str   = std::string("idx") + std::to_string(id);
+    std::string info_str  = std::string("iInfo") + std::to_string(id);
+    std::string id_str    = std::string("sh_id_") + std::to_string(id) + "_";
+    std::string shift_str = std::string("shift") + std::to_string(id) + "_";
+
+    for (int i = 0; i < 4; i++) {
+        kerStream << "int " << id_str << i << " = __circular_mod(id" << i
+                  << " + " << shift_str << i << ", " << info_str << ".dims["
+                  << i << "]);\n";
+    }
+
+    kerStream << "int " << idx_str << " = (" << id_str << "3 < " << info_str
+              << ".dims[3]) * " << info_str << ".strides[3] * " << id_str
+              << "3;\n";
+    kerStream << idx_str << " += (" << id_str << "2 < " << info_str
+              << ".dims[2]) * " << info_str << ".strides[2] * " << id_str
+              << "2;\n";
+    kerStream << idx_str << " += (" << id_str << "1 < " << info_str
+              << ".dims[1]) * " << info_str << ".strides[1] * " << id_str
+              << "1;\n";
+    kerStream << idx_str << " += (" << id_str << "0 < " << info_str
+              << ".dims[0]) * " << id_str << "0 + " << info_str << ".offset;\n";
+}
+
+inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
+                                  const std::string& type_str) {
+    kerStream << type_str << " val" << id << " = in" << id << "[idx" << id
+              << "];\n";
+}
+}  // namespace
+}  // namespace opencl
diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
new file mode 100644
index 0000000000..a645ea56f5
--- /dev/null
+++ b/src/backend/oneapi/join.cpp
@@ -0,0 +1,91 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <join.hpp>
+
+#include <algorithm>
+#include <stdexcept>
+#include <vector>
+
+using af::dim4;
+using common::half;
+using std::transform;
+using std::vector;
+
+namespace oneapi {
+dim4 calcOffset(const dim4 &dims, int dim) {
+    dim4 offset;
+    offset[0] = (dim == 0) ? dims[0] : 0;
+    offset[1] = (dim == 1) ? dims[1] : 0;
+    offset[2] = (dim == 2) ? dims[2] : 0;
+    offset[3] = (dim == 3) ? dims[3] : 0;
+    return offset;
+}
+
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> out = createEmptyArray<T>(af::dim4(1));
+    return out;
+}
+
+template<typename T>
+void join_wrapper(const int dim, Array<T> &out,
+                  const vector<Array<T>> &inputs) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<typename T>
+void join(Array<T> &out, const int dim, const vector<Array<T>> &inputs) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T)                                              \
+    template Array<T> join<T>(const int dim, const Array<T> &first, \
+                              const Array<T> &second);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
+
+#undef INSTANTIATE
+
+#define INSTANTIATE(T)                                   \
+    template void join<T>(Array<T> & out, const int dim, \
+                          const vector<Array<T>> &inputs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
+
+#undef INSTANTIATE
+}  // namespace oneapi
diff --git a/src/backend/oneapi/join.hpp b/src/backend/oneapi/join.hpp
new file mode 100644
index 0000000000..25763f063e
--- /dev/null
+++ b/src/backend/oneapi/join.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
+
+template<typename T>
+void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/KParam.hpp b/src/backend/oneapi/kernel/KParam.hpp
new file mode 100644
index 0000000000..b5bb98e850
--- /dev/null
+++ b/src/backend/oneapi/kernel/KParam.hpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#ifndef __KPARAM_H
+#define __KPARAM_H
+
+//#ifndef __OPENCL_VERSION__
+// Only define dim_t in host code. dim_t is defined when setting the program
+// options in program.cpp
+#include <af/defines.h>
+//#endif
+
+// Defines the size and shape of the data in the OpenCL buffer
+typedef struct {
+    dim_t dims[4];
+    dim_t strides[4];
+    dim_t offset;
+} KParam;
+
+#endif
diff --git a/src/backend/oneapi/logic.hpp b/src/backend/oneapi/logic.hpp
new file mode 100644
index 0000000000..e1706583e2
--- /dev/null
+++ b/src/backend/oneapi/logic.hpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <binary.hpp>
+#include <common/jit/BinaryNode.hpp>
+#include <err_oneapi.hpp>
+#include <optypes.hpp>
+#include <af/defines.h>
+#include <af/dim4.hpp>
+
+namespace oneapi {
+template<typename T, af_op_t op>
+Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
+                    const af::dim4 &odims) {
+    return common::createBinaryNode<char, T, op>(lhs, rhs, odims);
+}
+
+template<typename T, af_op_t op>
+Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
+               const af::dim4 &odims) {
+    return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/lookup.cpp b/src/backend/oneapi/lookup.cpp
new file mode 100644
index 0000000000..304ab9afa7
--- /dev/null
+++ b/src/backend/oneapi/lookup.cpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <lookup.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <af/dim4.hpp>
+
+using common::half;
+
+namespace oneapi {
+template<typename in_t, typename idx_t>
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<in_t> out = createEmptyArray<in_t>(af::dim4(1));
+    return out;
+}
+
+#define INSTANTIATE(T)                                                         \
+    template Array<T> lookup<T, float>(const Array<T> &, const Array<float> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, double>(                                       \
+        const Array<T> &, const Array<double> &, const unsigned);              \
+    template Array<T> lookup<T, int>(const Array<T> &, const Array<int> &,     \
+                                     const unsigned);                          \
+    template Array<T> lookup<T, unsigned>(                                     \
+        const Array<T> &, const Array<unsigned> &, const unsigned);            \
+    template Array<T> lookup<T, short>(const Array<T> &, const Array<short> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, ushort>(                                       \
+        const Array<T> &, const Array<ushort> &, const unsigned);              \
+    template Array<T> lookup<T, intl>(const Array<T> &, const Array<intl> &,   \
+                                      const unsigned);                         \
+    template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
+                                       const unsigned);                        \
+    template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
+                                      const unsigned)
+
+INSTANTIATE(float);
+INSTANTIATE(cfloat);
+INSTANTIATE(double);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
+INSTANTIATE(unsigned);
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(uchar);
+INSTANTIATE(char);
+INSTANTIATE(ushort);
+INSTANTIATE(short);
+INSTANTIATE(half);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/lookup.hpp b/src/backend/oneapi/lookup.hpp
new file mode 100644
index 0000000000..2fe9b0240c
--- /dev/null
+++ b/src/backend/oneapi/lookup.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename in_t, typename idx_t>
+Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
+                   const unsigned dim);
+}
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
new file mode 100644
index 0000000000..849fea1426
--- /dev/null
+++ b/src/backend/oneapi/lu.cpp
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <err_oneapi.hpp>
+#include <lu.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+#include <blas.hpp>
+#include <copy.hpp>
+#include <platform.hpp>
+
+namespace oneapi {
+
+Array<int> convertPivot(int *ipiv, int in_sz, int out_sz) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<int> out = createEmptyArray<in_t>(af::dim4(1));
+    return out;
+}
+
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<int> out = createEmptyArray<in_t>(af::dim4(1));
+    return out;
+}
+
+bool isLAPACKAvailable() { return true; }
+
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
+
+INSTANTIATE_LU(float)
+INSTANTIATE_LU(cfloat)
+INSTANTIATE_LU(double)
+INSTANTIATE_LU(cdouble)
+
+}  // namespace oneapi
+
+#else  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend", AF_ERR_NOT_CONFIGURED);
+}
+
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
+    ONEAPI_NOT_SUPPORTED("");
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend", AF_ERR_NOT_CONFIGURED);
+}
+
+bool isLAPACKAvailable() { return false; }
+
+#define INSTANTIATE_LU(T)                                        \
+    template Array<int> lu_inplace<T>(Array<T> & in,             \
+                                      const bool convert_pivot); \
+    template void lu<T>(Array<T> & lower, Array<T> & upper,      \
+                        Array<int> & pivot, const Array<T> &in);
+
+INSTANTIATE_LU(float)
+INSTANTIATE_LU(cfloat)
+INSTANTIATE_LU(double)
+INSTANTIATE_LU(cdouble)
+
+}  // namespace oneapi
+
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/lu.hpp b/src/backend/oneapi/lu.hpp
new file mode 100644
index 0000000000..8ab1f25a7a
--- /dev/null
+++ b/src/backend/oneapi/lu.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
+        const Array<T> &in);
+
+template<typename T>
+Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
+
+bool isLAPACKAvailable();
+}  // namespace oneapi
diff --git a/src/backend/oneapi/match_template.cpp b/src/backend/oneapi/match_template.cpp
new file mode 100644
index 0000000000..6a0182f7bd
--- /dev/null
+++ b/src/backend/oneapi/match_template.cpp
@@ -0,0 +1,38 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <match_template.hpp>
+
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+
+template<typename inType, typename outType>
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg,
+                              const af::matchType mType) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<outType> out = createEmptyArray<outType>(sImg.dims());
+    return out;
+}
+
+#define INSTANTIATE(in_t, out_t)                       \
+    template Array<out_t> match_template<in_t, out_t>( \
+        const Array<in_t> &, const Array<in_t> &, const af::matchType);
+
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(char, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(short, float)
+INSTANTIATE(ushort, float)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/match_template.hpp b/src/backend/oneapi/match_template.hpp
new file mode 100644
index 0000000000..9e79f3e19b
--- /dev/null
+++ b/src/backend/oneapi/match_template.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/defines.h>
+
+namespace oneapi {
+template<typename inType, typename outType>
+Array<outType> match_template(const Array<inType> &sImg,
+                              const Array<inType> &tImg,
+                              const af::matchType mType);
+}
diff --git a/src/backend/oneapi/math.cpp b/src/backend/oneapi/math.cpp
new file mode 100644
index 0000000000..a3b9d07e7a
--- /dev/null
+++ b/src/backend/oneapi/math.cpp
@@ -0,0 +1,53 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include "math.hpp"
+#include <common/half.hpp>
+
+namespace oneapi {
+cfloat operator+(cfloat lhs, cfloat rhs) {
+    //cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
+    cfloat res;
+    return res;
+}
+
+cdouble operator+(cdouble lhs, cdouble rhs) {
+    //cdouble res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
+    cdouble res;
+    return res;
+}
+
+cfloat operator*(cfloat lhs, cfloat rhs) {
+    cfloat out;
+    //out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
+    //out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
+    return out;
+}
+
+cdouble operator*(cdouble lhs, cdouble rhs) {
+    cdouble out;
+    //out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
+    //out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
+    return out;
+}
+
+cfloat division(cfloat lhs, double rhs) {
+    cfloat retVal;
+    //retVal.s[0] = real(lhs) / rhs;
+    //retVal.s[1] = imag(lhs) / rhs;
+    return retVal;
+}
+
+cdouble division(cdouble lhs, double rhs) {
+    cdouble retVal;
+    //retVal.s[0] = real(lhs) / rhs;
+    //retVal.s[1] = imag(lhs) / rhs;
+    return retVal;
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
new file mode 100644
index 0000000000..2b4182d811
--- /dev/null
+++ b/src/backend/oneapi/math.hpp
@@ -0,0 +1,155 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/defines.hpp>
+#include <common/half.hpp>
+#include <af/defines.h>
+
+#include <backend.hpp>
+#include <types.hpp>
+
+#include <algorithm>
+#include <complex>
+#include <limits>
+
+#if defined(__GNUC__) || defined(__GNUG__)
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
+#else
+/* Other */
+#endif
+
+namespace oneapi {
+
+template<typename T>
+static inline T abs(T val) {
+    return std::abs(val);
+}
+template<typename T>
+static inline T min(T lhs, T rhs) {
+    return std::min(lhs, rhs);
+}
+template<typename T>
+static inline T max(T lhs, T rhs) {
+    return std::max(lhs, rhs);
+}
+
+template<typename T>
+static inline T division(T lhs, double rhs) {
+    return lhs / rhs;
+}
+cfloat division(cfloat lhs, double rhs);
+cdouble division(cdouble lhs, double rhs);
+
+template<>
+inline cfloat max<cfloat>(cfloat lhs, cfloat rhs) {
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+template<>
+inline cdouble max<cdouble>(cdouble lhs, cdouble rhs) {
+    return abs(lhs) > abs(rhs) ? lhs : rhs;
+}
+
+template<>
+inline cfloat min<cfloat>(cfloat lhs, cfloat rhs) {
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+template<>
+inline cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
+    return abs(lhs) < abs(rhs) ? lhs : rhs;
+}
+
+template<typename T>
+static T scalar(double val) {
+    return (T)(val);
+}
+
+template<>
+inline cfloat scalar<cfloat>(double val) {
+    cfloat cval(static_cast<float>(val));
+    // cval.real() = (float)val;
+    // cval.imag() = 0;
+    return cval;
+}
+
+template<>
+inline cdouble scalar<cdouble>(double val) {
+    cdouble cval(val);
+    return cval;
+}
+
+template<typename To, typename Ti>
+static To scalar(Ti real, Ti imag) {
+    To cval(real, imag);
+    return cval;
+}
+
+template<typename T>
+inline T maxval() {
+    return std::numeric_limits<T>::max();
+}
+template<typename T>
+inline T minval() {
+    return std::numeric_limits<T>::min();
+}
+template<>
+inline float maxval() {
+    return std::numeric_limits<float>::infinity();
+}
+template<>
+inline double maxval() {
+    return std::numeric_limits<double>::infinity();
+}
+
+template<>
+inline common::half maxval() {
+    return std::numeric_limits<common::half>::infinity();
+}
+
+template<>
+inline float minval() {
+    return -std::numeric_limits<float>::infinity();
+}
+
+template<>
+inline double minval() {
+    return -std::numeric_limits<double>::infinity();
+}
+template<>
+inline common::half minval() {
+    return -std::numeric_limits<common::half>::infinity();
+}
+
+template<typename T>
+static inline T real(T in) {
+    return std::real(in);
+}
+
+template<typename T>
+static inline T imag(T in) {
+    return std::imag(in);
+}
+
+inline common::half operator+(common::half lhs, common::half rhs) noexcept {
+    return common::half(static_cast<float>(lhs) + static_cast<float>(rhs));
+}
+}  // namespace oneapi
+
+
+#if defined(__GNUC__) || defined(__GNUG__)
+/* GCC/G++, Clang/LLVM, Intel ICC */
+#pragma GCC diagnostic pop
+#else
+/* Other */
+#endif
diff --git a/src/backend/oneapi/max.cpp b/src/backend/oneapi/max.cpp
new file mode 100644
index 0000000000..4ae8efeaee
--- /dev/null
+++ b/src/backend/oneapi/max.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// max
+INSTANTIATE(af_max_t, float, float)
+INSTANTIATE(af_max_t, double, double)
+INSTANTIATE(af_max_t, cfloat, cfloat)
+INSTANTIATE(af_max_t, cdouble, cdouble)
+INSTANTIATE(af_max_t, int, int)
+INSTANTIATE(af_max_t, uint, uint)
+INSTANTIATE(af_max_t, intl, intl)
+INSTANTIATE(af_max_t, uintl, uintl)
+INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, uchar, uchar)
+INSTANTIATE(af_max_t, short, short)
+INSTANTIATE(af_max_t, ushort, ushort)
+INSTANTIATE(af_max_t, half, half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/mean.cpp b/src/backend/oneapi/mean.cpp
new file mode 100644
index 0000000000..2fb632eb75
--- /dev/null
+++ b/src/backend/oneapi/mean.cpp
@@ -0,0 +1,94 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <mean.hpp>
+#include <err_oneapi.hpp>
+
+#include <common/half.hpp>
+// #include <kernel/mean.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+using common::half;
+using std::swap;
+
+namespace oneapi {
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in) {
+
+    ONEAPI_NOT_SUPPORTED("mean Not supported");
+
+    return To(0);
+    // return kernel::meanAll<Ti, Tw, To>(in);
+}
+
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts) {
+
+    ONEAPI_NOT_SUPPORTED("mean Not supported");
+
+    return T(0);
+    // return kernel::meanAllWeighted<T, Tw>(in, wts);
+}
+
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim) {
+
+    ONEAPI_NOT_SUPPORTED("mean Not supported");
+
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    // kernel::mean<Ti, Tw, To>(out, in, dim);
+    return out;
+}
+
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim) {
+
+    ONEAPI_NOT_SUPPORTED("mean Not supported");
+
+    dim4 odims   = in.dims();
+    odims[dim]   = 1;
+    Array<T> out = createEmptyArray<T>(odims);
+    // kernel::meanWeighted<T, Tw, T>(out, in, wts, dim);
+    return out;
+}
+
+#define INSTANTIATE(Ti, Tw, To)                        \
+    template To mean<Ti, Tw, To>(const Array<Ti>& in); \
+    template Array<To> mean<Ti, Tw, To>(const Array<Ti>& in, const int dim);
+
+INSTANTIATE(double, double, double);
+INSTANTIATE(float, float, float);
+INSTANTIATE(int, float, float);
+INSTANTIATE(unsigned, float, float);
+INSTANTIATE(intl, double, double);
+INSTANTIATE(uintl, double, double);
+INSTANTIATE(short, float, float);
+INSTANTIATE(ushort, float, float);
+INSTANTIATE(uchar, float, float);
+INSTANTIATE(char, float, float);
+INSTANTIATE(cfloat, float, cfloat);
+INSTANTIATE(cdouble, double, cdouble);
+INSTANTIATE(half, float, half);
+INSTANTIATE(half, float, float);
+
+#define INSTANTIATE_WGT(T, Tw)                                              \
+    template T mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts);       \
+    template Array<T> mean<T, Tw>(const Array<T>& in, const Array<Tw>& wts, \
+                                  const int dim);
+
+INSTANTIATE_WGT(double, double);
+INSTANTIATE_WGT(float, float);
+INSTANTIATE_WGT(cfloat, float);
+INSTANTIATE_WGT(cdouble, double);
+INSTANTIATE_WGT(half, float);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/mean.hpp b/src/backend/oneapi/mean.hpp
new file mode 100644
index 0000000000..c682fa8d5f
--- /dev/null
+++ b/src/backend/oneapi/mean.hpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename Ti, typename Tw, typename To>
+To mean(const Array<Ti>& in);
+
+template<typename T, typename Tw>
+T mean(const Array<T>& in, const Array<Tw>& wts);
+
+template<typename Ti, typename Tw, typename To>
+Array<To> mean(const Array<Ti>& in, const int dim);
+
+template<typename T, typename Tw>
+Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/meanshift.cpp b/src/backend/oneapi/meanshift.cpp
new file mode 100644
index 0000000000..61823f1467
--- /dev/null
+++ b/src/backend/oneapi/meanshift.cpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/meanshift.hpp>
+#include <meanshift.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+template<typename T>
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor) {
+
+    ONEAPI_NOT_SUPPORTED("meanshift Not supported");
+
+    const dim4 &dims = in.dims();
+    Array<T> out     = createEmptyArray<T>(dims);
+    // kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma, numIterations,
+    //                      isColor);
+    return out;
+}
+
+#define INSTANTIATE(T)                                              \
+    template Array<T> meanshift<T>(const Array<T> &, const float &, \
+                                   const float &, const unsigned &, \
+                                   const bool &);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/meanshift.hpp b/src/backend/oneapi/meanshift.hpp
new file mode 100644
index 0000000000..014c0f2468
--- /dev/null
+++ b/src/backend/oneapi/meanshift.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
+                   const float &chromaticSigma, const unsigned &numIterations,
+                   const bool &isColor);
+}
diff --git a/src/backend/oneapi/medfilt.cpp b/src/backend/oneapi/medfilt.cpp
new file mode 100644
index 0000000000..526f505244
--- /dev/null
+++ b/src/backend/oneapi/medfilt.cpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/medfilt.hpp>
+#include <medfilt.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType pad) {
+
+    ONEAPI_NOT_SUPPORTED("medfilt1 Not supported");
+
+    // ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
+    // ARG_ASSERT(2, (w_wid % 2 != 0));
+
+    const dim4 &dims = in.dims();
+
+    Array<T> out = createEmptyArray<T>(dims);
+
+    // kernel::medfilt1<T>(out, in, w_wid, pad);
+
+    return out;
+}
+
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType pad) {
+
+    ONEAPI_NOT_SUPPORTED("medfilt2 Not supported");
+
+    // ARG_ASSERT(2, (w_len % 2 != 0));
+    // ARG_ASSERT(2, (w_len <= kernel::MAX_MEDFILTER2_LEN));
+
+    Array<T> out = createEmptyArray<T>(in.dims());
+    // kernel::medfilt2<T>(out, in, pad, w_len, w_wid);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                 \
+    template Array<T> medfilt1<T>(const Array<T> &in, const int w_wid, \
+                                  const af::borderType);               \
+    template Array<T> medfilt2<T>(const Array<T> &in, const int w_len, \
+                                  const int w_wid, const af::borderType);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/medfilt.hpp b/src/backend/oneapi/medfilt.hpp
new file mode 100644
index 0000000000..1e356a23bb
--- /dev/null
+++ b/src/backend/oneapi/medfilt.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> medfilt1(const Array<T> &in, const int w_wid,
+                  const af::borderType edge_pad);
+
+template<typename T>
+Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
+                  const af::borderType edge_pad);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
new file mode 100644
index 0000000000..2f869d3147
--- /dev/null
+++ b/src/backend/oneapi/memory.cpp
@@ -0,0 +1,351 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/Logger.hpp>
+#include <common/MemoryManagerBase.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <errorcodes.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
+#include <spdlog/spdlog.h>
+#include <types.hpp>
+#include <af/dim4.hpp>
+
+#include <utility>
+
+using common::bytesToString;
+
+using af::dim4;
+using std::function;
+using std::move;
+using std::unique_ptr;
+
+namespace oneapi {
+float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
+float getMemoryPressureThreshold() {
+    return memoryManager().getMemoryPressureThreshold();
+}
+
+bool jitTreeExceedsMemoryPressure(size_t bytes) {
+    return memoryManager().jitTreeExceedsMemoryPressure(bytes);
+}
+
+void setMemStepSize(size_t step_bytes) {
+    memoryManager().setMemStepSize(step_bytes);
+}
+
+size_t getMemStepSize() { return memoryManager().getMemStepSize(); }
+
+void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
+
+void shutdownMemoryManager() { memoryManager().shutdown(); }
+
+void shutdownPinnedMemoryManager() { /*pinnedMemoryManager().shutdown();*/ }
+
+void printMemInfo(const char *msg, const int device) {
+    memoryManager().printInfo(msg, device);
+}
+
+template<typename T>
+// unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
+//unique_ptr<int, function<void(int *)>> memAlloc(
+std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>> memAlloc(
+    const size_t &elements) {
+    ONEAPI_NOT_SUPPORTED("memAlloc Not supported");
+    //return unique_ptr<int, function<void(int *)>>();
+    return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>();
+    // // TODO: make memAlloc aware of array shapes
+    // if (elements) {
+    //     dim4 dims(elements);
+    //     void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    //     auto buf  = static_cast<cl_mem>(ptr);
+    //     cl::Buffer *bptr = new cl::Buffer(buf, true);
+    //     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(bptr,
+    //                                                                 bufferFree);
+    // } else {
+    //     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(nullptr,
+    //                                                                 bufferFree);
+    // }
+}
+
+void *memAllocUser(const size_t &bytes) {
+
+    ONEAPI_NOT_SUPPORTED("memAllocUser Not supported");
+    return nullptr;
+
+    // dim4 dims(bytes);
+    // void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
+    // auto buf  = static_cast<cl_mem>(ptr);
+    // return new cl::Buffer(buf, true);
+}
+
+template<typename T>
+void memFree(T *ptr) {
+
+    ONEAPI_NOT_SUPPORTED("memFree Not supported");
+
+    // cl::Buffer *buf = reinterpret_cast<cl::Buffer *>(ptr);
+    // cl_mem mem      = static_cast<cl_mem>((*buf)());
+    // delete buf;
+    // return memoryManager().unlock(static_cast<void *>(mem), false);
+}
+
+void memFreeUser(void *ptr) {
+
+    ONEAPI_NOT_SUPPORTED("memFreeUser Not supported");
+
+    // cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
+    // cl_mem mem      = (*buf)();
+    // delete buf;
+    // memoryManager().unlock(mem, true);
+}
+
+template<typename T>
+sycl::buffer<T> *bufferAlloc(const size_t &bytes) {
+
+    ONEAPI_NOT_SUPPORTED("bufferAlloc Not supported");
+    return nullptr;
+
+    // dim4 dims(bytes);
+    // if (bytes) {
+    //     void *ptr       = memoryManager().alloc(false, 1, dims.get(), 1);
+    //     cl_mem mem      = static_cast<cl_mem>(ptr);
+    //     cl::Buffer *buf = new cl::Buffer(mem, true);
+    //     return buf;
+    // } else {
+    //     return nullptr;
+    // }
+}
+
+template<typename T>
+void bufferFree(sycl::buffer<T> *buf) {
+
+    ONEAPI_NOT_SUPPORTED("bufferFree Not supported");
+
+    // if (buf) {
+    //     cl_mem mem = (*buf)();
+    //     delete buf;
+    //     memoryManager().unlock(static_cast<void *>(mem), false);
+    // }
+}
+
+template<typename T>
+void memLock(const sycl::buffer<T> *ptr) {
+
+    ONEAPI_NOT_SUPPORTED("memLock Not supported");
+
+    // cl_mem mem = static_cast<cl_mem>((*ptr)());
+    // memoryManager().userLock(static_cast<void *>(mem));
+}
+
+template<typename T>
+void memUnlock(const sycl::buffer<T> *ptr) {
+
+    ONEAPI_NOT_SUPPORTED("memUnlock Not supported");
+
+    // cl_mem mem = static_cast<cl_mem>((*ptr)());
+    // memoryManager().userUnlock(static_cast<void *>(mem));
+}
+
+bool isLocked(const void *ptr) {
+    return memoryManager().isUserLocked(const_cast<void *>(ptr));
+}
+
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes, size_t *lock_buffers) {
+    memoryManager().usageInfo(alloc_bytes, alloc_buffers, lock_bytes,
+                              lock_buffers);
+}
+
+template<typename T>
+T *pinnedAlloc(const size_t &elements) {
+
+    ONEAPI_NOT_SUPPORTED("pinnedAlloc Not supported");
+
+    // // TODO: make pinnedAlloc aware of array shapes
+    // dim4 dims(elements);
+    // void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    return static_cast<T *>(nullptr);
+}
+
+template<typename T>
+void pinnedFree(T *ptr) {
+    //pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
+}
+
+//template unique_ptr<int, function<void(int *)>> memAlloc<T>(
+#define INSTANTIATE(T)                                                                          \
+    template std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>> memAlloc( \
+        const size_t &elements);                                                                \
+    template void memFree(T *ptr);                                                              \
+    template T *pinnedAlloc(const size_t &elements);                                            \
+    template void pinnedFree(T *ptr);                                                           \
+    template void bufferFree(sycl::buffer<T> *buf);                                             \
+    template void memLock(const sycl::buffer<T> *buf);                                          \
+    template void memUnlock(const sycl::buffer<T> *buf);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(common::half)
+
+Allocator::Allocator() { logger = common::loggerFactory("mem"); }
+
+void Allocator::shutdown() {
+
+    ONEAPI_NOT_SUPPORTED("Allocator::shutdown Not supported");
+
+    // for (int n = 0; n < opencl::getDeviceCount(); n++) {
+    //     try {
+    //         opencl::setDevice(n);
+    //         shutdownMemoryManager();
+    //     } catch (const AfError &err) {
+    //         continue;  // Do not throw any errors while shutting down
+    //     }
+    // }
+}
+
+int Allocator::getActiveDeviceId() {
+
+    ONEAPI_NOT_SUPPORTED("Allocator::getActiveDeviceId Not supported");
+
+    return 0;
+    // return opencl::getActiveDeviceId();
+}
+
+size_t Allocator::getMaxMemorySize(int id) {
+
+    ONEAPI_NOT_SUPPORTED("Allocator::getMaxMemorySize Not supported");
+
+    return 0;
+    // return opencl::getDeviceMemorySize(id);
+}
+
+void *Allocator::nativeAlloc(const size_t bytes) {
+
+    ONEAPI_NOT_SUPPORTED("Allocator::nativeAlloc Not supported");
+    return nullptr;
+
+    // cl_int err = CL_SUCCESS;
+    // auto ptr   = static_cast<void *>(clCreateBuffer(
+    //     getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
+    //     bytes, nullptr, &err));
+
+    // if (err != CL_SUCCESS) {
+    //     auto str = fmt::format("Failed to allocate device memory of size {}",
+    //                            bytesToString(bytes));
+    //     AF_ERROR(str, AF_ERR_NO_MEM);
+    // }
+
+    // AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
+    // return ptr;
+}
+
+void Allocator::nativeFree(void *ptr) {
+
+    ONEAPI_NOT_SUPPORTED("Allocator::nativeFree Not supported");
+
+    // cl_mem buffer = static_cast<cl_mem>(ptr);
+    // AF_TRACE("nativeFree:          {}", ptr);
+    // cl_int err = clReleaseMemObject(buffer);
+    // if (err != CL_SUCCESS) {
+    //     AF_ERROR("Failed to release device memory.", AF_ERR_RUNTIME);
+    // }
+}
+
+AllocatorPinned::AllocatorPinned() : pinnedMaps(oneapi::getDeviceCount()) {
+    logger = common::loggerFactory("mem");
+}
+
+void AllocatorPinned::shutdown() {
+
+    ONEAPI_NOT_SUPPORTED("AllocatorPinned::shutdown Not supported");
+
+//     for (int n = 0; n < opencl::getDeviceCount(); n++) {
+//         opencl::setDevice(n);
+//         shutdownPinnedMemoryManager();
+//         auto currIterator = pinnedMaps[n].begin();
+//         auto endIterator  = pinnedMaps[n].end();
+//         while (currIterator != endIterator) {
+//             pinnedMaps[n].erase(currIterator++);
+//         }
+//     }
+}
+
+int AllocatorPinned::getActiveDeviceId() {
+
+    ONEAPI_NOT_SUPPORTED("AllocatorPinned::getActiveDeviceId Not supported");
+    return 0;
+
+    // opencl::getActiveDeviceId();
+}
+
+size_t AllocatorPinned::getMaxMemorySize(int id) {
+
+    ONEAPI_NOT_SUPPORTED("AllocatorPinned::getMaxMemorySize Not supported");
+    return 0;
+    // return opencl::getDeviceMemorySize(id);
+}
+
+void *AllocatorPinned::nativeAlloc(const size_t bytes) {
+
+    ONEAPI_NOT_SUPPORTED("AllocatorPinned::nativeAlloc Not supported");
+    return nullptr;
+//     void *ptr = NULL;
+
+//     cl_int err = CL_SUCCESS;
+//     auto buf   = clCreateBuffer(getContext()(), CL_MEM_ALLOC_HOST_PTR, bytes,
+//                               nullptr, &err);
+//     if (err != CL_SUCCESS) {
+//         AF_ERROR("Failed to allocate pinned memory.", AF_ERR_NO_MEM);
+//     }
+
+//     ptr = clEnqueueMapBuffer(getQueue()(), buf, CL_TRUE,
+//                              CL_MAP_READ | CL_MAP_WRITE, 0, bytes, 0, nullptr,
+//                              nullptr, &err);
+//     if (err != CL_SUCCESS) {
+//         AF_ERROR("Failed to map pinned memory", AF_ERR_RUNTIME);
+//     }
+//     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
+//     pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, new cl::Buffer(buf));
+//     return ptr;
+}
+
+void AllocatorPinned::nativeFree(void *ptr) {
+
+    ONEAPI_NOT_SUPPORTED("AllocatorPinned::nativeFree Not supported");
+
+    // AF_TRACE("Pinned::nativeFree:          {}", ptr);
+    // int n     = opencl::getActiveDeviceId();
+    // auto &map = pinnedMaps[n];
+    // auto iter = map.find(ptr);
+
+    // if (iter != map.end()) {
+    //     cl::Buffer *buf = map[ptr];
+    //     if (cl_int err = getQueue().enqueueUnmapMemObject(*buf, ptr)) {
+    //         getLogger()->warn(
+    //             "Pinned::nativeFree: Error unmapping pinned memory({}:{}). "
+    //             "Ignoring",
+    //             err, getErrorMessage(err));
+    //     }
+    //     delete buf;
+    //     map.erase(iter);
+    // }
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
new file mode 100644
index 0000000000..2e18a13ae4
--- /dev/null
+++ b/src/backend/oneapi/memory.hpp
@@ -0,0 +1,94 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <common/AllocatorInterface.hpp>
+
+#include <cstdlib>
+#include <functional>
+#include <map>
+#include <memory>
+#include <vector>
+
+
+namespace oneapi {
+template<typename T>
+sycl::buffer<T> *bufferAlloc(const size_t &bytes);
+
+template<typename T>
+void bufferFree(sycl::buffer<T> *buf);
+
+template<typename T>
+using bufptr =
+  std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>;
+
+template<typename T>
+bufptr<T> memAlloc(const size_t &elements);
+void *memAllocUser(const size_t &bytes);
+
+// Need these as 2 separate function and not a default argument
+// This is because it is used as the deleter in shared pointer
+// which cannot support default arguments
+template<typename T>
+void memFree(T *ptr);
+void memFreeUser(void *ptr);
+
+template<typename T>
+void memLock(const sycl::buffer<T> *ptr);
+
+template<typename T>
+void memUnlock(const sycl::buffer<T> *ptr);
+
+  bool isLocked(const void *ptr);
+
+  template<typename T>
+  T *pinnedAlloc(const size_t &elements);
+  template<typename T>
+  void pinnedFree(T *ptr);
+
+  void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                        size_t *lock_bytes, size_t *lock_buffers);
+  void signalMemoryCleanup();
+  void shutdownMemoryManager();
+  void pinnedGarbageCollect();
+
+  void printMemInfo(const char *msg, const int device);
+
+  float getMemoryPressure();
+  float getMemoryPressureThreshold();
+  bool jitTreeExceedsMemoryPressure(size_t bytes);
+  void setMemStepSize(size_t step_bytes);
+  size_t getMemStepSize(void);
+
+  class Allocator final : public common::memory::AllocatorInterface {
+     public:
+      Allocator();
+      ~Allocator() = default;
+      void shutdown() override;
+      int getActiveDeviceId() override;
+      size_t getMaxMemorySize(int id) override;
+      void *nativeAlloc(const size_t bytes) override;
+      void nativeFree(void *ptr) override;
+};
+
+class AllocatorPinned final : public common::memory::AllocatorInterface {
+   public:
+    AllocatorPinned();
+    ~AllocatorPinned() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
+
+   private:
+    std::vector<std::map<void *, void *>> pinnedMaps;
+};
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/min.cpp b/src/backend/oneapi/min.cpp
new file mode 100644
index 0000000000..3afa0d9787
--- /dev/null
+++ b/src/backend/oneapi/min.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// min
+INSTANTIATE(af_min_t, float, float)
+INSTANTIATE(af_min_t, double, double)
+INSTANTIATE(af_min_t, cfloat, cfloat)
+INSTANTIATE(af_min_t, cdouble, cdouble)
+INSTANTIATE(af_min_t, int, int)
+INSTANTIATE(af_min_t, uint, uint)
+INSTANTIATE(af_min_t, intl, intl)
+INSTANTIATE(af_min_t, uintl, uintl)
+INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, uchar, uchar)
+INSTANTIATE(af_min_t, short, short)
+INSTANTIATE(af_min_t, ushort, ushort)
+INSTANTIATE(af_min_t, half, half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/moments.cpp b/src/backend/oneapi/moments.cpp
new file mode 100644
index 0000000000..aa595c9269
--- /dev/null
+++ b/src/backend/oneapi/moments.cpp
@@ -0,0 +1,57 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+// #include <debug_opencl.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/moments.hpp>
+
+namespace oneapi {
+
+static inline unsigned bitCount(unsigned v) {
+    v = v - ((v >> 1U) & 0x55555555U);
+    v = (v & 0x33333333U) + ((v >> 2U) & 0x33333333U);
+    return (((v + (v >> 4U)) & 0xF0F0F0FU) * 0x1010101U) >> 24U;
+}
+
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment) {
+
+    ONEAPI_NOT_SUPPORTED("moments Not supported");
+
+    in.eval();
+    dim4 odims, idims = in.dims();
+    dim_t moments_dim = bitCount(moment);
+
+    odims[0] = moments_dim;
+    odims[1] = 1;
+    odims[2] = idims[2];
+    odims[3] = idims[3];
+
+    Array<float> out = createValueArray<float>(odims, 0.f);
+    out.eval();
+
+    // kernel::moments<T>(out, in, moment);
+    return out;
+}
+
+#define INSTANTIATE(T)                                   \
+    template Array<float> moments<T>(const Array<T> &in, \
+                                     const af_moment_type moment);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(ushort)
+INSTANTIATE(short)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/moments.hpp b/src/backend/oneapi/moments.hpp
new file mode 100644
index 0000000000..6201ccb897
--- /dev/null
+++ b/src/backend/oneapi/moments.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<float> moments(const Array<T> &in, const af_moment_type moment);
+}
diff --git a/src/backend/oneapi/morph.cpp b/src/backend/oneapi/morph.cpp
new file mode 100644
index 0000000000..de38b446ac
--- /dev/null
+++ b/src/backend/oneapi/morph.cpp
@@ -0,0 +1,70 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/morph.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+#include <morph.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
+
+    ONEAPI_NOT_SUPPORTED("morph Not supported");
+
+    // const dim4 mdims = mask.dims();
+    // if (mdims[0] != mdims[1]) {
+    //     OPENCL_NOT_SUPPORTED("Rectangular masks are not suported");
+    // }
+    // if (mdims[0] > 19) {
+    //     OPENCL_NOT_SUPPORTED("Kernels > 19x19 are not supported");
+    // }
+    const dim4 dims = in.dims();
+    Array<T> out    = createEmptyArray<T>(dims);
+    // kernel::morph<T>(out, in, mask, isDilation);
+    return out;
+}
+
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation) {
+
+    ONEAPI_NOT_SUPPORTED("morph3d Not supported");
+
+    // const dim4 mdims = mask.dims();
+    // if (mdims[0] != mdims[1] || mdims[0] != mdims[2]) {
+    //     OPENCL_NOT_SUPPORTED("Only cubic masks are supported");
+    // }
+    // if (mdims[0] > 7) {
+    //     OPENCL_NOT_SUPPORTED("Kernels > 7x7x7 masks are not supported");
+    // }
+    Array<T> out = createEmptyArray<T>(in.dims());
+    // kernel::morph3d<T>(out, in, mask, isDilation);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                    \
+    template Array<T> morph<T>(const Array<T> &, const Array<T> &, bool); \
+    template Array<T> morph3d<T>(const Array<T> &, const Array<T> &, bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/morph.hpp b/src/backend/oneapi/morph.hpp
new file mode 100644
index 0000000000..086baf2a90
--- /dev/null
+++ b/src/backend/oneapi/morph.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
+
+template<typename T>
+Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/nearest_neighbour.cpp b/src/backend/oneapi/nearest_neighbour.cpp
new file mode 100644
index 0000000000..e4705f1126
--- /dev/null
+++ b/src/backend/oneapi/nearest_neighbour.cpp
@@ -0,0 +1,89 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/nearest_neighbour.hpp>
+#include <math.hpp>
+#include <topk.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+// nsing cl::Device;
+
+namespace oneapi {
+
+template<typename T, typename To, af_match_type dist_type>
+void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
+                        const Array<T>& query, const Array<T>& train,
+                        const uint dist_dim, const uint n_dist) {
+
+    ONEAPI_NOT_SUPPORTED("nearest_neighbour_ Not supported");
+
+    uint sample_dim   = (dist_dim == 0) ? 1 : 0;
+    const dim4& qDims = query.dims();
+    const dim4& tDims = train.dims();
+
+    const dim4 outDims(n_dist, qDims[sample_dim]);
+    const dim4 distDims(tDims[sample_dim], qDims[sample_dim]);
+
+    Array<To> tmp_dists = createEmptyArray<To>(distDims);
+
+    idx  = createEmptyArray<uint>(outDims);
+    dist = createEmptyArray<To>(outDims);
+
+    Array<T> queryT = dist_dim == 0 ? transpose(query, false) : query;
+    Array<T> trainT = dist_dim == 0 ? transpose(train, false) : train;
+
+    // kernel::allDistances<T, To>(tmp_dists, queryT, trainT, 1, dist_type);
+
+    topk(dist, idx, tmp_dists, n_dist, 0, AF_TOPK_MIN);
+}
+
+template<typename T, typename To>
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist, const af_match_type dist_type) {
+    switch (dist_type) {
+        case AF_SAD:
+            nearest_neighbour_<T, To, AF_SAD>(idx, dist, query, train, dist_dim,
+                                              n_dist);
+            break;
+        case AF_SSD:
+            nearest_neighbour_<T, To, AF_SSD>(idx, dist, query, train, dist_dim,
+                                              n_dist);
+            break;
+        case AF_SHD:
+            nearest_neighbour_<T, To, AF_SHD>(idx, dist, query, train, dist_dim,
+                                              n_dist);
+            break;
+        default: AF_ERROR("Unsupported dist_type", AF_ERR_NOT_CONFIGURED);
+    }
+}
+
+#define INSTANTIATE(T, To)                                             \
+    template void nearest_neighbour<T, To>(                            \
+        Array<uint> & idx, Array<To> & dist, const Array<T>& query,    \
+        const Array<T>& train, const uint dist_dim, const uint n_dist, \
+        const af_match_type dist_type);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, uint)
+INSTANTIATE(intl, intl)
+INSTANTIATE(uintl, uintl)
+INSTANTIATE(short, int)
+INSTANTIATE(ushort, uint)
+INSTANTIATE(uchar, uint)
+
+INSTANTIATE(uintl, uint)  // For Hamming
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/nearest_neighbour.hpp b/src/backend/oneapi/nearest_neighbour.hpp
new file mode 100644
index 0000000000..f16b709d8e
--- /dev/null
+++ b/src/backend/oneapi/nearest_neighbour.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/features.h>
+
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename To>
+void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
+                       const Array<T>& train, const uint dist_dim,
+                       const uint n_dist,
+                       const af_match_type dist_type = AF_SSD);
+
+}
diff --git a/src/backend/oneapi/orb.cpp b/src/backend/oneapi/orb.cpp
new file mode 100644
index 0000000000..db7bd31207
--- /dev/null
+++ b/src/backend/oneapi/orb.cpp
@@ -0,0 +1,69 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/orb.hpp>
+#include <math.hpp>
+#include <af/dim4.hpp>
+#include <af/features.h>
+
+using af::dim4;
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename convAccT>
+unsigned orb(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
+             Array<float> &ori_out, Array<float> &size_out,
+             Array<uint> &desc_out, const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img) {
+
+    ONEAPI_NOT_SUPPORTED("orb Not supported");
+    return 0;
+
+    // unsigned nfeat;
+
+    // Param x;
+    // Param y;
+    // Param score;
+    // Param ori;
+    // Param size;
+    // Param desc;
+
+    // kernel::orb<T, convAccT>(&nfeat, x, y, score, ori, size, desc, image,
+    //                          fast_thr, max_feat, scl_fctr, levels, blur_img);
+
+    // if (nfeat > 0) {
+    //     const dim4 out_dims(nfeat);
+    //     const dim4 desc_dims(8, nfeat);
+
+    //     x_out     = createParamArray<float>(x, true);
+    //     y_out     = createParamArray<float>(y, true);
+    //     score_out = createParamArray<float>(score, true);
+    //     ori_out   = createParamArray<float>(ori, true);
+    //     size_out  = createParamArray<float>(size, true);
+    //     desc_out  = createParamArray<unsigned>(desc, true);
+    // }
+
+    // return nfeat;
+}
+
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned orb<T, convAccT>(                                       \
+        Array<float> & x, Array<float> & y, Array<float> & score,             \
+        Array<float> & ori, Array<float> & size, Array<uint> & desc,          \
+        const Array<T> &image, const float fast_thr, const unsigned max_feat, \
+        const float scl_fctr, const unsigned levels, const bool blur_img);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/orb.hpp b/src/backend/oneapi/orb.hpp
new file mode 100644
index 0000000000..aa1fe324bb
--- /dev/null
+++ b/src/backend/oneapi/orb.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/features.h>
+
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename convAccT>
+unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
+             Array<float> &orientation, Array<float> &size,
+             Array<unsigned> &desc, const Array<T> &image, const float fast_thr,
+             const unsigned max_feat, const float scl_fctr,
+             const unsigned levels, const bool blur_img);
+
+}
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
new file mode 100644
index 0000000000..ef28dadbdb
--- /dev/null
+++ b/src/backend/oneapi/platform.cpp
@@ -0,0 +1,462 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/graphics_common.hpp>
+#include <GraphicsResourceManager.hpp>
+#include <blas.hpp>
+#include <common/DefaultMemoryManager.hpp>
+#include <common/Logger.hpp>
+#include <common/host_memory.hpp>
+#include <common/util.hpp>
+#include <device_manager.hpp>
+#include <err_oneapi.hpp>
+#include <errorcodes.hpp>
+#include <version.hpp>
+#include <af/version.h>
+#include <memory.hpp>
+
+#ifdef OS_MAC
+#include <OpenGL/CGLCurrent.h>
+#endif
+
+#include <cctype>
+#include <cstdlib>
+#include <functional>
+#include <map>
+#include <mutex>
+#include <sstream>
+#include <string>
+#include <thread>
+#include <utility>
+#include <vector>
+
+using sycl::queue;
+using sycl::context;
+using sycl::device;
+using sycl::platform;
+using std::begin;
+using std::call_once;
+using std::end;
+using std::endl;
+using std::find_if;
+using std::get;
+using std::make_pair;
+using std::make_unique;
+using std::map;
+using std::move;
+using std::once_flag;
+using std::ostringstream;
+using std::pair;
+using std::string;
+using std::to_string;
+using std::unique_ptr;
+using std::vector;
+
+using common::memory::MemoryManagerBase;
+using oneapi::Allocator;
+using oneapi::AllocatorPinned;
+
+namespace oneapi {
+
+static string get_system() {
+    string arch = (sizeof(void*) == 4) ? "32-bit " : "64-bit ";
+
+    return arch +
+#if defined(OS_LNX)
+           "Linux";
+#elif defined(OS_WIN)
+           "Windows";
+#elif defined(OS_MAC)
+           "Mac OSX";
+#endif
+}
+
+int getBackend() { return AF_BACKEND_OPENCL; }
+
+bool verify_present(const string& pname, const string ref) {
+    auto iter =
+        search(begin(pname), end(pname), begin(ref), end(ref),
+               [](const string::value_type& l, const string::value_type& r) {
+                   return tolower(l) == tolower(r);
+               });
+
+    return iter != end(pname);
+}
+
+static string platformMap(string& platStr) {
+    using strmap_t                = map<string, string>;
+    static const strmap_t platMap = {
+        make_pair("NVIDIA CUDA", "NVIDIA"),
+        make_pair("Intel(R) OpenCL", "INTEL"),
+        make_pair("AMD Accelerated Parallel Processing", "AMD"),
+        make_pair("Intel Gen OCL Driver", "BEIGNET"),
+        make_pair("Intel(R) OpenCL HD Graphics", "INTEL"),
+        make_pair("Apple", "APPLE"),
+        make_pair("Portable Computing Language", "POCL"),
+    };
+
+    auto idx = platMap.find(platStr);
+
+    if (idx == platMap.end()) {
+        return platStr;
+    } else {
+        return idx->second;
+    }
+}
+
+/*
+afcl::platform getPlatformEnum(cl::Device dev) {
+    string pname = getPlatformName(dev);
+    if (verify_present(pname, "AMD"))
+        return AFCL_PLATFORM_AMD;
+    else if (verify_present(pname, "NVIDIA"))
+        return AFCL_PLATFORM_NVIDIA;
+    else if (verify_present(pname, "INTEL"))
+        return AFCL_PLATFORM_INTEL;
+    else if (verify_present(pname, "APPLE"))
+        return AFCL_PLATFORM_APPLE;
+    else if (verify_present(pname, "BEIGNET"))
+        return AFCL_PLATFORM_BEIGNET;
+    else if (verify_present(pname, "POCL"))
+        return AFCL_PLATFORM_POCL;
+    return AFCL_PLATFORM_UNKNOWN;
+}
+*/
+
+string getDeviceInfo() noexcept {
+    ONEAPI_NOT_SUPPORTED("");
+    return "";
+}
+
+string getPlatformName(const sycl::device& device) {
+    ONEAPI_NOT_SUPPORTED("");
+    return "";
+}
+
+typedef pair<unsigned, unsigned> device_id_t;
+
+pair<unsigned, unsigned>& tlocalActiveDeviceId() {
+    // First element is active context id
+    // Second element is active queue id
+    thread_local device_id_t activeDeviceId(0, 0);
+
+    return activeDeviceId;
+}
+
+void setActiveContext(int device) {
+    tlocalActiveDeviceId() = make_pair(device, device);
+}
+
+int getDeviceCount() noexcept {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+void init() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+unsigned getActiveDeviceId() {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+/*
+int getDeviceIdFromNativeId(cl_device_id id) {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    int nDevices = static_cast<int>(devMngr.mDevices.size());
+    int devId    = 0;
+    for (devId = 0; devId < nDevices; ++devId) {
+        if (id == devMngr.mDevices[devId]->operator()()) { break; }
+    }
+
+    return devId;
+}
+*/
+
+int getActiveDeviceType() {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+int getActivePlatform() {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+const context& getContext() {
+    ONEAPI_NOT_SUPPORTED("");
+    sycl::context c;
+    return c;
+    /*
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return *(devMngr.mContexts[get<0>(devId)]);
+    */
+}
+
+sycl::queue& getQueue() {
+    sycl::queue q;
+    return q; 
+    /*
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return *(devMngr.mQueues[get<1>(devId)]);
+    */
+}
+
+const sycl::device& getDevice(int id) {
+    sycl::device d;
+    return d;
+    /*
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    if (id == -1) { id = get<1>(devId); }
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+    return *(devMngr.mDevices[id]);
+    */
+}
+
+size_t getDeviceMemorySize(int device) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+size_t getHostMemorySize() { return common::getHostMemorySize(); }
+
+/*
+cl_device_type getDeviceType() {
+    const sycl::device& device = getDevice();
+    cl_device_type type        = device.getInfo<CL_DEVICE_TYPE>();
+    return type;
+}
+*/
+
+bool isHostUnifiedMemory(const sycl::device& device) {
+    ONEAPI_NOT_SUPPORTED("");
+    return false;
+}
+
+bool OpenCLCPUOffload(bool forceOffloadOSX) {
+    ONEAPI_NOT_SUPPORTED("");
+    return false;
+}
+
+bool isGLSharingSupported() {
+    ONEAPI_NOT_SUPPORTED("");
+    return false;
+}
+
+bool isDoubleSupported(unsigned device) {
+    ONEAPI_NOT_SUPPORTED("");
+    return false;
+}
+
+bool isHalfSupported(unsigned device) {
+    ONEAPI_NOT_SUPPORTED("");
+    return false;
+}
+
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+int setDevice(int device) {
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+}
+
+void sync(int device) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void setDeviceContext(sycl::device dev, sycl::context ctx) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void removeDeviceContext(sycl::device dev, sycl::context ctx) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+bool synchronize_calls() {
+    return false;
+}
+
+int& getMaxJitSize() {
+#if defined(OS_MAC)
+    constexpr int MAX_JIT_LEN = 50;
+#else
+    constexpr int MAX_JIT_LEN = 100;
+#endif
+    thread_local int length = 0;
+    if (length <= 0) {
+        string env_var = getEnvVar("AF_OPENCL_MAX_JIT_LEN");
+        if (!env_var.empty()) {
+            int input_len = stoi(env_var);
+            length        = input_len > 0 ? input_len : MAX_JIT_LEN;
+        } else {
+            length = MAX_JIT_LEN;
+        }
+    }
+    return length;
+}
+
+bool& evalFlag() {
+    ONEAPI_NOT_SUPPORTED("");
+    thread_local bool flag = true;
+    return flag;
+}
+
+MemoryManagerBase& memoryManager() {
+    static once_flag flag;
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    call_once(flag, [&]() {
+        // By default, create an instance of the default memory manager
+        inst.memManager = make_unique<common::DefaultMemoryManager>(
+            getDeviceCount(), common::MAX_BUFFERS,
+            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG);
+        // Set the memory manager's device memory manager
+        unique_ptr<Allocator> deviceMemoryManager;
+        deviceMemoryManager = make_unique<Allocator>();
+        inst.memManager->setAllocator(move(deviceMemoryManager));
+        inst.memManager->initialize();
+    });
+
+    return *(inst.memManager.get());
+}
+
+/*
+MemoryManagerBase& pinnedMemoryManager() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+*/
+
+void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void resetMemoryManager() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void setMemoryManagerPinned(unique_ptr<MemoryManagerBase> mgr) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+void resetMemoryManagerPinned() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+graphics::ForgeManager& forgeManager() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+GraphicsResourceManager& interopManager() {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+}  // namespace oneapi
+
+/*
+using namespace oneapi;
+
+af_err afcl_get_device_type(afcl_device_type* res) {
+    try {
+        *res = static_cast<afcl_device_type>(getActiveDeviceType());
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_get_platform(afcl_platform* res) {
+    try {
+        *res = static_cast<afcl_platform>(getActivePlatform());
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_get_context(cl_context* ctx, const bool retain) {
+    try {
+        *ctx = getContext()();
+        if (retain) { clRetainContext(*ctx); }
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_get_queue(cl_command_queue* queue, const bool retain) {
+    try {
+        *queue = getQueue()();
+        if (retain) { clRetainCommandQueue(*queue); }
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_get_device_id(cl_device_id* id) {
+    try {
+        *id = getDevice()();
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_set_device_id(cl_device_id id) {
+    try {
+        setDevice(getDeviceIdFromNativeId(id));
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_add_device_context(cl_device_id dev, cl_context ctx,
+                               cl_command_queue que) {
+    try {
+        addDeviceContext(dev, ctx, que);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_set_device_context(cl_device_id dev, cl_context ctx) {
+    try {
+        setDeviceContext(dev, ctx);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+
+af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx) {
+    try {
+        removeDeviceContext(dev, ctx);
+    }
+    CATCHALL;
+    return AF_SUCCESS;
+}
+*/
\ No newline at end of file
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
new file mode 100644
index 0000000000..da33f35690
--- /dev/null
+++ b/src/backend/oneapi/platform.hpp
@@ -0,0 +1,121 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+//#include <af/oneapi.h>
+
+#include <memory>
+#include <string>
+
+// Forward declarations
+namespace spdlog {
+class logger;
+}
+
+namespace graphics {
+class ForgeManager;
+}
+
+namespace common {
+namespace memory {
+class MemoryManagerBase;
+}
+}  // namespace common
+
+using common::memory::MemoryManagerBase;
+
+namespace oneapi {
+
+// Forward declarations
+class GraphicsResourceManager;
+class PlanCache;  // clfft
+
+bool verify_present(const std::string& pname, const std::string ref);
+
+int getBackend();
+
+std::string getDeviceInfo() noexcept;
+
+int getDeviceCount() noexcept;
+
+void init();
+
+unsigned getActiveDeviceId();
+
+int& getMaxJitSize();
+
+const sycl::context& getContext();
+
+sycl::queue& getQueue();
+
+const sycl::device& getDevice(int id = -1);
+
+size_t getDeviceMemorySize(int device);
+
+size_t getHostMemorySize();
+
+//sycl::device::is_cpu,is_gpu,is_accelerator
+//cl_device_type getDeviceType();
+
+bool isHostUnifiedMemory(const sycl::device& device);
+
+bool OneAPICPUOffload(bool forceOffloadOSX = true);
+
+bool isGLSharingSupported();
+
+bool isDoubleSupported(unsigned device);
+
+// Returns true if 16-bit precision floats are supported by the device
+bool isHalfSupported(unsigned device);
+
+void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute);
+
+std::string getPlatformName(const sycl::device& device);
+
+int setDevice(int device);
+
+void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que);
+
+void setDeviceContext(sycl::device dev, sycl::context ctx);
+
+void removeDeviceContext(sycl::device dev, sycl::context ctx);
+
+void sync(int device);
+
+bool synchronize_calls();
+
+int getActiveDeviceType();
+
+int getActivePlatform();
+
+bool& evalFlag();
+
+MemoryManagerBase& memoryManager();
+
+void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+
+void resetMemoryManager();
+
+MemoryManagerBase& pinnedMemoryManager();
+
+void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+
+void resetMemoryManagerPinned();
+
+graphics::ForgeManager& forgeManager();
+
+GraphicsResourceManager& interopManager();
+
+//afcl::platform getPlatformEnum(cl::Device dev);
+
+void setActiveContext(int device);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/plot.cpp b/src/backend/oneapi/plot.cpp
new file mode 100644
index 0000000000..544cc61568
--- /dev/null
+++ b/src/backend/oneapi/plot.cpp
@@ -0,0 +1,79 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+// #include <GraphicsResourceManager.hpp>
+// #include <debug_opencl.hpp>
+#include <err_oneapi.hpp>
+#include <plot.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T>
+void copy_plot(const Array<T> &P, fg_plot plot) {
+    ONEAPI_NOT_SUPPORTED("copy_plot Not supported");
+
+    // ForgeModule &_ = graphics::forgePlugin();
+    // if (isGLSharingSupported()) {
+    //     CheckGL("Begin OpenCL resource copy");
+    //     const cl::Buffer *d_P = P.get();
+    //     unsigned bytes        = 0;
+    //     FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
+
+    //     auto res = interopManager().getPlotResources(plot);
+
+    //     std::vector<cl::Memory> shared_objects;
+    //     shared_objects.push_back(*(res[0].get()));
+
+    //     glFinish();
+
+    //     // Use of events:
+    //     // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+    //     cl::Event event;
+
+    //     getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+    //     event.wait();
+    //     getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+    //                                  &event);
+    //     getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+    //     event.wait();
+
+    //     CL_DEBUG_FINISH(getQueue());
+    //     CheckGL("End OpenCL resource copy");
+    // } else {
+    //     unsigned bytes = 0, buffer = 0;
+    //     FG_CHECK(_.fg_get_plot_vertex_buffer(&buffer, plot));
+    //     FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
+
+    //     CheckGL("Begin OpenCL fallback-resource copy");
+    //     glBindBuffer(GL_ARRAY_BUFFER, buffer);
+    //     auto *ptr =
+    //         static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
+    //     if (ptr) {
+    //         getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
+    //         glUnmapBuffer(GL_ARRAY_BUFFER);
+    //     }
+    //     glBindBuffer(GL_ARRAY_BUFFER, 0);
+    //     CheckGL("End OpenCL fallback-resource copy");
+    // }
+}
+
+#define INSTANTIATE(T) template void copy_plot<T>(const Array<T> &, fg_plot);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/plot.hpp b/src/backend/oneapi/plot.hpp
new file mode 100644
index 0000000000..c7c922e270
--- /dev/null
+++ b/src/backend/oneapi/plot.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/graphics_common.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void copy_plot(const Array<T> &P, fg_plot plot);
+
+}
diff --git a/src/backend/oneapi/print.hpp b/src/backend/oneapi/print.hpp
new file mode 100644
index 0000000000..787df41df2
--- /dev/null
+++ b/src/backend/oneapi/print.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <backend.hpp>
+#include <ostream>
+
+namespace oneapi {
+static std::ostream& operator<<(std::ostream& out, const cfloat& var) {
+    out << "(" << std::real(var) << "," << std::imag(var) << ")";
+    return out;
+}
+
+static std::ostream& operator<<(std::ostream& out, const cdouble& var) {
+    out << "(" << std::real(var) << "," << std::imag(var) << ")";
+    return out;
+}
+}  // namespace oneapi
diff --git a/src/backend/oneapi/product.cpp b/src/backend/oneapi/product.cpp
new file mode 100644
index 0000000000..6d449e1fa7
--- /dev/null
+++ b/src/backend/oneapi/product.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// sum
+INSTANTIATE(af_mul_t, float, float)
+INSTANTIATE(af_mul_t, double, double)
+INSTANTIATE(af_mul_t, cfloat, cfloat)
+INSTANTIATE(af_mul_t, cdouble, cdouble)
+INSTANTIATE(af_mul_t, int, int)
+INSTANTIATE(af_mul_t, uint, uint)
+INSTANTIATE(af_mul_t, intl, intl)
+INSTANTIATE(af_mul_t, uintl, uintl)
+INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, uchar, uint)
+INSTANTIATE(af_mul_t, short, int)
+INSTANTIATE(af_mul_t, ushort, uint)
+INSTANTIATE(af_mul_t, half, float)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/qr.cpp b/src/backend/oneapi/qr.cpp
new file mode 100644
index 0000000000..80fa226994
--- /dev/null
+++ b/src/backend/oneapi/qr.cpp
@@ -0,0 +1,142 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <qr.hpp>
+
+#include <err_oneapi.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA) && !defined(AF_ONEAPI)
+
+#include <blas.hpp>
+#include <copy.hpp>
+#include <cpu/cpu_qr.hpp>
+#include <identity.hpp>
+// #include <kernel/triangle.hpp>
+#include <magma/magma.h>
+#include <magma/magma_data.h>
+#include <magma/magma_helper.h>
+#include <platform.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig) {
+    if (OpenCLCPUOffload()) { return cpu::qr(q, r, t, orig); }
+
+    const dim4 NullShape(0, 0, 0, 0);
+
+    dim4 iDims = orig.dims();
+    int M      = iDims[0];
+    int N      = iDims[1];
+
+    dim4 endPadding(M - iDims[0], max(M, N) - iDims[1], 0, 0);
+    Array<T> in =
+        (endPadding == NullShape
+             ? copyArray(orig)
+             : padArrayBorders(orig, NullShape, endPadding, AF_PAD_ZERO));
+    in.resetDims(iDims);
+
+    int MN = std::min(M, N);
+    int NB = magma_get_geqrf_nb<T>(M);
+
+    int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
+    Array<T> tmp = createEmptyArray<T>(dim4(NUM));
+
+    std::vector<T> h_tau(MN);
+
+    int info           = 0;
+    cl::Buffer *in_buf = in.get();
+    cl::Buffer *dT     = tmp.get();
+
+    magma_geqrf3_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
+                        &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
+                        &info);
+
+    r = createEmptyArray<T>(in.dims());
+    kernel::triangle<T>(r, in, true, false);
+
+    cl::Buffer *r_buf = r.get();
+    magmablas_swapdblk<T>(MN - 1, NB, (*r_buf)(), r.getOffset(), r.strides()[1],
+                          1, (*dT)(), tmp.getOffset() + MN * NB, NB, 0,
+                          getQueue()());
+
+    q = in;  // No need to copy
+    q.resetDims(dim4(M, M));
+    cl::Buffer *q_buf = q.get();
+
+    magma_ungqr_gpu<T>(q.dims()[0], q.dims()[1], std::min(M, N), (*q_buf)(),
+                       q.getOffset(), q.strides()[1], &h_tau[0], (*dT)(),
+                       tmp.getOffset(), NB, getQueue()(), &info);
+
+    t = createHostDataArray(dim4(MN), &h_tau[0]);
+}
+
+template<typename T>
+Array<T> qr_inplace(Array<T> &in) {
+    if (OpenCLCPUOffload()) { return cpu::qr_inplace(in); }
+
+    dim4 iDims = in.dims();
+    int M      = iDims[0];
+    int N      = iDims[1];
+    int MN     = std::min(M, N);
+
+    getQueue().finish();  // FIXME: Does this need to be here?
+    cl::CommandQueue Queue2(getContext(), getDevice());
+    cl_command_queue queues[] = {getQueue()(), Queue2()};
+
+    std::vector<T> h_tau(MN);
+    cl::Buffer *in_buf = in.get();
+
+    int info = 0;
+    magma_geqrf2_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
+                        &h_tau[0], queues, &info);
+
+    Array<T> t = createHostDataArray(dim4(MN), &h_tau[0]);
+    return t;
+}
+
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
+
+INSTANTIATE_QR(float)
+INSTANTIATE_QR(cfloat)
+INSTANTIATE_QR(double)
+INSTANTIATE_QR(cdouble)
+
+}  // namespace oneapi
+
+#else  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
+    AF_ERROR("Linear Algebra is disabled on OneAPI", AF_ERR_NOT_CONFIGURED);
+}
+
+template<typename T>
+Array<T> qr_inplace(Array<T> &in) {
+    AF_ERROR("Linear Algebra is disabled on OneAPI", AF_ERR_NOT_CONFIGURED);
+}
+
+#define INSTANTIATE_QR(T)                                         \
+    template Array<T> qr_inplace<T>(Array<T> & in);               \
+    template void qr<T>(Array<T> & q, Array<T> & r, Array<T> & t, \
+                        const Array<T> &in);
+
+INSTANTIATE_QR(float)
+INSTANTIATE_QR(cfloat)
+INSTANTIATE_QR(double)
+INSTANTIATE_QR(cdouble)
+
+}  // namespace oneapi
+
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/qr.hpp b/src/backend/oneapi/qr.hpp
new file mode 100644
index 0000000000..3ae750cf70
--- /dev/null
+++ b/src/backend/oneapi/qr.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig);
+
+template<typename T>
+Array<T> qr_inplace(Array<T> &in);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/random_engine.cpp b/src/backend/oneapi/random_engine.cpp
new file mode 100644
index 0000000000..9e9e7ba305
--- /dev/null
+++ b/src/backend/oneapi/random_engine.cpp
@@ -0,0 +1,160 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreemengt can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+#include <common/half.hpp>
+// #include <kernel/random_engine.hpp>
+#include <af/dim4.hpp>
+
+using common::half;
+
+namespace oneapi {
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> &tbl) {
+
+    ONEAPI_NOT_SUPPORTED("initMersenneState Not supported");
+
+    // kernel::initMersenneState(*state.get(), *tbl.get(), seed);
+}
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter) {
+
+    ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");
+
+    Array<T> out = createEmptyArray<T>(dims);
+    // kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,
+    //                                     counter);
+    return out;
+}
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl &seed,
+                            uintl &counter) {
+
+    ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");
+
+    Array<T> out = createEmptyArray<T>(dims);
+    // kernel::normalDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,
+    //                                    counter);
+    return out;
+}
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state) {
+
+    ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");
+
+    Array<T> out = createEmptyArray<T>(dims);
+    // kernel::uniformDistributionMT<T>(
+    //     *out.get(), out.elements(), *state.get(), *pos.get(), *sh1.get(),
+    //     *sh2.get(), mask, *recursion_table.get(), *temper_table.get());
+    return out;
+}
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state) {
+
+    ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");
+
+    Array<T> out = createEmptyArray<T>(dims);
+    // kernel::normalDistributionMT<T>(
+    //     *out.get(), out.elements(), *state.get(), *pos.get(), *sh1.get(),
+    //     *sh2.get(), mask, *recursion_table.get(), *temper_table.get());
+    return out;
+}
+
+#define INSTANTIATE_UNIFORM(T)                                   \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl &seed, uintl &counter);                      \
+    template Array<T> uniformDistribution<T>(                    \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
+
+#define INSTANTIATE_NORMAL(T)                                    \
+    template Array<T> normalDistribution<T>(                     \
+        const af::dim4 &dims, const af_random_engine_type type,  \
+        const uintl &seed, uintl &counter);                      \
+    template Array<T> normalDistribution<T>(                     \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,  \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table, \
+        Array<uint> temper_table, Array<uint> state);
+
+#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                    \
+    template<>                                                                 \
+    Array<T> uniformDistribution<T>(const af::dim4 &dims,                      \
+                                    const af_random_engine_type type,          \
+                                    const uintl &seed, uintl &counter) {       \
+        ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");             \
+        Array<T> out    = createEmptyArray<T>(dims);                           \
+        return out;                                                            \
+    }                                                                          \
+    template<>                                                                 \
+    Array<T> uniformDistribution<T>(                                           \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,                \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,               \
+        Array<uint> temper_table, Array<uint> state) {                         \
+        Array<T> out    = createEmptyArray<T>(dims);                           \
+        return out;                                                            \
+    }
+
+#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                    \
+    template<>                                                                \
+    Array<T> normalDistribution<T>(const af::dim4 &dims,                      \
+                                   const af_random_engine_type type,          \
+                                   const uintl &seed, uintl &counter) {       \
+        ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");             \
+        Array<T> out    = createEmptyArray<T>(dims);                          \
+        return out;                                                           \
+    }                                                                         \
+    template<>                                                                \
+    Array<T> normalDistribution<T>(                                           \
+        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,               \
+        Array<uint> sh2, uint mask, Array<uint> recursion_table,              \
+        Array<uint> temper_table, Array<uint> state) {                        \
+        ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");             \
+        Array<T> out    = createEmptyArray<T>(dims);                          \
+        return out;                                                           \
+    }
+
+INSTANTIATE_UNIFORM(float)
+INSTANTIATE_UNIFORM(double)
+INSTANTIATE_UNIFORM(int)
+INSTANTIATE_UNIFORM(uint)
+INSTANTIATE_UNIFORM(intl)
+INSTANTIATE_UNIFORM(uintl)
+INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(uchar)
+INSTANTIATE_UNIFORM(short)
+INSTANTIATE_UNIFORM(ushort)
+INSTANTIATE_UNIFORM(half)
+
+INSTANTIATE_NORMAL(float)
+INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(half)
+
+COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
+COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
+
+COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
+COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/random_engine.hpp b/src/backend/oneapi/random_engine.hpp
new file mode 100644
index 0000000000..0839d387b8
--- /dev/null
+++ b/src/backend/oneapi/random_engine.hpp
@@ -0,0 +1,41 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <backend.hpp>
+#include <af/defines.h>
+
+namespace oneapi {
+void initMersenneState(Array<uint> &state, const uintl seed,
+                       const Array<uint> &tbl);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims,
+                            const af_random_engine_type type, const uintl &seed,
+                            uintl &counter);
+
+template<typename T>
+Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
+                             Array<uint> sh1, Array<uint> sh2, uint mask,
+                             Array<uint> recursion_table,
+                             Array<uint> temper_table, Array<uint> state);
+
+template<typename T>
+Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
+                            Array<uint> sh1, Array<uint> sh2, uint mask,
+                            Array<uint> recursion_table,
+                            Array<uint> temper_table, Array<uint> state);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/range.cpp b/src/backend/oneapi/range.cpp
new file mode 100644
index 0000000000..e47a9cc664
--- /dev/null
+++ b/src/backend/oneapi/range.cpp
@@ -0,0 +1,57 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+// #include <kernel/range.hpp>
+#include <range.hpp>
+#include <err_oneapi.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim) {
+
+    ONEAPI_NOT_SUPPORTED("range Not supported");
+
+    // Set dimension along which the sequence should be
+    // Other dimensions are simply tiled
+    int _seq_dim = seq_dim;
+    if (seq_dim < 0) {
+        _seq_dim = 0;  // column wise sequence
+    }
+
+    if (_seq_dim < 0 || _seq_dim > 3) {
+        AF_ERROR("Invalid rep selection", AF_ERR_ARG);
+    }
+
+    Array<T> out = createEmptyArray<T>(dim);
+    // kernel::range<T>(out, _seq_dim);
+
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> range<T>(const af::dim4& dims, const int seq_dims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/range.hpp b/src/backend/oneapi/range.hpp
new file mode 100644
index 0000000000..7191152fb1
--- /dev/null
+++ b/src/backend/oneapi/range.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> range(const dim4& dim, const int seq_dim = -1);
+}
diff --git a/src/backend/oneapi/reduce.hpp b/src/backend/oneapi/reduce.hpp
new file mode 100644
index 0000000000..668fa1ac72
--- /dev/null
+++ b/src/backend/oneapi/reduce.hpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <optypes.hpp>
+
+namespace oneapi {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
+                 double nanval = 0);
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan = false, double nanval = 0);
+
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
+                     double nanval = 0);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
new file mode 100644
index 0000000000..d2763c92ac
--- /dev/null
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+//#include <kernel/reduce.hpp>
+//#include <kernel/reduce_by_key.hpp>
+#include <reduce.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+
+using af::dim4;
+using std::swap;
+namespace oneapi {
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
+                 double nanval) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<To> out = createEmptyArray<To>(1);
+    return out;
+
+}
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
+                   const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
+                   bool change_nan, double nanval) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+template<af_op_t op, typename Ti, typename To>
+Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<To> out = createEmptyArray<To>(1);
+    return out;
+}
+
+}  // namespace oneapi
+
+#define INSTANTIATE(Op, Ti, To)                                                \
+    template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim,  \
+                                          bool change_nan, double nanval);     \
+    template void reduce_by_key<Op, Ti, int, To>(                              \
+        Array<int> & keys_out, Array<To> & vals_out, const Array<int> &keys,   \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template void reduce_by_key<Op, Ti, uint, To>(                             \
+        Array<uint> & keys_out, Array<To> & vals_out, const Array<uint> &keys, \
+        const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
+    template Array<To> reduce_all<Op, Ti, To>(const Array<Ti> &in,             \
+                                              bool change_nan, double nanval);
diff --git a/src/backend/oneapi/regions.cpp b/src/backend/oneapi/regions.cpp
new file mode 100644
index 0000000000..cc74fb9543
--- /dev/null
+++ b/src/backend/oneapi/regions.cpp
@@ -0,0 +1,42 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/regions.hpp>
+#include <regions.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
+
+    ONEAPI_NOT_SUPPORTED("regions Not supported");
+
+    const af::dim4 &dims = in.dims();
+    Array<T> out         = createEmptyArray<T>(dims);
+    // kernel::regions<T>(out, in, connectivity == AF_CONNECTIVITY_8, 2);
+    return out;
+}
+
+#define INSTANTIATE(T)                                  \
+    template Array<T> regions<T>(const Array<char> &in, \
+                                 af_connectivity connectivity);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/regions.hpp b/src/backend/oneapi/regions.hpp
new file mode 100644
index 0000000000..585f7e6e14
--- /dev/null
+++ b/src/backend/oneapi/regions.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> regions(const Array<char> &in, af_connectivity connectivity);
+
+}
diff --git a/src/backend/oneapi/reorder.cpp b/src/backend/oneapi/reorder.cpp
new file mode 100644
index 0000000000..7cced14197
--- /dev/null
+++ b/src/backend/oneapi/reorder.cpp
@@ -0,0 +1,52 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/reorder.hpp>
+#include <reorder.hpp>
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
+
+    ONEAPI_NOT_SUPPORTED("reorder Not supported");
+
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims(0);
+    for (int i = 0; i < 4; i++) { oDims[i] = iDims[rdims[i]]; }
+
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    // kernel::reorder<T>(out, in, rdims.get());
+
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> reorder<T>(const Array<T> &in, const af::dim4 &rdims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/reorder.hpp b/src/backend/oneapi/reorder.hpp
new file mode 100644
index 0000000000..eb2cc8ef9c
--- /dev/null
+++ b/src/backend/oneapi/reorder.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
+}
diff --git a/src/backend/oneapi/reshape.cpp b/src/backend/oneapi/reshape.cpp
new file mode 100644
index 0000000000..9331038986
--- /dev/null
+++ b/src/backend/oneapi/reshape.cpp
@@ -0,0 +1,82 @@
+
+/*******************************************************
+ * Copyright (c) 2020, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+
+#include <common/half.hpp>
+// #include <kernel/memcopy.hpp>
+
+using common::half;
+
+namespace oneapi {
+
+template<typename inType, typename outType>
+Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
+                       outType defaultValue, double scale) {
+
+    ONEAPI_NOT_SUPPORTED("reshape Not supported");
+
+    Array<outType> out = createEmptyArray<outType>(outDims);
+    // kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale,
+    //                               in.dims() == outDims);
+    return out;
+}
+
+#define INSTANTIATE(SRC_T)                                                    \
+    template Array<float> reshape<SRC_T, float>(Array<SRC_T> const &,         \
+                                                dim4 const &, float, double); \
+    template Array<double> reshape<SRC_T, double>(                            \
+        Array<SRC_T> const &, dim4 const &, double, double);                  \
+    template Array<cfloat> reshape<SRC_T, cfloat>(                            \
+        Array<SRC_T> const &, dim4 const &, cfloat, double);                  \
+    template Array<cdouble> reshape<SRC_T, cdouble>(                          \
+        Array<SRC_T> const &, dim4 const &, cdouble, double);                 \
+    template Array<int> reshape<SRC_T, int>(Array<SRC_T> const &,             \
+                                            dim4 const &, int, double);       \
+    template Array<uint> reshape<SRC_T, uint>(Array<SRC_T> const &,           \
+                                              dim4 const &, uint, double);    \
+    template Array<intl> reshape<SRC_T, intl>(Array<SRC_T> const &,           \
+                                              dim4 const &, intl, double);    \
+    template Array<uintl> reshape<SRC_T, uintl>(Array<SRC_T> const &,         \
+                                                dim4 const &, uintl, double); \
+    template Array<short> reshape<SRC_T, short>(Array<SRC_T> const &,         \
+                                                dim4 const &, short, double); \
+    template Array<ushort> reshape<SRC_T, ushort>(                            \
+        Array<SRC_T> const &, dim4 const &, ushort, double);                  \
+    template Array<uchar> reshape<SRC_T, uchar>(Array<SRC_T> const &,         \
+                                                dim4 const &, uchar, double); \
+    template Array<char> reshape<SRC_T, char>(Array<SRC_T> const &,           \
+                                              dim4 const &, char, double);    \
+    template Array<half> reshape<SRC_T, half>(Array<SRC_T> const &,           \
+                                              dim4 const &, half, double);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(half)
+
+#define INSTANTIATE_COMPLEX(SRC_T)                           \
+    template Array<cfloat> reshape<SRC_T, cfloat>(           \
+        Array<SRC_T> const &, dim4 const &, cfloat, double); \
+    template Array<cdouble> reshape<SRC_T, cdouble>(         \
+        Array<SRC_T> const &, dim4 const &, cdouble, double);
+
+INSTANTIATE_COMPLEX(cfloat)
+INSTANTIATE_COMPLEX(cdouble)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/resize.cpp b/src/backend/oneapi/resize.cpp
new file mode 100644
index 0000000000..89bdea49b1
--- /dev/null
+++ b/src/backend/oneapi/resize.cpp
@@ -0,0 +1,48 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/resize.hpp>
+#include <resize.hpp>
+#include <af/dim4.hpp>
+#include <stdexcept>
+
+namespace oneapi {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method) {
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    ONEAPI_NOT_SUPPORTED("resize Not supported");
+
+    // kernel::resize<T>(out, in, method);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                 \
+    template Array<T> resize<T>(const Array<T> &in, const dim_t odim0, \
+                                const dim_t odim1,                     \
+                                const af_interp_type method);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/resize.hpp b/src/backend/oneapi/resize.hpp
new file mode 100644
index 0000000000..77b5972588
--- /dev/null
+++ b/src/backend/oneapi/resize.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
+                const af_interp_type method);
+}
diff --git a/src/backend/oneapi/rotate.cpp b/src/backend/oneapi/rotate.cpp
new file mode 100644
index 0000000000..fc49dd6baa
--- /dev/null
+++ b/src/backend/oneapi/rotate.cpp
@@ -0,0 +1,59 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <rotate.hpp>
+#include <err_oneapi.hpp>
+
+// #include <kernel/rotate.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method) {
+
+    ONEAPI_NOT_SUPPORTED("rotate Not supported");
+
+    Array<T> out = createEmptyArray<T>(odims);
+
+    // switch (method) {
+    //     case AF_INTERP_NEAREST:
+    //     case AF_INTERP_LOWER:
+    //         kernel::rotate<T>(out, in, theta, method, 1);
+    //         break;
+    //     case AF_INTERP_BILINEAR:
+    //     case AF_INTERP_BILINEAR_COSINE:
+    //         kernel::rotate<T>(out, in, theta, method, 2);
+    //         break;
+    //     case AF_INTERP_BICUBIC:
+    //     case AF_INTERP_BICUBIC_SPLINE:
+    //         kernel::rotate<T>(out, in, theta, method, 3);
+    //         break;
+    //     default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+    // }
+    return out;
+}
+
+#define INSTANTIATE(T)                                              \
+    template Array<T> rotate(const Array<T> &in, const float theta, \
+                             const af::dim4 &odims,                 \
+                             const af_interp_type method);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/rotate.hpp b/src/backend/oneapi/rotate.hpp
new file mode 100644
index 0000000000..369bbd2521
--- /dev/null
+++ b/src/backend/oneapi/rotate.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
+                const af_interp_type method);
+}
diff --git a/src/backend/oneapi/scalar.hpp b/src/backend/oneapi/scalar.hpp
new file mode 100644
index 0000000000..fee814f9f2
--- /dev/null
+++ b/src/backend/oneapi/scalar.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/jit/ScalarNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> createScalarNode(const dim4 &size, const T val) {
+    return createNodeArray<T>(size,
+                              std::make_shared<common::ScalarNode<T>>(val));
+}
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/scan.cpp b/src/backend/oneapi/scan.cpp
new file mode 100644
index 0000000000..572746035c
--- /dev/null
+++ b/src/backend/oneapi/scan.cpp
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <scan.hpp>
+#include <err_oneapi.hpp>
+
+// #include <kernel/scan_dim.hpp>
+// #include <kernel/scan_first.hpp>
+
+namespace oneapi {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
+
+    ONEAPI_NOT_SUPPORTED("scan Not supported");
+
+    Array<To> out = createEmptyArray<To>(in.dims());
+
+    // Param Out = out;
+    // Param In  = in;
+
+    // if (dim == 0) {
+    //     kernel::scanFirst<Ti, To, op>(Out, In, inclusiveScan);
+    // } else {
+    //     kernel::scanDim<Ti, To, op>(Out, In, dim, inclusiveScan);
+    // }
+
+    return out;
+}
+
+#define INSTANTIATE_SCAN(ROp, Ti, To) \
+    template Array<To> scan<ROp, Ti, To>(const Array<Ti>&, const int, bool);
+
+#define INSTANTIATE_SCAN_ALL(ROp)           \
+    INSTANTIATE_SCAN(ROp, float, float)     \
+    INSTANTIATE_SCAN(ROp, double, double)   \
+    INSTANTIATE_SCAN(ROp, cfloat, cfloat)   \
+    INSTANTIATE_SCAN(ROp, cdouble, cdouble) \
+    INSTANTIATE_SCAN(ROp, int, int)         \
+    INSTANTIATE_SCAN(ROp, uint, uint)       \
+    INSTANTIATE_SCAN(ROp, intl, intl)       \
+    INSTANTIATE_SCAN(ROp, uintl, uintl)     \
+    INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, uchar, uint)      \
+    INSTANTIATE_SCAN(ROp, short, int)       \
+    INSTANTIATE_SCAN(ROp, ushort, uint)
+
+INSTANTIATE_SCAN(af_notzero_t, char, uint)
+INSTANTIATE_SCAN_ALL(af_add_t)
+INSTANTIATE_SCAN_ALL(af_mul_t)
+INSTANTIATE_SCAN_ALL(af_min_t)
+INSTANTIATE_SCAN_ALL(af_max_t)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/scan.hpp b/src/backend/oneapi/scan.hpp
new file mode 100644
index 0000000000..5e8508a8da
--- /dev/null
+++ b/src/backend/oneapi/scan.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <optypes.hpp>
+
+namespace oneapi {
+template<af_op_t op, typename Ti, typename To>
+Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
+}
diff --git a/src/backend/oneapi/scan_by_key.cpp b/src/backend/oneapi/scan_by_key.cpp
new file mode 100644
index 0000000000..08a4969905
--- /dev/null
+++ b/src/backend/oneapi/scan_by_key.cpp
@@ -0,0 +1,65 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+#include <scan.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+
+// #include <kernel/scan_dim_by_key.hpp>
+// #include <kernel/scan_first_by_key.hpp>
+
+namespace oneapi {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan) {
+
+    ONEAPI_NOT_SUPPORTED("scan Not supported");
+
+    Array<To> out = createEmptyArray<To>(in.dims());
+
+    // Param Out = out;
+    // Param Key = key;
+    // Param In  = in;
+
+    // if (dim == 0) {
+    //     // kernel::scanFirstByKey<Ti, Tk, To, op>(Out, In, Key, inclusive_scan);
+    // } else {
+    //     // kernel::scanDimByKey<Ti, Tk, To, op>(Out, In, Key, dim, inclusive_scan);
+    // }
+    return out;
+}
+
+#define INSTANTIATE_SCAN_BY_KEY(ROp, Ti, Tk, To)                  \
+    template Array<To> scan<ROp, Ti, Tk, To>(                     \
+        const Array<Tk>& key, const Array<Ti>& in, const int dim, \
+        bool inclusive_scan);
+
+#define INSTANTIATE_SCAN_BY_KEY_ALL(ROp, Tk)           \
+    INSTANTIATE_SCAN_BY_KEY(ROp, float, Tk, float)     \
+    INSTANTIATE_SCAN_BY_KEY(ROp, double, Tk, double)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cfloat, Tk, cfloat)   \
+    INSTANTIATE_SCAN_BY_KEY(ROp, cdouble, Tk, cdouble) \
+    INSTANTIATE_SCAN_BY_KEY(ROp, int, Tk, int)         \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uint, Tk, uint)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, intl, Tk, intl)       \
+    INSTANTIATE_SCAN_BY_KEY(ROp, uintl, Tk, uintl)
+
+#define INSTANTIATE_SCAN_BY_KEY_OP(ROp)    \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, int)  \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uint) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, intl) \
+    INSTANTIATE_SCAN_BY_KEY_ALL(ROp, uintl)
+
+INSTANTIATE_SCAN_BY_KEY_OP(af_add_t)
+INSTANTIATE_SCAN_BY_KEY_OP(af_mul_t)
+INSTANTIATE_SCAN_BY_KEY_OP(af_min_t)
+INSTANTIATE_SCAN_BY_KEY_OP(af_max_t)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/scan_by_key.hpp b/src/backend/oneapi/scan_by_key.hpp
new file mode 100644
index 0000000000..556d59f922
--- /dev/null
+++ b/src/backend/oneapi/scan_by_key.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <optypes.hpp>
+
+namespace oneapi {
+template<af_op_t op, typename Ti, typename Tk, typename To>
+Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
+               bool inclusive_scan = true);
+}
diff --git a/src/backend/oneapi/select.cpp b/src/backend/oneapi/select.cpp
new file mode 100644
index 0000000000..f15e2ab61c
--- /dev/null
+++ b/src/backend/oneapi/select.cpp
@@ -0,0 +1,145 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+// #include <kernel/select.hpp>
+#include <select.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <common/jit/NaryNode.hpp>
+#include <err_oneapi.hpp>
+#include <scalar.hpp>
+
+#include <nonstd/span.hpp>
+#include <memory>
+
+using af::dim4;
+
+using common::half;
+using common::NaryNode;
+
+using std::make_shared;
+using std::max;
+
+namespace oneapi {
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const dim4 &odims) {
+
+    ONEAPI_NOT_SUPPORTED("createSelectNode Not supported");
+
+    auto cond_node   = cond.getNode();
+    auto a_node      = a.getNode();
+    auto b_node      = b.getNode();
+    auto a_height    = a_node->getHeight();
+    auto b_height    = b_node->getHeight();
+    auto cond_height = cond_node->getHeight();
+    const int height = max(max(a_height, b_height), cond_height) + 1;
+
+    auto node = make_shared<NaryNode>(
+        NaryNode(static_cast<af::dtype>(af::dtype_traits<T>::af_type), "__select",
+                 3, {{cond_node, a_node, b_node}}, af_select_t, height));
+    std::array<common::Node *, 1> nodes{node.get()};
+    if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
+        if (a_height > max(b_height, cond_height)) {
+            a.eval();
+        } else if (b_height > cond_height) {
+            b.eval();
+        } else {
+            cond.eval();
+        }
+        return createSelectNode<T>(cond, a, b, odims);
+    }
+    return createNodeArray<T>(odims, node);
+}
+
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const T &b_val, const dim4 &odims) {
+
+    ONEAPI_NOT_SUPPORTED("createSelectNode Not supported");
+
+    auto cond_node   = cond.getNode();
+    auto a_node      = a.getNode();
+    Array<T> b       = createScalarNode<T>(odims, b_val);
+    auto b_node      = b.getNode();
+    auto a_height    = a_node->getHeight();
+    auto b_height    = b_node->getHeight();
+    auto cond_height = cond_node->getHeight();
+    const int height = max(max(a_height, b_height), cond_height) + 1;
+
+    auto node = make_shared<NaryNode>(NaryNode(
+        static_cast<af::dtype>(af::dtype_traits<T>::af_type),
+        (flip ? "__not_select" : "__select"), 3, {{cond_node, a_node, b_node}},
+        (flip ? af_not_select_t : af_select_t), height));
+
+    std::array<common::Node *, 1> nodes{node.get()};
+    if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
+        if (a_height > max(b_height, cond_height)) {
+            a.eval();
+        } else if (b_height > cond_height) {
+            b.eval();
+        } else {
+            cond.eval();
+        }
+        return createSelectNode<T, flip>(cond, a, b_val, odims);
+    }
+    return createNodeArray<T>(odims, node);
+}
+
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b) {
+    ONEAPI_NOT_SUPPORTED("select Not supported");
+
+    // kernel::select<T>(out, cond, a, b, out.ndims());
+}
+
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const T &b) {
+    ONEAPI_NOT_SUPPORTED("select_scalar Not supported");
+
+    // kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
+}
+
+#define INSTANTIATE(T)                                                   \
+    template Array<T> createSelectNode<T>(                               \
+        const Array<char> &cond, const Array<T> &a, const Array<T> &b,   \
+        const af::dim4 &odims);                                          \
+    template Array<T> createSelectNode<T, true>(                         \
+        const Array<char> &cond, const Array<T> &a, const T &b_val,      \
+        const af::dim4 &odims);                                          \
+    template Array<T> createSelectNode<T, false>(                        \
+        const Array<char> &cond, const Array<T> &a, const T &b_val,      \
+        const af::dim4 &odims);                                          \
+    template void select<T>(Array<T> & out, const Array<char> &cond,     \
+                            const Array<T> &a, const Array<T> &b);       \
+    template void select_scalar<T, true>(Array<T> & out,                 \
+                                         const Array<char> &cond,        \
+                                         const Array<T> &a, const T &b); \
+    template void select_scalar<T, false>(Array<T> & out,                \
+                                          const Array<char> &cond,       \
+                                          const Array<T> &a, const T &b)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(cfloat);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
+INSTANTIATE(uint);
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(char);
+INSTANTIATE(uchar);
+INSTANTIATE(short);
+INSTANTIATE(ushort);
+INSTANTIATE(half);
+
+#undef INSTANTIATE
+}  // namespace oneapi
diff --git a/src/backend/oneapi/select.hpp b/src/backend/oneapi/select.hpp
new file mode 100644
index 0000000000..00d0eb06c6
--- /dev/null
+++ b/src/backend/oneapi/select.hpp
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+#include <Array.hpp>
+#include <af/dim4.hpp>
+
+namespace oneapi {
+template<typename T>
+void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+            const Array<T> &b);
+
+template<typename T, bool flip>
+void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
+                   const T &b);
+
+template<typename T>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const Array<T> &b, const af::dim4 &odims);
+
+template<typename T, bool flip>
+Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
+                          const T &b_val, const af::dim4 &odims);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/set.cpp b/src/backend/oneapi/set.cpp
new file mode 100644
index 0000000000..2001729eca
--- /dev/null
+++ b/src/backend/oneapi/set.cpp
@@ -0,0 +1,157 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+#include <set.hpp>
+#include <sort.hpp>
+#include <af/dim4.hpp>
+#include <common/deprecated.hpp>
+
+namespace oneapi {
+using af::dim4;
+
+using std::conditional;
+using std::is_same;
+template<typename T>
+using ltype_t = typename conditional<is_same<T, intl>::value, cl_long, T>::type;
+
+template<typename T>
+using type_t =
+    typename conditional<is_same<T, uintl>::value, cl_ulong, ltype_t<T>>::type;
+
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
+
+    ONEAPI_NOT_SUPPORTED("setUnique Not supported");
+    return createEmptyArray<T>(dim4(1, 1, 1, 1));
+
+    // try {
+    //     Array<T> out = copyArray<T>(in);
+
+    //     compute::command_queue queue(getQueue()());
+
+    //     compute::buffer out_data((*out.get())());
+
+    //     compute::buffer_iterator<type_t<T>> begin(out_data, 0);
+    //     compute::buffer_iterator<type_t<T>> end(out_data, out.elements());
+
+    //     if (!is_sorted) { compute::sort(begin, end, queue); }
+
+    //     end = compute::unique(begin, end, queue);
+
+    //     out.resetDims(dim4(std::distance(begin, end), 1, 1, 1));
+
+    //     return out;
+    // } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique) {
+
+    ONEAPI_NOT_SUPPORTED("setUnion Not supported");
+    return createEmptyArray<T>(dim4(1, 1, 1, 1));
+
+    // try {
+    //     Array<T> unique_first  = first;
+    //     Array<T> unique_second = second;
+
+    //     if (!is_unique) {
+    //         unique_first  = setUnique(first, false);
+    //         unique_second = setUnique(second, false);
+    //     }
+
+    //     size_t out_size = unique_first.elements() + unique_second.elements();
+    //     Array<T> out    = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
+
+    //     compute::command_queue queue(getQueue()());
+
+    //     compute::buffer first_data((*unique_first.get())());
+    //     compute::buffer second_data((*unique_second.get())());
+    //     compute::buffer out_data((*out.get())());
+
+    //     compute::buffer_iterator<type_t<T>> first_begin(first_data, 0);
+    //     compute::buffer_iterator<type_t<T>> first_end(first_data,
+    //                                                   unique_first.elements());
+    //     compute::buffer_iterator<type_t<T>> second_begin(second_data, 0);
+    //     compute::buffer_iterator<type_t<T>> second_end(
+    //         second_data, unique_second.elements());
+    //     compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
+
+    //     compute::buffer_iterator<type_t<T>> out_end = compute::set_union(
+    //         first_begin, first_end, second_begin, second_end, out_begin, queue);
+
+    //     out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
+    //     return out;
+
+    // } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique) {
+
+    ONEAPI_NOT_SUPPORTED("setIntersect Not supported");
+    return createEmptyArray<T>(dim4(1, 1, 1, 1));
+
+    // try {
+    //     Array<T> unique_first  = first;
+    //     Array<T> unique_second = second;
+
+    //     if (!is_unique) {
+    //         unique_first  = setUnique(first, false);
+    //         unique_second = setUnique(second, false);
+    //     }
+
+    //     size_t out_size =
+    //         std::max(unique_first.elements(), unique_second.elements());
+    //     Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
+
+    //     compute::command_queue queue(getQueue()());
+
+    //     compute::buffer first_data((*unique_first.get())());
+    //     compute::buffer second_data((*unique_second.get())());
+    //     compute::buffer out_data((*out.get())());
+
+    //     compute::buffer_iterator<type_t<T>> first_begin(first_data, 0);
+    //     compute::buffer_iterator<type_t<T>> first_end(first_data,
+    //                                                   unique_first.elements());
+    //     compute::buffer_iterator<type_t<T>> second_begin(second_data, 0);
+    //     compute::buffer_iterator<type_t<T>> second_end(
+    //         second_data, unique_second.elements());
+    //     compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
+
+    //     compute::buffer_iterator<type_t<T>> out_end = compute::set_intersection(
+    //         first_begin, first_end, second_begin, second_end, out_begin, queue);
+
+    //     out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
+    //     return out;
+    // } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+#define INSTANTIATE(T)                                                        \
+    template Array<T> setUnique<T>(const Array<T> &in, const bool is_sorted); \
+    template Array<T> setUnion<T>(                                            \
+        const Array<T> &first, const Array<T> &second, const bool is_unique); \
+    template Array<T> setIntersect<T>(                                        \
+        const Array<T> &first, const Array<T> &second, const bool is_unique);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/set.hpp b/src/backend/oneapi/set.hpp
new file mode 100644
index 0000000000..7836873639
--- /dev/null
+++ b/src/backend/oneapi/set.hpp
@@ -0,0 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> setUnique(const Array<T> &in, const bool is_sorted);
+
+template<typename T>
+Array<T> setUnion(const Array<T> &first, const Array<T> &second,
+                  const bool is_unique);
+
+template<typename T>
+Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
+                      const bool is_unique);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/shift.cpp b/src/backend/oneapi/shift.cpp
new file mode 100644
index 0000000000..b3941f1960
--- /dev/null
+++ b/src/backend/oneapi/shift.cpp
@@ -0,0 +1,73 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <shift.hpp>
+
+#include <common/jit/ShiftNodeBase.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+using af::dim4;
+using common::Node_ptr;
+using common::ShiftNodeBase;
+using std::array;
+using std::make_shared;
+using std::static_pointer_cast;
+using std::string;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]) {
+    ONEAPI_NOT_SUPPORTED("");
+    Array<T> o = createEmptyArray<T>(dim4(1));
+    return o;
+    /*
+    // Shift should only be the first node in the JIT tree.
+    // Force input to be evaluated so that in is always a buffer.
+    in.eval();
+
+    string name_str("Sh");
+    name_str += shortname<T>(true);
+    const dim4 &iDims = in.dims();
+    dim4 oDims        = iDims;
+
+    array<int, 4> shifts{};
+    for (int i = 0; i < 4; i++) {
+        // sdims_[i] will always be positive and always [0, oDims[i]].
+        // Negative shifts are converted to position by going the other way
+        // round
+        shifts[i] = -(sdims[i] % static_cast<int>(oDims[i])) +
+                    oDims[i] * (sdims[i] > 0);
+        assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
+    }
+
+    auto node = make_shared<ShiftNode>(
+        static_cast<af::dtype>(dtype_traits<T>::af_type),
+        static_pointer_cast<BufferNode>(in.getNode()), shifts);
+    return createNodeArray<T>(oDims, common::Node_ptr(node));
+    */
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> shift<T>(const Array<T> &in, const int sdims[4]);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+}  // namespace opencl
diff --git a/src/backend/oneapi/shift.hpp b/src/backend/oneapi/shift.hpp
new file mode 100644
index 0000000000..f236018321
--- /dev/null
+++ b/src/backend/oneapi/shift.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> shift(const Array<T> &in, const int sdims[4]);
+}
diff --git a/src/backend/oneapi/sift.cpp b/src/backend/oneapi/sift.cpp
new file mode 100644
index 0000000000..af2f7bf10d
--- /dev/null
+++ b/src/backend/oneapi/sift.cpp
@@ -0,0 +1,76 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sift.hpp>
+
+// #include <kernel/sift.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+
+using af::dim4;
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename convAccT>
+unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
+              Array<float>& ori_out, Array<float>& size_out,
+              Array<float>& desc_out, const Array<T>& in,
+              const unsigned n_layers, const float contrast_thr,
+              const float edge_thr, const float init_sigma,
+              const bool double_input, const float img_scale,
+              const float feature_ratio, const bool compute_GLOH) {
+
+    ONEAPI_NOT_SUPPORTED("sift Not supported");
+    return 0;
+
+    // unsigned nfeat_out;
+    // unsigned desc_len;
+
+    // Param x;
+    // Param y;
+    // Param score;
+    // Param ori;
+    // Param size;
+    // Param desc;
+
+    // kernel::sift<T, convAccT>(&nfeat_out, &desc_len, x, y, score, ori, size,
+    //                           desc, in, n_layers, contrast_thr, edge_thr,
+    //                           init_sigma, double_input, img_scale,
+    //                           feature_ratio, compute_GLOH);
+
+    // if (nfeat_out > 0) {
+    //     const dim4 out_dims(nfeat_out);
+    //     const dim4 desc_dims(desc_len, nfeat_out);
+
+    //     x_out     = createParamArray<float>(x, true);
+    //     y_out     = createParamArray<float>(y, true);
+    //     score_out = createParamArray<float>(score, true);
+    //     ori_out   = createParamArray<float>(ori, true);
+    //     size_out  = createParamArray<float>(size, true);
+    //     desc_out  = createParamArray<float>(desc, true);
+    // }
+
+    // return nfeat_out;
+}
+
+#define INSTANTIATE(T, convAccT)                                              \
+    template unsigned sift<T, convAccT>(                                      \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        Array<float> & ori_out, Array<float> & size_out,                      \
+        Array<float> & desc_out, const Array<T>& in, const unsigned n_layers, \
+        const float contrast_thr, const float edge_thr,                       \
+        const float init_sigma, const bool double_input,                      \
+        const float img_scale, const float feature_ratio,                     \
+        const bool compute_GLOH);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sift.hpp b/src/backend/oneapi/sift.hpp
new file mode 100644
index 0000000000..5c2a33dca6
--- /dev/null
+++ b/src/backend/oneapi/sift.hpp
@@ -0,0 +1,26 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/features.h>
+
+using af::features;
+
+namespace oneapi {
+
+template<typename T, typename convAccT>
+unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
+              Array<float>& ori, Array<float>& size, Array<float>& desc,
+              const Array<T>& in, const unsigned n_layers,
+              const float contrast_thr, const float edge_thr,
+              const float init_sigma, const bool double_input,
+              const float img_scale, const float feature_ratio,
+              const bool compute_GLOH);
+
+}
diff --git a/src/backend/oneapi/sobel.cpp b/src/backend/oneapi/sobel.cpp
new file mode 100644
index 0000000000..f76b8685db
--- /dev/null
+++ b/src/backend/oneapi/sobel.cpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/sobel.hpp>
+#include <sobel.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename Ti, typename To>
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size) {
+
+    ONEAPI_NOT_SUPPORTED("sobelDerivatives Not supported");
+
+    Array<To> dx = createEmptyArray<To>(img.dims());
+    Array<To> dy = createEmptyArray<To>(img.dims());
+
+    // switch (ker_size) {
+    //     case 3: kernel::sobel<Ti, To, 3>(dx, dy, img); break;
+    // }
+
+    return std::make_pair(dx, dy);
+}
+
+#define INSTANTIATE(Ti, To)                                    \
+    template std::pair<Array<To>, Array<To>> sobelDerivatives( \
+        const Array<Ti> &img, const unsigned &ker_size);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(int, int)
+INSTANTIATE(uint, int)
+INSTANTIATE(char, int)
+INSTANTIATE(uchar, int)
+INSTANTIATE(short, int)
+INSTANTIATE(ushort, int)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sobel.hpp b/src/backend/oneapi/sobel.hpp
new file mode 100644
index 0000000000..94d3e06879
--- /dev/null
+++ b/src/backend/oneapi/sobel.hpp
@@ -0,0 +1,19 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <utility>
+
+namespace oneapi {
+
+template<typename Ti, typename To>
+std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
+                                                 const unsigned &ker_size);
+
+}
diff --git a/src/backend/oneapi/solve.cpp b/src/backend/oneapi/solve.cpp
new file mode 100644
index 0000000000..b38461d0f1
--- /dev/null
+++ b/src/backend/oneapi/solve.cpp
@@ -0,0 +1,368 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <solve.hpp>
+
+#include <err_oneapi.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA) && !defined(AF_ONEAPI)
+#include <blas.hpp>
+#include <copy.hpp>
+#include <cpu/cpu_solve.hpp>
+#include <lu.hpp>
+#include <magma/magma.h>
+#include <magma/magma_blas.h>
+#include <magma/magma_data.h>
+#include <magma/magma_helper.h>
+#include <math.hpp>
+#include <platform.hpp>
+#include <transpose.hpp>
+#include <af/opencl.h>
+
+#include <algorithm>
+#include <vector>
+
+using cl::Buffer;
+using std::min;
+using std::vector;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
+
+    ONEAPI_NOT_SUPPORTED("solveLU Not supported");
+
+    if (OpenCLCPUOffload()) { return cpu::solveLU(A, pivot, b, options); }
+
+    int N    = A.dims()[0];
+    int NRHS = b.dims()[1];
+
+    vector<int> ipiv(N);
+    copyData(&ipiv[0], pivot);
+
+    Array<T> B = copyArray<T>(b);
+
+    const Buffer *A_buf = A.get();
+    Buffer *B_buf       = B.get();
+
+    int info = 0;
+    magma_getrs_gpu<T>(MagmaNoTrans, N, NRHS, (*A_buf)(), A.getOffset(),
+                       A.strides()[1], &ipiv[0], (*B_buf)(), B.getOffset(),
+                       B.strides()[1], getQueue()(), &info);
+    return B;
+}
+
+template<typename T>
+Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
+
+    ONEAPI_NOT_SUPPORTED("generalSolve Not supported");
+
+    // dim4 aDims = a.dims();
+    // int batchz = aDims[2];
+    // int batchw = aDims[3];
+
+    // Array<T> A = copyArray<T>(a);
+    Array<T> B = copyArray<T>(b);
+
+    // for (int i = 0; i < batchw; i++) {
+    //     for (int j = 0; j < batchz; j++) {
+    //         int M  = aDims[0];
+    //         int N  = aDims[1];
+    //         int MN = min(M, N);
+    //         vector<int> ipiv(MN);
+
+    //         Buffer *A_buf      = A.get();
+    //         int info           = 0;
+    //         cl_command_queue q = getQueue()();
+    //         auto aoffset =
+    //             A.getOffset() + j * A.strides()[2] + i * A.strides()[3];
+    //         magma_getrf_gpu<T>(M, N, (*A_buf)(), aoffset, A.strides()[1],
+    //                            &ipiv[0], q, &info);
+
+    //         Buffer *B_buf = B.get();
+    //         int K         = B.dims()[1];
+
+    //         auto boffset =
+    //             B.getOffset() + j * B.strides()[2] + i * B.strides()[3];
+    //         magma_getrs_gpu<T>(MagmaNoTrans, M, K, (*A_buf)(), aoffset,
+    //                            A.strides()[1], &ipiv[0], (*B_buf)(), boffset,
+    //                            B.strides()[1], q, &info);
+    //     }
+    // }
+    return B;
+}
+
+template<typename T>
+Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
+
+    ONEAPI_NOT_SUPPORTED("leastSquares Not supported");
+
+    int M  = a.dims()[0];
+    int N  = a.dims()[1];
+    int K  = b.dims()[1];
+    int MN = min(M, N);
+
+    Array<T> B = createEmptyArray<T>(dim4());
+    gpu_blas_trsm_func<T> gpu_blas_trsm;
+
+    cl_event event;
+    cl_command_queue queue = getQueue()();
+
+    if (M < N) {
+#define UNMQR 0  // FIXME: UNMQR == 1 should be faster but does not work
+
+        // Least squres for this case is solved using the following
+        // solve(A, B) == matmul(Q, Xpad);
+        // Where:
+        // Xpad == pad(Xt, N - M, 1);
+        // Xt   == tri_solve(R1, B);
+        // R1   == R(seq(M), seq(M));
+        // transpose(A) == matmul(Q, R);
+
+        // QR is performed on the transpose of A
+        Array<T> A = transpose<T>(a, true);
+
+#if UNMQR
+        const dim4 NullShape(0, 0, 0, 0);
+        dim4 endPadding(N - b.dims()[0], K - b.dims()[1], 0, 0);
+        B = (endPadding == NullShape
+                 ? copyArray(b)
+                 : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
+        B.resetDims(dim4(M, K));
+#else
+        B = copyArray<T>(b);
+#endif
+
+        int NB       = magma_get_geqrf_nb<T>(A.dims()[1]);
+        int NUM      = (2 * MN + ((M + 31) / 32) * 32) * NB;
+        Array<T> tmp = createEmptyArray<T>(dim4(NUM));
+
+        vector<T> h_tau(MN);
+
+        int info   = 0;
+        Buffer *dA = A.get();
+        Buffer *dT = tmp.get();
+        Buffer *dB = B.get();
+
+        magma_geqrf3_gpu<T>(A.dims()[0], A.dims()[1], (*dA)(), A.getOffset(),
+                            A.strides()[1], &h_tau[0], (*dT)(), tmp.getOffset(),
+                            getQueue()(), &info);
+
+        A.resetDims(dim4(M, M));
+
+        magmablas_swapdblk<T>(MN - 1, NB, (*dA)(), A.getOffset(),
+                              A.strides()[1], 1, (*dT)(),
+                              tmp.getOffset() + MN * NB, NB, 0, queue);
+
+        OPENCL_BLAS_CHECK(
+            gpu_blas_trsm(OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                          OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
+                          B.dims()[0], B.dims()[1], scalar<T>(1), (*dA)(),
+                          A.getOffset(), A.strides()[1], (*dB)(), B.getOffset(),
+                          B.strides()[1], 1, &queue, 0, nullptr, &event));
+
+        magmablas_swapdblk<T>(MN - 1, NB, (*dT)(), tmp.getOffset() + MN * NB,
+                              NB, 0, (*dA)(), A.getOffset(), A.strides()[1], 1,
+                              queue);
+
+#if UNMQR
+        int lwork = (B.dims()[0] - A.dims()[0] + NB) * (B.dims()[1] + 2 * NB);
+        vector<T> h_work(lwork);
+        B.resetDims(dim4(N, K));
+        magma_unmqr_gpu<T>(MagmaLeft, MagmaNoTrans, B.dims()[0], B.dims()[1],
+                           A.dims()[0], (*dA)(), A.getOffset(), A.strides()[1],
+                           &h_tau[0], (*dB)(), B.getOffset(), B.strides()[1],
+                           &h_work[0], lwork, (*dT)(), tmp.getOffset(), NB,
+                           queue, &info);
+#else
+        A.resetDims(dim4(N, M));
+        magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], min(M, N), (*dA)(),
+                           A.getOffset(), A.strides()[1], &h_tau[0], (*dT)(),
+                           tmp.getOffset(), NB, queue, &info);
+
+        Array<T> B_new = createEmptyArray<T>(dim4(A.dims()[0], B.dims()[1]));
+        T alpha        = scalar<T>(1.0);
+        T beta         = scalar<T>(0.0);
+        gemm<T>(B_new, AF_MAT_NONE, AF_MAT_NONE, &alpha, A, B, &beta);
+        B = B_new;
+#endif
+    } else if (M > N) {
+        // Least squres for this case is solved using the following
+        // solve(A, B) == tri_solve(R1, Bt);
+        // Where:
+        // R1 == R(seq(N), seq(N));
+        // Bt == matmul(transpose(Q1), B);
+        // Q1 == Q(span, seq(N));
+        // A  == matmul(Q, R);
+
+        Array<T> A = copyArray<T>(a);
+        B          = copyArray(b);
+
+        int MN = min(M, N);
+        int NB = magma_get_geqrf_nb<T>(M);
+
+        int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
+        Array<T> tmp = createEmptyArray<T>(dim4(NUM));
+
+        vector<T> h_tau(NUM);
+
+        int info      = 0;
+        Buffer *A_buf = A.get();
+        Buffer *B_buf = B.get();
+        Buffer *dT    = tmp.get();
+
+        magma_geqrf3_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
+                            &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
+                            &info);
+
+        int NRHS   = B.dims()[1];
+        int lhwork = (M - N + NB) * (NRHS + NB) + NRHS * NB;
+
+        vector<T> h_work(lhwork);
+        h_work[0] = scalar<T>(lhwork);
+
+        magma_unmqr_gpu<T>(MagmaLeft, MagmaConjTrans, M, NRHS, N, (*A_buf)(),
+                           A.getOffset(), A.strides()[1], &h_tau[0], (*B_buf)(),
+                           B.getOffset(), B.strides()[1], &h_work[0], lhwork,
+                           (*dT)(), tmp.getOffset(), NB, queue, &info);
+
+        magmablas_swapdblk<T>(MN - 1, NB, (*A_buf)(), A.getOffset(),
+                              A.strides()[1], 1, (*dT)(),
+                              tmp.getOffset() + NB * MN, NB, 0, queue);
+
+        if (getActivePlatform() == AFCL_PLATFORM_NVIDIA) {
+            Array<T> AT    = transpose<T>(A, true);
+            Buffer *AT_buf = AT.get();
+            OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+                OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
+                scalar<T>(1), (*AT_buf)(), AT.getOffset(), AT.strides()[1],
+                (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0,
+                nullptr, &event));
+        } else {
+            OPENCL_BLAS_CHECK(gpu_blas_trsm(
+                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
+                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
+                scalar<T>(1), (*A_buf)(), A.getOffset(), A.strides()[1],
+                (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0,
+                nullptr, &event));
+        }
+        B.resetDims(dim4(N, K));
+    }
+
+    return B;
+}
+
+template<typename T>
+Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
+                       const af_mat_prop options) {
+    gpu_blas_trsm_func<T> gpu_blas_trsm;
+
+    Array<T> B = copyArray<T>(b);
+
+    int N    = B.dims()[0];
+    int NRHS = B.dims()[1];
+
+    const Buffer *A_buf = A.get();
+    Buffer *B_buf       = B.get();
+
+    cl_event event         = 0;
+    cl_command_queue queue = getQueue()();
+
+    if (getActivePlatform() == AFCL_PLATFORM_NVIDIA &&
+        (options & AF_MAT_UPPER)) {
+        Array<T> AT = transpose<T>(A, true);
+
+        cl::Buffer *AT_buf = AT.get();
+        OPENCL_BLAS_CHECK(gpu_blas_trsm(
+            OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
+            OPENCL_BLAS_CONJ_TRANS,
+            options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL
+                                       : OPENCL_BLAS_NON_UNIT_DIAGONAL,
+            N, NRHS, scalar<T>(1), (*AT_buf)(), AT.getOffset(), AT.strides()[1],
+            (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0, nullptr,
+            &event));
+    } else {
+        OPENCL_BLAS_CHECK(gpu_blas_trsm(
+            OPENCL_BLAS_SIDE_LEFT,
+            options & AF_MAT_LOWER ? OPENCL_BLAS_TRIANGLE_LOWER
+                                   : OPENCL_BLAS_TRIANGLE_UPPER,
+            OPENCL_BLAS_NO_TRANS,
+            options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL
+                                       : OPENCL_BLAS_NON_UNIT_DIAGONAL,
+            N, NRHS, scalar<T>(1), (*A_buf)(), A.getOffset(), A.strides()[1],
+            (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0, nullptr,
+            &event));
+    }
+
+    return B;
+}
+
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
+    if (OpenCLCPUOffload()) { return cpu::solve(a, b, options); }
+
+    if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
+        return triangleSolve<T>(a, b, options);
+    }
+
+    if (a.dims()[0] == a.dims()[1]) {
+        return generalSolve<T>(a, b);
+    } else {
+        return leastSquares<T>(a, b);
+    }
+}
+
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
+    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
+
+INSTANTIATE_SOLVE(float)
+INSTANTIATE_SOLVE(cfloat)
+INSTANTIATE_SOLVE(double)
+INSTANTIATE_SOLVE(cdouble)
+}  // namespace oneapi
+
+#else  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+template<typename T>
+Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options) {
+    AF_ERROR("Linear Algebra is disabled on OneAPI", AF_ERR_NOT_CONFIGURED);
+}
+
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options) {
+    AF_ERROR("Linear Algebra is disabled on OneAPI", AF_ERR_NOT_CONFIGURED);
+}
+
+#define INSTANTIATE_SOLVE(T)                                                 \
+    template Array<T> solve<T>(const Array<T> &a, const Array<T> &b,         \
+                               const af_mat_prop options);                   \
+    template Array<T> solveLU<T>(const Array<T> &A, const Array<int> &pivot, \
+                                 const Array<T> &b,                          \
+                                 const af_mat_prop options);
+
+INSTANTIATE_SOLVE(float)
+INSTANTIATE_SOLVE(cfloat)
+INSTANTIATE_SOLVE(double)
+INSTANTIATE_SOLVE(cdouble)
+
+}  // namespace oneapi
+
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/solve.hpp b/src/backend/oneapi/solve.hpp
new file mode 100644
index 0000000000..330605aa35
--- /dev/null
+++ b/src/backend/oneapi/solve.hpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> solve(const Array<T> &a, const Array<T> &b,
+               const af_mat_prop options = AF_MAT_NONE);
+
+template<typename T>
+Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
+                 const af_mat_prop options = AF_MAT_NONE);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
new file mode 100644
index 0000000000..f9c13b7429
--- /dev/null
+++ b/src/backend/oneapi/sort.cpp
@@ -0,0 +1,67 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/sort.hpp>
+#include <math.hpp>
+#include <reorder.hpp>
+#include <sort.hpp>
+#include <stdexcept>
+
+namespace oneapi {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
+
+    ONEAPI_NOT_SUPPORTED("sort Not supported");
+
+    try {
+        Array<T> out = copyArray<T>(in);
+        // switch (dim) {
+        //     case 0: kernel::sort0<T>(out, isAscending); break;
+        //     case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+        //     case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+        //     case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
+        //     default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        // }
+
+        if (dim != 0) {
+            af::dim4 preorderDims = out.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0]  = out.dims()[dim];
+            for (int i = 1; i <= static_cast<int>(dim); i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i]    = out.dims()[i - 1];
+            }
+
+            out.setDataDims(preorderDims);
+            out = reorder<T>(out, reorderDims);
+        }
+        return out;
+    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+#define INSTANTIATE(T)                                                \
+    template Array<T> sort<T>(const Array<T> &in, const unsigned dim, \
+                              bool isAscending);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sort.hpp b/src/backend/oneapi/sort.hpp
new file mode 100644
index 0000000000..ae7fdc9e6a
--- /dev/null
+++ b/src/backend/oneapi/sort.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
+}
diff --git a/src/backend/oneapi/sort_by_key.cpp b/src/backend/oneapi/sort_by_key.cpp
new file mode 100644
index 0000000000..f2a140c338
--- /dev/null
+++ b/src/backend/oneapi/sort_by_key.cpp
@@ -0,0 +1,55 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+//#include <kernel/sort_by_key.hpp>
+#include <math.hpp>
+#include <reorder.hpp>
+#include <sort_by_key.hpp>
+#include <stdexcept>
+
+namespace oneapi {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const unsigned dim, bool isAscending) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(Tk, Tv)                                        \
+    template void sort_by_key<Tk, Tv>(                             \
+        Array<Tk> & okey, Array<Tv> & oval, const Array<Tk> &ikey, \
+        const Array<Tv> &ival, const uint dim, bool isAscending);
+
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
+
+INSTANTIATE1(float)
+INSTANTIATE1(double)
+INSTANTIATE1(int)
+INSTANTIATE1(uint)
+INSTANTIATE1(short)
+INSTANTIATE1(ushort)
+INSTANTIATE1(char)
+INSTANTIATE1(uchar)
+INSTANTIATE1(intl)
+INSTANTIATE1(uintl)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sort_by_key.hpp b/src/backend/oneapi/sort_by_key.hpp
new file mode 100644
index 0000000000..2ba2c67ba3
--- /dev/null
+++ b/src/backend/oneapi/sort_by_key.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename Tk, typename Tv>
+void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
+                 const Array<Tv> &ival, const unsigned dim, bool isAscending);
+}
diff --git a/src/backend/oneapi/sort_index.cpp b/src/backend/oneapi/sort_index.cpp
new file mode 100644
index 0000000000..ebf5ce65f7
--- /dev/null
+++ b/src/backend/oneapi/sort_index.cpp
@@ -0,0 +1,82 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/sort_by_key.hpp>
+#include <math.hpp>
+#include <range.hpp>
+#include <reorder.hpp>
+#include <sort_index.hpp>
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
+                const uint dim, bool isAscending) {
+
+    ONEAPI_NOT_SUPPORTED("sort_index Not supported");
+
+    try {
+        // okey contains values, oval contains indices
+        okey = copyArray<T>(in);
+        oval = range<uint>(in.dims(), dim);
+        oval.eval();
+
+        // switch (dim) {
+        //     case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
+        //     case 1:
+        //     case 2:
+        //     case 3:
+        //         kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending);
+        //         break;
+        //     default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        // }
+
+        if (dim != 0) {
+            af::dim4 preorderDims = okey.dims();
+            af::dim4 reorderDims(0, 1, 2, 3);
+            reorderDims[dim] = 0;
+            preorderDims[0]  = okey.dims()[dim];
+            for (uint i = 1; i <= dim; i++) {
+                reorderDims[i - 1] = i;
+                preorderDims[i]    = okey.dims()[i - 1];
+            }
+
+            okey.setDataDims(preorderDims);
+            oval.setDataDims(preorderDims);
+
+            okey = reorder<T>(okey, reorderDims);
+            oval = reorder<uint>(oval, reorderDims);
+        }
+    } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+}
+
+#define INSTANTIATE(T)                                              \
+    template void sort_index<T>(Array<T> & val, Array<uint> & idx,  \
+                                const Array<T> &in, const uint dim, \
+                                bool isAscending);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sort_index.hpp b/src/backend/oneapi/sort_index.hpp
new file mode 100644
index 0000000000..2e7f262e62
--- /dev/null
+++ b/src/backend/oneapi/sort_index.hpp
@@ -0,0 +1,16 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void sort_index(Array<T> &okey, Array<unsigned> &oval, const Array<T> &in,
+                const unsigned dim, bool isAscending);
+}
diff --git a/src/backend/oneapi/sparse.cpp b/src/backend/oneapi/sparse.cpp
new file mode 100644
index 0000000000..70de66f6ee
--- /dev/null
+++ b/src/backend/oneapi/sparse.cpp
@@ -0,0 +1,225 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+// #include <kernel/sparse.hpp>
+#include <sparse.hpp>
+
+#include <arith.hpp>
+#include <common/cast.hpp>
+#include <common/moddims.hpp>
+#include <complex.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <range.hpp>
+#include <reduce.hpp>
+#include <where.hpp>
+
+#include <stdexcept>
+#include <string>
+
+namespace oneapi {
+
+using namespace common;
+
+// Partial template specialization of sparseConvertDenseToStorage for COO
+// However, template specialization is not allowed
+template<typename T>
+SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("sparseConvertDenseToCOO Not supported");
+    in.eval();
+
+    Array<uint> nonZeroIdx_ = where<T>(in);
+    Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
+
+    dim_t nNZ = nonZeroIdx.elements();
+
+    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
+    constDim.eval();
+
+    Array<int> rowIdx =
+        arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx =
+        arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+
+    Array<T> values = copyArray<T>(in);
+    values          = modDims(values, dim4(values.elements()));
+    values          = lookup<T, int>(values, nonZeroIdx, 0);
+
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+                                         AF_STORAGE_COO);
+}
+
+template<typename T, af_storage stype>
+SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_) {
+    ONEAPI_NOT_SUPPORTED("sparseConvertDenseToStorage Not supported");
+    in_.eval();
+
+    uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in_));
+
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
+    sparse_.eval();
+
+    Array<T> &values   = sparse_.getValues();
+    Array<int> &rowIdx = sparse_.getRowIdx();
+    Array<int> &colIdx = sparse_.getColIdx();
+
+    // kernel::dense2csr<T>(values, rowIdx, colIdx, in_);
+
+    return sparse_;
+}
+
+// Partial template specialization of sparseConvertStorageToDense for COO
+// However, template specialization is not allowed
+template<typename T>
+Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
+    ONEAPI_NOT_SUPPORTED("sparseConvertCOOToDense Not supported");
+    in.eval();
+
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    dense.eval();
+
+    const Array<T> values   = in.getValues();
+    const Array<int> rowIdx = in.getRowIdx();
+    const Array<int> colIdx = in.getColIdx();
+
+    // kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
+
+    return dense;
+}
+
+template<typename T, af_storage stype>
+Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
+    ONEAPI_NOT_SUPPORTED("sparseConvertStorageToDense Not supported");
+
+    if (stype != AF_STORAGE_CSR) {
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
+    }
+
+    in_.eval();
+
+    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
+    dense_.eval();
+
+    const Array<T> &values   = in_.getValues();
+    const Array<int> &rowIdx = in_.getRowIdx();
+    const Array<int> &colIdx = in_.getColIdx();
+
+    if (stype == AF_STORAGE_CSR) {
+      // kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
+    } else {
+        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
+    }
+
+    return dense_;
+}
+
+template<typename T, af_storage dest, af_storage src>
+SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
+    ONEAPI_NOT_SUPPORTED("sparseConvertStorageToStorage Not supported");
+    in.eval();
+
+    SparseArray<T> converted = createEmptySparseArray<T>(
+        in.dims(), static_cast<int>(in.getNNZ()), dest);
+    converted.eval();
+
+    if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+        Array<int> index = range<int>(in.getNNZ(), 0);
+        index.eval();
+
+        Array<T> &ovalues         = converted.getValues();
+        Array<int> &orowIdx       = converted.getRowIdx();
+        Array<int> &ocolIdx       = converted.getColIdx();
+        const Array<T> &ivalues   = in.getValues();
+        const Array<int> &irowIdx = in.getRowIdx();
+        const Array<int> &icolIdx = in.getColIdx();
+
+        // kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+        //                    index);
+
+    } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+        Array<int> index = range<int>(in.getNNZ(), 0);
+        index.eval();
+
+        Array<T> &ovalues         = converted.getValues();
+        Array<int> &orowIdx       = converted.getRowIdx();
+        Array<int> &ocolIdx       = converted.getColIdx();
+        const Array<T> &ivalues   = in.getValues();
+        const Array<int> &irowIdx = in.getRowIdx();
+        const Array<int> &icolIdx = in.getColIdx();
+
+        Array<int> rowCopy = copyArray<int>(irowIdx);
+        rowCopy.eval();
+
+        // kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+        //                    index, rowCopy, in.dims()[0]);
+
+    } else {
+        // Should never come here
+        AF_ERROR("OpenCL Backend invalid conversion combination",
+                 AF_ERR_NOT_SUPPORTED);
+    }
+
+    return converted;
+}
+
+#define INSTANTIATE_TO_STORAGE(T, S)                     \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSR>( \
+        const SparseArray<T> &in);                       \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_CSC>( \
+        const SparseArray<T> &in);                       \
+    template SparseArray<T>                              \
+    sparseConvertStorageToStorage<T, S, AF_STORAGE_COO>( \
+        const SparseArray<T> &in);
+
+#define INSTANTIATE_COO_SPECIAL(T)                                 \
+    template<>                                                     \
+    SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_COO>( \
+        const Array<T> &in) {                                      \
+        return sparseConvertDenseToCOO<T>(in);                     \
+    }                                                              \
+    template<>                                                     \
+    Array<T> sparseConvertStorageToDense<T, AF_STORAGE_COO>(       \
+        const SparseArray<T> &in) {                                \
+        return sparseConvertCOOToDense<T>(in);                     \
+    }
+
+#define INSTANTIATE_SPARSE(T)                                               \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSR>( \
+        const Array<T> &in);                                                \
+    template SparseArray<T> sparseConvertDenseToStorage<T, AF_STORAGE_CSC>( \
+        const Array<T> &in);                                                \
+                                                                            \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSR>(       \
+        const SparseArray<T> &in);                                          \
+    template Array<T> sparseConvertStorageToDense<T, AF_STORAGE_CSC>(       \
+        const SparseArray<T> &in);                                          \
+                                                                            \
+    INSTANTIATE_COO_SPECIAL(T)                                              \
+                                                                            \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSR)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_CSC)                               \
+    INSTANTIATE_TO_STORAGE(T, AF_STORAGE_COO)
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+#undef INSTANTIATE_TO_STORAGE
+#undef INSTANTIATE_COO_SPECIAL
+#undef INSTANTIATE_SPARSE
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sparse.hpp b/src/backend/oneapi/sparse.hpp
new file mode 100644
index 0000000000..3958dcea3b
--- /dev/null
+++ b/src/backend/oneapi/sparse.hpp
@@ -0,0 +1,27 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <common/SparseArray.hpp>
+
+namespace oneapi {
+
+template<typename T, af_storage stype>
+common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
+
+template<typename T, af_storage stype>
+Array<T> sparseConvertStorageToDense(const common::SparseArray<T> &in);
+
+template<typename T, af_storage dest, af_storage src>
+common::SparseArray<T> sparseConvertStorageToStorage(
+    const common::SparseArray<T> &in);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sparse_arith.cpp b/src/backend/oneapi/sparse_arith.cpp
new file mode 100644
index 0000000000..40e9e24ff4
--- /dev/null
+++ b/src/backend/oneapi/sparse_arith.cpp
@@ -0,0 +1,180 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+// #include <kernel/sparse_arith.hpp>
+#include <sparse.hpp>
+#include <err_oneapi.hpp>
+
+#include <stdexcept>
+#include <string>
+
+#include <arith.hpp>
+#include <common/cast.hpp>
+#include <common/err_common.hpp>
+#include <complex.hpp>
+#include <copy.hpp>
+#include <lookup.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <scan.hpp>
+#include <where.hpp>
+
+namespace oneapi {
+
+using namespace common;
+using std::numeric_limits;
+
+template<typename T>
+T getInf() {
+    return scalar<T>(numeric_limits<T>::infinity());
+}
+
+template<>
+cfloat getInf() {
+    return scalar<cfloat, float>(
+        NAN, NAN);  // Matches behavior of complex division by 0 in OpenCL
+}
+
+template<>
+cdouble getInf() {
+    return scalar<cdouble, double>(
+        NAN, NAN);  // Matches behavior of complex division by 0 in OpenCL
+}
+
+template<typename T, af_op_t op>
+Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
+                  const bool reverse) {
+    ONEAPI_NOT_SUPPORTED("arithOpD Not supported");
+    lhs.eval();
+    rhs.eval();
+
+    Array<T> out  = createEmptyArray<T>(dim4(0));
+    Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
+    switch (op) {
+        case af_add_t: out = copyArray<T>(rhs); break;
+        case af_sub_t:
+            out = reverse ? copyArray<T>(rhs)
+                          : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
+            break;
+        default: out = copyArray<T>(rhs);
+    }
+    out.eval();
+    switch (lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            // kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
+            //                                 lhs.getRowIdx(), lhs.getColIdx(),
+            //                                 rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            // kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
+            //                                 lhs.getRowIdx(), lhs.getColIdx(),
+            //                                 rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+template<typename T, af_op_t op>
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
+                       const bool reverse) {
+    ONEAPI_NOT_SUPPORTED("arithOp Not supported");
+    lhs.eval();
+    rhs.eval();
+
+    SparseArray<T> out = createArrayDataSparseArray<T>(
+        lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+        lhs.getStorage(), true);
+    out.eval();
+    switch (lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            // kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
+            //                                 out.getColIdx(), rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            // kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
+            //                                 out.getColIdx(), rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
+}
+
+template<typename T, af_op_t op>
+SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
+    ONEAPI_NOT_SUPPORTED("arithOp Not supported");
+    lhs.eval();
+    rhs.eval();
+    af::storage sfmt = lhs.getStorage();
+
+    const dim4 &ldims = lhs.dims();
+
+    const uint M = ldims[0];
+    const uint N = ldims[1];
+
+    const dim_t nnzA = lhs.getNNZ();
+    const dim_t nnzB = rhs.getNNZ();
+
+    auto temp = createValueArray<int>(dim4(M + 1), scalar<int>(0));
+    temp.eval();
+
+    unsigned nnzC = 0;
+    // kernel::csrCalcOutNNZ(temp, nnzC, M, N, nnzA, lhs.getRowIdx(),
+    //                       lhs.getColIdx(), nnzB, rhs.getRowIdx(),
+    //                       rhs.getColIdx());
+
+    auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
+
+    auto outColIdx = createEmptyArray<int>(dim4(nnzC));
+    auto outValues = createEmptyArray<T>(dim4(nnzC));
+
+    // kernel::ssArithCSR<T, op>(outValues, outColIdx, outRowIdx, M, N, nnzA,
+    //                           lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+    //                           nnzB, rhs.getValues(), rhs.getRowIdx(),
+    //                           rhs.getColIdx());
+
+    SparseArray<T> retVal = createArrayDataSparseArray(
+        ldims, outValues, outRowIdx, outColIdx, sfmt);
+    return retVal;
+}
+
+#define INSTANTIATE(T)                                                         \
+    template Array<T> arithOpD<T, af_add_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_sub_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_mul_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template Array<T> arithOpD<T, af_div_t>(                                   \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_mul_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_div_t>(                              \
+        const SparseArray<T> &lhs, const Array<T> &rhs, const bool reverse);   \
+    template SparseArray<T> arithOp<T, af_add_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs); \
+    template SparseArray<T> arithOp<T, af_sub_t>(                              \
+        const common::SparseArray<T> &lhs, const common::SparseArray<T> &rhs);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sparse_arith.hpp b/src/backend/oneapi/sparse_arith.hpp
new file mode 100644
index 0000000000..589620c314
--- /dev/null
+++ b/src/backend/oneapi/sparse_arith.hpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/SparseArray.hpp>
+#include <optypes.hpp>
+#include <sparse.hpp>
+
+namespace oneapi {
+
+// These two functions cannot be overloaded by return type.
+// So have to give them separate names.
+template<typename T, af_op_t op>
+Array<T> arithOpD(const common::SparseArray<T> &lhs, const Array<T> &rhs,
+                  const bool reverse = false);
+
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const Array<T> &rhs, const bool reverse = false);
+
+template<typename T, af_op_t op>
+common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
+                               const common::SparseArray<T> &rhs);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sparse_blas.cpp b/src/backend/oneapi/sparse_blas.cpp
new file mode 100644
index 0000000000..bc06759dde
--- /dev/null
+++ b/src/backend/oneapi/sparse_blas.cpp
@@ -0,0 +1,99 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <sparse_blas.hpp>
+
+// #include <kernel/cscmm.hpp>
+// #include <kernel/cscmv.hpp>
+// #include <kernel/csrmm.hpp>
+// #include <kernel/csrmv.hpp>
+
+#include <cassert>
+#include <stdexcept>
+#include <string>
+
+#include <common/err_common.hpp>
+#include <complex.hpp>
+#include <err_oneapi.hpp>
+#include <math.hpp>
+#include <platform.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+#include <cpu/cpu_sparse_blas.hpp>
+#endif  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+using namespace common;
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
+                af_mat_prop optLhs, af_mat_prop optRhs) {
+  ONEAPI_NOT_SUPPORTED("sparse matmul Not supported");
+#if defined(WITH_LINEAR_ALGEBRA)
+    if (OpenCLCPUOffload(
+            false)) {  // Do not force offload gemm on OSX Intel devices
+        return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
+    }
+#endif
+
+    int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+    // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+    static const int rColDim =
+        1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
+
+    dim4 lDims = lhs.dims();
+    dim4 rDims = rhsIn.dims();
+    int M      = lDims[lRowDim];
+    int N      = rDims[rColDim];
+    // int K = lDims[lColDim];
+
+    const Array<T> rhs =
+        (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) : rhsIn;
+    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+
+    static const T alpha = scalar<T>(1.0);
+    static const T beta  = scalar<T>(0.0);
+
+    const Array<T>& values   = lhs.getValues();
+    const Array<int>& rowIdx = lhs.getRowIdx();
+    const Array<int>& colIdx = lhs.getColIdx();
+
+    if (optLhs == AF_MAT_NONE) {
+        // if (N == 1) {
+        //     kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
+        // } else {
+        //     kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
+        // }
+    } else {
+        // // CSR transpose is a CSC matrix
+        // if (N == 1) {
+        //     kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
+        //                   optLhs == AF_MAT_CTRANS);
+        // } else {
+        //     kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha, beta,
+        //                      optLhs == AF_MAT_CTRANS);
+        // }
+    }
+    return out;
+}
+
+#define INSTANTIATE_SPARSE(T)                                            \
+    template Array<T> matmul<T>(const common::SparseArray<T>& lhs,       \
+                                const Array<T>& rhs, af_mat_prop optLhs, \
+                                af_mat_prop optRhs);
+
+INSTANTIATE_SPARSE(float)
+INSTANTIATE_SPARSE(double)
+INSTANTIATE_SPARSE(cfloat)
+INSTANTIATE_SPARSE(cdouble)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sparse_blas.hpp b/src/backend/oneapi/sparse_blas.hpp
new file mode 100644
index 0000000000..d187a4422a
--- /dev/null
+++ b/src/backend/oneapi/sparse_blas.hpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/SparseArray.hpp>
+#include <sparse.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
+                af_mat_prop optLhs, af_mat_prop optRhs);
+
+}
diff --git a/src/backend/oneapi/sum.cpp b/src/backend/oneapi/sum.cpp
new file mode 100644
index 0000000000..30850564e8
--- /dev/null
+++ b/src/backend/oneapi/sum.cpp
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/half.hpp>
+#include "reduce_impl.hpp"
+
+using common::half;
+
+namespace oneapi {
+// sum
+INSTANTIATE(af_add_t, float, float)
+INSTANTIATE(af_add_t, double, double)
+INSTANTIATE(af_add_t, cfloat, cfloat)
+INSTANTIATE(af_add_t, cdouble, cdouble)
+INSTANTIATE(af_add_t, int, int)
+INSTANTIATE(af_add_t, int, float)
+INSTANTIATE(af_add_t, uint, uint)
+INSTANTIATE(af_add_t, uint, float)
+INSTANTIATE(af_add_t, intl, intl)
+INSTANTIATE(af_add_t, intl, double)
+INSTANTIATE(af_add_t, uintl, uintl)
+INSTANTIATE(af_add_t, uintl, double)
+INSTANTIATE(af_add_t, char, int)
+INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, uchar, uint)
+INSTANTIATE(af_add_t, uchar, float)
+INSTANTIATE(af_add_t, short, int)
+INSTANTIATE(af_add_t, short, float)
+INSTANTIATE(af_add_t, ushort, uint)
+INSTANTIATE(af_add_t, ushort, float)
+INSTANTIATE(af_add_t, half, half)
+INSTANTIATE(af_add_t, half, float)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/surface.cpp b/src/backend/oneapi/surface.cpp
new file mode 100644
index 0000000000..7efebfc43c
--- /dev/null
+++ b/src/backend/oneapi/surface.cpp
@@ -0,0 +1,81 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+// #include <GraphicsResourceManager.hpp>
+// #include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <surface.hpp>
+
+using af::dim4;
+// using cl::Memory;
+using std::vector;
+
+namespace oneapi {
+
+template<typename T>
+void copy_surface(const Array<T> &P, fg_surface surface) {
+    ONEAPI_NOT_SUPPORTED("copy_surface Not supported");
+    // ForgeModule &_ = graphics::forgePlugin();
+    // if (isGLSharingSupported()) {
+    //     CheckGL("Begin OpenCL resource copy");
+    //     const cl::Buffer *d_P = P.get();
+    //     unsigned bytes        = 0;
+    //     FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
+
+    //     auto res = interopManager().getSurfaceResources(surface);
+
+    //     vector<Memory> shared_objects;
+    //     shared_objects.push_back(*(res[0].get()));
+
+    //     glFinish();
+
+    //     // Use of events:
+    //     // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+    //     cl::Event event;
+
+    //     getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
+    //     event.wait();
+    //     getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+    //                                  &event);
+    //     getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
+    //     event.wait();
+
+    //     CL_DEBUG_FINISH(getQueue());
+    //     CheckGL("End OpenCL resource copy");
+    // } else {
+    //     unsigned bytes = 0, buffer = 0;
+    //     FG_CHECK(_.fg_get_surface_vertex_buffer(&buffer, surface));
+    //     FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
+
+    //     CheckGL("Begin OpenCL fallback-resource copy");
+    //     glBindBuffer(GL_ARRAY_BUFFER, buffer);
+    //     auto *ptr =
+    //         static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
+    //     if (ptr) {
+    //         getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
+    //         glUnmapBuffer(GL_ARRAY_BUFFER);
+    //     }
+    //     glBindBuffer(GL_ARRAY_BUFFER, 0);
+    //     CheckGL("End OpenCL fallback-resource copy");
+    // }
+}
+
+#define INSTANTIATE(T) \
+    template void copy_surface<T>(const Array<T> &, fg_surface);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/surface.hpp b/src/backend/oneapi/surface.hpp
new file mode 100644
index 0000000000..0c4110fd36
--- /dev/null
+++ b/src/backend/oneapi/surface.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/graphics_common.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void copy_surface(const Array<T> &P, fg_surface surface);
+
+}
diff --git a/src/backend/oneapi/susan.cpp b/src/backend/oneapi/susan.cpp
new file mode 100644
index 0000000000..e6fe536918
--- /dev/null
+++ b/src/backend/oneapi/susan.cpp
@@ -0,0 +1,75 @@
+/*******************************************************
+ * Copyright (c) 2022, Arrayfire
+ * all rights reserved.
+ *
+ * This file is distributed under 3-clause bsd license.
+ * the complete license agreement can be obtained at:
+ * http://Arrayfire.com/licenses/bsd-3-clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/susan.hpp>
+#include <af/features.h>
+#include <algorithm>
+#include <cmath>
+
+using af::features;
+using std::vector;
+
+namespace oneapi {
+
+template<typename T>
+unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
+               const Array<T> &in, const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge) {
+    dim4 idims = in.dims();
+
+    const unsigned corner_lim = in.elements() * feature_ratio;
+    Array<float> x_corners    = createEmptyArray<float>({corner_lim});
+    Array<float> y_corners    = createEmptyArray<float>({corner_lim});
+    Array<float> resp_corners = createEmptyArray<float>({corner_lim});
+
+    // auto resp = memAlloc<float>(in.elements());
+
+    ONEAPI_NOT_SUPPORTED("");
+    return 0;
+
+    // kernel::susan<T>(resp.get(), in.get(), in.getOffset(), idims[0], idims[1],
+    //                  diff_thr, geom_thr, edge, radius);
+
+    // unsigned corners_found = kernel::nonMaximal<T>(
+    //     x_corners.get(), y_corners.get(), resp_corners.get(), idims[0],
+    //     idims[1], resp.get(), edge, corner_lim);
+
+    // const unsigned corners_out = std::min(corners_found, corner_lim);
+    // if (corners_out == 0) {
+    //     x_out    = createEmptyArray<float>(dim4());
+    //     y_out    = createEmptyArray<float>(dim4());
+    //     resp_out = createEmptyArray<float>(dim4());
+    // } else {
+    //     vector<af_seq> idx{{0., static_cast<double>(corners_out - 1.0), 1.}};
+    //     x_out    = createSubArray(x_corners, idx);
+    //     y_out    = createSubArray(y_corners, idx);
+    //     resp_out = createSubArray(resp_corners, idx);
+    // }
+    // return corners_out;
+}
+
+#define INSTANTIATE(T)                                                        \
+    template unsigned susan<T>(                                               \
+        Array<float> & x_out, Array<float> & y_out, Array<float> & score_out, \
+        const Array<T> &in, const unsigned radius, const float diff_thr,      \
+        const float geom_thr, const float feature_ratio, const unsigned edge);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace oneap
diff --git a/src/backend/oneapi/susan.hpp b/src/backend/oneapi/susan.hpp
new file mode 100644
index 0000000000..8510117dea
--- /dev/null
+++ b/src/backend/oneapi/susan.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, Arrayfire
+ * all rights reserved.
+ *
+ * This file is distributed under 3-clause bsd license.
+ * the complete license agreement can be obtained at:
+ * http://Arrayfire.com/licenses/bsd-3-clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <af/features.h>
+
+using af::features;
+
+namespace oneapi {
+
+template<typename T>
+unsigned susan(Array<float> &x_out, Array<float> &y_out,
+               Array<float> &score_out, const Array<T> &in,
+               const unsigned radius, const float diff_thr,
+               const float geom_thr, const float feature_ratio,
+               const unsigned edge);
+
+}
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
new file mode 100644
index 0000000000..8fef95ba6c
--- /dev/null
+++ b/src/backend/oneapi/svd.cpp
@@ -0,0 +1,268 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <blas.hpp>
+#include <copy.hpp>
+#include <err_oneapi.hpp>  // error check functions and Macros
+#include <math.hpp>
+#include <reduce.hpp>
+#include <svd.hpp>  // oneapi backend function header
+#include <transpose.hpp>
+
+#if defined(WITH_LINEAR_ALGEBRA)
+
+#include <cpu/cpu_svd.hpp>
+#include <magma/magma.h>
+#include <magma/magma_cpu_lapack.h>
+#include <magma/magma_helper.h>
+#include <platform.hpp>
+
+namespace oneapi {
+
+template<typename Tr>
+Tr calc_scale(Tr From, Tr To) {
+    // FIXME: I am not sure this is correct, removing this for now
+#if 0
+    //http://www.netlib.org/lapack/explore-3.1.1-html/dlascl.f.html
+    cpu_lapack_lamch_func<Tr> cpu_lapack_lamch;
+
+    Tr S = cpu_lapack_lamch('S');
+    Tr B = 1.0 / S;
+
+    Tr FromCopy = From, ToCopy = To;
+
+    Tr Mul = 1;
+
+    while (true) {
+        Tr From1 = FromCopy * S, To1 = ToCopy / B;
+        if (std::abs(From1) > std::abs(ToCopy) && ToCopy != 0) {
+            Mul *= S;
+            FromCopy = From1;
+        } else if (std::abs(To1) > std::abs(FromCopy)) {
+            Mul *= B;
+            ToCopy = To1;
+        } else {
+            Mul *= (ToCopy) / (FromCopy);
+            break;
+        }
+    }
+
+    return Mul;
+#else
+    return To / From;
+#endif
+}
+
+template<typename T, typename Tr>
+void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
+         bool want_vectors = true) {
+    ONEAPI_NOT_SUPPORTED("");
+    dim4 idims    = arrA.dims();
+    dim4 istrides = arrA.strides();
+
+    const int m      = static_cast<int>(idims[0]);
+    const int n      = static_cast<int>(idims[1]);
+    const int ldda   = static_cast<int>(istrides[1]);
+    const int lda    = m;
+    const int min_mn = std::min(m, n);
+    const int ldu    = m;
+    const int ldvt   = n;
+
+    const int nb    = magma_get_gebrd_nb<T>(n);
+    const int lwork = (m + n) * nb;
+
+    cpu_lapack_lacpy_func<T> cpu_lapack_lacpy;
+    cpu_lapack_bdsqr_work_func<T> cpu_lapack_bdsqr_work;
+    cpu_lapack_ungbr_work_func<T> cpu_lapack_ungbr_work;
+    cpu_lapack_lamch_func<Tr> cpu_lapack_lamch;
+
+    // Get machine constants
+    static const double eps    = cpu_lapack_lamch('P');
+    static const double smlnum = std::sqrt(cpu_lapack_lamch('S')) / eps;
+    static const double bignum = 1. / smlnum;
+
+    Tr anrm = abs(getScalar<T>(reduce_all<af_max_t, T, T>(arrA)));
+
+    T scale                = scalar<T>(1);
+    static const int ione  = 1;
+    static const int izero = 0;
+
+    bool iscl = false;
+    if (anrm > 0. && anrm < smlnum) {
+        iscl  = true;
+        scale = scalar<T>(calc_scale<Tr>(anrm, smlnum));
+    } else if (anrm > bignum) {
+        iscl  = true;
+        scale = scalar<T>(calc_scale<Tr>(anrm, bignum));
+    }
+
+    if (iscl == 1) { multiply_inplace(arrA, abs(scale)); }
+
+    int nru  = 0;
+    int ncvt = 0;
+
+    // Instead of copying U, S, VT, and A to the host and copying the results
+    // back to the device, create a pointer that's mapped to device memory where
+    // the computation can directly happen
+    T *mappedA = static_cast<T *>(getQueue().enqueueMapBuffer(
+        *arrA.get(), CL_FALSE, CL_MAP_READ, sizeof(T) * arrA.getOffset(),
+        sizeof(T) * arrA.elements()));
+    std::vector<T> tauq(min_mn), taup(min_mn);
+    std::vector<T> work(lwork);
+    Tr *mappedS0 = (Tr *)getQueue().enqueueMapBuffer(
+        *arrS.get(), CL_TRUE, CL_MAP_WRITE, sizeof(Tr) * arrS.getOffset(),
+        sizeof(Tr) * arrS.elements());
+    std::vector<Tr> s1(min_mn - 1);
+    std::vector<Tr> rwork(5 * min_mn);
+
+    int info = 0;
+
+    // Bidiagonalize A
+    // (CWorkspace: need 2*N + M, prefer 2*N + (M + N)*NB)
+    // (RWorkspace: need N)
+    magma_gebrd_hybrid<T>(m, n, mappedA, lda, (*arrA.get())(), arrA.getOffset(),
+                          ldda, (void *)mappedS0, static_cast<void *>(&s1[0]),
+                          &tauq[0], &taup[0], &work[0], lwork, getQueue()(),
+                          &info, false);
+
+    T *mappedU = nullptr, *mappedVT = nullptr;
+    std::vector<T> cdummy(1);
+
+    if (want_vectors) {
+        mappedU  = static_cast<T *>(getQueue().enqueueMapBuffer(
+            *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
+            sizeof(T) * arrU.elements()));
+        mappedVT = static_cast<T *>(getQueue().enqueueMapBuffer(
+            *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
+            sizeof(T) * arrVT.elements()));
+
+        // If left singular vectors desired in U, copy result to U
+        // and generate left bidiagonalizing vectors in U
+        // (CWorkspace: need 2*N + NCU, prefer 2*N + NCU*NB)
+        // (RWorkspace: 0)
+        LAPACKE_CHECK(cpu_lapack_lacpy('L', m, n, mappedA, lda, mappedU, ldu));
+
+        int ncu = m;
+        LAPACKE_CHECK(cpu_lapack_ungbr_work('Q', m, ncu, n, mappedU, ldu,
+                                            &tauq[0], &work[0], lwork));
+
+        // If right singular vectors desired in VT, copy result to
+        // VT and generate right bidiagonalizing vectors in VT
+        // (CWorkspace: need 3*N-1, prefer 2*N + (N-1)*NB)
+        // (RWorkspace: 0)
+        LAPACKE_CHECK(
+            cpu_lapack_lacpy('U', n, n, mappedA, lda, mappedVT, ldvt));
+        LAPACKE_CHECK(cpu_lapack_ungbr_work('P', n, n, n, mappedVT, ldvt,
+                                            &taup[0], &work[0], lwork));
+
+        nru  = m;
+        ncvt = n;
+    }
+    getQueue().enqueueUnmapMemObject(*arrA.get(), mappedA);
+
+    // Perform bidiagonal QR iteration, if desired, computing
+    // left singular vectors in U and computing right singular
+    // vectors in VT
+    // (CWorkspace: need 0)
+    // (RWorkspace: need BDSPAC)
+    LAPACKE_CHECK(cpu_lapack_bdsqr_work('U', n, ncvt, nru, izero, mappedS0,
+                                        &s1[0], mappedVT, ldvt, mappedU, ldu,
+                                        &cdummy[0], ione, &rwork[0]));
+
+    if (want_vectors) {
+        getQueue().enqueueUnmapMemObject(*arrU.get(), mappedU);
+        getQueue().enqueueUnmapMemObject(*arrVT.get(), mappedVT);
+    }
+
+    getQueue().enqueueUnmapMemObject(*arrS.get(), mappedS0);
+
+    if (iscl == 1) {
+        Tr rscale = scalar<Tr>(1);
+        if (anrm > bignum) {
+            rscale = calc_scale<Tr>(bignum, anrm);
+        } else if (anrm < smlnum) {
+            rscale = calc_scale<Tr>(smlnum, anrm);
+        }
+        multiply_inplace(arrS, rscale);
+    }
+}
+
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    // if (OpenCLCPUOffload()) { return cpu::svdInPlace(s, u, vt, in); }
+
+    // svd<T, Tr>(u, s, vt, in, true);
+}
+
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+
+    // if (OpenCLCPUOffload()) { return cpu::svd(s, u, vt, in); }
+
+    // dim4 iDims = in.dims();
+    // int M      = iDims[0];
+    // int N      = iDims[1];
+
+    // if (M >= N) {
+    //     Array<T> in_copy = copyArray(in);
+    //     svdInPlace(s, u, vt, in_copy);
+    // } else {
+    //     Array<T> in_trans = transpose(in, true);
+    //     svdInPlace(s, vt, u, in_trans);
+    //     transpose_inplace(u, true);
+    //     transpose_inplace(vt, true);
+    // }
+}
+
+#define INSTANTIATE(T, Tr)                                               \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
+
+}  // namespace opencl
+
+#else  // WITH_LINEAR_ALGEBRA
+
+namespace oneapi {
+
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    AF_ERROR("Linear Algebra is disabled on OneAPI", AF_ERR_NOT_CONFIGURED);
+}
+
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
+    ONEAPI_NOT_SUPPORTED("");
+    AF_ERROR("Linear Algebra is disabled on OneAPI", AF_ERR_NOT_CONFIGURED);
+}
+
+#define INSTANTIATE(T, Tr)                                               \
+    template void svd<T, Tr>(Array<Tr> & s, Array<T> & u, Array<T> & vt, \
+                             const Array<T> &in);                        \
+    template void svdInPlace<T, Tr>(Array<Tr> & s, Array<T> & u,         \
+                                    Array<T> & vt, Array<T> & in);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
+
+}  // namespace oneapi
+
+#endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/svd.hpp b/src/backend/oneapi/svd.hpp
new file mode 100644
index 0000000000..297c899be6
--- /dev/null
+++ b/src/backend/oneapi/svd.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T, typename Tr>
+void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
+
+template<typename T, typename Tr>
+void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/tile.cpp b/src/backend/oneapi/tile.cpp
new file mode 100644
index 0000000000..5aac53265b
--- /dev/null
+++ b/src/backend/oneapi/tile.cpp
@@ -0,0 +1,51 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+//#include <kernel/tile.hpp>
+#include <tile.hpp>
+#include <err_oneapi.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
+    const af::dim4 &iDims = in.dims();
+    af::dim4 oDims        = iDims;
+    oDims *= tileDims;
+
+    Array<T> out = createEmptyArray<T>(oDims);
+
+    ONEAPI_NOT_SUPPORTED("tile Not supported");
+    // kernel::tile<T>(out, in);
+
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> tile<T>(const Array<T> &in, const af::dim4 &tileDims);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/tile.hpp b/src/backend/oneapi/tile.hpp
new file mode 100644
index 0000000000..0ad5a9869a
--- /dev/null
+++ b/src/backend/oneapi/tile.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
+}
diff --git a/src/backend/oneapi/topk.cpp b/src/backend/oneapi/topk.cpp
new file mode 100644
index 0000000000..06d4218221
--- /dev/null
+++ b/src/backend/oneapi/topk.cpp
@@ -0,0 +1,182 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <index.hpp>
+#include <sort.hpp>
+#include <sort_index.hpp>
+#include <types.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include <vector>
+
+// using cl::Buffer;
+// using cl::Event;
+using common::half;
+
+using std::iota;
+using std::min;
+using std::partial_sort_copy;
+using std::transform;
+using std::vector;
+
+namespace oneapi {
+vector<af_index_t> indexForTopK(const int k) {
+    af_index_t idx;
+    idx.idx.seq = af_seq{0.0, static_cast<double>(k) - 1.0, 1.0};
+    idx.isSeq   = true;
+    idx.isBatch = false;
+
+    af_index_t sp;
+    sp.idx.seq = af_span;
+    sp.isSeq   = true;
+    sp.isBatch = false;
+
+    return vector<af_index_t>({idx, sp, sp, sp});
+}
+
+template<typename T>
+void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order) {
+
+    ONEAPI_NOT_SUPPORTED("topk Not supported");
+
+    // if (getDeviceType() == CL_DEVICE_TYPE_CPU) {
+    //     // This branch optimizes for CPU devices by first mapping the buffer
+    //     // and calling partial sort on the buffer
+
+    //     // TODO(umar): implement this in the kernel namespace
+
+    //     // The out_dims is of size k along the dimension of the topk operation
+    //     // and the same as the input dimension otherwise.
+    //     dim4 out_dims(1);
+    //     int ndims = in.dims().ndims();
+    //     for (int i = 0; i < ndims; i++) {
+    //         if (i == dim) {
+    //             out_dims[i] = min(k, (int)in.dims()[i]);
+    //         } else {
+    //             out_dims[i] = in.dims()[i];
+    //         }
+    //     }
+
+    //     auto values          = createEmptyArray<T>(out_dims);
+    //     auto indices         = createEmptyArray<unsigned>(out_dims);
+    //     const Buffer* in_buf = in.get();
+    //     Buffer* ibuf         = indices.get();
+    //     Buffer* vbuf         = values.get();
+
+    //     cl::Event ev_in, ev_val, ev_ind;
+
+    //     T* ptr     = static_cast<T*>(getQueue().enqueueMapBuffer(
+    //         *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
+    //         nullptr, &ev_in));
+    //     uint* iptr = static_cast<uint*>(getQueue().enqueueMapBuffer(
+    //         *ibuf, CL_FALSE, CL_MAP_READ | CL_MAP_WRITE, 0, k * sizeof(uint),
+    //         nullptr, &ev_ind));
+    //     T* vptr    = static_cast<T*>(getQueue().enqueueMapBuffer(
+    //         *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
+
+    //     vector<uint> idx(in.elements());
+
+    //     // Create a linear index
+    //     iota(begin(idx), end(idx), 0);
+    //     cl::Event::waitForEvents({ev_in, ev_ind});
+
+    //     int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
+    //     for (int i = 0; i < iter; i++) {
+    //         auto idx_itr = begin(idx) + i * in.strides()[1];
+    //         auto kiptr   = iptr + k * i;
+
+    //         if (order & AF_TOPK_MIN) {
+    //             if (order & AF_TOPK_STABLE) {
+    //                 partial_sort_copy(
+    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+    //                     [ptr](const uint lhs, const uint rhs) -> bool {
+    //                         return (compute_t<T>(ptr[lhs]) <
+    //                                 compute_t<T>(ptr[rhs]))
+    //                                    ? true
+    //                                : compute_t<T>(ptr[lhs]) ==
+    //                                        compute_t<T>(ptr[rhs])
+    //                                    ? (lhs < rhs)
+    //                                    : false;
+    //                     });
+    //             } else {
+    //                 // Sort the top k values in each column
+    //                 partial_sort_copy(
+    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+    //                     [ptr](const uint lhs, const uint rhs) -> bool {
+    //                         return compute_t<T>(ptr[lhs]) <
+    //                                compute_t<T>(ptr[rhs]);
+    //                     });
+    //             }
+    //         } else {
+    //             if (order & AF_TOPK_STABLE) {
+    //                 partial_sort_copy(
+    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+    //                     [ptr](const uint lhs, const uint rhs) -> bool {
+    //                         return (compute_t<T>(ptr[lhs]) >
+    //                                 compute_t<T>(ptr[rhs]))
+    //                                    ? true
+    //                                : compute_t<T>(ptr[lhs]) ==
+    //                                        compute_t<T>(ptr[rhs])
+    //                                    ? (lhs < rhs)
+    //                                    : false;
+    //                     });
+    //             } else {
+    //                 partial_sort_copy(
+    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
+    //                     [ptr](const uint lhs, const uint rhs) -> bool {
+    //                         return compute_t<T>(ptr[lhs]) >
+    //                                compute_t<T>(ptr[rhs]);
+    //                     });
+    //             }
+    //         }
+    //         ev_val.wait();
+
+    //         auto kvptr = vptr + k * i;
+    //         for (int j = 0; j < k; j++) {
+    //             // Update the value arrays with the original values
+    //             kvptr[j] = ptr[kiptr[j]];
+    //             // Convert linear indices back to column indices
+    //             kiptr[j] -= i * in.strides()[1];
+    //         }
+    //     }
+
+    //     getQueue().enqueueUnmapMemObject(*ibuf, iptr);
+    //     getQueue().enqueueUnmapMemObject(*vbuf, vptr);
+    //     getQueue().enqueueUnmapMemObject(*in_buf, ptr);
+
+    //     vals = values;
+    //     idxs = indices;
+    // } else {
+    //     auto values  = createEmptyArray<T>(in.dims());
+    //     auto indices = createEmptyArray<unsigned>(in.dims());
+    //     sort_index(values, indices, in, dim, order & AF_TOPK_MIN);
+    //     auto indVec = indexForTopK(k);
+    //     vals        = index<T>(values, indVec.data());
+    //     idxs        = index<unsigned>(indices, indVec.data());
+    // }
+}
+
+#define INSTANTIATE(T)                                                  \
+    template void topk<T>(Array<T>&, Array<unsigned>&, const Array<T>&, \
+                          const int, const int, const af::topkFunction);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(long long)
+INSTANTIATE(unsigned long long)
+INSTANTIATE(half)
+}  // namespace oneapi
diff --git a/src/backend/oneapi/topk.hpp b/src/backend/oneapi/topk.hpp
new file mode 100644
index 0000000000..8390733751
--- /dev/null
+++ b/src/backend/oneapi/topk.hpp
@@ -0,0 +1,14 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+namespace oneapi {
+template<typename T>
+void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
+          const int k, const int dim, const af::topkFunction order);
+}
diff --git a/src/backend/oneapi/traits.hpp b/src/backend/oneapi/traits.hpp
new file mode 100644
index 0000000000..61fab0663c
--- /dev/null
+++ b/src/backend/oneapi/traits.hpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/defines.hpp>
+#include <common/traits.hpp>
+#include <types.hpp>
+
+#include <sstream>
+#include <string>
+
+namespace af {
+
+template<typename T>
+static bool iscplx() {
+    return false;
+}
+template<>
+inline bool iscplx<oneapi::cfloat>() {
+    return true;
+}
+template<>
+inline bool iscplx<oneapi::cdouble>() {
+    return true;
+}
+
+template<typename T>
+inline std::string scalar_to_option(const T &val) {
+    using namespace common;
+    using namespace std;
+    return to_string(+val);
+}
+
+template<>
+inline std::string scalar_to_option<cl_float2>(const cl_float2 &val) {
+    std::ostringstream ss;
+    ss << val.s[0] << "," << val.s[1];
+    return ss.str();
+}
+
+template<>
+inline std::string scalar_to_option<cl_double2>(const cl_double2 &val) {
+    std::ostringstream ss;
+    ss << val.s[0] << "," << val.s[1];
+    return ss.str();
+}
+}  // namespace af
+
+using af::dtype_traits;
diff --git a/src/backend/oneapi/transform.cpp b/src/backend/oneapi/transform.cpp
new file mode 100644
index 0000000000..79cb584264
--- /dev/null
+++ b/src/backend/oneapi/transform.cpp
@@ -0,0 +1,58 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <transform.hpp>
+
+// #include <kernel/transform.hpp>
+#include <err_oneapi.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const af_interp_type method, const bool inverse,
+               const bool perspective) {
+    ONEAPI_NOT_SUPPORTED("transform Not supported");
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            // kernel::transform<T>(out, in, tf, inverse, perspective, method, 1);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            // kernel::transform<T>(out, in, tf, inverse, perspective, method, 2);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            // kernel::transform<T>(out, in, tf, inverse, perspective, method, 3);
+            break;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+    }
+}
+
+#define INSTANTIATE(T)                                                       \
+    template void transform(Array<T> &out, const Array<T> &in,               \
+                            const Array<float> &tf,                          \
+                            const af_interp_type method, const bool inverse, \
+                            const bool perspective);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/transform.hpp b/src/backend/oneapi/transform.hpp
new file mode 100644
index 0000000000..4433518055
--- /dev/null
+++ b/src/backend/oneapi/transform.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
+               const af_interp_type method, const bool inverse,
+               const bool perspective);
+}
diff --git a/src/backend/oneapi/transpose.cpp b/src/backend/oneapi/transpose.cpp
new file mode 100644
index 0000000000..8384a6bfa1
--- /dev/null
+++ b/src/backend/oneapi/transpose.cpp
@@ -0,0 +1,54 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+// #include <kernel/transpose.hpp>
+#include <transpose.hpp>
+#include <err_oneapi.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> transpose(const Array<T> &in, const bool conjugate) {
+    const dim4 &inDims = in.dims();
+    dim4 outDims       = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
+    Array<T> out       = createEmptyArray<T>(outDims);
+
+    // const bool is32multiple =
+    //     inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+
+    ONEAPI_NOT_SUPPORTED("transpose Not supported");
+    // kernel::transpose<T>(out, in, getQueue(), conjugate, is32multiple);
+
+    return out;
+}
+
+#define INSTANTIATE(T) \
+    template Array<T> transpose(const Array<T> &in, const bool conjugate);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/transpose.hpp b/src/backend/oneapi/transpose.hpp
new file mode 100644
index 0000000000..16056bb6c5
--- /dev/null
+++ b/src/backend/oneapi/transpose.hpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+Array<T> transpose(const Array<T> &in, const bool conjugate);
+
+template<typename T>
+void transpose_inplace(Array<T> &in, const bool conjugate);
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/transpose_inplace.cpp b/src/backend/oneapi/transpose_inplace.cpp
new file mode 100644
index 0000000000..2792a4200b
--- /dev/null
+++ b/src/backend/oneapi/transpose_inplace.cpp
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+//#include <kernel/transpose_inplace.hpp>
+#include <transpose.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+void transpose_inplace(Array<T> &in, const bool conjugate) {
+    ONEAPI_NOT_SUPPORTED("");
+}
+
+#define INSTANTIATE(T) \
+    template void transpose_inplace(Array<T> &in, const bool conjugate);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(uchar)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/triangle.cpp b/src/backend/oneapi/triangle.cpp
new file mode 100644
index 0000000000..ad22dcaa6c
--- /dev/null
+++ b/src/backend/oneapi/triangle.cpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+// #include <kernel/triangle.hpp>
+#include <triangle.hpp>
+#include <err_oneapi.hpp>
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <af/dim4.hpp>
+
+using af::dim4;
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag) {
+    ONEAPI_NOT_SUPPORTED("triangle Not supported");
+    // kernel::triangle<T>(out, in, is_upper, is_unit_diag);
+}
+
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag) {
+    Array<T> out = createEmptyArray<T>(in.dims());
+    triangle<T>(out, in, is_upper, is_unit_diag);
+    return out;
+}
+
+#define INSTANTIATE(T)                                                  \
+    template void triangle<T>(Array<T> &, const Array<T> &, const bool, \
+                              const bool);                              \
+    template Array<T> triangle<T>(const Array<T> &, const bool, const bool);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(char)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+
+}  // namespace opencl
diff --git a/src/backend/oneapi/triangle.hpp b/src/backend/oneapi/triangle.hpp
new file mode 100644
index 0000000000..0dc1a48a11
--- /dev/null
+++ b/src/backend/oneapi/triangle.hpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
+              const bool is_unit_diag);
+
+template<typename T>
+Array<T> triangle(const Array<T> &in, const bool is_upper,
+                  const bool is_unit_diag);
+}  // namespace oneapi
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
new file mode 100644
index 0000000000..945d1366c7
--- /dev/null
+++ b/src/backend/oneapi/types.hpp
@@ -0,0 +1,163 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <common/kernel_type.hpp>
+#include <common/traits.hpp>
+#include <af/compilers.h>
+#include <af/traits.hpp>
+
+#include <algorithm>
+#include <array>
+#include <complex>
+#include <string>
+
+namespace common {
+/// This is a CPU based half which need to be converted into floats before they
+/// are used
+template<>
+struct kernel_type<common::half> {
+    using data = common::half;
+
+    // These are the types within a kernel
+    using native = float;
+
+    using compute = float;
+};
+}  // namespace common
+
+namespace oneapi {
+using cdouble = std::complex<double>;
+using cfloat  = std::complex<float>;
+using intl    = long long;
+using uchar   = cl_uchar;
+using uint    = cl_uint;
+using uintl   = unsigned long long;
+using ushort  = cl_ushort;
+
+template<typename T>
+using compute_t = typename common::kernel_type<T>::compute;
+
+template<typename T>
+using data_t = typename common::kernel_type<T>::data;
+
+template<typename T>
+struct ToNumStr {
+    std::string operator()(T val);
+    template<typename CONVERSION_TYPE>
+    std::string operator()(CONVERSION_TYPE val);
+};
+
+namespace {
+template<typename T>
+inline const char *shortname(bool caps = false) {
+    return caps ? "X" : "x";
+}
+
+template<>
+inline const char *shortname<float>(bool caps) {
+    return caps ? "S" : "s";
+}
+template<>
+inline const char *shortname<double>(bool caps) {
+    return caps ? "D" : "d";
+}
+template<>
+inline const char *shortname<cfloat>(bool caps) {
+    return caps ? "C" : "c";
+}
+template<>
+inline const char *shortname<cdouble>(bool caps) {
+    return caps ? "Z" : "z";
+}
+template<>
+inline const char *shortname<int>(bool caps) {
+    return caps ? "I" : "i";
+}
+template<>
+inline const char *shortname<uint>(bool caps) {
+    return caps ? "U" : "u";
+}
+template<>
+inline const char *shortname<char>(bool caps) {
+    return caps ? "J" : "j";
+}
+template<>
+inline const char *shortname<uchar>(bool caps) {
+    return caps ? "V" : "v";
+}
+template<>
+inline const char *shortname<intl>(bool caps) {
+    return caps ? "L" : "l";
+}
+template<>
+inline const char *shortname<uintl>(bool caps) {
+    return caps ? "K" : "k";
+}
+template<>
+inline const char *shortname<short>(bool caps) {
+    return caps ? "P" : "p";
+}
+template<>
+inline const char *shortname<ushort>(bool caps) {
+    return caps ? "Q" : "q";
+}
+
+template<typename T>
+inline const char *getFullName() {
+    return af::dtype_traits<T>::getName();
+}
+
+template<>
+inline const char *getFullName<cfloat>() {
+    return "float2";
+}
+
+template<>
+inline const char *getFullName<cdouble>() {
+    return "double2";
+}
+}  // namespace
+
+#if 0
+template<typename... ARGS>
+AF_CONSTEXPR const char *getTypeBuildDefinition() {
+    using common::half;
+    using std::any_of;
+    using std::array;
+    using std::begin;
+    using std::end;
+    using std::is_same;
+    array<bool, sizeof...(ARGS)> is_half    = {is_same<ARGS, half>::value...};
+    array<bool, sizeof...(ARGS)> is_double  = {is_same<ARGS, double>::value...};
+    array<bool, sizeof...(ARGS)> is_cdouble = {
+        is_same<ARGS, cdouble>::value...};
+
+    bool half_def =
+        any_of(begin(is_half), end(is_half), [](bool val) { return val; });
+    bool double_def =
+        any_of(begin(is_double), end(is_double), [](bool val) { return val; });
+    bool cdouble_def = any_of(begin(is_cdouble), end(is_cdouble),
+                              [](bool val) { return val; });
+
+    if (half_def && (double_def || cdouble_def)) {
+        return " -D USE_HALF -D USE_DOUBLE";
+    } else if (half_def) {
+        return " -D USE_HALF";
+    } else if (double_def || cdouble_def) {
+        return " -D USE_DOUBLE";
+    } else {
+        return "";
+    }
+}
+#endif
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/unary.hpp b/src/backend/oneapi/unary.hpp
new file mode 100644
index 0000000000..0e8a267c07
--- /dev/null
+++ b/src/backend/oneapi/unary.hpp
@@ -0,0 +1,111 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Array.hpp>
+#include <common/jit/UnaryNode.hpp>
+#include <math.hpp>
+#include <optypes.hpp>
+#include <af/traits.hpp>
+
+namespace oneapi {
+
+template<af_op_t op>
+static const char *unaryName();
+
+#define UNARY_DECL(OP, FNAME)                     \
+    template<>                                    \
+    inline const char *unaryName<af_##OP##_t>() { \
+        return FNAME;                             \
+    }
+
+#define UNARY_FN(OP) UNARY_DECL(OP, #OP)
+
+UNARY_FN(sin)
+UNARY_FN(cos)
+UNARY_FN(tan)
+
+UNARY_FN(asin)
+UNARY_FN(acos)
+UNARY_FN(atan)
+
+UNARY_FN(sinh)
+UNARY_FN(cosh)
+UNARY_FN(tanh)
+
+UNARY_FN(asinh)
+UNARY_FN(acosh)
+UNARY_FN(atanh)
+
+UNARY_FN(exp)
+UNARY_DECL(sigmoid, "__sigmoid")
+UNARY_FN(expm1)
+UNARY_FN(erf)
+UNARY_FN(erfc)
+
+UNARY_FN(tgamma)
+UNARY_FN(lgamma)
+
+UNARY_FN(log)
+UNARY_FN(log1p)
+UNARY_FN(log10)
+UNARY_FN(log2)
+
+UNARY_FN(sqrt)
+UNARY_FN(rsqrt)
+UNARY_FN(cbrt)
+
+UNARY_FN(trunc)
+UNARY_FN(round)
+UNARY_FN(signbit)
+UNARY_FN(ceil)
+UNARY_FN(floor)
+
+UNARY_FN(isinf)
+UNARY_FN(isnan)
+UNARY_FN(iszero)
+UNARY_DECL(noop, "__noop")
+
+UNARY_DECL(bitnot, "__bitnot")
+
+#undef UNARY_FN
+
+template<typename T, af_op_t op>
+Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
+    using common::Node;
+    using common::Node_ptr;
+    using std::array;
+
+    auto createUnary = [](array<Node_ptr, 1> &operands) {
+        return common::Node_ptr(new common::UnaryNode(
+            static_cast<af::dtype>(af::dtype_traits<T>::af_type),
+            unaryName<op>(), operands[0], op));
+    };
+
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    Node_ptr out = common::createNaryNode<T, 1>(outDim, createUnary, {&in});
+    return createNodeArray<T>(outDim, out);
+}
+
+template<typename T, af_op_t op>
+Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
+    using common::Node_ptr;
+
+    auto createUnary = [](std::array<Node_ptr, 1> &operands) {
+        return Node_ptr(new common::UnaryNode(
+            static_cast<af::dtype>(af::dtype_traits<char>::af_type),
+            unaryName<op>(), operands[0], op));
+    };
+
+    if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
+    Node_ptr out = common::createNaryNode<T, 1>(outDim, createUnary, {&in});
+    return createNodeArray<char>(outDim, out);
+}
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/unwrap.cpp b/src/backend/oneapi/unwrap.cpp
new file mode 100644
index 0000000000..200da9d307
--- /dev/null
+++ b/src/backend/oneapi/unwrap.cpp
@@ -0,0 +1,63 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/unwrap.hpp>
+#include <unwrap.hpp>
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const dim_t dx, const dim_t dy, const bool is_column) {
+    af::dim4 idims = in.dims();
+
+    dim_t nx = 1 + (idims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    dim_t ny = 1 + (idims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
+
+    af::dim4 odims(wx * wy, nx * ny, idims[2], idims[3]);
+
+    if (!is_column) { std::swap(odims[0], odims[1]); }
+
+    Array<T> outArray = createEmptyArray<T>(odims);
+    ONEAPI_NOT_SUPPORTED("unwrap Not supported");
+    // kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, dx, dy, nx,
+    //                   is_column);
+
+    return outArray;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> unwrap<T>(                                            \
+        const Array<T> &in, const dim_t wx, const dim_t wy, const dim_t sx, \
+        const dim_t sy, const dim_t px, const dim_t py, const dim_t dx,     \
+        const dim_t dy, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+}  // namespace opencl
diff --git a/src/backend/oneapi/unwrap.hpp b/src/backend/oneapi/unwrap.hpp
new file mode 100644
index 0000000000..beab1dca4c
--- /dev/null
+++ b/src/backend/oneapi/unwrap.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
+                const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+                const dim_t dx, const dim_t dy, const bool is_column);
+}
diff --git a/src/backend/oneapi/vector_field.cpp b/src/backend/oneapi/vector_field.cpp
new file mode 100644
index 0000000000..40c7be146d
--- /dev/null
+++ b/src/backend/oneapi/vector_field.cpp
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <GraphicsResourceManager.hpp>
+#include <err_oneapi.hpp>
+#include <vector_field.hpp>
+
+using af::dim4;
+
+namespace oneapi {
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       fg_vector_field vfield) {
+}
+
+#define INSTANTIATE(T)                                                     \
+    template void copy_vector_field<T>(const Array<T> &, const Array<T> &, \
+                                       fg_vector_field);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+INSTANTIATE(uchar)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/vector_field.hpp b/src/backend/oneapi/vector_field.hpp
new file mode 100644
index 0000000000..2c2a9b565b
--- /dev/null
+++ b/src/backend/oneapi/vector_field.hpp
@@ -0,0 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/graphics_common.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void copy_vector_field(const Array<T> &points, const Array<T> &directions,
+                       fg_vector_field vfield);
+}
diff --git a/src/backend/oneapi/where.cpp b/src/backend/oneapi/where.cpp
new file mode 100644
index 0000000000..4dc3e42565
--- /dev/null
+++ b/src/backend/oneapi/where.cpp
@@ -0,0 +1,44 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/where.hpp>
+#include <where.hpp>
+#include <af/dim4.hpp>
+#include <complex>
+
+namespace oneapi {
+
+template<typename T>
+Array<uint> where(const Array<T> &in) {
+    //Param<uint> Out;
+    // Param<T> In = in;
+    ONEAPI_NOT_SUPPORTED("where Not supported");
+    // kernel::where<T>(Out, In);
+    //return createParamArray<uint>(Out, true);
+    return createEmptyArray<uint>(af::dim4(1));
+}
+
+#define INSTANTIATE(T) template Array<uint> where<T>(const Array<T> &in);
+
+INSTANTIATE(float)
+INSTANTIATE(cfloat)
+INSTANTIATE(double)
+INSTANTIATE(cdouble)
+INSTANTIATE(char)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+
+}  // namespace opencl
diff --git a/src/backend/oneapi/where.hpp b/src/backend/oneapi/where.hpp
new file mode 100644
index 0000000000..a63ca73cb9
--- /dev/null
+++ b/src/backend/oneapi/where.hpp
@@ -0,0 +1,15 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+template<typename T>
+Array<uint> where(const Array<T>& in);
+}
diff --git a/src/backend/oneapi/wrap.cpp b/src/backend/oneapi/wrap.cpp
new file mode 100644
index 0000000000..5dd0d7d78f
--- /dev/null
+++ b/src/backend/oneapi/wrap.cpp
@@ -0,0 +1,76 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+#include <err_oneapi.hpp>
+// #include <kernel/wrap.hpp>
+#include <math.hpp>
+#include <wrap.hpp>
+#include <stdexcept>
+
+using common::half;
+
+namespace oneapi {
+
+template<typename T>
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column) {
+  ONEAPI_NOT_SUPPORTED("wrap Not supported");
+  // kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
+}
+
+#define INSTANTIATE(T)                                                        \
+    template void wrap<T>(Array<T> & out, const Array<T> &in, const dim_t wx, \
+                          const dim_t wy, const dim_t sx, const dim_t sy,     \
+                          const dim_t px, const dim_t py,                     \
+                          const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(cfloat)
+INSTANTIATE(cdouble)
+INSTANTIATE(int)
+INSTANTIATE(uint)
+INSTANTIATE(intl)
+INSTANTIATE(uintl)
+INSTANTIATE(uchar)
+INSTANTIATE(char)
+INSTANTIATE(short)
+INSTANTIATE(ushort)
+#undef INSTANTIATE
+
+template<typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column) {
+    af::dim4 idims = in.dims();
+    af::dim4 odims(ox, oy, idims[2], idims[3]);
+    Array<T> out = createValueArray<T>(odims, scalar<T>(0));
+
+    // kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy, is_column);
+    ONEAPI_NOT_SUPPORTED("wrap_dilated Not supported");
+    return out;
+}
+
+#define INSTANTIATE(T)                                                      \
+    template Array<T> wrap_dilated<T>(                                      \
+        const Array<T> &in, const dim_t ox, const dim_t oy, const dim_t wx, \
+        const dim_t wy, const dim_t sx, const dim_t sy, const dim_t px,     \
+        const dim_t py, const dim_t dx, const dim_t dy, const bool is_column);
+
+INSTANTIATE(float)
+INSTANTIATE(double)
+INSTANTIATE(half)
+#undef INSTANTIATE
+
+}  // namespace opencl
diff --git a/src/backend/oneapi/wrap.hpp b/src/backend/oneapi/wrap.hpp
new file mode 100644
index 0000000000..ae831a9bb1
--- /dev/null
+++ b/src/backend/oneapi/wrap.hpp
@@ -0,0 +1,24 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Array.hpp>
+
+namespace oneapi {
+
+template<typename T>
+void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column);
+
+template<typename T>
+Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
+                      const dim_t wx, const dim_t wy, const dim_t sx,
+                      const dim_t sy, const dim_t px, const dim_t py,
+                      const dim_t dx, const dim_t dy, const bool is_column);
+}  // namespace oneapi
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index d1bbebbdeb..e2a580a1c1 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -95,6 +95,10 @@ if(AF_BUILD_OPENCL)
   list(APPEND enabled_backends "opencl")
 endif(AF_BUILD_OPENCL)
 
+if(AF_BUILD_ONEAPI)
+  list(APPEND enabled_backends "oneapi")
+endif(AF_BUILD_ONEAPI)
+
 if(AF_BUILD_UNIFIED)
   list(APPEND enabled_backends "unified")
 endif(AF_BUILD_UNIFIED)

From acc2a9db37fee228990103d5d759ee1d1d2ee78d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 13 Sep 2022 22:17:35 -0400
Subject: [PATCH 2297/2677] basic implementation of device_manager

---
 include/af/defines.h                  |   3 +-
 include/af/oneapi.h                   | 443 ++++++++++++++++++++++++++
 src/backend/oneapi/device_manager.cpp | 155 ++++++++-
 src/backend/oneapi/device_manager.hpp |  11 +-
 src/backend/oneapi/memory.hpp         |  60 ++--
 src/backend/oneapi/platform.cpp       |  38 ++-
 src/backend/oneapi/platform.hpp       |   2 +-
 7 files changed, 648 insertions(+), 64 deletions(-)
 create mode 100644 include/af/oneapi.h

diff --git a/include/af/defines.h b/include/af/defines.h
index 611a025375..da6c5591de 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -414,7 +414,8 @@ typedef enum {
     AF_BACKEND_DEFAULT = 0,  ///< Default backend order: OpenCL -> CUDA -> CPU
     AF_BACKEND_CPU     = 1,  ///< CPU a.k.a sequential algorithms
     AF_BACKEND_CUDA    = 2,  ///< CUDA Compute Backend
-    AF_BACKEND_OPENCL  = 4   ///< OpenCL Compute Backend
+    AF_BACKEND_OPENCL  = 4,  ///< OpenCL Compute Backend
+    AF_BACKEND_ONEAPI  = 8   ///< OneAPI Compute Backend
 } af_backend;
 #endif
 
diff --git a/include/af/oneapi.h b/include/af/oneapi.h
new file mode 100644
index 0000000000..5400a34d1a
--- /dev/null
+++ b/include/af/oneapi.h
@@ -0,0 +1,443 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <af/defines.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if AF_API_VERSION >= 39
+typedef enum
+{
+    //AF_ONEAPI_DEVICE_TYPE_CPU     = sycl::info::device_type::cpu,
+    //AF_ONEAPI_DEVICE_TYPE_GPU     = sycl::info::device_type::gpu,
+    //AF_ONEAPI_DEVICE_TYPE_ACC     = sycl::info::device_type::accelerator
+    //AF_ONEAPI_DEVICE_TYPE_UNKNOWN = -1
+    AF_ONEAPI_DEVICE_TYPE_CPU     = 0,
+    AF_ONEAPI_DEVICE_TYPE_GPU     = 1,
+    AF_ONEAPI_DEVICE_TYPE_ACC     = 2,
+    AF_ONEAPI_DEVICE_TYPE_UNKNOWN = -1
+} af_oneapi_device_type;
+#endif
+
+#if AF_API_VERSION >= 39
+typedef enum
+{
+    AF_ONEAPI_PLATFORM_AMD     = 0,
+    AF_ONEAPI_PLATFORM_APPLE   = 1,
+    AF_ONEAPI_PLATFORM_INTEL   = 2,
+    AF_ONEAPI_PLATFORM_NVIDIA  = 3,
+    AF_ONEAPI_PLATFORM_BEIGNET = 4,
+    AF_ONEAPI_PLATFORM_POCL    = 5,
+    AF_ONEAPI_PLATFORM_UNKNOWN = -1
+} af_oneapi_platform;
+#endif
+
+#if 0
+/**
+    \ingroup opencl_mat
+    @{
+*/
+/**
+  Get a handle to ArrayFire's OpenCL context
+
+  \param[out] ctx the current context being used by ArrayFire
+  \param[in] retain if true calls clRetainContext prior to returning the context
+  \returns \ref af_err error code
+
+  \note Set \p retain to true if this value will be passed to a cl::Context constructor
+*/
+AFAPI af_err afcl_get_context(cl_context *ctx, const bool retain);
+
+/**
+  Get a handle to ArrayFire's OpenCL command queue
+
+  \param[out] queue the current command queue being used by ArrayFire
+  \param[in] retain if true calls clRetainCommandQueue prior to returning the context
+  \returns \ref af_err error code
+
+  \note Set \p retain to true if this value will be passed to a cl::CommandQueue constructor
+*/
+AFAPI af_err afcl_get_queue(cl_command_queue *queue, const bool retain);
+
+/**
+   Get the device ID for ArrayFire's current active device
+
+   \param[out] id the cl_device_id of the current device
+   \returns \ref af_err error code
+*/
+AFAPI af_err afcl_get_device_id(cl_device_id *id);
+
+#if AF_API_VERSION >= 39
+/**
+   Set ArrayFire's active device based on \p id of type cl_device_id
+
+   \param[in] id the cl_device_id of the device to be set as active device
+   \returns \ref af_err error code
+*/
+AFAPI af_err afcl_set_device_id(cl_device_id id);
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Push user provided device control constructs into the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to use an
+   user generated OpenCL context and related objects for ArrayFire operations.
+
+   \param[in] dev is the OpenCL device for which user provided context will be used by ArrayFire
+   \param[in] ctx is the user provided OpenCL cl_context to be used by ArrayFire
+   \param[in] que is the user provided OpenCL cl_command_queue to be used by ArrayFire. If this
+                  parameter is NULL, then we create a command queue for the user using the OpenCL
+                  context they provided us.
+
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
+*/
+AFAPI af_err afcl_add_device_context(cl_device_id dev, cl_context ctx, cl_command_queue que);
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Set active device using cl_context and cl_device_id
+
+   \param[in] dev is the OpenCL device id that is to be set as Active device inside ArrayFire
+   \param[in] ctx is the OpenCL cl_context being used by ArrayFire
+*/
+AFAPI af_err afcl_set_device_context(cl_device_id dev, cl_context ctx);
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Remove the user provided device control constructs from the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to remove an already
+   pushed user generated OpenCL context and related objects.
+
+   \param[in] dev is the OpenCL device id that has to be popped
+   \param[in] ctx is the cl_context object to be removed from ArrayFire pool
+
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
+*/
+AFAPI af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx);
+#endif
+
+#if AF_API_VERSION >= 39
+ Ge
+  t the type of the current device
+*/
+AFAPI af_err afcl_get_device_type(afcl_device_type *res);
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Get the platform of the current device
+*/
+AFAPI af_err afcl_get_platform(afcl_platform *res);
+#endif
+
+/**
+  @}
+*/
+#endif //if 0 comment
+
+#ifdef __cplusplus
+}
+#endif
+
+#ifdef __cplusplus
+
+#include <af/array.h>
+#include <af/dim4.hpp>
+#include <af/exception.h>
+#include <af/device.h>
+#include <stdio.h>
+
+namespace afoneapi
+{
+
+#if 0
+ /**
+     \addtogroup opencl_mat
+     @{
+ */
+
+ /**
+ Get a handle to ArrayFire's OpenCL context
+
+ \param[in] retain if true calls clRetainContext prior to returning the context
+ \returns the current context being used by ArrayFire
+
+ \note Set \p retain to true if this value will be passed to a cl::Context constructor
+ */
+ static inline cl_context getContext(bool retain = false)
+ {
+     cl_context ctx;
+     af_err err = afcl_get_context(&ctx, retain);
+     if (err != AF_SUCCESS) throw af::exception("Failed to get OpenCL context from arrayfire");
+     return ctx;
+ }
+
+ /**
+ Get a handle to ArrayFire's OpenCL command queue
+
+ \param[in] retain if true calls clRetainCommandQueue prior to returning the context
+ \returns the current command queue being used by ArrayFire
+
+ \note Set \p retain to true if this value will be passed to a cl::CommandQueue constructor
+ */
+ static inline cl_command_queue getQueue(bool retain = false)
+ {
+     cl_command_queue queue;
+     af_err err = afcl_get_queue(&queue, retain);
+     if (err != AF_SUCCESS) throw af::exception("Failed to get OpenCL command queue from arrayfire");
+     return queue;
+ }
+
+ /**
+    Get the device ID for ArrayFire's current active device
+    \returns the cl_device_id of the current device
+ */
+ static inline cl_device_id getDeviceId()
+ {
+     cl_device_id id;
+     af_err err = afcl_get_device_id(&id);
+     if (err != AF_SUCCESS) throw af::exception("Failed to get OpenCL device ID");
+
+     return id;
+ }
+
+#if AF_API_VERSION >= 39
+ /**
+   Set ArrayFire's active device based on \p id of type cl_device_id
+
+   \param[in] id the cl_device_id of the device to be set as active device
+ */
+ static inline void setDeviceId(cl_device_id id)
+ {
+     af_err err = afcl_set_device_id(id);
+     if (err != AF_SUCCESS) throw af::exception("Failed to set OpenCL device as active device");
+ }
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Push user provided device control constructs into the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to use an
+   user generated OpenCL context and related objects for ArrayFire operations.
+
+   \param[in] dev is the OpenCL device for which user provided context will be used by ArrayFire
+   \param[in] ctx is the user provided OpenCL cl_context to be used by ArrayFire
+   \param[in] que is the user provided OpenCL cl_command_queue to be used by ArrayFire. If this
+                  parameter is NULL, then we create a command queue for the user using the OpenCL
+                  context they provided us.
+
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
+*/
+static inline void addDevice(cl_device_id dev, cl_context ctx, cl_command_queue que)
+{
+    af_err err = afcl_add_device_context(dev, ctx, que);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to push user provided device/context to ArrayFire pool");
+}
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Set active device using cl_context and cl_device_id
+
+   \param[in] dev is the OpenCL device id that is to be set as Active device inside ArrayFire
+   \param[in] ctx is the OpenCL cl_context being used by ArrayFire
+*/
+static inline void setDevice(cl_device_id dev, cl_context ctx)
+{
+    af_err err = afcl_set_device_context(dev, ctx);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to set device based on cl_device_id & cl_context");
+}
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Remove the user provided device control constructs from the ArrayFire device manager pool
+
+   This function should be used only when the user would like ArrayFire to remove an already
+   pushed user generated OpenCL context and related objects.
+
+   \param[in] dev is the OpenCL device id that has to be popped
+   \param[in] ctx is the cl_context object to be removed from ArrayFire pool
+
+   \note ArrayFire does not take control of releasing the objects passed to it. The user needs to release them appropriately.
+*/
+static inline void deleteDevice(cl_device_id dev, cl_context ctx)
+{
+    af_err err = afcl_delete_device_context(dev, ctx);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to remove the requested device from ArrayFire device pool");
+}
+#endif
+
+
+#if AF_API_VERSION >= 39
+ typedef afcl_device_type deviceType;
+ typedef afcl_platform platform;
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Get the type of the current device
+*/
+static inline deviceType getDeviceType()
+{
+    afcl_device_type res = AFCL_DEVICE_TYPE_UNKNOWN;
+    af_err err = afcl_get_device_type(&res);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to get OpenCL device type");
+    return res;
+}
+#endif
+
+#if AF_API_VERSION >= 39
+/**
+   Get a vendor enumeration for the current platform
+*/
+static inline platform getPlatform()
+{
+    afcl_platform res = AFCL_PLATFORM_UNKNOWN;
+    af_err err = afcl_get_platform(&res);
+    if (err!=AF_SUCCESS) throw af::exception("Failed to get OpenCL platform");
+    return res;
+}
+#endif
+
+ /**
+ Create an af::array object from an OpenCL cl_mem buffer
+
+ \param[in] idims the dimensions of the buffer
+ \param[in] buf the OpenCL memory object
+ \param[in] type the data type contained in the buffer
+ \param[in] retain if true, instructs ArrayFire to retain the memory object
+ \returns an array object created from the OpenCL buffer
+
+ \note Set \p retain to true if the memory originates from a cl::Buffer object
+  */
+ static inline af::array array(af::dim4 idims, cl_mem buf, af::dtype type, bool retain=false)
+ {
+     const unsigned ndims = (unsigned)idims.ndims();
+     const dim_t *dims = idims.get();
+
+     cl_context context;
+     cl_int clerr = clGetMemObjectInfo(buf, CL_MEM_CONTEXT, sizeof(cl_context), &context, NULL);
+     if (clerr != CL_SUCCESS) {
+         throw af::exception("Failed to get context from cl_mem object \"buf\" ");
+     }
+
+     if (context != getContext()) {
+         throw(af::exception("Context mismatch between input \"buf\" and arrayfire"));
+     }
+
+
+     if (retain) clerr = clRetainMemObject(buf);
+
+     af_array out;
+     af_err err = af_device_array(&out, buf, ndims, dims, type);
+
+     if (err != AF_SUCCESS || clerr != CL_SUCCESS) {
+         if (retain && clerr == CL_SUCCESS) clReleaseMemObject(buf);
+         throw af::exception("Failed to create device array");
+     }
+
+     return af::array(out);
+ }
+
+ /**
+ Create an af::array object from an OpenCL cl_mem buffer
+
+ \param[in] dim0 the length of the first dimension of the buffer
+ \param[in] buf the OpenCL memory object
+ \param[in] type the data type contained in the buffer
+ \param[in] retain if true, instructs ArrayFire to retain the memory object
+ \returns an array object created from the OpenCL buffer
+
+ \note Set \p retain to true if the memory originates from a cl::Buffer object
+  */
+ static inline af::array array(dim_t dim0,
+                               cl_mem buf, af::dtype type, bool retain=false)
+ {
+     return afcl::array(af::dim4(dim0), buf, type, retain);
+ }
+
+ /**
+ Create an af::array object from an OpenCL cl_mem buffer
+
+ \param[in] dim0 the length of the first dimension of the buffer
+ \param[in] dim1 the length of the second dimension of the buffer
+ \param[in] buf the OpenCL memory object
+ \param[in] type the data type contained in the buffer
+ \param[in] retain if true, instructs ArrayFire to retain the memory object
+ \returns an array object created from the OpenCL buffer
+
+ \note Set \p retain to true if the memory originates from a cl::Buffer object
+  */
+ static inline af::array array(dim_t dim0, dim_t dim1,
+                               cl_mem buf, af::dtype type, bool retain=false)
+ {
+     return afcl::array(af::dim4(dim0, dim1), buf, type, retain);
+ }
+
+ /**
+ Create an af::array object from an OpenCL cl_mem buffer
+
+ \param[in] dim0 the length of the first dimension of the buffer
+ \param[in] dim1 the length of the second dimension of the buffer
+ \param[in] dim2 the length of the third dimension of the buffer
+ \param[in] buf the OpenCL memory object
+ \param[in] type the data type contained in the buffer
+ \param[in] retain if true, instructs ArrayFire to retain the memory object
+ \returns an array object created from the OpenCL buffer
+
+ \note Set \p retain to true if the memory originates from a cl::Buffer object
+  */
+ static inline af::array array(dim_t dim0, dim_t dim1,
+                               dim_t dim2,
+                               cl_mem buf, af::dtype type, bool retain=false)
+ {
+     return afcl::array(af::dim4(dim0, dim1, dim2), buf, type, retain);
+ }
+
+ /**
+ Create an af::array object from an OpenCL cl_mem buffer
+
+ \param[in] dim0 the length of the first dimension of the buffer
+ \param[in] dim1 the length of the second dimension of the buffer
+ \param[in] dim2 the length of the third dimension of the buffer
+ \param[in] dim3 the length of the fourth dimension of the buffer
+ \param[in] buf the OpenCL memory object
+ \param[in] type the data type contained in the buffer
+ \param[in] retain if true, instructs ArrayFire to retain the memory object
+ \returns an array object created from the OpenCL buffer
+
+ \note Set \p retain to true if the memory originates from a cl::Buffer object
+  */
+ static inline af::array array(dim_t dim0, dim_t dim1,
+                               dim_t dim2, dim_t dim3,
+                               cl_mem buf, af::dtype type, bool retain=false)
+ {
+     return afcl::array(af::dim4(dim0, dim1, dim2, dim3), buf, type, retain);
+ }
+
+/**
+   @}
+*/
+#endif //#IF 0 tmp comment
+
+}
+
+
+#endif
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 5ef59d2682..d4750defae 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -10,8 +10,8 @@
 #include <common/graphics_common.hpp>
 
 #include <GraphicsResourceManager.hpp>
+#include <platform.hpp> //TODO: blas.hpp? y tho, also Array.hpp 
 #include <common/DefaultMemoryManager.hpp>
-#include <common/MemoryManagerBase.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
@@ -20,13 +20,9 @@
 #include <err_oneapi.hpp>
 //#include <errorcodes.hpp>
 #include <version.hpp>
-//#include <af/oneapi.h>
+#include <af/oneapi.h>
 #include <af/version.h>
-#include <memory>
-
-#ifdef OS_MAC
-#include <OpenGL/CGLCurrent.h>
-#endif
+#include <memory.hpp>
 
 #include <algorithm>
 #include <iterator>
@@ -43,48 +39,175 @@ using std::stringstream;
 using std::unique_ptr;
 using std::vector;
 using sycl::device;
+using sycl::platform;
 
 namespace oneapi {
 
-bool checkExtnAvailability(const device& pDevice, const string& pName) {
-    ONEAPI_NOT_SUPPORTED("");
-    return false;
+static inline bool compare_default(const unique_ptr<sycl::device>& ldev,
+                                   const unique_ptr<sycl::device>& rdev) {
+    //TODO: update sorting criteria
+    //select according to something applicable to oneapi backend
+    auto l_mem = ldev->get_info<sycl::info::device::local_mem_size>();
+    auto r_mem = rdev->get_info<sycl::info::device::local_mem_size>();
+    return l_mem > r_mem;
 }
 
 DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , mUserDeviceOffset(0)
     , fgMngr(nullptr) {
+    vector<sycl::platform> platforms;
+    try {
+        platforms = sycl::platform::get_platforms();
+    } catch (sycl::exception& err) {
+        AF_ERROR(
+            "No sycl platforms found on this system. Ensure you have "
+            "installed the device driver as well as the runtime.",
+            AF_ERR_RUNTIME);
+    }
+
+    fgMngr = std::make_unique<graphics::ForgeManager>();
+
+    AF_TRACE("Found {} sycl platforms", platforms.size());
+    // Iterate through platforms, get all available devices and store them
+    for (auto& platform : platforms) {
+        vector<sycl::device> current_devices;
+        try {
+            current_devices = platform.get_devices();
+        } catch(sycl::exception& err) {
+            printf("DeviceManager::DeviceManager() exception: %s\n", err.what());
+            throw;
+        }
+        AF_TRACE("Found {} devices on platform {}", current_devices.size(),
+                 platform.get_info<sycl::info::platform::name>());
+
+        for (auto& dev : current_devices) {
+            mDevices.emplace_back(make_unique<sycl::device>(dev));
+            AF_TRACE("Found device {} on platform {}",
+                     dev.get_info<sycl::info::device::name>(),
+                     platform.get_info<sycl::info::platform::name>());
+        }
+    }
+
+    int nDevices = mDevices.size();
+    AF_TRACE("Found {} sycl devices", nDevices);
+
+    if (nDevices == 0) { AF_ERROR("No sycl devices found", AF_ERR_RUNTIME); }
+
+    // Sort sycl devices based on default criteria
+    stable_sort(mDevices.begin(), mDevices.end(), compare_default);
+
+    auto devices = move(mDevices);
+    mDevices.clear();
+
+    // Create contexts and queues once the sort is done
+    for (int i = 0; i < nDevices; i++) {
+        try{
+            mContexts.push_back(make_unique<sycl::context>(*devices[i]));
+            mQueues.push_back(make_unique<sycl::queue>(
+                *mContexts.back(), *devices[i]));
+            mIsGLSharingOn.push_back(false);
+            //TODO:
+            //mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
+            //mPlatforms.push_back(getPlatformEnum(*devices[i]));
+            mDevices.emplace_back(std::move(devices[i]));
+        } catch (sycl::exception& err) {
+            AF_TRACE("Error creating context for device {} with error {}\n",
+                     devices[i]->get_info<sycl::info::device::name>(), err.what());
+        }
+    }
+    nDevices = mDevices.size();
+
+    bool default_device_set = false;
+    string deviceENV        = getEnvVar("AF_ONEAPI_DEFAULT_DEVICE");
+    if (!deviceENV.empty()) {
+        //TODO: handle default device from env variable
+    }
+
+    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
+    if (!default_device_set && !deviceENV.empty()) {
+        //TODO: handle default device by type env variable
+    }
+
+    // Define AF_DISABLE_GRAPHICS with any value to disable initialization
+    string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
+    if (fgMngr->plugin().isLoaded() && noGraphicsENV.empty()) {
+        //TODO: handle forge shared contexts
+    }
+
+    mUserDeviceOffset = mDevices.size();
+
+    // TODO: init other needed libraries?
+    // blas? program cache?
+    // AF_TRACE("Default device: {}", getActiveDeviceId());
 }
 
 spdlog::logger* DeviceManager::getLogger() { return logger.get(); }
 
 DeviceManager& DeviceManager::getInstance() {
-    ONEAPI_NOT_SUPPORTED("");
     static auto* my_instance = new DeviceManager();
     return *my_instance;
 }
 
 void DeviceManager::setMemoryManager(
     std::unique_ptr<MemoryManagerBase> newMgr) {
-    ONEAPI_NOT_SUPPORTED("");
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a memory manager and the default memory
+    // manager still hasn't been initialized, so initialize it anyways so we
+    // don't inadvertently reset to it when we first call memoryManager()
+    memoryManager();
+    // Calls shutdown() on the existing memory manager.
+    if (memManager) { memManager->shutdownAllocator(); }
+    memManager = std::move(newMgr);
+    // Set the backend memory manager for this new manager to register native
+    // functions correctly.
+    std::unique_ptr<oneapi::Allocator> deviceMemoryManager(
+        new oneapi::Allocator());
+    memManager->setAllocator(std::move(deviceMemoryManager));
+    memManager->initialize();
 }
 
 void DeviceManager::resetMemoryManager() {
-    ONEAPI_NOT_SUPPORTED("");
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_ONEAPI_MEM_DEBUG));
+    setMemoryManager(std::move(mgr));
 }
 
 void DeviceManager::setMemoryManagerPinned(
     std::unique_ptr<MemoryManagerBase> newMgr) {
-    ONEAPI_NOT_SUPPORTED("");
+    std::lock_guard<std::mutex> l(mutex);
+    // It's possible we're setting a pinned memory manager and the default
+    // memory manager still hasn't been initialized, so initialize it anyways so
+    // we don't inadvertently reset to it when we first call
+    // pinnedMemoryManager()
+    pinnedMemoryManager();
+    // Calls shutdown() on the existing memory manager.
+    if (pinnedMemManager) { pinnedMemManager->shutdownAllocator(); }
+    // Set the backend pinned memory manager for this new manager to register
+    // native functions correctly.
+    pinnedMemManager = std::move(newMgr);
+    std::unique_ptr<oneapi::AllocatorPinned> deviceMemoryManager(
+        new oneapi::AllocatorPinned());
+    pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
+    pinnedMemManager->initialize();
 }
 
 void DeviceManager::resetMemoryManagerPinned() {
-    ONEAPI_NOT_SUPPORTED("");
+    // Replace with default memory manager
+    std::unique_ptr<MemoryManagerBase> mgr(
+        new common::DefaultMemoryManager(getDeviceCount(), common::MAX_BUFFERS,
+                                         AF_MEM_DEBUG || AF_ONEAPI_MEM_DEBUG));
+    setMemoryManagerPinned(std::move(mgr));
 }
 
 DeviceManager::~DeviceManager() {
-    ONEAPI_NOT_SUPPORTED("");
+    for (int i = 0; i < getDeviceCount(); ++i) { gfxManagers[i] = nullptr; }
+    memManager       = nullptr;
+    pinnedMemManager = nullptr;
+
+    // TODO: cleanup mQueues, mContexts, mDevices??
 }
 
 void DeviceManager::markDeviceForInterop(const int device,
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index b4f291afc2..ab6804789a 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -15,15 +15,10 @@
 #include <string>
 #include <vector>
 
-#ifndef AF_OPENCL_MEM_DEBUG
-#define AF_OPENCL_MEM_DEBUG 0
+#ifndef AF_ONEAPI_MEM_DEBUG
+#define AF_ONEAPI_MEM_DEBUG 0
 #endif
 
-namespace boost {
-template<typename T>
-class shared_ptr;
-}  // namespace boost
-
 namespace spdlog {
 class logger;
 }
@@ -71,8 +66,6 @@ class DeviceManager {
 
     friend GraphicsResourceManager& interopManager();
 
-    //friend PlanCache& fftManager();
-
     friend void addKernelToCache(int device, const std::string& key,
                                  const kc_entry_t entry);
 
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index 2e18a13ae4..bb0e9f181e 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -45,36 +45,36 @@ void memLock(const sycl::buffer<T> *ptr);
 template<typename T>
 void memUnlock(const sycl::buffer<T> *ptr);
 
-  bool isLocked(const void *ptr);
-
-  template<typename T>
-  T *pinnedAlloc(const size_t &elements);
-  template<typename T>
-  void pinnedFree(T *ptr);
-
-  void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
-                        size_t *lock_bytes, size_t *lock_buffers);
-  void signalMemoryCleanup();
-  void shutdownMemoryManager();
-  void pinnedGarbageCollect();
-
-  void printMemInfo(const char *msg, const int device);
-
-  float getMemoryPressure();
-  float getMemoryPressureThreshold();
-  bool jitTreeExceedsMemoryPressure(size_t bytes);
-  void setMemStepSize(size_t step_bytes);
-  size_t getMemStepSize(void);
-
-  class Allocator final : public common::memory::AllocatorInterface {
-     public:
-      Allocator();
-      ~Allocator() = default;
-      void shutdown() override;
-      int getActiveDeviceId() override;
-      size_t getMaxMemorySize(int id) override;
-      void *nativeAlloc(const size_t bytes) override;
-      void nativeFree(void *ptr) override;
+bool isLocked(const void *ptr);
+
+template<typename T>
+T *pinnedAlloc(const size_t &elements);
+template<typename T>
+void pinnedFree(T *ptr);
+
+void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
+                      size_t *lock_bytes, size_t *lock_buffers);
+void signalMemoryCleanup();
+void shutdownMemoryManager();
+void pinnedGarbageCollect();
+
+void printMemInfo(const char *msg, const int device);
+
+float getMemoryPressure();
+float getMemoryPressureThreshold();
+bool jitTreeExceedsMemoryPressure(size_t bytes);
+void setMemStepSize(size_t step_bytes);
+size_t getMemStepSize(void);
+
+class Allocator final : public common::memory::AllocatorInterface {
+    public:
+    Allocator();
+    ~Allocator() = default;
+    void shutdown() override;
+    int getActiveDeviceId() override;
+    size_t getMaxMemorySize(int id) override;
+    void *nativeAlloc(const size_t bytes) override;
+    void nativeFree(void *ptr) override;
 };
 
 class AllocatorPinned final : public common::memory::AllocatorInterface {
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index ef28dadbdb..31e32117f5 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -40,6 +40,7 @@ using sycl::queue;
 using sycl::context;
 using sycl::device;
 using sycl::platform;
+
 using std::begin;
 using std::call_once;
 using std::end;
@@ -77,8 +78,9 @@ static string get_system() {
 #endif
 }
 
-int getBackend() { return AF_BACKEND_OPENCL; }
+int getBackend() { return AF_BACKEND_ONEAPI; }
 
+/*
 bool verify_present(const string& pname, const string ref) {
     auto iter =
         search(begin(pname), end(pname), begin(ref), end(ref),
@@ -109,6 +111,7 @@ static string platformMap(string& platStr) {
         return idx->second;
     }
 }
+*/
 
 /*
 afcl::platform getPlatformEnum(cl::Device dev) {
@@ -153,8 +156,15 @@ void setActiveContext(int device) {
     tlocalActiveDeviceId() = make_pair(device, device);
 }
 
-int getDeviceCount() noexcept {
-    ONEAPI_NOT_SUPPORTED("");
+int getDeviceCount() noexcept try {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+    return static_cast<int>(devMngr.mQueues.size());
+} catch (const AfError& err) {
+    UNUSED(err);
+    // If device manager threw an error then return 0 because no platforms
+    // were found
     return 0;
 }
 
@@ -339,7 +349,7 @@ MemoryManagerBase& memoryManager() {
         // By default, create an instance of the default memory manager
         inst.memManager = make_unique<common::DefaultMemoryManager>(
             getDeviceCount(), common::MAX_BUFFERS,
-            AF_MEM_DEBUG || AF_OPENCL_MEM_DEBUG);
+            AF_MEM_DEBUG || AF_ONEAPI_MEM_DEBUG);
         // Set the memory manager's device memory manager
         unique_ptr<Allocator> deviceMemoryManager;
         deviceMemoryManager = make_unique<Allocator>();
@@ -350,11 +360,25 @@ MemoryManagerBase& memoryManager() {
     return *(inst.memManager.get());
 }
 
-/*
 MemoryManagerBase& pinnedMemoryManager() {
-    ONEAPI_NOT_SUPPORTED("");
+    static once_flag flag;
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    call_once(flag, [&]() {
+        // By default, create an instance of the default memory manager
+        inst.pinnedMemManager = make_unique<common::DefaultMemoryManager>(
+            getDeviceCount(), common::MAX_BUFFERS,
+            AF_MEM_DEBUG || AF_ONEAPI_MEM_DEBUG);
+        // Set the memory manager's device memory manager
+        unique_ptr<AllocatorPinned> deviceMemoryManager;
+        deviceMemoryManager = make_unique<AllocatorPinned>();
+        inst.pinnedMemManager->setAllocator(move(deviceMemoryManager));
+        inst.pinnedMemManager->initialize();
+    });
+
+    return *(inst.pinnedMemManager.get());
 }
-*/
 
 void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
     ONEAPI_NOT_SUPPORTED("");
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index da33f35690..c1eea64837 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -10,7 +10,7 @@
 #pragma once
 
 #include <CL/sycl.hpp>
-//#include <af/oneapi.h>
+#include <af/oneapi.h>
 
 #include <memory>
 #include <string>

From 2aa02581787e0bbc3ece5d197f49dce5c4ce4d8d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 15 Sep 2022 16:54:48 -0400
Subject: [PATCH 2298/2677] basic platform.cpp implementation

---
 include/af/oneapi.h                   |  11 +-
 src/backend/oneapi/device_manager.hpp |  10 +-
 src/backend/oneapi/platform.cpp       | 349 ++++++++++++++++++++------
 src/backend/oneapi/platform.hpp       |   2 +-
 4 files changed, 285 insertions(+), 87 deletions(-)

diff --git a/include/af/oneapi.h b/include/af/oneapi.h
index 5400a34d1a..baf28bf73b 100644
--- a/include/af/oneapi.h
+++ b/include/af/oneapi.h
@@ -19,13 +19,9 @@ extern "C" {
 #if AF_API_VERSION >= 39
 typedef enum
 {
-    //AF_ONEAPI_DEVICE_TYPE_CPU     = sycl::info::device_type::cpu,
-    //AF_ONEAPI_DEVICE_TYPE_GPU     = sycl::info::device_type::gpu,
-    //AF_ONEAPI_DEVICE_TYPE_ACC     = sycl::info::device_type::accelerator
-    //AF_ONEAPI_DEVICE_TYPE_UNKNOWN = -1
-    AF_ONEAPI_DEVICE_TYPE_CPU     = 0,
-    AF_ONEAPI_DEVICE_TYPE_GPU     = 1,
-    AF_ONEAPI_DEVICE_TYPE_ACC     = 2,
+    AF_ONEAPI_DEVICE_TYPE_CPU     = (int)sycl::info::device_type::cpu,
+    AF_ONEAPI_DEVICE_TYPE_GPU     = (int)sycl::info::device_type::gpu,
+    AF_ONEAPI_DEVICE_TYPE_ACC     = (int)sycl::info::device_type::accelerator,
     AF_ONEAPI_DEVICE_TYPE_UNKNOWN = -1
 } af_oneapi_device_type;
 #endif
@@ -33,6 +29,7 @@ typedef enum
 #if AF_API_VERSION >= 39
 typedef enum
 {
+    //TODO: update? are these relevant in sycl
     AF_ONEAPI_PLATFORM_AMD     = 0,
     AF_ONEAPI_PLATFORM_APPLE   = 1,
     AF_ONEAPI_PLATFORM_INTEL   = 2,
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index ab6804789a..f6530dcbd9 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -98,14 +98,12 @@ class DeviceManager {
 
     friend int setDevice(int device);
 
-/*
-    friend void addDeviceContext(cl_device_id dev, cl_context ctx,
-                                 cl_command_queue que);
+    friend void addDeviceContext(sycl::device dev, sycl::context ctx,
+                                 sycl::queue que);
 
-    friend void setDeviceContext(cl_device_id dev, cl_context ctx);
+    friend void setDeviceContext(sycl::device dev, sycl::context ctx);
 
-    friend void removeDeviceContext(cl_device_id dev, cl_context ctx);
-*/
+    friend void removeDeviceContext(sycl::device dev, sycl::context ctx);
 
     friend int getActiveDeviceType();
 
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index 31e32117f5..c466ff60af 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -80,7 +80,6 @@ static string get_system() {
 
 int getBackend() { return AF_BACKEND_ONEAPI; }
 
-/*
 bool verify_present(const string& pname, const string ref) {
     auto iter =
         search(begin(pname), end(pname), begin(ref), end(ref),
@@ -91,6 +90,7 @@ bool verify_present(const string& pname, const string ref) {
     return iter != end(pname);
 }
 
+//TODO: update to new platforms?
 static string platformMap(string& platStr) {
     using strmap_t                = map<string, string>;
     static const strmap_t platMap = {
@@ -111,35 +111,80 @@ static string platformMap(string& platStr) {
         return idx->second;
     }
 }
-*/
 
-/*
-afcl::platform getPlatformEnum(cl::Device dev) {
+af_oneapi_platform getPlatformEnum(sycl::device dev) {
     string pname = getPlatformName(dev);
     if (verify_present(pname, "AMD"))
-        return AFCL_PLATFORM_AMD;
+        return AF_ONEAPI_PLATFORM_AMD;
     else if (verify_present(pname, "NVIDIA"))
-        return AFCL_PLATFORM_NVIDIA;
+        return AF_ONEAPI_PLATFORM_NVIDIA;
     else if (verify_present(pname, "INTEL"))
-        return AFCL_PLATFORM_INTEL;
+        return AF_ONEAPI_PLATFORM_INTEL;
     else if (verify_present(pname, "APPLE"))
-        return AFCL_PLATFORM_APPLE;
+        return AF_ONEAPI_PLATFORM_APPLE;
     else if (verify_present(pname, "BEIGNET"))
-        return AFCL_PLATFORM_BEIGNET;
+        return AF_ONEAPI_PLATFORM_BEIGNET;
     else if (verify_present(pname, "POCL"))
-        return AFCL_PLATFORM_POCL;
-    return AFCL_PLATFORM_UNKNOWN;
+        return AF_ONEAPI_PLATFORM_POCL;
+    return AF_ONEAPI_PLATFORM_UNKNOWN;
 }
-*/
 
 string getDeviceInfo() noexcept {
-    ONEAPI_NOT_SUPPORTED("");
-    return "";
+    ostringstream info;
+    info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
+         << ", build " << AF_REVISION << ")\n";
+
+    vector<sycl::device*> devices;
+    try {
+        DeviceManager& devMngr = DeviceManager::getInstance();
+
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        unsigned nDevices = 0;
+        for (auto& device : devMngr.mDevices) {
+            //const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
+
+            string dstr = device->get_info<sycl::info::device::name>();
+            bool show_braces =
+                (static_cast<unsigned>(getActiveDeviceId()) == nDevices);
+
+            string id = (show_braces ? string("[") : "-") +
+                        to_string(nDevices) + (show_braces ? string("]") : "-");
+
+            size_t msize = device->get_info<sycl::info::device::global_mem_size>();
+            info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
+                 << ", " << msize / 1048576 << " MB";
+#ifndef NDEBUG
+            info << " -- ";
+            string devVersion = device->get_info<sycl::info::device::version>();
+            string driVersion = device->get_info<sycl::info::device::driver_version>();
+            info << devVersion;
+            info << " -- Device driver " << driVersion;
+            info
+                << " -- FP64 Support: "
+                << (device->get_info<sycl::info::device::preferred_vector_width_double>() >
+                            0
+                        ? "True"
+                        : "False");
+            info << " -- Unified Memory ("
+                 << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
+#endif
+            info << endl;
+
+            nDevices++;
+        }
+    } catch (const AfError& err) {
+        UNUSED(err);
+        info << "No platforms found.\n";
+        // Don't throw an exception here. Info should pass even if the system
+        // doesn't have the correct drivers installed.
+    }
+    return info.str();
 }
 
 string getPlatformName(const sycl::device& device) {
-    ONEAPI_NOT_SUPPORTED("");
-    return "";
+    std::string platStr = device.get_platform().get_info<sycl::info::platform::name>();
+    //return platformMap(platStr);
+    return platStr;
 }
 
 typedef pair<unsigned, unsigned> device_id_t;
@@ -169,12 +214,14 @@ int getDeviceCount() noexcept try {
 }
 
 void init() {
-    ONEAPI_NOT_SUPPORTED("");
+    thread_local const DeviceManager& devMngr = DeviceManager::getInstance();
+    UNUSED(devMngr);
 }
 
 unsigned getActiveDeviceId() {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
+    // Second element is the queue id, which is
+    // what we mean by active device id in opencl backend
+    return get<1>(tlocalActiveDeviceId());
 }
 
 /*
@@ -186,27 +233,35 @@ int getDeviceIdFromNativeId(cl_device_id id) {
     int nDevices = static_cast<int>(devMngr.mDevices.size());
     int devId    = 0;
     for (devId = 0; devId < nDevices; ++devId) {
-        if (id == devMngr.mDevices[devId]->operator()()) { break; }
+        //TODO: how to get cl_device_id from sycl::device
+        if (id == devMngr.mDevices[devId]->get()) { return devId; }
     }
-
-    return devId;
+    // TODO: reasonable if no match??
+    return -1;
 }
 */
 
 int getActiveDeviceType() {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mDeviceTypes[get<1>(devId)];
 }
 
 int getActivePlatform() {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mPlatforms[get<1>(devId)];
 }
-const context& getContext() {
-    ONEAPI_NOT_SUPPORTED("");
-    sycl::context c;
-    return c;
-    /*
+
+const sycl::context& getContext() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -214,13 +269,9 @@ const context& getContext() {
     common::lock_guard_t lock(devMngr.deviceMutex);
 
     return *(devMngr.mContexts[get<0>(devId)]);
-    */
 }
 
 sycl::queue& getQueue() {
-    sycl::queue q;
-    return q; 
-    /*
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
@@ -228,13 +279,9 @@ sycl::queue& getQueue() {
     common::lock_guard_t lock(devMngr.deviceMutex);
 
     return *(devMngr.mQueues[get<1>(devId)]);
-    */
 }
 
 const sycl::device& getDevice(int id) {
-    sycl::device d;
-    return d;
-    /*
     device_id_t& devId = tlocalActiveDeviceId();
 
     if (id == -1) { id = get<1>(devId); }
@@ -243,47 +290,87 @@ const sycl::device& getDevice(int id) {
 
     common::lock_guard_t lock(devMngr.deviceMutex);
     return *(devMngr.mDevices[id]);
-    */
 }
 
 size_t getDeviceMemorySize(int device) {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    sycl::device dev;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        // Assuming devices don't deallocate or are invalidated during execution
+        dev = *devMngr.mDevices[device];
+    }
+    size_t msize = dev.get_info<sycl::info::device::global_mem_size>();
+    return msize;
 }
 
 size_t getHostMemorySize() { return common::getHostMemorySize(); }
 
-/*
-cl_device_type getDeviceType() {
+sycl::info::device_type getDeviceType() {
     const sycl::device& device = getDevice();
-    cl_device_type type        = device.getInfo<CL_DEVICE_TYPE>();
+    sycl::info::device_type type = device.get_info<sycl::info::device::device_type>();
     return type;
 }
-*/
 
 bool isHostUnifiedMemory(const sycl::device& device) {
-    ONEAPI_NOT_SUPPORTED("");
-    return false;
-}
-
-bool OpenCLCPUOffload(bool forceOffloadOSX) {
-    ONEAPI_NOT_SUPPORTED("");
-    return false;
+    return device.get_info<sycl::info::device::host_unified_memory>();
+}
+
+bool OneAPICPUOffload(bool forceOffloadOSX) {
+    static const bool offloadEnv = getEnvVar("AF_ONEAPI_CPU_OFFLOAD") != "0";
+    bool offload                 = false;
+    if (offloadEnv) { offload = isHostUnifiedMemory(getDevice()); }
+#if OS_MAC
+    // FORCED OFFLOAD FOR LAPACK FUNCTIONS ON OSX UNIFIED MEMORY DEVICES
+    //
+    // On OSX Unified Memory devices (Intel), always offload LAPACK but not GEMM
+    // irrespective of the AF_OPENCL_CPU_OFFLOAD value
+    // From GEMM, OpenCLCPUOffload(false) is called which will render the
+    // variable inconsequential to the returned result.
+    //
+    // Issue https://github.com/arrayfire/arrayfire/issues/662
+    //
+    // Make sure device has unified memory
+    bool osx_offload = isHostUnifiedMemory(getDevice());
+    // Force condition
+    offload = osx_offload && (offload || forceOffloadOSX);
+#else
+    UNUSED(forceOffloadOSX);
+#endif
+    return offload;
 }
 
 bool isGLSharingSupported() {
-    ONEAPI_NOT_SUPPORTED("");
-    return false;
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mIsGLSharingOn[get<1>(devId)];
 }
 
 bool isDoubleSupported(unsigned device) {
-    ONEAPI_NOT_SUPPORTED("");
-    return false;
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    sycl::device dev;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        dev = *devMngr.mDevices[device];
+    }
+    return dev.has(sycl::aspect::fp64);
 }
 
 bool isHalfSupported(unsigned device) {
-    ONEAPI_NOT_SUPPORTED("");
-    return false;
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    sycl::device dev;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        dev = *devMngr.mDevices[device];
+    }
+    return dev.has(sycl::aspect::fp16);
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
@@ -291,28 +378,133 @@ void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
 }
 
 int setDevice(int device) {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    if (device >= static_cast<int>(devMngr.mQueues.size()) ||
+        device >= static_cast<int>(DeviceManager::MAX_DEVICES)) {
+        return -1;
+    } else {
+        int old = getActiveDeviceId();
+        setActiveContext(device);
+        return old;
+    }
 }
 
 void sync(int device) {
-    ONEAPI_NOT_SUPPORTED("");
+    int currDevice = getActiveDeviceId();
+    setDevice(device);
+    getQueue().wait();
+    setDevice(currDevice);
 }
 
 void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que) {
-    ONEAPI_NOT_SUPPORTED("");
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    int nDevices = 0;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+
+        auto tDevice  = make_unique<sycl::device>(dev);
+        auto tContext = make_unique<sycl::context>(ctx);
+        // queue atleast has implicit context and device if created
+        auto tQueue   = make_unique<sycl::queue>(que);
+
+        devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
+        // FIXME: add OpenGL Interop for user provided contexts later
+        devMngr.mIsGLSharingOn.push_back(false);
+        devMngr.mDeviceTypes.push_back(
+            static_cast<int>(tDevice->get_info<sycl::info::device::device_type>()));
+
+        devMngr.mDevices.push_back(move(tDevice));
+        devMngr.mContexts.push_back(move(tContext));
+        devMngr.mQueues.push_back(move(tQueue));
+        nDevices = static_cast<int>(devMngr.mDevices.size()) - 1;
+
+        //TODO: cache?
+    }
+
+    // Last/newly added device needs memory management
+    memoryManager().addMemoryManagement(nDevices);
 }
 
 void setDeviceContext(sycl::device dev, sycl::context ctx) {
-    ONEAPI_NOT_SUPPORTED("");
+    // FIXME: add OpenGL Interop for user provided contexts later
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    const int dCount = static_cast<int>(devMngr.mDevices.size());
+    for (int i = 0; i < dCount; ++i) {
+        if (*devMngr.mDevices[i] == dev &&
+            *devMngr.mContexts[i] == ctx) {
+            setActiveContext(i);
+            return;
+        }
+    }
+    AF_ERROR("No matching device found", AF_ERR_ARG);
 }
 
 void removeDeviceContext(sycl::device dev, sycl::context ctx) {
-    ONEAPI_NOT_SUPPORTED("");
+    if (getDevice() == dev && getContext() == ctx) {
+        AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
+    }
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    int deleteIdx = -1;
+    {
+        common::lock_guard_t lock(devMngr.deviceMutex);
+
+        const int dCount = static_cast<int>(devMngr.mDevices.size());
+        for (int i = 0; i < dCount; ++i) {
+            if (*devMngr.mDevices[i] == dev &&
+                *devMngr.mContexts[i] == ctx) {
+                deleteIdx = i;
+                break;
+            }
+        }
+    }
+
+    if (deleteIdx < static_cast<int>(devMngr.mUserDeviceOffset)) {
+        AF_ERROR("Cannot pop ArrayFire internal devices", AF_ERR_ARG);
+    } else if (deleteIdx == -1) {
+        AF_ERROR("No matching device found", AF_ERR_ARG);
+    } else {
+        // remove memory management for device added by user outside of the lock
+        memoryManager().removeMemoryManagement(deleteIdx);
+
+        common::lock_guard_t lock(devMngr.deviceMutex);
+        // FIXME: this case can potentially cause issues due to the
+        // modification of the device pool stl containers.
+
+        // IF the current active device is enumerated at a position
+        // that lies ahead of the device that has been requested
+        // to be removed. We just pop the entries from pool since it
+        // has no side effects.
+        devMngr.mDevices.erase(devMngr.mDevices.begin() + deleteIdx);
+        devMngr.mContexts.erase(devMngr.mContexts.begin() + deleteIdx);
+        devMngr.mQueues.erase(devMngr.mQueues.begin() + deleteIdx);
+        devMngr.mPlatforms.erase(devMngr.mPlatforms.begin() + deleteIdx);
+
+        // FIXME: add OpenGL Interop for user provided contexts later
+        devMngr.mIsGLSharingOn.erase(devMngr.mIsGLSharingOn.begin() +
+                                     deleteIdx);
+
+        // OTHERWISE, update(decrement) the thread local active device ids
+        device_id_t& devId = tlocalActiveDeviceId();
+
+        if (deleteIdx < static_cast<int>(devId.first)) {
+            device_id_t newVals = make_pair(devId.first - 1, devId.second - 1);
+            devId               = newVals;
+        }
+    }
 }
 
 bool synchronize_calls() {
-    return false;
+    static const bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
+    return sync;
 }
 
 int& getMaxJitSize() {
@@ -335,7 +527,6 @@ int& getMaxJitSize() {
 }
 
 bool& evalFlag() {
-    ONEAPI_NOT_SUPPORTED("");
     thread_local bool flag = true;
     return flag;
 }
@@ -381,32 +572,44 @@ MemoryManagerBase& pinnedMemoryManager() {
 }
 
 void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
-    ONEAPI_NOT_SUPPORTED("");
+    return DeviceManager::getInstance().setMemoryManager(move(mgr));
 }
 
 void resetMemoryManager() {
-    ONEAPI_NOT_SUPPORTED("");
+    return DeviceManager::getInstance().resetMemoryManagerPinned();
 }
 
 void setMemoryManagerPinned(unique_ptr<MemoryManagerBase> mgr) {
-    ONEAPI_NOT_SUPPORTED("");
+    return DeviceManager::getInstance().setMemoryManagerPinned(move(mgr));
 }
 
 void resetMemoryManagerPinned() {
-    ONEAPI_NOT_SUPPORTED("");
+    return DeviceManager::getInstance().resetMemoryManagerPinned();
 }
 
 graphics::ForgeManager& forgeManager() {
-    ONEAPI_NOT_SUPPORTED("");
+    return *(DeviceManager::getInstance().fgMngr);
 }
 
 GraphicsResourceManager& interopManager() {
-    ONEAPI_NOT_SUPPORTED("");
+    static once_flag initFlags[DeviceManager::MAX_DEVICES];
+
+    int id = getActiveDeviceId();
+
+    DeviceManager& inst = DeviceManager::getInstance();
+
+    call_once(initFlags[id], [&] {
+        inst.gfxManagers[id] = make_unique<GraphicsResourceManager>();
+    });
+
+    return *(inst.gfxManagers[id].get());
 }
 
 }  // namespace oneapi
 
 /*
+//TODO: select which external api functions to expose and add to header+implement
+
 using namespace oneapi;
 
 af_err afcl_get_device_type(afcl_device_type* res) {
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index c1eea64837..d82868454e 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -63,7 +63,7 @@ size_t getDeviceMemorySize(int device);
 size_t getHostMemorySize();
 
 //sycl::device::is_cpu,is_gpu,is_accelerator
-//cl_device_type getDeviceType();
+sycl::info::device_type getDeviceType();
 
 bool isHostUnifiedMemory(const sycl::device& device);
 

From 37a0b4cd4625296572597e27a20c3b7192bf1c54 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 19 Sep 2022 20:40:38 -0400
Subject: [PATCH 2299/2677] adds more methods to Array.cpp implementation

---
 src/backend/oneapi/Array.cpp      | 116 ++++++++++++++++--------------
 src/backend/oneapi/CMakeLists.txt |   1 +
 src/backend/oneapi/Param.cpp      |  30 ++++++++
 src/backend/oneapi/Param.hpp      |   6 +-
 src/backend/oneapi/memory.cpp     |   4 +-
 test/CMakeLists.txt               |   6 +-
 6 files changed, 103 insertions(+), 60 deletions(-)
 create mode 100644 src/backend/oneapi/Param.cpp

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 5f53e37052..b62bc8ea3e 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -122,11 +122,14 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
     static_assert(
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");
-    // TODO(oneapi): Copy to buffer
     //getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
-    //sizeof(T) * info.elements(), in_data);
+                                  //sizeof(T) * info.elements(), in_data);
+    getQueue().submit([&] (sycl::handler &h) {
+        h.copy(in_data, data->get_access(h));
+    }).wait();
 }
 
+
 template<typename T>
 Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
                 bool copy)
@@ -139,12 +142,9 @@ Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
     , node()
     , owner(true) {
     if (copy) {
-      //clRetainMemObject(mem);
-      //buffer src_buf = buffer(mem);
-        // TODO(oneapi): copy buffer
-        ONEAPI_NOT_SUPPORTED("Buffer constructor not implamented");
-        //getQueue().enqueueCopyBuffer(src_buf, *data.get(), src_offset, 0,
-        //sizeof(T) * info.elements());
+        getQueue().submit([&] (sycl::handler &h) {
+            h.copy(mem->get_access(h), data->get_access(h));
+        }).wait();
     }
 }
 
@@ -180,16 +180,16 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
     : info(getActiveDeviceId(), dims, offset_, strides,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(is_device ? (new buffer<T>(*reinterpret_cast<buffer<T>*>(
-                                                                    const_cast<T *>(in_data))))
+                                        const_cast<T *>(in_data))))
                      : (memAlloc<T>(info.elements()).release()),
            bufferFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {
     if (!is_device) {
-      ONEAPI_NOT_SUPPORTED("Write to buffer from Host");
-          //getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
-          //sizeof(T) * info.total(), in_data);
+        getQueue().submit([&] (sycl::handler &h) {
+            h.copy(in_data, data->get_access(h));
+        }).wait();
     }
 }
 
@@ -198,18 +198,20 @@ void Array<T>::eval() {
     if (isReady()) { return; }
 
     this->setId(getActiveDeviceId());
-    data = std::shared_ptr<buffer<T>>(memAlloc<T>(info.elements()).release(),
+    data = std::shared_ptr<sycl::buffer<T>>(memAlloc<T>(info.elements()).release(),
                                        bufferFree<T>);
 
-    ONEAPI_NOT_SUPPORTED("JIT Not supported");
     // Do not replace this with cast operator
-    Param<T> info; //= {{dims()[0], dims()[1], dims()[2], dims()[3]},
-                  // {strides()[0], strides()[1], strides()[2], strides()[3]},
-                  // 0};
+    KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
+                   {strides()[0], strides()[1], strides()[2], strides()[3]},
+                   0};
+
+    Param<T> res{data.get(), info};
 
-    Param<T> res;// = {data.get(), info};
 
-    evalNodes(res, getNode().get());
+    //TODO: implement
+    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
+    //evalNodes(res, getNode().get());
     node.reset();
 }
 
@@ -232,43 +234,44 @@ void evalMultiple(vector<Array<T> *> arrays) {
     vector<Array<T> *> output_arrays;
     vector<Node *> nodes;
 
-    ONEAPI_NOT_SUPPORTED("JIT Not supported");
-    // // Check if all the arrays have the same dimension
-    // auto it = std::adjacent_find(begin(arrays), end(arrays),
-    //                              [](const Array<T> *l, const Array<T> *r) {
-    //                                  return l->dims() != r->dims();
-    //                              });
-
-    // // If they are not the same. eval individually
-    // if (it != end(arrays)) {
-    //     for (auto ptr : arrays) { ptr->eval(); }
-    //     return;
-    // }
+    // Check if all the arrays have the same dimension
+    auto it = std::adjacent_find(begin(arrays), end(arrays),
+                                 [](const Array<T> *l, const Array<T> *r) {
+                                     return l->dims() != r->dims();
+                                 });
+
+    // If they are not the same. eval individually
+    if (it != end(arrays)) {
+        for (auto ptr : arrays) { ptr->eval(); }
+        return;
+    }
 
-    // for (Array<T> *array : arrays) {
-    //     if (array->isReady()) { continue; }
+    for (Array<T> *array : arrays) {
+        if (array->isReady()) { continue; }
 
-    //     const ArrayInfo info = array->info;
+        const ArrayInfo info = array->info;
 
-    //     array->setId(getActiveDeviceId());
-    //     array->data = std::shared_ptr<buffer<T>>(
-    //         memAlloc<T>(info.elements()).release(), bufferFree<T>);
+        array->setId(getActiveDeviceId());
+        array->data = std::shared_ptr<buffer<T>>(
+            memAlloc<T>(info.elements()).release(), bufferFree<T>);
 
-    //     // Do not replace this with cast operator
-    //     Param<T> kInfo = {
-    //         {info.dims()[0], info.dims()[1], info.dims()[2], info.dims()[3]},
-    //         {info.strides()[0], info.strides()[1], info.strides()[2],
-    //          info.strides()[3]},
-    //         0};
+        // Do not replace this with cast operator
+        KParam kInfo = {
+            {info.dims()[0], info.dims()[1], info.dims()[2], info.dims()[3]},
+            {info.strides()[0], info.strides()[1], info.strides()[2],
+             info.strides()[3]},
+            0};
 
-    //     outputs.emplace_back(array->data.get(), kInfo);
-    //     output_arrays.push_back(array);
-    //     nodes.push_back(array->getNode().get());
-    // }
+        outputs.emplace_back(array->data.get(), kInfo);
+        output_arrays.push_back(array);
+        nodes.push_back(array->getNode().get());
+    }
 
-    // evalNodes(outputs, nodes);
+    //TODO: implement
+    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
+    //evalNodes(outputs, nodes);
 
-    // for (Array<T> *array : output_arrays) { array->node.reset(); }
+    for (Array<T> *array : output_arrays) { array->node.reset(); }
 }
 
 template<typename T>
@@ -383,12 +386,16 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     return kJITHeuristics::Pass;
 }
 
+//Doesn't make sense with sycl::buffer
+//TODO: accessors? or return sycl::buffer?
+//TODO: return accessor.get_pointer() for access::target::global_buffer or (host_buffer?)
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const buffer<T> *buf = arr.device();
     //if (!buf) { return NULL; }
     //memLock(buf);
     //cl_mem mem = (*buf)();
+    ONEAPI_NOT_SUPPORTED("pointer to sycl::buffer should be accessor");
     return (void *)buf;
 }
 
@@ -474,8 +481,12 @@ template<typename T>
 void writeHostDataArray(Array<T> &arr, const T *const data,
                         const size_t bytes) {
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
-
-    ONEAPI_NOT_SUPPORTED("writeHostDataArray Not supported");
+    getQueue().submit([&] (sycl::handler &h) {
+        buffer<T> &buf = *arr.get();
+        //auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
+        auto offset_acc = buf.get_access(h);
+        h.copy(data, offset_acc);
+    }).wait();
     //getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(), bytes,
     //data);
 }
@@ -505,14 +516,11 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
 
 template<typename T>
 size_t Array<T>::getAllocatedBytes() const {
-    return 0;
-    /*
     if (!isReady()) { return 0; }
     size_t bytes = memoryManager().allocated(data.get());
     // External device pointer
     if (bytes == 0 && data.get()) { return data_dims.elements() * sizeof(T); }
     return bytes;
-    */
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 61ce0f1eae..ed95713b67 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -16,6 +16,7 @@ add_library(afoneapi
   GraphicsResourceManager.cpp
   GraphicsResourceManager.hpp
   Module.hpp
+  Param.cpp
   Param.hpp
   all.cpp
   anisotropic_diffusion.cpp
diff --git a/src/backend/oneapi/Param.cpp b/src/backend/oneapi/Param.cpp
new file mode 100644
index 0000000000..c5d2b16762
--- /dev/null
+++ b/src/backend/oneapi/Param.cpp
@@ -0,0 +1,30 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <kernel/KParam.hpp>
+#include <platform.hpp>
+#include <af/defines.h>
+
+namespace oneapi {
+
+template<typename T>
+Param<T> makeParam(sycl::buffer<T> &mem, int off, const int dims[4],
+                const int strides[4]) {
+    Param<T> out;
+    out.data        = &mem;
+    out.info.offset = off;
+    for (int i = 0; i < 4; i++) {
+        out.info.dims[i]    = dims[i];
+        out.info.strides[i] = strides[i];
+    }
+    return out;
+}
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 0536d3dc0c..b65e28f2e7 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -23,9 +23,11 @@ struct Param {
     Param(Param&& other)                 = default;
 
     // AF_DEPRECATED("Use Array<T>")
-    Param();
+    Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
+
     // AF_DEPRECATED("Use Array<T>")
-    Param(sycl::buffer<T>* data_, KParam info_);
+    Param(sycl::buffer<T> *data_, KParam info_) : data(data_), info(info_) {}
+
     ~Param() = default;
 };
 
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 2f869d3147..0eca0c9d7a 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -58,9 +58,7 @@ template<typename T>
 //unique_ptr<int, function<void(int *)>> memAlloc(
 std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>> memAlloc(
     const size_t &elements) {
-    ONEAPI_NOT_SUPPORTED("memAlloc Not supported");
-    //return unique_ptr<int, function<void(int *)>>();
-    return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>();
+    return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(new sycl::buffer<T>(sycl::range(elements)), bufferFree<T>);
     // // TODO: make memAlloc aware of array shapes
     // if (elements) {
     //     dim4 dims(elements);
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index e2a580a1c1..aa46bdaebb 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -20,7 +20,7 @@ if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
 elseif(NOT TARGET GTest::gtest)
   af_dep_check_and_populate(${gtest_prefix}
     URI https://github.com/google/googletest.git
-    REF release-1.8.1
+    REF release-1.12.1
   )
 
   # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
@@ -32,6 +32,7 @@ elseif(NOT TARGET GTest::gtest)
     set(BUILD_SHARED_LIBS OFF)
   endif()
 
+  add_definitions(-DGTEST_HAS_SEH=OFF)
   add_subdirectory(${${gtest_prefix}_SOURCE_DIR} ${${gtest_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
   set_target_properties(gtest gtest_main
     PROPERTIES
@@ -44,6 +45,9 @@ elseif(NOT TARGET GTest::gtest)
       target_compile_options(gtest_main PRIVATE -Wno-maybe-uninitialized)
     endif()
   endif()
+  if(WIN32)
+      target_compile_options(gtest PRIVATE -Wno-error=ignored-attributes)
+  endif()
 
   # Hide gtest project variables
   mark_as_advanced(

From b31309dff54ba4e6670d6f05b04d0c796030650a Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 20 Sep 2022 19:06:30 -0400
Subject: [PATCH 2300/2677] remove exception in bufferFree

---
 src/backend/oneapi/Array.cpp  |  3 ++-
 src/backend/oneapi/copy.cpp   | 33 ++++++++++++++++++++++++++++++++-
 src/backend/oneapi/memory.cpp |  6 +++---
 3 files changed, 37 insertions(+), 5 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index b62bc8ea3e..f9d8e8e3e7 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -128,7 +128,7 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
         h.copy(in_data, data->get_access(h));
     }).wait();
 }
-
+   
 
 template<typename T>
 Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
@@ -484,6 +484,7 @@ void writeHostDataArray(Array<T> &arr, const T *const data,
     getQueue().submit([&] (sycl::handler &h) {
         buffer<T> &buf = *arr.get();
         //auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
+        //TODO: offset accessor
         auto offset_acc = buf.get_access(h);
         h.copy(data, offset_acc);
     }).wait();
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 5e708bb593..6ffd6bd05c 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -21,7 +21,38 @@ namespace oneapi {
 
 template<typename T>
 void copyData(T *data, const Array<T> &A) {
-    ONEAPI_NOT_SUPPORTED("");
+    /*
+    if (A.elements() == 0) { return; }
+
+    // FIXME: Merge this with copyArray
+    A.eval();
+
+    dim_t offset = 0;
+    sycl::buffer<T>* buf;
+    Array<T> out = A;
+
+    if (A.isLinear() ||  // No offsets, No strides
+        A.ndims() == 1   // Simple offset, no strides.
+    ) {
+        buf    = A.get();
+        offset = A.getOffset();
+    } else {
+        // FIXME: Think about implementing eval
+        out    = copyArray(A);
+        buf    = out.get();
+        offset = 0;
+    }sycl::access::target::device>
+
+    // FIXME: Add checks
+    getQueue().submit([&] (sycl::handler &h) {
+        //auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
+        //TODO: offset accessor
+        auto offset_acc = buf->get_access(h);
+        h.copy(offset_acc, data);
+    }).wait();
+    //getQueue().enqueueReadBuffer(buf, CL_TRUE, sizeof(T) * offset,
+                                 //sizeof(T) * A.elements(), data);
+    */
 }
 
 template<typename T>
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 0eca0c9d7a..add529c8cc 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -124,9 +124,9 @@ sycl::buffer<T> *bufferAlloc(const size_t &bytes) {
 
 template<typename T>
 void bufferFree(sycl::buffer<T> *buf) {
-
-    ONEAPI_NOT_SUPPORTED("bufferFree Not supported");
-
+    if(buf) {
+        delete buf;
+    }
     // if (buf) {
     //     cl_mem mem = (*buf)();
     //     delete buf;

From bb5de0ae2e4a8eca153483381c93451d89a82068 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 21 Sep 2022 20:43:57 -0400
Subject: [PATCH 2301/2677] array tests passing for non-JIT non-device
 operations

---
 src/backend/oneapi/copy.cpp          | 91 ++++++++++++++++++++++------
 src/backend/oneapi/random_engine.cpp |  2 +-
 2 files changed, 75 insertions(+), 18 deletions(-)

diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 6ffd6bd05c..474dfe849f 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -21,14 +21,13 @@ namespace oneapi {
 
 template<typename T>
 void copyData(T *data, const Array<T> &A) {
-    /*
     if (A.elements() == 0) { return; }
 
     // FIXME: Merge this with copyArray
     A.eval();
 
     dim_t offset = 0;
-    sycl::buffer<T>* buf;
+    const sycl::buffer<T>* buf;
     Array<T> out = A;
 
     if (A.isLinear() ||  // No offsets, No strides
@@ -41,43 +40,92 @@ void copyData(T *data, const Array<T> &A) {
         out    = copyArray(A);
         buf    = out.get();
         offset = 0;
-    }sycl::access::target::device>
+    }
 
     // FIXME: Add checks
-    getQueue().submit([&] (sycl::handler &h) {
-        //auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
-        //TODO: offset accessor
-        auto offset_acc = buf->get_access(h);
+    getQueue().submit([=] (sycl::handler &h) {
+        sycl::range rr(A.elements());
+        sycl::id offset_id(offset);
+        auto offset_acc = const_cast<sycl::buffer<T>*>(buf)->get_access(h, rr, offset_id);
         h.copy(offset_acc, data);
     }).wait();
-    //getQueue().enqueueReadBuffer(buf, CL_TRUE, sizeof(T) * offset,
-                                 //sizeof(T) * A.elements(), data);
-    */
 }
 
 template<typename T>
 Array<T> copyArray(const Array<T> &A) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
+    Array<T> out = createEmptyArray<T>(A.dims());
+    if (A.elements() == 0) { return out; }
+
+    dim_t offset = A.getOffset();
+    if (A.isLinear()) {
+        // FIXME: Add checks
+
+        const sycl::buffer<T>* A_buf = A.get();
+        sycl::buffer<T>* out_buf = out.get();
+
+        getQueue().submit([=] (sycl::handler &h) {
+            sycl::range rr(A.elements());
+            sycl::id offset_id(offset);
+            auto offset_acc_A  = const_cast<sycl::buffer<T>*>(A_buf)->get_access(h, rr, offset_id);
+            auto acc_out = out_buf->get_access(h);
+
+            h.copy(offset_acc_A, acc_out);
+        }).wait();
+    } else {
+        ONEAPI_NOT_SUPPORTED("");
+        /*
+        TODO:
+        kernel::memcopy<T>(*out.get(), out.strides().get(), *A.get(),
+                           A.dims().get(), A.strides().get(), offset,
+                           (uint)A.ndims());
+        */
+    }
     return out;
 }
 
 template<typename T>
 void multiply_inplace(Array<T> &in, double val) {
     ONEAPI_NOT_SUPPORTED("");
+    //TODO:
+    //kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val, true);
 }
 
 template<typename inType, typename outType>
 struct copyWrapper {
     void operator()(Array<outType> &out, Array<inType> const &in) {
-        ONEAPI_NOT_SUPPORTED("");
+        //TODO:
+        //kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
+                                      //1, in.dims() == out.dims());
     }
 };
 
 template<typename T>
 struct copyWrapper<T, T> {
     void operator()(Array<T> &out, Array<T> const &in) {
-        ONEAPI_NOT_SUPPORTED("");
+        if (out.isLinear() && in.isLinear() &&
+            out.elements() == in.elements()) {
+
+            dim_t in_offset  = in.getOffset() * sizeof(T);
+            dim_t out_offset = out.getOffset() * sizeof(T);
+
+            const sycl::buffer<T>* in_buf = in.get();
+            sycl::buffer<T>* out_buf = out.get();
+
+            getQueue().submit([=] (sycl::handler &h) {
+                sycl::range rr(in.elements());
+                sycl::id in_offset_id(in_offset);
+                sycl::id out_offset_id(out_offset);
+
+                auto offset_acc_in  = const_cast<sycl::buffer<T>*>(in_buf)->get_access(h, rr, in_offset_id);
+                auto offset_acc_out = out_buf->get_access(h, rr, out_offset_id);
+
+                h.copy(offset_acc_in, offset_acc_out);
+            }).wait();
+        } else {
+            //TODO:
+            //kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1,
+                               //in.dims() == out.dims());
+        }
     }
 };
 
@@ -85,7 +133,8 @@ template<typename inType, typename outType>
 void copyArray(Array<outType> &out, Array<inType> const &in) {
     static_assert(!(is_complex<inType>::value && !is_complex<outType>::value),
                   "Cannot copy from complex value to a non complex value");
-    ONEAPI_NOT_SUPPORTED("");
+    copyWrapper<inType, outType> copyFn;
+    copyFn(out, in);
 }
 
 #define INSTANTIATE(T)                                         \
@@ -158,8 +207,16 @@ INSTANTIATE_COPY_ARRAY_COMPLEX(cdouble)
 
 template<typename T>
 T getScalar(const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
-    return (T)0;
+    T retVal{};
+
+    getQueue().submit([=] (sycl::handler &h) {
+        sycl::range rr(1);
+        sycl::id offset_id(in.getOffset());
+        auto acc_in  = const_cast<sycl::buffer<T>*>(in.get())->get_access(h, rr, offset_id);
+        h.copy(acc_in, (void*)&retVal);
+    }).wait();
+
+    return retVal;
 }
 
 #define INSTANTIATE_GETSCALAR(T) template T getScalar(const Array<T> &in);
diff --git a/src/backend/oneapi/random_engine.cpp b/src/backend/oneapi/random_engine.cpp
index 9e9e7ba305..db56d21638 100644
--- a/src/backend/oneapi/random_engine.cpp
+++ b/src/backend/oneapi/random_engine.cpp
@@ -29,7 +29,7 @@ Array<T> uniformDistribution(const af::dim4 &dims,
                              const af_random_engine_type type,
                              const uintl &seed, uintl &counter) {
 
-    ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");
+    //ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");
 
     Array<T> out = createEmptyArray<T>(dims);
     // kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,

From 4bfac8e8677e3d375a9665842910357822488f69 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 21 Sep 2022 21:02:06 -0400
Subject: [PATCH 2302/2677] turn off oneapi backend by default

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 60df46c5a3..72b2ca4317 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -76,7 +76,7 @@ include(config_ccache)
 option(AF_BUILD_CPU      "Build ArrayFire with a CPU backend"        ON)
 option(AF_BUILD_CUDA     "Build ArrayFire with a CUDA backend"       ${CUDA_FOUND})
 option(AF_BUILD_OPENCL   "Build ArrayFire with a OpenCL backend"     ${OpenCL_FOUND})
-option(AF_BUILD_ONEAPI   "Build ArrayFire with a oneAPI backend"     ${IntelDPCPP_FOUND})
+option(AF_BUILD_ONEAPI   "Build ArrayFire with a oneAPI backend"     OFF)
 option(AF_BUILD_UNIFIED  "Build Backend-Independent ArrayFire API"   ON)
 option(AF_BUILD_DOCS     "Create ArrayFire Documentation"            ${DOXYGEN_FOUND})
 option(AF_BUILD_EXAMPLES "Build Examples"                            ON)

From 4d65e6c8cb28fd29e29e915ed72015fa1866f05b Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 28 Sep 2022 21:23:31 -0400
Subject: [PATCH 2303/2677] adds first pass copy, iota, range kernels

---
 src/backend/oneapi/CMakeLists.txt     |   7 +
 src/backend/oneapi/copy.cpp           |  21 +-
 src/backend/oneapi/iota.cpp           |  12 +-
 src/backend/oneapi/kernel/iota.hpp    | 109 +++++++++
 src/backend/oneapi/kernel/memcopy.hpp | 318 ++++++++++++++++++++++++++
 src/backend/oneapi/kernel/range.hpp   | 119 ++++++++++
 src/backend/oneapi/range.cpp          |   9 +-
 7 files changed, 574 insertions(+), 21 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/iota.hpp
 create mode 100644 src/backend/oneapi/kernel/memcopy.hpp
 create mode 100644 src/backend/oneapi/kernel/range.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index ed95713b67..8cf9384b9c 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -202,6 +202,13 @@ add_library(afoneapi
   wrap.hpp
   )
 
+target_sources(afoneapi
+  PRIVATE
+    kernel/KParam.hpp
+    kernel/iota.hpp
+    kernel/memcopy.hpp
+)
+
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
 
 arrayfire_set_default_cxx_flags(afoneapi)
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 474dfe849f..622268eb91 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -9,6 +9,7 @@
 #include <copy.hpp>
 
 #include <Array.hpp>
+#include <kernel/memcopy.hpp>
 #include <common/complex.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
@@ -72,30 +73,23 @@ Array<T> copyArray(const Array<T> &A) {
             h.copy(offset_acc_A, acc_out);
         }).wait();
     } else {
-        ONEAPI_NOT_SUPPORTED("");
-        /*
-        TODO:
-        kernel::memcopy<T>(*out.get(), out.strides().get(), *A.get(),
+        kernel::memcopy<T>(out.get(), out.strides().get(), A.get(),
                            A.dims().get(), A.strides().get(), offset,
                            (uint)A.ndims());
-        */
     }
     return out;
 }
 
 template<typename T>
 void multiply_inplace(Array<T> &in, double val) {
-    ONEAPI_NOT_SUPPORTED("");
-    //TODO:
-    //kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val, true);
+    kernel::copy<T, T>(in, in, in.ndims(), scalar<T>(0), val, true);
 }
 
 template<typename inType, typename outType>
 struct copyWrapper {
     void operator()(Array<outType> &out, Array<inType> const &in) {
-        //TODO:
-        //kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
-                                      //1, in.dims() == out.dims());
+        kernel::copy<inType, outType>(out, in, in.ndims(), scalar<outType>(0),
+                                      1, in.dims() == out.dims());
     }
 };
 
@@ -122,9 +116,8 @@ struct copyWrapper<T, T> {
                 h.copy(offset_acc_in, offset_acc_out);
             }).wait();
         } else {
-            //TODO:
-            //kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1,
-                               //in.dims() == out.dims());
+            kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1,
+                               in.dims() == out.dims());
         }
     }
 };
diff --git a/src/backend/oneapi/iota.cpp b/src/backend/oneapi/iota.cpp
index 92fbbd2ede..bb6380993b 100644
--- a/src/backend/oneapi/iota.cpp
+++ b/src/backend/oneapi/iota.cpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <iota.hpp>
+#include <kernel/iota.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
@@ -20,10 +21,19 @@ using common::half;
 namespace oneapi {
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
-    ONEAPI_NOT_SUPPORTED("");
     dim4 outdims = dims * tile_dims;
 
     Array<T> out = createEmptyArray<T>(outdims);
+    kernel::iota<T>(out, dims);
+    return out;
+}
+
+template<>
+Array<half> iota(const dim4 &dims, const dim4 &tile_dims) {
+    ONEAPI_NOT_SUPPORTED("");
+    dim4 outdims = dims * tile_dims;
+
+    Array<half> out = createEmptyArray<half>(outdims);
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
new file mode 100644
index 0000000000..223a990d34
--- /dev/null
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -0,0 +1,109 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+#include <common/kernel_cache.hpp>
+#include <traits.hpp>
+#include <af/dim4.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class iotaKernel {
+public:
+    iotaKernel(sycl::accessor<T> out, KParam oinfo,
+               const int s0, const int s1, const int s2, const int s3,
+               const int blocksPerMatX, const int blocksPerMatY, 
+               sycl::stream debug) :
+        out_(out), oinfo_(oinfo),
+        s0_(s0), s1_(s1), s2_(s2), s3_(s3),
+        blocksPerMatX_(blocksPerMatX), blocksPerMatY_(blocksPerMatY),
+        debug_(debug) {}
+
+    void operator() (sycl::nd_item<2> it) const {
+        //printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
+        //debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) << "]" << sycl::stream_manipulator::endl;
+
+        sycl::group gg = it.get_group();
+        const int oz = gg.get_group_id(0) / blocksPerMatX_;
+        const int ow = gg.get_group_id(1) / blocksPerMatY_;
+
+        const int blockIdx_x = gg.get_group_id(0) - oz * blocksPerMatX_;
+        const int blockIdx_y = gg.get_group_id(1) - ow * blocksPerMatY_;
+
+        const int xx = it.get_local_id(0) + blockIdx_x * gg.get_local_range(0);
+        const int yy = it.get_local_id(1) + blockIdx_y * gg.get_local_range(1);
+
+        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] || oz >= oinfo_.dims[2] ||
+            ow >= oinfo_.dims[3])
+            return;
+
+        const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
+
+        T val = static_cast<T>((ow % s3_) * s2_ * s1_ * s0_);
+        val +=  static_cast<T>((oz % s2_) * s1_ * s0_);
+
+        const int incy = blocksPerMatY_ * gg.get_local_range(1);
+        const int incx = blocksPerMatX_ * gg.get_local_range(0);
+
+        for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
+            T valY   = val + (oy % s1_) * s0_;
+            int oyzw = ozw + oy * oinfo_.strides[1];
+            for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
+                int oidx = oyzw + ox;
+                out_[oidx] = valY + (ox % s0_);
+            }
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    KParam oinfo_;
+    int s0_, s1_, s2_, s3_;
+    int blocksPerMatX_, blocksPerMatY_;
+    sycl::stream debug_;
+};
+
+template<typename T>
+void iota(Param<T> out, const af::dim4& sdims) {
+    constexpr int IOTA_TX = 32;
+    constexpr int IOTA_TY = 8;
+    constexpr int TILEX   = 512;
+    constexpr int TILEY   = 32;
+
+    sycl::range<2> local(IOTA_TX, IOTA_TY);
+
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    sycl::range<2> global(local[0] * blocksPerMatX * out.info.dims[2],
+                          local[1] * blocksPerMatY * out.info.dims[3]);
+    sycl::nd_range<2> ndrange(global, local);
+
+    getQueue().submit([=] (sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+
+        sycl::stream debug_stream(2048, 128, h);
+
+        h.parallel_for(ndrange, iotaKernel<T>(out_acc, out.info,
+            static_cast<int>(sdims[0]), static_cast<int>(sdims[1]),
+            static_cast<int>(sdims[2]), static_cast<int>(sdims[3]),
+            blocksPerMatX, blocksPerMatY, debug_stream));
+    });
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
new file mode 100644
index 0000000000..2fae4238b2
--- /dev/null
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -0,0 +1,318 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <CL/sycl.hpp>
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+//#include <common/kernel_cache.hpp>
+#include <common/traits.hpp>
+#include <traits.hpp>
+
+#include <algorithm>
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+typedef struct {
+    dim_t dim[4];
+} dims_t;
+
+template<typename T>
+class memCopy {
+public:
+    memCopy(sycl::accessor<T> out, dims_t ostrides, 
+            sycl::accessor<T> in, dims_t idims, dims_t istrides,
+            int offset, int groups_0, int groups_1, sycl::stream debug) :
+                out_(out), ostrides_(ostrides), in_(in), idims_(idims), istrides_(istrides),
+                offset_(offset), groups_0_(groups_0), groups_1_(groups_1), debug_(debug) {}
+
+    void operator() (sycl::nd_item<2> it) const {
+        //printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
+        //debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) << "]" << sycl::stream_manipulator::endl;
+        const int lid0 = it.get_local_id(0);
+        const int lid1 = it.get_local_id(1);
+
+        sycl::group gg = it.get_group();
+        const int id2        = gg.get_group_id(0) / groups_0_;
+        const int id3        = gg.get_group_id(1) / groups_1_;
+        const int group_id_0 = gg.get_group_id(0) - groups_0_ * id2;
+        const int group_id_1 = gg.get_group_id(1) - groups_1_ * id3;
+        const int id0        = group_id_0 * gg.get_local_range(0) + lid0;
+        const int id1        = group_id_1 * gg.get_local_range(1) + lid1;
+
+        debug_ << "[" << id0 <<  "," << id1 <<  "," << id2 <<  "," << id3 <<  "]" << sycl::stream_manipulator::endl;
+
+        T* iptr = in_.get_pointer();
+        iptr += offset_;
+        // FIXME: Do more work per work group
+
+        T* optr = out_.get_pointer();
+        optr +=
+            id3 * ostrides_.dim[3] + id2 * ostrides_.dim[2] + id1 * ostrides_.dim[1];
+        iptr += id3 * istrides_.dim[3] + id2 * istrides_.dim[2] + id1 * istrides_.dim[1];
+
+        int istride0 = istrides_.dim[0];
+        if (id0 < idims_.dim[0] && id1 < idims_.dim[1] && id2 < idims_.dim[2] &&
+            id3 < idims_.dim[3]) {
+            optr[id0] = iptr[id0 * istride0];
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_, in_;
+    dims_t ostrides_, idims_, istrides_;
+    int offset_, groups_0_, groups_1_;
+    sycl::stream debug_;
+};
+
+
+constexpr uint DIM0 = 32;
+constexpr uint DIM1 = 8;
+
+template<typename T>
+void memcopy(sycl::buffer<T>* out, const dim_t *ostrides, const sycl::buffer<T>* in,
+             const dim_t *idims, const dim_t *istrides, int offset,
+             uint ndims) {
+    
+    dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
+    dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
+    dims_t _idims    = {{idims[0], idims[1], idims[2], idims[3]}};
+
+    size_t local_size[2] = { DIM0, DIM1 };
+    if (ndims == 1) {
+        local_size[0] *= local_size[1];
+        local_size[1] = 1;
+    }
+
+    int groups_0 = divup(idims[0], local_size[0]);
+    int groups_1 = divup(idims[1], local_size[1]);
+
+    sycl::range<2> local(local_size[0], local_size[1]);
+    sycl::range<2> global(groups_0 * idims[2] * local_size[0],
+                          groups_1 * idims[3] * local_size[1]);
+    sycl::nd_range<2> ndrange(global, local);
+
+    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0), ndrange.get_global_range().get(1), ndrange.get_local_range().get(0), ndrange.get_local_range().get(1));
+    printf("<%d, %d> ", ndrange.get_group_range().get(0), ndrange.get_group_range().get(1));
+    getQueue().submit([=] (sycl::handler &h) {
+        auto out_acc = out->get_access(h);
+        auto in_acc  = const_cast<sycl::buffer<T>*>(in)->get_access(h);
+
+        sycl::stream debug_stream(2048, 128, h);
+
+        h.parallel_for(ndrange, memCopy<T>(
+            out_acc, _ostrides, 
+            in_acc, _idims, _istrides,
+            offset, groups_0, groups_1, debug_stream));
+    });
+}
+
+template<typename T>
+static T scale(T value, double factor) {
+    return (T)(double(value) * factor);
+}
+
+template<>
+cfloat scale<cfloat>(cfloat value, double factor) {
+    return (cfloat)(value.real() * factor, value.imag() * factor);
+}
+
+template<>
+cdouble scale<cdouble>(cdouble value, double factor) {
+    return (cdouble)(value.real() * factor, value.imag() * factor);
+}
+
+template<typename inType, typename outType>
+outType convertType(inType value) {
+    return static_cast<outType>(value);
+}
+
+template<>
+char convertType<compute_t<common::half>, char>(
+    compute_t<common::half> value) {
+    return (char)((short)value);
+}
+
+template<>
+compute_t<common::half>
+convertType<char, compute_t<common::half>>(char value) {
+    return compute_t<common::half>(value);
+}
+
+template<>
+unsigned char
+convertType<compute_t<common::half>, unsigned char>(
+    compute_t<common::half> value) {
+    return (unsigned char)((short)value);
+}
+
+template<>
+compute_t<common::half>
+convertType<unsigned char, compute_t<common::half>>(unsigned char value) {
+    return compute_t<common::half>(value);
+}
+
+template<>
+cdouble convertType<cfloat, cdouble>(cfloat value) {
+    return cdouble(value.real(), value.imag());
+}
+
+template<>
+cfloat convertType<cdouble, cfloat>(cdouble value) {
+    return cfloat(value.real(), value.imag());
+}
+
+#define OTHER_SPECIALIZATIONS(IN_T)                         \
+    template<>                                              \
+    cfloat convertType<IN_T, cfloat>(IN_T value) {          \
+        return cfloat(static_cast<float>(value), 0.0f);     \
+    }                                                       \
+                                                            \
+    template<>                                              \
+    cdouble convertType<IN_T, cdouble>(IN_T value) {        \
+        return cdouble(static_cast<double>(value), 0.0);    \
+    }
+
+OTHER_SPECIALIZATIONS(float)
+OTHER_SPECIALIZATIONS(double)
+OTHER_SPECIALIZATIONS(int)
+OTHER_SPECIALIZATIONS(uint)
+OTHER_SPECIALIZATIONS(intl)
+OTHER_SPECIALIZATIONS(uintl)
+OTHER_SPECIALIZATIONS(short)
+OTHER_SPECIALIZATIONS(ushort)
+OTHER_SPECIALIZATIONS(uchar)
+OTHER_SPECIALIZATIONS(char)
+OTHER_SPECIALIZATIONS(common::half)
+
+template<typename inType, typename outType, bool SAMEDIMS>
+class reshapeCopy {
+public:
+    reshapeCopy(sycl::accessor<outType> dst, KParam oInfo, 
+                sycl::accessor<inType> src, KParam iInfo,
+                outType default_value, float factor, dims_t trgt,
+                int blk_x, int blk_y, sycl::stream debug) : 
+            dst_(dst), oInfo_(oInfo), src_(src), iInfo_(iInfo),
+            default_value_(default_value), factor_(factor), trgt_(trgt),
+            blk_x_(blk_x), blk_y_(blk_y), debug_(debug) {}
+
+    void operator() (sycl::nd_item<2> it) const {
+
+        const uint lx = it.get_local_id(0);
+        const uint ly = it.get_local_id(1);
+
+        sycl::group gg = it.get_group();
+        uint gz         = gg.get_group_id(0) / blk_x_;
+        uint gw         = gg.get_group_id(1) / blk_y_;
+        uint blockIdx_x = gg.get_group_id(0) - (blk_x_)*gz;
+        uint blockIdx_y = gg.get_group_id(1) - (blk_y_)*gw;
+        uint gx         = blockIdx_x * gg.get_local_range(0) + lx;
+        uint gy         = blockIdx_y * gg.get_local_range(1) + ly;
+
+        const inType* srcptr  = src_.get_pointer();
+        outType* dstptr = dst_.get_pointer();
+
+        const inType *in =
+            srcptr + (gw * iInfo_.strides[3] + gz * iInfo_.strides[2] +
+                      gy * iInfo_.strides[1] + iInfo_.offset);
+        outType *out = dstptr + (gw * oInfo_.strides[3] + gz * oInfo_.strides[2] +
+                                 gy * oInfo_.strides[1] + oInfo_.offset);
+
+        uint istride0 = iInfo_.strides[0];
+        uint ostride0 = oInfo_.strides[0];
+
+        if (gy < oInfo_.dims[1] && gz < oInfo_.dims[2] && gw < oInfo_.dims[3]) {
+            int loop_offset = gg.get_local_range(0) * blk_x_;
+            bool cond = gy < trgt_.dim[1] && gz < trgt_.dim[2] && gw < trgt_.dim[3];
+            for (int rep = gx; rep < oInfo_.dims[0]; rep += loop_offset) {
+                outType temp = default_value_;
+                if (SAMEDIMS || (rep < trgt_.dim[0] && cond)) {
+                    temp = convertType<inType, outType>(
+                        scale<inType>(in[rep * istride0], factor_));
+                }
+                out[rep * ostride0] = temp;
+            }
+        }
+    }
+
+protected:
+    sycl::accessor<outType> dst_;
+    sycl::accessor<inType> src_;
+    KParam oInfo_, iInfo_;
+    outType default_value_;
+    float factor_;
+    dims_t trgt_;
+    int blk_x_, blk_y_;
+    sycl::stream debug_;
+};
+
+template<typename inType, typename outType>
+void copy(Param<outType> dst, const Param<inType> src, const int ndims,
+          const outType default_value, const double factor,
+          const bool same_dims) {
+    using std::string;
+    
+    sycl::range<2> local(DIM0, DIM1);
+    size_t local_size[] = {DIM0, DIM1};
+
+    local_size[0] *= local_size[1];
+    if (ndims == 1) { local_size[1] = 1; }
+
+    int blk_x = divup(dst.info.dims[0], local_size[0]);
+    int blk_y = divup(dst.info.dims[1], local_size[1]);
+
+    sycl::range<2> global(blk_x * dst.info.dims[2] * DIM0,
+                          blk_y * dst.info.dims[3] * DIM1);
+
+    sycl::nd_range<2> ndrange(global, local);
+    printf("reshape wat?\n");
+    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0), ndrange.get_global_range().get(1), ndrange.get_local_range().get(0), ndrange.get_local_range().get(1));
+    printf("<%d, %d> ", ndrange.get_group_range().get(0), ndrange.get_group_range().get(1));
+
+    dims_t trgt_dims;
+    if (same_dims) {
+        trgt_dims = {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2],
+                      dst.info.dims[3]}};
+    } else {
+        dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
+        dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
+        dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
+        dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
+        trgt_dims    = {{trgt_i, trgt_j, trgt_k, trgt_l}};
+    }
+
+    getQueue().submit([=] (sycl::handler &h) {
+        auto dst_acc = dst.data->get_access(h);
+        auto src_acc  = const_cast<sycl::buffer<inType>*>(src.data)->get_access(h);
+
+        sycl::stream debug_stream(2048, 128, h);
+
+        if(same_dims) {
+            h.parallel_for(ndrange, reshapeCopy<inType, outType, true>(
+                    dst_acc, dst.info,
+                    src_acc, src.info,
+                    default_value, (float)factor, trgt_dims,
+                    blk_x, blk_y, debug_stream));
+        } else {
+            h.parallel_for(ndrange, reshapeCopy<inType, outType, false>(
+                    dst_acc, dst.info,
+                    src_acc, src.info,
+                    default_value, (float)factor, trgt_dims,
+                    blk_x, blk_y, debug_stream));
+        }
+    });
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
new file mode 100644
index 0000000000..0ad4797730
--- /dev/null
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -0,0 +1,119 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+#include <common/kernel_cache.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+#include <af/dim4.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class rangeOp {
+public:
+    rangeOp(sycl::accessor<T> out, KParam oinfo, const int dim,
+               const int blocksPerMatX, const int blocksPerMatY, 
+               sycl::stream debug) :
+        out_(out), oinfo_(oinfo), dim_(dim),
+        blocksPerMatX_(blocksPerMatX), blocksPerMatY_(blocksPerMatY),
+        debug_(debug) {}
+
+    void operator() (sycl::nd_item<2> it) const {
+        //printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
+        //debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) << "]" << sycl::stream_manipulator::endl;
+
+        const int mul0 = (dim_ == 0);
+        const int mul1 = (dim_ == 1);
+        const int mul2 = (dim_ == 2);
+        const int mul3 = (dim_ == 3);
+
+        sycl::group g = it.get_group();
+        const int oz = g.get_group_id(0) / blocksPerMatX_;
+        const int ow = g.get_group_id(1) / blocksPerMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - oz * blocksPerMatX_;
+        const int blockIdx_y = g.get_group_id(1) - ow * blocksPerMatY_;
+
+        const int xx = it.get_local_id(0) + blockIdx_x * it.get_local_range(0);
+        const int yy = it.get_local_id(1) + blockIdx_y * it.get_local_range(1);
+
+        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] || oz >= oinfo_.dims[2] ||
+            ow >= oinfo_.dims[3])
+            return;
+
+        const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
+
+        const int incy = blocksPerMatY_ * g.get_local_range(1);
+        const int incx = blocksPerMatX_ * g.get_local_range(0);
+
+        T valZW = (mul3 * ow) + (mul2 * oz);
+
+        T* optr = out_.get_pointer();
+        for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
+           T valYZW = valZW + (mul1 * oy);
+            int oyzw = ozw + oy * oinfo_.strides[1];
+            for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
+                int oidx = oyzw + ox;
+                T val    = valYZW + (mul0 * ox);
+
+                optr[oidx] = val;
+            }
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    KParam oinfo_;
+    int dim_;
+    int blocksPerMatX_, blocksPerMatY_;
+    sycl::stream debug_;
+};
+
+
+template<typename T>
+void range(Param<T> out, const int dim) {
+    constexpr int RANGE_TX    = 32;
+    constexpr int RANGE_TY    = 8;
+    constexpr int RANGE_TILEX = 512;
+    constexpr int RANGE_TILEY = 32;
+
+    sycl::range<2> local(RANGE_TX, RANGE_TY);
+
+    int blocksPerMatX = divup(out.info.dims[0], RANGE_TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], RANGE_TILEY);
+    sycl::range<2> global(local[0] * blocksPerMatX * out.info.dims[2],
+                          local[1] * blocksPerMatY * out.info.dims[3]);
+    sycl::nd_range<2> ndrange(global, local);
+
+    getQueue().submit([=] (sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+
+        sycl::stream debug_stream(2048, 128, h);
+
+        h.parallel_for(ndrange, rangeOp<T>(out_acc, out.info,
+            dim, blocksPerMatX, blocksPerMatY, debug_stream));
+    });
+}
+
+template<>
+void range(Param<common::half> out, const int dim) {
+    ONEAPI_NOT_SUPPORTED("TODO: fix common::half support");
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/range.cpp b/src/backend/oneapi/range.cpp
index e47a9cc664..015ae955db 100644
--- a/src/backend/oneapi/range.cpp
+++ b/src/backend/oneapi/range.cpp
@@ -6,14 +6,14 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-// #include <kernel/range.hpp>
 #include <range.hpp>
-#include <err_oneapi.hpp>
+#include <kernel/range.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
 #include <math.hpp>
+
 #include <stdexcept>
 
 using common::half;
@@ -21,9 +21,6 @@ using common::half;
 namespace oneapi {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim) {
-
-    ONEAPI_NOT_SUPPORTED("range Not supported");
-
     // Set dimension along which the sequence should be
     // Other dimensions are simply tiled
     int _seq_dim = seq_dim;
@@ -36,7 +33,7 @@ Array<T> range(const dim4& dim, const int seq_dim) {
     }
 
     Array<T> out = createEmptyArray<T>(dim);
-    // kernel::range<T>(out, _seq_dim);
+    kernel::range<T>(out, _seq_dim);
 
     return out;
 }

From d35f77c4955dd26b05488f317bf5f3dac07f0bee Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Thu, 29 Sep 2022 13:36:18 -0400
Subject: [PATCH 2304/2677] oneapi/transpose: kernels passes all but 5 tests
 (123 pass)

missing uniform random function elsewhere in arrayfire. maxdims fails
like everywhere else. gfor fails.
---
 src/backend/oneapi/CMakeLists.txt       |   2 +
 src/backend/oneapi/kernel/transpose.hpp | 154 ++++++++++++++++++++++++
 src/backend/oneapi/transpose.cpp        |  10 +-
 3 files changed, 160 insertions(+), 6 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/transpose.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 8cf9384b9c..a511d05077 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -207,6 +207,8 @@ target_sources(afoneapi
     kernel/KParam.hpp
     kernel/iota.hpp
     kernel/memcopy.hpp
+    kernel/range.hpp
+    kernel/transpose.hpp
 )
 
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
new file mode 100644
index 0000000000..8cc0c66fa5
--- /dev/null
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -0,0 +1,154 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+constexpr int TILE_DIM  = 32;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = 256 / TILE_DIM;
+
+template<typename T>
+T getConjugate(const T &in) {
+    // For non-complex types return same
+    return in;
+}
+
+template<>
+cfloat getConjugate(const cfloat &in) {
+    return std::conj(in);
+}
+
+template<>
+cdouble getConjugate(const cdouble &in) {
+    return std::conj(in);
+}
+
+template <typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+
+template <typename T>
+class transposeKernel {
+public:
+  transposeKernel(sycl::accessor<T> oData, const KParam out,
+                  const sycl::accessor<T> iData, const KParam in,
+                  const int blocksPerMatX, const int blocksPerMatY,
+                  const bool conjugate, const bool IS32MULTIPLE,
+                  local_accessor<T, 1> shrdMem,
+                  sycl::stream debugStream) :
+    oData_(oData), out_(out), iData_(iData), in_(in), blocksPerMatX_(blocksPerMatX),
+    blocksPerMatY_(blocksPerMatY), conjugate_(conjugate), IS32MULTIPLE_(IS32MULTIPLE), shrdMem_(shrdMem), debugStream_(debugStream) {}
+  void operator() (sycl::nd_item<2> it) const {
+      const int shrdStride = TILE_DIM + 1;
+
+      const int oDim0 = out_.dims[0];
+      const int oDim1 = out_.dims[1];
+      const int iDim0 = in_.dims[0];
+      const int iDim1 = in_.dims[1];
+
+      // calculate strides
+      const int oStride1 = out_.strides[1];
+      const int iStride1 = in_.strides[1];
+
+      const int lx = it.get_local_id(0);
+      const int ly = it.get_local_id(1);
+
+      // batch based block Id
+      sycl::group g = it.get_group();
+      const int batchId_x  = g.get_group_id(0) / blocksPerMatX_;
+      const int blockIdx_x = (g.get_group_id(0) - batchId_x * blocksPerMatX_);
+
+      const int batchId_y  = g.get_group_id(1) / blocksPerMatY_;
+      const int blockIdx_y = (g.get_group_id(1) - batchId_y * blocksPerMatY_);
+
+      const int x0 = TILE_DIM * blockIdx_x;
+      const int y0 = TILE_DIM * blockIdx_y;
+
+      // calculate global in_dices
+      int gx = lx + x0;
+      int gy = ly + y0;
+
+      // offset in_ and out_ based on batch id
+      // also add the subBuffer offsets
+      T *iDataPtr = iData_.get_pointer(), *oDataPtr = oData_.get_pointer();
+      iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] + in_.offset;
+      oDataPtr +=
+        batchId_x * out_.strides[2] + batchId_y * out_.strides[3] + out_.offset;
+
+      for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+        int gy_ = gy + repeat;
+        if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+          shrdMem_[(ly + repeat) * shrdStride + lx] =
+            iDataPtr[gy_ * iStride1 + gx];
+      }
+      it.barrier();
+
+      gx = lx + y0;
+      gy = ly + x0;
+
+      for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+        int gy_ = gy + repeat;
+        if (IS32MULTIPLE_ || (gx < oDim0 && gy_ < oDim1)) {
+          const T val = shrdMem_[lx * shrdStride + ly + repeat];
+          oDataPtr[gy_ * oStride1 + gx] = conjugate_ ? getConjugate(val) : val;
+        }
+      }
+  }
+private:
+  sycl::accessor<T> oData_;
+  KParam out_;
+  sycl::accessor<T> iData_;
+  KParam in_;
+  int blocksPerMatX_;
+  int blocksPerMatY_;
+  sycl::stream debugStream_;
+  bool conjugate_;
+  bool IS32MULTIPLE_;
+  local_accessor<T, 1> shrdMem_;
+};
+
+template<typename T>
+void transpose(Param<T> out, const Param<T> in, const bool conjugate, const bool IS32MULTIPLE) {
+    auto local = sycl::range{THREADS_X, THREADS_Y};
+
+    const int blk_x = divup(in.info.dims[0], TILE_DIM);
+    const int blk_y = divup(in.info.dims[1], TILE_DIM);
+
+    auto global = sycl::range{blk_x * local[0] * in.info.dims[2],
+                              blk_y * local[1] * in.info.dims[3]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto r = in.data->get_access(h);
+        auto q = out.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        auto shrdMem = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       transposeKernel<T>(q, out.info,
+                                          r, in.info,
+                                          blk_x, blk_y,
+                                          conjugate, IS32MULTIPLE,
+                                          shrdMem, debugStream));
+    }).wait();
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/transpose.cpp b/src/backend/oneapi/transpose.cpp
index 8384a6bfa1..0985bc48fa 100644
--- a/src/backend/oneapi/transpose.cpp
+++ b/src/backend/oneapi/transpose.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-// #include <kernel/transpose.hpp>
+#include <kernel/transpose.hpp>
 #include <transpose.hpp>
 #include <err_oneapi.hpp>
 
@@ -25,11 +25,9 @@ Array<T> transpose(const Array<T> &in, const bool conjugate) {
     dim4 outDims       = dim4(inDims[1], inDims[0], inDims[2], inDims[3]);
     Array<T> out       = createEmptyArray<T>(outDims);
 
-    // const bool is32multiple =
-    //     inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
-
-    ONEAPI_NOT_SUPPORTED("transpose Not supported");
-    // kernel::transpose<T>(out, in, getQueue(), conjugate, is32multiple);
+    const bool is32multiple =
+        inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+    kernel::transpose<T>(out, in, conjugate, is32multiple);
 
     return out;
 }

From d06eb6024a0c41f84b39c1f420d22070852649aa Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 29 Sep 2022 17:46:40 -0400
Subject: [PATCH 2305/2677] adds ONEAPI_DEBUG_FINISH(q)

---
 src/backend/oneapi/debug_oneapi.hpp     | 25 +++++++++++++++++++++++++
 src/backend/oneapi/kernel/iota.hpp      |  2 ++
 src/backend/oneapi/kernel/memcopy.hpp   |  3 +++
 src/backend/oneapi/kernel/range.hpp     |  2 ++
 src/backend/oneapi/kernel/transpose.hpp |  4 +++-
 5 files changed, 35 insertions(+), 1 deletion(-)
 create mode 100644 src/backend/oneapi/debug_oneapi.hpp

diff --git a/src/backend/oneapi/debug_oneapi.hpp b/src/backend/oneapi/debug_oneapi.hpp
new file mode 100644
index 0000000000..ea7cf992ee
--- /dev/null
+++ b/src/backend/oneapi/debug_oneapi.hpp
@@ -0,0 +1,25 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <platform.hpp>
+
+#ifndef NDEBUG
+
+#define ONEAPI_DEBUG_FINISH(Q) Q.wait_and_throw()
+
+#else
+
+#define ONEAPI_DEBUG_FINISH(Q)                                   \
+    do {                                                         \
+        if (oneapi::synchronize_calls()) { Q.wait_and_throw(); } \
+    } while (false);
+
+#endif
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 223a990d34..5141726cdb 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
 #include <traits.hpp>
 #include <af/dim4.hpp>
 
@@ -103,6 +104,7 @@ void iota(Param<T> out, const af::dim4& sdims) {
             static_cast<int>(sdims[2]), static_cast<int>(sdims[3]),
             blocksPerMatX, blocksPerMatY, debug_stream));
     });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 2fae4238b2..4376ae0121 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -15,6 +15,7 @@
 #include <common/half.hpp>
 //#include <common/kernel_cache.hpp>
 #include <common/traits.hpp>
+#include <debug_oneapi.hpp>
 #include <traits.hpp>
 
 #include <algorithm>
@@ -116,6 +117,7 @@ void memcopy(sycl::buffer<T>* out, const dim_t *ostrides, const sycl::buffer<T>*
             in_acc, _idims, _istrides,
             offset, groups_0, groups_1, debug_stream));
     });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
@@ -312,6 +314,7 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
                     blk_x, blk_y, debug_stream));
         }
     });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index 0ad4797730..3a0c447035 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 #include <af/dim4.hpp>
@@ -108,6 +109,7 @@ void range(Param<T> out, const int dim) {
         h.parallel_for(ndrange, rangeOp<T>(out_acc, out.info,
             dim, blocksPerMatX, blocksPerMatY, debug_stream));
     });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<>
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index 8cc0c66fa5..ef87bc77b2 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <err_oneapi.hpp>
+#include <debug_oneapi.hpp>
 #include <traits.hpp>
 
 #include <string>
@@ -147,7 +148,8 @@ void transpose(Param<T> out, const Param<T> in, const bool conjugate, const bool
                                           blk_x, blk_y,
                                           conjugate, IS32MULTIPLE,
                                           shrdMem, debugStream));
-    }).wait();
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 }  // namespace kernel

From 870a7c79f184f1caf21bad252cd33188e99787f6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 30 Sep 2022 13:52:34 -0400
Subject: [PATCH 2306/2677] adds assign kernel to oneapi backend

---
 src/backend/oneapi/CMakeLists.txt    |   1 +
 src/backend/oneapi/assign.cpp        |  42 ++++++++-
 src/backend/oneapi/kernel/assign.hpp | 136 +++++++++++++++++++++++++++
 3 files changed, 178 insertions(+), 1 deletion(-)
 create mode 100644 src/backend/oneapi/kernel/assign.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index a511d05077..ee3798f503 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -205,6 +205,7 @@ add_library(afoneapi
 target_sources(afoneapi
   PRIVATE
     kernel/KParam.hpp
+    kernel/assign.hpp
     kernel/iota.hpp
     kernel/memcopy.hpp
     kernel/range.hpp
diff --git a/src/backend/oneapi/assign.cpp b/src/backend/oneapi/assign.cpp
index 06e0f63abf..a41365101c 100644
--- a/src/backend/oneapi/assign.cpp
+++ b/src/backend/oneapi/assign.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <assign.hpp>
+#include <kernel/assign.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
@@ -23,7 +24,46 @@ namespace oneapi {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
-    ONEAPI_NOT_SUPPORTED("");
+    kernel::AssignKernelParam_t p;
+    std::vector<af_seq> seqs(4, af_span);
+    // create seq vector to retrieve output
+    // dimensions, offsets & offsets
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
+    }
+
+    // retrieve dimensions, strides and offsets
+    const dim4& dDims = out.dims();
+    // retrieve dimensions & strides for array
+    // to which rhs is being copied to
+    dim4 dstOffs  = toOffset(seqs, dDims);
+    dim4 dstStrds = toStride(seqs, dDims);
+
+    for (dim_t i = 0; i < 4; ++i) {
+        p.isSeq[i] = idxrs[i].isSeq;
+        p.offs[i]  = dstOffs[i];
+        p.strds[i] = dstStrds[i];
+    }
+
+    sycl::buffer<uint>* bPtrs[4];
+
+    std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
+    // look through indexs to read af_array indexs
+    for (dim_t x = 0; x < 4; ++x) {
+        // set index pointers were applicable
+        if (!p.isSeq[x]) {
+            idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
+            bPtrs[x]   = idxArrs[x].get();
+        } else {
+            // alloc an 1-element buffer to avoid OpenCL from failing using
+            // direct buffer allocation as opposed to mem manager to avoid
+            // reference count desprepancies between different backends
+            static auto* empty = new sycl::buffer<uint>(sycl::range{1});
+            bPtrs[x] = empty;
+        }
+    }
+
+    kernel::assign<T>(out, rhs, p, bPtrs);
     return;
 }
 
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
new file mode 100644
index 0000000000..9896306cac
--- /dev/null
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -0,0 +1,136 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+typedef struct {
+    int offs[4];
+    int strds[4];
+    char isSeq[4];
+} AssignKernelParam_t;
+
+static int trimIndex(int idx, const int len) {
+    int ret_val = idx;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
+    }
+    return ret_val;
+}
+
+template<typename T>
+class assignKernel {
+public:
+    assignKernel(sycl::accessor<T> out, KParam oInfo,
+                sycl::accessor<T> in, KParam iInfo, AssignKernelParam_t p,
+                sycl::accessor<uint> ptr0, sycl::accessor<uint> ptr1, 
+                sycl::accessor<uint> ptr2, sycl::accessor<uint> ptr3,
+                const int nBBS0, const int nBBS1, sycl::stream debug) :
+        out_(out), oInfo_(oInfo), in_(in), iInfo_(iInfo), p_(p),
+        ptr0_(ptr0), ptr1_(ptr1), ptr2_(ptr2), ptr3_(ptr3),
+        nBBS0_(nBBS0), nBBS1_(nBBS1), debug_(debug) {}
+
+
+    void operator() (sycl::nd_item<2> it) const {
+        // retrive booleans that tell us which index to use
+        const bool s0 = p_.isSeq[0];
+        const bool s1 = p_.isSeq[1];
+        const bool s2 = p_.isSeq[2];
+        const bool s3 = p_.isSeq[3];
+
+        sycl::group g = it.get_group();
+        const int gz = g.get_group_id(0) / nBBS0_;
+        const int gw = g.get_group_id(1) / nBBS1_;
+        const int gx =
+            g.get_local_range(0) * (g.get_group_id(0) - gz * nBBS0_) + it.get_local_id(0);
+        const int gy =
+            g.get_local_range(1) * (g.get_group_id(1) - gw * nBBS1_) + it.get_local_id(1);
+        if (gx < iInfo_.dims[0] && gy < iInfo_.dims[1] && gz < iInfo_.dims[2] &&
+            gw < iInfo_.dims[3]) {
+            // calculate pointer offsets for input
+            int i = p_.strds[0] *
+                    trimIndex(s0 ? gx + p_.offs[0] : ptr0_[gx], oInfo_.dims[0]);
+            int j = p_.strds[1] *
+                    trimIndex(s1 ? gy + p_.offs[1] : ptr1_[gy], oInfo_.dims[1]);
+            int k = p_.strds[2] *
+                    trimIndex(s2 ? gz + p_.offs[2] : ptr2_[gz], oInfo_.dims[2]);
+            int l = p_.strds[3] *
+                    trimIndex(s3 ? gw + p_.offs[3] : ptr3_[gw], oInfo_.dims[3]);
+            
+            T* iptr = in_.get_pointer();
+            // offset input and output pointers
+            const T* src =
+                iptr +
+                (gx * iInfo_.strides[0] + gy * iInfo_.strides[1] +
+                 gz * iInfo_.strides[2] + gw * iInfo_.strides[3] + iInfo_.offset);
+
+            T* optr = out_.get_pointer();
+            T* dst = optr + (i + j + k + l) + oInfo_.offset;
+            // set the output
+            dst[0] = src[0];
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_, in_;
+    KParam oInfo_, iInfo_;
+    AssignKernelParam_t p_;
+    sycl::accessor<uint> ptr0_, ptr1_, ptr2_, ptr3_;
+    const int nBBS0_, nBBS1_;
+    sycl::stream debug_;
+};
+
+template<typename T>
+void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
+            sycl::buffer<uint>* bPtr[4]) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
+
+    sycl::range<2> local(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    sycl::range<2> global(blk_x * in.info.dims[2] * THREADS_X,
+                          blk_y * in.info.dims[3] * THREADS_Y);
+
+    getQueue().submit([=] (sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        auto bptr0 = bPtr[0]->get_access(h);
+        auto bptr1 = bPtr[1]->get_access(h);
+        auto bptr2 = bPtr[2]->get_access(h);
+        auto bptr3 = bPtr[3]->get_access(h);
+
+        sycl::stream debug_stream(2048, 128, h);
+
+        h.parallel_for(sycl::nd_range<2>(global, local), assignKernel<T>(
+            out_acc, out.info, in_acc, in.info, 
+            p, bptr0, bptr1, bptr2, bptr3,
+            blk_x, blk_y, debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi

From b0d33df27598c3f1864578abbe03e787756b24ff Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 3 Oct 2022 21:38:37 -0400
Subject: [PATCH 2307/2677] adds RNG, needs half support

---
 src/backend/oneapi/Array.hpp                  |   6 +-
 src/backend/oneapi/CMakeLists.txt             |   5 +
 src/backend/oneapi/kernel/random_engine.hpp   | 205 +++++
 .../oneapi/kernel/random_engine_mersenne.hpp  | 337 ++++++++
 .../oneapi/kernel/random_engine_philox.hpp    | 183 ++++
 .../oneapi/kernel/random_engine_threefry.hpp  | 248 ++++++
 .../oneapi/kernel/random_engine_write.hpp     | 804 ++++++++++++++++++
 src/backend/oneapi/random_engine.cpp          |  86 +-
 src/backend/oneapi/types.hpp                  |   6 +-
 9 files changed, 1805 insertions(+), 75 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/random_engine.hpp
 create mode 100644 src/backend/oneapi/kernel/random_engine_mersenne.hpp
 create mode 100644 src/backend/oneapi/kernel/random_engine_philox.hpp
 create mode 100644 src/backend/oneapi/kernel/random_engine_threefry.hpp
 create mode 100644 src/backend/oneapi/kernel/random_engine_write.hpp

diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index eb010385d4..47c3c8bc7d 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -18,9 +18,9 @@
 //#include <err_opencl.hpp>
 //#include <jit/BufferNode.hpp>
 //#include <memory.hpp>
-//#include <platform.hpp>
-//#include <traits.hpp>
-//#include <types.hpp>
+#include <platform.hpp>
+#include <traits.hpp>
+#include <types.hpp>
 
 //#include <af/dim4.hpp>
 
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index ee3798f503..adb97f30ff 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -208,6 +208,11 @@ target_sources(afoneapi
     kernel/assign.hpp
     kernel/iota.hpp
     kernel/memcopy.hpp
+    kernel/random_engine.hpp
+    kernel/random_engine_write.hpp
+    kernel/random_engine_mersenne.hpp
+    kernel/random_engine_philox.hpp
+    kernel/random_engine_threefry.hpp
     kernel/range.hpp
     kernel/transpose.hpp
 )
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
new file mode 100644
index 0000000000..4597b33a3a
--- /dev/null
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -0,0 +1,205 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <af/defines.h>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <random_engine.hpp>
+#include <kernel/random_engine_write.hpp>
+#include <kernel/random_engine_mersenne.hpp>
+#include <kernel/random_engine_philox.hpp>
+#include <kernel/random_engine_threefry.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+
+#include <functional>
+#include <string>
+#include <vector>
+
+static const int N          = 351;
+static const int TABLE_SIZE = 16;
+static const int MAX_BLOCKS = 32;
+static const int STATE_SIZE = (256 * 3);
+
+namespace oneapi {
+namespace kernel {
+
+static const uint THREADS = 256;
+static const uint THREADS_PER_GROUP = 256;
+static const uint THREADS_X         = 32;
+static const uint THREADS_Y         = THREADS_PER_GROUP / THREADS_X;
+static const uint REPEAT            = 32;
+
+template<typename T>
+void uniformDistributionCBRNG(Param<T> out,  const size_t elements,
+                              const af_random_engine_type type,
+                              const uintl &seed, uintl &counter) {
+    int threads          = THREADS;
+    int elementsPerBlock = threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks           = divup(elements, elementsPerBlock);
+    uint hi              = seed >> 32;
+    uint lo              = seed;
+    uint hic             = counter >> 32;
+    uint loc             = counter;
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            getQueue().submit([=] (sycl::handler &h) {
+                auto out_acc = out.data->get_access(h);
+
+                sycl::stream debug_stream(2048, 128, h);
+                h.parallel_for(ndrange, 
+                    uniformPhilox<T>(out_acc,
+                                     hi, lo, hic, loc,
+                                     elementsPerBlock, elements,
+                                     debug_stream));
+            });
+            ONEAPI_DEBUG_FINISH(getQueue());
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            getQueue().submit([=] (sycl::handler &h) {
+                auto out_acc = out.data->get_access(h);
+
+                sycl::stream debug_stream(2048, 128, h);
+                h.parallel_for(ndrange, 
+                    uniformThreefry<T>(out_acc,
+                                       hi, lo, hic, loc,
+                                       elementsPerBlock, elements,
+                                       debug_stream));
+            });
+            ONEAPI_DEBUG_FINISH(getQueue());
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
+    counter += elements;
+}
+
+template<typename T>
+void normalDistributionCBRNG(Param<T> out, const size_t elements,
+                             const af_random_engine_type type,
+                             const uintl &seed, uintl &counter) {
+    int threads          = THREADS;
+    int elementsPerBlock = threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks           = divup(elements, elementsPerBlock);
+    uint hi              = seed >> 32;
+    uint lo              = seed;
+    uint hic             = counter >> 32;
+    uint loc             = counter;
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
+    switch (type) {
+        case AF_RANDOM_ENGINE_PHILOX_4X32_10:
+            getQueue().submit([=] (sycl::handler &h) {
+                auto out_acc = out.data->get_access(h);
+
+                sycl::stream debug_stream(2048, 128, h);
+                h.parallel_for(ndrange, 
+                    normalPhilox<T>(out_acc,
+                                    hi, lo, hic, loc,
+                                    elementsPerBlock, elements,
+                                    debug_stream));
+            });
+            break;
+        case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
+            getQueue().submit([=] (sycl::handler &h) {
+                auto out_acc = out.data->get_access(h);
+
+                sycl::stream debug_stream(2048, 128, h);
+                h.parallel_for(ndrange, 
+                    normalThreefry<T>(out_acc,
+                                      hi, lo, hic, loc,
+                                      elementsPerBlock, elements,
+                                      debug_stream));
+            });
+            break;
+        default:
+            AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
+    counter += elements;
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void uniformDistributionMT(Param<T> out, const size_t elements,
+                           Param<uint> state, Param<uint> pos, Param<uint> sh1,
+                           Param<uint> sh2, const uint mask,
+                           Param<uint> recursion_table,
+                           Param<uint> temper_table) {
+    int threads                = THREADS;
+    int min_elements_per_block = 32 * threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks                 = divup(elements, min_elements_per_block);
+    blocks                     = (blocks > BLOCKS) ? BLOCKS : blocks;
+    uint elementsPerBlock      = divup(elements, blocks);
+
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
+    getQueue().submit([=] (sycl::handler &h) {
+        auto out_acc       = out.data->get_access(h);
+        auto state_acc     = state.data->get_access(h);
+        auto pos_acc       = pos.data->get_access(h);
+        auto sh1_acc       = sh1.data->get_access(h);
+        auto sh2_acc       = sh2.data->get_access(h);
+        auto recursion_acc = sh2.data->get_access(h);
+        auto temper_acc    = sh2.data->get_access(h);
+
+        auto lstate_acc     = local_accessor<uint, 1>(STATE_SIZE, h);
+        auto lrecursion_acc = local_accessor<uint, 1>(TABLE_SIZE, h);
+        auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
+
+        sycl::stream debug_stream(2048, 128, h);
+        h.parallel_for(ndrange, 
+            uniformMersenne<T>(out_acc,
+                               state_acc, pos_acc, sh1_acc, sh2_acc, mask, 
+                               recursion_acc, temper_acc,
+                               lstate_acc, lrecursion_acc, ltemper_acc,
+                               elementsPerBlock, elements, debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void normalDistributionMT(Param<T> out, const size_t elements,
+                          Param<uint> state, Param<uint> pos, Param<uint> sh1,
+                          Param<uint> sh2, const uint mask,
+                          Param<uint> recursion_table, Param<uint> temper_table) {
+    int threads                = THREADS;
+    int min_elements_per_block = 32 * threads * 4 * sizeof(uint) / sizeof(T);
+    int blocks                 = divup(elements, min_elements_per_block);
+    blocks                     = (blocks > BLOCKS) ? BLOCKS : blocks;
+    uint elementsPerBlock      = divup(elements, blocks);
+
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
+    getQueue().submit([=] (sycl::handler &h) {
+        auto out_acc       = out.data->get_access(h);
+        auto state_acc     = state.data->get_access(h);
+        auto pos_acc       = pos.data->get_access(h);
+        auto sh1_acc       = sh1.data->get_access(h);
+        auto sh2_acc       = sh2.data->get_access(h);
+        auto recursion_acc = sh2.data->get_access(h);
+        auto temper_acc    = sh2.data->get_access(h);
+
+        auto lstate_acc     = local_accessor<uint, 1>(STATE_SIZE, h);
+        auto lrecursion_acc = local_accessor<uint, 1>(TABLE_SIZE, h);
+        auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
+
+        sycl::stream debug_stream(2048, 128, h);
+        h.parallel_for(ndrange, 
+            normalMersenne<T>(out_acc,
+                              state_acc, pos_acc, sh1_acc, sh2_acc, mask, 
+                              recursion_acc, temper_acc,
+                              lstate_acc, lrecursion_acc, ltemper_acc,
+                              elementsPerBlock, elements, debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
new file mode 100644
index 0000000000..9fd8985ccf
--- /dev/null
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -0,0 +1,337 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/********************************************************
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.
+ * Copyright (c) 2011, 2012 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University, The Uinversity
+ *       of Tokyo nor the names of its contributors may be used to
+ *       endorse or promote products derived from this software without
+ *       specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *******************************************************/
+#pragma once
+#include <kernel/random_engine_write.hpp>
+
+namespace oneapi {
+namespace kernel {
+
+constexpr int N          = 351;
+constexpr int BLOCKS     = 32;
+constexpr int STATE_SIZE = (256 * 3);
+constexpr int TABLE_SIZE = 16;
+
+template <typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+
+
+// Utils
+static inline void read_table(uint *const sharedTable,
+                              const uint *const table,
+                              size_t groupId, size_t localId) {
+    const uint *const t = table + (groupId * TABLE_SIZE);
+    if (localId < TABLE_SIZE) { sharedTable[localId] = t[localId]; }
+}
+
+static inline void state_read(uint *const state,
+                              const uint *const gState,
+                              size_t groupRange, size_t groupId, size_t localId) {
+    const uint *const g                 = gState + (groupId * N);
+    state[STATE_SIZE - N + localId] = g[localId];
+    if (localId < N - groupRange) {
+        state[STATE_SIZE - N + groupRange + localId] =
+            g[groupRange + localId];
+    }
+}
+
+static inline void state_write(uint *const gState,
+                               const uint *const state,
+                               size_t groupRange, size_t groupId, size_t localId) {
+    uint *const g = gState + (groupId * N);
+    g[localId] = state[STATE_SIZE - N + localId];
+    if (localId < N - groupRange) {
+        g[groupRange + localId] =
+            state[STATE_SIZE - N + groupRange + localId];
+    }
+}
+
+static inline uint recursion(const uint *const recursion_table,
+                             const uint mask, const uint sh1,
+                             const uint sh2, const uint x1,
+                             const uint x2, uint y) {
+    uint x = (x1 & mask) ^ x2;
+    x ^= x << sh1;
+    y        = x ^ (y >> sh2);
+    uint mat = recursion_table[y & 0x0f];
+    return y ^ mat;
+}
+
+static inline uint temper(const uint *const temper_table,
+                          const uint v, uint t) {
+    t ^= t >> 16;
+    t ^= t >> 8;
+    uint mat = temper_table[t & 0x0f];
+    return v ^ mat;
+}
+
+// Initialization
+class initMersenneKernel {
+public:
+    initMersenneKernel(sycl::accessor<uint> state,
+                       sycl::accessor<uint> tbl,
+                       local_accessor<uint, 1> lstate,
+                       uintl seed, sycl::stream debug_stream) :
+            state_(state), tbl_(tbl), lstate_(lstate), seed_(seed), debug_(debug_stream) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        const uint *ltbl = tbl_.get_pointer() + (TABLE_SIZE * g.get_group_id(0));
+        uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
+        uint tmp         = hidden_seed;
+        tmp += tmp >> 16;
+        tmp += tmp >> 8;
+        tmp &= 0xff;
+        tmp |= tmp << 8;
+        tmp |= tmp << 16;
+        lstate_[it.get_local_id(0)] = tmp;
+        it.barrier();
+        if (it.get_local_id(0) == 0) {
+            lstate_[0] = seed_;
+            lstate_[1] = hidden_seed;
+            for (int i = 1; i < N; ++i) {
+                lstate_[i] ^=
+                    ((uint)(1812433253) * (lstate_[i - 1] ^ (lstate_[i - 1] >> 30)) + i);
+            }
+        }
+        it.barrier();
+        state_[N * g.get_group_id(0) + it.get_local_id(0)] = lstate_[it.get_local_id(0)];
+    }
+
+protected:
+    sycl::accessor<uint> state_, tbl_;
+    local_accessor<uint, 1> lstate_;
+    uintl seed_;
+    sycl::stream debug_;
+};
+
+void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
+    sycl::nd_range<1> ndrange({BLOCKS * N}, {N});
+    getQueue().submit([=] (sycl::handler &h) {
+        auto state_acc = state.data->get_access(h);
+        auto tbl_acc = tbl.data->get_access(h);
+        auto lstate_acc = local_accessor<uint, 1>(N, h);
+
+        sycl::stream debug_stream(2048, 128, h);
+        h.parallel_for(ndrange, 
+            initMersenneKernel(state_acc,
+                               tbl_acc, lstate_acc,
+                               seed, debug_stream));
+    });
+    //TODO: do we need to sync before using Mersenne generators?
+    //force wait() here?
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+
+
+template<typename T>
+class uniformMersenne {
+public:
+    uniformMersenne(sycl::accessor<T> out, sycl::accessor<uint> gState,
+                    sycl::accessor<uint> pos_tbl,
+                    sycl::accessor<uint> sh1_tbl,
+                    sycl::accessor<uint> sh2_tbl, uint mask,
+                    sycl::accessor<uint> g_recursion_table,
+                    sycl::accessor<uint> g_temper_table,
+                    //local memory caches of global state
+                    local_accessor<uint, 1> state,
+                    local_accessor<uint, 1> recursion_table,
+                    local_accessor<uint, 1> temper_table,
+                    uint elementsPerBlock, size_t elements,
+                    sycl::stream debug) :
+            out_(out), gState_(gState),
+            pos_tbl_(pos_tbl), sh1_tbl_(sh1_tbl), sh2_tbl_(sh2_tbl), mask_(mask),
+            g_recursion_table_(g_recursion_table), g_temper_table_(g_temper_table),
+            state_(state), recursion_table_(recursion_table), temper_table_(temper_table),
+            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+        uint start                    = g.get_group_id(0) * elementsPerBlock_;
+        uint end                      = start + elementsPerBlock_;
+        end                           = (end > elements_) ? elements_ : end;
+        int elementsPerBlockIteration = (g.get_local_range(0) * 4 * sizeof(uint)) / sizeof(T);
+        int iter = divup((end - start), elementsPerBlockIteration);
+
+        uint pos = pos_tbl_[it.get_group(0)];
+        uint sh1 = sh1_tbl_[it.get_group(0)];
+        uint sh2 = sh2_tbl_[it.get_group(0)];
+        state_read(state_.get_pointer(), gState_.get_pointer(), g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+        read_table(recursion_table_.get_pointer(), g_recursion_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
+        read_table(temper_table_.get_pointer(), g_temper_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
+        it.barrier();
+
+        uint index = start;
+        uint o[4];
+        int offsetX1 = (STATE_SIZE - N + it.get_local_id(0)) % STATE_SIZE;
+        int offsetX2 = (STATE_SIZE - N + it.get_local_id(0) + 1) % STATE_SIZE;
+        int offsetY  = (STATE_SIZE - N + it.get_local_id(0) + pos) % STATE_SIZE;
+        int offsetT  = (STATE_SIZE - N + it.get_local_id(0) + pos - 1) % STATE_SIZE;
+        int offsetO  = it.get_local_id(0);
+
+        for (int i = 0; i < iter; ++i) {
+            for (int ii = 0; ii < 4; ++ii) {
+                uint r = recursion(recursion_table_.get_pointer(), mask_, sh1, sh2, state_[offsetX1],
+                                   state_[offsetX2], state_[offsetY]);
+                state_[offsetO] = r;
+                o[ii]          = temper(temper_table_.get_pointer(), r, state_[offsetT]);
+                offsetX1       = (offsetX1 + g.get_local_range(0)) % STATE_SIZE;
+                offsetX2       = (offsetX2 + g.get_local_range(0)) % STATE_SIZE;
+                offsetY        = (offsetY + g.get_local_range(0)) % STATE_SIZE;
+                offsetT        = (offsetT + g.get_local_range(0)) % STATE_SIZE;
+                offsetO        = (offsetO + g.get_local_range(0)) % STATE_SIZE;
+                it.barrier();
+            }
+            if (i == iter - 1) {
+                partialWriteOut128Bytes(out_.get_pointer(), index + it.get_local_id(0), g.get_local_range(0),
+                                        o[0], o[1], o[2], o[3], elements_);
+            } else {
+                writeOut128Bytes(out_.get_pointer(), index + it.get_local_id(0),
+                                 g.get_local_range(0), o[0], o[1], o[2], o[3]);
+            }
+            index += elementsPerBlockIteration;
+        }
+        state_write(gState_.get_pointer(), state_.get_pointer(), 
+                    g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    sycl::accessor<uint> gState_;
+    sycl::accessor<uint> pos_tbl_, sh1_tbl_, sh2_tbl_;
+    uint mask_;
+    sycl::accessor<uint> g_recursion_table_, g_temper_table_;
+    local_accessor<uint, 1> state_, recursion_table_, temper_table_;
+    uint elementsPerBlock_;
+    size_t elements_;
+    sycl::stream debug_;
+};
+
+template<typename T>
+class normalMersenne {
+public:
+    normalMersenne(sycl::accessor<T> out, sycl::accessor<uint> gState,
+                   sycl::accessor<uint> pos_tbl,
+                   sycl::accessor<uint> sh1_tbl,
+                   sycl::accessor<uint> sh2_tbl, uint mask,
+                   sycl::accessor<uint> g_recursion_table,
+                   sycl::accessor<uint> g_temper_table,
+                   //local memory caches of global state
+                   local_accessor<uint, 1> state,
+                   local_accessor<uint, 1> recursion_table,
+                   local_accessor<uint, 1> temper_table,
+                   uint elementsPerBlock, size_t elements,
+                   sycl::stream debug) :
+            out_(out), gState_(gState),
+            pos_tbl_(pos_tbl), sh1_tbl_(sh1_tbl), sh2_tbl_(sh2_tbl), mask_(mask),
+            g_recursion_table_(g_recursion_table), g_temper_table_(g_temper_table),
+            state_(state), recursion_table_(recursion_table), temper_table_(temper_table),
+            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+        uint start                    = g.get_group_id(0) * elementsPerBlock_;
+        uint end                      = start + elementsPerBlock_;
+        end                           = (end > elements_) ? elements_ : end;
+        int elementsPerBlockIteration = (g.get_local_range(0) * 4 * sizeof(uint)) / sizeof(T);
+        int iter = divup((end - start), elementsPerBlockIteration);
+
+        uint pos = pos_tbl_[it.get_group(0)];
+        uint sh1 = sh1_tbl_[it.get_group(0)];
+        uint sh2 = sh2_tbl_[it.get_group(0)];
+        state_read(state_.get_pointer(), gState_.get_pointer(), g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+        read_table(recursion_table_.get_pointer(), g_recursion_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
+        read_table(temper_table_.get_pointer(), g_temper_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
+        it.barrier();
+
+        uint index = start;
+        uint o[4];
+        int offsetX1 = (STATE_SIZE - N + it.get_local_id(0)) % STATE_SIZE;
+        int offsetX2 = (STATE_SIZE - N + it.get_local_id(0) + 1) % STATE_SIZE;
+        int offsetY  = (STATE_SIZE - N + it.get_local_id(0) + pos) % STATE_SIZE;
+        int offsetT  = (STATE_SIZE - N + it.get_local_id(0) + pos - 1) % STATE_SIZE;
+        int offsetO  = it.get_local_id(0);
+
+        for (int i = 0; i < iter; ++i) {
+            for (int ii = 0; ii < 4; ++ii) {
+                uint r = recursion(recursion_table_.get_pointer(), mask_, sh1, sh2, state_[offsetX1],
+                                   state_[offsetX2], state_[offsetY]);
+                state_[offsetO] = r;
+                o[ii]          = temper(temper_table_.get_pointer(), r, state_[offsetT]);
+                offsetX1       = (offsetX1 + g.get_local_range(0)) % STATE_SIZE;
+                offsetX2       = (offsetX2 + g.get_local_range(0)) % STATE_SIZE;
+                offsetY        = (offsetY + g.get_local_range(0)) % STATE_SIZE;
+                offsetT        = (offsetT + g.get_local_range(0)) % STATE_SIZE;
+                offsetO        = (offsetO + g.get_local_range(0)) % STATE_SIZE;
+                it.barrier();
+            }
+            if (i == iter - 1) {
+                partialBoxMullerWriteOut128Bytes(out_.get_pointer(), index + it.get_local_id(0), 
+                                        g.get_local_range(0), o[0], o[1], o[2], o[3], elements_);
+            } else {
+                boxMullerWriteOut128Bytes(out_.get_pointer(), index + it.get_local_id(0),
+                                          g.get_local_range(0), o[0], o[1], o[2], o[3]);
+            }
+            index += elementsPerBlockIteration;
+        }
+        state_write(gState_.get_pointer(), state_.get_pointer(), 
+                    g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    sycl::accessor<uint> gState_;
+    sycl::accessor<uint> pos_tbl_, sh1_tbl_, sh2_tbl_;
+    uint mask_;
+    sycl::accessor<uint> g_recursion_table_, g_temper_table_;
+    local_accessor<uint, 1> state_, recursion_table_, temper_table_;
+    uint elementsPerBlock_;
+    size_t elements_;
+    sycl::stream debug_;
+};
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/random_engine_philox.hpp b/src/backend/oneapi/kernel/random_engine_philox.hpp
new file mode 100644
index 0000000000..3cb3dbd95b
--- /dev/null
+++ b/src/backend/oneapi/kernel/random_engine_philox.hpp
@@ -0,0 +1,183 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+#pragma once
+#include <kernel/random_engine_write.hpp>
+
+namespace oneapi {
+namespace kernel {
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/philox.hpp
+
+constexpr uint m4x32_0 = 0xD2511F53;
+constexpr uint m4x32_1 = 0xCD9E8D57;
+constexpr uint w32_0   = 0x9E3779B9;
+constexpr uint w32_1   = 0xBB67AE85;
+
+static inline void mulhilo(uint a, uint b, uint &hi, uint &lo) {
+    hi = sycl::mul_hi(a, b);
+    lo = a * b;
+}
+
+static inline void philoxBump(uint k[2]) {
+    k[0] += w32_0;
+    k[1] += w32_1;
+}
+
+static inline void philoxRound(const uint m0, const uint m1,
+                               const uint k[2], uint c[4]) {
+    uint hi0, lo0, hi1, lo1;
+    mulhilo(m0, c[0], hi0, lo0);
+    mulhilo(m1, c[2], hi1, lo1);
+    c[0] = hi1 ^ c[1] ^ k[0];
+    c[1] = lo1;
+    c[2] = hi0 ^ c[3] ^ k[1];
+    c[3] = lo0;
+}
+
+static inline void philox(uint key[2], uint ctr[4]) {
+    // 10 Rounds
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+    philoxBump(key);
+    philoxRound(m4x32_0, m4x32_1, key, ctr);
+}
+
+template<typename T>
+class uniformPhilox {
+public:
+    uniformPhilox(sycl::accessor<T> out,
+                  uint hi, uint lo, uint hic, uint loc,
+                  uint elementsPerBlock, uint elements,
+                  sycl::stream debug_stream) :
+            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
+            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        //debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) << "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
+        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+        uint key[2] = {lo_, hi_};
+        uint ctr[4] = {loc_, hic_, 0, 0};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc_);
+        ctr[2] += (ctr[1] < hic_);
+        T* optr = out_.get_pointer();
+        if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
+            philox(key, ctr);
+            writeOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1], ctr[2], ctr[3]);
+        } else {
+            philox(key, ctr);
+            partialWriteOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1], ctr[2], ctr[3],
+                                    elements_);
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    uint hi_, lo_, hic_, loc_;
+    uint elementsPerBlock_, elements_;
+    sycl::stream debug_;
+};
+
+template<typename T>
+class normalPhilox {
+public:
+    normalPhilox(sycl::accessor<T> out,
+                  uint hi, uint lo, uint hic, uint loc,
+                  uint elementsPerBlock, uint elements,
+                  sycl::stream debug_stream) :
+            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
+            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+        //debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) << "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
+
+        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+        uint key[2] = {lo_, hi_};
+        uint ctr[4] = {loc_, hic_, 0, 0};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc_);
+        ctr[2] += (ctr[1] < hic_);
+
+        philox(key, ctr);
+
+        T* optr = out_.get_pointer();
+        if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
+            boxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1], ctr[2], ctr[3]);
+        } else {
+            partialBoxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), 
+                                             ctr[0], ctr[1], ctr[2], ctr[3], elements_);
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    uint hi_, lo_, hic_, loc_;
+    uint elementsPerBlock_, elements_;
+    sycl::stream debug_;
+};
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/random_engine_threefry.hpp b/src/backend/oneapi/kernel/random_engine_threefry.hpp
new file mode 100644
index 0000000000..bb93e299bc
--- /dev/null
+++ b/src/backend/oneapi/kernel/random_engine_threefry.hpp
@@ -0,0 +1,248 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/*******************************************************
+ * Modified version of Random123 library:
+ * https://www.deshawresearch.com/downloads/download_random123.cgi/
+ * The original copyright can be seen here:
+ *
+ * RANDOM123 LICENSE AGREEMENT
+ *
+ * Copyright 2010-2011, D. E. Shaw Research. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions, and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions, and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * Neither the name of D. E. Shaw Research nor the names of its contributors
+ * may be used to endorse or promote products derived from this software
+ * without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+ * TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************/
+
+#pragma once
+#include <kernel/random_engine_write.hpp>
+
+namespace oneapi {
+namespace kernel {
+// Utils
+// Source of these constants :
+// github.com/DEShawResearch/Random123-Boost/blob/master/boost/random/threefry.hpp
+
+static const uint SKEIN_KS_PARITY32 = 0x1BD11BDA;
+
+static const uint R0 = 13;
+static const uint R1 = 15;
+static const uint R2 = 26;
+static const uint R3 = 6;
+static const uint R4 = 17;
+static const uint R5 = 29;
+static const uint R6 = 16;
+static const uint R7 = 24;
+
+
+static inline void setSkeinParity(uint *ptr) {
+    *ptr = SKEIN_KS_PARITY32;
+}
+
+static inline uint rotL(uint x, uint N) {
+    return (x << (N & 31)) | (x >> ((32 - N) & 31));
+}
+
+void threefry(uint k[2], uint c[2], uint X[2]) {
+    uint ks[3];
+
+    setSkeinParity(&ks[2]);
+    ks[0] = k[0];
+    X[0]  = c[0];
+    ks[2] ^= k[0];
+    ks[1] = k[1];
+    X[1]  = c[1];
+    ks[2] ^= k[1];
+
+    X[0] += ks[0];
+    X[1] += ks[1];
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=1) */
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 1; /* X[2-1] += r  */
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=2) */
+    X[0] += ks[2];
+    X[1] += ks[0];
+    X[1] += 2;
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R0);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R1);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R2);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R3);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=3) */
+    X[0] += ks[0];
+    X[1] += ks[1];
+    X[1] += 3;
+
+    X[0] += X[1];
+    X[1] = rotL(X[1], R4);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R5);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R6);
+    X[1] ^= X[0];
+    X[0] += X[1];
+    X[1] = rotL(X[1], R7);
+    X[1] ^= X[0];
+
+    /* InjectKey(r=4) */
+    X[0] += ks[1];
+    X[1] += ks[2];
+    X[1] += 4;
+}
+
+template<typename T>
+class uniformThreefry {
+public:
+    uniformThreefry(sycl::accessor<T> out,
+                    uint hi, uint lo, uint hic, uint loc,
+                    uint elementsPerBlock, uint elements,
+                    sycl::stream debug_stream) :
+            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
+            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+
+        uint key[2] = {lo_, hi_};
+        uint ctr[4] = {loc_, hic_, 0, 0};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc_);
+        uint o[4];
+
+        threefry(key, ctr, o);
+        uint step = elementsPerBlock_ / 2;
+        ctr[0] += step;
+        ctr[1] += (ctr[0] < step);
+        threefry(key, ctr, o + 2);
+
+        T* optr = out_.get_pointer();
+        if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
+            writeOut128Bytes(optr, index, g.get_local_range(0), o[0], o[1], o[2], o[3]);
+        } else {
+            partialWriteOut128Bytes(optr, index, g.get_local_range(0), 
+                                    o[0], o[1], o[2], o[3], elements_);
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    uint hi_, lo_, hic_, loc_;
+    uint elementsPerBlock_, elements_;
+    sycl::stream debug_;
+};
+
+template<typename T>
+class normalThreefry {
+public:
+    normalThreefry(sycl::accessor<T> out,
+                    uint hi, uint lo, uint hic, uint loc,
+                    uint elementsPerBlock, uint elements,
+                    sycl::stream debug_stream) :
+            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
+            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+
+        uint key[2] = {lo_, hi_};
+        uint ctr[4] = {loc_, hic_, 0, 0};
+        ctr[0] += index;
+        ctr[1] += (ctr[0] < loc_);
+        uint o[4];
+
+        threefry(key, ctr, o);
+        uint step = elementsPerBlock_ / 2;
+        ctr[0] += step;
+        ctr[1] += (ctr[0] < step);
+        threefry(key, ctr, o + 2);
+
+        T* optr = out_.get_pointer();
+        if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
+            boxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), o[0], o[1], o[2], o[3]);
+        } else {
+            partialBoxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), 
+                                             o[0], o[1], o[2], o[3], elements_);
+        }
+    }
+
+protected:
+    sycl::accessor<T> out_;
+    uint hi_, lo_, hic_, loc_;
+    uint elementsPerBlock_, elements_;
+    sycl::stream debug_;
+};
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
new file mode 100644
index 0000000000..9e943eaad2
--- /dev/null
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -0,0 +1,804 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+#include <CL/sycl.hpp>
+
+namespace oneapi {
+namespace kernel {
+
+//TODO: !!!! half functions still need to be ported !!!!
+
+
+//// Conversion to half adapted from Random123
+//// #define HALF_FACTOR (1.0f) / (std::numeric_limits<ushort>::max() + (1.0f))
+//// #define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
+////
+//// NOTE: The following constants for half were calculated using the formulas
+//// above. This is done so that we can avoid unnecessary computations because the
+//// __half datatype is not a constexprable type. This prevents the compiler from
+//// peforming these operations at compile time.
+//#define HALF_FACTOR __ushort_as_half(0x100u)
+//#define HALF_HALF_FACTOR __ushort_as_half(0x80)
+//
+//// Conversion to half adapted from Random123
+////#define SIGNED_HALF_FACTOR                                \
+//    //((1.0f) / (std::numeric_limits<short>::max() + (1.0f)))
+////#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
+////
+//// NOTE: The following constants for half were calculated using the formulas
+//// above. This is done so that we can avoid unnecessary computations because the
+//// __half datatype is not a constexprable type. This prevents the compiler from
+//// peforming these operations at compile time
+//#define SIGNED_HALF_FACTOR __ushort_as_half(0x200u)
+//#define SIGNED_HALF_HALF_FACTOR __ushort_as_half(0x100u)
+//
+///// This is the largest integer representable by fp16. We need to
+///// make sure that the value converted from ushort is smaller than this
+///// value to avoid generating infinity
+//constexpr ushort max_int_before_infinity = 65504;
+//
+//// Generates rationals in (0, 1]
+//__device__ static __half oneMinusGetHalf01(uint num) {
+//    // convert to ushort before the min operation
+//    ushort v = min(max_int_before_infinity, ushort(num));
+//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
+//    return (1.0f - __half2float(__hfma(__ushort2half_rn(v), HALF_FACTOR,
+//                                       HALF_HALF_FACTOR)));
+//#else
+//    __half out = __ushort_as_half(0x3c00u) /*1.0h*/ -
+//                 __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
+//    if (__hisinf(out)) printf("val: %d ushort: %d\n", num, v);
+//    return out;
+//#endif
+//}
+//
+//// Generates rationals in (0, 1]
+//__device__ static __half getHalf01(uint num) {
+//    // convert to ushort before the min operation
+//    ushort v = min(max_int_before_infinity, ushort(num));
+//    return __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
+//}
+//
+//// Generates rationals in (-1, 1]
+//__device__ static __half getHalfNegative11(uint num) {
+//    // convert to ushort before the min operation
+//    ushort v = min(max_int_before_infinity, ushort(num));
+//    return __hfma(__ushort2half_rn(v), SIGNED_HALF_FACTOR,
+//                  SIGNED_HALF_HALF_FACTOR);
+//}
+//
+// Generates rationals in (0, 1]
+static float getFloat01(uint num) {
+    // Conversion to floats adapted from Random123
+    constexpr float factor =
+        ((1.0f) /
+         (static_cast<float>(std::numeric_limits<unsigned int>::max()) +
+          (1.0f)));
+    constexpr float half_factor = ((0.5f) * factor);
+
+    return sycl::fma(static_cast<float>(num), factor, half_factor);
+}
+
+// Generates rationals in (-1, 1]
+static float getFloatNegative11(uint num) {
+    // Conversion to floats adapted from Random123
+    constexpr float factor =
+        ((1.0) /
+         (static_cast<double>(std::numeric_limits<int>::max()) + (1.0)));
+    constexpr float half_factor = ((0.5f) * factor);
+
+    return sycl::fma(static_cast<float>(num), factor, half_factor);
+}
+
+// Generates rationals in (0, 1]
+static double getDouble01(uint num1, uint num2) {
+    uint64_t n1 = num1;
+    uint64_t n2 = num2;
+    n1 <<= 32;
+    uint64_t num = n1 | n2;
+    constexpr double factor =
+        ((1.0) / (std::numeric_limits<unsigned long long>::max() +
+                  static_cast<double>(1.0)));
+    constexpr double half_factor((0.5) * factor);
+
+    return sycl::fma(static_cast<double>(num), factor, half_factor);
+}
+
+// Conversion to doubles adapted from Random123
+constexpr double signed_factor =
+    ((1.0l) / (std::numeric_limits<long long>::max() + (1.0l)));
+constexpr double half_factor = ((0.5) * signed_factor);
+
+// Generates rationals in (-1, 1]
+static double getDoubleNegative11(uint num1, uint num2) {
+    uint32_t arr[2] = {num2, num1};
+    uint64_t num;
+
+    memcpy(&num, arr, sizeof(uint64_t));
+    return sycl::fma(static_cast<double>(num), signed_factor, half_factor);
+}
+
+namespace {
+//
+//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+//#define HALF_MATH_FUNC(OP, HALF_OP)    \
+//    template<>                         \
+//    __device__ __half OP(__half val) { \
+//        return ::HALF_OP(val);         \
+//    }
+//#else
+//#define HALF_MATH_FUNC(OP, HALF_OP)     \
+//    template<>                          \
+//    __device__ __half OP(__half val) {  \
+//        float fval = __half2float(val); \
+//        return __float2half(OP(fval));  \
+//    }
+//#endif
+//
+//#define MATH_FUNC(OP, DOUBLE_OP, FLOAT_OP, HALF_OP) \
+//    template<typename T>                            \
+//    __device__ T OP(T val);                         \
+//    template<>                                      \
+//    __device__ double OP(double val) {              \
+//        return ::DOUBLE_OP(val);                    \
+//    }                                               \
+//    template<>                                      \
+//    __device__ float OP(float val) {                \
+//        return ::FLOAT_OP(val);                     \
+//    }                                               \
+//    HALF_MATH_FUNC(OP, HALF_OP)
+//
+//MATH_FUNC(log, log, logf, hlog)
+//MATH_FUNC(sqrt, sqrt, sqrtf, hsqrt)
+//MATH_FUNC(sin, sin, sinf, hsin)
+//MATH_FUNC(cos, cos, cosf, hcos)
+//
+//template<typename T>
+//__device__ void sincos(T val, T *sptr, T *cptr);
+//
+//template<>
+//__device__ void sincos(double val, double *sptr, double *cptr) {
+//    ::sincos(val, sptr, cptr);
+//}
+//
+//template<>
+//__device__ void sincos(float val, float *sptr, float *cptr) {
+//    sincosf(val, sptr, cptr);
+//}
+//
+//template<>
+//__device__ void sincos(__half val, __half *sptr, __half *cptr) {
+//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+//    *sptr = sin(val);
+//    *cptr = cos(val);
+//#else
+//    float s, c;
+//    float fval = __half2float(val);
+//    sincos(fval, &s, &c);
+//    *sptr = __float2half(s);
+//    *cptr = __float2half(c);
+//#endif
+//}
+//
+template<typename T>
+void sincospi(T val, T *sptr, T *cptr) {
+    *sptr = sycl::sinpi(val);
+    *cptr = sycl::cospi(val);
+}
+
+//template<>
+//__device__ void sincospi(__half val, __half *sptr, __half *cptr) {
+//    // CUDA cannot make __half into a constexpr as of CUDA 11 so we are
+//    // converting this offline
+//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+//    const __half pi_val = __ushort_as_half(0x4248);  // 0x4248 == 3.14062h
+//    val *= pi_val;
+//    *sptr = sin(val);
+//    *cptr = cos(val);
+//#else
+//    float fval = __half2float(val);
+//    float s, c;
+//    sincospi(fval, &s, &c);
+//    *sptr = __float2half(s);
+//    *cptr = __float2half(c);
+//#endif
+//}
+//
+}  // namespace
+//
+template<typename T>
+constexpr T neg_two() {
+    return -2.0;
+}
+//
+//template<typename T>
+//constexpr __device__ T two_pi() {
+//    return 2.0 * PI_VAL;
+//};
+//
+template<typename Tc>
+static void boxMullerTransform(cfloat *const cOut,
+                               const Tc &r1, const Tc &r2) {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
+    Tc r = sycl::sqrt(neg_two<Tc>() * sycl::log(r2));
+    Tc s, c;
+
+    // Multiplying by PI instead of 2*PI seems to yeild a better distribution
+    // even though the original boxMuller algorithm calls for 2 * PI
+    // sincos(two_pi<Tc>() * r1, &s, &c);
+    sincospi(r1, &s, &c);
+    cOut->real(static_cast<float>(r * s));
+    cOut->imag(static_cast<float>(r * c));
+}
+
+template<typename Tc>
+static void boxMullerTransform(cdouble *const cOut,
+                               const Tc &r1, const Tc &r2) {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
+    Tc r = sycl::sqrt(neg_two<Tc>() * sycl::log(r2));
+    Tc s, c;
+
+    // Multiplying by PI instead of 2*PI seems to yeild a better distribution
+    // even though the original boxMuller algorithm calls for 2 * PI
+    // sincos(two_pi<Tc>() * r1, &s, &c);
+    sincospi(r1, &s, &c);
+    cOut->real(static_cast<double>(r * s));
+    cOut->imag(static_cast<double>(r * c));
+}
+
+template<typename Td, typename Tc>
+static void boxMullerTransform(Td *const out1, Td *const out2,
+                               const Tc &r1, const Tc &r2) {
+    /*
+     * The log of a real value x where 0 < x < 1 is negative.
+     */
+    Tc r = sycl::sqrt(neg_two<Tc>() * sycl::log(r2));
+    Tc s, c;
+
+    // Multiplying by PI instead of 2*PI seems to yeild a better distribution
+    // even though the original boxMuller algorithm calls for 2 * PI
+    // sincos(two_pi<Tc>() * r1, &s, &c);
+    sincospi(r1, &s, &c);
+    *out1 = static_cast<Td>(r * s);
+    *out2 = static_cast<Td>(r * c);
+}
+//template<>
+//__device__ void boxMullerTransform<common::half, __half>(
+//    common::half *const out1, common::half *const out2, const __half &r1,
+//    const __half &r2) {
+//    float o1, o2;
+//    float fr1 = __half2float(r1);
+//    float fr2 = __half2float(r2);
+//    boxMullerTransform(&o1, &o2, fr1, fr2);
+//    *out1 = o1;
+//    *out2 = o2;
+//}
+
+// Writes without boundary checking
+static void writeOut128Bytes(uchar *out, const uint &index, const uint groupSz,
+                             const uint &r1, const uint &r2,
+                             const uint &r3, const uint &r4) {
+    out[index]                = r1;
+    out[index + groupSz]      = r1 >> 8;
+    out[index + 2 * groupSz]  = r1 >> 16;
+    out[index + 3 * groupSz]  = r1 >> 24;
+    out[index + 4 * groupSz]  = r2;
+    out[index + 5 * groupSz]  = r2 >> 8;
+    out[index + 6 * groupSz]  = r2 >> 16;
+    out[index + 7 * groupSz]  = r2 >> 24;
+    out[index + 8 * groupSz]  = r3;
+    out[index + 9 * groupSz]  = r3 >> 8;
+    out[index + 10 * groupSz] = r3 >> 16;
+    out[index + 11 * groupSz] = r3 >> 24;
+    out[index + 12 * groupSz] = r4;
+    out[index + 13 * groupSz] = r4 >> 8;
+    out[index + 14 * groupSz] = r4 >> 16;
+    out[index + 15 * groupSz] = r4 >> 24;
+}
+
+static void writeOut128Bytes(char *out, const uint &index, const uint groupSz,
+                             const uint &r1, const uint &r2,
+                             const uint &r3, const uint &r4) {
+    out[index]                = (r1)&0x1;
+    out[index + groupSz]      = (r1 >> 1) & 0x1;
+    out[index + 2 * groupSz]  = (r1 >> 2) & 0x1;
+    out[index + 3 * groupSz]  = (r1 >> 3) & 0x1;
+    out[index + 4 * groupSz]  = (r2)&0x1;
+    out[index + 5 * groupSz]  = (r2 >> 1) & 0x1;
+    out[index + 6 * groupSz]  = (r2 >> 2) & 0x1;
+    out[index + 7 * groupSz]  = (r2 >> 3) & 0x1;
+    out[index + 8 * groupSz]  = (r3)&0x1;
+    out[index + 9 * groupSz]  = (r3 >> 1) & 0x1;
+    out[index + 10 * groupSz] = (r3 >> 2) & 0x1;
+    out[index + 11 * groupSz] = (r3 >> 3) & 0x1;
+    out[index + 12 * groupSz] = (r4)&0x1;
+    out[index + 13 * groupSz] = (r4 >> 1) & 0x1;
+    out[index + 14 * groupSz] = (r4 >> 2) & 0x1;
+    out[index + 15 * groupSz] = (r4 >> 3) & 0x1;
+}
+
+static void writeOut128Bytes(short *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]               = r1;
+    out[index + groupSz]     = r1 >> 16;
+    out[index + 2 * groupSz] = r2;
+    out[index + 3 * groupSz] = r2 >> 16;
+    out[index + 4 * groupSz] = r3;
+    out[index + 5 * groupSz] = r3 >> 16;
+    out[index + 6 * groupSz] = r4;
+    out[index + 7 * groupSz] = r4 >> 16;
+}
+
+static void writeOut128Bytes(ushort *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((short *)(out), index, groupSz, r1, r2, r3, r4);
+}
+
+static void writeOut128Bytes(int *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]               = r1;
+    out[index + groupSz]     = r2;
+    out[index + 2 * groupSz] = r3;
+    out[index + 3 * groupSz] = r4;
+}
+
+static void writeOut128Bytes(uint *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((int *)(out), index, groupSz, r1, r2, r3, r4);
+}
+
+static void writeOut128Bytes(intl *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    intl c1              = r2;
+    c1                   = (c1 << 32) | r1;
+    intl c2              = r4;
+    c2                   = (c2 << 32) | r3;
+    out[index]           = c1;
+    out[index + groupSz] = c2;
+}
+
+static void writeOut128Bytes(uintl *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((intl *)(out), index, groupSz, r1, r2, r3, r4);
+}
+
+static void writeOut128Bytes(float *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]               = 1.f - getFloat01(r1);
+    out[index + groupSz]     = 1.f - getFloat01(r2);
+    out[index + 2 * groupSz] = 1.f - getFloat01(r3);
+    out[index + 3 * groupSz] = 1.f - getFloat01(r4);
+}
+
+static void writeOut128Bytes(cfloat *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index] = {1.f - getFloat01(r1), 1.f - getFloat01(r2)};
+    out[index + groupSz] = {1.f - getFloat01(r3), 1.f - getFloat01(r4)};
+}
+
+static void writeOut128Bytes(double *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index]           = 1.0 - getDouble01(r1, r2);
+    out[index + groupSz] = 1.0 - getDouble01(r3, r4);
+}
+
+static void writeOut128Bytes(cdouble *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    out[index] = {1.0 - getDouble01(r1, r2), 1.0 - getDouble01(r3, r4)};
+}
+
+static void writeOut128Bytes(common::half *out, const uint &index, const uint groupSz,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+   //out[index]               = oneMinusGetHalf01(r1);
+   //out[index + groupSz]     = oneMinusGetHalf01(r1 >> 16);
+   //out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
+   //out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
+   //out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
+   //out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
+   //out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
+   //out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
+}
+
+// Normalized writes without boundary checking
+
+static void boxMullerWriteOut128Bytes(float *out, const uint &index, const uint groupSz,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], &out[index + groupSz],
+                       getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&out[index + 2 * groupSz],
+                       &out[index + 3 * groupSz],
+                       getFloatNegative11(r3),
+                       getFloat01(r4));
+}
+
+static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index, const uint groupSz,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&out[index + groupSz], getFloatNegative11(r3), getFloat01(r4));
+}
+
+static void boxMullerWriteOut128Bytes(double *out, const uint &index, const uint groupSz,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], &out[index + groupSz],
+                       getDoubleNegative11(r1, r2), getDouble01(r3, r4));
+}
+
+static void boxMullerWriteOut128Bytes(cdouble *out,
+                                                 const uint &index, const uint groupSz,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+    boxMullerTransform(&out[index], getDoubleNegative11(r1, r2), getDouble01(r3, r4));
+}
+
+static void boxMullerWriteOut128Bytes(common::half *out,
+                                                 const uint &index, const uint groupSz,
+                                                 const uint &r1, const uint &r2,
+                                                 const uint &r3,
+                                                 const uint &r4) {
+//   boxMullerTransform(&out[index], &out[index + groupSz],
+//                      getHalfNegative11(r1), getHalf01(r1 >> 16));
+//   boxMullerTransform(&out[index + 2 * groupSz],
+//                      &out[index + 3 * groupSz], getHalfNegative11(r2),
+//                      getHalf01(r2 >> 16));
+//   boxMullerTransform(&out[index + 4 * groupSz],
+//                      &out[index + 5 * groupSz], getHalfNegative11(r3),
+//                      getHalf01(r3 >> 16));
+//   boxMullerTransform(&out[index + 6 * groupSz],
+//                      &out[index + 7 * groupSz], getHalfNegative11(r4),
+//                      getHalf01(r4 >> 16));
+}
+
+// Writes with boundary checking
+
+static void partialWriteOut128Bytes(uchar *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + groupSz < elements) { out[index + groupSz] = r1 >> 8; }
+    if (index + 2 * groupSz < elements) {
+        out[index + 2 * groupSz] = r1 >> 16;
+    }
+    if (index + 3 * groupSz < elements) {
+        out[index + 3 * groupSz] = r1 >> 24;
+    }
+    if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = r2; }
+    if (index + 5 * groupSz < elements) {
+        out[index + 5 * groupSz] = r2 >> 8;
+    }
+    if (index + 6 * groupSz < elements) {
+        out[index + 6 * groupSz] = r2 >> 16;
+    }
+    if (index + 7 * groupSz < elements) {
+        out[index + 7 * groupSz] = r2 >> 24;
+    }
+    if (index + 8 * groupSz < elements) { out[index + 8 * groupSz] = r3; }
+    if (index + 9 * groupSz < elements) {
+        out[index + 9 * groupSz] = r3 >> 8;
+    }
+    if (index + 10 * groupSz < elements) {
+        out[index + 10 * groupSz] = r3 >> 16;
+    }
+    if (index + 11 * groupSz < elements) {
+        out[index + 11 * groupSz] = r3 >> 24;
+    }
+    if (index + 12 * groupSz < elements) {
+        out[index + 12 * groupSz] = r4;
+    }
+    if (index + 13 * groupSz < elements) {
+        out[index + 13 * groupSz] = r4 >> 8;
+    }
+    if (index + 14 * groupSz < elements) {
+        out[index + 14 * groupSz] = r4 >> 16;
+    }
+    if (index + 15 * groupSz < elements) {
+        out[index + 15 * groupSz] = r4 >> 24;
+    }
+}
+
+static void partialWriteOut128Bytes(char *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = (r1)&0x1; }
+    if (index + groupSz < elements) {
+        out[index + groupSz] = (r1 >> 1) & 0x1;
+    }
+    if (index + 2 * groupSz < elements) {
+        out[index + 2 * groupSz] = (r1 >> 2) & 0x1;
+    }
+    if (index + 3 * groupSz < elements) {
+        out[index + 3 * groupSz] = (r1 >> 3) & 0x1;
+    }
+    if (index + 4 * groupSz < elements) {
+        out[index + 4 * groupSz] = (r2)&0x1;
+    }
+    if (index + 5 * groupSz < elements) {
+        out[index + 5 * groupSz] = (r2 >> 1) & 0x1;
+    }
+    if (index + 6 * groupSz < elements) {
+        out[index + 6 * groupSz] = (r2 >> 2) & 0x1;
+    }
+    if (index + 7 * groupSz < elements) {
+        out[index + 7 * groupSz] = (r2 >> 3) & 0x1;
+    }
+    if (index + 8 * groupSz < elements) {
+        out[index + 8 * groupSz] = (r3)&0x1;
+    }
+    if (index + 9 * groupSz < elements) {
+        out[index + 9 * groupSz] = (r3 >> 1) & 0x1;
+    }
+    if (index + 10 * groupSz < elements) {
+        out[index + 10 * groupSz] = (r3 >> 2) & 0x1;
+    }
+    if (index + 11 * groupSz < elements) {
+        out[index + 11 * groupSz] = (r3 >> 3) & 0x1;
+    }
+    if (index + 12 * groupSz < elements) {
+        out[index + 12 * groupSz] = (r4)&0x1;
+    }
+    if (index + 13 * groupSz < elements) {
+        out[index + 13 * groupSz] = (r4 >> 1) & 0x1;
+    }
+    if (index + 14 * groupSz < elements) {
+        out[index + 14 * groupSz] = (r4 >> 2) & 0x1;
+    }
+    if (index + 15 * groupSz < elements) {
+        out[index + 15 * groupSz] = (r4 >> 3) & 0x1;
+    }
+}
+
+static void partialWriteOut128Bytes(short *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + groupSz < elements) { out[index + groupSz] = r1 >> 16; }
+    if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = r2; }
+    if (index + 3 * groupSz < elements) {
+        out[index + 3 * groupSz] = r2 >> 16;
+    }
+    if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = r3; }
+    if (index + 5 * groupSz < elements) {
+        out[index + 5 * groupSz] = r3 >> 16;
+    }
+    if (index + 6 * groupSz < elements) { out[index + 6 * groupSz] = r4; }
+    if (index + 7 * groupSz < elements) {
+        out[index + 7 * groupSz] = r4 >> 16;
+    }
+}
+
+static void partialWriteOut128Bytes(ushort *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((short *)(out), index, groupSz, r1, r2, r3, r4, elements);
+}
+
+static void partialWriteOut128Bytes(int *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + groupSz < elements) { out[index + groupSz] = r2; }
+    if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = r3; }
+    if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = r4; }
+}
+
+static void partialWriteOut128Bytes(uint *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((int *)(out), index, groupSz, r1, r2, r3, r4, elements);
+}
+
+static void partialWriteOut128Bytes(intl *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    intl c1 = r2;
+    c1      = (c1 << 32) | r1;
+    intl c2 = r4;
+    c2      = (c2 << 32) | r3;
+    if (index < elements) { out[index] = c1; }
+    if (index + groupSz < elements) { out[index + groupSz] = c2; }
+}
+
+static void partialWriteOut128Bytes(uintl *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((intl *)(out), index, groupSz, r1, r2, r3, r4, elements);
+}
+
+static void partialWriteOut128Bytes(float *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = 1.f - getFloat01(r1); }
+    if (index + groupSz < elements) {
+        out[index + groupSz] = 1.f - getFloat01(r2);
+    }
+    if (index + 2 * groupSz < elements) {
+        out[index + 2 * groupSz] = 1.f - getFloat01(r3);
+    }
+    if (index + 3 * groupSz < elements) {
+        out[index + 3 * groupSz] = 1.f - getFloat01(r4);
+    }
+}
+
+static void partialWriteOut128Bytes(cfloat *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) {
+        out[index] = {1.f - getFloat01(r1), 1.f - getFloat01(r2)};
+    }
+    if (index + groupSz < elements) {
+        out[index + groupSz] = {1.f - getFloat01(r3), 1.f - getFloat01(r4)};
+    }
+}
+
+static void partialWriteOut128Bytes(double *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) { out[index] = 1.0 - getDouble01(r1, r2); }
+    if (index + groupSz < elements) {
+        out[index + groupSz] = 1.0 - getDouble01(r3, r4);
+    }
+}
+
+static void partialWriteOut128Bytes(cdouble *out, const uint &index, const uint groupSz,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    if (index < elements) {
+        out[index] = {1.0 - getDouble01(r1, r2), 1.0 - getDouble01(r3, r4)};
+    }
+}
+
+// Normalized writes with boundary checking
+static void partialBoxMullerWriteOut128Bytes(
+    float *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    float n1, n2, n3, n4;
+    boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
+    if (index < elements) { out[index] = n1; }
+    if (index + groupSz < elements) { out[index + groupSz] = n2; }
+    if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = n3; }
+    if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = n4; }
+}
+
+static void partialBoxMullerWriteOut128Bytes(
+    cfloat *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    float n1, n2, n3, n4;
+    boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
+    boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
+    if (index < elements) {
+        out[index] = {n1, n2};
+    }
+    if (index + groupSz < elements) {
+        out[index + groupSz] = {n3, n4};
+    }
+}
+
+static void partialBoxMullerWriteOut128Bytes(
+    double *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    double n1, n2;
+    boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
+    if (index < elements) { out[index] = n1; }
+    if (index + groupSz < elements) { out[index + groupSz] = n2; }
+}
+
+static void partialBoxMullerWriteOut128Bytes(
+    cdouble *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+    double n1, n2;
+    boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
+    if (index < elements) {
+        out[index] = {n1, n2};
+    }
+}
+
+static void partialWriteOut128Bytes(common::half *out, 
+                                             const uint &index, const uint groupSz,
+                                             const uint &r1, const uint &r2,
+                                             const uint &r3, const uint &r4,
+                                             const uint &elements) {
+//  if (index < elements) { out[index] = oneMinusGetHalf01(r1); }
+//  if (index + groupSz < elements) {
+//      out[index + groupSz] = oneMinusGetHalf01(r1 >> 16);
+//  }
+//  if (index + 2 * groupSz < elements) {
+//      out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
+//  }
+//  if (index + 3 * groupSz < elements) {
+//      out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
+//  }
+//  if (index + 4 * groupSz < elements) {
+//      out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
+//  }
+//  if (index + 5 * groupSz < elements) {
+//      out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
+//  }
+//  if (index + 6 * groupSz < elements) {
+//      out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
+//  }
+//  if (index + 7 * groupSz < elements) {
+//      out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
+//  }
+}
+
+
+// Normalized writes with boundary checking
+static void partialBoxMullerWriteOut128Bytes(
+    common::half *out, const uint &index, const uint groupSz,
+    const uint &r1, const uint &r2,
+    const uint &r3, const uint &r4, const uint &elements) {
+//    common::half n[8];
+//    boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
+//                       getHalf01(r1 >> 16));
+//    boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
+//                       getHalf01(r2 >> 16));
+//    boxMullerTransform(n + 4, n + 5, getHalfNegative11(r3),
+//                       getHalf01(r3 >> 16));
+//    boxMullerTransform(n + 6, n + 7, getHalfNegative11(r4),
+//                       getHalf01(r4 >> 16));
+//    if (index < elements) { out[index] = n[0]; }
+//    if (index + groupSz < elements) { out[index + groupSz] = n[1]; }
+//    if (index + 2 * groupSz < elements) {
+//        out[index + 2 * groupSz] = n[2];
+//    }
+//    if (index + 3 * groupSz < elements) {
+//        out[index + 3 * groupSz] = n[3];
+//    }
+//    if (index + 4 * groupSz < elements) {
+//        out[index + 4 * groupSz] = n[4];
+//    }
+//    if (index + 5 * groupSz < elements) {
+//        out[index + 5 * groupSz] = n[5];
+//    }
+//    if (index + 6 * groupSz < elements) {
+//        out[index + 6 * groupSz] = n[6];
+//    }
+//    if (index + 7 * groupSz < elements) {
+//        out[index + 7 * groupSz] = n[7];
+//    }
+}
+
+} // namespace kernel
+} // namespace oneapi
diff --git a/src/backend/oneapi/random_engine.cpp b/src/backend/oneapi/random_engine.cpp
index db56d21638..5f8231706e 100644
--- a/src/backend/oneapi/random_engine.cpp
+++ b/src/backend/oneapi/random_engine.cpp
@@ -10,18 +10,16 @@
 #include <Array.hpp>
 #include <err_oneapi.hpp>
 #include <common/half.hpp>
-// #include <kernel/random_engine.hpp>
+#include <af/defines.h>
 #include <af/dim4.hpp>
+#include <kernel/random_engine.hpp>
 
 using common::half;
 
 namespace oneapi {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl) {
-
-    ONEAPI_NOT_SUPPORTED("initMersenneState Not supported");
-
-    // kernel::initMersenneState(*state.get(), *tbl.get(), seed);
+     kernel::initMersenneState(state, tbl, seed);
 }
 
 template<typename T>
@@ -29,11 +27,9 @@ Array<T> uniformDistribution(const af::dim4 &dims,
                              const af_random_engine_type type,
                              const uintl &seed, uintl &counter) {
 
-    //ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");
-
     Array<T> out = createEmptyArray<T>(dims);
-    // kernel::uniformDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,
-    //                                     counter);
+    kernel::uniformDistributionCBRNG<T>(out, out.elements(), type, seed,
+                                        counter);
     return out;
 }
 
@@ -41,12 +37,9 @@ template<typename T>
 Array<T> normalDistribution(const af::dim4 &dims,
                             const af_random_engine_type type, const uintl &seed,
                             uintl &counter) {
-
-    ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");
-
     Array<T> out = createEmptyArray<T>(dims);
-    // kernel::normalDistributionCBRNG<T>(*out.get(), out.elements(), type, seed,
-    //                                    counter);
+    kernel::normalDistributionCBRNG<T>(out, out.elements(), type, seed,
+                                       counter);
     return out;
 }
 
@@ -55,13 +48,10 @@ Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
                              Array<uint> sh1, Array<uint> sh2, uint mask,
                              Array<uint> recursion_table,
                              Array<uint> temper_table, Array<uint> state) {
-
-    ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");
-
     Array<T> out = createEmptyArray<T>(dims);
-    // kernel::uniformDistributionMT<T>(
-    //     *out.get(), out.elements(), *state.get(), *pos.get(), *sh1.get(),
-    //     *sh2.get(), mask, *recursion_table.get(), *temper_table.get());
+    kernel::uniformDistributionMT<T>(
+        out, out.elements(), state, pos, sh1,
+        sh2, mask, recursion_table, temper_table);
     return out;
 }
 
@@ -70,13 +60,10 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
                             Array<uint> sh1, Array<uint> sh2, uint mask,
                             Array<uint> recursion_table,
                             Array<uint> temper_table, Array<uint> state) {
-
-    ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");
-
     Array<T> out = createEmptyArray<T>(dims);
-    // kernel::normalDistributionMT<T>(
-    //     *out.get(), out.elements(), *state.get(), *pos.get(), *sh1.get(),
-    //     *sh2.get(), mask, *recursion_table.get(), *temper_table.get());
+    kernel::normalDistributionMT<T>(
+        out, out.elements(), state, pos, sh1,
+        sh2, mask, recursion_table, temper_table);
     return out;
 }
 
@@ -98,45 +85,10 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
         Array<uint> sh2, uint mask, Array<uint> recursion_table, \
         Array<uint> temper_table, Array<uint> state);
 
-#define COMPLEX_UNIFORM_DISTRIBUTION(T, TR)                                    \
-    template<>                                                                 \
-    Array<T> uniformDistribution<T>(const af::dim4 &dims,                      \
-                                    const af_random_engine_type type,          \
-                                    const uintl &seed, uintl &counter) {       \
-        ONEAPI_NOT_SUPPORTED("uniformDistribution Not supported");             \
-        Array<T> out    = createEmptyArray<T>(dims);                           \
-        return out;                                                            \
-    }                                                                          \
-    template<>                                                                 \
-    Array<T> uniformDistribution<T>(                                           \
-        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,                \
-        Array<uint> sh2, uint mask, Array<uint> recursion_table,               \
-        Array<uint> temper_table, Array<uint> state) {                         \
-        Array<T> out    = createEmptyArray<T>(dims);                           \
-        return out;                                                            \
-    }
-
-#define COMPLEX_NORMAL_DISTRIBUTION(T, TR)                                    \
-    template<>                                                                \
-    Array<T> normalDistribution<T>(const af::dim4 &dims,                      \
-                                   const af_random_engine_type type,          \
-                                   const uintl &seed, uintl &counter) {       \
-        ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");             \
-        Array<T> out    = createEmptyArray<T>(dims);                          \
-        return out;                                                           \
-    }                                                                         \
-    template<>                                                                \
-    Array<T> normalDistribution<T>(                                           \
-        const af::dim4 &dims, Array<uint> pos, Array<uint> sh1,               \
-        Array<uint> sh2, uint mask, Array<uint> recursion_table,              \
-        Array<uint> temper_table, Array<uint> state) {                        \
-        ONEAPI_NOT_SUPPORTED("normalDistribution Not supported");             \
-        Array<T> out    = createEmptyArray<T>(dims);                          \
-        return out;                                                           \
-    }
-
 INSTANTIATE_UNIFORM(float)
 INSTANTIATE_UNIFORM(double)
+INSTANTIATE_UNIFORM(cfloat)
+INSTANTIATE_UNIFORM(cdouble)
 INSTANTIATE_UNIFORM(int)
 INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
@@ -149,12 +101,8 @@ INSTANTIATE_UNIFORM(half)
 
 INSTANTIATE_NORMAL(float)
 INSTANTIATE_NORMAL(double)
+INSTANTIATE_NORMAL(cdouble)
+INSTANTIATE_NORMAL(cfloat)
 INSTANTIATE_NORMAL(half)
 
-COMPLEX_UNIFORM_DISTRIBUTION(cdouble, double)
-COMPLEX_UNIFORM_DISTRIBUTION(cfloat, float)
-
-COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
-COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
-
 }  // namespace oneapi
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
index 945d1366c7..10bd0e64c7 100644
--- a/src/backend/oneapi/types.hpp
+++ b/src/backend/oneapi/types.hpp
@@ -38,10 +38,10 @@ namespace oneapi {
 using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
 using intl    = long long;
-using uchar   = cl_uchar;
-using uint    = cl_uint;
+using uchar   = unsigned char;
+using uint    = unsigned int;
 using uintl   = unsigned long long;
-using ushort  = cl_ushort;
+using ushort  = unsigned short;
 
 template<typename T>
 using compute_t = typename common::kernel_type<T>::compute;

From 17ec326ab154078e12a1b35f55267a3727349b53 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Wed, 5 Oct 2022 08:46:50 -0400
Subject: [PATCH 2308/2677] transpose_inplace ported. passes all. see below.

test Transpose/0.TranposeIP_10 takes a much longer time to run
that
    cpu. investigate
---
 src/backend/oneapi/CMakeLists.txt             |   1 +
 .../oneapi/kernel/transpose_inplace.hpp       | 189 ++++++++++++++++++
 src/backend/oneapi/transpose_inplace.cpp      |   9 +-
 3 files changed, 197 insertions(+), 2 deletions(-)
 create mode 100755 src/backend/oneapi/kernel/transpose_inplace.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index adb97f30ff..f069b6a433 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -215,6 +215,7 @@ target_sources(afoneapi
     kernel/random_engine_threefry.hpp
     kernel/range.hpp
     kernel/transpose.hpp
+    kernel/transpose_inplace.hpp
 )
 
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
new file mode 100755
index 0000000000..c5230f364e
--- /dev/null
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -0,0 +1,189 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <err_oneapi.hpp>
+#include <debug_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+T static getConjugate(const T &in) {
+    // For non-complex types return same
+    return in;
+}
+
+template<>
+cfloat static getConjugate(const cfloat &in) {
+    return std::conj(in);
+}
+
+template<>
+cdouble static getConjugate(const cdouble &in) {
+    return std::conj(in);
+}
+
+#define doOp(v) (conjugate_ ? getConjugate((v)) : (v))
+
+constexpr int TILE_DIM  = 16;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = 256 / TILE_DIM;
+
+template <typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+
+template <typename T>
+class transposeInPlaceKernel {
+public:
+  transposeInPlaceKernel(const sycl::accessor<T> iData, const KParam in,
+                         const int blocksPerMatX, const int blocksPerMatY,
+                         const bool conjugate, const bool IS32MULTIPLE,
+                         local_accessor<T, 1> shrdMem_s, local_accessor<T, 1> shrdMem_d,
+                         sycl::stream debugStream) :
+    iData_(iData), in_(in), blocksPerMatX_(blocksPerMatX),
+    blocksPerMatY_(blocksPerMatY), conjugate_(conjugate), IS32MULTIPLE_(IS32MULTIPLE), shrdMem_s_(shrdMem_s), shrdMem_d_(shrdMem_d), debugStream_(debugStream) {}
+  void operator() (sycl::nd_item<2> it) const {
+    const int shrdStride = TILE_DIM + 1;
+
+    // create variables to hold output dimensions
+    const int iDim0 = in_.dims[0];
+    const int iDim1 = in_.dims[1];
+
+    // calculate strides
+    const int iStride1 = in_.strides[1];
+
+    const int lx = it.get_local_id(0);
+    const int ly = it.get_local_id(1);
+
+    // batch based block Id
+    sycl::group g = it.get_group();
+    const int batchId_x  = g.get_group_id(0) / blocksPerMatX_;
+    const int blockIdx_x = (g.get_group_id(0) - batchId_x * blocksPerMatX_);
+
+    const int batchId_y  = g.get_group_id(1) / blocksPerMatY_;
+    const int blockIdx_y = (g.get_group_id(1) - batchId_y * blocksPerMatY_);
+
+    const int x0 = TILE_DIM * blockIdx_x;
+    const int y0 = TILE_DIM * blockIdx_y;
+
+    T *iDataPtr = iData_.get_pointer();
+    iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] + in_.offset;
+
+    if (blockIdx_y > blockIdx_x) {
+        // calculate global indices
+        int gx = lx + x0;
+        int gy = ly + y0;
+        int dx = lx + y0;
+        int dy = ly + x0;
+
+        // Copy to shared memory
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+              shrdMem_s_[(ly + repeat) * shrdStride + lx] =
+                iDataPtr[gy_ * iStride1 + gx];
+
+            int dy_ = dy + repeat;
+            if (IS32MULTIPLE_ || (dx < iDim0 && dy_ < iDim1))
+                shrdMem_d_[(ly + repeat) * shrdStride + lx] =
+                    iDataPtr[dy_ * iStride1 + dx];
+        }
+
+        it.barrier();
+
+        // Copy from shared memory to global memory
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int dy_ = dy + repeat;
+            if (IS32MULTIPLE_ || (dx < iDim0 && dy_ < iDim1))
+                iDataPtr[dy_ * iStride1 + dx] =
+                    doOp(shrdMem_s_[(ly + repeat) + (shrdStride * lx)]);
+
+            int gy_ = gy + repeat;
+            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                iDataPtr[gy_ * iStride1 + gx] =
+                    doOp(shrdMem_d_[(ly + repeat) + (shrdStride * lx)]);
+        }
+
+    } else if (blockIdx_y == blockIdx_x) {
+        // calculate global indices
+        int gx = lx + x0;
+        int gy = ly + y0;
+
+        // Copy to shared memory
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_s_[(ly + repeat) * shrdStride + lx] =
+                    iDataPtr[gy_ * iStride1 + gx];
+        }
+
+        it.barrier();
+
+        // Copy from shared memory to global memory
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                iDataPtr[gy_ * iStride1 + gx] =
+                    doOp(shrdMem_s_[(ly + repeat) + (shrdStride * lx)]);
+        }
+    }
+  }
+private:
+  sycl::accessor<T> iData_;
+  KParam in_;
+  int blocksPerMatX_;
+  int blocksPerMatY_;
+  sycl::stream debugStream_;
+  bool conjugate_;
+  bool IS32MULTIPLE_;
+  local_accessor<T, 1> shrdMem_s_;
+  local_accessor<T, 1> shrdMem_d_;
+};
+
+template<typename T>
+void transpose_inplace(Param<T> in, const bool conjugate, const bool IS32MULTIPLE)
+{
+    auto local = sycl::range{THREADS_X, THREADS_Y};
+
+    int blk_x = divup(in.info.dims[0], TILE_DIM);
+    int blk_y = divup(in.info.dims[1], TILE_DIM);
+
+    auto global = sycl::range{blk_x * local[0] * in.info.dims[2],
+                              blk_y * local[1] * in.info.dims[3]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto r = in.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        auto shrdMem_s = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
+        auto shrdMem_d = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       transposeInPlaceKernel<T>(r, in.info,
+                                                 blk_x, blk_y,
+                                                 conjugate, IS32MULTIPLE,
+                                                 shrdMem_s, shrdMem_d,
+                                                 debugStream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/transpose_inplace.cpp b/src/backend/oneapi/transpose_inplace.cpp
index 2792a4200b..52a62d7837 100644
--- a/src/backend/oneapi/transpose_inplace.cpp
+++ b/src/backend/oneapi/transpose_inplace.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
-//#include <kernel/transpose_inplace.hpp>
+#include <kernel/transpose_inplace.hpp>
 #include <transpose.hpp>
 #include <af/dim4.hpp>
 
@@ -21,7 +21,12 @@ namespace oneapi {
 
 template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate) {
-    ONEAPI_NOT_SUPPORTED("");
+    const dim4 &inDims = in.dims();
+
+    const bool is32multiple =
+        inDims[0] % kernel::TILE_DIM == 0 && inDims[1] % kernel::TILE_DIM == 0;
+
+    kernel::transpose_inplace<T>(in, conjugate, is32multiple);
 }
 
 #define INSTANTIATE(T) \

From edc57407a23373f78849862ecaa624ff399e5db9 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Wed, 5 Oct 2022 11:29:40 -0400
Subject: [PATCH 2309/2677] triangle ported. passes all but gfor. see below.

---
 src/backend/oneapi/CMakeLists.txt      |   1 +
 src/backend/oneapi/kernel/triangle.hpp | 111 +++++++++++++++++++++++++
 src/backend/oneapi/triangle.cpp        |   5 +-
 3 files changed, 114 insertions(+), 3 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/triangle.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index f069b6a433..4559aa9292 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -216,6 +216,7 @@ target_sources(afoneapi
     kernel/range.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
+    kernel/triangle.hpp
 )
 
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
diff --git a/src/backend/oneapi/kernel/triangle.hpp b/src/backend/oneapi/kernel/triangle.hpp
new file mode 100644
index 0000000000..4f71ce1243
--- /dev/null
+++ b/src/backend/oneapi/kernel/triangle.hpp
@@ -0,0 +1,111 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <err_oneapi.hpp>
+#include <debug_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template <typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+
+template <typename T>
+class triangleKernel {
+public:
+  triangleKernel(sycl::accessor<T> rAcc, KParam rinfo, sycl::accessor<T> iAcc,
+                 KParam iinfo, const int groups_x, const int groups_y,
+                 const bool is_upper, const bool is_unit_diag) :
+    rAcc_(rAcc), rinfo_(rinfo), iAcc_(iAcc), iinfo_(iinfo), groups_x_(groups_x), groups_y_(groups_y), is_upper_(is_upper), is_unit_diag_(is_unit_diag) {}
+  void operator() (sycl::nd_item<2> it) const {
+    sycl::group g = it.get_group();
+    const int oz = g.get_group_id(0) / groups_x_;
+    const int ow = g.get_group_id(1) / groups_y_;
+
+    const int groupId_0 = g.get_group_id(0) - oz * groups_x_;
+    const int groupId_1 = g.get_group_id(1) - ow * groups_y_;
+
+    const int xx = it.get_local_id(0) + groupId_0 * it.get_local_range(0);
+    const int yy = it.get_local_id(1) + groupId_1 * it.get_local_range(1);
+
+    const int incy = groups_y_ * it.get_local_range(1);
+    const int incx = groups_x_ * it.get_local_range(0);
+
+    T *d_r = rAcc_.get_pointer();
+    const T *d_i = iAcc_.get_pointer() + iinfo_.offset;
+
+    if (oz < rinfo_.dims[2] && ow < rinfo_.dims[3]) {
+        d_i = d_i + oz * iinfo_.strides[2] + ow * iinfo_.strides[3];
+        d_r = d_r + oz * rinfo_.strides[2] + ow * rinfo_.strides[3];
+
+        for (int oy = yy; oy < rinfo_.dims[1]; oy += incy) {
+            const T *Yd_i = d_i + oy * iinfo_.strides[1];
+            T *Yd_r       = d_r + oy * rinfo_.strides[1];
+
+            for (int ox = xx; ox < rinfo_.dims[0]; ox += incx) {
+                bool cond         = is_upper_ ? (oy >= ox) : (oy <= ox);
+                bool do_unit_diag = is_unit_diag_ && (oy == ox);
+                if (cond) {
+                    Yd_r[ox] = do_unit_diag ? (T)(1) : Yd_i[ox];
+                } else {
+                    Yd_r[ox] = (T)(0);
+                }
+            }
+        }
+    }
+  }
+private:
+  sycl::accessor<T> rAcc_;
+  KParam rinfo_;
+  sycl::accessor<T> iAcc_;
+  KParam iinfo_;
+  const int groups_x_;
+  const int groups_y_;
+  const bool is_upper_;
+  const bool is_unit_diag_;
+};
+
+template<typename T>
+void triangle(Param<T> out, const Param<T> in, bool is_upper, bool is_unit_diag) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 128;
+    constexpr unsigned TILEY = 32;
+
+    auto local = sycl::range{TX, TY};
+
+    int groups_x = divup(out.info.dims[0], TILEX);
+    int groups_y = divup(out.info.dims[1], TILEY);
+
+    auto global = sycl::range{groups_x * out.info.dims[2] * local[0],
+                              groups_y * out.info.dims[3] * local[1]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto iAcc = in.data->get_access(h);
+        auto rAcc = out.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       triangleKernel<T>(rAcc, out.info, iAcc, in.info, groups_x, groups_y,
+                                         is_upper, is_unit_diag));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/triangle.cpp b/src/backend/oneapi/triangle.cpp
index ad22dcaa6c..f514b8d64b 100644
--- a/src/backend/oneapi/triangle.cpp
+++ b/src/backend/oneapi/triangle.cpp
@@ -6,7 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-// #include <kernel/triangle.hpp>
+#include <kernel/triangle.hpp>
 #include <triangle.hpp>
 #include <err_oneapi.hpp>
 
@@ -22,8 +22,7 @@ namespace oneapi {
 template<typename T>
 void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
               const bool is_unit_diag) {
-    ONEAPI_NOT_SUPPORTED("triangle Not supported");
-    // kernel::triangle<T>(out, in, is_upper, is_unit_diag);
+    kernel::triangle<T>(out, in, is_upper, is_unit_diag);
 }
 
 template<typename T>

From ef9898dd557b5c03e7720d1770524a1ddb926a61 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 5 Oct 2022 11:23:52 -0400
Subject: [PATCH 2310/2677] Apply clang-format to oneapi backend

---
 src/api/c/det.cpp                             |   2 +-
 src/backend/common/EventBase.hpp              |   4 +-
 src/backend/common/Logger.hpp                 |   1 -
 src/backend/common/forge_loader.hpp           |   1 -
 src/backend/common/jit/BufferNodeBase.hpp     |   2 +-
 src/backend/common/jit/Node.hpp               |  48 +-
 src/backend/oneapi/Array.cpp                  | 113 ++--
 src/backend/oneapi/Array.hpp                  |  81 +--
 src/backend/oneapi/Event.cpp                  |  20 +-
 src/backend/oneapi/Event.hpp                  |  18 +-
 .../oneapi/GraphicsResourceManager.hpp        |   1 -
 src/backend/oneapi/Kernel.hpp                 |  17 +-
 src/backend/oneapi/Module.hpp                 |  11 +-
 src/backend/oneapi/Param.cpp                  |   2 +-
 src/backend/oneapi/Param.hpp                  |   2 +-
 src/backend/oneapi/anisotropic_diffusion.cpp  |   2 +-
 src/backend/oneapi/approx.cpp                 |  25 +-
 src/backend/oneapi/assign.cpp                 |   2 +-
 src/backend/oneapi/bilateral.cpp              |   3 +-
 src/backend/oneapi/blas.cpp                   |   6 +-
 src/backend/oneapi/compile_module.cpp         |  21 +-
 src/backend/oneapi/copy.cpp                   |  86 +--
 src/backend/oneapi/copy.hpp                   |   2 +-
 src/backend/oneapi/device_manager.cpp         |  34 +-
 src/backend/oneapi/device_manager.hpp         |   6 +-
 src/backend/oneapi/diff.cpp                   |   2 +-
 src/backend/oneapi/errorcodes.cpp             |   5 +-
 src/backend/oneapi/exampleFunction.cpp        |   7 +-
 src/backend/oneapi/fft.cpp                    |   3 +-
 src/backend/oneapi/fftconvolve.cpp            |   2 +-
 src/backend/oneapi/gradient.cpp               |   2 +-
 src/backend/oneapi/homography.cpp             |   2 +-
 src/backend/oneapi/inverse.cpp                |   3 +-
 src/backend/oneapi/jit/BufferNode.hpp         |   7 +-
 src/backend/oneapi/jit/kernel_generators.hpp  |  17 +-
 src/backend/oneapi/kernel/assign.hpp          |  64 ++-
 src/backend/oneapi/kernel/iota.hpp            |  57 +-
 src/backend/oneapi/kernel/memcopy.hpp         | 179 +++---
 src/backend/oneapi/kernel/random_engine.hpp   |  95 ++--
 .../oneapi/kernel/random_engine_mersenne.hpp  | 260 +++++----
 .../oneapi/kernel/random_engine_philox.hpp    |  71 ++-
 .../oneapi/kernel/random_engine_threefry.hpp  |  61 +-
 .../oneapi/kernel/random_engine_write.hpp     | 536 +++++++++---------
 src/backend/oneapi/kernel/range.hpp           |  44 +-
 src/backend/oneapi/kernel/transpose.hpp       | 182 +++---
 .../oneapi/kernel/transpose_inplace.hpp       | 236 ++++----
 src/backend/oneapi/kernel/triangle.hpp        | 121 ++--
 src/backend/oneapi/lu.cpp                     |   6 +-
 src/backend/oneapi/math.cpp                   |  20 +-
 src/backend/oneapi/math.hpp                   |   1 -
 src/backend/oneapi/mean.cpp                   |   6 +-
 src/backend/oneapi/meanshift.cpp              |   4 +-
 src/backend/oneapi/medfilt.cpp                |   2 -
 src/backend/oneapi/memory.cpp                 | 109 ++--
 src/backend/oneapi/memory.hpp                 |   5 +-
 src/backend/oneapi/moments.cpp                |   1 -
 src/backend/oneapi/morph.cpp                  |   2 -
 src/backend/oneapi/nearest_neighbour.cpp      |   1 -
 src/backend/oneapi/orb.cpp                    |   1 -
 src/backend/oneapi/platform.cpp               |  52 +-
 src/backend/oneapi/platform.hpp               |   4 +-
 src/backend/oneapi/plot.cpp                   |   9 +-
 src/backend/oneapi/random_engine.cpp          |  17 +-
 src/backend/oneapi/range.cpp                  |   2 +-
 src/backend/oneapi/reduce_impl.hpp            |   1 -
 src/backend/oneapi/regions.cpp                |   1 -
 src/backend/oneapi/reorder.cpp                |   1 -
 src/backend/oneapi/reshape.cpp                |   1 -
 src/backend/oneapi/rotate.cpp                 |   3 +-
 src/backend/oneapi/scan.cpp                   |   3 +-
 src/backend/oneapi/scan_by_key.cpp            |   7 +-
 src/backend/oneapi/select.cpp                 |   8 +-
 src/backend/oneapi/set.cpp                    |  23 +-
 src/backend/oneapi/shift.cpp                  |   2 +-
 src/backend/oneapi/sift.cpp                   |   1 -
 src/backend/oneapi/sobel.cpp                  |   1 -
 src/backend/oneapi/solve.cpp                  |   3 -
 src/backend/oneapi/sort.cpp                   |   1 -
 src/backend/oneapi/sort_index.cpp             |  11 +-
 src/backend/oneapi/sparse.cpp                 |   8 +-
 src/backend/oneapi/sparse_arith.cpp           |   8 +-
 src/backend/oneapi/sparse_blas.cpp            |   2 +-
 src/backend/oneapi/surface.cpp                |   9 +-
 src/backend/oneapi/susan.cpp                  |   5 +-
 src/backend/oneapi/svd.cpp                    |   6 +-
 src/backend/oneapi/tile.cpp                   |   2 +-
 src/backend/oneapi/topk.cpp                   |   7 +-
 src/backend/oneapi/transform.cpp              |   9 +-
 src/backend/oneapi/transpose.cpp              |   2 +-
 src/backend/oneapi/triangle.cpp               |   5 +-
 src/backend/oneapi/unwrap.cpp                 |   2 +-
 src/backend/oneapi/vector_field.cpp           |   3 +-
 src/backend/oneapi/where.cpp                  |   6 +-
 src/backend/oneapi/wrap.cpp                   |   9 +-
 94 files changed, 1476 insertions(+), 1382 deletions(-)
 mode change 100755 => 100644 src/backend/oneapi/kernel/transpose_inplace.hpp

diff --git a/src/api/c/det.cpp b/src/api/c/det.cpp
index 0d0e5cc1d7..8507675b85 100644
--- a/src/api/c/det.cpp
+++ b/src/api/c/det.cpp
@@ -24,9 +24,9 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
-using detail::scalar;
 using detail::imag;
 using detail::real;
+using detail::scalar;
 
 template<typename T>
 T det(const af_array a) {
diff --git a/src/backend/common/EventBase.hpp b/src/backend/common/EventBase.hpp
index 874ec5b6c6..82ad049061 100644
--- a/src/backend/common/EventBase.hpp
+++ b/src/backend/common/EventBase.hpp
@@ -36,8 +36,8 @@ class EventBase {
 
     /// \brief Event destructor. Calls the destroy event call on the native API
     ~EventBase() noexcept {
-      //if (e_)
-          NativeEventPolicy::destroyEvent(&e_);
+        // if (e_)
+        NativeEventPolicy::destroyEvent(&e_);
     }
 
     /// \brief Creates the event object by calling the native create API
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index 50e74ae03b..5241dc9126 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -46,7 +46,6 @@
 /* Other */
 #endif
 
-
 namespace common {
 std::shared_ptr<spdlog::logger> loggerFactory(const std::string& name);
 std::string bytesToString(size_t bytes);
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index 1e3edc7125..c87e98690c 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -43,7 +43,6 @@
 /* Other */
 #endif
 
-
 class ForgeModule : public common::DependencyModule {
    public:
     ForgeModule();
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 6b3d56162b..a7d6747036 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -12,8 +12,8 @@
 #include <common/jit/Node.hpp>
 #include <jit/kernel_generators.hpp>
 
-#include <sstream>
 #include <cstring>
+#include <sstream>
 
 namespace common {
 
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 3062935909..bbe3fcb859 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -71,18 +71,18 @@ using Node_ptr = std::shared_ptr<Node>;
 
 static const char *getFullName(af::dtype type) {
     switch (type) {
-    case f32: return detail::getFullName<float>();
-    case f64: return detail::getFullName<double>();
-    case c32: return detail::getFullName<detail::cfloat>();
-    case c64: return detail::getFullName<detail::cdouble>();
-    case u32: return detail::getFullName<unsigned>();
-    case s32: return detail::getFullName<int>();
-    case u64: return detail::getFullName<unsigned long long>();
-    case s64: return detail::getFullName<long long>();
-    case u16: return detail::getFullName<unsigned short>();
-    case s16: return detail::getFullName<short>();
-    case b8: return  detail::getFullName<char>();
-    case u8: return  detail::getFullName<unsigned char>();
+        case f32: return detail::getFullName<float>();
+        case f64: return detail::getFullName<double>();
+        case c32: return detail::getFullName<detail::cfloat>();
+        case c64: return detail::getFullName<detail::cdouble>();
+        case u32: return detail::getFullName<unsigned>();
+        case s32: return detail::getFullName<int>();
+        case u64: return detail::getFullName<unsigned long long>();
+        case s64: return detail::getFullName<long long>();
+        case u16: return detail::getFullName<unsigned short>();
+        case s16: return detail::getFullName<short>();
+        case b8: return detail::getFullName<char>();
+        case u8: return detail::getFullName<unsigned char>();
         case f16: return "half";
     }
     return "";
@@ -90,18 +90,18 @@ static const char *getFullName(af::dtype type) {
 
 static const char *getShortName(af::dtype type) {
     switch (type) {
-    case f32: return detail::shortname<float>();
-    case f64: return detail::shortname<double>();
-    case c32: return detail::shortname<detail::cfloat>();
-    case c64: return detail::shortname<detail::cdouble>();
-    case u32: return detail::shortname<unsigned>();
-    case s32: return detail::shortname<int>();
-    case u64: return detail::shortname<unsigned long long>();
-    case s64: return detail::shortname<long long>();
-    case u16: return detail::shortname<unsigned short>();
-    case s16: return detail::shortname<short>();
-    case b8: return  detail::shortname<char>();
-    case u8: return  detail::shortname<unsigned char>();
+        case f32: return detail::shortname<float>();
+        case f64: return detail::shortname<double>();
+        case c32: return detail::shortname<detail::cfloat>();
+        case c64: return detail::shortname<detail::cdouble>();
+        case u32: return detail::shortname<unsigned>();
+        case s32: return detail::shortname<int>();
+        case u64: return detail::shortname<unsigned long long>();
+        case s64: return detail::shortname<long long>();
+        case u16: return detail::shortname<unsigned short>();
+        case s16: return detail::shortname<short>();
+        case b8: return detail::shortname<char>();
+        case u8: return detail::shortname<unsigned char>();
         case f16: return "h";
     }
     return "";
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index f9d8e8e3e7..db4bce10e3 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -12,10 +12,10 @@
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <common/jit/ScalarNode.hpp>
-#include <jit/BufferNode.hpp>
 #include <common/util.hpp>
-#include <err_oneapi.hpp>
 #include <copy.hpp>
+#include <err_oneapi.hpp>
+#include <jit/BufferNode.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <scalar.hpp>
@@ -36,11 +36,11 @@
 using af::dim4;
 using af::dtype_traits;
 
-using oneapi::jit::BufferNode;
 using common::half;
 using common::Node;
 using common::Node_ptr;
 using common::NodeIterator;
+using oneapi::jit::BufferNode;
 
 using nonstd::span;
 using std::accumulate;
@@ -122,29 +122,29 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
     static_assert(
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");
-    //getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
-                                  //sizeof(T) * info.elements(), in_data);
-    getQueue().submit([&] (sycl::handler &h) {
-        h.copy(in_data, data->get_access(h));
-    }).wait();
+    // getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
+    // sizeof(T) * info.elements(), in_data);
+    getQueue()
+        .submit([&](sycl::handler &h) { h.copy(in_data, data->get_access(h)); })
+        .wait();
 }
-   
 
 template<typename T>
 Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
                 bool copy)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(
-           copy ? memAlloc<T>(info.elements()).release() : new buffer<T>(*mem),
-          bufferFree<T>)
+    , data(copy ? memAlloc<T>(info.elements()).release() : new buffer<T>(*mem),
+           bufferFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {
     if (copy) {
-        getQueue().submit([&] (sycl::handler &h) {
-            h.copy(mem->get_access(h), data->get_access(h));
-        }).wait();
+        getQueue()
+            .submit([&](sycl::handler &h) {
+                h.copy(mem->get_access(h), data->get_access(h));
+            })
+            .wait();
     }
 }
 
@@ -168,7 +168,7 @@ Array<T>::Array(Param<T> &tmp, bool owner_)
                 tmp.info.strides[3]),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(
-           tmp.data, owner_ ? bufferFree<T> : [](buffer<T> * /*unused*/) {})
+          tmp.data, owner_ ? bufferFree<T> : [](buffer<T> * /*unused*/) {})
     , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
                      tmp.info.dims[3]))
     , node()
@@ -179,17 +179,18 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
                 const T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(is_device ? (new buffer<T>(*reinterpret_cast<buffer<T>*>(
-                                        const_cast<T *>(in_data))))
+    , data(is_device ? (new buffer<T>(*reinterpret_cast<buffer<T> *>(
+                           const_cast<T *>(in_data))))
                      : (memAlloc<T>(info.elements()).release()),
            bufferFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {
     if (!is_device) {
-        getQueue().submit([&] (sycl::handler &h) {
-            h.copy(in_data, data->get_access(h));
-        }).wait();
+        getQueue()
+            .submit(
+                [&](sycl::handler &h) { h.copy(in_data, data->get_access(h)); })
+            .wait();
     }
 }
 
@@ -198,8 +199,8 @@ void Array<T>::eval() {
     if (isReady()) { return; }
 
     this->setId(getActiveDeviceId());
-    data = std::shared_ptr<sycl::buffer<T>>(memAlloc<T>(info.elements()).release(),
-                                       bufferFree<T>);
+    data = std::shared_ptr<sycl::buffer<T>>(
+        memAlloc<T>(info.elements()).release(), bufferFree<T>);
 
     // Do not replace this with cast operator
     KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
@@ -208,10 +209,9 @@ void Array<T>::eval() {
 
     Param<T> res{data.get(), info};
 
-
-    //TODO: implement
+    // TODO: implement
     ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
-    //evalNodes(res, getNode().get());
+    // evalNodes(res, getNode().get());
     node.reset();
 }
 
@@ -267,9 +267,9 @@ void evalMultiple(vector<Array<T> *> arrays) {
         nodes.push_back(array->getNode().get());
     }
 
-    //TODO: implement
+    // TODO: implement
     ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
-    //evalNodes(outputs, nodes);
+    // evalNodes(outputs, nodes);
 
     for (Array<T> *array : output_arrays) { array->node.reset(); }
 }
@@ -342,7 +342,8 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     //         (3 * sizeof(uint));
 
     //     const cl::Device &device = getDevice();
-    //     size_t max_param_size = device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
+    //     size_t max_param_size =
+    //     device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
     //     // typical values:
     //     //   NVIDIA     = 4096
     //     //   AMD        = 3520  (AMD A10 iGPU = 1024)
@@ -375,7 +376,8 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     //     }
     //     isBufferLimit = jitTreeExceedsMemoryPressure(info.total_buffer_size);
 
-    //     size_t param_size = (info.num_buffers * (sizeof(Param<T>) + sizeof(T *)) +
+    //     size_t param_size = (info.num_buffers * (sizeof(Param<T>) + sizeof(T
+    //     *)) +
     //                          info.param_scalar_size);
 
     //     bool isParamLimit = param_size >= max_param_size;
@@ -386,15 +388,16 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     return kJITHeuristics::Pass;
 }
 
-//Doesn't make sense with sycl::buffer
-//TODO: accessors? or return sycl::buffer?
-//TODO: return accessor.get_pointer() for access::target::global_buffer or (host_buffer?)
+// Doesn't make sense with sycl::buffer
+// TODO: accessors? or return sycl::buffer?
+// TODO: return accessor.get_pointer() for access::target::global_buffer or
+// (host_buffer?)
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const buffer<T> *buf = arr.device();
-    //if (!buf) { return NULL; }
-    //memLock(buf);
-    //cl_mem mem = (*buf)();
+    // if (!buf) { return NULL; }
+    // memLock(buf);
+    // cl_mem mem = (*buf)();
     ONEAPI_NOT_SUPPORTED("pointer to sycl::buffer should be accessor");
     return (void *)buf;
 }
@@ -451,7 +454,7 @@ Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
     verifyTypeSupport<T>();
 
     bool copy_device = false;
-    return Array<T>(dims, static_cast<buffer<T>*>(data), 0, copy_device);
+    return Array<T>(dims, static_cast<buffer<T> *>(data), 0, copy_device);
 }
 
 template<typename T>
@@ -481,15 +484,17 @@ template<typename T>
 void writeHostDataArray(Array<T> &arr, const T *const data,
                         const size_t bytes) {
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
-    getQueue().submit([&] (sycl::handler &h) {
-        buffer<T> &buf = *arr.get();
-        //auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
-        //TODO: offset accessor
-        auto offset_acc = buf.get_access(h);
-        h.copy(data, offset_acc);
-    }).wait();
-    //getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(), bytes,
-    //data);
+    getQueue()
+        .submit([&](sycl::handler &h) {
+            buffer<T> &buf = *arr.get();
+            // auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
+            // TODO: offset accessor
+            auto offset_acc = buf.get_access(h);
+            h.copy(data, offset_acc);
+        })
+        .wait();
+    // getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(),
+    // bytes, data);
 }
 
 template<typename T>
@@ -499,14 +504,14 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
     buffer<T> &buf = *arr.get();
 
-    //clRetainMemObject(
+    // clRetainMemObject(
     //    reinterpret_cast<buffer<T> *>(const_cast<void *>(data)));
-    //buffer<T> data_buf =
+    // buffer<T> data_buf =
     //  buffer<T>(reinterpret_cast<buffer<T>*>(const_cast<void *>(data)));
 
     ONEAPI_NOT_SUPPORTED("writeDeviceDataArray not supported");
-    //getQueue().enqueueCopyBuffer(data_buf, buf, 0,
-    //static_cast<size_t>(arr.getOffset()), bytes);
+    // getQueue().enqueueCopyBuffer(data_buf, buf, 0,
+    // static_cast<size_t>(arr.getOffset()), bytes);
 }
 
 template<typename T>
@@ -530,7 +535,7 @@ size_t Array<T>::getAllocatedBytes() const {
     template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
     template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
     template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
-    template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);           \
+    template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);        \
     template Array<T> createSubArray<T>(                                      \
         const Array<T> &parent, const vector<af_seq> &index, bool copy);      \
     template void destroyArray<T>(Array<T> * A);                              \
@@ -538,13 +543,13 @@ size_t Array<T>::getAllocatedBytes() const {
     template Array<T>::Array(const dim4 &dims, const dim4 &strides,           \
                              dim_t offset, const T *const in_data,            \
                              bool is_device);                                 \
-    template Array<T>::Array(const dim4 &dims, buffer<T>* mem, size_t src_offset, \
-                             bool copy);                                      \
+    template Array<T>::Array(const dim4 &dims, buffer<T> *mem,                \
+                             size_t src_offset, bool copy);                   \
     template Node_ptr Array<T>::getNode();                                    \
     template Node_ptr Array<T>::getNode() const;                              \
     template void Array<T>::eval();                                           \
     template void Array<T>::eval() const;                                     \
-    template buffer<T> *Array<T>::device();                             \
+    template buffer<T> *Array<T>::device();                                   \
     template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
                                         const size_t bytes);                  \
     template void writeDeviceDataArray<T>(                                    \
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index 47c3c8bc7d..ae7234fb02 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -46,33 +46,33 @@ class Array;
 template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays);
 
-  template<typename T>
+template<typename T>
 void evalNodes(Param<T> &out, common::Node *node);
 
-  template<typename T>
-  void evalNodes(std::vector<Param<T>> &outputs,
-                 const std::vector<common::Node *> &nodes);
+template<typename T>
+void evalNodes(std::vector<Param<T>> &outputs,
+               const std::vector<common::Node *> &nodes);
 
-  /// Creates a new Array object on the heap and returns a reference to it.
-  template<typename T>
-  Array<T> createNodeArray(const af::dim4 &dims, common::Node_ptr node);
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createNodeArray(const af::dim4 &dims, common::Node_ptr node);
 
-  /// Creates a new Array object on the heap and returns a reference to it.
-  template<typename T>
-  Array<T> createValueArray(const af::dim4 &dims, const T &value);
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createValueArray(const af::dim4 &dims, const T &value);
 
-  /// Creates a new Array object on the heap and returns a reference to it.
-  template<typename T>
-  Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
+/// Creates a new Array object on the heap and returns a reference to it.
+template<typename T>
+Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
-  template<typename T>
-  Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
+template<typename T>
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
 
-  template<typename T>
-  Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
-                              dim_t offset, const T *const in_data,
-                              bool is_device) {
-      return Array<T>(dims, strides, offset, in_data, is_device);
+template<typename T>
+Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
+                            dim_t offset, const T *const in_data,
+                            bool is_device) {
+    return Array<T>(dims, strides, offset, in_data, is_device);
 }
 
 /// Copies data to an existing Array object from a host pointer
@@ -122,13 +122,13 @@ void *getDevicePtr(const Array<T> &arr);
 
 template<typename T>
 void *getRawPtr(const Array<T> &arr) {
-  //const sycl::buffer<T> *buf = arr.get();
-  //if (!buf) return NULL;
-  //cl_mem mem = (*buf)();
-  //return (void *)mem;
+    // const sycl::buffer<T> *buf = arr.get();
+    // if (!buf) return NULL;
+    // cl_mem mem = (*buf)();
+    // return (void *)mem;
 
-  // TODO:
-  return nullptr;
+    // TODO:
+    return nullptr;
 }
 
 template<typename T>
@@ -159,7 +159,8 @@ class Array {
     explicit Array(const af::dim4 &dims, common::Node_ptr n);
     explicit Array(const af::dim4 &dims, const T *const in_data);
 
-  explicit Array(const af::dim4 &dims, sycl::buffer<T>* const mem, size_t offset, bool copy);
+    explicit Array(const af::dim4 &dims, sycl::buffer<T> *const mem,
+                   size_t offset, bool copy);
 
    public:
     Array(const Array<T> &other) = default;
@@ -267,14 +268,14 @@ class Array {
         return out;
     }
 
-  operator KParam() const {
-      KParam kinfo = {
-          {dims()[0], dims()[1], dims()[2], dims()[3]},
-          {strides()[0], strides()[1], strides()[2], strides()[3]},
-          getOffset()};
+    operator KParam() const {
+        KParam kinfo = {
+            {dims()[0], dims()[1], dims()[2], dims()[3]},
+            {strides()[0], strides()[1], strides()[2], strides()[3]},
+            getOffset()};
 
-      return kinfo;
-  }
+        return kinfo;
+    }
 
     common::Node_ptr getNode() const;
     common::Node_ptr getNode();
@@ -285,16 +286,16 @@ class Array {
         if (!isReady()) eval();
         auto func = [data = data](void *ptr) {
             if (ptr != nullptr) {
-              //cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);
-              //UNUSED(err);
+                // cl_int err = getQueue().enqueueUnmapMemObject(*data, ptr);
+                // UNUSED(err);
                 ptr = nullptr;
             }
         };
 
-        //T *ptr = (T *)getQueue().enqueueMapBuffer(
-            //*static_cast<const sycl::buffer<T> *>(get()), CL_TRUE, map_flags,
-            //getOffset() * sizeof(T), elements() * sizeof(T), nullptr, nullptr,
-            //nullptr);
+        // T *ptr = (T *)getQueue().enqueueMapBuffer(
+        //*static_cast<const sycl::buffer<T> *>(get()), CL_TRUE, map_flags,
+        // getOffset() * sizeof(T), elements() * sizeof(T), nullptr, nullptr,
+        // nullptr);
 
         return mapped_ptr<T>(nullptr, func);
     }
diff --git a/src/backend/oneapi/Event.cpp b/src/backend/oneapi/Event.cpp
index 7e08c2fd44..a86d74f8ab 100644
--- a/src/backend/oneapi/Event.cpp
+++ b/src/backend/oneapi/Event.cpp
@@ -9,11 +9,11 @@
 
 #include <Event.hpp>
 
+#include <err_oneapi.hpp>
 #include <events.hpp>
 #include <platform.hpp>
 #include <af/event.h>
 #include <memory>
-#include <err_oneapi.hpp>
 
 #include <memory>
 
@@ -42,18 +42,18 @@ af_event createEvent() {
 
 void markEventOnActiveQueue(af_event eventHandle) {
     ONEAPI_NOT_SUPPORTED("");
-    //Event& event = getEvent(eventHandle);
+    // Event& event = getEvent(eventHandle);
     //// Use the currently-active stream
-    //if (event.mark(getQueue()()) != CL_SUCCESS) {
+    // if (event.mark(getQueue()()) != CL_SUCCESS) {
     //    AF_ERROR("Could not mark event on active queue", AF_ERR_RUNTIME);
     //}
 }
 
 void enqueueWaitOnActiveQueue(af_event eventHandle) {
     ONEAPI_NOT_SUPPORTED("");
-    //Event& event = getEvent(eventHandle);
+    // Event& event = getEvent(eventHandle);
     //// Use the currently-active stream
-    //if (event.enqueueWait(getQueue()()) != CL_SUCCESS) {
+    // if (event.enqueueWait(getQueue()()) != CL_SUCCESS) {
     //    AF_ERROR("Could not enqueue wait on active queue for event",
     //             AF_ERR_RUNTIME);
     //}
@@ -61,8 +61,8 @@ void enqueueWaitOnActiveQueue(af_event eventHandle) {
 
 void block(af_event eventHandle) {
     ONEAPI_NOT_SUPPORTED("");
-    //Event& event = getEvent(eventHandle);
-    //if (event.block() != CL_SUCCESS) {
+    // Event& event = getEvent(eventHandle);
+    // if (event.block() != CL_SUCCESS) {
     //    AF_ERROR("Could not block on active queue for event", AF_ERR_RUNTIME);
     //}
 }
@@ -70,9 +70,9 @@ void block(af_event eventHandle) {
 af_event createAndMarkEvent() {
     ONEAPI_NOT_SUPPORTED("");
     return 0;
-    //af_event handle = createEvent();
-    //markEventOnActiveQueue(handle);
-    //return handle;
+    // af_event handle = createEvent();
+    // markEventOnActiveQueue(handle);
+    // return handle;
 }
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/Event.hpp b/src/backend/oneapi/Event.hpp
index bc143283d0..ff600ebbcb 100644
--- a/src/backend/oneapi/Event.hpp
+++ b/src/backend/oneapi/Event.hpp
@@ -17,7 +17,7 @@ class OneAPIEventPolicy {
    public:
     using EventType = sycl::event;
     using QueueType = sycl::queue;
-    //using ErrorType = sycl::exception; //does this make sense
+    // using ErrorType = sycl::exception; //does this make sense
     using ErrorType = int;
 
     static ErrorType createAndMarkEvent(EventType *e) noexcept {
@@ -26,23 +26,23 @@ class OneAPIEventPolicy {
     }
 
     static ErrorType markEvent(EventType *e, QueueType stream) noexcept {
-        //return clEnqueueMarkerWithWaitList(stream, 0, nullptr, e);
-      return 0;
+        // return clEnqueueMarkerWithWaitList(stream, 0, nullptr, e);
+        return 0;
     }
 
     static ErrorType waitForEvent(EventType *e, QueueType stream) noexcept {
-        //return clEnqueueMarkerWithWaitList(stream, 1, e, nullptr);
-      return 0;
+        // return clEnqueueMarkerWithWaitList(stream, 1, e, nullptr);
+        return 0;
     }
 
     static ErrorType syncForEvent(EventType *e) noexcept {
-        //return clWaitForEvents(1, e);
-      return 0;
+        // return clWaitForEvents(1, e);
+        return 0;
     }
 
     static ErrorType destroyEvent(EventType *e) noexcept {
-        //return clReleaseEvent(*e);
-      return 0;
+        // return clReleaseEvent(*e);
+        return 0;
     }
 };
 
diff --git a/src/backend/oneapi/GraphicsResourceManager.hpp b/src/backend/oneapi/GraphicsResourceManager.hpp
index bdc889708a..6374f1ef7e 100644
--- a/src/backend/oneapi/GraphicsResourceManager.hpp
+++ b/src/backend/oneapi/GraphicsResourceManager.hpp
@@ -15,7 +15,6 @@
 #include <memory>
 #include <vector>
 
-
 namespace oneapi {
 class GraphicsResourceManager
     : public common::InteropManager<GraphicsResourceManager, std::byte> {
diff --git a/src/backend/oneapi/Kernel.hpp b/src/backend/oneapi/Kernel.hpp
index 823fc511ef..704237de24 100644
--- a/src/backend/oneapi/Kernel.hpp
+++ b/src/backend/oneapi/Kernel.hpp
@@ -12,8 +12,8 @@
 #include <common/KernelInterface.hpp>
 #include <common/Logger.hpp>
 
-#include <backend.hpp>
 #include <CL/sycl.hpp>
+#include <backend.hpp>
 #include <string>
 
 namespace oneapi {
@@ -44,7 +44,8 @@ class Kernel
     using KernelType = sycl::kernel;
     using DevPtrType<T> = sycl::buffer<T>*;
     using BaseClass =
-        common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType<T>>;
+        common::KernelInterface<ModuleType, KernelType, Enqueuer,
+DevPtrType<T>>;
 
     Kernel() : BaseClass("", nullptr, cl::Kernel{nullptr, false}) {}
     Kernel(std::string name, ModuleType mod, KernelType ker)
@@ -55,7 +56,8 @@ class Kernel
     DevPtrType<T> getDevPtr(const char* name) final;
     // clang-format on
 
-    void copyToReadOnly(DevPtrType<T> dst, DevPtrType<T> src, size_t bytes) final;
+    void copyToReadOnly(DevPtrType<T> dst, DevPtrType<T> src, size_t bytes)
+final;
 
     void setFlag(DevPtrType<T> dst, int* scalarValPtr,
                  const bool syncCopy = false) final;
@@ -66,12 +68,13 @@ class Kernel
 
 class Kernel {
    public:
-    using ModuleType = const sycl::kernel_bundle<sycl::bundle_state::executable> *;
+    using ModuleType =
+        const sycl::kernel_bundle<sycl::bundle_state::executable>*;
     using KernelType = sycl::kernel;
-  template<typename T>
+    template<typename T>
     using DevPtrType = sycl::buffer<T>*;
-    //using BaseClass =
-        //common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType<T>>;
+    // using BaseClass =
+    // common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType<T>>;
 
     Kernel() {}
     Kernel(std::string name, ModuleType mod, KernelType ker) {}
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
index 1c34306d68..0aa1cc790d 100644
--- a/src/backend/oneapi/Module.hpp
+++ b/src/backend/oneapi/Module.hpp
@@ -9,14 +9,15 @@
 
 #pragma once
 
-#include <common/ModuleInterface.hpp>
 #include <CL/sycl.hpp>
-
+#include <common/ModuleInterface.hpp>
 
 namespace oneapi {
 
 /// oneapi backend wrapper for cl::Program object
-  class Module : public common::ModuleInterface<sycl::kernel_bundle<sycl::bundle_state::executable>> {
+class Module
+    : public common::ModuleInterface<
+          sycl::kernel_bundle<sycl::bundle_state::executable>> {
    public:
     using ModuleType = sycl::kernel_bundle<sycl::bundle_state::executable>;
     using BaseClass  = common::ModuleInterface<ModuleType>;
@@ -32,8 +33,8 @@ namespace oneapi {
 
     /// Unload the module
     void unload() final {
-      // TODO(oneapi): Unload kernel/program
-      ;
+        // TODO(oneapi): Unload kernel/program
+        ;
     }
 };
 
diff --git a/src/backend/oneapi/Param.cpp b/src/backend/oneapi/Param.cpp
index c5d2b16762..87a539ce67 100644
--- a/src/backend/oneapi/Param.cpp
+++ b/src/backend/oneapi/Param.cpp
@@ -16,7 +16,7 @@ namespace oneapi {
 
 template<typename T>
 Param<T> makeParam(sycl::buffer<T> &mem, int off, const int dims[4],
-                const int strides[4]) {
+                   const int strides[4]) {
     Param<T> out;
     out.data        = &mem;
     out.info.offset = off;
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index b65e28f2e7..4a0d6ff9cc 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -26,7 +26,7 @@ struct Param {
     Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
 
     // AF_DEPRECATED("Use Array<T>")
-    Param(sycl::buffer<T> *data_, KParam info_) : data(data_), info(info_) {}
+    Param(sycl::buffer<T>* data_, KParam info_) : data(data_), info(info_) {}
 
     ~Param() = default;
 };
diff --git a/src/backend/oneapi/anisotropic_diffusion.cpp b/src/backend/oneapi/anisotropic_diffusion.cpp
index c063736c21..a68b8aaa8f 100644
--- a/src/backend/oneapi/anisotropic_diffusion.cpp
+++ b/src/backend/oneapi/anisotropic_diffusion.cpp
@@ -9,8 +9,8 @@
 
 #include <Array.hpp>
 #include <anisotropic_diffusion.hpp>
-#include <err_oneapi.hpp>
 #include <copy.hpp>
+#include <err_oneapi.hpp>
 #include <af/dim4.hpp>
 
 namespace oneapi {
diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index df22448704..e11216d00c 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -15,24 +15,23 @@ template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const int xdim, const Tp &xi_beg, const Tp &xi_step,
              const af_interp_type method, const float offGrid) {
-
     ONEAPI_NOT_SUPPORTED("");
     return;
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            //kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    //method, 1);
+            // kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step,
+            // offGrid, method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            //kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    //method, 2);
+            // kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step,
+            // offGrid, method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            //kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    //method, 3);
+            // kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step,
+            // offGrid, method, 3);
             break;
         default: break;
     }
@@ -49,22 +48,22 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            //kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
-                                    //yi_beg, yi_step, offGrid, method, 1);
+            // kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+            // ydim, yi_beg, yi_step, offGrid, method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            //kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
-                                    //yi_beg, yi_step, offGrid, method, 2);
+            // kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+            // ydim, yi_beg, yi_step, offGrid, method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            //kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
-                                    //yi_beg, yi_step, offGrid, method, 3);
+            // kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+            // ydim, yi_beg, yi_step, offGrid, method, 3);
             break;
         default: break;
     }
diff --git a/src/backend/oneapi/assign.cpp b/src/backend/oneapi/assign.cpp
index a41365101c..5517793411 100644
--- a/src/backend/oneapi/assign.cpp
+++ b/src/backend/oneapi/assign.cpp
@@ -59,7 +59,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
             // direct buffer allocation as opposed to mem manager to avoid
             // reference count desprepancies between different backends
             static auto* empty = new sycl::buffer<uint>(sycl::range{1});
-            bPtrs[x] = empty;
+            bPtrs[x]           = empty;
         }
     }
 
diff --git a/src/backend/oneapi/bilateral.cpp b/src/backend/oneapi/bilateral.cpp
index 4fef2afd5e..59b050d2bf 100644
--- a/src/backend/oneapi/bilateral.cpp
+++ b/src/backend/oneapi/bilateral.cpp
@@ -9,8 +9,8 @@
 
 #include <Array.hpp>
 #include <bilateral.hpp>
-#include <af/dim4.hpp>
 #include <err_oneapi.hpp>
+#include <af/dim4.hpp>
 
 using af::dim4;
 
@@ -22,7 +22,6 @@ Array<outType> bilateral(const Array<inType> &in, const float &sSigma,
     ONEAPI_NOT_SUPPORTED("");
     Array<outType> out = createEmptyArray<outType>(in.dims());
     return out;
-
 }
 
 #define INSTANTIATE(inT, outT)                                    \
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 852b277870..c8e8d69c98 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -26,9 +26,11 @@ using common::half;
 
 namespace oneapi {
 
-void initBlas() { /*gpu_blas_init();*/ }
+void initBlas() { /*gpu_blas_init();*/
+}
 
-void deInitBlas() { /*gpu_blas_deinit();*/ }
+void deInitBlas() { /*gpu_blas_deinit();*/
+}
 
 template<typename T>
 void gemm_fallback(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index a682ac7bfd..cc85d37005 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -30,13 +30,13 @@
 
 using common::loggerFactory;
 using fmt::format;
-//using oneapi::getActiveDeviceId;
-//using oneapi::getDevice;
-using sycl::kernel_bundle;
-using sycl::bundle_state;
+// using oneapi::getActiveDeviceId;
+// using oneapi::getDevice;
 using oneapi::Kernel;
 using oneapi::Module;
 using spdlog::logger;
+using sycl::bundle_state;
+using sycl::kernel_bundle;
 
 using std::begin;
 using std::end;
@@ -71,8 +71,8 @@ string getProgramBuildLog(const kernel_bundle<bundle_state::executable> &prog) {
 
 namespace oneapi {
 
-//const static string DEFAULT_MACROS_STR(
-    //"\n\
+// const static string DEFAULT_MACROS_STR(
+//"\n\
                                            //#ifdef USE_DOUBLE\n\
                                            //#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
                                            //#endif\n                     \
@@ -82,15 +82,16 @@ namespace oneapi {
                                            //#define half short\n          \
                                            //#endif\n                      \
                                            //#ifndef M_PI\n               \
-                                           //#define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
+                                           //#define
+// M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n
+//\
                                            //#endif\n                     \
                                            //");
 
 /*
 get_kernel_bundle<>() needs sycl::context
-kernel_bundle<bundle_state::executable> buildProgram(const vector<string> &kernelSources,
-                           const vector<string> &compileOpts) {
-    ONEAPI_NOT_SUPPORTED("");
+kernel_bundle<bundle_state::executable> buildProgram(const vector<string>
+&kernelSources, const vector<string> &compileOpts) { ONEAPI_NOT_SUPPORTED("");
     kernel_bundle<bundle_state::executable> bb;
     return bb;
 }
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 622268eb91..d852480342 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -9,10 +9,10 @@
 #include <copy.hpp>
 
 #include <Array.hpp>
-#include <kernel/memcopy.hpp>
 #include <common/complex.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/memcopy.hpp>
 #include <math.hpp>
 
 using common::half;
@@ -28,7 +28,7 @@ void copyData(T *data, const Array<T> &A) {
     A.eval();
 
     dim_t offset = 0;
-    const sycl::buffer<T>* buf;
+    const sycl::buffer<T> *buf;
     Array<T> out = A;
 
     if (A.isLinear() ||  // No offsets, No strides
@@ -44,12 +44,15 @@ void copyData(T *data, const Array<T> &A) {
     }
 
     // FIXME: Add checks
-    getQueue().submit([=] (sycl::handler &h) {
-        sycl::range rr(A.elements());
-        sycl::id offset_id(offset);
-        auto offset_acc = const_cast<sycl::buffer<T>*>(buf)->get_access(h, rr, offset_id);
-        h.copy(offset_acc, data);
-    }).wait();
+    getQueue()
+        .submit([=](sycl::handler &h) {
+            sycl::range rr(A.elements());
+            sycl::id offset_id(offset);
+            auto offset_acc = const_cast<sycl::buffer<T> *>(buf)->get_access(
+                h, rr, offset_id);
+            h.copy(offset_acc, data);
+        })
+        .wait();
 }
 
 template<typename T>
@@ -61,17 +64,21 @@ Array<T> copyArray(const Array<T> &A) {
     if (A.isLinear()) {
         // FIXME: Add checks
 
-        const sycl::buffer<T>* A_buf = A.get();
-        sycl::buffer<T>* out_buf = out.get();
-
-        getQueue().submit([=] (sycl::handler &h) {
-            sycl::range rr(A.elements());
-            sycl::id offset_id(offset);
-            auto offset_acc_A  = const_cast<sycl::buffer<T>*>(A_buf)->get_access(h, rr, offset_id);
-            auto acc_out = out_buf->get_access(h);
-
-            h.copy(offset_acc_A, acc_out);
-        }).wait();
+        const sycl::buffer<T> *A_buf = A.get();
+        sycl::buffer<T> *out_buf     = out.get();
+
+        getQueue()
+            .submit([=](sycl::handler &h) {
+                sycl::range rr(A.elements());
+                sycl::id offset_id(offset);
+                auto offset_acc_A =
+                    const_cast<sycl::buffer<T> *>(A_buf)->get_access(h, rr,
+                                                                     offset_id);
+                auto acc_out = out_buf->get_access(h);
+
+                h.copy(offset_acc_A, acc_out);
+            })
+            .wait();
     } else {
         kernel::memcopy<T>(out.get(), out.strides().get(), A.get(),
                            A.dims().get(), A.strides().get(), offset,
@@ -98,23 +105,27 @@ struct copyWrapper<T, T> {
     void operator()(Array<T> &out, Array<T> const &in) {
         if (out.isLinear() && in.isLinear() &&
             out.elements() == in.elements()) {
-
             dim_t in_offset  = in.getOffset() * sizeof(T);
             dim_t out_offset = out.getOffset() * sizeof(T);
 
-            const sycl::buffer<T>* in_buf = in.get();
-            sycl::buffer<T>* out_buf = out.get();
+            const sycl::buffer<T> *in_buf = in.get();
+            sycl::buffer<T> *out_buf      = out.get();
 
-            getQueue().submit([=] (sycl::handler &h) {
-                sycl::range rr(in.elements());
-                sycl::id in_offset_id(in_offset);
-                sycl::id out_offset_id(out_offset);
+            getQueue()
+                .submit([=](sycl::handler &h) {
+                    sycl::range rr(in.elements());
+                    sycl::id in_offset_id(in_offset);
+                    sycl::id out_offset_id(out_offset);
 
-                auto offset_acc_in  = const_cast<sycl::buffer<T>*>(in_buf)->get_access(h, rr, in_offset_id);
-                auto offset_acc_out = out_buf->get_access(h, rr, out_offset_id);
+                    auto offset_acc_in =
+                        const_cast<sycl::buffer<T> *>(in_buf)->get_access(
+                            h, rr, in_offset_id);
+                    auto offset_acc_out =
+                        out_buf->get_access(h, rr, out_offset_id);
 
-                h.copy(offset_acc_in, offset_acc_out);
-            }).wait();
+                    h.copy(offset_acc_in, offset_acc_out);
+                })
+                .wait();
         } else {
             kernel::copy<T, T>(out, in, in.ndims(), scalar<T>(0), 1,
                                in.dims() == out.dims());
@@ -202,12 +213,15 @@ template<typename T>
 T getScalar(const Array<T> &in) {
     T retVal{};
 
-    getQueue().submit([=] (sycl::handler &h) {
-        sycl::range rr(1);
-        sycl::id offset_id(in.getOffset());
-        auto acc_in  = const_cast<sycl::buffer<T>*>(in.get())->get_access(h, rr, offset_id);
-        h.copy(acc_in, (void*)&retVal);
-    }).wait();
+    getQueue()
+        .submit([=](sycl::handler &h) {
+            sycl::range rr(1);
+            sycl::id offset_id(in.getOffset());
+            auto acc_in = const_cast<sycl::buffer<T> *>(in.get())->get_access(
+                h, rr, offset_id);
+            h.copy(acc_in, (void *)&retVal);
+        })
+        .wait();
 
     return retVal;
 }
diff --git a/src/backend/oneapi/copy.hpp b/src/backend/oneapi/copy.hpp
index 00f01a8ac4..30d6196aa2 100644
--- a/src/backend/oneapi/copy.hpp
+++ b/src/backend/oneapi/copy.hpp
@@ -54,7 +54,7 @@ Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
 
     auto ret = createEmptyArray<T>(oDims);
 
-    //kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
+    // kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
 
     return ret;
 }
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index d4750defae..d8315eac38 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -10,7 +10,6 @@
 #include <common/graphics_common.hpp>
 
 #include <GraphicsResourceManager.hpp>
-#include <platform.hpp> //TODO: blas.hpp? y tho, also Array.hpp 
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
@@ -18,11 +17,12 @@
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <err_oneapi.hpp>
+#include <platform.hpp>  //TODO: blas.hpp? y tho, also Array.hpp
 //#include <errorcodes.hpp>
+#include <memory.hpp>
 #include <version.hpp>
 #include <af/oneapi.h>
 #include <af/version.h>
-#include <memory.hpp>
 
 #include <algorithm>
 #include <iterator>
@@ -45,8 +45,8 @@ namespace oneapi {
 
 static inline bool compare_default(const unique_ptr<sycl::device>& ldev,
                                    const unique_ptr<sycl::device>& rdev) {
-    //TODO: update sorting criteria
-    //select according to something applicable to oneapi backend
+    // TODO: update sorting criteria
+    // select according to something applicable to oneapi backend
     auto l_mem = ldev->get_info<sycl::info::device::local_mem_size>();
     auto r_mem = rdev->get_info<sycl::info::device::local_mem_size>();
     return l_mem > r_mem;
@@ -74,8 +74,9 @@ DeviceManager::DeviceManager()
         vector<sycl::device> current_devices;
         try {
             current_devices = platform.get_devices();
-        } catch(sycl::exception& err) {
-            printf("DeviceManager::DeviceManager() exception: %s\n", err.what());
+        } catch (sycl::exception& err) {
+            printf("DeviceManager::DeviceManager() exception: %s\n",
+                   err.what());
             throw;
         }
         AF_TRACE("Found {} devices on platform {}", current_devices.size(),
@@ -102,18 +103,19 @@ DeviceManager::DeviceManager()
 
     // Create contexts and queues once the sort is done
     for (int i = 0; i < nDevices; i++) {
-        try{
+        try {
             mContexts.push_back(make_unique<sycl::context>(*devices[i]));
-            mQueues.push_back(make_unique<sycl::queue>(
-                *mContexts.back(), *devices[i]));
+            mQueues.push_back(
+                make_unique<sycl::queue>(*mContexts.back(), *devices[i]));
             mIsGLSharingOn.push_back(false);
-            //TODO:
-            //mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
-            //mPlatforms.push_back(getPlatformEnum(*devices[i]));
+            // TODO:
+            // mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
+            // mPlatforms.push_back(getPlatformEnum(*devices[i]));
             mDevices.emplace_back(std::move(devices[i]));
         } catch (sycl::exception& err) {
             AF_TRACE("Error creating context for device {} with error {}\n",
-                     devices[i]->get_info<sycl::info::device::name>(), err.what());
+                     devices[i]->get_info<sycl::info::device::name>(),
+                     err.what());
         }
     }
     nDevices = mDevices.size();
@@ -121,18 +123,18 @@ DeviceManager::DeviceManager()
     bool default_device_set = false;
     string deviceENV        = getEnvVar("AF_ONEAPI_DEFAULT_DEVICE");
     if (!deviceENV.empty()) {
-        //TODO: handle default device from env variable
+        // TODO: handle default device from env variable
     }
 
     deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
     if (!default_device_set && !deviceENV.empty()) {
-        //TODO: handle default device by type env variable
+        // TODO: handle default device by type env variable
     }
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
     string noGraphicsENV = getEnvVar("AF_DISABLE_GRAPHICS");
     if (fgMngr->plugin().isLoaded() && noGraphicsENV.empty()) {
-        //TODO: handle forge shared contexts
+        // TODO: handle forge shared contexts
     }
 
     mUserDeviceOffset = mDevices.size();
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index f6530dcbd9..d84994226c 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -77,7 +77,7 @@ class DeviceManager {
 
     friend int getDeviceCount() noexcept;
 
-    //friend int getDeviceIdFromNativeId(cl_device_id id);
+    // friend int getDeviceIdFromNativeId(cl_device_id id);
 
     friend const sycl::context& getContext();
 
@@ -147,8 +147,8 @@ class DeviceManager {
     std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
     std::mutex mutex;
 
-  //using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
-  //std::vector<BoostProgCache*> mBoostProgCacheVector;
+    // using BoostProgCache = boost::shared_ptr<boost::compute::program_cache>;
+    // std::vector<BoostProgCache*> mBoostProgCacheVector;
 };
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/diff.cpp b/src/backend/oneapi/diff.cpp
index 7dfffc1881..71e331a122 100644
--- a/src/backend/oneapi/diff.cpp
+++ b/src/backend/oneapi/diff.cpp
@@ -10,9 +10,9 @@
 #include <Array.hpp>
 #include <diff.hpp>
 //#include <kernel/diff.hpp>
+#include <err_oneapi.hpp>
 #include <af/dim4.hpp>
 #include <stdexcept>
-#include <err_oneapi.hpp>
 
 namespace oneapi {
 
diff --git a/src/backend/oneapi/errorcodes.cpp b/src/backend/oneapi/errorcodes.cpp
index 615bbb94e7..cf7152fa00 100644
--- a/src/backend/oneapi/errorcodes.cpp
+++ b/src/backend/oneapi/errorcodes.cpp
@@ -7,12 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <errorcodes.hpp>
 #include <err_oneapi.hpp>
-
+#include <errorcodes.hpp>
 
 std::string getErrorMessage(int error_code) {
     ONEAPI_NOT_SUPPORTED("");
-    //return boost::compute::opencl_error::to_string(error_code);
+    // return boost::compute::opencl_error::to_string(error_code);
     return "";
 }
diff --git a/src/backend/oneapi/exampleFunction.cpp b/src/backend/oneapi/exampleFunction.cpp
index dc5c6a8680..bc5c52b031 100644
--- a/src/backend/oneapi/exampleFunction.cpp
+++ b/src/backend/oneapi/exampleFunction.cpp
@@ -16,8 +16,9 @@
 #include <err_oneapi.hpp>  // error check functions and Macros
                            // specific to oneapi backend
 
-//#include <kernel/exampleFunction.hpp>  // this header under the folder src/oneapi/kernel
-                                       // defines the OneAPI kernel wrapper
+//#include <kernel/exampleFunction.hpp>  // this header under the folder
+// src/oneapi/kernel
+// defines the OneAPI kernel wrapper
 // function to which the main computation of your
 // algorithm should be relayed to
 
@@ -41,7 +42,7 @@ Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
     // can create.
 
     // Relay the actual computation to OneAPI kernel wrapper
-    //kernel::exampleFunc<T>(out, a, b, method);
+    // kernel::exampleFunc<T>(out, a, b, method);
 
     return out;  // return the result
 }
diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index 684cc860b7..1591e4b4cf 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -19,8 +19,7 @@ using af::dim4;
 
 namespace oneapi {
 
-void setFFTPlanCacheSize(size_t numPlans) {
-}
+void setFFTPlanCacheSize(size_t numPlans) {}
 
 /*
 template<typename T>
diff --git a/src/backend/oneapi/fftconvolve.cpp b/src/backend/oneapi/fftconvolve.cpp
index 5a2a64d869..dad10f492e 100644
--- a/src/backend/oneapi/fftconvolve.cpp
+++ b/src/backend/oneapi/fftconvolve.cpp
@@ -11,9 +11,9 @@
 
 #include <Array.hpp>
 #include <common/dispatch.hpp>
+#include <err_oneapi.hpp>
 #include <fft.hpp>
 #include <af/dim4.hpp>
-#include <err_oneapi.hpp>
 
 #include <cmath>
 #include <type_traits>
diff --git a/src/backend/oneapi/gradient.cpp b/src/backend/oneapi/gradient.cpp
index 0755b7a691..40b557a4ae 100644
--- a/src/backend/oneapi/gradient.cpp
+++ b/src/backend/oneapi/gradient.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <gradient.hpp>
 #include <err_oneapi.hpp>
+#include <gradient.hpp>
 //#include <kernel/gradient.hpp>
 #include <math.hpp>
 #include <stdexcept>
diff --git a/src/backend/oneapi/homography.cpp b/src/backend/oneapi/homography.cpp
index e9b08cc475..5060cd50ae 100644
--- a/src/backend/oneapi/homography.cpp
+++ b/src/backend/oneapi/homography.cpp
@@ -10,8 +10,8 @@
 #include <homography.hpp>
 
 #include <arith.hpp>
-#include <af/dim4.hpp>
 #include <err_oneapi.hpp>
+#include <af/dim4.hpp>
 
 #include <algorithm>
 #include <limits>
diff --git a/src/backend/oneapi/inverse.cpp b/src/backend/oneapi/inverse.cpp
index 60026719db..079250d4f7 100644
--- a/src/backend/oneapi/inverse.cpp
+++ b/src/backend/oneapi/inverse.cpp
@@ -39,7 +39,8 @@ namespace oneapi {
 template<typename T>
 Array<T> inverse(const Array<T> &in) {
     ONEAPI_NOT_SUPPORTED("");
-    AF_ERROR("Linear Algebra is disabled on OneAPI backend", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend",
+             AF_ERR_NOT_CONFIGURED);
 }
 
 #define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
index 2e6ef7fe34..9925ec7211 100644
--- a/src/backend/oneapi/jit/BufferNode.hpp
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -14,10 +14,11 @@
 
 namespace oneapi {
 namespace jit {
-  template<typename T>
-  using BufferNode = common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, KParam>;
+template<typename T>
+using BufferNode =
+    common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, KParam>;
 }
-}  // namespace opencl
+}  // namespace oneapi
 
 namespace common {
 
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index 607d85ce98..a49b25de0c 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -35,15 +35,16 @@ inline int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
     const std::shared_ptr<sycl::buffer<T>>& ptr, const KParam& info) {
-  // TODO(oneapi)
-  ONEAPI_NOT_SUPPORTED("ERROR");
-  //setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
-  //sizeof(cl_mem));
+    // TODO(oneapi)
+    ONEAPI_NOT_SUPPORTED("ERROR");
+    // setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
+    // sizeof(cl_mem));
     if (is_linear) {
-      //setArg(start_id + 1, static_cast<const void*>(&info.offset),
-      //sizeof(dim_t));
+        // setArg(start_id + 1, static_cast<const void*>(&info.offset),
+        // sizeof(dim_t));
     } else {
-      //setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam));
+        // setArg(start_id + 1, static_cast<const void*>(&info),
+        // sizeof(KParam));
     }
     return start_id + 2;
 }
@@ -109,4 +110,4 @@ inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
               << "];\n";
 }
 }  // namespace
-}  // namespace opencl
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 9896306cac..7a75735f50 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -40,18 +40,26 @@ static int trimIndex(int idx, const int len) {
 
 template<typename T>
 class assignKernel {
-public:
-    assignKernel(sycl::accessor<T> out, KParam oInfo,
-                sycl::accessor<T> in, KParam iInfo, AssignKernelParam_t p,
-                sycl::accessor<uint> ptr0, sycl::accessor<uint> ptr1, 
-                sycl::accessor<uint> ptr2, sycl::accessor<uint> ptr3,
-                const int nBBS0, const int nBBS1, sycl::stream debug) :
-        out_(out), oInfo_(oInfo), in_(in), iInfo_(iInfo), p_(p),
-        ptr0_(ptr0), ptr1_(ptr1), ptr2_(ptr2), ptr3_(ptr3),
-        nBBS0_(nBBS0), nBBS1_(nBBS1), debug_(debug) {}
-
-
-    void operator() (sycl::nd_item<2> it) const {
+   public:
+    assignKernel(sycl::accessor<T> out, KParam oInfo, sycl::accessor<T> in,
+                 KParam iInfo, AssignKernelParam_t p, sycl::accessor<uint> ptr0,
+                 sycl::accessor<uint> ptr1, sycl::accessor<uint> ptr2,
+                 sycl::accessor<uint> ptr3, const int nBBS0, const int nBBS1,
+                 sycl::stream debug)
+        : out_(out)
+        , oInfo_(oInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , p_(p)
+        , ptr0_(ptr0)
+        , ptr1_(ptr1)
+        , ptr2_(ptr2)
+        , ptr3_(ptr3)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
         // retrive booleans that tell us which index to use
         const bool s0 = p_.isSeq[0];
         const bool s1 = p_.isSeq[1];
@@ -59,12 +67,14 @@ class assignKernel {
         const bool s3 = p_.isSeq[3];
 
         sycl::group g = it.get_group();
-        const int gz = g.get_group_id(0) / nBBS0_;
-        const int gw = g.get_group_id(1) / nBBS1_;
+        const int gz  = g.get_group_id(0) / nBBS0_;
+        const int gw  = g.get_group_id(1) / nBBS1_;
         const int gx =
-            g.get_local_range(0) * (g.get_group_id(0) - gz * nBBS0_) + it.get_local_id(0);
+            g.get_local_range(0) * (g.get_group_id(0) - gz * nBBS0_) +
+            it.get_local_id(0);
         const int gy =
-            g.get_local_range(1) * (g.get_group_id(1) - gw * nBBS1_) + it.get_local_id(1);
+            g.get_local_range(1) * (g.get_group_id(1) - gw * nBBS1_) +
+            it.get_local_id(1);
         if (gx < iInfo_.dims[0] && gy < iInfo_.dims[1] && gz < iInfo_.dims[2] &&
             gw < iInfo_.dims[3]) {
             // calculate pointer offsets for input
@@ -76,22 +86,22 @@ class assignKernel {
                     trimIndex(s2 ? gz + p_.offs[2] : ptr2_[gz], oInfo_.dims[2]);
             int l = p_.strds[3] *
                     trimIndex(s3 ? gw + p_.offs[3] : ptr3_[gw], oInfo_.dims[3]);
-            
+
             T* iptr = in_.get_pointer();
             // offset input and output pointers
             const T* src =
-                iptr +
-                (gx * iInfo_.strides[0] + gy * iInfo_.strides[1] +
-                 gz * iInfo_.strides[2] + gw * iInfo_.strides[3] + iInfo_.offset);
+                iptr + (gx * iInfo_.strides[0] + gy * iInfo_.strides[1] +
+                        gz * iInfo_.strides[2] + gw * iInfo_.strides[3] +
+                        iInfo_.offset);
 
             T* optr = out_.get_pointer();
-            T* dst = optr + (i + j + k + l) + oInfo_.offset;
+            T* dst  = optr + (i + j + k + l) + oInfo_.offset;
             // set the output
             dst[0] = src[0];
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_, in_;
     KParam oInfo_, iInfo_;
     AssignKernelParam_t p_;
@@ -114,7 +124,7 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
     sycl::range<2> global(blk_x * in.info.dims[2] * THREADS_X,
                           blk_y * in.info.dims[3] * THREADS_Y);
 
-    getQueue().submit([=] (sycl::handler &h) {
+    getQueue().submit([=](sycl::handler& h) {
         auto out_acc = out.data->get_access(h);
         auto in_acc  = in.data->get_access(h);
 
@@ -125,10 +135,10 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
 
         sycl::stream debug_stream(2048, 128, h);
 
-        h.parallel_for(sycl::nd_range<2>(global, local), assignKernel<T>(
-            out_acc, out.info, in_acc, in.info, 
-            p, bptr0, bptr1, bptr2, bptr3,
-            blk_x, blk_y, debug_stream));
+        h.parallel_for(
+            sycl::nd_range<2>(global, local),
+            assignKernel<T>(out_acc, out.info, in_acc, in.info, p, bptr0, bptr1,
+                            bptr2, bptr3, blk_x, blk_y, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 5141726cdb..d4672dfd0d 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -25,23 +25,28 @@ namespace kernel {
 
 template<typename T>
 class iotaKernel {
-public:
-    iotaKernel(sycl::accessor<T> out, KParam oinfo,
-               const int s0, const int s1, const int s2, const int s3,
-               const int blocksPerMatX, const int blocksPerMatY, 
-               sycl::stream debug) :
-        out_(out), oinfo_(oinfo),
-        s0_(s0), s1_(s1), s2_(s2), s3_(s3),
-        blocksPerMatX_(blocksPerMatX), blocksPerMatY_(blocksPerMatY),
-        debug_(debug) {}
-
-    void operator() (sycl::nd_item<2> it) const {
-        //printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
-        //debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) << "]" << sycl::stream_manipulator::endl;
+   public:
+    iotaKernel(sycl::accessor<T> out, KParam oinfo, const int s0, const int s1,
+               const int s2, const int s3, const int blocksPerMatX,
+               const int blocksPerMatY, sycl::stream debug)
+        : out_(out)
+        , oinfo_(oinfo)
+        , s0_(s0)
+        , s1_(s1)
+        , s2_(s2)
+        , s3_(s3)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        // printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
+        // debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) <<
+        // "]" << sycl::stream_manipulator::endl;
 
         sycl::group gg = it.get_group();
-        const int oz = gg.get_group_id(0) / blocksPerMatX_;
-        const int ow = gg.get_group_id(1) / blocksPerMatY_;
+        const int oz   = gg.get_group_id(0) / blocksPerMatX_;
+        const int ow   = gg.get_group_id(1) / blocksPerMatY_;
 
         const int blockIdx_x = gg.get_group_id(0) - oz * blocksPerMatX_;
         const int blockIdx_y = gg.get_group_id(1) - ow * blocksPerMatY_;
@@ -49,14 +54,14 @@ class iotaKernel {
         const int xx = it.get_local_id(0) + blockIdx_x * gg.get_local_range(0);
         const int yy = it.get_local_id(1) + blockIdx_y * gg.get_local_range(1);
 
-        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] || oz >= oinfo_.dims[2] ||
-            ow >= oinfo_.dims[3])
+        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] ||
+            oz >= oinfo_.dims[2] || ow >= oinfo_.dims[3])
             return;
 
         const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
 
         T val = static_cast<T>((ow % s3_) * s2_ * s1_ * s0_);
-        val +=  static_cast<T>((oz % s2_) * s1_ * s0_);
+        val += static_cast<T>((oz % s2_) * s1_ * s0_);
 
         const int incy = blocksPerMatY_ * gg.get_local_range(1);
         const int incx = blocksPerMatX_ * gg.get_local_range(0);
@@ -65,13 +70,13 @@ class iotaKernel {
             T valY   = val + (oy % s1_) * s0_;
             int oyzw = ozw + oy * oinfo_.strides[1];
             for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
-                int oidx = oyzw + ox;
+                int oidx   = oyzw + ox;
                 out_[oidx] = valY + (ox % s0_);
             }
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     KParam oinfo_;
     int s0_, s1_, s2_, s3_;
@@ -94,15 +99,17 @@ void iota(Param<T> out, const af::dim4& sdims) {
                           local[1] * blocksPerMatY * out.info.dims[3]);
     sycl::nd_range<2> ndrange(global, local);
 
-    getQueue().submit([=] (sycl::handler &h) {
+    getQueue().submit([=](sycl::handler& h) {
         auto out_acc = out.data->get_access(h);
 
         sycl::stream debug_stream(2048, 128, h);
 
-        h.parallel_for(ndrange, iotaKernel<T>(out_acc, out.info,
-            static_cast<int>(sdims[0]), static_cast<int>(sdims[1]),
-            static_cast<int>(sdims[2]), static_cast<int>(sdims[3]),
-            blocksPerMatX, blocksPerMatY, debug_stream));
+        h.parallel_for(
+            ndrange, iotaKernel<T>(
+                         out_acc, out.info, static_cast<int>(sdims[0]),
+                         static_cast<int>(sdims[1]), static_cast<int>(sdims[2]),
+                         static_cast<int>(sdims[3]), blocksPerMatX,
+                         blocksPerMatY, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 4376ae0121..3f3fdce1ae 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -31,20 +31,28 @@ typedef struct {
 
 template<typename T>
 class memCopy {
-public:
-    memCopy(sycl::accessor<T> out, dims_t ostrides, 
-            sycl::accessor<T> in, dims_t idims, dims_t istrides,
-            int offset, int groups_0, int groups_1, sycl::stream debug) :
-                out_(out), ostrides_(ostrides), in_(in), idims_(idims), istrides_(istrides),
-                offset_(offset), groups_0_(groups_0), groups_1_(groups_1), debug_(debug) {}
-
-    void operator() (sycl::nd_item<2> it) const {
-        //printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
-        //debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) << "]" << sycl::stream_manipulator::endl;
+   public:
+    memCopy(sycl::accessor<T> out, dims_t ostrides, sycl::accessor<T> in,
+            dims_t idims, dims_t istrides, int offset, int groups_0,
+            int groups_1, sycl::stream debug)
+        : out_(out)
+        , ostrides_(ostrides)
+        , in_(in)
+        , idims_(idims)
+        , istrides_(istrides)
+        , offset_(offset)
+        , groups_0_(groups_0)
+        , groups_1_(groups_1)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        // printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
+        // debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) <<
+        // "]" << sycl::stream_manipulator::endl;
         const int lid0 = it.get_local_id(0);
         const int lid1 = it.get_local_id(1);
 
-        sycl::group gg = it.get_group();
+        sycl::group gg       = it.get_group();
         const int id2        = gg.get_group_id(0) / groups_0_;
         const int id3        = gg.get_group_id(1) / groups_1_;
         const int group_id_0 = gg.get_group_id(0) - groups_0_ * id2;
@@ -52,16 +60,18 @@ class memCopy {
         const int id0        = group_id_0 * gg.get_local_range(0) + lid0;
         const int id1        = group_id_1 * gg.get_local_range(1) + lid1;
 
-        debug_ << "[" << id0 <<  "," << id1 <<  "," << id2 <<  "," << id3 <<  "]" << sycl::stream_manipulator::endl;
+        debug_ << "[" << id0 << "," << id1 << "," << id2 << "," << id3 << "]"
+               << sycl::stream_manipulator::endl;
 
-        T* iptr = in_.get_pointer();
+        T *iptr = in_.get_pointer();
         iptr += offset_;
         // FIXME: Do more work per work group
 
-        T* optr = out_.get_pointer();
-        optr +=
-            id3 * ostrides_.dim[3] + id2 * ostrides_.dim[2] + id1 * ostrides_.dim[1];
-        iptr += id3 * istrides_.dim[3] + id2 * istrides_.dim[2] + id1 * istrides_.dim[1];
+        T *optr = out_.get_pointer();
+        optr += id3 * ostrides_.dim[3] + id2 * ostrides_.dim[2] +
+                id1 * ostrides_.dim[1];
+        iptr += id3 * istrides_.dim[3] + id2 * istrides_.dim[2] +
+                id1 * istrides_.dim[1];
 
         int istride0 = istrides_.dim[0];
         if (id0 < idims_.dim[0] && id1 < idims_.dim[1] && id2 < idims_.dim[2] &&
@@ -70,27 +80,25 @@ class memCopy {
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_, in_;
     dims_t ostrides_, idims_, istrides_;
     int offset_, groups_0_, groups_1_;
     sycl::stream debug_;
 };
 
-
 constexpr uint DIM0 = 32;
 constexpr uint DIM1 = 8;
 
 template<typename T>
-void memcopy(sycl::buffer<T>* out, const dim_t *ostrides, const sycl::buffer<T>* in,
-             const dim_t *idims, const dim_t *istrides, int offset,
-             uint ndims) {
-    
+void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
+             const sycl::buffer<T> *in, const dim_t *idims,
+             const dim_t *istrides, int offset, uint ndims) {
     dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
     dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
     dims_t _idims    = {{idims[0], idims[1], idims[2], idims[3]}};
 
-    size_t local_size[2] = { DIM0, DIM1 };
+    size_t local_size[2] = {DIM0, DIM1};
     if (ndims == 1) {
         local_size[0] *= local_size[1];
         local_size[1] = 1;
@@ -104,18 +112,20 @@ void memcopy(sycl::buffer<T>* out, const dim_t *ostrides, const sycl::buffer<T>*
                           groups_1 * idims[3] * local_size[1]);
     sycl::nd_range<2> ndrange(global, local);
 
-    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0), ndrange.get_global_range().get(1), ndrange.get_local_range().get(0), ndrange.get_local_range().get(1));
-    printf("<%d, %d> ", ndrange.get_group_range().get(0), ndrange.get_group_range().get(1));
-    getQueue().submit([=] (sycl::handler &h) {
+    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0),
+           ndrange.get_global_range().get(1), ndrange.get_local_range().get(0),
+           ndrange.get_local_range().get(1));
+    printf("<%d, %d> ", ndrange.get_group_range().get(0),
+           ndrange.get_group_range().get(1));
+    getQueue().submit([=](sycl::handler &h) {
         auto out_acc = out->get_access(h);
-        auto in_acc  = const_cast<sycl::buffer<T>*>(in)->get_access(h);
+        auto in_acc  = const_cast<sycl::buffer<T> *>(in)->get_access(h);
 
         sycl::stream debug_stream(2048, 128, h);
 
-        h.parallel_for(ndrange, memCopy<T>(
-            out_acc, _ostrides, 
-            in_acc, _idims, _istrides,
-            offset, groups_0, groups_1, debug_stream));
+        h.parallel_for(ndrange,
+                       memCopy<T>(out_acc, _ostrides, in_acc, _idims, _istrides,
+                                  offset, groups_0, groups_1, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -141,27 +151,24 @@ outType convertType(inType value) {
 }
 
 template<>
-char convertType<compute_t<common::half>, char>(
-    compute_t<common::half> value) {
+char convertType<compute_t<common::half>, char>(compute_t<common::half> value) {
     return (char)((short)value);
 }
 
 template<>
-compute_t<common::half>
-convertType<char, compute_t<common::half>>(char value) {
+compute_t<common::half> convertType<char, compute_t<common::half>>(char value) {
     return compute_t<common::half>(value);
 }
 
 template<>
-unsigned char
-convertType<compute_t<common::half>, unsigned char>(
+unsigned char convertType<compute_t<common::half>, unsigned char>(
     compute_t<common::half> value) {
     return (unsigned char)((short)value);
 }
 
 template<>
-compute_t<common::half>
-convertType<unsigned char, compute_t<common::half>>(unsigned char value) {
+compute_t<common::half> convertType<unsigned char, compute_t<common::half>>(
+    unsigned char value) {
     return compute_t<common::half>(value);
 }
 
@@ -175,15 +182,15 @@ cfloat convertType<cdouble, cfloat>(cdouble value) {
     return cfloat(value.real(), value.imag());
 }
 
-#define OTHER_SPECIALIZATIONS(IN_T)                         \
-    template<>                                              \
-    cfloat convertType<IN_T, cfloat>(IN_T value) {          \
-        return cfloat(static_cast<float>(value), 0.0f);     \
-    }                                                       \
-                                                            \
-    template<>                                              \
-    cdouble convertType<IN_T, cdouble>(IN_T value) {        \
-        return cdouble(static_cast<double>(value), 0.0);    \
+#define OTHER_SPECIALIZATIONS(IN_T)                      \
+    template<>                                           \
+    cfloat convertType<IN_T, cfloat>(IN_T value) {       \
+        return cfloat(static_cast<float>(value), 0.0f);  \
+    }                                                    \
+                                                         \
+    template<>                                           \
+    cdouble convertType<IN_T, cdouble>(IN_T value) {     \
+        return cdouble(static_cast<double>(value), 0.0); \
     }
 
 OTHER_SPECIALIZATIONS(float)
@@ -200,21 +207,27 @@ OTHER_SPECIALIZATIONS(common::half)
 
 template<typename inType, typename outType, bool SAMEDIMS>
 class reshapeCopy {
-public:
-    reshapeCopy(sycl::accessor<outType> dst, KParam oInfo, 
-                sycl::accessor<inType> src, KParam iInfo,
-                outType default_value, float factor, dims_t trgt,
-                int blk_x, int blk_y, sycl::stream debug) : 
-            dst_(dst), oInfo_(oInfo), src_(src), iInfo_(iInfo),
-            default_value_(default_value), factor_(factor), trgt_(trgt),
-            blk_x_(blk_x), blk_y_(blk_y), debug_(debug) {}
-
-    void operator() (sycl::nd_item<2> it) const {
-
+   public:
+    reshapeCopy(sycl::accessor<outType> dst, KParam oInfo,
+                sycl::accessor<inType> src, KParam iInfo, outType default_value,
+                float factor, dims_t trgt, int blk_x, int blk_y,
+                sycl::stream debug)
+        : dst_(dst)
+        , oInfo_(oInfo)
+        , src_(src)
+        , iInfo_(iInfo)
+        , default_value_(default_value)
+        , factor_(factor)
+        , trgt_(trgt)
+        , blk_x_(blk_x)
+        , blk_y_(blk_y)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
         const uint lx = it.get_local_id(0);
         const uint ly = it.get_local_id(1);
 
-        sycl::group gg = it.get_group();
+        sycl::group gg  = it.get_group();
         uint gz         = gg.get_group_id(0) / blk_x_;
         uint gw         = gg.get_group_id(1) / blk_y_;
         uint blockIdx_x = gg.get_group_id(0) - (blk_x_)*gz;
@@ -222,21 +235,23 @@ class reshapeCopy {
         uint gx         = blockIdx_x * gg.get_local_range(0) + lx;
         uint gy         = blockIdx_y * gg.get_local_range(1) + ly;
 
-        const inType* srcptr  = src_.get_pointer();
-        outType* dstptr = dst_.get_pointer();
+        const inType *srcptr = src_.get_pointer();
+        outType *dstptr      = dst_.get_pointer();
 
         const inType *in =
             srcptr + (gw * iInfo_.strides[3] + gz * iInfo_.strides[2] +
                       gy * iInfo_.strides[1] + iInfo_.offset);
-        outType *out = dstptr + (gw * oInfo_.strides[3] + gz * oInfo_.strides[2] +
-                                 gy * oInfo_.strides[1] + oInfo_.offset);
+        outType *out =
+            dstptr + (gw * oInfo_.strides[3] + gz * oInfo_.strides[2] +
+                      gy * oInfo_.strides[1] + oInfo_.offset);
 
         uint istride0 = iInfo_.strides[0];
         uint ostride0 = oInfo_.strides[0];
 
         if (gy < oInfo_.dims[1] && gz < oInfo_.dims[2] && gw < oInfo_.dims[3]) {
             int loop_offset = gg.get_local_range(0) * blk_x_;
-            bool cond = gy < trgt_.dim[1] && gz < trgt_.dim[2] && gw < trgt_.dim[3];
+            bool cond =
+                gy < trgt_.dim[1] && gz < trgt_.dim[2] && gw < trgt_.dim[3];
             for (int rep = gx; rep < oInfo_.dims[0]; rep += loop_offset) {
                 outType temp = default_value_;
                 if (SAMEDIMS || (rep < trgt_.dim[0] && cond)) {
@@ -248,7 +263,7 @@ class reshapeCopy {
         }
     }
 
-protected:
+   protected:
     sycl::accessor<outType> dst_;
     sycl::accessor<inType> src_;
     KParam oInfo_, iInfo_;
@@ -264,7 +279,7 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
           const outType default_value, const double factor,
           const bool same_dims) {
     using std::string;
-    
+
     sycl::range<2> local(DIM0, DIM1);
     size_t local_size[] = {DIM0, DIM1};
 
@@ -279,8 +294,11 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
 
     sycl::nd_range<2> ndrange(global, local);
     printf("reshape wat?\n");
-    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0), ndrange.get_global_range().get(1), ndrange.get_local_range().get(0), ndrange.get_local_range().get(1));
-    printf("<%d, %d> ", ndrange.get_group_range().get(0), ndrange.get_group_range().get(1));
+    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0),
+           ndrange.get_global_range().get(1), ndrange.get_local_range().get(0),
+           ndrange.get_local_range().get(1));
+    printf("<%d, %d> ", ndrange.get_group_range().get(0),
+           ndrange.get_group_range().get(1));
 
     dims_t trgt_dims;
     if (same_dims) {
@@ -294,24 +312,23 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
         trgt_dims    = {{trgt_i, trgt_j, trgt_k, trgt_l}};
     }
 
-    getQueue().submit([=] (sycl::handler &h) {
+    getQueue().submit([=](sycl::handler &h) {
         auto dst_acc = dst.data->get_access(h);
-        auto src_acc  = const_cast<sycl::buffer<inType>*>(src.data)->get_access(h);
+        auto src_acc =
+            const_cast<sycl::buffer<inType> *>(src.data)->get_access(h);
 
         sycl::stream debug_stream(2048, 128, h);
 
-        if(same_dims) {
+        if (same_dims) {
             h.parallel_for(ndrange, reshapeCopy<inType, outType, true>(
-                    dst_acc, dst.info,
-                    src_acc, src.info,
-                    default_value, (float)factor, trgt_dims,
-                    blk_x, blk_y, debug_stream));
+                                        dst_acc, dst.info, src_acc, src.info,
+                                        default_value, (float)factor, trgt_dims,
+                                        blk_x, blk_y, debug_stream));
         } else {
             h.parallel_for(ndrange, reshapeCopy<inType, outType, false>(
-                    dst_acc, dst.info,
-                    src_acc, src.info,
-                    default_value, (float)factor, trgt_dims,
-                    blk_x, blk_y, debug_stream));
+                                        dst_acc, dst.info, src_acc, src.info,
+                                        default_value, (float)factor, trgt_dims,
+                                        blk_x, blk_y, debug_stream));
         }
     });
     ONEAPI_DEBUG_FINISH(getQueue());
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
index 4597b33a3a..8c9b5e9251 100644
--- a/src/backend/oneapi/kernel/random_engine.hpp
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -8,18 +8,18 @@
  ********************************************************/
 #pragma once
 
-#include <af/defines.h>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
-#include <random_engine.hpp>
-#include <kernel/random_engine_write.hpp>
 #include <kernel/random_engine_mersenne.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
+#include <kernel/random_engine_write.hpp>
+#include <random_engine.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+#include <af/defines.h>
 
 #include <functional>
 #include <string>
@@ -33,14 +33,14 @@ static const int STATE_SIZE = (256 * 3);
 namespace oneapi {
 namespace kernel {
 
-static const uint THREADS = 256;
+static const uint THREADS           = 256;
 static const uint THREADS_PER_GROUP = 256;
 static const uint THREADS_X         = 32;
 static const uint THREADS_Y         = THREADS_PER_GROUP / THREADS_X;
 static const uint REPEAT            = 32;
 
 template<typename T>
-void uniformDistributionCBRNG(Param<T> out,  const size_t elements,
+void uniformDistributionCBRNG(Param<T> out, const size_t elements,
                               const af_random_engine_type type,
                               const uintl &seed, uintl &counter) {
     int threads          = THREADS;
@@ -50,31 +50,30 @@ void uniformDistributionCBRNG(Param<T> out,  const size_t elements,
     uint lo              = seed;
     uint hic             = counter >> 32;
     uint loc             = counter;
-    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
+                              sycl::range<1>(threads));
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
-            getQueue().submit([=] (sycl::handler &h) {
+            getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
                 sycl::stream debug_stream(2048, 128, h);
-                h.parallel_for(ndrange, 
-                    uniformPhilox<T>(out_acc,
-                                     hi, lo, hic, loc,
-                                     elementsPerBlock, elements,
-                                     debug_stream));
+                h.parallel_for(
+                    ndrange,
+                    uniformPhilox<T>(out_acc, hi, lo, hic, loc,
+                                     elementsPerBlock, elements, debug_stream));
             });
             ONEAPI_DEBUG_FINISH(getQueue());
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
-            getQueue().submit([=] (sycl::handler &h) {
+            getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
                 sycl::stream debug_stream(2048, 128, h);
-                h.parallel_for(ndrange, 
-                    uniformThreefry<T>(out_acc,
-                                       hi, lo, hic, loc,
-                                       elementsPerBlock, elements,
-                                       debug_stream));
+                h.parallel_for(ndrange,
+                               uniformThreefry<T>(out_acc, hi, lo, hic, loc,
+                                                  elementsPerBlock, elements,
+                                                  debug_stream));
             });
             ONEAPI_DEBUG_FINISH(getQueue());
             break;
@@ -95,30 +94,29 @@ void normalDistributionCBRNG(Param<T> out, const size_t elements,
     uint lo              = seed;
     uint hic             = counter >> 32;
     uint loc             = counter;
-    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
+                              sycl::range<1>(threads));
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
-            getQueue().submit([=] (sycl::handler &h) {
+            getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
                 sycl::stream debug_stream(2048, 128, h);
-                h.parallel_for(ndrange, 
-                    normalPhilox<T>(out_acc,
-                                    hi, lo, hic, loc,
-                                    elementsPerBlock, elements,
-                                    debug_stream));
+                h.parallel_for(
+                    ndrange,
+                    normalPhilox<T>(out_acc, hi, lo, hic, loc, elementsPerBlock,
+                                    elements, debug_stream));
             });
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
-            getQueue().submit([=] (sycl::handler &h) {
+            getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
                 sycl::stream debug_stream(2048, 128, h);
-                h.parallel_for(ndrange, 
-                    normalThreefry<T>(out_acc,
-                                      hi, lo, hic, loc,
-                                      elementsPerBlock, elements,
-                                      debug_stream));
+                h.parallel_for(ndrange,
+                               normalThreefry<T>(out_acc, hi, lo, hic, loc,
+                                                 elementsPerBlock, elements,
+                                                 debug_stream));
             });
             break;
         default:
@@ -140,8 +138,9 @@ void uniformDistributionMT(Param<T> out, const size_t elements,
     blocks                     = (blocks > BLOCKS) ? BLOCKS : blocks;
     uint elementsPerBlock      = divup(elements, blocks);
 
-    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
-    getQueue().submit([=] (sycl::handler &h) {
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
+                              sycl::range<1>(threads));
+    getQueue().submit([=](sycl::handler &h) {
         auto out_acc       = out.data->get_access(h);
         auto state_acc     = state.data->get_access(h);
         auto pos_acc       = pos.data->get_access(h);
@@ -155,12 +154,11 @@ void uniformDistributionMT(Param<T> out, const size_t elements,
         auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
 
         sycl::stream debug_stream(2048, 128, h);
-        h.parallel_for(ndrange, 
-            uniformMersenne<T>(out_acc,
-                               state_acc, pos_acc, sh1_acc, sh2_acc, mask, 
-                               recursion_acc, temper_acc,
-                               lstate_acc, lrecursion_acc, ltemper_acc,
-                               elementsPerBlock, elements, debug_stream));
+        h.parallel_for(ndrange, uniformMersenne<T>(
+                                    out_acc, state_acc, pos_acc, sh1_acc,
+                                    sh2_acc, mask, recursion_acc, temper_acc,
+                                    lstate_acc, lrecursion_acc, ltemper_acc,
+                                    elementsPerBlock, elements, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -169,15 +167,17 @@ template<typename T>
 void normalDistributionMT(Param<T> out, const size_t elements,
                           Param<uint> state, Param<uint> pos, Param<uint> sh1,
                           Param<uint> sh2, const uint mask,
-                          Param<uint> recursion_table, Param<uint> temper_table) {
+                          Param<uint> recursion_table,
+                          Param<uint> temper_table) {
     int threads                = THREADS;
     int min_elements_per_block = 32 * threads * 4 * sizeof(uint) / sizeof(T);
     int blocks                 = divup(elements, min_elements_per_block);
     blocks                     = (blocks > BLOCKS) ? BLOCKS : blocks;
     uint elementsPerBlock      = divup(elements, blocks);
 
-    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads), sycl::range<1>(threads));
-    getQueue().submit([=] (sycl::handler &h) {
+    sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
+                              sycl::range<1>(threads));
+    getQueue().submit([=](sycl::handler &h) {
         auto out_acc       = out.data->get_access(h);
         auto state_acc     = state.data->get_access(h);
         auto pos_acc       = pos.data->get_access(h);
@@ -191,12 +191,11 @@ void normalDistributionMT(Param<T> out, const size_t elements,
         auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
 
         sycl::stream debug_stream(2048, 128, h);
-        h.parallel_for(ndrange, 
-            normalMersenne<T>(out_acc,
-                              state_acc, pos_acc, sh1_acc, sh2_acc, mask, 
-                              recursion_acc, temper_acc,
-                              lstate_acc, lrecursion_acc, ltemper_acc,
-                              elementsPerBlock, elements, debug_stream));
+        h.parallel_for(ndrange, normalMersenne<T>(
+                                    out_acc, state_acc, pos_acc, sh1_acc,
+                                    sh2_acc, mask, recursion_acc, temper_acc,
+                                    lstate_acc, lrecursion_acc, ltemper_acc,
+                                    elementsPerBlock, elements, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
index 9fd8985ccf..6a429feee9 100644
--- a/src/backend/oneapi/kernel/random_engine_mersenne.hpp
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -52,44 +52,40 @@ constexpr int BLOCKS     = 32;
 constexpr int STATE_SIZE = (256 * 3);
 constexpr int TABLE_SIZE = 16;
 
-template <typename T, int dimensions>
+template<typename T, int dimensions>
 using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
-
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
 
 // Utils
-static inline void read_table(uint *const sharedTable,
-                              const uint *const table,
+static inline void read_table(uint *const sharedTable, const uint *const table,
                               size_t groupId, size_t localId) {
     const uint *const t = table + (groupId * TABLE_SIZE);
     if (localId < TABLE_SIZE) { sharedTable[localId] = t[localId]; }
 }
 
-static inline void state_read(uint *const state,
-                              const uint *const gState,
-                              size_t groupRange, size_t groupId, size_t localId) {
-    const uint *const g                 = gState + (groupId * N);
+static inline void state_read(uint *const state, const uint *const gState,
+                              size_t groupRange, size_t groupId,
+                              size_t localId) {
+    const uint *const g             = gState + (groupId * N);
     state[STATE_SIZE - N + localId] = g[localId];
     if (localId < N - groupRange) {
-        state[STATE_SIZE - N + groupRange + localId] =
-            g[groupRange + localId];
+        state[STATE_SIZE - N + groupRange + localId] = g[groupRange + localId];
     }
 }
 
-static inline void state_write(uint *const gState,
-                               const uint *const state,
-                               size_t groupRange, size_t groupId, size_t localId) {
+static inline void state_write(uint *const gState, const uint *const state,
+                               size_t groupRange, size_t groupId,
+                               size_t localId) {
     uint *const g = gState + (groupId * N);
-    g[localId] = state[STATE_SIZE - N + localId];
+    g[localId]    = state[STATE_SIZE - N + localId];
     if (localId < N - groupRange) {
-        g[groupRange + localId] =
-            state[STATE_SIZE - N + groupRange + localId];
+        g[groupRange + localId] = state[STATE_SIZE - N + groupRange + localId];
     }
 }
 
-static inline uint recursion(const uint *const recursion_table,
-                             const uint mask, const uint sh1,
-                             const uint sh2, const uint x1,
+static inline uint recursion(const uint *const recursion_table, const uint mask,
+                             const uint sh1, const uint sh2, const uint x1,
                              const uint x2, uint y) {
     uint x = (x1 & mask) ^ x2;
     x ^= x << sh1;
@@ -98,8 +94,8 @@ static inline uint recursion(const uint *const recursion_table,
     return y ^ mat;
 }
 
-static inline uint temper(const uint *const temper_table,
-                          const uint v, uint t) {
+static inline uint temper(const uint *const temper_table, const uint v,
+                          uint t) {
     t ^= t >> 16;
     t ^= t >> 8;
     uint mat = temper_table[t & 0x0f];
@@ -108,17 +104,21 @@ static inline uint temper(const uint *const temper_table,
 
 // Initialization
 class initMersenneKernel {
-public:
-    initMersenneKernel(sycl::accessor<uint> state,
-                       sycl::accessor<uint> tbl,
-                       local_accessor<uint, 1> lstate,
-                       uintl seed, sycl::stream debug_stream) :
-            state_(state), tbl_(tbl), lstate_(lstate), seed_(seed), debug_(debug_stream) {}
+   public:
+    initMersenneKernel(sycl::accessor<uint> state, sycl::accessor<uint> tbl,
+                       local_accessor<uint, 1> lstate, uintl seed,
+                       sycl::stream debug_stream)
+        : state_(state)
+        , tbl_(tbl)
+        , lstate_(lstate)
+        , seed_(seed)
+        , debug_(debug_stream) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
 
-        const uint *ltbl = tbl_.get_pointer() + (TABLE_SIZE * g.get_group_id(0));
+        const uint *ltbl =
+            tbl_.get_pointer() + (TABLE_SIZE * g.get_group_id(0));
         uint hidden_seed = ltbl[4] ^ (ltbl[8] << 16);
         uint tmp         = hidden_seed;
         tmp += tmp >> 16;
@@ -132,15 +132,17 @@ class initMersenneKernel {
             lstate_[0] = seed_;
             lstate_[1] = hidden_seed;
             for (int i = 1; i < N; ++i) {
-                lstate_[i] ^=
-                    ((uint)(1812433253) * (lstate_[i - 1] ^ (lstate_[i - 1] >> 30)) + i);
+                lstate_[i] ^= ((uint)(1812433253) *
+                                   (lstate_[i - 1] ^ (lstate_[i - 1] >> 30)) +
+                               i);
             }
         }
         it.barrier();
-        state_[N * g.get_group_id(0) + it.get_local_id(0)] = lstate_[it.get_local_id(0)];
+        state_[N * g.get_group_id(0) + it.get_local_id(0)] =
+            lstate_[it.get_local_id(0)];
     }
 
-protected:
+   protected:
     sycl::accessor<uint> state_, tbl_;
     local_accessor<uint, 1> lstate_;
     uintl seed_;
@@ -149,59 +151,68 @@ class initMersenneKernel {
 
 void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
     sycl::nd_range<1> ndrange({BLOCKS * N}, {N});
-    getQueue().submit([=] (sycl::handler &h) {
-        auto state_acc = state.data->get_access(h);
-        auto tbl_acc = tbl.data->get_access(h);
+    getQueue().submit([=](sycl::handler &h) {
+        auto state_acc  = state.data->get_access(h);
+        auto tbl_acc    = tbl.data->get_access(h);
         auto lstate_acc = local_accessor<uint, 1>(N, h);
 
         sycl::stream debug_stream(2048, 128, h);
-        h.parallel_for(ndrange, 
-            initMersenneKernel(state_acc,
-                               tbl_acc, lstate_acc,
-                               seed, debug_stream));
+        h.parallel_for(ndrange,
+                       initMersenneKernel(state_acc, tbl_acc, lstate_acc, seed,
+                                          debug_stream));
     });
-    //TODO: do we need to sync before using Mersenne generators?
-    //force wait() here?
+    // TODO: do we need to sync before using Mersenne generators?
+    // force wait() here?
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
-
-
 template<typename T>
 class uniformMersenne {
-public:
+   public:
     uniformMersenne(sycl::accessor<T> out, sycl::accessor<uint> gState,
-                    sycl::accessor<uint> pos_tbl,
-                    sycl::accessor<uint> sh1_tbl,
+                    sycl::accessor<uint> pos_tbl, sycl::accessor<uint> sh1_tbl,
                     sycl::accessor<uint> sh2_tbl, uint mask,
                     sycl::accessor<uint> g_recursion_table,
                     sycl::accessor<uint> g_temper_table,
-                    //local memory caches of global state
+                    // local memory caches of global state
                     local_accessor<uint, 1> state,
                     local_accessor<uint, 1> recursion_table,
-                    local_accessor<uint, 1> temper_table,
-                    uint elementsPerBlock, size_t elements,
-                    sycl::stream debug) :
-            out_(out), gState_(gState),
-            pos_tbl_(pos_tbl), sh1_tbl_(sh1_tbl), sh2_tbl_(sh2_tbl), mask_(mask),
-            g_recursion_table_(g_recursion_table), g_temper_table_(g_temper_table),
-            state_(state), recursion_table_(recursion_table), temper_table_(temper_table),
-            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug) {}
+                    local_accessor<uint, 1> temper_table, uint elementsPerBlock,
+                    size_t elements, sycl::stream debug)
+        : out_(out)
+        , gState_(gState)
+        , pos_tbl_(pos_tbl)
+        , sh1_tbl_(sh1_tbl)
+        , sh2_tbl_(sh2_tbl)
+        , mask_(mask)
+        , g_recursion_table_(g_recursion_table)
+        , g_temper_table_(g_temper_table)
+        , state_(state)
+        , recursion_table_(recursion_table)
+        , temper_table_(temper_table)
+        , elementsPerBlock_(elementsPerBlock)
+        , elements_(elements)
+        , debug_(debug) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
-        uint start                    = g.get_group_id(0) * elementsPerBlock_;
-        uint end                      = start + elementsPerBlock_;
-        end                           = (end > elements_) ? elements_ : end;
-        int elementsPerBlockIteration = (g.get_local_range(0) * 4 * sizeof(uint)) / sizeof(T);
+        uint start    = g.get_group_id(0) * elementsPerBlock_;
+        uint end      = start + elementsPerBlock_;
+        end           = (end > elements_) ? elements_ : end;
+        int elementsPerBlockIteration =
+            (g.get_local_range(0) * 4 * sizeof(uint)) / sizeof(T);
         int iter = divup((end - start), elementsPerBlockIteration);
 
         uint pos = pos_tbl_[it.get_group(0)];
         uint sh1 = sh1_tbl_[it.get_group(0)];
         uint sh2 = sh2_tbl_[it.get_group(0)];
-        state_read(state_.get_pointer(), gState_.get_pointer(), g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
-        read_table(recursion_table_.get_pointer(), g_recursion_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
-        read_table(temper_table_.get_pointer(), g_temper_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
+        state_read(state_.get_pointer(), gState_.get_pointer(),
+                   g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+        read_table(recursion_table_.get_pointer(),
+                   g_recursion_table_.get_pointer(), g.get_group_id(0),
+                   it.get_local_id(0));
+        read_table(temper_table_.get_pointer(), g_temper_table_.get_pointer(),
+                   g.get_group_id(0), it.get_local_id(0));
         it.barrier();
 
         uint index = start;
@@ -209,36 +220,40 @@ class uniformMersenne {
         int offsetX1 = (STATE_SIZE - N + it.get_local_id(0)) % STATE_SIZE;
         int offsetX2 = (STATE_SIZE - N + it.get_local_id(0) + 1) % STATE_SIZE;
         int offsetY  = (STATE_SIZE - N + it.get_local_id(0) + pos) % STATE_SIZE;
-        int offsetT  = (STATE_SIZE - N + it.get_local_id(0) + pos - 1) % STATE_SIZE;
-        int offsetO  = it.get_local_id(0);
+        int offsetT =
+            (STATE_SIZE - N + it.get_local_id(0) + pos - 1) % STATE_SIZE;
+        int offsetO = it.get_local_id(0);
 
         for (int i = 0; i < iter; ++i) {
             for (int ii = 0; ii < 4; ++ii) {
-                uint r = recursion(recursion_table_.get_pointer(), mask_, sh1, sh2, state_[offsetX1],
-                                   state_[offsetX2], state_[offsetY]);
+                uint r = recursion(recursion_table_.get_pointer(), mask_, sh1,
+                                   sh2, state_[offsetX1], state_[offsetX2],
+                                   state_[offsetY]);
                 state_[offsetO] = r;
-                o[ii]          = temper(temper_table_.get_pointer(), r, state_[offsetT]);
-                offsetX1       = (offsetX1 + g.get_local_range(0)) % STATE_SIZE;
-                offsetX2       = (offsetX2 + g.get_local_range(0)) % STATE_SIZE;
-                offsetY        = (offsetY + g.get_local_range(0)) % STATE_SIZE;
-                offsetT        = (offsetT + g.get_local_range(0)) % STATE_SIZE;
-                offsetO        = (offsetO + g.get_local_range(0)) % STATE_SIZE;
+                o[ii] = temper(temper_table_.get_pointer(), r, state_[offsetT]);
+                offsetX1 = (offsetX1 + g.get_local_range(0)) % STATE_SIZE;
+                offsetX2 = (offsetX2 + g.get_local_range(0)) % STATE_SIZE;
+                offsetY  = (offsetY + g.get_local_range(0)) % STATE_SIZE;
+                offsetT  = (offsetT + g.get_local_range(0)) % STATE_SIZE;
+                offsetO  = (offsetO + g.get_local_range(0)) % STATE_SIZE;
                 it.barrier();
             }
             if (i == iter - 1) {
-                partialWriteOut128Bytes(out_.get_pointer(), index + it.get_local_id(0), g.get_local_range(0),
-                                        o[0], o[1], o[2], o[3], elements_);
+                partialWriteOut128Bytes(
+                    out_.get_pointer(), index + it.get_local_id(0),
+                    g.get_local_range(0), o[0], o[1], o[2], o[3], elements_);
             } else {
                 writeOut128Bytes(out_.get_pointer(), index + it.get_local_id(0),
                                  g.get_local_range(0), o[0], o[1], o[2], o[3]);
             }
             index += elementsPerBlockIteration;
         }
-        state_write(gState_.get_pointer(), state_.get_pointer(), 
-                    g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+        state_write(gState_.get_pointer(), state_.get_pointer(),
+                    g.get_local_range(0), g.get_group_id(0),
+                    it.get_local_id(0));
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     sycl::accessor<uint> gState_;
     sycl::accessor<uint> pos_tbl_, sh1_tbl_, sh2_tbl_;
@@ -252,39 +267,51 @@ class uniformMersenne {
 
 template<typename T>
 class normalMersenne {
-public:
+   public:
     normalMersenne(sycl::accessor<T> out, sycl::accessor<uint> gState,
-                   sycl::accessor<uint> pos_tbl,
-                   sycl::accessor<uint> sh1_tbl,
+                   sycl::accessor<uint> pos_tbl, sycl::accessor<uint> sh1_tbl,
                    sycl::accessor<uint> sh2_tbl, uint mask,
                    sycl::accessor<uint> g_recursion_table,
                    sycl::accessor<uint> g_temper_table,
-                   //local memory caches of global state
+                   // local memory caches of global state
                    local_accessor<uint, 1> state,
                    local_accessor<uint, 1> recursion_table,
-                   local_accessor<uint, 1> temper_table,
-                   uint elementsPerBlock, size_t elements,
-                   sycl::stream debug) :
-            out_(out), gState_(gState),
-            pos_tbl_(pos_tbl), sh1_tbl_(sh1_tbl), sh2_tbl_(sh2_tbl), mask_(mask),
-            g_recursion_table_(g_recursion_table), g_temper_table_(g_temper_table),
-            state_(state), recursion_table_(recursion_table), temper_table_(temper_table),
-            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug) {}
+                   local_accessor<uint, 1> temper_table, uint elementsPerBlock,
+                   size_t elements, sycl::stream debug)
+        : out_(out)
+        , gState_(gState)
+        , pos_tbl_(pos_tbl)
+        , sh1_tbl_(sh1_tbl)
+        , sh2_tbl_(sh2_tbl)
+        , mask_(mask)
+        , g_recursion_table_(g_recursion_table)
+        , g_temper_table_(g_temper_table)
+        , state_(state)
+        , recursion_table_(recursion_table)
+        , temper_table_(temper_table)
+        , elementsPerBlock_(elementsPerBlock)
+        , elements_(elements)
+        , debug_(debug) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
-        uint start                    = g.get_group_id(0) * elementsPerBlock_;
-        uint end                      = start + elementsPerBlock_;
-        end                           = (end > elements_) ? elements_ : end;
-        int elementsPerBlockIteration = (g.get_local_range(0) * 4 * sizeof(uint)) / sizeof(T);
+        uint start    = g.get_group_id(0) * elementsPerBlock_;
+        uint end      = start + elementsPerBlock_;
+        end           = (end > elements_) ? elements_ : end;
+        int elementsPerBlockIteration =
+            (g.get_local_range(0) * 4 * sizeof(uint)) / sizeof(T);
         int iter = divup((end - start), elementsPerBlockIteration);
 
         uint pos = pos_tbl_[it.get_group(0)];
         uint sh1 = sh1_tbl_[it.get_group(0)];
         uint sh2 = sh2_tbl_[it.get_group(0)];
-        state_read(state_.get_pointer(), gState_.get_pointer(), g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
-        read_table(recursion_table_.get_pointer(), g_recursion_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
-        read_table(temper_table_.get_pointer(), g_temper_table_.get_pointer(), g.get_group_id(0), it.get_local_id(0));
+        state_read(state_.get_pointer(), gState_.get_pointer(),
+                   g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+        read_table(recursion_table_.get_pointer(),
+                   g_recursion_table_.get_pointer(), g.get_group_id(0),
+                   it.get_local_id(0));
+        read_table(temper_table_.get_pointer(), g_temper_table_.get_pointer(),
+                   g.get_group_id(0), it.get_local_id(0));
         it.barrier();
 
         uint index = start;
@@ -292,36 +319,41 @@ class normalMersenne {
         int offsetX1 = (STATE_SIZE - N + it.get_local_id(0)) % STATE_SIZE;
         int offsetX2 = (STATE_SIZE - N + it.get_local_id(0) + 1) % STATE_SIZE;
         int offsetY  = (STATE_SIZE - N + it.get_local_id(0) + pos) % STATE_SIZE;
-        int offsetT  = (STATE_SIZE - N + it.get_local_id(0) + pos - 1) % STATE_SIZE;
-        int offsetO  = it.get_local_id(0);
+        int offsetT =
+            (STATE_SIZE - N + it.get_local_id(0) + pos - 1) % STATE_SIZE;
+        int offsetO = it.get_local_id(0);
 
         for (int i = 0; i < iter; ++i) {
             for (int ii = 0; ii < 4; ++ii) {
-                uint r = recursion(recursion_table_.get_pointer(), mask_, sh1, sh2, state_[offsetX1],
-                                   state_[offsetX2], state_[offsetY]);
+                uint r = recursion(recursion_table_.get_pointer(), mask_, sh1,
+                                   sh2, state_[offsetX1], state_[offsetX2],
+                                   state_[offsetY]);
                 state_[offsetO] = r;
-                o[ii]          = temper(temper_table_.get_pointer(), r, state_[offsetT]);
-                offsetX1       = (offsetX1 + g.get_local_range(0)) % STATE_SIZE;
-                offsetX2       = (offsetX2 + g.get_local_range(0)) % STATE_SIZE;
-                offsetY        = (offsetY + g.get_local_range(0)) % STATE_SIZE;
-                offsetT        = (offsetT + g.get_local_range(0)) % STATE_SIZE;
-                offsetO        = (offsetO + g.get_local_range(0)) % STATE_SIZE;
+                o[ii] = temper(temper_table_.get_pointer(), r, state_[offsetT]);
+                offsetX1 = (offsetX1 + g.get_local_range(0)) % STATE_SIZE;
+                offsetX2 = (offsetX2 + g.get_local_range(0)) % STATE_SIZE;
+                offsetY  = (offsetY + g.get_local_range(0)) % STATE_SIZE;
+                offsetT  = (offsetT + g.get_local_range(0)) % STATE_SIZE;
+                offsetO  = (offsetO + g.get_local_range(0)) % STATE_SIZE;
                 it.barrier();
             }
             if (i == iter - 1) {
-                partialBoxMullerWriteOut128Bytes(out_.get_pointer(), index + it.get_local_id(0), 
-                                        g.get_local_range(0), o[0], o[1], o[2], o[3], elements_);
+                partialBoxMullerWriteOut128Bytes(
+                    out_.get_pointer(), index + it.get_local_id(0),
+                    g.get_local_range(0), o[0], o[1], o[2], o[3], elements_);
             } else {
-                boxMullerWriteOut128Bytes(out_.get_pointer(), index + it.get_local_id(0),
-                                          g.get_local_range(0), o[0], o[1], o[2], o[3]);
+                boxMullerWriteOut128Bytes(
+                    out_.get_pointer(), index + it.get_local_id(0),
+                    g.get_local_range(0), o[0], o[1], o[2], o[3]);
             }
             index += elementsPerBlockIteration;
         }
-        state_write(gState_.get_pointer(), state_.get_pointer(), 
-                    g.get_local_range(0), g.get_group_id(0), it.get_local_id(0));
+        state_write(gState_.get_pointer(), state_.get_pointer(),
+                    g.get_local_range(0), g.get_group_id(0),
+                    it.get_local_id(0));
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     sycl::accessor<uint> gState_;
     sycl::accessor<uint> pos_tbl_, sh1_tbl_, sh2_tbl_;
diff --git a/src/backend/oneapi/kernel/random_engine_philox.hpp b/src/backend/oneapi/kernel/random_engine_philox.hpp
index 3cb3dbd95b..e43cfa31e5 100644
--- a/src/backend/oneapi/kernel/random_engine_philox.hpp
+++ b/src/backend/oneapi/kernel/random_engine_philox.hpp
@@ -58,7 +58,7 @@ constexpr uint m4x32_1 = 0xCD9E8D57;
 constexpr uint w32_0   = 0x9E3779B9;
 constexpr uint w32_1   = 0xBB67AE85;
 
-static inline void mulhilo(uint a, uint b, uint &hi, uint &lo) {
+static inline void mulhilo(uint a, uint b, uint& hi, uint& lo) {
     hi = sycl::mul_hi(a, b);
     lo = a * b;
 }
@@ -68,8 +68,8 @@ static inline void philoxBump(uint k[2]) {
     k[1] += w32_1;
 }
 
-static inline void philoxRound(const uint m0, const uint m1,
-                               const uint k[2], uint c[4]) {
+static inline void philoxRound(const uint m0, const uint m1, const uint k[2],
+                               uint c[4]) {
     uint hi0, lo0, hi1, lo1;
     mulhilo(m0, c[0], hi0, lo0);
     mulhilo(m1, c[2], hi1, lo1);
@@ -104,19 +104,25 @@ static inline void philox(uint key[2], uint ctr[4]) {
 
 template<typename T>
 class uniformPhilox {
-public:
-    uniformPhilox(sycl::accessor<T> out,
-                  uint hi, uint lo, uint hic, uint loc,
+   public:
+    uniformPhilox(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
                   uint elementsPerBlock, uint elements,
-                  sycl::stream debug_stream) :
-            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
-            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+                  sycl::stream debug_stream)
+        : out_(out)
+        , hi_(hi)
+        , lo_(lo)
+        , hic_(hic)
+        , loc_(loc)
+        , elementsPerBlock_(elementsPerBlock)
+        , elements_(elements)
+        , debug_(debug_stream) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
 
-        //debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) << "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
-        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+        // debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) <<
+        // "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
+        uint index = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
         uint key[2] = {lo_, hi_};
         uint ctr[4] = {loc_, hic_, 0, 0};
         ctr[0] += index;
@@ -125,15 +131,16 @@ class uniformPhilox {
         T* optr = out_.get_pointer();
         if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
             philox(key, ctr);
-            writeOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1], ctr[2], ctr[3]);
+            writeOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1],
+                             ctr[2], ctr[3]);
         } else {
             philox(key, ctr);
-            partialWriteOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1], ctr[2], ctr[3],
-                                    elements_);
+            partialWriteOut128Bytes(optr, index, g.get_local_range(0), ctr[0],
+                                    ctr[1], ctr[2], ctr[3], elements_);
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
@@ -142,19 +149,25 @@ class uniformPhilox {
 
 template<typename T>
 class normalPhilox {
-public:
-    normalPhilox(sycl::accessor<T> out,
-                  uint hi, uint lo, uint hic, uint loc,
-                  uint elementsPerBlock, uint elements,
-                  sycl::stream debug_stream) :
-            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
-            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+   public:
+    normalPhilox(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
+                 uint elementsPerBlock, uint elements,
+                 sycl::stream debug_stream)
+        : out_(out)
+        , hi_(hi)
+        , lo_(lo)
+        , hic_(hic)
+        , loc_(loc)
+        , elementsPerBlock_(elementsPerBlock)
+        , elements_(elements)
+        , debug_(debug_stream) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
-        //debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) << "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
+        // debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) <<
+        // "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
 
-        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+        uint index = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
         uint key[2] = {lo_, hi_};
         uint ctr[4] = {loc_, hic_, 0, 0};
         ctr[0] += index;
@@ -165,14 +178,16 @@ class normalPhilox {
 
         T* optr = out_.get_pointer();
         if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
-            boxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), ctr[0], ctr[1], ctr[2], ctr[3]);
+            boxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), ctr[0],
+                                      ctr[1], ctr[2], ctr[3]);
         } else {
-            partialBoxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), 
-                                             ctr[0], ctr[1], ctr[2], ctr[3], elements_);
+            partialBoxMullerWriteOut128Bytes(optr, index, g.get_local_range(0),
+                                             ctr[0], ctr[1], ctr[2], ctr[3],
+                                             elements_);
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
diff --git a/src/backend/oneapi/kernel/random_engine_threefry.hpp b/src/backend/oneapi/kernel/random_engine_threefry.hpp
index bb93e299bc..931e60ef63 100644
--- a/src/backend/oneapi/kernel/random_engine_threefry.hpp
+++ b/src/backend/oneapi/kernel/random_engine_threefry.hpp
@@ -64,10 +64,7 @@ static const uint R5 = 29;
 static const uint R6 = 16;
 static const uint R7 = 24;
 
-
-static inline void setSkeinParity(uint *ptr) {
-    *ptr = SKEIN_KS_PARITY32;
-}
+static inline void setSkeinParity(uint* ptr) { *ptr = SKEIN_KS_PARITY32; }
 
 static inline uint rotL(uint x, uint N) {
     return (x << (N & 31)) | (x >> ((32 - N) & 31));
@@ -162,17 +159,22 @@ void threefry(uint k[2], uint c[2], uint X[2]) {
 
 template<typename T>
 class uniformThreefry {
-public:
-    uniformThreefry(sycl::accessor<T> out,
-                    uint hi, uint lo, uint hic, uint loc,
+   public:
+    uniformThreefry(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
                     uint elementsPerBlock, uint elements,
-                    sycl::stream debug_stream) :
-            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
-            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+                    sycl::stream debug_stream)
+        : out_(out)
+        , hi_(hi)
+        , lo_(lo)
+        , hic_(hic)
+        , loc_(loc)
+        , elementsPerBlock_(elementsPerBlock)
+        , elements_(elements)
+        , debug_(debug_stream) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
-        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+        uint index = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
 
         uint key[2] = {lo_, hi_};
         uint ctr[4] = {loc_, hic_, 0, 0};
@@ -188,14 +190,15 @@ class uniformThreefry {
 
         T* optr = out_.get_pointer();
         if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
-            writeOut128Bytes(optr, index, g.get_local_range(0), o[0], o[1], o[2], o[3]);
+            writeOut128Bytes(optr, index, g.get_local_range(0), o[0], o[1],
+                             o[2], o[3]);
         } else {
-            partialWriteOut128Bytes(optr, index, g.get_local_range(0), 
-                                    o[0], o[1], o[2], o[3], elements_);
+            partialWriteOut128Bytes(optr, index, g.get_local_range(0), o[0],
+                                    o[1], o[2], o[3], elements_);
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
@@ -204,17 +207,22 @@ class uniformThreefry {
 
 template<typename T>
 class normalThreefry {
-public:
-    normalThreefry(sycl::accessor<T> out,
-                    uint hi, uint lo, uint hic, uint loc,
-                    uint elementsPerBlock, uint elements,
-                    sycl::stream debug_stream) :
-            out_(out), hi_(hi), lo_(lo), hic_(hic), loc_(loc),
-            elementsPerBlock_(elementsPerBlock), elements_(elements), debug_(debug_stream) {}
+   public:
+    normalThreefry(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
+                   uint elementsPerBlock, uint elements,
+                   sycl::stream debug_stream)
+        : out_(out)
+        , hi_(hi)
+        , lo_(lo)
+        , hic_(hic)
+        , loc_(loc)
+        , elementsPerBlock_(elementsPerBlock)
+        , elements_(elements)
+        , debug_(debug_stream) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
-        uint index  = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
+        uint index = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
 
         uint key[2] = {lo_, hi_};
         uint ctr[4] = {loc_, hic_, 0, 0};
@@ -230,14 +238,15 @@ class normalThreefry {
 
         T* optr = out_.get_pointer();
         if (g.get_group_id(0) != (g.get_group_range(0) - 1)) {
-            boxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), o[0], o[1], o[2], o[3]);
+            boxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), o[0],
+                                      o[1], o[2], o[3]);
         } else {
-            partialBoxMullerWriteOut128Bytes(optr, index, g.get_local_range(0), 
+            partialBoxMullerWriteOut128Bytes(optr, index, g.get_local_range(0),
                                              o[0], o[1], o[2], o[3], elements_);
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 9e943eaad2..3b2857b92f 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -12,17 +12,16 @@
 namespace oneapi {
 namespace kernel {
 
-//TODO: !!!! half functions still need to be ported !!!!
-
+// TODO: !!!! half functions still need to be ported !!!!
 
 //// Conversion to half adapted from Random123
 //// #define HALF_FACTOR (1.0f) / (std::numeric_limits<ushort>::max() + (1.0f))
 //// #define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
 ////
 //// NOTE: The following constants for half were calculated using the formulas
-//// above. This is done so that we can avoid unnecessary computations because the
-//// __half datatype is not a constexprable type. This prevents the compiler from
-//// peforming these operations at compile time.
+//// above. This is done so that we can avoid unnecessary computations because
+/// the / __half datatype is not a constexprable type. This prevents the
+/// compiler from / peforming these operations at compile time.
 //#define HALF_FACTOR __ushort_as_half(0x100u)
 //#define HALF_HALF_FACTOR __ushort_as_half(0x80)
 //
@@ -32,16 +31,16 @@ namespace kernel {
 ////#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
 ////
 //// NOTE: The following constants for half were calculated using the formulas
-//// above. This is done so that we can avoid unnecessary computations because the
-//// __half datatype is not a constexprable type. This prevents the compiler from
-//// peforming these operations at compile time
+//// above. This is done so that we can avoid unnecessary computations because
+/// the / __half datatype is not a constexprable type. This prevents the
+/// compiler from / peforming these operations at compile time
 //#define SIGNED_HALF_FACTOR __ushort_as_half(0x200u)
 //#define SIGNED_HALF_HALF_FACTOR __ushort_as_half(0x100u)
 //
 ///// This is the largest integer representable by fp16. We need to
 ///// make sure that the value converted from ushort is smaller than this
 ///// value to avoid generating infinity
-//constexpr ushort max_int_before_infinity = 65504;
+// constexpr ushort max_int_before_infinity = 65504;
 //
 //// Generates rationals in (0, 1]
 //__device__ static __half oneMinusGetHalf01(uint num) {
@@ -154,25 +153,25 @@ namespace {
 //    }                                               \
 //    HALF_MATH_FUNC(OP, HALF_OP)
 //
-//MATH_FUNC(log, log, logf, hlog)
-//MATH_FUNC(sqrt, sqrt, sqrtf, hsqrt)
-//MATH_FUNC(sin, sin, sinf, hsin)
-//MATH_FUNC(cos, cos, cosf, hcos)
+// MATH_FUNC(log, log, logf, hlog)
+// MATH_FUNC(sqrt, sqrt, sqrtf, hsqrt)
+// MATH_FUNC(sin, sin, sinf, hsin)
+// MATH_FUNC(cos, cos, cosf, hcos)
 //
-//template<typename T>
+// template<typename T>
 //__device__ void sincos(T val, T *sptr, T *cptr);
 //
-//template<>
+// template<>
 //__device__ void sincos(double val, double *sptr, double *cptr) {
 //    ::sincos(val, sptr, cptr);
 //}
 //
-//template<>
+// template<>
 //__device__ void sincos(float val, float *sptr, float *cptr) {
 //    sincosf(val, sptr, cptr);
 //}
 //
-//template<>
+// template<>
 //__device__ void sincos(__half val, __half *sptr, __half *cptr) {
 //#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
 //    *sptr = sin(val);
@@ -192,7 +191,7 @@ void sincospi(T val, T *sptr, T *cptr) {
     *cptr = sycl::cospi(val);
 }
 
-//template<>
+// template<>
 //__device__ void sincospi(__half val, __half *sptr, __half *cptr) {
 //    // CUDA cannot make __half into a constexpr as of CUDA 11 so we are
 //    // converting this offline
@@ -217,14 +216,13 @@ constexpr T neg_two() {
     return -2.0;
 }
 //
-//template<typename T>
-//constexpr __device__ T two_pi() {
+// template<typename T>
+// constexpr __device__ T two_pi() {
 //    return 2.0 * PI_VAL;
 //};
 //
 template<typename Tc>
-static void boxMullerTransform(cfloat *const cOut,
-                               const Tc &r1, const Tc &r2) {
+static void boxMullerTransform(cfloat *const cOut, const Tc &r1, const Tc &r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
@@ -240,8 +238,8 @@ static void boxMullerTransform(cfloat *const cOut,
 }
 
 template<typename Tc>
-static void boxMullerTransform(cdouble *const cOut,
-                               const Tc &r1, const Tc &r2) {
+static void boxMullerTransform(cdouble *const cOut, const Tc &r1,
+                               const Tc &r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
@@ -257,8 +255,8 @@ static void boxMullerTransform(cdouble *const cOut,
 }
 
 template<typename Td, typename Tc>
-static void boxMullerTransform(Td *const out1, Td *const out2,
-                               const Tc &r1, const Tc &r2) {
+static void boxMullerTransform(Td *const out1, Td *const out2, const Tc &r1,
+                               const Tc &r2) {
     /*
      * The log of a real value x where 0 < x < 1 is negative.
      */
@@ -272,7 +270,7 @@ static void boxMullerTransform(Td *const out1, Td *const out2,
     *out1 = static_cast<Td>(r * s);
     *out2 = static_cast<Td>(r * c);
 }
-//template<>
+// template<>
 //__device__ void boxMullerTransform<common::half, __half>(
 //    common::half *const out1, common::half *const out2, const __half &r1,
 //    const __half &r2) {
@@ -286,8 +284,8 @@ static void boxMullerTransform(Td *const out1, Td *const out2,
 
 // Writes without boundary checking
 static void writeOut128Bytes(uchar *out, const uint &index, const uint groupSz,
-                             const uint &r1, const uint &r2,
-                             const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     out[index]                = r1;
     out[index + groupSz]      = r1 >> 8;
     out[index + 2 * groupSz]  = r1 >> 16;
@@ -307,8 +305,8 @@ static void writeOut128Bytes(uchar *out, const uint &index, const uint groupSz,
 }
 
 static void writeOut128Bytes(char *out, const uint &index, const uint groupSz,
-                             const uint &r1, const uint &r2,
-                             const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     out[index]                = (r1)&0x1;
     out[index + groupSz]      = (r1 >> 1) & 0x1;
     out[index + 2 * groupSz]  = (r1 >> 2) & 0x1;
@@ -328,8 +326,8 @@ static void writeOut128Bytes(char *out, const uint &index, const uint groupSz,
 }
 
 static void writeOut128Bytes(short *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     out[index]               = r1;
     out[index + groupSz]     = r1 >> 16;
     out[index + 2 * groupSz] = r2;
@@ -341,14 +339,14 @@ static void writeOut128Bytes(short *out, const uint &index, const uint groupSz,
 }
 
 static void writeOut128Bytes(ushort *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     writeOut128Bytes((short *)(out), index, groupSz, r1, r2, r3, r4);
 }
 
 static void writeOut128Bytes(int *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     out[index]               = r1;
     out[index + groupSz]     = r2;
     out[index + 2 * groupSz] = r3;
@@ -356,14 +354,14 @@ static void writeOut128Bytes(int *out, const uint &index, const uint groupSz,
 }
 
 static void writeOut128Bytes(uint *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     writeOut128Bytes((int *)(out), index, groupSz, r1, r2, r3, r4);
 }
 
 static void writeOut128Bytes(intl *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     intl c1              = r2;
     c1                   = (c1 << 32) | r1;
     intl c2              = r4;
@@ -373,14 +371,14 @@ static void writeOut128Bytes(intl *out, const uint &index, const uint groupSz,
 }
 
 static void writeOut128Bytes(uintl *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     writeOut128Bytes((intl *)(out), index, groupSz, r1, r2, r3, r4);
 }
 
 static void writeOut128Bytes(float *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     out[index]               = 1.f - getFloat01(r1);
     out[index + groupSz]     = 1.f - getFloat01(r2);
     out[index + 2 * groupSz] = 1.f - getFloat01(r3);
@@ -388,131 +386,115 @@ static void writeOut128Bytes(float *out, const uint &index, const uint groupSz,
 }
 
 static void writeOut128Bytes(cfloat *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
-    out[index] = {1.f - getFloat01(r1), 1.f - getFloat01(r2)};
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
+    out[index]           = {1.f - getFloat01(r1), 1.f - getFloat01(r2)};
     out[index + groupSz] = {1.f - getFloat01(r3), 1.f - getFloat01(r4)};
 }
 
 static void writeOut128Bytes(double *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
     out[index]           = 1.0 - getDouble01(r1, r2);
     out[index + groupSz] = 1.0 - getDouble01(r3, r4);
 }
 
-static void writeOut128Bytes(cdouble *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
+static void writeOut128Bytes(cdouble *out, const uint &index,
+                             const uint groupSz, const uint &r1, const uint &r2,
+                             const uint &r3, const uint &r4) {
     out[index] = {1.0 - getDouble01(r1, r2), 1.0 - getDouble01(r3, r4)};
 }
 
-static void writeOut128Bytes(common::half *out, const uint &index, const uint groupSz,
-                                        const uint &r1, const uint &r2,
-                                        const uint &r3, const uint &r4) {
-   //out[index]               = oneMinusGetHalf01(r1);
-   //out[index + groupSz]     = oneMinusGetHalf01(r1 >> 16);
-   //out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
-   //out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
-   //out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
-   //out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
-   //out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
-   //out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
+static void writeOut128Bytes(common::half *out, const uint &index,
+                             const uint groupSz, const uint &r1, const uint &r2,
+                             const uint &r3, const uint &r4) {
+    // out[index]               = oneMinusGetHalf01(r1);
+    // out[index + groupSz]     = oneMinusGetHalf01(r1 >> 16);
+    // out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
+    // out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
+    // out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
+    // out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
+    // out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
+    // out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
 }
 
 // Normalized writes without boundary checking
 
-static void boxMullerWriteOut128Bytes(float *out, const uint &index, const uint groupSz,
-                                                 const uint &r1, const uint &r2,
-                                                 const uint &r3,
-                                                 const uint &r4) {
+static void boxMullerWriteOut128Bytes(float *out, const uint &index,
+                                      const uint groupSz, const uint &r1,
+                                      const uint &r2, const uint &r3,
+                                      const uint &r4) {
     boxMullerTransform(&out[index], &out[index + groupSz],
                        getFloatNegative11(r1), getFloat01(r2));
-    boxMullerTransform(&out[index + 2 * groupSz],
-                       &out[index + 3 * groupSz],
-                       getFloatNegative11(r3),
-                       getFloat01(r4));
+    boxMullerTransform(&out[index + 2 * groupSz], &out[index + 3 * groupSz],
+                       getFloatNegative11(r3), getFloat01(r4));
 }
 
-static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index, const uint groupSz,
-                                                 const uint &r1, const uint &r2,
-                                                 const uint &r3,
-                                                 const uint &r4) {
+static void boxMullerWriteOut128Bytes(cfloat *out, const uint &index,
+                                      const uint groupSz, const uint &r1,
+                                      const uint &r2, const uint &r3,
+                                      const uint &r4) {
     boxMullerTransform(&out[index], getFloatNegative11(r1), getFloat01(r2));
-    boxMullerTransform(&out[index + groupSz], getFloatNegative11(r3), getFloat01(r4));
+    boxMullerTransform(&out[index + groupSz], getFloatNegative11(r3),
+                       getFloat01(r4));
 }
 
-static void boxMullerWriteOut128Bytes(double *out, const uint &index, const uint groupSz,
-                                                 const uint &r1, const uint &r2,
-                                                 const uint &r3,
-                                                 const uint &r4) {
+static void boxMullerWriteOut128Bytes(double *out, const uint &index,
+                                      const uint groupSz, const uint &r1,
+                                      const uint &r2, const uint &r3,
+                                      const uint &r4) {
     boxMullerTransform(&out[index], &out[index + groupSz],
                        getDoubleNegative11(r1, r2), getDouble01(r3, r4));
 }
 
-static void boxMullerWriteOut128Bytes(cdouble *out,
-                                                 const uint &index, const uint groupSz,
-                                                 const uint &r1, const uint &r2,
-                                                 const uint &r3,
-                                                 const uint &r4) {
-    boxMullerTransform(&out[index], getDoubleNegative11(r1, r2), getDouble01(r3, r4));
-}
-
-static void boxMullerWriteOut128Bytes(common::half *out,
-                                                 const uint &index, const uint groupSz,
-                                                 const uint &r1, const uint &r2,
-                                                 const uint &r3,
-                                                 const uint &r4) {
-//   boxMullerTransform(&out[index], &out[index + groupSz],
-//                      getHalfNegative11(r1), getHalf01(r1 >> 16));
-//   boxMullerTransform(&out[index + 2 * groupSz],
-//                      &out[index + 3 * groupSz], getHalfNegative11(r2),
-//                      getHalf01(r2 >> 16));
-//   boxMullerTransform(&out[index + 4 * groupSz],
-//                      &out[index + 5 * groupSz], getHalfNegative11(r3),
-//                      getHalf01(r3 >> 16));
-//   boxMullerTransform(&out[index + 6 * groupSz],
-//                      &out[index + 7 * groupSz], getHalfNegative11(r4),
-//                      getHalf01(r4 >> 16));
+static void boxMullerWriteOut128Bytes(cdouble *out, const uint &index,
+                                      const uint groupSz, const uint &r1,
+                                      const uint &r2, const uint &r3,
+                                      const uint &r4) {
+    boxMullerTransform(&out[index], getDoubleNegative11(r1, r2),
+                       getDouble01(r3, r4));
+}
+
+static void boxMullerWriteOut128Bytes(common::half *out, const uint &index,
+                                      const uint groupSz, const uint &r1,
+                                      const uint &r2, const uint &r3,
+                                      const uint &r4) {
+    //   boxMullerTransform(&out[index], &out[index + groupSz],
+    //                      getHalfNegative11(r1), getHalf01(r1 >> 16));
+    //   boxMullerTransform(&out[index + 2 * groupSz],
+    //                      &out[index + 3 * groupSz], getHalfNegative11(r2),
+    //                      getHalf01(r2 >> 16));
+    //   boxMullerTransform(&out[index + 4 * groupSz],
+    //                      &out[index + 5 * groupSz], getHalfNegative11(r3),
+    //                      getHalf01(r3 >> 16));
+    //   boxMullerTransform(&out[index + 6 * groupSz],
+    //                      &out[index + 7 * groupSz], getHalfNegative11(r4),
+    //                      getHalf01(r4 >> 16));
 }
 
 // Writes with boundary checking
 
-static void partialWriteOut128Bytes(uchar *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(uchar *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = r1; }
     if (index + groupSz < elements) { out[index + groupSz] = r1 >> 8; }
-    if (index + 2 * groupSz < elements) {
-        out[index + 2 * groupSz] = r1 >> 16;
-    }
-    if (index + 3 * groupSz < elements) {
-        out[index + 3 * groupSz] = r1 >> 24;
-    }
+    if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = r1 >> 16; }
+    if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = r1 >> 24; }
     if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = r2; }
-    if (index + 5 * groupSz < elements) {
-        out[index + 5 * groupSz] = r2 >> 8;
-    }
-    if (index + 6 * groupSz < elements) {
-        out[index + 6 * groupSz] = r2 >> 16;
-    }
-    if (index + 7 * groupSz < elements) {
-        out[index + 7 * groupSz] = r2 >> 24;
-    }
+    if (index + 5 * groupSz < elements) { out[index + 5 * groupSz] = r2 >> 8; }
+    if (index + 6 * groupSz < elements) { out[index + 6 * groupSz] = r2 >> 16; }
+    if (index + 7 * groupSz < elements) { out[index + 7 * groupSz] = r2 >> 24; }
     if (index + 8 * groupSz < elements) { out[index + 8 * groupSz] = r3; }
-    if (index + 9 * groupSz < elements) {
-        out[index + 9 * groupSz] = r3 >> 8;
-    }
+    if (index + 9 * groupSz < elements) { out[index + 9 * groupSz] = r3 >> 8; }
     if (index + 10 * groupSz < elements) {
         out[index + 10 * groupSz] = r3 >> 16;
     }
     if (index + 11 * groupSz < elements) {
         out[index + 11 * groupSz] = r3 >> 24;
     }
-    if (index + 12 * groupSz < elements) {
-        out[index + 12 * groupSz] = r4;
-    }
+    if (index + 12 * groupSz < elements) { out[index + 12 * groupSz] = r4; }
     if (index + 13 * groupSz < elements) {
         out[index + 13 * groupSz] = r4 >> 8;
     }
@@ -524,23 +506,19 @@ static void partialWriteOut128Bytes(uchar *out, const uint &index, const uint gr
     }
 }
 
-static void partialWriteOut128Bytes(char *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(char *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = (r1)&0x1; }
-    if (index + groupSz < elements) {
-        out[index + groupSz] = (r1 >> 1) & 0x1;
-    }
+    if (index + groupSz < elements) { out[index + groupSz] = (r1 >> 1) & 0x1; }
     if (index + 2 * groupSz < elements) {
         out[index + 2 * groupSz] = (r1 >> 2) & 0x1;
     }
     if (index + 3 * groupSz < elements) {
         out[index + 3 * groupSz] = (r1 >> 3) & 0x1;
     }
-    if (index + 4 * groupSz < elements) {
-        out[index + 4 * groupSz] = (r2)&0x1;
-    }
+    if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = (r2)&0x1; }
     if (index + 5 * groupSz < elements) {
         out[index + 5 * groupSz] = (r2 >> 1) & 0x1;
     }
@@ -550,9 +528,7 @@ static void partialWriteOut128Bytes(char *out, const uint &index, const uint gro
     if (index + 7 * groupSz < elements) {
         out[index + 7 * groupSz] = (r2 >> 3) & 0x1;
     }
-    if (index + 8 * groupSz < elements) {
-        out[index + 8 * groupSz] = (r3)&0x1;
-    }
+    if (index + 8 * groupSz < elements) { out[index + 8 * groupSz] = (r3)&0x1; }
     if (index + 9 * groupSz < elements) {
         out[index + 9 * groupSz] = (r3 >> 1) & 0x1;
     }
@@ -576,54 +552,50 @@ static void partialWriteOut128Bytes(char *out, const uint &index, const uint gro
     }
 }
 
-static void partialWriteOut128Bytes(short *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(short *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = r1; }
     if (index + groupSz < elements) { out[index + groupSz] = r1 >> 16; }
     if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = r2; }
-    if (index + 3 * groupSz < elements) {
-        out[index + 3 * groupSz] = r2 >> 16;
-    }
+    if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = r2 >> 16; }
     if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = r3; }
-    if (index + 5 * groupSz < elements) {
-        out[index + 5 * groupSz] = r3 >> 16;
-    }
+    if (index + 5 * groupSz < elements) { out[index + 5 * groupSz] = r3 >> 16; }
     if (index + 6 * groupSz < elements) { out[index + 6 * groupSz] = r4; }
-    if (index + 7 * groupSz < elements) {
-        out[index + 7 * groupSz] = r4 >> 16;
-    }
+    if (index + 7 * groupSz < elements) { out[index + 7 * groupSz] = r4 >> 16; }
 }
 
-static void partialWriteOut128Bytes(ushort *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
-    partialWriteOut128Bytes((short *)(out), index, groupSz, r1, r2, r3, r4, elements);
+static void partialWriteOut128Bytes(ushort *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
+    partialWriteOut128Bytes((short *)(out), index, groupSz, r1, r2, r3, r4,
+                            elements);
 }
 
-static void partialWriteOut128Bytes(int *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(int *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = r1; }
     if (index + groupSz < elements) { out[index + groupSz] = r2; }
     if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = r3; }
     if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = r4; }
 }
 
-static void partialWriteOut128Bytes(uint *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
-    partialWriteOut128Bytes((int *)(out), index, groupSz, r1, r2, r3, r4, elements);
+static void partialWriteOut128Bytes(uint *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
+    partialWriteOut128Bytes((int *)(out), index, groupSz, r1, r2, r3, r4,
+                            elements);
 }
 
-static void partialWriteOut128Bytes(intl *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(intl *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     intl c1 = r2;
     c1      = (c1 << 32) | r1;
     intl c2 = r4;
@@ -632,17 +604,18 @@ static void partialWriteOut128Bytes(intl *out, const uint &index, const uint gro
     if (index + groupSz < elements) { out[index + groupSz] = c2; }
 }
 
-static void partialWriteOut128Bytes(uintl *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
-    partialWriteOut128Bytes((intl *)(out), index, groupSz, r1, r2, r3, r4, elements);
+static void partialWriteOut128Bytes(uintl *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
+    partialWriteOut128Bytes((intl *)(out), index, groupSz, r1, r2, r3, r4,
+                            elements);
 }
 
-static void partialWriteOut128Bytes(float *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(float *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = 1.f - getFloat01(r1); }
     if (index + groupSz < elements) {
         out[index + groupSz] = 1.f - getFloat01(r2);
@@ -655,10 +628,10 @@ static void partialWriteOut128Bytes(float *out, const uint &index, const uint gr
     }
 }
 
-static void partialWriteOut128Bytes(cfloat *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(cfloat *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) {
         out[index] = {1.f - getFloat01(r1), 1.f - getFloat01(r2)};
     }
@@ -667,29 +640,31 @@ static void partialWriteOut128Bytes(cfloat *out, const uint &index, const uint g
     }
 }
 
-static void partialWriteOut128Bytes(double *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(double *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = 1.0 - getDouble01(r1, r2); }
     if (index + groupSz < elements) {
         out[index + groupSz] = 1.0 - getDouble01(r3, r4);
     }
 }
 
-static void partialWriteOut128Bytes(cdouble *out, const uint &index, const uint groupSz,
-                                               const uint &r1, const uint &r2,
-                                               const uint &r3, const uint &r4,
-                                               const uint &elements) {
+static void partialWriteOut128Bytes(cdouble *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
     if (index < elements) {
         out[index] = {1.0 - getDouble01(r1, r2), 1.0 - getDouble01(r3, r4)};
     }
 }
 
 // Normalized writes with boundary checking
-static void partialBoxMullerWriteOut128Bytes(
-    float *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
-    const uint &r3, const uint &r4, const uint &elements) {
+static void partialBoxMullerWriteOut128Bytes(float *out, const uint &index,
+                                             const uint groupSz, const uint &r1,
+                                             const uint &r2, const uint &r3,
+                                             const uint &r4,
+                                             const uint &elements) {
     float n1, n2, n3, n4;
     boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
     boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
@@ -699,23 +674,23 @@ static void partialBoxMullerWriteOut128Bytes(
     if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = n4; }
 }
 
-static void partialBoxMullerWriteOut128Bytes(
-    cfloat *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
-    const uint &r3, const uint &r4, const uint &elements) {
+static void partialBoxMullerWriteOut128Bytes(cfloat *out, const uint &index,
+                                             const uint groupSz, const uint &r1,
+                                             const uint &r2, const uint &r3,
+                                             const uint &r4,
+                                             const uint &elements) {
     float n1, n2, n3, n4;
     boxMullerTransform(&n1, &n2, getFloatNegative11(r1), getFloat01(r2));
     boxMullerTransform(&n3, &n4, getFloatNegative11(r3), getFloat01(r4));
-    if (index < elements) {
-        out[index] = {n1, n2};
-    }
-    if (index + groupSz < elements) {
-        out[index + groupSz] = {n3, n4};
-    }
+    if (index < elements) { out[index] = {n1, n2}; }
+    if (index + groupSz < elements) { out[index + groupSz] = {n3, n4}; }
 }
 
-static void partialBoxMullerWriteOut128Bytes(
-    double *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
-    const uint &r3, const uint &r4, const uint &elements) {
+static void partialBoxMullerWriteOut128Bytes(double *out, const uint &index,
+                                             const uint groupSz, const uint &r1,
+                                             const uint &r2, const uint &r3,
+                                             const uint &r4,
+                                             const uint &elements) {
     double n1, n2;
     boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
                        getDouble01(r3, r4));
@@ -723,82 +698,79 @@ static void partialBoxMullerWriteOut128Bytes(
     if (index + groupSz < elements) { out[index + groupSz] = n2; }
 }
 
-static void partialBoxMullerWriteOut128Bytes(
-    cdouble *out, const uint &index, const uint groupSz, const uint &r1, const uint &r2,
-    const uint &r3, const uint &r4, const uint &elements) {
+static void partialBoxMullerWriteOut128Bytes(cdouble *out, const uint &index,
+                                             const uint groupSz, const uint &r1,
+                                             const uint &r2, const uint &r3,
+                                             const uint &r4,
+                                             const uint &elements) {
     double n1, n2;
     boxMullerTransform(&n1, &n2, getDoubleNegative11(r1, r2),
                        getDouble01(r3, r4));
-    if (index < elements) {
-        out[index] = {n1, n2};
-    }
-}
-
-static void partialWriteOut128Bytes(common::half *out, 
-                                             const uint &index, const uint groupSz,
-                                             const uint &r1, const uint &r2,
-                                             const uint &r3, const uint &r4,
-                                             const uint &elements) {
-//  if (index < elements) { out[index] = oneMinusGetHalf01(r1); }
-//  if (index + groupSz < elements) {
-//      out[index + groupSz] = oneMinusGetHalf01(r1 >> 16);
-//  }
-//  if (index + 2 * groupSz < elements) {
-//      out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
-//  }
-//  if (index + 3 * groupSz < elements) {
-//      out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
-//  }
-//  if (index + 4 * groupSz < elements) {
-//      out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
-//  }
-//  if (index + 5 * groupSz < elements) {
-//      out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
-//  }
-//  if (index + 6 * groupSz < elements) {
-//      out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
-//  }
-//  if (index + 7 * groupSz < elements) {
-//      out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
-//  }
+    if (index < elements) { out[index] = {n1, n2}; }
+}
+
+static void partialWriteOut128Bytes(common::half *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
+    //  if (index < elements) { out[index] = oneMinusGetHalf01(r1); }
+    //  if (index + groupSz < elements) {
+    //      out[index + groupSz] = oneMinusGetHalf01(r1 >> 16);
+    //  }
+    //  if (index + 2 * groupSz < elements) {
+    //      out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
+    //  }
+    //  if (index + 3 * groupSz < elements) {
+    //      out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
+    //  }
+    //  if (index + 4 * groupSz < elements) {
+    //      out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
+    //  }
+    //  if (index + 5 * groupSz < elements) {
+    //      out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
+    //  }
+    //  if (index + 6 * groupSz < elements) {
+    //      out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
+    //  }
+    //  if (index + 7 * groupSz < elements) {
+    //      out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
+    //  }
 }
 
-
 // Normalized writes with boundary checking
 static void partialBoxMullerWriteOut128Bytes(
-    common::half *out, const uint &index, const uint groupSz,
-    const uint &r1, const uint &r2,
-    const uint &r3, const uint &r4, const uint &elements) {
-//    common::half n[8];
-//    boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
-//                       getHalf01(r1 >> 16));
-//    boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
-//                       getHalf01(r2 >> 16));
-//    boxMullerTransform(n + 4, n + 5, getHalfNegative11(r3),
-//                       getHalf01(r3 >> 16));
-//    boxMullerTransform(n + 6, n + 7, getHalfNegative11(r4),
-//                       getHalf01(r4 >> 16));
-//    if (index < elements) { out[index] = n[0]; }
-//    if (index + groupSz < elements) { out[index + groupSz] = n[1]; }
-//    if (index + 2 * groupSz < elements) {
-//        out[index + 2 * groupSz] = n[2];
-//    }
-//    if (index + 3 * groupSz < elements) {
-//        out[index + 3 * groupSz] = n[3];
-//    }
-//    if (index + 4 * groupSz < elements) {
-//        out[index + 4 * groupSz] = n[4];
-//    }
-//    if (index + 5 * groupSz < elements) {
-//        out[index + 5 * groupSz] = n[5];
-//    }
-//    if (index + 6 * groupSz < elements) {
-//        out[index + 6 * groupSz] = n[6];
-//    }
-//    if (index + 7 * groupSz < elements) {
-//        out[index + 7 * groupSz] = n[7];
-//    }
-}
-
-} // namespace kernel
-} // namespace oneapi
+    common::half *out, const uint &index, const uint groupSz, const uint &r1,
+    const uint &r2, const uint &r3, const uint &r4, const uint &elements) {
+    //    common::half n[8];
+    //    boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
+    //                       getHalf01(r1 >> 16));
+    //    boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
+    //                       getHalf01(r2 >> 16));
+    //    boxMullerTransform(n + 4, n + 5, getHalfNegative11(r3),
+    //                       getHalf01(r3 >> 16));
+    //    boxMullerTransform(n + 6, n + 7, getHalfNegative11(r4),
+    //                       getHalf01(r4 >> 16));
+    //    if (index < elements) { out[index] = n[0]; }
+    //    if (index + groupSz < elements) { out[index + groupSz] = n[1]; }
+    //    if (index + 2 * groupSz < elements) {
+    //        out[index + 2 * groupSz] = n[2];
+    //    }
+    //    if (index + 3 * groupSz < elements) {
+    //        out[index + 3 * groupSz] = n[3];
+    //    }
+    //    if (index + 4 * groupSz < elements) {
+    //        out[index + 4 * groupSz] = n[4];
+    //    }
+    //    if (index + 5 * groupSz < elements) {
+    //        out[index + 5 * groupSz] = n[5];
+    //    }
+    //    if (index + 6 * groupSz < elements) {
+    //        out[index + 6 * groupSz] = n[6];
+    //    }
+    //    if (index + 7 * groupSz < elements) {
+    //        out[index + 7 * groupSz] = n[7];
+    //    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index 3a0c447035..d3106c5e7b 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -26,17 +26,21 @@ namespace kernel {
 
 template<typename T>
 class rangeOp {
-public:
+   public:
     rangeOp(sycl::accessor<T> out, KParam oinfo, const int dim,
-               const int blocksPerMatX, const int blocksPerMatY, 
-               sycl::stream debug) :
-        out_(out), oinfo_(oinfo), dim_(dim),
-        blocksPerMatX_(blocksPerMatX), blocksPerMatY_(blocksPerMatY),
-        debug_(debug) {}
-
-    void operator() (sycl::nd_item<2> it) const {
-        //printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
-        //debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) << "]" << sycl::stream_manipulator::endl;
+            const int blocksPerMatX, const int blocksPerMatY,
+            sycl::stream debug)
+        : out_(out)
+        , oinfo_(oinfo)
+        , dim_(dim)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        // printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
+        // debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) <<
+        // "]" << sycl::stream_manipulator::endl;
 
         const int mul0 = (dim_ == 0);
         const int mul1 = (dim_ == 1);
@@ -44,8 +48,8 @@ class rangeOp {
         const int mul3 = (dim_ == 3);
 
         sycl::group g = it.get_group();
-        const int oz = g.get_group_id(0) / blocksPerMatX_;
-        const int ow = g.get_group_id(1) / blocksPerMatY_;
+        const int oz  = g.get_group_id(0) / blocksPerMatX_;
+        const int ow  = g.get_group_id(1) / blocksPerMatY_;
 
         const int blockIdx_x = g.get_group_id(0) - oz * blocksPerMatX_;
         const int blockIdx_y = g.get_group_id(1) - ow * blocksPerMatY_;
@@ -53,8 +57,8 @@ class rangeOp {
         const int xx = it.get_local_id(0) + blockIdx_x * it.get_local_range(0);
         const int yy = it.get_local_id(1) + blockIdx_y * it.get_local_range(1);
 
-        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] || oz >= oinfo_.dims[2] ||
-            ow >= oinfo_.dims[3])
+        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] ||
+            oz >= oinfo_.dims[2] || ow >= oinfo_.dims[3])
             return;
 
         const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
@@ -66,7 +70,7 @@ class rangeOp {
 
         T* optr = out_.get_pointer();
         for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
-           T valYZW = valZW + (mul1 * oy);
+            T valYZW = valZW + (mul1 * oy);
             int oyzw = ozw + oy * oinfo_.strides[1];
             for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
                 int oidx = oyzw + ox;
@@ -77,7 +81,7 @@ class rangeOp {
         }
     }
 
-protected:
+   protected:
     sycl::accessor<T> out_;
     KParam oinfo_;
     int dim_;
@@ -85,7 +89,6 @@ class rangeOp {
     sycl::stream debug_;
 };
 
-
 template<typename T>
 void range(Param<T> out, const int dim) {
     constexpr int RANGE_TX    = 32;
@@ -101,13 +104,14 @@ void range(Param<T> out, const int dim) {
                           local[1] * blocksPerMatY * out.info.dims[3]);
     sycl::nd_range<2> ndrange(global, local);
 
-    getQueue().submit([=] (sycl::handler &h) {
+    getQueue().submit([=](sycl::handler& h) {
         auto out_acc = out.data->get_access(h);
 
         sycl::stream debug_stream(2048, 128, h);
 
-        h.parallel_for(ndrange, rangeOp<T>(out_acc, out.info,
-            dim, blocksPerMatX, blocksPerMatY, debug_stream));
+        h.parallel_for(ndrange,
+                       rangeOp<T>(out_acc, out.info, dim, blocksPerMatX,
+                                  blocksPerMatY, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index ef87bc77b2..b2bc48a407 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <err_oneapi.hpp>
 #include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
 #include <traits.hpp>
 
 #include <string>
@@ -41,92 +41,104 @@ cdouble getConjugate(const cdouble &in) {
     return std::conj(in);
 }
 
-template <typename T, int dimensions>
+template<typename T, int dimensions>
 using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
 
-template <typename T>
+template<typename T>
 class transposeKernel {
-public:
-  transposeKernel(sycl::accessor<T> oData, const KParam out,
-                  const sycl::accessor<T> iData, const KParam in,
-                  const int blocksPerMatX, const int blocksPerMatY,
-                  const bool conjugate, const bool IS32MULTIPLE,
-                  local_accessor<T, 1> shrdMem,
-                  sycl::stream debugStream) :
-    oData_(oData), out_(out), iData_(iData), in_(in), blocksPerMatX_(blocksPerMatX),
-    blocksPerMatY_(blocksPerMatY), conjugate_(conjugate), IS32MULTIPLE_(IS32MULTIPLE), shrdMem_(shrdMem), debugStream_(debugStream) {}
-  void operator() (sycl::nd_item<2> it) const {
-      const int shrdStride = TILE_DIM + 1;
-
-      const int oDim0 = out_.dims[0];
-      const int oDim1 = out_.dims[1];
-      const int iDim0 = in_.dims[0];
-      const int iDim1 = in_.dims[1];
-
-      // calculate strides
-      const int oStride1 = out_.strides[1];
-      const int iStride1 = in_.strides[1];
-
-      const int lx = it.get_local_id(0);
-      const int ly = it.get_local_id(1);
-
-      // batch based block Id
-      sycl::group g = it.get_group();
-      const int batchId_x  = g.get_group_id(0) / blocksPerMatX_;
-      const int blockIdx_x = (g.get_group_id(0) - batchId_x * blocksPerMatX_);
-
-      const int batchId_y  = g.get_group_id(1) / blocksPerMatY_;
-      const int blockIdx_y = (g.get_group_id(1) - batchId_y * blocksPerMatY_);
-
-      const int x0 = TILE_DIM * blockIdx_x;
-      const int y0 = TILE_DIM * blockIdx_y;
-
-      // calculate global in_dices
-      int gx = lx + x0;
-      int gy = ly + y0;
-
-      // offset in_ and out_ based on batch id
-      // also add the subBuffer offsets
-      T *iDataPtr = iData_.get_pointer(), *oDataPtr = oData_.get_pointer();
-      iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] + in_.offset;
-      oDataPtr +=
-        batchId_x * out_.strides[2] + batchId_y * out_.strides[3] + out_.offset;
-
-      for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-        int gy_ = gy + repeat;
-        if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
-          shrdMem_[(ly + repeat) * shrdStride + lx] =
-            iDataPtr[gy_ * iStride1 + gx];
-      }
-      it.barrier();
-
-      gx = lx + y0;
-      gy = ly + x0;
-
-      for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-        int gy_ = gy + repeat;
-        if (IS32MULTIPLE_ || (gx < oDim0 && gy_ < oDim1)) {
-          const T val = shrdMem_[lx * shrdStride + ly + repeat];
-          oDataPtr[gy_ * oStride1 + gx] = conjugate_ ? getConjugate(val) : val;
+   public:
+    transposeKernel(sycl::accessor<T> oData, const KParam out,
+                    const sycl::accessor<T> iData, const KParam in,
+                    const int blocksPerMatX, const int blocksPerMatY,
+                    const bool conjugate, const bool IS32MULTIPLE,
+                    local_accessor<T, 1> shrdMem, sycl::stream debugStream)
+        : oData_(oData)
+        , out_(out)
+        , iData_(iData)
+        , in_(in)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY)
+        , conjugate_(conjugate)
+        , IS32MULTIPLE_(IS32MULTIPLE)
+        , shrdMem_(shrdMem)
+        , debugStream_(debugStream) {}
+    void operator()(sycl::nd_item<2> it) const {
+        const int shrdStride = TILE_DIM + 1;
+
+        const int oDim0 = out_.dims[0];
+        const int oDim1 = out_.dims[1];
+        const int iDim0 = in_.dims[0];
+        const int iDim1 = in_.dims[1];
+
+        // calculate strides
+        const int oStride1 = out_.strides[1];
+        const int iStride1 = in_.strides[1];
+
+        const int lx = it.get_local_id(0);
+        const int ly = it.get_local_id(1);
+
+        // batch based block Id
+        sycl::group g        = it.get_group();
+        const int batchId_x  = g.get_group_id(0) / blocksPerMatX_;
+        const int blockIdx_x = (g.get_group_id(0) - batchId_x * blocksPerMatX_);
+
+        const int batchId_y  = g.get_group_id(1) / blocksPerMatY_;
+        const int blockIdx_y = (g.get_group_id(1) - batchId_y * blocksPerMatY_);
+
+        const int x0 = TILE_DIM * blockIdx_x;
+        const int y0 = TILE_DIM * blockIdx_y;
+
+        // calculate global in_dices
+        int gx = lx + x0;
+        int gy = ly + y0;
+
+        // offset in_ and out_ based on batch id
+        // also add the subBuffer offsets
+        T *iDataPtr = iData_.get_pointer(), *oDataPtr = oData_.get_pointer();
+        iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] +
+                    in_.offset;
+        oDataPtr += batchId_x * out_.strides[2] + batchId_y * out_.strides[3] +
+                    out_.offset;
+
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                shrdMem_[(ly + repeat) * shrdStride + lx] =
+                    iDataPtr[gy_ * iStride1 + gx];
+        }
+        it.barrier();
+
+        gx = lx + y0;
+        gy = ly + x0;
+
+        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+            int gy_ = gy + repeat;
+            if (IS32MULTIPLE_ || (gx < oDim0 && gy_ < oDim1)) {
+                const T val = shrdMem_[lx * shrdStride + ly + repeat];
+                oDataPtr[gy_ * oStride1 + gx] =
+                    conjugate_ ? getConjugate(val) : val;
+            }
         }
-      }
-  }
-private:
-  sycl::accessor<T> oData_;
-  KParam out_;
-  sycl::accessor<T> iData_;
-  KParam in_;
-  int blocksPerMatX_;
-  int blocksPerMatY_;
-  sycl::stream debugStream_;
-  bool conjugate_;
-  bool IS32MULTIPLE_;
-  local_accessor<T, 1> shrdMem_;
+    }
+
+   private:
+    sycl::accessor<T> oData_;
+    KParam out_;
+    sycl::accessor<T> iData_;
+    KParam in_;
+    int blocksPerMatX_;
+    int blocksPerMatY_;
+    sycl::stream debugStream_;
+    bool conjugate_;
+    bool IS32MULTIPLE_;
+    local_accessor<T, 1> shrdMem_;
 };
 
 template<typename T>
-void transpose(Param<T> out, const Param<T> in, const bool conjugate, const bool IS32MULTIPLE) {
+void transpose(Param<T> out, const Param<T> in, const bool conjugate,
+               const bool IS32MULTIPLE) {
     auto local = sycl::range{THREADS_X, THREADS_Y};
 
     const int blk_x = divup(in.info.dims[0], TILE_DIM);
@@ -142,12 +154,10 @@ void transpose(Param<T> out, const Param<T> in, const bool conjugate, const bool
 
         auto shrdMem = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 
-        h.parallel_for(sycl::nd_range{global, local},
-                       transposeKernel<T>(q, out.info,
-                                          r, in.info,
-                                          blk_x, blk_y,
-                                          conjugate, IS32MULTIPLE,
-                                          shrdMem, debugStream));
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            transposeKernel<T>(q, out.info, r, in.info, blk_x, blk_y, conjugate,
+                               IS32MULTIPLE, shrdMem, debugStream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
old mode 100755
new mode 100644
index c5230f364e..81313b50da
--- a/src/backend/oneapi/kernel/transpose_inplace.hpp
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -12,14 +12,13 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
-#include <err_oneapi.hpp>
 #include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
 #include <traits.hpp>
 
 #include <string>
 #include <vector>
 
-
 namespace oneapi {
 namespace kernel {
 
@@ -45,121 +44,132 @@ constexpr int TILE_DIM  = 16;
 constexpr int THREADS_X = TILE_DIM;
 constexpr int THREADS_Y = 256 / TILE_DIM;
 
-template <typename T, int dimensions>
+template<typename T, int dimensions>
 using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
 
-template <typename T>
+template<typename T>
 class transposeInPlaceKernel {
-public:
-  transposeInPlaceKernel(const sycl::accessor<T> iData, const KParam in,
-                         const int blocksPerMatX, const int blocksPerMatY,
-                         const bool conjugate, const bool IS32MULTIPLE,
-                         local_accessor<T, 1> shrdMem_s, local_accessor<T, 1> shrdMem_d,
-                         sycl::stream debugStream) :
-    iData_(iData), in_(in), blocksPerMatX_(blocksPerMatX),
-    blocksPerMatY_(blocksPerMatY), conjugate_(conjugate), IS32MULTIPLE_(IS32MULTIPLE), shrdMem_s_(shrdMem_s), shrdMem_d_(shrdMem_d), debugStream_(debugStream) {}
-  void operator() (sycl::nd_item<2> it) const {
-    const int shrdStride = TILE_DIM + 1;
-
-    // create variables to hold output dimensions
-    const int iDim0 = in_.dims[0];
-    const int iDim1 = in_.dims[1];
-
-    // calculate strides
-    const int iStride1 = in_.strides[1];
-
-    const int lx = it.get_local_id(0);
-    const int ly = it.get_local_id(1);
-
-    // batch based block Id
-    sycl::group g = it.get_group();
-    const int batchId_x  = g.get_group_id(0) / blocksPerMatX_;
-    const int blockIdx_x = (g.get_group_id(0) - batchId_x * blocksPerMatX_);
-
-    const int batchId_y  = g.get_group_id(1) / blocksPerMatY_;
-    const int blockIdx_y = (g.get_group_id(1) - batchId_y * blocksPerMatY_);
-
-    const int x0 = TILE_DIM * blockIdx_x;
-    const int y0 = TILE_DIM * blockIdx_y;
-
-    T *iDataPtr = iData_.get_pointer();
-    iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] + in_.offset;
-
-    if (blockIdx_y > blockIdx_x) {
-        // calculate global indices
-        int gx = lx + x0;
-        int gy = ly + y0;
-        int dx = lx + y0;
-        int dy = ly + x0;
-
-        // Copy to shared memory
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy + repeat;
-            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
-              shrdMem_s_[(ly + repeat) * shrdStride + lx] =
-                iDataPtr[gy_ * iStride1 + gx];
-
-            int dy_ = dy + repeat;
-            if (IS32MULTIPLE_ || (dx < iDim0 && dy_ < iDim1))
-                shrdMem_d_[(ly + repeat) * shrdStride + lx] =
-                    iDataPtr[dy_ * iStride1 + dx];
-        }
-
-        it.barrier();
-
-        // Copy from shared memory to global memory
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int dy_ = dy + repeat;
-            if (IS32MULTIPLE_ || (dx < iDim0 && dy_ < iDim1))
-                iDataPtr[dy_ * iStride1 + dx] =
-                    doOp(shrdMem_s_[(ly + repeat) + (shrdStride * lx)]);
-
-            int gy_ = gy + repeat;
-            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
-                iDataPtr[gy_ * iStride1 + gx] =
-                    doOp(shrdMem_d_[(ly + repeat) + (shrdStride * lx)]);
-        }
-
-    } else if (blockIdx_y == blockIdx_x) {
-        // calculate global indices
-        int gx = lx + x0;
-        int gy = ly + y0;
-
-        // Copy to shared memory
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy + repeat;
-            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
-                shrdMem_s_[(ly + repeat) * shrdStride + lx] =
-                    iDataPtr[gy_ * iStride1 + gx];
-        }
-
-        it.barrier();
-
-        // Copy from shared memory to global memory
-        for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
-            int gy_ = gy + repeat;
-            if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
-                iDataPtr[gy_ * iStride1 + gx] =
-                    doOp(shrdMem_s_[(ly + repeat) + (shrdStride * lx)]);
+   public:
+    transposeInPlaceKernel(const sycl::accessor<T> iData, const KParam in,
+                           const int blocksPerMatX, const int blocksPerMatY,
+                           const bool conjugate, const bool IS32MULTIPLE,
+                           local_accessor<T, 1> shrdMem_s,
+                           local_accessor<T, 1> shrdMem_d,
+                           sycl::stream debugStream)
+        : iData_(iData)
+        , in_(in)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY)
+        , conjugate_(conjugate)
+        , IS32MULTIPLE_(IS32MULTIPLE)
+        , shrdMem_s_(shrdMem_s)
+        , shrdMem_d_(shrdMem_d)
+        , debugStream_(debugStream) {}
+    void operator()(sycl::nd_item<2> it) const {
+        const int shrdStride = TILE_DIM + 1;
+
+        // create variables to hold output dimensions
+        const int iDim0 = in_.dims[0];
+        const int iDim1 = in_.dims[1];
+
+        // calculate strides
+        const int iStride1 = in_.strides[1];
+
+        const int lx = it.get_local_id(0);
+        const int ly = it.get_local_id(1);
+
+        // batch based block Id
+        sycl::group g        = it.get_group();
+        const int batchId_x  = g.get_group_id(0) / blocksPerMatX_;
+        const int blockIdx_x = (g.get_group_id(0) - batchId_x * blocksPerMatX_);
+
+        const int batchId_y  = g.get_group_id(1) / blocksPerMatY_;
+        const int blockIdx_y = (g.get_group_id(1) - batchId_y * blocksPerMatY_);
+
+        const int x0 = TILE_DIM * blockIdx_x;
+        const int y0 = TILE_DIM * blockIdx_y;
+
+        T *iDataPtr = iData_.get_pointer();
+        iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] +
+                    in_.offset;
+
+        if (blockIdx_y > blockIdx_x) {
+            // calculate global indices
+            int gx = lx + x0;
+            int gy = ly + y0;
+            int dx = lx + y0;
+            int dy = ly + x0;
+
+            // Copy to shared memory
+            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+                int gy_ = gy + repeat;
+                if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                    shrdMem_s_[(ly + repeat) * shrdStride + lx] =
+                        iDataPtr[gy_ * iStride1 + gx];
+
+                int dy_ = dy + repeat;
+                if (IS32MULTIPLE_ || (dx < iDim0 && dy_ < iDim1))
+                    shrdMem_d_[(ly + repeat) * shrdStride + lx] =
+                        iDataPtr[dy_ * iStride1 + dx];
+            }
+
+            it.barrier();
+
+            // Copy from shared memory to global memory
+            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+                int dy_ = dy + repeat;
+                if (IS32MULTIPLE_ || (dx < iDim0 && dy_ < iDim1))
+                    iDataPtr[dy_ * iStride1 + dx] =
+                        doOp(shrdMem_s_[(ly + repeat) + (shrdStride * lx)]);
+
+                int gy_ = gy + repeat;
+                if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                    iDataPtr[gy_ * iStride1 + gx] =
+                        doOp(shrdMem_d_[(ly + repeat) + (shrdStride * lx)]);
+            }
+
+        } else if (blockIdx_y == blockIdx_x) {
+            // calculate global indices
+            int gx = lx + x0;
+            int gy = ly + y0;
+
+            // Copy to shared memory
+            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+                int gy_ = gy + repeat;
+                if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                    shrdMem_s_[(ly + repeat) * shrdStride + lx] =
+                        iDataPtr[gy_ * iStride1 + gx];
+            }
+
+            it.barrier();
+
+            // Copy from shared memory to global memory
+            for (int repeat = 0; repeat < TILE_DIM; repeat += THREADS_Y) {
+                int gy_ = gy + repeat;
+                if (IS32MULTIPLE_ || (gx < iDim0 && gy_ < iDim1))
+                    iDataPtr[gy_ * iStride1 + gx] =
+                        doOp(shrdMem_s_[(ly + repeat) + (shrdStride * lx)]);
+            }
         }
     }
-  }
-private:
-  sycl::accessor<T> iData_;
-  KParam in_;
-  int blocksPerMatX_;
-  int blocksPerMatY_;
-  sycl::stream debugStream_;
-  bool conjugate_;
-  bool IS32MULTIPLE_;
-  local_accessor<T, 1> shrdMem_s_;
-  local_accessor<T, 1> shrdMem_d_;
+
+   private:
+    sycl::accessor<T> iData_;
+    KParam in_;
+    int blocksPerMatX_;
+    int blocksPerMatY_;
+    sycl::stream debugStream_;
+    bool conjugate_;
+    bool IS32MULTIPLE_;
+    local_accessor<T, 1> shrdMem_s_;
+    local_accessor<T, 1> shrdMem_d_;
 };
 
 template<typename T>
-void transpose_inplace(Param<T> in, const bool conjugate, const bool IS32MULTIPLE)
-{
+void transpose_inplace(Param<T> in, const bool conjugate,
+                       const bool IS32MULTIPLE) {
     auto local = sycl::range{THREADS_X, THREADS_Y};
 
     int blk_x = divup(in.info.dims[0], TILE_DIM);
@@ -176,11 +186,9 @@ void transpose_inplace(Param<T> in, const bool conjugate, const bool IS32MULTIPL
         auto shrdMem_d = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 
         h.parallel_for(sycl::nd_range{global, local},
-                       transposeInPlaceKernel<T>(r, in.info,
-                                                 blk_x, blk_y,
-                                                 conjugate, IS32MULTIPLE,
-                                                 shrdMem_s, shrdMem_d,
-                                                 debugStream));
+                       transposeInPlaceKernel<T>(
+                           r, in.info, blk_x, blk_y, conjugate, IS32MULTIPLE,
+                           shrdMem_s, shrdMem_d, debugStream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/triangle.hpp b/src/backend/oneapi/kernel/triangle.hpp
index 4f71ce1243..cf9c3e22a3 100644
--- a/src/backend/oneapi/kernel/triangle.hpp
+++ b/src/backend/oneapi/kernel/triangle.hpp
@@ -11,8 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <err_oneapi.hpp>
 #include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
 #include <traits.hpp>
 
 #include <string>
@@ -21,67 +21,77 @@
 namespace oneapi {
 namespace kernel {
 
-template <typename T, int dimensions>
+template<typename T, int dimensions>
 using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write, sycl::access::target::local>;
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
 
-template <typename T>
+template<typename T>
 class triangleKernel {
-public:
-  triangleKernel(sycl::accessor<T> rAcc, KParam rinfo, sycl::accessor<T> iAcc,
-                 KParam iinfo, const int groups_x, const int groups_y,
-                 const bool is_upper, const bool is_unit_diag) :
-    rAcc_(rAcc), rinfo_(rinfo), iAcc_(iAcc), iinfo_(iinfo), groups_x_(groups_x), groups_y_(groups_y), is_upper_(is_upper), is_unit_diag_(is_unit_diag) {}
-  void operator() (sycl::nd_item<2> it) const {
-    sycl::group g = it.get_group();
-    const int oz = g.get_group_id(0) / groups_x_;
-    const int ow = g.get_group_id(1) / groups_y_;
-
-    const int groupId_0 = g.get_group_id(0) - oz * groups_x_;
-    const int groupId_1 = g.get_group_id(1) - ow * groups_y_;
-
-    const int xx = it.get_local_id(0) + groupId_0 * it.get_local_range(0);
-    const int yy = it.get_local_id(1) + groupId_1 * it.get_local_range(1);
-
-    const int incy = groups_y_ * it.get_local_range(1);
-    const int incx = groups_x_ * it.get_local_range(0);
-
-    T *d_r = rAcc_.get_pointer();
-    const T *d_i = iAcc_.get_pointer() + iinfo_.offset;
-
-    if (oz < rinfo_.dims[2] && ow < rinfo_.dims[3]) {
-        d_i = d_i + oz * iinfo_.strides[2] + ow * iinfo_.strides[3];
-        d_r = d_r + oz * rinfo_.strides[2] + ow * rinfo_.strides[3];
-
-        for (int oy = yy; oy < rinfo_.dims[1]; oy += incy) {
-            const T *Yd_i = d_i + oy * iinfo_.strides[1];
-            T *Yd_r       = d_r + oy * rinfo_.strides[1];
-
-            for (int ox = xx; ox < rinfo_.dims[0]; ox += incx) {
-                bool cond         = is_upper_ ? (oy >= ox) : (oy <= ox);
-                bool do_unit_diag = is_unit_diag_ && (oy == ox);
-                if (cond) {
-                    Yd_r[ox] = do_unit_diag ? (T)(1) : Yd_i[ox];
-                } else {
-                    Yd_r[ox] = (T)(0);
+   public:
+    triangleKernel(sycl::accessor<T> rAcc, KParam rinfo, sycl::accessor<T> iAcc,
+                   KParam iinfo, const int groups_x, const int groups_y,
+                   const bool is_upper, const bool is_unit_diag)
+        : rAcc_(rAcc)
+        , rinfo_(rinfo)
+        , iAcc_(iAcc)
+        , iinfo_(iinfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , is_upper_(is_upper)
+        , is_unit_diag_(is_unit_diag) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int oz  = g.get_group_id(0) / groups_x_;
+        const int ow  = g.get_group_id(1) / groups_y_;
+
+        const int groupId_0 = g.get_group_id(0) - oz * groups_x_;
+        const int groupId_1 = g.get_group_id(1) - ow * groups_y_;
+
+        const int xx = it.get_local_id(0) + groupId_0 * it.get_local_range(0);
+        const int yy = it.get_local_id(1) + groupId_1 * it.get_local_range(1);
+
+        const int incy = groups_y_ * it.get_local_range(1);
+        const int incx = groups_x_ * it.get_local_range(0);
+
+        T *d_r       = rAcc_.get_pointer();
+        const T *d_i = iAcc_.get_pointer() + iinfo_.offset;
+
+        if (oz < rinfo_.dims[2] && ow < rinfo_.dims[3]) {
+            d_i = d_i + oz * iinfo_.strides[2] + ow * iinfo_.strides[3];
+            d_r = d_r + oz * rinfo_.strides[2] + ow * rinfo_.strides[3];
+
+            for (int oy = yy; oy < rinfo_.dims[1]; oy += incy) {
+                const T *Yd_i = d_i + oy * iinfo_.strides[1];
+                T *Yd_r       = d_r + oy * rinfo_.strides[1];
+
+                for (int ox = xx; ox < rinfo_.dims[0]; ox += incx) {
+                    bool cond         = is_upper_ ? (oy >= ox) : (oy <= ox);
+                    bool do_unit_diag = is_unit_diag_ && (oy == ox);
+                    if (cond) {
+                        Yd_r[ox] = do_unit_diag ? (T)(1) : Yd_i[ox];
+                    } else {
+                        Yd_r[ox] = (T)(0);
+                    }
                 }
             }
         }
     }
-  }
-private:
-  sycl::accessor<T> rAcc_;
-  KParam rinfo_;
-  sycl::accessor<T> iAcc_;
-  KParam iinfo_;
-  const int groups_x_;
-  const int groups_y_;
-  const bool is_upper_;
-  const bool is_unit_diag_;
+
+   private:
+    sycl::accessor<T> rAcc_;
+    KParam rinfo_;
+    sycl::accessor<T> iAcc_;
+    KParam iinfo_;
+    const int groups_x_;
+    const int groups_y_;
+    const bool is_upper_;
+    const bool is_unit_diag_;
 };
 
 template<typename T>
-void triangle(Param<T> out, const Param<T> in, bool is_upper, bool is_unit_diag) {
+void triangle(Param<T> out, const Param<T> in, bool is_upper,
+              bool is_unit_diag) {
     constexpr unsigned TX    = 32;
     constexpr unsigned TY    = 8;
     constexpr unsigned TILEX = 128;
@@ -100,9 +110,10 @@ void triangle(Param<T> out, const Param<T> in, bool is_upper, bool is_unit_diag)
         auto rAcc = out.data->get_access(h);
         sycl::stream debugStream(128, 128, h);
 
-        h.parallel_for(sycl::nd_range{global, local},
-                       triangleKernel<T>(rAcc, out.info, iAcc, in.info, groups_x, groups_y,
-                                         is_upper, is_unit_diag));
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            triangleKernel<T>(rAcc, out.info, iAcc, in.info, groups_x, groups_y,
+                              is_upper, is_unit_diag));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
index 849fea1426..170efca58c 100644
--- a/src/backend/oneapi/lu.cpp
+++ b/src/backend/oneapi/lu.cpp
@@ -59,13 +59,15 @@ template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
         const Array<T> &in) {
     ONEAPI_NOT_SUPPORTED("");
-    AF_ERROR("Linear Algebra is disabled on OneAPI backend", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend",
+             AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     ONEAPI_NOT_SUPPORTED("");
-    AF_ERROR("Linear Algebra is disabled on OneAPI backend", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend",
+             AF_ERR_NOT_CONFIGURED);
 }
 
 bool isLAPACKAvailable() { return false; }
diff --git a/src/backend/oneapi/math.cpp b/src/backend/oneapi/math.cpp
index a3b9d07e7a..e9c1666960 100644
--- a/src/backend/oneapi/math.cpp
+++ b/src/backend/oneapi/math.cpp
@@ -12,42 +12,42 @@
 
 namespace oneapi {
 cfloat operator+(cfloat lhs, cfloat rhs) {
-    //cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
+    // cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
     cfloat res;
     return res;
 }
 
 cdouble operator+(cdouble lhs, cdouble rhs) {
-    //cdouble res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
+    // cdouble res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
     cdouble res;
     return res;
 }
 
 cfloat operator*(cfloat lhs, cfloat rhs) {
     cfloat out;
-    //out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
-    //out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
+    // out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
+    // out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
     return out;
 }
 
 cdouble operator*(cdouble lhs, cdouble rhs) {
     cdouble out;
-    //out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
-    //out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
+    // out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
+    // out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
     return out;
 }
 
 cfloat division(cfloat lhs, double rhs) {
     cfloat retVal;
-    //retVal.s[0] = real(lhs) / rhs;
-    //retVal.s[1] = imag(lhs) / rhs;
+    // retVal.s[0] = real(lhs) / rhs;
+    // retVal.s[1] = imag(lhs) / rhs;
     return retVal;
 }
 
 cdouble division(cdouble lhs, double rhs) {
     cdouble retVal;
-    //retVal.s[0] = real(lhs) / rhs;
-    //retVal.s[1] = imag(lhs) / rhs;
+    // retVal.s[0] = real(lhs) / rhs;
+    // retVal.s[1] = imag(lhs) / rhs;
     return retVal;
 }
 }  // namespace oneapi
diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index 2b4182d811..584efa1d14 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -146,7 +146,6 @@ inline common::half operator+(common::half lhs, common::half rhs) noexcept {
 }
 }  // namespace oneapi
 
-
 #if defined(__GNUC__) || defined(__GNUG__)
 /* GCC/G++, Clang/LLVM, Intel ICC */
 #pragma GCC diagnostic pop
diff --git a/src/backend/oneapi/mean.cpp b/src/backend/oneapi/mean.cpp
index 2fb632eb75..41d72a547e 100644
--- a/src/backend/oneapi/mean.cpp
+++ b/src/backend/oneapi/mean.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <mean.hpp>
 #include <err_oneapi.hpp>
+#include <mean.hpp>
 
 #include <common/half.hpp>
 // #include <kernel/mean.hpp>
@@ -21,7 +21,6 @@ using std::swap;
 namespace oneapi {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
-
     ONEAPI_NOT_SUPPORTED("mean Not supported");
 
     return To(0);
@@ -30,7 +29,6 @@ To mean(const Array<Ti>& in) {
 
 template<typename T, typename Tw>
 T mean(const Array<T>& in, const Array<Tw>& wts) {
-
     ONEAPI_NOT_SUPPORTED("mean Not supported");
 
     return T(0);
@@ -39,7 +37,6 @@ T mean(const Array<T>& in, const Array<Tw>& wts) {
 
 template<typename Ti, typename Tw, typename To>
 Array<To> mean(const Array<Ti>& in, const int dim) {
-
     ONEAPI_NOT_SUPPORTED("mean Not supported");
 
     dim4 odims    = in.dims();
@@ -51,7 +48,6 @@ Array<To> mean(const Array<Ti>& in, const int dim) {
 
 template<typename T, typename Tw>
 Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim) {
-
     ONEAPI_NOT_SUPPORTED("mean Not supported");
 
     dim4 odims   = in.dims();
diff --git a/src/backend/oneapi/meanshift.cpp b/src/backend/oneapi/meanshift.cpp
index 61823f1467..fa352ed5c1 100644
--- a/src/backend/oneapi/meanshift.cpp
+++ b/src/backend/oneapi/meanshift.cpp
@@ -20,12 +20,12 @@ template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor) {
-
     ONEAPI_NOT_SUPPORTED("meanshift Not supported");
 
     const dim4 &dims = in.dims();
     Array<T> out     = createEmptyArray<T>(dims);
-    // kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma, numIterations,
+    // kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma,
+    // numIterations,
     //                      isColor);
     return out;
 }
diff --git a/src/backend/oneapi/medfilt.cpp b/src/backend/oneapi/medfilt.cpp
index 526f505244..1729573628 100644
--- a/src/backend/oneapi/medfilt.cpp
+++ b/src/backend/oneapi/medfilt.cpp
@@ -20,7 +20,6 @@ namespace oneapi {
 template<typename T>
 Array<T> medfilt1(const Array<T> &in, const int w_wid,
                   const af::borderType pad) {
-
     ONEAPI_NOT_SUPPORTED("medfilt1 Not supported");
 
     // ARG_ASSERT(2, (w_wid <= kernel::MAX_MEDFILTER1_LEN));
@@ -38,7 +37,6 @@ Array<T> medfilt1(const Array<T> &in, const int w_wid,
 template<typename T>
 Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
                   const af::borderType pad) {
-
     ONEAPI_NOT_SUPPORTED("medfilt2 Not supported");
 
     // ARG_ASSERT(2, (w_len % 2 != 0));
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index add529c8cc..314e1fd0a8 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -47,7 +47,8 @@ void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
 void shutdownMemoryManager() { memoryManager().shutdown(); }
 
-void shutdownPinnedMemoryManager() { /*pinnedMemoryManager().shutdown();*/ }
+void shutdownPinnedMemoryManager() { /*pinnedMemoryManager().shutdown();*/
+}
 
 void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
@@ -55,10 +56,11 @@ void printMemInfo(const char *msg, const int device) {
 
 template<typename T>
 // unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc(
-//unique_ptr<int, function<void(int *)>> memAlloc(
-std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>> memAlloc(
-    const size_t &elements) {
-    return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(new sycl::buffer<T>(sycl::range(elements)), bufferFree<T>);
+// unique_ptr<int, function<void(int *)>> memAlloc(
+std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>
+memAlloc(const size_t &elements) {
+    return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(
+        new sycl::buffer<T>(sycl::range(elements)), bufferFree<T>);
     // // TODO: make memAlloc aware of array shapes
     // if (elements) {
     //     dim4 dims(elements);
@@ -74,7 +76,6 @@ std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>> memAllo
 }
 
 void *memAllocUser(const size_t &bytes) {
-
     ONEAPI_NOT_SUPPORTED("memAllocUser Not supported");
     return nullptr;
 
@@ -86,7 +87,6 @@ void *memAllocUser(const size_t &bytes) {
 
 template<typename T>
 void memFree(T *ptr) {
-
     ONEAPI_NOT_SUPPORTED("memFree Not supported");
 
     // cl::Buffer *buf = reinterpret_cast<cl::Buffer *>(ptr);
@@ -96,7 +96,6 @@ void memFree(T *ptr) {
 }
 
 void memFreeUser(void *ptr) {
-
     ONEAPI_NOT_SUPPORTED("memFreeUser Not supported");
 
     // cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
@@ -107,7 +106,6 @@ void memFreeUser(void *ptr) {
 
 template<typename T>
 sycl::buffer<T> *bufferAlloc(const size_t &bytes) {
-
     ONEAPI_NOT_SUPPORTED("bufferAlloc Not supported");
     return nullptr;
 
@@ -124,9 +122,7 @@ sycl::buffer<T> *bufferAlloc(const size_t &bytes) {
 
 template<typename T>
 void bufferFree(sycl::buffer<T> *buf) {
-    if(buf) {
-        delete buf;
-    }
+    if (buf) { delete buf; }
     // if (buf) {
     //     cl_mem mem = (*buf)();
     //     delete buf;
@@ -136,7 +132,6 @@ void bufferFree(sycl::buffer<T> *buf) {
 
 template<typename T>
 void memLock(const sycl::buffer<T> *ptr) {
-
     ONEAPI_NOT_SUPPORTED("memLock Not supported");
 
     // cl_mem mem = static_cast<cl_mem>((*ptr)());
@@ -145,7 +140,6 @@ void memLock(const sycl::buffer<T> *ptr) {
 
 template<typename T>
 void memUnlock(const sycl::buffer<T> *ptr) {
-
     ONEAPI_NOT_SUPPORTED("memUnlock Not supported");
 
     // cl_mem mem = static_cast<cl_mem>((*ptr)());
@@ -164,7 +158,6 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-
     ONEAPI_NOT_SUPPORTED("pinnedAlloc Not supported");
 
     // // TODO: make pinnedAlloc aware of array shapes
@@ -175,18 +168,19 @@ T *pinnedAlloc(const size_t &elements) {
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    //pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
+    // pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
-//template unique_ptr<int, function<void(int *)>> memAlloc<T>(
-#define INSTANTIATE(T)                                                                          \
-    template std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>> memAlloc( \
-        const size_t &elements);                                                                \
-    template void memFree(T *ptr);                                                              \
-    template T *pinnedAlloc(const size_t &elements);                                            \
-    template void pinnedFree(T *ptr);                                                           \
-    template void bufferFree(sycl::buffer<T> *buf);                                             \
-    template void memLock(const sycl::buffer<T> *buf);                                          \
+// template unique_ptr<int, function<void(int *)>> memAlloc<T>(
+#define INSTANTIATE(T)                                               \
+    template std::unique_ptr<sycl::buffer<T>,                        \
+                             std::function<void(sycl::buffer<T> *)>> \
+    memAlloc(const size_t &elements);                                \
+    template void memFree(T *ptr);                                   \
+    template T *pinnedAlloc(const size_t &elements);                 \
+    template void pinnedFree(T *ptr);                                \
+    template void bufferFree(sycl::buffer<T> *buf);                  \
+    template void memLock(const sycl::buffer<T> *buf);               \
     template void memUnlock(const sycl::buffer<T> *buf);
 
 INSTANTIATE(float)
@@ -206,7 +200,6 @@ INSTANTIATE(common::half)
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {
-
     ONEAPI_NOT_SUPPORTED("Allocator::shutdown Not supported");
 
     // for (int n = 0; n < opencl::getDeviceCount(); n++) {
@@ -220,7 +213,6 @@ void Allocator::shutdown() {
 }
 
 int Allocator::getActiveDeviceId() {
-
     ONEAPI_NOT_SUPPORTED("Allocator::getActiveDeviceId Not supported");
 
     return 0;
@@ -228,7 +220,6 @@ int Allocator::getActiveDeviceId() {
 }
 
 size_t Allocator::getMaxMemorySize(int id) {
-
     ONEAPI_NOT_SUPPORTED("Allocator::getMaxMemorySize Not supported");
 
     return 0;
@@ -236,7 +227,6 @@ size_t Allocator::getMaxMemorySize(int id) {
 }
 
 void *Allocator::nativeAlloc(const size_t bytes) {
-
     ONEAPI_NOT_SUPPORTED("Allocator::nativeAlloc Not supported");
     return nullptr;
 
@@ -256,7 +246,6 @@ void *Allocator::nativeAlloc(const size_t bytes) {
 }
 
 void Allocator::nativeFree(void *ptr) {
-
     ONEAPI_NOT_SUPPORTED("Allocator::nativeFree Not supported");
 
     // cl_mem buffer = static_cast<cl_mem>(ptr);
@@ -272,22 +261,20 @@ AllocatorPinned::AllocatorPinned() : pinnedMaps(oneapi::getDeviceCount()) {
 }
 
 void AllocatorPinned::shutdown() {
-
     ONEAPI_NOT_SUPPORTED("AllocatorPinned::shutdown Not supported");
 
-//     for (int n = 0; n < opencl::getDeviceCount(); n++) {
-//         opencl::setDevice(n);
-//         shutdownPinnedMemoryManager();
-//         auto currIterator = pinnedMaps[n].begin();
-//         auto endIterator  = pinnedMaps[n].end();
-//         while (currIterator != endIterator) {
-//             pinnedMaps[n].erase(currIterator++);
-//         }
-//     }
+    //     for (int n = 0; n < opencl::getDeviceCount(); n++) {
+    //         opencl::setDevice(n);
+    //         shutdownPinnedMemoryManager();
+    //         auto currIterator = pinnedMaps[n].begin();
+    //         auto endIterator  = pinnedMaps[n].end();
+    //         while (currIterator != endIterator) {
+    //             pinnedMaps[n].erase(currIterator++);
+    //         }
+    //     }
 }
 
 int AllocatorPinned::getActiveDeviceId() {
-
     ONEAPI_NOT_SUPPORTED("AllocatorPinned::getActiveDeviceId Not supported");
     return 0;
 
@@ -295,38 +282,36 @@ int AllocatorPinned::getActiveDeviceId() {
 }
 
 size_t AllocatorPinned::getMaxMemorySize(int id) {
-
     ONEAPI_NOT_SUPPORTED("AllocatorPinned::getMaxMemorySize Not supported");
     return 0;
     // return opencl::getDeviceMemorySize(id);
 }
 
 void *AllocatorPinned::nativeAlloc(const size_t bytes) {
-
     ONEAPI_NOT_SUPPORTED("AllocatorPinned::nativeAlloc Not supported");
     return nullptr;
-//     void *ptr = NULL;
-
-//     cl_int err = CL_SUCCESS;
-//     auto buf   = clCreateBuffer(getContext()(), CL_MEM_ALLOC_HOST_PTR, bytes,
-//                               nullptr, &err);
-//     if (err != CL_SUCCESS) {
-//         AF_ERROR("Failed to allocate pinned memory.", AF_ERR_NO_MEM);
-//     }
-
-//     ptr = clEnqueueMapBuffer(getQueue()(), buf, CL_TRUE,
-//                              CL_MAP_READ | CL_MAP_WRITE, 0, bytes, 0, nullptr,
-//                              nullptr, &err);
-//     if (err != CL_SUCCESS) {
-//         AF_ERROR("Failed to map pinned memory", AF_ERR_RUNTIME);
-//     }
-//     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
-//     pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, new cl::Buffer(buf));
-//     return ptr;
+    //     void *ptr = NULL;
+
+    //     cl_int err = CL_SUCCESS;
+    //     auto buf   = clCreateBuffer(getContext()(), CL_MEM_ALLOC_HOST_PTR,
+    //     bytes,
+    //                               nullptr, &err);
+    //     if (err != CL_SUCCESS) {
+    //         AF_ERROR("Failed to allocate pinned memory.", AF_ERR_NO_MEM);
+    //     }
+
+    //     ptr = clEnqueueMapBuffer(getQueue()(), buf, CL_TRUE,
+    //                              CL_MAP_READ | CL_MAP_WRITE, 0, bytes, 0,
+    //                              nullptr, nullptr, &err);
+    //     if (err != CL_SUCCESS) {
+    //         AF_ERROR("Failed to map pinned memory", AF_ERR_RUNTIME);
+    //     }
+    //     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
+    //     pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, new
+    //     cl::Buffer(buf)); return ptr;
 }
 
 void AllocatorPinned::nativeFree(void *ptr) {
-
     ONEAPI_NOT_SUPPORTED("AllocatorPinned::nativeFree Not supported");
 
     // AF_TRACE("Pinned::nativeFree:          {}", ptr);
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index bb0e9f181e..2ed71fdd19 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -16,7 +16,6 @@
 #include <memory>
 #include <vector>
 
-
 namespace oneapi {
 template<typename T>
 sycl::buffer<T> *bufferAlloc(const size_t &bytes);
@@ -26,7 +25,7 @@ void bufferFree(sycl::buffer<T> *buf);
 
 template<typename T>
 using bufptr =
-  std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>;
+    std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>;
 
 template<typename T>
 bufptr<T> memAlloc(const size_t &elements);
@@ -67,7 +66,7 @@ void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 
 class Allocator final : public common::memory::AllocatorInterface {
-    public:
+   public:
     Allocator();
     ~Allocator() = default;
     void shutdown() override;
diff --git a/src/backend/oneapi/moments.cpp b/src/backend/oneapi/moments.cpp
index aa595c9269..119e01cbc9 100644
--- a/src/backend/oneapi/moments.cpp
+++ b/src/backend/oneapi/moments.cpp
@@ -22,7 +22,6 @@ static inline unsigned bitCount(unsigned v) {
 
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment) {
-
     ONEAPI_NOT_SUPPORTED("moments Not supported");
 
     in.eval();
diff --git a/src/backend/oneapi/morph.cpp b/src/backend/oneapi/morph.cpp
index de38b446ac..adef3be8d6 100644
--- a/src/backend/oneapi/morph.cpp
+++ b/src/backend/oneapi/morph.cpp
@@ -21,7 +21,6 @@ namespace oneapi {
 
 template<typename T>
 Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
-
     ONEAPI_NOT_SUPPORTED("morph Not supported");
 
     // const dim4 mdims = mask.dims();
@@ -39,7 +38,6 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
 
 template<typename T>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation) {
-
     ONEAPI_NOT_SUPPORTED("morph3d Not supported");
 
     // const dim4 mdims = mask.dims();
diff --git a/src/backend/oneapi/nearest_neighbour.cpp b/src/backend/oneapi/nearest_neighbour.cpp
index e4705f1126..30bc6d90d3 100644
--- a/src/backend/oneapi/nearest_neighbour.cpp
+++ b/src/backend/oneapi/nearest_neighbour.cpp
@@ -24,7 +24,6 @@ template<typename T, typename To, af_match_type dist_type>
 void nearest_neighbour_(Array<uint>& idx, Array<To>& dist,
                         const Array<T>& query, const Array<T>& train,
                         const uint dist_dim, const uint n_dist) {
-
     ONEAPI_NOT_SUPPORTED("nearest_neighbour_ Not supported");
 
     uint sample_dim   = (dist_dim == 0) ? 1 : 0;
diff --git a/src/backend/oneapi/orb.cpp b/src/backend/oneapi/orb.cpp
index db7bd31207..aaca439632 100644
--- a/src/backend/oneapi/orb.cpp
+++ b/src/backend/oneapi/orb.cpp
@@ -25,7 +25,6 @@ unsigned orb(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
              Array<uint> &desc_out, const Array<T> &image, const float fast_thr,
              const unsigned max_feat, const float scl_fctr,
              const unsigned levels, const bool blur_img) {
-
     ONEAPI_NOT_SUPPORTED("orb Not supported");
     return 0;
 
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index c466ff60af..b65ad6698d 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -7,19 +7,19 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/graphics_common.hpp>
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
+#include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <err_oneapi.hpp>
 #include <errorcodes.hpp>
+#include <memory.hpp>
 #include <version.hpp>
 #include <af/version.h>
-#include <memory.hpp>
 
 #ifdef OS_MAC
 #include <OpenGL/CGLCurrent.h>
@@ -36,10 +36,10 @@
 #include <utility>
 #include <vector>
 
-using sycl::queue;
 using sycl::context;
 using sycl::device;
 using sycl::platform;
+using sycl::queue;
 
 using std::begin;
 using std::call_once;
@@ -90,7 +90,7 @@ bool verify_present(const string& pname, const string ref) {
     return iter != end(pname);
 }
 
-//TODO: update to new platforms?
+// TODO: update to new platforms?
 static string platformMap(string& platStr) {
     using strmap_t                = map<string, string>;
     static const strmap_t platMap = {
@@ -141,7 +141,7 @@ string getDeviceInfo() noexcept {
         common::lock_guard_t lock(devMngr.deviceMutex);
         unsigned nDevices = 0;
         for (auto& device : devMngr.mDevices) {
-            //const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
+            // const Platform platform(device->getInfo<CL_DEVICE_PLATFORM>());
 
             string dstr = device->get_info<sycl::info::device::name>();
             bool show_braces =
@@ -150,21 +150,22 @@ string getDeviceInfo() noexcept {
             string id = (show_braces ? string("[") : "-") +
                         to_string(nDevices) + (show_braces ? string("]") : "-");
 
-            size_t msize = device->get_info<sycl::info::device::global_mem_size>();
+            size_t msize =
+                device->get_info<sycl::info::device::global_mem_size>();
             info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
                  << ", " << msize / 1048576 << " MB";
 #ifndef NDEBUG
             info << " -- ";
             string devVersion = device->get_info<sycl::info::device::version>();
-            string driVersion = device->get_info<sycl::info::device::driver_version>();
+            string driVersion =
+                device->get_info<sycl::info::device::driver_version>();
             info << devVersion;
             info << " -- Device driver " << driVersion;
-            info
-                << " -- FP64 Support: "
-                << (device->get_info<sycl::info::device::preferred_vector_width_double>() >
-                            0
-                        ? "True"
-                        : "False");
+            info << " -- FP64 Support: "
+                 << (device->get_info<sycl::info::device::
+                                          preferred_vector_width_double>() > 0
+                         ? "True"
+                         : "False");
             info << " -- Unified Memory ("
                  << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif
@@ -182,8 +183,9 @@ string getDeviceInfo() noexcept {
 }
 
 string getPlatformName(const sycl::device& device) {
-    std::string platStr = device.get_platform().get_info<sycl::info::platform::name>();
-    //return platformMap(platStr);
+    std::string platStr =
+        device.get_platform().get_info<sycl::info::platform::name>();
+    // return platformMap(platStr);
     return platStr;
 }
 
@@ -309,7 +311,8 @@ size_t getHostMemorySize() { return common::getHostMemorySize(); }
 
 sycl::info::device_type getDeviceType() {
     const sycl::device& device = getDevice();
-    sycl::info::device_type type = device.get_info<sycl::info::device::device_type>();
+    sycl::info::device_type type =
+        device.get_info<sycl::info::device::device_type>();
     return type;
 }
 
@@ -409,20 +412,20 @@ void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que) {
         auto tDevice  = make_unique<sycl::device>(dev);
         auto tContext = make_unique<sycl::context>(ctx);
         // queue atleast has implicit context and device if created
-        auto tQueue   = make_unique<sycl::queue>(que);
+        auto tQueue = make_unique<sycl::queue>(que);
 
         devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
-        devMngr.mDeviceTypes.push_back(
-            static_cast<int>(tDevice->get_info<sycl::info::device::device_type>()));
+        devMngr.mDeviceTypes.push_back(static_cast<int>(
+            tDevice->get_info<sycl::info::device::device_type>()));
 
         devMngr.mDevices.push_back(move(tDevice));
         devMngr.mContexts.push_back(move(tContext));
         devMngr.mQueues.push_back(move(tQueue));
         nDevices = static_cast<int>(devMngr.mDevices.size()) - 1;
 
-        //TODO: cache?
+        // TODO: cache?
     }
 
     // Last/newly added device needs memory management
@@ -437,8 +440,7 @@ void setDeviceContext(sycl::device dev, sycl::context ctx) {
 
     const int dCount = static_cast<int>(devMngr.mDevices.size());
     for (int i = 0; i < dCount; ++i) {
-        if (*devMngr.mDevices[i] == dev &&
-            *devMngr.mContexts[i] == ctx) {
+        if (*devMngr.mDevices[i] == dev && *devMngr.mContexts[i] == ctx) {
             setActiveContext(i);
             return;
         }
@@ -459,8 +461,7 @@ void removeDeviceContext(sycl::device dev, sycl::context ctx) {
 
         const int dCount = static_cast<int>(devMngr.mDevices.size());
         for (int i = 0; i < dCount; ++i) {
-            if (*devMngr.mDevices[i] == dev &&
-                *devMngr.mContexts[i] == ctx) {
+            if (*devMngr.mDevices[i] == dev && *devMngr.mContexts[i] == ctx) {
                 deleteIdx = i;
                 break;
             }
@@ -608,7 +609,8 @@ GraphicsResourceManager& interopManager() {
 }  // namespace oneapi
 
 /*
-//TODO: select which external api functions to expose and add to header+implement
+//TODO: select which external api functions to expose and add to
+header+implement
 
 using namespace oneapi;
 
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index d82868454e..46d24393f3 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -62,7 +62,7 @@ size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
 
-//sycl::device::is_cpu,is_gpu,is_accelerator
+// sycl::device::is_cpu,is_gpu,is_accelerator
 sycl::info::device_type getDeviceType();
 
 bool isHostUnifiedMemory(const sycl::device& device);
@@ -114,7 +114,7 @@ graphics::ForgeManager& forgeManager();
 
 GraphicsResourceManager& interopManager();
 
-//afcl::platform getPlatformEnum(cl::Device dev);
+// afcl::platform getPlatformEnum(cl::Device dev);
 
 void setActiveContext(int device);
 
diff --git a/src/backend/oneapi/plot.cpp b/src/backend/oneapi/plot.cpp
index 544cc61568..6abf9896a3 100644
--- a/src/backend/oneapi/plot.cpp
+++ b/src/backend/oneapi/plot.cpp
@@ -36,12 +36,14 @@ void copy_plot(const Array<T> &P, fg_plot plot) {
     //     glFinish();
 
     //     // Use of events:
-    //     // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+    //     //
+    //     https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
     //     cl::Event event;
 
     //     getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
     //     event.wait();
-    //     getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+    //     getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes,
+    //     NULL,
     //                                  &event);
     //     getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
     //     event.wait();
@@ -56,7 +58,8 @@ void copy_plot(const Array<T> &P, fg_plot plot) {
     //     CheckGL("Begin OpenCL fallback-resource copy");
     //     glBindBuffer(GL_ARRAY_BUFFER, buffer);
     //     auto *ptr =
-    //         static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
+    //         static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER,
+    //         GL_WRITE_ONLY));
     //     if (ptr) {
     //         getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
     //         glUnmapBuffer(GL_ARRAY_BUFFER);
diff --git a/src/backend/oneapi/random_engine.cpp b/src/backend/oneapi/random_engine.cpp
index 5f8231706e..cff66a7170 100644
--- a/src/backend/oneapi/random_engine.cpp
+++ b/src/backend/oneapi/random_engine.cpp
@@ -8,25 +8,24 @@
  ********************************************************/
 
 #include <Array.hpp>
-#include <err_oneapi.hpp>
 #include <common/half.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/random_engine.hpp>
 #include <af/defines.h>
 #include <af/dim4.hpp>
-#include <kernel/random_engine.hpp>
 
 using common::half;
 
 namespace oneapi {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl) {
-     kernel::initMersenneState(state, tbl, seed);
+    kernel::initMersenneState(state, tbl, seed);
 }
 
 template<typename T>
 Array<T> uniformDistribution(const af::dim4 &dims,
                              const af_random_engine_type type,
                              const uintl &seed, uintl &counter) {
-
     Array<T> out = createEmptyArray<T>(dims);
     kernel::uniformDistributionCBRNG<T>(out, out.elements(), type, seed,
                                         counter);
@@ -49,9 +48,8 @@ Array<T> uniformDistribution(const af::dim4 &dims, Array<uint> pos,
                              Array<uint> recursion_table,
                              Array<uint> temper_table, Array<uint> state) {
     Array<T> out = createEmptyArray<T>(dims);
-    kernel::uniformDistributionMT<T>(
-        out, out.elements(), state, pos, sh1,
-        sh2, mask, recursion_table, temper_table);
+    kernel::uniformDistributionMT<T>(out, out.elements(), state, pos, sh1, sh2,
+                                     mask, recursion_table, temper_table);
     return out;
 }
 
@@ -61,9 +59,8 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
                             Array<uint> recursion_table,
                             Array<uint> temper_table, Array<uint> state) {
     Array<T> out = createEmptyArray<T>(dims);
-    kernel::normalDistributionMT<T>(
-        out, out.elements(), state, pos, sh1,
-        sh2, mask, recursion_table, temper_table);
+    kernel::normalDistributionMT<T>(out, out.elements(), state, pos, sh1, sh2,
+                                    mask, recursion_table, temper_table);
     return out;
 }
 
diff --git a/src/backend/oneapi/range.cpp b/src/backend/oneapi/range.cpp
index 015ae955db..e5498d12d8 100644
--- a/src/backend/oneapi/range.cpp
+++ b/src/backend/oneapi/range.cpp
@@ -6,8 +6,8 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-#include <range.hpp>
 #include <kernel/range.hpp>
+#include <range.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index d2763c92ac..0300fa99b0 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -24,7 +24,6 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
     ONEAPI_NOT_SUPPORTED("");
     Array<To> out = createEmptyArray<To>(1);
     return out;
-
 }
 
 template<af_op_t op, typename Ti, typename Tk, typename To>
diff --git a/src/backend/oneapi/regions.cpp b/src/backend/oneapi/regions.cpp
index cc74fb9543..73ebccc46e 100644
--- a/src/backend/oneapi/regions.cpp
+++ b/src/backend/oneapi/regions.cpp
@@ -19,7 +19,6 @@ namespace oneapi {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity) {
-
     ONEAPI_NOT_SUPPORTED("regions Not supported");
 
     const af::dim4 &dims = in.dims();
diff --git a/src/backend/oneapi/reorder.cpp b/src/backend/oneapi/reorder.cpp
index 7cced14197..fe5bf98854 100644
--- a/src/backend/oneapi/reorder.cpp
+++ b/src/backend/oneapi/reorder.cpp
@@ -19,7 +19,6 @@ using common::half;
 namespace oneapi {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
-
     ONEAPI_NOT_SUPPORTED("reorder Not supported");
 
     const af::dim4 &iDims = in.dims();
diff --git a/src/backend/oneapi/reshape.cpp b/src/backend/oneapi/reshape.cpp
index 9331038986..87a7e7d28e 100644
--- a/src/backend/oneapi/reshape.cpp
+++ b/src/backend/oneapi/reshape.cpp
@@ -21,7 +21,6 @@ namespace oneapi {
 template<typename inType, typename outType>
 Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                        outType defaultValue, double scale) {
-
     ONEAPI_NOT_SUPPORTED("reshape Not supported");
 
     Array<outType> out = createEmptyArray<outType>(outDims);
diff --git a/src/backend/oneapi/rotate.cpp b/src/backend/oneapi/rotate.cpp
index fc49dd6baa..37f8abbe00 100644
--- a/src/backend/oneapi/rotate.cpp
+++ b/src/backend/oneapi/rotate.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <rotate.hpp>
 #include <err_oneapi.hpp>
+#include <rotate.hpp>
 
 // #include <kernel/rotate.hpp>
 
@@ -16,7 +16,6 @@ namespace oneapi {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method) {
-
     ONEAPI_NOT_SUPPORTED("rotate Not supported");
 
     Array<T> out = createEmptyArray<T>(odims);
diff --git a/src/backend/oneapi/scan.cpp b/src/backend/oneapi/scan.cpp
index 572746035c..c71564cc65 100644
--- a/src/backend/oneapi/scan.cpp
+++ b/src/backend/oneapi/scan.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <scan.hpp>
 #include <err_oneapi.hpp>
+#include <scan.hpp>
 
 // #include <kernel/scan_dim.hpp>
 // #include <kernel/scan_first.hpp>
@@ -16,7 +16,6 @@
 namespace oneapi {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
-
     ONEAPI_NOT_SUPPORTED("scan Not supported");
 
     Array<To> out = createEmptyArray<To>(in.dims());
diff --git a/src/backend/oneapi/scan_by_key.cpp b/src/backend/oneapi/scan_by_key.cpp
index 08a4969905..555817819c 100644
--- a/src/backend/oneapi/scan_by_key.cpp
+++ b/src/backend/oneapi/scan_by_key.cpp
@@ -20,7 +20,6 @@ namespace oneapi {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
                bool inclusive_scan) {
-
     ONEAPI_NOT_SUPPORTED("scan Not supported");
 
     Array<To> out = createEmptyArray<To>(in.dims());
@@ -30,9 +29,11 @@ Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
     // Param In  = in;
 
     // if (dim == 0) {
-    //     // kernel::scanFirstByKey<Ti, Tk, To, op>(Out, In, Key, inclusive_scan);
+    //     // kernel::scanFirstByKey<Ti, Tk, To, op>(Out, In, Key,
+    //     inclusive_scan);
     // } else {
-    //     // kernel::scanDimByKey<Ti, Tk, To, op>(Out, In, Key, dim, inclusive_scan);
+    //     // kernel::scanDimByKey<Ti, Tk, To, op>(Out, In, Key, dim,
+    //     inclusive_scan);
     // }
     return out;
 }
diff --git a/src/backend/oneapi/select.cpp b/src/backend/oneapi/select.cpp
index f15e2ab61c..beea59a771 100644
--- a/src/backend/oneapi/select.cpp
+++ b/src/backend/oneapi/select.cpp
@@ -30,7 +30,6 @@ namespace oneapi {
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const Array<T> &b, const dim4 &odims) {
-
     ONEAPI_NOT_SUPPORTED("createSelectNode Not supported");
 
     auto cond_node   = cond.getNode();
@@ -41,9 +40,9 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     auto cond_height = cond_node->getHeight();
     const int height = max(max(a_height, b_height), cond_height) + 1;
 
-    auto node = make_shared<NaryNode>(
-        NaryNode(static_cast<af::dtype>(af::dtype_traits<T>::af_type), "__select",
-                 3, {{cond_node, a_node, b_node}}, af_select_t, height));
+    auto node = make_shared<NaryNode>(NaryNode(
+        static_cast<af::dtype>(af::dtype_traits<T>::af_type), "__select", 3,
+        {{cond_node, a_node, b_node}}, af_select_t, height));
     std::array<common::Node *, 1> nodes{node.get()};
     if (detail::passesJitHeuristics<T>(nodes) != kJITHeuristics::Pass) {
         if (a_height > max(b_height, cond_height)) {
@@ -61,7 +60,6 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const T &b_val, const dim4 &odims) {
-
     ONEAPI_NOT_SUPPORTED("createSelectNode Not supported");
 
     auto cond_node   = cond.getNode();
diff --git a/src/backend/oneapi/set.cpp b/src/backend/oneapi/set.cpp
index 2001729eca..01fa0a6bcf 100644
--- a/src/backend/oneapi/set.cpp
+++ b/src/backend/oneapi/set.cpp
@@ -8,12 +8,12 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/deprecated.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
 #include <set.hpp>
 #include <sort.hpp>
 #include <af/dim4.hpp>
-#include <common/deprecated.hpp>
 
 namespace oneapi {
 using af::dim4;
@@ -29,7 +29,6 @@ using type_t =
 
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
-
     ONEAPI_NOT_SUPPORTED("setUnique Not supported");
     return createEmptyArray<T>(dim4(1, 1, 1, 1));
 
@@ -50,13 +49,13 @@ Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
     //     out.resetDims(dim4(std::distance(begin, end), 1, 1, 1));
 
     //     return out;
-    // } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    // } catch (const std::exception &ex) { AF_ERROR(ex.what(),
+    // AF_ERR_INTERNAL); }
 }
 
 template<typename T>
 Array<T> setUnion(const Array<T> &first, const Array<T> &second,
                   const bool is_unique) {
-
     ONEAPI_NOT_SUPPORTED("setUnion Not supported");
     return createEmptyArray<T>(dim4(1, 1, 1, 1));
 
@@ -87,18 +86,19 @@ Array<T> setUnion(const Array<T> &first, const Array<T> &second,
     //     compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
 
     //     compute::buffer_iterator<type_t<T>> out_end = compute::set_union(
-    //         first_begin, first_end, second_begin, second_end, out_begin, queue);
+    //         first_begin, first_end, second_begin, second_end, out_begin,
+    //         queue);
 
     //     out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
     //     return out;
 
-    // } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    // } catch (const std::exception &ex) { AF_ERROR(ex.what(),
+    // AF_ERR_INTERNAL); }
 }
 
 template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique) {
-
     ONEAPI_NOT_SUPPORTED("setIntersect Not supported");
     return createEmptyArray<T>(dim4(1, 1, 1, 1));
 
@@ -129,12 +129,15 @@ Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
     //         second_data, unique_second.elements());
     //     compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
 
-    //     compute::buffer_iterator<type_t<T>> out_end = compute::set_intersection(
-    //         first_begin, first_end, second_begin, second_end, out_begin, queue);
+    //     compute::buffer_iterator<type_t<T>> out_end =
+    //     compute::set_intersection(
+    //         first_begin, first_end, second_begin, second_end, out_begin,
+    //         queue);
 
     //     out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
     //     return out;
-    // } catch (const std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+    // } catch (const std::exception &ex) { AF_ERROR(ex.what(),
+    // AF_ERR_INTERNAL); }
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/oneapi/shift.cpp b/src/backend/oneapi/shift.cpp
index b3941f1960..e4ada40a5c 100644
--- a/src/backend/oneapi/shift.cpp
+++ b/src/backend/oneapi/shift.cpp
@@ -70,4 +70,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
-}  // namespace opencl
+}  // namespace oneapi
diff --git a/src/backend/oneapi/sift.cpp b/src/backend/oneapi/sift.cpp
index af2f7bf10d..9197c23d14 100644
--- a/src/backend/oneapi/sift.cpp
+++ b/src/backend/oneapi/sift.cpp
@@ -26,7 +26,6 @@ unsigned sift(Array<float>& x_out, Array<float>& y_out, Array<float>& score_out,
               const float edge_thr, const float init_sigma,
               const bool double_input, const float img_scale,
               const float feature_ratio, const bool compute_GLOH) {
-
     ONEAPI_NOT_SUPPORTED("sift Not supported");
     return 0;
 
diff --git a/src/backend/oneapi/sobel.cpp b/src/backend/oneapi/sobel.cpp
index f76b8685db..7d722e7f4d 100644
--- a/src/backend/oneapi/sobel.cpp
+++ b/src/backend/oneapi/sobel.cpp
@@ -20,7 +20,6 @@ namespace oneapi {
 template<typename Ti, typename To>
 std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
                                                  const unsigned &ker_size) {
-
     ONEAPI_NOT_SUPPORTED("sobelDerivatives Not supported");
 
     Array<To> dx = createEmptyArray<To>(img.dims());
diff --git a/src/backend/oneapi/solve.cpp b/src/backend/oneapi/solve.cpp
index b38461d0f1..ee662de210 100644
--- a/src/backend/oneapi/solve.cpp
+++ b/src/backend/oneapi/solve.cpp
@@ -37,7 +37,6 @@ namespace oneapi {
 template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options) {
-
     ONEAPI_NOT_SUPPORTED("solveLU Not supported");
 
     if (OpenCLCPUOffload()) { return cpu::solveLU(A, pivot, b, options); }
@@ -62,7 +61,6 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
 
 template<typename T>
 Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
-
     ONEAPI_NOT_SUPPORTED("generalSolve Not supported");
 
     // dim4 aDims = a.dims();
@@ -102,7 +100,6 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
 
 template<typename T>
 Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
-
     ONEAPI_NOT_SUPPORTED("leastSquares Not supported");
 
     int M  = a.dims()[0];
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
index f9c13b7429..b5e0eb73fd 100644
--- a/src/backend/oneapi/sort.cpp
+++ b/src/backend/oneapi/sort.cpp
@@ -19,7 +19,6 @@
 namespace oneapi {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
-
     ONEAPI_NOT_SUPPORTED("sort Not supported");
 
     try {
diff --git a/src/backend/oneapi/sort_index.cpp b/src/backend/oneapi/sort_index.cpp
index ebf5ce65f7..6600db9f7c 100644
--- a/src/backend/oneapi/sort_index.cpp
+++ b/src/backend/oneapi/sort_index.cpp
@@ -24,7 +24,6 @@ namespace oneapi {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
                 const uint dim, bool isAscending) {
-
     ONEAPI_NOT_SUPPORTED("sort_index Not supported");
 
     try {
@@ -34,12 +33,10 @@ void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
         oval.eval();
 
         // switch (dim) {
-        //     case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
-        //     case 1:
-        //     case 2:
-        //     case 3:
-        //         kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending);
-        //         break;
+        //     case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending);
+        //     break; case 1: case 2: case 3:
+        //         kernel::sortByKeyBatched<T, uint>(okey, oval, dim,
+        //         isAscending); break;
         //     default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
         // }
 
diff --git a/src/backend/oneapi/sparse.cpp b/src/backend/oneapi/sparse.cpp
index 70de66f6ee..ba776efb18 100644
--- a/src/backend/oneapi/sparse.cpp
+++ b/src/backend/oneapi/sparse.cpp
@@ -115,7 +115,7 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
     const Array<int> &colIdx = in_.getColIdx();
 
     if (stype == AF_STORAGE_CSR) {
-      // kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
+        // kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
     } else {
         AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
                  AF_ERR_NOT_SUPPORTED);
@@ -144,7 +144,8 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
         const Array<int> &irowIdx = in.getRowIdx();
         const Array<int> &icolIdx = in.getColIdx();
 
-        // kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+        // kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
+        // icolIdx,
         //                    index);
 
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
@@ -161,7 +162,8 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
         Array<int> rowCopy = copyArray<int>(irowIdx);
         rowCopy.eval();
 
-        // kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+        // kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
+        // icolIdx,
         //                    index, rowCopy, in.dims()[0]);
 
     } else {
diff --git a/src/backend/oneapi/sparse_arith.cpp b/src/backend/oneapi/sparse_arith.cpp
index 40e9e24ff4..2bb14c2b1d 100644
--- a/src/backend/oneapi/sparse_arith.cpp
+++ b/src/backend/oneapi/sparse_arith.cpp
@@ -8,8 +8,8 @@
  ********************************************************/
 
 // #include <kernel/sparse_arith.hpp>
-#include <sparse.hpp>
 #include <err_oneapi.hpp>
+#include <sparse.hpp>
 
 #include <stdexcept>
 #include <string>
@@ -141,9 +141,9 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     auto outValues = createEmptyArray<T>(dim4(nnzC));
 
     // kernel::ssArithCSR<T, op>(outValues, outColIdx, outRowIdx, M, N, nnzA,
-    //                           lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-    //                           nnzB, rhs.getValues(), rhs.getRowIdx(),
-    //                           rhs.getColIdx());
+    //                           lhs.getValues(), lhs.getRowIdx(),
+    //                           lhs.getColIdx(), nnzB, rhs.getValues(),
+    //                           rhs.getRowIdx(), rhs.getColIdx());
 
     SparseArray<T> retVal = createArrayDataSparseArray(
         ldims, outValues, outRowIdx, outColIdx, sfmt);
diff --git a/src/backend/oneapi/sparse_blas.cpp b/src/backend/oneapi/sparse_blas.cpp
index bc06759dde..a7c04abc40 100644
--- a/src/backend/oneapi/sparse_blas.cpp
+++ b/src/backend/oneapi/sparse_blas.cpp
@@ -37,7 +37,7 @@ using namespace common;
 template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
-  ONEAPI_NOT_SUPPORTED("sparse matmul Not supported");
+    ONEAPI_NOT_SUPPORTED("sparse matmul Not supported");
 #if defined(WITH_LINEAR_ALGEBRA)
     if (OpenCLCPUOffload(
             false)) {  // Do not force offload gemm on OSX Intel devices
diff --git a/src/backend/oneapi/surface.cpp b/src/backend/oneapi/surface.cpp
index 7efebfc43c..38ad3388f5 100644
--- a/src/backend/oneapi/surface.cpp
+++ b/src/backend/oneapi/surface.cpp
@@ -37,12 +37,14 @@ void copy_surface(const Array<T> &P, fg_surface surface) {
     //     glFinish();
 
     //     // Use of events:
-    //     // https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
+    //     //
+    //     https://www.khronos.org/registry/cl/sdk/1.1/docs/man/xhtml/clEnqueueReleaseGLObjects.html
     //     cl::Event event;
 
     //     getQueue().enqueueAcquireGLObjects(&shared_objects, NULL, &event);
     //     event.wait();
-    //     getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes, NULL,
+    //     getQueue().enqueueCopyBuffer(*d_P, *(res[0].get()), 0, 0, bytes,
+    //     NULL,
     //                                  &event);
     //     getQueue().enqueueReleaseGLObjects(&shared_objects, NULL, &event);
     //     event.wait();
@@ -57,7 +59,8 @@ void copy_surface(const Array<T> &P, fg_surface surface) {
     //     CheckGL("Begin OpenCL fallback-resource copy");
     //     glBindBuffer(GL_ARRAY_BUFFER, buffer);
     //     auto *ptr =
-    //         static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY));
+    //         static_cast<GLubyte *>(glMapBuffer(GL_ARRAY_BUFFER,
+    //         GL_WRITE_ONLY));
     //     if (ptr) {
     //         getQueue().enqueueReadBuffer(*P.get(), CL_TRUE, 0, bytes, ptr);
     //         glUnmapBuffer(GL_ARRAY_BUFFER);
diff --git a/src/backend/oneapi/susan.cpp b/src/backend/oneapi/susan.cpp
index e6fe536918..94173b3e4c 100644
--- a/src/backend/oneapi/susan.cpp
+++ b/src/backend/oneapi/susan.cpp
@@ -36,7 +36,8 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     ONEAPI_NOT_SUPPORTED("");
     return 0;
 
-    // kernel::susan<T>(resp.get(), in.get(), in.getOffset(), idims[0], idims[1],
+    // kernel::susan<T>(resp.get(), in.get(), in.getOffset(), idims[0],
+    // idims[1],
     //                  diff_thr, geom_thr, edge, radius);
 
     // unsigned corners_found = kernel::nonMaximal<T>(
@@ -72,4 +73,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}  // namespace oneap
+}  // namespace oneapi
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
index 8fef95ba6c..8a886983f9 100644
--- a/src/backend/oneapi/svd.cpp
+++ b/src/backend/oneapi/svd.cpp
@@ -137,8 +137,8 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
 
     if (want_vectors) {
         mappedU  = static_cast<T *>(getQueue().enqueueMapBuffer(
-            *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
-            sizeof(T) * arrU.elements()));
+             *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
+             sizeof(T) * arrU.elements()));
         mappedVT = static_cast<T *>(getQueue().enqueueMapBuffer(
             *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
             sizeof(T) * arrVT.elements()));
@@ -234,7 +234,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
-}  // namespace opencl
+}  // namespace oneapi
 
 #else  // WITH_LINEAR_ALGEBRA
 
diff --git a/src/backend/oneapi/tile.cpp b/src/backend/oneapi/tile.cpp
index 5aac53265b..384c0f0710 100644
--- a/src/backend/oneapi/tile.cpp
+++ b/src/backend/oneapi/tile.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 //#include <kernel/tile.hpp>
-#include <tile.hpp>
 #include <err_oneapi.hpp>
+#include <tile.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
diff --git a/src/backend/oneapi/topk.cpp b/src/backend/oneapi/topk.cpp
index 06d4218221..8d963ac4c2 100644
--- a/src/backend/oneapi/topk.cpp
+++ b/src/backend/oneapi/topk.cpp
@@ -48,7 +48,6 @@ vector<af_index_t> indexForTopK(const int k) {
 template<typename T>
 void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order) {
-
     ONEAPI_NOT_SUPPORTED("topk Not supported");
 
     // if (getDeviceType() == CL_DEVICE_TYPE_CPU) {
@@ -57,7 +56,8 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
 
     //     // TODO(umar): implement this in the kernel namespace
 
-    //     // The out_dims is of size k along the dimension of the topk operation
+    //     // The out_dims is of size k along the dimension of the topk
+    //     operation
     //     // and the same as the input dimension otherwise.
     //     dim4 out_dims(1);
     //     int ndims = in.dims().ndims();
@@ -84,7 +84,8 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
     //         *ibuf, CL_FALSE, CL_MAP_READ | CL_MAP_WRITE, 0, k * sizeof(uint),
     //         nullptr, &ev_ind));
     //     T* vptr    = static_cast<T*>(getQueue().enqueueMapBuffer(
-    //         *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
+    //         *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr,
+    //         &ev_val));
 
     //     vector<uint> idx(in.elements());
 
diff --git a/src/backend/oneapi/transform.cpp b/src/backend/oneapi/transform.cpp
index 79cb584264..732ba39cc0 100644
--- a/src/backend/oneapi/transform.cpp
+++ b/src/backend/oneapi/transform.cpp
@@ -22,15 +22,18 @@ void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            // kernel::transform<T>(out, in, tf, inverse, perspective, method, 1);
+            // kernel::transform<T>(out, in, tf, inverse, perspective, method,
+            // 1);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            // kernel::transform<T>(out, in, tf, inverse, perspective, method, 2);
+            // kernel::transform<T>(out, in, tf, inverse, perspective, method,
+            // 2);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            // kernel::transform<T>(out, in, tf, inverse, perspective, method, 3);
+            // kernel::transform<T>(out, in, tf, inverse, perspective, method,
+            // 3);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
diff --git a/src/backend/oneapi/transpose.cpp b/src/backend/oneapi/transpose.cpp
index 0985bc48fa..cef137b561 100644
--- a/src/backend/oneapi/transpose.cpp
+++ b/src/backend/oneapi/transpose.cpp
@@ -6,9 +6,9 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#include <err_oneapi.hpp>
 #include <kernel/transpose.hpp>
 #include <transpose.hpp>
-#include <err_oneapi.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
diff --git a/src/backend/oneapi/triangle.cpp b/src/backend/oneapi/triangle.cpp
index f514b8d64b..afe0c27b7f 100644
--- a/src/backend/oneapi/triangle.cpp
+++ b/src/backend/oneapi/triangle.cpp
@@ -7,8 +7,9 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #include <kernel/triangle.hpp>
-#include <triangle.hpp>
+
 #include <err_oneapi.hpp>
+#include <triangle.hpp>
 
 #include <Array.hpp>
 #include <common/half.hpp>
@@ -52,4 +53,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 
-}  // namespace opencl
+}  // namespace oneapi
diff --git a/src/backend/oneapi/unwrap.cpp b/src/backend/oneapi/unwrap.cpp
index 200da9d307..cbb2910ef7 100644
--- a/src/backend/oneapi/unwrap.cpp
+++ b/src/backend/oneapi/unwrap.cpp
@@ -60,4 +60,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 #undef INSTANTIATE
 
-}  // namespace opencl
+}  // namespace oneapi
diff --git a/src/backend/oneapi/vector_field.cpp b/src/backend/oneapi/vector_field.cpp
index 40c7be146d..d42c86c270 100644
--- a/src/backend/oneapi/vector_field.cpp
+++ b/src/backend/oneapi/vector_field.cpp
@@ -18,8 +18,7 @@ namespace oneapi {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
-                       fg_vector_field vfield) {
-}
+                       fg_vector_field vfield) {}
 
 #define INSTANTIATE(T)                                                     \
     template void copy_vector_field<T>(const Array<T> &, const Array<T> &, \
diff --git a/src/backend/oneapi/where.cpp b/src/backend/oneapi/where.cpp
index 4dc3e42565..df9267df72 100644
--- a/src/backend/oneapi/where.cpp
+++ b/src/backend/oneapi/where.cpp
@@ -18,11 +18,11 @@ namespace oneapi {
 
 template<typename T>
 Array<uint> where(const Array<T> &in) {
-    //Param<uint> Out;
+    // Param<uint> Out;
     // Param<T> In = in;
     ONEAPI_NOT_SUPPORTED("where Not supported");
     // kernel::where<T>(Out, In);
-    //return createParamArray<uint>(Out, true);
+    // return createParamArray<uint>(Out, true);
     return createEmptyArray<uint>(af::dim4(1));
 }
 
@@ -41,4 +41,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 
-}  // namespace opencl
+}  // namespace oneapi
diff --git a/src/backend/oneapi/wrap.cpp b/src/backend/oneapi/wrap.cpp
index 5dd0d7d78f..e3a9b2fc1f 100644
--- a/src/backend/oneapi/wrap.cpp
+++ b/src/backend/oneapi/wrap.cpp
@@ -24,8 +24,8 @@ template<typename T>
 void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
           const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
           const bool is_column) {
-  ONEAPI_NOT_SUPPORTED("wrap Not supported");
-  // kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
+    ONEAPI_NOT_SUPPORTED("wrap Not supported");
+    // kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
 #define INSTANTIATE(T)                                                        \
@@ -57,7 +57,8 @@ Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
     af::dim4 odims(ox, oy, idims[2], idims[3]);
     Array<T> out = createValueArray<T>(odims, scalar<T>(0));
 
-    // kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy, is_column);
+    // kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy,
+    // is_column);
     ONEAPI_NOT_SUPPORTED("wrap_dilated Not supported");
     return out;
 }
@@ -73,4 +74,4 @@ INSTANTIATE(double)
 INSTANTIATE(half)
 #undef INSTANTIATE
 
-}  // namespace opencl
+}  // namespace oneapi

From 87cfde362389c515815f380501aac36a3c867ddc Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Wed, 5 Oct 2022 16:39:41 -0400
Subject: [PATCH 2311/2677] diagonal port to oneapi. its tests pass except
 *LargeDim* and *GFOR*

---
 src/backend/oneapi/CMakeLists.txt      |   1 +
 src/backend/oneapi/diagonal.cpp        |   9 +-
 src/backend/oneapi/kernel/diagonal.hpp | 163 +++++++++++++++++++++++++
 3 files changed, 170 insertions(+), 3 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/diagonal.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 4559aa9292..10dec177d4 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -206,6 +206,7 @@ target_sources(afoneapi
   PRIVATE
     kernel/KParam.hpp
     kernel/assign.hpp
+    kernel/diagonal.hpp
     kernel/iota.hpp
     kernel/memcopy.hpp
     kernel/random_engine.hpp
diff --git a/src/backend/oneapi/diagonal.cpp b/src/backend/oneapi/diagonal.cpp
index f22b2440c2..b9d443c662 100644
--- a/src/backend/oneapi/diagonal.cpp
+++ b/src/backend/oneapi/diagonal.cpp
@@ -11,7 +11,7 @@
 #include <common/half.hpp>
 #include <diagonal.hpp>
 #include <err_oneapi.hpp>
-//#include <kernel/diagonal.hpp>
+#include <kernel/diagonal.hpp>
 #include <math.hpp>
 #include <af/dim4.hpp>
 
@@ -20,20 +20,23 @@ using common::half;
 namespace oneapi {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
-    ONEAPI_NOT_SUPPORTED("");
     int size     = in.dims()[0] + std::abs(num);
     int batch    = in.dims()[1];
     Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
+
+    kernel::diagCreate<T>(out, in, num);
+
     return out;
 }
 
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num) {
-    ONEAPI_NOT_SUPPORTED("");
     const dim_t *idims = in.dims().get();
     dim_t size         = std::min(idims[0], idims[1]) - std::abs(num);
     Array<T> out       = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
 
+    kernel::diagExtract<T>(out, in, num);
+
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/diagonal.hpp b/src/backend/oneapi/kernel/diagonal.hpp
new file mode 100644
index 0000000000..4668fee5bd
--- /dev/null
+++ b/src/backend/oneapi/kernel/diagonal.hpp
@@ -0,0 +1,163 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename T>
+class diagCreateKernel {
+   public:
+    diagCreateKernel(sycl::accessor<T> oData, KParam oInfo,
+                     sycl::accessor<T> iData, KParam iInfo, int num,
+                     int groups_x)
+        : oData_(oData)
+        , oInfo_(oInfo)
+        , iData_(iData)
+        , iInfo_(iInfo)
+        , num_(num)
+        , groups_x_(groups_x) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g      = it.get_group();
+        unsigned idz       = g.get_group_id(0) / groups_x_;
+        unsigned groupId_x = g.get_group_id(0) - idz * groups_x_;
+
+        unsigned idx = it.get_local_id(0) + groupId_x * g.get_local_range(0);
+        unsigned idy = it.get_global_id(1);
+
+        if (idx >= oInfo_.dims[0] || idy >= oInfo_.dims[1] ||
+            idz >= oInfo_.dims[2])
+            return;
+
+        T *optr = oData_.get_pointer();
+        optr += idz * oInfo_.strides[2] + idy * oInfo_.strides[1] + idx;
+
+        const T *iptr = iData_.get_pointer();
+        iptr +=
+            idz * iInfo_.strides[1] + ((num_ > 0) ? idx : idy) + iInfo_.offset;
+
+        T val = (idx == (idy - num_)) ? *iptr : (T)(0);
+        *optr = val;
+    }
+
+   private:
+    sycl::accessor<T> oData_;
+    KParam oInfo_;
+    sycl::accessor<T> iData_;
+    KParam iInfo_;
+    int num_;
+    int groups_x_;
+};
+
+template<typename T>
+static void diagCreate(Param<T> out, Param<T> in, int num) {
+    auto local   = sycl::range{32, 8};
+    int groups_x = divup(out.info.dims[0], local[0]);
+    int groups_y = divup(out.info.dims[1], local[1]);
+    auto global  = sycl::range{groups_x * local[0] * out.info.dims[2],
+                              groups_y * local[1]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto oData = out.data->get_access(h);
+        auto iData = in.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       diagCreateKernel<T>(oData, out.info, iData, in.info, num,
+                                           groups_x));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+class diagExtractKernel {
+   public:
+    diagExtractKernel(sycl::accessor<T> oData, KParam oInfo,
+                      sycl::accessor<T> iData, KParam iInfo, int num,
+                      int groups_z)
+        : oData_(oData)
+        , oInfo_(oInfo)
+        , iData_(iData)
+        , iInfo_(iInfo)
+        , num_(num)
+        , groups_z_(groups_z) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        unsigned idw  = g.get_group_id(1) / groups_z_;
+        unsigned idz  = g.get_group_id(1) - idw * groups_z_;
+
+        unsigned idx = it.get_global_id(0);
+
+        if (idx >= oInfo_.dims[0] || idz >= oInfo_.dims[2] ||
+            idw >= oInfo_.dims[3])
+            return;
+
+        T *optr = oData_.get_pointer();
+        optr += idz * oInfo_.strides[2] + idw * oInfo_.strides[3] + idx;
+
+        if (idx >= iInfo_.dims[0] || idx >= iInfo_.dims[1]) {
+            *optr = (T)(0);
+            return;
+        }
+
+        int i_off = (num_ > 0) ? (num_ * iInfo_.strides[1] + idx)
+                               : (idx - num_) + iInfo_.offset;
+
+        const T *iptr = iData_.get_pointer();
+        iptr += idz * iInfo_.strides[2] + idw * iInfo_.strides[3] + i_off;
+
+        *optr = iptr[idx * iInfo_.strides[1]];
+    }
+
+   private:
+    sycl::accessor<T> oData_;
+    KParam oInfo_;
+    sycl::accessor<T> iData_;
+    KParam iInfo_;
+    int num_;
+    int groups_z_;
+};
+
+template<typename T>
+static void diagExtract(Param<T> out, Param<T> in, int num) {
+    auto local   = sycl::range{256, 1};
+    int groups_x = divup(out.info.dims[0], local[0]);
+    int groups_z = out.info.dims[2];
+    auto global  = sycl::range{groups_x * local[0],
+                              groups_z * local[1] * out.info.dims[3]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto oData = out.data->get_access(h);
+        auto iData = in.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       diagExtractKernel<T>(oData, out.info, iData, in.info,
+                                            num, groups_z));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi

From eb31c7f2c9016c8ad6c32159ebb5588cf73c2fde Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Fri, 7 Oct 2022 12:49:24 -0400
Subject: [PATCH 2312/2677] diff port to oneapi. its tests pass except
 *LargeDim* and *GFOR* (#3304)

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt  |   1 +
 src/backend/oneapi/diff.cpp        |   7 +-
 src/backend/oneapi/kernel/diff.hpp | 124 +++++++++++++++++++++++++++++
 3 files changed, 127 insertions(+), 5 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/diff.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 10dec177d4..67f9ec8b23 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -207,6 +207,7 @@ target_sources(afoneapi
     kernel/KParam.hpp
     kernel/assign.hpp
     kernel/diagonal.hpp
+    kernel/diff.hpp
     kernel/iota.hpp
     kernel/memcopy.hpp
     kernel/random_engine.hpp
diff --git a/src/backend/oneapi/diff.cpp b/src/backend/oneapi/diff.cpp
index 71e331a122..ad9da16697 100644
--- a/src/backend/oneapi/diff.cpp
+++ b/src/backend/oneapi/diff.cpp
@@ -9,8 +9,7 @@
 
 #include <Array.hpp>
 #include <diff.hpp>
-//#include <kernel/diff.hpp>
-#include <err_oneapi.hpp>
+#include <kernel/diff.hpp>
 #include <af/dim4.hpp>
 #include <stdexcept>
 
@@ -18,7 +17,6 @@ namespace oneapi {
 
 template<typename T>
 Array<T> diff(const Array<T> &in, const int dim, const bool isDiff2) {
-    ONEAPI_NOT_SUPPORTED("");
     const af::dim4 &iDims = in.dims();
     af::dim4 oDims        = iDims;
     oDims[dim] -= (isDiff2 + 1);
@@ -27,18 +25,17 @@ Array<T> diff(const Array<T> &in, const int dim, const bool isDiff2) {
         throw std::runtime_error("Elements are 0");
     }
     Array<T> out = createEmptyArray<T>(oDims);
+    kernel::diff<T>(out, in, in.ndims(), dim, isDiff2);
     return out;
 }
 
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim) {
-    ONEAPI_NOT_SUPPORTED("");
     return diff<T>(in, dim, false);
 }
 
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim) {
-    ONEAPI_NOT_SUPPORTED("");
     return diff<T>(in, dim, true);
 }
 
diff --git a/src/backend/oneapi/kernel/diff.hpp b/src/backend/oneapi/kernel/diff.hpp
new file mode 100644
index 0000000000..d624cd5283
--- /dev/null
+++ b/src/backend/oneapi/kernel/diff.hpp
@@ -0,0 +1,124 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename T>
+class diffKernel {
+   public:
+    diffKernel(sycl::accessor<T> outAcc, const sycl::accessor<T> inAcc,
+               const KParam op, const KParam ip, const int oElem,
+               const int blocksPerMatX, const int blocksPerMatY,
+               const bool isDiff2, const unsigned DIM)
+        : outAcc_(outAcc)
+        , inAcc_(inAcc)
+        , op_(op)
+        , ip_(ip)
+        , oElem_(oElem)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY)
+        , isDiff2_(isDiff2)
+        , DIM_(DIM) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int idz = g.get_group_id(0) / blocksPerMatX_;
+        const int idw = g.get_group_id(1) / blocksPerMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - idz * blocksPerMatX_;
+        const int blockIdx_y = g.get_group_id(1) - idw * blocksPerMatY_;
+
+        const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+        const int idy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+        if (idx >= op_.dims[0] || idy >= op_.dims[1] || idz >= op_.dims[2] ||
+            idw >= op_.dims[3])
+            return;
+
+        int iMem0 = idw * ip_.strides[3] + idz * ip_.strides[2] +
+                    idy * ip_.strides[1] + idx;
+        int iMem1 = iMem0 + ip_.strides[DIM_];
+        int iMem2 = iMem1 + ip_.strides[DIM_];
+
+        int oMem = idw * op_.strides[3] + idz * op_.strides[2] +
+                   idy * op_.strides[1] + idx;
+
+        iMem2 *= isDiff2_;
+
+        T *out      = outAcc_.get_pointer();
+        const T *in = inAcc_.get_pointer() + ip_.offset;
+        if (isDiff2_ == 0) {
+            out[oMem] = in[iMem1] - in[iMem0];
+        } else {
+            out[oMem] = in[iMem2] - in[iMem1] - in[iMem1] + in[iMem0];
+        }
+
+        // diff_this(out, in + ip.offset, oMem, iMem0, iMem1, iMem2);
+    }
+
+   private:
+    sycl::accessor<T> outAcc_;
+    const sycl::accessor<T> inAcc_;
+    const KParam op_;
+    const KParam ip_;
+    const int oElem_;
+    const int blocksPerMatX_;
+    const int blocksPerMatY_;
+    const bool isDiff2_;
+    const unsigned DIM_;
+};
+
+template<typename T>
+void diff(Param<T> out, const Param<T> in, const unsigned indims,
+          const unsigned dim, const bool isDiff2) {
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+
+    auto local = sycl::range{TX, TY};
+    if (dim == 0 && indims == 1) { local = sycl::range{TX * TY, 1}; }
+
+    int blocksPerMatX = divup(out.info.dims[0], local[0]);
+    int blocksPerMatY = divup(out.info.dims[1], local[1]);
+    auto global       = sycl::range{local[0] * blocksPerMatX * out.info.dims[2],
+                              local[1] * blocksPerMatY * out.info.dims[3]};
+
+    const int oElem = out.info.dims[0] * out.info.dims[1] * out.info.dims[2] *
+                      out.info.dims[3];
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto inAcc  = in.data->get_access(h);
+        auto outAcc = out.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            diffKernel<T>(outAcc, inAcc, out.info, in.info, oElem,
+                          blocksPerMatX, blocksPerMatY, isDiff2, dim));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi

From 9b4642e19dde97e79d59e3702a09e29013d49d98 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Oct 2022 15:13:21 -0400
Subject: [PATCH 2313/2677] Fix several warnings in oneAPI backend

---
 extern/half/include/half.hpp                  |  14 ++
 src/backend/common/Logger.hpp                 |   1 +
 src/backend/oneapi/Array.cpp                  |   8 +-
 src/backend/oneapi/Module.hpp                 |   6 +-
 src/backend/oneapi/iota.cpp                   |   7 +-
 src/backend/oneapi/jit/kernel_generators.hpp  |   5 +-
 src/backend/oneapi/kernel/assign.hpp          |   2 +-
 src/backend/oneapi/kernel/memcopy.hpp         |  23 +-
 .../oneapi/kernel/random_engine_write.hpp     |   8 +-
 src/backend/oneapi/kernel/transpose.hpp       |   2 +-
 .../oneapi/kernel/transpose_inplace.hpp       |   8 +-
 src/backend/oneapi/platform.cpp               |   5 +-
 src/backend/oneapi/sparse.cpp                 | 208 +++++++++---------
 src/backend/oneapi/sparse_arith.cpp           | 140 ++++++------
 src/backend/oneapi/sparse_blas.cpp            |  95 ++++----
 test/CMakeLists.txt                           |  26 ++-
 test/arrayfire_test.cpp                       |   1 +
 17 files changed, 287 insertions(+), 272 deletions(-)

diff --git a/extern/half/include/half.hpp b/extern/half/include/half.hpp
index ab70791db9..e8dfc1995a 100644
--- a/extern/half/include/half.hpp
+++ b/extern/half/include/half.hpp
@@ -403,7 +403,14 @@ namespace half_float
 		template<typename T> bool builtin_isinf(T arg)
 		{
 		#if HALF_ENABLE_CPP11_CMATH
+#ifdef __clang__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wtautological-constant-compare"
+#endif
 			return std::isinf(arg);
+#ifdef __clang__
+#pragma GCC diagnostic pop
+#endif
 		#elif defined(_MSC_VER)
 			return !::_finite(static_cast<double>(arg)) && !::_isnan(static_cast<double>(arg));
 		#else
@@ -419,7 +426,14 @@ namespace half_float
 		template<typename T> bool builtin_isnan(T arg)
 		{
 		#if HALF_ENABLE_CPP11_CMATH
+#ifdef __clang__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wtautological-constant-compare"
+#endif
 			return std::isnan(arg);
+#ifdef __clang__
+#pragma GCC diagnostic pop
+#endif
 		#elif defined(_MSC_VER)
 			return ::_isnan(static_cast<double>(arg)) != 0;
 		#else
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index 5241dc9126..4b7b4d419e 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -17,6 +17,7 @@
 /* Clang/LLVM */
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wignored-attributes"
+#pragma clang diagnostic ignored "-Wtautological-constant-compare"
 #elif defined(__ICC) || defined(__INTEL_COMPILER)
 /* Intel ICC/ICPC */
 // Fix the warning code here, if any
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index db4bce10e3..bd5676fd01 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -313,12 +313,12 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
             return kJITHeuristics::TreeHeight;
         }
     }
+    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
 
-    bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
-    auto platform      = getActivePlatform();
+    // bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
+    // auto platform      = getActivePlatform();
 
     // The Apple platform can have the nvidia card or the AMD card
-    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
     // bool isIntel = platform == AFCL_PLATFORM_INTEL;
 
     // /// Intels param_size limit is much smaller than the other platforms
@@ -502,8 +502,6 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
                           const size_t bytes) {
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
 
-    buffer<T> &buf = *arr.get();
-
     // clRetainMemObject(
     //    reinterpret_cast<buffer<T> *>(const_cast<void *>(data)));
     // buffer<T> data_buf =
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
index 0aa1cc790d..5637fa5d06 100644
--- a/src/backend/oneapi/Module.hpp
+++ b/src/backend/oneapi/Module.hpp
@@ -17,9 +17,9 @@ namespace oneapi {
 /// oneapi backend wrapper for cl::Program object
 class Module
     : public common::ModuleInterface<
-          sycl::kernel_bundle<sycl::bundle_state::executable>> {
+          sycl::kernel_bundle<sycl::bundle_state::executable>*> {
    public:
-    using ModuleType = sycl::kernel_bundle<sycl::bundle_state::executable>;
+    using ModuleType = sycl::kernel_bundle<sycl::bundle_state::executable>*;
     using BaseClass  = common::ModuleInterface<ModuleType>;
 
     /// \brief Create an uninitialized Module
@@ -29,7 +29,7 @@ class Module
     Module(ModuleType mod) : BaseClass(mod) {}
 
     /// \brief Unload module
-    operator bool() const final { return get().empty(); }
+    operator bool() const final { return get()->empty(); }
 
     /// Unload the module
     void unload() final {
diff --git a/src/backend/oneapi/iota.cpp b/src/backend/oneapi/iota.cpp
index bb6380993b..18077e5199 100644
--- a/src/backend/oneapi/iota.cpp
+++ b/src/backend/oneapi/iota.cpp
@@ -31,10 +31,10 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
 template<>
 Array<half> iota(const dim4 &dims, const dim4 &tile_dims) {
     ONEAPI_NOT_SUPPORTED("");
-    dim4 outdims = dims * tile_dims;
+    // dim4 outdims = dims * tile_dims;
 
-    Array<half> out = createEmptyArray<half>(outdims);
-    return out;
+    // Array<half> out = createEmptyArray<half>(outdims);
+    // return out;
 }
 
 #define INSTANTIATE(T) \
@@ -49,5 +49,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
-INSTANTIATE(half)
 }  // namespace oneapi
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index a49b25de0c..202403f4cb 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -18,8 +18,9 @@ namespace oneapi {
 namespace {
 
 /// Creates a string that will be used to declare the parameter of kernel
-void generateParamDeclaration(std::stringstream& kerStream, int id,
-                              bool is_linear, const std::string& m_type_str) {
+inline void generateParamDeclaration(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& m_type_str) {
     if (is_linear) {
         kerStream << "__global " << m_type_str << " *in" << id
                   << ", dim_t iInfo" << id << "_offset, \n";
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 7a75735f50..d4cc7e2b6c 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -47,8 +47,8 @@ class assignKernel {
                  sycl::accessor<uint> ptr3, const int nBBS0, const int nBBS1,
                  sycl::stream debug)
         : out_(out)
-        , oInfo_(oInfo)
         , in_(in)
+        , oInfo_(oInfo)
         , iInfo_(iInfo)
         , p_(p)
         , ptr0_(ptr0)
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 3f3fdce1ae..701060820f 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -36,8 +36,8 @@ class memCopy {
             dims_t idims, dims_t istrides, int offset, int groups_0,
             int groups_1, sycl::stream debug)
         : out_(out)
-        , ostrides_(ostrides)
         , in_(in)
+        , ostrides_(ostrides)
         , idims_(idims)
         , istrides_(istrides)
         , offset_(offset)
@@ -46,9 +46,6 @@ class memCopy {
         , debug_(debug) {}
 
     void operator()(sycl::nd_item<2> it) const {
-        // printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
-        // debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) <<
-        // "]" << sycl::stream_manipulator::endl;
         const int lid0 = it.get_local_id(0);
         const int lid1 = it.get_local_id(1);
 
@@ -112,11 +109,6 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
                           groups_1 * idims[3] * local_size[1]);
     sycl::nd_range<2> ndrange(global, local);
 
-    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0),
-           ndrange.get_global_range().get(1), ndrange.get_local_range().get(0),
-           ndrange.get_local_range().get(1));
-    printf("<%d, %d> ", ndrange.get_group_range().get(0),
-           ndrange.get_group_range().get(1));
     getQueue().submit([=](sycl::handler &h) {
         auto out_acc = out->get_access(h);
         auto in_acc  = const_cast<sycl::buffer<T> *>(in)->get_access(h);
@@ -137,12 +129,13 @@ static T scale(T value, double factor) {
 
 template<>
 cfloat scale<cfloat>(cfloat value, double factor) {
-    return (cfloat)(value.real() * factor, value.imag() * factor);
+    return cfloat{static_cast<float>(value.real() * factor),
+                  static_cast<float>(value.imag() * factor)};
 }
 
 template<>
 cdouble scale<cdouble>(cdouble value, double factor) {
-    return (cdouble)(value.real() * factor, value.imag() * factor);
+    return cdouble{value.real() * factor, value.imag() * factor};
 }
 
 template<typename inType, typename outType>
@@ -213,8 +206,8 @@ class reshapeCopy {
                 float factor, dims_t trgt, int blk_x, int blk_y,
                 sycl::stream debug)
         : dst_(dst)
-        , oInfo_(oInfo)
         , src_(src)
+        , oInfo_(oInfo)
         , iInfo_(iInfo)
         , default_value_(default_value)
         , factor_(factor)
@@ -293,12 +286,6 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
                           blk_y * dst.info.dims[3] * DIM1);
 
     sycl::nd_range<2> ndrange(global, local);
-    printf("reshape wat?\n");
-    printf("<%d, %d> <%d, %d>\n", ndrange.get_global_range().get(0),
-           ndrange.get_global_range().get(1), ndrange.get_local_range().get(0),
-           ndrange.get_local_range().get(1));
-    printf("<%d, %d> ", ndrange.get_group_range().get(0),
-           ndrange.get_group_range().get(1));
 
     dims_t trgt_dims;
     if (same_dims) {
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 3b2857b92f..09f7a9c6e5 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -102,8 +102,9 @@ static double getDouble01(uint num1, uint num2) {
     n1 <<= 32;
     uint64_t num = n1 | n2;
     constexpr double factor =
-        ((1.0) / (std::numeric_limits<unsigned long long>::max() +
-                  static_cast<double>(1.0)));
+        ((1.0) /
+         (static_cast<double>(std::numeric_limits<unsigned long long>::max()) +
+          static_cast<double>(1.0)));
     constexpr double half_factor((0.5) * factor);
 
     return sycl::fma(static_cast<double>(num), factor, half_factor);
@@ -111,7 +112,8 @@ static double getDouble01(uint num1, uint num2) {
 
 // Conversion to doubles adapted from Random123
 constexpr double signed_factor =
-    ((1.0l) / (std::numeric_limits<long long>::max() + (1.0l)));
+    ((1.0l) / (static_cast<long double>(std::numeric_limits<long long>::max()) +
+               (1.0l)));
 constexpr double half_factor = ((0.5) * signed_factor);
 
 // Generates rationals in (-1, 1]
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index b2bc48a407..8c7fef325f 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -130,10 +130,10 @@ class transposeKernel {
     KParam in_;
     int blocksPerMatX_;
     int blocksPerMatY_;
-    sycl::stream debugStream_;
     bool conjugate_;
     bool IS32MULTIPLE_;
     local_accessor<T, 1> shrdMem_;
+    sycl::stream debugStream_;
 };
 
 template<typename T>
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
index 81313b50da..108b9596f9 100644
--- a/src/backend/oneapi/kernel/transpose_inplace.hpp
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -23,18 +23,18 @@ namespace oneapi {
 namespace kernel {
 
 template<typename T>
-T static getConjugate(const T &in) {
+static T getConjugate(const T &in) {
     // For non-complex types return same
     return in;
 }
 
 template<>
-cfloat static getConjugate(const cfloat &in) {
+cfloat getConjugate(const cfloat &in) {
     return std::conj(in);
 }
 
 template<>
-cdouble static getConjugate(const cdouble &in) {
+cdouble getConjugate(const cdouble &in) {
     return std::conj(in);
 }
 
@@ -160,11 +160,11 @@ class transposeInPlaceKernel {
     KParam in_;
     int blocksPerMatX_;
     int blocksPerMatY_;
-    sycl::stream debugStream_;
     bool conjugate_;
     bool IS32MULTIPLE_;
     local_accessor<T, 1> shrdMem_s_;
     local_accessor<T, 1> shrdMem_d_;
+    sycl::stream debugStream_;
 };
 
 template<typename T>
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index b65ad6698d..f2128c5ac5 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -91,7 +91,7 @@ bool verify_present(const string& pname, const string ref) {
 }
 
 // TODO: update to new platforms?
-static string platformMap(string& platStr) {
+inline string platformMap(string& platStr) {
     using strmap_t                = map<string, string>;
     static const strmap_t platMap = {
         make_pair("NVIDIA CUDA", "NVIDIA"),
@@ -134,7 +134,6 @@ string getDeviceInfo() noexcept {
     info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
          << ", build " << AF_REVISION << ")\n";
 
-    vector<sycl::device*> devices;
     try {
         DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -688,4 +687,4 @@ af_err afcl_delete_device_context(cl_device_id dev, cl_context ctx) {
     CATCHALL;
     return AF_SUCCESS;
 }
-*/
\ No newline at end of file
+*/
diff --git a/src/backend/oneapi/sparse.cpp b/src/backend/oneapi/sparse.cpp
index ba776efb18..18e1d48e81 100644
--- a/src/backend/oneapi/sparse.cpp
+++ b/src/backend/oneapi/sparse.cpp
@@ -35,46 +35,46 @@ using namespace common;
 template<typename T>
 SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in) {
     ONEAPI_NOT_SUPPORTED("sparseConvertDenseToCOO Not supported");
-    in.eval();
+    // in.eval();
 
-    Array<uint> nonZeroIdx_ = where<T>(in);
-    Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
+    // Array<uint> nonZeroIdx_ = where<T>(in);
+    // Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
 
-    dim_t nNZ = nonZeroIdx.elements();
+    // dim_t nNZ = nonZeroIdx.elements();
 
-    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
-    constDim.eval();
+    // Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
+    // constDim.eval();
 
-    Array<int> rowIdx =
-        arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
-    Array<int> colIdx =
-        arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    // Array<int> rowIdx =
+    //     arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    // Array<int> colIdx =
+    //     arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
-    Array<T> values = copyArray<T>(in);
-    values          = modDims(values, dim4(values.elements()));
-    values          = lookup<T, int>(values, nonZeroIdx, 0);
+    // Array<T> values = copyArray<T>(in);
+    // values          = modDims(values, dim4(values.elements()));
+    // values          = lookup<T, int>(values, nonZeroIdx, 0);
 
-    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
-                                         AF_STORAGE_COO);
+    // return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+    //                                      AF_STORAGE_COO);
 }
 
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_) {
     ONEAPI_NOT_SUPPORTED("sparseConvertDenseToStorage Not supported");
-    in_.eval();
-
-    uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in_));
-
-    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
-    sparse_.eval();
-
-    Array<T> &values   = sparse_.getValues();
-    Array<int> &rowIdx = sparse_.getRowIdx();
-    Array<int> &colIdx = sparse_.getColIdx();
+    //     in_.eval();
+    //
+    //     uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in_));
+    //
+    //     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ,
+    //     stype); sparse_.eval();
+    //
+    //     Array<T> &values   = sparse_.getValues();
+    //     Array<int> &rowIdx = sparse_.getRowIdx();
+    //     Array<int> &colIdx = sparse_.getColIdx();
 
     // kernel::dense2csr<T>(values, rowIdx, colIdx, in_);
 
-    return sparse_;
+    // return sparse_;
 }
 
 // Partial template specialization of sparseConvertStorageToDense for COO
@@ -82,97 +82,97 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_) {
 template<typename T>
 Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
     ONEAPI_NOT_SUPPORTED("sparseConvertCOOToDense Not supported");
-    in.eval();
-
-    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
-    dense.eval();
-
-    const Array<T> values   = in.getValues();
-    const Array<int> rowIdx = in.getRowIdx();
-    const Array<int> colIdx = in.getColIdx();
+    //    in.eval();
+    //
+    //    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    //    dense.eval();
+    //
+    //    const Array<T> values   = in.getValues();
+    //    const Array<int> rowIdx = in.getRowIdx();
+    //    const Array<int> colIdx = in.getColIdx();
 
     // kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
 
-    return dense;
+    // return dense;
 }
 
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
     ONEAPI_NOT_SUPPORTED("sparseConvertStorageToDense Not supported");
-
-    if (stype != AF_STORAGE_CSR) {
-        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
-                 AF_ERR_NOT_SUPPORTED);
-    }
-
-    in_.eval();
-
-    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
-    dense_.eval();
-
-    const Array<T> &values   = in_.getValues();
-    const Array<int> &rowIdx = in_.getRowIdx();
-    const Array<int> &colIdx = in_.getColIdx();
-
-    if (stype == AF_STORAGE_CSR) {
-        // kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
-    } else {
-        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
-                 AF_ERR_NOT_SUPPORTED);
-    }
-
-    return dense_;
+    //
+    //    if (stype != AF_STORAGE_CSR) {
+    //        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
+    //                 AF_ERR_NOT_SUPPORTED);
+    //    }
+    //
+    //    in_.eval();
+    //
+    //    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
+    //    dense_.eval();
+    //
+    //    const Array<T> &values   = in_.getValues();
+    //    const Array<int> &rowIdx = in_.getRowIdx();
+    //    const Array<int> &colIdx = in_.getColIdx();
+    //
+    //    if (stype == AF_STORAGE_CSR) {
+    //        // kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
+    //    } else {
+    //        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
+    //                 AF_ERR_NOT_SUPPORTED);
+    //    }
+    //
+    //    return dense_;
 }
 
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
     ONEAPI_NOT_SUPPORTED("sparseConvertStorageToStorage Not supported");
-    in.eval();
-
-    SparseArray<T> converted = createEmptySparseArray<T>(
-        in.dims(), static_cast<int>(in.getNNZ()), dest);
-    converted.eval();
-
-    if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
-        Array<int> index = range<int>(in.getNNZ(), 0);
-        index.eval();
-
-        Array<T> &ovalues         = converted.getValues();
-        Array<int> &orowIdx       = converted.getRowIdx();
-        Array<int> &ocolIdx       = converted.getColIdx();
-        const Array<T> &ivalues   = in.getValues();
-        const Array<int> &irowIdx = in.getRowIdx();
-        const Array<int> &icolIdx = in.getColIdx();
-
-        // kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
-        // icolIdx,
-        //                    index);
-
-    } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-        Array<int> index = range<int>(in.getNNZ(), 0);
-        index.eval();
-
-        Array<T> &ovalues         = converted.getValues();
-        Array<int> &orowIdx       = converted.getRowIdx();
-        Array<int> &ocolIdx       = converted.getColIdx();
-        const Array<T> &ivalues   = in.getValues();
-        const Array<int> &irowIdx = in.getRowIdx();
-        const Array<int> &icolIdx = in.getColIdx();
-
-        Array<int> rowCopy = copyArray<int>(irowIdx);
-        rowCopy.eval();
-
-        // kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
-        // icolIdx,
-        //                    index, rowCopy, in.dims()[0]);
-
-    } else {
-        // Should never come here
-        AF_ERROR("OpenCL Backend invalid conversion combination",
-                 AF_ERR_NOT_SUPPORTED);
-    }
-
-    return converted;
+    // in.eval();
+
+    // SparseArray<T> converted = createEmptySparseArray<T>(
+    //     in.dims(), static_cast<int>(in.getNNZ()), dest);
+    // converted.eval();
+
+    // if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+    //     Array<int> index = range<int>(in.getNNZ(), 0);
+    //     index.eval();
+
+    //    Array<T> &ovalues         = converted.getValues();
+    //    Array<int> &orowIdx       = converted.getRowIdx();
+    //    Array<int> &ocolIdx       = converted.getColIdx();
+    //    const Array<T> &ivalues   = in.getValues();
+    //    const Array<int> &irowIdx = in.getRowIdx();
+    //    const Array<int> &icolIdx = in.getColIdx();
+
+    //    // kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
+    //    // icolIdx,
+    //    //                    index);
+
+    //} else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+    //    Array<int> index = range<int>(in.getNNZ(), 0);
+    //    index.eval();
+
+    //    Array<T> &ovalues         = converted.getValues();
+    //    Array<int> &orowIdx       = converted.getRowIdx();
+    //    Array<int> &ocolIdx       = converted.getColIdx();
+    //    const Array<T> &ivalues   = in.getValues();
+    //    const Array<int> &irowIdx = in.getRowIdx();
+    //    const Array<int> &icolIdx = in.getColIdx();
+
+    //    Array<int> rowCopy = copyArray<int>(irowIdx);
+    //    rowCopy.eval();
+
+    //    kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
+    //    icolIdx,
+    //                       index, rowCopy, in.dims()[0]);
+
+    //} else {
+    //    // Should never come here
+    //    AF_ERROR("OpenCL Backend invalid conversion combination",
+    //             AF_ERR_NOT_SUPPORTED);
+    //}
+
+    // return converted;
 }
 
 #define INSTANTIATE_TO_STORAGE(T, S)                     \
diff --git a/src/backend/oneapi/sparse_arith.cpp b/src/backend/oneapi/sparse_arith.cpp
index 2bb14c2b1d..e39bed14e4 100644
--- a/src/backend/oneapi/sparse_arith.cpp
+++ b/src/backend/oneapi/sparse_arith.cpp
@@ -51,103 +51,103 @@ template<typename T, af_op_t op>
 Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
                   const bool reverse) {
     ONEAPI_NOT_SUPPORTED("arithOpD Not supported");
-    lhs.eval();
-    rhs.eval();
-
-    Array<T> out  = createEmptyArray<T>(dim4(0));
-    Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
-    switch (op) {
-        case af_add_t: out = copyArray<T>(rhs); break;
-        case af_sub_t:
-            out = reverse ? copyArray<T>(rhs)
-                          : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
-            break;
-        default: out = copyArray<T>(rhs);
-    }
-    out.eval();
-    switch (lhs.getStorage()) {
-        case AF_STORAGE_CSR:
-            // kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
-            //                                 lhs.getRowIdx(), lhs.getColIdx(),
-            //                                 rhs, reverse);
-            break;
-        case AF_STORAGE_COO:
-            // kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
-            //                                 lhs.getRowIdx(), lhs.getColIdx(),
-            //                                 rhs, reverse);
-            break;
-        default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
-                     AF_ERR_NOT_SUPPORTED);
-    }
-
-    return out;
+    // lhs.eval();
+    // rhs.eval();
+
+    // Array<T> out  = createEmptyArray<T>(dim4(0));
+    // Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
+    // switch (op) {
+    //     case af_add_t: out = copyArray<T>(rhs); break;
+    //     case af_sub_t:
+    //         out = reverse ? copyArray<T>(rhs)
+    //                       : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
+    //         break;
+    //     default: out = copyArray<T>(rhs);
+    // }
+    // out.eval();
+    // switch (lhs.getStorage()) {
+    //     case AF_STORAGE_CSR:
+    //         kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
+    //                                         lhs.getRowIdx(), lhs.getColIdx(),
+    //                                         rhs, reverse);
+    //         break;
+    //     case AF_STORAGE_COO:
+    //         kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
+    //                                         lhs.getRowIdx(), lhs.getColIdx(),
+    //                                         rhs, reverse);
+    //         break;
+    //     default:
+    //         AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+    //                  AF_ERR_NOT_SUPPORTED);
+    // }
+
+    // return out;
 }
 
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
                        const bool reverse) {
     ONEAPI_NOT_SUPPORTED("arithOp Not supported");
-    lhs.eval();
-    rhs.eval();
-
-    SparseArray<T> out = createArrayDataSparseArray<T>(
-        lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-        lhs.getStorage(), true);
-    out.eval();
-    switch (lhs.getStorage()) {
-        case AF_STORAGE_CSR:
-            // kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
-            //                                 out.getColIdx(), rhs, reverse);
-            break;
-        case AF_STORAGE_COO:
-            // kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
-            //                                 out.getColIdx(), rhs, reverse);
-            break;
-        default:
-            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
-                     AF_ERR_NOT_SUPPORTED);
-    }
-
-    return out;
+    // lhs.eval();
+    // rhs.eval();
+
+    // SparseArray<T> out = createArrayDataSparseArray<T>(
+    //     lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+    //     lhs.getStorage(), true);
+    // out.eval();
+    // switch (lhs.getStorage()) {
+    //     case AF_STORAGE_CSR:
+    //         kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
+    //                                         out.getColIdx(), rhs, reverse);
+    //         break;
+    //     case AF_STORAGE_COO:
+    //         kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
+    //                                         out.getColIdx(), rhs, reverse);
+    //         break;
+    //     default:
+    //         AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+    //                  AF_ERR_NOT_SUPPORTED);
+    // }
+
+    // return out;
 }
 
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     ONEAPI_NOT_SUPPORTED("arithOp Not supported");
-    lhs.eval();
-    rhs.eval();
-    af::storage sfmt = lhs.getStorage();
+    // lhs.eval();
+    // rhs.eval();
+    // af::storage sfmt = lhs.getStorage();
 
-    const dim4 &ldims = lhs.dims();
+    // const dim4 &ldims = lhs.dims();
 
-    const uint M = ldims[0];
-    const uint N = ldims[1];
+    // const uint M = ldims[0];
+    // const uint N = ldims[1];
 
-    const dim_t nnzA = lhs.getNNZ();
-    const dim_t nnzB = rhs.getNNZ();
+    // const dim_t nnzA = lhs.getNNZ();
+    // const dim_t nnzB = rhs.getNNZ();
 
-    auto temp = createValueArray<int>(dim4(M + 1), scalar<int>(0));
-    temp.eval();
+    // auto temp = createValueArray<int>(dim4(M + 1), scalar<int>(0));
+    // temp.eval();
 
-    unsigned nnzC = 0;
+    // unsigned nnzC = 0;
     // kernel::csrCalcOutNNZ(temp, nnzC, M, N, nnzA, lhs.getRowIdx(),
     //                       lhs.getColIdx(), nnzB, rhs.getRowIdx(),
     //                       rhs.getColIdx());
 
-    auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
+    // auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
 
-    auto outColIdx = createEmptyArray<int>(dim4(nnzC));
-    auto outValues = createEmptyArray<T>(dim4(nnzC));
+    // auto outColIdx = createEmptyArray<int>(dim4(nnzC));
+    // auto outValues = createEmptyArray<T>(dim4(nnzC));
 
     // kernel::ssArithCSR<T, op>(outValues, outColIdx, outRowIdx, M, N, nnzA,
     //                           lhs.getValues(), lhs.getRowIdx(),
     //                           lhs.getColIdx(), nnzB, rhs.getValues(),
     //                           rhs.getRowIdx(), rhs.getColIdx());
 
-    SparseArray<T> retVal = createArrayDataSparseArray(
-        ldims, outValues, outRowIdx, outColIdx, sfmt);
-    return retVal;
+    // SparseArray<T> retVal = createArrayDataSparseArray(
+    //     ldims, outValues, outRowIdx, outColIdx, sfmt);
+    // return retVal;
 }
 
 #define INSTANTIATE(T)                                                         \
diff --git a/src/backend/oneapi/sparse_blas.cpp b/src/backend/oneapi/sparse_blas.cpp
index a7c04abc40..b9fcd6fb52 100644
--- a/src/backend/oneapi/sparse_blas.cpp
+++ b/src/backend/oneapi/sparse_blas.cpp
@@ -38,52 +38,55 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
     ONEAPI_NOT_SUPPORTED("sparse matmul Not supported");
-#if defined(WITH_LINEAR_ALGEBRA)
-    if (OpenCLCPUOffload(
-            false)) {  // Do not force offload gemm on OSX Intel devices
-        return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
-    }
-#endif
-
-    int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
-    // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
-    static const int rColDim =
-        1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
-
-    dim4 lDims = lhs.dims();
-    dim4 rDims = rhsIn.dims();
-    int M      = lDims[lRowDim];
-    int N      = rDims[rColDim];
-    // int K = lDims[lColDim];
-
-    const Array<T> rhs =
-        (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) : rhsIn;
-    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
-
-    static const T alpha = scalar<T>(1.0);
-    static const T beta  = scalar<T>(0.0);
-
-    const Array<T>& values   = lhs.getValues();
-    const Array<int>& rowIdx = lhs.getRowIdx();
-    const Array<int>& colIdx = lhs.getColIdx();
-
-    if (optLhs == AF_MAT_NONE) {
-        // if (N == 1) {
-        //     kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
-        // } else {
-        //     kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha, beta);
-        // }
-    } else {
-        // // CSR transpose is a CSC matrix
-        // if (N == 1) {
-        //     kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
-        //                   optLhs == AF_MAT_CTRANS);
-        // } else {
-        //     kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha, beta,
-        //                      optLhs == AF_MAT_CTRANS);
-        // }
-    }
-    return out;
+    //#if defined(WITH_LINEAR_ALGEBRA)
+    //    if (OpenCLCPUOffload(
+    //            false)) {  // Do not force offload gemm on OSX Intel devices
+    //        return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
+    //    }
+    //#endif
+    //
+    //    int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+    //    // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+    //    static const int rColDim =
+    //        1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
+    //
+    //    dim4 lDims = lhs.dims();
+    //    dim4 rDims = rhsIn.dims();
+    //    int M      = lDims[lRowDim];
+    //    int N      = rDims[rColDim];
+    //    // int K = lDims[lColDim];
+    //
+    //    const Array<T> rhs =
+    //        (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) :
+    //        rhsIn;
+    //    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    //
+    //    static const T alpha = scalar<T>(1.0);
+    //    static const T beta  = scalar<T>(0.0);
+    //
+    //    const Array<T>& values   = lhs.getValues();
+    //    const Array<int>& rowIdx = lhs.getRowIdx();
+    //    const Array<int>& colIdx = lhs.getColIdx();
+    //
+    //    if (optLhs == AF_MAT_NONE) {
+    //        if (N == 1) {
+    //            kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
+    //        } else {
+    //            kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha,
+    //            beta);
+    //        }
+    //    } else {
+    //        // CSR transpose is a CSC matrix
+    //        if (N == 1) {
+    //            kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
+    //                          optLhs == AF_MAT_CTRANS);
+    //        } else {
+    //            kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha,
+    //            beta,
+    //                             optLhs == AF_MAT_CTRANS);
+    //        }
+    //    }
+    //    return out;
 }
 
 #define INSTANTIATE_SPARSE(T)                                            \
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index aa46bdaebb..7fcc708d32 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -107,6 +107,7 @@ if(AF_BUILD_UNIFIED)
   list(APPEND enabled_backends "unified")
 endif(AF_BUILD_UNIFIED)
 
+check_cxx_compiler_flag("-Wtautological-constant-compare" has_tautological_constant_compare_flag)
 
 add_library(arrayfire_test STATIC
   testHelpers.hpp
@@ -120,11 +121,15 @@ target_include_directories(arrayfire_test
     ${ArrayFire_SOURCE_DIR}/extern/half/include
   )
 
+# The tautological-constant-compare warning is always thrown for std::nan
+# and std::info calls. Its unnecessarily verbose.
+target_compile_options(arrayfire_test
+  PRIVATE
+    $<$<BOOL:${has_tautological_constant_compare_flag}>:-Wno-tautological-constant-compare>
+    $<$<CXX_COMPILER_ID:MSVC>: /bigobj
+                               /EHsc>
+  )
 if(WIN32)
-  target_compile_options(arrayfire_test
-    PRIVATE
-      /bigobj
-      /EHsc)
   target_compile_definitions(arrayfire_test
     PRIVATE
       WIN32_LEAN_AND_MEAN
@@ -185,6 +190,15 @@ function(make_test)
         arrayfire_test
       )
 
+    # The tautological-constant-compare warning is always thrown for std::nan
+    # and std::info calls. Its unnecessarily verbose.
+    target_compile_options(${target}
+      PRIVATE
+        $<$<BOOL:${has_tautological_constant_compare_flag}>:-Wno-tautological-constant-compare>
+        $<$<CXX_COMPILER_ID:MSVC>: /bigobj
+                                   /EHsc>
+      )
+
     if(${backend} STREQUAL "unified")
       target_link_libraries(${target}
         PRIVATE
@@ -221,10 +235,6 @@ function(make_test)
         )
     endif()
     if(WIN32)
-      target_compile_options(${target}
-        PRIVATE
-          /bigobj
-          /EHsc)
       target_compile_definitions(${target}
         PRIVATE
           WIN32_LEAN_AND_MEAN
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 6a7f6e7000..fda3d887d6 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -56,6 +56,7 @@ std::ostream &operator<<(std::ostream &os, af::Backend bk) {
         case AF_BACKEND_CPU: os << "AF_BACKEND_CPU"; break;
         case AF_BACKEND_CUDA: os << "AF_BACKEND_CUDA"; break;
         case AF_BACKEND_OPENCL: os << "AF_BACKEND_OPENCL"; break;
+        case AF_BACKEND_ONEAPI: os << "AF_BACKEND_ONEAPI"; break;
         case AF_BACKEND_DEFAULT: os << "AF_BACKEND_DEFAULT"; break;
     }
     return os;

From 227b1b10b3a0144cd005574c2a40663b2689a847 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Oct 2022 15:14:38 -0400
Subject: [PATCH 2314/2677] Add support for oneAPI to the unified backend

---
 src/api/cpp/array.cpp                       | 9 +++++++++
 src/api/unified/symbol_manager.cpp          | 8 ++++++--
 src/backend/common/DefaultMemoryManager.hpp | 8 ++++----
 src/backend/common/jit/BufferNodeBase.hpp   | 2 +-
 4 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 5889c0d99c..832c2999e5 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -286,6 +286,15 @@ array::~array() {
                     release_func(get());
                     break;
                 }
+                case AF_BACKEND_ONEAPI: {
+                    static auto *oneapi_handle = unified::getActiveHandle();
+                    static auto release_func =
+                        reinterpret_cast<af_release_array_ptr>(
+                            common::getFunctionPointer(oneapi_handle,
+                                                       "af_release_array"));
+                    release_func(get());
+                    break;
+                }
                 case AF_BACKEND_DEFAULT:
                     assert(1 != 1 &&
                            "AF_BACKEND_DEFAULT cannot be set as a backend for "
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index 8e1f846c54..ca11238773 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -68,6 +68,9 @@ string getBkndLibName(const af_backend backend) {
         case AF_BACKEND_CPU:
             ret = string(LIB_AF_BKND_PREFIX) + "afcpu" + LIB_AF_BKND_SUFFIX;
             break;
+        case AF_BACKEND_ONEAPI:
+            ret = string(LIB_AF_BKND_PREFIX) + "afoneapi" + LIB_AF_BKND_SUFFIX;
+            break;
         default: assert(1 != 1 && "Invalid backend");
     }
     return ret;
@@ -78,6 +81,7 @@ string getBackendDirectoryName(const af_backend backend) {
         case AF_BACKEND_CUDA: ret = "cuda"; break;
         case AF_BACKEND_OPENCL: ret = "opencl"; break;
         case AF_BACKEND_CPU: ret = "cpu"; break;
+        case AF_BACKEND_ONEAPI: ret = "oneapi"; break;
         default: assert(1 != 1 && "Invalid backend");
     }
     return ret;
@@ -185,8 +189,8 @@ AFSymbolManager::AFSymbolManager()
     , backendsAvailable(0)
     , logger(loggerFactory("unified")) {
     // In order of priority.
-    static const af_backend order[] = {AF_BACKEND_CUDA, AF_BACKEND_OPENCL,
-                                       AF_BACKEND_CPU};
+    static const af_backend order[] = {AF_BACKEND_CUDA, AF_BACKEND_ONEAPI,
+                                       AF_BACKEND_OPENCL, AF_BACKEND_CPU};
 
     LibHandle handle    = nullptr;
     af::Backend backend = AF_BACKEND_DEFAULT;
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index 0881f318a1..83af36d390 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -121,11 +121,11 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
     ~DefaultMemoryManager() = default;
 
    protected:
-    DefaultMemoryManager()                                  = delete;
-    DefaultMemoryManager(const DefaultMemoryManager &other) = delete;
-    DefaultMemoryManager(DefaultMemoryManager &&other)      = default;
+    DefaultMemoryManager()                                             = delete;
+    DefaultMemoryManager(const DefaultMemoryManager &other)            = delete;
+    DefaultMemoryManager(DefaultMemoryManager &&other)                 = delete;
     DefaultMemoryManager &operator=(const DefaultMemoryManager &other) = delete;
-    DefaultMemoryManager &operator=(DefaultMemoryManager &&other) = default;
+    DefaultMemoryManager &operator=(DefaultMemoryManager &&other)      = delete;
     common::mutex_t memory_mutex;
     // backend-specific
     std::vector<memory_info> memory;
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index a7d6747036..9633b2a867 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -93,7 +93,7 @@ class BufferNodeBase : public common::Node {
 
     size_t getBytes() const final { return m_bytes; }
 
-    size_t getHash() const noexcept {
+    size_t getHash() const noexcept override {
         size_t out = 0;
         auto ptr   = m_data.get();
         std::memcpy(&out, &ptr, std::max(sizeof(Node *), sizeof(size_t)));

From a976c076ffb7ac984cbb34654dc8f3a12c5db367 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 12 Oct 2022 13:11:27 -0400
Subject: [PATCH 2315/2677] Use span instead of vector in getKernel APIs

---
 CMakeLists.txt                                |  1 +
 src/backend/common/TemplateArg.cpp            |  5 +++
 src/backend/common/TemplateArg.hpp            | 14 +++++++
 src/backend/common/TemplateTypename.hpp       | 16 ++++---
 src/backend/common/compile_module.hpp         |  7 ++--
 src/backend/common/kernel_cache.cpp           | 18 ++++----
 src/backend/common/kernel_cache.hpp           |  9 ++--
 src/backend/common/util.cpp                   |  6 ++-
 src/backend/common/util.hpp                   |  5 ++-
 src/backend/cuda/compile_module.cpp           |  7 ++--
 src/backend/cuda/jit.cpp                      |  3 +-
 .../cuda/kernel/anisotropic_diffusion.hpp     |  9 ++--
 src/backend/cuda/kernel/approx.hpp            | 14 +++----
 src/backend/cuda/kernel/assign.hpp            |  5 ++-
 src/backend/cuda/kernel/bilateral.hpp         |  6 +--
 src/backend/cuda/kernel/canny.hpp             | 28 +++++++------
 src/backend/cuda/kernel/convolve.hpp          | 40 ++++++++++--------
 src/backend/cuda/kernel/diagonal.hpp          | 10 +++--
 src/backend/cuda/kernel/diff.hpp              |  7 ++--
 src/backend/cuda/kernel/exampleFunction.hpp   |  8 ++--
 src/backend/cuda/kernel/fftconvolve.hpp       | 26 ++++++------
 src/backend/cuda/kernel/flood_fill.hpp        | 13 +++---
 src/backend/cuda/kernel/gradient.hpp          |  9 ++--
 src/backend/cuda/kernel/histogram.hpp         |  8 ++--
 src/backend/cuda/kernel/hsv_rgb.hpp           |  6 +--
 src/backend/cuda/kernel/identity.hpp          |  5 ++-
 src/backend/cuda/kernel/iir.hpp               |  7 ++--
 src/backend/cuda/kernel/index.hpp             |  4 +-
 src/backend/cuda/kernel/iota.hpp              |  4 +-
 src/backend/cuda/kernel/ireduce.hpp           | 18 ++++----
 src/backend/cuda/kernel/lookup.hpp            | 14 +++----
 src/backend/cuda/kernel/lu_split.hpp          |  8 ++--
 src/backend/cuda/kernel/match_template.hpp    |  6 +--
 src/backend/cuda/kernel/meanshift.hpp         | 10 ++---
 src/backend/cuda/kernel/medfilt.hpp           | 17 ++++----
 src/backend/cuda/kernel/memcopy.hpp           | 41 +++++++++---------
 src/backend/cuda/kernel/moments.hpp           |  5 ++-
 src/backend/cuda/kernel/morph.hpp             | 18 ++++----
 src/backend/cuda/kernel/pad_array_borders.hpp |  8 ++--
 src/backend/cuda/kernel/range.hpp             |  4 +-
 src/backend/cuda/kernel/reorder.hpp           |  5 ++-
 src/backend/cuda/kernel/resize.hpp            |  6 +--
 src/backend/cuda/kernel/rotate.hpp            |  6 +--
 src/backend/cuda/kernel/scan_dim.hpp          | 18 ++++----
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 20 ++++-----
 src/backend/cuda/kernel/scan_first.hpp        | 18 ++++----
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 22 +++++-----
 src/backend/cuda/kernel/select.hpp            | 12 +++---
 src/backend/cuda/kernel/sobel.hpp             | 11 ++---
 src/backend/cuda/kernel/sparse.hpp            |  6 +--
 src/backend/cuda/kernel/sparse_arith.hpp      | 26 ++++++------
 src/backend/cuda/kernel/susan.hpp             |  9 ++--
 src/backend/cuda/kernel/tile.hpp              |  4 +-
 src/backend/cuda/kernel/transform.hpp         |  5 ++-
 src/backend/cuda/kernel/transpose.hpp         | 10 ++---
 src/backend/cuda/kernel/transpose_inplace.hpp | 10 ++---
 src/backend/cuda/kernel/triangle.hpp          |  8 ++--
 src/backend/cuda/kernel/unwrap.hpp            |  6 +--
 src/backend/cuda/kernel/where.hpp             |  4 +-
 src/backend/cuda/kernel/wrap.hpp              | 12 +++---
 src/backend/opencl/compile_module.cpp         | 12 +++---
 src/backend/opencl/jit.cpp                    |  3 +-
 .../opencl/kernel/anisotropic_diffusion.hpp   |  6 +--
 src/backend/opencl/kernel/approx.hpp          | 10 +++--
 src/backend/opencl/kernel/assign.hpp          | 11 +++--
 src/backend/opencl/kernel/bilateral.hpp       |  6 +--
 src/backend/opencl/kernel/canny.hpp           | 22 +++++-----
 .../opencl/kernel/convolve/conv2_impl.hpp     |  5 ++-
 .../opencl/kernel/convolve/conv_common.hpp    |  5 ++-
 .../opencl/kernel/convolve_separable.cpp      | 13 +++---
 src/backend/opencl/kernel/cscmm.hpp           |  9 ++--
 src/backend/opencl/kernel/cscmv.hpp           | 11 +++--
 src/backend/opencl/kernel/csrmm.hpp           |  9 ++--
 src/backend/opencl/kernel/csrmv.hpp           | 12 +++---
 src/backend/opencl/kernel/diagonal.hpp        | 22 +++++-----
 src/backend/opencl/kernel/diff.hpp            | 14 +++----
 src/backend/opencl/kernel/exampleFunction.hpp | 18 ++++----
 src/backend/opencl/kernel/fast.hpp            | 21 +++++-----
 src/backend/opencl/kernel/fftconvolve.hpp     | 33 +++++++--------
 src/backend/opencl/kernel/flood_fill.hpp      | 42 +++++++++----------
 src/backend/opencl/kernel/gradient.hpp        | 11 +++--
 src/backend/opencl/kernel/harris.hpp          | 18 ++++----
 src/backend/opencl/kernel/histogram.hpp       |  6 +--
 src/backend/opencl/kernel/homography.hpp      | 28 ++++++-------
 src/backend/opencl/kernel/hsv_rgb.hpp         |  9 ++--
 src/backend/opencl/kernel/identity.hpp        | 11 +++--
 src/backend/opencl/kernel/iir.hpp             | 13 +++---
 src/backend/opencl/kernel/index.hpp           | 10 ++---
 src/backend/opencl/kernel/iota.hpp            |  9 ++--
 src/backend/opencl/kernel/ireduce.hpp         | 26 ++++++------
 src/backend/opencl/kernel/laset.hpp           | 14 +++----
 src/backend/opencl/kernel/laset_band.hpp      |  6 +--
 src/backend/opencl/kernel/laswp.hpp           | 13 +++---
 src/backend/opencl/kernel/lookup.hpp          | 12 +++---
 src/backend/opencl/kernel/lu_split.hpp        | 14 +++----
 src/backend/opencl/kernel/match_template.hpp  | 11 +++--
 src/backend/opencl/kernel/mean.hpp            | 13 +++---
 src/backend/opencl/kernel/meanshift.hpp       | 11 +++--
 src/backend/opencl/kernel/medfilt.hpp         | 22 +++++-----
 src/backend/opencl/kernel/memcopy.hpp         | 18 ++++----
 src/backend/opencl/kernel/moments.hpp         | 11 +++--
 src/backend/opencl/kernel/morph.hpp           |  6 ++-
 .../opencl/kernel/nearest_neighbour.hpp       |  7 ++--
 src/backend/opencl/kernel/orb.hpp             | 12 ++++--
 .../opencl/kernel/pad_array_borders.hpp       |  5 ++-
 src/backend/opencl/kernel/random_engine.hpp   |  7 ++--
 src/backend/opencl/kernel/range.hpp           | 11 +++--
 src/backend/opencl/kernel/reduce.hpp          | 33 +++++++--------
 src/backend/opencl/kernel/reduce_by_key.hpp   | 42 ++++++++++---------
 src/backend/opencl/kernel/regions.hpp         |  9 ++--
 src/backend/opencl/kernel/reorder.hpp         | 11 +++--
 src/backend/opencl/kernel/resize.hpp          | 10 ++---
 src/backend/opencl/kernel/rotate.hpp          |  5 ++-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  2 +
 src/backend/opencl/kernel/scan_dim.hpp        |  4 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  3 +-
 src/backend/opencl/kernel/scan_first.hpp      |  4 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |  3 +-
 src/backend/opencl/kernel/select.hpp          | 28 ++++++-------
 src/backend/opencl/kernel/sift.hpp            | 25 +++++------
 src/backend/opencl/kernel/sobel.hpp           |  4 +-
 src/backend/opencl/kernel/sparse.hpp          | 24 +++++------
 src/backend/opencl/kernel/sparse_arith.hpp    | 12 +++---
 src/backend/opencl/kernel/susan.hpp           |  8 ++--
 src/backend/opencl/kernel/swapdblk.hpp        |  4 +-
 src/backend/opencl/kernel/tile.hpp            |  3 +-
 src/backend/opencl/kernel/transform.hpp       |  6 +--
 src/backend/opencl/kernel/transpose.hpp       |  4 +-
 .../opencl/kernel/transpose_inplace.hpp       |  6 +--
 src/backend/opencl/kernel/triangle.hpp        |  4 +-
 src/backend/opencl/kernel/unwrap.hpp          |  4 +-
 src/backend/opencl/kernel/where.hpp           |  4 +-
 src/backend/opencl/kernel/wrap.hpp            |  9 ++--
 133 files changed, 805 insertions(+), 751 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 72b2ca4317..5689a8094b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -322,6 +322,7 @@ else()
   set_target_properties(bin2cpp
     PROPERTIES
       CXX_STANDARD 17)
+  target_link_libraries(bin2cpp PRIVATE nonstd::span-lite)
 
   # NOSPDLOG is used to remove the spdlog dependency from bin2cpp
   target_compile_definitions(bin2cpp PRIVATE NOSPDLOG)
diff --git a/src/backend/common/TemplateArg.cpp b/src/backend/common/TemplateArg.cpp
index 740138b337..8cff5c4e24 100644
--- a/src/backend/common/TemplateArg.cpp
+++ b/src/backend/common/TemplateArg.cpp
@@ -33,6 +33,11 @@ template string toString<float>(float);
 template string toString<double>(double);
 template string toString<long double>(long double);
 
+template<>
+string toString(TemplateArg arg) {
+    return arg._tparam;
+}
+
 template<>
 string toString(bool val) {
     return string(val ? "true" : "false");
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
index d82d30e12a..a7dfbe4ceb 100644
--- a/src/backend/common/TemplateArg.hpp
+++ b/src/backend/common/TemplateArg.hpp
@@ -9,10 +9,15 @@
 
 #pragma once
 
+#include <array>
 #include <string>
 #include <utility>
 
 #include <optypes.hpp>
+#include <traits.hpp>
+
+template<typename T>
+class TemplateTypename;
 
 template<typename T>
 std::string toString(T value);
@@ -24,10 +29,19 @@ struct TemplateArg {
 
     TemplateArg(std::string str) : _tparam(std::move(str)) {}
 
+    template<typename T>
+    constexpr TemplateArg(TemplateTypename<T> arg) noexcept : _tparam(arg) {}
+
     template<typename T>
     constexpr TemplateArg(T value) noexcept : _tparam(toString(value)) {}
 };
 
+template<typename... Targs>
+std::array<TemplateArg, sizeof...(Targs)> TemplateArgs(Targs &&...args) {
+    return std::array<TemplateArg, sizeof...(Targs)>{
+        std::forward<Targs>(args)...};
+}
+
 #define DefineKey(arg) " -D " #arg
 #define DefineValue(arg) " -D " #arg "=" + toString(arg)
 #define DefineKeyValue(key, arg) " -D " #key "=" + toString(arg)
diff --git a/src/backend/common/TemplateTypename.hpp b/src/backend/common/TemplateTypename.hpp
index 6191348aae..682070510a 100644
--- a/src/backend/common/TemplateTypename.hpp
+++ b/src/backend/common/TemplateTypename.hpp
@@ -19,14 +19,18 @@ struct TemplateTypename {
     operator TemplateArg() const noexcept {
         return {std::string(dtype_traits<T>::getName())};
     }
+    operator std::string() const noexcept {
+        return {std::string(dtype_traits<T>::getName())};
+    }
 };
 
-#define SPECIALIZE(TYPE, NAME)                      \
-    template<>                                      \
-    struct TemplateTypename<TYPE> {                 \
-        operator TemplateArg() const noexcept {     \
-            return TemplateArg(std::string(#NAME)); \
-        }                                           \
+#define SPECIALIZE(TYPE, NAME)                                  \
+    template<>                                                  \
+    struct TemplateTypename<TYPE> {                             \
+        operator TemplateArg() const noexcept {                 \
+            return TemplateArg(std::string(#NAME));             \
+        }                                                       \
+        operator std::string() const noexcept { return #NAME; } \
     }
 
 SPECIALIZE(unsigned char, detail::uchar);
diff --git a/src/backend/common/compile_module.hpp b/src/backend/common/compile_module.hpp
index dc8a0b7dd0..c2abe76ecd 100644
--- a/src/backend/common/compile_module.hpp
+++ b/src/backend/common/compile_module.hpp
@@ -14,6 +14,7 @@
 #include <Module.hpp>
 #include <backend.hpp>
 
+#include <nonstd/span.hpp>
 #include <string>
 #include <vector>
 
@@ -43,9 +44,9 @@ namespace common {
 ///
 /// \returns Backend specific binary module that contains associated kernel
 detail::Module compileModule(const std::string& moduleKey,
-                             const std::vector<std::string>& sources,
-                             const std::vector<std::string>& options,
-                             const std::vector<std::string>& kInstances,
+                             nonstd::span<const std::string> sources,
+                             nonstd::span<const std::string> options,
+                             nonstd::span<const std::string> kInstances,
                              const bool isJIT);
 
 /// \brief Load module binary from disk cache
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 869ea8d5e9..ff2b53c787 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -15,6 +15,7 @@
 #include <device_manager.hpp>
 #include <platform.hpp>
 
+#include <nonstd/span.hpp>
 #include <algorithm>
 #include <shared_mutex>
 #include <string>
@@ -24,11 +25,15 @@
 using detail::Kernel;
 using detail::Module;
 
+using nonstd::span;
+using std::array;
 using std::back_inserter;
+using std::shared_lock;
 using std::shared_timed_mutex;
 using std::string;
 using std::to_string;
 using std::transform;
+using std::unique_lock;
 using std::unordered_map;
 using std::vector;
 
@@ -48,17 +53,16 @@ ModuleMap& getCache(const int device) {
 }
 
 Module findModule(const int device, const size_t& key) {
-    std::shared_lock<shared_timed_mutex> readLock(getCacheMutex(device));
+    shared_lock<shared_timed_mutex> readLock(getCacheMutex(device));
     auto& cache = getCache(device);
     auto iter   = cache.find(key);
     if (iter != cache.end()) { return iter->second; }
     return Module{};
 }
 
-Kernel getKernel(const string& kernelName,
-                 const vector<common::Source>& sources,
-                 const vector<TemplateArg>& targs,
-                 const vector<string>& options, const bool sourceIsJIT) {
+Kernel getKernel(const string& kernelName, span<const common::Source> sources,
+                 span<const TemplateArg> targs, span<const string> options,
+                 const bool sourceIsJIT) {
     string tInstance = kernelName;
 
 #if defined(AF_CUDA)
@@ -116,10 +120,10 @@ Kernel getKernel(const string& kernelName,
                 sources_str.push_back({s.ptr, s.length});
             }
             currModule = compileModule(to_string(moduleKeyDisk), sources_str,
-                                       options, {tInstance}, sourceIsJIT);
+                                       options, array{tInstance}, sourceIsJIT);
         }
 
-        std::unique_lock<shared_timed_mutex> writeLock(getCacheMutex(device));
+        unique_lock<shared_timed_mutex> writeLock(getCacheMutex(device));
         auto& cache = getCache(device);
         auto iter   = cache.find(moduleKeyCache);
         if (iter == cache.end()) {
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index c63c4278a4..b021919a21 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -17,6 +17,7 @@
 #include <common/TemplateTypename.hpp>
 #include <common/util.hpp>
 
+#include <nonstd/span.hpp>
 #include <string>
 #include <vector>
 
@@ -46,7 +47,7 @@ namespace common {
 /// Example Usage: transpose
 ///
 /// \code
-/// auto transpose = getKernel("cuda::transpose", {transpase_cuh_src},
+/// auto transpose = getKernel("cuda::transpose", std::array{transpase_cuh_src},
 ///         {
 ///           TemplateTypename<T>(),
 ///           TemplateArg(conjugate),
@@ -70,9 +71,9 @@ namespace common {
 ///            the kernel compilation.
 ///
 detail::Kernel getKernel(const std::string& kernelName,
-                         const std::vector<common::Source>& sources,
-                         const std::vector<TemplateArg>& templateArgs,
-                         const std::vector<std::string>& options = {},
+                         nonstd::span<const common::Source> sources,
+                         nonstd::span<const TemplateArg> templateArgs,
+                         nonstd::span<const std::string> options = {},
                          const bool sourceIsJIT                  = false);
 
 /// \brief Lookup a Module that matches the given key
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index a5af7f80e6..bac4cb573d 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -23,6 +23,7 @@
 #include <common/util.hpp>
 #include <af/defines.h>
 
+#include <nonstd/span.hpp>
 #include <sys/stat.h>
 #include <algorithm>
 #include <cstdio>
@@ -34,6 +35,7 @@
 #include <thread>
 #include <vector>
 
+using nonstd::span;
 using std::accumulate;
 using std::hash;
 using std::ofstream;
@@ -248,13 +250,13 @@ size_t deterministicHash(const string& data, const size_t prevHash) {
     return deterministicHash(data.data(), data.size(), prevHash);
 }
 
-size_t deterministicHash(const vector<string>& list, const size_t prevHash) {
+size_t deterministicHash(span<const string> list, const size_t prevHash) {
     size_t hash = prevHash;
     for (auto s : list) { hash = deterministicHash(s.data(), s.size(), hash); }
     return hash;
 }
 
-size_t deterministicHash(const vector<common::Source>& list) {
+size_t deterministicHash(span<const common::Source> list) {
     // Combine the different source codes, via their hashes
     size_t hash = FNV1A_BASE_OFFSET;
     for (auto s : list) {
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index c0f712ec0e..fb6c195af6 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -12,6 +12,7 @@
 
 #include <af/defines.h>
 
+#include <nonstd/span.hpp>
 #include <iosfwd>
 #include <string>
 #include <vector>
@@ -78,8 +79,8 @@ std::size_t deterministicHash(const std::string& data,
                               const std::size_t prevHash = FNV1A_BASE_OFFSET);
 
 // This concatenates strings in the vector and computes hash
-std::size_t deterministicHash(const std::vector<std::string>& list,
+std::size_t deterministicHash(nonstd::span<const std::string> list,
                               const std::size_t prevHash = FNV1A_BASE_OFFSET);
 
 // This concatenates hashes of multiple sources
-std::size_t deterministicHash(const std::vector<common::Source>& list);
+std::size_t deterministicHash(nonstd::span<const common::Source> list);
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index cbc7d98517..ee10077477 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -64,6 +64,7 @@
 using namespace cuda;
 
 using detail::Module;
+using nonstd::span;
 using std::accumulate;
 using std::array;
 using std::back_insert_iterator;
@@ -140,9 +141,9 @@ string getKernelCacheFilename(const int device, const string &key) {
 
 namespace common {
 
-Module compileModule(const string &moduleKey, const vector<string> &sources,
-                     const vector<string> &opts,
-                     const vector<string> &kInstances, const bool sourceIsJIT) {
+Module compileModule(const string &moduleKey, span<const string> sources,
+                     span<const string> opts, span<const string> kInstances,
+                     const bool sourceIsJIT) {
     nvrtcProgram prog;
     if (sourceIsJIT) {
         constexpr const char *header_names[] = {
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 6904d0673d..37ff605cb4 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -318,7 +318,8 @@ static CUfunction getKernel(const vector<Node*>& output_nodes,
         const common::Source jit_src{jitKer.c_str(), jitKer.size(),
                                      deterministicHash(jitKer)};
 
-        return common::getKernel(funcName, {jit_src}, {}, {}, true).get();
+        return common::getKernel(funcName, std::array{jit_src}, {}, {}, true)
+            .get();
     }
     return common::getKernel(entry, funcName, true).get();
 }
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index 32e10b9942..1c247bb499 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -27,10 +27,11 @@ template<typename T>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
     auto diffUpdate = common::getKernel(
-        "cuda::diffUpdate", {anisotropic_diffusion_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(fftype), TemplateArg(isMCDE)},
-        {DefineValue(THREADS_X), DefineValue(THREADS_Y),
-         DefineValue(YDIM_LOAD)});
+        "cuda::diffUpdate", std::array{anisotropic_diffusion_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(fftype),
+                     TemplateArg(isMCDE)),
+        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y),
+                   DefineValue(YDIM_LOAD)});
 
     dim3 threads(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 47473a4f03..66dea16fe6 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -27,10 +27,10 @@ template<typename Ty, typename Tp>
 void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, const float offGrid,
              const af::interpType method, const int order) {
-    auto approx1 =
-        common::getKernel("cuda::approx1", {approx1_cuh_src},
-                          {TemplateTypename<Ty>(), TemplateTypename<Tp>(),
-                           TemplateArg(xdim), TemplateArg(order)});
+    auto approx1 = common::getKernel(
+        "cuda::approx1", std::array{approx1_cuh_src},
+        TemplateArgs(TemplateTypename<Ty>(), TemplateTypename<Tp>(),
+                     TemplateArg(xdim), TemplateArg(order)));
 
     dim3 threads(THREADS, 1, 1);
     int blocksPerMat = divup(yo.dims[0], threads.x);
@@ -57,9 +57,9 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const Tp &yi_beg, const Tp &yi_step, const float offGrid,
              const af::interpType method, const int order) {
     auto approx2 = common::getKernel(
-        "cuda::approx2", {approx2_cuh_src},
-        {TemplateTypename<Ty>(), TemplateTypename<Tp>(), TemplateArg(xdim),
-         TemplateArg(ydim), TemplateArg(order)});
+        "cuda::approx2", std::array{approx2_cuh_src},
+        TemplateArgs(TemplateTypename<Ty>(), TemplateTypename<Tp>(),
+                     TemplateArg(xdim), TemplateArg(ydim), TemplateArg(order)));
 
     dim3 threads(TX, TY, 1);
     int blocksPerMatX = divup(zo.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 9632892cc4..523dad2505 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -22,8 +22,9 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    auto assignKer = common::getKernel("cuda::assign", {assign_cuh_src},
-                                       {TemplateTypename<T>()});
+    auto assignKer =
+        common::getKernel("cuda::assign", std::array{assign_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index a7788a5deb..357b57a8bc 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -23,9 +23,9 @@ template<typename inType, typename outType>
 void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
     auto bilateral = common::getKernel(
-        "cuda::bilateral", {bilateral_cuh_src},
-        {TemplateTypename<inType>(), TemplateTypename<outType>()},
-        {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        "cuda::bilateral", std::array{bilateral_cuh_src},
+        TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>()),
+        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 4dd6ce739c..cc63a029c4 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -27,9 +27,10 @@ template<typename T>
 void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
     auto nonMaxSuppress = common::getKernel(
-        "cuda::nonMaxSuppression", {canny_cuh_src}, {TemplateTypename<T>()},
-        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        "cuda::nonMaxSuppression", std::array{canny_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
@@ -48,17 +49,20 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
 template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     auto initEdgeOut = common::getKernel(
-        "cuda::initEdgeOut", {canny_cuh_src}, {TemplateTypename<T>()},
-        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        "cuda::initEdgeOut", std::array{canny_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto edgeTrack = common::getKernel(
-        "cuda::edgeTrack", {canny_cuh_src}, {TemplateTypename<T>()},
-        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        "cuda::edgeTrack", std::array{canny_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto suppressLeftOver = common::getKernel(
-        "cuda::suppressLeftOver", {canny_cuh_src}, {TemplateTypename<T>()},
-        {DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-         DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        "cuda::suppressLeftOver", std::array{canny_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 40485d0148..7b105ef842 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -101,9 +101,11 @@ template<typename T, typename aT>
 void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     auto convolve1 = common::getKernel(
-        "cuda::convolve1", {convolve1_cuh_src},
-        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
-        {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS)});
+        "cuda::convolve1", std::array{convolve1_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
+                     TemplateArg(expand)),
+        std::array{DefineValue(MAX_CONV1_FILTER_LEN),
+                   DefineValue(CONV_THREADS)});
 
     prepareKernelArgs<T>(p, out.dims, filt.dims, 1);
 
@@ -156,11 +158,11 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
     }
 
     auto convolve2 = common::getKernel(
-        "cuda::convolve2", {convolve2_cuh_src},
-        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand),
-         TemplateArg(f0), TemplateArg(f1)},
-        {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
-         DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
+        "cuda::convolve2", std::array{convolve2_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
+                     TemplateArg(expand), TemplateArg(f0), TemplateArg(f1)),
+        std::array{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+                   DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
 
     // FIXME: case where filter array is strided
     auto constMemPtr = convolve2.getDevPtr(conv_c_name);
@@ -201,11 +203,12 @@ template<typename T, typename aT>
 void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     auto convolve3 = common::getKernel(
-        "cuda::convolve3", {convolve3_cuh_src},
-        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(expand)},
-        {DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
-         DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
-         DefineValue(CONV3_CUBE_Z)});
+        "cuda::convolve3", std::array{convolve3_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
+                     TemplateArg(expand)),
+        std::array{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+                   DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
+                   DefineValue(CONV3_CUBE_Z)});
 
     prepareKernelArgs<T>(p, out.dims, filt.dims, 3);
 
@@ -305,11 +308,12 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     }
 
     auto convolve2_separable = common::getKernel(
-        "cuda::convolve2_separable", {convolve_separable_cuh_src},
-        {TemplateTypename<T>(), TemplateTypename<aT>(), TemplateArg(conv_dim),
-         TemplateArg(expand), TemplateArg(fLen)},
-        {DefineValue(MAX_SCONV_FILTER_LEN), DefineValue(SCONV_THREADS_X),
-         DefineValue(SCONV_THREADS_Y)});
+        "cuda::convolve2_separable", std::array{convolve_separable_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
+                     TemplateArg(conv_dim), TemplateArg(expand),
+                     TemplateArg(fLen)),
+        std::array{DefineValue(MAX_SCONV_FILTER_LEN),
+                   DefineValue(SCONV_THREADS_X), DefineValue(SCONV_THREADS_Y)});
 
     dim3 threads(SCONV_THREADS_X, SCONV_THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index 93b974420e..87ba53965b 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -20,8 +20,9 @@ namespace kernel {
 
 template<typename T>
 void diagCreate(Param<T> out, CParam<T> in, int num) {
-    auto genDiagMat = common::getKernel(
-        "cuda::createDiagonalMat", {diagonal_cuh_src}, {TemplateTypename<T>()});
+    auto genDiagMat = common::getKernel("cuda::createDiagonalMat",
+                                        std::array{diagonal_cuh_src},
+                                        TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
@@ -45,8 +46,9 @@ void diagCreate(Param<T> out, CParam<T> in, int num) {
 
 template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int num) {
-    auto extractDiag = common::getKernel(
-        "cuda::extractDiagonal", {diagonal_cuh_src}, {TemplateTypename<T>()});
+    auto extractDiag =
+        common::getKernel("cuda::extractDiagonal", std::array{diagonal_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(256, 1);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index 1d3d4c5278..fb157af798 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -24,9 +24,10 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
     constexpr unsigned TX = 16;
     constexpr unsigned TY = 16;
 
-    auto diff = common::getKernel(
-        "cuda::diff", {diff_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(dim), TemplateArg(isDiff2)});
+    auto diff =
+        common::getKernel("cuda::diff", std::array{diff_cuh_src},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(dim),
+                                       TemplateArg(isDiff2)));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 64229c88d7..019b8c9743 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -27,11 +27,9 @@ static const unsigned TY = 16;  // Kernel Launch Config Values
 
 template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
-    auto exampleFunc =
-        common::getKernel("cuda::exampleFunc", {exampleFunction_cuh_src},
-                          {
-                              TemplateTypename<T>(),
-                          });
+    auto exampleFunc = common::getKernel("cuda::exampleFunc",
+                                         std::array{exampleFunction_cuh_src},
+                                         TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
 
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index df6836c8af..6ca9569206 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -23,12 +23,12 @@ static const int THREADS = 256;
 template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
-    auto packData =
-        common::getKernel("cuda::packData", {fftconvolve_cuh_src},
-                          {TemplateTypename<convT>(), TemplateTypename<T>()});
-    auto padArray =
-        common::getKernel("cuda::padArray", {fftconvolve_cuh_src},
-                          {TemplateTypename<convT>(), TemplateTypename<T>()});
+    auto packData = common::getKernel(
+        "cuda::packData", std::array{fftconvolve_cuh_src},
+        TemplateArgs(TemplateTypename<convT>(), TemplateTypename<T>()));
+    auto padArray = common::getKernel(
+        "cuda::padArray", std::array{fftconvolve_cuh_src},
+        TemplateArgs(TemplateTypename<convT>(), TemplateTypename<T>()));
 
     dim_t *sd = sig.dims;
 
@@ -67,9 +67,9 @@ void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
 template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
-    auto cplxMul =
-        common::getKernel("cuda::complexMultiply", {fftconvolve_cuh_src},
-                          {TemplateTypename<convT>(), TemplateArg(kind)});
+    auto cplxMul = common::getKernel(
+        "cuda::complexMultiply", std::array{fftconvolve_cuh_src},
+        TemplateArgs(TemplateTypename<convT>(), TemplateArg(kind)));
 
     int sig_packed_elem    = 1;
     int filter_packed_elem = 1;
@@ -100,10 +100,10 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
                          CParam<T> filter, bool expand, int rank) {
     constexpr bool RoundResult = std::is_integral<T>::value;
 
-    auto reorderOut =
-        common::getKernel("cuda::reorderOutput", {fftconvolve_cuh_src},
-                          {TemplateTypename<T>(), TemplateTypename<convT>(),
-                           TemplateArg(expand), TemplateArg(RoundResult)});
+    auto reorderOut = common::getKernel(
+        "cuda::reorderOutput", std::array{fftconvolve_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateTypename<convT>(),
+                     TemplateArg(expand), TemplateArg(RoundResult)));
 
     dim_t *sd    = sig.dims;
     int fftScale = 1;
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index b6f9615a6c..ad6366a286 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -45,13 +45,16 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds = common::getKernel("cuda::initSeeds", {flood_fill_cuh_src},
-                                       {TemplateTypename<T>()});
+    auto initSeeds =
+        common::getKernel("cuda::initSeeds", std::array{flood_fill_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
     auto floodStep = common::getKernel(
-        "cuda::floodStep", {flood_fill_cuh_src}, {TemplateTypename<T>()},
-        {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        "cuda::floodStep", std::array{flood_fill_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto finalizeOutput = common::getKernel(
-        "cuda::finalizeOutput", {flood_fill_cuh_src}, {TemplateTypename<T>()});
+        "cuda::finalizeOutput", std::array{flood_fill_cuh_src},
+        TemplateArgs(TemplateTypename<T>()));
 
     EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
                       getActiveStream());
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index f413faec2d..8f1306e2b0 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -15,6 +15,8 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/gradient_cuh.hpp>
 
+#include <array>
+
 namespace cuda {
 namespace kernel {
 
@@ -23,9 +25,10 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     constexpr unsigned TX = 32;
     constexpr unsigned TY = 8;
 
-    auto gradient = common::getKernel("cuda::gradient", {gradient_cuh_src},
-                                      {TemplateTypename<T>()},
-                                      {DefineValue(TX), DefineValue(TY)});
+    auto gradient =
+        common::getKernel("cuda::gradient", std::array{gradient_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()),
+                          std::array{DefineValue(TX), DefineValue(TY)});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index bdf7d2283e..4e4fe8c901 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -23,10 +23,10 @@ constexpr int THRD_LOAD = 16;
 template<typename T>
 void histogram(Param<uint> out, CParam<T> in, int nbins, float minval,
                float maxval, bool isLinear) {
-    auto histogram =
-        common::getKernel("cuda::histogram", {histogram_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(isLinear)},
-                          {DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
+    auto histogram = common::getKernel(
+        "cuda::histogram", std::array{histogram_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(isLinear)),
+        std::array{DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
     dim3 threads(kernel::THREADS_X, 1);
 
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index ec3f0098eb..a10a6ade93 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -21,9 +21,9 @@ static const int THREADS_Y = 16;
 
 template<typename T>
 void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
-    auto hsvrgbConverter =
-        common::getKernel("cuda::hsvrgbConverter", {hsv_rgb_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(isHSV2RGB)});
+    auto hsvrgbConverter = common::getKernel(
+        "cuda::hsvrgbConverter", std::array{hsv_rgb_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(isHSV2RGB)));
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index ae92d7535c..58e369823b 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -20,8 +20,9 @@ namespace kernel {
 
 template<typename T>
 void identity(Param<T> out) {
-    auto identity = common::getKernel("cuda::identity", {identity_cuh_src},
-                                      {TemplateTypename<T>()});
+    auto identity =
+        common::getKernel("cuda::identity", std::array{identity_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index 985e623249..38b9ece04d 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -22,9 +22,10 @@ template<typename T, bool batch_a>
 void iir(Param<T> y, CParam<T> c, CParam<T> a) {
     constexpr int MAX_A_SIZE = 1024;
 
-    auto iir = common::getKernel("cuda::iir", {iir_cuh_src},
-                                 {TemplateTypename<T>(), TemplateArg(batch_a)},
-                                 {DefineValue(MAX_A_SIZE)});
+    auto iir = common::getKernel(
+        "cuda::iir", std::array{iir_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(batch_a)),
+        std::array{DefineValue(MAX_A_SIZE)});
 
     const int blocks_y = y.dims[1];
     const int blocks_x = y.dims[2];
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 589245213f..5a44f4be6f 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -21,8 +21,8 @@ namespace kernel {
 
 template<typename T>
 void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
-    auto index = common::getKernel("cuda::index", {index_cuh_src},
-                                   {TemplateTypename<T>()});
+    auto index = common::getKernel("cuda::index", std::array{index_cuh_src},
+                                   TemplateArgs(TemplateTypename<T>()));
     dim3 threads;
     switch (out.dims[1]) {
         case 1: threads.y = 1; break;
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 0b5cd61b78..d108bc2a25 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -26,8 +26,8 @@ void iota(Param<T> out, const af::dim4 &sdims) {
     constexpr unsigned TILEX   = 512;
     constexpr unsigned TILEY   = 32;
 
-    auto iota = common::getKernel("cuda::iota", {iota_cuh_src},
-                                  {TemplateTypename<T>()});
+    auto iota = common::getKernel("cuda::iota", std::array{iota_cuh_src},
+                                  TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index f1fd13d054..b57ba5d29b 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -37,10 +37,10 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     blocks.y = divup(blocks.y, blocks.z);
 
     auto ireduceDim = common::getKernel(
-        "cuda::ireduceDim", {ireduce_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
-         TemplateArg(is_first), TemplateArg(threads_y)},
-        {DefineValue(THREADS_X)});
+        "cuda::ireduceDim", std::array{ireduce_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
+                     TemplateArg(is_first), TemplateArg(threads_y)),
+        std::array{DefineValue(THREADS_X)});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -104,11 +104,11 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
     uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
     // threads_x can take values 32, 64, 128, 256
-    auto ireduceFirst =
-        common::getKernel("cuda::ireduceFirst", {ireduce_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(op),
-                           TemplateArg(is_first), TemplateArg(threads_x)},
-                          {DefineValue(THREADS_PER_BLOCK)});
+    auto ireduceFirst = common::getKernel(
+        "cuda::ireduceFirst", std::array{ireduce_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(op),
+                     TemplateArg(is_first), TemplateArg(threads_x)),
+        std::array{DefineValue(THREADS_PER_BLOCK)});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index 4f4758dca3..bca81cdebc 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -43,9 +43,9 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         dim3 blocks(blks, 1);
 
         auto lookup1d = common::getKernel(
-            "cuda::lookup1D", {lookup_cuh_src},
-            {TemplateTypename<in_t>(), TemplateTypename<idx_t>()},
-            {DefineValue(THREADS), DefineValue(THRD_LOAD)});
+            "cuda::lookup1D", std::array{lookup_cuh_src},
+            TemplateArgs(TemplateTypename<in_t>(), TemplateTypename<idx_t>()),
+            std::array{DefineValue(THREADS), DefineValue(THRD_LOAD)});
 
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -63,10 +63,10 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         blocks.z = divup(blocks.y, maxBlocksY);
         blocks.y = divup(blocks.y, blocks.z);
 
-        auto lookupnd =
-            common::getKernel("cuda::lookupND", {lookup_cuh_src},
-                              {TemplateTypename<in_t>(),
-                               TemplateTypename<idx_t>(), TemplateArg(dim)});
+        auto lookupnd = common::getKernel(
+            "cuda::lookupND", std::array{lookup_cuh_src},
+            TemplateArgs(TemplateTypename<in_t>(), TemplateTypename<idx_t>(),
+                         TemplateArg(dim)));
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
         lookupnd(qArgs, out, in, indices, blks_x, blks_y);
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 72def543e3..8e74c6fbe5 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -15,6 +15,8 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/lu_split_cuh.hpp>
 
+#include <array>
+
 namespace cuda {
 namespace kernel {
 
@@ -28,9 +30,9 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
     const bool sameDims =
         lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
-    auto luSplit =
-        common::getKernel("cuda::luSplit", {lu_split_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(sameDims)});
+    auto luSplit = common::getKernel(
+        "cuda::luSplit", std::array{lu_split_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(sameDims)));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 31d75e1bd6..3969bfd453 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -25,9 +25,9 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
                    CParam<inType> tmplt, const af::matchType mType,
                    bool needMean) {
     auto matchTemplate = common::getKernel(
-        "cuda::matchTemplate", {match_template_cuh_src},
-        {TemplateTypename<inType>(), TemplateTypename<outType>(),
-         TemplateArg(mType), TemplateArg(needMean)});
+        "cuda::matchTemplate", std::array{match_template_cuh_src},
+        TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
+                     TemplateArg(mType), TemplateArg(needMean)));
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index ffa3cba76b..530279fd1b 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -13,6 +13,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/meanshift_cuh.hpp>
 
+#include <array>
 #include <type_traits>
 
 namespace cuda {
@@ -27,11 +28,10 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
     typedef typename std::conditional<std::is_same<T, double>::value, double,
                                       float>::type AccType;
     auto meanshift = common::getKernel(
-        "cuda::meanshift", {meanshift_cuh_src},
-        {
-            TemplateTypename<AccType>(), TemplateTypename<T>(),
-            TemplateArg((IsColor ? 3 : 1))  // channels
-        });
+        "cuda::meanshift", std::array{meanshift_cuh_src},
+        TemplateArgs(TemplateTypename<AccType>(), TemplateTypename<T>(),
+                     TemplateArg((IsColor ? 3 : 1))  // channels
+                     ));
 
     static dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 3095db1a46..c0062ccc2f 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -26,11 +26,11 @@ template<typename T>
 void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
               int w_wid) {
     UNUSED(w_wid);
-    auto medfilt2 =
-        common::getKernel("cuda::medfilt2", {medfilt_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(pad),
-                           TemplateArg(w_len), TemplateArg(w_wid)},
-                          {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto medfilt2 = common::getKernel(
+        "cuda::medfilt2", std::array{medfilt_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
+                     TemplateArg(w_len), TemplateArg(w_wid)),
+        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
@@ -46,9 +46,10 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
 
 template<typename T>
 void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
-    auto medfilt1 = common::getKernel(
-        "cuda::medfilt1", {medfilt_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(pad), TemplateArg(w_wid)});
+    auto medfilt1 =
+        common::getKernel("cuda::medfilt1", std::array{medfilt_cuh_src},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
+                                       TemplateArg(w_wid)));
 
     const dim3 threads(THREADS_X);
 
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index f37252c633..7a971bddb0 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -126,35 +126,40 @@ void memcopy(Param<T> out, CParam<T> in, dim_t indims) {
     // Conversion to cuda base vector types.
     switch (sizeofNewT) {
         case 1: {
-            auto memCopy{
-                common::getKernel(kernelName, {memcopy_cuh_src}, {"char"})};
+            auto memCopy{common::getKernel(kernelName,
+                                           std::array{memcopy_cuh_src},
+                                           TemplateArgs(TemplateArg("char")))};
             memCopy(qArgs, Param<char>((char *)out.ptr, out.dims, out.strides),
                     CParam<char>((const char *)in.ptr, in.dims, in.strides));
         } break;
         case 2: {
-            auto memCopy{
-                common::getKernel(kernelName, {memcopy_cuh_src}, {"short"})};
+            auto memCopy{common::getKernel(kernelName,
+                                           std::array{memcopy_cuh_src},
+                                           TemplateArgs(TemplateArg("short")))};
             memCopy(qArgs,
                     Param<short>((short *)out.ptr, out.dims, out.strides),
                     CParam<short>((const short *)in.ptr, in.dims, in.strides));
         } break;
         case 4: {
-            auto memCopy{
-                common::getKernel(kernelName, {memcopy_cuh_src}, {"float"})};
+            auto memCopy{common::getKernel(kernelName,
+                                           std::array{memcopy_cuh_src},
+                                           TemplateArgs(TemplateArg("float")))};
             memCopy(qArgs,
                     Param<float>((float *)out.ptr, out.dims, out.strides),
                     CParam<float>((const float *)in.ptr, in.dims, in.strides));
         } break;
         case 8: {
             auto memCopy{
-                common::getKernel(kernelName, {memcopy_cuh_src}, {"float2"})};
+                common::getKernel(kernelName, std::array{memcopy_cuh_src},
+                                  TemplateArgs(TemplateArg("float2")))};
             memCopy(
                 qArgs, Param<float2>((float2 *)out.ptr, out.dims, out.strides),
                 CParam<float2>((const float2 *)in.ptr, in.dims, in.strides));
         } break;
         case 16: {
             auto memCopy{
-                common::getKernel(kernelName, {memcopy_cuh_src}, {"float4"})};
+                common::getKernel(kernelName, std::array{memcopy_cuh_src},
+                                  TemplateArgs(TemplateArg("float4")))};
             memCopy(
                 qArgs, Param<float4>((float4 *)out.ptr, out.dims, out.strides),
                 CParam<float4>((const float4 *)in.ptr, in.dims, in.strides));
@@ -188,18 +193,14 @@ void copy(Param<outType> dst, CParam<inType> src, dim_t ondims,
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    auto copy{common::getKernel(th.loop0 ? "cuda::scaledCopyLoop0"
-                                : th.loop2 | th.loop3
-                                    ? "cuda::scaledCopyLoop123"
-                                : th.loop1 ? "cuda::scaledCopyLoop1"
-                                           : "cuda::scaledCopy",
-                                {copy_cuh_src},
-                                {
-                                    TemplateTypename<inType>(),
-                                    TemplateTypename<outType>(),
-                                    TemplateArg(same_dims),
-                                    TemplateArg(factor != 1.0),
-                                })};
+    auto copy{common::getKernel(
+        th.loop0              ? "cuda::scaledCopyLoop0"
+        : th.loop2 | th.loop3 ? "cuda::scaledCopyLoop123"
+        : th.loop1            ? "cuda::scaledCopyLoop1"
+                              : "cuda::scaledCopy",
+        std::array{copy_cuh_src},
+        TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
+                     TemplateArg(same_dims), TemplateArg(factor != 1.0)))};
 
     copy(qArgs, dst, src, default_value, factor);
 
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 03f536eaeb..2af86afef6 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -21,8 +21,9 @@ static const int THREADS = 128;
 
 template<typename T>
 void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
-    auto moments = common::getKernel("cuda::moments", {moments_cuh_src},
-                                     {TemplateTypename<T>()});
+    auto moments =
+        common::getKernel("cuda::moments", std::array{moments_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index d9ae0ea37f..1202850f40 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -31,11 +31,10 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
     auto morph = common::getKernel(
-        "cuda::morph", {morph_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(SeLength)},
-        {
-            DefineValue(MAX_MORPH_FILTER_LEN),
-        });
+        "cuda::morph", std::array{morph_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(isDilation),
+                     TemplateArg(SeLength)),
+        std::array{DefineValue(MAX_MORPH_FILTER_LEN)});
 
     morph.copyToReadOnly(morph.getDevPtr("cFilter"),
                          reinterpret_cast<CUdeviceptr>(mask.ptr),
@@ -68,11 +67,10 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     }
 
     auto morph3D = common::getKernel(
-        "cuda::morph3D", {morph_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(isDilation), TemplateArg(windLen)},
-        {
-            DefineValue(MAX_MORPH_FILTER_LEN),
-        });
+        "cuda::morph3D", std::array{morph_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(isDilation),
+                     TemplateArg(windLen)),
+        std::array{DefineValue(MAX_MORPH_FILTER_LEN)});
 
     morph3D.copyToReadOnly(
         morph3D.getDevPtr("cFilter"), reinterpret_cast<CUdeviceptr>(mask.ptr),
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index decc7a5ae2..b55bd419c5 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -16,6 +16,8 @@
 #include <nvrtc_kernel_headers/pad_array_borders_cuh.hpp>
 #include <af/defines.h>
 
+#include <array>
+
 namespace cuda {
 namespace kernel {
 
@@ -25,9 +27,9 @@ static const int PADB_THREADS_Y = 8;
 template<typename T>
 void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
                 const af::borderType btype) {
-    auto padBorders =
-        common::getKernel("cuda::padBorders", {pad_array_borders_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(btype)});
+    auto padBorders = common::getKernel(
+        "cuda::padBorders", std::array{pad_array_borders_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(btype)));
 
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 4364d3e6a6..cb1f8e13e4 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -25,8 +25,8 @@ void range(Param<T> out, const int dim) {
     constexpr unsigned RANGE_TILEX = 512;
     constexpr unsigned RANGE_TILEY = 32;
 
-    auto range = common::getKernel("cuda::range", {range_cuh_src},
-                                   {TemplateTypename<T>()});
+    auto range = common::getKernel("cuda::range", std::array{range_cuh_src},
+                                   TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index fc6920ab7f..cb10ad3cb0 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -25,8 +25,9 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    auto reorder = common::getKernel("cuda::reorder", {reorder_cuh_src},
-                                     {TemplateTypename<T>()});
+    auto reorder =
+        common::getKernel("cuda::reorder", std::array{reorder_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index 7c5504c75b..231dab781b 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -23,9 +23,9 @@ static const unsigned TY = 16;
 
 template<typename T>
 void resize(Param<T> out, CParam<T> in, af_interp_type method) {
-    auto resize =
-        common::getKernel("cuda::resize", {resize_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(method)});
+    auto resize = common::getKernel(
+        "cuda::resize", std::array{resize_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(method)));
 
     dim3 threads(TX, TY, 1);
     dim3 blocks(divup(out.dims[0], threads.x), divup(out.dims[1], threads.y));
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 648e126230..5c86b57edf 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -32,9 +32,9 @@ typedef struct {
 template<typename T>
 void rotate(Param<T> out, CParam<T> in, const float theta,
             const af::interpType method, const int order) {
-    auto rotate =
-        common::getKernel("cuda::rotate", {rotate_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(order)});
+    auto rotate = common::getKernel(
+        "cuda::rotate", std::array{rotate_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(order)));
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index dafa280267..88c62e175e 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -25,11 +25,12 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                               const uint threads_y, const dim_t blocks_all[4],
                               int dim, bool isFinalPass, bool inclusive_scan) {
     auto scan_dim = common::getKernel(
-        "cuda::scan_dim", {scan_dim_cuh_src},
-        {TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(op),
-         TemplateArg(dim), TemplateArg(isFinalPass), TemplateArg(threads_y),
-         TemplateArg(inclusive_scan)},
-        {DefineValue(THREADS_X)});
+        "cuda::scan_dim", std::array{scan_dim_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>(),
+                     TemplateArg(op), TemplateArg(dim),
+                     TemplateArg(isFinalPass), TemplateArg(threads_y),
+                     TemplateArg(inclusive_scan)),
+        std::array{DefineValue(THREADS_X)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -52,9 +53,10 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
                                const uint threads_y, const dim_t blocks_all[4],
                                int dim, bool inclusive_scan) {
-    auto scan_dim_bcast = common::getKernel(
-        "cuda::scan_dim_bcast", {scan_dim_cuh_src},
-        {TemplateTypename<To>(), TemplateArg(op), TemplateArg(dim)});
+    auto scan_dim_bcast =
+        common::getKernel("cuda::scan_dim_bcast", std::array{scan_dim_cuh_src},
+                          TemplateArgs(TemplateTypename<To>(), TemplateArg(op),
+                                       TemplateArg(dim)));
 
     dim3 threads(THREADS_X, threads_y);
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index e3a618d125..0754e1fc22 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -32,10 +32,10 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
     auto scanbykey_dim_nonfinal = common::getKernel(
-        "cuda::scanbykey_dim_nonfinal", {scan_dim_by_key_cuh_src},
-        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
-         TemplateArg(op)},
-        {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+        "cuda::scanbykey_dim_nonfinal", std::array{scan_dim_by_key_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
+                     TemplateTypename<To>(), TemplateArg(op)),
+        std::array{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -56,10 +56,10 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
     auto scanbykey_dim_final = common::getKernel(
-        "cuda::scanbykey_dim_final", {scan_dim_by_key_cuh_src},
-        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
-         TemplateArg(op)},
-        {DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+        "cuda::scanbykey_dim_final", std::array{scan_dim_by_key_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
+                     TemplateTypename<To>(), TemplateArg(op)),
+        std::array{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -78,8 +78,8 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
     auto scanbykey_dim_bcast = common::getKernel(
-        "cuda::scanbykey_dim_bcast", {scan_dim_by_key_cuh_src},
-        {TemplateTypename<To>(), TemplateArg(op)});
+        "cuda::scanbykey_dim_bcast", std::array{scan_dim_by_key_cuh_src},
+        TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index f400f4b5d3..0fe6ce1d5f 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -25,12 +25,12 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                                 const uint blocks_x, const uint blocks_y,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
-    auto scan_first =
-        common::getKernel("cuda::scan_first", {scan_first_cuh_src},
-                          {TemplateTypename<Ti>(), TemplateTypename<To>(),
-                           TemplateArg(op), TemplateArg(isFinalPass),
-                           TemplateArg(threads_x), TemplateArg(inclusive_scan)},
-                          {DefineValue(THREADS_PER_BLOCK)});
+    auto scan_first = common::getKernel(
+        "cuda::scan_first", std::array{scan_first_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>(),
+                     TemplateArg(op), TemplateArg(isFinalPass),
+                     TemplateArg(threads_x), TemplateArg(inclusive_scan)),
+        std::array{DefineValue(THREADS_PER_BLOCK)});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -51,9 +51,9 @@ template<typename To, af_op_t op>
 static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
-    auto scan_first_bcast =
-        common::getKernel("cuda::scan_first_bcast", {scan_first_cuh_src},
-                          {TemplateTypename<To>(), TemplateArg(op)});
+    auto scan_first_bcast = common::getKernel(
+        "cuda::scan_first_bcast", std::array{scan_first_cuh_src},
+        TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index b5e2d070e1..6f9fbd36dd 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -30,10 +30,11 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
     auto scanbykey_first_nonfinal = common::getKernel(
-        "cuda::scanbykey_first_nonfinal", {scan_first_by_key_cuh_src},
-        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
-         TemplateArg(op)},
-        {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
+        "cuda::scanbykey_first_nonfinal", std::array{scan_first_by_key_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
+                     TemplateTypename<To>(), TemplateArg(op)),
+        std::array{DefineValue(THREADS_PER_BLOCK),
+                   DefineKeyValue(DIMX, threads_x)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
@@ -51,10 +52,11 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
     auto scanbykey_first_final = common::getKernel(
-        "cuda::scanbykey_first_final", {scan_first_by_key_cuh_src},
-        {TemplateTypename<Ti>(), TemplateTypename<Tk>(), TemplateTypename<To>(),
-         TemplateArg(op)},
-        {DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)});
+        "cuda::scanbykey_first_final", std::array{scan_first_by_key_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
+                     TemplateTypename<To>(), TemplateArg(op)),
+        std::array{DefineValue(THREADS_PER_BLOCK),
+                   DefineKeyValue(DIMX, threads_x)});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
@@ -71,8 +73,8 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
     auto scanbykey_first_bcast = common::getKernel(
-        "cuda::scanbykey_first_bcast", {scan_first_by_key_cuh_src},
-        {TemplateTypename<To>(), TemplateArg(op)});
+        "cuda::scanbykey_first_bcast", std::array{scan_first_by_key_cuh_src},
+        TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 6f8972e04f..ceec068e96 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -29,9 +29,9 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     bool is_same = true;
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
-    auto select =
-        common::getKernel("cuda::select", {select_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(is_same)});
+    auto select = common::getKernel(
+        "cuda::select", std::array{select_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(is_same)));
 
     dim3 threads(DIMX, DIMY);
 
@@ -59,9 +59,9 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
 template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b,
                    int ndims, bool flip) {
-    auto selectScalar =
-        common::getKernel("cuda::selectScalar", {select_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(flip)});
+    auto selectScalar = common::getKernel(
+        "cuda::selectScalar", std::array{select_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(flip)));
 
     dim3 threads(DIMX, DIMY);
 
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index 0c2f5a5324..943d8d520e 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -26,13 +26,10 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
            const unsigned& ker_size) {
     UNUSED(ker_size);
 
-    auto sobel3x3 =
-        common::getKernel("cuda::sobel3x3", {sobel_cuh_src},
-                          {
-                              TemplateTypename<Ti>(),
-                              TemplateTypename<To>(),
-                          },
-                          {DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto sobel3x3 = common::getKernel(
+        "cuda::sobel3x3", std::array{sobel_cuh_src},
+        TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>()),
+        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 797b7fec5f..66109b2934 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -23,9 +23,9 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
                CParam<int> colIdx) {
     constexpr int reps = 4;
 
-    auto coo2Dense =
-        common::getKernel("cuda::coo2Dense", {sparse_cuh_src},
-                          {TemplateTypename<T>()}, {DefineValue(reps)});
+    auto coo2Dense = common::getKernel(
+        "cuda::coo2Dense", std::array{sparse_cuh_src},
+        TemplateArgs(TemplateTypename<T>()), std::array{DefineValue(reps)});
 
     dim3 threads(256, 1, 1);
 
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 0f2f4ac70d..fb66e19a79 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -27,9 +27,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     auto csrArithDSD =
-        common::getKernel("cuda::csrArithDSD", {sparse_arith_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(op)},
-                          {DefineValue(TX), DefineValue(TY)});
+        common::getKernel("cuda::csrArithDSD", std::array{sparse_arith_cuh_src},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+                          std::array{DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -46,9 +46,10 @@ void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
-    auto cooArithDSD = common::getKernel(
-        "cuda::cooArithDSD", {sparse_arith_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
+    auto cooArithDSD =
+        common::getKernel("cuda::cooArithDSD", std::array{sparse_arith_cuh_src},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+                          std::array{DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
@@ -66,9 +67,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
     auto csrArithSSD =
-        common::getKernel("cuda::csrArithSSD", {sparse_arith_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(op)},
-                          {DefineValue(TX), DefineValue(TY)});
+        common::getKernel("cuda::csrArithSSD", std::array{sparse_arith_cuh_src},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+                          std::array{DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -85,9 +86,10 @@ void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
-    auto cooArithSSD = common::getKernel(
-        "cuda::cooArithSSD", {sparse_arith_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(op)}, {DefineValue(THREADS)});
+    auto cooArithSSD =
+        common::getKernel("cuda::cooArithSSD", std::array{sparse_arith_cuh_src},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+                          std::array{DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 6d45a41058..e8246b5249 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -26,8 +26,9 @@ void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
     auto susan = common::getKernel(
-        "cuda::susan", {susan_cuh_src}, {TemplateTypename<T>()},
-        {DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
+        "cuda::susan", std::array{susan_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
@@ -45,8 +46,8 @@ template<typename T>
 void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
-    auto nonMax = common::getKernel("cuda::nonMax", {susan_cuh_src},
-                                    {TemplateTypename<T>()});
+    auto nonMax = common::getKernel("cuda::nonMax", std::array{susan_cuh_src},
+                                    TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 8edebf3991..5656fcf8e1 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -25,8 +25,8 @@ void tile(Param<T> out, CParam<T> in) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    auto tile = common::getKernel("cuda::tile", {tile_cuh_src},
-                                  {TemplateTypename<T>()});
+    auto tile = common::getKernel("cuda::tile", std::array{tile_cuh_src},
+                                  TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index df9bf32c8b..489063cc8a 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -31,8 +31,9 @@ template<typename T>
 void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
                const bool perspective, const af::interpType method, int order) {
     auto transform = common::getKernel(
-        "cuda::transform", {transform_cuh_src},
-        {TemplateTypename<T>(), TemplateArg(inverse), TemplateArg(order)});
+        "cuda::transform", std::array{transform_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(inverse),
+                     TemplateArg(order)));
 
     const unsigned int nImg2  = in.dims[2];
     const unsigned int nImg3  = in.dims[3];
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 3a5101a37d..aca9efb9c6 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -25,11 +25,11 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T>
 void transpose(Param<T> out, CParam<T> in, const bool conjugate,
                const bool is32multiple) {
-    auto transpose =
-        common::getKernel("cuda::transpose", {transpose_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(conjugate),
-                           TemplateArg(is32multiple)},
-                          {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+    auto transpose = common::getKernel(
+        "cuda::transpose", std::array{transpose_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(conjugate),
+                     TemplateArg(is32multiple)),
+        std::array{DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index 0ba76f19da..d603a08653 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -25,11 +25,11 @@ static const int THREADS_Y = 256 / TILE_DIM;
 template<typename T>
 void transpose_inplace(Param<T> in, const bool conjugate,
                        const bool is32multiple) {
-    auto transposeIP =
-        common::getKernel("cuda::transposeIP", {transpose_inplace_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(conjugate),
-                           TemplateArg(is32multiple)},
-                          {DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+    auto transposeIP = common::getKernel(
+        "cuda::transposeIP", std::array{transpose_inplace_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(conjugate),
+                     TemplateArg(is32multiple)),
+        std::array{DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
 
     // dimensions passed to this function should be input dimensions
     // any necessary transformations and dimension related calculations are
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index b49601ce51..e6efac7be6 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -25,10 +25,10 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
     constexpr unsigned TILEX = 128;
     constexpr unsigned TILEY = 32;
 
-    auto triangle =
-        common::getKernel("cuda::triangle", {triangle_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(is_upper),
-                           TemplateArg(is_unit_diag)});
+    auto triangle = common::getKernel(
+        "cuda::triangle", std::array{triangle_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(is_upper),
+                     TemplateArg(is_unit_diag)));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 8e171ac816..15f74df963 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -23,9 +23,9 @@ template<typename T>
 void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
             const int sx, const int sy, const int px, const int py,
             const int dx, const int dy, const int nx, const bool is_column) {
-    auto unwrap =
-        common::getKernel("cuda::unwrap", {unwrap_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(is_column)});
+    auto unwrap = common::getKernel(
+        "cuda::unwrap", std::array{unwrap_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     dim3 threads, blocks;
     int reps;
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 66555253c0..bf992648d3 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -23,8 +23,8 @@ namespace kernel {
 
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
-    auto where = common::getKernel("cuda::where", {where_cuh_src},
-                                   {TemplateTypename<T>()});
+    auto where = common::getKernel("cuda::where", std::array{where_cuh_src},
+                                   TemplateArgs(TemplateTypename<T>()));
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 33a32a6ef3..7185ea38bb 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -22,9 +22,9 @@ namespace kernel {
 template<typename T>
 void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
           const int sy, const int px, const int py, const bool is_column) {
-    auto wrap =
-        common::getKernel("cuda::wrap", {wrap_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(is_column)});
+    auto wrap = common::getKernel(
+        "cuda::wrap", std::array{wrap_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
     int ny = (out.dims[1] + 2 * py - wy) / sy + 1;
@@ -51,9 +51,9 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                   const dim_t sx, const dim_t sy, const dim_t px,
                   const dim_t py, const dim_t dx, const dim_t dy,
                   const bool is_column) {
-    auto wrap =
-        common::getKernel("cuda::wrap_dilated", {wrap_cuh_src},
-                          {TemplateTypename<T>(), TemplateArg(is_column)});
+    auto wrap = common::getKernel(
+        "cuda::wrap_dilated", std::array{wrap_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     int ny = 1 + (out.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 999632d55a..4a85ce292e 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -17,6 +17,7 @@
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
 #include <kernel_headers/KParam.hpp>
+#include <nonstd/span.hpp>
 #include <platform.hpp>
 #include <traits.hpp>
 
@@ -32,6 +33,7 @@ using cl::Error;
 using cl::Program;
 using common::loggerFactory;
 using fmt::format;
+using nonstd::span;
 using opencl::getActiveDeviceId;
 using opencl::getDevice;
 using opencl::Kernel;
@@ -99,8 +101,8 @@ const static string DEFAULT_MACROS_STR(
                                            #endif\n                     \
                                            ");
 
-Program buildProgram(const vector<string> &kernelSources,
-                     const vector<string> &compileOpts) {
+Program buildProgram(span<const string> kernelSources,
+                     span<const string> compileOpts) {
     Program retVal;
     try {
         static const string defaults =
@@ -151,9 +153,9 @@ string getKernelCacheFilename(const int device, const string &key) {
 
 namespace common {
 
-Module compileModule(const string &moduleKey, const vector<string> &sources,
-                     const vector<string> &options,
-                     const vector<string> &kInstances, const bool isJIT) {
+Module compileModule(const string &moduleKey, span<const string> sources,
+                     span<const string> options, span<const string> kInstances,
+                     const bool isJIT) {
     UNUSED(kInstances);
     UNUSED(isJIT);
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 18a89e00a7..d475f32b71 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -278,7 +278,8 @@ cl::Kernel getKernel(const vector<Node*>& output_nodes,
         if (isHalfSupported(device)) {
             options.emplace_back(DefineKey(USE_HALF));
         }
-        return common::getKernel(funcName, {jit_cl_src, jitKer_cl_src}, {},
+        return common::getKernel(funcName,
+                                 std::array{jit_cl_src, jitKer_cl_src}, {},
                                  options, true)
             .get();
     }
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index e7d18136dd..84af9db4a7 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -49,9 +49,9 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto diffUpdate =
-        common::getKernel("aisoDiffUpdate", {anisotropic_diffusion_cl_src},
-                          tmpltArgs, compileOpts);
+    auto diffUpdate = common::getKernel(
+        "aisoDiffUpdate", std::array{anisotropic_diffusion_cl_src}, tmpltArgs,
+        compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index be569fbf61..1d702ed090 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -72,8 +72,9 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim);
 
-    auto approx1 = common::getKernel("approx1", {interp_cl_src, approx1_cl_src},
-                                     tmpltArgs, compileOpts);
+    auto approx1 =
+        common::getKernel("approx1", std::array{interp_cl_src, approx1_cl_src},
+                          tmpltArgs, compileOpts);
 
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
@@ -110,8 +111,9 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim, ydim);
 
-    auto approx2 = common::getKernel("approx2", {interp_cl_src, approx2_cl_src},
-                                     tmpltArgs, compileOpts);
+    auto approx2 =
+        common::getKernel("approx2", std::array{interp_cl_src, approx2_cl_src},
+                          tmpltArgs, compileOpts);
 
     NDRange local(TX, TY, 1);
     dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 568ec9b185..0b9ae34472 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -34,16 +34,15 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto assign =
-        common::getKernel("assignKernel", {assign_cl_src}, targs, options);
+    auto assign = common::getKernel("assignKernel", std::array{assign_cl_src},
+                                    targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 168fbcea6d..a191d53815 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -32,7 +32,7 @@ void bilateral(Param out, const Param in, const float s_sigma,
     constexpr bool UseNativeExp = !std::is_same<inType, double>::value ||
                                   std::is_same<inType, cdouble>::value;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<inType>(),
         TemplateTypename<outType>(),
     };
@@ -43,8 +43,8 @@ void bilateral(Param out, const Param in, const float s_sigma,
     if (UseNativeExp) { options.emplace_back(DefineKey(USE_NATIVE_EXP)); }
     options.emplace_back(getTypeBuildDefinition<inType>());
 
-    auto bilateralOp =
-        common::getKernel("bilateral", {bilateral_cl_src}, targs, options);
+    auto bilateralOp = common::getKernel(
+        "bilateral", std::array{bilateral_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 3c82b9df4f..7444ac00aa 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -41,9 +41,9 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto nonMaxOp = common::getKernel("nonMaxSuppressionKernel",
-                                      {nonmax_suppression_cl_src},
-                                      {TemplateTypename<T>()}, options);
+    auto nonMaxOp = common::getKernel(
+        "nonMaxSuppressionKernel", std::array{nonmax_suppression_cl_src},
+        TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -74,8 +74,9 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto initOp = common::getKernel("initEdgeOutKernel", {trace_edge_cl_src},
-                                    {TemplateTypename<T>()}, options);
+    auto initOp =
+        common::getKernel("initEdgeOutKernel", std::array{trace_edge_cl_src},
+                          TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -106,9 +107,9 @@ void suppressLeftOver(Param output) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalOp =
-        common::getKernel("suppressLeftOverKernel", {trace_edge_cl_src},
-                          {TemplateTypename<T>()}, options);
+    auto finalOp = common::getKernel(
+        "suppressLeftOverKernel", std::array{trace_edge_cl_src},
+        TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -142,8 +143,9 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto edgeTraceOp = common::getKernel("edgeTrackKernel", {trace_edge_cl_src},
-                                         {TemplateTypename<T>()}, options);
+    auto edgeTraceOp =
+        common::getKernel("edgeTrackKernel", std::array{trace_edge_cl_src},
+                          TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index abe95ae896..61f9d1d56d 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -50,8 +50,9 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto convolve = common::getKernel("convolve", {ops_cl_src, convolve_cl_src},
-                                      tmpltArgs, compileOpts);
+    auto convolve =
+        common::getKernel("convolve", std::array{ops_cl_src, convolve_cl_src},
+                          tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, *param.impulse, filter.info,
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 92cf5858e7..987e623dcf 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -113,8 +113,9 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto convolve = common::getKernel("convolve", {ops_cl_src, convolve_cl_src},
-                                      tmpltArgs, compileOpts);
+    auto convolve =
+        common::getKernel("convolve", std::array{ops_cl_src, convolve_cl_src},
+                          tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, cl::Local(param.loc_size),
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 85b9bfadb9..7017170e41 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -44,12 +44,12 @@ void convSep(Param out, const Param signal, const Param filter,
     const size_t C1_SIZE = (THREADS_Y + 2 * (fLen - 1)) * THREADS_X;
     size_t locSize       = (conv_dim == 0 ? C0_SIZE : C1_SIZE);
 
-    std::vector<TemplateArg> tmpltArgs = {
+    std::array<TemplateArg, 5> tmpltArgs = {
         TemplateTypename<T>(), TemplateTypename<accType>(),
         TemplateArg(conv_dim), TemplateArg(expand),
         TemplateArg(fLen),
     };
-    std::vector<std::string> compileOpts = {
+    std::array<std::string, 11> compileOpts = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(Ti, dtype_traits<T>::getName()),
         DefineKeyValue(To, dtype_traits<accType>::getName()),
@@ -60,12 +60,11 @@ void convSep(Param out, const Param signal, const Param filter,
         DefineKeyFromStr(binOpName<af_mul_t>()),
         DefineKeyValue(IS_CPLX, (IsComplex ? 1 : 0)),
         DefineKeyValue(LOCAL_MEM_SIZE, locSize),
-    };
-    compileOpts.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto conv =
-        common::getKernel("convolve", {ops_cl_src, convolve_separable_cl_src},
-                          tmpltArgs, compileOpts);
+    auto conv = common::getKernel(
+        "convolve", std::array{ops_cl_src, convolve_separable_cl_src},
+        tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 7047af13aa..9857133f9d 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -38,13 +38,13 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 7> targs = {
         TemplateTypename<T>(),       TemplateArg(use_alpha),
         TemplateArg(use_beta),       TemplateArg(is_conj),
         TemplateArg(rows_per_group), TemplateArg(cols_per_group),
         TemplateArg(threads),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 9> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
         DefineKeyValue(USE_BETA, use_beta),
@@ -53,11 +53,10 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
         DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
         DefineKeyValue(COLS_PER_GROUP, cols_per_group),
         DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
     auto cscmmNN =
-        common::getKernel("cscmm_nn", {cscmm_cl_src}, targs, options);
+        common::getKernel("cscmm_nn", std::array{cscmm_cl_src}, targs, options);
 
     cl::NDRange local(threads, 1);
     int M = out.info.dims[0];
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 5d948783fb..a3b66714c3 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -38,12 +38,12 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
 
     cl::NDRange local(THREADS_PER_GROUP);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 6> targs = {
         TemplateTypename<T>(),       TemplateArg(use_alpha),
         TemplateArg(use_beta),       TemplateArg(is_conj),
         TemplateArg(rows_per_group), TemplateArg(local[0]),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 8> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
         DefineKeyValue(USE_BETA, use_beta),
@@ -51,11 +51,10 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
         DefineKeyValue(THREADS, local[0]),
         DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
         DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto cscmvBlock =
-        common::getKernel("cscmv_block", {cscmv_cl_src}, targs, options);
+    auto cscmvBlock = common::getKernel("cscmv_block", std::array{cscmv_cl_src},
+                                        targs, options);
 
     int K        = colIdx.info.dims[0] - 1;
     int M        = out.info.dims[0];
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index a9b7b8fb95..42b5cc093a 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -38,25 +38,24 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<T>(),
         TemplateArg(use_alpha),
         TemplateArg(use_beta),
         TemplateArg(use_greedy),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 7> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
         DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(USE_GREEDY, use_greedy),
         DefineValue(THREADS_PER_GROUP),
         DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
     // FIXME: Switch to perf (thread vs block) baesd kernel
     auto csrmm_nt_func =
-        common::getKernel("csrmm_nt", {csrmm_cl_src}, targs, options);
+        common::getKernel("csrmm_nt", std::array{csrmm_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index d6b52ff6b4..2d7abaa190 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -43,24 +43,24 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
 
     cl::NDRange local(THREADS_PER_GROUP);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 5> targs = {
         TemplateTypename<T>(),   TemplateArg(use_alpha), TemplateArg(use_beta),
         TemplateArg(use_greedy), TemplateArg(local[0]),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 7> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
         DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(USE_GREEDY, use_greedy),
         DefineKeyValue(THREADS, local[0]),
         DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
     auto csrmv =
         (is_csrmv_block
-             ? common::getKernel("csrmv_thread", {csrmv_cl_src}, targs, options)
-             : common::getKernel("csrmv_block", {csrmv_cl_src}, targs,
+             ? common::getKernel("csrmv_thread", std::array{csrmv_cl_src},
+                                 targs, options)
+             : common::getKernel("csrmv_block", std::array{csrmv_cl_src}, targs,
                                  options));
 
     int M = rowIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 4ed94e2ba6..e4320aa6dc 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -27,17 +27,16 @@ namespace kernel {
 
 template<typename T>
 static void diagCreate(Param out, Param in, int num) {
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 3> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto diagCreate = common::getKernel("diagCreateKernel",
-                                        {diag_create_cl_src}, targs, options);
+    auto diagCreate = common::getKernel(
+        "diagCreateKernel", std::array{diag_create_cl_src}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
@@ -52,17 +51,16 @@ static void diagCreate(Param out, Param in, int num) {
 
 template<typename T>
 static void diagExtract(Param out, Param in, int num) {
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 3> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto diagExtract = common::getKernel("diagExtractKernel",
-                                         {diag_extract_cl_src}, targs, options);
+    auto diagExtract = common::getKernel(
+        "diagExtractKernel", std::array{diag_extract_cl_src}, targs, options);
 
     cl::NDRange local(256, 1);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 02251f6d41..c249e55d94 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -28,20 +28,18 @@ void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
     constexpr int TX = 16;
     constexpr int TY = 16;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 3> targs = {
         TemplateTypename<T>(),
         TemplateArg(dim),
         TemplateArg(isDiff2),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineKeyValue(DIM, dim),
+    std::array<std::string, 4> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineKeyValue(DIM, dim),
         DefineKeyValue(isDiff2, (isDiff2 ? 1 : 0)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto diffOp =
-        common::getKernel("diff_kernel", {diff_cl_src}, targs, options);
+    auto diffOp = common::getKernel("diff_kernel", std::array{diff_cl_src},
+                                    targs, options);
 
     cl::NDRange local(TX, TY, 1);
     if (dim == 0 && indims == 1) { local = cl::NDRange(TX * TY, 1, 1); }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 98ff024060..4b5e506c13 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -43,25 +43,25 @@ template<typename T>
 void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
     // Compilation options for compiling OpenCL kernel.
     // Go to common/kernel_cache.hpp to find details on this.
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
 
     // Compilation options for compiling OpenCL kernel.
     // Go to common/kernel_cache.hpp to find details on this.
-    std::vector<std::string> options = {
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
 
-    // The following templated function can take variable
-    // number of template parameters and if one of them is double
-    // precision, it will enable necessary constants, flags, ops
-    // in opencl kernel compilation stage
-    options.emplace_back(getTypeBuildDefinition<T>());
+        // The following templated function can take variable
+        // number of template parameters and if one of them is double
+        // precision, it will enable necessary constants, flags, ops
+        // in opencl kernel compilation stage
+        getTypeBuildDefinition<T>()};
 
     // Fetch the Kernel functor, go to common/kernel_cache.hpp
     // to find details of this function
-    auto exOp = common::getKernel("example", {example_cl_src}, targs, options);
+    auto exOp = common::getKernel("example", std::array{example_cl_src}, targs,
+                                  options);
 
     // configure work group parameters
     cl::NDRange local(THREADS_X, THREADS_Y);
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 1ef1ca46ff..9b4fc4341f 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -33,24 +33,23 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
     constexpr int FAST_THREADS_NONMAX_X = 32;
     constexpr int FAST_THREADS_NONMAX_Y = 8;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 3> targs = {
         TemplateTypename<T>(),
         TemplateArg(arc_length),
         TemplateArg(nonmax),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 4> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(ARC_LENGTH, arc_length),
         DefineKeyValue(NONMAX, static_cast<unsigned>(nonmax)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
-
-    auto locate =
-        common::getKernel("locate_features", {fast_cl_src}, targs, options);
-    auto nonMax =
-        common::getKernel("non_max_counts", {fast_cl_src}, targs, options);
-    auto getFeat =
-        common::getKernel("get_features", {fast_cl_src}, targs, options);
+        getTypeBuildDefinition<T>()};
+
+    auto locate  = common::getKernel("locate_features", std::array{fast_cl_src},
+                                     targs, options);
+    auto nonMax  = common::getKernel("non_max_counts", std::array{fast_cl_src},
+                                     targs, options);
+    auto getFeat = common::getKernel("get_features", std::array{fast_cl_src},
+                                     targs, options);
 
     const unsigned max_feat =
         ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 157c779936..222bde02e8 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -70,25 +70,24 @@ void packDataHelper(Param packed, Param sig, Param filter, const int rank,
     constexpr auto ctDType =
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 3> targs = {
         TemplateTypename<T>(),
         TemplateTypename<convT>(),
         TemplateArg(IsTypeDouble),
     };
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
+        getTypeBuildDefinition<T, convT>()};
     if (ctDType == c32) {
         options.emplace_back(DefineKeyValue(CONVT, "float"));
     } else if (ctDType == c64 && IsTypeDouble) {
         options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
-    options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto packData = common::getKernel("pack_data", {fftconvolve_pack_cl_src},
-                                      targs, options);
-    auto padArray = common::getKernel("pad_array", {fftconvolve_pack_cl_src},
-                                      targs, options);
+    auto packData = common::getKernel(
+        "pack_data", std::array{fftconvolve_pack_cl_src}, targs, options);
+    auto padArray = common::getKernel(
+        "pad_array", std::array{fftconvolve_pack_cl_src}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
@@ -129,7 +128,7 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
     constexpr auto ctDType =
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 3> targs = {
         TemplateTypename<T>(),
         TemplateTypename<convT>(),
         TemplateArg(IsTypeDouble),
@@ -140,16 +139,16 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
         DefineKeyValue(AF_BATCH_LHS, static_cast<int>(AF_BATCH_LHS)),
         DefineKeyValue(AF_BATCH_RHS, static_cast<int>(AF_BATCH_RHS)),
         DefineKeyValue(AF_BATCH_SAME, static_cast<int>(AF_BATCH_SAME)),
-    };
+        getTypeBuildDefinition<T, convT>()};
     if (ctDType == c32) {
         options.emplace_back(DefineKeyValue(CONVT, "float"));
     } else if (ctDType == c64 && IsTypeDouble) {
         options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
-    options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto cplxMul = common::getKernel(
-        "complex_multiply", {fftconvolve_multiply_cl_src}, targs, options);
+    auto cplxMul = common::getKernel("complex_multiply",
+                                     std::array{fftconvolve_multiply_cl_src},
+                                     targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
@@ -179,7 +178,7 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
         static_cast<af_dtype>(dtype_traits<convT>::af_type);
     constexpr bool RoundResult = std::is_integral<T>::value;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 5> targs = {
         TemplateTypename<T>(),     TemplateTypename<convT>(),
         TemplateArg(IsTypeDouble), TemplateArg(RoundResult),
         TemplateArg(expand),
@@ -188,16 +187,16 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(ROUND_OUT, static_cast<int>(RoundResult)),
         DefineKeyValue(EXPAND, static_cast<int>(expand)),
-    };
+        getTypeBuildDefinition<T, convT>()};
     if (ctDType == c32) {
         options.emplace_back(DefineKeyValue(CONVT, "float"));
     } else if (ctDType == c64 && IsTypeDouble) {
         options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
-    options.emplace_back(getTypeBuildDefinition<T, convT>());
 
-    auto reorder = common::getKernel(
-        "reorder_output", {fftconvolve_reorder_cl_src}, targs, options);
+    auto reorder = common::getKernel("reorder_output",
+                                     std::array{fftconvolve_reorder_cl_src},
+                                     targs, options);
 
     int fftScale = 1;
 
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 4061db1472..45b8dc7bf7 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -33,15 +33,13 @@ constexpr int ZERO      = 0;
 
 template<typename T>
 void initSeeds(Param out, const Param seedsx, const Param seedsy) {
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(VALID),
-        DefineKey(INIT_SEEDS),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+    std::array<std::string, 4> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(VALID),
+        DefineKey(INIT_SEEDS), getTypeBuildDefinition<T>()};
 
-    auto initSeeds = common::getKernel("init_seeds", {flood_fill_cl_src},
-                                       {TemplateTypename<T>()}, options);
+    auto initSeeds =
+        common::getKernel("init_seeds", std::array{flood_fill_cl_src},
+                          TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(kernel::THREADS, 1, 1);
     cl::NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
 
@@ -52,16 +50,14 @@ void initSeeds(Param out, const Param seedsx, const Param seedsy) {
 
 template<typename T>
 void finalizeOutput(Param out, const T newValue) {
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(VALID),
-        DefineValue(ZERO),
-        DefineKey(FINALIZE_OUTPUT),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
-
-    auto finalizeOut = common::getKernel("finalize_output", {flood_fill_cl_src},
-                                         {TemplateTypename<T>()}, options);
+    std::array<std::string, 5> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(VALID),
+        DefineValue(ZERO), DefineKey(FINALIZE_OUTPUT),
+        getTypeBuildDefinition<T>()};
+
+    auto finalizeOut =
+        common::getKernel("finalize_output", std::array{flood_fill_cl_src},
+                          TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
                        divup(out.info.dims[1], local[1]) * local[1], 1);
@@ -77,7 +73,7 @@ void floodFill(Param out, const Param image, const Param seedsx,
     constexpr int RADIUS = 1;
 
     UNUSED(nlookup);
-    std::vector<std::string> options = {
+    std::array<std::string, 11> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(RADIUS),
         DefineValue(VALID),
@@ -89,11 +85,11 @@ void floodFill(Param out, const Param image, const Param seedsx,
         DefineKeyValue(GROUP_SIZE, (THREADS_Y * THREADS_X)),
         DefineKeyValue(AF_IS_PLATFORM_NVIDIA,
                        (int)(AFCL_PLATFORM_NVIDIA == getActivePlatform())),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto floodStep = common::getKernel("flood_step", {flood_fill_cl_src},
-                                       {TemplateTypename<T>()}, options);
+    auto floodStep =
+        common::getKernel("flood_step", std::array{flood_fill_cl_src},
+                          TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
                        divup(out.info.dims[1], local[1]) * local[1], 1);
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index f18e2a965f..ad7ce75c84 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -29,20 +29,19 @@ void gradient(Param grad0, Param grad1, const Param in) {
     constexpr int TX = 32;
     constexpr int TY = 8;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 6> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(TX),
         DefineValue(TY),
         DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
         DefineKeyValue(CPLX, static_cast<int>(af::iscplx<T>())),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto gradOp =
-        common::getKernel("gradient", {gradient_cl_src}, targs, options);
+    auto gradOp = common::getKernel("gradient", std::array{gradient_cl_src},
+                                    targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 3b3bedb3a9..eb57c8ad71 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -62,20 +62,22 @@ void conv_helper(Array<T> &ixx, Array<T> &ixy, Array<T> &iyy,
 
 template<typename T>
 std::array<Kernel, 4> getHarrisKernels() {
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
     return {
-        common::getKernel("second_order_deriv", {harris_cl_src}, targs,
+        common::getKernel("second_order_deriv", std::array{harris_cl_src},
+                          targs, options),
+        common::getKernel("keep_corners", std::array{harris_cl_src}, targs,
+                          options),
+        common::getKernel("harris_responses", std::array{harris_cl_src}, targs,
+                          options),
+        common::getKernel("non_maximal", std::array{harris_cl_src}, targs,
                           options),
-        common::getKernel("keep_corners", {harris_cl_src}, targs, options),
-        common::getKernel("harris_responses", {harris_cl_src}, targs, options),
-        common::getKernel("non_maximal", {harris_cl_src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index b14fe5c0b3..03a2c2c892 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -29,7 +29,7 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     constexpr int THREADS_X = 256;
     constexpr int THRD_LOAD = 16;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(isLinear),
     };
@@ -41,8 +41,8 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     options.emplace_back(getTypeBuildDefinition<T>());
     if (isLinear) { options.emplace_back(DefineKey(IS_LINEAR)); }
 
-    auto histogram =
-        common::getKernel("histogram", {histogram_cl_src}, targs, options);
+    auto histogram = common::getKernel(
+        "histogram", std::array{histogram_cl_src}, targs, options);
 
     int nElems  = in.info.dims[0] * in.info.dims[1];
     int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 4585d7636e..34f1b2c7e9 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -31,16 +31,14 @@ constexpr int HG_THREADS   = 256;
 
 template<typename T>
 std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
-    std::vector<TemplateArg> targs   = {TemplateTypename<T>(),
+    std::array<TemplateArg, 2> targs = {TemplateTypename<T>(),
                                         TemplateArg(htype)};
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
-    options.emplace_back(
+        getTypeBuildDefinition<T>(),
         DefineKeyValue(EPS, (std::is_same<T, double>::value
                                  ? std::numeric_limits<double>::epsilon()
-                                 : std::numeric_limits<float>::epsilon())));
+                                 : std::numeric_limits<float>::epsilon()))};
     if (htype == AF_HOMOGRAPHY_RANSAC) {
         options.emplace_back(DefineKey(RANSAC));
     }
@@ -51,16 +49,16 @@ std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
         options.emplace_back(DefineKey(IS_CPU));
     }
     return {
-        common::getKernel("compute_homography", {homography_cl_src}, targs,
-                          options),
-        common::getKernel("eval_homography", {homography_cl_src}, targs,
-                          options),
-        common::getKernel("compute_median", {homography_cl_src}, targs,
-                          options),
-        common::getKernel("find_min_median", {homography_cl_src}, targs,
-                          options),
-        common::getKernel("compute_lmeds_inliers", {homography_cl_src}, targs,
-                          options),
+        common::getKernel("compute_homography", std::array{homography_cl_src},
+                          targs, options),
+        common::getKernel("eval_homography", std::array{homography_cl_src},
+                          targs, options),
+        common::getKernel("compute_median", std::array{homography_cl_src},
+                          targs, options),
+        common::getKernel("find_min_median", std::array{homography_cl_src},
+                          targs, options),
+        common::getKernel("compute_lmeds_inliers",
+                          std::array{homography_cl_src}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index e0afe9f14e..5e30938b17 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -27,18 +27,17 @@ void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(isHSV2RGB),
     };
     std::vector<std::string> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
     if (isHSV2RGB) { options.emplace_back(DefineKey(isHSV2RGB)); }
 
-    auto convert =
-        common::getKernel("hsvrgbConvert", {hsv_rgb_cl_src}, targs, options);
+    auto convert = common::getKernel(
+        "hsvrgbConvert", std::array{hsv_rgb_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 6ae1aa2eb0..6369beb3ce 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -27,18 +27,17 @@ namespace kernel {
 
 template<typename T>
 static void identity(Param out) {
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 4> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(ONE, af::scalar_to_option(scalar<T>(1))),
         DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto identityOp =
-        common::getKernel("identity_kernel", {identity_cl_src}, targs, options);
+    auto identityOp = common::getKernel(
+        "identity_kernel", std::array{identity_cl_src}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index a2b3942b81..2bbb407fe9 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -29,19 +29,18 @@ void iir(Param y, Param c, Param a) {
     // allocted outside
     constexpr int MAX_A_SIZE = (1024 * sizeof(double)) / sizeof(T);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(batch_a),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(MAX_A_SIZE),
+    std::array<std::string, 5> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(MAX_A_SIZE),
         DefineKeyValue(BATCH_A, batch_a),
         DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto iir = common::getKernel("iir_kernel", {iir_cl_src}, targs, options);
+    auto iir =
+        common::getKernel("iir_kernel", std::array{iir_cl_src}, targs, options);
 
     const int groups_y = y.info.dims[1];
     const int groups_x = y.info.dims[2];
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 3215ee22b5..881f000697 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -31,13 +31,13 @@ typedef struct {
 template<typename T>
 void index(Param out, const Param in, const IndexKernelParam_t& p,
            cl::Buffer* bPtr[4]) {
-    std::vector<std::string> options = {
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto index    = common::getKernel("indexKernel", {index_cl_src},
-                                      {TemplateTypename<T>()}, options);
+    auto index =
+        common::getKernel("indexKernel", std::array{index_cl_src},
+                          TemplateArgs(TemplateTypename<T>()), options);
     int threads_x = 256;
     int threads_y = 1;
     cl::NDRange local(threads_x, threads_y);
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index b0aced9524..cbf490fbf0 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -31,13 +31,12 @@ void iota(Param out, const af::dim4& sdims) {
     constexpr int TILEX   = 512;
     constexpr int TILEY   = 32;
 
-    std::vector<std::string> options = {
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto iota = common::getKernel("iota_kernel", {iota_cl_src},
-                                  {TemplateTypename<T>()}, options);
+    auto iota = common::getKernel("iota_kernel", std::array{iota_cl_src},
+                                  TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(IOTA_TX, IOTA_TY, 1);
 
     int blocksPerMatX = divup(out.info.dims[0], TILEX);
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index d6a89f03d5..5bdd55c180 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -33,11 +33,11 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
                         const int dim, const int threads_y, const bool is_first,
                         const uint groups_all[4], Param rlen) {
     ToNumStr<T> toNumStr;
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 5> targs = {
         TemplateTypename<T>(), TemplateArg(dim),       TemplateArg(op),
         TemplateArg(is_first), TemplateArg(threads_y),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 9> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(kDim, dim),
         DefineKeyValue(DIMY, threads_y),
@@ -46,12 +46,11 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<T>()),
         DefineKeyValue(IS_FIRST, is_first),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto ireduceDim =
-        common::getKernel("ireduce_dim_kernel",
-                          {iops_cl_src, ireduce_dim_cl_src}, targs, options);
+    auto ireduceDim = common::getKernel(
+        "ireduce_dim_kernel", std::array{iops_cl_src, ireduce_dim_cl_src},
+        targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -109,13 +108,13 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
                           const bool is_first, const uint groups_x,
                           const uint groups_y, Param rlen) {
     ToNumStr<T> toNumStr;
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<T>(),
         TemplateArg(op),
         TemplateArg(is_first),
         TemplateArg(threads_x),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 8> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(DIMX, threads_x),
         DefineValue(THREADS_PER_GROUP),
@@ -123,12 +122,11 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<T>()),
         DefineKeyValue(IS_FIRST, is_first),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto ireduceFirst =
-        common::getKernel("ireduce_first_kernel",
-                          {iops_cl_src, ireduce_first_cl_src}, targs, options);
+    auto ireduceFirst = common::getKernel(
+        "ireduce_first_kernel", std::array{iops_cl_src, ireduce_first_cl_src},
+        targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 07399511e6..fb52f3571f 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -46,20 +46,18 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
     constexpr int BLK_X = 64;
     constexpr int BLK_Y = 32;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(uplo),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(BLK_X),
+    std::array<std::string, 5> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(BLK_X),
         DefineValue(BLK_Y),
         DefineKeyValue(IS_CPLX, static_cast<int>(af::iscplx<T>())),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto lasetOp =
-        common::getKernel(laset_name<uplo>(), {laset_cl_src}, targs, options);
+    auto lasetOp = common::getKernel(laset_name<uplo>(),
+                                     std::array{laset_cl_src}, targs, options);
 
     int groups_x = (m - 1) / BLK_X + 1;
     int groups_y = (n - 1) / BLK_Y + 1;
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 1043310f70..9ceffec9e0 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -36,15 +36,15 @@ void laset_band(int m, int  n, int k,
 {
     static const std::string src(laset_band_cl, laset_band_cl_len);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(), TemplateArg(uplo),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 4> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(NB),
         DefineKeyValue(IS_CPLX, static_cast<int>(af::iscplx<T>())),
+        getTypeBuildDefinition<T>()
     };
-    options.emplace_back(getTypeBuildDefinition<T>());
 
     auto lasetBandOp = common::getKernel(laset_band_name<uplo>(), {src}, targs, options);
 
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index ace55aacfe..0fd58eb961 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -34,16 +34,15 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
            const int *ipiv, int inci, cl::CommandQueue &queue) {
     constexpr int NTHREADS = 256;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(MAX_PIVOTS),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+    std::array<std::string, 3> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(MAX_PIVOTS),
+        getTypeBuildDefinition<T>()};
 
-    auto laswpOp = common::getKernel("laswp", {laswp_cl_src}, targs, options);
+    auto laswpOp =
+        common::getKernel("laswp", std::array{laswp_cl_src}, targs, options);
 
     int groups = divup(n, NTHREADS);
     cl::NDRange local(NTHREADS);
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index f00ef8a8bb..ed82d58b6a 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -29,17 +29,15 @@ void lookup(Param out, const Param in, const Param indices,
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 3> targs = {
         TemplateTypename<in_t>(),
         TemplateTypename<idx_t>(),
         TemplateArg(dim),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 4> options = {
         DefineKeyValue(in_t, dtype_traits<in_t>::getName()),
         DefineKeyValue(idx_t, dtype_traits<idx_t>::getName()),
-        DefineKeyValue(DIM, dim),
-    };
-    options.emplace_back(getTypeBuildDefinition<in_t, idx_t>());
+        DefineKeyValue(DIM, dim), getTypeBuildDefinition<in_t, idx_t>()};
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
@@ -49,8 +47,8 @@ void lookup(Param out, const Param in, const Param indices,
     cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
                        blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp =
-        common::getKernel("lookupND", {lookup_cl_src}, targs, options);
+    auto arrIdxOp = common::getKernel("lookupND", std::array{lookup_cl_src},
+                                      targs, options);
 
     arrIdxOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index f2ac2d983d..e27eb78955 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -30,20 +30,18 @@ void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
     constexpr unsigned TILEX = 128;
     constexpr unsigned TILEY = 32;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(same_dims),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(same_dims),
+    std::array<std::string, 5> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(same_dims),
         DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
         DefineKeyValue(ONE, af::scalar_to_option(scalar<T>(1))),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto luSplit =
-        common::getKernel("luSplit", {lu_split_cl_src}, targs, options);
+    auto luSplit = common::getKernel("luSplit", std::array{lu_split_cl_src},
+                                     targs, options);
 
     cl::NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index f32fd722ef..5b7c471c33 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -28,13 +28,13 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<inType>(),
         TemplateTypename<outType>(),
         TemplateArg(mType),
         TemplateArg(needMean),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 14> options = {
         DefineKeyValue(inType, dtype_traits<inType>::getName()),
         DefineKeyValue(outType, dtype_traits<outType>::getName()),
         DefineKeyValue(MATCH_T, static_cast<int>(mType)),
@@ -48,11 +48,10 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
         DefineKeyValue(AF_NCC, static_cast<int>(AF_NCC)),
         DefineKeyValue(AF_ZNCC, static_cast<int>(AF_ZNCC)),
         DefineKeyValue(AF_SHD, static_cast<int>(AF_SHD)),
-    };
-    options.emplace_back(getTypeBuildDefinition<outType>());
+        getTypeBuildDefinition<outType>()};
 
-    auto matchImgOp = common::getKernel("matchTemplate", {matchTemplate_cl_src},
-                                        targs, options);
+    auto matchImgOp = common::getKernel(
+        "matchTemplate", std::array{matchTemplate_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 35bcee0fef..3149da3280 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -108,7 +108,7 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
     ToNumStr<Tw> twNumStr;
     common::Transform<uint, Tw, af_add_t> transform_weight;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 7> targs = {
         TemplateTypename<Ti>(),     TemplateTypename<To>(),
         TemplateTypename<Tw>(),     TemplateArg(dim),
         TemplateArg(threads_y),     TemplateArg(input_weight),
@@ -124,13 +124,13 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
         DefineKeyValue(init_To, toNumStr(common::Binary<To, af_add_t>::init())),
         DefineKeyValue(init_Tw, twNumStr(transform_weight(0))),
         DefineKeyValue(one_Tw, twNumStr(transform_weight(1))),
-    };
-    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+        getTypeBuildDefinition<Ti, To>()};
     if (input_weight) { options.emplace_back(DefineKey(INPUT_WEIGHT)); }
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
     auto meanOp = common::getKernel(
-        "meanDim", {mean_ops_cl_src, mean_dim_cl_src}, targs, options);
+        "meanDim", std::array{mean_ops_cl_src, mean_dim_cl_src}, targs,
+        options);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -202,7 +202,7 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
     ToNumStr<Tw> twNumStr;
     common::Transform<uint, Tw, af_add_t> transform_weight;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 6> targs = {
         TemplateTypename<Ti>(),    TemplateTypename<To>(),
         TemplateTypename<Tw>(),    TemplateArg(threads_x),
         TemplateArg(input_weight), TemplateArg(output_weight),
@@ -222,7 +222,8 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
     auto meanOp = common::getKernel(
-        "meanFirst", {mean_ops_cl_src, mean_first_cl_src}, targs, options);
+        "meanFirst", std::array{mean_ops_cl_src, mean_first_cl_src}, targs,
+        options);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index a616f6abc0..fb92f18866 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -32,19 +32,18 @@ void meanshift(Param out, const Param in, const float spatialSigma,
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(is_color),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 4> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(AccType, dtype_traits<AccType>::getName()),
         DefineKeyValue(MAX_CHANNELS, (is_color ? 3 : 1)),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto meanshiftOp =
-        common::getKernel("meanshift", {meanshift_cl_src}, targs, options);
+    auto meanshiftOp = common::getKernel(
+        "meanshift", std::array{meanshift_cl_src}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index af1d4f3615..e8af452eda 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -35,22 +35,21 @@ void medfilt1(Param out, const Param in, const unsigned w_wid,
     const int ARR_SIZE = (w_wid - w_wid / 2) + 1;
     size_t loc_size    = (THREADS_X + w_wid - 1) * sizeof(T);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(pad),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 7> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(pad, static_cast<int>(pad)),
         DefineKeyValue(AF_PAD_ZERO, static_cast<int>(AF_PAD_ZERO)),
         DefineKeyValue(AF_PAD_SYM, static_cast<int>(AF_PAD_SYM)),
         DefineValue(ARR_SIZE),
         DefineValue(w_wid),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto medfiltOp =
-        common::getKernel("medfilt1", {medfilt1_cl_src}, targs, options);
+    auto medfiltOp = common::getKernel("medfilt1", std::array{medfilt1_cl_src},
+                                       targs, options);
 
     cl::NDRange local(THREADS_X, 1, 1);
 
@@ -71,13 +70,13 @@ void medfilt2(Param out, const Param in, const af_border_type pad,
     const size_t loc_size =
         (THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1) * sizeof(T);
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<T>(),
         TemplateArg(pad),
         TemplateArg(w_len),
         TemplateArg(w_wid),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 8> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(pad, static_cast<int>(pad)),
         DefineKeyValue(AF_PAD_ZERO, static_cast<int>(AF_PAD_ZERO)),
@@ -85,11 +84,10 @@ void medfilt2(Param out, const Param in, const af_border_type pad,
         DefineValue(ARR_SIZE),
         DefineValue(w_wid),
         DefineValue(w_len),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto medfiltOp =
-        common::getKernel("medfilt2", {medfilt2_cl_src}, targs, options);
+    auto medfiltOp = common::getKernel("medfilt2", std::array{medfilt2_cl_src},
+                                       targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index 159fe4d35a..e4091fea53 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -23,9 +23,6 @@
 #include <string>
 #include <vector>
 
-using std::string;
-using std::vector;
-
 namespace opencl {
 namespace kernel {
 typedef struct {
@@ -149,7 +146,7 @@ void memcopy(const cl::Buffer& b_out, const dim4& ostrides,
         : th.loop1 ? th.loop3 ? "memCopyLoop13" : "memCopyLoop1"
         : th.loop3 ? "memCopyLoop3"
                    : "memCopy"};  // Conversion to  base vector types.
-    const char* tArg{
+    TemplateArg tArg{
         sizeofNewT == 1   ? "char"
         : sizeofNewT == 2 ? "short"
         : sizeofNewT == 4 ? "float"
@@ -157,8 +154,9 @@ void memcopy(const cl::Buffer& b_out, const dim4& ostrides,
         : sizeofNewT == 16
             ? "float4"
             : "type is larger than 16 bytes, which is unsupported"};
-    auto memCopy{common::getKernel(kernelName, {memcopy_cl_src}, {tArg},
-                                   {DefineKeyValue(T, tArg)})};
+    auto memCopy{common::getKernel(kernelName, std::array{memcopy_cl_src},
+                                   std::array{tArg},
+                                   std::array{DefineKeyValue(T, tArg)})};
     const cl::NDRange local{th.genLocal(memCopy.get())};
     const cl::NDRange global{th.genGlobal(local)};
 
@@ -209,12 +207,12 @@ void copy(const Param out, const Param in, dim_t ondims,
                                 std::is_same<inType, cdouble>::value};
     const char* factorType[]{"float", "double"};
 
-    const std::vector<TemplateArg> targs{
+    const std::array<TemplateArg, 5> targs{
         TemplateTypename<inType>(), TemplateTypename<outType>(),
         TemplateArg(same_dims),     TemplateArg(factorType[factorTypeIdx]),
         TemplateArg(factor != 1.0),
     };
-    const std::vector<std::string> options{
+    const std::array<std::string, 8> options{
         DefineKeyValue(inType, dtype_traits<inType>::getName()),
         DefineKeyValue(outType, dtype_traits<outType>::getName()),
         std::string(" -D inType_") + dtype_traits<inType>::getName(),
@@ -222,7 +220,7 @@ void copy(const Param out, const Param in, dim_t ondims,
         DefineKeyValue(SAME_DIMS, static_cast<int>(same_dims)),
         std::string(" -D factorType=") + factorType[factorTypeIdx],
         std::string((factor != 1.0) ? " -D FACTOR" : " -D NOFACTOR"),
-        {getTypeBuildDefinition<inType, outType>()},
+        getTypeBuildDefinition<inType, outType>(),
     };
 
     threadsMgt<int> th(odims_.dims, ondims_, 1, 1, totalSize, sizeof(outType));
@@ -230,7 +228,7 @@ void copy(const Param out, const Param in, dim_t ondims,
                                   : th.loop3 ? "scaledCopyLoop13"
                                   : th.loop1 ? "scaledCopyLoop1"
                                              : "scaledCopy",
-                                  {copy_cl_src}, targs, options);
+                                  std::array{copy_cl_src}, targs, options);
     const cl::NDRange local{th.genLocal(copy.get())};
     const cl::NDRange global{th.genGlobal(local)};
 
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index facabba3ff..6da71b9833 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -28,18 +28,17 @@ template<typename T>
 void moments(Param out, const Param in, af_moment_type moment) {
     constexpr int THREADS = 128;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(out.info.dims[0]),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 3> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(MOMENTS_SZ, out.info.dims[0]),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto momentsOp =
-        common::getKernel("moments", {moments_cl_src}, targs, options);
+    auto momentsOp = common::getKernel("moments", std::array{moments_cl_src},
+                                       targs, options);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(in.info.dims[1] * local[0],
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index a89b729613..43b5d6d443 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -55,7 +55,8 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::getKernel("morph", {morph_cl_src}, targs, options);
+    auto morphOp =
+        common::getKernel("morph", std::array{morph_cl_src}, targs, options);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -114,7 +115,8 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp = common::getKernel("morph3d", {morph_cl_src}, targs, options);
+    auto morphOp =
+        common::getKernel("morph3d", std::array{morph_cl_src}, targs, options);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index f8e523f03c..841a844038 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -45,7 +45,7 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
     unsigned unroll_len = nextpow2(feat_len);
     if (unroll_len != feat_len) unroll_len = 0;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<T>(),
         TemplateArg(dist_type),
         TemplateArg(use_lmem),
@@ -70,8 +70,9 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
         options.emplace_back(DefineKeyValue(DISTOP, "_shd_"));
         options.emplace_back(DefineKey(__SHD__));
     }
-    auto hmOp = common::getKernel("knnAllDistances", {nearest_neighbour_cl_src},
-                                  targs, options);
+    auto hmOp =
+        common::getKernel("knnAllDistances",
+                          std::array{nearest_neighbour_cl_src}, targs, options);
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index b755644e37..f2e72c7317 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -87,10 +87,14 @@ std::array<Kernel, 4> getOrbKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("harris_response", {orb_cl_src}, targs, compileOpts),
-        common::getKernel("keep_features", {orb_cl_src}, targs, compileOpts),
-        common::getKernel("centroid_angle", {orb_cl_src}, targs, compileOpts),
-        common::getKernel("extract_orb", {orb_cl_src}, targs, compileOpts),
+        common::getKernel("harris_response", std::array{orb_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("keep_features", std::array{orb_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("centroid_angle", std::array{orb_cl_src}, targs,
+                          compileOpts),
+        common::getKernel("extract_orb", std::array{orb_cl_src}, targs,
+                          compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index 567f2d33b4..4d18b06099 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -45,8 +45,9 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto pad = common::getKernel("padBorders", {pad_array_borders_cl_src},
-                                 tmpltArgs, compileOpts);
+    auto pad =
+        common::getKernel("padBorders", std::array{pad_array_borders_cl_src},
+                          tmpltArgs, compileOpts);
 
     NDRange local(PADB_THREADS_X, PADB_THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 21f932ba28..c15f9e292f 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -56,7 +56,7 @@ static Kernel getRandomEngineKernel(const af_random_engine_type type,
         default:
             AF_ERROR("Random Engine Type Not Supported", AF_ERR_NOT_SUPPORTED);
     }
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(kerIdx),
     };
@@ -162,8 +162,9 @@ void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
     cl::NDRange local(THREADS_PER_GROUP, 1);
     cl::NDRange global(local[0] * MAX_BLOCKS, 1);
 
-    auto initOp = common::getKernel("mersenneInitState",
-                                    {random_engine_mersenne_init_cl_src}, {});
+    auto initOp =
+        common::getKernel("mersenneInitState",
+                          std::array{random_engine_mersenne_init_cl_src}, {});
     initOp(cl::EnqueueArgs(getQueue(), global, local), state, table, seed);
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index b8eb75dfe6..d4a5acbd33 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -30,14 +30,13 @@ void range(Param out, const int dim) {
     constexpr int RANGE_TILEX = 512;
     constexpr int RANGE_TILEY = 32;
 
-    std::vector<TemplateArg> targs   = {TemplateTypename<T>()};
-    std::vector<std::string> options = {
+    std::array<TemplateArg, 1> targs   = {TemplateTypename<T>()};
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto rangeOp =
-        common::getKernel("range_kernel", {range_cl_src}, targs, options);
+    auto rangeOp = common::getKernel("range_kernel", std::array{range_cl_src},
+                                     targs, options);
 
     cl::NDRange local(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index f3c8022b71..f52d044bcb 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -38,11 +38,11 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
                        const uint groups_all[4], int change_nan,
                        double nanval) {
     ToNumStr<To> toNumStr;
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 5> targs = {
         TemplateTypename<Ti>(), TemplateTypename<To>(), TemplateArg(dim),
         TemplateArg(op),        TemplateArg(threads_y),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 10> options = {
         DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
         DefineKeyValue(To, dtype_traits<To>::getName()),
         DefineKeyValue(T, "To"),
@@ -52,11 +52,11 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
-    };
-    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+        getTypeBuildDefinition<Ti, To>()};
 
     auto reduceDim = common::getKernel(
-        "reduce_dim_kernel", {ops_cl_src, reduce_dim_cl_src}, targs, options);
+        "reduce_dim_kernel", std::array{ops_cl_src, reduce_dim_cl_src}, targs,
+        options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -115,13 +115,13 @@ void reduceAllLauncher(Param out, Param in, const uint groups_x,
                        const uint groups_y, const uint threads_x,
                        int change_nan, double nanval) {
     ToNumStr<To> toNumStr;
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<Ti>(),
         TemplateTypename<To>(),
         TemplateArg(op),
         TemplateArg(threads_x),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 9> options = {
         DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
         DefineKeyValue(To, dtype_traits<To>::getName()),
         DefineKeyValue(T, "To"),
@@ -130,11 +130,11 @@ void reduceAllLauncher(Param out, Param in, const uint groups_x,
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
-    };
-    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+        getTypeBuildDefinition<Ti, To>()};
 
     auto reduceAll = common::getKernel(
-        "reduce_all_kernel", {ops_cl_src, reduce_all_cl_src}, targs, options);
+        "reduce_all_kernel", std::array{ops_cl_src, reduce_all_cl_src}, targs,
+        options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
@@ -163,13 +163,13 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
                          const uint groups_y, const uint threads_x,
                          int change_nan, double nanval) {
     ToNumStr<To> toNumStr;
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 4> targs = {
         TemplateTypename<Ti>(),
         TemplateTypename<To>(),
         TemplateArg(op),
         TemplateArg(threads_x),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 9> options = {
         DefineKeyValue(Ti, dtype_traits<Ti>::getName()),
         DefineKeyValue(To, dtype_traits<To>::getName()),
         DefineKeyValue(T, "To"),
@@ -178,12 +178,11 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
         DefineKeyValue(CPLX, af::iscplx<Ti>()),
-    };
-    options.emplace_back(getTypeBuildDefinition<Ti, To>());
+        getTypeBuildDefinition<Ti, To>()};
 
-    auto reduceFirst =
-        common::getKernel("reduce_first_kernel",
-                          {ops_cl_src, reduce_first_cl_src}, targs, options);
+    auto reduceFirst = common::getKernel(
+        "reduce_first_kernel", std::array{ops_cl_src, reduce_first_cl_src},
+        targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index ec841dafc4..79779ca320 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -65,7 +65,8 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
 
     auto reduceBlocksByKeyDim = common::getKernel(
         "reduce_blocks_by_key_dim",
-        {ops_cl_src, reduce_blocks_by_key_dim_cl_src}, tmpltArgs, compileOpts);
+        std::array{ops_cl_src, reduce_blocks_by_key_dim_cl_src}, tmpltArgs,
+        compileOpts);
     int numBlocks = divup(n, threads_x);
 
     cl::NDRange local(threads_x);
@@ -105,10 +106,10 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto reduceBlocksByKeyFirst =
-        common::getKernel("reduce_blocks_by_key_first",
-                          {ops_cl_src, reduce_blocks_by_key_first_cl_src},
-                          tmpltArgs, compileOpts);
+    auto reduceBlocksByKeyFirst = common::getKernel(
+        "reduce_blocks_by_key_first",
+        std::array{ops_cl_src, reduce_blocks_by_key_first_cl_src}, tmpltArgs,
+        compileOpts);
     int numBlocks = divup(n, threads_x);
 
     cl::NDRange local(threads_x);
@@ -146,9 +147,10 @@ void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduce = common::getKernel(
-        "final_boundary_reduce", {ops_cl_src, reduce_by_key_boundary_cl_src},
-        tmpltArgs, compileOpts);
+    auto finalBoundaryReduce =
+        common::getKernel("final_boundary_reduce",
+                          std::array{ops_cl_src, reduce_by_key_boundary_cl_src},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks);
@@ -184,10 +186,10 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduceDim =
-        common::getKernel("final_boundary_reduce_dim",
-                          {ops_cl_src, reduce_by_key_boundary_dim_cl_src},
-                          tmpltArgs, compileOpts);
+    auto finalBoundaryReduceDim = common::getKernel(
+        "final_boundary_reduce_dim",
+        std::array{ops_cl_src, reduce_by_key_boundary_dim_cl_src}, tmpltArgs,
+        compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks,
@@ -220,9 +222,9 @@ void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto compact =
-        common::getKernel("compact", {ops_cl_src, reduce_by_key_compact_cl_src},
-                          tmpltArgs, compileOpts);
+    auto compact = common::getKernel(
+        "compact", std::array{ops_cl_src, reduce_by_key_compact_cl_src},
+        tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
@@ -256,7 +258,7 @@ void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto compactDim = common::getKernel(
-        "compact_dim", {ops_cl_src, reduce_by_key_compact_dim_cl_src},
+        "compact_dim", std::array{ops_cl_src, reduce_by_key_compact_dim_cl_src},
         tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
@@ -285,10 +287,10 @@ void testNeedsReduction(cl::Buffer needs_reduction, cl::Buffer needs_boundary,
         DefineKeyValue(DIMX, threads_x),
     };
 
-    auto testIfNeedsReduction =
-        common::getKernel("test_needs_reduction",
-                          {ops_cl_src, reduce_by_key_needs_reduction_cl_src},
-                          tmpltArgs, compileOpts);
+    auto testIfNeedsReduction = common::getKernel(
+        "test_needs_reduction",
+        std::array{ops_cl_src, reduce_by_key_needs_reduction_cl_src}, tmpltArgs,
+        compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks);
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 0baa0abfaf..710ccdf64b 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -66,9 +66,12 @@ std::array<Kernel, 3> getRegionsKernels(const bool full_conn,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("initial_label", {regions_cl_src}, targs, options),
-        common::getKernel("final_relabel", {regions_cl_src}, targs, options),
-        common::getKernel("update_equiv", {regions_cl_src}, targs, options),
+        common::getKernel("initial_label", std::array{regions_cl_src}, targs,
+                          options),
+        common::getKernel("final_relabel", std::array{regions_cl_src}, targs,
+                          options),
+        common::getKernel("update_equiv", std::array{regions_cl_src}, targs,
+                          options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 550ff127cc..e2dc87f481 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -28,16 +28,15 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
     constexpr int TILEX = 512;
     constexpr int TILEY = 32;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 1> targs = {
         TemplateTypename<T>(),
     };
-    std::vector<std::string> options = {
+    std::array<std::string, 2> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+        getTypeBuildDefinition<T>()};
 
-    auto reorderOp =
-        common::getKernel("reorder_kernel", {reorder_cl_src}, targs, options);
+    auto reorderOp = common::getKernel(
+        "reorder_kernel", std::array{reorder_cl_src}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index 0e55caa4e7..ae0184a4a1 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -40,7 +40,7 @@ void resize(Param out, const Param in, const af_interp_type method) {
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(method),
     };
@@ -48,12 +48,10 @@ void resize(Param out, const Param in, const af_interp_type method) {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(VT, dtype_traits<vtype_t<T>>::getName()),
         DefineKeyValue(WT, dtype_traits<wtype_t<BT>>::getName()),
-        DefineKeyValue(CPLX, (IsComplex ? 1 : 0)),
-    };
+        DefineKeyValue(CPLX, (IsComplex ? 1 : 0)), getTypeBuildDefinition<T>()};
     if (IsComplex) {
         options.emplace_back(DefineKeyValue(TB, dtype_traits<BT>::getName()));
     }
-    options.emplace_back(getTypeBuildDefinition<T>());
 
     switch (method) {
         case AF_INTERP_NEAREST:
@@ -68,8 +66,8 @@ void resize(Param out, const Param in, const af_interp_type method) {
         default: break;
     }
 
-    auto resizeOp =
-        common::getKernel("resize_kernel", {resize_cl_src}, targs, options);
+    auto resizeOp = common::getKernel(
+        "resize_kernel", std::array{resize_cl_src}, targs, options);
 
     cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 2edf47cf91..999a7f25a5 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -79,8 +79,9 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto rotate = common::getKernel(
-        "rotateKernel", {interp_cl_src, rotate_cl_src}, tmpltArgs, compileOpts);
+    auto rotate = common::getKernel("rotateKernel",
+                                    std::array{interp_cl_src, rotate_cl_src},
+                                    tmpltArgs, compileOpts);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index a59904cfe7..e5d0de3a97 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -40,6 +40,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:nonstd::span-lite,INTERFACE_INCLUDE_DIRECTORIES>
         ${ArrayFire_BINARY_DIR}/include
       )
     if(TARGET Forge::forge)
@@ -80,6 +81,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         ${opencl_compile_definitions}
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>
         $<TARGET_PROPERTY:af_spdlog,INTERFACE_COMPILE_DEFINITIONS>
+        $<TARGET_PROPERTY:nonstd::span-lite,INTERFACE_COMPILE_DEFINITIONS>
         TYPE=${SBK_BINARY_OP} AFDLL)
     target_sources(opencl_scan_by_key
       INTERFACE $<TARGET_OBJECTS:opencl_scan_by_key_${SBK_BINARY_OP}>)
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index c246711c47..00c4cfc8ef 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -57,8 +57,8 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {ops_cl_src, scan_dim_cl_src}, tmpltArgs,
-                             compileOpts);
+    return common::getKernel(key, std::array{ops_cl_src, scan_dim_cl_src},
+                             tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index b73c30ec07..8376c3a876 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -57,7 +57,8 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {ops_cl_src, scan_dim_by_key_cl_src},
+    return common::getKernel(key,
+                             std::array{ops_cl_src, scan_dim_by_key_cl_src},
                              tmpltArgs, compileOpts);
 }
 
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index d4c03d041c..a8031ecc5e 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -58,8 +58,8 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {ops_cl_src, scan_first_cl_src}, tmpltArgs,
-                             compileOpts);
+    return common::getKernel(key, std::array{ops_cl_src, scan_first_cl_src},
+                             tmpltArgs, compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index 3deee884b3..f8835e18a8 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -61,7 +61,8 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, {ops_cl_src, scan_first_by_key_cl_src},
+    return common::getKernel(key,
+                             std::array{ops_cl_src, scan_first_by_key_cl_src},
                              tmpltArgs, compileOpts);
 }
 
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 743f200d5c..69602817a9 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -29,18 +29,16 @@ constexpr int REPEAT = 64;
 template<typename T>
 void selectLauncher(Param out, Param cond, Param a, Param b, const int ndims,
                     const bool is_same) {
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(is_same),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(is_same),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+    std::array<std::string, 3> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(is_same),
+        getTypeBuildDefinition<T>()};
 
-    auto selectOp =
-        common::getKernel("select_kernel", {select_cl_src}, targs, options);
+    auto selectOp = common::getKernel(
+        "select_kernel", std::array{select_cl_src}, targs, options);
 
     int threads[] = {DIMX, DIMY};
 
@@ -74,18 +72,16 @@ void select(Param out, Param cond, Param a, Param b, int ndims) {
 template<typename T>
 void select_scalar(Param out, Param cond, Param a, const T b, const int ndims,
                    const bool flip) {
-    std::vector<TemplateArg> targs = {
+    std::array<TemplateArg, 2> targs = {
         TemplateTypename<T>(),
         TemplateArg(flip),
     };
-    std::vector<std::string> options = {
-        DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineValue(flip),
-    };
-    options.emplace_back(getTypeBuildDefinition<T>());
+    std::array<std::string, 3> options = {
+        DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(flip),
+        getTypeBuildDefinition<T>()};
 
-    auto selectOp = common::getKernel("select_scalar_kernel", {select_cl_src},
-                                      targs, options);
+    auto selectOp = common::getKernel(
+        "select_scalar_kernel", std::array{select_cl_src}, targs, options);
 
     int threads[] = {DIMX, DIMY};
 
diff --git a/src/backend/opencl/kernel/sift.hpp b/src/backend/opencl/kernel/sift.hpp
index 4b1609514e..90b063b2d0 100644
--- a/src/backend/opencl/kernel/sift.hpp
+++ b/src/backend/opencl/kernel/sift.hpp
@@ -355,19 +355,20 @@ std::array<Kernel, 7> getSiftKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("sub", {sift_nonfree_cl_src}, targs, compileOpts),
-        common::getKernel("detectExtrema", {sift_nonfree_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("interpolateExtrema", {sift_nonfree_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("calcOrientation", {sift_nonfree_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("removeDuplicates", {sift_nonfree_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("computeDescriptor", {sift_nonfree_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("computeGLOHDescriptor", {sift_nonfree_cl_src}, targs,
+        common::getKernel("sub", std::array{sift_nonfree_cl_src}, targs,
                           compileOpts),
+        common::getKernel("detectExtrema", std::array{sift_nonfree_cl_src},
+                          targs, compileOpts),
+        common::getKernel("interpolateExtrema", std::array{sift_nonfree_cl_src},
+                          targs, compileOpts),
+        common::getKernel("calcOrientation", std::array{sift_nonfree_cl_src},
+                          targs, compileOpts),
+        common::getKernel("removeDuplicates", std::array{sift_nonfree_cl_src},
+                          targs, compileOpts),
+        common::getKernel("computeDescriptor", std::array{sift_nonfree_cl_src},
+                          targs, compileOpts),
+        common::getKernel("computeGLOHDescriptor",
+                          std::array{sift_nonfree_cl_src}, targs, compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index d68b2dc933..8e0c406f4a 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -38,8 +38,8 @@ void sobel(Param dx, Param dy, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto sobel =
-        common::getKernel("sobel3x3", {sobel_cl_src}, targs, compileOpts);
+    auto sobel = common::getKernel("sobel3x3", std::array{sobel_cl_src}, targs,
+                                   compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index e938ed2f46..6cfed4b554 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -42,8 +42,8 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto coo2dense = common::getKernel("coo2Dense", {coo2dense_cl_src},
-                                       tmpltArgs, compileOpts);
+    auto coo2dense = common::getKernel(
+        "coo2Dense", std::array{coo2dense_cl_src}, tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_PER_GROUP, 1, 1);
 
@@ -75,8 +75,8 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2dense = common::getKernel("csr2Dense", {csr2dense_cl_src},
-                                       tmpltArgs, compileOpts);
+    auto csr2dense = common::getKernel(
+        "csr2Dense", std::array{csr2dense_cl_src}, tmpltArgs, compileOpts);
 
     cl::NDRange local(threads, 1);
     int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
@@ -101,8 +101,8 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto dense2Csr = common::getKernel("dense2Csr", {dense2csr_cl_src},
-                                       tmpltArgs, compileOpts);
+    auto dense2Csr = common::getKernel(
+        "dense2Csr", std::array{dense2csr_cl_src}, tmpltArgs, compileOpts);
 
     int num_rows = dense.info.dims[0];
     int num_cols = dense.info.dims[1];
@@ -146,8 +146,8 @@ void swapIndex(Param ovalues, Param oindex, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapIndex = common::getKernel("swapIndex", {csr2coo_cl_src}, tmpltArgs,
-                                       compileOpts);
+    auto swapIndex = common::getKernel("swapIndex", std::array{csr2coo_cl_src},
+                                       tmpltArgs, compileOpts);
 
     cl::NDRange global(ovalues.info.dims[0], 1, 1);
 
@@ -168,8 +168,8 @@ void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2coo =
-        common::getKernel("csr2Coo", {csr2coo_cl_src}, tmpltArgs, compileOpts);
+    auto csr2coo = common::getKernel("csr2Coo", std::array{csr2coo_cl_src},
+                                     tmpltArgs, compileOpts);
 
     const int MAX_GROUPS = 4096;
     int M                = irowIdx.info.dims[0] - 1;
@@ -208,8 +208,8 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csrReduce = common::getKernel("csrReduce", {csr2coo_cl_src}, tmpltArgs,
-                                       compileOpts);
+    auto csrReduce = common::getKernel("csrReduce", std::array{csr2coo_cl_src},
+                                       tmpltArgs, compileOpts);
 
     // Now we need to sort this into column major
     kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 25ae4e3db5..f10b3327a0 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -50,7 +50,7 @@ auto fetchKernel(const std::string key, const common::Source &additionalSrc,
     constexpr bool IsComplex =
         std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
-    std::vector<TemplateArg> tmpltArgs = {
+    std::array<TemplateArg, 2> tmpltArgs = {
         TemplateTypename<T>(),
         TemplateArg(op),
     };
@@ -62,8 +62,9 @@ auto fetchKernel(const std::string key, const common::Source &additionalSrc,
     options.emplace_back(getTypeBuildDefinition<T>());
     options.insert(std::end(options), std::begin(additionalOptions),
                    std::end(additionalOptions));
-    return common::getKernel(key, {sparse_arith_common_cl_src, additionalSrc},
-                             tmpltArgs, options);
+    return common::getKernel(
+        key, std::array{sparse_arith_common_cl_src, additionalSrc}, tmpltArgs,
+        options);
 }
 
 template<typename T, af_op_t op>
@@ -142,8 +143,9 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
         TemplateTypename<uint>(),
     };
 
-    auto calcNNZ = common::getKernel(
-        "csr_calc_out_nnz", {ssarith_calc_out_nnz_cl_src}, tmpltArgs, {});
+    auto calcNNZ = common::getKernel("csr_calc_out_nnz",
+                                     std::array{ssarith_calc_out_nnz_cl_src},
+                                     tmpltArgs, {});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index 7ebb1a20ec..d3cdfb8af2 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -48,8 +48,8 @@ void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto susan = common::getKernel("susan_responses", {susan_cl_src}, targs,
-                                   compileOpts);
+    auto susan = common::getKernel("susan_responses", std::array{susan_cl_src},
+                                   targs, compileOpts);
 
     cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
     cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
@@ -74,8 +74,8 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto nonMax =
-        common::getKernel("non_maximal", {susan_cl_src}, targs, compileOpts);
+    auto nonMax = common::getKernel("non_maximal", std::array{susan_cl_src},
+                                    targs, compileOpts);
 
     unsigned corners_found = 0;
     auto d_corners_found   = memAlloc<unsigned>(1);
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 106db3c4d2..ff875e25da 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -41,8 +41,8 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapdblk =
-        common::getKernel("swapdblk", {swapdblk_cl_src}, targs, compileOpts);
+    auto swapdblk = common::getKernel("swapdblk", std::array{swapdblk_cl_src},
+                                      targs, compileOpts);
 
     int nblocks = n / nb;
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index e0b268e594..cc65a1fc54 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -41,7 +41,8 @@ void tile(Param out, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto tile = common::getKernel("tile", {tile_cl_src}, targs, compileOpts);
+    auto tile =
+        common::getKernel("tile", std::array{tile_cl_src}, targs, compileOpts);
 
     NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index c107361771..a64468ea26 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -79,9 +79,9 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto transform =
-        common::getKernel("transformKernel", {interp_cl_src, transform_cl_src},
-                          tmpltArgs, compileOpts);
+    auto transform = common::getKernel(
+        "transformKernel", std::array{interp_cl_src, transform_cl_src},
+        tmpltArgs, compileOpts);
 
     const int nImg2 = in.info.dims[2];
     const int nImg3 = in.info.dims[3];
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 39b775d0cc..87e6b65fee 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -48,8 +48,8 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto transpose = common::getKernel("transpose", {transpose_cl_src},
-                                       tmpltArgs, compileOpts);
+    auto transpose = common::getKernel(
+        "transpose", std::array{transpose_cl_src}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index f53340fd26..06020a6e3c 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -48,9 +48,9 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto transpose =
-        common::getKernel("transpose_inplace", {transpose_inplace_cl_src},
-                          tmpltArgs, compileOpts);
+    auto transpose = common::getKernel("transpose_inplace",
+                                       std::array{transpose_inplace_cl_src},
+                                       tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 0421b09e8d..8380894b07 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -51,8 +51,8 @@ void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto triangle = common::getKernel("triangle", {triangle_cl_src}, tmpltArgs,
-                                      compileOpts);
+    auto triangle = common::getKernel("triangle", std::array{triangle_cl_src},
+                                      tmpltArgs, compileOpts);
 
     NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index d525015772..68d6846893 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -46,8 +46,8 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto unwrap =
-        common::getKernel("unwrap", {unwrap_cl_src}, tmpltArgs, compileOpts);
+    auto unwrap = common::getKernel("unwrap", std::array{unwrap_cl_src},
+                                    tmpltArgs, compileOpts);
 
     dim_t TX = 1, TY = 1;
     dim_t BX       = 1;
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 3cc9601e4d..9c17143398 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -45,8 +45,8 @@ static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto getIdx = common::getKernel("get_out_idx", {where_cl_src}, tmpltArgs,
-                                    compileOpts);
+    auto getIdx = common::getKernel("get_out_idx", std::array{where_cl_src},
+                                    tmpltArgs, compileOpts);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(local[0] * groups_x * in.info.dims[2],
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index ba202a48c3..72797bd5f5 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -46,8 +46,8 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto wrap =
-        common::getKernel("wrap", {wrap_cl_src}, tmpltArgs, compileOpts);
+    auto wrap = common::getKernel("wrap", std::array{wrap_cl_src}, tmpltArgs,
+                                  compileOpts);
 
     dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
     dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
@@ -91,8 +91,9 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto dilatedWrap = common::getKernel("wrap_dilated", {wrap_dilated_cl_src},
-                                         tmpltArgs, compileOpts);
+    auto dilatedWrap =
+        common::getKernel("wrap_dilated", std::array{wrap_dilated_cl_src},
+                          tmpltArgs, compileOpts);
 
     dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;

From 416bb5bbb0d425cfe827d9a90faf5f80d86595a0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 17 Oct 2022 12:23:36 -0400
Subject: [PATCH 2316/2677] Fix the way we encode backendId for unified backend

The way we were formatting the backend ID was incorrect and failed when we had
more than 3 backends. With the new oneAPI backend, this mechanism was failing
and causing errors.
---
 src/backend/common/ArrayInfo.cpp | 18 +++++-------------
 src/backend/common/ArrayInfo.hpp |  3 ++-
 2 files changed, 7 insertions(+), 14 deletions(-)

diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index 6a0ca86086..c2c6a842f2 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -38,26 +38,18 @@ unsigned ArrayInfo::getDevId() const {
 }
 
 void ArrayInfo::setId(int id) const {
-    // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
-    // for CPU, CUDA and OpenCL respectively
-    // See ArrayInfo.hpp for more
-    unsigned backendId =
-        detail::getBackend() >> 1U;  // Convert enums 1, 2, 4 to ints 0, 1, 2
-    const_cast<ArrayInfo *>(this)->setId(id | 1 << (backendId + 8U));
+    const_cast<ArrayInfo *>(this)->setId(id);
 }
 
 void ArrayInfo::setId(int id) {
-    // 1 << (backendId + 8) sets the 9th, 10th or 11th bit of devId to 1
-    // for CPU, CUDA and OpenCL respectively
-    // See ArrayInfo.hpp for more
-    unsigned backendId =
-        detail::getBackend() >> 1U;  // Convert enums 1, 2, 4 to ints 0, 1, 2
-    devId = id | 1U << (backendId + 8U);
+    /// Shift the backend flag to the end of the devId integer
+    unsigned backendId = detail::getBackend();
+    devId              = id | backendId << 8U;
 }
 
 af_backend ArrayInfo::getBackendId() const {
     // devId >> 8 converts the backend info to 1, 2, 4 which are enums
-    // for CPU, CUDA and OpenCL respectively
+    // for CPU, CUDA, OpenCL, and oneAPI respectively
     // See ArrayInfo.hpp for more
     unsigned backendId = devId >> 8U;
     return static_cast<af_backend>(backendId);
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index 7f5516e5a4..f2a99c0b1e 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -28,7 +28,8 @@ class ArrayInfo {
     // The devId variable stores information about the deviceId as well as the
     // backend. The 8 LSBs (0-7) are used to store the device ID. The 09th LSB
     // is set to 1 if backend is CPU The 10th LSB is set to 1 if backend is CUDA
-    // The 11th LSB is set to 1 if backend is OpenCL
+    // The 11th LSB is set to 1 if backend is OpenCL The 12th LSB is set to 1
+    // for oneAPI
     // This information can be retrieved directly from an af_array by doing
     //     int* devId = reinterpret_cast<int*>(a); // a is an af_array
     //     af_backend backendID = *devId >> 8;   // Returns 1, 2, 4 for CPU,

From 0bb2f7de2a3804cced3b75f28adc5b857c1407b4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 17 Oct 2022 12:26:09 -0400
Subject: [PATCH 2317/2677] Remove extra print from the memcpy kernel in oneAPI

---
 src/backend/oneapi/kernel/memcopy.hpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 701060820f..2bb2443cb2 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -57,9 +57,6 @@ class memCopy {
         const int id0        = group_id_0 * gg.get_local_range(0) + lid0;
         const int id1        = group_id_1 * gg.get_local_range(1) + lid1;
 
-        debug_ << "[" << id0 << "," << id1 << "," << id2 << "," << id3 << "]"
-               << sycl::stream_manipulator::endl;
-
         T *iptr = in_.get_pointer();
         iptr += offset_;
         // FIXME: Do more work per work group

From 9f0829b737bf2828e3a00b37ddd1d268ef382936 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 20 Oct 2022 16:27:37 -0400
Subject: [PATCH 2318/2677] Fix backend_index and NUM_BACKENDS constants in
 unified

---
 src/api/unified/symbol_manager.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index b77f7e9bbe..3106bfa2ae 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -23,7 +23,7 @@
 
 namespace unified {
 
-const int NUM_BACKENDS = 3;
+const int NUM_BACKENDS = 4;
 
 #define UNIFIED_ERROR_LOAD_LIB()                                       \
     AF_RETURN_ERROR(                                                   \
@@ -37,6 +37,7 @@ static inline int backend_index(af::Backend be) {
         case AF_BACKEND_CPU: return 0;
         case AF_BACKEND_CUDA: return 1;
         case AF_BACKEND_OPENCL: return 2;
+        case AF_BACKEND_ONEAPI: return 3;
         default: return -1;
     }
 }

From 3b9b820f4ed1ea5fa54015602fc91cfd6dce4dbc Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Fri, 21 Oct 2022 15:04:18 -0400
Subject: [PATCH 2319/2677] histogram ported to oneapi. (#3305)

* histogram ported to oneapi. 50/62 tests pass. see below

    Batch tests fail with value errors:
      Histogram/1.40Bins0min100max_Batch, where TypeParam = float
      Histogram/3.40Bins0min100max_Batch, where TypeParam = int
      Histogram/4.40Bins0min100max_Batch, where TypeParam = unsigned int
      Histogram/5.40Bins0min100max_Batch, where TypeParam = char
      Histogram/6.40Bins0min100max_Batch, where TypeParam = unsigned char
      Histogram/7.40Bins0min100max_Batch, where TypeParam = short
      Histogram/8.40Bins0min100max_Batch, where TypeParam = unsigned short
      Histogram/9.40Bins0min100max_Batch, where TypeParam = long long
      Histogram/10.40Bins0min100max_Batch, where TypeParam = unsigned long long

    Tests fail because reductions do not function for test harness:
      Histogram.SNIPPET_hist_nominmax
      Histogram.SNIPPET_histequal

    GFOR, LargeBins expected to fail (getMaxMemorySize not OneAPI supported):
      histogram.GFOR
      histogram.LargeBins

    IndexedArray expected to fail without JIT support:
      LargeBins

Authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt       |   1 +
 src/backend/oneapi/histogram.cpp        |   7 +-
 src/backend/oneapi/kernel/histogram.hpp | 167 ++++++++++++++++++++++++
 3 files changed, 173 insertions(+), 2 deletions(-)
 create mode 100755 src/backend/oneapi/kernel/histogram.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 67f9ec8b23..2ab7314581 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -209,6 +209,7 @@ target_sources(afoneapi
     kernel/diagonal.hpp
     kernel/diff.hpp
     kernel/iota.hpp
+    kernel/histogram.hpp
     kernel/memcopy.hpp
     kernel/random_engine.hpp
     kernel/random_engine_write.hpp
diff --git a/src/backend/oneapi/histogram.cpp b/src/backend/oneapi/histogram.cpp
index cf85c4e844..62ccd879af 100644
--- a/src/backend/oneapi/histogram.cpp
+++ b/src/backend/oneapi/histogram.cpp
@@ -11,6 +11,7 @@
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
 #include <histogram.hpp>
+#include <kernel/histogram.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
@@ -22,10 +23,12 @@ template<typename T>
 Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
                       const double &minval, const double &maxval,
                       const bool isLinear) {
-    ONEAPI_NOT_SUPPORTED("");
     const dim4 &dims = in.dims();
     dim4 outDims     = dim4(nbins, 1, dims[2], dims[3]);
-    Array<uint> out  = createValueArray<uint>(outDims, uint(0));
+    // Array<uint> out  = createValueArray<uint>(outDims, uint(0));
+    // \TODO revert createEmptyArray to createValueArray once JIT functions
+    Array<uint> out = createEmptyArray<uint>(outDims);
+    kernel::histogram<T>(out, in, nbins, minval, maxval, isLinear);
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
new file mode 100755
index 0000000000..bc9f74f88c
--- /dev/null
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -0,0 +1,167 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+#define MAX_BINS 4000
+#define THREADS_X 256
+#define THRD_LOAD 16
+
+// using memory_order = memory_order;
+// using memory_scope = memory_scope;
+
+template<typename T>
+using local_atomic_ref =
+    sycl::atomic_ref<T, sycl::memory_order::relaxed,
+                     sycl::memory_scope::work_group,
+                     sycl::access::address_space::local_space>;
+
+template<typename T>
+using global_atomic_ref =
+    sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
+                     sycl::access::address_space::global_space>;
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename T>
+class histogramKernel {
+   public:
+    histogramKernel(sycl::accessor<uint> d_dst, KParam oInfo,
+                    const sycl::accessor<T> d_src, KParam iInfo,
+                    local_accessor<uint, 1> localMemAcc, int len, int nbins,
+                    float minval, float maxval, int nBBS, const bool isLinear)
+        : d_dst_(d_dst)
+        , oInfo_(oInfo)
+        , d_src_(d_src)
+        , iInfo_(iInfo)
+        , localMemAcc_(localMemAcc)
+        , len_(len)
+        , nbins_(nbins)
+        , minval_(minval)
+        , maxval_(maxval)
+        , nBBS_(nBBS)
+        , isLinear_(isLinear) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        unsigned b2   = g.get_group_id(0) / nBBS_;
+        int start     = (g.get_group_id(0) - b2 * nBBS_) * THRD_LOAD *
+                        g.get_local_range(0) +
+                    it.get_local_id(0);
+        int end = fmin((int)(start + THRD_LOAD * g.get_local_range(0)), len_);
+
+        // offset input and output to account for batch ops
+        const T *in = d_src_.get_pointer() + b2 * iInfo_.strides[2] +
+                      g.get_group_id(1) * iInfo_.strides[3] + iInfo_.offset;
+        uint outOffset =
+            b2 * oInfo_.strides[2] + g.get_group_id(1) * oInfo_.strides[3];
+
+        float dx = (maxval_ - minval_) / (float)nbins_;
+
+        bool use_global = nbins_ > MAX_BINS;
+
+        if (!use_global) {
+            for (int i = it.get_local_id(0); i < nbins_;
+                 i += g.get_local_range(0))
+                localMemAcc_[i] = 0;
+            it.barrier();
+        }
+
+        for (int row = start; row < end; row += g.get_local_range(0)) {
+            const int i0  = row % iInfo_.dims[0];
+            const int i1  = row / iInfo_.dims[0];
+            const int idx = isLinear_ ? row : i0 + i1 * iInfo_.strides[1];
+
+            int bin = (int)(((float)in[idx] - minval_) / dx);
+            bin     = fmax(bin, 0);
+            bin     = fmin(bin, (int)nbins_ - 1);
+
+            if (use_global) {
+                global_atomic_ref<uint>(d_dst_[outOffset + bin])++;
+            } else {
+                local_atomic_ref<uint>(localMemAcc_[bin])++;
+            }
+        }
+
+        if (!use_global) {
+            it.barrier();
+            for (int i = it.get_local_id(0); i < nbins_;
+                 i += g.get_local_range(0)) {
+                global_atomic_ref<uint>(d_dst_[outOffset + i]) +=
+                    localMemAcc_[i];
+            }
+        }
+    }
+
+   private:
+    sycl::accessor<uint> d_dst_;
+    KParam oInfo_;
+    sycl::accessor<T> d_src_;
+    KParam iInfo_;
+    local_accessor<uint, 1> localMemAcc_;
+    int len_;
+    int nbins_;
+    float minval_;
+    float maxval_;
+    int nBBS_;
+    bool isLinear_;
+};
+
+template<typename T>
+void histogram(Param<uint> out, const Param<T> in, int nbins, float minval,
+               float maxval, bool isLinear) {
+    int nElems  = in.info.dims[0] * in.info.dims[1];
+    int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
+    int locSize = nbins <= MAX_BINS ? (nbins * sizeof(uint)) : 1;
+
+    auto local           = sycl::range{THREADS_X, 1};
+    const size_t global0 = blk_x * in.info.dims[2] * THREADS_X;
+    const size_t global1 = in.info.dims[3];
+    auto global          = sycl::range{global0, global1};
+
+    // \TODO drop this first memset once createEmptyArray is reverted back to
+    //       createValueArray in ../histogram.cpp
+    getQueue()
+        .submit([&](sycl::handler &h) {
+            auto outAcc = out.data->get_access(h);
+            h.parallel_for(sycl::range<1>{(size_t)nbins},
+                           [=](sycl::id<1> idx) { outAcc[idx[0]] = 0; });
+        })
+        .wait();
+    getQueue().submit([&](sycl::handler &h) {
+        auto inAcc  = in.data->get_access(h);
+        auto outAcc = out.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        auto localMem = local_accessor<uint, 1>(locSize, h);
+
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            histogramKernel<T>(outAcc, out.info, inAcc, in.info, localMem,
+                               nElems, nbins, minval, maxval, blk_x, isLinear));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi

From 0f9a29b678fab650d2ab22bce1988342daa8c392 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 21 Oct 2022 14:53:40 -0400
Subject: [PATCH 2320/2677] Add driver minimums for CUDA 11.8 toolkit

---
 src/backend/cuda/device_manager.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 354a216741..221534f6dc 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -96,6 +96,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {11080, 9, 0, 0},
     {11070, 8, 7, 0},
     {11060, 8, 6, 0},
     {11050, 8, 6, 0},
@@ -131,6 +132,7 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {11080, 450.80f, 452.39f},
         {11070, 450.80f, 452.39f},
         {11060, 450.80f, 452.39f},
         {11050, 450.80f, 452.39f},
@@ -159,7 +161,7 @@ static ComputeCapabilityToStreamingProcessors gpus[] = {
     {0x21, 48},  {0x30, 192}, {0x32, 192}, {0x35, 192}, {0x37, 192},
     {0x50, 128}, {0x52, 128}, {0x53, 128}, {0x60, 64},  {0x61, 128},
     {0x62, 128}, {0x70, 64},  {0x75, 64},  {0x80, 64},  {0x86, 128},
-    {0x87, 128}, {-1, -1},
+    {0x87, 128}, {0x89, 128}, {0x90, 128}, {-1, -1},
 };
 
 // pulled from CUTIL from CUDA SDK

From f79efb9330044db1355ea0d6983c7ae24b76cc9a Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 24 Oct 2022 21:10:43 -0400
Subject: [PATCH 2321/2677] adds shared memory based reduction to oneapi
 backend

---
 src/api/c/optypes.hpp                       |   4 +-
 src/backend/common/Binary.hpp               |  16 +-
 src/backend/oneapi/CMakeLists.txt           |   4 +
 src/backend/oneapi/copy.cpp                 |  14 +-
 src/backend/oneapi/device_manager.cpp       |  33 ++-
 src/backend/oneapi/kernel/reduce.hpp        | 112 ++++++++
 src/backend/oneapi/kernel/reduce_all.hpp    | 288 ++++++++++++++++++++
 src/backend/oneapi/kernel/reduce_config.hpp |  22 ++
 src/backend/oneapi/kernel/reduce_dim.hpp    | 236 ++++++++++++++++
 src/backend/oneapi/kernel/reduce_first.hpp  | 236 ++++++++++++++++
 src/backend/oneapi/platform.cpp             |   6 +-
 src/backend/oneapi/reduce_impl.hpp          |  12 +-
 12 files changed, 945 insertions(+), 38 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/reduce.hpp
 create mode 100644 src/backend/oneapi/kernel/reduce_all.hpp
 create mode 100644 src/backend/oneapi/kernel/reduce_config.hpp
 create mode 100644 src/backend/oneapi/kernel/reduce_dim.hpp
 create mode 100644 src/backend/oneapi/kernel/reduce_first.hpp

diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index aeb90e1dcd..696ae07668 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-typedef enum {
+typedef enum af_op_t : int {
     af_none_t = -1,
     af_add_t  = 0,
     af_sub_t,
@@ -100,4 +100,4 @@ typedef enum {
     af_rsqrt_t,
 
     af_moddims_t
-} af_op_t;
+};
diff --git a/src/backend/common/Binary.hpp b/src/backend/common/Binary.hpp
index 6eeaad2058..ca500ac865 100644
--- a/src/backend/common/Binary.hpp
+++ b/src/backend/common/Binary.hpp
@@ -78,15 +78,17 @@ template<>
 struct Binary<char, af_min_t> {
     static __DH__ char init() { return 1; }
 
-    __DH__ char operator()(char lhs, char rhs) { return min(lhs > 0, rhs > 0); }
+    __DH__ char operator()(char lhs, char rhs) {
+        return detail::min(lhs > 0, rhs > 0);
+    }
 };
 
-#define SPECIALIZE_COMPLEX_MIN(T, Tr)                               \
-    template<>                                                      \
-    struct Binary<T, af_min_t> {                                    \
-        static __DH__ T init() { return scalar<T>(maxval<Tr>()); }  \
-                                                                    \
-        __DH__ T operator()(T lhs, T rhs) { return min(lhs, rhs); } \
+#define SPECIALIZE_COMPLEX_MIN(T, Tr)                                       \
+    template<>                                                              \
+    struct Binary<T, af_min_t> {                                            \
+        static __DH__ T init() { return scalar<T>(maxval<Tr>()); }          \
+                                                                            \
+        __DH__ T operator()(T lhs, T rhs) { return detail::min(lhs, rhs); } \
     };
 
 SPECIALIZE_COMPLEX_MIN(cfloat, float)
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 2ab7314581..d9ae78f742 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -217,6 +217,10 @@ target_sources(afoneapi
     kernel/random_engine_philox.hpp
     kernel/random_engine_threefry.hpp
     kernel/range.hpp
+    kernel/reduce.hpp
+    kernel/reduce_all.hpp
+    kernel/reduce_first.hpp
+    kernel/reduce_dim.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index d852480342..f24db5650c 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -213,13 +213,15 @@ template<typename T>
 T getScalar(const Array<T> &in) {
     T retVal{};
 
+    sycl::buffer retBuffer(&retVal, {1},
+                           {sycl::property::buffer::use_host_ptr()});
+
     getQueue()
-        .submit([=](sycl::handler &h) {
-            sycl::range rr(1);
-            sycl::id offset_id(in.getOffset());
-            auto acc_in = const_cast<sycl::buffer<T> *>(in.get())->get_access(
-                h, rr, offset_id);
-            h.copy(acc_in, (void *)&retVal);
+        .submit([&](sycl::handler &h) {
+            auto acc_in  = in.getData()->get_access(h, sycl::range{1},
+                                                    sycl::id{in.getOffset()});
+            auto acc_out = retBuffer.get_access();
+            h.copy(acc_in, acc_out);
         })
         .wait();
 
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index d8315eac38..ed97248dcb 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -47,8 +47,8 @@ static inline bool compare_default(const unique_ptr<sycl::device>& ldev,
                                    const unique_ptr<sycl::device>& rdev) {
     // TODO: update sorting criteria
     // select according to something applicable to oneapi backend
-    auto l_mem = ldev->get_info<sycl::info::device::local_mem_size>();
-    auto r_mem = rdev->get_info<sycl::info::device::local_mem_size>();
+    auto l_mem = ldev->get_info<sycl::info::device::global_mem_size>();
+    auto r_mem = rdev->get_info<sycl::info::device::global_mem_size>();
     return l_mem > r_mem;
 }
 
@@ -103,19 +103,22 @@ DeviceManager::DeviceManager()
 
     // Create contexts and queues once the sort is done
     for (int i = 0; i < nDevices; i++) {
-        try {
-            mContexts.push_back(make_unique<sycl::context>(*devices[i]));
-            mQueues.push_back(
-                make_unique<sycl::queue>(*mContexts.back(), *devices[i]));
-            mIsGLSharingOn.push_back(false);
-            // TODO:
-            // mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
-            // mPlatforms.push_back(getPlatformEnum(*devices[i]));
-            mDevices.emplace_back(std::move(devices[i]));
-        } catch (sycl::exception& err) {
-            AF_TRACE("Error creating context for device {} with error {}\n",
-                     devices[i]->get_info<sycl::info::device::name>(),
-                     err.what());
+        if (devices[i]->is_gpu() || devices[i]->is_cpu() ||
+            !devices[i]->is_accelerator()) {
+            try {
+                mContexts.push_back(make_unique<sycl::context>(*devices[i]));
+                mQueues.push_back(
+                    make_unique<sycl::queue>(*mContexts.back(), *devices[i]));
+                mIsGLSharingOn.push_back(false);
+                // TODO:
+                // mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
+                // mPlatforms.push_back(getPlatformEnum(*devices[i]));
+                mDevices.emplace_back(std::move(devices[i]));
+            } catch (sycl::exception& err) {
+                AF_TRACE("Error creating context for device {} with error {}\n",
+                         devices[i]->get_info<sycl::info::device::name>(),
+                         err.what());
+            }
         }
     }
     nDevices = mDevices.size();
diff --git a/src/backend/oneapi/kernel/reduce.hpp b/src/backend/oneapi/kernel/reduce.hpp
new file mode 100644
index 0000000000..9db0561b0a
--- /dev/null
+++ b/src/backend/oneapi/kernel/reduce.hpp
@@ -0,0 +1,112 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_all.hpp>
+#include <kernel/reduce_config.hpp>
+#include <kernel/reduce_dim.hpp>
+#include <kernel/reduce_first.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+
+#include <algorithm>
+#include <climits>
+#include <complex>
+#include <iostream>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_cpu_dispatch(Param<To> out, Param<Ti> in, int dim, bool change_nan,
+                         double nanval) {
+    // TODO: use kernels optimized for SIMD-based subgroup sizes
+    reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_gpu_dispatch(Param<To> out, Param<Ti> in, int dim, bool change_nan,
+                         double nanval) {
+    // TODO: use kernels optimized for gpu subgroup sizes
+    reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_default_dispatch(Param<To> out, Param<Ti> in, int dim,
+                             bool change_nan, double nanval) {
+    switch (dim) {
+        case 0:
+            return reduce_first_default<Ti, To, op>(out, in, change_nan,
+                                                    nanval);
+        case 1:
+            return reduce_dim_default<Ti, To, op, 1>(out, in, change_nan,
+                                                     nanval);
+        case 2:
+            return reduce_dim_default<Ti, To, op, 2>(out, in, change_nan,
+                                                     nanval);
+        case 3:
+            return reduce_dim_default<Ti, To, op, 3>(out, in, change_nan,
+                                                     nanval);
+    }
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce(Param<To> out, Param<Ti> in, int dim, bool change_nan,
+            double nanval) {
+    // TODO: logic to dispatch to different kernels depending on device type
+    if (getQueue().get_device().is_cpu()) {
+        reduce_cpu_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+    } else if (getQueue().get_device().is_gpu()) {
+        reduce_gpu_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+    } else {
+        reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+    }
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_all(Param<To> out, Param<Ti> in, bool change_nan, double nanval) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+    bool is_linear = (in.info.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.info.strides[k] ==
+                      (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    }
+
+    if (is_linear) {
+        in.info.dims[0] = in_elements;
+        for (int k = 1; k < 4; k++) {
+            in.info.dims[k]    = 1;
+            in.info.strides[k] = in_elements;
+        }
+    }
+
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    // TODO: perf REPEAT, consider removing or runtime eval
+    // max problem size < SM resident threads, don't use REPEAT
+    uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.info.dims[1], threads_y);
+
+    reduce_all_launcher_default<Ti, To, op>(out, in, blocks_x, blocks_y,
+                                            threads_x, change_nan, nanval);
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
new file mode 100644
index 0000000000..be22e94c90
--- /dev/null
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -0,0 +1,288 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_config.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+
+#include <algorithm>
+#include <climits>
+#include <complex>
+#include <iostream>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename T>
+using global_atomic_ref =
+    sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
+                     sycl::access::address_space::global_space>;
+
+template<typename Ti, typename To, af_op_t op>
+class reduceAllKernelSMEM {
+   public:
+    reduceAllKernelSMEM(sycl::accessor<To> out, KParam oInfo,
+                        sycl::accessor<unsigned> retCount,
+                        sycl::accessor<To> tmp, KParam tmpInfo,
+                        sycl::accessor<Ti> in, KParam iInfo, uint DIMX,
+                        uint groups_x, uint groups_y, uint repeat,
+                        bool change_nan, To nanval,
+                        local_accessor<compute_t<To>, 1> s_ptr,
+                        local_accessor<bool, 1> amLast, sycl::stream debug)
+        : out_(out)
+        , oInfo_(oInfo)
+        , retCount_(retCount)
+        , tmp_(tmp)
+        , tmpInfo_(tmpInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , DIMX_(DIMX)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , repeat_(repeat)
+        , change_nan_(change_nan)
+        , nanval_(nanval)
+        , s_ptr_(s_ptr)
+        , amLast_(amLast)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * DIMX_ + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid = groupId_x * g.get_local_range(0) * repeat_ + lidx;
+        const uint yid = groupId_y * g.get_local_range(1) + lidy;
+
+        common::Binary<compute_t<To>, op> reduce;
+        common::Transform<Ti, compute_t<To>, op> transform;
+
+        const data_t<Ti> *const iptr =
+            in_.get_pointer() + wid * iInfo_.strides[3] +
+            zid * iInfo_.strides[2] + yid * iInfo_.strides[1] + iInfo_.offset;
+
+        bool cond = (yid < iInfo_.dims[1]) && (zid < iInfo_.dims[2]) &&
+                    (wid < iInfo_.dims[3]);
+
+        dim_t last = (xid + repeat_ * DIMX_);
+        int lim    = sycl::min(last, iInfo_.dims[0]);
+
+        compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
+        for (int id = xid; cond && id < lim; id += DIMX_) {
+            compute_t<To> in_val = transform(iptr[id]);
+            if (change_nan_)
+                in_val = !IS_NAN(in_val) ? in_val
+                                         : static_cast<compute_t<To>>(nanval_);
+            out_val = reduce(in_val, out_val);
+        }
+
+        s_ptr_[lid] = out_val;
+
+        group_barrier(g);
+
+        if (THREADS_PER_BLOCK == 256) {
+            if (lid < 128) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 128]);
+            group_barrier(g);
+        }
+
+        if (THREADS_PER_BLOCK >= 128) {
+            if (lid < 64) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 64]);
+            group_barrier(g);
+        }
+
+        if (THREADS_PER_BLOCK >= 64) {
+            if (lid < 32) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 32]);
+            group_barrier(g);
+        }
+
+        // TODO: replace with subgroup operations in optimized kernels
+        if (lid < 16) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 16]);
+        group_barrier(g);
+
+        if (lid < 8) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 8]);
+        group_barrier(g);
+
+        if (lid < 4) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 4]);
+        group_barrier(g);
+
+        if (lid < 2) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 2]);
+        group_barrier(g);
+
+        if (lid < 1) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 1]);
+        group_barrier(g);
+
+        const unsigned total_blocks =
+            (g.get_group_range(0) * g.get_group_range(1));
+        const int uubidx =
+            (g.get_group_range(0) * g.get_group_id(1)) + g.get_group_id(0);
+        if (cond && lid == 0) {
+            if (total_blocks != 1) {
+                tmp_[uubidx] = s_ptr_[0];
+            } else {
+                out_[0] = s_ptr_[0];
+            }
+        }
+
+        // Last block to perform final reduction
+        if (total_blocks > 1) {
+            sycl::atomic_fence(sycl::memory_order::seq_cst,
+                               sycl::memory_scope::device);
+
+            // thread 0 takes a ticket
+            if (lid == 0) {
+                unsigned int ticket = global_atomic_ref<uint>(retCount_[0])++;
+                // If the ticket ID == number of blocks, we are the last block
+                amLast_[0] = (ticket == (total_blocks - 1));
+            }
+            group_barrier(g);
+
+            if (amLast_[0]) {
+                int i   = lid;
+                out_val = common::Binary<compute_t<To>, op>::init();
+
+                while (i < total_blocks) {
+                    compute_t<To> in_val = compute_t<To>(tmp_[i]);
+                    out_val              = reduce(in_val, out_val);
+                    i += THREADS_PER_BLOCK;
+                }
+
+                s_ptr_[lid] = out_val;
+                group_barrier(g);
+
+                // reduce final block
+                if (THREADS_PER_BLOCK == 256) {
+                    if (lid < 128)
+                        s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 128]);
+                    group_barrier(g);
+                }
+
+                if (THREADS_PER_BLOCK >= 128) {
+                    if (lid < 64)
+                        s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 64]);
+                    group_barrier(g);
+                }
+
+                if (THREADS_PER_BLOCK >= 64) {
+                    if (lid < 32)
+                        s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 32]);
+                    group_barrier(g);
+                }
+
+                if (lid < 16)
+                    s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 16]);
+                group_barrier(g);
+
+                if (lid < 8) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 8]);
+                group_barrier(g);
+
+                if (lid < 4) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 4]);
+                group_barrier(g);
+
+                if (lid < 2) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 2]);
+                group_barrier(g);
+
+                if (lid < 1) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 1]);
+                group_barrier(g);
+
+                if (lid == 0) {
+                    out_[0] = s_ptr_[0];
+
+                    // reset retirement count so that next run succeeds
+                    retCount_[0] = 0;
+                }
+            }
+        }
+    }
+
+   protected:
+    sycl::accessor<To> out_;
+    sycl::accessor<unsigned> retCount_;
+    sycl::accessor<To> tmp_;
+    KParam oInfo_, tmpInfo_, iInfo_;
+    sycl::accessor<Ti> in_;
+    uint DIMX_, repeat_;
+    uint groups_x_, groups_y_;
+    bool change_nan_;
+    To nanval_;
+    local_accessor<compute_t<To>, 1> s_ptr_;
+    local_accessor<bool, 1> amLast_;
+    sycl::stream debug_;
+};
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
+                                 const uint groups_x, const uint groups_y,
+                                 const uint threads_x, bool change_nan,
+                                 double nanval) {
+    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
+                          groups_y * in.info.dims[3] * local[1]);
+
+    uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
+
+    long tmp_elements = groups_x * in.info.dims[2] * groups_y * in.info.dims[3];
+    if (tmp_elements > UINT_MAX) {
+        AF_ERROR(
+            "Too many blocks requested (typeof(retirementCount) == unsigned)",
+            AF_ERR_RUNTIME);
+    }
+    Array<To> tmp = createEmptyArray<To>(tmp_elements);
+    // TODO: JIT dependency
+    // Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
+    Array<unsigned> retirementCount = createEmptyArray<unsigned>(1);
+    getQueue().submit([=](sycl::handler &h) {
+        auto acc = retirementCount.getData()->get_access(h);
+        h.single_task([=] { acc[0] = 0; });
+    });
+
+    getQueue()
+        .submit([=](sycl::handler &h) {
+            auto out_acc      = out.data->get_access(h);
+            auto retCount_acc = retirementCount.getData()->get_access(h);
+            auto tmp_acc      = tmp.getData()->get_access(h);
+            auto in_acc       = in.data->get_access(h);
+
+            sycl::stream debug_stream(2048 * 256, 128, h);
+
+            auto shrdMem =
+                local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+            auto amLast = local_accessor<bool, 1>(1, h);
+            h.parallel_for(
+                sycl::nd_range<2>(global, local),
+                reduceAllKernelSMEM<Ti, To, op>(
+                    out_acc, out.info, retCount_acc, tmp_acc, (KParam)tmp,
+                    in_acc, in.info, threads_x, groups_x, groups_y, repeat,
+                    change_nan, scalar<To>(nanval), shrdMem, amLast,
+                    debug_stream));
+        })
+        .wait_and_throw();
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
\ No newline at end of file
diff --git a/src/backend/oneapi/kernel/reduce_config.hpp b/src/backend/oneapi/kernel/reduce_config.hpp
new file mode 100644
index 0000000000..827497967b
--- /dev/null
+++ b/src/backend/oneapi/kernel/reduce_config.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace oneapi {
+namespace kernel {
+
+// TODO: are different values more appropriate for reduce on oneapi?
+static const uint THREADS_PER_BLOCK = 256;
+static const uint THREADS_X         = 32;
+static const uint THREADS_Y         = THREADS_PER_BLOCK / THREADS_X;
+static const uint REPEAT            = 32;
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
new file mode 100644
index 0000000000..5105fb8b1c
--- /dev/null
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -0,0 +1,236 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_config.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+
+#include <algorithm>
+#include <climits>
+#include <complex>
+#include <iostream>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
+class reduceDimKernelSMEM {
+   public:
+    reduceDimKernelSMEM(sycl::accessor<To> out, KParam oInfo,
+                        sycl::accessor<Ti> in, KParam iInfo, uint groups_x,
+                        uint groups_y, uint offset_dim, bool change_nan,
+                        To nanval, local_accessor<compute_t<To>, 1> s_val,
+                        sycl::stream debug)
+        : out_(out)
+        , oInfo_(oInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , offset_dim_(offset_dim)
+        , change_nan_(change_nan)
+        , nanval_(nanval)
+        , s_val_(s_val)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) + lidx;
+        const uint yid       = groupId_y;
+
+        uint ids[4] = {xid, yid, zid, wid};
+
+        data_t<To> *const optr =
+            out_.get_pointer() + ids[3] * oInfo_.strides[3] +
+            ids[2] * oInfo_.strides[2] + ids[1] * oInfo_.strides[1] + ids[0];
+
+        const uint groupIdx_dim = ids[dim];
+        ids[dim]                = ids[dim] * g.get_local_range(1) + lidy;
+
+        const data_t<Ti> *iptr =
+            in_.get_pointer() + ids[3] * iInfo_.strides[3] +
+            ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] + ids[0];
+
+        const uint id_dim_in   = ids[dim];
+        const uint istride_dim = iInfo_.strides[dim];
+        bool is_valid          = (ids[0] < iInfo_.dims[0]) &&
+                        (ids[1] < iInfo_.dims[1]) &&
+                        (ids[2] < iInfo_.dims[2]) && (ids[3] < iInfo_.dims[3]);
+
+        common::Binary<compute_t<To>, op> reduce;
+        common::Transform<Ti, compute_t<To>, op> transform;
+
+        compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
+        for (int id = id_dim_in; is_valid && (id < iInfo_.dims[dim]);
+             id += offset_dim_ * g.get_local_range(1)) {
+            compute_t<To> in_val = transform(*iptr);
+            if (change_nan_)
+                in_val = !IS_NAN(in_val) ? in_val
+                                         : static_cast<compute_t<To>>(nanval_);
+            out_val = reduce(in_val, out_val);
+            iptr += offset_dim_ * g.get_local_range(1) * istride_dim;
+        }
+
+        s_val_[lid] = out_val;
+
+        it.barrier();
+        compute_t<To> *s_ptr = s_val_.get_pointer() + lid;
+
+        if (DIMY == 8) {
+            if (lidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
+            it.barrier();
+        }
+
+        if (DIMY >= 4) {
+            if (lidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
+            it.barrier();
+        }
+
+        if (DIMY >= 2) {
+            if (lidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
+            it.barrier();
+        }
+
+        if (lidy == 0 && is_valid && (groupIdx_dim < oInfo_.dims[dim])) {
+            *optr = data_t<To>(*s_ptr);
+        }
+    }
+
+   protected:
+    sycl::accessor<To> out_;
+    KParam oInfo_, iInfo_;
+    sycl::accessor<Ti> in_;
+    uint groups_x_, groups_y_, offset_dim_;
+    bool change_nan_;
+    To nanval_;
+    local_accessor<compute_t<To>, 1> s_val_;
+    sycl::stream debug_;
+};
+
+template<typename Ti, typename To, af_op_t op, uint dim>
+void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
+                                 const uint threads_y,
+                                 const dim_t blocks_dim[4], bool change_nan,
+                                 double nanval) {
+    sycl::range<2> local(THREADS_X, threads_y);
+    sycl::range<2> global(blocks_dim[0] * blocks_dim[2] * local[0],
+                          blocks_dim[1] * blocks_dim[3] * local[1]);
+
+    getQueue().submit([=](sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        auto shrdMem =
+            local_accessor<compute_t<To>, 1>(THREADS_X * threads_y, h);
+
+        switch (threads_y) {
+            case 8:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceDimKernelSMEM<Ti, To, op, dim, 8>(
+                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim], change_nan,
+                        scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+            case 4:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceDimKernelSMEM<Ti, To, op, dim, 4>(
+                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim], change_nan,
+                        scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+            case 2:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceDimKernelSMEM<Ti, To, op, dim, 2>(
+                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim], change_nan,
+                        scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+            case 1:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceDimKernelSMEM<Ti, To, op, dim, 1>(
+                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
+                        blocks_dim[1], blocks_dim[dim], change_nan,
+                        scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename To, af_op_t op, int dim>
+void reduce_dim_default(Param<To> out, Param<Ti> in, bool change_nan,
+                        double nanval) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = THREADS_X;
+
+    dim_t blocks_dim[] = {divup(in.info.dims[0], threads_x), in.info.dims[1],
+                          in.info.dims[2], in.info.dims[3]};
+    blocks_dim[dim]    = divup(in.info.dims[dim], threads_y * REPEAT);
+
+    Param<To> tmp = out;
+    bufptr<To> tmp_alloc;
+    if (blocks_dim[dim] > 1) {
+        tmp.info.dims[dim] = blocks_dim[dim];
+        int tmp_elements   = tmp.info.dims[0] * tmp.info.dims[1] *
+                           tmp.info.dims[2] * tmp.info.dims[3];
+
+        tmp_alloc = memAlloc<To>(tmp_elements);
+        tmp.data  = tmp_alloc.get();
+
+        tmp.info.dims[dim] = blocks_dim[dim];
+        for (int k = dim + 1; k < 4; k++)
+            tmp.info.strides[k] *= blocks_dim[dim];
+    }
+
+    reduce_dim_launcher_default<Ti, To, op, dim>(tmp, in, threads_y, blocks_dim,
+                                                 change_nan, nanval);
+
+    if (blocks_dim[dim] > 1) {
+        blocks_dim[dim] = 1;
+
+        if (op == af_notzero_t) {
+            reduce_dim_launcher_default<To, To, af_add_t, dim>(
+                out, tmp, threads_y, blocks_dim, change_nan, nanval);
+        } else {
+            reduce_dim_launcher_default<To, To, op, dim>(
+                out, tmp, threads_y, blocks_dim, change_nan, nanval);
+        }
+    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
new file mode 100644
index 0000000000..cd096e69e1
--- /dev/null
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -0,0 +1,236 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_config.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+
+#include <algorithm>
+#include <climits>
+#include <complex>
+#include <iostream>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename Ti, typename To, af_op_t op, uint DIMX>
+class reduceFirstKernelSMEM {
+   public:
+    reduceFirstKernelSMEM(sycl::accessor<To> out, KParam oInfo,
+                          sycl::accessor<Ti> in, KParam iInfo, uint groups_x,
+                          uint groups_y, uint repeat, bool change_nan,
+                          To nanval, local_accessor<compute_t<To>, 1> s_val,
+                          sycl::stream debug)
+        : out_(out)
+        , oInfo_(oInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , repeat_(repeat)
+        , change_nan_(change_nan)
+        , nanval_(nanval)
+        , s_val_(s_val)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid = groupId_x * g.get_local_range(0) * repeat_ + lidx;
+        const uint yid = groupId_y * g.get_local_range(1) + lidy;
+
+        common::Binary<compute_t<To>, op> reduce;
+        common::Transform<Ti, compute_t<To>, op> transform;
+
+        const data_t<Ti> *const iptr =
+            in_.get_pointer() + wid * iInfo_.strides[3] +
+            zid * iInfo_.strides[2] + yid * iInfo_.strides[1] + iInfo_.offset;
+
+        data_t<To> *const optr = out_.get_pointer() + wid * oInfo_.strides[3] +
+                                 zid * oInfo_.strides[2] +
+                                 yid * oInfo_.strides[1];
+
+        bool cond = (yid < iInfo_.dims[1]) && (zid < iInfo_.dims[2]) &&
+                    (wid < iInfo_.dims[3]);
+
+        dim_t last = (xid + repeat_ * DIMX);
+        int lim    = sycl::min(last, iInfo_.dims[0]);
+
+        compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
+        for (int id = xid; cond && id < lim; id += DIMX) {
+            compute_t<To> in_val = transform(iptr[id]);
+            if (change_nan_)
+                in_val = !IS_NAN(in_val) ? in_val
+                                         : static_cast<compute_t<To>>(nanval_);
+            out_val = reduce(in_val, out_val);
+        }
+
+        s_val_[lid] = out_val;
+
+        it.barrier();
+        compute_t<To> *s_ptr = s_val_.get_pointer() + lidy * DIMX;
+
+        if (DIMX == 256) {
+            if (lidx < 128)
+                s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 128]);
+            it.barrier();
+        }
+
+        if (DIMX >= 128) {
+            if (lidx < 64) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 64]);
+            it.barrier();
+        }
+
+        if (DIMX >= 64) {
+            if (lidx < 32) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 32]);
+            it.barrier();
+        }
+
+        // TODO: replace with subgroup operations in optimized kernels
+        if (lidx < 16) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 16]);
+        it.barrier();
+
+        if (lidx < 8) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 8]);
+        it.barrier();
+
+        if (lidx < 4) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 4]);
+        it.barrier();
+
+        if (lidx < 2) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 2]);
+        it.barrier();
+
+        if (lidx < 1) s_ptr[lidx] = reduce(s_ptr[lidx], s_ptr[lidx + 1]);
+        it.barrier();
+
+        if (cond && lidx == 0) optr[groupId_x] = data_t<To>(s_ptr[lidx]);
+    }
+
+   protected:
+    sycl::accessor<To> out_;
+    KParam oInfo_, iInfo_;
+    sycl::accessor<Ti> in_;
+    uint groups_x_, groups_y_, repeat_;
+    bool change_nan_;
+    To nanval_;
+    local_accessor<compute_t<To>, 1> s_val_;
+    sycl::stream debug_;
+};
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
+                                   const uint groups_x, const uint groups_y,
+                                   const uint threads_x, bool change_nan,
+                                   double nanval) {
+    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
+                          groups_y * in.info.dims[3] * local[1]);
+
+    uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
+
+    getQueue().submit([=](sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        auto shrdMem = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+
+        switch (threads_x) {
+            case 32:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               reduceFirstKernelSMEM<Ti, To, op, 32>(
+                                   out_acc, out.info, in_acc, in.info, groups_x,
+                                   groups_y, repeat, change_nan,
+                                   scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+            case 64:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               reduceFirstKernelSMEM<Ti, To, op, 64>(
+                                   out_acc, out.info, in_acc, in.info, groups_x,
+                                   groups_y, repeat, change_nan,
+                                   scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+            case 128:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               reduceFirstKernelSMEM<Ti, To, op, 128>(
+                                   out_acc, out.info, in_acc, in.info, groups_x,
+                                   groups_y, repeat, change_nan,
+                                   scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+            case 256:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               reduceFirstKernelSMEM<Ti, To, op, 256>(
+                                   out_acc, out.info, in_acc, in.info, groups_x,
+                                   groups_y, repeat, change_nan,
+                                   scalar<To>(nanval), shrdMem, debug_stream));
+                break;
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_first_default(Param<To> out, Param<Ti> in, bool change_nan,
+                          double nanval) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.info.dims[1], threads_y);
+
+    Param<To> tmp = out;
+    bufptr<To> tmp_alloc;
+    if (blocks_x > 1) {
+        tmp_alloc = memAlloc<To>(blocks_x * in.info.dims[1] * in.info.dims[2] *
+                                 in.info.dims[3]);
+        tmp.data  = tmp_alloc.get();
+
+        tmp.info.dims[0] = blocks_x;
+        for (int k = 1; k < 4; k++) tmp.info.strides[k] *= blocks_x;
+    }
+
+    reduce_first_launcher_default<Ti, To, op>(tmp, in, blocks_x, blocks_y,
+                                              threads_x, change_nan, nanval);
+
+    if (blocks_x > 1) {
+        // FIXME: Is there an alternative to the if condition?
+        if (op == af_notzero_t) {
+            reduce_first_launcher_default<To, To, af_add_t>(
+                out, tmp, 1, blocks_y, threads_x, change_nan, nanval);
+        } else {
+            reduce_first_launcher_default<To, To, op>(
+                out, tmp, 1, blocks_y, threads_x, change_nan, nanval);
+        }
+    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index f2128c5ac5..d32d9e8d46 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -355,13 +355,11 @@ bool isGLSharingSupported() {
 
 bool isDoubleSupported(unsigned device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
-
-    sycl::device dev;
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
-        dev = *devMngr.mDevices[device];
+        sycl::device& dev = *devMngr.mDevices[device];
+        return dev.has(sycl::aspect::fp64);
     }
-    return dev.has(sycl::aspect::fp64);
 }
 
 bool isHalfSupported(unsigned device) {
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index 0300fa99b0..007fbccac4 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <err_oneapi.hpp>
-//#include <kernel/reduce.hpp>
+#include <kernel/reduce.hpp>
 //#include <kernel/reduce_by_key.hpp>
 #include <reduce.hpp>
 #include <af/dim4.hpp>
@@ -17,12 +17,16 @@
 
 using af::dim4;
 using std::swap;
+
 namespace oneapi {
+
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
                  double nanval) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<To> out = createEmptyArray<To>(1);
+    dim4 odims    = in.dims();
+    odims[dim]    = 1;
+    Array<To> out = createEmptyArray<To>(odims);
+    kernel::reduce<Ti, To, op>(out, in, dim, change_nan, nanval);
     return out;
 }
 
@@ -35,8 +39,8 @@ void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
 
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
-    ONEAPI_NOT_SUPPORTED("");
     Array<To> out = createEmptyArray<To>(1);
+    kernel::reduce_all<Ti, To, op>(out, in, change_nan, nanval);
     return out;
 }
 

From a4c022081456c7c96df4ae102d63b1760b72f66c Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 1 Nov 2022 19:43:22 -0400
Subject: [PATCH 2322/2677] adds mean kernel

---
 src/backend/oneapi/CMakeLists.txt        |   1 +
 src/backend/oneapi/kernel/mean.hpp       | 789 +++++++++++++++++++++++
 src/backend/oneapi/kernel/reduce_all.hpp |  40 +-
 src/backend/oneapi/mean.cpp              |  20 +-
 4 files changed, 813 insertions(+), 37 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/mean.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index d9ae78f742..e8df95c5ed 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -211,6 +211,7 @@ target_sources(afoneapi
     kernel/iota.hpp
     kernel/histogram.hpp
     kernel/memcopy.hpp
+    kernel/mean.hpp
     kernel/random_engine.hpp
     kernel/random_engine_write.hpp
     kernel/random_engine_mersenne.hpp
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
new file mode 100644
index 0000000000..f63f46096c
--- /dev/null
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -0,0 +1,789 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+//#include <copy.hpp>?
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_config.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+
+#include <iostream>
+#include <memory>
+#include <vector>
+
+namespace oneapi {
+
+/*
+TODO: port half
+__device__ auto operator*(float lhs, __half rhs) -> __half {
+    return __float2half(lhs * __half2float(rhs));
+}
+
+__device__ auto operator/(__half lhs, float rhs) -> __half {
+    return __float2half(__half2float(lhs) / rhs);
+}
+*/
+
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename To, typename Tw>
+void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
+    if (((*l_wt) != (Tw)0) || (r_wt != (Tw)0)) {
+        Tw l_scale = (*l_wt);
+        (*l_wt) += r_wt;
+        l_scale = l_scale / (*l_wt);
+
+        Tw r_scale = r_wt / (*l_wt);
+        (*lhs)     = (l_scale * *lhs) + (r_scale * rhs);
+    }
+}
+
+template<typename Ti, typename Tw, typename To, uint dim, uint DIMY>
+class meanDimKernelSMEM {
+   public:
+    meanDimKernelSMEM(sycl::accessor<To> out, KParam oInfo,
+                      sycl::accessor<Tw> owt, KParam owInfo,
+                      sycl::accessor<Ti> in, KParam iInfo,
+                      sycl::accessor<Tw> iwt, KParam iwInfo, uint groups_x,
+                      uint groups_y, uint offset_dim,
+                      local_accessor<compute_t<To>, 1> s_val,
+                      local_accessor<compute_t<Tw>, 1> s_idx,
+                      sycl::stream debug, bool input_weight, bool output_weight)
+        : out_(out)
+        , oInfo_(oInfo)
+        , owt_(owt)
+        , owInfo_(owInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , iwt_(iwt)
+        , iwInfo_(iwInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , offset_dim_(offset_dim)
+        , s_val_(s_val)
+        , s_idx_(s_idx)
+        , debug_(debug)
+        , input_weight_(input_weight)
+        , output_weight_(output_weight) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid        = g.get_group_id(0) / groups_x_;
+        const uint wid        = g.get_group_id(1) / groups_y_;
+        const uint groupIdx_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupIdx_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid        = groupIdx_x * g.get_local_range(0) + lidx;
+        const uint yid =
+            groupIdx_y;  // yid  of output. updated for input later.
+
+        uint ids[4] = {xid, yid, zid, wid};
+
+        const Ti *iptr = in_.get_pointer();
+        To *optr       = out_.get_pointer();
+
+        uint ooffset = ids[3] * oInfo_.strides[3] + ids[2] * oInfo_.strides[2] +
+                       ids[1] * oInfo_.strides[1] + ids[0];
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before
+        // offsetting in
+        optr += ooffset;
+
+        const uint blockIdx_dim = ids[dim];
+        ids[dim]                = ids[dim] * g.get_local_range(1) + lidy;
+
+        uint ioffset = ids[3] * iInfo_.strides[3] + ids[2] * iInfo_.strides[2] +
+                       ids[1] * iInfo_.strides[1] + ids[0];
+        iptr += ioffset;
+
+        const Tw *iwptr;
+        Tw *owptr;
+
+        if (output_weight_) owptr = owt_.get_pointer() + ooffset;
+        if (input_weight_) iwptr = iwt_.get_pointer() + ioffset;
+
+        const uint id_dim_in   = ids[dim];
+        const uint istride_dim = iInfo_.strides[dim];
+
+        bool is_valid = (ids[0] < iInfo_.dims[0]) &&
+                        (ids[1] < iInfo_.dims[1]) &&
+                        (ids[2] < iInfo_.dims[2]) && (ids[3] < iInfo_.dims[3]);
+
+        common::Transform<Ti, compute_t<To>, af_add_t> transform;
+
+        compute_t<To> val    = common::Binary<compute_t<To>, af_add_t>::init();
+        compute_t<Tw> weight = common::Binary<compute_t<Tw>, af_add_t>::init();
+
+        if (is_valid && id_dim_in < iInfo_.dims[dim]) {
+            val = transform(*iptr);
+            if (iwptr != NULL) {
+                weight = *iwptr;
+            } else {
+                weight = (Tw)1;
+            }
+        }
+
+        const uint id_dim_in_start =
+            id_dim_in + offset_dim_ * g.get_local_range(0);
+
+        for (int id = id_dim_in_start; is_valid && (id < iInfo_.dims[dim]);
+             id += offset_dim_ * g.get_local_range(0)) {
+            iptr = iptr + offset_dim_ * g.get_local_range(0) * istride_dim;
+            if (input_weight_) {
+                iwptr =
+                    iwptr + offset_dim_ * g.get_local_range(0) * istride_dim;
+                stable_mean(&val, &weight, transform(*iptr),
+                            compute_t<Tw>(*iwptr));
+            } else {
+                // Faster version of stable_mean when iwptr is NULL
+                val    = val + (transform(*iptr) - val) / (weight + (Tw)1);
+                weight = weight + (Tw)1;
+            }
+        }
+
+        s_val_[lid] = val;
+        s_idx_[lid] = weight;
+
+        compute_t<To> *s_vptr = s_val_.get_pointer() + lid;
+        compute_t<Tw> *s_iptr = s_idx_.get_pointer() + lid;
+        group_barrier(g);
+
+        if (DIMY == 8) {
+            if (lidy < 4) {
+                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 4],
+                            s_iptr[THREADS_X * 4]);
+            }
+            group_barrier(g);
+        }
+
+        if (DIMY >= 4) {
+            if (lidy < 2) {
+                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 2],
+                            s_iptr[THREADS_X * 2]);
+            }
+            group_barrier(g);
+        }
+
+        if (DIMY >= 2) {
+            if (lidy < 1) {
+                stable_mean(s_vptr, s_iptr, s_vptr[THREADS_X * 1],
+                            s_iptr[THREADS_X * 1]);
+            }
+            group_barrier(g);
+        }
+
+        if (lidy == 0 && is_valid && (blockIdx_dim < oInfo_.dims[dim])) {
+            *optr = *s_vptr;
+            if (output_weight_) *owptr = *s_iptr;
+        }
+    }
+
+   protected:
+    sycl::accessor<To> out_;
+    sycl::accessor<Tw> owt_;
+    sycl::accessor<Ti> in_;
+    sycl::accessor<Tw> iwt_;
+    KParam oInfo_, owInfo_, iInfo_, iwInfo_;
+    const uint groups_x_, groups_y_, offset_dim_;
+    local_accessor<compute_t<To>, 1> s_val_;
+    local_accessor<compute_t<Tw>, 1> s_idx_;
+    bool input_weight_, output_weight_;
+    sycl::stream debug_;
+};
+
+template<typename Ti, typename Tw, typename To, int dim>
+void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
+                       Param<Tw> iwt, const uint threads_y,
+                       const dim_t blocks_dim[4]) {
+    sycl::range<2> local(THREADS_X, threads_y);
+    sycl::range<2> global(blocks_dim[0] * blocks_dim[2] * local[0],
+                          blocks_dim[1] * blocks_dim[3] * local[1]);
+
+    sycl::buffer<Tw, 1> empty(sycl::range<1>{1});
+    getQueue().submit([&](sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 2048, 2048, h);
+
+        auto s_val = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+        auto s_idx = local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
+
+        bool input_weight = ((iwt.info.dims[0] * iwt.info.dims[1] *
+                              iwt.info.dims[2] * iwt.info.dims[3]) != 0);
+
+        bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
+                               owt.info.dims[2] * owt.info.dims[3]) != 0);
+
+        auto owt_acc =
+            (output_weight) ? owt.data->get_access(h) : empty.get_access(h);
+        auto iwt_acc =
+            (input_weight) ? iwt.data->get_access(h) : empty.get_access(h);
+
+        switch (threads_y) {
+            case 8:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                                   out_acc, out.info, owt_acc, owt.info, in_acc,
+                                   in.info, iwt_acc, iwt.info, blocks_dim[0],
+                                   blocks_dim[1], blocks_dim[dim], s_val, s_idx,
+                                   debug_stream, input_weight, output_weight));
+                break;
+            case 4:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                                   out_acc, out.info, owt_acc, owt.info, in_acc,
+                                   in.info, iwt_acc, iwt.info, blocks_dim[0],
+                                   blocks_dim[1], blocks_dim[dim], s_val, s_idx,
+                                   debug_stream, input_weight, output_weight));
+                break;
+            case 2:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                                   out_acc, out.info, owt_acc, owt.info, in_acc,
+                                   in.info, iwt_acc, iwt.info, blocks_dim[0],
+                                   blocks_dim[1], blocks_dim[dim], s_val, s_idx,
+                                   debug_stream, input_weight, output_weight));
+                break;
+            case 1:
+                h.parallel_for(sycl::nd_range<2>(global, local),
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                                   out_acc, out.info, owt_acc, owt.info, in_acc,
+                                   in.info, iwt_acc, iwt.info, blocks_dim[0],
+                                   blocks_dim[1], blocks_dim[dim], s_val, s_idx,
+                                   debug_stream, input_weight, output_weight));
+                break;
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename Tw, typename To, int dim>
+void mean_dim(Param<To> out, Param<Ti> in, Param<Tw> iwt) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = THREADS_X;
+
+    dim_t blocks_dim[] = {divup(in.info.dims[0], threads_x), in.info.dims[1],
+                          in.info.dims[2], in.info.dims[3]};
+
+    blocks_dim[dim] = divup(in.info.dims[dim], threads_y * REPEAT);
+
+    Array<To> tmpOut = createEmptyArray<To>(dim4());
+    Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
+
+    if (blocks_dim[dim] > 1) {
+        dim4 dims(4, out.info.dims);
+        dims[dim] = blocks_dim[dim];
+        tmpOut    = createEmptyArray<To>(dims);
+        tmpWt     = createEmptyArray<Tw>(dims);
+    } else {
+        tmpOut = createParamArray(out, false);
+    }
+
+    mean_dim_launcher<Ti, Tw, To, dim>(tmpOut, tmpWt, in, iwt, threads_y,
+                                       blocks_dim);
+
+    if (blocks_dim[dim] > 1) {
+        blocks_dim[dim] = 1;
+
+        Array<Tw> owt = createEmptyArray<Tw>(dim4());
+        mean_dim_launcher<To, Tw, To, dim>(out, owt, tmpOut, tmpWt, threads_y,
+                                           blocks_dim);
+    }
+}
+
+// Calculate mean along the first dimension. If wt is an empty Param, use
+// weight as 1 and treat it as count. If owt is empty Param, do not write
+// temporary reduced counts/weights to it.
+template<typename Ti, typename Tw, typename To>
+class meanFirstKernelSMEM {
+   public:
+    meanFirstKernelSMEM(sycl::accessor<To> out, KParam oInfo,
+                        sycl::accessor<Tw> owt, KParam owInfo,
+                        sycl::accessor<Ti> in, KParam iInfo,
+                        sycl::accessor<Tw> iwt, KParam iwInfo, const uint DIMX,
+                        const uint groups_x, const uint groups_y,
+                        const uint repeat,
+                        local_accessor<compute_t<To>, 1> s_val,
+                        local_accessor<compute_t<Tw>, 1> s_idx,
+                        sycl::stream debug, bool input_weight,
+                        bool output_weight)
+        : out_(out)
+        , oInfo_(oInfo)
+        , owt_(owt)
+        , owInfo_(owInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , iwt_(iwt)
+        , iwInfo_(iwInfo)
+        , DIMX_(DIMX)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , repeat_(repeat)
+        , s_val_(s_val)
+        , s_idx_(s_idx)
+        , debug_(debug)
+        , input_weight_(input_weight)
+        , output_weight_(output_weight) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * DIMX_ + lidx;
+
+        const uint zid        = g.get_group_id(0) / groups_x_;
+        const uint wid        = g.get_group_id(1) / groups_y_;
+        const uint groupIdx_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupIdx_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid = groupIdx_x * g.get_local_range(0) * repeat_ + lidx;
+        const uint yid = groupIdx_y * g.get_local_range(1) + lidy;
+
+        const Ti *iptr = in_.get_pointer();
+        To *optr       = out_.get_pointer();
+
+        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
+                yid * iInfo_.strides[1];
+        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] +
+                yid * oInfo_.strides[1];
+
+        const Tw *iwptr;
+        Tw *owptr;
+        if (input_weight_)
+            iwptr = iwt_.get_pointer() + wid * iwInfo_.strides[3] +
+                    zid * iwInfo_.strides[2] + yid * iwInfo_.strides[1];
+
+        if (output_weight_)
+            owptr = owt_.get_pointer() + wid * oInfo_.strides[3] +
+                    zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+
+        bool cond = (yid < iInfo_.dims[1] && zid < iInfo_.dims[2] &&
+                     wid < iInfo_.dims[3]);
+
+        int lim = sycl::min((dim_t)(xid + repeat_ * DIMX_), iInfo_.dims[0]);
+
+        common::Transform<Ti, compute_t<To>, af_add_t> transform;
+
+        compute_t<To> val    = common::Binary<compute_t<To>, af_add_t>::init();
+        compute_t<Tw> weight = common::Binary<compute_t<Tw>, af_add_t>::init();
+
+        if (cond && xid < lim) {
+            val = transform(iptr[xid]);
+            if (input_weight_) {
+                weight = iwptr[xid];
+            } else {
+                weight = (Tw)1;
+            }
+        }
+
+        if (input_weight_) {
+            for (int id = xid + DIMX_; cond && id < lim; id += DIMX_) {
+                stable_mean(&val, &weight, transform(iptr[id]),
+                            compute_t<Tw>(iwptr[id]));
+            }
+        } else {
+            for (int id = xid + DIMX_; cond && id < lim; id += DIMX_) {
+                // Faster version of stable_mean when iwptr is NULL
+                val    = val + (transform(iptr[id]) - val) / (weight + (Tw)1);
+                weight = weight + (Tw)1;
+            }
+        }
+
+        s_val_[lid] = val;
+        s_idx_[lid] = weight;
+        group_barrier(g);
+
+        compute_t<To> *s_vptr = s_val_.get_pointer() + lidy * DIMX_;
+        compute_t<Tw> *s_iptr = s_idx_.get_pointer() + lidy * DIMX_;
+
+        if (DIMX_ == 256) {
+            if (lidx < 128) {
+                stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 128],
+                            s_iptr[lidx + 128]);
+            }
+            group_barrier(g);
+        }
+
+        if (DIMX_ >= 128) {
+            if (lidx < 64) {
+                stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 64],
+                            s_iptr[lidx + 64]);
+            }
+            group_barrier(g);
+        }
+
+        if (DIMX_ >= 64) {
+            if (lidx < 32) {
+                stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 32],
+                            s_iptr[lidx + 32]);
+            }
+            group_barrier(g);
+        }
+
+        if (lidx < 16) {
+            stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 16],
+                        s_iptr[lidx + 16]);
+        }
+        group_barrier(g);
+
+        if (lidx < 8) {
+            stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 8],
+                        s_iptr[lidx + 8]);
+        }
+        group_barrier(g);
+
+        if (lidx < 4) {
+            stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 4],
+                        s_iptr[lidx + 4]);
+        }
+        group_barrier(g);
+
+        if (lidx < 2) {
+            stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 2],
+                        s_iptr[lidx + 2]);
+        }
+        group_barrier(g);
+
+        if (lidx < 1) {
+            stable_mean(s_vptr + lidx, s_iptr + lidx, s_vptr[lidx + 1],
+                        s_iptr[lidx + 1]);
+        }
+        group_barrier(g);
+
+        if (cond && lidx == 0) {
+            optr[groupIdx_x] = s_vptr[0];
+            if (output_weight_) owptr[groupIdx_x] = s_iptr[0];
+        }
+    }
+
+   protected:
+    sycl::accessor<To> out_;
+    sycl::accessor<Tw> owt_;
+    sycl::accessor<Ti> in_;
+    sycl::accessor<Tw> iwt_;
+    KParam oInfo_, owInfo_, iInfo_, iwInfo_;
+    const uint DIMX_, groups_x_, groups_y_, repeat_;
+    local_accessor<compute_t<To>, 1> s_val_;
+    local_accessor<compute_t<Tw>, 1> s_idx_;
+    bool input_weight_, output_weight_;
+    sycl::stream debug_;
+};
+
+template<typename Ti, typename Tw, typename To>
+void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
+                         Param<Tw> iwt, const uint groups_x,
+                         const uint groups_y, const uint threads_x) {
+    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
+                          groups_y * in.info.dims[3] * local[1]);
+
+    uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
+
+    sycl::buffer<Tw, 1> empty(sycl::range<1>{1});
+    getQueue().submit([&](sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 2048, 2048, h);
+
+        auto s_val = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+        auto s_idx = local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
+
+        bool input_weight = ((iwt.info.dims[0] * iwt.info.dims[1] *
+                              iwt.info.dims[2] * iwt.info.dims[3]) != 0);
+
+        bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
+                               owt.info.dims[2] * owt.info.dims[3]) != 0);
+
+        auto owt_acc =
+            (output_weight) ? owt.data->get_access(h) : empty.get_access(h);
+        auto iwt_acc =
+            (input_weight) ? iwt.data->get_access(h) : empty.get_access(h);
+
+        h.parallel_for(
+            sycl::nd_range<2>(global, local),
+            meanFirstKernelSMEM<Ti, Tw, To>(
+                out_acc, out.info, owt_acc, owt.info, in_acc, in.info, iwt_acc,
+                iwt.info, threads_x, groups_x, groups_y, repeat, s_val, s_idx,
+                debug_stream, input_weight, output_weight));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean_first(Param<To> out, Param<Ti> in, Param<Tw> iwt) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint blocks_y = divup(in.info.dims[1], threads_y);
+
+    Array<To> tmpOut = createEmptyArray<To>(dim4());
+    Array<Tw> tmpWt  = createEmptyArray<Tw>(dim4());
+    if (blocks_x > 1) {
+        tmpOut = createEmptyArray<To>(
+            {blocks_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+        tmpWt = createEmptyArray<Tw>(
+            {blocks_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+    } else {
+        tmpOut = createParamArray(out, false);
+    }
+
+    mean_first_launcher<Ti, Tw, To>(tmpOut, tmpWt, in, iwt, blocks_x, blocks_y,
+                                    threads_x);
+
+    if (blocks_x > 1) {
+        Param<Tw> owt;
+        owt.data = nullptr;
+        mean_first_launcher<To, Tw, To>(out, owt, tmpOut, tmpWt, 1, blocks_y,
+                                        threads_x);
+    }
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean_weighted(Param<To> out, Param<Ti> in, Param<Tw> iwt, int dim) {
+    switch (dim) {
+        case 0: return mean_first<Ti, Tw, To>(out, in, iwt);
+        case 1: return mean_dim<Ti, Tw, To, 1>(out, in, iwt);
+        case 2: return mean_dim<Ti, Tw, To, 2>(out, in, iwt);
+        case 3: return mean_dim<Ti, Tw, To, 3>(out, in, iwt);
+    }
+}
+
+template<typename Ti, typename Tw, typename To>
+void mean(Param<To> out, Param<Ti> in, int dim) {
+    Param<Tw> dummy_weight;
+    mean_weighted<Ti, Tw, To>(out, in, dummy_weight, dim);
+}
+
+template<typename T, typename Tw>
+T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096) {
+        bool in_is_linear = (in.info.strides[0] == 1);
+        bool wt_is_linear = (iwt.info.strides[0] == 1);
+        for (int k = 1; k < 4; k++) {
+            in_is_linear &= (in.info.strides[k] ==
+                             (in.info.strides[k - 1] * in.info.dims[k - 1]));
+            wt_is_linear &= (iwt.info.strides[k] ==
+                             (iwt.info.strides[k - 1] * iwt.info.dims[k - 1]));
+        }
+
+        if (in_is_linear && wt_is_linear) {
+            in.info.dims[0] = in_elements;
+            for (int k = 1; k < 4; k++) {
+                in.info.dims[k]    = 1;
+                in.info.strides[k] = in_elements;
+            }
+
+            for (int k = 0; k < 4; k++) {
+                iwt.info.dims[k]    = in.info.dims[k];
+                iwt.info.strides[k] = in.info.strides[k];
+            }
+        }
+
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+        uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.info.dims[1], threads_y);
+
+        Array<T> tmpOut = createEmptyArray<T>(
+            {blocks_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+        Array<Tw> tmpWt = createEmptyArray<Tw>(
+            {blocks_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+
+        int tmp_elements = tmpOut.elements();
+
+        mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x,
+                                      blocks_y, threads_x);
+
+        std::vector<T> h_ptr(tmp_elements);
+        std::vector<Tw> h_wptr(tmp_elements);
+        sycl::buffer hBuffer(h_ptr.data(), {tmp_elements},
+                             {sycl::property::buffer::use_host_ptr()});
+        sycl::buffer hwBuffer(h_wptr.data(), {tmp_elements},
+                              {sycl::property::buffer::use_host_ptr()});
+
+        auto e1 = getQueue().submit([&](sycl::handler &h) {
+            auto acc_in =
+                tmpOut.getData()->get_access(h, sycl::range{tmp_elements});
+            auto acc_out = hBuffer.get_access();
+            h.copy(acc_in, acc_out);
+        });
+        auto e2 = getQueue().submit([&](sycl::handler &h) {
+            auto acc_in =
+                tmpWt.getData()->get_access(h, sycl::range{tmp_elements});
+            auto acc_out = hwBuffer.get_access();
+            h.copy(acc_in, acc_out);
+        });
+        e1.wait();
+        e2.wait();
+
+        compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
+
+        for (int i = 1; i < tmp_elements; i++) {
+            stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
+                        compute_t<Tw>(h_wptr[i]));
+        }
+
+        return static_cast<T>(val);
+    } else {
+        std::vector<T> h_ptr(in_elements);
+        std::vector<Tw> h_wptr(in_elements);
+
+        sycl::buffer hBuffer(h_ptr.data(), {in_elements},
+                             {sycl::property::buffer::use_host_ptr()});
+        sycl::buffer hwBuffer(h_wptr.data(), {in_elements},
+                              {sycl::property::buffer::use_host_ptr()});
+
+        auto e1 = getQueue().submit([&](sycl::handler &h) {
+            auto acc_in  = in.data->get_access(h, sycl::range{in_elements});
+            auto acc_out = hBuffer.get_access();
+            h.copy(acc_in, acc_out);
+        });
+        auto e2 = getQueue().submit([&](sycl::handler &h) {
+            auto acc_in  = iwt.data->get_access(h, sycl::range{in_elements});
+            auto acc_out = hwBuffer.get_access();
+            h.copy(acc_in, acc_out);
+        });
+        e1.wait();
+        e2.wait();
+
+        compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
+        for (int i = 1; i < in_elements; i++) {
+            stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
+                        compute_t<Tw>(h_wptr[i]));
+        }
+
+        return static_cast<T>(val);
+    }
+}
+
+template<typename Ti, typename Tw, typename To>
+To mean_all(Param<Ti> in) {
+    using std::unique_ptr;
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+    bool is_linear = (in.info.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.info.strides[k] ==
+                      (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    }
+
+    // FIXME: Use better heuristics to get to the optimum number
+    if (in_elements > 4096 || !is_linear) {
+        if (is_linear) {
+            in.info.dims[0] = in_elements;
+            for (int k = 1; k < 4; k++) {
+                in.info.dims[k]    = 1;
+                in.info.strides[k] = in_elements;
+            }
+        }
+
+        uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+        threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+        uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+        uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+        uint blocks_y = divup(in.info.dims[1], threads_y);
+
+        dim4 outDims(blocks_x, in.info.dims[1], in.info.dims[2],
+                     in.info.dims[3]);
+
+        Array<To> tmpOut = createEmptyArray<To>(outDims);
+        Array<Tw> tmpCt  = createEmptyArray<Tw>(outDims);
+
+        Param<Tw> iwt;
+        mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x,
+                                        blocks_y, threads_x);
+
+        int tmp_elements = tmpOut.elements();
+        std::vector<To> h_ptr(tmp_elements);
+        std::vector<Tw> h_cptr(tmp_elements);
+
+        sycl::buffer hBuffer(h_ptr.data(), {tmp_elements},
+                             {sycl::property::buffer::use_host_ptr()});
+        sycl::buffer hcBuffer(h_cptr.data(), {tmp_elements},
+                              {sycl::property::buffer::use_host_ptr()});
+
+        auto e1 = getQueue().submit([&](sycl::handler &h) {
+            auto acc_in =
+                tmpOut.getData()->get_access(h, sycl::range{tmp_elements});
+            auto acc_out = hBuffer.get_access();
+            h.copy(acc_in, acc_out);
+        });
+        auto e2 = getQueue().submit([&](sycl::handler &h) {
+            auto acc_in =
+                tmpCt.getData()->get_access(h, sycl::range{tmp_elements});
+            auto acc_out = hcBuffer.get_access();
+            h.copy(acc_in, acc_out);
+        });
+        e1.wait();
+        e2.wait();
+
+        compute_t<To> val    = static_cast<compute_t<To>>(h_ptr[0]);
+        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_cptr[0]);
+
+        for (int i = 1; i < tmp_elements; i++) {
+            stable_mean(&val, &weight, compute_t<To>(h_ptr[i]),
+                        compute_t<Tw>(h_cptr[i]));
+        }
+
+        return static_cast<To>(val);
+    } else {
+        std::vector<Ti> h_ptr(in_elements);
+        sycl::buffer outBuffer(h_ptr.data(), {in_elements},
+                               {sycl::property::buffer::use_host_ptr()});
+
+        getQueue()
+            .submit([&](sycl::handler &h) {
+                auto acc_in  = in.data->get_access(h);
+                auto acc_out = outBuffer.get_access();
+                h.copy(acc_in, acc_out);
+            })
+            .wait();
+
+        common::Transform<Ti, compute_t<To>, af_add_t> transform;
+        compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
+
+        compute_t<To> val    = transform(h_ptr[0]);
+        compute_t<Tw> weight = count;
+        for (int i = 1; i < in_elements; i++) {
+            stable_mean(&val, &weight, transform(h_ptr[i]), count);
+        }
+
+        return static_cast<To>(val);
+    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index be22e94c90..372dc931fb 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -260,29 +260,25 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
         h.single_task([=] { acc[0] = 0; });
     });
 
-    getQueue()
-        .submit([=](sycl::handler &h) {
-            auto out_acc      = out.data->get_access(h);
-            auto retCount_acc = retirementCount.getData()->get_access(h);
-            auto tmp_acc      = tmp.getData()->get_access(h);
-            auto in_acc       = in.data->get_access(h);
-
-            sycl::stream debug_stream(2048 * 256, 128, h);
-
-            auto shrdMem =
-                local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
-            auto amLast = local_accessor<bool, 1>(1, h);
-            h.parallel_for(
-                sycl::nd_range<2>(global, local),
-                reduceAllKernelSMEM<Ti, To, op>(
-                    out_acc, out.info, retCount_acc, tmp_acc, (KParam)tmp,
-                    in_acc, in.info, threads_x, groups_x, groups_y, repeat,
-                    change_nan, scalar<To>(nanval), shrdMem, amLast,
-                    debug_stream));
-        })
-        .wait_and_throw();
+    getQueue().submit([=](sycl::handler &h) {
+        auto out_acc      = out.data->get_access(h);
+        auto retCount_acc = retirementCount.getData()->get_access(h);
+        auto tmp_acc      = tmp.getData()->get_access(h);
+        auto in_acc       = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        auto shrdMem = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+        auto amLast  = local_accessor<bool, 1>(1, h);
+        h.parallel_for(
+            sycl::nd_range<2>(global, local),
+            reduceAllKernelSMEM<Ti, To, op>(
+                out_acc, out.info, retCount_acc, tmp_acc, (KParam)tmp, in_acc,
+                in.info, threads_x, groups_x, groups_y, repeat, change_nan,
+                scalar<To>(nanval), shrdMem, amLast, debug_stream));
+    });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 }  // namespace kernel
-}  // namespace oneapi
\ No newline at end of file
+}  // namespace oneapi
diff --git a/src/backend/oneapi/mean.cpp b/src/backend/oneapi/mean.cpp
index 41d72a547e..85c4bc0576 100644
--- a/src/backend/oneapi/mean.cpp
+++ b/src/backend/oneapi/mean.cpp
@@ -11,7 +11,7 @@
 #include <mean.hpp>
 
 #include <common/half.hpp>
-// #include <kernel/mean.hpp>
+#include <kernel/mean.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
@@ -21,39 +21,29 @@ using std::swap;
 namespace oneapi {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
-    ONEAPI_NOT_SUPPORTED("mean Not supported");
-
-    return To(0);
-    // return kernel::meanAll<Ti, Tw, To>(in);
+    return kernel::mean_all<Ti, Tw, To>(in);
 }
 
 template<typename T, typename Tw>
 T mean(const Array<T>& in, const Array<Tw>& wts) {
-    ONEAPI_NOT_SUPPORTED("mean Not supported");
-
-    return T(0);
-    // return kernel::meanAllWeighted<T, Tw>(in, wts);
+    return kernel::mean_all_weighted<T, Tw>(in, wts);
 }
 
 template<typename Ti, typename Tw, typename To>
 Array<To> mean(const Array<Ti>& in, const int dim) {
-    ONEAPI_NOT_SUPPORTED("mean Not supported");
-
     dim4 odims    = in.dims();
     odims[dim]    = 1;
     Array<To> out = createEmptyArray<To>(odims);
-    // kernel::mean<Ti, Tw, To>(out, in, dim);
+    kernel::mean<Ti, Tw, To>(out, in, dim);
     return out;
 }
 
 template<typename T, typename Tw>
 Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim) {
-    ONEAPI_NOT_SUPPORTED("mean Not supported");
-
     dim4 odims   = in.dims();
     odims[dim]   = 1;
     Array<T> out = createEmptyArray<T>(odims);
-    // kernel::meanWeighted<T, Tw, T>(out, in, wts, dim);
+    kernel::mean_weighted<T, Tw, T>(out, in, wts, dim);
     return out;
 }
 

From bb9edfdaf639a8891d4cafc2b5483946fd1aafa2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 3 Nov 2022 00:47:45 -0400
Subject: [PATCH 2323/2677] adds where kernel, scan_first dependency

---
 src/backend/oneapi/CMakeLists.txt        |   2 +
 src/backend/oneapi/kernel/scan_first.hpp | 303 +++++++++++++++++++++++
 src/backend/oneapi/kernel/where.hpp      | 180 ++++++++++++++
 src/backend/oneapi/scan.cpp              |  16 +-
 src/backend/oneapi/where.cpp             |  12 +-
 5 files changed, 497 insertions(+), 16 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/scan_first.hpp
 create mode 100644 src/backend/oneapi/kernel/where.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index e8df95c5ed..9b866a729f 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -222,9 +222,11 @@ target_sources(afoneapi
     kernel/reduce_all.hpp
     kernel/reduce_first.hpp
     kernel/reduce_dim.hpp
+    kernel/scan_first.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
+    kernel/where.hpp
 )
 
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
new file mode 100644
index 0000000000..886cd2f977
--- /dev/null
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -0,0 +1,303 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_config.hpp>
+#include <memory.hpp>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename Ti, typename To, af_op_t op>
+class scanFirstKernel {
+public:
+    scanFirstKernel(sycl::accessor<To> out_acc, KParam oInfo,
+                    sycl::accessor<To> tmp_acc, KParam tInfo,
+                    sycl::accessor<Ti> in_acc, KParam iInfo,
+                    const uint groups_x, const uint groups_y, const uint lim,
+                    const bool isFinalPass, const uint DIMX, const bool inclusive_scan,
+                    local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp,
+                    sycl::stream debug_stream) :
+        out_acc_(out_acc), oInfo_(oInfo),
+        tmp_acc_(tmp_acc), tInfo_(tInfo),
+        in_acc_(in_acc),   iInfo_(iInfo),
+        groups_x_(groups_x), groups_y_(groups_y), lim_(lim),
+        isFinalPass_(isFinalPass), DIMX_(DIMX), inclusive_scan_(inclusive_scan),
+        s_val_(s_val), s_tmp_(s_tmp), debug_stream_(debug_stream) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) * lim_ + lidx;
+        const uint yid       = groupId_y * g.get_local_range(1) + lidy;
+
+        bool cond_yzw =
+            (yid < oInfo_.dims[1]) && (zid < oInfo_.dims[2]) && (wid < oInfo_.dims[3]);
+
+        //if (!cond_yzw) return;  // retire warps early TODO: move
+
+        const Ti *iptr = in_acc_.get_pointer();
+        To *optr       = out_acc_.get_pointer();
+        To *tptr       = tmp_acc_.get_pointer();
+
+        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] + yid * iInfo_.strides[1];
+        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+        tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] + yid * tInfo_.strides[1];
+
+        To *sptr = s_val_.get_pointer() + lidy * (2 * DIMX_ + 1);
+
+        common::Transform<Ti, To, op> transform;
+        common::Binary<To, op> binop;
+
+        const To init = common::Binary<To, op>::init();
+        int id        = xid;
+        To val        = init;
+
+        const bool isLast = (lidx == (DIMX_ - 1));
+        for (int k = 0; k < lim_; k++) {
+            if (isLast) s_tmp_[lidy] = val;
+
+            bool cond  = (id < oInfo_.dims[0]) && cond_yzw;
+            val        = cond ? transform(iptr[id]) : init;
+            /*
+            if constexpr(std::is_fundamental<To>::value) {
+                debug_stream_ << id<<":"<<val <<", "<< sycl::stream_manipulator::endl;
+            }
+            */
+            sptr[lidx] = val;
+            group_barrier(g);
+
+            int start = 0;
+            #pragma unroll
+            for (int off = 1; off < DIMX_; off *= 2) {
+                if (lidx >= off) val = binop(val, sptr[(start - off) + lidx]);
+                start              = DIMX_ - start;
+                sptr[start + lidx] = val;
+
+                group_barrier(g);
+            }
+
+            val = binop(val, s_tmp_[lidy]);
+
+            if (inclusive_scan_) {
+                if (cond && cond_yzw) {
+                    //debug_stream_ << "oi0 ";
+                     optr[id] = val; }
+            } else {
+                if (cond_yzw && id == (oInfo_.dims[0] - 1)) {
+                    optr[0] = init;
+                } else if (cond_yzw && id < (oInfo_.dims[0] - 1)) {
+                    //debug_stream_ << "oe0 ";
+                    optr[id + 1] = val;
+                }
+            }
+            id += g.get_local_range(0);
+            group_barrier(g);
+        }
+
+        if (!isFinalPass_ && isLast && cond_yzw) {
+            //debug_stream_ << "ot ";
+             tptr[groupId_x] = val; }
+    }
+
+protected:
+    sycl::accessor<To> out_acc_;
+    sycl::accessor<To> tmp_acc_;
+    sycl::accessor<Ti> in_acc_; 
+    KParam oInfo_, tInfo_, iInfo_;
+    const uint groups_x_, groups_y_, lim_, DIMX_;
+    const bool isFinalPass_, inclusive_scan_;
+    local_accessor<To, 1> s_val_;
+    local_accessor<To, 1> s_tmp_;
+    sycl::stream debug_stream_;
+};
+
+template<typename To, af_op_t op>
+class scanFirstBcastKernel {
+public: 
+    scanFirstBcastKernel(sycl::accessor<To> out_acc, KParam oInfo,
+                         sycl::accessor<To> tmp_acc, KParam tInfo,
+                         const uint groups_x, const uint groups_y, const uint lim,
+                         const bool inclusive_scan, sycl::stream debug_stream) :
+        out_acc_(out_acc), oInfo_(oInfo),
+        tmp_acc_(tmp_acc), tInfo_(tInfo),
+        groups_x_(groups_x), groups_y_(groups_y), lim_(lim),
+        inclusive_scan_(inclusive_scan), debug_stream_(debug_stream) {}
+    
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) * lim_ + lidx;
+        const uint yid       = groupId_y * g.get_local_range(1) + lidy;
+
+        if (groupId_x == 0) return;
+
+        bool cond =
+            (yid < oInfo_.dims[1]) && (zid < oInfo_.dims[2]) && (wid < oInfo_.dims[3]);
+        if (!cond) return;
+
+        To *optr       = out_acc_.get_pointer();
+        const To *tptr = tmp_acc_.get_pointer();
+
+        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+        tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] + yid * tInfo_.strides[1];
+
+        common::Binary<To, op> binop;
+        To accum = tptr[groupId_x - 1];
+
+        // Shift broadcast one step to the right for exclusive scan (#2366)
+        int offset = !inclusive_scan_;
+        for (int k = 0, id = xid + offset; k < lim_ && id < oInfo_.dims[0];
+            k++, id += g.get_group_range(0)) {
+            optr[id] = binop(accum, optr[id]);
+        }
+    }
+protected:
+    sycl::accessor<To> out_acc_; 
+    sycl::accessor<To> tmp_acc_;
+    KParam oInfo_, tInfo_;
+    const uint groups_x_, groups_y_, lim_;
+    const bool inclusive_scan_;
+    sycl::stream debug_stream_;
+};
+
+
+template<typename Ti, typename To, af_op_t op>
+static void scan_first_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
+                                const uint groups_x, const uint groups_y,
+                                const uint threads_x, bool isFinalPass,
+                                bool inclusive_scan) {
+    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * out.info.dims[2] * local[0], 
+                          groups_y * out.info.dims[3] * local[1]);
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+    getQueue().submit([&] (sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto tmp_acc = tmp.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        const int DIMY            = THREADS_PER_BLOCK / threads_x;
+        const int SHARED_MEM_SIZE = (2 * threads_x + 1) * (DIMY);
+        auto s_val = local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
+        auto s_tmp = local_accessor<compute_t<To>, 1>(DIMY, h);
+
+        //TODO threads_x as template arg for #pragma unroll?
+        h.parallel_for(sycl::nd_range<2>(global, local), 
+            scanFirstKernel<Ti, To, op>(
+                    out_acc, out.info,
+                    tmp_acc, tmp.info,
+                    in_acc, in.info,
+                    groups_x, groups_y, lim,
+                    isFinalPass, threads_x, inclusive_scan,
+                    s_val, s_tmp, debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename To, af_op_t op>
+static void bcast_first_launcher(Param<To> out, Param<To> tmp,
+                                 const uint groups_x, const uint groups_y,
+                                 const uint threads_x, bool inclusive_scan) {
+    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * out.info.dims[2] * local[0], 
+                          groups_y * out.info.dims[3] * local[1]);
+    uint lim = divup(out.info.dims[0], (threads_x * groups_x));
+
+    getQueue().submit([&] (sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto tmp_acc = tmp.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        const int DIMY            = THREADS_PER_BLOCK / threads_x;
+        const int SHARED_MEM_SIZE = (2 * threads_x + 1) * (DIMY);
+        auto s_val = local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
+        auto s_tmp = local_accessor<compute_t<To>, 1>(DIMY, h);
+
+        h.parallel_for(sycl::nd_range<2>(global, local), 
+            scanFirstBcastKernel<To, op>(
+                    out_acc, out.info,
+                    tmp_acc, tmp.info,
+                    groups_x, groups_y, lim,
+                    inclusive_scan, debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename To, af_op_t op>
+static void scan_first(Param<To> out, Param<Ti> in, bool inclusive_scan) {
+    uint threads_x = nextpow2(std::max(32u, (uint)out.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint groups_x = divup(out.info.dims[0], threads_x * REPEAT);
+    uint groups_y = divup(out.info.dims[1], threads_y);
+
+    if (groups_x == 1) {
+        scan_first_launcher<Ti, To, op>(out, out, in, groups_x, groups_y,
+                                        threads_x, true, inclusive_scan);
+    } else {
+        Param<To> tmp = out;
+
+        tmp.info.dims[0]    = groups_x;
+        tmp.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++)
+            tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+
+        int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+        auto tmp_alloc   = memAlloc<To>(tmp_elements);
+        tmp.data          = tmp_alloc.get();
+
+        scan_first_launcher<Ti, To, op>(out, tmp, in, groups_x, groups_y,
+                                        threads_x, false, inclusive_scan);
+
+        // FIXME: Is there an alternative to the if condition ?
+        if (op == af_notzero_t) {
+            scan_first_launcher<To, To, af_add_t>(tmp, tmp, tmp, 1, groups_y,
+                                                  threads_x, true, true);
+        } else {
+            scan_first_launcher<To, To, op>(tmp, tmp, tmp, 1, groups_y,
+                                            threads_x, true, true);
+        }
+
+        bcast_first_launcher<To, op>(out, tmp, groups_x, groups_y, threads_x,
+                                     inclusive_scan);
+    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
\ No newline at end of file
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
new file mode 100644
index 0000000000..67c7500ee6
--- /dev/null
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -0,0 +1,180 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <memory.hpp>
+#include <kernel/reduce_config.hpp>
+#include <kernel/scan_first.hpp>
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <math.hpp>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class whereKernel {
+public:
+    whereKernel(sycl::accessor<uint> out_acc,  KParam oInfo,
+                sycl::accessor<uint> otmp_acc, KParam otInfo,
+                sycl::accessor<uint> rtmp_acc, KParam rtInfo,
+                sycl::accessor<T> in_acc,      KParam iInfo,
+                uint groups_x, uint groups_y, uint lim, sycl::stream debug) : 
+        out_acc_(out_acc), oInfo_(oInfo), otmp_acc_(otmp_acc), otInfo_(otInfo),
+        rtmp_acc_(rtmp_acc), rtInfo_(rtInfo), in_acc_(in_acc), iInfo_(iInfo),
+        groups_x_(groups_x), groups_y_(groups_y), lim_(lim), debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) * lim_ + lidx;
+        const uint yid       = groupId_y * g.get_local_range(1) + lidy;
+
+        const uint *otptr = otmp_acc_.get_pointer();
+        const uint *rtptr = rtmp_acc_.get_pointer();
+        const T *iptr     = in_acc_.get_pointer();
+
+        const uint off =
+            wid * otInfo_.strides[3] + zid * otInfo_.strides[2] + yid * otInfo_.strides[1];
+        const uint bid = wid * rtInfo_.strides[3] + zid * rtInfo_.strides[2] +
+                        yid * rtInfo_.strides[1] + groupId_x;
+
+        otptr +=
+            wid * otInfo_.strides[3] + zid * otInfo_.strides[2] + yid * otInfo_.strides[1];
+        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] + yid * iInfo_.strides[1];
+
+        bool cond =
+            (yid < otInfo_.dims[1]) && (zid < otInfo_.dims[2]) && (wid < otInfo_.dims[3]);
+        T zero = scalar<T>(0);
+
+        if (!cond) return;
+
+        uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
+
+        for (uint k = 0, id = xid; k < lim_ && id < otInfo_.dims[0];
+            k++, id += g.get_local_range(0)) {
+            uint idx = otptr[id] + accum;
+            if (iptr[id] != zero) out_acc_[idx - 1] = (off + id);
+        }
+    }
+protected:
+    sycl::accessor<uint> out_acc_;
+    sycl::accessor<uint> otmp_acc_;
+    sycl::accessor<uint> rtmp_acc_;
+    sycl::accessor<T> in_acc_;
+    KParam oInfo_, otInfo_, rtInfo_, iInfo_;
+    uint groups_x_, groups_y_, lim_;
+    sycl::stream debug_;
+};
+
+template<typename T>
+static void where(Param<uint> &out, Param<T> in) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
+    uint threads_y = THREADS_PER_BLOCK / threads_x;
+
+    uint groups_x = divup((uint)in.info.dims[0], (uint)(threads_x * REPEAT));
+    uint groups_y = divup(in.info.dims[1], threads_y);
+
+    Param<uint> rtmp;
+    Param<uint> otmp;
+    rtmp.info.dims[0]    = groups_x;
+    otmp.info.dims[0]    = in.info.dims[0];
+    rtmp.info.strides[0] = 1;
+    otmp.info.strides[0] = 1;
+
+    for (int k = 1; k < 4; k++) {
+        rtmp.info.dims[k]    = in.info.dims[k];
+        rtmp.info.strides[k] = rtmp.info.strides[k - 1] * rtmp.info.dims[k - 1];
+
+        otmp.info.dims[k]    = in.info.dims[k];
+        otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
+    }
+
+    int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
+    int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
+    auto rtmp_alloc   = memAlloc<uint>(rtmp_elements);
+    auto otmp_alloc   = memAlloc<uint>(otmp_elements);
+    rtmp.data          = rtmp_alloc.get();
+    otmp.data          = otmp_alloc.get();
+
+    scan_first_launcher<T, uint, af_notzero_t>(
+        otmp, rtmp, in, groups_x, groups_y, threads_x, false, true);
+
+    // Linearize the dimensions and perform scan
+    Param<uint> ltmp = rtmp;
+    ltmp.info.dims[0]     = rtmp_elements;
+    for (int k = 1; k < 4; k++) {
+        ltmp.info.dims[k]    = 1;
+        ltmp.info.strides[k] = rtmp_elements;
+    }
+
+    scan_first<uint, uint, af_add_t>(ltmp, ltmp, true);
+
+    // Get output size and allocate output
+    uint total;
+    sycl::buffer retBuffer(&total, {1},
+        {sycl::property::buffer::use_host_ptr()});
+
+    getQueue()
+        .submit([&](sycl::handler &h) {
+            auto acc_in = rtmp.data->get_access(h, sycl::range{1}, sycl::id{rtmp_elements - 1});
+            auto acc_out = retBuffer.get_access();
+            h.copy(acc_in, acc_out);
+    }).wait();
+
+    auto out_alloc = memAlloc<uint>(total);
+    out.data        = out_alloc.get();
+
+    out.info.dims[0]    = total;
+    out.info.strides[0] = 1;
+    for (int k = 1; k < 4; k++) {
+        out.info.dims[k]    = 1;
+        out.info.strides[k] = total;
+    }
+
+    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * in.info.dims[2] * local[0], 
+                          groups_y * in.info.dims[3] * local[1]);
+    uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
+
+    getQueue().submit([&] (sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto otmp_acc = otmp.data->get_access(h);
+        auto rtmp_acc = rtmp.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+        h.parallel_for(sycl::nd_range<2>(global, local), 
+            whereKernel<T>(
+                    out_acc, out.info,
+                    otmp_acc, otmp.info,
+                    rtmp_acc, rtmp.info,
+                    in_acc, in.info,
+                    groups_x, groups_y, lim,
+                    debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+    out_alloc.release();
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/scan.cpp b/src/backend/oneapi/scan.cpp
index c71564cc65..51183cfe8c 100644
--- a/src/backend/oneapi/scan.cpp
+++ b/src/backend/oneapi/scan.cpp
@@ -11,23 +11,21 @@
 #include <scan.hpp>
 
 // #include <kernel/scan_dim.hpp>
-// #include <kernel/scan_first.hpp>
+#include <kernel/scan_first.hpp>
 
 namespace oneapi {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
-    ONEAPI_NOT_SUPPORTED("scan Not supported");
-
     Array<To> out = createEmptyArray<To>(in.dims());
 
-    // Param Out = out;
-    // Param In  = in;
+    Param<To> Out = out;
+    Param<Ti> In  = in;
 
-    // if (dim == 0) {
-    //     kernel::scanFirst<Ti, To, op>(Out, In, inclusiveScan);
-    // } else {
+    if (dim == 0) {
+        kernel::scan_first<Ti, To, op>(Out, In, inclusiveScan);
+    } else {
     //     kernel::scanDim<Ti, To, op>(Out, In, dim, inclusiveScan);
-    // }
+    }
 
     return out;
 }
diff --git a/src/backend/oneapi/where.cpp b/src/backend/oneapi/where.cpp
index df9267df72..2965cbe883 100644
--- a/src/backend/oneapi/where.cpp
+++ b/src/backend/oneapi/where.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/where.hpp>
+#include <kernel/where.hpp>
 #include <where.hpp>
 #include <af/dim4.hpp>
 #include <complex>
@@ -18,12 +18,10 @@ namespace oneapi {
 
 template<typename T>
 Array<uint> where(const Array<T> &in) {
-    // Param<uint> Out;
-    // Param<T> In = in;
-    ONEAPI_NOT_SUPPORTED("where Not supported");
-    // kernel::where<T>(Out, In);
-    // return createParamArray<uint>(Out, true);
-    return createEmptyArray<uint>(af::dim4(1));
+    Param<uint> Out;
+    Param<T> In = in;
+    kernel::where<T>(Out, In);
+    return createParamArray<uint>(Out, true);
 }
 
 #define INSTANTIATE(T) template Array<uint> where<T>(const Array<T> &in);

From 9469aee07a33984ad4a6647165fb1c290c89e07b Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 4 Nov 2022 14:12:24 -0400
Subject: [PATCH 2324/2677] adds scan_dim kernels

---
 src/backend/oneapi/CMakeLists.txt            |   1 +
 src/backend/oneapi/kernel/default_config.hpp |  21 ++
 src/backend/oneapi/kernel/mean.hpp           |   7 +-
 src/backend/oneapi/kernel/reduce.hpp         |   6 +-
 src/backend/oneapi/kernel/reduce_all.hpp     |  21 +-
 src/backend/oneapi/kernel/reduce_config.hpp  |   3 +
 src/backend/oneapi/kernel/reduce_dim.hpp     |  19 +-
 src/backend/oneapi/kernel/reduce_first.hpp   |  11 +-
 src/backend/oneapi/kernel/scan_dim.hpp       | 349 +++++++++++++++++++
 src/backend/oneapi/kernel/scan_first.hpp     | 154 ++++----
 src/backend/oneapi/kernel/where.hpp          |  89 ++---
 src/backend/oneapi/scan.cpp                  |  11 +-
 12 files changed, 544 insertions(+), 148 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/default_config.hpp
 create mode 100644 src/backend/oneapi/kernel/scan_dim.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 9b866a729f..4b0f9f0a29 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -223,6 +223,7 @@ target_sources(afoneapi
     kernel/reduce_first.hpp
     kernel/reduce_dim.hpp
     kernel/scan_first.hpp
+    kernel/scan_dim.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
diff --git a/src/backend/oneapi/kernel/default_config.hpp b/src/backend/oneapi/kernel/default_config.hpp
new file mode 100644
index 0000000000..c279fd98bb
--- /dev/null
+++ b/src/backend/oneapi/kernel/default_config.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+namespace oneapi {
+namespace kernel {
+
+static const uint THREADS_PER_BLOCK = 256;
+static const uint THREADS_X         = 32;
+static const uint THREADS_Y         = THREADS_PER_BLOCK / THREADS_X;
+static const uint REPEAT            = 32;
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index f63f46096c..3f7b5c3fee 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -16,6 +16,7 @@
 //#include <copy.hpp>?
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/default_config.hpp>
 #include <kernel/reduce_config.hpp>
 #include <math.hpp>
 #include <memory.hpp>
@@ -253,7 +254,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                 break;
             case 4:
                 h.parallel_for(sycl::nd_range<2>(global, local),
-                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 4>(
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
@@ -261,7 +262,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                 break;
             case 2:
                 h.parallel_for(sycl::nd_range<2>(global, local),
-                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 2>(
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
@@ -269,7 +270,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                 break;
             case 1:
                 h.parallel_for(sycl::nd_range<2>(global, local),
-                               meanDimKernelSMEM<Ti, Tw, To, dim, 8>(
+                               meanDimKernelSMEM<Ti, Tw, To, dim, 1>(
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
diff --git a/src/backend/oneapi/kernel/reduce.hpp b/src/backend/oneapi/kernel/reduce.hpp
index 9db0561b0a..cae5c9854c 100644
--- a/src/backend/oneapi/kernel/reduce.hpp
+++ b/src/backend/oneapi/kernel/reduce.hpp
@@ -96,12 +96,12 @@ void reduce_all(Param<To> out, Param<Ti> in, bool change_nan, double nanval) {
     }
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
-    uint threads_y = THREADS_PER_BLOCK / threads_x;
+    threads_x      = std::min(threads_x, creduce::THREADS_PER_BLOCK);
+    uint threads_y = creduce::THREADS_PER_BLOCK / threads_x;
 
     // TODO: perf REPEAT, consider removing or runtime eval
     // max problem size < SM resident threads, don't use REPEAT
-    uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint blocks_x = divup(in.info.dims[0], threads_x * creduce::REPEAT);
     uint blocks_y = divup(in.info.dims[1], threads_y);
 
     reduce_all_launcher_default<Ti, To, op>(out, in, blocks_x, blocks_y,
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 372dc931fb..6fdf008e69 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -105,17 +105,17 @@ class reduceAllKernelSMEM {
 
         group_barrier(g);
 
-        if (THREADS_PER_BLOCK == 256) {
+        if (creduce::THREADS_PER_BLOCK == 256) {
             if (lid < 128) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 128]);
             group_barrier(g);
         }
 
-        if (THREADS_PER_BLOCK >= 128) {
+        if (creduce::THREADS_PER_BLOCK >= 128) {
             if (lid < 64) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 64]);
             group_barrier(g);
         }
 
-        if (THREADS_PER_BLOCK >= 64) {
+        if (creduce::THREADS_PER_BLOCK >= 64) {
             if (lid < 32) s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 32]);
             group_barrier(g);
         }
@@ -168,26 +168,26 @@ class reduceAllKernelSMEM {
                 while (i < total_blocks) {
                     compute_t<To> in_val = compute_t<To>(tmp_[i]);
                     out_val              = reduce(in_val, out_val);
-                    i += THREADS_PER_BLOCK;
+                    i += creduce::THREADS_PER_BLOCK;
                 }
 
                 s_ptr_[lid] = out_val;
                 group_barrier(g);
 
                 // reduce final block
-                if (THREADS_PER_BLOCK == 256) {
+                if (creduce::THREADS_PER_BLOCK == 256) {
                     if (lid < 128)
                         s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 128]);
                     group_barrier(g);
                 }
 
-                if (THREADS_PER_BLOCK >= 128) {
+                if (creduce::THREADS_PER_BLOCK >= 128) {
                     if (lid < 64)
                         s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 64]);
                     group_barrier(g);
                 }
 
-                if (THREADS_PER_BLOCK >= 64) {
+                if (creduce::THREADS_PER_BLOCK >= 64) {
                     if (lid < 32)
                         s_ptr_[lid] = reduce(s_ptr_[lid], s_ptr_[lid + 32]);
                     group_barrier(g);
@@ -239,7 +239,7 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
                                  const uint groups_x, const uint groups_y,
                                  const uint threads_x, bool change_nan,
                                  double nanval) {
-    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> local(threads_x, creduce::THREADS_PER_BLOCK / threads_x);
     sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
                           groups_y * in.info.dims[3] * local[1]);
 
@@ -268,8 +268,9 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
-        auto shrdMem = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
-        auto amLast  = local_accessor<bool, 1>(1, h);
+        auto shrdMem =
+            local_accessor<compute_t<To>, 1>(creduce::THREADS_PER_BLOCK, h);
+        auto amLast = local_accessor<bool, 1>(1, h);
         h.parallel_for(
             sycl::nd_range<2>(global, local),
             reduceAllKernelSMEM<Ti, To, op>(
diff --git a/src/backend/oneapi/kernel/reduce_config.hpp b/src/backend/oneapi/kernel/reduce_config.hpp
index 827497967b..a7d185de75 100644
--- a/src/backend/oneapi/kernel/reduce_config.hpp
+++ b/src/backend/oneapi/kernel/reduce_config.hpp
@@ -12,11 +12,14 @@
 namespace oneapi {
 namespace kernel {
 
+namespace creduce {
 // TODO: are different values more appropriate for reduce on oneapi?
 static const uint THREADS_PER_BLOCK = 256;
 static const uint THREADS_X         = 32;
 static const uint THREADS_Y         = THREADS_PER_BLOCK / THREADS_X;
 static const uint REPEAT            = 32;
 
+}  // namespace creduce
+
 }  // namespace kernel
 }  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 5105fb8b1c..3f9e365b8b 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -105,17 +105,20 @@ class reduceDimKernelSMEM {
         compute_t<To> *s_ptr = s_val_.get_pointer() + lid;
 
         if (DIMY == 8) {
-            if (lidy < 4) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 4]);
+            if (lidy < 4)
+                *s_ptr = reduce(*s_ptr, s_ptr[creduce::THREADS_X * 4]);
             it.barrier();
         }
 
         if (DIMY >= 4) {
-            if (lidy < 2) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 2]);
+            if (lidy < 2)
+                *s_ptr = reduce(*s_ptr, s_ptr[creduce::THREADS_X * 2]);
             it.barrier();
         }
 
         if (DIMY >= 2) {
-            if (lidy < 1) *s_ptr = reduce(*s_ptr, s_ptr[THREADS_X * 1]);
+            if (lidy < 1)
+                *s_ptr = reduce(*s_ptr, s_ptr[creduce::THREADS_X * 1]);
             it.barrier();
         }
 
@@ -140,7 +143,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                                  const uint threads_y,
                                  const dim_t blocks_dim[4], bool change_nan,
                                  double nanval) {
-    sycl::range<2> local(THREADS_X, threads_y);
+    sycl::range<2> local(creduce::THREADS_X, threads_y);
     sycl::range<2> global(blocks_dim[0] * blocks_dim[2] * local[0],
                           blocks_dim[1] * blocks_dim[3] * local[1]);
 
@@ -151,7 +154,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
         sycl::stream debug_stream(2048 * 256, 128, h);
 
         auto shrdMem =
-            local_accessor<compute_t<To>, 1>(THREADS_X * threads_y, h);
+            local_accessor<compute_t<To>, 1>(creduce::THREADS_X * threads_y, h);
 
         switch (threads_y) {
             case 8:
@@ -194,12 +197,12 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
 template<typename Ti, typename To, af_op_t op, int dim>
 void reduce_dim_default(Param<To> out, Param<Ti> in, bool change_nan,
                         double nanval) {
-    uint threads_y = std::min(THREADS_Y, nextpow2(in.info.dims[dim]));
-    uint threads_x = THREADS_X;
+    uint threads_y = std::min(creduce::THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = creduce::THREADS_X;
 
     dim_t blocks_dim[] = {divup(in.info.dims[0], threads_x), in.info.dims[1],
                           in.info.dims[2], in.info.dims[3]};
-    blocks_dim[dim]    = divup(in.info.dims[dim], threads_y * REPEAT);
+    blocks_dim[dim]    = divup(in.info.dims[dim], threads_y * creduce::REPEAT);
 
     Param<To> tmp = out;
     bufptr<To> tmp_alloc;
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index cd096e69e1..ebf55fb63e 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -148,7 +148,7 @@ void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
                                    const uint groups_x, const uint groups_y,
                                    const uint threads_x, bool change_nan,
                                    double nanval) {
-    sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> local(threads_x, creduce::THREADS_PER_BLOCK / threads_x);
     sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
                           groups_y * in.info.dims[3] * local[1]);
 
@@ -160,7 +160,8 @@ void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
-        auto shrdMem = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+        auto shrdMem =
+            local_accessor<compute_t<To>, 1>(creduce::THREADS_PER_BLOCK, h);
 
         switch (threads_x) {
             case 32:
@@ -200,10 +201,10 @@ template<typename Ti, typename To, af_op_t op>
 void reduce_first_default(Param<To> out, Param<Ti> in, bool change_nan,
                           double nanval) {
     uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
-    threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
-    uint threads_y = THREADS_PER_BLOCK / threads_x;
+    threads_x      = std::min(threads_x, creduce::THREADS_PER_BLOCK);
+    uint threads_y = creduce::THREADS_PER_BLOCK / threads_x;
 
-    uint blocks_x = divup(in.info.dims[0], threads_x * REPEAT);
+    uint blocks_x = divup(in.info.dims[0], threads_x * creduce::REPEAT);
     uint blocks_y = divup(in.info.dims[1], threads_y);
 
     Param<To> tmp = out;
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
new file mode 100644
index 0000000000..1db981ca9a
--- /dev/null
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -0,0 +1,349 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/default_config.hpp>
+#include <memory.hpp>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename Ti, typename To, af_op_t op, int dim>
+class scanDimKernel {
+   public:
+    scanDimKernel(sycl::accessor<To> out_acc, KParam oInfo,
+                  sycl::accessor<To> tmp_acc, KParam tInfo,
+                  sycl::accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
+                  const uint groups_y, const uint blocks_dim, const uint lim,
+                  const bool isFinalPass, const uint DIMY,
+                  const bool inclusive_scan, local_accessor<To, 1> s_val,
+                  local_accessor<To, 1> s_tmp, sycl::stream debug)
+        : out_acc_(out_acc)
+        , oInfo_(oInfo)
+        , tmp_acc_(tmp_acc)
+        , tInfo_(tInfo)
+        , in_acc_(in_acc)
+        , iInfo_(iInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , blocks_dim_(blocks_dim)
+        , lim_(lim)
+        , isFinalPass_(isFinalPass)
+        , DIMY_(DIMY)
+        , inclusive_scan_(inclusive_scan)
+        , s_val_(s_val)
+        , s_tmp_(s_tmp)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) + lidx;
+        const uint yid       = groupId_y;
+
+        int ids[4] = {xid, yid, zid, wid};
+
+        const Ti *iptr = in_acc_.get_pointer();
+        To *optr       = out_acc_.get_pointer();
+        To *tptr       = tmp_acc_.get_pointer();
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before
+        // offsetting in
+        tptr += ids[3] * tInfo_.strides[3] + ids[2] * tInfo_.strides[2] +
+                ids[1] * tInfo_.strides[1] + ids[0];
+
+        const int groupIdx_dim = ids[dim];
+        ids[dim]               = ids[dim] * g.get_local_range(1) * lim_ + lidy;
+
+        optr += ids[3] * oInfo_.strides[3] + ids[2] * oInfo_.strides[2] +
+                ids[1] * oInfo_.strides[1] + ids[0];
+        iptr += ids[3] * iInfo_.strides[3] + ids[2] * iInfo_.strides[2] +
+                ids[1] * iInfo_.strides[1] + ids[0];
+        int id_dim        = ids[dim];
+        const int out_dim = oInfo_.dims[dim];
+
+        bool is_valid = (ids[0] < oInfo_.dims[0]) &&
+                        (ids[1] < oInfo_.dims[1]) &&
+                        (ids[2] < oInfo_.dims[2]) && (ids[3] < oInfo_.dims[3]);
+
+        const int ostride_dim = oInfo_.strides[dim];
+        const int istride_dim = iInfo_.strides[dim];
+
+        To *sptr = s_val_.get_pointer() + lid;
+
+        common::Transform<Ti, To, op> transform;
+        common::Binary<To, op> binop;
+
+        const To init = common::Binary<To, op>::init();
+        To val        = init;
+
+        const bool isLast = (lidy == (DIMY_ - 1));
+
+        for (int k = 0; k < lim_; k++) {
+            if (isLast) s_tmp_[lidx] = val;
+
+            bool cond = (is_valid) && (id_dim < out_dim);
+            val       = cond ? transform(*iptr) : init;
+            *sptr     = val;
+            group_barrier(g);
+
+            int start = 0;
+#pragma unroll
+            for (int off = 1; off < DIMY_; off *= 2) {
+                if (lidy >= off)
+                    val = binop(val, sptr[(start - off) * (int)THREADS_X]);
+                start                   = DIMY_ - start;
+                sptr[start * THREADS_X] = val;
+
+                group_barrier(g);
+            }
+
+            val = binop(val, s_tmp_[lidx]);
+            if (inclusive_scan_) {
+                if (cond) { *optr = val; }
+            } else if (is_valid) {
+                if (id_dim == (out_dim - 1)) {
+                    *(optr - (id_dim * ostride_dim)) = init;
+                } else if (id_dim < (out_dim - 1)) {
+                    *(optr + ostride_dim) = val;
+                }
+            }
+            id_dim += g.get_local_range(1);
+            iptr += g.get_local_range(1) * istride_dim;
+            optr += g.get_local_range(1) * ostride_dim;
+            group_barrier(g);
+        }
+
+        if (!isFinalPass_ && is_valid && (groupIdx_dim < tInfo_.dims[dim]) &&
+            isLast) {
+            *tptr = val;
+        }
+    }
+
+   protected:
+    sycl::accessor<To> out_acc_;
+    sycl::accessor<To> tmp_acc_;
+    sycl::accessor<Ti> in_acc_;
+    KParam oInfo_, tInfo_, iInfo_;
+    const uint groups_x_, groups_y_, blocks_dim_, lim_, DIMY_;
+    const bool isFinalPass_, inclusive_scan_;
+    local_accessor<To, 1> s_val_;
+    local_accessor<To, 1> s_tmp_;
+    sycl::stream debug_;
+};
+
+template<typename To, af_op_t op, int dim>
+class scanDimBcastKernel {
+   public:
+    scanDimBcastKernel(sycl::accessor<To> out_acc, KParam oInfo,
+                       sycl::accessor<To> tmp_acc, KParam tInfo,
+                       const uint groups_x, const uint groups_y,
+                       const uint groups_dim, const uint lim,
+                       const bool inclusive_scan, sycl::stream debug)
+        : out_acc_(out_acc)
+        , oInfo_(oInfo)
+        , tmp_acc_(tmp_acc)
+        , tInfo_(tInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , groups_dim_(groups_dim)
+        , lim_(lim)
+        , inclusive_scan_(inclusive_scan)
+        , debug_(debug) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) + lidx;
+        const uint yid       = groupId_y;
+
+        int ids[4] = {xid, yid, zid, wid};
+
+        const To *tptr = tmp_acc_.get_pointer();
+        To *optr       = out_acc_.get_pointer();
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before
+        // offsetting in
+        tptr += ids[3] * tInfo_.strides[3] + ids[2] * tInfo_.strides[2] +
+                ids[1] * tInfo_.strides[1] + ids[0];
+
+        const int groupIdx_dim = ids[dim];
+        ids[dim]               = ids[dim] * g.get_local_range(1) * lim_ + lidy;
+
+        optr += ids[3] * oInfo_.strides[3] + ids[2] * oInfo_.strides[2] +
+                ids[1] * oInfo_.strides[1] + ids[0];
+        const int id_dim  = ids[dim];
+        const int out_dim = oInfo_.dims[dim];
+
+        // Shift broadcast one step to the right for exclusive scan (#2366)
+        int offset = inclusive_scan_ ? 0 : oInfo_.strides[dim];
+        optr += offset;
+
+        bool is_valid = (ids[0] < oInfo_.dims[0]) &&
+                        (ids[1] < oInfo_.dims[1]) &&
+                        (ids[2] < oInfo_.dims[2]) && (ids[3] < oInfo_.dims[3]);
+
+        if (!is_valid) return;
+        if (groupIdx_dim == 0) return;
+
+        To accum = *(tptr - tInfo_.strides[dim]);
+
+        common::Binary<To, op> binop;
+        const int ostride_dim = oInfo_.strides[dim];
+
+        for (int k = 0, id = id_dim; is_valid && k < lim_ && (id < out_dim);
+             k++, id += g.get_local_range(1)) {
+            *optr = binop(*optr, accum);
+            optr += g.get_local_range(1) * ostride_dim;
+        }
+    }
+
+   protected:
+    sycl::accessor<To> out_acc_;
+    sycl::accessor<To> tmp_acc_;
+    KParam oInfo_, tInfo_;
+    const uint groups_x_, groups_y_, groups_dim_, lim_;
+    const bool inclusive_scan_;
+    sycl::stream debug_;
+};
+
+template<typename Ti, typename To, af_op_t op, int dim>
+static void scan_dim_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
+                              const uint threads_y, const dim_t blocks_all[4],
+                              bool isFinalPass, bool inclusive_scan) {
+    sycl::range<2> local(THREADS_X, threads_y);
+    sycl::range<2> global(blocks_all[0] * blocks_all[2] * local[0],
+                          blocks_all[1] * blocks_all[3] * local[1]);
+
+    uint lim = divup(out.info.dims[dim], (threads_y * blocks_all[dim]));
+
+    getQueue().submit([&](sycl::handler &h) {
+        // TODO: specify access modes in all kernels
+        auto out_acc = out.data->get_access(h);
+        auto tmp_acc = tmp.data->get_access(h);
+        auto in_acc  = in.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        auto s_val =
+            local_accessor<compute_t<To>, 1>(THREADS_X * threads_y * 2, h);
+        auto s_tmp = local_accessor<compute_t<To>, 1>(THREADS_X, h);
+
+        h.parallel_for(
+            sycl::nd_range<2>(global, local),
+            scanDimKernel<Ti, To, op, dim>(
+                out_acc, out.info, tmp_acc, tmp.info, in_acc, in.info,
+                blocks_all[0], blocks_all[1], blocks_all[dim], lim, isFinalPass,
+                threads_y, inclusive_scan, s_val, s_tmp, debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename To, af_op_t op, int dim>
+static void bcast_dim_launcher(Param<To> out, Param<To> tmp,
+                               const uint threads_y, const dim_t blocks_all[4],
+                               bool inclusive_scan) {
+    sycl::range<2> local(THREADS_X, threads_y);
+    sycl::range<2> global(blocks_all[0] * blocks_all[2] * local[0],
+                          blocks_all[1] * blocks_all[3] * local[1]);
+
+    uint lim = divup(out.info.dims[dim], (threads_y * blocks_all[dim]));
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto out_acc = out.data->get_access(h);
+        auto tmp_acc = tmp.data->get_access(h);
+
+        sycl::stream debug_stream(2048 * 256, 128, h);
+
+        h.parallel_for(sycl::nd_range<2>(global, local),
+                       scanDimBcastKernel<To, op, dim>(
+                           out_acc, out.info, tmp_acc, tmp.info, blocks_all[0],
+                           blocks_all[1], blocks_all[dim], lim, inclusive_scan,
+                           debug_stream));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename To, af_op_t op, int dim>
+static void scan_dim(Param<To> out, Param<Ti> in, bool inclusive_scan) {
+    uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
+    uint threads_x = THREADS_X;
+
+    dim_t blocks_all[] = {divup(out.info.dims[0], threads_x), out.info.dims[1],
+                          out.info.dims[2], out.info.dims[3]};
+
+    blocks_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
+
+    if (blocks_all[dim] == 1) {
+        scan_dim_launcher<Ti, To, op, dim>(out, out, in, threads_y, blocks_all,
+                                           true, inclusive_scan);
+    } else {
+        Param<To> tmp = out;
+
+        tmp.info.dims[dim]  = blocks_all[dim];
+        tmp.info.strides[0] = 1;
+        for (int k = 1; k < 4; k++)
+            tmp.info.strides[k] =
+                tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+
+        int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
+        auto tmp_alloc   = memAlloc<To>(tmp_elements);
+        tmp.data         = tmp_alloc.get();
+
+        scan_dim_launcher<Ti, To, op, dim>(out, tmp, in, threads_y, blocks_all,
+                                           false, inclusive_scan);
+
+        int bdim        = blocks_all[dim];
+        blocks_all[dim] = 1;
+
+        // FIXME: Is there an alternative to the if condition ?
+        if (op == af_notzero_t) {
+            scan_dim_launcher<To, To, af_add_t, dim>(tmp, tmp, tmp, threads_y,
+                                                     blocks_all, true, true);
+        } else {
+            scan_dim_launcher<To, To, op, dim>(tmp, tmp, tmp, threads_y,
+                                               blocks_all, true, true);
+        }
+
+        blocks_all[dim] = bdim;
+        bcast_dim_launcher<To, op, dim>(out, tmp, threads_y, blocks_all,
+                                        inclusive_scan);
+    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 886cd2f977..0efed3de48 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -14,7 +14,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
-#include <kernel/reduce_config.hpp>
+#include <kernel/default_config.hpp>
 #include <memory.hpp>
 
 namespace oneapi {
@@ -27,23 +27,32 @@ using local_accessor =
 
 template<typename Ti, typename To, af_op_t op>
 class scanFirstKernel {
-public:
+   public:
     scanFirstKernel(sycl::accessor<To> out_acc, KParam oInfo,
                     sycl::accessor<To> tmp_acc, KParam tInfo,
                     sycl::accessor<Ti> in_acc, KParam iInfo,
                     const uint groups_x, const uint groups_y, const uint lim,
-                    const bool isFinalPass, const uint DIMX, const bool inclusive_scan,
-                    local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp,
-                    sycl::stream debug_stream) :
-        out_acc_(out_acc), oInfo_(oInfo),
-        tmp_acc_(tmp_acc), tInfo_(tInfo),
-        in_acc_(in_acc),   iInfo_(iInfo),
-        groups_x_(groups_x), groups_y_(groups_y), lim_(lim),
-        isFinalPass_(isFinalPass), DIMX_(DIMX), inclusive_scan_(inclusive_scan),
-        s_val_(s_val), s_tmp_(s_tmp), debug_stream_(debug_stream) {}
+                    const bool isFinalPass, const uint DIMX,
+                    const bool inclusive_scan, local_accessor<To, 1> s_val,
+                    local_accessor<To, 1> s_tmp, sycl::stream debug_stream)
+        : out_acc_(out_acc)
+        , oInfo_(oInfo)
+        , tmp_acc_(tmp_acc)
+        , tInfo_(tInfo)
+        , in_acc_(in_acc)
+        , iInfo_(iInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , lim_(lim)
+        , isFinalPass_(isFinalPass)
+        , DIMX_(DIMX)
+        , inclusive_scan_(inclusive_scan)
+        , s_val_(s_val)
+        , s_tmp_(s_tmp)
+        , debug_stream_(debug_stream) {}
 
     void operator()(sycl::nd_item<2> it) const {
-        sycl::group g = it.get_group();
+        sycl::group g   = it.get_group();
         const uint lidx = it.get_local_id(0);
         const uint lidy = it.get_local_id(1);
         const uint lid  = lidy * g.get_local_range(0) + lidx;
@@ -55,18 +64,21 @@ class scanFirstKernel {
         const uint xid       = groupId_x * g.get_local_range(0) * lim_ + lidx;
         const uint yid       = groupId_y * g.get_local_range(1) + lidy;
 
-        bool cond_yzw =
-            (yid < oInfo_.dims[1]) && (zid < oInfo_.dims[2]) && (wid < oInfo_.dims[3]);
+        bool cond_yzw = (yid < oInfo_.dims[1]) && (zid < oInfo_.dims[2]) &&
+                        (wid < oInfo_.dims[3]);
 
-        //if (!cond_yzw) return;  // retire warps early TODO: move
+        // if (!cond_yzw) return;  // retire warps early TODO: move
 
         const Ti *iptr = in_acc_.get_pointer();
         To *optr       = out_acc_.get_pointer();
         To *tptr       = tmp_acc_.get_pointer();
 
-        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] + yid * iInfo_.strides[1];
-        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
-        tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] + yid * tInfo_.strides[1];
+        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
+                yid * iInfo_.strides[1];
+        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] +
+                yid * oInfo_.strides[1];
+        tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] +
+                yid * tInfo_.strides[1];
 
         To *sptr = s_val_.get_pointer() + lidy * (2 * DIMX_ + 1);
 
@@ -83,16 +95,11 @@ class scanFirstKernel {
 
             bool cond  = (id < oInfo_.dims[0]) && cond_yzw;
             val        = cond ? transform(iptr[id]) : init;
-            /*
-            if constexpr(std::is_fundamental<To>::value) {
-                debug_stream_ << id<<":"<<val <<", "<< sycl::stream_manipulator::endl;
-            }
-            */
             sptr[lidx] = val;
             group_barrier(g);
 
             int start = 0;
-            #pragma unroll
+#pragma unroll
             for (int off = 1; off < DIMX_; off *= 2) {
                 if (lidx >= off) val = binop(val, sptr[(start - off) + lidx]);
                 start              = DIMX_ - start;
@@ -104,14 +111,12 @@ class scanFirstKernel {
             val = binop(val, s_tmp_[lidy]);
 
             if (inclusive_scan_) {
-                if (cond && cond_yzw) {
-                    //debug_stream_ << "oi0 ";
-                     optr[id] = val; }
+                if (cond) { optr[id] = val; }
             } else {
                 if (cond_yzw && id == (oInfo_.dims[0] - 1)) {
                     optr[0] = init;
                 } else if (cond_yzw && id < (oInfo_.dims[0] - 1)) {
-                    //debug_stream_ << "oe0 ";
+                    // debug_stream_ << "oe0 ";
                     optr[id + 1] = val;
                 }
             }
@@ -120,14 +125,15 @@ class scanFirstKernel {
         }
 
         if (!isFinalPass_ && isLast && cond_yzw) {
-            //debug_stream_ << "ot ";
-             tptr[groupId_x] = val; }
+            // debug_stream_ << "ot ";
+            tptr[groupId_x] = val;
+        }
     }
 
-protected:
+   protected:
     sycl::accessor<To> out_acc_;
     sycl::accessor<To> tmp_acc_;
-    sycl::accessor<Ti> in_acc_; 
+    sycl::accessor<Ti> in_acc_;
     KParam oInfo_, tInfo_, iInfo_;
     const uint groups_x_, groups_y_, lim_, DIMX_;
     const bool isFinalPass_, inclusive_scan_;
@@ -138,18 +144,24 @@ class scanFirstKernel {
 
 template<typename To, af_op_t op>
 class scanFirstBcastKernel {
-public: 
+   public:
     scanFirstBcastKernel(sycl::accessor<To> out_acc, KParam oInfo,
                          sycl::accessor<To> tmp_acc, KParam tInfo,
-                         const uint groups_x, const uint groups_y, const uint lim,
-                         const bool inclusive_scan, sycl::stream debug_stream) :
-        out_acc_(out_acc), oInfo_(oInfo),
-        tmp_acc_(tmp_acc), tInfo_(tInfo),
-        groups_x_(groups_x), groups_y_(groups_y), lim_(lim),
-        inclusive_scan_(inclusive_scan), debug_stream_(debug_stream) {}
-    
+                         const uint groups_x, const uint groups_y,
+                         const uint lim, const bool inclusive_scan,
+                         sycl::stream debug_stream)
+        : out_acc_(out_acc)
+        , oInfo_(oInfo)
+        , tmp_acc_(tmp_acc)
+        , tInfo_(tInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , lim_(lim)
+        , inclusive_scan_(inclusive_scan)
+        , debug_stream_(debug_stream) {}
+
     void operator()(sycl::nd_item<2> it) const {
-        sycl::group g = it.get_group();
+        sycl::group g   = it.get_group();
         const uint lidx = it.get_local_id(0);
         const uint lidy = it.get_local_id(1);
         const uint lid  = lidy * g.get_local_range(0) + lidx;
@@ -163,15 +175,17 @@ class scanFirstBcastKernel {
 
         if (groupId_x == 0) return;
 
-        bool cond =
-            (yid < oInfo_.dims[1]) && (zid < oInfo_.dims[2]) && (wid < oInfo_.dims[3]);
+        bool cond = (yid < oInfo_.dims[1]) && (zid < oInfo_.dims[2]) &&
+                    (wid < oInfo_.dims[3]);
         if (!cond) return;
 
         To *optr       = out_acc_.get_pointer();
         const To *tptr = tmp_acc_.get_pointer();
 
-        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
-        tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] + yid * tInfo_.strides[1];
+        optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] +
+                yid * oInfo_.strides[1];
+        tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] +
+                yid * tInfo_.strides[1];
 
         common::Binary<To, op> binop;
         To accum = tptr[groupId_x - 1];
@@ -179,12 +193,13 @@ class scanFirstBcastKernel {
         // Shift broadcast one step to the right for exclusive scan (#2366)
         int offset = !inclusive_scan_;
         for (int k = 0, id = xid + offset; k < lim_ && id < oInfo_.dims[0];
-            k++, id += g.get_group_range(0)) {
+             k++, id += g.get_group_range(0)) {
             optr[id] = binop(accum, optr[id]);
         }
     }
-protected:
-    sycl::accessor<To> out_acc_; 
+
+   protected:
+    sycl::accessor<To> out_acc_;
     sycl::accessor<To> tmp_acc_;
     KParam oInfo_, tInfo_;
     const uint groups_x_, groups_y_, lim_;
@@ -192,18 +207,17 @@ class scanFirstBcastKernel {
     sycl::stream debug_stream_;
 };
 
-
 template<typename Ti, typename To, af_op_t op>
 static void scan_first_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
                                 const uint groups_x, const uint groups_y,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
     sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
-    sycl::range<2> global(groups_x * out.info.dims[2] * local[0], 
+    sycl::range<2> global(groups_x * out.info.dims[2] * local[0],
                           groups_y * out.info.dims[3] * local[1]);
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    getQueue().submit([&] (sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         auto out_acc = out.data->get_access(h);
         auto tmp_acc = tmp.data->get_access(h);
         auto in_acc  = in.data->get_access(h);
@@ -215,15 +229,13 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
         auto s_val = local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
         auto s_tmp = local_accessor<compute_t<To>, 1>(DIMY, h);
 
-        //TODO threads_x as template arg for #pragma unroll?
-        h.parallel_for(sycl::nd_range<2>(global, local), 
+        // TODO threads_x as template arg for #pragma unroll?
+        h.parallel_for(
+            sycl::nd_range<2>(global, local),
             scanFirstKernel<Ti, To, op>(
-                    out_acc, out.info,
-                    tmp_acc, tmp.info,
-                    in_acc, in.info,
-                    groups_x, groups_y, lim,
-                    isFinalPass, threads_x, inclusive_scan,
-                    s_val, s_tmp, debug_stream));
+                out_acc, out.info, tmp_acc, tmp.info, in_acc, in.info, groups_x,
+                groups_y, lim, isFinalPass, threads_x, inclusive_scan, s_val,
+                s_tmp, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -233,27 +245,20 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp,
                                  const uint groups_x, const uint groups_y,
                                  const uint threads_x, bool inclusive_scan) {
     sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
-    sycl::range<2> global(groups_x * out.info.dims[2] * local[0], 
+    sycl::range<2> global(groups_x * out.info.dims[2] * local[0],
                           groups_y * out.info.dims[3] * local[1]);
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
-    getQueue().submit([&] (sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         auto out_acc = out.data->get_access(h);
         auto tmp_acc = tmp.data->get_access(h);
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
-        const int DIMY            = THREADS_PER_BLOCK / threads_x;
-        const int SHARED_MEM_SIZE = (2 * threads_x + 1) * (DIMY);
-        auto s_val = local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
-        auto s_tmp = local_accessor<compute_t<To>, 1>(DIMY, h);
-
-        h.parallel_for(sycl::nd_range<2>(global, local), 
-            scanFirstBcastKernel<To, op>(
-                    out_acc, out.info,
-                    tmp_acc, tmp.info,
-                    groups_x, groups_y, lim,
-                    inclusive_scan, debug_stream));
+        h.parallel_for(sycl::nd_range<2>(global, local),
+                       scanFirstBcastKernel<To, op>(
+                           out_acc, out.info, tmp_acc, tmp.info, groups_x,
+                           groups_y, lim, inclusive_scan, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -276,11 +281,12 @@ static void scan_first(Param<To> out, Param<Ti> in, bool inclusive_scan) {
         tmp.info.dims[0]    = groups_x;
         tmp.info.strides[0] = 1;
         for (int k = 1; k < 4; k++)
-            tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
+            tmp.info.strides[k] =
+                tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
 
         int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
         auto tmp_alloc   = memAlloc<To>(tmp_elements);
-        tmp.data          = tmp_alloc.get();
+        tmp.data         = tmp_alloc.get();
 
         scan_first_launcher<Ti, To, op>(out, tmp, in, groups_x, groups_y,
                                         threads_x, false, inclusive_scan);
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index 67c7500ee6..c927c995da 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -12,9 +12,9 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
-#include <memory.hpp>
-#include <kernel/reduce_config.hpp>
+#include <kernel/default_config.hpp>
 #include <kernel/scan_first.hpp>
+#include <memory.hpp>
 
 #include <Param.hpp>
 #include <backend.hpp>
@@ -25,18 +25,27 @@ namespace kernel {
 
 template<typename T>
 class whereKernel {
-public:
-    whereKernel(sycl::accessor<uint> out_acc,  KParam oInfo,
+   public:
+    whereKernel(sycl::accessor<uint> out_acc, KParam oInfo,
                 sycl::accessor<uint> otmp_acc, KParam otInfo,
                 sycl::accessor<uint> rtmp_acc, KParam rtInfo,
-                sycl::accessor<T> in_acc,      KParam iInfo,
-                uint groups_x, uint groups_y, uint lim, sycl::stream debug) : 
-        out_acc_(out_acc), oInfo_(oInfo), otmp_acc_(otmp_acc), otInfo_(otInfo),
-        rtmp_acc_(rtmp_acc), rtInfo_(rtInfo), in_acc_(in_acc), iInfo_(iInfo),
-        groups_x_(groups_x), groups_y_(groups_y), lim_(lim), debug_(debug) {}
+                sycl::accessor<T> in_acc, KParam iInfo, uint groups_x,
+                uint groups_y, uint lim, sycl::stream debug)
+        : out_acc_(out_acc)
+        , oInfo_(oInfo)
+        , otmp_acc_(otmp_acc)
+        , otInfo_(otInfo)
+        , rtmp_acc_(rtmp_acc)
+        , rtInfo_(rtInfo)
+        , in_acc_(in_acc)
+        , iInfo_(iInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , lim_(lim)
+        , debug_(debug) {}
 
     void operator()(sycl::nd_item<2> it) const {
-        sycl::group g = it.get_group();
+        sycl::group g   = it.get_group();
         const uint lidx = it.get_local_id(0);
         const uint lidy = it.get_local_id(1);
         const uint lid  = lidy * g.get_local_range(0) + lidx;
@@ -52,17 +61,18 @@ class whereKernel {
         const uint *rtptr = rtmp_acc_.get_pointer();
         const T *iptr     = in_acc_.get_pointer();
 
-        const uint off =
-            wid * otInfo_.strides[3] + zid * otInfo_.strides[2] + yid * otInfo_.strides[1];
+        const uint off = wid * otInfo_.strides[3] + zid * otInfo_.strides[2] +
+                         yid * otInfo_.strides[1];
         const uint bid = wid * rtInfo_.strides[3] + zid * rtInfo_.strides[2] +
-                        yid * rtInfo_.strides[1] + groupId_x;
+                         yid * rtInfo_.strides[1] + groupId_x;
 
-        otptr +=
-            wid * otInfo_.strides[3] + zid * otInfo_.strides[2] + yid * otInfo_.strides[1];
-        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] + yid * iInfo_.strides[1];
+        otptr += wid * otInfo_.strides[3] + zid * otInfo_.strides[2] +
+                 yid * otInfo_.strides[1];
+        iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
+                yid * iInfo_.strides[1];
 
-        bool cond =
-            (yid < otInfo_.dims[1]) && (zid < otInfo_.dims[2]) && (wid < otInfo_.dims[3]);
+        bool cond = (yid < otInfo_.dims[1]) && (zid < otInfo_.dims[2]) &&
+                    (wid < otInfo_.dims[3]);
         T zero = scalar<T>(0);
 
         if (!cond) return;
@@ -70,12 +80,13 @@ class whereKernel {
         uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
 
         for (uint k = 0, id = xid; k < lim_ && id < otInfo_.dims[0];
-            k++, id += g.get_local_range(0)) {
+             k++, id += g.get_local_range(0)) {
             uint idx = otptr[id] + accum;
             if (iptr[id] != zero) out_acc_[idx - 1] = (off + id);
         }
     }
-protected:
+
+   protected:
     sycl::accessor<uint> out_acc_;
     sycl::accessor<uint> otmp_acc_;
     sycl::accessor<uint> rtmp_acc_;
@@ -113,15 +124,15 @@ static void where(Param<uint> &out, Param<T> in) {
     int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
     auto rtmp_alloc   = memAlloc<uint>(rtmp_elements);
     auto otmp_alloc   = memAlloc<uint>(otmp_elements);
-    rtmp.data          = rtmp_alloc.get();
-    otmp.data          = otmp_alloc.get();
+    rtmp.data         = rtmp_alloc.get();
+    otmp.data         = otmp_alloc.get();
 
     scan_first_launcher<T, uint, af_notzero_t>(
         otmp, rtmp, in, groups_x, groups_y, threads_x, false, true);
 
     // Linearize the dimensions and perform scan
-    Param<uint> ltmp = rtmp;
-    ltmp.info.dims[0]     = rtmp_elements;
+    Param<uint> ltmp  = rtmp;
+    ltmp.info.dims[0] = rtmp_elements;
     for (int k = 1; k < 4; k++) {
         ltmp.info.dims[k]    = 1;
         ltmp.info.strides[k] = rtmp_elements;
@@ -132,17 +143,19 @@ static void where(Param<uint> &out, Param<T> in) {
     // Get output size and allocate output
     uint total;
     sycl::buffer retBuffer(&total, {1},
-        {sycl::property::buffer::use_host_ptr()});
+                           {sycl::property::buffer::use_host_ptr()});
 
     getQueue()
         .submit([&](sycl::handler &h) {
-            auto acc_in = rtmp.data->get_access(h, sycl::range{1}, sycl::id{rtmp_elements - 1});
+            auto acc_in  = rtmp.data->get_access(h, sycl::range{1},
+                                                 sycl::id{rtmp_elements - 1});
             auto acc_out = retBuffer.get_access();
             h.copy(acc_in, acc_out);
-    }).wait();
+        })
+        .wait();
 
     auto out_alloc = memAlloc<uint>(total);
-    out.data        = out_alloc.get();
+    out.data       = out_alloc.get();
 
     out.info.dims[0]    = total;
     out.info.strides[0] = 1;
@@ -152,25 +165,21 @@ static void where(Param<uint> &out, Param<T> in) {
     }
 
     sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
-    sycl::range<2> global(groups_x * in.info.dims[2] * local[0], 
+    sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
                           groups_y * in.info.dims[3] * local[1]);
     uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
 
-    getQueue().submit([&] (sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
+    getQueue().submit([&](sycl::handler &h) {
+        auto out_acc  = out.data->get_access(h);
         auto otmp_acc = otmp.data->get_access(h);
         auto rtmp_acc = rtmp.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        auto in_acc   = in.data->get_access(h);
 
         sycl::stream debug_stream(2048 * 256, 128, h);
-        h.parallel_for(sycl::nd_range<2>(global, local), 
-            whereKernel<T>(
-                    out_acc, out.info,
-                    otmp_acc, otmp.info,
-                    rtmp_acc, rtmp.info,
-                    in_acc, in.info,
-                    groups_x, groups_y, lim,
-                    debug_stream));
+        h.parallel_for(sycl::nd_range<2>(global, local),
+                       whereKernel<T>(out_acc, out.info, otmp_acc, otmp.info,
+                                      rtmp_acc, rtmp.info, in_acc, in.info,
+                                      groups_x, groups_y, lim, debug_stream));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
     out_alloc.release();
diff --git a/src/backend/oneapi/scan.cpp b/src/backend/oneapi/scan.cpp
index 51183cfe8c..81b7494d68 100644
--- a/src/backend/oneapi/scan.cpp
+++ b/src/backend/oneapi/scan.cpp
@@ -10,7 +10,7 @@
 #include <err_oneapi.hpp>
 #include <scan.hpp>
 
-// #include <kernel/scan_dim.hpp>
+#include <kernel/scan_dim.hpp>
 #include <kernel/scan_first.hpp>
 
 namespace oneapi {
@@ -21,10 +21,11 @@ Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
     Param<To> Out = out;
     Param<Ti> In  = in;
 
-    if (dim == 0) {
-        kernel::scan_first<Ti, To, op>(Out, In, inclusiveScan);
-    } else {
-    //     kernel::scanDim<Ti, To, op>(Out, In, dim, inclusiveScan);
+    switch (dim) {
+        case 0: kernel::scan_first<Ti, To, op>(Out, In, inclusiveScan); break;
+        case 1: kernel::scan_dim<Ti, To, op, 1>(Out, In, inclusiveScan); break;
+        case 2: kernel::scan_dim<Ti, To, op, 2>(Out, In, inclusiveScan); break;
+        case 3: kernel::scan_dim<Ti, To, op, 3>(Out, In, inclusiveScan); break;
     }
 
     return out;

From 16f22445a1faf4c6a7e211bdd354d3db17d137bf Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 4 Nov 2022 18:43:18 -0400
Subject: [PATCH 2325/2677] corrects accessor types

---
 src/backend/oneapi/kernel/iota.hpp         | 38 +++++++-------
 src/backend/oneapi/kernel/mean.hpp         | 61 +++++++++++-----------
 src/backend/oneapi/kernel/reduce_all.hpp   | 18 ++++---
 src/backend/oneapi/kernel/reduce_dim.hpp   | 18 ++++---
 src/backend/oneapi/kernel/reduce_first.hpp | 18 ++++---
 src/backend/oneapi/kernel/scan_dim.hpp     | 36 +++++++------
 src/backend/oneapi/kernel/scan_first.hpp   | 46 +++++++++-------
 src/backend/oneapi/kernel/where.hpp        | 30 ++++++-----
 8 files changed, 152 insertions(+), 113 deletions(-)

diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index d4672dfd0d..ee0b16d23a 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -12,7 +12,6 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
-#include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
 #include <af/dim4.hpp>
@@ -40,10 +39,6 @@ class iotaKernel {
         , debug_(debug) {}
 
     void operator()(sycl::nd_item<2> it) const {
-        // printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
-        // debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) <<
-        // "]" << sycl::stream_manipulator::endl;
-
         sycl::group gg = it.get_group();
         const int oz   = gg.get_group_id(0) / blocksPerMatX_;
         const int ow   = gg.get_group_id(1) / blocksPerMatY_;
@@ -99,19 +94,26 @@ void iota(Param<T> out, const af::dim4& sdims) {
                           local[1] * blocksPerMatY * out.info.dims[3]);
     sycl::nd_range<2> ndrange(global, local);
 
-    getQueue().submit([=](sycl::handler& h) {
-        auto out_acc = out.data->get_access(h);
-
-        sycl::stream debug_stream(2048, 128, h);
-
-        h.parallel_for(
-            ndrange, iotaKernel<T>(
-                         out_acc, out.info, static_cast<int>(sdims[0]),
-                         static_cast<int>(sdims[1]), static_cast<int>(sdims[2]),
-                         static_cast<int>(sdims[3]), blocksPerMatX,
-                         blocksPerMatY, debug_stream));
-    });
-    ONEAPI_DEBUG_FINISH(getQueue());
+    try {
+        getQueue()
+            .submit([=](sycl::handler& h) {
+                auto out_acc = out.data->get_access(h);
+
+                sycl::stream debug_stream(2048, 128, h);
+
+                h.parallel_for(
+                    ndrange,
+                    iotaKernel<T>(out_acc, out.info, static_cast<int>(sdims[0]),
+                                  static_cast<int>(sdims[1]),
+                                  static_cast<int>(sdims[2]),
+                                  static_cast<int>(sdims[3]), blocksPerMatX,
+                                  blocksPerMatY, debug_stream));
+            })
+            .wait();
+        ONEAPI_DEBUG_FINISH(getQueue());
+    } catch (sycl::exception& e) {
+        std::cout << e.what() << std::endl;
+    } catch (std::exception& e) { std::cout << e.what() << std::endl; }
 }
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 3f7b5c3fee..f64fc94b96 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -45,6 +45,12 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename To, typename Tw>
 void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
     if (((*l_wt) != (Tw)0) || (r_wt != (Tw)0)) {
@@ -60,12 +66,11 @@ void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
 template<typename Ti, typename Tw, typename To, uint dim, uint DIMY>
 class meanDimKernelSMEM {
    public:
-    meanDimKernelSMEM(sycl::accessor<To> out, KParam oInfo,
-                      sycl::accessor<Tw> owt, KParam owInfo,
-                      sycl::accessor<Ti> in, KParam iInfo,
-                      sycl::accessor<Tw> iwt, KParam iwInfo, uint groups_x,
-                      uint groups_y, uint offset_dim,
-                      local_accessor<compute_t<To>, 1> s_val,
+    meanDimKernelSMEM(write_accessor<To> out, KParam oInfo,
+                      write_accessor<Tw> owt, KParam owInfo,
+                      read_accessor<Ti> in, KParam iInfo, read_accessor<Tw> iwt,
+                      KParam iwInfo, uint groups_x, uint groups_y,
+                      uint offset_dim, local_accessor<compute_t<To>, 1> s_val,
                       local_accessor<compute_t<Tw>, 1> s_idx,
                       sycl::stream debug, bool input_weight, bool output_weight)
         : out_(out)
@@ -202,10 +207,10 @@ class meanDimKernelSMEM {
     }
 
    protected:
-    sycl::accessor<To> out_;
-    sycl::accessor<Tw> owt_;
-    sycl::accessor<Ti> in_;
-    sycl::accessor<Tw> iwt_;
+    write_accessor<To> out_;
+    write_accessor<Tw> owt_;
+    read_accessor<Ti> in_;
+    read_accessor<Tw> iwt_;
     KParam oInfo_, owInfo_, iInfo_, iwInfo_;
     const uint groups_x_, groups_y_, offset_dim_;
     local_accessor<compute_t<To>, 1> s_val_;
@@ -224,8 +229,8 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
 
     sycl::buffer<Tw, 1> empty(sycl::range<1>{1});
     getQueue().submit([&](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 2048, 2048, h);
 
@@ -238,10 +243,8 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                                owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-        auto owt_acc =
-            (output_weight) ? owt.data->get_access(h) : empty.get_access(h);
-        auto iwt_acc =
-            (input_weight) ? iwt.data->get_access(h) : empty.get_access(h);
+        write_accessor<Tw> owt_acc{(output_weight) ? *owt.data : empty, h};
+        read_accessor<Tw> iwt_acc{(input_weight) ? *iwt.data : empty, h};
 
         switch (threads_y) {
             case 8:
@@ -321,10 +324,10 @@ void mean_dim(Param<To> out, Param<Ti> in, Param<Tw> iwt) {
 template<typename Ti, typename Tw, typename To>
 class meanFirstKernelSMEM {
    public:
-    meanFirstKernelSMEM(sycl::accessor<To> out, KParam oInfo,
-                        sycl::accessor<Tw> owt, KParam owInfo,
-                        sycl::accessor<Ti> in, KParam iInfo,
-                        sycl::accessor<Tw> iwt, KParam iwInfo, const uint DIMX,
+    meanFirstKernelSMEM(write_accessor<To> out, KParam oInfo,
+                        write_accessor<Tw> owt, KParam owInfo,
+                        read_accessor<Ti> in, KParam iInfo,
+                        read_accessor<Tw> iwt, KParam iwInfo, const uint DIMX,
                         const uint groups_x, const uint groups_y,
                         const uint repeat,
                         local_accessor<compute_t<To>, 1> s_val,
@@ -480,10 +483,10 @@ class meanFirstKernelSMEM {
     }
 
    protected:
-    sycl::accessor<To> out_;
-    sycl::accessor<Tw> owt_;
-    sycl::accessor<Ti> in_;
-    sycl::accessor<Tw> iwt_;
+    write_accessor<To> out_;
+    write_accessor<Tw> owt_;
+    read_accessor<Ti> in_;
+    read_accessor<Tw> iwt_;
     KParam oInfo_, owInfo_, iInfo_, iwInfo_;
     const uint DIMX_, groups_x_, groups_y_, repeat_;
     local_accessor<compute_t<To>, 1> s_val_;
@@ -504,8 +507,8 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
 
     sycl::buffer<Tw, 1> empty(sycl::range<1>{1});
     getQueue().submit([&](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 2048, 2048, h);
 
@@ -518,10 +521,8 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                                owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-        auto owt_acc =
-            (output_weight) ? owt.data->get_access(h) : empty.get_access(h);
-        auto iwt_acc =
-            (input_weight) ? iwt.data->get_access(h) : empty.get_access(h);
+        write_accessor<Tw> owt_acc{(output_weight) ? *owt.data : empty, h};
+        read_accessor<Tw> iwt_acc{(input_weight) ? *iwt.data : empty, h};
 
         h.parallel_for(
             sycl::nd_range<2>(global, local),
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 6fdf008e69..8ad65d7948 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -38,13 +38,19 @@ using global_atomic_ref =
     sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
                      sycl::access::address_space::global_space>;
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename Ti, typename To, af_op_t op>
 class reduceAllKernelSMEM {
    public:
-    reduceAllKernelSMEM(sycl::accessor<To> out, KParam oInfo,
+    reduceAllKernelSMEM(write_accessor<To> out, KParam oInfo,
                         sycl::accessor<unsigned> retCount,
                         sycl::accessor<To> tmp, KParam tmpInfo,
-                        sycl::accessor<Ti> in, KParam iInfo, uint DIMX,
+                        read_accessor<Ti> in, KParam iInfo, uint DIMX,
                         uint groups_x, uint groups_y, uint repeat,
                         bool change_nan, To nanval,
                         local_accessor<compute_t<To>, 1> s_ptr,
@@ -220,11 +226,11 @@ class reduceAllKernelSMEM {
     }
 
    protected:
-    sycl::accessor<To> out_;
+    write_accessor<To> out_;
     sycl::accessor<unsigned> retCount_;
     sycl::accessor<To> tmp_;
+    read_accessor<Ti> in_;
     KParam oInfo_, tmpInfo_, iInfo_;
-    sycl::accessor<Ti> in_;
     uint DIMX_, repeat_;
     uint groups_x_, groups_y_;
     bool change_nan_;
@@ -261,10 +267,10 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
     });
 
     getQueue().submit([=](sycl::handler &h) {
-        auto out_acc      = out.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
         auto retCount_acc = retirementCount.getData()->get_access(h);
         auto tmp_acc      = tmp.getData()->get_access(h);
-        auto in_acc       = in.data->get_access(h);
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 3f9e365b8b..b5e4252651 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -33,11 +33,17 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
 class reduceDimKernelSMEM {
    public:
-    reduceDimKernelSMEM(sycl::accessor<To> out, KParam oInfo,
-                        sycl::accessor<Ti> in, KParam iInfo, uint groups_x,
+    reduceDimKernelSMEM(write_accessor<To> out, KParam oInfo,
+                        read_accessor<Ti> in, KParam iInfo, uint groups_x,
                         uint groups_y, uint offset_dim, bool change_nan,
                         To nanval, local_accessor<compute_t<To>, 1> s_val,
                         sycl::stream debug)
@@ -128,9 +134,9 @@ class reduceDimKernelSMEM {
     }
 
    protected:
-    sycl::accessor<To> out_;
+    write_accessor<To> out_;
     KParam oInfo_, iInfo_;
-    sycl::accessor<Ti> in_;
+    read_accessor<Ti> in_;
     uint groups_x_, groups_y_, offset_dim_;
     bool change_nan_;
     To nanval_;
@@ -148,8 +154,8 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                           blocks_dim[1] * blocks_dim[3] * local[1]);
 
     getQueue().submit([=](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index ebf55fb63e..6bfe177148 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -33,11 +33,17 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename Ti, typename To, af_op_t op, uint DIMX>
 class reduceFirstKernelSMEM {
    public:
-    reduceFirstKernelSMEM(sycl::accessor<To> out, KParam oInfo,
-                          sycl::accessor<Ti> in, KParam iInfo, uint groups_x,
+    reduceFirstKernelSMEM(write_accessor<To> out, KParam oInfo,
+                          read_accessor<Ti> in, KParam iInfo, uint groups_x,
                           uint groups_y, uint repeat, bool change_nan,
                           To nanval, local_accessor<compute_t<To>, 1> s_val,
                           sycl::stream debug)
@@ -133,9 +139,9 @@ class reduceFirstKernelSMEM {
     }
 
    protected:
-    sycl::accessor<To> out_;
+    write_accessor<To> out_;
     KParam oInfo_, iInfo_;
-    sycl::accessor<Ti> in_;
+    read_accessor<Ti> in_;
     uint groups_x_, groups_y_, repeat_;
     bool change_nan_;
     To nanval_;
@@ -155,8 +161,8 @@ void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
     uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
 
     getQueue().submit([=](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index 1db981ca9a..f617c782dc 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -25,12 +25,18 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename Ti, typename To, af_op_t op, int dim>
 class scanDimKernel {
    public:
-    scanDimKernel(sycl::accessor<To> out_acc, KParam oInfo,
-                  sycl::accessor<To> tmp_acc, KParam tInfo,
-                  sycl::accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
+    scanDimKernel(write_accessor<To> out_acc, KParam oInfo,
+                  write_accessor<To> tmp_acc, KParam tInfo,
+                  read_accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
                   const uint groups_y, const uint blocks_dim, const uint lim,
                   const bool isFinalPass, const uint DIMY,
                   const bool inclusive_scan, local_accessor<To, 1> s_val,
@@ -147,9 +153,9 @@ class scanDimKernel {
     }
 
    protected:
-    sycl::accessor<To> out_acc_;
-    sycl::accessor<To> tmp_acc_;
-    sycl::accessor<Ti> in_acc_;
+    write_accessor<To> out_acc_;
+    write_accessor<To> tmp_acc_;
+    read_accessor<Ti> in_acc_;
     KParam oInfo_, tInfo_, iInfo_;
     const uint groups_x_, groups_y_, blocks_dim_, lim_, DIMY_;
     const bool isFinalPass_, inclusive_scan_;
@@ -161,8 +167,8 @@ class scanDimKernel {
 template<typename To, af_op_t op, int dim>
 class scanDimBcastKernel {
    public:
-    scanDimBcastKernel(sycl::accessor<To> out_acc, KParam oInfo,
-                       sycl::accessor<To> tmp_acc, KParam tInfo,
+    scanDimBcastKernel(write_accessor<To> out_acc, KParam oInfo,
+                       read_accessor<To> tmp_acc, KParam tInfo,
                        const uint groups_x, const uint groups_y,
                        const uint groups_dim, const uint lim,
                        const bool inclusive_scan, sycl::stream debug)
@@ -233,8 +239,8 @@ class scanDimBcastKernel {
     }
 
    protected:
-    sycl::accessor<To> out_acc_;
-    sycl::accessor<To> tmp_acc_;
+    write_accessor<To> out_acc_;
+    read_accessor<To> tmp_acc_;
     KParam oInfo_, tInfo_;
     const uint groups_x_, groups_y_, groups_dim_, lim_;
     const bool inclusive_scan_;
@@ -253,9 +259,9 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
 
     getQueue().submit([&](sycl::handler &h) {
         // TODO: specify access modes in all kernels
-        auto out_acc = out.data->get_access(h);
-        auto tmp_acc = tmp.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        write_accessor<To> tmp_acc{*tmp.data, h};
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
@@ -284,8 +290,8 @@ static void bcast_dim_launcher(Param<To> out, Param<To> tmp,
     uint lim = divup(out.info.dims[dim], (threads_y * blocks_all[dim]));
 
     getQueue().submit([&](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto tmp_acc = tmp.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        read_accessor<To> tmp_acc{*tmp.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 0efed3de48..9a377ca2d9 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -25,16 +25,22 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename Ti, typename To, af_op_t op>
 class scanFirstKernel {
    public:
-    scanFirstKernel(sycl::accessor<To> out_acc, KParam oInfo,
-                    sycl::accessor<To> tmp_acc, KParam tInfo,
-                    sycl::accessor<Ti> in_acc, KParam iInfo,
-                    const uint groups_x, const uint groups_y, const uint lim,
-                    const bool isFinalPass, const uint DIMX,
-                    const bool inclusive_scan, local_accessor<To, 1> s_val,
-                    local_accessor<To, 1> s_tmp, sycl::stream debug_stream)
+    scanFirstKernel(write_accessor<To> out_acc, KParam oInfo,
+                    write_accessor<To> tmp_acc, KParam tInfo,
+                    read_accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
+                    const uint groups_y, const uint lim, const bool isFinalPass,
+                    const uint DIMX, const bool inclusive_scan,
+                    local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp,
+                    sycl::stream debug_stream)
         : out_acc_(out_acc)
         , oInfo_(oInfo)
         , tmp_acc_(tmp_acc)
@@ -131,9 +137,9 @@ class scanFirstKernel {
     }
 
    protected:
-    sycl::accessor<To> out_acc_;
-    sycl::accessor<To> tmp_acc_;
-    sycl::accessor<Ti> in_acc_;
+    write_accessor<To> out_acc_;
+    write_accessor<To> tmp_acc_;
+    read_accessor<Ti> in_acc_;
     KParam oInfo_, tInfo_, iInfo_;
     const uint groups_x_, groups_y_, lim_, DIMX_;
     const bool isFinalPass_, inclusive_scan_;
@@ -145,8 +151,8 @@ class scanFirstKernel {
 template<typename To, af_op_t op>
 class scanFirstBcastKernel {
    public:
-    scanFirstBcastKernel(sycl::accessor<To> out_acc, KParam oInfo,
-                         sycl::accessor<To> tmp_acc, KParam tInfo,
+    scanFirstBcastKernel(write_accessor<To> out_acc, KParam oInfo,
+                         read_accessor<To> tmp_acc, KParam tInfo,
                          const uint groups_x, const uint groups_y,
                          const uint lim, const bool inclusive_scan,
                          sycl::stream debug_stream)
@@ -199,8 +205,8 @@ class scanFirstBcastKernel {
     }
 
    protected:
-    sycl::accessor<To> out_acc_;
-    sycl::accessor<To> tmp_acc_;
+    write_accessor<To> out_acc_;
+    read_accessor<To> tmp_acc_;
     KParam oInfo_, tInfo_;
     const uint groups_x_, groups_y_, lim_;
     const bool inclusive_scan_;
@@ -218,9 +224,9 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
     getQueue().submit([&](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto tmp_acc = tmp.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        write_accessor<To> tmp_acc{*tmp.data, h};
+        read_accessor<Ti> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
@@ -250,8 +256,8 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp,
     uint lim = divup(out.info.dims[0], (threads_x * groups_x));
 
     getQueue().submit([&](sycl::handler &h) {
-        auto out_acc = out.data->get_access(h);
-        auto tmp_acc = tmp.data->get_access(h);
+        write_accessor<To> out_acc{*out.data, h};
+        read_accessor<To> tmp_acc{*tmp.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
 
@@ -306,4 +312,4 @@ static void scan_first(Param<To> out, Param<Ti> in, bool inclusive_scan) {
 }
 
 }  // namespace kernel
-}  // namespace oneapi
\ No newline at end of file
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index c927c995da..e55c72b367 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -23,13 +23,19 @@
 namespace oneapi {
 namespace kernel {
 
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
 template<typename T>
 class whereKernel {
    public:
-    whereKernel(sycl::accessor<uint> out_acc, KParam oInfo,
-                sycl::accessor<uint> otmp_acc, KParam otInfo,
-                sycl::accessor<uint> rtmp_acc, KParam rtInfo,
-                sycl::accessor<T> in_acc, KParam iInfo, uint groups_x,
+    whereKernel(write_accessor<uint> out_acc, KParam oInfo,
+                read_accessor<uint> otmp_acc, KParam otInfo,
+                read_accessor<uint> rtmp_acc, KParam rtInfo,
+                read_accessor<T> in_acc, KParam iInfo, uint groups_x,
                 uint groups_y, uint lim, sycl::stream debug)
         : out_acc_(out_acc)
         , oInfo_(oInfo)
@@ -87,10 +93,10 @@ class whereKernel {
     }
 
    protected:
-    sycl::accessor<uint> out_acc_;
-    sycl::accessor<uint> otmp_acc_;
-    sycl::accessor<uint> rtmp_acc_;
-    sycl::accessor<T> in_acc_;
+    write_accessor<uint> out_acc_;
+    read_accessor<uint> otmp_acc_;
+    read_accessor<uint> rtmp_acc_;
+    read_accessor<T> in_acc_;
     KParam oInfo_, otInfo_, rtInfo_, iInfo_;
     uint groups_x_, groups_y_, lim_;
     sycl::stream debug_;
@@ -170,10 +176,10 @@ static void where(Param<uint> &out, Param<T> in) {
     uint lim = divup(otmp.info.dims[0], (threads_x * groups_x));
 
     getQueue().submit([&](sycl::handler &h) {
-        auto out_acc  = out.data->get_access(h);
-        auto otmp_acc = otmp.data->get_access(h);
-        auto rtmp_acc = rtmp.data->get_access(h);
-        auto in_acc   = in.data->get_access(h);
+        write_accessor<uint> out_acc{*out.data, h};
+        read_accessor<uint> otmp_acc{*otmp.data, h};
+        read_accessor<uint> rtmp_acc{*rtmp.data, h};
+        read_accessor<T> in_acc{*in.data, h};
 
         sycl::stream debug_stream(2048 * 256, 128, h);
         h.parallel_for(sycl::nd_range<2>(global, local),

From 013b196e22df9eb872e10f0228ae7d484ec4269d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 7 Nov 2022 16:57:37 -0500
Subject: [PATCH 2326/2677] Fix errors on Linux builds for reduce, scan, and
 where

---
 src/api/c/optypes.hpp                    |  2 +-
 src/backend/oneapi/copy.cpp              |  5 +++--
 src/backend/oneapi/kernel/mean.hpp       |  8 +++----
 src/backend/oneapi/kernel/reduce.hpp     | 28 ++++++++++++------------
 src/backend/oneapi/kernel/scan_dim.hpp   | 12 +++++-----
 src/backend/oneapi/kernel/scan_first.hpp | 10 ++++-----
 src/backend/oneapi/kernel/where.hpp      | 13 +++++------
 7 files changed, 38 insertions(+), 40 deletions(-)

diff --git a/src/api/c/optypes.hpp b/src/api/c/optypes.hpp
index 696ae07668..44f1fd68d6 100644
--- a/src/api/c/optypes.hpp
+++ b/src/api/c/optypes.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-typedef enum af_op_t : int {
+enum af_op_t : int {
     af_none_t = -1,
     af_add_t  = 0,
     af_sub_t,
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index f24db5650c..f49689a423 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -218,8 +218,9 @@ T getScalar(const Array<T> &in) {
 
     getQueue()
         .submit([&](sycl::handler &h) {
-            auto acc_in  = in.getData()->get_access(h, sycl::range{1},
-                                                    sycl::id{in.getOffset()});
+            auto acc_in = in.getData()->get_access(
+                h, sycl::range{1},
+                sycl::id{static_cast<uintl>(in.getOffset())});
             auto acc_out = retBuffer.get_access();
             h.copy(acc_in, acc_out);
         })
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index f64fc94b96..8a2e07d93c 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -583,7 +583,7 @@ void mean(Param<To> out, Param<Ti> in, int dim) {
 
 template<typename T, typename Tw>
 T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
-    int in_elements =
+    uintl in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
     // FIXME: Use better heuristics to get to the optimum number
     if (in_elements > 4096) {
@@ -621,7 +621,7 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         Array<Tw> tmpWt = createEmptyArray<Tw>(
             {blocks_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
 
-        int tmp_elements = tmpOut.elements();
+        uintl tmp_elements = tmpOut.elements();
 
         mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x,
                                       blocks_y, threads_x);
@@ -693,7 +693,7 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
 template<typename Ti, typename Tw, typename To>
 To mean_all(Param<Ti> in) {
     using std::unique_ptr;
-    int in_elements =
+    uintl in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
     bool is_linear = (in.info.strides[0] == 1);
     for (int k = 1; k < 4; k++) {
@@ -728,7 +728,7 @@ To mean_all(Param<Ti> in) {
         mean_first_launcher<Ti, Tw, To>(tmpOut, tmpCt, in, iwt, blocks_x,
                                         blocks_y, threads_x);
 
-        int tmp_elements = tmpOut.elements();
+        uintl tmp_elements = tmpOut.elements();
         std::vector<To> h_ptr(tmp_elements);
         std::vector<Tw> h_cptr(tmp_elements);
 
diff --git a/src/backend/oneapi/kernel/reduce.hpp b/src/backend/oneapi/kernel/reduce.hpp
index cae5c9854c..6fa38e0269 100644
--- a/src/backend/oneapi/kernel/reduce.hpp
+++ b/src/backend/oneapi/kernel/reduce.hpp
@@ -31,20 +31,6 @@
 namespace oneapi {
 namespace kernel {
 
-template<typename Ti, typename To, af_op_t op>
-void reduce_cpu_dispatch(Param<To> out, Param<Ti> in, int dim, bool change_nan,
-                         double nanval) {
-    // TODO: use kernels optimized for SIMD-based subgroup sizes
-    reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
-}
-
-template<typename Ti, typename To, af_op_t op>
-void reduce_gpu_dispatch(Param<To> out, Param<Ti> in, int dim, bool change_nan,
-                         double nanval) {
-    // TODO: use kernels optimized for gpu subgroup sizes
-    reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
-}
-
 template<typename Ti, typename To, af_op_t op>
 void reduce_default_dispatch(Param<To> out, Param<Ti> in, int dim,
                              bool change_nan, double nanval) {
@@ -64,6 +50,20 @@ void reduce_default_dispatch(Param<To> out, Param<Ti> in, int dim,
     }
 }
 
+template<typename Ti, typename To, af_op_t op>
+void reduce_cpu_dispatch(Param<To> out, Param<Ti> in, int dim, bool change_nan,
+                         double nanval) {
+    // TODO: use kernels optimized for SIMD-based subgroup sizes
+    reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+}
+
+template<typename Ti, typename To, af_op_t op>
+void reduce_gpu_dispatch(Param<To> out, Param<Ti> in, int dim, bool change_nan,
+                         double nanval) {
+    // TODO: use kernels optimized for gpu subgroup sizes
+    reduce_default_dispatch<Ti, To, op>(out, in, dim, change_nan, nanval);
+}
+
 template<typename Ti, typename To, af_op_t op>
 void reduce(Param<To> out, Param<Ti> in, int dim, bool change_nan,
             double nanval) {
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index f617c782dc..8c1a6e9140 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -42,17 +42,17 @@ class scanDimKernel {
                   const bool inclusive_scan, local_accessor<To, 1> s_val,
                   local_accessor<To, 1> s_tmp, sycl::stream debug)
         : out_acc_(out_acc)
-        , oInfo_(oInfo)
         , tmp_acc_(tmp_acc)
-        , tInfo_(tInfo)
         , in_acc_(in_acc)
+        , oInfo_(oInfo)
+        , tInfo_(tInfo)
         , iInfo_(iInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , blocks_dim_(blocks_dim)
         , lim_(lim)
-        , isFinalPass_(isFinalPass)
         , DIMY_(DIMY)
+        , isFinalPass_(isFinalPass)
         , inclusive_scan_(inclusive_scan)
         , s_val_(s_val)
         , s_tmp_(s_tmp)
@@ -71,7 +71,7 @@ class scanDimKernel {
         const uint xid       = groupId_x * g.get_local_range(0) + lidx;
         const uint yid       = groupId_y;
 
-        int ids[4] = {xid, yid, zid, wid};
+        uint ids[4] = {xid, yid, zid, wid};
 
         const Ti *iptr = in_acc_.get_pointer();
         To *optr       = out_acc_.get_pointer();
@@ -173,8 +173,8 @@ class scanDimBcastKernel {
                        const uint groups_dim, const uint lim,
                        const bool inclusive_scan, sycl::stream debug)
         : out_acc_(out_acc)
-        , oInfo_(oInfo)
         , tmp_acc_(tmp_acc)
+        , oInfo_(oInfo)
         , tInfo_(tInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
@@ -195,7 +195,7 @@ class scanDimBcastKernel {
         const uint xid       = groupId_x * g.get_local_range(0) + lidx;
         const uint yid       = groupId_y;
 
-        int ids[4] = {xid, yid, zid, wid};
+        uint ids[4] = {xid, yid, zid, wid};
 
         const To *tptr = tmp_acc_.get_pointer();
         To *optr       = out_acc_.get_pointer();
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 9a377ca2d9..a7fe567c75 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -42,16 +42,16 @@ class scanFirstKernel {
                     local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp,
                     sycl::stream debug_stream)
         : out_acc_(out_acc)
-        , oInfo_(oInfo)
         , tmp_acc_(tmp_acc)
-        , tInfo_(tInfo)
         , in_acc_(in_acc)
+        , oInfo_(oInfo)
+        , tInfo_(tInfo)
         , iInfo_(iInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , lim_(lim)
-        , isFinalPass_(isFinalPass)
         , DIMX_(DIMX)
+        , isFinalPass_(isFinalPass)
         , inclusive_scan_(inclusive_scan)
         , s_val_(s_val)
         , s_tmp_(s_tmp)
@@ -61,7 +61,6 @@ class scanFirstKernel {
         sycl::group g   = it.get_group();
         const uint lidx = it.get_local_id(0);
         const uint lidy = it.get_local_id(1);
-        const uint lid  = lidy * g.get_local_range(0) + lidx;
 
         const uint zid       = g.get_group_id(0) / groups_x_;
         const uint wid       = g.get_group_id(1) / groups_y_;
@@ -157,8 +156,8 @@ class scanFirstBcastKernel {
                          const uint lim, const bool inclusive_scan,
                          sycl::stream debug_stream)
         : out_acc_(out_acc)
-        , oInfo_(oInfo)
         , tmp_acc_(tmp_acc)
+        , oInfo_(oInfo)
         , tInfo_(tInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
@@ -170,7 +169,6 @@ class scanFirstBcastKernel {
         sycl::group g   = it.get_group();
         const uint lidx = it.get_local_id(0);
         const uint lidy = it.get_local_id(1);
-        const uint lid  = lidy * g.get_local_range(0) + lidx;
 
         const uint zid       = g.get_group_id(0) / groups_x_;
         const uint wid       = g.get_group_id(1) / groups_y_;
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index e55c72b367..cb8887fb84 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -54,7 +54,6 @@ class whereKernel {
         sycl::group g   = it.get_group();
         const uint lidx = it.get_local_id(0);
         const uint lidy = it.get_local_id(1);
-        const uint lid  = lidy * g.get_local_range(0) + lidx;
 
         const uint zid       = g.get_group_id(0) / groups_x_;
         const uint wid       = g.get_group_id(1) / groups_y_;
@@ -126,12 +125,12 @@ static void where(Param<uint> &out, Param<T> in) {
         otmp.info.strides[k] = otmp.info.strides[k - 1] * otmp.info.dims[k - 1];
     }
 
-    int rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
-    int otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
-    auto rtmp_alloc   = memAlloc<uint>(rtmp_elements);
-    auto otmp_alloc   = memAlloc<uint>(otmp_elements);
-    rtmp.data         = rtmp_alloc.get();
-    otmp.data         = otmp_alloc.get();
+    uintl rtmp_elements = rtmp.info.strides[3] * rtmp.info.dims[3];
+    uintl otmp_elements = otmp.info.strides[3] * otmp.info.dims[3];
+    auto rtmp_alloc     = memAlloc<uint>(rtmp_elements);
+    auto otmp_alloc     = memAlloc<uint>(otmp_elements);
+    rtmp.data           = rtmp_alloc.get();
+    otmp.data           = otmp_alloc.get();
 
     scan_first_launcher<T, uint, af_notzero_t>(
         otmp, rtmp, in, groups_x, groups_y, threads_x, false, true);

From 3775fd7390da1a552be6f1b22be572d30e146295 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Sat, 8 Oct 2022 09:06:43 -0400
Subject: [PATCH 2327/2677] approx1 port to oneapi. tests out aside from Subs,
 JIT, Memory

---
 src/backend/oneapi/CMakeLists.txt    |   1 +
 src/backend/oneapi/approx.cpp        |  15 +-
 src/backend/oneapi/kernel/approx.hpp | 309 +++++++++++++++++++++++++++
 3 files changed, 317 insertions(+), 8 deletions(-)
 create mode 100755 src/backend/oneapi/kernel/approx.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 4b0f9f0a29..c2c78dc9c6 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -205,6 +205,7 @@ add_library(afoneapi
 target_sources(afoneapi
   PRIVATE
     kernel/KParam.hpp
+    kernel/approx.hpp
     kernel/assign.hpp
     kernel/diagonal.hpp
     kernel/diff.hpp
diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index e11216d00c..f25132f073 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -9,29 +9,28 @@
 
 #include <approx.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/approx.hpp>
 
 namespace oneapi {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
              const int xdim, const Tp &xi_beg, const Tp &xi_step,
              const af_interp_type method, const float offGrid) {
-    ONEAPI_NOT_SUPPORTED("");
-    return;
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            // kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step,
-            // offGrid, method, 1);
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            // kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step,
-            // offGrid, method, 2);
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            // kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step,
-            // offGrid, method, 3);
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 3);
             break;
         default: break;
     }
diff --git a/src/backend/oneapi/kernel/approx.hpp b/src/backend/oneapi/kernel/approx.hpp
new file mode 100755
index 0000000000..de96866f99
--- /dev/null
+++ b/src/backend/oneapi/kernel/approx.hpp
@@ -0,0 +1,309 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+constexpr int TILE_DIM  = 32;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = 256 / TILE_DIM;
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename Ty, typename Tp>
+class approx1Kernel {
+   public:
+    approx1Kernel(sycl::accessor<Ty> d_yo, const KParam yo,
+                  sycl::accessor<Ty> d_yi, const KParam yi,
+                  sycl::accessor<Tp> d_xo, const KParam xo, const Tp xi_beg,
+                  const Tp xi_step_reproc, const Ty offGrid,
+                  const int blocksMatX, const int batch, const int method,
+                  const int XDIM, const int INTERP_ORDER)
+        : d_yo_(d_yo)
+        , yo_(yo)
+        , d_yi_(d_yi)
+        , yi_(yi)
+        , d_xo_(d_xo)
+        , xo_(xo)
+        , xi_beg_(xi_beg)
+        , xi_step_reproc_(xi_step_reproc)
+        , offGrid_(offGrid)
+        , blocksMatX_(blocksMatX)
+        , batch_(batch)
+        , method_(method)
+        , XDIM_(XDIM)
+        , INTERP_ORDER_(INTERP_ORDER) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int idw = g.get_group_id(1) / yo_.dims[2];
+        const int idz = g.get_group_id(1) - idw * yo_.dims[2];
+
+        const int idy        = g.get_group_id(0) / blocksMatX_;
+        const int blockIdx_x = g.get_group_id(0) - idy * blocksMatX_;
+        const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+
+        if (idx >= yo_.dims[0] || idy >= yo_.dims[1] || idz >= yo_.dims[2] ||
+            idw >= yo_.dims[3])
+            return;
+
+        // FIXME: Only cubic interpolation is doing clamping
+        // We need to make it consistent across all methods
+        // Not changing the behavior because tests will fail
+        const bool doclamp = INTERP_ORDER_ == 3;
+
+        bool is_off[] = {xo_.dims[0] > 1, xo_.dims[1] > 1, xo_.dims[2] > 1,
+                         xo_.dims[3] > 1};
+
+        const int yo_idx = idw * yo_.strides[3] + idz * yo_.strides[2] +
+                           idy * yo_.strides[1] + idx + yo_.offset;
+
+        int xo_idx = idx * is_off[0] + xo_.offset;
+        if (batch_) {
+            xo_idx += idw * xo_.strides[3] * is_off[3];
+            xo_idx += idz * xo_.strides[2] * is_off[2];
+            xo_idx += idy * xo_.strides[1] * is_off[1];
+        }
+
+        const Tp x = (d_xo_[xo_idx] - xi_beg_) * xi_step_reproc_;
+
+#pragma unroll
+        for (int flagIdx = 0; flagIdx < 4; ++flagIdx) {
+            is_off[flagIdx] = true;
+        }
+        is_off[XDIM_] = false;
+
+        if (x < 0 || yi_.dims[XDIM_] < x + 1) {
+            d_yo_[yo_idx] = offGrid_;
+            return;
+        }
+
+        int yi_idx = idx * is_off[0] + yi_.offset;
+        yi_idx += idw * yi_.strides[3] * is_off[3];
+        yi_idx += idz * yi_.strides[2] * is_off[2];
+        yi_idx += idy * yi_.strides[1] * is_off[1];
+
+        if (INTERP_ORDER_ == 1)
+            interp1o1(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
+                      doclamp, 1);
+        if (INTERP_ORDER_ == 2)
+            interp1o2(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
+                      doclamp, 1);
+        if (INTERP_ORDER_ == 3)
+            interp1o3(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
+                      doclamp, 1);
+    }
+
+    void interp1o1(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        Ty zero = (Ty)0;
+
+        const int x_lim    = in.dims[XDIM_];
+        const int x_stride = in.strides[XDIM_];
+
+        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        bool cond = xid >= 0 && xid < x_lim;
+        if (doclamp) xid = fmax(0, fmin(xid, x_lim));
+
+        const int idx = ioff + xid * x_stride;
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            d_out[ooff + n * out.strides[batch_dim]] =
+                (doclamp || cond) ? d_in[idx_n] : zero;
+        }
+    }
+
+#if IS_CPLX
+#if USE_DOUBLE
+    typedef double ScalarTy;
+#else
+    typedef float ScalarTy;
+#endif
+    Ty __mulrc(ScalarTy s, Ty v) {
+        InterpInTy out = {s * v.x, s * v.y};
+        return out;
+    }
+#define MULRC(a, b) __mulrc(a, b)
+#define MULCR(a, b) __mulrc(b, a)
+#else
+#define MULRC(a, b) (a) * (b)
+#define MULCR(a, b) (a) * (b)
+#endif
+
+    Ty linearInterpFunc(Ty val[2], Tp ratio) const {
+        return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
+    }
+
+    Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio) const {
+        Ty res[2];
+        res[0] = linearInterpFunc(val[0], xratio);
+        res[1] = linearInterpFunc(val[1], xratio);
+        return linearInterpFunc(res, yratio);
+    }
+
+    Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
+        Ty a0, a1, a2, a3;
+        if (spline) {
+            a0 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)1.5, val[1]) +
+                 MULRC((Tp)-1.5, val[2]) + MULRC((Tp)0.5, val[3]);
+
+            a1 = MULRC((Tp)1.0, val[0]) + MULRC((Tp)-2.5, val[1]) +
+                 MULRC((Tp)2.0, val[2]) + MULRC((Tp)-0.5, val[3]);
+
+            a2 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)0.5, val[2]);
+
+            a3 = val[1];
+        } else {
+            a0 = val[3] - val[2] - val[0] + val[1];
+            a1 = val[0] - val[1] - a0;
+            a2 = val[2] - val[0];
+            a3 = val[1];
+        }
+
+        Tp xratio2 = xratio * xratio;
+        Tp xratio3 = xratio2 * xratio;
+
+        return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
+    }
+
+    Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio,
+                         bool spline) const {
+        Ty res[4];
+        res[0] = cubicInterpFunc(val[0], xratio, spline);
+        res[1] = cubicInterpFunc(val[1], xratio, spline);
+        res[2] = cubicInterpFunc(val[2], xratio, spline);
+        res[3] = cubicInterpFunc(val[3], xratio, spline);
+        return cubicInterpFunc(res, yratio, spline);
+    }
+
+    void interp1o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        const int grid_x = floor(x);    // nearest grid
+        const Tp off_x   = x - grid_x;  // fractional offset
+
+        const int x_lim    = in.dims[XDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int idx      = ioff + grid_x * x_stride;
+
+        Ty zero      = (Ty)0;
+        bool cond[2] = {true, grid_x + 1 < x_lim};
+        int offx[2]  = {0, cond[1] ? 1 : 0};
+        Tp ratio     = off_x;
+        if (method == AF_INTERP_LINEAR_COSINE) {
+            ratio = (1 - cos(ratio * (Tp)M_PI)) / 2;
+        }
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            Ty val[2] = {
+                (doclamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
+                (doclamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
+
+            d_out[ooff + n * out.strides[batch_dim]] =
+                linearInterpFunc(val, ratio);
+        }
+    }
+
+    void interp1o3(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        const int grid_x = floor(x);    // nearest grid
+        const Tp off_x   = x - grid_x;  // fractional offset
+
+        const int x_lim    = in.dims[XDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int idx      = ioff + grid_x * x_stride;
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                        grid_x + 2 < x_lim};
+        int off[4]   = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
+                      cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+        Ty zero = (Ty)0;
+
+        for (int n = 0; n < batch; n++) {
+            Ty val[4];
+            int idx_n = idx + n * in.strides[batch_dim];
+            for (int i = 0; i < 4; i++) {
+                val[i] = (doclamp || cond[i]) ? d_in[idx_n + off[i] * x_stride]
+                                              : zero;
+            }
+            bool spline = method == AF_INTERP_CUBIC_SPLINE;
+            d_out[ooff + n * out.strides[batch_dim]] =
+                cubicInterpFunc(val, off_x, spline);
+        }
+    }
+
+   private:
+    sycl::accessor<Ty> d_yo_;
+    const KParam yo_;
+    sycl::accessor<Ty> d_yi_;
+    const KParam yi_;
+    sycl::accessor<Tp> d_xo_;
+    const KParam xo_;
+    const Tp xi_beg_;
+    const Tp xi_step_reproc_;
+    const Ty offGrid_;
+    const int blocksMatX_;
+    const int batch_;
+    const int method_;
+    const int XDIM_;
+    const int INTERP_ORDER_;
+};
+
+template<typename Ty, typename Tp>
+void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
+             const int xdim, const Tp xi_beg, const Tp xi_step,
+             const float offGrid, const af_interp_type method,
+             const int order) {
+    constexpr int THREADS = 256;
+
+    auto local         = sycl::range{THREADS, 1};
+    dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
+    auto global        = sycl::range{blocksPerMat * local[0] * yo.info.dims[1],
+                              yo.info.dims[2] * yo.info.dims[3] * local[1]};
+
+    // Passing bools to opencl kernels is not allowed
+    bool batch =
+        !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto yoAcc = yo.data->get_access(h);
+        auto yiAcc = yi.data->get_access(h);
+        auto xoAcc = xo.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            approx1Kernel<Ty, Tp>(yoAcc, yo.info, yiAcc, yi.info, xoAcc,
+                                  xo.info, xi_beg, Tp(1) / xi_step, (Ty)offGrid,
+                                  (int)blocksPerMat, (int)batch, (int)method,
+                                  xdim, order));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi

From 55583412363c22210ed40601df5ba84e5f18c8de Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Mon, 10 Oct 2022 13:45:31 -0400
Subject: [PATCH 2328/2677] approx2 port to oneapi. tests out aside from Subs,
 JIT, Memory

---
 src/backend/oneapi/approx.cpp        |  14 +-
 src/backend/oneapi/arith.hpp         |   3 +
 src/backend/oneapi/kernel/approx.hpp | 344 +++++++++++++++++++++++++--
 3 files changed, 336 insertions(+), 25 deletions(-)

diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index f25132f073..43ff5f7dcf 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -42,27 +42,25 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
              const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
              const Tp &yi_step, const af_interp_type method,
              const float offGrid) {
-    ONEAPI_NOT_SUPPORTED("");
-    return;
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            // kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-            // ydim, yi_beg, yi_step, offGrid, method, 1);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                    ydim, yi_beg, yi_step, offGrid, method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            // kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-            // ydim, yi_beg, yi_step, offGrid, method, 2);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                    ydim, yi_beg, yi_step, offGrid, method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            // kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-            // ydim, yi_beg, yi_step, offGrid, method, 3);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                    ydim, yi_beg, yi_step, offGrid, method, 3);
             break;
         default: break;
     }
diff --git a/src/backend/oneapi/arith.hpp b/src/backend/oneapi/arith.hpp
index 2a004b5766..1311d4d607 100644
--- a/src/backend/oneapi/arith.hpp
+++ b/src/backend/oneapi/arith.hpp
@@ -11,6 +11,7 @@
 
 #include <Array.hpp>
 #include <common/jit/BinaryNode.hpp>
+#include <err_oneapi.hpp>
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 
@@ -19,12 +20,14 @@ namespace oneapi {
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &&lhs, const Array<T> &&rhs,
                  const af::dim4 &odims) {
+    ONEAPI_NOT_SUPPORTED(__FUNCTION__);
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
+    ONEAPI_NOT_SUPPORTED(__FUNCTION__);
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/approx.hpp b/src/backend/oneapi/kernel/approx.hpp
index de96866f99..15033317dd 100755
--- a/src/backend/oneapi/kernel/approx.hpp
+++ b/src/backend/oneapi/kernel/approx.hpp
@@ -155,13 +155,6 @@ class approx1Kernel {
         return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
     }
 
-    Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio) const {
-        Ty res[2];
-        res[0] = linearInterpFunc(val[0], xratio);
-        res[1] = linearInterpFunc(val[1], xratio);
-        return linearInterpFunc(res, yratio);
-    }
-
     Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
         Ty a0, a1, a2, a3;
         if (spline) {
@@ -187,16 +180,6 @@ class approx1Kernel {
         return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
     }
 
-    Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio,
-                         bool spline) const {
-        Ty res[4];
-        res[0] = cubicInterpFunc(val[0], xratio, spline);
-        res[1] = cubicInterpFunc(val[1], xratio, spline);
-        res[2] = cubicInterpFunc(val[2], xratio, spline);
-        res[3] = cubicInterpFunc(val[3], xratio, spline);
-        return cubicInterpFunc(res, yratio, spline);
-    }
-
     void interp1o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
                    sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
                    int method, int batch, bool doclamp, int batch_dim) const {
@@ -305,5 +288,332 @@ void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
+template<typename Ty, typename Tp>
+class approx2Kernel {
+   public:
+    approx2Kernel(sycl::accessor<Ty> d_zo, const KParam zo,
+                  sycl::accessor<Ty> d_zi, const KParam zi,
+                  sycl::accessor<Tp> d_xo, const KParam xo,
+                  sycl::accessor<Tp> d_yo, const KParam yo, const Tp xi_beg,
+                  const Tp xi_step_reproc, const Tp yi_beg,
+                  const Tp yi_step_reproc, const Ty offGrid,
+                  const int blocksMatX, const int blocksMatY, const int batch,
+                  int method, const int XDIM, const int YDIM,
+                  const int INTERP_ORDER)
+        : d_zo_(d_zo)
+        , zo_(zo)
+        , d_zi_(d_zi)
+        , zi_(zi)
+        , d_xo_(d_xo)
+        , xo_(xo)
+        , d_yo_(d_yo)
+        , yo_(yo)
+        , xi_beg_(xi_beg)
+        , xi_step_reproc_(xi_step_reproc)
+        , yi_beg_(yi_beg)
+        , yi_step_reproc_(yi_step_reproc)
+        , offGrid_(offGrid)
+        , blocksMatX_(blocksMatX)
+        , blocksMatY_(blocksMatY)
+        , batch_(batch)
+        , method_(method)
+        , XDIM_(XDIM)
+        , YDIM_(YDIM)
+        , INTERP_ORDER_(INTERP_ORDER) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int idz = g.get_group_id(0) / blocksMatX_;
+        const int idw = g.get_group_id(1) / blocksMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - idz * blocksMatX_;
+        const int blockIdx_y = g.get_group_id(1) - idw * blocksMatY_;
+
+        const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+        const int idy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+        if (idx >= zo_.dims[0] || idy >= zo_.dims[1] || idz >= zo_.dims[2] ||
+            idw >= zo_.dims[3])
+            return;
+
+        // FIXME: Only cubic interpolation is doing clamping
+        // We need to make it consistent across all methods
+        // Not changing the behavior because tests will fail
+        const bool doclamp = INTERP_ORDER_ == 3;
+
+        bool is_off[] = {xo_.dims[0] > 1, xo_.dims[1] > 1, xo_.dims[2] > 1,
+                         xo_.dims[3] > 1};
+
+        const int zo_idx = idw * zo_.strides[3] + idz * zo_.strides[2] +
+                           idy * zo_.strides[1] + idx + zo_.offset;
+        int xo_idx =
+            idy * xo_.strides[1] * is_off[1] + idx * is_off[0] + xo_.offset;
+
+        int yo_idx =
+            idy * yo_.strides[1] * is_off[1] + idx * is_off[0] + yo_.offset;
+        if (batch_) {
+            xo_idx += idw * xo_.strides[3] * is_off[3] +
+                      idz * xo_.strides[2] * is_off[2];
+            yo_idx += idw * yo_.strides[3] * is_off[3] +
+                      idz * yo_.strides[2] * is_off[2];
+        }
+
+#pragma unroll
+        for (int flagIdx = 0; flagIdx < 4; ++flagIdx) {
+            is_off[flagIdx] = true;
+        }
+        is_off[XDIM_] = false;
+        is_off[YDIM_] = false;
+
+        const Tp x = (d_xo_[xo_idx] - xi_beg_) * xi_step_reproc_;
+        const Tp y = (d_yo_[yo_idx] - yi_beg_) * yi_step_reproc_;
+
+        if (x < 0 || y < 0 || zi_.dims[XDIM_] < x + 1 ||
+            zi_.dims[YDIM_] < y + 1) {
+            d_zo_[zo_idx] = offGrid_;
+            return;
+        }
+
+        int zi_idx =
+            idy * zi_.strides[1] * is_off[1] + idx * is_off[0] + zi_.offset;
+        zi_idx +=
+            idw * zi_.strides[3] * is_off[3] + idz * zi_.strides[2] * is_off[2];
+
+        if (INTERP_ORDER_ == 1)
+            interp2o1(d_zo_, zo_, zo_idx, d_zi_, zi_, zi_idx, x, y, method_, 1,
+                      doclamp, 2);
+        if (INTERP_ORDER_ == 2)
+            interp2o2(d_zo_, zo_, zo_idx, d_zi_, zi_, zi_idx, x, y, method_, 1,
+                      doclamp, 2);
+        if (INTERP_ORDER_ == 3)
+            interp2o3(d_zo_, zo_, zo_idx, d_zi_, zi_, zi_idx, x, y, method_, 1,
+                      doclamp, 2);
+    }
+
+    Ty linearInterpFunc(Ty val[2], Tp ratio) const {
+        return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
+    }
+
+    Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio) const {
+        Ty res[2];
+        res[0] = linearInterpFunc(val[0], xratio);
+        res[1] = linearInterpFunc(val[1], xratio);
+        return linearInterpFunc(res, yratio);
+    }
+
+    Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
+        Ty a0, a1, a2, a3;
+        if (spline) {
+            a0 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)1.5, val[1]) +
+                 MULRC((Tp)-1.5, val[2]) + MULRC((Tp)0.5, val[3]);
+
+            a1 = MULRC((Tp)1.0, val[0]) + MULRC((Tp)-2.5, val[1]) +
+                 MULRC((Tp)2.0, val[2]) + MULRC((Tp)-0.5, val[3]);
+
+            a2 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)0.5, val[2]);
+
+            a3 = val[1];
+        } else {
+            a0 = val[3] - val[2] - val[0] + val[1];
+            a1 = val[0] - val[1] - a0;
+            a2 = val[2] - val[0];
+            a3 = val[1];
+        }
+
+        Tp xratio2 = xratio * xratio;
+        Tp xratio3 = xratio2 * xratio;
+
+        return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
+    }
+
+    Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio,
+                         bool spline) const {
+        Ty res[4];
+        res[0] = cubicInterpFunc(val[0], xratio, spline);
+        res[1] = cubicInterpFunc(val[1], xratio, spline);
+        res[2] = cubicInterpFunc(val[2], xratio, spline);
+        res[3] = cubicInterpFunc(val[3], xratio, spline);
+        return cubicInterpFunc(res, yratio, spline);
+    }
+
+    void interp2o1(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x, Tp y,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+
+        const int x_lim    = in.dims[XDIM_];
+        const int y_lim    = in.dims[YDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int y_stride = in.strides[YDIM_];
+
+        if (doclamp) {
+            xid = fmax(0, fmin(xid, x_lim));
+            yid = fmax(0, fmin(yid, y_lim));
+        }
+        const int idx = ioff + yid * y_stride + xid * x_stride;
+
+        bool condX = xid >= 0 && xid < x_lim;
+        bool condY = yid >= 0 && yid < y_lim;
+
+        Ty zero = (Ty)0;
+        ;
+        bool cond = condX && condY;
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            d_out[ooff + n * out.strides[batch_dim]] =
+                (doclamp || cond) ? d_in[idx_n] : zero;
+        }
+    }
+
+    void interp2o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x, Tp y,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        const int grid_x = floor(x);
+        const Tp off_x   = x - grid_x;
+
+        const int grid_y = floor(y);
+        const Tp off_y   = y - grid_y;
+
+        const int x_lim    = in.dims[XDIM_];
+        const int y_lim    = in.dims[YDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int y_stride = in.strides[YDIM_];
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
+
+        bool condX[2] = {true, x + 1 < x_lim};
+        bool condY[2] = {true, y + 1 < y_lim};
+        int offx[2]   = {0, condX[1] ? 1 : 0};
+        int offy[2]   = {0, condY[1] ? 1 : 0};
+
+        Tp xratio = off_x, yratio = off_y;
+        if (method == AF_INTERP_LINEAR_COSINE) {
+            xratio = (1 - cos(xratio * (Tp)M_PI)) / 2;
+            yratio = (1 - cos(yratio * (Tp)M_PI)) / 2;
+        }
+
+        Ty zero = (Ty)0;
+        ;
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            Ty val[2][2];
+            for (int j = 0; j < 2; j++) {
+                int off_y = idx_n + offy[j] * y_stride;
+                for (int i = 0; i < 2; i++) {
+                    bool cond = (doclamp || (condX[i] && condY[j]));
+                    val[j][i] = cond ? d_in[off_y + offx[i] * x_stride] : zero;
+                }
+            }
+            d_out[ooff + n * out.strides[batch_dim]] =
+                bilinearInterpFunc(val, xratio, yratio);
+        }
+    }
+
+    void interp2o3(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x, Tp y,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        const int grid_x = floor(x);
+        const Tp off_x   = x - grid_x;
+
+        const int grid_y = floor(y);
+        const Tp off_y   = y - grid_y;
+
+        const int x_lim    = in.dims[XDIM_];
+        const int y_lim    = in.dims[YDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int y_stride = in.strides[YDIM_];
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
+
+        // used for setting values at boundaries
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                         grid_x + 2 < x_lim};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim,
+                         grid_y + 2 < y_lim};
+        int offX[4]   = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0,
+                       condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int offY[4]   = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0,
+                       condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+        Ty zero = (Ty)0;
+        ;
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            // for bicubic interpolation, work with 4x4 val at a time
+            Ty val[4][4];
+#pragma unroll
+            for (int j = 0; j < 4; j++) {
+                int ioff_j = idx_n + offY[j] * y_stride;
+#pragma unroll
+                for (int i = 0; i < 4; i++) {
+                    bool cond = (doclamp || (condX[i] && condY[j]));
+                    val[j][i] = cond ? d_in[ioff_j + offX[i] * x_stride] : zero;
+                }
+            }
+            bool spline = method == AF_INTERP_CUBIC_SPLINE ||
+                          method == AF_INTERP_BICUBIC_SPLINE;
+            d_out[ooff + n * out.strides[batch_dim]] =
+                bicubicInterpFunc(val, off_x, off_y, spline);
+        }
+    }
+
+   private:
+    sycl::accessor<Ty> d_zo_;
+    const KParam zo_;
+    sycl::accessor<Ty> d_zi_;
+    const KParam zi_;
+    sycl::accessor<Tp> d_xo_;
+    const KParam xo_;
+    sycl::accessor<Tp> d_yo_;
+    const KParam yo_;
+    const Tp xi_beg_;
+    const Tp xi_step_reproc_;
+    const Tp yi_beg_;
+    const Tp yi_step_reproc_;
+    const Ty offGrid_;
+    const int blocksMatX_;
+    const int blocksMatY_;
+    const int batch_;
+    int method_;
+    const int XDIM_;
+    const int YDIM_;
+    const int INTERP_ORDER_;
+};
+
+template<typename Ty, typename Tp>
+void approx2(Param<Ty> zo, const Param<Ty> zi, const Param<Tp> xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Param<Tp> yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const float offGrid,
+             const af_interp_type method, const int order) {
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+
+    auto local          = sycl::range{TX, TY};
+    dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
+    dim_t blocksPerMatY = divup(zo.info.dims[1], local[1]);
+    auto global = sycl::range{blocksPerMatX * local[0] * zo.info.dims[2],
+                              blocksPerMatY * local[1] * zo.info.dims[3]};
+
+    // Passing bools to opencl kernels is not allowed
+    bool batch = !(xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto zoAcc = zo.data->get_access(h);
+        auto ziAcc = zi.data->get_access(h);
+        auto xoAcc = xo.data->get_access(h);
+        auto yoAcc = yo.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            approx2Kernel<Ty, Tp>(
+                zoAcc, zo.info, ziAcc, zi.info, xoAcc, xo.info, yoAcc, yo.info,
+                xi_beg, Tp(1) / xi_step, yi_beg, Tp(1) / yi_step, (Ty)offGrid,
+                static_cast<int>(blocksPerMatX),
+                static_cast<int>(blocksPerMatY), static_cast<int>(batch),
+                static_cast<int>(method), xdim, ydim, order));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
 }  // namespace kernel
 }  // namespace oneapi

From 1157453a2749f55ca20f84d29fe87ff841338296 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Mon, 10 Oct 2022 14:27:01 -0400
Subject: [PATCH 2329/2677] format

---
 src/backend/oneapi/approx.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index 43ff5f7dcf..c96764194c 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -45,22 +45,22 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                    ydim, yi_beg, yi_step, offGrid, method, 1);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 1);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                    ydim, yi_beg, yi_step, offGrid, method, 2);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 2);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
-                                    ydim, yi_beg, yi_step, offGrid, method, 3);
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 3);
             break;
         default: break;
     }

From 3fc5bd93d2f5cf87b3b5442e678e85a504e47c64 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Tue, 11 Oct 2022 10:13:29 -0400
Subject: [PATCH 2330/2677] split oneapi approx into approx1 and approx2
 separate sources

---
 src/backend/oneapi/CMakeLists.txt             |   6 +-
 src/backend/oneapi/approx.cpp                 |   3 +-
 src/backend/oneapi/approx1.cpp                |  49 +++
 src/backend/oneapi/approx2.cpp                |  56 ++++
 src/backend/oneapi/kernel/approx1.hpp         | 278 ++++++++++++++++++
 .../oneapi/kernel/{approx.hpp => approx2.hpp} | 261 +---------------
 6 files changed, 392 insertions(+), 261 deletions(-)
 create mode 100644 src/backend/oneapi/approx1.cpp
 create mode 100644 src/backend/oneapi/approx2.cpp
 create mode 100644 src/backend/oneapi/kernel/approx1.hpp
 rename src/backend/oneapi/kernel/{approx.hpp => approx2.hpp} (59%)
 mode change 100755 => 100644

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index c2c78dc9c6..e70af234c3 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -22,7 +22,8 @@ add_library(afoneapi
   anisotropic_diffusion.cpp
   anisotropic_diffusion.hpp
   any.cpp
-  approx.cpp
+  approx1.cpp
+  approx2.cpp
   approx.hpp
   arith.hpp
   assign.cpp
@@ -205,7 +206,8 @@ add_library(afoneapi
 target_sources(afoneapi
   PRIVATE
     kernel/KParam.hpp
-    kernel/approx.hpp
+    kernel/approx1.hpp
+    kernel/approx2.hpp
     kernel/assign.hpp
     kernel/diagonal.hpp
     kernel/diff.hpp
diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index c96764194c..da153301d2 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -9,7 +9,8 @@
 
 #include <approx.hpp>
 #include <err_oneapi.hpp>
-#include <kernel/approx.hpp>
+#include <kernel/approx1.hpp>
+#include <kernel/approx2.hpp>
 
 namespace oneapi {
 template<typename Ty, typename Tp>
diff --git a/src/backend/oneapi/approx1.cpp b/src/backend/oneapi/approx1.cpp
new file mode 100644
index 0000000000..cee2aa9b15
--- /dev/null
+++ b/src/backend/oneapi/approx1.cpp
@@ -0,0 +1,49 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <approx.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/approx1.hpp>
+
+namespace oneapi {
+template<typename Ty, typename Tp>
+void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const af_interp_type method, const float offGrid) {
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 1);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 2);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
+                                    method, 3);
+            break;
+        default: break;
+    }
+}
+
+#define INSTANTIATE(Ty, Tp)                                       \
+    template void approx1<Ty, Tp>(                                \
+        Array<Ty> & yo, const Array<Ty> &yi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const af_interp_type method, const float offGrid);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/approx2.cpp b/src/backend/oneapi/approx2.cpp
new file mode 100644
index 0000000000..e22d5406ee
--- /dev/null
+++ b/src/backend/oneapi/approx2.cpp
@@ -0,0 +1,56 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#include <approx.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/approx2.hpp>
+
+namespace oneapi {
+template<typename Ty, typename Tp>
+void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
+             const int xdim, const Tp &xi_beg, const Tp &xi_step,
+             const Array<Tp> &yo, const int ydim, const Tp &yi_beg,
+             const Tp &yi_step, const af_interp_type method,
+             const float offGrid) {
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 1);
+            break;
+        case AF_INTERP_LINEAR:
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_LINEAR_COSINE:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 2);
+            break;
+        case AF_INTERP_CUBIC:
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_CUBIC_SPLINE:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
+                                    yi_beg, yi_step, offGrid, method, 3);
+            break;
+        default: break;
+    }
+}
+
+#define INSTANTIATE(Ty, Tp)                                       \
+    template void approx2<Ty, Tp>(                                \
+        Array<Ty> & zo, const Array<Ty> &zi, const Array<Tp> &xo, \
+        const int xdim, const Tp &xi_beg, const Tp &xi_step,      \
+        const Array<Tp> &yo, const int ydim, const Tp &yi_beg,    \
+        const Tp &yi_step, const af_interp_type method, const float offGrid);
+
+INSTANTIATE(float, float)
+INSTANTIATE(double, double)
+INSTANTIATE(cfloat, float)
+INSTANTIATE(cdouble, double)
+
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/approx1.hpp b/src/backend/oneapi/kernel/approx1.hpp
new file mode 100644
index 0000000000..ad2ac257ea
--- /dev/null
+++ b/src/backend/oneapi/kernel/approx1.hpp
@@ -0,0 +1,278 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+#define MULRC(a, b) (a) * (b)
+#define MULCR(a, b) (a) * (b)
+
+namespace oneapi {
+namespace kernel {
+
+constexpr int TILE_DIM  = 32;
+constexpr int THREADS_X = TILE_DIM;
+constexpr int THREADS_Y = 256 / TILE_DIM;
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename Ty, typename Tp>
+class approx1Kernel {
+   public:
+    approx1Kernel(sycl::accessor<Ty> d_yo, const KParam yo,
+                  sycl::accessor<Ty> d_yi, const KParam yi,
+                  sycl::accessor<Tp> d_xo, const KParam xo, const Tp xi_beg,
+                  const Tp xi_step_reproc, const Ty offGrid,
+                  const int blocksMatX, const int batch, const int method,
+                  const int XDIM, const int INTERP_ORDER)
+        : d_yo_(d_yo)
+        , yo_(yo)
+        , d_yi_(d_yi)
+        , yi_(yi)
+        , d_xo_(d_xo)
+        , xo_(xo)
+        , xi_beg_(xi_beg)
+        , xi_step_reproc_(xi_step_reproc)
+        , offGrid_(offGrid)
+        , blocksMatX_(blocksMatX)
+        , batch_(batch)
+        , method_(method)
+        , XDIM_(XDIM)
+        , INTERP_ORDER_(INTERP_ORDER) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int idw = g.get_group_id(1) / yo_.dims[2];
+        const int idz = g.get_group_id(1) - idw * yo_.dims[2];
+
+        const int idy        = g.get_group_id(0) / blocksMatX_;
+        const int blockIdx_x = g.get_group_id(0) - idy * blocksMatX_;
+        const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+
+        if (idx >= yo_.dims[0] || idy >= yo_.dims[1] || idz >= yo_.dims[2] ||
+            idw >= yo_.dims[3])
+            return;
+
+        // FIXME: Only cubic interpolation is doing clamping
+        // We need to make it consistent across all methods
+        // Not changing the behavior because tests will fail
+        const bool doclamp = INTERP_ORDER_ == 3;
+
+        bool is_off[] = {xo_.dims[0] > 1, xo_.dims[1] > 1, xo_.dims[2] > 1,
+                         xo_.dims[3] > 1};
+
+        const int yo_idx = idw * yo_.strides[3] + idz * yo_.strides[2] +
+                           idy * yo_.strides[1] + idx + yo_.offset;
+
+        int xo_idx = idx * is_off[0] + xo_.offset;
+        if (batch_) {
+            xo_idx += idw * xo_.strides[3] * is_off[3];
+            xo_idx += idz * xo_.strides[2] * is_off[2];
+            xo_idx += idy * xo_.strides[1] * is_off[1];
+        }
+
+        const Tp x = (d_xo_[xo_idx] - xi_beg_) * xi_step_reproc_;
+
+#pragma unroll
+        for (int flagIdx = 0; flagIdx < 4; ++flagIdx) {
+            is_off[flagIdx] = true;
+        }
+        is_off[XDIM_] = false;
+
+        if (x < 0 || yi_.dims[XDIM_] < x + 1) {
+            d_yo_[yo_idx] = offGrid_;
+            return;
+        }
+
+        int yi_idx = idx * is_off[0] + yi_.offset;
+        yi_idx += idw * yi_.strides[3] * is_off[3];
+        yi_idx += idz * yi_.strides[2] * is_off[2];
+        yi_idx += idy * yi_.strides[1] * is_off[1];
+
+        if (INTERP_ORDER_ == 1)
+            interp1o1(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
+                      doclamp, 1);
+        if (INTERP_ORDER_ == 2)
+            interp1o2(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
+                      doclamp, 1);
+        if (INTERP_ORDER_ == 3)
+            interp1o3(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
+                      doclamp, 1);
+    }
+
+    void interp1o1(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        Ty zero = (Ty)0;
+
+        const int x_lim    = in.dims[XDIM_];
+        const int x_stride = in.strides[XDIM_];
+
+        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        bool cond = xid >= 0 && xid < x_lim;
+        if (doclamp) xid = fmax(0, fmin(xid, x_lim));
+
+        const int idx = ioff + xid * x_stride;
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            d_out[ooff + n * out.strides[batch_dim]] =
+                (doclamp || cond) ? d_in[idx_n] : zero;
+        }
+    }
+
+    Ty linearInterpFunc(Ty val[2], Tp ratio) const {
+        return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
+    }
+
+    Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
+        Ty a0, a1, a2, a3;
+        if (spline) {
+            a0 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)1.5, val[1]) +
+                 MULRC((Tp)-1.5, val[2]) + MULRC((Tp)0.5, val[3]);
+
+            a1 = MULRC((Tp)1.0, val[0]) + MULRC((Tp)-2.5, val[1]) +
+                 MULRC((Tp)2.0, val[2]) + MULRC((Tp)-0.5, val[3]);
+
+            a2 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)0.5, val[2]);
+
+            a3 = val[1];
+        } else {
+            a0 = val[3] - val[2] - val[0] + val[1];
+            a1 = val[0] - val[1] - a0;
+            a2 = val[2] - val[0];
+            a3 = val[1];
+        }
+
+        Tp xratio2 = xratio * xratio;
+        Tp xratio3 = xratio2 * xratio;
+
+        return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
+    }
+
+    void interp1o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        const int grid_x = floor(x);    // nearest grid
+        const Tp off_x   = x - grid_x;  // fractional offset
+
+        const int x_lim    = in.dims[XDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int idx      = ioff + grid_x * x_stride;
+
+        Ty zero      = (Ty)0;
+        bool cond[2] = {true, grid_x + 1 < x_lim};
+        int offx[2]  = {0, cond[1] ? 1 : 0};
+        Tp ratio     = off_x;
+        if (method == AF_INTERP_LINEAR_COSINE) {
+            ratio = (1 - cos(ratio * (Tp)M_PI)) / 2;
+        }
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * in.strides[batch_dim];
+            Ty val[2] = {
+                (doclamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
+                (doclamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
+
+            d_out[ooff + n * out.strides[batch_dim]] =
+                linearInterpFunc(val, ratio);
+        }
+    }
+
+    void interp1o3(sycl::accessor<Ty> d_out, KParam out, int ooff,
+                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
+                   int method, int batch, bool doclamp, int batch_dim) const {
+        const int grid_x = floor(x);    // nearest grid
+        const Tp off_x   = x - grid_x;  // fractional offset
+
+        const int x_lim    = in.dims[XDIM_];
+        const int x_stride = in.strides[XDIM_];
+        const int idx      = ioff + grid_x * x_stride;
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                        grid_x + 2 < x_lim};
+        int off[4]   = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
+                      cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+        Ty zero = (Ty)0;
+
+        for (int n = 0; n < batch; n++) {
+            Ty val[4];
+            int idx_n = idx + n * in.strides[batch_dim];
+            for (int i = 0; i < 4; i++) {
+                val[i] = (doclamp || cond[i]) ? d_in[idx_n + off[i] * x_stride]
+                                              : zero;
+            }
+            bool spline = method == AF_INTERP_CUBIC_SPLINE;
+            d_out[ooff + n * out.strides[batch_dim]] =
+                cubicInterpFunc(val, off_x, spline);
+        }
+    }
+
+   private:
+    sycl::accessor<Ty> d_yo_;
+    const KParam yo_;
+    sycl::accessor<Ty> d_yi_;
+    const KParam yi_;
+    sycl::accessor<Tp> d_xo_;
+    const KParam xo_;
+    const Tp xi_beg_;
+    const Tp xi_step_reproc_;
+    const Ty offGrid_;
+    const int blocksMatX_;
+    const int batch_;
+    const int method_;
+    const int XDIM_;
+    const int INTERP_ORDER_;
+};
+
+template<typename Ty, typename Tp>
+void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
+             const int xdim, const Tp xi_beg, const Tp xi_step,
+             const float offGrid, const af_interp_type method,
+             const int order) {
+    constexpr int THREADS = 256;
+
+    auto local         = sycl::range{THREADS, 1};
+    dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
+    auto global        = sycl::range{blocksPerMat * local[0] * yo.info.dims[1],
+                              yo.info.dims[2] * yo.info.dims[3] * local[1]};
+
+    // Passing bools to opencl kernels is not allowed
+    bool batch =
+        !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
+
+    getQueue().submit([&](sycl::handler &h) {
+        auto yoAcc = yo.data->get_access(h);
+        auto yiAcc = yi.data->get_access(h);
+        auto xoAcc = xo.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            approx1Kernel<Ty, Tp>(yoAcc, yo.info, yiAcc, yi.info, xoAcc,
+                                  xo.info, xi_beg, Tp(1) / xi_step, (Ty)offGrid,
+                                  (int)blocksPerMat, (int)batch, (int)method,
+                                  xdim, order));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/approx.hpp b/src/backend/oneapi/kernel/approx2.hpp
old mode 100755
new mode 100644
similarity index 59%
rename from src/backend/oneapi/kernel/approx.hpp
rename to src/backend/oneapi/kernel/approx2.hpp
index 15033317dd..5df6111d6a
--- a/src/backend/oneapi/kernel/approx.hpp
+++ b/src/backend/oneapi/kernel/approx2.hpp
@@ -18,6 +18,9 @@
 #include <string>
 #include <vector>
 
+#define MULRC(a, b) (a) * (b)
+#define MULCR(a, b) (a) * (b)
+
 namespace oneapi {
 namespace kernel {
 
@@ -30,264 +33,6 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
-template<typename Ty, typename Tp>
-class approx1Kernel {
-   public:
-    approx1Kernel(sycl::accessor<Ty> d_yo, const KParam yo,
-                  sycl::accessor<Ty> d_yi, const KParam yi,
-                  sycl::accessor<Tp> d_xo, const KParam xo, const Tp xi_beg,
-                  const Tp xi_step_reproc, const Ty offGrid,
-                  const int blocksMatX, const int batch, const int method,
-                  const int XDIM, const int INTERP_ORDER)
-        : d_yo_(d_yo)
-        , yo_(yo)
-        , d_yi_(d_yi)
-        , yi_(yi)
-        , d_xo_(d_xo)
-        , xo_(xo)
-        , xi_beg_(xi_beg)
-        , xi_step_reproc_(xi_step_reproc)
-        , offGrid_(offGrid)
-        , blocksMatX_(blocksMatX)
-        , batch_(batch)
-        , method_(method)
-        , XDIM_(XDIM)
-        , INTERP_ORDER_(INTERP_ORDER) {}
-    void operator()(sycl::nd_item<2> it) const {
-        sycl::group g = it.get_group();
-        const int idw = g.get_group_id(1) / yo_.dims[2];
-        const int idz = g.get_group_id(1) - idw * yo_.dims[2];
-
-        const int idy        = g.get_group_id(0) / blocksMatX_;
-        const int blockIdx_x = g.get_group_id(0) - idy * blocksMatX_;
-        const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
-
-        if (idx >= yo_.dims[0] || idy >= yo_.dims[1] || idz >= yo_.dims[2] ||
-            idw >= yo_.dims[3])
-            return;
-
-        // FIXME: Only cubic interpolation is doing clamping
-        // We need to make it consistent across all methods
-        // Not changing the behavior because tests will fail
-        const bool doclamp = INTERP_ORDER_ == 3;
-
-        bool is_off[] = {xo_.dims[0] > 1, xo_.dims[1] > 1, xo_.dims[2] > 1,
-                         xo_.dims[3] > 1};
-
-        const int yo_idx = idw * yo_.strides[3] + idz * yo_.strides[2] +
-                           idy * yo_.strides[1] + idx + yo_.offset;
-
-        int xo_idx = idx * is_off[0] + xo_.offset;
-        if (batch_) {
-            xo_idx += idw * xo_.strides[3] * is_off[3];
-            xo_idx += idz * xo_.strides[2] * is_off[2];
-            xo_idx += idy * xo_.strides[1] * is_off[1];
-        }
-
-        const Tp x = (d_xo_[xo_idx] - xi_beg_) * xi_step_reproc_;
-
-#pragma unroll
-        for (int flagIdx = 0; flagIdx < 4; ++flagIdx) {
-            is_off[flagIdx] = true;
-        }
-        is_off[XDIM_] = false;
-
-        if (x < 0 || yi_.dims[XDIM_] < x + 1) {
-            d_yo_[yo_idx] = offGrid_;
-            return;
-        }
-
-        int yi_idx = idx * is_off[0] + yi_.offset;
-        yi_idx += idw * yi_.strides[3] * is_off[3];
-        yi_idx += idz * yi_.strides[2] * is_off[2];
-        yi_idx += idy * yi_.strides[1] * is_off[1];
-
-        if (INTERP_ORDER_ == 1)
-            interp1o1(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
-                      doclamp, 1);
-        if (INTERP_ORDER_ == 2)
-            interp1o2(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
-                      doclamp, 1);
-        if (INTERP_ORDER_ == 3)
-            interp1o3(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
-                      doclamp, 1);
-    }
-
-    void interp1o1(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        Ty zero = (Ty)0;
-
-        const int x_lim    = in.dims[XDIM_];
-        const int x_stride = in.strides[XDIM_];
-
-        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        bool cond = xid >= 0 && xid < x_lim;
-        if (doclamp) xid = fmax(0, fmin(xid, x_lim));
-
-        const int idx = ioff + xid * x_stride;
-
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            d_out[ooff + n * out.strides[batch_dim]] =
-                (doclamp || cond) ? d_in[idx_n] : zero;
-        }
-    }
-
-#if IS_CPLX
-#if USE_DOUBLE
-    typedef double ScalarTy;
-#else
-    typedef float ScalarTy;
-#endif
-    Ty __mulrc(ScalarTy s, Ty v) {
-        InterpInTy out = {s * v.x, s * v.y};
-        return out;
-    }
-#define MULRC(a, b) __mulrc(a, b)
-#define MULCR(a, b) __mulrc(b, a)
-#else
-#define MULRC(a, b) (a) * (b)
-#define MULCR(a, b) (a) * (b)
-#endif
-
-    Ty linearInterpFunc(Ty val[2], Tp ratio) const {
-        return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
-    }
-
-    Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
-        Ty a0, a1, a2, a3;
-        if (spline) {
-            a0 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)1.5, val[1]) +
-                 MULRC((Tp)-1.5, val[2]) + MULRC((Tp)0.5, val[3]);
-
-            a1 = MULRC((Tp)1.0, val[0]) + MULRC((Tp)-2.5, val[1]) +
-                 MULRC((Tp)2.0, val[2]) + MULRC((Tp)-0.5, val[3]);
-
-            a2 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)0.5, val[2]);
-
-            a3 = val[1];
-        } else {
-            a0 = val[3] - val[2] - val[0] + val[1];
-            a1 = val[0] - val[1] - a0;
-            a2 = val[2] - val[0];
-            a3 = val[1];
-        }
-
-        Tp xratio2 = xratio * xratio;
-        Tp xratio3 = xratio2 * xratio;
-
-        return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
-    }
-
-    void interp1o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        const int grid_x = floor(x);    // nearest grid
-        const Tp off_x   = x - grid_x;  // fractional offset
-
-        const int x_lim    = in.dims[XDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int idx      = ioff + grid_x * x_stride;
-
-        Ty zero      = (Ty)0;
-        bool cond[2] = {true, grid_x + 1 < x_lim};
-        int offx[2]  = {0, cond[1] ? 1 : 0};
-        Tp ratio     = off_x;
-        if (method == AF_INTERP_LINEAR_COSINE) {
-            ratio = (1 - cos(ratio * (Tp)M_PI)) / 2;
-        }
-
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            Ty val[2] = {
-                (doclamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
-                (doclamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
-
-            d_out[ooff + n * out.strides[batch_dim]] =
-                linearInterpFunc(val, ratio);
-        }
-    }
-
-    void interp1o3(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        const int grid_x = floor(x);    // nearest grid
-        const Tp off_x   = x - grid_x;  // fractional offset
-
-        const int x_lim    = in.dims[XDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int idx      = ioff + grid_x * x_stride;
-
-        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
-                        grid_x + 2 < x_lim};
-        int off[4]   = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
-                      cond[3] ? 2 : (cond[2] ? 1 : 0)};
-
-        Ty zero = (Ty)0;
-
-        for (int n = 0; n < batch; n++) {
-            Ty val[4];
-            int idx_n = idx + n * in.strides[batch_dim];
-            for (int i = 0; i < 4; i++) {
-                val[i] = (doclamp || cond[i]) ? d_in[idx_n + off[i] * x_stride]
-                                              : zero;
-            }
-            bool spline = method == AF_INTERP_CUBIC_SPLINE;
-            d_out[ooff + n * out.strides[batch_dim]] =
-                cubicInterpFunc(val, off_x, spline);
-        }
-    }
-
-   private:
-    sycl::accessor<Ty> d_yo_;
-    const KParam yo_;
-    sycl::accessor<Ty> d_yi_;
-    const KParam yi_;
-    sycl::accessor<Tp> d_xo_;
-    const KParam xo_;
-    const Tp xi_beg_;
-    const Tp xi_step_reproc_;
-    const Ty offGrid_;
-    const int blocksMatX_;
-    const int batch_;
-    const int method_;
-    const int XDIM_;
-    const int INTERP_ORDER_;
-};
-
-template<typename Ty, typename Tp>
-void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
-             const int xdim, const Tp xi_beg, const Tp xi_step,
-             const float offGrid, const af_interp_type method,
-             const int order) {
-    constexpr int THREADS = 256;
-
-    auto local         = sycl::range{THREADS, 1};
-    dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
-    auto global        = sycl::range{blocksPerMat * local[0] * yo.info.dims[1],
-                              yo.info.dims[2] * yo.info.dims[3] * local[1]};
-
-    // Passing bools to opencl kernels is not allowed
-    bool batch =
-        !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
-
-    getQueue().submit([&](sycl::handler &h) {
-        auto yoAcc = yo.data->get_access(h);
-        auto yiAcc = yi.data->get_access(h);
-        auto xoAcc = xo.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
-
-        h.parallel_for(
-            sycl::nd_range{global, local},
-            approx1Kernel<Ty, Tp>(yoAcc, yo.info, yiAcc, yi.info, xoAcc,
-                                  xo.info, xi_beg, Tp(1) / xi_step, (Ty)offGrid,
-                                  (int)blocksPerMat, (int)batch, (int)method,
-                                  xdim, order));
-    });
-    ONEAPI_DEBUG_FINISH(getQueue());
-}
-
 template<typename Ty, typename Tp>
 class approx2Kernel {
    public:

From af3ef613ec43bbc66bc6f66283746bb789ac7450 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 7 Nov 2022 21:20:33 -0500
Subject: [PATCH 2331/2677] extract interp functor from approx1/approx2 for
 reuse in other kernels

---
 src/backend/oneapi/CMakeLists.txt     |   1 +
 src/backend/oneapi/approx.cpp         |  12 +-
 src/backend/oneapi/approx1.cpp        |  12 +-
 src/backend/oneapi/approx2.cpp        |  12 +-
 src/backend/oneapi/kernel/approx1.hpp | 241 +++++-------------
 src/backend/oneapi/kernel/approx2.hpp | 298 +++++-----------------
 src/backend/oneapi/kernel/interp.hpp  | 342 ++++++++++++++++++++++++++
 7 files changed, 489 insertions(+), 429 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/interp.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index e70af234c3..d6bfaae598 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -211,6 +211,7 @@ target_sources(afoneapi
     kernel/assign.hpp
     kernel/diagonal.hpp
     kernel/diff.hpp
+    kernel/interp.hpp
     kernel/iota.hpp
     kernel/histogram.hpp
     kernel/memcopy.hpp
diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index da153301d2..4ad0c27d9b 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -20,18 +20,18 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    method, 1);
+            kernel::approx1<Ty, Tp, 1>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    method, 2);
+            kernel::approx1<Ty, Tp, 2>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    method, 3);
+            kernel::approx1<Ty, Tp, 3>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         default: break;
     }
diff --git a/src/backend/oneapi/approx1.cpp b/src/backend/oneapi/approx1.cpp
index cee2aa9b15..8906f57016 100644
--- a/src/backend/oneapi/approx1.cpp
+++ b/src/backend/oneapi/approx1.cpp
@@ -18,18 +18,18 @@ void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    method, 1);
+            kernel::approx1<Ty, Tp, 1>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_LINEAR_COSINE:
-            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    method, 2);
+            kernel::approx1<Ty, Tp, 2>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_CUBIC_SPLINE:
-            kernel::approx1<Ty, Tp>(yo, yi, xo, xdim, xi_beg, xi_step, offGrid,
-                                    method, 3);
+            kernel::approx1<Ty, Tp, 3>(yo, yi, xo, xdim, xi_beg, xi_step,
+                                       offGrid, method);
             break;
         default: break;
     }
diff --git a/src/backend/oneapi/approx2.cpp b/src/backend/oneapi/approx2.cpp
index e22d5406ee..3330aaa42f 100644
--- a/src/backend/oneapi/approx2.cpp
+++ b/src/backend/oneapi/approx2.cpp
@@ -20,22 +20,22 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
-                                    yi_beg, yi_step, offGrid, method, 1);
+            kernel::approx2<Ty, Tp, 1>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         case AF_INTERP_LINEAR:
         case AF_INTERP_BILINEAR:
         case AF_INTERP_LINEAR_COSINE:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
-                                    yi_beg, yi_step, offGrid, method, 2);
+            kernel::approx2<Ty, Tp, 2>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         case AF_INTERP_CUBIC:
         case AF_INTERP_BICUBIC:
         case AF_INTERP_CUBIC_SPLINE:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::approx2<Ty, Tp>(zo, zi, xo, xdim, xi_beg, xi_step, yo, ydim,
-                                    yi_beg, yi_step, offGrid, method, 3);
+            kernel::approx2<Ty, Tp, 3>(zo, zi, xo, xdim, xi_beg, xi_step, yo,
+                                       ydim, yi_beg, yi_step, offGrid, method);
             break;
         default: break;
     }
diff --git a/src/backend/oneapi/kernel/approx1.hpp b/src/backend/oneapi/kernel/approx1.hpp
index ad2ac257ea..95b4ceb65c 100644
--- a/src/backend/oneapi/kernel/approx1.hpp
+++ b/src/backend/oneapi/kernel/approx1.hpp
@@ -13,14 +13,14 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/interp.hpp>
+#include <af/constants.h>
+// #include <kernel/default_config.hpp>
 #include <traits.hpp>
 
 #include <string>
 #include <vector>
 
-#define MULRC(a, b) (a) * (b)
-#define MULCR(a, b) (a) * (b)
-
 namespace oneapi {
 namespace kernel {
 
@@ -33,58 +33,64 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
-template<typename Ty, typename Tp>
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename Ty, typename Tp, int order>
 class approx1Kernel {
    public:
-    approx1Kernel(sycl::accessor<Ty> d_yo, const KParam yo,
-                  sycl::accessor<Ty> d_yi, const KParam yi,
-                  sycl::accessor<Tp> d_xo, const KParam xo, const Tp xi_beg,
+    approx1Kernel(write_accessor<Ty> d_yo, const KParam yoInfo,
+                  read_accessor<Ty> d_yi, const KParam yiInfo,
+                  read_accessor<Tp> d_xo, const KParam xoInfo, const Tp xi_beg,
                   const Tp xi_step_reproc, const Ty offGrid,
-                  const int blocksMatX, const int batch, const int method,
-                  const int XDIM, const int INTERP_ORDER)
+                  const int blocksMatX, const af_interp_type method,
+                  const bool batch, const int XDIM)
         : d_yo_(d_yo)
-        , yo_(yo)
+        , yoInfo_(yoInfo)
         , d_yi_(d_yi)
-        , yi_(yi)
+        , yiInfo_(yiInfo)
         , d_xo_(d_xo)
-        , xo_(xo)
+        , xoInfo_(xoInfo)
         , xi_beg_(xi_beg)
         , xi_step_reproc_(xi_step_reproc)
         , offGrid_(offGrid)
         , blocksMatX_(blocksMatX)
-        , batch_(batch)
         , method_(method)
-        , XDIM_(XDIM)
-        , INTERP_ORDER_(INTERP_ORDER) {}
+        , batch_(batch)
+        , XDIM_(XDIM) {}
+
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
-        const int idw = g.get_group_id(1) / yo_.dims[2];
-        const int idz = g.get_group_id(1) - idw * yo_.dims[2];
+        const int idw = g.get_group_id(1) / yoInfo_.dims[2];
+        const int idz = g.get_group_id(1) - idw * yoInfo_.dims[2];
 
         const int idy        = g.get_group_id(0) / blocksMatX_;
         const int blockIdx_x = g.get_group_id(0) - idy * blocksMatX_;
         const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
 
-        if (idx >= yo_.dims[0] || idy >= yo_.dims[1] || idz >= yo_.dims[2] ||
-            idw >= yo_.dims[3])
+        if (idx >= yoInfo_.dims[0] || idy >= yoInfo_.dims[1] ||
+            idz >= yoInfo_.dims[2] || idw >= yoInfo_.dims[3])
             return;
 
         // FIXME: Only cubic interpolation is doing clamping
         // We need to make it consistent across all methods
         // Not changing the behavior because tests will fail
-        const bool doclamp = INTERP_ORDER_ == 3;
+        const bool doclamp = order == 3;
 
-        bool is_off[] = {xo_.dims[0] > 1, xo_.dims[1] > 1, xo_.dims[2] > 1,
-                         xo_.dims[3] > 1};
+        bool is_off[] = {xoInfo_.dims[0] > 1, xoInfo_.dims[1] > 1,
+                         xoInfo_.dims[2] > 1, xoInfo_.dims[3] > 1};
 
-        const int yo_idx = idw * yo_.strides[3] + idz * yo_.strides[2] +
-                           idy * yo_.strides[1] + idx + yo_.offset;
+        const int yo_idx = idw * yoInfo_.strides[3] + idz * yoInfo_.strides[2] +
+                           idy * yoInfo_.strides[1] + idx + yoInfo_.offset;
 
-        int xo_idx = idx * is_off[0] + xo_.offset;
+        int xo_idx = idx * is_off[0] + xoInfo_.offset;
         if (batch_) {
-            xo_idx += idw * xo_.strides[3] * is_off[3];
-            xo_idx += idz * xo_.strides[2] * is_off[2];
-            xo_idx += idy * xo_.strides[1] * is_off[1];
+            xo_idx += idw * xoInfo_.strides[3] * is_off[3];
+            xo_idx += idz * xoInfo_.strides[2] * is_off[2];
+            xo_idx += idy * xoInfo_.strides[1] * is_off[1];
         }
 
         const Tp x = (d_xo_[xo_idx] - xi_beg_) * xi_step_reproc_;
@@ -95,181 +101,62 @@ class approx1Kernel {
         }
         is_off[XDIM_] = false;
 
-        if (x < 0 || yi_.dims[XDIM_] < x + 1) {
+        if (x < 0 || yiInfo_.dims[XDIM_] < x + 1) {
             d_yo_[yo_idx] = offGrid_;
             return;
         }
 
-        int yi_idx = idx * is_off[0] + yi_.offset;
-        yi_idx += idw * yi_.strides[3] * is_off[3];
-        yi_idx += idz * yi_.strides[2] * is_off[2];
-        yi_idx += idy * yi_.strides[1] * is_off[1];
-
-        if (INTERP_ORDER_ == 1)
-            interp1o1(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
-                      doclamp, 1);
-        if (INTERP_ORDER_ == 2)
-            interp1o2(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
-                      doclamp, 1);
-        if (INTERP_ORDER_ == 3)
-            interp1o3(d_yo_, yo_, yo_idx, d_yi_, yi_, yi_idx, x, method_, 1,
-                      doclamp, 1);
-    }
-
-    void interp1o1(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        Ty zero = (Ty)0;
-
-        const int x_lim    = in.dims[XDIM_];
-        const int x_stride = in.strides[XDIM_];
-
-        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        bool cond = xid >= 0 && xid < x_lim;
-        if (doclamp) xid = fmax(0, fmin(xid, x_lim));
-
-        const int idx = ioff + xid * x_stride;
-
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            d_out[ooff + n * out.strides[batch_dim]] =
-                (doclamp || cond) ? d_in[idx_n] : zero;
-        }
-    }
-
-    Ty linearInterpFunc(Ty val[2], Tp ratio) const {
-        return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
-    }
-
-    Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
-        Ty a0, a1, a2, a3;
-        if (spline) {
-            a0 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)1.5, val[1]) +
-                 MULRC((Tp)-1.5, val[2]) + MULRC((Tp)0.5, val[3]);
-
-            a1 = MULRC((Tp)1.0, val[0]) + MULRC((Tp)-2.5, val[1]) +
-                 MULRC((Tp)2.0, val[2]) + MULRC((Tp)-0.5, val[3]);
-
-            a2 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)0.5, val[2]);
-
-            a3 = val[1];
-        } else {
-            a0 = val[3] - val[2] - val[0] + val[1];
-            a1 = val[0] - val[1] - a0;
-            a2 = val[2] - val[0];
-            a3 = val[1];
-        }
-
-        Tp xratio2 = xratio * xratio;
-        Tp xratio3 = xratio2 * xratio;
-
-        return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
-    }
-
-    void interp1o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        const int grid_x = floor(x);    // nearest grid
-        const Tp off_x   = x - grid_x;  // fractional offset
-
-        const int x_lim    = in.dims[XDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int idx      = ioff + grid_x * x_stride;
-
-        Ty zero      = (Ty)0;
-        bool cond[2] = {true, grid_x + 1 < x_lim};
-        int offx[2]  = {0, cond[1] ? 1 : 0};
-        Tp ratio     = off_x;
-        if (method == AF_INTERP_LINEAR_COSINE) {
-            ratio = (1 - cos(ratio * (Tp)M_PI)) / 2;
-        }
+        int yi_idx = idx * is_off[0] + yiInfo_.offset;
+        yi_idx += idw * yiInfo_.strides[3] * is_off[3];
+        yi_idx += idz * yiInfo_.strides[2] * is_off[2];
+        yi_idx += idy * yiInfo_.strides[1] * is_off[1];
 
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            Ty val[2] = {
-                (doclamp || cond[0]) ? d_in[idx_n + offx[0] * x_stride] : zero,
-                (doclamp || cond[1]) ? d_in[idx_n + offx[1] * x_stride] : zero};
-
-            d_out[ooff + n * out.strides[batch_dim]] =
-                linearInterpFunc(val, ratio);
-        }
-    }
-
-    void interp1o3(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        const int grid_x = floor(x);    // nearest grid
-        const Tp off_x   = x - grid_x;  // fractional offset
-
-        const int x_lim    = in.dims[XDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int idx      = ioff + grid_x * x_stride;
-
-        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
-                        grid_x + 2 < x_lim};
-        int off[4]   = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
-                      cond[3] ? 2 : (cond[2] ? 1 : 0)};
-
-        Ty zero = (Ty)0;
-
-        for (int n = 0; n < batch; n++) {
-            Ty val[4];
-            int idx_n = idx + n * in.strides[batch_dim];
-            for (int i = 0; i < 4; i++) {
-                val[i] = (doclamp || cond[i]) ? d_in[idx_n + off[i] * x_stride]
-                                              : zero;
-            }
-            bool spline = method == AF_INTERP_CUBIC_SPLINE;
-            d_out[ooff + n * out.strides[batch_dim]] =
-                cubicInterpFunc(val, off_x, spline);
-        }
+        Interp1<Ty, Tp, order> interp;
+        interp(d_yo_, yoInfo_, yo_idx, d_yi_, yiInfo_, yi_idx, x, XDIM_,
+               method_, 1, doclamp);
     }
 
-   private:
-    sycl::accessor<Ty> d_yo_;
-    const KParam yo_;
-    sycl::accessor<Ty> d_yi_;
-    const KParam yi_;
-    sycl::accessor<Tp> d_xo_;
-    const KParam xo_;
+   protected:
+    write_accessor<Ty> d_yo_;
+    const KParam yoInfo_;
+    read_accessor<Ty> d_yi_;
+    const KParam yiInfo_;
+    read_accessor<Tp> d_xo_;
+    const KParam xoInfo_;
     const Tp xi_beg_;
     const Tp xi_step_reproc_;
     const Ty offGrid_;
     const int blocksMatX_;
-    const int batch_;
-    const int method_;
+    const af_interp_type method_;
+    const bool batch_;
     const int XDIM_;
-    const int INTERP_ORDER_;
 };
 
-template<typename Ty, typename Tp>
+template<typename Ty, typename Tp, int order>
 void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
              const int xdim, const Tp xi_beg, const Tp xi_step,
-             const float offGrid, const af_interp_type method,
-             const int order) {
+             const float offGrid, const af_interp_type method) {
     constexpr int THREADS = 256;
 
-    auto local         = sycl::range{THREADS, 1};
-    dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
-    auto global        = sycl::range{blocksPerMat * local[0] * yo.info.dims[1],
+    auto local        = sycl::range{THREADS, 1};
+    uint blocksPerMat = divup(yo.info.dims[0], local[0]);
+    auto global       = sycl::range{blocksPerMat * local[0] * yo.info.dims[1],
                               yo.info.dims[2] * yo.info.dims[3] * local[1]};
 
-    // Passing bools to opencl kernels is not allowed
     bool batch =
         !(xo.info.dims[1] == 1 && xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
     getQueue().submit([&](sycl::handler &h) {
-        auto yoAcc = yo.data->get_access(h);
-        auto yiAcc = yi.data->get_access(h);
-        auto xoAcc = xo.data->get_access(h);
+        write_accessor<Ty> yoAcc{*yo.data, h};
+        read_accessor<Ty> yiAcc{*yi.data, h};
+        read_accessor<Tp> xoAcc{*xo.data, h};
         sycl::stream debugStream(128, 128, h);
 
-        h.parallel_for(
-            sycl::nd_range{global, local},
-            approx1Kernel<Ty, Tp>(yoAcc, yo.info, yiAcc, yi.info, xoAcc,
-                                  xo.info, xi_beg, Tp(1) / xi_step, (Ty)offGrid,
-                                  (int)blocksPerMat, (int)batch, (int)method,
-                                  xdim, order));
+        h.parallel_for(sycl::nd_range{global, local},
+                       approx1Kernel<Ty, Tp, order>(
+                           yoAcc, yo.info, yiAcc, yi.info, xoAcc, xo.info,
+                           xi_beg, Tp(1) / xi_step, (Ty)offGrid,
+                           (uint)blocksPerMat, method, batch, xdim));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/approx2.hpp b/src/backend/oneapi/kernel/approx2.hpp
index 5df6111d6a..94b2f7060c 100644
--- a/src/backend/oneapi/kernel/approx2.hpp
+++ b/src/backend/oneapi/kernel/approx2.hpp
@@ -13,14 +13,14 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/interp.hpp>
+#include <af/constants.h>
+// #include <kernel/default_config.hpp>
 #include <traits.hpp>
 
 #include <string>
 #include <vector>
 
-#define MULRC(a, b) (a) * (b)
-#define MULCR(a, b) (a) * (b)
-
 namespace oneapi {
 namespace kernel {
 
@@ -33,26 +33,31 @@ using local_accessor =
     sycl::accessor<T, dimensions, sycl::access::mode::read_write,
                    sycl::access::target::local>;
 
-template<typename Ty, typename Tp>
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename Ty, typename Tp, int order>
 class approx2Kernel {
    public:
-    approx2Kernel(sycl::accessor<Ty> d_zo, const KParam zo,
-                  sycl::accessor<Ty> d_zi, const KParam zi,
-                  sycl::accessor<Tp> d_xo, const KParam xo,
-                  sycl::accessor<Tp> d_yo, const KParam yo, const Tp xi_beg,
+    approx2Kernel(write_accessor<Ty> d_zo, const KParam zo,
+                  read_accessor<Ty> d_zi, const KParam zi,
+                  read_accessor<Tp> d_xo, const KParam xo,
+                  read_accessor<Tp> d_yo, const KParam yo, const Tp xi_beg,
                   const Tp xi_step_reproc, const Tp yi_beg,
                   const Tp yi_step_reproc, const Ty offGrid,
-                  const int blocksMatX, const int blocksMatY, const int batch,
-                  int method, const int XDIM, const int YDIM,
-                  const int INTERP_ORDER)
+                  const int blocksMatX, const int blocksMatY, const bool batch,
+                  const af_interp_type method, const int XDIM, const int YDIM)
         : d_zo_(d_zo)
-        , zo_(zo)
+        , zoInfo_(zo)
         , d_zi_(d_zi)
-        , zi_(zi)
+        , ziInfo_(zi)
         , d_xo_(d_xo)
-        , xo_(xo)
+        , xoInfo_(xo)
         , d_yo_(d_yo)
-        , yo_(yo)
+        , yoInfo_(yo)
         , xi_beg_(xi_beg)
         , xi_step_reproc_(xi_step_reproc)
         , yi_beg_(yi_beg)
@@ -63,8 +68,8 @@ class approx2Kernel {
         , batch_(batch)
         , method_(method)
         , XDIM_(XDIM)
-        , YDIM_(YDIM)
-        , INTERP_ORDER_(INTERP_ORDER) {}
+        , YDIM_(YDIM) {}
+
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
         const int idz = g.get_group_id(0) / blocksMatX_;
@@ -76,30 +81,30 @@ class approx2Kernel {
         const int idx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
         const int idy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
 
-        if (idx >= zo_.dims[0] || idy >= zo_.dims[1] || idz >= zo_.dims[2] ||
-            idw >= zo_.dims[3])
+        if (idx >= zoInfo_.dims[0] || idy >= zoInfo_.dims[1] ||
+            idz >= zoInfo_.dims[2] || idw >= zoInfo_.dims[3])
             return;
 
         // FIXME: Only cubic interpolation is doing clamping
         // We need to make it consistent across all methods
         // Not changing the behavior because tests will fail
-        const bool doclamp = INTERP_ORDER_ == 3;
+        const bool doclamp = order == 3;
 
-        bool is_off[] = {xo_.dims[0] > 1, xo_.dims[1] > 1, xo_.dims[2] > 1,
-                         xo_.dims[3] > 1};
+        bool is_off[] = {xoInfo_.dims[0] > 1, xoInfo_.dims[1] > 1,
+                         xoInfo_.dims[2] > 1, xoInfo_.dims[3] > 1};
 
-        const int zo_idx = idw * zo_.strides[3] + idz * zo_.strides[2] +
-                           idy * zo_.strides[1] + idx + zo_.offset;
-        int xo_idx =
-            idy * xo_.strides[1] * is_off[1] + idx * is_off[0] + xo_.offset;
+        const int zo_idx = idw * zoInfo_.strides[3] + idz * zoInfo_.strides[2] +
+                           idy * zoInfo_.strides[1] + idx + zoInfo_.offset;
+        int xo_idx = idy * xoInfo_.strides[1] * is_off[1] + idx * is_off[0] +
+                     xoInfo_.offset;
 
-        int yo_idx =
-            idy * yo_.strides[1] * is_off[1] + idx * is_off[0] + yo_.offset;
+        int yo_idx = idy * yoInfo_.strides[1] * is_off[1] + idx * is_off[0] +
+                     yoInfo_.offset;
         if (batch_) {
-            xo_idx += idw * xo_.strides[3] * is_off[3] +
-                      idz * xo_.strides[2] * is_off[2];
-            yo_idx += idw * yo_.strides[3] * is_off[3] +
-                      idz * yo_.strides[2] * is_off[2];
+            xo_idx += idw * xoInfo_.strides[3] * is_off[3] +
+                      idz * xoInfo_.strides[2] * is_off[2];
+            yo_idx += idw * yoInfo_.strides[3] * is_off[3] +
+                      idz * yoInfo_.strides[2] * is_off[2];
         }
 
 #pragma unroll
@@ -112,203 +117,31 @@ class approx2Kernel {
         const Tp x = (d_xo_[xo_idx] - xi_beg_) * xi_step_reproc_;
         const Tp y = (d_yo_[yo_idx] - yi_beg_) * yi_step_reproc_;
 
-        if (x < 0 || y < 0 || zi_.dims[XDIM_] < x + 1 ||
-            zi_.dims[YDIM_] < y + 1) {
+        if (x < 0 || y < 0 || ziInfo_.dims[XDIM_] < x + 1 ||
+            ziInfo_.dims[YDIM_] < y + 1) {
             d_zo_[zo_idx] = offGrid_;
             return;
         }
 
-        int zi_idx =
-            idy * zi_.strides[1] * is_off[1] + idx * is_off[0] + zi_.offset;
-        zi_idx +=
-            idw * zi_.strides[3] * is_off[3] + idz * zi_.strides[2] * is_off[2];
-
-        if (INTERP_ORDER_ == 1)
-            interp2o1(d_zo_, zo_, zo_idx, d_zi_, zi_, zi_idx, x, y, method_, 1,
-                      doclamp, 2);
-        if (INTERP_ORDER_ == 2)
-            interp2o2(d_zo_, zo_, zo_idx, d_zi_, zi_, zi_idx, x, y, method_, 1,
-                      doclamp, 2);
-        if (INTERP_ORDER_ == 3)
-            interp2o3(d_zo_, zo_, zo_idx, d_zi_, zi_, zi_idx, x, y, method_, 1,
-                      doclamp, 2);
-    }
-
-    Ty linearInterpFunc(Ty val[2], Tp ratio) const {
-        return MULRC((1 - ratio), val[0]) + MULRC(ratio, val[1]);
-    }
-
-    Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio) const {
-        Ty res[2];
-        res[0] = linearInterpFunc(val[0], xratio);
-        res[1] = linearInterpFunc(val[1], xratio);
-        return linearInterpFunc(res, yratio);
-    }
-
-    Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) const {
-        Ty a0, a1, a2, a3;
-        if (spline) {
-            a0 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)1.5, val[1]) +
-                 MULRC((Tp)-1.5, val[2]) + MULRC((Tp)0.5, val[3]);
-
-            a1 = MULRC((Tp)1.0, val[0]) + MULRC((Tp)-2.5, val[1]) +
-                 MULRC((Tp)2.0, val[2]) + MULRC((Tp)-0.5, val[3]);
-
-            a2 = MULRC((Tp)-0.5, val[0]) + MULRC((Tp)0.5, val[2]);
-
-            a3 = val[1];
-        } else {
-            a0 = val[3] - val[2] - val[0] + val[1];
-            a1 = val[0] - val[1] - a0;
-            a2 = val[2] - val[0];
-            a3 = val[1];
-        }
-
-        Tp xratio2 = xratio * xratio;
-        Tp xratio3 = xratio2 * xratio;
-
-        return MULCR(a0, xratio3) + MULCR(a1, xratio2) + MULCR(a2, xratio) + a3;
-    }
-
-    Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio,
-                         bool spline) const {
-        Ty res[4];
-        res[0] = cubicInterpFunc(val[0], xratio, spline);
-        res[1] = cubicInterpFunc(val[1], xratio, spline);
-        res[2] = cubicInterpFunc(val[2], xratio, spline);
-        res[3] = cubicInterpFunc(val[3], xratio, spline);
-        return cubicInterpFunc(res, yratio, spline);
-    }
-
-    void interp2o1(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x, Tp y,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
-
-        const int x_lim    = in.dims[XDIM_];
-        const int y_lim    = in.dims[YDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int y_stride = in.strides[YDIM_];
-
-        if (doclamp) {
-            xid = fmax(0, fmin(xid, x_lim));
-            yid = fmax(0, fmin(yid, y_lim));
-        }
-        const int idx = ioff + yid * y_stride + xid * x_stride;
-
-        bool condX = xid >= 0 && xid < x_lim;
-        bool condY = yid >= 0 && yid < y_lim;
-
-        Ty zero = (Ty)0;
-        ;
-        bool cond = condX && condY;
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            d_out[ooff + n * out.strides[batch_dim]] =
-                (doclamp || cond) ? d_in[idx_n] : zero;
-        }
-    }
-
-    void interp2o2(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x, Tp y,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        const int grid_x = floor(x);
-        const Tp off_x   = x - grid_x;
-
-        const int grid_y = floor(y);
-        const Tp off_y   = y - grid_y;
-
-        const int x_lim    = in.dims[XDIM_];
-        const int y_lim    = in.dims[YDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int y_stride = in.strides[YDIM_];
-        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
-
-        bool condX[2] = {true, x + 1 < x_lim};
-        bool condY[2] = {true, y + 1 < y_lim};
-        int offx[2]   = {0, condX[1] ? 1 : 0};
-        int offy[2]   = {0, condY[1] ? 1 : 0};
-
-        Tp xratio = off_x, yratio = off_y;
-        if (method == AF_INTERP_LINEAR_COSINE) {
-            xratio = (1 - cos(xratio * (Tp)M_PI)) / 2;
-            yratio = (1 - cos(yratio * (Tp)M_PI)) / 2;
-        }
-
-        Ty zero = (Ty)0;
-        ;
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            Ty val[2][2];
-            for (int j = 0; j < 2; j++) {
-                int off_y = idx_n + offy[j] * y_stride;
-                for (int i = 0; i < 2; i++) {
-                    bool cond = (doclamp || (condX[i] && condY[j]));
-                    val[j][i] = cond ? d_in[off_y + offx[i] * x_stride] : zero;
-                }
-            }
-            d_out[ooff + n * out.strides[batch_dim]] =
-                bilinearInterpFunc(val, xratio, yratio);
-        }
-    }
-
-    void interp2o3(sycl::accessor<Ty> d_out, KParam out, int ooff,
-                   sycl::accessor<Ty> d_in, KParam in, int ioff, Tp x, Tp y,
-                   int method, int batch, bool doclamp, int batch_dim) const {
-        const int grid_x = floor(x);
-        const Tp off_x   = x - grid_x;
-
-        const int grid_y = floor(y);
-        const Tp off_y   = y - grid_y;
+        int zi_idx = idy * ziInfo_.strides[1] * is_off[1] + idx * is_off[0] +
+                     ziInfo_.offset;
+        zi_idx += idw * ziInfo_.strides[3] * is_off[3] +
+                  idz * ziInfo_.strides[2] * is_off[2];
 
-        const int x_lim    = in.dims[XDIM_];
-        const int y_lim    = in.dims[YDIM_];
-        const int x_stride = in.strides[XDIM_];
-        const int y_stride = in.strides[YDIM_];
-        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
-
-        // used for setting values at boundaries
-        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
-                         grid_x + 2 < x_lim};
-        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim,
-                         grid_y + 2 < y_lim};
-        int offX[4]   = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0,
-                       condX[3] ? 2 : (condX[2] ? 1 : 0)};
-        int offY[4]   = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0,
-                       condY[3] ? 2 : (condY[2] ? 1 : 0)};
-
-        Ty zero = (Ty)0;
-        ;
-        for (int n = 0; n < batch; n++) {
-            int idx_n = idx + n * in.strides[batch_dim];
-            // for bicubic interpolation, work with 4x4 val at a time
-            Ty val[4][4];
-#pragma unroll
-            for (int j = 0; j < 4; j++) {
-                int ioff_j = idx_n + offY[j] * y_stride;
-#pragma unroll
-                for (int i = 0; i < 4; i++) {
-                    bool cond = (doclamp || (condX[i] && condY[j]));
-                    val[j][i] = cond ? d_in[ioff_j + offX[i] * x_stride] : zero;
-                }
-            }
-            bool spline = method == AF_INTERP_CUBIC_SPLINE ||
-                          method == AF_INTERP_BICUBIC_SPLINE;
-            d_out[ooff + n * out.strides[batch_dim]] =
-                bicubicInterpFunc(val, off_x, off_y, spline);
-        }
+        Interp2<Ty, Tp, order> interp;
+        interp(d_zo_, zoInfo_, zo_idx, d_zi_, ziInfo_, zi_idx, x, y, XDIM_,
+               YDIM_, method_, 1, doclamp);
     }
 
-   private:
-    sycl::accessor<Ty> d_zo_;
-    const KParam zo_;
-    sycl::accessor<Ty> d_zi_;
-    const KParam zi_;
-    sycl::accessor<Tp> d_xo_;
-    const KParam xo_;
-    sycl::accessor<Tp> d_yo_;
-    const KParam yo_;
+   protected:
+    write_accessor<Ty> d_zo_;
+    const KParam zoInfo_;
+    read_accessor<Ty> d_zi_;
+    const KParam ziInfo_;
+    read_accessor<Tp> d_xo_;
+    const KParam xoInfo_;
+    read_accessor<Tp> d_yo_;
+    const KParam yoInfo_;
     const Tp xi_beg_;
     const Tp xi_step_reproc_;
     const Tp yi_beg_;
@@ -317,18 +150,17 @@ class approx2Kernel {
     const int blocksMatX_;
     const int blocksMatY_;
     const int batch_;
-    int method_;
+    af::interpType method_;
     const int XDIM_;
     const int YDIM_;
-    const int INTERP_ORDER_;
 };
 
-template<typename Ty, typename Tp>
+template<typename Ty, typename Tp, int order>
 void approx2(Param<Ty> zo, const Param<Ty> zi, const Param<Tp> xo,
              const int xdim, const Tp &xi_beg, const Tp &xi_step,
              const Param<Tp> yo, const int ydim, const Tp &yi_beg,
              const Tp &yi_step, const float offGrid,
-             const af_interp_type method, const int order) {
+             const af_interp_type method) {
     constexpr int TX = 16;
     constexpr int TY = 16;
 
@@ -342,20 +174,18 @@ void approx2(Param<Ty> zo, const Param<Ty> zi, const Param<Tp> xo,
     bool batch = !(xo.info.dims[2] == 1 && xo.info.dims[3] == 1);
 
     getQueue().submit([&](sycl::handler &h) {
-        auto zoAcc = zo.data->get_access(h);
-        auto ziAcc = zi.data->get_access(h);
-        auto xoAcc = xo.data->get_access(h);
-        auto yoAcc = yo.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
+        write_accessor<Ty> zoAcc{*zo.data, h};
+        read_accessor<Ty> ziAcc{*zi.data, h};
+        read_accessor<Tp> xoAcc{*xo.data, h};
+        read_accessor<Tp> yoAcc{*yo.data, h};
 
         h.parallel_for(
             sycl::nd_range{global, local},
-            approx2Kernel<Ty, Tp>(
+            approx2Kernel<Ty, Tp, order>(
                 zoAcc, zo.info, ziAcc, zi.info, xoAcc, xo.info, yoAcc, yo.info,
                 xi_beg, Tp(1) / xi_step, yi_beg, Tp(1) / yi_step, (Ty)offGrid,
                 static_cast<int>(blocksPerMatX),
-                static_cast<int>(blocksPerMatY), static_cast<int>(batch),
-                static_cast<int>(method), xdim, ydim, order));
+                static_cast<int>(blocksPerMatY), batch, method, xdim, ydim));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
new file mode 100644
index 0000000000..1e3ac19287
--- /dev/null
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -0,0 +1,342 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <math.hpp>
+#include <types.hpp>
+#include <algorithm>
+
+namespace oneapi {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+struct itype_t {
+    typedef float wtype;
+    typedef float vtype;
+};
+
+template<>
+struct itype_t<double> {
+    typedef double wtype;
+    typedef double vtype;
+};
+
+template<>
+struct itype_t<cfloat> {
+    typedef float wtype;
+    typedef cfloat vtype;
+};
+
+template<>
+struct itype_t<cdouble> {
+    typedef double wtype;
+    typedef cdouble vtype;
+};
+
+template<typename Ty, typename Tp>
+Ty linearInterpFunc(Ty val[2], Tp ratio) {
+    return (1 - ratio) * val[0] + ratio * val[1];
+}
+
+template<typename Ty, typename Tp>
+Ty bilinearInterpFunc(Ty val[2][2], Tp xratio, Tp yratio) {
+    Ty res[2];
+    res[0] = linearInterpFunc(val[0], xratio);
+    res[1] = linearInterpFunc(val[1], xratio);
+    return linearInterpFunc(res, yratio);
+}
+
+template<typename Ty, typename Tp>
+inline static Ty cubicInterpFunc(Ty val[4], Tp xratio, bool spline) {
+    Ty a0, a1, a2, a3;
+    if (spline) {
+        a0 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(1.5) * val[1] +
+             scalar<Ty>(-1.5) * val[2] + scalar<Ty>(0.5) * val[3];
+
+        a1 = scalar<Ty>(1.0) * val[0] + scalar<Ty>(-2.5) * val[1] +
+             scalar<Ty>(2.0) * val[2] + scalar<Ty>(-0.5) * val[3];
+
+        a2 = scalar<Ty>(-0.5) * val[0] + scalar<Ty>(0.5) * val[2];
+
+        a3 = val[1];
+    } else {
+        a0 = val[3] - val[2] - val[0] + val[1];
+        a1 = val[0] - val[1] - a0;
+        a2 = val[2] - val[0];
+        a3 = val[1];
+    }
+
+    Tp xratio2 = xratio * xratio;
+    Tp xratio3 = xratio2 * xratio;
+
+    return a0 * xratio3 + a1 * xratio2 + a2 * xratio + a3;
+}
+
+template<typename Ty, typename Tp>
+inline static Ty bicubicInterpFunc(Ty val[4][4], Tp xratio, Tp yratio,
+                                   bool spline) {
+    Ty res[4];
+    res[0] = cubicInterpFunc(val[0], xratio, spline);
+    res[1] = cubicInterpFunc(val[1], xratio, spline);
+    res[2] = cubicInterpFunc(val[2], xratio, spline);
+    res[3] = cubicInterpFunc(val[3], xratio, spline);
+    return cubicInterpFunc(res, yratio, spline);
+}
+
+template<typename Ty, typename Tp, int order>
+struct Interp1 {};
+
+template<typename Ty, typename Tp>
+struct Interp1<Ty, Tp, 1> {
+    void operator()(write_accessor<Ty> out, KParam oInfo, int ooff,
+                    read_accessor<Ty> in, KParam iInfo, int ioff, Tp x,
+                    int xdim, af::interpType method, int batch, bool clamp,
+                    int batch_dim = 1) {
+        Ty zero = scalar<Ty>(0);
+
+        const int x_lim    = iInfo.dims[xdim];
+        const int x_stride = iInfo.strides[xdim];
+
+        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        bool cond = xid >= 0 && xid < x_lim;
+        if (clamp) xid = std::max((int)0, std::min(xid, x_lim));
+
+        const int idx = ioff + xid * x_stride;
+
+        for (int n = 0; n < batch; n++) {
+            Ty outval =
+                (cond || clamp) ? in[idx + n * iInfo.strides[batch_dim]] : zero;
+            out[ooff + n * oInfo.strides[batch_dim]] = outval;
+        }
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp1<Ty, Tp, 2> {
+    void operator()(write_accessor<Ty> out, KParam oInfo, int ooff,
+                    read_accessor<Ty> in, KParam iInfo, int ioff, Tp x,
+                    int xdim, af::interpType method, int batch, bool clamp,
+                    int batch_dim = 1) {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
+        const int grid_x = floor(x);    // nearest grid
+        const WT off_x   = x - grid_x;  // fractional offset
+
+        const int x_lim    = iInfo.dims[xdim];
+        const int x_stride = iInfo.strides[xdim];
+        const int idx      = ioff + grid_x * x_stride;
+
+        bool cond[2] = {true, grid_x + 1 < x_lim};
+        int offx[2]  = {0, cond[1] ? 1 : 0};
+        WT ratio     = off_x;
+        if (method == AF_INTERP_LINEAR_COSINE) {
+            // Smooth the factional part with cosine
+            ratio = (1 - cos(ratio * af::Pi)) / 2;
+        }
+
+        Ty zero = scalar<Ty>(0);
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * iInfo.strides[batch_dim];
+            VT val[2] = {
+                (clamp || cond[0]) ? in[idx_n + offx[0] * x_stride] : zero,
+                (clamp || cond[1]) ? in[idx_n + offx[1] * x_stride] : zero};
+            out[ooff + n * oInfo.strides[batch_dim]] =
+                linearInterpFunc(val, ratio);
+        }
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp1<Ty, Tp, 3> {
+    void operator()(write_accessor<Ty> out, KParam oInfo, int ooff,
+                    read_accessor<Ty> in, KParam iInfo, int ioff, Tp x,
+                    int xdim, af::interpType method, int batch, bool clamp,
+                    int batch_dim = 1) {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
+        const int grid_x = floor(x);    // nearest grid
+        const WT off_x   = x - grid_x;  // fractional offset
+
+        const int x_lim    = iInfo.dims[xdim];
+        const int x_stride = iInfo.strides[xdim];
+        const int idx      = ioff + grid_x * x_stride;
+
+        bool cond[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                        grid_x + 2 < x_lim};
+        int offx[4]  = {cond[0] ? -1 : 0, 0, cond[2] ? 1 : 0,
+                       cond[3] ? 2 : (cond[2] ? 1 : 0)};
+
+        bool spline = method == AF_INTERP_CUBIC_SPLINE;
+        Ty zero     = scalar<Ty>(0);
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * iInfo.strides[batch_dim];
+            VT val[4];
+            for (int i = 0; i < 4; i++) {
+                val[i] =
+                    (clamp || cond[i]) ? in[idx_n + offx[i] * x_stride] : zero;
+            }
+            out[ooff + n * oInfo.strides[batch_dim]] =
+                cubicInterpFunc(val, off_x, spline);
+        }
+    }
+};
+
+template<typename Ty, typename Tp, int order>
+struct Interp2 {};
+
+template<typename Ty, typename Tp>
+struct Interp2<Ty, Tp, 1> {
+    void operator()(write_accessor<Ty> out, KParam oInfo, int ooff,
+                    read_accessor<Ty> in, KParam iInfo, int ioff, Tp x, Tp y,
+                    int xdim, int ydim, af::interpType method, int batch,
+                    bool clamp, int batch_dim = 2) {
+        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+
+        const int x_lim    = iInfo.dims[xdim];
+        const int y_lim    = iInfo.dims[ydim];
+        const int x_stride = iInfo.strides[xdim];
+        const int y_stride = iInfo.strides[ydim];
+
+        if (clamp) {
+            xid = std::max(0, std::min(xid, (int)iInfo.dims[xdim]));
+            yid = std::max(0, std::min(yid, (int)iInfo.dims[ydim]));
+        }
+
+        const int idx = ioff + yid * y_stride + xid * x_stride;
+
+        bool condX = xid >= 0 && xid < x_lim;
+        bool condY = yid >= 0 && yid < y_lim;
+
+        Ty zero   = scalar<Ty>(0);
+        bool cond = condX && condY;
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * iInfo.strides[batch_dim];
+            Ty val    = (clamp || cond) ? in[idx_n] : zero;
+            out[ooff + n * oInfo.strides[batch_dim]] = val;
+        }
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp2<Ty, Tp, 2> {
+    void operator()(write_accessor<Ty> out, KParam oInfo, int ooff,
+                    read_accessor<Ty> in, KParam iInfo, int ioff, Tp x, Tp y,
+                    int xdim, int ydim, af::interpType method, int batch,
+                    bool clamp, int batch_dim = 2) {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
+        const int grid_x = floor(x);
+        const WT off_x   = x - grid_x;
+
+        const int grid_y = floor(y);
+        const WT off_y   = y - grid_y;
+
+        const int x_lim    = iInfo.dims[xdim];
+        const int y_lim    = iInfo.dims[ydim];
+        const int x_stride = iInfo.strides[xdim];
+        const int y_stride = iInfo.strides[ydim];
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
+
+        bool condX[2] = {true, x + 1 < x_lim};
+        bool condY[2] = {true, y + 1 < y_lim};
+        int offx[2]   = {0, condX[1] ? 1 : 0};
+        int offy[2]   = {0, condY[1] ? 1 : 0};
+
+        WT xratio = off_x, yratio = off_y;
+        if (method == AF_INTERP_LINEAR_COSINE ||
+            method == AF_INTERP_BILINEAR_COSINE) {
+            // Smooth the factional part with cosine
+            xratio = (1 - cos(xratio * af::Pi)) / 2;
+            yratio = (1 - cos(yratio * af::Pi)) / 2;
+        }
+
+        Ty zero = scalar<Ty>(0);
+
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * iInfo.strides[batch_dim];
+            VT val[2][2];
+            for (int j = 0; j < 2; j++) {
+                int ioff_j = idx_n + offy[j] * y_stride;
+                for (int i = 0; i < 2; i++) {
+                    bool cond = clamp || (condX[i] && condY[j]);
+                    val[j][i] = (cond) ? in[ioff_j + offx[i] * x_stride] : zero;
+                }
+            }
+            out[ooff + n * oInfo.strides[batch_dim]] =
+                bilinearInterpFunc(val, xratio, yratio);
+        }
+    }
+};
+
+template<typename Ty, typename Tp>
+struct Interp2<Ty, Tp, 3> {
+    void operator()(write_accessor<Ty> out, KParam oInfo, int ooff,
+                    read_accessor<Ty> in, KParam iInfo, int ioff, Tp x, Tp y,
+                    int xdim, int ydim, af::interpType method, int batch,
+                    bool clamp, int batch_dim = 2) {
+        typedef typename itype_t<Tp>::wtype WT;
+        typedef typename itype_t<Ty>::vtype VT;
+
+        const int grid_x = floor(x);
+        const WT off_x   = x - grid_x;
+
+        const int grid_y = floor(y);
+        const WT off_y   = y - grid_y;
+
+        const int x_lim    = iInfo.dims[xdim];
+        const int y_lim    = iInfo.dims[ydim];
+        const int x_stride = iInfo.strides[xdim];
+        const int y_stride = iInfo.strides[ydim];
+        const int idx      = ioff + grid_y * y_stride + grid_x * x_stride;
+
+        // used for setting values at boundaries
+        bool condX[4] = {grid_x - 1 >= 0, true, grid_x + 1 < x_lim,
+                         grid_x + 2 < x_lim};
+        bool condY[4] = {grid_y - 1 >= 0, true, grid_y + 1 < y_lim,
+                         grid_y + 2 < y_lim};
+        int offX[4]   = {condX[0] ? -1 : 0, 0, condX[2] ? 1 : 0,
+                       condX[3] ? 2 : (condX[2] ? 1 : 0)};
+        int offY[4]   = {condY[0] ? -1 : 0, 0, condY[2] ? 1 : 0,
+                       condY[3] ? 2 : (condY[2] ? 1 : 0)};
+
+        // for bicubic interpolation, work with 4x4 val at a time
+        Ty zero     = scalar<Ty>(0);
+        bool spline = (method == AF_INTERP_CUBIC_SPLINE ||
+                       method == AF_INTERP_BICUBIC_SPLINE);
+        for (int n = 0; n < batch; n++) {
+            int idx_n = idx + n * iInfo.strides[batch_dim];
+            VT val[4][4];
+#pragma unroll
+            for (int j = 0; j < 4; j++) {
+                int ioff_j = idx_n + offY[j] * y_stride;
+#pragma unroll
+                for (int i = 0; i < 4; i++) {
+                    bool cond = clamp || (condX[i] && condY[j]);
+                    val[j][i] = (cond) ? in[ioff_j + offX[i] * x_stride] : zero;
+                }
+            }
+
+            out[ooff + n * oInfo.strides[batch_dim]] =
+                bicubicInterpFunc(val, off_x, off_y, spline);
+        }
+    }
+};
+
+}  // namespace oneapi

From 51a4f6936a1ef0ecb93914ff1d464a72d111e3b1 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Tue, 11 Oct 2022 13:32:39 -0400
Subject: [PATCH 2332/2677] bilateral port to oneapi. tests pass except GFOR
 b/c of missing JIT

---
 src/backend/oneapi/CMakeLists.txt       |   1 +
 src/backend/oneapi/bilateral.cpp        |   3 +-
 src/backend/oneapi/kernel/bilateral.hpp | 217 ++++++++++++++++++++++++
 3 files changed, 220 insertions(+), 1 deletion(-)
 create mode 100755 src/backend/oneapi/kernel/bilateral.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index d6bfaae598..0561573a44 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -209,6 +209,7 @@ target_sources(afoneapi
     kernel/approx1.hpp
     kernel/approx2.hpp
     kernel/assign.hpp
+    kernel/bilateral.hpp
     kernel/diagonal.hpp
     kernel/diff.hpp
     kernel/interp.hpp
diff --git a/src/backend/oneapi/bilateral.cpp b/src/backend/oneapi/bilateral.cpp
index 59b050d2bf..75b97d5509 100644
--- a/src/backend/oneapi/bilateral.cpp
+++ b/src/backend/oneapi/bilateral.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <bilateral.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/bilateral.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
@@ -19,8 +20,8 @@ namespace oneapi {
 template<typename inType, typename outType>
 Array<outType> bilateral(const Array<inType> &in, const float &sSigma,
                          const float &cSigma) {
-    ONEAPI_NOT_SUPPORTED("");
     Array<outType> out = createEmptyArray<outType>(in.dims());
+    kernel::bilateral<inType, outType>(out, in, sSigma, cSigma);
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
new file mode 100755
index 0000000000..aba8b93d87
--- /dev/null
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -0,0 +1,217 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename outType, bool USE_NATIVE_EXP>
+auto exp_native_nonnative(float in) {
+    if constexpr (USE_NATIVE_EXP)
+        return sycl::native::exp(in);
+    else
+        return exp(in);
+}
+
+template<typename outType, typename inType, bool USE_NATIVE_EXP>
+class bilateralKernel {
+   public:
+    bilateralKernel(sycl::accessor<outType> d_dst, KParam oInfo,
+                    sycl::accessor<inType> d_src, KParam iInfo,
+                    local_accessor<outType, 1> localMem,
+                    local_accessor<outType, 1> gauss2d, float sigma_space,
+                    float sigma_color, int gaussOff, int nBBS0, int nBBS1)
+        : d_dst_(d_dst)
+        , oInfo_(oInfo)
+        , d_src_(d_src)
+        , iInfo_(iInfo)
+        , localMem_(localMem)
+        , gauss2d_(gauss2d)
+        , sigma_space_(sigma_space)
+        , sigma_color_(sigma_color)
+        , gaussOff_(gaussOff)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g                   = it.get_group();
+        const int radius                = fmax((int)(sigma_space_ * 1.5f), 1);
+        const int padding               = 2 * radius;
+        const int window_size           = padding + 1;
+        const int shrdLen               = g.get_local_range(0) + padding;
+        const float variance_range      = sigma_color_ * sigma_color_;
+        const float variance_space      = sigma_space_ * sigma_space_;
+        const float variance_space_neg2 = -2.0 * variance_space;
+        const float inv_variance_range_neg2 = -0.5 / (variance_range);
+
+        // gfor batch offsets
+        unsigned b2 = g.get_group_id(0) / nBBS0_;
+        unsigned b3 = g.get_group_id(1) / nBBS1_;
+
+        const inType* in =
+            d_src_.get_pointer() +
+            (b2 * iInfo_.strides[2] + b3 * iInfo_.strides[3] + iInfo_.offset);
+        outType* out = d_dst_.get_pointer() +
+                       (b2 * oInfo_.strides[2] + b3 * oInfo_.strides[3]);
+
+        int lx = it.get_local_id(0);
+        int ly = it.get_local_id(1);
+
+        const int gx =
+            g.get_local_range(0) * (g.get_group_id(0) - b2 * nBBS0_) + lx;
+        const int gy =
+            g.get_local_range(1) * (g.get_group_id(1) - b3 * nBBS1_) + ly;
+
+        // generate gauss2d_ spatial variance values for block
+        if (lx < window_size && ly < window_size) {
+            int x = lx - radius;
+            int y = ly - radius;
+            gauss2d_[ly * window_size + lx] =
+                exp_native_nonnative<outType, USE_NATIVE_EXP>(
+                    ((x * x) + (y * y)) / variance_space_neg2);
+        }
+
+        int s0 = iInfo_.strides[0];
+        int s1 = iInfo_.strides[1];
+        int d0 = iInfo_.dims[0];
+        int d1 = iInfo_.dims[1];
+        // pull image to local memory
+        for (int b = ly, gy2 = gy; b < shrdLen;
+             b += g.get_local_range(1), gy2 += g.get_local_range(1)) {
+            // move row_set g.get_local_range(1) along coloumns
+            for (int a = lx, gx2 = gx; a < shrdLen;
+                 a += g.get_local_range(0), gx2 += g.get_local_range(0)) {
+                load2LocalMem(localMem_, in, a, b, shrdLen, d0, d1,
+                              gx2 - radius, gy2 - radius, s1, s0);
+            }
+        }
+
+        it.barrier();
+
+        if (gx < iInfo_.dims[0] && gy < iInfo_.dims[1]) {
+            lx += radius;
+            ly += radius;
+            outType center_color = localMem_[ly * shrdLen + lx];
+            outType res          = 0;
+            outType norm         = 0;
+
+            int joff = (ly - radius) * shrdLen + (lx - radius);
+            int goff = 0;
+
+#pragma unroll
+            for (int wj = 0; wj < window_size; ++wj) {
+#pragma unroll
+                for (int wi = 0; wi < window_size; ++wi) {
+                    outType tmp_color = localMem_[joff + wi];
+                    const outType c   = center_color - tmp_color;
+                    outType gauss_range =
+                        exp_native_nonnative<outType, USE_NATIVE_EXP>(
+                            c * c * inv_variance_range_neg2);
+                    outType weight = gauss2d_[goff + wi] * gauss_range;
+                    norm += weight;
+                    res += tmp_color * weight;
+                }
+                joff += shrdLen;
+                goff += window_size;
+            }
+            out[gy * oInfo_.strides[1] + gx] = res / norm;
+        }
+    }
+
+    int lIdx(int x, int y, int stride1, int stride0) const {
+        return (y * stride1 + x * stride0);
+    }
+
+    void load2LocalMem(local_accessor<outType, 1> shrd, const inType* in,
+                       int lx, int ly, int shrdStride, int dim0, int dim1,
+                       int gx, int gy, int inStride1, int inStride0) const {
+        int gx_ = std::clamp(gx, 0, dim0 - 1);
+        int gy_ = std::clamp(gy, 0, dim1 - 1);
+        shrd[lIdx(lx, ly, shrdStride, 1)] =
+            (outType)in[lIdx(gx_, gy_, inStride1, inStride0)];
+    }
+
+   private:
+    sycl::accessor<outType> d_dst_;
+    KParam oInfo_;
+    sycl::accessor<inType> d_src_;
+    KParam iInfo_;
+    local_accessor<outType, 1> localMem_;
+    local_accessor<outType, 1> gauss2d_;
+    float sigma_space_;
+    float sigma_color_;
+    int gaussOff_;
+    int nBBS0_;
+    int nBBS1_;
+};
+
+template<typename inType, typename outType>
+void bilateral(Param<outType> out, const Param<inType> in, const float s_sigma,
+               const float c_sigma) {
+    constexpr int THREADS_X     = 16;
+    constexpr int THREADS_Y     = 16;
+    constexpr bool UseNativeExp = !std::is_same<inType, double>::value ||
+                                  std::is_same<inType, cdouble>::value;
+
+    auto local = sycl::range{THREADS_X, THREADS_Y};
+
+    const int blk_x = divup(in.info.dims[0], THREADS_X);
+    const int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    auto global = sycl::range{(size_t)(blk_x * in.info.dims[2] * THREADS_X),
+                              (size_t)(blk_y * in.info.dims[3] * THREADS_Y)};
+
+    // calculate local memory size
+    int radius          = (int)std::max(s_sigma * 1.5f, 1.f);
+    int num_shrd_elems  = (THREADS_X + 2 * radius) * (THREADS_Y + 2 * radius);
+    int num_gauss_elems = (2 * radius + 1) * (2 * radius + 1);
+    size_t localMemSize = (num_shrd_elems + num_gauss_elems) * sizeof(outType);
+    size_t MaxLocalSize =
+        getQueue().get_device().get_info<sycl::info::device::local_mem_size>();
+    if (localMemSize > MaxLocalSize) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nOneAPI Bilateral filter doesn't support %f spatial sigma\n",
+                 s_sigma);
+        ONEAPI_NOT_SUPPORTED(errMessage);
+    }
+
+    getQueue().submit([&](sycl::handler& h) {
+        auto inAcc  = in.data->get_access(h);
+        auto outAcc = out.data->get_access(h);
+        sycl::stream debugStream(128, 128, h);
+
+        auto localMem = local_accessor<outType, 1>(num_shrd_elems, h);
+        auto gauss2d  = local_accessor<outType, 1>(num_shrd_elems, h);
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       bilateralKernel<outType, inType, UseNativeExp>(
+                           outAcc, out.info, inAcc, in.info, localMem, gauss2d,
+                           s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi

From 46735cd7cd5ccb5e983338fc6d8527c8ba0eb892 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Fri, 11 Nov 2022 13:09:16 -0500
Subject: [PATCH 2333/2677] fix: interp.hpp missing af/constants.h

---
 src/backend/oneapi/kernel/interp.hpp | 1 +
 1 file changed, 1 insertion(+)
 mode change 100644 => 100755 src/backend/oneapi/kernel/interp.hpp

diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
old mode 100644
new mode 100755
index 1e3ac19287..778aff8202
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -11,6 +11,7 @@
 #include <math.hpp>
 #include <types.hpp>
 #include <algorithm>
+#include <af/constants.h>
 
 namespace oneapi {
 

From 8d4f680e6b5e79531dc5596b7e6382ee2e0d857e Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Fri, 11 Nov 2022 13:13:42 -0500
Subject: [PATCH 2334/2677] formatting

---
 src/backend/oneapi/kernel/interp.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
index 778aff8202..6f43fb52f2 100755
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -10,8 +10,8 @@
 #include <Param.hpp>
 #include <math.hpp>
 #include <types.hpp>
-#include <algorithm>
 #include <af/constants.h>
+#include <algorithm>
 
 namespace oneapi {
 

From dbc33fc7065d1a2dfae177dec0a204acbb722146 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 14 Nov 2022 16:01:26 -0500
Subject: [PATCH 2335/2677] Fix documentation for af_clamp

---
 docs/details/arith.dox | 7 ++++++-
 include/af/arith.h     | 6 +++---
 test/clamp.cpp         | 8 ++++----
 3 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index f53de09a87..8461ecd100 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -190,7 +190,6 @@ Bitwise xor operation of two inputs
 Minimum of two inputs.
 
 
-
 \defgroup arith_func_max max
 
 \ingroup numeric_mat
@@ -198,6 +197,12 @@ Minimum of two inputs.
 Maximum of two inputs.
 
 
+\defgroup arith_func_clamp clamp
+
+\ingroup numeric_mat
+
+Limits the range of the in array to the values between lo and hi
+
 
 \defgroup arith_func_rem rem
 
diff --git a/include/af/arith.h b/include/af/arith.h
index 319bda674b..89bd39bd64 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -888,16 +888,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for max of two arrays
+       C Interface for clamp
 
-       \param[out] out will contain the values from \p clamped between \p lo and \p hi
+       \param[out] out will contain the values from \p in clamped between \p lo and \p hi
        \param[in] in Input array
        \param[in] lo Value for lower limit
        \param[in] hi Value for upper limit
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_max
+       \ingroup arith_func_clamp
     */
     AFAPI af_err af_clamp(af_array *out, const af_array in,
                           const af_array lo, const af_array hi, const bool batch);
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 7f888a56ac..d27ad3a16d 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -144,7 +144,7 @@ TEST_P(ClampFloatingPoint, Basic) {
     ASSERT_ARRAYS_NEAR(gold_, out, 1e-5);
 }
 
-TEST(ClampTests, FloatArrayArray) {
+TEST(Clamp, FloatArrayArray) {
     array in = randu(num, f32);
     array lo = randu(num, f32) / 10;        // Ensure lo <= 0.1
     array hi = 1.0 - randu(num, f32) / 10;  // Ensure hi >= 0.9
@@ -165,7 +165,7 @@ TEST(ClampTests, FloatArrayArray) {
     }
 }
 
-TEST(ClampTests, FloatArrayScalar) {
+TEST(Clamp, FloatArrayScalar) {
     array in = randu(num, f32);
     array lo = randu(num, f32) / 10;  // Ensure lo <= 0.1
     float hi = 0.9;
@@ -185,7 +185,7 @@ TEST(ClampTests, FloatArrayScalar) {
     }
 }
 
-TEST(ClampTests, FloatScalarArray) {
+TEST(Clamp, FloatScalarArray) {
     array in = randu(num, f32);
     float lo = 0.1;
     array hi = 1.0 - randu(num, f32) / 10;  // Ensure hi >= 0.9
@@ -205,7 +205,7 @@ TEST(ClampTests, FloatScalarArray) {
     }
 }
 
-TEST(ClampTests, FloatScalarScalar) {
+TEST(Clamp, FloatScalarScalar) {
     array in = randu(num, f32);
     float lo = 0.1;
     float hi = 0.9;

From af95a357f6f6ff584b3e2dc4da9fca7a25a75ba7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 14 Nov 2022 16:44:37 -0500
Subject: [PATCH 2336/2677] Avoid installing system forge when
 AF_INSTALL_STANDALONE not set

The install target was copying the forge library installed on the system. This
is not expected because the install command only copies the artifacts generated
by the project and not libraries installed on the system. We do want system
libraries to be installed when AF_INSTALL_STANDALONE is enabled. This commit
addresses both of these issues.
---
 CMakeModules/AFconfigure_forge_dep.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/AFconfigure_forge_dep.cmake b/CMakeModules/AFconfigure_forge_dep.cmake
index 6944d9e9f1..8bf27d3a9e 100644
--- a/CMakeModules/AFconfigure_forge_dep.cmake
+++ b/CMakeModules/AFconfigure_forge_dep.cmake
@@ -75,7 +75,8 @@ else(AF_BUILD_FORGE)
 
     if(TARGET Forge::forge)
         get_target_property(fg_lib_type Forge::forge TYPE)
-        if(NOT ${fg_lib_type} STREQUAL "STATIC_LIBRARY")
+        if(NOT ${fg_lib_type} STREQUAL "STATIC_LIBRARY" AND
+           AF_INSTALL_STANDALONE)
             install(FILES
                     $<TARGET_FILE:Forge::forge>
                     $<$<PLATFORM_ID:Linux>:$<TARGET_SONAME_FILE:Forge::forge>>

From 35a88d9992d4698a0a77dc79e5724babd04d4023 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 18 Nov 2022 16:58:22 -0500
Subject: [PATCH 2337/2677] Fix ireduce failure in clang 14 due to b8 RNG
 optimization

The random number generator for b8 was producing incorrect results on clang 14
due to loop unrolling. This commit addresses the underlying issue caused by
ineffective indexing on the b8 RNG and updates one ireduce test to use the
ASSERT_VEC_ARRAYS_EQ function
---
 src/backend/cpu/kernel/random_engine.hpp |   4 +-
 test/ireduce.cpp                         | 125 ++++++++++++-----------
 2 files changed, 66 insertions(+), 63 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 29484e26da..6eaa862031 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -99,8 +99,8 @@ double getDouble01(uint *val, uint index) {
 
 template<>
 char transform<char>(uint *val, uint index) {
-    char v = val[index >> 2] >> (8 << (index & 3));
-    v      = (v & 0x1) ? 1 : 0;
+    char v = val[index >> 2] >> (index & 3);
+    v = v & 0x1;
     return v;
 }
 
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 92596528d4..1e55b9ac23 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -32,67 +32,70 @@ using af::span;
 using std::complex;
 using std::vector;
 
-#define MINMAXOP(fn, ty)                                        \
-    TEST(IndexedReduce, fn##_##ty##_0) {                        \
-        SUPPORTED_TYPE_CHECK(ty);                               \
-        dtype dty    = (dtype)dtype_traits<ty>::af_type;        \
-        const int nx = 10000;                                   \
-        const int ny = 100;                                     \
-        array in     = randu(nx, ny, dty);                      \
-        array val, idx;                                         \
-        fn(val, idx, in, 0);                                    \
-                                                                \
-        ty *h_in    = in.host<ty>();                            \
-        ty *h_in_st = h_in;                                     \
-        ty *h_val   = val.host<ty>();                           \
-        uint *h_idx = idx.host<uint>();                         \
-        for (int i = 0; i < ny; i++) {                          \
-            ty tmp = *std::fn##_element(h_in, h_in + nx);       \
-            ASSERT_EQ(tmp, h_val[i]) << "for index" << i;       \
-            ASSERT_EQ(h_in[h_idx[i]], tmp) << "for index" << i; \
-            h_in += nx;                                         \
-        }                                                       \
-        af_free_host(h_in_st);                                  \
-        af_free_host(h_val);                                    \
-        af_free_host(h_idx);                                    \
-    }                                                           \
-    TEST(IndexedReduce, fn##_##ty##_1) {                        \
-        SUPPORTED_TYPE_CHECK(ty);                               \
-        dtype dty    = (dtype)dtype_traits<ty>::af_type;        \
-        const int nx = 100;                                     \
-        const int ny = 100;                                     \
-        array in     = randu(nx, ny, dty);                      \
-        array val, idx;                                         \
-        fn(val, idx, in, 1);                                    \
-                                                                \
-        ty *h_in    = in.host<ty>();                            \
-        ty *h_val   = val.host<ty>();                           \
-        uint *h_idx = idx.host<uint>();                         \
-        for (int i = 0; i < nx; i++) {                          \
-            ty val = h_val[i];                                  \
-            for (int j = 0; j < ny; j++) {                      \
-                ty tmp = std::fn(val, h_in[j * nx + i]);        \
-                ASSERT_EQ(tmp, val);                            \
-            }                                                   \
-            ASSERT_EQ(val, h_in[h_idx[i] * nx + i]);            \
-        }                                                       \
-        af_free_host(h_in);                                     \
-        af_free_host(h_val);                                    \
-        af_free_host(h_idx);                                    \
-    }                                                           \
-    TEST(IndexedReduce, fn##_##ty##_all) {                      \
-        SUPPORTED_TYPE_CHECK(ty);                               \
-        dtype dty     = (dtype)dtype_traits<ty>::af_type;       \
-        const int num = 100000;                                 \
-        array in      = randu(num, dty);                        \
-        ty val;                                                 \
-        uint idx;                                               \
-        fn<ty>(&val, &idx, in);                                 \
-        ty *h_in = in.host<ty>();                               \
-        ty tmp   = *std::fn##_element(h_in, h_in + num);        \
-        ASSERT_EQ(tmp, val);                                    \
-        ASSERT_EQ(tmp, h_in[idx]);                              \
-        af_free_host(h_in);                                     \
+#define MINMAXOP(fn, ty)                                         \
+    TEST(IndexedReduce, fn##_##ty##_0) {                         \
+        SUPPORTED_TYPE_CHECK(ty);                                \
+        dtype dty    = (dtype)dtype_traits<ty>::af_type;         \
+        const int nx = 10;                                       \
+        const int ny = 100;                                      \
+        array in     = randu(nx, ny, dty);                       \
+        array val, idx;                                          \
+        fn(val, idx, in, 0);                                     \
+                                                                 \
+        ty *h_in    = in.host<ty>();                             \
+        ty *h_in_st = h_in;                                      \
+        uint *h_idx = idx.host<uint>();                          \
+        vector<ty> gold;                                         \
+        vector<ty> igold;                                        \
+        gold.reserve(ny);                                        \
+        igold.reserve(ny);                                       \
+        for (int i = 0; i < ny; i++) {                           \
+            gold.push_back(*std::fn##_element(h_in, h_in + nx)); \
+            igold.push_back(h_in[h_idx[i]]);                     \
+            h_in += nx;                                          \
+        }                                                        \
+        ASSERT_VEC_ARRAY_EQ(gold, af::dim4(1, ny), val);         \
+        ASSERT_VEC_ARRAY_EQ(igold, af::dim4(1, ny), val);        \
+        af_free_host(h_in_st);                                   \
+        af_free_host(h_idx);                                     \
+    }                                                            \
+    TEST(IndexedReduce, fn##_##ty##_1) {                         \
+        SUPPORTED_TYPE_CHECK(ty);                                \
+        dtype dty    = (dtype)dtype_traits<ty>::af_type;         \
+        const int nx = 100;                                      \
+        const int ny = 100;                                      \
+        array in     = randu(nx, ny, dty);                       \
+        array val, idx;                                          \
+        fn(val, idx, in, 1);                                     \
+                                                                 \
+        ty *h_in    = in.host<ty>();                             \
+        ty *h_val   = val.host<ty>();                            \
+        uint *h_idx = idx.host<uint>();                          \
+        for (int i = 0; i < nx; i++) {                           \
+            ty val = h_val[i];                                   \
+            for (int j = 0; j < ny; j++) {                       \
+                ty tmp = std::fn(val, h_in[j * nx + i]);         \
+                ASSERT_EQ(tmp, val);                             \
+            }                                                    \
+            ASSERT_EQ(val, h_in[h_idx[i] * nx + i]);             \
+        }                                                        \
+        af_free_host(h_in);                                      \
+        af_free_host(h_val);                                     \
+        af_free_host(h_idx);                                     \
+    }                                                            \
+    TEST(IndexedReduce, fn##_##ty##_all) {                       \
+        SUPPORTED_TYPE_CHECK(ty);                                \
+        dtype dty     = (dtype)dtype_traits<ty>::af_type;        \
+        const int num = 100000;                                  \
+        array in      = randu(num, dty);                         \
+        ty val;                                                  \
+        uint idx;                                                \
+        fn<ty>(&val, &idx, in);                                  \
+        ty *h_in = in.host<ty>();                                \
+        ty tmp   = *std::fn##_element(h_in, h_in + num);         \
+        ASSERT_EQ(tmp, val);                                     \
+        ASSERT_EQ(tmp, h_in[idx]);                               \
+        af_free_host(h_in);                                      \
     }
 
 MINMAXOP(min, float)

From d8900ea6b56ca8b442973067be1204c50c9a0aec Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 18 Nov 2022 17:22:17 -0500
Subject: [PATCH 2338/2677] Fix b8 RNG indexing so that the entire range of ctr
 is used

Previously the b8 RNG was only using the lest significant bits for the RNG
this is probably okay but it made the CPU indexing difficult. This commit
ensures that the LSB of each of the 4 integers are used instead of only
the first integer
---
 src/backend/cpu/kernel/random_engine.hpp      | 12 +++--
 src/backend/cuda/kernel/random_engine.hpp     | 48 +++++++++----------
 .../oneapi/kernel/random_engine_write.hpp     | 48 +++++++++----------
 .../opencl/kernel/random_engine_write.cl      | 48 +++++++++----------
 test/random.cpp                               |  2 +-
 5 files changed, 81 insertions(+), 77 deletions(-)

diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 6eaa862031..6f55f69719 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -99,14 +99,18 @@ double getDouble01(uint *val, uint index) {
 
 template<>
 char transform<char>(uint *val, uint index) {
-    char v = val[index >> 2] >> (index & 3);
-    v = v & 0x1;
+    char v = 0;
+    memcpy(&v, static_cast<char *>(static_cast<void *>(val)) + index,
+           sizeof(char));
+    v &= 0x1;
     return v;
 }
 
 template<>
 uchar transform<uchar>(uint *val, uint index) {
-    uchar v = val[index >> 2] >> (index << 3);
+    uchar v = 0;
+    memcpy(&v, static_cast<uchar *>(static_cast<void *>(val)) + index,
+           sizeof(uchar));
     return v;
 }
 
@@ -210,7 +214,7 @@ void philoxUniform(T *out, size_t elements, const uintl seed, uintl counter) {
 
                 // Use the same ctr array for each of the 4 locations,
                 // but each of the location gets a different ctr value
-                for (size_t buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
+                for (uint buf_idx = 0; buf_idx < NUM_WRITES; ++buf_idx) {
                     size_t out_idx = iter + buf_idx * WRITE_STRIDE + i + j;
                     if (out_idx < elements) {
                         out[out_idx] = transform<T>(ctr, buf_idx);
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index e52e78d354..31f9a711ed 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -315,21 +315,21 @@ __device__ static void writeOut128Bytes(char *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
     out[index]                   = (r1)&0x1;
-    out[index + blockDim.x]      = (r1 >> 1) & 0x1;
-    out[index + 2 * blockDim.x]  = (r1 >> 2) & 0x1;
-    out[index + 3 * blockDim.x]  = (r1 >> 3) & 0x1;
+    out[index + blockDim.x]      = (r1 >> 8) & 0x1;
+    out[index + 2 * blockDim.x]  = (r1 >> 16) & 0x1;
+    out[index + 3 * blockDim.x]  = (r1 >> 24) & 0x1;
     out[index + 4 * blockDim.x]  = (r2)&0x1;
-    out[index + 5 * blockDim.x]  = (r2 >> 1) & 0x1;
-    out[index + 6 * blockDim.x]  = (r2 >> 2) & 0x1;
-    out[index + 7 * blockDim.x]  = (r2 >> 3) & 0x1;
+    out[index + 5 * blockDim.x]  = (r2 >> 8) & 0x1;
+    out[index + 6 * blockDim.x]  = (r2 >> 16) & 0x1;
+    out[index + 7 * blockDim.x]  = (r2 >> 24) & 0x1;
     out[index + 8 * blockDim.x]  = (r3)&0x1;
-    out[index + 9 * blockDim.x]  = (r3 >> 1) & 0x1;
-    out[index + 10 * blockDim.x] = (r3 >> 2) & 0x1;
-    out[index + 11 * blockDim.x] = (r3 >> 3) & 0x1;
+    out[index + 9 * blockDim.x]  = (r3 >> 8) & 0x1;
+    out[index + 10 * blockDim.x] = (r3 >> 16) & 0x1;
+    out[index + 11 * blockDim.x] = (r3 >> 24) & 0x1;
     out[index + 12 * blockDim.x] = (r4)&0x1;
-    out[index + 13 * blockDim.x] = (r4 >> 1) & 0x1;
-    out[index + 14 * blockDim.x] = (r4 >> 2) & 0x1;
-    out[index + 15 * blockDim.x] = (r4 >> 3) & 0x1;
+    out[index + 13 * blockDim.x] = (r4 >> 8) & 0x1;
+    out[index + 14 * blockDim.x] = (r4 >> 16) & 0x1;
+    out[index + 15 * blockDim.x] = (r4 >> 24) & 0x1;
 }
 
 __device__ static void writeOut128Bytes(short *out, const uint &index,
@@ -540,49 +540,49 @@ __device__ static void partialWriteOut128Bytes(char *out, const uint &index,
                                                const uint &elements) {
     if (index < elements) { out[index] = (r1)&0x1; }
     if (index + blockDim.x < elements) {
-        out[index + blockDim.x] = (r1 >> 1) & 0x1;
+        out[index + blockDim.x] = (r1 >> 8) & 0x1;
     }
     if (index + 2 * blockDim.x < elements) {
-        out[index + 2 * blockDim.x] = (r1 >> 2) & 0x1;
+        out[index + 2 * blockDim.x] = (r1 >> 16) & 0x1;
     }
     if (index + 3 * blockDim.x < elements) {
-        out[index + 3 * blockDim.x] = (r1 >> 3) & 0x1;
+        out[index + 3 * blockDim.x] = (r1 >> 24) & 0x1;
     }
     if (index + 4 * blockDim.x < elements) {
         out[index + 4 * blockDim.x] = (r2)&0x1;
     }
     if (index + 5 * blockDim.x < elements) {
-        out[index + 5 * blockDim.x] = (r2 >> 1) & 0x1;
+        out[index + 5 * blockDim.x] = (r2 >> 8) & 0x1;
     }
     if (index + 6 * blockDim.x < elements) {
-        out[index + 6 * blockDim.x] = (r2 >> 2) & 0x1;
+        out[index + 6 * blockDim.x] = (r2 >> 16) & 0x1;
     }
     if (index + 7 * blockDim.x < elements) {
-        out[index + 7 * blockDim.x] = (r2 >> 3) & 0x1;
+        out[index + 7 * blockDim.x] = (r2 >> 24) & 0x1;
     }
     if (index + 8 * blockDim.x < elements) {
         out[index + 8 * blockDim.x] = (r3)&0x1;
     }
     if (index + 9 * blockDim.x < elements) {
-        out[index + 9 * blockDim.x] = (r3 >> 1) & 0x1;
+        out[index + 9 * blockDim.x] = (r3 >> 8) & 0x1;
     }
     if (index + 10 * blockDim.x < elements) {
-        out[index + 10 * blockDim.x] = (r3 >> 2) & 0x1;
+        out[index + 10 * blockDim.x] = (r3 >> 16) & 0x1;
     }
     if (index + 11 * blockDim.x < elements) {
-        out[index + 11 * blockDim.x] = (r3 >> 3) & 0x1;
+        out[index + 11 * blockDim.x] = (r3 >> 24) & 0x1;
     }
     if (index + 12 * blockDim.x < elements) {
         out[index + 12 * blockDim.x] = (r4)&0x1;
     }
     if (index + 13 * blockDim.x < elements) {
-        out[index + 13 * blockDim.x] = (r4 >> 1) & 0x1;
+        out[index + 13 * blockDim.x] = (r4 >> 8) & 0x1;
     }
     if (index + 14 * blockDim.x < elements) {
-        out[index + 14 * blockDim.x] = (r4 >> 2) & 0x1;
+        out[index + 14 * blockDim.x] = (r4 >> 16) & 0x1;
     }
     if (index + 15 * blockDim.x < elements) {
-        out[index + 15 * blockDim.x] = (r4 >> 3) & 0x1;
+        out[index + 15 * blockDim.x] = (r4 >> 24) & 0x1;
     }
 }
 
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 09f7a9c6e5..824feb95b8 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -310,21 +310,21 @@ static void writeOut128Bytes(char *out, const uint &index, const uint groupSz,
                              const uint &r1, const uint &r2, const uint &r3,
                              const uint &r4) {
     out[index]                = (r1)&0x1;
-    out[index + groupSz]      = (r1 >> 1) & 0x1;
-    out[index + 2 * groupSz]  = (r1 >> 2) & 0x1;
-    out[index + 3 * groupSz]  = (r1 >> 3) & 0x1;
+    out[index + groupSz]      = (r1 >> 8) & 0x1;
+    out[index + 2 * groupSz]  = (r1 >> 16) & 0x1;
+    out[index + 3 * groupSz]  = (r1 >> 24) & 0x1;
     out[index + 4 * groupSz]  = (r2)&0x1;
-    out[index + 5 * groupSz]  = (r2 >> 1) & 0x1;
-    out[index + 6 * groupSz]  = (r2 >> 2) & 0x1;
-    out[index + 7 * groupSz]  = (r2 >> 3) & 0x1;
+    out[index + 5 * groupSz]  = (r2 >> 8) & 0x1;
+    out[index + 6 * groupSz]  = (r2 >> 16) & 0x1;
+    out[index + 7 * groupSz]  = (r2 >> 24) & 0x1;
     out[index + 8 * groupSz]  = (r3)&0x1;
-    out[index + 9 * groupSz]  = (r3 >> 1) & 0x1;
-    out[index + 10 * groupSz] = (r3 >> 2) & 0x1;
-    out[index + 11 * groupSz] = (r3 >> 3) & 0x1;
+    out[index + 9 * groupSz]  = (r3 >> 8) & 0x1;
+    out[index + 10 * groupSz] = (r3 >> 16) & 0x1;
+    out[index + 11 * groupSz] = (r3 >> 24) & 0x1;
     out[index + 12 * groupSz] = (r4)&0x1;
-    out[index + 13 * groupSz] = (r4 >> 1) & 0x1;
-    out[index + 14 * groupSz] = (r4 >> 2) & 0x1;
-    out[index + 15 * groupSz] = (r4 >> 3) & 0x1;
+    out[index + 13 * groupSz] = (r4 >> 8) & 0x1;
+    out[index + 14 * groupSz] = (r4 >> 16) & 0x1;
+    out[index + 15 * groupSz] = (r4 >> 24) & 0x1;
 }
 
 static void writeOut128Bytes(short *out, const uint &index, const uint groupSz,
@@ -513,44 +513,44 @@ static void partialWriteOut128Bytes(char *out, const uint &index,
                                     const uint &r2, const uint &r3,
                                     const uint &r4, const uint &elements) {
     if (index < elements) { out[index] = (r1)&0x1; }
-    if (index + groupSz < elements) { out[index + groupSz] = (r1 >> 1) & 0x1; }
+    if (index + groupSz < elements) { out[index + groupSz] = (r1 >> 8) & 0x1; }
     if (index + 2 * groupSz < elements) {
-        out[index + 2 * groupSz] = (r1 >> 2) & 0x1;
+        out[index + 2 * groupSz] = (r1 >> 16) & 0x1;
     }
     if (index + 3 * groupSz < elements) {
-        out[index + 3 * groupSz] = (r1 >> 3) & 0x1;
+        out[index + 3 * groupSz] = (r1 >> 24) & 0x1;
     }
     if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = (r2)&0x1; }
     if (index + 5 * groupSz < elements) {
-        out[index + 5 * groupSz] = (r2 >> 1) & 0x1;
+        out[index + 5 * groupSz] = (r2 >> 8) & 0x1;
     }
     if (index + 6 * groupSz < elements) {
-        out[index + 6 * groupSz] = (r2 >> 2) & 0x1;
+        out[index + 6 * groupSz] = (r2 >> 16) & 0x1;
     }
     if (index + 7 * groupSz < elements) {
-        out[index + 7 * groupSz] = (r2 >> 3) & 0x1;
+        out[index + 7 * groupSz] = (r2 >> 24) & 0x1;
     }
     if (index + 8 * groupSz < elements) { out[index + 8 * groupSz] = (r3)&0x1; }
     if (index + 9 * groupSz < elements) {
-        out[index + 9 * groupSz] = (r3 >> 1) & 0x1;
+        out[index + 9 * groupSz] = (r3 >> 8) & 0x1;
     }
     if (index + 10 * groupSz < elements) {
-        out[index + 10 * groupSz] = (r3 >> 2) & 0x1;
+        out[index + 10 * groupSz] = (r3 >> 16) & 0x1;
     }
     if (index + 11 * groupSz < elements) {
-        out[index + 11 * groupSz] = (r3 >> 3) & 0x1;
+        out[index + 11 * groupSz] = (r3 >> 24) & 0x1;
     }
     if (index + 12 * groupSz < elements) {
         out[index + 12 * groupSz] = (r4)&0x1;
     }
     if (index + 13 * groupSz < elements) {
-        out[index + 13 * groupSz] = (r4 >> 1) & 0x1;
+        out[index + 13 * groupSz] = (r4 >> 8) & 0x1;
     }
     if (index + 14 * groupSz < elements) {
-        out[index + 14 * groupSz] = (r4 >> 2) & 0x1;
+        out[index + 14 * groupSz] = (r4 >> 16) & 0x1;
     }
     if (index + 15 * groupSz < elements) {
-        out[index + 15 * groupSz] = (r4 >> 3) & 0x1;
+        out[index + 15 * groupSz] = (r4 >> 24) & 0x1;
     }
 }
 
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index e61610b24a..8711987e44 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -50,21 +50,21 @@ void writeOut128Bytes_uchar(global uchar *out, uint index, uint r1, uint r2,
 void writeOut128Bytes_char(global char *out, uint index, uint r1, uint r2,
                            uint r3, uint r4) {
     out[index]                = (r1)&0x1;
-    out[index + THREADS]      = (r1 >> 1) & 0x1;
-    out[index + 2 * THREADS]  = (r1 >> 2) & 0x1;
-    out[index + 3 * THREADS]  = (r1 >> 3) & 0x1;
+    out[index + THREADS]      = (r1 >> 8) & 0x1;
+    out[index + 2 * THREADS]  = (r1 >> 16) & 0x1;
+    out[index + 3 * THREADS]  = (r1 >> 24) & 0x1;
     out[index + 4 * THREADS]  = (r2)&0x1;
-    out[index + 5 * THREADS]  = (r2 >> 1) & 0x1;
-    out[index + 6 * THREADS]  = (r2 >> 2) & 0x1;
-    out[index + 7 * THREADS]  = (r2 >> 3) & 0x1;
+    out[index + 5 * THREADS]  = (r2 >> 8) & 0x1;
+    out[index + 6 * THREADS]  = (r2 >> 16) & 0x1;
+    out[index + 7 * THREADS]  = (r2 >> 24) & 0x1;
     out[index + 8 * THREADS]  = (r3)&0x1;
-    out[index + 9 * THREADS]  = (r3 >> 1) & 0x1;
-    out[index + 10 * THREADS] = (r3 >> 2) & 0x1;
-    out[index + 11 * THREADS] = (r3 >> 3) & 0x1;
+    out[index + 9 * THREADS]  = (r3 >> 8) & 0x1;
+    out[index + 10 * THREADS] = (r3 >> 16) & 0x1;
+    out[index + 11 * THREADS] = (r3 >> 24) & 0x1;
     out[index + 12 * THREADS] = (r4)&0x1;
-    out[index + 13 * THREADS] = (r4 >> 1) & 0x1;
-    out[index + 14 * THREADS] = (r4 >> 2) & 0x1;
-    out[index + 15 * THREADS] = (r4 >> 3) & 0x1;
+    out[index + 13 * THREADS] = (r4 >> 8) & 0x1;
+    out[index + 14 * THREADS] = (r4 >> 16) & 0x1;
+    out[index + 15 * THREADS] = (r4 >> 24) & 0x1;
 }
 
 void writeOut128Bytes_short(global short *out, uint index, uint r1, uint r2,
@@ -187,44 +187,44 @@ void partialWriteOut128Bytes_uchar(global uchar *out, uint index, uint r1,
 void partialWriteOut128Bytes_char(global char *out, uint index, uint r1,
                                   uint r2, uint r3, uint r4, uint elements) {
     if (index < elements) { out[index] = (r1)&0x1; }
-    if (index + THREADS < elements) { out[index + THREADS] = (r1 >> 1) & 0x1; }
+    if (index + THREADS < elements) { out[index + THREADS] = (r1 >> 8) & 0x1; }
     if (index + 2 * THREADS < elements) {
-        out[index + 2 * THREADS] = (r1 >> 2) & 0x1;
+        out[index + 2 * THREADS] = (r1 >> 16) & 0x1;
     }
     if (index + 3 * THREADS < elements) {
-        out[index + 3 * THREADS] = (r1 >> 3) & 0x1;
+        out[index + 3 * THREADS] = (r1 >> 24) & 0x1;
     }
     if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = (r2)&0x1; }
     if (index + 5 * THREADS < elements) {
-        out[index + 5 * THREADS] = (r2 >> 1) & 0x1;
+        out[index + 5 * THREADS] = (r2 >> 8) & 0x1;
     }
     if (index + 6 * THREADS < elements) {
-        out[index + 6 * THREADS] = (r2 >> 2) & 0x1;
+        out[index + 6 * THREADS] = (r2 >> 16) & 0x1;
     }
     if (index + 7 * THREADS < elements) {
-        out[index + 7 * THREADS] = (r2 >> 3) & 0x1;
+        out[index + 7 * THREADS] = (r2 >> 24) & 0x1;
     }
     if (index + 8 * THREADS < elements) { out[index + 8 * THREADS] = (r3)&0x1; }
     if (index + 9 * THREADS < elements) {
-        out[index + 9 * THREADS] = (r3 >> 1) & 0x1;
+        out[index + 9 * THREADS] = (r3 >> 8) & 0x1;
     }
     if (index + 10 * THREADS < elements) {
-        out[index + 10 * THREADS] = (r3 >> 2) & 0x1;
+        out[index + 10 * THREADS] = (r3 >> 16) & 0x1;
     }
     if (index + 11 * THREADS < elements) {
-        out[index + 11 * THREADS] = (r3 >> 3) & 0x1;
+        out[index + 11 * THREADS] = (r3 >> 24) & 0x1;
     }
     if (index + 12 * THREADS < elements) {
         out[index + 12 * THREADS] = (r4)&0x1;
     }
     if (index + 13 * THREADS < elements) {
-        out[index + 13 * THREADS] = (r4 >> 1) & 0x1;
+        out[index + 13 * THREADS] = (r4 >> 8) & 0x1;
     }
     if (index + 14 * THREADS < elements) {
-        out[index + 14 * THREADS] = (r4 >> 2) & 0x1;
+        out[index + 14 * THREADS] = (r4 >> 16) & 0x1;
     }
     if (index + 15 * THREADS < elements) {
-        out[index + 15 * THREADS] = (r4 >> 3) & 0x1;
+        out[index + 15 * THREADS] = (r4 >> 24) & 0x1;
     }
 }
 
diff --git a/test/random.cpp b/test/random.cpp
index df65ac8006..d0860b70f2 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -36,7 +36,7 @@ class Random : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
-                         uintl, unsigned char, af_half>
+                         uintl, unsigned char, char, af_half>
     TestTypes;
 
 // register the type list

From 84046ca61a672ad50e6224f877b0f1df84fdbb11 Mon Sep 17 00:00:00 2001
From: ktdq <105746631+ktdq@users.noreply.github.com>
Date: Sun, 20 Nov 2022 00:52:49 -0500
Subject: [PATCH 2339/2677] Support 64bit hamming distance (#3314)

* support 64bit hamming distance on CUDA
* CPU support for 64 bit __popc in hamming distance
* adds hammingMatcher tests for uintll type

Co-authored-by: syurkevi <stefan@arrayfire.com>
---
 src/backend/cpu/kernel/nearest_neighbour.hpp  |  3 +-
 src/backend/cuda/kernel/nearest_neighbour.hpp |  2 +-
 test/hamming.cpp                              | 38 +++++++++++++++++++
 3 files changed, 41 insertions(+), 2 deletions(-)

diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 599c04356b..39b005c4ed 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -17,6 +17,7 @@ namespace kernel {
 
 #include <intrin.h>
 #define __builtin_popcount __popcnt
+#define __builtin_popcountll __popcnt64
 
 #endif
 
@@ -44,7 +45,7 @@ struct dist_op<uint, To, AF_SHD> {
 
 template<typename To>
 struct dist_op<uintl, To, AF_SHD> {
-    To operator()(uintl v1, uintl v2) { return __builtin_popcount(v1 ^ v2); }
+    To operator()(uintl v1, uintl v2) { return __builtin_popcountll(v1 ^ v2); }
 };
 
 template<typename To>
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index f615a733db..170f81868a 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -52,7 +52,7 @@ struct dist_op<uint, To, AF_SHD> {
 
 template<typename To>
 struct dist_op<uintl, To, AF_SHD> {
-    __device__ To operator()(uintl v1, uintl v2) { return __popc(v1 ^ v2); }
+    __device__ To operator()(uintl v1, uintl v2) { return __popcll(v1 ^ v2); }
 };
 
 template<typename To>
diff --git a/test/hamming.cpp b/test/hamming.cpp
index 763e0f7774..b14a33db0a 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -153,3 +153,41 @@ TEST(HammingMatcher, CPP) {
     delete[] outIdx;
     delete[] outDist;
 }
+
+TEST(HammingMatcher64bit, CPP) {
+    using af::array;
+    using af::dim4;
+
+    vector<dim4> numDims;
+    vector<vector<unsigned long long>> in;
+    vector<vector<unsigned long long>> tests;
+
+    readTests<unsigned long long, unsigned long long, int>(
+        TEST_DIR "/hamming/hamming_500_5000_dim0_u32.test", numDims, in, tests);
+
+    dim4 qDims = numDims[0];
+    dim4 tDims = numDims[1];
+
+    array query(qDims, &(in[0].front()));
+    array train(tDims, &(in[1].front()));
+
+    array idx, dist;
+    hammingMatcher(idx, dist, query, train, 0, 1);
+
+    vector<unsigned long long> goldIdx  = tests[0];
+    vector<unsigned long long> goldDist = tests[1];
+    size_t nElems                       = goldIdx.size();
+    uint *outIdx                        = new uint[nElems];
+    uint *outDist                       = new uint[nElems];
+
+    idx.host(outIdx);
+    dist.host(outDist);
+
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(goldDist[elIter], outDist[elIter])
+            << "at: " << elIter << endl;
+    }
+
+    delete[] outIdx;
+    delete[] outDist;
+}

From 5a11efe8ca64ffbef367917da87a7400cccca7bb Mon Sep 17 00:00:00 2001
From: guillaume <schmidg109@gmail.com>
Date: Mon, 19 Sep 2022 08:13:12 +0200
Subject: [PATCH 2340/2677] Fixes local issue with to_string. Refactor out hash
 funcitons

The arguments provided to OpenCL uses the C++ standard library
function std::to_string(). This function uses the locale to render
it's argument to a string.

It is a problem when arrayfire is used in a
software initialised with non "C" locale.

For instance, on a French computer, to_string(1.0) will output the
string "1,0000000".
This string is provided to OpenCL kernels, generating a syntax error.

The most portable way to fix this problem is to use a local ostringstream
imbued withe "C" locale.

An Other way would be to use C++17 to_chars function, as it only renders it
argument with "C" locale, without impact from the application or
system locale.

The patch is pretty simple, it changes the toString() function to use
the stringstream in src/backend/common/TemplateArg.cpp and changed the
to_string calls to this toString function in types.cpp.
---
 CMakeLists.txt                           |   8 +-
 CMakeModules/bin2cpp.cpp                 |   4 +-
 src/api/c/device.cpp                     |   3 +
 src/api/c/type_util.hpp                  |   2 -
 src/api/unified/symbol_manager.cpp       |   1 +
 src/backend/common/CMakeLists.txt        |   4 +-
 src/backend/common/Source.hpp            |  17 ++
 src/backend/common/TemplateArg.cpp       | 295 ----------------------
 src/backend/common/TemplateArg.hpp       |  19 +-
 src/backend/common/deterministicHash.cpp |  47 ++++
 src/backend/common/deterministicHash.hpp |  36 +++
 src/backend/common/err_common.cpp        |  20 +-
 src/backend/common/graphics_common.cpp   |   1 +
 src/backend/common/half.cpp              |   6 +
 src/backend/common/half.hpp              |   1 +
 src/backend/common/jit/Node.cpp          |   1 +
 src/backend/common/jit/NodeIO.hpp        |   7 +-
 src/backend/common/kernel_cache.cpp      |   3 +-
 src/backend/common/kernel_cache.hpp      |   1 +
 src/backend/common/util.cpp              | 302 +++++++++++++++++++++--
 src/backend/common/util.hpp              |  39 +--
 src/backend/cpu/platform.cpp             |   2 +
 src/backend/cpu/queue.hpp                |   2 +-
 src/backend/cuda/compile_module.cpp      |   1 +
 src/backend/cuda/cudnnModule.cpp         |   1 +
 src/backend/cuda/device_manager.cpp      |   2 +
 src/backend/cuda/jit.cpp                 |   3 +
 src/backend/cuda/platform.cpp            |   2 +
 src/backend/opencl/compile_module.cpp    |   2 +
 src/backend/opencl/device_manager.cpp    |   1 +
 src/backend/opencl/jit.cpp               |   2 +
 src/backend/opencl/platform.cpp          |   2 +
 src/backend/opencl/types.cpp             |  27 +-
 33 files changed, 465 insertions(+), 399 deletions(-)
 create mode 100644 src/backend/common/Source.hpp
 delete mode 100644 src/backend/common/TemplateArg.cpp
 create mode 100644 src/backend/common/deterministicHash.cpp
 create mode 100644 src/backend/common/deterministicHash.hpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5689a8094b..099e9a72ae 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -317,15 +317,15 @@ if(CMAKE_CROSSCOMPILING)
                        "directory and build the bin2cpp target.")
   endif()
 else()
-  add_executable(bin2cpp ${ArrayFire_SOURCE_DIR}/CMakeModules/bin2cpp.cpp
-                         ${ArrayFire_SOURCE_DIR}/src/backend/common/util.cpp)
+  add_executable(bin2cpp CMakeModules/bin2cpp.cpp
+                         src/backend/common/deterministicHash.cpp
+                         src/backend/common/deterministicHash.hpp
+                         src/backend/common/Source.hpp)
   set_target_properties(bin2cpp
     PROPERTIES
       CXX_STANDARD 17)
   target_link_libraries(bin2cpp PRIVATE nonstd::span-lite)
 
-  # NOSPDLOG is used to remove the spdlog dependency from bin2cpp
-  target_compile_definitions(bin2cpp PRIVATE NOSPDLOG)
   if(WIN32)
     target_compile_definitions(bin2cpp PRIVATE OS_WIN)
   elseif(APPLE)
diff --git a/CMakeModules/bin2cpp.cpp b/CMakeModules/bin2cpp.cpp
index 217b3efe14..3426b1ebed 100644
--- a/CMakeModules/bin2cpp.cpp
+++ b/CMakeModules/bin2cpp.cpp
@@ -28,7 +28,7 @@
 #include <utility>
 #include <vector>
 
-#include <common/util.hpp>
+#include <common/deterministicHash.hpp>
 
 using namespace std;
 using std::cout;
@@ -275,7 +275,7 @@ int main(int argc, const char *const *const argv) {
 
     cout << "#pragma once\n";
     cout << "#include <cstddef>\n";          // defines size_t
-    cout << "#include <common/util.hpp>\n";  // defines common::Source
+    cout << "#include <common/Source.hpp>\n";  // defines common::Source
 
     int ns_cnt = 0;
     int level  = 0;
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index cf65bfd81c..57c61be4c3 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -28,7 +28,10 @@
 #include <string>
 
 using af::dim4;
+using common::getCacheDirectory;
+using common::getEnvVar;
 using common::half;
+using common::JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/type_util.hpp b/src/api/c/type_util.hpp
index 1fa7dd7c87..4214882492 100644
--- a/src/api/c/type_util.hpp
+++ b/src/api/c/type_util.hpp
@@ -10,8 +10,6 @@
 #pragma once
 #include <af/defines.h>
 
-const char *getName(af_dtype type);
-
 // uchar to number converters
 template<typename T>
 struct ToNum {
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index ca11238773..a2efc6ee59 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -26,6 +26,7 @@
 #include <dlfcn.h>
 #endif
 
+using common::getEnvVar;
 using common::getErrorMessage;
 using common::getFunctionPointer;
 using common::loadLibrary;
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 8f553814e7..1487d99c44 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -40,9 +40,9 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/MemoryManagerBase.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/MersenneTwister.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/ModuleInterface.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/Source.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateTypename.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
@@ -53,6 +53,8 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/complex.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/constants.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/defines.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/deterministicHash.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/deterministicHash.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dim4.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dispatch.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/dispatch.hpp
diff --git a/src/backend/common/Source.hpp b/src/backend/common/Source.hpp
new file mode 100644
index 0000000000..000c2809d2
--- /dev/null
+++ b/src/backend/common/Source.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+namespace common {
+struct Source {
+    const char* ptr;           // Pointer to the kernel source
+    const std::size_t length;  // Length of the kernel source
+    const std::size_t hash;    // hash value for the source *ptr;
+};
+}  // namespace common
diff --git a/src/backend/common/TemplateArg.cpp b/src/backend/common/TemplateArg.cpp
deleted file mode 100644
index 8cff5c4e24..0000000000
--- a/src/backend/common/TemplateArg.cpp
+++ /dev/null
@@ -1,295 +0,0 @@
-/*******************************************************
- * Copyright (c) 2020, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#include <common/TemplateArg.hpp>
-
-#include <common/internal_enums.hpp>
-#include <optypes.hpp>
-#include <af/defines.h>
-
-#include <cstdlib>
-#include <string>
-
-using std::string;
-
-template<typename T>
-string toString(T value) {
-    return std::to_string(value);
-}
-
-template string toString<int>(int);
-template string toString<long>(long);
-template string toString<long long>(long long);
-template string toString<unsigned>(unsigned);
-template string toString<unsigned long>(unsigned long);
-template string toString<unsigned long long>(unsigned long long);
-template string toString<float>(float);
-template string toString<double>(double);
-template string toString<long double>(long double);
-
-template<>
-string toString(TemplateArg arg) {
-    return arg._tparam;
-}
-
-template<>
-string toString(bool val) {
-    return string(val ? "true" : "false");
-}
-
-template<>
-string toString(const char* str) {
-    return string(str);
-}
-
-template<>
-string toString(const string str) {
-    return str;
-}
-
-template<>
-string toString(unsigned short val) {
-    return std::to_string((unsigned int)(val));
-}
-
-template<>
-string toString(short val) {
-    return std::to_string(int(val));
-}
-
-template<>
-string toString(unsigned char val) {
-    return std::to_string((unsigned int)(val));
-}
-
-template<>
-string toString(char val) {
-    return std::to_string(int(val));
-}
-
-string getOpEnumStr(af_op_t val) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(af_add_t);
-        CASE_STMT(af_sub_t);
-        CASE_STMT(af_mul_t);
-        CASE_STMT(af_div_t);
-
-        CASE_STMT(af_and_t);
-        CASE_STMT(af_or_t);
-        CASE_STMT(af_eq_t);
-        CASE_STMT(af_neq_t);
-        CASE_STMT(af_lt_t);
-        CASE_STMT(af_le_t);
-        CASE_STMT(af_gt_t);
-        CASE_STMT(af_ge_t);
-
-        CASE_STMT(af_bitnot_t);
-        CASE_STMT(af_bitor_t);
-        CASE_STMT(af_bitand_t);
-        CASE_STMT(af_bitxor_t);
-        CASE_STMT(af_bitshiftl_t);
-        CASE_STMT(af_bitshiftr_t);
-
-        CASE_STMT(af_min_t);
-        CASE_STMT(af_max_t);
-        CASE_STMT(af_cplx2_t);
-        CASE_STMT(af_atan2_t);
-        CASE_STMT(af_pow_t);
-        CASE_STMT(af_hypot_t);
-
-        CASE_STMT(af_sin_t);
-        CASE_STMT(af_cos_t);
-        CASE_STMT(af_tan_t);
-        CASE_STMT(af_asin_t);
-        CASE_STMT(af_acos_t);
-        CASE_STMT(af_atan_t);
-
-        CASE_STMT(af_sinh_t);
-        CASE_STMT(af_cosh_t);
-        CASE_STMT(af_tanh_t);
-        CASE_STMT(af_asinh_t);
-        CASE_STMT(af_acosh_t);
-        CASE_STMT(af_atanh_t);
-
-        CASE_STMT(af_exp_t);
-        CASE_STMT(af_expm1_t);
-        CASE_STMT(af_erf_t);
-        CASE_STMT(af_erfc_t);
-
-        CASE_STMT(af_log_t);
-        CASE_STMT(af_log10_t);
-        CASE_STMT(af_log1p_t);
-        CASE_STMT(af_log2_t);
-
-        CASE_STMT(af_sqrt_t);
-        CASE_STMT(af_cbrt_t);
-
-        CASE_STMT(af_abs_t);
-        CASE_STMT(af_cast_t);
-        CASE_STMT(af_cplx_t);
-        CASE_STMT(af_real_t);
-        CASE_STMT(af_imag_t);
-        CASE_STMT(af_conj_t);
-
-        CASE_STMT(af_floor_t);
-        CASE_STMT(af_ceil_t);
-        CASE_STMT(af_round_t);
-        CASE_STMT(af_trunc_t);
-        CASE_STMT(af_signbit_t);
-
-        CASE_STMT(af_rem_t);
-        CASE_STMT(af_mod_t);
-
-        CASE_STMT(af_tgamma_t);
-        CASE_STMT(af_lgamma_t);
-
-        CASE_STMT(af_notzero_t);
-
-        CASE_STMT(af_iszero_t);
-        CASE_STMT(af_isinf_t);
-        CASE_STMT(af_isnan_t);
-
-        CASE_STMT(af_sigmoid_t);
-
-        CASE_STMT(af_noop_t);
-
-        CASE_STMT(af_select_t);
-        CASE_STMT(af_not_select_t);
-        CASE_STMT(af_rsqrt_t);
-        CASE_STMT(af_moddims_t);
-
-        CASE_STMT(af_none_t);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_op_t val) {
-    return getOpEnumStr(val);
-}
-
-template<>
-string toString(af_interp_type p) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (p) {
-        CASE_STMT(AF_INTERP_NEAREST);
-        CASE_STMT(AF_INTERP_LINEAR);
-        CASE_STMT(AF_INTERP_BILINEAR);
-        CASE_STMT(AF_INTERP_CUBIC);
-        CASE_STMT(AF_INTERP_LOWER);
-        CASE_STMT(AF_INTERP_LINEAR_COSINE);
-        CASE_STMT(AF_INTERP_BILINEAR_COSINE);
-        CASE_STMT(AF_INTERP_BICUBIC);
-        CASE_STMT(AF_INTERP_CUBIC_SPLINE);
-        CASE_STMT(AF_INTERP_BICUBIC_SPLINE);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_border_type p) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (p) {
-        CASE_STMT(AF_PAD_ZERO);
-        CASE_STMT(AF_PAD_SYM);
-        CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
-        CASE_STMT(AF_PAD_PERIODIC);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_moment_type p) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (p) {
-        CASE_STMT(AF_MOMENT_M00);
-        CASE_STMT(AF_MOMENT_M01);
-        CASE_STMT(AF_MOMENT_M10);
-        CASE_STMT(AF_MOMENT_M11);
-        CASE_STMT(AF_MOMENT_FIRST_ORDER);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_match_type p) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (p) {
-        CASE_STMT(AF_SAD);
-        CASE_STMT(AF_ZSAD);
-        CASE_STMT(AF_LSAD);
-        CASE_STMT(AF_SSD);
-        CASE_STMT(AF_ZSSD);
-        CASE_STMT(AF_LSSD);
-        CASE_STMT(AF_NCC);
-        CASE_STMT(AF_ZNCC);
-        CASE_STMT(AF_SHD);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_flux_function p) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (p) {
-        CASE_STMT(AF_FLUX_QUADRATIC);
-        CASE_STMT(AF_FLUX_EXPONENTIAL);
-        CASE_STMT(AF_FLUX_DEFAULT);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(AF_BATCH_KIND val) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_BATCH_NONE);
-        CASE_STMT(AF_BATCH_LHS);
-        CASE_STMT(AF_BATCH_RHS);
-        CASE_STMT(AF_BATCH_SAME);
-        CASE_STMT(AF_BATCH_DIFF);
-        CASE_STMT(AF_BATCH_UNSUPPORTED);
-    }
-#undef CASE_STMT
-    return retVal;
-}
-
-template<>
-string toString(af_homography_type val) {
-    const char* retVal = NULL;
-#define CASE_STMT(v) \
-    case v: retVal = #v; break
-    switch (val) {
-        CASE_STMT(AF_HOMOGRAPHY_RANSAC);
-        CASE_STMT(AF_HOMOGRAPHY_LMEDS);
-    }
-#undef CASE_STMT
-    return retVal;
-}
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
index a7dfbe4ceb..3a92bf643e 100644
--- a/src/backend/common/TemplateArg.hpp
+++ b/src/backend/common/TemplateArg.hpp
@@ -9,21 +9,15 @@
 
 #pragma once
 
+#include <common/util.hpp>
+
 #include <array>
 #include <string>
 #include <utility>
 
-#include <optypes.hpp>
-#include <traits.hpp>
-
 template<typename T>
 class TemplateTypename;
 
-template<typename T>
-std::string toString(T value);
-
-std::string getOpEnumStr(af_op_t val);
-
 struct TemplateArg {
     std::string _tparam;
 
@@ -33,7 +27,8 @@ struct TemplateArg {
     constexpr TemplateArg(TemplateTypename<T> arg) noexcept : _tparam(arg) {}
 
     template<typename T>
-    constexpr TemplateArg(T value) noexcept : _tparam(toString(value)) {}
+    constexpr TemplateArg(T value) noexcept
+        : _tparam(common::toString(value)) {}
 };
 
 template<typename... Targs>
@@ -43,6 +38,6 @@ std::array<TemplateArg, sizeof...(Targs)> TemplateArgs(Targs &&...args) {
 }
 
 #define DefineKey(arg) " -D " #arg
-#define DefineValue(arg) " -D " #arg "=" + toString(arg)
-#define DefineKeyValue(key, arg) " -D " #key "=" + toString(arg)
-#define DefineKeyFromStr(arg) toString(" -D " + std::string(arg))
+#define DefineValue(arg) " -D " #arg "=" + common::toString(arg)
+#define DefineKeyValue(key, arg) " -D " #key "=" + common::toString(arg)
+#define DefineKeyFromStr(arg) " -D " + std::string(arg)
diff --git a/src/backend/common/deterministicHash.cpp b/src/backend/common/deterministicHash.cpp
new file mode 100644
index 0000000000..0529f7c58b
--- /dev/null
+++ b/src/backend/common/deterministicHash.cpp
@@ -0,0 +1,47 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/deterministicHash.hpp>
+
+#include <nonstd/span.hpp>
+#include <numeric>
+#include <string>
+
+using nonstd::span;
+using std::accumulate;
+using std::string;
+
+size_t deterministicHash(const void* data, size_t byteSize, size_t prevHash) {
+    // Fowler-Noll-Vo "1a" 32 bit hash
+    // https://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function
+    const auto* byteData = static_cast<const uint8_t*>(data);
+    return accumulate(
+        byteData, byteData + byteSize, prevHash,
+        [&](size_t hash, uint8_t data) { return (hash ^ data) * FNV1A_PRIME; });
+}
+
+size_t deterministicHash(const string& data, const size_t prevHash) {
+    return deterministicHash(data.data(), data.size(), prevHash);
+}
+
+size_t deterministicHash(span<const string> list, const size_t prevHash) {
+    size_t hash = prevHash;
+    for (auto s : list) { hash = deterministicHash(s.data(), s.size(), hash); }
+    return hash;
+}
+
+size_t deterministicHash(span<const common::Source> list) {
+    // Combine the different source codes, via their hashes
+    size_t hash = FNV1A_BASE_OFFSET;
+    for (auto s : list) {
+        size_t h = s.hash ? s.hash : deterministicHash(s.ptr, s.length);
+        hash     = deterministicHash(&h, sizeof(size_t), hash);
+    }
+    return hash;
+}
diff --git a/src/backend/common/deterministicHash.hpp b/src/backend/common/deterministicHash.hpp
new file mode 100644
index 0000000000..25b43a8893
--- /dev/null
+++ b/src/backend/common/deterministicHash.hpp
@@ -0,0 +1,36 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <nonstd/span.hpp>
+#include <string>
+
+#include <common/Source.hpp>
+
+/// Return the FNV-1a hash of the provided bata.
+///
+/// \param[in] data Binary data to hash
+/// \param[in] byteSize Size of the data in bytes
+/// \param[in] optional prevHash Hash of previous parts when string is split
+///
+/// \returns An unsigned integer representing the hash of the data
+constexpr std::size_t FNV1A_BASE_OFFSET = 0x811C9DC5;
+constexpr std::size_t FNV1A_PRIME       = 0x01000193;
+std::size_t deterministicHash(const void* data, std::size_t byteSize,
+                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
+
+// This is just a wrapper around the above function.
+std::size_t deterministicHash(const std::string& data,
+                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
+
+// This concatenates strings in the vector and computes hash
+std::size_t deterministicHash(nonstd::span<const std::string> list,
+                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
+
+// This concatenates hashes of multiple sources
+std::size_t deterministicHash(nonstd::span<const common::Source> list);
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 7a19bcb941..58bc0a9ced 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -26,15 +26,17 @@
 #include <platform.hpp>
 #endif
 
+using boost::stacktrace::stacktrace;
 using std::move;
 using std::string;
 using std::stringstream;
 
+using common::getEnvVar;
+using common::getName;
 using common::is_stacktrace_enabled;
 
 AfError::AfError(const char *const func, const char *const file, const int line,
-                 const char *const message, af_err err,
-                 boost::stacktrace::stacktrace st)
+                 const char *const message, af_err err, stacktrace st)
     : logic_error(message)
     , functionName(func)
     , fileName(file)
@@ -43,8 +45,7 @@ AfError::AfError(const char *const func, const char *const file, const int line,
     , error(err) {}
 
 AfError::AfError(string func, string file, const int line,
-                 const string &message, af_err err,
-                 boost::stacktrace::stacktrace st)
+                 const string &message, af_err err, stacktrace st)
     : logic_error(message)
     , functionName(move(func))
     , fileName(move(file))
@@ -64,7 +65,7 @@ AfError::~AfError() noexcept = default;
 
 TypeError::TypeError(const char *const func, const char *const file,
                      const int line, const int index, const af_dtype type,
-                     boost::stacktrace::stacktrace st)
+                     stacktrace st)
     : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE, move(st))
     , errTypeName(getName(type))
     , argIndex(index) {}
@@ -75,8 +76,7 @@ int TypeError::getArgIndex() const noexcept { return argIndex; }
 
 ArgumentError::ArgumentError(const char *const func, const char *const file,
                              const int line, const int index,
-                             const char *const expectString,
-                             boost::stacktrace::stacktrace st)
+                             const char *const expectString, stacktrace st)
     : AfError(func, file, line, "Invalid argument", AF_ERR_ARG, move(st))
     , expected(expectString)
     , argIndex(index) {}
@@ -89,7 +89,7 @@ int ArgumentError::getArgIndex() const noexcept { return argIndex; }
 
 SupportError::SupportError(const char *const func, const char *const file,
                            const int line, const char *const back,
-                           boost::stacktrace::stacktrace st)
+                           stacktrace st)
     : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED,
               move(st))
     , backend(back) {}
@@ -99,7 +99,7 @@ const string &SupportError::getBackendName() const noexcept { return backend; }
 DimensionError::DimensionError(const char *const func, const char *const file,
                                const int line, const int index,
                                const char *const expectString,
-                               const boost::stacktrace::stacktrace &st)
+                               const stacktrace &st)
     : AfError(func, file, line, "Invalid size", AF_ERR_SIZE, st)
     , expected(expectString)
     , argIndex(index) {}
@@ -111,7 +111,7 @@ const string &DimensionError::getExpectedCondition() const noexcept {
 int DimensionError::getArgIndex() const noexcept { return argIndex; }
 
 af_err set_global_error_string(const string &msg, af_err err) {
-    std::string perr = getEnvVar("AF_PRINT_ERRORS");
+    string perr = getEnvVar("AF_PRINT_ERRORS");
     if (!perr.empty()) {
         if (perr != "0") { fprintf(stderr, "%s\n", msg.c_str()); }
     }
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index d1a572a153..75fe4c002c 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -15,6 +15,7 @@
 #include <mutex>
 #include <utility>
 
+using common::getEnvVar;
 using std::make_pair;
 using std::string;
 
diff --git a/src/backend/common/half.cpp b/src/backend/common/half.cpp
index 96c5ef4ff9..3e41699c72 100644
--- a/src/backend/common/half.cpp
+++ b/src/backend/common/half.cpp
@@ -1,9 +1,15 @@
 
 #include <common/half.hpp>
+#include <common/util.hpp>
 
 namespace common {
 std::ostream &operator<<(std::ostream &os, const half &val) {
     os << float(val);
     return os;
 }
+
+template<>
+std::string toString(const half val) {
+    return common::toString(static_cast<float>(val));
+}
 }  // namespace common
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 7904598eb8..bd5f143c28 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -47,6 +47,7 @@ using uint16_t = unsigned short;
 #include <type_traits>
 
 #include <limits>
+
 #endif
 
 namespace common {
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index c637926d79..71d88424f5 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <common/defines.hpp>
+#include <common/deterministicHash.hpp>
 #include <common/jit/Node.hpp>
 #include <common/util.hpp>
 
diff --git a/src/backend/common/jit/NodeIO.hpp b/src/backend/common/jit/NodeIO.hpp
index 050c8e3a7c..bd4346f465 100644
--- a/src/backend/common/jit/NodeIO.hpp
+++ b/src/backend/common/jit/NodeIO.hpp
@@ -9,10 +9,9 @@
 
 #pragma once
 #include <common/jit/Node.hpp>
-#include <common/util.hpp>
 #include <spdlog/fmt/bundled/format.h>
 
-#include <common/TemplateArg.hpp>
+#include <common/util.hpp>
 
 template<>
 struct fmt::formatter<af::dtype> : fmt::formatter<char> {
@@ -69,9 +68,9 @@ struct fmt::formatter<common::Node> {
             if (isBuffer(node)) {
                 format_to(ctx.out(), "buffer ");
             } else if (isScalar(node)) {
-                format_to(ctx.out(), "scalar ", getOpEnumStr(node.getOp()));
+                format_to(ctx.out(), "scalar ", common::toString(node.getOp()));
             } else {
-                format_to(ctx.out(), "{} ", getOpEnumStr(node.getOp()));
+                format_to(ctx.out(), "{} ", common::toString(node.getOp()));
             }
         }
         if (type) format_to(ctx.out(), "{} ", node.getType());
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index ff2b53c787..1fb81ad293 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -10,13 +10,12 @@
 #if !defined(AF_CPU) && !defined(AF_ONEAPI)
 
 #include <common/compile_module.hpp>
+#include <common/deterministicHash.hpp>
 #include <common/kernel_cache.hpp>
-#include <common/util.hpp>
 #include <device_manager.hpp>
 #include <platform.hpp>
 
 #include <nonstd/span.hpp>
-#include <algorithm>
 #include <shared_mutex>
 #include <string>
 #include <unordered_map>
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index b021919a21..eb1b90f47b 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -14,6 +14,7 @@
 #include <Kernel.hpp>
 #include <Module.hpp>
 #include <backend.hpp>
+#include <common/Source.hpp>
 #include <common/TemplateTypename.hpp>
 #include <common/util.hpp>
 
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index bac4cb573d..f6d39a864e 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -15,39 +15,53 @@
 #include <unistd.h>
 #endif
 
-#ifndef NOSPDLOG
 #include <common/Logger.hpp>
-#endif
-
+#include <common/TemplateArg.hpp>
 #include <common/defines.hpp>
 #include <common/util.hpp>
+#include <optypes.hpp>
 #include <af/defines.h>
 
 #include <nonstd/span.hpp>
 #include <sys/stat.h>
+
 #include <algorithm>
+#include <array>
 #include <cstdio>
 #include <cstdlib>
 #include <cstring>
 #include <fstream>
 #include <numeric>
+#include <sstream>
 #include <string>
 #include <thread>
 #include <vector>
 
+#ifdef __has_include
+#if __has_include(<charconv>)
+#include <charconv>
+#endif
+#if __has_include(<version>)
+#include <version>
+#endif
+#endif
+
 using nonstd::span;
 using std::accumulate;
+using std::array;
 using std::hash;
 using std::ofstream;
 using std::once_flag;
 using std::rename;
 using std::size_t;
 using std::string;
+using std::stringstream;
 using std::thread;
 using std::to_string;
 using std::uint8_t;
 using std::vector;
 
+namespace common {
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
 string& ltrim(string& s) {
@@ -237,31 +251,273 @@ string makeTempFilename() {
         hash<string>{}(to_string(threadID) + "_" + to_string(fileCount)));
 }
 
-size_t deterministicHash(const void* data, size_t byteSize, size_t prevHash) {
-    // Fowler-Noll-Vo "1a" 32 bit hash
-    // https://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function
-    const auto* byteData = static_cast<const uint8_t*>(data);
-    return accumulate(
-        byteData, byteData + byteSize, prevHash,
-        [&](size_t hash, uint8_t data) { return (hash ^ data) * FNV1A_PRIME; });
+template<typename T>
+string toString(T value) {
+#ifdef __cpp_lib_to_chars
+    array<char, 128> out;
+    if (auto [ptr, ec] = std::to_chars(out.data(), out.data() + 128, value);
+        ec == std::errc()) {
+        return string(out.data(), ptr);
+    } else {
+        return string("#error invalid conversion");
+    }
+#else
+    stringstream ss;
+    ss.imbue(std::locale::classic());
+    ss << value;
+    return ss.str();
+#endif
+}
+
+template string toString<int>(int);
+template string toString<unsigned short>(unsigned short);
+template string toString<short>(short);
+template string toString<unsigned char>(unsigned char);
+template string toString<char>(char);
+template string toString<long>(long);
+template string toString<long long>(long long);
+template string toString<unsigned>(unsigned);
+template string toString<unsigned long>(unsigned long);
+template string toString<unsigned long long>(unsigned long long);
+template string toString<float>(float);
+template string toString<double>(double);
+template string toString<long double>(long double);
+
+template<>
+string toString(TemplateArg arg) {
+    return arg._tparam;
+}
+
+template<>
+string toString(bool val) {
+    return string(val ? "true" : "false");
+}
+
+template<>
+string toString(const char* str) {
+    return string(str);
+}
+
+template<>
+string toString(const string str) {
+    return str;
+}
+
+template<>
+string toString(af_op_t val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(af_add_t);
+        CASE_STMT(af_sub_t);
+        CASE_STMT(af_mul_t);
+        CASE_STMT(af_div_t);
+
+        CASE_STMT(af_and_t);
+        CASE_STMT(af_or_t);
+        CASE_STMT(af_eq_t);
+        CASE_STMT(af_neq_t);
+        CASE_STMT(af_lt_t);
+        CASE_STMT(af_le_t);
+        CASE_STMT(af_gt_t);
+        CASE_STMT(af_ge_t);
+
+        CASE_STMT(af_bitnot_t);
+        CASE_STMT(af_bitor_t);
+        CASE_STMT(af_bitand_t);
+        CASE_STMT(af_bitxor_t);
+        CASE_STMT(af_bitshiftl_t);
+        CASE_STMT(af_bitshiftr_t);
+
+        CASE_STMT(af_min_t);
+        CASE_STMT(af_max_t);
+        CASE_STMT(af_cplx2_t);
+        CASE_STMT(af_atan2_t);
+        CASE_STMT(af_pow_t);
+        CASE_STMT(af_hypot_t);
+
+        CASE_STMT(af_sin_t);
+        CASE_STMT(af_cos_t);
+        CASE_STMT(af_tan_t);
+        CASE_STMT(af_asin_t);
+        CASE_STMT(af_acos_t);
+        CASE_STMT(af_atan_t);
+
+        CASE_STMT(af_sinh_t);
+        CASE_STMT(af_cosh_t);
+        CASE_STMT(af_tanh_t);
+        CASE_STMT(af_asinh_t);
+        CASE_STMT(af_acosh_t);
+        CASE_STMT(af_atanh_t);
+
+        CASE_STMT(af_exp_t);
+        CASE_STMT(af_expm1_t);
+        CASE_STMT(af_erf_t);
+        CASE_STMT(af_erfc_t);
+
+        CASE_STMT(af_log_t);
+        CASE_STMT(af_log10_t);
+        CASE_STMT(af_log1p_t);
+        CASE_STMT(af_log2_t);
+
+        CASE_STMT(af_sqrt_t);
+        CASE_STMT(af_cbrt_t);
+
+        CASE_STMT(af_abs_t);
+        CASE_STMT(af_cast_t);
+        CASE_STMT(af_cplx_t);
+        CASE_STMT(af_real_t);
+        CASE_STMT(af_imag_t);
+        CASE_STMT(af_conj_t);
+
+        CASE_STMT(af_floor_t);
+        CASE_STMT(af_ceil_t);
+        CASE_STMT(af_round_t);
+        CASE_STMT(af_trunc_t);
+        CASE_STMT(af_signbit_t);
+
+        CASE_STMT(af_rem_t);
+        CASE_STMT(af_mod_t);
+
+        CASE_STMT(af_tgamma_t);
+        CASE_STMT(af_lgamma_t);
+
+        CASE_STMT(af_notzero_t);
+
+        CASE_STMT(af_iszero_t);
+        CASE_STMT(af_isinf_t);
+        CASE_STMT(af_isnan_t);
+
+        CASE_STMT(af_sigmoid_t);
+
+        CASE_STMT(af_noop_t);
+
+        CASE_STMT(af_select_t);
+        CASE_STMT(af_not_select_t);
+        CASE_STMT(af_rsqrt_t);
+        CASE_STMT(af_moddims_t);
+
+        CASE_STMT(af_none_t);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_interp_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_INTERP_NEAREST);
+        CASE_STMT(AF_INTERP_LINEAR);
+        CASE_STMT(AF_INTERP_BILINEAR);
+        CASE_STMT(AF_INTERP_CUBIC);
+        CASE_STMT(AF_INTERP_LOWER);
+        CASE_STMT(AF_INTERP_LINEAR_COSINE);
+        CASE_STMT(AF_INTERP_BILINEAR_COSINE);
+        CASE_STMT(AF_INTERP_BICUBIC);
+        CASE_STMT(AF_INTERP_CUBIC_SPLINE);
+        CASE_STMT(AF_INTERP_BICUBIC_SPLINE);
+    }
+#undef CASE_STMT
+    return retVal;
 }
 
-size_t deterministicHash(const string& data, const size_t prevHash) {
-    return deterministicHash(data.data(), data.size(), prevHash);
+template<>
+string toString(af_border_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_PAD_ZERO);
+        CASE_STMT(AF_PAD_SYM);
+        CASE_STMT(AF_PAD_CLAMP_TO_EDGE);
+        CASE_STMT(AF_PAD_PERIODIC);
+    }
+#undef CASE_STMT
+    return retVal;
 }
 
-size_t deterministicHash(span<const string> list, const size_t prevHash) {
-    size_t hash = prevHash;
-    for (auto s : list) { hash = deterministicHash(s.data(), s.size(), hash); }
-    return hash;
+template<>
+string toString(af_moment_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_MOMENT_M00);
+        CASE_STMT(AF_MOMENT_M01);
+        CASE_STMT(AF_MOMENT_M10);
+        CASE_STMT(AF_MOMENT_M11);
+        CASE_STMT(AF_MOMENT_FIRST_ORDER);
+    }
+#undef CASE_STMT
+    return retVal;
 }
 
-size_t deterministicHash(span<const common::Source> list) {
-    // Combine the different source codes, via their hashes
-    size_t hash = FNV1A_BASE_OFFSET;
-    for (auto s : list) {
-        size_t h = s.hash ? s.hash : deterministicHash(s.ptr, s.length);
-        hash     = deterministicHash(&h, sizeof(size_t), hash);
+template<>
+string toString(af_match_type p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_SAD);
+        CASE_STMT(AF_ZSAD);
+        CASE_STMT(AF_LSAD);
+        CASE_STMT(AF_SSD);
+        CASE_STMT(AF_ZSSD);
+        CASE_STMT(AF_LSSD);
+        CASE_STMT(AF_NCC);
+        CASE_STMT(AF_ZNCC);
+        CASE_STMT(AF_SHD);
     }
-    return hash;
+#undef CASE_STMT
+    return retVal;
 }
+
+template<>
+string toString(af_flux_function p) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (p) {
+        CASE_STMT(AF_FLUX_QUADRATIC);
+        CASE_STMT(AF_FLUX_EXPONENTIAL);
+        CASE_STMT(AF_FLUX_DEFAULT);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(AF_BATCH_KIND val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(AF_BATCH_NONE);
+        CASE_STMT(AF_BATCH_LHS);
+        CASE_STMT(AF_BATCH_RHS);
+        CASE_STMT(AF_BATCH_SAME);
+        CASE_STMT(AF_BATCH_DIFF);
+        CASE_STMT(AF_BATCH_UNSUPPORTED);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+template<>
+string toString(af_homography_type val) {
+    const char* retVal = NULL;
+#define CASE_STMT(v) \
+    case v: retVal = #v; break
+    switch (val) {
+        CASE_STMT(AF_HOMOGRAPHY_RANSAC);
+        CASE_STMT(AF_HOMOGRAPHY_LMEDS);
+    }
+#undef CASE_STMT
+    return retVal;
+}
+
+}  // namespace common
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index fb6c195af6..896223e140 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -10,21 +10,12 @@
 /// This file contains platform independent utility functions
 #pragma once
 
+#include <optypes.hpp>
 #include <af/defines.h>
 
-#include <nonstd/span.hpp>
-#include <iosfwd>
 #include <string>
-#include <vector>
 
 namespace common {
-struct Source {
-    const char* ptr;           // Pointer to the kernel source
-    const std::size_t length;  // Length of the kernel source
-    const std::size_t hash;    // hash value for the source *ptr;
-};
-}  // namespace common
-
 /// The environment variable that determines where the runtime kernels
 /// will be stored on the file system
 constexpr const char* JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME =
@@ -62,25 +53,9 @@ std::string makeTempFilename();
 
 const char* getName(af_dtype type);
 
-/// Return the FNV-1a hash of the provided bata.
-///
-/// \param[in] data Binary data to hash
-/// \param[in] byteSize Size of the data in bytes
-/// \param[in] optional prevHash Hash of previous parts when string is split
-///
-/// \returns An unsigned integer representing the hash of the data
-constexpr std::size_t FNV1A_BASE_OFFSET = 0x811C9DC5;
-constexpr std::size_t FNV1A_PRIME       = 0x01000193;
-std::size_t deterministicHash(const void* data, std::size_t byteSize,
-                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
-
-// This is just a wrapper around the above function.
-std::size_t deterministicHash(const std::string& data,
-                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
-
-// This concatenates strings in the vector and computes hash
-std::size_t deterministicHash(nonstd::span<const std::string> list,
-                              const std::size_t prevHash = FNV1A_BASE_OFFSET);
-
-// This concatenates hashes of multiple sources
-std::size_t deterministicHash(nonstd::span<const common::Source> list);
+std::string getOpEnumStr(af_op_t val);
+
+template<typename T>
+std::string toString(T value);
+
+}  // namespace common
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 3f83956b91..5bb28a41ec 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -21,6 +21,8 @@
 #include <sstream>
 #include <string>
 
+using common::getEnvVar;
+using common::ltrim;
 using common::memory::MemoryManagerBase;
 using std::endl;
 using std::ostringstream;
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 2a0db9d638..97142f4f1a 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -56,7 +56,7 @@ class queue {
     queue()
         : count(0)
         , sync_calls(__SYNCHRONOUS_ARCH == 1 ||
-                     getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
+                     common::getEnvVar("AF_SYNCHRONOUS_CALLS") == "1") {}
 
     template<typename F, typename... Args>
     void enqueue(const F func, Args &&...args) {
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index ee10077477..3f5bd17d84 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -12,6 +12,7 @@
 
 #include <Module.hpp>
 #include <common/Logger.hpp>
+#include <common/deterministicHash.hpp>
 #include <common/internal_enums.hpp>
 #include <common/util.hpp>
 #include <device_manager.hpp>
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index b76b0c65fe..4a2f3e792c 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <tuple>
 
+using common::int_version_to_string;
 using common::Version;
 using std::make_tuple;
 using std::string;
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 221534f6dc..f556d08cce 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -46,6 +46,8 @@
 #include <thread>
 #include <utility>
 
+using common::getEnvVar;
+using common::int_version_to_string;
 using std::begin;
 using std::end;
 using std::find;
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 37ff605cb4..02cf3c367d 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <Kernel.hpp>
+#include <common/deterministicHash.hpp>
 #include <common/half.hpp>
 #include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
@@ -34,6 +35,7 @@
 #include <vector>
 
 using common::findModule;
+using common::getEnvVar;
 using common::getFuncName;
 using common::half;
 using common::ModdimNode;
@@ -42,6 +44,7 @@ using common::Node_ids;
 using common::Node_map_t;
 using common::Node_ptr;
 using common::NodeIterator;
+using common::saveKernel;
 
 using std::array;
 using std::equal;
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index fa412101f0..13d10564bf 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -60,6 +60,8 @@ using std::to_string;
 using std::unique_ptr;
 using std::vector;
 
+using common::getEnvVar;
+using common::int_version_to_string;
 using common::unique_handle;
 using common::memory::MemoryManagerBase;
 using cuda::Allocator;
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 4a85ce292e..83d66eb740 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -13,6 +13,7 @@
 #include <cl2hpp.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
+#include <common/deterministicHash.hpp>
 #include <common/util.hpp>
 #include <debug_opencl.hpp>
 #include <err_opencl.hpp>
@@ -31,6 +32,7 @@
 
 using cl::Error;
 using cl::Program;
+using common::getEnvVar;
 using common::loggerFactory;
 using fmt::format;
 using nonstd::span;
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 6452ee590e..0a543f4297 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -44,6 +44,7 @@ using cl::CommandQueue;
 using cl::Context;
 using cl::Device;
 using cl::Platform;
+using common::getEnvVar;
 using std::begin;
 using std::end;
 using std::find;
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index d475f32b71..9a49c8c5f7 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 #include <common/compile_module.hpp>
+#include <common/deterministicHash.hpp>
 #include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
 #include <common/jit/NodeIterator.hpp>
@@ -40,6 +41,7 @@ using common::Node_ids;
 using common::Node_map_t;
 using common::Node_ptr;
 using common::NodeIterator;
+using common::saveKernel;
 
 using cl::Kernel;
 using cl::NDRange;
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 0f0f19764b..6bcc2e55ae 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -66,6 +66,8 @@ using std::to_string;
 using std::unique_ptr;
 using std::vector;
 
+using common::getEnvVar;
+using common::ltrim;
 using common::memory::MemoryManagerBase;
 using opencl::Allocator;
 using opencl::AllocatorPinned;
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index a7d255a987..aba15fe693 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -10,6 +10,7 @@
 #include <types.hpp>
 
 #include <common/half.hpp>
+#include <common/util.hpp>
 #include <type_util.hpp>
 
 #include <cmath>
@@ -17,35 +18,39 @@
 #include <string>
 
 using common::half;
+using common::toString;
+
+using std::isinf;
+using std::stringstream;
 
 namespace opencl {
 
 template<typename T>
 inline std::string ToNumStr<T>::operator()(T val) {
     ToNum<T> toNum;
-    return std::to_string(toNum(val));
+    return toString(toNum(val));
 }
 
 template<>
 std::string ToNumStr<float>::operator()(float val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (std::isinf(val)) { return val < 0.f ? NINF : PINF; }
-    return std::to_string(val);
+    if (isinf(val)) { return val < 0.f ? NINF : PINF; }
+    return toString(val);
 }
 
 template<>
 std::string ToNumStr<double>::operator()(double val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (std::isinf(val)) { return val < 0. ? NINF : PINF; }
-    return std::to_string(val);
+    if (isinf(val)) { return val < 0. ? NINF : PINF; }
+    return toString(val);
 }
 
 template<>
 std::string ToNumStr<cfloat>::operator()(cfloat val) {
     ToNumStr<float> realStr;
-    std::stringstream s;
+    stringstream s;
     s << "{" << realStr(val.s[0]) << "," << realStr(val.s[1]) << "}";
     return s.str();
 }
@@ -53,7 +58,7 @@ std::string ToNumStr<cfloat>::operator()(cfloat val) {
 template<>
 std::string ToNumStr<cdouble>::operator()(cdouble val) {
     ToNumStr<double> realStr;
-    std::stringstream s;
+    stringstream s;
     s << "{" << realStr(val.s[0]) << "," << realStr(val.s[1]) << "}";
     return s.str();
 }
@@ -64,8 +69,8 @@ std::string ToNumStr<half>::operator()(half val) {
     using namespace common;
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (common::isinf(val)) { return val < 0.f ? NINF : PINF; }
-    return common::to_string(val);
+    if (isinf(val)) { return val < 0.f ? NINF : PINF; }
+    return toString(val);
 }
 
 template<>
@@ -73,8 +78,8 @@ template<>
 std::string ToNumStr<half>::operator()<float>(float val) {
     static const char *PINF = "+INFINITY";
     static const char *NINF = "-INFINITY";
-    if (common::isinf(half(val))) { return val < 0.f ? NINF : PINF; }
-    return std::to_string(val);
+    if (isinf(half(val))) { return val < 0.f ? NINF : PINF; }
+    return toString(val);
 }
 
 #define INSTANTIATE(TYPE) template struct ToNumStr<TYPE>

From a4bb0a5f19bc882c8fb952f7c8c5289cbde0a8cb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 19 Nov 2022 09:57:47 -0500
Subject: [PATCH 2341/2677] Add compilers to GitHub actions matrix. Update
 Ubuntu versions

---
 .github/workflows/unix_cpu_build.yml | 28 +++++++++++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 114799bbca..6085718c1d 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -20,11 +20,16 @@ jobs:
             matrix:
                 blas_backend: [Atlas, MKL, OpenBLAS]
                 os: [ubuntu-18.04, ubuntu-20.04, macos-latest]
+                compiler: [gcc, clang, icx]
                 exclude:
                     - os: macos-latest
                       blas_backend: Atlas
                     - os: macos-latest
                       blas_backend: MKL
+                    - blas_backend: Atlas
+                      compiler: icx
+                    - blas_backend: OpenBLAS
+                      compiler: icx
         steps:
             - name: Checkout Repository
               uses: actions/checkout@master
@@ -43,6 +48,7 @@ jobs:
               if: matrix.os != 'macos-latest'
               env:
                   OS_NAME: ${{ matrix.os }}
+                  CC: ${{ matrix.compiler }}
               run: |
                   cmake_suffix=$(if [ $OS_NAME == 'macos-latest' ]; then echo "Darwin-x86_64"; else echo "Linux-x86_64"; fi)
                   cmake_url=$(echo "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VER}/cmake-${CMAKE_VER}-${cmake_suffix}.tar.gz")
@@ -54,6 +60,17 @@ jobs:
                   cmake_osx_dir=$(echo "${cmake_install_dir}/CMake.app/Contents/bin")
                   cmake_dir=$(if [ $OS_NAME == 'macos-latest' ]; then echo "${cmake_osx_dir}"; else echo "${cmake_lnx_dir}"; fi)
                   echo "CMAKE_PROGRAM=$(pwd)/${cmake_dir}/cmake" >> $GITHUB_ENV
+                  case "$CC" in
+                    'gcc')
+                        echo "CXX=g++" >> $GITHUB_ENV
+                        ;;
+                    'clang')
+                        echo "CXX=clang++" >> $GITHUB_ENV
+                        ;;
+                    'icx')
+                        echo "CXX=icpx" >> $GITHUB_ENV
+                        ;;
+                  esac
 
             - name: Install Dependencies for Macos
               if: matrix.os == 'macos-latest'
@@ -62,7 +79,7 @@ jobs:
                   echo "CMAKE_PROGRAM=cmake" >> $GITHUB_ENV
 
             - name: Install Common Dependencies for Ubuntu
-              if: matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-18.04'
+              if: matrix.os == 'ubuntu-18.04' || matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-22.04'
               run: |
                   sudo add-apt-repository ppa:mhier/libboost-latest
                   sudo apt-get -qq update
@@ -78,12 +95,15 @@ jobs:
 
             - name: Install MKL for Ubuntu
               if: matrix.os != 'macos-latest' && matrix.blas_backend == 'MKL'
+              env:
+                  CC: ${{ matrix.compiler }}
               run: |
                   wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
                   sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
                   sudo sh -c 'echo deb https://apt.repos.intel.com/oneapi all main > /etc/apt/sources.list.d/oneAPI.list'
                   sudo apt-get -qq update
                   sudo apt-get install -y intel-oneapi-mkl-devel
+                  if [ "$CC" == 'icx' ]; then sudo apt-get install -y intel-oneapi-compiler-dpcpp-cpp; fi
                   echo "MKLROOT=/opt/intel/oneapi/mkl/latest" >> ${GITHUB_ENV}
 
             - name: Install OpenBLAS for Ubuntu
@@ -94,6 +114,8 @@ jobs:
               env:
                   USE_MKL: ${{ matrix.blas_backend == 'MKL' }}
                   BLAS_BACKEND: ${{ matrix.blas_backend }}
+                  CC: ${{ matrix.compiler }}
+                  OS_NAME: ${{ matrix.os }}
               run: |
                   ref=$(echo ${GITHUB_REF} | awk '/refs\/pull\/[0-9]+\/merge/{print $0}')
                   prnum=$(echo $ref | awk '{split($0, a, "/"); print a[3]}')
@@ -103,6 +125,7 @@ jobs:
                   backend=$(if [ "$USE_MKL" == 1 ]; then echo "Intel-MKL"; else echo "FFTW/LAPACK/BLAS"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
                   cmake_rpath=$(if [ $OS_NAME == 'macos-latest' ]; then echo "-DCMAKE_INSTALL_RPATH=/opt/arrayfire/lib"; fi)
+                  if [ "$CC" == 'icx' ]; then source /opt/intel/oneapi/setvars.sh intel64; fi
                   mkdir build && cd build && unset VCPKG_ROOT
                   ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
@@ -115,6 +138,9 @@ jobs:
                   echo "CTEST_DASHBOARD=${dashboard}" >> $GITHUB_ENV
 
             - name: Build and Test
+              env:
+                  CC: ${{ matrix.compiler }}
               run: |
                   cd ${GITHUB_WORKSPACE}/build
+                  if [ "$CC" == 'icx' ]; then source /opt/intel/oneapi/setvars.sh intel64; fi
                   ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2

From 45b6a3f585e92190802f4d360d75c0a12caf4782 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Nov 2022 19:39:46 -0500
Subject: [PATCH 2342/2677] Add support for fast math compiler flags when
 building ArrayFire

---
 CMakeLists.txt                   |  1 +
 CMakeModules/InternalUtils.cmake | 22 +++++++++++++++++++++-
 test/CMakeLists.txt              |  6 ++++++
 test/approx1.cpp                 |  6 ++++++
 test/half.cpp                    |  2 ++
 test/imageio.cpp                 |  6 +++---
 test/ireduce.cpp                 |  4 ++++
 test/meanvar.cpp                 |  4 ++--
 test/median.cpp                  |  5 +++--
 test/reduce.cpp                  |  9 +++++++++
 test/replace.cpp                 |  1 +
 test/select.cpp                  |  1 +
 test/testHelpers.hpp             | 15 ++++++++++++---
 test/threading.cpp               |  2 +-
 14 files changed, 72 insertions(+), 12 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 099e9a72ae..2424d9162f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -92,6 +92,7 @@ option(AF_WITH_STATIC_MKL "Link against static Intel MKL libraries" OFF)
 option(AF_WITH_STATIC_CUDA_NUMERIC_LIBS "Link libafcuda with static numeric libraries(cublas, cufft, etc.)" OFF)
 option(AF_WITH_SPDLOG_HEADER_ONLY "Build ArrayFire with header only version of spdlog" OFF)
 option(AF_WITH_FMT_HEADER_ONLY "Build ArrayFire with header only version of fmt" OFF)
+option(AF_WITH_FAST_MATH "Use lower precision but high performance numeric optimizations" OFF)
 
 if(AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   option(AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS "Prune CUDA static libraries to reduce binary size.(WARNING: May break some libs on older CUDA toolkits for some compute arch)" OFF)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index dde0756aaa..f5bb077e57 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -25,6 +25,13 @@ if(WIN32)
   check_cxx_compiler_flag(/permissive- cxx_compliance)
 endif()
 
+check_cxx_compiler_flag(-ffast-math has_cxx_fast_math)
+check_cxx_compiler_flag("-fp-model fast" has_cxx_fp_model)
+check_cxx_compiler_flag(-fno-errno-math has_cxx_no_errno_math)
+check_cxx_compiler_flag(-fno-trapping-math  has_cxx_no_trapping_math)
+check_cxx_compiler_flag(-fno-signed-zeros  has_cxx_no_signed_zeros)
+check_cxx_compiler_flag(-mno-ieee-fp has_cxx_no_ieee_fp)
+
 function(arrayfire_set_default_cxx_flags target)
   target_compile_options(${target}
     PRIVATE
@@ -51,7 +58,19 @@ function(arrayfire_set_default_cxx_flags target)
               # ignored attribute warnings in the OpenCL
               # headers
               $<$<BOOL:${has_ignored_attributes_flag}>:-Wno-ignored-attributes>
-              $<$<BOOL:${has_all_warnings_flag}>:-Wall>>
+              $<$<BOOL:${has_all_warnings_flag}>:-Wall>
+
+              $<$<BOOL:${AF_WITH_FAST_MATH}>:
+                  $<$<BOOL:${has_cxx_fast_math}>:-ffast-math>
+                  $<$<BOOL:${has_cxx_no_errno_math}>:-fno-errno-math>
+                  $<$<BOOL:${has_cxx_no_trapping_math}>:-fno-trapping-math>
+                  $<$<BOOL:${has_cxx_no_signed_zeros}>:-fno-signed-zeros>
+                  $<$<BOOL:${has_cxx_no_ieee_fp}>:-mno-ieee-fp>
+                  >
+
+              $<$<NOT:$<BOOL:${AF_WITH_FAST_MATH}>>:
+                    $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>>
+                      >
     )
 
   target_compile_definitions(${target}
@@ -65,6 +84,7 @@ function(arrayfire_set_default_cxx_flags target)
 
       $<$<BOOL:${AF_WITH_LOGGING}>:           AF_WITH_LOGGING>
       $<$<BOOL:${AF_CACHE_KERNELS_TO_DISK}>:  AF_CACHE_KERNELS_TO_DISK>
+      $<$<BOOL:${AF_WITH_FAST_MATH}>:         AF_WITH_FAST_MATH>
   )
 endfunction()
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 7fcc708d32..8492ca574c 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -124,6 +124,10 @@ target_include_directories(arrayfire_test
 # The tautological-constant-compare warning is always thrown for std::nan
 # and std::info calls. Its unnecessarily verbose.
 target_compile_options(arrayfire_test
+  PUBLIC
+    # Intel compilers use fast math by default and ignore special floating point
+    # values like NaN and Infs.
+    $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>
   PRIVATE
     $<$<BOOL:${has_tautological_constant_compare_flag}>:-Wno-tautological-constant-compare>
     $<$<CXX_COMPILER_ID:MSVC>: /bigobj
@@ -137,6 +141,8 @@ if(WIN32)
 endif()
 
 target_compile_definitions(arrayfire_test
+  PUBLIC
+    $<$<BOOL:${AF_WITH_FAST_MATH}>:AF_WITH_FAST_MATH>
   PRIVATE
     TEST_RESULT_IMAGE_DIR="${CMAKE_BINARY_DIR}/test/"
     USE_MTX)
diff --git a/test/approx1.cpp b/test/approx1.cpp
index ed7bf83066..143f66bd71 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -777,6 +777,9 @@ TEST(Approx1, CPPUniformInvalidStepSize) {
 // specified by the user, ArrayFire will assume a regular grid with a
 // starting index of 0 and a step value of 1.
 TEST(Approx1, CPPInfCheck) {
+#ifdef __INTEL_LLVM_COMPILER
+    SKIP_IF_FAST_MATH_ENABLED();
+#endif
     array sampled(seq(0.0, 5.0, 0.5));
     sampled(0) = af::Inf;
     seq xo(0.0, 2.0, 0.25);
@@ -799,6 +802,9 @@ TEST(Approx1, CPPInfCheck) {
 }
 
 TEST(Approx1, CPPUniformInfCheck) {
+#ifdef __INTEL_LLVM_COMPILER
+    SKIP_IF_FAST_MATH_ENABLED();
+#endif
     array sampled(seq(10.0, 50.0, 10.0));
     sampled(0) = af::Inf;
     seq xo(0.0, 8.0, 2.0);
diff --git a/test/half.cpp b/test/half.cpp
index 18fcdb4077..33ae4eae4a 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -87,6 +87,7 @@ TEST(Half, arith) {
 
 TEST(Half, isInf) {
     SUPPORTED_TYPE_CHECK(af_half);
+    SKIP_IF_FAST_MATH_ENABLED();
     half_float::half hinf = std::numeric_limits<half_float::half>::infinity();
 
     vector<half_float::half> input(2, half_float::half(0));
@@ -105,6 +106,7 @@ TEST(Half, isInf) {
 
 TEST(Half, isNan) {
     SUPPORTED_TYPE_CHECK(af_half);
+    SKIP_IF_FAST_MATH_ENABLED();
     half_float::half hnan = std::numeric_limits<half_float::half>::quiet_NaN();
 
     vector<half_float::half> input(2, half_float::half(0));
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 6d3de9f45b..4869e50e15 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -289,7 +289,7 @@ TEST(ImageIO, SaveImage16CPP) {
     dim4 dims(16, 24, 3);
 
     array input     = randu(dims, u16);
-    array input_255 = (input / 257).as(u16);
+    array input_255 = floor(input.as(f32) / 257);
 
     std::string testname  = getTestName() + "_" + getBackendName();
     std::string imagename = "saveImage16CPP_" + testname + ".png";
@@ -297,9 +297,9 @@ TEST(ImageIO, SaveImage16CPP) {
     saveImage(imagename.c_str(), input);
 
     array img = loadImage(imagename.c_str(), true);
-    ASSERT_EQ(img.type(), f32);  // loadImage should always return float
 
-    ASSERT_FALSE(anyTrue<bool>(abs(img - input_255)));
+    ASSERT_EQ(img.type(), f32);  // loadImage should always return float
+    ASSERT_IMAGES_NEAR(input_255, img, 0.001);
 }
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 1e55b9ac23..2ebd951d46 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -192,6 +192,7 @@ TEST(IndexedReduce, MaxReduceDimensionHasSingleValue) {
 }
 
 TEST(IndexedReduce, MinNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     float test_data[] = {1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f};
     int rows          = 4;
     int cols          = 2;
@@ -218,6 +219,7 @@ TEST(IndexedReduce, MinNaN) {
 }
 
 TEST(IndexedReduce, MaxNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     float test_data[] = {1.f, NAN, 5.f, 0.1f, NAN, -0.5f, NAN, 0.f};
     int rows          = 4;
     int cols          = 2;
@@ -244,6 +246,7 @@ TEST(IndexedReduce, MaxNaN) {
 }
 
 TEST(IndexedReduce, MinCplxNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
                               NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
@@ -279,6 +282,7 @@ TEST(IndexedReduce, MinCplxNaN) {
 }
 
 TEST(IndexedReduce, MaxCplxNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
                               NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 81f3fb8099..bd79c4015a 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -131,7 +131,7 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T>> {
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.5f);
         } else if (is_same_type<float, outType<T>>::value ||
                    is_same_type<cfloat, outType<T>>::value) {
-            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.0016f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
         } else {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.00001f);
@@ -171,7 +171,7 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T>> {
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.5f);
         } else if (is_same_type<float, outType<T>>::value ||
                    is_same_type<cfloat, outType<T>>::value) {
-            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.001f);
+            ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.0016f);
             ASSERT_VEC_ARRAY_NEAR(test.variance_, outDim, var, 0.2f);
         } else {
             ASSERT_VEC_ARRAY_NEAR(test.mean_, outDim, mean, 0.00001f);
diff --git a/test/median.cpp b/test/median.cpp
index 332dbe8d70..c55251e66c 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -93,20 +93,21 @@ void median_test(int nx, int ny = 1, int nz = 1, int nw = 1) {
 
     if (sa.dims(dim) % 2 == 1) {
         mSeq[dim] = mSeq[dim] - 1.0;
+        sa        = sa.as((af_dtype)dtype_traits<To>::af_type);
         verify    = sa(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     } else {
         dim_t sdim[4] = {0};
         sdim[dim]     = 1;
         sa            = sa.as((af_dtype)dtype_traits<To>::af_type);
         array sas     = shift(sa, sdim[0], sdim[1], sdim[2], sdim[3]);
-        verify        = ((sa + sas) / 2)(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
+        verify = ((sa + sas) / To(2))(mSeq[0], mSeq[1], mSeq[2], mSeq[3]);
     }
 
     // Test Part
     array out = median(a, dim);
 
     ASSERT_EQ(out.dims() == verify.dims(), true);
-    ASSERT_NEAR(0, sum<double>(abs(out - verify)), 1e-5);
+    ASSERT_ARRAYS_EQ(verify, out);
 }
 
 #define MEDIAN_FLAT(To, Ti)                                                    \
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 31845b8d0c..5afdf70648 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -779,6 +779,7 @@ TEST(ReduceByKey, countReduceByKey) {
 }
 
 TEST(ReduceByKey, ReduceByKeyNans) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const static int testSz      = 8;
     const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
     const float testVals[testSz] = {0, 7, NAN, 6, 2, 5, 3, 4};
@@ -1072,6 +1073,7 @@ TYPED_TEST(Reduce, Test_Any_Global) {
 }
 
 TEST(MinMax, MinMaxNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const int num      = 10000;
     array A            = randu(num);
     A(where(A < 0.25)) = NaN;
@@ -1095,6 +1097,7 @@ TEST(MinMax, MinMaxNaN) {
 }
 
 TEST(MinMax, MinCplxNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     float real_wnan_data[] = {0.005f, NAN, -6.3f, NAN,      -0.5f,
                               NAN,    NAN, 0.2f,  -1205.4f, 8.9f};
 
@@ -1122,6 +1125,7 @@ TEST(MinMax, MinCplxNaN) {
 }
 
 TEST(MinMax, MaxCplxNaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     // 4th element is unusually large to cover the case where
     //  one part holds the largest value among the array,
     //  and the other part is NaN.
@@ -1158,6 +1162,7 @@ TEST(MinMax, MaxCplxNaN) {
 }
 
 TEST(Count, NaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const int num = 10000;
     array A       = round(5 * randu(num));
     array B       = A;
@@ -1168,6 +1173,7 @@ TEST(Count, NaN) {
 }
 
 TEST(Sum, NaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const int num      = 10000;
     array A            = randu(num);
     A(where(A < 0.25)) = NaN;
@@ -1187,6 +1193,7 @@ TEST(Sum, NaN) {
 }
 
 TEST(Product, NaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const int num = 5;
     array A       = randu(num);
     A(2)          = NaN;
@@ -1206,6 +1213,7 @@ TEST(Product, NaN) {
 }
 
 TEST(AnyAll, NaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const int num = 10000;
     array A       = (randu(num) > 0.5).as(f32);
     array B       = A;
@@ -2263,6 +2271,7 @@ TYPED_TEST(Reduce, Test_Any_Global_Array) {
 
 
 TEST(Reduce, Test_Sum_Global_Array_nanval) {
+    SKIP_IF_FAST_MATH_ENABLED();
     const int num = 100000;
     array a = af::randn(num, 2, 34, 4);
     a(1, 0, 0, 0) = NAN;
diff --git a/test/replace.cpp b/test/replace.cpp
index 1d0a758489..14e679436b 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -113,6 +113,7 @@ TYPED_TEST(Replace, Simple) { replaceTest<TypeParam>(dim4(1024, 1024)); }
 TYPED_TEST(Replace, Scalar) { replaceScalarTest<TypeParam>(dim4(5, 5)); }
 
 TEST(Replace, NaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     dim4 dims(1000, 1250);
     dtype ty = f32;
 
diff --git a/test/select.cpp b/test/select.cpp
index a147bb3039..0b6724d8fa 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -130,6 +130,7 @@ TYPED_TEST(Select, LeftScalar) {
 }
 
 TEST(Select, NaN) {
+    SKIP_IF_FAST_MATH_ENABLED();
     dim4 dims(1000, 1250);
     dtype ty = f32;
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index faf7162a3b..69240883ac 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -223,9 +223,18 @@ bool noDoubleTests(af::dtype ty);
 
 bool noHalfTests(af::dtype ty);
 
-#define SUPPORTED_TYPE_CHECK(type)                                        \
-    if (noDoubleTests((af_dtype)af::dtype_traits<type>::af_type)) return; \
-    if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type)) return;
+#define SUPPORTED_TYPE_CHECK(type)                                \
+    if (noDoubleTests((af_dtype)af::dtype_traits<type>::af_type)) \
+        GTEST_SKIP() << "Device doesn't support Doubles";         \
+    if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type))   \
+        GTEST_SKIP() << "Device doesn't support Half";
+
+#ifdef AF_WITH_FAST_MATH
+#define SKIP_IF_FAST_MATH_ENABLED() \
+    GTEST_SKIP() << "ArrayFire compiled with AF_WITH_FAST_MATH"
+#else
+#define SKIP_IF_FAST_MATH_ENABLED()
+#endif
 
 bool noImageIOTests();
 
diff --git a/test/threading.cpp b/test/threading.cpp
index f26047ce95..96dd894e4f 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -53,7 +53,7 @@ void calc(ArithOp opcode, array op1, array op2, float outValue,
     vector<float> out(res.elements());
     res.host((void*)out.data());
 
-    for (unsigned i = 0; i < out.size(); ++i) ASSERT_EQ(out[i], outValue);
+    for (unsigned i = 0; i < out.size(); ++i) ASSERT_FLOAT_EQ(out[i], outValue);
     af::sync();
 }
 

From dd6ac75471e27e776b8820b5846b40ba06f817d3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Nov 2022 19:40:13 -0500
Subject: [PATCH 2343/2677] Convert vector to array in addInterpEnumOptions.
 Fix clang warnings

---
 CMakeModules/InternalUtils.cmake             |  5 +++++
 examples/benchmarks/pi.cpp                   |  4 ++--
 src/api/c/blas.cpp                           |  8 ++++----
 src/backend/common/TemplateArg.hpp           |  2 +-
 src/backend/cuda/jit.cpp                     |  2 ++
 src/backend/cuda/join.cpp                    |  2 +-
 src/backend/cuda/kernel/memcopy.hpp          |  8 ++++----
 src/backend/oneapi/device_manager.cpp        |  2 ++
 src/backend/oneapi/kernel/mean.hpp           | 20 ++++++++++----------
 src/backend/oneapi/kernel/reduce_all.hpp     |  6 +++---
 src/backend/oneapi/kernel/reduce_dim.hpp     |  2 +-
 src/backend/oneapi/kernel/reduce_first.hpp   |  2 +-
 src/backend/oneapi/kernel/where.hpp          |  6 +++---
 src/backend/oneapi/platform.cpp              |  4 +++-
 src/backend/opencl/jit.cpp                   |  2 ++
 src/backend/opencl/jit/kernel_generators.hpp |  5 +++--
 src/backend/opencl/join.cpp                  |  2 +-
 src/backend/opencl/kernel/homography.hpp     |  2 +-
 src/backend/opencl/kernel/interp.hpp         |  4 ++--
 src/backend/opencl/kernel/memcopy.hpp        |  7 ++++---
 test/CMakeLists.txt                          | 18 +++---------------
 test/arrayfire_test.cpp                      |  4 ++--
 22 files changed, 60 insertions(+), 57 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index f5bb077e57..c698e3d290 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -31,6 +31,8 @@ check_cxx_compiler_flag(-fno-errno-math has_cxx_no_errno_math)
 check_cxx_compiler_flag(-fno-trapping-math  has_cxx_no_trapping_math)
 check_cxx_compiler_flag(-fno-signed-zeros  has_cxx_no_signed_zeros)
 check_cxx_compiler_flag(-mno-ieee-fp has_cxx_no_ieee_fp)
+check_cxx_compiler_flag(-Wno-unqualified-std-cast-call has_cxx_unqualified_std_cast_call)
+check_cxx_compiler_flag(-Werror=reorder-ctor has_cxx_error_reorder_ctor)
 
 function(arrayfire_set_default_cxx_flags target)
   target_compile_options(${target}
@@ -46,6 +48,7 @@ function(arrayfire_set_default_cxx_flags target)
                                           /wd4668
                                           /wd4710
                                           /wd4505
+                                          /we5038
                                           /bigobj
                                           /EHsc
                                           # MSVC incorrectly sets the cplusplus to 199711L even if the compiler supports
@@ -59,6 +62,8 @@ function(arrayfire_set_default_cxx_flags target)
               # headers
               $<$<BOOL:${has_ignored_attributes_flag}>:-Wno-ignored-attributes>
               $<$<BOOL:${has_all_warnings_flag}>:-Wall>
+              $<$<BOOL:${has_cxx_unqualified_std_cast_call}>:-Wno-unqualified-std-cast-call>
+              $<$<BOOL:${has_cxx_error_reorder_ctor}>:-Werror=reorder-ctor>
 
               $<$<BOOL:${AF_WITH_FAST_MATH}>:
                   $<$<BOOL:${has_cxx_fast_math}>:-ffast-math>
diff --git a/examples/benchmarks/pi.cpp b/examples/benchmarks/pi.cpp
index 8913f36bc1..d4a550b78a 100644
--- a/examples/benchmarks/pi.cpp
+++ b/examples/benchmarks/pi.cpp
@@ -35,8 +35,8 @@ static double pi_device() {
 static double pi_host() {
     int count = 0;
     for (int i = 0; i < samples; ++i) {
-        float x = float(rand()) / RAND_MAX;
-        float y = float(rand()) / RAND_MAX;
+        float x = float(rand()) / float(RAND_MAX);
+        float y = float(rand()) / float(RAND_MAX);
         if (sqrt(x * x + y * y) < 1) count++;
     }
     return 4.0 * count / samples;
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index d34d55fd4a..0afd4f79b2 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -254,8 +254,8 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                 break;
             }
             case c32: {
-                cfloat alpha = {1.f, 0.f};
-                cfloat beta  = {0.f, 0.f};
+                cfloat alpha{1.f, 0.f};
+                cfloat beta{0.f, 0.f};
 
                 AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
                                  &beta));
@@ -269,8 +269,8 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                 break;
             }
             case c64: {
-                cdouble alpha = {1.0, 0.0};
-                cdouble beta  = {0.0, 0.0};
+                cdouble alpha{1.0, 0.0};
+                cdouble beta{0.0, 0.0};
                 AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
                                  &beta));
                 break;
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
index 3a92bf643e..a26df012ca 100644
--- a/src/backend/common/TemplateArg.hpp
+++ b/src/backend/common/TemplateArg.hpp
@@ -16,7 +16,7 @@
 #include <utility>
 
 template<typename T>
-class TemplateTypename;
+struct TemplateTypename;
 
 struct TemplateArg {
     std::string _tparam;
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 02cf3c367d..4dab53a877 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -334,6 +334,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     assert(outputs.size() == output_nodes.size());
     dim_t* outDims{outputs[0].dims};
     dim_t* outStrides{outputs[0].strides};
+#ifndef NDEBUG
     for_each(
         begin(outputs)++, end(outputs),
         [outDims, outStrides](Param<T>& output) {
@@ -341,6 +342,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                    equal(output.strides, output.strides + AF_MAX_DIMS,
                          outStrides));
         });
+#endif
 
     dim_t ndims{outDims[3] > 1   ? 4
                 : outDims[2] > 1 ? 3
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index a605867863..7f65773d0a 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -53,7 +53,7 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     //              will be called twice
     if (fdims.dims[jdim] == sdims.dims[jdim]) {
         const size_t L2CacheSize{getL2CacheSize(getActiveDeviceId())};
-        if (!(first.isReady() | second.isReady()) ||
+        if (!(first.isReady() || second.isReady()) ||
             (fdims.elements() * sizeof(T) * 2 * 2 < L2CacheSize)) {
             // Both arrays have same size & everything fits into the cache,
             // so treat in 1 JIT kernel, iso individual copies which is
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 7a971bddb0..1592d62ec9 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -194,10 +194,10 @@ void copy(Param<outType> dst, CParam<inType> src, dim_t ondims,
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
     auto copy{common::getKernel(
-        th.loop0              ? "cuda::scaledCopyLoop0"
-        : th.loop2 | th.loop3 ? "cuda::scaledCopyLoop123"
-        : th.loop1            ? "cuda::scaledCopyLoop1"
-                              : "cuda::scaledCopy",
+        th.loop0                 ? "cuda::scaledCopyLoop0"
+        : (th.loop2 || th.loop3) ? "cuda::scaledCopyLoop123"
+        : th.loop1               ? "cuda::scaledCopyLoop1"
+                                 : "cuda::scaledCopy",
         std::array{copy_cuh_src},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
                      TemplateArg(same_dims), TemplateArg(factor != 1.0)))};
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index ed97248dcb..4588369637 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -30,10 +30,12 @@
 #include <string>
 #include <vector>
 
+using common::getEnvVar;
 using std::begin;
 using std::end;
 using std::find;
 using std::make_unique;
+using std::move;
 using std::string;
 using std::stringstream;
 using std::unique_ptr;
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 8a2e07d93c..17d2eb2164 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -74,21 +74,21 @@ class meanDimKernelSMEM {
                       local_accessor<compute_t<Tw>, 1> s_idx,
                       sycl::stream debug, bool input_weight, bool output_weight)
         : out_(out)
-        , oInfo_(oInfo)
         , owt_(owt)
-        , owInfo_(owInfo)
         , in_(in)
-        , iInfo_(iInfo)
         , iwt_(iwt)
+        , oInfo_(oInfo)
+        , owInfo_(owInfo)
+        , iInfo_(iInfo)
         , iwInfo_(iwInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , offset_dim_(offset_dim)
         , s_val_(s_val)
         , s_idx_(s_idx)
-        , debug_(debug)
         , input_weight_(input_weight)
-        , output_weight_(output_weight) {}
+        , output_weight_(output_weight)
+        , debug_(debug) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -335,12 +335,12 @@ class meanFirstKernelSMEM {
                         sycl::stream debug, bool input_weight,
                         bool output_weight)
         : out_(out)
-        , oInfo_(oInfo)
         , owt_(owt)
-        , owInfo_(owInfo)
         , in_(in)
-        , iInfo_(iInfo)
         , iwt_(iwt)
+        , oInfo_(oInfo)
+        , owInfo_(owInfo)
+        , iInfo_(iInfo)
         , iwInfo_(iwInfo)
         , DIMX_(DIMX)
         , groups_x_(groups_x)
@@ -348,9 +348,9 @@ class meanFirstKernelSMEM {
         , repeat_(repeat)
         , s_val_(s_val)
         , s_idx_(s_idx)
-        , debug_(debug)
         , input_weight_(input_weight)
-        , output_weight_(output_weight) {}
+        , output_weight_(output_weight)
+        , debug_(debug) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 8ad65d7948..1a318e8bc5 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -56,16 +56,16 @@ class reduceAllKernelSMEM {
                         local_accessor<compute_t<To>, 1> s_ptr,
                         local_accessor<bool, 1> amLast, sycl::stream debug)
         : out_(out)
-        , oInfo_(oInfo)
         , retCount_(retCount)
         , tmp_(tmp)
-        , tmpInfo_(tmpInfo)
         , in_(in)
+        , oInfo_(oInfo)
+        , tmpInfo_(tmpInfo)
         , iInfo_(iInfo)
         , DIMX_(DIMX)
+        , repeat_(repeat)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
-        , repeat_(repeat)
         , change_nan_(change_nan)
         , nanval_(nanval)
         , s_ptr_(s_ptr)
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index b5e4252651..6efb6851b1 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -49,8 +49,8 @@ class reduceDimKernelSMEM {
                         sycl::stream debug)
         : out_(out)
         , oInfo_(oInfo)
-        , in_(in)
         , iInfo_(iInfo)
+        , in_(in)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , offset_dim_(offset_dim)
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index 6bfe177148..a4094f8cb9 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -49,8 +49,8 @@ class reduceFirstKernelSMEM {
                           sycl::stream debug)
         : out_(out)
         , oInfo_(oInfo)
-        , in_(in)
         , iInfo_(iInfo)
+        , in_(in)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , repeat_(repeat)
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index cb8887fb84..4158641dce 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -38,12 +38,12 @@ class whereKernel {
                 read_accessor<T> in_acc, KParam iInfo, uint groups_x,
                 uint groups_y, uint lim, sycl::stream debug)
         : out_acc_(out_acc)
-        , oInfo_(oInfo)
         , otmp_acc_(otmp_acc)
-        , otInfo_(otInfo)
         , rtmp_acc_(rtmp_acc)
-        , rtInfo_(rtInfo)
         , in_acc_(in_acc)
+        , oInfo_(oInfo)
+        , otInfo_(otInfo)
+        , rtInfo_(rtInfo)
         , iInfo_(iInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index d32d9e8d46..c16a4afff9 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -59,6 +59,8 @@ using std::to_string;
 using std::unique_ptr;
 using std::vector;
 
+using common::getEnvVar;
+using common::ltrim;
 using common::memory::MemoryManagerBase;
 using oneapi::Allocator;
 using oneapi::AllocatorPinned;
@@ -316,7 +318,7 @@ sycl::info::device_type getDeviceType() {
 }
 
 bool isHostUnifiedMemory(const sycl::device& device) {
-    return device.get_info<sycl::info::device::host_unified_memory>();
+    return device.has(sycl::aspect::usm_host_allocations);
 }
 
 bool OneAPICPUOffload(bool forceOffloadOSX) {
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 9a49c8c5f7..dddf1ecd0d 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -295,6 +295,7 @@ void evalNodes(vector<Param>& outputs, const vector<Node*>& output_nodes) {
     KParam& out_info{outputs[0].info};
     dim_t* outDims{out_info.dims};
     dim_t* outStrides{out_info.strides};
+#ifndef NDEBUG
     for_each(begin(outputs)++, end(outputs),
              [outDims, outStrides](Param& output) {
                  assert(equal(output.info.dims, output.info.dims + AF_MAX_DIMS,
@@ -302,6 +303,7 @@ void evalNodes(vector<Param>& outputs, const vector<Node*>& output_nodes) {
                         equal(output.info.strides,
                               output.info.strides + AF_MAX_DIMS, outStrides));
              });
+#endif
 
     dim_t ndims{outDims[3] > 1   ? 4
                 : outDims[2] > 1 ? 3
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index fe87ebc21b..5c111fdedb 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -16,8 +16,9 @@ namespace opencl {
 namespace {
 
 /// Creates a string that will be used to declare the parameter of kernel
-void generateParamDeclaration(std::stringstream& kerStream, int id,
-                              bool is_linear, const std::string& m_type_str) {
+inline void generateParamDeclaration(std::stringstream& kerStream, int id,
+                                     bool is_linear,
+                                     const std::string& m_type_str) {
     if (is_linear) {
         kerStream << "__global " << m_type_str << " *in" << id
                   << ", dim_t iInfo" << id << "_offset, \n";
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 2d166b693e..7eda4fc307 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -51,7 +51,7 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     //              will be called twice
     if (fdims.dims[jdim] == sdims.dims[jdim]) {
         const size_t L2CacheSize{getL2CacheSize(opencl::getDevice())};
-        if (!(first.isReady() | second.isReady()) ||
+        if (!(first.isReady() || second.isReady()) ||
             (fdims.elements() * sizeof(T) * 2 * 2 < L2CacheSize)) {
             // Both arrays have same size & everything fits into the cache,
             // so thread in 1 JIT kernel, iso individual copies which is
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 34f1b2c7e9..2c192ef6b7 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -193,7 +193,7 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
                                      sizeof(unsigned), &inliersH);
 
         bufferFree(totalInliers.data);
-    } else if (htype == AF_HOMOGRAPHY_RANSAC) {
+    } else /* if (htype == AF_HOMOGRAPHY_RANSAC) */ {
         unsigned blockIdx;
         inliersH = kernel::ireduceAll<unsigned, af_max_t>(&blockIdx, inliers);
 
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
index 370e500322..0c3a744c42 100644
--- a/src/backend/opencl/kernel/interp.hpp
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -12,14 +12,14 @@
 #include <common/TemplateArg.hpp>
 #include <af/defines.h>
 
+#include <array>
 #include <string>
-#include <vector>
 
 namespace opencl {
 namespace kernel {
 
 static void addInterpEnumOptions(std::vector<std::string>& options) {
-    std::vector<std::string> enOpts = {
+    static std::array<std::string, 10> enOpts = {
         DefineKeyValue(AF_INTERP_NEAREST, static_cast<int>(AF_INTERP_NEAREST)),
         DefineKeyValue(AF_INTERP_LINEAR, static_cast<int>(AF_INTERP_LINEAR)),
         DefineKeyValue(AF_INTERP_BILINEAR,
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index e4091fea53..c63d1e42b3 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -47,9 +47,10 @@ typedef struct {
 //  - maximum obtained vectorization.
 //  - All the parameters are updated accordingly
 //
-static unsigned vectorizeShape(const unsigned maxVectorWidth, int dims[4],
-                               int istrides[4], int& indims, dim_t& ioffset,
-                               int ostrides[4], dim_t& ooffset) {
+static inline unsigned vectorizeShape(const unsigned maxVectorWidth,
+                                      int dims[4], int istrides[4], int& indims,
+                                      dim_t& ioffset, int ostrides[4],
+                                      dim_t& ooffset) {
     unsigned vectorWidth{1};
     if ((maxVectorWidth != 1) & (istrides[0] == 1) & (ostrides[0] == 1)) {
         // - Only adjacent items can be vectorized into a base vector type
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 8492ca574c..5177293c9f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -107,8 +107,6 @@ if(AF_BUILD_UNIFIED)
   list(APPEND enabled_backends "unified")
 endif(AF_BUILD_UNIFIED)
 
-check_cxx_compiler_flag("-Wtautological-constant-compare" has_tautological_constant_compare_flag)
-
 add_library(arrayfire_test STATIC
   testHelpers.hpp
   arrayfire_test.cpp)
@@ -127,9 +125,10 @@ target_compile_options(arrayfire_test
   PUBLIC
     # Intel compilers use fast math by default and ignore special floating point
     # values like NaN and Infs.
-    $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>
+    $<$<COMPILE_LANGUAGE:CXX>:
+      $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>
+      $<$<BOOL:${has_cxx_unqualified_std_cast_call}>:-Wno-unqualified-std-cast-call>>
   PRIVATE
-    $<$<BOOL:${has_tautological_constant_compare_flag}>:-Wno-tautological-constant-compare>
     $<$<CXX_COMPILER_ID:MSVC>: /bigobj
                                /EHsc>
   )
@@ -196,11 +195,8 @@ function(make_test)
         arrayfire_test
       )
 
-    # The tautological-constant-compare warning is always thrown for std::nan
-    # and std::info calls. Its unnecessarily verbose.
     target_compile_options(${target}
       PRIVATE
-        $<$<BOOL:${has_tautological_constant_compare_flag}>:-Wno-tautological-constant-compare>
         $<$<CXX_COMPILER_ID:MSVC>: /bigobj
                                    /EHsc>
       )
@@ -364,14 +360,6 @@ if(CUDA_FOUND)
     set(cuda_test_backends "cuda" "unified")
     if(${backend} IN_LIST cuda_test_backends)
       set(target test_cuda_${backend})
-      if(${CMAKE_VERSION} VERSION_LESS 3.5.2)
-        cuda_include_directories(
-          ${ArrayFire_SOURCE_DIR}/include
-          ${ArrayFire_BINARY_DIR}/include
-          ${ArrayFire_SOURCE_DIR}/extern/half/include
-          ${CMAKE_CURRENT_SOURCE_DIR}
-        )
-      endif()
       add_executable(${target} cuda.cu)
       target_include_directories(${target} PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index fda3d887d6..b9e73b0458 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -1119,7 +1119,6 @@ bool compareArraysRMSD(dim_t data_size, T *gold, T *data, double tolerance) {
 INSTANTIATE(float);
 INSTANTIATE(double);
 INSTANTIATE(char);
-INSTANTIATE(unsigned char);
 #undef INSTANTIATE
 
 TestOutputArrayInfo::TestOutputArrayInfo()
@@ -1368,7 +1367,8 @@ af::array cpu_randu(const af::dim4 dims) {
 
     std::vector<BT> out(elements);
     for (size_t i = 0; i < elements; i++) {
-        out[i] = isTypeFloat ? (BT)(rand()) / RAND_MAX : rand() % 100;
+        out[i] = isTypeFloat ? (BT)(rand()) / static_cast<double>(RAND_MAX)
+                             : rand() % 100;
     }
 
     return af::array(dims, (T *)&out[0]);

From 65e67404e570ff64ce3969c06c3c459d9f9aff95 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 21 Nov 2022 20:28:05 -0500
Subject: [PATCH 2344/2677] Refactor GitHub workflows

---
 .github/workflows/clang-format-lint.yml | 38 -------------------
 .github/workflows/docs_build.yml        | 44 ----------------------
 .github/workflows/unix_cpu_build.yml    | 49 +++++++++++++++++++++++++
 3 files changed, 49 insertions(+), 82 deletions(-)
 delete mode 100644 .github/workflows/clang-format-lint.yml
 delete mode 100644 .github/workflows/docs_build.yml

diff --git a/.github/workflows/clang-format-lint.yml b/.github/workflows/clang-format-lint.yml
deleted file mode 100644
index 25e79545ac..0000000000
--- a/.github/workflows/clang-format-lint.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-on:
-  push:
-    branches:
-    - master
-  pull_request:
-    branches:
-    - master
-
-name: ci
-
-jobs:
-  clang-format:
-      name: Clang Format Lint
-      runs-on: ubuntu-latest
-      steps:
-          - name: Checkout Respository
-            uses: actions/checkout@master
-
-          - name: Check Sources
-            uses: DoozyX/clang-format-lint-action@v0.14
-            with:
-              source: './src'
-              extensions: 'h,cpp,hpp'
-              clangFormatVersion: 14
-
-          - name: Check Tests
-            uses: DoozyX/clang-format-lint-action@v0.14
-            with:
-              source: './test'
-              extensions: 'h,cpp,hpp'
-              clangFormatVersion: 14
-
-          - name: Check Examples
-            uses: DoozyX/clang-format-lint-action@v0.14
-            with:
-              source: './examples'
-              extensions: 'h,cpp,hpp'
-              clangFormatVersion: 14
diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
deleted file mode 100644
index 38091d113a..0000000000
--- a/.github/workflows/docs_build.yml
+++ /dev/null
@@ -1,44 +0,0 @@
-on:
-  push:
-    branches:
-    - master
-  pull_request:
-    branches:
-    - master
-
-name: ci
-
-jobs:
-    build_documentation:
-        name: Documentation
-        runs-on: ubuntu-18.04
-        env:
-          DOXYGEN_VER: 1.8.18
-        steps:
-            - name: Checkout Repository
-              uses: actions/checkout@master
-
-            - name: Install Doxygen
-              run: |
-                  wget --quiet https://sourceforge.net/projects/doxygen/files/rel-${DOXYGEN_VER}/doxygen-${DOXYGEN_VER}.linux.bin.tar.gz
-                  mkdir doxygen
-                  tar -xf doxygen-${DOXYGEN_VER}.linux.bin.tar.gz -C doxygen --strip 1
-
-            - name: Install Boost
-              run: |
-                  sudo add-apt-repository ppa:mhier/libboost-latest
-                  sudo apt-get -qq update
-                  sudo apt-get install -y libboost1.74-dev
-
-            - name: Configure
-              run: |
-                  mkdir build && cd build && unset VCPKG_ROOT
-                  cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
-                        -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
-                        -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
-                        -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen ..
-
-            - name: Build
-              run: |
-                  cd ${GITHUB_WORKSPACE}/build
-                  cmake --build . --target docs
diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 6085718c1d..3c0e566d6f 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -9,9 +9,58 @@ on:
 name: ci
 
 jobs:
+    clang-format:
+        name: Clang Format Lint
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout Respository
+              uses: actions/checkout@master
+
+            - name: Check Sources
+              uses: DoozyX/clang-format-lint-action@v0.14
+              with:
+                source: './src ./test ./examples'
+                extensions: 'h,cpp,hpp'
+                clangFormatVersion: 14
+
+    documentation:
+        name: Documentation
+        runs-on: ubuntu-18.04
+        env:
+          DOXYGEN_VER: 1.8.18
+        steps:
+            - name: Checkout Repository
+              uses: actions/checkout@master
+
+            - name: Install Doxygen
+              run: |
+                  wget --quiet https://sourceforge.net/projects/doxygen/files/rel-${DOXYGEN_VER}/doxygen-${DOXYGEN_VER}.linux.bin.tar.gz
+                  mkdir doxygen
+                  tar -xf doxygen-${DOXYGEN_VER}.linux.bin.tar.gz -C doxygen --strip 1
+
+            - name: Install Boost
+              run: |
+                  sudo add-apt-repository ppa:mhier/libboost-latest
+                  sudo apt-get -qq update
+                  sudo apt-get install -y libboost1.74-dev
+
+            - name: Configure
+              run: |
+                  mkdir build && cd build && unset VCPKG_ROOT
+                  cmake -DAF_BUILD_CPU:BOOL=OFF -DAF_BUILD_CUDA:BOOL=OFF \
+                        -DAF_BUILD_OPENCL:BOOL=OFF -DAF_BUILD_UNIFIED:BOOL=OFF \
+                        -DAF_BUILD_EXAMPLES:BOOL=OFF -DBUILD_TESTING:BOOL=OFF \
+                        -DDOXYGEN_EXECUTABLE:FILEPATH=${GITHUB_WORKSPACE}/doxygen/bin/doxygen ..
+
+            - name: Build
+              run: |
+                  cd ${GITHUB_WORKSPACE}/build
+                  cmake --build . --target docs
+
     build_cpu:
         name: CPU
         runs-on: ${{ matrix.os }}
+        needs: [clang-format, documentation]
         env:
           NINJA_VER: 1.10.2
           CMAKE_VER: 3.10.2

From ad47660dc574f884e2f4e84473b177fb4f489d58 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 22 Nov 2022 14:29:59 -0500
Subject: [PATCH 2345/2677] Add CMake targets and exports for afoneapi

The oneAPI target was not creating CMake configuration files. This caused a
problem with the print_info target when no other backends were built because
CMake didn't include the ArrayFire include directories and libraries.
---
 CMakeLists.txt                    |  2 +-
 src/backend/oneapi/CMakeLists.txt | 13 +++++++++++++
 test/CMakeLists.txt               |  2 ++
 3 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2424d9162f..b779d929f5 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -470,7 +470,7 @@ install(DIRECTORY "${ArrayFire_SOURCE_DIR}/LICENSES/"
     DESTINATION LICENSES
     COMPONENT licenses)
 
-foreach(backend CPU CUDA OpenCL Unified)
+foreach(backend CPU CUDA OpenCL oneAPI Unified)
   string(TOUPPER ${backend} upper_backend)
   string(TOLOWER ${backend} lower_backend)
   if(AF_BUILD_${upper_backend})
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 0561573a44..4fb6f3c0a9 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -265,6 +265,19 @@ target_link_libraries(afoneapi
     -fsycl
   )
 
+af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})
+
+install(TARGETS afoneapi
+  EXPORT ArrayFireoneAPITargets
+  COMPONENT oneapi
+  PUBLIC_HEADER DESTINATION af
+  RUNTIME DESTINATION ${AF_INSTALL_BIN_DIR}
+  LIBRARY DESTINATION ${AF_INSTALL_LIB_DIR}
+  ARCHIVE DESTINATION ${AF_INSTALL_LIB_DIR}
+  FRAMEWORK DESTINATION framework
+  INCLUDES DESTINATION ${AF_INSTALL_INC_DIR}
+)
+
 source_group(include REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/include/*)
 source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5177293c9f..50fcadaf5b 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -478,6 +478,8 @@ elseif(AF_BUILD_CUDA)
   target_link_libraries(print_info ArrayFire::afcuda)
 elseif(AF_BUILD_CPU)
   target_link_libraries(print_info ArrayFire::afcpu)
+elseif(AF_BUILD_ONEAPI)
+  target_link_libraries(print_info ArrayFire::afoneapi)
 endif()
 
 make_test(SRC jit_test_api.cpp)

From 5cf3169d86a39da47446d67be54924816406ca99 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 23 Nov 2022 18:04:05 -0500
Subject: [PATCH 2346/2677] Remove reinterpret casts for conversions to void*

---
 src/api/c/device.cpp             |  5 +++--
 src/api/c/error.cpp              |  5 ++++-
 src/api/c/print.cpp              |  7 ++++---
 src/api/c/sparse.cpp             |  2 +-
 src/api/unified/error.cpp        |  9 ++++++---
 src/backend/common/ArrayInfo.cpp |  5 +++--
 src/backend/cuda/Kernel.hpp      |  2 +-
 src/backend/oneapi/Array.cpp     | 12 +++++++-----
 src/backend/opencl/api.cpp       | 14 +++++++++++++-
 9 files changed, 42 insertions(+), 19 deletions(-)

diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 57c61be4c3..b619a867f2 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -167,8 +167,9 @@ af_err af_info_string(char** str, const bool verbose) {
     UNUSED(verbose);  // TODO(umar): Add something useful
     try {
         std::string infoStr = getDeviceInfo();
-        af_alloc_host(reinterpret_cast<void**>(str),
-                      sizeof(char) * (infoStr.size() + 1));
+        void* halloc_ptr    = nullptr;
+        af_alloc_host(&halloc_ptr, sizeof(char) * (infoStr.size() + 1));
+        memcpy(str, &halloc_ptr, sizeof(void*));
 
         // Need to do a deep copy
         // str.c_str wont cut it
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 8ede0ee9c0..4dd1ff190f 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -13,6 +13,7 @@
 #include <af/util.h>
 
 #include <algorithm>
+#include <cstring>
 #include <string>
 
 void af_get_last_error(char **str, dim_t *len) {
@@ -26,7 +27,9 @@ void af_get_last_error(char **str, dim_t *len) {
         return;
     }
 
-    af_alloc_host(reinterpret_cast<void **>(str), sizeof(char) * (slen + 1));
+    void *halloc_ptr = nullptr;
+    af_alloc_host(&halloc_ptr, sizeof(char) * (slen + 1));
+    memcpy(str, &halloc_ptr, sizeof(void *));
     global_error_string.copy(*str, slen);
 
     (*str)[slen]        = '\0';
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index ef749e970f..85f30dc028 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -278,9 +278,10 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
                 default: TYPE_ERROR(1, type);
             }
         }
-        std::string str = ss.str();
-        af_alloc_host(reinterpret_cast<void **>(output),
-                      sizeof(char) * (str.size() + 1));
+        std::string str  = ss.str();
+        void *halloc_ptr = nullptr;
+        af_alloc_host(&halloc_ptr, sizeof(char) * (str.size() + 1));
+        memcpy(output, &halloc_ptr, sizeof(void *));
         str.copy(*output, str.size());
         (*output)[str.size()] = '\0';  // don't forget the terminating 0
     }
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index d1a737f488..714a0c1d15 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -31,7 +31,7 @@ using detail::sparseConvertDenseToStorage;
 const SparseArrayBase &getSparseArrayBase(const af_array in,
                                           bool device_check) {
     const SparseArrayBase *base =
-        static_cast<SparseArrayBase *>(reinterpret_cast<void *>(in));
+        static_cast<SparseArrayBase *>(static_cast<void *>(in));
 
     if (!base->isSparse()) {
         AF_ERROR(
diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index de6fad63e9..9fd89c0166 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -28,8 +28,9 @@ void af_get_last_error(char **str, dim_t *len) {
             return;
         }
 
-        af_alloc_host(reinterpret_cast<void **>(str),
-                      sizeof(char) * (slen + 1));
+        void *in = nullptr;
+        af_alloc_host(&in, sizeof(char) * (slen + 1));
+        memcpy(str, &in, sizeof(void *));
         global_error_string.copy(*str, slen);
 
         (*str)[slen]        = '\0';
@@ -39,7 +40,9 @@ void af_get_last_error(char **str, dim_t *len) {
     } else {
         // If false, the error is coming from active backend.
         typedef void (*af_func)(char **, dim_t *);
-        auto func = reinterpret_cast<af_func>(LOAD_SYMBOL());
+        void *vfn    = LOAD_SYMBOL();
+        af_func func = nullptr;
+        memcpy(&func, vfn, sizeof(void *));
         func(str, len);
     }
 }
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index c2c6a842f2..f079bac8ef 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -11,6 +11,7 @@
 #include <common/err_common.hpp>
 #include <common/traits.hpp>
 #include <algorithm>
+#include <cstring>
 #include <functional>
 #include <numeric>
 
@@ -173,8 +174,8 @@ dim4 toStride(const vector<af_seq> &seqs, const af::dim4 &parentDims) {
 
 const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
                          bool device_check) {
-    const ArrayInfo *info =
-        static_cast<ArrayInfo *>(reinterpret_cast<void *>(arr));
+    const ArrayInfo *info = nullptr;
+    memcpy(&info, &arr, sizeof(af_array));
 
     // Check Sparse -> If false, then both standard Array<T> and SparseArray<T>
     // are accepted Otherwise only regular Array<T> is accepted
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index 1e2459bc73..a728940d97 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -29,7 +29,7 @@ struct Enqueuer {
     template<typename... Args>
     void operator()(std::string name, void* ker, const EnqueueArgs& qArgs,
                     Args... args) {
-        void* params[] = {reinterpret_cast<void*>(&args)...};
+        void* params[] = {static_cast<void*>(&args)...};
         for (auto& event : qArgs.mEvents) {
             CU_CHECK(cuStreamWaitEvent(qArgs.mStream, event, 0));
         }
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index bd5676fd01..24330ee3ae 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -179,14 +179,16 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
                 const T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(is_device ? (new buffer<T>(*reinterpret_cast<buffer<T> *>(
-                           const_cast<T *>(in_data))))
-                     : (memAlloc<T>(info.elements()).release()),
-           bufferFree<T>)
+    , data()
     , data_dims(dims)
     , node()
     , owner(true) {
-    if (!is_device) {
+    if (is_device) {
+        buffer<T> *ptr;
+        std::memcpy(&ptr, in_data, sizeof(buffer<T> *));
+        data = make_shared<buffer<T>>(*ptr);
+    } else {
+        data = memAlloc<T>(info.elements());
         getQueue()
             .submit(
                 [&](sycl::handler &h) { h.copy(in_data, data->get_access(h)); })
diff --git a/src/backend/opencl/api.cpp b/src/backend/opencl/api.cpp
index 04b73eff4f..df3f6783a1 100644
--- a/src/backend/opencl/api.cpp
+++ b/src/backend/opencl/api.cpp
@@ -1,11 +1,23 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
 #include <af/array.h>
 #include <af/opencl.h>
+#include <cstring>
 
 namespace af {
 template<>
 AFAPI cl_mem *array::device() const {
     auto *mem_ptr = new cl_mem;
-    af_err err = af_get_device_ptr(reinterpret_cast<void **>(mem_ptr), get());
+    void *dptr    = nullptr;
+    af_err err    = af_get_device_ptr(&dptr, get());
+    memcpy(mem_ptr, &dptr, sizeof(void *));
     if (err != AF_SUCCESS) {
         throw af::exception("Failed to get cl_mem from array object");
     }

From 9890fb0ef77ba1665aa90309e314e6cc9c71a92c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 23 Nov 2022 18:30:23 -0500
Subject: [PATCH 2347/2677] Rename version.hpp to build_version.hpp

---
 CMakeLists.txt                                        | 5 -----
 CMakeModules/Version.cmake                            | 4 ++--
 CMakeModules/{version.hpp.in => build_version.hpp.in} | 2 +-
 src/api/c/version.cpp                                 | 2 +-
 src/api/unified/CMakeLists.txt                        | 1 -
 src/backend/common/CMakeLists.txt                     | 3 +--
 src/backend/common/jit/Node.cpp                       | 2 +-
 src/backend/cpu/CMakeLists.txt                        | 2 +-
 src/backend/cpu/platform.cpp                          | 2 +-
 src/backend/cuda/CMakeLists.txt                       | 2 +-
 src/backend/cuda/device_manager.cpp                   | 2 +-
 src/backend/cuda/platform.cpp                         | 2 +-
 src/backend/oneapi/CMakeLists.txt                     | 2 +-
 src/backend/oneapi/device_manager.cpp                 | 2 +-
 src/backend/oneapi/platform.cpp                       | 2 +-
 src/backend/opencl/CMakeLists.txt                     | 2 +-
 src/backend/opencl/device_manager.cpp                 | 2 +-
 src/backend/opencl/platform.cpp                       | 2 +-
 18 files changed, 17 insertions(+), 24 deletions(-)
 rename CMakeModules/{version.hpp.in => build_version.hpp.in} (92%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b779d929f5..f67cecee36 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -302,11 +302,6 @@ af_dep_check_and_populate(${assets_prefix}
 )
 set(ASSETS_DIR ${${assets_prefix}_SOURCE_DIR})
 
-configure_file(
-    ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
-    ${ArrayFire_BINARY_DIR}/version.hpp
-)
-
 # when crosscompiling use the bin2cpp file from the native bin directory
 if(CMAKE_CROSSCOMPILING)
   set(NATIVE_BIN_DIR "NATIVE_BIN_DIR-NOTFOUND"
diff --git a/CMakeModules/Version.cmake b/CMakeModules/Version.cmake
index 54c0ac8174..2269bd73f2 100644
--- a/CMakeModules/Version.cmake
+++ b/CMakeModules/Version.cmake
@@ -49,6 +49,6 @@ configure_file(
 )
 
 configure_file(
-    ${ArrayFire_SOURCE_DIR}/CMakeModules/version.hpp.in
-    ${ArrayFire_BINARY_DIR}/src/backend/version.hpp
+    ${ArrayFire_SOURCE_DIR}/CMakeModules/build_version.hpp.in
+    ${ArrayFire_BINARY_DIR}/src/backend/build_version.hpp
 )
diff --git a/CMakeModules/version.hpp.in b/CMakeModules/build_version.hpp.in
similarity index 92%
rename from CMakeModules/version.hpp.in
rename to CMakeModules/build_version.hpp.in
index f4c9ec6150..d3b881f8d9 100644
--- a/CMakeModules/version.hpp.in
+++ b/CMakeModules/build_version.hpp.in
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2022, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
diff --git a/src/api/c/version.cpp b/src/api/c/version.cpp
index ce471bd9d1..47b6952427 100644
--- a/src/api/c/version.cpp
+++ b/src/api/c/version.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <version.hpp>
+#include <build_version.hpp>
 #include <af/util.h>
 
 af_err af_get_version(int *major, int *minor, int *patch) {
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index 67b6b80dd2..a17c6618f1 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -78,7 +78,6 @@ target_include_directories(af
   PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c
     ${ArrayFire_SOURCE_DIR}/src/api/unified
-    ${ArrayFire_BINARY_DIR}
     $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
     $<$<BOOL:${OpenCL_FOUND}>: $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>>
     $<$<BOOL:${CUDA_FOUND}>:  ${CUDA_INCLUDE_DIRS}>
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 1487d99c44..7b26e11194 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -78,7 +78,6 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/unique_handle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/util.hpp
-    ${ArrayFire_BINARY_DIR}/version.hpp
   )
 
 if(WIN32)
@@ -115,7 +114,7 @@ endif()
 target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
-    ${ArrayFire_BINARY_DIR})
+    ${ArrayFire_BINARY_DIR}/src/backend)
 
 target_include_directories(afcommon_interface
   SYSTEM INTERFACE
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 71d88424f5..ed24b9c1f8 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <build_version.hpp>
 #include <common/defines.hpp>
 #include <common/deterministicHash.hpp>
 #include <common/jit/Node.hpp>
 #include <common/util.hpp>
 
-#include <version.hpp>
 #include <sstream>
 #include <string>
 #include <vector>
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 04d0d3390b..83005c8e62 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -356,5 +356,5 @@ source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
 source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
 source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*)
-source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
+source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/src/backend/build_version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
 source_group("" FILES CMakeLists.txt)
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 5bb28a41ec..8676054136 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -7,12 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <build_version.hpp>
 #include <common/MemoryManagerBase.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <device_manager.hpp>
 #include <platform.hpp>
-#include <version.hpp>
 #include <af/version.h>
 
 #include <cctype>
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index e1f47b2947..4d3ac5051e 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -861,7 +861,7 @@ source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
 source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
 source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/thrust_sort_by_key/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/*)
-source_group("generated files"  FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h
+source_group("generated files"  FILES ${ArrayFire_BINARY_DIR}/src/backend/build_version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h
                                 REGULAR_EXPRESSION ${CMAKE_CURRENT_BINARY_DIR}/${kernel_headers_dir}/*)
 source_group("" FILES CMakeLists.txt)
 
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index f556d08cce..4b946a7fee 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -12,6 +12,7 @@
 #endif
 
 #include <GraphicsResourceManager.hpp>
+#include <build_version.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/MemoryManagerBase.hpp>
@@ -26,7 +27,6 @@
 #include <memory.hpp>
 #include <platform.hpp>
 #include <spdlog/spdlog.h>
-#include <version.hpp>
 #include <af/cuda.h>
 #include <af/version.h>
 // cuda_gl_interop.h does not include OpenGL headers for ARM
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 13d10564bf..7e82f76843 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -17,6 +17,7 @@
 #endif
 
 #include <GraphicsResourceManager.hpp>
+#include <build_version.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
@@ -36,7 +37,6 @@
 #include <memory.hpp>
 #include <spdlog/spdlog.h>
 #include <utility.hpp>
-#include <version.hpp>
 #include <af/cuda.h>
 #include <af/device.h>
 #include <af/version.h>
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 4fb6f3c0a9..f67764e21d 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -283,5 +283,5 @@ source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
 source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
 source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*)
-source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
+source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/src/backend/build_version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
 source_group("" FILES CMakeLists.txt)
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 4588369637..48201b7ebc 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -19,8 +19,8 @@
 #include <err_oneapi.hpp>
 #include <platform.hpp>  //TODO: blas.hpp? y tho, also Array.hpp
 //#include <errorcodes.hpp>
+#include <build_version.hpp>
 #include <memory.hpp>
-#include <version.hpp>
 #include <af/oneapi.h>
 #include <af/version.h>
 
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index c16a4afff9..4e22f742ae 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -9,6 +9,7 @@
 
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
+#include <build_version.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/graphics_common.hpp>
@@ -18,7 +19,6 @@
 #include <err_oneapi.hpp>
 #include <errorcodes.hpp>
 #include <memory.hpp>
-#include <version.hpp>
 #include <af/version.h>
 
 #ifdef OS_MAC
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a827c55193..8df8ff6aaa 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -544,4 +544,4 @@ source_group(api\\cpp REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/cpp/*)
 source_group(api\\c   REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/api/c/*)
 source_group(backend  REGULAR_EXPRESSION ${ArrayFire_SOURCE_DIR}/src/backend/common/*|${CMAKE_CURRENT_SOURCE_DIR}/*)
 source_group(backend\\kernel  REGULAR_EXPRESSION ${CMAKE_CURRENT_SOURCE_DIR}/kernel/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/*|${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_by_key/*)
-source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
+source_group("generated files" FILES ${ArrayFire_BINARY_DIR}/src/backend/build_version.hpp ${ArrayFire_BINARY_DIR}/include/af/version.h)
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 0a543f4297..a9cfbc02e2 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -13,6 +13,7 @@
 
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
+#include <build_version.hpp>
 #include <clfft.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
@@ -22,7 +23,6 @@
 #include <device_manager.hpp>
 #include <err_opencl.hpp>
 #include <errorcodes.hpp>
-#include <version.hpp>
 #include <af/opencl.h>
 #include <af/version.h>
 #include <memory>
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 6bcc2e55ae..04859ad40a 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -13,6 +13,7 @@
 
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
+#include <build_version.hpp>
 #include <clfft.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
@@ -22,7 +23,6 @@
 #include <err_opencl.hpp>
 #include <errorcodes.hpp>
 #include <platform.hpp>
-#include <version.hpp>
 #include <af/version.h>
 
 #ifdef OS_MAC

From d59f70d1547294db6ec27a1a460a2d01e5b4ada3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 23 Nov 2022 18:50:17 -0500
Subject: [PATCH 2348/2677] Fix target_include_directory to specify system
 headers

---
 CMakeLists.txt                                |  7 ++++-
 CMakeModules/build_clFFT.cmake                |  5 ++++
 src/api/c/CMakeLists.txt                      |  2 +-
 src/api/cpp/CMakeLists.txt                    |  8 +++--
 src/api/unified/CMakeLists.txt                |  5 +++-
 src/backend/common/CMakeLists.txt             | 14 ++++-----
 src/backend/cpu/CMakeLists.txt                |  8 +++--
 .../cpu/kernel/sort_by_key/CMakeLists.txt     |  8 +++--
 src/backend/cuda/CMakeLists.txt               |  9 ++++--
 src/backend/oneapi/CMakeLists.txt             |  6 +++-
 src/backend/opencl/CMakeLists.txt             |  3 +-
 .../opencl/kernel/scan_by_key/CMakeLists.txt  |  2 +-
 .../opencl/kernel/sort_by_key/CMakeLists.txt  | 30 +++++++++----------
 test/CMakeLists.txt                           | 18 +++++++----
 14 files changed, 79 insertions(+), 46 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f67cecee36..440f28ae18 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -261,7 +261,7 @@ else()
   )
   add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
-  target_include_directories(af_spdlog INTERFACE "${${spdlog_prefix}_SOURCE_DIR}/include")
+  target_include_directories(af_spdlog SYSTEM INTERFACE "${${spdlog_prefix}_SOURCE_DIR}/include")
   if(TARGET fmt::fmt)
     set_target_properties(af_spdlog
       PROPERTIES
@@ -294,6 +294,11 @@ if(NOT TARGET nonstd::span-lite)
     REF "ccf2351"
     )
   add_subdirectory(${span-lite_SOURCE_DIR} EXCLUDE_FROM_ALL)
+  get_property(span_include_dir
+    TARGET span-lite
+    PROPERTY INTERFACE_INCLUDE_DIRECTORIES)
+  set_target_properties(span-lite
+    PROPERTIES INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${span_include_dir}")
 endif()
 
 af_dep_check_and_populate(${assets_prefix}
diff --git a/CMakeModules/build_clFFT.cmake b/CMakeModules/build_clFFT.cmake
index d4f3081e63..b3e56137bf 100644
--- a/CMakeModules/build_clFFT.cmake
+++ b/CMakeModules/build_clFFT.cmake
@@ -13,6 +13,11 @@ af_dep_check_and_populate(${clfft_prefix}
 set(current_build_type ${BUILD_SHARED_LIBS})
 set(BUILD_SHARED_LIBS OFF)
 add_subdirectory(${${clfft_prefix}_SOURCE_DIR}/src ${${clfft_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+get_property(clfft_include_dir
+  TARGET clFFT
+  PROPERTY INTERFACE_INCLUDE_DIRECTORIES)
+set_target_properties(clFFT
+  PROPERTIES INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${clfft_include_dir}")
 
 # OpenCL targets need this flag to avoid ignored attribute warnings in the
 # OpenCL headers
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 0830402a1f..8dcf7c3d5b 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -175,7 +175,7 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
     target_compile_definitions(c_api_interface INTERFACE FREEIMAGE_STATIC)
     target_link_libraries(c_api_interface INTERFACE FreeImage::FreeImage_STATIC)
   else ()
-    target_include_directories(c_api_interface INTERFACE $<TARGET_PROPERTY:FreeImage::FreeImage,INTERFACE_INCLUDE_DIRECTORIES>)
+    target_include_directories(c_api_interface SYSTEM INTERFACE $<TARGET_PROPERTY:FreeImage::FreeImage,INTERFACE_INCLUDE_DIRECTORIES>)
     if (WIN32 AND AF_INSTALL_STANDALONE)
       install(FILES $<TARGET_FILE:FreeImage::FreeImage>
         DESTINATION ${AF_INSTALL_BIN_DIR}
diff --git a/src/api/cpp/CMakeLists.txt b/src/api/cpp/CMakeLists.txt
index 1df8c7ff77..e33a8b320d 100644
--- a/src/api/cpp/CMakeLists.txt
+++ b/src/api/cpp/CMakeLists.txt
@@ -89,8 +89,10 @@ target_sources(cpp_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/ycbcr_rgb.cpp
 )
 
+target_include_directories(cpp_api_interface
+  SYSTEM INTERFACE
+    ${ArrayFire_SOURCE_DIR}/extern/half/include)
+
 target_include_directories(cpp_api_interface
   INTERFACE
-    ${CMAKE_SOURCE_DIR}/src/api/c
-    ${ArrayFire_SOURCE_DIR}/extern/half/include
-)
+    ${CMAKE_SOURCE_DIR}/src/api/c)
diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index a17c6618f1..ca6805c7a4 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -77,7 +77,10 @@ target_include_directories(af
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${ArrayFire_SOURCE_DIR}/src/api/c
-    ${ArrayFire_SOURCE_DIR}/src/api/unified
+    ${ArrayFire_SOURCE_DIR}/src/api/unified)
+
+target_include_directories(af
+  SYSTEM PRIVATE
     $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
     $<$<BOOL:${OpenCL_FOUND}>: $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>>
     $<$<BOOL:${CUDA_FOUND}>:  ${CUDA_INCLUDE_DIRS}>
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 7b26e11194..795e5df44c 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -111,25 +111,25 @@ if(AF_BUILD_FORGE AND NOT Forge_FOUND)
   add_dependencies(afcommon_interface forge)
 endif()
 
+target_include_directories(afcommon_interface
+  SYSTEM INTERFACE
+    $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>)
+
 target_include_directories(afcommon_interface
   INTERFACE
     ${ArrayFire_SOURCE_DIR}/src/backend
     ${ArrayFire_BINARY_DIR}/src/backend)
 
-target_include_directories(afcommon_interface
-  SYSTEM INTERFACE
-    $<$<PLATFORM_ID:Darwin>:${OPENGL_INCLUDE_DIR}>
-  )
 if(TARGET Forge::forge)
   target_include_directories(afcommon_interface
     SYSTEM INTERFACE
-    $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
+      $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
   )
 else()
   target_include_directories(afcommon_interface
     SYSTEM INTERFACE
-    ${${forge_prefix}_SOURCE_DIR}/include
-    ${${forge_prefix}_BINARY_DIR}/include
+      ${${forge_prefix}_SOURCE_DIR}/include
+      ${${forge_prefix}_BINARY_DIR}/include
   )
 endif()
 
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index 83005c8e62..fc84101de4 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -292,9 +292,11 @@ target_include_directories(afcpu
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${CMAKE_CURRENT_SOURCE_DIR}
-    ${${threads_prefix}_SOURCE_DIR}/include
-    ${CBLAS_INCLUDE_DIR}
-  )
+    ${${threads_prefix}_SOURCE_DIR}/include)
+
+target_include_directories(afcpu
+  SYSTEM PRIVATE
+    ${CBLAS_INCLUDE_DIR})
 
 target_compile_definitions(afcpu
   PRIVATE
diff --git a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
index 3c894b37f5..752501fabc 100644
--- a/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/cpu/kernel/sort_by_key/CMakeLists.txt
@@ -29,20 +29,22 @@ foreach(SBK_TYPE ${SBK_TYPES})
       FOLDER "Generated Targets")
 
   arrayfire_set_default_cxx_flags(cpu_sort_by_key_${SBK_TYPE})
-  # TODO(umar): This should just use the include directories from the
-  # afcpu_static target
+
   target_include_directories(cpu_sort_by_key_${SBK_TYPE}
     PUBLIC
       .
       ../../api/c
       ${ArrayFire_SOURCE_DIR}/include
       ${ArrayFire_BINARY_DIR}/include
-      $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
     PRIVATE
       ../common
       ..
       threads)
 
+  target_include_directories(cpu_sort_by_key_${SBK_TYPE}
+    SYSTEM PRIVATE
+      $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>)
+
   set_target_properties(cpu_sort_by_key_${SBK_TYPE} PROPERTIES POSITION_INDEPENDENT_CODE ON)
   target_sources(cpu_sort_by_key
     INTERFACE $<TARGET_OBJECTS:cpu_sort_by_key_${SBK_TYPE}>)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 4d3ac5051e..8490c541a0 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -668,13 +668,16 @@ target_include_directories (afcuda
     $<BUILD_INTERFACE:${ArrayFire_BINARY_DIR}/include>
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
-    $<$<BOOL:${AF_WITH_CUDNN}>:${cuDNN_INCLUDE_DIRS}>
-    ${CUDA_INCLUDE_DIRS}
     ${ArrayFire_SOURCE_DIR}/src/api/c
     ${CMAKE_CURRENT_SOURCE_DIR}
     ${CMAKE_CURRENT_SOURCE_DIR}/kernel
     ${CMAKE_CURRENT_SOURCE_DIR}/jit
-    ${CMAKE_CURRENT_BINARY_DIR}
+    ${CMAKE_CURRENT_BINARY_DIR})
+
+target_include_directories (afcuda
+  SYSTEM PRIVATE
+    $<$<BOOL:${AF_WITH_CUDNN}>:${cuDNN_INCLUDE_DIRS}>
+    ${CUDA_INCLUDE_DIRS}
 )
 
 target_link_libraries(afcuda
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index f67764e21d..3036a20b2f 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -239,6 +239,10 @@ add_library(ArrayFire::afoneapi ALIAS afoneapi)
 
 arrayfire_set_default_cxx_flags(afoneapi)
 
+target_include_directories(afoneapi
+  SYSTEM PRIVATE
+    ${SYCL_INCLUDE_DIR})
+
 target_include_directories(afoneapi
   PUBLIC
     $<BUILD_INTERFACE:${ArrayFire_SOURCE_DIR}/include>
@@ -246,7 +250,7 @@ target_include_directories(afoneapi
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${CMAKE_CURRENT_SOURCE_DIR}
-    ${SYCL_INCLUDE_DIR}
+
   )
 
 target_compile_options(afoneapi
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 8df8ff6aaa..069609b95e 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -480,8 +480,9 @@ if(LAPACK_FOUND OR BUILD_WITH_MKL)
     endif()
 
     target_include_directories(afopencl
-      PRIVATE
+      SYSTEM PRIVATE
         ${CBLAS_INCLUDE_DIR})
+
     target_link_libraries(afopencl
       PRIVATE
         ${CBLAS_LIBRARIES}
diff --git a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
index e5d0de3a97..316e946a31 100644
--- a/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/scan_by_key/CMakeLists.txt
@@ -40,7 +40,7 @@ foreach(SBK_BINARY_OP ${SBK_BINARY_OPS})
         $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:nonstd::span-lite,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:nonstd::span-lite,INTERFACE_SYSTEM_INCLUDE_DIRECTORIES>
         ${ArrayFire_BINARY_DIR}/include
       )
     if(TARGET Forge::forge)
diff --git a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
index 2853d75cd9..e2ad168138 100644
--- a/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/opencl/kernel/sort_by_key/CMakeLists.txt
@@ -22,46 +22,46 @@ foreach(SBK_TYPE ${SBK_TYPES})
     add_dependencies(opencl_sort_by_key_${SBK_TYPE}
                         ${cl_kernel_targets} OpenCL::cl2hpp Boost::boost)
 
+    target_include_directories(opencl_sort_by_key_${SBK_TYPE}
+      SYSTEM PRIVATE
+        ${span-lite_SOURCE_DIR}/include
+        $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>)
+
     target_include_directories(opencl_sort_by_key_${SBK_TYPE}
       PRIVATE
         .
         ..
-        magma
         ../../api/c
         ../common
         ../../../include
-        ${span-lite_SOURCE_DIR}/include
+        magma
+        ${ArrayFire_BINARY_DIR}/include
         ${CMAKE_CURRENT_BINARY_DIR})
 
-    target_include_directories(opencl_sort_by_key_${SBK_TYPE}
-      SYSTEM PRIVATE
-        $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:OpenCL::cl2hpp,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>
-        $<TARGET_PROPERTY:af_spdlog,INTERFACE_INCLUDE_DIRECTORIES>
-        ${ArrayFire_BINARY_DIR}/include
-      )
     if(TARGET Forge::forge)
       target_include_directories(opencl_sort_by_key_${SBK_TYPE}
         SYSTEM INTERFACE
-        $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
+          $<TARGET_PROPERTY:Forge::forge,INCLUDE_DIRECTORIES>
       )
     else()
       target_include_directories(opencl_sort_by_key_${SBK_TYPE}
         SYSTEM INTERFACE
-        ${${forge_prefix}_SOURCE_DIR}/include
-        ${${forge_prefix}_BINARY_DIR}/include
+          ${${forge_prefix}_SOURCE_DIR}/include
+          ${${forge_prefix}_BINARY_DIR}/include
       )
     endif()
     if(TARGET glad::glad)
       target_include_directories(opencl_sort_by_key_${SBK_TYPE}
         SYSTEM INTERFACE
-        $<TARGET_PROPERTY:glad::glad,INTERFACE_INCLUDE_DIRECTORIES>
+          $<TARGET_PROPERTY:glad::glad,INTERFACE_INCLUDE_DIRECTORIES>
       )
     else()
       target_include_directories(opencl_sort_by_key_${SBK_TYPE}
         SYSTEM INTERFACE
-        $<TARGET_PROPERTY:af_glad,INTERFACE_INCLUDE_DIRECTORIES>
+          $<TARGET_PROPERTY:af_glad,INTERFACE_INCLUDE_DIRECTORIES>
       )
     endif()
 
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 50fcadaf5b..1ff1d94041 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -115,7 +115,10 @@ target_include_directories(arrayfire_test
   PRIVATE
     ${CMAKE_CURRENT_LIST_DIR}
     ${ArrayFire_SOURCE_DIR}/include
-    ${ArrayFire_BINARY_DIR}/include
+    ${ArrayFire_BINARY_DIR}/include)
+
+target_include_directories(arrayfire_test
+  SYSTEM PRIVATE
     ${ArrayFire_SOURCE_DIR}/extern/half/include
   )
 
@@ -185,9 +188,10 @@ function(make_test)
     add_executable(${target} ${mt_args_SRC})
     target_include_directories(${target}
       PRIVATE
-        ${ArrayFire_SOURCE_DIR}/extern/half/include
         ${CMAKE_SOURCE_DIR}
         ${CMAKE_CURRENT_SOURCE_DIR}
+      SYSTEM PRIVATE
+        ${ArrayFire_SOURCE_DIR}/extern/half/include
       )
     target_link_libraries(${target}
       PRIVATE
@@ -361,10 +365,12 @@ if(CUDA_FOUND)
     if(${backend} IN_LIST cuda_test_backends)
       set(target test_cuda_${backend})
       add_executable(${target} cuda.cu)
-      target_include_directories(${target} PRIVATE
-        ${ArrayFire_SOURCE_DIR}/extern/half/include
-        ${CMAKE_SOURCE_DIR}
-        ${CMAKE_CURRENT_SOURCE_DIR})
+      target_include_directories(${target}
+        PRIVATE
+          ${CMAKE_SOURCE_DIR}
+          ${CMAKE_CURRENT_SOURCE_DIR}
+        SYSTEM PRIVATE
+          ${ArrayFire_SOURCE_DIR}/extern/half/include)
       if(${backend} STREQUAL "unified")
         target_link_libraries(${target}
           ArrayFire::af)

From 021fab268972edb47dab371abc36003fe1b64d67 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 23 Nov 2022 18:53:20 -0500
Subject: [PATCH 2349/2677] Fix cl2hpp deprecated header warning

---
 src/backend/opencl/cl2hpp.hpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/backend/opencl/cl2hpp.hpp b/src/backend/opencl/cl2hpp.hpp
index ef6f80037b..729710d420 100644
--- a/src/backend/opencl/cl2hpp.hpp
+++ b/src/backend/opencl/cl2hpp.hpp
@@ -19,6 +19,14 @@ AF_DEPRECATED_WARNINGS_OFF
 #if __GNUC__ >= 8
 #pragma GCC diagnostic ignored "-Wcatch-value="
 #endif
+#ifdef __has_include
+#if __has_include(<CL/opencl.hpp>)
+#include <CL/opencl.hpp>
+#else
 #include <CL/cl2.hpp>
+#endif
+#else
+#include <CL/cl2.hpp>
+#endif
 AF_DEPRECATED_WARNINGS_ON
 #pragma GCC diagnostic pop

From 5d13f3835cfea7c57626c7934a0d000c2112a9fa Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 25 Nov 2022 17:28:18 -0500
Subject: [PATCH 2350/2677] Pass fast math flags to nvcc, NVRTC and OpenCL

---
 src/backend/cuda/CMakeLists.txt       |  1 +
 src/backend/cuda/compile_module.cpp   |  4 +++
 src/backend/cuda/kernel/jit.cuh       |  5 +++
 src/backend/cuda/math.hpp             | 35 ++++++++++-----------
 src/backend/opencl/compile_module.cpp |  4 +++
 src/backend/opencl/math.hpp           | 45 ++++++++++-----------------
 test/reduce.cpp                       |  1 +
 7 files changed, 48 insertions(+), 47 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 8490c541a0..ece17d962f 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -636,6 +636,7 @@ endif()
 
 target_compile_options(afcuda
   PRIVATE
+    $<$<BOOL:${AF_WITH_FAST_MATH}>:$<$<COMPILE_LANGUAGE:CUDA>:-use_fast_math>>
     $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
     $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe --diag_suppress=unrecognized_gcc_pragma>
     $<$<COMPILE_LANGUAGE:CUDA>: $<$<CXX_COMPILER_ID:MSVC>:  -Xcompiler=/wd4251
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 3f5bd17d84..de22e8c493 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -261,6 +261,10 @@ Module compileModule(const string &moduleKey, span<const string> sources,
         arch.data(),
         "--std=c++14",
         "--device-as-default-execution-space",
+#ifdef AF_WITH_FAST_MATH
+        "--use_fast_math",
+        "-DAF_WITH_FAST_MATH",
+#endif
 #if !(defined(NDEBUG) || defined(__aarch64__) || defined(__LP64__))
         "--device-debug",
         "--generate-line-info"
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 4681c151ed..cf69146114 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -59,8 +59,13 @@ typedef cuDoubleComplex cdouble;
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
 
+#ifdef AF_WITH_FAST_MATH
+#define __pow(lhs, rhs) \
+    static_cast<double>(pow(static_cast<double>(lhs), static_cast<double>(rhs)));
+#else
 #define __pow(lhs, rhs) \
     __float2int_rn(pow(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
+#endif
 #define __powll(lhs, rhs) \
     __double2ll_rn(pow(__ll2double_rn(lhs), __ll2double_rn(rhs)))
 #define __powul(lhs, rhs) \
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 5987017fa7..23aa1a449b 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -32,6 +32,12 @@
 
 namespace cuda {
 
+#ifdef AF_WITH_FAST_MATH
+constexpr bool fast_math = true;
+#else
+constexpr bool fast_math = false;
+#endif
+
 template<typename T>
 static inline __DH__ T abs(T val) {
     return ::abs(val);
@@ -138,29 +144,22 @@ __DH__ static To scalar(Ti real, Ti imag) {
 }
 
 #ifndef __CUDA_ARCH__
+
 template<typename T>
 inline T maxval() {
-    return std::numeric_limits<T>::max();
+    if constexpr (std::is_floating_point_v<T> && !fast_math) {
+        return std::numeric_limits<T>::infinity();
+    } else {
+        return std::numeric_limits<T>::max();
+    }
 }
 template<typename T>
 inline T minval() {
-    return std::numeric_limits<T>::min();
-}
-template<>
-inline float maxval() {
-    return std::numeric_limits<float>::infinity();
-}
-template<>
-inline double maxval() {
-    return std::numeric_limits<double>::infinity();
-}
-template<>
-inline float minval() {
-    return -std::numeric_limits<float>::infinity();
-}
-template<>
-inline double minval() {
-    return -std::numeric_limits<double>::infinity();
+    if constexpr (std::is_floating_point_v<T> && !fast_math) {
+        return -std::numeric_limits<T>::infinity();
+    } else {
+        return std::numeric_limits<T>::lowest();
+    }
 }
 #else
 template<typename T>
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 83d66eb740..f931bb554a 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -126,6 +126,10 @@ Program buildProgram(span<const string> kernelSources,
         ostringstream options;
         for (auto &opt : compileOpts) { options << opt; }
 
+#ifdef AF_WITH_FAST_MATH
+        options << " -cl-fast-relaxed-math -DAF_WITH_FAST_MATH";
+#endif
+
         retVal.build({device}, (cl_std + defaults + options.str()).c_str());
     } catch (Error &err) {
         if (err.err() == CL_BUILD_PROGRAM_FAILURE) {
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index e1e9c28f12..e7cf8d1928 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -106,40 +106,27 @@ static To scalar(Ti real, Ti imag) {
     return cval;
 }
 
+#ifdef AF_WITH_FAST_MATH
+constexpr bool fast_math = true;
+#else
+constexpr bool fast_math = false;
+#endif
+
 template<typename T>
 inline T maxval() {
-    return std::numeric_limits<T>::max();
+    if constexpr (std::is_floating_point_v<T> && !fast_math) {
+        return std::numeric_limits<T>::infinity();
+    } else {
+        return std::numeric_limits<T>::max();
+    }
 }
 template<typename T>
 inline T minval() {
-    return std::numeric_limits<T>::min();
-}
-template<>
-inline float maxval() {
-    return std::numeric_limits<float>::infinity();
-}
-template<>
-inline double maxval() {
-    return std::numeric_limits<double>::infinity();
-}
-
-template<>
-inline common::half maxval() {
-    return std::numeric_limits<common::half>::infinity();
-}
-
-template<>
-inline float minval() {
-    return -std::numeric_limits<float>::infinity();
-}
-
-template<>
-inline double minval() {
-    return -std::numeric_limits<double>::infinity();
-}
-template<>
-inline common::half minval() {
-    return -std::numeric_limits<common::half>::infinity();
+    if constexpr (std::is_floating_point_v<T> && !fast_math) {
+        return -std::numeric_limits<T>::infinity();
+    } else {
+        return std::numeric_limits<T>::lowest();
+    }
 }
 
 static inline double real(cdouble in) { return in.s[0]; }
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 5afdf70648..ef5b33bb1c 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2296,6 +2296,7 @@ TEST(Reduce, Test_Sum_Global_Array_nanval) {
 }
 
 TEST(Reduce, nanval_issue_3255) {
+    SKIP_IF_FAST_MATH_ENABLED();
     char *info_str;
     af_array  ikeys, ivals, okeys, ovals;
     dim_t dims[1] = {8};

From 218173939f60fa4eb2d46e3738309b2f39744f78 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 25 Nov 2022 17:30:36 -0500
Subject: [PATCH 2351/2677] Set cublasMathMode and Atomic mode when
 AF_WITH_FAST_MATH is set

---
 src/backend/cuda/platform.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 7e82f76843..d3b7c2efd9 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -94,6 +94,12 @@ unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
         // call outside of call_once scope.
         CUBLAS_CHECK(
             cublasSetStream(handles[deviceId], cuda::getStream(deviceId)));
+#ifdef AF_WITH_FAST_MATH
+        CUBLAS_CHECK(
+            cublasSetMathMode(handles[deviceId], CUBLAS_TF32_TENSOR_OP_MATH));
+        CUBLAS_CHECK(
+            cublasSetAtomicsMode(handles[deviceId], CUBLAS_ATOMICS_ALLOWED));
+#endif
     });
 
     return &handles[deviceId];

From 921799bfe324956602a0df9b3f034e67108ff416 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Sat, 12 Nov 2022 12:18:07 -0500
Subject: [PATCH 2352/2677] wrap ported to oneapi. most tests fail due to
 missing jit

---
 src/backend/oneapi/CMakeLists.txt          |   2 +
 src/backend/oneapi/kernel/wrap.hpp         | 162 +++++++++++++++++++
 src/backend/oneapi/kernel/wrap_dilated.hpp | 177 +++++++++++++++++++++
 src/backend/oneapi/wrap.cpp                |  10 +-
 4 files changed, 345 insertions(+), 6 deletions(-)
 create mode 100755 src/backend/oneapi/kernel/wrap.hpp
 create mode 100755 src/backend/oneapi/kernel/wrap_dilated.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 3036a20b2f..826f144a83 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -233,6 +233,8 @@ target_sources(afoneapi
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
     kernel/where.hpp
+    kernel/wrap.hpp
+    kernel/wrap_dilated.hpp
 )
 
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
new file mode 100755
index 0000000000..0cac661ba6
--- /dev/null
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -0,0 +1,162 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/default_config.hpp>
+#include <math.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
+                                      sycl::access::target::local>;
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+class wrapCreateKernel {
+   public:
+    wrapCreateKernel(write_accessor<T> optrAcc, KParam out,
+                     read_accessor<T> iptrAcc, KParam in, const int wx,
+                     const int wy, const int sx, const int sy, const int px,
+                     const int py, const int nx, const int ny, int groups_x,
+                     int groups_y, const bool is_column)
+        : optrAcc_(optrAcc)
+        , out_(out)
+        , iptrAcc_(iptrAcc)
+        , in_(in)
+        , wx_(wx)
+        , wy_(wy)
+        , sx_(sx)
+        , sy_(sy)
+        , px_(px)
+        , py_(py)
+        , nx_(nx)
+        , ny_(ny)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , is_column_(is_column) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        int idx2 = g.get_group_id(0) / groups_x_;
+        int idx3 = g.get_group_id(1) / groups_y_;
+
+        int groupId_x = g.get_group_id(0) - idx2 * groups_x_;
+        int groupId_y = g.get_group_id(1) - idx3 * groups_y_;
+
+        int oidx0 = it.get_local_id(0) + g.get_local_range(0) * groupId_x;
+        int oidx1 = it.get_local_id(1) + g.get_local_range(1) * groupId_y;
+
+        T *optr = optrAcc_.get_pointer() + idx2 * out_.strides[2] +
+                  idx3 * out_.strides[3] + out_.offset;
+        T *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
+                  idx3 * in_.strides[3] + in_.offset;
+
+        if (oidx0 >= out_.dims[0] || oidx1 >= out_.dims[1]) return;
+
+        int pidx0 = oidx0 + px_;
+        int pidx1 = oidx1 + py_;
+
+        // The last time a value appears in_ the unwrapped index is padded_index
+        // / stride Each previous index has the value appear "stride" locations
+        // earlier We work our way back from the last index
+
+        const int x_end = fmin(pidx0 / sx_, nx_ - 1);
+        const int y_end = fmin(pidx1 / sy_, ny_ - 1);
+
+        const int x_off = pidx0 - sx_ * x_end;
+        const int y_off = pidx1 - sy_ * y_end;
+
+        T val   = (T)0;
+        int idx = 1;
+
+        for (int y = y_end, yo = y_off; y >= 0 && yo < wy_; yo += sy_, y--) {
+            int win_end_y = yo * wx_;
+            int dim_end_y = y * nx_;
+
+            for (int x = x_end, xo = x_off; x >= 0 && xo < wx_;
+                 xo += sx_, x--) {
+                int win_end = win_end_y + xo;
+                int dim_end = dim_end_y + x;
+
+                if (is_column_) {
+                    idx = dim_end * in_.strides[1] + win_end;
+                } else {
+                    idx = dim_end + win_end * in_.strides[1];
+                }
+
+                // No need to include anything special for complex
+                // Add for complex numbers is just vector add of reals
+                // Might need to change if we generalize add to more binary ops
+                val = val + iptr[idx];
+            }
+        }
+
+        optr[oidx1 * out_.strides[1] + oidx0] = val;
+    }
+
+   private:
+    write_accessor<T> optrAcc_;
+    KParam out_;
+    read_accessor<T> iptrAcc_;
+    KParam in_;
+    const int wx_;
+    const int wy_;
+    const int sx_;
+    const int sy_;
+    const int px_;
+    const int py_;
+    const int nx_;
+    const int ny_;
+    int groups_x_;
+    int groups_y_;
+    const bool is_column_;
+};
+
+template<typename T>
+void wrap(Param<T> out, const Param<T> in, const dim_t wx, const dim_t wy,
+          const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+          const bool is_column) {
+    dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
+    dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
+
+    auto local = sycl::range{THREADS_X, THREADS_Y};
+
+    dim_t groups_x = divup(out.info.dims[0], local[0]);
+    dim_t groups_y = divup(out.info.dims[1], local[1]);
+
+    auto global = sycl::range{groups_x * local[0] * out.info.dims[2],
+                              groups_y * local[1]};
+
+    auto Q = getQueue();
+    Q.submit([&](sycl::handler &h) {
+        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor inAcc{*in.data, h, sycl::read_only};
+        h.parallel_for(sycl::nd_range{global, local},
+                       wrapCreateKernel<T>(outAcc, out.info, inAcc, in.info, wx,
+                                           wy, sx, sy, px, py, nx, ny, groups_x,
+                                           groups_y, is_column));
+    });
+    ONEAPI_DEBUG_FINISH(Q);
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/wrap_dilated.hpp b/src/backend/oneapi/kernel/wrap_dilated.hpp
new file mode 100755
index 0000000000..12760a57c6
--- /dev/null
+++ b/src/backend/oneapi/kernel/wrap_dilated.hpp
@@ -0,0 +1,177 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/default_config.hpp>
+#include <math.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
+                                      sycl::access::target::local>;
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+class wrapDilatedCreateKernel {
+   public:
+    wrapDilatedCreateKernel(write_accessor<T> optrAcc, KParam out,
+                            read_accessor<T> iptrAcc, KParam in, const int wx,
+                            const int wy, const int sx, const int sy,
+                            const int px, const int py, const int dx,
+                            const int dy, const int nx, const int ny,
+                            int groups_x, int groups_y, const bool is_column)
+        : optrAcc_(optrAcc)
+        , out_(out)
+        , iptrAcc_(iptrAcc)
+        , in_(in)
+        , wx_(wx)
+        , wy_(wy)
+        , sx_(sx)
+        , sy_(sy)
+        , px_(px)
+        , py_(py)
+        , dx_(dx)
+        , dy_(dy)
+        , nx_(nx)
+        , ny_(ny)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , is_column_(is_column) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        int idx2 = g.get_group_id(0) / groups_x_;
+        int idx3 = g.get_group_id(1) / groups_y_;
+
+        int groupId_x = g.get_group_id(0) - idx2 * groups_x_;
+        int groupId_y = g.get_group_id(1) - idx3 * groups_y_;
+
+        int oidx0 = it.get_local_id(0) + g.get_local_range(0) * groupId_x;
+        int oidx1 = it.get_local_id(1) + g.get_local_range(1) * groupId_y;
+
+        T *optr = optrAcc_.get_pointer() + idx2 * out_.strides[2] +
+                  idx3 * out_.strides[3];
+        T *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
+                  idx3 * in_.strides[3] + in_.offset;
+
+        if (oidx0 >= out_.dims[0] || oidx1 >= out_.dims[1]) return;
+
+        int eff_wx = wx_ + (wx_ - 1) * (dx_ - 1);
+        int eff_wy = wy_ + (wy_ - 1) * (dy_ - 1);
+
+        int pidx0 = oidx0 + px_;
+        int pidx1 = oidx1 + py_;
+
+        // The last time a value appears in_ the unwrapped index is padded_index
+        // / stride Each previous index has the value appear "stride" locations
+        // earlier We work our way back from the last index
+
+        const int y_start = (pidx1 < eff_wy) ? 0 : (pidx1 - eff_wy) / sy_ + 1;
+        const int y_end   = fmin(pidx1 / sy_ + 1, ny_);
+
+        const int x_start = (pidx0 < eff_wx) ? 0 : (pidx0 - eff_wx) / sx_ + 1;
+        const int x_end   = fmin(pidx0 / sx_ + 1, nx_);
+
+        T val   = (T)0;
+        int idx = 1;
+
+        for (int y = y_start; y < y_end; y++) {
+            int fy      = (pidx1 - y * sy_);
+            bool yvalid = (fy % dy_ == 0) && (y < ny_);
+            fy /= dy_;
+
+            int win_end_y = fy * wx_;
+            int dim_end_y = y * nx_;
+
+            for (int x = x_start; x < x_end; x++) {
+                int fx      = (pidx0 - x * sx_);
+                bool xvalid = (fx % dx_ == 0) && (x < nx_);
+                fx /= dx_;
+
+                int win_end = win_end_y + fx;
+                int dim_end = dim_end_y + x;
+
+                if (is_column_) {
+                    idx = dim_end * in_.strides[1] + win_end;
+                } else {
+                    idx = dim_end + win_end * in_.strides[1];
+                }
+
+                T ival;
+                ival = (yvalid && xvalid) ? iptr[idx] : (T)0;
+                val  = val + ival;
+            }
+        }
+
+        optr[oidx1 * out_.strides[1] + oidx0] = val;
+    }
+
+   private:
+    write_accessor<T> optrAcc_;
+    KParam out_;
+    read_accessor<T> iptrAcc_;
+    KParam in_;
+    const int wx_;
+    const int wy_;
+    const int sx_;
+    const int sy_;
+    const int px_;
+    const int py_;
+    const int dx_;
+    const int dy_;
+    const int nx_;
+    const int ny_;
+    int groups_x_;
+    int groups_y_;
+    const bool is_column_;
+};
+
+template<typename T>
+void wrap_dilated(Param<T> out, const Param<T> in, const dim_t wx,
+                  const dim_t wy, const dim_t sx, const dim_t sy,
+                  const dim_t px, const dim_t py, const dim_t dx,
+                  const dim_t dy, const bool is_column) {
+    dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
+    dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;
+
+    auto local = sycl::range{THREADS_X, THREADS_Y};
+
+    dim_t groups_x = divup(out.info.dims[0], local[0]);
+    dim_t groups_y = divup(out.info.dims[1], local[1]);
+
+    auto global = sycl::range{local[0] * groups_x * out.info.dims[2],
+                              local[1] * groups_y * out.info.dims[3]};
+
+    auto Q = getQueue();
+    Q.submit([&](sycl::handler &h) {
+        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor inAcc{*in.data, h, sycl::read_only};
+        h.parallel_for(sycl::nd_range{global, local},
+                       wrapDilatedCreateKernel<T>(
+                           outAcc, out.info, inAcc, in.info, wx, wy, sx, sy, px,
+                           py, dx, dy, nx, ny, groups_x, groups_y, is_column));
+    });
+    ONEAPI_DEBUG_FINISH(Q);
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/wrap.cpp b/src/backend/oneapi/wrap.cpp
index e3a9b2fc1f..b00b61efef 100644
--- a/src/backend/oneapi/wrap.cpp
+++ b/src/backend/oneapi/wrap.cpp
@@ -11,7 +11,8 @@
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/wrap.hpp>
+#include <kernel/wrap.hpp>
+#include <kernel/wrap_dilated.hpp>
 #include <math.hpp>
 #include <wrap.hpp>
 #include <stdexcept>
@@ -24,8 +25,7 @@ template<typename T>
 void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
           const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
           const bool is_column) {
-    ONEAPI_NOT_SUPPORTED("wrap Not supported");
-    // kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
+    kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
 }
 
 #define INSTANTIATE(T)                                                        \
@@ -57,9 +57,7 @@ Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
     af::dim4 odims(ox, oy, idims[2], idims[3]);
     Array<T> out = createValueArray<T>(odims, scalar<T>(0));
 
-    // kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy,
-    // is_column);
-    ONEAPI_NOT_SUPPORTED("wrap_dilated Not supported");
+    kernel::wrap_dilated<T>(out, in, wx, wy, sx, sy, px, py, dx, dy, is_column);
     return out;
 }
 

From 052778ff48c83500d400ec3d63004524e864f405 Mon Sep 17 00:00:00 2001
From: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Date: Fri, 11 Nov 2022 14:29:50 -0500
Subject: [PATCH 2353/2677] unwrap ported to oneapi. all tests pass

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt    |   1 +
 src/backend/oneapi/kernel/unwrap.hpp | 170 +++++++++++++++++++++++++++
 src/backend/oneapi/unwrap.cpp        |   7 +-
 3 files changed, 174 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 src/backend/oneapi/CMakeLists.txt
 create mode 100755 src/backend/oneapi/kernel/unwrap.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
old mode 100644
new mode 100755
index 826f144a83..dcff3b35e9
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -232,6 +232,7 @@ target_sources(afoneapi
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
+    kernel/unwrap.hpp
     kernel/where.hpp
     kernel/wrap.hpp
     kernel/wrap_dilated.hpp
diff --git a/src/backend/oneapi/kernel/unwrap.hpp b/src/backend/oneapi/kernel/unwrap.hpp
new file mode 100755
index 0000000000..475e55b66c
--- /dev/null
+++ b/src/backend/oneapi/kernel/unwrap.hpp
@@ -0,0 +1,170 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/default_config.hpp>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class unwrapCreateKernel {
+   public:
+    unwrapCreateKernel(sycl::accessor<T, 1, sycl::access::mode::write> d_out,
+                       const KParam out,
+                       sycl::accessor<T, 1, sycl::access::mode::read> d_in,
+                       const KParam in, const int wx, const int wy,
+                       const int sx, const int sy, const int px, const int py,
+                       const int dx, const int dy, const int nx, const int reps,
+                       const bool IS_COLUMN)
+        : d_out_(d_out)
+        , out_(out)
+        , d_in_(d_in)
+        , in_(in)
+        , wx_(wx)
+        , wy_(wy)
+        , sx_(sx)
+        , sy_(sy)
+        , px_(px)
+        , py_(py)
+        , dx_(dx)
+        , dy_(dy)
+        , nx_(nx)
+        , reps_(reps)
+        , IS_COLUMN_(IS_COLUMN) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        // Compute channel and volume
+        const int w = g.get_group_id(1) / in_.dims[2];
+        const int z = g.get_group_id(1) - w * in_.dims[2];
+
+        if (w >= in_.dims[3] || z >= in_.dims[2]) return;
+
+        // Compute offset for channel and volume
+        const int cOut = w * out_.strides[3] + z * out_.strides[2];
+        const int cIn  = w * in_.strides[3] + z * in_.strides[2];
+
+        // Compute the output column index
+        const int id = IS_COLUMN_ ? (g.get_group_id(0) * g.get_local_range(1) +
+                                     it.get_local_id(1))
+                                  : it.get_global_id(0);
+
+        if (id >= (IS_COLUMN_ ? out_.dims[1] : out_.dims[0])) return;
+
+        // Compute the starting index of window in_ x and y of input
+        const int startx = (id % nx_) * sx_;
+        const int starty = (id / nx_) * sy_;
+
+        const int spx = startx - px_;
+        const int spy = starty - py_;
+
+        // Offset the global pointers to the respective starting indices
+        T *optr = d_out_.get_pointer() + cOut +
+                  id * (IS_COLUMN_ ? out_.strides[1] : 1);
+        const T *iptr = d_in_.get_pointer() + cIn + in_.offset;
+
+        bool cond = (spx >= 0 && spx + (wx_ * dx_) < in_.dims[0] && spy >= 0 &&
+                     spy + (wy_ * dy_) < in_.dims[1]);
+
+        // Compute output index local to column
+        int outIdx = IS_COLUMN_ ? it.get_local_id(0) : it.get_local_id(1);
+        const int oStride =
+            IS_COLUMN_ ? it.get_local_range(0) : it.get_local_range(1);
+
+        for (int i = 0; i < reps_; i++) {
+            if (outIdx >= (IS_COLUMN_ ? out_.dims[0] : out_.dims[1])) return;
+
+            // Compute input index local to window
+            const int y = outIdx / wx_;
+            const int x = outIdx % wx_;
+
+            const int xpad = spx + x * dx_;
+            const int ypad = spy + y * dy_;
+
+            // Copy
+            T val = (T)0;
+            if (cond || (xpad >= 0 && xpad < in_.dims[0] && ypad >= 0 &&
+                         ypad < in_.dims[1])) {
+                const int inIdx = ypad * in_.strides[1] + xpad * in_.strides[0];
+                val             = iptr[inIdx];
+            }
+
+            if (IS_COLUMN_) {
+                optr[outIdx] = val;
+            } else {
+                optr[outIdx * out_.strides[1]] = val;
+            }
+
+            outIdx += oStride;
+        }
+    }
+
+   private:
+    sycl::accessor<T, 1, sycl::access::mode::write> d_out_;
+    const KParam out_;
+    sycl::accessor<T, 1, sycl::access::mode::read> d_in_;
+    const KParam in_;
+    const int wx_;
+    const int wy_;
+    const int sx_;
+    const int sy_;
+    const int px_;
+    const int py_;
+    const int dx_;
+    const int dy_;
+    const int nx_;
+    const int reps_;
+    const bool IS_COLUMN_;
+};
+
+template<typename T>
+void unwrap(Param<T> out, const Param<T> in, const dim_t wx, const dim_t wy,
+            const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
+            const dim_t dx, const dim_t dy, const dim_t nx,
+            const bool IS_COLUMN) {
+    dim_t TX = 1, TY = 1;
+    dim_t BX       = 1;
+    const dim_t BY = out.info.dims[2] * out.info.dims[3];
+    int reps       = 1;
+
+    if (IS_COLUMN) {
+        TX   = std::min(THREADS_PER_BLOCK, nextpow2(out.info.dims[0]));
+        TY   = THREADS_PER_BLOCK / TX;
+        BX   = divup(out.info.dims[1], TY);
+        reps = divup((wx * wy), TX);
+    } else {
+        TX   = THREADS_X;
+        TY   = THREADS_X;
+        BX   = divup(out.info.dims[0], TX);
+        reps = divup((wx * wy), TY);
+    }
+
+    auto local  = sycl::range(TX, TY);
+    auto global = sycl::range(local[0] * BX, local[1] * BY);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_out{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor d_in{*in.data, h, sycl::read_only};
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            unwrapCreateKernel<T>(d_out, out.info, d_in, in.info, wx, wy, sx,
+                                  sy, px, py, dx, dy, nx, reps, IS_COLUMN));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/unwrap.cpp b/src/backend/oneapi/unwrap.cpp
index cbb2910ef7..bfb21aef17 100644
--- a/src/backend/oneapi/unwrap.cpp
+++ b/src/backend/oneapi/unwrap.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/unwrap.hpp>
+#include <kernel/unwrap.hpp>
 #include <unwrap.hpp>
 #include <stdexcept>
 
@@ -32,9 +32,8 @@ Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
     if (!is_column) { std::swap(odims[0], odims[1]); }
 
     Array<T> outArray = createEmptyArray<T>(odims);
-    ONEAPI_NOT_SUPPORTED("unwrap Not supported");
-    // kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, dx, dy, nx,
-    //                   is_column);
+    kernel::unwrap<T>(outArray, in, wx, wy, sx, sy, px, py, dx, dy, nx,
+                      is_column);
 
     return outArray;
 }

From 138f12e9f181b8a7bd013323137931aec0f3bd59 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Tue, 29 Nov 2022 12:52:55 -0500
Subject: [PATCH 2354/2677] convolve{1,2,3} oneapi port redo (#3327)

* convolve{1,2,3}. fails separable, unwrap, double, jit. as expected


Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/convolve.cpp         |  58 ++++++-
 src/backend/oneapi/kernel/convolve.hpp  | 145 +++++++++++++++++
 src/backend/oneapi/kernel/convolve1.hpp | 174 +++++++++++++++++++++
 src/backend/oneapi/kernel/convolve2.hpp | 193 +++++++++++++++++++++++
 src/backend/oneapi/kernel/convolve3.hpp | 199 ++++++++++++++++++++++++
 5 files changed, 765 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 src/backend/oneapi/convolve.cpp
 create mode 100755 src/backend/oneapi/kernel/convolve.hpp
 create mode 100755 src/backend/oneapi/kernel/convolve1.hpp
 create mode 100755 src/backend/oneapi/kernel/convolve2.hpp
 create mode 100755 src/backend/oneapi/kernel/convolve3.hpp

diff --git a/src/backend/oneapi/convolve.cpp b/src/backend/oneapi/convolve.cpp
old mode 100644
new mode 100755
index 94e6d48d09..a7a2fc9aee
--- a/src/backend/oneapi/convolve.cpp
+++ b/src/backend/oneapi/convolve.cpp
@@ -14,6 +14,7 @@
 #include <common/moddims.hpp>
 #include <convolve.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/convolve.hpp>
 #include <reorder.hpp>
 #include <transpose.hpp>
 #include <unwrap.hpp>
@@ -33,8 +34,56 @@ namespace oneapi {
 template<typename T, typename accT>
 Array<T> convolve(Array<T> const &signal, Array<accT> const &filter,
                   AF_BATCH_KIND kind, const int rank, const bool expand) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
+    const dim4 &sDims = signal.dims();
+    const dim4 &fDims = filter.dims();
+
+    dim4 oDims(1);
+    if (expand) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
+            } else {
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
+            }
+        }
+    } else {
+        oDims = sDims;
+        if (kind == AF_BATCH_RHS) {
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
+        }
+    }
+
+    Array<T> out    = createEmptyArray<T>(oDims);
+    bool callKernel = true;
+
+    dim_t MCFL2 = kernel::MAX_CONV2_FILTER_LEN;
+    dim_t MCFL3 = kernel::MAX_CONV3_FILTER_LEN;
+    switch (rank) {
+        case 1:
+            if (fDims[0] > kernel::MAX_CONV1_FILTER_LEN) { callKernel = false; }
+            break;
+        case 2:
+            if ((fDims[0] * fDims[1]) > (MCFL2 * MCFL2)) { callKernel = false; }
+            break;
+        case 3:
+            if ((fDims[0] * fDims[1] * fDims[2]) > (MCFL3 * MCFL3 * MCFL3)) {
+                callKernel = false;
+            }
+            break;
+        default: AF_ERROR("rank only supports values 1-3.", AF_ERR_UNKNOWN);
+    }
+
+    if (!callKernel) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\nOneAPI N Dimensional Convolution doesn't support "
+                 "%llux%llux%llu kernel\n",
+                 fDims[0], fDims[1], fDims[2]);
+        ONEAPI_NOT_SUPPORTED(errMessage);
+    }
+
+    kernel::convolve_nd<T, accT>(out, signal, filter, kind, rank, expand);
+
     return out;
 }
 
@@ -60,8 +109,9 @@ template<typename T>
 Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
                           const dim4 &stride, const dim4 &padding,
                           const dim4 &dilation) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
+    Array<T> out =
+        convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
+
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/convolve.hpp b/src/backend/oneapi/kernel/convolve.hpp
new file mode 100755
index 0000000000..39abe603ad
--- /dev/null
+++ b/src/backend/oneapi/kernel/convolve.hpp
@@ -0,0 +1,145 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <af/defines.h>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+// below shared MAX_*_LEN's are calculated based on
+// a maximum shared memory configuration of 48KB per block
+// considering complex types as well
+constexpr int MAX_CONV1_FILTER_LEN = 129;
+constexpr int MAX_CONV2_FILTER_LEN = 17;
+constexpr int MAX_CONV3_FILTER_LEN = 5;
+
+constexpr int MAX_SCONV_FILTER_LEN = 31;
+
+constexpr int THREADS   = 256;
+constexpr int THREADS_X = 16;
+constexpr int THREADS_Y = 16;
+constexpr int CUBE_X    = 8;
+constexpr int CUBE_Y    = 8;
+constexpr int CUBE_Z    = 4;
+
+template<typename aT>
+struct conv_kparam_t {
+    sycl::range<3> global{0, 0, 0};
+    sycl::range<3> local{0, 0, 0};
+    size_t loc_size;
+    int nBBS0;
+    int nBBS1;
+    bool outHasNoOffset;
+    bool inHasNoOffset;
+    bool launchMoreBlocks;
+    int o[3];
+    int s[3];
+    sycl::buffer<aT> *impulse;
+};
+
+template<typename T>
+T binOp(T lhs, T rhs) {
+    return lhs * rhs;
+}
+
+template<typename aT>
+void prepareKernelArgs(conv_kparam_t<aT> &param, dim_t *oDims,
+                       const dim_t *fDims, const int rank) {
+    using sycl::range;
+
+    int batchDims[4] = {1, 1, 1, 1};
+    for (int i = rank; i < 4; ++i) {
+        batchDims[i] = (param.launchMoreBlocks ? 1 : oDims[i]);
+    }
+
+    if (rank == 1) {
+        param.local    = range<3>{THREADS, 1, 1};
+        param.nBBS0    = divup(oDims[0], THREADS);
+        param.nBBS1    = batchDims[2];
+        param.global   = range<3>(param.nBBS0 * THREADS * batchDims[1],
+                                param.nBBS1 * batchDims[3], 1);
+        param.loc_size = (THREADS + 2 * (fDims[0] - 1));
+    } else if (rank == 2) {
+        param.local  = range<3>{THREADS_X, THREADS_Y, 1};
+        param.nBBS0  = divup(oDims[0], THREADS_X);
+        param.nBBS1  = divup(oDims[1], THREADS_Y);
+        param.global = range<3>(param.nBBS0 * THREADS_X * batchDims[2],
+                                param.nBBS1 * THREADS_Y * batchDims[3], 1);
+    } else if (rank == 3) {
+        param.local    = range<3>{CUBE_X, CUBE_Y, CUBE_Z};
+        param.nBBS0    = divup(oDims[0], CUBE_X);
+        param.nBBS1    = divup(oDims[1], CUBE_Y);
+        int blk_z      = divup(oDims[2], CUBE_Z);
+        param.global   = range<3>(param.nBBS0 * CUBE_X * batchDims[3],
+                                param.nBBS1 * CUBE_Y, blk_z * CUBE_Z);
+        param.loc_size = (CUBE_X + 2 * (fDims[0] - 1)) *
+                         (CUBE_Y + 2 * (fDims[1] - 1)) *
+                         (CUBE_Z + 2 * (fDims[2] - 1));
+    }
+}
+
+template<typename T>
+void memcpyBuffer(sycl::buffer<T, 1> &dest, sycl::buffer<T, 1> &src,
+                  const size_t n, const size_t srcOffset) {
+    getQueue().submit([&](auto &h) {
+        sycl::accessor srcAcc{src, h, sycl::range{n}, sycl::id{srcOffset},
+                              sycl::read_only};
+        sycl::accessor destAcc{
+            dest,         h, sycl::range{n}, sycl::id{0}, sycl::write_only,
+            sycl::no_init};
+        h.copy(srcAcc, destAcc);
+    });
+}
+
+template<typename T>
+using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
+                                      sycl::access::target::local>;
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+#include "convolve1.hpp"
+#include "convolve2.hpp"
+#include "convolve3.hpp"
+
+template<typename T, typename aT>
+void convolve_nd(Param<T> out, const Param<T> signal, const Param<aT> filter,
+                 AF_BATCH_KIND kind, const int rank, const bool expand) {
+    conv_kparam_t<aT> param;
+
+    for (int i = 0; i < 3; ++i) {
+        param.o[i] = 0;
+        param.s[i] = 0;
+    }
+    param.launchMoreBlocks = kind == AF_BATCH_SAME || kind == AF_BATCH_RHS;
+    param.outHasNoOffset   = kind == AF_BATCH_LHS || kind == AF_BATCH_NONE;
+    param.inHasNoOffset    = kind != AF_BATCH_SAME;
+
+    prepareKernelArgs<aT>(param, out.info.dims, filter.info.dims, rank);
+
+    switch (rank) {
+        case 1: conv1<T, aT>(param, out, signal, filter, expand); break;
+        case 2: conv2<T, aT>(param, out, signal, filter, expand); break;
+        case 3: conv3<T, aT>(param, out, signal, filter, expand); break;
+    }
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/convolve1.hpp b/src/backend/oneapi/kernel/convolve1.hpp
new file mode 100755
index 0000000000..1383bb4591
--- /dev/null
+++ b/src/backend/oneapi/kernel/convolve1.hpp
@@ -0,0 +1,174 @@
+template<typename T, typename aT>
+class conv1HelperCreateKernel {
+   public:
+    conv1HelperCreateKernel(write_accessor<T> out, KParam oInfo,
+                            read_accessor<T> signal, KParam sInfo,
+                            local_accessor<aT> localMem,
+                            read_accessor<aT> impulse, KParam fInfo, int nBBS0,
+                            int nBBS1, int ostep1, int ostep2, int ostep3,
+                            int sstep1, int sstep2, int sstep3,
+                            const bool expand)
+        : out_(out)
+        , oInfo_(oInfo)
+        , signal_(signal)
+        , sInfo_(sInfo)
+        , localMem_(localMem)
+        , impulse_(impulse)
+        , fInfo_(fInfo)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1)
+        , ostep1_(ostep1)
+        , ostep2_(ostep2)
+        , ostep3_(ostep3)
+        , sstep1_(sstep1)
+        , sstep2_(sstep2)
+        , sstep3_(sstep3)
+        , expand_(expand) {}
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g = it.get_group();
+
+        int fLen          = fInfo_.dims[0];
+        int padding       = fLen - 1;
+        int shrdLen       = g.get_local_range(0) + 2 * padding;
+        const unsigned b1 = g.get_group_id(0) / nBBS0_;
+        const unsigned b0 = g.get_group_id(0) - nBBS0_ * b1;
+        const unsigned b3 = g.get_group_id(1) / nBBS1_;
+        const unsigned b2 = g.get_group_id(1) - nBBS1_ * b3;
+
+        T *dst =
+            out_.get_pointer() +
+            (b1 * oInfo_.strides[1] + /* activated with batched input signal_ */
+             ostep1_ *
+                 oInfo_.strides[1] +  /* activated with batched input filter */
+             b2 * oInfo_.strides[2] + /* activated with batched input signal_ */
+             ostep2_ *
+                 oInfo_.strides[2] +  /* activated with batched input filter */
+             b3 * oInfo_.strides[3] + /* activated with batched input signal_ */
+             ostep3_ *
+                 oInfo_.strides[3]); /* activated with batched input filter */
+
+        T const *src =
+            signal_.get_pointer() + sInfo_.offset +
+            (b1 * sInfo_.strides[1] + /* activated with batched input signal_ */
+             sstep1_ *
+                 sInfo_.strides[1] +  /* activated with batched input filter */
+             b2 * sInfo_.strides[2] + /* activated with batched input signal_ */
+             sstep2_ *
+                 sInfo_.strides[2] +  /* activated with batched input filter */
+             b3 * sInfo_.strides[3] + /* activated with batched input signal_ */
+             sstep3_ *
+                 sInfo_.strides[3]); /* activated with batched input filter */
+
+        int gx = g.get_local_range(0) * b0;
+
+        for (int i = it.get_local_id(0); i < shrdLen;
+             i += g.get_local_range(0)) {
+            int idx      = gx - padding + i;
+            localMem_[i] = (idx >= 0 && idx < sInfo_.dims[0])
+                               ? src[idx * sInfo_.strides[0]]
+                               : (T)(0);
+        }
+        it.barrier();
+        gx += it.get_local_id(0);
+
+        if (gx >= 0 && gx < oInfo_.dims[0]) {
+            int lx   = it.get_local_id(0) + padding + (expand_ ? 0 : fLen >> 1);
+            aT accum = (aT)(0);
+            for (int f = 0; f < fLen; ++f) {
+                // binOp will do MUL_OP for convolution operation
+                accum = accum + binOp((aT)localMem_[lx - f], (aT)impulse_[f]);
+            }
+            dst[gx] = (T)accum;
+        }
+    }
+
+   private:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    read_accessor<T> signal_;
+    KParam sInfo_;
+    local_accessor<aT> localMem_;
+    read_accessor<aT> impulse_;
+    KParam fInfo_;
+    int nBBS0_;
+    int nBBS1_;
+    int ostep1_;
+    int ostep2_;
+    int ostep3_;
+    int sstep1_;
+    int sstep2_;
+    int sstep3_;
+    const bool expand_;
+};
+
+template<typename T, typename aT>
+void conv1Helper(const conv_kparam_t<aT> &param, Param<T> &out,
+                 const Param<T> &signal, const Param<aT> &filter,
+                 const int rank, const bool expand) {
+    auto Q = getQueue();
+    Q.submit([&](auto &h) {
+        sycl::accessor<aT, 1, sycl::access::mode::read_write,
+                       sycl::access::target::local>
+            localMem(param.loc_size, h);
+        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor signalAcc{*signal.data, h, sycl::read_only};
+        sycl::accessor impulseAcc{*param.impulse, h, sycl::read_only};
+        h.parallel_for(
+            sycl::nd_range{param.global, param.local},
+            conv1HelperCreateKernel<T, aT>(
+                outAcc, out.info, signalAcc, signal.info, localMem, impulseAcc,
+                filter.info, param.nBBS0, param.nBBS1, param.o[0], param.o[1],
+                param.o[2], param.s[0], param.s[1], param.s[2], expand));
+    });
+    ONEAPI_DEBUG_FINISH(Q);
+}
+
+template<typename T, typename aT>
+void conv1(conv_kparam_t<aT> &p, Param<T> &out, const Param<T> &sig,
+           const Param<aT> &filt, const bool expand) {
+    const size_t se_size = filt.info.dims[0];
+    sycl::buffer<aT> impulse{sycl::range(filt.info.dims[0])};
+    int f0Off = filt.info.offset;
+    for (int b3 = 0; b3 < filt.info.dims[3]; ++b3) {
+        int f3Off = b3 * filt.info.strides[3];
+
+        for (int b2 = 0; b2 < filt.info.dims[2]; ++b2) {
+            int f2Off = b2 * filt.info.strides[2];
+
+            for (int b1 = 0; b1 < filt.info.dims[1]; ++b1) {
+                int f1Off = b1 * filt.info.strides[1];
+
+                const size_t srcOffset = f0Off + f1Off + f2Off + f3Off;
+                memcpyBuffer(impulse, *filt.data, se_size, srcOffset);
+                p.impulse = &impulse;
+
+                p.o[0] = (p.outHasNoOffset ? 0 : b1);
+                p.o[1] = (p.outHasNoOffset ? 0 : b2);
+                p.o[2] = (p.outHasNoOffset ? 0 : b3);
+                p.s[0] = (p.inHasNoOffset ? 0 : b1);
+                p.s[1] = (p.inHasNoOffset ? 0 : b2);
+                p.s[2] = (p.inHasNoOffset ? 0 : b3);
+
+                conv1Helper<T, aT>(p, out, sig, filt, 1, expand);
+            }
+        }
+    }
+}
+
+#define INSTANTIATE_CONV1(T, aT)                                    \
+    template void conv1<T, aT>(conv_kparam_t<aT> &, Param<T> &,     \
+                               const Param<T> &, const Param<aT> &, \
+                               const bool);
+
+INSTANTIATE_CONV1(cdouble, cdouble)
+INSTANTIATE_CONV1(cfloat, cfloat)
+INSTANTIATE_CONV1(double, double)
+INSTANTIATE_CONV1(float, float)
+INSTANTIATE_CONV1(uint, float)
+INSTANTIATE_CONV1(int, float)
+INSTANTIATE_CONV1(uchar, float)
+INSTANTIATE_CONV1(char, float)
+INSTANTIATE_CONV1(ushort, float)
+INSTANTIATE_CONV1(short, float)
+INSTANTIATE_CONV1(uintl, float)
+INSTANTIATE_CONV1(intl, float)
diff --git a/src/backend/oneapi/kernel/convolve2.hpp b/src/backend/oneapi/kernel/convolve2.hpp
new file mode 100755
index 0000000000..5232b225ff
--- /dev/null
+++ b/src/backend/oneapi/kernel/convolve2.hpp
@@ -0,0 +1,193 @@
+template<typename T, typename aT>
+class conv2HelperCreateKernel {
+   public:
+    conv2HelperCreateKernel(write_accessor<T> out, KParam oInfo,
+                            read_accessor<T> signal, KParam sInfo,
+                            read_accessor<aT> impulse, KParam fInfo, int nBBS0,
+                            int nBBS1, int ostep2, int ostep3, int sstep2,
+                            int sstep3, local_accessor<aT> localMem,
+                            const int f0, const int f1, const bool expand)
+        : out_(out)
+        , oInfo_(oInfo)
+        , signal_(signal)
+        , sInfo_(sInfo)
+        , impulse_(impulse)
+        , fInfo_(fInfo)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1)
+        , ostep2_(ostep2)
+        , ostep3_(ostep3)
+        , sstep2_(sstep2)
+        , sstep3_(sstep3)
+        , localMem_(localMem)
+        , f0_(f0)
+        , f1_(f1)
+        , expand_(expand) {}
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g = it.get_group();
+
+        int radius0  = f0_ - 1;
+        int radius1  = f1_ - 1;
+        int padding0 = 2 * radius0;
+        int padding1 = 2 * radius1;
+        int shrdLen0 = g.get_local_range(0) + padding0;
+        int shrdLen1 = g.get_local_range(1) + padding1;
+
+        unsigned b0 = g.get_group_id(0) / nBBS0_;
+        unsigned b1 = g.get_group_id(1) / nBBS1_;
+
+        T *dst =
+            out_.get_pointer() +
+            (b0 * oInfo_.strides[2] + /* activated with batched input signal_ */
+             ostep2_ *
+                 oInfo_.strides[2] +  /* activated with batched input filter */
+             b1 * oInfo_.strides[3] + /* activated with batched input signal_ */
+             ostep3_ *
+                 oInfo_.strides[3]); /* activated with batched input filter */
+
+        const T *src =
+            signal_.get_pointer() + sInfo_.offset +
+            (b0 * sInfo_.strides[2] + /* activated with batched input signal_ */
+             sstep2_ *
+                 sInfo_.strides[2] +  /* activated with batched input filter */
+             b1 * sInfo_.strides[3] + /* activated with batched input signal_ */
+             sstep3_ *
+                 sInfo_.strides[3]); /* activated with batched input filter */
+
+        int lx = it.get_local_id(0);
+        int ly = it.get_local_id(1);
+        int gx = g.get_local_range(0) * (g.get_group_id(0) - b0 * nBBS0_) + lx;
+        int gy = g.get_local_range(1) * (g.get_group_id(1) - b1 * nBBS1_) + ly;
+
+        // below loops are traditional loops, they only run multiple
+        // times filter length is more than launch size
+        int s0 = sInfo_.strides[0];
+        int s1 = sInfo_.strides[1];
+        int d0 = sInfo_.dims[0];
+        int d1 = sInfo_.dims[1];
+        for (int b = ly, gy2 = gy; b < shrdLen1;
+             b += g.get_local_range(1), gy2 += g.get_local_range(1)) {
+            int j     = gy2 - radius1;
+            bool is_j = j >= 0 && j < d1;
+            // move row_set g.get_local_range(1) along coloumns
+            for (int a = lx, gx2 = gx; a < shrdLen0;
+                 a += g.get_local_range(0), gx2 += g.get_local_range(0)) {
+                int i     = gx2 - radius0;
+                bool is_i = i >= 0 && i < d0;
+                localMem_[b * shrdLen0 + a] =
+                    (is_i && is_j ? src[i * s0 + j * s1] : (T)(0));
+            }
+        }
+        it.barrier();
+
+        if (gx < oInfo_.dims[0] && gy < oInfo_.dims[1]) {
+            int ci = lx + radius0 + (expand_ ? 0 : f0_ >> 1);
+            int cj = ly + radius1 + (expand_ ? 0 : f1_ >> 1);
+
+            aT accum = (aT)(0);
+            for (int fj = 0; fj < f1_; ++fj) {
+                for (int fi = 0; fi < f0_; ++fi) {
+                    aT f_val = impulse_[fj * f0_ + fi];
+                    T s_val  = localMem_[(cj - fj) * shrdLen0 + (ci - fi)];
+
+                    // binOp will do MUL_OP for convolution operation
+                    accum = accum + binOp((aT)s_val, (aT)f_val);
+                }
+            }
+            dst[gy * oInfo_.strides[1] + gx] = (T)accum;
+        }
+    }
+
+   private:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    read_accessor<T> signal_;
+    KParam sInfo_;
+    read_accessor<aT> impulse_;
+    KParam fInfo_;
+    int nBBS0_;
+    int nBBS1_;
+    int ostep2_;
+    int ostep3_;
+    int sstep2_;
+    int sstep3_;
+    local_accessor<aT> localMem_;
+    const int f0_;
+    const int f1_;
+    const bool expand_;
+};
+
+template<typename T, typename aT>
+void conv2Helper(const conv_kparam_t<aT> &param, Param<T> out,
+                 const Param<T> signal, const Param<aT> filter,
+                 const bool expand) {
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    const int f0 = filter.info.dims[0];
+    const int f1 = filter.info.dims[1];
+    const size_t LOC_SIZE =
+        (THREADS_X + 2 * (f0 - 1)) * (THREADS_Y + 2 * (f1 - 1));
+
+    auto Q = getQueue();
+    Q.submit([&](auto &h) {
+        sycl::accessor<aT, 1, sycl::access::mode::read_write,
+                       sycl::access::target::local>
+            localMem(LOC_SIZE, h);
+        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor signalAcc{*signal.data, h, sycl::read_only};
+        sycl::accessor impulseAcc{*param.impulse, h, sycl::read_only};
+        h.parallel_for(
+            sycl::nd_range{param.global, param.local},
+            conv2HelperCreateKernel<T, aT>(
+                outAcc, out.info, signalAcc, signal.info, impulseAcc,
+                filter.info, param.nBBS0, param.nBBS1, param.o[1], param.o[2],
+                param.s[1], param.s[2], localMem, f0, f1, expand));
+    });
+    ONEAPI_DEBUG_FINISH(Q);
+}
+
+template<typename T, typename aT>
+void conv2(conv_kparam_t<aT> &p, Param<T> &out, const Param<T> &sig,
+           const Param<aT> &filt, const bool expand) {
+    size_t se_size = filt.info.dims[0] * filt.info.dims[1];
+    sycl::buffer<aT> impulse{sycl::range(se_size)};
+    int f0Off = filt.info.offset;
+
+    for (int b3 = 0; b3 < filt.info.dims[3]; ++b3) {
+        int f3Off = b3 * filt.info.strides[3];
+
+        for (int b2 = 0; b2 < filt.info.dims[2]; ++b2) {
+            int f2Off = b2 * filt.info.strides[2];
+
+            const size_t srcOffset = f2Off + f3Off + f0Off;
+            memcpyBuffer(impulse, *filt.data, se_size, srcOffset);
+            p.impulse = &impulse;
+
+            p.o[1] = (p.outHasNoOffset ? 0 : b2);
+            p.o[2] = (p.outHasNoOffset ? 0 : b3);
+            p.s[1] = (p.inHasNoOffset ? 0 : b2);
+            p.s[2] = (p.inHasNoOffset ? 0 : b3);
+
+            conv2Helper<T, aT>(p, out, sig, filt, expand);
+        }
+    }
+}
+
+#define INSTANTIATE_CONV2(T, aT)                                    \
+    template void conv2<T, aT>(conv_kparam_t<aT> &, Param<T> &,     \
+                               const Param<T> &, const Param<aT> &, \
+                               const bool);
+
+INSTANTIATE_CONV2(char, float)
+INSTANTIATE_CONV2(cfloat, cfloat)
+INSTANTIATE_CONV2(cdouble, cdouble)
+INSTANTIATE_CONV2(float, float)
+INSTANTIATE_CONV2(double, double)
+INSTANTIATE_CONV2(short, float)
+INSTANTIATE_CONV2(int, float)
+INSTANTIATE_CONV2(intl, float)
+INSTANTIATE_CONV2(ushort, float)
+INSTANTIATE_CONV2(uint, float)
+INSTANTIATE_CONV2(uintl, float)
+INSTANTIATE_CONV2(uchar, float)
diff --git a/src/backend/oneapi/kernel/convolve3.hpp b/src/backend/oneapi/kernel/convolve3.hpp
new file mode 100755
index 0000000000..d9a93affef
--- /dev/null
+++ b/src/backend/oneapi/kernel/convolve3.hpp
@@ -0,0 +1,199 @@
+int index(int i, int j, int k, int jstride, int kstride) {
+    return i + j * jstride + k * kstride;
+}
+
+template<typename T, typename aT>
+class conv3HelperCreateKernel {
+   public:
+    conv3HelperCreateKernel(write_accessor<T> out, KParam oInfo,
+                            read_accessor<T> signal, KParam sInfo,
+                            local_accessor<aT> localMem,
+                            read_accessor<aT> impulse, KParam fInfo, int nBBS0,
+                            int nBBS1, int ostep1, int ostep2, int ostep3,
+                            int sstep1, int sstep2, int sstep3,
+                            const bool EXPAND)
+        : out_(out)
+        , oInfo_(oInfo)
+        , signal_(signal)
+        , sInfo_(sInfo)
+        , localMem_(localMem)
+        , impulse_(impulse)
+        , fInfo_(fInfo)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1)
+        , ostep1_(ostep1)
+        , ostep2_(ostep2)
+        , ostep3_(ostep3)
+        , sstep1_(sstep1)
+        , sstep2_(sstep2)
+        , sstep3_(sstep3)
+        , EXPAND_(EXPAND) {}
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g = it.get_group();
+        int fLen0     = fInfo_.dims[0];
+        int fLen1     = fInfo_.dims[1];
+        int fLen2     = fInfo_.dims[2];
+        int radius0   = fLen0 - 1;
+        int radius1   = fLen1 - 1;
+        int radius2   = fLen2 - 1;
+        int shrdLen0  = g.get_local_range(0) + 2 * radius0;
+        int shrdLen1  = g.get_local_range(1) + 2 * radius1;
+        int shrdLen2  = g.get_local_range(2) + 2 * radius2;
+        int skStride  = shrdLen0 * shrdLen1;
+        int fStride   = fLen0 * fLen1;
+        unsigned b2   = g.get_group_id(0) / nBBS0_;
+
+        T *dst =
+            out_.get_pointer() +
+            (b2 * oInfo_.strides[3] + /* activated with batched input signal_ */
+             ostep3_ *
+                 oInfo_.strides[3]); /* activated with batched input filter */
+
+        const T *src =
+            signal_.get_pointer() + sInfo_.offset +
+            (b2 * sInfo_.strides[3] + /* activated with batched input signal_ */
+             sstep3_ *
+                 sInfo_.strides[3]); /* activated with batched input filter */
+
+        int lx  = it.get_local_id(0);
+        int ly  = it.get_local_id(1);
+        int lz  = it.get_local_id(2);
+        int gx  = g.get_local_range(0) * (g.get_group_id(0) - b2 * nBBS0_) + lx;
+        int gy  = g.get_local_range(1) * g.get_group_id(1) + ly;
+        int gz  = g.get_local_range(2) * g.get_group_id(2) + lz;
+        int lx2 = lx + g.get_local_range(0);
+        int ly2 = ly + g.get_local_range(1);
+        int lz2 = lz + g.get_local_range(2);
+        int gx2 = gx + g.get_local_range(0);
+        int gy2 = gy + g.get_local_range(1);
+        int gz2 = gz + g.get_local_range(2);
+
+        int s0 = sInfo_.strides[0];
+        int s1 = sInfo_.strides[1];
+        int s2 = sInfo_.strides[2];
+        int d0 = sInfo_.dims[0];
+        int d1 = sInfo_.dims[1];
+        int d2 = sInfo_.dims[2];
+
+        for (int c = lz, gz2 = gz; c < shrdLen2;
+             c += g.get_local_range(2), gz2 += g.get_local_range(2)) {
+            int k     = gz2 - radius2;
+            bool is_k = k >= 0 && k < d2;
+            for (int b = ly, gy2 = gy; b < shrdLen1;
+                 b += g.get_local_range(1), gy2 += g.get_local_range(1)) {
+                int j     = gy2 - radius1;
+                bool is_j = j >= 0 && j < d1;
+                for (int a = lx, gx2 = gx; a < shrdLen0;
+                     a += g.get_local_range(0), gx2 += g.get_local_range(0)) {
+                    int i     = gx2 - radius0;
+                    bool is_i = i >= 0 && i < d0;
+                    localMem_[c * skStride + b * shrdLen0 + a] =
+                        (is_i && is_j && is_k ? src[i * s0 + j * s1 + k * s2]
+                                              : (T)(0));
+                }
+            }
+        }
+        it.barrier();
+
+        if (gx < oInfo_.dims[0] && gy < oInfo_.dims[1] && gz < oInfo_.dims[2]) {
+            int ci = lx + radius0 + (EXPAND_ ? 0 : fLen0 >> 1);
+            int cj = ly + radius1 + (EXPAND_ ? 0 : fLen1 >> 1);
+            int ck = lz + radius2 + (EXPAND_ ? 0 : fLen2 >> 1);
+
+            aT accum = (aT)(0);
+            for (int fk = 0; fk < fLen2; ++fk) {
+                for (int fj = 0; fj < fLen1; ++fj) {
+                    for (int fi = 0; fi < fLen0; ++fi) {
+                        aT f_val = impulse_[index(fi, fj, fk, fLen0, fStride)];
+                        T s_val  = localMem_[index(ci - fi, cj - fj, ck - fk,
+                                                   shrdLen0, skStride)];
+
+                        // binOp will do MUL_OP for convolution operation
+                        accum = accum + binOp((aT)s_val, (aT)f_val);
+                    }
+                }
+            }
+            dst[index(gx, gy, gz, oInfo_.strides[1], oInfo_.strides[2])] =
+                (T)accum;
+        }
+    }
+
+   private:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    read_accessor<T> signal_;
+    KParam sInfo_;
+    local_accessor<aT> localMem_;
+    read_accessor<aT> impulse_;
+    KParam fInfo_;
+    int nBBS0_;
+    int nBBS1_;
+    int ostep1_;
+    int ostep2_;
+    int ostep3_;
+    int sstep1_;
+    int sstep2_;
+    int sstep3_;
+    const bool EXPAND_;
+};
+
+template<typename T, typename aT>
+void conv3Helper(const conv_kparam_t<aT> &param, Param<T> &out,
+                 const Param<T> &signal, const Param<aT> &impulse,
+                 const int rank, const bool EXPAND) {
+    auto Q = getQueue();
+    Q.submit([&](auto &h) {
+        sycl::accessor<aT, 1, sycl::access::mode::read_write,
+                       sycl::access::target::local>
+            localMem(param.loc_size, h);
+        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor signalAcc{*signal.data, h, sycl::read_only};
+        sycl::accessor impulseAcc{*param.impulse, h, sycl::read_only};
+        h.parallel_for(
+            sycl::nd_range{param.global, param.local},
+            conv3HelperCreateKernel<T, aT>(
+                outAcc, out.info, signalAcc, signal.info, localMem, impulseAcc,
+                impulse.info, param.nBBS0, param.nBBS1, param.o[0], param.o[1],
+                param.o[2], param.s[0], param.s[1], param.s[2], EXPAND));
+    });
+    ONEAPI_DEBUG_FINISH(Q);
+}
+
+template<typename T, typename aT>
+void conv3(conv_kparam_t<aT> &p, Param<T> &out, const Param<T> &sig,
+           const Param<aT> &filt, const bool expand) {
+    size_t se_size = filt.info.dims[0] * filt.info.dims[1] * filt.info.dims[2];
+    sycl::buffer<aT> impulse{sycl::range(se_size)};
+    int f0Off = filt.info.offset;
+
+    for (int b3 = 0; b3 < filt.info.dims[3]; ++b3) {
+        int f3Off = b3 * filt.info.strides[3];
+
+        const size_t srcOffset = f3Off + f0Off;
+        memcpyBuffer(impulse, *filt.data, se_size, srcOffset);
+        p.impulse = &impulse;
+
+        p.o[2] = (p.outHasNoOffset ? 0 : b3);
+        p.s[2] = (p.inHasNoOffset ? 0 : b3);
+
+        conv3Helper<T, aT>(p, out, sig, filt, 3, expand);
+    }
+}
+
+#define INSTANTIATE_CONV3(T, aT)                                    \
+    template void conv3<T, aT>(conv_kparam_t<aT> &, Param<T> &,     \
+                               const Param<T> &, const Param<aT> &, \
+                               const bool);
+
+INSTANTIATE_CONV3(cdouble, cdouble)
+INSTANTIATE_CONV3(cfloat, cfloat)
+INSTANTIATE_CONV3(double, double)
+INSTANTIATE_CONV3(float, float)
+INSTANTIATE_CONV3(uint, float)
+INSTANTIATE_CONV3(int, float)
+INSTANTIATE_CONV3(uchar, float)
+INSTANTIATE_CONV3(char, float)
+INSTANTIATE_CONV3(ushort, float)
+INSTANTIATE_CONV3(short, float)
+INSTANTIATE_CONV3(uintl, float)
+INSTANTIATE_CONV3(intl, float)

From a230ef46c27588cffe204bb5c465dba30bc08cdd Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Wed, 21 Dec 2022 18:02:03 -0500
Subject: [PATCH 2355/2677] reorder oneapi port (#3332)

* reorder ported to oneapi

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
Co-authored-by: syurkevi <stefan@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt     |   1 +
 src/backend/oneapi/kernel/reorder.hpp | 130 ++++++++++++++++++++++++++
 src/backend/oneapi/reorder.cpp        |   6 +-
 3 files changed, 133 insertions(+), 4 deletions(-)
 create mode 100755 src/backend/oneapi/kernel/reorder.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index dcff3b35e9..9abca35940 100755
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -227,6 +227,7 @@ target_sources(afoneapi
     kernel/reduce_all.hpp
     kernel/reduce_first.hpp
     kernel/reduce_dim.hpp
+    kernel/reorder.hpp
     kernel/scan_first.hpp
     kernel/scan_dim.hpp
     kernel/transpose.hpp
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
new file mode 100755
index 0000000000..2eb7484db2
--- /dev/null
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -0,0 +1,130 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+// #include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
+                                      sycl::access::target::local>;
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+class reorderCreateKernel {
+   public:
+    reorderCreateKernel(write_accessor<T> out, read_accessor<T> in,
+                        const KParam op, const KParam ip, const int d0,
+                        const int d1, const int d2, const int d3,
+                        const int blocksPerMatX, const int blocksPerMatY)
+        : out_(out)
+        , in_(in)
+        , op_(op)
+        , ip_(ip)
+        , d0_(d0)
+        , d1_(d1)
+        , d2_(d2)
+        , d3_(d3)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        auto g = it.get_group();
+
+        const int oz = g.get_group_id(0) / blocksPerMatX_;
+        const int ow = g.get_group_id(1) / blocksPerMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - oz * blocksPerMatX_;
+        const int blockIdx_y = g.get_group_id(1) - ow * blocksPerMatY_;
+
+        const int xx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+        const int yy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+        bool valid = (xx < op_.dims[0] && yy < op_.dims[1] &&
+                      oz < op_.dims[2] && ow < op_.dims[3]);
+
+        const int incy = blocksPerMatY_ * g.get_local_range(1);
+        const int incx = blocksPerMatX_ * g.get_local_range(0);
+
+        const int o_off    = ow * op_.strides[3] + oz * op_.strides[2];
+        const int rdims[4] = {d0_, d1_, d2_, d3_};
+        int ods[4]         = {xx, yy, oz, ow};
+        int ids[4]         = {0};
+
+        ids[rdims[3]] = ow;
+        ids[rdims[2]] = oz;
+
+        for (int oy = yy; oy < op_.dims[1]; oy += incy) {
+            ids[rdims[1]] = oy;
+            for (int ox = xx; ox < op_.dims[0]; ox += incx) {
+                ids[rdims[0]] = ox;
+
+                const int oIdx = o_off + oy * op_.strides[1] + ox;
+
+                const int iIdx = ids[3] * ip_.strides[3] +
+                                 ids[2] * ip_.strides[2] +
+                                 ids[1] * ip_.strides[1] + ids[0];
+
+                if (valid) { out_[oIdx] = in_[ip_.offset + iIdx]; }
+            }
+        }
+    }
+
+   private:
+    write_accessor<T> out_;
+    read_accessor<T> in_;
+    const KParam op_;
+    const KParam ip_;
+    const int d0_;
+    const int d1_;
+    const int d2_;
+    const int d3_;
+    const int blocksPerMatX_;
+    const int blocksPerMatY_;
+};
+
+template<typename T>
+void reorder(Param<T> out, const Param<T> in, const dim_t* rdims) {
+    constexpr int TX    = 32;
+    constexpr int TY    = 8;
+    constexpr int TILEX = 512;
+    constexpr int TILEY = 32;
+
+    auto local = sycl::range{TX, TY};
+
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    auto global       = sycl::range{local[0] * blocksPerMatX * out.info.dims[2],
+                              local[1] * blocksPerMatY * out.info.dims[3]};
+
+    getQueue().submit([&](sycl::handler& h) {
+        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor inAcc{*in.data, h, sycl::read_only};
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       reorderCreateKernel<T>(
+                           outAcc, inAcc, out.info, in.info, rdims[0], rdims[1],
+                           rdims[2], rdims[3], blocksPerMatX, blocksPerMatY));
+    });
+}
+}  // namespace kernel
+}  // namespace oneapi
diff --git a/src/backend/oneapi/reorder.cpp b/src/backend/oneapi/reorder.cpp
index fe5bf98854..fc5c7f26a7 100644
--- a/src/backend/oneapi/reorder.cpp
+++ b/src/backend/oneapi/reorder.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/reorder.hpp>
+#include <kernel/reorder.hpp>
 #include <reorder.hpp>
 #include <stdexcept>
 
@@ -19,15 +19,13 @@ using common::half;
 namespace oneapi {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
-    ONEAPI_NOT_SUPPORTED("reorder Not supported");
-
     const af::dim4 &iDims = in.dims();
     af::dim4 oDims(0);
     for (int i = 0; i < 4; i++) { oDims[i] = iDims[rdims[i]]; }
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    // kernel::reorder<T>(out, in, rdims.get());
+    kernel::reorder<T>(out, in, rdims.get());
 
     return out;
 }

From 60231723cb7ce7c57f5af10040b8f21eb0411c22 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 15 Dec 2022 19:15:59 -0500
Subject: [PATCH 2356/2677] Put all internal symbols in the arrayfire namespace

There were some conflicts in the new cuda and oneapi version. This needed to be
done because the namespaces we used can conflict with other libraries.
---
 CMakeModules/FileToString.cmake               |   7 +-
 src/api/c/CMakeLists.txt                      |   1 +
 src/api/c/anisotropic_diffusion.cpp           |   2 +-
 src/api/c/array.cpp                           |  84 +------
 src/api/c/assign.cpp                          |  19 +-
 src/api/c/binary.cpp                          |  22 +-
 src/api/c/blas.cpp                            |  25 +-
 src/api/c/canny.cpp                           |  10 +-
 src/api/c/cast.cpp                            |   4 +-
 src/api/c/cholesky.cpp                        |   1 +
 src/api/c/clamp.cpp                           |   2 +-
 src/api/c/complex.cpp                         |   2 +-
 src/api/c/confidence_connected.cpp            |  12 +-
 src/api/c/convolve.cpp                        |  10 +-
 src/api/c/corrcoef.cpp                        |   2 +-
 src/api/c/covariance.cpp                      |   2 +-
 src/api/c/data.cpp                            |  15 +-
 src/api/c/deconvolution.cpp                   |   2 +-
 src/api/c/device.cpp                          |   9 +-
 src/api/c/diff.cpp                            |   2 +
 src/api/c/error.cpp                           |   2 +-
 src/api/c/exampleFunction.cpp                 |   2 +-
 src/api/c/fftconvolve.cpp                     |   2 +-
 src/api/c/flip.cpp                            |   5 +-
 src/api/c/gradient.cpp                        |   1 +
 src/api/c/handle.cpp                          | 116 +++++++++
 src/api/c/handle.hpp                          |  24 +-
 src/api/c/hist.cpp                            |  11 +-
 src/api/c/histeq.cpp                          |   4 +-
 src/api/c/histogram.cpp                       |   4 +-
 src/api/c/image.cpp                           |  10 +-
 src/api/c/imageio.cpp                         |  17 ++
 src/api/c/imageio2.cpp                        |  13 ++
 src/api/c/imageio_helper.h                    |   3 +
 src/api/c/imgproc_common.hpp                  |   2 +
 src/api/c/index.cpp                           |  10 +-
 src/api/c/indexing_common.hpp                 |   2 +
 src/api/c/internal.cpp                        |   2 +-
 src/api/c/join.cpp                            |   2 +-
 src/api/c/mean.cpp                            |   6 +-
 src/api/c/memory.cpp                          |   2 +-
 src/api/c/memoryapi.hpp                       |   2 +-
 src/api/c/moddims.cpp                         |   6 +-
 src/api/c/morph.cpp                           |   4 +-
 src/api/c/pinverse.cpp                        |   4 +-
 src/api/c/plot.cpp                            |  16 +-
 src/api/c/print.cpp                           |   6 +-
 src/api/c/random.cpp                          |  20 +-
 src/api/c/reduce.cpp                          |   2 +-
 src/api/c/reorder.cpp                         |   2 +-
 src/api/c/replace.cpp                         |   3 +-
 src/api/c/rgb_gray.cpp                        |   4 +-
 src/api/c/sat.cpp                             |   3 +-
 src/api/c/select.cpp                          |   2 +-
 src/api/c/sparse.cpp                          | 220 ++++++++++--------
 src/api/c/sparse_handle.hpp                   |   6 +
 src/api/c/stdev.cpp                           |   2 +-
 src/api/c/surface.cpp                         |  17 +-
 src/api/c/tile.cpp                            |   5 +-
 src/api/c/topk.cpp                            |   2 +-
 src/api/c/transpose.cpp                       |   2 +-
 src/api/c/unary.cpp                           |   2 +-
 src/api/c/var.cpp                             |   4 +-
 src/api/c/vector_field.cpp                    |  16 +-
 src/api/c/window.cpp                          |   4 +-
 src/api/cpp/array.cpp                         |  30 +--
 src/api/unified/device.cpp                    |  10 +-
 src/api/unified/symbol_manager.cpp            |  16 +-
 src/api/unified/symbol_manager.hpp            |  28 ++-
 src/backend/common/AllocatorInterface.hpp     |   4 +-
 src/backend/common/ArrayInfo.cpp              |  28 ++-
 src/backend/common/Binary.hpp                 |   2 +
 src/backend/common/DefaultMemoryManager.cpp   |   2 +
 src/backend/common/DefaultMemoryManager.hpp   |   4 +-
 src/backend/common/DependencyModule.cpp       |   4 +-
 src/backend/common/DependencyModule.hpp       |   2 +
 src/backend/common/EventBase.hpp              |   2 +
 src/backend/common/FFTPlanCache.hpp           |   2 +
 src/backend/common/HandleBase.hpp             |   2 +
 src/backend/common/InteropManager.hpp         |  20 +-
 src/backend/common/KernelInterface.hpp        |   2 +
 src/backend/common/Logger.cpp                 |   2 +
 src/backend/common/Logger.hpp                 |   2 +
 src/backend/common/MemoryManagerBase.hpp      |   4 +-
 src/backend/common/MersenneTwister.hpp        |   2 +
 src/backend/common/ModuleInterface.hpp        |   2 +
 src/backend/common/Source.hpp                 |   2 +
 src/backend/common/SparseArray.cpp            |   2 +
 src/backend/common/SparseArray.hpp            |   2 +
 src/backend/common/TemplateArg.hpp            |   7 +-
 src/backend/common/TemplateTypename.hpp       |   4 +-
 src/backend/common/Transform.hpp              |   2 +
 src/backend/common/cast.cpp                   |   8 +-
 src/backend/common/cast.hpp                   |  10 +-
 src/backend/common/compile_module.hpp         |   2 +
 src/backend/common/complex.hpp                |   2 +
 src/backend/common/defines.hpp                |   2 +
 src/backend/common/deterministicHash.cpp      |   2 +-
 src/backend/common/deterministicHash.hpp      |   3 +-
 src/backend/common/err_common.cpp             |   8 +-
 src/backend/common/err_common.hpp             |   4 +-
 src/backend/common/forge_loader.hpp           |  10 +-
 src/backend/common/graphics_common.cpp        |  13 +-
 src/backend/common/graphics_common.hpp        |  15 +-
 src/backend/common/half.cpp                   |   2 +
 src/backend/common/half.hpp                   | 109 +++++----
 src/backend/common/host_memory.cpp            |   2 +
 src/backend/common/host_memory.hpp            |   4 +-
 src/backend/common/indexing_helpers.hpp       |   2 +
 src/backend/common/jit/BinaryNode.cpp         |   2 +
 src/backend/common/jit/BinaryNode.hpp         |   2 +
 src/backend/common/jit/BufferNodeBase.hpp     |   2 +
 src/backend/common/jit/ModdimNode.hpp         |   2 +
 src/backend/common/jit/NaryNode.hpp           |   2 +
 src/backend/common/jit/Node.cpp               |   9 +-
 src/backend/common/jit/Node.hpp               |  18 +-
 src/backend/common/jit/NodeIO.hpp             |  14 +-
 src/backend/common/jit/NodeIterator.hpp       |   4 +-
 src/backend/common/jit/ScalarNode.hpp         |   2 +
 src/backend/common/jit/ShiftNodeBase.hpp      |   2 +
 src/backend/common/jit/UnaryNode.hpp          |   2 +
 src/backend/common/kernel_cache.cpp           |   2 +
 src/backend/common/kernel_cache.hpp           |   5 +-
 src/backend/common/kernel_type.hpp            |   2 +
 src/backend/common/moddims.cpp                |  16 +-
 src/backend/common/moddims.hpp                |   2 +
 src/backend/common/module_loading.hpp         |   2 +
 src/backend/common/module_loading_unix.cpp    |   2 +
 src/backend/common/module_loading_windows.cpp |   2 +
 src/backend/common/sparse_helpers.hpp         |   2 +
 src/backend/common/tile.hpp                   |   2 +
 src/backend/common/traits.hpp                 |   6 +-
 src/backend/common/unique_handle.hpp          |   6 +-
 src/backend/common/util.cpp                   |   2 +
 src/backend/common/util.hpp                   |   2 +
 src/backend/cpu/Array.cpp                     |  14 +-
 src/backend/cpu/Array.hpp                     |   2 +
 src/backend/cpu/Event.cpp                     |   2 +
 src/backend/cpu/Event.hpp                     |   2 +
 src/backend/cpu/Param.hpp                     |   2 +
 src/backend/cpu/ParamIterator.hpp             |   2 +
 src/backend/cpu/anisotropic_diffusion.cpp     |   2 +
 src/backend/cpu/anisotropic_diffusion.hpp     |   2 +
 src/backend/cpu/approx.cpp                    |   2 +
 src/backend/cpu/approx.hpp                    |   2 +
 src/backend/cpu/arith.hpp                     |   2 +
 src/backend/cpu/assign.cpp                    |   4 +-
 src/backend/cpu/assign.hpp                    |   2 +
 src/backend/cpu/backend.hpp                   |   2 +-
 src/backend/cpu/bilateral.cpp                 |   2 +
 src/backend/cpu/bilateral.hpp                 |   4 +-
 src/backend/cpu/binary.hpp                    |   2 +
 src/backend/cpu/blas.cpp                      |   8 +-
 src/backend/cpu/blas.hpp                      |   2 +
 src/backend/cpu/canny.cpp                     |   2 +
 src/backend/cpu/canny.hpp                     |   2 +
 src/backend/cpu/cast.hpp                      |  18 +-
 src/backend/cpu/cholesky.cpp                  |   4 +
 src/backend/cpu/cholesky.hpp                  |   2 +
 src/backend/cpu/complex.hpp                   |   2 +
 src/backend/cpu/convolve.cpp                  |   8 +-
 src/backend/cpu/convolve.hpp                  |   2 +
 src/backend/cpu/copy.cpp                      |   7 +-
 src/backend/cpu/copy.hpp                      |   2 +
 src/backend/cpu/device_manager.cpp            |   6 +-
 src/backend/cpu/device_manager.hpp            |   8 +-
 src/backend/cpu/diagonal.cpp                  |   9 +-
 src/backend/cpu/diagonal.hpp                  |   2 +
 src/backend/cpu/diff.cpp                      |   2 +
 src/backend/cpu/diff.hpp                      |   2 +
 src/backend/cpu/exampleFunction.cpp           |   2 +
 src/backend/cpu/exampleFunction.hpp           |   4 +-
 src/backend/cpu/fast.cpp                      |   2 +
 src/backend/cpu/fast.hpp                      |   2 +
 src/backend/cpu/fft.cpp                       |   2 +
 src/backend/cpu/fft.hpp                       |   2 +
 src/backend/cpu/fftconvolve.cpp               |   2 +
 src/backend/cpu/fftconvolve.hpp               |   4 +-
 src/backend/cpu/flood_fill.cpp                |   2 +
 src/backend/cpu/flood_fill.hpp                |   2 +
 src/backend/cpu/gradient.cpp                  |   2 +
 src/backend/cpu/gradient.hpp                  |   4 +-
 src/backend/cpu/harris.cpp                    |   2 +
 src/backend/cpu/harris.hpp                    |   4 +-
 src/backend/cpu/hist_graphics.cpp             |   8 +-
 src/backend/cpu/hist_graphics.hpp             |   4 +-
 src/backend/cpu/histogram.cpp                 |   4 +-
 src/backend/cpu/histogram.hpp                 |   4 +-
 src/backend/cpu/homography.cpp                |   2 +
 src/backend/cpu/homography.hpp                |   4 +-
 src/backend/cpu/hsv_rgb.cpp                   |   2 +
 src/backend/cpu/hsv_rgb.hpp                   |   2 +
 src/backend/cpu/identity.cpp                  |   5 +-
 src/backend/cpu/identity.hpp                  |   4 +-
 src/backend/cpu/iir.cpp                       |   2 +
 src/backend/cpu/iir.hpp                       |   4 +-
 src/backend/cpu/image.cpp                     |   8 +-
 src/backend/cpu/image.hpp                     |   4 +-
 src/backend/cpu/index.cpp                     |   5 +-
 src/backend/cpu/index.hpp                     |   4 +-
 src/backend/cpu/inverse.cpp                   |   4 +
 src/backend/cpu/inverse.hpp                   |   4 +-
 src/backend/cpu/iota.cpp                      |   5 +-
 src/backend/cpu/iota.hpp                      |   4 +-
 src/backend/cpu/ireduce.cpp                   |   4 +-
 src/backend/cpu/ireduce.hpp                   |   2 +
 src/backend/cpu/jit/BinaryNode.hpp            |   3 +-
 src/backend/cpu/jit/BufferNode.hpp            |   2 +
 src/backend/cpu/jit/Node.hpp                  |   4 +-
 src/backend/cpu/jit/ScalarNode.hpp            |   3 +-
 src/backend/cpu/jit/UnaryNode.hpp             |   5 +-
 src/backend/cpu/join.cpp                      |   4 +-
 src/backend/cpu/join.hpp                      |   2 +
 src/backend/cpu/kernel/Array.hpp              |  16 +-
 .../cpu/kernel/anisotropic_diffusion.hpp      |   2 +
 src/backend/cpu/kernel/approx.hpp             |   2 +
 src/backend/cpu/kernel/assign.hpp             |   2 +
 src/backend/cpu/kernel/bilateral.hpp          |   2 +
 src/backend/cpu/kernel/canny.hpp              |   2 +
 src/backend/cpu/kernel/convolve.hpp           |   2 +
 src/backend/cpu/kernel/copy.hpp               |   2 +
 src/backend/cpu/kernel/diagonal.hpp           |   2 +
 src/backend/cpu/kernel/diff.hpp               |   2 +
 src/backend/cpu/kernel/dot.hpp                |   2 +
 src/backend/cpu/kernel/exampleFunction.hpp    |   2 +
 src/backend/cpu/kernel/fast.hpp               |   2 +
 src/backend/cpu/kernel/fftconvolve.hpp        |   2 +
 src/backend/cpu/kernel/flood_fill.hpp         |   2 +
 src/backend/cpu/kernel/gradient.hpp           |   2 +
 src/backend/cpu/kernel/harris.hpp             |   2 +
 src/backend/cpu/kernel/histogram.hpp          |   2 +
 src/backend/cpu/kernel/hsv_rgb.hpp            |   2 +
 src/backend/cpu/kernel/identity.hpp           |   2 +
 src/backend/cpu/kernel/iir.hpp                |   2 +
 src/backend/cpu/kernel/index.hpp              |   2 +
 src/backend/cpu/kernel/interp.hpp             |   2 +
 src/backend/cpu/kernel/iota.hpp               |   2 +
 src/backend/cpu/kernel/ireduce.hpp            |   2 +
 src/backend/cpu/kernel/join.hpp               |   2 +
 src/backend/cpu/kernel/lookup.hpp             |   2 +
 src/backend/cpu/kernel/lu.hpp                 |   2 +
 src/backend/cpu/kernel/match_template.hpp     |   2 +
 src/backend/cpu/kernel/mean.hpp               |   2 +
 src/backend/cpu/kernel/meanshift.hpp          |   2 +
 src/backend/cpu/kernel/medfilt.hpp            |   2 +
 src/backend/cpu/kernel/moments.hpp            |   2 +
 src/backend/cpu/kernel/morph.hpp              |   2 +
 src/backend/cpu/kernel/nearest_neighbour.hpp  |   2 +
 src/backend/cpu/kernel/orb.hpp                |   2 +
 src/backend/cpu/kernel/pad_array_borders.hpp  |   2 +
 src/backend/cpu/kernel/random_engine.hpp      |  19 +-
 .../cpu/kernel/random_engine_mersenne.hpp     |   2 +
 .../cpu/kernel/random_engine_philox.hpp       |   2 +
 .../cpu/kernel/random_engine_threefry.hpp     |   2 +
 src/backend/cpu/kernel/range.hpp              |   2 +
 src/backend/cpu/kernel/reduce.hpp             |   2 +
 src/backend/cpu/kernel/regions.hpp            |   2 +
 src/backend/cpu/kernel/reorder.hpp            |   2 +
 src/backend/cpu/kernel/resize.hpp             |   2 +
 src/backend/cpu/kernel/rotate.hpp             |   2 +
 src/backend/cpu/kernel/scan.hpp               |   2 +
 src/backend/cpu/kernel/scan_by_key.hpp        |   2 +
 src/backend/cpu/kernel/select.hpp             |   2 +
 src/backend/cpu/kernel/shift.hpp              |   2 +
 src/backend/cpu/kernel/sift.hpp               |   2 +
 src/backend/cpu/kernel/sobel.hpp              |   2 +
 src/backend/cpu/kernel/sort.hpp               |   2 +
 src/backend/cpu/kernel/sort_by_key.hpp        |   2 +
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |   2 +
 src/backend/cpu/kernel/sort_by_key_impl.hpp   |   3 +
 src/backend/cpu/kernel/sort_helper.hpp        |   2 +
 src/backend/cpu/kernel/sparse.hpp             |   2 +
 src/backend/cpu/kernel/sparse_arith.hpp       |   2 +
 src/backend/cpu/kernel/susan.hpp              |   2 +
 src/backend/cpu/kernel/tile.hpp               |   2 +
 src/backend/cpu/kernel/transform.hpp          |   2 +
 src/backend/cpu/kernel/transpose.hpp          |   2 +
 src/backend/cpu/kernel/triangle.hpp           |   2 +
 src/backend/cpu/kernel/unwrap.hpp             |   2 +
 src/backend/cpu/kernel/wrap.hpp               |   2 +
 src/backend/cpu/logic.hpp                     |   2 +
 src/backend/cpu/lookup.cpp                    |   4 +-
 src/backend/cpu/lookup.hpp                    |   4 +-
 src/backend/cpu/lu.cpp                        |   6 +
 src/backend/cpu/lu.hpp                        |   2 +
 src/backend/cpu/match_template.cpp            |   2 +
 src/backend/cpu/match_template.hpp            |   4 +-
 src/backend/cpu/math.cpp                      |   2 +
 src/backend/cpu/math.hpp                      |  10 +-
 src/backend/cpu/mean.cpp                      |   4 +-
 src/backend/cpu/mean.hpp                      |   2 +
 src/backend/cpu/meanshift.cpp                 |   2 +
 src/backend/cpu/meanshift.hpp                 |   4 +-
 src/backend/cpu/medfilt.cpp                   |   2 +
 src/backend/cpu/medfilt.hpp                   |   2 +
 src/backend/cpu/memory.cpp                    |   6 +-
 src/backend/cpu/memory.hpp                    |   4 +-
 src/backend/cpu/moments.cpp                   |   2 +
 src/backend/cpu/moments.hpp                   |   4 +-
 src/backend/cpu/morph.cpp                     |   2 +
 src/backend/cpu/morph.hpp                     |   2 +
 src/backend/cpu/nearest_neighbour.cpp         |   2 +
 src/backend/cpu/nearest_neighbour.hpp         |   4 +-
 src/backend/cpu/orb.cpp                       |   2 +
 src/backend/cpu/orb.hpp                       |   4 +-
 src/backend/cpu/platform.cpp                  |  13 +-
 src/backend/cpu/platform.hpp                  |  15 +-
 src/backend/cpu/plot.cpp                      |   7 +-
 src/backend/cpu/plot.hpp                      |   4 +-
 src/backend/cpu/print.hpp                     |   4 +-
 src/backend/cpu/qr.cpp                        |   6 +
 src/backend/cpu/qr.hpp                        |   2 +
 src/backend/cpu/queue.hpp                     |   2 +
 src/backend/cpu/random_engine.cpp             |   4 +-
 src/backend/cpu/random_engine.hpp             |   2 +
 src/backend/cpu/range.cpp                     |   4 +-
 src/backend/cpu/range.hpp                     |   4 +-
 src/backend/cpu/reduce.cpp                    |  11 +-
 src/backend/cpu/reduce.hpp                    |   2 +
 src/backend/cpu/regions.cpp                   |   2 +
 src/backend/cpu/regions.hpp                   |   4 +-
 src/backend/cpu/reorder.cpp                   |   4 +-
 src/backend/cpu/reorder.hpp                   |   4 +-
 src/backend/cpu/reshape.cpp                   |   4 +-
 src/backend/cpu/resize.cpp                    |   2 +
 src/backend/cpu/resize.hpp                    |   4 +-
 src/backend/cpu/rotate.cpp                    |   2 +
 src/backend/cpu/rotate.hpp                    |   4 +-
 src/backend/cpu/scan.cpp                      |   2 +
 src/backend/cpu/scan.hpp                      |   4 +-
 src/backend/cpu/scan_by_key.cpp               |   2 +
 src/backend/cpu/scan_by_key.hpp               |   4 +-
 src/backend/cpu/select.cpp                    |   4 +-
 src/backend/cpu/select.hpp                    |   2 +
 src/backend/cpu/set.cpp                       |   2 +
 src/backend/cpu/set.hpp                       |   2 +
 src/backend/cpu/shift.cpp                     |   2 +
 src/backend/cpu/shift.hpp                     |   4 +-
 src/backend/cpu/sift.cpp                      |   2 +
 src/backend/cpu/sift.hpp                      |   4 +-
 src/backend/cpu/sobel.cpp                     |   2 +
 src/backend/cpu/sobel.hpp                     |   4 +-
 src/backend/cpu/solve.cpp                     |   6 +
 src/backend/cpu/solve.hpp                     |   2 +
 src/backend/cpu/sort.cpp                      |   2 +
 src/backend/cpu/sort.hpp                      |   4 +-
 src/backend/cpu/sort_by_key.cpp               |   2 +
 src/backend/cpu/sort_by_key.hpp               |   4 +-
 src/backend/cpu/sort_index.cpp                |   2 +
 src/backend/cpu/sort_index.hpp                |   4 +-
 src/backend/cpu/sparse.cpp                    |  10 +-
 src/backend/cpu/sparse.hpp                    |   2 +
 src/backend/cpu/sparse_arith.cpp              |   8 +-
 src/backend/cpu/sparse_arith.hpp              |   2 +
 src/backend/cpu/sparse_blas.cpp               |   2 +
 src/backend/cpu/sparse_blas.hpp               |   4 +-
 src/backend/cpu/surface.cpp                   |   7 +-
 src/backend/cpu/surface.hpp                   |   4 +-
 src/backend/cpu/susan.cpp                     |   2 +
 src/backend/cpu/susan.hpp                     |   4 +-
 src/backend/cpu/svd.cpp                       |   6 +
 src/backend/cpu/svd.hpp                       |   2 +
 src/backend/cpu/tile.cpp                      |   4 +-
 src/backend/cpu/tile.hpp                      |   4 +-
 src/backend/cpu/topk.cpp                      |   4 +-
 src/backend/cpu/topk.hpp                      |   4 +-
 src/backend/cpu/transform.cpp                 |   2 +
 src/backend/cpu/transform.hpp                 |   4 +-
 src/backend/cpu/transpose.cpp                 |   4 +-
 src/backend/cpu/transpose.hpp                 |   2 +
 src/backend/cpu/triangle.cpp                  |   4 +-
 src/backend/cpu/triangle.hpp                  |   2 +
 src/backend/cpu/types.hpp                     |   7 +-
 src/backend/cpu/unary.hpp                     |   2 +
 src/backend/cpu/unwrap.cpp                    |   4 +-
 src/backend/cpu/unwrap.hpp                    |   4 +-
 src/backend/cpu/utility.hpp                   |   2 +
 src/backend/cpu/vector_field.cpp              |   7 +-
 src/backend/cpu/vector_field.hpp              |   4 +-
 src/backend/cpu/where.cpp                     |   2 +
 src/backend/cpu/where.hpp                     |   4 +-
 src/backend/cpu/wrap.cpp                      |   4 +-
 src/backend/cpu/wrap.hpp                      |   2 +
 src/backend/cuda/Array.cpp                    |  24 +-
 src/backend/cuda/Array.hpp                    |   3 +
 src/backend/cuda/CMakeLists.txt               |   4 +-
 src/backend/cuda/EnqueueArgs.hpp              |   2 +
 src/backend/cuda/Event.cpp                    |   2 +
 src/backend/cuda/Event.hpp                    |   2 +
 src/backend/cuda/GraphicsResourceManager.cpp  |   2 +
 src/backend/cuda/GraphicsResourceManager.hpp  |   2 +
 src/backend/cuda/Kernel.cpp                   |  15 +-
 src/backend/cuda/Kernel.hpp                   |   2 +
 src/backend/cuda/LookupTable1D.hpp            |   2 +
 src/backend/cuda/Module.hpp                   |   2 +
 src/backend/cuda/Param.hpp                    |   2 +
 src/backend/cuda/ThrustAllocator.cuh          |   3 +
 src/backend/cuda/ThrustArrayFirePolicy.hpp    |  16 +-
 src/backend/cuda/all.cu                       |   6 +-
 src/backend/cuda/anisotropic_diffusion.cpp    |   2 +
 src/backend/cuda/anisotropic_diffusion.hpp    |   4 +-
 src/backend/cuda/any.cu                       |   6 +-
 src/backend/cuda/approx.cpp                   |   2 +
 src/backend/cuda/approx.hpp                   |   2 +
 src/backend/cuda/arith.hpp                    |   2 +
 src/backend/cuda/assign.cpp                   |   4 +-
 src/backend/cuda/assign.hpp                   |   4 +-
 src/backend/cuda/assign_kernel_param.hpp      |   2 +
 src/backend/cuda/backend.hpp                  |   6 +-
 src/backend/cuda/bilateral.cpp                |   2 +
 src/backend/cuda/bilateral.hpp                |   4 +-
 src/backend/cuda/binary.hpp                   |   2 +
 src/backend/cuda/blas.cu                      |   6 +-
 src/backend/cuda/blas.hpp                     |   2 +
 src/backend/cuda/canny.cpp                    |   2 +
 src/backend/cuda/canny.hpp                    |   2 +
 src/backend/cuda/cast.hpp                     |   2 +
 src/backend/cuda/cholesky.cpp                 |   2 +
 src/backend/cuda/cholesky.hpp                 |   2 +
 src/backend/cuda/compile_module.cpp           |  22 +-
 src/backend/cuda/complex.hpp                  |   2 +
 src/backend/cuda/convolve.cpp                 |   4 +-
 src/backend/cuda/convolve.hpp                 |   2 +
 src/backend/cuda/convolveNN.cpp               |  10 +-
 src/backend/cuda/copy.cpp                     |  15 +-
 src/backend/cuda/copy.hpp                     |   2 +
 src/backend/cuda/count.cu                     |   6 +-
 src/backend/cuda/cublas.cpp                   |   2 +
 src/backend/cuda/cublas.hpp                   |   4 +-
 src/backend/cuda/cudaDataType.hpp             |   2 +
 src/backend/cuda/cudnn.cpp                    |   2 +
 src/backend/cuda/cudnn.hpp                    |  10 +-
 src/backend/cuda/cudnnModule.cpp              |   6 +-
 src/backend/cuda/cudnnModule.hpp              |   2 +
 src/backend/cuda/cufft.cu                     |   6 +-
 src/backend/cuda/cufft.hpp                    |  26 ++-
 src/backend/cuda/cusolverDn.cpp               |   2 +
 src/backend/cuda/cusolverDn.hpp               |   4 +-
 src/backend/cuda/cusparse.cpp                 |   2 +
 src/backend/cuda/cusparse.hpp                 |  14 +-
 src/backend/cuda/cusparseModule.cpp           |   2 +
 src/backend/cuda/cusparseModule.hpp           |   4 +-
 .../cuda/cusparse_descriptor_helpers.hpp      |   2 +
 src/backend/cuda/debug_cuda.hpp               |  44 ++--
 src/backend/cuda/device_manager.cpp           |  12 +-
 src/backend/cuda/device_manager.hpp           |   8 +-
 src/backend/cuda/diagonal.cpp                 |   4 +-
 src/backend/cuda/diagonal.hpp                 |   2 +
 src/backend/cuda/diff.cpp                     |   2 +
 src/backend/cuda/diff.hpp                     |   2 +
 src/backend/cuda/dims_param.hpp               |   2 +
 src/backend/cuda/exampleFunction.cpp          |   2 +
 src/backend/cuda/exampleFunction.hpp          |   4 +-
 src/backend/cuda/fast.cu                      |   2 +
 src/backend/cuda/fast.hpp                     |   4 +-
 src/backend/cuda/fast_pyramid.cpp             |   2 +
 src/backend/cuda/fast_pyramid.hpp             |   4 +-
 src/backend/cuda/fft.cu                       |   8 +-
 src/backend/cuda/fft.hpp                      |   2 +
 src/backend/cuda/fftconvolve.cpp              |   2 +
 src/backend/cuda/fftconvolve.hpp              |   4 +-
 src/backend/cuda/flood_fill.cpp               |   2 +
 src/backend/cuda/flood_fill.hpp               |   2 +
 src/backend/cuda/gradient.cpp                 |   2 +
 src/backend/cuda/gradient.hpp                 |   4 +-
 src/backend/cuda/harris.cu                    |   2 +
 src/backend/cuda/harris.hpp                   |   4 +-
 src/backend/cuda/hist_graphics.cpp            |  10 +-
 src/backend/cuda/hist_graphics.hpp            |   4 +-
 src/backend/cuda/histogram.cpp                |   4 +-
 src/backend/cuda/histogram.hpp                |   4 +-
 src/backend/cuda/homography.cu                |   2 +
 src/backend/cuda/homography.hpp               |   4 +-
 src/backend/cuda/hsv_rgb.cpp                  |   2 +
 src/backend/cuda/hsv_rgb.hpp                  |   2 +
 src/backend/cuda/identity.cpp                 |   4 +-
 src/backend/cuda/identity.hpp                 |   4 +-
 src/backend/cuda/iir.cpp                      |   2 +
 src/backend/cuda/iir.hpp                      |   4 +-
 src/backend/cuda/image.cpp                    |   9 +-
 src/backend/cuda/image.hpp                    |   4 +-
 src/backend/cuda/index.cpp                    |   4 +-
 src/backend/cuda/index.hpp                    |   4 +-
 src/backend/cuda/inverse.cpp                  |   2 +
 src/backend/cuda/inverse.hpp                  |   4 +-
 src/backend/cuda/iota.cpp                     |   4 +-
 src/backend/cuda/iota.hpp                     |   4 +-
 src/backend/cuda/ireduce.cpp                  |   4 +-
 src/backend/cuda/ireduce.hpp                  |   2 +
 src/backend/cuda/jit.cpp                      |  26 ++-
 src/backend/cuda/jit/BufferNode.hpp           |   6 +-
 src/backend/cuda/jit/kernel_generators.hpp    |   2 +
 src/backend/cuda/join.cpp                     |   8 +-
 src/backend/cuda/join.hpp                     |   2 +
 .../cuda/kernel/anisotropic_diffusion.cuh     |  61 +++--
 .../cuda/kernel/anisotropic_diffusion.hpp     |  10 +-
 src/backend/cuda/kernel/approx.hpp            |  20 +-
 src/backend/cuda/kernel/approx1.cuh           |   2 +
 src/backend/cuda/kernel/approx2.cuh           |   2 +
 src/backend/cuda/kernel/assign.cuh            |   7 +-
 src/backend/cuda/kernel/assign.hpp            |  11 +-
 src/backend/cuda/kernel/atomics.hpp           |   2 +
 src/backend/cuda/kernel/bilateral.cuh         |  23 +-
 src/backend/cuda/kernel/bilateral.hpp         |   7 +-
 src/backend/cuda/kernel/canny.cuh             |  93 ++++----
 src/backend/cuda/kernel/canny.hpp             |  10 +-
 src/backend/cuda/kernel/config.hpp            |   2 +
 src/backend/cuda/kernel/convolve.hpp          |  14 +-
 src/backend/cuda/kernel/convolve1.cuh         |  16 +-
 src/backend/cuda/kernel/convolve2.cuh         |  19 +-
 src/backend/cuda/kernel/convolve3.cuh         |  17 +-
 .../cuda/kernel/convolve_separable.cpp        |   2 +
 .../cuda/kernel/convolve_separable.cuh        |   8 +-
 src/backend/cuda/kernel/copy.cuh              |  11 +-
 src/backend/cuda/kernel/diagonal.cuh          |   2 +
 src/backend/cuda/kernel/diagonal.hpp          |  20 +-
 src/backend/cuda/kernel/diff.cuh              |   2 +
 src/backend/cuda/kernel/diff.hpp              |  11 +-
 src/backend/cuda/kernel/exampleFunction.cuh   |   4 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |   6 +-
 src/backend/cuda/kernel/fast.hpp              |   9 +-
 src/backend/cuda/kernel/fftconvolve.cuh       |   2 +
 src/backend/cuda/kernel/fftconvolve.hpp       |  10 +-
 src/backend/cuda/kernel/flood_fill.cuh        |  64 ++---
 src/backend/cuda/kernel/flood_fill.hpp        |  14 +-
 src/backend/cuda/kernel/gradient.cuh          |  11 +-
 src/backend/cuda/kernel/gradient.hpp          |  17 +-
 src/backend/cuda/kernel/harris.hpp            |  18 +-
 src/backend/cuda/kernel/histogram.cuh         |  16 +-
 src/backend/cuda/kernel/histogram.hpp         |   4 +-
 src/backend/cuda/kernel/homography.hpp        |  18 +-
 src/backend/cuda/kernel/hsv_rgb.cuh           |   7 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |  11 +-
 src/backend/cuda/kernel/identity.cuh          |   2 +
 src/backend/cuda/kernel/identity.hpp          |  15 +-
 src/backend/cuda/kernel/iir.cuh               |   2 +
 src/backend/cuda/kernel/iir.hpp               |   4 +-
 src/backend/cuda/kernel/index.cuh             |   7 +-
 src/backend/cuda/kernel/index.hpp             |  14 +-
 src/backend/cuda/kernel/interp.hpp            |   2 +
 src/backend/cuda/kernel/iota.cuh              |   2 +
 src/backend/cuda/kernel/iota.hpp              |  14 +-
 src/backend/cuda/kernel/ireduce.cuh           |   4 +-
 src/backend/cuda/kernel/ireduce.hpp           |  36 ++-
 src/backend/cuda/kernel/jit.cuh               |  28 +--
 src/backend/cuda/kernel/lookup.cuh            |   2 +
 src/backend/cuda/kernel/lookup.hpp            |   8 +-
 src/backend/cuda/kernel/lu_split.cuh          |   2 +
 src/backend/cuda/kernel/lu_split.hpp          |   4 +-
 src/backend/cuda/kernel/match_template.cuh    |   9 +-
 src/backend/cuda/kernel/match_template.hpp    |   4 +-
 src/backend/cuda/kernel/mean.hpp              |  63 +++--
 src/backend/cuda/kernel/meanshift.cuh         |   9 +-
 src/backend/cuda/kernel/meanshift.hpp         |   4 +-
 src/backend/cuda/kernel/medfilt.cuh           |  26 +--
 src/backend/cuda/kernel/medfilt.hpp           |  12 +-
 src/backend/cuda/kernel/memcopy.cuh           |   2 +
 src/backend/cuda/kernel/memcopy.hpp           |  23 +-
 src/backend/cuda/kernel/moments.cuh           |   8 +-
 src/backend/cuda/kernel/moments.hpp           |   8 +-
 src/backend/cuda/kernel/morph.cuh             |   6 +-
 src/backend/cuda/kernel/morph.hpp             |   6 +-
 src/backend/cuda/kernel/nearest_neighbour.hpp |   2 +
 src/backend/cuda/kernel/orb.hpp               |  21 +-
 src/backend/cuda/kernel/orb_patch.hpp         |   2 +
 src/backend/cuda/kernel/pad_array_borders.cuh |  20 +-
 src/backend/cuda/kernel/pad_array_borders.hpp |   4 +-
 src/backend/cuda/kernel/random_engine.hpp     |   2 +
 .../cuda/kernel/random_engine_mersenne.hpp    |   2 +
 .../cuda/kernel/random_engine_philox.hpp      |   2 +
 .../cuda/kernel/random_engine_threefry.hpp    |   2 +
 src/backend/cuda/kernel/range.cuh             |   2 +
 src/backend/cuda/kernel/range.hpp             |  14 +-
 src/backend/cuda/kernel/reduce.hpp            |  23 +-
 src/backend/cuda/kernel/reduce_by_key.hpp     |   2 +
 src/backend/cuda/kernel/regions.hpp           |  40 ++--
 src/backend/cuda/kernel/reorder.cuh           |   2 +
 src/backend/cuda/kernel/reorder.hpp           |  15 +-
 src/backend/cuda/kernel/resize.cuh            |  36 ++-
 src/backend/cuda/kernel/resize.hpp            |   4 +-
 src/backend/cuda/kernel/rotate.cuh            |   4 +-
 src/backend/cuda/kernel/rotate.hpp            |   4 +-
 .../kernel/scan_by_key/scan_by_key_impl.cpp   |   2 +
 src/backend/cuda/kernel/scan_dim.cuh          |   2 +
 src/backend/cuda/kernel/scan_dim.hpp          |  26 +--
 src/backend/cuda/kernel/scan_dim_by_key.cuh   |   2 +
 src/backend/cuda/kernel/scan_dim_by_key.hpp   |   2 +
 .../cuda/kernel/scan_dim_by_key_impl.hpp      |  11 +-
 src/backend/cuda/kernel/scan_first.cuh        |   2 +
 src/backend/cuda/kernel/scan_first.hpp        |  20 +-
 src/backend/cuda/kernel/scan_first_by_key.cuh |  14 +-
 src/backend/cuda/kernel/scan_first_by_key.hpp |   2 +
 .../cuda/kernel/scan_first_by_key_impl.hpp    |  11 +-
 src/backend/cuda/kernel/select.cuh            |   2 +
 src/backend/cuda/kernel/select.hpp            |  13 +-
 src/backend/cuda/kernel/shared.hpp            |   4 +
 src/backend/cuda/kernel/shfl_intrinsics.hpp   |  39 ++--
 src/backend/cuda/kernel/sift.hpp              |  48 ++--
 src/backend/cuda/kernel/sobel.cuh             |  19 +-
 src/backend/cuda/kernel/sobel.hpp             |   4 +-
 src/backend/cuda/kernel/sort.hpp              |   2 +
 src/backend/cuda/kernel/sort_by_key.hpp       |   2 +
 src/backend/cuda/kernel/sparse.cuh            |   2 +
 src/backend/cuda/kernel/sparse.hpp            |   4 +-
 src/backend/cuda/kernel/sparse_arith.cuh      |   2 +
 src/backend/cuda/kernel/sparse_arith.hpp      |  34 +--
 src/backend/cuda/kernel/susan.cuh             |   2 +
 src/backend/cuda/kernel/susan.hpp             |  14 +-
 .../cuda/kernel/thrust_sort_by_key.hpp        |   2 +
 .../thrust_sort_by_key_impl.cu                |   2 +
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |   2 +
 src/backend/cuda/kernel/tile.cuh              |   2 +
 src/backend/cuda/kernel/tile.hpp              |  14 +-
 src/backend/cuda/kernel/topk.hpp              |   2 +
 src/backend/cuda/kernel/transform.cuh         |  20 +-
 src/backend/cuda/kernel/transform.hpp         |   4 +-
 src/backend/cuda/kernel/transpose.cuh         |   7 +-
 src/backend/cuda/kernel/transpose.hpp         |  11 +-
 src/backend/cuda/kernel/transpose_inplace.cuh |   4 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |   4 +-
 src/backend/cuda/kernel/triangle.cuh          |   2 +
 src/backend/cuda/kernel/triangle.hpp          |  11 +-
 src/backend/cuda/kernel/unwrap.cuh            |   2 +
 src/backend/cuda/kernel/unwrap.hpp            |  11 +-
 src/backend/cuda/kernel/where.cuh             |   9 +-
 src/backend/cuda/kernel/where.hpp             |  16 +-
 src/backend/cuda/kernel/wrap.cuh              |   2 +
 src/backend/cuda/kernel/wrap.hpp              |  20 +-
 src/backend/cuda/logic.hpp                    |   2 +
 src/backend/cuda/lookup.cpp                   |   4 +-
 src/backend/cuda/lookup.hpp                   |   4 +-
 src/backend/cuda/lu.cpp                       |   2 +
 src/backend/cuda/lu.hpp                       |   2 +
 src/backend/cuda/match_template.cpp           |   2 +
 src/backend/cuda/match_template.hpp           |   4 +-
 src/backend/cuda/math.hpp                     |  17 +-
 src/backend/cuda/max.cu                       |   6 +-
 src/backend/cuda/mean.cu                      |   6 +-
 src/backend/cuda/mean.hpp                     |   2 +
 src/backend/cuda/meanshift.cpp                |   2 +
 src/backend/cuda/meanshift.hpp                |   4 +-
 src/backend/cuda/medfilt.cpp                  |   2 +
 src/backend/cuda/medfilt.hpp                  |   2 +
 src/backend/cuda/memory.cpp                   |  16 +-
 src/backend/cuda/memory.hpp                   |   6 +-
 src/backend/cuda/min.cu                       |   6 +-
 src/backend/cuda/minmax_op.hpp                |   2 +
 src/backend/cuda/moments.cpp                  |   2 +
 src/backend/cuda/moments.hpp                  |   4 +-
 src/backend/cuda/morph.cpp                    |   2 +
 src/backend/cuda/morph.hpp                    |   2 +
 src/backend/cuda/nearest_neighbour.cu         |   2 +
 src/backend/cuda/nearest_neighbour.hpp        |   4 +-
 src/backend/cuda/orb.cu                       |   2 +
 src/backend/cuda/orb.hpp                      |   4 +-
 src/backend/cuda/pad_array_borders.cpp        |   2 +
 src/backend/cuda/platform.cpp                 |  54 ++---
 src/backend/cuda/platform.hpp                 |  15 +-
 src/backend/cuda/plot.cpp                     |   9 +-
 src/backend/cuda/plot.hpp                     |   4 +-
 src/backend/cuda/print.hpp                    |   2 +
 src/backend/cuda/product.cu                   |   6 +-
 src/backend/cuda/qr.cpp                       |   2 +
 src/backend/cuda/qr.hpp                       |   2 +
 src/backend/cuda/random_engine.cu             |   4 +-
 src/backend/cuda/random_engine.hpp            |   2 +
 src/backend/cuda/range.cpp                    |   4 +-
 src/backend/cuda/range.hpp                    |   4 +-
 src/backend/cuda/reduce.hpp                   |   2 +
 src/backend/cuda/reduce_impl.hpp              |   2 +
 src/backend/cuda/regions.cu                   |   2 +
 src/backend/cuda/regions.hpp                  |   4 +-
 src/backend/cuda/reorder.cpp                  |   4 +-
 src/backend/cuda/reorder.hpp                  |   4 +-
 src/backend/cuda/reshape.cpp                  |   4 +-
 src/backend/cuda/resize.cpp                   |   2 +
 src/backend/cuda/resize.hpp                   |   4 +-
 src/backend/cuda/rotate.cpp                   |   2 +
 src/backend/cuda/rotate.hpp                   |   4 +-
 src/backend/cuda/scalar.hpp                   |   2 +
 src/backend/cuda/scan.cpp                     |   2 +
 src/backend/cuda/scan.hpp                     |   4 +-
 src/backend/cuda/scan_by_key.cpp              |   2 +
 src/backend/cuda/scan_by_key.hpp              |   4 +-
 src/backend/cuda/select.cpp                   |   8 +-
 src/backend/cuda/select.hpp                   |   2 +
 src/backend/cuda/set.cu                       |   4 +-
 src/backend/cuda/set.hpp                      |   2 +
 src/backend/cuda/shift.cpp                    |   8 +-
 src/backend/cuda/shift.hpp                    |   4 +-
 src/backend/cuda/sift.cu                      |   2 +
 src/backend/cuda/sift.hpp                     |   4 +-
 src/backend/cuda/sobel.cpp                    |   2 +
 src/backend/cuda/sobel.hpp                    |   4 +-
 src/backend/cuda/solve.cu                     |   8 +-
 src/backend/cuda/solve.hpp                    |   2 +
 src/backend/cuda/sort.cu                      |   2 +
 src/backend/cuda/sort.hpp                     |   4 +-
 src/backend/cuda/sort_by_key.cu               |   2 +
 src/backend/cuda/sort_by_key.hpp              |   4 +-
 src/backend/cuda/sort_index.cu                |   2 +
 src/backend/cuda/sort_index.hpp               |   4 +-
 src/backend/cuda/sparse.cu                    |   8 +-
 src/backend/cuda/sparse.hpp                   |   2 +
 src/backend/cuda/sparse_arith.cu              |   8 +-
 src/backend/cuda/sparse_arith.hpp             |   2 +
 src/backend/cuda/sparse_blas.cu               |   2 +
 src/backend/cuda/sparse_blas.hpp              |   4 +-
 src/backend/cuda/sum.cu                       |   6 +-
 src/backend/cuda/surface.cpp                  |   9 +-
 src/backend/cuda/surface.hpp                  |   4 +-
 src/backend/cuda/susan.cpp                    |   2 +
 src/backend/cuda/susan.hpp                    |   4 +-
 src/backend/cuda/svd.cpp                      |   2 +
 src/backend/cuda/svd.hpp                      |   2 +
 src/backend/cuda/threadsMgt.hpp               |  10 +-
 src/backend/cuda/thrust_utils.hpp             |  27 ++-
 src/backend/cuda/tile.cpp                     |   4 +-
 src/backend/cuda/tile.hpp                     |   4 +-
 src/backend/cuda/topk.cu                      |   4 +-
 src/backend/cuda/topk.hpp                     |   4 +-
 src/backend/cuda/transform.cpp                |   2 +
 src/backend/cuda/transform.hpp                |   4 +-
 src/backend/cuda/transpose.cpp                |   4 +-
 src/backend/cuda/transpose.hpp                |   2 +
 src/backend/cuda/transpose_inplace.cpp        |   4 +-
 src/backend/cuda/triangle.cpp                 |   4 +-
 src/backend/cuda/triangle.hpp                 |   2 +
 src/backend/cuda/types.hpp                    |  14 +-
 src/backend/cuda/unary.hpp                    |   8 +-
 src/backend/cuda/unwrap.cpp                   |   4 +-
 src/backend/cuda/unwrap.hpp                   |   4 +-
 src/backend/cuda/utility.cpp                  |   2 +
 src/backend/cuda/utility.hpp                  |   2 +
 src/backend/cuda/vector_field.cpp             |   9 +-
 src/backend/cuda/vector_field.hpp             |   4 +-
 src/backend/cuda/where.cpp                    |   2 +
 src/backend/cuda/where.hpp                    |   4 +-
 src/backend/cuda/wrap.cpp                     |   4 +-
 src/backend/cuda/wrap.hpp                     |   2 +
 src/backend/opencl/Array.cpp                  |  12 +-
 src/backend/opencl/Array.hpp                  |   2 +
 src/backend/opencl/CMakeLists.txt             |   2 +-
 src/backend/opencl/Event.cpp                  |   2 +
 src/backend/opencl/Event.hpp                  |   2 +
 .../opencl/GraphicsResourceManager.cpp        |   2 +
 .../opencl/GraphicsResourceManager.hpp        |   2 +
 src/backend/opencl/Kernel.cpp                 |   2 +
 src/backend/opencl/Kernel.hpp                 |   2 +
 src/backend/opencl/Module.hpp                 |   2 +
 src/backend/opencl/Param.cpp                  |   2 +
 src/backend/opencl/Param.hpp                  |   2 +
 src/backend/opencl/all.cpp                    |   4 +-
 src/backend/opencl/anisotropic_diffusion.cpp  |   2 +
 src/backend/opencl/anisotropic_diffusion.hpp  |   4 +-
 src/backend/opencl/any.cpp                    |   4 +-
 src/backend/opencl/approx.cpp                 |   2 +
 src/backend/opencl/approx.hpp                 |   2 +
 src/backend/opencl/arith.hpp                  |   2 +
 src/backend/opencl/assign.cpp                 |   4 +-
 src/backend/opencl/assign.hpp                 |   4 +-
 src/backend/opencl/backend.hpp                |   2 +-
 src/backend/opencl/bilateral.cpp              |   2 +
 src/backend/opencl/bilateral.hpp              |   4 +-
 src/backend/opencl/binary.hpp                 |   2 +
 src/backend/opencl/blas.cpp                   |   4 +-
 src/backend/opencl/blas.hpp                   |   2 +
 src/backend/opencl/canny.cpp                  |   2 +
 src/backend/opencl/canny.hpp                  |   2 +
 src/backend/opencl/cast.hpp                   |   2 +
 src/backend/opencl/cholesky.cpp               |   4 +
 src/backend/opencl/cholesky.hpp               |   2 +
 src/backend/opencl/clfft.cpp                  |   2 +
 src/backend/opencl/clfft.hpp                  |   2 +
 src/backend/opencl/compile_module.cpp         |  44 ++--
 src/backend/opencl/complex.hpp                |   2 +
 src/backend/opencl/convolve.cpp               |   8 +-
 src/backend/opencl/convolve.hpp               |   2 +
 src/backend/opencl/convolve_separable.cpp     |   2 +
 src/backend/opencl/copy.cpp                   |   6 +-
 src/backend/opencl/copy.hpp                   |   2 +
 src/backend/opencl/count.cpp                  |   4 +-
 src/backend/opencl/cpu/cpu_blas.cpp           |   4 +-
 src/backend/opencl/cpu/cpu_blas.hpp           |   4 +-
 src/backend/opencl/cpu/cpu_cholesky.cpp       |   2 +
 src/backend/opencl/cpu/cpu_cholesky.hpp       |   2 +
 src/backend/opencl/cpu/cpu_helper.hpp         |   4 +-
 src/backend/opencl/cpu/cpu_inverse.cpp        |   2 +
 src/backend/opencl/cpu/cpu_inverse.hpp        |   4 +-
 src/backend/opencl/cpu/cpu_lu.cpp             |   2 +
 src/backend/opencl/cpu/cpu_lu.hpp             |   2 +
 src/backend/opencl/cpu/cpu_qr.cpp             |   2 +
 src/backend/opencl/cpu/cpu_qr.hpp             |   2 +
 src/backend/opencl/cpu/cpu_solve.cpp          |   2 +
 src/backend/opencl/cpu/cpu_solve.hpp          |   2 +
 src/backend/opencl/cpu/cpu_sparse_blas.cpp    |   4 +-
 src/backend/opencl/cpu/cpu_sparse_blas.hpp    |   8 +-
 src/backend/opencl/cpu/cpu_svd.cpp            |   2 +
 src/backend/opencl/cpu/cpu_svd.hpp            |   2 +
 src/backend/opencl/cpu/cpu_triangle.hpp       |   2 +
 src/backend/opencl/device_manager.cpp         |   6 +-
 src/backend/opencl/device_manager.hpp         |  33 +--
 src/backend/opencl/diagonal.cpp               |   4 +-
 src/backend/opencl/diagonal.hpp               |   2 +
 src/backend/opencl/diff.cpp                   |   2 +
 src/backend/opencl/diff.hpp                   |   2 +
 src/backend/opencl/exampleFunction.cpp        |   2 +
 src/backend/opencl/exampleFunction.hpp        |   4 +-
 src/backend/opencl/fast.cpp                   |   2 +
 src/backend/opencl/fast.hpp                   |   4 +-
 src/backend/opencl/fft.cpp                    |   2 +
 src/backend/opencl/fft.hpp                    |   2 +
 src/backend/opencl/fftconvolve.cpp            |   2 +
 src/backend/opencl/fftconvolve.hpp            |   4 +-
 src/backend/opencl/flood_fill.cpp             |   2 +
 src/backend/opencl/flood_fill.hpp             |   2 +
 src/backend/opencl/gradient.cpp               |   2 +
 src/backend/opencl/gradient.hpp               |   4 +-
 src/backend/opencl/harris.cpp                 |   2 +
 src/backend/opencl/harris.hpp                 |   4 +-
 src/backend/opencl/hist_graphics.cpp          |   7 +-
 src/backend/opencl/hist_graphics.hpp          |   4 +-
 src/backend/opencl/histogram.cpp              |   4 +-
 src/backend/opencl/histogram.hpp              |   4 +-
 src/backend/opencl/homography.cpp             |   2 +
 src/backend/opencl/homography.hpp             |   4 +-
 src/backend/opencl/hsv_rgb.cpp                |   2 +
 src/backend/opencl/hsv_rgb.hpp                |   2 +
 src/backend/opencl/identity.cpp               |   4 +-
 src/backend/opencl/identity.hpp               |   4 +-
 src/backend/opencl/iir.cpp                    |   2 +
 src/backend/opencl/iir.hpp                    |   4 +-
 src/backend/opencl/image.cpp                  |   7 +-
 src/backend/opencl/image.hpp                  |   4 +-
 src/backend/opencl/index.cpp                  |   4 +-
 src/backend/opencl/index.hpp                  |   4 +-
 src/backend/opencl/inverse.cpp                |   4 +
 src/backend/opencl/inverse.hpp                |   4 +-
 src/backend/opencl/iota.cpp                   |   4 +-
 src/backend/opencl/iota.hpp                   |   4 +-
 src/backend/opencl/ireduce.cpp                |   4 +-
 src/backend/opencl/ireduce.hpp                |   2 +
 src/backend/opencl/jit.cpp                    |  20 +-
 src/backend/opencl/jit/BufferNode.hpp         |   4 +-
 src/backend/opencl/jit/kernel_generators.hpp  |   2 +
 src/backend/opencl/join.cpp                   |   8 +-
 src/backend/opencl/join.hpp                   |   2 +
 .../opencl/kernel/anisotropic_diffusion.hpp   |   2 +
 src/backend/opencl/kernel/approx.hpp          |   2 +
 src/backend/opencl/kernel/assign.hpp          |   2 +
 src/backend/opencl/kernel/bilateral.hpp       |   2 +
 src/backend/opencl/kernel/canny.hpp           |   2 +
 src/backend/opencl/kernel/config.cpp          |   2 +
 src/backend/opencl/kernel/config.hpp          |   2 +
 src/backend/opencl/kernel/convolve.hpp        |   2 +
 src/backend/opencl/kernel/convolve/conv1.cpp  |   2 +
 .../opencl/kernel/convolve/conv2_b8.cpp       |   2 +
 .../opencl/kernel/convolve/conv2_c32.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_c64.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_f32.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_f64.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_impl.hpp     |   2 +
 .../opencl/kernel/convolve/conv2_s16.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_s32.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_s64.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_u16.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_u32.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_u64.cpp      |   2 +
 .../opencl/kernel/convolve/conv2_u8.cpp       |   2 +
 src/backend/opencl/kernel/convolve/conv3.cpp  |   2 +
 .../opencl/kernel/convolve/conv_common.hpp    |   2 +
 .../opencl/kernel/convolve_separable.cpp      |   2 +
 .../opencl/kernel/convolve_separable.hpp      |   2 +
 src/backend/opencl/kernel/cscmm.hpp           |   4 +-
 src/backend/opencl/kernel/cscmv.hpp           |   4 +-
 src/backend/opencl/kernel/csrmm.hpp           |   4 +-
 src/backend/opencl/kernel/csrmv.hpp           |   4 +-
 src/backend/opencl/kernel/diagonal.hpp        |   6 +-
 src/backend/opencl/kernel/diff.hpp            |   2 +
 src/backend/opencl/kernel/exampleFunction.hpp |   2 +
 src/backend/opencl/kernel/fast.hpp            |   2 +
 src/backend/opencl/kernel/fftconvolve.hpp     |   2 +
 src/backend/opencl/kernel/flood_fill.hpp      |   2 +
 src/backend/opencl/kernel/gradient.hpp        |   6 +-
 src/backend/opencl/kernel/harris.hpp          |   2 +
 src/backend/opencl/kernel/histogram.hpp       |   2 +
 src/backend/opencl/kernel/homography.hpp      |   2 +
 src/backend/opencl/kernel/hsv_rgb.hpp         |   2 +
 src/backend/opencl/kernel/identity.hpp        |   6 +-
 src/backend/opencl/kernel/iir.hpp             |   4 +-
 src/backend/opencl/kernel/index.hpp           |   2 +
 src/backend/opencl/kernel/interp.hpp          |   2 +
 src/backend/opencl/kernel/iota.hpp            |   2 +
 src/backend/opencl/kernel/ireduce.hpp         |   6 +-
 src/backend/opencl/kernel/laset.hpp           |   4 +-
 src/backend/opencl/kernel/laset_band.hpp      |   4 +-
 src/backend/opencl/kernel/laswp.hpp           |   2 +
 src/backend/opencl/kernel/lookup.hpp          |   2 +
 src/backend/opencl/kernel/lu_split.hpp        |   6 +-
 src/backend/opencl/kernel/match_template.hpp  |   2 +
 src/backend/opencl/kernel/mean.hpp            |   2 +
 src/backend/opencl/kernel/meanshift.hpp       |   2 +
 src/backend/opencl/kernel/medfilt.hpp         |   2 +
 src/backend/opencl/kernel/memcopy.hpp         |   2 +
 src/backend/opencl/kernel/moments.hpp         |   2 +
 src/backend/opencl/kernel/morph.hpp           |   2 +
 .../opencl/kernel/nearest_neighbour.hpp       |   2 +
 src/backend/opencl/kernel/orb.hpp             |   2 +
 .../opencl/kernel/pad_array_borders.hpp       |   2 +
 src/backend/opencl/kernel/random_engine.hpp   |   2 +
 src/backend/opencl/kernel/range.hpp           |   2 +
 src/backend/opencl/kernel/reduce.hpp          |   8 +-
 src/backend/opencl/kernel/reduce_by_key.hpp   |  14 +-
 src/backend/opencl/kernel/regions.hpp         |   2 +
 src/backend/opencl/kernel/reorder.hpp         |   2 +
 src/backend/opencl/kernel/resize.hpp          |   2 +
 src/backend/opencl/kernel/rotate.hpp          |   2 +
 .../kernel/scan_by_key/scan_by_key_impl.cpp   |   2 +
 src/backend/opencl/kernel/scan_dim.hpp        |   4 +-
 src/backend/opencl/kernel/scan_dim_by_key.hpp |   2 +
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |   4 +-
 src/backend/opencl/kernel/scan_first.hpp      |   4 +-
 .../opencl/kernel/scan_first_by_key.hpp       |   2 +
 .../opencl/kernel/scan_first_by_key_impl.hpp  |   4 +-
 src/backend/opencl/kernel/select.hpp          |   4 +-
 src/backend/opencl/kernel/sift.hpp            |   2 +
 src/backend/opencl/kernel/sobel.hpp           |   2 +
 src/backend/opencl/kernel/sort.hpp            |   2 +
 src/backend/opencl/kernel/sort_by_key.hpp     |   2 +
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |   2 +
 .../opencl/kernel/sort_by_key_impl.hpp        |   4 +-
 src/backend/opencl/kernel/sort_helper.hpp     |   2 +
 src/backend/opencl/kernel/sparse.hpp          |   2 +
 src/backend/opencl/kernel/sparse_arith.hpp    |   4 +-
 src/backend/opencl/kernel/susan.hpp           |   2 +
 src/backend/opencl/kernel/swapdblk.hpp        |   2 +
 src/backend/opencl/kernel/tile.hpp            |   2 +
 src/backend/opencl/kernel/transform.hpp       |   2 +
 src/backend/opencl/kernel/transpose.hpp       |   4 +-
 .../opencl/kernel/transpose_inplace.hpp       |   4 +-
 src/backend/opencl/kernel/triangle.hpp        |   4 +-
 src/backend/opencl/kernel/unwrap.hpp          |   2 +
 src/backend/opencl/kernel/where.hpp           |   4 +-
 src/backend/opencl/kernel/wrap.hpp            |   2 +
 src/backend/opencl/logic.hpp                  |   2 +
 src/backend/opencl/lookup.cpp                 |   4 +-
 src/backend/opencl/lookup.hpp                 |   4 +-
 src/backend/opencl/lu.cpp                     |   4 +
 src/backend/opencl/lu.hpp                     |   2 +
 src/backend/opencl/magma/geqrf2.cpp           |   2 +-
 src/backend/opencl/magma/getrs.cpp            |   2 +-
 src/backend/opencl/magma/labrd.cpp            |   2 +-
 src/backend/opencl/magma/laset.cpp            |  10 +-
 src/backend/opencl/magma/laswp.cpp            |   3 +-
 src/backend/opencl/magma/magma_blas.h         |   4 +-
 src/backend/opencl/magma/magma_blas_clblast.h |   6 +-
 src/backend/opencl/magma/magma_data.h         |   4 +-
 src/backend/opencl/magma/swapdblk.cpp         |   4 +-
 src/backend/opencl/magma/transpose.cpp        |   4 +-
 .../opencl/magma/transpose_inplace.cpp        |   4 +-
 src/backend/opencl/match_template.cpp         |   2 +
 src/backend/opencl/match_template.hpp         |   4 +-
 src/backend/opencl/math.cpp                   |   2 +
 src/backend/opencl/math.hpp                   |  16 +-
 src/backend/opencl/max.cpp                    |   4 +-
 src/backend/opencl/mean.cpp                   |   4 +-
 src/backend/opencl/mean.hpp                   |   2 +
 src/backend/opencl/meanshift.cpp              |   2 +
 src/backend/opencl/meanshift.hpp              |   4 +-
 src/backend/opencl/medfilt.cpp                |   2 +
 src/backend/opencl/medfilt.hpp                |   2 +
 src/backend/opencl/memory.cpp                 |   4 +-
 src/backend/opencl/memory.hpp                 |   6 +-
 src/backend/opencl/min.cpp                    |   4 +-
 src/backend/opencl/moments.cpp                |   2 +
 src/backend/opencl/moments.hpp                |   4 +-
 src/backend/opencl/morph.cpp                  |   2 +
 src/backend/opencl/morph.hpp                  |   2 +
 src/backend/opencl/nearest_neighbour.cpp      |   2 +
 src/backend/opencl/nearest_neighbour.hpp      |   4 +-
 src/backend/opencl/orb.cpp                    |   2 +
 src/backend/opencl/orb.hpp                    |   4 +-
 src/backend/opencl/platform.cpp               |  16 +-
 src/backend/opencl/platform.hpp               |  15 +-
 src/backend/opencl/plot.cpp                   |   6 +-
 src/backend/opencl/plot.hpp                   |   4 +-
 src/backend/opencl/print.hpp                  |   2 +
 src/backend/opencl/product.cpp                |   4 +-
 src/backend/opencl/qr.cpp                     |   4 +
 src/backend/opencl/qr.hpp                     |   2 +
 src/backend/opencl/random_engine.cpp          |   4 +-
 src/backend/opencl/random_engine.hpp          |   2 +
 src/backend/opencl/range.cpp                  |   4 +-
 src/backend/opencl/range.hpp                  |   4 +-
 src/backend/opencl/reduce.hpp                 |   2 +
 src/backend/opencl/reduce_impl.hpp            |   2 +
 src/backend/opencl/regions.cpp                |   2 +
 src/backend/opencl/regions.hpp                |   4 +-
 src/backend/opencl/reorder.cpp                |   4 +-
 src/backend/opencl/reorder.hpp                |   4 +-
 src/backend/opencl/reshape.cpp                |   4 +-
 src/backend/opencl/resize.cpp                 |   2 +
 src/backend/opencl/resize.hpp                 |   4 +-
 src/backend/opencl/rotate.cpp                 |   2 +
 src/backend/opencl/rotate.hpp                 |   4 +-
 src/backend/opencl/scalar.hpp                 |   2 +
 src/backend/opencl/scan.cpp                   |   2 +
 src/backend/opencl/scan.hpp                   |   4 +-
 src/backend/opencl/scan_by_key.cpp            |   2 +
 src/backend/opencl/scan_by_key.hpp            |   4 +-
 src/backend/opencl/select.cpp                 |   6 +-
 src/backend/opencl/select.hpp                 |   2 +
 src/backend/opencl/set.cpp                    |   2 +
 src/backend/opencl/set.hpp                    |   2 +
 src/backend/opencl/shift.cpp                  |   8 +-
 src/backend/opencl/shift.hpp                  |   4 +-
 src/backend/opencl/sift.cpp                   |   2 +
 src/backend/opencl/sift.hpp                   |   4 +-
 src/backend/opencl/sobel.cpp                  |   2 +
 src/backend/opencl/sobel.hpp                  |   4 +-
 src/backend/opencl/solve.cpp                  |   4 +
 src/backend/opencl/solve.hpp                  |   2 +
 src/backend/opencl/sort.cpp                   |   2 +
 src/backend/opencl/sort.hpp                   |   4 +-
 src/backend/opencl/sort_by_key.cpp            |   2 +
 src/backend/opencl/sort_by_key.hpp            |   4 +-
 src/backend/opencl/sort_index.cpp             |   4 +-
 src/backend/opencl/sort_index.hpp             |   4 +-
 src/backend/opencl/sparse.cpp                 |   2 +
 src/backend/opencl/sparse.hpp                 |   2 +
 src/backend/opencl/sparse_arith.cpp           |   2 +
 src/backend/opencl/sparse_arith.hpp           |   2 +
 src/backend/opencl/sparse_blas.cpp            |   2 +
 src/backend/opencl/sparse_blas.hpp            |   4 +-
 src/backend/opencl/sum.cpp                    |   4 +-
 src/backend/opencl/surface.cpp                |   6 +-
 src/backend/opencl/surface.hpp                |   4 +-
 src/backend/opencl/susan.cpp                  |   2 +
 src/backend/opencl/susan.hpp                  |   4 +-
 src/backend/opencl/svd.cpp                    |   4 +
 src/backend/opencl/svd.hpp                    |   2 +
 src/backend/opencl/threadsMgt.hpp             |   4 +-
 src/backend/opencl/tile.cpp                   |   4 +-
 src/backend/opencl/tile.hpp                   |   4 +-
 src/backend/opencl/topk.cpp                   |   4 +-
 src/backend/opencl/topk.hpp                   |   8 +-
 src/backend/opencl/traits.hpp                 |  19 +-
 src/backend/opencl/transform.cpp              |   2 +
 src/backend/opencl/transform.hpp              |   4 +-
 src/backend/opencl/transpose.cpp              |   4 +-
 src/backend/opencl/transpose.hpp              |   2 +
 src/backend/opencl/transpose_inplace.cpp      |   4 +-
 src/backend/opencl/triangle.cpp               |   4 +-
 src/backend/opencl/triangle.hpp               |   2 +
 src/backend/opencl/types.cpp                  |   6 +-
 src/backend/opencl/types.hpp                  |   6 +-
 src/backend/opencl/unary.hpp                  |   8 +-
 src/backend/opencl/unwrap.cpp                 |   4 +-
 src/backend/opencl/unwrap.hpp                 |   4 +-
 src/backend/opencl/vector_field.cpp           |   6 +-
 src/backend/opencl/vector_field.hpp           |   4 +-
 src/backend/opencl/where.cpp                  |   2 +
 src/backend/opencl/where.hpp                  |   4 +-
 src/backend/opencl/wrap.cpp                   |   4 +-
 src/backend/opencl/wrap.hpp                   |   2 +
 1065 files changed, 4081 insertions(+), 1883 deletions(-)
 create mode 100644 src/api/c/handle.cpp

diff --git a/CMakeModules/FileToString.cmake b/CMakeModules/FileToString.cmake
index 6092c9176c..5491c8b126 100644
--- a/CMakeModules/FileToString.cmake
+++ b/CMakeModules/FileToString.cmake
@@ -45,6 +45,7 @@ function(FILE_TO_STRING)
         endif(RTCS_NULLTERM)
 
         string(REPLACE "." "_" var_name ${var_name})
+        string(REPLACE "\ " "_" namespace_name ${RTCS_NAMESPACE})
 
         set(_output_path "${CMAKE_CURRENT_BINARY_DIR}/${RTCS_OUTPUT_DIR}")
         if(RTCS_WITH_EXTENSION)
@@ -66,9 +67,9 @@ function(FILE_TO_STRING)
 
         list(APPEND _output_files ${_output_file})
     endforeach()
-    add_custom_target(${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target DEPENDS ${_output_files})
-    set_target_properties(${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target PROPERTIES FOLDER "Generated Targets")
+    add_custom_target(${namespace_name}_${RTCS_OUTPUT_DIR}_bin_target DEPENDS ${_output_files})
+    set_target_properties(${namespace_name}_${RTCS_OUTPUT_DIR}_bin_target PROPERTIES FOLDER "Generated Targets")
 
     set("${RTCS_VARNAME}" ${_output_files} PARENT_SCOPE)
-    set("${RTCS_TARGETS}" ${RTCS_NAMESPACE}_${RTCS_OUTPUT_DIR}_bin_target PARENT_SCOPE)
+    set("${RTCS_TARGETS}" ${namespace_name}_${RTCS_OUTPUT_DIR}_bin_target PARENT_SCOPE)
 endfunction(FILE_TO_STRING)
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 8dcf7c3d5b..870d687382 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -88,6 +88,7 @@ target_sources(c_api_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/gaussian_kernel.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/gradient.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/hamming.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/handle.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/handle.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/harris.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/hist.cpp
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index fd2f83c5c1..3c77f8644c 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -24,7 +24,7 @@
 #include <type_traits>
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::createEmptyArray;
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 8cb79bfae8..e9a0f68603 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -17,8 +17,14 @@
 #include <af/sparse.h>
 
 using af::dim4;
-using common::half;
-using common::SparseArrayBase;
+using arrayfire::copyData;
+using arrayfire::copySparseArray;
+using arrayfire::getSparseArrayBase;
+using arrayfire::releaseHandle;
+using arrayfire::releaseSparseHandle;
+using arrayfire::retainSparseHandle;
+using arrayfire::common::half;
+using arrayfire::common::SparseArrayBase;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
@@ -27,48 +33,6 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
-af_array createHandle(const dim4 &d, af_dtype dtype) {
-    // clang-format off
-    switch (dtype) {
-        case f32: return createHandle<float  >(d);
-        case c32: return createHandle<cfloat >(d);
-        case f64: return createHandle<double >(d);
-        case c64: return createHandle<cdouble>(d);
-        case b8:  return createHandle<char   >(d);
-        case s32: return createHandle<int    >(d);
-        case u32: return createHandle<uint   >(d);
-        case u8:  return createHandle<uchar  >(d);
-        case s64: return createHandle<intl   >(d);
-        case u64: return createHandle<uintl  >(d);
-        case s16: return createHandle<short  >(d);
-        case u16: return createHandle<ushort >(d);
-        case f16: return createHandle<half   >(d);
-        default: TYPE_ERROR(3, dtype);
-    }
-    // clang-format on
-}
-
-af_array createHandleFromValue(const dim4 &d, double val, af_dtype dtype) {
-    // clang-format off
-    switch (dtype) {
-        case f32: return createHandleFromValue<float  >(d, val);
-        case c32: return createHandleFromValue<cfloat >(d, val);
-        case f64: return createHandleFromValue<double >(d, val);
-        case c64: return createHandleFromValue<cdouble>(d, val);
-        case b8:  return createHandleFromValue<char   >(d, val);
-        case s32: return createHandleFromValue<int    >(d, val);
-        case u32: return createHandleFromValue<uint   >(d, val);
-        case u8:  return createHandleFromValue<uchar  >(d, val);
-        case s64: return createHandleFromValue<intl   >(d, val);
-        case u64: return createHandleFromValue<uintl  >(d, val);
-        case s16: return createHandleFromValue<short  >(d, val);
-        case u16: return createHandleFromValue<ushort >(d, val);
-        case f16: return createHandleFromValue<half   >(d, val);
-        default: TYPE_ERROR(3, dtype);
-    }
-    // clang-format on
-}
-
 af_err af_get_data_ptr(void *data, const af_array arr) {
     try {
         af_dtype type = getInfo(arr).getType();
@@ -291,38 +255,6 @@ af_err af_release_array(af_array arr) {
     return AF_SUCCESS;
 }
 
-af_array retain(const af_array in) {
-    const ArrayInfo &info = getInfo(in, false, false);
-    af_dtype ty           = info.getType();
-
-    if (info.isSparse()) {
-        switch (ty) {
-            case f32: return retainSparseHandle<float>(in);
-            case f64: return retainSparseHandle<double>(in);
-            case c32: return retainSparseHandle<detail::cfloat>(in);
-            case c64: return retainSparseHandle<detail::cdouble>(in);
-            default: TYPE_ERROR(1, ty);
-        }
-    } else {
-        switch (ty) {
-            case f32: return retainHandle<float>(in);
-            case f64: return retainHandle<double>(in);
-            case s32: return retainHandle<int>(in);
-            case u32: return retainHandle<uint>(in);
-            case u8: return retainHandle<uchar>(in);
-            case c32: return retainHandle<detail::cfloat>(in);
-            case c64: return retainHandle<detail::cdouble>(in);
-            case b8: return retainHandle<char>(in);
-            case s64: return retainHandle<intl>(in);
-            case u64: return retainHandle<uintl>(in);
-            case s16: return retainHandle<short>(in);
-            case u16: return retainHandle<ushort>(in);
-            case f16: return retainHandle<half>(in);
-            default: TYPE_ERROR(1, ty);
-        }
-    }
-}
-
 af_err af_retain_array(af_array *out, const af_array in) {
     try {
         *out = retain(in);
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 20aa69e629..e53b43a6c5 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -30,12 +30,13 @@ using std::swap;
 using std::vector;
 
 using af::dim4;
-using common::convert2Canonical;
-using common::createSpanIndex;
-using common::half;
-using common::if_complex;
-using common::if_real;
-using common::modDims;
+using arrayfire::common::convert2Canonical;
+using arrayfire::common::createSpanIndex;
+using arrayfire::common::half;
+using arrayfire::common::if_complex;
+using arrayfire::common::if_real;
+using arrayfire::common::modDims;
+using arrayfire::common::tile;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
@@ -77,9 +78,9 @@ static void assign(Array<Tout>& out, const vector<af_seq> seqs,
 
         // If both out and in are vectors of equal elements,
         // reshape in to out dims
-        Array<Tin> in_ =
-            in.elements() == 1 ? common::tile(in, oDims) : modDims(in, oDims);
-        auto dst = createSubArray<Tout>(out, seqs, false);
+        Array<Tin> in_ = in.elements() == 1 ? arrayfire::common::tile(in, oDims)
+                                            : modDims(in, oDims);
+        auto dst       = createSubArray<Tout>(out, seqs, false);
 
         copyArray<Tin, Tout>(dst, in_);
     } else {
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index fc24fd64eb..b9f9393421 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -30,9 +30,13 @@
 
 using af::dim4;
 using af::dtype;
-using common::half;
-using common::modDims;
-using common::tile;
+using arrayfire::castSparse;
+using arrayfire::getSparseArray;
+using arrayfire::getSparseArrayBase;
+using arrayfire::common::half;
+using arrayfire::common::modDims;
+using arrayfire::common::SparseArrayBase;
+using arrayfire::common::tile;
 using detail::arithOp;
 using detail::arithOpD;
 using detail::Array;
@@ -84,8 +88,10 @@ static inline af_array arithOpBroadcast(const af_array lhs,
         }
     }
 
-    Array<T> lhst = common::tile<T>(modDims(getArray<T>(lhs), lshape), ltile);
-    Array<T> rhst = common::tile<T>(modDims(getArray<T>(rhs), rshape), rtile);
+    Array<T> lhst =
+        arrayfire::common::tile<T>(modDims(getArray<T>(lhs), lshape), ltile);
+    Array<T> rhst =
+        arrayfire::common::tile<T>(modDims(getArray<T>(rhs), rshape), rtile);
 
     return getHandle(arithOp<T, op>(lhst, rhst, odims));
 }
@@ -199,8 +205,8 @@ template<af_op_t op>
 static af_err af_arith_sparse(af_array *out, const af_array lhs,
                               const af_array rhs) {
     try {
-        const common::SparseArrayBase linfo = getSparseArrayBase(lhs);
-        const common::SparseArrayBase rinfo = getSparseArrayBase(rhs);
+        const SparseArrayBase linfo = getSparseArrayBase(lhs);
+        const SparseArrayBase rinfo = getSparseArrayBase(rhs);
 
         ARG_ASSERT(1, (linfo.getStorage() == rinfo.getStorage()));
         ARG_ASSERT(1, (linfo.dims() == rinfo.dims()));
@@ -227,7 +233,7 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs,
                                     const af_array rhs,
                                     const bool reverse = false) {
     try {
-        const common::SparseArrayBase linfo = getSparseArrayBase(lhs);
+        const SparseArrayBase linfo = getSparseArrayBase(lhs);
         if (linfo.ndims() > 2) {
             AF_ERROR(
                 "Sparse-Dense arithmetic operations cannot be used in batch "
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 0afd4f79b2..0946d42083 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -25,13 +25,16 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
-using common::half;
-using common::SparseArrayBase;
+using arrayfire::getSparseArray;
+using arrayfire::getSparseArrayBase;
+using arrayfire::common::half;
+using arrayfire::common::SparseArrayBase;
 using detail::cdouble;
 using detail::cfloat;
 using detail::gemm;
 using detail::matmul;
 
+namespace {
 template<typename T>
 static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
                                     af_mat_prop optLhs, af_mat_prop optRhs) {
@@ -54,6 +57,16 @@ static inline af_array dot(const af_array lhs, const af_array rhs,
         dot<T>(getArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
+template<typename T>
+static inline T dotAll(af_array out) {
+    T res{};
+    AF_CHECK(af_eval(out));
+    AF_CHECK(af_get_data_ptr((void *)&res, out));
+    return res;
+}
+
+}  // namespace
+
 af_err af_sparse_matmul(af_array *out, const af_array lhs, const af_array rhs,
                         const af_mat_prop optLhs, const af_mat_prop optRhs) {
     try {
@@ -327,14 +340,6 @@ af_err af_dot(af_array *out, const af_array lhs, const af_array rhs,
     return AF_SUCCESS;
 }
 
-template<typename T>
-static inline T dotAll(af_array out) {
-    T res{};
-    AF_CHECK(af_eval(out));
-    AF_CHECK(af_get_data_ptr((void *)&res, out));
-    return res;
-}
-
 af_err af_dot_all(double *rval, double *ival, const af_array lhs,
                   const af_array rhs, const af_mat_prop optLhs,
                   const af_mat_prop optRhs) {
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index 625ce748fa..ae1fa8add9 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -36,8 +36,8 @@
 #include <vector>
 
 using af::dim4;
-using common::cast;
-using common::tile;
+using arrayfire::common::cast;
+using arrayfire::common::tile;
 using detail::arithOp;
 using detail::Array;
 using detail::convolve2;
@@ -62,6 +62,7 @@ using std::make_pair;
 using std::pair;
 using std::vector;
 
+namespace {
 Array<float> gradientMagnitude(const Array<float>& gx, const Array<float>& gy,
                                const bool& isf) {
     using detail::abs;
@@ -138,7 +139,8 @@ Array<float> otsuThreshold(const Array<float>& in, const unsigned NUM_BINS,
 
     ireduce<af_max_t, float>(thresh, locs, sigmas, 0);
 
-    return cast<float, uint>(common::tile(locs, dim4(inDims[0], inDims[1])));
+    return cast<float, uint>(
+        arrayfire::common::tile(locs, dim4(inDims[0], inDims[1])));
 }
 
 Array<float> normalize(const Array<float>& supEdges, const float minVal,
@@ -219,6 +221,8 @@ af_array cannyHelper(const Array<T>& in, const float t1,
     return getHandle(edgeTrackingByHysteresis(swpair.first, swpair.second));
 }
 
+}  // namespace
+
 af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
                 const float t1, const float t2, const unsigned sw,
                 const bool isf) {
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index c4f66cdf34..20e47a1a2d 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -22,7 +22,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::castSparse;
+using arrayfire::getHandle;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
diff --git a/src/api/c/cholesky.cpp b/src/api/c/cholesky.cpp
index 4dd8fdc20f..1a662c649f 100644
--- a/src/api/c/cholesky.cpp
+++ b/src/api/c/cholesky.cpp
@@ -17,6 +17,7 @@
 #include <af/defines.h>
 #include <af/lapack.h>
 
+using arrayfire::getArray;
 using detail::cdouble;
 using detail::cfloat;
 
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index f0da3323eb..fb821d3bf3 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -22,7 +22,7 @@
 #include <af/defines.h>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index 1732aaf4bc..c7a4c4e2bc 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -22,7 +22,7 @@
 #include <complex.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::conj;
diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index b42decc227..ceb8ca7b75 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -24,8 +24,10 @@
 #include <type_traits>
 
 using af::dim4;
-using common::cast;
-using common::createSpanIndex;
+using arrayfire::common::cast;
+using arrayfire::common::convRange;
+using arrayfire::common::createSpanIndex;
+using arrayfire::common::integralImage;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
@@ -122,10 +124,10 @@ af_array ccHelper(const Array<T>& img, const Array<uint>& seedx,
     Array<uint> x_     = arithOp<uint, af_add_t>(seedx, radii, seedDims);
     Array<uint> _y     = arithOp<uint, af_sub_t>(seedy, radiip, seedDims);
     Array<uint> y_     = arithOp<uint, af_add_t>(seedy, radii, seedDims);
-    Array<CT> in       = common::convRange<CT, T>(img, CT(1), CT(2));
+    Array<CT> in       = convRange<CT, T>(img, CT(1), CT(2));
     Array<CT> in_2     = arithOp<CT, af_mul_t>(in, in, inDims);
-    Array<CT> I1       = common::integralImage<CT>(in);
-    Array<CT> I2       = common::integralImage<CT>(in_2);
+    Array<CT> I1       = integralImage<CT>(in);
+    Array<CT> I2       = integralImage<CT>(in_2);
     Array<CT> S1       = sum(I1, _x, x_, _y, y_);
     Array<CT> S2       = sum(I2, _x, x_, _y, y_);
     CT totSum          = getScalar<CT>(reduce_all<af_add_t, CT, CT>(S1));
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 9a496633b0..abbcd2f71b 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -25,8 +25,8 @@
 #include <cstdio>
 
 using af::dim4;
-using common::cast;
-using common::half;
+using arrayfire::common::cast;
+using arrayfire::common::half;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
@@ -54,8 +54,10 @@ inline af_array convolve2(const af_array &s, const af_array &c_f,
     const Array<accT> signal    = castArray<accT>(s);
 
     if (colFilter.isScalar() && rowFilter.isScalar()) {
-        Array<accT> colArray = common::tile(colFilter, signal.dims());
-        Array<accT> rowArray = common::tile(rowFilter, signal.dims());
+        Array<accT> colArray =
+            arrayfire::common::tile(colFilter, signal.dims());
+        Array<accT> rowArray =
+            arrayfire::common::tile(rowFilter, signal.dims());
 
         Array<accT> filter =
             arithOp<accT, af_mul_t>(colArray, rowArray, signal.dims());
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index 0efc503cd4..fd767fb0ba 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -24,7 +24,7 @@
 #include <cmath>
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::getScalar;
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index 80108c4b0b..f364558b11 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -23,7 +23,7 @@
 #include "stats.h"
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index f231c7b300..60ede3d4f6 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -26,7 +26,7 @@
 #include <af/util.h>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createValueArray;
@@ -40,19 +40,6 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
-dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
-    DIM_ASSERT(1, ndims >= 1);
-
-    dim4 d(1, 1, 1, 1);
-
-    for (unsigned i = 0; i < ndims; i++) {
-        d[i] = dims[i];
-        DIM_ASSERT(2, dims[i] >= 1);
-    }
-
-    return d;
-}
-
 // Strong Exception Guarantee
 af_err af_constant(af_array *result, const double value, const unsigned ndims,
                    const dim_t *const dims, const af_dtype type) {
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index 21180b2d8b..d5327d1efe 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -33,7 +33,7 @@
 #include <vector>
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index b619a867f2..1b6ef9fb93 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -28,10 +28,11 @@
 #include <string>
 
 using af::dim4;
-using common::getCacheDirectory;
-using common::getEnvVar;
-using common::half;
-using common::JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME;
+using arrayfire::getSparseArray;
+using arrayfire::common::getCacheDirectory;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::half;
+using arrayfire::common::JIT_KERNEL_CACHE_DIRECTORY_ENV_NAME;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index 3fb1cee150..c579f0b53e 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -16,6 +16,8 @@
 #include <af/defines.h>
 
 using af::dim4;
+using arrayfire::getArray;
+using arrayfire::getHandle;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
diff --git a/src/api/c/error.cpp b/src/api/c/error.cpp
index 4dd1ff190f..91a84b3ff3 100644
--- a/src/api/c/error.cpp
+++ b/src/api/c/error.cpp
@@ -39,7 +39,7 @@ void af_get_last_error(char **str, dim_t *len) {
 }
 
 af_err af_set_enable_stacktrace(int is_enabled) {
-    common::is_stacktrace_enabled() = is_enabled;
+    arrayfire::common::is_stacktrace_enabled() = is_enabled;
 
     return AF_SUCCESS;
 }
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index a304a6d963..4a7a52f6bd 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -41,7 +41,7 @@ af_array example(const af_array& a, const af_array& b,
     // getArray<T> function is defined in handle.hpp
     // and it returns backend specific Array, namely one of the following
     //      * cpu::Array<T>
-    //      * cuda::Array<T>
+    //      * arrayfire::cuda::Array<T>
     //      * opencl::Array<T>
     // getHandle<T> function is defined in handle.hpp takes one of the
     // above backend specific detail::Array<T> and returns the
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 58cbc9e2c4..bbcb2d2a1d 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -26,7 +26,7 @@
 #include <vector>
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 4b0bf15ef2..080af47aac 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -18,8 +18,9 @@
 #include <cassert>
 
 using af::dim4;
-using common::flip;
-using common::half;
+using arrayfire::getArray;
+using arrayfire::common::flip;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/gradient.cpp b/src/api/c/gradient.cpp
index 419039ad11..e99f4e6e64 100644
--- a/src/api/c/gradient.cpp
+++ b/src/api/c/gradient.cpp
@@ -16,6 +16,7 @@
 #include <af/image.h>
 
 using af::dim4;
+using arrayfire::getArray;
 using detail::cdouble;
 using detail::cfloat;
 
diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
new file mode 100644
index 0000000000..392e120fca
--- /dev/null
+++ b/src/api/c/handle.cpp
@@ -0,0 +1,116 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <handle.hpp>
+
+#include <backend.hpp>
+#include <sparse_handle.hpp>
+
+#include <af/dim4.hpp>
+
+using af::dim4;
+using arrayfire::common::half;
+using detail::cdouble;
+using detail::cfloat;
+using detail::intl;
+using detail::uchar;
+using detail::uint;
+using detail::uintl;
+using detail::ushort;
+
+namespace arrayfire {
+
+af_array retain(const af_array in) {
+    const ArrayInfo &info = getInfo(in, false, false);
+    af_dtype ty           = info.getType();
+
+    if (info.isSparse()) {
+        switch (ty) {
+            case f32: return retainSparseHandle<float>(in);
+            case f64: return retainSparseHandle<double>(in);
+            case c32: return retainSparseHandle<detail::cfloat>(in);
+            case c64: return retainSparseHandle<detail::cdouble>(in);
+            default: TYPE_ERROR(1, ty);
+        }
+    } else {
+        switch (ty) {
+            case f32: return retainHandle<float>(in);
+            case f64: return retainHandle<double>(in);
+            case s32: return retainHandle<int>(in);
+            case u32: return retainHandle<uint>(in);
+            case u8: return retainHandle<uchar>(in);
+            case c32: return retainHandle<detail::cfloat>(in);
+            case c64: return retainHandle<detail::cdouble>(in);
+            case b8: return retainHandle<char>(in);
+            case s64: return retainHandle<intl>(in);
+            case u64: return retainHandle<uintl>(in);
+            case s16: return retainHandle<short>(in);
+            case u16: return retainHandle<ushort>(in);
+            case f16: return retainHandle<half>(in);
+            default: TYPE_ERROR(1, ty);
+        }
+    }
+}
+
+af_array createHandle(const dim4 &d, af_dtype dtype) {
+    // clang-format off
+    switch (dtype) {
+        case f32: return createHandle<float  >(d);
+        case c32: return createHandle<cfloat >(d);
+        case f64: return createHandle<double >(d);
+        case c64: return createHandle<cdouble>(d);
+        case b8:  return createHandle<char   >(d);
+        case s32: return createHandle<int    >(d);
+        case u32: return createHandle<uint   >(d);
+        case u8:  return createHandle<uchar  >(d);
+        case s64: return createHandle<intl   >(d);
+        case u64: return createHandle<uintl  >(d);
+        case s16: return createHandle<short  >(d);
+        case u16: return createHandle<ushort >(d);
+        case f16: return createHandle<half   >(d);
+        default: TYPE_ERROR(3, dtype);
+    }
+    // clang-format on
+}
+
+af_array createHandleFromValue(const dim4 &d, double val, af_dtype dtype) {
+    // clang-format off
+    switch (dtype) {
+        case f32: return createHandleFromValue<float  >(d, val);
+        case c32: return createHandleFromValue<cfloat >(d, val);
+        case f64: return createHandleFromValue<double >(d, val);
+        case c64: return createHandleFromValue<cdouble>(d, val);
+        case b8:  return createHandleFromValue<char   >(d, val);
+        case s32: return createHandleFromValue<int    >(d, val);
+        case u32: return createHandleFromValue<uint   >(d, val);
+        case u8:  return createHandleFromValue<uchar  >(d, val);
+        case s64: return createHandleFromValue<intl   >(d, val);
+        case u64: return createHandleFromValue<uintl  >(d, val);
+        case s16: return createHandleFromValue<short  >(d, val);
+        case u16: return createHandleFromValue<ushort >(d, val);
+        case f16: return createHandleFromValue<half   >(d, val);
+        default: TYPE_ERROR(3, dtype);
+    }
+    // clang-format on
+}
+
+dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
+    DIM_ASSERT(1, ndims >= 1);
+
+    dim4 d(1, 1, 1, 1);
+
+    for (unsigned i = 0; i < ndims; i++) {
+        d[i] = dims[i];
+        DIM_ASSERT(2, dims[i] >= 1);
+    }
+
+    return d;
+}
+
+}  // namespace arrayfire
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 2499c9781a..4b73293cb3 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -20,8 +20,7 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
-const ArrayInfo &getInfo(const af_array arr, bool sparse_check = true,
-                         bool device_check = true);
+namespace arrayfire {
 
 af_array retain(const af_array in);
 
@@ -31,10 +30,14 @@ af_array createHandle(const af::dim4 &d, af_dtype dtype);
 
 af_array createHandleFromValue(const af::dim4 &d, double val, af_dtype dtype);
 
+namespace common {
+const ArrayInfo &getInfo(const af_array arr, bool sparse_check = true,
+                         bool device_check = true);
+
 template<typename To>
 detail::Array<To> castArray(const af_array &in);
 
-namespace {
+}  // namespace common
 
 template<typename T>
 const detail::Array<T> &getArray(const af_array &arr) {
@@ -119,4 +122,17 @@ detail::Array<T> &getCopyOnWriteArray(const af_array &arr) {
     return *A;
 }
 
-}  // namespace
+}  // namespace arrayfire
+
+using arrayfire::copyArray;
+using arrayfire::copyData;
+using arrayfire::createHandle;
+using arrayfire::createHandleFromData;
+using arrayfire::createHandleFromValue;
+using arrayfire::getArray;
+using arrayfire::getHandle;
+using arrayfire::releaseHandle;
+using arrayfire::retain;
+using arrayfire::verifyDims;
+using arrayfire::common::castArray;
+using arrayfire::common::getInfo;
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 4b74e33cdf..350d97416d 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -18,6 +18,12 @@
 #include <reduce.hpp>
 #include <af/graphics.h>
 
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+using arrayfire::common::getGLType;
+using arrayfire::common::makeContextCurrent;
+using arrayfire::common::step_round;
 using detail::Array;
 using detail::copy_histogram;
 using detail::forgeManager;
@@ -25,13 +31,12 @@ using detail::getScalar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
-using graphics::ForgeManager;
 
 template<typename T>
 fg_chart setup_histogram(fg_window const window, const af_array in,
                          const double minval, const double maxval,
                          const af_cell* const props) {
-    ForgeModule& _ = graphics::forgePlugin();
+    ForgeModule& _ = forgePlugin();
 
     const Array<T> histogramInput = getArray<T>(in);
     dim_t nBins                   = histogramInput.elements();
@@ -133,7 +138,7 @@ af_err af_draw_hist(const af_window window, const af_array X,
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index 8fef8a2684..da2a7579d8 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -23,8 +23,8 @@
 #include <af/index.h>
 
 using af::dim4;
-using common::cast;
-using common::modDims;
+using arrayfire::common::cast;
+using arrayfire::common::modDims;
 using detail::arithOp;
 using detail::Array;
 using detail::createValueArray;
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index f04f4a23df..aa2744bb6c 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -79,8 +79,8 @@ af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
                                           info.isLinear());
                 break;
             case f16:
-                output = histogram<common::half>(in, nbins, minval, maxval,
-                                                 info.isLinear());
+                output = histogram<arrayfire::common::half>(
+                    in, nbins, minval, maxval, info.isLinear());
                 break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 4b93727d01..533612f45d 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -27,7 +27,12 @@
 #include <limits>
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+using arrayfire::common::getGLType;
+using arrayfire::common::makeContextCurrent;
 using detail::arithOp;
 using detail::Array;
 using detail::copy_image;
@@ -36,7 +41,6 @@ using detail::forgeManager;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
-using graphics::ForgeManager;
 
 template<typename T>
 Array<T> normalizePerType(const Array<T>& in) {
@@ -101,7 +105,7 @@ af_err af_draw_image(const af_window window, const af_array in,
             default: TYPE_ERROR(1, type);
         }
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         auto gridDims  = forgeManager().getWindowGrid(window);
         FG_CHECK(_.fg_set_window_colormap(window, (fg_color_map)props->cmap));
         if (props->col > -1 && props->row > -1) {
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index ba0a024d9e..41e713e631 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -35,6 +35,16 @@
 #include <string>
 
 using af::dim4;
+using arrayfire::AFFI_GRAY;
+using arrayfire::AFFI_RGB;
+using arrayfire::AFFI_RGBA;
+using arrayfire::bitmap_ptr;
+using arrayfire::channel_split;
+using arrayfire::FI_CHANNELS;
+using arrayfire::FreeImage_Module;
+using arrayfire::FreeImageErrorHandler;
+using arrayfire::getFreeImagePlugin;
+using arrayfire::make_bitmap_ptr;
 using detail::pinnedAlloc;
 using detail::pinnedFree;
 using detail::uchar;
@@ -43,6 +53,8 @@ using detail::ushort;
 using std::string;
 using std::swap;
 
+namespace arrayfire {
+
 template<typename T, FI_CHANNELS fi_color, FI_CHANNELS fo_color>
 static af_err readImage(af_array* rImage, const uchar* pSrcLine,
                         const int nSrcPitch, const uint fi_w, const uint fi_h) {
@@ -213,11 +225,14 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
     return err;
 }
 
+}  // namespace arrayfire
+
 ////////////////////////////////////////////////////////////////////////////////
 // File IO
 ////////////////////////////////////////////////////////////////////////////////
 // Load image from disk.
 af_err af_load_image(af_array* out, const char* filename, const bool isColor) {
+    using arrayfire::readImage;
     try {
         ARG_ASSERT(1, filename != NULL);
 
@@ -707,6 +722,7 @@ af_err af_save_image(const char* filename, const af_array in_) {
 ////////////////////////////////////////////////////////////////////////////////
 /// Load image from memory.
 af_err af_load_image_memory(af_array* out, const void* ptr) {
+    using arrayfire::readImage;
     try {
         ARG_ASSERT(1, ptr != NULL);
 
@@ -1075,4 +1091,5 @@ af_err af_delete_image_memory(void *ptr) {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
                     AF_ERR_NOT_CONFIGURED);
 }
+}  // namespace arrayfire
 #endif  // WITH_FREEIMAGE
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index f1edab6d7e..7130202397 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -32,12 +32,23 @@
 #include <string>
 
 using af::dim4;
+using arrayfire::AFFI_GRAY;
+using arrayfire::AFFI_RGB;
+using arrayfire::AFFI_RGBA;
+using arrayfire::bitmap_ptr;
+using arrayfire::channel_split;
+using arrayfire::FI_CHANNELS;
+using arrayfire::FreeImage_Module;
+using arrayfire::FreeImageErrorHandler;
+using arrayfire::getFreeImagePlugin;
+using arrayfire::make_bitmap_ptr;
 using detail::pinnedAlloc;
 using detail::pinnedFree;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
 
+namespace {
 template<typename T, FI_CHANNELS fi_color>
 static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
                           const int nSrcPitch, const uint fi_w,
@@ -116,6 +127,8 @@ FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type) {
     return FIT_BITMAP;
 }
 
+}  // namespace
+
 ////////////////////////////////////////////////////////////////////////////////
 // File IO
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/src/api/c/imageio_helper.h b/src/api/c/imageio_helper.h
index 787a391e59..e9ef818bf3 100644
--- a/src/api/c/imageio_helper.h
+++ b/src/api/c/imageio_helper.h
@@ -21,6 +21,8 @@
 #include <functional>
 #include <memory>
 
+namespace arrayfire {
+
 class FreeImage_Module {
     common::DependencyModule module;
 
@@ -102,3 +104,4 @@ static af_err channel_split(const af_array rgb, const af::dim4 &dims,
 }
 
 #endif
+}
diff --git a/src/api/c/imgproc_common.hpp b/src/api/c/imgproc_common.hpp
index 214fbe6c7a..f4abcb0907 100644
--- a/src/api/c/imgproc_common.hpp
+++ b/src/api/c/imgproc_common.hpp
@@ -19,6 +19,7 @@
 
 #include <cmath>
 
+namespace arrayfire {
 namespace common {
 
 template<typename To, typename Ti = To>
@@ -78,3 +79,4 @@ detail::Array<To> convRange(const detail::Array<Ti>& in,
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 0f36e0b463..1c7484f2bf 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -32,10 +32,10 @@ using std::swap;
 using std::vector;
 
 using af::dim4;
-using common::convert2Canonical;
-using common::createSpanIndex;
-using common::flat;
-using common::half;
+using arrayfire::common::convert2Canonical;
+using arrayfire::common::createSpanIndex;
+using arrayfire::common::flat;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::index;
@@ -45,6 +45,7 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
+namespace arrayfire {
 namespace common {
 af_index_t createSpanIndex() {
     static af_index_t s = [] {
@@ -64,6 +65,7 @@ af_seq convert2Canonical(const af_seq s, const dim_t len) {
     return af_seq{begin, end, s.step};
 }
 }  // namespace common
+}  // namespace arrayfire
 
 template<typename T>
 static af_array indexBySeqs(const af_array& src,
diff --git a/src/api/c/indexing_common.hpp b/src/api/c/indexing_common.hpp
index ae5ea3958a..85a5d9562a 100644
--- a/src/api/c/indexing_common.hpp
+++ b/src/api/c/indexing_common.hpp
@@ -11,6 +11,7 @@
 
 #include <af/index.h>
 
+namespace arrayfire {
 namespace common {
 /// Creates a af_index_t object that represents a af_span value
 af_index_t createSpanIndex();
@@ -39,3 +40,4 @@ af_index_t createSpanIndex();
 /// s{-1, 2, -1};    will return the sequence af_seq(9,2,-1)
 af_seq convert2Canonical(const af_seq s, const dim_t len);
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 219942cc1e..38c0c96dfe 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -20,7 +20,7 @@
 #include <cstring>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createStridedArray;
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index a31a728874..4c47fbe495 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -20,7 +20,7 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index 2dfb7bdbf2..af9021983e 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -23,7 +23,7 @@
 #include "stats.h"
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
@@ -160,7 +160,9 @@ af_err af_mean_all(double *realVal, double *imagVal, const af_array in) {
             case u16: *realVal = mean<ushort, float>(in); break;
             case u8: *realVal = mean<uchar, float>(in); break;
             case b8: *realVal = mean<char, float>(in); break;
-            case f16: *realVal = mean<common::half, float>(in); break;
+            case f16:
+                *realVal = mean<arrayfire::common::half, float>(in);
+                break;
             case c32: {
                 cfloat tmp = mean<cfloat, cfloat>(in);
                 *realVal   = real(tmp);
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 2958d6c90c..a689f92a91 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -26,7 +26,7 @@
 #include <utility>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::createDeviceDataArray;
diff --git a/src/api/c/memoryapi.hpp b/src/api/c/memoryapi.hpp
index 945b0fb287..a52947dce0 100644
--- a/src/api/c/memoryapi.hpp
+++ b/src/api/c/memoryapi.hpp
@@ -22,7 +22,7 @@
  * on a af_memory_manager via calls to a MemoryManagerBase
  */
 class MemoryManagerFunctionWrapper final
-    : public common::memory::MemoryManagerBase {
+    : public arrayfire::common::MemoryManagerBase {
     af_memory_manager handle_;
 
    public:
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 5f07c6bf8b..4f6f0f310d 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -18,7 +18,7 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
@@ -30,11 +30,11 @@ using detail::ushort;
 namespace {
 template<typename T>
 af_array modDims(const af_array in, const dim4& newDims) {
-    return getHandle(common::modDims(getArray<T>(in), newDims));
+    return getHandle(arrayfire::common::modDims(getArray<T>(in), newDims));
 }
 template<typename T>
 af_array flat(const af_array in) {
-    return getHandle(common::flat(getArray<T>(in)));
+    return getHandle(arrayfire::common::flat(getArray<T>(in)));
 }
 }  // namespace
 
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index 948effd652..efaf6cc53a 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -24,8 +24,8 @@
 #include <af/image.h>
 
 using af::dim4;
-using common::cast;
-using common::flip;
+using arrayfire::common::cast;
+using arrayfire::common::flip;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/pinverse.cpp b/src/api/c/pinverse.cpp
index 05d2d92fba..55c5cf8d7d 100644
--- a/src/api/c/pinverse.cpp
+++ b/src/api/c/pinverse.cpp
@@ -32,8 +32,8 @@
 
 using af::dim4;
 using af::dtype_traits;
-using common::cast;
-using common::modDims;
+using arrayfire::common::cast;
+using arrayfire::common::modDims;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 677fda370a..b60448593f 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -23,6 +23,13 @@
 #include <transpose.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+using arrayfire::common::getFGMarker;
+using arrayfire::common::getGLType;
+using arrayfire::common::makeContextCurrent;
+using arrayfire::common::step_round;
 using detail::Array;
 using detail::copy_plot;
 using detail::forgeManager;
@@ -30,14 +37,13 @@ using detail::reduce;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
-using namespace graphics;
 
 // Requires in_ to be in either [order, n] or [n, order] format
 template<typename T, int order>
 fg_chart setup_plot(fg_window window, const af_array in_,
                     const af_cell* const props, fg_plot_type ptype,
                     fg_marker_type mtype) {
-    ForgeModule& _ = graphics::forgePlugin();
+    ForgeModule& _ = forgePlugin();
 
     Array<T> in = getArray<T>(in_);
 
@@ -168,7 +174,7 @@ af_err plotWrapper(const af_window window, const af_array in,
 
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
@@ -240,7 +246,7 @@ af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
@@ -307,7 +313,7 @@ af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 85f30dc028..48fea73b48 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -30,7 +30,9 @@
 
 #include <af/index.h>
 
-using common::half;
+using arrayfire::getSparseArray;
+using arrayfire::common::half;
+using arrayfire::common::SparseArray;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
@@ -115,7 +117,7 @@ static void print(const char *exp, af_array arr, const int precision,
 template<typename T>
 static void printSparse(const char *exp, af_array arr, const int precision,
                         std::ostream &os = std::cout, bool transpose = true) {
-    common::SparseArray<T> sparse = getSparseArray<T>(arr);
+    SparseArray<T> sparse = getSparseArray<T>(arr);
     std::string name("No Name Sparse Array");
 
     if (exp != NULL) { name = std::string(exp); }
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 8d65c4b718..f1a85b2891 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -23,16 +23,16 @@
 #include <memory>
 
 using af::dim4;
-using common::half;
-using common::mask;
-using common::MaxBlocks;
-using common::MtStateLength;
-using common::pos;
-using common::recursion_tbl;
-using common::sh1;
-using common::sh2;
-using common::TableLength;
-using common::temper_tbl;
+using arrayfire::common::half;
+using arrayfire::common::mask;
+using arrayfire::common::MaxBlocks;
+using arrayfire::common::MtStateLength;
+using arrayfire::common::pos;
+using arrayfire::common::recursion_tbl;
+using arrayfire::common::sh1;
+using arrayfire::common::sh2;
+using arrayfire::common::TableLength;
+using arrayfire::common::temper_tbl;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 1849255257..8e1e670506 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -21,7 +21,7 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index c367430809..b283c800bf 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -20,7 +20,7 @@
 #include <af/data.h>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index bd4814157a..b8fdd75e02 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -22,7 +22,8 @@
 #include <select.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::getCopyOnWriteArray;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 3c189af5df..3bea06e855 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -23,7 +23,7 @@
 #include <math.hpp>
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::createEmptyArray;
@@ -75,7 +75,7 @@ static af_array gray2rgb(const af_array& in, const float r, const float g,
                          const float b) {
     if (r == 1.0 && g == 1.0 && b == 1.0) {
         dim4 tileDims(1, 1, 3, 1);
-        return getHandle(common::tile(getArray<T>(in), tileDims));
+        return getHandle(arrayfire::common::tile(getArray<T>(in), tileDims));
     }
 
     af_array mod_input = 0;
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index 8012cfaaba..3ff72abacc 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -14,6 +14,7 @@
 #include <af/image.h>
 
 using af::dim4;
+using arrayfire::common::integralImage;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
@@ -24,7 +25,7 @@ using detail::ushort;
 
 template<typename To, typename Ti>
 inline af_array sat(const af_array& in) {
-    return getHandle<To>(common::integralImage<To, Ti>(getArray<Ti>(in)));
+    return getHandle<To>(integralImage<To, Ti>(getArray<Ti>(in)));
 }
 
 af_err af_sat(af_array* out, const af_array in) {
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index 31d7facbcd..dec47166e7 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -20,7 +20,7 @@
 #include <af/defines.h>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 714a0c1d15..917864dcaf 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -20,14 +20,21 @@
 #include <af/sparse.h>
 
 using af::dim4;
-using common::createEmptySparseArray;
-using common::SparseArray;
-using common::SparseArrayBase;
+using arrayfire::getSparseArray;
+using arrayfire::retainSparseHandle;
+using arrayfire::common::createArrayDataSparseArray;
+using arrayfire::common::createDeviceDataSparseArray;
+using arrayfire::common::createEmptySparseArray;
+using arrayfire::common::createHostDataSparseArray;
+using arrayfire::common::SparseArray;
+using arrayfire::common::SparseArrayBase;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::sparseConvertDenseToStorage;
 
+namespace arrayfire {
+
 const SparseArrayBase &getSparseArrayBase(const af_array in,
                                           bool device_check) {
     const SparseArrayBase *base =
@@ -54,12 +61,119 @@ template<typename T>
 af_array createSparseArrayFromData(const dim4 &dims, const af_array values,
                                    const af_array rowIdx, const af_array colIdx,
                                    const af::storage stype) {
-    SparseArray<T> sparse = common::createArrayDataSparseArray(
+    SparseArray<T> sparse = createArrayDataSparseArray(
         dims, getArray<T>(values), getArray<int>(rowIdx), getArray<int>(colIdx),
         stype);
     return getHandle(sparse);
 }
 
+template<typename T>
+af_array createSparseArrayFromPtr(const af::dim4 &dims, const dim_t nNZ,
+                                  const T *const values,
+                                  const int *const rowIdx,
+                                  const int *const colIdx,
+                                  const af::storage stype,
+                                  const af::source source) {
+    if (nNZ) {
+        switch (source) {
+            case afHost:
+                return getHandle(createHostDataSparseArray(
+                    dims, nNZ, values, rowIdx, colIdx, stype));
+                break;
+            case afDevice:
+                return getHandle(createDeviceDataSparseArray(
+                    dims, nNZ, const_cast<T *>(values),
+                    const_cast<int *>(rowIdx), const_cast<int *>(colIdx),
+                    stype));
+                break;
+        }
+    }
+
+    return getHandle(createEmptySparseArray<T>(dims, nNZ, stype));
+}
+
+template<typename T>
+af_array createSparseArrayFromDense(const af_array _in,
+                                    const af_storage stype) {
+    const Array<T> in = getArray<T>(_in);
+
+    switch (stype) {
+        case AF_STORAGE_CSR:
+            return getHandle(
+                sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(in));
+        case AF_STORAGE_COO:
+            return getHandle(
+                sparseConvertDenseToStorage<T, AF_STORAGE_COO>(in));
+        case AF_STORAGE_CSC:
+            // return getHandle(sparseConvertDenseToStorage<T,
+            // AF_STORAGE_CSC>(in));
+        default:
+            AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
+    }
+}
+
+template<typename T>
+af_array sparseConvertStorage(const af_array in_,
+                              const af_storage destStorage) {
+    const SparseArray<T> in = getSparseArray<T>(in_);
+
+    if (destStorage == AF_STORAGE_DENSE) {
+        // Returns a regular af_array, not sparse
+        switch (in.getStorage()) {
+            case AF_STORAGE_CSR:
+                return getHandle(
+                    detail::sparseConvertStorageToDense<T, AF_STORAGE_CSR>(in));
+            case AF_STORAGE_COO:
+                return getHandle(
+                    detail::sparseConvertStorageToDense<T, AF_STORAGE_COO>(in));
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    } else if (destStorage == AF_STORAGE_CSR) {
+        // Returns a sparse af_array
+        switch (in.getStorage()) {
+            case AF_STORAGE_CSR: return retainSparseHandle<T>(in_);
+            case AF_STORAGE_COO:
+                return getHandle(
+                    detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR,
+                                                          AF_STORAGE_COO>(in));
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    } else if (destStorage == AF_STORAGE_COO) {
+        // Returns a sparse af_array
+        switch (in.getStorage()) {
+            case AF_STORAGE_CSR:
+                return getHandle(
+                    detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO,
+                                                          AF_STORAGE_CSR>(in));
+            case AF_STORAGE_COO: return retainSparseHandle<T>(in_);
+            default:
+                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
+        }
+    }
+
+    // Shoud never come here
+    return NULL;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Get Functions
+////////////////////////////////////////////////////////////////////////////////
+template<typename T>
+af_array getSparseValues(const af_array in) {
+    return getHandle(getSparseArray<T>(in).getValues());
+}
+
+}  // namespace arrayfire
+
+using arrayfire::createSparseArrayFromData;
+using arrayfire::createSparseArrayFromDense;
+using arrayfire::createSparseArrayFromPtr;
+using arrayfire::getSparseArrayBase;
+using arrayfire::getSparseValues;
+using arrayfire::sparseConvertStorage;
+
 af_err af_create_sparse_array(af_array *out, const dim_t nRows,
                               const dim_t nCols, const af_array values,
                               const af_array rowIdx, const af_array colIdx,
@@ -132,31 +246,6 @@ af_err af_create_sparse_array(af_array *out, const dim_t nRows,
     return AF_SUCCESS;
 }
 
-template<typename T>
-af_array createSparseArrayFromPtr(const af::dim4 &dims, const dim_t nNZ,
-                                  const T *const values,
-                                  const int *const rowIdx,
-                                  const int *const colIdx,
-                                  const af::storage stype,
-                                  const af::source source) {
-    if (nNZ) {
-        switch (source) {
-            case afHost:
-                return getHandle(common::createHostDataSparseArray(
-                    dims, nNZ, values, rowIdx, colIdx, stype));
-                break;
-            case afDevice:
-                return getHandle(common::createDeviceDataSparseArray(
-                    dims, nNZ, const_cast<T *>(values),
-                    const_cast<int *>(rowIdx), const_cast<int *>(colIdx),
-                    stype));
-                break;
-        }
-    }
-
-    return getHandle(createEmptySparseArray<T>(dims, nNZ, stype));
-}
-
 af_err af_create_sparse_array_from_ptr(
     af_array *out, const dim_t nRows, const dim_t nCols, const dim_t nNZ,
     const void *const values, const int *const rowIdx, const int *const colIdx,
@@ -211,26 +300,6 @@ af_err af_create_sparse_array_from_ptr(
     return AF_SUCCESS;
 }
 
-template<typename T>
-af_array createSparseArrayFromDense(const af_array _in,
-                                    const af_storage stype) {
-    const Array<T> in = getArray<T>(_in);
-
-    switch (stype) {
-        case AF_STORAGE_CSR:
-            return getHandle(
-                sparseConvertDenseToStorage<T, AF_STORAGE_CSR>(in));
-        case AF_STORAGE_COO:
-            return getHandle(
-                sparseConvertDenseToStorage<T, AF_STORAGE_COO>(in));
-        case AF_STORAGE_CSC:
-            // return getHandle(sparseConvertDenseToStorage<T,
-            // AF_STORAGE_CSC>(in));
-        default:
-            AF_ERROR("Storage type is out of range/unsupported", AF_ERR_ARG);
-    }
-}
-
 af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
                                          const af_storage stype) {
     try {
@@ -274,51 +343,6 @@ af_err af_create_sparse_array_from_dense(af_array *out, const af_array in,
     return AF_SUCCESS;
 }
 
-template<typename T>
-af_array sparseConvertStorage(const af_array in_,
-                              const af_storage destStorage) {
-    const SparseArray<T> in = getSparseArray<T>(in_);
-
-    if (destStorage == AF_STORAGE_DENSE) {
-        // Returns a regular af_array, not sparse
-        switch (in.getStorage()) {
-            case AF_STORAGE_CSR:
-                return getHandle(
-                    detail::sparseConvertStorageToDense<T, AF_STORAGE_CSR>(in));
-            case AF_STORAGE_COO:
-                return getHandle(
-                    detail::sparseConvertStorageToDense<T, AF_STORAGE_COO>(in));
-            default:
-                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
-        }
-    } else if (destStorage == AF_STORAGE_CSR) {
-        // Returns a sparse af_array
-        switch (in.getStorage()) {
-            case AF_STORAGE_CSR: return retainSparseHandle<T>(in_);
-            case AF_STORAGE_COO:
-                return getHandle(
-                    detail::sparseConvertStorageToStorage<T, AF_STORAGE_CSR,
-                                                          AF_STORAGE_COO>(in));
-            default:
-                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
-        }
-    } else if (destStorage == AF_STORAGE_COO) {
-        // Returns a sparse af_array
-        switch (in.getStorage()) {
-            case AF_STORAGE_CSR:
-                return getHandle(
-                    detail::sparseConvertStorageToStorage<T, AF_STORAGE_COO,
-                                                          AF_STORAGE_CSR>(in));
-            case AF_STORAGE_COO: return retainSparseHandle<T>(in_);
-            default:
-                AF_ERROR("Invalid storage type of input array", AF_ERR_ARG);
-        }
-    }
-
-    // Shoud never come here
-    return NULL;
-}
-
 af_err af_sparse_convert_to(af_array *out, const af_array in,
                             const af_storage destStorage) {
     try {
@@ -398,14 +422,6 @@ af_err af_sparse_to_dense(af_array *out, const af_array in) {
     return AF_SUCCESS;
 }
 
-////////////////////////////////////////////////////////////////////////////////
-// Get Functions
-////////////////////////////////////////////////////////////////////////////////
-template<typename T>
-af_array getSparseValues(const af_array in) {
-    return getHandle(getSparseArray<T>(in).getValues());
-}
-
 af_err af_sparse_get_info(af_array *values, af_array *rows, af_array *cols,
                           af_storage *stype, const af_array in) {
     try {
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index 72b251473b..e99bbb36e5 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -20,6 +20,8 @@
 
 #include <common/SparseArray.hpp>
 
+namespace arrayfire {
+
 const common::SparseArrayBase &getSparseArrayBase(const af_array in,
                                                   bool device_check = true);
 
@@ -86,3 +88,7 @@ static af_array copySparseArray(const af_array in) {
     const common::SparseArray<T> &inArray = getSparseArray<T>(in);
     return getHandle<T>(common::copySparseArray<T>(inArray));
 }
+
+}  // namespace arrayfire
+
+using arrayfire::getHandle;
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 3be779e544..7f64bf3355 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -26,7 +26,7 @@
 #include "stats.h"
 
 using af::dim4;
-using common::cast;
+using arrayfire::common::cast;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 58cc9476aa..62ef46e0e2 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -24,7 +24,13 @@
 #include <surface.hpp>
 
 using af::dim4;
-using common::modDims;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+using arrayfire::common::getGLType;
+using arrayfire::common::makeContextCurrent;
+using arrayfire::common::modDims;
+using arrayfire::common::step_round;
 using detail::Array;
 using detail::copy_surface;
 using detail::createEmptyArray;
@@ -34,13 +40,12 @@ using detail::reduce_all;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
-using namespace graphics;
 
 template<typename T>
 fg_chart setup_surface(fg_window window, const af_array xVals,
                        const af_array yVals, const af_array zVals,
                        const af_cell* const props) {
-    ForgeModule& _ = graphics::forgePlugin();
+    ForgeModule& _ = forgePlugin();
     Array<T> xIn   = getArray<T>(xVals);
     Array<T> yIn   = getArray<T>(yVals);
     Array<T> zIn   = getArray<T>(zVals);
@@ -58,13 +63,13 @@ fg_chart setup_surface(fg_window window, const af_array xVals,
         xIn = modDims(xIn, xIn.elements());
         // Now tile along second dimension
         dim4 x_tdims(1, Y_dims[0], 1, 1);
-        xIn = common::tile(xIn, x_tdims);
+        xIn = arrayfire::common::tile(xIn, x_tdims);
 
         // Convert yIn to a row vector
         yIn = modDims(yIn, dim4(1, yIn.elements()));
         // Now tile along first dimension
         dim4 y_tdims(X_dims[0], 1, 1, 1);
-        yIn = common::tile(yIn, y_tdims);
+        yIn = arrayfire::common::tile(yIn, y_tdims);
     }
 
     // Flatten xIn, yIn and zIn into row vectors
@@ -191,7 +196,7 @@ af_err af_draw_surface(const af_window window, const af_array xVals,
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index 443419b540..ce512e9958 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -20,7 +20,8 @@
 #include <af/data.h>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
+using arrayfire::common::tile;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
@@ -33,7 +34,7 @@ using detail::ushort;
 
 template<typename T>
 static inline af_array tile(const af_array in, const af::dim4 &tileDims) {
-    return getHandle(common::tile<T>(getArray<T>(in), tileDims));
+    return getHandle(arrayfire::common::tile<T>(getArray<T>(in), tileDims));
 }
 
 af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims) {
diff --git a/src/api/c/topk.cpp b/src/api/c/topk.cpp
index 9375d857c0..c8a303afea 100644
--- a/src/api/c/topk.cpp
+++ b/src/api/c/topk.cpp
@@ -17,7 +17,7 @@
 #include <handle.hpp>
 #include <topk.hpp>
 
-using common::half;
+using arrayfire::common::half;
 using detail::createEmptyArray;
 using detail::uint;
 
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index a92fe77e91..82ae18fef2 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -19,7 +19,7 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 95e48d75bc..af18031eab 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -31,7 +31,7 @@
 #include <af/defines.h>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index efbbfc8a70..c82c1ca0cd 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -26,8 +26,8 @@
 #include <tuple>
 
 using af::dim4;
-using common::cast;
-using common::half;
+using arrayfire::common::cast;
+using arrayfire::common::half;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index fa48328462..a6bd0e07cc 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -23,6 +23,12 @@
 #include <vector>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+using arrayfire::common::getGLType;
+using arrayfire::common::makeContextCurrent;
+using arrayfire::common::step_round;
 using detail::Array;
 using detail::copy_vector_field;
 using detail::createEmptyArray;
@@ -34,14 +40,12 @@ using detail::uint;
 using detail::ushort;
 using std::vector;
 
-using namespace graphics;
-
 template<typename T>
 fg_chart setup_vector_field(fg_window window, const vector<af_array>& points,
                             const vector<af_array>& directions,
                             const af_cell* const props,
                             const bool transpose_ = true) {
-    ForgeModule& _ = graphics::forgePlugin();
+    ForgeModule& _ = forgePlugin();
     vector<Array<T>> pnts;
     vector<Array<T>> dirs;
 
@@ -184,7 +188,7 @@ af_err vectorFieldWrapper(const af_window window, const af_array points,
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
@@ -291,7 +295,7 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
         }
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
@@ -386,7 +390,7 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
 
         auto gridDims = forgeManager().getWindowGrid(window);
 
-        ForgeModule& _ = graphics::forgePlugin();
+        ForgeModule& _ = forgePlugin();
         if (props->col > -1 && props->row > -1) {
             FG_CHECK(_.fg_draw_chart_to_cell(
                 window, gridDims.first, gridDims.second,
diff --git a/src/api/c/window.cpp b/src/api/c/window.cpp
index 5f9d6e1c43..fe9fea5ba0 100644
--- a/src/api/c/window.cpp
+++ b/src/api/c/window.cpp
@@ -15,8 +15,10 @@
 #include <common/graphics_common.hpp>
 #include <platform.hpp>
 
+using arrayfire::common::ForgeManager;
+using arrayfire::common::forgePlugin;
+using arrayfire::common::step_round;
 using detail::forgeManager;
-using namespace graphics;
 
 af_err af_create_window(af_window* out, const int width, const int height,
                         const char* const title) {
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 832c2999e5..1d61c63c2d 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -36,6 +36,7 @@
 #ifdef AF_UNIFIED
 #include <symbol_manager.hpp>
 #include <af/backend.h>
+using arrayfire::common::getFunctionPointer;
 #endif
 
 #include <memory>
@@ -255,43 +256,46 @@ array::~array() {
         std::add_pointer<decltype(af_release_array)>::type;
 
     if (get()) {
-        af_backend backend = unified::getActiveBackend();
+        af_backend backend = arrayfire::unified::getActiveBackend();
         af_err err         = af_get_backend_id(&backend, get());
         if (!err) {
             switch (backend) {
                 case AF_BACKEND_CPU: {
-                    static auto *cpu_handle = unified::getActiveHandle();
+                    static auto *cpu_handle =
+                        arrayfire::unified::getActiveHandle();
                     static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
-                            common::getFunctionPointer(cpu_handle,
-                                                       "af_release_array"));
+                            getFunctionPointer(cpu_handle, "af_release_array"));
                     release_func(get());
                     break;
                 }
                 case AF_BACKEND_OPENCL: {
-                    static auto *opencl_handle = unified::getActiveHandle();
+                    static auto *opencl_handle =
+                        arrayfire::unified::getActiveHandle();
                     static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
-                            common::getFunctionPointer(opencl_handle,
-                                                       "af_release_array"));
+                            getFunctionPointer(opencl_handle,
+                                               "af_release_array"));
                     release_func(get());
                     break;
                 }
                 case AF_BACKEND_CUDA: {
-                    static auto *cuda_handle = unified::getActiveHandle();
+                    static auto *cuda_handle =
+                        arrayfire::unified::getActiveHandle();
                     static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
-                            common::getFunctionPointer(cuda_handle,
-                                                       "af_release_array"));
+                            getFunctionPointer(cuda_handle,
+                                               "af_release_array"));
                     release_func(get());
                     break;
                 }
                 case AF_BACKEND_ONEAPI: {
-                    static auto *oneapi_handle = unified::getActiveHandle();
+                    static auto *oneapi_handle =
+                        arrayfire::unified::getActiveHandle();
                     static auto release_func =
                         reinterpret_cast<af_release_array_ptr>(
-                            common::getFunctionPointer(oneapi_handle,
-                                                       "af_release_array"));
+                            getFunctionPointer(oneapi_handle,
+                                               "af_release_array"));
                     release_func(get());
                     break;
                 }
diff --git a/src/api/unified/device.cpp b/src/api/unified/device.cpp
index 826d44a83d..96b14d621e 100644
--- a/src/api/unified/device.cpp
+++ b/src/api/unified/device.cpp
@@ -14,16 +14,18 @@
 #include "symbol_manager.hpp"
 
 af_err af_set_backend(const af_backend bknd) {
-    return unified::setBackend(bknd);
+    return arrayfire::unified::setBackend(bknd);
 }
 
 af_err af_get_backend_count(unsigned *num_backends) {
-    *num_backends = unified::AFSymbolManager::getInstance().getBackendCount();
+    *num_backends =
+        arrayfire::unified::AFSymbolManager::getInstance().getBackendCount();
     return AF_SUCCESS;
 }
 
 af_err af_get_available_backends(int *result) {
-    *result = unified::AFSymbolManager::getInstance().getAvailableBackends();
+    *result = arrayfire::unified::AFSymbolManager::getInstance()
+                  .getAvailableBackends();
     return AF_SUCCESS;
 }
 
@@ -39,7 +41,7 @@ af_err af_get_device_id(int *device, const af_array in) {
 }
 
 af_err af_get_active_backend(af_backend *result) {
-    *result = unified::getActiveBackend();
+    *result = arrayfire::unified::getActiveBackend();
     return AF_SUCCESS;
 }
 
diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index a2efc6ee59..d3aed5f498 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -26,16 +26,17 @@
 #include <dlfcn.h>
 #endif
 
-using common::getEnvVar;
-using common::getErrorMessage;
-using common::getFunctionPointer;
-using common::loadLibrary;
-using common::loggerFactory;
-
+using arrayfire::common::getEnvVar;
+using arrayfire::common::getErrorMessage;
+using arrayfire::common::getFunctionPointer;
+using arrayfire::common::loadLibrary;
+using arrayfire::common::loggerFactory;
+using arrayfire::common::unloadLibrary;
 using std::extent;
 using std::function;
 using std::string;
 
+namespace arrayfire {
 namespace unified {
 
 #if defined(OS_WIN)
@@ -222,7 +223,7 @@ AFSymbolManager::AFSymbolManager()
 
 AFSymbolManager::~AFSymbolManager() {
     for (auto& bkndHandle : bkndHandles) {
-        if (bkndHandle) { common::unloadLibrary(bkndHandle); }
+        if (bkndHandle) { unloadLibrary(bkndHandle); }
     }
 }
 
@@ -252,3 +253,4 @@ af_err setBackend(af::Backend bknd) {
 }
 
 }  // namespace unified
+}  // namespace arrayfire
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index 3106bfa2ae..df5d77705c 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <unordered_map>
 
+namespace arrayfire {
 namespace unified {
 
 const int NUM_BACKENDS = 4;
@@ -123,6 +124,7 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 }
 
 }  // namespace unified
+}  // namespace arrayfire
 
 /// Checks if the active backend and the af_arrays are the same.
 ///
@@ -133,27 +135,28 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 /// \param[in] Any number of af_arrays or pointer to af_arrays
 #define CHECK_ARRAYS(...)                                                     \
     do {                                                                      \
-        af_backend backendId = unified::getActiveBackend();                   \
-        if (!unified::checkArrays(backendId, __VA_ARGS__))                    \
+        af_backend backendId = arrayfire::unified::getActiveBackend();        \
+        if (!arrayfire::unified::checkArrays(backendId, __VA_ARGS__))         \
             AF_RETURN_ERROR("Input array does not belong to current backend", \
                             AF_ERR_ARR_BKND_MISMATCH);                        \
     } while (0)
 
 #define CALL(FUNCTION, ...)                                                      \
     using af_func                  = std::add_pointer<decltype(FUNCTION)>::type; \
-    thread_local af_backend index_ = unified::getActiveBackend();                \
-    if (unified::getActiveHandle()) {                                            \
-        thread_local af_func func = (af_func)common::getFunctionPointer(         \
-            unified::getActiveHandle(), __func__);                               \
+    thread_local af_backend index_ = arrayfire::unified::getActiveBackend();     \
+    if (arrayfire::unified::getActiveHandle()) {                                 \
+        thread_local af_func func =                                              \
+            (af_func)arrayfire::common::getFunctionPointer(                      \
+                arrayfire::unified::getActiveHandle(), __func__);                \
         if (!func) {                                                             \
             AF_RETURN_ERROR(                                                     \
                 "requested symbol name could not be found in loaded library.",   \
                 AF_ERR_LOAD_LIB);                                                \
         }                                                                        \
-        if (index_ != unified::getActiveBackend()) {                             \
-            index_ = unified::getActiveBackend();                                \
-            func   = (af_func)common::getFunctionPointer(                        \
-                  unified::getActiveHandle(), __func__);                         \
+        if (index_ != arrayfire::unified::getActiveBackend()) {                  \
+            index_ = arrayfire::unified::getActiveBackend();                     \
+            func   = (af_func)arrayfire::common::getFunctionPointer(             \
+                  arrayfire::unified::getActiveHandle(), __func__);              \
         }                                                                        \
         return func(__VA_ARGS__);                                                \
     } else {                                                                     \
@@ -163,5 +166,6 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
 
 #define CALL_NO_PARAMS(FUNCTION) CALL(FUNCTION)
 
-#define LOAD_SYMBOL() \
-    common::getFunctionPointer(unified::getActiveHandle(), __FUNCTION__)
+#define LOAD_SYMBOL()                      \
+    arrayfire::common::getFunctionPointer( \
+        arrayfire::unified::getActiveHandle(), __FUNCTION__)
diff --git a/src/backend/common/AllocatorInterface.hpp b/src/backend/common/AllocatorInterface.hpp
index 0a7d34393f..0df799efdb 100644
--- a/src/backend/common/AllocatorInterface.hpp
+++ b/src/backend/common/AllocatorInterface.hpp
@@ -15,8 +15,8 @@
 namespace spdlog {
 class logger;
 }
+namespace arrayfire {
 namespace common {
-namespace memory {
 
 /**
  * An interface that provides backend-specific memory management functions,
@@ -39,5 +39,5 @@ class AllocatorInterface {
     std::shared_ptr<spdlog::logger> logger;
 };
 
-}  // namespace memory
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index f079bac8ef..b83380fe88 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -87,23 +87,27 @@ bool ArrayInfo::isVector() const {
     return singular_dims == AF_MAX_DIMS - 1 && non_singular_dims == 1;
 }
 
-bool ArrayInfo::isComplex() const { return common::isComplex(type); }
+bool ArrayInfo::isComplex() const { return arrayfire::common::isComplex(type); }
 
-bool ArrayInfo::isReal() const { return common::isReal(type); }
+bool ArrayInfo::isReal() const { return arrayfire::common::isReal(type); }
 
-bool ArrayInfo::isDouble() const { return common::isDouble(type); }
+bool ArrayInfo::isDouble() const { return arrayfire::common::isDouble(type); }
 
-bool ArrayInfo::isSingle() const { return common::isSingle(type); }
+bool ArrayInfo::isSingle() const { return arrayfire::common::isSingle(type); }
 
-bool ArrayInfo::isHalf() const { return common::isHalf(type); }
+bool ArrayInfo::isHalf() const { return arrayfire::common::isHalf(type); }
 
-bool ArrayInfo::isRealFloating() const { return common::isRealFloating(type); }
+bool ArrayInfo::isRealFloating() const {
+    return arrayfire::common::isRealFloating(type);
+}
 
-bool ArrayInfo::isFloating() const { return common::isFloating(type); }
+bool ArrayInfo::isFloating() const {
+    return arrayfire::common::isFloating(type);
+}
 
-bool ArrayInfo::isInteger() const { return common::isInteger(type); }
+bool ArrayInfo::isInteger() const { return arrayfire::common::isInteger(type); }
 
-bool ArrayInfo::isBool() const { return common::isBool(type); }
+bool ArrayInfo::isBool() const { return arrayfire::common::isBool(type); }
 
 bool ArrayInfo::isLinear() const {
     if (ndims() == 1) { return dim_strides[0] == 1; }
@@ -172,6 +176,9 @@ dim4 toStride(const vector<af_seq> &seqs, const af::dim4 &parentDims) {
     return out;
 }
 
+namespace arrayfire {
+namespace common {
+
 const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
                          bool device_check) {
     const ArrayInfo *info = nullptr;
@@ -188,3 +195,6 @@ const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
 
     return *info;
 }
+
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/Binary.hpp b/src/backend/common/Binary.hpp
index ca500ac865..6ad8654f83 100644
--- a/src/backend/common/Binary.hpp
+++ b/src/backend/common/Binary.hpp
@@ -18,6 +18,7 @@
 
 #include "optypes.hpp"
 
+namespace arrayfire {
 namespace common {
 
 using namespace detail;  // NOLINT
@@ -124,3 +125,4 @@ SPECIALIZE_COMPLEX_MAX(cdouble, double)
 #undef SPECIALIZE_COMPLEX_MAX
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index 3ac5ab7324..d4aae2138e 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -28,6 +28,7 @@ using std::stoi;
 using std::string;
 using std::vector;
 
+namespace arrayfire {
 namespace common {
 
 DefaultMemoryManager::memory_info &
@@ -374,3 +375,4 @@ void DefaultMemoryManager::setMemStepSize(size_t new_step_size) {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/DefaultMemoryManager.hpp b/src/backend/common/DefaultMemoryManager.hpp
index 83af36d390..60fa10a8c9 100644
--- a/src/backend/common/DefaultMemoryManager.hpp
+++ b/src/backend/common/DefaultMemoryManager.hpp
@@ -16,6 +16,7 @@
 #include <unordered_map>
 #include <vector>
 
+namespace arrayfire {
 namespace common {
 
 constexpr unsigned MAX_BUFFERS = 1000;
@@ -23,7 +24,7 @@ constexpr size_t ONE_GB        = 1 << 30;
 
 using uptr_t = std::unique_ptr<void, std::function<void(void *)>>;
 
-class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
+class DefaultMemoryManager final : public common::MemoryManagerBase {
     size_t mem_step_size;
     unsigned max_buffers;
 
@@ -134,3 +135,4 @@ class DefaultMemoryManager final : public common::memory::MemoryManagerBase {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index bdb5b27e0a..6511c54e67 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -20,7 +20,7 @@
 #include <dlfcn.h>
 #endif
 
-using common::Version;
+using arrayfire::common::Version;
 using std::make_tuple;
 using std::string;
 using std::to_string;
@@ -87,6 +87,7 @@ vector<string> libNames(const std::string& name, const string& suffix,
 #error "Unsupported platform"
 #endif
 
+namespace arrayfire {
 namespace common {
 
 DependencyModule::DependencyModule(const char* plugin_file_name,
@@ -168,3 +169,4 @@ spdlog::logger* DependencyModule::getLogger() const noexcept {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 923ba96a47..41cc64569e 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -22,6 +22,7 @@
 namespace spdlog {
 class logger;
 }
+namespace arrayfire {
 namespace common {
 
 using Version = std::tuple<int, int, int>;  // major, minor, patch
@@ -75,6 +76,7 @@ class DependencyModule {
 };
 
 }  // namespace common
+}  // namespace arrayfire
 
 /// Creates a function pointer
 #define MODULE_MEMBER(NAME) decltype(&::NAME) NAME
diff --git a/src/backend/common/EventBase.hpp b/src/backend/common/EventBase.hpp
index 82ad049061..6356e4e1af 100644
--- a/src/backend/common/EventBase.hpp
+++ b/src/backend/common/EventBase.hpp
@@ -9,6 +9,7 @@
 #pragma once
 #include <utility>
 
+namespace arrayfire {
 namespace common {
 
 template<typename NativeEventPolicy>
@@ -81,3 +82,4 @@ class EventBase {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/FFTPlanCache.hpp b/src/backend/common/FFTPlanCache.hpp
index bd341032a2..8ae853480d 100644
--- a/src/backend/common/FFTPlanCache.hpp
+++ b/src/backend/common/FFTPlanCache.hpp
@@ -13,6 +13,7 @@
 #include <string>
 #include <utility>
 
+namespace arrayfire {
 namespace common {
 // FFTPlanCache caches backend specific fft plans in FIFO order
 //
@@ -70,3 +71,4 @@ class FFTPlanCache {
     plan_cache_t mCache;
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/HandleBase.hpp b/src/backend/common/HandleBase.hpp
index 4ffaf4dca1..713ae6f71f 100644
--- a/src/backend/common/HandleBase.hpp
+++ b/src/backend/common/HandleBase.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace common {
 template<typename T, typename H>
 class HandleBase {
@@ -28,6 +29,7 @@ class HandleBase {
     HandleBase& operator=(HandleBase&& h) = default;
 };
 }  // namespace common
+}  // namespace arrayfire
 
 #define CREATE_HANDLE(NAME, TYPE, CREATE_FUNCTION, DESTROY_FUNCTION,  \
                       CHECK_FUNCTION)                                 \
diff --git a/src/backend/common/InteropManager.hpp b/src/backend/common/InteropManager.hpp
index c784ae94aa..efdc76adb6 100644
--- a/src/backend/common/InteropManager.hpp
+++ b/src/backend/common/InteropManager.hpp
@@ -18,6 +18,7 @@
 #include <memory>
 #include <vector>
 
+namespace arrayfire {
 namespace common {
 template<class T, typename R>
 class InteropManager {
@@ -42,8 +43,7 @@ class InteropManager {
     res_vec_t getImageResources(const fg_window image) {
         if (mInteropMap.find(image) == mInteropMap.end()) {
             uint32_t buffer;
-            FG_CHECK(
-                graphics::forgePlugin().fg_get_pixel_buffer(&buffer, image));
+            FG_CHECK(common::forgePlugin().fg_get_pixel_buffer(&buffer, image));
             mInteropMap[image] =
                 static_cast<T *>(this)->registerResources({buffer});
         }
@@ -53,8 +53,8 @@ class InteropManager {
     res_vec_t getPlotResources(const fg_plot plot) {
         if (mInteropMap.find(plot) == mInteropMap.end()) {
             uint32_t buffer;
-            FG_CHECK(graphics::forgePlugin().fg_get_plot_vertex_buffer(&buffer,
-                                                                       plot));
+            FG_CHECK(
+                common::forgePlugin().fg_get_plot_vertex_buffer(&buffer, plot));
             mInteropMap[plot] =
                 static_cast<T *>(this)->registerResources({buffer});
         }
@@ -64,7 +64,7 @@ class InteropManager {
     res_vec_t getHistogramResources(const fg_histogram histogram) {
         if (mInteropMap.find(histogram) == mInteropMap.end()) {
             uint32_t buffer;
-            FG_CHECK(graphics::forgePlugin().fg_get_histogram_vertex_buffer(
+            FG_CHECK(common::forgePlugin().fg_get_histogram_vertex_buffer(
                 &buffer, histogram));
             mInteropMap[histogram] =
                 static_cast<T *>(this)->registerResources({buffer});
@@ -75,7 +75,7 @@ class InteropManager {
     res_vec_t getSurfaceResources(const fg_surface surface) {
         if (mInteropMap.find(surface) == mInteropMap.end()) {
             uint32_t buffer;
-            FG_CHECK(graphics::forgePlugin().fg_get_surface_vertex_buffer(
+            FG_CHECK(common::forgePlugin().fg_get_surface_vertex_buffer(
                 &buffer, surface));
             mInteropMap[surface] =
                 static_cast<T *>(this)->registerResources({buffer});
@@ -86,11 +86,10 @@ class InteropManager {
     res_vec_t getVectorFieldResources(const fg_vector_field field) {
         if (mInteropMap.find(field) == mInteropMap.end()) {
             uint32_t verts, dirs;
-            FG_CHECK(graphics::forgePlugin().fg_get_vector_field_vertex_buffer(
+            FG_CHECK(common::forgePlugin().fg_get_vector_field_vertex_buffer(
                 &verts, field));
-            FG_CHECK(
-                graphics::forgePlugin().fg_get_vector_field_direction_buffer(
-                    &dirs, field));
+            FG_CHECK(common::forgePlugin().fg_get_vector_field_direction_buffer(
+                &dirs, field));
             mInteropMap[field] =
                 static_cast<T *>(this)->registerResources({verts, dirs});
         }
@@ -108,3 +107,4 @@ class InteropManager {
     res_map_t mInteropMap;
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
index 537c2a7a86..5eeb8710fd 100644
--- a/src/backend/common/KernelInterface.hpp
+++ b/src/backend/common/KernelInterface.hpp
@@ -12,6 +12,7 @@
 #include <cstddef>
 #include <string>
 
+namespace arrayfire {
 namespace common {
 
 /// Kernel Interface that should be implemented by each backend
@@ -101,3 +102,4 @@ class KernelInterface {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/Logger.cpp b/src/backend/common/Logger.cpp
index ac488cd40b..3081eab672 100644
--- a/src/backend/common/Logger.cpp
+++ b/src/backend/common/Logger.cpp
@@ -29,6 +29,7 @@ using spdlog::get;
 using spdlog::logger;
 using spdlog::stdout_logger_mt;
 
+namespace arrayfire {
 namespace common {
 
 shared_ptr<logger> loggerFactory(const string& name) {
@@ -62,3 +63,4 @@ string bytesToString(size_t bytes) {
     return fmt::format("{:.3g} {}", fbytes, units[count]);
 }
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index 4b7b4d419e..a004e773fb 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -47,10 +47,12 @@
 /* Other */
 #endif
 
+namespace arrayfire {
 namespace common {
 std::shared_ptr<spdlog::logger> loggerFactory(const std::string& name);
 std::string bytesToString(size_t bytes);
 }  // namespace common
+}  // namespace arrayfire
 
 #ifdef AF_WITH_LOGGING
 #define AF_STR_H(x) #x
diff --git a/src/backend/common/MemoryManagerBase.hpp b/src/backend/common/MemoryManagerBase.hpp
index c338db1020..569154695e 100644
--- a/src/backend/common/MemoryManagerBase.hpp
+++ b/src/backend/common/MemoryManagerBase.hpp
@@ -19,8 +19,8 @@ namespace spdlog {
 class logger;
 }
 
+namespace arrayfire {
 namespace common {
-namespace memory {
 /**
  * A internal base interface for a memory manager which is exposed to AF
  * internals. Externally, both the default AF memory manager implementation and
@@ -89,5 +89,5 @@ class MemoryManagerBase {
     std::unique_ptr<AllocatorInterface> nmi_;
 };
 
-}  // namespace memory
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/MersenneTwister.hpp b/src/backend/common/MersenneTwister.hpp
index 2810a1da0c..a96e271a01 100644
--- a/src/backend/common/MersenneTwister.hpp
+++ b/src/backend/common/MersenneTwister.hpp
@@ -51,6 +51,7 @@
 
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace common {
 const dim_t MaxBlocks     = 32;
 const dim_t TableLength   = 16 * MaxBlocks;
@@ -261,3 +262,4 @@ static unsigned temper_tbl[] = {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/ModuleInterface.hpp b/src/backend/common/ModuleInterface.hpp
index 167c3b2304..2c3127abb2 100644
--- a/src/backend/common/ModuleInterface.hpp
+++ b/src/backend/common/ModuleInterface.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace common {
 
 /// Instances of this object are stored in jit kernel cache
@@ -44,3 +45,4 @@ class ModuleInterface {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/Source.hpp b/src/backend/common/Source.hpp
index 000c2809d2..2199b389da 100644
--- a/src/backend/common/Source.hpp
+++ b/src/backend/common/Source.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #pragma once
 
+namespace arrayfire {
 namespace common {
 struct Source {
     const char* ptr;           // Pointer to the kernel source
@@ -15,3 +16,4 @@ struct Source {
     const std::size_t hash;    // hash value for the source *ptr;
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index 06156ad3f6..ac91a29f31 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -27,6 +27,7 @@ using detail::getActiveDeviceId;
 using detail::scalar;
 using detail::writeDeviceDataArray;
 
+namespace arrayfire {
 namespace common {
 ////////////////////////////////////////////////////////////////////////////
 // Sparse Array Base Implementations
@@ -260,3 +261,4 @@ INSTANTIATE(cdouble);
 #undef INSTANTIATE
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 2dbcdbd3e0..860f7814ac 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -16,6 +16,7 @@
 #include <cstddef>
 #include <vector>
 
+namespace arrayfire {
 namespace common {
 
 template<typename T>
@@ -248,3 +249,4 @@ class SparseArray {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/TemplateArg.hpp b/src/backend/common/TemplateArg.hpp
index a26df012ca..238c912de2 100644
--- a/src/backend/common/TemplateArg.hpp
+++ b/src/backend/common/TemplateArg.hpp
@@ -28,7 +28,7 @@ struct TemplateArg {
 
     template<typename T>
     constexpr TemplateArg(T value) noexcept
-        : _tparam(common::toString(value)) {}
+        : _tparam(arrayfire::common::toString(value)) {}
 };
 
 template<typename... Targs>
@@ -38,6 +38,7 @@ std::array<TemplateArg, sizeof...(Targs)> TemplateArgs(Targs &&...args) {
 }
 
 #define DefineKey(arg) " -D " #arg
-#define DefineValue(arg) " -D " #arg "=" + common::toString(arg)
-#define DefineKeyValue(key, arg) " -D " #key "=" + common::toString(arg)
+#define DefineValue(arg) " -D " #arg "=" + arrayfire::common::toString(arg)
+#define DefineKeyValue(key, arg) \
+    " -D " #key "=" + arrayfire::common::toString(arg)
 #define DefineKeyFromStr(arg) " -D " + std::string(arg)
diff --git a/src/backend/common/TemplateTypename.hpp b/src/backend/common/TemplateTypename.hpp
index 682070510a..47286af899 100644
--- a/src/backend/common/TemplateTypename.hpp
+++ b/src/backend/common/TemplateTypename.hpp
@@ -17,10 +17,10 @@
 template<typename T>
 struct TemplateTypename {
     operator TemplateArg() const noexcept {
-        return {std::string(dtype_traits<T>::getName())};
+        return {std::string(af::dtype_traits<T>::getName())};
     }
     operator std::string() const noexcept {
-        return {std::string(dtype_traits<T>::getName())};
+        return {std::string(af::dtype_traits<T>::getName())};
     }
 };
 
diff --git a/src/backend/common/Transform.hpp b/src/backend/common/Transform.hpp
index 4fb2a127f1..3d56cf0209 100644
--- a/src/backend/common/Transform.hpp
+++ b/src/backend/common/Transform.hpp
@@ -19,6 +19,7 @@
 
 #include "optypes.hpp"
 
+namespace arrayfire {
 namespace common {
 
 using namespace detail;  // NOLINT
@@ -61,3 +62,4 @@ struct Transform<Ti, To, af_notzero_t> {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/cast.cpp b/src/backend/common/cast.cpp
index f02267ecd0..cc98f0504f 100644
--- a/src/backend/common/cast.cpp
+++ b/src/backend/common/cast.cpp
@@ -10,7 +10,7 @@
 #include <common/cast.hpp>
 #include <handle.hpp>
 
-using common::half;
+using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
@@ -19,6 +19,9 @@ using detail::uint;
 using detail::uintl;
 using detail::ushort;
 
+namespace arrayfire {
+namespace common {
+
 template<typename To>
 detail::Array<To> castArray(const af_array &in) {
     const ArrayInfo &info = getInfo(in);
@@ -60,3 +63,6 @@ template detail::Array<uintl> castArray(const af_array &in);
 template detail::Array<short> castArray(const af_array &in);
 template detail::Array<ushort> castArray(const af_array &in);
 template detail::Array<half> castArray(const af_array &in);
+
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/cast.hpp b/src/backend/common/cast.hpp
index d80caacfe6..4186a03914 100644
--- a/src/backend/common/cast.hpp
+++ b/src/backend/common/cast.hpp
@@ -17,6 +17,7 @@
 #include <jit/UnaryNode.hpp>
 #endif
 
+namespace arrayfire {
 namespace common {
 /// This function determines if consecutive cast operations should be
 /// removed from a JIT AST.
@@ -71,7 +72,7 @@ struct CastWrapper {
     }
 
     detail::Array<To> operator()(const detail::Array<Ti> &in) {
-        using cpu::jit::UnaryNode;
+        using detail::jit::UnaryNode;
 
         common::Node_ptr in_node = in.getNode();
         constexpr af::dtype to_dtype =
@@ -118,11 +119,11 @@ struct CastWrapper {
     }
 
     detail::Array<To> operator()(const detail::Array<Ti> &in) {
-        using common::UnaryNode;
+        using arrayfire::common::UnaryNode;
         detail::CastOp<To, Ti> cop;
         common::Node_ptr in_node = in.getNode();
         constexpr af::dtype to_dtype =
-            static_cast<af::dtype>(dtype_traits<To>::af_type);
+            static_cast<af::dtype>(af::dtype_traits<To>::af_type);
         constexpr af::dtype in_dtype =
             static_cast<af::dtype>(af::dtype_traits<Ti>::af_type);
 
@@ -137,7 +138,7 @@ struct CastWrapper {
             if (in_node_unary && in_node_unary->getOp() == af_cast_t) {
                 // child child's output type is the input type of the child
                 AF_TRACE("Cast optimiztion performed by removing cast to {}",
-                         dtype_traits<Ti>::getName());
+                         af::dtype_traits<Ti>::getName());
                 auto in_child_node = in_node_unary->getChildren()[0];
                 if (in_child_node->getType() == to_dtype) {
                     // ignore the input node and simply connect a noop node from
@@ -182,3 +183,4 @@ auto cast(const detail::Array<Ti> &in)
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/compile_module.hpp b/src/backend/common/compile_module.hpp
index c2abe76ecd..2f12f6386b 100644
--- a/src/backend/common/compile_module.hpp
+++ b/src/backend/common/compile_module.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace common {
 
 /// \brief Backend specific source compilation implementation
@@ -63,5 +64,6 @@ detail::Module loadModuleFromDisk(const int device,
                                   const bool isJIT);
 
 }  // namespace common
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/common/complex.hpp b/src/backend/common/complex.hpp
index cb5a4cdabf..b7663580dc 100644
--- a/src/backend/common/complex.hpp
+++ b/src/backend/common/complex.hpp
@@ -13,6 +13,7 @@
 
 #include <type_traits>
 
+namespace arrayfire {
 namespace common {
 
 // The value returns true if the type is a complex type. False otherwise
@@ -39,3 +40,4 @@ using if_real =
     typename std::enable_if<is_complex<T>::value == false, TYPE>::type;
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/defines.hpp b/src/backend/common/defines.hpp
index c72c7b1b32..5c7eadc6ce 100644
--- a/src/backend/common/defines.hpp
+++ b/src/backend/common/defines.hpp
@@ -63,7 +63,9 @@ using LibHandle = void*;
 #define AF_MEM_DEBUG 0
 #endif
 
+namespace arrayfire {
 namespace common {
 using mutex_t      = std::mutex;
 using lock_guard_t = std::lock_guard<mutex_t>;
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/deterministicHash.cpp b/src/backend/common/deterministicHash.cpp
index 0529f7c58b..2280d4cbbb 100644
--- a/src/backend/common/deterministicHash.cpp
+++ b/src/backend/common/deterministicHash.cpp
@@ -36,7 +36,7 @@ size_t deterministicHash(span<const string> list, const size_t prevHash) {
     return hash;
 }
 
-size_t deterministicHash(span<const common::Source> list) {
+size_t deterministicHash(span<const arrayfire::common::Source> list) {
     // Combine the different source codes, via their hashes
     size_t hash = FNV1A_BASE_OFFSET;
     for (auto s : list) {
diff --git a/src/backend/common/deterministicHash.hpp b/src/backend/common/deterministicHash.hpp
index 25b43a8893..fa950bc2a5 100644
--- a/src/backend/common/deterministicHash.hpp
+++ b/src/backend/common/deterministicHash.hpp
@@ -33,4 +33,5 @@ std::size_t deterministicHash(nonstd::span<const std::string> list,
                               const std::size_t prevHash = FNV1A_BASE_OFFSET);
 
 // This concatenates hashes of multiple sources
-std::size_t deterministicHash(nonstd::span<const common::Source> list);
+std::size_t deterministicHash(
+    nonstd::span<const arrayfire::common::Source> list);
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 58bc0a9ced..68514bac29 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -31,9 +31,9 @@ using std::move;
 using std::string;
 using std::stringstream;
 
-using common::getEnvVar;
-using common::getName;
-using common::is_stacktrace_enabled;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::getName;
+using arrayfire::common::is_stacktrace_enabled;
 
 AfError::AfError(const char *const func, const char *const file, const int line,
                  const char *const message, af_err err, stacktrace st)
@@ -222,6 +222,7 @@ const char *af_err_to_string(const af_err err) {
            "case in af_err_to_string.";
 }
 
+namespace arrayfire {
 namespace common {
 
 bool &is_stacktrace_enabled() noexcept {
@@ -230,3 +231,4 @@ bool &is_stacktrace_enabled() noexcept {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 6adf600cf6..a2c55742e0 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -210,8 +210,10 @@ af_err set_global_error_string(const std::string& msg,
 static const int MAX_ERR_SIZE = 1024;
 std::string& get_global_error_string() noexcept;
 
+namespace arrayfire {
 namespace common {
 
 bool& is_stacktrace_enabled() noexcept;
 
-}  // namespace common
+}
+}  // namespace arrayfire
diff --git a/src/backend/common/forge_loader.hpp b/src/backend/common/forge_loader.hpp
index c87e98690c..6fcdd625ef 100644
--- a/src/backend/common/forge_loader.hpp
+++ b/src/backend/common/forge_loader.hpp
@@ -43,7 +43,10 @@
 /* Other */
 #endif
 
-class ForgeModule : public common::DependencyModule {
+namespace arrayfire {
+namespace common {
+
+class ForgeModule : public DependencyModule {
    public:
     ForgeModule();
 
@@ -117,9 +120,7 @@ class ForgeModule : public common::DependencyModule {
     MODULE_MEMBER(fg_err_to_string);
 };
 
-namespace graphics {
 ForgeModule& forgePlugin();
-}
 
 #define FG_CHECK(fn)                                        \
     do {                                                    \
@@ -128,3 +129,6 @@ ForgeModule& forgePlugin();
             AF_ERROR("forge call failed", AF_ERR_INTERNAL); \
         }                                                   \
     } while (0);
+
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 75fe4c002c..07084c43b2 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -15,10 +15,13 @@
 #include <mutex>
 #include <utility>
 
-using common::getEnvVar;
+using arrayfire::common::getEnvVar;
 using std::make_pair;
 using std::string;
 
+namespace arrayfire {
+namespace common {
+
 /// Dynamically loads forge function pointer at runtime
 #define FG_MODULE_FUNCTION_INIT(NAME) \
     NAME = DependencyModule::getSymbol<decltype(&::NAME)>(#NAME)
@@ -175,7 +178,7 @@ size_t getTypeSize(GLenum type) {
 }
 
 void makeContextCurrent(fg_window window) {
-    FG_CHECK(graphics::forgePlugin().fg_make_window_current(window));
+    FG_CHECK(common::forgePlugin().fg_make_window_current(window));
     CheckGL("End makeContextCurrent");
 }
 
@@ -235,8 +238,6 @@ double step_round(const double in, const bool dir) {
     return mag * mult;
 }
 
-namespace graphics {
-
 ForgeModule& forgePlugin() { return detail::forgeManager().plugin(); }
 
 ForgeManager::ForgeManager() : mPlugin(new ForgeModule()) {}
@@ -519,4 +520,6 @@ void ForgeManager::setChartAxesOverride(const fg_chart chart, bool flag) {
     }
     mChartAxesOverrideMap[chart] = flag;
 }
-}  // namespace graphics
+
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/graphics_common.hpp b/src/backend/common/graphics_common.hpp
index 6db366f323..ec59033fcb 100644
--- a/src/backend/common/graphics_common.hpp
+++ b/src/backend/common/graphics_common.hpp
@@ -17,6 +17,9 @@
 #include <utility>
 #include <vector>
 
+namespace arrayfire {
+namespace common {
+
 // default to f32(float) type
 template<typename T>
 fg_dtype getGLType();
@@ -25,7 +28,8 @@ fg_dtype getGLType();
 // Returns 1 if an OpenGL error occurred, 0 otherwise.
 GLenum glErrorCheck(const char* msg, const char* file, int line);
 
-#define CheckGL(msg) glErrorCheck(msg, __AF_FILENAME__, __LINE__)
+#define CheckGL(msg) \
+    arrayfire::common::glErrorCheck(msg, __AF_FILENAME__, __LINE__)
 
 fg_marker_type getFGMarker(const af_marker_type af_marker);
 
@@ -33,8 +37,6 @@ void makeContextCurrent(fg_window window);
 
 double step_round(const double in, const bool dir);
 
-namespace graphics {
-
 /// \brief The singleton manager class for Forge resources
 ///
 /// Only device manager class can create objects of this class.
@@ -59,7 +61,7 @@ class ForgeManager {
     ForgeManager& operator=(ForgeManager&&)      = delete;
 
     /// \brief Module used to invoke forge API calls
-    ForgeModule& plugin();
+    common::ForgeModule& plugin();
 
     /// \brief The main window with which all other windows share GL context
     fg_window getMainWindow();
@@ -294,7 +296,7 @@ class ForgeManager {
     using SurfaceMapIterator   = std::map<ChartKey, SurfacePtr>::iterator;
     using VecFieldMapIterator  = std::map<ChartKey, VectorFieldPtr>::iterator;
 
-    std::unique_ptr<ForgeModule> mPlugin;
+    std::unique_ptr<common::ForgeModule> mPlugin;
     std::unique_ptr<Window, Window::Deleter> mMainWindow;
 
     std::map<fg_window, ChartList> mChartMap;
@@ -307,4 +309,5 @@ class ForgeManager {
     std::map<fg_chart, bool> mChartAxesOverrideMap;
 };
 
-}  // namespace graphics
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/half.cpp b/src/backend/common/half.cpp
index 3e41699c72..249346b038 100644
--- a/src/backend/common/half.cpp
+++ b/src/backend/common/half.cpp
@@ -2,6 +2,7 @@
 #include <common/half.hpp>
 #include <common/util.hpp>
 
+namespace arrayfire {
 namespace common {
 std::ostream &operator<<(std::ostream &os, const half &val) {
     os << float(val);
@@ -13,3 +14,4 @@ std::string toString(const half val) {
     return common::toString(static_cast<float>(val));
 }
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index bd5f143c28..8080dcffa1 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -50,6 +50,7 @@ using uint16_t = unsigned short;
 
 #endif
 
+namespace arrayfire {
 namespace common {
 
 #if defined(__CUDA_ARCH__)
@@ -807,20 +808,22 @@ static constexpr binary_t binary = binary_t{};
 
 class half;
 
-AF_CONSTEXPR __DH__ static inline bool operator==(common::half lhs,
-                                                  common::half rhs) noexcept;
-AF_CONSTEXPR __DH__ static inline bool operator!=(common::half lhs,
-                                                  common::half rhs) noexcept;
-__DH__ static inline bool operator<(common::half lhs,
-                                    common::half rhs) noexcept;
-__DH__ static inline bool operator<(common::half lhs, float rhs) noexcept;
+AF_CONSTEXPR __DH__ static inline bool operator==(
+    arrayfire::common::half lhs, arrayfire::common::half rhs) noexcept;
+AF_CONSTEXPR __DH__ static inline bool operator!=(
+    arrayfire::common::half lhs, arrayfire::common::half rhs) noexcept;
+__DH__ static inline bool operator<(arrayfire::common::half lhs,
+                                    arrayfire::common::half rhs) noexcept;
+__DH__ static inline bool operator<(arrayfire::common::half lhs,
+                                    float rhs) noexcept;
 AF_CONSTEXPR __DH__ static inline bool isinf(half val) noexcept;
 
 /// Classification implementation.
 /// \param arg value to classify
 /// \retval true if not a number
 /// \retval false else
-AF_CONSTEXPR __DH__ static inline bool isnan(common::half val) noexcept;
+AF_CONSTEXPR __DH__ static inline bool isnan(
+    arrayfire::common::half val) noexcept;
 
 class alignas(2) half {
     native_half_t data_ = native_half_t();
@@ -970,22 +973,26 @@ class alignas(2) half {
 
     friend AF_CONSTEXPR __DH__ bool operator==(half lhs, half rhs) noexcept;
     friend AF_CONSTEXPR __DH__ bool operator!=(half lhs, half rhs) noexcept;
-    friend __DH__ bool operator<(common::half lhs, common::half rhs) noexcept;
-    friend __DH__ bool operator<(common::half lhs, float rhs) noexcept;
+    friend __DH__ bool operator<(arrayfire::common::half lhs,
+                                 arrayfire::common::half rhs) noexcept;
+    friend __DH__ bool operator<(arrayfire::common::half lhs,
+                                 float rhs) noexcept;
     friend AF_CONSTEXPR __DH__ bool isinf(half val) noexcept;
     friend AF_CONSTEXPR __DH__ inline bool isnan(half val) noexcept;
 
-    AF_CONSTEXPR __DH__ common::half operator-() const {
+    AF_CONSTEXPR __DH__ arrayfire::common::half operator-() const {
 #if __CUDA_ARCH__ >= 530
-        return common::half(__hneg(data_));
+        return arrayfire::common::half(__hneg(data_));
 #elif defined(__CUDA_ARCH__)
-        return common::half(-(__half2float(data_)));
+        return arrayfire::common::half(-(__half2float(data_)));
 #else
-        return common::half(internal::binary, data_ ^ 0x8000);
+        return arrayfire::common::half(internal::binary, data_ ^ 0x8000);
 #endif
     }
 
-    AF_CONSTEXPR __DH__ common::half operator+() const { return *this; }
+    AF_CONSTEXPR __DH__ arrayfire::common::half operator+() const {
+        return *this;
+    }
 
     AF_CONSTEXPR static half infinity() {
         half out;
@@ -998,8 +1005,8 @@ class alignas(2) half {
     }
 };
 
-AF_CONSTEXPR __DH__ static inline bool operator==(common::half lhs,
-                                                  common::half rhs) noexcept {
+AF_CONSTEXPR __DH__ static inline bool operator==(
+    arrayfire::common::half lhs, arrayfire::common::half rhs) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __heq(lhs.data_, rhs.data_);
 #elif defined(__CUDA_ARCH__)
@@ -1010,8 +1017,8 @@ AF_CONSTEXPR __DH__ static inline bool operator==(common::half lhs,
 #endif
 }
 
-AF_CONSTEXPR __DH__ static inline bool operator!=(common::half lhs,
-                                                  common::half rhs) noexcept {
+AF_CONSTEXPR __DH__ static inline bool operator!=(
+    arrayfire::common::half lhs, arrayfire::common::half rhs) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __hne(lhs.data_, rhs.data_);
 #else
@@ -1019,8 +1026,8 @@ AF_CONSTEXPR __DH__ static inline bool operator!=(common::half lhs,
 #endif
 }
 
-__DH__ static inline bool operator<(common::half lhs,
-                                    common::half rhs) noexcept {
+__DH__ static inline bool operator<(arrayfire::common::half lhs,
+                                    arrayfire::common::half rhs) noexcept {
 #if __CUDA_ARCH__ >= 530
     return __hlt(lhs.data_, rhs.data_);
 #elif defined(__CUDA_ARCH__)
@@ -1033,7 +1040,8 @@ __DH__ static inline bool operator<(common::half lhs,
 #endif
 }
 
-__DH__ static inline bool operator<(common::half lhs, float rhs) noexcept {
+__DH__ static inline bool operator<(arrayfire::common::half lhs,
+                                    float rhs) noexcept {
 #if defined(__CUDA_ARCH__)
     return __half2float(lhs.data_) < rhs;
 #else
@@ -1054,6 +1062,7 @@ static inline std::string to_string(const half&& val) {
 #endif
 
 }  // namespace common
+}  // namespace arrayfire
 
 #if !defined(__NVCC__) && !defined(__CUDACC_RTC__)
 //#endif
@@ -1063,7 +1072,7 @@ namespace std {
 /// Because of the underlying single-precision implementation of many
 /// operations, it inherits some properties from `std::numeric_limits<float>`.
 template<>
-class numeric_limits<common::half> : public numeric_limits<float> {
+class numeric_limits<arrayfire::common::half> : public numeric_limits<float> {
    public:
     /// Supports signed values.
     static constexpr bool is_signed = true;
@@ -1120,60 +1129,70 @@ class numeric_limits<common::half> : public numeric_limits<float> {
     static constexpr int max_exponent10 = 4;
 
     /// Smallest positive normal value.
-    static AF_CONSTEXPR __DH__ common::half min() noexcept {
-        return common::half(common::internal::binary, 0x0400);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half min() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x0400);
     }
 
     /// Smallest finite value.
-    static AF_CONSTEXPR __DH__ common::half lowest() noexcept {
-        return common::half(common::internal::binary, 0xFBFF);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half lowest() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0xFBFF);
     }
 
     /// Largest finite value.
-    static AF_CONSTEXPR __DH__ common::half max() noexcept {
-        return common::half(common::internal::binary, 0x7BFF);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half max() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x7BFF);
     }
 
     /// Difference between one and next representable value.
-    static AF_CONSTEXPR __DH__ common::half epsilon() noexcept {
-        return common::half(common::internal::binary, 0x1400);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half epsilon() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x1400);
     }
 
     /// Maximum rounding error.
-    static AF_CONSTEXPR __DH__ common::half round_error() noexcept {
-        return common::half(
-            common::internal::binary,
+    static AF_CONSTEXPR __DH__ arrayfire::common::half round_error() noexcept {
+        return arrayfire::common::half(
+            arrayfire::common::internal::binary,
             (round_style == std::round_to_nearest) ? 0x3800 : 0x3C00);
     }
 
     /// Positive infinity.
-    static AF_CONSTEXPR __DH__ common::half infinity() noexcept {
-        return common::half(common::internal::binary, 0x7C00);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half infinity() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x7C00);
     }
 
     /// Quiet NaN.
-    static AF_CONSTEXPR __DH__ common::half quiet_NaN() noexcept {
-        return common::half(common::internal::binary, 0x7FFF);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half quiet_NaN() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x7FFF);
     }
 
     /// Signalling NaN.
-    static AF_CONSTEXPR __DH__ common::half signaling_NaN() noexcept {
-        return common::half(common::internal::binary, 0x7DFF);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half
+    signaling_NaN() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x7DFF);
     }
 
     /// Smallest positive subnormal value.
-    static AF_CONSTEXPR __DH__ common::half denorm_min() noexcept {
-        return common::half(common::internal::binary, 0x0001);
+    static AF_CONSTEXPR __DH__ arrayfire::common::half denorm_min() noexcept {
+        return arrayfire::common::half(arrayfire::common::internal::binary,
+                                       0x0001);
     }
 };
 
 /// Hash function for half-precision floats.
 /// This is only defined if C++11 `std::hash` is supported and enabled.
 template<>
-struct hash<common::half>  //: unary_function<common::half,size_t>
+struct hash<
+    arrayfire::common::half>  //: unary_function<arrayfire::common::half,size_t>
 {
     /// Type of function argument.
-    typedef common::half argument_type;
+    typedef arrayfire::common::half argument_type;
 
     /// Function return type.
     typedef size_t result_type;
@@ -1191,6 +1210,7 @@ struct hash<common::half>  //: unary_function<common::half,size_t>
 }  // namespace std
 #endif
 
+namespace arrayfire {
 namespace common {
 AF_CONSTEXPR __DH__ static bool isinf(half val) noexcept {
 #if __CUDA_ARCH__ >= 530
@@ -1213,3 +1233,4 @@ AF_CONSTEXPR __DH__ static inline bool isnan(half val) noexcept {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/host_memory.cpp b/src/backend/common/host_memory.cpp
index 51a01e2164..0e213cb7e5 100644
--- a/src/backend/common/host_memory.cpp
+++ b/src/backend/common/host_memory.cpp
@@ -26,6 +26,7 @@
 #define NOMEMORYSIZE
 #endif
 
+namespace arrayfire {
 namespace common {
 
 #ifdef NOMEMORYSIZE
@@ -109,3 +110,4 @@ size_t getHostMemorySize() {
 
 #endif  // NOMEMORYSIZE
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/host_memory.hpp b/src/backend/common/host_memory.hpp
index 69557fb576..ead8a8c54e 100644
--- a/src/backend/common/host_memory.hpp
+++ b/src/backend/common/host_memory.hpp
@@ -10,8 +10,10 @@
 #pragma once
 #include <cstddef>
 
+namespace arrayfire {
 namespace common {
 
 size_t getHostMemorySize();
 
-}
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/indexing_helpers.hpp b/src/backend/common/indexing_helpers.hpp
index 46e33492bb..9482fa639c 100644
--- a/src/backend/common/indexing_helpers.hpp
+++ b/src/backend/common/indexing_helpers.hpp
@@ -15,6 +15,7 @@
 
 #include <array>
 
+namespace arrayfire {
 namespace common {
 
 // will generate indexes to flip input array
@@ -34,3 +35,4 @@ static detail::Array<T> flip(const detail::Array<T>& in,
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
index 1277aa10be..84c5597e31 100644
--- a/src/backend/common/jit/BinaryNode.cpp
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -18,6 +18,7 @@ using detail::createNodeArray;
 
 using std::make_shared;
 
+namespace arrayfire {
 namespace common {
 #ifdef AF_CPU
 template<typename To, typename Ti, af_op_t op>
@@ -152,3 +153,4 @@ INSTANTIATE_LOGIC(af_ge_t);
 #undef INSTANTIATE
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/BinaryNode.hpp b/src/backend/common/jit/BinaryNode.hpp
index bfc68bd8ea..e250382745 100644
--- a/src/backend/common/jit/BinaryNode.hpp
+++ b/src/backend/common/jit/BinaryNode.hpp
@@ -13,6 +13,7 @@
 
 #include <cmath>
 
+namespace arrayfire {
 namespace common {
 class BinaryNode : public NaryNode {
    public:
@@ -28,3 +29,4 @@ detail::Array<To> createBinaryNode(const detail::Array<Ti> &lhs,
                                    const af::dim4 &odims);
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 9633b2a867..5af3a216d0 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -15,6 +15,7 @@
 #include <cstring>
 #include <sstream>
 
+namespace arrayfire {
 namespace common {
 
 template<typename DataType, typename ParamType>
@@ -118,3 +119,4 @@ class BufferNodeBase : public common::Node {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/ModdimNode.hpp b/src/backend/common/jit/ModdimNode.hpp
index 209593df5c..b0f7d927a6 100644
--- a/src/backend/common/jit/ModdimNode.hpp
+++ b/src/backend/common/jit/ModdimNode.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <common/jit/NaryNode.hpp>
 
+namespace arrayfire {
 namespace common {
 
 class ModdimNode : public NaryNode {
@@ -30,3 +31,4 @@ class ModdimNode : public NaryNode {
     }
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 5e97e249dd..0d78b9e86c 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <utility>
 
+namespace arrayfire {
 namespace common {
 
 class NaryNode : public Node {
@@ -136,3 +137,4 @@ common::Node_ptr createNaryNode(
     return ptr;
 }
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index ed24b9c1f8..0e67228f91 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -19,6 +19,7 @@
 
 using std::vector;
 
+namespace arrayfire {
 namespace common {
 
 int Node::getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes,
@@ -76,9 +77,11 @@ auto isScalar(const Node &ptr) -> bool { return ptr.isScalar(); }
 bool Node::isLinear(const dim_t dims[4]) const { return true; }
 
 }  // namespace common
+}  // namespace arrayfire
 
-size_t std::hash<common::Node *>::operator()(
-    common::Node *const node) const noexcept {
-    common::Node *const node_ptr = static_cast<common::Node *const>(node);
+size_t std::hash<arrayfire::common::Node *>::operator()(
+    arrayfire::common::Node *const node) const noexcept {
+    arrayfire::common::Node *const node_ptr =
+        static_cast<arrayfire::common::Node *const>(node);
     return node_ptr->getHash();
 }
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index bbe3fcb859..9ed090fbaa 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -31,29 +31,34 @@ enum class kJITHeuristics {
     MemoryPressure      = 3  /* eval due to memory pressure */
 };
 
+namespace arrayfire {
 namespace common {
 class Node;
-}
+}  // namespace common
+}  // namespace arrayfire
 
 #ifdef AF_CPU
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
 template<typename T>
 void evalMultiple(std::vector<Param<T>> arrays,
                   std::vector<std::shared_ptr<common::Node>> output_nodes_);
-}
+}  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
 #endif
 
 namespace std {
 template<>
-struct hash<common::Node *> {
+struct hash<arrayfire::common::Node *> {
     /// Calls the getHash function of the Node pointer
-    size_t operator()(common::Node *const n) const noexcept;
+    size_t operator()(arrayfire::common::Node *const n) const noexcept;
 };
 }  // namespace std
 
+namespace arrayfire {
 namespace common {
 class Node;
 struct Node_ids;
@@ -288,8 +293,8 @@ class Node {
 
 #ifdef AF_CPU
     template<typename U>
-    friend void cpu::kernel::evalMultiple(
-        std::vector<cpu::Param<U>> arrays,
+    friend void arrayfire::cpu::kernel::evalMultiple(
+        std::vector<arrayfire::cpu::Param<U>> arrays,
         std::vector<common::Node_ptr> output_nodes_);
 
     virtual void setShape(af::dim4 new_shape) { UNUSED(new_shape); }
@@ -313,3 +318,4 @@ auto isBuffer(const Node &ptr) -> bool;
 auto isScalar(const Node &ptr) -> bool;
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/NodeIO.hpp b/src/backend/common/jit/NodeIO.hpp
index bd4346f465..ac149d98d9 100644
--- a/src/backend/common/jit/NodeIO.hpp
+++ b/src/backend/common/jit/NodeIO.hpp
@@ -17,13 +17,13 @@ template<>
 struct fmt::formatter<af::dtype> : fmt::formatter<char> {
     template<typename FormatContext>
     auto format(const af::dtype& p, FormatContext& ctx) -> decltype(ctx.out()) {
-        format_to(ctx.out(), "{}", getName(p));
+        format_to(ctx.out(), "{}", arrayfire::common::getName(p));
         return ctx.out();
     }
 };
 
 template<>
-struct fmt::formatter<common::Node> {
+struct fmt::formatter<arrayfire::common::Node> {
     // Presentation format: 'p' - pointer, 't' - type.
     // char presentation;
     bool pointer;
@@ -58,7 +58,7 @@ struct fmt::formatter<common::Node> {
     // Formats the point p using the parsed format specification (presentation)
     // stored in this formatter.
     template<typename FormatContext>
-    auto format(const common::Node& node, FormatContext& ctx)
+    auto format(const arrayfire::common::Node& node, FormatContext& ctx)
         -> decltype(ctx.out()) {
         // ctx.out() is an output iterator to write to.
 
@@ -68,15 +68,17 @@ struct fmt::formatter<common::Node> {
             if (isBuffer(node)) {
                 format_to(ctx.out(), "buffer ");
             } else if (isScalar(node)) {
-                format_to(ctx.out(), "scalar ", common::toString(node.getOp()));
+                format_to(ctx.out(), "scalar ",
+                          arrayfire::common::toString(node.getOp()));
             } else {
-                format_to(ctx.out(), "{} ", common::toString(node.getOp()));
+                format_to(ctx.out(), "{} ",
+                          arrayfire::common::toString(node.getOp()));
             }
         }
         if (type) format_to(ctx.out(), "{} ", node.getType());
         if (children) {
             int count;
-            for (count = 0; count < common::Node::kMaxChildren &&
+            for (count = 0; count < arrayfire::common::Node::kMaxChildren &&
                             node.m_children[count].get() != nullptr;
                  count++) {}
             if (count > 0) {
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index e2883079a1..82e916c7ef 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -14,6 +14,7 @@
 #include <iterator>
 #include <vector>
 
+namespace arrayfire {
 namespace common {
 
 /// A node iterator that performs a breadth first traversal of the node tree
@@ -28,7 +29,7 @@ class NodeIterator {
 
    private:
     std::vector<pointer> tree;
-    size_t index;
+    size_t index = 0;
 
     /// Copies the children of the \p n Node to the end of the tree vector
     void copy_children_to_end(Node* n) {
@@ -101,3 +102,4 @@ class NodeIterator {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 126e8860f7..3a530a6911 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -16,6 +16,7 @@
 #include <types.hpp>
 #include <iomanip>
 
+namespace arrayfire {
 namespace common {
 
 template<typename T>
@@ -94,3 +95,4 @@ class ScalarNode : public common::Node {
 };
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index df42002576..bbc0f5863f 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -20,6 +20,7 @@
 #include <sstream>
 #include <string>
 
+namespace arrayfire {
 namespace common {
 
 template<typename BufferNode>
@@ -115,3 +116,4 @@ class ShiftNodeBase : public Node {
     }
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/jit/UnaryNode.hpp b/src/backend/common/jit/UnaryNode.hpp
index d7470a3378..c847bd9f91 100644
--- a/src/backend/common/jit/UnaryNode.hpp
+++ b/src/backend/common/jit/UnaryNode.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <common/jit/NaryNode.hpp>
 
+namespace arrayfire {
 namespace common {
 
 class UnaryNode : public NaryNode {
@@ -24,3 +25,4 @@ class UnaryNode : public NaryNode {
     }
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/kernel_cache.cpp b/src/backend/common/kernel_cache.cpp
index 1fb81ad293..423204ba6b 100644
--- a/src/backend/common/kernel_cache.cpp
+++ b/src/backend/common/kernel_cache.cpp
@@ -36,6 +36,7 @@ using std::unique_lock;
 using std::unordered_map;
 using std::vector;
 
+namespace arrayfire {
 namespace common {
 
 using ModuleMap = unordered_map<size_t, Module>;
@@ -140,5 +141,6 @@ Kernel getKernel(const string& kernelName, span<const common::Source> sources,
 }
 
 }  // namespace common
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index eb1b90f47b..bef3b6b577 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace common {
 
 /// \brief Find/Create-Cache a Kernel that fits the given criteria
@@ -48,7 +49,8 @@ namespace common {
 /// Example Usage: transpose
 ///
 /// \code
-/// auto transpose = getKernel("cuda::transpose", std::array{transpase_cuh_src},
+/// auto transpose = getKernel("arrayfire::cuda::transpose",
+/// std::array{transpase_cuh_src},
 ///         {
 ///           TemplateTypename<T>(),
 ///           TemplateArg(conjugate),
@@ -103,5 +105,6 @@ detail::Kernel getKernel(const detail::Module& mod, const std::string& name,
                          const bool sourceWasJIT);
 
 }  // namespace common
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/common/kernel_type.hpp b/src/backend/common/kernel_type.hpp
index d61f796f67..9d833b7e4b 100644
--- a/src/backend/common/kernel_type.hpp
+++ b/src/backend/common/kernel_type.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace common {
 
 /// \brief Maps a type between its data representation and the type used
@@ -33,3 +34,4 @@ struct kernel_type {
     using native = compute;
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/moddims.cpp b/src/backend/common/moddims.cpp
index 50f9fc6846..6fbd99650e 100644
--- a/src/backend/common/moddims.cpp
+++ b/src/backend/common/moddims.cpp
@@ -22,11 +22,12 @@ using std::make_shared;
 using std::shared_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace common {
 template<typename T>
 Array<T> moddimOp(const Array<T> &in, af::dim4 outDim) {
-    using common::Node;
-    using common::Node_ptr;
+    using arrayfire::common::Node;
+    using arrayfire::common::Node_ptr;
     using std::array;
 
     auto createModdim = [outDim](array<Node_ptr, 1> &operands) {
@@ -80,18 +81,19 @@ detail::Array<T> flat(const detail::Array<T> &in) {
 }
 
 }  // namespace common
+}  // namespace arrayfire
 
-#define INSTANTIATE(TYPE)                                        \
-    template detail::Array<TYPE> common::modDims<TYPE>(          \
-        const detail::Array<TYPE> &in, const af::dim4 &newDims); \
-    template detail::Array<TYPE> common::flat<TYPE>(             \
+#define INSTANTIATE(TYPE)                                          \
+    template detail::Array<TYPE> arrayfire::common::modDims<TYPE>( \
+        const detail::Array<TYPE> &in, const af::dim4 &newDims);   \
+    template detail::Array<TYPE> arrayfire::common::flat<TYPE>(    \
         const detail::Array<TYPE> &in)
 
 INSTANTIATE(float);
 INSTANTIATE(double);
 INSTANTIATE(detail::cfloat);
 INSTANTIATE(detail::cdouble);
-INSTANTIATE(common::half);
+INSTANTIATE(arrayfire::common::half);
 INSTANTIATE(unsigned char);
 INSTANTIATE(char);
 INSTANTIATE(unsigned short);
diff --git a/src/backend/common/moddims.hpp b/src/backend/common/moddims.hpp
index a132db018c..c127407753 100644
--- a/src/backend/common/moddims.hpp
+++ b/src/backend/common/moddims.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace common {
 
 /// Modifies the shape of the Array<T> object to \p newDims
@@ -39,3 +40,4 @@ template<typename T>
 detail::Array<T> flat(const detail::Array<T> &in);
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/module_loading.hpp b/src/backend/common/module_loading.hpp
index 5a28c5bb9e..c64231a49a 100644
--- a/src/backend/common/module_loading.hpp
+++ b/src/backend/common/module_loading.hpp
@@ -9,6 +9,7 @@
 
 #include <common/defines.hpp>
 
+namespace arrayfire {
 namespace common {
 
 void* getFunctionPointer(LibHandle handle, const char* symbolName);
@@ -20,3 +21,4 @@ void unloadLibrary(LibHandle handle);
 std::string getErrorMessage();
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/module_loading_unix.cpp b/src/backend/common/module_loading_unix.cpp
index 81dc4e391c..8380cdf3b1 100644
--- a/src/backend/common/module_loading_unix.cpp
+++ b/src/backend/common/module_loading_unix.cpp
@@ -15,6 +15,7 @@
 #include <string>
 using std::string;
 
+namespace arrayfire {
 namespace common {
 
 void* getFunctionPointer(LibHandle handle, const char* symbolName) {
@@ -35,3 +36,4 @@ string getErrorMessage() {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/module_loading_windows.cpp b/src/backend/common/module_loading_windows.cpp
index 7415792951..bccf1e9bbc 100644
--- a/src/backend/common/module_loading_windows.cpp
+++ b/src/backend/common/module_loading_windows.cpp
@@ -15,6 +15,7 @@
 
 using std::string;
 
+namespace arrayfire {
 namespace common {
 
 void* getFunctionPointer(LibHandle handle, const char* symbolName) {
@@ -40,3 +41,4 @@ string getErrorMessage() {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/sparse_helpers.hpp b/src/backend/common/sparse_helpers.hpp
index 7a370bc38c..daec047eb3 100644
--- a/src/backend/common/sparse_helpers.hpp
+++ b/src/backend/common/sparse_helpers.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace common {
 
 class SparseArrayBase;
@@ -60,3 +61,4 @@ template<typename T>
 SparseArray<T> copySparseArray(const SparseArray<T> &other);
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/tile.hpp b/src/backend/common/tile.hpp
index 512d14b62b..b6ccdd2f60 100644
--- a/src/backend/common/tile.hpp
+++ b/src/backend/common/tile.hpp
@@ -17,6 +17,7 @@
 
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace common {
 
 /// duplicates the elements of an Array<T> array.
@@ -46,3 +47,4 @@ detail::Array<T> tile(const detail::Array<T> &in, const af::dim4 tileDims) {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/traits.hpp b/src/backend/common/traits.hpp
index cfd07b8a0e..2b9090727c 100644
--- a/src/backend/common/traits.hpp
+++ b/src/backend/common/traits.hpp
@@ -16,6 +16,7 @@ template<typename T>
 struct dtype_traits;
 }
 
+namespace arrayfire {
 namespace common {
 class half;
 
@@ -69,12 +70,13 @@ constexpr bool isFloating(af::dtype type) {
 
 }  // namespace
 }  // namespace common
+}  // namespace arrayfire
 
 namespace af {
 template<>
-struct dtype_traits<common::half> {
+struct dtype_traits<arrayfire::common::half> {
     enum { af_type = f16, ctype = f16 };
-    typedef common::half base_type;
+    typedef arrayfire::common::half base_type;
     static const char *getName() { return "half"; }
 };
 }  // namespace af
diff --git a/src/backend/common/unique_handle.hpp b/src/backend/common/unique_handle.hpp
index 0c3fe8fe6f..c55e2ddf81 100644
--- a/src/backend/common/unique_handle.hpp
+++ b/src/backend/common/unique_handle.hpp
@@ -12,6 +12,7 @@
 
 #include <utility>
 
+namespace arrayfire {
 namespace common {
 
 template<typename T>
@@ -117,8 +118,10 @@ unique_handle<T> make_handle(Args... args) {
 }
 
 }  // namespace common
+}  // namespace arrayfire
 
 #define DEFINE_HANDLER(HANDLE_TYPE, HCREATOR, HDESTROYER)            \
+    namespace arrayfire {                                            \
     namespace common {                                               \
     template<>                                                       \
     class ResourceHandler<HANDLE_TYPE> {                             \
@@ -131,4 +134,5 @@ unique_handle<T> make_handle(Args... args) {
             return HDESTROYER(handle);                               \
         }                                                            \
     };                                                               \
-    }  // namespace common
+    }                                                                \
+    }
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index f6d39a864e..a4cc1e2421 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -61,6 +61,7 @@ using std::to_string;
 using std::uint8_t;
 using std::vector;
 
+namespace arrayfire {
 namespace common {
 // http://stackoverflow.com/questions/216823/whats-the-best-way-to-trim-stdstring/217605#217605
 // trim from start
@@ -521,3 +522,4 @@ string toString(af_homography_type val) {
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index 896223e140..ce154775f9 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -15,6 +15,7 @@
 
 #include <string>
 
+namespace arrayfire {
 namespace common {
 /// The environment variable that determines where the runtime kernels
 /// will be stored on the file system
@@ -59,3 +60,4 @@ template<typename T>
 std::string toString(T value);
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 159fd2aa7c..9498fa36aa 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -38,12 +38,12 @@
 #include <utility>
 
 using af::dim4;
-using common::half;
-using common::Node;
-using common::Node_map_t;
-using common::Node_ptr;
-using common::NodeIterator;
-using cpu::jit::BufferNode;
+using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_map_t;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::cpu::jit::BufferNode;
 
 using nonstd::span;
 using std::accumulate;
@@ -55,6 +55,7 @@ using std::make_shared;
 using std::move;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -368,3 +369,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 8db2ee7e44..120d24b373 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -28,6 +28,7 @@
 #include <memory>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 
 namespace jit {
@@ -291,3 +292,4 @@ class Array {
 };
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/Event.cpp b/src/backend/cpu/Event.cpp
index e0c67519d9..8cdf94338c 100644
--- a/src/backend/cpu/Event.cpp
+++ b/src/backend/cpu/Event.cpp
@@ -18,6 +18,7 @@
 
 using std::make_unique;
 
+namespace arrayfire {
 namespace cpu {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(cpu::queue& queue) {
@@ -68,3 +69,4 @@ af_event createAndMarkEvent() {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/Event.hpp b/src/backend/cpu/Event.hpp
index 2d15039cfb..103bc3e9ee 100644
--- a/src/backend/cpu/Event.hpp
+++ b/src/backend/cpu/Event.hpp
@@ -14,6 +14,7 @@
 
 #include <type_traits>
 
+namespace arrayfire {
 namespace cpu {
 
 class CPUEventPolicy {
@@ -58,3 +59,4 @@ void block(af_event eventHandle);
 af_event createAndMarkEvent();
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/Param.hpp b/src/backend/cpu/Param.hpp
index 20686c4430..55b507876a 100644
--- a/src/backend/cpu/Param.hpp
+++ b/src/backend/cpu/Param.hpp
@@ -12,6 +12,7 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 /// \brief Constant parameter object who's memory cannot be modified. Params
@@ -153,3 +154,4 @@ CParam<T> toParam(const Array<T> &val) noexcept {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/ParamIterator.hpp b/src/backend/cpu/ParamIterator.hpp
index ba2189bdeb..3d6427853e 100644
--- a/src/backend/cpu/ParamIterator.hpp
+++ b/src/backend/cpu/ParamIterator.hpp
@@ -16,6 +16,7 @@
 #include <iterator>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 
 /// A Param iterator that iterates through a Param object
@@ -137,3 +138,4 @@ ParamIterator<const T> end(CParam<T>& param) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/anisotropic_diffusion.cpp b/src/backend/cpu/anisotropic_diffusion.cpp
index 97818aea50..7d38cbe5ab 100644
--- a/src/backend/cpu/anisotropic_diffusion.cpp
+++ b/src/backend/cpu/anisotropic_diffusion.cpp
@@ -11,6 +11,7 @@
 #include <kernel/anisotropic_diffusion.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
@@ -33,3 +34,4 @@ void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
 INSTANTIATE(double)
 INSTANTIATE(float)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/anisotropic_diffusion.hpp b/src/backend/cpu/anisotropic_diffusion.hpp
index bf82cbde46..76d1f9ddcf 100644
--- a/src/backend/cpu/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/anisotropic_diffusion.hpp
@@ -9,6 +9,7 @@
 
 #include "af/defines.h"
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 class Array;
@@ -18,3 +19,4 @@ void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/approx.cpp b/src/backend/cpu/approx.cpp
index 1d027eba2c..f65cd18961 100644
--- a/src/backend/cpu/approx.cpp
+++ b/src/backend/cpu/approx.cpp
@@ -12,6 +12,7 @@
 #include <platform.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename Ty, typename Tp>
@@ -88,3 +89,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/approx.hpp b/src/backend/cpu/approx.hpp
index 21a79bcb54..893250a824 100644
--- a/src/backend/cpu/approx.hpp
+++ b/src/backend/cpu/approx.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -23,3 +24,4 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
              const Tp &yi_step, const af_interp_type method,
              const float offGrid);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/arith.hpp b/src/backend/cpu/arith.hpp
index 7a8e5a2402..131f9ae64a 100644
--- a/src/backend/cpu/arith.hpp
+++ b/src/backend/cpu/arith.hpp
@@ -13,6 +13,7 @@
 #include <common/jit/BinaryNode.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, af_op_t op>
@@ -28,3 +29,4 @@ Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index 0f32fab35d..cfeb5e168e 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -28,6 +28,7 @@
 using af::dim4;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
@@ -69,6 +70,7 @@ INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
-INSTANTIATE(common::half)
+INSTANTIATE(arrayfire::common::half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/assign.hpp b/src/backend/cpu/assign.hpp
index 8a9536c14d..ccbdec5ddf 100644
--- a/src/backend/cpu/assign.hpp
+++ b/src/backend/cpu/assign.hpp
@@ -9,6 +9,7 @@
 
 #include <af/index.h>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 class Array;
@@ -17,3 +18,4 @@ template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/backend.hpp b/src/backend/cpu/backend.hpp
index 744fa8f290..ba9f9677d3 100644
--- a/src/backend/cpu/backend.hpp
+++ b/src/backend/cpu/backend.hpp
@@ -21,4 +21,4 @@
 
 #include "types.hpp"
 
-namespace detail = cpu;
+namespace detail = arrayfire::cpu;
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 995e464302..027afb2c3b 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename inType, typename outType>
@@ -42,3 +43,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/bilateral.hpp b/src/backend/cpu/bilateral.hpp
index 543f7eeff0..1cb6edb1e1 100644
--- a/src/backend/cpu/bilateral.hpp
+++ b/src/backend/cpu/bilateral.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename inType, typename outType>
 Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
                          const float &chromaticSigma);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/binary.hpp b/src/backend/cpu/binary.hpp
index 1af89bd3a6..3d130ba520 100644
--- a/src/backend/cpu/binary.hpp
+++ b/src/backend/cpu/binary.hpp
@@ -14,6 +14,7 @@
 #include <types.hpp>
 #include <cmath>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename To, typename Ti, af_op_t op>
@@ -151,3 +152,4 @@ NUMERIC_FN(af_atan2_t, atan2)
 NUMERIC_FN(af_hypot_t, hypot)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index 463c3e8fe1..b7d158eb21 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -34,12 +34,13 @@
 #include <vector>
 
 using af::dtype_traits;
-using common::cast;
-using common::half;
-using common::is_complex;
+using arrayfire::common::cast;
+using arrayfire::common::half;
+using arrayfire::common::is_complex;
 using std::conditional;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 
 // clang-format off
@@ -392,3 +393,4 @@ INSTANTIATE_DOT(cfloat);
 INSTANTIATE_DOT(cdouble);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 956ba6a963..1043a567e9 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -34,3 +35,4 @@ Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/canny.cpp b/src/backend/cpu/canny.cpp
index 55ac39049a..17f242c0fc 100644
--- a/src/backend/cpu/canny.cpp
+++ b/src/backend/cpu/canny.cpp
@@ -15,6 +15,7 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+namespace arrayfire {
 namespace cpu {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -35,3 +36,4 @@ Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
     return out;
 }
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/canny.hpp b/src/backend/cpu/canny.hpp
index e2910fd2a1..7f21d89fe5 100644
--- a/src/backend/cpu/canny.hpp
+++ b/src/backend/cpu/canny.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -17,3 +18,4 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
 Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
                                      const Array<char>& weak);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index 992030407a..dd756eb2b3 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -17,6 +17,7 @@
 #include <af/dim4.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename To, typename Ti>
@@ -33,8 +34,8 @@ struct UnOp<To, Ti, af_cast_t> {
 /// TODO(umar): make a macro to reduce repeat code
 
 template<typename To>
-struct UnOp<To, common::half, af_cast_t> {
-    typedef common::half Ti;
+struct UnOp<To, arrayfire::common::half, af_cast_t> {
+    typedef arrayfire::common::half Ti;
 
     void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
         for (int i = 0; i < lim; i++) {
@@ -49,8 +50,8 @@ struct UnOp<To, common::half, af_cast_t> {
 };
 
 template<typename Ti>
-struct UnOp<common::half, Ti, af_cast_t> {
-    typedef common::half To;
+struct UnOp<arrayfire::common::half, Ti, af_cast_t> {
+    typedef arrayfire::common::half To;
 
     void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
         for (int i = 0; i < lim; i++) {
@@ -65,8 +66,8 @@ struct UnOp<common::half, Ti, af_cast_t> {
 };
 
 template<>
-struct UnOp<common::half, std::complex<float>, af_cast_t> {
-    typedef common::half To;
+struct UnOp<arrayfire::common::half, std::complex<float>, af_cast_t> {
+    typedef arrayfire::common::half To;
     typedef std::complex<float> Ti;
 
     void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
@@ -82,8 +83,8 @@ struct UnOp<common::half, std::complex<float>, af_cast_t> {
 };
 
 template<>
-struct UnOp<common::half, std::complex<double>, af_cast_t> {
-    typedef common::half To;
+struct UnOp<arrayfire::common::half, std::complex<double>, af_cast_t> {
+    typedef arrayfire::common::half To;
     typedef std::complex<double> Ti;
 
     void eval(jit::array<To> &out, const jit::array<Ti> &in, int lim) {
@@ -153,3 +154,4 @@ CAST_B8(uchar)
 CAST_B8(char)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/cholesky.cpp b/src/backend/cpu/cholesky.cpp
index c4588d3b3e..cd478ad75e 100644
--- a/src/backend/cpu/cholesky.cpp
+++ b/src/backend/cpu/cholesky.cpp
@@ -24,6 +24,7 @@
 #include <triangle.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -87,9 +88,11 @@ INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -113,5 +116,6 @@ INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/cpu/cholesky.hpp b/src/backend/cpu/cholesky.hpp
index 9317718d72..5b1247be4d 100644
--- a/src/backend/cpu/cholesky.hpp
+++ b/src/backend/cpu/cholesky.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
@@ -16,3 +17,4 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/complex.hpp b/src/backend/cpu/complex.hpp
index 4d262f7565..44dc574377 100644
--- a/src/backend/cpu/complex.hpp
+++ b/src/backend/cpu/complex.hpp
@@ -15,6 +15,7 @@
 #include <af/dim4.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename To, typename Ti>
@@ -83,3 +84,4 @@ Array<T> conj(const Array<T> &in) {
     return createNodeArray<T>(in.dims(), move(node));
 }
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index d760b724b9..a57ace15f6 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -28,10 +28,11 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::flip;
-using common::half;
-using common::modDims;
+using arrayfire::common::flip;
+using arrayfire::common::half;
+using arrayfire::common::modDims;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename accT>
@@ -256,3 +257,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/convolve.hpp b/src/backend/cpu/convolve.hpp
index e2490e9c96..66963a1d58 100644
--- a/src/backend/cpu/convolve.hpp
+++ b/src/backend/cpu/convolve.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <common/defines.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename accT>
@@ -38,3 +39,4 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index 0790454957..b1d0985680 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -23,9 +23,11 @@
 #include <cstdio>
 #include <cstring>
 
-using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
-using common::is_complex;
+using arrayfire::common::half;  // NOLINT(misc-unused-using-decls) bug in
+                                // clang-tidy
+using arrayfire::common::is_complex;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -150,3 +152,4 @@ INSTANTIATE_GETSCALAR(short)
 INSTANTIATE_GETSCALAR(ushort)
 INSTANTIATE_GETSCALAR(half)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/copy.hpp b/src/backend/cpu/copy.hpp
index 8aade1fe04..6e68bff2b7 100644
--- a/src/backend/cpu/copy.hpp
+++ b/src/backend/cpu/copy.hpp
@@ -17,6 +17,7 @@ namespace af {
 class dim4;
 }
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -73,3 +74,4 @@ void multiply_inplace(Array<T> &in, double val);
 template<typename T>
 T getScalar(const Array<T> &in);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/device_manager.cpp b/src/backend/cpu/device_manager.cpp
index a95d9f5a5c..e2d5ed6f68 100644
--- a/src/backend/cpu/device_manager.cpp
+++ b/src/backend/cpu/device_manager.cpp
@@ -17,7 +17,7 @@
 #include <cctype>
 #include <sstream>
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 using std::string;
 
 #ifdef CPUID_CAPABLE
@@ -119,11 +119,12 @@ CPUInfo::CPUInfo()
 
 #endif
 
+namespace arrayfire {
 namespace cpu {
 
 DeviceManager::DeviceManager()
     : queues(MAX_QUEUES)
-    , fgMngr(new graphics::ForgeManager())
+    , fgMngr(new common::ForgeManager())
     , memManager(new common::DefaultMemoryManager(
           getDeviceCount(), common::MAX_BUFFERS,
           AF_MEM_DEBUG || AF_CPU_MEM_DEBUG)) {
@@ -180,3 +181,4 @@ void DeviceManager::resetMemoryManagerPinned() {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/device_manager.hpp b/src/backend/cpu/device_manager.hpp
index 3015ae05f6..a67c611d24 100644
--- a/src/backend/cpu/device_manager.hpp
+++ b/src/backend/cpu/device_manager.hpp
@@ -15,7 +15,7 @@
 #include <mutex>
 #include <string>
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
 #ifndef AF_CPU_MEM_DEBUG
 #define AF_CPU_MEM_DEBUG 0
@@ -86,6 +86,7 @@ class CPUInfo {
     bool mIsHTT;
 };
 
+namespace arrayfire {
 namespace cpu {
 
 class DeviceManager {
@@ -117,7 +118,7 @@ class DeviceManager {
 
     void resetMemoryManagerPinned();
 
-    friend graphics::ForgeManager& forgeManager();
+    friend arrayfire::common::ForgeManager& forgeManager();
 
     void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
 
@@ -136,10 +137,11 @@ class DeviceManager {
 
     // Attributes
     std::vector<queue> queues;
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<arrayfire::common::ForgeManager> fgMngr;
     const CPUInfo cinfo;
     std::unique_ptr<MemoryManagerBase> memManager;
     std::mutex mutex;
 };
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index 9a8c61fc48..eddd8c0a49 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -19,10 +19,12 @@
 #include <algorithm>
 #include <cstdlib>
 
-using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
-using std::abs;      // NOLINT(misc-unused-using-decls) bug in clang-tidy
-using std::min;      // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using arrayfire::common::half;  // NOLINT(misc-unused-using-decls) bug in
+                                // clang-tidy
+using std::abs;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using std::min;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -66,3 +68,4 @@ INSTANTIATE_DIAGONAL(ushort)
 INSTANTIATE_DIAGONAL(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/diagonal.hpp b/src/backend/cpu/diagonal.hpp
index f58ce6fcdb..8a3807b913 100644
--- a/src/backend/cpu/diagonal.hpp
+++ b/src/backend/cpu/diagonal.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num);
@@ -16,3 +17,4 @@ Array<T> diagCreate(const Array<T> &in, const int num);
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index a64b7dbe3c..8e9c67cae1 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -15,6 +15,7 @@
 
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -61,3 +62,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/diff.hpp b/src/backend/cpu/diff.hpp
index 32913b9391..7a50aec7c2 100644
--- a/src/backend/cpu/diff.hpp
+++ b/src/backend/cpu/diff.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim);
@@ -16,3 +17,4 @@ Array<T> diff1(const Array<T> &in, const int dim);
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index f912cf7d66..ee7b847524 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -21,6 +21,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -61,3 +62,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/exampleFunction.hpp b/src/backend/cpu/exampleFunction.hpp
index 822ad57186..19a3d151ef 100644
--- a/src/backend/cpu/exampleFunction.hpp
+++ b/src/backend/cpu/exampleFunction.hpp
@@ -10,8 +10,10 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
                          const af_someenum_t method);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index 057cf96552..b8ac38eeaf 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -23,6 +23,7 @@
 using af::dim4;
 using std::ceil;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -124,3 +125,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/fast.hpp b/src/backend/cpu/fast.hpp
index d588246916..7d22621bb4 100644
--- a/src/backend/cpu/fast.hpp
+++ b/src/backend/cpu/fast.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 class Array;
@@ -18,3 +19,4 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const unsigned edge);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/fft.cpp b/src/backend/cpu/fft.cpp
index fafc178c29..31515d0f99 100644
--- a/src/backend/cpu/fft.cpp
+++ b/src/backend/cpu/fft.cpp
@@ -22,6 +22,7 @@
 using af::dim4;
 using std::array;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -229,3 +230,4 @@ INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/fft.hpp b/src/backend/cpu/fft.hpp
index fbdf7af339..383690ca21 100644
--- a/src/backend/cpu/fft.hpp
+++ b/src/backend/cpu/fft.hpp
@@ -15,6 +15,7 @@ namespace af {
 class dim4;
 }
 
+namespace arrayfire {
 namespace cpu {
 
 void setFFTPlanCacheSize(size_t numPlans);
@@ -28,3 +29,4 @@ Array<Tc> fft_r2c(const Array<Tr> &in, const int rank);
 template<typename Tr, typename Tc>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 20047cf5b9..728238c1ef 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -25,6 +25,7 @@ using af::dim4;
 using std::array;
 using std::ceil;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convT>
@@ -214,3 +215,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/fftconvolve.hpp b/src/backend/cpu/fftconvolve.hpp
index a2b9845dfd..8a21fbe958 100644
--- a/src/backend/cpu/fftconvolve.hpp
+++ b/src/backend/cpu/fftconvolve.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/flood_fill.cpp b/src/backend/cpu/flood_fill.cpp
index 7a08663ef3..2ea32df803 100644
--- a/src/backend/cpu/flood_fill.cpp
+++ b/src/backend/cpu/flood_fill.cpp
@@ -14,6 +14,7 @@
 
 using af::connectivity;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -38,3 +39,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/flood_fill.hpp b/src/backend/cpu/flood_fill.hpp
index 8bd4623328..8ac52fbec1 100644
--- a/src/backend/cpu/flood_fill.hpp
+++ b/src/backend/cpu/flood_fill.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
@@ -19,3 +20,4 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
                    const T lowValue, const T highValue,
                    const af::connectivity nlookup = AF_CONNECTIVITY_8);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/gradient.cpp b/src/backend/cpu/gradient.cpp
index 711cd72c49..d328e9f7e4 100644
--- a/src/backend/cpu/gradient.cpp
+++ b/src/backend/cpu/gradient.cpp
@@ -16,6 +16,7 @@
 #include <queue.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -33,3 +34,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/gradient.hpp b/src/backend/cpu/gradient.hpp
index cc18462ba1..d73ecafccf 100644
--- a/src/backend/cpu/gradient.hpp
+++ b/src/backend/cpu/gradient.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/harris.cpp b/src/backend/cpu/harris.cpp
index 29fddc5417..cf7f41ecbf 100644
--- a/src/backend/cpu/harris.cpp
+++ b/src/backend/cpu/harris.cpp
@@ -21,6 +21,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convAccT>
@@ -148,3 +149,4 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/harris.hpp b/src/backend/cpu/harris.hpp
index c2f587b18d..b42f8cd4f8 100644
--- a/src/backend/cpu/harris.hpp
+++ b/src/backend/cpu/harris.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
                 const float sigma, const unsigned filter_len,
                 const float k_thr);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 4c68d6858e..7635004c91 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -12,11 +12,16 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     data.eval();
     getQueue().sync();
 
@@ -43,3 +48,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/hist_graphics.hpp b/src/backend/cpu/hist_graphics.hpp
index 1fd68a1adb..8971645496 100644
--- a/src/backend/cpu/hist_graphics.hpp
+++ b/src/backend/cpu/hist_graphics.hpp
@@ -12,9 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index 2b044efd02..e2f8e15433 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -16,8 +16,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -55,3 +56,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/histogram.hpp b/src/backend/cpu/histogram.hpp
index 650b59d621..086baf50f0 100644
--- a/src/backend/cpu/histogram.hpp
+++ b/src/backend/cpu/histogram.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
                       const double &minval, const double &maxval,
                       const bool isLinear);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/homography.cpp b/src/backend/cpu/homography.cpp
index 9fbdf9fead..9be88a2e02 100644
--- a/src/backend/cpu/homography.cpp
+++ b/src/backend/cpu/homography.cpp
@@ -33,6 +33,7 @@ using std::round;
 using std::sqrt;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -420,3 +421,4 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/homography.hpp b/src/backend/cpu/homography.hpp
index 25acd7cb23..76ac8bbf86 100644
--- a/src/backend/cpu/homography.hpp
+++ b/src/backend/cpu/homography.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -18,4 +19,5 @@ int homography(Array<T> &H, const Array<float> &x_src,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/hsv_rgb.cpp b/src/backend/cpu/hsv_rgb.cpp
index da3cf25e54..cf278862d0 100644
--- a/src/backend/cpu/hsv_rgb.cpp
+++ b/src/backend/cpu/hsv_rgb.cpp
@@ -14,6 +14,7 @@
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -42,3 +43,4 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/hsv_rgb.hpp b/src/backend/cpu/hsv_rgb.hpp
index eac988b035..3d0929c22b 100644
--- a/src/backend/cpu/hsv_rgb.hpp
+++ b/src/backend/cpu/hsv_rgb.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index ded01b348e..05695d7629 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -15,8 +15,10 @@
 #include <queue.hpp>
 #include <af/dim4.hpp>
 
-using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using arrayfire::common::half;  // NOLINT(misc-unused-using-decls) bug in
+                                // clang-tidy
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -46,3 +48,4 @@ INSTANTIATE_IDENTITY(ushort)
 INSTANTIATE_IDENTITY(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/identity.hpp b/src/backend/cpu/identity.hpp
index 805214585c..5a77fa2d9a 100644
--- a/src/backend/cpu/identity.hpp
+++ b/src/backend/cpu/identity.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> identity(const dim4& dim);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/iir.cpp b/src/backend/cpu/iir.cpp
index e1f6c0e4e4..9d3fcfc966 100644
--- a/src/backend/cpu/iir.cpp
+++ b/src/backend/cpu/iir.cpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -49,3 +50,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/iir.hpp b/src/backend/cpu/iir.hpp
index 2286fd91e6..4075c48b43 100644
--- a/src/backend/cpu/iir.hpp
+++ b/src/backend/cpu/iir.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index 4b5e3cd486..f11a2db4ca 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -17,11 +17,16 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
 
     CheckGL("Before CopyArrayToImage");
     const T *d_X = in.get();
@@ -50,3 +55,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/image.hpp b/src/backend/cpu/image.hpp
index 06493f6850..2dd41e585e 100644
--- a/src/backend/cpu/image.hpp
+++ b/src/backend/cpu/image.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 9a2172569e..315406b46d 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -21,9 +21,11 @@
 #include <vector>
 
 using af::dim4;
-using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using arrayfire::common::half;  // NOLINT(misc-unused-using-decls) bug in
+                                // clang-tidy
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -77,3 +79,4 @@ INSTANTIATE(short)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/index.hpp b/src/backend/cpu/index.hpp
index d397db3ed7..14a6692db1 100644
--- a/src/backend/cpu/index.hpp
+++ b/src/backend/cpu/index.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/inverse.cpp b/src/backend/cpu/inverse.cpp
index 47230f21d3..20543d027c 100644
--- a/src/backend/cpu/inverse.cpp
+++ b/src/backend/cpu/inverse.cpp
@@ -25,6 +25,7 @@
 #include <queue.hpp>
 #include <solve.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -76,9 +77,11 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -94,5 +97,6 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/cpu/inverse.hpp b/src/backend/cpu/inverse.hpp
index 460b2fd954..476388cb68 100644
--- a/src/backend/cpu/inverse.hpp
+++ b/src/backend/cpu/inverse.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> inverse(const Array<T> &in);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 38fb1c292b..1e7155bcd9 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -15,8 +15,10 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-using common::half;  // NOLINT(misc-unused-using-decls) bug in clang-tidy
+using arrayfire::common::half;  // NOLINT(misc-unused-using-decls) bug in
+                                // clang-tidy
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -45,3 +47,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/iota.hpp b/src/backend/cpu/iota.hpp
index c8551a14c4..9921933cbf 100644
--- a/src/backend/cpu/iota.hpp
+++ b/src/backend/cpu/iota.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 44b4b302be..435d6ea44d 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -18,8 +18,9 @@
 #include <complex>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<af_op_t op, typename T>
@@ -125,3 +126,4 @@ INSTANTIATE(af_max_t, ushort)
 INSTANTIATE(af_max_t, half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/ireduce.hpp b/src/backend/cpu/ireduce.hpp
index 39258a284e..301ee65e53 100644
--- a/src/backend/cpu/ireduce.hpp
+++ b/src/backend/cpu/ireduce.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
@@ -22,3 +23,4 @@ void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 0ce7e348f4..8c1cc39d68 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -17,6 +17,7 @@
 #include <array>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 
 namespace jit {
@@ -92,5 +93,5 @@ class BinaryNode : public TNode<compute_t<To>> {
 };
 
 }  // namespace jit
-
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index ac789dc2ee..e6be492b7f 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -18,6 +18,7 @@
 #include <sstream>
 #include <string>
 
+namespace arrayfire {
 namespace cpu {
 
 namespace jit {
@@ -179,3 +180,4 @@ class BufferNode : public TNode<T> {
 
 }  // namespace jit
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index 51ec0646ae..b3914cbc70 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -24,6 +24,7 @@ template<typename T>
 class NodeIterator;
 }
 
+namespace arrayfire {
 namespace cpu {
 
 namespace jit {
@@ -38,7 +39,7 @@ template<typename T>
 class TNode : public common::Node {
    public:
     alignas(16) jit::array<compute_t<T>> m_val;
-    using common::Node::m_children;
+    using arrayfire::common::Node::m_children;
 
    public:
     TNode(T val, const int height,
@@ -53,3 +54,4 @@ class TNode : public common::Node {
 };
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index 79a9f40f22..a6d7eff5df 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -12,6 +12,7 @@
 #include <vector>
 #include "Node.hpp"
 
+namespace arrayfire {
 namespace cpu {
 
 namespace jit {
@@ -62,5 +63,5 @@ class ScalarNode : public TNode<T> {
     bool isScalar() const final { return true; }
 };
 }  // namespace jit
-
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 527d078dcc..9ae8e0aa94 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -16,6 +16,7 @@
 #include <jit/BufferNode.hpp>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 template<typename To, typename Ti, af_op_t op>
 struct UnOp {
@@ -28,7 +29,7 @@ namespace jit {
 template<typename To, typename Ti, af_op_t op>
 class UnaryNode : public TNode<To> {
    protected:
-    using common::Node::m_children;
+    using arrayfire::common::Node::m_children;
     UnOp<To, Ti, op> m_op;
 
    public:
@@ -70,5 +71,5 @@ class UnaryNode : public TNode<To> {
 };
 
 }  // namespace jit
-
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index 52f73747e2..e9fed65df1 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -16,8 +16,9 @@
 
 #include <algorithm>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -97,3 +98,4 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/join.hpp b/src/backend/cpu/join.hpp
index efabe9c8a5..f13bea2fed 100644
--- a/src/backend/cpu/join.hpp
+++ b/src/backend/cpu/join.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
@@ -17,3 +18,4 @@ Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 template<typename T>
 void join(Array<T> &output, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/Array.hpp b/src/backend/cpu/kernel/Array.hpp
index e13548aa60..7af4e35555 100644
--- a/src/backend/cpu/kernel/Array.hpp
+++ b/src/backend/cpu/kernel/Array.hpp
@@ -18,6 +18,7 @@
 #include <platform.hpp>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -25,7 +26,7 @@ namespace kernel {
 std::vector<std::shared_ptr<common::Node>> cloneNodes(
     const std::vector<common::Node *> &node_index_map,
     const std::vector<common::Node_ids> &ids) {
-    using common::Node;
+    using arrayfire::common::Node;
     // find all moddims in the tree
     std::vector<std::shared_ptr<Node>> node_clones;
     node_clones.reserve(node_index_map.size());
@@ -45,7 +46,7 @@ std::vector<std::shared_ptr<common::Node>> cloneNodes(
 /// new shape
 void propagateModdimsShape(
     std::vector<std::shared_ptr<common::Node>> &node_clones) {
-    using common::NodeIterator;
+    using arrayfire::common::NodeIterator;
     for (auto &node : node_clones) {
         if (node->getOp() == af_moddims_t) {
             common::ModdimNode *mn =
@@ -67,7 +68,7 @@ void propagateModdimsShape(
 /// Removes node_index_map whos operation matchs a unary operation \p op.
 void removeNodeOfOperation(
     std::vector<std::shared_ptr<common::Node>> &node_index_map, af_op_t op) {
-    using common::Node;
+    using arrayfire::common::Node;
 
     for (size_t nid = 0; nid < node_index_map.size(); nid++) {
         auto &node = node_index_map[nid];
@@ -124,10 +125,10 @@ std::vector<TNode<T> *> getClonedOutputNodes(
 template<typename T>
 void evalMultiple(std::vector<Param<T>> arrays,
                   std::vector<common::Node_ptr> output_nodes_) {
-    using common::ModdimNode;
-    using common::Node;
-    using common::Node_map_t;
-    using common::NodeIterator;
+    using arrayfire::common::ModdimNode;
+    using arrayfire::common::Node;
+    using arrayfire::common::Node_map_t;
+    using arrayfire::common::NodeIterator;
 
     af::dim4 odims = arrays[0].dims();
     af::dim4 ostrs = arrays[0].strides();
@@ -205,3 +206,4 @@ void evalMultiple(std::vector<Param<T>> arrays,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/anisotropic_diffusion.hpp b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
index 0a8e773f00..1acad4857c 100644
--- a/src/backend/cpu/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cpu/kernel/anisotropic_diffusion.hpp
@@ -20,6 +20,7 @@ using std::exp;
 using std::pow;
 using std::sqrt;
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -188,3 +189,4 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/approx.hpp b/src/backend/cpu/kernel/approx.hpp
index 35f3a2bd78..826b124fdb 100644
--- a/src/backend/cpu/kernel/approx.hpp
+++ b/src/backend/cpu/kernel/approx.hpp
@@ -12,6 +12,7 @@
 #include <math.hpp>
 #include "interp.hpp"
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -137,3 +138,4 @@ void approx2(Param<InT> zo, CParam<InT> zi, CParam<LocT> xo, const int xdim,
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/assign.hpp b/src/backend/cpu/kernel/assign.hpp
index 8a055db0c5..4605f5d000 100644
--- a/src/backend/cpu/kernel/assign.hpp
+++ b/src/backend/cpu/kernel/assign.hpp
@@ -19,6 +19,7 @@
 
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -81,3 +82,4 @@ void assign(Param<T> out, af::dim4 dDims, CParam<T> rhs,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/bilateral.hpp b/src/backend/cpu/kernel/bilateral.hpp
index a2f316d15f..72d8edd12c 100644
--- a/src/backend/cpu/kernel/bilateral.hpp
+++ b/src/backend/cpu/kernel/bilateral.hpp
@@ -13,6 +13,7 @@
 #include <utility.hpp>
 #include <cmath>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -86,3 +87,4 @@ void bilateral(Param<OutT> out, CParam<InT> in, float const s_sigma,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/canny.hpp b/src/backend/cpu/kernel/canny.hpp
index ebf3474cf8..e68b73cfb6 100644
--- a/src/backend/cpu/kernel/canny.hpp
+++ b/src/backend/cpu/kernel/canny.hpp
@@ -13,6 +13,7 @@
 #include <cassert>
 #include <list>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 template<typename T>
@@ -182,3 +183,4 @@ void edgeTrackingHysteresis(Param<T> out, CParam<T> strong, CParam<T> weak) {
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/convolve.hpp b/src/backend/cpu/kernel/convolve.hpp
index 1bb67b569f..62381dd749 100644
--- a/src/backend/cpu/kernel/convolve.hpp
+++ b/src/backend/cpu/kernel/convolve.hpp
@@ -12,6 +12,7 @@
 #include <math.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -289,3 +290,4 @@ void convolve2(Param<InT> out, CParam<InT> signal, CParam<AccT> c_filter,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/copy.hpp b/src/backend/cpu/kernel/copy.hpp
index 618d5deb22..9506ed7d70 100644
--- a/src/backend/cpu/kernel/copy.hpp
+++ b/src/backend/cpu/kernel/copy.hpp
@@ -15,6 +15,7 @@
 
 #include <cstring>  //memcpy
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -160,3 +161,4 @@ void copy(Param<OutT> dst, CParam<InT> src) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/diagonal.hpp b/src/backend/cpu/kernel/diagonal.hpp
index e5de90f41d..388bd4c459 100644
--- a/src/backend/cpu/kernel/diagonal.hpp
+++ b/src/backend/cpu/kernel/diagonal.hpp
@@ -13,6 +13,7 @@
 
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -62,3 +63,4 @@ void diagExtract(Param<T> out, CParam<T> in, int const num) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/diff.hpp b/src/backend/cpu/kernel/diff.hpp
index 9e2e8a4e21..b1ed5642b6 100644
--- a/src/backend/cpu/kernel/diff.hpp
+++ b/src/backend/cpu/kernel/diff.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -80,3 +81,4 @@ void diff2(Param<T> out, CParam<T> in, int const dim) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/dot.hpp b/src/backend/cpu/kernel/dot.hpp
index 8946534bb8..74ea9087c3 100644
--- a/src/backend/cpu/kernel/dot.hpp
+++ b/src/backend/cpu/kernel/dot.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -49,3 +50,4 @@ void dot(Param<T> output, CParam<T> lhs, CParam<T> rhs, af_mat_prop optLhs,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/exampleFunction.hpp b/src/backend/cpu/kernel/exampleFunction.hpp
index 853f96e60c..6b263830ab 100644
--- a/src/backend/cpu/kernel/exampleFunction.hpp
+++ b/src/backend/cpu/kernel/exampleFunction.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -47,3 +48,4 @@ void exampleFunction(Param<T> out, CParam<T> a, CParam<T> b,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/fast.hpp b/src/backend/cpu/kernel/fast.hpp
index b168021903..341ddbe701 100644
--- a/src/backend/cpu/kernel/fast.hpp
+++ b/src/backend/cpu/kernel/fast.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -215,3 +216,4 @@ void non_maximal(CParam<float> score, CParam<float> x_in, CParam<float> y_in,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/fftconvolve.hpp b/src/backend/cpu/kernel/fftconvolve.hpp
index d6c6f8493e..13109502c7 100644
--- a/src/backend/cpu/kernel/fftconvolve.hpp
+++ b/src/backend/cpu/kernel/fftconvolve.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -251,3 +252,4 @@ void reorder(Param<T> out, Param<convT> packed, CParam<T> filter,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/flood_fill.hpp b/src/backend/cpu/kernel/flood_fill.hpp
index 045564ef44..121adc87e6 100644
--- a/src/backend/cpu/kernel/flood_fill.hpp
+++ b/src/backend/cpu/kernel/flood_fill.hpp
@@ -15,6 +15,7 @@
 #include <queue>
 #include <utility>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -119,3 +120,4 @@ void floodFill(Param<T> out, CParam<T> in, CParam<uint> x, CParam<uint> y,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/gradient.hpp b/src/backend/cpu/kernel/gradient.hpp
index 35f1fa8248..407f4fc6da 100644
--- a/src/backend/cpu/kernel/gradient.hpp
+++ b/src/backend/cpu/kernel/gradient.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -84,3 +85,4 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/harris.hpp b/src/backend/cpu/kernel/harris.hpp
index 7ea9350642..4b717c6187 100644
--- a/src/backend/cpu/kernel/harris.hpp
+++ b/src/backend/cpu/kernel/harris.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -118,3 +119,4 @@ static void keep_corners(Param<float> xOut, Param<float> yOut,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/histogram.hpp b/src/backend/cpu/kernel/histogram.hpp
index 4b18f94b5b..fb90631c52 100644
--- a/src/backend/cpu/kernel/histogram.hpp
+++ b/src/backend/cpu/kernel/histogram.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <types.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -47,3 +48,4 @@ void histogram(Param<uint> out, CParam<T> in, const unsigned nbins,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/hsv_rgb.hpp b/src/backend/cpu/kernel/hsv_rgb.hpp
index dd75815be2..1bf4c387bc 100644
--- a/src/backend/cpu/kernel/hsv_rgb.hpp
+++ b/src/backend/cpu/kernel/hsv_rgb.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <cmath>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -117,3 +118,4 @@ void rgb2hsv(Param<T> out, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/identity.hpp b/src/backend/cpu/kernel/identity.hpp
index 1c3b1cf12e..a00a2cc83c 100644
--- a/src/backend/cpu/kernel/identity.hpp
+++ b/src/backend/cpu/kernel/identity.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -32,3 +33,4 @@ void identity(Param<T> out) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/iir.hpp b/src/backend/cpu/kernel/iir.hpp
index b355c7dcbb..515d778f5d 100644
--- a/src/backend/cpu/kernel/iir.hpp
+++ b/src/backend/cpu/kernel/iir.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -52,3 +53,4 @@ void iir(Param<T> y, Param<T> c, CParam<T> a) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index 605d1009d9..2a6a6d9bc4 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -12,6 +12,7 @@
 #include <utility.hpp>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -64,3 +65,4 @@ void index(Param<T> out, CParam<T> in, const af::dim4 dDims,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/interp.hpp b/src/backend/cpu/kernel/interp.hpp
index b0a9c18f5e..d316b22f19 100644
--- a/src/backend/cpu/kernel/interp.hpp
+++ b/src/backend/cpu/kernel/interp.hpp
@@ -13,6 +13,7 @@
 #include <af/constants.h>
 #include <type_traits>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -349,3 +350,4 @@ struct Interp2<InT, LocT, 3> {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/iota.hpp b/src/backend/cpu/kernel/iota.hpp
index e59151b82b..ef575a8166 100644
--- a/src/backend/cpu/kernel/iota.hpp
+++ b/src/backend/cpu/kernel/iota.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -39,3 +40,4 @@ void iota(Param<T> output, const af::dim4& sdims) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index c04cbc7409..9c371498c7 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -12,6 +12,7 @@
 #include <common/Binary.hpp>
 #include <algorithm>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -104,3 +105,4 @@ struct ireduce_dim<op, T, 0> {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/join.hpp b/src/backend/cpu/kernel/join.hpp
index a81f8801fa..800ded1270 100644
--- a/src/backend/cpu/kernel/join.hpp
+++ b/src/backend/cpu/kernel/join.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -60,3 +61,4 @@ void join(const int dim, Param<T> out, const std::vector<CParam<T>> inputs,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/lookup.hpp b/src/backend/cpu/kernel/lookup.hpp
index fe333eb8cd..f968e48ff8 100644
--- a/src/backend/cpu/kernel/lookup.hpp
+++ b/src/backend/cpu/kernel/lookup.hpp
@@ -12,6 +12,7 @@
 #include <utility.hpp>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -60,3 +61,4 @@ void lookup(Param<InT> out, CParam<InT> input, CParam<IndexT> indices,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/lu.hpp b/src/backend/cpu/kernel/lu.hpp
index c1473a7918..170289919c 100644
--- a/src/backend/cpu/kernel/lu.hpp
+++ b/src/backend/cpu/kernel/lu.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -73,3 +74,4 @@ void convertPivot(Param<int> p, Param<int> pivot) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/match_template.hpp b/src/backend/cpu/kernel/match_template.hpp
index d2463bf3b0..bed6ef5354 100644
--- a/src/backend/cpu/kernel/match_template.hpp
+++ b/src/backend/cpu/kernel/match_template.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -140,3 +141,4 @@ void matchTemplate(Param<OutT> out, CParam<InT> sImg, CParam<InT> tImg) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/mean.hpp b/src/backend/cpu/kernel/mean.hpp
index 86f30e515c..c15773687e 100644
--- a/src/backend/cpu/kernel/mean.hpp
+++ b/src/backend/cpu/kernel/mean.hpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <common/Transform.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -123,3 +124,4 @@ struct mean_dim<Ti, Tw, To, 0> {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/meanshift.hpp b/src/backend/cpu/kernel/meanshift.hpp
index 141153bb75..490fb93af6 100644
--- a/src/backend/cpu/kernel/meanshift.hpp
+++ b/src/backend/cpu/kernel/meanshift.hpp
@@ -13,6 +13,7 @@
 #include <type_traits>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 template<typename T, bool IsColor>
@@ -139,3 +140,4 @@ void meanShift(Param<T> out, CParam<T> in, const float spatialSigma,
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/medfilt.hpp b/src/backend/cpu/kernel/medfilt.hpp
index 269348cee5..cd998adf05 100644
--- a/src/backend/cpu/kernel/medfilt.hpp
+++ b/src/backend/cpu/kernel/medfilt.hpp
@@ -14,6 +14,7 @@
 #include <algorithm>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -202,3 +203,4 @@ void medfilt2(Param<T> out, CParam<T> in, dim_t w_len, dim_t w_wid) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/moments.hpp b/src/backend/cpu/kernel/moments.hpp
index f67b2deb48..0f3e6611eb 100644
--- a/src/backend/cpu/kernel/moments.hpp
+++ b/src/backend/cpu/kernel/moments.hpp
@@ -13,6 +13,7 @@
 #include <utility.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -58,3 +59,4 @@ void moments(Param<float> output, CParam<T> input, af_moment_type moment) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/morph.hpp b/src/backend/cpu/kernel/morph.hpp
index 1142940ba6..563420e57f 100644
--- a/src/backend/cpu/kernel/morph.hpp
+++ b/src/backend/cpu/kernel/morph.hpp
@@ -13,6 +13,7 @@
 #include <utility.hpp>
 #include <limits>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 template<typename T>
@@ -143,3 +144,4 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask) {
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/nearest_neighbour.hpp b/src/backend/cpu/kernel/nearest_neighbour.hpp
index 39b005c4ed..af94d03ec4 100644
--- a/src/backend/cpu/kernel/nearest_neighbour.hpp
+++ b/src/backend/cpu/kernel/nearest_neighbour.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -98,3 +99,4 @@ void nearest_neighbour(Param<To> dists, CParam<T> query, CParam<T> train,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/orb.hpp b/src/backend/cpu/kernel/orb.hpp
index df36f3655b..385f71abb6 100644
--- a/src/backend/cpu/kernel/orb.hpp
+++ b/src/backend/cpu/kernel/orb.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -281,3 +282,4 @@ void extract_orb(unsigned* desc_out, const unsigned n_feat, float* x_in_out,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/pad_array_borders.hpp b/src/backend/cpu/kernel/pad_array_borders.hpp
index 5d9ea155a3..8b44c9d425 100644
--- a/src/backend/cpu/kernel/pad_array_borders.hpp
+++ b/src/backend/cpu/kernel/pad_array_borders.hpp
@@ -14,6 +14,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 namespace {
@@ -130,3 +131,4 @@ void padBorders(Param<T> out, CParam<T> in, const dim4 lBoundPadSize,
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 6f55f69719..09c2bff20c 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -25,6 +25,7 @@
 using std::array;
 using std::memcpy;
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 // Utils
@@ -70,21 +71,21 @@ static float getFloatNegative11(uint *val, uint index) {
 }
 
 // Generates rationals in [0, 1)
-common::half getHalf01(uint *val, uint index) {
+arrayfire::common::half getHalf01(uint *val, uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return static_cast<common::half>(
+    return static_cast<arrayfire::common::half>(
         fmaf(v, unsigned_half_factor, unsigned_half_half_factor));
 }
 
 // Generates rationals in (-1, 1]
-static common::half getHalfNegative11(uint *val, uint index) {
+static arrayfire::common::half getHalfNegative11(uint *val, uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
     // Conversion to half adapted from Random123
     constexpr float factor =
         ((1.0f) / (std::numeric_limits<short>::max() + (1.0f)));
     constexpr float half_factor = ((0.5f) * factor);
 
-    return static_cast<common::half>(fmaf(v, factor, half_factor));
+    return static_cast<arrayfire::common::half>(fmaf(v, factor, half_factor));
 }
 
 // Generates rationals in [0, 1)
@@ -154,9 +155,10 @@ double transform<double>(uint *val, uint index) {
 }
 
 template<>
-common::half transform<common::half>(uint *val, uint index) {
+arrayfire::common::half transform<arrayfire::common::half>(uint *val,
+                                                           uint index) {
     float v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
-    return static_cast<common::half>(
+    return static_cast<arrayfire::common::half>(
         1.f - fmaf(v, unsigned_half_factor, unsigned_half_half_factor));
 }
 
@@ -274,8 +276,8 @@ void boxMullerTransform(uint val[4], float *temp) {
                               getFloat01(val, 3));
 }
 
-void boxMullerTransform(uint val[4], common::half *temp) {
-    using common::half;
+void boxMullerTransform(uint val[4], arrayfire::common::half *temp) {
+    using arrayfire::common::half;
     boxMullerTransform<half>(&temp[0], &temp[1], getHalfNegative11(val, 0),
                              getHalf01(val, 1));
     boxMullerTransform<half>(&temp[2], &temp[3], getHalfNegative11(val, 2),
@@ -416,3 +418,4 @@ void normalDistributionCBRNG(T *out, size_t elements,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/random_engine_mersenne.hpp b/src/backend/cpu/kernel/random_engine_mersenne.hpp
index ada96f231e..5087621b26 100644
--- a/src/backend/cpu/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cpu/kernel/random_engine_mersenne.hpp
@@ -44,6 +44,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -117,3 +118,4 @@ void initMersenneState(uint* const state, const uint* const tbl,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/random_engine_philox.hpp b/src/backend/cpu/kernel/random_engine_philox.hpp
index 7b2efd45f9..f1a82014df 100644
--- a/src/backend/cpu/kernel/random_engine_philox.hpp
+++ b/src/backend/cpu/kernel/random_engine_philox.hpp
@@ -47,6 +47,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 // Utils
@@ -103,3 +104,4 @@ void philox(uint* const key, uint* const ctr) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/random_engine_threefry.hpp b/src/backend/cpu/kernel/random_engine_threefry.hpp
index 8affc5bcaa..df728c9a81 100644
--- a/src/backend/cpu/kernel/random_engine_threefry.hpp
+++ b/src/backend/cpu/kernel/random_engine_threefry.hpp
@@ -46,6 +46,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 // Utils
@@ -156,3 +157,4 @@ static inline void threefry(uint k[2], uint c[2], uint X[2]) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/range.hpp b/src/backend/cpu/kernel/range.hpp
index dd6995386f..8d93d384be 100644
--- a/src/backend/cpu/kernel/range.hpp
+++ b/src/backend/cpu/kernel/range.hpp
@@ -13,6 +13,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -48,3 +49,4 @@ void range(Param<T> output) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/reduce.hpp b/src/backend/cpu/kernel/reduce.hpp
index db39dbc8b8..de685b426a 100644
--- a/src/backend/cpu/kernel/reduce.hpp
+++ b/src/backend/cpu/kernel/reduce.hpp
@@ -13,6 +13,7 @@
 #include <common/Transform.hpp>
 #include <common/half.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -200,3 +201,4 @@ struct reduce_all {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/regions.hpp b/src/backend/cpu/kernel/regions.hpp
index 40aa507b74..fab7398720 100644
--- a/src/backend/cpu/kernel/regions.hpp
+++ b/src/backend/cpu/kernel/regions.hpp
@@ -13,6 +13,7 @@
 #include <map>
 #include <set>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -167,3 +168,4 @@ void regions(Param<T> out, CParam<char> in, af_connectivity connectivity) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/reorder.hpp b/src/backend/cpu/kernel/reorder.hpp
index b038d4920b..ccaf8efc72 100644
--- a/src/backend/cpu/kernel/reorder.hpp
+++ b/src/backend/cpu/kernel/reorder.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -48,3 +49,4 @@ void reorder(Param<T> out, CParam<T> in, const af::dim4 oDims,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/resize.hpp b/src/backend/cpu/kernel/resize.hpp
index 0a3d3a0e33..d5e1a3f6b9 100644
--- a/src/backend/cpu/kernel/resize.hpp
+++ b/src/backend/cpu/kernel/resize.hpp
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <af/traits.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -173,3 +174,4 @@ void resize(Param<T> out, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/rotate.hpp b/src/backend/cpu/kernel/rotate.hpp
index af2e21f31d..67a34a9e71 100644
--- a/src/backend/cpu/kernel/rotate.hpp
+++ b/src/backend/cpu/kernel/rotate.hpp
@@ -16,6 +16,7 @@
 
 using af::dtype_traits;
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -89,3 +90,4 @@ void rotate(Param<T> output, CParam<T> input, const float theta,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/scan.hpp b/src/backend/cpu/kernel/scan.hpp
index 6e6cc84d54..3ad4e04688 100644
--- a/src/backend/cpu/kernel/scan.hpp
+++ b/src/backend/cpu/kernel/scan.hpp
@@ -12,6 +12,7 @@
 #include <common/Binary.hpp>
 #include <common/Transform.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -72,3 +73,4 @@ struct scan_dim<op, Ti, To, 0, inclusive_scan> {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/scan_by_key.hpp b/src/backend/cpu/kernel/scan_by_key.hpp
index d4546377e0..4639dfcda7 100644
--- a/src/backend/cpu/kernel/scan_by_key.hpp
+++ b/src/backend/cpu/kernel/scan_by_key.hpp
@@ -12,6 +12,7 @@
 #include <common/Binary.hpp>
 #include <common/Transform.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -86,3 +87,4 @@ struct scan_dim_by_key<op, Ti, Tk, To, 0> {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/select.hpp b/src/backend/cpu/kernel/select.hpp
index 88a95fd5bc..dcc3c8855c 100644
--- a/src/backend/cpu/kernel/select.hpp
+++ b/src/backend/cpu/kernel/select.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -120,3 +121,4 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/shift.hpp b/src/backend/cpu/kernel/shift.hpp
index ea844439e9..223c3081a0 100644
--- a/src/backend/cpu/kernel/shift.hpp
+++ b/src/backend/cpu/kernel/shift.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <cassert>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -63,3 +64,4 @@ void shift(Param<T> out, CParam<T> in, const af::dim4 sdims) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sift.hpp b/src/backend/cpu/kernel/sift.hpp
index e7d4821e37..ee1eb046a7 100644
--- a/src/backend/cpu/kernel/sift.hpp
+++ b/src/backend/cpu/kernel/sift.hpp
@@ -26,6 +26,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 static const float PI_VAL = 3.14159265358979323846f;
@@ -1053,3 +1054,4 @@ unsigned sift_impl(Array<float>& x, Array<float>& y, Array<float>& score,
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sobel.hpp b/src/backend/cpu/kernel/sobel.hpp
index 1bf3203874..54315203d4 100644
--- a/src/backend/cpu/kernel/sobel.hpp
+++ b/src/backend/cpu/kernel/sobel.hpp
@@ -14,6 +14,7 @@
 
 #include <cassert>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -73,3 +74,4 @@ void derivative(Param<To> output, CParam<Ti> input) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sort.hpp b/src/backend/cpu/kernel/sort.hpp
index 5c0bf21a99..0e4c91aa56 100644
--- a/src/backend/cpu/kernel/sort.hpp
+++ b/src/backend/cpu/kernel/sort.hpp
@@ -15,6 +15,7 @@
 #include <functional>
 #include <numeric>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -45,3 +46,4 @@ void sort0Iterative(Param<T> val, bool isAscending) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sort_by_key.hpp b/src/backend/cpu/kernel/sort_by_key.hpp
index 9f67a570c0..785a25b378 100644
--- a/src/backend/cpu/kernel/sort_by_key.hpp
+++ b/src/backend/cpu/kernel/sort_by_key.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -25,3 +26,4 @@ void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending);
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index c1ae75110e..6ac6875f3e 100644
--- a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -11,8 +11,10 @@
 
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 INSTANTIATE1(TYPE)
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index c10ac89747..acd7524a9b 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -20,6 +20,7 @@
 #include <tuple>
 #include <utility>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -171,5 +172,7 @@ void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)
+
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sort_helper.hpp b/src/backend/cpu/kernel/sort_helper.hpp
index 955460bf86..ff301c0e0a 100644
--- a/src/backend/cpu/kernel/sort_helper.hpp
+++ b/src/backend/cpu/kernel/sort_helper.hpp
@@ -10,6 +10,7 @@
 #include <err_cpu.hpp>
 #include <tuple>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 template<typename Tk, typename Tv>
@@ -60,3 +61,4 @@ struct KIPCompareK {
 };
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sparse.hpp b/src/backend/cpu/kernel/sparse.hpp
index a8b796a702..9cf8074d80 100644
--- a/src/backend/cpu/kernel/sparse.hpp
+++ b/src/backend/cpu/kernel/sparse.hpp
@@ -15,6 +15,7 @@
 #include <algorithm>
 #include <tuple>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -173,3 +174,4 @@ void coo2csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/sparse_arith.hpp b/src/backend/cpu/kernel/sparse_arith.hpp
index 2c4afcfb8f..07eae80aca 100644
--- a/src/backend/cpu/kernel/sparse_arith.hpp
+++ b/src/backend/cpu/kernel/sparse_arith.hpp
@@ -13,6 +13,7 @@
 
 #include <cmath>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -223,3 +224,4 @@ void sparseArithOp(Param<T> oVals, Param<int> oColIdx, CParam<int> oRowIdx,
 }
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/susan.hpp b/src/backend/cpu/kernel/susan.hpp
index 13dee51519..161f185f8b 100644
--- a/src/backend/cpu/kernel/susan.hpp
+++ b/src/backend/cpu/kernel/susan.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -94,3 +95,4 @@ void non_maximal(Param<float> xcoords, Param<float> ycoords,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/tile.hpp b/src/backend/cpu/kernel/tile.hpp
index 5fdaba9db7..bb533889ac 100644
--- a/src/backend/cpu/kernel/tile.hpp
+++ b/src/backend/cpu/kernel/tile.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -48,3 +49,4 @@ void tile(Param<T> out, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/transform.hpp b/src/backend/cpu/kernel/transform.hpp
index f0e388cbe7..bfa1485629 100644
--- a/src/backend/cpu/kernel/transform.hpp
+++ b/src/backend/cpu/kernel/transform.hpp
@@ -14,6 +14,7 @@
 #include <type_traits>
 #include "interp.hpp"
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -140,3 +141,4 @@ void transform(Param<T> output, CParam<T> input, CParam<float> transform,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/transpose.hpp b/src/backend/cpu/kernel/transpose.hpp
index 6ea41b65df..5c9a254401 100644
--- a/src/backend/cpu/kernel/transpose.hpp
+++ b/src/backend/cpu/kernel/transpose.hpp
@@ -12,6 +12,7 @@
 #include <err_cpu.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -178,3 +179,4 @@ void transpose_inplace(Param<T> in, const bool conjugate) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/triangle.hpp b/src/backend/cpu/kernel/triangle.hpp
index 40ba7e4591..3c6051ce0b 100644
--- a/src/backend/cpu/kernel/triangle.hpp
+++ b/src/backend/cpu/kernel/triangle.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -56,3 +57,4 @@ void triangle(Param<T> out, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/unwrap.hpp b/src/backend/cpu/kernel/unwrap.hpp
index 2b4e4f662d..e9cd6675a3 100644
--- a/src/backend/cpu/kernel/unwrap.hpp
+++ b/src/backend/cpu/kernel/unwrap.hpp
@@ -12,6 +12,7 @@
 #include <err_cpu.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -80,3 +81,4 @@ void unwrap_dim(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/kernel/wrap.hpp b/src/backend/cpu/kernel/wrap.hpp
index 6b574ee158..0a6eb63a5d 100644
--- a/src/backend/cpu/kernel/wrap.hpp
+++ b/src/backend/cpu/kernel/wrap.hpp
@@ -14,6 +14,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cpu {
 namespace kernel {
 
@@ -144,3 +145,4 @@ void wrap_dim_dilated(Param<T> out, CParam<T> in, const dim_t wx,
 
 }  // namespace kernel
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/logic.hpp b/src/backend/cpu/logic.hpp
index b5ed91f615..40a90e0167 100644
--- a/src/backend/cpu/logic.hpp
+++ b/src/backend/cpu/logic.hpp
@@ -14,6 +14,7 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, af_op_t op>
@@ -28,3 +29,4 @@ Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 9eda1f9253..8a5c40d55c 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -14,8 +14,9 @@
 #include <queue.hpp>
 #include <cstdlib>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
@@ -69,3 +70,4 @@ INSTANTIATE(ushort);
 INSTANTIATE(short);
 INSTANTIATE(half);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/lookup.hpp b/src/backend/cpu/lookup.hpp
index cd5f72a78d..c21a757d10 100644
--- a/src/backend/cpu/lookup.hpp
+++ b/src/backend/cpu/lookup.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/lu.cpp b/src/backend/cpu/lu.cpp
index 22a3a25d57..43df22e90c 100644
--- a/src/backend/cpu/lu.cpp
+++ b/src/backend/cpu/lu.cpp
@@ -23,6 +23,7 @@
 #include <cassert>
 #include <iostream>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -88,9 +89,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
 bool isLAPACKAvailable() { return true; }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -107,9 +110,11 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
 bool isLAPACKAvailable() { return false; }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 #define INSTANTIATE_LU(T)                                        \
@@ -124,3 +129,4 @@ INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/lu.hpp b/src/backend/cpu/lu.hpp
index 4092d4445c..d114d4f2b4 100644
--- a/src/backend/cpu/lu.hpp
+++ b/src/backend/cpu/lu.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
@@ -19,3 +20,4 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
 bool isLAPACKAvailable();
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index 5b609ad0a7..d3cfb26b4a 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename To, typename Ti>
@@ -55,3 +56,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/match_template.hpp b/src/backend/cpu/match_template.hpp
index ebe78e6023..6fbbec0a9e 100644
--- a/src/backend/cpu/match_template.hpp
+++ b/src/backend/cpu/match_template.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
                               const Array<inType> &tImg,
                               const af::matchType mType);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/math.cpp b/src/backend/cpu/math.cpp
index 04e426e48a..07b037a30a 100644
--- a/src/backend/cpu/math.cpp
+++ b/src/backend/cpu/math.cpp
@@ -10,6 +10,7 @@
 #include <math.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cpu {
 
 uint abs(uint val) { return val; }
@@ -39,3 +40,4 @@ cdouble max(cdouble lhs, cdouble rhs) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index f55530f531..d2735acd2a 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -18,6 +18,7 @@
 #include <limits>
 #include <numeric>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 static inline T abs(T val) {
@@ -76,8 +77,8 @@ inline double maxval() {
     return std::numeric_limits<double>::infinity();
 }
 template<>
-inline common::half maxval() {
-    return std::numeric_limits<common::half>::infinity();
+inline arrayfire::common::half maxval() {
+    return std::numeric_limits<arrayfire::common::half>::infinity();
 }
 template<>
 inline float minval() {
@@ -88,8 +89,8 @@ inline double minval() {
     return -std::numeric_limits<double>::infinity();
 }
 template<>
-inline common::half minval() {
-    return -std::numeric_limits<common::half>::infinity();
+inline arrayfire::common::half minval() {
+    return -std::numeric_limits<arrayfire::common::half>::infinity();
 }
 
 template<typename T>
@@ -113,3 +114,4 @@ inline double imag(cdouble in) noexcept { return std::imag(in); }
 inline float imag(cfloat in) noexcept { return std::imag(in); }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index 6da92b98e2..6a256113f7 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -19,8 +19,9 @@
 #include <complex>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename Ti, typename Tw, typename To>
@@ -159,3 +160,4 @@ INSTANTIATE_WGT(cdouble, double);
 INSTANTIATE_WGT(half, float);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/mean.hpp b/src/backend/cpu/mean.hpp
index ecc481c203..7079a91528 100644
--- a/src/backend/cpu/mean.hpp
+++ b/src/backend/cpu/mean.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename Ti, typename Tw, typename To>
 Array<To> mean(const Array<Ti>& in, const int dim);
@@ -22,3 +23,4 @@ T mean(const Array<T>& in, const Array<Tw>& wts);
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index e8a0f55ba4..d52b56a99e 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -21,6 +21,7 @@
 using af::dim4;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
@@ -55,3 +56,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/meanshift.hpp b/src/backend/cpu/meanshift.hpp
index b8ba8d2c24..c17d922414 100644
--- a/src/backend/cpu/meanshift.hpp
+++ b/src/backend/cpu/meanshift.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index cb24b81c43..53497be8c9 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -19,6 +19,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -67,3 +68,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/medfilt.hpp b/src/backend/cpu/medfilt.hpp
index 25f3ff2fe6..5d9f8e688c 100644
--- a/src/backend/cpu/medfilt.hpp
+++ b/src/backend/cpu/medfilt.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -20,3 +21,4 @@ Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
                   const af::borderType edge_pad);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index f64bed56ff..440680b48d 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -22,12 +22,13 @@
 #include <utility>
 
 using af::dim4;
-using common::bytesToString;
-using common::half;
+using arrayfire::common::bytesToString;
+using arrayfire::common::half;
 using std::function;
 using std::move;
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace cpu {
 float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
 float getMemoryPressureThreshold() {
@@ -156,3 +157,4 @@ void Allocator::nativeFree(void *ptr) {
     free(ptr);  // NOLINT(hicpp-no-malloc)
 }
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index bdd7365559..a45ca06ec1 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -14,6 +14,7 @@
 #include <functional>
 #include <memory>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
@@ -52,7 +53,7 @@ bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 
-class Allocator final : public common::memory::AllocatorInterface {
+class Allocator final : public common::AllocatorInterface {
    public:
     Allocator();
     ~Allocator() = default;
@@ -64,3 +65,4 @@ class Allocator final : public common::memory::AllocatorInterface {
 };
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index aedb9bc214..bd5c520eac 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -14,6 +14,7 @@
 #include <queue.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 
 static inline unsigned bitCount(unsigned v) {
@@ -54,3 +55,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/moments.hpp b/src/backend/cpu/moments.hpp
index 20a4ff4ed0..43793307da 100644
--- a/src/backend/cpu/moments.hpp
+++ b/src/backend/cpu/moments.hpp
@@ -10,7 +10,9 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index eca2424cb5..add13de416 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation) {
@@ -70,3 +71,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/morph.hpp b/src/backend/cpu/morph.hpp
index cf9e46bd9f..d1fabb47f7 100644
--- a/src/backend/cpu/morph.hpp
+++ b/src/backend/cpu/morph.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
@@ -16,3 +17,4 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 template<typename T>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 916d43d416..2979090dd9 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename To>
@@ -73,3 +74,4 @@ INSTANTIATE(short, int)
 INSTANTIATE(uintl, uint)  // For Hamming
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/nearest_neighbour.hpp b/src/backend/cpu/nearest_neighbour.hpp
index 22e190cb16..0c5bd401d9 100644
--- a/src/backend/cpu/nearest_neighbour.hpp
+++ b/src/backend/cpu/nearest_neighbour.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename To>
@@ -17,4 +18,5 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
                        const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/orb.cpp b/src/backend/cpu/orb.cpp
index 0a415c5cee..f03eb6427b 100644
--- a/src/backend/cpu/orb.cpp
+++ b/src/backend/cpu/orb.cpp
@@ -37,6 +37,7 @@ using std::sqrt;
 using std::unique_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convAccT>
@@ -292,3 +293,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/orb.hpp b/src/backend/cpu/orb.hpp
index cfb5904935..8bdd7a92c0 100644
--- a/src/backend/cpu/orb.hpp
+++ b/src/backend/cpu/orb.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              const unsigned max_feat, const float scl_fctr,
              const unsigned levels, const bool blur_img);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index 8676054136..dc73e76f17 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -21,15 +21,17 @@
 #include <sstream>
 #include <string>
 
-using common::getEnvVar;
-using common::ltrim;
-using common::memory::MemoryManagerBase;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::ltrim;
+using arrayfire::common::MemoryManagerBase;
 using std::endl;
 using std::ostringstream;
 using std::stoi;
 using std::string;
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace cpu {
 
 static string get_system() {
@@ -174,8 +176,7 @@ void resetMemoryManagerPinned() {
     return DeviceManager::getInstance().resetMemoryManagerPinned();
 }
 
-graphics::ForgeManager& forgeManager() {
-    return *(DeviceManager::getInstance().fgMngr);
-}
+ForgeManager& forgeManager() { return *(DeviceManager::getInstance().fgMngr); }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index f50e16461b..b02a1ca118 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -12,18 +12,16 @@
 #include <queue.hpp>
 #include <string>
 
-namespace graphics {
-class ForgeManager;
-}
-
+namespace arrayfire {
 namespace common {
-namespace memory {
+class ForgeManager;
 class MemoryManagerBase;
-}
 }  // namespace common
+}  // namespace arrayfire
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
+namespace arrayfire {
 namespace cpu {
 
 int getBackend();
@@ -67,6 +65,7 @@ void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
 
 void resetMemoryManagerPinned();
 
-graphics::ForgeManager& forgeManager();
+arrayfire::common::ForgeManager& forgeManager();
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index bc4afa5059..abf1a7b397 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -15,12 +15,16 @@
 #include <queue.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     P.eval();
     getQueue().sync();
 
@@ -47,3 +51,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/plot.hpp b/src/backend/cpu/plot.hpp
index f64ec8966c..11063e22f4 100644
--- a/src/backend/cpu/plot.hpp
+++ b/src/backend/cpu/plot.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/print.hpp b/src/backend/cpu/print.hpp
index 9d9d8da4f1..52e3e62877 100644
--- a/src/backend/cpu/print.hpp
+++ b/src/backend/cpu/print.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+namespace arrayfire {
 namespace cpu {
 // Nothing here
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/qr.cpp b/src/backend/cpu/qr.cpp
index 7cf0595eff..61d6305438 100644
--- a/src/backend/cpu/qr.cpp
+++ b/src/backend/cpu/qr.cpp
@@ -22,6 +22,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -108,9 +109,11 @@ Array<T> qr_inplace(Array<T> &in) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -124,9 +127,11 @@ Array<T> qr_inplace(Array<T> &in) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 #define INSTANTIATE_QR(T)                                         \
@@ -140,3 +145,4 @@ INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/qr.hpp b/src/backend/cpu/qr.hpp
index b8a43d4d02..4a3290e61c 100644
--- a/src/backend/cpu/qr.hpp
+++ b/src/backend/cpu/qr.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
@@ -16,3 +17,4 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
 template<typename T>
 Array<T> qr_inplace(Array<T> &in);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 97142f4f1a..594396a78e 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -48,6 +48,7 @@ using event_impl = threads::event;
 
 #endif
 
+namespace arrayfire {
 namespace cpu {
 
 /// Wraps the async_queue class
@@ -108,3 +109,4 @@ class queue_event {
     operator bool() const noexcept { return event_; }
 };
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index d6f6e7c792..3e1c8745c8 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -12,8 +12,9 @@
 #include <kernel/random_engine.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl) {
@@ -164,3 +165,4 @@ COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)  // NOLINT
 COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)    // NOLINT
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/random_engine.hpp b/src/backend/cpu/random_engine.hpp
index e2e490167d..adfa7b9fc6 100644
--- a/src/backend/cpu/random_engine.hpp
+++ b/src/backend/cpu/random_engine.hpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cpu {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl);
@@ -41,3 +42,4 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
                             Array<uint> recursion_table,
                             Array<uint> temper_table, Array<uint> state);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index b2fc132547..3b782837e0 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -19,8 +19,9 @@
 #include <numeric>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -59,3 +60,4 @@ INSTANTIATE(short)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/range.hpp b/src/backend/cpu/range.hpp
index 9b30f261f7..b6d0f58bd9 100644
--- a/src/backend/cpu/range.hpp
+++ b/src/backend/cpu/range.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim = -1);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index e1baf5daea..6ce141b316 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -21,11 +21,12 @@
 #include <functional>
 
 using af::dim4;
-using common::Binary;
-using common::half;
-using common::Transform;
-using cpu::cdouble;
+using arrayfire::common::Binary;
+using arrayfire::common::half;
+using arrayfire::common::Transform;
+using arrayfire::cpu::cdouble;
 
+namespace arrayfire {
 namespace common {
 
 template<>
@@ -38,7 +39,6 @@ struct Binary<cdouble, af_add_t> {
 };
 
 }  // namespace common
-
 namespace cpu {
 
 template<af_op_t op, typename Ti, typename To>
@@ -250,3 +250,4 @@ INSTANTIATE(af_and_t, ushort, char)
 INSTANTIATE(af_and_t, half, char)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/reduce.hpp b/src/backend/cpu/reduce.hpp
index 3db9b0cc8a..8ff97c51a6 100644
--- a/src/backend/cpu/reduce.hpp
+++ b/src/backend/cpu/reduce.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
@@ -24,3 +25,4 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
                      double nanval = 0);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/regions.cpp b/src/backend/cpu/regions.cpp
index 0f6612768d..821a5285c3 100644
--- a/src/backend/cpu/regions.cpp
+++ b/src/backend/cpu/regions.cpp
@@ -21,6 +21,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -43,3 +44,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/regions.hpp b/src/backend/cpu/regions.hpp
index 0e2ce0f319..b1c06b1911 100644
--- a/src/backend/cpu/regions.hpp
+++ b/src/backend/cpu/regions.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 83d2038f38..67233542bd 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -14,8 +14,9 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -47,3 +48,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/reorder.hpp b/src/backend/cpu/reorder.hpp
index bc689f74c2..5dee87f401 100644
--- a/src/backend/cpu/reorder.hpp
+++ b/src/backend/cpu/reorder.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/reshape.cpp b/src/backend/cpu/reshape.cpp
index 7844f3a596..b2d46eb066 100644
--- a/src/backend/cpu/reshape.cpp
+++ b/src/backend/cpu/reshape.cpp
@@ -14,6 +14,7 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void multiply_inplace(Array<T> &in, double val) {
@@ -82,7 +83,7 @@ INSTANTIATE_PAD_ARRAY(uchar)
 INSTANTIATE_PAD_ARRAY(char)
 INSTANTIATE_PAD_ARRAY(ushort)
 INSTANTIATE_PAD_ARRAY(short)
-INSTANTIATE_PAD_ARRAY(common::half)
+INSTANTIATE_PAD_ARRAY(arrayfire::common::half)
 
 #define INSTANTIATE_PAD_ARRAY_COMPLEX(SRC_T)                 \
     template Array<cfloat> reshape<SRC_T, cfloat>(           \
@@ -93,3 +94,4 @@ INSTANTIATE_PAD_ARRAY(common::half)
 INSTANTIATE_PAD_ARRAY_COMPLEX(cfloat)
 INSTANTIATE_PAD_ARRAY_COMPLEX(cdouble)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index f5850bb106..4f899d89d8 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -14,6 +14,7 @@
 #include <queue.hpp>
 #include <resize.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -58,3 +59,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/resize.hpp b/src/backend/cpu/resize.hpp
index 83852f1e29..d31290daf5 100644
--- a/src/backend/cpu/resize.hpp
+++ b/src/backend/cpu/resize.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 7a0fada05f..0e9806a2af 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -13,6 +13,7 @@
 #include <queue.hpp>
 #include <rotate.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -58,3 +59,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/rotate.hpp b/src/backend/cpu/rotate.hpp
index 094bc24f92..cf18a7df56 100644
--- a/src/backend/cpu/rotate.hpp
+++ b/src/backend/cpu/rotate.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index f4412168d1..af5c4d9efe 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<af_op_t op, typename Ti, typename To>
@@ -93,3 +94,4 @@ INSTANTIATE_SCAN_ALL(af_mul_t)
 INSTANTIATE_SCAN_ALL(af_min_t)
 INSTANTIATE_SCAN_ALL(af_max_t)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/scan.hpp b/src/backend/cpu/scan.hpp
index 431c46b1f9..45cd171092 100644
--- a/src/backend/cpu/scan.hpp
+++ b/src/backend/cpu/scan.hpp
@@ -10,7 +10,9 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/scan_by_key.cpp b/src/backend/cpu/scan_by_key.cpp
index ef7a9d3036..f869098ffd 100644
--- a/src/backend/cpu/scan_by_key.cpp
+++ b/src/backend/cpu/scan_by_key.cpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
@@ -64,3 +65,4 @@ INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_mul_t)
 INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_min_t)
 INSTANTIATE_SCAN_BY_KEY_ALL_OP(af_max_t)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/scan_by_key.hpp b/src/backend/cpu/scan_by_key.hpp
index 3bc934d529..414840dc35 100644
--- a/src/backend/cpu/scan_by_key.hpp
+++ b/src/backend/cpu/scan_by_key.hpp
@@ -10,8 +10,10 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
                bool inclusive_scan = true);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index a801bb5e86..96849cecd1 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -15,8 +15,9 @@
 #include <queue.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -56,3 +57,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/select.hpp b/src/backend/cpu/select.hpp
index b92a8d36c5..1ed5d3969b 100644
--- a/src/backend/cpu/select.hpp
+++ b/src/backend/cpu/select.hpp
@@ -9,6 +9,7 @@
 #pragma once
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
@@ -34,3 +35,4 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
     return out;
 }
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index d4bb1612e3..838ad7675e 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -19,6 +19,7 @@
 #include <complex>
 #include <vector>
 
+namespace arrayfire {
 namespace cpu {
 
 using af::dim4;
@@ -126,3 +127,4 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/set.hpp b/src/backend/cpu/set.hpp
index 762a7329db..086fcc6866 100644
--- a/src/backend/cpu/set.hpp
+++ b/src/backend/cpu/set.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted);
@@ -22,3 +23,4 @@ template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index 5126cda592..f8942f641f 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -13,6 +13,7 @@
 #include <queue.hpp>
 #include <shift.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -42,3 +43,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/shift.hpp b/src/backend/cpu/shift.hpp
index 4f992e7fb0..0e298f16ae 100644
--- a/src/backend/cpu/shift.hpp
+++ b/src/backend/cpu/shift.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sift.cpp b/src/backend/cpu/sift.cpp
index 3b7e6b554c..246505a206 100644
--- a/src/backend/cpu/sift.cpp
+++ b/src/backend/cpu/sift.cpp
@@ -13,6 +13,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convAccT>
@@ -41,3 +42,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sift.hpp b/src/backend/cpu/sift.hpp
index 66f0d191bb..804e52eb27 100644
--- a/src/backend/cpu/sift.hpp
+++ b/src/backend/cpu/sift.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename convAccT>
@@ -23,4 +24,5 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 76ecf17dc6..68bddee784 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename Ti, typename To>
@@ -48,3 +49,4 @@ INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sobel.hpp b/src/backend/cpu/sobel.hpp
index dcd41b9366..ad1082d18e 100644
--- a/src/backend/cpu/sobel.hpp
+++ b/src/backend/cpu/sobel.hpp
@@ -10,10 +10,12 @@
 #include <Array.hpp>
 #include <utility>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename Ti, typename To>
 std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
                                                  const unsigned &ker_size);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/solve.cpp b/src/backend/cpu/solve.cpp
index 52843d2fae..0e8d863817 100644
--- a/src/backend/cpu/solve.cpp
+++ b/src/backend/cpu/solve.cpp
@@ -26,6 +26,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -322,9 +323,11 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -344,9 +347,11 @@ Array<T> solve(const Array<T> &a, const Array<T> &b,
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 #define INSTANTIATE_SOLVE(T)                                                 \
@@ -362,3 +367,4 @@ INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/solve.hpp b/src/backend/cpu/solve.hpp
index 2469a39451..c63ec1252b 100644
--- a/src/backend/cpu/solve.hpp
+++ b/src/backend/cpu/solve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options = AF_MAT_NONE);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index 50f44dcae9..e5067a8dba 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -21,6 +21,7 @@
 #include <algorithm>
 #include <functional>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, int dim>
@@ -104,3 +105,4 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sort.hpp b/src/backend/cpu/sort.hpp
index 4ec954685c..c22dab7c7d 100644
--- a/src/backend/cpu/sort.hpp
+++ b/src/backend/cpu/sort.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index e69672e6a4..169b598558 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -17,6 +17,7 @@
 #include <reorder.hpp>
 #include <sort_by_key.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename Tk, typename Tv>
@@ -88,3 +89,4 @@ INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sort_by_key.hpp b/src/backend/cpu/sort_by_key.hpp
index a8c6fc2078..8ed3bb63f4 100644
--- a/src/backend/cpu/sort_by_key.hpp
+++ b/src/backend/cpu/sort_by_key.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
                  const Array<Tv> &ival, const unsigned dim, bool isAscending);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index c7ec0b8c05..cec724c85d 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -21,6 +21,7 @@
 #include <algorithm>
 #include <numeric>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -81,3 +82,4 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sort_index.hpp b/src/backend/cpu/sort_index.hpp
index e4a3cbf775..b0b50fbf87 100644
--- a/src/backend/cpu/sort_index.hpp
+++ b/src/backend/cpu/sort_index.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void sort_index(Array<T> &okey, Array<unsigned> &oval, const Array<T> &in,
                 const unsigned dim, bool isAscending);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sparse.cpp b/src/backend/cpu/sparse.cpp
index 30c7475292..3641c96a90 100644
--- a/src/backend/cpu/sparse.cpp
+++ b/src/backend/cpu/sparse.cpp
@@ -28,14 +28,15 @@
 
 #include <functional>
 
-using common::cast;
+using arrayfire::common::cast;
 using std::function;
 
+namespace arrayfire {
 namespace cpu {
 
-using common::createArrayDataSparseArray;
-using common::createEmptySparseArray;
-using common::SparseArray;
+using arrayfire::common::createArrayDataSparseArray;
+using arrayfire::common::createEmptySparseArray;
+using arrayfire::common::SparseArray;
 
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
@@ -161,3 +162,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_SPARSE
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sparse.hpp b/src/backend/cpu/sparse.hpp
index 9246a529a1..8709fe199d 100644
--- a/src/backend/cpu/sparse.hpp
+++ b/src/backend/cpu/sparse.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T, af_storage stype>
 common::SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in);
@@ -23,3 +24,4 @@ template<typename T, af_storage dest, af_storage src>
 common::SparseArray<T> sparseConvertStorageToStorage(
     const common::SparseArray<T> &in);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sparse_arith.cpp b/src/backend/cpu/sparse_arith.cpp
index f07d9c57c4..d6d7e5391e 100644
--- a/src/backend/cpu/sparse_arith.cpp
+++ b/src/backend/cpu/sparse_arith.cpp
@@ -27,11 +27,12 @@
 #include <string>
 #include <vector>
 
-using common::createArrayDataSparseArray;
-using common::createEmptySparseArray;
-using common::SparseArray;
+using arrayfire::common::createArrayDataSparseArray;
+using arrayfire::common::createEmptySparseArray;
+using arrayfire::common::SparseArray;
 using std::numeric_limits;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -166,3 +167,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sparse_arith.hpp b/src/backend/cpu/sparse_arith.hpp
index f37f55a42d..2563802c4d 100644
--- a/src/backend/cpu/sparse_arith.hpp
+++ b/src/backend/cpu/sparse_arith.hpp
@@ -14,6 +14,7 @@
 #include <optypes.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace cpu {
 // These two functions cannot be overloaded by return type.
 // So have to give them separate names.
@@ -29,3 +30,4 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sparse_blas.cpp b/src/backend/cpu/sparse_blas.cpp
index dcb8158d9a..d6bd338575 100644
--- a/src/backend/cpu/sparse_blas.cpp
+++ b/src/backend/cpu/sparse_blas.cpp
@@ -26,6 +26,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace cpu {
 
 #ifdef USE_MKL
@@ -462,3 +463,4 @@ INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/sparse_blas.hpp b/src/backend/cpu/sparse_blas.hpp
index 54da96c282..f59ef83d60 100644
--- a/src/backend/cpu/sparse_blas.hpp
+++ b/src/backend/cpu/sparse_blas.hpp
@@ -11,10 +11,12 @@
 #include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index 7eb1034d49..e861dbeac7 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -15,12 +15,16 @@
 #include <surface.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = common::forgePlugin();
     P.eval();
     getQueue().sync();
 
@@ -48,3 +52,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/surface.hpp b/src/backend/cpu/surface.hpp
index 8437d45e18..1bcf57fac3 100644
--- a/src/backend/cpu/surface.hpp
+++ b/src/backend/cpu/surface.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 7f69925b16..0d79078988 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -19,6 +19,7 @@
 using af::features;
 using std::shared_ptr;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -77,3 +78,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/susan.hpp b/src/backend/cpu/susan.hpp
index 29504b8f2b..af6640e195 100644
--- a/src/backend/cpu/susan.hpp
+++ b/src/backend/cpu/susan.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -21,4 +22,5 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out,
                const float geom_thr, const float feature_ratio,
                const unsigned edge);
 
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/svd.cpp b/src/backend/cpu/svd.cpp
index 7093689812..75804d240b 100644
--- a/src/backend/cpu/svd.cpp
+++ b/src/backend/cpu/svd.cpp
@@ -18,6 +18,7 @@
 #include <platform.hpp>
 #include <queue.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 #define SVD_FUNC_DEF(FUNC)            \
@@ -85,9 +86,11 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T, typename Tr>
@@ -101,9 +104,11 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace cpu {
 
 #define INSTANTIATE_SVD(T, Tr)                                           \
@@ -118,3 +123,4 @@ INSTANTIATE_SVD(cfloat, float)
 INSTANTIATE_SVD(cdouble, double)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/svd.hpp b/src/backend/cpu/svd.hpp
index 2019ea57c5..ba667d2032 100644
--- a/src/backend/cpu/svd.hpp
+++ b/src/backend/cpu/svd.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T, typename Tr>
 void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
@@ -16,3 +17,4 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index 9d951badf8..d2a8d3ab7c 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -14,8 +14,9 @@
 #include <common/half.hpp>
 #include <platform.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -53,3 +54,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/tile.hpp b/src/backend/cpu/tile.hpp
index 4e71919789..eee387cb87 100644
--- a/src/backend/cpu/tile.hpp
+++ b/src/backend/cpu/tile.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/topk.cpp b/src/backend/cpu/topk.cpp
index a87d257a8c..0103c3586b 100644
--- a/src/backend/cpu/topk.cpp
+++ b/src/backend/cpu/topk.cpp
@@ -18,12 +18,13 @@
 #include <numeric>
 #include <vector>
 
-using common::half;
+using arrayfire::common::half;
 using std::iota;
 using std::min;
 using std::partial_sort_copy;
 using std::vector;
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
@@ -130,3 +131,4 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(half)
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/topk.hpp b/src/backend/cpu/topk.hpp
index 75cb5e7cfe..0383e13fcf 100644
--- a/src/backend/cpu/topk.hpp
+++ b/src/backend/cpu/topk.hpp
@@ -7,8 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index f03dd57919..9a57424250 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -13,6 +13,7 @@
 #include <platform.hpp>
 #include <transform.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -63,3 +64,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/transform.hpp b/src/backend/cpu/transform.hpp
index e00284980a..1df2b38934 100644
--- a/src/backend/cpu/transform.hpp
+++ b/src/backend/cpu/transform.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 4617f19b97..7cd713afd6 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -18,8 +18,9 @@
 #include <utility>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -58,3 +59,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/transpose.hpp b/src/backend/cpu/transpose.hpp
index 27337bd0fb..565f89cc6c 100644
--- a/src/backend/cpu/transpose.hpp
+++ b/src/backend/cpu/transpose.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 6440a286b4..8e3b0569b2 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -15,8 +15,9 @@
 
 #include <functional>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -63,3 +64,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/triangle.hpp b/src/backend/cpu/triangle.hpp
index 8178767b45..01e55f7c0b 100644
--- a/src/backend/cpu/triangle.hpp
+++ b/src/backend/cpu/triangle.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> triangle(const Array<T> &in, const bool is_upper,
                   const bool is_unit_diag);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index d0263fbf0b..27a678af82 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -11,6 +11,7 @@
 #include <common/kernel_type.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cpu {
 
 namespace {
@@ -49,8 +50,8 @@ struct kernel_type;
 class half;
 
 template<>
-struct kernel_type<common::half> {
-    using data = common::half;
+struct kernel_type<arrayfire::common::half> {
+    using data = arrayfire::common::half;
 
     // These are the types within a kernel
     using native = float;
@@ -58,3 +59,5 @@ struct kernel_type<common::half> {
     using compute = float;
 };
 }  // namespace common
+
+}  // namespace arrayfire
diff --git a/src/backend/cpu/unary.hpp b/src/backend/cpu/unary.hpp
index 3a1c7677dd..620ed26e8c 100644
--- a/src/backend/cpu/unary.hpp
+++ b/src/backend/cpu/unary.hpp
@@ -14,6 +14,7 @@
 #include <optypes.hpp>
 #include <cmath>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -120,3 +121,4 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 }
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index ce062b6b8a..49086fad49 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -15,8 +15,9 @@
 #include <platform.hpp>
 #include <unwrap.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
@@ -62,3 +63,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/unwrap.hpp b/src/backend/cpu/unwrap.hpp
index 260605734d..fcfad88f6f 100644
--- a/src/backend/cpu/unwrap.hpp
+++ b/src/backend/cpu/unwrap.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
                 const dim_t dx, const dim_t dy, const bool is_column);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/utility.hpp b/src/backend/cpu/utility.hpp
index f7d74f9162..9cd3de96f0 100644
--- a/src/backend/cpu/utility.hpp
+++ b/src/backend/cpu/utility.hpp
@@ -13,6 +13,7 @@
 #include <cmath>
 #include "backend.hpp"
 
+namespace arrayfire {
 namespace cpu {
 static inline dim_t trimIndex(int const& idx, dim_t const& len) {
     int ret_val = idx;
@@ -47,3 +48,4 @@ void gaussian1D(T* out, int const dim, double sigma = 0.0) {
     for (int k = 0; k < dim; k++) out[k] /= sum;
 }
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index 2f9f2d34e4..2a7549de81 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -15,13 +15,17 @@
 #include <vector_field.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     points.eval();
     directions.eval();
     getQueue().sync();
@@ -59,3 +63,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/vector_field.hpp b/src/backend/cpu/vector_field.hpp
index c25a1501e4..a64414e781 100644
--- a/src/backend/cpu/vector_field.hpp
+++ b/src/backend/cpu/vector_field.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 14dbdddfa5..3eb65015f0 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -21,6 +21,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -77,3 +78,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/where.hpp b/src/backend/cpu/where.hpp
index 8ec35b1526..35c671c2b0 100644
--- a/src/backend/cpu/where.hpp
+++ b/src/backend/cpu/where.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 template<typename T>
 Array<uint> where(const Array<T>& in);
-}
+}  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index 6a6c887faa..d502bc85ad 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -15,8 +15,9 @@
 #include <platform.hpp>
 #include <wrap.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -84,3 +85,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cpu/wrap.hpp b/src/backend/cpu/wrap.hpp
index bcfe18ef5e..0bec7c8727 100644
--- a/src/backend/cpu/wrap.hpp
+++ b/src/backend/cpu/wrap.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cpu {
 
 template<typename T>
@@ -22,3 +23,4 @@ Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
                       const dim_t sy, const dim_t px, const dim_t py,
                       const dim_t dx, const dim_t dy, const bool is_column);
 }  // namespace cpu
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index c6347d1bbe..ea5a7e971a 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -24,11 +24,11 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
-using common::Node;
-using common::Node_ptr;
-using common::NodeIterator;
-using cuda::jit::BufferNode;
+using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::cuda::jit::BufferNode;
 
 using nonstd::span;
 using std::accumulate;
@@ -36,6 +36,7 @@ using std::move;
 using std::shared_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -87,14 +88,14 @@ Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");
     if (!is_device) {
-        CUDA_CHECK(
-            cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                            cudaMemcpyHostToDevice, cuda::getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(data.get(), in_data,
+                                   dims.elements() * sizeof(T),
+                                   cudaMemcpyHostToDevice, getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     } else if (copy_device) {
         CUDA_CHECK(
             cudaMemcpyAsync(data.get(), in_data, dims.elements() * sizeof(T),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 }
@@ -407,7 +408,7 @@ void writeHostDataArray(Array<T> &arr, const T *const data,
     T *ptr = arr.get();
 
     CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyHostToDevice,
-                               cuda::getActiveStream()));
+                               getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 }
 
@@ -419,7 +420,7 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
     T *ptr = arr.get();
 
     CUDA_CHECK(cudaMemcpyAsync(ptr, data, bytes, cudaMemcpyDeviceToDevice,
-                               cuda::getActiveStream()));
+                               getActiveStream()));
 }
 
 template<typename T>
@@ -473,3 +474,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 52dbed7aeb..07e06f0681 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -25,7 +25,9 @@
 #include <nonstd/span.hpp>
 #include <vector>
 
+namespace arrayfire {
 namespace cuda {
+
 using af::dim4;
 
 template<typename T>
@@ -287,3 +289,4 @@ class Array {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index ece17d962f..5e0119d93d 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -129,7 +129,7 @@ file_to_string(
     EXTENSION "hpp"
     OUTPUT_DIR "kernel_headers"
     TARGETS jit_kernel_targets
-    NAMESPACE "cuda"
+    NAMESPACE "arrayfire cuda"
     WITH_EXTENSION
     )
 
@@ -222,7 +222,7 @@ file_to_string(
     EXTENSION "hpp"
     OUTPUT_DIR "nvrtc_kernel_headers"
     TARGETS nvrtc_kernel_targets
-    NAMESPACE "cuda"
+    NAMESPACE "arrayfire cuda"
     WITH_EXTENSION
     NULLTERM
     )
diff --git a/src/backend/cuda/EnqueueArgs.hpp b/src/backend/cuda/EnqueueArgs.hpp
index 9dbac7eaa7..f3fb608b4c 100644
--- a/src/backend/cuda/EnqueueArgs.hpp
+++ b/src/backend/cuda/EnqueueArgs.hpp
@@ -14,6 +14,7 @@
 
 #include <vector>
 
+namespace arrayfire {
 namespace cuda {
 
 ///
@@ -51,3 +52,4 @@ struct EnqueueArgs {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Event.cpp b/src/backend/cuda/Event.cpp
index 0b0d9618e8..fb5fbff170 100644
--- a/src/backend/cuda/Event.cpp
+++ b/src/backend/cuda/Event.cpp
@@ -17,6 +17,7 @@
 
 #include <memory>
 
+namespace arrayfire {
 namespace cuda {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(cudaStream_t queue) {
@@ -69,3 +70,4 @@ af_event createAndMarkEvent() {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Event.hpp b/src/backend/cuda/Event.hpp
index b6600934e4..2db9679aca 100644
--- a/src/backend/cuda/Event.hpp
+++ b/src/backend/cuda/Event.hpp
@@ -13,6 +13,7 @@
 #include <cuda_runtime_api.h>
 #include <af/event.h>
 
+namespace arrayfire {
 namespace cuda {
 
 class CUDARuntimeEventPolicy {
@@ -64,3 +65,4 @@ void block(af_event eventHandle);
 af_event createAndMarkEvent();
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/GraphicsResourceManager.cpp b/src/backend/cuda/GraphicsResourceManager.cpp
index 5778f72658..cca78f286f 100644
--- a/src/backend/cuda/GraphicsResourceManager.cpp
+++ b/src/backend/cuda/GraphicsResourceManager.cpp
@@ -16,6 +16,7 @@
 #include <err_cuda.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 GraphicsResourceManager::ShrdResVector
 GraphicsResourceManager::registerResources(
@@ -43,3 +44,4 @@ GraphicsResourceManager::registerResources(
     return output;
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/GraphicsResourceManager.hpp b/src/backend/cuda/GraphicsResourceManager.hpp
index ba05c2dbe3..dde6a30ab5 100644
--- a/src/backend/cuda/GraphicsResourceManager.hpp
+++ b/src/backend/cuda/GraphicsResourceManager.hpp
@@ -15,6 +15,7 @@
 #include <map>
 #include <vector>
 
+namespace arrayfire {
 namespace cuda {
 class GraphicsResourceManager
     : public common::InteropManager<GraphicsResourceManager,
@@ -31,3 +32,4 @@ class GraphicsResourceManager
     void operator=(GraphicsResourceManager const &);
 };
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Kernel.cpp b/src/backend/cuda/Kernel.cpp
index f2f64bdeb0..d72672a1fc 100644
--- a/src/backend/cuda/Kernel.cpp
+++ b/src/backend/cuda/Kernel.cpp
@@ -11,6 +11,7 @@
 
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 Kernel::DevPtrType Kernel::getDevPtr(const char* name) {
@@ -22,22 +23,22 @@ Kernel::DevPtrType Kernel::getDevPtr(const char* name) {
 
 void Kernel::copyToReadOnly(Kernel::DevPtrType dst, Kernel::DevPtrType src,
                             size_t bytes) {
-    CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, cuda::getActiveStream()));
+    CU_CHECK(cuMemcpyDtoDAsync(dst, src, bytes, getActiveStream()));
 }
 
 void Kernel::setFlag(Kernel::DevPtrType dst, int* scalarValPtr,
                      const bool syncCopy) {
-    CU_CHECK(cuMemcpyHtoDAsync(dst, scalarValPtr, sizeof(int),
-                               cuda::getActiveStream()));
-    if (syncCopy) { CU_CHECK(cuStreamSynchronize(cuda::getActiveStream())); }
+    CU_CHECK(
+        cuMemcpyHtoDAsync(dst, scalarValPtr, sizeof(int), getActiveStream()));
+    if (syncCopy) { CU_CHECK(cuStreamSynchronize(getActiveStream())); }
 }
 
 int Kernel::getFlag(Kernel::DevPtrType src) {
     int retVal = 0;
-    CU_CHECK(
-        cuMemcpyDtoHAsync(&retVal, src, sizeof(int), cuda::getActiveStream()));
-    CU_CHECK(cuStreamSynchronize(cuda::getActiveStream()));
+    CU_CHECK(cuMemcpyDtoHAsync(&retVal, src, sizeof(int), getActiveStream()));
+    CU_CHECK(cuStreamSynchronize(getActiveStream()));
     return retVal;
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index a728940d97..b5375f6ad2 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -18,6 +18,7 @@
 #include <cstdlib>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 
 struct Enqueuer {
@@ -72,3 +73,4 @@ class Kernel
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/LookupTable1D.hpp b/src/backend/cuda/LookupTable1D.hpp
index ffbfb0f4c8..f688ac4b7e 100644
--- a/src/backend/cuda/LookupTable1D.hpp
+++ b/src/backend/cuda/LookupTable1D.hpp
@@ -14,6 +14,7 @@
 
 #include <type_traits>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -64,3 +65,4 @@ class LookupTable1D {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Module.hpp b/src/backend/cuda/Module.hpp
index ceefd2f94e..b5eb028765 100644
--- a/src/backend/cuda/Module.hpp
+++ b/src/backend/cuda/Module.hpp
@@ -17,6 +17,7 @@
 #include <string>
 #include <unordered_map>
 
+namespace arrayfire {
 namespace cuda {
 
 /// CUDA backend wrapper for CUmodule
@@ -57,3 +58,4 @@ class Module : public common::ModuleInterface<CUmodule> {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index cd1651cae5..817d601eaa 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -13,6 +13,7 @@
 #include <types.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -77,3 +78,4 @@ class CParam {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/ThrustAllocator.cuh b/src/backend/cuda/ThrustAllocator.cuh
index 917cc5e9ba..21152e6059 100644
--- a/src/backend/cuda/ThrustAllocator.cuh
+++ b/src/backend/cuda/ThrustAllocator.cuh
@@ -16,7 +16,9 @@
 // Below Class definition is found at the following URL
 // http://stackoverflow.com/questions/9007343/mix-custom-memory-managment-and-thrust-in-cuda
 
+namespace arrayfire {
 namespace cuda {
+
 template<typename T>
 struct ThrustAllocator : thrust::device_malloc_allocator<T> {
     // shorthand for the name of the base class
@@ -41,3 +43,4 @@ struct ThrustAllocator : thrust::device_malloc_allocator<T> {
     }
 };
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
index 6787d405de..189ee558b3 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.hpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -12,8 +12,10 @@
 #include <backend.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
+#include <thrust/memory.h>
 #include <thrust/system/cuda/execution_policy.h>
 
+namespace arrayfire {
 namespace cuda {
 struct ThrustArrayFirePolicy
     : thrust::cuda::execution_policy<ThrustArrayFirePolicy> {};
@@ -22,7 +24,7 @@ template<typename T>
 thrust::pair<thrust::pointer<T, ThrustArrayFirePolicy>, std::ptrdiff_t>
 get_temporary_buffer(ThrustArrayFirePolicy, std::ptrdiff_t n) {
     thrust::pointer<T, ThrustArrayFirePolicy> result(
-        cuda::memAlloc<T>(n / sizeof(T)).release());
+        arrayfire::cuda::memAlloc<T>(n / sizeof(T)).release());
 
     return thrust::make_pair(result, n);
 }
@@ -33,25 +35,27 @@ inline void return_temporary_buffer(ThrustArrayFirePolicy, Pointer p) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
 
 namespace thrust {
 namespace cuda_cub {
 template<>
-__DH__ inline cudaStream_t get_stream<::cuda::ThrustArrayFirePolicy>(
-    execution_policy<::cuda::ThrustArrayFirePolicy> &) {
+__DH__ inline cudaStream_t get_stream<arrayfire::cuda::ThrustArrayFirePolicy>(
+    execution_policy<arrayfire::cuda::ThrustArrayFirePolicy> &) {
 #if defined(__CUDA_ARCH__)
     return 0;
 #else
-    return ::cuda::getActiveStream();
+    return arrayfire::cuda::getActiveStream();
 #endif
 }
 
 __DH__
-inline cudaError_t synchronize_stream(const ::cuda::ThrustArrayFirePolicy &) {
+inline cudaError_t synchronize_stream(
+    const arrayfire::cuda::ThrustArrayFirePolicy &) {
 #if defined(__CUDA_ARCH__)
     return cudaSuccess;
 #else
-    return cudaStreamSynchronize(::cuda::getActiveStream());
+    return cudaStreamSynchronize(arrayfire::cuda::getActiveStream());
 #endif
 }
 
diff --git a/src/backend/cuda/all.cu b/src/backend/cuda/all.cu
index b681a87384..3ff42ad599 100644
--- a/src/backend/cuda/all.cu
+++ b/src/backend/cuda/all.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // alltrue
 INSTANTIATE(af_and_t, float, char)
@@ -28,3 +29,4 @@ INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
 INSTANTIATE(af_and_t, half, char)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/anisotropic_diffusion.cpp b/src/backend/cuda/anisotropic_diffusion.cpp
index 3d6294ed46..45b84b8b6f 100644
--- a/src/backend/cuda/anisotropic_diffusion.cpp
+++ b/src/backend/cuda/anisotropic_diffusion.cpp
@@ -12,6 +12,7 @@
 #include <kernel/anisotropic_diffusion.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
@@ -29,3 +30,4 @@ void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
 INSTANTIATE(double)
 INSTANTIATE(float)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/anisotropic_diffusion.hpp b/src/backend/cuda/anisotropic_diffusion.hpp
index 4dca3740f2..6e9c2e4c1c 100644
--- a/src/backend/cuda/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/anisotropic_diffusion.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/any.cu b/src/backend/cuda/any.cu
index 2da5d3349f..34092c94d3 100644
--- a/src/backend/cuda/any.cu
+++ b/src/backend/cuda/any.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // anytrue
 INSTANTIATE(af_or_t, float, char)
@@ -28,3 +29,4 @@ INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
 INSTANTIATE(af_or_t, half, char)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/approx.cpp b/src/backend/cuda/approx.cpp
index 0c1bc0bb1f..b9bd55e78d 100644
--- a/src/backend/cuda/approx.cpp
+++ b/src/backend/cuda/approx.cpp
@@ -13,6 +13,7 @@
 #include <kernel/approx.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -49,3 +50,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/approx.hpp b/src/backend/cuda/approx.hpp
index 0d459970f1..c72d2cbe9b 100644
--- a/src/backend/cuda/approx.hpp
+++ b/src/backend/cuda/approx.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -22,3 +23,4 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
              const Tp &yi_step, const af_interp_type method,
              const float offGrid);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/arith.hpp b/src/backend/cuda/arith.hpp
index f478ecf6c0..67e39f54f4 100644
--- a/src/backend/cuda/arith.hpp
+++ b/src/backend/cuda/arith.hpp
@@ -13,6 +13,7 @@
 #include <common/jit/BinaryNode.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, af_op_t op>
@@ -27,3 +28,4 @@ Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/assign.cpp b/src/backend/cuda/assign.cpp
index 8c910fceb6..67bcbd1291 100644
--- a/src/backend/cuda/assign.cpp
+++ b/src/backend/cuda/assign.cpp
@@ -17,8 +17,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -78,3 +79,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/assign.hpp b/src/backend/cuda/assign.hpp
index 1e2eff86bf..be2f725e90 100644
--- a/src/backend/cuda/assign.hpp
+++ b/src/backend/cuda/assign.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/assign_kernel_param.hpp b/src/backend/cuda/assign_kernel_param.hpp
index 6587465ce2..0591ca80ad 100644
--- a/src/backend/cuda/assign_kernel_param.hpp
+++ b/src/backend/cuda/assign_kernel_param.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cuda {
 
 typedef struct {
@@ -21,3 +22,4 @@ typedef struct {
 using IndexKernelParam = AssignKernelParam;
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/backend.hpp b/src/backend/cuda/backend.hpp
index 33ce38d384..149353ca21 100644
--- a/src/backend/cuda/backend.hpp
+++ b/src/backend/cuda/backend.hpp
@@ -24,6 +24,8 @@
 #endif
 #endif
 
-namespace cuda {}
+namespace arrayfire {
+namespace cuda {}  // namespace cuda
+}  // namespace arrayfire
 
-namespace detail = cuda;
+namespace detail = arrayfire::cuda;
diff --git a/src/backend/cuda/bilateral.cpp b/src/backend/cuda/bilateral.cpp
index 12b2907b4f..f9f828018d 100644
--- a/src/backend/cuda/bilateral.cpp
+++ b/src/backend/cuda/bilateral.cpp
@@ -14,6 +14,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename inType, typename outType>
@@ -38,3 +39,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/bilateral.hpp b/src/backend/cuda/bilateral.hpp
index 35fa575500..63cdaee7af 100644
--- a/src/backend/cuda/bilateral.hpp
+++ b/src/backend/cuda/bilateral.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename inType, typename outType>
 Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
                          const float &chromaticSigma);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index ad3b95bb89..20f2bea9a6 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -11,6 +11,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename To, typename Ti, af_op_t op>
@@ -125,3 +126,4 @@ struct BinOp<To, Ti, af_hypot_t> {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/blas.cu b/src/backend/cuda/blas.cu
index bb88c60feb..6c88ea002a 100644
--- a/src/backend/cuda/blas.cu
+++ b/src/backend/cuda/blas.cu
@@ -33,11 +33,12 @@
 #include <string>
 #include <vector>
 
-using common::half;
-using common::kernel_type;
+using arrayfire::common::half;
+using arrayfire::common::kernel_type;
 using std::is_same;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 
 cublasOperation_t toCblasTranspose(af_mat_prop opt) {
@@ -373,3 +374,4 @@ INSTANTIATE_TRSM(double)
 INSTANTIATE_TRSM(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index ce1aac1f3a..dc4382d013 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
@@ -36,3 +37,4 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans = AF_MAT_NONE,
           bool is_upper = false, bool is_left = true, bool is_unit = false);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/canny.cpp b/src/backend/cuda/canny.cpp
index a967aaf3ee..ebf8ba2e04 100644
--- a/src/backend/cuda/canny.cpp
+++ b/src/backend/cuda/canny.cpp
@@ -14,6 +14,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -30,3 +31,4 @@ Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
     return out;
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/canny.hpp b/src/backend/cuda/canny.hpp
index bbd90a9ca2..7f8142493b 100644
--- a/src/backend/cuda/canny.hpp
+++ b/src/backend/cuda/canny.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -17,3 +18,4 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
 Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
                                      const Array<char>& weak);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index cfcc9a8042..9328dd5052 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -17,6 +17,7 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename To, typename Ti>
@@ -84,3 +85,4 @@ struct CastOp<unsigned char, common::half> {
 #undef CAST_CFN
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cholesky.cpp b/src/backend/cuda/cholesky.cpp
index 2757d50e26..7c48dbb40c 100644
--- a/src/backend/cuda/cholesky.cpp
+++ b/src/backend/cuda/cholesky.cpp
@@ -21,6 +21,7 @@
 #include <common/err_common.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // cusolverStatus_t cusolverDn<>potrf_bufferSize(
@@ -124,3 +125,4 @@ INSTANTIATE_CH(cfloat)
 INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cholesky.hpp b/src/backend/cuda/cholesky.hpp
index 82bfcc3580..4a97aab757 100644
--- a/src/backend/cuda/cholesky.hpp
+++ b/src/backend/cuda/cholesky.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
@@ -16,3 +17,4 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index de22e8c493..3fddb93d95 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -62,8 +62,12 @@
 #include <utility>
 #include <vector>
 
-using namespace cuda;
-
+using arrayfire::common::getCacheDirectory;
+using arrayfire::common::makeTempFilename;
+using arrayfire::common::removeFile;
+using arrayfire::common::renameFile;
+using arrayfire::cuda::getComputeCapability;
+using arrayfire::cuda::getDeviceProp;
 using detail::Module;
 using nonstd::span;
 using std::accumulate;
@@ -127,7 +131,8 @@ constexpr size_t linkLogSize = 2048;
     } while (0)
 
 spdlog::logger *getLogger() {
-    static std::shared_ptr<spdlog::logger> logger(common::loggerFactory("jit"));
+    static std::shared_ptr<spdlog::logger> logger(
+        arrayfire::common::loggerFactory("jit"));
     return logger.get();
 }
 
@@ -140,12 +145,14 @@ string getKernelCacheFilename(const int device, const string &key) {
            to_string(AF_API_VERSION_CURRENT) + ".bin";
 }
 
+namespace arrayfire {
 namespace common {
 
 Module compileModule(const string &moduleKey, span<const string> sources,
                      span<const string> opts, span<const string> kInstances,
                      const bool sourceIsJIT) {
     nvrtcProgram prog;
+    using namespace arrayfire::cuda;
     if (sourceIsJIT) {
         constexpr const char *header_names[] = {
             "utility",
@@ -252,8 +259,8 @@ Module compileModule(const string &moduleKey, span<const string> sources,
                                        includeNames));
     }
 
-    int device       = cuda::getActiveDeviceId();
-    auto computeFlag = cuda::getComputeCapability(device);
+    int device       = getActiveDeviceId();
+    auto computeFlag = getComputeCapability(device);
     array<char, 32> arch;
     snprintf(arch.data(), arch.size(), "--gpu-architecture=compute_%d%d",
              computeFlag.first, computeFlag.second);
@@ -482,8 +489,8 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
     return retVal;
 }
 
-Kernel getKernel(const Module &mod, const string &nameExpr,
-                 const bool sourceWasJIT) {
+arrayfire::cuda::Kernel getKernel(const Module &mod, const string &nameExpr,
+                                  const bool sourceWasJIT) {
     std::string name  = (sourceWasJIT ? nameExpr : mod.mangledName(nameExpr));
     CUfunction kernel = nullptr;
     CU_CHECK(cuModuleGetFunction(&kernel, mod.get(), name.c_str()));
@@ -491,3 +498,4 @@ Kernel getKernel(const Module &mod, const string &nameExpr,
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index 68b5313150..d9d143ddbf 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -14,6 +14,7 @@
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename To, typename Ti>
 Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
@@ -87,3 +88,4 @@ Array<T> conj(const Array<T> &in) {
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 2fe0b8d653..3a33c6f64f 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -18,10 +18,11 @@
 #include <type_traits>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using std::conditional;
 using std::is_same;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename accT>
@@ -103,3 +104,4 @@ INSTANTIATE(intl, float)
 #undef INSTANTIATE
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/convolve.hpp b/src/backend/cuda/convolve.hpp
index 636031b30d..b7faa73f00 100644
--- a/src/backend/cuda/convolve.hpp
+++ b/src/backend/cuda/convolve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename accT>
@@ -37,3 +38,4 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 075817925e..47dbe634cb 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -33,16 +33,17 @@
 #include <vector>
 
 using af::dim4;
-using common::flip;
-using common::half;
-using common::make_handle;
-using common::modDims;
+using arrayfire::common::flip;
+using arrayfire::common::half;
+using arrayfire::common::make_handle;
+using arrayfire::common::modDims;
 using std::conditional;
 using std::is_same;
 using std::pair;
 using std::tie;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 
 #ifdef WITH_CUDNN
@@ -536,3 +537,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index dbcf1284fe..f8472a7dfb 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -16,9 +16,10 @@
 #include <kernel/memcopy.hpp>
 #include <math.hpp>
 
-using common::half;
-using common::is_complex;
+using arrayfire::common::half;
+using arrayfire::common::is_complex;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -26,7 +27,7 @@ void copyData(T *data, const Array<T> &src) {
     if (src.elements() > 0) {
         Array<T> lin = src.isReady() && src.isLinear() ? src : copyArray(src);
         // out is now guaranteed linear
-        auto stream = cuda::getActiveStream();
+        auto stream = getActiveStream();
         CUDA_CHECK(cudaMemcpyAsync(data, lin.get(), lin.elements() * sizeof(T),
                                    cudaMemcpyDeviceToHost, stream));
         CUDA_CHECK(cudaStreamSynchronize(stream));
@@ -76,7 +77,7 @@ struct copyWrapper<T, T> {
                     if (dst.isLinear() && src.isLinear()) {
                         CUDA_CHECK(cudaMemcpyAsync(
                             dst.get(), src.get(), src.elements() * sizeof(T),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                            cudaMemcpyDeviceToDevice, getActiveStream()));
                     } else {
                         kernel::memcopy<T>(dst, src, src.ndims());
                     }
@@ -173,9 +174,8 @@ template<typename T>
 T getScalar(const Array<T> &src) {
     T retVal{};
     CUDA_CHECK(cudaMemcpyAsync(&retVal, src.get(), sizeof(T),
-                               cudaMemcpyDeviceToHost,
-                               cuda::getActiveStream()));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+                               cudaMemcpyDeviceToHost, getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
     return retVal;
 }
 
@@ -196,3 +196,4 @@ INSTANTIATE_GETSCALAR(ushort)
 INSTANTIATE_GETSCALAR(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/copy.hpp b/src/backend/cuda/copy.hpp
index 143e6f0888..454e50679e 100644
--- a/src/backend/cuda/copy.hpp
+++ b/src/backend/cuda/copy.hpp
@@ -10,6 +10,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 // Copies(blocking) data from an Array<T> object to a contiguous host side
 // pointer.
@@ -60,3 +61,4 @@ void multiply_inplace(Array<T> &in, double val);
 template<typename T>
 T getScalar(const Array<T> &in);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/count.cu b/src/backend/cuda/count.cu
index c15c543cdb..373def999c 100644
--- a/src/backend/cuda/count.cu
+++ b/src/backend/cuda/count.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // count
 INSTANTIATE(af_notzero_t, float, uint)
@@ -28,3 +29,4 @@ INSTANTIATE(af_notzero_t, char, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, half, uint)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cublas.cpp b/src/backend/cuda/cublas.cpp
index 4f024b8117..31111deda4 100644
--- a/src/backend/cuda/cublas.cpp
+++ b/src/backend/cuda/cublas.cpp
@@ -12,6 +12,7 @@
 #include <common/err_common.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 const char* errorString(cublasStatus_t err) {
     switch (err) {
@@ -32,3 +33,4 @@ const char* errorString(cublasStatus_t err) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cublas.hpp b/src/backend/cuda/cublas.hpp
index da93d41791..d0611263d8 100644
--- a/src/backend/cuda/cublas.hpp
+++ b/src/backend/cuda/cublas.hpp
@@ -15,6 +15,7 @@
 
 DEFINE_HANDLER(cublasHandle_t, cublasCreate, cublasDestroy);
 
+namespace arrayfire {
 namespace cuda {
 
 const char* errorString(cublasStatus_t err);
@@ -25,9 +26,10 @@ const char* errorString(cublasStatus_t err);
         if (_error != CUBLAS_STATUS_SUCCESS) {                              \
             char _err_msg[1024];                                            \
             snprintf(_err_msg, sizeof(_err_msg), "CUBLAS Error (%d): %s\n", \
-                     (int)(_error), cuda::errorString(_error));             \
+                     (int)(_error), arrayfire::cuda::errorString(_error));  \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                            \
         }                                                                   \
     } while (0)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cudaDataType.hpp b/src/backend/cuda/cudaDataType.hpp
index 4e1d874e97..1da3429e60 100644
--- a/src/backend/cuda/cudaDataType.hpp
+++ b/src/backend/cuda/cudaDataType.hpp
@@ -13,6 +13,7 @@
 #include <library_types.h>  // cudaDataType enum
 #include <types.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -66,3 +67,4 @@ inline cudaDataType_t getComputeType<common::half>() {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index f75769d8f6..aa5ffd2db4 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -12,6 +12,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 const char *errorString(cudnnStatus_t err) {
@@ -297,3 +298,4 @@ cudnnStatus_t cudnnConvolutionBackwardFilter(
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cudnn.hpp b/src/backend/cuda/cudnn.hpp
index 4fae40692e..5cd8f5f7e6 100644
--- a/src/backend/cuda/cudnn.hpp
+++ b/src/backend/cuda/cudnn.hpp
@@ -16,15 +16,16 @@
 #include <af/dim4.hpp>
 
 // clang-format off
-DEFINE_HANDLER(cudnnHandle_t, cuda::getCudnnPlugin().cudnnCreate, cuda::getCudnnPlugin().cudnnDestroy);
+DEFINE_HANDLER(cudnnHandle_t, arrayfire::cuda::getCudnnPlugin().cudnnCreate, arrayfire::cuda::getCudnnPlugin().cudnnDestroy);
 
-DEFINE_HANDLER(cudnnTensorDescriptor_t, cuda::getCudnnPlugin().cudnnCreateTensorDescriptor, cuda::getCudnnPlugin().cudnnDestroyTensorDescriptor);
+DEFINE_HANDLER(cudnnTensorDescriptor_t, arrayfire::cuda::getCudnnPlugin().cudnnCreateTensorDescriptor, arrayfire::cuda::getCudnnPlugin().cudnnDestroyTensorDescriptor);
 
-DEFINE_HANDLER(cudnnFilterDescriptor_t, cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
+DEFINE_HANDLER(cudnnFilterDescriptor_t, arrayfire::cuda::getCudnnPlugin().cudnnCreateFilterDescriptor, arrayfire::cuda::getCudnnPlugin().cudnnDestroyFilterDescriptor);
 
-DEFINE_HANDLER(cudnnConvolutionDescriptor_t, cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
+DEFINE_HANDLER(cudnnConvolutionDescriptor_t, arrayfire::cuda::getCudnnPlugin().cudnnCreateConvolutionDescriptor, arrayfire::cuda::getCudnnPlugin().cudnnDestroyConvolutionDescriptor);
 // clang-format on
 
+namespace arrayfire {
 namespace cuda {
 
 const char *errorString(cudnnStatus_t err);
@@ -184,3 +185,4 @@ cudnnStatus_t cudnnConvolutionBackwardFilter(
     const cudnnFilterDescriptor_t dwDesc, void *dw);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 4a2f3e792c..596516bbe5 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -18,11 +18,12 @@
 #include <string>
 #include <tuple>
 
-using common::int_version_to_string;
-using common::Version;
+using arrayfire::common::int_version_to_string;
+using arrayfire::common::Version;
 using std::make_tuple;
 using std::string;
 
+namespace arrayfire {
 namespace cuda {
 
 // clang-format off
@@ -165,3 +166,4 @@ cudnnModule& getCudnnPlugin() noexcept {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index aafefa6b84..54c4b3b708 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -61,6 +61,7 @@ cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
     cudnnConvolutionBwdFilterAlgo_t* algo);
 #endif
 
+namespace arrayfire {
 namespace cuda {
 
 class cudnnModule {
@@ -111,3 +112,4 @@ class cudnnModule {
 cudnnModule& getCudnnPlugin() noexcept;
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cufft.cu b/src/backend/cuda/cufft.cu
index 9dd976e9fe..69d7229b6b 100644
--- a/src/backend/cuda/cufft.cu
+++ b/src/backend/cuda/cufft.cu
@@ -12,6 +12,7 @@
 #include <memory.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 const char *_cufftGetResultString(cufftResult res) {
     switch (res) {
@@ -94,7 +95,7 @@ SharedPlan findPlan(int rank, int *n, int *inembed, int istride, int idist,
     sprintf(key_str_temp, "%d:%d", (int)type, batch);
     key_string.append(std::string(key_str_temp));
 
-    PlanCache &planner = cuda::fftManager();
+    PlanCache &planner = arrayfire::cuda::fftManager();
     SharedPlan retVal  = planner.find(key_string);
 
     if (retVal) return retVal;
@@ -105,7 +106,7 @@ SharedPlan findPlan(int rank, int *n, int *inembed, int istride, int idist,
 
     // If plan creation fails, clean up the memory we hold on to and try again
     if (res != CUFFT_SUCCESS) {
-        cuda::signalMemoryCleanup();
+        arrayfire::cuda::signalMemoryCleanup();
         CUFFT_CHECK(cufftPlanMany(temp, rank, n, inembed, istride, idist,
                                   onembed, ostride, odist, type, batch));
     }
@@ -120,3 +121,4 @@ SharedPlan findPlan(int rank, int *n, int *inembed, int istride, int idist,
     return retVal;
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cufft.hpp b/src/backend/cuda/cufft.hpp
index 937af94759..80ba06c8f5 100644
--- a/src/backend/cuda/cufft.hpp
+++ b/src/backend/cuda/cufft.hpp
@@ -17,6 +17,7 @@
 
 DEFINE_HANDLER(cufftHandle, cufftCreate, cufftDestroy);
 
+namespace arrayfire {
 namespace cuda {
 
 typedef cufftHandle PlanType;
@@ -35,16 +36,17 @@ class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
 };
 
 }  // namespace cuda
-
-#define CUFFT_CHECK(fn)                                           \
-    do {                                                          \
-        cufftResult _cufft_res = fn;                              \
-        if (_cufft_res != CUFFT_SUCCESS) {                        \
-            char cufft_res_msg[1024];                             \
-            snprintf(cufft_res_msg, sizeof(cufft_res_msg),        \
-                     "cuFFT Error (%d): %s\n", (int)(_cufft_res), \
-                     cuda::_cufftGetResultString(_cufft_res));    \
-                                                                  \
-            AF_ERROR(cufft_res_msg, AF_ERR_INTERNAL);             \
-        }                                                         \
+}  // namespace arrayfire
+
+#define CUFFT_CHECK(fn)                                                   \
+    do {                                                                  \
+        cufftResult _cufft_res = fn;                                      \
+        if (_cufft_res != CUFFT_SUCCESS) {                                \
+            char cufft_res_msg[1024];                                     \
+            snprintf(cufft_res_msg, sizeof(cufft_res_msg),                \
+                     "cuFFT Error (%d): %s\n", (int)(_cufft_res),         \
+                     arrayfire::cuda::_cufftGetResultString(_cufft_res)); \
+                                                                          \
+            AF_ERROR(cufft_res_msg, AF_ERR_INTERNAL);                     \
+        }                                                                 \
     } while (0)
diff --git a/src/backend/cuda/cusolverDn.cpp b/src/backend/cuda/cusolverDn.cpp
index afe88d3374..3cbfec6898 100644
--- a/src/backend/cuda/cusolverDn.cpp
+++ b/src/backend/cuda/cusolverDn.cpp
@@ -13,6 +13,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 const char *errorString(cusolverStatus_t err) {
     switch (err) {
@@ -42,3 +43,4 @@ const char *errorString(cusolverStatus_t err) {
     }
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cusolverDn.hpp b/src/backend/cuda/cusolverDn.hpp
index e643934930..e9edab58b5 100644
--- a/src/backend/cuda/cusolverDn.hpp
+++ b/src/backend/cuda/cusolverDn.hpp
@@ -14,6 +14,7 @@
 
 DEFINE_HANDLER(cusolverDnHandle_t, cusolverDnCreate, cusolverDnDestroy);
 
+namespace arrayfire {
 namespace cuda {
 
 const char* errorString(cusolverStatus_t err);
@@ -24,10 +25,11 @@ const char* errorString(cusolverStatus_t err);
         if (_error != CUSOLVER_STATUS_SUCCESS) {                              \
             char _err_msg[1024];                                              \
             snprintf(_err_msg, sizeof(_err_msg), "CUSOLVER Error (%d): %s\n", \
-                     (int)(_error), cuda::errorString(_error));               \
+                     (int)(_error), arrayfire::cuda::errorString(_error));    \
                                                                               \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
         }                                                                     \
     } while (0)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cusparse.cpp b/src/backend/cuda/cusparse.cpp
index a2471d6267..224d798327 100644
--- a/src/backend/cuda/cusparse.cpp
+++ b/src/backend/cuda/cusparse.cpp
@@ -12,6 +12,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 const char* errorString(cusparseStatus_t err) {
     switch (err) {
@@ -38,3 +39,4 @@ const char* errorString(cusparseStatus_t err) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index b7a332a856..467b2a82ec 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -16,15 +16,16 @@
 #include <cusparse_v2.h>
 
 // clang-format off
-DEFINE_HANDLER(cusparseHandle_t, cuda::getCusparsePlugin().cusparseCreate, cuda::getCusparsePlugin().cusparseDestroy);
-DEFINE_HANDLER(cusparseMatDescr_t, cuda::getCusparsePlugin().cusparseCreateMatDescr, cuda::getCusparsePlugin().cusparseDestroyMatDescr);
+DEFINE_HANDLER(cusparseHandle_t, arrayfire::cuda::getCusparsePlugin().cusparseCreate, arrayfire::cuda::getCusparsePlugin().cusparseDestroy);
+DEFINE_HANDLER(cusparseMatDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateMatDescr, arrayfire::cuda::getCusparsePlugin().cusparseDestroyMatDescr);
 #if defined(AF_USE_NEW_CUSPARSE_API)
-DEFINE_HANDLER(cusparseSpMatDescr_t, cuda::getCusparsePlugin().cusparseCreateCsr, cuda::getCusparsePlugin().cusparseDestroySpMat);
-DEFINE_HANDLER(cusparseDnVecDescr_t, cuda::getCusparsePlugin().cusparseCreateDnVec, cuda::getCusparsePlugin().cusparseDestroyDnVec);
-DEFINE_HANDLER(cusparseDnMatDescr_t, cuda::getCusparsePlugin().cusparseCreateDnMat, cuda::getCusparsePlugin().cusparseDestroyDnMat);
+DEFINE_HANDLER(cusparseSpMatDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateCsr, arrayfire::cuda::getCusparsePlugin().cusparseDestroySpMat);
+DEFINE_HANDLER(cusparseDnVecDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateDnVec, arrayfire::cuda::getCusparsePlugin().cusparseDestroyDnVec);
+DEFINE_HANDLER(cusparseDnMatDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateDnMat, arrayfire::cuda::getCusparsePlugin().cusparseDestroyDnMat);
 #endif
 // clang-format on
 
+namespace arrayfire {
 namespace cuda {
 
 const char* errorString(cusparseStatus_t err);
@@ -35,10 +36,11 @@ const char* errorString(cusparseStatus_t err);
         if (_error != CUSPARSE_STATUS_SUCCESS) {                              \
             char _err_msg[1024];                                              \
             snprintf(_err_msg, sizeof(_err_msg), "CUSPARSE Error (%d): %s\n", \
-                     (int)(_error), cuda::errorString(_error));               \
+                     (int)(_error), arrayfire::cuda::errorString(_error));    \
                                                                               \
             AF_ERROR(_err_msg, AF_ERR_INTERNAL);                              \
         }                                                                     \
     } while (0)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cusparseModule.cpp b/src/backend/cuda/cusparseModule.cpp
index e7b8105221..bc049fcb01 100644
--- a/src/backend/cuda/cusparseModule.cpp
+++ b/src/backend/cuda/cusparseModule.cpp
@@ -15,6 +15,7 @@
 #include <cuda.h>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 
 cusparseModule::cusparseModule()
@@ -133,3 +134,4 @@ cusparseModule& getCusparsePlugin() noexcept {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cusparseModule.hpp b/src/backend/cuda/cusparseModule.hpp
index 57878c2cf8..ac7e826a13 100644
--- a/src/backend/cuda/cusparseModule.hpp
+++ b/src/backend/cuda/cusparseModule.hpp
@@ -13,9 +13,10 @@
 #include <cuda.h>
 #include <cusparse_v2.h>
 
+namespace arrayfire {
 namespace cuda {
 class cusparseModule {
-    common::DependencyModule module;
+    arrayfire::common::DependencyModule module;
 
    public:
     cusparseModule();
@@ -94,3 +95,4 @@ class cusparseModule {
 cusparseModule& getCusparsePlugin() noexcept;
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/cusparse_descriptor_helpers.hpp b/src/backend/cuda/cusparse_descriptor_helpers.hpp
index 3e94f89f47..41e369b0d8 100644
--- a/src/backend/cuda/cusparse_descriptor_helpers.hpp
+++ b/src/backend/cuda/cusparse_descriptor_helpers.hpp
@@ -17,6 +17,7 @@
 
 #include <utility>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -44,5 +45,6 @@ auto denMatDescriptor(const Array<T> &in) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/cuda/debug_cuda.hpp b/src/backend/cuda/debug_cuda.hpp
index 25f266c268..555944a5ed 100644
--- a/src/backend/cuda/debug_cuda.hpp
+++ b/src/backend/cuda/debug_cuda.hpp
@@ -13,6 +13,7 @@
 #include <platform.hpp>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel_logger {
 
@@ -22,6 +23,7 @@ inline auto getLogger() {
 }
 }  // namespace kernel_logger
 }  // namespace cuda
+}  // namespace arrayfire
 
 template<>
 struct fmt::formatter<dim3> : fmt::formatter<std::string> {
@@ -33,16 +35,17 @@ struct fmt::formatter<dim3> : fmt::formatter<std::string> {
     }
 };
 
-#define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...)                     \
-    do {                                                                      \
-        {                                                                     \
-            using namespace cuda::kernel_logger;                              \
-            AF_TRACE(                                                         \
-                "Launching {}: Blocks: [{}] Threads: [{}] "                   \
-                "Shared Memory: {}",                                          \
-                #fn, blks, thrds, smem_size);                                 \
-        }                                                                     \
-        fn<<<blks, thrds, smem_size, cuda::getActiveStream()>>>(__VA_ARGS__); \
+#define CUDA_LAUNCH_SMEM(fn, blks, thrds, smem_size, ...)                   \
+    do {                                                                    \
+        {                                                                   \
+            using namespace arrayfire::cuda::kernel_logger;                 \
+            AF_TRACE(                                                       \
+                "Launching {}: Blocks: [{}] Threads: [{}] "                 \
+                "Shared Memory: {}",                                        \
+                #fn, blks, thrds, smem_size);                               \
+        }                                                                   \
+        fn<<<blks, thrds, smem_size, arrayfire::cuda::getActiveStream()>>>( \
+            __VA_ARGS__);                                                   \
     } while (false)
 
 #define CUDA_LAUNCH(fn, blks, thrds, ...) \
@@ -51,18 +54,21 @@ struct fmt::formatter<dim3> : fmt::formatter<std::string> {
 // FIXME: Add a special flag for debug
 #ifndef NDEBUG
 
-#define POST_LAUNCH_CHECK() \
-    do { CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); } while (0)
+#define POST_LAUNCH_CHECK()                                                    \
+    do {                                                                       \
+        CUDA_CHECK(cudaStreamSynchronize(arrayfire::cuda::getActiveStream())); \
+    } while (0)
 
 #else
 
-#define POST_LAUNCH_CHECK()                                             \
-    do {                                                                \
-        if (cuda::synchronize_calls()) {                                \
-            CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        } else {                                                        \
-            CUDA_CHECK(cudaPeekAtLastError());                          \
-        }                                                               \
+#define POST_LAUNCH_CHECK()                                                 \
+    do {                                                                    \
+        if (arrayfire::cuda::synchronize_calls()) {                         \
+            CUDA_CHECK(                                                     \
+                cudaStreamSynchronize(arrayfire::cuda::getActiveStream())); \
+        } else {                                                            \
+            CUDA_CHECK(cudaPeekAtLastError());                              \
+        }                                                                   \
     } while (0)
 
 #endif
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 4b946a7fee..5f79b00abf 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -46,8 +46,8 @@
 #include <thread>
 #include <utility>
 
-using common::getEnvVar;
-using common::int_version_to_string;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::int_version_to_string;
 using std::begin;
 using std::end;
 using std::find;
@@ -57,6 +57,7 @@ using std::pair;
 using std::string;
 using std::stringstream;
 
+namespace arrayfire {
 namespace cuda {
 
 struct cuNVRTCcompute {
@@ -380,7 +381,7 @@ void DeviceManager::setMemoryManager(
     memManager = std::move(newMgr);
     // Set the backend memory manager for this new manager to register native
     // functions correctly.
-    std::unique_ptr<cuda::Allocator> deviceMemoryManager(new cuda::Allocator());
+    std::unique_ptr<cuda::Allocator> deviceMemoryManager(new Allocator());
     memManager->setAllocator(std::move(deviceMemoryManager));
     memManager->initialize();
 }
@@ -407,7 +408,7 @@ void DeviceManager::setMemoryManagerPinned(
     // functions correctly.
     pinnedMemManager = std::move(newMgr);
     std::unique_ptr<cuda::AllocatorPinned> deviceMemoryManager(
-        new cuda::AllocatorPinned());
+        new AllocatorPinned());
     pinnedMemManager->setAllocator(std::move(deviceMemoryManager));
     pinnedMemManager->initialize();
 }
@@ -547,7 +548,7 @@ DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , cuDevices(0)
     , nDevices(0)
-    , fgMngr(new graphics::ForgeManager()) {
+    , fgMngr(new arrayfire::common::ForgeManager()) {
     try {
         checkCudaVsDriverVersion();
 
@@ -726,3 +727,4 @@ int DeviceManager::setActiveDevice(int device, int nId) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index 5ea6d3a2f6..9275386011 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -17,12 +17,13 @@
 #include <utility>
 #include <vector>
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
 #ifndef AF_CUDA_MEM_DEBUG
 #define AF_CUDA_MEM_DEBUG 0
 #endif
 
+namespace arrayfire {
 namespace cuda {
 
 struct cudaDevice_t {
@@ -66,7 +67,7 @@ class DeviceManager {
 
     void resetMemoryManagerPinned();
 
-    friend graphics::ForgeManager& forgeManager();
+    friend arrayfire::common::ForgeManager& forgeManager();
 
     friend GraphicsResourceManager& interopManager();
 
@@ -122,7 +123,7 @@ class DeviceManager {
     int nDevices;
     cudaStream_t streams[MAX_DEVICES]{};
 
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<arrayfire::common::ForgeManager> fgMngr;
 
     std::unique_ptr<MemoryManagerBase> memManager;
 
@@ -134,3 +135,4 @@ class DeviceManager {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/diagonal.cpp b/src/backend/cuda/diagonal.cpp
index 2a2f07b594..cbf3180a70 100644
--- a/src/backend/cuda/diagonal.cpp
+++ b/src/backend/cuda/diagonal.cpp
@@ -15,8 +15,9 @@
 #include <math.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
@@ -59,3 +60,4 @@ INSTANTIATE_DIAGONAL(ushort)
 INSTANTIATE_DIAGONAL(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/diagonal.hpp b/src/backend/cuda/diagonal.hpp
index c6e2aff5fd..a1a9828a2a 100644
--- a/src/backend/cuda/diagonal.hpp
+++ b/src/backend/cuda/diagonal.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num);
@@ -16,3 +17,4 @@ Array<T> diagCreate(const Array<T> &in, const int num);
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/diff.cpp b/src/backend/cuda/diff.cpp
index f67a0eabda..55bb68ece0 100644
--- a/src/backend/cuda/diff.cpp
+++ b/src/backend/cuda/diff.cpp
@@ -13,6 +13,7 @@
 #include <kernel/diff.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -60,3 +61,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/diff.hpp b/src/backend/cuda/diff.hpp
index 30ac6661e9..c2b4900862 100644
--- a/src/backend/cuda/diff.hpp
+++ b/src/backend/cuda/diff.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim);
@@ -16,3 +17,4 @@ Array<T> diff1(const Array<T> &in, const int dim);
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/dims_param.hpp b/src/backend/cuda/dims_param.hpp
index 3692a68838..273eaf13cb 100644
--- a/src/backend/cuda/dims_param.hpp
+++ b/src/backend/cuda/dims_param.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cuda {
 
 typedef struct {
@@ -16,3 +17,4 @@ typedef struct {
 } dims_t;
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/exampleFunction.cpp b/src/backend/cuda/exampleFunction.cpp
index f4b7a7fc8f..b94f9f8e54 100644
--- a/src/backend/cuda/exampleFunction.cpp
+++ b/src/backend/cuda/exampleFunction.cpp
@@ -26,6 +26,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -65,3 +66,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/exampleFunction.hpp b/src/backend/cuda/exampleFunction.hpp
index b0c20927ab..d0e9938dda 100644
--- a/src/backend/cuda/exampleFunction.hpp
+++ b/src/backend/cuda/exampleFunction.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
                          const af_someenum_t method);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index d4f00274bc..7744d4b6d6 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -19,6 +19,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -66,3 +67,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fast.hpp b/src/backend/cuda/fast.hpp
index 84f509c5aa..d60c671634 100644
--- a/src/backend/cuda/fast.hpp
+++ b/src/backend/cuda/fast.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -20,4 +21,5 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const bool non_max, const float feature_ratio,
               const unsigned edge);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fast_pyramid.cpp b/src/backend/cuda/fast_pyramid.cpp
index 8d14cf752c..97228af248 100644
--- a/src/backend/cuda/fast_pyramid.cpp
+++ b/src/backend/cuda/fast_pyramid.cpp
@@ -18,6 +18,7 @@
 using af::dim4;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -124,3 +125,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fast_pyramid.hpp b/src/backend/cuda/fast_pyramid.hpp
index ceac076d95..af8e902ea2 100644
--- a/src/backend/cuda/fast_pyramid.hpp
+++ b/src/backend/cuda/fast_pyramid.hpp
@@ -13,6 +13,7 @@
 
 #include <vector>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void fast_pyramid(std::vector<unsigned> &feat_pyr,
@@ -23,4 +24,5 @@ void fast_pyramid(std::vector<unsigned> &feat_pyr,
                   const float fast_thr, const unsigned max_feat,
                   const float scl_fctr, const unsigned levels,
                   const unsigned patch_size);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fft.cu b/src/backend/cuda/fft.cu
index 4254b719bf..800e6571d2 100644
--- a/src/backend/cuda/fft.cu
+++ b/src/backend/cuda/fft.cu
@@ -23,6 +23,7 @@ using af::dim4;
 using std::array;
 using std::string;
 
+namespace arrayfire {
 namespace cuda {
 void setFFTPlanCacheSize(size_t numPlans) {
     fftManager().setMaxCacheSize(numPlans);
@@ -84,7 +85,7 @@ void fft_inplace(Array<T> &in, const int rank, const bool direction) {
                  (cufftType)cufft_transform<T>::type, batch);
 
     cufft_transform<T> transform;
-    CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
+    CUFFT_CHECK(cufftSetStream(*plan.get(), getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (T *)in.get(), in.get(),
                           direction ? CUFFT_FORWARD : CUFFT_INVERSE));
 }
@@ -114,7 +115,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
                  (cufftType)cufft_real_transform<Tc, Tr>::type, batch);
 
     cufft_real_transform<Tc, Tr> transform;
-    CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
+    CUFFT_CHECK(cufftSetStream(*plan.get(), getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (Tr *)in.get(), out.get()));
     return out;
 }
@@ -140,7 +141,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
                  istrides[rank], out_embed.data(), ostrides[0], ostrides[rank],
                  (cufftType)cufft_real_transform<Tr, Tc>::type, batch);
 
-    CUFFT_CHECK(cufftSetStream(*plan.get(), cuda::getActiveStream()));
+    CUFFT_CHECK(cufftSetStream(*plan.get(), getActiveStream()));
     CUFFT_CHECK(transform(*plan.get(), (Tc *)in.get(), out.get()));
     return out;
 }
@@ -159,3 +160,4 @@ INSTANTIATE(cdouble)
 INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fft.hpp b/src/backend/cuda/fft.hpp
index c9ff79877a..5cc2bf42e4 100644
--- a/src/backend/cuda/fft.hpp
+++ b/src/backend/cuda/fft.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 void setFFTPlanCacheSize(size_t numPlans);
@@ -23,3 +24,4 @@ template<typename Tr, typename Tc>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index 36a449256a..7c50c0838c 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -21,6 +21,7 @@ using std::conditional;
 using std::is_integral;
 using std::is_same;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -117,3 +118,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/fftconvolve.hpp b/src/backend/cuda/fftconvolve.hpp
index f7cf19a199..c158bdaa3d 100644
--- a/src/backend/cuda/fftconvolve.hpp
+++ b/src/backend/cuda/fftconvolve.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/flood_fill.cpp b/src/backend/cuda/flood_fill.cpp
index 1442ba2619..2165f8a6c8 100644
--- a/src/backend/cuda/flood_fill.cpp
+++ b/src/backend/cuda/flood_fill.cpp
@@ -12,6 +12,7 @@
 #include <err_cuda.hpp>
 #include <kernel/flood_fill.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -36,3 +37,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/flood_fill.hpp b/src/backend/cuda/flood_fill.hpp
index b4d432feec..6716abeae7 100644
--- a/src/backend/cuda/flood_fill.hpp
+++ b/src/backend/cuda/flood_fill.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
@@ -19,3 +20,4 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
                    const T lowValue, const T highValue,
                    const af::connectivity nlookup = AF_CONNECTIVITY_8);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/gradient.cpp b/src/backend/cuda/gradient.cpp
index 0fdd4941ee..b7274a736f 100644
--- a/src/backend/cuda/gradient.cpp
+++ b/src/backend/cuda/gradient.cpp
@@ -16,6 +16,7 @@
 
 #include <stdexcept>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
@@ -31,3 +32,4 @@ INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/gradient.hpp b/src/backend/cuda/gradient.hpp
index 1378fba097..46ff6db000 100644
--- a/src/backend/cuda/gradient.hpp
+++ b/src/backend/cuda/gradient.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/harris.cu b/src/backend/cuda/harris.cu
index 375b9e1570..1c9c9a482c 100644
--- a/src/backend/cuda/harris.cu
+++ b/src/backend/cuda/harris.cu
@@ -16,6 +16,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename convAccT>
@@ -55,3 +56,4 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/harris.hpp b/src/backend/cuda/harris.hpp
index ce51eaf3de..4cf4fc8084 100644
--- a/src/backend/cuda/harris.hpp
+++ b/src/backend/cuda/harris.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
                 const float sigma, const unsigned filter_len,
                 const float k_thr);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index d415a12aad..6678281db6 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -14,11 +14,16 @@
 #include <err_cuda.hpp>
 #include <hist_graphics.hpp>
 
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist) {
-    auto stream = cuda::getActiveStream();
+    auto stream = getActiveStream();
     if (DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = data.get();
 
@@ -36,7 +41,7 @@ void copy_histogram(const Array<T> &data, fg_histogram hist) {
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule &_ = graphics::forgePlugin();
+        ForgeModule &_ = common::forgePlugin();
         unsigned bytes = 0, buffer = 0;
         FG_CHECK(_.fg_get_histogram_vertex_buffer(&buffer, hist));
         FG_CHECK(_.fg_get_histogram_vertex_buffer_size(&bytes, hist));
@@ -67,3 +72,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/hist_graphics.hpp b/src/backend/cuda/hist_graphics.hpp
index 10cae9ae94..348d84ba3c 100644
--- a/src/backend/cuda/hist_graphics.hpp
+++ b/src/backend/cuda/hist_graphics.hpp
@@ -12,9 +12,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/histogram.cpp b/src/backend/cuda/histogram.cpp
index a2680de686..ca7e6ced86 100644
--- a/src/backend/cuda/histogram.cpp
+++ b/src/backend/cuda/histogram.cpp
@@ -15,8 +15,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -48,3 +49,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/histogram.hpp b/src/backend/cuda/histogram.hpp
index b07453f083..f9498d422c 100644
--- a/src/backend/cuda/histogram.hpp
+++ b/src/backend/cuda/histogram.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
                       const double &minval, const double &maxval,
                       const bool isLinear);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/homography.cu b/src/backend/cuda/homography.cu
index b8525dee8e..7b70064902 100644
--- a/src/backend/cuda/homography.cu
+++ b/src/backend/cuda/homography.cu
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 #define RANSACConfidence 0.99f
@@ -64,3 +65,4 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/homography.hpp b/src/backend/cuda/homography.hpp
index 38ad486e93..95c4bdf853 100644
--- a/src/backend/cuda/homography.hpp
+++ b/src/backend/cuda/homography.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -18,4 +19,5 @@ int homography(Array<T> &H, const Array<float> &x_src,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/hsv_rgb.cpp b/src/backend/cuda/hsv_rgb.cpp
index 13d1a95187..d4eda7ef58 100644
--- a/src/backend/cuda/hsv_rgb.cpp
+++ b/src/backend/cuda/hsv_rgb.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -39,3 +40,4 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/hsv_rgb.hpp b/src/backend/cuda/hsv_rgb.hpp
index 7758ce5181..26288245e6 100644
--- a/src/backend/cuda/hsv_rgb.hpp
+++ b/src/backend/cuda/hsv_rgb.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/identity.cpp b/src/backend/cuda/identity.cpp
index 293489c216..995b09a9d9 100644
--- a/src/backend/cuda/identity.cpp
+++ b/src/backend/cuda/identity.cpp
@@ -14,8 +14,9 @@
 #include <debug_cuda.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> identity(const dim4& dims) {
@@ -42,3 +43,4 @@ INSTANTIATE_IDENTITY(ushort)
 INSTANTIATE_IDENTITY(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/identity.hpp b/src/backend/cuda/identity.hpp
index 77b58f6ab7..f03d9f6199 100644
--- a/src/backend/cuda/identity.hpp
+++ b/src/backend/cuda/identity.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> identity(const dim4& dim);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/iir.cpp b/src/backend/cuda/iir.cpp
index 616411805a..63a662b885 100644
--- a/src/backend/cuda/iir.cpp
+++ b/src/backend/cuda/iir.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
@@ -56,3 +57,4 @@ INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/iir.hpp b/src/backend/cuda/iir.hpp
index f2ff082d2a..1ad18333f3 100644
--- a/src/backend/cuda/iir.hpp
+++ b/src/backend/cuda/iir.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index d247322201..810d36d968 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -18,12 +18,16 @@
 #include <image.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image) {
-    auto stream = cuda::getActiveStream();
+    auto stream = getActiveStream();
     if (DeviceManager::checkGraphicsInteropCapability()) {
         auto res = interopManager().getImageResources(image);
 
@@ -39,7 +43,7 @@ void copy_image(const Array<T> &in, fg_image image) {
         POST_LAUNCH_CHECK();
         CheckGL("After cuda resource copy");
     } else {
-        ForgeModule &_ = graphics::forgePlugin();
+        ForgeModule &_ = common::forgePlugin();
         CheckGL("Begin CUDA fallback-resource copy");
         unsigned data_size = 0, buffer = 0;
         FG_CHECK(_.fg_get_image_size(&data_size, image));
@@ -72,3 +76,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/image.hpp b/src/backend/cuda/image.hpp
index e97d78aaa7..2a98743dd4 100644
--- a/src/backend/cuda/image.hpp
+++ b/src/backend/cuda/image.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/index.cpp b/src/backend/cuda/index.cpp
index 0974e71dbb..88a95da73b 100644
--- a/src/backend/cuda/index.cpp
+++ b/src/backend/cuda/index.cpp
@@ -18,8 +18,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -85,3 +86,4 @@ INSTANTIATE(short)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/index.hpp b/src/backend/cuda/index.hpp
index 3a439c9941..5966078eaf 100644
--- a/src/backend/cuda/index.hpp
+++ b/src/backend/cuda/index.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/inverse.cpp b/src/backend/cuda/inverse.cpp
index 22c1ae88b3..db7059d4a9 100644
--- a/src/backend/cuda/inverse.cpp
+++ b/src/backend/cuda/inverse.cpp
@@ -13,6 +13,7 @@
 #include <identity.hpp>
 #include <solve.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -29,3 +30,4 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/inverse.hpp b/src/backend/cuda/inverse.hpp
index 27ba153175..7c662b8cda 100644
--- a/src/backend/cuda/inverse.hpp
+++ b/src/backend/cuda/inverse.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> inverse(const Array<T> &in);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/iota.cpp b/src/backend/cuda/iota.cpp
index f79cb6c492..d9afef41c5 100644
--- a/src/backend/cuda/iota.cpp
+++ b/src/backend/cuda/iota.cpp
@@ -15,8 +15,9 @@
 #include <math.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
@@ -42,3 +43,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/iota.hpp b/src/backend/cuda/iota.hpp
index bbc01a94e8..5232fdddbc 100644
--- a/src/backend/cuda/iota.hpp
+++ b/src/backend/cuda/iota.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/ireduce.cpp b/src/backend/cuda/ireduce.cpp
index abbea5514d..94cd340a66 100644
--- a/src/backend/cuda/ireduce.cpp
+++ b/src/backend/cuda/ireduce.cpp
@@ -19,8 +19,9 @@
 #include <complex>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<af_op_t op, typename T>
@@ -79,3 +80,4 @@ INSTANTIATE(af_max_t, char)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/ireduce.hpp b/src/backend/cuda/ireduce.hpp
index 69f25be476..f65eb863a4 100644
--- a/src/backend/cuda/ireduce.hpp
+++ b/src/backend/cuda/ireduce.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
@@ -22,3 +23,4 @@ void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 4dab53a877..2ffc2f72cf 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -34,17 +34,17 @@
 #include <string>
 #include <vector>
 
-using common::findModule;
-using common::getEnvVar;
-using common::getFuncName;
-using common::half;
-using common::ModdimNode;
-using common::Node;
-using common::Node_ids;
-using common::Node_map_t;
-using common::Node_ptr;
-using common::NodeIterator;
-using common::saveKernel;
+using arrayfire::common::findModule;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::getFuncName;
+using arrayfire::common::half;
+using arrayfire::common::ModdimNode;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ids;
+using arrayfire::common::Node_map_t;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::common::saveKernel;
 
 using std::array;
 using std::equal;
@@ -56,6 +56,7 @@ using std::stringstream;
 using std::to_string;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 using jit::BufferNode;
 
@@ -498,7 +499,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     for (auto& out : outputs) { args.push_back(static_cast<void*>(&out)); }
 
     {
-        using namespace cuda::kernel_logger;
+        using namespace arrayfire::cuda::kernel_logger;
         AF_TRACE(
             "Launching : Dims: [{},{},{},{}] Blocks: [{}] "
             "Threads: [{}] threads: {}",
@@ -564,3 +565,4 @@ template void evalNodes<ushort>(vector<Param<ushort>>& out,
 template void evalNodes<half>(vector<Param<half>>& out,
                               const vector<Node*>& node);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
index 21601f2a03..195353fdd8 100644
--- a/src/backend/cuda/jit/BufferNode.hpp
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -11,12 +11,12 @@
 #include <common/jit/BufferNodeBase.hpp>
 #include "../Param.hpp"
 
+namespace arrayfire {
 namespace cuda {
 namespace jit {
 template<typename T>
 using BufferNode = common::BufferNodeBase<std::shared_ptr<T>, Param<T>>;
-}
-
+}  // namespace jit
 }  // namespace cuda
 
 namespace common {
@@ -32,3 +32,5 @@ bool BufferNodeBase<DataType, ParamType>::operator==(
 }
 
 }  // namespace common
+
+}  // namespace arrayfire
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index cc67ac6996..f675faf4b4 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -16,6 +16,7 @@
 #include <sstream>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 
 namespace {
@@ -104,3 +105,4 @@ inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
 
 }  // namespace
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 7f65773d0a..3eed6f7fb5 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -19,11 +19,12 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
-using common::Node;
-using common::Node_ptr;
+using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -234,3 +235,4 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/join.hpp b/src/backend/cuda/join.hpp
index cf74076b8a..18767feae9 100644
--- a/src/backend/cuda/join.hpp
+++ b/src/backend/cuda/join.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
@@ -16,3 +17,4 @@ Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 template<typename T>
 void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.cuh b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
index cdb5c59121..cd393474aa 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.cuh
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
@@ -10,24 +10,22 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
-__forceinline__ __device__
-int index(const int x, const int y, const int dim0,
-          const int dim1, const int stride0, const int stride1) {
+__forceinline__ __device__ int index(const int x, const int y, const int dim0,
+                                     const int dim1, const int stride0,
+                                     const int stride1) {
     return clamp(x, 0, dim0 - 1) * stride0 + clamp(y, 0, dim1 - 1) * stride1;
 }
 
-__device__
-float quadratic(const float value) { return 1.0 / (1.0 + value); }
+__device__ float quadratic(const float value) { return 1.0 / (1.0 + value); }
 
 template<af_flux_function FluxEnum>
-__device__
-float gradientUpdate(const float mct, const float C,
-                     const float S, const float N,
-                     const float W, const float E,
-                     const float SE, const float SW,
-                     const float NE, const float NW) {
+__device__ float gradientUpdate(const float mct, const float C, const float S,
+                                const float N, const float W, const float E,
+                                const float SE, const float SW, const float NE,
+                                const float NW) {
     float delta = 0;
 
     float dx, dy, df, db, cx, cxd;
@@ -69,11 +67,10 @@ float gradientUpdate(const float mct, const float C,
     return delta;
 }
 
-__device__
-float curvatureUpdate(const float mct, const float C, const float S,
-                      const float N, const float W, const float E,
-                      const float SE, const float SW, const float NE,
-                      const float NW) {
+__device__ float curvatureUpdate(const float mct, const float C, const float S,
+                                 const float N, const float W, const float E,
+                                 const float SE, const float SW, const float NE,
+                                 const float NW) {
     float delta     = 0;
     float prop_grad = 0;
 
@@ -131,11 +128,10 @@ float curvatureUpdate(const float mct, const float C, const float S,
 }
 
 template<typename T, af_flux_function FluxEnum, bool isMCDE>
-__global__
-void diffUpdate(Param<T> inout, const float dt, const float mct,
-                const unsigned blkX, const unsigned blkY) {
-    const unsigned RADIUS = 1;
-    const unsigned SHRD_MEM_WIDTH = THREADS_X + 2 * RADIUS;
+__global__ void diffUpdate(Param<T> inout, const float dt, const float mct,
+                           const unsigned blkX, const unsigned blkY) {
+    const unsigned RADIUS          = 1;
+    const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2 * RADIUS;
     const unsigned SHRD_MEM_HEIGHT = THREADS_Y * YDIM_LOAD + 2 * RADIUS;
 
     __shared__ float shrdMem[SHRD_MEM_HEIGHT][SHRD_MEM_WIDTH];
@@ -152,7 +148,7 @@ void diffUpdate(Param<T> inout, const float dt, const float mct,
     const int b3 = blockIdx.y / blkY;
 
     const int gx = blockDim.x * (blockIdx.x - b2 * blkX) + lx;
-          int gy = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
+    int gy       = blockDim.y * (blockIdx.y - b3 * blkY) + ly;
 
     T* img = (T*)inout.ptr + (b3 * inout.strides[3] + b2 * inout.strides[2]);
 
@@ -162,7 +158,7 @@ void diffUpdate(Param<T> inout, const float dt, const float mct,
 #pragma unroll
         for (int a = lx, gx2 = gx - RADIUS; a < SHRD_MEM_WIDTH;
              a += blockDim.x, gx2 += blockDim.x) {
-            shrdMem[b][a] = img[ index(gx2, gy2, l0, l1, s0, s1) ];
+            shrdMem[b][a] = img[index(gx2, gy2, l0, l1, s0, s1)];
         }
     }
     __syncthreads();
@@ -171,19 +167,19 @@ void diffUpdate(Param<T> inout, const float dt, const float mct,
     int j = ly + RADIUS;
 
 #pragma unroll
-    for (int ld = 0; ld < YDIM_LOAD; ++ld, j+= blockDim.y, gy += blockDim.y) {
-        float C = shrdMem[j][i];
+    for (int ld = 0; ld < YDIM_LOAD; ++ld, j += blockDim.y, gy += blockDim.y) {
+        float C     = shrdMem[j][i];
         float delta = 0.0f;
         if (isMCDE) {
             delta = curvatureUpdate(
-                    mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
-                    shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
-                    shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1]);
+                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1]);
         } else {
             delta = gradientUpdate<FluxEnum>(
-                    mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
-                    shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
-                    shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1]);
+                mct, C, shrdMem[j][i + 1], shrdMem[j][i - 1], shrdMem[j - 1][i],
+                shrdMem[j + 1][i], shrdMem[j + 1][i + 1], shrdMem[j - 1][i + 1],
+                shrdMem[j + 1][i - 1], shrdMem[j - 1][i - 1]);
         }
         if (gy < l1 && gx < l0) {
             img[gx * s0 + gy * s1] = (T)(C + delta * dt);
@@ -191,4 +187,5 @@ void diffUpdate(Param<T> inout, const float dt, const float mct,
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index 1c247bb499..e727d7ca4c 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -16,6 +16,7 @@
 #include <nvrtc_kernel_headers/anisotropic_diffusion_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -27,7 +28,8 @@ template<typename T>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
     auto diffUpdate = common::getKernel(
-        "cuda::diffUpdate", std::array{anisotropic_diffusion_cuh_src},
+        "arrayfire::cuda::diffUpdate",
+        std::array{anisotropic_diffusion_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(fftype),
                      TemplateArg(isMCDE)),
         std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y),
@@ -40,9 +42,8 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
 
     dim3 blocks(blkX * inout.dims[2], blkY * inout.dims[3], 1);
 
-    const int maxBlkY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    const int blkZ = divup(blocks.y, maxBlkY);
+    const int maxBlkY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    const int blkZ    = divup(blocks.y, maxBlkY);
 
     if (blkZ > 1) {
         blocks.y = maxBlkY;
@@ -58,3 +59,4 @@ void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index 66dea16fe6..db705da687 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -15,6 +15,7 @@
 #include <nvrtc_kernel_headers/approx2_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -28,7 +29,7 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, const float offGrid,
              const af::interpType method, const int order) {
     auto approx1 = common::getKernel(
-        "cuda::approx1", std::array{approx1_cuh_src},
+        "arrayfire::cuda::approx1", std::array{approx1_cuh_src},
         TemplateArgs(TemplateTypename<Ty>(), TemplateTypename<Tp>(),
                      TemplateArg(xdim), TemplateArg(order)));
 
@@ -38,10 +39,9 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
 
     bool batch = !(xo.dims[1] == 1 && xo.dims[2] == 1 && xo.dims[3] == 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -57,7 +57,7 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const Tp &yi_beg, const Tp &yi_step, const float offGrid,
              const af::interpType method, const int order) {
     auto approx2 = common::getKernel(
-        "cuda::approx2", std::array{approx2_cuh_src},
+        "arrayfire::cuda::approx2", std::array{approx2_cuh_src},
         TemplateArgs(TemplateTypename<Ty>(), TemplateTypename<Tp>(),
                      TemplateArg(xdim), TemplateArg(ydim), TemplateArg(order)));
 
@@ -68,10 +68,9 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
 
     bool batch = !(xo.dims[2] == 1 && xo.dims[3] == 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -83,3 +82,4 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/approx1.cuh b/src/backend/cuda/kernel/approx1.cuh
index 6ef6a837a4..9ccf95e504 100644
--- a/src/backend/cuda/kernel/approx1.cuh
+++ b/src/backend/cuda/kernel/approx1.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <interp.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Ty, typename Tp, int xdim, int order>
@@ -69,3 +70,4 @@ __global__ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/approx2.cuh b/src/backend/cuda/kernel/approx2.cuh
index 191a4e8919..7d4179643e 100644
--- a/src/backend/cuda/kernel/approx2.cuh
+++ b/src/backend/cuda/kernel/approx2.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <interp.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Ty, typename Tp, int xdim, int ydim, int order>
@@ -74,3 +75,4 @@ __global__ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/assign.cuh b/src/backend/cuda/kernel/assign.cuh
index 102d42ec99..ddf159288b 100644
--- a/src/backend/cuda/kernel/assign.cuh
+++ b/src/backend/cuda/kernel/assign.cuh
@@ -13,12 +13,12 @@
 #include <assign_kernel_param.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
-__global__ void assign(Param<T> out, CParam<T> in,
-                       const cuda::AssignKernelParam p, const int nBBS0,
-                       const int nBBS1) {
+__global__ void assign(Param<T> out, CParam<T> in, const AssignKernelParam p,
+                       const int nBBS0, const int nBBS1) {
     // retrieve index pointers
     // these can be 0 where af_array index is not used
     const uint* ptr0 = p.ptr[0];
@@ -60,3 +60,4 @@ __global__ void assign(Param<T> out, CParam<T> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 523dad2505..75c24e874c 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -14,6 +14,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/assign_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -23,7 +24,7 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
     constexpr int THREADS_Y = 8;
 
     auto assignKer =
-        common::getKernel("cuda::assign", std::array{assign_cuh_src},
+        common::getKernel("arrayfire::cuda::assign", std::array{assign_cuh_src},
                           TemplateArgs(TemplateTypename<T>()));
 
     const dim3 threads(THREADS_X, THREADS_Y);
@@ -33,10 +34,9 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
 
     dim3 blocks(blks_x * in.dims[2], blks_y * in.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -47,3 +47,4 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/atomics.hpp b/src/backend/cuda/kernel/atomics.hpp
index 47ed2f4747..cea1678e59 100644
--- a/src/backend/cuda/kernel/atomics.hpp
+++ b/src/backend/cuda/kernel/atomics.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 template<typename T>
@@ -49,3 +50,4 @@ __device__ cdouble atomicAdd<cdouble>(cdouble *ptr, cdouble val) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/bilateral.cuh b/src/backend/cuda/kernel/bilateral.cuh
index fb618005ac..6fdfbd1a3d 100644
--- a/src/backend/cuda/kernel/bilateral.cuh
+++ b/src/backend/cuda/kernel/bilateral.cuh
@@ -11,28 +11,26 @@
 #include <math.hpp>
 #include <shared.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
-inline __device__
-int lIdx(int x, int y, int stride1, int stride0) {
+inline __device__ int lIdx(int x, int y, int stride1, int stride0) {
     return (y * stride1 + x * stride0);
 }
 
 template<typename inType, typename outType>
-inline __device__
-void load2ShrdMem(outType *shrd, const inType *const in,
-                  int lx, int ly, int shrdStride, int dim0,
-                  int dim1, int gx, int gy, int inStride1,
-                  int inStride0) {
+inline __device__ void load2ShrdMem(outType *shrd, const inType *const in,
+                                    int lx, int ly, int shrdStride, int dim0,
+                                    int dim1, int gx, int gy, int inStride1,
+                                    int inStride0) {
     shrd[ly * shrdStride + lx] = in[lIdx(
         clamp(gx, 0, dim0 - 1), clamp(gy, 0, dim1 - 1), inStride1, inStride0)];
 }
 
 template<typename inType, typename outType>
-__global__
-void bilateral(Param<outType> out, CParam<inType> in,
-               float sigma_space, float sigma_color,
-               int gaussOff, int nBBS0, int nBBS1) {
+__global__ void bilateral(Param<outType> out, CParam<inType> in,
+                          float sigma_space, float sigma_color, int gaussOff,
+                          int nBBS0, int nBBS1) {
     SharedMemory<outType> shared;
     outType *localMem = shared.getPointer();
     outType *gauss2d  = localMem + gaussOff;
@@ -110,4 +108,5 @@ void bilateral(Param<outType> out, CParam<inType> in,
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index 357b57a8bc..cf19eeb97c 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -13,6 +13,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/bilateral_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -23,7 +24,7 @@ template<typename inType, typename outType>
 void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
     auto bilateral = common::getKernel(
-        "cuda::bilateral", std::array{bilateral_cuh_src},
+        "arrayfire::cuda::bilateral", std::array{bilateral_cuh_src},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>()),
         std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
@@ -41,8 +42,7 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
     size_t total_shrd_size =
         sizeof(outType) * (num_shrd_elems + num_gauss_elems);
 
-    size_t MAX_SHRD_SIZE =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock;
+    size_t MAX_SHRD_SIZE = getDeviceProp(getActiveDeviceId()).sharedMemPerBlock;
     if (total_shrd_size > MAX_SHRD_SIZE) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
@@ -60,3 +60,4 @@ void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/canny.cuh b/src/backend/cuda/kernel/canny.cuh
index 27c758d1c4..bdd9ac2217 100644
--- a/src/backend/cuda/kernel/canny.cuh
+++ b/src/backend/cuda/kernel/canny.cuh
@@ -15,17 +15,17 @@
 // the breath first search algorithm
 __device__ int hasChanged = 0;
 
+namespace arrayfire {
 namespace cuda {
 
-__forceinline__ __device__
-int lIdx(int x, int y, int stride0, int stride1) {
+__forceinline__ __device__ int lIdx(int x, int y, int stride0, int stride1) {
     return (x * stride0 + y * stride1);
 }
 
 template<typename T>
-__global__
-void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
-                       CParam<T> dy, unsigned nBBS0, unsigned nBBS1) {
+__global__ void nonMaxSuppression(Param<float> output, CParam<T> in,
+                                  CParam<T> dx, CParam<T> dy, unsigned nBBS0,
+                                  unsigned nBBS1) {
     const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Coloumns
     const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
 
@@ -46,8 +46,7 @@ void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
 
     // Offset input and output pointers to second pixel of second coloumn/row
     // to skip the border
-    const T* mag = (const T*)in.ptr +
-                   (b2 * in.strides[2] + b3 * in.strides[3]);
+    const T* mag = (const T*)in.ptr + (b2 * in.strides[2] + b3 * in.strides[3]);
     const T* dX = (const T*)dx.ptr + (b2 * dx.strides[2] + b3 * dx.strides[3]) +
                   dx.strides[1] + 1;
     const T* dY = (const T*)dy.ptr + (b2 * dy.strides[2] + b3 * dy.strides[3]) +
@@ -63,8 +62,7 @@ void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
 #pragma unroll
         for (int a = lx, gx2 = gx; a < SHRD_MEM_WIDTH && gx2 < in.dims[0];
              a += blockDim.x, gx2 += blockDim.x)
-            shrdMem[b][a] =
-                mag[lIdx(gx2, gy2, in.strides[0], in.strides[1])];
+            shrdMem[b][a] = mag[lIdx(gx2, gy2, in.strides[0], in.strides[1])];
 
     int i = lx + 1;
     int j = ly + 1;
@@ -143,9 +141,8 @@ void nonMaxSuppression(Param<float> output, CParam<T> in, CParam<T> dx,
 }
 
 template<typename T>
-__global__
-void initEdgeOut(Param<T> output, CParam<T> strong, CParam<T> weak,
-                 unsigned nBBS0, unsigned nBBS1) {
+__global__ void initEdgeOut(Param<T> output, CParam<T> strong, CParam<T> weak,
+                            unsigned nBBS0, unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = blockIdx.x / nBBS0;
     const unsigned b3 = blockIdx.y / nBBS1;
@@ -175,8 +172,7 @@ void initEdgeOut(Param<T> output, CParam<T> strong, CParam<T> weak,
      (i) < (SHRD_MEM_WIDTH - 1))
 
 template<typename T>
-__global__
-void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
+__global__ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
     const unsigned SHRD_MEM_WIDTH  = THREADS_X + 2;  // Cols
     const unsigned SHRD_MEM_HEIGHT = THREADS_Y + 2;  // Rows
 
@@ -226,25 +222,24 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
     int continueIter = 1;
 
     while (continueIter) {
-
-      int nw ,no ,ne ,we ,ea ,sw ,so ,se;
-
-      if(outMem[j][i] == WEAK) {
-        nw = outMem[j - 1][i - 1];
-        no = outMem[j - 1][i];
-        ne = outMem[j - 1][i + 1];
-        we = outMem[j    ][i - 1];
-        ea = outMem[j    ][i + 1];
-        sw = outMem[j + 1][i - 1];
-        so = outMem[j + 1][i];
-        se = outMem[j + 1][i + 1];
-
-        bool hasStrongNeighbour =
-            nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
-            se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
-
-        if (hasStrongNeighbour) outMem[j][i] = STRONG;
-      }
+        int nw, no, ne, we, ea, sw, so, se;
+
+        if (outMem[j][i] == WEAK) {
+            nw = outMem[j - 1][i - 1];
+            no = outMem[j - 1][i];
+            ne = outMem[j - 1][i + 1];
+            we = outMem[j][i - 1];
+            ea = outMem[j][i + 1];
+            sw = outMem[j + 1][i - 1];
+            so = outMem[j + 1][i];
+            se = outMem[j + 1][i + 1];
+
+            bool hasStrongNeighbour =
+                nw == STRONG || no == STRONG || ne == STRONG || ea == STRONG ||
+                se == STRONG || so == STRONG || sw == STRONG || we == STRONG;
+
+            if (hasStrongNeighbour) outMem[j][i] = STRONG;
+        }
 
         __syncthreads();
 
@@ -252,17 +247,17 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
         // This search however ignores 1-pixel border encompassing the
         // shared memory tile region.
         bool hasWeakNeighbour = false;
-        if(outMem[j][i] == STRONG) {
-          nw = outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
-          no = outMem[j - 1][i    ] == WEAK && VALID_BLOCK_IDX(j - 1, i);
-          ne = outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
-          we = outMem[j    ][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
-          ea = outMem[j    ][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
-          sw = outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
-          so = outMem[j + 1][i    ] == WEAK && VALID_BLOCK_IDX(j + 1, i);
-          se = outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
-
-          hasWeakNeighbour = nw || no || ne || ea || se || so || sw || we;
+        if (outMem[j][i] == STRONG) {
+            nw = outMem[j - 1][i - 1] == WEAK && VALID_BLOCK_IDX(j - 1, i - 1);
+            no = outMem[j - 1][i] == WEAK && VALID_BLOCK_IDX(j - 1, i);
+            ne = outMem[j - 1][i + 1] == WEAK && VALID_BLOCK_IDX(j - 1, i + 1);
+            we = outMem[j][i - 1] == WEAK && VALID_BLOCK_IDX(j, i - 1);
+            ea = outMem[j][i + 1] == WEAK && VALID_BLOCK_IDX(j, i + 1);
+            sw = outMem[j + 1][i - 1] == WEAK && VALID_BLOCK_IDX(j + 1, i - 1);
+            so = outMem[j + 1][i] == WEAK && VALID_BLOCK_IDX(j + 1, i);
+            se = outMem[j + 1][i + 1] == WEAK && VALID_BLOCK_IDX(j + 1, i + 1);
+
+            hasWeakNeighbour = nw || no || ne || ea || se || so || sw || we;
         }
 
         continueIter = __syncthreads_or(hasWeakNeighbour);
@@ -291,12 +286,13 @@ void edgeTrack(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
 
     // Update output with shared memory result
     if (gx < (output.dims[0] - 2) && gy < (output.dims[1] - 2))
-        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1]) + output.strides[1] + 1] = outMem[j][i];
+        oPtr[lIdx(gx, gy, output.strides[0], output.strides[1]) +
+             output.strides[1] + 1] = outMem[j][i];
 }
 
 template<typename T>
-__global__
-void suppressLeftOver(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
+__global__ void suppressLeftOver(Param<T> output, unsigned nBBS0,
+                                 unsigned nBBS1) {
     // batch offsets for 3rd and 4th dimension
     const unsigned b2 = blockIdx.x / nBBS0;
     const unsigned b3 = blockIdx.y / nBBS1;
@@ -317,4 +313,5 @@ void suppressLeftOver(Param<T> output, unsigned nBBS0, unsigned nBBS1) {
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index cc63a029c4..61af04ba6c 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -13,6 +13,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/canny_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -27,7 +28,7 @@ template<typename T>
 void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
     auto nonMaxSuppress = common::getKernel(
-        "cuda::nonMaxSuppression", std::array{canny_cuh_src},
+        "arrayfire::cuda::nonMaxSuppression", std::array{canny_cuh_src},
         TemplateArgs(TemplateTypename<T>()),
         std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
                    DefineValue(THREADS_X), DefineValue(THREADS_Y)});
@@ -49,17 +50,17 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
 template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     auto initEdgeOut = common::getKernel(
-        "cuda::initEdgeOut", std::array{canny_cuh_src},
+        "arrayfire::cuda::initEdgeOut", std::array{canny_cuh_src},
         TemplateArgs(TemplateTypename<T>()),
         std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
                    DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto edgeTrack = common::getKernel(
-        "cuda::edgeTrack", std::array{canny_cuh_src},
+        "arrayfire::cuda::edgeTrack", std::array{canny_cuh_src},
         TemplateArgs(TemplateTypename<T>()),
         std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
                    DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto suppressLeftOver = common::getKernel(
-        "cuda::suppressLeftOver", std::array{canny_cuh_src},
+        "arrayfire::cuda::suppressLeftOver", std::array{canny_cuh_src},
         TemplateArgs(TemplateTypename<T>()),
         std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
                    DefineValue(THREADS_X), DefineValue(THREADS_Y)});
@@ -92,3 +93,4 @@ void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/config.hpp b/src/backend/cuda/kernel/config.hpp
index 975d6ff987..9bef1d7784 100644
--- a/src/backend/cuda/kernel/config.hpp
+++ b/src/backend/cuda/kernel/config.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -18,3 +19,4 @@ static const uint THREADS_Y         = THREADS_PER_BLOCK / THREADS_X;
 static const uint REPEAT            = 32;
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 7b105ef842..8183805e7c 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -20,6 +20,7 @@
 #include <nvrtc_kernel_headers/convolve_separable_cuh.hpp>
 #include <traits.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -63,8 +64,7 @@ void prepareKernelArgs(conv_kparam_t& params, dim_t oDims[], dim_t fDims[],
         batchDims[i] = (params.launchMoreBlocks ? 1 : oDims[i]);
     }
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
     if (baseDim == 1) {
         params.mThreads = dim3(CONV_THREADS, 1);
         params.mBlk_x   = divup(oDims[0], params.mThreads.x);
@@ -101,7 +101,7 @@ template<typename T, typename aT>
 void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     auto convolve1 = common::getKernel(
-        "cuda::convolve1", std::array{convolve1_cuh_src},
+        "arrayfire::cuda::convolve1", std::array{convolve1_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(expand)),
         std::array{DefineValue(MAX_CONV1_FILTER_LEN),
@@ -158,7 +158,7 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
     }
 
     auto convolve2 = common::getKernel(
-        "cuda::convolve2", std::array{convolve2_cuh_src},
+        "arrayfire::cuda::convolve2", std::array{convolve2_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(expand), TemplateArg(f0), TemplateArg(f1)),
         std::array{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
@@ -203,7 +203,7 @@ template<typename T, typename aT>
 void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     auto convolve3 = common::getKernel(
-        "cuda::convolve3", std::array{convolve3_cuh_src},
+        "arrayfire::cuda::convolve3", std::array{convolve3_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(expand)),
         std::array{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
@@ -308,7 +308,8 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     }
 
     auto convolve2_separable = common::getKernel(
-        "cuda::convolve2_separable", std::array{convolve_separable_cuh_src},
+        "arrayfire::cuda::convolve2_separable",
+        std::array{convolve_separable_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(conv_dim), TemplateArg(expand),
                      TemplateArg(fLen)),
@@ -335,3 +336,4 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/convolve1.cuh b/src/backend/cuda/kernel/convolve1.cuh
index 765703cf99..f82c85427c 100644
--- a/src/backend/cuda/kernel/convolve1.cuh
+++ b/src/backend/cuda/kernel/convolve1.cuh
@@ -11,17 +11,16 @@
 #include <math.hpp>
 #include <shared.hpp>
 
-__constant__ char
-    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
-            sizeof(double)];
+__constant__ char cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
+                          sizeof(double)];
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename aT, bool expand>
-__global__
-void convolve1(Param<T> out, CParam<T> signal,
-               int fLen, int nBBS0, int nBBS1,
-               int o1, int o2, int o3, int s1, int s2, int s3) {
+__global__ void convolve1(Param<T> out, CParam<T> signal, int fLen, int nBBS0,
+                          int nBBS1, int o1, int o2, int o3, int s1, int s2,
+                          int s3) {
     SharedMemory<T> shared;
     T *shrdMem = shared.getPointer();
 
@@ -74,4 +73,5 @@ void convolve1(Param<T> out, CParam<T> signal,
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/convolve2.cuh b/src/backend/cuda/kernel/convolve2.cuh
index 7bd8fa4375..3699cb9e51 100644
--- a/src/backend/cuda/kernel/convolve2.cuh
+++ b/src/backend/cuda/kernel/convolve2.cuh
@@ -10,16 +10,15 @@
 #include <Param.hpp>
 #include <math.hpp>
 
-__constant__ char
-    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
-            sizeof(double)];
+__constant__ char cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
+                          sizeof(double)];
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename aT, bool expand, int fLen0, int fLen1>
-__global__
-void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
-               int o2, int o3, int s2, int s3) {
+__global__ void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
+                          int o2, int o3, int s2, int s3) {
     const size_t C_SIZE = (CONV2_THREADS_X + 2 * (fLen0 - 1)) *
                           (CONV2_THREADS_Y + 2 * (fLen1 - 1));
     __shared__ T shrdMem[C_SIZE];
@@ -51,8 +50,9 @@ void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
     int lx = threadIdx.x;
     int ly = threadIdx.y;
     int gx = CONV2_THREADS_X * (blockIdx.x - b0 * nBBS0) + lx;
-    int gy = CONV2_THREADS_Y *
-                 ((blockIdx.y + blockIdx.z * gridDim.y) - b1 * nBBS1) + ly;
+    int gy =
+        CONV2_THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) - b1 * nBBS1) +
+        ly;
 
     if (b1 >= out.dims[3]) return;
 
@@ -97,4 +97,5 @@ void convolve2(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1,
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/convolve3.cuh b/src/backend/cuda/kernel/convolve3.cuh
index 08e671692c..18ad939054 100644
--- a/src/backend/cuda/kernel/convolve3.cuh
+++ b/src/backend/cuda/kernel/convolve3.cuh
@@ -11,21 +11,19 @@
 #include <math.hpp>
 #include <shared.hpp>
 
-__constant__ char
-    cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
-            sizeof(double)];
+__constant__ char cFilter[2 * (2 * (MAX_CONV1_FILTER_LEN - 1) + CONV_THREADS) *
+                          sizeof(double)];
 
+namespace arrayfire {
 namespace cuda {
 
-__inline__
-int index(int i, int j, int k, int jstride, int kstride) {
+__inline__ int index(int i, int j, int k, int jstride, int kstride) {
     return i + j * jstride + k * kstride;
 }
 
 template<typename T, typename aT, bool expand>
-__global__
-void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
-               int fLen2, int nBBS, int o3, int s3) {
+__global__ void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
+                          int fLen2, int nBBS, int o3, int s3) {
     SharedMemory<T> shared;
 
     T *shrdMem   = shared.getPointer();
@@ -109,4 +107,5 @@ void convolve3(Param<T> out, CParam<T> signal, int fLen0, int fLen1,
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/convolve_separable.cpp b/src/backend/cuda/kernel/convolve_separable.cpp
index c95f48afeb..3c18a02240 100644
--- a/src/backend/cuda/kernel/convolve_separable.cpp
+++ b/src/backend/cuda/kernel/convolve_separable.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 #include <kernel/convolve.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -29,3 +30,4 @@ INSTANTIATE(intl, float)
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/convolve_separable.cuh b/src/backend/cuda/kernel/convolve_separable.cuh
index 8a2e076dec..ead157df92 100644
--- a/src/backend/cuda/kernel/convolve_separable.cuh
+++ b/src/backend/cuda/kernel/convolve_separable.cuh
@@ -14,11 +14,12 @@ __constant__ char sFilter[2 * SCONV_THREADS_Y *
                           (2 * (MAX_SCONV_FILTER_LEN - 1) + SCONV_THREADS_X) *
                           sizeof(double)];
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename accType, int conv_dim, bool expand, int fLen>
-__global__
-void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1) {
+__global__ void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0,
+                                    int nBBS1) {
     const int smem_len =
         (conv_dim == 0 ? (SCONV_THREADS_X + 2 * (fLen - 1)) * SCONV_THREADS_Y
                        : (SCONV_THREADS_Y + 2 * (fLen - 1)) * SCONV_THREADS_X);
@@ -96,4 +97,5 @@ void convolve2_separable(Param<T> out, CParam<T> signal, int nBBS0, int nBBS1) {
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/copy.cuh b/src/backend/cuda/kernel/copy.cuh
index 5c6b6e485a..9e771e8c52 100644
--- a/src/backend/cuda/kernel/copy.cuh
+++ b/src/backend/cuda/kernel/copy.cuh
@@ -14,6 +14,7 @@
 #include <dims_param.hpp>
 #include <types.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -49,15 +50,14 @@ convertType<char, compute_t<common::half>>(char value) {
 }
 
 template<>
-__inline__ __device__ cuda::uchar
-convertType<compute_t<common::half>, cuda::uchar>(
-    compute_t<common::half> value) {
-    return (cuda::uchar)((short)value);
+__inline__ __device__ uchar
+convertType<compute_t<common::half>, uchar>(compute_t<common::half> value) {
+    return (uchar)((short)value);
 }
 
 template<>
 __inline__ __device__ compute_t<common::half>
-convertType<cuda::uchar, compute_t<common::half>>(cuda::uchar value) {
+convertType<uchar, compute_t<common::half>>(uchar value) {
     return compute_t<common::half>(value);
 }
 
@@ -290,3 +290,4 @@ __global__ void scaledCopyLoop123(Param<outType> out, CParam<inType> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/diagonal.cuh b/src/backend/cuda/kernel/diagonal.cuh
index d337c8f2a1..6e47af5b22 100644
--- a/src/backend/cuda/kernel/diagonal.cuh
+++ b/src/backend/cuda/kernel/diagonal.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -53,3 +54,4 @@ __global__ void extractDiagonal(Param<T> out, CParam<T> in, int num,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index 87ba53965b..4ffb6fa4ff 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -15,12 +15,13 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/diagonal_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 void diagCreate(Param<T> out, CParam<T> in, int num) {
-    auto genDiagMat = common::getKernel("cuda::createDiagonalMat",
+    auto genDiagMat = common::getKernel("arrayfire::cuda::createDiagonalMat",
                                         std::array{diagonal_cuh_src},
                                         TemplateArgs(TemplateTypename<T>()));
 
@@ -29,8 +30,7 @@ void diagCreate(Param<T> out, CParam<T> in, int num) {
     int blocks_y = divup(out.dims[1], threads.y);
     dim3 blocks(blocks_x * out.dims[2], blocks_y);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+    const int maxBlocksY    = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
     const int blocksPerMatZ = divup(blocks.y, maxBlocksY);
     if (blocksPerMatZ > 1) {
         blocks.y = maxBlocksY;
@@ -46,19 +46,18 @@ void diagCreate(Param<T> out, CParam<T> in, int num) {
 
 template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int num) {
-    auto extractDiag =
-        common::getKernel("cuda::extractDiagonal", std::array{diagonal_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto extractDiag = common::getKernel("arrayfire::cuda::extractDiagonal",
+                                         std::array{diagonal_cuh_src},
+                                         TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(256, 1);
     int blocks_x = divup(out.dims[0], threads.x);
     int blocks_z = out.dims[2];
     dim3 blocks(blocks_x, out.dims[3] * blocks_z);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -69,3 +68,4 @@ void diagExtract(Param<T> out, CParam<T> in, int num) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/diff.cuh b/src/backend/cuda/kernel/diff.cuh
index 2f6305eb0f..fc02296b5c 100644
--- a/src/backend/cuda/kernel/diff.cuh
+++ b/src/backend/cuda/kernel/diff.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool D>
@@ -58,3 +59,4 @@ __global__ void diff(Param<T> out, CParam<T> in, const unsigned oElem,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index fb157af798..c547e0e933 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/diff_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,7 +26,7 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
     constexpr unsigned TY = 16;
 
     auto diff =
-        common::getKernel("cuda::diff", std::array{diff_cuh_src},
+        common::getKernel("arrayfire::cuda::diff", std::array{diff_cuh_src},
                           TemplateArgs(TemplateTypename<T>(), TemplateArg(dim),
                                        TemplateArg(isDiff2)));
 
@@ -39,10 +40,9 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
 
     const int oElem = out.dims[0] * out.dims[1] * out.dims[2] * out.dims[3];
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -52,3 +52,4 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/exampleFunction.cuh b/src/backend/cuda/kernel/exampleFunction.cuh
index 9670d89ef6..e0a4ddffd6 100644
--- a/src/backend/cuda/kernel/exampleFunction.cuh
+++ b/src/backend/cuda/kernel/exampleFunction.cuh
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -34,4 +35,5 @@ __global__ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b,
     }
 }
 
-} //namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 019b8c9743..730c309a86 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -18,6 +18,7 @@
 
 #include <nvrtc_kernel_headers/exampleFunction_cuh.hpp>  //kernel generated by nvrtc
 
+namespace arrayfire {
 namespace cuda {
 
 namespace kernel {
@@ -27,7 +28,7 @@ static const unsigned TY = 16;  // Kernel Launch Config Values
 
 template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
-    auto exampleFunc = common::getKernel("cuda::exampleFunc",
+    auto exampleFunc = common::getKernel("arrayfire::cuda::exampleFunc",
                                          std::array{exampleFunction_cuh_src},
                                          TemplateArgs(TemplateTypename<T>()));
 
@@ -43,7 +44,7 @@ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     // on your CUDA kernels needs such as shared memory etc.
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
-    // Call the kernel functor retrieved using common::getKernel
+    // Call the kernel functor retrieved using arrayfire::common::getKernel
     exampleFunc(qArgs, c, a, b, p);
 
     POST_LAUNCH_CHECK();  // Macro for post kernel launch checks
@@ -52,3 +53,4 @@ void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/fast.hpp b/src/backend/cuda/kernel/fast.hpp
index 3521f8cfcb..7b54162b42 100644
--- a/src/backend/cuda/kernel/fast.hpp
+++ b/src/backend/cuda/kernel/fast.hpp
@@ -17,6 +17,7 @@
 #include <memory.hpp>
 #include <cub/block/block_reduce.cuh>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -397,7 +398,7 @@ void fast(unsigned *out_feat, float **x_out, float **y_out, float **score_out,
 
     unsigned *d_total = (unsigned *)(d_score.get() + (indims[0] * indims[1]));
     CUDA_CHECK(
-        cudaMemsetAsync(d_total, 0, sizeof(unsigned), cuda::getActiveStream()));
+        cudaMemsetAsync(d_total, 0, sizeof(unsigned), getActiveStream()));
     auto d_counts  = memAlloc<unsigned>(blocks.x * blocks.y);
     auto d_offsets = memAlloc<unsigned>(blocks.x * blocks.y);
 
@@ -415,9 +416,8 @@ void fast(unsigned *out_feat, float **x_out, float **y_out, float **score_out,
     // Dimensions of output array
     unsigned total;
     CUDA_CHECK(cudaMemcpyAsync(&total, d_total, sizeof(unsigned),
-                               cudaMemcpyDeviceToHost,
-                               cuda::getActiveStream()));
-    CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+                               cudaMemcpyDeviceToHost, getActiveStream()));
+    CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
     total = total < max_feat ? total : max_feat;
 
     if (total > 0) {
@@ -444,3 +444,4 @@ void fast(unsigned *out_feat, float **x_out, float **y_out, float **score_out,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/fftconvolve.cuh b/src/backend/cuda/kernel/fftconvolve.cuh
index c5df6a1df4..350a7b299f 100644
--- a/src/backend/cuda/kernel/fftconvolve.cuh
+++ b/src/backend/cuda/kernel/fftconvolve.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/internal_enums.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename To, typename Ti>
@@ -218,3 +219,4 @@ __global__ void reorderOutput(Param<To> out, Param<Ti> in, CParam<To> filter,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index 6ca9569206..cf45bc18a4 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/fftconvolve_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -24,10 +25,10 @@ template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
     auto packData = common::getKernel(
-        "cuda::packData", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::packData", std::array{fftconvolve_cuh_src},
         TemplateArgs(TemplateTypename<convT>(), TemplateTypename<T>()));
     auto padArray = common::getKernel(
-        "cuda::padArray", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::padArray", std::array{fftconvolve_cuh_src},
         TemplateArgs(TemplateTypename<convT>(), TemplateTypename<T>()));
 
     dim_t *sd = sig.dims;
@@ -68,7 +69,7 @@ template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
     auto cplxMul = common::getKernel(
-        "cuda::complexMultiply", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::complexMultiply", std::array{fftconvolve_cuh_src},
         TemplateArgs(TemplateTypename<convT>(), TemplateArg(kind)));
 
     int sig_packed_elem    = 1;
@@ -101,7 +102,7 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     constexpr bool RoundResult = std::is_integral<T>::value;
 
     auto reorderOut = common::getKernel(
-        "cuda::reorderOutput", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::reorderOutput", std::array{fftconvolve_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<convT>(),
                      TemplateArg(expand), TemplateArg(RoundResult)));
 
@@ -125,3 +126,4 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/flood_fill.cuh b/src/backend/cuda/kernel/flood_fill.cuh
index bab68916ec..ede793c0d3 100644
--- a/src/backend/cuda/kernel/flood_fill.cuh
+++ b/src/backend/cuda/kernel/flood_fill.cuh
@@ -8,14 +8,15 @@
  ********************************************************/
 
 #include <Param.hpp>
-#include <af/defines.h>
 #include <math.hpp>
+#include <af/defines.h>
 
 /// doAnotherLaunch is a variable in kernel space
 /// used to track the convergence of
 /// the breath first search algorithm
 __device__ int doAnotherLaunch = 0;
 
+namespace arrayfire {
 namespace cuda {
 
 /// Output array is set to the following values during the progression
@@ -27,24 +28,33 @@ namespace cuda {
 ///
 /// Once, the algorithm is finished, output is reset
 /// to either zero or \p newValue for all valid pixels.
-template<typename T> constexpr T VALID() { return T(2); }
-template<typename T> constexpr T INVALID() { return T(1); }
-template<typename T> constexpr T ZERO() { return T(0); }
+template<typename T>
+constexpr T VALID() {
+    return T(2);
+}
+template<typename T>
+constexpr T INVALID() {
+    return T(1);
+}
+template<typename T>
+constexpr T ZERO() {
+    return T(0);
+}
 
 template<typename T>
-__global__
-void initSeeds(Param<T> out, CParam<uint> seedsx, CParam<uint> seedsy) {
+__global__ void initSeeds(Param<T> out, CParam<uint> seedsx,
+                          CParam<uint> seedsy) {
     uint idx = blockDim.x * blockIdx.x + threadIdx.x;
     if (idx < seedsx.elements()) {
-        uint x = seedsx.ptr[ idx ];
-        uint y = seedsy.ptr[ idx ];
-        out.ptr[ x + y * out.dims[0] ] = VALID<T>();
+        uint x                       = seedsx.ptr[idx];
+        uint y                       = seedsy.ptr[idx];
+        out.ptr[x + y * out.dims[0]] = VALID<T>();
     }
 }
 
 template<typename T>
-__global__
-void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
+__global__ void floodStep(Param<T> out, CParam<T> img, T lowValue,
+                          T highValue) {
     constexpr int RADIUS      = 1;
     constexpr int SMEM_WIDTH  = THREADS_X + 2 * RADIUS;
     constexpr int SMEM_HEIGHT = THREADS_Y + 2 * RADIUS;
@@ -61,7 +71,7 @@ void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
     const int s1 = out.strides[1];
 
     const T *iptr = (const T *)img.ptr;
-          T *optr = (T *)out.ptr;
+    T *optr       = (T *)out.ptr;
 #pragma unroll
     for (int b = ly, gy2 = gy; b < SMEM_HEIGHT;
          b += blockDim.y, gy2 += blockDim.y) {
@@ -71,14 +81,14 @@ void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
             int x      = gx2 - RADIUS;
             int y      = gy2 - RADIUS;
             bool inROI = (x >= 0 && x < d0 && y >= 0 && y < d1);
-            smem[b][a] = (inROI ? optr[ x*s0+y*s1 ] : INVALID<T>());
+            smem[b][a] = (inROI ? optr[x * s0 + y * s1] : INVALID<T>());
         }
     }
     int i = lx + RADIUS;
     int j = ly + RADIUS;
 
-    T tImgVal = iptr[(clamp(gx, 0, int(img.dims[0]-1)) * img.strides[0] +
-                      clamp(gy, 0, int(img.dims[1]-1)) * img.strides[1])];
+    T tImgVal = iptr[(clamp(gx, 0, int(img.dims[0] - 1)) * img.strides[0] +
+                      clamp(gy, 0, int(img.dims[1] - 1)) * img.strides[1])];
     const int isPxBtwnThresholds =
         (tImgVal >= lowValue && tImgVal <= highValue);
     __syncthreads();
@@ -86,7 +96,7 @@ void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
     T origOutVal      = smem[j][i];
     bool blockChanged = false;
     bool isBorderPxl  = (lx == 0 || ly == 0 || lx == (blockDim.x - 1) ||
-                         ly == (blockDim.y - 1));
+                        ly == (blockDim.y - 1));
     do {
         int validNeighbors = 0;
 #pragma unroll
@@ -100,16 +110,14 @@ void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
         __syncthreads();
 
         bool outChanged = (smem[j][i] == ZERO<T>() && (validNeighbors > 0));
-        if (outChanged) {
-            smem[j][i] = T(isPxBtwnThresholds + INVALID<T>());
-        }
+        if (outChanged) { smem[j][i] = T(isPxBtwnThresholds + INVALID<T>()); }
         blockChanged = __syncthreads_or(int(outChanged));
     } while (blockChanged);
 
     T newOutVal = smem[j][i];
 
-    bool borderChanged = (isBorderPxl &&
-                          newOutVal != origOutVal && newOutVal == VALID<T>());
+    bool borderChanged =
+        (isBorderPxl && newOutVal != origOutVal && newOutVal == VALID<T>());
 
     borderChanged = __syncthreads_or(int(borderChanged));
 
@@ -120,21 +128,19 @@ void floodStep(Param<T> out, CParam<T> img, T lowValue, T highValue) {
         doAnotherLaunch = 1;
     }
 
-    if (gx < d0 && gy < d1) {
-        optr[ (gx*s0 + gy*s1) ] = smem[j][i];
-    }
+    if (gx < d0 && gy < d1) { optr[(gx * s0 + gy * s1)] = smem[j][i]; }
 }
 
 template<typename T>
-__global__
-void finalizeOutput(Param<T> out, T newValue) {
+__global__ void finalizeOutput(Param<T> out, T newValue) {
     uint gx = blockDim.x * blockIdx.x + threadIdx.x;
     uint gy = blockDim.y * blockIdx.y + threadIdx.y;
     if (gx < out.dims[0] && gy < out.dims[1]) {
-        uint idx = gx * out.strides[0] + gy * out.strides[1];
-        T val = out.ptr[idx];
+        uint idx     = gx * out.strides[0] + gy * out.strides[1];
+        T val        = out.ptr[idx];
         out.ptr[idx] = (val == VALID<T>() ? newValue : ZERO<T>());
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index ad6366a286..29f5741a04 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -16,6 +16,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/flood_fill_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -37,7 +38,7 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
                const T highValue, const af::connectivity nlookup) {
     UNUSED(nlookup);
     if (sharedMemRequiredByFloodFill<T>() >
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).sharedMemPerBlock) {
+        getDeviceProp(getActiveDeviceId()).sharedMemPerBlock) {
         char errMessage[256];
         snprintf(errMessage, sizeof(errMessage),
                  "\nCurrent thread's CUDA device doesn't have sufficient "
@@ -45,15 +46,15 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds =
-        common::getKernel("cuda::initSeeds", std::array{flood_fill_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto initSeeds = common::getKernel("arrayfire::cuda::initSeeds",
+                                       std::array{flood_fill_cuh_src},
+                                       TemplateArgs(TemplateTypename<T>()));
     auto floodStep = common::getKernel(
-        "cuda::floodStep", std::array{flood_fill_cuh_src},
+        "arrayfire::cuda::floodStep", std::array{flood_fill_cuh_src},
         TemplateArgs(TemplateTypename<T>()),
         std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
     auto finalizeOutput = common::getKernel(
-        "cuda::finalizeOutput", std::array{flood_fill_cuh_src},
+        "arrayfire::cuda::finalizeOutput", std::array{flood_fill_cuh_src},
         TemplateArgs(TemplateTypename<T>()));
 
     EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
@@ -81,3 +82,4 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/gradient.cuh b/src/backend/cuda/kernel/gradient.cuh
index 94051dc6a8..19ec419887 100644
--- a/src/backend/cuda/kernel/gradient.cuh
+++ b/src/backend/cuda/kernel/gradient.cuh
@@ -12,14 +12,14 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 #define sidx(y, x) scratch[y + 1][x + 1]
 
 template<typename T>
 __global__ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in,
-                         const int blocksPerMatX,
-                         const int blocksPerMatY) {
+                         const int blocksPerMatX, const int blocksPerMatY) {
     const int idz = blockIdx.x / blocksPerMatX;
     const int idw = (blockIdx.y + blockIdx.z * gridDim.y) / blocksPerMatY;
 
@@ -63,9 +63,9 @@ __global__ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in,
     // Cols
     if (threadIdx.y == 0) {
         // Y-1
-        sidx(-1, threadIdx.x) = (cond || idy == 0)
-                                    ? sidx(0, threadIdx.x)
-                                    : in.ptr[iIdx - in.strides[1]];
+        sidx(-1, threadIdx.x)   = (cond || idy == 0)
+                                      ? sidx(0, threadIdx.x)
+                                      : in.ptr[iIdx - in.strides[1]];
         sidx(ymax, threadIdx.x) = (cond || (idy + ymax) >= in.dims[1])
                                       ? sidx(ymax - 1, threadIdx.x)
                                       : in.ptr[iIdx + ymax * in.strides[1]];
@@ -90,3 +90,4 @@ __global__ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index 8f1306e2b0..a6f2a8a6b9 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -17,6 +17,7 @@
 
 #include <array>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,10 +26,10 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     constexpr unsigned TX = 32;
     constexpr unsigned TY = 8;
 
-    auto gradient =
-        common::getKernel("cuda::gradient", std::array{gradient_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()),
-                          std::array{DefineValue(TX), DefineValue(TY)});
+    auto gradient = common::getKernel(
+        "arrayfire::cuda::gradient", std::array{gradient_cuh_src},
+        TemplateArgs(TemplateTypename<T>()),
+        std::array{DefineValue(TX), DefineValue(TY)});
 
     dim3 threads(TX, TY, 1);
 
@@ -36,10 +37,9 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     int blocksPerMatY = divup(in.dims[1], TY);
     dim3 blocks(blocksPerMatX * in.dims[2], blocksPerMatY * in.dims[3], 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -49,3 +49,4 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/harris.hpp b/src/backend/cuda/kernel/harris.hpp
index e8fe490b52..e956f02441 100644
--- a/src/backend/cuda/kernel/harris.hpp
+++ b/src/backend/cuda/kernel/harris.hpp
@@ -23,6 +23,7 @@
 
 #include <vector>
 
+namespace arrayfire {
 namespace cuda {
 
 namespace kernel {
@@ -176,9 +177,9 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
     int filter_elem   = filter.strides[3] * filter.dims[3];
     auto filter_alloc = memAlloc<convAccT>(filter_elem);
     filter.ptr        = filter_alloc.get();
-    CUDA_CHECK(cudaMemcpyAsync(
-        filter.ptr, h_filter.data(), filter_elem * sizeof(convAccT),
-        cudaMemcpyHostToDevice, cuda::getActiveStream()));
+    CUDA_CHECK(cudaMemcpyAsync(filter.ptr, h_filter.data(),
+                               filter_elem * sizeof(convAccT),
+                               cudaMemcpyHostToDevice, getActiveStream()));
 
     const unsigned border_len = filter_len / 2 + 1;
 
@@ -238,7 +239,7 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
 
     auto d_corners_found = memAlloc<unsigned>(1);
     CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
-                               cuda::getActiveStream()));
+                               getActiveStream()));
 
     auto d_x_corners    = memAlloc<float>(corner_lim);
     auto d_y_corners    = memAlloc<float>(corner_lim);
@@ -265,7 +266,7 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
     unsigned corners_found = 0;
     CUDA_CHECK(cudaMemcpyAsync(&corners_found, d_corners_found.get(),
                                sizeof(unsigned), cudaMemcpyDeviceToHost,
-                               cuda::getActiveStream()));
+                               getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     *corners_out =
@@ -327,13 +328,13 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
 
         CUDA_CHECK(cudaMemcpyAsync(
             *x_out, d_x_corners.get(), *corners_out * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *y_out, d_y_corners.get(), *corners_out * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *resp_out, d_resp_corners.get(), *corners_out * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
 
         x_out_alloc.release();
         y_out_alloc.release();
@@ -349,3 +350,4 @@ void harris(unsigned* corners_out, float** x_out, float** y_out,
 }  // namespace kernel
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/histogram.cuh b/src/backend/cuda/kernel/histogram.cuh
index 3cd68a1485..258dc6ff3c 100644
--- a/src/backend/cuda/kernel/histogram.cuh
+++ b/src/backend/cuda/kernel/histogram.cuh
@@ -12,6 +12,7 @@
 #include <shared.hpp>
 #include <types.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool isLinear>
@@ -21,9 +22,10 @@ __global__ void histogram(Param<uint> out, CParam<T> in, int len, int nbins,
     uint *shrdMem = shared.getPointer();
 
     // offset input and output to account for batch ops
-    unsigned b2   = blockIdx.x / nBBS;
-    const data_t<T> *iptr = in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
-    uint *optr    = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
+    unsigned b2 = blockIdx.x / nBBS;
+    const data_t<T> *iptr =
+        in.ptr + b2 * in.strides[2] + blockIdx.y * in.strides[3];
+    uint *optr = out.ptr + b2 * out.strides[2] + blockIdx.y * out.strides[3];
 
     int start = (blockIdx.x - b2 * nBBS) * THRD_LOAD * blockDim.x + threadIdx.x;
     int end   = min((start + THRD_LOAD * blockDim.x), len);
@@ -45,9 +47,10 @@ __global__ void histogram(Param<uint> out, CParam<T> in, int len, int nbins,
             isLinear
                 ? row
                 : ((row % in.dims[0]) + (row / in.dims[0]) * in.strides[1]);
-        int bin = (int)(static_cast<float>(compute_t<T>(iptr[idx]) - minvalT) / step);
-        bin     = (bin < 0) ? 0 : bin;
-        bin     = (bin >= nbins) ? (nbins - 1) : bin;
+        int bin =
+            (int)(static_cast<float>(compute_t<T>(iptr[idx]) - minvalT) / step);
+        bin = (bin < 0) ? 0 : bin;
+        bin = (bin >= nbins) ? (nbins - 1) : bin;
 
         if (use_global) {
             atomicAdd((optr + bin), 1);
@@ -66,3 +69,4 @@ __global__ void histogram(Param<uint> out, CParam<T> in, int len, int nbins,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index 4e4fe8c901..b9a9945c99 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -13,6 +13,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/histogram_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -24,7 +25,7 @@ template<typename T>
 void histogram(Param<uint> out, CParam<T> in, int nbins, float minval,
                float maxval, bool isLinear) {
     auto histogram = common::getKernel(
-        "cuda::histogram", std::array{histogram_cuh_src},
+        "arrayfire::cuda::histogram", std::array{histogram_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isLinear)),
         std::array{DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
 
@@ -45,3 +46,4 @@ void histogram(Param<uint> out, CParam<T> in, int nbins, float minval,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/homography.hpp b/src/backend/cuda/kernel/homography.hpp
index aaad7af358..72627f84a8 100644
--- a/src/backend/cuda/kernel/homography.hpp
+++ b/src/backend/cuda/kernel/homography.hpp
@@ -17,6 +17,7 @@
 
 #include <cfloat>
 
+namespace arrayfire {
 namespace cuda {
 
 namespace kernel {
@@ -553,25 +554,25 @@ int computeH(Param<T> bestH, Param<T> H, Param<float> err, CParam<float> x_src,
 
             CUDA_CHECK(cudaMemcpyAsync(&minMedian, finalMedian.get(),
                                        sizeof(float), cudaMemcpyDeviceToHost,
-                                       cuda::getActiveStream()));
+                                       getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(&minIdx, finalIdx.get(),
                                        sizeof(unsigned), cudaMemcpyDeviceToHost,
-                                       cuda::getActiveStream()));
+                                       getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         } else {
             CUDA_CHECK(cudaMemcpyAsync(&minMedian, median.get(), sizeof(float),
                                        cudaMemcpyDeviceToHost,
-                                       cuda::getActiveStream()));
+                                       getActiveStream()));
             CUDA_CHECK(cudaMemcpyAsync(&minIdx, idx.get(), sizeof(unsigned),
                                        cudaMemcpyDeviceToHost,
-                                       cuda::getActiveStream()));
+                                       getActiveStream()));
             CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         }
 
         // Copy best homography to output
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + minIdx * 9, 9 * sizeof(T),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         blocks = dim3(divup(nsamples, threads.x));
         // sync stream for the device to host copies to be visible for
@@ -588,7 +589,7 @@ int computeH(Param<T> bestH, Param<T> H, Param<float> err, CParam<float> x_src,
 
         CUDA_CHECK(cudaMemcpyAsync(&inliersH, totalInliers.get(),
                                    sizeof(unsigned), cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     } else if (htype == AF_HOMOGRAPHY_RANSAC) {
@@ -597,11 +598,11 @@ int computeH(Param<T> bestH, Param<T> H, Param<float> err, CParam<float> x_src,
         // Copies back index and number of inliers of best homography estimation
         CUDA_CHECK(cudaMemcpyAsync(&idxH, idx.get() + blockIdx,
                                    sizeof(unsigned), cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(bestH.ptr, H.ptr + idxH * 9, 9 * sizeof(T),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
     }
 
     // sync stream for the device to host copies to be visible for
@@ -614,3 +615,4 @@ int computeH(Param<T> bestH, Param<T> H, Param<float> err, CParam<float> x_src,
 }  // namespace kernel
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/hsv_rgb.cuh b/src/backend/cuda/kernel/hsv_rgb.cuh
index ca7322777c..9ffcf0cc61 100644
--- a/src/backend/cuda/kernel/hsv_rgb.cuh
+++ b/src/backend/cuda/kernel/hsv_rgb.cuh
@@ -9,11 +9,11 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool isHSV2RGB>
-__global__
-void hsvrgbConverter(Param<T> out, CParam<T> in, int nBBS) {
+__global__ void hsvrgbConverter(Param<T> out, CParam<T> in, int nBBS) {
     // batch offsets
     unsigned batchId = blockIdx.x / nBBS;
     const T* src     = (const T*)in.ptr + (batchId * in.strides[3]);
@@ -81,4 +81,5 @@ void hsvrgbConverter(Param<T> out, CParam<T> in, int nBBS) {
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index a10a6ade93..fe89bb34cb 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -13,6 +13,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/hsv_rgb_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -22,7 +23,7 @@ static const int THREADS_Y = 16;
 template<typename T>
 void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
     auto hsvrgbConverter = common::getKernel(
-        "cuda::hsvrgbConverter", std::array{hsv_rgb_cuh_src},
+        "arrayfire::cuda::hsvrgbConverter", std::array{hsv_rgb_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isHSV2RGB)));
 
     const dim3 threads(THREADS_X, THREADS_Y);
@@ -34,10 +35,9 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
     // parameter would be along 4th dimension
     dim3 blocks(blk_x * in.dims[3], blk_y);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
     hsvrgbConverter(qArgs, out, in, blk_x);
@@ -46,3 +46,4 @@ void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/identity.cuh b/src/backend/cuda/kernel/identity.cuh
index 22ba3709d6..e8868f0a9a 100644
--- a/src/backend/cuda/kernel/identity.cuh
+++ b/src/backend/cuda/kernel/identity.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -39,3 +40,4 @@ __global__ void identity(Param<T> out, int blocks_x, int blocks_y) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 58e369823b..42fe1707e8 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -15,24 +15,24 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/identity_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 void identity(Param<T> out) {
-    auto identity =
-        common::getKernel("cuda::identity", std::array{identity_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto identity = common::getKernel("arrayfire::cuda::identity",
+                                      std::array{identity_cuh_src},
+                                      TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
     int blocks_y = divup(out.dims[1], threads.y);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -41,3 +41,4 @@ void identity(Param<T> out) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/iir.cuh b/src/backend/cuda/kernel/iir.cuh
index edd18062eb..e5b195f77a 100644
--- a/src/backend/cuda/kernel/iir.cuh
+++ b/src/backend/cuda/kernel/iir.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool batch_a>
@@ -67,3 +68,4 @@ __global__ void iir(Param<T> y, CParam<T> c, CParam<T> a, const int blocks_y) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index 38b9ece04d..f0f58512d8 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/iir_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -23,7 +24,7 @@ void iir(Param<T> y, CParam<T> c, CParam<T> a) {
     constexpr int MAX_A_SIZE = 1024;
 
     auto iir = common::getKernel(
-        "cuda::iir", std::array{iir_cuh_src},
+        "arrayfire::cuda::iir", std::array{iir_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(batch_a)),
         std::array{DefineValue(MAX_A_SIZE)});
 
@@ -43,3 +44,4 @@ void iir(Param<T> y, CParam<T> c, CParam<T> a) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/index.cuh b/src/backend/cuda/kernel/index.cuh
index 643fe87837..37b6b63d46 100644
--- a/src/backend/cuda/kernel/index.cuh
+++ b/src/backend/cuda/kernel/index.cuh
@@ -13,12 +13,12 @@
 #include <assign_kernel_param.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
-__global__ void index(Param<T> out, CParam<T> in,
-                      const cuda::IndexKernelParam p, const int nBBS0,
-                      const int nBBS1) {
+__global__ void index(Param<T> out, CParam<T> in, const IndexKernelParam p,
+                      const int nBBS0, const int nBBS1) {
     // retrieve index pointers
     // these can be 0 where af_array index is not used
     const uint* ptr0 = p.ptr[0];
@@ -60,3 +60,4 @@ __global__ void index(Param<T> out, CParam<T> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 5a44f4be6f..63d318408e 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -16,13 +16,15 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/index_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
-    auto index = common::getKernel("cuda::index", std::array{index_cuh_src},
-                                   TemplateArgs(TemplateTypename<T>()));
+    auto index =
+        common::getKernel("arrayfire::cuda::index", std::array{index_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
     dim3 threads;
     switch (out.dims[1]) {
         case 1: threads.y = 1; break;
@@ -38,10 +40,9 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
 
     dim3 blocks(blks_x * out.dims[2], blks_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -51,3 +52,4 @@ void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/interp.hpp b/src/backend/cuda/kernel/interp.hpp
index 8101fba41e..39fb7a77ff 100644
--- a/src/backend/cuda/kernel/interp.hpp
+++ b/src/backend/cuda/kernel/interp.hpp
@@ -9,6 +9,7 @@
 
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -328,3 +329,4 @@ struct Interp2<Ty, Tp, xdim, ydim, 3> {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/iota.cuh b/src/backend/cuda/kernel/iota.cuh
index 1554e08096..ce0ec56168 100644
--- a/src/backend/cuda/kernel/iota.cuh
+++ b/src/backend/cuda/kernel/iota.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -51,3 +52,4 @@ __global__ void iota(Param<T> out, const int s0, const int s1, const int s2,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index d108bc2a25..7624f68559 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -16,6 +16,7 @@
 #include <nvrtc_kernel_headers/iota_cuh.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,8 +27,9 @@ void iota(Param<T> out, const af::dim4 &sdims) {
     constexpr unsigned TILEX   = 512;
     constexpr unsigned TILEY   = 32;
 
-    auto iota = common::getKernel("cuda::iota", std::array{iota_cuh_src},
-                                  TemplateArgs(TemplateTypename<T>()));
+    auto iota =
+        common::getKernel("arrayfire::cuda::iota", std::array{iota_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
@@ -36,10 +38,9 @@ void iota(Param<T> out, const af::dim4 &sdims) {
 
     dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -50,3 +51,4 @@ void iota(Param<T> out, const af::dim4 &sdims) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/ireduce.cuh b/src/backend/cuda/kernel/ireduce.cuh
index 1c6cd63b60..6c59a360b1 100644
--- a/src/backend/cuda/kernel/ireduce.cuh
+++ b/src/backend/cuda/kernel/ireduce.cuh
@@ -13,6 +13,7 @@
 #include <common/Binary.hpp>
 #include <minmax_op.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
@@ -176,7 +177,7 @@ __global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
     const uint *rlenptr   = (rlen.ptr) ? rlen.ptr + wid * rlen.strides[3] +
                                            zid * rlen.strides[2] +
                                            yid * rlen.strides[1]
-                                     : nullptr;
+                                       : nullptr;
 
     iptr += wid * in.strides[3] + zid * in.strides[2] + yid * in.strides[1];
     optr += wid * out.strides[3] + zid * out.strides[2] + yid * out.strides[1];
@@ -251,3 +252,4 @@ __global__ static void ireduceFirst(Param<T> out, uint *olptr, CParam<T> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index b57ba5d29b..91539469eb 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -20,6 +20,7 @@
 
 #include <memory>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -31,13 +32,12 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     dim3 blocks(blocks_dim[0] * blocks_dim[2], blocks_dim[1] * blocks_dim[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     auto ireduceDim = common::getKernel(
-        "cuda::ireduceDim", std::array{ireduce_cuh_src},
+        "arrayfire::cuda::ireduceDim", std::array{ireduce_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
                      TemplateArg(is_first), TemplateArg(threads_y)),
         std::array{DefineValue(THREADS_X)});
@@ -96,16 +96,15 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
                             CParam<uint> rlen) {
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * in.dims[2], blocks_y * in.dims[3]);
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
     // threads_x can take values 32, 64, 128, 256
     auto ireduceFirst = common::getKernel(
-        "cuda::ireduceFirst", std::array{ireduce_cuh_src},
+        "arrayfire::cuda::ireduceFirst", std::array{ireduce_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op),
                      TemplateArg(is_first), TemplateArg(threads_x)),
         std::array{DefineValue(THREADS_PER_BLOCK)});
@@ -218,12 +217,11 @@ T ireduce_all(uint *idx, CParam<T> in) {
         uint *h_lptr_raw = h_lptr.get();
 
         CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, tmp.ptr, tmp_elements * sizeof(T),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
-        CUDA_CHECK(
-            cudaMemcpyAsync(h_lptr_raw, tlptr, tmp_elements * sizeof(uint),
-                            cudaMemcpyDeviceToHost, cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
+        CUDA_CHECK(cudaMemcpyAsync(h_lptr_raw, tlptr,
+                                   tmp_elements * sizeof(uint),
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         if (!is_linear) {
             // Converting n-d index into a linear index
@@ -248,9 +246,8 @@ T ireduce_all(uint *idx, CParam<T> in) {
         unique_ptr<T[]> h_ptr(new T[in_elements]);
         T *h_ptr_raw = h_ptr.get();
         CUDA_CHECK(cudaMemcpyAsync(h_ptr_raw, in.ptr, in_elements * sizeof(T),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
         MinMaxOp<op, T> Op(h_ptr_raw[0], 0);
         for (int i = 1; i < in_elements; i++) { Op(h_ptr_raw[i], i); }
@@ -262,3 +259,4 @@ T ireduce_all(uint *idx, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index cf69146114..3d66c02f24 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -42,8 +42,8 @@ typedef cuDoubleComplex cdouble;
 #define __neq(lhs, rhs) (lhs) != (rhs)
 
 #define __conj(in) (in)
-#define __real(in)(in)
-#define __imag(in)(0)
+#define __real(in) (in)
+#define __imag(in) (0)
 #define __abs(in) abs(in)
 #define __sigmoid(in) (1.0 / (1 + exp(-(in))))
 
@@ -60,8 +60,9 @@ typedef cuDoubleComplex cdouble;
 #define __mod(lhs, rhs) ((lhs) % (rhs))
 
 #ifdef AF_WITH_FAST_MATH
-#define __pow(lhs, rhs) \
-    static_cast<double>(pow(static_cast<double>(lhs), static_cast<double>(rhs)));
+#define __pow(lhs, rhs)  \
+    static_cast<double>( \
+        pow(static_cast<double>(lhs), static_cast<double>(rhs)));
 #else
 #define __pow(lhs, rhs) \
     __float2int_rn(pow(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
@@ -185,7 +186,7 @@ __device__ cdouble __cdiv(cdouble lhs, cdouble rhs) {
     double rhs_x       = inv_rhs_abs * rhs.x;
     double rhs_y       = inv_rhs_abs * rhs.y;
     cdouble out        = {lhs.x * rhs_x + lhs.y * rhs_y,
-                   lhs.y * rhs_x - lhs.x * rhs_y};
+                          lhs.y * rhs_x - lhs.x * rhs_y};
     out.x *= inv_rhs_abs;
     out.y *= inv_rhs_abs;
     return out;
@@ -200,20 +201,17 @@ __device__ cdouble __cmax(cdouble lhs, cdouble rhs) {
 }
 
 template<typename T>
-static __device__ __inline__
-int iszero(T a) {
-  return a == T(0);
+static __device__ __inline__ int iszero(T a) {
+    return a == T(0);
 }
 
 template<typename T>
-static __device__ __inline__
-int __isinf(const T in) {
+static __device__ __inline__ int __isinf(const T in) {
     return isinf(in);
 }
 
 template<>
-__device__ __inline__
-int __isinf<__half>(const __half in) {
+__device__ __inline__ int __isinf<__half>(const __half in) {
 #if __CUDA_ARCH__ >= 530
     return __hisinf(in);
 #else
@@ -222,14 +220,12 @@ int __isinf<__half>(const __half in) {
 }
 
 template<typename T>
-static __device__ __inline__
-int __isnan(const T in) {
+static __device__ __inline__ int __isnan(const T in) {
     return isnan(in);
 }
 
 template<>
-__device__ __inline__
-int __isnan<__half>(const __half in) {
+__device__ __inline__ int __isnan<__half>(const __half in) {
 #if __CUDA_ARCH__ >= 530
     return __hisnan(in);
 #else
diff --git a/src/backend/cuda/kernel/lookup.cuh b/src/backend/cuda/kernel/lookup.cuh
index 6613095ae6..753ea8c6db 100644
--- a/src/backend/cuda/kernel/lookup.cuh
+++ b/src/backend/cuda/kernel/lookup.cuh
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename in_t, typename idx_t>
@@ -68,3 +69,4 @@ __global__ void lookupND(Param<in_t> out, CParam<in_t> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index bca81cdebc..b4395980f0 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/lookup_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -43,7 +44,7 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         dim3 blocks(blks, 1);
 
         auto lookup1d = common::getKernel(
-            "cuda::lookup1D", std::array{lookup_cuh_src},
+            "arrayfire::cuda::lookup1D", std::array{lookup_cuh_src},
             TemplateArgs(TemplateTypename<in_t>(), TemplateTypename<idx_t>()),
             std::array{DefineValue(THREADS), DefineValue(THRD_LOAD)});
 
@@ -59,12 +60,12 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         dim3 blocks(blks_x * out.dims[2], blks_y * out.dims[3]);
 
         const int maxBlocksY =
-            cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
+            getDeviceProp(getActiveDeviceId()).maxGridSize[1];
         blocks.z = divup(blocks.y, maxBlocksY);
         blocks.y = divup(blocks.y, blocks.z);
 
         auto lookupnd = common::getKernel(
-            "cuda::lookupND", std::array{lookup_cuh_src},
+            "arrayfire::cuda::lookupND", std::array{lookup_cuh_src},
             TemplateArgs(TemplateTypename<in_t>(), TemplateTypename<idx_t>(),
                          TemplateArg(dim)));
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
@@ -76,3 +77,4 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/lu_split.cuh b/src/backend/cuda/kernel/lu_split.cuh
index 4299419382..f2f892bbce 100644
--- a/src/backend/cuda/kernel/lu_split.cuh
+++ b/src/backend/cuda/kernel/lu_split.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool same_dims>
@@ -62,3 +63,4 @@ __global__ void luSplit(Param<T> lower, Param<T> upper, Param<T> in,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 8e74c6fbe5..1d2a185276 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -17,6 +17,7 @@
 
 #include <array>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -31,7 +32,7 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
         lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
     auto luSplit = common::getKernel(
-        "cuda::luSplit", std::array{lu_split_cuh_src},
+        "arrayfire::cuda::luSplit", std::array{lu_split_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(sameDims)));
 
     dim3 threads(TX, TY, 1);
@@ -48,3 +49,4 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/match_template.cuh b/src/backend/cuda/kernel/match_template.cuh
index daffdb9ceb..16cf172e1b 100644
--- a/src/backend/cuda/kernel/match_template.cuh
+++ b/src/backend/cuda/kernel/match_template.cuh
@@ -9,12 +9,12 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename inType, typename outType, af::matchType mType, bool needMean>
-__global__
-void matchTemplate(Param<outType> out, CParam<inType> srch,
-                   CParam<inType> tmplt, int nBBS0, int nBBS1) {
+__global__ void matchTemplate(Param<outType> out, CParam<inType> srch,
+                              CParam<inType> tmplt, int nBBS0, int nBBS1) {
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
 
@@ -118,4 +118,5 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index 3969bfd453..c9754473ae 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -14,6 +14,7 @@
 #include <nvrtc_kernel_headers/match_template_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,7 +26,7 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
                    CParam<inType> tmplt, const af::matchType mType,
                    bool needMean) {
     auto matchTemplate = common::getKernel(
-        "cuda::matchTemplate", std::array{match_template_cuh_src},
+        "arrayfire::cuda::matchTemplate", std::array{match_template_cuh_src},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
                      TemplateArg(mType), TemplateArg(needMean)));
 
@@ -43,3 +44,4 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/mean.hpp b/src/backend/cuda/kernel/mean.hpp
index c981d59656..a26eeac7fd 100644
--- a/src/backend/cuda/kernel/mean.hpp
+++ b/src/backend/cuda/kernel/mean.hpp
@@ -24,6 +24,7 @@
 #include <memory>
 #include <vector>
 
+namespace arrayfire {
 namespace cuda {
 
 __device__ auto operator*(float lhs, __half rhs) -> __half {
@@ -476,16 +477,13 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
         std::vector<T> h_ptr(tmp_elements);
         std::vector<Tw> h_wptr(tmp_elements);
 
-        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.get(),
-                                   tmp_elements * sizeof(T),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), tmpWt.get(),
-                                   tmp_elements * sizeof(Tw),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(
-            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_ptr.data(), tmpOut.get(), tmp_elements * sizeof(T),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_wptr.data(), tmpWt.get(), tmp_elements * sizeof(Tw),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaStreamSynchronize(getStream(getActiveDeviceId())));
 
         compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
         compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
@@ -500,16 +498,13 @@ T mean_all_weighted(CParam<T> in, CParam<Tw> iwt) {
         std::vector<T> h_ptr(in_elements);
         std::vector<Tw> h_wptr(in_elements);
 
-        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr,
-                                   in_elements * sizeof(T),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaMemcpyAsync(h_wptr.data(), iwt.ptr,
-                                   in_elements * sizeof(Tw),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(
-            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_ptr.data(), in.ptr, in_elements * sizeof(T),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_wptr.data(), iwt.ptr, in_elements * sizeof(Tw),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaStreamSynchronize(getStream(getActiveDeviceId())));
 
         compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
         compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
@@ -561,16 +556,13 @@ To mean_all(CParam<Ti> in) {
         std::vector<To> h_ptr(tmp_elements);
         std::vector<Tw> h_cptr(tmp_elements);
 
-        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), tmpOut.get(),
-                                   tmp_elements * sizeof(To),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(cudaMemcpyAsync(h_cptr.data(), tmpCt.get(),
-                                   tmp_elements * sizeof(Tw),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(
-            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_ptr.data(), tmpOut.get(), tmp_elements * sizeof(To),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_cptr.data(), tmpCt.get(), tmp_elements * sizeof(Tw),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaStreamSynchronize(getStream(getActiveDeviceId())));
 
         compute_t<To> val    = static_cast<compute_t<To>>(h_ptr[0]);
         compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_cptr[0]);
@@ -584,12 +576,10 @@ To mean_all(CParam<Ti> in) {
     } else {
         std::vector<Ti> h_ptr(in_elements);
 
-        CUDA_CHECK(cudaMemcpyAsync(h_ptr.data(), in.ptr,
-                                   in_elements * sizeof(Ti),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getStream(cuda::getActiveDeviceId())));
-        CUDA_CHECK(
-            cudaStreamSynchronize(cuda::getStream(cuda::getActiveDeviceId())));
+        CUDA_CHECK(cudaMemcpyAsync(
+            h_ptr.data(), in.ptr, in_elements * sizeof(Ti),
+            cudaMemcpyDeviceToHost, getStream(getActiveDeviceId())));
+        CUDA_CHECK(cudaStreamSynchronize(getStream(getActiveDeviceId())));
 
         common::Transform<Ti, compute_t<To>, af_add_t> transform;
         compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
@@ -606,3 +596,4 @@ To mean_all(CParam<Ti> in) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/meanshift.cuh b/src/backend/cuda/kernel/meanshift.cuh
index 4e599385e3..240c853f46 100644
--- a/src/backend/cuda/kernel/meanshift.cuh
+++ b/src/backend/cuda/kernel/meanshift.cuh
@@ -10,12 +10,12 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename AccType, typename T, int channels>
-__global__
-void meanshift(Param<T> out, CParam<T> in, int radius, float cvar,
-               uint numIters, int nBBS0, int nBBS1) {
+__global__ void meanshift(Param<T> out, CParam<T> in, int radius, float cvar,
+                          uint numIters, int nBBS0, int nBBS1) {
     unsigned b2 = blockIdx.x / nBBS0;
     unsigned b3 = blockIdx.y / nBBS1;
     const T* iptr =
@@ -126,4 +126,5 @@ void meanshift(Param<T> out, CParam<T> in, int radius, float cvar,
               ch * out.strides[2])] = currentCenterColors[ch];
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index 530279fd1b..c1882c91fc 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -16,6 +16,7 @@
 #include <array>
 #include <type_traits>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -28,7 +29,7 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
     typedef typename std::conditional<std::is_same<T, double>::value, double,
                                       float>::type AccType;
     auto meanshift = common::getKernel(
-        "cuda::meanshift", std::array{meanshift_cuh_src},
+        "arrayfire::cuda::meanshift", std::array{meanshift_cuh_src},
         TemplateArgs(TemplateTypename<AccType>(), TemplateTypename<T>(),
                      TemplateArg((IsColor ? 3 : 1))  // channels
                      ));
@@ -52,3 +53,4 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/medfilt.cuh b/src/backend/cuda/kernel/medfilt.cuh
index d04c9ec1db..e2d513cf95 100644
--- a/src/backend/cuda/kernel/medfilt.cuh
+++ b/src/backend/cuda/kernel/medfilt.cuh
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <shared.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // Exchange trick: Morgan McGuire, ShaderX 2008
@@ -20,16 +21,14 @@ namespace cuda {
         b     = max(tmp, b); \
     }
 
-__forceinline__ __device__
-int lIdx(int x, int y, int stride1, int stride0) {
+__forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
     return (y * stride1 + x * stride0);
 }
 
 template<typename T, af::borderType pad>
-__device__
-void load2ShrdMem(T* shrd, const T* in, int lx, int ly,
-                  int shrdStride, int dim0, int dim1, int gx, int gy,
-                  int inStride1, int inStride0) {
+__device__ void load2ShrdMem(T* shrd, const T* in, int lx, int ly,
+                             int shrdStride, int dim0, int dim1, int gx, int gy,
+                             int inStride1, int inStride0) {
     switch (pad) {
         case AF_PAD_ZERO: {
             if (gx < 0 || gx >= dim0 || gy < 0 || gy >= dim1)
@@ -51,9 +50,8 @@ void load2ShrdMem(T* shrd, const T* in, int lx, int ly,
 }
 
 template<typename T, af::borderType pad>
-__device__
-void load2ShrdMem_1d(T* shrd, const T* in, int lx, int dim0, int gx,
-                     int inStride0) {
+__device__ void load2ShrdMem_1d(T* shrd, const T* in, int lx, int dim0, int gx,
+                                int inStride0) {
     switch (pad) {
         case AF_PAD_ZERO: {
             if (gx < 0 || gx >= dim0)
@@ -71,8 +69,7 @@ void load2ShrdMem_1d(T* shrd, const T* in, int lx, int dim0, int gx,
 }
 
 template<typename T, af::borderType pad, unsigned w_len, unsigned w_wid>
-__global__
-void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1) {
+__global__ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1) {
     __shared__ T shrdMem[(THREADS_X + w_len - 1) * (THREADS_Y + w_wid - 1)];
 
     // calculate necessary offset and window parameters
@@ -182,8 +179,8 @@ void medfilt2(Param<T> out, CParam<T> in, int nBBS0, int nBBS1) {
 }
 
 template<typename T, af::borderType pad, unsigned ARR_SIZE>
-__global__
-void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0) {
+__global__ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid,
+                         int nBBS0) {
     SharedMemory<T> shared;
     T* shrdMem = shared.getPointer();
 
@@ -285,4 +282,5 @@ void medfilt1(Param<T> out, CParam<T> in, unsigned w_wid, int nBBS0) {
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index c0062ccc2f..69920b5ac0 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -14,6 +14,7 @@
 #include <nvrtc_kernel_headers/medfilt_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -27,7 +28,7 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
               int w_wid) {
     UNUSED(w_wid);
     auto medfilt2 = common::getKernel(
-        "cuda::medfilt2", std::array{medfilt_cuh_src},
+        "arrayfire::cuda::medfilt2", std::array{medfilt_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
                      TemplateArg(w_len), TemplateArg(w_wid)),
         std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
@@ -46,10 +47,10 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
 
 template<typename T>
 void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
-    auto medfilt1 =
-        common::getKernel("cuda::medfilt1", std::array{medfilt_cuh_src},
-                          TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
-                                       TemplateArg(w_wid)));
+    auto medfilt1 = common::getKernel(
+        "arrayfire::cuda::medfilt1", std::array{medfilt_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
+                     TemplateArg(w_wid)));
 
     const dim3 threads(THREADS_X);
 
@@ -66,3 +67,4 @@ void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/memcopy.cuh b/src/backend/cuda/kernel/memcopy.cuh
index ecef444cce..b078a48aea 100644
--- a/src/backend/cuda/kernel/memcopy.cuh
+++ b/src/backend/cuda/kernel/memcopy.cuh
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // memCopy without looping, so dim3 has to be 1.
@@ -223,3 +224,4 @@ __global__ void memCopyLoop123(Param<T> out, CParam<T> in) {
     }
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index 1592d62ec9..b75cc39c86 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -20,6 +20,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -116,12 +117,13 @@ void memcopy(Param<T> out, CParam<T> in, dim_t indims) {
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
     // select the kernel with the necessary loopings
-    const char *kernelName{th.loop0   ? "cuda::memCopyLoop0"
-                           : th.loop2 ? "cuda::memCopyLoop123"
-                           : th.loop1 ? th.loop3 ? "cuda::memCopyLoop13"
-                                                 : "cuda::memCopyLoop1"
-                           : th.loop3 ? "cuda::memCopyLoop3"
-                                      : "cuda::memCopy"};
+    const char *kernelName{th.loop0   ? "arrayfire::cuda::memCopyLoop0"
+                           : th.loop2 ? "arrayfire::cuda::memCopyLoop123"
+                           : th.loop1 ? th.loop3
+                                            ? "arrayfire::cuda::memCopyLoop13"
+                                            : "arrayfire::cuda::memCopyLoop1"
+                           : th.loop3 ? "arrayfire::cuda::memCopyLoop3"
+                                      : "arrayfire::cuda::memCopy"};
 
     // Conversion to cuda base vector types.
     switch (sizeofNewT) {
@@ -194,10 +196,10 @@ void copy(Param<outType> dst, CParam<inType> src, dim_t ondims,
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
     auto copy{common::getKernel(
-        th.loop0                 ? "cuda::scaledCopyLoop0"
-        : (th.loop2 || th.loop3) ? "cuda::scaledCopyLoop123"
-        : th.loop1               ? "cuda::scaledCopyLoop1"
-                                 : "cuda::scaledCopy",
+        th.loop0                 ? "arrayfire::cuda::scaledCopyLoop0"
+        : (th.loop2 || th.loop3) ? "arrayfire::cuda::scaledCopyLoop123"
+        : th.loop1               ? "arrayfire::cuda::scaledCopyLoop1"
+                                 : "arrayfire::cuda::scaledCopy",
         std::array{copy_cuh_src},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
                      TemplateArg(same_dims), TemplateArg(factor != 1.0)))};
@@ -208,3 +210,4 @@ void copy(Param<outType> dst, CParam<inType> src, dim_t ondims,
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/moments.cuh b/src/backend/cuda/kernel/moments.cuh
index 765b15d2a8..12703a6343 100644
--- a/src/backend/cuda/kernel/moments.cuh
+++ b/src/backend/cuda/kernel/moments.cuh
@@ -9,11 +9,12 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
-__global__
-void moments(Param<float> out, CParam<T> in, af::momentType moment, const bool pBatch) {
+__global__ void moments(Param<float> out, CParam<T> in, af::momentType moment,
+                        const bool pBatch) {
     const dim_t idw = blockIdx.y / in.dims[2];
     const dim_t idz = blockIdx.y - idw * in.dims[2];
 
@@ -56,4 +57,5 @@ void moments(Param<float> out, CParam<T> in, af::momentType moment, const bool p
         atomicAdd(offset, blk_moment_sum[threadIdx.x]);
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index 2af86afef6..ece6627c71 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -14,6 +14,7 @@
 #include <nvrtc_kernel_headers/moments_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -21,9 +22,9 @@ static const int THREADS = 128;
 
 template<typename T>
 void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
-    auto moments =
-        common::getKernel("cuda::moments", std::array{moments_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto moments = common::getKernel("arrayfire::cuda::moments",
+                                     std::array{moments_cuh_src},
+                                     TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
@@ -40,3 +41,4 @@ void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/morph.cuh b/src/backend/cuda/kernel/morph.cuh
index 086c4508ea..34e7a10e1c 100644
--- a/src/backend/cuda/kernel/morph.cuh
+++ b/src/backend/cuda/kernel/morph.cuh
@@ -20,6 +20,7 @@
 __constant__ char
     cFilter[MAX_MORPH_FILTER_LEN * MAX_MORPH_FILTER_LEN * sizeof(double)];
 
+namespace arrayfire {
 namespace cuda {
 
 __forceinline__ __device__ int lIdx(int x, int y, int stride1, int stride0) {
@@ -101,7 +102,7 @@ __global__ void morph(Param<T> out, CParam<T> in, int nBBS0, int nBBS1,
 
     const T* d_filt = (const T*)cFilter;
     T acc           = isDilation ? common::Binary<T, af_max_t>::init()
-                       : common::Binary<T, af_min_t>::init();
+                                 : common::Binary<T, af_min_t>::init();
 #pragma unroll
     for (int wj = 0; wj < windLen; ++wj) {
         int joff   = wj * windLen;
@@ -197,7 +198,7 @@ __global__ void morph3D(Param<T> out, CParam<T> in, int nBBS) {
 
     const T* d_filt = (const T*)cFilter;
     T acc           = isDilation ? common::Binary<T, af_max_t>::init()
-                       : common::Binary<T, af_min_t>::init();
+                                 : common::Binary<T, af_min_t>::init();
 #pragma unroll
     for (int wk = 0; wk < windLen; ++wk) {
         int koff   = wk * se_area;
@@ -227,3 +228,4 @@ __global__ void morph3D(Param<T> out, CParam<T> in, int nBBS) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 1202850f40..4936d659b4 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -15,6 +15,7 @@
 
 #include <limits>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -31,7 +32,7 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
     auto morph = common::getKernel(
-        "cuda::morph", std::array{morph_cuh_src},
+        "arrayfire::cuda::morph", std::array{morph_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isDilation),
                      TemplateArg(SeLength)),
         std::array{DefineValue(MAX_MORPH_FILTER_LEN)});
@@ -67,7 +68,7 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     }
 
     auto morph3D = common::getKernel(
-        "cuda::morph3D", std::array{morph_cuh_src},
+        "arrayfire::cuda::morph3D", std::array{morph_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isDilation),
                      TemplateArg(windLen)),
         std::array{DefineValue(MAX_MORPH_FILTER_LEN)});
@@ -97,3 +98,4 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/nearest_neighbour.hpp b/src/backend/cuda/kernel/nearest_neighbour.hpp
index 170f81868a..a628c18a48 100644
--- a/src/backend/cuda/kernel/nearest_neighbour.hpp
+++ b/src/backend/cuda/kernel/nearest_neighbour.hpp
@@ -15,6 +15,7 @@
 #include <memory.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 namespace kernel {
@@ -188,3 +189,4 @@ void all_distances(Param<To> dist, CParam<T> query, CParam<T> train,
 }  // namespace kernel
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/orb.hpp b/src/backend/cuda/kernel/orb.hpp
index 672da31fc3..c1df7620f5 100644
--- a/src/backend/cuda/kernel/orb.hpp
+++ b/src/backend/cuda/kernel/orb.hpp
@@ -21,6 +21,7 @@
 using std::unique_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -291,7 +292,7 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
     // distribution instead of using the reference one
     // CUDA_CHECK(cudaMemcpyToSymbolAsync(d_ref_pat, h_ref_pat, 256 * 4 *
     // sizeof(int), 0,
-    // cudaMemcpyHostToDevice, cuda::getActiveStream()));
+    // cudaMemcpyHostToDevice, getActiveStream()));
 
     vector<float*> d_score_pyr(max_levels);
     vector<float*> d_ori_pyr(max_levels);
@@ -311,8 +312,7 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
         gauss_filter = createHostDataArray<convAccT>(gauss_dim, h_gauss.data());
         CUDA_CHECK(cudaMemcpyAsync(gauss_filter.get(), h_gauss.data(),
                                    h_gauss.size() * sizeof(convAccT),
-                                   cudaMemcpyHostToDevice,
-                                   cuda::getActiveStream()));
+                                   cudaMemcpyHostToDevice, getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     }
 
@@ -378,7 +378,7 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
         unsigned* d_desc_lvl = memAlloc<unsigned>(feat_pyr[i] * 8).release();
         CUDA_CHECK(cudaMemsetAsync(d_desc_lvl, 0,
                                    feat_pyr[i] * 8 * sizeof(unsigned),
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         // Compute ORB descriptors
         threads = dim3(THREADS_X, THREADS_Y);
@@ -419,23 +419,23 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
 
         CUDA_CHECK(cudaMemcpyAsync(
             *d_x + offset, d_x_pyr[i], feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_y + offset, d_y_pyr[i], feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_score + offset, d_score_pyr[i], feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_ori + offset, d_ori_pyr[i], feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_size + offset, d_size_pyr[i], feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_desc + (offset * 8), d_desc_pyr[i],
                                    feat_pyr[i] * 8 * sizeof(unsigned),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         memFree(d_x_pyr[i]);
         memFree(d_y_pyr[i]);
@@ -451,3 +451,4 @@ void orb(unsigned* out_feat, float** d_x, float** d_y, float** d_score,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/orb_patch.hpp b/src/backend/cuda/kernel/orb_patch.hpp
index 6dfe3fb037..8a384c24ad 100644
--- a/src/backend/cuda/kernel/orb_patch.hpp
+++ b/src/backend/cuda/kernel/orb_patch.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cuda {
 
 // Reference pattern, generated for a patch size of 31x31, as suggested by
@@ -94,3 +95,4 @@ int d_ref_pat[REF_PAT_LENGTH] = {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/pad_array_borders.cuh b/src/backend/cuda/kernel/pad_array_borders.cuh
index 20e8ac6bc7..73df3261a7 100644
--- a/src/backend/cuda/kernel/pad_array_borders.cuh
+++ b/src/backend/cuda/kernel/pad_array_borders.cuh
@@ -11,30 +11,29 @@
 #include <math.hpp>
 #include <utility.hpp>
 
-namespace  cuda {
+namespace arrayfire {
+namespace cuda {
 
 template<af::borderType BType>
-__device__
-int idxByndEdge(const int i, const int lb, const int len) {
+__device__ int idxByndEdge(const int i, const int lb, const int len) {
     uint retVal;
     switch (BType) {
-        case AF_PAD_SYM: retVal = trimIndex(i-lb, len); break;
+        case AF_PAD_SYM: retVal = trimIndex(i - lb, len); break;
         case AF_PAD_CLAMP_TO_EDGE: retVal = clamp(i - lb, 0, len - 1); break;
         case AF_PAD_PERIODIC: {
             int rem   = (i - lb) % len;
             bool cond = rem < 0;
             retVal    = cond * (rem + len) + (1 - cond) * rem;
         } break;
-        default: retVal = 0; break; // AF_PAD_ZERO
+        default: retVal = 0; break;  // AF_PAD_ZERO
     }
     return retVal;
 }
 
 template<typename T, af::borderType BType>
-__global__
-void padBorders(Param<T> out, CParam<T> in, const int l0,
-               const int l1, const int l2, const int l3,
-               unsigned blk_x, unsigned blk_y) {
+__global__ void padBorders(Param<T> out, CParam<T> in, const int l0,
+                           const int l1, const int l2, const int l3,
+                           unsigned blk_x, unsigned blk_y) {
     const int lx = threadIdx.x;
     const int ly = threadIdx.y;
     const int k  = blockIdx.x / blk_x;
@@ -86,4 +85,5 @@ void padBorders(Param<T> out, CParam<T> in, const int l0,
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index b55bd419c5..85acaabb26 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -18,6 +18,7 @@
 
 #include <array>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -28,7 +29,7 @@ template<typename T>
 void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
                 const af::borderType btype) {
     auto padBorders = common::getKernel(
-        "cuda::padBorders", std::array{pad_array_borders_cuh_src},
+        "arrayfire::cuda::padBorders", std::array{pad_array_borders_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(btype)));
 
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
@@ -48,3 +49,4 @@ void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 31f9a711ed..7fddcbfd20 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -21,6 +21,7 @@
 
 #include <limits>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
@@ -1101,3 +1102,4 @@ void normalDistributionCBRNG(T *out, size_t elements,
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/random_engine_mersenne.hpp b/src/backend/cuda/kernel/random_engine_mersenne.hpp
index 6e8862574e..5b288bc6b4 100644
--- a/src/backend/cuda/kernel/random_engine_mersenne.hpp
+++ b/src/backend/cuda/kernel/random_engine_mersenne.hpp
@@ -42,6 +42,7 @@
  * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *******************************************************/
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -128,3 +129,4 @@ void initMersenneState(uint *state, const uint *tbl, uintl seed) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/random_engine_philox.hpp b/src/backend/cuda/kernel/random_engine_philox.hpp
index 4648617a8a..8124416e03 100644
--- a/src/backend/cuda/kernel/random_engine_philox.hpp
+++ b/src/backend/cuda/kernel/random_engine_philox.hpp
@@ -46,6 +46,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // Utils
@@ -102,3 +103,4 @@ static inline __device__ void philox(uint key[2], uint ctr[4]) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/random_engine_threefry.hpp b/src/backend/cuda/kernel/random_engine_threefry.hpp
index dbafbfae44..a2bbbcaec1 100644
--- a/src/backend/cuda/kernel/random_engine_threefry.hpp
+++ b/src/backend/cuda/kernel/random_engine_threefry.hpp
@@ -46,6 +46,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // Utils
@@ -160,3 +161,4 @@ __device__ void threefry(uint k[2], uint c[2], uint X[2]) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/range.cuh b/src/backend/cuda/kernel/range.cuh
index 8e703b356f..753bbad174 100644
--- a/src/backend/cuda/kernel/range.cuh
+++ b/src/backend/cuda/kernel/range.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -56,3 +57,4 @@ __global__ void range(Param<T> out, const int dim, const int blocksPerMatX,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index cb1f8e13e4..2e222f6e21 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/range_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,8 +26,9 @@ void range(Param<T> out, const int dim) {
     constexpr unsigned RANGE_TILEX = 512;
     constexpr unsigned RANGE_TILEY = 32;
 
-    auto range = common::getKernel("cuda::range", std::array{range_cuh_src},
-                                   TemplateArgs(TemplateTypename<T>()));
+    auto range =
+        common::getKernel("arrayfire::cuda::range", std::array{range_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
@@ -34,10 +36,9 @@ void range(Param<T> out, const int dim) {
     int blocksPerMatY = divup(out.dims[1], RANGE_TILEY);
     dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -47,3 +48,4 @@ void range(Param<T> out, const int dim) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/reduce.hpp b/src/backend/cuda/kernel/reduce.hpp
index fb51a72851..c3cf279b39 100644
--- a/src/backend/cuda/kernel/reduce.hpp
+++ b/src/backend/cuda/kernel/reduce.hpp
@@ -26,6 +26,7 @@
 
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -117,10 +118,9 @@ void reduce_dim_launcher(Param<To> out, CParam<Ti> in, const uint threads_y,
 
     dim3 blocks(blocks_dim[0] * blocks_dim[2], blocks_dim[1] * blocks_dim[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     switch (threads_y) {
         case 8:
@@ -390,10 +390,9 @@ void reduce_all_launcher(Param<To> out, CParam<Ti> in, const uint blocks_x,
 
     uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     long tmp_elements = blocks.x * blocks.y * blocks.z;
     if (tmp_elements > UINT_MAX) {
@@ -438,10 +437,9 @@ void reduce_first_launcher(Param<To> out, CParam<Ti> in, const uint blocks_x,
 
     uint repeat = divup(in.dims[0], (blocks_x * threads_x));
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     switch (threads_x) {
         case 32:
@@ -546,3 +544,4 @@ void reduce_all(Param<To> out, CParam<Ti> in, bool change_nan, double nanval) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index 72b5c7b146..ea015aaff2 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -27,6 +27,7 @@ using std::unique_ptr;
 
 const static unsigned int FULL_MASK = 0xFFFFFFFF;
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -637,3 +638,4 @@ __global__ static void reduce_blocks_dim_by_key(
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index 7a459a6fb9..b1fe3f7c8d 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -34,14 +34,15 @@ __device__ static int continue_flag = 1;
 
 // Wrapper function for texture fetch
 template<typename T>
-static inline __device__ T fetch(const int n, cuda::Param<T> equiv_map,
+static inline __device__ T fetch(const int n,
+                                 arrayfire::cuda::Param<T> equiv_map,
                                  cudaTextureObject_t tex) {
     return tex1Dfetch<T>(tex, n);
 }
 
 template<>
 __device__ inline double fetch<double>(const int n,
-                                       cuda::Param<double> equiv_map,
+                                       arrayfire::cuda::Param<double> equiv_map,
                                        cudaTextureObject_t tex) {
     return equiv_map.ptr[n];
 }
@@ -49,8 +50,8 @@ __device__ inline double fetch<double>(const int n,
 // The initial label kernel distinguishes between valid (nonzero)
 // pixels and "background" (zero) pixels.
 template<typename T, int n_per_thread>
-__global__ static void initial_label(cuda::Param<T> equiv_map,
-                                     cuda::CParam<char> bin) {
+__global__ static void initial_label(arrayfire::cuda::Param<T> equiv_map,
+                                     arrayfire::cuda::CParam<char> bin) {
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
 
@@ -70,8 +71,9 @@ __global__ static void initial_label(cuda::Param<T> equiv_map,
 }
 
 template<typename T, int n_per_thread>
-__global__ static void final_relabel(cuda::Param<T> equiv_map,
-                                     cuda::CParam<char> bin, const T* d_tmp) {
+__global__ static void final_relabel(arrayfire::cuda::Param<T> equiv_map,
+                                     arrayfire::cuda::CParam<char> bin,
+                                     const T* d_tmp) {
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
     const int base_y = (blockIdx.y * blockDim.y * n_per_thread) + threadIdx.y;
 
@@ -96,8 +98,8 @@ __global__ static void final_relabel(cuda::Param<T> equiv_map,
 // do not choose zero, which indicates invalid.
 template<typename T>
 __device__ __inline__ static T relabel(const T a, const T b) {
-    T aa = (a == 0) ? cuda::maxval<T>() : a;
-    T bb = (b == 0) ? cuda::maxval<T>() : b;
+    T aa = (a == 0) ? arrayfire::cuda::maxval<T>() : a;
+    T bb = (b == 0) ? arrayfire::cuda::maxval<T>() : b;
     return min(aa, bb);
 }
 
@@ -120,7 +122,7 @@ struct warp_count {
 // Number of elements to handle per thread in each dimension
 // int n_per_thread = 2; // 2x2 per thread = 4 total elems per thread
 template<typename T, int block_dim, int n_per_thread, bool full_conn>
-__global__ static void update_equiv(cuda::Param<T> equiv_map,
+__global__ static void update_equiv(arrayfire::cuda::Param<T> equiv_map,
                                     const cudaTextureObject_t tex) {
     // Basic coordinates
     const int base_x = (blockIdx.x * blockDim.x * n_per_thread) + threadIdx.x;
@@ -346,8 +348,9 @@ struct clamp_to_one : public thrust::unary_function<T, T> {
 };
 
 template<typename T, bool full_conn, int n_per_thread>
-void regions(cuda::Param<T> out, cuda::CParam<char> in,
+void regions(arrayfire::cuda::Param<T> out, arrayfire::cuda::CParam<char> in,
              cudaTextureObject_t tex) {
+    using arrayfire::cuda::getActiveStream;
     const dim3 threads(THREADS_X, THREADS_Y);
 
     const int blk_x = divup(in.dims[0], threads.x * 2);
@@ -363,9 +366,9 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in,
 
     while (h_continue) {
         h_continue = 0;
-        CUDA_CHECK(cudaMemcpyToSymbolAsync(
-            continue_flag, &h_continue, sizeof(int), 0, cudaMemcpyHostToDevice,
-            cuda::getActiveStream()));
+        CUDA_CHECK(
+            cudaMemcpyToSymbolAsync(continue_flag, &h_continue, sizeof(int), 0,
+                                    cudaMemcpyHostToDevice, getActiveStream()));
 
         CUDA_LAUNCH((update_equiv<T, 16, n_per_thread, full_conn>), blocks,
                     threads, out, tex);
@@ -374,8 +377,8 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in,
 
         CUDA_CHECK(cudaMemcpyFromSymbolAsync(
             &h_continue, continue_flag, sizeof(int), 0, cudaMemcpyDeviceToHost,
-            cuda::getActiveStream()));
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
+            getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
     }
 
     // Now, perform the final relabeling.  This converts the equivalency
@@ -383,10 +386,9 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in,
     // component to being sequentially numbered components starting at
     // 1.
     int size = in.dims[0] * in.dims[1];
-    auto tmp = cuda::memAlloc<T>(size);
+    auto tmp = arrayfire::cuda::memAlloc<T>(size);
     CUDA_CHECK(cudaMemcpyAsync(tmp.get(), out.ptr, size * sizeof(T),
-                               cudaMemcpyDeviceToDevice,
-                               cuda::getActiveStream()));
+                               cudaMemcpyDeviceToDevice, getActiveStream()));
 
     // Wrap raw device ptr
     thrust::device_ptr<T> wrapped_tmp = thrust::device_pointer_cast(tmp.get());
@@ -405,7 +407,7 @@ void regions(cuda::Param<T> out, cuda::CParam<char> in,
     // post-processing of labels is required.
     if (num_bins <= 2) return;
 
-    cuda::ThrustVector<T> labels(num_bins);
+    arrayfire::cuda::ThrustVector<T> labels(num_bins);
 
     // Find the end of each section of values
     thrust::counting_iterator<T> search_begin(0);
diff --git a/src/backend/cuda/kernel/reorder.cuh b/src/backend/cuda/kernel/reorder.cuh
index 617943cc87..4f1db7bf3a 100644
--- a/src/backend/cuda/kernel/reorder.cuh
+++ b/src/backend/cuda/kernel/reorder.cuh
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -56,3 +57,4 @@ __global__ void reorder(Param<T> out, CParam<T> in, const int d0, const int d1,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index cb10ad3cb0..e2b83e4ab8 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/reorder_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,9 +26,9 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    auto reorder =
-        common::getKernel("cuda::reorder", std::array{reorder_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto reorder = common::getKernel("arrayfire::cuda::reorder",
+                                     std::array{reorder_cuh_src},
+                                     TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);
 
@@ -35,10 +36,9 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     int blocksPerMatY = divup(out.dims[1], TILEY);
     dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -49,3 +49,4 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/resize.cuh b/src/backend/cuda/kernel/resize.cuh
index 22a0d1d159..8186804dae 100644
--- a/src/backend/cuda/kernel/resize.cuh
+++ b/src/backend/cuda/kernel/resize.cuh
@@ -10,15 +10,15 @@
 #include <Param.hpp>
 #include <interp.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // nearest-neighbor resampling
 template<typename T>
-__host__ __device__
-void resize_n(Param<T> out, CParam<T> in, const int o_off,
-              const int i_off, const int blockIdx_x,
-              const int blockIdx_y, const float xf,
-              const float yf) {
+__host__ __device__ void resize_n(Param<T> out, CParam<T> in, const int o_off,
+                                  const int i_off, const int blockIdx_x,
+                                  const int blockIdx_y, const float xf,
+                                  const float yf) {
     const int ox = threadIdx.x + blockIdx_x * blockDim.x;
     const int oy = threadIdx.y + blockIdx_y * blockDim.y;
 
@@ -35,11 +35,10 @@ void resize_n(Param<T> out, CParam<T> in, const int o_off,
 
 // bilinear resampling
 template<typename T>
-__host__ __device__
-void resize_b(Param<T> out, CParam<T> in, const int o_off,
-              const int i_off, const int blockIdx_x,
-              const int blockIdx_y, const float xf_,
-              const float yf_) {
+__host__ __device__ void resize_b(Param<T> out, CParam<T> in, const int o_off,
+                                  const int i_off, const int blockIdx_x,
+                                  const int blockIdx_y, const float xf_,
+                                  const float yf_) {
     const int ox = threadIdx.x + blockIdx_x * blockDim.x;
     const int oy = threadIdx.y + blockIdx_y * blockDim.y;
 
@@ -78,11 +77,10 @@ void resize_b(Param<T> out, CParam<T> in, const int o_off,
 
 // lower resampling
 template<typename T>
-__host__ __device__
-void resize_l(Param<T> out, CParam<T> in, const int o_off,
-              const int i_off, const int blockIdx_x,
-              const int blockIdx_y, const float xf,
-              const float yf) {
+__host__ __device__ void resize_l(Param<T> out, CParam<T> in, const int o_off,
+                                  const int i_off, const int blockIdx_x,
+                                  const int blockIdx_y, const float xf,
+                                  const float yf) {
     const int ox = threadIdx.x + blockIdx_x * blockDim.x;
     const int oy = threadIdx.y + blockIdx_y * blockDim.y;
 
@@ -98,9 +96,8 @@ void resize_l(Param<T> out, CParam<T> in, const int o_off,
 }
 
 template<typename T, af::interpType method>
-__global__
-void resize(Param<T> out, CParam<T> in, const int b0,
-            const int b1, const float xf, const float yf) {
+__global__ void resize(Param<T> out, CParam<T> in, const int b0, const int b1,
+                       const float xf, const float yf) {
     const int bIdx = blockIdx.x / b0;
     const int bIdy = blockIdx.y / b1;
     // channel adjustment
@@ -119,4 +116,5 @@ void resize(Param<T> out, CParam<T> in, const int b0,
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index 231dab781b..254e23e7d3 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -14,6 +14,7 @@
 #include <nvrtc_kernel_headers/resize_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -24,7 +25,7 @@ static const unsigned TY = 16;
 template<typename T>
 void resize(Param<T> out, CParam<T> in, af_interp_type method) {
     auto resize = common::getKernel(
-        "cuda::resize", std::array{resize_cuh_src},
+        "arrayfire::cuda::resize", std::array{resize_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(method)));
 
     dim3 threads(TX, TY, 1);
@@ -46,3 +47,4 @@ void resize(Param<T> out, CParam<T> in, af_interp_type method) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/rotate.cuh b/src/backend/cuda/kernel/rotate.cuh
index bd76c490e6..f6fa755ac2 100644
--- a/src/backend/cuda/kernel/rotate.cuh
+++ b/src/backend/cuda/kernel/rotate.cuh
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <interp.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 typedef struct {
@@ -68,4 +69,5 @@ __global__ void rotate(Param<T> out, CParam<T> in, const tmat_t t,
     interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index 5c86b57edf..b31218047c 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -16,6 +16,7 @@
 #include <nvrtc_kernel_headers/rotate_cuh.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -33,7 +34,7 @@ template<typename T>
 void rotate(Param<T> out, CParam<T> in, const float theta,
             const af::interpType method, const int order) {
     auto rotate = common::getKernel(
-        "cuda::rotate", std::array{rotate_cuh_src},
+        "arrayfire::cuda::rotate", std::array{rotate_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(order)));
 
     const float c = cos(-theta), s = sin(-theta);
@@ -85,3 +86,4 @@ void rotate(Param<T> out, CParam<T> in, const float theta,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp
index 6b88c5e8e0..b1480e6628 100644
--- a/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp
+++ b/src/backend/cuda/kernel/scan_by_key/scan_by_key_impl.cpp
@@ -14,6 +14,7 @@
 // The line below is read by CMake to determenine the instantiations
 // SBK_BINARY_OPS:af_add_t af_mul_t af_max_t af_min_t
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // clang-format off
@@ -22,3 +23,4 @@ INSTANTIATE_SCAN_DIM_BY_KEY_OP( @SBK_BINARY_OP@ )
 // clang-format on
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_dim.cuh b/src/backend/cuda/kernel/scan_dim.cuh
index 3f019bb084..a7f4066c80 100644
--- a/src/backend/cuda/kernel/scan_dim.cuh
+++ b/src/backend/cuda/kernel/scan_dim.cuh
@@ -13,6 +13,7 @@
 #include <common/Transform.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Ti, typename To, af_op_t op, int dim, bool isFinalPass,
@@ -168,3 +169,4 @@ __global__ void scan_dim_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index 88c62e175e..a85c15a5ed 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -17,6 +17,7 @@
 #include <nvrtc_kernel_headers/scan_dim_cuh.hpp>
 #include "config.hpp"
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,7 +26,7 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                               const uint threads_y, const dim_t blocks_all[4],
                               int dim, bool isFinalPass, bool inclusive_scan) {
     auto scan_dim = common::getKernel(
-        "cuda::scan_dim", std::array{scan_dim_cuh_src},
+        "arrayfire::cuda::scan_dim", std::array{scan_dim_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>(),
                      TemplateArg(op), TemplateArg(dim),
                      TemplateArg(isFinalPass), TemplateArg(threads_y),
@@ -36,10 +37,9 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
@@ -53,19 +53,18 @@ template<typename To, af_op_t op>
 static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
                                const uint threads_y, const dim_t blocks_all[4],
                                int dim, bool inclusive_scan) {
-    auto scan_dim_bcast =
-        common::getKernel("cuda::scan_dim_bcast", std::array{scan_dim_cuh_src},
-                          TemplateArgs(TemplateTypename<To>(), TemplateArg(op),
-                                       TemplateArg(dim)));
+    auto scan_dim_bcast = common::getKernel(
+        "arrayfire::cuda::scan_dim_bcast", std::array{scan_dim_cuh_src},
+        TemplateArgs(TemplateTypename<To>(), TemplateArg(op),
+                     TemplateArg(dim)));
 
     dim3 threads(THREADS_X, threads_y);
 
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     uint lim = divup(out.dims[dim], (threads_y * blocks_all[dim]));
 
@@ -124,3 +123,4 @@ static void scan_dim(Param<To> out, CParam<Ti> in, int dim,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.cuh b/src/backend/cuda/kernel/scan_dim_by_key.cuh
index 0c5875c2e1..06de7c1ae1 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_dim_by_key.cuh
@@ -12,6 +12,7 @@
 #include <common/Transform.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Tk>
@@ -368,3 +369,4 @@ __global__ void scanbykey_dim_bcast(Param<To> out, CParam<To> tmp,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_dim_by_key.hpp b/src/backend/cuda/kernel/scan_dim_by_key.hpp
index a36b95be39..05092499d6 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 template<typename Ti, typename Tk, typename To, af_op_t op>
@@ -18,3 +19,4 @@ void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim,
                      bool inclusive_scan);
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 0754e1fc22..0dda0b872f 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -21,6 +21,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -32,7 +33,8 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
     auto scanbykey_dim_nonfinal = common::getKernel(
-        "cuda::scanbykey_dim_nonfinal", std::array{scan_dim_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_dim_nonfinal",
+        std::array{scan_dim_by_key_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
         std::array{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
@@ -56,7 +58,8 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
     auto scanbykey_dim_final = common::getKernel(
-        "cuda::scanbykey_dim_final", std::array{scan_dim_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_dim_final",
+        std::array{scan_dim_by_key_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
         std::array{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
@@ -78,7 +81,8 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
     auto scanbykey_dim_bcast = common::getKernel(
-        "cuda::scanbykey_dim_bcast", std::array{scan_dim_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_dim_bcast",
+        std::array{scan_dim_by_key_cuh_src},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
@@ -167,3 +171,4 @@ void scan_dim_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key, int dim,
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uintl)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_first.cuh b/src/backend/cuda/kernel/scan_first.cuh
index 1bd3b52a53..31abbd57a5 100644
--- a/src/backend/cuda/kernel/scan_first.cuh
+++ b/src/backend/cuda/kernel/scan_first.cuh
@@ -13,6 +13,7 @@
 #include <common/Transform.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Ti, typename To, af_op_t op, bool isFinalPass, uint DIMX,
@@ -134,3 +135,4 @@ __global__ void scan_first_bcast(Param<To> out, CParam<To> tmp, uint blocks_x,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index 0fe6ce1d5f..fec9d4be7a 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -17,6 +17,7 @@
 #include <nvrtc_kernel_headers/scan_first_cuh.hpp>
 #include "config.hpp"
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,7 +27,7 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
     auto scan_first = common::getKernel(
-        "cuda::scan_first", std::array{scan_first_cuh_src},
+        "arrayfire::cuda::scan_first", std::array{scan_first_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>(),
                      TemplateArg(op), TemplateArg(isFinalPass),
                      TemplateArg(threads_x), TemplateArg(inclusive_scan)),
@@ -35,10 +36,9 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
@@ -52,16 +52,15 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
     auto scan_first_bcast = common::getKernel(
-        "cuda::scan_first_bcast", std::array{scan_first_cuh_src},
+        "arrayfire::cuda::scan_first_bcast", std::array{scan_first_cuh_src},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     uint lim = divup(out.dims[0], (threads_x * blocks_x));
 
@@ -114,3 +113,4 @@ static void scan_first(Param<To> out, CParam<Ti> in, bool inclusive_scan) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_first_by_key.cuh b/src/backend/cuda/kernel/scan_first_by_key.cuh
index ec894127a0..8f876e2470 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.cuh
+++ b/src/backend/cuda/kernel/scan_first_by_key.cuh
@@ -12,6 +12,7 @@
 #include <common/Transform.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Tk>
@@ -118,9 +119,9 @@ __global__ void scanbykey_first_nonfinal(Param<To> out, Param<To> tmp,
 #pragma unroll
         for (int off = 1; off < DIMX; off *= 2) {
             if (tidx >= off) {
-                val = sfptr[start + tidx]
-                          ? val
-                          : binop(val, sptr[(start - off) + tidx]);
+                val  = sfptr[start + tidx]
+                           ? val
+                           : binop(val, sptr[(start - off) + tidx]);
                 flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
             }
             start               = DIMX - start;
@@ -248,9 +249,9 @@ __global__ void scanbykey_first_final(Param<To> out, CParam<Ti> in,
 #pragma unroll
         for (int off = 1; off < DIMX; off *= 2) {
             if (tidx >= off) {
-                val = sfptr[start + tidx]
-                          ? val
-                          : binop(val, sptr[(start - off) + tidx]);
+                val  = sfptr[start + tidx]
+                           ? val
+                           : binop(val, sptr[(start - off) + tidx]);
                 flag = sfptr[start + tidx] | sfptr[(start - off) + tidx];
             }
             start               = DIMX - start;
@@ -313,3 +314,4 @@ __global__ void scanbykey_first_bcast(Param<To> out, Param<To> tmp,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_first_by_key.hpp b/src/backend/cuda/kernel/scan_first_by_key.hpp
index 41ae8d83c5..80491a1c65 100644
--- a/src/backend/cuda/kernel/scan_first_by_key.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key.hpp
@@ -11,6 +11,7 @@
 #include <Param.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 template<typename Ti, typename Tk, typename To, af_op_t op>
@@ -18,3 +19,4 @@ void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                        bool inclusive_scan);
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 6f9fbd36dd..16abf56b3e 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -20,6 +20,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -30,7 +31,8 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    const uint blocks_x, const uint blocks_y,
                                    const uint threads_x, bool inclusive_scan) {
     auto scanbykey_first_nonfinal = common::getKernel(
-        "cuda::scanbykey_first_nonfinal", std::array{scan_first_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_first_nonfinal",
+        std::array{scan_first_by_key_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
         std::array{DefineValue(THREADS_PER_BLOCK),
@@ -52,7 +54,8 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
     auto scanbykey_first_final = common::getKernel(
-        "cuda::scanbykey_first_final", std::array{scan_first_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_first_final",
+        std::array{scan_first_by_key_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
         std::array{DefineValue(THREADS_PER_BLOCK),
@@ -73,7 +76,8 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
     auto scanbykey_first_bcast = common::getKernel(
-        "cuda::scanbykey_first_bcast", std::array{scan_first_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_first_bcast",
+        std::array{scan_first_by_key_cuh_src},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -154,3 +158,4 @@ void scan_first_by_key(Param<To> out, CParam<Ti> in, CParam<Tk> key,
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uintl)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/select.cuh b/src/backend/cuda/kernel/select.cuh
index 36ab8e4991..c5988594cd 100644
--- a/src/backend/cuda/kernel/select.cuh
+++ b/src/backend/cuda/kernel/select.cuh
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 int getOffset(dim_t *dims, dim_t *strides, dim_t *refdims, int ids[4]) {
@@ -99,3 +100,4 @@ __global__ void selectScalar(Param<T> out, CParam<char> cond, CParam<T> a, T b,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index ceec068e96..1b6d78fa8f 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -16,6 +16,7 @@
 #include <math.hpp>
 #include <nvrtc_kernel_headers/select_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -30,7 +31,7 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
     auto select = common::getKernel(
-        "cuda::select", std::array{select_cuh_src},
+        "arrayfire::cuda::select", std::array{select_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_same)));
 
     dim3 threads(DIMX, DIMY);
@@ -45,10 +46,9 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
 
     dim3 blocks(blk_x * out.dims[2], blk_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -60,7 +60,7 @@ template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b,
                    int ndims, bool flip) {
     auto selectScalar = common::getKernel(
-        "cuda::selectScalar", std::array{select_cuh_src},
+        "arrayfire::cuda::selectScalar", std::array{select_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(flip)));
 
     dim3 threads(DIMX, DIMY);
@@ -83,3 +83,4 @@ void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/shared.hpp b/src/backend/cuda/kernel/shared.hpp
index 5ad92be9da..55d9f70a64 100644
--- a/src/backend/cuda/kernel/shared.hpp
+++ b/src/backend/cuda/kernel/shared.hpp
@@ -11,6 +11,7 @@
 
 #ifdef __CUDACC_RTC__
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 struct SharedMemory {
@@ -20,9 +21,11 @@ struct SharedMemory {
     }
 };
 }  // namespace cuda
+}  // namespace arrayfire
 
 #else
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -58,5 +61,6 @@ SPECIALIZE(uintl)
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/cuda/kernel/shfl_intrinsics.hpp b/src/backend/cuda/kernel/shfl_intrinsics.hpp
index ef12aafe29..687abf5144 100644
--- a/src/backend/cuda/kernel/shfl_intrinsics.hpp
+++ b/src/backend/cuda/kernel/shfl_intrinsics.hpp
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -51,25 +52,24 @@ __device__ T shfl_down_sync(unsigned mask, T var, int delta) {
 }
 // specialization for cfloat
 template<>
-inline __device__ cuda::cfloat shfl_down_sync(unsigned mask, cuda::cfloat var,
-                                              int delta) {
+inline __device__ cfloat shfl_down_sync(unsigned mask, cfloat var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    cuda::cfloat res = {__shfl_down_sync(mask, var.x, delta),
-                        __shfl_down_sync(mask, var.y, delta)};
+    cfloat res = {__shfl_down_sync(mask, var.x, delta),
+                  __shfl_down_sync(mask, var.y, delta)};
 #else
-    cuda::cfloat res  = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
+    cfloat res  = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
 #endif
     return res;
 }
 // specialization for cdouble
 template<>
-inline __device__ cuda::cdouble shfl_down_sync(unsigned mask, cuda::cdouble var,
-                                               int delta) {
+inline __device__ cdouble shfl_down_sync(unsigned mask, cdouble var,
+                                         int delta) {
 #if (CUDA_VERSION >= 9000)
-    cuda::cdouble res = {__shfl_down_sync(mask, var.x, delta),
-                         __shfl_down_sync(mask, var.y, delta)};
+    cdouble res = {__shfl_down_sync(mask, var.x, delta),
+                   __shfl_down_sync(mask, var.y, delta)};
 #else
-    cuda::cdouble res = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
+    cdouble res = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
 #endif
     return res;
 }
@@ -85,28 +85,27 @@ __device__ T shfl_up_sync(unsigned mask, T var, int delta) {
 }
 // specialization for cfloat
 template<>
-inline __device__ cuda::cfloat shfl_up_sync(unsigned mask, cuda::cfloat var,
-                                            int delta) {
+inline __device__ cfloat shfl_up_sync(unsigned mask, cfloat var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    cuda::cfloat res = {__shfl_up_sync(mask, var.x, delta),
-                        __shfl_up_sync(mask, var.y, delta)};
+    cfloat res = {__shfl_up_sync(mask, var.x, delta),
+                  __shfl_up_sync(mask, var.y, delta)};
 #else
-    cuda::cfloat res  = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
+    cfloat res  = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
 #endif
     return res;
 }
 // specialization for cdouble
 template<>
-inline __device__ cuda::cdouble shfl_up_sync(unsigned mask, cuda::cdouble var,
-                                             int delta) {
+inline __device__ cdouble shfl_up_sync(unsigned mask, cdouble var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    cuda::cdouble res = {__shfl_up_sync(mask, var.x, delta),
-                         __shfl_up_sync(mask, var.y, delta)};
+    cdouble res = {__shfl_up_sync(mask, var.x, delta),
+                   __shfl_up_sync(mask, var.y, delta)};
 #else
-    cuda::cdouble res = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
+    cdouble res = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
 #endif
     return res;
 }
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sift.hpp b/src/backend/cuda/kernel/sift.hpp
index 509267402b..9c3e3bf7b8 100644
--- a/src/backend/cuda/kernel/sift.hpp
+++ b/src/backend/cuda/kernel/sift.hpp
@@ -35,6 +35,7 @@
 
 #include <cfloat>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -1066,10 +1067,9 @@ std::vector<Array<T>> buildGaussPyr(Param<T> init_img, const unsigned n_octaves,
             const unsigned imel   = tmp_pyr[idx].elements();
             const unsigned offset = imel * l;
 
-            CUDA_CHECK(cudaMemcpyAsync(gauss_pyr[o].get() + offset,
-                                       tmp_pyr[idx].get(), imel * sizeof(T),
-                                       cudaMemcpyDeviceToDevice,
-                                       cuda::getActiveStream()));
+            CUDA_CHECK(cudaMemcpyAsync(
+                gauss_pyr[o].get() + offset, tmp_pyr[idx].get(),
+                imel * sizeof(T), cudaMemcpyDeviceToDevice, getActiveStream()));
         }
     }
     return gauss_pyr;
@@ -1103,9 +1103,9 @@ std::vector<Array<T>> buildDoGPyr(std::vector<Array<T>>& gauss_pyr,
 
 template<typename T>
 void update_permutation(thrust::device_ptr<T>& keys,
-                        cuda::ThrustVector<int>& permutation) {
+                        arrayfire::cuda::ThrustVector<int>& permutation) {
     // temporary storage for keys
-    cuda::ThrustVector<T> temp(permutation.size());
+    arrayfire::cuda::ThrustVector<T> temp(permutation.size());
 
     // permute the keys with the current reordering
     THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(),
@@ -1118,9 +1118,9 @@ void update_permutation(thrust::device_ptr<T>& keys,
 
 template<typename T>
 void apply_permutation(thrust::device_ptr<T>& keys,
-                       cuda::ThrustVector<int>& permutation) {
+                       arrayfire::cuda::ThrustVector<int>& permutation) {
     // copy keys to temporary vector
-    cuda::ThrustVector<T> temp(keys, keys + permutation.size());
+    arrayfire::cuda::ThrustVector<T> temp(keys, keys + permutation.size());
 
     // permute the keys
     THRUST_SELECT((thrust::gather), permutation.begin(), permutation.end(),
@@ -1175,7 +1175,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
         const unsigned max_feat = ceil(imel * feature_ratio);
 
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         uptr<float> d_extrema_x        = memAlloc<float>(max_feat);
         uptr<float> d_extrema_y        = memAlloc<float>(max_feat);
@@ -1200,14 +1200,14 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
         unsigned extrema_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&extrema_feat, d_count.get(),
                                    sizeof(unsigned), cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         extrema_feat = min(extrema_feat, max_feat);
 
         if (extrema_feat == 0) { continue; }
 
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         auto d_interp_x        = memAlloc<float>(extrema_feat);
         auto d_interp_y        = memAlloc<float>(extrema_feat);
@@ -1229,12 +1229,12 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
         unsigned interp_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&interp_feat, d_count.get(),
                                    sizeof(unsigned), cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         interp_feat = min(interp_feat, max_feat);
 
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         if (interp_feat == 0) { continue; }
 
@@ -1249,7 +1249,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
         thrust::device_ptr<float> interp_size_ptr =
             thrust::device_pointer_cast(d_interp_size.get());
 
-        cuda::ThrustVector<int> permutation(interp_feat);
+        arrayfire::cuda::ThrustVector<int> permutation(interp_feat);
         thrust::sequence(permutation.begin(), permutation.end());
 
         update_permutation<float>(interp_size_ptr, permutation);
@@ -1282,11 +1282,10 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
 
         unsigned nodup_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&nodup_feat, d_count.get(), sizeof(unsigned),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         CUDA_CHECK(cudaMemsetAsync(d_count.get(), 0, sizeof(unsigned),
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
 
         const unsigned max_oriented_feat = nodup_feat * 3;
 
@@ -1315,7 +1314,7 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
         unsigned oriented_feat = 0;
         CUDA_CHECK(cudaMemcpyAsync(&oriented_feat, d_count.get(),
                                    sizeof(unsigned), cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         oriented_feat = min(oriented_feat, max_oriented_feat);
 
@@ -1377,25 +1376,25 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
 
         CUDA_CHECK(cudaMemcpyAsync(
             *d_x + offset, d_x_pyr[i].get(), feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_y + offset, d_y_pyr[i].get(), feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(*d_score + offset, d_response_pyr[i].get(),
                                    feat_pyr[i] * sizeof(float),
                                    cudaMemcpyDeviceToDevice,
-                                   cuda::getActiveStream()));
+                                   getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_ori + offset, d_ori_pyr[i].get(), feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(
             *d_size + offset, d_size_pyr[i].get(), feat_pyr[i] * sizeof(float),
-            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+            cudaMemcpyDeviceToDevice, getActiveStream()));
 
         CUDA_CHECK(
             cudaMemcpyAsync(*d_desc + (offset * desc_len), d_desc_pyr[i].get(),
                             feat_pyr[i] * desc_len * sizeof(float),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                            cudaMemcpyDeviceToDevice, getActiveStream()));
 
         offset += feat_pyr[i];
     }
@@ -1407,3 +1406,4 @@ void sift(unsigned* out_feat, unsigned* out_dlen, float** d_x, float** d_y,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sobel.cuh b/src/backend/cuda/kernel/sobel.cuh
index 1ed9b7b0af..03e333c414 100644
--- a/src/backend/cuda/kernel/sobel.cuh
+++ b/src/backend/cuda/kernel/sobel.cuh
@@ -10,18 +10,18 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
-__device__
-int reflect101(int index, int endIndex) {
+__device__ int reflect101(int index, int endIndex) {
     return abs(endIndex - abs(endIndex - index));
 }
 
 template<typename Ti>
 __device__ Ti load2ShrdMem(const Ti* in, int d0, int d1, int gx, int gy,
                            int inStride1, int inStride0) {
-    int idx = reflect101(gx, d0-1) * inStride0 +
-              reflect101(gy, d1-1) * inStride1;
+    int idx =
+        reflect101(gx, d0 - 1) * inStride0 + reflect101(gy, d1 - 1) * inStride1;
     return in[idx];
 }
 
@@ -77,14 +77,15 @@ __global__ void sobel3x3(Param<To> dx, Param<To> dy, CParam<Ti> in, int nBBS0,
         float NE = shrdMem[_i][j_];
         float SE = shrdMem[i_][j_];
 
-        float t1  = shrdMem[_i][j];
-        float t2  = shrdMem[i_][j];
+        float t1                       = shrdMem[_i][j];
+        float t2                       = shrdMem[i_][j];
         dxptr[gy * dx.strides[1] + gx] = (SW + SE - (NW + NE) + 2 * (t2 - t1));
 
-        t1 = shrdMem[i][_j];
-        t2 = shrdMem[i][j_];
+        t1                             = shrdMem[i][_j];
+        t2                             = shrdMem[i][j_];
         dyptr[gy * dy.strides[1] + gx] = (NE + SE - (NW + SW) + 2 * (t2 - t1));
     }
 }
 
-} // namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index 943d8d520e..130625c11b 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/sobel_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -27,7 +28,7 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
     UNUSED(ker_size);
 
     auto sobel3x3 = common::getKernel(
-        "cuda::sobel3x3", std::array{sobel_cuh_src},
+        "arrayfire::cuda::sobel3x3", std::array{sobel_cuh_src},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>()),
         std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
 
@@ -49,3 +50,4 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sort.hpp b/src/backend/cuda/kernel/sort.hpp
index f99dcdf4ba..23ee41b820 100644
--- a/src/backend/cuda/kernel/sort.hpp
+++ b/src/backend/cuda/kernel/sort.hpp
@@ -18,6 +18,7 @@
 #include <thrust/sort.h>
 #include <thrust_utils.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // Wrapper functions
@@ -80,3 +81,4 @@ void sort0(Param<T> val, bool isAscending) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sort_by_key.hpp b/src/backend/cuda/kernel/sort_by_key.hpp
index e2edb286e3..aea6bebb85 100644
--- a/src/backend/cuda/kernel/sort_by_key.hpp
+++ b/src/backend/cuda/kernel/sort_by_key.hpp
@@ -17,6 +17,7 @@
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // Wrapper functions
@@ -95,3 +96,4 @@ void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sparse.cuh b/src/backend/cuda/kernel/sparse.cuh
index 81ad141f26..bdf0e20884 100644
--- a/src/backend/cuda/kernel/sparse.cuh
+++ b/src/backend/cuda/kernel/sparse.cuh
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -33,3 +34,4 @@ __global__ void coo2Dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 66109b2934..efed1ed6d7 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/sparse_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -24,7 +25,7 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
     constexpr int reps = 4;
 
     auto coo2Dense = common::getKernel(
-        "cuda::coo2Dense", std::array{sparse_cuh_src},
+        "arrayfire::cuda::coo2Dense", std::array{sparse_cuh_src},
         TemplateArgs(TemplateTypename<T>()), std::array{DefineValue(reps)});
 
     dim3 threads(256, 1, 1);
@@ -39,3 +40,4 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sparse_arith.cuh b/src/backend/cuda/kernel/sparse_arith.cuh
index a5d51bc8cc..5357805abe 100644
--- a/src/backend/cuda/kernel/sparse_arith.cuh
+++ b/src/backend/cuda/kernel/sparse_arith.cuh
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, af_op_t op>
@@ -152,3 +153,4 @@ __global__ void cooArithSSD(Param<T> values, Param<int> rowIdx,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index fb66e19a79..13dd5ddb7e 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -16,6 +16,7 @@
 #include <nvrtc_kernel_headers/sparse_arith_cuh.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,10 +27,10 @@ constexpr unsigned THREADS = TX * TY;
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
-    auto csrArithDSD =
-        common::getKernel("cuda::csrArithDSD", std::array{sparse_arith_cuh_src},
-                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-                          std::array{DefineValue(TX), DefineValue(TY)});
+    auto csrArithDSD = common::getKernel(
+        "arrayfire::cuda::csrArithDSD", std::array{sparse_arith_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+        std::array{DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -46,10 +47,10 @@ void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
-    auto cooArithDSD =
-        common::getKernel("cuda::cooArithDSD", std::array{sparse_arith_cuh_src},
-                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-                          std::array{DefineValue(THREADS)});
+    auto cooArithDSD = common::getKernel(
+        "arrayfire::cuda::cooArithDSD", std::array{sparse_arith_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+        std::array{DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
@@ -66,10 +67,10 @@ void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
-    auto csrArithSSD =
-        common::getKernel("cuda::csrArithSSD", std::array{sparse_arith_cuh_src},
-                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-                          std::array{DefineValue(TX), DefineValue(TY)});
+    auto csrArithSSD = common::getKernel(
+        "arrayfire::cuda::csrArithSSD", std::array{sparse_arith_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+        std::array{DefineValue(TX), DefineValue(TY)});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -86,10 +87,10 @@ void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
-    auto cooArithSSD =
-        common::getKernel("cuda::cooArithSSD", std::array{sparse_arith_cuh_src},
-                          TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-                          std::array{DefineValue(THREADS)});
+    auto cooArithSSD = common::getKernel(
+        "arrayfire::cuda::cooArithSSD", std::array{sparse_arith_cuh_src},
+        TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
+        std::array{DefineValue(THREADS)});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
@@ -105,3 +106,4 @@ void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/susan.cuh b/src/backend/cuda/kernel/susan.cuh
index 0f23264454..e2a706e000 100644
--- a/src/backend/cuda/kernel/susan.cuh
+++ b/src/backend/cuda/kernel/susan.cuh
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <shared.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 inline __device__ int max_val(const int x, const int y) { return max(x, y); }
@@ -121,3 +122,4 @@ __global__ void nonMax(float* x_out, float* y_out, float* resp_out,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index e8246b5249..42082bd221 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/susan_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,7 +27,7 @@ void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
     auto susan = common::getKernel(
-        "cuda::susan", std::array{susan_cuh_src},
+        "arrayfire::cuda::susan", std::array{susan_cuh_src},
         TemplateArgs(TemplateTypename<T>()),
         std::array{DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
 
@@ -46,8 +47,9 @@ template<typename T>
 void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
-    auto nonMax = common::getKernel("cuda::nonMax", std::array{susan_cuh_src},
-                                    TemplateArgs(TemplateTypename<T>()));
+    auto nonMax =
+        common::getKernel("arrayfire::cuda::nonMax", std::array{susan_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
@@ -55,7 +57,7 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
 
     auto d_corners_found = memAlloc<unsigned>(1);
     CUDA_CHECK(cudaMemsetAsync(d_corners_found.get(), 0, sizeof(unsigned),
-                               cuda::getActiveStream()));
+                               getActiveStream()));
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -64,10 +66,10 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
     POST_LAUNCH_CHECK();
 
     CUDA_CHECK(cudaMemcpyAsync(count, d_corners_found.get(), sizeof(unsigned),
-                               cudaMemcpyDeviceToHost,
-                               cuda::getActiveStream()));
+                               cudaMemcpyDeviceToHost, getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 }
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key.hpp b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
index cb5cb376b1..9bf2a9b7a3 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Param.hpp>
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // Wrapper functions
@@ -16,3 +17,4 @@ template<typename Tk, typename Tv>
 void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending);
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
index 50996bb12e..19b291356c 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
@@ -14,6 +14,7 @@
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar
 // SBK_INSTS:0 1
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // clang-format off
@@ -21,3 +22,4 @@ namespace kernel {
 // clang-format on
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index 99d9ee7d9a..e4695ac48e 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -13,6 +13,7 @@
 #include <thrust_utils.hpp>
 #include <types.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 // Wrapper functions
@@ -50,3 +51,4 @@ void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/tile.cuh b/src/backend/cuda/kernel/tile.cuh
index dd5047c46a..705ac70647 100644
--- a/src/backend/cuda/kernel/tile.cuh
+++ b/src/backend/cuda/kernel/tile.cuh
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -52,3 +53,4 @@ __global__ void tile(Param<T> out, CParam<T> in, const int blocksPerMatX,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 5656fcf8e1..035cc39437 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/tile_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -25,8 +26,9 @@ void tile(Param<T> out, CParam<T> in) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    auto tile = common::getKernel("cuda::tile", std::array{tile_cuh_src},
-                                  TemplateArgs(TemplateTypename<T>()));
+    auto tile =
+        common::getKernel("arrayfire::cuda::tile", std::array{tile_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);
 
@@ -34,10 +36,9 @@ void tile(Param<T> out, CParam<T> in) {
     int blocksPerMatY = divup(out.dims[1], TILEY);
     dim3 blocks(blocksPerMatX * out.dims[2], blocksPerMatY * out.dims[3], 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -47,3 +48,4 @@ void tile(Param<T> out, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 0d71d4949c..9418a9162d 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -22,6 +22,7 @@
 
 using cub::BlockRadixSort;
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 static const int TOPK_THRDS_PER_BLK = 256;
@@ -190,3 +191,4 @@ inline void topk(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals,
 }
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/transform.cuh b/src/backend/cuda/kernel/transform.cuh
index 7bece00265..f2d2f2c909 100644
--- a/src/backend/cuda/kernel/transform.cuh
+++ b/src/backend/cuda/kernel/transform.cuh
@@ -13,11 +13,12 @@
 __constant__ float
     c_tmat[3072];  // Allows 512 Affine Transforms and 340 Persp. Transforms
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
-__device__
-void calc_transf_inverse(T *txo, const T *txi, const bool perspective) {
+__device__ void calc_transf_inverse(T *txo, const T *txi,
+                                    const bool perspective) {
     if (perspective) {
         txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
         txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
@@ -56,13 +57,11 @@ void calc_transf_inverse(T *txo, const T *txi, const bool perspective) {
 }
 
 template<typename T, bool inverse, int order>
-__global__
-void transform(Param<T> out, CParam<T> in,
-               const int nImg2, const int nImg3,
-               const int nTfs2, const int nTfs3,
-               const int batchImg2,
-               const int blocksXPerImage, const int blocksYPerImage,
-               const bool perspective, af::interpType method) {
+__global__ void transform(Param<T> out, CParam<T> in, const int nImg2,
+                          const int nImg3, const int nTfs2, const int nTfs3,
+                          const int batchImg2, const int blocksXPerImage,
+                          const int blocksYPerImage, const bool perspective,
+                          af::interpType method) {
     // Image Ids
     const int imgId2 = blockIdx.x / blocksXPerImage;
     const int imgId3 = blockIdx.y / blocksYPerImage;
@@ -171,4 +170,5 @@ void transform(Param<T> out, CParam<T> in,
     interp(out, loco, in, inoff, xidi, yidi, method, limages, clamp);
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 489063cc8a..4ed94d7949 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -18,6 +18,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -31,7 +32,7 @@ template<typename T>
 void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
                const bool perspective, const af::interpType method, int order) {
     auto transform = common::getKernel(
-        "cuda::transform", std::array{transform_cuh_src},
+        "arrayfire::cuda::transform", std::array{transform_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(inverse),
                      TemplateArg(order)));
 
@@ -74,3 +75,4 @@ void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/transpose.cuh b/src/backend/cuda/kernel/transpose.cuh
index 1307a043b3..444a61b819 100644
--- a/src/backend/cuda/kernel/transpose.cuh
+++ b/src/backend/cuda/kernel/transpose.cuh
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool conjugate>
@@ -21,8 +22,7 @@ __device__ T doOp(T in) {
 }
 
 template<typename T, bool conjugate, bool is32Multiple>
-__global__ void transpose(Param<T> out, CParam<T> in,
-                          const int blocksPerMatX,
+__global__ void transpose(Param<T> out, CParam<T> in, const int blocksPerMatX,
                           const int blocksPerMatY) {
     __shared__ T shrdMem[TILE_DIM][TILE_DIM + 1];
 
@@ -75,4 +75,5 @@ __global__ void transpose(Param<T> out, CParam<T> in,
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index aca9efb9c6..7ec97b7127 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/transpose_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,7 +27,7 @@ template<typename T>
 void transpose(Param<T> out, CParam<T> in, const bool conjugate,
                const bool is32multiple) {
     auto transpose = common::getKernel(
-        "cuda::transpose", std::array{transpose_cuh_src},
+        "arrayfire::cuda::transpose", std::array{transpose_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(conjugate),
                      TemplateArg(is32multiple)),
         std::array{DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
@@ -36,10 +37,9 @@ void transpose(Param<T> out, CParam<T> in, const bool conjugate,
     int blk_x = divup(in.dims[0], TILE_DIM);
     int blk_y = divup(in.dims[1], TILE_DIM);
     dim3 blocks(blk_x * in.dims[2], blk_y * in.dims[3]);
-    const int maxBlocksY =
-        cuda::getDeviceProp(getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -50,3 +50,4 @@ void transpose(Param<T> out, CParam<T> in, const bool conjugate,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/transpose_inplace.cuh b/src/backend/cuda/kernel/transpose_inplace.cuh
index 733db729c0..8d0b3cdb04 100644
--- a/src/backend/cuda/kernel/transpose_inplace.cuh
+++ b/src/backend/cuda/kernel/transpose_inplace.cuh
@@ -10,6 +10,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool conjugate>
@@ -117,4 +118,5 @@ __global__ void transposeIP(Param<T> in, const int blocksPerMatX,
     }
 }
 
-} //namespace cuda
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index d603a08653..b5374b6025 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/transpose_inplace_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,7 +27,7 @@ template<typename T>
 void transpose_inplace(Param<T> in, const bool conjugate,
                        const bool is32multiple) {
     auto transposeIP = common::getKernel(
-        "cuda::transposeIP", std::array{transpose_inplace_cuh_src},
+        "arrayfire::cuda::transposeIP", std::array{transpose_inplace_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(conjugate),
                      TemplateArg(is32multiple)),
         std::array{DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
@@ -49,3 +50,4 @@ void transpose_inplace(Param<T> in, const bool conjugate,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/triangle.cuh b/src/backend/cuda/kernel/triangle.cuh
index 44d3342f2b..841a7c636f 100644
--- a/src/backend/cuda/kernel/triangle.cuh
+++ b/src/backend/cuda/kernel/triangle.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool is_upper, bool is_unit_diag>
@@ -59,3 +60,4 @@ __global__ void triangle(Param<T> r, CParam<T> in, const int blocksPerMatX,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index e6efac7be6..3c1841a324 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -15,6 +15,7 @@
 #include <debug_cuda.hpp>
 #include <nvrtc_kernel_headers/triangle_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -26,7 +27,7 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
     constexpr unsigned TILEY = 32;
 
     auto triangle = common::getKernel(
-        "cuda::triangle", std::array{triangle_cuh_src},
+        "arrayfire::cuda::triangle", std::array{triangle_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_upper),
                      TemplateArg(is_unit_diag)));
 
@@ -36,10 +37,9 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
     int blocksPerMatY = divup(r.dims[1], TILEY);
     dim3 blocks(blocksPerMatX * r.dims[2], blocksPerMatY * r.dims[3], 1);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -49,3 +49,4 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/unwrap.cuh b/src/backend/cuda/kernel/unwrap.cuh
index b8668356b0..415727a281 100644
--- a/src/backend/cuda/kernel/unwrap.cuh
+++ b/src/backend/cuda/kernel/unwrap.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool is_column>
@@ -79,3 +80,4 @@ __global__ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 15f74df963..6105b8b0a1 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -16,6 +16,7 @@
 #include <kernel/config.hpp>
 #include <nvrtc_kernel_headers/unwrap_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -24,7 +25,7 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
             const int sx, const int sy, const int px, const int py,
             const int dx, const int dy, const int nx, const bool is_column) {
     auto unwrap = common::getKernel(
-        "cuda::unwrap", std::array{unwrap_cuh_src},
+        "arrayfire::cuda::unwrap", std::array{unwrap_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     dim3 threads, blocks;
@@ -44,10 +45,9 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
         reps = divup((wx * wy), threads.y);
     }
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -57,3 +57,4 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/where.cuh b/src/backend/cuda/kernel/where.cuh
index ac1f81cfa9..a9e31d2739 100644
--- a/src/backend/cuda/kernel/where.cuh
+++ b/src/backend/cuda/kernel/where.cuh
@@ -11,12 +11,12 @@
 #include <backend.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
-__global__
-void where(uint *optr, CParam<uint> otmp, CParam<uint> rtmp, CParam<T> in,
-           uint blocks_x, uint blocks_y, uint lim) {
+__global__ void where(uint *optr, CParam<uint> otmp, CParam<uint> rtmp,
+                      CParam<T> in, uint blocks_x, uint blocks_y, uint lim) {
     const uint tidx = threadIdx.x;
     const uint tidy = threadIdx.y;
 
@@ -56,4 +56,5 @@ void where(uint *optr, CParam<uint> otmp, CParam<uint> rtmp, CParam<T> in,
     }
 }
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index bf992648d3..0dddc456b9 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -18,13 +18,15 @@
 #include "config.hpp"
 #include "scan_first.hpp"
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
-    auto where = common::getKernel("cuda::where", std::array{where_cuh_src},
-                                   TemplateArgs(TemplateTypename<T>()));
+    auto where =
+        common::getKernel("arrayfire::cuda::where", std::array{where_cuh_src},
+                          TemplateArgs(TemplateTypename<T>()));
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
@@ -72,7 +74,7 @@ static void where(Param<uint> &out, CParam<T> in) {
     uint total;
     CUDA_CHECK(cudaMemcpyAsync(&total, rtmp.ptr + rtmp_elements - 1,
                                sizeof(uint), cudaMemcpyDeviceToHost,
-                               cuda::getActiveStream()));
+                               getActiveStream()));
     CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
 
     auto out_alloc = memAlloc<uint>(total);
@@ -90,10 +92,9 @@ static void where(Param<uint> &out, CParam<T> in) {
 
     uint lim = divup(otmp.dims[0], (threads_x * blocks_x));
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
     where(qArgs, out.ptr, otmp, rtmp, in, blocks_x, blocks_y, lim);
@@ -104,3 +105,4 @@ static void where(Param<uint> &out, CParam<T> in) {
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/wrap.cuh b/src/backend/cuda/kernel/wrap.cuh
index f8f1db20ca..9200d78f13 100644
--- a/src/backend/cuda/kernel/wrap.cuh
+++ b/src/backend/cuda/kernel/wrap.cuh
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, bool is_column>
@@ -144,3 +145,4 @@ __global__ void wrap_dilated(Param<T> out, CParam<T> in, const int wx,
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 7185ea38bb..37b9e97cf9 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -16,6 +16,7 @@
 #include <kernel/config.hpp>
 #include <nvrtc_kernel_headers/wrap_cuh.hpp>
 
+namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
@@ -23,7 +24,7 @@ template<typename T>
 void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
           const int sy, const int px, const int py, const bool is_column) {
     auto wrap = common::getKernel(
-        "cuda::wrap", std::array{wrap_cuh_src},
+        "arrayfire::cuda::wrap", std::array{wrap_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
@@ -35,10 +36,9 @@ void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
 
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -52,7 +52,7 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                   const dim_t py, const dim_t dx, const dim_t dy,
                   const bool is_column) {
     auto wrap = common::getKernel(
-        "cuda::wrap_dilated", std::array{wrap_cuh_src},
+        "arrayfire::cuda::wrap_dilated", std::array{wrap_cuh_src},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
@@ -64,10 +64,9 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
 
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
-    const int maxBlocksY =
-        cuda::getDeviceProp(cuda::getActiveDeviceId()).maxGridSize[1];
-    blocks.z = divup(blocks.y, maxBlocksY);
-    blocks.y = divup(blocks.y, blocks.z);
+    const int maxBlocksY = getDeviceProp(getActiveDeviceId()).maxGridSize[1];
+    blocks.z             = divup(blocks.y, maxBlocksY);
+    blocks.y             = divup(blocks.y, blocks.z);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -78,3 +77,4 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
 
 }  // namespace kernel
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/logic.hpp b/src/backend/cuda/logic.hpp
index e32a15548f..88c11b3d09 100644
--- a/src/backend/cuda/logic.hpp
+++ b/src/backend/cuda/logic.hpp
@@ -11,6 +11,7 @@
 #include <common/jit/BinaryNode.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
@@ -24,3 +25,4 @@ Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/lookup.cpp b/src/backend/cuda/lookup.cpp
index f5e6bebc69..133db5ba26 100644
--- a/src/backend/cuda/lookup.cpp
+++ b/src/backend/cuda/lookup.cpp
@@ -14,8 +14,9 @@
 #include <err_cuda.hpp>
 #include <kernel/lookup.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
@@ -72,3 +73,4 @@ INSTANTIATE(short);
 INSTANTIATE(ushort);
 INSTANTIATE(half);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/lookup.hpp b/src/backend/cuda/lookup.hpp
index 0a3c25414a..0dc298805b 100644
--- a/src/backend/cuda/lookup.hpp
+++ b/src/backend/cuda/lookup.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/lu.cpp b/src/backend/cuda/lu.cpp
index cf3dcc11ea..addae1e7ba 100644
--- a/src/backend/cuda/lu.cpp
+++ b/src/backend/cuda/lu.cpp
@@ -18,6 +18,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cuda {
 
 // cusolverStatus_t CUDENSEAPI cusolverDn<>getrf_bufferSize(
@@ -147,3 +148,4 @@ INSTANTIATE_LU(cfloat)
 INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/lu.hpp b/src/backend/cuda/lu.hpp
index 335d6b3376..7ed639bef4 100644
--- a/src/backend/cuda/lu.hpp
+++ b/src/backend/cuda/lu.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
@@ -19,3 +20,4 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
 bool isLAPACKAvailable();
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/match_template.cpp b/src/backend/cuda/match_template.cpp
index 19043b7cb7..d82137bb5c 100644
--- a/src/backend/cuda/match_template.cpp
+++ b/src/backend/cuda/match_template.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename inType, typename outType>
@@ -42,3 +43,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/match_template.hpp b/src/backend/cuda/match_template.hpp
index a7f24fc833..fe98cea5e9 100644
--- a/src/backend/cuda/match_template.hpp
+++ b/src/backend/cuda/match_template.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
                               const Array<inType> &tImg,
                               const af::matchType mType);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 23aa1a449b..4c48e6990f 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -30,6 +30,7 @@
 #include <cuda_fp16.h>
 #include <math_constants.h>
 
+namespace arrayfire {
 namespace cuda {
 
 #ifdef AF_WITH_FAST_MATH
@@ -393,14 +394,20 @@ template<typename T>
 constexpr const __DH__ T clamp(const T value, const T lo, const T hi) {
     return clamp(value, lo, hi, [](auto lhs, auto rhs) { return lhs < rhs; });
 }
-
 }  // namespace cuda
+}  // namespace arrayfire
 
-__SDH__ bool operator==(cuda::cfloat a, cuda::cfloat b) {
+__SDH__ bool operator==(arrayfire::cuda::cfloat a, arrayfire::cuda::cfloat b) {
     return (a.x == b.x) && (a.y == b.y);
 }
-__SDH__ bool operator!=(cuda::cfloat a, cuda::cfloat b) { return !(a == b); }
-__SDH__ bool operator==(cuda::cdouble a, cuda::cdouble b) {
+__SDH__ bool operator!=(arrayfire::cuda::cfloat a, arrayfire::cuda::cfloat b) {
+    return !(a == b);
+}
+__SDH__ bool operator==(arrayfire::cuda::cdouble a,
+                        arrayfire::cuda::cdouble b) {
     return (a.x == b.x) && (a.y == b.y);
 }
-__SDH__ bool operator!=(cuda::cdouble a, cuda::cdouble b) { return !(a == b); }
+__SDH__ bool operator!=(arrayfire::cuda::cdouble a,
+                        arrayfire::cuda::cdouble b) {
+    return !(a == b);
+}
diff --git a/src/backend/cuda/max.cu b/src/backend/cuda/max.cu
index 337262dc15..03f712b303 100644
--- a/src/backend/cuda/max.cu
+++ b/src/backend/cuda/max.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // max
 INSTANTIATE(af_max_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
 INSTANTIATE(af_max_t, half, half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/mean.cu b/src/backend/cuda/mean.cu
index cf692ea48c..9b1eea74e9 100644
--- a/src/backend/cuda/mean.cu
+++ b/src/backend/cuda/mean.cu
@@ -11,15 +11,16 @@
 #include <af/dim4.hpp>
 
 #undef _GLIBCXX_USE_INT128
+#include <common/half.hpp>
 #include <err_cuda.hpp>
 #include <kernel/mean.hpp>
 #include <mean.hpp>
 #include <complex>
-#include <common/half.hpp>
 
-using common::half;
 using af::dim4;
+using arrayfire::common::half;
 using std::swap;
+namespace arrayfire {
 namespace cuda {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
@@ -80,3 +81,4 @@ INSTANTIATE_WGT(cdouble, double);
 INSTANTIATE_WGT(half, float);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/mean.hpp b/src/backend/cuda/mean.hpp
index 7871bb2aab..af1810550c 100644
--- a/src/backend/cuda/mean.hpp
+++ b/src/backend/cuda/mean.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in);
@@ -24,3 +25,4 @@ template<typename T, typename Tw>
 Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/meanshift.cpp b/src/backend/cuda/meanshift.cpp
index c2f552df2b..d72d1aa041 100644
--- a/src/backend/cuda/meanshift.cpp
+++ b/src/backend/cuda/meanshift.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
@@ -43,3 +44,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/meanshift.hpp b/src/backend/cuda/meanshift.hpp
index d27ff71279..267a978cb1 100644
--- a/src/backend/cuda/meanshift.hpp
+++ b/src/backend/cuda/meanshift.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/medfilt.cpp b/src/backend/cuda/medfilt.cpp
index 6561419ddd..c80c95c21f 100644
--- a/src/backend/cuda/medfilt.cpp
+++ b/src/backend/cuda/medfilt.cpp
@@ -16,6 +16,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -62,3 +63,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/medfilt.hpp b/src/backend/cuda/medfilt.hpp
index 9fa6868859..e9bc1d2f2d 100644
--- a/src/backend/cuda/medfilt.hpp
+++ b/src/backend/cuda/medfilt.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -20,3 +21,4 @@ Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
                   const af::borderType edge_pad);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 969574a1c4..6c86a6244a 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -28,11 +28,12 @@
 #include <mutex>
 
 using af::dim4;
-using common::bytesToString;
-using common::half;
+using arrayfire::common::bytesToString;
+using arrayfire::common::half;
 
 using std::move;
 
+namespace arrayfire {
 namespace cuda {
 float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
 float getMemoryPressureThreshold() {
@@ -136,9 +137,9 @@ template void memFree(void *ptr);
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {
-    for (int n = 0; n < cuda::getDeviceCount(); n++) {
+    for (int n = 0; n < getDeviceCount(); n++) {
         try {
-            cuda::setDevice(n);
+            setDevice(n);
             shutdownMemoryManager();
         } catch (const AfError &err) {
             continue;  // Do not throw any errors while shutting down
@@ -148,9 +149,7 @@ void Allocator::shutdown() {
 
 int Allocator::getActiveDeviceId() { return cuda::getActiveDeviceId(); }
 
-size_t Allocator::getMaxMemorySize(int id) {
-    return cuda::getDeviceMemorySize(id);
-}
+size_t Allocator::getMaxMemorySize(int id) { return getDeviceMemorySize(id); }
 
 void *Allocator::nativeAlloc(const size_t bytes) {
     void *ptr = NULL;
@@ -175,7 +174,7 @@ int AllocatorPinned::getActiveDeviceId() {
 
 size_t AllocatorPinned::getMaxMemorySize(int id) {
     UNUSED(id);
-    return cuda::getHostMemorySize();
+    return getHostMemorySize();
 }
 
 void *AllocatorPinned::nativeAlloc(const size_t bytes) {
@@ -191,3 +190,4 @@ void AllocatorPinned::nativeFree(void *ptr) {
     if (err != cudaErrorCudartUnloading) { CUDA_CHECK(err); }
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index d033ba0443..935c788769 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -14,6 +14,7 @@
 #include <functional>
 #include <memory>
 
+namespace arrayfire {
 namespace cuda {
 float getMemoryPressure();
 float getMemoryPressureThreshold();
@@ -58,7 +59,7 @@ bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 
-class Allocator final : public common::memory::AllocatorInterface {
+class Allocator final : public arrayfire::common::AllocatorInterface {
    public:
     Allocator();
     ~Allocator() = default;
@@ -73,7 +74,7 @@ class Allocator final : public common::memory::AllocatorInterface {
 // So we pass 1 as numDevices to the constructor so that it creates 1 vector
 // of memory_info
 // When allocating and freeing, it doesn't really matter which device is active
-class AllocatorPinned final : public common::memory::AllocatorInterface {
+class AllocatorPinned final : public arrayfire::common::AllocatorInterface {
    public:
     AllocatorPinned();
     ~AllocatorPinned() = default;
@@ -85,3 +86,4 @@ class AllocatorPinned final : public common::memory::AllocatorInterface {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/min.cu b/src/backend/cuda/min.cu
index 30ad8bc186..72a3f1beef 100644
--- a/src/backend/cuda/min.cu
+++ b/src/backend/cuda/min.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // min
 INSTANTIATE(af_min_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
 INSTANTIATE(af_min_t, half, half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/minmax_op.hpp b/src/backend/cuda/minmax_op.hpp
index 83040d7248..4fcc995c0b 100644
--- a/src/backend/cuda/minmax_op.hpp
+++ b/src/backend/cuda/minmax_op.hpp
@@ -11,6 +11,7 @@
 
 #include <common/Binary.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -83,3 +84,4 @@ struct MinMaxOp<af_max_t, T> {
 };
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/moments.cpp b/src/backend/cuda/moments.cpp
index a8c1a53ab7..34c8cf753f 100644
--- a/src/backend/cuda/moments.cpp
+++ b/src/backend/cuda/moments.cpp
@@ -14,6 +14,7 @@
 #include <err_cuda.hpp>
 #include <kernel/moments.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 static inline unsigned bitCount(unsigned v) {
@@ -56,3 +57,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/moments.hpp b/src/backend/cuda/moments.hpp
index d8361d8896..54791ac590 100644
--- a/src/backend/cuda/moments.hpp
+++ b/src/backend/cuda/moments.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/morph.cpp b/src/backend/cuda/morph.cpp
index ba4cf98683..a49fd5a40e 100644
--- a/src/backend/cuda/morph.cpp
+++ b/src/backend/cuda/morph.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -57,3 +58,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/morph.hpp b/src/backend/cuda/morph.hpp
index b1276dfbf2..7b072ef669 100644
--- a/src/backend/cuda/morph.hpp
+++ b/src/backend/cuda/morph.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
@@ -16,3 +17,4 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 template<typename T>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index 53e22a29fc..ca6a11a1c6 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename To>
@@ -73,3 +74,4 @@ INSTANTIATE(ushort, uint)
 INSTANTIATE(uintl, uint)  // For Hamming
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/nearest_neighbour.hpp b/src/backend/cuda/nearest_neighbour.hpp
index 8de98e6924..a1e8bd21bf 100644
--- a/src/backend/cuda/nearest_neighbour.hpp
+++ b/src/backend/cuda/nearest_neighbour.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename To>
@@ -20,4 +21,5 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
                        const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/orb.cu b/src/backend/cuda/orb.cu
index 86e463ed42..83da734ce2 100644
--- a/src/backend/cuda/orb.cu
+++ b/src/backend/cuda/orb.cu
@@ -21,6 +21,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename convAccT>
@@ -99,3 +100,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/orb.hpp b/src/backend/cuda/orb.hpp
index e7a03ad9e1..c40a1f9026 100644
--- a/src/backend/cuda/orb.hpp
+++ b/src/backend/cuda/orb.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              const unsigned max_feat, const float scl_fctr,
              const unsigned levels, const bool blur_img);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/pad_array_borders.cpp b/src/backend/cuda/pad_array_borders.cpp
index 2250f7f363..bf41b5f2e7 100644
--- a/src/backend/cuda/pad_array_borders.cpp
+++ b/src/backend/cuda/pad_array_borders.cpp
@@ -14,6 +14,7 @@
 #include <err_cuda.hpp>
 #include <kernel/pad_array_borders.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> padArrayBorders(Array<T> const& in, dim4 const& lowerBoundPadding,
@@ -53,3 +54,4 @@ INSTANTIATE_PAD_ARRAY_BORDERS(ushort)
 INSTANTIATE_PAD_ARRAY_BORDERS(short)
 INSTANTIATE_PAD_ARRAY_BORDERS(common::half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index d3b7c2efd9..5ad8c27a7f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -60,13 +60,14 @@ using std::to_string;
 using std::unique_ptr;
 using std::vector;
 
-using common::getEnvVar;
-using common::int_version_to_string;
-using common::unique_handle;
-using common::memory::MemoryManagerBase;
-using cuda::Allocator;
-using cuda::AllocatorPinned;
-
+using arrayfire::common::getEnvVar;
+using arrayfire::common::int_version_to_string;
+using arrayfire::common::MemoryManagerBase;
+using arrayfire::common::unique_handle;
+using arrayfire::cuda::Allocator;
+using arrayfire::cuda::AllocatorPinned;
+
+namespace arrayfire {
 namespace cuda {
 
 static string get_system() {
@@ -92,8 +93,7 @@ unique_handle<cublasHandle_t> *cublasManager(const int deviceId) {
         // TODO(pradeep) When multiple streams per device
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
-        CUBLAS_CHECK(
-            cublasSetStream(handles[deviceId], cuda::getStream(deviceId)));
+        CUBLAS_CHECK(cublasSetStream(handles[deviceId], getStream(deviceId)));
 #ifdef AF_WITH_FAST_MATH
         CUBLAS_CHECK(
             cublasSetMathMode(handles[deviceId], CUBLAS_TF32_TENSOR_OP_MATH));
@@ -128,7 +128,7 @@ unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
         AF_ERROR(error_msg, AF_ERR_RUNTIME);
     }
     CUDNN_CHECK(getCudnnPlugin().cudnnSetStream(cudnnHandles[deviceId],
-                                                cuda::getStream(deviceId)));
+                                                getStream(deviceId)));
 
     return handle;
 }
@@ -152,14 +152,14 @@ unique_handle<cusolverDnHandle_t> *cusolverManager(const int deviceId) {
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
         CUSOLVER_CHECK(
-            cusolverDnSetStream(handles[deviceId], cuda::getStream(deviceId)));
+            cusolverDnSetStream(handles[deviceId], getStream(deviceId)));
     });
     // TODO(pradeep) prior to this change, stream was being synced in get solver
     // handle because of some cusolver bug. Re-enable that if this change
     // doesn't work and sovler tests fail.
     // https://gist.github.com/shehzan10/414c3d04a40e7c4a03ed3c2e1b9072e7
     // cuSolver Streams patch:
-    // CUDA_CHECK(cudaStreamSynchronize(cuda::getStream(deviceId)));
+    // CUDA_CHECK(cudaStreamSynchronize(getStream(deviceId)));
 
     return &handles[deviceId];
 }
@@ -175,7 +175,7 @@ unique_handle<cusparseHandle_t> *cusparseManager(const int deviceId) {
         // is added to CUDA backend, move the cublasSetStream
         // call outside of call_once scope.
         CUSPARSE_CHECK(
-            _.cusparseSetStream(handles[deviceId], cuda::getStream(deviceId)));
+            _.cusparseSetStream(handles[deviceId], getStream(deviceId)));
     });
     return &handles[deviceId];
 }
@@ -486,7 +486,7 @@ void resetMemoryManagerPinned() {
     return DeviceManager::getInstance().resetMemoryManagerPinned();
 }
 
-graphics::ForgeManager &forgeManager() {
+arrayfire::common::ForgeManager &forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
@@ -504,11 +504,9 @@ GraphicsResourceManager &interopManager() {
     return *(inst.gfxManagers[id].get());
 }
 
-PlanCache &fftManager() {
-    return *(cufftManager(cuda::getActiveDeviceId()).get());
-}
+PlanCache &fftManager() { return *(cufftManager(getActiveDeviceId()).get()); }
 
-BlasHandle blasHandle() { return *cublasManager(cuda::getActiveDeviceId()); }
+BlasHandle blasHandle() { return *cublasManager(getActiveDeviceId()); }
 
 #ifdef WITH_CUDNN
 cudnnHandle_t nnHandle() {
@@ -519,7 +517,7 @@ cudnnHandle_t nnHandle() {
     static cudnnModule keep_me_to_avoid_exceptions_exceptions =
         getCudnnPlugin();
     static unique_handle<cudnnHandle_t> *handle =
-        nnManager(cuda::getActiveDeviceId());
+        nnManager(getActiveDeviceId());
     if (*handle) {
         return *handle;
     } else {
@@ -528,13 +526,9 @@ cudnnHandle_t nnHandle() {
 }
 #endif
 
-SolveHandle solverDnHandle() {
-    return *cusolverManager(cuda::getActiveDeviceId());
-}
+SolveHandle solverDnHandle() { return *cusolverManager(getActiveDeviceId()); }
 
-SparseHandle sparseHandle() {
-    return *cusparseManager(cuda::getActiveDeviceId());
-}
+SparseHandle sparseHandle() { return *cusparseManager(getActiveDeviceId()); }
 
 void sync(int device) {
     int currDevice = getActiveDeviceId();
@@ -554,10 +548,11 @@ bool &evalFlag() {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
 
 af_err afcu_get_stream(cudaStream_t *stream, int id) {
     try {
-        *stream = cuda::getStream(id);
+        *stream = arrayfire::cuda::getStream(id);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -565,7 +560,7 @@ af_err afcu_get_stream(cudaStream_t *stream, int id) {
 
 af_err afcu_get_native_id(int *nativeid, int id) {
     try {
-        *nativeid = cuda::getDeviceNativeId(id);
+        *nativeid = arrayfire::cuda::getDeviceNativeId(id);
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -573,7 +568,8 @@ af_err afcu_get_native_id(int *nativeid, int id) {
 
 af_err afcu_set_native_id(int nativeid) {
     try {
-        cuda::setDevice(cuda::getDeviceIdFromNativeId(nativeid));
+        arrayfire::cuda::setDevice(
+            arrayfire::cuda::getDeviceIdFromNativeId(nativeid));
     }
     CATCHALL;
     return AF_SUCCESS;
@@ -581,7 +577,7 @@ af_err afcu_set_native_id(int nativeid) {
 
 af_err afcu_cublasSetMathMode(cublasMath_t mode) {
     try {
-        CUBLAS_CHECK(cublasSetMathMode(cuda::blasHandle(), mode));
+        CUBLAS_CHECK(cublasSetMathMode(arrayfire::cuda::blasHandle(), mode));
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index bbdf5a8d6d..946c6addf1 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -38,18 +38,16 @@ namespace spdlog {
 class logger;
 }
 
-namespace graphics {
-class ForgeManager;
-}
-
+namespace arrayfire {
 namespace common {
-namespace memory {
+class ForgeManager;
 class MemoryManagerBase;
-}
 }  // namespace common
+}  // namespace arrayfire
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
+namespace arrayfire {
 namespace cuda {
 
 class GraphicsResourceManager;
@@ -132,7 +130,7 @@ void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
 
 void resetMemoryManagerPinned();
 
-graphics::ForgeManager& forgeManager();
+arrayfire::common::ForgeManager& forgeManager();
 
 GraphicsResourceManager& interopManager();
 
@@ -149,3 +147,4 @@ SolveHandle solverDnHandle();
 SparseHandle sparseHandle();
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index c454b0dff1..e012377305 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -15,12 +15,16 @@
 #include <plot.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot) {
-    auto stream = cuda::getActiveStream();
+    auto stream = getActiveStream();
     if (DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
@@ -38,7 +42,7 @@ void copy_plot(const Array<T> &P, fg_plot plot) {
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule &_ = graphics::forgePlugin();
+        ForgeModule &_ = common::forgePlugin();
         unsigned bytes = 0, buffer = 0;
         FG_CHECK(_.fg_get_plot_vertex_buffer(&buffer, plot));
         FG_CHECK(_.fg_get_plot_vertex_buffer_size(&bytes, plot));
@@ -69,3 +73,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/plot.hpp b/src/backend/cuda/plot.hpp
index 7b0a7473f3..ff0739105d 100644
--- a/src/backend/cuda/plot.hpp
+++ b/src/backend/cuda/plot.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/print.hpp b/src/backend/cuda/print.hpp
index 97fe7a22ff..2343992350 100644
--- a/src/backend/cuda/print.hpp
+++ b/src/backend/cuda/print.hpp
@@ -12,6 +12,7 @@
 #include <types.hpp>
 #include <ostream>
 
+namespace arrayfire {
 namespace cuda {
 static std::ostream& operator<<(std::ostream& out, const cfloat& var) {
     out << "(" << var.x << "," << var.y << ")";
@@ -23,3 +24,4 @@ static std::ostream& operator<<(std::ostream& out, const cdouble& var) {
     return out;
 }
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/product.cu b/src/backend/cuda/product.cu
index 42a38dae3a..c4fff43b93 100644
--- a/src/backend/cuda/product.cu
+++ b/src/backend/cuda/product.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // mul
 INSTANTIATE(af_mul_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
 INSTANTIATE(af_mul_t, half, float)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/qr.cpp b/src/backend/cuda/qr.cpp
index 3663f43570..c28a41523f 100644
--- a/src/backend/cuda/qr.cpp
+++ b/src/backend/cuda/qr.cpp
@@ -18,6 +18,7 @@
 #include <memory.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // cusolverStatus_t cusolverDn<>geqrf_bufferSize(
@@ -183,3 +184,4 @@ INSTANTIATE_QR(cfloat)
 INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/qr.hpp b/src/backend/cuda/qr.hpp
index 450a3555a6..46121cc211 100644
--- a/src/backend/cuda/qr.hpp
+++ b/src/backend/cuda/qr.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
@@ -16,3 +17,4 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in);
 template<typename T>
 Array<T> qr_inplace(Array<T> &in);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index d03eb51e91..a63ead0bf8 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -13,8 +13,9 @@
 #include <af/dim4.hpp>
 #include <cassert>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl) {
@@ -158,3 +159,4 @@ COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
 COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/random_engine.hpp b/src/backend/cuda/random_engine.hpp
index ca7bd1a233..8062f6feb7 100644
--- a/src/backend/cuda/random_engine.hpp
+++ b/src/backend/cuda/random_engine.hpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl);
@@ -39,3 +40,4 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
                             Array<uint> recursion_table,
                             Array<uint> temper_table, Array<uint> state);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/range.cpp b/src/backend/cuda/range.cpp
index 54cc76268e..55a2553649 100644
--- a/src/backend/cuda/range.cpp
+++ b/src/backend/cuda/range.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim) {
@@ -52,3 +53,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/range.hpp b/src/backend/cuda/range.hpp
index 904fe139a9..7ad50970aa 100644
--- a/src/backend/cuda/range.hpp
+++ b/src/backend/cuda/range.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim = -1);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/reduce.hpp b/src/backend/cuda/reduce.hpp
index d606153650..70f7cf848d 100644
--- a/src/backend/cuda/reduce.hpp
+++ b/src/backend/cuda/reduce.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
@@ -24,3 +25,4 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
                      double nanval = 0);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index 0c4e2e3e87..eb8a5b9a48 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -27,6 +27,7 @@
 using af::dim4;
 using std::swap;
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
@@ -360,6 +361,7 @@ Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
 
 #define INSTANTIATE(Op, Ti, To)                                                \
     template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim,  \
diff --git a/src/backend/cuda/regions.cu b/src/backend/cuda/regions.cu
index a79717a5bf..7de5c54c05 100644
--- a/src/backend/cuda/regions.cu
+++ b/src/backend/cuda/regions.cu
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -73,3 +74,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/regions.hpp b/src/backend/cuda/regions.hpp
index f94b2f7f79..34959c4f62 100644
--- a/src/backend/cuda/regions.hpp
+++ b/src/backend/cuda/regions.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/reorder.cpp b/src/backend/cuda/reorder.cpp
index fcc0e6a830..c81fd02f6a 100644
--- a/src/backend/cuda/reorder.cpp
+++ b/src/backend/cuda/reorder.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -51,3 +52,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/reorder.hpp b/src/backend/cuda/reorder.hpp
index 525b50001f..bda5fc449c 100644
--- a/src/backend/cuda/reorder.hpp
+++ b/src/backend/cuda/reorder.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/reshape.cpp b/src/backend/cuda/reshape.cpp
index 8d48000457..9d6e57549f 100644
--- a/src/backend/cuda/reshape.cpp
+++ b/src/backend/cuda/reshape.cpp
@@ -13,8 +13,9 @@
 #include <common/half.hpp>
 #include <kernel/memcopy.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename inType, typename outType>
@@ -77,3 +78,4 @@ INSTANTIATE_COMPLEX(cfloat)
 INSTANTIATE_COMPLEX(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/resize.cpp b/src/backend/cuda/resize.cpp
index 25678976e3..97dc8a7da8 100644
--- a/src/backend/cuda/resize.cpp
+++ b/src/backend/cuda/resize.cpp
@@ -13,6 +13,7 @@
 #include <err_cuda.hpp>
 #include <kernel/resize.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
@@ -45,3 +46,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/resize.hpp b/src/backend/cuda/resize.hpp
index 602a071b24..ee2f1a0117 100644
--- a/src/backend/cuda/resize.hpp
+++ b/src/backend/cuda/resize.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/rotate.cpp b/src/backend/cuda/rotate.cpp
index 7c26164a8c..2f46894aef 100644
--- a/src/backend/cuda/rotate.cpp
+++ b/src/backend/cuda/rotate.cpp
@@ -12,6 +12,7 @@
 #include <kernel/rotate.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -40,3 +41,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/rotate.hpp b/src/backend/cuda/rotate.hpp
index 0686fd40bd..a9e271de04 100644
--- a/src/backend/cuda/rotate.hpp
+++ b/src/backend/cuda/rotate.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/scalar.hpp b/src/backend/cuda/scalar.hpp
index c08c201a73..250062b535 100644
--- a/src/backend/cuda/scalar.hpp
+++ b/src/backend/cuda/scalar.hpp
@@ -13,6 +13,7 @@
 #include <optypes.hpp>
 #include <memory>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -33,3 +34,4 @@ Array<T> createScalarNode(const dim4 &size, const T val) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/scan.cpp b/src/backend/cuda/scan.cpp
index c6f2da12d2..10002cbbad 100644
--- a/src/backend/cuda/scan.cpp
+++ b/src/backend/cuda/scan.cpp
@@ -17,6 +17,7 @@
 #include <scan.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
@@ -56,3 +57,4 @@ INSTANTIATE_SCAN_ALL(af_mul_t)
 INSTANTIATE_SCAN_ALL(af_min_t)
 INSTANTIATE_SCAN_ALL(af_max_t)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/scan.hpp b/src/backend/cuda/scan.hpp
index 4ee9e84d5c..b26202fba7 100644
--- a/src/backend/cuda/scan.hpp
+++ b/src/backend/cuda/scan.hpp
@@ -10,7 +10,9 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/scan_by_key.cpp b/src/backend/cuda/scan_by_key.cpp
index 30ae778a3d..b7d476cc56 100644
--- a/src/backend/cuda/scan_by_key.cpp
+++ b/src/backend/cuda/scan_by_key.cpp
@@ -16,6 +16,7 @@
 #include <scan_by_key.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
@@ -57,3 +58,4 @@ INSTANTIATE_SCAN_OP(af_mul_t)
 INSTANTIATE_SCAN_OP(af_min_t)
 INSTANTIATE_SCAN_OP(af_max_t)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/scan_by_key.hpp b/src/backend/cuda/scan_by_key.hpp
index 366453b3ad..5b95c75978 100644
--- a/src/backend/cuda/scan_by_key.hpp
+++ b/src/backend/cuda/scan_by_key.hpp
@@ -10,8 +10,10 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
                bool inclusive_scan);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index 739e150c05..b13df55bfe 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -18,12 +18,13 @@
 
 #include <memory>
 
-using common::half;
-using common::NaryNode;
-using common::Node_ptr;
+using arrayfire::common::half;
+using arrayfire::common::NaryNode;
+using arrayfire::common::Node_ptr;
 using std::make_shared;
 using std::max;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -132,3 +133,4 @@ INSTANTIATE(ushort);
 INSTANTIATE(half);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/select.hpp b/src/backend/cuda/select.hpp
index 6552ca3ccd..530aab097f 100644
--- a/src/backend/cuda/select.hpp
+++ b/src/backend/cuda/select.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
@@ -27,3 +28,4 @@ template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const T &b_val, const af::dim4 &odims);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index a768c31e15..fbbbc28c0a 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -10,9 +10,9 @@
 #include <Array.hpp>
 #include <copy.hpp>
 #include <debug_cuda.hpp>
-#include <thrust_utils.hpp>
 #include <set.hpp>
 #include <sort.hpp>
+#include <thrust_utils.hpp>
 #include <af/dim4.hpp>
 
 #include <thrust/device_ptr.h>
@@ -22,6 +22,7 @@
 
 #include <algorithm>
 
+namespace arrayfire {
 namespace cuda {
 using af::dim4;
 
@@ -127,3 +128,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/set.hpp b/src/backend/cuda/set.hpp
index 7b72447bcf..872599ad40 100644
--- a/src/backend/cuda/set.hpp
+++ b/src/backend/cuda/set.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted);
@@ -21,3 +22,4 @@ template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index f83bba9802..82aab5e1fe 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -17,16 +17,17 @@
 
 using af::dim4;
 
-using common::Node_ptr;
-using common::ShiftNodeBase;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::ShiftNodeBase;
 
-using cuda::jit::BufferNode;
+using arrayfire::cuda::jit::BufferNode;
 
 using std::array;
 using std::make_shared;
 using std::static_pointer_cast;
 using std::string;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 using ShiftNode = ShiftNodeBase<BufferNode<T>>;
@@ -74,3 +75,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/shift.hpp b/src/backend/cuda/shift.hpp
index e651c2b0d3..68c4ccd9bf 100644
--- a/src/backend/cuda/shift.hpp
+++ b/src/backend/cuda/shift.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sift.cu b/src/backend/cuda/sift.cu
index 78314981cd..dbfb46a63b 100644
--- a/src/backend/cuda/sift.cu
+++ b/src/backend/cuda/sift.cu
@@ -14,6 +14,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename convAccT>
@@ -71,3 +72,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sift.hpp b/src/backend/cuda/sift.hpp
index 1ec8638b41..a177c345ae 100644
--- a/src/backend/cuda/sift.hpp
+++ b/src/backend/cuda/sift.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, typename convAccT>
@@ -23,4 +24,5 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sobel.cpp b/src/backend/cuda/sobel.cpp
index c58bb17974..5200f69a45 100644
--- a/src/backend/cuda/sobel.cpp
+++ b/src/backend/cuda/sobel.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Ti, typename To>
@@ -42,3 +43,4 @@ INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sobel.hpp b/src/backend/cuda/sobel.hpp
index 4cba95b4cf..f566459138 100644
--- a/src/backend/cuda/sobel.hpp
+++ b/src/backend/cuda/sobel.hpp
@@ -10,10 +10,12 @@
 #include <Array.hpp>
 #include <utility>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename Ti, typename To>
 std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
                                                  const unsigned &ker_size);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index f9e80efdf0..f762785818 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -23,6 +23,7 @@
 #include <qr.hpp>
 #include <transpose.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // cublasStatus_t cublas<>getrsBatched( cublasHandle_t handle,
@@ -271,8 +272,10 @@ Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
         }
     }
 
-    unique_mem_ptr aBatched_device_mem(pinnedAlloc<char>(bytes), pinnedFree<char>);
-    unique_mem_ptr bBatched_device_mem(pinnedAlloc<char>(bytes), pinnedFree<char>);
+    unique_mem_ptr aBatched_device_mem(pinnedAlloc<char>(bytes),
+                                       pinnedFree<char>);
+    unique_mem_ptr bBatched_device_mem(pinnedAlloc<char>(bytes),
+                                       pinnedFree<char>);
 
     T **aBatched_device_ptrs = (T **)aBatched_device_mem.get();
     T **bBatched_device_ptrs = (T **)bBatched_device_mem.get();
@@ -477,3 +480,4 @@ INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/solve.hpp b/src/backend/cuda/solve.hpp
index 72c80000d0..20205aa771 100644
--- a/src/backend/cuda/solve.hpp
+++ b/src/backend/cuda/solve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options = AF_MAT_NONE);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 8596c3b894..9970ddd8b2 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -16,6 +16,7 @@
 #include <sort.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
@@ -59,3 +60,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sort.hpp b/src/backend/cuda/sort.hpp
index 74473bb981..f6b8832f01 100644
--- a/src/backend/cuda/sort.hpp
+++ b/src/backend/cuda/sort.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sort_by_key.cu b/src/backend/cuda/sort_by_key.cu
index 4cc64e2aed..bd19d16240 100644
--- a/src/backend/cuda/sort_by_key.cu
+++ b/src/backend/cuda/sort_by_key.cu
@@ -16,6 +16,7 @@
 #include <sort_by_key.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace cuda {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
@@ -82,3 +83,4 @@ INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sort_by_key.hpp b/src/backend/cuda/sort_by_key.hpp
index 5eb7c1e716..e44badc6a8 100644
--- a/src/backend/cuda/sort_by_key.hpp
+++ b/src/backend/cuda/sort_by_key.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
                  const Array<Tv> &ival, const unsigned dim, bool isAscending);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 9d1a88822e..039e77a147 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -17,6 +17,7 @@
 #include <sort_index.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
@@ -69,3 +70,4 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sort_index.hpp b/src/backend/cuda/sort_index.hpp
index 970e7c9b48..1355f9ea8a 100644
--- a/src/backend/cuda/sort_index.hpp
+++ b/src/backend/cuda/sort_index.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void sort_index(Array<T> &val, Array<unsigned> &idx, const Array<T> &in,
                 const unsigned dim, bool isAscending);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 27b805e9ea..6dec35090c 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -25,6 +25,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 
 using namespace common;
@@ -307,7 +308,7 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
         CUDA_CHECK(
             cudaMemcpyAsync(converted.getColIdx().get(), in.getColIdx().get(),
                             in.getColIdx().elements() * sizeof(int),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                            cudaMemcpyDeviceToDevice, getActiveStream()));
 
         // cusparse function to expand compressed row into coordinate
         CUSPARSE_CHECK(_.cusparseXcsr2coo(
@@ -374,11 +375,11 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
         CUDA_CHECK(
             cudaMemcpyAsync(converted.getValues().get(), cooT.getValues().get(),
                             cooT.getValues().elements() * sizeof(T),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                            cudaMemcpyDeviceToDevice, getActiveStream()));
         CUDA_CHECK(
             cudaMemcpyAsync(converted.getColIdx().get(), cooT.getColIdx().get(),
                             cooT.getColIdx().elements() * sizeof(int),
-                            cudaMemcpyDeviceToDevice, cuda::getActiveStream()));
+                            cudaMemcpyDeviceToDevice, getActiveStream()));
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(_.cusparseXcoo2csr(
@@ -446,3 +447,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_SPARSE
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sparse.hpp b/src/backend/cuda/sparse.hpp
index 5b571d4eb9..ae4f42ccf6 100644
--- a/src/backend/cuda/sparse.hpp
+++ b/src/backend/cuda/sparse.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T, af_storage stype>
@@ -25,3 +26,4 @@ common::SparseArray<T> sparseConvertStorageToStorage(
     const common::SparseArray<T> &in);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index a41c356397..63bda7f733 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -26,6 +26,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 
 using namespace common;
@@ -235,11 +236,9 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
         nnzC = *nnzcDevHostPtr;
     } else {
         CUDA_CHECK(cudaMemcpyAsync(&nnzC, csrRowPtrC + M, sizeof(int),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
         CUDA_CHECK(cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int),
-                                   cudaMemcpyDeviceToHost,
-                                   cuda::getActiveStream()));
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         nnzC -= baseC;
     }
@@ -295,3 +294,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sparse_arith.hpp b/src/backend/cuda/sparse_arith.hpp
index bd1839d058..a3628df405 100644
--- a/src/backend/cuda/sparse_arith.hpp
+++ b/src/backend/cuda/sparse_arith.hpp
@@ -12,6 +12,7 @@
 #include <optypes.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 // These two functions cannot be overloaded by return type.
@@ -28,3 +29,4 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sparse_blas.cu b/src/backend/cuda/sparse_blas.cu
index 33a2957a62..965186a915 100644
--- a/src/backend/cuda/sparse_blas.cu
+++ b/src/backend/cuda/sparse_blas.cu
@@ -22,6 +22,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace cuda {
 
 cusparseOperation_t toCusparseTranspose(af_mat_prop opt) {
@@ -222,3 +223,4 @@ INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sparse_blas.hpp b/src/backend/cuda/sparse_blas.hpp
index 3ff5e38520..d4b41defd0 100644
--- a/src/backend/cuda/sparse_blas.hpp
+++ b/src/backend/cuda/sparse_blas.hpp
@@ -10,10 +10,12 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/sum.cu b/src/backend/cuda/sum.cu
index 3dcd357700..44cfec9449 100644
--- a/src/backend/cuda/sum.cu
+++ b/src/backend/cuda/sum.cu
@@ -7,11 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "reduce_impl.hpp"
 #include <common/half.hpp>
+#include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 // sum
 INSTANTIATE(af_add_t, float, float)
@@ -38,3 +39,4 @@ INSTANTIATE(af_add_t, half, half)
 INSTANTIATE(af_add_t, half, float)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index ca38716f39..bef751239b 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -15,12 +15,16 @@
 #include <surface.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface) {
-    auto stream = cuda::getActiveStream();
+    auto stream = getActiveStream();
     if (DeviceManager::checkGraphicsInteropCapability()) {
         const T *d_P = P.get();
 
@@ -38,7 +42,7 @@ void copy_surface(const Array<T> &P, fg_surface surface) {
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule &_ = graphics::forgePlugin();
+        ForgeModule &_ = forgePlugin();
         unsigned bytes = 0, buffer = 0;
         FG_CHECK(_.fg_get_surface_vertex_buffer(&buffer, surface));
         FG_CHECK(_.fg_get_surface_vertex_buffer_size(&bytes, surface));
@@ -70,3 +74,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/surface.hpp b/src/backend/cuda/surface.hpp
index a9fef84fb6..896344c73b 100644
--- a/src/backend/cuda/surface.hpp
+++ b/src/backend/cuda/surface.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/susan.cpp b/src/backend/cuda/susan.cpp
index 1f2a367e88..4d0fcc078c 100644
--- a/src/backend/cuda/susan.cpp
+++ b/src/backend/cuda/susan.cpp
@@ -18,6 +18,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -78,3 +79,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/susan.hpp b/src/backend/cuda/susan.hpp
index bc27d5bc7f..2266320485 100644
--- a/src/backend/cuda/susan.hpp
+++ b/src/backend/cuda/susan.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -19,4 +20,5 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
                const Array<T> &in, const unsigned radius, const float diff_thr,
                const float geom_thr, const float feature_ratio,
                const unsigned edge);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/svd.cpp b/src/backend/cuda/svd.cpp
index 7c51fefc51..6ec71739ba 100644
--- a/src/backend/cuda/svd.cpp
+++ b/src/backend/cuda/svd.cpp
@@ -19,6 +19,7 @@
 
 #include <cusolverDn.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 cusolverStatus_t gesvd_buf_func(cusolverDnHandle_t /*handle*/, int /*m*/,
@@ -114,3 +115,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/svd.hpp b/src/backend/cuda/svd.hpp
index 39192f95bb..21cd52b684 100644
--- a/src/backend/cuda/svd.hpp
+++ b/src/backend/cuda/svd.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T, typename Tr>
 void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
@@ -16,3 +17,4 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/threadsMgt.hpp b/src/backend/cuda/threadsMgt.hpp
index 06fccdb0a3..147dff5586 100644
--- a/src/backend/cuda/threadsMgt.hpp
+++ b/src/backend/cuda/threadsMgt.hpp
@@ -11,13 +11,14 @@
 #include <common/dispatch.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace cuda {
 // OVERALL USAGE (With looping):
 // ...                                                      // OWN CODE
 // threadsMgt<T> th(...);                                   // backend.hpp
 // const dim3 threads{th.genThreads()};                     // backend.hpp
 // const dim3 blocks{th.genBlocks(threads,..)};             // backend.hpp
-// cuda::Kernel KER{GETKERNEL(..., th.loop0, th.loop1, th.loop2,
+// arrayfire::cuda::Kernel KER{GETKERNEL(..., th.loop0, th.loop1, th.loop2,
 //                               th.loop3)};                // OWN CODE
 // KER(threads,blocks,...);                                 // OWN CODE
 // ...                                                      // OWN CODE
@@ -27,8 +28,8 @@ namespace cuda {
 // threadsMgt<T> th(...);                                   // backend.hpp
 // const dim3 threads{th.genThreads()};                     // backend.hpp
 // const dim3 blocks{th.genBlocksFull(threads,...)};        // backend.hpp
-// cuda::Kernel KER{GETKERNEL(...)};                        // OWN CODE
-// KER(threads,blocks,...);                                 // OWN CODE
+// arrayfire::cuda::Kernel KER{GETKERNEL(...)};             // OWN
+// CODE KER(threads,blocks,...);                            // OWN CODE
 // ...                                                      // OWN CODE
 template<typename T>
 class threadsMgt {
@@ -324,4 +325,5 @@ inline dim3 threadsMgt<T>::genBlocks(const dim3& threads,
 
     return blocks;
 };
-}  // namespace cuda
\ No newline at end of file
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/thrust_utils.hpp b/src/backend/cuda/thrust_utils.hpp
index ed468b74a5..8aafbc1752 100644
--- a/src/backend/cuda/thrust_utils.hpp
+++ b/src/backend/cuda/thrust_utils.hpp
@@ -13,29 +13,32 @@
 #include <thrust/version.h>
 #include <ThrustAllocator.cuh>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
-using ThrustVector = thrust::device_vector<T, cuda::ThrustAllocator<T>>;
-}
+using ThrustVector = thrust::device_vector<T, ThrustAllocator<T>>;
+}  // namespace cuda
+}  // namespace arrayfire
 
 #if THRUST_MAJOR_VERSION >= 1 && THRUST_MINOR_VERSION >= 8
 
-#define THRUST_SELECT(fn, ...) fn(cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
+#define THRUST_SELECT(fn, ...) \
+    fn(arrayfire::cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
 #define THRUST_SELECT_OUT(res, fn, ...) \
-    res = fn(cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
+    res = fn(arrayfire::cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
 
 #else
 
-#define THRUST_SELECT(fn, ...)                                      \
-    do {                                                            \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        fn(__VA_ARGS__);                                            \
+#define THRUST_SELECT(fn, ...)                                                 \
+    do {                                                                       \
+        CUDA_CHECK(cudaStreamSynchronize(arrayfire::cuda::getActiveStream())); \
+        fn(__VA_ARGS__);                                                       \
     } while (0)
 
-#define THRUST_SELECT_OUT(res, fn, ...)                             \
-    do {                                                            \
-        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream())); \
-        res = fn(__VA_ARGS__);                                      \
+#define THRUST_SELECT_OUT(res, fn, ...)                                        \
+    do {                                                                       \
+        CUDA_CHECK(cudaStreamSynchronize(arrayfire::cuda::getActiveStream())); \
+        res = fn(__VA_ARGS__);                                                 \
     } while (0)
 
 #endif
diff --git a/src/backend/cuda/tile.cpp b/src/backend/cuda/tile.cpp
index 4b2839232e..f93982eb43 100644
--- a/src/backend/cuda/tile.cpp
+++ b/src/backend/cuda/tile.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
@@ -54,3 +55,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/tile.hpp b/src/backend/cuda/tile.hpp
index d58795a629..888e77aa13 100644
--- a/src/backend/cuda/tile.hpp
+++ b/src/backend/cuda/tile.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/topk.cu b/src/backend/cuda/topk.cu
index 5901c5e5b1..12dde72684 100644
--- a/src/backend/cuda/topk.cu
+++ b/src/backend/cuda/topk.cu
@@ -13,8 +13,9 @@
 #include <topk.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void topk(Array<T>& ovals, Array<uint>& oidxs, const Array<T>& ivals,
@@ -40,3 +41,4 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(half)
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/topk.hpp b/src/backend/cuda/topk.hpp
index 3b87427eb3..f3c27f433c 100644
--- a/src/backend/cuda/topk.hpp
+++ b/src/backend/cuda/topk.hpp
@@ -8,8 +8,10 @@
  ********************************************************/
 
 #include <Array.hpp>
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/transform.cpp b/src/backend/cuda/transform.cpp
index a143d74963..baba9b1a04 100644
--- a/src/backend/cuda/transform.cpp
+++ b/src/backend/cuda/transform.cpp
@@ -12,6 +12,7 @@
 #include <kernel/transform.hpp>
 #include <utility.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -42,3 +43,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/transform.hpp b/src/backend/cuda/transform.hpp
index ee3596d3ef..8e9e4b6990 100644
--- a/src/backend/cuda/transform.hpp
+++ b/src/backend/cuda/transform.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/transpose.cpp b/src/backend/cuda/transpose.cpp
index 25f882b667..faa4659b68 100644
--- a/src/backend/cuda/transpose.cpp
+++ b/src/backend/cuda/transpose.cpp
@@ -14,8 +14,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -52,3 +53,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/transpose.hpp b/src/backend/cuda/transpose.hpp
index 5a26aa8b14..e612754323 100644
--- a/src/backend/cuda/transpose.hpp
+++ b/src/backend/cuda/transpose.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/transpose_inplace.cpp b/src/backend/cuda/transpose_inplace.cpp
index d0c9163f89..ff89730d47 100644
--- a/src/backend/cuda/transpose_inplace.cpp
+++ b/src/backend/cuda/transpose_inplace.cpp
@@ -14,8 +14,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -44,3 +45,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/triangle.cpp b/src/backend/cuda/triangle.cpp
index 8e5f7eec76..4ec0a04e6f 100644
--- a/src/backend/cuda/triangle.cpp
+++ b/src/backend/cuda/triangle.cpp
@@ -15,8 +15,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -53,3 +54,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/triangle.hpp b/src/backend/cuda/triangle.hpp
index 801dfdd900..98c3480126 100644
--- a/src/backend/cuda/triangle.hpp
+++ b/src/backend/cuda/triangle.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> triangle(const Array<T> &in, const bool is_upper,
                   const bool is_unit_diag);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 91bcdbbda7..34815cba66 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -13,9 +13,11 @@
 #include <cuComplex.h>
 #include <cuda_fp16.h>
 
+namespace arrayfire {
 namespace common {
 class half;
-}
+}  // namespace common
+}  // namespace arrayfire
 
 #ifdef __CUDACC_RTC__
 
@@ -27,6 +29,7 @@ using dim_t = long long;
 
 #endif  //__CUDACC_RTC__
 
+namespace arrayfire {
 namespace cuda {
 
 using cdouble = cuDoubleComplex;
@@ -99,7 +102,7 @@ inline const char *shortname<ushort>(bool caps) {
     return caps ? "Q" : "q";
 }
 template<>
-inline const char *shortname<common::half>(bool caps) {
+inline const char *shortname<arrayfire::common::half>(bool caps) {
     return caps ? "H" : "h";
 }
 
@@ -133,9 +136,7 @@ inline const char *getFullName<common::half>() {
 }  // namespace
 #endif  //__CUDACC_RTC__
 
-//#ifndef __CUDACC_RTC__
 }  // namespace cuda
-//#endif  //__CUDACC_RTC__
 
 namespace common {
 
@@ -143,8 +144,8 @@ template<typename T>
 struct kernel_type;
 
 template<>
-struct kernel_type<common::half> {
-    using data = common::half;
+struct kernel_type<arrayfire::common::half> {
+    using data = arrayfire::common::half;
 
 #ifdef __CUDA_ARCH__
 
@@ -170,3 +171,4 @@ struct kernel_type<common::half> {
 #endif  // __CUDA_ARCH__
 };
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/cuda/unary.hpp b/src/backend/cuda/unary.hpp
index a94c84dfa2..5fd9e48f52 100644
--- a/src/backend/cuda/unary.hpp
+++ b/src/backend/cuda/unary.hpp
@@ -14,6 +14,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<af_op_t op>
@@ -78,8 +79,8 @@ UNARY_DECL(noop, "__noop")
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using common::Node;
-    using common::Node_ptr;
+    using arrayfire::common::Node;
+    using arrayfire::common::Node_ptr;
     using std::array;
 
     auto createUnary = [](array<Node_ptr, 1> &operands) {
@@ -95,7 +96,7 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using common::Node_ptr;
+    using arrayfire::common::Node_ptr;
 
     auto createUnary = [](std::array<Node_ptr, 1> &operands) {
         return Node_ptr(new common::UnaryNode(
@@ -109,3 +110,4 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/unwrap.cpp b/src/backend/cuda/unwrap.cpp
index 0f9b4dd0c1..6eae7d428b 100644
--- a/src/backend/cuda/unwrap.cpp
+++ b/src/backend/cuda/unwrap.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -62,3 +63,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/unwrap.hpp b/src/backend/cuda/unwrap.hpp
index 1a348d93e2..dbb1f8ee24 100644
--- a/src/backend/cuda/unwrap.hpp
+++ b/src/backend/cuda/unwrap.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
                 const dim_t dx, const dim_t dy, const bool is_column);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/utility.cpp b/src/backend/cuda/utility.cpp
index a315f4d28d..724f546326 100644
--- a/src/backend/cuda/utility.cpp
+++ b/src/backend/cuda/utility.cpp
@@ -11,6 +11,7 @@
 
 #include <err_cuda.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 int interpOrder(const af_interp_type p) noexcept {
@@ -31,3 +32,4 @@ int interpOrder(const af_interp_type p) noexcept {
 }
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/utility.hpp b/src/backend/cuda/utility.hpp
index bf602eacc9..d3ff338bf6 100644
--- a/src/backend/cuda/utility.hpp
+++ b/src/backend/cuda/utility.hpp
@@ -12,6 +12,7 @@
 #include <backend.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace cuda {
 
 [[gnu::unused]] static __DH__ dim_t trimIndex(const int &idx,
@@ -30,3 +31,4 @@ namespace cuda {
 int interpOrder(const af_interp_type p) noexcept;
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index eba52ad532..2868979772 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -15,13 +15,17 @@
 #include <vector_field.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield) {
-    auto stream = cuda::getActiveStream();
+    auto stream = getActiveStream();
     if (DeviceManager::checkGraphicsInteropCapability()) {
         auto res = interopManager().getVectorFieldResources(vfield);
         cudaGraphicsResource_t resources[2] = {*res[0].get(), *res[1].get()};
@@ -54,7 +58,7 @@ void copy_vector_field(const Array<T> &points, const Array<T> &directions,
 
         POST_LAUNCH_CHECK();
     } else {
-        ForgeModule &_ = graphics::forgePlugin();
+        ForgeModule &_ = forgePlugin();
         CheckGL("Begin CUDA fallback-resource copy");
         unsigned size1 = 0, size2 = 0;
         unsigned buff1 = 0, buff2 = 0;
@@ -104,3 +108,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/vector_field.hpp b/src/backend/cuda/vector_field.hpp
index abb375bcbc..086e1bbf27 100644
--- a/src/backend/cuda/vector_field.hpp
+++ b/src/backend/cuda/vector_field.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/where.cpp b/src/backend/cuda/where.cpp
index fd39c88eb6..efd488d26e 100644
--- a/src/backend/cuda/where.cpp
+++ b/src/backend/cuda/where.cpp
@@ -16,6 +16,7 @@
 #include <where.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<uint> where(const Array<T> &in) {
@@ -40,3 +41,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/where.hpp b/src/backend/cuda/where.hpp
index 6a2069f344..a2e9ccdab6 100644
--- a/src/backend/cuda/where.hpp
+++ b/src/backend/cuda/where.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 Array<uint> where(const Array<T>& in);
-}
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/wrap.cpp b/src/backend/cuda/wrap.cpp
index 76834e6a10..d8963cacd9 100644
--- a/src/backend/cuda/wrap.cpp
+++ b/src/backend/cuda/wrap.cpp
@@ -18,8 +18,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace cuda {
 
 template<typename T>
@@ -74,3 +75,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/wrap.hpp b/src/backend/cuda/wrap.hpp
index d324975379..312b24a23e 100644
--- a/src/backend/cuda/wrap.hpp
+++ b/src/backend/cuda/wrap.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace cuda {
 template<typename T>
 void wrap(Array<T> &out, const Array<T> &in, const dim_t wx, const dim_t wy,
@@ -21,3 +22,4 @@ Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
                       const dim_t sy, const dim_t px, const dim_t py,
                       const dim_t dx, const dim_t dy, const bool is_column);
 }  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index f3dd8d97ed..225e9686ac 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -39,11 +39,11 @@ using af::dtype_traits;
 
 using cl::Buffer;
 
-using common::half;
-using common::Node;
-using common::Node_ptr;
-using common::NodeIterator;
-using opencl::jit::BufferNode;
+using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::opencl::jit::BufferNode;
 
 using nonstd::span;
 using std::accumulate;
@@ -52,6 +52,7 @@ using std::make_shared;
 using std::shared_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 shared_ptr<BufferNode> bufferNodePtr() {
@@ -549,3 +550,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index d3362cfa9a..2d2ca97c94 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -34,6 +34,7 @@ template<typename T>
 class SparseArray;
 }
 
+namespace arrayfire {
 namespace opencl {
 typedef std::shared_ptr<cl::Buffer> Buffer_ptr;
 using af::dim4;
@@ -315,3 +316,4 @@ class Array {
 };
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 069609b95e..cf31204415 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -28,7 +28,7 @@ file_to_string(
     EXTENSION "hpp"
     OUTPUT_DIR ${kernel_headers_dir}
     TARGETS cl_kernel_targets
-    NAMESPACE "opencl"
+    NAMESPACE "arrayfire opencl"
     )
 
 set(opencl_compile_definitions
diff --git a/src/backend/opencl/Event.cpp b/src/backend/opencl/Event.cpp
index 21523891d9..bc93b60a62 100644
--- a/src/backend/opencl/Event.cpp
+++ b/src/backend/opencl/Event.cpp
@@ -20,6 +20,7 @@
 using std::make_unique;
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace opencl {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(cl::CommandQueue& queue) {
@@ -70,3 +71,4 @@ af_event createAndMarkEvent() {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Event.hpp b/src/backend/opencl/Event.hpp
index 51505d5489..c8420a9dff 100644
--- a/src/backend/opencl/Event.hpp
+++ b/src/backend/opencl/Event.hpp
@@ -12,6 +12,7 @@
 #include <common/EventBase.hpp>
 #include <af/event.h>
 
+namespace arrayfire {
 namespace opencl {
 class OpenCLEventPolicy {
    public:
@@ -57,3 +58,4 @@ void block(af_event eventHandle);
 af_event createAndMarkEvent();
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/GraphicsResourceManager.cpp b/src/backend/opencl/GraphicsResourceManager.cpp
index e2cd64150f..fe1f703a5f 100644
--- a/src/backend/opencl/GraphicsResourceManager.cpp
+++ b/src/backend/opencl/GraphicsResourceManager.cpp
@@ -10,6 +10,7 @@
 #include <GraphicsResourceManager.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 GraphicsResourceManager::ShrdResVector
 GraphicsResourceManager::registerResources(
@@ -25,3 +26,4 @@ GraphicsResourceManager::registerResources(
     return output;
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/GraphicsResourceManager.hpp b/src/backend/opencl/GraphicsResourceManager.hpp
index 618e46e2f4..130a564df1 100644
--- a/src/backend/opencl/GraphicsResourceManager.hpp
+++ b/src/backend/opencl/GraphicsResourceManager.hpp
@@ -18,6 +18,7 @@ namespace cl {
 class Buffer;
 }
 
+namespace arrayfire {
 namespace opencl {
 class GraphicsResourceManager
     : public common::InteropManager<GraphicsResourceManager, cl::Buffer> {
@@ -33,3 +34,4 @@ class GraphicsResourceManager
     void operator=(GraphicsResourceManager const&);
 };
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Kernel.cpp b/src/backend/opencl/Kernel.cpp
index a096979f9a..b5d818b6d2 100644
--- a/src/backend/opencl/Kernel.cpp
+++ b/src/backend/opencl/Kernel.cpp
@@ -14,6 +14,7 @@
 #include <common/defines.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 Kernel::DevPtrType Kernel::getDevPtr(const char* name) {
@@ -39,3 +40,4 @@ int Kernel::getFlag(Kernel::DevPtrType src) {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index 92eb28be1e..e3a05e7da8 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -16,6 +16,7 @@
 #include <cl2hpp.hpp>
 #include <string>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel_logger {
 inline auto getLogger() -> spdlog::logger* {
@@ -63,3 +64,4 @@ class Kernel
 };
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Module.hpp b/src/backend/opencl/Module.hpp
index c918797699..b8a8d6a3b5 100644
--- a/src/backend/opencl/Module.hpp
+++ b/src/backend/opencl/Module.hpp
@@ -13,6 +13,7 @@
 
 #include <cl2hpp.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 /// OpenCL backend wrapper for cl::Program object
@@ -35,3 +36,4 @@ class Module : public common::ModuleInterface<cl::Program> {
 };
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Param.cpp b/src/backend/opencl/Param.cpp
index 25358310ae..3b791c96ea 100644
--- a/src/backend/opencl/Param.cpp
+++ b/src/backend/opencl/Param.cpp
@@ -12,6 +12,7 @@
 #include <platform.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace opencl {
 Param::Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
 Param::Param(cl::Buffer *data_, KParam info_) : data(data_), info(info_) {}
@@ -28,3 +29,4 @@ Param makeParam(cl::Buffer &mem, int off, const int dims[4],
     return out;
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index 6cf63f356b..aaf19dea62 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -12,6 +12,7 @@
 #include <cl2hpp.hpp>
 #include <kernel/KParam.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 struct Param {
@@ -32,3 +33,4 @@ struct Param {
 Param makeParam(cl::Buffer& mem, int off, const int dims[4],
                 const int strides[4]);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/all.cpp b/src/backend/opencl/all.cpp
index 5825b3af4a..2d2a1d4717 100644
--- a/src/backend/opencl/all.cpp
+++ b/src/backend/opencl/all.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // alltrue
 INSTANTIATE(af_and_t, float, char)
@@ -28,3 +29,4 @@ INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
 INSTANTIATE(af_and_t, half, char)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/anisotropic_diffusion.cpp b/src/backend/opencl/anisotropic_diffusion.cpp
index e71a78cfc8..19e065c14f 100644
--- a/src/backend/opencl/anisotropic_diffusion.cpp
+++ b/src/backend/opencl/anisotropic_diffusion.cpp
@@ -13,6 +13,7 @@
 #include <kernel/anisotropic_diffusion.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
@@ -33,3 +34,4 @@ void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
 INSTANTIATE(double)
 INSTANTIATE(float)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/anisotropic_diffusion.hpp b/src/backend/opencl/anisotropic_diffusion.hpp
index 816cae3359..a1a76a29dc 100644
--- a/src/backend/opencl/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/anisotropic_diffusion.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/any.cpp b/src/backend/opencl/any.cpp
index c9668f3451..ce36f8ed90 100644
--- a/src/backend/opencl/any.cpp
+++ b/src/backend/opencl/any.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // anytrue
 INSTANTIATE(af_or_t, float, char)
@@ -28,3 +29,4 @@ INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
 INSTANTIATE(af_or_t, half, char)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/approx.cpp b/src/backend/opencl/approx.cpp
index dc4f851e4f..cc8c6994a9 100644
--- a/src/backend/opencl/approx.cpp
+++ b/src/backend/opencl/approx.cpp
@@ -11,6 +11,7 @@
 
 #include <kernel/approx.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -83,3 +84,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/approx.hpp b/src/backend/opencl/approx.hpp
index addb8fe73c..5a2b7e3212 100644
--- a/src/backend/opencl/approx.hpp
+++ b/src/backend/opencl/approx.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -22,3 +23,4 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
              const Tp &yi_step, const af_interp_type method,
              const float offGrid);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/arith.hpp b/src/backend/opencl/arith.hpp
index 48bab53038..932a86d814 100644
--- a/src/backend/opencl/arith.hpp
+++ b/src/backend/opencl/arith.hpp
@@ -14,6 +14,7 @@
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, af_op_t op>
@@ -28,3 +29,4 @@ Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index b11a2398a9..9e0f8074a3 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -18,8 +18,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -87,3 +88,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/assign.hpp b/src/backend/opencl/assign.hpp
index 4dd07541d5..6283ad8ceb 100644
--- a/src/backend/opencl/assign.hpp
+++ b/src/backend/opencl/assign.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/backend.hpp b/src/backend/opencl/backend.hpp
index 527d379168..30392a7b9a 100644
--- a/src/backend/opencl/backend.hpp
+++ b/src/backend/opencl/backend.hpp
@@ -21,4 +21,4 @@
 
 #include "types.hpp"
 
-namespace detail = opencl;
+namespace detail = arrayfire::opencl;
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index d75f62d2fc..21ec82e2b6 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -14,6 +14,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename inType, typename outType>
@@ -38,3 +39,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/bilateral.hpp b/src/backend/opencl/bilateral.hpp
index ab9775f3b2..05fd52c429 100644
--- a/src/backend/opencl/bilateral.hpp
+++ b/src/backend/opencl/bilateral.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename inType, typename outType>
 Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
                          const float &chromaticSigma);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 700a1b3c49..02291d566a 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename To, typename Ti, af_op_t op>
@@ -125,3 +126,4 @@ struct BinOp<To, Ti, af_hypot_t> {
 };
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 263d07bd9f..45b4149599 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -26,8 +26,9 @@
 #include <cpu/cpu_blas.hpp>
 #include <magma/magma_blas.h>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 void initBlas() { gpu_blas_init(); }
@@ -164,3 +165,4 @@ INSTANTIATE_DOT(cdouble)
 INSTANTIATE_DOT(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index 22c2e1ec02..4416960f46 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -14,6 +14,7 @@
 // functions. They can be implemented in different back-ends,
 // such as CLBlast or clBLAS.
 
+namespace arrayfire {
 namespace opencl {
 
 void initBlas();
@@ -40,3 +41,4 @@ template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/canny.cpp b/src/backend/opencl/canny.cpp
index ab2ec78c2f..cf4965fd5c 100644
--- a/src/backend/opencl/canny.cpp
+++ b/src/backend/opencl/canny.cpp
@@ -14,6 +14,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -34,3 +35,4 @@ Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
     return out;
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/canny.hpp b/src/backend/opencl/canny.hpp
index 173937b521..e7ad6dda0d 100644
--- a/src/backend/opencl/canny.hpp
+++ b/src/backend/opencl/canny.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -17,3 +18,4 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
 Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
                                      const Array<char>& weak);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index 3f3a0c1001..999d6188d9 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -18,6 +18,7 @@
 #include <af/dim4.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename To, typename Ti>
@@ -71,3 +72,4 @@ struct CastOp<cdouble, cdouble> {
 #undef CAST_CFN
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cholesky.cpp b/src/backend/opencl/cholesky.cpp
index eac4490baf..4d140ba099 100644
--- a/src/backend/opencl/cholesky.cpp
+++ b/src/backend/opencl/cholesky.cpp
@@ -17,6 +17,7 @@
 #include <magma/magma.h>
 #include <triangle.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -58,9 +59,11 @@ INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -84,5 +87,6 @@ INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cholesky.hpp b/src/backend/opencl/cholesky.hpp
index aa4e56bf29..be1805bc96 100644
--- a/src/backend/opencl/cholesky.hpp
+++ b/src/backend/opencl/cholesky.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
@@ -16,3 +17,4 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/clfft.cpp b/src/backend/opencl/clfft.cpp
index 21ef1f37d7..68a17cbd50 100644
--- a/src/backend/opencl/clfft.cpp
+++ b/src/backend/opencl/clfft.cpp
@@ -18,6 +18,7 @@
 using std::make_unique;
 using std::string;
 
+namespace arrayfire {
 namespace opencl {
 const char *_clfftGetResultString(clfftStatus st) {
     switch (st) {
@@ -178,3 +179,4 @@ SharedPlan findPlan(clfftLayout iLayout, clfftLayout oLayout, clfftDim rank,
     return retVal;
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/clfft.hpp b/src/backend/opencl/clfft.hpp
index f0f1bc28f6..c7b9d9949f 100644
--- a/src/backend/opencl/clfft.hpp
+++ b/src/backend/opencl/clfft.hpp
@@ -15,6 +15,7 @@
 
 #include <cstdio>
 
+namespace arrayfire {
 namespace opencl {
 typedef clfftPlanHandle PlanType;
 typedef std::shared_ptr<PlanType> SharedPlan;
@@ -34,6 +35,7 @@ class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
                                size_t batch);
 };
 }  // namespace opencl
+}  // namespace arrayfire
 
 #define CLFFT_CHECK(fn)                                          \
     do {                                                         \
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index f931bb554a..32ea5809f5 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -30,16 +30,16 @@
 #include <string>
 #include <vector>
 
+using arrayfire::common::getEnvVar;
+using arrayfire::common::loggerFactory;
+using arrayfire::opencl::getActiveDeviceId;
+using arrayfire::opencl::getDevice;
+using arrayfire::opencl::Kernel;
+using arrayfire::opencl::Module;
 using cl::Error;
 using cl::Program;
-using common::getEnvVar;
-using common::loggerFactory;
 using fmt::format;
 using nonstd::span;
-using opencl::getActiveDeviceId;
-using opencl::getDevice;
-using opencl::Kernel;
-using opencl::Module;
 using spdlog::logger;
 
 using std::begin;
@@ -86,6 +86,7 @@ string getProgramBuildLog(const Program &prog) {
         AF_ERROR(build_error, AF_ERR_INTERNAL);                      \
     } while (0)
 
+namespace arrayfire {
 namespace opencl {
 
 const static string DEFAULT_MACROS_STR(
@@ -141,9 +142,10 @@ Program buildProgram(span<const string> kernelSources,
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 string getKernelCacheFilename(const int device, const string &key) {
-    auto &dev = opencl::getDevice(device);
+    auto &dev = arrayfire::opencl::getDevice(device);
 
     unsigned vendorId = dev.getInfo<CL_DEVICE_VENDOR_ID>();
     auto devName      = dev.getInfo<CL_DEVICE_NAME>();
@@ -157,6 +159,7 @@ string getKernelCacheFilename(const int device, const string &key) {
            to_string(AF_API_VERSION_CURRENT) + ".bin";
 }
 
+namespace arrayfire {
 namespace common {
 
 Module compileModule(const string &moduleKey, span<const string> sources,
@@ -166,11 +169,11 @@ Module compileModule(const string &moduleKey, span<const string> sources,
     UNUSED(isJIT);
 
     auto compileBegin = high_resolution_clock::now();
-    auto program      = opencl::buildProgram(sources, options);
+    auto program      = arrayfire::opencl::buildProgram(sources, options);
     auto compileEnd   = high_resolution_clock::now();
 
 #ifdef AF_CACHE_KERNELS_TO_DISK
-    const int device             = opencl::getActiveDeviceId();
+    const int device             = arrayfire::opencl::getActiveDeviceId();
     const string &cacheDirectory = getCacheDirectory();
     if (!cacheDirectory.empty()) {
         const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
@@ -202,15 +205,17 @@ Module compileModule(const string &moduleKey, span<const string> sources,
             // before the current thread.
             if (!renameFile(tempFile, cacheFile)) { removeFile(tempFile); }
         } catch (const cl::Error &e) {
-            AF_TRACE("{{{:<20} : Failed to fetch opencl binary for {}, {}}}",
-                     moduleKey,
-                     opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
-                     e.what());
+            AF_TRACE(
+                "{{{:<20} : Failed to fetch opencl binary for {}, {}}}",
+                moduleKey,
+                arrayfire::opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
+                e.what());
         } catch (const std::ios_base::failure &e) {
-            AF_TRACE("{{{:<20} : Failed writing binary to {} for {}, {}}}",
-                     moduleKey, cacheFile,
-                     opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
-                     e.what());
+            AF_TRACE(
+                "{{{:<20} : Failed writing binary to {} for {}, {}}}",
+                moduleKey, cacheFile,
+                arrayfire::opencl::getDevice(device).getInfo<CL_DEVICE_NAME>(),
+                e.what());
         }
     }
 #endif
@@ -228,7 +233,7 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
     const string &cacheDirectory = getCacheDirectory();
     if (cacheDirectory.empty()) return Module{};
 
-    auto &dev              = opencl::getDevice(device);
+    auto &dev              = arrayfire::opencl::getDevice(device);
     const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
                              getKernelCacheFilename(device, moduleKey);
     Program program;
@@ -255,7 +260,7 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
         if (recomputedHash != clbinHash) {
             AF_ERROR("Binary on disk seems to be corrupted", AF_ERR_LOAD_SYM);
         }
-        program = Program(opencl::getContext(), {dev}, {clbin});
+        program = Program(arrayfire::opencl::getContext(), {dev}, {clbin});
         program.build();
 
         AF_TRACE("{{{:<20} : loaded from {} for {} }}", moduleKey, cacheFile,
@@ -293,3 +298,4 @@ Kernel getKernel(const Module &mod, const string &nameExpr,
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/opencl/complex.hpp b/src/backend/opencl/complex.hpp
index 124d3b49ca..a4306c7be3 100644
--- a/src/backend/opencl/complex.hpp
+++ b/src/backend/opencl/complex.hpp
@@ -15,6 +15,7 @@
 #include <traits.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename To, typename Ti>
 Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
@@ -88,3 +89,4 @@ Array<T> conj(const Array<T> &in) {
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index a4924303f3..edc28e4e35 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -24,11 +24,12 @@
 #include <vector>
 
 using af::dim4;
-using common::flip;
-using common::half;
-using common::modDims;
+using arrayfire::common::flip;
+using arrayfire::common::half;
+using arrayfire::common::modDims;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename accT>
@@ -249,3 +250,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/convolve.hpp b/src/backend/opencl/convolve.hpp
index 6e52ed6e56..0cf040c417 100644
--- a/src/backend/opencl/convolve.hpp
+++ b/src/backend/opencl/convolve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename accT>
@@ -37,3 +38,4 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index fc337e718f..03da468ac4 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -16,6 +16,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename accT>
@@ -72,3 +73,4 @@ INSTANTIATE(intl, float)
 INSTANTIATE(uintl, float)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index cfb5e5b61d..970deae518 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -15,9 +15,10 @@
 #include <err_opencl.hpp>
 #include <math.hpp>
 
-using common::half;
-using common::is_complex;
+using arrayfire::common::half;
+using arrayfire::common::is_complex;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -209,3 +210,4 @@ INSTANTIATE_GETSCALAR(ushort)
 INSTANTIATE_GETSCALAR(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/copy.hpp b/src/backend/opencl/copy.hpp
index 9f6b19bcae..1b8576a5d9 100644
--- a/src/backend/opencl/copy.hpp
+++ b/src/backend/opencl/copy.hpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <kernel/pad_array_borders.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void copyData(T *data, const Array<T> &A);
@@ -65,3 +66,4 @@ void multiply_inplace(Array<T> &in, double val);
 template<typename T>
 T getScalar(const Array<T> &in);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/count.cpp b/src/backend/opencl/count.cpp
index fd1f6b3381..80f12e68cd 100644
--- a/src/backend/opencl/count.cpp
+++ b/src/backend/opencl/count.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // count
 INSTANTIATE(af_notzero_t, float, uint)
@@ -28,3 +29,4 @@ INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
 INSTANTIATE(af_notzero_t, half, uint)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_blas.cpp b/src/backend/opencl/cpu/cpu_blas.cpp
index 8f80b044f3..8fbef46443 100644
--- a/src/backend/opencl/cpu/cpu_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_blas.cpp
@@ -16,7 +16,7 @@
 #include <math.hpp>
 #include <traits.hpp>
 
-using common::is_complex;
+using arrayfire::common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
@@ -25,6 +25,7 @@ using std::enable_if;
 using std::is_floating_point;
 using std::remove_const;
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -246,4 +247,5 @@ INSTANTIATE_GEMM(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_blas.hpp b/src/backend/opencl/cpu/cpu_blas.hpp
index b39d8ae205..ae44d0ea91 100644
--- a/src/backend/opencl/cpu/cpu_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_blas.hpp
@@ -9,11 +9,13 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
 template<typename T>
 void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
           const Array<T> &lhs, const Array<T> &rhs, const T *beta);
-}
+}  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_cholesky.cpp b/src/backend/opencl/cpu/cpu_cholesky.cpp
index fc066bd710..8878c8adf2 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.cpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.cpp
@@ -13,6 +13,7 @@
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_triangle.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -81,4 +82,5 @@ INSTANTIATE_CH(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_cholesky.hpp b/src/backend/opencl/cpu/cpu_cholesky.hpp
index 3fdecfcd4a..489221304c 100644
--- a/src/backend/opencl/cpu/cpu_cholesky.hpp
+++ b/src/backend/opencl/cpu/cpu_cholesky.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper);
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_helper.hpp b/src/backend/opencl/cpu/cpu_helper.hpp
index b614e53be1..0f979d1f90 100644
--- a/src/backend/opencl/cpu/cpu_helper.hpp
+++ b/src/backend/opencl/cpu/cpu_helper.hpp
@@ -20,8 +20,8 @@
 //********************************************************/
 #if defined(WITH_LINEAR_ALGEBRA)
 
-#define lapack_complex_float opencl::cfloat
-#define lapack_complex_double opencl::cdouble
+#define lapack_complex_float arrayfire::opencl::cfloat
+#define lapack_complex_double arrayfire::opencl::cdouble
 #define LAPACK_PREFIX LAPACKE_
 #define ORDER_TYPE int
 #define AF_LAPACK_COL_MAJOR LAPACK_COL_MAJOR
diff --git a/src/backend/opencl/cpu/cpu_inverse.cpp b/src/backend/opencl/cpu/cpu_inverse.cpp
index 7adcacc17c..b31e70b857 100644
--- a/src/backend/opencl/cpu/cpu_inverse.cpp
+++ b/src/backend/opencl/cpu/cpu_inverse.cpp
@@ -13,6 +13,7 @@
 #include <cpu/cpu_inverse.hpp>
 #include <cpu/cpu_lu.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -68,4 +69,5 @@ INSTANTIATE(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_inverse.hpp b/src/backend/opencl/cpu/cpu_inverse.hpp
index b5be9e1ee0..04ed32b7d4 100644
--- a/src/backend/opencl/cpu/cpu_inverse.hpp
+++ b/src/backend/opencl/cpu/cpu_inverse.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 template<typename T>
 Array<T> inverse(const Array<T> &in);
-}
+}  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_lu.cpp b/src/backend/opencl/cpu/cpu_lu.cpp
index 7793a3590e..a754535025 100644
--- a/src/backend/opencl/cpu/cpu_lu.cpp
+++ b/src/backend/opencl/cpu/cpu_lu.cpp
@@ -16,6 +16,7 @@
 
 #include <numeric>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -156,4 +157,5 @@ INSTANTIATE_LU(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_lu.hpp b/src/backend/opencl/cpu/cpu_lu.hpp
index f3cf4aaa1d..936add16e3 100644
--- a/src/backend/opencl/cpu/cpu_lu.hpp
+++ b/src/backend/opencl/cpu/cpu_lu.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 template<typename T>
@@ -19,3 +20,4 @@ template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_qr.cpp b/src/backend/opencl/cpu/cpu_qr.cpp
index fd5526792d..1e1b926d0f 100644
--- a/src/backend/opencl/cpu/cpu_qr.cpp
+++ b/src/backend/opencl/cpu/cpu_qr.cpp
@@ -13,6 +13,7 @@
 #include <cpu/cpu_qr.hpp>
 #include <cpu/cpu_triangle.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -115,4 +116,5 @@ INSTANTIATE_QR(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_qr.hpp b/src/backend/opencl/cpu/cpu_qr.hpp
index 5d755dbd0b..d9c9345115 100644
--- a/src/backend/opencl/cpu/cpu_qr.hpp
+++ b/src/backend/opencl/cpu/cpu_qr.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> qr_inplace(Array<T> &in);
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_solve.cpp b/src/backend/opencl/cpu/cpu_solve.cpp
index 8b2cd79f64..4e0349d2dc 100644
--- a/src/backend/opencl/cpu/cpu_solve.cpp
+++ b/src/backend/opencl/cpu/cpu_solve.cpp
@@ -18,6 +18,7 @@
 #include <algorithm>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -313,4 +314,5 @@ INSTANTIATE_SOLVE(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_solve.hpp b/src/backend/opencl/cpu/cpu_solve.hpp
index 9ef13caa8f..1223a96531 100644
--- a/src/backend/opencl/cpu/cpu_solve.hpp
+++ b/src/backend/opencl/cpu/cpu_solve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 template<typename T>
@@ -20,3 +21,4 @@ Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options = AF_MAT_NONE);
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.cpp b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
index 0699c44717..66fba7cdbe 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.cpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.cpp
@@ -20,7 +20,7 @@
 #include <stdexcept>
 #include <string>
 
-using common::is_complex;
+using arrayfire::common::is_complex;
 
 using std::add_const;
 using std::add_pointer;
@@ -30,6 +30,7 @@ using std::is_floating_point;
 using std::is_same;
 using std::remove_const;
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -487,4 +488,5 @@ INSTANTIATE_SPARSE(cdouble)
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_sparse_blas.hpp b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
index 90e53e30d6..dee21c7c01 100644
--- a/src/backend/opencl/cpu/cpu_sparse_blas.hpp
+++ b/src/backend/opencl/cpu/cpu_sparse_blas.hpp
@@ -18,10 +18,11 @@
 using sp_cfloat  = MKL_Complex8;
 using sp_cdouble = MKL_Complex16;
 #else
-using sp_cfloat  = opencl::cfloat;
-using sp_cdouble = opencl::cdouble;
+using sp_cfloat  = arrayfire::opencl::cfloat;
+using sp_cdouble = arrayfire::opencl::cdouble;
 #endif
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -29,5 +30,6 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T> lhs, const Array<T> rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
-}
+}  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_svd.cpp b/src/backend/opencl/cpu/cpu_svd.cpp
index 2b0e23db1e..6d865e8520 100644
--- a/src/backend/opencl/cpu/cpu_svd.cpp
+++ b/src/backend/opencl/cpu/cpu_svd.cpp
@@ -12,6 +12,7 @@
 #include <cpu/cpu_helper.hpp>
 #include <cpu/cpu_svd.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -93,4 +94,5 @@ INSTANTIATE_SVD(cfloat, float)
 INSTANTIATE_SVD(cdouble, double)
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/cpu/cpu_svd.hpp b/src/backend/opencl/cpu/cpu_svd.hpp
index 783c1664fe..2cb163de43 100644
--- a/src/backend/opencl/cpu/cpu_svd.hpp
+++ b/src/backend/opencl/cpu/cpu_svd.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 template<typename T, typename Tr>
@@ -18,3 +19,4 @@ template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/cpu/cpu_triangle.hpp b/src/backend/opencl/cpu/cpu_triangle.hpp
index 51bc242428..6bf2a4ceda 100644
--- a/src/backend/opencl/cpu/cpu_triangle.hpp
+++ b/src/backend/opencl/cpu/cpu_triangle.hpp
@@ -13,6 +13,7 @@
 
 #include <math.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace cpu {
 
@@ -50,6 +51,7 @@ void triangle(T *o, const T *i, const dim4 odm, const dim4 ost,
 
 }  // namespace cpu
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index a9cfbc02e2..c1fa920a97 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -40,11 +40,11 @@
 #include <string>
 #include <vector>
 
+using arrayfire::common::getEnvVar;
 using cl::CommandQueue;
 using cl::Context;
 using cl::Device;
 using cl::Platform;
-using common::getEnvVar;
 using std::begin;
 using std::end;
 using std::find;
@@ -54,6 +54,7 @@ using std::stringstream;
 using std::unique_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 
 #if defined(OS_MAC)
@@ -197,7 +198,7 @@ DeviceManager::DeviceManager()
         }
 #endif
     }
-    fgMngr = std::make_unique<graphics::ForgeManager>();
+    fgMngr = std::make_unique<arrayfire::common::ForgeManager>();
 
     // This is all we need because the sort takes care of the order of devices
 #ifdef OS_MAC
@@ -543,3 +544,4 @@ void DeviceManager::markDeviceForInterop(const int device,
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index b68297b511..8789675fe2 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -40,18 +40,16 @@ namespace spdlog {
 class logger;
 }
 
-namespace graphics {
-class ForgeManager;
-}
-
+namespace arrayfire {
 namespace common {
-namespace memory {
+class ForgeManager;
 class MemoryManagerBase;
-}
 }  // namespace common
+}  // namespace arrayfire
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
+namespace arrayfire {
 namespace opencl {
 
 // opencl namespace forward declarations
@@ -60,27 +58,31 @@ struct kc_entry_t;  // kernel cache entry
 class PlanCache;    // clfft
 
 class DeviceManager {
-    friend MemoryManagerBase& memoryManager();
+    friend arrayfire::common::MemoryManagerBase& memoryManager();
 
-    friend void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+    friend void setMemoryManager(
+        std::unique_ptr<arrayfire::common::MemoryManagerBase> mgr);
 
-    void setMemoryManager(std::unique_ptr<MemoryManagerBase> mgr);
+    void setMemoryManager(
+        std::unique_ptr<arrayfire::common::MemoryManagerBase> mgr);
 
     friend void resetMemoryManager();
 
     void resetMemoryManager();
 
-    friend MemoryManagerBase& pinnedMemoryManager();
+    friend arrayfire::common::MemoryManagerBase& pinnedMemoryManager();
 
-    friend void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+    friend void setMemoryManagerPinned(
+        std::unique_ptr<arrayfire::common::MemoryManagerBase> mgr);
 
-    void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
+    void setMemoryManagerPinned(
+        std::unique_ptr<arrayfire::common::MemoryManagerBase> mgr);
 
     friend void resetMemoryManagerPinned();
 
     void resetMemoryManagerPinned();
 
-    friend graphics::ForgeManager& forgeManager();
+    friend arrayfire::common::ForgeManager& forgeManager();
 
     friend GraphicsResourceManager& interopManager();
 
@@ -163,7 +165,7 @@ class DeviceManager {
     std::vector<int> mPlatforms;
     unsigned mUserDeviceOffset;
 
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<arrayfire::common::ForgeManager> fgMngr;
     std::unique_ptr<MemoryManagerBase> memManager;
     std::unique_ptr<MemoryManagerBase> pinnedMemManager;
     std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
@@ -175,3 +177,4 @@ class DeviceManager {
 };
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 96624f90b7..094906a77a 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -15,8 +15,9 @@
 #include <math.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
@@ -59,3 +60,4 @@ INSTANTIATE_DIAGONAL(ushort)
 INSTANTIATE_DIAGONAL(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/diagonal.hpp b/src/backend/opencl/diagonal.hpp
index 2d08df817e..5ba6daed79 100644
--- a/src/backend/opencl/diagonal.hpp
+++ b/src/backend/opencl/diagonal.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num);
@@ -16,3 +17,4 @@ Array<T> diagCreate(const Array<T> &in, const int num);
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/diff.cpp b/src/backend/opencl/diff.cpp
index 8c99eee837..020365d24c 100644
--- a/src/backend/opencl/diff.cpp
+++ b/src/backend/opencl/diff.cpp
@@ -13,6 +13,7 @@
 #include <af/dim4.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -56,3 +57,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(char)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/diff.hpp b/src/backend/opencl/diff.hpp
index d670ebcf33..ff60455fe8 100644
--- a/src/backend/opencl/diff.hpp
+++ b/src/backend/opencl/diff.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim);
@@ -16,3 +17,4 @@ Array<T> diff1(const Array<T> &in, const int dim);
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/exampleFunction.cpp b/src/backend/opencl/exampleFunction.cpp
index fd0f7c3e18..10af977382 100644
--- a/src/backend/opencl/exampleFunction.cpp
+++ b/src/backend/opencl/exampleFunction.cpp
@@ -23,6 +23,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -62,3 +63,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/exampleFunction.hpp b/src/backend/opencl/exampleFunction.hpp
index 2ee89e8f42..35f844dc4e 100644
--- a/src/backend/opencl/exampleFunction.hpp
+++ b/src/backend/opencl/exampleFunction.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
                          const af_someenum_t method);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index faf9914b96..bfe6c84177 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -16,6 +16,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -57,3 +58,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/fast.hpp b/src/backend/opencl/fast.hpp
index 2eda909eb1..4a1d7cc3cd 100644
--- a/src/backend/opencl/fast.hpp
+++ b/src/backend/opencl/fast.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -20,4 +21,5 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const bool non_max, const float feature_ratio,
               const unsigned edge);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/fft.cpp b/src/backend/opencl/fft.cpp
index 071ef4b9e4..36ebd70a63 100644
--- a/src/backend/opencl/fft.cpp
+++ b/src/backend/opencl/fft.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 void setFFTPlanCacheSize(size_t numPlans) {
@@ -167,3 +168,4 @@ INSTANTIATE(cdouble)
 INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/fft.hpp b/src/backend/opencl/fft.hpp
index 28adbdfbfa..f071b9a8c5 100644
--- a/src/backend/opencl/fft.hpp
+++ b/src/backend/opencl/fft.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 void setFFTPlanCacheSize(size_t numPlans);
@@ -23,3 +24,4 @@ template<typename Tr, typename Tc>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index 10b3015b6b..a4f8b1f1f1 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -25,6 +25,7 @@ using std::is_integral;
 using std::is_same;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -143,3 +144,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/fftconvolve.hpp b/src/backend/opencl/fftconvolve.hpp
index fde659d2b0..a00f978adc 100644
--- a/src/backend/opencl/fftconvolve.hpp
+++ b/src/backend/opencl/fftconvolve.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/flood_fill.cpp b/src/backend/opencl/flood_fill.cpp
index 500a9219db..b57de824bd 100644
--- a/src/backend/opencl/flood_fill.cpp
+++ b/src/backend/opencl/flood_fill.cpp
@@ -12,6 +12,7 @@
 #include <err_opencl.hpp>
 #include <kernel/flood_fill.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -36,3 +37,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/flood_fill.hpp b/src/backend/opencl/flood_fill.hpp
index 0cdea7fd62..b4210c2d57 100644
--- a/src/backend/opencl/flood_fill.hpp
+++ b/src/backend/opencl/flood_fill.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
@@ -19,3 +20,4 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
                    const T lowValue, const T highValue,
                    const af::connectivity nlookup = AF_CONNECTIVITY_8);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/gradient.cpp b/src/backend/opencl/gradient.cpp
index 0ecf94f06b..711e579295 100644
--- a/src/backend/opencl/gradient.cpp
+++ b/src/backend/opencl/gradient.cpp
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
@@ -28,3 +29,4 @@ INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/gradient.hpp b/src/backend/opencl/gradient.hpp
index c5108ae93f..88d663f436 100644
--- a/src/backend/opencl/gradient.hpp
+++ b/src/backend/opencl/gradient.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/harris.cpp b/src/backend/opencl/harris.cpp
index eedb054add..ce2f21fced 100644
--- a/src/backend/opencl/harris.cpp
+++ b/src/backend/opencl/harris.cpp
@@ -16,6 +16,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename convAccT>
@@ -53,3 +54,4 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/harris.hpp b/src/backend/opencl/harris.hpp
index b68dfbf098..73ac64bbfd 100644
--- a/src/backend/opencl/harris.hpp
+++ b/src/backend/opencl/harris.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
                 const float sigma, const unsigned filter_len,
                 const float k_thr);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index a1875686bc..6c2a06e0b1 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -13,11 +13,15 @@
 #include <err_opencl.hpp>
 #include <hist_graphics.hpp>
 
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = data.get();
@@ -73,3 +77,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/hist_graphics.hpp b/src/backend/opencl/hist_graphics.hpp
index fa49bfe43f..40dd57e5e9 100644
--- a/src/backend/opencl/hist_graphics.hpp
+++ b/src/backend/opencl/hist_graphics.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 7963d07d3c..7c3d432228 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -15,8 +15,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -48,3 +49,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/histogram.hpp b/src/backend/opencl/histogram.hpp
index 583a8150cd..5b0c21e970 100644
--- a/src/backend/opencl/histogram.hpp
+++ b/src/backend/opencl/histogram.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
                       const double &minval, const double &maxval,
                       const bool isLinear);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/homography.cpp b/src/backend/opencl/homography.cpp
index 9153336471..1bd958de55 100644
--- a/src/backend/opencl/homography.cpp
+++ b/src/backend/opencl/homography.cpp
@@ -19,6 +19,7 @@
 using af::dim4;
 using std::numeric_limits;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -74,3 +75,4 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/homography.hpp b/src/backend/opencl/homography.hpp
index 3453abc11f..2fa7c76690 100644
--- a/src/backend/opencl/homography.hpp
+++ b/src/backend/opencl/homography.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -18,4 +19,5 @@ int homography(Array<T> &H, const Array<float> &x_src,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/hsv_rgb.cpp b/src/backend/opencl/hsv_rgb.cpp
index 5ca8521236..06ab6b9856 100644
--- a/src/backend/opencl/hsv_rgb.cpp
+++ b/src/backend/opencl/hsv_rgb.cpp
@@ -11,6 +11,7 @@
 
 #include <kernel/hsv_rgb.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -35,3 +36,4 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/hsv_rgb.hpp b/src/backend/opencl/hsv_rgb.hpp
index fbbaf66569..4c87fa9479 100644
--- a/src/backend/opencl/hsv_rgb.hpp
+++ b/src/backend/opencl/hsv_rgb.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/identity.cpp b/src/backend/opencl/identity.cpp
index 27a092448c..9d9ae55718 100644
--- a/src/backend/opencl/identity.cpp
+++ b/src/backend/opencl/identity.cpp
@@ -14,8 +14,9 @@
 #include <debug_opencl.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> identity(const dim4& dims) {
@@ -42,3 +43,4 @@ INSTANTIATE_IDENTITY(ushort)
 INSTANTIATE_IDENTITY(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/identity.hpp b/src/backend/opencl/identity.hpp
index cb5512d1b5..0a401099b8 100644
--- a/src/backend/opencl/identity.hpp
+++ b/src/backend/opencl/identity.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> identity(const dim4& dim);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/iir.cpp b/src/backend/opencl/iir.cpp
index 63d34be2bd..9b53708212 100644
--- a/src/backend/opencl/iir.cpp
+++ b/src/backend/opencl/iir.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
@@ -57,3 +58,4 @@ INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/iir.hpp b/src/backend/opencl/iir.hpp
index c278a86b05..0b939ab3fe 100644
--- a/src/backend/opencl/iir.hpp
+++ b/src/backend/opencl/iir.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index 15b6a614a6..cffc2b8194 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -16,11 +16,15 @@
 #include <stdexcept>
 #include <vector>
 
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
+
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin opencl resource copy");
 
@@ -80,3 +84,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/image.hpp b/src/backend/opencl/image.hpp
index 7f4d37efa5..f9ee5db1eb 100644
--- a/src/backend/opencl/image.hpp
+++ b/src/backend/opencl/image.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index a5d00b8373..0911229936 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -16,8 +16,9 @@
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -87,3 +88,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/index.hpp b/src/backend/opencl/index.hpp
index b0d933a4f3..2164305a62 100644
--- a/src/backend/opencl/index.hpp
+++ b/src/backend/opencl/index.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/inverse.cpp b/src/backend/opencl/inverse.cpp
index c5b62a861f..860c449c3c 100644
--- a/src/backend/opencl/inverse.cpp
+++ b/src/backend/opencl/inverse.cpp
@@ -15,6 +15,7 @@
 #include <cpu/cpu_inverse.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -34,9 +35,11 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -52,5 +55,6 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/opencl/inverse.hpp b/src/backend/opencl/inverse.hpp
index 9316532a1a..1695798720 100644
--- a/src/backend/opencl/inverse.hpp
+++ b/src/backend/opencl/inverse.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> inverse(const Array<T> &in);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/iota.cpp b/src/backend/opencl/iota.cpp
index ebd0b5824d..de69ca6595 100644
--- a/src/backend/opencl/iota.cpp
+++ b/src/backend/opencl/iota.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
@@ -43,3 +44,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/iota.hpp b/src/backend/opencl/iota.hpp
index 5552e63332..26869554b8 100644
--- a/src/backend/opencl/iota.hpp
+++ b/src/backend/opencl/iota.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index 86ff0fd1db..ca4c916f63 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -17,8 +17,9 @@
 #include <complex>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<af_op_t op, typename T>
@@ -77,3 +78,4 @@ INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
 INSTANTIATE(af_max_t, half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/ireduce.hpp b/src/backend/opencl/ireduce.hpp
index 05bea7bd19..1b60a7a745 100644
--- a/src/backend/opencl/ireduce.hpp
+++ b/src/backend/opencl/ireduce.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
@@ -22,3 +23,4 @@ void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index dddf1ecd0d..30a942d2dd 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -33,15 +33,15 @@
 #include <string>
 #include <vector>
 
-using common::findModule;
-using common::getFuncName;
-using common::ModdimNode;
-using common::Node;
-using common::Node_ids;
-using common::Node_map_t;
-using common::Node_ptr;
-using common::NodeIterator;
-using common::saveKernel;
+using arrayfire::common::findModule;
+using arrayfire::common::getFuncName;
+using arrayfire::common::ModdimNode;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ids;
+using arrayfire::common::Node_map_t;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::common::saveKernel;
 
 using cl::Kernel;
 using cl::NDRange;
@@ -56,6 +56,7 @@ using std::stringstream;
 using std::to_string;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 using jit::BufferNode;
 
@@ -490,3 +491,4 @@ void evalNodes(Param& out, Node* node) {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/jit/BufferNode.hpp b/src/backend/opencl/jit/BufferNode.hpp
index 0746c0538e..e188fb429f 100644
--- a/src/backend/opencl/jit/BufferNode.hpp
+++ b/src/backend/opencl/jit/BufferNode.hpp
@@ -13,10 +13,11 @@
 
 #include <memory>
 
+namespace arrayfire {
 namespace opencl {
 namespace jit {
 using BufferNode = common::BufferNodeBase<std::shared_ptr<cl::Buffer>, KParam>;
-}
+}  // namespace jit
 }  // namespace opencl
 
 namespace common {
@@ -32,3 +33,4 @@ bool BufferNodeBase<DataType, ParamType>::operator==(
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index 5c111fdedb..d4700260c4 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -11,6 +11,7 @@
 #include <sstream>
 #include <string>
 
+namespace arrayfire {
 namespace opencl {
 
 namespace {
@@ -106,3 +107,4 @@ inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
 }
 }  // namespace
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 7eda4fc307..22875d0e61 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -19,11 +19,12 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
-using common::Node;
-using common::Node_ptr;
+using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
@@ -252,3 +253,4 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/join.hpp b/src/backend/opencl/join.hpp
index ea101d03f2..9caf52d863 100644
--- a/src/backend/opencl/join.hpp
+++ b/src/backend/opencl/join.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
@@ -16,3 +17,4 @@ Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 template<typename T>
 void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index 84af9db4a7..bf13bb4cd5 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -68,3 +69,4 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 1d702ed090..797ac19d4b 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -133,3 +134,4 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 0b9ae34472..447e4e8c60 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -59,3 +60,4 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index a191d53815..832611dcdb 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -77,3 +78,4 @@ void bilateral(Param out, const Param in, const float s_sigma,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 7444ac00aa..3659e1fb4b 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 constexpr int THREADS_X = 16;
@@ -174,3 +175,4 @@ void edgeTrackingHysteresis(Param output, const Param strong,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/config.cpp b/src/backend/opencl/kernel/config.cpp
index 97d91c510a..363a876d95 100644
--- a/src/backend/opencl/kernel/config.cpp
+++ b/src/backend/opencl/kernel/config.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include "config.hpp"
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -22,3 +23,4 @@ std::ostream& operator<<(std::ostream& out, const cdouble& var) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/config.hpp b/src/backend/opencl/kernel/config.hpp
index 38a47399a4..9e3d07868a 100644
--- a/src/backend/opencl/kernel/config.hpp
+++ b/src/backend/opencl/kernel/config.hpp
@@ -11,6 +11,7 @@
 #include <types.hpp>
 #include <ostream>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -24,3 +25,4 @@ static const uint THREADS_Y         = THREADS_PER_GROUP / THREADS_X;
 static const uint REPEAT            = 32;
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve.hpp b/src/backend/opencl/kernel/convolve.hpp
index 6c9e2e5d6d..39d2c77564 100644
--- a/src/backend/opencl/kernel/convolve.hpp
+++ b/src/backend/opencl/kernel/convolve.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <kernel/convolve/conv_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 namespace kernel {
@@ -57,3 +58,4 @@ void convolve_nd(Param out, const Param signal, const Param filter,
 }  // namespace kernel
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv1.cpp b/src/backend/opencl/kernel/convolve/conv1.cpp
index d870faaf80..10ae600888 100644
--- a/src/backend/opencl/kernel/convolve/conv1.cpp
+++ b/src/backend/opencl/kernel/convolve/conv1.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -66,3 +67,4 @@ INSTANTIATE(intl, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_b8.cpp b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
index c9e61d1fee..18c41628a6 100644
--- a/src/backend/opencl/kernel/convolve/conv2_b8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_b8.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(char, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_c32.cpp b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
index 53b05d2cea..5be66c8040 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c32.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(cfloat, cfloat)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_c64.cpp b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
index e8a5af8a4f..87e787ceed 100644
--- a/src/backend/opencl/kernel/convolve/conv2_c64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_c64.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(cdouble, cdouble)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_f32.cpp b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
index 2f92484942..89dc63dd6d 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f32.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(float, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_f64.cpp b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
index 84dd2ac4bb..97a8044cdd 100644
--- a/src/backend/opencl/kernel/convolve/conv2_f64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_f64.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(double, double)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 61f9d1d56d..59f0523de8 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -12,6 +12,7 @@
 #include <common/kernel_cache.hpp>
 #include <kernel/convolve/conv_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -95,3 +96,4 @@ void conv2(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_s16.cpp b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
index 2a8b7866d3..d5c1e5cc3d 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s16.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(short, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_s32.cpp b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
index 4fa785d738..dc621d45f5 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s32.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(int, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_s64.cpp b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
index 93dca03a3b..cdfde44ab1 100644
--- a/src/backend/opencl/kernel/convolve/conv2_s64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_s64.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(intl, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_u16.cpp b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
index ad06327135..05b525ea5c 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u16.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u16.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(ushort, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_u32.cpp b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
index 6ad074843e..c4b6667c32 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u32.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u32.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(uint, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_u64.cpp b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
index d682084197..b7f410bc9c 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u64.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u64.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(uintl, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv2_u8.cpp b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
index 23879b269d..bfe74b4c6b 100644
--- a/src/backend/opencl/kernel/convolve/conv2_u8.cpp
+++ b/src/backend/opencl/kernel/convolve/conv2_u8.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv2_impl.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -16,3 +17,4 @@ INSTANTIATE(uchar, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv3.cpp b/src/backend/opencl/kernel/convolve/conv3.cpp
index 411ff85372..9a1baf9c6b 100644
--- a/src/backend/opencl/kernel/convolve/conv3.cpp
+++ b/src/backend/opencl/kernel/convolve/conv3.cpp
@@ -9,6 +9,7 @@
 
 #include <kernel/convolve/conv_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -53,3 +54,4 @@ INSTANTIATE(intl, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 987e623dcf..93c4781976 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -136,3 +137,4 @@ void conv3(conv_kparam_t& p, Param& out, const Param& sig, const Param& filt,
            const bool expand);
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 7017170e41..6f7611428b 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -103,3 +104,4 @@ INSTANTIATE(intl, float)
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve_separable.hpp b/src/backend/opencl/kernel/convolve_separable.hpp
index 0d7feddd44..2651856c92 100644
--- a/src/backend/opencl/kernel/convolve_separable.hpp
+++ b/src/backend/opencl/kernel/convolve_separable.hpp
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -25,3 +26,4 @@ void convSep(Param out, const Param sig, const Param filt, const int cDim,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 9857133f9d..4fb0cc3479 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -52,7 +53,7 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
         DefineKeyValue(THREADS, threads),
         DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
         DefineKeyValue(COLS_PER_GROUP, cols_per_group),
-        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+        DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
     auto cscmmNN =
@@ -74,3 +75,4 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index a3b66714c3..675176e393 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -23,6 +23,7 @@
 
 #include <string>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -50,7 +51,7 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
         DefineKeyValue(IS_CONJ, is_conj),
         DefineKeyValue(THREADS, local[0]),
         DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
-        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+        DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
     auto cscmvBlock = common::getKernel("cscmv_block", std::array{cscmv_cl_src},
@@ -68,3 +69,4 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index 42b5cc093a..a786f7cafb 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -50,7 +51,7 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
         DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(USE_GREEDY, use_greedy),
         DefineValue(THREADS_PER_GROUP),
-        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+        DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
     // FIXME: Switch to perf (thread vs block) baesd kernel
@@ -76,3 +77,4 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 2d7abaa190..3c948f0177 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -53,7 +54,7 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
         DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(USE_GREEDY, use_greedy),
         DefineKeyValue(THREADS, local[0]),
-        DefineKeyValue(IS_CPLX, (af::iscplx<T>() ? 1 : 0)),
+        DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
     auto csrmv =
@@ -87,3 +88,4 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index e4320aa6dc..9f2ded02c7 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -32,7 +33,7 @@ static void diagCreate(Param out, Param in, int num) {
     };
     std::array<std::string, 3> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
     auto diagCreate = common::getKernel(
@@ -56,7 +57,7 @@ static void diagExtract(Param out, Param in, int num) {
     };
     std::array<std::string, 3> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
     auto diagExtract = common::getKernel(
@@ -75,3 +76,4 @@ static void diagExtract(Param out, Param in, int num) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index c249e55d94..817bd92bac 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -58,3 +59,4 @@ void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 4b5e506c13..8de171e908 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -33,6 +33,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -82,3 +83,4 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 9b4fc4341f..5e75bd1995 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -145,3 +146,4 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index 222bde02e8..c43e750a89 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -227,3 +228,4 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 45b8dc7bf7..d0af9aa7c9 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -114,3 +115,4 @@ void floodFill(Param out, const Param image, const Param seedsx,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index ad7ce75c84..cab0a98abf 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -36,8 +37,8 @@ void gradient(Param grad0, Param grad1, const Param in) {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(TX),
         DefineValue(TY),
-        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
-        DefineKeyValue(CPLX, static_cast<int>(af::iscplx<T>())),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(CPLX, static_cast<int>(iscplx<T>())),
         getTypeBuildDefinition<T>()};
 
     auto gradOp = common::getKernel("gradient", std::array{gradient_cl_src},
@@ -57,3 +58,4 @@ void gradient(Param grad0, Param grad1, const Param in) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index eb57c8ad71..942fb44d1b 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -26,6 +26,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -274,3 +275,4 @@ void harris(unsigned *corners_out, Param &x_out, Param &y_out, Param &resp_out,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index 03a2c2c892..a05bad05f6 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -58,3 +59,4 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 2c192ef6b7..328f39d753 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -23,6 +23,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 constexpr int HG_THREADS_X = 16;
@@ -213,3 +214,4 @@ int computeH(Param bestH, Param H, Param err, Param x_src, Param y_src,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 5e30938b17..1f46cc5085 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -54,3 +55,4 @@ void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 6369beb3ce..19afcdaea7 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -32,8 +33,8 @@ static void identity(Param out) {
     };
     std::array<std::string, 4> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineKeyValue(ONE, af::scalar_to_option(scalar<T>(1))),
-        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ONE, scalar_to_option(scalar<T>(1))),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
     auto identityOp = common::getKernel(
@@ -52,3 +53,4 @@ static void identity(Param out) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 2bbb407fe9..7786197da4 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -36,7 +37,7 @@ void iir(Param y, Param c, Param a) {
     std::array<std::string, 5> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(MAX_A_SIZE),
         DefineKeyValue(BATCH_A, batch_a),
-        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
     auto iir =
@@ -63,3 +64,4 @@ void iir(Param y, Param c, Param a) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 881f000697..6a496d1ade 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -63,3 +64,4 @@ void index(Param out, const Param in, const IndexKernelParam_t& p,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/interp.hpp b/src/backend/opencl/kernel/interp.hpp
index 0c3a744c42..d827bedc5a 100644
--- a/src/backend/opencl/kernel/interp.hpp
+++ b/src/backend/opencl/kernel/interp.hpp
@@ -15,6 +15,7 @@
 #include <array>
 #include <string>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -40,3 +41,4 @@ static void addInterpEnumOptions(std::vector<std::string>& options) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index cbf490fbf0..3308ee23e1 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -52,3 +53,4 @@ void iota(Param out, const af::dim4& sdims) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 5bdd55c180..775ee044d7 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -25,6 +25,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -44,7 +45,7 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
         DefineValue(THREADS_X),
         DefineKeyValue(init, toNumStr(common::Binary<T, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<T>()),
+        DefineKeyValue(CPLX, iscplx<T>()),
         DefineKeyValue(IS_FIRST, is_first),
         getTypeBuildDefinition<T>()};
 
@@ -120,7 +121,7 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
         DefineValue(THREADS_PER_GROUP),
         DefineKeyValue(init, toNumStr(common::Binary<T, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<T>()),
+        DefineKeyValue(CPLX, iscplx<T>()),
         DefineKeyValue(IS_FIRST, is_first),
         getTypeBuildDefinition<T>()};
 
@@ -333,3 +334,4 @@ T ireduceAll(uint *loc, Param in) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index fb52f3571f..504cf9244f 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -53,7 +54,7 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
     std::array<std::string, 5> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(BLK_X),
         DefineValue(BLK_Y),
-        DefineKeyValue(IS_CPLX, static_cast<int>(af::iscplx<T>())),
+        DefineKeyValue(IS_CPLX, static_cast<int>(iscplx<T>())),
         getTypeBuildDefinition<T>()};
 
     auto lasetOp = common::getKernel(laset_name<uplo>(),
@@ -74,3 +75,4 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/laset_band.hpp b/src/backend/opencl/kernel/laset_band.hpp
index 9ceffec9e0..daa1f73b0c 100644
--- a/src/backend/opencl/kernel/laset_band.hpp
+++ b/src/backend/opencl/kernel/laset_band.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -42,7 +43,7 @@ void laset_band(int m, int  n, int k,
     std::array<std::string, 4> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineValue(NB),
-        DefineKeyValue(IS_CPLX, static_cast<int>(af::iscplx<T>())),
+        DefineKeyValue(IS_CPLX, static_cast<int>(iscplx<T>())),
         getTypeBuildDefinition<T>()
     };
 
@@ -68,3 +69,4 @@ void laset_band(int m, int  n, int k,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 0fd58eb961..5db0b388ff 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -69,3 +70,4 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index ed82d58b6a..1e99e82780 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -57,3 +58,4 @@ void lookup(Param out, const Param in, const Param indices,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index e27eb78955..65fc511415 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -36,8 +37,8 @@ void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
     };
     std::array<std::string, 5> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(same_dims),
-        DefineKeyValue(ZERO, af::scalar_to_option(scalar<T>(0))),
-        DefineKeyValue(ONE, af::scalar_to_option(scalar<T>(1))),
+        DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
+        DefineKeyValue(ONE, scalar_to_option(scalar<T>(1))),
         getTypeBuildDefinition<T>()};
 
     auto luSplit = common::getKernel("luSplit", std::array{lu_split_cl_src},
@@ -64,3 +65,4 @@ void luSplit(Param lower, Param upper, const Param in) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index 5b7c471c33..21041eb73b 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -67,3 +68,4 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 3149da3280..13f74453a8 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -27,6 +27,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -467,3 +468,4 @@ To meanAll(Param in) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index fb92f18866..24fa61374d 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -66,3 +67,4 @@ void meanshift(Param out, const Param in, const float spatialSigma,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index e8af452eda..d38943e50d 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -103,3 +104,4 @@ void medfilt2(Param out, const Param in, const af_border_type pad,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index c63d1e42b3..d9fe825107 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -23,6 +23,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 typedef struct {
@@ -249,3 +250,4 @@ void copy(const Param out, const Param in, dim_t ondims,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 6da71b9833..3f269686c3 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -53,3 +54,4 @@ void moments(Param out, const Param in, af_moment_type moment) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 43b5d6d443..730a424eed 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -145,3 +146,4 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index 841a844038..b4f7e5fa36 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -94,3 +95,4 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index f2e72c7317..b3e4014d05 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -44,6 +44,7 @@
 /* Other */
 #endif
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -498,6 +499,7 @@ void orb(unsigned* out_feat, Param& x_out, Param& y_out, Param& score_out,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
 
 #if defined(__clang__)
 /* Clang/LLVM */
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index 4d18b06099..8e75e5fbd5 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 static const int PADB_THREADS_X = 16;
@@ -65,3 +66,4 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index c15f9e292f..96c230f133 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -30,6 +30,7 @@ static const int TABLE_SIZE = 16;
 static const int MAX_BLOCKS = 32;
 static const int STATE_SIZE = (256 * 3);
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 static const uint THREADS = 256;
@@ -170,3 +171,4 @@ void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index d4a5acbd33..ddb946d307 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -51,3 +52,4 @@ void range(Param out, const int dim) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index f52d044bcb..21db6e2edc 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -30,6 +30,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -51,7 +52,7 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
         DefineValue(THREADS_X),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         getTypeBuildDefinition<Ti, To>()};
 
     auto reduceDim = common::getKernel(
@@ -129,7 +130,7 @@ void reduceAllLauncher(Param out, Param in, const uint groups_x,
         DefineValue(THREADS_PER_GROUP),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         getTypeBuildDefinition<Ti, To>()};
 
     auto reduceAll = common::getKernel(
@@ -177,7 +178,7 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
         DefineValue(THREADS_PER_GROUP),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         getTypeBuildDefinition<Ti, To>()};
 
     auto reduceFirst = common::getKernel(
@@ -271,3 +272,4 @@ void reduceAll(Param out, Param in, int change_nan, double nanval) {
 }  // namespace kernel
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index 79779ca320..eeb0e119df 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -36,6 +36,7 @@
 
 namespace compute = boost::compute;
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -59,7 +60,7 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(DIM, dim),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
@@ -102,7 +103,7 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(DIMX, threads_x),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
@@ -143,7 +144,7 @@ void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(DIMX, threads_x),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<To>()),
+        DefineKeyValue(CPLX, iscplx<To>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
@@ -182,7 +183,7 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
         DefineKeyValue(DIM, dim),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<To>()),
+        DefineKeyValue(CPLX, iscplx<To>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
@@ -218,7 +219,7 @@ void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
         DefineKeyValue(To, dtype_traits<To>::getName()),
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
-        DefineKeyValue(CPLX, af::iscplx<To>()),
+        DefineKeyValue(CPLX, iscplx<To>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
@@ -253,7 +254,7 @@ void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
         DefineKeyValue(T, "To"),
         DefineKeyValue(DIMX, threads_x),
         DefineKeyValue(DIM, dim),
-        DefineKeyValue(CPLX, af::iscplx<To>()),
+        DefineKeyValue(CPLX, iscplx<To>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
@@ -572,3 +573,4 @@ void reduceByKey(Array<Tk> &keys_out, Array<To> &vals_out,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 710ccdf64b..63716ba8ea 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -37,6 +37,7 @@ AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -195,3 +196,4 @@ void regions(Param out, Param in, const bool full_conn,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index e2dc87f481..9322647cd2 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -53,3 +54,4 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index ae0184a4a1..bc813393c5 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -87,3 +88,4 @@ void resize(Param out, const Param in, const af_interp_type method) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index 999a7f25a5..dec52c8962 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -131,3 +132,4 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
index db44fb59c7..46cac6723d 100644
--- a/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/scan_by_key/scan_by_key_impl.cpp
@@ -15,9 +15,11 @@
 // The line below is read by CMake to determenine the instantiations
 // SBK_BINARY_OPS:af_add_t af_mul_t af_max_t af_min_t
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 INSTANTIATE_SCAN_FIRST_BY_KEY_OP(TYPE)
 INSTANTIATE_SCAN_DIM_BY_KEY_OP(TYPE)
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 00c4cfc8ef..2edc7f68c0 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -23,6 +23,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename Ti, typename To, af_op_t op>
@@ -51,7 +52,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
         DefineValue(THREADS_X),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         DefineKeyValue(IS_FINAL_PASS, (isFinalPass ? 1 : 0)),
         DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
     };
@@ -156,3 +157,4 @@ static void scanDim(Param out, const Param in, const int dim,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_dim_by_key.hpp b/src/backend/opencl/kernel/scan_dim_by_key.hpp
index d975fbe03e..f698c4176d 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key.hpp
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename Ti, typename Tk, typename To, af_op_t op>
@@ -18,3 +19,4 @@ void scanDimByKey(Param out, const Param in, const Param key, int dim,
                   const bool inclusive_scan);
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 8376c3a876..3d9745923c 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -25,6 +25,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename Ti, typename Tk, typename To, af_op_t op>
@@ -51,7 +52,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
         DefineValue(THREADS_X),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         DefineKeyValue(calculateFlags, (calculateFlags ? 1 : 0)),
         DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
     };
@@ -210,3 +211,4 @@ void scanDimByKey(Param out, const Param in, const Param key, int dim,
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_DIM_BY_KEY_TYPES(ROp, uintl)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index a8031ecc5e..4354d27b49 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -23,6 +23,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -52,7 +53,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
         DefineKeyFromStr(binOpName<op>()),
         DefineValue(SHARED_MEM_SIZE),
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         DefineKeyValue(IS_FINAL_PASS, (isFinalPass ? 1 : 0)),
         DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
     };
@@ -152,3 +153,4 @@ static void scanFirst(Param &out, const Param &in,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_first_by_key.hpp b/src/backend/opencl/kernel/scan_first_by_key.hpp
index 609e918f56..1e520bcebb 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key.hpp
@@ -11,6 +11,7 @@
 
 #include <Param.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename Ti, typename Tk, typename To, af_op_t op>
@@ -18,3 +19,4 @@ void scanFirstByKey(Param &out, const Param &in, const Param &key,
                     const bool inclusive_scan);
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index f8835e18a8..d0351add52 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -23,6 +23,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -55,7 +56,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
         DefineKeyValue(init, toNumStr(common::Binary<To, op>::init())),
         DefineValue(SHARED_MEM_SIZE),
         DefineKeyFromStr(binOpName<op>()),
-        DefineKeyValue(CPLX, af::iscplx<Ti>()),
+        DefineKeyValue(CPLX, iscplx<Ti>()),
         DefineKeyValue(calculateFlags, (calculateFlags ? 1 : 0)),
         DefineKeyValue(INCLUSIVE_SCAN, inclusiveScan),
     };
@@ -206,3 +207,4 @@ void scanFirstByKey(Param &out, const Param &in, const Param &key,
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, intl) \
     INSTANTIATE_SCAN_FIRST_BY_KEY_TYPES(ROp, uintl)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index 69602817a9..fc37e6cb86 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -17,9 +17,10 @@
 #include <math.hpp>
 #include <traits.hpp>
 
+#include <array>
 #include <string>
-#include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 constexpr uint DIMX  = 32;
@@ -103,3 +104,4 @@ void select_scalar(Param out, Param cond, Param a, const T b, const int ndims,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sift.hpp b/src/backend/opencl/kernel/sift.hpp
index 90b063b2d0..d5b248f007 100644
--- a/src/backend/opencl/kernel/sift.hpp
+++ b/src/backend/opencl/kernel/sift.hpp
@@ -38,6 +38,7 @@ AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -729,3 +730,4 @@ void sift(unsigned* out_feat, unsigned* out_dlen, Param& x_out, Param& y_out,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 8e0c406f4a..9e92213adf 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename Ti, typename To, unsigned ker_size>
@@ -58,3 +59,4 @@ void sobel(Param dx, Param dy, const Param in) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sort.hpp b/src/backend/opencl/kernel/sort.hpp
index a55eb2b966..dd8bbe1390 100644
--- a/src/backend/opencl/kernel/sort.hpp
+++ b/src/backend/opencl/kernel/sort.hpp
@@ -26,6 +26,7 @@ AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -128,3 +129,4 @@ void sort0(Param val, bool isAscending) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sort_by_key.hpp b/src/backend/opencl/kernel/sort_by_key.hpp
index 7a25662667..4333a7830c 100644
--- a/src/backend/opencl/kernel/sort_by_key.hpp
+++ b/src/backend/opencl/kernel/sort_by_key.hpp
@@ -13,6 +13,7 @@
 #include <debug_opencl.hpp>
 #include <traits.hpp>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename Tk, typename Tv>
@@ -25,3 +26,4 @@ template<typename Tk, typename Tv>
 void sort0ByKey(Param pKey, Param pVal, bool isAscending);
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index ab20be6a33..dd74cccc7e 100644
--- a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -11,8 +11,10 @@
 
 // SBK_TYPES:float double int uint intl uintl short ushort char uchar half
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 INSTANTIATE1(TYPE)
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index 2d6f84493b..a070a60c67 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -36,7 +36,7 @@ AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
-using common::half;
+using arrayfire::common::half;
 
 template<typename Tk, typename Tv, bool isAscending>
 inline boost::compute::function<bool(const std::pair<Tk, Tv>,
@@ -79,6 +79,7 @@ INSTANTIATE_FLIP(cl_ulong, ULONG_MAX)
 
 #undef INSTANTIATE_FLIP
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 static const int copyPairIter = 4;
@@ -254,3 +255,4 @@ void sort0ByKey(Param pKey, Param pVal, bool isAscending) {
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sort_helper.hpp b/src/backend/opencl/kernel/sort_helper.hpp
index 1c9db6cab7..971b4077e9 100644
--- a/src/backend/opencl/kernel/sort_helper.hpp
+++ b/src/backend/opencl/kernel/sort_helper.hpp
@@ -14,6 +14,7 @@
 #include <debug_opencl.hpp>
 #include <type_traits>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -44,3 +45,4 @@ using type_t = typename std::conditional<std::is_same<T, uintl>::value,
                                          cl_ulong, ltype_t<T>>::type;
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 6cfed4b554..f7ef69e248 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -27,6 +27,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -227,3 +228,4 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index f10b3327a0..048a6d4876 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -25,6 +25,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -171,7 +172,7 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
 
     auto arithOp = fetchKernel<T, op>(
         "ssarith_csr", sp_sp_arith_csr_cl_src,
-        {DefineKeyValue(IDENTITY_VALUE, af::scalar_to_option(iden_val))});
+        {DefineKeyValue(IDENTITY_VALUE, scalar_to_option(iden_val))});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
@@ -184,3 +185,4 @@ void ssArithCSR(Param oVals, Param oColIdx, const Param oRowIdx, const uint M,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index d3cdfb8af2..d407755f31 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 constexpr unsigned SUSAN_THREADS_X = 16;
@@ -95,3 +96,4 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index ff875e25da..820db15094 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -20,6 +20,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -81,3 +82,4 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index cc65a1fc54..fa097ba58f 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -57,3 +58,4 @@ void tile(Param out, const Param in) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index a64468ea26..a3f81fd75b 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -24,6 +24,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -109,3 +110,4 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 87e6b65fee..3397596179 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -43,7 +44,7 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
         DefineValue(TILE_DIM),
         DefineValue(THREADS_Y),
         DefineValue(IS32MULTIPLE),
-        DefineKeyValue(DOCONJUGATE, (conjugate && af::iscplx<T>())),
+        DefineKeyValue(DOCONJUGATE, (conjugate && iscplx<T>())),
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
@@ -66,3 +67,4 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index 06020a6e3c..b55f2e4d43 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -43,7 +44,7 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
         DefineValue(TILE_DIM),
         DefineValue(THREADS_Y),
         DefineValue(IS32MULTIPLE),
-        DefineKeyValue(DOCONJUGATE, (conjugate && af::iscplx<T>())),
+        DefineKeyValue(DOCONJUGATE, (conjugate && iscplx<T>())),
         DefineKeyValue(T, dtype_traits<T>::getName()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
@@ -69,3 +70,4 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index 8380894b07..c0be0de33f 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -21,12 +21,13 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
 template<typename T>
 void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
-    using af::scalar_to_option;
+    using arrayfire::opencl::scalar_to_option;
     using cl::EnqueueArgs;
     using cl::NDRange;
     using std::string;
@@ -68,3 +69,4 @@ void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 68d6846893..08e535f713 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -79,3 +80,4 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 9c17143398..88e89fd26b 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -23,6 +23,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 template<typename T>
@@ -41,7 +42,7 @@ static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
     vector<string> compileOpts = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(ZERO, toNumStr(scalar<T>(0))),
-        DefineKeyValue(CPLX, af::iscplx<T>()),
+        DefineKeyValue(CPLX, iscplx<T>()),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
@@ -132,3 +133,4 @@ static void where(Param &out, Param &in) {
 }
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index 72797bd5f5..b527cd8bce 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace opencl {
 namespace kernel {
 
@@ -118,3 +119,4 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
 
 }  // namespace kernel
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/logic.hpp b/src/backend/opencl/logic.hpp
index b7132ac01c..78efdcadd3 100644
--- a/src/backend/opencl/logic.hpp
+++ b/src/backend/opencl/logic.hpp
@@ -15,6 +15,7 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
@@ -28,3 +29,4 @@ Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 724538604e..2fee6f6ae0 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -15,8 +15,9 @@
 #include <err_opencl.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
@@ -71,3 +72,4 @@ INSTANTIATE(ushort);
 INSTANTIATE(short);
 INSTANTIATE(half);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/lookup.hpp b/src/backend/opencl/lookup.hpp
index 5164648cfa..abf10d5902 100644
--- a/src/backend/opencl/lookup.hpp
+++ b/src/backend/opencl/lookup.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/lu.cpp b/src/backend/opencl/lu.cpp
index 8fe05b3bf6..ff6f54d0d9 100644
--- a/src/backend/opencl/lu.cpp
+++ b/src/backend/opencl/lu.cpp
@@ -18,6 +18,7 @@
 #include <magma/magma.h>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 Array<int> convertPivot(int *ipiv, int in_sz, int out_sz) {
@@ -91,9 +92,11 @@ INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -121,5 +124,6 @@ INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/lu.hpp b/src/backend/opencl/lu.hpp
index 6ba417baa7..2186aef62e 100644
--- a/src/backend/opencl/lu.hpp
+++ b/src/backend/opencl/lu.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
@@ -19,3 +20,4 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
 bool isLAPACKAvailable();
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/magma/geqrf2.cpp b/src/backend/opencl/magma/geqrf2.cpp
index bcb71ad51f..daba1f4328 100644
--- a/src/backend/opencl/magma/geqrf2.cpp
+++ b/src/backend/opencl/magma/geqrf2.cpp
@@ -230,7 +230,7 @@ magma_int_t magma_geqrf2_gpu(magma_int_t m, magma_int_t n, cl_mem dA,
     }
     */
 
-    cl_mem buffer = clCreateBuffer(opencl::getContext()(),
+    cl_mem buffer = clCreateBuffer(arrayfire::opencl::getContext()(),
                                    CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR,
                                    sizeof(Ty) * lwork, NULL, NULL);
     work          = (Ty *)clEnqueueMapBuffer(queue[0], buffer, CL_TRUE,
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index 1f4578db6b..a689408a26 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -165,7 +165,7 @@ magma_int_t magma_getrs_gpu(magma_trans_t trans, magma_int_t n,
             : (trans == MagmaTrans ? OPENCL_BLAS_TRANS
                                    : OPENCL_BLAS_CONJ_TRANS);
 
-    bool cond  = opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
+    bool cond  = arrayfire::opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
     cl_mem dAT = 0;
     if (nrhs > 1 && cond) {
         magma_malloc<Ty>(&dAT, n * n);
diff --git a/src/backend/opencl/magma/labrd.cpp b/src/backend/opencl/magma/labrd.cpp
index 010a3675a7..c2f5fd0698 100644
--- a/src/backend/opencl/magma/labrd.cpp
+++ b/src/backend/opencl/magma/labrd.cpp
@@ -203,7 +203,7 @@ magma_int_t magma_labrd_gpu(magma_int_t m, magma_int_t n, magma_int_t nb, Ty *a,
 
     using Tr = typename af::dtype_traits<Ty>::base_type;
 
-    constexpr bool is_cplx = common::is_complex<Ty>::value;
+    constexpr bool is_cplx = arrayfire::common::is_complex<Ty>::value;
 
     Tr *d = (Tr *)_d;
     Tr *e = (Tr *)_e;
diff --git a/src/backend/opencl/magma/laset.cpp b/src/backend/opencl/magma/laset.cpp
index a08b7af2fa..520bdea59e 100644
--- a/src/backend/opencl/magma/laset.cpp
+++ b/src/backend/opencl/magma/laset.cpp
@@ -60,6 +60,7 @@ template<typename T>
 void magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n, T offdiag,
                      T diag, cl_mem dA, size_t dA_offset, magma_int_t ldda,
                      magma_queue_t queue) {
+    using arrayfire::opencl::kernel::laset;
     magma_int_t info = 0;
     if (uplo != MagmaLower && uplo != MagmaUpper && uplo != MagmaFull) {
         info = -1;
@@ -79,14 +80,11 @@ void magmablas_laset(magma_uplo_t uplo, magma_int_t m, magma_int_t n, T offdiag,
 
     switch (uplo) {
         case MagmaFull:
-            return opencl::kernel::laset<T, 0>(m, n, offdiag, diag, dA,
-                                               dA_offset, ldda, queue);
+            return laset<T, 0>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
         case MagmaLower:
-            return opencl::kernel::laset<T, 1>(m, n, offdiag, diag, dA,
-                                               dA_offset, ldda, queue);
+            return laset<T, 1>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
         case MagmaUpper:
-            return opencl::kernel::laset<T, 2>(m, n, offdiag, diag, dA,
-                                               dA_offset, ldda, queue);
+            return laset<T, 2>(m, n, offdiag, diag, dA, dA_offset, ldda, queue);
         default: return;
     }
 }
diff --git a/src/backend/opencl/magma/laswp.cpp b/src/backend/opencl/magma/laswp.cpp
index 53f4cccbea..14d24e61c7 100644
--- a/src/backend/opencl/magma/laswp.cpp
+++ b/src/backend/opencl/magma/laswp.cpp
@@ -78,7 +78,8 @@ void magmablas_laswp(magma_int_t n, cl_mem dAT, size_t dAT_offset,
     }
 
     cl::CommandQueue q(queue, true);
-    opencl::kernel::laswp<T>(n, dAT, dAT_offset, ldda, k1, k2, ipiv, inci, q);
+    arrayfire::opencl::kernel::laswp<T>(n, dAT, dAT_offset, ldda, k1, k2, ipiv,
+                                        inci, q);
 }
 
 #define INSTANTIATE(T)                                                  \
diff --git a/src/backend/opencl/magma/magma_blas.h b/src/backend/opencl/magma/magma_blas.h
index d34d04c29a..62f3290121 100644
--- a/src/backend/opencl/magma/magma_blas.h
+++ b/src/backend/opencl/magma/magma_blas.h
@@ -17,8 +17,8 @@
 #include <types.hpp>
 #include "magma_common.h"
 
-using opencl::cdouble;
-using opencl::cfloat;
+using arrayfire::opencl::cdouble;
+using arrayfire::opencl::cfloat;
 
 template<typename T>
 struct gpu_blas_gemm_func;
diff --git a/src/backend/opencl/magma/magma_blas_clblast.h b/src/backend/opencl/magma/magma_blas_clblast.h
index 905b5fc723..bb2bfbeee5 100644
--- a/src/backend/opencl/magma/magma_blas_clblast.h
+++ b/src/backend/opencl/magma/magma_blas_clblast.h
@@ -60,7 +60,7 @@ struct CLBlastType<cdouble> {
     using Type = std::complex<double>;
 };
 template<>
-struct CLBlastType<common::half> {
+struct CLBlastType<arrayfire::common::half> {
     using Type = cl_half;
 };
 
@@ -78,7 +78,7 @@ double inline toCLBlastConstant(const double val) {
     return val;
 }
 template<>
-cl_half inline toCLBlastConstant(const common::half val) {
+cl_half inline toCLBlastConstant(const arrayfire::common::half val) {
     cl_half out;
     memcpy(&out, &val, sizeof(cl_half));
     return out;
@@ -98,7 +98,7 @@ struct CLBlastBasicType {
     using Type = T;
 };
 template<>
-struct CLBlastBasicType<common::half> {
+struct CLBlastBasicType<arrayfire::common::half> {
     using Type = cl_half;
 };
 template<>
diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 4d6834b42e..69bd5e36a8 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -71,8 +71,8 @@ static magma_int_t magma_malloc(magma_ptr* ptrPtr, int num) {
     // size
     if (size == 0) size = sizeof(T);
     cl_int err;
-    *ptrPtr = clCreateBuffer(opencl::getContext()(), CL_MEM_READ_WRITE, size,
-                             NULL, &err);
+    *ptrPtr = clCreateBuffer(arrayfire::opencl::getContext()(),
+                             CL_MEM_READ_WRITE, size, NULL, &err);
     if (err != CL_SUCCESS) { return MAGMA_ERR_DEVICE_ALLOC; }
     return MAGMA_SUCCESS;
 }
diff --git a/src/backend/opencl/magma/swapdblk.cpp b/src/backend/opencl/magma/swapdblk.cpp
index d6751b2c0f..6a669a54ce 100644
--- a/src/backend/opencl/magma/swapdblk.cpp
+++ b/src/backend/opencl/magma/swapdblk.cpp
@@ -16,8 +16,8 @@ void magmablas_swapdblk(magma_int_t n, magma_int_t nb, cl_mem dA,
                         magma_int_t inca, cl_mem dB, magma_int_t dB_offset,
                         magma_int_t lddb, magma_int_t incb,
                         magma_queue_t queue) {
-    opencl::kernel::swapdblk<T>(n, nb, dA, dA_offset, ldda, inca, dB, dB_offset,
-                                lddb, incb, queue);
+    arrayfire::opencl::kernel::swapdblk<T>(n, nb, dA, dA_offset, ldda, inca, dB,
+                                           dB_offset, lddb, incb, queue);
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/opencl/magma/transpose.cpp b/src/backend/opencl/magma/transpose.cpp
index e9ff2243ca..a33d440f95 100644
--- a/src/backend/opencl/magma/transpose.cpp
+++ b/src/backend/opencl/magma/transpose.cpp
@@ -54,10 +54,10 @@
 #include "kernel/transpose.hpp"
 #include "magma_data.h"
 
+using arrayfire::opencl::makeParam;
+using arrayfire::opencl::kernel::transpose;
 using cl::Buffer;
 using cl::CommandQueue;
-using opencl::makeParam;
-using opencl::kernel::transpose;
 
 template<typename T>
 void magmablas_transpose(magma_int_t m, magma_int_t n, cl_mem dA,
diff --git a/src/backend/opencl/magma/transpose_inplace.cpp b/src/backend/opencl/magma/transpose_inplace.cpp
index 21770f98be..7705edb7b3 100644
--- a/src/backend/opencl/magma/transpose_inplace.cpp
+++ b/src/backend/opencl/magma/transpose_inplace.cpp
@@ -54,10 +54,10 @@
 #include "kernel/transpose_inplace.hpp"
 #include "magma_data.h"
 
+using arrayfire::opencl::makeParam;
+using arrayfire::opencl::kernel::transpose_inplace;
 using cl::Buffer;
 using cl::CommandQueue;
-using opencl::makeParam;
-using opencl::kernel::transpose_inplace;
 
 template<typename T>
 void magmablas_transpose_inplace(magma_int_t n, cl_mem dA, size_t dA_offset,
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index 8b2d0dd025..f97bc6d353 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -11,6 +11,7 @@
 
 #include <kernel/match_template.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename inType, typename outType>
@@ -41,3 +42,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/match_template.hpp b/src/backend/opencl/match_template.hpp
index bf2a76f55d..7b493d2ca0 100644
--- a/src/backend/opencl/match_template.hpp
+++ b/src/backend/opencl/match_template.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace opencl {
 template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
                               const Array<inType> &tImg,
                               const af::matchType mType);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/math.cpp b/src/backend/opencl/math.cpp
index 31c09c3b96..bbe78dfc94 100644
--- a/src/backend/opencl/math.cpp
+++ b/src/backend/opencl/math.cpp
@@ -10,6 +10,7 @@
 #include "math.hpp"
 #include <common/half.hpp>
 
+namespace arrayfire {
 namespace opencl {
 cfloat operator+(cfloat lhs, cfloat rhs) {
     cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
@@ -53,3 +54,4 @@ cdouble division(cdouble lhs, double rhs) {
     return retVal;
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index e7cf8d1928..e4745d9e92 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -28,6 +28,7 @@
 /* Other */
 #endif
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -142,19 +143,22 @@ cfloat operator*(cfloat lhs, cfloat rhs);
 cdouble operator*(cdouble lhs, cdouble rhs);
 common::half operator+(common::half lhs, common::half rhs) noexcept;
 }  // namespace opencl
+}  // namespace arrayfire
 
-static inline bool operator==(opencl::cfloat lhs, opencl::cfloat rhs) noexcept {
+static inline bool operator==(arrayfire::opencl::cfloat lhs,
+                              arrayfire::opencl::cfloat rhs) noexcept {
     return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
 }
-static inline bool operator!=(opencl::cfloat lhs, opencl::cfloat rhs) noexcept {
+static inline bool operator!=(arrayfire::opencl::cfloat lhs,
+                              arrayfire::opencl::cfloat rhs) noexcept {
     return !(lhs == rhs);
 }
-static inline bool operator==(opencl::cdouble lhs,
-                              opencl::cdouble rhs) noexcept {
+static inline bool operator==(arrayfire::opencl::cdouble lhs,
+                              arrayfire::opencl::cdouble rhs) noexcept {
     return (lhs.s[0] == rhs.s[0]) && (lhs.s[1] == rhs.s[1]);
 }
-static inline bool operator!=(opencl::cdouble lhs,
-                              opencl::cdouble rhs) noexcept {
+static inline bool operator!=(arrayfire::opencl::cdouble lhs,
+                              arrayfire::opencl::cdouble rhs) noexcept {
     return !(lhs == rhs);
 }
 
diff --git a/src/backend/opencl/max.cpp b/src/backend/opencl/max.cpp
index d4a7640acf..b2a2cdfdf0 100644
--- a/src/backend/opencl/max.cpp
+++ b/src/backend/opencl/max.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // max
 INSTANTIATE(af_max_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
 INSTANTIATE(af_max_t, half, half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
index adce4be841..7bd586e587 100644
--- a/src/backend/opencl/mean.cpp
+++ b/src/backend/opencl/mean.cpp
@@ -14,9 +14,10 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using std::swap;
 
+namespace arrayfire {
 namespace opencl {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
@@ -77,3 +78,4 @@ INSTANTIATE_WGT(cdouble, double);
 INSTANTIATE_WGT(half, float);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/mean.hpp b/src/backend/opencl/mean.hpp
index 7f98f439d8..61f44aa86a 100644
--- a/src/backend/opencl/mean.hpp
+++ b/src/backend/opencl/mean.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in);
@@ -24,3 +25,4 @@ template<typename T, typename Tw>
 Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index bceed64bb1..3c6f140c98 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
@@ -43,3 +44,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/meanshift.hpp b/src/backend/opencl/meanshift.hpp
index eafd6dbd93..54e8dd588f 100644
--- a/src/backend/opencl/meanshift.hpp
+++ b/src/backend/opencl/meanshift.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 0e63834253..66a4c6969e 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -59,3 +60,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/medfilt.hpp b/src/backend/opencl/medfilt.hpp
index 0a010c3154..439282b1f1 100644
--- a/src/backend/opencl/medfilt.hpp
+++ b/src/backend/opencl/medfilt.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -20,3 +21,4 @@ Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
                   const af::borderType edge_pad);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 8dab1f428b..6c37d873a2 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -20,13 +20,14 @@
 
 #include <utility>
 
-using common::bytesToString;
+using arrayfire::common::bytesToString;
 
 using af::dim4;
 using std::function;
 using std::move;
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace opencl {
 float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
 float getMemoryPressureThreshold() {
@@ -272,3 +273,4 @@ void AllocatorPinned::nativeFree(void *ptr) {
     }
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index ba7e340d32..4f618d7956 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -20,6 +20,7 @@ namespace cl {
 class Buffer;  // Forward declaration of cl::Buffer from CL/cl2.hpp
 }
 
+namespace arrayfire {
 namespace opencl {
 cl::Buffer *bufferAlloc(const size_t &bytes);
 void bufferFree(cl::Buffer *buf);
@@ -60,7 +61,7 @@ bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 
-class Allocator final : public common::memory::AllocatorInterface {
+class Allocator final : public common::AllocatorInterface {
    public:
     Allocator();
     ~Allocator() = default;
@@ -71,7 +72,7 @@ class Allocator final : public common::memory::AllocatorInterface {
     void nativeFree(void *ptr) override;
 };
 
-class AllocatorPinned final : public common::memory::AllocatorInterface {
+class AllocatorPinned final : public common::AllocatorInterface {
    public:
     AllocatorPinned();
     ~AllocatorPinned() = default;
@@ -86,3 +87,4 @@ class AllocatorPinned final : public common::memory::AllocatorInterface {
 };
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/min.cpp b/src/backend/opencl/min.cpp
index 69aa38efae..9cc6a09272 100644
--- a/src/backend/opencl/min.cpp
+++ b/src/backend/opencl/min.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // min
 INSTANTIATE(af_min_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
 INSTANTIATE(af_min_t, half, half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index ef378762e2..0b03d203c9 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -12,6 +12,7 @@
 #include <err_opencl.hpp>
 #include <kernel/moments.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 static inline unsigned bitCount(unsigned v) {
@@ -52,3 +53,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/moments.hpp b/src/backend/opencl/moments.hpp
index 90666f710a..c0e3cb4058 100644
--- a/src/backend/opencl/moments.hpp
+++ b/src/backend/opencl/moments.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/morph.cpp b/src/backend/opencl/morph.cpp
index 10ac7397c5..e77b7a063c 100644
--- a/src/backend/opencl/morph.cpp
+++ b/src/backend/opencl/morph.cpp
@@ -16,6 +16,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -61,3 +62,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/morph.hpp b/src/backend/opencl/morph.hpp
index 9435abef85..aee753c8d7 100644
--- a/src/backend/opencl/morph.hpp
+++ b/src/backend/opencl/morph.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
@@ -16,3 +17,4 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 template<typename T>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index fc3727b860..535be4083f 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -18,6 +18,7 @@
 using af::dim4;
 using cl::Device;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename To, af_match_type dist_type>
@@ -84,3 +85,4 @@ INSTANTIATE(uchar, uint)
 INSTANTIATE(uintl, uint)  // For Hamming
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/nearest_neighbour.hpp b/src/backend/opencl/nearest_neighbour.hpp
index 2f64436874..65a7a3d1c5 100644
--- a/src/backend/opencl/nearest_neighbour.hpp
+++ b/src/backend/opencl/nearest_neighbour.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename To>
@@ -20,4 +21,5 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
                        const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/orb.cpp b/src/backend/opencl/orb.cpp
index 44971f9d02..5e1d2b42d0 100644
--- a/src/backend/opencl/orb.cpp
+++ b/src/backend/opencl/orb.cpp
@@ -17,6 +17,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename convAccT>
@@ -63,3 +64,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/orb.hpp b/src/backend/opencl/orb.hpp
index 6b5906ae18..012113886e 100644
--- a/src/backend/opencl/orb.hpp
+++ b/src/backend/opencl/orb.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              const unsigned max_feat, const float scl_fctr,
              const unsigned levels, const bool blur_img);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 04859ad40a..c040c04b09 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -66,12 +66,13 @@ using std::to_string;
 using std::unique_ptr;
 using std::vector;
 
-using common::getEnvVar;
-using common::ltrim;
-using common::memory::MemoryManagerBase;
-using opencl::Allocator;
-using opencl::AllocatorPinned;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::ltrim;
+using arrayfire::common::MemoryManagerBase;
+using arrayfire::opencl::Allocator;
+using arrayfire::opencl::AllocatorPinned;
 
+namespace arrayfire {
 namespace opencl {
 
 static string get_system() {
@@ -645,7 +646,7 @@ void resetMemoryManagerPinned() {
     return DeviceManager::getInstance().resetMemoryManagerPinned();
 }
 
-graphics::ForgeManager& forgeManager() {
+arrayfire::common::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
@@ -670,8 +671,9 @@ PlanCache& fftManager() {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
 
-using namespace opencl;
+using namespace arrayfire::opencl;
 
 af_err afcl_get_device_type(afcl_device_type* res) {
     try {
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index fa937b0e0f..07eca8f856 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -29,18 +29,18 @@ namespace spdlog {
 class logger;
 }
 
-namespace graphics {
+namespace arrayfire {
+namespace common {
+
 class ForgeManager;
-}
 
-namespace common {
-namespace memory {
 class MemoryManagerBase;
-}
 }  // namespace common
+}  // namespace arrayfire
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
+namespace arrayfire {
 namespace opencl {
 
 // Forward declarations
@@ -165,7 +165,7 @@ void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
 
 void resetMemoryManagerPinned();
 
-graphics::ForgeManager& forgeManager();
+arrayfire::common::ForgeManager& forgeManager();
 
 GraphicsResourceManager& interopManager();
 
@@ -176,3 +176,4 @@ afcl::platform getPlatformEnum(cl::Device dev);
 void setActiveContext(int device);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index bf4a1e7370..cc7f93262e 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -14,12 +14,15 @@
 #include <plot.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
@@ -75,3 +78,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/plot.hpp b/src/backend/opencl/plot.hpp
index 1d8c2e9f10..4a6849e01a 100644
--- a/src/backend/opencl/plot.hpp
+++ b/src/backend/opencl/plot.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/print.hpp b/src/backend/opencl/print.hpp
index d78e1a36a2..40919135a7 100644
--- a/src/backend/opencl/print.hpp
+++ b/src/backend/opencl/print.hpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <ostream>
 
+namespace arrayfire {
 namespace opencl {
 static std::ostream& operator<<(std::ostream& out, const cfloat& var) {
     out << "(" << var.s[0] << "," << var.s[1] << ")";
@@ -22,3 +23,4 @@ static std::ostream& operator<<(std::ostream& out, const cdouble& var) {
     return out;
 }
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/product.cpp b/src/backend/opencl/product.cpp
index 3ea554e2f6..f13a9b9ae3 100644
--- a/src/backend/opencl/product.cpp
+++ b/src/backend/opencl/product.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // sum
 INSTANTIATE(af_mul_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
 INSTANTIATE(af_mul_t, half, float)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/qr.cpp b/src/backend/opencl/qr.cpp
index 3588147aed..bb8d5c1205 100644
--- a/src/backend/opencl/qr.cpp
+++ b/src/backend/opencl/qr.cpp
@@ -23,6 +23,7 @@
 #include <magma/magma_helper.h>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -112,9 +113,11 @@ INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -138,5 +141,6 @@ INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/qr.hpp b/src/backend/opencl/qr.hpp
index b202aec88a..6c7b564ebc 100644
--- a/src/backend/opencl/qr.hpp
+++ b/src/backend/opencl/qr.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig);
@@ -16,3 +17,4 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig);
 template<typename T>
 Array<T> qr_inplace(Array<T> &in);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index c112df4196..f2110c8be0 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -12,8 +12,9 @@
 #include <kernel/random_engine.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl) {
@@ -153,3 +154,4 @@ COMPLEX_NORMAL_DISTRIBUTION(cdouble, double)
 COMPLEX_NORMAL_DISTRIBUTION(cfloat, float)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/random_engine.hpp b/src/backend/opencl/random_engine.hpp
index 279db75fc1..93c190942e 100644
--- a/src/backend/opencl/random_engine.hpp
+++ b/src/backend/opencl/random_engine.hpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace opencl {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl);
@@ -39,3 +40,4 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
                             Array<uint> recursion_table,
                             Array<uint> temper_table, Array<uint> state);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/range.cpp b/src/backend/opencl/range.cpp
index b98d9ba584..92340d34eb 100644
--- a/src/backend/opencl/range.cpp
+++ b/src/backend/opencl/range.cpp
@@ -15,8 +15,9 @@
 #include <math.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim) {
@@ -51,3 +52,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/range.hpp b/src/backend/opencl/range.hpp
index 610d31933f..e34f302536 100644
--- a/src/backend/opencl/range.hpp
+++ b/src/backend/opencl/range.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim = -1);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/reduce.hpp b/src/backend/opencl/reduce.hpp
index 4c9581c761..8660f9f1d8 100644
--- a/src/backend/opencl/reduce.hpp
+++ b/src/backend/opencl/reduce.hpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
@@ -25,3 +26,4 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
                      double nanval = 0);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/reduce_impl.hpp b/src/backend/opencl/reduce_impl.hpp
index 4211dc9050..7b68187e4e 100644
--- a/src/backend/opencl/reduce_impl.hpp
+++ b/src/backend/opencl/reduce_impl.hpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 using std::swap;
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
@@ -44,6 +45,7 @@ Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #define INSTANTIATE(Op, Ti, To)                                                \
     template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim,  \
diff --git a/src/backend/opencl/regions.cpp b/src/backend/opencl/regions.cpp
index 66d67ee448..06df18dd4c 100644
--- a/src/backend/opencl/regions.cpp
+++ b/src/backend/opencl/regions.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -37,3 +38,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/regions.hpp b/src/backend/opencl/regions.hpp
index 89eab2714c..1c4d26f6c0 100644
--- a/src/backend/opencl/regions.hpp
+++ b/src/backend/opencl/regions.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/reorder.cpp b/src/backend/opencl/reorder.cpp
index 720d415883..da485911e6 100644
--- a/src/backend/opencl/reorder.cpp
+++ b/src/backend/opencl/reorder.cpp
@@ -14,8 +14,9 @@
 #include <reorder.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
@@ -47,3 +48,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/reorder.hpp b/src/backend/opencl/reorder.hpp
index bd49a074f9..6aa860c769 100644
--- a/src/backend/opencl/reorder.hpp
+++ b/src/backend/opencl/reorder.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/reshape.cpp b/src/backend/opencl/reshape.cpp
index 0ec77e27bc..78c83cc086 100644
--- a/src/backend/opencl/reshape.cpp
+++ b/src/backend/opencl/reshape.cpp
@@ -13,8 +13,9 @@
 #include <common/half.hpp>
 #include <kernel/memcopy.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename inType, typename outType>
@@ -77,3 +78,4 @@ INSTANTIATE_COMPLEX(cfloat)
 INSTANTIATE_COMPLEX(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/resize.cpp b/src/backend/opencl/resize.cpp
index 67257cc214..ee7776b82f 100644
--- a/src/backend/opencl/resize.cpp
+++ b/src/backend/opencl/resize.cpp
@@ -13,6 +13,7 @@
 #include <af/dim4.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
@@ -42,3 +43,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/resize.hpp b/src/backend/opencl/resize.hpp
index 0741be36b5..bec5bc8ce3 100644
--- a/src/backend/opencl/resize.hpp
+++ b/src/backend/opencl/resize.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/rotate.cpp b/src/backend/opencl/rotate.cpp
index a7f969e55e..46caa65c88 100644
--- a/src/backend/opencl/rotate.cpp
+++ b/src/backend/opencl/rotate.cpp
@@ -11,6 +11,7 @@
 
 #include <kernel/rotate.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
@@ -53,3 +54,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/rotate.hpp b/src/backend/opencl/rotate.hpp
index 94916e7441..dddc164718 100644
--- a/src/backend/opencl/rotate.hpp
+++ b/src/backend/opencl/rotate.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/scalar.hpp b/src/backend/opencl/scalar.hpp
index 420b38144d..1e497af867 100644
--- a/src/backend/opencl/scalar.hpp
+++ b/src/backend/opencl/scalar.hpp
@@ -12,6 +12,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -21,3 +22,4 @@ Array<T> createScalarNode(const dim4 &size, const T val) {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index c069beb537..0fc36366ef 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -12,6 +12,7 @@
 #include <kernel/scan_dim.hpp>
 #include <kernel/scan_first.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
@@ -52,3 +53,4 @@ INSTANTIATE_SCAN_ALL(af_mul_t)
 INSTANTIATE_SCAN_ALL(af_min_t)
 INSTANTIATE_SCAN_ALL(af_max_t)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/scan.hpp b/src/backend/opencl/scan.hpp
index d72f86dc64..77fef74c02 100644
--- a/src/backend/opencl/scan.hpp
+++ b/src/backend/opencl/scan.hpp
@@ -10,7 +10,9 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/scan_by_key.cpp b/src/backend/opencl/scan_by_key.cpp
index 606a1b00f9..8af8d2a31b 100644
--- a/src/backend/opencl/scan_by_key.cpp
+++ b/src/backend/opencl/scan_by_key.cpp
@@ -16,6 +16,7 @@
 #include <kernel/scan_dim_by_key.hpp>
 #include <kernel/scan_first_by_key.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
@@ -60,3 +61,4 @@ INSTANTIATE_SCAN_BY_KEY_OP(af_mul_t)
 INSTANTIATE_SCAN_BY_KEY_OP(af_min_t)
 INSTANTIATE_SCAN_BY_KEY_OP(af_max_t)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/scan_by_key.hpp b/src/backend/opencl/scan_by_key.hpp
index 58fb5cacdd..f2ad2b2fc7 100644
--- a/src/backend/opencl/scan_by_key.hpp
+++ b/src/backend/opencl/scan_by_key.hpp
@@ -10,8 +10,10 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
                bool inclusive_scan = true);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index d652df25c6..bbafbe989c 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -20,12 +20,13 @@
 
 using af::dim4;
 
-using common::half;
-using common::NaryNode;
+using arrayfire::common::half;
+using arrayfire::common::NaryNode;
 
 using std::make_shared;
 using std::max;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
@@ -133,3 +134,4 @@ INSTANTIATE(half);
 
 #undef INSTANTIATE
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/select.hpp b/src/backend/opencl/select.hpp
index 4dbd0635da..a026f9c04d 100644
--- a/src/backend/opencl/select.hpp
+++ b/src/backend/opencl/select.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
@@ -27,3 +28,4 @@ template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const T &b_val, const af::dim4 &odims);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index 30aa475a01..195cf23047 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -24,6 +24,7 @@ AF_DEPRECATED_WARNINGS_ON
 
 namespace compute = boost::compute;
 
+namespace arrayfire {
 namespace opencl {
 using af::dim4;
 
@@ -152,3 +153,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/set.hpp b/src/backend/opencl/set.hpp
index e67acc1ffd..2a3ea83594 100644
--- a/src/backend/opencl/set.hpp
+++ b/src/backend/opencl/set.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted);
@@ -21,3 +22,4 @@ template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index 0266c5e6d5..512c113ed1 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -14,14 +14,15 @@
 #include <traits.hpp>
 
 using af::dim4;
-using common::Node_ptr;
-using common::ShiftNodeBase;
-using opencl::jit::BufferNode;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::ShiftNodeBase;
+using arrayfire::opencl::jit::BufferNode;
 using std::array;
 using std::make_shared;
 using std::static_pointer_cast;
 using std::string;
 
+namespace arrayfire {
 namespace opencl {
 using ShiftNode = ShiftNodeBase<BufferNode>;
 
@@ -68,3 +69,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/shift.hpp b/src/backend/opencl/shift.hpp
index 5ee21f063c..1797d6d1a7 100644
--- a/src/backend/opencl/shift.hpp
+++ b/src/backend/opencl/shift.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sift.cpp b/src/backend/opencl/sift.cpp
index aa4dea46e5..d4b32c3820 100644
--- a/src/backend/opencl/sift.cpp
+++ b/src/backend/opencl/sift.cpp
@@ -15,6 +15,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename convAccT>
@@ -69,3 +70,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sift.hpp b/src/backend/opencl/sift.hpp
index 3544405315..078841bf69 100644
--- a/src/backend/opencl/sift.hpp
+++ b/src/backend/opencl/sift.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename convAccT>
@@ -23,4 +24,5 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sobel.cpp b/src/backend/opencl/sobel.cpp
index 9716140019..e718021b42 100644
--- a/src/backend/opencl/sobel.cpp
+++ b/src/backend/opencl/sobel.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename Ti, typename To>
@@ -44,3 +45,4 @@ INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sobel.hpp b/src/backend/opencl/sobel.hpp
index 63b25bd316..74ccb2ebcf 100644
--- a/src/backend/opencl/sobel.hpp
+++ b/src/backend/opencl/sobel.hpp
@@ -10,10 +10,12 @@
 #include <Array.hpp>
 #include <utility>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename Ti, typename To>
 std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
                                                  const unsigned &ker_size);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index ad73e21d27..60d8f3a59b 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -32,6 +32,7 @@ using cl::Buffer;
 using std::min;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -325,9 +326,11 @@ INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 }  // namespace opencl
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -355,5 +358,6 @@ INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/solve.hpp b/src/backend/opencl/solve.hpp
index c2b22810e4..390871856c 100644
--- a/src/backend/opencl/solve.hpp
+++ b/src/backend/opencl/solve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options = AF_MAT_NONE);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index e73f4db312..8b977316f1 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -16,6 +16,7 @@
 #include <sort.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
@@ -62,3 +63,4 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sort.hpp b/src/backend/opencl/sort.hpp
index 91e57b560c..092995aeec 100644
--- a/src/backend/opencl/sort.hpp
+++ b/src/backend/opencl/sort.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
index f98a70e057..2e4b2dd616 100644
--- a/src/backend/opencl/sort_by_key.cpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -16,6 +16,7 @@
 #include <sort_by_key.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace opencl {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
@@ -83,3 +84,4 @@ INSTANTIATE1(uchar)
 INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sort_by_key.hpp b/src/backend/opencl/sort_by_key.hpp
index a1e616c3e5..78223de9be 100644
--- a/src/backend/opencl/sort_by_key.hpp
+++ b/src/backend/opencl/sort_by_key.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
                  const Array<Tv> &ival, const unsigned dim, bool isAscending);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index 869dd7bdc0..9c92f8406c 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -18,8 +18,9 @@
 #include <sort_index.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
@@ -77,3 +78,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sort_index.hpp b/src/backend/opencl/sort_index.hpp
index 573a61d247..0979a1aa37 100644
--- a/src/backend/opencl/sort_index.hpp
+++ b/src/backend/opencl/sort_index.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void sort_index(Array<T> &okey, Array<unsigned> &oval, const Array<T> &in,
                 const unsigned dim, bool isAscending);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sparse.cpp b/src/backend/opencl/sparse.cpp
index 580822d5d1..de220563f7 100644
--- a/src/backend/opencl/sparse.cpp
+++ b/src/backend/opencl/sparse.cpp
@@ -26,6 +26,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace opencl {
 
 using namespace common;
@@ -217,3 +218,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_SPARSE
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sparse.hpp b/src/backend/opencl/sparse.hpp
index e8496a533e..32a118df0e 100644
--- a/src/backend/opencl/sparse.hpp
+++ b/src/backend/opencl/sparse.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, af_storage stype>
@@ -25,3 +26,4 @@ common::SparseArray<T> sparseConvertStorageToStorage(
     const common::SparseArray<T> &in);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sparse_arith.cpp b/src/backend/opencl/sparse_arith.cpp
index 5de05b873a..cfc868b0a6 100644
--- a/src/backend/opencl/sparse_arith.cpp
+++ b/src/backend/opencl/sparse_arith.cpp
@@ -24,6 +24,7 @@
 #include <scan.hpp>
 #include <where.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 using namespace common;
@@ -174,3 +175,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sparse_arith.hpp b/src/backend/opencl/sparse_arith.hpp
index c0ac32c180..3d45738c76 100644
--- a/src/backend/opencl/sparse_arith.hpp
+++ b/src/backend/opencl/sparse_arith.hpp
@@ -12,6 +12,7 @@
 #include <optypes.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 // These two functions cannot be overloaded by return type.
@@ -28,3 +29,4 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sparse_blas.cpp b/src/backend/opencl/sparse_blas.cpp
index 4b214e821e..42b6547127 100644
--- a/src/backend/opencl/sparse_blas.cpp
+++ b/src/backend/opencl/sparse_blas.cpp
@@ -30,6 +30,7 @@
 #include <cpu/cpu_sparse_blas.hpp>
 #endif  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 using namespace common;
@@ -96,3 +97,4 @@ INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sparse_blas.hpp b/src/backend/opencl/sparse_blas.hpp
index 788fe3fd3c..f51eeac9b4 100644
--- a/src/backend/opencl/sparse_blas.hpp
+++ b/src/backend/opencl/sparse_blas.hpp
@@ -11,10 +11,12 @@
 #include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/sum.cpp b/src/backend/opencl/sum.cpp
index fc02b072c9..890280ba92 100644
--- a/src/backend/opencl/sum.cpp
+++ b/src/backend/opencl/sum.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 // sum
 INSTANTIATE(af_add_t, float, float)
@@ -37,3 +38,4 @@ INSTANTIATE(af_add_t, ushort, float)
 INSTANTIATE(af_add_t, half, half)
 INSTANTIATE(af_add_t, half, float)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index d1ab53196d..a0de95fb19 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -14,14 +14,17 @@
 #include <surface.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 using cl::Memory;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_P = P.get();
@@ -78,3 +81,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/surface.hpp b/src/backend/opencl/surface.hpp
index 6eedbfec66..62a1095a84 100644
--- a/src/backend/opencl/surface.hpp
+++ b/src/backend/opencl/surface.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/susan.cpp b/src/backend/opencl/susan.cpp
index 35f22a953b..6bd78e2540 100644
--- a/src/backend/opencl/susan.cpp
+++ b/src/backend/opencl/susan.cpp
@@ -17,6 +17,7 @@
 using af::features;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -70,3 +71,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/susan.hpp b/src/backend/opencl/susan.hpp
index a82fa4418b..ca6c779c8a 100644
--- a/src/backend/opencl/susan.hpp
+++ b/src/backend/opencl/susan.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -21,4 +22,5 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out,
                const float geom_thr, const float feature_ratio,
                const unsigned edge);
 
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 5c7aed92c4..7bda5306ca 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -24,6 +24,7 @@
 #include <magma/magma_helper.h>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename Tr>
@@ -231,9 +232,11 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T, typename Tr>
@@ -258,5 +261,6 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace opencl
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/opencl/svd.hpp b/src/backend/opencl/svd.hpp
index 6dd4eb6dc6..ddf3f4a1bb 100644
--- a/src/backend/opencl/svd.hpp
+++ b/src/backend/opencl/svd.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T, typename Tr>
 void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
@@ -16,3 +17,4 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/threadsMgt.hpp b/src/backend/opencl/threadsMgt.hpp
index 4fb3838e5b..1fdc136613 100644
--- a/src/backend/opencl/threadsMgt.hpp
+++ b/src/backend/opencl/threadsMgt.hpp
@@ -13,6 +13,7 @@
 #include <platform.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace opencl {
 // OVERALL USAGE (With looping):
 // ...                                                      // OWN CODE
@@ -325,4 +326,5 @@ inline cl::NDRange threadsMgt<T>::genGlobal(const cl::NDRange& local) const {
         return genGlobalFull(local);
     }
 };
-}  // namespace opencl
\ No newline at end of file
+}  // namespace opencl
+}  // namespace arrayfire
\ No newline at end of file
diff --git a/src/backend/opencl/tile.cpp b/src/backend/opencl/tile.cpp
index c3e2604970..14e2d5beac 100644
--- a/src/backend/opencl/tile.cpp
+++ b/src/backend/opencl/tile.cpp
@@ -13,8 +13,9 @@
 #include <common/half.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
@@ -47,3 +48,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/tile.hpp b/src/backend/opencl/tile.hpp
index 8326b034e2..172cbadbed 100644
--- a/src/backend/opencl/tile.hpp
+++ b/src/backend/opencl/tile.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 5fcf157946..9ff966ed65 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -20,9 +20,9 @@
 #include <numeric>
 #include <vector>
 
+using arrayfire::common::half;
 using cl::Buffer;
 using cl::Event;
-using common::half;
 
 using std::iota;
 using std::min;
@@ -30,6 +30,7 @@ using std::partial_sort_copy;
 using std::transform;
 using std::vector;
 
+namespace arrayfire {
 namespace opencl {
 vector<af_index_t> indexForTopK(const int k) {
     af_index_t idx;
@@ -177,3 +178,4 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(half)
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/topk.hpp b/src/backend/opencl/topk.hpp
index 5767d8a0d2..d4c67878e7 100644
--- a/src/backend/opencl/topk.hpp
+++ b/src/backend/opencl/topk.hpp
@@ -7,8 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <Array.hpp>
+
+#include <af/defines.h>
+
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 6610c7aee1..00af1d17b0 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -19,36 +19,40 @@
 namespace af {
 
 template<>
-struct dtype_traits<opencl::cfloat> {
+struct dtype_traits<arrayfire::opencl::cfloat> {
     enum { af_type = c32 };
     typedef float base_type;
     static const char *getName() { return "float2"; }
 };
 
 template<>
-struct dtype_traits<opencl::cdouble> {
+struct dtype_traits<arrayfire::opencl::cdouble> {
     enum { af_type = c64 };
     typedef double base_type;
     static const char *getName() { return "double2"; }
 };
+}  // namespace af
+
+namespace arrayfire {
+namespace opencl {
 
 template<typename T>
 static bool iscplx() {
     return false;
 }
 template<>
-inline bool iscplx<opencl::cfloat>() {
+inline bool iscplx<cfloat>() {
     return true;
 }
 template<>
-inline bool iscplx<opencl::cdouble>() {
+inline bool iscplx<cdouble>() {
     return true;
 }
 
 template<typename T>
 inline std::string scalar_to_option(const T &val) {
-    using namespace common;
-    using namespace std;
+    using namespace arrayfire::common;
+    using std::to_string;
     return to_string(+val);
 }
 
@@ -65,6 +69,7 @@ inline std::string scalar_to_option<cl_double2>(const cl_double2 &val) {
     ss << val.s[0] << "," << val.s[1];
     return ss.str();
 }
-}  // namespace af
 
 using af::dtype_traits;
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 253ff6ccb4..14ee03c962 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -11,6 +11,7 @@
 
 #include <kernel/transform.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -54,3 +55,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/transform.hpp b/src/backend/opencl/transform.hpp
index 809294fc6f..50c1455be0 100644
--- a/src/backend/opencl/transform.hpp
+++ b/src/backend/opencl/transform.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index 819e73fb29..a25fa9be28 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -14,8 +14,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -50,3 +51,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/transpose.hpp b/src/backend/opencl/transpose.hpp
index f9d363f11b..7bb1f66bbf 100644
--- a/src/backend/opencl/transpose.hpp
+++ b/src/backend/opencl/transpose.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index 4ee4a740cd..dc23873814 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -14,8 +14,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -46,3 +47,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index 9713c906c8..cb781eeef4 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -14,8 +14,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -52,3 +53,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/triangle.hpp b/src/backend/opencl/triangle.hpp
index d616337c7e..51061d51b8 100644
--- a/src/backend/opencl/triangle.hpp
+++ b/src/backend/opencl/triangle.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> triangle(const Array<T> &in, const bool is_upper,
                   const bool is_unit_diag);
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index aba15fe693..35c2b5745a 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -17,12 +17,13 @@
 #include <sstream>
 #include <string>
 
-using common::half;
-using common::toString;
+using arrayfire::common::half;
+using arrayfire::common::toString;
 
 using std::isinf;
 using std::stringstream;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -101,3 +102,4 @@ INSTANTIATE(half);
 #undef INSTANTIATE
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index e88086b262..2bc96996aa 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -18,6 +18,7 @@
 #include <array>
 #include <string>
 
+namespace arrayfire {
 namespace common {
 /// This is a CPU based half which need to be converted into floats before they
 /// are used
@@ -31,7 +32,9 @@ struct kernel_type<common::half> {
     using compute = float;
 };
 }  // namespace common
+}  // namespace arrayfire
 
+namespace arrayfire {
 namespace opencl {
 using cdouble = cl_double2;
 using cfloat  = cl_float2;
@@ -127,7 +130,7 @@ inline const char *getFullName<cdouble>() {
 
 template<typename... ARGS>
 AF_CONSTEXPR const char *getTypeBuildDefinition() {
-    using common::half;
+    using arrayfire::common::half;
     using std::any_of;
     using std::array;
     using std::begin;
@@ -157,3 +160,4 @@ AF_CONSTEXPR const char *getTypeBuildDefinition() {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/unary.hpp b/src/backend/opencl/unary.hpp
index 65da1b690b..9ff2fea8c6 100644
--- a/src/backend/opencl/unary.hpp
+++ b/src/backend/opencl/unary.hpp
@@ -13,6 +13,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<af_op_t op>
@@ -77,8 +78,8 @@ UNARY_DECL(bitnot, "__bitnot")
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using common::Node;
-    using common::Node_ptr;
+    using arrayfire::common::Node;
+    using arrayfire::common::Node_ptr;
     using std::array;
 
     auto createUnary = [](array<Node_ptr, 1> &operands) {
@@ -94,7 +95,7 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using common::Node_ptr;
+    using arrayfire::common::Node_ptr;
 
     auto createUnary = [](std::array<Node_ptr, 1> &operands) {
         return Node_ptr(new common::UnaryNode(
@@ -108,3 +109,4 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 }
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/unwrap.cpp b/src/backend/opencl/unwrap.cpp
index 26c720e3c1..c6c7a12d4f 100644
--- a/src/backend/opencl/unwrap.cpp
+++ b/src/backend/opencl/unwrap.cpp
@@ -14,8 +14,9 @@
 #include <unwrap.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -60,3 +61,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/unwrap.hpp b/src/backend/opencl/unwrap.hpp
index 35b6b617f5..f65e324c67 100644
--- a/src/backend/opencl/unwrap.hpp
+++ b/src/backend/opencl/unwrap.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
                 const dim_t dx, const dim_t dy, const bool is_column);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index 508ff0ded9..e470f73c9a 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -14,13 +14,16 @@
 #include <vector_field.hpp>
 
 using af::dim4;
+using arrayfire::common::ForgeModule;
+using arrayfire::common::forgePlugin;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield) {
-    ForgeModule &_ = graphics::forgePlugin();
+    ForgeModule &_ = common::forgePlugin();
     if (isGLSharingSupported()) {
         CheckGL("Begin OpenCL resource copy");
         const cl::Buffer *d_points     = points.get();
@@ -101,3 +104,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/vector_field.hpp b/src/backend/opencl/vector_field.hpp
index 2c3447aa4a..33d4d61dff 100644
--- a/src/backend/opencl/vector_field.hpp
+++ b/src/backend/opencl/vector_field.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/where.cpp b/src/backend/opencl/where.cpp
index 4ad6a870d9..c3ac797454 100644
--- a/src/backend/opencl/where.cpp
+++ b/src/backend/opencl/where.cpp
@@ -14,6 +14,7 @@
 #include <af/dim4.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<uint> where(const Array<T> &in) {
@@ -39,3 +40,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/where.hpp b/src/backend/opencl/where.hpp
index c67a235e66..a5ee5feca4 100644
--- a/src/backend/opencl/where.hpp
+++ b/src/backend/opencl/where.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 template<typename T>
 Array<uint> where(const Array<T>& in);
-}
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 76847e1988..42d684857a 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -16,8 +16,9 @@
 #include <wrap.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -72,3 +73,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/wrap.hpp b/src/backend/opencl/wrap.hpp
index 7a7815caa1..cceb47ee43 100644
--- a/src/backend/opencl/wrap.hpp
+++ b/src/backend/opencl/wrap.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace opencl {
 
 template<typename T>
@@ -22,3 +23,4 @@ Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
                       const dim_t sy, const dim_t px, const dim_t py,
                       const dim_t dx, const dim_t dy, const bool is_column);
 }  // namespace opencl
+}  // namespace arrayfire

From 5e66211164521f61ce9793abef134d7209d4d6e0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 31 Dec 2022 14:25:06 -0500
Subject: [PATCH 2357/2677] Move oneapi namespace into the arrayfire namespace

---
 src/backend/oneapi/Array.cpp                  | 14 +++---
 src/backend/oneapi/Array.hpp                  |  2 +
 src/backend/oneapi/Event.cpp                  |  2 +
 src/backend/oneapi/Event.hpp                  |  2 +
 .../oneapi/GraphicsResourceManager.cpp        |  2 +
 .../oneapi/GraphicsResourceManager.hpp        |  2 +
 src/backend/oneapi/Kernel.hpp                 |  2 +
 src/backend/oneapi/Module.hpp                 |  2 +
 src/backend/oneapi/Param.cpp                  |  2 +
 src/backend/oneapi/Param.hpp                  |  2 +
 src/backend/oneapi/all.cpp                    |  4 +-
 src/backend/oneapi/anisotropic_diffusion.cpp  |  2 +
 src/backend/oneapi/anisotropic_diffusion.hpp  |  4 +-
 src/backend/oneapi/any.cpp                    |  4 +-
 src/backend/oneapi/approx.cpp                 |  2 +
 src/backend/oneapi/approx.hpp                 |  2 +
 src/backend/oneapi/approx1.cpp                |  2 +
 src/backend/oneapi/approx2.cpp                |  2 +
 src/backend/oneapi/arith.hpp                  |  2 +
 src/backend/oneapi/assign.cpp                 |  4 +-
 src/backend/oneapi/assign.hpp                 |  4 +-
 src/backend/oneapi/backend.hpp                |  2 +-
 src/backend/oneapi/bilateral.cpp              |  2 +
 src/backend/oneapi/bilateral.hpp              |  4 +-
 src/backend/oneapi/binary.hpp                 |  2 +
 src/backend/oneapi/blas.cpp                   |  4 +-
 src/backend/oneapi/blas.hpp                   |  2 +
 src/backend/oneapi/canny.cpp                  |  2 +
 src/backend/oneapi/canny.hpp                  |  2 +
 src/backend/oneapi/cast.hpp                   |  2 +
 src/backend/oneapi/cholesky.cpp               |  4 ++
 src/backend/oneapi/cholesky.hpp               |  2 +
 src/backend/oneapi/compile_module.cpp         | 12 +++--
 src/backend/oneapi/complex.hpp                |  2 +
 src/backend/oneapi/convolve.cpp               |  8 ++--
 src/backend/oneapi/convolve.hpp               |  2 +
 src/backend/oneapi/convolve_separable.cpp     |  2 +
 src/backend/oneapi/copy.cpp                   |  6 ++-
 src/backend/oneapi/copy.hpp                   |  2 +
 src/backend/oneapi/count.cpp                  |  4 +-
 src/backend/oneapi/device_manager.cpp         |  7 ++-
 src/backend/oneapi/device_manager.hpp         | 17 ++++---
 src/backend/oneapi/diagonal.cpp               |  4 +-
 src/backend/oneapi/diagonal.hpp               |  2 +
 src/backend/oneapi/diff.cpp                   |  2 +
 src/backend/oneapi/diff.hpp                   |  2 +
 src/backend/oneapi/exampleFunction.cpp        |  2 +
 src/backend/oneapi/exampleFunction.hpp        |  4 +-
 src/backend/oneapi/fast.cpp                   |  2 +
 src/backend/oneapi/fast.hpp                   |  4 +-
 src/backend/oneapi/fft.cpp                    |  2 +
 src/backend/oneapi/fft.hpp                    |  2 +
 src/backend/oneapi/fftconvolve.cpp            |  2 +
 src/backend/oneapi/fftconvolve.hpp            |  4 +-
 src/backend/oneapi/flood_fill.cpp             |  2 +
 src/backend/oneapi/flood_fill.hpp             |  4 +-
 src/backend/oneapi/gradient.cpp               |  2 +
 src/backend/oneapi/gradient.hpp               |  4 +-
 src/backend/oneapi/harris.cpp                 |  2 +
 src/backend/oneapi/harris.hpp                 |  4 +-
 src/backend/oneapi/hist_graphics.cpp          |  2 +
 src/backend/oneapi/hist_graphics.hpp          |  4 +-
 src/backend/oneapi/histogram.cpp              |  4 +-
 src/backend/oneapi/histogram.hpp              |  4 +-
 src/backend/oneapi/homography.cpp             |  2 +
 src/backend/oneapi/homography.hpp             |  4 +-
 src/backend/oneapi/hsv_rgb.cpp                |  2 +
 src/backend/oneapi/hsv_rgb.hpp                |  2 +
 src/backend/oneapi/identity.cpp               |  4 +-
 src/backend/oneapi/identity.hpp               |  4 +-
 src/backend/oneapi/iir.cpp                    |  2 +
 src/backend/oneapi/iir.hpp                    |  4 +-
 src/backend/oneapi/image.cpp                  |  2 +
 src/backend/oneapi/image.hpp                  |  5 +-
 src/backend/oneapi/index.cpp                  |  4 +-
 src/backend/oneapi/index.hpp                  |  4 +-
 src/backend/oneapi/inverse.cpp                |  4 ++
 src/backend/oneapi/inverse.hpp                |  4 +-
 src/backend/oneapi/iota.cpp                   |  4 +-
 src/backend/oneapi/iota.hpp                   |  4 +-
 src/backend/oneapi/ireduce.cpp                |  4 +-
 src/backend/oneapi/ireduce.hpp                |  2 +
 src/backend/oneapi/jit.cpp                    | 10 ++--
 src/backend/oneapi/jit/BufferNode.hpp         |  2 +
 src/backend/oneapi/jit/kernel_generators.hpp  |  2 +
 src/backend/oneapi/join.cpp                   |  4 +-
 src/backend/oneapi/join.hpp                   |  2 +
 src/backend/oneapi/kernel/approx1.hpp         |  2 +
 src/backend/oneapi/kernel/approx2.hpp         |  2 +
 src/backend/oneapi/kernel/assign.hpp          |  2 +
 src/backend/oneapi/kernel/bilateral.hpp       |  2 +
 src/backend/oneapi/kernel/convolve.hpp        |  2 +
 src/backend/oneapi/kernel/default_config.hpp  |  2 +
 src/backend/oneapi/kernel/diagonal.hpp        |  2 +
 src/backend/oneapi/kernel/diff.hpp            |  2 +
 src/backend/oneapi/kernel/histogram.hpp       |  2 +
 src/backend/oneapi/kernel/interp.hpp          |  2 +
 src/backend/oneapi/kernel/iota.hpp            |  2 +
 src/backend/oneapi/kernel/mean.hpp            |  2 +
 src/backend/oneapi/kernel/memcopy.hpp         | 21 +++++----
 src/backend/oneapi/kernel/random_engine.hpp   |  2 +
 .../oneapi/kernel/random_engine_mersenne.hpp  |  2 +
 .../oneapi/kernel/random_engine_philox.hpp    |  2 +
 .../oneapi/kernel/random_engine_threefry.hpp  |  2 +
 .../oneapi/kernel/random_engine_write.hpp     | 46 ++++++++++---------
 src/backend/oneapi/kernel/range.hpp           |  6 ++-
 src/backend/oneapi/kernel/reduce.hpp          |  2 +
 src/backend/oneapi/kernel/reduce_all.hpp      |  2 +
 src/backend/oneapi/kernel/reduce_config.hpp   |  2 +
 src/backend/oneapi/kernel/reduce_dim.hpp      |  2 +
 src/backend/oneapi/kernel/reduce_first.hpp    |  2 +
 src/backend/oneapi/kernel/reorder.hpp         |  2 +
 src/backend/oneapi/kernel/scan_dim.hpp        |  2 +
 src/backend/oneapi/kernel/scan_first.hpp      |  2 +
 src/backend/oneapi/kernel/transpose.hpp       |  2 +
 .../oneapi/kernel/transpose_inplace.hpp       |  2 +
 src/backend/oneapi/kernel/triangle.hpp        |  2 +
 src/backend/oneapi/kernel/unwrap.hpp          |  2 +
 src/backend/oneapi/kernel/where.hpp           |  2 +
 src/backend/oneapi/kernel/wrap.hpp            |  2 +
 src/backend/oneapi/kernel/wrap_dilated.hpp    |  2 +
 src/backend/oneapi/logic.hpp                  |  2 +
 src/backend/oneapi/lookup.cpp                 |  4 +-
 src/backend/oneapi/lookup.hpp                 |  4 +-
 src/backend/oneapi/lu.cpp                     |  4 ++
 src/backend/oneapi/lu.hpp                     |  2 +
 src/backend/oneapi/match_template.cpp         |  2 +
 src/backend/oneapi/match_template.hpp         |  4 +-
 src/backend/oneapi/math.cpp                   |  2 +
 src/backend/oneapi/math.hpp                   | 16 ++++---
 src/backend/oneapi/max.cpp                    |  4 +-
 src/backend/oneapi/mean.cpp                   |  4 +-
 src/backend/oneapi/mean.hpp                   |  2 +
 src/backend/oneapi/meanshift.cpp              |  2 +
 src/backend/oneapi/meanshift.hpp              |  4 +-
 src/backend/oneapi/medfilt.cpp                |  2 +
 src/backend/oneapi/medfilt.hpp                |  2 +
 src/backend/oneapi/memory.cpp                 |  6 ++-
 src/backend/oneapi/memory.hpp                 |  6 ++-
 src/backend/oneapi/min.cpp                    |  4 +-
 src/backend/oneapi/moments.cpp                |  2 +
 src/backend/oneapi/moments.hpp                |  4 +-
 src/backend/oneapi/morph.cpp                  |  2 +
 src/backend/oneapi/morph.hpp                  |  2 +
 src/backend/oneapi/nearest_neighbour.cpp      |  2 +
 src/backend/oneapi/nearest_neighbour.hpp      |  5 +-
 src/backend/oneapi/orb.cpp                    |  2 +
 src/backend/oneapi/orb.hpp                    |  4 +-
 src/backend/oneapi/platform.cpp               | 14 +++---
 src/backend/oneapi/platform.hpp               | 15 +++---
 src/backend/oneapi/plot.cpp                   |  4 +-
 src/backend/oneapi/plot.hpp                   |  4 +-
 src/backend/oneapi/print.hpp                  |  2 +
 src/backend/oneapi/product.cpp                |  4 +-
 src/backend/oneapi/qr.cpp                     |  4 ++
 src/backend/oneapi/qr.hpp                     |  2 +
 src/backend/oneapi/random_engine.cpp          |  4 +-
 src/backend/oneapi/random_engine.hpp          |  2 +
 src/backend/oneapi/range.cpp                  |  4 +-
 src/backend/oneapi/range.hpp                  |  4 +-
 src/backend/oneapi/reduce.hpp                 |  2 +
 src/backend/oneapi/reduce_impl.hpp            |  2 +
 src/backend/oneapi/regions.cpp                |  2 +
 src/backend/oneapi/regions.hpp                |  4 +-
 src/backend/oneapi/reorder.cpp                |  4 +-
 src/backend/oneapi/reorder.hpp                |  4 +-
 src/backend/oneapi/reshape.cpp                |  4 +-
 src/backend/oneapi/resize.cpp                 |  2 +
 src/backend/oneapi/resize.hpp                 |  4 +-
 src/backend/oneapi/rotate.cpp                 |  2 +
 src/backend/oneapi/rotate.hpp                 |  4 +-
 src/backend/oneapi/scalar.hpp                 |  2 +
 src/backend/oneapi/scan.cpp                   |  2 +
 src/backend/oneapi/scan.hpp                   |  4 +-
 src/backend/oneapi/scan_by_key.cpp            |  2 +
 src/backend/oneapi/scan_by_key.hpp            |  4 +-
 src/backend/oneapi/select.cpp                 |  6 ++-
 src/backend/oneapi/select.hpp                 |  2 +
 src/backend/oneapi/set.cpp                    |  2 +
 src/backend/oneapi/set.hpp                    |  2 +
 src/backend/oneapi/shift.cpp                  |  6 ++-
 src/backend/oneapi/shift.hpp                  |  4 +-
 src/backend/oneapi/sift.cpp                   |  2 +
 src/backend/oneapi/sift.hpp                   |  4 +-
 src/backend/oneapi/sobel.cpp                  |  2 +
 src/backend/oneapi/sobel.hpp                  |  4 +-
 src/backend/oneapi/solve.cpp                  |  4 ++
 src/backend/oneapi/solve.hpp                  |  2 +
 src/backend/oneapi/sort.cpp                   |  2 +
 src/backend/oneapi/sort.hpp                   |  4 +-
 src/backend/oneapi/sort_by_key.cpp            |  2 +
 src/backend/oneapi/sort_by_key.hpp            |  4 +-
 src/backend/oneapi/sort_index.cpp             |  4 +-
 src/backend/oneapi/sort_index.hpp             |  4 +-
 src/backend/oneapi/sparse.cpp                 |  2 +
 src/backend/oneapi/sparse.hpp                 |  2 +
 src/backend/oneapi/sparse_arith.cpp           |  2 +
 src/backend/oneapi/sparse_arith.hpp           |  2 +
 src/backend/oneapi/sparse_blas.cpp            |  2 +
 src/backend/oneapi/sparse_blas.hpp            |  4 +-
 src/backend/oneapi/sum.cpp                    |  4 +-
 src/backend/oneapi/surface.cpp                |  4 +-
 src/backend/oneapi/surface.hpp                |  4 +-
 src/backend/oneapi/susan.cpp                  |  2 +
 src/backend/oneapi/susan.hpp                  |  4 +-
 src/backend/oneapi/svd.cpp                    |  4 ++
 src/backend/oneapi/svd.hpp                    |  2 +
 src/backend/oneapi/tile.cpp                   |  4 +-
 src/backend/oneapi/tile.hpp                   |  5 +-
 src/backend/oneapi/topk.cpp                   |  4 +-
 src/backend/oneapi/topk.hpp                   |  5 +-
 src/backend/oneapi/traits.hpp                 |  6 +--
 src/backend/oneapi/transform.cpp              |  2 +
 src/backend/oneapi/transform.hpp              |  4 +-
 src/backend/oneapi/transpose.cpp              |  4 +-
 src/backend/oneapi/transpose.hpp              |  2 +
 src/backend/oneapi/transpose_inplace.cpp      |  4 +-
 src/backend/oneapi/triangle.cpp               |  4 +-
 src/backend/oneapi/triangle.hpp               |  2 +
 src/backend/oneapi/types.hpp                  |  6 ++-
 src/backend/oneapi/unary.hpp                  |  8 ++--
 src/backend/oneapi/unwrap.cpp                 |  4 +-
 src/backend/oneapi/unwrap.hpp                 |  4 +-
 src/backend/oneapi/vector_field.cpp           |  2 +
 src/backend/oneapi/vector_field.hpp           |  4 +-
 src/backend/oneapi/where.cpp                  |  2 +
 src/backend/oneapi/where.hpp                  |  4 +-
 src/backend/oneapi/wrap.cpp                   |  4 +-
 src/backend/oneapi/wrap.hpp                   |  2 +
 229 files changed, 648 insertions(+), 180 deletions(-)
 mode change 100755 => 100644 src/backend/oneapi/convolve.cpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/bilateral.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/convolve.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/histogram.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/interp.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/reorder.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/unwrap.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/wrap.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/wrap_dilated.hpp

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 24330ee3ae..16ab7e5b5a 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -36,11 +36,11 @@
 using af::dim4;
 using af::dtype_traits;
 
-using common::half;
-using common::Node;
-using common::Node_ptr;
-using common::NodeIterator;
-using oneapi::jit::BufferNode;
+using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::oneapi::jit::BufferNode;
 
 using nonstd::span;
 using std::accumulate;
@@ -51,6 +51,7 @@ using std::vector;
 
 using sycl::buffer;
 
+namespace arrayfire {
 namespace oneapi {
 namespace {
 template<typename T>
@@ -77,7 +78,7 @@ void verifyTypeSupport<cdouble>() {
 }
 
 template<>
-void verifyTypeSupport<common::half>() {
+void verifyTypeSupport<arrayfire::common::half>() {
     if (!isHalfSupported(getActiveDeviceId())) {
         AF_ERROR("Half precision not supported", AF_ERR_NO_HALF);
     }
@@ -575,3 +576,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index ae7234fb02..c3e0d38b98 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -35,6 +35,7 @@ template<typename T>
 class SparseArray;
 }
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -326,3 +327,4 @@ class Array {
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Event.cpp b/src/backend/oneapi/Event.cpp
index a86d74f8ab..056c6cf950 100644
--- a/src/backend/oneapi/Event.cpp
+++ b/src/backend/oneapi/Event.cpp
@@ -20,6 +20,7 @@
 using std::make_unique;
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace oneapi {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(sycl::queue& queue) {
@@ -76,3 +77,4 @@ af_event createAndMarkEvent() {
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Event.hpp b/src/backend/oneapi/Event.hpp
index ff600ebbcb..1bdedf34ad 100644
--- a/src/backend/oneapi/Event.hpp
+++ b/src/backend/oneapi/Event.hpp
@@ -12,6 +12,7 @@
 #include <common/EventBase.hpp>
 #include <af/event.h>
 
+namespace arrayfire {
 namespace oneapi {
 class OneAPIEventPolicy {
    public:
@@ -62,3 +63,4 @@ void block(af_event eventHandle);
 af_event createAndMarkEvent();
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/GraphicsResourceManager.cpp b/src/backend/oneapi/GraphicsResourceManager.cpp
index 8cf078e8be..cb03ce0a4f 100644
--- a/src/backend/oneapi/GraphicsResourceManager.cpp
+++ b/src/backend/oneapi/GraphicsResourceManager.cpp
@@ -10,6 +10,7 @@
 #include <GraphicsResourceManager.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 GraphicsResourceManager::ShrdResVector
 GraphicsResourceManager::registerResources(
@@ -18,3 +19,4 @@ GraphicsResourceManager::registerResources(
     return output;
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/GraphicsResourceManager.hpp b/src/backend/oneapi/GraphicsResourceManager.hpp
index 6374f1ef7e..1f19c6f8c0 100644
--- a/src/backend/oneapi/GraphicsResourceManager.hpp
+++ b/src/backend/oneapi/GraphicsResourceManager.hpp
@@ -15,6 +15,7 @@
 #include <memory>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 class GraphicsResourceManager
     : public common::InteropManager<GraphicsResourceManager, std::byte> {
@@ -30,3 +31,4 @@ class GraphicsResourceManager
     void operator=(GraphicsResourceManager const&);
 };
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Kernel.hpp b/src/backend/oneapi/Kernel.hpp
index 704237de24..e36e202387 100644
--- a/src/backend/oneapi/Kernel.hpp
+++ b/src/backend/oneapi/Kernel.hpp
@@ -16,6 +16,7 @@
 #include <backend.hpp>
 #include <string>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel_logger {
 inline auto getLogger() -> spdlog::logger* {
@@ -91,3 +92,4 @@ class Kernel {
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
index 5637fa5d06..c4de202761 100644
--- a/src/backend/oneapi/Module.hpp
+++ b/src/backend/oneapi/Module.hpp
@@ -12,6 +12,7 @@
 #include <CL/sycl.hpp>
 #include <common/ModuleInterface.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 /// oneapi backend wrapper for cl::Program object
@@ -39,3 +40,4 @@ class Module
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Param.cpp b/src/backend/oneapi/Param.cpp
index 87a539ce67..6528f707f4 100644
--- a/src/backend/oneapi/Param.cpp
+++ b/src/backend/oneapi/Param.cpp
@@ -12,6 +12,7 @@
 #include <platform.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -28,3 +29,4 @@ Param<T> makeParam(sycl::buffer<T> &mem, int off, const int dims[4],
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 4a0d6ff9cc..01088f86b7 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -12,6 +12,7 @@
 #include <CL/sycl.hpp>
 #include <kernel/KParam.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -36,3 +37,4 @@ template<typename T>
 Param<T> makeParam(sycl::buffer<T>& mem, int off, const int dims[4],
                    const int strides[4]);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/all.cpp b/src/backend/oneapi/all.cpp
index e74df9806c..ad09e4aff1 100644
--- a/src/backend/oneapi/all.cpp
+++ b/src/backend/oneapi/all.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // alltrue
 INSTANTIATE(af_and_t, float, char)
@@ -28,3 +29,4 @@ INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
 INSTANTIATE(af_and_t, half, char)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/anisotropic_diffusion.cpp b/src/backend/oneapi/anisotropic_diffusion.cpp
index a68b8aaa8f..912ee6d986 100644
--- a/src/backend/oneapi/anisotropic_diffusion.cpp
+++ b/src/backend/oneapi/anisotropic_diffusion.cpp
@@ -13,6 +13,7 @@
 #include <err_oneapi.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
@@ -29,3 +30,4 @@ void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
 INSTANTIATE(double)
 INSTANTIATE(float)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/anisotropic_diffusion.hpp b/src/backend/oneapi/anisotropic_diffusion.hpp
index e71d8928ef..71ed5a9bc4 100644
--- a/src/backend/oneapi/anisotropic_diffusion.hpp
+++ b/src/backend/oneapi/anisotropic_diffusion.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void anisotropicDiffusion(Array<T>& inout, const float dt, const float mct,
                           const af::fluxFunction fftype,
                           const af::diffusionEq eq);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/any.cpp b/src/backend/oneapi/any.cpp
index 3a3e62431f..bdf600e9a9 100644
--- a/src/backend/oneapi/any.cpp
+++ b/src/backend/oneapi/any.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // anytrue
 INSTANTIATE(af_or_t, float, char)
@@ -28,3 +29,4 @@ INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
 INSTANTIATE(af_or_t, half, char)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/approx.cpp b/src/backend/oneapi/approx.cpp
index 4ad0c27d9b..825c9072fb 100644
--- a/src/backend/oneapi/approx.cpp
+++ b/src/backend/oneapi/approx.cpp
@@ -12,6 +12,7 @@
 #include <kernel/approx1.hpp>
 #include <kernel/approx2.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -84,3 +85,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/approx.hpp b/src/backend/oneapi/approx.hpp
index 68d06967eb..b895dac8aa 100644
--- a/src/backend/oneapi/approx.hpp
+++ b/src/backend/oneapi/approx.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -22,3 +23,4 @@ void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
              const Tp &yi_step, const af_interp_type method,
              const float offGrid);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/approx1.cpp b/src/backend/oneapi/approx1.cpp
index 8906f57016..0271d0a4ed 100644
--- a/src/backend/oneapi/approx1.cpp
+++ b/src/backend/oneapi/approx1.cpp
@@ -10,6 +10,7 @@
 #include <err_oneapi.hpp>
 #include <kernel/approx1.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Ty, typename Tp>
 void approx1(Array<Ty> &yo, const Array<Ty> &yi, const Array<Tp> &xo,
@@ -47,3 +48,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/approx2.cpp b/src/backend/oneapi/approx2.cpp
index 3330aaa42f..e491a5be5e 100644
--- a/src/backend/oneapi/approx2.cpp
+++ b/src/backend/oneapi/approx2.cpp
@@ -10,6 +10,7 @@
 #include <err_oneapi.hpp>
 #include <kernel/approx2.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Ty, typename Tp>
 void approx2(Array<Ty> &zo, const Array<Ty> &zi, const Array<Tp> &xo,
@@ -54,3 +55,4 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/arith.hpp b/src/backend/oneapi/arith.hpp
index 1311d4d607..8f31a5383e 100644
--- a/src/backend/oneapi/arith.hpp
+++ b/src/backend/oneapi/arith.hpp
@@ -15,6 +15,7 @@
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, af_op_t op>
@@ -31,3 +32,4 @@ Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/assign.cpp b/src/backend/oneapi/assign.cpp
index 5517793411..0f2b96e5d5 100644
--- a/src/backend/oneapi/assign.cpp
+++ b/src/backend/oneapi/assign.cpp
@@ -18,8 +18,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -86,3 +87,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/assign.hpp b/src/backend/oneapi/assign.hpp
index 7cb69fb9f4..cb26fd515b 100644
--- a/src/backend/oneapi/assign.hpp
+++ b/src/backend/oneapi/assign.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/backend.hpp b/src/backend/oneapi/backend.hpp
index 5c805903c5..3366912b3b 100644
--- a/src/backend/oneapi/backend.hpp
+++ b/src/backend/oneapi/backend.hpp
@@ -19,4 +19,4 @@
 #define __DH__
 #endif
 
-namespace detail = oneapi;
+namespace detail = arrayfire::oneapi;
diff --git a/src/backend/oneapi/bilateral.cpp b/src/backend/oneapi/bilateral.cpp
index 75b97d5509..d7d5dd33b9 100644
--- a/src/backend/oneapi/bilateral.cpp
+++ b/src/backend/oneapi/bilateral.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename inType, typename outType>
@@ -39,3 +40,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/bilateral.hpp b/src/backend/oneapi/bilateral.hpp
index 14a221f48f..f88145cd7b 100644
--- a/src/backend/oneapi/bilateral.hpp
+++ b/src/backend/oneapi/bilateral.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename inType, typename outType>
 Array<outType> bilateral(const Array<inType> &in, const float &spatialSigma,
                          const float &chromaticSigma);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/binary.hpp b/src/backend/oneapi/binary.hpp
index b0d02195b6..a9bc4900e8 100644
--- a/src/backend/oneapi/binary.hpp
+++ b/src/backend/oneapi/binary.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename To, typename Ti, af_op_t op>
@@ -125,3 +126,4 @@ struct BinOp<To, Ti, af_hypot_t> {
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index c8e8d69c98..4a3b5e180d 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -22,8 +22,9 @@
 #include <complex>
 #include <vector>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 void initBlas() { /*gpu_blas_init();*/
@@ -84,3 +85,4 @@ INSTANTIATE_DOT(cdouble)
 INSTANTIATE_DOT(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/blas.hpp b/src/backend/oneapi/blas.hpp
index 7371d4884f..605b3f6d6c 100644
--- a/src/backend/oneapi/blas.hpp
+++ b/src/backend/oneapi/blas.hpp
@@ -13,6 +13,7 @@
 // This file contains the common interface for OneAPI BLAS
 // functions
 
+namespace arrayfire {
 namespace oneapi {
 
 void initBlas();
@@ -39,3 +40,4 @@ template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/canny.cpp b/src/backend/oneapi/canny.cpp
index ac85af2e1b..4e9e7fceb2 100644
--- a/src/backend/oneapi/canny.cpp
+++ b/src/backend/oneapi/canny.cpp
@@ -13,6 +13,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -26,3 +27,4 @@ Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/canny.hpp b/src/backend/oneapi/canny.hpp
index 25f7f5458b..c9bbe36edd 100644
--- a/src/backend/oneapi/canny.hpp
+++ b/src/backend/oneapi/canny.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 Array<float> nonMaximumSuppression(const Array<float>& mag,
                                    const Array<float>& gx,
@@ -17,3 +18,4 @@ Array<float> nonMaximumSuppression(const Array<float>& mag,
 Array<char> edgeTrackingByHysteresis(const Array<char>& strong,
                                      const Array<char>& weak);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/cast.hpp b/src/backend/oneapi/cast.hpp
index aef3711589..c9b015c4f2 100644
--- a/src/backend/oneapi/cast.hpp
+++ b/src/backend/oneapi/cast.hpp
@@ -18,6 +18,7 @@
 #include <af/dim4.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename To, typename Ti>
@@ -71,3 +72,4 @@ struct CastOp<cdouble, cdouble> {
 #undef CAST_CFN
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/cholesky.cpp b/src/backend/oneapi/cholesky.cpp
index bd6b286654..905a3208c5 100644
--- a/src/backend/oneapi/cholesky.cpp
+++ b/src/backend/oneapi/cholesky.cpp
@@ -15,6 +15,7 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 //#include <triangle.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -40,9 +41,11 @@ INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -66,5 +69,6 @@ INSTANTIATE_CH(double)
 INSTANTIATE_CH(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/cholesky.hpp b/src/backend/oneapi/cholesky.hpp
index d934beb566..ab2bef5cc8 100644
--- a/src/backend/oneapi/cholesky.hpp
+++ b/src/backend/oneapi/cholesky.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
@@ -16,3 +17,4 @@ Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper);
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index cc85d37005..39783a3c53 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -28,12 +28,12 @@
 #include <string>
 #include <vector>
 
-using common::loggerFactory;
+using arrayfire::common::loggerFactory;
+using arrayfire::oneapi::Kernel;
+using arrayfire::oneapi::Module;
 using fmt::format;
-// using oneapi::getActiveDeviceId;
-// using oneapi::getDevice;
-using oneapi::Kernel;
-using oneapi::Module;
+// using arrayfire::oneapi::getActiveDeviceId;
+// using arrayfire::oneapi::getDevice;
 using spdlog::logger;
 using sycl::bundle_state;
 using sycl::kernel_bundle;
@@ -69,6 +69,7 @@ string getProgramBuildLog(const kernel_bundle<bundle_state::executable> &prog) {
 //        AF_ERROR(build_error, AF_ERR_INTERNAL);                      \
 //    } while (0)
 
+namespace arrayfire {
 namespace oneapi {
 
 // const static string DEFAULT_MACROS_STR(
@@ -98,6 +99,7 @@ kernel_bundle<bundle_state::executable> buildProgram(const vector<string>
 */
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 string getKernelCacheFilename(const int device, const string &key) {
     ONEAPI_NOT_SUPPORTED("");
diff --git a/src/backend/oneapi/complex.hpp b/src/backend/oneapi/complex.hpp
index c087959b42..c480fa6474 100644
--- a/src/backend/oneapi/complex.hpp
+++ b/src/backend/oneapi/complex.hpp
@@ -15,6 +15,7 @@
 #include <traits.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename To, typename Ti>
 Array<To> cplx(const Array<Ti> &lhs, const Array<Ti> &rhs,
@@ -88,3 +89,4 @@ Array<T> conj(const Array<T> &in) {
     return createNodeArray<T>(in.dims(), common::Node_ptr(node));
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/convolve.cpp b/src/backend/oneapi/convolve.cpp
old mode 100755
new mode 100644
index a7a2fc9aee..ac940f501d
--- a/src/backend/oneapi/convolve.cpp
+++ b/src/backend/oneapi/convolve.cpp
@@ -24,11 +24,12 @@
 #include <vector>
 
 using af::dim4;
-using common::flip;
-using common::half;
-using common::modDims;
+using arrayfire::common::flip;
+using arrayfire::common::half;
+using arrayfire::common::modDims;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename accT>
@@ -173,3 +174,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/convolve.hpp b/src/backend/oneapi/convolve.hpp
index 7fbf2e86a1..6551416170 100644
--- a/src/backend/oneapi/convolve.hpp
+++ b/src/backend/oneapi/convolve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename accT>
@@ -37,3 +38,4 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> &convolved_output, af::dim4 stride,
                              af::dim4 padding, af::dim4 dilation);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/convolve_separable.cpp b/src/backend/oneapi/convolve_separable.cpp
index d9b1e1f64a..969aff66e2 100644
--- a/src/backend/oneapi/convolve_separable.cpp
+++ b/src/backend/oneapi/convolve_separable.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename accT>
@@ -43,3 +44,4 @@ INSTANTIATE(intl, float)
 INSTANTIATE(uintl, float)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index f49689a423..23106f7dd1 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -15,9 +15,10 @@
 #include <kernel/memcopy.hpp>
 #include <math.hpp>
 
-using common::half;
-using common::is_complex;
+using arrayfire::common::half;
+using arrayfire::common::is_complex;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -246,3 +247,4 @@ INSTANTIATE_GETSCALAR(ushort)
 INSTANTIATE_GETSCALAR(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/copy.hpp b/src/backend/oneapi/copy.hpp
index 30d6196aa2..048c89260a 100644
--- a/src/backend/oneapi/copy.hpp
+++ b/src/backend/oneapi/copy.hpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 //#include <kernel/pad_array_borders.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void copyData(T *data, const Array<T> &A);
@@ -65,3 +66,4 @@ void multiply_inplace(Array<T> &in, double val);
 template<typename T>
 T getScalar(const Array<T> &in);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/count.cpp b/src/backend/oneapi/count.cpp
index d50f35b694..f8ef354169 100644
--- a/src/backend/oneapi/count.cpp
+++ b/src/backend/oneapi/count.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // count
 INSTANTIATE(af_notzero_t, float, uint)
@@ -28,3 +29,4 @@ INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
 INSTANTIATE(af_notzero_t, half, uint)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 48201b7ebc..54878e3fea 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -30,7 +30,8 @@
 #include <string>
 #include <vector>
 
-using common::getEnvVar;
+using arrayfire::common::ForgeManager;
+using arrayfire::common::getEnvVar;
 using std::begin;
 using std::end;
 using std::find;
@@ -43,6 +44,7 @@ using std::vector;
 using sycl::device;
 using sycl::platform;
 
+namespace arrayfire {
 namespace oneapi {
 
 static inline bool compare_default(const unique_ptr<sycl::device>& ldev,
@@ -68,7 +70,7 @@ DeviceManager::DeviceManager()
             AF_ERR_RUNTIME);
     }
 
-    fgMngr = std::make_unique<graphics::ForgeManager>();
+    fgMngr = std::make_unique<ForgeManager>();
 
     AF_TRACE("Found {} sycl platforms", platforms.size());
     // Iterate through platforms, get all available devices and store them
@@ -223,3 +225,4 @@ void DeviceManager::markDeviceForInterop(const int device,
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index d84994226c..df14603147 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -23,18 +23,16 @@ namespace spdlog {
 class logger;
 }
 
-namespace graphics {
-class ForgeManager;
-}
-
+namespace arrayfire {
 namespace common {
-namespace memory {
+class ForgeManager;
 class MemoryManagerBase;
-}
 }  // namespace common
+}  // namespace arrayfire
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
+namespace arrayfire {
 namespace oneapi {
 
 // opencl namespace forward declarations
@@ -62,7 +60,7 @@ class DeviceManager {
 
     void resetMemoryManagerPinned();
 
-    friend graphics::ForgeManager& forgeManager();
+    friend arrayfire::common::ForgeManager& forgeManager();
 
     friend GraphicsResourceManager& interopManager();
 
@@ -141,7 +139,7 @@ class DeviceManager {
     std::vector<int> mPlatforms;
     unsigned mUserDeviceOffset;
 
-    std::unique_ptr<graphics::ForgeManager> fgMngr;
+    std::unique_ptr<arrayfire::common::ForgeManager> fgMngr;
     std::unique_ptr<MemoryManagerBase> memManager;
     std::unique_ptr<MemoryManagerBase> pinnedMemManager;
     std::unique_ptr<GraphicsResourceManager> gfxManagers[MAX_DEVICES];
@@ -152,3 +150,4 @@ class DeviceManager {
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/diagonal.cpp b/src/backend/oneapi/diagonal.cpp
index b9d443c662..a18d024585 100644
--- a/src/backend/oneapi/diagonal.cpp
+++ b/src/backend/oneapi/diagonal.cpp
@@ -15,8 +15,9 @@
 #include <math.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num) {
@@ -59,3 +60,4 @@ INSTANTIATE_DIAGONAL(ushort)
 INSTANTIATE_DIAGONAL(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/diagonal.hpp b/src/backend/oneapi/diagonal.hpp
index 28b4f46df6..1329cdd9d2 100644
--- a/src/backend/oneapi/diagonal.hpp
+++ b/src/backend/oneapi/diagonal.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> diagCreate(const Array<T> &in, const int num);
@@ -16,3 +17,4 @@ Array<T> diagCreate(const Array<T> &in, const int num);
 template<typename T>
 Array<T> diagExtract(const Array<T> &in, const int num);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/diff.cpp b/src/backend/oneapi/diff.cpp
index ad9da16697..a3c37f6a4a 100644
--- a/src/backend/oneapi/diff.cpp
+++ b/src/backend/oneapi/diff.cpp
@@ -13,6 +13,7 @@
 #include <af/dim4.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -56,3 +57,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(char)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/diff.hpp b/src/backend/oneapi/diff.hpp
index d7f5aaf477..9679f90c59 100644
--- a/src/backend/oneapi/diff.hpp
+++ b/src/backend/oneapi/diff.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> diff1(const Array<T> &in, const int dim);
@@ -16,3 +17,4 @@ Array<T> diff1(const Array<T> &in, const int dim);
 template<typename T>
 Array<T> diff2(const Array<T> &in, const int dim);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/exampleFunction.cpp b/src/backend/oneapi/exampleFunction.cpp
index bc5c52b031..9e6d81e9d5 100644
--- a/src/backend/oneapi/exampleFunction.cpp
+++ b/src/backend/oneapi/exampleFunction.cpp
@@ -24,6 +24,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -64,3 +65,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/exampleFunction.hpp b/src/backend/oneapi/exampleFunction.hpp
index 7f51018f83..5e5978a057 100644
--- a/src/backend/oneapi/exampleFunction.hpp
+++ b/src/backend/oneapi/exampleFunction.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> exampleFunction(const Array<T> &a, const Array<T> &b,
                          const af_someenum_t method);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/fast.cpp b/src/backend/oneapi/fast.cpp
index 25f8c47e6a..cb9ae28d4c 100644
--- a/src/backend/oneapi/fast.cpp
+++ b/src/backend/oneapi/fast.cpp
@@ -15,6 +15,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -42,3 +43,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/fast.hpp b/src/backend/oneapi/fast.hpp
index 19667cf49e..4f9c7cf7f4 100644
--- a/src/backend/oneapi/fast.hpp
+++ b/src/backend/oneapi/fast.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -20,4 +21,5 @@ unsigned fast(Array<float> &x_out, Array<float> &y_out, Array<float> &score_out,
               const bool non_max, const float feature_ratio,
               const unsigned edge);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index 1591e4b4cf..9ccdcfcb86 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 void setFFTPlanCacheSize(size_t numPlans) {}
@@ -103,3 +104,4 @@ INSTANTIATE(cdouble)
 INSTANTIATE_REAL(float, cfloat)
 INSTANTIATE_REAL(double, cdouble)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/fft.hpp b/src/backend/oneapi/fft.hpp
index 57de589db2..0138970ba9 100644
--- a/src/backend/oneapi/fft.hpp
+++ b/src/backend/oneapi/fft.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 void setFFTPlanCacheSize(size_t numPlans);
@@ -23,3 +24,4 @@ template<typename Tr, typename Tc>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/fftconvolve.cpp b/src/backend/oneapi/fftconvolve.cpp
index dad10f492e..c4aea5689c 100644
--- a/src/backend/oneapi/fftconvolve.cpp
+++ b/src/backend/oneapi/fftconvolve.cpp
@@ -26,6 +26,7 @@ using std::is_integral;
 using std::is_same;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -80,3 +81,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/fftconvolve.hpp b/src/backend/oneapi/fftconvolve.hpp
index 7eac7750aa..88ad3c9b9d 100644
--- a/src/backend/oneapi/fftconvolve.hpp
+++ b/src/backend/oneapi/fftconvolve.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/flood_fill.cpp b/src/backend/oneapi/flood_fill.cpp
index a336a441ec..2d9d22d696 100644
--- a/src/backend/oneapi/flood_fill.cpp
+++ b/src/backend/oneapi/flood_fill.cpp
@@ -11,6 +11,7 @@
 
 #include <err_oneapi.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -34,3 +35,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/flood_fill.hpp b/src/backend/oneapi/flood_fill.hpp
index 6590f33e59..00ddce1b70 100644
--- a/src/backend/oneapi/flood_fill.hpp
+++ b/src/backend/oneapi/flood_fill.hpp
@@ -12,10 +12,12 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
                    const Array<uint>& seedsY, const T newValue,
                    const T lowValue, const T highValue,
                    const af::connectivity nlookup = AF_CONNECTIVITY_8);
-}  // namespace oneapi
+}
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/gradient.cpp b/src/backend/oneapi/gradient.cpp
index 40b557a4ae..dc45b67cc6 100644
--- a/src/backend/oneapi/gradient.cpp
+++ b/src/backend/oneapi/gradient.cpp
@@ -14,6 +14,7 @@
 #include <math.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
@@ -29,3 +30,4 @@ INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/gradient.hpp b/src/backend/oneapi/gradient.hpp
index e5ebff012c..b90fb6ecc7 100644
--- a/src/backend/oneapi/gradient.hpp
+++ b/src/backend/oneapi/gradient.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/harris.cpp b/src/backend/oneapi/harris.cpp
index ef6b844fd4..d266a18bad 100644
--- a/src/backend/oneapi/harris.cpp
+++ b/src/backend/oneapi/harris.cpp
@@ -15,6 +15,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename convAccT>
@@ -38,3 +39,4 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/harris.hpp b/src/backend/oneapi/harris.hpp
index 8eeef1dcc3..eba87bd404 100644
--- a/src/backend/oneapi/harris.hpp
+++ b/src/backend/oneapi/harris.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned harris(Array<float> &x_out, Array<float> &y_out,
                 const float sigma, const unsigned filter_len,
                 const float k_thr);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/hist_graphics.cpp b/src/backend/oneapi/hist_graphics.cpp
index 12d9bb2b33..3b280592b1 100644
--- a/src/backend/oneapi/hist_graphics.cpp
+++ b/src/backend/oneapi/hist_graphics.cpp
@@ -12,6 +12,7 @@
 #include <err_oneapi.hpp>
 #include <hist_graphics.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -30,3 +31,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/hist_graphics.hpp b/src/backend/oneapi/hist_graphics.hpp
index 4be3935750..578a9bde70 100644
--- a/src/backend/oneapi/hist_graphics.hpp
+++ b/src/backend/oneapi/hist_graphics.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_histogram(const Array<T> &data, fg_histogram hist);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/histogram.cpp b/src/backend/oneapi/histogram.cpp
index 62ccd879af..4036a5229b 100644
--- a/src/backend/oneapi/histogram.cpp
+++ b/src/backend/oneapi/histogram.cpp
@@ -15,8 +15,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -50,3 +51,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/histogram.hpp b/src/backend/oneapi/histogram.hpp
index f899faffbe..67be10a0d3 100644
--- a/src/backend/oneapi/histogram.hpp
+++ b/src/backend/oneapi/histogram.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
                       const double &minval, const double &maxval,
                       const bool isLinear);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/homography.cpp b/src/backend/oneapi/homography.cpp
index 5060cd50ae..2bf05ef672 100644
--- a/src/backend/oneapi/homography.cpp
+++ b/src/backend/oneapi/homography.cpp
@@ -19,6 +19,7 @@
 using af::dim4;
 using std::numeric_limits;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -42,3 +43,4 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/homography.hpp b/src/backend/oneapi/homography.hpp
index 6c4e54be66..456b692330 100644
--- a/src/backend/oneapi/homography.hpp
+++ b/src/backend/oneapi/homography.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -18,4 +19,5 @@ int homography(Array<T> &H, const Array<float> &x_src,
                const af_homography_type htype, const float inlier_thr,
                const unsigned iterations);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/hsv_rgb.cpp b/src/backend/oneapi/hsv_rgb.cpp
index 6902f0f6c2..fb9d86b5ec 100644
--- a/src/backend/oneapi/hsv_rgb.cpp
+++ b/src/backend/oneapi/hsv_rgb.cpp
@@ -11,6 +11,7 @@
 
 #include <err_oneapi.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -35,3 +36,4 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/hsv_rgb.hpp b/src/backend/oneapi/hsv_rgb.hpp
index e46da55a80..73abd86410 100644
--- a/src/backend/oneapi/hsv_rgb.hpp
+++ b/src/backend/oneapi/hsv_rgb.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> rgb2hsv(const Array<T>& in);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/identity.cpp b/src/backend/oneapi/identity.cpp
index ccb633aef2..c7db8e7d44 100644
--- a/src/backend/oneapi/identity.cpp
+++ b/src/backend/oneapi/identity.cpp
@@ -13,8 +13,9 @@
 #include <err_oneapi.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> identity(const dim4& dims) {
@@ -41,3 +42,4 @@ INSTANTIATE_IDENTITY(ushort)
 INSTANTIATE_IDENTITY(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/identity.hpp b/src/backend/oneapi/identity.hpp
index b9fed4aa03..4b1057d04a 100644
--- a/src/backend/oneapi/identity.hpp
+++ b/src/backend/oneapi/identity.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> identity(const dim4& dim);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/iir.cpp b/src/backend/oneapi/iir.cpp
index 9051e34b5f..e0223ca6f1 100644
--- a/src/backend/oneapi/iir.cpp
+++ b/src/backend/oneapi/iir.cpp
@@ -18,6 +18,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
@@ -35,3 +36,4 @@ INSTANTIATE(double)
 INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/iir.hpp b/src/backend/oneapi/iir.hpp
index 6f7d052119..3c50f539ee 100644
--- a/src/backend/oneapi/iir.hpp
+++ b/src/backend/oneapi/iir.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/image.cpp b/src/backend/oneapi/image.cpp
index 8406294a44..723c29fb8b 100644
--- a/src/backend/oneapi/image.cpp
+++ b/src/backend/oneapi/image.cpp
@@ -15,6 +15,7 @@
 #include <stdexcept>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -34,3 +35,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/image.hpp b/src/backend/oneapi/image.hpp
index 5647efea36..6e644a3e48 100644
--- a/src/backend/oneapi/image.hpp
+++ b/src/backend/oneapi/image.hpp
@@ -10,9 +10,10 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_image(const Array<T> &in, fg_image image);
-
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/index.cpp b/src/backend/oneapi/index.cpp
index 481da0f9ec..03a6b74c56 100644
--- a/src/backend/oneapi/index.cpp
+++ b/src/backend/oneapi/index.cpp
@@ -15,8 +15,9 @@
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -44,3 +45,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/index.hpp b/src/backend/oneapi/index.hpp
index d8fdb674b5..cebd4c3ea5 100644
--- a/src/backend/oneapi/index.hpp
+++ b/src/backend/oneapi/index.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/index.h>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/inverse.cpp b/src/backend/oneapi/inverse.cpp
index 079250d4f7..97d91f4db4 100644
--- a/src/backend/oneapi/inverse.cpp
+++ b/src/backend/oneapi/inverse.cpp
@@ -14,6 +14,7 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -31,9 +32,11 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -51,5 +54,6 @@ INSTANTIATE(double)
 INSTANTIATE(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #endif
diff --git a/src/backend/oneapi/inverse.hpp b/src/backend/oneapi/inverse.hpp
index 2011950ed1..5b37d94978 100644
--- a/src/backend/oneapi/inverse.hpp
+++ b/src/backend/oneapi/inverse.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> inverse(const Array<T> &in);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/iota.cpp b/src/backend/oneapi/iota.cpp
index 18077e5199..84bf693f1b 100644
--- a/src/backend/oneapi/iota.cpp
+++ b/src/backend/oneapi/iota.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
@@ -50,3 +51,4 @@ INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/iota.hpp b/src/backend/oneapi/iota.hpp
index fe9b1cdf8c..ffce49d1bd 100644
--- a/src/backend/oneapi/iota.hpp
+++ b/src/backend/oneapi/iota.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/ireduce.cpp b/src/backend/oneapi/ireduce.cpp
index cf97ad3a4a..6cca678b20 100644
--- a/src/backend/oneapi/ireduce.cpp
+++ b/src/backend/oneapi/ireduce.cpp
@@ -16,8 +16,9 @@
 #include <complex>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<af_op_t op, typename T>
@@ -76,3 +77,4 @@ INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
 INSTANTIATE(af_max_t, half)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/ireduce.hpp b/src/backend/oneapi/ireduce.hpp
index 3ae1b6c476..99a1e45aac 100644
--- a/src/backend/oneapi/ireduce.hpp
+++ b/src/backend/oneapi/ireduce.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
@@ -22,3 +23,4 @@ void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index c957c86c1d..3233f97430 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -29,16 +29,17 @@
 #include <string>
 #include <vector>
 
-using common::getFuncName;
-using common::Node;
-using common::Node_ids;
-using common::Node_map_t;
+using arrayfire::common::getFuncName;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ids;
+using arrayfire::common::Node_map_t;
 
 using std::string;
 using std::stringstream;
 using std::to_string;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 
 string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
@@ -69,3 +70,4 @@ void evalNodes(Param &out, Node *node) {
 */
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
index 9925ec7211..5f8ead77e0 100644
--- a/src/backend/oneapi/jit/BufferNode.hpp
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -12,6 +12,7 @@
 
 #include <memory>
 
+namespace arrayfire {
 namespace oneapi {
 namespace jit {
 template<typename T>
@@ -33,3 +34,4 @@ bool BufferNodeBase<DataType, ParamType>::operator==(
 }
 
 }  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index 202403f4cb..b3753955b9 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -13,6 +13,7 @@
 
 #include <err_oneapi.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 namespace {
@@ -112,3 +113,4 @@ inline void generateShiftNodeRead(std::stringstream& kerStream, int id,
 }
 }  // namespace
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
index a645ea56f5..9e8aa2f743 100644
--- a/src/backend/oneapi/join.cpp
+++ b/src/backend/oneapi/join.cpp
@@ -17,10 +17,11 @@
 #include <vector>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using std::transform;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 dim4 calcOffset(const dim4 &dims, int dim) {
     dim4 offset;
@@ -89,3 +90,4 @@ INSTANTIATE(half)
 
 #undef INSTANTIATE
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/join.hpp b/src/backend/oneapi/join.hpp
index 25763f063e..818047cae2 100644
--- a/src/backend/oneapi/join.hpp
+++ b/src/backend/oneapi/join.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
@@ -16,3 +17,4 @@ Array<T> join(const int dim, const Array<T> &first, const Array<T> &second);
 template<typename T>
 void join(Array<T> &out, const int dim, const std::vector<Array<T>> &inputs);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/approx1.hpp b/src/backend/oneapi/kernel/approx1.hpp
index 95b4ceb65c..f520719749 100644
--- a/src/backend/oneapi/kernel/approx1.hpp
+++ b/src/backend/oneapi/kernel/approx1.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -163,3 +164,4 @@ void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/approx2.hpp b/src/backend/oneapi/kernel/approx2.hpp
index 94b2f7060c..5b7e509f9b 100644
--- a/src/backend/oneapi/kernel/approx2.hpp
+++ b/src/backend/oneapi/kernel/approx2.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -192,3 +193,4 @@ void approx2(Param<Ty> zo, const Param<Ty> zi, const Param<Tp> xo,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index d4cc7e2b6c..162c1d5254 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -17,6 +17,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -144,3 +145,4 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
 }
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
old mode 100755
new mode 100644
index aba8b93d87..3814084c1b
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -215,3 +216,4 @@ void bilateral(Param<outType> out, const Param<inType> in, const float s_sigma,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/convolve.hpp b/src/backend/oneapi/kernel/convolve.hpp
old mode 100755
new mode 100644
index 39abe603ad..9f868ce729
--- a/src/backend/oneapi/kernel/convolve.hpp
+++ b/src/backend/oneapi/kernel/convolve.hpp
@@ -17,6 +17,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -143,3 +144,4 @@ void convolve_nd(Param<T> out, const Param<T> signal, const Param<aT> filter,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/default_config.hpp b/src/backend/oneapi/kernel/default_config.hpp
index c279fd98bb..c2ed8ae3dc 100644
--- a/src/backend/oneapi/kernel/default_config.hpp
+++ b/src/backend/oneapi/kernel/default_config.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -19,3 +20,4 @@ static const uint REPEAT            = 32;
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/diagonal.hpp b/src/backend/oneapi/kernel/diagonal.hpp
index 4668fee5bd..a21c1abd11 100644
--- a/src/backend/oneapi/kernel/diagonal.hpp
+++ b/src/backend/oneapi/kernel/diagonal.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -161,3 +162,4 @@ static void diagExtract(Param<T> out, Param<T> in, int num) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/diff.hpp b/src/backend/oneapi/kernel/diff.hpp
index d624cd5283..bd3d925d3b 100644
--- a/src/backend/oneapi/kernel/diff.hpp
+++ b/src/backend/oneapi/kernel/diff.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -122,3 +123,4 @@ void diff(Param<T> out, const Param<T> in, const unsigned indims,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
old mode 100755
new mode 100644
index bc9f74f88c..99ee437ae3
--- a/src/backend/oneapi/kernel/histogram.hpp
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -165,3 +166,4 @@ void histogram(Param<uint> out, const Param<T> in, int nbins, float minval,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
old mode 100755
new mode 100644
index 6f43fb52f2..af430ca031
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -13,6 +13,7 @@
 #include <af/constants.h>
 #include <algorithm>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -341,3 +342,4 @@ struct Interp2<Ty, Tp, 3> {
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index ee0b16d23a..956bbc401a 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -118,3 +119,4 @@ void iota(Param<T> out, const af::dim4& sdims) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 17d2eb2164..d0361a18dc 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -25,6 +25,7 @@
 #include <memory>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 
 /*
@@ -789,3 +790,4 @@ To mean_all(Param<Ti> in) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 2bb2443cb2..efe577c9ce 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -22,6 +22,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -141,25 +142,28 @@ outType convertType(inType value) {
 }
 
 template<>
-char convertType<compute_t<common::half>, char>(compute_t<common::half> value) {
+char convertType<compute_t<arrayfire::common::half>, char>(
+    compute_t<arrayfire::common::half> value) {
     return (char)((short)value);
 }
 
 template<>
-compute_t<common::half> convertType<char, compute_t<common::half>>(char value) {
-    return compute_t<common::half>(value);
+compute_t<arrayfire::common::half>
+convertType<char, compute_t<arrayfire::common::half>>(char value) {
+    return compute_t<arrayfire::common::half>(value);
 }
 
 template<>
-unsigned char convertType<compute_t<common::half>, unsigned char>(
-    compute_t<common::half> value) {
+unsigned char convertType<compute_t<arrayfire::common::half>, unsigned char>(
+    compute_t<arrayfire::common::half> value) {
     return (unsigned char)((short)value);
 }
 
 template<>
-compute_t<common::half> convertType<unsigned char, compute_t<common::half>>(
+compute_t<arrayfire::common::half>
+convertType<unsigned char, compute_t<arrayfire::common::half>>(
     unsigned char value) {
-    return compute_t<common::half>(value);
+    return compute_t<arrayfire::common::half>(value);
 }
 
 template<>
@@ -193,7 +197,7 @@ OTHER_SPECIALIZATIONS(short)
 OTHER_SPECIALIZATIONS(ushort)
 OTHER_SPECIALIZATIONS(uchar)
 OTHER_SPECIALIZATIONS(char)
-OTHER_SPECIALIZATIONS(common::half)
+OTHER_SPECIALIZATIONS(arrayfire::common::half)
 
 template<typename inType, typename outType, bool SAMEDIMS>
 class reshapeCopy {
@@ -320,3 +324,4 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
index 8c9b5e9251..d86700a7fb 100644
--- a/src/backend/oneapi/kernel/random_engine.hpp
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -30,6 +30,7 @@ static const int TABLE_SIZE = 16;
 static const int MAX_BLOCKS = 32;
 static const int STATE_SIZE = (256 * 3);
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -202,3 +203,4 @@ void normalDistributionMT(Param<T> out, const size_t elements,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
index 6a429feee9..e0a0f57c8d 100644
--- a/src/backend/oneapi/kernel/random_engine_mersenne.hpp
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -44,6 +44,7 @@
 #pragma once
 #include <kernel/random_engine_write.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -367,3 +368,4 @@ class normalMersenne {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/random_engine_philox.hpp b/src/backend/oneapi/kernel/random_engine_philox.hpp
index e43cfa31e5..b5887aa16e 100644
--- a/src/backend/oneapi/kernel/random_engine_philox.hpp
+++ b/src/backend/oneapi/kernel/random_engine_philox.hpp
@@ -47,6 +47,7 @@
 #pragma once
 #include <kernel/random_engine_write.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 // Utils
@@ -196,3 +197,4 @@ class normalPhilox {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/random_engine_threefry.hpp b/src/backend/oneapi/kernel/random_engine_threefry.hpp
index 931e60ef63..2e8b6e0d16 100644
--- a/src/backend/oneapi/kernel/random_engine_threefry.hpp
+++ b/src/backend/oneapi/kernel/random_engine_threefry.hpp
@@ -47,6 +47,7 @@
 #pragma once
 #include <kernel/random_engine_write.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 // Utils
@@ -255,3 +256,4 @@ class normalThreefry {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 824feb95b8..426b518eba 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -9,6 +9,7 @@
 #pragma once
 #include <CL/sycl.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -273,15 +274,11 @@ static void boxMullerTransform(Td *const out1, Td *const out2, const Tc &r1,
     *out2 = static_cast<Td>(r * c);
 }
 // template<>
-//__device__ void boxMullerTransform<common::half, __half>(
-//    common::half *const out1, common::half *const out2, const __half &r1,
-//    const __half &r2) {
-//    float o1, o2;
-//    float fr1 = __half2float(r1);
-//    float fr2 = __half2float(r2);
-//    boxMullerTransform(&o1, &o2, fr1, fr2);
-//    *out1 = o1;
-//    *out2 = o2;
+//__device__ void boxMullerTransform<arrayfire::common::half, __half>(
+//    arrayfire::common::half *const out1, arrayfire::common::half *const out2,
+//    const __half &r1, const __half &r2) { float o1, o2; float fr1 =
+//    __half2float(r1); float fr2 = __half2float(r2); boxMullerTransform(&o1,
+//    &o2, fr1, fr2); *out1 = o1; *out2 = o2;
 //}
 
 // Writes without boundary checking
@@ -407,7 +404,7 @@ static void writeOut128Bytes(cdouble *out, const uint &index,
     out[index] = {1.0 - getDouble01(r1, r2), 1.0 - getDouble01(r3, r4)};
 }
 
-static void writeOut128Bytes(common::half *out, const uint &index,
+static void writeOut128Bytes(arrayfire::common::half *out, const uint &index,
                              const uint groupSz, const uint &r1, const uint &r2,
                              const uint &r3, const uint &r4) {
     // out[index]               = oneMinusGetHalf01(r1);
@@ -457,10 +454,10 @@ static void boxMullerWriteOut128Bytes(cdouble *out, const uint &index,
                        getDouble01(r3, r4));
 }
 
-static void boxMullerWriteOut128Bytes(common::half *out, const uint &index,
-                                      const uint groupSz, const uint &r1,
-                                      const uint &r2, const uint &r3,
-                                      const uint &r4) {
+static void boxMullerWriteOut128Bytes(arrayfire::common::half *out,
+                                      const uint &index, const uint groupSz,
+                                      const uint &r1, const uint &r2,
+                                      const uint &r3, const uint &r4) {
     //   boxMullerTransform(&out[index], &out[index + groupSz],
     //                      getHalfNegative11(r1), getHalf01(r1 >> 16));
     //   boxMullerTransform(&out[index + 2 * groupSz],
@@ -711,10 +708,11 @@ static void partialBoxMullerWriteOut128Bytes(cdouble *out, const uint &index,
     if (index < elements) { out[index] = {n1, n2}; }
 }
 
-static void partialWriteOut128Bytes(common::half *out, const uint &index,
-                                    const uint groupSz, const uint &r1,
-                                    const uint &r2, const uint &r3,
-                                    const uint &r4, const uint &elements) {
+static void partialWriteOut128Bytes(arrayfire::common::half *out,
+                                    const uint &index, const uint groupSz,
+                                    const uint &r1, const uint &r2,
+                                    const uint &r3, const uint &r4,
+                                    const uint &elements) {
     //  if (index < elements) { out[index] = oneMinusGetHalf01(r1); }
     //  if (index + groupSz < elements) {
     //      out[index + groupSz] = oneMinusGetHalf01(r1 >> 16);
@@ -740,10 +738,13 @@ static void partialWriteOut128Bytes(common::half *out, const uint &index,
 }
 
 // Normalized writes with boundary checking
-static void partialBoxMullerWriteOut128Bytes(
-    common::half *out, const uint &index, const uint groupSz, const uint &r1,
-    const uint &r2, const uint &r3, const uint &r4, const uint &elements) {
-    //    common::half n[8];
+static void partialBoxMullerWriteOut128Bytes(arrayfire::common::half *out,
+                                             const uint &index,
+                                             const uint groupSz, const uint &r1,
+                                             const uint &r2, const uint &r3,
+                                             const uint &r4,
+                                             const uint &elements) {
+    //    arrayfire::common::half n[8];
     //    boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
     //                       getHalf01(r1 >> 16));
     //    boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
@@ -776,3 +777,4 @@ static void partialBoxMullerWriteOut128Bytes(
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index d3106c5e7b..cce47881f2 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -117,9 +118,10 @@ void range(Param<T> out, const int dim) {
 }
 
 template<>
-void range(Param<common::half> out, const int dim) {
-    ONEAPI_NOT_SUPPORTED("TODO: fix common::half support");
+void range(Param<arrayfire::common::half> out, const int dim) {
+    ONEAPI_NOT_SUPPORTED("TODO: fix arrayfire::common::half support");
 }
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reduce.hpp b/src/backend/oneapi/kernel/reduce.hpp
index 6fa38e0269..6807a68396 100644
--- a/src/backend/oneapi/kernel/reduce.hpp
+++ b/src/backend/oneapi/kernel/reduce.hpp
@@ -28,6 +28,7 @@
 #include <iostream>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -110,3 +111,4 @@ void reduce_all(Param<To> out, Param<Ti> in, bool change_nan, double nanval) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 1a318e8bc5..eb8b206a02 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -25,6 +25,7 @@
 #include <iostream>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -289,3 +290,4 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reduce_config.hpp b/src/backend/oneapi/kernel/reduce_config.hpp
index a7d185de75..ca892f4cc8 100644
--- a/src/backend/oneapi/kernel/reduce_config.hpp
+++ b/src/backend/oneapi/kernel/reduce_config.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -23,3 +24,4 @@ static const uint REPEAT            = 32;
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 6efb6851b1..bfb4f808aa 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -25,6 +25,7 @@
 #include <iostream>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -243,3 +244,4 @@ void reduce_dim_default(Param<To> out, Param<Ti> in, bool change_nan,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index a4094f8cb9..94553f2b07 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -25,6 +25,7 @@
 #include <iostream>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -241,3 +242,4 @@ void reduce_first_default(Param<To> out, Param<Ti> in, bool change_nan,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
old mode 100755
new mode 100644
index 2eb7484db2..6aa6cd39c0
--- a/src/backend/oneapi/kernel/reorder.hpp
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -128,3 +129,4 @@ void reorder(Param<T> out, const Param<T> in, const dim_t* rdims) {
 }
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index 8c1a6e9140..eb0683791c 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -17,6 +17,7 @@
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -353,3 +354,4 @@ static void scan_dim(Param<To> out, Param<Ti> in, bool inclusive_scan) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index a7fe567c75..78039dd36d 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -17,6 +17,7 @@
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -311,3 +312,4 @@ static void scan_first(Param<To> out, Param<Ti> in, bool inclusive_scan) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index 8c7fef325f..0fac0bacb7 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -164,3 +165,4 @@ void transpose(Param<T> out, const Param<T> in, const bool conjugate,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
index 108b9596f9..d397436dfc 100644
--- a/src/backend/oneapi/kernel/transpose_inplace.hpp
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -195,3 +196,4 @@ void transpose_inplace(Param<T> in, const bool conjugate,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/triangle.hpp b/src/backend/oneapi/kernel/triangle.hpp
index cf9c3e22a3..96fdeb3d88 100644
--- a/src/backend/oneapi/kernel/triangle.hpp
+++ b/src/backend/oneapi/kernel/triangle.hpp
@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -120,3 +121,4 @@ void triangle(Param<T> out, const Param<T> in, bool is_upper,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/unwrap.hpp b/src/backend/oneapi/kernel/unwrap.hpp
old mode 100755
new mode 100644
index 475e55b66c..a6fa8ee64e
--- a/src/backend/oneapi/kernel/unwrap.hpp
+++ b/src/backend/oneapi/kernel/unwrap.hpp
@@ -15,6 +15,7 @@
 #include <debug_oneapi.hpp>
 #include <kernel/default_config.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -168,3 +169,4 @@ void unwrap(Param<T> out, const Param<T> in, const dim_t wx, const dim_t wy,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index 4158641dce..d9ee535eb6 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -20,6 +20,7 @@
 #include <backend.hpp>
 #include <math.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -192,3 +193,4 @@ static void where(Param<uint> &out, Param<T> in) {
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
old mode 100755
new mode 100644
index 0cac661ba6..e574b4a127
--- a/src/backend/oneapi/kernel/wrap.hpp
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -160,3 +161,4 @@ void wrap(Param<T> out, const Param<T> in, const dim_t wx, const dim_t wy,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/wrap_dilated.hpp b/src/backend/oneapi/kernel/wrap_dilated.hpp
old mode 100755
new mode 100644
index 12760a57c6..c479316968
--- a/src/backend/oneapi/kernel/wrap_dilated.hpp
+++ b/src/backend/oneapi/kernel/wrap_dilated.hpp
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
@@ -175,3 +176,4 @@ void wrap_dilated(Param<T> out, const Param<T> in, const dim_t wx,
 
 }  // namespace kernel
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/logic.hpp b/src/backend/oneapi/logic.hpp
index e1706583e2..650d079159 100644
--- a/src/backend/oneapi/logic.hpp
+++ b/src/backend/oneapi/logic.hpp
@@ -15,6 +15,7 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T, af_op_t op>
 Array<char> logicOp(const Array<T> &lhs, const Array<T> &rhs,
@@ -28,3 +29,4 @@ Array<T> bitOp(const Array<T> &lhs, const Array<T> &rhs,
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/lookup.cpp b/src/backend/oneapi/lookup.cpp
index 304ab9afa7..101dc90c1d 100644
--- a/src/backend/oneapi/lookup.cpp
+++ b/src/backend/oneapi/lookup.cpp
@@ -14,8 +14,9 @@
 #include <err_oneapi.hpp>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
@@ -61,3 +62,4 @@ INSTANTIATE(ushort);
 INSTANTIATE(short);
 INSTANTIATE(half);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/lookup.hpp b/src/backend/oneapi/lookup.hpp
index 2fe9b0240c..78d8da1ac1 100644
--- a/src/backend/oneapi/lookup.hpp
+++ b/src/backend/oneapi/lookup.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
index 170efca58c..b1d0b4b746 100644
--- a/src/backend/oneapi/lu.cpp
+++ b/src/backend/oneapi/lu.cpp
@@ -15,6 +15,7 @@
 #include <copy.hpp>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 Array<int> convertPivot(int *ipiv, int in_sz, int out_sz) {
@@ -50,9 +51,11 @@ INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -84,5 +87,6 @@ INSTANTIATE_LU(double)
 INSTANTIATE_LU(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/lu.hpp b/src/backend/oneapi/lu.hpp
index 8ab1f25a7a..a6b1eeb982 100644
--- a/src/backend/oneapi/lu.hpp
+++ b/src/backend/oneapi/lu.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
@@ -19,3 +20,4 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot = true);
 
 bool isLAPACKAvailable();
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/match_template.cpp b/src/backend/oneapi/match_template.cpp
index 6a0182f7bd..28794ff2eb 100644
--- a/src/backend/oneapi/match_template.cpp
+++ b/src/backend/oneapi/match_template.cpp
@@ -11,6 +11,7 @@
 
 #include <err_oneapi.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename inType, typename outType>
@@ -36,3 +37,4 @@ INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/match_template.hpp b/src/backend/oneapi/match_template.hpp
index 9e79f3e19b..84ea6d337a 100644
--- a/src/backend/oneapi/match_template.hpp
+++ b/src/backend/oneapi/match_template.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename inType, typename outType>
 Array<outType> match_template(const Array<inType> &sImg,
                               const Array<inType> &tImg,
                               const af::matchType mType);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/math.cpp b/src/backend/oneapi/math.cpp
index e9c1666960..a673f9293b 100644
--- a/src/backend/oneapi/math.cpp
+++ b/src/backend/oneapi/math.cpp
@@ -10,6 +10,7 @@
 #include "math.hpp"
 #include <common/half.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 cfloat operator+(cfloat lhs, cfloat rhs) {
     // cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
@@ -51,3 +52,4 @@ cdouble division(cdouble lhs, double rhs) {
     return retVal;
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index 584efa1d14..063d82f370 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -28,6 +28,7 @@
 /* Other */
 #endif
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -113,8 +114,8 @@ inline double maxval() {
 }
 
 template<>
-inline common::half maxval() {
-    return std::numeric_limits<common::half>::infinity();
+inline arrayfire::common::half maxval() {
+    return std::numeric_limits<arrayfire::common::half>::infinity();
 }
 
 template<>
@@ -127,8 +128,8 @@ inline double minval() {
     return -std::numeric_limits<double>::infinity();
 }
 template<>
-inline common::half minval() {
-    return -std::numeric_limits<common::half>::infinity();
+inline arrayfire::common::half minval() {
+    return -std::numeric_limits<arrayfire::common::half>::infinity();
 }
 
 template<typename T>
@@ -141,10 +142,13 @@ static inline T imag(T in) {
     return std::imag(in);
 }
 
-inline common::half operator+(common::half lhs, common::half rhs) noexcept {
-    return common::half(static_cast<float>(lhs) + static_cast<float>(rhs));
+inline arrayfire::common::half operator+(arrayfire::common::half lhs,
+                                         arrayfire::common::half rhs) noexcept {
+    return arrayfire::common::half(static_cast<float>(lhs) +
+                                   static_cast<float>(rhs));
 }
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #if defined(__GNUC__) || defined(__GNUG__)
 /* GCC/G++, Clang/LLVM, Intel ICC */
diff --git a/src/backend/oneapi/max.cpp b/src/backend/oneapi/max.cpp
index 4ae8efeaee..8b6ef71a10 100644
--- a/src/backend/oneapi/max.cpp
+++ b/src/backend/oneapi/max.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // max
 INSTANTIATE(af_max_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
 INSTANTIATE(af_max_t, half, half)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/mean.cpp b/src/backend/oneapi/mean.cpp
index 85c4bc0576..09763bb739 100644
--- a/src/backend/oneapi/mean.cpp
+++ b/src/backend/oneapi/mean.cpp
@@ -15,9 +15,10 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 using std::swap;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in) {
@@ -78,3 +79,4 @@ INSTANTIATE_WGT(cdouble, double);
 INSTANTIATE_WGT(half, float);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/mean.hpp b/src/backend/oneapi/mean.hpp
index c682fa8d5f..1ff66440b5 100644
--- a/src/backend/oneapi/mean.hpp
+++ b/src/backend/oneapi/mean.hpp
@@ -10,6 +10,7 @@
 #pragma once
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Ti, typename Tw, typename To>
 To mean(const Array<Ti>& in);
@@ -24,3 +25,4 @@ template<typename T, typename Tw>
 Array<T> mean(const Array<T>& in, const Array<Tw>& wts, const int dim);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/meanshift.cpp b/src/backend/oneapi/meanshift.cpp
index fa352ed5c1..de517e700f 100644
--- a/src/backend/oneapi/meanshift.cpp
+++ b/src/backend/oneapi/meanshift.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
@@ -46,3 +47,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/meanshift.hpp b/src/backend/oneapi/meanshift.hpp
index 014c0f2468..dbe26b4c85 100644
--- a/src/backend/oneapi/meanshift.hpp
+++ b/src/backend/oneapi/meanshift.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/medfilt.cpp b/src/backend/oneapi/medfilt.cpp
index 1729573628..3b1ff319c5 100644
--- a/src/backend/oneapi/medfilt.cpp
+++ b/src/backend/oneapi/medfilt.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -63,3 +64,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/medfilt.hpp b/src/backend/oneapi/medfilt.hpp
index 1e356a23bb..eb459f7dd9 100644
--- a/src/backend/oneapi/medfilt.hpp
+++ b/src/backend/oneapi/medfilt.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -20,3 +21,4 @@ Array<T> medfilt2(const Array<T> &in, const int w_len, const int w_wid,
                   const af::borderType edge_pad);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 314e1fd0a8..e87812e5b4 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -20,13 +20,14 @@
 
 #include <utility>
 
-using common::bytesToString;
+using arrayfire::common::bytesToString;
 
 using af::dim4;
 using std::function;
 using std::move;
 using std::unique_ptr;
 
+namespace arrayfire {
 namespace oneapi {
 float getMemoryPressure() { return memoryManager().getMemoryPressure(); }
 float getMemoryPressureThreshold() {
@@ -195,7 +196,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
-INSTANTIATE(common::half)
+INSTANTIATE(arrayfire::common::half)
 
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
@@ -332,3 +333,4 @@ void AllocatorPinned::nativeFree(void *ptr) {
     // }
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index 2ed71fdd19..bcb8c1dabf 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -16,6 +16,7 @@
 #include <memory>
 #include <vector>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 sycl::buffer<T> *bufferAlloc(const size_t &bytes);
@@ -65,7 +66,7 @@ bool jitTreeExceedsMemoryPressure(size_t bytes);
 void setMemStepSize(size_t step_bytes);
 size_t getMemStepSize(void);
 
-class Allocator final : public common::memory::AllocatorInterface {
+class Allocator final : public common::AllocatorInterface {
    public:
     Allocator();
     ~Allocator() = default;
@@ -76,7 +77,7 @@ class Allocator final : public common::memory::AllocatorInterface {
     void nativeFree(void *ptr) override;
 };
 
-class AllocatorPinned final : public common::memory::AllocatorInterface {
+class AllocatorPinned final : public common::AllocatorInterface {
    public:
     AllocatorPinned();
     ~AllocatorPinned() = default;
@@ -91,3 +92,4 @@ class AllocatorPinned final : public common::memory::AllocatorInterface {
 };
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/min.cpp b/src/backend/oneapi/min.cpp
index 3afa0d9787..ea9900543c 100644
--- a/src/backend/oneapi/min.cpp
+++ b/src/backend/oneapi/min.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // min
 INSTANTIATE(af_min_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
 INSTANTIATE(af_min_t, half, half)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/moments.cpp b/src/backend/oneapi/moments.cpp
index 119e01cbc9..50efe4ccd5 100644
--- a/src/backend/oneapi/moments.cpp
+++ b/src/backend/oneapi/moments.cpp
@@ -12,6 +12,7 @@
 #include <err_oneapi.hpp>
 // #include <kernel/moments.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 static inline unsigned bitCount(unsigned v) {
@@ -54,3 +55,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/moments.hpp b/src/backend/oneapi/moments.hpp
index 6201ccb897..3dcf1e194f 100644
--- a/src/backend/oneapi/moments.hpp
+++ b/src/backend/oneapi/moments.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<float> moments(const Array<T> &in, const af_moment_type moment);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/morph.cpp b/src/backend/oneapi/morph.cpp
index adef3be8d6..44fe6a6529 100644
--- a/src/backend/oneapi/morph.cpp
+++ b/src/backend/oneapi/morph.cpp
@@ -17,6 +17,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -66,3 +67,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/morph.hpp b/src/backend/oneapi/morph.hpp
index 086baf2a90..47d3399f87 100644
--- a/src/backend/oneapi/morph.hpp
+++ b/src/backend/oneapi/morph.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
@@ -16,3 +17,4 @@ Array<T> morph(const Array<T> &in, const Array<T> &mask, bool isDilation);
 template<typename T>
 Array<T> morph3d(const Array<T> &in, const Array<T> &mask, bool isDilation);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/nearest_neighbour.cpp b/src/backend/oneapi/nearest_neighbour.cpp
index 30bc6d90d3..7a34ba0fba 100644
--- a/src/backend/oneapi/nearest_neighbour.cpp
+++ b/src/backend/oneapi/nearest_neighbour.cpp
@@ -18,6 +18,7 @@
 using af::dim4;
 // nsing cl::Device;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename To, af_match_type dist_type>
@@ -86,3 +87,4 @@ INSTANTIATE(uchar, uint)
 INSTANTIATE(uintl, uint)  // For Hamming
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/nearest_neighbour.hpp b/src/backend/oneapi/nearest_neighbour.hpp
index f16b709d8e..1af9889b00 100644
--- a/src/backend/oneapi/nearest_neighbour.hpp
+++ b/src/backend/oneapi/nearest_neighbour.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename To>
@@ -19,5 +20,5 @@ void nearest_neighbour(Array<uint>& idx, Array<To>& dist, const Array<T>& query,
                        const Array<T>& train, const uint dist_dim,
                        const uint n_dist,
                        const af_match_type dist_type = AF_SSD);
-
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/orb.cpp b/src/backend/oneapi/orb.cpp
index aaca439632..b00cf0395f 100644
--- a/src/backend/oneapi/orb.cpp
+++ b/src/backend/oneapi/orb.cpp
@@ -17,6 +17,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename convAccT>
@@ -66,3 +67,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/orb.hpp b/src/backend/oneapi/orb.hpp
index aa1fe324bb..ab29a6813b 100644
--- a/src/backend/oneapi/orb.hpp
+++ b/src/backend/oneapi/orb.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename convAccT>
@@ -21,4 +22,5 @@ unsigned orb(Array<float> &x, Array<float> &y, Array<float> &score,
              const unsigned max_feat, const float scl_fctr,
              const unsigned levels, const bool blur_img);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index 4e22f742ae..c0f3a0d08e 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -59,12 +59,13 @@ using std::to_string;
 using std::unique_ptr;
 using std::vector;
 
-using common::getEnvVar;
-using common::ltrim;
-using common::memory::MemoryManagerBase;
-using oneapi::Allocator;
-using oneapi::AllocatorPinned;
+using arrayfire::common::getEnvVar;
+using arrayfire::common::ltrim;
+using arrayfire::common::MemoryManagerBase;
+using arrayfire::oneapi::Allocator;
+using arrayfire::oneapi::AllocatorPinned;
 
+namespace arrayfire {
 namespace oneapi {
 
 static string get_system() {
@@ -587,7 +588,7 @@ void resetMemoryManagerPinned() {
     return DeviceManager::getInstance().resetMemoryManagerPinned();
 }
 
-graphics::ForgeManager& forgeManager() {
+arrayfire::common::ForgeManager& forgeManager() {
     return *(DeviceManager::getInstance().fgMngr);
 }
 
@@ -606,6 +607,7 @@ GraphicsResourceManager& interopManager() {
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 /*
 //TODO: select which external api functions to expose and add to
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index 46d24393f3..aa58ea5a7e 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -20,18 +20,16 @@ namespace spdlog {
 class logger;
 }
 
-namespace graphics {
-class ForgeManager;
-}
-
+namespace arrayfire {
 namespace common {
-namespace memory {
 class MemoryManagerBase;
-}
+class ForgeManager;
 }  // namespace common
+}  // namespace arrayfire
 
-using common::memory::MemoryManagerBase;
+using arrayfire::common::MemoryManagerBase;
 
+namespace arrayfire {
 namespace oneapi {
 
 // Forward declarations
@@ -110,7 +108,7 @@ void setMemoryManagerPinned(std::unique_ptr<MemoryManagerBase> mgr);
 
 void resetMemoryManagerPinned();
 
-graphics::ForgeManager& forgeManager();
+arrayfire::common::ForgeManager& forgeManager();
 
 GraphicsResourceManager& interopManager();
 
@@ -119,3 +117,4 @@ GraphicsResourceManager& interopManager();
 void setActiveContext(int device);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/plot.cpp b/src/backend/oneapi/plot.cpp
index 6abf9896a3..d2fa041291 100644
--- a/src/backend/oneapi/plot.cpp
+++ b/src/backend/oneapi/plot.cpp
@@ -15,13 +15,14 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot) {
     ONEAPI_NOT_SUPPORTED("copy_plot Not supported");
 
-    // ForgeModule &_ = graphics::forgePlugin();
+    // ForgeModule &_ = common::forgePlugin();
     // if (isGLSharingSupported()) {
     //     CheckGL("Begin OpenCL resource copy");
     //     const cl::Buffer *d_P = P.get();
@@ -80,3 +81,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/plot.hpp b/src/backend/oneapi/plot.hpp
index c7c922e270..ed8bd5e118 100644
--- a/src/backend/oneapi/plot.hpp
+++ b/src/backend/oneapi/plot.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_plot(const Array<T> &P, fg_plot plot);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/print.hpp b/src/backend/oneapi/print.hpp
index 787df41df2..0e487278d5 100644
--- a/src/backend/oneapi/print.hpp
+++ b/src/backend/oneapi/print.hpp
@@ -11,6 +11,7 @@
 #include <backend.hpp>
 #include <ostream>
 
+namespace arrayfire {
 namespace oneapi {
 static std::ostream& operator<<(std::ostream& out, const cfloat& var) {
     out << "(" << std::real(var) << "," << std::imag(var) << ")";
@@ -22,3 +23,4 @@ static std::ostream& operator<<(std::ostream& out, const cdouble& var) {
     return out;
 }
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/product.cpp b/src/backend/oneapi/product.cpp
index 6d449e1fa7..bc3f9421ae 100644
--- a/src/backend/oneapi/product.cpp
+++ b/src/backend/oneapi/product.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // sum
 INSTANTIATE(af_mul_t, float, float)
@@ -28,3 +29,4 @@ INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
 INSTANTIATE(af_mul_t, half, float)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/qr.cpp b/src/backend/oneapi/qr.cpp
index 80fa226994..32bf559f4c 100644
--- a/src/backend/oneapi/qr.cpp
+++ b/src/backend/oneapi/qr.cpp
@@ -23,6 +23,7 @@
 #include <magma/magma_helper.h>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -112,9 +113,11 @@ INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -138,5 +141,6 @@ INSTANTIATE_QR(double)
 INSTANTIATE_QR(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/qr.hpp b/src/backend/oneapi/qr.hpp
index 3ae750cf70..ad8ed882a0 100644
--- a/src/backend/oneapi/qr.hpp
+++ b/src/backend/oneapi/qr.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig);
@@ -16,3 +17,4 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig);
 template<typename T>
 Array<T> qr_inplace(Array<T> &in);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/random_engine.cpp b/src/backend/oneapi/random_engine.cpp
index cff66a7170..7045dcc8cc 100644
--- a/src/backend/oneapi/random_engine.cpp
+++ b/src/backend/oneapi/random_engine.cpp
@@ -14,8 +14,9 @@
 #include <af/defines.h>
 #include <af/dim4.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl) {
@@ -103,3 +104,4 @@ INSTANTIATE_NORMAL(cfloat)
 INSTANTIATE_NORMAL(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/random_engine.hpp b/src/backend/oneapi/random_engine.hpp
index 0839d387b8..7738294d06 100644
--- a/src/backend/oneapi/random_engine.hpp
+++ b/src/backend/oneapi/random_engine.hpp
@@ -13,6 +13,7 @@
 #include <backend.hpp>
 #include <af/defines.h>
 
+namespace arrayfire {
 namespace oneapi {
 void initMersenneState(Array<uint> &state, const uintl seed,
                        const Array<uint> &tbl);
@@ -39,3 +40,4 @@ Array<T> normalDistribution(const af::dim4 &dims, Array<uint> pos,
                             Array<uint> recursion_table,
                             Array<uint> temper_table, Array<uint> state);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/range.cpp b/src/backend/oneapi/range.cpp
index e5498d12d8..caa8ed48bc 100644
--- a/src/backend/oneapi/range.cpp
+++ b/src/backend/oneapi/range.cpp
@@ -16,8 +16,9 @@
 
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim) {
@@ -52,3 +53,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/range.hpp b/src/backend/oneapi/range.hpp
index 7191152fb1..6a997c6787 100644
--- a/src/backend/oneapi/range.hpp
+++ b/src/backend/oneapi/range.hpp
@@ -10,7 +10,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> range(const dim4& dim, const int seq_dim = -1);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/reduce.hpp b/src/backend/oneapi/reduce.hpp
index 668fa1ac72..6d6ab31670 100644
--- a/src/backend/oneapi/reduce.hpp
+++ b/src/backend/oneapi/reduce.hpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<af_op_t op, typename Ti, typename To>
 Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan = false,
@@ -25,3 +26,4 @@ template<af_op_t op, typename Ti, typename To>
 Array<To> reduce_all(const Array<Ti> &in, bool change_nan = false,
                      double nanval = 0);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index 007fbccac4..898f77d006 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -18,6 +18,7 @@
 using af::dim4;
 using std::swap;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<af_op_t op, typename Ti, typename To>
@@ -45,6 +46,7 @@ Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #define INSTANTIATE(Op, Ti, To)                                                \
     template Array<To> reduce<Op, Ti, To>(const Array<Ti> &in, const int dim,  \
diff --git a/src/backend/oneapi/regions.cpp b/src/backend/oneapi/regions.cpp
index 73ebccc46e..983b3b9000 100644
--- a/src/backend/oneapi/regions.cpp
+++ b/src/backend/oneapi/regions.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -39,3 +40,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/regions.hpp b/src/backend/oneapi/regions.hpp
index 585f7e6e14..34e90f2918 100644
--- a/src/backend/oneapi/regions.hpp
+++ b/src/backend/oneapi/regions.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 Array<T> regions(const Array<char> &in, af_connectivity connectivity);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/reorder.cpp b/src/backend/oneapi/reorder.cpp
index fc5c7f26a7..d62db984e9 100644
--- a/src/backend/oneapi/reorder.cpp
+++ b/src/backend/oneapi/reorder.cpp
@@ -14,8 +14,9 @@
 #include <reorder.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims) {
@@ -47,3 +48,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/reorder.hpp b/src/backend/oneapi/reorder.hpp
index eb2cc8ef9c..a587bc9de3 100644
--- a/src/backend/oneapi/reorder.hpp
+++ b/src/backend/oneapi/reorder.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> reorder(const Array<T> &in, const af::dim4 &rdims);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/reshape.cpp b/src/backend/oneapi/reshape.cpp
index 87a7e7d28e..768a167480 100644
--- a/src/backend/oneapi/reshape.cpp
+++ b/src/backend/oneapi/reshape.cpp
@@ -14,8 +14,9 @@
 #include <common/half.hpp>
 // #include <kernel/memcopy.hpp>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename inType, typename outType>
@@ -79,3 +80,4 @@ INSTANTIATE_COMPLEX(cfloat)
 INSTANTIATE_COMPLEX(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/resize.cpp b/src/backend/oneapi/resize.cpp
index 89bdea49b1..6d8d3307ab 100644
--- a/src/backend/oneapi/resize.cpp
+++ b/src/backend/oneapi/resize.cpp
@@ -14,6 +14,7 @@
 #include <af/dim4.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
@@ -46,3 +47,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/resize.hpp b/src/backend/oneapi/resize.hpp
index 77b5972588..4cd7aa39aa 100644
--- a/src/backend/oneapi/resize.hpp
+++ b/src/backend/oneapi/resize.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
                 const af_interp_type method);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/rotate.cpp b/src/backend/oneapi/rotate.cpp
index 37f8abbe00..b5cd2fa6e3 100644
--- a/src/backend/oneapi/rotate.cpp
+++ b/src/backend/oneapi/rotate.cpp
@@ -12,6 +12,7 @@
 
 // #include <kernel/rotate.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
@@ -56,3 +57,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/rotate.hpp b/src/backend/oneapi/rotate.hpp
index 369bbd2521..ee6114da0d 100644
--- a/src/backend/oneapi/rotate.hpp
+++ b/src/backend/oneapi/rotate.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/scalar.hpp b/src/backend/oneapi/scalar.hpp
index fee814f9f2..9e5ac25704 100644
--- a/src/backend/oneapi/scalar.hpp
+++ b/src/backend/oneapi/scalar.hpp
@@ -12,6 +12,7 @@
 #include <math.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -21,3 +22,4 @@ Array<T> createScalarNode(const dim4 &size, const T val) {
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/scan.cpp b/src/backend/oneapi/scan.cpp
index 81b7494d68..f7151ce076 100644
--- a/src/backend/oneapi/scan.cpp
+++ b/src/backend/oneapi/scan.cpp
@@ -13,6 +13,7 @@
 #include <kernel/scan_dim.hpp>
 #include <kernel/scan_first.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
@@ -54,3 +55,4 @@ INSTANTIATE_SCAN_ALL(af_mul_t)
 INSTANTIATE_SCAN_ALL(af_min_t)
 INSTANTIATE_SCAN_ALL(af_max_t)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/scan.hpp b/src/backend/oneapi/scan.hpp
index 5e8508a8da..59522a8c4b 100644
--- a/src/backend/oneapi/scan.hpp
+++ b/src/backend/oneapi/scan.hpp
@@ -10,7 +10,9 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<af_op_t op, typename Ti, typename To>
 Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan = true);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/scan_by_key.cpp b/src/backend/oneapi/scan_by_key.cpp
index 555817819c..dabca1815a 100644
--- a/src/backend/oneapi/scan_by_key.cpp
+++ b/src/backend/oneapi/scan_by_key.cpp
@@ -16,6 +16,7 @@
 // #include <kernel/scan_dim_by_key.hpp>
 // #include <kernel/scan_first_by_key.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
@@ -64,3 +65,4 @@ INSTANTIATE_SCAN_BY_KEY_OP(af_mul_t)
 INSTANTIATE_SCAN_BY_KEY_OP(af_min_t)
 INSTANTIATE_SCAN_BY_KEY_OP(af_max_t)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/scan_by_key.hpp b/src/backend/oneapi/scan_by_key.hpp
index 556d59f922..7512f479c1 100644
--- a/src/backend/oneapi/scan_by_key.hpp
+++ b/src/backend/oneapi/scan_by_key.hpp
@@ -10,8 +10,10 @@
 #include <Array.hpp>
 #include <optypes.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<af_op_t op, typename Ti, typename Tk, typename To>
 Array<To> scan(const Array<Tk>& key, const Array<Ti>& in, const int dim,
                bool inclusive_scan = true);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/select.cpp b/src/backend/oneapi/select.cpp
index beea59a771..08458b9778 100644
--- a/src/backend/oneapi/select.cpp
+++ b/src/backend/oneapi/select.cpp
@@ -20,12 +20,13 @@
 
 using af::dim4;
 
-using common::half;
-using common::NaryNode;
+using arrayfire::common::half;
+using arrayfire::common::NaryNode;
 
 using std::make_shared;
 using std::max;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
@@ -141,3 +142,4 @@ INSTANTIATE(half);
 
 #undef INSTANTIATE
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/select.hpp b/src/backend/oneapi/select.hpp
index 00d0eb06c6..754a0ec44d 100644
--- a/src/backend/oneapi/select.hpp
+++ b/src/backend/oneapi/select.hpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
@@ -27,3 +28,4 @@ template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const T &b_val, const af::dim4 &odims);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/set.cpp b/src/backend/oneapi/set.cpp
index 01fa0a6bcf..a76363f10b 100644
--- a/src/backend/oneapi/set.cpp
+++ b/src/backend/oneapi/set.cpp
@@ -15,6 +15,7 @@
 #include <sort.hpp>
 #include <af/dim4.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 using af::dim4;
 
@@ -158,3 +159,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/set.hpp b/src/backend/oneapi/set.hpp
index 7836873639..85d3386489 100644
--- a/src/backend/oneapi/set.hpp
+++ b/src/backend/oneapi/set.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted);
@@ -21,3 +22,4 @@ template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/shift.cpp b/src/backend/oneapi/shift.cpp
index e4ada40a5c..d72477c770 100644
--- a/src/backend/oneapi/shift.cpp
+++ b/src/backend/oneapi/shift.cpp
@@ -14,13 +14,14 @@
 #include <traits.hpp>
 
 using af::dim4;
-using common::Node_ptr;
-using common::ShiftNodeBase;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::ShiftNodeBase;
 using std::array;
 using std::make_shared;
 using std::static_pointer_cast;
 using std::string;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -71,3 +72,4 @@ INSTANTIATE(char)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/shift.hpp b/src/backend/oneapi/shift.hpp
index f236018321..1c808479d0 100644
--- a/src/backend/oneapi/shift.hpp
+++ b/src/backend/oneapi/shift.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sift.cpp b/src/backend/oneapi/sift.cpp
index 9197c23d14..72dccab12d 100644
--- a/src/backend/oneapi/sift.cpp
+++ b/src/backend/oneapi/sift.cpp
@@ -16,6 +16,7 @@
 using af::dim4;
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename convAccT>
@@ -73,3 +74,4 @@ INSTANTIATE(float, float)
 INSTANTIATE(double, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sift.hpp b/src/backend/oneapi/sift.hpp
index 5c2a33dca6..ae656a73fd 100644
--- a/src/backend/oneapi/sift.hpp
+++ b/src/backend/oneapi/sift.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename convAccT>
@@ -23,4 +24,5 @@ unsigned sift(Array<float>& x, Array<float>& y, Array<float>& score,
               const float img_scale, const float feature_ratio,
               const bool compute_GLOH);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sobel.cpp b/src/backend/oneapi/sobel.cpp
index 7d722e7f4d..54ba117be7 100644
--- a/src/backend/oneapi/sobel.cpp
+++ b/src/backend/oneapi/sobel.cpp
@@ -15,6 +15,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename Ti, typename To>
@@ -46,3 +47,4 @@ INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sobel.hpp b/src/backend/oneapi/sobel.hpp
index 94d3e06879..44e2356dc5 100644
--- a/src/backend/oneapi/sobel.hpp
+++ b/src/backend/oneapi/sobel.hpp
@@ -10,10 +10,12 @@
 #include <Array.hpp>
 #include <utility>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename Ti, typename To>
 std::pair<Array<To>, Array<To>> sobelDerivatives(const Array<Ti> &img,
                                                  const unsigned &ker_size);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/solve.cpp b/src/backend/oneapi/solve.cpp
index ee662de210..a4082c0d1f 100644
--- a/src/backend/oneapi/solve.cpp
+++ b/src/backend/oneapi/solve.cpp
@@ -32,6 +32,7 @@ using cl::Buffer;
 using std::min;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -331,9 +332,11 @@ INSTANTIATE_SOLVE(cfloat)
 INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -361,5 +364,6 @@ INSTANTIATE_SOLVE(double)
 INSTANTIATE_SOLVE(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/solve.hpp b/src/backend/oneapi/solve.hpp
index 330605aa35..acea9327b4 100644
--- a/src/backend/oneapi/solve.hpp
+++ b/src/backend/oneapi/solve.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> solveLU(const Array<T> &a, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options = AF_MAT_NONE);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
index b5e0eb73fd..599d23c896 100644
--- a/src/backend/oneapi/sort.cpp
+++ b/src/backend/oneapi/sort.cpp
@@ -16,6 +16,7 @@
 #include <sort.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
@@ -64,3 +65,4 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sort.hpp b/src/backend/oneapi/sort.hpp
index ae7fdc9e6a..73512ed973 100644
--- a/src/backend/oneapi/sort.hpp
+++ b/src/backend/oneapi/sort.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sort_by_key.cpp b/src/backend/oneapi/sort_by_key.cpp
index f2a140c338..f7b5beca91 100644
--- a/src/backend/oneapi/sort_by_key.cpp
+++ b/src/backend/oneapi/sort_by_key.cpp
@@ -16,6 +16,7 @@
 #include <sort_by_key.hpp>
 #include <stdexcept>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
@@ -53,3 +54,4 @@ INSTANTIATE1(uchar)
 INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sort_by_key.hpp b/src/backend/oneapi/sort_by_key.hpp
index 2ba2c67ba3..665fdccaca 100644
--- a/src/backend/oneapi/sort_by_key.hpp
+++ b/src/backend/oneapi/sort_by_key.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
                  const Array<Tv> &ival, const unsigned dim, bool isAscending);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sort_index.cpp b/src/backend/oneapi/sort_index.cpp
index 6600db9f7c..c0df0fb9de 100644
--- a/src/backend/oneapi/sort_index.cpp
+++ b/src/backend/oneapi/sort_index.cpp
@@ -18,8 +18,9 @@
 #include <sort_index.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
@@ -77,3 +78,4 @@ INSTANTIATE(uintl)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sort_index.hpp b/src/backend/oneapi/sort_index.hpp
index 2e7f262e62..30d6db07b9 100644
--- a/src/backend/oneapi/sort_index.hpp
+++ b/src/backend/oneapi/sort_index.hpp
@@ -9,8 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void sort_index(Array<T> &okey, Array<unsigned> &oval, const Array<T> &in,
                 const unsigned dim, bool isAscending);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse.cpp b/src/backend/oneapi/sparse.cpp
index 18e1d48e81..37e5826430 100644
--- a/src/backend/oneapi/sparse.cpp
+++ b/src/backend/oneapi/sparse.cpp
@@ -26,6 +26,7 @@
 #include <stdexcept>
 #include <string>
 
+namespace arrayfire {
 namespace oneapi {
 
 using namespace common;
@@ -225,3 +226,4 @@ INSTANTIATE_SPARSE(cdouble)
 #undef INSTANTIATE_SPARSE
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse.hpp b/src/backend/oneapi/sparse.hpp
index 3958dcea3b..e7440fc405 100644
--- a/src/backend/oneapi/sparse.hpp
+++ b/src/backend/oneapi/sparse.hpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <common/SparseArray.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, af_storage stype>
@@ -25,3 +26,4 @@ common::SparseArray<T> sparseConvertStorageToStorage(
     const common::SparseArray<T> &in);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse_arith.cpp b/src/backend/oneapi/sparse_arith.cpp
index e39bed14e4..856d300553 100644
--- a/src/backend/oneapi/sparse_arith.cpp
+++ b/src/backend/oneapi/sparse_arith.cpp
@@ -25,6 +25,7 @@
 #include <scan.hpp>
 #include <where.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 using namespace common;
@@ -178,3 +179,4 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse_arith.hpp b/src/backend/oneapi/sparse_arith.hpp
index 589620c314..b35d4963e1 100644
--- a/src/backend/oneapi/sparse_arith.hpp
+++ b/src/backend/oneapi/sparse_arith.hpp
@@ -12,6 +12,7 @@
 #include <optypes.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 // These two functions cannot be overloaded by return type.
@@ -28,3 +29,4 @@ template<typename T, af_op_t op>
 common::SparseArray<T> arithOp(const common::SparseArray<T> &lhs,
                                const common::SparseArray<T> &rhs);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse_blas.cpp b/src/backend/oneapi/sparse_blas.cpp
index b9fcd6fb52..6d414c8ee0 100644
--- a/src/backend/oneapi/sparse_blas.cpp
+++ b/src/backend/oneapi/sparse_blas.cpp
@@ -30,6 +30,7 @@
 #include <cpu/cpu_sparse_blas.hpp>
 #endif  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 using namespace common;
@@ -100,3 +101,4 @@ INSTANTIATE_SPARSE(cfloat)
 INSTANTIATE_SPARSE(cdouble)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse_blas.hpp b/src/backend/oneapi/sparse_blas.hpp
index d187a4422a..a5acc6ffc0 100644
--- a/src/backend/oneapi/sparse_blas.hpp
+++ b/src/backend/oneapi/sparse_blas.hpp
@@ -11,10 +11,12 @@
 #include <common/SparseArray.hpp>
 #include <sparse.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhs,
                 af_mat_prop optLhs, af_mat_prop optRhs);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sum.cpp b/src/backend/oneapi/sum.cpp
index 30850564e8..fb20ce6121 100644
--- a/src/backend/oneapi/sum.cpp
+++ b/src/backend/oneapi/sum.cpp
@@ -10,8 +10,9 @@
 #include <common/half.hpp>
 #include "reduce_impl.hpp"
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 // sum
 INSTANTIATE(af_add_t, float, float)
@@ -37,3 +38,4 @@ INSTANTIATE(af_add_t, ushort, float)
 INSTANTIATE(af_add_t, half, half)
 INSTANTIATE(af_add_t, half, float)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/surface.cpp b/src/backend/oneapi/surface.cpp
index 38ad3388f5..2a8d604772 100644
--- a/src/backend/oneapi/surface.cpp
+++ b/src/backend/oneapi/surface.cpp
@@ -17,12 +17,13 @@ using af::dim4;
 // using cl::Memory;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface) {
     ONEAPI_NOT_SUPPORTED("copy_surface Not supported");
-    // ForgeModule &_ = graphics::forgePlugin();
+    // ForgeModule &_ = common::forgePlugin();
     // if (isGLSharingSupported()) {
     //     CheckGL("Begin OpenCL resource copy");
     //     const cl::Buffer *d_P = P.get();
@@ -82,3 +83,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/surface.hpp b/src/backend/oneapi/surface.hpp
index 0c4110fd36..2d868301e0 100644
--- a/src/backend/oneapi/surface.hpp
+++ b/src/backend/oneapi/surface.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_surface(const Array<T> &P, fg_surface surface);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/susan.cpp b/src/backend/oneapi/susan.cpp
index 94173b3e4c..437259681c 100644
--- a/src/backend/oneapi/susan.cpp
+++ b/src/backend/oneapi/susan.cpp
@@ -17,6 +17,7 @@
 using af::features;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -74,3 +75,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/susan.hpp b/src/backend/oneapi/susan.hpp
index 8510117dea..1a0c4ffe8c 100644
--- a/src/backend/oneapi/susan.hpp
+++ b/src/backend/oneapi/susan.hpp
@@ -12,6 +12,7 @@
 
 using af::features;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -21,4 +22,5 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out,
                const float geom_thr, const float feature_ratio,
                const unsigned edge);
 
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
index 8a886983f9..fad4c2f35b 100644
--- a/src/backend/oneapi/svd.cpp
+++ b/src/backend/oneapi/svd.cpp
@@ -24,6 +24,7 @@
 #include <magma/magma_helper.h>
 #include <platform.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename Tr>
@@ -235,9 +236,11 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #else  // WITH_LINEAR_ALGEBRA
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T, typename Tr>
@@ -264,5 +267,6 @@ INSTANTIATE(cfloat, float)
 INSTANTIATE(cdouble, double)
 
 }  // namespace oneapi
+}  // namespace arrayfire
 
 #endif  // WITH_LINEAR_ALGEBRA
diff --git a/src/backend/oneapi/svd.hpp b/src/backend/oneapi/svd.hpp
index 297c899be6..4b001d2ad0 100644
--- a/src/backend/oneapi/svd.hpp
+++ b/src/backend/oneapi/svd.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T, typename Tr>
 void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
@@ -16,3 +17,4 @@ void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in);
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/tile.cpp b/src/backend/oneapi/tile.cpp
index 384c0f0710..5f2c38c475 100644
--- a/src/backend/oneapi/tile.cpp
+++ b/src/backend/oneapi/tile.cpp
@@ -14,8 +14,9 @@
 #include <common/half.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
@@ -49,3 +50,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/tile.hpp b/src/backend/oneapi/tile.hpp
index 0ad5a9869a..f11e2aa711 100644
--- a/src/backend/oneapi/tile.hpp
+++ b/src/backend/oneapi/tile.hpp
@@ -9,7 +9,10 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> tile(const Array<T> &in, const af::dim4 &tileDims);
-}
+
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/topk.cpp b/src/backend/oneapi/topk.cpp
index 8d963ac4c2..35c0b66975 100644
--- a/src/backend/oneapi/topk.cpp
+++ b/src/backend/oneapi/topk.cpp
@@ -22,7 +22,7 @@
 
 // using cl::Buffer;
 // using cl::Event;
-using common::half;
+using arrayfire::common::half;
 
 using std::iota;
 using std::min;
@@ -30,6 +30,7 @@ using std::partial_sort_copy;
 using std::transform;
 using std::vector;
 
+namespace arrayfire {
 namespace oneapi {
 vector<af_index_t> indexForTopK(const int k) {
     af_index_t idx;
@@ -181,3 +182,4 @@ INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(half)
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/topk.hpp b/src/backend/oneapi/topk.hpp
index 8390733751..fa816b9ca7 100644
--- a/src/backend/oneapi/topk.hpp
+++ b/src/backend/oneapi/topk.hpp
@@ -7,8 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void topk(Array<T>& keys, Array<unsigned>& vals, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order);
-}
+
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/traits.hpp b/src/backend/oneapi/traits.hpp
index 61fab0663c..57e1949082 100644
--- a/src/backend/oneapi/traits.hpp
+++ b/src/backend/oneapi/traits.hpp
@@ -23,17 +23,17 @@ static bool iscplx() {
     return false;
 }
 template<>
-inline bool iscplx<oneapi::cfloat>() {
+inline bool iscplx<arrayfire::oneapi::cfloat>() {
     return true;
 }
 template<>
-inline bool iscplx<oneapi::cdouble>() {
+inline bool iscplx<arrayfire::oneapi::cdouble>() {
     return true;
 }
 
 template<typename T>
 inline std::string scalar_to_option(const T &val) {
-    using namespace common;
+    using namespace arrayfire::common;
     using namespace std;
     return to_string(+val);
 }
diff --git a/src/backend/oneapi/transform.cpp b/src/backend/oneapi/transform.cpp
index 732ba39cc0..720dfa1654 100644
--- a/src/backend/oneapi/transform.cpp
+++ b/src/backend/oneapi/transform.cpp
@@ -12,6 +12,7 @@
 // #include <kernel/transform.hpp>
 #include <err_oneapi.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -59,3 +60,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/transform.hpp b/src/backend/oneapi/transform.hpp
index 4433518055..ea62f261b0 100644
--- a/src/backend/oneapi/transform.hpp
+++ b/src/backend/oneapi/transform.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/transpose.cpp b/src/backend/oneapi/transpose.cpp
index cef137b561..580573125f 100644
--- a/src/backend/oneapi/transpose.cpp
+++ b/src/backend/oneapi/transpose.cpp
@@ -15,8 +15,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -50,3 +51,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/transpose.hpp b/src/backend/oneapi/transpose.hpp
index 16056bb6c5..88ca4abce0 100644
--- a/src/backend/oneapi/transpose.hpp
+++ b/src/backend/oneapi/transpose.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -18,3 +19,4 @@ template<typename T>
 void transpose_inplace(Array<T> &in, const bool conjugate);
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/transpose_inplace.cpp b/src/backend/oneapi/transpose_inplace.cpp
index 52a62d7837..ddbb14e419 100644
--- a/src/backend/oneapi/transpose_inplace.cpp
+++ b/src/backend/oneapi/transpose_inplace.cpp
@@ -15,8 +15,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -47,3 +48,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/triangle.cpp b/src/backend/oneapi/triangle.cpp
index afe0c27b7f..e418c15b93 100644
--- a/src/backend/oneapi/triangle.cpp
+++ b/src/backend/oneapi/triangle.cpp
@@ -16,8 +16,9 @@
 #include <af/dim4.hpp>
 
 using af::dim4;
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -54,3 +55,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(half)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/triangle.hpp b/src/backend/oneapi/triangle.hpp
index 0dc1a48a11..d56a26c126 100644
--- a/src/backend/oneapi/triangle.hpp
+++ b/src/backend/oneapi/triangle.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void triangle(Array<T> &out, const Array<T> &in, const bool is_upper,
@@ -18,3 +19,4 @@ template<typename T>
 Array<T> triangle(const Array<T> &in, const bool is_upper,
                   const bool is_unit_diag);
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
index 10bd0e64c7..dacfd85f01 100644
--- a/src/backend/oneapi/types.hpp
+++ b/src/backend/oneapi/types.hpp
@@ -20,6 +20,7 @@
 #include <complex>
 #include <string>
 
+namespace arrayfire {
 namespace common {
 /// This is a CPU based half which need to be converted into floats before they
 /// are used
@@ -33,7 +34,9 @@ struct kernel_type<common::half> {
     using compute = float;
 };
 }  // namespace common
+}  // namespace arrayfire
 
+namespace arrayfire {
 namespace oneapi {
 using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
@@ -130,7 +133,7 @@ inline const char *getFullName<cdouble>() {
 #if 0
 template<typename... ARGS>
 AF_CONSTEXPR const char *getTypeBuildDefinition() {
-    using common::half;
+    using arrayfire::common::half;
     using std::any_of;
     using std::array;
     using std::begin;
@@ -161,3 +164,4 @@ AF_CONSTEXPR const char *getTypeBuildDefinition() {
 #endif
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/unary.hpp b/src/backend/oneapi/unary.hpp
index 0e8a267c07..2c9ccf54ce 100644
--- a/src/backend/oneapi/unary.hpp
+++ b/src/backend/oneapi/unary.hpp
@@ -14,6 +14,7 @@
 #include <optypes.hpp>
 #include <af/traits.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<af_op_t op>
@@ -78,8 +79,8 @@ UNARY_DECL(bitnot, "__bitnot")
 
 template<typename T, af_op_t op>
 Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using common::Node;
-    using common::Node_ptr;
+    using arrayfire::common::Node;
+    using arrayfire::common::Node_ptr;
     using std::array;
 
     auto createUnary = [](array<Node_ptr, 1> &operands) {
@@ -95,7 +96,7 @@ Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 
 template<typename T, af_op_t op>
 Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
-    using common::Node_ptr;
+    using arrayfire::common::Node_ptr;
 
     auto createUnary = [](std::array<Node_ptr, 1> &operands) {
         return Node_ptr(new common::UnaryNode(
@@ -109,3 +110,4 @@ Array<char> checkOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
 }
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/unwrap.cpp b/src/backend/oneapi/unwrap.cpp
index bfb21aef17..15d60afe5d 100644
--- a/src/backend/oneapi/unwrap.cpp
+++ b/src/backend/oneapi/unwrap.cpp
@@ -14,8 +14,9 @@
 #include <unwrap.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -60,3 +61,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/unwrap.hpp b/src/backend/oneapi/unwrap.hpp
index beab1dca4c..9977e99af4 100644
--- a/src/backend/oneapi/unwrap.hpp
+++ b/src/backend/oneapi/unwrap.hpp
@@ -9,9 +9,11 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> unwrap(const Array<T> &in, const dim_t wx, const dim_t wy,
                 const dim_t sx, const dim_t sy, const dim_t px, const dim_t py,
                 const dim_t dx, const dim_t dy, const bool is_column);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/vector_field.cpp b/src/backend/oneapi/vector_field.cpp
index d42c86c270..92f310698a 100644
--- a/src/backend/oneapi/vector_field.cpp
+++ b/src/backend/oneapi/vector_field.cpp
@@ -14,6 +14,7 @@
 
 using af::dim4;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -33,3 +34,4 @@ INSTANTIATE(ushort)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/vector_field.hpp b/src/backend/oneapi/vector_field.hpp
index 2c2a9b565b..b6bf83a52e 100644
--- a/src/backend/oneapi/vector_field.hpp
+++ b/src/backend/oneapi/vector_field.hpp
@@ -10,9 +10,11 @@
 #include <Array.hpp>
 #include <common/graphics_common.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 void copy_vector_field(const Array<T> &points, const Array<T> &directions,
                        fg_vector_field vfield);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/where.cpp b/src/backend/oneapi/where.cpp
index 2965cbe883..bc9e45a515 100644
--- a/src/backend/oneapi/where.cpp
+++ b/src/backend/oneapi/where.cpp
@@ -14,6 +14,7 @@
 #include <af/dim4.hpp>
 #include <complex>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -40,3 +41,4 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/where.hpp b/src/backend/oneapi/where.hpp
index a63ca73cb9..e4b1b0b87f 100644
--- a/src/backend/oneapi/where.hpp
+++ b/src/backend/oneapi/where.hpp
@@ -9,7 +9,9 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<uint> where(const Array<T>& in);
-}
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/wrap.cpp b/src/backend/oneapi/wrap.cpp
index b00b61efef..1400db07f0 100644
--- a/src/backend/oneapi/wrap.cpp
+++ b/src/backend/oneapi/wrap.cpp
@@ -17,8 +17,9 @@
 #include <wrap.hpp>
 #include <stdexcept>
 
-using common::half;
+using arrayfire::common::half;
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -73,3 +74,4 @@ INSTANTIATE(half)
 #undef INSTANTIATE
 
 }  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/wrap.hpp b/src/backend/oneapi/wrap.hpp
index ae831a9bb1..245632cbca 100644
--- a/src/backend/oneapi/wrap.hpp
+++ b/src/backend/oneapi/wrap.hpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
@@ -22,3 +23,4 @@ Array<T> wrap_dilated(const Array<T> &in, const dim_t ox, const dim_t oy,
                       const dim_t sy, const dim_t px, const dim_t py,
                       const dim_t dx, const dim_t dy, const bool is_column);
 }  // namespace oneapi
+}  // namespace arrayfire

From ab1027dbfbfbb70a1ab30c7a5b46f7d9422c95bd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Dec 2022 16:50:01 -0500
Subject: [PATCH 2358/2677] Fix af_spdlog target for non-header-only builds

---
 CMakeLists.txt | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 440f28ae18..96498f9a2d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -261,16 +261,20 @@ else()
   )
   add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
-  target_include_directories(af_spdlog SYSTEM INTERFACE "${${spdlog_prefix}_SOURCE_DIR}/include")
   if(TARGET fmt::fmt)
     set_target_properties(af_spdlog
       PROPERTIES
         INTERFACE_COMPILE_DEFINITIONS "SPDLOG_FMT_EXTERNAL")
   endif()
+
   if(AF_WITH_SPDLOG_HEADER_ONLY)
     set_target_properties(af_spdlog
       PROPERTIES
-        INTERFACE_COMPILE_DEFINITIONS "$<TARGET_PROPERTY:af_spdlog,INTERFACE_COMPILE_DEFINITIONS>;SPDLOG_HEADER_ONLY")
+        INTERFACE_LINK_LIBRARIES "spdlog_header_only")
+  else()
+    set_target_properties(af_spdlog
+      PROPERTIES
+        INTERFACE_LINK_LIBRARIES "spdlog")
   endif()
 endif()
 

From a7d772f20fce330c00e258619c3b0b27e9bf7de7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Dec 2022 16:56:33 -0500
Subject: [PATCH 2359/2677] Make CUDA libraries for dynamic linking private

---
 src/backend/cuda/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 5e0119d93d..aa9f3fc037 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -588,7 +588,7 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
 
 else()
   target_link_libraries(afcuda
-    PUBLIC
+    PRIVATE
       ${CUDA_CUBLAS_LIBRARIES}
       ${CUDA_CUFFT_LIBRARIES}
       ${CUDA_cusolver_LIBRARY}

From 529e98b49c40131173f261c071e3fcdfe482742e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 31 Dec 2022 16:39:19 -0500
Subject: [PATCH 2360/2677] Add Version class to manage external version
 printing and conparisons

---
 src/backend/common/ArrayFireTypesIO.hpp |  52 ++++++++++++
 src/backend/common/CMakeLists.txt       |   1 +
 src/backend/common/DependencyModule.cpp |  55 +++++++-----
 src/backend/common/DependencyModule.hpp |  18 ++--
 src/backend/common/Version.hpp          |  76 +++++++++++++++++
 src/backend/common/util.cpp             |   5 --
 src/backend/common/util.hpp             |   2 -
 src/backend/cuda/convolveNN.cpp         |   4 +-
 src/backend/cuda/cudnn.cpp              |   4 +-
 src/backend/cuda/cudnnModule.cpp        | 107 ++++++++++++++----------
 src/backend/cuda/cudnnModule.hpp        |   5 +-
 src/backend/cuda/cusparseModule.cpp     |  18 ++++
 src/backend/cuda/device_manager.cpp     |  44 +++++-----
 13 files changed, 282 insertions(+), 109 deletions(-)
 create mode 100644 src/backend/common/Version.hpp

diff --git a/src/backend/common/ArrayFireTypesIO.hpp b/src/backend/common/ArrayFireTypesIO.hpp
index 234df93b43..2d6b514a3e 100644
--- a/src/backend/common/ArrayFireTypesIO.hpp
+++ b/src/backend/common/ArrayFireTypesIO.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+#include <common/Version.hpp>
 #include <spdlog/fmt/bundled/ranges.h>
 #include <spdlog/fmt/ostr.h>
 #include <af/seq.h>
@@ -35,3 +36,54 @@ struct fmt::formatter<af_seq> {
         return format_to(ctx.out(), "({} -({})-> {})", p.begin, p.step, p.end);
     }
 };
+
+template<>
+struct fmt::formatter<arrayfire::common::Version> {
+    // show major version
+    bool show_major = false;
+    // show minor version
+    bool show_minor = false;
+    // show patch version
+    bool show_patch = false;
+
+    // Parses format specifications of the form ['M' | 'm' | 'p'].
+    constexpr auto parse(format_parse_context& ctx) -> decltype(ctx.begin()) {
+        auto it = ctx.begin(), end = ctx.end();
+        if (it == end || *it == '}') {
+            show_major = show_minor = show_patch = true;
+            return it;
+        }
+        do {
+            switch (*it) {
+                case 'M': show_major = true; break;
+                case 'm': show_minor = true; break;
+                case 'p': show_patch = true; break;
+                default: throw format_error("invalid format");
+            }
+            ++it;
+        } while (it != end && *it != '}');
+        return ctx.begin();
+    }
+
+    // Formats the point p using the parsed format specification (presentation)
+    // stored in this formatter.
+    template<typename FormatContext>
+    auto format(const arrayfire::common::Version& ver, FormatContext& ctx)
+        -> decltype(ctx.out()) {
+        // ctx.out() is an output iterator to write to.
+        // if (ver.major == -1) return format_to(ctx.out(), "N/A");
+        if (ver.minor == -1) show_minor = false;
+        if (ver.patch == -1) show_patch = false;
+        if (show_major && !show_minor && !show_patch) {
+            return format_to(ctx.out(), "{}", ver.major);
+        }
+        if (show_major && show_minor && !show_patch) {
+            return format_to(ctx.out(), "{}.{}", ver.major, ver.minor);
+        }
+        if (show_major && show_minor && show_patch) {
+            return format_to(ctx.out(), "{}.{}.{}", ver.major, ver.minor,
+                             ver.patch);
+        }
+        return ctx.out();
+    }
+};
diff --git a/src/backend/common/CMakeLists.txt b/src/backend/common/CMakeLists.txt
index 795e5df44c..b33ea2598e 100644
--- a/src/backend/common/CMakeLists.txt
+++ b/src/backend/common/CMakeLists.txt
@@ -45,6 +45,7 @@ target_sources(afcommon_interface
     ${CMAKE_CURRENT_SOURCE_DIR}/SparseArray.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateArg.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/TemplateTypename.hpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/Version.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/blas_headers.hpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cast.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/cast.hpp
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index 6511c54e67..d8552e450d 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -7,8 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/ArrayFireTypesIO.hpp>
 #include <common/DependencyModule.hpp>
 #include <common/Logger.hpp>
+#include <common/Version.hpp>
 #include <common/module_loading.hpp>
 
 #include <algorithm>
@@ -26,8 +28,6 @@ using std::string;
 using std::to_string;
 using std::vector;
 
-constexpr Version NullVersion{-1, -1, -1};
-
 #ifdef OS_WIN
 #include <Windows.h>
 
@@ -35,7 +35,7 @@ static const char* librarySuffix = ".dll";
 
 namespace {
 vector<string> libNames(const std::string& name, const string& suffix,
-                        const Version& ver = NullVersion) {
+                        const Version& ver = arrayfire::common::NullVersion) {
     UNUSED(ver);  // Windows DLL files are not version suffixed
     return {name + suffix + librarySuffix};
 }
@@ -48,11 +48,11 @@ static const char* libraryPrefix = "lib";
 
 namespace {
 vector<string> libNames(const std::string& name, const string& suffix,
-                        const Version& ver = NullVersion) {
+                        const Version& ver = arrayfire::common::NullVersion) {
     UNUSED(suffix);
     const string noVerName = libraryPrefix + name + librarySuffix;
-    if (ver != NullVersion) {
-        const string infix = "." + to_string(std::get<0>(ver)) + ".";
+    if (ver != arrayfire::common::NullVersion) {
+        const string infix = "." + to_string(ver.major) + ".";
         return {libraryPrefix + name + infix + librarySuffix, noVerName};
     } else {
         return {noVerName};
@@ -67,15 +67,14 @@ static const char* libraryPrefix = "lib";
 
 namespace {
 vector<string> libNames(const std::string& name, const string& suffix,
-                        const Version& ver = NullVersion) {
+                        const Version& ver = arrayfire::common::NullVersion) {
     UNUSED(suffix);
     const string noVerName = libraryPrefix + name + librarySuffix;
-    if (ver != NullVersion) {
-        const string soname("." + to_string(std::get<0>(ver)));
+    if (ver != arrayfire::common::NullVersion) {
+        const string soname("." + to_string(ver.major));
 
-        const string vsfx = "." + to_string(std::get<0>(ver)) + "." +
-                            to_string(std::get<1>(ver)) + "." +
-                            to_string(std::get<2>(ver));
+        const string vsfx = "." + to_string(ver.major) + "." +
+                            to_string(ver.minor) + "." + to_string(ver.patch);
         return {noVerName + vsfx, noVerName + soname, noVerName};
     } else {
         return {noVerName};
@@ -92,7 +91,9 @@ namespace common {
 
 DependencyModule::DependencyModule(const char* plugin_file_name,
                                    const char** paths)
-    : handle(nullptr), logger(common::loggerFactory("platform")) {
+    : handle(nullptr)
+    , logger(common::loggerFactory("platform"))
+    , version(-1, -1) {
     // TODO(umar): Implement handling of non-standard paths
     UNUSED(paths);
     if (plugin_file_name) {
@@ -107,12 +108,14 @@ DependencyModule::DependencyModule(const char* plugin_file_name,
     }
 }
 
-DependencyModule::DependencyModule(const vector<string>& plugin_base_file_name,
-                                   const vector<string>& suffixes,
-                                   const vector<string>& paths,
-                                   const size_t verListSize,
-                                   const Version* versions)
-    : handle(nullptr), logger(common::loggerFactory("platform")) {
+DependencyModule::DependencyModule(
+    const vector<string>& plugin_base_file_name, const vector<string>& suffixes,
+    const vector<string>& paths, const size_t verListSize,
+    const Version* versions,
+    std::function<Version(const LibHandle&)> versionFunction)
+    : handle(nullptr)
+    , logger(common::loggerFactory("platform"))
+    , version(-1, -1) {
     for (const string& base_name : plugin_base_file_name) {
         for (const string& path : paths) {
             UNUSED(path);
@@ -128,7 +131,12 @@ DependencyModule::DependencyModule(const vector<string>& plugin_base_file_name,
                         AF_TRACE("Attempting to load: {}", fileName);
                         handle = loadLibrary(fileName.c_str());
                         if (handle) {
-                            AF_TRACE("Found: {}", fileName);
+                            if (versionFunction) {
+                                version = versionFunction(handle);
+                                AF_TRACE("Found: {}({})", fileName, version);
+                            } else {
+                                AF_TRACE("Found: {}", fileName);
+                            }
                             return;
                         }
                     }
@@ -138,7 +146,12 @@ DependencyModule::DependencyModule(const vector<string>& plugin_base_file_name,
                 AF_TRACE("Attempting to load: {}", fileNames[0]);
                 handle = loadLibrary(fileNames[0].c_str());
                 if (handle) {
-                    AF_TRACE("Found: {}", fileNames[0]);
+                    if (versionFunction) {
+                        version = versionFunction(handle);
+                        AF_TRACE("Found: {}({})", fileNames[0], version);
+                    } else {
+                        AF_TRACE("Found: {}", fileNames[0]);
+                    }
                     return;
                 }
             }
diff --git a/src/backend/common/DependencyModule.hpp b/src/backend/common/DependencyModule.hpp
index 41cc64569e..6473a4d3bd 100644
--- a/src/backend/common/DependencyModule.hpp
+++ b/src/backend/common/DependencyModule.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/Logger.hpp>
+#include <common/Version.hpp>
 #include <common/defines.hpp>
 #include <common/module_loading.hpp>
 
@@ -25,8 +26,6 @@ class logger;
 namespace arrayfire {
 namespace common {
 
-using Version = std::tuple<int, int, int>;  // major, minor, patch
-
 /// Allows you to create classes which dynamically load dependencies at runtime
 ///
 /// Creates a dependency module which will dynamically load a library
@@ -37,6 +36,7 @@ class DependencyModule {
     LibHandle handle;
     std::shared_ptr<spdlog::logger> logger;
     std::vector<void*> functions;
+    Version version;
 
    public:
     /// Loads the library \p plugin_file_name from the \p paths locations
@@ -47,11 +47,12 @@ class DependencyModule {
     DependencyModule(const char* plugin_file_name,
                      const char** paths = nullptr);
 
-    DependencyModule(const std::vector<std::string>& plugin_base_file_name,
-                     const std::vector<std::string>& suffixes,
-                     const std::vector<std::string>& paths,
-                     const size_t verListSize = 0,
-                     const Version* versions  = nullptr);
+    DependencyModule(
+        const std::vector<std::string>& plugin_base_file_name,
+        const std::vector<std::string>& suffixes,
+        const std::vector<std::string>& paths, const size_t verListSize = 0,
+        const Version* versions                                  = nullptr,
+        std::function<Version(const LibHandle&)> versionFunction = {});
 
     ~DependencyModule() noexcept;
 
@@ -68,6 +69,9 @@ class DependencyModule {
     /// Returns true if all of the symbols for the module were loaded
     bool symbolsLoaded() const noexcept;
 
+    /// Returns the version of the module
+    Version getVersion() const noexcept { return version; }
+
     /// Returns the last error message that occurred because of loading the
     /// library
     static std::string getErrorMessage() noexcept;
diff --git a/src/backend/common/Version.hpp b/src/backend/common/Version.hpp
new file mode 100644
index 0000000000..0b88444222
--- /dev/null
+++ b/src/backend/common/Version.hpp
@@ -0,0 +1,76 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <string>
+
+// Some compilers create these macros in the header. Causes
+// some errors in the Version struct constructor
+#ifdef major
+#undef major
+#endif
+#ifdef minor
+#undef minor
+#endif
+
+namespace arrayfire {
+namespace common {
+struct Version {
+    int major = -1;
+    int minor = -1;
+    int patch = -1;
+
+    /// Checks if the major version is defined before minor and minor is defined
+    /// before patch
+    constexpr static bool validate(int major_, int minor_,
+                                   int patch_) noexcept {
+        return !(major_ < 0 && (minor_ >= 0 || patch_ >= 0)) &&
+               !(minor_ < 0 && patch_ >= 0);
+    }
+
+    constexpr Version(const int ver_major, const int ver_minor = -1,
+                      const int ver_patch = -1) noexcept
+        : major(ver_major), minor(ver_minor), patch(ver_patch) {}
+};
+
+constexpr bool operator==(const Version& lhs, const Version& rhs) {
+    return lhs.major == rhs.major && lhs.minor == rhs.minor &&
+           lhs.patch == rhs.patch;
+}
+
+constexpr bool operator!=(const Version& lhs, const Version& rhs) {
+    return !(lhs == rhs);
+}
+
+constexpr static Version NullVersion{-1, -1, -1};
+
+constexpr bool operator<(const Version& lhs, const Version& rhs) {
+    if (lhs == NullVersion || rhs == NullVersion) return false;
+    if (lhs.major != -1 && rhs.major != -1 && lhs.major < rhs.major)
+        return true;
+    if (lhs.minor != -1 && rhs.minor != -1 && lhs.minor < rhs.minor)
+        return true;
+    if (lhs.patch != -1 && rhs.patch != -1 && lhs.patch < rhs.patch)
+        return true;
+    return false;
+}
+
+inline Version fromCudaVersion(size_t version_int) {
+    return {static_cast<int>(version_int / 1000),
+            static_cast<int>(version_int % 1000) / 10,
+            static_cast<int>(version_int % 10)};
+}
+
+inline std::string int_version_to_string(int version) {
+    return std::to_string(version / 1000) + "." +
+           std::to_string(static_cast<int>((version % 1000) / 10.));
+}
+
+}  // namespace common
+}  // namespace arrayfire
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index a4cc1e2421..2d4a8e5ea0 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -136,11 +136,6 @@ void saveKernel(const string& funcName, const string& jit_ker,
     fclose(f);
 }
 
-string int_version_to_string(int version) {
-    return to_string(version / 1000) + "." +
-           to_string(static_cast<int>((version % 1000) / 10.));
-}
-
 #if defined(OS_WIN)
 string getTemporaryDirectory() {
     DWORD bufSize = 261;  // limit according to GetTempPath documentation
diff --git a/src/backend/common/util.hpp b/src/backend/common/util.hpp
index ce154775f9..8a1ad42838 100644
--- a/src/backend/common/util.hpp
+++ b/src/backend/common/util.hpp
@@ -30,8 +30,6 @@ std::string& ltrim(std::string& s);
 void saveKernel(const std::string& funcName, const std::string& jit_ker,
                 const std::string& ext);
 
-std::string int_version_to_string(int version);
-
 std::string& getCacheDirectory();
 
 bool directoryExists(const std::string& path);
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 47dbe634cb..4988d807f3 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -70,7 +70,7 @@ pair<cudnnConvolutionFwdAlgo_t, size_t> getForwardAlgorithm(
     size_t workspace_bytes = 0;
 
     auto version = getCudnnPlugin().getVersion();
-    if (std::get<0>(version) >= 8) {
+    if (version.major >= 8) {
         int maxAlgoCount = 0;
         CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithmMaxCount(
             cudnn, &maxAlgoCount));
@@ -419,7 +419,7 @@ pair<cudnnConvolutionBwdFilterAlgo_t, size_t> getBackwardFilterAlgorithm(
     size_t workspace_bytes = 0;
 
     auto version = getCudnnPlugin().getVersion();
-    if (std::get<0>(version) >= 8) {
+    if (version.major >= 8) {
         int maxAlgoCount = 0;
         CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
             cudnn, &maxAlgoCount));
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index aa5ffd2db4..b6fd903729 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -238,7 +238,7 @@ cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
     cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
     cudnnConvolutionFwdAlgo_t *algo) {
     auto version = getCudnnPlugin().getVersion();
-    if (std::get<0>(version) < 8) {
+    if (version.major < 8) {
         return getCudnnPlugin().cudnnGetConvolutionForwardAlgorithm(
             handle, xDesc, wDesc, convDesc, yDesc, preference,
             memoryLimitInBytes, algo);
@@ -259,7 +259,7 @@ cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
     cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
     cudnnConvolutionBwdFilterAlgo_t *algo) {
     auto version = getCudnnPlugin().getVersion();
-    if (std::get<0>(version) < 8) {
+    if (version.major < 8) {
         return getCudnnPlugin().cudnnGetConvolutionBackwardFilterAlgorithm(
             handle, xDesc, dyDesc, convDesc, dwDesc, preference,
             memoryLimitInBytes, algo);
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 596516bbe5..657c867156 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -7,10 +7,12 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <cudnnModule.hpp>
+
+#include <common/ArrayFireTypesIO.hpp>
 #include <common/Logger.hpp>
 #include <common/err_common.hpp>
 #include <common/util.hpp>
-#include <cudnnModule.hpp>
 #include <device_manager.hpp>
 #include <utility.hpp>
 
@@ -18,7 +20,7 @@
 #include <string>
 #include <tuple>
 
-using arrayfire::common::int_version_to_string;
+using arrayfire::common::fromCudaVersion;
 using arrayfire::common::Version;
 using std::make_tuple;
 using std::string;
@@ -29,17 +31,17 @@ namespace cuda {
 // clang-format off
 // Latest version from each minor releases are enlisted below
 constexpr std::array<common::Version, 11> cudnnVersions = {
-    make_tuple(8, 0,  1),
-    make_tuple(7, 6,  5),
-    make_tuple(7, 5,  1),
-    make_tuple(7, 4,  2),
-    make_tuple(7, 3,  1),
-    make_tuple(7, 2,  1),
-    make_tuple(7, 1,  4),
-    make_tuple(7, 0,  5),
-    make_tuple(6, 0, 21),
-    make_tuple(5, 1, 10),
-    make_tuple(4, 0,  7)
+    Version(8, 0,  1),
+    Version(7, 6,  5),
+    Version(7, 5,  1),
+    Version(7, 4,  2),
+    Version(7, 3,  1),
+    Version(7, 2,  1),
+    Version(7, 1,  4),
+    Version(7, 0,  5),
+    Version(6, 0, 21),
+    Version(5, 1, 10),
+    Version(4, 0,  7)
 };
 // clang-format on
 
@@ -47,22 +49,32 @@ spdlog::logger* cudnnModule::getLogger() const noexcept {
     return module.getLogger();
 }
 
-auto cudnnVersionComponents(size_t version) {
-    size_t major = version / 1000;
-    size_t minor = (version - (major * 1000)) / 100;
-    size_t patch = (version - (major * 1000) - (minor * 100));
-    return make_tuple(major, minor, patch);
+Version cudnnVersionComponents(size_t version) {
+    int major = static_cast<int>(version / 1000);
+    int minor = static_cast<int>((version - (major * 1000)) / 100);
+    int patch = static_cast<int>(version - (major * 1000) - (minor * 100));
+    return {major, minor, patch};
 }
 
-auto cudaRuntimeVersionComponents(size_t version) {
-    auto major = version / 1000;
-    auto minor = (version - (major * 1000)) / 10;
-    return make_tuple(major, minor);
+Version cudaRuntimeVersionComponents(size_t version) {
+    int major = static_cast<int>(version / 1000);
+    int minor = static_cast<int>((version - (major * 1000)) / 10);
+    int patch =
+        static_cast<int>((version - (major * 1000) - (minor * 10)) / 10);
+    return {major, minor, patch};
+}
+
+Version getCudnnVersion(const LibHandle& handle) {
+    std::function<size_t()> fptr(reinterpret_cast<size_t (*)()>(
+        common::getFunctionPointer(handle, "cudnnGetVersion")));
+    size_t v = fptr();
+
+    return cudnnVersionComponents(v);
 }
 
 cudnnModule::cudnnModule()
-    : module({"cudnn"}, {"", "64_7", "64_8", "64_6", "64_5", "64_4"}, {""},
-             cudnnVersions.size(), cudnnVersions.data()) {
+    : module({"cudnn"}, {"", "64_8", "64_7", "64_6", "64_5", "64_4"}, {""},
+             cudnnVersions.size(), cudnnVersions.data(), getCudnnVersion) {
     if (!module.isLoaded()) {
         AF_TRACE(
             "WARNING: Unable to load cuDNN: {}"
@@ -77,39 +89,41 @@ cudnnModule::cudnnModule()
 
     MODULE_FUNCTION_INIT(cudnnGetVersion);
 
-    int rtmajor, rtminor;
-    size_t cudnn_version          = this->cudnnGetVersion();
-    size_t cudnn_rtversion        = 0;
-    std::tie(major, minor, patch) = cudnnVersionComponents(cudnn_version);
+    size_t cudnn_rtversion_val = 0;
 
-    if (cudnn_version >= 6000) {
-        MODULE_FUNCTION_INIT(cudnnGetCudartVersion);
-        cudnn_rtversion = this->cudnnGetCudartVersion();
-    } else {
+    Version cudnn_version = module.getVersion();
+    if (cudnn_version < Version(6)) {
         AF_TRACE(
-            "Warning: This version of cuDNN({}.{}) does not support "
+            "Warning: This version of cuDNN({}) does not support "
             "cudnnGetCudartVersion. No runtime checks performed.",
-            major, minor);
+            cudnn_version);
+    } else {
+        MODULE_FUNCTION_INIT(cudnnGetCudartVersion);
+        cudnn_rtversion_val = this->cudnnGetCudartVersion();
     }
 
-    std::tie(rtmajor, rtminor) = cudaRuntimeVersionComponents(cudnn_rtversion);
+    Version cudnn_rtversion = cudaRuntimeVersionComponents(cudnn_rtversion_val);
+
+    AF_TRACE("cuDNN Version: {} cuDNN CUDA Runtime: {}", cudnn_version,
+             cudnn_rtversion);
 
-    AF_TRACE("cuDNN Version: {}.{}.{} cuDNN CUDA Runtime: {}.{}", major, minor,
-             patch, rtmajor, rtminor);
+    Version compiled_cudnn_version = fromCudaVersion(CUDNN_VERSION);
 
     // Check to see if the version of cuDNN ArrayFire was compiled against
     // is compatible with the version loaded at runtime
-    if (CUDNN_VERSION <= 6000 && cudnn_version > CUDNN_VERSION) {
+    if (compiled_cudnn_version.major <= 6 &&
+        compiled_cudnn_version < cudnn_version) {
         string error_msg = fmt::format(
             "ArrayFire was compiled with an older version of cuDNN({}.{}) that "
             "does not support the version that was loaded at runtime({}.{}).",
-            CUDNN_MAJOR, CUDNN_MINOR, major, minor);
+            CUDNN_MAJOR, CUDNN_MINOR, cudnn_version.major, cudnn_version.minor);
         AF_ERROR(error_msg, AF_ERR_NOT_SUPPORTED);
     }
 
-    int afcuda_runtime = 0;
-    cudaRuntimeGetVersion(&afcuda_runtime);
-    if (afcuda_runtime != static_cast<int>(cudnn_rtversion)) {
+    int afcuda_runtime_version = 0;
+    cudaRuntimeGetVersion(&afcuda_runtime_version);
+    Version afcuda_runtime = fromCudaVersion(afcuda_runtime_version);
+    if (afcuda_runtime != cudnn_rtversion) {
         getLogger()->warn(
             "WARNING: ArrayFire CUDA Runtime({}) and cuDNN CUDA "
             "Runtime({}) do not match. For maximum compatibility, make sure "
@@ -117,8 +131,7 @@ cudnnModule::cudnnModule()
             // NOTE: the int version formats from CUDA and cuDNN are different
             // so we are using int_version_to_string for the ArrayFire CUDA
             // runtime
-            int_version_to_string(afcuda_runtime),
-            int_version_to_string(cudnn_rtversion));
+            afcuda_runtime, cudnn_rtversion);
     }
 
     MODULE_FUNCTION_INIT(cudnnConvolutionBackwardData);
@@ -139,14 +152,16 @@ cudnnModule::cudnnModule()
     MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterWorkspaceSize);
     MODULE_FUNCTION_INIT(cudnnFindConvolutionForwardAlgorithm);
     MODULE_FUNCTION_INIT(cudnnFindConvolutionBackwardFilterAlgorithm);
-    if (major < 8) {
+    if (cudnn_version.major < 8) {
         MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardAlgorithm);
         MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterAlgorithm);
     }
     MODULE_FUNCTION_INIT(cudnnGetConvolutionNdForwardOutputDim);
     MODULE_FUNCTION_INIT(cudnnSetConvolution2dDescriptor);
     MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor);
-    if (major == 4) { MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor_v4); }
+    if (cudnn_version.major == 4) {
+        MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor_v4);
+    }
     MODULE_FUNCTION_INIT(cudnnSetStream);
     MODULE_FUNCTION_INIT(cudnnSetTensor4dDescriptor);
 
diff --git a/src/backend/cuda/cudnnModule.hpp b/src/backend/cuda/cudnnModule.hpp
index 54c4b3b708..26856f69d7 100644
--- a/src/backend/cuda/cudnnModule.hpp
+++ b/src/backend/cuda/cudnnModule.hpp
@@ -66,7 +66,6 @@ namespace cuda {
 
 class cudnnModule {
     common::DependencyModule module;
-    int major{}, minor{}, patch{};
 
    public:
     cudnnModule();
@@ -102,9 +101,7 @@ class cudnnModule {
     spdlog::logger* getLogger() const noexcept;
 
     /// Returns the version of the cuDNN loaded at runtime
-    std::tuple<int, int, int> getVersion() const noexcept {
-        return std::make_tuple(major, minor, patch);
-    }
+    common::Version getVersion() const noexcept { return module.getVersion(); }
 
     bool isLoaded() const noexcept { return module.isLoaded(); }
 };
diff --git a/src/backend/cuda/cusparseModule.cpp b/src/backend/cuda/cusparseModule.cpp
index bc049fcb01..7d470f00e9 100644
--- a/src/backend/cuda/cusparseModule.cpp
+++ b/src/backend/cuda/cusparseModule.cpp
@@ -8,16 +8,34 @@
  ********************************************************/
 
 #include <cusparseModule.hpp>
+#include <cusparse.hpp>
 
+#include <common/Version.hpp>
 #include <common/err_common.hpp>
 #include <af/defines.h>
 
 #include <cuda.h>
 #include <string>
 
+using arrayfire::common::Version;
+
 namespace arrayfire {
 namespace cuda {
 
+common::Version getCusparseVersion(const LibHandle& handle) {
+    std::function<cusparseStatus_t(libraryPropertyType, int*)> fptr(
+        reinterpret_cast<cusparseStatus_t (*)(libraryPropertyType, int*)>(
+            common::getFunctionPointer(handle, "cusparseGetProperty")));
+
+    int major, minor, patch;
+    CUSPARSE_CHECK(fptr(MAJOR_VERSION, &major));
+    CUSPARSE_CHECK(fptr(MINOR_VERSION, &minor));
+    CUSPARSE_CHECK(fptr(PATCH_LEVEL, &patch));
+
+    Version out{major, minor, patch};
+    return out;
+}
+
 cusparseModule::cusparseModule()
     :
 #ifdef AF_cusparse_STATIC_LINKING
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 5f79b00abf..8e7ca0e7d2 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -7,12 +7,15 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <device_manager.hpp>
+
 #if defined(OS_WIN)
 #include <windows.h>
 #endif
 
 #include <GraphicsResourceManager.hpp>
 #include <build_version.hpp>
+#include <common/ArrayFireTypesIO.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/MemoryManagerBase.hpp>
@@ -21,7 +24,6 @@
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
 #include <cublas_v2.h>  // needed for af/cuda.h
-#include <device_manager.hpp>
 #include <driver.h>
 #include <err_cuda.hpp>
 #include <memory.hpp>
@@ -46,8 +48,8 @@
 #include <thread>
 #include <utility>
 
+using arrayfire::common::fromCudaVersion;
 using arrayfire::common::getEnvVar;
-using arrayfire::common::int_version_to_string;
 using std::begin;
 using std::end;
 using std::find;
@@ -202,7 +204,7 @@ bool checkDeviceWithRuntime(int runtime, pair<int, int> compute) {
                 "create an issue or a pull request on the ArrayFire repository "
                 "to update the Toolkit2MaxCompute array with this version of "
                 "the CUDA Runtime. Continuing.",
-                int_version_to_string(runtime));
+                fromCudaVersion(runtime));
         return true;
     }
 
@@ -264,7 +266,7 @@ void checkAndSetDevMaxCompute(pair<int, int> &computeCapability) {
                     "Please create an issue or a pull request on the ArrayFire "
                     "repository to update the Toolkit2MaxCompute array with "
                     "this version of the CUDA Runtime.",
-                    int_version_to_string(rtCudaVer), originalCompute.first,
+                    fromCudaVersion(rtCudaVer), originalCompute.first,
                     originalCompute.second, computeCapability.first,
                     computeCapability.second, computeCapability.first,
                     computeCapability.second);
@@ -451,14 +453,15 @@ void debugRuntimeCheck(spdlog::logger *logger, int runtime_version,
     // display a message in the trace. Do not throw an error unless this is
     // a debug build
     if (runtime_it == end(CudaToDriverVersion)) {
-        char buf[256];
-        char err_msg[] =
-            "CUDA runtime version(%s) not recognized. Please create an issue "
+        constexpr size_t buf_size = 256;
+        char buf[buf_size];
+        const char *err_msg =
+            "CUDA runtime version({}) not recognized. Please create an issue "
             "or a pull request on the ArrayFire repository to update the "
             "CudaToDriverVersion variable with this version of the CUDA "
             "runtime.\n";
-        snprintf(buf, 256, err_msg,
-                 int_version_to_string(runtime_version).c_str());
+        fmt::format_to_n(buf, buf_size, err_msg,
+                         fromCudaVersion(runtime_version));
         AF_TRACE("{}", buf);
 #ifndef NDEBUG
         AF_ERROR(buf, AF_ERR_RUNTIME);
@@ -471,7 +474,7 @@ void debugRuntimeCheck(spdlog::logger *logger, int runtime_version,
             "array. Please create an issue or a pull request on the ArrayFire "
             "repository to update the CudaToDriverVersion variable with this "
             "version of the CUDA runtime.\n",
-            int_version_to_string(driver_version).c_str());
+            fromCudaVersion(driver_version));
     }
 }
 
@@ -486,17 +489,17 @@ void DeviceManager::checkCudaVsDriverVersion() {
     CUDA_CHECK(cudaRuntimeGetVersion(&runtime));
 
     AF_TRACE("CUDA Driver supports up to CUDA {} ArrayFire CUDA Runtime {}",
-             int_version_to_string(driver), int_version_to_string(runtime));
+             fromCudaVersion(driver), fromCudaVersion(runtime));
 
     debugRuntimeCheck(getLogger(), runtime, driver);
 
     if (runtime > driver) {
         string msg =
-            "ArrayFire was built with CUDA %s which requires GPU driver "
-            "version %.2f or later. Please download and install the latest "
+            "ArrayFire was built with CUDA {} which requires GPU driver "
+            "version {Mm} or later. Please download and install the latest "
             "drivers from https://www.nvidia.com/drivers for your GPU. "
             "Alternatively, you could rebuild ArrayFire with CUDA Toolkit "
-            "version %s to use the current drivers.";
+            "version {} to use the current drivers.";
 
         auto runtime_it =
             find_if(begin(CudaToDriverVersion), end(CudaToDriverVersion),
@@ -504,18 +507,19 @@ void DeviceManager::checkCudaVsDriverVersion() {
                         return runtime == ver.version;
                     });
 
+        constexpr size_t buf_size = 1024;
         // If the runtime version is not part of the CudaToDriverVersion
         // array, display a message in the trace. Do not throw an error
         // unless this is a debug build
         if (runtime_it == end(CudaToDriverVersion)) {
-            char buf[1024];
+            char buf[buf_size];
             char err_msg[] =
                 "CUDA runtime version(%s) not recognized. Please create an "
                 "issue or a pull request on the ArrayFire repository to "
                 "update the CudaToDriverVersion variable with this "
                 "version of the CUDA Toolkit.";
-            snprintf(buf, 1024, err_msg,
-                     int_version_to_string(runtime).c_str());
+            snprintf(buf, buf_size, err_msg,
+                     fmt::format("{}", fromCudaVersion(runtime)).c_str());
             AF_TRACE("{}", buf);
             return;
         }
@@ -527,9 +531,9 @@ void DeviceManager::checkCudaVsDriverVersion() {
             runtime_it->unix_min_version;
 #endif
 
-        char buf[1024];
-        snprintf(buf, 1024, msg.c_str(), int_version_to_string(runtime).c_str(),
-                 minimumDriverVersion, int_version_to_string(driver).c_str());
+        char buf[buf_size];
+        fmt::format_to_n(buf, buf_size, msg, fromCudaVersion(runtime),
+                         minimumDriverVersion, fromCudaVersion(driver));
 
         AF_ERROR(buf, AF_ERR_DRIVER);
     }

From 66ca6e92adc49d76c237029592218c1de204369d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 31 Dec 2022 17:13:03 -0500
Subject: [PATCH 2361/2677] Update AF_ASSERT_ARRAYS_[EQ,NEAR] to accept sparse
 arrays

AF_ASSERT_ARRAY_* now accept sparse arrays and can be compared
to dense arrays now
---
 test/arrayfire_test.cpp | 282 ++++++++++++++++++++++++++++++++++++++--
 test/sparse_arith.cpp   |  68 ++--------
 test/sparse_common.hpp  |   2 +-
 test/sparse_convert.cpp |  30 +----
 4 files changed, 284 insertions(+), 98 deletions(-)

diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index b9e73b0458..a8f8a34562 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -40,6 +40,7 @@
 
 using af::af_cdouble;
 using af::af_cfloat;
+using std::vector;
 
 bool operator==(const af_half &lhs, const af_half &rhs) {
     return lhs.data_ == rhs.data_;
@@ -1390,6 +1391,116 @@ INSTANTIATE(long long);
 INSTANTIATE(unsigned long long);
 #undef INSTANTIATE
 
+template<typename T>
+struct sparseCooValue {
+    int row = 0;
+    int col = 0;
+    T value = 0;
+    sparseCooValue(int r, int c, T v) : row(r), col(c), value(v) {}
+};
+
+template<typename T>
+void swap(sparseCooValue<T> &lhs, sparseCooValue<T> &rhs) {
+    std::swap(lhs.row, rhs.row);
+    std::swap(lhs.col, rhs.col);
+    std::swap(lhs.value, rhs.value);
+}
+
+template<typename T>
+bool operator<(const sparseCooValue<T> &lhs, const sparseCooValue<T> &rhs) {
+    if (lhs.row < rhs.row) {
+        return true;
+    } else if (lhs.row == rhs.row && lhs.col < rhs.col) {
+        return true;
+    } else {
+        return false;
+    }
+}
+
+template<typename T>
+std::ostream &operator<<(std::ostream &os, const sparseCooValue<T> &val) {
+    os << "(" << val.row << ", " << val.col << "): " << val.value;
+    return os;
+}
+
+template<typename T>
+bool isZero(const sparseCooValue<T> &val) {
+    return val.value == 0.;
+}
+
+template<typename T>
+vector<sparseCooValue<T>> toCooVector(const af::array &arr) {
+    vector<sparseCooValue<T>> out;
+    if (arr.issparse()) {
+        switch (sparseGetStorage(arr)) {
+            case AF_STORAGE_COO: {
+                dim_t nnz = sparseGetNNZ(arr);
+                vector<int> row(nnz), col(nnz);
+                vector<T> values(nnz);
+                sparseGetValues(arr).host(values.data());
+                sparseGetRowIdx(arr).host(row.data());
+                sparseGetColIdx(arr).host(col.data());
+                out.reserve(nnz);
+                for (int i = 0; i < nnz; i++) {
+                    out.emplace_back(row[i], col[i], values[i]);
+                }
+            } break;
+            case AF_STORAGE_CSR: {
+                dim_t nnz = sparseGetNNZ(arr);
+                vector<int> row(arr.dims(0) + 1), col(nnz);
+                vector<T> values(nnz);
+                sparseGetValues(arr).host(values.data());
+                sparseGetRowIdx(arr).host(row.data());
+                sparseGetColIdx(arr).host(col.data());
+                out.reserve(nnz);
+                for (int i = 0; i < row.size() - 1; i++) {
+                    for (int r = row[i]; r < row[i + 1]; r++) {
+                        out.emplace_back(i, col[r], values[r]);
+                    }
+                }
+            } break;
+            case AF_STORAGE_CSC: {
+                dim_t nnz = sparseGetNNZ(arr);
+                vector<int> row(nnz), col(arr.dims(1) + 1);
+                vector<T> values(nnz);
+                sparseGetValues(arr).host(values.data());
+                sparseGetRowIdx(arr).host(row.data());
+                sparseGetColIdx(arr).host(col.data());
+                out.reserve(nnz);
+                for (int i = 0; i < col.size() - 1; i++) {
+                    for (int c = col[i]; c < col[i + 1]; c++) {
+                        out.emplace_back(row[c], i, values[c]);
+                    }
+                }
+            } break;
+            default: throw std::logic_error("NOT SUPPORTED");
+        }
+    } else {
+        vector<T> values(arr.elements());
+        arr.host(values.data());
+        int M = arr.dims(0), N = arr.dims(1);
+        for (int j = 0; j < N; j++) {
+            for (int i = 0; i < M; i++) {
+                if (std::fpclassify(real(values[j * M + i])) == FP_ZERO) {
+                    out.emplace_back(i, j, values[j * M + i]);
+                }
+            }
+        }
+    }
+
+    // Remove zero elements from result to ensure that only non-zero elements
+    // are compared
+    out.erase(std::remove_if(out.begin(), out.end(), isZero<T>), out.end());
+    std::sort(begin(out), end(out));
+    return out;
+}
+
+template<typename T>
+bool operator==(const sparseCooValue<T> &lhs, sparseCooValue<T> &rhs) {
+    return lhs.row == rhs.row && lhs.col == rhs.col &&
+           cmp(lhs.value, rhs.value);
+}
+
 template<typename T>
 std::string printContext(const std::vector<T> &hGold, std::string goldName,
                          const std::vector<T> &hOut, std::string outName,
@@ -1495,6 +1606,92 @@ std::string printContext(const std::vector<T> &hGold, std::string goldName,
     return os.str();
 }
 
+template<typename T>
+std::string printContext(const std::vector<sparseCooValue<T>> &hGold,
+                         std::string goldName,
+                         const std::vector<sparseCooValue<T>> &hOut,
+                         std::string outName, af::dim4 arrDims,
+                         af::dim4 arrStrides, dim_t idx) {
+    std::ostringstream os;
+
+    af::dim4 coords = unravelIdx(idx, arrDims, arrStrides);
+    dim_t ctxWidth  = 5;
+
+    // Coordinates that span dim0
+    af::dim4 coordsMinBound = coords;
+    coordsMinBound[0]       = 0;
+    af::dim4 coordsMaxBound = coords;
+    coordsMaxBound[0]       = arrDims[0] - 1;
+
+    // dim0 positions that can be displayed
+    dim_t dim0Start = std::max<dim_t>(0LL, idx - ctxWidth);
+    dim_t dim0End   = std::min<dim_t>(idx + ctxWidth + 1LL, hGold.size());
+
+    int setwval = 9;
+    // Linearized indices of values in vectors that can be displayed
+    dim_t vecStartIdx =
+        std::max<dim_t>(ravelIdx(coordsMinBound, arrStrides), idx - ctxWidth);
+    os << "Idx: ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << elem << "]";
+        } else {
+            os << std::setw(setwval) << elem;
+        }
+    }
+    os << "\nRow: ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << hGold[elem].row << "]";
+        } else {
+            os << std::setw(setwval) << hGold[elem].row;
+        }
+    }
+    os << "\n     ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << hOut[elem].row << "]";
+        } else {
+            os << std::setw(setwval) << hOut[elem].row;
+        }
+    }
+    os << "\nCol: ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << hGold[elem].col << "]";
+        } else {
+            os << std::setw(setwval) << hGold[elem].col;
+        }
+    }
+    os << "\n     ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << hOut[elem].col << "]";
+        } else {
+            os << std::setw(setwval) << hOut[elem].col;
+        }
+    }
+
+    os << "\nValue: ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << hGold[elem].value << "]";
+        } else {
+            os << std::setw(setwval) << hGold[elem].value;
+        }
+    }
+    os << "\n       ";
+    for (int elem = dim0Start; elem < dim0End; elem++) {
+        if (elem == idx) {
+            os << std::setw(setwval - 2) << "[" << hOut[elem].value << "]";
+        } else {
+            os << std::setw(setwval) << hOut[elem].value;
+        }
+    }
+
+    return os.str();
+}
+
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const std::vector<T> &a, af::dim4 aDims,
@@ -1502,6 +1699,7 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       float maxAbsDiff, IntegerTag) {
     UNUSED(maxAbsDiff);
     typedef typename std::vector<T>::const_iterator iter;
+
     std::pair<iter, iter> mismatches =
         std::mismatch(a.begin(), a.end(), b.begin());
     iter bItr = mismatches.second;
@@ -1525,7 +1723,7 @@ struct absMatch {
     absMatch(float diff) : diff_(diff) {}
 
     template<typename T>
-    bool operator()(T lhs, T rhs) {
+    bool operator()(const T &lhs, const T &rhs) const {
         if (diff_ > 0) {
             using half_float::abs;
             using std::abs;
@@ -1537,25 +1735,26 @@ struct absMatch {
 };
 
 template<>
-bool absMatch::operator()<af::af_cfloat>(af::af_cfloat lhs, af::af_cfloat rhs) {
+bool absMatch::operator()<af::af_cfloat>(const af::af_cfloat &lhs,
+                                         const af::af_cfloat &rhs) const {
     return af::abs(rhs - lhs) <= diff_;
 }
 
 template<>
-bool absMatch::operator()<af::af_cdouble>(af::af_cdouble lhs,
-                                          af::af_cdouble rhs) {
+bool absMatch::operator()<af::af_cdouble>(const af::af_cdouble &lhs,
+                                          const af::af_cdouble &rhs) const {
     return af::abs(rhs - lhs) <= diff_;
 }
 
 template<>
-bool absMatch::operator()<std::complex<float>>(std::complex<float> lhs,
-                                               std::complex<float> rhs) {
+bool absMatch::operator()<std::complex<float>>(
+    const std::complex<float> &lhs, const std::complex<float> &rhs) const {
     return std::abs(rhs - lhs) <= diff_;
 }
 
 template<>
-bool absMatch::operator()<std::complex<double>>(std::complex<double> lhs,
-                                                std::complex<double> rhs) {
+bool absMatch::operator()<std::complex<double>>(
+    const std::complex<double> &lhs, const std::complex<double> &rhs) const {
     return std::abs(rhs - lhs) <= diff_;
 }
 
@@ -1597,6 +1796,53 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
     }
 }
 
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      const std::vector<sparseCooValue<T>> &a,
+                                      af::dim4 aDims,
+                                      const std::vector<sparseCooValue<T>> &b,
+                                      af::dim4 bDims, float maxAbsDiff,
+                                      IntegerTag) {
+    return ::testing::AssertionFailure() << "Unsupported sparse type\n";
+}
+template<typename T>
+::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
+                                      const std::vector<sparseCooValue<T>> &a,
+                                      af::dim4 aDims,
+                                      const std::vector<sparseCooValue<T>> &b,
+                                      af::dim4 bDims, float maxAbsDiff,
+                                      FloatTag) {
+    typedef typename std::vector<sparseCooValue<T>>::const_iterator iter;
+    // TODO(mark): Modify equality for float
+
+    const absMatch diff(maxAbsDiff);
+    std::pair<iter, iter> mismatches = std::mismatch(
+        a.begin(), a.end(), b.begin(),
+        [&diff](const sparseCooValue<T> &lhs, const sparseCooValue<T> &rhs) {
+            return lhs.row == rhs.row && lhs.col == rhs.col &&
+                   diff(lhs.value, rhs.value);
+        });
+
+    iter aItr = mismatches.first;
+    iter bItr = mismatches.second;
+
+    if (aItr == a.end()) {
+        return ::testing::AssertionSuccess();
+    } else {
+        dim_t idx       = std::distance(b.begin(), bItr);
+        af::dim4 coords = unravelIdx(idx, bDims, calcStrides(bDims));
+
+        af::dim4 aStrides = calcStrides(aDims);
+
+        ::testing::AssertionResult result =
+            ::testing::AssertionFailure()
+            << "VALUE DIFFERS at " << idx << ":\n"
+            << printContext(a, aName, b, bName, aDims, aStrides, idx);
+
+        return result;
+    }
+}
+
 template<typename T>
 ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
                                       const af::array &a, const af::array &b,
@@ -1606,13 +1852,21 @@ ::testing::AssertionResult elemWiseEq(std::string aName, std::string bName,
         FloatTag, IntegerTag>::type TagType;
     TagType tag;
 
-    std::vector<T> hA(static_cast<size_t>(a.elements()));
-    a.host(hA.data());
+    if (a.issparse() || b.issparse()) {
+        vector<sparseCooValue<T>> hA = toCooVector<T>(a);
+        vector<sparseCooValue<T>> hB = toCooVector<T>(b);
 
-    std::vector<T> hB(static_cast<size_t>(b.elements()));
-    b.host(hB.data());
-    return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(), maxAbsDiff,
-                         tag);
+        return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(),
+                             maxAbsDiff, tag);
+    } else {
+        std::vector<T> hA(static_cast<size_t>(a.elements()));
+        a.host(hA.data());
+
+        std::vector<T> hB(static_cast<size_t>(b.elements()));
+        b.host(hB.data());
+        return elemWiseEq<T>(aName, bName, hA, a.dims(), hB, b.dims(),
+                             maxAbsDiff, tag);
+    }
 }
 
 template<typename T>
diff --git a/test/sparse_arith.cpp b/test/sparse_arith.cpp
index 5f08340530..8415effed5 100644
--- a/test/sparse_arith.cpp
+++ b/test/sparse_arith.cpp
@@ -91,41 +91,6 @@ struct arith_op<af_div_t> {
     array operator()(array v1, array v2) { return v1 / v2; }
 };
 
-template<typename T>
-void sparseCompare(array A, array B, const double eps) {
-// This macro is used to check if either value is finite and then call assert
-// If neither value is finite, then they can be assumed to be equal to either
-// inf or nan
-#define ASSERT_FINITE_EQ(V1, V2)                  \
-    if (std::isfinite(V1) || std::isfinite(V2)) { \
-        ASSERT_NEAR(V1, V2, eps) << "at : " << i; \
-    }
-
-    array AValues = sparseGetValues(A);
-    array ARowIdx = sparseGetRowIdx(A);
-    array AColIdx = sparseGetColIdx(A);
-
-    array BValues = sparseGetValues(B);
-    array BRowIdx = sparseGetRowIdx(B);
-    array BColIdx = sparseGetColIdx(B);
-
-    // Verify row and col indices
-    ASSERT_EQ(0, max<int>(ARowIdx - BRowIdx));
-    ASSERT_EQ(0, max<int>(AColIdx - BColIdx));
-
-    T* ptrA = AValues.host<T>();
-    T* ptrB = BValues.host<T>();
-    for (int i = 0; i < AValues.elements(); i++) {
-        ASSERT_FINITE_EQ(real(ptrA[i]), real(ptrB[i]));
-
-        if (A.iscomplex()) { ASSERT_FINITE_EQ(imag(ptrA[i]), imag(ptrB[i])); }
-    }
-    freeHost(ptrA);
-    freeHost(ptrB);
-
-#undef ASSERT_FINITE_EQ
-}
-
 template<typename T, af_op_t op>
 void sparseArithTester(const int m, const int n, int factor, const double eps) {
     deviceGC();
@@ -154,17 +119,10 @@ void sparseArithTester(const int m, const int n, int factor, const double eps) {
     array revO = arith_op<op>()(B, OA);
     array revD = arith_op<op>()(B, A);
 
-    ASSERT_NEAR(0, sum<double>(abs(real(resR - resD))) / (m * n), eps);
-    ASSERT_NEAR(0, sum<double>(abs(imag(resR - resD))) / (m * n), eps);
-
-    ASSERT_NEAR(0, sum<double>(abs(real(resO - resD))) / (m * n), eps);
-    ASSERT_NEAR(0, sum<double>(abs(imag(resO - resD))) / (m * n), eps);
-
-    ASSERT_NEAR(0, sum<double>(abs(real(revR - revD))) / (m * n), eps);
-    ASSERT_NEAR(0, sum<double>(abs(imag(revR - revD))) / (m * n), eps);
-
-    ASSERT_NEAR(0, sum<double>(abs(real(revO - revD))) / (m * n), eps);
-    ASSERT_NEAR(0, sum<double>(abs(imag(revO - revD))) / (m * n), eps);
+    ASSERT_ARRAYS_NEAR(resD, resR, eps);
+    ASSERT_ARRAYS_NEAR(resD, resO, eps);
+    ASSERT_ARRAYS_NEAR(revD, revR, eps);
+    ASSERT_ARRAYS_NEAR(revD, revO, eps);
 }
 
 // Mul
@@ -200,11 +158,11 @@ void sparseArithTesterMul(const int m, const int n, int factor,
 
         // Check resR against conR
         array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
-        sparseCompare<T>(resR, conR, eps);
+        ASSERT_ARRAYS_NEAR(resR, conR, eps);
 
         // Check resO against conO
         array conO = sparseConvertTo(resR, AF_STORAGE_COO);
-        sparseCompare<T>(resO, conO, eps);
+        ASSERT_ARRAYS_NEAR(resO, conO, eps);
     }
 
     // Reverse
@@ -219,11 +177,11 @@ void sparseArithTesterMul(const int m, const int n, int factor,
 
         // Check resR against conR
         array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
-        sparseCompare<T>(resR, conR, eps);
+        ASSERT_ARRAYS_NEAR(resR, conR, eps);
 
         // Check resO against conO
         array conO = sparseConvertTo(resR, AF_STORAGE_COO);
-        sparseCompare<T>(resO, conO, eps);
+        ASSERT_ARRAYS_NEAR(resO, conO, eps);
     }
 }
 
@@ -266,11 +224,11 @@ void sparseArithTesterDiv(const int m, const int n, int factor,
 
     // Check resR against conR
     array conR = sparseConvertTo(resR, AF_STORAGE_CSR);
-    sparseCompare<T>(resR, conR, eps);
+    ASSERT_ARRAYS_EQ(resR, conR);
 
     // Check resO against conO
     array conO = sparseConvertTo(resR, AF_STORAGE_COO);
-    sparseCompare<T>(resO, conO, eps);
+    ASSERT_ARRAYS_EQ(resO, conO);
 }
 
 #define ARITH_TESTS_OPS(T, M, N, F, EPS)              \
@@ -325,11 +283,11 @@ void ssArithmetic(const int m, const int n, int factor, const double eps) {
     // Arith Op
     array resS = binOp(spA, spB);
     array resD = binOp(A, B);
+    ASSERT_ARRAYS_NEAR(resD, resS, eps);
+
     array revS = binOp(spB, spA);
     array revD = binOp(B, A);
-
-    ASSERT_ARRAYS_NEAR(resD, dense(resS), eps);
-    ASSERT_ARRAYS_NEAR(revD, dense(revS), eps);
+    ASSERT_ARRAYS_NEAR(revD, revS, eps);
 }
 
 #define SP_SP_ARITH_TEST(type, m, n, factor, eps)           \
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index bc95871b68..41dd3fd05d 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -161,7 +161,7 @@ static void convertCSR(const int M, const int N, const double ratio,
     af::array s  = af::sparse(a, AF_STORAGE_CSR);
     af::array aa = af::dense(s);
 
-    ASSERT_EQ(0, af::max<double>(af::abs(a - aa)));
+    ASSERT_ARRAYS_EQ(a, aa);
 }
 
 // This test essentially verifies that the sparse structures have the correct
diff --git a/test/sparse_convert.cpp b/test/sparse_convert.cpp
index 04599e03ca..7e8b927542 100644
--- a/test/sparse_convert.cpp
+++ b/test/sparse_convert.cpp
@@ -78,34 +78,8 @@ void sparseConvertTester(const int m, const int n, int factor) {
     // Create the dest type from dense - gold
     array dA = sparse(A, dest);
 
-    // Verify nnZ
-    dim_t dNNZ   = sparseGetNNZ(dA);
-    dim_t s2dNNZ = sparseGetNNZ(s2d);
-
-    ASSERT_EQ(dNNZ, s2dNNZ);
-
-    // Verify Types
-    af_storage dType   = sparseGetStorage(dA);
-    af_storage s2dType = sparseGetStorage(s2d);
-
-    ASSERT_EQ(dType, s2dType);
-
-    // Get the individual arrays and verify equality
-    array dValues = sparseGetValues(dA);
-    array dRowIdx = sparseGetRowIdx(dA);
-    array dColIdx = sparseGetColIdx(dA);
-
-    array s2dValues = sparseGetValues(s2d);
-    array s2dRowIdx = sparseGetRowIdx(s2d);
-    array s2dColIdx = sparseGetColIdx(s2d);
-
-    // Verify values
-    ASSERT_EQ(0, max<double>(real(dValues - s2dValues)));
-    ASSERT_EQ(0, max<double>(imag(dValues - s2dValues)));
-
-    // Verify row and col indices
-    ASSERT_EQ(0, max<int>(dRowIdx - s2dRowIdx));
-    ASSERT_EQ(0, max<int>(dColIdx - s2dColIdx));
+    ASSERT_ARRAYS_EQ(dA, s2d);
+    ASSERT_ARRAYS_EQ(A, s2d);
 }
 
 #define CONVERT_TESTS_TYPES(T, STYPE, DTYPE, SUFFIX, M, N, F) \

From 8b6a4acbbe5b983bf94bfc5b0f3ba4ee1b24e478 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 31 Dec 2022 20:20:22 -0500
Subject: [PATCH 2362/2677] Add support for CUDA 12

---
 src/backend/common/ArrayFireTypesIO.hpp       |   1 -
 src/backend/cuda/cusparseModule.cpp           |  72 +++++---
 src/backend/cuda/cusparseModule.hpp           |  71 ++++---
 .../cuda/cusparse_descriptor_helpers.hpp      |   9 +-
 src/backend/cuda/device_manager.cpp           |   4 +-
 src/backend/cuda/sparse.cu                    | 173 +++++++++++++-----
 src/backend/cuda/sparse_arith.cu              | 118 ++++++------
 src/backend/cuda/sparse_blas.cu               |  27 ++-
 src/backend/cuda/thrust_utils.hpp             |  18 --
 9 files changed, 316 insertions(+), 177 deletions(-)

diff --git a/src/backend/common/ArrayFireTypesIO.hpp b/src/backend/common/ArrayFireTypesIO.hpp
index 2d6b514a3e..81b73f9988 100644
--- a/src/backend/common/ArrayFireTypesIO.hpp
+++ b/src/backend/common/ArrayFireTypesIO.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 #include <common/Version.hpp>
-#include <spdlog/fmt/bundled/ranges.h>
 #include <spdlog/fmt/ostr.h>
 #include <af/seq.h>
 
diff --git a/src/backend/cuda/cusparseModule.cpp b/src/backend/cuda/cusparseModule.cpp
index 7d470f00e9..84daa25460 100644
--- a/src/backend/cuda/cusparseModule.cpp
+++ b/src/backend/cuda/cusparseModule.cpp
@@ -7,11 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <cusparseModule.hpp>
 #include <cusparse.hpp>
+#include <cusparseModule.hpp>
 
 #include <common/Version.hpp>
 #include <common/err_common.hpp>
+#include <cusparse.hpp>
+#include <platform.hpp>
 #include <af/defines.h>
 
 #include <cuda.h>
@@ -41,7 +43,8 @@ cusparseModule::cusparseModule()
 #ifdef AF_cusparse_STATIC_LINKING
     module(nullptr, nullptr)
 #else
-    module({"cusparse"}, {"64_11", "64_10", "64_9", "64_8"}, {""})
+    module({"cusparse"}, {"64_12", "64_11", "64_10", "64_9", "64_8"}, {""}, 0,
+           nullptr, getCusparseVersion)
 #endif
 {
 #ifdef AF_cusparse_STATIC_LINKING
@@ -62,11 +65,44 @@ cusparseModule::cusparseModule()
     }
 #endif
 
+    MODULE_FUNCTION_INIT(cusparseGetVersion);
+
+#if CUSPARSE_VERSION < 11300
     MODULE_FUNCTION_INIT(cusparseCcsc2dense);
     MODULE_FUNCTION_INIT(cusparseCcsr2dense);
     MODULE_FUNCTION_INIT(cusparseCdense2csc);
     MODULE_FUNCTION_INIT(cusparseCdense2csr);
     MODULE_FUNCTION_INIT(cusparseCgthr);
+    MODULE_FUNCTION_INIT(cusparseDcsc2dense);
+    MODULE_FUNCTION_INIT(cusparseDcsr2dense);
+    MODULE_FUNCTION_INIT(cusparseDdense2csc);
+    MODULE_FUNCTION_INIT(cusparseDdense2csr);
+    MODULE_FUNCTION_INIT(cusparseDgthr);
+    MODULE_FUNCTION_INIT(cusparseScsc2dense);
+    MODULE_FUNCTION_INIT(cusparseScsr2dense);
+    MODULE_FUNCTION_INIT(cusparseSdense2csc);
+    MODULE_FUNCTION_INIT(cusparseSdense2csr);
+    MODULE_FUNCTION_INIT(cusparseSgthr);
+    MODULE_FUNCTION_INIT(cusparseZcsc2dense);
+    MODULE_FUNCTION_INIT(cusparseZcsr2dense);
+    MODULE_FUNCTION_INIT(cusparseZdense2csc);
+    MODULE_FUNCTION_INIT(cusparseZdense2csr);
+    MODULE_FUNCTION_INIT(cusparseZgthr);
+#else
+    MODULE_FUNCTION_INIT(cusparseCreateCsc);
+    MODULE_FUNCTION_INIT(cusparseSparseToDense_bufferSize);
+    MODULE_FUNCTION_INIT(cusparseSparseToDense);
+    MODULE_FUNCTION_INIT(cusparseDenseToSparse_bufferSize);
+    MODULE_FUNCTION_INIT(cusparseDenseToSparse_analysis);
+    MODULE_FUNCTION_INIT(cusparseDenseToSparse_convert);
+    MODULE_FUNCTION_INIT(cusparseSpMatGetSize);
+    MODULE_FUNCTION_INIT(cusparseCsrSetPointers);
+    MODULE_FUNCTION_INIT(cusparseCscSetPointers);
+    MODULE_FUNCTION_INIT(cusparseSetPointerMode);
+    MODULE_FUNCTION_INIT(cusparseXcsrsort_bufferSizeExt);
+    MODULE_FUNCTION_INIT(cusparseXcsrsort);
+#endif
+
     MODULE_FUNCTION_INIT(cusparseCnnz);
     MODULE_FUNCTION_INIT(cusparseCreateCsr);
     MODULE_FUNCTION_INIT(cusparseCreateDnMat);
@@ -74,25 +110,15 @@ cusparseModule::cusparseModule()
     MODULE_FUNCTION_INIT(cusparseCreateIdentityPermutation);
     MODULE_FUNCTION_INIT(cusparseCreate);
     MODULE_FUNCTION_INIT(cusparseCreateMatDescr);
-    MODULE_FUNCTION_INIT(cusparseDcsc2dense);
-    MODULE_FUNCTION_INIT(cusparseDcsr2dense);
-    MODULE_FUNCTION_INIT(cusparseDdense2csc);
-    MODULE_FUNCTION_INIT(cusparseDdense2csr);
     MODULE_FUNCTION_INIT(cusparseDestroyDnMat);
     MODULE_FUNCTION_INIT(cusparseDestroyDnVec);
     MODULE_FUNCTION_INIT(cusparseDestroy);
     MODULE_FUNCTION_INIT(cusparseDestroyMatDescr);
     MODULE_FUNCTION_INIT(cusparseDestroySpMat);
-    MODULE_FUNCTION_INIT(cusparseDgthr);
     MODULE_FUNCTION_INIT(cusparseDnnz);
-    MODULE_FUNCTION_INIT(cusparseScsc2dense);
-    MODULE_FUNCTION_INIT(cusparseScsr2dense);
-    MODULE_FUNCTION_INIT(cusparseSdense2csc);
-    MODULE_FUNCTION_INIT(cusparseSdense2csr);
     MODULE_FUNCTION_INIT(cusparseSetMatIndexBase);
     MODULE_FUNCTION_INIT(cusparseSetMatType);
     MODULE_FUNCTION_INIT(cusparseSetStream);
-    MODULE_FUNCTION_INIT(cusparseSgthr);
     MODULE_FUNCTION_INIT(cusparseSnnz);
     MODULE_FUNCTION_INIT(cusparseSpMM_bufferSize);
     MODULE_FUNCTION_INIT(cusparseSpMM);
@@ -103,14 +129,14 @@ cusparseModule::cusparseModule()
     MODULE_FUNCTION_INIT(cusparseXcoosortByColumn);
     MODULE_FUNCTION_INIT(cusparseXcoosortByRow);
     MODULE_FUNCTION_INIT(cusparseXcsr2coo);
-#if CUDA_VERSION >= 11000
-    MODULE_FUNCTION_INIT(cusparseXcsrgeam2Nnz);
-#else
+#if CUSPARSE_VERSION < 11000
     MODULE_FUNCTION_INIT(cusparseXcsrgeamNnz);
-#endif
-    MODULE_FUNCTION_INIT(cusparseZcsc2dense);
-    MODULE_FUNCTION_INIT(cusparseZcsr2dense);
-#if CUDA_VERSION >= 11000
+    MODULE_FUNCTION_INIT(cusparseScsrgeam);
+    MODULE_FUNCTION_INIT(cusparseDcsrgeam);
+    MODULE_FUNCTION_INIT(cusparseCcsrgeam);
+    MODULE_FUNCTION_INIT(cusparseZcsrgeam);
+#else
+    MODULE_FUNCTION_INIT(cusparseXcsrgeam2Nnz);
     MODULE_FUNCTION_INIT(cusparseScsrgeam2_bufferSizeExt);
     MODULE_FUNCTION_INIT(cusparseScsrgeam2);
     MODULE_FUNCTION_INIT(cusparseDcsrgeam2_bufferSizeExt);
@@ -119,15 +145,7 @@ cusparseModule::cusparseModule()
     MODULE_FUNCTION_INIT(cusparseCcsrgeam2);
     MODULE_FUNCTION_INIT(cusparseZcsrgeam2_bufferSizeExt);
     MODULE_FUNCTION_INIT(cusparseZcsrgeam2);
-#else
-    MODULE_FUNCTION_INIT(cusparseScsrgeam);
-    MODULE_FUNCTION_INIT(cusparseDcsrgeam);
-    MODULE_FUNCTION_INIT(cusparseCcsrgeam);
-    MODULE_FUNCTION_INIT(cusparseZcsrgeam);
 #endif
-    MODULE_FUNCTION_INIT(cusparseZdense2csc);
-    MODULE_FUNCTION_INIT(cusparseZdense2csr);
-    MODULE_FUNCTION_INIT(cusparseZgthr);
     MODULE_FUNCTION_INIT(cusparseZnnz);
 
 #ifndef AF_cusparse_STATIC_LINKING
diff --git a/src/backend/cuda/cusparseModule.hpp b/src/backend/cuda/cusparseModule.hpp
index ac7e826a13..5f63cec285 100644
--- a/src/backend/cuda/cusparseModule.hpp
+++ b/src/backend/cuda/cusparseModule.hpp
@@ -22,37 +22,61 @@ class cusparseModule {
     cusparseModule();
     ~cusparseModule() = default;
 
+    MODULE_MEMBER(cusparseGetVersion);
+
+#if CUSPARSE_VERSION < 11300
     MODULE_MEMBER(cusparseCcsc2dense);
     MODULE_MEMBER(cusparseCcsr2dense);
     MODULE_MEMBER(cusparseCdense2csc);
     MODULE_MEMBER(cusparseCdense2csr);
     MODULE_MEMBER(cusparseCgthr);
-    MODULE_MEMBER(cusparseCnnz);
-    MODULE_MEMBER(cusparseCreateCsr);
-    MODULE_MEMBER(cusparseCreateDnMat);
-    MODULE_MEMBER(cusparseCreateDnVec);
-    MODULE_MEMBER(cusparseCreateIdentityPermutation);
-    MODULE_MEMBER(cusparseCreate);
-    MODULE_MEMBER(cusparseCreateMatDescr);
     MODULE_MEMBER(cusparseDcsc2dense);
     MODULE_MEMBER(cusparseDcsr2dense);
     MODULE_MEMBER(cusparseDdense2csc);
     MODULE_MEMBER(cusparseDdense2csr);
+    MODULE_MEMBER(cusparseDgthr);
+    MODULE_MEMBER(cusparseScsc2dense);
+    MODULE_MEMBER(cusparseScsr2dense);
+    MODULE_MEMBER(cusparseSdense2csc);
+    MODULE_MEMBER(cusparseSdense2csr);
+    MODULE_MEMBER(cusparseSgthr);
+    MODULE_MEMBER(cusparseZcsc2dense);
+    MODULE_MEMBER(cusparseZcsr2dense);
+    MODULE_MEMBER(cusparseZdense2csc);
+    MODULE_MEMBER(cusparseZdense2csr);
+    MODULE_MEMBER(cusparseZgthr);
+#else
+    MODULE_MEMBER(cusparseCreateCsc);
+    MODULE_MEMBER(cusparseSparseToDense);
+    MODULE_MEMBER(cusparseSparseToDense_bufferSize);
+    MODULE_MEMBER(cusparseDenseToSparse_bufferSize);
+    MODULE_MEMBER(cusparseDenseToSparse_analysis);
+    MODULE_MEMBER(cusparseDenseToSparse_convert);
+    MODULE_MEMBER(cusparseSpMatGetSize);
+    MODULE_MEMBER(cusparseCsrSetPointers);
+    MODULE_MEMBER(cusparseCscSetPointers);
+    MODULE_MEMBER(cusparseGather);
+    MODULE_MEMBER(cusparseSetPointerMode);
+    MODULE_MEMBER(cusparseXcsrsort_bufferSizeExt);
+    MODULE_MEMBER(cusparseXcsrsort);
+#endif
+
+    MODULE_MEMBER(cusparseCreateCsr);
     MODULE_MEMBER(cusparseDestroyDnMat);
     MODULE_MEMBER(cusparseDestroyDnVec);
     MODULE_MEMBER(cusparseDestroy);
     MODULE_MEMBER(cusparseDestroyMatDescr);
     MODULE_MEMBER(cusparseDestroySpMat);
-    MODULE_MEMBER(cusparseDgthr);
+    MODULE_MEMBER(cusparseCnnz);
+    MODULE_MEMBER(cusparseCreateDnMat);
+    MODULE_MEMBER(cusparseCreateDnVec);
+    MODULE_MEMBER(cusparseCreateIdentityPermutation);
+    MODULE_MEMBER(cusparseCreate);
+    MODULE_MEMBER(cusparseCreateMatDescr);
     MODULE_MEMBER(cusparseDnnz);
-    MODULE_MEMBER(cusparseScsc2dense);
-    MODULE_MEMBER(cusparseScsr2dense);
-    MODULE_MEMBER(cusparseSdense2csc);
-    MODULE_MEMBER(cusparseSdense2csr);
     MODULE_MEMBER(cusparseSetMatIndexBase);
     MODULE_MEMBER(cusparseSetMatType);
     MODULE_MEMBER(cusparseSetStream);
-    MODULE_MEMBER(cusparseSgthr);
     MODULE_MEMBER(cusparseSnnz);
     MODULE_MEMBER(cusparseSpMM_bufferSize);
     MODULE_MEMBER(cusparseSpMM);
@@ -63,11 +87,14 @@ class cusparseModule {
     MODULE_MEMBER(cusparseXcoosortByColumn);
     MODULE_MEMBER(cusparseXcoosortByRow);
     MODULE_MEMBER(cusparseXcsr2coo);
-    MODULE_MEMBER(cusparseZcsc2dense);
-    MODULE_MEMBER(cusparseZcsr2dense);
 
-#if CUDA_VERSION >= 11000
-    MODULE_MEMBER(cusparseXcsrgeam2Nnz);
+#if CUSPARSE_VERSION < 11000
+    MODULE_MEMBER(cusparseCcsrgeam);
+    MODULE_MEMBER(cusparseDcsrgeam);
+    MODULE_MEMBER(cusparseScsrgeam);
+    MODULE_MEMBER(cusparseZcsrgeam);
+    MODULE_MEMBER(cusparseXcsrgeamNnz);
+#else
     MODULE_MEMBER(cusparseCcsrgeam2_bufferSizeExt);
     MODULE_MEMBER(cusparseCcsrgeam2);
     MODULE_MEMBER(cusparseDcsrgeam2_bufferSizeExt);
@@ -76,17 +103,9 @@ class cusparseModule {
     MODULE_MEMBER(cusparseScsrgeam2);
     MODULE_MEMBER(cusparseZcsrgeam2_bufferSizeExt);
     MODULE_MEMBER(cusparseZcsrgeam2);
-#else
-    MODULE_MEMBER(cusparseXcsrgeamNnz);
-    MODULE_MEMBER(cusparseCcsrgeam);
-    MODULE_MEMBER(cusparseDcsrgeam);
-    MODULE_MEMBER(cusparseScsrgeam);
-    MODULE_MEMBER(cusparseZcsrgeam);
+    MODULE_MEMBER(cusparseXcsrgeam2Nnz);
 #endif
 
-    MODULE_MEMBER(cusparseZdense2csc);
-    MODULE_MEMBER(cusparseZdense2csr);
-    MODULE_MEMBER(cusparseZgthr);
     MODULE_MEMBER(cusparseZnnz);
 
     spdlog::logger* getLogger() const noexcept;
diff --git a/src/backend/cuda/cusparse_descriptor_helpers.hpp b/src/backend/cuda/cusparse_descriptor_helpers.hpp
index 41e369b0d8..99d474cdbb 100644
--- a/src/backend/cuda/cusparse_descriptor_helpers.hpp
+++ b/src/backend/cuda/cusparse_descriptor_helpers.hpp
@@ -13,6 +13,7 @@
 // CUDA Toolkit 10.0 or later
 
 #include <common/unique_handle.hpp>
+#include <cudaDataType.hpp>
 #include <cusparse.hpp>
 
 #include <utility>
@@ -21,8 +22,9 @@ namespace arrayfire {
 namespace cuda {
 
 template<typename T>
-auto csrMatDescriptor(const common::SparseArray<T> &in) {
+auto cusparseDescriptor(const common::SparseArray<T> &in) {
     auto dims = in.dims();
+
     return common::make_handle<cusparseSpMatDescr_t>(
         dims[0], dims[1], in.getNNZ(), (void *)(in.getRowIdx().get()),
         (void *)(in.getColIdx().get()), (void *)(in.getValues().get()),
@@ -38,9 +40,10 @@ auto denVecDescriptor(const Array<T> &in) {
 
 template<typename T>
 auto denMatDescriptor(const Array<T> &in) {
-    auto dims = in.dims();
+    auto dims    = in.dims();
+    auto strides = in.strides();
     return common::make_handle<cusparseDnMatDescr_t>(
-        dims[0], dims[1], dims[0], (void *)(in.get()), getType<T>(),
+        dims[0], dims[1], strides[1], (void *)in.get(), getType<T>(),
         CUSPARSE_ORDER_COL);
 }
 
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 8e7ca0e7d2..4f0d534b8d 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12000, 9, 0, 0},
     {11080, 9, 0, 0},
     {11070, 8, 7, 0},
     {11060, 8, 6, 0},
@@ -137,6 +138,7 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {12000, 525.60f, 527.41f},
         {11080, 450.80f, 452.39f},
         {11070, 450.80f, 452.39f},
         {11060, 450.80f, 452.39f},
@@ -159,7 +161,7 @@ static const ToolkitDriverVersions
 
 // Vector of minimum supported compute versions for CUDA toolkit (i+1).*
 // where i is the index of the vector
-static const std::array<int, 11> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3}};
+static const std::array<int, 12> minSV{{1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 5}};
 
 static ComputeCapabilityToStreamingProcessors gpus[] = {
     {0x10, 8},   {0x11, 8},   {0x12, 8},   {0x13, 8},   {0x20, 32},
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index 6dec35090c..dd6d8d22b7 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -14,8 +14,11 @@
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
+#include <cudaDataType.hpp>
 #include <cusparse.hpp>
 #include <cusparseModule.hpp>
+#include <cusparse_descriptor_helpers.hpp>
+#include <handle.hpp>
 #include <kernel/sparse.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
@@ -129,6 +132,9 @@ struct gthr_func_def_t {
             _.cusparse##PREFIX##FUNC);                                      \
     }
 
+/// Newer versions of cusparse use matrix descriptor instead of types encoded in
+/// their names
+#if CUSPARSE_VERSION < 11300
 SPARSE_FUNC_DEF(dense2csr)
 SPARSE_FUNC(dense2csr, float, S)
 SPARSE_FUNC(dense2csr, double, D)
@@ -153,17 +159,18 @@ SPARSE_FUNC(csc2dense, double, D)
 SPARSE_FUNC(csc2dense, cfloat, C)
 SPARSE_FUNC(csc2dense, cdouble, Z)
 
-SPARSE_FUNC_DEF(nnz)
-SPARSE_FUNC(nnz, float, S)
-SPARSE_FUNC(nnz, double, D)
-SPARSE_FUNC(nnz, cfloat, C)
-SPARSE_FUNC(nnz, cdouble, Z)
-
 SPARSE_FUNC_DEF(gthr)
 SPARSE_FUNC(gthr, float, S)
 SPARSE_FUNC(gthr, double, D)
 SPARSE_FUNC(gthr, cfloat, C)
 SPARSE_FUNC(gthr, cdouble, Z)
+#endif
+
+SPARSE_FUNC_DEF(nnz)
+SPARSE_FUNC(nnz, float, S)
+SPARSE_FUNC(nnz, double, D)
+SPARSE_FUNC(nnz, cfloat, C)
+SPARSE_FUNC(nnz, cdouble, Z)
 
 #undef SPARSE_FUNC
 #undef SPARSE_FUNC_DEF
@@ -198,6 +205,7 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     const int N = in.dims()[1];
 
     cusparseModule &_ = getCusparsePlugin();
+#if CUSPARSE_VERSION < 11300
     // Create Sparse Matrix Descriptor
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(_.cusparseCreateMatDescr(&descr));
@@ -232,20 +240,97 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     }
     Array<T> values = createEmptyArray<T>(dim4(nNZ));
 
-    if (stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR) {
         CUSPARSE_CHECK(dense2csr_func<T>()(
             sparseHandle(), M, N, descr, in.get(), in.strides()[1],
             nnzPerDir.get(), values.get(), rowIdx.get(), colIdx.get()));
-    else
+    } else {
         CUSPARSE_CHECK(dense2csc_func<T>()(
             sparseHandle(), M, N, descr, in.get(), in.strides()[1],
             nnzPerDir.get(), values.get(), rowIdx.get(), colIdx.get()));
-
+    }
     // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(_.cusparseDestroyMatDescr(descr));
 
     return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
                                          stype);
+#else
+    auto matA = denMatDescriptor(in);
+    cusparseSpMatDescr_t matB;
+
+    auto d_csr_offsets = createEmptyArray<int>(M + 1);
+
+    if (stype == AF_STORAGE_CSR) {
+        // Create sparse matrix B in CSR format
+        CUSPARSE_CHECK(
+            _.cusparseCreateCsr(&matB, M, N, 0, d_csr_offsets.get(), nullptr,
+                                nullptr, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
+                                CUSPARSE_INDEX_BASE_ZERO, getType<T>()));
+    } else {
+        CUSPARSE_CHECK(
+            _.cusparseCreateCsc(&matB, M, N, 0, d_csr_offsets.get(), nullptr,
+                                nullptr, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
+                                CUSPARSE_INDEX_BASE_ZERO, getType<T>()));
+    }
+
+    // allocate an external buffer if needed
+    size_t bufferSize;
+    CUSPARSE_CHECK(_.cusparseDenseToSparse_bufferSize(
+        sparseHandle(), matA, matB, CUSPARSE_DENSETOSPARSE_ALG_DEFAULT,
+        &bufferSize));
+
+    auto dBuffer = memAlloc<char>(bufferSize);
+
+    // execute Sparse to Dense conversion
+    CUSPARSE_CHECK(_.cusparseDenseToSparse_analysis(
+        sparseHandle(), matA, matB, CUSPARSE_DENSETOSPARSE_ALG_DEFAULT,
+        dBuffer.get()));
+    // get number of non-zero elements
+    int64_t num_rows_tmp, num_cols_tmp, nnz;
+    CUSPARSE_CHECK(
+        _.cusparseSpMatGetSize(matB, &num_rows_tmp, &num_cols_tmp, &nnz));
+
+    auto d_csr_columns = createEmptyArray<int>(nnz);
+    auto d_csr_values  = createEmptyArray<T>(nnz);
+    // allocate CSR column indices and values
+    // reset offsets, column indices, and values pointers
+    if (stype == AF_STORAGE_CSR) {
+        // Create sparse matrix B in CSR format
+        // reset offsets, column indices, and values pointers
+        CUSPARSE_CHECK(_.cusparseCsrSetPointers(matB, d_csr_offsets.get(),
+                                                d_csr_columns.get(),
+                                                d_csr_values.get()));
+
+    } else {
+        // reset offsets, column indices, and values pointers
+        CUSPARSE_CHECK(_.cusparseCscSetPointers(matB, d_csr_offsets.get(),
+                                                d_csr_columns.get(),
+                                                d_csr_values.get()));
+    }
+    // execute Sparse to Dense conversion
+    CUSPARSE_CHECK(_.cusparseDenseToSparse_convert(
+        sparseHandle(), matA, matB, CUSPARSE_DENSETOSPARSE_ALG_DEFAULT,
+        dBuffer.get()));
+
+    if (stype == AF_STORAGE_CSR) {
+        size_t pBufferSizeInBytes = 0;
+        auto desc                 = make_handle<cusparseMatDescr_t>();
+        CUSPARSE_CHECK(_.cusparseXcsrsort_bufferSizeExt(
+            sparseHandle(), M, N, nnz, d_csr_offsets.get(), d_csr_columns.get(),
+            &pBufferSizeInBytes));
+        auto pBuffer = memAlloc<char>(pBufferSizeInBytes);
+        Array<int> P = createEmptyArray<int>(nnz);
+        CUSPARSE_CHECK(
+            _.cusparseCreateIdentityPermutation(sparseHandle(), nnz, P.get()));
+        CUSPARSE_CHECK(_.cusparseXcsrsort(
+            sparseHandle(), M, N, nnz, desc, (int *)d_csr_offsets.get(),
+            (int *)d_csr_columns.get(), P.get(), pBuffer.get()));
+        d_csr_values = lookup(d_csr_values, P, 0);
+    }
+
+    return createArrayDataSparseArray<T>(in.dims(), d_csr_values, d_csr_offsets,
+                                         d_csr_columns, stype, false);
+#endif
 }
 
 // Partial template specialization of sparseConvertStorageToDense for COO
@@ -266,7 +351,8 @@ Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
     // Create Sparse Matrix Descriptor
-    cusparseModule &_        = getCusparsePlugin();
+    cusparseModule &_ = getCusparsePlugin();
+#if CUSPARSE_VERSION < 11300
     cusparseMatDescr_t descr = 0;
     CUSPARSE_CHECK(_.cusparseCreateMatDescr(&descr));
     _.cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
@@ -277,19 +363,36 @@ Array<T> sparseConvertStorageToDense(const SparseArray<T> &in) {
     Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
     int d_strides1 = dense.strides()[1];
 
-    if (stype == AF_STORAGE_CSR)
+    if (stype == AF_STORAGE_CSR) {
         CUSPARSE_CHECK(
             csr2dense_func<T>()(sparseHandle(), M, N, descr,
                                 in.getValues().get(), in.getRowIdx().get(),
                                 in.getColIdx().get(), dense.get(), d_strides1));
-    else
+    } else {
         CUSPARSE_CHECK(
             csc2dense_func<T>()(sparseHandle(), M, N, descr,
                                 in.getValues().get(), in.getRowIdx().get(),
                                 in.getColIdx().get(), dense.get(), d_strides1));
+    }
 
     // Destory Sparse Matrix Descriptor
     CUSPARSE_CHECK(_.cusparseDestroyMatDescr(descr));
+#else
+    unique_handle<cusparseSpMatDescr_t> inhandle = cusparseDescriptor(in);
+
+    Array<T> dense = createEmptyArray<T>(in.dims());
+    unique_handle<cusparseDnMatDescr_t> outhandle = denMatDescriptor(dense);
+
+    size_t bufferSize = 0;
+    _.cusparseSparseToDense_bufferSize(sparseHandle(), inhandle, outhandle,
+                                       CUSPARSE_SPARSETODENSE_ALG_DEFAULT,
+                                       &bufferSize);
+
+    auto dBuffer = memAlloc<char>(bufferSize);
+    _.cusparseSparseToDense(sparseHandle(), inhandle, outhandle,
+                            CUSPARSE_SPARSETODENSE_ALG_DEFAULT, dBuffer.get());
+
+#endif
 
     return dense;
 }
@@ -321,27 +424,27 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
             sparseHandle(), in.dims()[0], in.dims()[1], nNZ,
             converted.getRowIdx().get(), converted.getColIdx().get(),
             &pBufferSizeInBytes));
-        shared_ptr<char> pBuffer(memAlloc<char>(pBufferSizeInBytes).release(),
-                                 memFree<char>);
+        auto pBuffer = memAlloc<char>(pBufferSizeInBytes);
 
-        shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
+        // shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
+        Array<int> P = createEmptyArray<int>(nNZ);
         CUSPARSE_CHECK(
             _.cusparseCreateIdentityPermutation(sparseHandle(), nNZ, P.get()));
 
-        CUSPARSE_CHECK(_.cusparseXcoosortByColumn(
+        CUSPARSE_CHECK(_.cusparseXcoosortByRow(
             sparseHandle(), in.dims()[0], in.dims()[1], nNZ,
             converted.getRowIdx().get(), converted.getColIdx().get(), P.get(),
-            (void *)pBuffer.get()));
+            pBuffer.get()));
 
-        CUSPARSE_CHECK(gthr_func<T>()(sparseHandle(), nNZ, in.getValues().get(),
-                                      converted.getValues().get(), P.get(),
-                                      CUSPARSE_INDEX_BASE_ZERO));
+        converted.getValues() = lookup<T, int>(in.getValues(), P, 0);
 
     } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
         // The cusparse csr sort function is not behaving correctly.
         // So the work around is to convert the COO into row major and then
         // convert it to CSR
 
+        int M = in.dims()[0];
+        int N = in.dims()[1];
         // Deep copy input into temporary COO Row Major
         SparseArray<T> cooT = createArrayDataSparseArray<T>(
             in.dims(), in.getValues(), in.getRowIdx(), in.getColIdx(),
@@ -351,39 +454,27 @@ SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
         {
             size_t pBufferSizeInBytes = 0;
             CUSPARSE_CHECK(_.cusparseXcoosort_bufferSizeExt(
-                sparseHandle(), cooT.dims()[0], cooT.dims()[1], nNZ,
-                cooT.getRowIdx().get(), cooT.getColIdx().get(),
-                &pBufferSizeInBytes));
-            shared_ptr<char> pBuffer(
-                memAlloc<char>(pBufferSizeInBytes).release(), memFree<char>);
+                sparseHandle(), M, N, nNZ, cooT.getRowIdx().get(),
+                cooT.getColIdx().get(), &pBufferSizeInBytes));
+            auto pBuffer = memAlloc<char>(pBufferSizeInBytes);
 
-            shared_ptr<int> P(memAlloc<int>(nNZ).release(), memFree<int>);
+            Array<int> P = createEmptyArray<int>(nNZ);
             CUSPARSE_CHECK(_.cusparseCreateIdentityPermutation(sparseHandle(),
                                                                nNZ, P.get()));
 
             CUSPARSE_CHECK(_.cusparseXcoosortByRow(
-                sparseHandle(), cooT.dims()[0], cooT.dims()[1], nNZ,
-                cooT.getRowIdx().get(), cooT.getColIdx().get(), P.get(),
-                (void *)pBuffer.get()));
+                sparseHandle(), M, N, nNZ, cooT.getRowIdx().get(),
+                cooT.getColIdx().get(), P.get(), pBuffer.get()));
 
-            CUSPARSE_CHECK(gthr_func<T>()(
-                sparseHandle(), nNZ, in.getValues().get(),
-                cooT.getValues().get(), P.get(), CUSPARSE_INDEX_BASE_ZERO));
+            converted.getValues() = lookup<T, int>(in.getValues(), P, 0);
         }
 
         // Copy values and colIdx as is
-        CUDA_CHECK(
-            cudaMemcpyAsync(converted.getValues().get(), cooT.getValues().get(),
-                            cooT.getValues().elements() * sizeof(T),
-                            cudaMemcpyDeviceToDevice, getActiveStream()));
-        CUDA_CHECK(
-            cudaMemcpyAsync(converted.getColIdx().get(), cooT.getColIdx().get(),
-                            cooT.getColIdx().elements() * sizeof(int),
-                            cudaMemcpyDeviceToDevice, getActiveStream()));
+        copyArray<int, int>(converted.getColIdx(), cooT.getColIdx());
 
         // cusparse function to compress row from coordinate
         CUSPARSE_CHECK(_.cusparseXcoo2csr(
-            sparseHandle(), cooT.getRowIdx().get(), nNZ, cooT.dims()[0],
+            sparseHandle(), cooT.getRowIdx().get(), nNZ, M,
             converted.getRowIdx().get(), CUSPARSE_INDEX_BASE_ZERO));
 
         // No need to call CSRSORT
diff --git a/src/backend/cuda/sparse_arith.cu b/src/backend/cuda/sparse_arith.cu
index 63bda7f733..8a60aba4d3 100644
--- a/src/backend/cuda/sparse_arith.cu
+++ b/src/backend/cuda/sparse_arith.cu
@@ -7,6 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <kernel/sparse_arith.hpp>
 #include <sparse_arith.hpp>
 
 #include <arith.hpp>
@@ -16,11 +17,13 @@
 #include <complex.hpp>
 #include <copy.hpp>
 #include <cusparse.hpp>
-#include <kernel/sparse_arith.hpp>
+#include <cusparse_descriptor_helpers.hpp>
+#include <handle.hpp>
 #include <lookup.hpp>
 #include <math.hpp>
 #include <platform.hpp>
 #include <sparse.hpp>
+#include <sparse_handle.hpp>
 #include <where.hpp>
 
 #include <stdexcept>
@@ -123,10 +126,10 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
         return _.cusparse##INFIX##FUNC;          \
     }
 
-#if CUDA_VERSION >= 11000
+#if CUSPARSE_VERSION >= 11000
 
 template<typename T>
-using csrgeam2_buffer_size_def = cusparseStatus_t (*)(
+using csrgeam2_bufferSizeExt_def = cusparseStatus_t (*)(
     cusparseHandle_t, int, int, const T *, const cusparseMatDescr_t, int,
     const T *, const int *, const int *, const T *, const cusparseMatDescr_t,
     int, const T *, const int *, const int *, const cusparseMatDescr_t,
@@ -134,21 +137,21 @@ using csrgeam2_buffer_size_def = cusparseStatus_t (*)(
 
 #define SPARSE_ARITH_OP_BUFFER_SIZE_FUNC_DEF(FUNC) \
     template<typename T>                           \
-    FUNC##_buffer_size_def<T> FUNC##_buffer_size_func();
+    FUNC##_def<T> FUNC##_func();
 
-SPARSE_ARITH_OP_BUFFER_SIZE_FUNC_DEF(csrgeam2);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC_DEF(csrgeam2_bufferSizeExt);
 
-#define SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(FUNC, TYPE, INFIX)        \
-    template<>                                                     \
-    FUNC##_buffer_size_def<TYPE> FUNC##_buffer_size_func<TYPE>() { \
-        cusparseModule &_ = getCusparsePlugin();                   \
-        return _.cusparse##INFIX##FUNC##_bufferSizeExt;            \
+#define SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(FUNC, TYPE, INFIX) \
+    template<>                                              \
+    FUNC##_def<TYPE> FUNC##_func<TYPE>() {                  \
+        cusparseModule &_ = getCusparsePlugin();            \
+        return _.cusparse##INFIX##FUNC;                     \
     }
 
-SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, float, S);
-SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, double, D);
-SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, cfloat, C);
-SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2, cdouble, Z);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2_bufferSizeExt, float, S);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2_bufferSizeExt, double, D);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2_bufferSizeExt, cfloat, C);
+SPARSE_ARITH_OP_BUFFER_SIZE_FUNC(csrgeam2_bufferSizeExt, cdouble, Z);
 
 template<typename T>
 using csrgeam2_def = cusparseStatus_t (*)(cusparseHandle_t, int, int, const T *,
@@ -188,11 +191,12 @@ SPARSE_ARITH_OP_FUNC(csrgeam, cdouble, Z);
 
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
-    lhs.eval();
-    rhs.eval();
+    cusparseModule &_ = getCusparsePlugin();
+    af::storage sfmt  = lhs.getStorage();
+    auto ldesc        = make_handle<cusparseMatDescr_t>();
+    auto rdesc        = make_handle<cusparseMatDescr_t>();
+    auto odesc        = make_handle<cusparseMatDescr_t>();
 
-    af::storage sfmt      = lhs.getStorage();
-    auto desc             = make_handle<cusparseMatDescr_t>();
     const dim4 ldims      = lhs.dims();
     const int M           = ldims[0];
     const int N           = ldims[1];
@@ -203,59 +207,63 @@ SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
     const int *csrRowPtrB = rhs.getRowIdx().get();
     const int *csrColPtrB = rhs.getColIdx().get();
 
-    auto outRowIdx = createEmptyArray<int>(dim4(M + 1));
+    int baseC, nnzC = M + 1;
 
-    int *csrRowPtrC = outRowIdx.get();
-    int baseC, nnzC;
-    int *nnzcDevHostPtr = &nnzC;
+    auto nnzDevHostPtr = memAlloc<int>(1);
+    auto outRowIdx     = createValueArray<int>(M + 1, 0);
 
-    T alpha           = scalar<T>(1);
-    T beta            = op == af_sub_t ? scalar<T>(-1) : alpha;
-    cusparseModule &_ = getCusparsePlugin();
+    T alpha = scalar<T>(1);
+    T beta  = op == af_sub_t ? scalar<T>(-1) : scalar<T>(1);
 
-#if CUDA_VERSION >= 11000
-    size_t pBufferSize = 0;
+    T *csrValC      = nullptr;
+    int *csrColIndC = nullptr;
 
-    csrgeam2_buffer_size_func<T>()(
-        sparseHandle(), M, N, &alpha, desc, nnzA, lhs.getValues().get(),
-        csrRowPtrA, csrColPtrA, &beta, desc, nnzB, rhs.getValues().get(),
-        csrRowPtrB, csrColPtrB, desc, NULL, csrRowPtrC, NULL, &pBufferSize);
+#if CUSPARSE_VERSION < 11000
+    CUSPARSE_CHECK(_.cusparseXcsrgeamNnz(
+        sparseHandle(), M, N, ldesc, nnzA, csrRowPtrA, csrColPtrA, rdesc, nnzB,
+        csrRowPtrB, csrColPtrB, odesc, outRowIdx.get(), nnzDevHostPtr.get()));
+#else
+    size_t pBufferSize = 0;
 
-    auto tmpBuffer = createEmptyArray<char>(dim4(pBufferSize));
+    CUSPARSE_CHECK(csrgeam2_bufferSizeExt_func<T>()(
+        sparseHandle(), M, N, &alpha, ldesc, nnzA, lhs.getValues().get(),
+        csrRowPtrA, csrColPtrA, &beta, rdesc, nnzB, rhs.getValues().get(),
+        csrRowPtrB, csrColPtrB, odesc, csrValC, outRowIdx.get(), csrColIndC,
+        &pBufferSize));
 
+    auto tmpBuffer = memAlloc<char>(pBufferSize);
     CUSPARSE_CHECK(_.cusparseXcsrgeam2Nnz(
-        sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
-        csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr,
+        sparseHandle(), M, N, ldesc, nnzA, csrRowPtrA, csrColPtrA, rdesc, nnzB,
+        csrRowPtrB, csrColPtrB, odesc, outRowIdx.get(), nnzDevHostPtr.get(),
         tmpBuffer.get()));
-#else
-    CUSPARSE_CHECK(_.cusparseXcsrgeamNnz(
-        sparseHandle(), M, N, desc, nnzA, csrRowPtrA, csrColPtrA, desc, nnzB,
-        csrRowPtrB, csrColPtrB, desc, csrRowPtrC, nnzcDevHostPtr));
 #endif
-    if (NULL != nnzcDevHostPtr) {
-        nnzC = *nnzcDevHostPtr;
+    if (NULL != nnzDevHostPtr) {
+        CUDA_CHECK(cudaMemcpyAsync(&nnzC, nnzDevHostPtr.get(), sizeof(int),
+                                   cudaMemcpyDeviceToHost, getActiveStream()));
+        CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
     } else {
-        CUDA_CHECK(cudaMemcpyAsync(&nnzC, csrRowPtrC + M, sizeof(int),
+        CUDA_CHECK(cudaMemcpyAsync(&nnzC, outRowIdx.get() + M, sizeof(int),
                                    cudaMemcpyDeviceToHost, getActiveStream()));
-        CUDA_CHECK(cudaMemcpyAsync(&baseC, csrRowPtrC, sizeof(int),
+        CUDA_CHECK(cudaMemcpyAsync(&baseC, outRowIdx.get(), sizeof(int),
                                    cudaMemcpyDeviceToHost, getActiveStream()));
         CUDA_CHECK(cudaStreamSynchronize(cuda::getActiveStream()));
         nnzC -= baseC;
     }
-
-    auto outColIdx = createEmptyArray<int>(dim4(nnzC));
-    auto outValues = createEmptyArray<T>(dim4(nnzC));
-#if CUDA_VERSION >= 11000
-    csrgeam2_func<T>()(sparseHandle(), M, N, &alpha, desc, nnzA,
-                       lhs.getValues().get(), csrRowPtrA, csrColPtrA, &beta,
-                       desc, nnzB, rhs.getValues().get(), csrRowPtrB,
-                       csrColPtrB, desc, outValues.get(), csrRowPtrC,
-                       outColIdx.get(), tmpBuffer.get());
+    auto outColIdx = createEmptyArray<int>(nnzC);
+    auto outValues = createEmptyArray<T>(nnzC);
+
+#if CUSPARSE_VERSION < 11000
+    CUSPARSE_CHECK(csrgeam_func<T>()(
+        sparseHandle(), M, N, &alpha, ldesc, nnzA, lhs.getValues().get(),
+        csrRowPtrA, csrColPtrA, &beta, rdesc, nnzB, rhs.getValues().get(),
+        csrRowPtrB, csrColPtrB, odesc, outValues.get(), outRowIdx.get(),
+        outColIdx.get()));
 #else
-    csrgeam_func<T>()(sparseHandle(), M, N, &alpha, desc, nnzA,
-                      lhs.getValues().get(), csrRowPtrA, csrColPtrA, &beta,
-                      desc, nnzB, rhs.getValues().get(), csrRowPtrB, csrColPtrB,
-                      desc, outValues.get(), csrRowPtrC, outColIdx.get());
+    CUSPARSE_CHECK(csrgeam2_func<T>()(
+        sparseHandle(), M, N, &alpha, ldesc, nnzA, lhs.getValues().get(),
+        csrRowPtrA, csrColPtrA, &beta, rdesc, nnzB, rhs.getValues().get(),
+        csrRowPtrB, csrColPtrB, odesc, outValues.get(), outRowIdx.get(),
+        outColIdx.get(), tmpBuffer.get()));
 #endif
     SparseArray<T> retVal = createArrayDataSparseArray(
         ldims, outValues, outRowIdx, outColIdx, sfmt);
diff --git a/src/backend/cuda/sparse_blas.cu b/src/backend/cuda/sparse_blas.cu
index 965186a915..f0ef6a45c3 100644
--- a/src/backend/cuda/sparse_blas.cu
+++ b/src/backend/cuda/sparse_blas.cu
@@ -36,6 +36,23 @@ cusparseOperation_t toCusparseTranspose(af_mat_prop opt) {
     return out;
 }
 
+#if CUSPARSE_VERSION < 11300
+#define AF_CUSPARSE_SPMV_CSR_ALG1 CUSPARSE_CSRMV_ALG1
+#define AF_CUSPARSE_SPMV_ALG_DEFAULT CUSPARSE_MV_ALG_DEFAULT
+#define AF_CUSPARSE_SPMM_CSR_ALG1 CUSPARSE_CSRMM_ALG1
+#define AF_CUSPARSE_SPMM_CSR_ALG1 CUSPARSE_CSRMM_ALG1
+#elif CUSPARSE_VERSION < 11400
+#define AF_CUSPARSE_SPMV_CSR_ALG1 CUSPARSE_CSRMV_ALG1
+#define AF_CUSPARSE_SPMV_ALG_DEFAULT CUSPARSE_MV_ALG_DEFAULT
+#define AF_CUSPARSE_SPMM_CSR_ALG1 CUSPARSE_SPMM_CSR_ALG1
+#define AF_CUSPARSE_SPMM_CSR_ALG1 CUSPARSE_SPMM_CSR_ALG1
+#else
+#define AF_CUSPARSE_SPMV_CSR_ALG1 CUSPARSE_SPMV_CSR_ALG1
+#define AF_CUSPARSE_SPMV_ALG_DEFAULT CUSPARSE_SPMV_ALG_DEFAULT
+#define AF_CUSPARSE_SPMM_CSR_ALG1 CUSPARSE_SPMM_CSR_ALG1
+#define AF_CUSPARSE_SPMM_CSR_ALG1 CUSPARSE_SPMM_CSR_ALG1
+#endif
+
 #if defined(AF_USE_NEW_CUSPARSE_API)
 
 template<typename T>
@@ -47,7 +64,7 @@ size_t spmvBufferSize(cusparseOperation_t opA, const T *alpha,
     cusparseModule &_ = getCusparsePlugin();
     CUSPARSE_CHECK(_.cusparseSpMV_bufferSize(
         sparseHandle(), opA, alpha, matA, vecX, beta, vecY, getComputeType<T>(),
-        CUSPARSE_CSRMV_ALG1, &retVal));
+        AF_CUSPARSE_SPMV_CSR_ALG1, &retVal));
     return retVal;
 }
 
@@ -58,7 +75,7 @@ void spmv(cusparseOperation_t opA, const T *alpha,
     cusparseModule &_ = getCusparsePlugin();
     CUSPARSE_CHECK(_.cusparseSpMV(sparseHandle(), opA, alpha, matA, vecX, beta,
                                   vecY, getComputeType<T>(),
-                                  CUSPARSE_MV_ALG_DEFAULT, buffer));
+                                  AF_CUSPARSE_SPMV_ALG_DEFAULT, buffer));
 }
 
 template<typename T>
@@ -70,7 +87,7 @@ size_t spmmBufferSize(cusparseOperation_t opA, cusparseOperation_t opB,
     cusparseModule &_ = getCusparsePlugin();
     CUSPARSE_CHECK(_.cusparseSpMM_bufferSize(
         sparseHandle(), opA, opB, alpha, matA, matB, beta, matC,
-        getComputeType<T>(), CUSPARSE_CSRMM_ALG1, &retVal));
+        getComputeType<T>(), AF_CUSPARSE_SPMM_CSR_ALG1, &retVal));
     return retVal;
 }
 
@@ -81,7 +98,7 @@ void spmm(cusparseOperation_t opA, cusparseOperation_t opB, const T *alpha,
     cusparseModule &_ = getCusparsePlugin();
     CUSPARSE_CHECK(_.cusparseSpMM(sparseHandle(), opA, opB, alpha, matA, matB,
                                   beta, matC, getComputeType<T>(),
-                                  CUSPARSE_CSRMM_ALG1, buffer));
+                                  AF_CUSPARSE_SPMM_CSR_ALG1, buffer));
 }
 
 #else
@@ -158,7 +175,7 @@ Array<T> matmul(const common::SparseArray<T> &lhs, const Array<T> &rhs,
 
 #if defined(AF_USE_NEW_CUSPARSE_API)
 
-    auto spMat = csrMatDescriptor<T>(lhs);
+    auto spMat = cusparseDescriptor<T>(lhs);
 
     if (rDims[rColDim] == 1) {
         auto dnVec = denVecDescriptor<T>(rhs);
diff --git a/src/backend/cuda/thrust_utils.hpp b/src/backend/cuda/thrust_utils.hpp
index 8aafbc1752..0646b934ba 100644
--- a/src/backend/cuda/thrust_utils.hpp
+++ b/src/backend/cuda/thrust_utils.hpp
@@ -20,25 +20,7 @@ using ThrustVector = thrust::device_vector<T, ThrustAllocator<T>>;
 }  // namespace cuda
 }  // namespace arrayfire
 
-#if THRUST_MAJOR_VERSION >= 1 && THRUST_MINOR_VERSION >= 8
-
 #define THRUST_SELECT(fn, ...) \
     fn(arrayfire::cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
 #define THRUST_SELECT_OUT(res, fn, ...) \
     res = fn(arrayfire::cuda::ThrustArrayFirePolicy(), __VA_ARGS__)
-
-#else
-
-#define THRUST_SELECT(fn, ...)                                                 \
-    do {                                                                       \
-        CUDA_CHECK(cudaStreamSynchronize(arrayfire::cuda::getActiveStream())); \
-        fn(__VA_ARGS__);                                                       \
-    } while (0)
-
-#define THRUST_SELECT_OUT(res, fn, ...)                                        \
-    do {                                                                       \
-        CUDA_CHECK(cudaStreamSynchronize(arrayfire::cuda::getActiveStream())); \
-        res = fn(__VA_ARGS__);                                                 \
-    } while (0)
-
-#endif

From 8263656d274125f853396b64d18f1d108969b54f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Jan 2023 15:42:57 -0500
Subject: [PATCH 2363/2677] Enable support for p2447 style span initialization

---
 CMakeLists.txt                                |  3 ++
 src/backend/common/kernel_cache.hpp           |  2 +-
 src/backend/cuda/jit.cpp                      |  3 +-
 .../cuda/kernel/anisotropic_diffusion.hpp     |  7 ++-
 src/backend/cuda/kernel/approx.hpp            |  4 +-
 src/backend/cuda/kernel/assign.hpp            |  2 +-
 src/backend/cuda/kernel/bilateral.hpp         |  4 +-
 src/backend/cuda/kernel/canny.hpp             | 24 +++++-----
 src/backend/cuda/kernel/convolve.hpp          | 26 +++++------
 src/backend/cuda/kernel/diagonal.hpp          |  4 +-
 src/backend/cuda/kernel/diff.hpp              |  2 +-
 src/backend/cuda/kernel/exampleFunction.hpp   |  2 +-
 src/backend/cuda/kernel/fftconvolve.hpp       |  8 ++--
 src/backend/cuda/kernel/flood_fill.hpp        | 16 +++----
 src/backend/cuda/kernel/gradient.hpp          |  8 ++--
 src/backend/cuda/kernel/histogram.hpp         |  4 +-
 src/backend/cuda/kernel/hsv_rgb.hpp           |  2 +-
 src/backend/cuda/kernel/identity.hpp          |  6 +--
 src/backend/cuda/kernel/iir.hpp               |  4 +-
 src/backend/cuda/kernel/index.hpp             |  5 +--
 src/backend/cuda/kernel/iota.hpp              |  5 +--
 src/backend/cuda/kernel/ireduce.hpp           |  8 ++--
 src/backend/cuda/kernel/lookup.hpp            |  6 +--
 src/backend/cuda/kernel/lu_split.hpp          |  2 +-
 src/backend/cuda/kernel/match_template.hpp    |  2 +-
 src/backend/cuda/kernel/meanshift.hpp         |  2 +-
 src/backend/cuda/kernel/medfilt.hpp           | 18 ++++----
 src/backend/cuda/kernel/memcopy.hpp           | 15 +++----
 src/backend/cuda/kernel/moments.hpp           |  6 +--
 src/backend/cuda/kernel/morph.hpp             |  8 ++--
 src/backend/cuda/kernel/pad_array_borders.hpp |  2 +-
 src/backend/cuda/kernel/range.hpp             |  5 +--
 src/backend/cuda/kernel/reorder.hpp           |  6 +--
 src/backend/cuda/kernel/resize.hpp            |  2 +-
 src/backend/cuda/kernel/rotate.hpp            |  2 +-
 src/backend/cuda/kernel/scan_dim.hpp          |  6 +--
 .../cuda/kernel/scan_dim_by_key_impl.hpp      | 13 +++---
 src/backend/cuda/kernel/scan_first.hpp        |  6 +--
 .../cuda/kernel/scan_first_by_key_impl.hpp    | 14 +++---
 src/backend/cuda/kernel/select.hpp            |  4 +-
 src/backend/cuda/kernel/sobel.hpp             |  4 +-
 src/backend/cuda/kernel/sparse.hpp            |  4 +-
 src/backend/cuda/kernel/sparse_arith.hpp      | 16 +++----
 src/backend/cuda/kernel/susan.hpp             | 10 ++---
 src/backend/cuda/kernel/tile.hpp              |  5 +--
 src/backend/cuda/kernel/transform.hpp         |  2 +-
 src/backend/cuda/kernel/transpose.hpp         |  4 +-
 src/backend/cuda/kernel/transpose_inplace.hpp |  4 +-
 src/backend/cuda/kernel/triangle.hpp          |  2 +-
 src/backend/cuda/kernel/unwrap.hpp            |  2 +-
 src/backend/cuda/kernel/where.hpp             |  5 +--
 src/backend/cuda/kernel/wrap.hpp              |  4 +-
 src/backend/opencl/jit.cpp                    |  3 +-
 .../opencl/kernel/anisotropic_diffusion.hpp   |  6 +--
 src/backend/opencl/kernel/approx.hpp          | 10 ++---
 src/backend/opencl/kernel/assign.hpp          |  4 +-
 src/backend/opencl/kernel/bilateral.hpp       |  4 +-
 src/backend/opencl/kernel/canny.hpp           | 12 ++---
 .../opencl/kernel/convolve/conv2_impl.hpp     |  5 +--
 .../opencl/kernel/convolve/conv_common.hpp    |  5 +--
 .../opencl/kernel/convolve_separable.cpp      |  6 +--
 src/backend/opencl/kernel/cscmm.hpp           |  2 +-
 src/backend/opencl/kernel/cscmv.hpp           |  4 +-
 src/backend/opencl/kernel/csrmm.hpp           |  2 +-
 src/backend/opencl/kernel/csrmv.hpp           |  9 ++--
 src/backend/opencl/kernel/diagonal.hpp        |  6 +--
 src/backend/opencl/kernel/diff.hpp            |  4 +-
 src/backend/opencl/kernel/exampleFunction.hpp |  4 +-
 src/backend/opencl/kernel/fast.hpp            | 12 ++---
 src/backend/opencl/kernel/fftconvolve.hpp     | 18 ++++----
 src/backend/opencl/kernel/flood_fill.hpp      |  6 +--
 src/backend/opencl/kernel/gradient.hpp        |  4 +-
 src/backend/opencl/kernel/harris.hpp          | 10 ++---
 src/backend/opencl/kernel/histogram.hpp       |  4 +-
 src/backend/opencl/kernel/homography.hpp      | 20 ++++-----
 src/backend/opencl/kernel/hsv_rgb.hpp         |  4 +-
 src/backend/opencl/kernel/identity.hpp        |  4 +-
 src/backend/opencl/kernel/iir.hpp             |  3 +-
 src/backend/opencl/kernel/index.hpp           |  2 +-
 src/backend/opencl/kernel/iota.hpp            |  2 +-
 src/backend/opencl/kernel/ireduce.hpp         | 12 ++---
 src/backend/opencl/kernel/laset.hpp           |  4 +-
 src/backend/opencl/kernel/laswp.hpp           |  3 +-
 src/backend/opencl/kernel/lookup.hpp          |  4 +-
 src/backend/opencl/kernel/lu_split.hpp        |  4 +-
 src/backend/opencl/kernel/match_template.hpp  |  2 +-
 src/backend/opencl/kernel/mean.hpp            |  6 +--
 src/backend/opencl/kernel/meanshift.hpp       |  4 +-
 src/backend/opencl/kernel/medfilt.hpp         |  8 ++--
 src/backend/opencl/kernel/memcopy.hpp         |  7 ++-
 src/backend/opencl/kernel/moments.hpp         |  4 +-
 src/backend/opencl/kernel/morph.hpp           |  5 +--
 .../opencl/kernel/nearest_neighbour.hpp       |  5 +--
 src/backend/opencl/kernel/orb.hpp             | 11 ++---
 .../opencl/kernel/pad_array_borders.hpp       |  5 +--
 src/backend/opencl/kernel/random_engine.hpp   |  5 +--
 src/backend/opencl/kernel/range.hpp           |  4 +-
 src/backend/opencl/kernel/reduce.hpp          | 12 +++--
 src/backend/opencl/kernel/reduce_by_key.hpp   | 45 +++++++++----------
 src/backend/opencl/kernel/regions.hpp         |  9 ++--
 src/backend/opencl/kernel/reorder.hpp         |  4 +-
 src/backend/opencl/kernel/resize.hpp          |  4 +-
 src/backend/opencl/kernel/rotate.hpp          |  6 +--
 src/backend/opencl/kernel/scan_dim.hpp        |  4 +-
 .../opencl/kernel/scan_dim_by_key_impl.hpp    |  3 +-
 src/backend/opencl/kernel/scan_first.hpp      |  4 +-
 .../opencl/kernel/scan_first_by_key_impl.hpp  |  3 +-
 src/backend/opencl/kernel/select.hpp          |  8 ++--
 src/backend/opencl/kernel/sift.hpp            | 23 +++++-----
 src/backend/opencl/kernel/sobel.hpp           |  4 +-
 src/backend/opencl/kernel/sparse.hpp          | 20 ++++-----
 src/backend/opencl/kernel/sparse_arith.hpp    | 10 ++---
 src/backend/opencl/kernel/susan.hpp           |  8 ++--
 src/backend/opencl/kernel/swapdblk.hpp        |  4 +-
 src/backend/opencl/kernel/tile.hpp            |  3 +-
 src/backend/opencl/kernel/transform.hpp       |  6 +--
 src/backend/opencl/kernel/transpose.hpp       |  4 +-
 .../opencl/kernel/transpose_inplace.hpp       |  6 +--
 src/backend/opencl/kernel/triangle.hpp        |  2 +-
 src/backend/opencl/kernel/unwrap.hpp          |  4 +-
 src/backend/opencl/kernel/where.hpp           |  4 +-
 src/backend/opencl/kernel/wrap.hpp            |  9 ++--
 122 files changed, 378 insertions(+), 429 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 96498f9a2d..d610bba1c5 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -303,6 +303,9 @@ if(NOT TARGET nonstd::span-lite)
     PROPERTY INTERFACE_INCLUDE_DIRECTORIES)
   set_target_properties(span-lite
     PROPERTIES INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${span_include_dir}")
+  set_target_properties(span-lite
+    PROPERTIES INTERFACE_COMPILE_DEFINITIONS "span_FEATURE_WITH_INITIALIZER_LIST_P2447=1")
+
 endif()
 
 af_dep_check_and_populate(${assets_prefix}
diff --git a/src/backend/common/kernel_cache.hpp b/src/backend/common/kernel_cache.hpp
index bef3b6b577..50602963b1 100644
--- a/src/backend/common/kernel_cache.hpp
+++ b/src/backend/common/kernel_cache.hpp
@@ -50,7 +50,7 @@ namespace common {
 ///
 /// \code
 /// auto transpose = getKernel("arrayfire::cuda::transpose",
-/// std::array{transpase_cuh_src},
+/// {{transpase_cuh_src}},
 ///         {
 ///           TemplateTypename<T>(),
 ///           TemplateArg(conjugate),
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 2ffc2f72cf..86b2b2e6a6 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -322,8 +322,7 @@ static CUfunction getKernel(const vector<Node*>& output_nodes,
         const common::Source jit_src{jitKer.c_str(), jitKer.size(),
                                      deterministicHash(jitKer)};
 
-        return common::getKernel(funcName, std::array{jit_src}, {}, {}, true)
-            .get();
+        return common::getKernel(funcName, {{jit_src}}, {}, {}, true).get();
     }
     return common::getKernel(entry, funcName, true).get();
 }
diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.hpp b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
index e727d7ca4c..f376b8842e 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.hpp
@@ -28,12 +28,11 @@ template<typename T>
 void anisotropicDiffusion(Param<T> inout, const float dt, const float mct,
                           const af::fluxFunction fftype, bool isMCDE) {
     auto diffUpdate = common::getKernel(
-        "arrayfire::cuda::diffUpdate",
-        std::array{anisotropic_diffusion_cuh_src},
+        "arrayfire::cuda::diffUpdate", {{anisotropic_diffusion_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(fftype),
                      TemplateArg(isMCDE)),
-        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y),
-                   DefineValue(YDIM_LOAD)});
+        {{DefineValue(THREADS_X), DefineValue(THREADS_Y),
+          DefineValue(YDIM_LOAD)}});
 
     dim3 threads(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/cuda/kernel/approx.hpp b/src/backend/cuda/kernel/approx.hpp
index db705da687..46490c06b1 100644
--- a/src/backend/cuda/kernel/approx.hpp
+++ b/src/backend/cuda/kernel/approx.hpp
@@ -29,7 +29,7 @@ void approx1(Param<Ty> yo, CParam<Ty> yi, CParam<Tp> xo, const int xdim,
              const Tp &xi_beg, const Tp &xi_step, const float offGrid,
              const af::interpType method, const int order) {
     auto approx1 = common::getKernel(
-        "arrayfire::cuda::approx1", std::array{approx1_cuh_src},
+        "arrayfire::cuda::approx1", {{approx1_cuh_src}},
         TemplateArgs(TemplateTypename<Ty>(), TemplateTypename<Tp>(),
                      TemplateArg(xdim), TemplateArg(order)));
 
@@ -57,7 +57,7 @@ void approx2(Param<Ty> zo, CParam<Ty> zi, CParam<Tp> xo, const int xdim,
              const Tp &yi_beg, const Tp &yi_step, const float offGrid,
              const af::interpType method, const int order) {
     auto approx2 = common::getKernel(
-        "arrayfire::cuda::approx2", std::array{approx2_cuh_src},
+        "arrayfire::cuda::approx2", {{approx2_cuh_src}},
         TemplateArgs(TemplateTypename<Ty>(), TemplateTypename<Tp>(),
                      TemplateArg(xdim), TemplateArg(ydim), TemplateArg(order)));
 
diff --git a/src/backend/cuda/kernel/assign.hpp b/src/backend/cuda/kernel/assign.hpp
index 75c24e874c..008de72d37 100644
--- a/src/backend/cuda/kernel/assign.hpp
+++ b/src/backend/cuda/kernel/assign.hpp
@@ -24,7 +24,7 @@ void assign(Param<T> out, CParam<T> in, const AssignKernelParam& p) {
     constexpr int THREADS_Y = 8;
 
     auto assignKer =
-        common::getKernel("arrayfire::cuda::assign", std::array{assign_cuh_src},
+        common::getKernel("arrayfire::cuda::assign", {{assign_cuh_src}},
                           TemplateArgs(TemplateTypename<T>()));
 
     const dim3 threads(THREADS_X, THREADS_Y);
diff --git a/src/backend/cuda/kernel/bilateral.hpp b/src/backend/cuda/kernel/bilateral.hpp
index cf19eeb97c..c32d946792 100644
--- a/src/backend/cuda/kernel/bilateral.hpp
+++ b/src/backend/cuda/kernel/bilateral.hpp
@@ -24,9 +24,9 @@ template<typename inType, typename outType>
 void bilateral(Param<outType> out, CParam<inType> in, float s_sigma,
                float c_sigma) {
     auto bilateral = common::getKernel(
-        "arrayfire::cuda::bilateral", std::array{bilateral_cuh_src},
+        "arrayfire::cuda::bilateral", {{bilateral_cuh_src}},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>()),
-        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        {{DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/canny.hpp b/src/backend/cuda/kernel/canny.hpp
index 61af04ba6c..ef3dc6c40c 100644
--- a/src/backend/cuda/kernel/canny.hpp
+++ b/src/backend/cuda/kernel/canny.hpp
@@ -28,10 +28,10 @@ template<typename T>
 void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
                        CParam<T> dy) {
     auto nonMaxSuppress = common::getKernel(
-        "arrayfire::cuda::nonMaxSuppression", std::array{canny_cuh_src},
+        "arrayfire::cuda::nonMaxSuppression", {{canny_cuh_src}},
         TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        {{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+          DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
@@ -50,20 +50,20 @@ void nonMaxSuppression(Param<T> output, CParam<T> magnitude, CParam<T> dx,
 template<typename T>
 void edgeTrackingHysteresis(Param<T> output, CParam<T> strong, CParam<T> weak) {
     auto initEdgeOut = common::getKernel(
-        "arrayfire::cuda::initEdgeOut", std::array{canny_cuh_src},
+        "arrayfire::cuda::initEdgeOut", {{canny_cuh_src}},
         TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        {{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+          DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
     auto edgeTrack = common::getKernel(
-        "arrayfire::cuda::edgeTrack", std::array{canny_cuh_src},
+        "arrayfire::cuda::edgeTrack", {{canny_cuh_src}},
         TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        {{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+          DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
     auto suppressLeftOver = common::getKernel(
-        "arrayfire::cuda::suppressLeftOver", std::array{canny_cuh_src},
+        "arrayfire::cuda::suppressLeftOver", {{canny_cuh_src}},
         TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
-                   DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        {{DefineValue(STRONG), DefineValue(WEAK), DefineValue(NOEDGE),
+          DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/convolve.hpp b/src/backend/cuda/kernel/convolve.hpp
index 8183805e7c..38339f2de2 100644
--- a/src/backend/cuda/kernel/convolve.hpp
+++ b/src/backend/cuda/kernel/convolve.hpp
@@ -101,11 +101,10 @@ template<typename T, typename aT>
 void convolve_1d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     auto convolve1 = common::getKernel(
-        "arrayfire::cuda::convolve1", std::array{convolve1_cuh_src},
+        "arrayfire::cuda::convolve1", {{convolve1_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(expand)),
-        std::array{DefineValue(MAX_CONV1_FILTER_LEN),
-                   DefineValue(CONV_THREADS)});
+        {{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS)}});
 
     prepareKernelArgs<T>(p, out.dims, filt.dims, 1);
 
@@ -158,11 +157,11 @@ void conv2Helper(const conv_kparam_t& p, Param<T> out, CParam<T> sig,
     }
 
     auto convolve2 = common::getKernel(
-        "arrayfire::cuda::convolve2", std::array{convolve2_cuh_src},
+        "arrayfire::cuda::convolve2", {{convolve2_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(expand), TemplateArg(f0), TemplateArg(f1)),
-        std::array{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
-                   DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)});
+        {{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+          DefineValue(CONV2_THREADS_X), DefineValue(CONV2_THREADS_Y)}});
 
     // FIXME: case where filter array is strided
     auto constMemPtr = convolve2.getDevPtr(conv_c_name);
@@ -203,12 +202,12 @@ template<typename T, typename aT>
 void convolve_3d(conv_kparam_t& p, Param<T> out, CParam<T> sig, CParam<aT> filt,
                  const bool expand) {
     auto convolve3 = common::getKernel(
-        "arrayfire::cuda::convolve3", std::array{convolve3_cuh_src},
+        "arrayfire::cuda::convolve3", {{convolve3_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(expand)),
-        std::array{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
-                   DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
-                   DefineValue(CONV3_CUBE_Z)});
+        {{DefineValue(MAX_CONV1_FILTER_LEN), DefineValue(CONV_THREADS),
+          DefineValue(CONV3_CUBE_X), DefineValue(CONV3_CUBE_Y),
+          DefineValue(CONV3_CUBE_Z)}});
 
     prepareKernelArgs<T>(p, out.dims, filt.dims, 3);
 
@@ -308,13 +307,12 @@ void convolve2(Param<T> out, CParam<T> signal, CParam<aT> filter, int conv_dim,
     }
 
     auto convolve2_separable = common::getKernel(
-        "arrayfire::cuda::convolve2_separable",
-        std::array{convolve_separable_cuh_src},
+        "arrayfire::cuda::convolve2_separable", {{convolve_separable_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<aT>(),
                      TemplateArg(conv_dim), TemplateArg(expand),
                      TemplateArg(fLen)),
-        std::array{DefineValue(MAX_SCONV_FILTER_LEN),
-                   DefineValue(SCONV_THREADS_X), DefineValue(SCONV_THREADS_Y)});
+        {{DefineValue(MAX_SCONV_FILTER_LEN), DefineValue(SCONV_THREADS_X),
+          DefineValue(SCONV_THREADS_Y)}});
 
     dim3 threads(SCONV_THREADS_X, SCONV_THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/diagonal.hpp b/src/backend/cuda/kernel/diagonal.hpp
index 4ffb6fa4ff..40b25e159e 100644
--- a/src/backend/cuda/kernel/diagonal.hpp
+++ b/src/backend/cuda/kernel/diagonal.hpp
@@ -22,7 +22,7 @@ namespace kernel {
 template<typename T>
 void diagCreate(Param<T> out, CParam<T> in, int num) {
     auto genDiagMat = common::getKernel("arrayfire::cuda::createDiagonalMat",
-                                        std::array{diagonal_cuh_src},
+                                        {{diagonal_cuh_src}},
                                         TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(32, 8);
@@ -47,7 +47,7 @@ void diagCreate(Param<T> out, CParam<T> in, int num) {
 template<typename T>
 void diagExtract(Param<T> out, CParam<T> in, int num) {
     auto extractDiag = common::getKernel("arrayfire::cuda::extractDiagonal",
-                                         std::array{diagonal_cuh_src},
+                                         {{diagonal_cuh_src}},
                                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(256, 1);
diff --git a/src/backend/cuda/kernel/diff.hpp b/src/backend/cuda/kernel/diff.hpp
index c547e0e933..cdce6eaf8f 100644
--- a/src/backend/cuda/kernel/diff.hpp
+++ b/src/backend/cuda/kernel/diff.hpp
@@ -26,7 +26,7 @@ void diff(Param<T> out, CParam<T> in, const int indims, const unsigned dim,
     constexpr unsigned TY = 16;
 
     auto diff =
-        common::getKernel("arrayfire::cuda::diff", std::array{diff_cuh_src},
+        common::getKernel("arrayfire::cuda::diff", {{diff_cuh_src}},
                           TemplateArgs(TemplateTypename<T>(), TemplateArg(dim),
                                        TemplateArg(isDiff2)));
 
diff --git a/src/backend/cuda/kernel/exampleFunction.hpp b/src/backend/cuda/kernel/exampleFunction.hpp
index 730c309a86..4f037eb771 100644
--- a/src/backend/cuda/kernel/exampleFunction.hpp
+++ b/src/backend/cuda/kernel/exampleFunction.hpp
@@ -29,7 +29,7 @@ static const unsigned TY = 16;  // Kernel Launch Config Values
 template<typename T>  // CUDA kernel wrapper function
 void exampleFunc(Param<T> c, CParam<T> a, CParam<T> b, const af_someenum_t p) {
     auto exampleFunc = common::getKernel("arrayfire::cuda::exampleFunc",
-                                         std::array{exampleFunction_cuh_src},
+                                         {{exampleFunction_cuh_src}},
                                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);  // set your cuda launch config for blocks
diff --git a/src/backend/cuda/kernel/fftconvolve.hpp b/src/backend/cuda/kernel/fftconvolve.hpp
index cf45bc18a4..da3657d4de 100644
--- a/src/backend/cuda/kernel/fftconvolve.hpp
+++ b/src/backend/cuda/kernel/fftconvolve.hpp
@@ -25,10 +25,10 @@ template<typename convT, typename T>
 void packDataHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                     CParam<T> sig, CParam<T> filter) {
     auto packData = common::getKernel(
-        "arrayfire::cuda::packData", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::packData", {{fftconvolve_cuh_src}},
         TemplateArgs(TemplateTypename<convT>(), TemplateTypename<T>()));
     auto padArray = common::getKernel(
-        "arrayfire::cuda::padArray", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::padArray", {{fftconvolve_cuh_src}},
         TemplateArgs(TemplateTypename<convT>(), TemplateTypename<T>()));
 
     dim_t *sd = sig.dims;
@@ -69,7 +69,7 @@ template<typename T, typename convT>
 void complexMultiplyHelper(Param<convT> sig_packed, Param<convT> filter_packed,
                            AF_BATCH_KIND kind) {
     auto cplxMul = common::getKernel(
-        "arrayfire::cuda::complexMultiply", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::complexMultiply", {{fftconvolve_cuh_src}},
         TemplateArgs(TemplateTypename<convT>(), TemplateArg(kind)));
 
     int sig_packed_elem    = 1;
@@ -102,7 +102,7 @@ void reorderOutputHelper(Param<T> out, Param<convT> packed, CParam<T> sig,
     constexpr bool RoundResult = std::is_integral<T>::value;
 
     auto reorderOut = common::getKernel(
-        "arrayfire::cuda::reorderOutput", std::array{fftconvolve_cuh_src},
+        "arrayfire::cuda::reorderOutput", {{fftconvolve_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateTypename<convT>(),
                      TemplateArg(expand), TemplateArg(RoundResult)));
 
diff --git a/src/backend/cuda/kernel/flood_fill.hpp b/src/backend/cuda/kernel/flood_fill.hpp
index 29f5741a04..03e3fd8fea 100644
--- a/src/backend/cuda/kernel/flood_fill.hpp
+++ b/src/backend/cuda/kernel/flood_fill.hpp
@@ -46,15 +46,15 @@ void floodFill(Param<T> out, CParam<T> image, CParam<uint> seedsx,
         CUDA_NOT_SUPPORTED(errMessage);
     }
 
-    auto initSeeds = common::getKernel("arrayfire::cuda::initSeeds",
-                                       std::array{flood_fill_cuh_src},
-                                       TemplateArgs(TemplateTypename<T>()));
-    auto floodStep = common::getKernel(
-        "arrayfire::cuda::floodStep", std::array{flood_fill_cuh_src},
-        TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto initSeeds =
+        common::getKernel("arrayfire::cuda::initSeeds", {{flood_fill_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()));
+    auto floodStep =
+        common::getKernel("arrayfire::cuda::floodStep", {{flood_fill_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()),
+                          {{DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
     auto finalizeOutput = common::getKernel(
-        "arrayfire::cuda::finalizeOutput", std::array{flood_fill_cuh_src},
+        "arrayfire::cuda::finalizeOutput", {{flood_fill_cuh_src}},
         TemplateArgs(TemplateTypename<T>()));
 
     EnqueueArgs qArgs(dim3(divup(seedsx.elements(), THREADS)), dim3(THREADS),
diff --git a/src/backend/cuda/kernel/gradient.hpp b/src/backend/cuda/kernel/gradient.hpp
index a6f2a8a6b9..3aaf250e60 100644
--- a/src/backend/cuda/kernel/gradient.hpp
+++ b/src/backend/cuda/kernel/gradient.hpp
@@ -26,10 +26,10 @@ void gradient(Param<T> grad0, Param<T> grad1, CParam<T> in) {
     constexpr unsigned TX = 32;
     constexpr unsigned TY = 8;
 
-    auto gradient = common::getKernel(
-        "arrayfire::cuda::gradient", std::array{gradient_cuh_src},
-        TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(TX), DefineValue(TY)});
+    auto gradient =
+        common::getKernel("arrayfire::cuda::gradient", {{gradient_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()),
+                          {{DefineValue(TX), DefineValue(TY)}});
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/histogram.hpp b/src/backend/cuda/kernel/histogram.hpp
index b9a9945c99..ddc0d7fae0 100644
--- a/src/backend/cuda/kernel/histogram.hpp
+++ b/src/backend/cuda/kernel/histogram.hpp
@@ -25,9 +25,9 @@ template<typename T>
 void histogram(Param<uint> out, CParam<T> in, int nbins, float minval,
                float maxval, bool isLinear) {
     auto histogram = common::getKernel(
-        "arrayfire::cuda::histogram", std::array{histogram_cuh_src},
+        "arrayfire::cuda::histogram", {{histogram_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isLinear)),
-        std::array{DefineValue(MAX_BINS), DefineValue(THRD_LOAD)});
+        {{DefineValue(MAX_BINS), DefineValue(THRD_LOAD)}});
 
     dim3 threads(kernel::THREADS_X, 1);
 
diff --git a/src/backend/cuda/kernel/hsv_rgb.hpp b/src/backend/cuda/kernel/hsv_rgb.hpp
index fe89bb34cb..83cae19e33 100644
--- a/src/backend/cuda/kernel/hsv_rgb.hpp
+++ b/src/backend/cuda/kernel/hsv_rgb.hpp
@@ -23,7 +23,7 @@ static const int THREADS_Y = 16;
 template<typename T>
 void hsv2rgb_convert(Param<T> out, CParam<T> in, bool isHSV2RGB) {
     auto hsvrgbConverter = common::getKernel(
-        "arrayfire::cuda::hsvrgbConverter", std::array{hsv_rgb_cuh_src},
+        "arrayfire::cuda::hsvrgbConverter", {{hsv_rgb_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isHSV2RGB)));
 
     const dim3 threads(THREADS_X, THREADS_Y);
diff --git a/src/backend/cuda/kernel/identity.hpp b/src/backend/cuda/kernel/identity.hpp
index 42fe1707e8..c3aea2dc8b 100644
--- a/src/backend/cuda/kernel/identity.hpp
+++ b/src/backend/cuda/kernel/identity.hpp
@@ -21,9 +21,9 @@ namespace kernel {
 
 template<typename T>
 void identity(Param<T> out) {
-    auto identity = common::getKernel("arrayfire::cuda::identity",
-                                      std::array{identity_cuh_src},
-                                      TemplateArgs(TemplateTypename<T>()));
+    auto identity =
+        common::getKernel("arrayfire::cuda::identity", {{identity_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(32, 8);
     int blocks_x = divup(out.dims[0], threads.x);
diff --git a/src/backend/cuda/kernel/iir.hpp b/src/backend/cuda/kernel/iir.hpp
index f0f58512d8..a17d205fd8 100644
--- a/src/backend/cuda/kernel/iir.hpp
+++ b/src/backend/cuda/kernel/iir.hpp
@@ -24,9 +24,9 @@ void iir(Param<T> y, CParam<T> c, CParam<T> a) {
     constexpr int MAX_A_SIZE = 1024;
 
     auto iir = common::getKernel(
-        "arrayfire::cuda::iir", std::array{iir_cuh_src},
+        "arrayfire::cuda::iir", {{iir_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(batch_a)),
-        std::array{DefineValue(MAX_A_SIZE)});
+        {{DefineValue(MAX_A_SIZE)}});
 
     const int blocks_y = y.dims[1];
     const int blocks_x = y.dims[2];
diff --git a/src/backend/cuda/kernel/index.hpp b/src/backend/cuda/kernel/index.hpp
index 63d318408e..d2a4d06d37 100644
--- a/src/backend/cuda/kernel/index.hpp
+++ b/src/backend/cuda/kernel/index.hpp
@@ -22,9 +22,8 @@ namespace kernel {
 
 template<typename T>
 void index(Param<T> out, CParam<T> in, const IndexKernelParam& p) {
-    auto index =
-        common::getKernel("arrayfire::cuda::index", std::array{index_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto index = common::getKernel("arrayfire::cuda::index", {{index_cuh_src}},
+                                   TemplateArgs(TemplateTypename<T>()));
     dim3 threads;
     switch (out.dims[1]) {
         case 1: threads.y = 1; break;
diff --git a/src/backend/cuda/kernel/iota.hpp b/src/backend/cuda/kernel/iota.hpp
index 7624f68559..1007ec2f1e 100644
--- a/src/backend/cuda/kernel/iota.hpp
+++ b/src/backend/cuda/kernel/iota.hpp
@@ -27,9 +27,8 @@ void iota(Param<T> out, const af::dim4 &sdims) {
     constexpr unsigned TILEX   = 512;
     constexpr unsigned TILEY   = 32;
 
-    auto iota =
-        common::getKernel("arrayfire::cuda::iota", std::array{iota_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto iota = common::getKernel("arrayfire::cuda::iota", {{iota_cuh_src}},
+                                  TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index 91539469eb..c394c01f83 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -37,10 +37,10 @@ void ireduce_dim_launcher(Param<T> out, uint *olptr, CParam<T> in,
     blocks.y             = divup(blocks.y, blocks.z);
 
     auto ireduceDim = common::getKernel(
-        "arrayfire::cuda::ireduceDim", std::array{ireduce_cuh_src},
+        "arrayfire::cuda::ireduceDim", {{ireduce_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op), TemplateArg(dim),
                      TemplateArg(is_first), TemplateArg(threads_y)),
-        std::array{DefineValue(THREADS_X)});
+        {{DefineValue(THREADS_X)}});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -104,10 +104,10 @@ void ireduce_first_launcher(Param<T> out, uint *olptr, CParam<T> in,
 
     // threads_x can take values 32, 64, 128, 256
     auto ireduceFirst = common::getKernel(
-        "arrayfire::cuda::ireduceFirst", std::array{ireduce_cuh_src},
+        "arrayfire::cuda::ireduceFirst", {{ireduce_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op),
                      TemplateArg(is_first), TemplateArg(threads_x)),
-        std::array{DefineValue(THREADS_PER_BLOCK)});
+        {{DefineValue(THREADS_PER_BLOCK)}});
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
diff --git a/src/backend/cuda/kernel/lookup.hpp b/src/backend/cuda/kernel/lookup.hpp
index b4395980f0..4d23596d6c 100644
--- a/src/backend/cuda/kernel/lookup.hpp
+++ b/src/backend/cuda/kernel/lookup.hpp
@@ -44,9 +44,9 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         dim3 blocks(blks, 1);
 
         auto lookup1d = common::getKernel(
-            "arrayfire::cuda::lookup1D", std::array{lookup_cuh_src},
+            "arrayfire::cuda::lookup1D", {{lookup_cuh_src}},
             TemplateArgs(TemplateTypename<in_t>(), TemplateTypename<idx_t>()),
-            std::array{DefineValue(THREADS), DefineValue(THRD_LOAD)});
+            {{DefineValue(THREADS), DefineValue(THRD_LOAD)}});
 
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
@@ -65,7 +65,7 @@ void lookup(Param<in_t> out, CParam<in_t> in, CParam<idx_t> indices, int nDims,
         blocks.y = divup(blocks.y, blocks.z);
 
         auto lookupnd = common::getKernel(
-            "arrayfire::cuda::lookupND", std::array{lookup_cuh_src},
+            "arrayfire::cuda::lookupND", {{lookup_cuh_src}},
             TemplateArgs(TemplateTypename<in_t>(), TemplateTypename<idx_t>(),
                          TemplateArg(dim)));
         EnqueueArgs qArgs(blocks, threads, getActiveStream());
diff --git a/src/backend/cuda/kernel/lu_split.hpp b/src/backend/cuda/kernel/lu_split.hpp
index 1d2a185276..467173c218 100644
--- a/src/backend/cuda/kernel/lu_split.hpp
+++ b/src/backend/cuda/kernel/lu_split.hpp
@@ -32,7 +32,7 @@ void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
         lower.dims[0] == in.dims[0] && lower.dims[1] == in.dims[1];
 
     auto luSplit = common::getKernel(
-        "arrayfire::cuda::luSplit", std::array{lu_split_cuh_src},
+        "arrayfire::cuda::luSplit", {{lu_split_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(sameDims)));
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/match_template.hpp b/src/backend/cuda/kernel/match_template.hpp
index c9754473ae..a605eabab5 100644
--- a/src/backend/cuda/kernel/match_template.hpp
+++ b/src/backend/cuda/kernel/match_template.hpp
@@ -26,7 +26,7 @@ void matchTemplate(Param<outType> out, CParam<inType> srch,
                    CParam<inType> tmplt, const af::matchType mType,
                    bool needMean) {
     auto matchTemplate = common::getKernel(
-        "arrayfire::cuda::matchTemplate", std::array{match_template_cuh_src},
+        "arrayfire::cuda::matchTemplate", {{match_template_cuh_src}},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
                      TemplateArg(mType), TemplateArg(needMean)));
 
diff --git a/src/backend/cuda/kernel/meanshift.hpp b/src/backend/cuda/kernel/meanshift.hpp
index c1882c91fc..600f456fb9 100644
--- a/src/backend/cuda/kernel/meanshift.hpp
+++ b/src/backend/cuda/kernel/meanshift.hpp
@@ -29,7 +29,7 @@ void meanshift(Param<T> out, CParam<T> in, const float spatialSigma,
     typedef typename std::conditional<std::is_same<T, double>::value, double,
                                       float>::type AccType;
     auto meanshift = common::getKernel(
-        "arrayfire::cuda::meanshift", std::array{meanshift_cuh_src},
+        "arrayfire::cuda::meanshift", {{meanshift_cuh_src}},
         TemplateArgs(TemplateTypename<AccType>(), TemplateTypename<T>(),
                      TemplateArg((IsColor ? 3 : 1))  // channels
                      ));
diff --git a/src/backend/cuda/kernel/medfilt.hpp b/src/backend/cuda/kernel/medfilt.hpp
index 69920b5ac0..20f3514ec6 100644
--- a/src/backend/cuda/kernel/medfilt.hpp
+++ b/src/backend/cuda/kernel/medfilt.hpp
@@ -27,11 +27,11 @@ template<typename T>
 void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
               int w_wid) {
     UNUSED(w_wid);
-    auto medfilt2 = common::getKernel(
-        "arrayfire::cuda::medfilt2", std::array{medfilt_cuh_src},
-        TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
-                     TemplateArg(w_len), TemplateArg(w_wid)),
-        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+    auto medfilt2 =
+        common::getKernel("arrayfire::cuda::medfilt2", {{medfilt_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
+                                       TemplateArg(w_len), TemplateArg(w_wid)),
+                          {{DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
@@ -47,10 +47,10 @@ void medfilt2(Param<T> out, CParam<T> in, const af::borderType pad, int w_len,
 
 template<typename T>
 void medfilt1(Param<T> out, CParam<T> in, const af::borderType pad, int w_wid) {
-    auto medfilt1 = common::getKernel(
-        "arrayfire::cuda::medfilt1", std::array{medfilt_cuh_src},
-        TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
-                     TemplateArg(w_wid)));
+    auto medfilt1 =
+        common::getKernel("arrayfire::cuda::medfilt1", {{medfilt_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>(), TemplateArg(pad),
+                                       TemplateArg(w_wid)));
 
     const dim3 threads(THREADS_X);
 
diff --git a/src/backend/cuda/kernel/memcopy.hpp b/src/backend/cuda/kernel/memcopy.hpp
index b75cc39c86..f4d39e6c64 100644
--- a/src/backend/cuda/kernel/memcopy.hpp
+++ b/src/backend/cuda/kernel/memcopy.hpp
@@ -128,23 +128,20 @@ void memcopy(Param<T> out, CParam<T> in, dim_t indims) {
     // Conversion to cuda base vector types.
     switch (sizeofNewT) {
         case 1: {
-            auto memCopy{common::getKernel(kernelName,
-                                           std::array{memcopy_cuh_src},
+            auto memCopy{common::getKernel(kernelName, {{memcopy_cuh_src}},
                                            TemplateArgs(TemplateArg("char")))};
             memCopy(qArgs, Param<char>((char *)out.ptr, out.dims, out.strides),
                     CParam<char>((const char *)in.ptr, in.dims, in.strides));
         } break;
         case 2: {
-            auto memCopy{common::getKernel(kernelName,
-                                           std::array{memcopy_cuh_src},
+            auto memCopy{common::getKernel(kernelName, {{memcopy_cuh_src}},
                                            TemplateArgs(TemplateArg("short")))};
             memCopy(qArgs,
                     Param<short>((short *)out.ptr, out.dims, out.strides),
                     CParam<short>((const short *)in.ptr, in.dims, in.strides));
         } break;
         case 4: {
-            auto memCopy{common::getKernel(kernelName,
-                                           std::array{memcopy_cuh_src},
+            auto memCopy{common::getKernel(kernelName, {{memcopy_cuh_src}},
                                            TemplateArgs(TemplateArg("float")))};
             memCopy(qArgs,
                     Param<float>((float *)out.ptr, out.dims, out.strides),
@@ -152,7 +149,7 @@ void memcopy(Param<T> out, CParam<T> in, dim_t indims) {
         } break;
         case 8: {
             auto memCopy{
-                common::getKernel(kernelName, std::array{memcopy_cuh_src},
+                common::getKernel(kernelName, {{memcopy_cuh_src}},
                                   TemplateArgs(TemplateArg("float2")))};
             memCopy(
                 qArgs, Param<float2>((float2 *)out.ptr, out.dims, out.strides),
@@ -160,7 +157,7 @@ void memcopy(Param<T> out, CParam<T> in, dim_t indims) {
         } break;
         case 16: {
             auto memCopy{
-                common::getKernel(kernelName, std::array{memcopy_cuh_src},
+                common::getKernel(kernelName, {{memcopy_cuh_src}},
                                   TemplateArgs(TemplateArg("float4")))};
             memCopy(
                 qArgs, Param<float4>((float4 *)out.ptr, out.dims, out.strides),
@@ -200,7 +197,7 @@ void copy(Param<outType> dst, CParam<inType> src, dim_t ondims,
         : (th.loop2 || th.loop3) ? "arrayfire::cuda::scaledCopyLoop123"
         : th.loop1               ? "arrayfire::cuda::scaledCopyLoop1"
                                  : "arrayfire::cuda::scaledCopy",
-        std::array{copy_cuh_src},
+        {{copy_cuh_src}},
         TemplateArgs(TemplateTypename<inType>(), TemplateTypename<outType>(),
                      TemplateArg(same_dims), TemplateArg(factor != 1.0)))};
 
diff --git a/src/backend/cuda/kernel/moments.hpp b/src/backend/cuda/kernel/moments.hpp
index ece6627c71..dcc1161b23 100644
--- a/src/backend/cuda/kernel/moments.hpp
+++ b/src/backend/cuda/kernel/moments.hpp
@@ -22,9 +22,9 @@ static const int THREADS = 128;
 
 template<typename T>
 void moments(Param<float> out, CParam<T> in, const af::momentType moment) {
-    auto moments = common::getKernel("arrayfire::cuda::moments",
-                                     std::array{moments_cuh_src},
-                                     TemplateArgs(TemplateTypename<T>()));
+    auto moments =
+        common::getKernel("arrayfire::cuda::moments", {{moments_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(THREADS, 1, 1);
     dim3 blocks(in.dims[1], in.dims[2] * in.dims[3]);
diff --git a/src/backend/cuda/kernel/morph.hpp b/src/backend/cuda/kernel/morph.hpp
index 4936d659b4..0aff8ff639 100644
--- a/src/backend/cuda/kernel/morph.hpp
+++ b/src/backend/cuda/kernel/morph.hpp
@@ -32,10 +32,10 @@ void morph(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     const int SeLength = (windLen <= 10 ? windLen : 0);
 
     auto morph = common::getKernel(
-        "arrayfire::cuda::morph", std::array{morph_cuh_src},
+        "arrayfire::cuda::morph", {{morph_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isDilation),
                      TemplateArg(SeLength)),
-        std::array{DefineValue(MAX_MORPH_FILTER_LEN)});
+        {{DefineValue(MAX_MORPH_FILTER_LEN)}});
 
     morph.copyToReadOnly(morph.getDevPtr("cFilter"),
                          reinterpret_cast<CUdeviceptr>(mask.ptr),
@@ -68,10 +68,10 @@ void morph3d(Param<T> out, CParam<T> in, CParam<T> mask, bool isDilation) {
     }
 
     auto morph3D = common::getKernel(
-        "arrayfire::cuda::morph3D", std::array{morph_cuh_src},
+        "arrayfire::cuda::morph3D", {{morph_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(isDilation),
                      TemplateArg(windLen)),
-        std::array{DefineValue(MAX_MORPH_FILTER_LEN)});
+        {{DefineValue(MAX_MORPH_FILTER_LEN)}});
 
     morph3D.copyToReadOnly(
         morph3D.getDevPtr("cFilter"), reinterpret_cast<CUdeviceptr>(mask.ptr),
diff --git a/src/backend/cuda/kernel/pad_array_borders.hpp b/src/backend/cuda/kernel/pad_array_borders.hpp
index 85acaabb26..b52fcf1401 100644
--- a/src/backend/cuda/kernel/pad_array_borders.hpp
+++ b/src/backend/cuda/kernel/pad_array_borders.hpp
@@ -29,7 +29,7 @@ template<typename T>
 void padBorders(Param<T> out, CParam<T> in, dim4 const lBoundPadding,
                 const af::borderType btype) {
     auto padBorders = common::getKernel(
-        "arrayfire::cuda::padBorders", std::array{pad_array_borders_cuh_src},
+        "arrayfire::cuda::padBorders", {{pad_array_borders_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(btype)));
 
     dim3 threads(kernel::PADB_THREADS_X, kernel::PADB_THREADS_Y);
diff --git a/src/backend/cuda/kernel/range.hpp b/src/backend/cuda/kernel/range.hpp
index 2e222f6e21..9b75276dc4 100644
--- a/src/backend/cuda/kernel/range.hpp
+++ b/src/backend/cuda/kernel/range.hpp
@@ -26,9 +26,8 @@ void range(Param<T> out, const int dim) {
     constexpr unsigned RANGE_TILEX = 512;
     constexpr unsigned RANGE_TILEY = 32;
 
-    auto range =
-        common::getKernel("arrayfire::cuda::range", std::array{range_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto range = common::getKernel("arrayfire::cuda::range", {{range_cuh_src}},
+                                   TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/cuda/kernel/reorder.hpp b/src/backend/cuda/kernel/reorder.hpp
index e2b83e4ab8..e54ebcf417 100644
--- a/src/backend/cuda/kernel/reorder.hpp
+++ b/src/backend/cuda/kernel/reorder.hpp
@@ -26,9 +26,9 @@ void reorder(Param<T> out, CParam<T> in, const dim_t *rdims) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    auto reorder = common::getKernel("arrayfire::cuda::reorder",
-                                     std::array{reorder_cuh_src},
-                                     TemplateArgs(TemplateTypename<T>()));
+    auto reorder =
+        common::getKernel("arrayfire::cuda::reorder", {{reorder_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/resize.hpp b/src/backend/cuda/kernel/resize.hpp
index 254e23e7d3..6129fe1e64 100644
--- a/src/backend/cuda/kernel/resize.hpp
+++ b/src/backend/cuda/kernel/resize.hpp
@@ -25,7 +25,7 @@ static const unsigned TY = 16;
 template<typename T>
 void resize(Param<T> out, CParam<T> in, af_interp_type method) {
     auto resize = common::getKernel(
-        "arrayfire::cuda::resize", std::array{resize_cuh_src},
+        "arrayfire::cuda::resize", {{resize_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(method)));
 
     dim3 threads(TX, TY, 1);
diff --git a/src/backend/cuda/kernel/rotate.hpp b/src/backend/cuda/kernel/rotate.hpp
index b31218047c..f1aa40585a 100644
--- a/src/backend/cuda/kernel/rotate.hpp
+++ b/src/backend/cuda/kernel/rotate.hpp
@@ -34,7 +34,7 @@ template<typename T>
 void rotate(Param<T> out, CParam<T> in, const float theta,
             const af::interpType method, const int order) {
     auto rotate = common::getKernel(
-        "arrayfire::cuda::rotate", std::array{rotate_cuh_src},
+        "arrayfire::cuda::rotate", {{rotate_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(order)));
 
     const float c = cos(-theta), s = sin(-theta);
diff --git a/src/backend/cuda/kernel/scan_dim.hpp b/src/backend/cuda/kernel/scan_dim.hpp
index a85c15a5ed..9fc32c61e9 100644
--- a/src/backend/cuda/kernel/scan_dim.hpp
+++ b/src/backend/cuda/kernel/scan_dim.hpp
@@ -26,12 +26,12 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                               const uint threads_y, const dim_t blocks_all[4],
                               int dim, bool isFinalPass, bool inclusive_scan) {
     auto scan_dim = common::getKernel(
-        "arrayfire::cuda::scan_dim", std::array{scan_dim_cuh_src},
+        "arrayfire::cuda::scan_dim", {{scan_dim_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>(),
                      TemplateArg(op), TemplateArg(dim),
                      TemplateArg(isFinalPass), TemplateArg(threads_y),
                      TemplateArg(inclusive_scan)),
-        std::array{DefineValue(THREADS_X)});
+        {{DefineValue(THREADS_X)}});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -54,7 +54,7 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp,
                                const uint threads_y, const dim_t blocks_all[4],
                                int dim, bool inclusive_scan) {
     auto scan_dim_bcast = common::getKernel(
-        "arrayfire::cuda::scan_dim_bcast", std::array{scan_dim_cuh_src},
+        "arrayfire::cuda::scan_dim_bcast", {{scan_dim_cuh_src}},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op),
                      TemplateArg(dim)));
 
diff --git a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
index 0dda0b872f..0a07b7fa1e 100644
--- a/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_dim_by_key_impl.hpp
@@ -33,11 +33,10 @@ static void scan_dim_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                        const dim_t blocks_all[4],
                                        bool inclusive_scan) {
     auto scanbykey_dim_nonfinal = common::getKernel(
-        "arrayfire::cuda::scanbykey_dim_nonfinal",
-        std::array{scan_dim_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_dim_nonfinal", {{scan_dim_by_key_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
-        std::array{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+        {{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)}});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -58,11 +57,10 @@ static void scan_dim_final_launcher(Param<To> out, CParam<Ti> in,
                                     const dim_t blocks_all[4],
                                     bool calculateFlags, bool inclusive_scan) {
     auto scanbykey_dim_final = common::getKernel(
-        "arrayfire::cuda::scanbykey_dim_final",
-        std::array{scan_dim_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_dim_final", {{scan_dim_by_key_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
-        std::array{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)});
+        {{DefineValue(THREADS_X), DefineKeyValue(DIMY, threads_y)}});
 
     dim3 threads(THREADS_X, threads_y);
 
@@ -81,8 +79,7 @@ static void bcast_dim_launcher(Param<To> out, CParam<To> tmp, Param<int> tlid,
                                const int dim, const uint threads_y,
                                const dim_t blocks_all[4]) {
     auto scanbykey_dim_bcast = common::getKernel(
-        "arrayfire::cuda::scanbykey_dim_bcast",
-        std::array{scan_dim_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_dim_bcast", {{scan_dim_by_key_cuh_src}},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
     dim3 threads(THREADS_X, threads_y);
     dim3 blocks(blocks_all[0] * blocks_all[2], blocks_all[1] * blocks_all[3]);
diff --git a/src/backend/cuda/kernel/scan_first.hpp b/src/backend/cuda/kernel/scan_first.hpp
index fec9d4be7a..868816f4ed 100644
--- a/src/backend/cuda/kernel/scan_first.hpp
+++ b/src/backend/cuda/kernel/scan_first.hpp
@@ -27,11 +27,11 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, CParam<Ti> in,
                                 const uint threads_x, bool isFinalPass,
                                 bool inclusive_scan) {
     auto scan_first = common::getKernel(
-        "arrayfire::cuda::scan_first", std::array{scan_first_cuh_src},
+        "arrayfire::cuda::scan_first", {{scan_first_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>(),
                      TemplateArg(op), TemplateArg(isFinalPass),
                      TemplateArg(threads_x), TemplateArg(inclusive_scan)),
-        std::array{DefineValue(THREADS_PER_BLOCK)});
+        {{DefineValue(THREADS_PER_BLOCK)}});
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
@@ -52,7 +52,7 @@ static void bcast_first_launcher(Param<To> out, CParam<To> tmp,
                                  const uint blocks_x, const uint blocks_y,
                                  const uint threads_x, bool inclusive_scan) {
     auto scan_first_bcast = common::getKernel(
-        "arrayfire::cuda::scan_first_bcast", std::array{scan_first_cuh_src},
+        "arrayfire::cuda::scan_first_bcast", {{scan_first_cuh_src}},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
 
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
diff --git a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
index 16abf56b3e..bf873fdd3d 100644
--- a/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/scan_first_by_key_impl.hpp
@@ -32,11 +32,10 @@ static void scan_nonfinal_launcher(Param<To> out, Param<To> tmp,
                                    const uint threads_x, bool inclusive_scan) {
     auto scanbykey_first_nonfinal = common::getKernel(
         "arrayfire::cuda::scanbykey_first_nonfinal",
-        std::array{scan_first_by_key_cuh_src},
+        {{scan_first_by_key_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
-        std::array{DefineValue(THREADS_PER_BLOCK),
-                   DefineKeyValue(DIMX, threads_x)});
+        {{DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)}});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
@@ -54,12 +53,10 @@ static void scan_final_launcher(Param<To> out, CParam<Ti> in, CParam<Tk> key,
                                 const uint threads_x, bool calculateFlags,
                                 bool inclusive_scan) {
     auto scanbykey_first_final = common::getKernel(
-        "arrayfire::cuda::scanbykey_first_final",
-        std::array{scan_first_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_first_final", {{scan_first_by_key_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<Tk>(),
                      TemplateTypename<To>(), TemplateArg(op)),
-        std::array{DefineValue(THREADS_PER_BLOCK),
-                   DefineKeyValue(DIMX, threads_x)});
+        {{DefineValue(THREADS_PER_BLOCK), DefineKeyValue(DIMX, threads_x)}});
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
 
@@ -76,8 +73,7 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp, Param<int> tlid,
                                  const dim_t blocks_x, const dim_t blocks_y,
                                  const uint threads_x) {
     auto scanbykey_first_bcast = common::getKernel(
-        "arrayfire::cuda::scanbykey_first_bcast",
-        std::array{scan_first_by_key_cuh_src},
+        "arrayfire::cuda::scanbykey_first_bcast", {{scan_first_by_key_cuh_src}},
         TemplateArgs(TemplateTypename<To>(), TemplateArg(op)));
     dim3 threads(threads_x, THREADS_PER_BLOCK / threads_x);
     dim3 blocks(blocks_x * out.dims[2], blocks_y * out.dims[3]);
diff --git a/src/backend/cuda/kernel/select.hpp b/src/backend/cuda/kernel/select.hpp
index 1b6d78fa8f..4df1d3da83 100644
--- a/src/backend/cuda/kernel/select.hpp
+++ b/src/backend/cuda/kernel/select.hpp
@@ -31,7 +31,7 @@ void select(Param<T> out, CParam<char> cond, CParam<T> a, CParam<T> b,
     for (int i = 0; i < 4; i++) { is_same &= (a.dims[i] == b.dims[i]); }
 
     auto select = common::getKernel(
-        "arrayfire::cuda::select", std::array{select_cuh_src},
+        "arrayfire::cuda::select", {{select_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_same)));
 
     dim3 threads(DIMX, DIMY);
@@ -60,7 +60,7 @@ template<typename T>
 void select_scalar(Param<T> out, CParam<char> cond, CParam<T> a, const T b,
                    int ndims, bool flip) {
     auto selectScalar = common::getKernel(
-        "arrayfire::cuda::selectScalar", std::array{select_cuh_src},
+        "arrayfire::cuda::selectScalar", {{select_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(flip)));
 
     dim3 threads(DIMX, DIMY);
diff --git a/src/backend/cuda/kernel/sobel.hpp b/src/backend/cuda/kernel/sobel.hpp
index 130625c11b..710b930404 100644
--- a/src/backend/cuda/kernel/sobel.hpp
+++ b/src/backend/cuda/kernel/sobel.hpp
@@ -28,9 +28,9 @@ void sobel(Param<To> dx, Param<To> dy, CParam<Ti> in,
     UNUSED(ker_size);
 
     auto sobel3x3 = common::getKernel(
-        "arrayfire::cuda::sobel3x3", std::array{sobel_cuh_src},
+        "arrayfire::cuda::sobel3x3", {{sobel_cuh_src}},
         TemplateArgs(TemplateTypename<Ti>(), TemplateTypename<To>()),
-        std::array{DefineValue(THREADS_X), DefineValue(THREADS_Y)});
+        {{DefineValue(THREADS_X), DefineValue(THREADS_Y)}});
 
     const dim3 threads(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index efed1ed6d7..6629d0fec6 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -25,8 +25,8 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
     constexpr int reps = 4;
 
     auto coo2Dense = common::getKernel(
-        "arrayfire::cuda::coo2Dense", std::array{sparse_cuh_src},
-        TemplateArgs(TemplateTypename<T>()), std::array{DefineValue(reps)});
+        "arrayfire::cuda::coo2Dense", {{sparse_cuh_src}},
+        TemplateArgs(TemplateTypename<T>()), {{DefineValue(reps)}});
 
     dim3 threads(256, 1, 1);
 
diff --git a/src/backend/cuda/kernel/sparse_arith.hpp b/src/backend/cuda/kernel/sparse_arith.hpp
index 13dd5ddb7e..b21d2130e5 100644
--- a/src/backend/cuda/kernel/sparse_arith.hpp
+++ b/src/backend/cuda/kernel/sparse_arith.hpp
@@ -28,9 +28,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     auto csrArithDSD = common::getKernel(
-        "arrayfire::cuda::csrArithDSD", std::array{sparse_arith_cuh_src},
+        "arrayfire::cuda::csrArithDSD", {{sparse_arith_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-        std::array{DefineValue(TX), DefineValue(TY)});
+        {{DefineValue(TX), DefineValue(TY)}});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -48,9 +48,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> out, CParam<T> values, CParam<int> rowIdx,
                       CParam<int> colIdx, CParam<T> rhs, const bool reverse) {
     auto cooArithDSD = common::getKernel(
-        "arrayfire::cuda::cooArithDSD", std::array{sparse_arith_cuh_src},
+        "arrayfire::cuda::cooArithDSD", {{sparse_arith_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-        std::array{DefineValue(THREADS)});
+        {{DefineValue(THREADS)}});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
@@ -68,9 +68,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
     auto csrArithSSD = common::getKernel(
-        "arrayfire::cuda::csrArithSSD", std::array{sparse_arith_cuh_src},
+        "arrayfire::cuda::csrArithSSD", {{sparse_arith_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-        std::array{DefineValue(TX), DefineValue(TY)});
+        {{DefineValue(TX), DefineValue(TY)}});
 
     // Each Y for threads does one row
     dim3 threads(TX, TY, 1);
@@ -88,9 +88,9 @@ template<typename T, af_op_t op>
 void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
                       CParam<T> rhs, const bool reverse) {
     auto cooArithSSD = common::getKernel(
-        "arrayfire::cuda::cooArithSSD", std::array{sparse_arith_cuh_src},
+        "arrayfire::cuda::cooArithSSD", {{sparse_arith_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(op)),
-        std::array{DefineValue(THREADS)});
+        {{DefineValue(THREADS)}});
 
     // Linear indexing with one elements per thread
     dim3 threads(THREADS, 1, 1);
diff --git a/src/backend/cuda/kernel/susan.hpp b/src/backend/cuda/kernel/susan.hpp
index 42082bd221..28a96a1e6d 100644
--- a/src/backend/cuda/kernel/susan.hpp
+++ b/src/backend/cuda/kernel/susan.hpp
@@ -26,10 +26,10 @@ template<typename T>
 void susan_responses(T* out, const T* in, const unsigned idim0,
                      const unsigned idim1, const int radius, const float t,
                      const float g, const unsigned edge) {
-    auto susan = common::getKernel(
-        "arrayfire::cuda::susan", std::array{susan_cuh_src},
-        TemplateArgs(TemplateTypename<T>()),
-        std::array{DefineValue(BLOCK_X), DefineValue(BLOCK_Y)});
+    auto susan =
+        common::getKernel("arrayfire::cuda::susan", {{susan_cuh_src}},
+                          TemplateArgs(TemplateTypename<T>()),
+                          {{DefineValue(BLOCK_X), DefineValue(BLOCK_Y)}});
 
     dim3 threads(BLOCK_X, BLOCK_Y);
     dim3 blocks(divup(idim0 - edge * 2, BLOCK_X),
@@ -48,7 +48,7 @@ void nonMaximal(float* x_out, float* y_out, float* resp_out, unsigned* count,
                 const unsigned idim0, const unsigned idim1, const T* resp_in,
                 const unsigned edge, const unsigned max_corners) {
     auto nonMax =
-        common::getKernel("arrayfire::cuda::nonMax", std::array{susan_cuh_src},
+        common::getKernel("arrayfire::cuda::nonMax", {{susan_cuh_src}},
                           TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(BLOCK_X, BLOCK_Y);
diff --git a/src/backend/cuda/kernel/tile.hpp b/src/backend/cuda/kernel/tile.hpp
index 035cc39437..e25bdce4b7 100644
--- a/src/backend/cuda/kernel/tile.hpp
+++ b/src/backend/cuda/kernel/tile.hpp
@@ -26,9 +26,8 @@ void tile(Param<T> out, CParam<T> in) {
     constexpr unsigned TILEX = 512;
     constexpr unsigned TILEY = 32;
 
-    auto tile =
-        common::getKernel("arrayfire::cuda::tile", std::array{tile_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto tile = common::getKernel("arrayfire::cuda::tile", {{tile_cuh_src}},
+                                  TemplateArgs(TemplateTypename<T>()));
 
     dim3 threads(TX, TY, 1);
 
diff --git a/src/backend/cuda/kernel/transform.hpp b/src/backend/cuda/kernel/transform.hpp
index 4ed94d7949..5405fcc9cc 100644
--- a/src/backend/cuda/kernel/transform.hpp
+++ b/src/backend/cuda/kernel/transform.hpp
@@ -32,7 +32,7 @@ template<typename T>
 void transform(Param<T> out, CParam<T> in, CParam<float> tf, const bool inverse,
                const bool perspective, const af::interpType method, int order) {
     auto transform = common::getKernel(
-        "arrayfire::cuda::transform", std::array{transform_cuh_src},
+        "arrayfire::cuda::transform", {{transform_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(inverse),
                      TemplateArg(order)));
 
diff --git a/src/backend/cuda/kernel/transpose.hpp b/src/backend/cuda/kernel/transpose.hpp
index 7ec97b7127..f84ff89b96 100644
--- a/src/backend/cuda/kernel/transpose.hpp
+++ b/src/backend/cuda/kernel/transpose.hpp
@@ -27,10 +27,10 @@ template<typename T>
 void transpose(Param<T> out, CParam<T> in, const bool conjugate,
                const bool is32multiple) {
     auto transpose = common::getKernel(
-        "arrayfire::cuda::transpose", std::array{transpose_cuh_src},
+        "arrayfire::cuda::transpose", {{transpose_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(conjugate),
                      TemplateArg(is32multiple)),
-        std::array{DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+        {{DefineValue(TILE_DIM), DefineValue(THREADS_Y)}});
 
     dim3 threads(kernel::THREADS_X, kernel::THREADS_Y);
 
diff --git a/src/backend/cuda/kernel/transpose_inplace.hpp b/src/backend/cuda/kernel/transpose_inplace.hpp
index b5374b6025..5ff28020c4 100644
--- a/src/backend/cuda/kernel/transpose_inplace.hpp
+++ b/src/backend/cuda/kernel/transpose_inplace.hpp
@@ -27,10 +27,10 @@ template<typename T>
 void transpose_inplace(Param<T> in, const bool conjugate,
                        const bool is32multiple) {
     auto transposeIP = common::getKernel(
-        "arrayfire::cuda::transposeIP", std::array{transpose_inplace_cuh_src},
+        "arrayfire::cuda::transposeIP", {{transpose_inplace_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(conjugate),
                      TemplateArg(is32multiple)),
-        std::array{DefineValue(TILE_DIM), DefineValue(THREADS_Y)});
+        {{DefineValue(TILE_DIM), DefineValue(THREADS_Y)}});
 
     // dimensions passed to this function should be input dimensions
     // any necessary transformations and dimension related calculations are
diff --git a/src/backend/cuda/kernel/triangle.hpp b/src/backend/cuda/kernel/triangle.hpp
index 3c1841a324..ba922a3115 100644
--- a/src/backend/cuda/kernel/triangle.hpp
+++ b/src/backend/cuda/kernel/triangle.hpp
@@ -27,7 +27,7 @@ void triangle(Param<T> r, CParam<T> in, bool is_upper, bool is_unit_diag) {
     constexpr unsigned TILEY = 32;
 
     auto triangle = common::getKernel(
-        "arrayfire::cuda::triangle", std::array{triangle_cuh_src},
+        "arrayfire::cuda::triangle", {{triangle_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_upper),
                      TemplateArg(is_unit_diag)));
 
diff --git a/src/backend/cuda/kernel/unwrap.hpp b/src/backend/cuda/kernel/unwrap.hpp
index 6105b8b0a1..20ad8e67e3 100644
--- a/src/backend/cuda/kernel/unwrap.hpp
+++ b/src/backend/cuda/kernel/unwrap.hpp
@@ -25,7 +25,7 @@ void unwrap(Param<T> out, CParam<T> in, const int wx, const int wy,
             const int sx, const int sy, const int px, const int py,
             const int dx, const int dy, const int nx, const bool is_column) {
     auto unwrap = common::getKernel(
-        "arrayfire::cuda::unwrap", std::array{unwrap_cuh_src},
+        "arrayfire::cuda::unwrap", {{unwrap_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     dim3 threads, blocks;
diff --git a/src/backend/cuda/kernel/where.hpp b/src/backend/cuda/kernel/where.hpp
index 0dddc456b9..0b500d4628 100644
--- a/src/backend/cuda/kernel/where.hpp
+++ b/src/backend/cuda/kernel/where.hpp
@@ -24,9 +24,8 @@ namespace kernel {
 
 template<typename T>
 static void where(Param<uint> &out, CParam<T> in) {
-    auto where =
-        common::getKernel("arrayfire::cuda::where", std::array{where_cuh_src},
-                          TemplateArgs(TemplateTypename<T>()));
+    auto where = common::getKernel("arrayfire::cuda::where", {{where_cuh_src}},
+                                   TemplateArgs(TemplateTypename<T>()));
 
     uint threads_x = nextpow2(std::max(32u, (uint)in.dims[0]));
     threads_x      = std::min(threads_x, THREADS_PER_BLOCK);
diff --git a/src/backend/cuda/kernel/wrap.hpp b/src/backend/cuda/kernel/wrap.hpp
index 37b9e97cf9..e95db0f3f3 100644
--- a/src/backend/cuda/kernel/wrap.hpp
+++ b/src/backend/cuda/kernel/wrap.hpp
@@ -24,7 +24,7 @@ template<typename T>
 void wrap(Param<T> out, CParam<T> in, const int wx, const int wy, const int sx,
           const int sy, const int px, const int py, const bool is_column) {
     auto wrap = common::getKernel(
-        "arrayfire::cuda::wrap", std::array{wrap_cuh_src},
+        "arrayfire::cuda::wrap", {{wrap_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     int nx = (out.dims[0] + 2 * px - wx) / sx + 1;
@@ -52,7 +52,7 @@ void wrap_dilated(Param<T> out, CParam<T> in, const dim_t wx, const dim_t wy,
                   const dim_t py, const dim_t dx, const dim_t dy,
                   const bool is_column) {
     auto wrap = common::getKernel(
-        "arrayfire::cuda::wrap_dilated", std::array{wrap_cuh_src},
+        "arrayfire::cuda::wrap_dilated", {{wrap_cuh_src}},
         TemplateArgs(TemplateTypename<T>(), TemplateArg(is_column)));
 
     int nx = 1 + (out.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 30a942d2dd..f7ba973032 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -281,8 +281,7 @@ cl::Kernel getKernel(const vector<Node*>& output_nodes,
         if (isHalfSupported(device)) {
             options.emplace_back(DefineKey(USE_HALF));
         }
-        return common::getKernel(funcName,
-                                 std::array{jit_cl_src, jitKer_cl_src}, {},
+        return common::getKernel(funcName, {{jit_cl_src, jitKer_cl_src}}, {},
                                  options, true)
             .get();
     }
diff --git a/src/backend/opencl/kernel/anisotropic_diffusion.hpp b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
index bf13bb4cd5..a8655be95e 100644
--- a/src/backend/opencl/kernel/anisotropic_diffusion.hpp
+++ b/src/backend/opencl/kernel/anisotropic_diffusion.hpp
@@ -50,9 +50,9 @@ void anisotropicDiffusion(Param inout, const float dt, const float mct,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto diffUpdate = common::getKernel(
-        "aisoDiffUpdate", std::array{anisotropic_diffusion_cl_src}, tmpltArgs,
-        compileOpts);
+    auto diffUpdate =
+        common::getKernel("aisoDiffUpdate", {{anisotropic_diffusion_cl_src}},
+                          tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y, 1);
 
diff --git a/src/backend/opencl/kernel/approx.hpp b/src/backend/opencl/kernel/approx.hpp
index 797ac19d4b..d23a590e7f 100644
--- a/src/backend/opencl/kernel/approx.hpp
+++ b/src/backend/opencl/kernel/approx.hpp
@@ -73,9 +73,8 @@ void approx1(Param yo, const Param yi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim);
 
-    auto approx1 =
-        common::getKernel("approx1", std::array{interp_cl_src, approx1_cl_src},
-                          tmpltArgs, compileOpts);
+    auto approx1 = common::getKernel(
+        "approx1", {{interp_cl_src, approx1_cl_src}}, tmpltArgs, compileOpts);
 
     NDRange local(THREADS, 1, 1);
     dim_t blocksPerMat = divup(yo.info.dims[0], local[0]);
@@ -112,9 +111,8 @@ void approx2(Param zo, const Param zi, const Param xo, const int xdim,
     };
     auto compileOpts = genCompileOptions<Ty, Tp>(order, xdim, ydim);
 
-    auto approx2 =
-        common::getKernel("approx2", std::array{interp_cl_src, approx2_cl_src},
-                          tmpltArgs, compileOpts);
+    auto approx2 = common::getKernel(
+        "approx2", {{interp_cl_src, approx2_cl_src}}, tmpltArgs, compileOpts);
 
     NDRange local(TX, TY, 1);
     dim_t blocksPerMatX = divup(zo.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/assign.hpp b/src/backend/opencl/kernel/assign.hpp
index 447e4e8c60..b7cd779027 100644
--- a/src/backend/opencl/kernel/assign.hpp
+++ b/src/backend/opencl/kernel/assign.hpp
@@ -42,8 +42,8 @@ void assign(Param out, const Param in, const AssignKernelParam_t& p,
         DefineKeyValue(T, dtype_traits<T>::getName()),
         getTypeBuildDefinition<T>()};
 
-    auto assign = common::getKernel("assignKernel", std::array{assign_cl_src},
-                                    targs, options);
+    auto assign =
+        common::getKernel("assignKernel", {{assign_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/bilateral.hpp b/src/backend/opencl/kernel/bilateral.hpp
index 832611dcdb..eba0f2bb10 100644
--- a/src/backend/opencl/kernel/bilateral.hpp
+++ b/src/backend/opencl/kernel/bilateral.hpp
@@ -44,8 +44,8 @@ void bilateral(Param out, const Param in, const float s_sigma,
     if (UseNativeExp) { options.emplace_back(DefineKey(USE_NATIVE_EXP)); }
     options.emplace_back(getTypeBuildDefinition<inType>());
 
-    auto bilateralOp = common::getKernel(
-        "bilateral", std::array{bilateral_cl_src}, targs, options);
+    auto bilateralOp =
+        common::getKernel("bilateral", {{bilateral_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/canny.hpp b/src/backend/opencl/kernel/canny.hpp
index 3659e1fb4b..bcc850e6ba 100644
--- a/src/backend/opencl/kernel/canny.hpp
+++ b/src/backend/opencl/kernel/canny.hpp
@@ -43,7 +43,7 @@ void nonMaxSuppression(Param output, const Param magnitude, const Param dx,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto nonMaxOp = common::getKernel(
-        "nonMaxSuppressionKernel", std::array{nonmax_suppression_cl_src},
+        "nonMaxSuppressionKernel", {{nonmax_suppression_cl_src}},
         TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
@@ -76,7 +76,7 @@ void initEdgeOut(Param output, const Param strong, const Param weak) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto initOp =
-        common::getKernel("initEdgeOutKernel", std::array{trace_edge_cl_src},
+        common::getKernel("initEdgeOutKernel", {{trace_edge_cl_src}},
                           TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
@@ -108,9 +108,9 @@ void suppressLeftOver(Param output) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto finalOp = common::getKernel(
-        "suppressLeftOverKernel", std::array{trace_edge_cl_src},
-        TemplateArgs(TemplateTypename<T>()), options);
+    auto finalOp =
+        common::getKernel("suppressLeftOverKernel", {{trace_edge_cl_src}},
+                          TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y, 1);
 
@@ -145,7 +145,7 @@ void edgeTrackingHysteresis(Param output, const Param strong,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto edgeTraceOp =
-        common::getKernel("edgeTrackKernel", std::array{trace_edge_cl_src},
+        common::getKernel("edgeTrackKernel", {{trace_edge_cl_src}},
                           TemplateArgs(TemplateTypename<T>()), options);
 
     NDRange threads(kernel::THREADS_X, kernel::THREADS_Y);
diff --git a/src/backend/opencl/kernel/convolve/conv2_impl.hpp b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
index 59f0523de8..9798714750 100644
--- a/src/backend/opencl/kernel/convolve/conv2_impl.hpp
+++ b/src/backend/opencl/kernel/convolve/conv2_impl.hpp
@@ -51,9 +51,8 @@ void conv2Helper(const conv_kparam_t& param, Param out, const Param signal,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto convolve =
-        common::getKernel("convolve", std::array{ops_cl_src, convolve_cl_src},
-                          tmpltArgs, compileOpts);
+    auto convolve = common::getKernel(
+        "convolve", {{ops_cl_src, convolve_cl_src}}, tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, *param.impulse, filter.info,
diff --git a/src/backend/opencl/kernel/convolve/conv_common.hpp b/src/backend/opencl/kernel/convolve/conv_common.hpp
index 93c4781976..bd93419c7c 100644
--- a/src/backend/opencl/kernel/convolve/conv_common.hpp
+++ b/src/backend/opencl/kernel/convolve/conv_common.hpp
@@ -114,9 +114,8 @@ void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto convolve =
-        common::getKernel("convolve", std::array{ops_cl_src, convolve_cl_src},
-                          tmpltArgs, compileOpts);
+    auto convolve = common::getKernel(
+        "convolve", {{ops_cl_src, convolve_cl_src}}, tmpltArgs, compileOpts);
 
     convolve(EnqueueArgs(getQueue(), param.global, param.local), *out.data,
              out.info, *signal.data, signal.info, cl::Local(param.loc_size),
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 6f7611428b..41bfa55dde 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -63,9 +63,9 @@ void convSep(Param out, const Param signal, const Param filter,
         DefineKeyValue(LOCAL_MEM_SIZE, locSize),
         getTypeBuildDefinition<T>()};
 
-    auto conv = common::getKernel(
-        "convolve", std::array{ops_cl_src, convolve_separable_cl_src},
-        tmpltArgs, compileOpts);
+    auto conv =
+        common::getKernel("convolve", {{ops_cl_src, convolve_separable_cl_src}},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/cscmm.hpp b/src/backend/opencl/kernel/cscmm.hpp
index 4fb0cc3479..a668025726 100644
--- a/src/backend/opencl/kernel/cscmm.hpp
+++ b/src/backend/opencl/kernel/cscmm.hpp
@@ -57,7 +57,7 @@ void cscmm_nn(Param out, const Param &values, const Param &colIdx,
         getTypeBuildDefinition<T>()};
 
     auto cscmmNN =
-        common::getKernel("cscmm_nn", std::array{cscmm_cl_src}, targs, options);
+        common::getKernel("cscmm_nn", {{cscmm_cl_src}}, targs, options);
 
     cl::NDRange local(threads, 1);
     int M = out.info.dims[0];
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 675176e393..88008480f8 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -54,8 +54,8 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
         DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
-    auto cscmvBlock = common::getKernel("cscmv_block", std::array{cscmv_cl_src},
-                                        targs, options);
+    auto cscmvBlock =
+        common::getKernel("cscmv_block", {{cscmv_cl_src}}, targs, options);
 
     int K        = colIdx.info.dims[0] - 1;
     int M        = out.info.dims[0];
diff --git a/src/backend/opencl/kernel/csrmm.hpp b/src/backend/opencl/kernel/csrmm.hpp
index a786f7cafb..60499bf877 100644
--- a/src/backend/opencl/kernel/csrmm.hpp
+++ b/src/backend/opencl/kernel/csrmm.hpp
@@ -56,7 +56,7 @@ void csrmm_nt(Param out, const Param &values, const Param &rowIdx,
 
     // FIXME: Switch to perf (thread vs block) baesd kernel
     auto csrmm_nt_func =
-        common::getKernel("csrmm_nt", std::array{csrmm_cl_src}, targs, options);
+        common::getKernel("csrmm_nt", {{csrmm_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_PER_GROUP, 1);
     int M = rowIdx.info.dims[0] - 1;
diff --git a/src/backend/opencl/kernel/csrmv.hpp b/src/backend/opencl/kernel/csrmv.hpp
index 3c948f0177..ca39ae4d32 100644
--- a/src/backend/opencl/kernel/csrmv.hpp
+++ b/src/backend/opencl/kernel/csrmv.hpp
@@ -58,11 +58,10 @@ void csrmv(Param out, const Param &values, const Param &rowIdx,
         getTypeBuildDefinition<T>()};
 
     auto csrmv =
-        (is_csrmv_block
-             ? common::getKernel("csrmv_thread", std::array{csrmv_cl_src},
-                                 targs, options)
-             : common::getKernel("csrmv_block", std::array{csrmv_cl_src}, targs,
-                                 options));
+        (is_csrmv_block ? common::getKernel("csrmv_thread", {{csrmv_cl_src}},
+                                            targs, options)
+                        : common::getKernel("csrmv_block", {{csrmv_cl_src}},
+                                            targs, options));
 
     int M = rowIdx.info.dims[0] - 1;
 
diff --git a/src/backend/opencl/kernel/diagonal.hpp b/src/backend/opencl/kernel/diagonal.hpp
index 9f2ded02c7..e8340fba03 100644
--- a/src/backend/opencl/kernel/diagonal.hpp
+++ b/src/backend/opencl/kernel/diagonal.hpp
@@ -36,8 +36,8 @@ static void diagCreate(Param out, Param in, int num) {
         DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
-    auto diagCreate = common::getKernel(
-        "diagCreateKernel", std::array{diag_create_cl_src}, targs, options);
+    auto diagCreate = common::getKernel("diagCreateKernel",
+                                        {{diag_create_cl_src}}, targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
@@ -61,7 +61,7 @@ static void diagExtract(Param out, Param in, int num) {
         getTypeBuildDefinition<T>()};
 
     auto diagExtract = common::getKernel(
-        "diagExtractKernel", std::array{diag_extract_cl_src}, targs, options);
+        "diagExtractKernel", {{diag_extract_cl_src}}, targs, options);
 
     cl::NDRange local(256, 1);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/diff.hpp b/src/backend/opencl/kernel/diff.hpp
index 817bd92bac..33ccbbfca8 100644
--- a/src/backend/opencl/kernel/diff.hpp
+++ b/src/backend/opencl/kernel/diff.hpp
@@ -39,8 +39,8 @@ void diff(Param out, const Param in, const unsigned indims, const unsigned dim,
         DefineKeyValue(isDiff2, (isDiff2 ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
-    auto diffOp = common::getKernel("diff_kernel", std::array{diff_cl_src},
-                                    targs, options);
+    auto diffOp =
+        common::getKernel("diff_kernel", {{diff_cl_src}}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
     if (dim == 0 && indims == 1) { local = cl::NDRange(TX * TY, 1, 1); }
diff --git a/src/backend/opencl/kernel/exampleFunction.hpp b/src/backend/opencl/kernel/exampleFunction.hpp
index 8de171e908..794c34670c 100644
--- a/src/backend/opencl/kernel/exampleFunction.hpp
+++ b/src/backend/opencl/kernel/exampleFunction.hpp
@@ -61,8 +61,8 @@ void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p) {
 
     // Fetch the Kernel functor, go to common/kernel_cache.hpp
     // to find details of this function
-    auto exOp = common::getKernel("example", std::array{example_cl_src}, targs,
-                                  options);
+    auto exOp =
+        common::getKernel("example", {{example_cl_src}}, targs, options);
 
     // configure work group parameters
     cl::NDRange local(THREADS_X, THREADS_Y);
diff --git a/src/backend/opencl/kernel/fast.hpp b/src/backend/opencl/kernel/fast.hpp
index 5e75bd1995..73351803b6 100644
--- a/src/backend/opencl/kernel/fast.hpp
+++ b/src/backend/opencl/kernel/fast.hpp
@@ -45,12 +45,12 @@ void fast(const unsigned arc_length, unsigned *out_feat, Param &x_out,
         DefineKeyValue(NONMAX, static_cast<unsigned>(nonmax)),
         getTypeBuildDefinition<T>()};
 
-    auto locate  = common::getKernel("locate_features", std::array{fast_cl_src},
-                                     targs, options);
-    auto nonMax  = common::getKernel("non_max_counts", std::array{fast_cl_src},
-                                     targs, options);
-    auto getFeat = common::getKernel("get_features", std::array{fast_cl_src},
-                                     targs, options);
+    auto locate =
+        common::getKernel("locate_features", {{fast_cl_src}}, targs, options);
+    auto nonMax =
+        common::getKernel("non_max_counts", {{fast_cl_src}}, targs, options);
+    auto getFeat =
+        common::getKernel("get_features", {{fast_cl_src}}, targs, options);
 
     const unsigned max_feat =
         ceil(in.info.dims[0] * in.info.dims[1] * feature_ratio);
diff --git a/src/backend/opencl/kernel/fftconvolve.hpp b/src/backend/opencl/kernel/fftconvolve.hpp
index c43e750a89..ab6fc944e7 100644
--- a/src/backend/opencl/kernel/fftconvolve.hpp
+++ b/src/backend/opencl/kernel/fftconvolve.hpp
@@ -85,10 +85,10 @@ void packDataHelper(Param packed, Param sig, Param filter, const int rank,
         options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
 
-    auto packData = common::getKernel(
-        "pack_data", std::array{fftconvolve_pack_cl_src}, targs, options);
-    auto padArray = common::getKernel(
-        "pad_array", std::array{fftconvolve_pack_cl_src}, targs, options);
+    auto packData = common::getKernel("pack_data", {{fftconvolve_pack_cl_src}},
+                                      targs, options);
+    auto padArray = common::getKernel("pad_array", {{fftconvolve_pack_cl_src}},
+                                      targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
@@ -147,9 +147,8 @@ void complexMultiplyHelper(Param packed, Param sig, Param filter,
         options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
 
-    auto cplxMul = common::getKernel("complex_multiply",
-                                     std::array{fftconvolve_multiply_cl_src},
-                                     targs, options);
+    auto cplxMul = common::getKernel(
+        "complex_multiply", {{fftconvolve_multiply_cl_src}}, targs, options);
 
     Param sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
@@ -195,9 +194,8 @@ void reorderOutputHelper(Param out, Param packed, Param sig, Param filter,
         options.emplace_back(DefineKeyValue(CONVT, "double"));
     }
 
-    auto reorder = common::getKernel("reorder_output",
-                                     std::array{fftconvolve_reorder_cl_src},
-                                     targs, options);
+    auto reorder = common::getKernel(
+        "reorder_output", {{fftconvolve_reorder_cl_src}}, targs, options);
 
     int fftScale = 1;
 
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index d0af9aa7c9..0b0b29fefe 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -39,7 +39,7 @@ void initSeeds(Param out, const Param seedsx, const Param seedsy) {
         DefineKey(INIT_SEEDS), getTypeBuildDefinition<T>()};
 
     auto initSeeds =
-        common::getKernel("init_seeds", std::array{flood_fill_cl_src},
+        common::getKernel("init_seeds", {{flood_fill_cl_src}},
                           TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(kernel::THREADS, 1, 1);
     cl::NDRange global(divup(seedsx.info.dims[0], local[0]) * local[0], 1, 1);
@@ -57,7 +57,7 @@ void finalizeOutput(Param out, const T newValue) {
         getTypeBuildDefinition<T>()};
 
     auto finalizeOut =
-        common::getKernel("finalize_output", std::array{flood_fill_cl_src},
+        common::getKernel("finalize_output", {{flood_fill_cl_src}},
                           TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
@@ -89,7 +89,7 @@ void floodFill(Param out, const Param image, const Param seedsx,
         getTypeBuildDefinition<T>()};
 
     auto floodStep =
-        common::getKernel("flood_step", std::array{flood_fill_cl_src},
+        common::getKernel("flood_step", {{flood_fill_cl_src}},
                           TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(kernel::THREADS_X, kernel::THREADS_Y, 1);
     cl::NDRange global(divup(out.info.dims[0], local[0]) * local[0],
diff --git a/src/backend/opencl/kernel/gradient.hpp b/src/backend/opencl/kernel/gradient.hpp
index cab0a98abf..6809f10c19 100644
--- a/src/backend/opencl/kernel/gradient.hpp
+++ b/src/backend/opencl/kernel/gradient.hpp
@@ -41,8 +41,8 @@ void gradient(Param grad0, Param grad1, const Param in) {
         DefineKeyValue(CPLX, static_cast<int>(iscplx<T>())),
         getTypeBuildDefinition<T>()};
 
-    auto gradOp = common::getKernel("gradient", std::array{gradient_cl_src},
-                                    targs, options);
+    auto gradOp =
+        common::getKernel("gradient", {{gradient_cl_src}}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/harris.hpp b/src/backend/opencl/kernel/harris.hpp
index 942fb44d1b..835c20c745 100644
--- a/src/backend/opencl/kernel/harris.hpp
+++ b/src/backend/opencl/kernel/harris.hpp
@@ -71,14 +71,12 @@ std::array<Kernel, 4> getHarrisKernels() {
         getTypeBuildDefinition<T>()};
 
     return {
-        common::getKernel("second_order_deriv", std::array{harris_cl_src},
-                          targs, options),
-        common::getKernel("keep_corners", std::array{harris_cl_src}, targs,
+        common::getKernel("second_order_deriv", {{harris_cl_src}}, targs,
                           options),
-        common::getKernel("harris_responses", std::array{harris_cl_src}, targs,
-                          options),
-        common::getKernel("non_maximal", std::array{harris_cl_src}, targs,
+        common::getKernel("keep_corners", {{harris_cl_src}}, targs, options),
+        common::getKernel("harris_responses", {{harris_cl_src}}, targs,
                           options),
+        common::getKernel("non_maximal", {{harris_cl_src}}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/histogram.hpp b/src/backend/opencl/kernel/histogram.hpp
index a05bad05f6..d138202240 100644
--- a/src/backend/opencl/kernel/histogram.hpp
+++ b/src/backend/opencl/kernel/histogram.hpp
@@ -42,8 +42,8 @@ void histogram(Param out, const Param in, int nbins, float minval, float maxval,
     options.emplace_back(getTypeBuildDefinition<T>());
     if (isLinear) { options.emplace_back(DefineKey(IS_LINEAR)); }
 
-    auto histogram = common::getKernel(
-        "histogram", std::array{histogram_cl_src}, targs, options);
+    auto histogram =
+        common::getKernel("histogram", {{histogram_cl_src}}, targs, options);
 
     int nElems  = in.info.dims[0] * in.info.dims[1];
     int blk_x   = divup(nElems, THRD_LOAD * THREADS_X);
diff --git a/src/backend/opencl/kernel/homography.hpp b/src/backend/opencl/kernel/homography.hpp
index 328f39d753..4c785b57a1 100644
--- a/src/backend/opencl/kernel/homography.hpp
+++ b/src/backend/opencl/kernel/homography.hpp
@@ -50,16 +50,16 @@ std::array<Kernel, 5> getHomographyKernels(const af_homography_type htype) {
         options.emplace_back(DefineKey(IS_CPU));
     }
     return {
-        common::getKernel("compute_homography", std::array{homography_cl_src},
-                          targs, options),
-        common::getKernel("eval_homography", std::array{homography_cl_src},
-                          targs, options),
-        common::getKernel("compute_median", std::array{homography_cl_src},
-                          targs, options),
-        common::getKernel("find_min_median", std::array{homography_cl_src},
-                          targs, options),
-        common::getKernel("compute_lmeds_inliers",
-                          std::array{homography_cl_src}, targs, options),
+        common::getKernel("compute_homography", {{homography_cl_src}}, targs,
+                          options),
+        common::getKernel("eval_homography", {{homography_cl_src}}, targs,
+                          options),
+        common::getKernel("compute_median", {{homography_cl_src}}, targs,
+                          options),
+        common::getKernel("find_min_median", {{homography_cl_src}}, targs,
+                          options),
+        common::getKernel("compute_lmeds_inliers", {{homography_cl_src}}, targs,
+                          options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/hsv_rgb.hpp b/src/backend/opencl/kernel/hsv_rgb.hpp
index 1f46cc5085..4ca85a4f74 100644
--- a/src/backend/opencl/kernel/hsv_rgb.hpp
+++ b/src/backend/opencl/kernel/hsv_rgb.hpp
@@ -37,8 +37,8 @@ void hsv2rgb_convert(Param out, const Param in, bool isHSV2RGB) {
         getTypeBuildDefinition<T>()};
     if (isHSV2RGB) { options.emplace_back(DefineKey(isHSV2RGB)); }
 
-    auto convert = common::getKernel(
-        "hsvrgbConvert", std::array{hsv_rgb_cl_src}, targs, options);
+    auto convert =
+        common::getKernel("hsvrgbConvert", {{hsv_rgb_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/identity.hpp b/src/backend/opencl/kernel/identity.hpp
index 19afcdaea7..32186164ef 100644
--- a/src/backend/opencl/kernel/identity.hpp
+++ b/src/backend/opencl/kernel/identity.hpp
@@ -37,8 +37,8 @@ static void identity(Param out) {
         DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
-    auto identityOp = common::getKernel(
-        "identity_kernel", std::array{identity_cl_src}, targs, options);
+    auto identityOp = common::getKernel("identity_kernel", {{identity_cl_src}},
+                                        targs, options);
 
     cl::NDRange local(32, 8);
     int groups_x = divup(out.info.dims[0], local[0]);
diff --git a/src/backend/opencl/kernel/iir.hpp b/src/backend/opencl/kernel/iir.hpp
index 7786197da4..34f9d2c0bf 100644
--- a/src/backend/opencl/kernel/iir.hpp
+++ b/src/backend/opencl/kernel/iir.hpp
@@ -40,8 +40,7 @@ void iir(Param y, Param c, Param a) {
         DefineKeyValue(ZERO, scalar_to_option(scalar<T>(0))),
         getTypeBuildDefinition<T>()};
 
-    auto iir =
-        common::getKernel("iir_kernel", std::array{iir_cl_src}, targs, options);
+    auto iir = common::getKernel("iir_kernel", {{iir_cl_src}}, targs, options);
 
     const int groups_y = y.info.dims[1];
     const int groups_x = y.info.dims[2];
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 6a496d1ade..9433893b96 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -37,7 +37,7 @@ void index(Param out, const Param in, const IndexKernelParam_t& p,
         getTypeBuildDefinition<T>()};
 
     auto index =
-        common::getKernel("indexKernel", std::array{index_cl_src},
+        common::getKernel("indexKernel", {{index_cl_src}},
                           TemplateArgs(TemplateTypename<T>()), options);
     int threads_x = 256;
     int threads_y = 1;
diff --git a/src/backend/opencl/kernel/iota.hpp b/src/backend/opencl/kernel/iota.hpp
index 3308ee23e1..24d5ad7924 100644
--- a/src/backend/opencl/kernel/iota.hpp
+++ b/src/backend/opencl/kernel/iota.hpp
@@ -36,7 +36,7 @@ void iota(Param out, const af::dim4& sdims) {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         getTypeBuildDefinition<T>()};
 
-    auto iota = common::getKernel("iota_kernel", std::array{iota_cl_src},
+    auto iota = common::getKernel("iota_kernel", {{iota_cl_src}},
                                   TemplateArgs(TemplateTypename<T>()), options);
     cl::NDRange local(IOTA_TX, IOTA_TY, 1);
 
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 775ee044d7..1bbcf08d2b 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -49,9 +49,9 @@ void ireduceDimLauncher(Param out, cl::Buffer *oidx, Param in, cl::Buffer *iidx,
         DefineKeyValue(IS_FIRST, is_first),
         getTypeBuildDefinition<T>()};
 
-    auto ireduceDim = common::getKernel(
-        "ireduce_dim_kernel", std::array{iops_cl_src, ireduce_dim_cl_src},
-        targs, options);
+    auto ireduceDim =
+        common::getKernel("ireduce_dim_kernel",
+                          {{iops_cl_src, ireduce_dim_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -125,9 +125,9 @@ void ireduceFirstLauncher(Param out, cl::Buffer *oidx, Param in,
         DefineKeyValue(IS_FIRST, is_first),
         getTypeBuildDefinition<T>()};
 
-    auto ireduceFirst = common::getKernel(
-        "ireduce_first_kernel", std::array{iops_cl_src, ireduce_first_cl_src},
-        targs, options);
+    auto ireduceFirst = common::getKernel("ireduce_first_kernel",
+                                          {{iops_cl_src, ireduce_first_cl_src}},
+                                          targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 504cf9244f..63e9a66526 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -57,8 +57,8 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
         DefineKeyValue(IS_CPLX, static_cast<int>(iscplx<T>())),
         getTypeBuildDefinition<T>()};
 
-    auto lasetOp = common::getKernel(laset_name<uplo>(),
-                                     std::array{laset_cl_src}, targs, options);
+    auto lasetOp =
+        common::getKernel(laset_name<uplo>(), {{laset_cl_src}}, targs, options);
 
     int groups_x = (m - 1) / BLK_X + 1;
     int groups_y = (n - 1) / BLK_Y + 1;
diff --git a/src/backend/opencl/kernel/laswp.hpp b/src/backend/opencl/kernel/laswp.hpp
index 5db0b388ff..7439f3680e 100644
--- a/src/backend/opencl/kernel/laswp.hpp
+++ b/src/backend/opencl/kernel/laswp.hpp
@@ -42,8 +42,7 @@ void laswp(int n, cl_mem in, size_t offset, int ldda, int k1, int k2,
         DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(MAX_PIVOTS),
         getTypeBuildDefinition<T>()};
 
-    auto laswpOp =
-        common::getKernel("laswp", std::array{laswp_cl_src}, targs, options);
+    auto laswpOp = common::getKernel("laswp", {{laswp_cl_src}}, targs, options);
 
     int groups = divup(n, NTHREADS);
     cl::NDRange local(NTHREADS);
diff --git a/src/backend/opencl/kernel/lookup.hpp b/src/backend/opencl/kernel/lookup.hpp
index 1e99e82780..3410c65266 100644
--- a/src/backend/opencl/kernel/lookup.hpp
+++ b/src/backend/opencl/kernel/lookup.hpp
@@ -48,8 +48,8 @@ void lookup(Param out, const Param in, const Param indices,
     cl::NDRange global(blk_x * out.info.dims[2] * THREADS_X,
                        blk_y * out.info.dims[3] * THREADS_Y);
 
-    auto arrIdxOp = common::getKernel("lookupND", std::array{lookup_cl_src},
-                                      targs, options);
+    auto arrIdxOp =
+        common::getKernel("lookupND", {{lookup_cl_src}}, targs, options);
 
     arrIdxOp(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
              *in.data, in.info, *indices.data, indices.info, blk_x, blk_y);
diff --git a/src/backend/opencl/kernel/lu_split.hpp b/src/backend/opencl/kernel/lu_split.hpp
index 65fc511415..019e02528b 100644
--- a/src/backend/opencl/kernel/lu_split.hpp
+++ b/src/backend/opencl/kernel/lu_split.hpp
@@ -41,8 +41,8 @@ void luSplitLauncher(Param lower, Param upper, const Param in, bool same_dims) {
         DefineKeyValue(ONE, scalar_to_option(scalar<T>(1))),
         getTypeBuildDefinition<T>()};
 
-    auto luSplit = common::getKernel("luSplit", std::array{lu_split_cl_src},
-                                     targs, options);
+    auto luSplit =
+        common::getKernel("luSplit", {{lu_split_cl_src}}, targs, options);
 
     cl::NDRange local(TX, TY);
 
diff --git a/src/backend/opencl/kernel/match_template.hpp b/src/backend/opencl/kernel/match_template.hpp
index 21041eb73b..8f43c99174 100644
--- a/src/backend/opencl/kernel/match_template.hpp
+++ b/src/backend/opencl/kernel/match_template.hpp
@@ -52,7 +52,7 @@ void matchTemplate(Param out, const Param srch, const Param tmplt,
         getTypeBuildDefinition<outType>()};
 
     auto matchImgOp = common::getKernel(
-        "matchTemplate", std::array{matchTemplate_cl_src}, targs, options);
+        "matchTemplate", {{matchTemplate_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/mean.hpp b/src/backend/opencl/kernel/mean.hpp
index 13f74453a8..bc80a23be9 100644
--- a/src/backend/opencl/kernel/mean.hpp
+++ b/src/backend/opencl/kernel/mean.hpp
@@ -130,8 +130,7 @@ void meanDimLauncher(Param out, Param owt, Param in, Param inWeight,
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
     auto meanOp = common::getKernel(
-        "meanDim", std::array{mean_ops_cl_src, mean_dim_cl_src}, targs,
-        options);
+        "meanDim", {{mean_ops_cl_src, mean_dim_cl_src}}, targs, options);
 
     NDRange local(THREADS_X, threads_y);
     NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -223,8 +222,7 @@ void meanFirstLauncher(Param out, Param owt, Param in, Param inWeight,
     if (output_weight) { options.emplace_back(DefineKey(OUTPUT_WEIGHT)); }
 
     auto meanOp = common::getKernel(
-        "meanFirst", std::array{mean_ops_cl_src, mean_first_cl_src}, targs,
-        options);
+        "meanFirst", {{mean_ops_cl_src, mean_first_cl_src}}, targs, options);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/meanshift.hpp b/src/backend/opencl/kernel/meanshift.hpp
index 24fa61374d..752e507262 100644
--- a/src/backend/opencl/kernel/meanshift.hpp
+++ b/src/backend/opencl/kernel/meanshift.hpp
@@ -43,8 +43,8 @@ void meanshift(Param out, const Param in, const float spatialSigma,
         DefineKeyValue(MAX_CHANNELS, (is_color ? 3 : 1)),
         getTypeBuildDefinition<T>()};
 
-    auto meanshiftOp = common::getKernel(
-        "meanshift", std::array{meanshift_cl_src}, targs, options);
+    auto meanshiftOp =
+        common::getKernel("meanshift", {{meanshift_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/medfilt.hpp b/src/backend/opencl/kernel/medfilt.hpp
index d38943e50d..abbd0ea5c7 100644
--- a/src/backend/opencl/kernel/medfilt.hpp
+++ b/src/backend/opencl/kernel/medfilt.hpp
@@ -49,8 +49,8 @@ void medfilt1(Param out, const Param in, const unsigned w_wid,
         DefineValue(w_wid),
         getTypeBuildDefinition<T>()};
 
-    auto medfiltOp = common::getKernel("medfilt1", std::array{medfilt1_cl_src},
-                                       targs, options);
+    auto medfiltOp =
+        common::getKernel("medfilt1", {{medfilt1_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, 1, 1);
 
@@ -87,8 +87,8 @@ void medfilt2(Param out, const Param in, const af_border_type pad,
         DefineValue(w_len),
         getTypeBuildDefinition<T>()};
 
-    auto medfiltOp = common::getKernel("medfilt2", std::array{medfilt2_cl_src},
-                                       targs, options);
+    auto medfiltOp =
+        common::getKernel("medfilt2", {{medfilt2_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/memcopy.hpp b/src/backend/opencl/kernel/memcopy.hpp
index d9fe825107..c27d8c39b6 100644
--- a/src/backend/opencl/kernel/memcopy.hpp
+++ b/src/backend/opencl/kernel/memcopy.hpp
@@ -156,9 +156,8 @@ void memcopy(const cl::Buffer& b_out, const dim4& ostrides,
         : sizeofNewT == 16
             ? "float4"
             : "type is larger than 16 bytes, which is unsupported"};
-    auto memCopy{common::getKernel(kernelName, std::array{memcopy_cl_src},
-                                   std::array{tArg},
-                                   std::array{DefineKeyValue(T, tArg)})};
+    auto memCopy{common::getKernel(kernelName, {{memcopy_cl_src}}, {{tArg}},
+                                   {{DefineKeyValue(T, tArg)}})};
     const cl::NDRange local{th.genLocal(memCopy.get())};
     const cl::NDRange global{th.genGlobal(local)};
 
@@ -230,7 +229,7 @@ void copy(const Param out, const Param in, dim_t ondims,
                                   : th.loop3 ? "scaledCopyLoop13"
                                   : th.loop1 ? "scaledCopyLoop1"
                                              : "scaledCopy",
-                                  std::array{copy_cl_src}, targs, options);
+                                  {{copy_cl_src}}, targs, options);
     const cl::NDRange local{th.genLocal(copy.get())};
     const cl::NDRange global{th.genGlobal(local)};
 
diff --git a/src/backend/opencl/kernel/moments.hpp b/src/backend/opencl/kernel/moments.hpp
index 3f269686c3..2ab1185516 100644
--- a/src/backend/opencl/kernel/moments.hpp
+++ b/src/backend/opencl/kernel/moments.hpp
@@ -38,8 +38,8 @@ void moments(Param out, const Param in, af_moment_type moment) {
         DefineKeyValue(MOMENTS_SZ, out.info.dims[0]),
         getTypeBuildDefinition<T>()};
 
-    auto momentsOp = common::getKernel("moments", std::array{moments_cl_src},
-                                       targs, options);
+    auto momentsOp =
+        common::getKernel("moments", {{moments_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS, 1, 1);
     cl::NDRange global(in.info.dims[1] * local[0],
diff --git a/src/backend/opencl/kernel/morph.hpp b/src/backend/opencl/kernel/morph.hpp
index 730a424eed..473de659f2 100644
--- a/src/backend/opencl/kernel/morph.hpp
+++ b/src/backend/opencl/kernel/morph.hpp
@@ -56,8 +56,7 @@ void morph(Param out, const Param in, const Param mask, bool isDilation) {
     };
     options.emplace_back(getTypeBuildDefinition<T>());
 
-    auto morphOp =
-        common::getKernel("morph", std::array{morph_cl_src}, targs, options);
+    auto morphOp = common::getKernel("morph", {{morph_cl_src}}, targs, options);
 
     NDRange local(THREADS_X, THREADS_Y);
 
@@ -117,7 +116,7 @@ void morph3d(Param out, const Param in, const Param mask, bool isDilation) {
     options.emplace_back(getTypeBuildDefinition<T>());
 
     auto morphOp =
-        common::getKernel("morph3d", std::array{morph_cl_src}, targs, options);
+        common::getKernel("morph3d", {{morph_cl_src}}, targs, options);
 
     NDRange local(CUBE_X, CUBE_Y, CUBE_Z);
 
diff --git a/src/backend/opencl/kernel/nearest_neighbour.hpp b/src/backend/opencl/kernel/nearest_neighbour.hpp
index b4f7e5fa36..cac36cab33 100644
--- a/src/backend/opencl/kernel/nearest_neighbour.hpp
+++ b/src/backend/opencl/kernel/nearest_neighbour.hpp
@@ -71,9 +71,8 @@ void allDistances(Param dist, Param query, Param train, const dim_t dist_dim,
         options.emplace_back(DefineKeyValue(DISTOP, "_shd_"));
         options.emplace_back(DefineKey(__SHD__));
     }
-    auto hmOp =
-        common::getKernel("knnAllDistances",
-                          std::array{nearest_neighbour_cl_src}, targs, options);
+    auto hmOp = common::getKernel("knnAllDistances",
+                                  {{nearest_neighbour_cl_src}}, targs, options);
 
     const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
 
diff --git a/src/backend/opencl/kernel/orb.hpp b/src/backend/opencl/kernel/orb.hpp
index b3e4014d05..5d4f523f16 100644
--- a/src/backend/opencl/kernel/orb.hpp
+++ b/src/backend/opencl/kernel/orb.hpp
@@ -88,14 +88,11 @@ std::array<Kernel, 4> getOrbKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("harris_response", std::array{orb_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("keep_features", std::array{orb_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("centroid_angle", std::array{orb_cl_src}, targs,
-                          compileOpts),
-        common::getKernel("extract_orb", std::array{orb_cl_src}, targs,
+        common::getKernel("harris_response", {{orb_cl_src}}, targs,
                           compileOpts),
+        common::getKernel("keep_features", {{orb_cl_src}}, targs, compileOpts),
+        common::getKernel("centroid_angle", {{orb_cl_src}}, targs, compileOpts),
+        common::getKernel("extract_orb", {{orb_cl_src}}, targs, compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/pad_array_borders.hpp b/src/backend/opencl/kernel/pad_array_borders.hpp
index 8e75e5fbd5..53ee36d8d8 100644
--- a/src/backend/opencl/kernel/pad_array_borders.hpp
+++ b/src/backend/opencl/kernel/pad_array_borders.hpp
@@ -46,9 +46,8 @@ void padBorders(Param out, const Param in, dim4 const& lBPadding,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto pad =
-        common::getKernel("padBorders", std::array{pad_array_borders_cl_src},
-                          tmpltArgs, compileOpts);
+    auto pad = common::getKernel("padBorders", {{pad_array_borders_cl_src}},
+                                 tmpltArgs, compileOpts);
 
     NDRange local(PADB_THREADS_X, PADB_THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/random_engine.hpp b/src/backend/opencl/kernel/random_engine.hpp
index 96c230f133..390be184eb 100644
--- a/src/backend/opencl/kernel/random_engine.hpp
+++ b/src/backend/opencl/kernel/random_engine.hpp
@@ -163,9 +163,8 @@ void initMersenneState(cl::Buffer state, cl::Buffer table, const uintl &seed) {
     cl::NDRange local(THREADS_PER_GROUP, 1);
     cl::NDRange global(local[0] * MAX_BLOCKS, 1);
 
-    auto initOp =
-        common::getKernel("mersenneInitState",
-                          std::array{random_engine_mersenne_init_cl_src}, {});
+    auto initOp = common::getKernel("mersenneInitState",
+                                    {{random_engine_mersenne_init_cl_src}}, {});
     initOp(cl::EnqueueArgs(getQueue(), global, local), state, table, seed);
     CL_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/opencl/kernel/range.hpp b/src/backend/opencl/kernel/range.hpp
index ddb946d307..3fb58a65ce 100644
--- a/src/backend/opencl/kernel/range.hpp
+++ b/src/backend/opencl/kernel/range.hpp
@@ -36,8 +36,8 @@ void range(Param out, const int dim) {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         getTypeBuildDefinition<T>()};
 
-    auto rangeOp = common::getKernel("range_kernel", std::array{range_cl_src},
-                                     targs, options);
+    auto rangeOp =
+        common::getKernel("range_kernel", {{range_cl_src}}, targs, options);
 
     cl::NDRange local(RANGE_TX, RANGE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/reduce.hpp b/src/backend/opencl/kernel/reduce.hpp
index 21db6e2edc..98982fe8f3 100644
--- a/src/backend/opencl/kernel/reduce.hpp
+++ b/src/backend/opencl/kernel/reduce.hpp
@@ -56,8 +56,7 @@ void reduceDimLauncher(Param out, Param in, const int dim, const uint threads_y,
         getTypeBuildDefinition<Ti, To>()};
 
     auto reduceDim = common::getKernel(
-        "reduce_dim_kernel", std::array{ops_cl_src, reduce_dim_cl_src}, targs,
-        options);
+        "reduce_dim_kernel", {{ops_cl_src, reduce_dim_cl_src}}, targs, options);
 
     cl::NDRange local(THREADS_X, threads_y);
     cl::NDRange global(groups_all[0] * groups_all[2] * local[0],
@@ -134,8 +133,7 @@ void reduceAllLauncher(Param out, Param in, const uint groups_x,
         getTypeBuildDefinition<Ti, To>()};
 
     auto reduceAll = common::getKernel(
-        "reduce_all_kernel", std::array{ops_cl_src, reduce_all_cl_src}, targs,
-        options);
+        "reduce_all_kernel", {{ops_cl_src, reduce_all_cl_src}}, targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
@@ -181,9 +179,9 @@ void reduceFirstLauncher(Param out, Param in, const uint groups_x,
         DefineKeyValue(CPLX, iscplx<Ti>()),
         getTypeBuildDefinition<Ti, To>()};
 
-    auto reduceFirst = common::getKernel(
-        "reduce_first_kernel", std::array{ops_cl_src, reduce_first_cl_src},
-        targs, options);
+    auto reduceFirst =
+        common::getKernel("reduce_first_kernel",
+                          {{ops_cl_src, reduce_first_cl_src}}, targs, options);
 
     cl::NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     cl::NDRange global(groups_x * in.info.dims[2] * local[0],
diff --git a/src/backend/opencl/kernel/reduce_by_key.hpp b/src/backend/opencl/kernel/reduce_by_key.hpp
index eeb0e119df..e80e3603c6 100644
--- a/src/backend/opencl/kernel/reduce_by_key.hpp
+++ b/src/backend/opencl/kernel/reduce_by_key.hpp
@@ -64,10 +64,10 @@ void reduceBlocksByKeyDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto reduceBlocksByKeyDim = common::getKernel(
-        "reduce_blocks_by_key_dim",
-        std::array{ops_cl_src, reduce_blocks_by_key_dim_cl_src}, tmpltArgs,
-        compileOpts);
+    auto reduceBlocksByKeyDim =
+        common::getKernel("reduce_blocks_by_key_dim",
+                          {{ops_cl_src, reduce_blocks_by_key_dim_cl_src}},
+                          tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
     cl::NDRange local(threads_x);
@@ -107,10 +107,10 @@ void reduceBlocksByKey(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto reduceBlocksByKeyFirst = common::getKernel(
-        "reduce_blocks_by_key_first",
-        std::array{ops_cl_src, reduce_blocks_by_key_first_cl_src}, tmpltArgs,
-        compileOpts);
+    auto reduceBlocksByKeyFirst =
+        common::getKernel("reduce_blocks_by_key_first",
+                          {{ops_cl_src, reduce_blocks_by_key_first_cl_src}},
+                          tmpltArgs, compileOpts);
     int numBlocks = divup(n, threads_x);
 
     cl::NDRange local(threads_x);
@@ -148,10 +148,9 @@ void finalBoundaryReduce(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduce =
-        common::getKernel("final_boundary_reduce",
-                          std::array{ops_cl_src, reduce_by_key_boundary_cl_src},
-                          tmpltArgs, compileOpts);
+    auto finalBoundaryReduce = common::getKernel(
+        "final_boundary_reduce", {{ops_cl_src, reduce_by_key_boundary_cl_src}},
+        tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks);
@@ -187,10 +186,10 @@ void finalBoundaryReduceDim(cl::Buffer *reduced_block_sizes, Param keys_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
-    auto finalBoundaryReduceDim = common::getKernel(
-        "final_boundary_reduce_dim",
-        std::array{ops_cl_src, reduce_by_key_boundary_dim_cl_src}, tmpltArgs,
-        compileOpts);
+    auto finalBoundaryReduceDim =
+        common::getKernel("final_boundary_reduce_dim",
+                          {{ops_cl_src, reduce_by_key_boundary_dim_cl_src}},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks,
@@ -224,8 +223,8 @@ void compact(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto compact = common::getKernel(
-        "compact", std::array{ops_cl_src, reduce_by_key_compact_cl_src},
-        tmpltArgs, compileOpts);
+        "compact", {{ops_cl_src, reduce_by_key_compact_cl_src}}, tmpltArgs,
+        compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks, vals_out.info.dims[1],
@@ -259,7 +258,7 @@ void compactDim(cl::Buffer *reduced_block_sizes, Param keys_out, Param vals_out,
     compileOpts.emplace_back(getTypeBuildDefinition<To>());
 
     auto compactDim = common::getKernel(
-        "compact_dim", std::array{ops_cl_src, reduce_by_key_compact_dim_cl_src},
+        "compact_dim", {{ops_cl_src, reduce_by_key_compact_dim_cl_src}},
         tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
@@ -288,10 +287,10 @@ void testNeedsReduction(cl::Buffer needs_reduction, cl::Buffer needs_boundary,
         DefineKeyValue(DIMX, threads_x),
     };
 
-    auto testIfNeedsReduction = common::getKernel(
-        "test_needs_reduction",
-        std::array{ops_cl_src, reduce_by_key_needs_reduction_cl_src}, tmpltArgs,
-        compileOpts);
+    auto testIfNeedsReduction =
+        common::getKernel("test_needs_reduction",
+                          {{ops_cl_src, reduce_by_key_needs_reduction_cl_src}},
+                          tmpltArgs, compileOpts);
 
     cl::NDRange local(threads_x);
     cl::NDRange global(threads_x * numBlocks);
diff --git a/src/backend/opencl/kernel/regions.hpp b/src/backend/opencl/kernel/regions.hpp
index 63716ba8ea..a082d165af 100644
--- a/src/backend/opencl/kernel/regions.hpp
+++ b/src/backend/opencl/kernel/regions.hpp
@@ -67,12 +67,9 @@ std::array<Kernel, 3> getRegionsKernels(const bool full_conn,
     options.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("initial_label", std::array{regions_cl_src}, targs,
-                          options),
-        common::getKernel("final_relabel", std::array{regions_cl_src}, targs,
-                          options),
-        common::getKernel("update_equiv", std::array{regions_cl_src}, targs,
-                          options),
+        common::getKernel("initial_label", {{regions_cl_src}}, targs, options),
+        common::getKernel("final_relabel", {{regions_cl_src}}, targs, options),
+        common::getKernel("update_equiv", {{regions_cl_src}}, targs, options),
     };
 }
 
diff --git a/src/backend/opencl/kernel/reorder.hpp b/src/backend/opencl/kernel/reorder.hpp
index 9322647cd2..469e8b77c3 100644
--- a/src/backend/opencl/kernel/reorder.hpp
+++ b/src/backend/opencl/kernel/reorder.hpp
@@ -36,8 +36,8 @@ void reorder(Param out, const Param in, const dim_t* rdims) {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         getTypeBuildDefinition<T>()};
 
-    auto reorderOp = common::getKernel(
-        "reorder_kernel", std::array{reorder_cl_src}, targs, options);
+    auto reorderOp =
+        common::getKernel("reorder_kernel", {{reorder_cl_src}}, targs, options);
 
     cl::NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/resize.hpp b/src/backend/opencl/kernel/resize.hpp
index bc813393c5..f201427ddf 100644
--- a/src/backend/opencl/kernel/resize.hpp
+++ b/src/backend/opencl/kernel/resize.hpp
@@ -67,8 +67,8 @@ void resize(Param out, const Param in, const af_interp_type method) {
         default: break;
     }
 
-    auto resizeOp = common::getKernel(
-        "resize_kernel", std::array{resize_cl_src}, targs, options);
+    auto resizeOp =
+        common::getKernel("resize_kernel", {{resize_cl_src}}, targs, options);
 
     cl::NDRange local(RESIZE_TX, RESIZE_TY, 1);
 
diff --git a/src/backend/opencl/kernel/rotate.hpp b/src/backend/opencl/kernel/rotate.hpp
index dec52c8962..a3d3f41cba 100644
--- a/src/backend/opencl/kernel/rotate.hpp
+++ b/src/backend/opencl/kernel/rotate.hpp
@@ -80,9 +80,9 @@ void rotate(Param out, const Param in, const float theta, af_interp_type method,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto rotate = common::getKernel("rotateKernel",
-                                    std::array{interp_cl_src, rotate_cl_src},
-                                    tmpltArgs, compileOpts);
+    auto rotate =
+        common::getKernel("rotateKernel", {{interp_cl_src, rotate_cl_src}},
+                          tmpltArgs, compileOpts);
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/opencl/kernel/scan_dim.hpp b/src/backend/opencl/kernel/scan_dim.hpp
index 2edc7f68c0..f9820f47cf 100644
--- a/src/backend/opencl/kernel/scan_dim.hpp
+++ b/src/backend/opencl/kernel/scan_dim.hpp
@@ -58,8 +58,8 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, std::array{ops_cl_src, scan_dim_cl_src},
-                             tmpltArgs, compileOpts);
+    return common::getKernel(key, {{ops_cl_src, scan_dim_cl_src}}, tmpltArgs,
+                             compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
index 3d9745923c..c4cc7959ff 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_dim_by_key_impl.hpp
@@ -58,8 +58,7 @@ static opencl::Kernel getScanDimKernel(const std::string key, int dim,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key,
-                             std::array{ops_cl_src, scan_dim_by_key_cl_src},
+    return common::getKernel(key, {{ops_cl_src, scan_dim_by_key_cl_src}},
                              tmpltArgs, compileOpts);
 }
 
diff --git a/src/backend/opencl/kernel/scan_first.hpp b/src/backend/opencl/kernel/scan_first.hpp
index 4354d27b49..569c361ef8 100644
--- a/src/backend/opencl/kernel/scan_first.hpp
+++ b/src/backend/opencl/kernel/scan_first.hpp
@@ -59,8 +59,8 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key, std::array{ops_cl_src, scan_first_cl_src},
-                             tmpltArgs, compileOpts);
+    return common::getKernel(key, {{ops_cl_src, scan_first_cl_src}}, tmpltArgs,
+                             compileOpts);
 }
 
 template<typename Ti, typename To, af_op_t op>
diff --git a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
index d0351add52..82674db44d 100644
--- a/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/scan_first_by_key_impl.hpp
@@ -62,8 +62,7 @@ static opencl::Kernel getScanFirstKernel(const std::string key,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    return common::getKernel(key,
-                             std::array{ops_cl_src, scan_first_by_key_cl_src},
+    return common::getKernel(key, {{ops_cl_src, scan_first_by_key_cl_src}},
                              tmpltArgs, compileOpts);
 }
 
diff --git a/src/backend/opencl/kernel/select.hpp b/src/backend/opencl/kernel/select.hpp
index fc37e6cb86..6de96e2cd6 100644
--- a/src/backend/opencl/kernel/select.hpp
+++ b/src/backend/opencl/kernel/select.hpp
@@ -38,8 +38,8 @@ void selectLauncher(Param out, Param cond, Param a, Param b, const int ndims,
         DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(is_same),
         getTypeBuildDefinition<T>()};
 
-    auto selectOp = common::getKernel(
-        "select_kernel", std::array{select_cl_src}, targs, options);
+    auto selectOp =
+        common::getKernel("select_kernel", {{select_cl_src}}, targs, options);
 
     int threads[] = {DIMX, DIMY};
 
@@ -81,8 +81,8 @@ void select_scalar(Param out, Param cond, Param a, const T b, const int ndims,
         DefineKeyValue(T, dtype_traits<T>::getName()), DefineValue(flip),
         getTypeBuildDefinition<T>()};
 
-    auto selectOp = common::getKernel(
-        "select_scalar_kernel", std::array{select_cl_src}, targs, options);
+    auto selectOp = common::getKernel("select_scalar_kernel", {{select_cl_src}},
+                                      targs, options);
 
     int threads[] = {DIMX, DIMY};
 
diff --git a/src/backend/opencl/kernel/sift.hpp b/src/backend/opencl/kernel/sift.hpp
index d5b248f007..01bfaa3926 100644
--- a/src/backend/opencl/kernel/sift.hpp
+++ b/src/backend/opencl/kernel/sift.hpp
@@ -356,20 +356,19 @@ std::array<Kernel, 7> getSiftKernels() {
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
     return {
-        common::getKernel("sub", std::array{sift_nonfree_cl_src}, targs,
+        common::getKernel("sub", {{sift_nonfree_cl_src}}, targs, compileOpts),
+        common::getKernel("detectExtrema", {{sift_nonfree_cl_src}}, targs,
                           compileOpts),
-        common::getKernel("detectExtrema", std::array{sift_nonfree_cl_src},
-                          targs, compileOpts),
-        common::getKernel("interpolateExtrema", std::array{sift_nonfree_cl_src},
-                          targs, compileOpts),
-        common::getKernel("calcOrientation", std::array{sift_nonfree_cl_src},
-                          targs, compileOpts),
-        common::getKernel("removeDuplicates", std::array{sift_nonfree_cl_src},
-                          targs, compileOpts),
-        common::getKernel("computeDescriptor", std::array{sift_nonfree_cl_src},
+        common::getKernel("interpolateExtrema", {{sift_nonfree_cl_src}}, targs,
+                          compileOpts),
+        common::getKernel("calcOrientation", {{sift_nonfree_cl_src}}, targs,
+                          compileOpts),
+        common::getKernel("removeDuplicates", {{sift_nonfree_cl_src}}, targs,
+                          compileOpts),
+        common::getKernel("computeDescriptor", {{sift_nonfree_cl_src}}, targs,
+                          compileOpts),
+        common::getKernel("computeGLOHDescriptor", {{sift_nonfree_cl_src}},
                           targs, compileOpts),
-        common::getKernel("computeGLOHDescriptor",
-                          std::array{sift_nonfree_cl_src}, targs, compileOpts),
     };
 }
 
diff --git a/src/backend/opencl/kernel/sobel.hpp b/src/backend/opencl/kernel/sobel.hpp
index 9e92213adf..9e7138f69d 100644
--- a/src/backend/opencl/kernel/sobel.hpp
+++ b/src/backend/opencl/kernel/sobel.hpp
@@ -39,8 +39,8 @@ void sobel(Param dx, Param dy, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<Ti>());
 
-    auto sobel = common::getKernel("sobel3x3", std::array{sobel_cl_src}, targs,
-                                   compileOpts);
+    auto sobel =
+        common::getKernel("sobel3x3", {{sobel_cl_src}}, targs, compileOpts);
 
     cl::NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index f7ef69e248..e1b29c986c 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -43,8 +43,8 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto coo2dense = common::getKernel(
-        "coo2Dense", std::array{coo2dense_cl_src}, tmpltArgs, compileOpts);
+    auto coo2dense = common::getKernel("coo2Dense", {{coo2dense_cl_src}},
+                                       tmpltArgs, compileOpts);
 
     cl::NDRange local(THREADS_PER_GROUP, 1, 1);
 
@@ -76,8 +76,8 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2dense = common::getKernel(
-        "csr2Dense", std::array{csr2dense_cl_src}, tmpltArgs, compileOpts);
+    auto csr2dense = common::getKernel("csr2Dense", {{csr2dense_cl_src}},
+                                       tmpltArgs, compileOpts);
 
     cl::NDRange local(threads, 1);
     int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
@@ -102,8 +102,8 @@ void dense2csr(Param values, Param rowIdx, Param colIdx, const Param dense) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto dense2Csr = common::getKernel(
-        "dense2Csr", std::array{dense2csr_cl_src}, tmpltArgs, compileOpts);
+    auto dense2Csr = common::getKernel("dense2Csr", {{dense2csr_cl_src}},
+                                       tmpltArgs, compileOpts);
 
     int num_rows = dense.info.dims[0];
     int num_cols = dense.info.dims[1];
@@ -147,7 +147,7 @@ void swapIndex(Param ovalues, Param oindex, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapIndex = common::getKernel("swapIndex", std::array{csr2coo_cl_src},
+    auto swapIndex = common::getKernel("swapIndex", {{csr2coo_cl_src}},
                                        tmpltArgs, compileOpts);
 
     cl::NDRange global(ovalues.info.dims[0], 1, 1);
@@ -169,8 +169,8 @@ void csr2coo(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csr2coo = common::getKernel("csr2Coo", std::array{csr2coo_cl_src},
-                                     tmpltArgs, compileOpts);
+    auto csr2coo = common::getKernel("csr2Coo", {{csr2coo_cl_src}}, tmpltArgs,
+                                     compileOpts);
 
     const int MAX_GROUPS = 4096;
     int M                = irowIdx.info.dims[0] - 1;
@@ -209,7 +209,7 @@ void coo2csr(Param ovalues, Param orowIdx, Param ocolIdx, const Param ivalues,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto csrReduce = common::getKernel("csrReduce", std::array{csr2coo_cl_src},
+    auto csrReduce = common::getKernel("csrReduce", {{csr2coo_cl_src}},
                                        tmpltArgs, compileOpts);
 
     // Now we need to sort this into column major
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 048a6d4876..313fa902d2 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -63,9 +63,8 @@ auto fetchKernel(const std::string key, const common::Source &additionalSrc,
     options.emplace_back(getTypeBuildDefinition<T>());
     options.insert(std::end(options), std::begin(additionalOptions),
                    std::end(additionalOptions));
-    return common::getKernel(
-        key, std::array{sparse_arith_common_cl_src, additionalSrc}, tmpltArgs,
-        options);
+    return common::getKernel(key, {{sparse_arith_common_cl_src, additionalSrc}},
+                             tmpltArgs, options);
 }
 
 template<typename T, af_op_t op>
@@ -144,9 +143,8 @@ static void csrCalcOutNNZ(Param outRowIdx, unsigned &nnzC, const uint M,
         TemplateTypename<uint>(),
     };
 
-    auto calcNNZ = common::getKernel("csr_calc_out_nnz",
-                                     std::array{ssarith_calc_out_nnz_cl_src},
-                                     tmpltArgs, {});
+    auto calcNNZ = common::getKernel(
+        "csr_calc_out_nnz", {{ssarith_calc_out_nnz_cl_src}}, tmpltArgs, {});
 
     cl::NDRange local(256, 1);
     cl::NDRange global(divup(M, local[0]) * local[0], 1, 1);
diff --git a/src/backend/opencl/kernel/susan.hpp b/src/backend/opencl/kernel/susan.hpp
index d407755f31..4b87b43a85 100644
--- a/src/backend/opencl/kernel/susan.hpp
+++ b/src/backend/opencl/kernel/susan.hpp
@@ -49,8 +49,8 @@ void susan(cl::Buffer* out, const cl::Buffer* in, const unsigned in_off,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto susan = common::getKernel("susan_responses", std::array{susan_cl_src},
-                                   targs, compileOpts);
+    auto susan = common::getKernel("susan_responses", {{susan_cl_src}}, targs,
+                                   compileOpts);
 
     cl::NDRange local(SUSAN_THREADS_X, SUSAN_THREADS_Y);
     cl::NDRange global(divup(idim0 - 2 * edge, local[0]) * local[0],
@@ -75,8 +75,8 @@ unsigned nonMaximal(cl::Buffer* x_out, cl::Buffer* y_out, cl::Buffer* resp_out,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto nonMax = common::getKernel("non_maximal", std::array{susan_cl_src},
-                                    targs, compileOpts);
+    auto nonMax =
+        common::getKernel("non_maximal", {{susan_cl_src}}, targs, compileOpts);
 
     unsigned corners_found = 0;
     auto d_corners_found   = memAlloc<unsigned>(1);
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 820db15094..0b8b43fb72 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -42,8 +42,8 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto swapdblk = common::getKernel("swapdblk", std::array{swapdblk_cl_src},
-                                      targs, compileOpts);
+    auto swapdblk =
+        common::getKernel("swapdblk", {{swapdblk_cl_src}}, targs, compileOpts);
 
     int nblocks = n / nb;
 
diff --git a/src/backend/opencl/kernel/tile.hpp b/src/backend/opencl/kernel/tile.hpp
index fa097ba58f..7c9b042372 100644
--- a/src/backend/opencl/kernel/tile.hpp
+++ b/src/backend/opencl/kernel/tile.hpp
@@ -42,8 +42,7 @@ void tile(Param out, const Param in) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto tile =
-        common::getKernel("tile", std::array{tile_cl_src}, targs, compileOpts);
+    auto tile = common::getKernel("tile", {{tile_cl_src}}, targs, compileOpts);
 
     NDRange local(TX, TY, 1);
 
diff --git a/src/backend/opencl/kernel/transform.hpp b/src/backend/opencl/kernel/transform.hpp
index a3f81fd75b..76a2dafa43 100644
--- a/src/backend/opencl/kernel/transform.hpp
+++ b/src/backend/opencl/kernel/transform.hpp
@@ -80,9 +80,9 @@ void transform(Param out, const Param in, const Param tf, bool isInverse,
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
     addInterpEnumOptions(compileOpts);
 
-    auto transform = common::getKernel(
-        "transformKernel", std::array{interp_cl_src, transform_cl_src},
-        tmpltArgs, compileOpts);
+    auto transform = common::getKernel("transformKernel",
+                                       {{interp_cl_src, transform_cl_src}},
+                                       tmpltArgs, compileOpts);
 
     const int nImg2 = in.info.dims[2];
     const int nImg3 = in.info.dims[3];
diff --git a/src/backend/opencl/kernel/transpose.hpp b/src/backend/opencl/kernel/transpose.hpp
index 3397596179..b6979cf6d5 100644
--- a/src/backend/opencl/kernel/transpose.hpp
+++ b/src/backend/opencl/kernel/transpose.hpp
@@ -49,8 +49,8 @@ void transpose(Param out, const Param in, cl::CommandQueue queue,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto transpose = common::getKernel(
-        "transpose", std::array{transpose_cl_src}, tmpltArgs, compileOpts);
+    auto transpose = common::getKernel("transpose", {{transpose_cl_src}},
+                                       tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/transpose_inplace.hpp b/src/backend/opencl/kernel/transpose_inplace.hpp
index b55f2e4d43..6ed5c1e5c4 100644
--- a/src/backend/opencl/kernel/transpose_inplace.hpp
+++ b/src/backend/opencl/kernel/transpose_inplace.hpp
@@ -49,9 +49,9 @@ void transpose_inplace(Param in, cl::CommandQueue& queue, const bool conjugate,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto transpose = common::getKernel("transpose_inplace",
-                                       std::array{transpose_inplace_cl_src},
-                                       tmpltArgs, compileOpts);
+    auto transpose =
+        common::getKernel("transpose_inplace", {{transpose_inplace_cl_src}},
+                          tmpltArgs, compileOpts);
 
     NDRange local(THREADS_X, THREADS_Y);
 
diff --git a/src/backend/opencl/kernel/triangle.hpp b/src/backend/opencl/kernel/triangle.hpp
index c0be0de33f..888ac21909 100644
--- a/src/backend/opencl/kernel/triangle.hpp
+++ b/src/backend/opencl/kernel/triangle.hpp
@@ -52,7 +52,7 @@ void triangle(Param out, const Param in, bool is_upper, bool is_unit_diag) {
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto triangle = common::getKernel("triangle", std::array{triangle_cl_src},
+    auto triangle = common::getKernel("triangle", {{triangle_cl_src}},
                                       tmpltArgs, compileOpts);
 
     NDRange local(TX, TY);
diff --git a/src/backend/opencl/kernel/unwrap.hpp b/src/backend/opencl/kernel/unwrap.hpp
index 08e535f713..7c3d71bb37 100644
--- a/src/backend/opencl/kernel/unwrap.hpp
+++ b/src/backend/opencl/kernel/unwrap.hpp
@@ -47,8 +47,8 @@ void unwrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto unwrap = common::getKernel("unwrap", std::array{unwrap_cl_src},
-                                    tmpltArgs, compileOpts);
+    auto unwrap =
+        common::getKernel("unwrap", {{unwrap_cl_src}}, tmpltArgs, compileOpts);
 
     dim_t TX = 1, TY = 1;
     dim_t BX       = 1;
diff --git a/src/backend/opencl/kernel/where.hpp b/src/backend/opencl/kernel/where.hpp
index 88e89fd26b..980cdfe13f 100644
--- a/src/backend/opencl/kernel/where.hpp
+++ b/src/backend/opencl/kernel/where.hpp
@@ -46,8 +46,8 @@ static void get_out_idx(cl::Buffer *out_data, Param &otmp, Param &rtmp,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto getIdx = common::getKernel("get_out_idx", std::array{where_cl_src},
-                                    tmpltArgs, compileOpts);
+    auto getIdx = common::getKernel("get_out_idx", {{where_cl_src}}, tmpltArgs,
+                                    compileOpts);
 
     NDRange local(threads_x, THREADS_PER_GROUP / threads_x);
     NDRange global(local[0] * groups_x * in.info.dims[2],
diff --git a/src/backend/opencl/kernel/wrap.hpp b/src/backend/opencl/kernel/wrap.hpp
index b527cd8bce..e664c7b472 100644
--- a/src/backend/opencl/kernel/wrap.hpp
+++ b/src/backend/opencl/kernel/wrap.hpp
@@ -47,8 +47,8 @@ void wrap(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto wrap = common::getKernel("wrap", std::array{wrap_cl_src}, tmpltArgs,
-                                  compileOpts);
+    auto wrap =
+        common::getKernel("wrap", {{wrap_cl_src}}, tmpltArgs, compileOpts);
 
     dim_t nx = (out.info.dims[0] + 2 * px - wx) / sx + 1;
     dim_t ny = (out.info.dims[1] + 2 * py - wy) / sy + 1;
@@ -92,9 +92,8 @@ void wrap_dilated(Param out, const Param in, const dim_t wx, const dim_t wy,
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 
-    auto dilatedWrap =
-        common::getKernel("wrap_dilated", std::array{wrap_dilated_cl_src},
-                          tmpltArgs, compileOpts);
+    auto dilatedWrap = common::getKernel(
+        "wrap_dilated", {{wrap_dilated_cl_src}}, tmpltArgs, compileOpts);
 
     dim_t nx = 1 + (out.info.dims[0] + 2 * px - (((wx - 1) * dx) + 1)) / sx;
     dim_t ny = 1 + (out.info.dims[1] + 2 * py - (((wy - 1) * dy) + 1)) / sy;

From 262eb94a112c28ad844b271b1db3e8f8a7bb8f4d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Jan 2023 16:27:07 -0500
Subject: [PATCH 2364/2677] Update compiers.h header to add if constexpr macro

---
 CMakeModules/InternalUtils.cmake |   5 ++
 CMakeModules/compilers.h         | 129 +++++++++++++++++++------------
 src/backend/common/half.hpp      | 120 ++++++++++++++--------------
 src/backend/cuda/math.hpp        |  10 +--
 4 files changed, 147 insertions(+), 117 deletions(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index c698e3d290..1d1c387245 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -187,6 +187,11 @@ macro(arrayfire_set_cmake_default_variables)
   #  #else
   #  #define AF_CONSTEXPR
   #  #endif
+  #  #if __cpp_if_constexpr || __cplusplus >= 201606L
+  #  #define AF_IF_CONSTEXPR if constexpr
+  #  #else
+  #  #define AF_IF_CONSTEXPR if
+  #  #endif
   #  ]=])
   #  include(WriteCompilerDetectionHeader)
   #  write_compiler_detection_header(
diff --git a/CMakeModules/compilers.h b/CMakeModules/compilers.h
index c247005c80..60480d86ee 100644
--- a/CMakeModules/compilers.h
+++ b/CMakeModules/compilers.h
@@ -16,19 +16,24 @@
 # define AF_COMPILER_IS_HP 0
 # define AF_COMPILER_IS_Compaq 0
 # define AF_COMPILER_IS_zOS 0
+# define AF_COMPILER_IS_IBMClang 0
 # define AF_COMPILER_IS_XLClang 0
 # define AF_COMPILER_IS_XL 0
 # define AF_COMPILER_IS_VisualAge 0
+# define AF_COMPILER_IS_NVHPC 0
 # define AF_COMPILER_IS_PGI 0
 # define AF_COMPILER_IS_Cray 0
 # define AF_COMPILER_IS_TI 0
+# define AF_COMPILER_IS_FujitsuClang 0
 # define AF_COMPILER_IS_Fujitsu 0
 # define AF_COMPILER_IS_GHS 0
+# define AF_COMPILER_IS_Tasking 0
 # define AF_COMPILER_IS_SCO 0
 # define AF_COMPILER_IS_ARMCC 0
 # define AF_COMPILER_IS_AppleClang 0
 # define AF_COMPILER_IS_ARMClang 0
 # define AF_COMPILER_IS_Clang 0
+# define AF_COMPILER_IS_LCC 0
 # define AF_COMPILER_IS_GNU 0
 # define AF_COMPILER_IS_MSVC 0
 # define AF_COMPILER_IS_ADSP 0
@@ -79,6 +84,10 @@
 # undef AF_COMPILER_IS_zOS
 # define AF_COMPILER_IS_zOS 1
 
+#elif defined(__open_xl__) && defined(__clang__)
+# undef AF_COMPILER_IS_IBMClang
+# define AF_COMPILER_IS_IBMClang 1
+
 #elif defined(__ibmxl__) && defined(__clang__)
 # undef AF_COMPILER_IS_XLClang
 # define AF_COMPILER_IS_XLClang 1
@@ -91,6 +100,10 @@
 # undef AF_COMPILER_IS_VisualAge
 # define AF_COMPILER_IS_VisualAge 1
 
+#elif defined(__NVCOMPILER)
+# undef AF_COMPILER_IS_NVHPC
+# define AF_COMPILER_IS_NVHPC 1
+
 #elif defined(__PGI)
 # undef AF_COMPILER_IS_PGI
 # define AF_COMPILER_IS_PGI 1
@@ -103,7 +116,11 @@
 # undef AF_COMPILER_IS_TI
 # define AF_COMPILER_IS_TI 1
 
-#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version)
+#elif defined(__CLANG_FUJITSU)
+# undef AF_COMPILER_IS_FujitsuClang
+# define AF_COMPILER_IS_FujitsuClang 1
+
+#elif defined(__FUJITSU)
 # undef AF_COMPILER_IS_Fujitsu
 # define AF_COMPILER_IS_Fujitsu 1
 
@@ -111,6 +128,10 @@
 # undef AF_COMPILER_IS_GHS
 # define AF_COMPILER_IS_GHS 1
 
+#elif defined(__TASKING__)
+# undef AF_COMPILER_IS_Tasking
+# define AF_COMPILER_IS_Tasking 1
+
 #elif defined(__SCO_VERSION__)
 # undef AF_COMPILER_IS_SCO
 # define AF_COMPILER_IS_SCO 1
@@ -131,6 +152,10 @@
 # undef AF_COMPILER_IS_Clang
 # define AF_COMPILER_IS_Clang 1
 
+#elif defined(__LCC__) && (defined(__GNUC__) || defined(__GNUG__) || defined(__MCST__))
+# undef AF_COMPILER_IS_LCC
+# define AF_COMPILER_IS_LCC 1
+
 #elif defined(__GNUC__) || defined(__GNUG__)
 # undef AF_COMPILER_IS_GNU
 # define AF_COMPILER_IS_GNU 1
@@ -139,7 +164,7 @@
 # undef AF_COMPILER_IS_MSVC
 # define AF_COMPILER_IS_MSVC 1
 
-#elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__)
+#elif defined(_ADI_COMPILER)
 # undef AF_COMPILER_IS_ADSP
 # define AF_COMPILER_IS_ADSP 1
 
@@ -202,12 +227,11 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
-#if ((__clang_major__ * 100) + __clang_minor__) >= 400 &&                      \
-    __has_feature(cxx_relaxed_constexpr)
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
-#else
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
-#endif
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 400 && __has_feature(cxx_relaxed_constexpr)
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#    else
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#    endif
 
 #  elif AF_COMPILER_IS_Clang
 
@@ -260,12 +284,11 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
-#if ((__clang_major__ * 100) + __clang_minor__) >= 301 &&                      \
-    __has_feature(cxx_relaxed_constexpr)
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
-#else
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
-#endif
+#    if ((__clang_major__ * 100) + __clang_minor__) >= 301 && __has_feature(cxx_relaxed_constexpr)
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#    else
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#    endif
 
 #  elif AF_COMPILER_IS_GNU
 
@@ -321,11 +344,11 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
-#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 500 && __cplusplus >= 201402L
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
-#else
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
-#endif
+#    if (__GNUC__ * 100 + __GNUC_MINOR__) >= 500 && __cplusplus >= 201402L
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#    else
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#    endif
 
 #  elif AF_COMPILER_IS_Intel
 
@@ -333,16 +356,25 @@
 #      error Unsupported compiler version
 #    endif
 
-  /* __INTEL_COMPILER = VRP */
-# define AF_COMPILER_VERSION_MAJOR (__INTEL_COMPILER/100)
-# define AF_COMPILER_VERSION_MINOR (__INTEL_COMPILER/10 % 10)
-# if defined(__INTEL_COMPILER_UPDATE)
-#  define AF_COMPILER_VERSION_PATCH (__INTEL_COMPILER_UPDATE)
+  /* __INTEL_COMPILER = VRP prior to 2021, and then VVVV for 2021 and later,
+     except that a few beta releases use the old format with V=2021.  */
+# if __INTEL_COMPILER < 2021 || __INTEL_COMPILER == 202110 || __INTEL_COMPILER == 202111
+#  define AF_COMPILER_VERSION_MAJOR (__INTEL_COMPILER/100)
+#  define AF_COMPILER_VERSION_MINOR (__INTEL_COMPILER/10 % 10)
+#  if defined(__INTEL_COMPILER_UPDATE)
+#   define AF_COMPILER_VERSION_PATCH (__INTEL_COMPILER_UPDATE)
+#  else
+#   define AF_COMPILER_VERSION_PATCH (__INTEL_COMPILER   % 10)
+#  endif
 # else
-#  define AF_COMPILER_VERSION_PATCH (__INTEL_COMPILER   % 10)
+#  define AF_COMPILER_VERSION_MAJOR (__INTEL_COMPILER)
+#  define AF_COMPILER_VERSION_MINOR (__INTEL_COMPILER_UPDATE)
+   /* The third version component from --version is an update index,
+      but no macro is provided for it.  */
+#  define AF_COMPILER_VERSION_PATCH (0)
 # endif
 # if defined(__INTEL_COMPILER_BUILD_DATE)
-  /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */
+   /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */
 #  define AF_COMPILER_VERSION_TWEAK (__INTEL_COMPILER_BUILD_DATE)
 # endif
 # if defined(_MSC_VER)
@@ -398,19 +430,11 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
-#if __cpp_constexpr >= 201304 ||                                               \
-    (__INTEL_COMPILER >= 1700 &&                                               \
-     ((__cplusplus >= 201300L) ||                                              \
-      ((__cplusplus == 201103L) && !defined(__INTEL_CXX11_MODE__)) ||          \
-      ((((__INTEL_COMPILER == 1500) && (__INTEL_COMPILER_UPDATE == 1))) &&     \
-       defined(__GXX_EXPERIMENTAL_CXX0X__) &&                                  \
-       !defined(__INTEL_CXX11_MODE__)) ||                                      \
-      (defined(__INTEL_CXX11_MODE__) && defined(__cpp_aggregate_nsdmi))) &&    \
-     !defined(_MSC_VER))
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
-#else
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
-#endif
+#    if __cpp_constexpr >= 201304 || (__INTEL_COMPILER >= 1700 && ((__cplusplus >= 201300L) || ((__cplusplus == 201103L) && !defined(__INTEL_CXX11_MODE__)) || ((((__INTEL_COMPILER == 1500) && (__INTEL_COMPILER_UPDATE == 1))) && defined(__GXX_EXPERIMENTAL_CXX0X__) && !defined(__INTEL_CXX11_MODE__) ) || (defined(__INTEL_CXX11_MODE__) && defined(__cpp_aggregate_nsdmi)) ) && !defined(_MSC_VER))
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#    else
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#    endif
 
 #  elif AF_COMPILER_IS_MSVC
 
@@ -470,11 +494,11 @@
 #      define AF_COMPILER_CXX_GENERALIZED_INITIALIZERS 0
 #    endif
 
-#if _MSC_VER >= 1911
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
-#else
-#define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
-#endif
+#    if _MSC_VER >= 1911
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 1
+#    else
+#      define AF_COMPILER_CXX_RELAXED_CONSTEXPR 0
+#    endif
 
 #  endif
 
@@ -511,11 +535,16 @@ template<> struct AFStaticAssert<true>{};
 
 #endif
 
-#if defined(AF_COMPILER_CXX_RELAXED_CONSTEXPR) &&                              \
-    AF_COMPILER_CXX_RELAXED_CONSTEXPR
-#define AF_CONSTEXPR constexpr
-#else
-#define AF_CONSTEXPR
-#endif
+  #if defined(AF_COMPILER_CXX_RELAXED_CONSTEXPR) && AF_COMPILER_CXX_RELAXED_CONSTEXPR
+  #define AF_CONSTEXPR constexpr
+  #else
+  #define AF_CONSTEXPR
+  #endif
+  #if defined(__cpp_if_constexpr) || __cplusplus >= 201606L
+  #define AF_IF_CONSTEXPR if constexpr
+  #else
+  #define AF_IF_CONSTEXPR if
+  #endif
+
 
 #endif
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 8080dcffa1..f653024fb1 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -129,12 +129,11 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
     if (S) value = -value;
     uint16_t bits = S << 15;
     if (value > 0xFFFF) {
-        if constexpr (R == std::round_toward_infinity)
-            bits |= (0x7C00 - S);
-        else if constexpr (R == std::round_toward_neg_infinity)
-            bits |= (0x7BFF + S);
-        else
-            bits |= (0x7BFF + (R != std::round_toward_zero));
+        AF_IF_CONSTEXPR(R == std::round_toward_infinity)
+        bits |= (0x7C00 - S);
+        else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) bits |=
+            (0x7BFF + S);
+        else bits |= (0x7BFF + (R != std::round_toward_zero));
     } else if (value) {
         uint32_t m = value, exp = 24;
         for (; m < 0x400; m <<= 1, --exp)
@@ -143,16 +142,16 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
             ;
         bits |= (exp << 10) + m;
         if (exp > 24) {
-            if constexpr (R == std::round_to_nearest)
-                bits += (value >> (exp - 25)) & 1
+            AF_IF_CONSTEXPR(R == std::round_to_nearest)
+            bits += (value >> (exp - 25)) & 1
 #if HALF_ROUND_TIES_TO_EVEN
-                        & (((((1 << (exp - 25)) - 1) & value) != 0) | bits)
+                    & (((((1 << (exp - 25)) - 1) & value) != 0) | bits)
 #endif
-                    ;
-            else if constexpr (R == std::round_toward_infinity)
-                bits += ((value & ((1 << (exp - 24)) - 1)) != 0) & !S;
-            else if constexpr (R == std::round_toward_neg_infinity)
-                bits += ((value & ((1 << (exp - 24)) - 1)) != 0) & S;
+                ;
+            else AF_IF_CONSTEXPR(R == std::round_toward_infinity) bits +=
+                ((value & ((1 << (exp - 24)) - 1)) != 0) & !S;
+            else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) bits +=
+                ((value & ((1 << (exp - 24)) - 1)) != 0) & S;
         }
     }
     return bits;
@@ -279,34 +278,33 @@ __DH__ native_half_t float2half_impl(float value) noexcept {
     uint16_t hbits =
         base_table[bits >> 23] +
         static_cast<uint16_t>((bits & 0x7FFFFF) >> shift_table[bits >> 23]);
-    if constexpr (R == std::round_to_nearest)
-        hbits +=
-            (((bits & 0x7FFFFF) >> (shift_table[bits >> 23] - 1)) |
-             (((bits >> 23) & 0xFF) == 102)) &
-            ((hbits & 0x7C00) != 0x7C00)
+    AF_IF_CONSTEXPR(R == std::round_to_nearest)
+    hbits +=
+        (((bits & 0x7FFFFF) >> (shift_table[bits >> 23] - 1)) |
+         (((bits >> 23) & 0xFF) == 102)) &
+        ((hbits & 0x7C00) != 0x7C00)
 #if HALF_ROUND_TIES_TO_EVEN
-            &
-            (((((static_cast<uint32>(1) << (shift_table[bits >> 23] - 1)) - 1) &
-               bits) != 0) |
-             hbits)
+        & (((((static_cast<uint32>(1) << (shift_table[bits >> 23] - 1)) - 1) &
+             bits) != 0) |
+           hbits)
 #endif
-            ;
-    else if constexpr (R == std::round_toward_zero)
-        hbits -= ((hbits & 0x7FFF) == 0x7C00) & ~shift_table[bits >> 23];
-    else if constexpr (R == std::round_toward_infinity)
-        hbits += ((((bits & 0x7FFFFF &
-                     ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) -
-                      1)) != 0) |
-                   (((bits >> 23) <= 102) & ((bits >> 23) != 0))) &
-                  (hbits < 0x7C00)) -
-                 ((hbits == 0xFC00) & ((bits >> 23) != 511));
-    else if constexpr (R == std::round_toward_neg_infinity)
-        hbits += ((((bits & 0x7FFFFF &
-                     ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) -
-                      1)) != 0) |
-                   (((bits >> 23) <= 358) & ((bits >> 23) != 256))) &
-                  (hbits < 0xFC00) & (hbits >> 15)) -
-                 ((hbits == 0x7C00) & ((bits >> 23) != 255));
+        ;
+    else AF_IF_CONSTEXPR(R == std::round_toward_zero) hbits -=
+        ((hbits & 0x7FFF) == 0x7C00) & ~shift_table[bits >> 23];
+    else AF_IF_CONSTEXPR(R == std::round_toward_infinity) hbits +=
+        ((((bits & 0x7FFFFF &
+            ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) - 1)) !=
+           0) |
+          (((bits >> 23) <= 102) & ((bits >> 23) != 0))) &
+         (hbits < 0x7C00)) -
+        ((hbits == 0xFC00) & ((bits >> 23) != 511));
+    else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) hbits +=
+        ((((bits & 0x7FFFFF &
+            ((static_cast<uint32_t>(1) << (shift_table[bits >> 23])) - 1)) !=
+           0) |
+          (((bits >> 23) <= 358) & ((bits >> 23) != 256))) &
+         (hbits < 0xFC00) & (hbits >> 15)) -
+        ((hbits == 0x7C00) & ((bits >> 23) != 255));
     return hbits;
 }
 
@@ -330,10 +328,10 @@ __DH__ native_half_t float2half_impl(double value) {
         return hbits | 0x7C00 |
                (0x3FF & -static_cast<unsigned>((bits & 0xFFFFFFFFFFFFF) != 0));
     if (exp > 1038) {
-        if constexpr (R == std::round_toward_infinity)
-            return hbits | (0x7C00 - (hbits >> 15));
-        if constexpr (R == std::round_toward_neg_infinity)
-            return hbits | (0x7BFF + (hbits >> 15));
+        AF_IF_CONSTEXPR(R == std::round_toward_infinity)
+        return hbits | (0x7C00 - (hbits >> 15));
+        AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity)
+        return hbits | (0x7BFF + (hbits >> 15));
         return hbits | (0x7BFF + (R != std::round_toward_zero));
     }
     int g = 0, s = lo != 0;
@@ -350,16 +348,16 @@ __DH__ native_half_t float2half_impl(double value) {
     } else {
         s |= hi != 0;
     }
-    if constexpr (R == std::round_to_nearest)
+    AF_IF_CONSTEXPR(R == std::round_to_nearest)
 #if HALF_ROUND_TIES_TO_EVEN
-        hbits += g & (s | hbits);
+    hbits += g & (s | hbits);
 #else
-        hbits += g;
+    hbits += g;
 #endif
-    else if constexpr (R == std::round_toward_infinity)
-        hbits += ~(hbits >> 15) & (s | g);
-    else if constexpr (R == std::round_toward_neg_infinity)
-        hbits += (hbits >> 15) & (g | s);
+    else AF_IF_CONSTEXPR(R == std::round_toward_infinity) hbits +=
+        ~(hbits >> 15) & (s | g);
+    else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) hbits +=
+        (hbits >> 15) & (g | s);
     return hbits;
 }
 
@@ -775,21 +773,21 @@ AF_CONSTEXPR T half2int(native_half_t value) {
         return (value & 0x8000) ? std::numeric_limits<T>::min()
                                 : std::numeric_limits<T>::max();
     if (e < 0x3800) {
-        if constexpr (R == std::round_toward_infinity)
-            return T(~(value >> 15) & (e != 0));
-        else if constexpr (R == std::round_toward_neg_infinity)
-            return -T(value > 0x8000);
+        AF_IF_CONSTEXPR(R == std::round_toward_infinity)
+        return T(~(value >> 15) & (e != 0));
+        else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) return -T(
+            value > 0x8000);
         return T();
     }
     unsigned int m = (value & 0x3FF) | 0x400;
     e >>= 10;
     if (e < 25) {
-        if constexpr (R == std::round_to_nearest)
-            m += (1 << (24 - e)) - (~(m >> (25 - e)) & E);
-        else if constexpr (R == std::round_toward_infinity)
-            m += ((value >> 15) - 1) & ((1 << (25 - e)) - 1U);
-        else if constexpr (R == std::round_toward_neg_infinity)
-            m += -(value >> 15) & ((1 << (25 - e)) - 1U);
+        AF_IF_CONSTEXPR(R == std::round_to_nearest)
+        m += (1 << (24 - e)) - (~(m >> (25 - e)) & E);
+        else AF_IF_CONSTEXPR(R == std::round_toward_infinity) m +=
+            ((value >> 15) - 1) & ((1 << (25 - e)) - 1U);
+        else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) m +=
+            -(value >> 15) & ((1 << (25 - e)) - 1U);
         m >>= 25 - e;
     } else
         m <<= e - 25;
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 4c48e6990f..31d7e5b51b 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -148,19 +148,17 @@ __DH__ static To scalar(Ti real, Ti imag) {
 
 template<typename T>
 inline T maxval() {
-    if constexpr (std::is_floating_point_v<T> && !fast_math) {
+    AF_IF_CONSTEXPR(std::is_floating_point_v<T> && !fast_math) {
         return std::numeric_limits<T>::infinity();
-    } else {
-        return std::numeric_limits<T>::max();
     }
+    else { return std::numeric_limits<T>::max(); }
 }
 template<typename T>
 inline T minval() {
-    if constexpr (std::is_floating_point_v<T> && !fast_math) {
+    AF_IF_CONSTEXPR(std::is_floating_point_v<T> && !fast_math) {
         return -std::numeric_limits<T>::infinity();
-    } else {
-        return std::numeric_limits<T>::lowest();
     }
+    else { return std::numeric_limits<T>::lowest(); }
 }
 #else
 template<typename T>

From 202cc76801db76b7608329d565729c694d102f4e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Jan 2023 16:29:53 -0500
Subject: [PATCH 2365/2677] Revert CUDA C++ standard to 14 to support older
 CUDA toolkits

---
 src/backend/cuda/CMakeLists.txt | 30 ++++++++++++++++++++++--------
 src/backend/cuda/math.hpp       |  4 ++--
 2 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index aa9f3fc037..c6617ffac5 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -598,15 +598,29 @@ endif()
 
 
 af_detect_and_set_cuda_architectures(afcuda)
-if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
-  set_target_properties(afcuda
-    PROPERTIES
-      CUDA_STANDARD 17
-      CUDA_STANDARD_REQUIRED ON)
+
+if(CUDA_VERSION VERSION_LESS 11.0)
+  if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
+    set_target_properties(afcuda
+      PROPERTIES
+        CUDA_STANDARD 14
+        CUDA_STANDARD_REQUIRED ON)
+  else()
+    target_compile_options(afcuda
+      PRIVATE
+        $<$<COMPILE_LANGUAGE:CUDA>:--std=c++14>)
+  endif()
 else()
-  target_compile_options(afcuda
-    PRIVATE
-      $<$<COMPILE_LANGUAGE:CUDA>:--std=c++17>)
+  if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.18")
+    set_target_properties(afcuda
+      PROPERTIES
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED ON)
+  else()
+    target_compile_options(afcuda
+      PRIVATE
+        $<$<COMPILE_LANGUAGE:CUDA>:--std=c++17>)
+  endif()
 endif()
 
 target_compile_definitions(afcuda
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 31d7e5b51b..f988372d27 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -148,14 +148,14 @@ __DH__ static To scalar(Ti real, Ti imag) {
 
 template<typename T>
 inline T maxval() {
-    AF_IF_CONSTEXPR(std::is_floating_point_v<T> && !fast_math) {
+    AF_IF_CONSTEXPR(std::is_floating_point<T>::value && !fast_math) {
         return std::numeric_limits<T>::infinity();
     }
     else { return std::numeric_limits<T>::max(); }
 }
 template<typename T>
 inline T minval() {
-    AF_IF_CONSTEXPR(std::is_floating_point_v<T> && !fast_math) {
+    AF_IF_CONSTEXPR(std::is_floating_point<T>::value && !fast_math) {
         return -std::numeric_limits<T>::infinity();
     }
     else { return std::numeric_limits<T>::lowest(); }

From 5c79a61f798b961da7045950d2dfcfee8e0e2385 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Jan 2023 16:37:04 -0500
Subject: [PATCH 2366/2677] Fix CUB include paths when CUDA 10.2 and lower
 toolkits are used

---
 src/backend/cuda/CMakeLists.txt | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index c6617ffac5..c031deebd9 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -114,11 +114,15 @@ mark_as_advanced(
     CUDA_architecture_build_targets)
 
 if(CUDA_VERSION_MAJOR VERSION_LESS 11)
-  af_dep_check_and_populate(${cub_prefix}
-    URI https://github.com/NVIDIA/cub.git
-    REF 1.10.0
-  )
-  cuda_include_directories(${${cub_prefix}_SOURCE_DIR})
+  find_package(CUB)
+  if(NOT TARGET CUB::CUB)
+    af_dep_check_and_populate(${cub_prefix}
+      URI https://github.com/NVIDIA/cub.git
+      REF 1.10.0
+    )
+    find_package(CUB REQUIRED
+      PATHS ${${cub_prefix}_SOURCE_DIR})
+  endif()
 endif()
 
 file(GLOB jit_src "kernel/jit.cuh")
@@ -596,6 +600,12 @@ else()
   )
 endif()
 
+if(CUDA_VERSION_MAJOR VERSION_LESS 11)
+  target_link_libraries(afcuda
+    PRIVATE
+      CUB::CUB
+  )
+endif()
 
 af_detect_and_set_cuda_architectures(afcuda)
 

From 65b7d11e1714bce66c1305a7957f5a20fa104a35 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Jan 2023 16:37:52 -0500
Subject: [PATCH 2367/2677] Set minimum toolkit version to 10.2

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d610bba1c5..ae7a3742a6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -48,7 +48,7 @@ endif()
 #Set Intel OpenMP as default MKL thread layer
 set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
 
-find_package(CUDA 9.0)
+find_package(CUDA 10.2)
 find_package(cuDNN 4.0)
 find_package(OpenCL 1.2)
 find_package(OpenGL)

From f917e3f038ddf8aec27871a04c3583e6a02df74f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 2 Jan 2023 19:47:34 -0500
Subject: [PATCH 2368/2677] Fix errors in the fmt library when printing const
 dim3 values

---
 src/backend/cuda/kernel/regions.hpp | 4 ++--
 src/backend/cuda/kernel/topk.hpp    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/kernel/regions.hpp b/src/backend/cuda/kernel/regions.hpp
index b1fe3f7c8d..d03aed4517 100644
--- a/src/backend/cuda/kernel/regions.hpp
+++ b/src/backend/cuda/kernel/regions.hpp
@@ -351,12 +351,12 @@ template<typename T, bool full_conn, int n_per_thread>
 void regions(arrayfire::cuda::Param<T> out, arrayfire::cuda::CParam<char> in,
              cudaTextureObject_t tex) {
     using arrayfire::cuda::getActiveStream;
-    const dim3 threads(THREADS_X, THREADS_Y);
+    dim3 threads(THREADS_X, THREADS_Y);
 
     const int blk_x = divup(in.dims[0], threads.x * 2);
     const int blk_y = divup(in.dims[1], threads.y * 2);
 
-    const dim3 blocks(blk_x, blk_y);
+    dim3 blocks(blk_x, blk_y);
 
     CUDA_LAUNCH((initial_label<T, n_per_thread>), blocks, threads, out, in);
 
diff --git a/src/backend/cuda/kernel/topk.hpp b/src/backend/cuda/kernel/topk.hpp
index 9418a9162d..22f7c34f93 100644
--- a/src/backend/cuda/kernel/topk.hpp
+++ b/src/backend/cuda/kernel/topk.hpp
@@ -120,7 +120,7 @@ static __global__ void kerTopkDim0(Param<T> ovals, Param<uint> oidxs,
 template<typename T>
 void topkDim0(Param<T> ovals, Param<uint> oidxs, CParam<T> ivals, const int k,
               const af::topkFunction order) {
-    const dim3 threads(TOPK_THRDS_PER_BLK, 1);
+    dim3 threads(TOPK_THRDS_PER_BLK, 1);
     const int thrdLoad = TOPK_IDX_THRD_LOAD;
 
     int numBlocksX = divup(ivals.dims[0], threads.x * thrdLoad);

From c3dd2369766a36b5ade11e547f0abe4b70e0a3ac Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 3 Jan 2023 15:54:21 -0500
Subject: [PATCH 2369/2677] Fix warning when building spdlog. Caused errors on
 GitHub actions

---
 CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ae7a3742a6..a966d75c41 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -272,6 +272,9 @@ else()
       PROPERTIES
         INTERFACE_LINK_LIBRARIES "spdlog_header_only")
   else()
+    target_compile_options(spdlog
+      PRIVATE
+        $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>)
     set_target_properties(af_spdlog
       PROPERTIES
         INTERFACE_LINK_LIBRARIES "spdlog")

From fa44d4a729dfd88f43220181db36e2afa75e7b71 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 3 Jan 2023 15:55:17 -0500
Subject: [PATCH 2370/2677] Cleanup advanced CMake variables and Gtest compile
 flags and def

---
 CMakeLists.txt      | 94 ++++++++++++++++++++++++++++++---------------
 test/CMakeLists.txt | 22 +++--------
 2 files changed, 68 insertions(+), 48 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a966d75c41..8985c797ff 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -173,37 +173,6 @@ if(DEFINED USE_CPU_MKL OR DEFINED USE_OPENCL_MKL)
   endif()
 endif()
 
-mark_as_advanced(
-  AF_BUILD_FRAMEWORK
-  AF_CACHE_KERNELS_TO_DISK
-  AF_INSTALL_STANDALONE
-  AF_WITH_CPUID
-  AF_WITH_LOGGING
-  AF_WITH_STACKTRACE
-  AF_WITH_STATIC_FREEIMAGE
-  AF_WITH_NONFREE
-  AF_WITH_IMAGEIO
-  AF_WITH_RELATIVE_TEST_DIR
-  AF_TEST_WITH_MTX_FILES
-  ArrayFire_DIR
-  Boost_INCLUDE_DIR
-  CLEAR CUDA_VERSION
-  CUDA_HOST_COMPILER
-  CUDA_SDK_ROOT_DIR
-  CUDA_USE_STATIC_CUDA_RUNTIME
-  CUDA_rt_LIBRARY
-  SPDLOG_BUILD_EXAMPLES
-  SPDLOG_BUILD_TESTING
-  ADDR2LINE_PROGRAM
-  Backtrace_LIBRARY
-  AF_WITH_STATIC_MKL
-  GIT
-  Forge_DIR
-  glad_DIR
-  spdlog_DIR
-  FG_BUILD_OFFLINE
-  )
-
 if(AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
   set(BLA_VENDOR "Intel10_64lp")
   if(MKL_THREAD_LAYER STREQUAL "Sequential")
@@ -603,6 +572,19 @@ include(CPackConfig)
 # for ArrayFire Development. They are marked hidden.
 # If VCPKG is not used, marking them is not harmful
 mark_as_advanced(
+  AF_BUILD_FRAMEWORK
+  AF_CACHE_KERNELS_TO_DISK
+  AF_INSTALL_STANDALONE
+  AF_WITH_CPUID
+  AF_WITH_LOGGING
+  AF_WITH_STACKTRACE
+  AF_WITH_STATIC_FREEIMAGE
+  AF_WITH_NONFREE
+  AF_WITH_IMAGEIO
+  AF_WITH_RELATIVE_TEST_DIR
+  AF_TEST_WITH_MTX_FILES
+  ArrayFire_DIR
+
   VCPKG_APPLOCAL_DEPS
   VCPKG_BOOTSTRAP_OPTIONS
   VCPKG_INSTALL_OPTIONS
@@ -618,4 +600,54 @@ mark_as_advanced(
   Z_VCPKG_PWSH_PATH
   Z_VCPKG_CL
   _VCPKG_INSTALLED_DIR
+
+  Boost_INCLUDE_DIR
+  CLEAR CUDA_VERSION
+  CUDA_HOST_COMPILER
+  CUDA_SDK_ROOT_DIR
+  CUDA_USE_STATIC_CUDA_RUNTIME
+  CUDA_rt_LIBRARY
+  SPDLOG_BUILD_EXAMPLES
+  SPDLOG_BUILD_TESTING
+  ADDR2LINE_PROGRAM
+  Backtrace_LIBRARY
+  AF_WITH_STATIC_MKL
+  GIT
+  Forge_DIR
+  glad_DIR
+  spdlog_DIR
+  FG_BUILD_OFFLINE
+  SPAN_LITE_COLOURISE_TEST
+  SPAN_LITE_EXPORT_PACKAGE
+  SPAN_LITE_OPT_BUILD_EXAMPLES
+  SPAN_LITE_OPT_BUILD_TESTS
+  SPAN_LITE_OPT_SELECT_NONSTD
+  SPAN_LITE_OPT_SELECT_STD
+  FETCHCONTENT_SOURCE_DIR_SPAN-LITE
+  SPDLOG_BUILD_ALL
+  SPDLOG_BUILD_BENCH
+  SPDLOG_BUILD_EXAMPLE
+  SPDLOG_BUILD_EXAMPLE_HO
+  SPDLOG_BUILD_SHARED
+  SPDLOG_BUILD_TESTS
+  SPDLOG_BUILD_TESTS_HO
+  SPDLOG_BUILD_WARNINGS
+  SPDLOG_CLOCK_COARSE
+  SPDLOG_DISABLE_DEFAULT_LOGGER
+  SPDLOG_ENABLE_PCH
+  SPDLOG_FMT_EXTERNAL
+  SPDLOG_FMT_EXTERNAL_HO
+  SPDLOG_INSTALL
+  SPDLOG_NO_ATOMIC_LEVELS
+  SPDLOG_NO_EXCEPTIONS
+  SPDLOG_NO_THREAD_ID
+  SPDLOG_NO_TLS
+  SPDLOG_PREVENT_CHILD_FD
+  SPDLOG_SANITIZE_ADDRESS
+  SPDLOG_TIDY
+  SPDLOG_WCHAR_FILENAMES
+  SPDLOG_WCHAR_SUPPORT
+  cub_include_dir
+  fmt_DIR
+  span-lite_DIR
   )
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 1ff1d94041..16ba6f71ec 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -22,33 +22,21 @@ elseif(NOT TARGET GTest::gtest)
     URI https://github.com/google/googletest.git
     REF release-1.12.1
   )
-
-  # gtest targets cmake version 2.6 which throws warnings for policy CMP0042 on
-  # newer cmakes. This sets the default global setting for that policy.
-  set(CMAKE_POLICY_DEFAULT_CMP0042 NEW)
   if(WIN32)
     set(gtest_force_shared_crt ON
         CACHE INTERNAL "Required so that the libs Runtime is not set to MT DLL")
     set(BUILD_SHARED_LIBS OFF)
   endif()
 
-  add_definitions(-DGTEST_HAS_SEH=OFF)
   add_subdirectory(${${gtest_prefix}_SOURCE_DIR} ${${gtest_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
-  set_target_properties(gtest gtest_main
+  target_compile_definitions(gtest PRIVATE GTEST_HAS_SEH=OFF)
+  set_target_properties(gtest
     PROPERTIES
       FOLDER "ExternalProjectTargets/gtest")
+  target_compile_options(gtest
+    PRIVATE
+      $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>)
   add_library(GTest::gtest ALIAS gtest)
-  if(UNIX)
-    if("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" AND
-      CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "10.3.0")
-      target_compile_options(gtest PRIVATE -Wno-maybe-uninitialized)
-      target_compile_options(gtest_main PRIVATE -Wno-maybe-uninitialized)
-    endif()
-  endif()
-  if(WIN32)
-      target_compile_options(gtest PRIVATE -Wno-error=ignored-attributes)
-  endif()
-
   # Hide gtest project variables
   mark_as_advanced(
     BUILD_SHARED_LIBS

From 08e7b64d954ad94b86f9bda4611a5708c168f013 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Jan 2023 16:12:11 -0500
Subject: [PATCH 2371/2677] Fix unused cusparse CSC code path in the CUDA
 backend

---
 src/backend/cuda/cusparse.hpp                 | 53 +++++++++++++++++--
 src/backend/cuda/cusparseModule.cpp           |  1 +
 src/backend/cuda/cusparseModule.hpp           |  1 +
 .../cuda/cusparse_descriptor_helpers.hpp      |  6 +--
 src/backend/cuda/sparse.cu                    | 38 ++++++-------
 test/sparse_common.hpp                        | 19 +++++++
 6 files changed, 92 insertions(+), 26 deletions(-)

diff --git a/src/backend/cuda/cusparse.hpp b/src/backend/cuda/cusparse.hpp
index 467b2a82ec..e7b5a51e33 100644
--- a/src/backend/cuda/cusparse.hpp
+++ b/src/backend/cuda/cusparse.hpp
@@ -9,17 +9,64 @@
 
 #pragma once
 
+#include <common/SparseArray.hpp>
 #include <common/defines.hpp>
-#include <common/err_common.hpp>
 #include <common/unique_handle.hpp>
+#include <cudaDataType.hpp>
 #include <cusparseModule.hpp>
 #include <cusparse_v2.h>
+#include <err_cuda.hpp>
+
+#if defined(AF_USE_NEW_CUSPARSE_API)
+namespace arrayfire {
+namespace cuda {
+
+template<typename T>
+cusparseStatus_t createSpMatDescr(
+    cusparseSpMatDescr_t *out, const arrayfire::common::SparseArray<T> &arr) {
+    auto &_ = arrayfire::cuda::getCusparsePlugin();
+    switch (arr.getStorage()) {
+        case AF_STORAGE_CSR: {
+            return _.cusparseCreateCsr(
+                out, arr.dims()[0], arr.dims()[1], arr.getNNZ(),
+                (void *)arr.getRowIdx().get(), (void *)arr.getColIdx().get(),
+                (void *)arr.getValues().get(), CUSPARSE_INDEX_32I,
+                CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, getType<T>());
+        }
+#if CUSPARSE_VERSION >= 11300
+        case AF_STORAGE_CSC: {
+            return _.cusparseCreateCsc(
+                out, arr.dims()[0], arr.dims()[1], arr.getNNZ(),
+                (void *)arr.getColIdx().get(), (void *)arr.getRowIdx().get(),
+                (void *)arr.getValues().get(), CUSPARSE_INDEX_32I,
+                CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, getType<T>());
+        }
+#else
+        case AF_STORAGE_CSC:
+            CUDA_NOT_SUPPORTED(
+                "Sparse not supported for CSC on this version of the CUDA "
+                "Toolkit");
+#endif
+        case AF_STORAGE_COO: {
+            return _.cusparseCreateCoo(
+                out, arr.dims()[0], arr.dims()[1], arr.getNNZ(),
+                (void *)arr.getColIdx().get(), (void *)arr.getRowIdx().get(),
+                (void *)arr.getValues().get(), CUSPARSE_INDEX_32I,
+                CUSPARSE_INDEX_BASE_ZERO, getType<T>());
+        }
+    }
+    return CUSPARSE_STATUS_SUCCESS;
+}
+
+}  // namespace cuda
+}  // namespace arrayfire
+#endif
 
 // clang-format off
 DEFINE_HANDLER(cusparseHandle_t, arrayfire::cuda::getCusparsePlugin().cusparseCreate, arrayfire::cuda::getCusparsePlugin().cusparseDestroy);
 DEFINE_HANDLER(cusparseMatDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateMatDescr, arrayfire::cuda::getCusparsePlugin().cusparseDestroyMatDescr);
 #if defined(AF_USE_NEW_CUSPARSE_API)
-DEFINE_HANDLER(cusparseSpMatDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateCsr, arrayfire::cuda::getCusparsePlugin().cusparseDestroySpMat);
+DEFINE_HANDLER(cusparseSpMatDescr_t, arrayfire::cuda::createSpMatDescr, arrayfire::cuda::getCusparsePlugin().cusparseDestroySpMat);
 DEFINE_HANDLER(cusparseDnVecDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateDnVec, arrayfire::cuda::getCusparsePlugin().cusparseDestroyDnVec);
 DEFINE_HANDLER(cusparseDnMatDescr_t, arrayfire::cuda::getCusparsePlugin().cusparseCreateDnMat, arrayfire::cuda::getCusparsePlugin().cusparseDestroyDnMat);
 #endif
@@ -28,7 +75,7 @@ DEFINE_HANDLER(cusparseDnMatDescr_t, arrayfire::cuda::getCusparsePlugin().cuspar
 namespace arrayfire {
 namespace cuda {
 
-const char* errorString(cusparseStatus_t err);
+const char *errorString(cusparseStatus_t err);
 
 #define CUSPARSE_CHECK(fn)                                                    \
     do {                                                                      \
diff --git a/src/backend/cuda/cusparseModule.cpp b/src/backend/cuda/cusparseModule.cpp
index 84daa25460..a7dba5dc77 100644
--- a/src/backend/cuda/cusparseModule.cpp
+++ b/src/backend/cuda/cusparseModule.cpp
@@ -105,6 +105,7 @@ cusparseModule::cusparseModule()
 
     MODULE_FUNCTION_INIT(cusparseCnnz);
     MODULE_FUNCTION_INIT(cusparseCreateCsr);
+    MODULE_FUNCTION_INIT(cusparseCreateCoo);
     MODULE_FUNCTION_INIT(cusparseCreateDnMat);
     MODULE_FUNCTION_INIT(cusparseCreateDnVec);
     MODULE_FUNCTION_INIT(cusparseCreateIdentityPermutation);
diff --git a/src/backend/cuda/cusparseModule.hpp b/src/backend/cuda/cusparseModule.hpp
index 5f63cec285..fc3bb09b76 100644
--- a/src/backend/cuda/cusparseModule.hpp
+++ b/src/backend/cuda/cusparseModule.hpp
@@ -61,6 +61,7 @@ class cusparseModule {
     MODULE_MEMBER(cusparseXcsrsort);
 #endif
 
+    MODULE_MEMBER(cusparseCreateCoo);
     MODULE_MEMBER(cusparseCreateCsr);
     MODULE_MEMBER(cusparseDestroyDnMat);
     MODULE_MEMBER(cusparseDestroyDnVec);
diff --git a/src/backend/cuda/cusparse_descriptor_helpers.hpp b/src/backend/cuda/cusparse_descriptor_helpers.hpp
index 99d474cdbb..340a049b11 100644
--- a/src/backend/cuda/cusparse_descriptor_helpers.hpp
+++ b/src/backend/cuda/cusparse_descriptor_helpers.hpp
@@ -25,11 +25,7 @@ template<typename T>
 auto cusparseDescriptor(const common::SparseArray<T> &in) {
     auto dims = in.dims();
 
-    return common::make_handle<cusparseSpMatDescr_t>(
-        dims[0], dims[1], in.getNNZ(), (void *)(in.getRowIdx().get()),
-        (void *)(in.getColIdx().get()), (void *)(in.getValues().get()),
-        CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO,
-        getType<T>());
+    return common::make_handle<cusparseSpMatDescr_t>(in);
 }
 
 template<typename T>
diff --git a/src/backend/cuda/sparse.cu b/src/backend/cuda/sparse.cu
index dd6d8d22b7..3c39c72695 100644
--- a/src/backend/cuda/sparse.cu
+++ b/src/backend/cuda/sparse.cu
@@ -258,17 +258,19 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     auto matA = denMatDescriptor(in);
     cusparseSpMatDescr_t matB;
 
-    auto d_csr_offsets = createEmptyArray<int>(M + 1);
+    Array<int> d_offsets = createEmptyArray<int>(0);
 
     if (stype == AF_STORAGE_CSR) {
+        d_offsets = createEmptyArray<int>(M + 1);
         // Create sparse matrix B in CSR format
         CUSPARSE_CHECK(
-            _.cusparseCreateCsr(&matB, M, N, 0, d_csr_offsets.get(), nullptr,
+            _.cusparseCreateCsr(&matB, M, N, 0, d_offsets.get(), nullptr,
                                 nullptr, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
                                 CUSPARSE_INDEX_BASE_ZERO, getType<T>()));
     } else {
+        d_offsets = createEmptyArray<int>(N + 1);
         CUSPARSE_CHECK(
-            _.cusparseCreateCsc(&matB, M, N, 0, d_csr_offsets.get(), nullptr,
+            _.cusparseCreateCsc(&matB, M, N, 0, d_offsets.get(), nullptr,
                                 nullptr, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
                                 CUSPARSE_INDEX_BASE_ZERO, getType<T>()));
     }
@@ -290,22 +292,20 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
     CUSPARSE_CHECK(
         _.cusparseSpMatGetSize(matB, &num_rows_tmp, &num_cols_tmp, &nnz));
 
-    auto d_csr_columns = createEmptyArray<int>(nnz);
-    auto d_csr_values  = createEmptyArray<T>(nnz);
+    auto d_ind    = createEmptyArray<int>(nnz);
+    auto d_values = createEmptyArray<T>(nnz);
     // allocate CSR column indices and values
     // reset offsets, column indices, and values pointers
     if (stype == AF_STORAGE_CSR) {
         // Create sparse matrix B in CSR format
         // reset offsets, column indices, and values pointers
-        CUSPARSE_CHECK(_.cusparseCsrSetPointers(matB, d_csr_offsets.get(),
-                                                d_csr_columns.get(),
-                                                d_csr_values.get()));
+        CUSPARSE_CHECK(_.cusparseCsrSetPointers(matB, d_offsets.get(),
+                                                d_ind.get(), d_values.get()));
 
     } else {
         // reset offsets, column indices, and values pointers
-        CUSPARSE_CHECK(_.cusparseCscSetPointers(matB, d_csr_offsets.get(),
-                                                d_csr_columns.get(),
-                                                d_csr_values.get()));
+        CUSPARSE_CHECK(_.cusparseCscSetPointers(matB, d_offsets.get(),
+                                                d_ind.get(), d_values.get()));
     }
     // execute Sparse to Dense conversion
     CUSPARSE_CHECK(_.cusparseDenseToSparse_convert(
@@ -316,20 +316,22 @@ SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in) {
         size_t pBufferSizeInBytes = 0;
         auto desc                 = make_handle<cusparseMatDescr_t>();
         CUSPARSE_CHECK(_.cusparseXcsrsort_bufferSizeExt(
-            sparseHandle(), M, N, nnz, d_csr_offsets.get(), d_csr_columns.get(),
+            sparseHandle(), M, N, nnz, d_offsets.get(), d_ind.get(),
             &pBufferSizeInBytes));
         auto pBuffer = memAlloc<char>(pBufferSizeInBytes);
         Array<int> P = createEmptyArray<int>(nnz);
         CUSPARSE_CHECK(
             _.cusparseCreateIdentityPermutation(sparseHandle(), nnz, P.get()));
         CUSPARSE_CHECK(_.cusparseXcsrsort(
-            sparseHandle(), M, N, nnz, desc, (int *)d_csr_offsets.get(),
-            (int *)d_csr_columns.get(), P.get(), pBuffer.get()));
-        d_csr_values = lookup(d_csr_values, P, 0);
+            sparseHandle(), M, N, nnz, desc, (int *)d_offsets.get(),
+            (int *)d_ind.get(), P.get(), pBuffer.get()));
+        d_values = lookup(d_values, P, 0);
+        return createArrayDataSparseArray<T>(in.dims(), d_values, d_offsets,
+                                             d_ind, stype, false);
+    } else {
+        return createArrayDataSparseArray<T>(in.dims(), d_values, d_ind,
+                                             d_offsets, stype, false);
     }
-
-    return createArrayDataSparseArray<T>(in.dims(), d_csr_values, d_csr_offsets,
-                                         d_csr_columns, stype, false);
 #endif
 }
 
diff --git a/test/sparse_common.hpp b/test/sparse_common.hpp
index 41dd3fd05d..5884871388 100644
--- a/test/sparse_common.hpp
+++ b/test/sparse_common.hpp
@@ -164,6 +164,25 @@ static void convertCSR(const int M, const int N, const double ratio,
     ASSERT_ARRAYS_EQ(a, aa);
 }
 
+template<typename T>
+static void convertCSC(const int M, const int N, const double ratio,
+                       int targetDevice = -1) {
+    if (targetDevice >= 0) af::setDevice(targetDevice);
+
+    SUPPORTED_TYPE_CHECK(T);
+#if 1
+    af::array a = cpu_randu<T>(af::dim4(M, N));
+#else
+    af::array a = af::randu(M, N);
+#endif
+    a = a * (a > ratio);
+
+    af::array s  = af::sparse(a, AF_STORAGE_CSC);
+    af::array aa = af::dense(s);
+
+    ASSERT_ARRAYS_EQ(a, aa);
+}
+
 // This test essentially verifies that the sparse structures have the correct
 // dimensions and indices using a very basic test
 template<af_storage stype>

From d453523443d3b304095b3699143784524509cab2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Jan 2023 18:59:52 -0500
Subject: [PATCH 2372/2677] Check the result of cuda error functions before
 using results

---
 src/backend/cuda/CMakeLists.txt     |  1 -
 src/backend/cuda/Kernel.hpp         |  2 +-
 src/backend/cuda/Module.hpp         |  2 +-
 src/backend/cuda/cu_check_macro.hpp | 30 -----------------------------
 src/backend/cuda/err_cuda.hpp       | 19 ++++++++++++++++++
 5 files changed, 21 insertions(+), 33 deletions(-)
 delete mode 100644 src/backend/cuda/cu_check_macro.hpp

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index c031deebd9..0dc208fd8b 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -406,7 +406,6 @@ add_library(afcuda
     convolveNN.cpp
     copy.cpp
     copy.hpp
-    cu_check_macro.hpp
     cublas.cpp
     cublas.hpp
 
diff --git a/src/backend/cuda/Kernel.hpp b/src/backend/cuda/Kernel.hpp
index b5375f6ad2..2199292080 100644
--- a/src/backend/cuda/Kernel.hpp
+++ b/src/backend/cuda/Kernel.hpp
@@ -14,7 +14,7 @@
 
 #include <EnqueueArgs.hpp>
 #include <backend.hpp>
-#include <cu_check_macro.hpp>
+#include <err_cuda.hpp>
 #include <cstdlib>
 #include <string>
 
diff --git a/src/backend/cuda/Module.hpp b/src/backend/cuda/Module.hpp
index b5eb028765..88881611fc 100644
--- a/src/backend/cuda/Module.hpp
+++ b/src/backend/cuda/Module.hpp
@@ -10,7 +10,7 @@
 #pragma once
 
 #include <common/ModuleInterface.hpp>
-#include <cu_check_macro.hpp>
+#include <err_cuda.hpp>
 
 #include <cuda.h>
 
diff --git a/src/backend/cuda/cu_check_macro.hpp b/src/backend/cuda/cu_check_macro.hpp
deleted file mode 100644
index a6b8d3f3e1..0000000000
--- a/src/backend/cuda/cu_check_macro.hpp
+++ /dev/null
@@ -1,30 +0,0 @@
-/*******************************************************
- * Copyright (c) 2020, ArrayFire
- * All rights reserved.
- *
- * This file is distributed under 3-clause BSD license.
- * The complete license agreement can be obtained at:
- * http://arrayfire.com/licenses/BSD-3-Clause
- ********************************************************/
-
-#pragma once
-
-#include <common/err_common.hpp>
-
-#include <cuda.h>
-
-#include <cstdio>
-
-#define CU_CHECK(fn)                                                      \
-    do {                                                                  \
-        CUresult res = fn;                                                \
-        if (res == CUDA_SUCCESS) break;                                   \
-        char cu_err_msg[1024];                                            \
-        const char* cu_err_name;                                          \
-        const char* cu_err_string;                                        \
-        cuGetErrorName(res, &cu_err_name);                                \
-        cuGetErrorString(res, &cu_err_string);                            \
-        snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
-                 cu_err_name, (int)(res), cu_err_string);                 \
-        AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
-    } while (0)
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 091b848283..77926cdd79 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -18,6 +18,25 @@
                            boost::stacktrace::stacktrace());                \
     } while (0)
 
+#define CU_CHECK(fn)                                                          \
+    do {                                                                      \
+        CUresult res = fn;                                                    \
+        if (res == CUDA_SUCCESS) break;                                       \
+        char cu_err_msg[1024];                                                \
+        const char* cu_err_name;                                              \
+        const char* cu_err_string;                                            \
+        CUresult nameErr, strErr;                                             \
+        nameErr = cuGetErrorName(res, &cu_err_name);                          \
+        strErr  = cuGetErrorString(res, &cu_err_string);                      \
+        if (nameErr == CUDA_SUCCESS && strErr == CUDA_SUCCESS) {              \
+            snprintf(cu_err_msg, sizeof(cu_err_msg), "CU Error %s(%d): %s\n", \
+                     cu_err_name, (int)(res), cu_err_string);                 \
+            AF_ERROR(cu_err_msg, AF_ERR_INTERNAL);                            \
+        } else {                                                              \
+            AF_ERROR("CU Unknown error.\n", AF_ERR_INTERNAL);                 \
+        }                                                                     \
+    } while (0)
+
 #define CUDA_CHECK(fn)                                               \
     do {                                                             \
         cudaError_t _cuda_error = fn;                                \

From b6f234e76812053d2b541c7085ec0ba7627ca463 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 9 Jan 2023 19:00:22 -0500
Subject: [PATCH 2373/2677] Clear the thread_local vectors and stringstream in
 case of exception

---
 src/backend/cuda/jit.cpp | 391 +++++++++++++++++++++------------------
 1 file changed, 209 insertions(+), 182 deletions(-)

diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 86b2b2e6a6..33a80adb50 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -226,67 +226,79 @@ struct Param {
     thread_local stringstream inOffsetsStream;
     thread_local stringstream opsStream;
     thread_local stringstream outrefStream;
+    thread_local stringstream kerStream;
 
-    int oid{0};
-    for (size_t i{0}; i < full_nodes.size(); i++) {
-        const auto& node{full_nodes[i]};
-        const auto& ids_curr{full_ids[i]};
-        // Generate input parameters, only needs current id
-        node->genParams(inParamStream, ids_curr.id, is_linear);
-        // Generate input offsets, only needs current id
-        node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
-        // Generate the core function body, needs children ids as well
-        node->genFuncs(opsStream, ids_curr);
-        for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
-             (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
-            // Generate also output parameters
-            outParamStream << (oid == 0 ? "" : ",\n") << "Param<"
-                           << full_nodes[ids_curr.id]->getTypeStr() << "> out"
-                           << oid;
-            // Generate code to write the output (offset already in ptr)
-            opsStream << "out" << oid << ".ptr[idx] = val" << ids_curr.id
-                      << ";\n";
-            ++oid;
+    string ret;
+    try {
+        int oid{0};
+        for (size_t i{0}; i < full_nodes.size(); i++) {
+            const auto& node{full_nodes[i]};
+            const auto& ids_curr{full_ids[i]};
+            // Generate input parameters, only needs current id
+            node->genParams(inParamStream, ids_curr.id, is_linear);
+            // Generate input offsets, only needs current id
+            node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
+            // Generate the core function body, needs children ids as well
+            node->genFuncs(opsStream, ids_curr);
+            for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
+                 (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
+                // Generate also output parameters
+                outParamStream << (oid == 0 ? "" : ",\n") << "Param<"
+                               << full_nodes[ids_curr.id]->getTypeStr()
+                               << "> out" << oid;
+                // Generate code to write the output (offset already in ptr)
+                opsStream << "out" << oid << ".ptr[idx] = val" << ids_curr.id
+                          << ";\n";
+                ++oid;
+            }
         }
-    }
-
-    outrefStream << "\n    const Param<"
-                 << full_nodes[output_ids[0]]->getTypeStr()
-                 << "> &outref = out0;";
 
-    // Put various blocks into a single stream
-    thread_local stringstream kerStream;
-    kerStream << typedefStr << includeFileStr << "\n\n"
-              << paramTStr << '\n'
-              << kernelVoid << funcName << "(\n"
-              << inParamStream.str() << outParamStream.str() << dimParams << ')'
-              << blockStart << outrefStream.str();
-    if (is_linear) {
-        kerStream << linearInit;
-        if (loop0) kerStream << linearLoop0Start;
-        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
-        if (loop0) kerStream << linearLoop0End;
-        kerStream << linearEnd;
-    } else {
-        if (loop0) {
-            kerStream << stridedLoop0Init << stridedLoop0Start;
+        outrefStream << "\n    const Param<"
+                     << full_nodes[output_ids[0]]->getTypeStr()
+                     << "> &outref = out0;";
+
+        // Put various blocks into a single stream
+        kerStream << typedefStr << includeFileStr << "\n\n"
+                  << paramTStr << '\n'
+                  << kernelVoid << funcName << "(\n"
+                  << inParamStream.str() << outParamStream.str() << dimParams
+                  << ')' << blockStart << outrefStream.str();
+        if (is_linear) {
+            kerStream << linearInit;
+            if (loop0) kerStream << linearLoop0Start;
+            kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+            if (loop0) kerStream << linearLoop0End;
+            kerStream << linearEnd;
         } else {
-            kerStream << stridedLoopNInit;
-            if (loop3) kerStream << stridedLoop3Init;
-            if (loop2) kerStream << stridedLoop2Init;
-            if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
-            if (loop2) kerStream << stridedLoop2Start;
-            if (loop3) kerStream << stridedLoop3Start;
+            if (loop0) {
+                kerStream << stridedLoop0Init << stridedLoop0Start;
+            } else {
+                kerStream << stridedLoopNInit;
+                if (loop3) kerStream << stridedLoop3Init;
+                if (loop2) kerStream << stridedLoop2Init;
+                if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
+                if (loop2) kerStream << stridedLoop2Start;
+                if (loop3) kerStream << stridedLoop3Start;
+            }
+            kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+            if (loop3) kerStream << stridedLoop3End;
+            if (loop2) kerStream << stridedLoop2End;
+            if (loop1) kerStream << stridedLoop1End;
+            if (loop0) kerStream << stridedLoop0End;
+            kerStream << stridedEnd;
         }
-        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
-        if (loop3) kerStream << stridedLoop3End;
-        if (loop2) kerStream << stridedLoop2End;
-        if (loop1) kerStream << stridedLoop1End;
-        if (loop0) kerStream << stridedLoop0End;
-        kerStream << stridedEnd;
+        kerStream << blockEnd;
+        ret = kerStream.str();
+    } catch (...) {
+        // Prepare for next round
+        inParamStream.str("");
+        outParamStream.str("");
+        inOffsetsStream.str("");
+        opsStream.str("");
+        outrefStream.str("");
+        kerStream.str("");
+        throw;
     }
-    kerStream << blockEnd;
-    const string ret{kerStream.str()};
 
     // Prepare for next round
     inParamStream.str("");
@@ -364,150 +376,165 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     thread_local vector<Node_ids> full_ids;
     thread_local vector<int> output_ids;
 
-    // Reserve some space to improve performance at smaller
-    // sizes
-    constexpr size_t CAP{1024};
-    if (full_nodes.capacity() < CAP) {
-        nodes.reserve(CAP);
-        output_ids.reserve(10);
-        full_nodes.reserve(CAP);
-        full_ids.reserve(CAP);
-    }
-
-    const af::dtype outputType{output_nodes[0]->getType()};
-    const size_t outputSizeofType{size_of(outputType)};
-    for (Node* node : output_nodes) {
-        assert(node->getType() == outputType);
-        const int id = node->getNodesMap(nodes, full_nodes, full_ids);
-        output_ids.push_back(id);
-    }
-
-    size_t inputSize{0};
-    unsigned nrInputs{0};
-    bool moddimsFound{false};
-    for (const Node* node : full_nodes) {
-        is_linear &= node->isLinear(outDims);
-        moddimsFound |= (node->getOp() == af_moddims_t);
-        if (node->isBuffer()) {
-            ++nrInputs;
-            inputSize += node->getBytes();
+    try {
+        // Reserve some space to improve performance at smaller
+        // sizes
+        constexpr size_t CAP{1024};
+        if (full_nodes.capacity() < CAP) {
+            nodes.reserve(CAP);
+            output_ids.reserve(10);
+            full_nodes.reserve(CAP);
+            full_ids.reserve(CAP);
         }
-    }
-    const size_t outputSize{numOutElems * outputSizeofType * nrOutputs};
-    const size_t totalSize{inputSize + outputSize};
-
-    bool emptyColumnsFound{false};
-    if (is_linear) {
-        outDims[0]    = numOutElems;
-        outDims[1]    = 1;
-        outDims[2]    = 1;
-        outDims[3]    = 1;
-        outStrides[0] = 1;
-        outStrides[1] = numOutElems;
-        outStrides[2] = numOutElems;
-        outStrides[3] = numOutElems;
-        ndims         = 1;
-    } else {
-        emptyColumnsFound = ndims > (outDims[0] == 1   ? 1
-                                     : outDims[1] == 1 ? 2
-                                     : outDims[2] == 1 ? 3
-                                                       : 4);
-    }
 
-    // Keep node_clones in scope, so that the nodes remain active for later
-    // referral in case moddims or Column elimination operations have to take
-    // place
-    vector<Node_ptr> node_clones;
-    if (moddimsFound | emptyColumnsFound) {
-        node_clones.reserve(full_nodes.size());
-        for (Node* node : full_nodes) {
-            node_clones.emplace_back(node->clone());
+        const af::dtype outputType{output_nodes[0]->getType()};
+        const size_t outputSizeofType{size_of(outputType)};
+        for (Node* node : output_nodes) {
+            assert(node->getType() == outputType);
+            const int id = node->getNodesMap(nodes, full_nodes, full_ids);
+            output_ids.push_back(id);
         }
 
-        for (const Node_ids& ids : full_ids) {
-            auto& children{node_clones[ids.id]->m_children};
-            for (int i{0}; i < Node::kMaxChildren && children[i] != nullptr;
-                 i++) {
-                children[i] = node_clones[ids.child_ids[i]];
+        size_t inputSize{0};
+        unsigned nrInputs{0};
+        bool moddimsFound{false};
+        for (const Node* node : full_nodes) {
+            is_linear &= node->isLinear(outDims);
+            moddimsFound |= (node->getOp() == af_moddims_t);
+            if (node->isBuffer()) {
+                ++nrInputs;
+                inputSize += node->getBytes();
             }
         }
+        const size_t outputSize{numOutElems * outputSizeofType * nrOutputs};
+        const size_t totalSize{inputSize + outputSize};
+
+        bool emptyColumnsFound{false};
+        if (is_linear) {
+            outDims[0]    = numOutElems;
+            outDims[1]    = 1;
+            outDims[2]    = 1;
+            outDims[3]    = 1;
+            outStrides[0] = 1;
+            outStrides[1] = numOutElems;
+            outStrides[2] = numOutElems;
+            outStrides[3] = numOutElems;
+            ndims         = 1;
+        } else {
+            emptyColumnsFound = ndims > (outDims[0] == 1   ? 1
+                                         : outDims[1] == 1 ? 2
+                                         : outDims[2] == 1 ? 3
+                                                           : 4);
+        }
 
-        if (moddimsFound) {
-            const auto isModdim{[](const Node_ptr& node) {
-                return node->getOp() == af_moddims_t;
-            }};
-            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
-                 (nodeIt = find_if(nodeIt, endIt, isModdim)) != endIt;
-                 ++nodeIt) {
-                const ModdimNode* mn{static_cast<ModdimNode*>(nodeIt->get())};
+        // Keep node_clones in scope, so that the nodes remain active for later
+        // referral in case moddims or Column elimination operations have to
+        // take place
+        vector<Node_ptr> node_clones;
+        if (moddimsFound | emptyColumnsFound) {
+            node_clones.reserve(full_nodes.size());
+            for (Node* node : full_nodes) {
+                node_clones.emplace_back(node->clone());
+            }
 
-                const auto new_strides{calcStrides(mn->m_new_shape)};
+            for (const Node_ids& ids : full_ids) {
+                auto& children{node_clones[ids.id]->m_children};
+                for (int i{0}; i < Node::kMaxChildren && children[i] != nullptr;
+                     i++) {
+                    children[i] = node_clones[ids.child_ids[i]];
+                }
+            }
+
+            if (moddimsFound) {
+                const auto isModdim{[](const Node_ptr& node) {
+                    return node->getOp() == af_moddims_t;
+                }};
+                for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                     (nodeIt = find_if(nodeIt, endIt, isModdim)) != endIt;
+                     ++nodeIt) {
+                    const ModdimNode* mn{
+                        static_cast<ModdimNode*>(nodeIt->get())};
+
+                    const auto new_strides{calcStrides(mn->m_new_shape)};
+                    const auto isBuffer{
+                        [](const Node& ptr) { return ptr.isBuffer(); }};
+                    for (NodeIterator<> it{nodeIt->get()},
+                         end{NodeIterator<>()};
+                         (it = find_if(it, end, isBuffer)) != end; ++it) {
+                        BufferNode<T>* buf{static_cast<BufferNode<T>*>(&(*it))};
+                        buf->m_param.dims[0]    = mn->m_new_shape[0];
+                        buf->m_param.dims[1]    = mn->m_new_shape[1];
+                        buf->m_param.dims[2]    = mn->m_new_shape[2];
+                        buf->m_param.dims[3]    = mn->m_new_shape[3];
+                        buf->m_param.strides[0] = new_strides[0];
+                        buf->m_param.strides[1] = new_strides[1];
+                        buf->m_param.strides[2] = new_strides[2];
+                        buf->m_param.strides[3] = new_strides[3];
+                    }
+                }
+            }
+            if (emptyColumnsFound) {
                 const auto isBuffer{
-                    [](const Node& ptr) { return ptr.isBuffer(); }};
-                for (NodeIterator<> it{nodeIt->get()}, end{NodeIterator<>()};
-                     (it = find_if(it, end, isBuffer)) != end; ++it) {
-                    BufferNode<T>* buf{static_cast<BufferNode<T>*>(&(*it))};
-                    buf->m_param.dims[0]    = mn->m_new_shape[0];
-                    buf->m_param.dims[1]    = mn->m_new_shape[1];
-                    buf->m_param.dims[2]    = mn->m_new_shape[2];
-                    buf->m_param.dims[3]    = mn->m_new_shape[3];
-                    buf->m_param.strides[0] = new_strides[0];
-                    buf->m_param.strides[1] = new_strides[1];
-                    buf->m_param.strides[2] = new_strides[2];
-                    buf->m_param.strides[3] = new_strides[3];
+                    [](const Node_ptr& node) { return node->isBuffer(); }};
+                for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                     (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
+                     ++nodeIt) {
+                    BufferNode<T>* buf{
+                        static_cast<BufferNode<T>*>(nodeIt->get())};
+                    removeEmptyColumns(outDims, ndims, buf->m_param.dims,
+                                       buf->m_param.strides);
                 }
+                for_each(++begin(outputs), end(outputs),
+                         [outDims, ndims](Param<T>& output) {
+                             removeEmptyColumns(outDims, ndims, output.dims,
+                                                output.strides);
+                         });
+                ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
             }
-        }
-        if (emptyColumnsFound) {
-            const auto isBuffer{
-                [](const Node_ptr& node) { return node->isBuffer(); }};
-            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
-                 (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
-                 ++nodeIt) {
-                BufferNode<T>* buf{static_cast<BufferNode<T>*>(nodeIt->get())};
-                removeEmptyColumns(outDims, ndims, buf->m_param.dims,
-                                   buf->m_param.strides);
+
+            full_nodes.clear();
+            for (Node_ptr& node : node_clones) {
+                full_nodes.push_back(node.get());
             }
-            for_each(++begin(outputs), end(outputs),
-                     [outDims, ndims](Param<T>& output) {
-                         removeEmptyColumns(outDims, ndims, output.dims,
-                                            output.strides);
-                     });
-            ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
         }
 
-        full_nodes.clear();
-        for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
-    }
-
-    threadsMgt<dim_t> th(outDims, ndims);
-    const dim3 threads{th.genThreads()};
-    const dim3 blocks{th.genBlocks(threads, nrInputs, nrOutputs, totalSize,
-                                   outputSizeofType)};
-    auto ker = getKernel(output_nodes, output_ids, full_nodes, full_ids,
-                         is_linear, th.loop0, th.loop1, th.loop2, th.loop3);
-
-    vector<void*> args;
-    for (const Node* node : full_nodes) {
-        node->setArgs(0, is_linear,
-                      [&](int /*id*/, const void* ptr, size_t /*size*/) {
-                          args.push_back(const_cast<void*>(ptr));
-                      });
-    }
+        threadsMgt<dim_t> th(outDims, ndims);
+        const dim3 threads{th.genThreads()};
+        const dim3 blocks{th.genBlocks(threads, nrInputs, nrOutputs, totalSize,
+                                       outputSizeofType)};
+        auto ker = getKernel(output_nodes, output_ids, full_nodes, full_ids,
+                             is_linear, th.loop0, th.loop1, th.loop2, th.loop3);
+
+        vector<void*> args;
+        for (const Node* node : full_nodes) {
+            node->setArgs(0, is_linear,
+                          [&](int /*id*/, const void* ptr, size_t /*size*/) {
+                              args.push_back(const_cast<void*>(ptr));
+                          });
+        }
 
-    for (auto& out : outputs) { args.push_back(static_cast<void*>(&out)); }
+        for (auto& out : outputs) { args.push_back(static_cast<void*>(&out)); }
 
-    {
-        using namespace arrayfire::cuda::kernel_logger;
-        AF_TRACE(
-            "Launching : Dims: [{},{},{},{}] Blocks: [{}] "
-            "Threads: [{}] threads: {}",
-            outDims[0], outDims[1], outDims[2], outDims[3], blocks, threads,
-            blocks.x * threads.x * blocks.y * threads.y * blocks.z * threads.z);
+        {
+            using namespace arrayfire::cuda::kernel_logger;
+            AF_TRACE(
+                "Launching : Dims: [{},{},{},{}] Blocks: [{}] "
+                "Threads: [{}] threads: {}",
+                outDims[0], outDims[1], outDims[2], outDims[3], blocks, threads,
+                blocks.x * threads.x * blocks.y * threads.y * blocks.z *
+                    threads.z);
+        }
+        CU_CHECK(cuLaunchKernel(ker, blocks.x, blocks.y, blocks.z, threads.x,
+                                threads.y, threads.z, 0, getActiveStream(),
+                                args.data(), NULL));
+    } catch (...) {
+        // Reset the thread local vectors
+        nodes.clear();
+        output_ids.clear();
+        full_nodes.clear();
+        full_ids.clear();
+        throw;
     }
-    CU_CHECK(cuLaunchKernel(ker, blocks.x, blocks.y, blocks.z, threads.x,
-                            threads.y, threads.z, 0, getActiveStream(),
-                            args.data(), NULL));
 
     // Reset the thread local vectors
     nodes.clear();

From c40eec3e8f7e724bfe1d1ff9f624af36d6a091d9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Nov 2022 00:36:18 -0500
Subject: [PATCH 2374/2677] Add type checks for tests and different types

---
 test/approx1.cpp        |  1 +
 test/approx2.cpp        |  2 ++
 test/arrayfire_test.cpp |  8 +++++++
 test/arrayio.cpp        |  3 +++
 test/basic.cpp          |  1 +
 test/binary.cpp         | 47 +++++++++++++++++++++++------------------
 test/canny.cpp          |  2 +-
 test/cast.cpp           | 10 +++++++++
 test/clamp.cpp          | 19 ++++++++++++-----
 test/fft.cpp            |  3 +++
 test/half.cpp           |  4 ++++
 test/replace.cpp        | 17 ++++++++++-----
 test/rng_quality.cpp    |  3 +--
 test/topk.cpp           |  2 +-
 test/wrap.cpp           |  1 +
 15 files changed, 88 insertions(+), 35 deletions(-)

diff --git a/test/approx1.cpp b/test/approx1.cpp
index 143f66bd71..af719d8c4d 100644
--- a/test/approx1.cpp
+++ b/test/approx1.cpp
@@ -968,6 +968,7 @@ template<typename T>
 class Approx1V2Simple : public Approx1V2<T> {
    protected:
     void SetUp() {
+        SUPPORTED_TYPE_CHECK(T);
         SimpleTestData data;
         this->setTestData(&data.h_gold.front(), data.gold_dims,
                           &data.h_in.front(), data.in_dims, &data.h_pos.front(),
diff --git a/test/approx2.cpp b/test/approx2.cpp
index 1b7901bf8d..bec8bd75cf 100644
--- a/test/approx2.cpp
+++ b/test/approx2.cpp
@@ -45,6 +45,7 @@ template<typename T>
 class Approx2 : public ::testing::Test {
    public:
     virtual void SetUp() {
+        SUPPORTED_TYPE_CHECK(T);
         subMat0.push_back(af_make_seq(0, 4, 1));
         subMat0.push_back(af_make_seq(2, 6, 1));
         subMat0.push_back(af_make_seq(0, 2, 1));
@@ -903,6 +904,7 @@ template<typename T>
 class Approx2V2Simple : public Approx2V2<T> {
    protected:
     void SetUp() {
+        SUPPORTED_TYPE_CHECK(T);
         SimpleTestData data;
         this->setTestData(&data.h_gold.front(), data.gold_dims,
                           &data.h_in.front(), data.in_dims,
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index a8f8a34562..cf776b6e2b 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -260,8 +260,16 @@ ::testing::AssertionResult assertImageEq(std::string aName, std::string bName,
     switch (arrDtype) {
         case u8: return imageEq<unsigned char>(aName, bName, a, b, maxAbsDiff);
         case b8: return imageEq<char>(aName, bName, a, b, maxAbsDiff);
+        case s32: return imageEq<int>(aName, bName, a, b, maxAbsDiff);
+        case u32: return imageEq<unsigned int>(aName, bName, a, b, maxAbsDiff);
         case f32: return imageEq<float>(aName, bName, a, b, maxAbsDiff);
         case f64: return imageEq<double>(aName, bName, a, b, maxAbsDiff);
+        case s16: return imageEq<short>(aName, bName, a, b, maxAbsDiff);
+        case u16:
+            return imageEq<unsigned short>(aName, bName, a, b, maxAbsDiff);
+        case u64:
+            return imageEq<unsigned long long>(aName, bName, a, b, maxAbsDiff);
+        case s64: return imageEq<long long>(aName, bName, a, b, maxAbsDiff);
         default: throw(AF_ERR_NOT_SUPPORTED);
     }
     return ::testing::AssertionSuccess();
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
index 7a578b612a..00d907a568 100644
--- a/test/arrayio.cpp
+++ b/test/arrayio.cpp
@@ -56,6 +56,7 @@ INSTANTIATE_TEST_SUITE_P(
 
 TEST_P(ArrayIOType, ReadType) {
     type_params p = GetParam();
+    if (noDoubleTests(p.type)) GTEST_SKIP() << "No double support.";
     array arr =
         readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(),
                   p.name.c_str());
@@ -65,6 +66,7 @@ TEST_P(ArrayIOType, ReadType) {
 
 TEST_P(ArrayIOType, ReadSize) {
     type_params p = GetParam();
+    if (noDoubleTests(p.type)) GTEST_SKIP() << "No double support.";
     array arr =
         readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(),
                   p.name.c_str());
@@ -89,6 +91,7 @@ void checkVals(array arr, double r, double i, af_dtype t) {
 
 TEST_P(ArrayIOType, ReadContent) {
     type_params p = GetParam();
+    if (noDoubleTests(p.type)) GTEST_SKIP() << "No double support.";
     array arr =
         readArray((string(TEST_DIR) + "/arrayio/" + p.name + ".arr").c_str(),
                   p.name.c_str());
diff --git a/test/basic.cpp b/test/basic.cpp
index c39e800408..ebb211c7b7 100644
--- a/test/basic.cpp
+++ b/test/basic.cpp
@@ -314,6 +314,7 @@ TEST(Assert, TestEqualsC) {
 }
 
 TEST(Assert, TestEqualsDiffTypes) {
+    SUPPORTED_TYPE_CHECK(double);
     array gold = constant(1, 10, 10, f64);
     array out  = constant(1, 10, 10);
 
diff --git a/test/binary.cpp b/test/binary.cpp
index b0c04a4c30..f6f9a8928f 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -360,20 +360,27 @@ TEST(BinaryTests, ISSUE_1762) {
 }
 
 template<typename T>
-class PowPrecisionTest : public ::testing::TestWithParam<T> {};
-
-#define DEF_TEST(Sx, T)                                      \
-    using PowPrecisionTest##Sx = PowPrecisionTest<T>;        \
-    TEST_P(PowPrecisionTest##Sx, Issue2304) {                \
-        T param     = GetParam();                            \
-        auto dtype  = (af_dtype)dtype_traits<T>::af_type;    \
-        af::array A = af::constant(param, 1, dtype);         \
-        af::array B = af::pow(A, 2);                         \
-        vector<T> hres(1, 0);                                \
-        B.host(&hres[0]);                                    \
-        std::fesetround(FE_TONEAREST);                       \
-        T gold = (T)std::rint(std::pow((double)param, 2.0)); \
-        ASSERT_EQ(hres[0], gold);                            \
+class PowPrecisionTest : public ::testing::TestWithParam<T> {
+    void SetUp() { SUPPORTED_TYPE_CHECK(T); }
+};
+
+#define DEF_TEST(Sx, T)                                                    \
+    using PowPrecisionTest##Sx = PowPrecisionTest<T>;                      \
+    TEST_P(PowPrecisionTest##Sx, Issue2304) {                              \
+        T param    = GetParam();                                           \
+        auto dtype = (af_dtype)dtype_traits<T>::af_type;                   \
+        if (noDoubleTests(dtype)) {                                        \
+            if (std::abs((double)param) > 10000)                           \
+                GTEST_SKIP()                                               \
+                    << "Skip larger values because double not supported."; \
+        }                                                                  \
+        af::array A = af::constant(param, 1, dtype);                       \
+        af::array B = af::pow(A, 2);                                       \
+        vector<T> hres(1, 0);                                              \
+        B.host(&hres[0]);                                                  \
+        std::fesetround(FE_TONEAREST);                                     \
+        T gold = (T)std::rint(std::pow((double)param, 2.0));               \
+        ASSERT_EQ(hres[0], gold);                                          \
     }
 
 DEF_TEST(ULong, unsigned long long)
@@ -429,15 +436,17 @@ class ResultType : public testing::TestWithParam<result_type_param> {
     af::array lhs;
     af::array rhs;
     af_dtype gold;
-    bool skip;
 
     void SetUp() {
         result_type_param params = GetParam();
         gold                     = params.result_;
-        skip                     = false;
         if (noHalfTests(params.result_) || noHalfTests(params.lhs_) ||
             noHalfTests(params.rhs_)) {
-            skip = true;
+            GTEST_SKIP() << "Half not supported on this device";
+            return;
+        } else if (noDoubleTests(params.result_) ||
+                   noDoubleTests(params.lhs_) || noDoubleTests(params.rhs_)) {
+            GTEST_SKIP() << "Double not supported on this device";
             return;
         }
         lhs = af::array(10, params.lhs_);
@@ -513,19 +522,15 @@ INSTANTIATE_TEST_SUITE_P(
 
 // clang-format off
 TEST_P(ResultType, Addition)       {
-    if (skip) return;
     ASSERT_EQ(gold, (lhs + rhs).type());
 }
 TEST_P(ResultType, Subtraction)    {
-    if (skip) return;
     ASSERT_EQ(gold, (lhs - rhs).type());
 }
 TEST_P(ResultType, Multiplication) {
-    if (skip) return;
     ASSERT_EQ(gold, (lhs * rhs).type());
 }
 TEST_P(ResultType, Division)       {
-    if (skip) return;
     ASSERT_EQ(gold, (lhs / rhs).type());
 }
 // clang-format on
diff --git a/test/canny.cpp b/test/canny.cpp
index 7e72d4e356..b34a4923b4 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -251,7 +251,7 @@ void cannyImageOtsuBatchTest(string pTestFile, const dim_t targetBatchCount) {
             canny(inputIm, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false);
         outIm *= 255.0;
 
-        ASSERT_IMAGES_NEAR(outIm.as(u8), goldIm, 1.0e-3);
+        ASSERT_IMAGES_NEAR(goldIm, outIm.as(u8), 1.0e-3);
     }
 }
 
diff --git a/test/cast.cpp b/test/cast.cpp
index 96178a470c..cb1f4e3f42 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -95,6 +95,8 @@ void cast_test_complex_real() {
 
 #define COMPLEX_REAL_TESTS(Ti, To)                      \
     TEST(CAST_TEST, Test_Complex_To_Real_##Ti##_##To) { \
+        SUPPORTED_TYPE_CHECK(Ti);                       \
+        SUPPORTED_TYPE_CHECK(To);                       \
         cast_test_complex_real<Ti, To>();               \
     }
 
@@ -106,6 +108,7 @@ COMPLEX_REAL_TESTS(cdouble, double)
 TEST(CAST_TEST, Test_JIT_DuplicateCastNoop) {
     // Does a trivial cast - check JIT kernel trace to ensure a __noop is
     // generated since we don't have a way to test it directly
+    SUPPORTED_TYPE_CHECK(double);
     af_dtype ta = (af_dtype)dtype_traits<float>::af_type;
     af_dtype tb = (af_dtype)dtype_traits<double>::af_type;
     dim4 dims(num, 1, 1, 1);
@@ -129,6 +132,7 @@ TEST(CAST_TEST, Test_JIT_DuplicateCastNoop) {
 
 TEST(Cast, ImplicitCast) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array a = randu(100, 100, f64);
     array b = a.as(f32);
 
@@ -138,6 +142,7 @@ TEST(Cast, ImplicitCast) {
 
 TEST(Cast, ConstantCast) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array a = constant(1, 100, f64);
     array b = a.as(f32);
 
@@ -147,6 +152,7 @@ TEST(Cast, ConstantCast) {
 
 TEST(Cast, OpCast) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array a = constant(1, 100, f64);
     a       = a + a;
     array b = a.as(f32);
@@ -156,6 +162,7 @@ TEST(Cast, OpCast) {
 }
 TEST(Cast, ImplicitCastIndexed) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array a = randu(100, 100, f64);
     array b = a(span, 1).as(f32);
     array c = max(abs(a(span, 1) - b));
@@ -164,6 +171,7 @@ TEST(Cast, ImplicitCastIndexed) {
 
 TEST(Cast, ImplicitCastIndexedNonLinear) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array a = randu(100, 100, f64);
     array b = a(seq(10, 20, 2), 1).as(f32);
     array c = max(abs(a(seq(10, 20, 2), 1) - b));
@@ -172,6 +180,7 @@ TEST(Cast, ImplicitCastIndexedNonLinear) {
 
 TEST(Cast, ImplicitCastIndexedNonLinearArray) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array a   = randu(100, 100, f64);
     array idx = seq(10, 20, 2);
     array b   = a(idx, 1).as(f32);
@@ -181,6 +190,7 @@ TEST(Cast, ImplicitCastIndexedNonLinearArray) {
 
 TEST(Cast, ImplicitCastIndexedAndScoped) {
     using namespace af;
+    SUPPORTED_TYPE_CHECK(double);
     array c;
     {
         array a = randu(100, 100, f64);
diff --git a/test/clamp.cpp b/test/clamp.cpp
index d27ad3a16d..1e0b04b7c2 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -51,8 +51,19 @@ class Clamp : public ::testing::TestWithParam<clamp_params> {
    public:
     void SetUp() {
         clamp_params params = GetParam();
-        if (noDoubleTests(params.in_type_)) return;
-        if (noHalfTests(params.in_type_)) return;
+        SUPPORTED_TYPE_CHECK(double);
+        if (noDoubleTests(params.in_type_))
+            GTEST_SKIP() << "Double not supported on this device";
+        if (noHalfTests(params.in_type_))
+            GTEST_SKIP() << "Half not supported on this device";
+        if (noDoubleTests(params.hi_type_))
+            GTEST_SKIP() << "Double not supported on this device";
+        if (noHalfTests(params.hi_type_))
+            GTEST_SKIP() << "Half not supported on this device";
+        if (noDoubleTests(params.lo_type_))
+            GTEST_SKIP() << "Double not supported on this device";
+        if (noHalfTests(params.lo_type_))
+            GTEST_SKIP() << "Half not supported on this device";
 
         in_ = randu(params.size_, params.in_type_);
         lo_ = randu(params.size_, params.lo_type_) / T(10);
@@ -138,9 +149,7 @@ INSTANTIATE_TEST_SUITE_P(
 
 TEST_P(ClampFloatingPoint, Basic) {
     clamp_params params = GetParam();
-    if (noDoubleTests(params.in_type_)) return;
-    if (noHalfTests(params.in_type_)) return;
-    array out = clamp(in_, lo_, hi_);
+    array out           = clamp(in_, lo_, hi_);
     ASSERT_ARRAYS_NEAR(gold_, out, 1e-5);
 }
 
diff --git a/test/fft.cpp b/test/fft.cpp
index 49176ca522..0af43dca2b 100644
--- a/test/fft.cpp
+++ b/test/fft.cpp
@@ -816,6 +816,7 @@ TEST_P(FFT2D, Real32ToComplexInputsPreserved) {
 }
 
 TEST_P(FFT2D, Real64ToComplexInputsPreserved) {
+    SUPPORTED_TYPE_CHECK(double);
     fft_params params = GetParam();
     af::array a       = af::randu(params.input_dims_, f64);
     af::array a_copy  = a.copy();
@@ -834,6 +835,7 @@ TEST_P(FFTC2R, Complex32ToRInputsPreserved) {
 }
 
 TEST_P(FFTC2R, Complex64ToRInputsPreserved) {
+    SUPPORTED_TYPE_CHECK(double);
     fft_params params = GetParam();
     af::array a       = af::randu(params.input_dims_, c64);
     af::array a_copy  = a.copy();
@@ -852,6 +854,7 @@ TEST_P(FFTND, Real32ToComplexInputsPreserved) {
 }
 
 TEST_P(FFTND, Real64ToComplexInputsPreserved) {
+    SUPPORTED_TYPE_CHECK(double);
     fft_params params = GetParam();
     af::array a       = af::randu(params.input_dims_, f64);
     af::array a_copy  = a.copy();
diff --git a/test/half.cpp b/test/half.cpp
index 33ae4eae4a..7f85950170 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -63,6 +63,10 @@ INSTANTIATE_TEST_SUITE_P(FromF16, HalfConvert,
 TEST_P(HalfConvert, convert) {
     SUPPORTED_TYPE_CHECK(af_half);
     convert_params params = GetParam();
+    if (noDoubleTests(params.to))
+        GTEST_SKIP() << "Double not supported on this device";
+    if (noDoubleTests(params.from))
+        GTEST_SKIP() << "Double not supported on this device";
 
     array from = af::constant(params.value, 3, 3, params.from);
     array to   = from.as(params.to);
diff --git a/test/replace.cpp b/test/replace.cpp
index 14e679436b..6d72cf7fc9 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -142,7 +142,8 @@ TEST(Replace, ISSUE_1249) {
     array a    = randu(dims);
     array b    = a.copy();
     replace(b, !cond, a - a * 0.9);
-    array c = a - a * cond * 0.9;
+    array c  = (a - a * 0.9);
+    c(!cond) = a(!cond);
 
     int num = (int)dims.elements();
     vector<float> hb(num);
@@ -151,7 +152,9 @@ TEST(Replace, ISSUE_1249) {
     b.host(&hb[0]);
     c.host(&hc[0]);
 
-    for (int i = 0; i < num; i++) { ASSERT_EQ(hc[i], hb[i]) << "at " << i; }
+    for (int i = 0; i < num; i++) {
+        ASSERT_FLOAT_EQ(hc[i], hb[i]) << "at " << i;
+    }
 }
 
 TEST(Replace, 4D) {
@@ -169,7 +172,9 @@ TEST(Replace, 4D) {
     b.host(&hb[0]);
     c.host(&hc[0]);
 
-    for (int i = 0; i < num; i++) { ASSERT_EQ(hc[i], hb[i]) << "at " << i; }
+    for (int i = 0; i < num; i++) {
+        ASSERT_FLOAT_EQ(hc[i], hb[i]) << "at " << i;
+    }
 }
 
 TEST(Replace, ISSUE_1683) {
@@ -187,12 +192,14 @@ TEST(Replace, ISSUE_1683) {
     B.host(hb.data());
 
     // Ensures A is not modified by replace
-    for (int i = 0; i < (int)A.elements(); i++) { ASSERT_EQ(ha1[i], ha2[i]); }
+    for (int i = 0; i < (int)A.elements(); i++) {
+        ASSERT_FLOAT_EQ(ha1[i], ha2[i]);
+    }
 
     // Ensures replace on B works as expected
     for (int i = 0; i < (int)B.elements(); i++) {
         float val = ha1[i * A.dims(0)];
         val       = val < 0.5 ? 0 : val;
-        ASSERT_EQ(val, hb[i]);
+        ASSERT_FLOAT_EQ(val, hb[i]);
     }
 }
diff --git a/test/rng_quality.cpp b/test/rng_quality.cpp
index 8274b1dfa9..92c264dfbb 100644
--- a/test/rng_quality.cpp
+++ b/test/rng_quality.cpp
@@ -20,6 +20,7 @@ class RandomEngine : public ::testing::Test {
     virtual void SetUp() {
         // Ensure all unlocked buffers are freed
         deviceGC();
+        SUPPORTED_TYPE_CHECK(T);
     }
 };
 
@@ -30,7 +31,6 @@ TYPED_TEST_SUITE(RandomEngine, TestTypesEngine);
 
 template<typename T>
 void testRandomEnginePeriod(randomEngineType type) {
-    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem  = 1024 * 1024;
@@ -88,7 +88,6 @@ double chi2_statistic<half_float::half>(array input, array expected,
 
 template<typename T>
 void testRandomEngineUniformChi2(randomEngineType type) {
-    SUPPORTED_TYPE_CHECK(T);
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
     int elem  = 256 * 1024 * 1024;
diff --git a/test/topk.cpp b/test/topk.cpp
index 86cf1287f9..58319f25e8 100644
--- a/test/topk.cpp
+++ b/test/topk.cpp
@@ -149,7 +149,7 @@ void topkTest(const int ndims, const dim_t* dims, const unsigned k,
             case f32:
                 EXPECT_FLOAT_EQ(outData[i], hovals[i]) << "at: " << i;
                 break;
-            default: EXPECT_EQ(outData[i], hovals[i]); break;
+            default: EXPECT_EQ(outData[i], hovals[i]) << "at: " << i; break;
         }
         ASSERT_EQ(outIdxs[i], hoidxs[i]) << "at: " << i;
     }
diff --git a/test/wrap.cpp b/test/wrap.cpp
index 91b57c4bc0..baff77c5b1 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -360,6 +360,7 @@ template<typename T>
 class WrapV2Simple : public WrapV2<T> {
    protected:
     void SetUp() {
+        SUPPORTED_TYPE_CHECK(T);
         this->releaseArrays();
         this->in_   = 0;
         this->gold_ = 0;

From f72233168f432d93c964f428e9dc894f93944085 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 29 Nov 2022 00:03:05 -0500
Subject: [PATCH 2375/2677] Fix fftconvolve so that floats are used for complex
 float values

---
 src/api/c/fftconvolve.cpp          | 9 +++++----
 src/backend/cuda/fftconvolve.cpp   | 9 +++++----
 src/backend/opencl/fftconvolve.cpp | 9 +++++----
 3 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index bbcb2d2a1d..f92a3fc655 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -49,10 +49,11 @@ using std::vector;
 template<typename T>
 af_array fftconvolve_fallback(const af_array signal, const af_array filter,
                               const bool expand, const int baseDim) {
-    using convT =
-        typename conditional<is_integral<T>::value || is_same<T, float>::value,
-                             float, double>::type;
-    using cT = typename conditional<is_same<convT, float>::value, cfloat,
+    using convT = typename conditional<is_integral<T>::value ||
+                                           is_same<T, float>::value ||
+                                           is_same<T, cfloat>::value,
+                                       float, double>::type;
+    using cT    = typename conditional<is_same<convT, float>::value, cfloat,
                                     cdouble>::type;
 
     const Array<cT> S = castArray<cT>(signal);
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index 7c50c0838c..ed22d0ea85 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -50,10 +50,11 @@ dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2, const int rank) {
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank) {
-    using convT =
-        typename conditional<is_integral<T>::value || is_same<T, float>::value,
-                             float, double>::type;
-    using cT = typename conditional<is_same<convT, float>::value, cfloat,
+    using convT = typename conditional<is_integral<T>::value ||
+                                           is_same<T, float>::value ||
+                                           is_same<T, cfloat>::value,
+                                       float, double>::type;
+    using cT    = typename conditional<is_same<convT, float>::value, cfloat,
                                     cdouble>::type;
 
     const dim4& sDims = signal.dims();
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index a4f8b1f1f1..f6b243baac 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -58,10 +58,11 @@ dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2, const dim_t rank) {
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank) {
-    using convT =
-        typename conditional<is_integral<T>::value || is_same<T, float>::value,
-                             float, double>::type;
-    using cT = typename conditional<is_same<convT, float>::value, cfloat,
+    using convT = typename conditional<is_integral<T>::value ||
+                                           is_same<T, float>::value ||
+                                           is_same<T, cfloat>::value,
+                                       float, double>::type;
+    using cT    = typename conditional<is_same<convT, float>::value, cfloat,
                                     cdouble>::type;
 
     const dim4& sDims = signal.dims();

From 000d4311ac5104ffeab679dda9acccbfff6da3d2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 29 Nov 2022 00:05:05 -0500
Subject: [PATCH 2376/2677] Add ifdef check around powll and powul functions in
 jit.cl

---
 src/backend/opencl/kernel/jit.cl | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/jit.cl b/src/backend/opencl/kernel/jit.cl
index c9c3b7eb8c..a0486106e2 100644
--- a/src/backend/opencl/kernel/jit.cl
+++ b/src/backend/opencl/kernel/jit.cl
@@ -107,12 +107,19 @@ float2 __cdivf(float2 lhs, float2 rhs) {
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
 
-#define __pow(lhs, rhs) \
+#define __pow(lhs, rhs)                                                 \
     convert_int_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#ifdef USE_DOUBLE
 #define __powll(lhs, rhs) \
     convert_long_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
 #define __powul(lhs, rhs) \
     convert_ulong_rte(pow(convert_double_rte(lhs), convert_double_rte(rhs)))
+#else
+#define __powll(lhs, rhs) \
+    convert_long_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#define __powul(lhs, rhs) \
+    convert_ulong_rte(pow(convert_float_rte(lhs), convert_float_rte(rhs)))
+#endif
 
 #ifdef USE_DOUBLE
 #define __powui(lhs, rhs) \

From a07246e41497d3eb87df1efe8b40373caaaf4ebf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 29 Nov 2022 02:13:15 -0500
Subject: [PATCH 2377/2677] Update cl2hpp tag and disable building cl2hpp if
 found on system

---
 CMakeModules/build_cl2hpp.cmake | 44 +++++++++++++++++----------------
 1 file changed, 23 insertions(+), 21 deletions(-)

diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 14c2646c2e..0a3fef2de0 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -13,28 +13,30 @@
 
 find_package(OpenCL)
 
-find_path(cl2hpp_header_file_path
-  NAMES CL/cl2.hpp
-  PATHS ${OpenCL_INCLUDE_PATHS})
-
-if(cl2hpp_header_file_path)
-  add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
-  add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
-
-  set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
-    INTERFACE_INCLUDE_DIRECTORIES ${cl2hpp_header_file_path})
-elseif (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
-  af_dep_check_and_populate(${cl2hpp_prefix}
-    URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
-    REF v2.0.12)
-
-  find_path(cl2hpp_var
+if(NOT TARGET OpenCL::cl2hpp)
+  find_path(cl2hpp_header_file_path
     NAMES CL/cl2.hpp
-    PATHS ${ArrayFire_BINARY_DIR}/extern/${cl2hpp_prefix}-src/include)
+    PATHS ${OpenCL_INCLUDE_PATHS})
 
-  add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
-  add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
+  if(cl2hpp_header_file_path)
+    add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
+    add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
 
-  set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
-    INTERFACE_INCLUDE_DIRECTORIES ${cl2hpp_var})
+    set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
+      INTERFACE_INCLUDE_DIRECTORIES ${cl2hpp_header_file_path})
+  elseif (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
+    af_dep_check_and_populate(${cl2hpp_prefix}
+      URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
+      REF v2022.09.30)
+
+    find_path(cl2hpp_var
+      NAMES CL/cl2.hpp
+      PATHS ${ArrayFire_BINARY_DIR}/extern/${cl2hpp_prefix}-src/include)
+
+    add_library(cl2hpp IMPORTED INTERFACE GLOBAL)
+    add_library(OpenCL::cl2hpp IMPORTED INTERFACE GLOBAL)
+
+    set_target_properties(cl2hpp OpenCL::cl2hpp PROPERTIES
+      INTERFACE_INCLUDE_DIRECTORIES ${cl2hpp_var})
+  endif()
 endif()

From 1e210f4da349e77bcf546a4083931353b5a03edc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 Nov 2022 02:25:49 -0500
Subject: [PATCH 2378/2677] Update the minimum required OpenCL version to 3.0

---
 CMakeLists.txt                                |   2 +-
 CMakeModules/FindOpenCL.cmake                 | 101 ++++++++++-----
 src/backend/opencl/CMakeLists.txt             | 116 +++++++++++++++++-
 src/backend/opencl/compile_module.cpp         |  16 +--
 src/backend/opencl/device_manager.cpp         |  27 ++--
 src/backend/opencl/device_manager.hpp         |   3 +
 .../kernel/reduce_blocks_by_key_first.cl      |  12 +-
 src/backend/opencl/platform.cpp               |  35 ++++--
 src/backend/opencl/platform.hpp               |   6 +-
 9 files changed, 240 insertions(+), 78 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8985c797ff..2fb83beed9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -50,7 +50,7 @@ set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for
 
 find_package(CUDA 10.2)
 find_package(cuDNN 4.0)
-find_package(OpenCL 1.2)
+find_package(OpenCL 3.0)
 find_package(OpenGL)
 find_package(glad CONFIG QUIET)
 find_package(FreeImage)
diff --git a/CMakeModules/FindOpenCL.cmake b/CMakeModules/FindOpenCL.cmake
index cdaeba20cc..3ac45a4a12 100644
--- a/CMakeModules/FindOpenCL.cmake
+++ b/CMakeModules/FindOpenCL.cmake
@@ -1,35 +1,43 @@
 # Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
 # file Copyright.txt or https://cmake.org/licensing for details.
 
-#.rst:
-# FindOpenCL
-# ----------
-#
-# Try to find OpenCL
-#
-# IMPORTED Targets
-# ^^^^^^^^^^^^^^^^
-#
-# This module defines :prop_tgt:`IMPORTED` target ``OpenCL::OpenCL``, if
-# OpenCL has been found.
-#
-# Result Variables
-# ^^^^^^^^^^^^^^^^
-#
-# This module defines the following variables::
-#
-#   OpenCL_FOUND          - True if OpenCL was found
-#   OpenCL_INCLUDE_DIRS   - include directories for OpenCL
-#   OpenCL_LIBRARIES      - link against this library to use OpenCL
-#   OpenCL_VERSION_STRING - Highest supported OpenCL version (eg. 1.2)
-#   OpenCL_VERSION_MAJOR  - The major version of the OpenCL implementation
-#   OpenCL_VERSION_MINOR  - The minor version of the OpenCL implementation
-#
-# The module will also define two cache variables::
-#
-#   OpenCL_INCLUDE_DIR    - the OpenCL include directory
-#   OpenCL_LIBRARY        - the path to the OpenCL library
-#
+#[=======================================================================[.rst:
+FindOpenCL
+----------
+
+.. versionadded:: 3.1
+
+Finds Open Computing Language (OpenCL)
+
+.. versionadded:: 3.10
+  Detection of OpenCL 2.1 and 2.2.
+
+IMPORTED Targets
+^^^^^^^^^^^^^^^^
+
+.. versionadded:: 3.7
+
+This module defines :prop_tgt:`IMPORTED` target ``OpenCL::OpenCL``, if
+OpenCL has been found.
+
+Result Variables
+^^^^^^^^^^^^^^^^
+
+This module defines the following variables::
+
+  OpenCL_FOUND          - True if OpenCL was found
+  OpenCL_INCLUDE_DIRS   - include directories for OpenCL
+  OpenCL_LIBRARIES      - link against this library to use OpenCL
+  OpenCL_VERSION_STRING - Highest supported OpenCL version (eg. 1.2)
+  OpenCL_VERSION_MAJOR  - The major version of the OpenCL implementation
+  OpenCL_VERSION_MINOR  - The minor version of the OpenCL implementation
+
+The module will also define two cache variables::
+
+  OpenCL_INCLUDE_DIR    - the OpenCL include directory
+  OpenCL_LIBRARY        - the path to the OpenCL library
+
+#]=======================================================================]
 
 function(_FIND_OPENCL_VERSION)
   include(CheckSymbolExists)
@@ -37,7 +45,7 @@ function(_FIND_OPENCL_VERSION)
   set(CMAKE_REQUIRED_QUIET ${OpenCL_FIND_QUIETLY})
 
   CMAKE_PUSH_CHECK_STATE()
-  foreach(VERSION "2_0" "1_2" "1_1" "1_0")
+  foreach(VERSION "3_0" "2_2" "2_1" "2_0" "1_2" "1_1" "1_0")
     set(CMAKE_REQUIRED_INCLUDES "${OpenCL_INCLUDE_DIR}")
 
     if(APPLE)
@@ -76,6 +84,9 @@ find_path(OpenCL_INCLUDE_DIR
     ENV NVSDKCOMPUTE_ROOT
     ENV CUDA_PATH
     ENV ATISTREAMSDKROOT
+    ENV OCL_ROOT
+    /usr/local/cuda
+    /opt/cuda
   PATH_SUFFIXES
     include
     OpenCL/common/inc
@@ -94,6 +105,7 @@ if(WIN32)
         ENV CUDA_PATH
         ENV NVSDKCOMPUTE_ROOT
         ENV ATISTREAMSDKROOT
+        ENV OCL_ROOT
       PATH_SUFFIXES
         "AMD APP/lib/x86"
         lib/x86
@@ -109,6 +121,7 @@ if(WIN32)
         ENV CUDA_PATH
         ENV NVSDKCOMPUTE_ROOT
         ENV ATISTREAMSDKROOT
+        ENV OCL_ROOT
       PATH_SUFFIXES
         "AMD APP/lib/x86_64"
         lib/x86_64
@@ -116,9 +129,31 @@ if(WIN32)
         OpenCL/common/lib/x64)
   endif()
 else()
-  find_library(OpenCL_LIBRARY
-    NAMES OpenCL
-    PATH_SUFFIXES lib64/)
+  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+    find_library(OpenCL_LIBRARY
+      NAMES OpenCL
+      PATHS
+        ENV AMDAPPSDKROOT
+        ENV CUDA_PATH
+        /usr/local/cuda
+        /opt/cuda
+      PATH_SUFFIXES
+        lib/x86
+        lib)
+  elseif(CMAKE_SIZEOF_VOID_P EQUAL 8)
+    find_library(OpenCL_LIBRARY
+      NAMES OpenCL
+      PATHS
+        ENV AMDAPPSDKROOT
+        ENV CUDA_PATH
+        /usr/local/cuda
+        /opt/cuda
+      PATH_SUFFIXES
+        lib/x86_64
+        lib/x64
+        lib
+        lib64)
+  endif()
 endif()
 
 set(OpenCL_LIBRARIES ${OpenCL_LIBRARY})
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index cf31204415..d82e00d7d5 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -18,7 +18,112 @@ generate_product_version(af_opencl_ver_res_file
   FILE_DESCRIPTION "OpenCL Backend Dynamic-link library"
 )
 
-file(GLOB kernel_src kernel/*.cl kernel/KParam.hpp)
+set(kernel_src
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/KParam.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/anisotropic_diffusion.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/approx1.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/approx2.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/assign.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/bilateral.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/convolve_separable.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/coo2dense.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/copy.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/cscmm.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/cscmv.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/csr2coo.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/csr2dense.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/csrmm.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/csrmv.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/dense2csr.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/diag_create.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/diag_extract.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/diff.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/example.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/fast.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/fftconvolve_multiply.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/fftconvolve_pack.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/fftconvolve_reorder.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/flood_fill.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/gradient.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/harris.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/histogram.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/homography.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/hsv_rgb.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/identity.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/iir.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/index.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/interp.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/iops.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/iota.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/ireduce_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/ireduce_first.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/jit.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/laset_band.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/laset.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/laswp.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/lookup.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/lu_split.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/matchTemplate.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/mean_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/mean_first.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/mean_ops.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/meanshift.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/medfilt1.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/medfilt2.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/memcopy.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/moments.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/morph.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/nearest_neighbour.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/nonmax_suppression.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/ops.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/orb.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/pad_array_borders.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/random_engine_mersenne.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/random_engine_mersenne_init.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/random_engine_philox.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/random_engine_threefry.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/random_engine_write.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/range.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_all.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_blocks_by_key_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_blocks_by_key_first.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_by_key_boundary.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_by_key_boundary_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_by_key_compact.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_by_key_compact_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_by_key_needs_reduction.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reduce_first.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/regions.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/reorder.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/resize.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/rotate.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim_by_key.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_dim.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first_by_key.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/scan_first.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/select.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sift_nonfree.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sobel.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sparse_arith_common.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sparse_arith_coo.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sparse_arith_csr.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/sp_sp_arith_csr.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/ssarith_calc_out_nnz.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/susan.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/swapdblk.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/tile.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/trace_edge.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transform.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/transpose_inplace.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/triangle.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/unwrap.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/where.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/wrap.cl
+  ${CMAKE_CURRENT_SOURCE_DIR}/kernel/wrap_dilated.cl
+)
 
 set( kernel_headers_dir "kernel_headers")
 
@@ -32,11 +137,10 @@ file_to_string(
     )
 
 set(opencl_compile_definitions
-        CL_TARGET_OPENCL_VERSION=120
-        CL_HPP_TARGET_OPENCL_VERSION=120
-        CL_HPP_MINIMUM_OPENCL_VERSION=120
-        CL_HPP_ENABLE_EXCEPTIONS
-        CL_USE_DEPRECATED_OPENCL_1_2_APIS)
+        CL_TARGET_OPENCL_VERSION=300
+        CL_HPP_TARGET_OPENCL_VERSION=300
+        CL_HPP_MINIMUM_OPENCL_VERSION=300
+        CL_HPP_ENABLE_EXCEPTIONS)
 
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/sort_by_key/CMakeLists.txt)
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 32ea5809f5..03fd41a196 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -108,15 +108,7 @@ Program buildProgram(span<const string> kernelSources,
                      span<const string> compileOpts) {
     Program retVal;
     try {
-        static const string defaults =
-            string(" -D dim_t=") + string(dtype_traits<dim_t>::getName());
-
         auto device = getDevice();
-
-        const string cl_std =
-            string(" -cl-std=CL") +
-            device.getInfo<CL_DEVICE_OPENCL_C_VERSION>().substr(9, 3);
-
         Program::Sources sources;
         sources.emplace_back(DEFAULT_MACROS_STR);
         sources.emplace_back(KParam_hpp, KParam_hpp_len);
@@ -126,12 +118,8 @@ Program buildProgram(span<const string> kernelSources,
 
         ostringstream options;
         for (auto &opt : compileOpts) { options << opt; }
-
-#ifdef AF_WITH_FAST_MATH
-        options << " -cl-fast-relaxed-math -DAF_WITH_FAST_MATH";
-#endif
-
-        retVal.build({device}, (cl_std + defaults + options.str()).c_str());
+        options << getActiveDeviceBaseBuildFlags();
+        retVal.build({device}, (options.str()).c_str());
     } catch (Error &err) {
         if (err.err() == CL_BUILD_PROGRAM_FAILURE) {
             THROW_BUILD_LOG_EXCEPTION(retVal);
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index c1fa920a97..2befa70744 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -49,6 +49,8 @@ using std::begin;
 using std::end;
 using std::find;
 using std::make_unique;
+using std::ostringstream;
+using std::sort;
 using std::string;
 using std::stringstream;
 using std::unique_ptr;
@@ -99,13 +101,6 @@ static inline bool compare_default(const unique_ptr<Device>& ldev,
         if (!is_l_curr_type && is_r_curr_type) { return false; }
     }
 
-    // For GPUs, this ensures discrete > integrated
-    auto is_l_integrated = ldev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-    auto is_r_integrated = rdev->getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-
-    if (!is_l_integrated && is_r_integrated) { return true; }
-    if (is_l_integrated && !is_r_integrated) { return false; }
-
     // At this point, the devices are of same type.
     // Sort based on emperical evidence of preferred platforms
 
@@ -263,6 +258,24 @@ DeviceManager::DeviceManager()
             mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
             mPlatforms.push_back(getPlatformEnum(*devices[i]));
             mDevices.emplace_back(std::move(devices[i]));
+
+            auto device_versions =
+                mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
+            sort(begin(device_versions), end(device_versions),
+                 [](const auto& lhs, const auto& rhs) {
+                     return lhs.version < rhs.version;
+                 });
+            cl_name_version max_version = device_versions.back();
+            ostringstream options;
+            options << fmt::format(" -cl-std=CL{}.{}",
+                                   CL_VERSION_MAJOR(max_version.version),
+                                   CL_VERSION_MINOR(max_version.version))
+                    << fmt::format(" -D dim_t={}",
+                                   dtype_traits<dim_t>::getName());
+#ifdef AF_WITH_FAST_MATH
+            options << " -cl-fast-relaxed-math";
+#endif
+            mBaseBuildFlags.push_back(options.str());
         } catch (const cl::Error& err) {
             AF_TRACE("Error creating context for device {} with error {}\n",
                      devices[i]->getInfo<CL_DEVICE_NAME>(), err.what());
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 8789675fe2..cce238533c 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -107,6 +107,8 @@ class DeviceManager {
 
     friend const cl::Device& getDevice(int id);
 
+    friend const std::string& getActiveDeviceBaseBuildFlags();
+
     friend size_t getDeviceMemorySize(int device);
 
     friend bool isGLSharingSupported();
@@ -161,6 +163,7 @@ class DeviceManager {
     std::vector<std::unique_ptr<cl::Context>> mContexts;
     std::vector<std::unique_ptr<cl::CommandQueue>> mQueues;
     std::vector<bool> mIsGLSharingOn;
+    std::vector<std::string> mBaseBuildFlags;
     std::vector<int> mDeviceTypes;
     std::vector<int> mPlatforms;
     unsigned mUserDeviceOffset;
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
index 5889288f82..e473244152 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -9,7 +9,7 @@
 
 // Starting from OpenCL 2.0, core profile includes work group level
 // inclusive scan operations, hence skip defining custom one
-#if __OPENCL_VERSION__ < 200
+#if !__opencl_c_work_group_collective_functions
 int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
     local int *active_buf;
 
@@ -29,7 +29,7 @@ int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
     int res = active_buf[lid];
     return res;
 }
-#endif  // __OPENCL_VERSION__ < 200
+#endif
 
 kernel void reduce_blocks_by_key_first(
     global int *reduced_block_sizes, __global Tk *oKeys, KParam oKInfo,
@@ -48,7 +48,7 @@ kernel void reduce_blocks_by_key_first(
     local Tk reduced_keys[DIMX];
     local To reduced_vals[DIMX];
     local int unique_ids[DIMX];
-#if __OPENCL_VERSION__ < 200
+#if !__opencl_c_work_group_collective_functions
     local int wg_temp[DIMX];
     local int unique_flags[DIMX];
 #endif
@@ -84,11 +84,11 @@ kernel void reduce_blocks_by_key_first(
     int eq_check    = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
     int unique_flag = (eq_check || (lid == 0)) && (gid < n);
 
-#if __OPENCL_VERSION__ < 200
+#if __opencl_c_work_group_collective_functions
+    int unique_id = work_group_scan_inclusive_add(unique_flag);
+#else
     unique_flags[lid] = unique_flag;
     int unique_id     = work_group_scan_inclusive_add(wg_temp, unique_flags);
-#else
-    int unique_id = work_group_scan_inclusive_add(unique_flag);
 #endif
     unique_ids[lid] = unique_id;
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index c040c04b09..26476b2057 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -174,8 +174,6 @@ string getDeviceInfo() noexcept {
                             0
                         ? "True"
                         : "False");
-            info << " -- Unified Memory ("
-                 << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif
             info << endl;
 
@@ -297,6 +295,14 @@ const cl::Device& getDevice(int id) {
     return *(devMngr.mDevices[id]);
 }
 
+const std::string& getActiveDeviceBaseBuildFlags() {
+    device_id_t& devId     = tlocalActiveDeviceId();
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+    return devMngr.mBaseBuildFlags[get<1>(devId)];
+}
+
 size_t getDeviceMemorySize(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -321,7 +327,7 @@ cl_device_type getDeviceType() {
 bool OpenCLCPUOffload(bool forceOffloadOSX) {
     static const bool offloadEnv = getEnvVar("AF_OPENCL_CPU_OFFLOAD") != "0";
     bool offload                 = false;
-    if (offloadEnv) { offload = isHostUnifiedMemory(getDevice()); }
+    if (offloadEnv) { offload = getDeviceType() == CL_DEVICE_TYPE_CPU; }
 #if OS_MAC
     // FORCED OFFLOAD FOR LAPACK FUNCTIONS ON OSX UNIFIED MEMORY DEVICES
     //
@@ -331,11 +337,9 @@ bool OpenCLCPUOffload(bool forceOffloadOSX) {
     // variable inconsequential to the returned result.
     //
     // Issue https://github.com/arrayfire/arrayfire/issues/662
-    //
-    // Make sure device has unified memory
-    bool osx_offload = isHostUnifiedMemory(getDevice());
     // Force condition
-    offload = osx_offload && (offload || forceOffloadOSX);
+    bool osx_offload = getDeviceType() == CL_DEVICE_TYPE_CPU;
+    offload          = osx_offload && (offload || forceOffloadOSX);
 #else
     UNUSED(forceOffloadOSX);
 #endif
@@ -475,6 +479,23 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
         devMngr.mQueues.push_back(move(tQueue));
         nDevices = static_cast<int>(devMngr.mDevices.size()) - 1;
 
+        auto device_versions =
+            devMngr.mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
+        sort(begin(device_versions), end(device_versions),
+             [](const auto& lhs, const auto& rhs) {
+                 return lhs.version < rhs.version;
+             });
+        cl_name_version max_version = device_versions.back();
+        ostringstream options;
+        options << fmt::format(" -cl-std=CL{}.{}",
+                               CL_VERSION_MAJOR(max_version.version),
+                               CL_VERSION_MINOR(max_version.version))
+                << fmt::format(" -D dim_t={}", dtype_traits<dim_t>::getName());
+#ifdef AF_WITH_FAST_MATH
+        options << " -cl-fast-relaxed-math";
+#endif
+        devMngr.mBaseBuildFlags.push_back(options.str());
+
         // cache the boost program_cache object, clean up done on program exit
         // not during removeDeviceContext
         namespace compute = boost::compute;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 07eca8f856..dba60388f7 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -67,6 +67,8 @@ cl::CommandQueue& getQueue();
 
 const cl::Device& getDevice(int id = -1);
 
+const std::string& getActiveDeviceBaseBuildFlags();
+
 size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
@@ -108,10 +110,6 @@ inline unsigned getMaxParallelThreads(const cl::Device& device) {
 
 cl_device_type getDeviceType();
 
-inline bool isHostUnifiedMemory(const cl::Device& device) {
-    return device.getInfo<CL_DEVICE_HOST_UNIFIED_MEMORY>();
-}
-
 bool OpenCLCPUOffload(bool forceOffloadOSX = true);
 
 bool isGLSharingSupported();

From 61cd88345fdac94eadedf18962ffada8593d00b1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 Nov 2022 03:43:16 -0500
Subject: [PATCH 2379/2677] Fix some errors due to pow in CUDA code with fast
 math

---
 .../cuda/kernel/anisotropic_diffusion.cuh     | 35 ++++++++++---------
 src/backend/cuda/kernel/jit.cuh               |  2 +-
 src/backend/cuda/kernel/susan.cuh             |  2 +-
 src/backend/cuda/math.hpp                     | 13 +++++++
 4 files changed, 33 insertions(+), 19 deletions(-)

diff --git a/src/backend/cuda/kernel/anisotropic_diffusion.cuh b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
index cd393474aa..8b108b434d 100644
--- a/src/backend/cuda/kernel/anisotropic_diffusion.cuh
+++ b/src/backend/cuda/kernel/anisotropic_diffusion.cuh
@@ -19,7 +19,8 @@ __forceinline__ __device__ int index(const int x, const int y, const int dim0,
     return clamp(x, 0, dim0 - 1) * stride0 + clamp(y, 0, dim1 - 1) * stride1;
 }
 
-__device__ float quadratic(const float value) { return 1.0 / (1.0 + value); }
+__device__
+float quadratic(const float value) { return 1.0f / (1.0f + value); }
 
 template<af_flux_function FluxEnum>
 __device__ float gradientUpdate(const float mct, const float C, const float S,
@@ -39,13 +40,13 @@ __device__ float gradientUpdate(const float mct, const float C, const float S,
     db = C - W;
 
     if (FluxEnum == AF_FLUX_EXPONENTIAL) {
-        cx  = expf((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
-        cxd = expf((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
+        cx  = expf((df * df + 0.25f * afpowf(dy + 0.5f * (SE - NE), 2)) * mct);
+        cxd = expf((db * db + 0.25f * afpowf(dy + 0.5f * (SW - NW), 2)) * mct);
     } else {
         cx =
-            quadratic((df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2)) * mct);
+            quadratic((df * df + 0.25f * afpowf(dy + 0.5f * (SE - NE), 2)) * mct);
         cxd =
-            quadratic((db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2)) * mct);
+            quadratic((db * db + 0.25f * afpowf(dy + 0.5f * (SW - NW), 2)) * mct);
     }
     delta += (cx * df - cxd * db);
 
@@ -54,13 +55,13 @@ __device__ float gradientUpdate(const float mct, const float C, const float S,
     db = C - N;
 
     if (FluxEnum == AF_FLUX_EXPONENTIAL) {
-        cx  = expf((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
-        cxd = expf((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
+        cx  = expf((df * df + 0.25f * afpowf(dx + 0.5f * (SE - SW), 2)) * mct);
+        cxd = expf((db * db + 0.25f * afpowf(dx + 0.5f * (NE - NW), 2)) * mct);
     } else {
         cx =
-            quadratic((df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2)) * mct);
+            quadratic((df * df + 0.25f * afpowf(dx + 0.5f * (SE - SW), 2)) * mct);
         cxd =
-            quadratic((db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2)) * mct);
+            quadratic((db * db + 0.25f * afpowf(dx + 0.5f * (NE - NW), 2)) * mct);
     }
     delta += (cx * df - cxd * db);
 
@@ -87,8 +88,8 @@ __device__ float curvatureUpdate(const float mct, const float C, const float S,
     df0 = df;
     db0 = db;
 
-    gmsqf = (df * df + 0.25f * powf(dy + 0.5f * (SE - NE), 2));
-    gmsqb = (db * db + 0.25f * powf(dy + 0.5f * (SW - NW), 2));
+    gmsqf = (df * df + 0.25f * afpowf(dy + 0.5f * (SE - NE), 2));
+    gmsqb = (db * db + 0.25f * afpowf(dy + 0.5f * (SW - NW), 2));
 
     gmf = sqrtf(1.0e-10 + gmsqf);
     gmb = sqrtf(1.0e-10 + gmsqb);
@@ -102,8 +103,8 @@ __device__ float curvatureUpdate(const float mct, const float C, const float S,
     df = S - C;
     db = C - N;
 
-    gmsqf = (df * df + 0.25f * powf(dx + 0.5f * (SE - SW), 2));
-    gmsqb = (db * db + 0.25f * powf(dx + 0.5f * (NE - NW), 2));
+    gmsqf = (df * df + 0.25f * afpowf(dx + 0.5f * (SE - SW), 2));
+    gmsqb = (db * db + 0.25f * afpowf(dx + 0.5f * (NE - NW), 2));
     gmf   = sqrtf(1.0e-10 + gmsqf);
     gmb   = sqrtf(1.0e-10 + gmsqb);
 
@@ -114,14 +115,14 @@ __device__ float curvatureUpdate(const float mct, const float C, const float S,
 
     if (delta > 0) {
         prop_grad +=
-            (powf(fminf(db0, 0.0f), 2.0f) + powf(fmaxf(df0, 0.0f), 2.0f));
+            (afpowf(fminf(db0, 0.0f), 2.0f) + afpowf(fmaxf(df0, 0.0f), 2.0f));
         prop_grad +=
-            (powf(fminf(db, 0.0f), 2.0f) + powf(fmaxf(df, 0.0f), 2.0f));
+            (afpowf(fminf(db, 0.0f), 2.0f) + afpowf(fmaxf(df, 0.0f), 2.0f));
     } else {
         prop_grad +=
-            (powf(fmaxf(db0, 0.0f), 2.0f) + powf(fminf(df0, 0.0f), 2.0f));
+            (afpowf(fmaxf(db0, 0.0f), 2.0f) + afpowf(fminf(df0, 0.0f), 2.0f));
         prop_grad +=
-            (powf(fmaxf(db, 0.0f), 2.0f) + powf(fminf(df, 0.0f), 2.0f));
+            (afpowf(fmaxf(db, 0.0f), 2.0f) + afpowf(fminf(df, 0.0f), 2.0f));
     }
 
     return sqrtf(prop_grad) * delta;
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 3d66c02f24..cfb5837719 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -65,7 +65,7 @@ typedef cuDoubleComplex cdouble;
         pow(static_cast<double>(lhs), static_cast<double>(rhs)));
 #else
 #define __pow(lhs, rhs) \
-    __float2int_rn(pow(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
+    __float2int_rn(powf(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
 #endif
 #define __powll(lhs, rhs) \
     __double2ll_rn(pow(__ll2double_rn(lhs), __ll2double_rn(rhs)))
diff --git a/src/backend/cuda/kernel/susan.cuh b/src/backend/cuda/kernel/susan.cuh
index e2a706e000..5bb7f28805 100644
--- a/src/backend/cuda/kernel/susan.cuh
+++ b/src/backend/cuda/kernel/susan.cuh
@@ -73,7 +73,7 @@ __global__ void susan(T* out, const T* in, const unsigned idim0,
                 if (i * i + j * j < rSqrd) {
                     float c       = m_0;
                     float m       = shrdMem[b * shrdLen + a];
-                    float exp_pow = powf((m - c) / t, 6.0f);
+                    float exp_pow = afpowf((m - c) / t, 6.0f);
                     float cM      = expf(-exp_pow);
                     nM += cM;
                 }
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index f988372d27..3562565a86 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -392,6 +392,19 @@ template<typename T>
 constexpr const __DH__ T clamp(const T value, const T lo, const T hi) {
     return clamp(value, lo, hi, [](auto lhs, auto rhs) { return lhs < rhs; });
 }
+
+#ifdef AF_WITH_FAST_MATH
+/// The pow function with fast math is constantly wrong with fast math
+/// so this function converts the operation to double when fast-math
+/// is used
+__device__ inline double afpowf(double x, double y) { return pow(x, y); }
+#else
+/// The pow function with fast math is constantly wrong with fast math
+/// so this function converts the operation to double when fast-math
+/// is used
+__device__ inline float afpowf(float x, float y) { return powf(x, y); }
+#endif
+
 }  // namespace cuda
 }  // namespace arrayfire
 

From 49a73f3e6fa021548a403e2d5fe2a30433643ca0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 30 Nov 2022 21:00:47 -0500
Subject: [PATCH 2380/2677] Add OpenCL version def to af/opencl.h. Remove
 FindOpenCL from test

---
 include/af/opencl.h                |   3 +
 test/CMakeModules/FindOpenCL.cmake | 190 -----------------------------
 2 files changed, 3 insertions(+), 190 deletions(-)
 delete mode 100644 test/CMakeModules/FindOpenCL.cmake

diff --git a/include/af/opencl.h b/include/af/opencl.h
index 27cc73e181..d055804d6d 100644
--- a/include/af/opencl.h
+++ b/include/af/opencl.h
@@ -8,6 +8,9 @@
  ********************************************************/
 
 #pragma once
+#ifndef CL_TARGET_OPENCL_VERSION
+#define CL_TARGET_OPENCL_VERSION 120
+#endif
 #if defined(__APPLE__) || defined(__MACOSX)
 #include <OpenCL/cl.h>
 #else
diff --git a/test/CMakeModules/FindOpenCL.cmake b/test/CMakeModules/FindOpenCL.cmake
deleted file mode 100644
index 4d4ef57bc3..0000000000
--- a/test/CMakeModules/FindOpenCL.cmake
+++ /dev/null
@@ -1,190 +0,0 @@
-#.rst:
-# FindOpenCL
-# ----------
-#
-# Try to find OpenCL
-#
-# Once done this will define::
-#
-#   OpenCL_FOUND          - True if OpenCL was found
-#   OpenCL_INCLUDE_DIRS   - include directories for OpenCL
-#   OpenCL_LIBRARIES      - link against this library to use OpenCL
-#   OpenCL_VERSION_STRING - Highest supported OpenCL version (eg. 1.2)
-#   OpenCL_VERSION_MAJOR  - The major version of the OpenCL implementation
-#   OpenCL_VERSION_MINOR  - The minor version of the OpenCL implementation
-#
-# The module will also define two cache variables::
-#
-#   OpenCL_INCLUDE_DIR    - the OpenCL include directory
-#   OpenCL_LIBRARY        - the path to the OpenCL library
-#
-
-#=============================================================================
-# From CMake 3.2
-# Copyright 2014 Matthaeus G. Chajdas
-#
-# Distributed under the OSI-approved BSD License (the "License");
-# see accompanying file Copyright.txt for details.
-#
-# This software is distributed WITHOUT ANY WARRANTY; without even the
-# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the License for more information.
-
-# CMake - Cross Platform Makefile Generator
-# Copyright 2000-2014 Kitware, Inc.
-# Copyright 2000-2011 Insight Software Consortium
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-#
-# * Neither the names of Kitware, Inc., the Insight Software Consortium,
-# nor the names of their contributors may be used to endorse or promote
-# products derived from this software without specific prior written
-# permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#=============================================================================
-
-function(_FIND_OPENCL_VERSION)
-  include(CheckSymbolExists)
-  include(CMakePushCheckState)
-  set(CMAKE_REQUIRED_QUIET ${OpenCL_FIND_QUIETLY})
-
-  CMAKE_PUSH_CHECK_STATE()
-  foreach(VERSION "2_0" "1_2" "1_1" "1_0")
-    set(CMAKE_REQUIRED_INCLUDES "${OpenCL_INCLUDE_DIR}")
-    if(APPLE)
-      CHECK_SYMBOL_EXISTS(
-        CL_VERSION_${VERSION}
-        "${OpenCL_INCLUDE_DIR}/OpenCL/cl.h"
-        OPENCL_VERSION_${VERSION})
-    else()
-      CHECK_SYMBOL_EXISTS(
-        CL_VERSION_${VERSION}
-        "${OpenCL_INCLUDE_DIR}/CL/cl.h"
-        OPENCL_VERSION_${VERSION})
-    endif()
-
-    if(OPENCL_VERSION_${VERSION})
-      string(REPLACE "_" "." VERSION "${VERSION}")
-      set(OpenCL_VERSION_STRING ${VERSION} PARENT_SCOPE)
-      string(REGEX MATCHALL "[0-9]+" version_components "${VERSION}")
-      list(GET version_components 0 major_version)
-      list(GET version_components 1 minor_version)
-      set(OpenCL_VERSION_MAJOR ${major_version} PARENT_SCOPE)
-      set(OpenCL_VERSION_MINOR ${minor_version} PARENT_SCOPE)
-      break()
-    endif()
-  endforeach()
-  CMAKE_POP_CHECK_STATE()
-endfunction()
-
-find_path(OpenCL_INCLUDE_DIR
-  NAMES
-    CL/cl.h OpenCL/cl.h
-  PATHS
-    ENV "PROGRAMFILES(X86)"
-    ENV NVSDKCOMPUTE_ROOT
-    ENV CUDA_PATH
-    ENV AMDAPPSDKROOT
-    ENV INTELOCLSDKROOT
-    ENV ATISTREAMSDKROOT
-  PATH_SUFFIXES
-    include
-    OpenCL/common/inc
-    "AMD APP/include")
-
-_FIND_OPENCL_VERSION()
-
-if(WIN32)
-  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
-    find_library(OpenCL_LIBRARY
-      NAMES OpenCL
-      PATHS
-        ENV "PROGRAMFILES(X86)"
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV ATISTREAMSDKROOT
-      PATH_SUFFIXES
-        "AMD APP/lib/x86"
-        lib/x86
-        lib/Win32
-        OpenCL/common/lib/Win32)
-  elseif(CMAKE_SIZEOF_VOID_P EQUAL 8)
-    find_library(OpenCL_LIBRARY
-      NAMES OpenCL
-      PATHS
-        ENV "PROGRAMFILES(X86)"
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV ATISTREAMSDKROOT
-      PATH_SUFFIXES
-        "AMD APP/lib/x86_64"
-        lib/x86_64
-        lib/x64
-        OpenCL/common/lib/x64)
-  endif()
-else()
-  find_library(OpenCL_LIBRARY
-    NAMES OpenCL
-    PATHS
-        ENV LD_LIBRARY_PATH
-        ENV AMDAPPSDKROOT
-        ENV INTELOCLSDKROOT
-        ENV CUDA_PATH
-        ENV NVSDKCOMPUTE_ROOT
-        ENV ATISTREAMSDKROOT
-        /usr/lib64
-        /usr/lib
-        /usr/local/lib64
-        /usr/local/lib
-        /sw/lib
-        /opt/local/lib
-    PATH_SUFFIXES
-        "AMD APP/lib/x86_64"
-        lib/x86_64
-        lib/x64
-        lib/
-        lib64/
-        x86_64-linux-gnu
-        arm-linux-gnueabihf
-    )
-endif()
-
-set(OpenCL_LIBRARIES ${OpenCL_LIBRARY})
-set(OpenCL_INCLUDE_DIRS ${OpenCL_INCLUDE_DIR})
-
-#include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
-find_package_handle_standard_args(
-  OpenCL
-  FOUND_VAR OpenCL_FOUND
-  REQUIRED_VARS OpenCL_LIBRARY OpenCL_INCLUDE_DIR
-  VERSION_VAR OpenCL_VERSION_STRING)
-
-mark_as_advanced(
-  OpenCL_INCLUDE_DIR
-  OpenCL_LIBRARY)
-

From 433348e598296018605f27ea5e43c31448d8f9f8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 2 Dec 2022 18:37:50 -0500
Subject: [PATCH 2381/2677] Allow OpenCL C device version checks on older
 platforms

---
 CMakeLists.txt                           |  2 +-
 src/backend/opencl/Array.cpp             |  2 +-
 src/backend/opencl/CMakeLists.txt        |  2 +-
 src/backend/opencl/device_manager.cpp    | 36 +++++++++++------
 src/backend/opencl/device_manager.hpp    |  9 ++++-
 src/backend/opencl/kernel/flood_fill.hpp |  4 +-
 src/backend/opencl/magma/getrs.cpp       |  3 +-
 src/backend/opencl/platform.cpp          | 51 +++++++++++++++++++-----
 src/backend/opencl/platform.hpp          |  4 +-
 src/backend/opencl/solve.cpp             |  4 +-
 10 files changed, 85 insertions(+), 32 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2fb83beed9..8985c797ff 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -50,7 +50,7 @@ set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for
 
 find_package(CUDA 10.2)
 find_package(cuDNN 4.0)
-find_package(OpenCL 3.0)
+find_package(OpenCL 1.2)
 find_package(OpenGL)
 find_package(glad CONFIG QUIET)
 find_package(FreeImage)
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 225e9686ac..811f5551e3 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -308,7 +308,7 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     }
 
     bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
-    auto platform      = getActivePlatform();
+    auto platform      = getActivePlatformVendor();
 
     // The Apple platform can have the nvidia card or the AMD card
     bool isIntel = platform == AFCL_PLATFORM_INTEL;
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index d82e00d7d5..d79cc95705 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -139,7 +139,7 @@ file_to_string(
 set(opencl_compile_definitions
         CL_TARGET_OPENCL_VERSION=300
         CL_HPP_TARGET_OPENCL_VERSION=300
-        CL_HPP_MINIMUM_OPENCL_VERSION=300
+        CL_HPP_MINIMUM_OPENCL_VERSION=110
         CL_HPP_ENABLE_EXCEPTIONS)
 
 include(kernel/scan_by_key/CMakeLists.txt)
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 2befa70744..69a0da4f2c 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -256,21 +256,33 @@ DeviceManager::DeviceManager()
                 *mContexts.back(), *devices[i], cl::QueueProperties::None));
             mIsGLSharingOn.push_back(false);
             mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
-            mPlatforms.push_back(getPlatformEnum(*devices[i]));
+            mPlatforms.push_back(
+                std::make_pair<std::unique_ptr<cl::Platform>, afcl_platform>(
+                    make_unique<cl::Platform>(device_platform, true),
+                    getPlatformEnum(*devices[i])));
             mDevices.emplace_back(std::move(devices[i]));
 
-            auto device_versions =
-                mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
-            sort(begin(device_versions), end(device_versions),
-                 [](const auto& lhs, const auto& rhs) {
-                     return lhs.version < rhs.version;
-                 });
-            cl_name_version max_version = device_versions.back();
+            auto platform_version =
+                mPlatforms.back().first->getInfo<CL_PLATFORM_VERSION>();
             ostringstream options;
-            options << fmt::format(" -cl-std=CL{}.{}",
-                                   CL_VERSION_MAJOR(max_version.version),
-                                   CL_VERSION_MINOR(max_version.version))
-                    << fmt::format(" -D dim_t={}",
+            if (platform_version.substr(7).c_str()[0] >= '3') {
+                auto device_versions =
+                    mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
+                sort(begin(device_versions), end(device_versions),
+                     [](const auto& lhs, const auto& rhs) {
+                         return lhs.version < rhs.version;
+                     });
+                cl_name_version max_version = device_versions.back();
+                options << fmt::format(" -cl-std=CL{}.{}",
+                                       CL_VERSION_MAJOR(max_version.version),
+                                       CL_VERSION_MINOR(max_version.version));
+            } else {
+                auto device_version =
+                    mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_VERSION>();
+                options << fmt::format(" -cl-std=CL{}",
+                                       device_version.substr(9, 3));
+            }
+            options << fmt::format(" -D dim_t={}",
                                    dtype_traits<dim_t>::getName());
 #ifdef AF_WITH_FAST_MATH
             options << " -cl-fast-relaxed-math";
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index cce238533c..4e06582da3 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -9,6 +9,8 @@
 
 #pragma once
 
+#include <af/opencl.h>
+
 #include <memory>
 #include <mutex>
 #include <string>
@@ -131,7 +133,9 @@ class DeviceManager {
 
     friend int getActiveDeviceType();
 
-    friend int getActivePlatform();
+    friend cl::Platform& getActivePlatform();
+
+    friend afcl::platform getActivePlatformVendor();
 
    public:
     static const int MAX_DEVICES = 32;
@@ -165,7 +169,8 @@ class DeviceManager {
     std::vector<bool> mIsGLSharingOn;
     std::vector<std::string> mBaseBuildFlags;
     std::vector<int> mDeviceTypes;
-    std::vector<int> mPlatforms;
+    std::vector<std::pair<std::unique_ptr<cl::Platform>, afcl::platform>>
+        mPlatforms;
     unsigned mUserDeviceOffset;
 
     std::unique_ptr<arrayfire::common::ForgeManager> fgMngr;
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 0b0b29fefe..793ae5adcd 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -84,8 +84,8 @@ void floodFill(Param out, const Param image, const Param seedsx,
         DefineKeyValue(LMEM_WIDTH, (THREADS_X + 2 * RADIUS)),
         DefineKeyValue(LMEM_HEIGHT, (THREADS_Y + 2 * RADIUS)),
         DefineKeyValue(GROUP_SIZE, (THREADS_Y * THREADS_X)),
-        DefineKeyValue(AF_IS_PLATFORM_NVIDIA,
-                       (int)(AFCL_PLATFORM_NVIDIA == getActivePlatform())),
+        DefineKeyValue(AF_IS_PLATFORM_NVIDIA, (int)(AFCL_PLATFORM_NVIDIA ==
+                                                    getActivePlatformVendor())),
         getTypeBuildDefinition<T>()};
 
     auto floodStep =
diff --git a/src/backend/opencl/magma/getrs.cpp b/src/backend/opencl/magma/getrs.cpp
index a689408a26..d945fa9def 100644
--- a/src/backend/opencl/magma/getrs.cpp
+++ b/src/backend/opencl/magma/getrs.cpp
@@ -165,7 +165,8 @@ magma_int_t magma_getrs_gpu(magma_trans_t trans, magma_int_t n,
             : (trans == MagmaTrans ? OPENCL_BLAS_TRANS
                                    : OPENCL_BLAS_CONJ_TRANS);
 
-    bool cond  = arrayfire::opencl::getActivePlatform() == AFCL_PLATFORM_NVIDIA;
+    bool cond =
+        arrayfire::opencl::getActivePlatformVendor() == AFCL_PLATFORM_NVIDIA;
     cl_mem dAT = 0;
     if (nrhs > 1 && cond) {
         magma_malloc<Ty>(&dAT, n * n);
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 26476b2057..ee2f1b83c6 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -255,15 +255,26 @@ int getActiveDeviceType() {
     return devMngr.mDeviceTypes[get<1>(devId)];
 }
 
-int getActivePlatform() {
+cl::Platform& getActivePlatform() {
     device_id_t& devId = tlocalActiveDeviceId();
 
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
 
-    return devMngr.mPlatforms[get<1>(devId)];
+    return *devMngr.mPlatforms[get<1>(devId)].first;
 }
+
+afcl::platform getActivePlatformVendor() {
+    device_id_t& devId = tlocalActiveDeviceId();
+
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mPlatforms[get<1>(devId)].second;
+}
+
 const Context& getContext() {
     device_id_t& devId = tlocalActiveDeviceId();
 
@@ -468,12 +479,17 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
         auto tQueue =
             (que == NULL ? make_unique<cl::CommandQueue>(*tContext, *tDevice)
                          : make_unique<cl::CommandQueue>(que, true));
-        devMngr.mPlatforms.push_back(getPlatformEnum(*tDevice));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
         devMngr.mDeviceTypes.push_back(
             static_cast<int>(tDevice->getInfo<CL_DEVICE_TYPE>()));
 
+        auto device_platform = tDevice->getInfo<CL_DEVICE_PLATFORM>();
+        devMngr.mPlatforms.push_back(
+            std::make_pair<std::unique_ptr<cl::Platform>, afcl_platform>(
+                make_unique<cl::Platform>(device_platform, true),
+                getPlatformEnum(*tDevice)));
+
         devMngr.mDevices.push_back(move(tDevice));
         devMngr.mContexts.push_back(move(tContext));
         devMngr.mQueues.push_back(move(tQueue));
@@ -485,12 +501,29 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
              [](const auto& lhs, const auto& rhs) {
                  return lhs.version < rhs.version;
              });
-        cl_name_version max_version = device_versions.back();
+
+        auto platform_version =
+            devMngr.mPlatforms.back().first->getInfo<CL_PLATFORM_VERSION>();
         ostringstream options;
-        options << fmt::format(" -cl-std=CL{}.{}",
-                               CL_VERSION_MAJOR(max_version.version),
-                               CL_VERSION_MINOR(max_version.version))
-                << fmt::format(" -D dim_t={}", dtype_traits<dim_t>::getName());
+        if (platform_version.substr(7).c_str()[0] >= '3') {
+            auto device_versions =
+                devMngr.mDevices.back()
+                    ->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
+            sort(begin(device_versions), end(device_versions),
+                 [](const auto& lhs, const auto& rhs) {
+                     return lhs.version < rhs.version;
+                 });
+            cl_name_version max_version = device_versions.back();
+            options << fmt::format(" -cl-std=CL{}.{}",
+                                   CL_VERSION_MAJOR(max_version.version),
+                                   CL_VERSION_MINOR(max_version.version));
+        } else {
+            auto device_version =
+                devMngr.mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_VERSION>();
+            options << fmt::format(" -cl-std=CL{}",
+                                   device_version.substr(9, 3));
+        }
+        options << fmt::format(" -D dim_t={}", dtype_traits<dim_t>::getName());
 #ifdef AF_WITH_FAST_MATH
         options << " -cl-fast-relaxed-math";
 #endif
@@ -706,7 +739,7 @@ af_err afcl_get_device_type(afcl_device_type* res) {
 
 af_err afcl_get_platform(afcl_platform* res) {
     try {
-        *res = static_cast<afcl_platform>(getActivePlatform());
+        *res = static_cast<afcl_platform>(getActivePlatformVendor());
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index dba60388f7..c7099bf818 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -147,7 +147,9 @@ bool synchronize_calls();
 
 int getActiveDeviceType();
 
-int getActivePlatform();
+cl::Platform& getActivePlatform();
+
+afcl::platform getActivePlatformVendor();
 
 bool& evalFlag();
 
diff --git a/src/backend/opencl/solve.cpp b/src/backend/opencl/solve.cpp
index 60d8f3a59b..e6e7aa99ea 100644
--- a/src/backend/opencl/solve.cpp
+++ b/src/backend/opencl/solve.cpp
@@ -230,7 +230,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
                               A.strides()[1], 1, (*dT)(),
                               tmp.getOffset() + NB * MN, NB, 0, queue);
 
-        if (getActivePlatform() == AFCL_PLATFORM_NVIDIA) {
+        if (getActivePlatformVendor() == AFCL_PLATFORM_NVIDIA) {
             Array<T> AT    = transpose<T>(A, true);
             Buffer *AT_buf = AT.get();
             OPENCL_BLAS_CHECK(gpu_blas_trsm(
@@ -269,7 +269,7 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
     cl_event event         = 0;
     cl_command_queue queue = getQueue()();
 
-    if (getActivePlatform() == AFCL_PLATFORM_NVIDIA &&
+    if (getActivePlatformVendor() == AFCL_PLATFORM_NVIDIA &&
         (options & AF_MAT_UPPER)) {
         Array<T> AT = transpose<T>(A, true);
 

From dcffa51a89377029816adf70ce86163adc1bf98d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 Jan 2023 15:05:43 -0500
Subject: [PATCH 2382/2677] Fix Version formatting function

---
 src/backend/common/ArrayFireTypesIO.hpp | 10 ++--------
 src/backend/cuda/device_manager.cpp     |  2 +-
 2 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/src/backend/common/ArrayFireTypesIO.hpp b/src/backend/common/ArrayFireTypesIO.hpp
index 81b73f9988..bf2585c92d 100644
--- a/src/backend/common/ArrayFireTypesIO.hpp
+++ b/src/backend/common/ArrayFireTypesIO.hpp
@@ -14,13 +14,10 @@
 
 template<>
 struct fmt::formatter<af_seq> {
-    // Parses format specifications of the form ['f' | 'e'].
     constexpr auto parse(format_parse_context& ctx) -> decltype(ctx.begin()) {
         return ctx.begin();
     }
 
-    // Formats the point p using the parsed format specification (presentation)
-    // stored in this formatter.
     template<typename FormatContext>
     auto format(const af_seq& p, FormatContext& ctx) -> decltype(ctx.out()) {
         // ctx.out() is an output iterator to write to.
@@ -61,16 +58,13 @@ struct fmt::formatter<arrayfire::common::Version> {
             }
             ++it;
         } while (it != end && *it != '}');
-        return ctx.begin();
+        return it;
     }
 
-    // Formats the point p using the parsed format specification (presentation)
-    // stored in this formatter.
     template<typename FormatContext>
     auto format(const arrayfire::common::Version& ver, FormatContext& ctx)
         -> decltype(ctx.out()) {
-        // ctx.out() is an output iterator to write to.
-        // if (ver.major == -1) return format_to(ctx.out(), "N/A");
+        if (ver.major == -1) return format_to(ctx.out(), "N/A");
         if (ver.minor == -1) show_minor = false;
         if (ver.patch == -1) show_patch = false;
         if (show_major && !show_minor && !show_patch) {
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 4f0d534b8d..00d2e68ee3 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -498,7 +498,7 @@ void DeviceManager::checkCudaVsDriverVersion() {
     if (runtime > driver) {
         string msg =
             "ArrayFire was built with CUDA {} which requires GPU driver "
-            "version {Mm} or later. Please download and install the latest "
+            "version {} or later. Please download and install the latest "
             "drivers from https://www.nvidia.com/drivers for your GPU. "
             "Alternatively, you could rebuild ArrayFire with CUDA Toolkit "
             "version {} to use the current drivers.";

From e5b1047f58f8524a9371e6aa24da2541ffb8c64f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 Jan 2023 15:06:09 -0500
Subject: [PATCH 2383/2677] Update convolve tests tolerances for floating point
 types

---
 test/convolve.cpp |  2 +-
 test/reduce.cpp   | 92 +++++++++++++++++++++++++----------------------
 2 files changed, 50 insertions(+), 44 deletions(-)

diff --git a/test/convolve.cpp b/test/convolve.cpp
index 5fb61e7ee0..8adeb40fd8 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -898,7 +898,7 @@ float tolerance();
 
 template<>
 float tolerance<float>() {
-    return 1e-4;
+    return 2e-3;
 }
 
 template<>
diff --git a/test/reduce.cpp b/test/reduce.cpp
index ef5b33bb1c..c6cc0d7d72 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2012,15 +2012,14 @@ vector<ragged_params *> genRaggedRangeTests() {
           ragged_range_data<unsigned, Tv, To>("ragged_range", 1024 * 1025, 3),
     };
 }
+// clang-format on
 
 vector<ragged_params *> generateAllTypesRagged() {
     vector<ragged_params *> out;
-    vector<vector<ragged_params *> > tmp{
-        genRaggedRangeTests<int, int>(),
-        genRaggedRangeTests<float, float>(),
+    vector<vector<ragged_params *>> tmp{
+        genRaggedRangeTests<int, int>(), genRaggedRangeTests<float, float>(),
         genRaggedRangeTests<double, double>(),
-        genRaggedRangeTests<half_float::half, half_float::half>()
-    };
+        genRaggedRangeTests<half_float::half, half_float::half>()};
 
     for (auto &v : tmp) { copy(begin(v), end(v), back_inserter(out)); }
     return out;
@@ -2032,7 +2031,7 @@ string testNameGeneratorRagged(
     af_dtype lt = info.param->lType_;
     af_dtype vt = info.param->vType_;
     size_t size = info.param->reduceDimLen_;
-    int rdim = info.param->reduceDim_;
+    int rdim    = info.param->reduceDim_;
     std::stringstream s;
     s << info.param->testname_ << "_lenType_" << lt << "_valueType_" << vt
       << "_size_" << size << "_reduceDim_" << rdim;
@@ -2040,8 +2039,8 @@ string testNameGeneratorRagged(
 }
 
 INSTANTIATE_TEST_SUITE_P(RaggedReduceTests, RaggedReduceMaxRangeP,
-                        ::testing::ValuesIn(generateAllTypesRagged()),
-                        testNameGeneratorRagged<RaggedReduceMaxRangeP>);
+                         ::testing::ValuesIn(generateAllTypesRagged()),
+                         testNameGeneratorRagged<RaggedReduceMaxRangeP>);
 
 TEST_P(RaggedReduceMaxRangeP, rangeMaxTest) {
     if (noHalfTests(GetParam()->vType_)) { return; }
@@ -2052,13 +2051,12 @@ TEST_P(RaggedReduceMaxRangeP, rangeMaxTest) {
 
     ASSERT_ARRAYS_EQ(valsReducedGold, ragged_max);
     ASSERT_ARRAYS_EQ(idxsReducedGold, idx);
-
 }
 
 TEST(ReduceByKey, ISSUE_2955) {
-    int N = 256;
-    af::array val = af::randu(N);
-    af::array key = af::range(af::dim4(N), 0, af::dtype::s32);
+    int N                  = 256;
+    af::array val          = af::randu(N);
+    af::array key          = af::range(af::dim4(N), 0, af::dtype::s32);
     key(seq(127, af::end)) = 1;
 
     af::array ok, ov;
@@ -2068,9 +2066,9 @@ TEST(ReduceByKey, ISSUE_2955) {
 }
 
 TEST(ReduceByKey, ISSUE_2955_dim) {
-    int N = 256;
-    af::array val = af::randu(8, N);
-    af::array key = af::range(af::dim4(N), 0, af::dtype::s32);
+    int N                  = 256;
+    af::array val          = af::randu(8, N);
+    af::array key          = af::range(af::dim4(N), 0, af::dtype::s32);
     key(seq(127, af::end)) = 1;
 
     af::array ok, ov;
@@ -2082,7 +2080,7 @@ TEST(ReduceByKey, ISSUE_2955_dim) {
 TEST(ReduceByKey, ISSUE_3062) {
     size_t N = 129;
 
-    af::array ones = af::constant(1, N, u32);
+    af::array ones  = af::constant(1, N, u32);
     af::array zeros = af::constant(0, N, u32);
 
     af::array okeys;
@@ -2095,7 +2093,7 @@ TEST(ReduceByKey, ISSUE_3062) {
     ASSERT_EQ(ovalues.scalar<unsigned>(), 129);
 
     // test reduction on non-zero dimension as well
-    ones = af::constant(1, 2, N, u32);
+    ones  = af::constant(1, 2, N, u32);
     zeros = af::constant(0, N, u32);
 
     af::sumByKey(okeys, ovalues, zeros, ones, 1);
@@ -2109,15 +2107,16 @@ TEST(Reduce, Test_Sum_Global_Array) {
     const int num = 513;
     array a       = af::randn(num, 2, 33, 4);
 
-    float res          = af::sum<float>(a);
-    array full_reduce  = af::sum<af::array>(a);
+    float res         = af::sum<float>(a);
+    array full_reduce = af::sum<af::array>(a);
 
     float *h_a = a.host<float>();
     float gold = 0.f;
 
     for (int i = 0; i < a.elements(); i++) { gold += h_a[i]; }
 
-    float max_error = std::numeric_limits<float>::epsilon() * (float)a.elements();
+    float max_error =
+        std::numeric_limits<float>::epsilon() * (float)a.elements();
     ASSERT_NEAR(gold, res, max_error);
     ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
     freeHost(h_a);
@@ -2127,15 +2126,16 @@ TEST(Reduce, Test_Product_Global_Array) {
     const int num = 512;
     array a       = 1 + (0.005 * af::randn(num, 2, 3, 4));
 
-    float res          = af::product<float>(a);
-    array full_reduce  = af::product<af::array>(a);
+    float res         = af::product<float>(a);
+    array full_reduce = af::product<af::array>(a);
 
     float *h_a = a.host<float>();
     float gold = 1.f;
 
     for (int i = 0; i < a.elements(); i++) { gold *= h_a[i]; }
 
-    float max_error = std::numeric_limits<float>::epsilon() * (float)a.elements();
+    float max_error =
+        std::numeric_limits<float>::epsilon() * (float)a.elements();
     ASSERT_NEAR(gold, res, max_error);
     ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
     freeHost(h_a);
@@ -2149,7 +2149,7 @@ TEST(Reduce, Test_Count_Global_Array) {
     int res       = count<int>(b);
     array res_arr = count<af::array>(b);
     char *h_b     = b.host<char>();
-    unsigned gold      = 0;
+    unsigned gold = 0;
 
     for (int i = 0; i < a.elements(); i++) { gold += h_b[i]; }
 
@@ -2204,15 +2204,17 @@ TYPED_TEST(Reduce, Test_All_Global_Array) {
         TypeParam res = allTrue<TypeParam>(a);
         array res_arr = allTrue<array>(a);
         typed_assert_eq((TypeParam) true, res, false);
-        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(), false);
+        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(),
+                        false);
 
         h_vals[3] = false;
         a         = array(2, num / 2, &h_vals.front());
 
-        res = allTrue<TypeParam>(a);
+        res     = allTrue<TypeParam>(a);
         res_arr = allTrue<array>(a);
         typed_assert_eq((TypeParam) false, res, false);
-        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(), false);
+        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(),
+                        false);
     }
 
     // false value location test
@@ -2225,7 +2227,8 @@ TYPED_TEST(Reduce, Test_All_Global_Array) {
         TypeParam res = allTrue<TypeParam>(a);
         array res_arr = allTrue<array>(a);
         typed_assert_eq((TypeParam) false, res, false);
-        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(), false);
+        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(),
+                        false);
 
         h_vals[i] = true;
     }
@@ -2243,14 +2246,16 @@ TYPED_TEST(Reduce, Test_Any_Global_Array) {
         TypeParam res = anyTrue<TypeParam>(a);
         array res_arr = anyTrue<array>(a);
         typed_assert_eq((TypeParam) false, res, false);
-        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(), false);
+        typed_assert_eq((TypeParam) false, (TypeParam)res_arr.scalar<char>(),
+                        false);
 
         h_vals[3] = true;
         a         = array(2, num / 2, &h_vals.front());
 
-        res = anyTrue<TypeParam>(a);
+        res     = anyTrue<TypeParam>(a);
         res_arr = anyTrue<array>(a);
-        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(), false);
+        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(),
+                        false);
     }
 
     // true value location test
@@ -2263,25 +2268,25 @@ TYPED_TEST(Reduce, Test_Any_Global_Array) {
         TypeParam res = anyTrue<TypeParam>(a);
         array res_arr = anyTrue<array>(a);
         typed_assert_eq((TypeParam) true, res, false);
-        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(), false);
+        typed_assert_eq((TypeParam) true, (TypeParam)res_arr.scalar<char>(),
+                        false);
 
         h_vals[i] = false;
     }
 }
 
-
 TEST(Reduce, Test_Sum_Global_Array_nanval) {
     SKIP_IF_FAST_MATH_ENABLED();
     const int num = 100000;
-    array a = af::randn(num, 2, 34, 4);
+    array a       = af::randn(num, 2, 34, 4);
     a(1, 0, 0, 0) = NAN;
     a(0, 1, 0, 0) = NAN;
     a(0, 0, 1, 0) = NAN;
     a(0, 0, 0, 1) = NAN;
 
-    double nanval = 0.2;
-    float res          = af::sum<float>(a, nanval);
-    array full_reduce  = af::sum<af::array>(a, nanval);
+    double nanval     = 0.2;
+    float res         = af::sum<float>(a, nanval);
+    array full_reduce = af::sum<af::array>(a, nanval);
 
     float *h_a = a.host<float>();
     float gold = 0.f;
@@ -2289,7 +2294,8 @@ TEST(Reduce, Test_Sum_Global_Array_nanval) {
     for (int i = 0; i < a.elements(); i++) {
         gold += (isnan(h_a[i])) ? nanval : h_a[i];
     }
-    float max_error = std::numeric_limits<float>::epsilon() * (float)a.elements();
+    float max_error =
+        std::numeric_limits<float>::epsilon() * (float)a.elements();
     ASSERT_NEAR(gold, res, max_error);
     ASSERT_NEAR(res, full_reduce.scalar<float>(), max_error);
     freeHost(h_a);
@@ -2298,16 +2304,16 @@ TEST(Reduce, Test_Sum_Global_Array_nanval) {
 TEST(Reduce, nanval_issue_3255) {
     SKIP_IF_FAST_MATH_ENABLED();
     char *info_str;
-    af_array  ikeys, ivals, okeys, ovals;
+    af_array ikeys, ivals, okeys, ovals;
     dim_t dims[1] = {8};
 
-    int ikeys_src[8] = {0, 0,  1, 1, 1,  2, 2,  0};
+    int ikeys_src[8] = {0, 0, 1, 1, 1, 2, 2, 0};
     af_create_array(&ikeys, ikeys_src, 1, dims, u32);
 
     int i;
-    for (i=0; i<8; i++) {
-        double ivals_src[8] = {1, 2,  3, 4, 5,  6, 7,  8};
-        ivals_src[i] = NAN;
+    for (i = 0; i < 8; i++) {
+        double ivals_src[8] = {1, 2, 3, 4, 5, 6, 7, 8};
+        ivals_src[i]        = NAN;
         af_create_array(&ivals, ivals_src, 1, dims, f64);
 
         af_product_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);

From dcbfb2dbe483edf7f318898454078b4ef6adccef Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 11 Jan 2023 15:24:54 -0500
Subject: [PATCH 2384/2677] Add support for building older OpenCL versions.

---
 src/backend/opencl/CMakeLists.txt     | 18 ++++--
 src/backend/opencl/device_manager.cpp | 34 ++++-------
 src/backend/opencl/platform.cpp       | 83 ++++++++++++++++-----------
 src/backend/opencl/platform.hpp       |  6 ++
 4 files changed, 80 insertions(+), 61 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index d79cc95705..5c694b632d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -136,11 +136,19 @@ file_to_string(
     NAMESPACE "arrayfire opencl"
     )
 
-set(opencl_compile_definitions
-        CL_TARGET_OPENCL_VERSION=300
-        CL_HPP_TARGET_OPENCL_VERSION=300
-        CL_HPP_MINIMUM_OPENCL_VERSION=110
-        CL_HPP_ENABLE_EXCEPTIONS)
+if(OpenCL_VERSION_MAJOR LESS 3)
+  set(opencl_compile_definitions
+    CL_TARGET_OPENCL_VERSION=120
+    CL_HPP_TARGET_OPENCL_VERSION=120
+    CL_HPP_MINIMUM_OPENCL_VERSION=120
+    CL_HPP_ENABLE_EXCEPTIONS)
+else()
+  set(opencl_compile_definitions
+    CL_TARGET_OPENCL_VERSION=300
+    CL_HPP_TARGET_OPENCL_VERSION=300
+    CL_HPP_MINIMUM_OPENCL_VERSION=110
+    CL_HPP_ENABLE_EXCEPTIONS)
+endif()
 
 include(kernel/scan_by_key/CMakeLists.txt)
 include(kernel/sort_by_key/CMakeLists.txt)
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 69a0da4f2c..a8ca6e96c9 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -15,8 +15,10 @@
 #include <blas.hpp>
 #include <build_version.hpp>
 #include <clfft.hpp>
+#include <common/ArrayFireTypesIO.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
+#include <common/Version.hpp>
 #include <common/defines.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
@@ -264,30 +266,18 @@ DeviceManager::DeviceManager()
 
             auto platform_version =
                 mPlatforms.back().first->getInfo<CL_PLATFORM_VERSION>();
-            ostringstream options;
-            if (platform_version.substr(7).c_str()[0] >= '3') {
-                auto device_versions =
-                    mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
-                sort(begin(device_versions), end(device_versions),
-                     [](const auto& lhs, const auto& rhs) {
-                         return lhs.version < rhs.version;
-                     });
-                cl_name_version max_version = device_versions.back();
-                options << fmt::format(" -cl-std=CL{}.{}",
-                                       CL_VERSION_MAJOR(max_version.version),
-                                       CL_VERSION_MINOR(max_version.version));
-            } else {
-                auto device_version =
-                    mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_VERSION>();
-                options << fmt::format(" -cl-std=CL{}",
-                                       device_version.substr(9, 3));
-            }
-            options << fmt::format(" -D dim_t={}",
-                                   dtype_traits<dim_t>::getName());
+            string options;
+            common::Version version =
+                getOpenCLCDeviceVersion(*mDevices[i]).back();
 #ifdef AF_WITH_FAST_MATH
-            options << " -cl-fast-relaxed-math";
+            options = fmt::format(
+                " -cl-std=CL{:Mm} -D dim_t={} -cl-fast-relaxed-math", version,
+                dtype_traits<dim_t>::getName());
+#else
+            options = fmt::format(" -cl-std=CL{:Mm} -D dim_t={}", version,
+                                  dtype_traits<dim_t>::getName());
 #endif
-            mBaseBuildFlags.push_back(options.str());
+            mBaseBuildFlags.push_back(options);
         } catch (const cl::Error& err) {
             AF_TRACE("Error creating context for device {} with error {}\n",
                      devices[i]->getInfo<CL_DEVICE_NAME>(), err.what());
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index ee2f1b83c6..7e94cb0bde 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -15,8 +15,10 @@
 #include <blas.hpp>
 #include <build_version.hpp>
 #include <clfft.hpp>
+#include <common/ArrayFireTypesIO.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
+#include <common/Version.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
 #include <device_manager.hpp>
@@ -69,6 +71,7 @@ using std::vector;
 using arrayfire::common::getEnvVar;
 using arrayfire::common::ltrim;
 using arrayfire::common::MemoryManagerBase;
+using arrayfire::common::Version;
 using arrayfire::opencl::Allocator;
 using arrayfire::opencl::AllocatorPinned;
 
@@ -121,7 +124,7 @@ static string platformMap(string& platStr) {
     }
 }
 
-afcl::platform getPlatformEnum(cl::Device dev) {
+afcl::platform getPlatformEnum(Device dev) {
     string pname = getPlatformName(dev);
     if (verify_present(pname, "AMD"))
         return AFCL_PLATFORM_AMD;
@@ -188,7 +191,7 @@ string getDeviceInfo() noexcept {
     return info.str();
 }
 
-string getPlatformName(const cl::Device& device) {
+string getPlatformName(const Device& device) {
     const Platform platform(device.getInfo<CL_DEVICE_PLATFORM>());
     string platStr = platform.getInfo<CL_PLATFORM_NAME>();
     return platformMap(platStr);
@@ -295,7 +298,7 @@ CommandQueue& getQueue() {
     return *(devMngr.mQueues[get<1>(devId)]);
 }
 
-const cl::Device& getDevice(int id) {
+const Device& getDevice(int id) {
     device_id_t& devId = tlocalActiveDeviceId();
 
     if (id == -1) { id = get<1>(devId); }
@@ -314,6 +317,40 @@ const std::string& getActiveDeviceBaseBuildFlags() {
     return devMngr.mBaseBuildFlags[get<1>(devId)];
 }
 
+vector<Version> getOpenCLCDeviceVersion(const Device& device) {
+    Platform device_platform(device.getInfo<CL_DEVICE_PLATFORM>(), false);
+    auto platform_version = device_platform.getInfo<CL_PLATFORM_VERSION>();
+    vector<Version> out;
+
+    /// The ifdef allows us to support BUILDING ArrayFire with older versions of
+    /// OpenCL where as the if condition in the ifdef allows us to support older
+    /// versions of OpenCL at runtime
+#ifdef CL_DEVICE_OPENCL_C_ALL_VERSIONS
+    if (platform_version.substr(7).c_str()[0] >= '3') {
+        vector<cl_name_version> device_versions =
+            device.getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
+        sort(begin(device_versions), end(device_versions),
+             [](const auto& lhs, const auto& rhs) {
+                 return lhs.version < rhs.version;
+             });
+        transform(begin(device_versions), end(device_versions),
+                  std::back_inserter(out), [](const cl_name_version& version) {
+                      return Version(CL_VERSION_MAJOR(version.version),
+                                     CL_VERSION_MINOR(version.version),
+                                     CL_VERSION_PATCH(version.version));
+                  });
+    } else {
+#endif
+        auto device_version = device.getInfo<CL_DEVICE_OPENCL_C_VERSION>();
+        int major           = atoi(device_version.substr(9, 1).c_str());
+        int minor           = atoi(device_version.substr(11, 1).c_str());
+        out.emplace_back(major, minor);
+#ifdef CL_DEVICE_OPENCL_C_ALL_VERSIONS
+    }
+#endif
+    return out;
+}
+
 size_t getDeviceMemorySize(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -495,39 +532,17 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
         devMngr.mQueues.push_back(move(tQueue));
         nDevices = static_cast<int>(devMngr.mDevices.size()) - 1;
 
-        auto device_versions =
-            devMngr.mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
-        sort(begin(device_versions), end(device_versions),
-             [](const auto& lhs, const auto& rhs) {
-                 return lhs.version < rhs.version;
-             });
-
-        auto platform_version =
-            devMngr.mPlatforms.back().first->getInfo<CL_PLATFORM_VERSION>();
-        ostringstream options;
-        if (platform_version.substr(7).c_str()[0] >= '3') {
-            auto device_versions =
-                devMngr.mDevices.back()
-                    ->getInfo<CL_DEVICE_OPENCL_C_ALL_VERSIONS>();
-            sort(begin(device_versions), end(device_versions),
-                 [](const auto& lhs, const auto& rhs) {
-                     return lhs.version < rhs.version;
-                 });
-            cl_name_version max_version = device_versions.back();
-            options << fmt::format(" -cl-std=CL{}.{}",
-                                   CL_VERSION_MAJOR(max_version.version),
-                                   CL_VERSION_MINOR(max_version.version));
-        } else {
-            auto device_version =
-                devMngr.mDevices.back()->getInfo<CL_DEVICE_OPENCL_C_VERSION>();
-            options << fmt::format(" -cl-std=CL{}",
-                                   device_version.substr(9, 3));
-        }
-        options << fmt::format(" -D dim_t={}", dtype_traits<dim_t>::getName());
+        auto versions = getOpenCLCDeviceVersion(*(devMngr.mDevices.back()));
 #ifdef AF_WITH_FAST_MATH
-        options << " -cl-fast-relaxed-math";
+        std::string options =
+            fmt::format(" -cl-std=CL{:Mm} -D dim_t={} -cl-fast-relaxed-math",
+                        versions.back(), dtype_traits<dim_t>::getName());
+#else
+        std::string options =
+            fmt::format(" -cl-std=CL{:Mm} -D dim_t={}", versions.back(),
+                        dtype_traits<dim_t>::getName());
 #endif
-        devMngr.mBaseBuildFlags.push_back(options.str());
+        devMngr.mBaseBuildFlags.push_back(options);
 
         // cache the boost program_cache object, clean up done on program exit
         // not during removeDeviceContext
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index c7099bf818..050e44f8c3 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -35,6 +35,8 @@ namespace common {
 class ForgeManager;
 
 class MemoryManagerBase;
+
+class Version;
 }  // namespace common
 }  // namespace arrayfire
 
@@ -69,6 +71,10 @@ const cl::Device& getDevice(int id = -1);
 
 const std::string& getActiveDeviceBaseBuildFlags();
 
+/// Returns the set of all OpenCL C Versions the device supports. The values
+/// are sorted from oldest to latest.
+std::vector<common::Version> getOpenCLCDeviceVersion(const cl::Device& device);
+
 size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();

From 61e980582fdeb32734f33ba2f1cf52194d6e8f90 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jan 2023 00:41:14 -0500
Subject: [PATCH 2385/2677] Fix meanvar tests to avoid segfaults for
 unsupported types

---
 test/arrayfire_test.cpp | 13 +++++++++
 test/meanvar.cpp        | 65 ++++++++++++++++++++++++++++++-----------
 2 files changed, 61 insertions(+), 17 deletions(-)

diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index cf776b6e2b..2128f7fbd3 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -430,10 +430,23 @@ INSTANTIATE(unsigned char, unsigned char, float);
 INSTANTIATE(short, short, float);
 INSTANTIATE(unsigned short, unsigned short, float);
 INSTANTIATE(half_float::half, half_float::half, float);
+INSTANTIATE(half_float::half, half_float::half, double);
 
+INSTANTIATE(af_cdouble, af_cdouble, double);
 INSTANTIATE(double, af_cdouble, float);
 INSTANTIATE(float, af_cfloat, float);
 INSTANTIATE(half_float::half, uint, uint);
+INSTANTIATE(float, float, double);
+INSTANTIATE(int, float, double);
+INSTANTIATE(unsigned int, float, double);
+INSTANTIATE(short, float, double);
+INSTANTIATE(unsigned short, float, double);
+INSTANTIATE(char, float, double);
+INSTANTIATE(unsigned char, float, double);
+INSTANTIATE(long long, double, double);
+INSTANTIATE(unsigned long long, double, double);
+INSTANTIATE(af_cfloat, af_cfloat, double);
+INSTANTIATE(half_float::half, float, double);
 
 #undef INSTANTIATE
 
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index bd79c4015a..08e4702481 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -27,6 +27,7 @@ using std::string;
 using std::vector;
 
 af_err init_err = af_init();
+
 template<typename T>
 struct elseType {
     typedef typename cond_type<is_same_type<T, uintl>::value ||
@@ -59,8 +60,9 @@ struct meanvar_test {
     vector<outType<T>> variance_;
 
     meanvar_test(string description, af_array in, af_array weights,
-                 af_var_bias bias, int dim, vector<double> &&mean,
-                 vector<double> &&variance)
+                 af_var_bias bias, int dim,
+                 vector<typename varOutType<T>::type> &&mean,
+                 vector<typename varOutType<T>::type> &&variance)
         : test_description_(description)
         , in_(0)
         , weights_(0)
@@ -73,8 +75,21 @@ struct meanvar_test {
         for (auto &v : mean) mean_.push_back((outType<T>)v);
         for (auto &v : variance) variance_.push_back((outType<T>)v);
     }
-    meanvar_test()                                   = default;
-    meanvar_test(meanvar_test<T> &&other)            = default;
+
+    meanvar_test(std::string name)
+        : test_description_(name), in_(0), weights_(0) {}
+
+    meanvar_test(meanvar_test<T> &&other)
+        : test_description_(other.test_description_)
+        , in_(other.in_)
+        , weights_(other.weights_)
+        , bias_(other.bias_)
+        , dim_(other.dim_)
+        , mean_(other.mean_)
+        , variance_(other.variance_) {
+        other.in_      = 0;
+        other.weights_ = 0;
+    }
     meanvar_test &operator=(meanvar_test<T> &&other) = default;
     meanvar_test &operator=(meanvar_test<T> &other)  = delete;
 
@@ -86,7 +101,7 @@ struct meanvar_test {
         , dim_(other.dim_)
         , mean_(other.mean_)
         , variance_(other.variance_) {
-        af_retain_array(&in_, other.in_);
+        if (other.in_) af_retain_array(&in_, other.in_);
         if (other.weights_) { af_retain_array(&weights_, other.weights_); }
     }
 
@@ -109,6 +124,7 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T>> {
    public:
     void meanvar_test_function(const meanvar_test<T> &test) {
         SUPPORTED_TYPE_CHECK(T);
+        SUPPORTED_TYPE_CHECK(outType<T>);
         af_array mean, var;
 
         // Cast to the expected type
@@ -145,6 +161,7 @@ class MeanVarTyped : public ::testing::TestWithParam<meanvar_test<T>> {
 
     void meanvar_cpp_test_function(const meanvar_test<T> &test) {
         SUPPORTED_TYPE_CHECK(T);
+        SUPPORTED_TYPE_CHECK(outType<T>);
         array mean, var;
 
         // Cast to the expected type
@@ -188,19 +205,28 @@ template<typename T>
 meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
                                  af_var_bias bias, int dim, int mean_index,
                                  int var_index, test_size size) {
+    if (noDoubleTests((af_dtype)af::dtype_traits<T>::af_type) ||
+        noDoubleTests((
+            af_dtype)af::dtype_traits<typename varOutType<T>::type>::af_type) ||
+        noHalfTests((af_dtype)af::dtype_traits<T>::af_type)) {
+        meanvar_test<T> out(name);
+        return out;
+    }
+
     vector<af_array> inputs;
-    vector<vector<double>> outputs;
+    vector<vector<typename varOutType<T>::type>> outputs;
     if (size == MEANVAR_SMALL) {
         vector<af::dim4> numDims_;
-        vector<vector<double>> in_;
-        vector<vector<double>> tests_;
-        readTests<double, typename varOutType<double>::type, double>(
+        vector<vector<T>> in_;
+        vector<vector<typename varOutType<T>::type>> tests_;
+        readTests<T, typename varOutType<T>::type, double>(
             TEST_DIR "/meanvar/meanvar.data", numDims_, in_, tests_);
 
         inputs.resize(in_.size());
         for (size_t i = 0; i < in_.size(); i++) {
             af_create_array(&inputs[i], &in_[i].front(), numDims_[i].ndims(),
-                            numDims_[i].get(), f64);
+                            numDims_[i].get(),
+                            (af_dtype)af::dtype_traits<T>::af_type);
         }
 
         outputs.resize(tests_.size());
@@ -219,21 +245,26 @@ meanvar_test<T> meanvar_test_gen(string name, int in_index, int weight_index,
             {50, 40, 1, 1}   // 5
         };
 
-        vector<double> large_(full_array_size);
+        vector<T> large_(full_array_size);
         for (size_t i = 0; i < large_.size(); i++) {
-            large_[i] = static_cast<double>(i);
+            large_[i] = static_cast<T>(i);
         }
 
         inputs.resize(dimensions.size());
         for (size_t i = 0; i < dimensions.size(); i++) {
             af_create_array(&inputs[i], &large_.front(), 4,
-                            dimensions[i].data(), f64);
+                            dimensions[i].data(),
+                            (af_dtype)af::dtype_traits<T>::af_type);
         }
 
-        outputs.push_back(vector<double>(1, 999.5));
-        outputs.push_back(vector<double>(1, 333500));
-        outputs.push_back({249.50, 749.50, 1249.50, 1749.50});
-        outputs.push_back(vector<double>(4, 20875));
+        outputs.push_back(
+            vector<typename varOutType<T>::type>(1, outType<T>(999.5)));
+        outputs.push_back(
+            vector<typename varOutType<T>::type>(1, outType<T>(333500)));
+        outputs.push_back({outType<T>(249.50), outType<T>(749.50),
+                           outType<T>(1249.50), outType<T>(1749.50)});
+        outputs.push_back(
+            vector<typename varOutType<T>::type>(4, outType<T>(20875)));
     }
     meanvar_test<T> out(name, inputs[in_index],
                         (weight_index == -1) ? empty : inputs[weight_index],

From d26d891633240e68b81fc9a75764345485591149 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jan 2023 00:56:28 -0500
Subject: [PATCH 2386/2677] Fix warnings related to Version class

---
 src/backend/common/ArrayFireTypesIO.hpp | 14 +++++++-------
 src/backend/common/DependencyModule.cpp |  9 +++++----
 src/backend/common/Version.hpp          | 25 +++++++++++++++----------
 src/backend/cuda/convolveNN.cpp         |  4 ++--
 src/backend/cuda/cudnn.cpp              |  4 ++--
 src/backend/cuda/cudnnModule.cpp        |  9 +++++----
 6 files changed, 36 insertions(+), 29 deletions(-)

diff --git a/src/backend/common/ArrayFireTypesIO.hpp b/src/backend/common/ArrayFireTypesIO.hpp
index bf2585c92d..8d36aa54c1 100644
--- a/src/backend/common/ArrayFireTypesIO.hpp
+++ b/src/backend/common/ArrayFireTypesIO.hpp
@@ -64,18 +64,18 @@ struct fmt::formatter<arrayfire::common::Version> {
     template<typename FormatContext>
     auto format(const arrayfire::common::Version& ver, FormatContext& ctx)
         -> decltype(ctx.out()) {
-        if (ver.major == -1) return format_to(ctx.out(), "N/A");
-        if (ver.minor == -1) show_minor = false;
-        if (ver.patch == -1) show_patch = false;
+        if (ver.major() == -1) return format_to(ctx.out(), "N/A");
+        if (ver.minor() == -1) show_minor = false;
+        if (ver.patch() == -1) show_patch = false;
         if (show_major && !show_minor && !show_patch) {
-            return format_to(ctx.out(), "{}", ver.major);
+            return format_to(ctx.out(), "{}", ver.major());
         }
         if (show_major && show_minor && !show_patch) {
-            return format_to(ctx.out(), "{}.{}", ver.major, ver.minor);
+            return format_to(ctx.out(), "{}.{}", ver.major(), ver.minor());
         }
         if (show_major && show_minor && show_patch) {
-            return format_to(ctx.out(), "{}.{}.{}", ver.major, ver.minor,
-                             ver.patch);
+            return format_to(ctx.out(), "{}.{}.{}", ver.major(), ver.minor(),
+                             ver.patch());
         }
         return ctx.out();
     }
diff --git a/src/backend/common/DependencyModule.cpp b/src/backend/common/DependencyModule.cpp
index d8552e450d..4ccb64bc9a 100644
--- a/src/backend/common/DependencyModule.cpp
+++ b/src/backend/common/DependencyModule.cpp
@@ -52,7 +52,7 @@ vector<string> libNames(const std::string& name, const string& suffix,
     UNUSED(suffix);
     const string noVerName = libraryPrefix + name + librarySuffix;
     if (ver != arrayfire::common::NullVersion) {
-        const string infix = "." + to_string(ver.major) + ".";
+        const string infix = "." + to_string(ver.major()) + ".";
         return {libraryPrefix + name + infix + librarySuffix, noVerName};
     } else {
         return {noVerName};
@@ -71,10 +71,11 @@ vector<string> libNames(const std::string& name, const string& suffix,
     UNUSED(suffix);
     const string noVerName = libraryPrefix + name + librarySuffix;
     if (ver != arrayfire::common::NullVersion) {
-        const string soname("." + to_string(ver.major));
+        const string soname("." + to_string(ver.major()));
 
-        const string vsfx = "." + to_string(ver.major) + "." +
-                            to_string(ver.minor) + "." + to_string(ver.patch);
+        const string vsfx = "." + to_string(ver.major()) + "." +
+                            to_string(ver.minor()) + "." +
+                            to_string(ver.patch());
         return {noVerName + vsfx, noVerName + soname, noVerName};
     } else {
         return {noVerName};
diff --git a/src/backend/common/Version.hpp b/src/backend/common/Version.hpp
index 0b88444222..55a6e79efb 100644
--- a/src/backend/common/Version.hpp
+++ b/src/backend/common/Version.hpp
@@ -21,11 +21,12 @@
 
 namespace arrayfire {
 namespace common {
-struct Version {
-    int major = -1;
-    int minor = -1;
-    int patch = -1;
+class Version {
+    int major_ = -1;
+    int minor_ = -1;
+    int patch_ = -1;
 
+   public:
     /// Checks if the major version is defined before minor and minor is defined
     /// before patch
     constexpr static bool validate(int major_, int minor_,
@@ -34,14 +35,18 @@ struct Version {
                !(minor_ < 0 && patch_ >= 0);
     }
 
+    constexpr int major() const { return major_; }
+    constexpr int minor() const { return minor_; }
+    constexpr int patch() const { return patch_; }
+
     constexpr Version(const int ver_major, const int ver_minor = -1,
                       const int ver_patch = -1) noexcept
-        : major(ver_major), minor(ver_minor), patch(ver_patch) {}
+        : major_(ver_major), minor_(ver_minor), patch_(ver_patch) {}
 };
 
 constexpr bool operator==(const Version& lhs, const Version& rhs) {
-    return lhs.major == rhs.major && lhs.minor == rhs.minor &&
-           lhs.patch == rhs.patch;
+    return lhs.major() == rhs.major() && lhs.minor() == rhs.minor() &&
+           lhs.patch() == rhs.patch();
 }
 
 constexpr bool operator!=(const Version& lhs, const Version& rhs) {
@@ -52,11 +57,11 @@ constexpr static Version NullVersion{-1, -1, -1};
 
 constexpr bool operator<(const Version& lhs, const Version& rhs) {
     if (lhs == NullVersion || rhs == NullVersion) return false;
-    if (lhs.major != -1 && rhs.major != -1 && lhs.major < rhs.major)
+    if (lhs.major() != -1 && rhs.major() != -1 && lhs.major() < rhs.major())
         return true;
-    if (lhs.minor != -1 && rhs.minor != -1 && lhs.minor < rhs.minor)
+    if (lhs.minor() != -1 && rhs.minor() != -1 && lhs.minor() < rhs.minor())
         return true;
-    if (lhs.patch != -1 && rhs.patch != -1 && lhs.patch < rhs.patch)
+    if (lhs.patch() != -1 && rhs.patch() != -1 && lhs.patch() < rhs.patch())
         return true;
     return false;
 }
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 4988d807f3..1110d81506 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -70,7 +70,7 @@ pair<cudnnConvolutionFwdAlgo_t, size_t> getForwardAlgorithm(
     size_t workspace_bytes = 0;
 
     auto version = getCudnnPlugin().getVersion();
-    if (version.major >= 8) {
+    if (version.major() >= 8) {
         int maxAlgoCount = 0;
         CUDNN_CHECK(cuda::cudnnGetConvolutionForwardAlgorithmMaxCount(
             cudnn, &maxAlgoCount));
@@ -419,7 +419,7 @@ pair<cudnnConvolutionBwdFilterAlgo_t, size_t> getBackwardFilterAlgorithm(
     size_t workspace_bytes = 0;
 
     auto version = getCudnnPlugin().getVersion();
-    if (version.major >= 8) {
+    if (version.major() >= 8) {
         int maxAlgoCount = 0;
         CUDNN_CHECK(cuda::cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(
             cudnn, &maxAlgoCount));
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index b6fd903729..39ee3305e6 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -238,7 +238,7 @@ cudnnStatus_t cudnnGetConvolutionForwardAlgorithm(
     cudnnConvolutionFwdPreference_t preference, size_t memoryLimitInBytes,
     cudnnConvolutionFwdAlgo_t *algo) {
     auto version = getCudnnPlugin().getVersion();
-    if (version.major < 8) {
+    if (version.major() < 8) {
         return getCudnnPlugin().cudnnGetConvolutionForwardAlgorithm(
             handle, xDesc, wDesc, convDesc, yDesc, preference,
             memoryLimitInBytes, algo);
@@ -259,7 +259,7 @@ cudnnStatus_t cudnnGetConvolutionBackwardFilterAlgorithm(
     cudnnConvolutionBwdFilterPreference_t preference, size_t memoryLimitInBytes,
     cudnnConvolutionBwdFilterAlgo_t *algo) {
     auto version = getCudnnPlugin().getVersion();
-    if (version.major < 8) {
+    if (version.major() < 8) {
         return getCudnnPlugin().cudnnGetConvolutionBackwardFilterAlgorithm(
             handle, xDesc, dyDesc, convDesc, dwDesc, preference,
             memoryLimitInBytes, algo);
diff --git a/src/backend/cuda/cudnnModule.cpp b/src/backend/cuda/cudnnModule.cpp
index 657c867156..66c4b4ab06 100644
--- a/src/backend/cuda/cudnnModule.cpp
+++ b/src/backend/cuda/cudnnModule.cpp
@@ -111,12 +111,13 @@ cudnnModule::cudnnModule()
 
     // Check to see if the version of cuDNN ArrayFire was compiled against
     // is compatible with the version loaded at runtime
-    if (compiled_cudnn_version.major <= 6 &&
+    if (compiled_cudnn_version.major() <= 6 &&
         compiled_cudnn_version < cudnn_version) {
         string error_msg = fmt::format(
             "ArrayFire was compiled with an older version of cuDNN({}.{}) that "
             "does not support the version that was loaded at runtime({}.{}).",
-            CUDNN_MAJOR, CUDNN_MINOR, cudnn_version.major, cudnn_version.minor);
+            CUDNN_MAJOR, CUDNN_MINOR, cudnn_version.major(),
+            cudnn_version.minor());
         AF_ERROR(error_msg, AF_ERR_NOT_SUPPORTED);
     }
 
@@ -152,14 +153,14 @@ cudnnModule::cudnnModule()
     MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterWorkspaceSize);
     MODULE_FUNCTION_INIT(cudnnFindConvolutionForwardAlgorithm);
     MODULE_FUNCTION_INIT(cudnnFindConvolutionBackwardFilterAlgorithm);
-    if (cudnn_version.major < 8) {
+    if (cudnn_version.major() < 8) {
         MODULE_FUNCTION_INIT(cudnnGetConvolutionForwardAlgorithm);
         MODULE_FUNCTION_INIT(cudnnGetConvolutionBackwardFilterAlgorithm);
     }
     MODULE_FUNCTION_INIT(cudnnGetConvolutionNdForwardOutputDim);
     MODULE_FUNCTION_INIT(cudnnSetConvolution2dDescriptor);
     MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor);
-    if (cudnn_version.major == 4) {
+    if (cudnn_version.major() == 4) {
         MODULE_FUNCTION_INIT(cudnnSetFilter4dDescriptor_v4);
     }
     MODULE_FUNCTION_INIT(cudnnSetStream);

From 4e8e9389b9338bae7a07b35fe43680cb864cf76d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jan 2023 14:36:14 -0500
Subject: [PATCH 2387/2677] Update vcpkg baseline to update OpenCL version

---
 .github/workflows/win_cpu_build.yml                      | 2 +-
 CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake | 2 +-
 vcpkg.json                                               | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index dc73cf7c28..8564bd03b8 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -13,7 +13,7 @@ jobs:
         name: CPU (fftw, OpenBLAS, windows-latest)
         runs-on: windows-latest
         env:
-          VCPKG_HASH: 6ca56aeb457f033d344a7106cb3f9f1abf8f4e98
+          VCPKG_HASH: f14984af3738e69f197bf0e647a8dca12de92996
           VCPKG_DEFAULT_TRIPLET: x64-windows
         steps:
             - name: Checkout Repository
diff --git a/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake b/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake
index ba8999d36e..f1a180065a 100644
--- a/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake
+++ b/CMakeModules/vcpkg/ports/lapack-reference/portfile.cmake
@@ -68,7 +68,7 @@ vcpkg_cmake_configure(
     OPTIONS
         "-DUSE_OPTIMIZED_BLAS=${USE_OPTIMIZED_BLAS}"
         "-DCBLAS=${CBLAS}"
-	"-DLAPACKE=ON"
+        "-DLAPACKE=ON"
         ${FORTRAN_CMAKE}
 )
 
diff --git a/vcpkg.json b/vcpkg.json
index 4562e14f80..72625d8fa9 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -77,5 +77,5 @@
             ]
         }
     },
-    "builtin-baseline": "6ca56aeb457f033d344a7106cb3f9f1abf8f4e98"
+    "builtin-baseline": "f14984af3738e69f197bf0e647a8dca12de92996"
 }

From 3bd7991fccab353e639ed5f2a1b91ffe2c9c3691 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jan 2023 14:38:57 -0500
Subject: [PATCH 2388/2677] Add group flags around LAPACKE libraries to avoid
 missing symbol errs

---
 src/backend/opencl/CMakeLists.txt | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 5c694b632d..8a0e55d2e4 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -595,11 +595,19 @@ if(LAPACK_FOUND OR BUILD_WITH_MKL)
       SYSTEM PRIVATE
         ${CBLAS_INCLUDE_DIR})
 
+    check_cxx_compiler_flag("-Wl,--start-group -Werror" group_flags)
+    if(group_flags)
+      set(START_GROUP -Wl,--start-group)
+      set(END_GROUP -Wl,--end-group)
+    endif()
     target_link_libraries(afopencl
       PRIVATE
-        ${CBLAS_LIBRARIES}
+        ${START_GROUP}
         ${LAPACK_LIBRARIES}
-        LAPACKE::LAPACKE)
+        LAPACKE::LAPACKE
+        ${CBLAS_LIBRARIES}
+        ${END_GROUP}
+      )
   endif()
 
   target_compile_definitions(afopencl PRIVATE WITH_LINEAR_ALGEBRA)

From 727a7960e28275bee1bbd3ce95c546c4725921c2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jan 2023 14:39:34 -0500
Subject: [PATCH 2389/2677] Fix extern half include directories command in
 cmake

---
 test/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 16ba6f71ec..dbd81ea6e7 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -177,7 +177,8 @@ function(make_test)
     target_include_directories(${target}
       PRIVATE
         ${CMAKE_SOURCE_DIR}
-        ${CMAKE_CURRENT_SOURCE_DIR}
+        ${CMAKE_CURRENT_SOURCE_DIR})
+    target_include_directories(${target}
       SYSTEM PRIVATE
         ${ArrayFire_SOURCE_DIR}/extern/half/include
       )

From 225a828fcd4e34076d61b1d20495d3acc9b9da8a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 20 Jan 2023 16:08:23 -0500
Subject: [PATCH 2390/2677] Fix error due to an extra brace during the
 namespace refactor

---
 src/api/c/imageio.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 41e713e631..be5f528922 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -1091,5 +1091,4 @@ af_err af_delete_image_memory(void *ptr) {
     AF_RETURN_ERROR("ArrayFire compiled without Image IO (FreeImage) support",
                     AF_ERR_NOT_CONFIGURED);
 }
-}  // namespace arrayfire
 #endif  // WITH_FREEIMAGE

From f9259985a144ec2b85af820ce12971f66da8a464 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 19 Dec 2022 21:40:04 -0500
Subject: [PATCH 2391/2677] use doxygen-awesome css theme

---
 docs/CMakeLists.txt                          |    3 +-
 docs/arrayfire.css                           |  196 --
 docs/doxygen-awesome-darkmode-toggle.js      |  157 ++
 docs/doxygen-awesome-fragment-copy-button.js |   85 +
 docs/doxygen-awesome-interactive-toc.js      |   81 +
 docs/doxygen-awesome-sidebar-only.css        |  115 +
 docs/doxygen-awesome.css                     | 2405 ++++++++++++++++++
 docs/doxygen.mk                              |  226 +-
 docs/header.htm                              |   74 +-
 9 files changed, 3063 insertions(+), 279 deletions(-)
 delete mode 100644 docs/arrayfire.css
 create mode 100644 docs/doxygen-awesome-darkmode-toggle.js
 create mode 100644 docs/doxygen-awesome-fragment-copy-button.js
 create mode 100644 docs/doxygen-awesome-interactive-toc.js
 create mode 100644 docs/doxygen-awesome-sidebar-only.css
 create mode 100644 docs/doxygen-awesome.css

diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 1310b3c87b..93ba6615e8 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -39,10 +39,9 @@ configure_file(
     ${DOCS_DIR}/details/examples.dox
 )
 ###########################################################
-
 add_custom_target(docs
     ALL
-    COMMAND ${DOXYGEN_EXECUTABLE} ${AF_DOCS_CONFIG_OUT}
+    COMMAND Doxygen::doxygen ${AF_DOCS_CONFIG_OUT}
     COMMAND cmake -E copy_directory ${ASSETS_DIR} ${CMAKE_CURRENT_BINARY_DIR}/html
     WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
     COMMENT "Generating Documentation"
diff --git a/docs/arrayfire.css b/docs/arrayfire.css
deleted file mode 100644
index 397e8089d5..0000000000
--- a/docs/arrayfire.css
+++ /dev/null
@@ -1,196 +0,0 @@
-/* The standard CSS for doxygen 1.8.5 */
-
-body, table, div, p, dl
-{
-    font            :   400 12px/22px Lucida Grande, Verdana, Geneva, Arial, sans-serif;
-}
-
-p
-{
-    padding-left    :   10px;
-}
-
-p code
-{
-    font-weight     :   bold;
-    background-color:   #F7F7F7;
-}
-
-/* @group Heading Levels */
-/* Increase the size of the page title */
-.title
-{
-    font-size       :   250%;
-}
-
-/* Remove space above line items */
-ul
-{
-    margin-top      :   0em;
-}
-
-/* Slightly pad subsections */
-h2, h3, h4, h5
-{
-    padding-left    :   10px;
-    margin-bottom   :   0px;
-}
-
-/* Margins on the left of the code */
-div.line
-{
-    margin-left :   15px;
-}
-
-a.code, a.code:visited, a.line, a.line:visited
-{
-    color       :   #4665A2;
-}
-
-a.codeRef, a.codeRef:visited, a.lineRef, a.lineRef:visited
-{
-    color       :   #4665A2;
-}
-
-/*image and image groups*/
-div.image_group
-{
-    text-align  :   center;
-}
-
-div.image_group > div
-{
-    display     :   inline-block;
-}
-
-div.scaled > img
-{
-    max-width   :   250px;
-}
-
-div.scaled > img:hover
-{
-    z-index             :   255; /* Hovered image to be shown on top of all */
-    background          :   #ffffff;
-    border              :   1px solid #000000;
-    -ms-transform       :   scale(2, 2);
-    -webkit-transform   :   scale(2, 2);
-    -moz-transform      :   scale(2, 2);
-    transform           :   scale(2, 2);
-}
-
-/*ArrayFire Feature Support Settings*/
-div.support
-{
-    text-align  :   right;
-}
-
-div.support *
-{
-    display     :   inline-block;
-    max-width   :   50px;
-}
-
-#under_logo
-{
-    font-size   :   2em;
-    max-width   :   25px;
-    color       :   #000000;
-}
-
-#projectbrief
-{
-    color       :   #555555
-}
-
-#projectlogo
-{
-    width       :   300px;
-    text-align  :   left;
-}
-
-#projectnumber
-{
-    max-width   :   25px;
-}
-
-#projectname
-{
-    font-size       :   3em;
-    max-width       :   25px;
-    color           :   #555555
-}
-
-#gsearch
-{
-    width       :   20%;
-}
-
-.tablist span
-{
-    font-weight     :   normal;
-    font-family     :   "Raleway","Helvetica Neue",Helvetica,sans-serif;
-    color           :   #FFFFFF;
-    text-shadow     :   none;
-}
-
-#side-nav {
-    height: 100%
-}
-
-#nav-tree
-{
-    background-color    : #F7F7F7;
-}
-
-div.toc
-{
-    background-color    : #F7F7F7;
-    border              : 1px solid #DFDFDF;
-}
-
-#nav-tree
-{
-    background-color    : #F7F7F7;
-}
-
-div.toc
-{
-    background-color    : #F7F7F7;
-    border              : 1px solid #DFDFDF;
-}
-
-.tablist a
-{
-    background-image:url('tab_b.png');
-}
-
-div.header
-{
-    background-image    :   none;
-    background-color    :   #F7F7F7;
-    border-bottom       :   1px solid #DFDFDF;
-}
-
-#nav-tree
-{
-    background-image    :   none;
-}
-
-.ui-resizable-e
-{
-    background  :   url("ftv2splitbar1.png") repeat scroll right center transparent;
-}
-
-div.fragment
-{
-    background-color    :   #F7F7F7;
-    border              :   1px solid #DFDFDF;
-}
-
-pre
-{
-    overflow            : hidden;
-}
-
-/* @end */
diff --git a/docs/doxygen-awesome-darkmode-toggle.js b/docs/doxygen-awesome-darkmode-toggle.js
new file mode 100644
index 0000000000..2032f02c0b
--- /dev/null
+++ b/docs/doxygen-awesome-darkmode-toggle.js
@@ -0,0 +1,157 @@
+/**
+
+Doxygen Awesome
+https://github.com/jothepro/doxygen-awesome-css
+
+MIT License
+
+Copyright (c) 2021 - 2022 jothepro
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+*/
+
+class DoxygenAwesomeDarkModeToggle extends HTMLElement {
+    // SVG icons from https://fonts.google.com/icons
+    // Licensed under the Apache 2.0 license:
+    // https://www.apache.org/licenses/LICENSE-2.0.html
+    static lightModeIcon = `<svg xmlns="http://www.w3.org/2000/svg" enable-background="new 0 0 24 24" height="24px" viewBox="0 0 24 24" width="24px" fill="#FCBF00"><rect fill="none" height="24" width="24"/><circle cx="12" cy="12" opacity=".3" r="3"/><path d="M12,9c1.65,0,3,1.35,3,3s-1.35,3-3,3s-3-1.35-3-3S10.35,9,12,9 M12,7c-2.76,0-5,2.24-5,5s2.24,5,5,5s5-2.24,5-5 S14.76,7,12,7L12,7z M2,13l2,0c0.55,0,1-0.45,1-1s-0.45-1-1-1l-2,0c-0.55,0-1,0.45-1,1S1.45,13,2,13z M20,13l2,0c0.55,0,1-0.45,1-1 s-0.45-1-1-1l-2,0c-0.55,0-1,0.45-1,1S19.45,13,20,13z M11,2v2c0,0.55,0.45,1,1,1s1-0.45,1-1V2c0-0.55-0.45-1-1-1S11,1.45,11,2z M11,20v2c0,0.55,0.45,1,1,1s1-0.45,1-1v-2c0-0.55-0.45-1-1-1C11.45,19,11,19.45,11,20z M5.99,4.58c-0.39-0.39-1.03-0.39-1.41,0 c-0.39,0.39-0.39,1.03,0,1.41l1.06,1.06c0.39,0.39,1.03,0.39,1.41,0s0.39-1.03,0-1.41L5.99,4.58z M18.36,16.95 c-0.39-0.39-1.03-0.39-1.41,0c-0.39,0.39-0.39,1.03,0,1.41l1.06,1.06c0.39,0.39,1.03,0.39,1.41,0c0.39-0.39,0.39-1.03,0-1.41 L18.36,16.95z M19.42,5.99c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06c-0.39,0.39-0.39,1.03,0,1.41 s1.03,0.39,1.41,0L19.42,5.99z M7.05,18.36c0.39-0.39,0.39-1.03,0-1.41c-0.39-0.39-1.03-0.39-1.41,0l-1.06,1.06 c-0.39,0.39-0.39,1.03,0,1.41s1.03,0.39,1.41,0L7.05,18.36z"/></svg>`
+    static darkModeIcon = `<svg xmlns="http://www.w3.org/2000/svg" enable-background="new 0 0 24 24" height="24px" viewBox="0 0 24 24" width="24px" fill="#FE9700"><rect fill="none" height="24" width="24"/><path d="M9.37,5.51C9.19,6.15,9.1,6.82,9.1,7.5c0,4.08,3.32,7.4,7.4,7.4c0.68,0,1.35-0.09,1.99-0.27 C17.45,17.19,14.93,19,12,19c-3.86,0-7-3.14-7-7C5,9.07,6.81,6.55,9.37,5.51z" opacity=".3"/><path d="M9.37,5.51C9.19,6.15,9.1,6.82,9.1,7.5c0,4.08,3.32,7.4,7.4,7.4c0.68,0,1.35-0.09,1.99-0.27C17.45,17.19,14.93,19,12,19 c-3.86,0-7-3.14-7-7C5,9.07,6.81,6.55,9.37,5.51z M12,3c-4.97,0-9,4.03-9,9s4.03,9,9,9s9-4.03,9-9c0-0.46-0.04-0.92-0.1-1.36 c-0.98,1.37-2.58,2.26-4.4,2.26c-2.98,0-5.4-2.42-5.4-5.4c0-1.81,0.89-3.42,2.26-4.4C12.92,3.04,12.46,3,12,3L12,3z"/></svg>`
+    static title = "Toggle Light/Dark Mode"
+
+    static prefersLightModeInDarkModeKey = "prefers-light-mode-in-dark-mode"
+    static prefersDarkModeInLightModeKey = "prefers-dark-mode-in-light-mode"
+
+    static _staticConstructor = function() {
+        DoxygenAwesomeDarkModeToggle.enableDarkMode(DoxygenAwesomeDarkModeToggle.userPreference)
+        // Update the color scheme when the browsers preference changes
+        // without user interaction on the website.
+        window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', event => {
+            DoxygenAwesomeDarkModeToggle.onSystemPreferenceChanged()
+        })
+        // Update the color scheme when the tab is made visible again.
+        // It is possible that the appearance was changed in another tab 
+        // while this tab was in the background.
+        document.addEventListener("visibilitychange", visibilityState => {
+            if (document.visibilityState === 'visible') {
+                DoxygenAwesomeDarkModeToggle.onSystemPreferenceChanged()
+            }
+        });
+    }()
+
+    static init() {
+        $(function() {
+            $(document).ready(function() {
+                const toggleButton = document.createElement('doxygen-awesome-dark-mode-toggle')
+                toggleButton.title = DoxygenAwesomeDarkModeToggle.title
+                toggleButton.updateIcon()
+
+                window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', event => {
+                    toggleButton.updateIcon()
+                })
+                document.addEventListener("visibilitychange", visibilityState => {
+                    if (document.visibilityState === 'visible') {
+                        toggleButton.updateIcon()
+                    }
+                });
+
+                $(document).ready(function(){
+                    document.getElementById("togglediv").parentNode.appendChild(toggleButton)
+                })
+                $(window).resize(function(){
+                    document.getElementById("togglediv").parentNode.appendChild(toggleButton)
+                })
+            })
+        })
+    }
+
+    constructor() {
+        super();
+        this.onclick=this.toggleDarkMode
+    }
+
+    /**
+     * @returns `true` for dark-mode, `false` for light-mode system preference
+     */
+    static get systemPreference() {
+        return window.matchMedia('(prefers-color-scheme: dark)').matches
+    }
+
+    /**
+     * @returns `true` for dark-mode, `false` for light-mode user preference
+     */
+    static get userPreference() {
+        return (!DoxygenAwesomeDarkModeToggle.systemPreference && localStorage.getItem(DoxygenAwesomeDarkModeToggle.prefersDarkModeInLightModeKey)) || 
+        (DoxygenAwesomeDarkModeToggle.systemPreference && !localStorage.getItem(DoxygenAwesomeDarkModeToggle.prefersLightModeInDarkModeKey))
+    }
+
+    static set userPreference(userPreference) {
+        DoxygenAwesomeDarkModeToggle.darkModeEnabled = userPreference
+        if(!userPreference) {
+            if(DoxygenAwesomeDarkModeToggle.systemPreference) {
+                localStorage.setItem(DoxygenAwesomeDarkModeToggle.prefersLightModeInDarkModeKey, true)
+            } else {
+                localStorage.removeItem(DoxygenAwesomeDarkModeToggle.prefersDarkModeInLightModeKey)
+            }
+        } else {
+            if(!DoxygenAwesomeDarkModeToggle.systemPreference) {
+                localStorage.setItem(DoxygenAwesomeDarkModeToggle.prefersDarkModeInLightModeKey, true)
+            } else {
+                localStorage.removeItem(DoxygenAwesomeDarkModeToggle.prefersLightModeInDarkModeKey)
+            }
+        }
+        DoxygenAwesomeDarkModeToggle.onUserPreferenceChanged()
+    }
+
+    static enableDarkMode(enable) {
+        if(enable) {
+            DoxygenAwesomeDarkModeToggle.darkModeEnabled = true
+            document.documentElement.classList.add("dark-mode")
+            document.documentElement.classList.remove("light-mode")
+        } else {
+            DoxygenAwesomeDarkModeToggle.darkModeEnabled = false
+            document.documentElement.classList.remove("dark-mode")
+            document.documentElement.classList.add("light-mode")
+        }
+    }
+
+    static onSystemPreferenceChanged() {
+        DoxygenAwesomeDarkModeToggle.darkModeEnabled = DoxygenAwesomeDarkModeToggle.userPreference
+        DoxygenAwesomeDarkModeToggle.enableDarkMode(DoxygenAwesomeDarkModeToggle.darkModeEnabled)
+    }
+
+    static onUserPreferenceChanged() {
+        DoxygenAwesomeDarkModeToggle.enableDarkMode(DoxygenAwesomeDarkModeToggle.darkModeEnabled)
+    }
+
+    toggleDarkMode() {
+        DoxygenAwesomeDarkModeToggle.userPreference = !DoxygenAwesomeDarkModeToggle.userPreference
+        this.updateIcon()
+    }
+
+    updateIcon() {
+        if(DoxygenAwesomeDarkModeToggle.darkModeEnabled) {
+            this.innerHTML = DoxygenAwesomeDarkModeToggle.darkModeIcon
+        } else {
+            this.innerHTML = DoxygenAwesomeDarkModeToggle.lightModeIcon
+        }
+    }
+}
+
+customElements.define("doxygen-awesome-dark-mode-toggle", DoxygenAwesomeDarkModeToggle);
diff --git a/docs/doxygen-awesome-fragment-copy-button.js b/docs/doxygen-awesome-fragment-copy-button.js
new file mode 100644
index 0000000000..7d06b348d6
--- /dev/null
+++ b/docs/doxygen-awesome-fragment-copy-button.js
@@ -0,0 +1,85 @@
+/**
+
+Doxygen Awesome
+https://github.com/jothepro/doxygen-awesome-css
+
+MIT License
+
+Copyright (c) 2022 jothepro
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+*/
+
+class DoxygenAwesomeFragmentCopyButton extends HTMLElement {
+    constructor() {
+        super();
+        this.onclick=this.copyContent
+    }
+    static title = "Copy to clipboard"
+    static copyIcon = `<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="24" height="24"><path d="M0 0h24v24H0V0z" fill="none"/><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg>`
+    static successIcon = `<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="24" height="24"><path d="M0 0h24v24H0V0z" fill="none"/><path d="M9 16.17L4.83 12l-1.42 1.41L9 19 21 7l-1.41-1.41L9 16.17z"/></svg>`
+    static successDuration = 980
+    static init() {
+        $(function() {
+            $(document).ready(function() {
+                if(navigator.clipboard) {
+                    const fragments = document.getElementsByClassName("fragment")
+                    for(const fragment of fragments) {
+                        const fragmentWrapper = document.createElement("div")
+                        fragmentWrapper.className = "doxygen-awesome-fragment-wrapper"
+                        const fragmentCopyButton = document.createElement("doxygen-awesome-fragment-copy-button")
+                        fragmentCopyButton.innerHTML = DoxygenAwesomeFragmentCopyButton.copyIcon
+                        fragmentCopyButton.title = DoxygenAwesomeFragmentCopyButton.title
+                
+                        fragment.parentNode.replaceChild(fragmentWrapper, fragment)
+                        fragmentWrapper.appendChild(fragment)
+                        fragmentWrapper.appendChild(fragmentCopyButton)
+            
+                    }
+                }
+            })
+        })
+    }
+
+
+    copyContent() {
+        const content = this.previousSibling.cloneNode(true)
+        // filter out line number from file listings
+        content.querySelectorAll(".lineno, .ttc").forEach((node) => {
+            node.remove()
+        })
+        let textContent = content.textContent
+        // remove trailing newlines that appear in file listings
+        let numberOfTrailingNewlines = 0
+        while(textContent.charAt(textContent.length - (numberOfTrailingNewlines + 1)) == '\n') {
+            numberOfTrailingNewlines++;
+        }
+        textContent = textContent.substring(0, textContent.length - numberOfTrailingNewlines)
+        navigator.clipboard.writeText(textContent);
+        this.classList.add("success")
+        this.innerHTML = DoxygenAwesomeFragmentCopyButton.successIcon
+        window.setTimeout(() => {
+            this.classList.remove("success")
+            this.innerHTML = DoxygenAwesomeFragmentCopyButton.copyIcon
+        }, DoxygenAwesomeFragmentCopyButton.successDuration);
+    }
+}
+
+customElements.define("doxygen-awesome-fragment-copy-button", DoxygenAwesomeFragmentCopyButton)
diff --git a/docs/doxygen-awesome-interactive-toc.js b/docs/doxygen-awesome-interactive-toc.js
new file mode 100644
index 0000000000..b049f57331
--- /dev/null
+++ b/docs/doxygen-awesome-interactive-toc.js
@@ -0,0 +1,81 @@
+/**
+
+Doxygen Awesome
+https://github.com/jothepro/doxygen-awesome-css
+
+MIT License
+
+Copyright (c) 2022 jothepro
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+*/
+
+class DoxygenAwesomeInteractiveToc {
+    static topOffset = 38
+    static hideMobileMenu = true
+    static headers = []
+
+    static init() {
+        window.addEventListener("load", () => {
+            let toc = document.querySelector(".contents > .toc")
+            if(toc) {
+                toc.classList.add("interactive")
+                if(!DoxygenAwesomeInteractiveToc.hideMobileMenu) {
+                    toc.classList.add("open")
+                }
+                document.querySelector(".contents > .toc > h3")?.addEventListener("click", () => {
+                    if(toc.classList.contains("open")) {
+                        toc.classList.remove("open")
+                    } else {
+                        toc.classList.add("open")
+                    }
+                })
+
+                document.querySelectorAll(".contents > .toc > ul a").forEach((node) => {
+                    let id = node.getAttribute("href").substring(1)
+                    DoxygenAwesomeInteractiveToc.headers.push({
+                        node: node,
+                        headerNode: document.getElementById(id)
+                    })
+
+                    document.getElementById("doc-content")?.addEventListener("scroll", () => {
+                        DoxygenAwesomeInteractiveToc.update()
+                    })
+                })
+                DoxygenAwesomeInteractiveToc.update()
+            }
+        })
+    }
+
+    static update() {
+        let active = DoxygenAwesomeInteractiveToc.headers[0]?.node
+        DoxygenAwesomeInteractiveToc.headers.forEach((header) => {
+            let position = header.headerNode.getBoundingClientRect().top
+            header.node.classList.remove("active")
+            header.node.classList.remove("aboveActive")
+            if(position < DoxygenAwesomeInteractiveToc.topOffset) {
+                active = header.node
+                active?.classList.add("aboveActive")
+            }
+        })
+        active?.classList.add("active")
+        active?.classList.remove("aboveActive")
+    }
+}
\ No newline at end of file
diff --git a/docs/doxygen-awesome-sidebar-only.css b/docs/doxygen-awesome-sidebar-only.css
new file mode 100644
index 0000000000..65e1a71fd2
--- /dev/null
+++ b/docs/doxygen-awesome-sidebar-only.css
@@ -0,0 +1,115 @@
+/**
+
+Doxygen Awesome
+https://github.com/jothepro/doxygen-awesome-css
+
+MIT License
+
+Copyright (c) 2021 jothepro
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ */
+
+html {
+    /* side nav width. MUST be = `TREEVIEW_WIDTH`.
+     * Make sure it is wide enough to contain the page title (logo + title + version)
+     */
+    --side-nav-fixed-width: 335px;
+    --menu-display: none;
+
+    --top-height: 170px;
+    --toc-sticky-top: -25px;
+    --toc-max-height: calc(100vh - 2 * var(--spacing-medium) - 25px);
+}
+
+#projectname {
+    white-space: nowrap;
+}
+
+
+@media screen and (min-width: 768px) {
+    html {
+        --searchbar-background: var(--page-background-color);
+    }
+
+    #side-nav {
+        min-width: var(--side-nav-fixed-width);
+        max-width: var(--side-nav-fixed-width);
+        top: var(--top-height);
+        overflow: visible;
+    }
+
+    #nav-tree, #side-nav {
+        height: calc(100vh - var(--top-height)) !important;
+    }
+
+    #nav-tree {
+        padding: 0;
+    }
+
+    #top {
+        display: block;
+        border-bottom: none;
+        height: var(--top-height);
+        margin-bottom: calc(0px - var(--top-height));
+        max-width: var(--side-nav-fixed-width);
+        overflow: hidden;
+        background: var(--side-nav-background);
+    }
+    #main-nav {
+        float: left;
+        padding-right: 0;
+    }
+
+    .ui-resizable-handle {
+        cursor: default;
+        width: 1px !important;
+        box-shadow: 0 calc(-2 * var(--top-height)) 0 0 var(--separator-color);
+    }
+
+    #nav-path {
+        position: fixed;
+        right: 0;
+        left: var(--side-nav-fixed-width);
+        bottom: 0;
+        width: auto;
+    }
+
+    #doc-content {
+        height: calc(100vh - 31px) !important;
+        padding-bottom: calc(3 * var(--spacing-large));
+        padding-top: calc(var(--top-height) - 80px);
+        box-sizing: border-box;
+        margin-left: var(--side-nav-fixed-width) !important;
+    }
+
+    #MSearchBox {
+        width: calc(var(--side-nav-fixed-width) - calc(2 * var(--spacing-medium)));
+    }
+
+    #MSearchField {
+        width: calc(var(--side-nav-fixed-width) - calc(2 * var(--spacing-medium)) - 65px);
+    }
+
+    #MSearchResultsWindow {
+        left: var(--spacing-medium) !important;
+        right: auto;
+    }
+}
diff --git a/docs/doxygen-awesome.css b/docs/doxygen-awesome.css
new file mode 100644
index 0000000000..e9a1553123
--- /dev/null
+++ b/docs/doxygen-awesome.css
@@ -0,0 +1,2405 @@
+/**
+
+Doxygen Awesome
+https://github.com/jothepro/doxygen-awesome-css
+
+MIT License
+
+Copyright (c) 2021 - 2022 jothepro
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+*/
+
+html {
+    /* primary theme color. This will affect the entire websites color scheme: links, arrows, labels, ... */
+    --primary-color: #1779c4;
+    --primary-dark-color: #335c80;
+    --primary-light-color: #70b1e9;
+
+    /* page base colors */
+    --page-background-color: #ffffff;
+    --page-foreground-color: #2f4153;
+    --page-secondary-foreground-color: #6f7e8e;
+
+    /* color for all separators on the website: hr, borders, ... */
+    --separator-color: #dedede;
+
+    /* border radius for all rounded components. Will affect many components, like dropdowns, memitems, codeblocks, ... */
+    --border-radius-large: 6px;
+    --border-radius-small: 3px;
+    --border-radius-medium: 5px;
+
+    /* default spacings. Most components reference these values for spacing, to provide uniform spacing on the page. */
+    --spacing-small: 5px;
+    --spacing-medium: 8px;
+    --spacing-large: 10px;
+
+    /* default box shadow used for raising an element above the normal content. Used in dropdowns, search result, ... */
+    --box-shadow: 0 2px 8px 0 rgba(0,0,0,.075);
+
+    --odd-color: rgba(0,0,0,.028);
+
+    /* font-families. will affect all text on the website
+     * font-family: the normal font for text, headlines, menus
+     * font-family-monospace: used for preformatted text in memtitle, code, fragments
+     */
+    --font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen,Ubuntu,Cantarell,Fira Sans,Droid Sans,Helvetica Neue,sans-serif;
+    --font-family-monospace: ui-monospace,SFMono-Regular,SF Mono,Menlo,Consolas,Liberation Mono,monospace;
+
+    /* font sizes */
+    --page-font-size: 15.6px;
+    --navigation-font-size: 14.4px;
+    --toc-font-size: 13.4px;
+    --code-font-size: 14px; /* affects code, fragment */
+    --title-font-size: 22px;
+
+    /* content text properties. These only affect the page content, not the navigation or any other ui elements */
+    --content-line-height: 25px;
+    /* The content is centered and constraint in it's width. To make the content fill the whole page, set the variable to auto.*/
+    --content-maxwidth: 1050px;
+    --table-line-height: 24px;
+    --toc-sticky-top: var(--spacing-medium);
+    --toc-width: 200px;
+    --toc-max-height: calc(100vh - 2 * var(--spacing-medium) - 85px);
+
+    /* colors for various content boxes: @warning, @note, @deprecated @bug */
+    --warning-color: #f8d1cc;
+    --warning-color-dark: #b61825;
+    --warning-color-darker: #75070f;
+    --note-color: #faf3d8;
+    --note-color-dark: #f3a600;
+    --note-color-darker: #5f4204;
+    --todo-color: #e4f3ff;
+    --todo-color-dark: #1879C4;
+    --todo-color-darker: #274a5c;
+    --deprecated-color: #ecf0f3;
+    --deprecated-color-dark: #5b6269;
+    --deprecated-color-darker: #43454a;
+    --bug-color: #e4dafd;
+    --bug-color-dark: #5b2bdd;
+    --bug-color-darker: #2a0d72;
+    --invariant-color: #d8f1e3;
+    --invariant-color-dark: #44b86f;
+    --invariant-color-darker: #265532;
+
+    /* blockquote colors */
+    --blockquote-background: #f8f9fa;
+    --blockquote-foreground: #636568;
+
+    /* table colors */
+    --tablehead-background: #f1f1f1;
+    --tablehead-foreground: var(--page-foreground-color);
+
+    /* menu-display: block | none
+     * Visibility of the top navigation on screens >= 768px. On smaller screen the menu is always visible.
+     * `GENERATE_TREEVIEW` MUST be enabled!
+     */
+    --menu-display: block;
+
+    --menu-focus-foreground: var(--page-background-color);
+    --menu-focus-background: var(--primary-color);
+    --menu-selected-background: rgba(0,0,0,.05);
+
+
+    --header-background: var(--page-background-color);
+    --header-foreground: var(--page-foreground-color);
+
+    /* searchbar colors */
+    --searchbar-background: var(--side-nav-background);
+    --searchbar-foreground: var(--page-foreground-color);
+
+    /* searchbar size
+     * (`searchbar-width` is only applied on screens >= 768px.
+     * on smaller screens the searchbar will always fill the entire screen width) */
+    --searchbar-height: 33px;
+    --searchbar-width: 210px;
+    --searchbar-border-radius: var(--searchbar-height);
+
+    /* code block colors */
+    --code-background: #f5f5f5;
+    --code-foreground: var(--page-foreground-color);
+
+    /* fragment colors */
+    --fragment-background: #F8F9FA;
+    --fragment-foreground: #37474F;
+    --fragment-keyword: #bb6bb2;
+    --fragment-keywordtype: #8258b3;
+    --fragment-keywordflow: #d67c3b;
+    --fragment-token: #438a59;
+    --fragment-comment: #969696;
+    --fragment-link: #5383d6;
+    --fragment-preprocessor: #46aaa5;
+    --fragment-linenumber-color: #797979;
+    --fragment-linenumber-background: #f4f4f5;
+    --fragment-linenumber-border: #e3e5e7;
+    --fragment-lineheight: 19px;
+
+    /* sidebar navigation (treeview) colors */
+    --side-nav-background: #fbfbfb;
+    --side-nav-foreground: var(--page-foreground-color);
+    --side-nav-arrow-opacity: 0;
+    --side-nav-arrow-hover-opacity: 0.9;
+
+    --toc-background: var(--side-nav-background);
+    --toc-foreground: var(--side-nav-foreground);
+
+    /* height of an item in any tree / collapsable table */
+    --tree-item-height: 27px;
+
+    --memname-font-size: var(--code-font-size);
+    --memtitle-font-size: 18px;
+
+    --webkit-scrollbar-size: 7px;
+    --webkit-scrollbar-padding: 4px;
+    --webkit-scrollbar-color: var(--separator-color);
+}
+
+@media screen and (max-width: 767px) {
+    html {
+        --page-font-size: 16px;
+        --navigation-font-size: 16px;
+        --toc-font-size: 15px;
+        --code-font-size: 15px; /* affects code, fragment */
+        --title-font-size: 22px;
+    }
+}
+
+@media (prefers-color-scheme: dark) {
+    html:not(.light-mode) {
+        color-scheme: dark;
+
+        --primary-color: #1982d2;
+        --primary-dark-color: #86a9c4;
+        --primary-light-color: #4779ac;
+
+        --box-shadow: 0 2px 8px 0 rgba(0,0,0,.35);
+
+        --odd-color: rgba(100,100,100,.06);
+
+        --menu-selected-background: rgba(0,0,0,.4);
+
+        --page-background-color: #1C1D1F;
+        --page-foreground-color: #d2dbde;
+        --page-secondary-foreground-color: #859399;
+        --separator-color: #38393b;
+        --side-nav-background: #252628;
+
+        --code-background: #2a2c2f;
+
+        --tablehead-background: #2a2c2f;
+    
+        --blockquote-background: #222325;
+        --blockquote-foreground: #7e8c92;
+
+        --warning-color: #2e1917;
+        --warning-color-dark: #ad2617;
+        --warning-color-darker: #f5b1aa;
+        --note-color: #3b2e04;
+        --note-color-dark: #f1b602;
+        --note-color-darker: #ceb670;
+        --todo-color: #163750;
+        --todo-color-dark: #1982D2;
+        --todo-color-darker: #dcf0fa;
+        --deprecated-color: #2e323b;
+        --deprecated-color-dark: #738396;
+        --deprecated-color-darker: #abb0bd;
+        --bug-color: #2a2536;
+        --bug-color-dark: #7661b3;
+        --bug-color-darker: #ae9ed6;
+        --invariant-color: #303a35;
+        --invariant-color-dark: #76ce96;
+        --invariant-color-darker: #cceed5;
+
+        --fragment-background: #282c34;
+        --fragment-foreground: #dbe4eb;
+        --fragment-keyword: #cc99cd;
+        --fragment-keywordtype: #ab99cd;
+        --fragment-keywordflow: #e08000;
+        --fragment-token: #7ec699;
+        --fragment-comment: #999999;
+        --fragment-link: #98c0e3;
+        --fragment-preprocessor: #65cabe;
+        --fragment-linenumber-color: #cccccc;
+        --fragment-linenumber-background: #35393c;
+        --fragment-linenumber-border: #1f1f1f;
+    }
+}
+
+/* dark mode variables are defined twice, to support both the dark-mode without and with doxygen-awesome-darkmode-toggle.js */
+html.dark-mode {
+    color-scheme: dark;
+
+    --primary-color: #1982d2;
+    --primary-dark-color: #86a9c4;
+    --primary-light-color: #4779ac;
+
+    --box-shadow: 0 2px 8px 0 rgba(0,0,0,.30);
+
+    --odd-color: rgba(100,100,100,.06);
+
+    --menu-selected-background: rgba(0,0,0,.4);
+
+    --page-background-color: #1C1D1F;
+    --page-foreground-color: #d2dbde;
+    --page-secondary-foreground-color: #859399;
+    --separator-color: #38393b;
+    --side-nav-background: #252628;
+
+    --code-background: #2a2c2f;
+
+    --tablehead-background: #2a2c2f;
+
+    --blockquote-background: #222325;
+    --blockquote-foreground: #7e8c92;
+
+    --warning-color: #2e1917;
+    --warning-color-dark: #ad2617;
+    --warning-color-darker: #f5b1aa;
+    --note-color: #3b2e04;
+    --note-color-dark: #f1b602;
+    --note-color-darker: #ceb670;
+    --todo-color: #163750;
+    --todo-color-dark: #1982D2;
+    --todo-color-darker: #dcf0fa;
+    --deprecated-color: #2e323b;
+    --deprecated-color-dark: #738396;
+    --deprecated-color-darker: #abb0bd;
+    --bug-color: #2a2536;
+    --bug-color-dark: #7661b3;
+    --bug-color-darker: #ae9ed6;
+    --invariant-color: #303a35;
+    --invariant-color-dark: #76ce96;
+    --invariant-color-darker: #cceed5;
+
+    --fragment-background: #282c34;
+    --fragment-foreground: #dbe4eb;
+    --fragment-keyword: #cc99cd;
+    --fragment-keywordtype: #ab99cd;
+    --fragment-keywordflow: #e08000;
+    --fragment-token: #7ec699;
+    --fragment-comment: #999999;
+    --fragment-link: #98c0e3;
+    --fragment-preprocessor: #65cabe;
+    --fragment-linenumber-color: #cccccc;
+    --fragment-linenumber-background: #35393c;
+    --fragment-linenumber-border: #1f1f1f;
+}
+
+body {
+    color: var(--page-foreground-color);
+    background-color: var(--page-background-color);
+    font-size: var(--page-font-size);
+}
+
+body, table, div, p, dl, #nav-tree .label, .title,
+.sm-dox a, .sm-dox a:hover, .sm-dox a:focus, #projectname,
+.SelectItem, #MSearchField, .navpath li.navelem a,
+.navpath li.navelem a:hover, p.reference, p.definition {
+    font-family: var(--font-family);
+}
+
+h1, h2, h3, h4, h5 {
+    margin-top: .9em;
+    font-weight: 600;
+    line-height: initial;
+}
+
+p, div, table, dl, p.reference, p.definition {
+    font-size: var(--page-font-size);
+}
+
+p.reference, p.definition {
+    color: var(--page-secondary-foreground-color);
+}
+
+a:link, a:visited, a:hover, a:focus, a:active {
+    color: var(--primary-color) !important;
+    font-weight: 500;
+}
+
+a.anchor {
+    scroll-margin-top: var(--spacing-large);
+    display: block;
+}
+
+/*
+ Title and top navigation
+ */
+
+#top {
+    background: var(--header-background);
+    border-bottom: 1px solid var(--separator-color);
+}
+
+@media screen and (min-width: 768px) {
+    #top {
+        display: flex;
+        flex-wrap: wrap;
+        justify-content: space-between;
+        align-items: center;
+    }
+}
+
+#main-nav {
+    flex-grow: 5;
+    padding: var(--spacing-small) var(--spacing-medium);
+}
+
+#titlearea {
+    width: auto;
+    padding: var(--spacing-medium) var(--spacing-large);
+    background: none;
+    color: var(--header-foreground);
+    border-bottom: none;
+}
+
+@media screen and (max-width: 767px) {
+    #titlearea {
+        padding-bottom: var(--spacing-small);
+    }
+}
+
+#titlearea table tbody tr {
+    height: auto !important;
+}
+
+#projectname {
+    font-size: var(--title-font-size);
+    font-weight: 600;
+}
+
+#projectnumber {
+    font-family: inherit;
+    font-size: 60%;
+}
+
+#projectbrief {
+    font-family: inherit;
+    font-size: 80%;
+}
+
+#projectlogo {
+    vertical-align: middle;
+}
+
+#projectlogo img {
+    max-height: calc(var(--title-font-size) * 2);
+    margin-right: var(--spacing-small);
+}
+
+.sm-dox, .tabs, .tabs2, .tabs3 {
+    background: none;
+    padding: 0;
+}
+
+.tabs, .tabs2, .tabs3 {
+    border-bottom: 1px solid var(--separator-color);
+    margin-bottom: -1px;
+}
+
+.main-menu-btn-icon, .main-menu-btn-icon:before, .main-menu-btn-icon:after {
+    background: var(--page-secondary-foreground-color);
+}
+
+@media screen and (max-width: 767px) {
+    .sm-dox a span.sub-arrow {
+        background: var(--code-background);
+    }
+
+    #main-menu a.has-submenu span.sub-arrow {
+        color: var(--page-secondary-foreground-color);
+        border-radius: var(--border-radius-medium);
+    }
+
+    #main-menu a.has-submenu:hover span.sub-arrow {
+        color: var(--page-foreground-color);
+    }
+}
+
+@media screen and (min-width: 768px) {
+    .sm-dox li, .tablist li {
+        display: var(--menu-display);
+    }
+
+    .sm-dox a span.sub-arrow {
+        border-color: var(--header-foreground) transparent transparent transparent;
+    }
+
+    .sm-dox a:hover span.sub-arrow {
+        border-color: var(--menu-focus-foreground) transparent transparent transparent;
+    }
+
+    .sm-dox ul a span.sub-arrow {
+        border-color: transparent transparent transparent var(--page-foreground-color);
+    }
+
+    .sm-dox ul a:hover span.sub-arrow {
+        border-color: transparent transparent transparent var(--menu-focus-foreground);
+    }
+}
+
+.sm-dox ul {
+    background: var(--page-background-color);
+    box-shadow: var(--box-shadow);
+    border: 1px solid var(--separator-color);
+    border-radius: var(--border-radius-medium) !important;
+    padding: var(--spacing-small);
+    animation: ease-out 150ms slideInMenu;
+}
+
+@keyframes slideInMenu {
+    from {
+        opacity: 0;
+        transform: translate(0px, -2px);
+    }
+
+    to {
+        opacity: 1;
+        transform: translate(0px, 0px);
+    }
+}
+
+.sm-dox ul a {
+    color: var(--page-foreground-color) !important;
+    background: var(--page-background-color);
+    font-size: var(--navigation-font-size);
+}
+
+.sm-dox>li>ul:after {
+    border-bottom-color: var(--page-background-color) !important;
+}
+
+.sm-dox>li>ul:before {
+    border-bottom-color: var(--separator-color) !important;
+}
+
+.sm-dox ul a:hover, .sm-dox ul a:active, .sm-dox ul a:focus {
+    font-size: var(--navigation-font-size) !important;
+    color: var(--menu-focus-foreground) !important;
+    text-shadow: none;
+    background-color: var(--menu-focus-background);
+    border-radius: var(--border-radius-small) !important;
+}
+
+.sm-dox a, .sm-dox a:focus, .tablist li, .tablist li a, .tablist li.current a {
+    text-shadow: none;
+    background: transparent;
+    background-image: none !important;
+    color: var(--header-foreground) !important;
+    font-weight: normal;
+    font-size: var(--navigation-font-size);
+    border-radius: var(--border-radius-small) !important;
+}
+
+.sm-dox a:focus {
+    outline: auto;
+}
+
+.sm-dox a:hover, .sm-dox a:active, .tablist li a:hover {
+    text-shadow: none;
+    font-weight: normal;
+    background: var(--menu-focus-background);
+    color: var(--menu-focus-foreground) !important;
+    border-radius: var(--border-radius-small) !important;
+    font-size: var(--navigation-font-size);
+}
+
+.tablist li.current {
+    border-radius: var(--border-radius-small);
+    background: var(--menu-selected-background);
+}
+
+.tablist li {
+    margin: var(--spacing-small) 0 var(--spacing-small) var(--spacing-small);
+}
+
+.tablist a {
+    padding: 0 var(--spacing-large);
+}
+
+
+/*
+ Search box
+ */
+
+#MSearchBox {
+    height: var(--searchbar-height);
+    background: var(--searchbar-background);
+    border-radius: var(--searchbar-border-radius);
+    border: 1px solid var(--separator-color);
+    overflow: hidden;
+    width: var(--searchbar-width);
+    position: relative;
+    box-shadow: none;
+    display: block;
+    margin-top: 0;
+}
+
+/* until Doxygen 1.9.4 */
+.left img#MSearchSelect {
+    left: 0;
+    user-select: none;
+    padding-left: 8px;
+}
+
+/* Doxygen 1.9.5 */
+.left span#MSearchSelect {
+    left: 0;
+    user-select: none;
+    margin-left: 8px;
+    padding: 0;
+}
+
+.left #MSearchSelect[src$=".png"] {
+    padding-left: 0
+}
+
+.SelectionMark {
+    user-select: none;
+}
+
+.tabs .left #MSearchSelect {
+    padding-left: 0;
+}
+
+.tabs #MSearchBox {
+    position: absolute;
+    right: var(--spacing-medium);
+}
+
+@media screen and (max-width: 767px) {
+    .tabs #MSearchBox {
+        position: relative;
+        right: 0;
+        margin-left: var(--spacing-medium);
+        margin-top: 0;
+    }
+}
+
+#MSearchSelectWindow, #MSearchResultsWindow {
+    z-index: 9999;
+}
+
+#MSearchBox.MSearchBoxActive {
+    border-color: var(--primary-color);
+    box-shadow: inset 0 0 0 1px var(--primary-color);
+}
+
+#main-menu > li:last-child {
+    margin-right: 0;
+}
+
+@media screen and (max-width: 767px) {
+    #main-menu > li:last-child {
+        height: 50px;
+    }
+}
+
+#MSearchField {
+    font-size: var(--navigation-font-size);
+    height: calc(var(--searchbar-height) - 2px);
+    background: transparent;
+    width: calc(var(--searchbar-width) - 64px);
+}
+
+.MSearchBoxActive #MSearchField {
+    color: var(--searchbar-foreground);
+}
+
+#MSearchSelect {
+    top: calc(calc(var(--searchbar-height) / 2) - 11px);
+}
+
+#MSearchBox span.left, #MSearchBox span.right {
+    background: none;
+    background-image: none;
+}
+
+#MSearchBox span.right {
+    padding-top: calc(calc(var(--searchbar-height) / 2) - 12px);
+    position: absolute;
+    right: var(--spacing-small);
+}
+
+.tabs #MSearchBox span.right {
+    top: calc(calc(var(--searchbar-height) / 2) - 12px);
+}
+
+@keyframes slideInSearchResults {
+    from {
+        opacity: 0;
+        transform: translate(0, 15px);
+    }
+
+    to {
+        opacity: 1;
+        transform: translate(0, 20px);
+    }
+}
+
+#MSearchResultsWindow {
+    left: auto !important;
+    right: var(--spacing-medium);
+    border-radius: var(--border-radius-large);
+    border: 1px solid var(--separator-color);
+    transform: translate(0, 20px);
+    box-shadow: var(--box-shadow);
+    animation: ease-out 280ms slideInSearchResults;
+    background: var(--page-background-color);
+}
+
+iframe#MSearchResults {
+    margin: 4px;
+}
+
+iframe {
+    color-scheme: normal;
+}
+
+@media (prefers-color-scheme: dark) {
+    html:not(.light-mode) iframe#MSearchResults {
+        filter: invert() hue-rotate(180deg);
+    }
+}
+
+html.dark-mode iframe#MSearchResults {
+    filter: invert() hue-rotate(180deg);
+}
+
+#MSearchResults .SRPage {
+    background-color: transparent;
+}
+
+#MSearchResults .SRPage .SREntry {
+    font-size: 10pt;
+    padding: var(--spacing-small) var(--spacing-medium);
+}
+
+#MSearchSelectWindow {
+    border: 1px solid var(--separator-color);
+    border-radius: var(--border-radius-medium);
+    box-shadow: var(--box-shadow);
+    background: var(--page-background-color);
+    padding-top: var(--spacing-small);
+    padding-bottom: var(--spacing-small);
+}
+
+#MSearchSelectWindow a.SelectItem {
+    font-size: var(--navigation-font-size);
+    line-height: var(--content-line-height);
+    margin: 0 var(--spacing-small);
+    border-radius: var(--border-radius-small);
+    color: var(--page-foreground-color) !important;
+    font-weight: normal;
+}
+
+#MSearchSelectWindow a.SelectItem:hover {
+    background: var(--menu-focus-background);
+    color: var(--menu-focus-foreground) !important;
+}
+
+@media screen and (max-width: 767px) {
+    #MSearchBox {
+        margin-top: var(--spacing-medium);
+        margin-bottom: var(--spacing-medium);
+        width: calc(100vw - 30px);
+    }
+
+    #main-menu > li:last-child {
+        float: none !important;
+    }
+
+    #MSearchField {
+        width: calc(100vw - 110px);
+    }
+
+    @keyframes slideInSearchResultsMobile {
+        from {
+            opacity: 0;
+            transform: translate(0, 15px);
+        }
+
+        to {
+            opacity: 1;
+            transform: translate(0, 20px);
+        }
+    }
+
+    #MSearchResultsWindow {
+        left: var(--spacing-medium) !important;
+        right: var(--spacing-medium);
+        overflow: auto;
+        transform: translate(0, 20px);
+        animation: ease-out 280ms slideInSearchResultsMobile;
+        width: auto !important;
+    }
+
+    /*
+     * Overwrites for fixing the searchbox on mobile in doxygen 1.9.2
+     */
+    label.main-menu-btn ~ #searchBoxPos1 {
+        top: 3px !important;
+        right: 6px !important;
+        left: 45px;
+        display: flex;
+    }
+
+    label.main-menu-btn ~ #searchBoxPos1 > #MSearchBox {
+        margin-top: 0;
+        margin-bottom: 0;
+        flex-grow: 2;
+        float: left;
+    }
+}
+
+/*
+ Tree view
+ */
+
+#side-nav {
+    padding: 0 !important;
+    background: var(--side-nav-background);
+}
+
+@media screen and (max-width: 767px) {
+    #side-nav {
+        display: none;
+    }
+
+    #doc-content {
+        margin-left: 0 !important;
+    }
+}
+
+#nav-tree {
+    background: transparent;
+}
+
+#nav-tree .label {
+    font-size: var(--navigation-font-size);
+}
+
+#nav-tree .item {
+    height: var(--tree-item-height);
+    line-height: var(--tree-item-height);
+}
+
+#nav-sync {
+    bottom: 12px;
+    right: 12px;
+    top: auto !important;
+    user-select: none;
+}
+
+#nav-tree .selected {
+    text-shadow: none;
+    background-image: none;
+    background-color: transparent;
+    position: relative;
+}
+
+#nav-tree .selected::after {
+    content: "";
+    position: absolute;
+    top: 1px;
+    bottom: 1px;
+    left: 0;
+    width: 4px;
+    border-radius: 0 var(--border-radius-small) var(--border-radius-small) 0;
+    background: var(--primary-color);
+}
+
+
+#nav-tree a {
+    color: var(--side-nav-foreground) !important;
+    font-weight: normal;
+}
+
+#nav-tree a:focus {
+    outline-style: auto;
+}
+
+#nav-tree .arrow {
+    opacity: var(--side-nav-arrow-opacity);
+}
+
+.arrow {
+    color: inherit;
+    cursor: pointer;
+    font-size: 45%;
+    vertical-align: middle;
+    margin-right: 2px;
+    font-family: serif;
+    height: auto;
+    text-align: right;
+}
+
+#nav-tree div.item:hover .arrow, #nav-tree a:focus .arrow {
+    opacity: var(--side-nav-arrow-hover-opacity);
+}
+
+#nav-tree .selected a {
+    color: var(--primary-color) !important;
+    font-weight: bolder;
+    font-weight: 600;
+}
+
+.ui-resizable-e {
+    background: var(--separator-color);
+    width: 1px;
+}
+
+/*
+ Contents
+ */
+
+div.header {
+    border-bottom: 1px solid var(--separator-color);
+    background-color: var(--page-background-color);
+    background-image: none;
+}
+
+@media screen and (min-width: 1000px) {
+    #doc-content > div > div.contents,
+    .PageDoc > div.contents {
+        display: flex;
+        flex-direction: row-reverse;
+        flex-wrap: nowrap;
+        align-items: flex-start;
+    }
+    
+    div.contents .textblock {
+        min-width: 200px;
+        flex-grow: 1;
+    }
+}
+
+div.contents, div.header .title, div.header .summary {
+    max-width: var(--content-maxwidth);
+}
+
+div.contents, div.header .title  {
+    line-height: initial;
+    margin: calc(var(--spacing-medium) + .2em) auto var(--spacing-medium) auto;
+}
+
+div.header .summary {
+    margin: var(--spacing-medium) auto 0 auto;
+}
+
+div.headertitle {
+    padding: 0;
+}
+
+div.header .title {
+    font-weight: 600;
+    font-size: 225%;
+    padding: var(--spacing-medium) var(--spacing-large);
+    word-break: break-word;
+}
+
+div.header .summary {
+    width: auto;
+    display: block;
+    float: none;
+    padding: 0 var(--spacing-large);
+}
+
+td.memSeparator {
+    border-color: var(--separator-color);
+}
+
+span.mlabel {
+    background: var(--primary-color);
+    border: none;
+    padding: 4px 9px;
+    border-radius: 12px;
+    margin-right: var(--spacing-medium);
+}
+
+span.mlabel:last-of-type {
+    margin-right: 2px;
+}
+
+div.contents {
+    padding: 0 var(--spacing-large);
+}
+
+div.contents p, div.contents li {
+    line-height: var(--content-line-height);
+}
+
+div.contents div.dyncontent {
+    margin: var(--spacing-medium) 0;
+}
+
+@media (prefers-color-scheme: dark) {
+    html:not(.light-mode) div.contents div.dyncontent img,
+    html:not(.light-mode) div.contents center img,
+    html:not(.light-mode) div.contents > table img,
+    html:not(.light-mode) div.contents div.dyncontent iframe,
+    html:not(.light-mode) div.contents center iframe,
+    html:not(.light-mode) div.contents table iframe {
+        filter: hue-rotate(180deg) invert();
+    }
+}
+
+html.dark-mode div.contents div.dyncontent img,
+html.dark-mode div.contents center img,
+html.dark-mode div.contents > table img,
+html.dark-mode div.contents div.dyncontent iframe,
+html.dark-mode div.contents center iframe,
+html.dark-mode div.contents table iframe {
+    filter: hue-rotate(180deg) invert();
+}
+
+h2.groupheader {
+    border-bottom: 0px;
+    color: var(--page-foreground-color);
+    box-shadow: 
+        100px 0 var(--page-background-color), 
+        -100px 0 var(--page-background-color),
+        100px 0.75px var(--separator-color),
+        -100px 0.75px var(--separator-color),
+        500px 0 var(--page-background-color), 
+        -500px 0 var(--page-background-color),
+        500px 0.75px var(--separator-color),
+        -500px 0.75px var(--separator-color),
+        900px 0 var(--page-background-color), 
+        -900px 0 var(--page-background-color),
+        900px 0.75px var(--separator-color),
+        -900px 0.75px var(--separator-color),
+        1400px 0 var(--page-background-color),
+        -1400px 0 var(--page-background-color), 
+        1400px 0.75px var(--separator-color),
+        -1400px 0.75px var(--separator-color),
+        1900px 0 var(--page-background-color),
+        -1900px 0 var(--page-background-color),
+        1900px 0.75px var(--separator-color),
+        -1900px 0.75px var(--separator-color);
+}
+
+blockquote {
+    margin: 0 var(--spacing-medium) 0 var(--spacing-medium);
+    padding: var(--spacing-small) var(--spacing-large);
+    background: var(--blockquote-background);
+    color: var(--blockquote-foreground);
+    border-left: 0;
+    overflow: visible;
+    border-radius: var(--border-radius-medium);
+    overflow: visible;
+    position: relative;
+}
+
+blockquote::before, blockquote::after {
+    font-weight: bold;
+    font-family: serif;
+    font-size: 360%;
+    opacity: .15;
+    position: absolute;
+}
+
+blockquote::before {
+    content: "“";
+    left: -10px;
+    top: 4px;
+}
+
+blockquote::after {
+    content: "”";
+    right: -8px;
+    bottom: -25px;
+}
+
+blockquote p {
+    margin: var(--spacing-small) 0 var(--spacing-medium) 0;
+}
+.paramname {
+    font-weight: 600;
+    color: var(--primary-dark-color);
+}
+
+.paramname > code {
+    border: 0;
+}
+
+table.params .paramname {
+    font-weight: 600;
+    font-family: var(--font-family-monospace);
+    font-size: var(--code-font-size);
+    padding-right: var(--spacing-small);
+    line-height: var(--table-line-height);
+}
+
+h1.glow, h2.glow, h3.glow, h4.glow, h5.glow, h6.glow {
+    text-shadow: 0 0 15px var(--primary-light-color);
+}
+
+.alphachar a {
+    color: var(--page-foreground-color);
+}
+
+/*
+ Table of Contents
+ */
+
+div.contents .toc {
+    max-height: var(--toc-max-height);
+    min-width: var(--toc-width);
+    border: 0;
+    border-left: 1px solid var(--separator-color);
+    border-radius: 0;
+    background-color: transparent;
+    box-shadow: none;
+    position: sticky;
+    top: var(--toc-sticky-top);
+    padding: 0 var(--spacing-large);
+    margin: var(--spacing-small) 0 var(--spacing-large) var(--spacing-large);
+}
+
+div.toc h3 {
+    color: var(--toc-foreground);
+    font-size: var(--navigation-font-size);
+    margin: var(--spacing-large) 0 var(--spacing-medium) 0;
+}
+
+div.toc li {
+    padding: 0;
+    background: none;
+    line-height: var(--toc-font-size);
+    margin: var(--toc-font-size) 0 0 0;
+}
+
+div.toc li::before {
+    display: none;
+}
+
+div.toc ul {
+    margin-top: 0
+}
+
+div.toc li a {
+    font-size: var(--toc-font-size);
+    color: var(--page-foreground-color) !important;
+    text-decoration: none;
+}
+
+div.toc li a:hover, div.toc li a.active {
+    color: var(--primary-color) !important;
+}
+
+div.toc li a.aboveActive {
+    color: var(--page-secondary-foreground-color) !important;
+}
+
+
+@media screen and (max-width: 999px) {
+    div.contents .toc {
+        max-height: 45vh;
+        float: none;
+        width: auto;
+        margin: 0 0 var(--spacing-medium) 0;
+        position: relative;
+        top: 0;
+        position: relative;
+        border: 1px solid var(--separator-color);
+        border-radius: var(--border-radius-medium);
+        background-color: var(--toc-background);
+        box-shadow: var(--box-shadow);
+    }
+
+    div.contents .toc.interactive {
+        max-height: calc(var(--navigation-font-size) + 2 * var(--spacing-large));
+        overflow: hidden;
+    }
+
+    div.contents .toc > h3 {
+        -webkit-tap-highlight-color: transparent;
+        cursor: pointer;
+        position: sticky;
+        top: 0;
+        background-color: var(--toc-background);
+        margin: 0;
+        padding: var(--spacing-large) 0;
+        display: block;
+    }
+
+    div.contents .toc.interactive > h3::before {
+        content: "";
+        width: 0; 
+        height: 0; 
+        border-left: 4px solid transparent;
+        border-right: 4px solid transparent;
+        border-top: 5px solid var(--primary-color);
+        display: inline-block;
+        margin-right: var(--spacing-small);
+        margin-bottom: calc(var(--navigation-font-size) / 4);
+        transform: rotate(-90deg);
+        transition: transform 0.25s ease-out;
+    }
+
+    div.contents .toc.interactive.open > h3::before {
+        transform: rotate(0deg);
+    }
+
+    div.contents .toc.interactive.open {
+        max-height: 45vh;
+        overflow: auto;
+        transition: max-height 0.2s ease-in-out;
+    }
+
+    div.contents .toc a, div.contents .toc a.active {
+        color: var(--primary-color) !important;
+    }
+
+    div.contents .toc a:hover {
+        text-decoration: underline;
+    }
+}
+
+/*
+ Code & Fragments
+ */
+
+code, div.fragment, pre.fragment {
+    border-radius: var(--border-radius-small);
+    border: 1px solid var(--separator-color);
+    overflow: hidden;
+}
+
+code {
+    display: inline;
+    background: var(--code-background);
+    color: var(--code-foreground);
+    padding: 2px 6px;
+}
+
+div.fragment, pre.fragment {
+    margin: var(--spacing-medium) 0;
+    padding: calc(var(--spacing-large) - (var(--spacing-large) / 6)) var(--spacing-large);
+    background: var(--fragment-background);
+    color: var(--fragment-foreground);
+    overflow-x: auto;
+}
+
+@media screen and (max-width: 767px) {
+    div.fragment, pre.fragment {
+        border-top-right-radius: 0;
+        border-bottom-right-radius: 0;
+        border-right: 0;
+    }
+
+    .contents > div.fragment,
+    .textblock > div.fragment,
+    .textblock > pre.fragment,
+    .contents > .doxygen-awesome-fragment-wrapper > div.fragment,
+    .textblock > .doxygen-awesome-fragment-wrapper > div.fragment,
+    .textblock > .doxygen-awesome-fragment-wrapper > pre.fragment {
+        margin: var(--spacing-medium) calc(0px - var(--spacing-large));
+        border-radius: 0;
+        border-left: 0;
+    }
+
+    .textblock li > .fragment,
+    .textblock li > .doxygen-awesome-fragment-wrapper > .fragment {
+        margin: var(--spacing-medium) calc(0px - var(--spacing-large));
+    }
+
+    .memdoc li > .fragment,
+    .memdoc li > .doxygen-awesome-fragment-wrapper > .fragment {
+        margin: var(--spacing-medium) calc(0px - var(--spacing-medium));
+    }
+
+    .textblock ul, .memdoc ul {
+        overflow: initial;
+    }
+
+    .memdoc > div.fragment,
+    .memdoc > pre.fragment,
+    dl dd > div.fragment,
+    dl dd pre.fragment,
+    .memdoc > .doxygen-awesome-fragment-wrapper > div.fragment,
+    .memdoc > .doxygen-awesome-fragment-wrapper > pre.fragment,
+    dl dd > .doxygen-awesome-fragment-wrapper > div.fragment,
+    dl dd .doxygen-awesome-fragment-wrapper > pre.fragment {
+        margin: var(--spacing-medium) calc(0px - var(--spacing-medium));
+        border-radius: 0;
+        border-left: 0;
+    }
+}
+
+code, code a, pre.fragment, div.fragment, div.fragment .line, div.fragment span, div.fragment .line a, div.fragment .line span {
+    font-family: var(--font-family-monospace);
+    font-size: var(--code-font-size) !important;
+}
+
+div.line:after {
+    margin-right: var(--spacing-medium);
+}
+
+div.fragment .line, pre.fragment {
+    white-space: pre;
+    word-wrap: initial;
+    line-height: var(--fragment-lineheight);
+}
+
+div.fragment span.keyword {
+    color: var(--fragment-keyword);
+}
+
+div.fragment span.keywordtype {
+    color: var(--fragment-keywordtype);
+}
+
+div.fragment span.keywordflow {
+    color: var(--fragment-keywordflow);
+}
+
+div.fragment span.stringliteral {
+    color: var(--fragment-token)
+}
+
+div.fragment span.comment {
+    color: var(--fragment-comment);
+}
+
+div.fragment a.code {
+    color: var(--fragment-link) !important;
+}
+
+div.fragment span.preprocessor {
+    color: var(--fragment-preprocessor);
+}
+
+div.fragment span.lineno {
+    display: inline-block;
+    width: 27px;
+    border-right: none;
+    background: var(--fragment-linenumber-background);
+    color: var(--fragment-linenumber-color);
+}
+
+div.fragment span.lineno a {
+    background: none;
+    color: var(--fragment-link) !important;
+}
+
+div.fragment .line:first-child .lineno {
+    box-shadow: -999999px 0px 0 999999px var(--fragment-linenumber-background), -999998px 0px 0 999999px var(--fragment-linenumber-border);
+}
+
+div.line {
+    border-radius: var(--border-radius-small);
+}
+
+div.line.glow {
+    background-color: var(--primary-light-color);
+    box-shadow: none;
+}
+
+/*
+ dl warning, attention, note, deprecated, bug, ...
+ */
+
+dl.bug dt a, dl.deprecated dt a, dl.todo dt a {
+    font-weight: bold !important;
+}
+
+dl.warning, dl.attention, dl.note, dl.deprecated, dl.bug, dl.invariant, dl.pre, dl.post, dl.todo, dl.remark {
+    padding: var(--spacing-medium);
+    margin: var(--spacing-medium) 0;
+    color: var(--page-background-color);
+    overflow: hidden;
+    margin-left: 0;
+    border-radius: var(--border-radius-small);
+}
+
+dl.section dd {
+    margin-bottom: 2px;
+}
+
+dl.warning, dl.attention {
+    background: var(--warning-color);
+    border-left: 8px solid var(--warning-color-dark);
+    color: var(--warning-color-darker);
+}
+
+dl.warning dt, dl.attention dt {
+    color: var(--warning-color-dark);
+}
+
+dl.note, dl.remark {
+    background: var(--note-color);
+    border-left: 8px solid var(--note-color-dark);
+    color: var(--note-color-darker);
+}
+
+dl.note dt, dl.remark dt {
+    color: var(--note-color-dark);
+}
+
+dl.todo {
+    background: var(--todo-color);
+    border-left: 8px solid var(--todo-color-dark);
+    color: var(--todo-color-darker);
+}
+
+dl.todo dt {
+    color: var(--todo-color-dark);
+}
+
+dl.bug dt a {
+    color: var(--todo-color-dark) !important;
+}
+
+dl.bug {
+    background: var(--bug-color);
+    border-left: 8px solid var(--bug-color-dark);
+    color: var(--bug-color-darker);
+}
+
+dl.bug dt a {
+    color: var(--bug-color-dark) !important;
+}
+
+dl.deprecated {
+    background: var(--deprecated-color);
+    border-left: 8px solid var(--deprecated-color-dark);
+    color: var(--deprecated-color-darker);
+}
+
+dl.deprecated dt a {
+    color: var(--deprecated-color-dark) !important;
+}
+
+dl.section dd, dl.bug dd, dl.deprecated dd, dl.todo dd {
+    margin-inline-start: 0px;
+}
+
+dl.invariant, dl.pre, dl.post {
+    background: var(--invariant-color);
+    border-left: 8px solid var(--invariant-color-dark);
+    color: var(--invariant-color-darker);
+}
+
+dl.invariant dt, dl.pre dt, dl.post dt {
+    color: var(--invariant-color-dark);
+}
+
+/*
+ memitem
+ */
+
+div.memdoc, div.memproto, h2.memtitle {
+    box-shadow: none;
+    background-image: none;
+    border: none;
+}
+
+div.memdoc {
+    padding: 0 var(--spacing-medium);
+    background: var(--page-background-color);
+}
+
+h2.memtitle, div.memitem {
+    border: 1px solid var(--separator-color);
+    box-shadow: var(--box-shadow);
+}
+
+h2.memtitle {
+    box-shadow: 0px var(--spacing-medium) 0 -1px var(--fragment-background), var(--box-shadow);
+}
+
+div.memitem {
+    transition: none;
+}
+
+div.memproto, h2.memtitle {
+    background: var(--fragment-background);
+}
+
+h2.memtitle {
+    font-weight: 500;
+    font-size: var(--memtitle-font-size);
+    font-family: var(--font-family-monospace);
+    border-bottom: none;
+    border-top-left-radius: var(--border-radius-medium);
+    border-top-right-radius: var(--border-radius-medium);
+    word-break: break-all;
+    position: relative;
+}
+
+h2.memtitle:after {
+    content: "";
+    display: block;
+    background: var(--fragment-background);
+    height: var(--spacing-medium);
+    bottom: calc(0px - var(--spacing-medium));
+    left: 0;
+    right: -14px;
+    position: absolute;
+    border-top-right-radius: var(--border-radius-medium);
+}
+
+h2.memtitle > span.permalink {
+    font-size: inherit;
+}
+
+h2.memtitle > span.permalink > a {
+    text-decoration: none;
+    padding-left: 3px;
+    margin-right: -4px;
+    user-select: none;
+    display: inline-block;
+    margin-top: -6px;
+}
+
+h2.memtitle > span.permalink > a:hover {
+    color: var(--primary-dark-color) !important;
+}
+
+a:target + h2.memtitle, a:target + h2.memtitle + div.memitem {
+    border-color: var(--primary-light-color);
+}
+
+div.memitem {
+    border-top-right-radius: var(--border-radius-medium);
+    border-bottom-right-radius: var(--border-radius-medium);
+    border-bottom-left-radius: var(--border-radius-medium);
+    overflow: hidden;
+    display: block !important;
+}
+
+div.memdoc {
+    border-radius: 0;
+}
+
+div.memproto {
+    border-radius: 0 var(--border-radius-small) 0 0;
+    overflow: auto;
+    border-bottom: 1px solid var(--separator-color);
+    padding: var(--spacing-medium);
+    margin-bottom: -1px;
+}
+
+div.memtitle {
+    border-top-right-radius: var(--border-radius-medium);
+    border-top-left-radius: var(--border-radius-medium);
+}
+
+div.memproto table.memname {
+    font-family: var(--font-family-monospace);
+    color: var(--page-foreground-color);
+    font-size: var(--memname-font-size);
+    text-shadow: none;
+}
+
+div.memproto div.memtemplate {
+    font-family: var(--font-family-monospace);
+    color: var(--primary-dark-color);
+    font-size: var(--memname-font-size);
+    margin-left: 2px;
+    text-shadow: none;
+}
+
+table.mlabels, table.mlabels > tbody {
+    display: block;
+}
+
+td.mlabels-left {
+    width: auto;
+}
+
+td.mlabels-right {
+    margin-top: 3px;
+    position: sticky;
+    left: 0;
+}
+
+table.mlabels > tbody > tr:first-child {
+    display: flex;
+    justify-content: space-between;
+    flex-wrap: wrap;
+}
+
+.memname, .memitem span.mlabels {
+    margin: 0
+}
+
+/*
+ reflist
+ */
+
+dl.reflist {
+    box-shadow: var(--box-shadow);
+    border-radius: var(--border-radius-medium);
+    border: 1px solid var(--separator-color);
+    overflow: hidden;
+    padding: 0;
+}
+
+
+dl.reflist dt, dl.reflist dd {
+    box-shadow: none;
+    text-shadow: none;
+    background-image: none;
+    border: none;
+    padding: 12px;
+}
+
+
+dl.reflist dt {
+    font-weight: 500;
+    border-radius: 0;
+    background: var(--code-background);
+    border-bottom: 1px solid var(--separator-color);
+    color: var(--page-foreground-color)
+}
+
+
+dl.reflist dd {
+    background: none;
+}
+
+/*
+ Table
+ */
+
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname),
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody {
+    display: inline-block;
+    max-width: 100%;
+}
+
+.contents > table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname):not(.classindex) {
+    margin-left: calc(0px - var(--spacing-large));
+    margin-right: calc(0px - var(--spacing-large));
+    max-width: calc(100% + 2 * var(--spacing-large));
+}
+
+table.fieldtable,
+table.markdownTable tbody,
+table.doxtable tbody {
+    border: none;
+    margin: var(--spacing-medium) 0;
+    box-shadow: 0 0 0 1px var(--separator-color);
+    border-radius: var(--border-radius-small);
+}
+
+table.doxtable caption {
+    display: block;
+}
+
+table.fieldtable {
+    border-collapse: collapse;
+    width: 100%;
+}
+
+th.markdownTableHeadLeft,
+th.markdownTableHeadRight,
+th.markdownTableHeadCenter,
+th.markdownTableHeadNone,
+table.doxtable th {
+    background: var(--tablehead-background);
+    color: var(--tablehead-foreground);
+    font-weight: 600;
+    font-size: var(--page-font-size);
+}
+
+th.markdownTableHeadLeft:first-child,
+th.markdownTableHeadRight:first-child,
+th.markdownTableHeadCenter:first-child,
+th.markdownTableHeadNone:first-child,
+table.doxtable tr th:first-child {
+    border-top-left-radius: var(--border-radius-small);
+}
+
+th.markdownTableHeadLeft:last-child,
+th.markdownTableHeadRight:last-child,
+th.markdownTableHeadCenter:last-child,
+th.markdownTableHeadNone:last-child,
+table.doxtable tr th:last-child {
+    border-top-right-radius: var(--border-radius-small);
+}
+
+table.markdownTable td,
+table.markdownTable th,
+table.fieldtable td,
+table.fieldtable th,
+table.doxtable td,
+table.doxtable th {
+    border: 1px solid var(--separator-color);
+    padding: var(--spacing-small) var(--spacing-medium);
+}
+
+table.markdownTable td:last-child,
+table.markdownTable th:last-child,
+table.fieldtable td:last-child,
+table.fieldtable th:last-child,
+table.doxtable td:last-child,
+table.doxtable th:last-child {
+    border-right: none;
+}
+
+table.markdownTable td:first-child,
+table.markdownTable th:first-child,
+table.fieldtable td:first-child,
+table.fieldtable th:first-child,
+table.doxtable td:first-child,
+table.doxtable th:first-child {
+    border-left: none;
+}
+
+table.markdownTable tr:first-child td,
+table.markdownTable tr:first-child th,
+table.fieldtable tr:first-child td,
+table.fieldtable tr:first-child th,
+table.doxtable tr:first-child td,
+table.doxtable tr:first-child th {
+    border-top: none;
+}
+
+table.markdownTable tr:last-child td,
+table.markdownTable tr:last-child th,
+table.fieldtable tr:last-child td,
+table.fieldtable tr:last-child th,
+table.doxtable tr:last-child td,
+table.doxtable tr:last-child th {
+    border-bottom: none;
+}
+
+table.markdownTable tr, table.doxtable tr {
+    border-bottom: 1px solid var(--separator-color);
+}
+
+table.markdownTable tr:last-child, table.doxtable tr:last-child {
+    border-bottom: none;
+}
+
+table.fieldtable th {
+    font-size: var(--page-font-size);
+    font-weight: 600;
+    background-image: none;
+    background-color: var(--tablehead-background);
+    color: var(--tablehead-foreground);
+}
+
+table.fieldtable td.fieldtype, .fieldtable td.fieldname, .fieldtable td.fielddoc, .fieldtable th {
+    border-bottom: 1px solid var(--separator-color);
+    border-right: 1px solid var(--separator-color);
+}
+
+table.fieldtable tr:last-child td:first-child {
+    border-bottom-left-radius: var(--border-radius-small);
+}
+
+table.fieldtable tr:last-child td:last-child {
+    border-bottom-right-radius: var(--border-radius-small);
+}
+
+.memberdecls td.glow, .fieldtable tr.glow {
+    background-color: var(--primary-light-color);
+    box-shadow: none;
+}
+
+table.memberdecls {
+    display: block;
+    -webkit-tap-highlight-color: transparent;
+}
+
+table.memberdecls tr[class^='memitem'] {
+    font-family: var(--font-family-monospace);
+    font-size: var(--code-font-size);
+}
+
+table.memberdecls tr[class^='memitem'] .memTemplParams {
+    font-family: var(--font-family-monospace);
+    font-size: var(--code-font-size);
+    color: var(--primary-dark-color);
+    white-space: normal;
+}
+
+table.memberdecls .memItemLeft,
+table.memberdecls .memItemRight,
+table.memberdecls .memTemplItemLeft,
+table.memberdecls .memTemplItemRight,
+table.memberdecls .memTemplParams {
+    transition: none;
+    padding-top: var(--spacing-small);
+    padding-bottom: var(--spacing-small);
+    border-top: 1px solid var(--separator-color);
+    border-bottom: 1px solid var(--separator-color);
+    background-color: var(--fragment-background);
+}
+
+table.memberdecls .memTemplItemLeft,
+table.memberdecls .memTemplItemRight {
+    padding-top: 2px;
+}
+
+table.memberdecls .memTemplParams {
+    border-bottom: 0;
+    border-left: 1px solid var(--separator-color);
+    border-right: 1px solid var(--separator-color);
+    border-radius: var(--border-radius-small) var(--border-radius-small) 0 0;
+    padding-bottom: var(--spacing-small);
+}
+
+table.memberdecls .memTemplItemLeft {
+    border-radius: 0 0 0 var(--border-radius-small);
+    border-left: 1px solid var(--separator-color);
+    border-top: 0;
+}
+
+table.memberdecls .memTemplItemRight {
+    border-radius: 0 0 var(--border-radius-small) 0;
+    border-right: 1px solid var(--separator-color);
+    padding-left: 0;
+    border-top: 0;
+}
+
+table.memberdecls .memItemLeft {
+    border-radius: var(--border-radius-small) 0 0 var(--border-radius-small);
+    border-left: 1px solid var(--separator-color);
+    padding-left: var(--spacing-medium);
+    padding-right: 0;
+}
+
+table.memberdecls .memItemRight  {
+    border-radius: 0 var(--border-radius-small) var(--border-radius-small) 0;
+    border-right: 1px solid var(--separator-color);
+    padding-right: var(--spacing-medium);
+    padding-left: 0;
+
+}
+
+table.memberdecls .mdescLeft, table.memberdecls .mdescRight {
+    background: none;
+    color: var(--page-foreground-color);
+    padding: var(--spacing-small) 0;
+}
+
+table.memberdecls .memItemLeft,
+table.memberdecls .memTemplItemLeft {
+    padding-right: var(--spacing-medium);
+}
+
+table.memberdecls .memSeparator {
+    background: var(--page-background-color);
+    height: var(--spacing-large);
+    border: 0;
+    transition: none;
+}
+
+table.memberdecls .groupheader {
+    margin-bottom: var(--spacing-large);
+}
+
+table.memberdecls .inherit_header td {
+    padding: 0 0 var(--spacing-medium) 0;
+    text-indent: -12px;
+    color: var(--page-secondary-foreground-color);
+}
+
+table.memberdecls img[src="closed.png"],
+table.memberdecls img[src="open.png"],
+div.dynheader img[src="open.png"],
+div.dynheader img[src="closed.png"] {
+    width: 0; 
+    height: 0; 
+    border-left: 4px solid transparent;
+    border-right: 4px solid transparent;
+    border-top: 5px solid var(--primary-color);
+    margin-top: 8px;
+    display: block;
+    float: left;
+    margin-left: -10px;
+    transition: transform 0.25s ease-out;
+}
+
+table.memberdecls img {
+    margin-right: 10px;
+}
+
+table.memberdecls img[src="closed.png"],
+div.dynheader img[src="closed.png"] {
+    transform: rotate(-90deg);
+    
+}
+
+.compoundTemplParams {
+    font-family: var(--font-family-monospace);
+    color: var(--primary-dark-color);
+    font-size: var(--code-font-size);
+}
+
+@media screen and (max-width: 767px) {
+
+    table.memberdecls .memItemLeft,
+    table.memberdecls .memItemRight,
+    table.memberdecls .mdescLeft,
+    table.memberdecls .mdescRight,
+    table.memberdecls .memTemplItemLeft,
+    table.memberdecls .memTemplItemRight,
+    table.memberdecls .memTemplParams {
+        display: block;
+        text-align: left;
+        padding-left: var(--spacing-large);
+        margin: 0 calc(0px - var(--spacing-large)) 0 calc(0px - var(--spacing-large));
+        border-right: none;
+        border-left: none;
+        border-radius: 0;
+        white-space: normal;
+    }
+
+    table.memberdecls .memItemLeft,
+    table.memberdecls .mdescLeft,
+    table.memberdecls .memTemplItemLeft {
+        border-bottom: 0;
+        padding-bottom: 0;
+    }
+
+    table.memberdecls .memTemplItemLeft {
+        padding-top: 0;
+    }
+
+    table.memberdecls .mdescLeft {
+        margin-bottom: calc(0px - var(--page-font-size));
+    }
+
+    table.memberdecls .memItemRight, 
+    table.memberdecls .mdescRight,
+    table.memberdecls .memTemplItemRight {
+        border-top: 0;
+        padding-top: 0;
+        padding-right: var(--spacing-large);
+        overflow-x: auto;
+    }
+
+    table.memberdecls tr[class^='memitem']:not(.inherit) {
+        display: block;
+        width: calc(100vw - 2 * var(--spacing-large));
+    }
+
+    table.memberdecls .mdescRight {
+        color: var(--page-foreground-color);
+    }
+
+    table.memberdecls tr.inherit {
+        visibility: hidden;
+    }
+
+    table.memberdecls tr[style="display: table-row;"] {
+        display: block !important;
+        visibility: visible;
+        width: calc(100vw - 2 * var(--spacing-large));
+        animation: fade .5s;
+    }
+
+    @keyframes fade {
+        0% {
+            opacity: 0;
+            max-height: 0;
+        }
+
+        100% {
+            opacity: 1;
+            max-height: 200px;
+        }
+    }
+}
+
+
+/*
+ Horizontal Rule
+ */
+
+hr {
+    margin-top: var(--spacing-large);
+    margin-bottom: var(--spacing-large);
+    height: 1px;
+    background-color: var(--separator-color);
+    border: 0;
+}
+
+.contents hr {
+    box-shadow: 100px 0 0 var(--separator-color),
+                -100px 0 0 var(--separator-color),
+                500px 0 0 var(--separator-color),
+                -500px 0 0 var(--separator-color),
+                1500px 0 0 var(--separator-color),
+                -1500px 0 0 var(--separator-color),
+                2000px 0 0 var(--separator-color),
+                -2000px 0 0 var(--separator-color);
+}
+
+.contents img, .contents .center, .contents center, .contents div.image object {
+    max-width: 100%;
+    overflow: auto;
+}
+
+@media screen and (max-width: 767px) {
+    .contents .dyncontent > .center, .contents > center {
+        margin-left: calc(0px - var(--spacing-large));
+        margin-right: calc(0px - var(--spacing-large));
+        max-width: calc(100% + 2 * var(--spacing-large));
+    }
+}
+
+/*
+ Directories
+ */
+div.directory {
+    border-top: 1px solid var(--separator-color);
+    border-bottom: 1px solid var(--separator-color);
+    width: auto;
+}
+
+table.directory {
+    font-family: var(--font-family);
+    font-size: var(--page-font-size);
+    font-weight: normal;
+    width: 100%;
+}
+
+table.directory td.entry, table.directory td.desc {
+    padding: calc(var(--spacing-small) / 2) var(--spacing-small);
+    line-height: var(--table-line-height);
+}
+
+table.directory tr.even td:last-child {
+    border-radius: 0 var(--border-radius-small) var(--border-radius-small) 0;
+}
+
+table.directory tr.even td:first-child {
+    border-radius: var(--border-radius-small) 0 0 var(--border-radius-small);
+}
+
+table.directory tr.even:last-child td:last-child {
+    border-radius: 0 var(--border-radius-small) 0 0;
+}
+
+table.directory tr.even:last-child td:first-child {
+    border-radius: var(--border-radius-small) 0 0 0;
+}
+
+table.directory td.desc {
+    min-width: 250px;
+}
+
+table.directory tr.even {
+    background-color: var(--odd-color);
+}
+
+table.directory tr.odd {
+    background-color: transparent;
+}
+
+.icona {
+    width: auto;
+    height: auto;
+    margin: 0 var(--spacing-small);
+}
+
+.icon {
+    background: var(--primary-color);
+    border-radius: var(--border-radius-small);
+    font-size: var(--page-font-size);
+    padding: calc(var(--page-font-size) / 5);
+    line-height: var(--page-font-size);
+    transform: scale(0.8);
+    height: auto;
+    width: var(--page-font-size);
+    user-select: none;
+}
+
+.iconfopen, .icondoc, .iconfclosed {
+    background-position: center;
+    margin-bottom: 0;
+    height: var(--table-line-height);
+}
+
+.icondoc {
+    filter: saturate(0.2);
+}
+
+@media screen and (max-width: 767px) {
+    div.directory {
+        margin-left: calc(0px - var(--spacing-large));
+        margin-right: calc(0px - var(--spacing-large));
+    }
+}
+
+@media (prefers-color-scheme: dark) {
+    html:not(.light-mode) .iconfopen, html:not(.light-mode) .iconfclosed {
+        filter: hue-rotate(180deg) invert();
+    }
+}
+
+html.dark-mode .iconfopen, html.dark-mode .iconfclosed {
+    filter: hue-rotate(180deg) invert();
+}
+
+/*
+ Class list
+ */
+
+.classindex dl.odd {
+    background: var(--odd-color);
+    border-radius: var(--border-radius-small);
+}
+
+.classindex dl.even {
+    background-color: transparent;
+}
+
+/* 
+ Class Index Doxygen 1.8 
+*/
+
+table.classindex {
+    margin-left: 0;
+    margin-right: 0;
+    width: 100%;
+}
+
+table.classindex table div.ah {
+    background-image: none;
+    background-color: initial;
+    border-color: var(--separator-color);
+    color: var(--page-foreground-color);
+    box-shadow: var(--box-shadow);
+    border-radius: var(--border-radius-large);
+    padding: var(--spacing-small);
+}
+
+div.qindex {
+    background-color: var(--odd-color);
+    border-radius: var(--border-radius-small);
+    border: 1px solid var(--separator-color);
+    padding: var(--spacing-small) 0;
+}
+
+/*
+  Footer and nav-path
+ */
+
+#nav-path {
+    width: 100%;
+}
+
+#nav-path ul {
+    background-image: none;
+    background: var(--page-background-color);
+    border: none;
+    border-top: 1px solid var(--separator-color);
+    border-bottom: 1px solid var(--separator-color);
+    border-bottom: 0;
+    box-shadow: 0 0.75px 0 var(--separator-color);
+    font-size: var(--navigation-font-size);
+}
+
+img.footer {
+    width: 60px;
+}
+
+.navpath li.footer {
+    color: var(--page-secondary-foreground-color);
+}
+
+address.footer {
+    color: var(--page-secondary-foreground-color);
+    margin-bottom: var(--spacing-large);
+}
+
+#nav-path li.navelem {
+    background-image: none;
+    display: flex;
+    align-items: center;
+}
+
+.navpath li.navelem a {
+    text-shadow: none;
+    display: inline-block;
+    color: var(--primary-color) !important;
+}
+
+.navpath li.navelem b {
+    color: var(--primary-dark-color);
+    font-weight: 500;
+}
+
+li.navelem {
+    padding: 0;
+    margin-left: -8px;
+}
+
+li.navelem:first-child {
+    margin-left: var(--spacing-large);
+}
+
+li.navelem:first-child:before {
+    display: none;
+}
+
+#nav-path li.navelem:after {
+    content: '';
+    border: 5px solid var(--page-background-color);
+    border-bottom-color: transparent;
+    border-right-color: transparent;
+    border-top-color: transparent;
+    transform: translateY(-1px) scaleY(4.2);
+    z-index: 10;
+    margin-left: 6px;
+}
+
+#nav-path li.navelem:before {
+    content: '';
+    border: 5px solid var(--separator-color);
+    border-bottom-color: transparent;
+    border-right-color: transparent;
+    border-top-color: transparent;
+    transform: translateY(-1px) scaleY(3.2);
+    margin-right: var(--spacing-small);
+}
+
+.navpath li.navelem a:hover {
+    color: var(--primary-color);
+}
+
+/*
+ Scrollbars for Webkit
+*/
+
+#nav-tree::-webkit-scrollbar,
+div.fragment::-webkit-scrollbar,
+pre.fragment::-webkit-scrollbar,
+div.memproto::-webkit-scrollbar,
+.contents center::-webkit-scrollbar,
+.contents .center::-webkit-scrollbar,
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody::-webkit-scrollbar,
+div.contents .toc::-webkit-scrollbar {
+    background: transparent;
+    width: calc(var(--webkit-scrollbar-size) + var(--webkit-scrollbar-padding) + var(--webkit-scrollbar-padding));
+    height: calc(var(--webkit-scrollbar-size) + var(--webkit-scrollbar-padding) + var(--webkit-scrollbar-padding));
+}
+
+#nav-tree::-webkit-scrollbar-thumb,
+div.fragment::-webkit-scrollbar-thumb,
+pre.fragment::-webkit-scrollbar-thumb,
+div.memproto::-webkit-scrollbar-thumb,
+.contents center::-webkit-scrollbar-thumb,
+.contents .center::-webkit-scrollbar-thumb,
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody::-webkit-scrollbar-thumb,
+div.contents .toc::-webkit-scrollbar-thumb {
+    background-color: transparent;
+    border: var(--webkit-scrollbar-padding) solid transparent;
+    border-radius: calc(var(--webkit-scrollbar-padding) + var(--webkit-scrollbar-padding));
+    background-clip: padding-box;  
+}
+
+#nav-tree:hover::-webkit-scrollbar-thumb,
+div.fragment:hover::-webkit-scrollbar-thumb,
+pre.fragment:hover::-webkit-scrollbar-thumb,
+div.memproto:hover::-webkit-scrollbar-thumb,
+.contents center:hover::-webkit-scrollbar-thumb,
+.contents .center:hover::-webkit-scrollbar-thumb,
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody:hover::-webkit-scrollbar-thumb,
+div.contents .toc:hover::-webkit-scrollbar-thumb {
+    background-color: var(--webkit-scrollbar-color);
+}
+
+#nav-tree::-webkit-scrollbar-track,
+div.fragment::-webkit-scrollbar-track,
+pre.fragment::-webkit-scrollbar-track,
+div.memproto::-webkit-scrollbar-track,
+.contents center::-webkit-scrollbar-track,
+.contents .center::-webkit-scrollbar-track,
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody::-webkit-scrollbar-track,
+div.contents .toc::-webkit-scrollbar-track {
+    background: transparent;
+}
+
+#nav-tree::-webkit-scrollbar-corner {
+    background-color: var(--side-nav-background);
+}
+
+#nav-tree,
+div.fragment,
+pre.fragment,
+div.memproto,
+.contents center,
+.contents .center,
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody,
+div.contents .toc {
+    overflow-x: auto;
+    overflow-x: overlay;
+}
+
+#nav-tree {
+    overflow-x: auto;
+    overflow-y: auto;
+    overflow-y: overlay;
+}
+
+/*
+ Scrollbars for Firefox
+*/
+
+#nav-tree,
+div.fragment,
+pre.fragment,
+div.memproto,
+.contents center,
+.contents .center,
+.contents table:not(.memberdecls):not(.mlabels):not(.fieldtable):not(.memname) tbody,
+div.contents .toc {
+    scrollbar-width: thin;
+}
+
+/*
+  Optional Dark mode toggle button
+*/
+
+doxygen-awesome-dark-mode-toggle {
+    display: inline-block;
+    margin: 0 0 0 var(--spacing-small);
+    padding: 0;
+    width: var(--searchbar-height);
+    height: var(--searchbar-height);
+    background: none;
+    border: none;
+    border-radius: var(--searchbar-height);
+    vertical-align: middle;
+    text-align: center;
+    line-height: var(--searchbar-height);
+    font-size: 22px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    user-select: none;
+    cursor: pointer;
+}
+
+doxygen-awesome-dark-mode-toggle > svg {
+    transition: transform .1s ease-in-out;
+}
+
+doxygen-awesome-dark-mode-toggle:active > svg {
+    transform: scale(.5);
+}
+
+doxygen-awesome-dark-mode-toggle:hover {
+    background-color: rgba(0,0,0,.03);
+}
+
+html.dark-mode doxygen-awesome-dark-mode-toggle:hover {
+    background-color: rgba(0,0,0,.18);
+}
+
+/*
+ Optional fragment copy button
+*/
+.doxygen-awesome-fragment-wrapper {
+    position: relative;
+}
+
+doxygen-awesome-fragment-copy-button {
+    opacity: 0;
+    background: var(--fragment-background);
+    width: 28px;
+    height: 28px;
+    position: absolute;
+    right: calc(var(--spacing-large) - (var(--spacing-large) / 2.5));
+    top: calc(var(--spacing-large) - (var(--spacing-large) / 2.5));
+    border: 1px solid var(--fragment-foreground);
+    cursor: pointer;
+    border-radius: var(--border-radius-small);
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+
+.doxygen-awesome-fragment-wrapper:hover doxygen-awesome-fragment-copy-button, doxygen-awesome-fragment-copy-button.success {
+    opacity: .28;
+}
+
+doxygen-awesome-fragment-copy-button:hover, doxygen-awesome-fragment-copy-button.success {
+    opacity: 1 !important;
+}
+
+doxygen-awesome-fragment-copy-button:active:not([class~=success]) svg {
+    transform: scale(.91);
+}
+
+doxygen-awesome-fragment-copy-button svg {
+    fill: var(--fragment-foreground);
+    width: 18px;
+    height: 18px;
+}
+
+doxygen-awesome-fragment-copy-button.success svg {
+    fill: rgb(14, 168, 14);
+}
+
+doxygen-awesome-fragment-copy-button.success {
+    border-color: rgb(14, 168, 14);
+}
+
+@media screen and (max-width: 767px) {
+    .textblock > .doxygen-awesome-fragment-wrapper > doxygen-awesome-fragment-copy-button,
+    .textblock li > .doxygen-awesome-fragment-wrapper > doxygen-awesome-fragment-copy-button,
+    .memdoc li > .doxygen-awesome-fragment-wrapper > doxygen-awesome-fragment-copy-button,
+    .memdoc > .doxygen-awesome-fragment-wrapper > doxygen-awesome-fragment-copy-button,
+    dl dd > .doxygen-awesome-fragment-wrapper > doxygen-awesome-fragment-copy-button {
+        right: 0;
+    }
+}
+
+/*
+ Optional paragraph link button
+*/
+
+a.anchorlink {
+    font-size: 90%;
+    margin-left: var(--spacing-small);
+    color: var(--page-foreground-color) !important;
+    text-decoration: none;
+    opacity: .15;
+    display: none;
+    transition: opacity .1s ease-in-out, color .1s ease-in-out;
+}
+
+a.anchorlink svg {
+    fill: var(--page-foreground-color);
+}
+
+h3 a.anchorlink svg, h4 a.anchorlink svg {
+    margin-bottom: -3px;
+    margin-top: -4px;
+}
+
+a.anchorlink:hover {
+    opacity: .45;
+}
+
+h2:hover a.anchorlink, h1:hover a.anchorlink, h3:hover a.anchorlink, h4:hover a.anchorlink  {
+    display: inline-block;
+}
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index b7eded0238..4ec7155d51 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1,4 +1,4 @@
-# Doxyfile 1.9.3
+# Doxyfile 1.9.5
 
 # This file describes the settings to be used by the documentation system
 # doxygen (www.doxygen.org) for a project.
@@ -12,6 +12,16 @@
 # For lists, items can also be appended using:
 # TAG += value [value, ...]
 # Values that contain spaces should be placed between quotes (\" \").
+#
+# Note:
+#
+# Use doxygen to compare the used configuration file with the template
+# configuration file:
+# doxygen -x [configFile]
+# Use doxygen to compare the used configuration file with the template
+# configuration file without replacing the environment variables or CMake type
+# replacement variables:
+# doxygen -x_noenv [configFile]
 
 #---------------------------------------------------------------------------
 # Project related configuration options
@@ -60,16 +70,28 @@ PROJECT_LOGO           = ${ASSETS_DIR}/arrayfire_logo.png
 
 OUTPUT_DIRECTORY       = ${CMAKE_CURRENT_BINARY_DIR}
 
-# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
-# directories (in 2 levels) under the output directory of each output format and
-# will distribute the generated files over these directories. Enabling this
+# If the CREATE_SUBDIRS tag is set to YES then doxygen will create up to 4096
+# sub-directories (in 2 levels) under the output directory of each output format
+# and will distribute the generated files over these directories. Enabling this
 # option can be useful when feeding doxygen a huge amount of source files, where
 # putting all generated files in the same directory would otherwise causes
-# performance problems for the file system.
+# performance problems for the file system. Adapt CREATE_SUBDIRS_LEVEL to
+# control the number of sub-directories.
 # The default value is: NO.
 
 CREATE_SUBDIRS         = NO
 
+# Controls the number of sub-directories that will be created when
+# CREATE_SUBDIRS tag is set to YES. Level 0 represents 16 directories, and every
+# level increment doubles the number of directories, resulting in 4096
+# directories at level 8 which is the default and also the maximum value. The
+# sub-directories are organized in 2 levels, the first level always has a fixed
+# numer of 16 directories.
+# Minimum value: 0, maximum value: 8, default value: 8.
+# This tag requires that the tag CREATE_SUBDIRS is set to YES.
+
+CREATE_SUBDIRS_LEVEL   = 8
+
 # If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
 # characters to appear in the names of generated files. If set to NO, non-ASCII
 # characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
@@ -81,14 +103,14 @@ ALLOW_UNICODE_NAMES    = NO
 # The OUTPUT_LANGUAGE tag is used to specify the language in which all
 # documentation generated by doxygen is written. Doxygen will use this
 # information to generate all constant output in the proper language.
-# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
-# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
-# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
-# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
-# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
-# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
-# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
-# Ukrainian and Vietnamese.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Bulgarian,
+# Catalan, Chinese, Chinese-Traditional, Croatian, Czech, Danish, Dutch, English
+# (United States), Esperanto, Farsi (Persian), Finnish, French, German, Greek,
+# Hindi, Hungarian, Indonesian, Italian, Japanese, Japanese-en (Japanese with
+# English messages), Korean, Korean-en (Korean with English messages), Latvian,
+# Lithuanian, Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese,
+# Romanian, Russian, Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish,
+# Swedish, Turkish, Ukrainian and Vietnamese.
 # The default value is: English.
 
 OUTPUT_LANGUAGE        = English
@@ -466,7 +488,7 @@ TYPEDEF_HIDES_STRUCT   = NO
 
 LOOKUP_CACHE_SIZE      = 0
 
-# The NUM_PROC_THREADS specifies the number threads doxygen is allowed to use
+# The NUM_PROC_THREADS specifies the number of threads doxygen is allowed to use
 # during processing. When set to 0 doxygen will based this on the number of
 # cores available in the system. You can set it explicitly to a value larger
 # than 0 to get more control over the balance between CPU load and processing
@@ -591,14 +613,15 @@ INTERNAL_DOCS          = NO
 # filesystem is case sensitive (i.e. it supports files in the same directory
 # whose names only differ in casing), the option must be set to YES to properly
 # deal with such files in case they appear in the input. For filesystems that
-# are not case sensitive the option should be be set to NO to properly deal with
+# are not case sensitive the option should be set to NO to properly deal with
 # output files written for symbols that only differ in casing, such as for two
 # classes, one named CLASS and the other named Class, and to also support
 # references to files without having to specify the exact matching casing. On
 # Windows (including Cygwin) and MacOS, users should typically set this option
 # to NO, whereas on Linux or other Unix flavors it should typically be set to
 # YES.
-# The default value is: system dependent.
+# Possible values are: SYSTEM, NO and YES.
+# The default value is: SYSTEM.
 
 CASE_SENSE_NAMES       = YES
 
@@ -865,10 +888,21 @@ WARN_AS_ERROR          = NO
 # and the warning text. Optionally the format may contain $version, which will
 # be replaced by the version of the file (if it could be obtained via
 # FILE_VERSION_FILTER)
+# See also: WARN_LINE_FORMAT
 # The default value is: $file:$line: $text.
 
 WARN_FORMAT            = "$file:$line: $text"
 
+# In the $text part of the WARN_FORMAT command it is possible that a reference
+# to a more specific place is given. To make it easier to jump to this place
+# (outside of doxygen) the user can define a custom "cut" / "paste" string.
+# Example:
+# WARN_LINE_FORMAT = "'vi $file +$line'"
+# See also: WARN_FORMAT
+# The default value is: at line $line of file $file.
+
+WARN_LINE_FORMAT       = "at line $line of file $file"
+
 # The WARN_LOGFILE tag can be used to specify a file to which warning and error
 # messages should be written. If left blank the output is written to standard
 # error (stderr). In case the file specified cannot be opened for writing the
@@ -898,10 +932,21 @@ INPUT                  = ${DOCS_DIR}/pages \
 # libiconv (or the iconv built into libc) for the transcoding. See the libiconv
 # documentation (see:
 # https://www.gnu.org/software/libiconv/) for the list of possible encodings.
+# See also: INPUT_FILE_ENCODING
 # The default value is: UTF-8.
 
 INPUT_ENCODING         = UTF-8
 
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses The INPUT_FILE_ENCODING tag can be used to specify
+# character encoding on a per file pattern basis. Doxygen will compare the file
+# name with each pattern and apply the encoding instead of the default
+# INPUT_ENCODING) if there is a match. The character encodings are a list of the
+# form: pattern=encoding (like *.php=ISO-8859-1). See cfg_input_encoding
+# "INPUT_ENCODING" for further information on supported encodings.
+
+INPUT_FILE_ENCODING    =
+
 # If the value of the INPUT tag contains directories, you can use the
 # FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
 # *.h) to filter out the source-files in the directories.
@@ -1009,6 +1054,11 @@ IMAGE_PATH             = ${ASSETS_DIR} \
 # code is scanned, but not when the output code is generated. If lines are added
 # or removed, the anchors will not be placed correctly.
 #
+# Note that doxygen will use the data processed and written to standard output
+# for further processing, therefore nothing else, like debug statements or used
+# commands (so in case of a Windows batch file always use @echo OFF), should be
+# written to standard output.
+#
 # Note that for custom extensions or not directly supported extensions you also
 # need to set EXTENSION_MAPPING for the extension otherwise the files are not
 # properly processed by doxygen.
@@ -1050,6 +1100,15 @@ FILTER_SOURCE_PATTERNS =
 
 USE_MDFILE_AS_MAINPAGE = ${DOCS_DIR}/pages/README.md
 
+# The Fortran standard specifies that for fixed formatted Fortran code all
+# characters from position 72 are to be considered as comment. A common
+# extension is to allow longer lines before the automatic comment starts. The
+# setting FORTRAN_COMMENT_AFTER will also make it possible that longer lines can
+# be processed before the automatic comment starts.
+# Minimum value: 7, maximum value: 10000, default value: 72.
+
+FORTRAN_COMMENT_AFTER  = 72
+
 #---------------------------------------------------------------------------
 # Configuration options related to source browsing
 #---------------------------------------------------------------------------
@@ -1136,6 +1195,46 @@ USE_HTAGS              = NO
 
 VERBATIM_HEADERS       = YES
 
+# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
+# clang parser (see:
+# http://clang.llvm.org/) for more accurate parsing at the cost of reduced
+# performance. This can be particularly helpful with template rich C++ code for
+# which doxygen's built-in parser lacks the necessary type information.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+# The default value is: NO.
+
+CLANG_ASSISTED_PARSING = NO
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES and the CLANG_ADD_INC_PATHS
+# tag is set to YES then doxygen will add the directory of each input to the
+# include path.
+# The default value is: YES.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_ADD_INC_PATHS    = YES
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_OPTIONS          =
+
+# If clang assisted parsing is enabled you can provide the clang parser with the
+# path to the directory containing a file called compile_commands.json. This
+# file is the compilation database (see:
+# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) containing the
+# options used when the source files were built. This is equivalent to
+# specifying the -p option to a clang tool, such as clang-check. These options
+# will then be passed to the parser. Any options specified with CLANG_OPTIONS
+# will be added as well.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+
+CLANG_DATABASE_PATH    =
+
 #---------------------------------------------------------------------------
 # Configuration options related to the alphabetical class index
 #---------------------------------------------------------------------------
@@ -1232,7 +1331,8 @@ HTML_STYLESHEET        =
 # list). For an example see the documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/arrayfire.css
+HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/doxygen-awesome.css \
+                         ${DOCS_DIR}/doxygen-awesome-sidebar-only.css
 
 # The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
 # other source files which should be copied to the HTML output directory. Note
@@ -1242,7 +1342,26 @@ HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/arrayfire.css
 # files will be copied as-is; there are no commands or markers available.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_EXTRA_FILES       =
+HTML_EXTRA_FILES       = ${DOCS_DIR}/doxygen-awesome-darkmode-toggle.js \
+                         ${DOCS_DIR}/doxygen-awesome-fragment-copy-button.js \
+                         ${DOCS_DIR}/doxygen-awesome-interactive-toc.js
+
+# The HTML_COLORSTYLE tag can be used to specify if the generated HTML output
+# should be rendered with a dark or light theme. Default setting AUTO_LIGHT
+# enables light output unless the user preference is dark output. Other options
+# are DARK to always use dark mode, LIGHT to always use light mode, AUTO_DARK to
+# default to dark mode unless the user prefers light mode, and TOGGLE to let the
+# user toggle between dark and light mode via a button.
+# Possible values are: LIGHT Always generate light output., DARK Always generate
+# dark output., AUTO_LIGHT Automatically set the mode according to the user
+# preference, use light mode if no preference is set (the default)., AUTO_DARK
+# Automatically set the mode according to the user preference, use dark mode if
+# no preference is set. and TOGGLE Allow to user to switch between light and
+# dark mode via a button..
+# The default value is: AUTO_LIGHT.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE        = LIGHT
 
 # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
 # will adjust the colors in the style sheet and background images according to
@@ -1571,7 +1690,7 @@ ENUM_VALUES_PER_LINE   = 4
 # Minimum value: 0, maximum value: 1500, default value: 250.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-TREEVIEW_WIDTH         = 250
+TREEVIEW_WIDTH         = 335
 
 # If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
 # external symbols imported via tag files in a separate window.
@@ -1607,17 +1726,6 @@ HTML_FORMULA_FORMAT    = png
 
 FORMULA_FONTSIZE       = 12
 
-# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
-# generated for formulas are transparent PNGs. Transparent PNGs are not
-# supported properly for IE 6.0, but are supported on all modern browsers.
-#
-# Note that when changing this option you need to delete any form_*.png files in
-# the HTML output directory before the changes have effect.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_TRANSPARENT    = YES
-
 # The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
 # to create new LaTeX commands to be used in formulas as building blocks. See
 # the section "Including formulas" for details.
@@ -2208,7 +2316,8 @@ SEARCH_INCLUDES        = NO
 
 # The INCLUDE_PATH tag can be used to specify one or more directories that
 # contain include files that are not input files but should be processed by the
-# preprocessor.
+# preprocessor. Note that the INCLUDE_PATH is not recursive, so the setting of
+# RECURSIVE has no effect here.
 # This tag requires that the tag SEARCH_INCLUDES is set to YES.
 
 INCLUDE_PATH           =
@@ -2336,26 +2445,38 @@ HAVE_DOT               = NO
 
 DOT_NUM_THREADS        = 0
 
-# When you want a differently looking font in the dot files that doxygen
-# generates you can specify the font name using DOT_FONTNAME. You need to make
-# sure dot is able to find the font, which can be done by putting it in a
-# standard location or by setting the DOTFONTPATH environment variable or by
-# setting DOT_FONTPATH to the directory containing the font.
-# The default value is: Helvetica.
+# DOT_COMMON_ATTR is common attributes for nodes, edges and labels of
+# subgraphs. When you want a differently looking font in the dot files that
+# doxygen generates you can specify fontname, fontcolor and fontsize attributes.
+# For details please see <a href=https://graphviz.org/doc/info/attrs.html>Node,
+# Edge and Graph Attributes specification</a> You need to make sure dot is able
+# to find the font, which can be done by putting it in a standard location or by
+# setting the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the
+# directory containing the font. Default graphviz fontsize is 14.
+# The default value is: fontname=Helvetica,fontsize=10.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTNAME           = Helvetica
+DOT_COMMON_ATTR        = "fontname=Helvetica,fontsize=10"
 
-# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
-# dot graphs.
-# Minimum value: 4, maximum value: 24, default value: 10.
+# DOT_EDGE_ATTR is concatenated with DOT_COMMON_ATTR. For elegant style you can
+# add 'arrowhead=open, arrowtail=open, arrowsize=0.5'. <a
+# href=https://graphviz.org/doc/info/arrows.html>Complete documentation about
+# arrows shapes.</a>
+# The default value is: labelfontname=Helvetica,labelfontsize=10.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTSIZE           = 10
+DOT_EDGE_ATTR          = "labelfontname=Helvetica,labelfontsize=10"
 
-# By default doxygen will tell dot to use the default font as specified with
-# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
-# the path where dot can find it using this tag.
+# DOT_NODE_ATTR is concatenated with DOT_COMMON_ATTR. For view without boxes
+# around nodes set 'shape=plain' or 'shape=plaintext' <a
+# href=https://www.graphviz.org/doc/info/shapes.html>Shapes specification</a>
+# The default value is: shape=box,height=0.2,width=0.4.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_NODE_ATTR          = "shape=box,height=0.2,width=0.4"
+
+# You can set the path where dot can find font specified with fontname in
+# DOT_COMMON_ATTR and others dot attributes.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_FONTPATH           =
@@ -2381,7 +2502,8 @@ CLASS_GRAPH            = YES
 COLLABORATION_GRAPH    = YES
 
 # If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
-# groups, showing the direct groups dependencies.
+# groups, showing the direct groups dependencies. See also the chapter Grouping
+# in the manual.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
@@ -2597,18 +2719,6 @@ DOT_GRAPH_MAX_NODES    = 50
 
 MAX_DOT_GRAPH_DEPTH    = 0
 
-# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
-# background. This is disabled by default, because dot on Windows does not seem
-# to support this out of the box.
-#
-# Warning: Depending on the platform used, enabling this option may lead to
-# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
-# read).
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_TRANSPARENT        = NO
-
 # Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output
 # files in one run (i.e. multiple -o and -T options on the command line). This
 # makes dot run faster, but since only newer versions of dot (>1.8.10) support
diff --git a/docs/header.htm b/docs/header.htm
index 5704d89dfb..7709ca014c 100644
--- a/docs/header.htm
+++ b/docs/header.htm
@@ -1,6 +1,6 @@
-<!-- HTML header for doxygen 1.9.3-->
-<!DOCTYPE html>
-<html xmlns="http://www.w3.org/1999/xhtml">
+<!-- HTML header for doxygen 1.9.5-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" lang="$langISO">
 <head>
 <!-- Global site tag (gtag.js) - Google Analytics -->
 <script async src="https://www.googletagmanager.com/gtag/js?id=UA-130950618-1"></script>
@@ -28,8 +28,17 @@
 $treeview
 $search
 $mathjax
+$darkmode
 <link href="$relpath^$stylesheet" rel="stylesheet" type="text/css" />
 $extrastylesheet
+<script type="text/javascript" src="$relpath^doxygen-awesome-darkmode-toggle.js"></script>
+<script type="text/javascript" src="$relpath^doxygen-awesome-fragment-copy-button.js"></script>
+<script type="text/javascript" src="$relpath^doxygen-awesome-interactive-toc.js"></script>
+<script type="text/javascript">
+    DoxygenAwesomeDarkModeToggle.init()
+    DoxygenAwesomeInteractiveToc.init()
+	DoxygenAwesomeFragmentCopyButton.init()
+</script>
 </head>
 <body>
 <!--BEGIN DISABLE_INDEX-->
@@ -42,45 +51,64 @@
 
 <!--BEGIN TITLEAREA-->
 <div id="titlearea">
-<table cellspacing="0" cellpadding="0" width="100%">
+<table cellspacing="2" cellpadding="2" width="100%">
  <tbody>
- <tr id="projectrow">
+  <tr id="projectrow">
   <!--BEGIN PROJECT_LOGO-->
-  <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/></td>
+  <td id="projectlogo"><a  href="index.html"><img alt="Logo" src="$relpath^$projectlogo"/></a></td>
   <!--END PROJECT_LOGO-->
-  <!--BEGIN PROJECT_NAME-->
-  <!--<td id="projectalign">
-   <div id="projectname">$projectname<!--BEGIN PROJECT_NUMBER--><span id="projectnumber">&#160;$projectnumber</span><!--END PROJECT_NUMBER-->
-   </div>
-   <!--BEGIN PROJECT_BRIEF--><div id="projectbrief">$projectbrief</div><!--END PROJECT_BRIEF-->
-  </td>-->
-  <!--END PROJECT_NAME-->
-  <!--BEGIN !PROJECT_NAME-->
-   <!--BEGIN PROJECT_BRIEF-->
-    <td>
-    <div id="projectbrief">$projectbrief</div>
-    </td>
-   <!--END PROJECT_BRIEF-->
+  </tr>
+  <!--BEGIN PROJECT_BRIEF-->
+  <tr id="projectrow">
+  <td>
+  <div id="projectbrief">$projectbrief</div>
+  </td>
+  </tr>
+  <!--END PROJECT_BRIEF-->
   <!--END !PROJECT_NAME-->
   <!--BEGIN DISABLE_INDEX-->
    <!--BEGIN SEARCHENGINE-->
      <!--BEGIN !FULL_SIDEBAR-->
-    <td>$searchbox</td>
+  <tr>   
      <!--END !FULL_SIDEBAR-->
    <!--END SEARCHENGINE-->
   <!--END DISABLE_INDEX-->
+  <div>
+    <style>
+	.cse input.gsc-input,input.gsc-input,.gsc-input-box-focus{
+		border-radius: var(--searchbar-border-radius) !important;
+		background-image:none !important;
+		color-scheme: light !important;
+		-webkit-box-sizing: border-box !important;
+		-moz-box-sizing: content-box !important;
+		box-sizing: content-box !important;
+		text-align: center !important;
+		border: none !important;
+		outline: none !important;
+	}
+	.gsc-clear-button {
+	    display:none !important;
+	}
+    </style>
     <td id="gsearch">
         <script async src="https://cse.google.com/cse.js?cx=004356362924927882526:zup3ehe-7bs"></script>
         <div class="gcse-search"></div>
     </td>
+  </div>
+ </tr>
+ <tr>
+  <td>
+    <div id="togglediv"></div>
+  </td>
  </tr>
   <!--BEGIN SEARCHENGINE-->
-   <!--BEGIN FULL_SIDEBAR-->
- <tr><td colspan="2">$searchbox</td></tr>
+  <!--BEGIN FULL_SIDEBAR-->
+   
+ 
    <!--END FULL_SIDEBAR-->
   <!--END SEARCHENGINE-->
  </tbody>
 </table>
 </div>
 <!--END TITLEAREA-->
-<!-- end header part -->
+<!-- end header part -->
\ No newline at end of file

From 8c7eff36460a2e943525e05ac875306157f14529 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 21 Dec 2022 17:50:52 -0500
Subject: [PATCH 2392/2677] fix exccessive padding w/gsearch on firefox

---
 docs/arrayfire.css | 22 ++++++++++++++++++++++
 docs/doxygen.mk    |  3 ++-
 docs/header.htm    | 18 +-----------------
 3 files changed, 25 insertions(+), 18 deletions(-)
 create mode 100644 docs/arrayfire.css

diff --git a/docs/arrayfire.css b/docs/arrayfire.css
new file mode 100644
index 0000000000..c9a0417fb0
--- /dev/null
+++ b/docs/arrayfire.css
@@ -0,0 +1,22 @@
+/*
+Overwrite google search bar .css to better match doxygen-awesome dark theme
+*/
+.cse input.gsc-input,input.gsc-input,.gsc_input-box,.gsc-input-box-focus{
+	border-radius: 4px !important;
+	background-image:none !important;
+	color-scheme: light !important;
+	-webkit-box-sizing: border-box !important;
+	-moz-box-sizing: content-box !important;
+	box-sizing: content-box !important;
+	border: none !important;
+	outline: none !important;
+}
+.gsc-control-cse {
+	padding: 0px !important;
+	border: none !important;
+	outline: none !important;
+	background-color: transparent !important;
+}
+.gsc-clear-button {
+	display:none !important;
+}
\ No newline at end of file
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 4ec7155d51..2e4da59f66 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1331,7 +1331,8 @@ HTML_STYLESHEET        =
 # list). For an example see the documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/doxygen-awesome.css \
+HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/arrayfire.css \
+                         ${DOCS_DIR}/doxygen-awesome.css \
                          ${DOCS_DIR}/doxygen-awesome-sidebar-only.css
 
 # The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
diff --git a/docs/header.htm b/docs/header.htm
index 7709ca014c..9d7542fe1b 100644
--- a/docs/header.htm
+++ b/docs/header.htm
@@ -55,7 +55,7 @@
  <tbody>
   <tr id="projectrow">
   <!--BEGIN PROJECT_LOGO-->
-  <td id="projectlogo"><a  href="index.html"><img alt="Logo" src="$relpath^$projectlogo"/></a></td>
+  <td id="projectlogo"><a  href="index.htm"><img alt="Logo" src="$relpath^$projectlogo"/></a></td>
   <!--END PROJECT_LOGO-->
   </tr>
   <!--BEGIN PROJECT_BRIEF-->
@@ -74,22 +74,6 @@
    <!--END SEARCHENGINE-->
   <!--END DISABLE_INDEX-->
   <div>
-    <style>
-	.cse input.gsc-input,input.gsc-input,.gsc-input-box-focus{
-		border-radius: var(--searchbar-border-radius) !important;
-		background-image:none !important;
-		color-scheme: light !important;
-		-webkit-box-sizing: border-box !important;
-		-moz-box-sizing: content-box !important;
-		box-sizing: content-box !important;
-		text-align: center !important;
-		border: none !important;
-		outline: none !important;
-	}
-	.gsc-clear-button {
-	    display:none !important;
-	}
-    </style>
     <td id="gsearch">
         <script async src="https://cse.google.com/cse.js?cx=004356362924927882526:zup3ehe-7bs"></script>
         <div class="gcse-search"></div>

From eb23625f43479b26661face4eb8f23af5bd52b7e Mon Sep 17 00:00:00 2001
From: John Melonakos <john@arrayfire.com>
Date: Thu, 29 Dec 2022 12:35:00 -0500
Subject: [PATCH 2393/2677] docs updates to arith, blas, data.. new examples

---
 docs/details/arith.dox    | 20 ++++++----
 docs/details/blas.dox     | 12 +++++-
 docs/details/data.dox     | 35 +++++------------
 docs/details/examples.dox | 58 +++++++++++++++++++++++++++
 include/af/arith.h        | 72 +++++++++++++++++-----------------
 include/af/blas.h         | 34 +++++++---------
 include/af/data.h         | 82 +++++++++++++++++++++++----------------
 test/complex.cpp          | 59 ++++++++++++++++++++++++++++
 test/getting_started.cpp  | 14 +++++++
 test/moddims.cpp          | 34 ++++++++++++++++
 test/range.cpp            | 38 ++++++++++++++++++
 test/reduce.cpp           | 38 ++++++++++++++++++
 test/transpose.cpp        | 22 +++++++++++
 13 files changed, 392 insertions(+), 126 deletions(-)
 create mode 100644 docs/details/examples.dox

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 8461ecd100..ca3968db68 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -190,6 +190,7 @@ Bitwise xor operation of two inputs
 Minimum of two inputs.
 
 
+
 \defgroup arith_func_max max
 
 \ingroup numeric_mat
@@ -197,12 +198,6 @@ Minimum of two inputs.
 Maximum of two inputs.
 
 
-\defgroup arith_func_clamp clamp
-
-\ingroup numeric_mat
-
-Limits the range of the in array to the values between lo and hi
-
 
 \defgroup arith_func_rem rem
 
@@ -385,7 +380,18 @@ atanh of input
 
 \ingroup complex_mat
 
-create complex arrays
+Create complex arrays.
+
+Complex arrays are created from any of the following four inputs:
+
+1. a single real array, returning zeros for the imaginary component. See `array b` in the example.
+2. two real arrays, one for the real component and one for the imaginary component. See `array c` in the example.
+3. a single real array for the real component and a single scalar for each imaginary component. See `array d` in the example.
+4. a single scalar for each real component and a single real array for the imaginary component. See `array e` in the example.
+
+__Examples:__
+
+\snippet test/complex.cpp ex_arith_func_complex
 
 
 
diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index 7ec09af9c3..3765ed446c 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -50,9 +50,17 @@ and restrictions.
 \ingroup blas_mat
 \ingroup manip_mat
 
-\brief Matrix Transpose
+\brief Transpose a matrix.
 
-Transposes a matrix
+Reverse or permute the dimensions of an array; returns the modified array. For an array a with two dimensions, `transpose(a)` gives the matrix transpose. For an array with more than two dimensions, the first two dimensions are transposed across higher dimensions.
+
+Set `conjugate=true` to perform the complex conjugate transpose of a matrix which interchanges the row and column index for each element, reflecting the elements across the main diagonal and negating the imaginary part of any complex numbers. For example, if `b = transpose(a, true)` and element `a(2, 1)` is `(1, 2)`, then element `b(1, 2)` is `(1, -2)`.
+
+In-place versions perform matrix transposition by reordering the input, reducing memory footprint.
+
+__Examples:__
+
+\snippet test/transpose.cpp ex_blas_func_transpose
 
 =======================================================================
 
diff --git a/docs/details/data.dox b/docs/details/data.dox
index f8db9586f0..99a94f1202 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -45,30 +45,11 @@ array a = identity(5, 3);
 
 \defgroup data_func_range range
 
-\brief Creates an array with [0, n] values along the seq_dim which is tiled across other dimensions
+\brief Create an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions.
 
-\code
-// Generates an array of [0, 4] along first dimension
-array a = range(dim4(5));        // a = [0,
-                                 //      1,
-                                 //      2,
-                                 //      3,
-                                 //      4]
-
-// Generates an array of [0, 4] along first dimension, tiled along second dimension
-array b = range(dim4(5, 2));     // a = [0, 0,
-                                 //      1, 1,
-                                 //      2, 2,
-                                 //      3, 3,
-                                 //      4, 4]
-
-// Generates an array of [0, 2] along second dimension, tiled along first dimension
-array c = range(dim4(5, 3), 1);  // c = [0, 1, 2,
-                                 //      0, 1, 2,
-                                 //      0, 1, 2,
-                                 //      0, 1, 2,
-                                 //      0, 1, 2]
-\endcode
+__Examples:__
+
+\snippet test/range.cpp ex_data_func_range
 
 \ingroup data_mat
 \ingroup arrayfire_func
@@ -259,9 +240,13 @@ Shifts the values in a circular fashion along the specified dimesion.
 
 \defgroup manip_func_moddims moddims
 
-\brief Modify the input dimensions without changing the data order
+\brief Modify the dimensions of an array without changing the order of its elements.
+
+This function only modifies array metadata and requires no computation. It is a NOOP.
+
+__Examples:__
 
-Simply modifies the metadata. This is a noop.
+\snippet test/moddims.cpp ex_data_func_moddims
 
 \ingroup manip_mat
 \ingroup arrayfire_func
diff --git a/docs/details/examples.dox b/docs/details/examples.dox
new file mode 100644
index 0000000000..a61ffbc271
--- /dev/null
+++ b/docs/details/examples.dox
@@ -0,0 +1,58 @@
+/**
+\example benchmarks/blas.cpp
+\example benchmarks/cg.cpp
+\example benchmarks/fft.cpp
+\example benchmarks/pi.cpp
+\example computer_vision/fast.cpp
+\example computer_vision/harris.cpp
+\example computer_vision/matching.cpp
+\example computer_vision/susan.cpp
+\example financial/black_scholes_options.cpp
+\example financial/heston_model.cpp
+\example financial/monte_carlo_options.cpp
+\example getting_started/convolve.cpp
+\example getting_started/integer.cpp
+\example getting_started/rainfall.cpp
+\example getting_started/vectorize.cpp
+\example graphics/conway.cpp
+\example graphics/conway_pretty.cpp
+\example graphics/field.cpp
+\example graphics/fractal.cpp
+\example graphics/gravity_sim.cpp
+\example graphics/histogram.cpp
+\example graphics/plot2d.cpp
+\example graphics/plot3.cpp
+\example graphics/surface.cpp
+\example helloworld/helloworld.cpp
+\example image_processing/adaptive_thresholding.cpp
+\example image_processing/binary_thresholding.cpp
+\example image_processing/brain_segmentation.cpp
+\example image_processing/confidence_connected_components.cpp
+\example image_processing/deconvolution.cpp
+\example image_processing/edge.cpp
+\example image_processing/filters.cpp
+\example image_processing/gradient_diffusion.cpp
+\example image_processing/image_demo.cpp
+\example image_processing/image_editing.cpp
+\example image_processing/morphing.cpp
+\example image_processing/optical_flow.cpp
+\example image_processing/pyramids.cpp
+\example lin_algebra/cholesky.cpp
+\example lin_algebra/lu.cpp
+\example lin_algebra/qr.cpp
+\example lin_algebra/svd.cpp
+\example machine_learning/bagging.cpp
+\example machine_learning/deep_belief_net.cpp
+\example machine_learning/geneticalgorithm.cpp
+\example machine_learning/kmeans.cpp
+\example machine_learning/knn.cpp
+\example machine_learning/logistic_regression.cpp
+\example machine_learning/naive_bayes.cpp
+\example machine_learning/neural_network.cpp
+\example machine_learning/perceptron.cpp
+\example machine_learning/rbm.cpp
+\example machine_learning/softmax_regression.cpp
+\example pde/swe.cpp
+\example unified/basic.cpp
+
+*/
diff --git a/include/af/arith.h b/include/af/arith.h
index 89bd39bd64..e2f695601d 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -259,36 +259,34 @@ namespace af
     AFAPI array atan2  (const double lhs, const array &rhs);
     /// @}
 
-    /// \ingroup trig_func_cplx2
+    /// \ingroup arith_func_cplx
     /// @{
-    /// C++ Interface for creating complex array from two inputs
+    /// C++ Interface for creating a complex array from a single real array.
     ///
-    /// Creates a complex number from two sets of inputs. The left hand side is
-    /// the real part and the right hand side is the imaginary part. This
-    /// function accepts two \ref af::array or one \ref af::array and a scalar
-    /// as nputs.
+    /// \param[in] in a real array
+    /// \return the returned complex array
+    AFAPI array complex(const array& in);
+ 
+    /// C++ Interface for creating a complex array from two real arrays.
     ///
-    /// \param[in] real is real value(s)
-    /// \param[in] imaginary is imaginary value(s)
-    /// \return complex array from inputs
-    /// \ingroup arith_func_cplx
-    AFAPI array complex(const array &real, const array &imaginary);
-
-    /// \copydoc complex(const array&, const array&)
-    /// \ingroup arith_func_cplx
-    AFAPI array complex(const array &real, const double imaginary);
-
-    /// \copydoc complex(const array&, const array&)
-    /// \ingroup arith_func_cplx
-    AFAPI array complex(const double real, const array &imaginary);
+    /// \param[in] real_ a real array to be assigned as the real component of the returned complex array
+    /// \param[in] imag_ a real array to be assigned as the imaginary component of the returned complex array
+    /// \return the returned complex array
+    AFAPI array complex(const array &real_, const array &imag_);
 
-    /// C++ Interface for creating complex array from real array
+    /// C++ Interface for creating a complex array from a single real array for the real component and a single scalar for each imaginary component.
     ///
-    /// \param[in] in is real array
-    /// \return complex array from \p in
+    /// \param[in] real_ a real array to be assigned as the real component of the returned complex array
+    /// \param[in] imag_ a single scalar to be assigned as the imaginary component of each value of the returned complex array
+    /// \return the returned complex array
+    AFAPI array complex(const array &real_, const double imag_);
+
+    /// C++ Interface for creating a complex array from a single scalar for each real component and a single real array for the imaginary component.
     ///
-    /// \ingroup arith_func_cplx
-    AFAPI array complex(const array &in);
+    /// \param[in] real_ a single scalar to be assigned as the real component of each value of the returned complex array
+    /// \param[in] imag_ a real array to be assigned as the imaginary component of the returned complex array
+    /// \return the returned complex array
+    AFAPI array complex(const double real_, const array &imag_);
     /// @}
 
     /// C++ Interface for getting real part from complex array
@@ -888,16 +886,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for clamp
+       C Interface for max of two arrays
 
-       \param[out] out will contain the values from \p in clamped between \p lo and \p hi
+       \param[out] out will contain the values from \p clamped between \p lo and \p hi
        \param[in] in Input array
        \param[in] lo Value for lower limit
        \param[in] hi Value for upper limit
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_clamp
+       \ingroup arith_func_max
     */
     AFAPI af_err af_clamp(af_array *out, const af_array in,
                           const af_array lo, const af_array hi, const bool batch);
@@ -1103,28 +1101,28 @@ extern "C" {
     AFAPI af_err af_atan2 (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for creating complex array from two input arrays
+       C Interface for creating a complex array from a single real array.
 
-       \param[out] out will contain the complex array generated from inputs
-       \param[in] real is real array
-       \param[in] imaginary is imaginary array
-       \param[in] batch specifies if operations need to be performed in batch mode
+       \param[out] out the returned complex array
+       \param[in] in a real array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cplx
     */
-    AFAPI af_err af_cplx2 (af_array *out, const af_array real, const af_array imaginary, const bool batch);
+    AFAPI af_err af_cplx(af_array* out, const af_array in);
 
     /**
-       C Interface for creating complex array from real array
+       C Interface for creating a complex array from two real arrays.
 
-       \param[out] out will contain complex array created from real input \p in
-       \param[in] in is real array
+       \param[out] out the returned complex array
+       \param[in] real a real array to be assigned as the real component of the returned complex array
+       \param[in] imag a real array to be assigned as the imaginary component of the returned complex array
+       \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cplx
     */
-    AFAPI af_err af_cplx    (af_array *out, const af_array in);
+    AFAPI af_err af_cplx2 (af_array *out, const af_array real, const af_array imag, const bool batch);
 
     /**
        C Interface for getting real part from complex array
diff --git a/include/af/blas.h b/include/af/blas.h
index 6023717d0e..d20986b215 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -181,24 +181,20 @@ namespace af
                     const matProp optRhs = AF_MAT_NONE);
 
     /**
-        \brief Transposes a matrix
+        \brief C++ Interface for transposing a matrix
 
-        \copydetails blas_func_transpose
-
-        \param[in] in Input Matrix
-        \param[in] conjugate If true a congugate transposition is performed
-        \return Transposed matrix
+        \param[in] in an input matrix
+        \param[in] conjugate if true, a conjugate transposition is performed
+        \return the transposed matrix
         \ingroup blas_func_transpose
     */
     AFAPI array transpose(const array &in, const bool conjugate = false);
 
     /**
-        \brief Transposes a matrix in-place
-
-        \copydetails blas_func_transpose
+        \brief C++ Interface for transposing a matrix in-place
 
-        \param[in,out] in is the matrix to be transposed in place
-        \param[in] conjugate If true a congugate transposition is performed
+        \param[in,out] in the matrix to be transposed in-place
+        \param[in] conjugate if true, a conjugate transposition is performed
 
         \ingroup blas_func_transpose
     */
@@ -356,13 +352,11 @@ extern "C" {
 #endif
 
     /**
-        \brief Transposes a matrix
+        \brief C Interface for transposing a matrix
 
-        This funciton will tranpose the matrix in.
-
-        \param[out] out The transposed matrix
-        \param[in] in Input matrix which will be transposed
-        \param[in] conjugate Perform a congugate transposition
+        \param[out] out the transposed matrix
+        \param[in] in an input matrix
+        \param[in] conjugate if true, a conjugate transposition is performed
 
         \return AF_SUCCESS if the process is successful.
         \ingroup blas_func_transpose
@@ -370,12 +364,10 @@ extern "C" {
     AFAPI af_err af_transpose(af_array *out, af_array in, const bool conjugate);
 
     /**
-        \brief Transposes a matrix in-place
-
-        \copydetails blas_func_transpose
+        \brief C Interface for transposing a matrix in-place
 
         \param[in,out] in is the matrix to be transposed in place
-        \param[in] conjugate If true a congugate transposition is performed
+        \param[in] conjugate if true, a conjugate transposition is performed
 
         \ingroup blas_func_transpose
     */
diff --git a/include/af/data.h b/include/af/data.h
index 6da90fe801..1559ea204f 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -144,25 +144,29 @@ namespace af
                          const dim_t d2, const dim_t d3, const dtype ty=f32);
 
     /**
-        \param[in] dims is dim4 for size of all dimensions
-        \param[in] seq_dim is dimesion along which [0, dim[seq_dim] - 1] is generated
-        \param[in] ty is the type of array to generate
+    *  C++ Interface for creating an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions of shape `dim4`.
+    *
+        \param[in] dims the `dim4` object describing the shape of the generated array
+        \param[in] seq_dim the dimesion along which `[0, dim[seq_dim] - 1]` is created
+        \param[in] ty the type of the generated array
 
-        \returns an array of integral range specified dimension and type
+        \returns the generated array
 
         \ingroup data_func_range
     */
     AFAPI array range(const dim4 &dims, const int seq_dim = -1, const dtype ty=f32);
 
     /**
-        \param[in] d0 is size of first dimension
-        \param[in] d1 is size of second dimension
-        \param[in] d2 is size of third dimension
-        \param[in] d3 is size of fourth dimension
-        \param[in] seq_dim is dimesion along which [0, dim[seq_dim] - 1] is generated
-        \param[in] ty is the type of array to generate
+    *  C++ Interface for creating an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions described by dimension parameters.
+    *
+        \param[in] d0 the size of first dimension
+        \param[in] d1 the size of second dimension
+        \param[in] d2 the size of third dimension
+        \param[in] d3 the size of fourth dimension
+        \param[in] seq_dim the dimesion along which `[0, dim[seq_dim] - 1]` is created
+        \param[in] ty the type of the generated array
 
-        \returns an array of integral range specified dimension and type
+        \returns the generated array
 
         \ingroup data_func_range
     */
@@ -295,35 +299,41 @@ namespace af
     AFAPI array shift(const array& in, const int x, const int y=0, const int z=0, const int w=0);
 
     /**
-        \param[in] in is the input array
-        \param[in] ndims is the number of dimensions
-        \param[in] dims is the array containing the new dimensions
+    * C++ Interface for modifying the dimensions of an input array to the shape specified by a `dim4` object
+    *
+        \param[in] in the input array
+        \param[in] dims the array of new dimension sizes
         \return the modded output
 
         \ingroup manip_func_moddims
     */
-    AFAPI array moddims(const array& in, const unsigned ndims, const dim_t * const dims);
+    AFAPI array moddims(const array& in, const dim4& dims);
 
     /**
-        \param[in] in is the input array
-        \param[in] dims is the new dimensions
+    * C++ Interface for modifying the dimensions of an input array to the shape specified by dimension length parameters
+    *
+        \param[in] in the input array
+        \param[in] d0 the new size of the first dimension
+        \param[in] d1 the new size of the second dimension (optional)
+        \param[in] d2 the new size of the third dimension (optional)
+        \param[in] d3 the new size of the fourth dimension (optional)
         \return the modded output
 
         \ingroup manip_func_moddims
     */
-    AFAPI array moddims(const array& in, const dim4& dims);
+    AFAPI array moddims(const array& in, const dim_t d0, const dim_t d1=1, const dim_t d2=1, const dim_t d3=1);
 
     /**
-        \param[in] in is the input array
-        \param[in] d0 specifies the new size of the first dimension
-        \param[in] d1 specifies the new size of the second dimension
-        \param[in] d2 specifies the new size of the third dimension
-        \param[in] d3 specifies the new size of the fourth dimension
-        \return the modded array
+    * C++ Interface for modifying the dimensions of an input array to the shape specified by an array of `ndims` dimensions
+    *
+        \param[in] in the input array
+        \param[in] ndims the number of dimensions
+        \param[in] dims the array of new dimension sizes
+        \return the modded output
 
         \ingroup manip_func_moddims
     */
-    AFAPI array moddims(const array& in, const dim_t d0, const dim_t d1=1, const dim_t d2=1, const dim_t d3=1);
+    AFAPI array moddims(const array& in, const unsigned ndims, const dim_t* const dims);
 
     /**
         \param[in] in is the input array
@@ -567,11 +577,13 @@ extern "C" {
     AFAPI af_err af_constant_ulong(af_array *arr, const unsigned long long val, const unsigned ndims, const dim_t * const dims);
 
     /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] seq_dim is dimension along which [0, dim[seq_dim] - 1] is generated
-        \param[in] type is the type of array to generate
+    * C Interface for creating an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions specified by an array of `ndims` dimensions.
+    *
+        \param[out] out the generated array
+        \param[in] ndims the size of dimension array `dims`
+        \param[in] dims the array containing the dimension sizes
+        \param[in] seq_dim the dimension along which `[0, dim[seq_dim] - 1]` is created
+        \param[in] type the type of the generated array
 
         \ingroup data_func_range
     */
@@ -693,10 +705,12 @@ extern "C" {
     AFAPI af_err af_shift(af_array *out, const af_array in, const int x, const int y, const int z, const int w);
 
     /**
-        \param[out] out is the modded array
-        \param[in] in is the input array
-        \param[in] ndims is the number of dimensions
-        \param[in] dims is the array containing the new dimensions
+    * C Interface for modifying the dimensions of an input array to the shape specified by an array of `ndims` dimensions
+    *
+        \param[out] out the modded output
+        \param[in] in the input array
+        \param[in] ndims the number of dimensions
+        \param[in] dims the array of new dimension sizes
 
         \ingroup manip_func_moddims
     */
diff --git a/test/complex.cpp b/test/complex.cpp
index 93a5d47b18..b63fd63bba 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -134,3 +134,62 @@ const int num = 10;
 COMPLEX_TESTS(float, float, float)
 COMPLEX_TESTS(double, double, double)
 COMPLEX_TESTS(float, double, double)
+
+TEST(Complex, SNIPPET_arith_func_complex) {
+    //! [ex_arith_func_complex]
+    //!
+    // Create a, a 2x3 array
+    array a = iota(dim4(2, 3));    // a = [0, 2, 4,
+                                   //      1, 3, 5]
+
+    // Create b from a single real array, returning zeros for the imaginary component
+    array b = complex(a);          // b = [(0, 0), (2, 0), (4, 0),
+                                   //      (1, 0), (3, 0), (5, 0)]
+
+    // Create c from two real arrays, one for the real component and one for the imaginary component
+    array c = complex(a, a);       // c = [(0, 0), (2, 2), (4, 4),
+                                   //      (1, 1), (3, 3), (5, 5)]
+
+    // Create d from a single real array for the real component and a single scalar for each imaginary component
+    array d = complex(a, 2);       // d = [(0, 2), (2, 2), (4, 2),
+                                   //      (1, 2), (3, 2), (5, 2)]
+
+    // Create e from a single scalar for each real component and a single real array for the imaginary component
+    array e = complex(2, a);       // e = [(2, 0), (2, 2), (2, 4),
+                                   //      (2, 1), (2, 3), (2, 5)]
+
+    //! [ex_arith_func_complex]
+
+    using std::complex;
+    using std::vector;
+    vector<float> ha(a.elements());
+    a.host(ha.data());
+
+    vector<cfloat> gold_b(a.elements());
+    for (int i = 0; i < a.elements(); i++) {
+        gold_b[i].real = ha[i];
+        gold_b[i].imag = 0;
+    }
+    ASSERT_VEC_ARRAY_EQ(gold_b, a.dims(), b);
+
+    vector<cfloat> gold_c(a.elements());
+    for (int i = 0; i < a.elements(); i++) {
+        gold_c[i].real = ha[i];
+        gold_c[i].imag = ha[i];
+    }
+    ASSERT_VEC_ARRAY_EQ(gold_c, a.dims(), c);
+
+    vector<cfloat> gold_d(a.elements());
+    for (int i = 0; i < a.elements(); i++) {
+        gold_d[i].real = ha[i];
+        gold_d[i].imag = 2;
+    }
+    ASSERT_VEC_ARRAY_EQ(gold_d, a.dims(), d);
+
+    vector<cfloat> gold_e(a.elements());
+    for (int i = 0; i < a.elements(); i++) {
+        gold_e[i].real = 2;
+        gold_e[i].imag = ha[i];
+    }
+    ASSERT_VEC_ARRAY_EQ(gold_e, a.dims(), e);
+}
\ No newline at end of file
diff --git a/test/getting_started.cpp b/test/getting_started.cpp
index ac77f58cf5..c9e73ef6b5 100644
--- a/test/getting_started.cpp
+++ b/test/getting_started.cpp
@@ -307,3 +307,17 @@ TEST(GettingStarted, SNIPPET_getting_started_constants) {
 
     ASSERT_LE(fabs(Pi - pi_est), 0.005);
 }
+
+TEST(GettingStarted, SNIPPET_JohnTest) {
+    array a = iota(dim4(2, 3));
+    array b = sum(a);     // sum across the first axis, same as sum(a, 0)
+    array c = sum(a, 1);  // sum across the second axis
+    array d = sum(a, 2);  // sum across the third axis
+    array e = sum(a, 3);  // sum acorss the fourth axis
+    // array f = sum(a, 4); fails due to stepping out of bounds
+    af_print(a);
+    af_print(b);
+    af_print(c);
+    af_print(d);
+    af_print(e);
+}
\ No newline at end of file
diff --git a/test/moddims.cpp b/test/moddims.cpp
index 9674c5a4f1..a7dea52a00 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -346,3 +346,37 @@ TEST(Moddims, JitMultipleModdimsThenTiled) {
     gold.eval();
     ASSERT_ARRAYS_EQ(gold, c);
 }
+
+TEST(Moddims, SNIPPET_data_func_moddims) {
+    // clang-format off
+    //! [ex_data_func_moddims]
+    //!
+    // Create a, a 2x3 array
+    array a = iota(dim4(2, 3));           // a = [0, 2, 4,
+                                          //      1, 3, 5]
+
+    // Create b by modifying the dimensions of a to the shape described by a dim4 object
+    array b = moddims(a, dim4(3, 2));     // b = [0, 3,
+                                          //      1, 4,
+                                          //      2, 5]
+
+    // Create c by modifying the dimensions of a to the shape described by dimension length parameters
+    array c = moddims(a, 3, 2);           // c = [0, 3,
+                                          //      1, 4,
+                                          //      2, 5]
+
+    // Create d by modifying the dimensions of a to the shape described by an array of ndims dimensions
+    vector<dim_t> x{3, 2};
+    array d = moddims(a, 2, x.data());    // d = [0, 3,
+                                          //      1, 4,
+                                          //      2, 5]
+
+    //! [ex_data_func_moddims]
+    // clang-format on
+
+    vector<float> gold_a{0, 1, 2, 3, 4, 5};
+
+    ASSERT_VEC_ARRAY_EQ(gold_a, dim4(3, 2), b);
+    ASSERT_VEC_ARRAY_EQ(gold_a, dim4(3, 2), c);
+    ASSERT_VEC_ARRAY_EQ(gold_a, dim4(3, 2), d);
+}
\ No newline at end of file
diff --git a/test/range.cpp b/test/range.cpp
index 4d90b8a42f..35708bde09 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -171,3 +171,41 @@ TEST(Range, CPP) {
     // Delete
     delete[] outData;
 }
+
+TEST(Range, SNIPPET_data_func_range) {
+    // clang-format off
+    //! [ex_data_func_range]
+    //!
+    // Generates an array of [0, 4] along first dimension
+    array a = range(dim4(5));          // a = [0,
+                                       //      1,
+                                       //      2,
+                                       //      3,
+                                       //      4]
+
+    // Generates an array of [0, 4] along first dimension, tiled along second dimension
+    array b = range(dim4(5, 2));       // b = [0, 0,
+                                       //      1, 1,
+                                       //      2, 2,
+                                       //      3, 3,
+                                       //      4, 4]
+
+    // Generates an array of [0, 2] along second dimension, tiled along first dimension
+    array c = range(dim4(5, 3), 1);    // c = [0, 1, 2,
+                                       //      0, 1, 2,
+                                       //      0, 1, 2,
+                                       //      0, 1, 2,
+                                       //      0, 1, 2]
+
+    //! [ex_data_func_range]
+    // clang-format on
+
+    using std::vector;
+    vector<float> gold_a{0, 1, 2, 3, 4};
+    vector<float> gold_b{0, 1, 2, 3, 4, 0, 1, 2, 3, 4};
+    vector<float> gold_c{0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
+
+    ASSERT_VEC_ARRAY_EQ(gold_a, a.dims(), a);
+    ASSERT_VEC_ARRAY_EQ(gold_b, b.dims(), b);
+    ASSERT_VEC_ARRAY_EQ(gold_c, c.dims(), c);
+}
diff --git a/test/reduce.cpp b/test/reduce.cpp
index c6cc0d7d72..fc16e60716 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -2330,3 +2330,41 @@ TEST(Reduce, nanval_issue_3255) {
     }
     ASSERT_SUCCESS(af_release_array(ikeys));
 }
+
+TEST(Reduce, SNIPPET_algorithm_func_sum) {
+    // clang-format off
+    //! [ex_algorithm_func_sum]
+    //
+    // Create a, a 2x3 array
+    array a = iota(dim4(2, 3));           // a = [0, 2, 4,
+                                          //      1, 3, 5]
+
+    // Create b by summing across the first dimension
+    array b = sum(a);        // sum across the first dimension, same as sum(a, 0)
+
+    // Create c by summing across the second dimension
+    array c = sum(a, 1);     // sum across the second dimension
+
+    // Create d by summing across the third dimension
+    array d = sum(a, 2);     // sum across the third dimension
+
+    // Create e by summing across the fouth dimension
+    array e = sum(a, 3);     // sum acorss the fourth dimension
+
+    // Summing across higher dimensions fails due to stepping out of bounds. For example,
+    // array f = sum(a0, 4)  // fails due to stepping out of bounds
+
+    //! [ex_algorithm_func_sum]
+    // clang-format on
+
+    using std::vector;
+    vector<float> gold_a{0, 1, 2, 3, 4, 5};
+    vector<float> gold_b{1, 5, 9};
+    vector<float> gold_c{6, 9};
+
+    ASSERT_VEC_ARRAY_EQ(gold_a, a.dims(), a);
+    ASSERT_VEC_ARRAY_EQ(gold_b, b.dims(), b);
+    ASSERT_VEC_ARRAY_EQ(gold_c, c.dims(), c);
+    ASSERT_VEC_ARRAY_EQ(gold_a, d.dims(), d);
+    ASSERT_VEC_ARRAY_EQ(gold_a, e.dims(), e);
+}
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 8bc0c1c6e9..72a32194fa 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -263,3 +263,25 @@ TEST(Transpose, GFOR) {
         ASSERT_EQ(max<double>(abs(c_ii - b_ii)) < 1E-5, true);
     }
 }
+
+TEST(Transpose, SNIPPET_blas_func_transpose) {
+    // clang-format off
+    //! [ex_blas_func_transpose]
+    //!
+    // Create a, a 2x3 array
+    array a = iota(dim4(2, 3));    // a = [0, 2, 4
+                                   //      1, 3, 5]
+
+    // Create b, the transpose of a
+    array b = transpose(a);        // b = [0, 1,
+                                   //      2, 3,
+                                   //      4, 5]
+
+    //! [ex_blas_func_transpose]
+    // clang-format on
+
+    using std::vector;
+    vector<float> gold_b{0, 2, 4, 1, 3, 5};
+
+    ASSERT_VEC_ARRAY_EQ(gold_b, b.dims(), b);
+}

From bac6b9302ad882756160d3f69868eebc80cd91df Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Wed, 11 Jan 2023 15:30:39 -0500
Subject: [PATCH 2394/2677] improves documentation for arith functions

---
 docs/details/arith.dox | 294 ++++++++-----
 include/af/arith.h     | 919 +++++++++++++++++++++--------------------
 2 files changed, 662 insertions(+), 551 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index ca3968db68..84f9a5c451 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -21,51 +21,39 @@
 
 \ingroup arith_mat
 
-Addition of two inputs.
+Add.
 
+Add two arrays.
 
 
-\defgroup arith_func_sub sub
-
-\ingroup arith_mat
-
-Subtract one input from another
 
-
-
-\defgroup arith_func_mul mul
+\defgroup arith_func_sub sub
 
 \ingroup arith_mat
 
-Multiply two inputs element wise
-
-
-
-\defgroup arith_func_div div
-
-\ingroup arith_mat
+Subtract.
 
-Divide one input by another
+Subtract one array from another array.
 
 
 
-\defgroup arith_func_shiftl bitshiftl
+\defgroup arith_func_mul mul
 
 \ingroup arith_mat
 
-Left shift an input
+Multiply.
 
-\copydoc arith_int_only
+Multiply two arrays.
 
 
 
-\defgroup arith_func_shiftr bitshiftr
+\defgroup arith_func_div div
 
 \ingroup arith_mat
 
-Right shift an input
+Divide.
 
-\copydoc arith_int_only
+Divide one array by another array.
 
 
 
@@ -73,7 +61,9 @@ Right shift an input
 
 \ingroup logic_mat
 
-Check if input is less than another
+Is less than.
+
+Check if the elements of one array are less than those of another array.
 
 
 
@@ -81,7 +71,9 @@ Check if input is less than another
 
 \ingroup logic_mat
 
-Check if input is greater than another
+Is greater than.
+
+Check if the elements of one array are greater than those of another array.
 
 
 
@@ -89,7 +81,9 @@ Check if input is greater than another
 
 \ingroup logic_mat
 
-Check if input is less than or equal to another
+Is less than or equal.
+
+Check if the elements of one array are less than or equal to those of another array.
 
 
 
@@ -97,7 +91,9 @@ Check if input is less than or equal to another
 
 \ingroup logic_mat
 
-Check if input is greater than or equal to another
+Is greater than or equal.
+
+Check if the elements of one array are greater than or equal to those of another array.
 
 
 
@@ -105,7 +101,9 @@ Check if input is greater than or equal to another
 
 \ingroup logic_mat
 
-Check if input two inputs are equal
+Is equal.
+
+Check if the elements of one array are equal to those of another array.
 
 
 
@@ -113,7 +111,9 @@ Check if input two inputs are equal
 
 \ingroup logic_mat
 
-Check if input two inputs are not equal
+Is not equal.
+
+Check if the elements of one array are not equal to those of another array.
 
 
 
@@ -122,13 +122,17 @@ Check if input two inputs are not equal
 
 \ingroup logic_mat
 
-Logical and of two inputs
+Logical AND.
+
+Evaluate the logical AND of two arrays.
 
 \defgroup arith_func_or or
 
 \ingroup logic_mat
 
-Logical or of two inputs
+Logical OR.
+
+Evaluate the logical OR of two arrays.
 
 
 
@@ -136,7 +140,9 @@ Logical or of two inputs
 
 \ingroup logic_mat
 
-Logical not of an input
+Logical NOT.
+
+Evaluate the logical NOT of an array.
 
 
 
@@ -144,14 +150,18 @@ Logical not of an input
 
 \ingroup numeric_mat
 
-Negative of an input
+Negative of an array.
+
+Negate an array.
 
 
 \defgroup arith_func_bitnot bitnot
 
 \ingroup logic_mat
 
-Bitwise not on the input
+Bitwise NOT.
+
+Evaluate the bitwise NOT of an array.
 
 \copydoc arith_int_only
 
@@ -160,7 +170,9 @@ Bitwise not on the input
 
 \ingroup logic_mat
 
-Bitwise and operation of two inputs
+Bitwise AND.
+
+Evaluate the bitwise AND of two arrays.
 
 \copydoc arith_int_only
 
@@ -169,7 +181,9 @@ Bitwise and operation of two inputs
 
 \ingroup logic_mat
 
-Bitwise or operation of two inputs
+Bitwise OR.
+
+Evaluate the bitwise OR of two arrays.
 
 \copydoc arith_int_only
 
@@ -178,17 +192,49 @@ Bitwise or operation of two inputs
 
 \ingroup logic_mat
 
-Bitwise xor operation of two inputs
+Bitwise XOR.
+
+Evaluate the bitwise XOR of two arrays.
 
 \copydoc arith_int_only
 
 
+\defgroup arith_func_shiftl bitshiftl
+
+\ingroup arith_mat
+
+Left shift on integer arrays.
+
+Shift the bits of integer arrays left.
+
+\copydoc arith_int_only
+
+
+\defgroup arith_func_shiftr bitshiftr
+
+\ingroup arith_mat
+
+Right shift on integer arrays.
+
+Shift the bits of integer arrays right.
+
+\copydoc arith_int_only
+
+
+\defgroup arith_func_cast cast
+
+\ingroup helper_mat
+
+Cast an array from one type to another.
+
+
 \defgroup arith_func_min min
 
 \ingroup numeric_mat
 
 Minimum of two inputs.
 
+Find the elementwise minimum between two arrays.
 
 
 \defgroup arith_func_max max
@@ -197,13 +243,16 @@ Minimum of two inputs.
 
 Maximum of two inputs.
 
+Find the elementwise maximum between two arrays.
 
 
 \defgroup arith_func_rem rem
 
 \ingroup numeric_mat
 
-Remainder operation
+Remainder.
+
+Find the remainder of a division.
 
 \copydoc arith_real_only
 
@@ -212,34 +261,41 @@ Remainder operation
 
 \ingroup numeric_mat
 
-Compute \f$x - n * y\f$ where n is quotient of \f$x / y\f$
+Modulus.
 
-\copydoc arith_real_only
+Find the modulus.
 
+\copydoc arith_real_only
 
 
 \defgroup arith_func_abs abs
 
-\brief Absolute value
+Absolute value.
 
-\ingroup numeric_mat
+Find the absolute value.
 
-Absolute value
+__Examples:__
+
+\snippet test/math.cpp ex_arith_func_abs
 
+\ingroup numeric_mat
 
 
 \defgroup arith_func_arg arg
 \ingroup numeric_mat
 
-\brief Phase of a number in the complex plane
+Phase angle.
 
+Find the phase angle (in radians) of a complex array.
 
 
 \defgroup arith_func_sign sign
 
 \ingroup numeric_mat
 
-Check if input is negative
+Sign.
+
+Find the sign of elements in an array.
 
 \copydoc arith_real_only
 
@@ -248,7 +304,9 @@ Check if input is negative
 
 \ingroup numeric_mat
 
-Round to nearest integer
+Round.
+
+Round numbers to the nearest integer.
 
 \copydoc arith_real_only
 
@@ -257,7 +315,9 @@ Round to nearest integer
 
 \ingroup numeric_mat
 
-Truncate to nearest integer
+Truncate.
+
+Truncate numbers to nearest integer.
 
 \copydoc arith_real_only
 
@@ -266,7 +326,9 @@ Truncate to nearest integer
 
 \ingroup numeric_mat
 
-Round to integer less than equal to current value
+Floor.
+
+Round to the integer less than or equal to the magnitude of the input value.
 
 \copydoc arith_real_only
 
@@ -275,7 +337,9 @@ Round to integer less than equal to current value
 
 \ingroup numeric_mat
 
-Round to integer greater than equal to current value
+Ceil.
+
+Round to the integer greater than or equal to the magnitude of the input value.
 
 \copydoc arith_real_only
 
@@ -284,7 +348,9 @@ Round to integer greater than equal to current value
 
 \ingroup numeric_mat
 
-Hypotenuse of the two inputs
+Hypotenuse.
+
+Find the length of the hypotenuse of two inputs.
 
 \copydoc arith_real_only
 
@@ -293,87 +359,114 @@ Hypotenuse of the two inputs
 
 \ingroup trig_mat
 
-sin of input
+Sine.
+
+Evaluate the sine function.
 
 
 \defgroup arith_func_cos cos
 
 \ingroup trig_mat
 
-cos of input
+Cosine.
 
+Evaluate the cosine function.
 
 
 \defgroup arith_func_tan tan/tan2
 
 \ingroup trig_mat
 
-tan of input
+Tangent.
+
+Evaluate the tangent function.
 
 
 \defgroup arith_func_asin asin
 
 \ingroup trig_mat
 
-arc sin of input
+Inverse sine (arc sine).
+
+Evaluate the inverse sine function.
 
 
 \defgroup arith_func_acos acos
-\brief Inverse cosine.
 
-\ingroup trig_mat
+Inverse cosine (arc cosine).
+
+Evaluate the inverse cosine function.
 
-arc cos of input
+The inverse of cosine so that, if `y = cos(x)`, then `x = arccos(y)`.
+
+__Examples:__
+
+\snippet test/math.cpp ex_arith_func_acos
+
+\ingroup trig_mat
 
 
 \defgroup arith_func_atan atan/atan2
 
 \ingroup trig_mat
 
-arc tan of input
+Inverse tangent (arc tangent).
+
+Evaluate the inverse tangent function.
 
 
 \defgroup arith_func_sinh sinh
 
 \ingroup hyper_mat
 
-sinh of input
+Hyperbolic sine.
+
+Evaluate the hyperbolic sine function.
 
 
 \defgroup arith_func_cosh cosh
 
 \ingroup hyper_mat
 
-cosh of input
+Hyperbolic cosine.
+
+Evaluate the hyperbolic cosine function.
 
 
 \defgroup arith_func_tanh tanh
 
 \ingroup hyper_mat
 
-tanh of input
+Hyperbolic tangent.
+
+Evaluate the hyperbolic tangent function.
 
 
 \defgroup arith_func_asinh asinh
 
 \ingroup hyper_mat
 
-asinh of input
+Inverse hyperbolic sine (area hyperbolic sine).
+
+Evaluate the inverse hyperbolic sine function.
 
 
 \defgroup arith_func_acosh acosh
-\brief Inverse hyperbolic cosine
 
 \ingroup hyper_mat
 
-acosh of input
+Inverse hyperbolic cosine (area hyperbolic cosine).
+
+Evaluate the inverse hyperbolic cosine function.
 
 
 \defgroup arith_func_atanh atanh
 
 \ingroup hyper_mat
 
-atanh of input
+Inverse hyperbolic tangent (area hyperbolic tangent).
+
+Evaluate the inverse hyperbolic tangent function.
 
 
 \defgroup arith_func_cplx complex
@@ -394,44 +487,41 @@ __Examples:__
 \snippet test/complex.cpp ex_arith_func_complex
 
 
-
 \defgroup arith_func_real real
 
 \ingroup complex_mat
 
-Get real part of complex arrays
-
+Find the real part of a complex array.
 
 
 \defgroup arith_func_imag imag
 
 \ingroup complex_mat
 
-Get imaginary part of complex arrays
-
+Find the imaginary part of a complex array.
 
 
 \defgroup arith_func_conjg conjg
 
 \ingroup complex_mat
 
-Get complex conjugate
-
+Complex conjugate.
 
+Find the complex conjugate of an input array.
 
 
 \defgroup arith_func_root root
 
 \ingroup explog_mat
 
-Find root of an input
+Find the nth root.
 
 
 \defgroup arith_func_pow pow
 
 \ingroup explog_mat
 
-Raise an array to a power
+Raise a base to a power (or exponent).
 
 If the input array has values beyond what a floating point type can represent, then there is no
 guarantee that the results will be accurate. The exact type mapping from integral types to floating
@@ -450,19 +540,26 @@ point types used to compute power is given below.
 The output array will be of the same type as input.
 
 
+\defgroup arith_func_sigmoid sigmoid
+
+Sigmoid function (logistical).
+
+Evaluate the logistical sigmoid function.
+
+
 
 \defgroup arith_func_exp exp
 
 \ingroup explog_mat
 
-Exponential of input
+Evaluate the exponential.
 
 
 \defgroup arith_func_expm1 expm1
 
 \ingroup explog_mat
 
-Exponential of input - 1
+Evaluate the exponential of an array minus 1, `exp(in) - 1`.
 
 \copydoc arith_real_only
 
@@ -471,7 +568,7 @@ Exponential of input - 1
 
 \ingroup explog_mat
 
-Error function value
+Evaluate the error function.
 
 \copydoc arith_real_only
 
@@ -481,7 +578,7 @@ Error function value
 
 \ingroup explog_mat
 
-Complementary Error function value
+Evaluate the complementary error function.
 
 \copydoc arith_real_only
 
@@ -490,14 +587,14 @@ Complementary Error function value
 
 \ingroup explog_mat
 
-Natural logarithm
+Evaluate the natural logarithm.
 
 
 \defgroup arith_func_log1p log1p
 
 \ingroup explog_mat
 
-Natural logarithm of (1 + in)
+Evaluate the natural logarithm of 1 + input, `ln(1+in)`.
 
 \copydoc arith_real_only
 
@@ -506,7 +603,16 @@ Natural logarithm of (1 + in)
 
 \ingroup explog_mat
 
-logarithm base 10
+Evaluate the base 10 logarithm.
+
+\copydoc arith_real_only
+
+
+\defgroup arith_func_log2 log2
+
+\ingroup explog_mat
+
+Evaluate the base 2 logarithm.
 
 \copydoc arith_real_only
 
@@ -515,23 +621,25 @@ logarithm base 10
 
 \ingroup explog_mat
 
-Square root of input arrays
+Find the square root.
+
 
 \defgroup arith_func_rsqrt rsqrt
 
 \ingroup explog_mat
 
-The reciprocal or inverse square root of input arrays
+Find the reciprocal square root.
 
 \f[ \frac{1}{\sqrt{x}} \f]
 
 \copydoc arith_real_only
 
+
 \defgroup arith_func_cbrt cbrt
 
 \ingroup explog_mat
 
-Cube root of input arrays
+Find the cube root.
 
 \copydoc arith_real_only
 
@@ -540,7 +648,7 @@ Cube root of input arrays
 
 \ingroup explog_mat
 
-Factorial function
+Find the factorial.
 
 \copydoc arith_real_only
 
@@ -549,7 +657,7 @@ Factorial function
 
 \ingroup explog_mat
 
-Gamma function
+Evaluate the gamma function.
 
 \copydoc arith_real_only
 
@@ -558,7 +666,7 @@ Gamma function
 
 \ingroup explog_mat
 
-Logarithm of absolute values of Gamma function
+Evaluate the logarithm of the absolute value of the gamma function.
 
 \copydoc arith_real_only
 
@@ -567,28 +675,22 @@ Logarithm of absolute values of Gamma function
 
 \ingroup helper_mat
 
-Check if values are zero
+Check if values are zero.
 
 
 \defgroup arith_func_isinf isinf
 
 \ingroup helper_mat
 
-Check if values are infinite
+Check if values are infinite.
 
 
 \defgroup arith_func_isnan isNan
 
 \ingroup helper_mat
 
-Check if values are Nan
-
-
-\defgroup arith_func_cast cast
-
-\ingroup helper_mat
+Check if values are NaN.
 
-Casting inputs from one type to another
 
 @}
 */
diff --git a/include/af/arith.h b/include/af/arith.h
index e2f695601d..789e54aab5 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -14,48 +14,70 @@ namespace af
 {
     class array;
 
-    /// \ingroup arith_func_min
-    /// @{
-    /// \brief C++ interface for min of two arrays
+    /// C++ Interface to find the elementwise minimum between two arrays.
     ///
-    /// \param[in] lhs first input
-    /// \param[in] rhs second input
+    /// \param[in] lhs input array
+    /// \param[in] rhs input array
     /// \return minimum of \p lhs and \p rhs
     ///
+    /// \ingroup arith_func_min
     AFAPI array min    (const array &lhs, const array &rhs);
 
-    /// \copydoc min(const array&, const array &)
+    /// C++ Interface to find the elementwise minimum between an array and a scalar value.
+    ///
+    /// \param[in] lhs input array
+    /// \param[in] rhs scalar value
+    /// \return minimum of \p lhs and \p rhs
+    ///
+    /// \ingroup arith_func_min
     AFAPI array min    (const array &lhs, const double rhs);
 
-    /// \copydoc min(const array&, const array &)
+    /// C++ Interface to find the elementwise minimum between an array and a scalar value.
+    ///
+    /// \param[in] lhs scalar value
+    /// \param[in] rhs input array
+    /// \return minimum of \p lhs and \p rhs
+    ///
+    /// \ingroup arith_func_min
     AFAPI array min    (const double lhs, const array &rhs);
-    /// @}
 
-    /// \ingroup arith_func_max
-    /// @{
-    /// C++ Interface for max of two arrays or an array and a scalar
+    /// C++ Interface to find the elementwise maximum between two arrays.
     ///
-    /// \param[in] lhs first input
-    /// \param[in] rhs second input
+    /// \param[in] lhs input array
+    /// \param[in] rhs input array
     /// \return maximum of \p lhs and \p rhs
+    ///
+    /// \ingroup arith_func_max
     AFAPI array max    (const array &lhs, const array &rhs);
 
-    /// \copydoc max(const array&, const array&)
+    /// C++ Interface to find the elementwise maximum between an array and a scalar value.
+    ///
+    /// \param[in] lhs input array
+    /// \param[in] rhs scalar value
+    /// \return maximum of \p lhs and \p rhs
+    ///
+    /// \ingroup arith_func_max
     AFAPI array max    (const array &lhs, const double rhs);
 
-    /// \copydoc max(const array&, const array&)
+    /// C++ Interface to find the elementwise maximum between an array and a scalar value.
+    ///
+    /// \param[in] lhs input array
+    /// \param[in] rhs scalar value
+    /// \return maximum of \p lhs and \p rhs
+    ///
+    /// \ingroup arith_func_max
     AFAPI array max    (const double lhs, const array &rhs);
-    /// @}
 
 #if AF_API_VERSION >= 34
-    /// \ingroup arith_func_clamp
     /// @{
-    /// C++ Interface for clamping an array between two values
+    /// C++ Interface to clamp an array between an upper and a lower limit.
     ///
-    /// \param[in] in Input array
-    /// \param[in] lo Value for lower limit
-    /// \param[in] hi  Value for upper limit
+    /// \param[in] in input array
+    /// \param[in] lo lower limit; can be an array or a scalar
+    /// \param[in] hi upper limit; can be an array or a scalar
     /// \return array containing values from \p in clamped between \p lo and \p hi
+    /// 
+    /// \ingroup arith_func_clamp
     AFAPI array clamp(const array &in, const array &lo, const array &hi);
 #endif
 
@@ -75,14 +97,14 @@ namespace af
 #endif
     /// @}
 
-    /// \ingroup arith_func_rem
     /// @{
-    /// C++ Interface for remainder when array divides array,
-    /// scalar divides array or array divides scalar
+    /// C++ Interface to find the remainder.
     ///
-    /// \param[in] lhs is numerator
-    /// \param[in] rhs is denominator
-    /// \return remainder when \p rhs divides \p lhs
+    /// \param[in] lhs numerator; can be an array or a scalar
+    /// \param[in] rhs denominator; can be an array or a scalar
+    /// \return remainder of \p lhs divided by \p rhs
+    /// 
+    /// \ingroup arith_func_rem
     AFAPI array rem    (const array &lhs, const array &rhs);
 
     /// \copydoc rem(const array&, const array&)
@@ -92,14 +114,14 @@ namespace af
     AFAPI array rem    (const double lhs, const array &rhs);
     /// @}
 
-    /// \ingroup arith_func_mod
     /// @{
-    /// C++ Interface for modulus when dividend and divisor are arrays
-    /// or one of them is scalar
+    /// C++ Interface to find the modulus.
     ///
-    /// \param[in] lhs is dividend
-    /// \param[in] rhs is divisor
+    /// \param[in] lhs dividend; can be an array or a scalar
+    /// \param[in] rhs divisor; can be an array or a scalar
     /// \return \p lhs modulo \p rhs
+    /// 
+    /// \ingroup arith_func_mod
     AFAPI array mod    (const array &lhs, const array &rhs);
 
     /// \copydoc mod(const array&, const array&)
@@ -109,68 +131,57 @@ namespace af
     AFAPI array mod    (const double lhs, const array &rhs);
     /// @}
 
-    /// C++ Interface for absolute value
+    /// C++ Interface to find the absolute value.
     ///
-    /// \param[in] in is input array
-    /// \return absolute value of \p in
+    /// \param[in] in input array
+    /// \return absolute value
     ///
     /// \ingroup arith_func_abs
     AFAPI array abs    (const array &in);
 
-    /**
-       C++ Interface for arg
-
-       \param[in] in is input array
-       \return phase of \p in
-
-       \ingroup arith_func_arg
-    */
+    /// C++ Interface to find the phase angle (in radians) of a complex array.
+    ///
+    /// \param[in] in input array, typically complex
+    /// \return phase angle (in radians)
+    /// 
+    /// \ingroup arith_func_arg
     AFAPI array arg    (const array &in);
 
-    /**
-       C++ Interface for getting the sign of input
-
-       \param[in] in is input array
-       \return the sign of each element of input
-
-       \note output is 1 for negative numbers and 0 for positive numbers
-
-       \ingroup arith_func_sign
-    */
+    /// C++ Interface to find the sign of elements in an array.
+    ///
+    /// \param[in] in input array
+    /// \return array containing 1's for negative values; 0's otherwise
+    /// 
+    /// \ingroup arith_func_sign
     AFAPI array sign  (const array &in);
 
-    ///C++ Interface for rounding an array of numbers
-    ///
-    ///\param[in] in is input array
-    ///\return values rounded to nearest integer
+    /// C++ Interface to round numbers.
     ///
-    ///\note The values are rounded to nearest integer
+    /// \param[in] in input array
+    /// \return numbers rounded to nearest integer
     ///
-    ///\ingroup arith_func_round
+    /// \ingroup arith_func_round
     AFAPI array round  (const array &in);
 
-    /**
-       C++ Interface for truncating an array of numbers
-
-       \param[in] in is input array
-       \return values truncated to nearest integer not greater than input values
-
-       \ingroup arith_func_trunc
-    */
+    /// C++ Interface to truncate numbers.
+    ///
+    /// \param[in] in input array
+    /// \return nearest integer not greater in magnitude than \p in
+    /// 
+    /// \ingroup arith_func_trunc
     AFAPI array trunc  (const array &in);
 
-
-    /// C++ Interface for flooring an array of numbers
+    /// C++ Interface to floor numbers.
     ///
-    /// \param[in] in is input array
+    /// \param[in] in input array
     /// \return values rounded to nearest integer less than or equal to current value
     ///
     /// \ingroup arith_func_floor
     AFAPI array floor  (const array &in);
 
-    /// C++ Interface for ceiling an array of numbers
+    /// C++ Interface to ceil numbers.
     ///
-    /// \param[in] in is input array
+    /// \param[in] in input array
     /// \return values rounded to nearest integer greater than or equal to current value
     ///
     /// \ingroup arith_func_ceil
@@ -178,14 +189,14 @@ namespace af
 
     /// \ingroup arith_func_hypot
     /// @{
-    /// \brief C++ Interface for getting length of hypotenuse of two inputs
+    /// C++ Interface to find the length of the hypotenuse of two inputs.
     ///
     /// Calculates the hypotenuse of two inputs. The inputs can be both arrays
     /// or an array and a scalar.
     ///
-    /// \param[in] lhs is the length of first side
-    /// \param[in] rhs is the length of second side
-    /// \return the length of the hypotenuse
+    /// \param[in] lhs length of first side
+    /// \param[in] rhs length of second side
+    /// \return length of the hypotenuse
     AFAPI array hypot  (const array &lhs, const array &rhs);
 
     /// \copydoc hypot(const array&, const array&)
@@ -195,61 +206,61 @@ namespace af
     AFAPI array hypot  (const double lhs, const array &rhs);
     /// @}
 
-    /// C++ Interface for sin
+    /// C++ Interface to evaluate the sine function.
     ///
-    /// \param[in] in is input array
-    /// \return sin of input
+    /// \param[in] in input array
+    /// \return sine
     ///
     /// \ingroup arith_func_sin
     AFAPI array sin    (const array &in);
 
-    /// C++ Interface for cos
+    /// C++ Interface to evaluate the cosine function.
     ///
-    /// \param[in] in is input array
-    /// \return cos of input
+    /// \param[in] in input array
+    /// \return cosine
     ///
     /// \ingroup arith_func_cos
     AFAPI array cos    (const array &in);
 
-    /// C++ Interface for tan
+    /// C++ Interface to evaluate the tangent function.
     ///
-    /// \param[in] in is input array
-    /// \return tan of input
+    /// \param[in] in input array
+    /// \return tangent
     ///
     /// \ingroup arith_func_tan
     AFAPI array tan    (const array &in);
 
-    /// C++ Interface for arc sin (sin inverse)
+    /// C++ Interface to evaluate the inverse sine function.
     ///
-    /// \param[in] in is input array
-    /// \return arc sin of input
+    /// \param[in] in input array
+    /// \return inverse sine
     ///
     /// \ingroup arith_func_asin
     AFAPI array asin   (const array &in);
 
-    /// C++ Interface for arc cos (cos inverse)
+    /// C++ Interface to evaluate the inverse cosine function.
     ///
-    /// \param[in] in is input array
-    /// \return arc cos of input
+    /// \param[in] in input array
+    /// \return inverse cosine
     ///
     /// \ingroup arith_func_acos
     AFAPI array acos   (const array &in);
 
-    /// C++ Interface for arc tan (tan inverse)
+    /// C++ Interface to evaluate the inverse tangent function.
     ///
-    /// \param[in] in is input array
-    /// \return arc tan of input
+    /// \param[in] in input array
+    /// \return inverse tangent
     ///
     /// \ingroup arith_func_atan
     AFAPI array atan   (const array &in);
 
     /// \ingroup arith_func_atan
     /// @{
-    /// C++ Interface for arc tan of two arrays
+    /// C++ Interface to evaluate the inverse tangent of two arrays.
     ///
     /// \param[in] lhs value of numerator
     /// \param[in] rhs value of denominator
-    /// \return arc tan of the inputs
+    /// \return inverse tangent of the inputs
     AFAPI array atan2  (const array &lhs, const array &rhs);
 
     /// \copydoc atan2(const array&, const array&)
@@ -259,29 +270,77 @@ namespace af
     AFAPI array atan2  (const double lhs, const array &rhs);
     /// @}
 
+    /// C++ Interface to evaluate the hyperbolic sine function.
+    ///
+    /// \param[in] in input array
+    /// \return hyperbolic sine
+    ///
+    /// \ingroup arith_func_sinh
+    AFAPI array sinh(const array& in);
+
+    /// C++ Interface to evaluate the hyperbolic cosine function.
+    ///
+    /// \param[in] in input array
+    /// \return hyperbolic cosine
+    ///
+    /// \ingroup arith_func_cosh
+    AFAPI array cosh(const array& in);
+
+    /// C++ Interface to evaluate the hyperbolic tangent function.
+    ///
+    /// \param[in] in input array
+    /// \return hyperbolic tangent
+    ///
+    /// \ingroup arith_func_tanh
+    AFAPI array tanh(const array& in);
+
+    /// C++ Interface to evaluate the inverse hyperbolic sine function.
+    ///
+    /// \param[in] in input array
+    /// \return inverse hyperbolic sine
+    ///
+    /// \ingroup arith_func_asinh
+    AFAPI array asinh(const array& in);
+
+    /// C++ Interface to evaluate the inverse hyperbolic cosine function.
+    ///
+    /// \param[in] in input array
+    /// \return inverse hyperbolic cosine
+    ///
+    /// \ingroup arith_func_acosh
+    AFAPI array acosh(const array& in);
+
+    /// C++ Interface to evaluate the inverse hyperbolic tangent function.
+    ///
+    /// \param[in] in input array
+    /// \return inverse hyperbolic tangent
+    ///
+    /// \ingroup arith_func_atanh
+    AFAPI array atanh(const array& in);
+
     /// \ingroup arith_func_cplx
     /// @{
-    /// C++ Interface for creating a complex array from a single real array.
+    /// C++ Interface to create a complex array from a single real array.
     ///
     /// \param[in] in a real array
     /// \return the returned complex array
     AFAPI array complex(const array& in);
  
-    /// C++ Interface for creating a complex array from two real arrays.
+    /// C++ Interface to create a complex array from two real arrays.
     ///
     /// \param[in] real_ a real array to be assigned as the real component of the returned complex array
     /// \param[in] imag_ a real array to be assigned as the imaginary component of the returned complex array
     /// \return the returned complex array
     AFAPI array complex(const array &real_, const array &imag_);
 
-    /// C++ Interface for creating a complex array from a single real array for the real component and a single scalar for each imaginary component.
+    /// C++ Interface to create a complex array from a single real array for the real component and a single scalar for each imaginary component.
     ///
     /// \param[in] real_ a real array to be assigned as the real component of the returned complex array
     /// \param[in] imag_ a single scalar to be assigned as the imaginary component of each value of the returned complex array
     /// \return the returned complex array
     AFAPI array complex(const array &real_, const double imag_);
 
-    /// C++ Interface for creating a complex array from a single scalar for each real component and a single real array for the imaginary component.
+    /// C++ Interface to create a complex array from a single scalar for each real component and a single real array for the imaginary component.
     ///
     /// \param[in] real_ a single scalar to be assigned as the real component of each value of the returned complex array
     /// \param[in] imag_ a real array to be assigned as the imaginary component of the returned complex array
@@ -289,100 +348,52 @@ namespace af
     AFAPI array complex(const double real_, const array &imag_);
     /// @}
 
-    /// C++ Interface for getting real part from complex array
+    /// C++ Interface to find the real part of a complex array.
     ///
-    /// \param[in] in is complex array
-    /// \return the real part of \p in
+    /// \param[in] in input complex array
+    /// \return real part
     ///
     /// \ingroup arith_func_real
     AFAPI array real   (const array &in);
 
-    /// C++ Interface for getting imaginary part from complex array
+    /// C++ Interface to find the imaginary part of a complex array.
     ///
-    /// \param[in] in is complex array
-    /// \return the imaginary part of \p in
+    /// \param[in] in input complex array
+    /// \return imaginary part
     ///
     /// \ingroup arith_func_imag
     AFAPI array imag   (const array &in);
 
-    /// C++ Interface for getting the complex conjugate of input array
+    /// C++ Interface to find the complex conjugate of an input array.
     ///
-    /// \param[in] in is complex array
-    /// \return the complex conjugate of \p in
+    /// \param[in] in input complex array
+    /// \return complex conjugate
     ///
     /// \ingroup arith_func_conjg
     AFAPI array conjg  (const array &in);
 
-    /// C++ Interface for sinh
-    ///
-    /// \param[in] in is input array
-    /// \return sinh of input
-    ///
-    /// \ingroup arith_func_sinh
-    AFAPI array sinh    (const array &in);
-
-    /// C++ Interface for cosh
-    ///
-    /// \param[in] in is input array
-    /// \return cosh of input
-    ///
-    /// \ingroup arith_func_cosh
-    AFAPI array cosh    (const array &in);
-
-    /// C++ Interface for tanh
-    ///
-    /// \param[in] in is input array
-    /// \return tanh of input
-    ///
-    /// \ingroup arith_func_tanh
-    AFAPI array tanh    (const array &in);
-
-    /// C++ Interface for sinh inverse
-    ///
-    /// \param[in] in is input array
-    /// \return sinh inverse of input
-    ///
-    /// \ingroup arith_func_asinh
-    AFAPI array asinh   (const array &in);
-
-    /// C++ Interface for cosh inverse
+    /// C++ Interface to find the nth root.
     ///
-    /// \param[in] in is input array
-    /// \return cosh inverse of input
-    ///
-    /// \ingroup arith_func_acosh
-    AFAPI array acosh   (const array &in);
-
-    /// C++ Interface for tanh inverse
-    ///
-    /// \param[in] in is input array
-    /// \return tanh inverse of input
-    ///
-    /// \ingroup arith_func_atanh
-    AFAPI array atanh   (const array &in);
-
-    /// C++ Interface for nth root
-    ///
-    /// \param[in] lhs is nth root
-    /// \param[in] rhs is value
+    /// \param[in] lhs nth root
+    /// \param[in] rhs value
     /// \return \p lhs th root of \p rhs
     ///
     /// \ingroup arith_func_root
     AFAPI array root    (const array &lhs, const array &rhs);
 
-    /// C++ Interface for nth root
+    /// C++ Interface to find the nth root.
     ///
-    /// \param[in] lhs is nth root
-    /// \param[in] rhs is value
+    /// \param[in] lhs nth root
+    /// \param[in] rhs value
     /// \return \p lhs th root of \p rhs
     ///
     /// \ingroup arith_func_root
     AFAPI array root    (const array &lhs, const double rhs);
 
-    /// C++ Interface for nth root
+    /// C++ Interface to find the nth root.
     ///
-    /// \param[in] lhs is nth root
-    /// \param[in] rhs is value
+    /// \param[in] lhs nth root
+    /// \param[in] rhs value
     /// \return \p lhs th root of \p rhs
     ///
     /// \ingroup arith_func_root
@@ -391,14 +402,13 @@ namespace af
 
     /// \ingroup arith_func_pow
     /// @{
-    /// \brief C++ Interface for power
+    /// C++ Interface to raise a base to a power (or exponent).
     ///
-    /// Computes the value of \p lhs raised to the power of \p rhs. The inputs
-    /// can be two arrays or an array and a scalar.
+    /// Computes the value of \p lhs raised to the power of \p rhs. The inputs can be two arrays or an array and a scalar.
     ///
-    /// \param[in] lhs is base
-    /// \param[in] rhs is exponent
-    /// \return \p lhs raised to power \p rhs
+    /// \param[in] lhs base
+    /// \param[in] rhs exponent
+    /// \return \p lhs raised to the power of \p rhs
     AFAPI array pow    (const array &lhs, const array &rhs);
 
     /// \copydoc pow(const array&, const array&)
@@ -407,161 +417,162 @@ namespace af
     /// \copydoc pow(const array&, const array&)
     AFAPI array pow    (const double lhs, const array &rhs);
 
-    /// C++ Interface for power of 2
+    /// C++ Interface to raise 2 to a power (or exponent).
     ///
-    /// \param[in] in is exponent
-    /// \return 2 raised to power of \p in
+    /// \param[in] in exponent
+    /// \return 2 raised to the power
     ///
     AFAPI array pow2    (const array &in);
     /// @}
 
 #if AF_API_VERSION >= 31
-    /// C++ Interface for calculating sigmoid function of an array
+    /// C++ Interface to evaluate the logistical sigmoid function.
     ///
-    /// \param[in] in is input
-    /// \return the sigmoid of \p in
+    /// \param[in] in input
+    /// \return sigmoid
+    /// 
+    /// \note Computes `1/(1+e^-x)`.
     ///
     /// \ingroup arith_func_sigmoid
     AFAPI array sigmoid (const array &in);
 #endif
 
-    /// C++ Interface for exponential of an array
+    /// C++ Interface to evaluate the exponential.
     ///
-    /// \param[in] in is exponent
-    /// \return the exponential of \p in
+    /// \param[in] in exponent
+    /// \return exponential
     ///
     /// \ingroup arith_func_exp
     AFAPI array exp    (const array &in);
 
-    /// C++ Interface for exponential of an array minus 1
+    /// C++ Interface to evaluate the exponential of an array minus 1, `exp(in) - 1`.
     ///
-    /// \param[in] in is exponent
-    /// \return the exponential of \p in - 1
+    /// \param[in] in exponent
+    /// \return the exponential minus 1
     ///
     /// \note This function is useful when \p in is small
     /// \ingroup arith_func_expm1
     AFAPI array expm1  (const array &in);
 
-    /// C++ Interface for error function value
+    /// C++ Interface to evaluate the error function.
     ///
-    /// \param[in] in is input
-    /// \return the error function value
+    /// \param[in] in input
+    /// \return error function
     ///
     /// \ingroup arith_func_erf
     AFAPI array erf    (const array &in);
 
-    /// C++ Interface for complementary error function value
+    /// C++ Interface to evaluate the complementary error function.
     ///
-    /// \param[in] in is input
-    /// \return the complementary error function value
+    /// \param[in] in input
+    /// \return complementary error function
     ///
     /// \ingroup arith_func_erfc
     AFAPI array erfc   (const array &in);
 
-    /// C++ Interface for natural logarithm
+    /// C++ Interface to evaluate the natural logarithm.
     ///
-    /// \param[in] in is input
-    /// \return the natural logarithm of input
+    /// \param[in] in input
+    /// \return natural logarithm
     ///
     /// \ingroup arith_func_log
     AFAPI array log    (const array &in);
 
-    /// C++ Interface for natural logarithm of 1 + input
+    /// C++ Interface to evaluate the natural logarithm of 1 + input, `ln(1+in)`.
     ///
-    /// \param[in] in is input
-    /// \return the natural logarithm of (1 + input)
+    /// \param[in] in input
+    /// \return natural logarithm of `1 + input`
     ///
     /// \note This function is useful when \p in is small
     /// \ingroup arith_func_log1p
     AFAPI array log1p  (const array &in);
 
-    /// C++ Interface for logarithm base 10
+    /// C++ Interface to evaluate the base 10 logarithm.
     ///
-    /// \param[in] in is input
-    /// \return the logarithm of input in base 10
+    /// \param[in] in input
+    /// \return base 10 logarithm
     ///
     /// \ingroup arith_func_log10
     AFAPI array log10  (const array &in);
 
-    /// C++ Interface for logarithm base 2
+    /// C++ Interface to evaluate the base 2 logarithm.
     ///
-    /// \param[in] in is input
-    /// \return the logarithm of input \p in base 2
+    /// \param[in] in input
+    /// \return base 2 logarithm
     ///
     /// \ingroup explog_func_log2
     AFAPI array log2   (const array &in);
 
-    /// C++ Interface for square root of input
+    /// C++ Interface to find the square root.
     ///
-    /// \param[in] in is input
-    /// \return the square root of input
+    /// \param[in] in input
+    /// \return square root
     ///
     /// \ingroup arith_func_sqrt
     AFAPI array sqrt   (const array &in);
 
 #if AF_API_VERSION >= 37
-    /// C++ Interface for reciprocal square root of input
+    /// C++ Interface to find the reciprocal square root.
     ///
-    /// \param[in] in is input
-    /// \return the reciprocal square root of input
+    /// \param[in] in input
+    /// \return reciprocal square root
     ///
     /// \ingroup arith_func_rsqrt
     AFAPI array rsqrt   (const array &in);
 #endif
 
-    /// C++ Interface for cube root of input
+    /// C++ Interface to find the cube root.
     ///
-    /// \param[in] in is input
-    /// \return the cube root of input
+    /// \param[in] in input
+    /// \return cube root
     ///
     /// \ingroup arith_func_cbrt
     AFAPI array cbrt   (const array &in);
 
+    /// C++ Interface to find the factorial.
     ///
-    /// C++ Interface for factorial of input
-    ///
-    /// \param[in] in is input
-    /// \return the factorial function of input
+    /// \param[in] in input
+    /// \return the factorial function
     ///
     /// \ingroup arith_func_factorial
     AFAPI array factorial (const array &in);
 
-    /// C++ Interface for gamma function of input
+    /// C++ Interface to evaluate the gamma function.
     ///
-    /// \param[in] in is input
-    /// \return the gamma function of input
+    /// \param[in] in input
+    /// \return gamma function
     ///
     /// \ingroup arith_func_tgamma
     AFAPI array tgamma (const array &in);
 
-    /// C++ Interface for logarithm of absolute value of gamma function of input
+    /// C++ Interface to evaluate the logarithm of the absolute value of the gamma function.
     ///
-    /// \param[in] in is input
-    /// \return the logarithm of absolute value of gamma function of input
+    /// \param[in] in input
+    /// \return logarithm of the absolute value of the gamma function
     ///
-    /// \ingroup arith_func_tgamma
+    /// \ingroup arith_func_lgamma
     AFAPI array lgamma (const array &in);
 
-    /// C++ Interface for checking if values are zero
+    /// C++ Interface to check if values are zero.
     ///
-    /// \param[in] in is input
-    /// \return array containing 1's where input is 0, and 0 otherwise.
+    /// \param[in] in input
+    /// \return array containing 1's where input is 0; 0's otherwise
     ///
     /// \ingroup arith_func_iszero
     AFAPI array iszero (const array &in);
 
-    /// C++ Interface for checking if values are Infinities
+    /// C++ Interface to check if values are infinite.
     ///
-    /// \param[in] in is input
-    /// \return array containing 1's where input is Inf or -Inf, and 0 otherwise.
+    /// \param[in] in input
+    /// \return array containing 1's where input is Inf or -Inf; 0's otherwise
     ///
     /// \ingroup arith_func_isinf
     AFAPI array isInf  (const array &in);
 
-    /// C++ Interface for checking if values are NaNs
+    /// C++ Interface to check if values are NaN.
     ///
-    /// \param[in] in is input
-    /// \return array containing 1's where input is NaN, and 0 otherwise.
+    /// \param[in] in input
+    /// \return array containing 1's where input is NaN; 0's otherwise
     ///
     /// \ingroup arith_func_isnan
     AFAPI array isNaN  (const array &in);
@@ -573,9 +584,9 @@ extern "C" {
 #endif
 
     /**
-       C Interface for adding arrays
+       C Interface to add two arrays.
 
-       \param[out] out will contain sum of \p lhs and \p rhs
+       \param[out] out sum of \p lhs and \p rhs
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -586,9 +597,9 @@ extern "C" {
     AFAPI af_err af_add   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for subtracting an array from another
+       C Interface to subtract one array from another array.
 
-       \param[out] out will contain result of \p lhs - \p rhs
+       \param[out] out subtraction of \p lhs - \p rhs
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -599,9 +610,9 @@ extern "C" {
     AFAPI af_err af_sub   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for multiplying two arrays
+       C Interface to multiply two arrays.
 
-       \param[out] out will contain the product of \p lhs and  \p rhs
+       \param[out] out product of \p lhs and \p rhs
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -612,9 +623,9 @@ extern "C" {
     AFAPI af_err af_mul   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for dividing an array by another
+       C Interface to divide one array by another array.
 
-       \param[out] out will contain result of \p lhs / \p rhs.
+       \param[out] out result of \p lhs / \p rhs.
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -625,9 +636,9 @@ extern "C" {
     AFAPI af_err af_div   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for checking if an array is less than another
+       C Interface to check if the elements of one array are less than those of another array.
 
-       \param[out] out will contain result of \p lhs < \p rhs. out is of type b8
+       \param[out] out result of \p lhs < \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -638,9 +649,9 @@ extern "C" {
     AFAPI af_err af_lt    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for checking if an array is greater than another
+       C Interface to check if the elements of one array are greater than those of another array.
 
-       \param[out] out will contain result of \p lhs > \p rhs. out is of type b8
+       \param[out] out result of \p lhs > \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -651,9 +662,9 @@ extern "C" {
     AFAPI af_err af_gt    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for checking if an array is less or equal to another
+       C Interface to check if the elements of one array are less than or equal to those of another array.
 
-       \param[out] out will contain result of \p lhs <= \p rhs. out is of type b8
+       \param[out] out result of \p lhs <= \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -664,9 +675,9 @@ extern "C" {
     AFAPI af_err af_le    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for checking if an array is greater or equal to another
+       C Interface to check if the elements of one array are greater than or equal to those of another array.
 
-       \param[out] out will contain result of \p lhs >= \p rhs. out is of type b8
+       \param[out] out result of \p lhs >= \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -677,9 +688,9 @@ extern "C" {
     AFAPI af_err af_ge    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for checking if an array is equal to another
+       C Interface to check if the elements of one array are equal to those of another array.
 
-       \param[out] out will contain result of \p lhs == \p rhs. out is of type b8
+       \param[out] out result of \p lhs == \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -690,9 +701,9 @@ extern "C" {
     AFAPI af_err af_eq    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for checking if an array is not equal to another
+       C Interface to check if the elements of one array are not equal to those of another array.
 
-       \param[out] out will contain result of \p lhs != \p rhs. out is of type b8
+       \param[out] out result of \p lhs != \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -703,9 +714,9 @@ extern "C" {
     AFAPI af_err af_neq   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for performing logical and on two arrays
+       C Interface to evaluate the logical AND of two arrays.
 
-       \param[out] out will contain result of \p lhs && \p rhs. out is of type b8
+       \param[out] out result of \p lhs && \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -716,9 +727,9 @@ extern "C" {
     AFAPI af_err af_and   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for performing logical or on two arrays
+       C Interface the evaluate the logical OR of two arrays.
 
-       \param[out] out will contain result of \p lhs || \p rhs. out is of type b8
+       \param[out] out result of \p lhs || \p rhs; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -729,10 +740,10 @@ extern "C" {
     AFAPI af_err af_or    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for performing logical not on input
+       C Interface to evaluate the logical NOT of an array.
 
-       \param[out] out will contain result of logical not of \p in. out is of type b8
-       \param[in] in is the input
+       \param[out] out result of logical NOT; type is b8
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_not
@@ -741,10 +752,10 @@ extern "C" {
 
 #if AF_API_VERSION >= 38
     /**
-       C Interface for performing bitwise not on input
+       C Interface to evaluate the bitwise NOT of an array.
 
-       \param[out] out will contain result of bitwise not of \p in.
-       \param[in] in is the input
+       \param[out] out result of bitwise NOT
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_bitnot
@@ -753,9 +764,9 @@ extern "C" {
 #endif
 
     /**
-       C Interface for performing bitwise and on two arrays
+       C Interface to evaluate the bitwise AND of two arrays.
 
-       \param[out] out will contain result of \p lhs & \p rhs
+       \param[out] out result of \p lhs & \p rhs
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -766,9 +777,9 @@ extern "C" {
     AFAPI af_err af_bitand   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for performing bitwise or on two arrays
+       C Interface to evaluate the bitwise OR of two arrays.
 
-       \param[out] out will contain result of \p lhs & \p rhs
+       \param[out] out result of \p lhs | \p rhs
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -779,9 +790,9 @@ extern "C" {
     AFAPI af_err af_bitor    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for performing bitwise xor on two arrays
+       C Interface to evaluate the bitwise XOR of two arrays.
 
-       \param[out] out will contain result of \p lhs ^ \p rhs
+       \param[out] out result of \p lhs ^ \p rhs
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -792,9 +803,9 @@ extern "C" {
     AFAPI af_err af_bitxor   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for left shift on integer arrays
+       C Interface to shift the bits of integer arrays left.
 
-       \param[out] out will contain result of the left shift
+       \param[out] out result of the left shift
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -805,9 +816,9 @@ extern "C" {
     AFAPI af_err af_bitshiftl(af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for right shift on integer arrays
+       C Interface to shift the bits of integer arrays right.
 
-       \param[out] out will contain result of the right shift
+       \param[out] out result of the right shift
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -818,7 +829,7 @@ extern "C" {
     AFAPI af_err af_bitshiftr(af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for casting an array from one type to another
+       C Interface to cast an array from one type to another.
 
        This function casts an af_array object from one type to another. If the
        type of the original array is the same as \p type then the same array is
@@ -847,11 +858,11 @@ extern "C" {
        | f16     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
        If you want to avoid this behavior use af_eval after the first cast
        operation. This will ensure that the cast operation is performed on the
-       af_array
+       af_array.
 
-       \param[out] out will contain the values in the specified type
-       \param[in] in is the input
-       \param[in] type is the target data type \ref af_dtype
+       \param[out] out values in the specified type
+       \param[in] in input
+       \param[in] type target data type \ref af_dtype
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cast
@@ -859,11 +870,11 @@ extern "C" {
     AFAPI af_err af_cast    (af_array *out, const af_array in, const af_dtype type);
 
     /**
-       C Interface for min of two arrays
+       C Interface to find the elementwise minimum between two arrays.
 
-       \param[out] out will contain minimum of \p lhs and \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
+       \param[out] out minimum of \p lhs and \p rhs
+       \param[in] lhs input array
+       \param[in] rhs input array
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -872,11 +883,11 @@ extern "C" {
     AFAPI af_err af_minof (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for max of two arrays
+       C Interface to find the elementwise minimum between an array and a scalar value.
 
-       \param[out] out will contain maximum of \p lhs and \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
+       \param[out] out maximum of \p lhs and \p rhs
+       \param[in] lhs input array
+       \param[in] rhs input array
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -886,27 +897,27 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for max of two arrays
+       C Interface to clamp an array between an upper and a lower limit.
 
-       \param[out] out will contain the values from \p clamped between \p lo and \p hi
-       \param[in] in Input array
-       \param[in] lo Value for lower limit
-       \param[in] hi Value for upper limit
+       \param[out] out array containing values from \p in clamped between \p lo and \p hi
+       \param[in] in input array
+       \param[in] lo lower limit array
+       \param[in] hi upper limit array
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_max
+       \ingroup arith_func_clamp
     */
     AFAPI af_err af_clamp(af_array *out, const af_array in,
                           const af_array lo, const af_array hi, const bool batch);
 #endif
 
     /**
-       C Interface for remainder
+       C Interface to find the remainder.
 
-       \param[out] out will contain the remainder of \p lhs divided by \p rhs
-       \param[in] lhs is numerator
-       \param[in] rhs is denominator
+       \param[out] out remainder of \p lhs divided by \p rhs
+       \param[in] lhs numerator
+       \param[in] rhs denominator
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -915,11 +926,11 @@ extern "C" {
     AFAPI af_err af_rem   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for modulus
+       C Interface to find the modulus.
 
-       \param[out] out will contain the output of \p lhs modulo \p rhs
-       \param[in] lhs is dividend
-       \param[in] rhs is divisor
+       \param[out] out \p lhs modulo \p rhs
+       \param[in] lhs dividend
+       \param[in] rhs divisor
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -928,10 +939,10 @@ extern "C" {
     AFAPI af_err af_mod   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for absolute value
+       C Interface to find the absolute value.
 
-       \param[out] out will contain the absolute value of \p in
-       \param[in] in is input array
+       \param[out] out absolute value
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_abs
@@ -939,10 +950,10 @@ extern "C" {
     AFAPI af_err af_abs     (af_array *out, const af_array in);
 
     /**
-       C Interface for finding the phase
+       C Interface to find the phase angle (in radians) of a complex array.
 
-       \param[out] out will the phase of \p in
-       \param[in] in is input array
+       \param[out] out phase angle (in radians)
+       \param[in] in input array, typically complex
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_arg
@@ -950,36 +961,32 @@ extern "C" {
     AFAPI af_err af_arg     (af_array *out, const af_array in);
 
     /**
-       C Interface for finding the sign of the input
+       C Interface to find the sign of elements in an array.
 
-       \param[out] out will contain the sign of each element of the input arrays
-       \param[in] in is input array
+       \param[out] out array containing 1's for negative values; 0's otherwise
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \note output is 1 for negative numbers and 0 for positive numbers
-
-       \ingroup arith_func_round
+       \ingroup arith_func_sign
     */
     AFAPI af_err af_sign   (af_array *out, const af_array in);
 
     /**
-       C Interface for rounding an array of numbers
+       C Interface to round numbers.
 
-       \param[out] out will contain values rounded to nearest integer
-       \param[in] in is input array
+       \param[out] out values rounded to nearest integer
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \note The values are rounded to nearest integer
-
        \ingroup arith_func_round
     */
     AFAPI af_err af_round   (af_array *out, const af_array in);
 
     /**
-       C Interface for truncating an array of numbers
+       C Interface to truncate numbers.
 
-       \param[out] out will contain values truncated to nearest integer not greater than input
-       \param[in] in is input array
+       \param[out] out nearest integer not greater in magnitude than \p in
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_trunc
@@ -987,10 +994,10 @@ extern "C" {
     AFAPI af_err af_trunc   (af_array *out, const af_array in);
 
     /**
-       C Interface for flooring an array of numbers
+       C Interface to floor numbers.
 
-       \param[out] out will contain values rounded to nearest integer less than or equal to in
-       \param[in] in is input array
+       \param[out] out values rounded to nearest integer less than or equal to \p in
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_floor
@@ -998,10 +1005,10 @@ extern "C" {
     AFAPI af_err af_floor   (af_array *out, const af_array in);
 
     /**
-       C Interface for ceiling an array of numbers
+       C Interface to ceil numbers.
 
-       \param[out] out will contain values rounded to nearest integer greater than or equal to in
-       \param[in] in is input array
+       \param[out] out values rounded to nearest integer greater than or equal to \p in
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_ceil
@@ -1009,11 +1016,11 @@ extern "C" {
     AFAPI af_err af_ceil    (af_array *out, const af_array in);
 
     /**
-       C Interface for getting length of hypotenuse of two arrays
+       C Interface to find the length of the hypotenuse of two inputs.
 
-       \param[out] out will contain the length of the hypotenuse
-       \param[in] lhs is the length of first side
-       \param[in] rhs is the length of second side
+       \param[out] out length of the hypotenuse
+       \param[in] lhs length of first side
+       \param[in] rhs length of second side
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -1022,10 +1029,10 @@ extern "C" {
     AFAPI af_err af_hypot (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for sin
+       C Interface to evaluate the sine function.
 
-       \param[out] out will contain sin of input
-       \param[in] in is input array
+       \param[out] out sine
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_sin
@@ -1033,10 +1040,10 @@ extern "C" {
     AFAPI af_err af_sin     (af_array *out, const af_array in);
 
     /**
-       C Interface for cos
+       C Interface to evaluate the cosine function.
 
-       \param[out] out will contain cos of input
-       \param[in] in is input array
+       \param[out] out cosine
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cos
@@ -1044,10 +1051,10 @@ extern "C" {
     AFAPI af_err af_cos     (af_array *out, const af_array in);
 
     /**
-       C Interface for tan
+       C Interface to evaluate the tangent function.
 
-       \param[out] out will contain tan of input
-       \param[in] in is input array
+       \param[out] out tangent
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_tan
@@ -1055,10 +1062,10 @@ extern "C" {
     AFAPI af_err af_tan     (af_array *out, const af_array in);
 
     /**
-       C Interface for arc sin
+       C Interface to evaluate the inverse sine function.
 
-       \param[out] out will contain arc sin of input
-       \param[in] in is input array
+       \param[out] out inverse sine
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_asin
@@ -1066,10 +1073,10 @@ extern "C" {
     AFAPI af_err af_asin    (af_array *out, const af_array in);
 
     /**
-       C Interface for arc cos
+       C Interface to evaluate the inverse cosine function.
 
-       \param[out] out will contain arc cos of input
-       \param[in] in is input array
+       \param[out] out inverse cos
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_acos
@@ -1077,10 +1084,10 @@ extern "C" {
     AFAPI af_err af_acos    (af_array *out, const af_array in);
 
     /**
-       C Interface for arc tan
+       C Interface to evaluate the inverse tangent function.
 
-       \param[out] out will contain arc tan of input
-       \param[in] in is input array
+       \param[out] out inverse tangent
+       \param[in] in input array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_atan
@@ -1088,11 +1095,11 @@ extern "C" {
     AFAPI af_err af_atan    (af_array *out, const af_array in);
 
     /**
-       C Interface for arc tan of two inputs
+       C Interface to evaluate the inverse tangent of two arrays.
 
-       \param[out] out will arc tan of the inputs
-       \param[in] lhs value of numerator
-       \param[in] rhs value of denominator
+       \param[out] out inverse tangent of two arrays
+       \param[in] lhs numerator
+       \param[in] rhs denominator
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -1101,10 +1108,10 @@ extern "C" {
     AFAPI af_err af_atan2 (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for creating a complex array from a single real array.
+       C Interface to create a complex array from a single real array.
 
-       \param[out] out the returned complex array
-       \param[in] in a real array
+       \param[out] out complex array
+       \param[in] in real array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cplx
@@ -1112,11 +1119,11 @@ extern "C" {
     AFAPI af_err af_cplx(af_array* out, const af_array in);
 
     /**
-       C Interface for creating a complex array from two real arrays.
+       C Interface to create a complex array from two real arrays.
 
-       \param[out] out the returned complex array
-       \param[in] real a real array to be assigned as the real component of the returned complex array
-       \param[in] imag a real array to be assigned as the imaginary component of the returned complex array
+       \param[out] out complex array
+       \param[in] real real array to be assigned as the real component of the returned complex array
+       \param[in] imag real array to be assigned as the imaginary component of the returned complex array
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -1125,10 +1132,10 @@ extern "C" {
     AFAPI af_err af_cplx2 (af_array *out, const af_array real, const af_array imag, const bool batch);
 
     /**
-       C Interface for getting real part from complex array
+       C Interface to find the real part of a complex array.
 
-       \param[out] out will contain the real part of \p in
-       \param[in] in is complex array
+       \param[out] out real part
+       \param[in] in complex array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_real
@@ -1136,10 +1143,10 @@ extern "C" {
     AFAPI af_err af_real    (af_array *out, const af_array in);
 
     /**
-       C Interface for getting imaginary part from complex array
+       C Interface to find the imaginary part of a complex array.
 
-       \param[out] out will contain the imaginary part of \p in
-       \param[in] in is complex array
+       \param[out] out imaginary part
+       \param[in] in complex array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_imag
@@ -1147,10 +1154,10 @@ extern "C" {
     AFAPI af_err af_imag    (af_array *out, const af_array in);
 
     /**
-       C Interface for getting the complex conjugate of input array
+       C Interface to find the complex conjugate of an input array.
 
-       \param[out] out will contain the complex conjugate of \p in
-       \param[in] in is complex array
+       \param[out] out complex conjugate
+       \param[in] in complex array
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_conjg
@@ -1158,10 +1165,10 @@ extern "C" {
     AFAPI af_err af_conjg   (af_array *out, const af_array in);
 
     /**
-       C Interface for sinh
+       C Interface to evaluate the hyperbolic sine function.
 
-       \param[out] out will contain sinh of input
-       \param[in] in is input array
+       \param[out] out hyperbolic sine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_sinh
@@ -1169,10 +1176,10 @@ extern "C" {
     AFAPI af_err af_sinh    (af_array *out, const af_array in);
 
     /**
-       C Interface for cosh
+       C Interface to evaluate the hyperbolic cosine function.
 
-       \param[out] out will contain cosh of input
-       \param[in] in is input array
+       \param[out] out hyperbolic cosine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cosh
@@ -1180,10 +1187,10 @@ extern "C" {
     AFAPI af_err af_cosh    (af_array *out, const af_array in);
 
     /**
-       C Interface for tanh
+       C Interface to evaluate the hyperbolic tangent function.
 
-       \param[out] out will contain tanh of input
-       \param[in] in is input array
+       \param[out] out hyperbolic tangent
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_tanh
@@ -1191,10 +1198,10 @@ extern "C" {
     AFAPI af_err af_tanh    (af_array *out, const af_array in);
 
     /**
-       C Interface for asinh
+       C Interface to evaluate the inverse hyperbolic sine function.
 
-       \param[out] out will contain inverse sinh of input
-       \param[in] in is input array
+       \param[out] out inverse hyperbolic sine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_asinh
@@ -1202,10 +1209,10 @@ extern "C" {
     AFAPI af_err af_asinh   (af_array *out, const af_array in);
 
     /**
-       C Interface for acosh
+       C Interface to evaluate the inverse hyperbolic cosine function.
 
-       \param[out] out will contain inverse cosh of input
-       \param[in] in is input array
+       \param[out] out inverse hyperbolic cosine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_acosh
@@ -1213,10 +1220,10 @@ extern "C" {
     AFAPI af_err af_acosh   (af_array *out, const af_array in);
 
     /**
-       C Interface for atanh
+       C Interface to evaluate the inverse hyperbolic tangent function.
 
-       \param[out] out will contain inverse tanh of input
-       \param[in] in is input array
+       \param[out] out inverse hyperbolic tangent
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_atanh
@@ -1224,11 +1231,11 @@ extern "C" {
     AFAPI af_err af_atanh   (af_array *out, const af_array in);
 
     /**
-       C Interface for root
+       C Interface to find the nth root.
 
-       \param[out] out will contain \p lhs th root of \p rhs
-       \param[in] lhs is nth root
-       \param[in] rhs is value
+       \param[out] out \p lhs th root of \p rhs
+       \param[in] lhs nth root
+       \param[in] rhs value
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -1238,11 +1245,11 @@ extern "C" {
 
 
     /**
-       C Interface for power
+       C Interface to raise a base to a power (or exponent).
 
-       \param[out] out will contain \p lhs raised to power \p rhs
-       \param[in] lhs is base
-       \param[in] rhs is exponent
+       \param[out] out \p lhs raised to the power of \p rhs
+       \param[in] lhs base
+       \param[in] rhs exponent
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -1251,45 +1258,47 @@ extern "C" {
     AFAPI af_err af_pow   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface for power of two
+       C Interface to raise 2 to a power (or exponent).
 
-       \param[out] out will contain the values of 2 to the power \p in
-       \param[in] in is exponent
+       \param[out] out 2 raised to the power of \p in
+       \param[in] in exponent
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_pow2
     */
     AFAPI af_err af_pow2     (af_array *out, const af_array in);
 
+#if AF_API_VERSION >= 31
     /**
-       C Interface for exponential of an array
+       C Interface to evaluate the logistical sigmoid function.
 
-       \param[out] out will contain the exponential of \p in
-       \param[in] in is exponent
+       \param[out] out output of the logistic sigmoid function
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_exp
+       \note Computes `1/(1+e^-x)`.
+
+       \ingroup arith_func_sigmoid
     */
-    AFAPI af_err af_exp     (af_array *out, const af_array in);
+    AFAPI af_err af_sigmoid(af_array* out, const af_array in);
+#endif
 
-#if AF_API_VERSION >= 31
     /**
-       C Interface for calculating sigmoid function of an array
+       C Interface to evaluate the exponential.
 
-       \param[out] out will contain the sigmoid of \p in
-       \param[in] in is input
+       \param[out] out e raised to the power of \p in
+       \param[in] in exponent
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_sigmoid
+       \ingroup arith_func_exp
     */
-    AFAPI af_err af_sigmoid (af_array *out, const af_array in);
-#endif
+    AFAPI af_err af_exp     (af_array *out, const af_array in);
 
     /**
-       C Interface for exponential of an array minus 1
+       C Interface to evaluate the exponential of an array minus 1, `exp(in) - 1`.
 
-       \param[out] out will contain the exponential of \p in - 1
-       \param[in] in is input
+       \param[out] out exponential of `in - 1`
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_expm1
@@ -1297,10 +1306,10 @@ extern "C" {
     AFAPI af_err af_expm1   (af_array *out, const af_array in);
 
     /**
-       C Interface for error function value
+       C Interface to evaluate the error function.
 
-       \param[out] out will contain the error function value of \p in
-       \param[in] in is input
+       \param[out] out error function value
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_erf
@@ -1308,10 +1317,10 @@ extern "C" {
     AFAPI af_err af_erf     (af_array *out, const af_array in);
 
     /**
-       C Interface for complementary error function value
+       C Interface to evaluate the complementary error function.
 
-       \param[out] out will contain the complementary error function value of \p in
-       \param[in] in is input
+       \param[out] out complementary error function
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_erfc
@@ -1319,10 +1328,10 @@ extern "C" {
     AFAPI af_err af_erfc    (af_array *out, const af_array in);
 
     /**
-       C Interface for natural logarithm
+       C Interface to evaluate the natural logarithm.
 
-       \param[out] out will contain the natural logarithm of \p in
-       \param[in] in is input
+       \param[out] out natural logarithm
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_log
@@ -1330,10 +1339,10 @@ extern "C" {
     AFAPI af_err af_log     (af_array *out, const af_array in);
 
     /**
-       C Interface for logarithm of (in + 1)
+       C Interface to evaluate the natural logarithm of 1 + input, `ln(1+in)`.
 
-       \param[out] out will contain the logarithm of of (in + 1)
-       \param[in] in is input
+       \param[out] out logarithm of `in + 1`
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_log1p
@@ -1341,10 +1350,10 @@ extern "C" {
     AFAPI af_err af_log1p   (af_array *out, const af_array in);
 
     /**
-       C Interface for logarithm base 10
+       C Interface to evaluate the base 10 logarithm.
 
-       \param[out] out will contain the base 10 logarithm of \p in
-       \param[in] in is input
+       \param[out] out base 10 logarithm
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_log10
@@ -1352,10 +1361,10 @@ extern "C" {
     AFAPI af_err af_log10   (af_array *out, const af_array in);
 
     /**
-       C Interface for logarithm base 2
+       C Interface to evaluate the base 2 logarithm.
 
-       \param[out] out will contain the base 2 logarithm of \p in
-       \param[in] in is input
+       \param[out] out base 2 logarithm
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup explog_func_log2
@@ -1363,10 +1372,10 @@ extern "C" {
     AFAPI af_err af_log2   (af_array *out, const af_array in);
 
     /**
-       C Interface for square root
+       C Interface to find the square root.
 
-       \param[out] out will contain the square root of \p in
-       \param[in] in is input
+       \param[out] out square root
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_sqrt
@@ -1375,10 +1384,10 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-      C Interface for reciprocal  square root
+      C Interface to find the reciprocal square root.
 
-      \param[out] out will contain the reciprocal square root of \p in
-      \param[in] in is input
+      \param[out] out reciprocal square root
+      \param[in] in input
       \return \ref AF_SUCCESS if the execution completes properly
 
       \ingroup arith_func_rsqrt
@@ -1386,10 +1395,10 @@ extern "C" {
     AFAPI af_err af_rsqrt    (af_array *out, const af_array in);
 #endif
     /**
-       C Interface for cube root
+       C Interface to find the cube root.
 
-       \param[out] out will contain the cube root of \p in
-       \param[in] in is input
+       \param[out] out cube root
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_cbrt
@@ -1397,10 +1406,10 @@ extern "C" {
     AFAPI af_err af_cbrt    (af_array *out, const af_array in);
 
     /**
-       C Interface for the factorial
+       C Interface to find the factorial.
 
-       \param[out] out will contain the result of factorial of \p in
-       \param[in] in is input
+       \param[out] out factorial
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_factorial
@@ -1408,10 +1417,10 @@ extern "C" {
     AFAPI af_err af_factorial   (af_array *out, const af_array in);
 
     /**
-       C Interface for the gamma function
+       C Interface to evaluate the gamma function.
 
-       \param[out] out will contain the result of gamma function of \p in
-       \param[in] in is input
+       \param[out] out gamma function
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_tgamma
@@ -1419,10 +1428,10 @@ extern "C" {
     AFAPI af_err af_tgamma   (af_array *out, const af_array in);
 
     /**
-       C Interface for the logarithm of absolute values of gamma function
+       C Interface to evaluate the logarithm of the absolute value of the gamma function.
 
-       \param[out] out will contain the result of logarithm of absolute values of gamma function of \p in
-       \param[in] in is input
+       \param[out] out logarithm of the absolute value of the gamma function
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
        \ingroup arith_func_lgamma
@@ -1430,10 +1439,10 @@ extern "C" {
     AFAPI af_err af_lgamma   (af_array *out, const af_array in);
 
     /**
-        C Interface for checking if values are zero
+        C Interface to check if values are zero.
 
-        \param[out] out will contain 1's where input is 0, and 0 otherwise.
-        \param[in] in is input
+        \param[out] out array containing 1's where input is 0; 0's otherwise
+        \param[in] in input
         \return \ref AF_SUCCESS if the execution completes properly
 
         \ingroup arith_func_iszero
@@ -1441,10 +1450,10 @@ extern "C" {
     AFAPI af_err af_iszero  (af_array *out, const af_array in);
 
     /**
-        C Interface for checking if values are infinities
+        C Interface to check if values are infinite.
 
-        \param[out] out will contain 1's where input is Inf or -Inf, and 0 otherwise.
-        \param[in] in is input
+        \param[out] out array containing 1's where input is Inf or -Inf; 0's otherwise
+        \param[in] in input
         \return \ref AF_SUCCESS if the execution completes properly
 
         \ingroup arith_func_isinf
@@ -1452,10 +1461,10 @@ extern "C" {
     AFAPI af_err af_isinf   (af_array *out, const af_array in);
 
     /**
-        C Interface for checking if values are NaNs
+        C Interface to check if values are NaN.
 
-        \param[out] out will contain 1's where input is NaN, and 0 otherwise.
-        \param[in] in is input
+        \param[out] out array containing 1's where input is NaN; 0's otherwise
+        \param[in] in input
         \return \ref AF_SUCCESS if the execution completes properly
 
         \ingroup arith_func_isnan

From 1af82bf2fb3e6c39bd5058cb919541309e05909e Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Thu, 12 Jan 2023 17:19:25 -0500
Subject: [PATCH 2395/2677] improves documentation for arith functions, round 2

---
 docs/details/arith.dox | 153 ++++++++---------------------------------
 include/af/arith.h     | 116 +++++++++++++++----------------
 2 files changed, 87 insertions(+), 182 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 84f9a5c451..ac8d265628 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -21,137 +21,109 @@
 
 \ingroup arith_mat
 
-Add.
-
 Add two arrays.
 
 
-
 \defgroup arith_func_sub sub
 
 \ingroup arith_mat
 
-Subtract.
-
 Subtract one array from another array.
 
 
-
 \defgroup arith_func_mul mul
 
 \ingroup arith_mat
 
-Multiply.
-
 Multiply two arrays.
 
 
-
 \defgroup arith_func_div div
 
 \ingroup arith_mat
 
-Divide.
-
 Divide one array by another array.
 
 
-
 \defgroup arith_func_lt lt
 
 \ingroup logic_mat
 
-Is less than.
+Less than, an elementwise comparison of two arrays.
 
 Check if the elements of one array are less than those of another array.
 
 
-
 \defgroup arith_func_gt gt
 
 \ingroup logic_mat
 
-Is greater than.
+Greater than comparison, an elementwise comparison of two arrays.
 
 Check if the elements of one array are greater than those of another array.
 
 
-
 \defgroup arith_func_le le
 
 \ingroup logic_mat
 
-Is less than or equal.
+Less than or equal to, an elementwise comparison of two arrays.
 
 Check if the elements of one array are less than or equal to those of another array.
 
 
-
 \defgroup arith_func_ge ge
 
 \ingroup logic_mat
 
-Is greater than or equal.
+Greater than or equal to, an elementwise comparison of two arrays.
 
 Check if the elements of one array are greater than or equal to those of another array.
 
 
-
 \defgroup arith_func_eq eq
 
 \ingroup logic_mat
 
-Is equal.
+\brief Equal to, an elementwise comparison of two arrays.
 
 Check if the elements of one array are equal to those of another array.
 
 
-
 \defgroup arith_func_neq neq
 
 \ingroup logic_mat
 
-Is not equal.
+\brief Not equal to, an elementwise comparison of two arrays.
 
 Check if the elements of one array are not equal to those of another array.
 
 
-
 \defgroup arith_func_and and
-\brief Logical AND
 
 \ingroup logic_mat
 
-Logical AND.
-
 Evaluate the logical AND of two arrays.
 
+
 \defgroup arith_func_or or
 
 \ingroup logic_mat
 
-Logical OR.
-
 Evaluate the logical OR of two arrays.
 
 
-
 \defgroup arith_func_not not
 
 \ingroup logic_mat
 
-Logical NOT.
-
 Evaluate the logical NOT of an array.
 
 
-
 \defgroup arith_func_neg neg
 
 \ingroup numeric_mat
 
-Negative of an array.
-
 Negate an array.
 
 
@@ -159,8 +131,6 @@ Negate an array.
 
 \ingroup logic_mat
 
-Bitwise NOT.
-
 Evaluate the bitwise NOT of an array.
 
 \copydoc arith_int_only
@@ -170,8 +140,6 @@ Evaluate the bitwise NOT of an array.
 
 \ingroup logic_mat
 
-Bitwise AND.
-
 Evaluate the bitwise AND of two arrays.
 
 \copydoc arith_int_only
@@ -181,8 +149,6 @@ Evaluate the bitwise AND of two arrays.
 
 \ingroup logic_mat
 
-Bitwise OR.
-
 Evaluate the bitwise OR of two arrays.
 
 \copydoc arith_int_only
@@ -192,8 +158,6 @@ Evaluate the bitwise OR of two arrays.
 
 \ingroup logic_mat
 
-Bitwise XOR.
-
 Evaluate the bitwise XOR of two arrays.
 
 \copydoc arith_int_only
@@ -203,8 +167,6 @@ Evaluate the bitwise XOR of two arrays.
 
 \ingroup arith_mat
 
-Left shift on integer arrays.
-
 Shift the bits of integer arrays left.
 
 \copydoc arith_int_only
@@ -214,8 +176,6 @@ Shift the bits of integer arrays left.
 
 \ingroup arith_mat
 
-Right shift on integer arrays.
-
 Shift the bits of integer arrays right.
 
 \copydoc arith_int_only
@@ -232,8 +192,6 @@ Cast an array from one type to another.
 
 \ingroup numeric_mat
 
-Minimum of two inputs.
-
 Find the elementwise minimum between two arrays.
 
 
@@ -241,16 +199,19 @@ Find the elementwise minimum between two arrays.
 
 \ingroup numeric_mat
 
-Maximum of two inputs.
-
 Find the elementwise maximum between two arrays.
 
 
-\defgroup arith_func_rem rem
+\defgroup arith_func_clamp clamp
 
 \ingroup numeric_mat
 
-Remainder.
+Clamp an array between an upper and a lower limit.
+
+
+\defgroup arith_func_rem rem
+
+\ingroup numeric_mat
 
 Find the remainder of a division.
 
@@ -261,8 +222,6 @@ Find the remainder of a division.
 
 \ingroup numeric_mat
 
-Modulus.
-
 Find the modulus.
 
 \copydoc arith_real_only
@@ -270,8 +229,6 @@ Find the modulus.
 
 \defgroup arith_func_abs abs
 
-Absolute value.
-
 Find the absolute value.
 
 __Examples:__
@@ -282,9 +239,8 @@ __Examples:__
 
 
 \defgroup arith_func_arg arg
-\ingroup numeric_mat
 
-Phase angle.
+\ingroup numeric_mat
 
 Find the phase angle (in radians) of a complex array.
 
@@ -293,8 +249,6 @@ Find the phase angle (in radians) of a complex array.
 
 \ingroup numeric_mat
 
-Sign.
-
 Find the sign of elements in an array.
 
 \copydoc arith_real_only
@@ -304,8 +258,6 @@ Find the sign of elements in an array.
 
 \ingroup numeric_mat
 
-Round.
-
 Round numbers to the nearest integer.
 
 \copydoc arith_real_only
@@ -315,8 +267,6 @@ Round numbers to the nearest integer.
 
 \ingroup numeric_mat
 
-Truncate.
-
 Truncate numbers to nearest integer.
 
 \copydoc arith_real_only
@@ -326,8 +276,6 @@ Truncate numbers to nearest integer.
 
 \ingroup numeric_mat
 
-Floor.
-
 Round to the integer less than or equal to the magnitude of the input value.
 
 \copydoc arith_real_only
@@ -337,8 +285,6 @@ Round to the integer less than or equal to the magnitude of the input value.
 
 \ingroup numeric_mat
 
-Ceil.
-
 Round to the integer greater than or equal to the magnitude of the input value.
 
 \copydoc arith_real_only
@@ -348,8 +294,6 @@ Round to the integer greater than or equal to the magnitude of the input value.
 
 \ingroup numeric_mat
 
-Hypotenuse.
-
 Find the length of the hypotenuse of two inputs.
 
 \copydoc arith_real_only
@@ -359,8 +303,6 @@ Find the length of the hypotenuse of two inputs.
 
 \ingroup trig_mat
 
-Sine.
-
 Evaluate the sine function.
 
 
@@ -368,17 +310,13 @@ Evaluate the sine function.
 
 \ingroup trig_mat
 
-Cosine.
-
 Evaluate the cosine function.
 
 
-\defgroup arith_func_tan tan/tan2
+\defgroup arith_func_tan tan
 
 \ingroup trig_mat
 
-Tangent.
-
 Evaluate the tangent function.
 
 
@@ -386,16 +324,12 @@ Evaluate the tangent function.
 
 \ingroup trig_mat
 
-Inverse sine (arc sine).
-
-Evaluate the inverse sine function.
+Evaluate the inverse sine function (arc sine).
 
 
 \defgroup arith_func_acos acos
 
-Inverse cosine (arc cosine).
-
-Evaluate the inverse cosine function.
+Evaluate the inverse cosine function (arc cosine).
 
 The inverse of cosine so that, if `y = cos(x)`, then `x = arccos(y)`.
 
@@ -410,17 +344,13 @@ __Examples:__
 
 \ingroup trig_mat
 
-Inverse tangent (arc tangent).
-
-Evaluate the inverse tangent function.
+Evaluate the inverse tangent function (arc tangent).
 
 
 \defgroup arith_func_sinh sinh
 
 \ingroup hyper_mat
 
-Hyperbolic sine.
-
 Evaluate the hyperbolic sine function.
 
 
@@ -428,8 +358,6 @@ Evaluate the hyperbolic sine function.
 
 \ingroup hyper_mat
 
-Hyperbolic cosine.
-
 Evaluate the hyperbolic cosine function.
 
 
@@ -437,8 +365,6 @@ Evaluate the hyperbolic cosine function.
 
 \ingroup hyper_mat
 
-Hyperbolic tangent.
-
 Evaluate the hyperbolic tangent function.
 
 
@@ -446,27 +372,21 @@ Evaluate the hyperbolic tangent function.
 
 \ingroup hyper_mat
 
-Inverse hyperbolic sine (area hyperbolic sine).
-
-Evaluate the inverse hyperbolic sine function.
+Evaluate the inverse hyperbolic sine function (area hyperbolic sine).
 
 
 \defgroup arith_func_acosh acosh
 
 \ingroup hyper_mat
 
-Inverse hyperbolic cosine (area hyperbolic cosine).
-
-Evaluate the inverse hyperbolic cosine function.
+Evaluate the inverse hyperbolic cosine function (area hyperbolic cosine).
 
 
 \defgroup arith_func_atanh atanh
 
 \ingroup hyper_mat
 
-Inverse hyperbolic tangent (area hyperbolic tangent).
-
-Evaluate the inverse hyperbolic tangent function.
+Evaluate the inverse hyperbolic tangent function (area hyperbolic tangent).
 
 
 \defgroup arith_func_cplx complex
@@ -505,8 +425,6 @@ Find the imaginary part of a complex array.
 
 \ingroup complex_mat
 
-Complex conjugate.
-
 Find the complex conjugate of an input array.
 
 
@@ -523,43 +441,31 @@ Find the nth root.
 
 Raise a base to a power (or exponent).
 
-If the input array has values beyond what a floating point type can represent, then there is no
-guarantee that the results will be accurate. The exact type mapping from integral types to floating
-point types used to compute power is given below.
 
-| Input Type         | Compute Type   |
-| :------------------| :--------------|
-| unsigned long long | double         |
-| long long          | double         |
-| unsigned int       | double         |
-| int                | double         |
-| unsigned short     | float          |
-| short              | float          |
-| unsigned char      | float          |
+\defgroup arith_func_pow pow2
 
-The output array will be of the same type as input.
+\ingroup explog_mat
 
+Raise 2 to a power (or exponent).
 
-\defgroup arith_func_sigmoid sigmoid
 
-Sigmoid function (logistical).
+\defgroup arith_func_sigmoid sigmoid
 
 Evaluate the logistical sigmoid function.
 
 
-
 \defgroup arith_func_exp exp
 
 \ingroup explog_mat
 
-Evaluate the exponential.
+Evaluate the exponential function.
 
 
 \defgroup arith_func_expm1 expm1
 
 \ingroup explog_mat
 
-Evaluate the exponential of an array minus 1, `exp(in) - 1`.
+Evaluate the exponential function of an array minus 1, `exp(in) - 1`.
 
 \copydoc arith_real_only
 
@@ -573,7 +479,6 @@ Evaluate the error function.
 \copydoc arith_real_only
 
 
-
 \defgroup arith_func_erfc erfc
 
 \ingroup explog_mat
@@ -685,7 +590,7 @@ Check if values are zero.
 Check if values are infinite.
 
 
-\defgroup arith_func_isnan isNan
+\defgroup arith_func_isnan isnan
 
 \ingroup helper_mat
 
diff --git a/include/af/arith.h b/include/af/arith.h
index 789e54aab5..f6f190f199 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -690,7 +690,7 @@ extern "C" {
     /**
        C Interface to check if the elements of one array are equal to those of another array.
 
-       \param[out] out result of \p lhs == \p rhs; type is b8
+       \param[out] out result of `lhs == rhs`; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -703,7 +703,7 @@ extern "C" {
     /**
        C Interface to check if the elements of one array are not equal to those of another array.
 
-       \param[out] out result of \p lhs != \p rhs; type is b8
+       \param[out] out result of `lhs != rhs`; type is b8
        \param[in] lhs first input
        \param[in] rhs second input
        \param[in] batch specifies if operations need to be performed in batch mode
@@ -1108,127 +1108,127 @@ extern "C" {
     AFAPI af_err af_atan2 (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to create a complex array from a single real array.
+       C Interface to evaluate the hyperbolic sine function.
 
-       \param[out] out complex array
-       \param[in] in real array
+       \param[out] out hyperbolic sine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_cplx
+       \ingroup arith_func_sinh
     */
-    AFAPI af_err af_cplx(af_array* out, const af_array in);
+    AFAPI af_err af_sinh    (af_array *out, const af_array in);
 
     /**
-       C Interface to create a complex array from two real arrays.
+       C Interface to evaluate the hyperbolic cosine function.
 
-       \param[out] out complex array
-       \param[in] real real array to be assigned as the real component of the returned complex array
-       \param[in] imag real array to be assigned as the imaginary component of the returned complex array
-       \param[in] batch specifies if operations need to be performed in batch mode
+       \param[out] out hyperbolic cosine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_cplx
+       \ingroup arith_func_cosh
     */
-    AFAPI af_err af_cplx2 (af_array *out, const af_array real, const af_array imag, const bool batch);
+    AFAPI af_err af_cosh    (af_array *out, const af_array in);
 
     /**
-       C Interface to find the real part of a complex array.
+       C Interface to evaluate the hyperbolic tangent function.
 
-       \param[out] out real part
-       \param[in] in complex array
+       \param[out] out hyperbolic tangent
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_real
+       \ingroup arith_func_tanh
     */
-    AFAPI af_err af_real    (af_array *out, const af_array in);
+    AFAPI af_err af_tanh    (af_array *out, const af_array in);
 
     /**
-       C Interface to find the imaginary part of a complex array.
+       C Interface to evaluate the inverse hyperbolic sine function.
 
-       \param[out] out imaginary part
-       \param[in] in complex array
+       \param[out] out inverse hyperbolic sine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_imag
+       \ingroup arith_func_asinh
     */
-    AFAPI af_err af_imag    (af_array *out, const af_array in);
+    AFAPI af_err af_asinh   (af_array *out, const af_array in);
 
     /**
-       C Interface to find the complex conjugate of an input array.
+       C Interface to evaluate the inverse hyperbolic cosine function.
 
-       \param[out] out complex conjugate
-       \param[in] in complex array
+       \param[out] out inverse hyperbolic cosine
+       \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_conjg
+       \ingroup arith_func_acosh
     */
-    AFAPI af_err af_conjg   (af_array *out, const af_array in);
+    AFAPI af_err af_acosh   (af_array *out, const af_array in);
 
     /**
-       C Interface to evaluate the hyperbolic sine function.
+       C Interface to evaluate the inverse hyperbolic tangent function.
 
-       \param[out] out hyperbolic sine
+       \param[out] out inverse hyperbolic tangent
        \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_sinh
+       \ingroup arith_func_atanh
     */
-    AFAPI af_err af_sinh    (af_array *out, const af_array in);
+    AFAPI af_err af_atanh   (af_array *out, const af_array in);
 
     /**
-       C Interface to evaluate the hyperbolic cosine function.
+       C Interface to create a complex array from a single real array.
 
-       \param[out] out hyperbolic cosine
-       \param[in] in input
+       \param[out] out complex array
+       \param[in] in real array
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_cosh
+       \ingroup arith_func_cplx
     */
-    AFAPI af_err af_cosh    (af_array *out, const af_array in);
+    AFAPI af_err af_cplx(af_array* out, const af_array in);
 
     /**
-       C Interface to evaluate the hyperbolic tangent function.
+       C Interface to create a complex array from two real arrays.
 
-       \param[out] out hyperbolic tangent
-       \param[in] in input
+       \param[out] out complex array
+       \param[in] real real array to be assigned as the real component of the returned complex array
+       \param[in] imag real array to be assigned as the imaginary component of the returned complex array
+       \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_tanh
+       \ingroup arith_func_cplx
     */
-    AFAPI af_err af_tanh    (af_array *out, const af_array in);
+    AFAPI af_err af_cplx2(af_array* out, const af_array real, const af_array imag, const bool batch);
 
     /**
-       C Interface to evaluate the inverse hyperbolic sine function.
+       C Interface to find the real part of a complex array.
 
-       \param[out] out inverse hyperbolic sine
-       \param[in] in input
+       \param[out] out real part
+       \param[in] in complex array
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_asinh
+       \ingroup arith_func_real
     */
-    AFAPI af_err af_asinh   (af_array *out, const af_array in);
+    AFAPI af_err af_real(af_array* out, const af_array in);
 
     /**
-       C Interface to evaluate the inverse hyperbolic cosine function.
+       C Interface to find the imaginary part of a complex array.
 
-       \param[out] out inverse hyperbolic cosine
-       \param[in] in input
+       \param[out] out imaginary part
+       \param[in] in complex array
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_acosh
+       \ingroup arith_func_imag
     */
-    AFAPI af_err af_acosh   (af_array *out, const af_array in);
+    AFAPI af_err af_imag(af_array* out, const af_array in);
 
     /**
-       C Interface to evaluate the inverse hyperbolic tangent function.
+       C Interface to find the complex conjugate of an input array.
 
-       \param[out] out inverse hyperbolic tangent
-       \param[in] in input
+       \param[out] out complex conjugate
+       \param[in] in complex array
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \ingroup arith_func_atanh
+       \ingroup arith_func_conjg
     */
-    AFAPI af_err af_atanh   (af_array *out, const af_array in);
+    AFAPI af_err af_conjg(af_array* out, const af_array in);
 
     /**
        C Interface to find the nth root.

From 2333815b524abaf8623f3b407cd68e96cc681c69 Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Fri, 13 Jan 2023 14:35:46 -0500
Subject: [PATCH 2396/2677] improves formatting of arith.dox

---
 docs/details/arith.dox | 148 ++++++++++++++++++++---------------------
 1 file changed, 73 insertions(+), 75 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index ac8d265628..4d0fee8ae3 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -1,52 +1,50 @@
 /*!
 \page arith_real_only arith_real
-
 \note This function supports real inputs only. Complex inputs are not yet supported.
-
 */
 
 /*!
 \page arith_int_only arith_int
-
 \note This function supports integer only.
-
 */
 
 
+
 /**
 \addtogroup arrayfire_func
 @{
 
-\defgroup arith_func_add add
 
+
+\defgroup arith_func_add add
 \ingroup arith_mat
 
 Add two arrays.
 
 
-\defgroup arith_func_sub sub
 
+\defgroup arith_func_sub sub
 \ingroup arith_mat
 
 Subtract one array from another array.
 
 
-\defgroup arith_func_mul mul
 
+\defgroup arith_func_mul mul
 \ingroup arith_mat
 
 Multiply two arrays.
 
 
-\defgroup arith_func_div div
 
+\defgroup arith_func_div div
 \ingroup arith_mat
 
 Divide one array by another array.
 
 
-\defgroup arith_func_lt lt
 
+\defgroup arith_func_lt lt
 \ingroup logic_mat
 
 Less than, an elementwise comparison of two arrays.
@@ -54,8 +52,8 @@ Less than, an elementwise comparison of two arrays.
 Check if the elements of one array are less than those of another array.
 
 
-\defgroup arith_func_gt gt
 
+\defgroup arith_func_gt gt
 \ingroup logic_mat
 
 Greater than comparison, an elementwise comparison of two arrays.
@@ -63,8 +61,8 @@ Greater than comparison, an elementwise comparison of two arrays.
 Check if the elements of one array are greater than those of another array.
 
 
-\defgroup arith_func_le le
 
+\defgroup arith_func_le le
 \ingroup logic_mat
 
 Less than or equal to, an elementwise comparison of two arrays.
@@ -73,7 +71,6 @@ Check if the elements of one array are less than or equal to those of another ar
 
 
 \defgroup arith_func_ge ge
-
 \ingroup logic_mat
 
 Greater than or equal to, an elementwise comparison of two arrays.
@@ -81,8 +78,8 @@ Greater than or equal to, an elementwise comparison of two arrays.
 Check if the elements of one array are greater than or equal to those of another array.
 
 
-\defgroup arith_func_eq eq
 
+\defgroup arith_func_eq eq
 \ingroup logic_mat
 
 \brief Equal to, an elementwise comparison of two arrays.
@@ -90,8 +87,8 @@ Check if the elements of one array are greater than or equal to those of another
 Check if the elements of one array are equal to those of another array.
 
 
-\defgroup arith_func_neq neq
 
+\defgroup arith_func_neq neq
 \ingroup logic_mat
 
 \brief Not equal to, an elementwise comparison of two arrays.
@@ -99,36 +96,36 @@ Check if the elements of one array are equal to those of another array.
 Check if the elements of one array are not equal to those of another array.
 
 
-\defgroup arith_func_and and
 
+\defgroup arith_func_and and
 \ingroup logic_mat
 
 Evaluate the logical AND of two arrays.
 
 
-\defgroup arith_func_or or
 
+\defgroup arith_func_or or
 \ingroup logic_mat
 
 Evaluate the logical OR of two arrays.
 
 
-\defgroup arith_func_not not
 
+\defgroup arith_func_not not
 \ingroup logic_mat
 
 Evaluate the logical NOT of an array.
 
 
-\defgroup arith_func_neg neg
 
+\defgroup arith_func_neg neg
 \ingroup numeric_mat
 
 Negate an array.
 
 
-\defgroup arith_func_bitnot bitnot
 
+\defgroup arith_func_bitnot bitnot
 \ingroup logic_mat
 
 Evaluate the bitwise NOT of an array.
@@ -136,8 +133,8 @@ Evaluate the bitwise NOT of an array.
 \copydoc arith_int_only
 
 
-\defgroup arith_func_bitand bitand
 
+\defgroup arith_func_bitand bitand
 \ingroup logic_mat
 
 Evaluate the bitwise AND of two arrays.
@@ -145,8 +142,8 @@ Evaluate the bitwise AND of two arrays.
 \copydoc arith_int_only
 
 
-\defgroup arith_func_bitor bitor
 
+\defgroup arith_func_bitor bitor
 \ingroup logic_mat
 
 Evaluate the bitwise OR of two arrays.
@@ -154,8 +151,8 @@ Evaluate the bitwise OR of two arrays.
 \copydoc arith_int_only
 
 
-\defgroup arith_func_bitxor bitxor
 
+\defgroup arith_func_bitxor bitxor
 \ingroup logic_mat
 
 Evaluate the bitwise XOR of two arrays.
@@ -163,8 +160,8 @@ Evaluate the bitwise XOR of two arrays.
 \copydoc arith_int_only
 
 
-\defgroup arith_func_shiftl bitshiftl
 
+\defgroup arith_func_shiftl bitshiftl
 \ingroup arith_mat
 
 Shift the bits of integer arrays left.
@@ -172,8 +169,8 @@ Shift the bits of integer arrays left.
 \copydoc arith_int_only
 
 
-\defgroup arith_func_shiftr bitshiftr
 
+\defgroup arith_func_shiftr bitshiftr
 \ingroup arith_mat
 
 Shift the bits of integer arrays right.
@@ -181,36 +178,36 @@ Shift the bits of integer arrays right.
 \copydoc arith_int_only
 
 
-\defgroup arith_func_cast cast
 
+\defgroup arith_func_cast cast
 \ingroup helper_mat
 
 Cast an array from one type to another.
 
 
-\defgroup arith_func_min min
 
+\defgroup arith_func_min min
 \ingroup numeric_mat
 
 Find the elementwise minimum between two arrays.
 
 
-\defgroup arith_func_max max
 
+\defgroup arith_func_max max
 \ingroup numeric_mat
 
 Find the elementwise maximum between two arrays.
 
 
-\defgroup arith_func_clamp clamp
 
+\defgroup arith_func_clamp clamp
 \ingroup numeric_mat
 
 Clamp an array between an upper and a lower limit.
 
 
-\defgroup arith_func_rem rem
 
+\defgroup arith_func_rem rem
 \ingroup numeric_mat
 
 Find the remainder of a division.
@@ -218,8 +215,8 @@ Find the remainder of a division.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_mod mod
 
+\defgroup arith_func_mod mod
 \ingroup numeric_mat
 
 Find the modulus.
@@ -227,7 +224,9 @@ Find the modulus.
 \copydoc arith_real_only
 
 
+
 \defgroup arith_func_abs abs
+\ingroup numeric_mat
 
 Find the absolute value.
 
@@ -235,18 +234,16 @@ __Examples:__
 
 \snippet test/math.cpp ex_arith_func_abs
 
-\ingroup numeric_mat
 
 
 \defgroup arith_func_arg arg
-
 \ingroup numeric_mat
 
 Find the phase angle (in radians) of a complex array.
 
 
-\defgroup arith_func_sign sign
 
+\defgroup arith_func_sign sign
 \ingroup numeric_mat
 
 Find the sign of elements in an array.
@@ -254,8 +251,8 @@ Find the sign of elements in an array.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_round round
 
+\defgroup arith_func_round round
 \ingroup numeric_mat
 
 Round numbers to the nearest integer.
@@ -263,8 +260,8 @@ Round numbers to the nearest integer.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_trunc trunc
 
+\defgroup arith_func_trunc trunc
 \ingroup numeric_mat
 
 Truncate numbers to nearest integer.
@@ -272,8 +269,8 @@ Truncate numbers to nearest integer.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_floor floor
 
+\defgroup arith_func_floor floor
 \ingroup numeric_mat
 
 Round to the integer less than or equal to the magnitude of the input value.
@@ -281,8 +278,8 @@ Round to the integer less than or equal to the magnitude of the input value.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_ceil ceil
 
+\defgroup arith_func_ceil ceil
 \ingroup numeric_mat
 
 Round to the integer greater than or equal to the magnitude of the input value.
@@ -290,8 +287,8 @@ Round to the integer greater than or equal to the magnitude of the input value.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_hypot hypot
 
+\defgroup arith_func_hypot hypot
 \ingroup numeric_mat
 
 Find the length of the hypotenuse of two inputs.
@@ -299,35 +296,37 @@ Find the length of the hypotenuse of two inputs.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_sin sin
 
+\defgroup arith_func_sin sin
 \ingroup trig_mat
 
 Evaluate the sine function.
 
 
-\defgroup arith_func_cos cos
 
+\defgroup arith_func_cos cos
 \ingroup trig_mat
 
 Evaluate the cosine function.
 
 
-\defgroup arith_func_tan tan
 
+\defgroup arith_func_tan tan
 \ingroup trig_mat
 
 Evaluate the tangent function.
 
 
-\defgroup arith_func_asin asin
 
+\defgroup arith_func_asin asin
 \ingroup trig_mat
 
 Evaluate the inverse sine function (arc sine).
 
 
+
 \defgroup arith_func_acos acos
+\ingroup trig_mat
 
 Evaluate the inverse cosine function (arc cosine).
 
@@ -337,60 +336,58 @@ __Examples:__
 
 \snippet test/math.cpp ex_arith_func_acos
 
-\ingroup trig_mat
 
 
 \defgroup arith_func_atan atan/atan2
-
 \ingroup trig_mat
 
 Evaluate the inverse tangent function (arc tangent).
 
 
-\defgroup arith_func_sinh sinh
 
+\defgroup arith_func_sinh sinh
 \ingroup hyper_mat
 
 Evaluate the hyperbolic sine function.
 
 
-\defgroup arith_func_cosh cosh
 
+\defgroup arith_func_cosh cosh
 \ingroup hyper_mat
 
 Evaluate the hyperbolic cosine function.
 
 
-\defgroup arith_func_tanh tanh
 
+\defgroup arith_func_tanh tanh
 \ingroup hyper_mat
 
 Evaluate the hyperbolic tangent function.
 
 
-\defgroup arith_func_asinh asinh
 
+\defgroup arith_func_asinh asinh
 \ingroup hyper_mat
 
 Evaluate the inverse hyperbolic sine function (area hyperbolic sine).
 
 
-\defgroup arith_func_acosh acosh
 
+\defgroup arith_func_acosh acosh
 \ingroup hyper_mat
 
 Evaluate the inverse hyperbolic cosine function (area hyperbolic cosine).
 
 
-\defgroup arith_func_atanh atanh
 
+\defgroup arith_func_atanh atanh
 \ingroup hyper_mat
 
 Evaluate the inverse hyperbolic tangent function (area hyperbolic tangent).
 
 
-\defgroup arith_func_cplx complex
 
+\defgroup arith_func_cplx complex
 \ingroup complex_mat
 
 Create complex arrays.
@@ -407,62 +404,62 @@ __Examples:__
 \snippet test/complex.cpp ex_arith_func_complex
 
 
-\defgroup arith_func_real real
 
+\defgroup arith_func_real real
 \ingroup complex_mat
 
 Find the real part of a complex array.
 
 
-\defgroup arith_func_imag imag
 
+\defgroup arith_func_imag imag
 \ingroup complex_mat
 
 Find the imaginary part of a complex array.
 
 
-\defgroup arith_func_conjg conjg
 
+\defgroup arith_func_conjg conjg
 \ingroup complex_mat
 
 Find the complex conjugate of an input array.
 
 
-\defgroup arith_func_root root
 
+\defgroup arith_func_root root
 \ingroup explog_mat
 
 Find the nth root.
 
 
-\defgroup arith_func_pow pow
 
+\defgroup arith_func_pow pow
 \ingroup explog_mat
 
 Raise a base to a power (or exponent).
 
 
-\defgroup arith_func_pow pow2
 
+\defgroup arith_func_pow pow2
 \ingroup explog_mat
 
 Raise 2 to a power (or exponent).
 
 
-\defgroup arith_func_sigmoid sigmoid
 
+\defgroup arith_func_sigmoid sigmoid
 Evaluate the logistical sigmoid function.
 
 
-\defgroup arith_func_exp exp
 
+\defgroup arith_func_exp exp
 \ingroup explog_mat
 
 Evaluate the exponential function.
 
 
-\defgroup arith_func_expm1 expm1
 
+\defgroup arith_func_expm1 expm1
 \ingroup explog_mat
 
 Evaluate the exponential function of an array minus 1, `exp(in) - 1`.
@@ -470,8 +467,8 @@ Evaluate the exponential function of an array minus 1, `exp(in) - 1`.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_erf erf
 
+\defgroup arith_func_erf erf
 \ingroup explog_mat
 
 Evaluate the error function.
@@ -479,8 +476,8 @@ Evaluate the error function.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_erfc erfc
 
+\defgroup arith_func_erfc erfc
 \ingroup explog_mat
 
 Evaluate the complementary error function.
@@ -488,15 +485,15 @@ Evaluate the complementary error function.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_log log
 
+\defgroup arith_func_log log
 \ingroup explog_mat
 
 Evaluate the natural logarithm.
 
 
-\defgroup arith_func_log1p log1p
 
+\defgroup arith_func_log1p log1p
 \ingroup explog_mat
 
 Evaluate the natural logarithm of 1 + input, `ln(1+in)`.
@@ -504,8 +501,8 @@ Evaluate the natural logarithm of 1 + input, `ln(1+in)`.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_log10 log10
 
+\defgroup arith_func_log10 log10
 \ingroup explog_mat
 
 Evaluate the base 10 logarithm.
@@ -513,8 +510,8 @@ Evaluate the base 10 logarithm.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_log2 log2
 
+\defgroup arith_func_log2 log2
 \ingroup explog_mat
 
 Evaluate the base 2 logarithm.
@@ -522,15 +519,15 @@ Evaluate the base 2 logarithm.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_sqrt sqrt
 
+\defgroup arith_func_sqrt sqrt
 \ingroup explog_mat
 
 Find the square root.
 
 
-\defgroup arith_func_rsqrt rsqrt
 
+\defgroup arith_func_rsqrt rsqrt
 \ingroup explog_mat
 
 Find the reciprocal square root.
@@ -540,8 +537,8 @@ Find the reciprocal square root.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_cbrt cbrt
 
+\defgroup arith_func_cbrt cbrt
 \ingroup explog_mat
 
 Find the cube root.
@@ -549,8 +546,8 @@ Find the cube root.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_factorial factorial
 
+\defgroup arith_func_factorial factorial
 \ingroup explog_mat
 
 Find the factorial.
@@ -558,8 +555,8 @@ Find the factorial.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_tgamma tgamma
 
+\defgroup arith_func_tgamma tgamma
 \ingroup explog_mat
 
 Evaluate the gamma function.
@@ -567,8 +564,8 @@ Evaluate the gamma function.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_lgamma lgamma
 
+\defgroup arith_func_lgamma lgamma
 \ingroup explog_mat
 
 Evaluate the logarithm of the absolute value of the gamma function.
@@ -576,26 +573,27 @@ Evaluate the logarithm of the absolute value of the gamma function.
 \copydoc arith_real_only
 
 
-\defgroup arith_func_iszero iszero
 
+\defgroup arith_func_iszero iszero
 \ingroup helper_mat
 
 Check if values are zero.
 
 
-\defgroup arith_func_isinf isinf
 
+\defgroup arith_func_isinf isinf
 \ingroup helper_mat
 
 Check if values are infinite.
 
 
-\defgroup arith_func_isnan isnan
 
+\defgroup arith_func_isnan isnan
 \ingroup helper_mat
 
 Check if values are NaN.
 
 
+
 @}
 */

From 2fb3c9e13f41436c030f59337122375004fe2167 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 20 Jan 2023 20:35:46 -0500
Subject: [PATCH 2397/2677] upgrade doxygen.mk to 1.9.6 for better
 compatibility with theme

---
 docs/details/examples.dox | 116 +++++++++++++++++++-------------------
 docs/doxygen.mk           |  49 +++++++++-------
 2 files changed, 88 insertions(+), 77 deletions(-)

diff --git a/docs/details/examples.dox b/docs/details/examples.dox
index a61ffbc271..1fd4451335 100644
--- a/docs/details/examples.dox
+++ b/docs/details/examples.dox
@@ -1,58 +1,58 @@
-/**
-\example benchmarks/blas.cpp
-\example benchmarks/cg.cpp
-\example benchmarks/fft.cpp
-\example benchmarks/pi.cpp
-\example computer_vision/fast.cpp
-\example computer_vision/harris.cpp
-\example computer_vision/matching.cpp
-\example computer_vision/susan.cpp
-\example financial/black_scholes_options.cpp
-\example financial/heston_model.cpp
-\example financial/monte_carlo_options.cpp
-\example getting_started/convolve.cpp
-\example getting_started/integer.cpp
-\example getting_started/rainfall.cpp
-\example getting_started/vectorize.cpp
-\example graphics/conway.cpp
-\example graphics/conway_pretty.cpp
-\example graphics/field.cpp
-\example graphics/fractal.cpp
-\example graphics/gravity_sim.cpp
-\example graphics/histogram.cpp
-\example graphics/plot2d.cpp
-\example graphics/plot3.cpp
-\example graphics/surface.cpp
-\example helloworld/helloworld.cpp
-\example image_processing/adaptive_thresholding.cpp
-\example image_processing/binary_thresholding.cpp
-\example image_processing/brain_segmentation.cpp
-\example image_processing/confidence_connected_components.cpp
-\example image_processing/deconvolution.cpp
-\example image_processing/edge.cpp
-\example image_processing/filters.cpp
-\example image_processing/gradient_diffusion.cpp
-\example image_processing/image_demo.cpp
-\example image_processing/image_editing.cpp
-\example image_processing/morphing.cpp
-\example image_processing/optical_flow.cpp
-\example image_processing/pyramids.cpp
-\example lin_algebra/cholesky.cpp
-\example lin_algebra/lu.cpp
-\example lin_algebra/qr.cpp
-\example lin_algebra/svd.cpp
-\example machine_learning/bagging.cpp
-\example machine_learning/deep_belief_net.cpp
-\example machine_learning/geneticalgorithm.cpp
-\example machine_learning/kmeans.cpp
-\example machine_learning/knn.cpp
-\example machine_learning/logistic_regression.cpp
-\example machine_learning/naive_bayes.cpp
-\example machine_learning/neural_network.cpp
-\example machine_learning/perceptron.cpp
-\example machine_learning/rbm.cpp
-\example machine_learning/softmax_regression.cpp
-\example pde/swe.cpp
-\example unified/basic.cpp
-
-*/
+/**
+\example benchmarks/blas.cpp
+\example benchmarks/cg.cpp
+\example benchmarks/fft.cpp
+\example benchmarks/pi.cpp
+\example computer_vision/fast.cpp
+\example computer_vision/harris.cpp
+\example computer_vision/matching.cpp
+\example computer_vision/susan.cpp
+\example financial/black_scholes_options.cpp
+\example financial/heston_model.cpp
+\example financial/monte_carlo_options.cpp
+\example getting_started/convolve.cpp
+\example getting_started/integer.cpp
+\example getting_started/rainfall.cpp
+\example getting_started/vectorize.cpp
+\example graphics/conway.cpp
+\example graphics/conway_pretty.cpp
+\example graphics/field.cpp
+\example graphics/fractal.cpp
+\example graphics/gravity_sim.cpp
+\example graphics/histogram.cpp
+\example graphics/plot2d.cpp
+\example graphics/plot3.cpp
+\example graphics/surface.cpp
+\example helloworld/helloworld.cpp
+\example image_processing/adaptive_thresholding.cpp
+\example image_processing/binary_thresholding.cpp
+\example image_processing/brain_segmentation.cpp
+\example image_processing/confidence_connected_components.cpp
+\example image_processing/deconvolution.cpp
+\example image_processing/edge.cpp
+\example image_processing/filters.cpp
+\example image_processing/gradient_diffusion.cpp
+\example image_processing/image_demo.cpp
+\example image_processing/image_editing.cpp
+\example image_processing/morphing.cpp
+\example image_processing/optical_flow.cpp
+\example image_processing/pyramids.cpp
+\example lin_algebra/cholesky.cpp
+\example lin_algebra/lu.cpp
+\example lin_algebra/qr.cpp
+\example lin_algebra/svd.cpp
+\example machine_learning/bagging.cpp
+\example machine_learning/deep_belief_net.cpp
+\example machine_learning/geneticalgorithm.cpp
+\example machine_learning/kmeans.cpp
+\example machine_learning/knn.cpp
+\example machine_learning/logistic_regression.cpp
+\example machine_learning/naive_bayes.cpp
+\example machine_learning/neural_network.cpp
+\example machine_learning/perceptron.cpp
+\example machine_learning/rbm.cpp
+\example machine_learning/softmax_regression.cpp
+\example pde/swe.cpp
+\example unified/basic.cpp
+
+*/
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 2e4da59f66..914ebb35b4 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1,4 +1,4 @@
-# Doxyfile 1.9.5
+# Doxyfile 1.9.6
 
 # This file describes the settings to be used by the documentation system
 # doxygen (www.doxygen.org) for a project.
@@ -86,7 +86,7 @@ CREATE_SUBDIRS         = NO
 # level increment doubles the number of directories, resulting in 4096
 # directories at level 8 which is the default and also the maximum value. The
 # sub-directories are organized in 2 levels, the first level always has a fixed
-# numer of 16 directories.
+# number of 16 directories.
 # Minimum value: 0, maximum value: 8, default value: 8.
 # This tag requires that the tag CREATE_SUBDIRS is set to YES.
 
@@ -582,7 +582,8 @@ HIDE_UNDOC_MEMBERS     = NO
 # If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
 # undocumented classes that are normally visible in the class hierarchy. If set
 # to NO, these classes will be included in the various overviews. This option
-# has no effect if EXTRACT_ALL is enabled.
+# will also hide undocumented C++ concepts if enabled. This option has no effect
+# if EXTRACT_ALL is enabled.
 # The default value is: NO.
 
 HIDE_UNDOC_CLASSES     = NO
@@ -873,6 +874,14 @@ WARN_IF_INCOMPLETE_DOC = YES
 
 WARN_NO_PARAMDOC       = YES
 
+# If WARN_IF_UNDOC_ENUM_VAL option is set to YES, doxygen will warn about
+# undocumented enumeration values. If set to NO, doxygen will accept
+# undocumented enumeration values. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: NO.
+
+WARN_IF_UNDOC_ENUM_VAL = NO
+
 # If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
 # a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS
 # then doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but
@@ -1246,10 +1255,11 @@ CLANG_DATABASE_PATH    =
 
 ALPHABETICAL_INDEX     = YES
 
-# In case all classes in a project start with a common prefix, all classes will
-# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
-# can be used to specify a prefix (or a list of prefixes) that should be ignored
-# while generating the index headers.
+# The IGNORE_PREFIX tag can be used to specify a prefix (or a list of prefixes)
+# that should be ignored while generating the index headers. The IGNORE_PREFIX
+# tag works for classes, function and member names. The entity will be placed in
+# the alphabetical list under the first letter of the entity name that remains
+# after removing the prefix.
 # This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
 
 IGNORE_PREFIX          = af_
@@ -1328,7 +1338,12 @@ HTML_STYLESHEET        =
 # Doxygen will copy the style sheet files to the output directory.
 # Note: The order of the extra style sheet files is of importance (e.g. the last
 # style sheet in the list overrules the setting of the previous ones in the
-# list). For an example see the documentation.
+# list).
+# Note: Since the styling of scrollbars can currently not be overruled in
+# Webkit/Chromium, the styling will be left out of the default doxygen.css if
+# one or more extra stylesheets have been specified. So if scrollbar
+# customization is desired it has to be added explicitly. For an example see the
+# documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_EXTRA_STYLESHEET  = ${DOCS_DIR}/arrayfire.css \
@@ -1348,17 +1363,13 @@ HTML_EXTRA_FILES       = ${DOCS_DIR}/doxygen-awesome-darkmode-toggle.js \
                          ${DOCS_DIR}/doxygen-awesome-interactive-toc.js
 
 # The HTML_COLORSTYLE tag can be used to specify if the generated HTML output
-# should be rendered with a dark or light theme. Default setting AUTO_LIGHT
-# enables light output unless the user preference is dark output. Other options
-# are DARK to always use dark mode, LIGHT to always use light mode, AUTO_DARK to
-# default to dark mode unless the user prefers light mode, and TOGGLE to let the
-# user toggle between dark and light mode via a button.
-# Possible values are: LIGHT Always generate light output., DARK Always generate
-# dark output., AUTO_LIGHT Automatically set the mode according to the user
-# preference, use light mode if no preference is set (the default)., AUTO_DARK
-# Automatically set the mode according to the user preference, use dark mode if
-# no preference is set. and TOGGLE Allow to user to switch between light and
-# dark mode via a button..
+# should be rendered with a dark or light theme.
+# Possible values are: LIGHT always generate light mode output, DARK always
+# generate dark mode output, AUTO_LIGHT automatically set the mode according to
+# the user preference, use light mode if no preference is set (the default),
+# AUTO_DARK automatically set the mode according to the user preference, use
+# dark mode if no preference is set and TOGGLE allow to user to switch between
+# light and dark mode via a button.
 # The default value is: AUTO_LIGHT.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 

From 461b694d5dd1c494ddd04253950395f2421c9ad0 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 23 Jan 2023 18:42:32 -0500
Subject: [PATCH 2398/2677] remove doxygen warnings

---
 docs/details/arith.dox         | 12 +----
 docs/details/image.dox         |  8 ++--
 docs/details/lapack.dox        |  2 +-
 docs/details/signal.dox        |  2 +-
 docs/pages/getting_started.md  |  4 +-
 docs/pages/release_notes.md    | 84 +++++++++++++++++-----------------
 docs/pages/using_on_linux.md   |  4 +-
 docs/pages/using_on_osx.md     |  4 +-
 docs/pages/using_on_windows.md |  2 +-
 include/af/image.h             |  8 ++--
 include/af/ml.h                |  4 +-
 include/af/util.h              |  2 +-
 test/complex.cpp               | 40 ++++++++--------
 13 files changed, 85 insertions(+), 91 deletions(-)

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 4d0fee8ae3..a7130647df 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -230,11 +230,6 @@ Find the modulus.
 
 Find the absolute value.
 
-__Examples:__
-
-\snippet test/math.cpp ex_arith_func_abs
-
-
 
 \defgroup arith_func_arg arg
 \ingroup numeric_mat
@@ -332,11 +327,6 @@ Evaluate the inverse cosine function (arc cosine).
 
 The inverse of cosine so that, if `y = cos(x)`, then `x = arccos(y)`.
 
-__Examples:__
-
-\snippet test/math.cpp ex_arith_func_acos
-
-
 
 \defgroup arith_func_atan atan/atan2
 \ingroup trig_mat
@@ -440,7 +430,7 @@ Raise a base to a power (or exponent).
 
 
 
-\defgroup arith_func_pow pow2
+\defgroup arith_func_pow2 pow2
 \ingroup explog_mat
 
 Raise 2 to a power (or exponent).
diff --git a/docs/details/image.dox b/docs/details/image.dox
index 73ae3239eb..a93f1ebaed 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -855,7 +855,7 @@ is described above, but the effect should be the same.
 \defgroup image_func_wrap wrap
 \ingroup image_mod_mat
 
-Performs the opposite of \ref unwrap().
+Performs the opposite of \ref af::unwrap().
 
 More specifically, wrap takes each column (or row if `is_column` is false) of the
 \f$m \times n\f$ input array and reshapes them into `wx` \f$\times\f$ `wy`
@@ -935,7 +935,7 @@ is visualized above, but the effect should be the same.
 \defgroup image_func_moments moments
 \ingroup moments_mat
 
-The \ref moments() function allows for finding different
+The \ref af::moments() function allows for finding different
 properties of image regions. Currently, ArrayFire calculates all first order moments.
 The moments are defined within the \ref af_moment_type enum.
 
@@ -1059,8 +1059,8 @@ explicitly.
 
 \brief Segment image based on similar pixel characteristics
 
-This filter is similar to \ref regions() (connected components) with additional
-criteria for segmentation. In \ref regions(), all connected (\ref af_connectivity)
+This filter is similar to \ref af::regions() (connected components) with additional
+criteria for segmentation. In \ref af::regions(), all connected (\ref af_connectivity)
 pixels connected are considered to be a single component. In this
 variation of connected components, pixels having similar pixel statistics
 of the neighborhoods around a given set of seed points are grouped together.
diff --git a/docs/details/lapack.dox b/docs/details/lapack.dox
index 8bf5d5a5ea..bf977b0c0c 100644
--- a/docs/details/lapack.dox
+++ b/docs/details/lapack.dox
@@ -141,7 +141,7 @@ following code snippet can be used:
 
 \snippet test/svd_dense.cpp ex_svd_reg
 
-When memory is a concern, and \f$A\f$ is dispensable, \ref svdInPlace() can be
+When memory is a concern, and \f$A\f$ is dispensable, \ref af::svdInPlace() can be
 used. However, this in-place version is currently limited to input arrays where
 \f$M \geq N\f$.
 
diff --git a/docs/details/signal.dox b/docs/details/signal.dox
index fa1b3130c5..e77da4f968 100644
--- a/docs/details/signal.dox
+++ b/docs/details/signal.dox
@@ -274,7 +274,7 @@ Given below is an example of this batch mode.
 
 
 The batching behavior of convolve2NN functions(\ref af_convolve2_nn() and
-\ref convolve2NN() ) is different from convolve2. The new functions can perform 2D
+\ref af::convolve2NN() ) is different from convolve2. The new functions can perform 2D
 convolution on 3D signals and filters in a way that is more aligned with
 convolutional neural networks.
 
diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index d10142269b..d958892c2e 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -18,7 +18,7 @@ achieve high throughput on most parallel architectures.
 
 ArrayFire provides one generic container object, the [array](\ref af::array)
 on which functions and mathematical operations are performed. The `array`
-can represent one of many different [basic data types](\ref af::af_dtype):
+can represent one of many different [basic data types](\ref af_dtype):
 
 * [f32](\ref f32) real single-precision (`float`)
 * [c32](\ref c32) complex single-precision (`cfloat`)
@@ -87,7 +87,7 @@ ArrayFire provides several functions to determine various aspects of arrays.
 This includes functions to print the contents, query the dimensions, and
 determine various other aspects of arrays.
 
-The [af_print](\ref af::af_print) function can be used to print arrays that
+The [af_print](\ref af_print) function can be used to print arrays that
 have already been generated or any expression involving arrays:
 
 \snippet test/getting_started.cpp ex_getting_started_print
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index fe893c564c..bc40f2a7b7 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1217,7 +1217,7 @@ Bug Fixes
       before returning pointer with asynchronous calls in CPU backend.
     * OpenCL Backend: [fix segfaults](https://github.com/arrayfire/arrayfire/issues/1324)
       when requested for device pointers on empty arrays.
-* Fixed \ref af::array::operator%() from using [rem to mod](https://github.com/arrayfire/arrayfire/issues/1318).
+* Fixed \ref af::operator%() from using [rem to mod](https://github.com/arrayfire/arrayfire/issues/1318).
 * Fixed [array destruction](https://github.com/arrayfire/arrayfire/issues/1321)
   when backends are switched in Unified API.
 * Fixed [indexing](https://github.com/arrayfire/arrayfire/issues/1331) after
@@ -1356,9 +1356,9 @@ Deprecations
 Documentation
 --------------
 
-* Fixes to documentation for \ref matchTemplate().
+* Fixes to documentation for \ref af::matchTemplate().
 * Improved documentation for deviceInfo.
-* Fixes to documentation for \ref exp().
+* Fixes to documentation for \ref af::exp().
 
 Known Issues
 ------------
@@ -1497,18 +1497,18 @@ Major Updates
 Function Additions
 ------------------
 * Unified Backend
-    * \ref setBackend() - Sets a backend as active
-    * \ref getBackendCount() - Gets the number of backends available for use
-    * \ref getAvailableBackends() - Returns information about available backends
-    * \ref getBackendId() - Gets the backend enum for an array
+    * \ref af::setBackend() - Sets a backend as active
+    * \ref af::getBackendCount() - Gets the number of backends available for use
+    * \ref af::getAvailableBackends() - Returns information about available backends
+    * \ref af::getBackendId() - Gets the backend enum for an array
 
 * Vision
-    * \ref homography() - Homography estimation
-    * \ref gloh() - GLOH Descriptor for SIFT
+    * \ref af::homography() - Homography estimation
+    * \ref af::gloh() - GLOH Descriptor for SIFT
 
 * Image Processing
-    * \ref loadImageNative() - Load an image as native data without modification
-    * \ref saveImageNative() - Save an image without modifying data or type
+    * \ref af::loadImageNative() - Load an image as native data without modification
+    * \ref af::saveImageNative() - Save an image without modifying data or type
 
 * Graphics
     * \ref af::Window::plot3() - 3-dimensional line plot
@@ -1522,26 +1522,26 @@ Function Additions
     * \ref af_release_indexers()
 
 * CUDA Backend Specific
-    * \ref setNativeId() - Set the CUDA device with given native id as active
+    * \ref afcu::setNativeId() - Set the CUDA device with given native id as active
         * ArrayFire uses a modified order for devices. The native id for a
           device can be retreived using `nvidia-smi`
 
 * OpenCL Backend Specific
-    * \ref setDeviceId() - Set the OpenCL device using the `clDeviceId`
+    * \ref afcl::setDeviceId() - Set the OpenCL device using the `clDeviceId`
 
 Other Improvements
 ------------------------
-* Added \ref c32 and \ref c64 support for \ref isNaN(), \ref isInf() and \ref iszero()
-* Added CPU information for `x86` and `x86_64` architectures in CPU backend's \ref info()
-* Batch support for \ref approx1() and \ref approx2()
+* Added \ref c32 and \ref c64 support for \ref af::isNaN(), \ref af::isInf() and \ref af::iszero()
+* Added CPU information for `x86` and `x86_64` architectures in CPU backend's \ref af::info()
+* Batch support for \ref af::approx1() and \ref af::approx2()
     * Now can be used with gfor as well
 * Added \ref s64 and \ref u64 support to:
-    * \ref sort() (along with sort index and sort by key)
-    * \ref setUnique(), \ref setUnion(), \ref setIntersect()
-    * \ref convolve() and \ref fftConvolve()
-    * \ref histogram() and \ref histEqual()
-    * \ref lookup()
-    * \ref mean()
+    * \ref af::sort() (along with sort index and sort by key)
+    * \ref af::setUnique(), \ref af::setUnion(), \ref af::setIntersect()
+    * \ref af::convolve() and \ref af::fftConvolve()
+    * \ref af::histogram() and \ref af::histEqual()
+    * \ref af::lookup()
+    * \ref af::mean()
 * Added \ref AF_MSG macro
 
 Build Improvements
@@ -1553,15 +1553,15 @@ Build Improvements
 
 Bug Fixes
 --------------
-* Fixed [memory leak](https://github.com/arrayfire/arrayfire/pull/1096) in \ref susan()
+* Fixed [memory leak](https://github.com/arrayfire/arrayfire/pull/1096) in \ref af::susan()
 * Fixed [failing test](https://github.com/arrayfire/arrayfire/commit/144a2db)
-  in \ref lower() and \ref upper() for CUDA compute 53
+  in \ref af::lower() and \ref af::upper() for CUDA compute 53
 * Fixed [bug](https://github.com/arrayfire/arrayfire/issues/1092) in CUDA for indexing out of bounds
-* Fixed [dims check](https://github.com/arrayfire/arrayfire/commit/6975da8) in \ref iota()
-* Fixed [out-of-bounds access](https://github.com/arrayfire/arrayfire/commit/7fc3856) in \ref sift()
-* Fixed [memory allocation](https://github.com/arrayfire/arrayfire/commit/5e88e4a) in \ref fast() OpenCL
+* Fixed [dims check](https://github.com/arrayfire/arrayfire/commit/6975da8) in \ref af::iota()
+* Fixed [out-of-bounds access](https://github.com/arrayfire/arrayfire/commit/7fc3856) in \ref af::sift()
+* Fixed [memory allocation](https://github.com/arrayfire/arrayfire/commit/5e88e4a) in \ref af::fast() OpenCL
 * Fixed [memory leak](https://github.com/arrayfire/arrayfire/pull/994) in image I/O functions
-* \ref dog() now returns float-point type arrays
+* \ref af::dog() now returns float-point type arrays
 
 Documentation Updates
 ---------------------
@@ -1664,10 +1664,10 @@ v3.1.0
 Function Additions
 ------------------
 * Computer Vision Functions
-    * \ref nearestNeighbour() - Nearest Neighbour with SAD, SSD and SHD distances
-    * \ref harris() - Harris Corner Detector
-    * \ref susan() - Susan Corner Detector
-    * \ref sift() - Scale Invariant Feature Transform (SIFT)
+    * \ref af::nearestNeighbour() - Nearest Neighbour with SAD, SSD and SHD distances
+    * \ref af::harris() - Harris Corner Detector
+    * \ref af::susan() - Susan Corner Detector
+    * \ref af::sift() - Scale Invariant Feature Transform (SIFT)
         * Method and apparatus for identifying scale invariant features"
           "in an image and use of same for locating an object in an image,\" David"
           "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application"
@@ -1677,7 +1677,7 @@ Function Additions
           "Columbia.")
         * SIFT is available for compiling but does not ship with ArrayFire
           hosted installers/pre-built libraries
-    * \ref dog() -  Difference of Gaussians
+    * \ref af::dog() -  Difference of Gaussians
 
 * Image Processing Functions
     * \ref ycbcr2rgb() and \ref rgb2ycbcr() - RGB <->YCbCr color space conversion
@@ -1803,20 +1803,20 @@ Bug Fixes
 --------------
 
 * Added missing symbols from the compatible API
-* Fixed a bug affecting corner rows and elements in \ref grad()
+* Fixed a bug affecting corner rows and elements in \ref af::grad()
 * Fixed linear interpolation bugs affecting large images in the following:
-    - \ref approx1()
-    - \ref approx2()
-    - \ref resize()
-    - \ref rotate()
-    - \ref scale()
-    - \ref skew()
-    - \ref transform()
+    - \ref af::approx1()
+    - \ref af::approx2()
+    - \ref af::resize()
+    - \ref af::rotate()
+    - \ref af::scale()
+    - \ref af::skew()
+    - \ref af::transform()
 
 Documentation
 -----------------
 
-* Added missing documentation for \ref constant()
+* Added missing documentation for \ref af::constant()
 * Added missing documentation for `array::scalar()`
 * Added supported input types for functions in `arith.h`
 
diff --git a/docs/pages/using_on_linux.md b/docs/pages/using_on_linux.md
index 4948763d77..0fcd23bba1 100644
--- a/docs/pages/using_on_linux.md
+++ b/docs/pages/using_on_linux.md
@@ -8,7 +8,7 @@ requirements are that you include the ArrayFire header directories and link with
 the ArrayFire library you intend to use i.e. CUDA, OpenCL, CPU, or Unified
 backends.
 
-## The big picture  {#big-picture}
+## The big picture  {#big-picture-linux}
 
 On Linux, we recommend installing ArrayFire to `/opt/arrayfire` directory. The
 installer will populate files in the following sub-directories:
@@ -57,7 +57,7 @@ apt install build-essential cmake cmake-curses-gui
 ## CMake
 
 We recommend that the CMake build system be used to create ArrayFire projects.
-As [discussed above](#big-picture), ArrayFire ships with a series of CMake
+As [discussed above](#big-picture-linux), ArrayFire ships with a series of CMake
 scripts to make finding and using our library easy.
 
 First create a file called `CMakeLists.txt` in your project directory:
diff --git a/docs/pages/using_on_osx.md b/docs/pages/using_on_osx.md
index 272898ec5e..e851509c4b 100644
--- a/docs/pages/using_on_osx.md
+++ b/docs/pages/using_on_osx.md
@@ -7,7 +7,7 @@ project using almost any editor, compiler, or build system. The only requirement
 is that you can include the ArrayFire header directory, and link with the
 ArrayFire library you intend to use.
 
-## <a name="big-picture"/> The big picture
+## The big picture {#big-picture-osx}
 
 By default, the ArrayFire OSX installer will place several files in your
 computer's `/opt/arrayfire` directory. The installer will populate this
@@ -33,7 +33,7 @@ CMake or Makefiles with CMake being our preferred build system.
 ## CMake {#CMake}
 
 The CMake build system can be used to create ArrayFire projects. As [discussed
-above](#big-picture), ArrayFire ships with a series of CMake scripts to make
+above](#big-picture-osx), ArrayFire ships with a series of CMake scripts to make
 finding and using our library easy.
 
 First create a file called `CMakeLists.txt` in your project directory:
diff --git a/docs/pages/using_on_windows.md b/docs/pages/using_on_windows.md
index 924fca2794..b178ad9c86 100644
--- a/docs/pages/using_on_windows.md
+++ b/docs/pages/using_on_windows.md
@@ -4,7 +4,7 @@ Using ArrayFire with Microsoft Windows and Visual Studio {#using_on_windows}
 If you have not already done so, please make sure you have installed,
 configured, and tested ArrayFire following the [installation instructions](#installing).
 
-# The big picture
+# The big picture {#big-picture-windows}
 
 The ArrayFire Windows installer creates the following:
 1. **AF_PATH** environment variable to point to the installation location. The
diff --git a/include/af/image.h b/include/af/image.h
index 5e32b551a9..b28d0b5395 100644
--- a/include/af/image.h
+++ b/include/af/image.h
@@ -602,7 +602,7 @@ AFAPI array unwrap(const array& in, const dim_t wx, const dim_t wy,
 
 #if AF_API_VERSION >= 31
 /**
-   C++ Interface for performing the opposite of \ref unwrap()
+   C++ Interface for performing the opposite of \ref unwrap
 
    \param[in]  in is the input array
    \param[in]  ox is the output's dimension 0 size
@@ -1487,7 +1487,7 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for performing the opposite of \ref unwrap()
+       C Interface for performing the opposite of \ref af::unwrap()
 
        \param[out] out is an array with the input's columns (or rows) reshaped as
                    patches
@@ -1506,7 +1506,7 @@ extern "C" {
        otherwise an appropriate error code is returned.
 
        \note Wrap is typically used to recompose an unwrapped image. If this is the
-             case, use the same parameters that were used in \ref unwrap(). Also
+             case, use the same parameters that were used in \ref af::unwrap(). Also
              use the original image size (before unwrap) for \p ox and \p oy.
        \note The window/patch size, \p wx \f$\times\f$ \p wy, must equal
              `input.dims(0)` (or `input.dims(1)` if \p is_column is false).
@@ -1552,7 +1552,7 @@ extern "C" {
        otherwise an appropriate error code is returned.
 
        \note Wrap is typically used to recompose an unwrapped image. If this is the
-             case, use the same parameters that were used in \ref unwrap(). Also
+             case, use the same parameters that were used in \ref af::unwrap(). Also
              use the original image size (before unwrap) for \p ox and \p oy.
        \note The window/patch size, \p wx \f$\times\f$ \p wy, must equal
              `input.dims(0)` (or `input.dims(1)` if \p is_column is false).
diff --git a/include/af/ml.h b/include/af/ml.h
index c341fd9a43..33feff9112 100644
--- a/include/af/ml.h
+++ b/include/af/ml.h
@@ -20,7 +20,7 @@ class dim4;
     /**
         C++ interface for calculating backward pass gradient of 2D convolution
         This function calculates the gradient with respect to the output
-        of the \ref convolve2NN() function that uses the machine learning
+        of the \ref convolve2NN function that uses the machine learning
         formulation for the dimensions of the signals and filters
 
         \param[in]  incoming_gradient gradients to be distributed in backwards pass
@@ -60,7 +60,7 @@ extern "C" {
     /**
         C interface for calculating backward pass gradient of 2D convolution
         This function calculates the gradient with respect to the output
-        of the \ref convolve2NN() function that uses the machine learning
+        of the \ref af::convolve2NN() function that uses the machine learning
         formulation for the dimensions of the signals and filters
 
         \param[out] out gradient wrt/gradType
diff --git a/include/af/util.h b/include/af/util.h
index 6075625de5..49a16b43ec 100644
--- a/include/af/util.h
+++ b/include/af/util.h
@@ -184,7 +184,7 @@ extern "C" {
 #if AF_API_VERSION >= 31
     /**
         \param[out] index is the index location of the array in the file
-        \param[in] key is an expression used as tag/key for the array during \ref readArray()
+        \param[in] key is an expression used as tag/key for the array during \ref af::readArray()
         \param[in] arr is the array to be written
         \param[in] filename is the path to the location on disk
         \param[in] append is used to append to an existing file when true and create or
diff --git a/test/complex.cpp b/test/complex.cpp
index b63fd63bba..fe8a60c0f9 100644
--- a/test/complex.cpp
+++ b/test/complex.cpp
@@ -139,24 +139,28 @@ TEST(Complex, SNIPPET_arith_func_complex) {
     //! [ex_arith_func_complex]
     //!
     // Create a, a 2x3 array
-    array a = iota(dim4(2, 3));    // a = [0, 2, 4,
-                                   //      1, 3, 5]
-
-    // Create b from a single real array, returning zeros for the imaginary component
-    array b = complex(a);          // b = [(0, 0), (2, 0), (4, 0),
-                                   //      (1, 0), (3, 0), (5, 0)]
-
-    // Create c from two real arrays, one for the real component and one for the imaginary component
-    array c = complex(a, a);       // c = [(0, 0), (2, 2), (4, 4),
-                                   //      (1, 1), (3, 3), (5, 5)]
-
-    // Create d from a single real array for the real component and a single scalar for each imaginary component
-    array d = complex(a, 2);       // d = [(0, 2), (2, 2), (4, 2),
-                                   //      (1, 2), (3, 2), (5, 2)]
-
-    // Create e from a single scalar for each real component and a single real array for the imaginary component
-    array e = complex(2, a);       // e = [(2, 0), (2, 2), (2, 4),
-                                   //      (2, 1), (2, 3), (2, 5)]
+    array a = iota(dim4(2, 3));  // a = [0, 2, 4,
+                                 //      1, 3, 5]
+
+    // Create b from a single real array, returning zeros for the imaginary
+    // component
+    array b = complex(a);  // b = [(0, 0), (2, 0), (4, 0),
+                           //      (1, 0), (3, 0), (5, 0)]
+
+    // Create c from two real arrays, one for the real component and one for the
+    // imaginary component
+    array c = complex(a, a);  // c = [(0, 0), (2, 2), (4, 4),
+                              //      (1, 1), (3, 3), (5, 5)]
+
+    // Create d from a single real array for the real component and a single
+    // scalar for each imaginary component
+    array d = complex(a, 2);  // d = [(0, 2), (2, 2), (4, 2),
+                              //      (1, 2), (3, 2), (5, 2)]
+
+    // Create e from a single scalar for each real component and a single real
+    // array for the imaginary component
+    array e = complex(2, a);  // e = [(2, 0), (2, 2), (2, 4),
+                              //      (2, 1), (2, 3), (2, 5)]
 
     //! [ex_arith_func_complex]
 

From 33935abd8bf537b98ec264472d9089c8480ec6b8 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 23 Jan 2023 20:33:22 -0500
Subject: [PATCH 2399/2677] slight tweaks to documentation wording

---
 docs/details/arith.dox    |  44 +++++++-------
 docs/details/blas.dox     |  18 ++++--
 docs/details/examples.dox |  58 ------------------
 include/af/arith.h        | 124 +++++++++++++++++++-------------------
 4 files changed, 97 insertions(+), 147 deletions(-)
 delete mode 100644 docs/details/examples.dox

diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index a7130647df..2e123f7ba8 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -19,28 +19,28 @@
 \defgroup arith_func_add add
 \ingroup arith_mat
 
-Add two arrays.
+Elementwise addition
 
 
 
 \defgroup arith_func_sub sub
 \ingroup arith_mat
 
-Subtract one array from another array.
+Elementwise subtraction
 
 
 
 \defgroup arith_func_mul mul
 \ingroup arith_mat
 
-Multiply two arrays.
+Elementwise multiply
 
 
 
 \defgroup arith_func_div div
 \ingroup arith_mat
 
-Divide one array by another array.
+Elementwise division
 
 
 
@@ -189,14 +189,14 @@ Cast an array from one type to another.
 \defgroup arith_func_min min
 \ingroup numeric_mat
 
-Find the elementwise minimum between two arrays.
+Returns the elementwise minimum between two arrays.
 
 
 
 \defgroup arith_func_max max
 \ingroup numeric_mat
 
-Find the elementwise maximum between two arrays.
+Returns the elementwise maximum between two arrays.
 
 
 
@@ -210,7 +210,7 @@ Clamp an array between an upper and a lower limit.
 \defgroup arith_func_rem rem
 \ingroup numeric_mat
 
-Find the remainder of a division.
+Calculate the remainder of a division.
 
 \copydoc arith_real_only
 
@@ -219,7 +219,7 @@ Find the remainder of a division.
 \defgroup arith_func_mod mod
 \ingroup numeric_mat
 
-Find the modulus.
+Calculate the modulus.
 
 \copydoc arith_real_only
 
@@ -228,20 +228,20 @@ Find the modulus.
 \defgroup arith_func_abs abs
 \ingroup numeric_mat
 
-Find the absolute value.
+Calculate the absolute value.
 
 
 \defgroup arith_func_arg arg
 \ingroup numeric_mat
 
-Find the phase angle (in radians) of a complex array.
+Calculate the phase angle (in radians) of a complex array.
 
 
 
 \defgroup arith_func_sign sign
 \ingroup numeric_mat
 
-Find the sign of elements in an array.
+Return the sign of elements in an array.
 
 \copydoc arith_real_only
 
@@ -268,7 +268,7 @@ Truncate numbers to nearest integer.
 \defgroup arith_func_floor floor
 \ingroup numeric_mat
 
-Round to the integer less than or equal to the magnitude of the input value.
+Rounds down to the greatest integer less than or equal to x.
 
 \copydoc arith_real_only
 
@@ -277,7 +277,7 @@ Round to the integer less than or equal to the magnitude of the input value.
 \defgroup arith_func_ceil ceil
 \ingroup numeric_mat
 
-Round to the integer greater than or equal to the magnitude of the input value.
+Rounds up to the least integer greater than or equal to x.
 
 \copydoc arith_real_only
 
@@ -286,7 +286,7 @@ Round to the integer greater than or equal to the magnitude of the input value.
 \defgroup arith_func_hypot hypot
 \ingroup numeric_mat
 
-Find the length of the hypotenuse of two inputs.
+Evaluate the length of the hypotenuse of two inputs.
 
 \copydoc arith_real_only
 
@@ -398,28 +398,28 @@ __Examples:__
 \defgroup arith_func_real real
 \ingroup complex_mat
 
-Find the real part of a complex array.
+Returns the real part of a complex array.
 
 
 
 \defgroup arith_func_imag imag
 \ingroup complex_mat
 
-Find the imaginary part of a complex array.
+Returns the imaginary part of a complex array.
 
 
 
 \defgroup arith_func_conjg conjg
 \ingroup complex_mat
 
-Find the complex conjugate of an input array.
+Evaluate the complex conjugate of an input array.
 
 
 
 \defgroup arith_func_root root
 \ingroup explog_mat
 
-Find the nth root.
+Evaluate the nth root.
 
 
 
@@ -513,14 +513,14 @@ Evaluate the base 2 logarithm.
 \defgroup arith_func_sqrt sqrt
 \ingroup explog_mat
 
-Find the square root.
+Evaluate the square root.
 
 
 
 \defgroup arith_func_rsqrt rsqrt
 \ingroup explog_mat
 
-Find the reciprocal square root.
+Evaluate the reciprocal square root.
 
 \f[ \frac{1}{\sqrt{x}} \f]
 
@@ -531,7 +531,7 @@ Find the reciprocal square root.
 \defgroup arith_func_cbrt cbrt
 \ingroup explog_mat
 
-Find the cube root.
+Evaluate the cube root.
 
 \copydoc arith_real_only
 
@@ -540,7 +540,7 @@ Find the cube root.
 \defgroup arith_func_factorial factorial
 \ingroup explog_mat
 
-Find the factorial.
+Evaluate the factorial.
 
 \copydoc arith_real_only
 
diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index 3765ed446c..b8757d81fb 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -52,11 +52,19 @@ and restrictions.
 
 \brief Transpose a matrix.
 
-Reverse or permute the dimensions of an array; returns the modified array. For an array a with two dimensions, `transpose(a)` gives the matrix transpose. For an array with more than two dimensions, the first two dimensions are transposed across higher dimensions.
-
-Set `conjugate=true` to perform the complex conjugate transpose of a matrix which interchanges the row and column index for each element, reflecting the elements across the main diagonal and negating the imaginary part of any complex numbers. For example, if `b = transpose(a, true)` and element `a(2, 1)` is `(1, 2)`, then element `b(1, 2)` is `(1, -2)`.
-
-In-place versions perform matrix transposition by reordering the input, reducing memory footprint.
+Reverse or permute the dimensions of an array; returns the modified array.
+For an array a with two dimensions, `transpose(a)` gives the matrix transpose.
+For an array with more than two dimensions, the first two dimensions are
+transposed across higher dimensions.
+
+Set `conjugate=true` to perform the complex conjugate transpose of a matrix
+which interchanges the row and column index for each element, reflecting the
+elements across the main diagonal and negating the imaginary part of any
+complex numbers. For example, if `b = transpose(a, true)` and element
+`a(2, 1)` is `(1, 2)`, then element `b(1, 2)` is `(1, -2)`.
+
+In-place versions perform matrix transposition by reordering the input,
+reducing memory footprint.
 
 __Examples:__
 
diff --git a/docs/details/examples.dox b/docs/details/examples.dox
deleted file mode 100644
index 1fd4451335..0000000000
--- a/docs/details/examples.dox
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
-\example benchmarks/blas.cpp
-\example benchmarks/cg.cpp
-\example benchmarks/fft.cpp
-\example benchmarks/pi.cpp
-\example computer_vision/fast.cpp
-\example computer_vision/harris.cpp
-\example computer_vision/matching.cpp
-\example computer_vision/susan.cpp
-\example financial/black_scholes_options.cpp
-\example financial/heston_model.cpp
-\example financial/monte_carlo_options.cpp
-\example getting_started/convolve.cpp
-\example getting_started/integer.cpp
-\example getting_started/rainfall.cpp
-\example getting_started/vectorize.cpp
-\example graphics/conway.cpp
-\example graphics/conway_pretty.cpp
-\example graphics/field.cpp
-\example graphics/fractal.cpp
-\example graphics/gravity_sim.cpp
-\example graphics/histogram.cpp
-\example graphics/plot2d.cpp
-\example graphics/plot3.cpp
-\example graphics/surface.cpp
-\example helloworld/helloworld.cpp
-\example image_processing/adaptive_thresholding.cpp
-\example image_processing/binary_thresholding.cpp
-\example image_processing/brain_segmentation.cpp
-\example image_processing/confidence_connected_components.cpp
-\example image_processing/deconvolution.cpp
-\example image_processing/edge.cpp
-\example image_processing/filters.cpp
-\example image_processing/gradient_diffusion.cpp
-\example image_processing/image_demo.cpp
-\example image_processing/image_editing.cpp
-\example image_processing/morphing.cpp
-\example image_processing/optical_flow.cpp
-\example image_processing/pyramids.cpp
-\example lin_algebra/cholesky.cpp
-\example lin_algebra/lu.cpp
-\example lin_algebra/qr.cpp
-\example lin_algebra/svd.cpp
-\example machine_learning/bagging.cpp
-\example machine_learning/deep_belief_net.cpp
-\example machine_learning/geneticalgorithm.cpp
-\example machine_learning/kmeans.cpp
-\example machine_learning/knn.cpp
-\example machine_learning/logistic_regression.cpp
-\example machine_learning/naive_bayes.cpp
-\example machine_learning/neural_network.cpp
-\example machine_learning/perceptron.cpp
-\example machine_learning/rbm.cpp
-\example machine_learning/softmax_regression.cpp
-\example pde/swe.cpp
-\example unified/basic.cpp
-
-*/
diff --git a/include/af/arith.h b/include/af/arith.h
index f6f190f199..ea9be6c328 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -98,7 +98,7 @@ namespace af
     /// @}
 
     /// @{
-    /// C++ Interface to find the remainder.
+    /// C++ Interface to calculate the remainder.
     ///
     /// \param[in] lhs numerator; can be an array or a scalar
     /// \param[in] rhs denominator; can be an array or a scalar
@@ -115,7 +115,7 @@ namespace af
     /// @}
 
     /// @{
-    /// C++ Interface to find the modulus.
+    /// C++ Interface to calculate the modulus.
     ///
     /// \param[in] lhs dividend; can be an array or a scalar
     /// \param[in] rhs divisor; can be an array or a scalar
@@ -131,7 +131,7 @@ namespace af
     AFAPI array mod    (const double lhs, const array &rhs);
     /// @}
 
-    /// C++ Interface to find the absolute value.
+    /// C++ Interface to calculate the absolute value.
     ///
     /// \param[in] in input array
     /// \return absolute value
@@ -139,7 +139,7 @@ namespace af
     /// \ingroup arith_func_abs
     AFAPI array abs    (const array &in);
 
-    /// C++ Interface to find the phase angle (in radians) of a complex array.
+    /// C++ Interface to calculate the phase angle (in radians) of a complex array.
     ///
     /// \param[in] in input array, typically complex
     /// \return phase angle (in radians)
@@ -147,7 +147,7 @@ namespace af
     /// \ingroup arith_func_arg
     AFAPI array arg    (const array &in);
 
-    /// C++ Interface to find the sign of elements in an array.
+    /// C++ Interface to return the sign of elements in an array.
     ///
     /// \param[in] in input array
     /// \return array containing 1's for negative values; 0's otherwise
@@ -189,7 +189,7 @@ namespace af
 
     /// \ingroup arith_func_hypot
     /// @{
-    /// C++ Interface to find the length of the hypotenuse of two inputs.
+    /// C++ Interface to calculate the length of the hypotenuse of two inputs.
     ///
     /// Calculates the hypotenuse of two inputs. The inputs can be both arrays
     /// or an array and a scalar.
@@ -348,7 +348,7 @@ namespace af
     AFAPI array complex(const double real_, const array &imag_);
     /// @}
 
-    /// C++ Interface to find the real part of a complex array.
+    /// C++ Interface to return the real part of a complex array.
     ///
     /// \param[in] in input complex array
     /// \return real part
@@ -356,7 +356,7 @@ namespace af
     /// \ingroup arith_func_real
     AFAPI array real   (const array &in);
 
-    /// C++ Interface to find the imaginary part of a complex array.
+    /// C++ Interface to return the imaginary part of a complex array.
     ///
     /// \param[in] in input complex array
     /// \return imaginary part
@@ -364,7 +364,7 @@ namespace af
     /// \ingroup arith_func_imag
     AFAPI array imag   (const array &in);
 
-    /// C++ Interface to find the complex conjugate of an input array.
+    /// C++ Interface to calculate the complex conjugate of an input array.
     ///
     /// \param[in] in input complex array
     /// \return complex conjugate
@@ -372,50 +372,50 @@ namespace af
     /// \ingroup arith_func_conjg
     AFAPI array conjg  (const array &in);
 
-    /// C++ Interface to find the nth root.
+    /// C++ Interface to evaluate the nth root.
     ///
-    /// \param[in] lhs nth root
-    /// \param[in] rhs value
-    /// \return \p lhs th root of \p rhs
+    /// \param[in] nth_root nth root
+    /// \param[in] value value
+    /// \return \p nth_root th root of \p value
     ///
     /// \ingroup arith_func_root
-    AFAPI array root    (const array &lhs, const array &rhs);
+    AFAPI array root    (const array &nth_root, const array &value);
 
-    /// C++ Interface to find the nth root.
+    /// C++ Interface to evaluate the nth root.
     ///
-    /// \param[in] lhs nth root
-    /// \param[in] rhs value
-    /// \return \p lhs th root of \p rhs
+    /// \param[in] nth_root nth root
+    /// \param[in] value value
+    /// \return \p nth_root th root of \p value
     ///
     /// \ingroup arith_func_root
-    AFAPI array root    (const array &lhs, const double rhs);
+    AFAPI array root    (const array &nth_root, const double value);
 
-    /// C++ Interface to find the nth root.
+    /// C++ Interface to evaluate the nth root.
     ///
-    /// \param[in] lhs nth root
-    /// \param[in] rhs value
-    /// \return \p lhs th root of \p rhs
+    /// \param[in] nth_root nth root
+    /// \param[in] value value
+    /// \return \p nth_root th root of \p value
     ///
     /// \ingroup arith_func_root
-    AFAPI array root    (const double lhs, const array &rhs);
+    AFAPI array root    (const double nth_root, const array &value);
 
 
     /// \ingroup arith_func_pow
     /// @{
     /// C++ Interface to raise a base to a power (or exponent).
     ///
-    /// Computes the value of \p lhs raised to the power of \p rhs. The inputs can be two arrays or an array and a scalar.
+    /// Computes the value of \p base raised to the power of \p exponent. The inputs can be two arrays or an array and a scalar.
     ///
-    /// \param[in] lhs base
-    /// \param[in] rhs exponent
-    /// \return \p lhs raised to the power of \p rhs
-    AFAPI array pow    (const array &lhs, const array &rhs);
+    /// \param[in] base base
+    /// \param[in] exponent exponent
+    /// \return \p base raised to the power of \p exponent
+    AFAPI array pow    (const array &base, const array &exponent);
 
     /// \copydoc pow(const array&, const array&)
-    AFAPI array pow    (const array &lhs, const double rhs);
+    AFAPI array pow    (const array &base, const double exponent);
 
     /// \copydoc pow(const array&, const array&)
-    AFAPI array pow    (const double lhs, const array &rhs);
+    AFAPI array pow    (const double base, const array &exponent);
 
     /// C++ Interface to raise 2 to a power (or exponent).
     ///
@@ -503,7 +503,7 @@ namespace af
     /// \ingroup explog_func_log2
     AFAPI array log2   (const array &in);
 
-    /// C++ Interface to find the square root.
+    /// C++ Interface to evaluate the square root.
     ///
     /// \param[in] in input
     /// \return square root
@@ -512,7 +512,7 @@ namespace af
     AFAPI array sqrt   (const array &in);
 
 #if AF_API_VERSION >= 37
-    /// C++ Interface to find the reciprocal square root.
+    /// C++ Interface to evaluate the reciprocal square root.
     ///
     /// \param[in] in input
     /// \return reciprocal square root
@@ -521,7 +521,7 @@ namespace af
     AFAPI array rsqrt   (const array &in);
 #endif
 
-    /// C++ Interface to find the cube root.
+    /// C++ Interface to evaluate the cube root.
     ///
     /// \param[in] in input
     /// \return cube root
@@ -529,7 +529,7 @@ namespace af
     /// \ingroup arith_func_cbrt
     AFAPI array cbrt   (const array &in);
 
-    /// C++ Interface to find the factorial.
+    /// C++ Interface to calculate the factorial.
     ///
     /// \param[in] in input
     /// \return the factorial function
@@ -553,7 +553,7 @@ namespace af
     /// \ingroup arith_func_lgamma
     AFAPI array lgamma (const array &in);
 
-    /// C++ Interface to check if values are zero.
+    /// C++ Interface to check which values are zero.
     ///
     /// \param[in] in input
     /// \return array containing 1's where input is 0; 0's otherwise
@@ -636,7 +636,7 @@ extern "C" {
     AFAPI af_err af_div   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if the elements of one array are less than those of another array.
+       C Interface to perform a less-than comparison between corresponding elements of two arrays.
 
        \param[out] out result of \p lhs < \p rhs; type is b8
        \param[in] lhs first input
@@ -649,7 +649,7 @@ extern "C" {
     AFAPI af_err af_lt    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if the elements of one array are greater than those of another array.
+       C Interface to perform a greater-than comparison between corresponding elements of two arrays.
 
        \param[out] out result of \p lhs > \p rhs; type is b8
        \param[in] lhs first input
@@ -662,7 +662,7 @@ extern "C" {
     AFAPI af_err af_gt    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if the elements of one array are less than or equal to those of another array.
+       C Interface to perform a less-than-or-equal comparison between corresponding elements of two arrays.
 
        \param[out] out result of \p lhs <= \p rhs; type is b8
        \param[in] lhs first input
@@ -675,7 +675,7 @@ extern "C" {
     AFAPI af_err af_le    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if the elements of one array are greater than or equal to those of another array.
+       C Interface to perform a greater-than-or-equal comparison between corresponding elements of two arrays.
 
        \param[out] out result of \p lhs >= \p rhs; type is b8
        \param[in] lhs first input
@@ -688,7 +688,7 @@ extern "C" {
     AFAPI af_err af_ge    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if the elements of one array are equal to those of another array.
+       C Interface to check if corresponding elements of two arrays are equal
 
        \param[out] out result of `lhs == rhs`; type is b8
        \param[in] lhs first input
@@ -701,7 +701,7 @@ extern "C" {
     AFAPI af_err af_eq    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if the elements of one array are not equal to those of another array.
+       C Interface to check if corresponding elements of two arrays are not equal
 
        \param[out] out result of `lhs != rhs`; type is b8
        \param[in] lhs first input
@@ -806,8 +806,8 @@ extern "C" {
        C Interface to shift the bits of integer arrays left.
 
        \param[out] out result of the left shift
-       \param[in] lhs first input
-       \param[in] rhs second input
+       \param[in] lhs values to shift
+       \param[in] rhs n bits to shift
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -819,8 +819,8 @@ extern "C" {
        C Interface to shift the bits of integer arrays right.
 
        \param[out] out result of the right shift
-       \param[in] lhs first input
-       \param[in] rhs second input
+       \param[in] lhs values to shift
+       \param[in] rhs n bits to shift
        \param[in] batch specifies if operations need to be performed in batch mode
        \return \ref AF_SUCCESS if the execution completes properly
 
@@ -913,7 +913,7 @@ extern "C" {
 #endif
 
     /**
-       C Interface to find the remainder.
+       C Interface to calculate the remainder.
 
        \param[out] out remainder of \p lhs divided by \p rhs
        \param[in] lhs numerator
@@ -926,7 +926,7 @@ extern "C" {
     AFAPI af_err af_rem   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to find the modulus.
+       C Interface to calculate the modulus.
 
        \param[out] out \p lhs modulo \p rhs
        \param[in] lhs dividend
@@ -939,7 +939,7 @@ extern "C" {
     AFAPI af_err af_mod   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to find the absolute value.
+       C Interface to calculate the absolute value.
 
        \param[out] out absolute value
        \param[in] in input array
@@ -950,7 +950,7 @@ extern "C" {
     AFAPI af_err af_abs     (af_array *out, const af_array in);
 
     /**
-       C Interface to find the phase angle (in radians) of a complex array.
+       C Interface to calculate the phase angle (in radians) of a complex array.
 
        \param[out] out phase angle (in radians)
        \param[in] in input array, typically complex
@@ -961,7 +961,7 @@ extern "C" {
     AFAPI af_err af_arg     (af_array *out, const af_array in);
 
     /**
-       C Interface to find the sign of elements in an array.
+       C Interface to calculate the sign of elements in an array.
 
        \param[out] out array containing 1's for negative values; 0's otherwise
        \param[in] in input array
@@ -1016,7 +1016,7 @@ extern "C" {
     AFAPI af_err af_ceil    (af_array *out, const af_array in);
 
     /**
-       C Interface to find the length of the hypotenuse of two inputs.
+       C Interface to calculate the length of the hypotenuse of two inputs.
 
        \param[out] out length of the hypotenuse
        \param[in] lhs length of first side
@@ -1198,7 +1198,7 @@ extern "C" {
     AFAPI af_err af_cplx2(af_array* out, const af_array real, const af_array imag, const bool batch);
 
     /**
-       C Interface to find the real part of a complex array.
+       C Interface to return the real part of a complex array.
 
        \param[out] out real part
        \param[in] in complex array
@@ -1209,7 +1209,7 @@ extern "C" {
     AFAPI af_err af_real(af_array* out, const af_array in);
 
     /**
-       C Interface to find the imaginary part of a complex array.
+       C Interface to return the imaginary part of a complex array.
 
        \param[out] out imaginary part
        \param[in] in complex array
@@ -1220,7 +1220,7 @@ extern "C" {
     AFAPI af_err af_imag(af_array* out, const af_array in);
 
     /**
-       C Interface to find the complex conjugate of an input array.
+       C Interface to evaluate the complex conjugate of an input array.
 
        \param[out] out complex conjugate
        \param[in] in complex array
@@ -1231,7 +1231,7 @@ extern "C" {
     AFAPI af_err af_conjg(af_array* out, const af_array in);
 
     /**
-       C Interface to find the nth root.
+       C Interface to evaluate the nth root.
 
        \param[out] out \p lhs th root of \p rhs
        \param[in] lhs nth root
@@ -1272,12 +1272,12 @@ extern "C" {
     /**
        C Interface to evaluate the logistical sigmoid function.
 
+       Computes `1/(1+e^-x)`.
+
        \param[out] out output of the logistic sigmoid function
        \param[in] in input
        \return \ref AF_SUCCESS if the execution completes properly
 
-       \note Computes `1/(1+e^-x)`.
-
        \ingroup arith_func_sigmoid
     */
     AFAPI af_err af_sigmoid(af_array* out, const af_array in);
@@ -1372,7 +1372,7 @@ extern "C" {
     AFAPI af_err af_log2   (af_array *out, const af_array in);
 
     /**
-       C Interface to find the square root.
+       C Interface to evaluate the square root.
 
        \param[out] out square root
        \param[in] in input
@@ -1384,7 +1384,7 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-      C Interface to find the reciprocal square root.
+      C Interface to evaluate the reciprocal square root.
 
       \param[out] out reciprocal square root
       \param[in] in input
@@ -1395,7 +1395,7 @@ extern "C" {
     AFAPI af_err af_rsqrt    (af_array *out, const af_array in);
 #endif
     /**
-       C Interface to find the cube root.
+       C Interface to evaluate the cube root.
 
        \param[out] out cube root
        \param[in] in input
@@ -1406,7 +1406,7 @@ extern "C" {
     AFAPI af_err af_cbrt    (af_array *out, const af_array in);
 
     /**
-       C Interface to find the factorial.
+       C Interface to calculate the factorial.
 
        \param[out] out factorial
        \param[in] in input

From 635718a121892719f4bb87e4b48552deba66f0bf Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Wed, 25 Jan 2023 12:02:04 -0500
Subject: [PATCH 2400/2677] pinned memory oneapi (#3356)

* supports pinned memory allocation on oneapi backend through USM

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/memory.cpp | 111 ++++++++--------------------------
 1 file changed, 26 insertions(+), 85 deletions(-)
 mode change 100644 => 100755 src/backend/oneapi/memory.cpp

diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
old mode 100644
new mode 100755
index e87812e5b4..80c589a5b0
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -48,8 +48,7 @@ void signalMemoryCleanup() { memoryManager().signalMemoryCleanup(); }
 
 void shutdownMemoryManager() { memoryManager().shutdown(); }
 
-void shutdownPinnedMemoryManager() { /*pinnedMemoryManager().shutdown();*/
-}
+void shutdownPinnedMemoryManager() { pinnedMemoryManager().shutdown(); }
 
 void printMemInfo(const char *msg, const int device) {
     memoryManager().printInfo(msg, device);
@@ -62,18 +61,6 @@ std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>
 memAlloc(const size_t &elements) {
     return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(
         new sycl::buffer<T>(sycl::range(elements)), bufferFree<T>);
-    // // TODO: make memAlloc aware of array shapes
-    // if (elements) {
-    //     dim4 dims(elements);
-    //     void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    //     auto buf  = static_cast<cl_mem>(ptr);
-    //     cl::Buffer *bptr = new cl::Buffer(buf, true);
-    //     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(bptr,
-    //                                                                 bufferFree);
-    // } else {
-    //     return unique_ptr<cl::Buffer, function<void(cl::Buffer *)>>(nullptr,
-    //                                                                 bufferFree);
-    // }
 }
 
 void *memAllocUser(const size_t &bytes) {
@@ -159,17 +146,15 @@ void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements) {
-    ONEAPI_NOT_SUPPORTED("pinnedAlloc Not supported");
-
-    // // TODO: make pinnedAlloc aware of array shapes
-    // dim4 dims(elements);
-    // void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    return static_cast<T *>(nullptr);
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    return static_cast<T *>(ptr);
 }
 
 template<typename T>
 void pinnedFree(T *ptr) {
-    // pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
+    pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
 }
 
 // template unique_ptr<int, function<void(int *)>> memAlloc<T>(
@@ -257,80 +242,36 @@ void Allocator::nativeFree(void *ptr) {
     // }
 }
 
-AllocatorPinned::AllocatorPinned() : pinnedMaps(oneapi::getDeviceCount()) {
-    logger = common::loggerFactory("mem");
-}
+AllocatorPinned::AllocatorPinned() { logger = common::loggerFactory("mem"); }
 
-void AllocatorPinned::shutdown() {
-    ONEAPI_NOT_SUPPORTED("AllocatorPinned::shutdown Not supported");
+void AllocatorPinned::shutdown() { shutdownPinnedMemoryManager(); }
 
-    //     for (int n = 0; n < opencl::getDeviceCount(); n++) {
-    //         opencl::setDevice(n);
-    //         shutdownPinnedMemoryManager();
-    //         auto currIterator = pinnedMaps[n].begin();
-    //         auto endIterator  = pinnedMaps[n].end();
-    //         while (currIterator != endIterator) {
-    //             pinnedMaps[n].erase(currIterator++);
-    //         }
-    //     }
-}
-
-int AllocatorPinned::getActiveDeviceId() {
-    ONEAPI_NOT_SUPPORTED("AllocatorPinned::getActiveDeviceId Not supported");
-    return 0;
-
-    // opencl::getActiveDeviceId();
-}
+int AllocatorPinned::getActiveDeviceId() { oneapi::getActiveDeviceId(); }
 
 size_t AllocatorPinned::getMaxMemorySize(int id) {
-    ONEAPI_NOT_SUPPORTED("AllocatorPinned::getMaxMemorySize Not supported");
-    return 0;
-    // return opencl::getDeviceMemorySize(id);
+    return oneapi::getDeviceMemorySize(id);
 }
 
 void *AllocatorPinned::nativeAlloc(const size_t bytes) {
-    ONEAPI_NOT_SUPPORTED("AllocatorPinned::nativeAlloc Not supported");
-    return nullptr;
-    //     void *ptr = NULL;
-
-    //     cl_int err = CL_SUCCESS;
-    //     auto buf   = clCreateBuffer(getContext()(), CL_MEM_ALLOC_HOST_PTR,
-    //     bytes,
-    //                               nullptr, &err);
-    //     if (err != CL_SUCCESS) {
-    //         AF_ERROR("Failed to allocate pinned memory.", AF_ERR_NO_MEM);
-    //     }
-
-    //     ptr = clEnqueueMapBuffer(getQueue()(), buf, CL_TRUE,
-    //                              CL_MAP_READ | CL_MAP_WRITE, 0, bytes, 0,
-    //                              nullptr, nullptr, &err);
-    //     if (err != CL_SUCCESS) {
-    //         AF_ERROR("Failed to map pinned memory", AF_ERR_RUNTIME);
-    //     }
-    //     AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
-    //     pinnedMaps[opencl::getActiveDeviceId()].emplace(ptr, new
-    //     cl::Buffer(buf)); return ptr;
+    void *ptr = NULL;
+    try {
+        ptr = sycl::malloc_host<unsigned char>(bytes, getQueue());
+    } catch (...) {
+        auto str = fmt::format("Failed to allocate device memory of size {}",
+                               bytesToString(bytes));
+        AF_ERROR(str, AF_ERR_NO_MEM);
+    }
+    AF_TRACE("Pinned::nativeAlloc: {:>7} {}", bytesToString(bytes), ptr);
+    return ptr;
 }
 
 void AllocatorPinned::nativeFree(void *ptr) {
-    ONEAPI_NOT_SUPPORTED("AllocatorPinned::nativeFree Not supported");
-
-    // AF_TRACE("Pinned::nativeFree:          {}", ptr);
-    // int n     = opencl::getActiveDeviceId();
-    // auto &map = pinnedMaps[n];
-    // auto iter = map.find(ptr);
-
-    // if (iter != map.end()) {
-    //     cl::Buffer *buf = map[ptr];
-    //     if (cl_int err = getQueue().enqueueUnmapMemObject(*buf, ptr)) {
-    //         getLogger()->warn(
-    //             "Pinned::nativeFree: Error unmapping pinned memory({}:{}). "
-    //             "Ignoring",
-    //             err, getErrorMessage(err));
-    //     }
-    //     delete buf;
-    //     map.erase(iter);
-    // }
+    AF_TRACE("Pinned::nativeFree:          {}", ptr);
+    try {
+        sycl::free(ptr, getQueue());
+    } catch (...) {
+        AF_ERROR("Failed to release device memory.", AF_ERR_RUNTIME);
+    }
 }
 }  // namespace oneapi
 }  // namespace arrayfire

From 12f63fadaa42f0690b5c6c459a77c8e0bcafe3b6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 25 Jan 2023 14:32:47 -0500
Subject: [PATCH 2401/2677] fix spdlog when external fmt found

---
 CMakeLists.txt | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8985c797ff..b049258552 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -60,7 +60,7 @@ find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
 find_package(MKL)
-find_package(spdlog QUIET ${AF_REQUIRED})
+find_package(spdlog QUIET ${AF_REQUIRED} NO_CMAKE_PACKAGE_REGISTRY)
 find_package(fmt QUIET ${AF_REQUIRED})
 find_package(span-lite QUIET)
 find_package(GTest)
@@ -228,14 +228,13 @@ else()
     URI https://github.com/gabime/spdlog.git
     REF v1.9.2
   )
-  add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
 
   if(TARGET fmt::fmt)
-    set_target_properties(af_spdlog
-      PROPERTIES
-        INTERFACE_COMPILE_DEFINITIONS "SPDLOG_FMT_EXTERNAL")
+    set(SPDLOG_FMT_EXTERNAL ON)
   endif()
 
+  add_subdirectory(${${spdlog_prefix}_SOURCE_DIR} ${${spdlog_prefix}_BINARY_DIR} EXCLUDE_FROM_ALL)
+
   if(AF_WITH_SPDLOG_HEADER_ONLY)
     set_target_properties(af_spdlog
       PROPERTIES

From 715e21fcd6e989793d01c5781908f221720e7d48 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Wed, 1 Feb 2023 14:54:51 -0500
Subject: [PATCH 2402/2677] opencl to oneapi function port batch 1 (#3358)

* gradient ported to oneapi. tests pass but MaxDims b/c missing jit
* meanshift passes tests up to missing ImageIO, JIT
* lookup compiles for oneapi. spot checked. no test in harness
* restore standard device selection in device manager
* select compiles. tests pass up to jit, randu, and maxdims
* updates tile.hpp
* rotate functions for float inputs
* resize
* approx1. mods to interp to use only float's not double's
* approx2 now passes on a770 (float only) gpu
* reorder ported again to ensure compatibility with a770
* transform
---------

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt       |   0
 src/backend/oneapi/gradient.cpp         |   4 +-
 src/backend/oneapi/kernel/convolve1.hpp |  10 +-
 src/backend/oneapi/kernel/convolve2.hpp |  10 +-
 src/backend/oneapi/kernel/convolve3.hpp |  10 +-
 src/backend/oneapi/kernel/gradient.hpp  | 163 +++++++++++++
 src/backend/oneapi/kernel/interp.hpp    |  28 +--
 src/backend/oneapi/kernel/lookup.hpp    | 133 ++++++++++
 src/backend/oneapi/kernel/meanshift.hpp | 229 ++++++++++++++++++
 src/backend/oneapi/kernel/reorder.hpp   |  46 ++--
 src/backend/oneapi/kernel/resize.hpp    | 230 ++++++++++++++++++
 src/backend/oneapi/kernel/rotate.hpp    | 217 +++++++++++++++++
 src/backend/oneapi/kernel/select.hpp    | 258 ++++++++++++++++++++
 src/backend/oneapi/kernel/tile.hpp      | 112 +++++++++
 src/backend/oneapi/kernel/transform.hpp | 307 ++++++++++++++++++++++++
 src/backend/oneapi/lookup.cpp           |  14 +-
 src/backend/oneapi/meanshift.cpp        |   9 +-
 src/backend/oneapi/memory.cpp           |   0
 src/backend/oneapi/resize.cpp           |   6 +-
 src/backend/oneapi/rotate.cpp           |  34 ++-
 src/backend/oneapi/select.cpp           |  13 +-
 src/backend/oneapi/tile.cpp             |   5 +-
 src/backend/oneapi/transform.cpp        |  12 +-
 23 files changed, 1741 insertions(+), 109 deletions(-)
 mode change 100755 => 100644 src/backend/oneapi/CMakeLists.txt
 mode change 100755 => 100644 src/backend/oneapi/kernel/convolve1.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/convolve2.hpp
 mode change 100755 => 100644 src/backend/oneapi/kernel/convolve3.hpp
 create mode 100644 src/backend/oneapi/kernel/gradient.hpp
 create mode 100644 src/backend/oneapi/kernel/lookup.hpp
 create mode 100644 src/backend/oneapi/kernel/meanshift.hpp
 create mode 100644 src/backend/oneapi/kernel/resize.hpp
 create mode 100644 src/backend/oneapi/kernel/rotate.hpp
 create mode 100644 src/backend/oneapi/kernel/select.hpp
 create mode 100644 src/backend/oneapi/kernel/tile.hpp
 create mode 100644 src/backend/oneapi/kernel/transform.hpp
 mode change 100755 => 100644 src/backend/oneapi/memory.cpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
old mode 100755
new mode 100644
diff --git a/src/backend/oneapi/gradient.cpp b/src/backend/oneapi/gradient.cpp
index dc45b67cc6..0ab39d7e8d 100644
--- a/src/backend/oneapi/gradient.cpp
+++ b/src/backend/oneapi/gradient.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <err_oneapi.hpp>
 #include <gradient.hpp>
-//#include <kernel/gradient.hpp>
+#include <kernel/gradient.hpp>
 #include <math.hpp>
 #include <stdexcept>
 
@@ -18,7 +18,7 @@ namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void gradient(Array<T> &grad0, Array<T> &grad1, const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
+    kernel::gradient<T>(grad0, grad1, in);
 }
 
 #define INSTANTIATE(T)                                            \
diff --git a/src/backend/oneapi/kernel/convolve1.hpp b/src/backend/oneapi/kernel/convolve1.hpp
old mode 100755
new mode 100644
index 1383bb4591..1d3df7ef3b
--- a/src/backend/oneapi/kernel/convolve1.hpp
+++ b/src/backend/oneapi/kernel/convolve1.hpp
@@ -107,12 +107,10 @@ void conv1Helper(const conv_kparam_t<aT> &param, Param<T> &out,
                  const int rank, const bool expand) {
     auto Q = getQueue();
     Q.submit([&](auto &h) {
-        sycl::accessor<aT, 1, sycl::access::mode::read_write,
-                       sycl::access::target::local>
-            localMem(param.loc_size, h);
-        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
-        sycl::accessor signalAcc{*signal.data, h, sycl::read_only};
-        sycl::accessor impulseAcc{*param.impulse, h, sycl::read_only};
+        local_accessor<aT> localMem(param.loc_size, h);
+        write_accessor<T> outAcc{*out.data, h};
+        read_accessor<T> signalAcc{*signal.data, h};
+        read_accessor<aT> impulseAcc{*param.impulse, h};
         h.parallel_for(
             sycl::nd_range{param.global, param.local},
             conv1HelperCreateKernel<T, aT>(
diff --git a/src/backend/oneapi/kernel/convolve2.hpp b/src/backend/oneapi/kernel/convolve2.hpp
old mode 100755
new mode 100644
index 5232b225ff..173405bdb8
--- a/src/backend/oneapi/kernel/convolve2.hpp
+++ b/src/backend/oneapi/kernel/convolve2.hpp
@@ -131,12 +131,10 @@ void conv2Helper(const conv_kparam_t<aT> &param, Param<T> out,
 
     auto Q = getQueue();
     Q.submit([&](auto &h) {
-        sycl::accessor<aT, 1, sycl::access::mode::read_write,
-                       sycl::access::target::local>
-            localMem(LOC_SIZE, h);
-        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
-        sycl::accessor signalAcc{*signal.data, h, sycl::read_only};
-        sycl::accessor impulseAcc{*param.impulse, h, sycl::read_only};
+        local_accessor<aT> localMem(LOC_SIZE, h);
+        write_accessor<T> outAcc{*out.data, h};
+        read_accessor<T> signalAcc{*signal.data, h};
+        read_accessor<aT> impulseAcc{*param.impulse, h};
         h.parallel_for(
             sycl::nd_range{param.global, param.local},
             conv2HelperCreateKernel<T, aT>(
diff --git a/src/backend/oneapi/kernel/convolve3.hpp b/src/backend/oneapi/kernel/convolve3.hpp
old mode 100755
new mode 100644
index d9a93affef..57f1538ddc
--- a/src/backend/oneapi/kernel/convolve3.hpp
+++ b/src/backend/oneapi/kernel/convolve3.hpp
@@ -143,12 +143,10 @@ void conv3Helper(const conv_kparam_t<aT> &param, Param<T> &out,
                  const int rank, const bool EXPAND) {
     auto Q = getQueue();
     Q.submit([&](auto &h) {
-        sycl::accessor<aT, 1, sycl::access::mode::read_write,
-                       sycl::access::target::local>
-            localMem(param.loc_size, h);
-        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
-        sycl::accessor signalAcc{*signal.data, h, sycl::read_only};
-        sycl::accessor impulseAcc{*param.impulse, h, sycl::read_only};
+        local_accessor<aT> localMem(param.loc_size, h);
+        write_accessor<T> outAcc{*out.data, h};
+        read_accessor<T> signalAcc{*signal.data, h};
+        read_accessor<aT> impulseAcc{*param.impulse, h};
         h.parallel_for(
             sycl::nd_range{param.global, param.local},
             conv3HelperCreateKernel<T, aT>(
diff --git a/src/backend/oneapi/kernel/gradient.hpp b/src/backend/oneapi/kernel/gradient.hpp
new file mode 100644
index 0000000000..fbaae20b51
--- /dev/null
+++ b/src/backend/oneapi/kernel/gradient.hpp
@@ -0,0 +1,163 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/default_config.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
+                                      sycl::access::target::local>;
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+#define sidx(y, x) scratch_[((y + 1) * (TX + 2)) + (x + 1)]
+
+template<typename T, int TX, int TY>
+class gradientCreateKernel {
+   public:
+    gradientCreateKernel(write_accessor<T> d_grad0, const KParam grad0,
+                         write_accessor<T> d_grad1, const KParam grad1,
+                         read_accessor<T> d_in, const KParam in,
+                         const int blocksPerMatX, const int blocksPerMatY,
+                         local_accessor<T> scratch)
+        : d_grad0_(d_grad0)
+        , grad0_(grad0)
+        , d_grad1_(d_grad1)
+        , grad1_(grad1)
+        , d_in_(d_in)
+        , in_(in)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY)
+        , scratch_(scratch) {}
+    void operator()(sycl::nd_item<2> it) const {
+        auto g = it.get_group();
+
+        const int idz = g.get_group_id(0) / blocksPerMatX_;
+        const int idw = g.get_group_id(1) / blocksPerMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - idz * blocksPerMatX_;
+        const int blockIdx_y = g.get_group_id(1) - idw * blocksPerMatY_;
+
+        const int xB = blockIdx_x * g.get_local_range(0);
+        const int yB = blockIdx_y * g.get_local_range(1);
+
+        const int tx = it.get_local_id(0);
+        const int ty = it.get_local_id(1);
+
+        const int idx = tx + xB;
+        const int idy = ty + yB;
+
+        const bool cond = (idx >= in_.dims[0] || idy >= in_.dims[1] ||
+                           idz >= in_.dims[2] || idw >= in_.dims[3]);
+
+        int xmax = (TX > (in_.dims[0] - xB)) ? (in_.dims[0] - xB) : TX;
+        int ymax = (TY > (in_.dims[1] - yB)) ? (in_.dims[1] - yB) : TY;
+
+        int iIdx = in_.offset + idw * in_.strides[3] + idz * in_.strides[2] +
+                   idy * in_.strides[1] + idx;
+
+        int g0dx = idw * grad0_.strides[3] + idz * grad0_.strides[2] +
+                   idy * grad0_.strides[1] + idx;
+
+        int g1dx = idw * grad1_.strides[3] + idz * grad1_.strides[2] +
+                   idy * grad1_.strides[1] + idx;
+
+        // Multipliers - 0.5 for interior, 1 for edge cases
+        typename std::conditional<std::is_same<T, std::complex<double>>::value,
+                                  double, float>::type
+            xf = 0.5 * (1 + (idx == 0 || idx >= (in_.dims[0] - 1))),
+            yf = 0.5 * (1 + (idy == 0 || idy >= (in_.dims[1] - 1)));
+
+        // Copy data to scratch space
+        T zero = (T)(0);
+        if (cond) {
+            sidx(ty, tx) = zero;
+        } else {
+            sidx(ty, tx) = d_in_[iIdx];
+        }
+
+        it.barrier();
+
+        // Copy buffer zone data. Corner (0,0) etc, are not used.
+        // Cols
+        if (ty == 0) {
+            // Y-1
+            sidx(-1, tx) =
+                (cond || idy == 0) ? sidx(0, tx) : d_in_[iIdx - in_.strides[1]];
+            sidx(ymax, tx) = (cond || (idy + ymax) >= in_.dims[1])
+                                 ? sidx(ymax - 1, tx)
+                                 : d_in_[iIdx + ymax * in_.strides[1]];
+        }
+        // Rows
+        if (tx == 0) {
+            sidx(ty, -1)   = (cond || idx == 0) ? sidx(ty, 0) : d_in_[iIdx - 1];
+            sidx(ty, xmax) = (cond || (idx + xmax) >= in_.dims[0])
+                                 ? sidx(ty, xmax - 1)
+                                 : d_in_[iIdx + xmax];
+        }
+
+        it.barrier();
+
+        if (cond) return;
+
+        d_grad0_[g0dx] = xf * (sidx(ty, tx + 1) - sidx(ty, tx - 1));
+        d_grad1_[g1dx] = yf * (sidx(ty + 1, tx) - sidx(ty - 1, tx));
+    }
+
+   private:
+    write_accessor<T> d_grad0_;
+    const KParam grad0_;
+    write_accessor<T> d_grad1_;
+    const KParam grad1_;
+    read_accessor<T> d_in_;
+    const KParam in_;
+    const int blocksPerMatX_;
+    const int blocksPerMatY_;
+    local_accessor<T> scratch_;
+};
+
+template<typename T>
+void gradient(Param<T> grad0, Param<T> grad1, const Param<T> in) {
+    constexpr int TX = 32;
+    constexpr int TY = 8;
+
+    auto local = sycl::range{TX, TY};
+
+    int blocksPerMatX = divup(in.info.dims[0], TX);
+    int blocksPerMatY = divup(in.info.dims[1], TY);
+    auto global       = sycl::range{local[0] * blocksPerMatX * in.info.dims[2],
+                              local[1] * blocksPerMatY * in.info.dims[3]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<T> grad0Acc{*grad0.data, h};
+        write_accessor<T> grad1Acc{*grad1.data, h};
+        read_accessor<T> inAcc{*in.data, h};
+        auto scratch = local_accessor<T>((TY + 2) * (TX + 2), h);
+        h.parallel_for(sycl::nd_range{global, local},
+                       gradientCreateKernel<T, TX, TY>(
+                           grad0Acc, grad0.info, grad1Acc, grad1.info, inAcc,
+                           in.info, blocksPerMatX, blocksPerMatY, scratch));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
index af430ca031..cefd67c992 100644
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -110,7 +110,7 @@ struct Interp1<Ty, Tp, 1> {
         const int x_lim    = iInfo.dims[xdim];
         const int x_stride = iInfo.strides[xdim];
 
-        int xid   = (method == AF_INTERP_LOWER ? floor(x) : round(x));
+        int xid = (method == AF_INTERP_LOWER ? sycl::floor(x) : sycl::round(x));
         bool cond = xid >= 0 && xid < x_lim;
         if (clamp) xid = std::max((int)0, std::min(xid, x_lim));
 
@@ -133,8 +133,8 @@ struct Interp1<Ty, Tp, 2> {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
-        const int grid_x = floor(x);    // nearest grid
-        const WT off_x   = x - grid_x;  // fractional offset
+        const int grid_x = sycl::floor(x);  // nearest grid
+        const WT off_x   = x - grid_x;      // fractional offset
 
         const int x_lim    = iInfo.dims[xdim];
         const int x_stride = iInfo.strides[xdim];
@@ -145,7 +145,7 @@ struct Interp1<Ty, Tp, 2> {
         WT ratio     = off_x;
         if (method == AF_INTERP_LINEAR_COSINE) {
             // Smooth the factional part with cosine
-            ratio = (1 - cos(ratio * af::Pi)) / 2;
+            ratio = (1 - sycl::cospi(ratio)) / 2;
         }
 
         Ty zero = scalar<Ty>(0);
@@ -170,8 +170,8 @@ struct Interp1<Ty, Tp, 3> {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
-        const int grid_x = floor(x);    // nearest grid
-        const WT off_x   = x - grid_x;  // fractional offset
+        const int grid_x = sycl::floor(x);  // nearest grid
+        const WT off_x   = x - grid_x;      // fractional offset
 
         const int x_lim    = iInfo.dims[xdim];
         const int x_stride = iInfo.strides[xdim];
@@ -206,8 +206,8 @@ struct Interp2<Ty, Tp, 1> {
                     read_accessor<Ty> in, KParam iInfo, int ioff, Tp x, Tp y,
                     int xdim, int ydim, af::interpType method, int batch,
                     bool clamp, int batch_dim = 2) {
-        int xid = (method == AF_INTERP_LOWER ? floor(x) : round(x));
-        int yid = (method == AF_INTERP_LOWER ? floor(y) : round(y));
+        int xid = (method == AF_INTERP_LOWER ? sycl::floor(x) : sycl::round(x));
+        int yid = (method == AF_INTERP_LOWER ? sycl::floor(y) : sycl::round(y));
 
         const int x_lim    = iInfo.dims[xdim];
         const int y_lim    = iInfo.dims[ydim];
@@ -244,10 +244,10 @@ struct Interp2<Ty, Tp, 2> {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
-        const int grid_x = floor(x);
+        const int grid_x = sycl::floor(x);
         const WT off_x   = x - grid_x;
 
-        const int grid_y = floor(y);
+        const int grid_y = sycl::floor(y);
         const WT off_y   = y - grid_y;
 
         const int x_lim    = iInfo.dims[xdim];
@@ -265,8 +265,8 @@ struct Interp2<Ty, Tp, 2> {
         if (method == AF_INTERP_LINEAR_COSINE ||
             method == AF_INTERP_BILINEAR_COSINE) {
             // Smooth the factional part with cosine
-            xratio = (1 - cos(xratio * af::Pi)) / 2;
-            yratio = (1 - cos(yratio * af::Pi)) / 2;
+            xratio = (1 - sycl::cospi(xratio)) / 2;
+            yratio = (1 - sycl::cospi(yratio)) / 2;
         }
 
         Ty zero = scalar<Ty>(0);
@@ -296,10 +296,10 @@ struct Interp2<Ty, Tp, 3> {
         typedef typename itype_t<Tp>::wtype WT;
         typedef typename itype_t<Ty>::vtype VT;
 
-        const int grid_x = floor(x);
+        const int grid_x = sycl::floor(x);
         const WT off_x   = x - grid_x;
 
-        const int grid_y = floor(y);
+        const int grid_y = sycl::floor(y);
         const WT off_y   = y - grid_y;
 
         const int x_lim    = iInfo.dims[xdim];
diff --git a/src/backend/oneapi/kernel/lookup.hpp b/src/backend/oneapi/kernel/lookup.hpp
new file mode 100644
index 0000000000..8baf14ad21
--- /dev/null
+++ b/src/backend/oneapi/kernel/lookup.hpp
@@ -0,0 +1,133 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+int trimIndex(int idx, const int len) {
+    int ret_val = idx;
+    if (ret_val < 0) {
+        int offset = (abs(ret_val) - 1) % len;
+        ret_val    = offset;
+    } else if (ret_val >= len) {
+        int offset = abs(ret_val) % len;
+        ret_val    = len - offset - 1;
+    }
+    return ret_val;
+}
+
+template<typename in_t, typename idx_t>
+class lookupNDCreateKernel {
+   public:
+    lookupNDCreateKernel(write_accessor<in_t> out, KParam oInfo,
+                         read_accessor<in_t> in, KParam iInfo,
+                         read_accessor<idx_t> indices, KParam idxInfo,
+                         int nBBS0, int nBBS1, const int DIM)
+        : out_(out)
+        , oInfo_(oInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , indices_(indices)
+        , idxInfo_(idxInfo)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1)
+        , DIM_(DIM) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        int lx = it.get_local_id(0);
+        int ly = it.get_local_id(1);
+
+        int gz = g.get_group_id(0) / nBBS0_;
+        int gw = g.get_group_id(1) / nBBS1_;
+
+        int gx = g.get_local_range(0) * (g.get_group_id(0) - gz * nBBS0_) + lx;
+        int gy = g.get_local_range(1) * (g.get_group_id(1) - gw * nBBS1_) + ly;
+
+        const idx_t *idxPtr = indices_.get_pointer();
+
+        int i = iInfo_.strides[0] *
+                (DIM_ == 0 ? trimIndex((int)idxPtr[gx], iInfo_.dims[0]) : gx);
+        int j = iInfo_.strides[1] *
+                (DIM_ == 1 ? trimIndex((int)idxPtr[gy], iInfo_.dims[1]) : gy);
+        int k = iInfo_.strides[2] *
+                (DIM_ == 2 ? trimIndex((int)idxPtr[gz], iInfo_.dims[2]) : gz);
+        int l = iInfo_.strides[3] *
+                (DIM_ == 3 ? trimIndex((int)idxPtr[gw], iInfo_.dims[3]) : gw);
+
+        const in_t *inPtr = in_.get_pointer() + (i + j + k + l) + iInfo_.offset;
+        in_t *outPtr =
+            out_.get_pointer() +
+            (gx * oInfo_.strides[0] + gy * oInfo_.strides[1] +
+             gz * oInfo_.strides[2] + gw * oInfo_.strides[3] + oInfo_.offset);
+
+        if (gx < oInfo_.dims[0] && gy < oInfo_.dims[1] && gz < oInfo_.dims[2] &&
+            gw < oInfo_.dims[3]) {
+            outPtr[0] = inPtr[0];
+        }
+    }
+
+   private:
+    write_accessor<in_t> out_;
+    KParam oInfo_;
+    read_accessor<in_t> in_;
+    KParam iInfo_;
+    read_accessor<idx_t> indices_;
+    KParam idxInfo_;
+    int nBBS0_;
+    int nBBS1_;
+    const int DIM_;
+};
+
+template<typename in_t, typename idx_t>
+void lookup(Param<in_t> out, const Param<in_t> in, const Param<idx_t> indices,
+            const unsigned dim) {
+    constexpr int THREADS_X = 32;
+    constexpr int THREADS_Y = 8;
+
+    auto local = sycl::range(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(out.info.dims[0], THREADS_X);
+    int blk_y = divup(out.info.dims[1], THREADS_Y);
+
+    auto global = sycl::range(blk_x * out.info.dims[2] * THREADS_X,
+                              blk_y * out.info.dims[3] * THREADS_Y);
+
+    getQueue().submit([&](auto &h) {
+        write_accessor<in_t> d_out{*out.data, h};
+        read_accessor<in_t> d_in{*in.data, h};
+        read_accessor<idx_t> d_indices{*indices.data, h};
+        h.parallel_for(sycl::nd_range{global, local},
+                       lookupNDCreateKernel<in_t, idx_t>(
+                           d_out, out.info, d_in, in.info, d_indices,
+                           indices.info, blk_x, blk_y, dim));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/meanshift.hpp b/src/backend/oneapi/kernel/meanshift.hpp
new file mode 100644
index 0000000000..8dfb96a3b7
--- /dev/null
+++ b/src/backend/oneapi/kernel/meanshift.hpp
@@ -0,0 +1,229 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+
+#include <algorithm>
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+inline int convert_int_rtz(float number) { return ((int)(number)); }
+
+template<typename T, typename AccType, const int MAX_CHANNELS>
+class meanshiftCreateKernel {
+   public:
+    meanshiftCreateKernel(write_accessor<T> d_dst, KParam oInfo,
+                          read_accessor<T> d_src, KParam iInfo, int radius,
+                          float cvar, unsigned numIters, int nBBS0, int nBBS1)
+        : d_dst_(d_dst)
+        , oInfo_(oInfo)
+        , d_src_(d_src)
+        , iInfo_(iInfo)
+        , radius_(radius)
+        , cvar_(cvar)
+        , numIters_(numIters)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        unsigned b2 = g.get_group_id(0) / nBBS0_;
+        unsigned b3 = g.get_group_id(1) / nBBS1_;
+        const int gx =
+            g.get_local_range(0) * (g.get_group_id(0) - b2 * nBBS0_) +
+            it.get_local_id(0);
+        const int gy =
+            g.get_local_range(1) * (g.get_group_id(1) - b3 * nBBS1_) +
+            it.get_local_id(1);
+
+        if (gx < iInfo_.dims[0] && gy < iInfo_.dims[1]) {
+            const T* iptr =
+                d_src_.get_pointer() + (b2 * iInfo_.strides[2] +
+                                        b3 * iInfo_.strides[3] + iInfo_.offset);
+            T* optr = d_dst_.get_pointer() +
+                      (b2 * oInfo_.strides[2] + b3 * oInfo_.strides[3]);
+
+            int meanPosI = gx;
+            int meanPosJ = gy;
+
+            T currentCenterColors[MAX_CHANNELS];
+            T tempColors[MAX_CHANNELS];
+
+            AccType currentMeanColors[MAX_CHANNELS];
+
+#pragma unroll
+            for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                currentCenterColors[ch] =
+                    iptr[gx * iInfo_.strides[0] + gy * iInfo_.strides[1] +
+                         ch * iInfo_.strides[2]];
+
+            const int dim0LenLmt = iInfo_.dims[0] - 1;
+            const int dim1LenLmt = iInfo_.dims[1] - 1;
+
+            // scope of meanshift iterationd begin
+            for (uint it = 0; it < numIters_; ++it) {
+                int oldMeanPosJ = meanPosJ;
+                int oldMeanPosI = meanPosI;
+                unsigned count  = 0;
+
+                int shift_x = 0;
+                int shift_y = 0;
+
+                for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                    currentMeanColors[ch] = 0;
+
+                for (int wj = -radius_; wj <= radius_; ++wj) {
+                    int hit_count = 0;
+                    int tj        = meanPosJ + wj;
+
+                    if (tj < 0 || tj > dim1LenLmt) continue;
+
+                    for (int wi = -radius_; wi <= radius_; ++wi) {
+                        int ti = meanPosI + wi;
+
+                        if (ti < 0 || ti > dim0LenLmt) continue;
+
+                        AccType norm = 0;
+#pragma unroll
+                        for (int ch = 0; ch < MAX_CHANNELS; ++ch) {
+                            unsigned idx = ti * iInfo_.strides[0] +
+                                           tj * iInfo_.strides[1] +
+                                           ch * iInfo_.strides[2];
+                            tempColors[ch] = iptr[idx];
+                            AccType diff   = (AccType)currentCenterColors[ch] -
+                                           (AccType)tempColors[ch];
+                            norm += (diff * diff);
+                        }
+
+                        if (norm <= cvar_) {
+#pragma unroll
+                            for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                                currentMeanColors[ch] +=
+                                    (AccType)tempColors[ch];
+
+                            shift_x += ti;
+                            ++hit_count;
+                        }
+                    }
+                    count += hit_count;
+                    shift_y += tj * hit_count;
+                }
+
+                if (count == 0) break;
+
+                const AccType fcount = 1 / (AccType)count;
+
+                meanPosI = convert_int_rtz(shift_x * fcount);
+                meanPosJ = convert_int_rtz(shift_y * fcount);
+
+#pragma unroll
+                for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                    currentMeanColors[ch] =
+                        convert_int_rtz(currentMeanColors[ch] * fcount);
+
+                AccType norm = 0;
+#pragma unroll
+                for (int ch = 0; ch < MAX_CHANNELS; ++ch) {
+                    AccType diff = (AccType)currentCenterColors[ch] -
+                                   currentMeanColors[ch];
+                    norm += (diff * diff);
+                }
+
+                bool stop =
+                    (meanPosJ == oldMeanPosJ && meanPosI == oldMeanPosI) ||
+                    ((abs(oldMeanPosJ - meanPosJ) +
+                      abs(oldMeanPosI - meanPosI)) +
+                     norm) <= 1;
+
+#pragma unroll
+                for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                    currentCenterColors[ch] = (T)(currentMeanColors[ch]);
+
+                if (stop) break;
+            }  // scope of meanshift iterations end
+
+#pragma unroll
+            for (int ch = 0; ch < MAX_CHANNELS; ++ch)
+                optr[gx * oInfo_.strides[0] + gy * oInfo_.strides[1] +
+                     ch * oInfo_.strides[2]] = currentCenterColors[ch];
+        }
+    }
+
+   private:
+    write_accessor<T> d_dst_;
+    KParam oInfo_;
+    read_accessor<T> d_src_;
+    KParam iInfo_;
+    int radius_;
+    float cvar_;
+    unsigned numIters_;
+    int nBBS0_;
+    int nBBS1_;
+};
+
+template<typename T>
+void meanshift(Param<T> out, const Param<T> in, const float spatialSigma,
+               const float chromaticSigma, const uint numIters,
+               const bool is_color) {
+    using AccType = typename std::conditional<std::is_same<T, double>::value,
+                                              double, float>::type;
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
+
+    const int MAX_CHANNELS = (is_color ? 3 : 1);
+
+    auto local = sycl::range(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(in.info.dims[0], THREADS_X);
+    int blk_y = divup(in.info.dims[1], THREADS_Y);
+
+    const int bCount = (is_color ? 1 : in.info.dims[2]);
+
+    auto global = sycl::range(bCount * blk_x * THREADS_X,
+                              in.info.dims[3] * blk_y * THREADS_Y);
+
+    // clamp spatial and chromatic sigma's
+    int radius = std::max((int)(spatialSigma * 1.5f), 1);
+
+    const float cvar = chromaticSigma * chromaticSigma;
+
+    getQueue().submit([&](auto& h) {
+        read_accessor<T> d_src{*in.data, h};
+        write_accessor<T> d_dst{*out.data, h};
+        if (MAX_CHANNELS == 3) {
+            h.parallel_for(sycl::nd_range{global, local},
+                           meanshiftCreateKernel<T, AccType, 3>(
+                               d_dst, out.info, d_src, in.info, radius, cvar,
+                               numIters, blk_x, blk_y));
+        } else {
+            h.parallel_for(sycl::nd_range{global, local},
+                           meanshiftCreateKernel<T, AccType, 1>(
+                               d_dst, out.info, d_src, in.info, radius, cvar,
+                               numIters, blk_x, blk_y));
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
index 6aa6cd39c0..c39ff556b7 100644
--- a/src/backend/oneapi/kernel/reorder.hpp
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2022, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -11,9 +11,8 @@
 
 #include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
-// #include <traits.hpp>
+#include <traits.hpp>
 
 #include <string>
 #include <vector>
@@ -22,9 +21,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
-                                      sycl::access::target::local>;
 template<typename T>
 using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
 template<typename T>
@@ -47,9 +43,8 @@ class reorderCreateKernel {
         , d3_(d3)
         , blocksPerMatX_(blocksPerMatX)
         , blocksPerMatY_(blocksPerMatY) {}
-
     void operator()(sycl::nd_item<2> it) const {
-        auto g = it.get_group();
+        sycl::group g = it.get_group();
 
         const int oz = g.get_group_id(0) / blocksPerMatX_;
         const int ow = g.get_group_id(1) / blocksPerMatY_;
@@ -66,10 +61,10 @@ class reorderCreateKernel {
         const int incy = blocksPerMatY_ * g.get_local_range(1);
         const int incx = blocksPerMatX_ * g.get_local_range(0);
 
-        const int o_off    = ow * op_.strides[3] + oz * op_.strides[2];
-        const int rdims[4] = {d0_, d1_, d2_, d3_};
-        int ods[4]         = {xx, yy, oz, ow};
-        int ids[4]         = {0};
+        const int o_off   = ow * op_.strides[3] + oz * op_.strides[2];
+        const int rdims[] = {d0_, d1_, d2_, d3_};
+        int ods[]         = {xx, yy, oz, ow};
+        int ids[4]        = {0};
 
         ids[rdims[3]] = ow;
         ids[rdims[2]] = oz;
@@ -110,22 +105,25 @@ void reorder(Param<T> out, const Param<T> in, const dim_t* rdims) {
     constexpr int TILEX = 512;
     constexpr int TILEY = 32;
 
-    auto local = sycl::range{TX, TY};
+    auto local = sycl::range(TX, TY);
 
     int blocksPerMatX = divup(out.info.dims[0], TILEX);
     int blocksPerMatY = divup(out.info.dims[1], TILEY);
-    auto global       = sycl::range{local[0] * blocksPerMatX * out.info.dims[2],
-                              local[1] * blocksPerMatY * out.info.dims[3]};
-
-    getQueue().submit([&](sycl::handler& h) {
-        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
-        sycl::accessor inAcc{*in.data, h, sycl::read_only};
-
-        h.parallel_for(sycl::nd_range{global, local},
-                       reorderCreateKernel<T>(
-                           outAcc, inAcc, out.info, in.info, rdims[0], rdims[1],
-                           rdims[2], rdims[3], blocksPerMatX, blocksPerMatY));
+    auto global       = sycl::range(local[0] * blocksPerMatX * out.info.dims[2],
+                                    local[1] * blocksPerMatY * out.info.dims[3]);
+
+    getQueue().submit([&](auto& h) {
+        read_accessor<T> d_in{*in.data, h};
+        write_accessor<T> d_out{*out.data, h};
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            reorderCreateKernel<T>(
+                d_out, d_in, out.info, in.info, static_cast<int>(rdims[0]),
+                static_cast<int>(rdims[1]), static_cast<int>(rdims[2]),
+                static_cast<int>(rdims[3]), blocksPerMatX, blocksPerMatY));
     });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
 }  // namespace oneapi
diff --git a/src/backend/oneapi/kernel/resize.hpp b/src/backend/oneapi/kernel/resize.hpp
new file mode 100644
index 0000000000..b44d878818
--- /dev/null
+++ b/src/backend/oneapi/kernel/resize.hpp
@@ -0,0 +1,230 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename AT, typename BT>
+BT mul(AT a, BT b) {
+    return a * b;
+}
+template<typename AT>
+std::complex<double> mul(AT a, std::complex<double> b) {
+    return std::complex<double>(a * b.real(), a * b.imag());
+}
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                          double, float>::type;
+
+template<typename T>
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
+                                          wtype_t<T>>::type;
+
+////////////////////////////////////////////////////////////////////////////////////
+// nearest-neighbor resampling
+template<typename T>
+void resize_n_(T* d_out, const KParam out, const T* d_in, const KParam in,
+               const int blockIdx_x, const int blockIdx_y, const float xf,
+               const float yf, sycl::nd_item<2>& it) {
+    sycl::group g = it.get_group();
+    int const ox  = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+    int const oy  = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+    // int ix = convert_int_rtp(ox * xf);
+    // int iy = convert_int_rtp(oy * yf);
+    int ix = sycl::round(ox * xf);
+    int iy = sycl::round(oy * yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    d_out[ox + oy * out.strides[1]] = d_in[ix + iy * in.strides[1]];
+}
+
+////////////////////////////////////////////////////////////////////////////////////
+// bilinear resampling
+template<typename T, typename VT>
+void resize_b_(T* d_out, const KParam out, const T* d_in, const KParam in,
+               const int blockIdx_x, const int blockIdx_y, const float xf_,
+               const float yf_, sycl::nd_item<2>& it) {
+    sycl::group g = it.get_group();
+
+    int const ox = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+    int const oy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+    float xf = ox * xf_;
+    float yf = oy * yf_;
+
+    int ix = sycl::floor(xf);
+
+    int iy = sycl::floor(yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    float b = xf - ix;
+    float a = yf - iy;
+
+    const int ix2 = (ix + 1) < in.dims[0] ? (ix + 1) : ix;
+    const int iy2 = (iy + 1) < in.dims[1] ? (iy + 1) : iy;
+
+    const VT p1 = d_in[ix + in.strides[1] * iy];
+    const VT p2 = d_in[ix + in.strides[1] * iy2];
+    const VT p3 = d_in[ix2 + in.strides[1] * iy];
+    const VT p4 = d_in[ix2 + in.strides[1] * iy2];
+
+    d_out[ox + oy * out.strides[1]] =
+        mul(((1.0f - a) * (1.0f - b)), p1) + mul(((a) * (1.0f - b)), p2) +
+        mul(((1.0f - a) * (b)), p3) + mul(((a) * (b)), p4);
+}
+
+////////////////////////////////////////////////////////////////////////////////////
+// lower resampling
+template<typename T>
+void resize_l_(T* d_out, const KParam out, const T* d_in, const KParam in,
+               const int blockIdx_x, const int blockIdx_y, const float xf,
+               const float yf, sycl::nd_item<2>& it) {
+    sycl::group g = it.get_group();
+
+    int const ox = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+    int const oy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+    int ix = (ox * xf);
+    int iy = (oy * yf);
+
+    if (ox >= out.dims[0] || oy >= out.dims[1]) { return; }
+    if (ix >= in.dims[0]) { ix = in.dims[0] - 1; }
+    if (iy >= in.dims[1]) { iy = in.dims[1] - 1; }
+
+    d_out[ox + oy * out.strides[1]] = d_in[ix + iy * in.strides[1]];
+}
+
+template<typename T, int method>
+class resizeCreateKernel {
+   public:
+    resizeCreateKernel(write_accessor<T> d_out, const KParam out,
+                       read_accessor<T> d_in, const KParam in, const int b0,
+                       const int b1, const float xf, const float yf)
+        : d_out_(d_out)
+        , out_(out)
+        , d_in_(d_in)
+        , in_(in)
+        , b0_(b0)
+        , b1_(b1)
+        , xf_(xf)
+        , yf_(yf) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        int bIdx = g.get_group_id(0) / b0_;
+        int bIdy = g.get_group_id(1) / b1_;
+        // batch adjustment
+        int i_off = bIdy * in_.strides[3] + bIdx * in_.strides[2] + in_.offset;
+        int o_off = bIdy * out_.strides[3] + bIdx * out_.strides[2];
+        int blockIdx_x = g.get_group_id(0) - bIdx * b0_;
+        int blockIdx_y = g.get_group_id(1) - bIdy * b1_;
+
+        switch (method) {
+            case AF_INTERP_NEAREST:
+                resize_n_<T>(d_out_.get_pointer() + o_off, out_,
+                             d_in_.get_pointer() + i_off, in_, blockIdx_x,
+                             blockIdx_y, xf_, yf_, it);
+                break;
+            case AF_INTERP_BILINEAR:
+                resize_b_<T, vtype_t<T>>(d_out_.get_pointer() + o_off, out_,
+                                         d_in_.get_pointer() + i_off, in_,
+                                         blockIdx_x, blockIdx_y, xf_, yf_, it);
+                break;
+            case AF_INTERP_LOWER:
+                resize_l_<T>(d_out_.get_pointer() + o_off, out_,
+                             d_in_.get_pointer() + i_off, in_, blockIdx_x,
+                             blockIdx_y, xf_, yf_, it);
+                break;
+        }
+    }
+
+   private:
+    write_accessor<T> d_out_;
+    const KParam out_;
+    read_accessor<T> d_in_;
+    const KParam in_;
+    const int b0_;
+    const int b1_;
+    const float xf_;
+    const float yf_;
+};
+
+template<typename T>
+void resize(Param<T> out, const Param<T> in, const af_interp_type method) {
+    constexpr int RESIZE_TX = 16;
+    constexpr int RESIZE_TY = 16;
+
+    auto local = sycl::range(RESIZE_TX, RESIZE_TY);
+
+    int blocksPerMatX = divup(out.info.dims[0], local[0]);
+    int blocksPerMatY = divup(out.info.dims[1], local[1]);
+    auto global       = sycl::range(local[0] * blocksPerMatX * in.info.dims[2],
+                                    local[1] * blocksPerMatY * in.info.dims[3]);
+
+    double xd = (double)in.info.dims[0] / (double)out.info.dims[0];
+    double yd = (double)in.info.dims[1] / (double)out.info.dims[1];
+
+    float xf = (float)xd, yf = (float)yd;
+
+    getQueue().submit([&](auto& h) {
+        read_accessor<T> d_in{*in.data, h};
+        write_accessor<T> d_out{*out.data, h};
+        switch (method) {
+            case AF_INTERP_NEAREST:
+                h.parallel_for(sycl::nd_range{global, local},
+                               resizeCreateKernel<T, AF_INTERP_NEAREST>(
+                                   d_out, out.info, d_in, in.info,
+                                   blocksPerMatX, blocksPerMatY, xf, yf));
+                break;
+            case AF_INTERP_BILINEAR:
+                h.parallel_for(sycl::nd_range{global, local},
+                               resizeCreateKernel<T, AF_INTERP_BILINEAR>(
+                                   d_out, out.info, d_in, in.info,
+                                   blocksPerMatX, blocksPerMatY, xf, yf));
+                break;
+            case AF_INTERP_LOWER:
+                h.parallel_for(sycl::nd_range{global, local},
+                               resizeCreateKernel<T, AF_INTERP_LOWER>(
+                                   d_out, out.info, d_in, in.info,
+                                   blocksPerMatX, blocksPerMatY, xf, yf));
+                break;
+            default: break;
+        }
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/rotate.hpp b/src/backend/oneapi/kernel/rotate.hpp
new file mode 100644
index 0000000000..61d736763a
--- /dev/null
+++ b/src/backend/oneapi/kernel/rotate.hpp
@@ -0,0 +1,217 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/interp.hpp>
+#include <math.hpp>
+#include <traits.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+typedef struct {
+    float tmat[6];
+} tmat_t;
+
+template<typename T>
+using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                          double, float>::type;
+
+template<typename T>
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
+                                          wtype_t<T>>::type;
+
+template<typename T, typename InterpInTy, typename InterpPosTy,
+         int INTERP_ORDER>
+class rotateCreateKernel {
+   public:
+    rotateCreateKernel(write_accessor<T> d_out, const KParam out,
+                       read_accessor<T> d_in, const KParam in, const tmat_t t,
+                       const int nimages, const int batches,
+                       const int blocksXPerImage, const int blocksYPerImage,
+                       af::interpType method)
+        : d_out_(d_out)
+        , out_(out)
+        , d_in_(d_in)
+        , in_(in)
+        , t_(t)
+        , nimages_(nimages)
+        , batches_(batches)
+        , blocksXPerImage_(blocksXPerImage)
+        , blocksYPerImage_(blocksYPerImage)
+        , method_(method)
+        , INTERP_ORDER_(INTERP_ORDER) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        // Compute which image set
+        const int setId      = g.get_group_id(0) / blocksXPerImage_;
+        const int blockIdx_x = g.get_group_id(0) - setId * blocksXPerImage_;
+
+        const int batch      = g.get_group_id(1) / blocksYPerImage_;
+        const int blockIdx_y = g.get_group_id(1) - batch * blocksYPerImage_;
+
+        // Get thread indices
+        const int xido = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+        const int yido = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+        const int limages =
+            std::min((int)out_.dims[2] - setId * nimages_, nimages_);
+
+        if (xido >= out_.dims[0] || yido >= out_.dims[1]) return;
+
+        InterpPosTy xidi = xido * t_.tmat[0] + yido * t_.tmat[1] + t_.tmat[2];
+        InterpPosTy yidi = xido * t_.tmat[3] + yido * t_.tmat[4] + t_.tmat[5];
+
+        int outoff = out_.offset + setId * nimages_ * out_.strides[2] +
+                     batch * out_.strides[3];
+        int inoff = in_.offset + setId * nimages_ * in_.strides[2] +
+                    batch * in_.strides[3];
+
+        const int loco = outoff + (yido * out_.strides[1] + xido);
+
+        InterpInTy zero = (InterpInTy)0;
+        if (INTERP_ORDER_ > 1) {
+            // Special conditions to deal with boundaries for bilinear and
+            // bicubic
+            // FIXME: Ideally this condition should be removed or be present for
+            // all  methods But tests are expecting a different behavior for
+            // bilinear and nearest
+            if (xidi < (InterpPosTy)-0.0001 || yidi < (InterpPosTy)-0.0001 ||
+                in_.dims[0] <= xidi || in_.dims[1] <= yidi) {
+                for (int i = 0; i < nimages_; i++) {
+                    d_out_[loco + i * out_.strides[2]] = zero;
+                }
+                return;
+            }
+        }
+
+        // FIXME: Nearest and lower do not do clamping, but other methods do
+        // Make it consistent
+        const bool doclamp = INTERP_ORDER_ != 1;
+        Interp2<T, InterpPosTy, 1> interp2;  // INTERP_ORDER> interp2;
+        interp2(d_out_, out_, loco, d_in_, in_, inoff, xidi, yidi, 0, 1,
+                method_, limages, doclamp, 2);
+    }
+
+   private:
+    write_accessor<T> d_out_;
+    const KParam out_;
+    read_accessor<T> d_in_;
+    const KParam in_;
+    const tmat_t t_;
+    const int nimages_;
+    const int batches_;
+    const int blocksXPerImage_;
+    const int blocksYPerImage_;
+    af::interpType method_;
+    const int INTERP_ORDER_;
+};
+
+template<typename T>
+void rotate(Param<T> out, const Param<T> in, const float theta,
+            af_interp_type method, int order) {
+    using std::string;
+
+    using BT = typename dtype_traits<T>::base_type;
+
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+
+    // Used for batching images
+    constexpr int TI = 4;
+    constexpr bool isComplex =
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
+
+    const float c = cos(-theta), s = sin(-theta);
+    float tx, ty;
+    {
+        const float nx = 0.5 * (in.info.dims[0] - 1);
+        const float ny = 0.5 * (in.info.dims[1] - 1);
+        const float mx = 0.5 * (out.info.dims[0] - 1);
+        const float my = 0.5 * (out.info.dims[1] - 1);
+        const float sx = (mx * c + my * -s);
+        const float sy = (mx * s + my * c);
+        tx             = -(sx - nx);
+        ty             = -(sy - ny);
+    }
+
+    // Rounding error. Anything more than 3 decimal points wont make a diff
+    tmat_t t;
+    t.tmat[0] = round(c * 1000) / 1000.0f;
+    t.tmat[1] = round(-s * 1000) / 1000.0f;
+    t.tmat[2] = round(tx * 1000) / 1000.0f;
+    t.tmat[3] = round(s * 1000) / 1000.0f;
+    t.tmat[4] = round(c * 1000) / 1000.0f;
+    t.tmat[5] = round(ty * 1000) / 1000.0f;
+
+    auto local = sycl::range(TX, TY);
+
+    int nimages               = in.info.dims[2];
+    int nbatches              = in.info.dims[3];
+    int global_x              = local[0] * divup(out.info.dims[0], local[0]);
+    int global_y              = local[1] * divup(out.info.dims[1], local[1]);
+    const int blocksXPerImage = global_x / local[0];
+    const int blocksYPerImage = global_y / local[1];
+
+    if (nimages > TI) {
+        int tile_images = divup(nimages, TI);
+        nimages         = TI;
+        global_x        = global_x * tile_images;
+    }
+    global_y *= nbatches;
+
+    auto global = sycl::range(global_x, global_y);
+
+    getQueue().submit([&](auto &h) {
+        read_accessor<T> d_in{*in.data, h};
+        write_accessor<T> d_out{*out.data, h};
+        switch (order) {
+            case 1:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    rotateCreateKernel<T, T, wtype_t<BT>, 1>(
+                        d_out, out.info, d_in, in.info, t, nimages, nbatches,
+                        blocksXPerImage, blocksYPerImage, method));
+                break;
+            case 2:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    rotateCreateKernel<T, T, wtype_t<BT>, 2>(
+                        d_out, out.info, d_in, in.info, t, nimages, nbatches,
+                        blocksXPerImage, blocksYPerImage, method));
+                break;
+            case 3:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    rotateCreateKernel<T, T, wtype_t<BT>, 3>(
+                        d_out, out.info, d_in, in.info, t, nimages, nbatches,
+                        blocksXPerImage, blocksYPerImage, method));
+                break;
+            default: throw std::string("invalid interpolation order");
+        }
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/select.hpp b/src/backend/oneapi/kernel/select.hpp
new file mode 100644
index 0000000000..618cea3437
--- /dev/null
+++ b/src/backend/oneapi/kernel/select.hpp
@@ -0,0 +1,258 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <math.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+constexpr uint DIMX  = 32;
+constexpr uint DIMY  = 8;
+constexpr int REPEAT = 64;
+
+int getOffset(const dim_t *dims, const dim_t *strides, const dim_t *refdims,
+              int ids[4]) {
+    int off = 0;
+    off += ids[3] * (dims[3] == refdims[3]) * strides[3];
+    off += ids[2] * (dims[2] == refdims[2]) * strides[2];
+    off += ids[1] * (dims[1] == refdims[1]) * strides[1];
+    return off;
+}
+
+template<typename T>
+class selectKernelCreateKernel {
+   public:
+    selectKernelCreateKernel(write_accessor<T> optr, KParam oinfo,
+                             read_accessor<char> cptr_, KParam cinfo,
+                             read_accessor<T> aptr_, KParam ainfo,
+                             read_accessor<T> bptr_, KParam binfo, int groups_0,
+                             int groups_1, const bool is_same)
+        : optr_(optr)
+        , oinfo_(oinfo)
+        , cptr__(cptr_)
+        , cinfo_(cinfo)
+        , aptr__(aptr_)
+        , ainfo_(ainfo)
+        , bptr__(bptr_)
+        , binfo_(binfo)
+        , groups_0_(groups_0)
+        , groups_1_(groups_1)
+        , is_same_(is_same) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        char *cptr = cptr__.get_pointer() + cinfo_.offset;
+        T *aptr    = aptr__.get_pointer() + ainfo_.offset;
+        T *bptr    = bptr__.get_pointer() + binfo_.offset;
+
+        const int idz = g.get_group_id(0) / groups_0_;
+        const int idw = g.get_group_id(1) / groups_1_;
+
+        const int group_id_0 = g.get_group_id(0) - idz * groups_0_;
+        const int group_id_1 = g.get_group_id(1) - idw * groups_1_;
+
+        const int idx0 = group_id_0 * g.get_local_range(0) + it.get_local_id(0);
+        const int idy  = group_id_1 * g.get_local_range(1) + it.get_local_id(1);
+
+        const int off = idw * oinfo_.strides[3] + idz * oinfo_.strides[2] +
+                        idy * oinfo_.strides[1];
+
+        const bool valid = (idw < oinfo_.dims[3] && idz < oinfo_.dims[2] &&
+                            idy < oinfo_.dims[1]);
+
+        int ids[] = {idx0, idy, idz, idw};
+
+        T *optr_pointer = optr_.get_pointer();
+        optr_pointer += off;
+        aptr += getOffset(ainfo_.dims, ainfo_.strides, oinfo_.dims, ids);
+        bptr += getOffset(binfo_.dims, binfo_.strides, oinfo_.dims, ids);
+        cptr += getOffset(cinfo_.dims, cinfo_.strides, oinfo_.dims, ids);
+
+        if (is_same_) {
+            for (int idx = idx0; idx < oinfo_.dims[0];
+                 idx += g.get_local_range(0) * groups_0_) {
+                if (valid)
+                    optr_pointer[idx] = (cptr[idx]) ? aptr[idx] : bptr[idx];
+            }
+        } else {
+            bool csame = cinfo_.dims[0] == oinfo_.dims[0];
+            bool asame = ainfo_.dims[0] == oinfo_.dims[0];
+            bool bsame = binfo_.dims[0] == oinfo_.dims[0];
+            for (int idx = idx0; idx < oinfo_.dims[0];
+                 idx += g.get_local_range(0) * groups_0_) {
+                if (valid)
+                    optr_pointer[idx] = (cptr[csame * idx]) ? aptr[asame * idx]
+                                                            : bptr[bsame * idx];
+            }
+        }
+    }
+
+   private:
+    write_accessor<T> optr_;
+    KParam oinfo_;
+    read_accessor<char> cptr__;
+    KParam cinfo_;
+    read_accessor<T> aptr__;
+    KParam ainfo_;
+    read_accessor<T> bptr__;
+    KParam binfo_;
+    int groups_0_;
+    int groups_1_;
+    const bool is_same_;
+};
+
+template<typename T>
+void selectLauncher(Param<T> out, Param<char> cond, Param<T> a, Param<T> b,
+                    const int ndims, const bool is_same) {
+    int threads[] = {DIMX, DIMY};
+
+    if (ndims == 1) {
+        threads[0] *= threads[1];
+        threads[1] = 1;
+    }
+
+    auto local = sycl::range(threads[0], threads[1]);
+
+    int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
+    int groups_1 = divup(out.info.dims[1], local[1]);
+
+    auto global = sycl::range(groups_0 * out.info.dims[2] * local[0],
+                              groups_1 * out.info.dims[3] * local[1]);
+
+    getQueue().submit([&](auto &h) {
+        write_accessor<T> d_out{*out.data, h};
+        read_accessor<char> d_cond{*cond.data, h};
+        read_accessor<T> d_a{*a.data, h};
+        read_accessor<T> d_b{*b.data, h};
+        h.parallel_for(sycl::nd_range{global, local},
+                       selectKernelCreateKernel<T>(
+                           d_out, out.info, d_cond, cond.info, d_a, a.info, d_b,
+                           b.info, groups_0, groups_1, is_same));
+    });
+}
+
+template<typename T>
+class selectScalarCreateKernel {
+   public:
+    selectScalarCreateKernel(write_accessor<T> optr, KParam oinfo,
+                             read_accessor<char> cptr_, KParam cinfo,
+                             read_accessor<T> aptr_, KParam ainfo, T b,
+                             int groups_0, int groups_1, const bool flip)
+        : optr_(optr)
+        , oinfo_(oinfo)
+        , cptr__(cptr_)
+        , cinfo_(cinfo)
+        , aptr__(aptr_)
+        , ainfo_(ainfo)
+        , b_(b)
+        , groups_0_(groups_0)
+        , groups_1_(groups_1)
+        , flip_(flip) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        char *cptr = cptr__.get_pointer() + cinfo_.offset;
+        T *aptr    = aptr__.get_pointer() + ainfo_.offset;
+
+        const int idz = g.get_group_id(0) / groups_0_;
+        const int idw = g.get_group_id(1) / groups_1_;
+
+        const int group_id_0 = g.get_group_id(0) - idz * groups_0_;
+        const int group_id_1 = g.get_group_id(1) - idw * groups_1_;
+
+        const int idx0 = group_id_0 * g.get_local_range(0) + it.get_local_id(0);
+        const int idy  = group_id_1 * g.get_local_range(1) + it.get_local_id(1);
+
+        const int off = idw * oinfo_.strides[3] + idz * oinfo_.strides[2] +
+                        idy * oinfo_.strides[1];
+
+        int ids[] = {idx0, idy, idz, idw};
+        optr_.get_pointer() += off;
+        aptr += getOffset(ainfo_.dims, ainfo_.strides, oinfo_.dims, ids);
+        cptr += getOffset(cinfo_.dims, cinfo_.strides, oinfo_.dims, ids);
+
+        if (idw >= oinfo_.dims[3] || idz >= oinfo_.dims[2] ||
+            idy >= oinfo_.dims[1]) {
+            return;
+        }
+
+        for (int idx = idx0; idx < oinfo_.dims[0];
+             idx += g.get_local_range(0) * groups_0_) {
+            optr_.get_pointer()[idx] = (cptr[idx] ^ flip_) ? aptr[idx] : b_;
+        }
+    }
+
+   private:
+    write_accessor<T> optr_;
+    KParam oinfo_;
+    read_accessor<char> cptr__;
+    KParam cinfo_;
+    read_accessor<T> aptr__;
+    KParam ainfo_;
+    T b_;
+    int groups_0_;
+    int groups_1_;
+    const bool flip_;
+};
+
+template<typename T>
+void select(Param<T> out, Param<char> cond, Param<T> a, Param<T> b, int ndims) {
+    bool is_same = true;
+    for (int i = 0; i < 4; i++) {
+        is_same &= (a.info.dims[i] == b.info.dims[i]);
+    }
+    selectLauncher<T>(out, cond, a, b, ndims, is_same);
+}
+
+template<typename T>
+void select_scalar(Param<T> out, Param<char> cond, Param<T> a, const T b,
+                   const int ndims, const bool flip) {
+    int threads[] = {DIMX, DIMY};
+
+    if (ndims == 1) {
+        threads[0] *= threads[1];
+        threads[1] = 1;
+    }
+
+    auto local = sycl::range(threads[0], threads[1]);
+
+    int groups_0 = divup(out.info.dims[0], REPEAT * local[0]);
+    int groups_1 = divup(out.info.dims[1], local[1]);
+
+    auto global = sycl::range(groups_0 * out.info.dims[2] * local[0],
+                              groups_1 * out.info.dims[3] * local[1]);
+
+    getQueue().submit([&](auto &h) {
+        write_accessor<T> d_out{*out.data, h};
+        read_accessor<char> d_cond{*cond.data, h};
+        read_accessor<T> d_a{*a.data, h};
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            selectScalarCreateKernel<T>(d_out, out.info, d_cond, cond.info, d_a,
+                                        a.info, b, groups_0, groups_1, flip));
+    });
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/tile.hpp b/src/backend/oneapi/kernel/tile.hpp
new file mode 100644
index 0000000000..24112442a9
--- /dev/null
+++ b/src/backend/oneapi/kernel/tile.hpp
@@ -0,0 +1,112 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+class tileCreateKernel {
+   public:
+    tileCreateKernel(write_accessor<T> out, read_accessor<T> in,
+                     const KParam op, const KParam ip, const int blocksPerMatX,
+                     const int blocksPerMatY)
+        : out_(out)
+        , in_(in)
+        , op_(op)
+        , ip_(ip)
+        , blocksPerMatX_(blocksPerMatX)
+        , blocksPerMatY_(blocksPerMatY) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        const int oz = g.get_group_id(0) / blocksPerMatX_;
+        const int ow = g.get_group_id(1) / blocksPerMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - oz * blocksPerMatX_;
+        const int blockIdx_y = g.get_group_id(1) - ow * blocksPerMatY_;
+
+        const int xx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+        const int yy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+        const bool valid = (xx < op_.dims[0] && yy < op_.dims[1] &&
+                            oz < op_.dims[2] && ow < op_.dims[3]);
+
+        const int iz  = oz % ip_.dims[2];
+        const int iw  = ow % ip_.dims[3];
+        const int izw = iw * ip_.strides[3] + iz * ip_.strides[2];
+        const int ozw = ow * op_.strides[3] + oz * op_.strides[2];
+
+        const int incy = blocksPerMatY_ * g.get_local_range(1);
+        const int incx = blocksPerMatX_ * g.get_local_range(0);
+
+        for (int oy = yy; oy < op_.dims[1]; oy += incy) {
+            const int iy = oy % ip_.dims[1];
+            for (int ox = xx; ox < op_.dims[0]; ox += incx) {
+                const int ix = ox % ip_.dims[0];
+
+                int iMem = izw + iy * ip_.strides[1] + ix;
+                int oMem = ozw + oy * op_.strides[1] + ox;
+
+                if (valid) out_[oMem] = in_[ip_.offset + iMem];
+            }
+        }
+    }
+
+   private:
+    write_accessor<T> out_;
+    read_accessor<T> in_;
+    const KParam op_;
+    const KParam ip_;
+    const int blocksPerMatX_;
+    const int blocksPerMatY_;
+};
+
+template<typename T>
+void tile(Param<T> out, const Param<T> in) {
+    constexpr int TX    = 32;
+    constexpr int TY    = 8;
+    constexpr int TILEX = 512;
+    constexpr int TILEY = 32;
+
+    auto local = sycl::range(TX, TY);
+
+    int blocksPerMatX = divup(out.info.dims[0], TILEX);
+    int blocksPerMatY = divup(out.info.dims[1], TILEY);
+    auto global       = sycl::range(local[0] * blocksPerMatX * out.info.dims[2],
+                                    local[1] * blocksPerMatY * out.info.dims[3]);
+
+    getQueue().submit([&](auto &h) {
+        write_accessor<T> d_out{*out.data, h};
+        read_accessor<T> d_in{*in.data, h};
+        h.parallel_for(sycl::nd_range{global, local},
+                       tileCreateKernel<T>(d_out, d_in, out.info, in.info,
+                                           blocksPerMatX, blocksPerMatY));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/transform.hpp b/src/backend/oneapi/kernel/transform.hpp
new file mode 100644
index 0000000000..b67a11c660
--- /dev/null
+++ b/src/backend/oneapi/kernel/transform.hpp
@@ -0,0 +1,307 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+// #include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+// #include <kernel/config.hpp>
+#include <kernel/interp.hpp>
+#include <math.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+using wtype_t = typename std::conditional<std::is_same<T, double>::value,
+                                          double, float>::type;
+
+template<typename T>
+using vtype_t = typename std::conditional<common::is_complex<T>::value, T,
+                                          wtype_t<T>>::type;
+
+template<bool PERSPECTIVE>
+void calc_transf_inverse(float *txo, const float *txi) {
+    if constexpr (PERSPECTIVE) {
+        txo[0] = txi[4] * txi[8] - txi[5] * txi[7];
+        txo[1] = -(txi[1] * txi[8] - txi[2] * txi[7]);
+        txo[2] = txi[1] * txi[5] - txi[2] * txi[4];
+
+        txo[3] = -(txi[3] * txi[8] - txi[5] * txi[6]);
+        txo[4] = txi[0] * txi[8] - txi[2] * txi[6];
+        txo[5] = -(txi[0] * txi[5] - txi[2] * txi[3]);
+
+        txo[6] = txi[3] * txi[7] - txi[4] * txi[6];
+        txo[7] = -(txi[0] * txi[7] - txi[1] * txi[6]);
+        txo[8] = txi[0] * txi[4] - txi[1] * txi[3];
+
+        float det = txi[0] * txo[0] + txi[1] * txo[3] + txi[2] * txo[6];
+
+        txo[0] /= det;
+        txo[1] /= det;
+        txo[2] /= det;
+        txo[3] /= det;
+        txo[4] /= det;
+        txo[5] /= det;
+        txo[6] /= det;
+        txo[7] /= det;
+        txo[8] /= det;
+    } else {
+        float det = txi[0] * txi[4] - txi[1] * txi[3];
+
+        txo[0] = txi[4] / det;
+        txo[1] = txi[3] / det;
+        txo[3] = txi[1] / det;
+        txo[4] = txi[0] / det;
+
+        txo[2] = txi[2] * -txo[0] + txi[5] * -txo[1];
+        txo[5] = txi[2] * -txo[3] + txi[5] * -txo[4];
+    }
+}
+
+template<typename T, typename InterpPosTy, bool PERSPECTIVE, int INTERP_ORDER>
+class transformCreateKernel {
+   public:
+    transformCreateKernel(write_accessor<T> d_out, const KParam out,
+                          read_accessor<T> d_in, const KParam in,
+                          read_accessor<float> c_tmat, const KParam tf,
+                          const int nImg2, const int nImg3, const int nTfs2,
+                          const int nTfs3, const int batchImg2,
+                          const int blocksXPerImage, const int blocksYPerImage,
+                          const af::interpType method, const bool INVERSE)
+        : d_out_(d_out)
+        , out_(out)
+        , d_in_(d_in)
+        , in_(in)
+        , c_tmat_(c_tmat)
+        , tf_(tf)
+        , nImg2_(nImg2)
+        , nImg3_(nImg3)
+        , nTfs2_(nTfs2)
+        , nTfs3_(nTfs3)
+        , batchImg2_(batchImg2)
+        , blocksXPerImage_(blocksXPerImage)
+        , blocksYPerImage_(blocksYPerImage)
+        , method_(method)
+        , INVERSE_(INVERSE) {}
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g = it.get_group();
+
+        // Image Ids
+        const int imgId2 = g.get_group_id(0) / blocksXPerImage_;
+        const int imgId3 = g.get_group_id(1) / blocksYPerImage_;
+
+        // Block in_ local image
+        const int blockIdx_x = g.get_group_id(0) - imgId2 * blocksXPerImage_;
+        const int blockIdx_y = g.get_group_id(1) - imgId3 * blocksYPerImage_;
+
+        // Get thread indices in_ local image
+        const int xido = blockIdx_x * g.get_local_range(0) + it.get_local_id(0);
+        const int yido = blockIdx_y * g.get_local_range(1) + it.get_local_id(1);
+
+        // Image iteration loop count for image batching
+        int limages = sycl::min(
+            sycl::max((int)(out_.dims[2] - imgId2 * nImg2_), 1), batchImg2_);
+
+        if (xido >= out_.dims[0] || yido >= out_.dims[1]) return;
+
+        // Index of transform
+        const int eTfs2 = sycl::max((nTfs2_ / nImg2_), 1);
+        const int eTfs3 = sycl::max((nTfs3_ / nImg3_), 1);
+
+        int t_idx3        = -1;  // init
+        int t_idx2        = -1;  // init
+        int t_idx2_offset = 0;
+
+        const int blockIdx_z = g.get_group_id(2);
+
+        if (nTfs3_ == 1) {
+            t_idx3 = 0;  // Always 0 as only 1 transform defined
+        } else {
+            if (nTfs3_ == nImg3_) {
+                t_idx3 =
+                    imgId3;  // One to one batch with all transforms defined
+            } else {
+                t_idx3 = blockIdx_z / eTfs2;  // Transform batched, calculate
+                t_idx2_offset = t_idx3 * nTfs2_;
+            }
+        }
+
+        if (nTfs2_ == 1) {
+            t_idx2 = 0;  // Always 0 as only 1 transform defined
+        } else {
+            if (nTfs2_ == nImg2_) {
+                t_idx2 =
+                    imgId2;  // One to one batch with all transforms defined
+            } else {
+                t_idx2 =
+                    blockIdx_z - t_idx2_offset;  // Transform batched, calculate
+            }
+        }
+
+        // Linear transform index
+        const int t_idx = t_idx2 + t_idx3 * nTfs2_;
+
+        // Global outoff
+        int outoff = out_.offset;
+        int inoff  = imgId2 * batchImg2_ * in_.strides[2] +
+                    imgId3 * in_.strides[3] + in_.offset;
+        if (nImg2_ == nTfs2_ || nImg2_ > 1) {  // One-to-One or Image on dim2
+            outoff += imgId2 * batchImg2_ * out_.strides[2];
+        } else {  // Transform batched on dim2
+            outoff += t_idx2 * out_.strides[2];
+        }
+
+        if (nImg3_ == nTfs3_ || nImg3_ > 1) {  // One-to-One or Image on dim3
+            outoff += imgId3 * out_.strides[3];
+        } else {  // Transform batched on dim2
+            outoff += t_idx3 * out_.strides[3];
+        }
+
+        // Transform is in_ global memory.
+        // Needs outoff to correct transform being processed.
+        const int transf_len = PERSPECTIVE ? 9 : 6;
+        using TMatTy =
+            typename std::conditional<PERSPECTIVE, float[9], float[6]>::type;
+        TMatTy tmat;
+        const float *tmat_ptr = c_tmat_.get_pointer() + t_idx * transf_len;
+
+        // We expect a inverse transform matrix by default
+        // If it is an forward transform, then we need its inverse
+        if (INVERSE_ == 1) {
+#pragma unroll 3
+            for (int i = 0; i < transf_len; i++) tmat[i] = tmat_ptr[i];
+        } else {
+            calc_transf_inverse<PERSPECTIVE>(tmat, tmat_ptr);
+        }
+
+        InterpPosTy xidi = xido * tmat[0] + yido * tmat[1] + tmat[2];
+        InterpPosTy yidi = xido * tmat[3] + yido * tmat[4] + tmat[5];
+
+        if constexpr (PERSPECTIVE) {
+            const InterpPosTy W = xido * tmat[6] + yido * tmat[7] + tmat[8];
+            xidi /= W;
+            yidi /= W;
+        }
+        const int loco = outoff + (yido * out_.strides[1] + xido);
+        // FIXME: Nearest and lower do not do clamping, but other methods do
+        // Make it consistent
+        const bool doclamp = INTERP_ORDER != 1;
+
+        T zero = (T)0;
+        if (xidi < (InterpPosTy)-0.0001f || yidi < (InterpPosTy)-0.0001f ||
+            in_.dims[0] <= xidi || in_.dims[1] <= yidi) {
+            for (int n = 0; n < limages; n++) {
+                d_out_[loco + n * out_.strides[2]] = zero;
+            }
+            return;
+        }
+
+        Interp2<T, InterpPosTy, INTERP_ORDER> interp2;
+        interp2(d_out_, out_, loco, d_in_, in_, inoff, xidi, yidi, 0, 1,
+                method_, limages, doclamp, 2);
+    }
+
+   private:
+    write_accessor<T> d_out_;
+    const KParam out_;
+    read_accessor<T> d_in_;
+    const KParam in_;
+    read_accessor<float> c_tmat_;
+    const KParam tf_;
+    const int nImg2_;
+    const int nImg3_;
+    const int nTfs2_;
+    const int nTfs3_;
+    const int batchImg2_;
+    const int blocksXPerImage_;
+    const int blocksYPerImage_;
+    const af::interpType method_;
+    const bool INVERSE_;
+};
+
+template<typename T>
+void transform(Param<T> out, const Param<T> in, const Param<float> tf,
+               bool isInverse, bool isPerspective, af_interp_type method,
+               int order) {
+    static int counter = 0;
+
+    using std::string;
+
+    using BT = typename dtype_traits<T>::base_type;
+
+    constexpr int TX = 16;
+    constexpr int TY = 16;
+    // Used for batching images
+    constexpr int TI = 4;
+    constexpr bool isComplex =
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
+        static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
+
+    const int nImg2 = in.info.dims[2];
+    const int nImg3 = in.info.dims[3];
+    const int nTfs2 = tf.info.dims[2];
+    const int nTfs3 = tf.info.dims[3];
+
+    auto local = sycl::range(TX, TY, 1);
+
+    int batchImg2 = 1;
+    if (nImg2 != nTfs2) batchImg2 = fmin(nImg2, TI);
+
+    const int blocksXPerImage = divup(out.info.dims[0], local[0]);
+    const int blocksYPerImage = divup(out.info.dims[1], local[1]);
+
+    int global_x = local[0] * blocksXPerImage * (nImg2 / batchImg2);
+    int global_y = local[1] * blocksYPerImage * nImg3;
+    int global_z =
+        local[2] * fmax((nTfs2 / nImg2), 1) * fmax((nTfs3 / nImg3), 1);
+
+    auto global = sycl::range(global_x, global_y, global_z);
+
+#define INVOKE(PERSPECTIVE, INTERP_ORDER)                                      \
+    h.parallel_for(                                                            \
+        sycl::nd_range{global, local},                                         \
+        transformCreateKernel<T, wtype_t<BT>, PERSPECTIVE, INTERP_ORDER>(      \
+            d_out, out.info, d_in, in.info, d_tf, tf.info, nImg2, nImg3,       \
+            nTfs2, nTfs3, batchImg2, blocksXPerImage, blocksYPerImage, method, \
+            isInverse));
+
+    getQueue().submit([&](auto &h) {
+        read_accessor<T> d_in{*in.data, h};
+        read_accessor<float> d_tf{*tf.data, h};
+        write_accessor<T> d_out{*out.data, h};
+
+        if (isPerspective == true && order == 1) INVOKE(true, 1);
+        if (isPerspective == true && order == 2) INVOKE(true, 2);
+        if (isPerspective == true && order == 3) INVOKE(true, 3);
+
+        if (isPerspective == false && order == 1) INVOKE(false, 1);
+        if (isPerspective == false && order == 2) INVOKE(false, 2);
+        if (isPerspective == false && order == 3) INVOKE(false, 3);
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/lookup.cpp b/src/backend/oneapi/lookup.cpp
index 101dc90c1d..9c87003375 100644
--- a/src/backend/oneapi/lookup.cpp
+++ b/src/backend/oneapi/lookup.cpp
@@ -12,6 +12,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/lookup.hpp>
 #include <af/dim4.hpp>
 
 using arrayfire::common::half;
@@ -21,8 +22,17 @@ namespace oneapi {
 template<typename in_t, typename idx_t>
 Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                    const unsigned dim) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<in_t> out = createEmptyArray<in_t>(af::dim4(1));
+    const dim4 &iDims = input.dims();
+
+    dim4 oDims(1);
+    for (int d = 0; d < 4; ++d) {
+        oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
+    }
+
+    Array<in_t> out = createEmptyArray<in_t>(oDims);
+
+    kernel::lookup<in_t, idx_t>(out, input, indices, dim);
+
     return out;
 }
 
diff --git a/src/backend/oneapi/meanshift.cpp b/src/backend/oneapi/meanshift.cpp
index de517e700f..1017b9074b 100644
--- a/src/backend/oneapi/meanshift.cpp
+++ b/src/backend/oneapi/meanshift.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/meanshift.hpp>
+#include <kernel/meanshift.hpp>
 #include <meanshift.hpp>
 #include <af/dim4.hpp>
 
@@ -21,13 +21,10 @@ template<typename T>
 Array<T> meanshift(const Array<T> &in, const float &spatialSigma,
                    const float &chromaticSigma, const unsigned &numIterations,
                    const bool &isColor) {
-    ONEAPI_NOT_SUPPORTED("meanshift Not supported");
-
     const dim4 &dims = in.dims();
     Array<T> out     = createEmptyArray<T>(dims);
-    // kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma,
-    // numIterations,
-    //                      isColor);
+    kernel::meanshift<T>(out, in, spatialSigma, chromaticSigma, numIterations,
+                         isColor);
     return out;
 }
 
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
old mode 100755
new mode 100644
diff --git a/src/backend/oneapi/resize.cpp b/src/backend/oneapi/resize.cpp
index 6d8d3307ab..005faf6b2b 100644
--- a/src/backend/oneapi/resize.cpp
+++ b/src/backend/oneapi/resize.cpp
@@ -9,7 +9,7 @@
 
 #include <Array.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/resize.hpp>
+#include <kernel/resize.hpp>
 #include <resize.hpp>
 #include <af/dim4.hpp>
 #include <stdexcept>
@@ -23,9 +23,7 @@ Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
     af::dim4 oDims(odim0, odim1, iDims[2], iDims[3]);
     Array<T> out = createEmptyArray<T>(oDims);
 
-    ONEAPI_NOT_SUPPORTED("resize Not supported");
-
-    // kernel::resize<T>(out, in, method);
+    kernel::resize<T>(out, in, method);
     return out;
 }
 
diff --git a/src/backend/oneapi/rotate.cpp b/src/backend/oneapi/rotate.cpp
index b5cd2fa6e3..10f1f93480 100644
--- a/src/backend/oneapi/rotate.cpp
+++ b/src/backend/oneapi/rotate.cpp
@@ -10,32 +10,30 @@
 #include <err_oneapi.hpp>
 #include <rotate.hpp>
 
-// #include <kernel/rotate.hpp>
+#include <kernel/rotate.hpp>
 
 namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> rotate(const Array<T> &in, const float theta, const af::dim4 &odims,
                 const af_interp_type method) {
-    ONEAPI_NOT_SUPPORTED("rotate Not supported");
-
     Array<T> out = createEmptyArray<T>(odims);
 
-    // switch (method) {
-    //     case AF_INTERP_NEAREST:
-    //     case AF_INTERP_LOWER:
-    //         kernel::rotate<T>(out, in, theta, method, 1);
-    //         break;
-    //     case AF_INTERP_BILINEAR:
-    //     case AF_INTERP_BILINEAR_COSINE:
-    //         kernel::rotate<T>(out, in, theta, method, 2);
-    //         break;
-    //     case AF_INTERP_BICUBIC:
-    //     case AF_INTERP_BICUBIC_SPLINE:
-    //         kernel::rotate<T>(out, in, theta, method, 3);
-    //         break;
-    //     default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
-    // }
+    switch (method) {
+        case AF_INTERP_NEAREST:
+        case AF_INTERP_LOWER:
+            kernel::rotate<T>(out, in, theta, method, 1);
+            break;
+        case AF_INTERP_BILINEAR:
+        case AF_INTERP_BILINEAR_COSINE:
+            kernel::rotate<T>(out, in, theta, method, 2);
+            break;
+        case AF_INTERP_BICUBIC:
+        case AF_INTERP_BICUBIC_SPLINE:
+            kernel::rotate<T>(out, in, theta, method, 3);
+            break;
+        default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
+    }
     return out;
 }
 
diff --git a/src/backend/oneapi/select.cpp b/src/backend/oneapi/select.cpp
index 08458b9778..8cb80c919d 100644
--- a/src/backend/oneapi/select.cpp
+++ b/src/backend/oneapi/select.cpp
@@ -13,6 +13,7 @@
 #include <common/half.hpp>
 #include <common/jit/NaryNode.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/select.hpp>
 #include <scalar.hpp>
 
 #include <nonstd/span.hpp>
@@ -31,8 +32,6 @@ namespace oneapi {
 template<typename T>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const Array<T> &b, const dim4 &odims) {
-    ONEAPI_NOT_SUPPORTED("createSelectNode Not supported");
-
     auto cond_node   = cond.getNode();
     auto a_node      = a.getNode();
     auto b_node      = b.getNode();
@@ -61,8 +60,6 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 template<typename T, bool flip>
 Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
                           const T &b_val, const dim4 &odims) {
-    ONEAPI_NOT_SUPPORTED("createSelectNode Not supported");
-
     auto cond_node   = cond.getNode();
     auto a_node      = a.getNode();
     Array<T> b       = createScalarNode<T>(odims, b_val);
@@ -94,17 +91,13 @@ Array<T> createSelectNode(const Array<char> &cond, const Array<T> &a,
 template<typename T>
 void select(Array<T> &out, const Array<char> &cond, const Array<T> &a,
             const Array<T> &b) {
-    ONEAPI_NOT_SUPPORTED("select Not supported");
-
-    // kernel::select<T>(out, cond, a, b, out.ndims());
+    kernel::select<T>(out, cond, a, b, out.ndims());
 }
 
 template<typename T, bool flip>
 void select_scalar(Array<T> &out, const Array<char> &cond, const Array<T> &a,
                    const T &b) {
-    ONEAPI_NOT_SUPPORTED("select_scalar Not supported");
-
-    // kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
+    kernel::select_scalar<T>(out, cond, a, b, out.ndims(), flip);
 }
 
 #define INSTANTIATE(T)                                                   \
diff --git a/src/backend/oneapi/tile.cpp b/src/backend/oneapi/tile.cpp
index 5f2c38c475..aca96e4ec6 100644
--- a/src/backend/oneapi/tile.cpp
+++ b/src/backend/oneapi/tile.cpp
@@ -6,8 +6,8 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
-//#include <kernel/tile.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/tile.hpp>
 #include <tile.hpp>
 
 #include <Array.hpp>
@@ -26,8 +26,7 @@ Array<T> tile(const Array<T> &in, const af::dim4 &tileDims) {
 
     Array<T> out = createEmptyArray<T>(oDims);
 
-    ONEAPI_NOT_SUPPORTED("tile Not supported");
-    // kernel::tile<T>(out, in);
+    kernel::tile<T>(out, in);
 
     return out;
 }
diff --git a/src/backend/oneapi/transform.cpp b/src/backend/oneapi/transform.cpp
index 720dfa1654..54b328f7fd 100644
--- a/src/backend/oneapi/transform.cpp
+++ b/src/backend/oneapi/transform.cpp
@@ -9,8 +9,8 @@
 
 #include <transform.hpp>
 
-// #include <kernel/transform.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/transform.hpp>
 
 namespace arrayfire {
 namespace oneapi {
@@ -19,22 +19,18 @@ template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective) {
-    ONEAPI_NOT_SUPPORTED("transform Not supported");
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            // kernel::transform<T>(out, in, tf, inverse, perspective, method,
-            // 1);
+            kernel::transform<T>(out, in, tf, inverse, perspective, method, 1);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            // kernel::transform<T>(out, in, tf, inverse, perspective, method,
-            // 2);
+            kernel::transform<T>(out, in, tf, inverse, perspective, method, 2);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            // kernel::transform<T>(out, in, tf, inverse, perspective, method,
-            // 3);
+            kernel::transform<T>(out, in, tf, inverse, perspective, method, 3);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }

From 04e27256a7be20853c62629a24dc224ccfe6f646 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 7 Jul 2022 12:13:27 -0400
Subject: [PATCH 2403/2677] Create a getQueueHandle function to unify backend
 code

The getQueueHandle function is necessary for the creation of a more uniform API
for the backend code. This allow us to combine the getStream and getQueue APIs
so that the same function can be used for both
---
 src/backend/cpu/platform.cpp          | 2 ++
 src/backend/cpu/platform.hpp          | 6 ++++++
 src/backend/cuda/platform.cpp         | 2 ++
 src/backend/cuda/platform.hpp         | 6 ++++++
 src/backend/oneapi/device_manager.hpp | 2 ++
 src/backend/oneapi/platform.cpp       | 8 ++++++++
 src/backend/oneapi/platform.hpp       | 6 ++++++
 src/backend/opencl/device_manager.hpp | 2 ++
 src/backend/opencl/platform.cpp       | 8 ++++++++
 src/backend/opencl/platform.hpp       | 6 ++++++
 10 files changed, 48 insertions(+)

diff --git a/src/backend/cpu/platform.cpp b/src/backend/cpu/platform.cpp
index dc73e76f17..a1dd7cd67b 100644
--- a/src/backend/cpu/platform.cpp
+++ b/src/backend/cpu/platform.cpp
@@ -148,6 +148,8 @@ queue& getQueue(int device) {
     return DeviceManager::getInstance().queues[device];
 }
 
+queue* getQueueHandle(int device) { return &getQueue(device); }
+
 void sync(int device) { getQueue(device).sync(); }
 
 bool& evalFlag() {
diff --git a/src/backend/cpu/platform.hpp b/src/backend/cpu/platform.hpp
index b02a1ca118..1f86639188 100644
--- a/src/backend/cpu/platform.hpp
+++ b/src/backend/cpu/platform.hpp
@@ -50,6 +50,12 @@ int setDevice(int device);
 
 queue& getQueue(int device = 0);
 
+/// Return a handle to the queue for the device.
+///
+/// \param[in] device The device of the returned queue
+/// \returns The handle to the queue
+queue* getQueueHandle(int device);
+
 void sync(int device);
 
 bool& evalFlag();
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 5ad8c27a7f..4b311f9808 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -396,6 +396,8 @@ cudaStream_t getStream(int device) {
 
 cudaStream_t getActiveStream() { return getStream(getActiveDeviceId()); }
 
+cudaStream_t getQueueHandle(int device) { return getStream(device); }
+
 size_t getDeviceMemorySize(int device) {
     return getDeviceProp(device).totalGlobalMem;
 }
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index 946c6addf1..cac1281b59 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -88,6 +88,12 @@ cudaStream_t getStream(int device);
 
 cudaStream_t getActiveStream();
 
+/// Return a handle to the stream for the device.
+///
+/// \param[in] device The device of the returned stream
+/// \returns The handle to the queue/stream
+cudaStream_t getQueueHandle(int device);
+
 size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index df14603147..36824539b2 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -81,6 +81,8 @@ class DeviceManager {
 
     friend sycl::queue& getQueue();
 
+    friend sycl::queue* getQueueHandle(int device_id);
+
     friend const sycl::device& getDevice(int id);
 
     friend size_t getDeviceMemorySize(int device);
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index c0f3a0d08e..ce3ad2e099 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -285,6 +285,14 @@ sycl::queue& getQueue() {
     return *(devMngr.mQueues[get<1>(devId)]);
 }
 
+sycl::queue* getQueueHandle(int device_id) {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return devMngr.mQueues[device_id].get();
+}
+
 const sycl::device& getDevice(int id) {
     device_id_t& devId = tlocalActiveDeviceId();
 
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index aa58ea5a7e..af579573d8 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -54,6 +54,12 @@ const sycl::context& getContext();
 
 sycl::queue& getQueue();
 
+/// Return a handle to the queue for the device.
+///
+/// \param[in] device The device of the returned queue
+/// \returns The handle to the queue
+sycl::queue* getQueueHandle(int device);
+
 const sycl::device& getDevice(int id = -1);
 
 size_t getDeviceMemorySize(int device);
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 4e06582da3..432758bd87 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -107,6 +107,8 @@ class DeviceManager {
 
     friend cl::CommandQueue& getQueue();
 
+    friend cl_command_queue getQueueHandle(int device_id);
+
     friend const cl::Device& getDevice(int id);
 
     friend const std::string& getActiveDeviceBaseBuildFlags();
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 7e94cb0bde..165eded95f 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -288,6 +288,14 @@ const Context& getContext() {
     return *(devMngr.mContexts[get<0>(devId)]);
 }
 
+cl_command_queue getQueueHandle(int device_id) {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return (*(devMngr.mQueues[device_id]))();
+}
+
 CommandQueue& getQueue() {
     device_id_t& devId = tlocalActiveDeviceId();
 
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 050e44f8c3..94ab6dff52 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -67,6 +67,12 @@ const cl::Context& getContext();
 
 cl::CommandQueue& getQueue();
 
+/// Return a cl_command_queue handle to the queue for the device.
+///
+/// \param[in] device The device of the returned queue
+/// \returns The cl_command_queue handle to the queue
+cl_command_queue getQueueHandle(int device_id);
+
 const cl::Device& getDevice(int id = -1);
 
 const std::string& getActiveDeviceBaseBuildFlags();

From e5132b0fb63f650bcb43cc36585efae4ed1ec7f5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 7 Jul 2022 12:16:47 -0400
Subject: [PATCH 2404/2677] Create a specilization for the pinnedAlloc function
 that returns void*

This function makes it easier to create a void* pointer to pinned memory. This
is necessary when you want to create type independent code that requires the use
of pinned memory.
---
 src/backend/cpu/memory.cpp    | 13 +++++++++++++
 src/backend/cuda/memory.cpp   | 13 +++++++++++++
 src/backend/oneapi/memory.cpp | 14 ++++++++++++++
 src/backend/opencl/memory.cpp | 13 +++++++++++++
 4 files changed, 53 insertions(+)

diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 440680b48d..7f0ba41965 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -121,6 +121,19 @@ INSTANTIATE(ushort)
 INSTANTIATE(short)
 INSTANTIATE(half)
 
+template<>
+void *pinnedAlloc<void>(const size_t &elements) {
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    void *ptr = memoryManager().alloc(false, 1, dims.get(), 1);
+    return ptr;
+}
+
+template<>
+void pinnedFree(void *ptr) {
+    memoryManager().unlock(ptr, false);
+}
+
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 6c86a6244a..13106fd5c1 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -132,6 +132,19 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(half)
 
+template<>
+void *pinnedAlloc<void>(const size_t &elements) {
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), 1);
+    return ptr;
+}
+
+template<>
+void pinnedFree(void *ptr) {
+    pinnedMemoryManager().unlock(ptr, false);
+}
+
 template void memFree(void *ptr);
 
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 80c589a5b0..56efa95785 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -183,6 +183,20 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(arrayfire::common::half)
 
+template<>
+void *pinnedAlloc<void>(const size_t &elements) {
+    ONEAPI_NOT_SUPPORTED("pinnedAlloc Not supported");
+
+    // // TODO: make pinnedAlloc aware of array shapes
+    // dim4 dims(elements);
+    // void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
+    return static_cast<void *>(nullptr);
+}
+template<>
+void pinnedFree(void *ptr) {
+    // pinnedMemoryManager().unlock(ptr, false);
+}
+
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 6c37d873a2..f1158dd91f 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -167,6 +167,19 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(common::half)
 
+template<>
+void *pinnedAlloc<void>(const size_t &elements) {
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), 1);
+    return ptr;
+}
+
+template<>
+void pinnedFree(void *ptr) {
+    pinnedMemoryManager().unlock(ptr, false);
+}
+
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {

From cd9686ee8e583d0e920eb929ce92cc2fbecf6d92 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 7 Jul 2022 16:00:09 -0400
Subject: [PATCH 2405/2677] Expose copy parameter to the createDeviceDataArray
 function

The copy parameter was not exposed ot the createDeviceDataArray, this
parameter determines weather we should use the pointer directly or
allocate a new array and copy data to it.
---
 src/backend/cpu/Array.cpp    |  8 +++--
 src/backend/cpu/Array.hpp    | 14 ++++++--
 src/backend/cuda/Array.cpp   | 18 +++++-----
 src/backend/cuda/Array.hpp   | 14 ++++++--
 src/backend/oneapi/Array.cpp | 64 ++++++++++++++++++------------------
 src/backend/oneapi/Array.hpp | 14 ++++++--
 src/backend/opencl/Array.cpp |  9 +++--
 src/backend/opencl/Array.hpp | 14 ++++++--
 8 files changed, 98 insertions(+), 57 deletions(-)

diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 9498fa36aa..c190c9b51d 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -217,8 +217,9 @@ Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
-    return Array<T>(dims, static_cast<T *>(data), true);
+Array<T> createDeviceDataArray(const dim4 &dims, void *data, bool copy) {
+    bool is_device = true;
+    return Array<T>(dims, static_cast<T *>(data), is_device, copy);
 }
 
 template<typename T>
@@ -330,7 +331,8 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
 #define INSTANTIATE(T)                                                        \
     template Array<T> createHostDataArray<T>(const dim4 &dims,                \
                                              const T *const data);            \
-    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
+    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data,  \
+                                               bool copy);                    \
     template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
     template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
     template Array<T> createSubArray<T>(                                      \
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 120d24b373..3c7b54c5ec 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -69,8 +69,17 @@ Array<T> createValueArray(const af::dim4 &dims, const T &value);
 template<typename T>
 Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
+/// Creates an Array<T> object from a device pointer.
+///
+/// \param[in] dims The shape of the resulting Array.
+/// \param[in] data The device pointer to the data
+/// \param[in] copy If true, memory will be allocated and the data will be
+///                 copied to the device. If false the data will be used
+///                 directly
+/// \returns The new Array<T> object based on the device pointer.
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data,
+                               bool copy = false);
 
 template<typename T>
 Array<T> createStridedArray(af::dim4 dims, af::dim4 strides, dim_t offset,
@@ -269,7 +278,8 @@ class Array {
     friend Array<T> createValueArray<T>(const af::dim4 &dims, const T &value);
     friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data,
+                                             bool copy);
     friend Array<T> createStridedArray<T>(af::dim4 dims, af::dim4 strides,
                                           dim_t offset, T *const in_data,
                                           bool is_device);
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index ea5a7e971a..2ced1ea214 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -71,10 +71,10 @@ Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
                 bool copy_device)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(
-          ((is_device & !copy_device) ? const_cast<T *>(in_data)
-                                      : memAlloc<T>(dims.elements()).release()),
-          memFree<T>)
+    , data(((is_device && !copy_device)
+                ? const_cast<T *>(in_data)
+                : memAlloc<T>(dims.elements()).release()),
+           memFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -338,11 +338,10 @@ Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+Array<T> createDeviceDataArray(const dim4 &dims, void *data, bool copy) {
     verifyTypeSupport<T>();
-    bool is_device   = true;
-    bool copy_device = false;
-    return Array<T>(dims, static_cast<T *>(data), is_device, copy_device);
+    bool is_device = true;
+    return Array<T>(dims, static_cast<T *>(data), is_device, copy);
 }
 
 template<typename T>
@@ -432,7 +431,8 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
 #define INSTANTIATE(T)                                                        \
     template Array<T> createHostDataArray<T>(const dim4 &size,                \
                                              const T *const data);            \
-    template Array<T> createDeviceDataArray<T>(const dim4 &size, void *data); \
+    template Array<T> createDeviceDataArray<T>(const dim4 &size, void *data,  \
+                                               bool copy);                    \
     template Array<T> createValueArray<T>(const dim4 &size, const T &value);  \
     template Array<T> createEmptyArray<T>(const dim4 &size);                  \
     template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);        \
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 07e06f0681..6c00910c9d 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -56,8 +56,17 @@ Array<T> createValueArray(const af::dim4 &dims, const T &value);
 template<typename T>
 Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
+/// Creates an Array<T> object from a device pointer.
+///
+/// \param[in] dims The shape of the resulting Array.
+/// \param[in] data The device pointer to the data
+/// \param[in] copy If true, memory will be allocated and the data will be
+///                 copied to the device. If false the data will be used
+///                 directly
+/// \returns The new Array<T> object based on the device pointer.
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data,
+                               bool copy = false);
 
 template<typename T>
 Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
@@ -268,7 +277,8 @@ class Array {
     friend Array<T> createValueArray<T>(const af::dim4 &size, const T &value);
     friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data,
+                                             bool copy);
     friend Array<T> createStridedArray<T>(const af::dim4 &dims,
                                           const af::dim4 &strides, dim_t offset,
                                           const T *const in_data,
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 16ab7e5b5a..ab880732e3 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -453,11 +453,10 @@ Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+Array<T> createDeviceDataArray(const dim4 &dims, void *data, bool copy) {
     verifyTypeSupport<T>();
 
-    bool copy_device = false;
-    return Array<T>(dims, static_cast<buffer<T> *>(data), 0, copy_device);
+    return Array<T>(dims, static_cast<buffer<T> *>(data), 0, copy);
 }
 
 template<typename T>
@@ -530,35 +529,36 @@ size_t Array<T>::getAllocatedBytes() const {
     return bytes;
 }
 
-#define INSTANTIATE(T)                                                        \
-    template Array<T> createHostDataArray<T>(const dim4 &dims,                \
-                                             const T *const data);            \
-    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
-    template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
-    template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
-    template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);        \
-    template Array<T> createSubArray<T>(                                      \
-        const Array<T> &parent, const vector<af_seq> &index, bool copy);      \
-    template void destroyArray<T>(Array<T> * A);                              \
-    template Array<T> createNodeArray<T>(const dim4 &dims, Node_ptr node);    \
-    template Array<T>::Array(const dim4 &dims, const dim4 &strides,           \
-                             dim_t offset, const T *const in_data,            \
-                             bool is_device);                                 \
-    template Array<T>::Array(const dim4 &dims, buffer<T> *mem,                \
-                             size_t src_offset, bool copy);                   \
-    template Node_ptr Array<T>::getNode();                                    \
-    template Node_ptr Array<T>::getNode() const;                              \
-    template void Array<T>::eval();                                           \
-    template void Array<T>::eval() const;                                     \
-    template buffer<T> *Array<T>::device();                                   \
-    template void writeHostDataArray<T>(Array<T> & arr, const T *const data,  \
-                                        const size_t bytes);                  \
-    template void writeDeviceDataArray<T>(                                    \
-        Array<T> & arr, const void *const data, const size_t bytes);          \
-    template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
-    template kJITHeuristics passesJitHeuristics<T>(span<Node *> node);        \
-    template void *getDevicePtr<T>(const Array<T> &arr);                      \
-    template void Array<T>::setDataDims(const dim4 &new_dims);                \
+#define INSTANTIATE(T)                                                       \
+    template Array<T> createHostDataArray<T>(const dim4 &dims,               \
+                                             const T *const data);           \
+    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data, \
+                                               bool copy);                   \
+    template Array<T> createValueArray<T>(const dim4 &dims, const T &value); \
+    template Array<T> createEmptyArray<T>(const dim4 &dims);                 \
+    template Array<T> createParamArray<T>(Param<T> & tmp, bool owner);       \
+    template Array<T> createSubArray<T>(                                     \
+        const Array<T> &parent, const vector<af_seq> &index, bool copy);     \
+    template void destroyArray<T>(Array<T> * A);                             \
+    template Array<T> createNodeArray<T>(const dim4 &dims, Node_ptr node);   \
+    template Array<T>::Array(const dim4 &dims, const dim4 &strides,          \
+                             dim_t offset, const T *const in_data,           \
+                             bool is_device);                                \
+    template Array<T>::Array(const dim4 &dims, buffer<T> *mem,               \
+                             size_t src_offset, bool copy);                  \
+    template Node_ptr Array<T>::getNode();                                   \
+    template Node_ptr Array<T>::getNode() const;                             \
+    template void Array<T>::eval();                                          \
+    template void Array<T>::eval() const;                                    \
+    template buffer<T> *Array<T>::device();                                  \
+    template void writeHostDataArray<T>(Array<T> & arr, const T *const data, \
+                                        const size_t bytes);                 \
+    template void writeDeviceDataArray<T>(                                   \
+        Array<T> & arr, const void *const data, const size_t bytes);         \
+    template void evalMultiple<T>(vector<Array<T> *> arrays);                \
+    template kJITHeuristics passesJitHeuristics<T>(span<Node *> node);       \
+    template void *getDevicePtr<T>(const Array<T> &arr);                     \
+    template void Array<T>::setDataDims(const dim4 &new_dims);               \
     template size_t Array<T>::getAllocatedBytes() const;
 
 INSTANTIATE(float)
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index c3e0d38b98..3d74a897ba 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -66,8 +66,17 @@ Array<T> createValueArray(const af::dim4 &dims, const T &value);
 template<typename T>
 Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
+/// Creates an Array<T> object from a device pointer.
+///
+/// \param[in] dims The shape of the resulting Array.
+/// \param[in] data The device pointer to the data
+/// \param[in] copy If true, memory will be allocated and the data will be
+///                 copied to the device. If false the data will be used
+///                 directly
+/// \returns The new Array<T> object based on the device pointer.
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data,
+                               bool copy = false);
 
 template<typename T>
 Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
@@ -306,7 +315,8 @@ class Array {
     friend Array<T> createValueArray<T>(const af::dim4 &dims, const T &value);
     friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data,
+                                             bool copy);
     friend Array<T> createStridedArray<T>(const af::dim4 &dims,
                                           const af::dim4 &strides, dim_t offset,
                                           const T *const in_data,
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 811f5551e3..810666b9a6 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -435,11 +435,9 @@ Array<T> createHostDataArray(const dim4 &dims, const T *const data) {
 }
 
 template<typename T>
-Array<T> createDeviceDataArray(const dim4 &dims, void *data) {
+Array<T> createDeviceDataArray(const dim4 &dims, void *data, bool copy) {
     verifyTypeSupport<T>();
-
-    bool copy_device = false;
-    return Array<T>(dims, static_cast<cl_mem>(data), 0, copy_device);
+    return Array<T>(dims, static_cast<cl_mem>(data), 0, copy);
 }
 
 template<typename T>
@@ -507,7 +505,8 @@ size_t Array<T>::getAllocatedBytes() const {
 #define INSTANTIATE(T)                                                        \
     template Array<T> createHostDataArray<T>(const dim4 &dims,                \
                                              const T *const data);            \
-    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data); \
+    template Array<T> createDeviceDataArray<T>(const dim4 &dims, void *data,  \
+                                               bool copy);                    \
     template Array<T> createValueArray<T>(const dim4 &dims, const T &value);  \
     template Array<T> createEmptyArray<T>(const dim4 &dims);                  \
     template Array<T> createParamArray<T>(Param & tmp, bool owner);           \
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 2d2ca97c94..6951021f19 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -60,8 +60,17 @@ Array<T> createValueArray(const af::dim4 &dims, const T &value);
 template<typename T>
 Array<T> createHostDataArray(const af::dim4 &dims, const T *const data);
 
+/// Creates an Array<T> object from a device pointer.
+///
+/// \param[in] dims The shape of the resulting Array.
+/// \param[in] data The device pointer to the data
+/// \param[in] copy If true, memory will be allocated and the data will be
+///                 copied to the device. If false the data will be used
+///                 directly
+/// \returns The new Array<T> object based on the device pointer.
 template<typename T>
-Array<T> createDeviceDataArray(const af::dim4 &dims, void *data);
+Array<T> createDeviceDataArray(const af::dim4 &dims, void *data,
+                               bool copy = false);
 
 template<typename T>
 Array<T> createStridedArray(const af::dim4 &dims, const af::dim4 &strides,
@@ -295,7 +304,8 @@ class Array {
     friend Array<T> createValueArray<T>(const af::dim4 &dims, const T &value);
     friend Array<T> createHostDataArray<T>(const af::dim4 &dims,
                                            const T *const data);
-    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data);
+    friend Array<T> createDeviceDataArray<T>(const af::dim4 &dims, void *data,
+                                             bool copy);
     friend Array<T> createStridedArray<T>(const af::dim4 &dims,
                                           const af::dim4 &strides, dim_t offset,
                                           const T *const in_data,

From 76a08fdcda579465766d8159caf6e998ee777417 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 15:38:30 -0400
Subject: [PATCH 2406/2677] Call getActiveStream to create a stream in the init
 function.

This is necessary because when creating a new event before an operaiton is
performed in ArrayFire, the cuda driver API will throw an error.
---
 src/backend/cuda/platform.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 4b311f9808..3fab99bb7f 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -360,7 +360,9 @@ int getDeviceCount() {
 void init() {
     thread_local auto err =
         cudaSetDevice(getDeviceNativeId(getActiveDeviceId()));
+    thread_local auto queue2 = getActiveStream();
     UNUSED(err);
+    UNUSED(queue2);
 }
 
 int getActiveDeviceId() { return tlocalActiveDeviceId(); }

From c83bcdf9fd3e36e97f20f81d0ee137dfa5ac2a99 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 8 Jul 2022 19:19:49 -0400
Subject: [PATCH 2407/2677] Add function to create an af_array from a device
 pointer

---
 src/api/c/array.cpp  |  1 +
 src/api/c/handle.cpp | 23 +++++++++++++++++++++++
 src/api/c/handle.hpp |  9 +++++++++
 3 files changed, 33 insertions(+)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index e9a0f68603..173c52171c 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -27,6 +27,7 @@ using arrayfire::common::half;
 using arrayfire::common::SparseArrayBase;
 using detail::cdouble;
 using detail::cfloat;
+using detail::createDeviceDataArray;
 using detail::intl;
 using detail::uchar;
 using detail::uint;
diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
index 392e120fca..0d9f3d2aec 100644
--- a/src/api/c/handle.cpp
+++ b/src/api/c/handle.cpp
@@ -18,6 +18,7 @@ using af::dim4;
 using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
+using detail::createDeviceDataArray;
 using detail::intl;
 using detail::uchar;
 using detail::uint;
@@ -100,6 +101,28 @@ af_array createHandleFromValue(const dim4 &d, double val, af_dtype dtype) {
     // clang-format on
 }
 
+af_array createHandleFromDeviceData(const af::dim4 &d, af_dtype dtype,
+                                    void *data) {
+    // clang-format off
+    switch (dtype) {
+        case f32: return getHandle(createDeviceDataArray<float  >(d, data, false));
+        case c32: return getHandle(createDeviceDataArray<cfloat >(d, data, false));
+        case f64: return getHandle(createDeviceDataArray<double >(d, data, false));
+        case c64: return getHandle(createDeviceDataArray<cdouble>(d, data, false));
+        case b8:  return getHandle(createDeviceDataArray<char   >(d, data, false));
+        case s32: return getHandle(createDeviceDataArray<int    >(d, data, false));
+        case u32: return getHandle(createDeviceDataArray<uint   >(d, data, false));
+        case u8:  return getHandle(createDeviceDataArray<uchar  >(d, data, false));
+        case s64: return getHandle(createDeviceDataArray<intl   >(d, data, false));
+        case u64: return getHandle(createDeviceDataArray<uintl  >(d, data, false));
+        case s16: return getHandle(createDeviceDataArray<short  >(d, data, false));
+        case u16: return getHandle(createDeviceDataArray<ushort >(d, data, false));
+        case f16: return getHandle(createDeviceDataArray<half   >(d, data, false));
+        default: TYPE_ERROR(2, dtype);
+    }
+    // clang-format on
+}
+
 dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
     DIM_ASSERT(1, ndims >= 1);
 
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 4b73293cb3..b19de9c143 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -30,6 +30,15 @@ af_array createHandle(const af::dim4 &d, af_dtype dtype);
 
 af_array createHandleFromValue(const af::dim4 &d, double val, af_dtype dtype);
 
+/// This function creates an af_array handle from memory handle on the device.
+///
+/// \param[in] d The shape of the new af_array
+/// \param[in] dtype The type of the new af_array
+/// \param[in] data The handle to the device memory
+/// \returns a new af_array with a view to the \p data pointer
+af_array createHandleFromDeviceData(const af::dim4 &d, af_dtype dtype,
+                                    void *data);
+
 namespace common {
 const ArrayInfo &getInfo(const af_array arr, bool sparse_check = true,
                          bool device_check = true);

From e93f000335494a8207b0ede5e09f4320c545fda4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 4 Aug 2022 15:12:21 -0400
Subject: [PATCH 2408/2677] Create EXPECT macros to the internal ArrayFire test
 macros

---
 test/testHelpers.hpp | 65 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)

diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 69240883ac..2382060ebf 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -406,6 +406,34 @@ ::testing::AssertionResult assertRefEq(std::string hA_name,
     ASSERT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
                         ACTUAL_ARR)
 
+/// Compares two af::array or af_arrays for their types, dims, and values
+/// (strict equality).
+///
+/// \param[in] EXPECTED The expected array of the assertion
+/// \param[in] ACTUAL The actual resulting array from the calculation
+#define EXPECT_ARRAYS_EQ(EXPECTED, ACTUAL) \
+    EXPECT_PRED_FORMAT2(assertArrayEq, EXPECTED, ACTUAL)
+
+/// Same as EXPECT_ARRAYS_EQ, but for cases when a "special" output array is
+/// given to the function.
+/// The special array can be null, a full-sized array, a subarray, or reordered
+/// Can only be used for testing C-API functions currently
+///
+/// \param[in] EXPECTED The expected array of the assertion
+/// \param[in] ACTUAL The actual resulting array from the calculation
+#define EXPECT_SPECIAL_ARRAYS_EQ(EXPECTED, ACTUAL, META) \
+    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED, ACTUAL, META)
+
+/// Compares a std::vector with an af::/af_array for their types, dims, and
+/// values (strict equality).
+///
+/// \param[in] EXPECTED_VEC The vector that represents the expected array
+/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
+/// \param[in] ACTUAL_ARR The actual resulting array from the calculation
+#define EXPECT_VEC_ARRAY_EQ(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR) \
+    EXPECT_PRED_FORMAT3(assertArrayEq, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
+                        ACTUAL_ARR)
+
 /// Compares two af::array or af_arrays for their type, dims, and values (with a
 /// given tolerance).
 ///
@@ -443,6 +471,43 @@ ::testing::AssertionResult assertRefEq(std::string hA_name,
     ASSERT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
                         ACTUAL_ARR, MAX_ABSDIFF)
 
+/// Compares two af::array or af_arrays for their type, dims, and values (with a
+/// given tolerance).
+///
+/// \param[in] EXPECTED Expected value of the assertion
+/// \param[in] ACTUAL Actual value of the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+///
+/// \NOTE: This macro will deallocate the af_arrays after the call
+#define EXPECT_ARRAYS_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
+    EXPECT_PRED_FORMAT3(assertArrayNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
+
+/// Compares two af::array or af_arrays for their type, dims, and values (with a
+/// given tolerance).
+///
+/// \param[in] EXPECTED Expected value of the assertion
+/// \param[in] ACTUAL Actual value of the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+///
+/// \NOTE: This macro will deallocate the af_arrays after the call
+#define EXPECT_IMAGES_NEAR(EXPECTED, ACTUAL, MAX_ABSDIFF) \
+    EXPECT_PRED_FORMAT3(assertImageNear, EXPECTED, ACTUAL, MAX_ABSDIFF)
+
+/// Compares a std::vector with an af::array for their dims and values (with a
+/// given tolerance).
+///
+/// \param[in] EXPECTED_VEC The vector that represents the expected array
+/// \param[in] EXPECTED_ARR_DIMS The dimensions of the expected array
+/// \param[in] ACTUAL_ARR The actual array from the calculation
+/// \param[in] MAX_ABSDIFF Expected maximum absolute difference between
+///            elements of EXPECTED and ACTUAL
+#define EXPECT_VEC_ARRAY_NEAR(EXPECTED_VEC, EXPECTED_ARR_DIMS, ACTUAL_ARR, \
+                              MAX_ABSDIFF)                                 \
+    EXPECT_PRED_FORMAT4(assertArrayNear, EXPECTED_VEC, EXPECTED_ARR_DIMS,  \
+                        ACTUAL_ARR, MAX_ABSDIFF)
+
 #define ASSERT_REF(arr, expected) \
     ASSERT_PRED_FORMAT2(assertRefEq, arr, expected)
 

From e7aa327c3442fceccc3974351e37aded1ff79a40 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 4 Aug 2022 15:58:47 -0400
Subject: [PATCH 2409/2677] Convert memFree and pinnedFree function to
 non-templated functions

---
 src/api/c/memory.cpp                 |  2 +-
 src/backend/cpu/Array.cpp            |  9 ++++-----
 src/backend/cpu/memory.cpp           | 29 ++++++++--------------------
 src/backend/cpu/memory.hpp           |  9 ++++-----
 src/backend/cpu/susan.cpp            | 12 ++++++------
 src/backend/cuda/Array.cpp           | 12 ++++++------
 src/backend/cuda/ThrustAllocator.cuh |  2 +-
 src/backend/cuda/memory.cpp          | 23 ++++------------------
 src/backend/cuda/memory.hpp          |  6 ++----
 src/backend/cuda/solve.cu            | 14 ++++++--------
 src/backend/oneapi/memory.cpp        | 14 +++-----------
 src/backend/oneapi/memory.hpp        |  7 +++----
 src/backend/opencl/memory.cpp        |  8 +++++---
 src/backend/opencl/memory.hpp        |  4 ++--
 14 files changed, 55 insertions(+), 96 deletions(-)

diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index a689f92a91..17ea0a4d73 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -308,7 +308,7 @@ af_err af_free_device_v2(void *ptr) {
 
 af_err af_free_pinned(void *ptr) {
     try {
-        pinnedFree<char>(static_cast<char *>(ptr));
+        pinnedFree(ptr);
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index c190c9b51d..88f4bcabee 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -67,7 +67,7 @@ template<typename T>
 Array<T>::Array(dim4 dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(memAlloc<T>(dims.elements()).release(), memFree<T>)
+    , data(memAlloc<T>(dims.elements()).release(), memFree)
     , data_dims(dims)
     , node()
     , owner(true) {}
@@ -79,7 +79,7 @@ Array<T>::Array(const dim4 &dims, T *const in_data, bool is_device,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data((is_device & !copy_device) ? in_data
                                       : memAlloc<T>(dims.elements()).release(),
-           memFree<T>)
+           memFree)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -123,8 +123,7 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
                 T *const in_data, bool is_device)
     : info(getActiveDeviceId(), dims, offset_, strides,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(is_device ? in_data : memAlloc<T>(info.total()).release(),
-           memFree<T>)
+    , data(is_device ? in_data : memAlloc<T>(info.total()).release(), memFree)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -180,7 +179,7 @@ void evalMultiple(vector<Array<T> *> array_ptrs) {
 
         array->setId(getActiveDeviceId());
         array->data =
-            shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
+            shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree);
 
         outputs.push_back(array);
         params.emplace_back(array->getData().get(), array->dims(),
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 7f0ba41965..9bbb41d458 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -54,12 +54,12 @@ void printMemInfo(const char *msg, const int device) {
 }
 
 template<typename T>
-unique_ptr<T[], function<void(T *)>> memAlloc(const size_t &elements) {
+unique_ptr<T[], function<void(void *)>> memAlloc(const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
     T *ptr = static_cast<T *>(
         memoryManager().alloc(false, 1, dims.get(), sizeof(T)));
-    return unique_ptr<T[], function<void(T *)>>(ptr, memFree<T>);
+    return unique_ptr<T[], function<void(void *)>>(ptr, memFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
@@ -68,10 +68,7 @@ void *memAllocUser(const size_t &bytes) {
     return ptr;
 }
 
-template<typename T>
-void memFree(T *ptr) {
-    return memoryManager().unlock(static_cast<void *>(ptr), false);
-}
+void memFree(void *ptr) { return memoryManager().unlock(ptr, false); }
 
 void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
@@ -95,17 +92,12 @@ T *pinnedAlloc(const size_t &elements) {
     return static_cast<T *>(ptr);
 }
 
-template<typename T>
-void pinnedFree(T *ptr) {
-    memoryManager().unlock(static_cast<void *>(ptr), false);
-}
+void pinnedFree(void *ptr) { memoryManager().unlock(ptr, false); }
 
-#define INSTANTIATE(T)                                                \
-    template std::unique_ptr<T[], std::function<void(T *)>> memAlloc( \
-        const size_t &elements);                                      \
-    template void memFree(T *ptr);                                    \
-    template T *pinnedAlloc(const size_t &elements);                  \
-    template void pinnedFree(T *ptr);
+#define INSTANTIATE(T)                                                   \
+    template std::unique_ptr<T[], std::function<void(void *)>> memAlloc( \
+        const size_t &elements);                                         \
+    template T *pinnedAlloc(const size_t &elements);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
@@ -129,11 +121,6 @@ void *pinnedAlloc<void>(const size_t &elements) {
     return ptr;
 }
 
-template<>
-void pinnedFree(void *ptr) {
-    memoryManager().unlock(ptr, false);
-}
-
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {
diff --git a/src/backend/cpu/memory.hpp b/src/backend/cpu/memory.hpp
index a45ca06ec1..908136d094 100644
--- a/src/backend/cpu/memory.hpp
+++ b/src/backend/cpu/memory.hpp
@@ -20,14 +20,14 @@ template<typename T>
 using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
 
 template<typename T>
-std::unique_ptr<T[], std::function<void(T *)>> memAlloc(const size_t &elements);
+std::unique_ptr<T[], std::function<void(void *)>> memAlloc(
+    const size_t &elements);
 void *memAllocUser(const size_t &bytes);
 
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-template<typename T>
-void memFree(T *ptr);
+void memFree(void *ptr);
 void memFreeUser(void *ptr);
 
 void memLock(const void *ptr);
@@ -36,8 +36,7 @@ bool isLocked(const void *ptr);
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements);
-template<typename T>
-void pinnedFree(T *ptr);
+void pinnedFree(void *ptr);
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers);
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 0d79078988..6ab2bfba78 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -30,12 +30,12 @@ unsigned susan(Array<float> &x_out, Array<float> &y_out, Array<float> &resp_out,
     dim4 idims                = in.dims();
     const unsigned corner_lim = in.elements() * feature_ratio;
 
-    auto x_corners     = createEmptyArray<float>(dim4(corner_lim));
-    auto y_corners     = createEmptyArray<float>(dim4(corner_lim));
-    auto resp_corners  = createEmptyArray<float>(dim4(corner_lim));
-    auto response      = createEmptyArray<T>(dim4(in.elements()));
-    auto corners_found = std::shared_ptr<unsigned>(
-        memAlloc<unsigned>(1).release(), memFree<unsigned>);
+    auto x_corners    = createEmptyArray<float>(dim4(corner_lim));
+    auto y_corners    = createEmptyArray<float>(dim4(corner_lim));
+    auto resp_corners = createEmptyArray<float>(dim4(corner_lim));
+    auto response     = createEmptyArray<T>(dim4(in.elements()));
+    auto corners_found =
+        std::shared_ptr<unsigned>(memAlloc<unsigned>(1).release(), memFree);
     corners_found.get()[0] = 0;
 
     getQueue().enqueue(kernel::susan_responses<T>, response, in, idims[0],
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 2ced1ea214..db03d1b3e5 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -61,7 +61,7 @@ Array<T>::Array(const af::dim4 &dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data((dims.elements() ? memAlloc<T>(dims.elements()).release() : nullptr),
-           memFree<T>)
+           memFree)
     , data_dims(dims)
     , node()
     , owner(true) {}
@@ -74,7 +74,7 @@ Array<T>::Array(const af::dim4 &dims, const T *const in_data, bool is_device,
     , data(((is_device && !copy_device)
                 ? const_cast<T *>(in_data)
                 : memAlloc<T>(dims.elements()).release()),
-           memFree<T>)
+           memFree)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -117,7 +117,7 @@ Array<T>::Array(Param<T> &tmp, bool owner_)
            af::dim4(tmp.strides[0], tmp.strides[1], tmp.strides[2],
                     tmp.strides[3]),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(tmp.ptr, owner_ ? std::function<void(T *)>(memFree<T>)
+    , data(tmp.ptr, owner_ ? std::function<void(T *)>(memFree)
                            : std::function<void(T *)>([](T * /*unused*/) {}))
     , data_dims(af::dim4(tmp.dims[0], tmp.dims[1], tmp.dims[2], tmp.dims[3]))
     , node()
@@ -143,7 +143,7 @@ Array<T>::Array(const af::dim4 &dims, const af::dim4 &strides, dim_t offset_,
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(is_device ? const_cast<T *>(in_data)
                      : memAlloc<T>(info.total()).release(),
-           memFree<T>)
+           memFree)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -161,7 +161,7 @@ void Array<T>::eval() {
     if (isReady()) { return; }
 
     this->setId(getActiveDeviceId());
-    this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree<T>);
+    this->data = shared_ptr<T>(memAlloc<T>(elements()).release(), memFree);
 
     Param<T> p(data.get(), dims().get(), strides().get());
     evalNodes<T>(p, node.get());
@@ -204,7 +204,7 @@ void evalMultiple(std::vector<Array<T> *> arrays) {
 
         array->setId(getActiveDeviceId());
         array->data =
-            shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree<T>);
+            shared_ptr<T>(memAlloc<T>(array->elements()).release(), memFree);
 
         output_params.emplace_back(array->getData().get(), array->dims().get(),
                                    array->strides().get());
diff --git a/src/backend/cuda/ThrustAllocator.cuh b/src/backend/cuda/ThrustAllocator.cuh
index 21152e6059..93a4a8fc6d 100644
--- a/src/backend/cuda/ThrustAllocator.cuh
+++ b/src/backend/cuda/ThrustAllocator.cuh
@@ -39,7 +39,7 @@ struct ThrustAllocator : thrust::device_malloc_allocator<T> {
 
     void deallocate(pointer p, size_type n) {
         UNUSED(n);
-        memFree<T>(p.get());  // delegate to ArrayFire allocator
+        memFree(p.get());  // delegate to ArrayFire allocator
     }
 };
 }  // namespace cuda
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index 13106fd5c1..dafbef1ce8 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -65,7 +65,7 @@ uptr<T> memAlloc(const size_t &elements) {
     // TODO: make memAlloc aware of array shapes
     dim4 dims(elements);
     void *ptr = memoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    return uptr<T>(static_cast<T *>(ptr), memFree<T>);
+    return uptr<T>(static_cast<T *>(ptr), memFree);
 }
 
 void *memAllocUser(const size_t &bytes) {
@@ -74,10 +74,7 @@ void *memAllocUser(const size_t &bytes) {
     return ptr;
 }
 
-template<typename T>
-void memFree(T *ptr) {
-    memoryManager().unlock(static_cast<void *>(ptr), false);
-}
+void memFree(void *ptr) { memoryManager().unlock(ptr, false); }
 
 void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
@@ -107,16 +104,11 @@ T *pinnedAlloc(const size_t &elements) {
     return static_cast<T *>(ptr);
 }
 
-template<typename T>
-void pinnedFree(T *ptr) {
-    pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
-}
+void pinnedFree(void *ptr) { pinnedMemoryManager().unlock(ptr, false); }
 
 #define INSTANTIATE(T)                                 \
     template uptr<T> memAlloc(const size_t &elements); \
-    template void memFree(T *ptr);                     \
-    template T *pinnedAlloc(const size_t &elements);   \
-    template void pinnedFree(T *ptr);
+    template T *pinnedAlloc(const size_t &elements);
 
 INSTANTIATE(float)
 INSTANTIATE(cfloat)
@@ -140,13 +132,6 @@ void *pinnedAlloc<void>(const size_t &elements) {
     return ptr;
 }
 
-template<>
-void pinnedFree(void *ptr) {
-    pinnedMemoryManager().unlock(ptr, false);
-}
-
-template void memFree(void *ptr);
-
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
 void Allocator::shutdown() {
diff --git a/src/backend/cuda/memory.hpp b/src/backend/cuda/memory.hpp
index 935c788769..039879a90e 100644
--- a/src/backend/cuda/memory.hpp
+++ b/src/backend/cuda/memory.hpp
@@ -19,8 +19,7 @@ namespace cuda {
 float getMemoryPressure();
 float getMemoryPressureThreshold();
 
-template<typename T>
-void memFree(T *ptr);
+void memFree(void *ptr);
 
 template<typename T>
 using uptr = std::unique_ptr<T[], std::function<void(T[])>>;
@@ -42,8 +41,7 @@ bool isLocked(const void *ptr);
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements);
-template<typename T>
-void pinnedFree(T *ptr);
+void pinnedFree(void *ptr);
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers);
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index f762785818..884d7735b1 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -251,12 +251,12 @@ Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
     int batch  = batchz * batchw;
 
     size_t bytes         = batch * sizeof(T *);
-    using unique_mem_ptr = std::unique_ptr<char, void (*)(char *)>;
+    using unique_mem_ptr = std::unique_ptr<char, void (*)(void *)>;
 
     unique_mem_ptr aBatched_host_mem(pinnedAlloc<char>(bytes),
-                                     pinnedFree<char>);
+                                     pinnedFree);
     unique_mem_ptr bBatched_host_mem(pinnedAlloc<char>(bytes),
-                                     pinnedFree<char>);
+                                     pinnedFree);
 
     T *a_ptr               = A.get();
     T *b_ptr               = B.get();
@@ -272,10 +272,8 @@ Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
         }
     }
 
-    unique_mem_ptr aBatched_device_mem(pinnedAlloc<char>(bytes),
-                                       pinnedFree<char>);
-    unique_mem_ptr bBatched_device_mem(pinnedAlloc<char>(bytes),
-                                       pinnedFree<char>);
+    unique_mem_ptr aBatched_device_mem(pinnedAlloc<char>(bytes), pinnedFree);
+    unique_mem_ptr bBatched_device_mem(pinnedAlloc<char>(bytes), pinnedFree);
 
     T **aBatched_device_ptrs = (T **)aBatched_device_mem.get();
     T **bBatched_device_ptrs = (T **)bBatched_device_mem.get();
@@ -299,7 +297,7 @@ Array<T> generalSolveBatched(const Array<T> &a, const Array<T> &b) {
 
     // getrs requires info to be host pointer
     unique_mem_ptr info_host_mem(pinnedAlloc<char>(batch * sizeof(int)),
-                                 pinnedFree<char>);
+                                 pinnedFree);
     CUBLAS_CHECK(getrsBatched_func<T>()(
         blasHandle(), CUBLAS_OP_N, N, NRHS, (const T **)aBatched_device_ptrs,
         A.strides()[1], pivots.get(), bBatched_device_ptrs, B.strides()[1],
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 56efa95785..17cfb37d32 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -73,8 +73,7 @@ void *memAllocUser(const size_t &bytes) {
     // return new cl::Buffer(buf, true);
 }
 
-template<typename T>
-void memFree(T *ptr) {
+void memFree(void *ptr) {
     ONEAPI_NOT_SUPPORTED("memFree Not supported");
 
     // cl::Buffer *buf = reinterpret_cast<cl::Buffer *>(ptr);
@@ -152,9 +151,8 @@ T *pinnedAlloc(const size_t &elements) {
     return static_cast<T *>(ptr);
 }
 
-template<typename T>
-void pinnedFree(T *ptr) {
-    pinnedMemoryManager().unlock(static_cast<void *>(ptr), false);
+void pinnedFree(void *ptr) {
+    pinnedMemoryManager().unlock(ptr, false);
 }
 
 // template unique_ptr<int, function<void(int *)>> memAlloc<T>(
@@ -162,9 +160,7 @@ void pinnedFree(T *ptr) {
     template std::unique_ptr<sycl::buffer<T>,                        \
                              std::function<void(sycl::buffer<T> *)>> \
     memAlloc(const size_t &elements);                                \
-    template void memFree(T *ptr);                                   \
     template T *pinnedAlloc(const size_t &elements);                 \
-    template void pinnedFree(T *ptr);                                \
     template void bufferFree(sycl::buffer<T> *buf);                  \
     template void memLock(const sycl::buffer<T> *buf);               \
     template void memUnlock(const sycl::buffer<T> *buf);
@@ -192,10 +188,6 @@ void *pinnedAlloc<void>(const size_t &elements) {
     // void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
     return static_cast<void *>(nullptr);
 }
-template<>
-void pinnedFree(void *ptr) {
-    // pinnedMemoryManager().unlock(ptr, false);
-}
 
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index bcb8c1dabf..809f219eb7 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -35,8 +35,7 @@ void *memAllocUser(const size_t &bytes);
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-template<typename T>
-void memFree(T *ptr);
+void memFree(void *ptr);
 void memFreeUser(void *ptr);
 
 template<typename T>
@@ -49,8 +48,8 @@ bool isLocked(const void *ptr);
 
 template<typename T>
 T *pinnedAlloc(const size_t &elements);
-template<typename T>
-void pinnedFree(T *ptr);
+
+void pinnedFree(void *ptr);
 
 void deviceMemoryInfo(size_t *alloc_bytes, size_t *alloc_buffers,
                       size_t *lock_bytes, size_t *lock_buffers);
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index f1158dd91f..68ae43c5e8 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -78,14 +78,17 @@ void *memAllocUser(const size_t &bytes) {
     return new cl::Buffer(buf, true);
 }
 
-template<typename T>
-void memFree(T *ptr) {
+void memFree(cl::Buffer *ptr) {
     cl::Buffer *buf = reinterpret_cast<cl::Buffer *>(ptr);
     cl_mem mem      = static_cast<cl_mem>((*buf)());
     delete buf;
     return memoryManager().unlock(static_cast<void *>(mem), false);
 }
 
+void memFree(cl_mem ptr) {
+    return memoryManager().unlock(static_cast<void *>(ptr), false);
+}
+
 void memFreeUser(void *ptr) {
     cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
     cl_mem mem      = (*buf)();
@@ -149,7 +152,6 @@ void pinnedFree(T *ptr) {
 #define INSTANTIATE(T)                                                         \
     template unique_ptr<cl::Buffer, function<void(cl::Buffer *)>> memAlloc<T>( \
         const size_t &elements);                                               \
-    template void memFree(T *ptr);                                             \
     template T *pinnedAlloc(const size_t &elements);                           \
     template void pinnedFree(T *ptr);
 
diff --git a/src/backend/opencl/memory.hpp b/src/backend/opencl/memory.hpp
index 4f618d7956..447f80bb83 100644
--- a/src/backend/opencl/memory.hpp
+++ b/src/backend/opencl/memory.hpp
@@ -34,8 +34,8 @@ void *memAllocUser(const size_t &bytes);
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-template<typename T>
-void memFree(T *ptr);
+void memFree(cl::Buffer *ptr);
+void memFree(cl_mem ptr);
 void memFreeUser(void *ptr);
 
 void memLock(const cl::Buffer *ptr);

From be685a9a0d8e977ec6e71519c0ce54478492bd21 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 5 Aug 2022 11:45:35 -0400
Subject: [PATCH 2410/2677] Update threads library for event behavior. Update
 event docs

---
 src/api/c/events.cpp           | 7 +------
 src/api/c/events.hpp           | 3 +--
 src/backend/cpu/CMakeLists.txt | 2 +-
 3 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/src/api/c/events.cpp b/src/api/c/events.cpp
index c3d7d5a773..112373672d 100644
--- a/src/api/c/events.cpp
+++ b/src/api/c/events.cpp
@@ -20,16 +20,11 @@ using detail::enqueueWaitOnActiveQueue;
 using detail::Event;
 using detail::markEventOnActiveQueue;
 
-Event &getEvent(af_event &handle) {
+Event &getEvent(af_event handle) {
     Event &event = *static_cast<Event *>(handle);
     return event;
 }
 
-const Event &getEvent(const af_event &handle) {
-    const Event &event = *static_cast<const Event *>(handle);
-    return event;
-}
-
 af_event getHandle(Event &event) { return static_cast<af_event>(&event); }
 
 af_err af_create_event(af_event *handle) {
diff --git a/src/api/c/events.hpp b/src/api/c/events.hpp
index b3d3eb398d..488cb204e4 100644
--- a/src/api/c/events.hpp
+++ b/src/api/c/events.hpp
@@ -15,5 +15,4 @@
 
 af_event getHandle(detail::Event& event);
 
-detail::Event& getEvent(af_event& eventHandle);
-const detail::Event& getEvent(const af_event& eventHandle);
+detail::Event& getEvent(af_event eventHandle);
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index fc84101de4..b8025d53a2 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -274,7 +274,7 @@ endif(AF_WITH_CPUID)
 
 af_dep_check_and_populate(${threads_prefix}
   URI https://github.com/arrayfire/threads.git
-  REF b666773940269179f19ef11c8f1eb77005e85d9a
+  REF 4d4a4f0384d1ac2f25b2c4fc1d57b9e25f4d6818
 )
 
 target_sources(afcpu

From 30676ee9d9826242fc1c46cbe8004d1fd3c79dcf Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 7 Aug 2022 09:21:42 -0400
Subject: [PATCH 2411/2677] Fix minor warnings and update clang-tidy

---
 src/.clang-tidy                         | 2 +-
 src/backend/common/jit/NodeIterator.hpp | 2 +-
 src/backend/cuda/reduce_impl.hpp        | 8 ++++----
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/.clang-tidy b/src/.clang-tidy
index a3e8a261dd..549c784606 100644
--- a/src/.clang-tidy
+++ b/src/.clang-tidy
@@ -1,5 +1,5 @@
 ---
-Checks:          'clang-diagnostic-*,clang-analyzer-*,*,-fuchsia-*,-cppcoreguidelines-*,-misc-misplaced-const,-hicpp-no-array-decay,-readability-implicit-bool-conversion,bugprone-*,performance-*,modernize-*,-llvm-header-guard,-hicpp-use-auto,-modernize-use-trailing-return-type,-hicpp-uppercase-literal-suffix,-hicpp-use-nullptr,-modernize-use-nullptr,-google-runtime-int,-llvm-include-order,-google-runtime-references,-readability-magic-numbers,-readability-isolate-declaration,-hicpp-vararg,-google-readability-todo,-bugprone-macro-parentheses,-misc-unused-using-decls,-readability-else-after-return,-hicpp-avoid-c-arrays,-modernize-avoid-c-arrays,-hicpp-braces-around-statements,-hicpp-noexcept-move'
+Checks:          'clang-diagnostic-*,clang-analyzer-*,*,-fuchsia-*,-cppcoreguidelines-*,-misc-misplaced-const,-hicpp-no-array-decay,-readability-implicit-bool-conversion,bugprone-*,performance-*,modernize-*,-llvm-header-guard,-hicpp-use-auto,-modernize-use-trailing-return-type,-hicpp-uppercase-literal-suffix,-hicpp-use-nullptr,-modernize-use-nullptr,-google-runtime-int,-llvm-include-order,-google-runtime-references,-readability-magic-numbers,-readability-isolate-declaration,-hicpp-vararg,-google-readability-todo,-bugprone-macro-parentheses,-misc-unused-using-decls,-readability-else-after-return,-hicpp-avoid-c-arrays,-modernize-avoid-c-arrays,-hicpp-braces-around-statements,-hicpp-noexcept-move,-llvmlibc-*,-altera-*,-hicpp-explicit-conversions'
 WarningsAsErrors: ''
 HeaderFilterRegex: ''
 AnalyzeTemporaryDtors: true
diff --git a/src/backend/common/jit/NodeIterator.hpp b/src/backend/common/jit/NodeIterator.hpp
index 82e916c7ef..7359316c65 100644
--- a/src/backend/common/jit/NodeIterator.hpp
+++ b/src/backend/common/jit/NodeIterator.hpp
@@ -46,7 +46,7 @@ class NodeIterator {
     /// NodeIterator Constructor
     ///
     /// \param[in] root The root node of the tree
-    NodeIterator(pointer root) : tree{root}, index(0) {
+    NodeIterator(pointer root) : tree{root} {
         tree.reserve(root->getHeight() * 8);
     }
 
diff --git a/src/backend/cuda/reduce_impl.hpp b/src/backend/cuda/reduce_impl.hpp
index eb8a5b9a48..bbb91d79d9 100644
--- a/src/backend/cuda/reduce_impl.hpp
+++ b/src/backend/cuda/reduce_impl.hpp
@@ -172,8 +172,8 @@ void reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
                         t_reduced_keys, t_reduced_vals, dim, folded_dim_sz);
             POST_LAUNCH_CHECK();
 
-            swap(t_reduced_keys, reduced_keys);
-            swap(t_reduced_vals, reduced_vals);
+            std::swap(t_reduced_keys, reduced_keys);
+            std::swap(t_reduced_vals, reduced_vals);
             reduce_host_event.block();
         }
     } while (needs_another_reduction_host ||
@@ -319,8 +319,8 @@ void reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
                         t_reduced_keys, t_reduced_vals, odims[2]);
             POST_LAUNCH_CHECK();
 
-            swap(t_reduced_keys, reduced_keys);
-            swap(t_reduced_vals, reduced_vals);
+            std::swap(t_reduced_keys, reduced_keys);
+            std::swap(t_reduced_vals, reduced_vals);
             reduce_host_event.block();
         }
     } while (needs_another_reduction_host ||

From bb5e46557caf3f3a4188bfc92f1d54efce61ede9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 20 Feb 2023 20:21:17 -0500
Subject: [PATCH 2412/2677] Add function that returns basic OpenCL build flags
 for each device

---
 src/backend/oneapi/device_manager.cpp | 23 ++++++++++++++++++++---
 src/backend/oneapi/device_manager.hpp |  3 +++
 src/backend/oneapi/platform.cpp       |  8 ++++++++
 src/backend/oneapi/platform.hpp       |  2 ++
 4 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 54878e3fea..c559fafbbd 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -10,6 +10,7 @@
 #include <common/graphics_common.hpp>
 
 #include <GraphicsResourceManager.hpp>
+#include <build_version.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
@@ -17,10 +18,8 @@
 #include <common/util.hpp>
 #include <device_manager.hpp>
 #include <err_oneapi.hpp>
-#include <platform.hpp>  //TODO: blas.hpp? y tho, also Array.hpp
-//#include <errorcodes.hpp>
-#include <build_version.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 #include <af/oneapi.h>
 #include <af/version.h>
 
@@ -44,6 +43,8 @@ using std::vector;
 using sycl::device;
 using sycl::platform;
 
+using af::dtype_traits;
+
 namespace arrayfire {
 namespace oneapi {
 
@@ -118,6 +119,22 @@ DeviceManager::DeviceManager()
                 // mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
                 // mPlatforms.push_back(getPlatformEnum(*devices[i]));
                 mDevices.emplace_back(std::move(devices[i]));
+
+                std::string options;
+#ifdef AF_WITH_FAST_MATH
+                options = fmt::format(" -D dim_t=CL3.0 -cl-fast-relaxed-math",
+                                      dtype_traits<dim_t>::getName());
+#else
+                options = fmt::format(" -cl-std=CL3.0 -D dim_t={}",
+                                      dtype_traits<dim_t>::getName());
+#endif
+                mBaseOpenCLBuildFlags.push_back(options);
+                if (mDevices.back()->has(sycl::aspect::fp64)) {
+                    mBaseOpenCLBuildFlags.back() += " -DUSE_DOUBLE";
+                }
+                if (mDevices.back()->has(sycl::aspect::fp16)) {
+                    mBaseOpenCLBuildFlags.back() += " -D USE_HALF";
+                }
             } catch (sycl::exception& err) {
                 AF_TRACE("Error creating context for device {} with error {}\n",
                          devices[i]->get_info<sycl::info::device::name>(),
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index 36824539b2..37c5cbe087 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -85,6 +85,8 @@ class DeviceManager {
 
     friend const sycl::device& getDevice(int id);
 
+    friend const std::string& getActiveDeviceBaseBuildFlags();
+
     friend size_t getDeviceMemorySize(int device);
 
     friend bool isGLSharingSupported();
@@ -137,6 +139,7 @@ class DeviceManager {
     std::vector<std::unique_ptr<sycl::context>> mContexts;
     std::vector<std::unique_ptr<sycl::queue>> mQueues;
     std::vector<bool> mIsGLSharingOn;
+    std::vector<std::string> mBaseOpenCLBuildFlags;
     std::vector<int> mDeviceTypes;
     std::vector<int> mPlatforms;
     unsigned mUserDeviceOffset;
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index ce3ad2e099..dc2c8a9766 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -304,6 +304,14 @@ const sycl::device& getDevice(int id) {
     return *(devMngr.mDevices[id]);
 }
 
+const std::string& getActiveDeviceBaseBuildFlags() {
+    device_id_t& devId     = tlocalActiveDeviceId();
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+    return devMngr.mBaseOpenCLBuildFlags[get<1>(devId)];
+}
+
 size_t getDeviceMemorySize(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index af579573d8..b508f6fc4e 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -62,6 +62,8 @@ sycl::queue* getQueueHandle(int device);
 
 const sycl::device& getDevice(int id = -1);
 
+const std::string& getActiveDeviceBaseBuildFlags();
+
 size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();

From 95d433717eb38841ae76cedb55d624d977dd9bd1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 20 Feb 2023 20:40:50 -0500
Subject: [PATCH 2413/2677] Refactor some JIT tests to use new style asserts

---
 test/jit.cpp | 37 ++++++++++++-------------------------
 1 file changed, 12 insertions(+), 25 deletions(-)

diff --git a/test/jit.cpp b/test/jit.cpp
index 101580a488..3848a22242 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -89,12 +89,7 @@ TEST(JIT, CPP_JIT_Reset_Binary) {
     array g = d - c;
     g.eval();
 
-    vector<float> hf(f.elements());
-    vector<float> hg(g.elements());
-    f.host(&hf[0]);
-    g.host(&hg[0]);
-
-    for (int i = 0; i < (int)f.elements(); i++) { ASSERT_EQ(hf[i], -hg[i]); }
+    ASSERT_ARRAYS_NEAR(f, -g, 1e-5);
 }
 
 TEST(JIT, CPP_JIT_Reset_Unary) {
@@ -109,12 +104,7 @@ TEST(JIT, CPP_JIT_Reset_Unary) {
     array g = d - c;
     g.eval();
 
-    vector<float> hf(f.elements());
-    vector<float> hg(g.elements());
-    f.host(&hf[0]);
-    g.host(&hg[0]);
-
-    for (int i = 0; i < (int)f.elements(); i++) { ASSERT_EQ(hf[i], -hg[i]); }
+    ASSERT_ARRAYS_EQ(f, -g);
 }
 
 TEST(JIT, CPP_Multi_linear) {
@@ -142,7 +132,7 @@ TEST(JIT, CPP_Multi_linear) {
     ASSERT_VEC_ARRAY_EQ(goldy, dim4(num), y);
 }
 
-TEST(JIT, CPP_strided) {
+TEST(JIT, CPP_gforSet_strided) {
     const int num = 1024;
     gforSet(true);
     array a = randu(num, 1, s32);
@@ -155,23 +145,23 @@ TEST(JIT, CPP_strided) {
 
     vector<int> ha(num);
     vector<int> hb(num);
-    vector<int> hx(num * num);
-    vector<int> hy(num * num);
 
     a.host(&ha[0]);
     b.host(&hb[0]);
-    x.host(&hx[0]);
-    y.host(&hy[0]);
 
+    vector<int> hapb(num * num);
+    vector<int> hamb(num * num);
     for (int j = 0; j < num; j++) {
         for (int i = 0; i < num; i++) {
-            ASSERT_EQ((ha[i] + hb[j]), hx[j * num + i]);
-            ASSERT_EQ((ha[i] - hb[j]), hy[j * num + i]);
+            hapb[j * num + i] = ha[i] + hb[j];
+            hamb[j * num + i] = ha[i] - hb[j];
         }
     }
+    ASSERT_VEC_ARRAY_EQ(hapb, dim4(num, num), x);
+    ASSERT_VEC_ARRAY_EQ(hamb, dim4(num, num), y);
 }
 
-TEST(JIT, CPP_Multi_strided) {
+TEST(JIT, CPP_gforSet_Multi_strided) {
     const int num = 1024;
     gforSet(true);
     array a = randu(num, 1, s32);
@@ -285,14 +275,11 @@ TEST(JIT, NonLinearLargeY) {
 
     a.host(ha.data());
     b.host(hb.data());
-    c.host(hc.data());
 
     for (int j = 0; j < d1; j++) {
-        for (int i = 0; i < d0; i++) {
-            ASSERT_EQ(hc[i + j * d0], ha[i] + hb[j])
-                << " at " << i << " , " << j;
-        }
+        for (int i = 0; i < d0; i++) { hc[i + j * d0] = ha[i] + hb[j]; }
     }
+    ASSERT_VEC_ARRAY_EQ(hc, dim4(d0, d1), c);
 }
 
 TEST(JIT, NonLinearLargeX) {

From ef1823bd25d16d46d5e5b23cad230c2d4e4bbf7d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 21 Feb 2023 20:26:52 -0500
Subject: [PATCH 2414/2677] Improve compile times by using more specific
 headers, etc.

---
 include/af/oneapi.h                           | 11 -----
 src/api/c/handle.cpp                          | 46 +++++++++++++++++
 src/api/c/handle.hpp                          | 26 +---------
 src/api/c/plot.cpp                            |  1 +
 src/api/c/vector_field.cpp                    |  1 +
 src/backend/common/ArrayInfo.cpp              | 42 ++++++++++++++++
 src/backend/common/ArrayInfo.hpp              | 40 +--------------
 src/backend/common/MemoryManagerBase.hpp      |  2 +-
 src/backend/common/err_common.cpp             | 14 +++---
 src/backend/common/err_common.hpp             |  3 +-
 src/backend/common/half.hpp                   |  5 ++
 src/backend/common/jit/BufferNodeBase.hpp     |  4 +-
 src/backend/oneapi/Array.cpp                  |  2 +
 src/backend/oneapi/Array.hpp                  | 27 +++++-----
 src/backend/oneapi/Event.hpp                  |  5 +-
 src/backend/oneapi/Param.hpp                  |  8 ++-
 src/backend/oneapi/backend.hpp                |  6 ++-
 src/backend/oneapi/device_manager.cpp         | 11 ++++-
 src/backend/oneapi/device_manager.hpp         | 13 +++--
 src/backend/oneapi/kernel/approx1.hpp         |  1 -
 src/backend/oneapi/kernel/assign.hpp          | 11 ++---
 src/backend/oneapi/kernel/bilateral.hpp       |  3 +-
 src/backend/oneapi/kernel/diagonal.hpp        |  2 -
 src/backend/oneapi/kernel/diff.hpp            |  1 -
 src/backend/oneapi/kernel/histogram.hpp       |  3 +-
 src/backend/oneapi/kernel/interp.hpp          |  3 ++
 src/backend/oneapi/kernel/iota.hpp            | 10 ++--
 src/backend/oneapi/kernel/mean.hpp            | 39 ++++++---------
 src/backend/oneapi/kernel/memcopy.hpp         | 25 +++-------
 src/backend/oneapi/kernel/random_engine.hpp   |  2 +-
 .../oneapi/kernel/random_engine_write.hpp     | 38 +++++++-------
 src/backend/oneapi/kernel/reduce_all.hpp      | 17 ++++---
 src/backend/oneapi/kernel/reduce_dim.hpp      | 17 +++----
 src/backend/oneapi/kernel/reduce_first.hpp    | 49 +++++++++----------
 src/backend/oneapi/kernel/reorder.hpp         |  1 -
 src/backend/oneapi/kernel/resize.hpp          |  2 +
 src/backend/oneapi/kernel/scan_dim.hpp        | 31 +++++-------
 src/backend/oneapi/kernel/scan_first.hpp      | 40 ++++++---------
 src/backend/oneapi/kernel/transpose.hpp       | 14 ++----
 src/backend/oneapi/kernel/triangle.hpp        |  1 -
 src/backend/oneapi/kernel/where.hpp           |  9 ++--
 src/backend/oneapi/memory.cpp                 |  9 ++--
 src/backend/oneapi/memory.hpp                 |  2 +
 src/backend/oneapi/platform.cpp               | 27 ++++++++--
 src/backend/oneapi/platform.hpp               | 21 ++++++--
 src/backend/oneapi/print.hpp                  |  2 +
 src/backend/oneapi/types.hpp                  |  3 +-
 47 files changed, 343 insertions(+), 307 deletions(-)

diff --git a/include/af/oneapi.h b/include/af/oneapi.h
index baf28bf73b..b6a3da15fa 100644
--- a/include/af/oneapi.h
+++ b/include/af/oneapi.h
@@ -9,23 +9,12 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <af/defines.h>
 
 #ifdef __cplusplus
 extern "C" {
 #endif
 
-#if AF_API_VERSION >= 39
-typedef enum
-{
-    AF_ONEAPI_DEVICE_TYPE_CPU     = (int)sycl::info::device_type::cpu,
-    AF_ONEAPI_DEVICE_TYPE_GPU     = (int)sycl::info::device_type::gpu,
-    AF_ONEAPI_DEVICE_TYPE_ACC     = (int)sycl::info::device_type::accelerator,
-    AF_ONEAPI_DEVICE_TYPE_UNKNOWN = -1
-} af_oneapi_device_type;
-#endif
-
 #if AF_API_VERSION >= 39
 typedef enum
 {
diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
index 0d9f3d2aec..a432d8a720 100644
--- a/src/api/c/handle.cpp
+++ b/src/api/c/handle.cpp
@@ -136,4 +136,50 @@ dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
     return d;
 }
 
+template<typename T>
+void releaseHandle(const af_array arr) {
+    auto &Arr      = getArray<T>(arr);
+    int old_device = detail::getActiveDeviceId();
+    int array_id   = Arr.getDevId();
+    if (array_id != old_device) {
+        detail::setDevice(array_id);
+        detail::destroyArray(static_cast<detail::Array<T> *>(arr));
+        detail::setDevice(old_device);
+    } else {
+        detail::destroyArray(static_cast<detail::Array<T> *>(arr));
+    }
+}
+
+template<typename T>
+detail::Array<T> &getCopyOnWriteArray(const af_array &arr) {
+    detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
+
+    if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
+        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+
+    ARG_ASSERT(0, A->isSparse() == false);
+
+    if (A->useCount() > 1) { *A = copyArray(*A); }
+
+    return *A;
+}
+
+#define INSTANTIATE(TYPE)                                  \
+    template void releaseHandle<TYPE>(const af_array arr); \
+    template detail::Array<TYPE> &getCopyOnWriteArray<TYPE>(const af_array &arr)
+
+INSTANTIATE(float);
+INSTANTIATE(double);
+INSTANTIATE(cfloat);
+INSTANTIATE(cdouble);
+INSTANTIATE(int);
+INSTANTIATE(uint);
+INSTANTIATE(intl);
+INSTANTIATE(uintl);
+INSTANTIATE(uchar);
+INSTANTIATE(char);
+INSTANTIATE(short);
+INSTANTIATE(ushort);
+INSTANTIATE(half);
+
 }  // namespace arrayfire
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index b19de9c143..97243ac353 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -104,32 +104,10 @@ af_array copyArray(const af_array in) {
 }
 
 template<typename T>
-void releaseHandle(const af_array arr) {
-    auto &Arr      = getArray<T>(arr);
-    int old_device = detail::getActiveDeviceId();
-    int array_id   = Arr.getDevId();
-    if (array_id != old_device) {
-        detail::setDevice(array_id);
-        detail::destroyArray(static_cast<detail::Array<T> *>(arr));
-        detail::setDevice(old_device);
-    } else {
-        detail::destroyArray(static_cast<detail::Array<T> *>(arr));
-    }
-}
+void releaseHandle(const af_array arr);
 
 template<typename T>
-detail::Array<T> &getCopyOnWriteArray(const af_array &arr) {
-    detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
-
-    if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
-        AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
-
-    ARG_ASSERT(0, A->isSparse() == false);
-
-    if (A->useCount() > 1) { *A = copyArray(*A); }
-
-    return *A;
-}
+detail::Array<T> &getCopyOnWriteArray(const af_array &arr);
 
 }  // namespace arrayfire
 
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index b60448593f..3cf03d05cf 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -17,6 +17,7 @@
 #include <common/graphics_common.hpp>
 #include <handle.hpp>
 #include <join.hpp>
+#include <platform.hpp>
 #include <plot.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index a6bd0e07cc..a46d1eed47 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -16,6 +16,7 @@
 #include <common/graphics_common.hpp>
 #include <handle.hpp>
 #include <join.hpp>
+#include <platform.hpp>
 #include <reduce.hpp>
 #include <transpose.hpp>
 #include <vector_field.hpp>
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index b83380fe88..d919c942f8 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -32,6 +32,48 @@ dim4 calcStrides(const dim4 &parentDim) {
     return out;
 }
 
+ArrayInfo::ArrayInfo(unsigned id, af::dim4 size, dim_t offset_, af::dim4 stride,
+                     af_dtype af_type)
+    : devId(id)
+    , type(af_type)
+    , dim_size(size)
+    , offset(offset_)
+    , dim_strides(stride)
+    , is_sparse(false) {
+    setId(id);
+    static_assert(std::is_move_assignable<ArrayInfo>::value,
+                  "ArrayInfo is not move assignable");
+    static_assert(std::is_move_constructible<ArrayInfo>::value,
+                  "ArrayInfo is not move constructible");
+    static_assert(
+        offsetof(ArrayInfo, devId) == 0,
+        "ArrayInfo::devId must be the first member variable of ArrayInfo. \
+                   devId is used to encode the backend into the integer. \
+                   This is then used in the unified backend to check mismatched arrays.");
+    static_assert(std::is_standard_layout<ArrayInfo>::value,
+                  "ArrayInfo must be a standard layout type");
+}
+
+ArrayInfo::ArrayInfo(unsigned id, af::dim4 size, dim_t offset_, af::dim4 stride,
+                     af_dtype af_type, bool sparse)
+    : devId(id)
+    , type(af_type)
+    , dim_size(size)
+    , offset(offset_)
+    , dim_strides(stride)
+    , is_sparse(sparse) {
+    setId(id);
+    static_assert(
+        offsetof(ArrayInfo, devId) == 0,
+        "ArrayInfo::devId must be the first member variable of ArrayInfo. \
+                   devId is used to encode the backend into the integer. \
+                   This is then used in the unified backend to check mismatched arrays.");
+    static_assert(std::is_nothrow_move_assignable<ArrayInfo>::value,
+                  "ArrayInfo is not nothrow move assignable");
+    static_assert(std::is_nothrow_move_constructible<ArrayInfo>::value,
+                  "ArrayInfo is not nothrow move constructible");
+}
+
 unsigned ArrayInfo::getDevId() const {
     // The actual device ID is only stored in the first 8 bits of devId
     // See ArrayInfo.hpp for more
diff --git a/src/backend/common/ArrayInfo.hpp b/src/backend/common/ArrayInfo.hpp
index f2a99c0b1e..aae9e7b6a7 100644
--- a/src/backend/common/ArrayInfo.hpp
+++ b/src/backend/common/ArrayInfo.hpp
@@ -49,44 +49,10 @@ class ArrayInfo {
 
    public:
     ArrayInfo(unsigned id, af::dim4 size, dim_t offset_, af::dim4 stride,
-              af_dtype af_type)
-        : devId(id)
-        , type(af_type)
-        , dim_size(size)
-        , offset(offset_)
-        , dim_strides(stride)
-        , is_sparse(false) {
-        setId(id);
-        static_assert(std::is_move_assignable<ArrayInfo>::value,
-                      "ArrayInfo is not move assignable");
-        static_assert(std::is_move_constructible<ArrayInfo>::value,
-                      "ArrayInfo is not move constructible");
-        static_assert(
-            offsetof(ArrayInfo, devId) == 0,
-            "ArrayInfo::devId must be the first member variable of ArrayInfo. \
-                   devId is used to encode the backend into the integer. \
-                   This is then used in the unified backend to check mismatched arrays.");
-    }
+              af_dtype af_type);
 
     ArrayInfo(unsigned id, af::dim4 size, dim_t offset_, af::dim4 stride,
-              af_dtype af_type, bool sparse)
-        : devId(id)
-        , type(af_type)
-        , dim_size(size)
-        , offset(offset_)
-        , dim_strides(stride)
-        , is_sparse(sparse) {
-        setId(id);
-        static_assert(
-            offsetof(ArrayInfo, devId) == 0,
-            "ArrayInfo::devId must be the first member variable of ArrayInfo. \
-                   devId is used to encode the backend into the integer. \
-                   This is then used in the unified backend to check mismatched arrays.");
-        static_assert(std::is_nothrow_move_assignable<ArrayInfo>::value,
-                      "ArrayInfo is not nothrow move assignable");
-        static_assert(std::is_nothrow_move_constructible<ArrayInfo>::value,
-                      "ArrayInfo is not nothrow move constructible");
-    }
+              af_dtype af_type, bool sparse);
 
     ArrayInfo()                       = default;
     ArrayInfo(const ArrayInfo& other) = default;
@@ -170,8 +136,6 @@ class ArrayInfo {
 
     bool isSparse() const;
 };
-static_assert(std::is_standard_layout<ArrayInfo>::value,
-              "ArrayInfo must be a standard layout type");
 
 af::dim4 toDims(const std::vector<af_seq>& seqs, const af::dim4& parentDims);
 
diff --git a/src/backend/common/MemoryManagerBase.hpp b/src/backend/common/MemoryManagerBase.hpp
index 569154695e..ceeb26c605 100644
--- a/src/backend/common/MemoryManagerBase.hpp
+++ b/src/backend/common/MemoryManagerBase.hpp
@@ -9,8 +9,8 @@
 
 #pragma once
 
-#include <Event.hpp>
 #include <common/AllocatorInterface.hpp>
+#include <af/defines.h>
 
 #include <cstddef>
 #include <memory>
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 68514bac29..c7dc95b8fd 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -40,16 +40,16 @@ AfError::AfError(const char *const func, const char *const file, const int line,
     : logic_error(message)
     , functionName(func)
     , fileName(file)
-    , st_(move(st))
+    , st_(std::move(st))
     , lineNumber(line)
     , error(err) {}
 
 AfError::AfError(string func, string file, const int line,
                  const string &message, af_err err, stacktrace st)
     : logic_error(message)
-    , functionName(move(func))
-    , fileName(move(file))
-    , st_(move(st))
+    , functionName(std::move(func))
+    , fileName(std::move(file))
+    , st_(std::move(st))
     , lineNumber(line)
     , error(err) {}
 
@@ -66,7 +66,7 @@ AfError::~AfError() noexcept = default;
 TypeError::TypeError(const char *const func, const char *const file,
                      const int line, const int index, const af_dtype type,
                      stacktrace st)
-    : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE, move(st))
+    : AfError(func, file, line, "Invalid data type", AF_ERR_TYPE, std::move(st))
     , errTypeName(getName(type))
     , argIndex(index) {}
 
@@ -77,7 +77,7 @@ int TypeError::getArgIndex() const noexcept { return argIndex; }
 ArgumentError::ArgumentError(const char *const func, const char *const file,
                              const int line, const int index,
                              const char *const expectString, stacktrace st)
-    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG, move(st))
+    : AfError(func, file, line, "Invalid argument", AF_ERR_ARG, std::move(st))
     , expected(expectString)
     , argIndex(index) {}
 
@@ -91,7 +91,7 @@ SupportError::SupportError(const char *const func, const char *const file,
                            const int line, const char *const back,
                            stacktrace st)
     : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED,
-              move(st))
+              std::move(st))
     , backend(back) {}
 
 const string &SupportError::getBackendName() const noexcept { return backend; }
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index a2c55742e0..79c9d029d7 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -17,11 +17,10 @@
 #include <common/defines.hpp>
 #include <af/defines.h>
 
-#include <cassert>
 #include <sstream>
 #include <stdexcept>
 #include <string>
-#include <vector>
+#include <utility>
 
 class AfError : public std::logic_error {
     std::string functionName;
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index f653024fb1..57545f4bcd 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -32,6 +32,10 @@
 #endif
 #endif
 
+#ifdef AF_ONEAPI
+#include <sycl/half_type.hpp>
+#endif
+
 #include <backend.hpp>
 
 #ifdef __CUDACC_RTC__
@@ -41,6 +45,7 @@ using uint16_t = unsigned short;
 #define AF_CONSTEXPR constexpr
 #else
 #include <af/compilers.h>
+#include <cmath>
 #include <cstring>
 #include <ostream>
 #include <string>
diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 5af3a216d0..061aa37a8c 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -8,11 +8,13 @@
  ********************************************************/
 
 #pragma once
-#include <backend.hpp>
 #include <common/jit/Node.hpp>
 #include <jit/kernel_generators.hpp>
 
+#include <backend.hpp>
+
 #include <cstring>
+#include <memory>
 #include <sstream>
 
 namespace arrayfire {
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index ab880732e3..a55915edb8 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -9,6 +9,8 @@
 
 #include <Array.hpp>
 
+#include <Param.hpp>
+#include <common/MemoryManagerBase.hpp>
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <common/jit/ScalarNode.hpp>
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index 3d74a897ba..d907cad92f 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -9,20 +9,14 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
-#include <Param.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
-#include <common/MemoryManagerBase.hpp>
-#include <common/jit/Node.hpp>
-//#include <err_opencl.hpp>
-//#include <jit/BufferNode.hpp>
-//#include <memory.hpp>
-#include <platform.hpp>
+#include <kernel/KParam.hpp>
 #include <traits.hpp>
 #include <types.hpp>
+#include <af/dim4.hpp>
 
-//#include <af/dim4.hpp>
+#include <sycl/buffer.hpp>
 
 #include <nonstd/span.hpp>
 #include <algorithm>
@@ -30,14 +24,25 @@
 #include <memory>
 #include <vector>
 
+enum class kJITHeuristics;
+
+namespace arrayfire {
 namespace common {
 template<typename T>
 class SparseArray;
-}
 
-namespace arrayfire {
+class Node;
+
+using Node_ptr = std::shared_ptr<Node>;
+
+}  // namespace common
 namespace oneapi {
 
+template<typename T>
+struct Param;
+template<typename T>
+struct AParam;
+
 template<typename T>
 using Buffer_ptr = std::shared_ptr<sycl::buffer<T>>;
 using af::dim4;
diff --git a/src/backend/oneapi/Event.hpp b/src/backend/oneapi/Event.hpp
index 1bdedf34ad..90aaf1b2ca 100644
--- a/src/backend/oneapi/Event.hpp
+++ b/src/backend/oneapi/Event.hpp
@@ -8,10 +8,13 @@
  ********************************************************/
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <common/EventBase.hpp>
+
 #include <af/event.h>
 
+#include <sycl/event.hpp>
+#include <sycl/queue.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 class OneAPIEventPolicy {
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 01088f86b7..cca1d519f6 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -9,8 +9,14 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <kernel/KParam.hpp>
+#include <types.hpp>
+
+#include <af/dim4.hpp>
+
+#include <sycl/accessor.hpp>
+#include <sycl/buffer.hpp>
+#include <sycl/handler.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/backend.hpp b/src/backend/oneapi/backend.hpp
index 3366912b3b..2eb14151d8 100644
--- a/src/backend/oneapi/backend.hpp
+++ b/src/backend/oneapi/backend.hpp
@@ -7,16 +7,18 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include "types.hpp"
 #ifdef __DH__
 #undef __DH__
 #endif
 
 #ifdef __CUDACC__
-#include <opencl_runtime.h>
 #define __DH__ __device__ __host__
 #else
 #define __DH__
 #endif
 
+namespace arrayfire {
+namespace oneapi {}
+}  // namespace arrayfire
+
 namespace detail = arrayfire::oneapi;
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index c559fafbbd..aea4398c66 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -7,22 +7,29 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/graphics_common.hpp>
+#include <device_manager.hpp>
 
 #include <GraphicsResourceManager.hpp>
 #include <build_version.hpp>
 #include <common/DefaultMemoryManager.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
+#include <common/graphics_common.hpp>
 #include <common/host_memory.hpp>
 #include <common/util.hpp>
-#include <device_manager.hpp>
 #include <err_oneapi.hpp>
 #include <memory.hpp>
 #include <platform.hpp>
 #include <af/oneapi.h>
 #include <af/version.h>
 
+#include <sycl/context.hpp>
+#include <sycl/device.hpp>
+#include <sycl/exception.hpp>
+#include <sycl/exception_list.hpp>
+#include <sycl/platform.hpp>
+#include <sycl/queue.hpp>
+
 #include <algorithm>
 #include <iterator>
 #include <sstream>
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index 37c5cbe087..198ddd07e0 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -9,7 +9,10 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
+#include <sycl/context.hpp>
+#include <sycl/device.hpp>
+#include <sycl/queue.hpp>
+
 #include <memory>
 #include <mutex>
 #include <string>
@@ -100,12 +103,12 @@ class DeviceManager {
 
     friend int setDevice(int device);
 
-    friend void addDeviceContext(sycl::device dev, sycl::context ctx,
-                                 sycl::queue que);
+    friend void addDeviceContext(sycl::device& dev, sycl::context& ctx,
+                                 sycl::queue& que);
 
-    friend void setDeviceContext(sycl::device dev, sycl::context ctx);
+    friend void setDeviceContext(sycl::device& dev, sycl::context& ctx);
 
-    friend void removeDeviceContext(sycl::device dev, sycl::context ctx);
+    friend void removeDeviceContext(sycl::device& dev, sycl::context& ctx);
 
     friend int getActiveDeviceType();
 
diff --git a/src/backend/oneapi/kernel/approx1.hpp b/src/backend/oneapi/kernel/approx1.hpp
index f520719749..4d9d039f1b 100644
--- a/src/backend/oneapi/kernel/approx1.hpp
+++ b/src/backend/oneapi/kernel/approx1.hpp
@@ -151,7 +151,6 @@ void approx1(Param<Ty> yo, const Param<Ty> yi, const Param<Tp> xo,
         write_accessor<Ty> yoAcc{*yo.data, h};
         read_accessor<Ty> yiAcc{*yi.data, h};
         read_accessor<Tp> xoAcc{*xo.data, h};
-        sycl::stream debugStream(128, 128, h);
 
         h.parallel_for(sycl::nd_range{global, local},
                        approx1Kernel<Ty, Tp, order>(
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 162c1d5254..0876b9e16c 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -45,8 +45,7 @@ class assignKernel {
     assignKernel(sycl::accessor<T> out, KParam oInfo, sycl::accessor<T> in,
                  KParam iInfo, AssignKernelParam_t p, sycl::accessor<uint> ptr0,
                  sycl::accessor<uint> ptr1, sycl::accessor<uint> ptr2,
-                 sycl::accessor<uint> ptr3, const int nBBS0, const int nBBS1,
-                 sycl::stream debug)
+                 sycl::accessor<uint> ptr3, const int nBBS0, const int nBBS1)
         : out_(out)
         , in_(in)
         , oInfo_(oInfo)
@@ -57,8 +56,7 @@ class assignKernel {
         , ptr2_(ptr2)
         , ptr3_(ptr3)
         , nBBS0_(nBBS0)
-        , nBBS1_(nBBS1)
-        , debug_(debug) {}
+        , nBBS1_(nBBS1) {}
 
     void operator()(sycl::nd_item<2> it) const {
         // retrive booleans that tell us which index to use
@@ -108,7 +106,6 @@ class assignKernel {
     AssignKernelParam_t p_;
     sycl::accessor<uint> ptr0_, ptr1_, ptr2_, ptr3_;
     const int nBBS0_, nBBS1_;
-    sycl::stream debug_;
 };
 
 template<typename T>
@@ -134,12 +131,10 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
         auto bptr2 = bPtr[2]->get_access(h);
         auto bptr3 = bPtr[3]->get_access(h);
 
-        sycl::stream debug_stream(2048, 128, h);
-
         h.parallel_for(
             sycl::nd_range<2>(global, local),
             assignKernel<T>(out_acc, out.info, in_acc, in.info, p, bptr0, bptr1,
-                            bptr2, bptr3, blk_x, blk_y, debug_stream));
+                            bptr2, bptr3, blk_x, blk_y));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
index 3814084c1b..cb3d323f07 100644
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -15,6 +15,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/builtins.hpp>
+
 #include <string>
 #include <vector>
 
@@ -200,7 +202,6 @@ void bilateral(Param<outType> out, const Param<inType> in, const float s_sigma,
     getQueue().submit([&](sycl::handler& h) {
         auto inAcc  = in.data->get_access(h);
         auto outAcc = out.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         auto localMem = local_accessor<outType, 1>(num_shrd_elems, h);
         auto gauss2d  = local_accessor<outType, 1>(num_shrd_elems, h);
diff --git a/src/backend/oneapi/kernel/diagonal.hpp b/src/backend/oneapi/kernel/diagonal.hpp
index a21c1abd11..c49d9871e3 100644
--- a/src/backend/oneapi/kernel/diagonal.hpp
+++ b/src/backend/oneapi/kernel/diagonal.hpp
@@ -82,7 +82,6 @@ static void diagCreate(Param<T> out, Param<T> in, int num) {
     getQueue().submit([&](sycl::handler &h) {
         auto oData = out.data->get_access(h);
         auto iData = in.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         h.parallel_for(sycl::nd_range{global, local},
                        diagCreateKernel<T>(oData, out.info, iData, in.info, num,
@@ -151,7 +150,6 @@ static void diagExtract(Param<T> out, Param<T> in, int num) {
     getQueue().submit([&](sycl::handler &h) {
         auto oData = out.data->get_access(h);
         auto iData = in.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         h.parallel_for(sycl::nd_range{global, local},
                        diagExtractKernel<T>(oData, out.info, iData, in.info,
diff --git a/src/backend/oneapi/kernel/diff.hpp b/src/backend/oneapi/kernel/diff.hpp
index bd3d925d3b..f5a73c8c40 100644
--- a/src/backend/oneapi/kernel/diff.hpp
+++ b/src/backend/oneapi/kernel/diff.hpp
@@ -111,7 +111,6 @@ void diff(Param<T> out, const Param<T> in, const unsigned indims,
     getQueue().submit([&](sycl::handler &h) {
         auto inAcc  = in.data->get_access(h);
         auto outAcc = out.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         h.parallel_for(
             sycl::nd_range{global, local},
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
index 99ee437ae3..ea6c4c229a 100644
--- a/src/backend/oneapi/kernel/histogram.hpp
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -15,6 +15,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/atomic_ref.hpp>
+
 #include <string>
 #include <vector>
 
@@ -152,7 +154,6 @@ void histogram(Param<uint> out, const Param<T> in, int nbins, float minval,
     getQueue().submit([&](sycl::handler &h) {
         auto inAcc  = in.data->get_access(h);
         auto outAcc = out.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         auto localMem = local_accessor<uint, 1>(locSize, h);
 
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
index cefd67c992..d6bb62b177 100644
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -11,6 +11,9 @@
 #include <math.hpp>
 #include <types.hpp>
 #include <af/constants.h>
+
+#include <sycl/builtins.hpp>
+
 #include <algorithm>
 
 namespace arrayfire {
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 956bbc401a..e326ff9416 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -28,7 +28,7 @@ class iotaKernel {
    public:
     iotaKernel(sycl::accessor<T> out, KParam oinfo, const int s0, const int s1,
                const int s2, const int s3, const int blocksPerMatX,
-               const int blocksPerMatY, sycl::stream debug)
+               const int blocksPerMatY)
         : out_(out)
         , oinfo_(oinfo)
         , s0_(s0)
@@ -36,8 +36,7 @@ class iotaKernel {
         , s2_(s2)
         , s3_(s3)
         , blocksPerMatX_(blocksPerMatX)
-        , blocksPerMatY_(blocksPerMatY)
-        , debug_(debug) {}
+        , blocksPerMatY_(blocksPerMatY) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group gg = it.get_group();
@@ -77,7 +76,6 @@ class iotaKernel {
     KParam oinfo_;
     int s0_, s1_, s2_, s3_;
     int blocksPerMatX_, blocksPerMatY_;
-    sycl::stream debug_;
 };
 
 template<typename T>
@@ -100,15 +98,13 @@ void iota(Param<T> out, const af::dim4& sdims) {
             .submit([=](sycl::handler& h) {
                 auto out_acc = out.data->get_access(h);
 
-                sycl::stream debug_stream(2048, 128, h);
-
                 h.parallel_for(
                     ndrange,
                     iotaKernel<T>(out_acc, out.info, static_cast<int>(sdims[0]),
                                   static_cast<int>(sdims[1]),
                                   static_cast<int>(sdims[2]),
                                   static_cast<int>(sdims[3]), blocksPerMatX,
-                                  blocksPerMatY, debug_stream));
+                                  blocksPerMatY));
             })
             .wait();
         ONEAPI_DEBUG_FINISH(getQueue());
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index d0361a18dc..3f3dbc378b 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -13,7 +13,6 @@
 #include <common/Transform.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
-//#include <copy.hpp>?
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
 #include <kernel/default_config.hpp>
@@ -21,7 +20,9 @@
 #include <math.hpp>
 #include <memory.hpp>
 
-#include <iostream>
+#include <sycl/builtins.hpp>
+#include <sycl/group_algorithm.hpp>
+
 #include <memory>
 #include <vector>
 
@@ -72,8 +73,8 @@ class meanDimKernelSMEM {
                       read_accessor<Ti> in, KParam iInfo, read_accessor<Tw> iwt,
                       KParam iwInfo, uint groups_x, uint groups_y,
                       uint offset_dim, local_accessor<compute_t<To>, 1> s_val,
-                      local_accessor<compute_t<Tw>, 1> s_idx,
-                      sycl::stream debug, bool input_weight, bool output_weight)
+                      local_accessor<compute_t<Tw>, 1> s_idx, bool input_weight,
+                      bool output_weight)
         : out_(out)
         , owt_(owt)
         , in_(in)
@@ -88,8 +89,7 @@ class meanDimKernelSMEM {
         , s_val_(s_val)
         , s_idx_(s_idx)
         , input_weight_(input_weight)
-        , output_weight_(output_weight)
-        , debug_(debug) {}
+        , output_weight_(output_weight) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -217,7 +217,6 @@ class meanDimKernelSMEM {
     local_accessor<compute_t<To>, 1> s_val_;
     local_accessor<compute_t<Tw>, 1> s_idx_;
     bool input_weight_, output_weight_;
-    sycl::stream debug_;
 };
 
 template<typename Ti, typename Tw, typename To, int dim>
@@ -233,8 +232,6 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 2048, 2048, h);
-
         auto s_val = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
         auto s_idx = local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
 
@@ -254,7 +251,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
-                                   debug_stream, input_weight, output_weight));
+                                   input_weight, output_weight));
                 break;
             case 4:
                 h.parallel_for(sycl::nd_range<2>(global, local),
@@ -262,7 +259,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
-                                   debug_stream, input_weight, output_weight));
+                                   input_weight, output_weight));
                 break;
             case 2:
                 h.parallel_for(sycl::nd_range<2>(global, local),
@@ -270,7 +267,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
-                                   debug_stream, input_weight, output_weight));
+                                   input_weight, output_weight));
                 break;
             case 1:
                 h.parallel_for(sycl::nd_range<2>(global, local),
@@ -278,7 +275,7 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                                    out_acc, out.info, owt_acc, owt.info, in_acc,
                                    in.info, iwt_acc, iwt.info, blocks_dim[0],
                                    blocks_dim[1], blocks_dim[dim], s_val, s_idx,
-                                   debug_stream, input_weight, output_weight));
+                                   input_weight, output_weight));
                 break;
         }
     });
@@ -333,8 +330,7 @@ class meanFirstKernelSMEM {
                         const uint repeat,
                         local_accessor<compute_t<To>, 1> s_val,
                         local_accessor<compute_t<Tw>, 1> s_idx,
-                        sycl::stream debug, bool input_weight,
-                        bool output_weight)
+                        bool input_weight, bool output_weight)
         : out_(out)
         , owt_(owt)
         , in_(in)
@@ -350,8 +346,7 @@ class meanFirstKernelSMEM {
         , s_val_(s_val)
         , s_idx_(s_idx)
         , input_weight_(input_weight)
-        , output_weight_(output_weight)
-        , debug_(debug) {}
+        , output_weight_(output_weight) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -387,7 +382,7 @@ class meanFirstKernelSMEM {
         bool cond = (yid < iInfo_.dims[1] && zid < iInfo_.dims[2] &&
                      wid < iInfo_.dims[3]);
 
-        int lim = sycl::min((dim_t)(xid + repeat_ * DIMX_), iInfo_.dims[0]);
+        int lim = min((dim_t)(xid + repeat_ * DIMX_), iInfo_.dims[0]);
 
         common::Transform<Ti, compute_t<To>, af_add_t> transform;
 
@@ -411,7 +406,8 @@ class meanFirstKernelSMEM {
         } else {
             for (int id = xid + DIMX_; cond && id < lim; id += DIMX_) {
                 // Faster version of stable_mean when iwptr is NULL
-                val    = val + (transform(iptr[id]) - val) / (weight + (Tw)1);
+                val = val + (transform(iptr[id]) - compute_t<To>(val)) /
+                                (weight + (Tw)1);
                 weight = weight + (Tw)1;
             }
         }
@@ -493,7 +489,6 @@ class meanFirstKernelSMEM {
     local_accessor<compute_t<To>, 1> s_val_;
     local_accessor<compute_t<Tw>, 1> s_idx_;
     bool input_weight_, output_weight_;
-    sycl::stream debug_;
 };
 
 template<typename Ti, typename Tw, typename To>
@@ -511,8 +506,6 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 2048, 2048, h);
-
         auto s_val = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
         auto s_idx = local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
 
@@ -530,7 +523,7 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
             meanFirstKernelSMEM<Ti, Tw, To>(
                 out_acc, out.info, owt_acc, owt.info, in_acc, in.info, iwt_acc,
                 iwt.info, threads_x, groups_x, groups_y, repeat, s_val, s_idx,
-                debug_stream, input_weight, output_weight));
+                input_weight, output_weight));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index efe577c9ce..294573b1bf 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -9,11 +9,9 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
-//#include <common/kernel_cache.hpp>
 #include <common/traits.hpp>
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
@@ -35,7 +33,7 @@ class memCopy {
    public:
     memCopy(sycl::accessor<T> out, dims_t ostrides, sycl::accessor<T> in,
             dims_t idims, dims_t istrides, int offset, int groups_0,
-            int groups_1, sycl::stream debug)
+            int groups_1)
         : out_(out)
         , in_(in)
         , ostrides_(ostrides)
@@ -43,8 +41,7 @@ class memCopy {
         , istrides_(istrides)
         , offset_(offset)
         , groups_0_(groups_0)
-        , groups_1_(groups_1)
-        , debug_(debug) {}
+        , groups_1_(groups_1) {}
 
     void operator()(sycl::nd_item<2> it) const {
         const int lid0 = it.get_local_id(0);
@@ -79,7 +76,6 @@ class memCopy {
     sycl::accessor<T> out_, in_;
     dims_t ostrides_, idims_, istrides_;
     int offset_, groups_0_, groups_1_;
-    sycl::stream debug_;
 };
 
 constexpr uint DIM0 = 32;
@@ -111,11 +107,9 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
         auto out_acc = out->get_access(h);
         auto in_acc  = const_cast<sycl::buffer<T> *>(in)->get_access(h);
 
-        sycl::stream debug_stream(2048, 128, h);
-
         h.parallel_for(ndrange,
                        memCopy<T>(out_acc, _ostrides, in_acc, _idims, _istrides,
-                                  offset, groups_0, groups_1, debug_stream));
+                                  offset, groups_0, groups_1));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -204,8 +198,7 @@ class reshapeCopy {
    public:
     reshapeCopy(sycl::accessor<outType> dst, KParam oInfo,
                 sycl::accessor<inType> src, KParam iInfo, outType default_value,
-                float factor, dims_t trgt, int blk_x, int blk_y,
-                sycl::stream debug)
+                float factor, dims_t trgt, int blk_x, int blk_y)
         : dst_(dst)
         , src_(src)
         , oInfo_(oInfo)
@@ -214,8 +207,7 @@ class reshapeCopy {
         , factor_(factor)
         , trgt_(trgt)
         , blk_x_(blk_x)
-        , blk_y_(blk_y)
-        , debug_(debug) {}
+        , blk_y_(blk_y) {}
 
     void operator()(sycl::nd_item<2> it) const {
         const uint lx = it.get_local_id(0);
@@ -265,7 +257,6 @@ class reshapeCopy {
     float factor_;
     dims_t trgt_;
     int blk_x_, blk_y_;
-    sycl::stream debug_;
 };
 
 template<typename inType, typename outType>
@@ -305,18 +296,16 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
         auto src_acc =
             const_cast<sycl::buffer<inType> *>(src.data)->get_access(h);
 
-        sycl::stream debug_stream(2048, 128, h);
-
         if (same_dims) {
             h.parallel_for(ndrange, reshapeCopy<inType, outType, true>(
                                         dst_acc, dst.info, src_acc, src.info,
                                         default_value, (float)factor, trgt_dims,
-                                        blk_x, blk_y, debug_stream));
+                                        blk_x, blk_y));
         } else {
             h.parallel_for(ndrange, reshapeCopy<inType, outType, false>(
                                         dst_acc, dst.info, src_acc, src.info,
                                         default_value, (float)factor, trgt_dims,
-                                        blk_x, blk_y, debug_stream));
+                                        blk_x, blk_y));
         }
     });
     ONEAPI_DEBUG_FINISH(getQueue());
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
index d86700a7fb..66e286fea9 100644
--- a/src/backend/oneapi/kernel/random_engine.hpp
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -8,8 +8,8 @@
  ********************************************************/
 #pragma once
 
+#include <Param.hpp>
 #include <common/dispatch.hpp>
-#include <common/half.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
 #include <kernel/random_engine_mersenne.hpp>
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 426b518eba..9769285d2f 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -23,8 +23,8 @@ namespace kernel {
 //// above. This is done so that we can avoid unnecessary computations because
 /// the / __half datatype is not a constexprable type. This prevents the
 /// compiler from / peforming these operations at compile time.
-//#define HALF_FACTOR __ushort_as_half(0x100u)
-//#define HALF_HALF_FACTOR __ushort_as_half(0x80)
+// #define HALF_FACTOR __ushort_as_half(0x100u)
+// #define HALF_HALF_FACTOR __ushort_as_half(0x80)
 //
 //// Conversion to half adapted from Random123
 ////#define SIGNED_HALF_FACTOR                                \
@@ -35,8 +35,8 @@ namespace kernel {
 //// above. This is done so that we can avoid unnecessary computations because
 /// the / __half datatype is not a constexprable type. This prevents the
 /// compiler from / peforming these operations at compile time
-//#define SIGNED_HALF_FACTOR __ushort_as_half(0x200u)
-//#define SIGNED_HALF_HALF_FACTOR __ushort_as_half(0x100u)
+// #define SIGNED_HALF_FACTOR __ushort_as_half(0x200u)
+// #define SIGNED_HALF_HALF_FACTOR __ushort_as_half(0x100u)
 //
 ///// This is the largest integer representable by fp16. We need to
 ///// make sure that the value converted from ushort is smaller than this
@@ -47,15 +47,15 @@ namespace kernel {
 //__device__ static __half oneMinusGetHalf01(uint num) {
 //    // convert to ushort before the min operation
 //    ushort v = min(max_int_before_infinity, ushort(num));
-//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
+// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
 //    return (1.0f - __half2float(__hfma(__ushort2half_rn(v), HALF_FACTOR,
 //                                       HALF_HALF_FACTOR)));
-//#else
+// #else
 //    __half out = __ushort_as_half(0x3c00u) /*1.0h*/ -
 //                 __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
 //    if (__hisinf(out)) printf("val: %d ushort: %d\n", num, v);
 //    return out;
-//#endif
+// #endif
 //}
 //
 //// Generates rationals in (0, 1]
@@ -128,22 +128,22 @@ static double getDoubleNegative11(uint num1, uint num2) {
 
 namespace {
 //
-//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
-//#define HALF_MATH_FUNC(OP, HALF_OP)    \
+// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+// #define HALF_MATH_FUNC(OP, HALF_OP)    \
 //    template<>                         \
 //    __device__ __half OP(__half val) { \
 //        return ::HALF_OP(val);         \
 //    }
-//#else
-//#define HALF_MATH_FUNC(OP, HALF_OP)     \
+// #else
+// #define HALF_MATH_FUNC(OP, HALF_OP)     \
 //    template<>                          \
 //    __device__ __half OP(__half val) {  \
 //        float fval = __half2float(val); \
 //        return __float2half(OP(fval));  \
 //    }
-//#endif
+// #endif
 //
-//#define MATH_FUNC(OP, DOUBLE_OP, FLOAT_OP, HALF_OP) \
+// #define MATH_FUNC(OP, DOUBLE_OP, FLOAT_OP, HALF_OP) \
 //    template<typename T>                            \
 //    __device__ T OP(T val);                         \
 //    template<>                                      \
@@ -176,16 +176,16 @@ namespace {
 //
 // template<>
 //__device__ void sincos(__half val, __half *sptr, __half *cptr) {
-//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
 //    *sptr = sin(val);
 //    *cptr = cos(val);
-//#else
+// #else
 //    float s, c;
 //    float fval = __half2float(val);
 //    sincos(fval, &s, &c);
 //    *sptr = __float2half(s);
 //    *cptr = __float2half(c);
-//#endif
+// #endif
 //}
 //
 template<typename T>
@@ -198,18 +198,18 @@ void sincospi(T val, T *sptr, T *cptr) {
 //__device__ void sincospi(__half val, __half *sptr, __half *cptr) {
 //    // CUDA cannot make __half into a constexpr as of CUDA 11 so we are
 //    // converting this offline
-//#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
 //    const __half pi_val = __ushort_as_half(0x4248);  // 0x4248 == 3.14062h
 //    val *= pi_val;
 //    *sptr = sin(val);
 //    *cptr = cos(val);
-//#else
+// #else
 //    float fval = __half2float(val);
 //    float s, c;
 //    sincospi(fval, &s, &c);
 //    *sptr = __float2half(s);
 //    *cptr = __float2half(c);
-//#endif
+// #endif
 //}
 //
 }  // namespace
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index eb8b206a02..14e5f757c5 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -19,6 +19,11 @@
 #include <math.hpp>
 #include <memory.hpp>
 
+#include <sycl/atomic_fence.hpp>
+#include <sycl/atomic_ref.hpp>
+#include <sycl/builtins.hpp>
+#include <sycl/group_algorithm.hpp>
+
 #include <algorithm>
 #include <climits>
 #include <complex>
@@ -55,7 +60,7 @@ class reduceAllKernelSMEM {
                         uint groups_x, uint groups_y, uint repeat,
                         bool change_nan, To nanval,
                         local_accessor<compute_t<To>, 1> s_ptr,
-                        local_accessor<bool, 1> amLast, sycl::stream debug)
+                        local_accessor<bool, 1> amLast)
         : out_(out)
         , retCount_(retCount)
         , tmp_(tmp)
@@ -70,8 +75,7 @@ class reduceAllKernelSMEM {
         , change_nan_(change_nan)
         , nanval_(nanval)
         , s_ptr_(s_ptr)
-        , amLast_(amLast)
-        , debug_(debug) {}
+        , amLast_(amLast) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -97,7 +101,7 @@ class reduceAllKernelSMEM {
                     (wid < iInfo_.dims[3]);
 
         dim_t last = (xid + repeat_ * DIMX_);
-        int lim    = sycl::min(last, iInfo_.dims[0]);
+        int lim    = min(last, iInfo_.dims[0]);
 
         compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
         for (int id = xid; cond && id < lim; id += DIMX_) {
@@ -238,7 +242,6 @@ class reduceAllKernelSMEM {
     To nanval_;
     local_accessor<compute_t<To>, 1> s_ptr_;
     local_accessor<bool, 1> amLast_;
-    sycl::stream debug_;
 };
 
 template<typename Ti, typename To, af_op_t op>
@@ -273,8 +276,6 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
         auto tmp_acc      = tmp.getData()->get_access(h);
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
         auto shrdMem =
             local_accessor<compute_t<To>, 1>(creduce::THREADS_PER_BLOCK, h);
         auto amLast = local_accessor<bool, 1>(1, h);
@@ -283,7 +284,7 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
             reduceAllKernelSMEM<Ti, To, op>(
                 out_acc, out.info, retCount_acc, tmp_acc, (KParam)tmp, in_acc,
                 in.info, threads_x, groups_x, groups_y, repeat, change_nan,
-                scalar<To>(nanval), shrdMem, amLast, debug_stream));
+                scalar<To>(nanval), shrdMem, amLast));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index bfb4f808aa..99f0452785 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -46,8 +46,7 @@ class reduceDimKernelSMEM {
     reduceDimKernelSMEM(write_accessor<To> out, KParam oInfo,
                         read_accessor<Ti> in, KParam iInfo, uint groups_x,
                         uint groups_y, uint offset_dim, bool change_nan,
-                        To nanval, local_accessor<compute_t<To>, 1> s_val,
-                        sycl::stream debug)
+                        To nanval, local_accessor<compute_t<To>, 1> s_val)
         : out_(out)
         , oInfo_(oInfo)
         , iInfo_(iInfo)
@@ -57,8 +56,7 @@ class reduceDimKernelSMEM {
         , offset_dim_(offset_dim)
         , change_nan_(change_nan)
         , nanval_(nanval)
-        , s_val_(s_val)
-        , debug_(debug) {}
+        , s_val_(s_val) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -142,7 +140,6 @@ class reduceDimKernelSMEM {
     bool change_nan_;
     To nanval_;
     local_accessor<compute_t<To>, 1> s_val_;
-    sycl::stream debug_;
 };
 
 template<typename Ti, typename To, af_op_t op, uint dim>
@@ -158,8 +155,6 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
         auto shrdMem =
             local_accessor<compute_t<To>, 1>(creduce::THREADS_X * threads_y, h);
 
@@ -170,7 +165,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                     reduceDimKernelSMEM<Ti, To, op, dim, 8>(
                         out_acc, out.info, in_acc, in.info, blocks_dim[0],
                         blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem, debug_stream));
+                        scalar<To>(nanval), shrdMem));
                 break;
             case 4:
                 h.parallel_for(
@@ -178,7 +173,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                     reduceDimKernelSMEM<Ti, To, op, dim, 4>(
                         out_acc, out.info, in_acc, in.info, blocks_dim[0],
                         blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem, debug_stream));
+                        scalar<To>(nanval), shrdMem));
                 break;
             case 2:
                 h.parallel_for(
@@ -186,7 +181,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                     reduceDimKernelSMEM<Ti, To, op, dim, 2>(
                         out_acc, out.info, in_acc, in.info, blocks_dim[0],
                         blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem, debug_stream));
+                        scalar<To>(nanval), shrdMem));
                 break;
             case 1:
                 h.parallel_for(
@@ -194,7 +189,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                     reduceDimKernelSMEM<Ti, To, op, dim, 1>(
                         out_acc, out.info, in_acc, in.info, blocks_dim[0],
                         blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem, debug_stream));
+                        scalar<To>(nanval), shrdMem));
                 break;
         }
     });
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index 94553f2b07..342f6f2530 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -46,8 +46,7 @@ class reduceFirstKernelSMEM {
     reduceFirstKernelSMEM(write_accessor<To> out, KParam oInfo,
                           read_accessor<Ti> in, KParam iInfo, uint groups_x,
                           uint groups_y, uint repeat, bool change_nan,
-                          To nanval, local_accessor<compute_t<To>, 1> s_val,
-                          sycl::stream debug)
+                          To nanval, local_accessor<compute_t<To>, 1> s_val)
         : out_(out)
         , oInfo_(oInfo)
         , iInfo_(iInfo)
@@ -57,8 +56,7 @@ class reduceFirstKernelSMEM {
         , repeat_(repeat)
         , change_nan_(change_nan)
         , nanval_(nanval)
-        , s_val_(s_val)
-        , debug_(debug) {}
+        , s_val_(s_val) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -147,7 +145,6 @@ class reduceFirstKernelSMEM {
     bool change_nan_;
     To nanval_;
     local_accessor<compute_t<To>, 1> s_val_;
-    sycl::stream debug_;
 };
 
 template<typename Ti, typename To, af_op_t op>
@@ -165,39 +162,37 @@ void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
         auto shrdMem =
             local_accessor<compute_t<To>, 1>(creduce::THREADS_PER_BLOCK, h);
 
         switch (threads_x) {
             case 32:
-                h.parallel_for(sycl::nd_range<2>(global, local),
-                               reduceFirstKernelSMEM<Ti, To, op, 32>(
-                                   out_acc, out.info, in_acc, in.info, groups_x,
-                                   groups_y, repeat, change_nan,
-                                   scalar<To>(nanval), shrdMem, debug_stream));
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceFirstKernelSMEM<Ti, To, op, 32>(
+                        out_acc, out.info, in_acc, in.info, groups_x, groups_y,
+                        repeat, change_nan, scalar<To>(nanval), shrdMem));
                 break;
             case 64:
-                h.parallel_for(sycl::nd_range<2>(global, local),
-                               reduceFirstKernelSMEM<Ti, To, op, 64>(
-                                   out_acc, out.info, in_acc, in.info, groups_x,
-                                   groups_y, repeat, change_nan,
-                                   scalar<To>(nanval), shrdMem, debug_stream));
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceFirstKernelSMEM<Ti, To, op, 64>(
+                        out_acc, out.info, in_acc, in.info, groups_x, groups_y,
+                        repeat, change_nan, scalar<To>(nanval), shrdMem));
                 break;
             case 128:
-                h.parallel_for(sycl::nd_range<2>(global, local),
-                               reduceFirstKernelSMEM<Ti, To, op, 128>(
-                                   out_acc, out.info, in_acc, in.info, groups_x,
-                                   groups_y, repeat, change_nan,
-                                   scalar<To>(nanval), shrdMem, debug_stream));
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceFirstKernelSMEM<Ti, To, op, 128>(
+                        out_acc, out.info, in_acc, in.info, groups_x, groups_y,
+                        repeat, change_nan, scalar<To>(nanval), shrdMem));
                 break;
             case 256:
-                h.parallel_for(sycl::nd_range<2>(global, local),
-                               reduceFirstKernelSMEM<Ti, To, op, 256>(
-                                   out_acc, out.info, in_acc, in.info, groups_x,
-                                   groups_y, repeat, change_nan,
-                                   scalar<To>(nanval), shrdMem, debug_stream));
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    reduceFirstKernelSMEM<Ti, To, op, 256>(
+                        out_acc, out.info, in_acc, in.info, groups_x, groups_y,
+                        repeat, change_nan, scalar<To>(nanval), shrdMem));
                 break;
         }
     });
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
index c39ff556b7..b643bb6fc8 100644
--- a/src/backend/oneapi/kernel/reorder.hpp
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -63,7 +63,6 @@ class reorderCreateKernel {
 
         const int o_off   = ow * op_.strides[3] + oz * op_.strides[2];
         const int rdims[] = {d0_, d1_, d2_, d3_};
-        int ods[]         = {xx, yy, oz, ow};
         int ids[4]        = {0};
 
         ids[rdims[3]] = ow;
diff --git a/src/backend/oneapi/kernel/resize.hpp b/src/backend/oneapi/kernel/resize.hpp
index b44d878818..5443815b75 100644
--- a/src/backend/oneapi/kernel/resize.hpp
+++ b/src/backend/oneapi/kernel/resize.hpp
@@ -15,6 +15,8 @@
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/builtins.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index eb0683791c..b4a2678dac 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -17,6 +17,9 @@
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
+#include <sycl/builtins.hpp>
+#include <sycl/group_algorithm.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
@@ -41,7 +44,7 @@ class scanDimKernel {
                   const uint groups_y, const uint blocks_dim, const uint lim,
                   const bool isFinalPass, const uint DIMY,
                   const bool inclusive_scan, local_accessor<To, 1> s_val,
-                  local_accessor<To, 1> s_tmp, sycl::stream debug)
+                  local_accessor<To, 1> s_tmp)
         : out_acc_(out_acc)
         , tmp_acc_(tmp_acc)
         , in_acc_(in_acc)
@@ -56,8 +59,7 @@ class scanDimKernel {
         , isFinalPass_(isFinalPass)
         , inclusive_scan_(inclusive_scan)
         , s_val_(s_val)
-        , s_tmp_(s_tmp)
-        , debug_(debug) {}
+        , s_tmp_(s_tmp) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -162,7 +164,6 @@ class scanDimKernel {
     const bool isFinalPass_, inclusive_scan_;
     local_accessor<To, 1> s_val_;
     local_accessor<To, 1> s_tmp_;
-    sycl::stream debug_;
 };
 
 template<typename To, af_op_t op, int dim>
@@ -172,7 +173,7 @@ class scanDimBcastKernel {
                        read_accessor<To> tmp_acc, KParam tInfo,
                        const uint groups_x, const uint groups_y,
                        const uint groups_dim, const uint lim,
-                       const bool inclusive_scan, sycl::stream debug)
+                       const bool inclusive_scan)
         : out_acc_(out_acc)
         , tmp_acc_(tmp_acc)
         , oInfo_(oInfo)
@@ -181,8 +182,7 @@ class scanDimBcastKernel {
         , groups_y_(groups_y)
         , groups_dim_(groups_dim)
         , lim_(lim)
-        , inclusive_scan_(inclusive_scan)
-        , debug_(debug) {}
+        , inclusive_scan_(inclusive_scan) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -245,7 +245,6 @@ class scanDimBcastKernel {
     KParam oInfo_, tInfo_;
     const uint groups_x_, groups_y_, groups_dim_, lim_;
     const bool inclusive_scan_;
-    sycl::stream debug_;
 };
 
 template<typename Ti, typename To, af_op_t op, int dim>
@@ -264,8 +263,6 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
         write_accessor<To> tmp_acc{*tmp.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
         auto s_val =
             local_accessor<compute_t<To>, 1>(THREADS_X * threads_y * 2, h);
         auto s_tmp = local_accessor<compute_t<To>, 1>(THREADS_X, h);
@@ -275,7 +272,7 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
             scanDimKernel<Ti, To, op, dim>(
                 out_acc, out.info, tmp_acc, tmp.info, in_acc, in.info,
                 blocks_all[0], blocks_all[1], blocks_all[dim], lim, isFinalPass,
-                threads_y, inclusive_scan, s_val, s_tmp, debug_stream));
+                threads_y, inclusive_scan, s_val, s_tmp));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -294,13 +291,11 @@ static void bcast_dim_launcher(Param<To> out, Param<To> tmp,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<To> tmp_acc{*tmp.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
-        h.parallel_for(sycl::nd_range<2>(global, local),
-                       scanDimBcastKernel<To, op, dim>(
-                           out_acc, out.info, tmp_acc, tmp.info, blocks_all[0],
-                           blocks_all[1], blocks_all[dim], lim, inclusive_scan,
-                           debug_stream));
+        h.parallel_for(
+            sycl::nd_range<2>(global, local),
+            scanDimBcastKernel<To, op, dim>(
+                out_acc, out.info, tmp_acc, tmp.info, blocks_all[0],
+                blocks_all[1], blocks_all[dim], lim, inclusive_scan));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 78039dd36d..777f8f205e 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -17,6 +17,9 @@
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
+#include <sycl/builtins.hpp>
+#include <sycl/group_algorithm.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
@@ -40,8 +43,7 @@ class scanFirstKernel {
                     read_accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
                     const uint groups_y, const uint lim, const bool isFinalPass,
                     const uint DIMX, const bool inclusive_scan,
-                    local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp,
-                    sycl::stream debug_stream)
+                    local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp)
         : out_acc_(out_acc)
         , tmp_acc_(tmp_acc)
         , in_acc_(in_acc)
@@ -55,8 +57,7 @@ class scanFirstKernel {
         , isFinalPass_(isFinalPass)
         , inclusive_scan_(inclusive_scan)
         , s_val_(s_val)
-        , s_tmp_(s_tmp)
-        , debug_stream_(debug_stream) {}
+        , s_tmp_(s_tmp) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -122,7 +123,6 @@ class scanFirstKernel {
                 if (cond_yzw && id == (oInfo_.dims[0] - 1)) {
                     optr[0] = init;
                 } else if (cond_yzw && id < (oInfo_.dims[0] - 1)) {
-                    // debug_stream_ << "oe0 ";
                     optr[id + 1] = val;
                 }
             }
@@ -130,10 +130,7 @@ class scanFirstKernel {
             group_barrier(g);
         }
 
-        if (!isFinalPass_ && isLast && cond_yzw) {
-            // debug_stream_ << "ot ";
-            tptr[groupId_x] = val;
-        }
+        if (!isFinalPass_ && isLast && cond_yzw) { tptr[groupId_x] = val; }
     }
 
    protected:
@@ -145,7 +142,6 @@ class scanFirstKernel {
     const bool isFinalPass_, inclusive_scan_;
     local_accessor<To, 1> s_val_;
     local_accessor<To, 1> s_tmp_;
-    sycl::stream debug_stream_;
 };
 
 template<typename To, af_op_t op>
@@ -154,8 +150,7 @@ class scanFirstBcastKernel {
     scanFirstBcastKernel(write_accessor<To> out_acc, KParam oInfo,
                          read_accessor<To> tmp_acc, KParam tInfo,
                          const uint groups_x, const uint groups_y,
-                         const uint lim, const bool inclusive_scan,
-                         sycl::stream debug_stream)
+                         const uint lim, const bool inclusive_scan)
         : out_acc_(out_acc)
         , tmp_acc_(tmp_acc)
         , oInfo_(oInfo)
@@ -163,8 +158,7 @@ class scanFirstBcastKernel {
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , lim_(lim)
-        , inclusive_scan_(inclusive_scan)
-        , debug_stream_(debug_stream) {}
+        , inclusive_scan_(inclusive_scan) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -209,7 +203,6 @@ class scanFirstBcastKernel {
     KParam oInfo_, tInfo_;
     const uint groups_x_, groups_y_, lim_;
     const bool inclusive_scan_;
-    sycl::stream debug_stream_;
 };
 
 template<typename Ti, typename To, af_op_t op>
@@ -227,20 +220,17 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
         write_accessor<To> tmp_acc{*tmp.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
         const int DIMY            = THREADS_PER_BLOCK / threads_x;
         const int SHARED_MEM_SIZE = (2 * threads_x + 1) * (DIMY);
         auto s_val = local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
         auto s_tmp = local_accessor<compute_t<To>, 1>(DIMY, h);
 
         // TODO threads_x as template arg for #pragma unroll?
-        h.parallel_for(
-            sycl::nd_range<2>(global, local),
-            scanFirstKernel<Ti, To, op>(
-                out_acc, out.info, tmp_acc, tmp.info, in_acc, in.info, groups_x,
-                groups_y, lim, isFinalPass, threads_x, inclusive_scan, s_val,
-                s_tmp, debug_stream));
+        h.parallel_for(sycl::nd_range<2>(global, local),
+                       scanFirstKernel<Ti, To, op>(
+                           out_acc, out.info, tmp_acc, tmp.info, in_acc,
+                           in.info, groups_x, groups_y, lim, isFinalPass,
+                           threads_x, inclusive_scan, s_val, s_tmp));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -258,12 +248,10 @@ static void bcast_first_launcher(Param<To> out, Param<To> tmp,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<To> tmp_acc{*tmp.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
-
         h.parallel_for(sycl::nd_range<2>(global, local),
                        scanFirstBcastKernel<To, op>(
                            out_acc, out.info, tmp_acc, tmp.info, groups_x,
-                           groups_y, lim, inclusive_scan, debug_stream));
+                           groups_y, lim, inclusive_scan));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index 0fac0bacb7..d22a6f4475 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -54,7 +54,7 @@ class transposeKernel {
                     const sycl::accessor<T> iData, const KParam in,
                     const int blocksPerMatX, const int blocksPerMatY,
                     const bool conjugate, const bool IS32MULTIPLE,
-                    local_accessor<T, 1> shrdMem, sycl::stream debugStream)
+                    local_accessor<T, 1> shrdMem)
         : oData_(oData)
         , out_(out)
         , iData_(iData)
@@ -63,8 +63,7 @@ class transposeKernel {
         , blocksPerMatY_(blocksPerMatY)
         , conjugate_(conjugate)
         , IS32MULTIPLE_(IS32MULTIPLE)
-        , shrdMem_(shrdMem)
-        , debugStream_(debugStream) {}
+        , shrdMem_(shrdMem) {}
     void operator()(sycl::nd_item<2> it) const {
         const int shrdStride = TILE_DIM + 1;
 
@@ -134,7 +133,6 @@ class transposeKernel {
     bool conjugate_;
     bool IS32MULTIPLE_;
     local_accessor<T, 1> shrdMem_;
-    sycl::stream debugStream_;
 };
 
 template<typename T>
@@ -151,14 +149,12 @@ void transpose(Param<T> out, const Param<T> in, const bool conjugate,
     getQueue().submit([&](sycl::handler &h) {
         auto r = in.data->get_access(h);
         auto q = out.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         auto shrdMem = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 
-        h.parallel_for(
-            sycl::nd_range{global, local},
-            transposeKernel<T>(q, out.info, r, in.info, blk_x, blk_y, conjugate,
-                               IS32MULTIPLE, shrdMem, debugStream));
+        h.parallel_for(sycl::nd_range{global, local},
+                       transposeKernel<T>(q, out.info, r, in.info, blk_x, blk_y,
+                                          conjugate, IS32MULTIPLE, shrdMem));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/triangle.hpp b/src/backend/oneapi/kernel/triangle.hpp
index 96fdeb3d88..2f65abe20c 100644
--- a/src/backend/oneapi/kernel/triangle.hpp
+++ b/src/backend/oneapi/kernel/triangle.hpp
@@ -109,7 +109,6 @@ void triangle(Param<T> out, const Param<T> in, bool is_upper,
     getQueue().submit([&](sycl::handler &h) {
         auto iAcc = in.data->get_access(h);
         auto rAcc = out.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
 
         h.parallel_for(
             sycl::nd_range{global, local},
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index d9ee535eb6..3d8fe3324f 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -37,7 +37,7 @@ class whereKernel {
                 read_accessor<uint> otmp_acc, KParam otInfo,
                 read_accessor<uint> rtmp_acc, KParam rtInfo,
                 read_accessor<T> in_acc, KParam iInfo, uint groups_x,
-                uint groups_y, uint lim, sycl::stream debug)
+                uint groups_y, uint lim)
         : out_acc_(out_acc)
         , otmp_acc_(otmp_acc)
         , rtmp_acc_(rtmp_acc)
@@ -48,8 +48,7 @@ class whereKernel {
         , iInfo_(iInfo)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
-        , lim_(lim)
-        , debug_(debug) {}
+        , lim_(lim) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g   = it.get_group();
@@ -99,7 +98,6 @@ class whereKernel {
     read_accessor<T> in_acc_;
     KParam oInfo_, otInfo_, rtInfo_, iInfo_;
     uint groups_x_, groups_y_, lim_;
-    sycl::stream debug_;
 };
 
 template<typename T>
@@ -181,11 +179,10 @@ static void where(Param<uint> &out, Param<T> in) {
         read_accessor<uint> rtmp_acc{*rtmp.data, h};
         read_accessor<T> in_acc{*in.data, h};
 
-        sycl::stream debug_stream(2048 * 256, 128, h);
         h.parallel_for(sycl::nd_range<2>(global, local),
                        whereKernel<T>(out_acc, out.info, otmp_acc, otmp.info,
                                       rtmp_acc, rtmp.info, in_acc, in.info,
-                                      groups_x, groups_y, lim, debug_stream));
+                                      groups_x, groups_y, lim));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
     out_alloc.release();
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 17cfb37d32..ee47082295 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -18,6 +18,9 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
+#include <sycl/builtins.hpp>
+#include <sycl/usm.hpp>
+
 #include <utility>
 
 using arrayfire::common::bytesToString;
@@ -151,9 +154,7 @@ T *pinnedAlloc(const size_t &elements) {
     return static_cast<T *>(ptr);
 }
 
-void pinnedFree(void *ptr) {
-    pinnedMemoryManager().unlock(ptr, false);
-}
+void pinnedFree(void *ptr) { pinnedMemoryManager().unlock(ptr, false); }
 
 // template unique_ptr<int, function<void(int *)>> memAlloc<T>(
 #define INSTANTIATE(T)                                               \
@@ -252,7 +253,7 @@ AllocatorPinned::AllocatorPinned() { logger = common::loggerFactory("mem"); }
 
 void AllocatorPinned::shutdown() { shutdownPinnedMemoryManager(); }
 
-int AllocatorPinned::getActiveDeviceId() { oneapi::getActiveDeviceId(); }
+int AllocatorPinned::getActiveDeviceId() { return oneapi::getActiveDeviceId(); }
 
 size_t AllocatorPinned::getMaxMemorySize(int id) {
     return oneapi::getDeviceMemorySize(id);
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index 809f219eb7..462c1498f1 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -10,6 +10,8 @@
 
 #include <common/AllocatorInterface.hpp>
 
+#include <sycl/buffer.hpp>
+
 #include <cstdlib>
 #include <functional>
 #include <map>
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index dc2c8a9766..b95b5326bc 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <platform.hpp>
+
 #include <GraphicsResourceManager.hpp>
 #include <blas.hpp>
 #include <build_version.hpp>
@@ -19,12 +21,15 @@
 #include <err_oneapi.hpp>
 #include <errorcodes.hpp>
 #include <memory.hpp>
+#include <af/oneapi.h>
 #include <af/version.h>
 
 #ifdef OS_MAC
 #include <OpenGL/CGLCurrent.h>
 #endif
 
+#include <sycl/platform.hpp>
+
 #include <cctype>
 #include <cstdlib>
 #include <functional>
@@ -418,7 +423,7 @@ void sync(int device) {
     setDevice(currDevice);
 }
 
-void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que) {
+void addDeviceContext(sycl::device& dev, sycl::context& ctx, sycl::queue& que) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     int nDevices = 0;
@@ -448,7 +453,7 @@ void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que) {
     memoryManager().addMemoryManagement(nDevices);
 }
 
-void setDeviceContext(sycl::device dev, sycl::context ctx) {
+void setDeviceContext(sycl::device& dev, sycl::context& ctx) {
     // FIXME: add OpenGL Interop for user provided contexts later
     DeviceManager& devMngr = DeviceManager::getInstance();
 
@@ -464,7 +469,7 @@ void setDeviceContext(sycl::device dev, sycl::context ctx) {
     AF_ERROR("No matching device found", AF_ERR_ARG);
 }
 
-void removeDeviceContext(sycl::device dev, sycl::context ctx) {
+void removeDeviceContext(sycl::device& dev, sycl::context& ctx) {
     if (getDevice() == dev && getContext() == ctx) {
         AF_ERROR("Cannot pop the device currently in use", AF_ERR_ARG);
     }
@@ -519,6 +524,22 @@ void removeDeviceContext(sycl::device dev, sycl::context ctx) {
     }
 }
 
+unsigned getMemoryBusWidth(const sycl::device& device) {
+    return device.get_info<sycl::info::device::global_mem_cache_line_size>();
+}
+
+size_t getL2CacheSize(const sycl::device& device) {
+    return device.get_info<sycl::info::device::global_mem_cache_line_size>();
+}
+
+unsigned getComputeUnits(const sycl::device& device) {
+    return device.get_info<sycl::info::device::max_compute_units>();
+}
+
+unsigned getMaxParallelThreads(const sycl::device& device) {
+    return getComputeUnits(device) * 2048;
+}
+
 bool synchronize_calls() {
     static const bool sync = getEnvVar("AF_SYNCHRONOUS_CALLS") == "1";
     return sync;
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index b508f6fc4e..de6ae498dc 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -9,9 +9,12 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <af/oneapi.h>
 
+#include <sycl/context.hpp>
+#include <sycl/device.hpp>
+#include <sycl/queue.hpp>
+
 #include <memory>
 #include <string>
 
@@ -68,6 +71,16 @@ size_t getDeviceMemorySize(int device);
 
 size_t getHostMemorySize();
 
+unsigned getMemoryBusWidth(const sycl::device& device);
+
+size_t getL2CacheSize(const sycl::device& device);
+
+unsigned getComputeUnits(const sycl::device& device);
+
+// maximum nr of threads the device really can run in parallel, without
+// scheduling
+unsigned getMaxParallelThreads(const sycl::device& device);
+
 // sycl::device::is_cpu,is_gpu,is_accelerator
 sycl::info::device_type getDeviceType();
 
@@ -88,11 +101,11 @@ std::string getPlatformName(const sycl::device& device);
 
 int setDevice(int device);
 
-void addDeviceContext(sycl::device dev, sycl::context ctx, sycl::queue que);
+void addDeviceContext(sycl::device& dev, sycl::context& ctx, sycl::queue& que);
 
-void setDeviceContext(sycl::device dev, sycl::context ctx);
+void setDeviceContext(sycl::device& dev, sycl::context& ctx);
 
-void removeDeviceContext(sycl::device dev, sycl::context ctx);
+void removeDeviceContext(sycl::device& dev, sycl::context& ctx);
 
 void sync(int device);
 
diff --git a/src/backend/oneapi/print.hpp b/src/backend/oneapi/print.hpp
index 0e487278d5..686445db49 100644
--- a/src/backend/oneapi/print.hpp
+++ b/src/backend/oneapi/print.hpp
@@ -9,6 +9,8 @@
 
 #pragma once
 #include <backend.hpp>
+#include <types.hpp>
+
 #include <ostream>
 
 namespace arrayfire {
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
index dacfd85f01..74d117a491 100644
--- a/src/backend/oneapi/types.hpp
+++ b/src/backend/oneapi/types.hpp
@@ -9,12 +9,13 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <common/kernel_type.hpp>
 #include <common/traits.hpp>
 #include <af/compilers.h>
 #include <af/traits.hpp>
 
+#include <sycl/aliases.hpp>
+
 #include <algorithm>
 #include <array>
 #include <complex>

From aed0ff5c626988340ee04e8d4751aab394b760c6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 6 Mar 2023 14:12:36 -0500
Subject: [PATCH 2415/2677] Update ToolkitDriverVersions for CUDA 12.1

---
 src/backend/cuda/device_manager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 00d2e68ee3..8000f2f635 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12010, 9, 0, 0},
     {12000, 9, 0, 0},
     {11080, 9, 0, 0},
     {11070, 8, 7, 0},
@@ -138,6 +139,7 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {12010, 525.60f, 527.41f},
         {12000, 525.60f, 527.41f},
         {11080, 450.80f, 452.39f},
         {11070, 450.80f, 452.39f},

From 0e06e99945264c6f1ac66b979b8320b6d2bc1670 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 Mar 2023 18:48:34 -0500
Subject: [PATCH 2416/2677] Improve half support for oneAPI

---
 src/backend/common/half.hpp                | 264 +++++++++++++--------
 src/backend/oneapi/Param.hpp               |   6 +
 src/backend/oneapi/kernel/reduce_all.hpp   |   6 +-
 src/backend/oneapi/kernel/reduce_dim.hpp   |  51 ++--
 src/backend/oneapi/kernel/reduce_first.hpp |  11 +-
 src/backend/oneapi/types.hpp               |   9 +-
 6 files changed, 208 insertions(+), 139 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 57545f4bcd..0de986ceb5 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -60,61 +60,126 @@ namespace common {
 
 #if defined(__CUDA_ARCH__)
 using native_half_t = __half;
+#elif defined(AF_ONEAPI)
+using native_half_t = sycl::half;
 #else
 using native_half_t = uint16_t;
 #endif
 
 #ifdef __CUDACC_RTC__
-template<typename T>
-AF_CONSTEXPR __DH__ native_half_t float2half(T value) {
-    return __float2half(value);
+template<std::float_round_style R = std::round_to_nearest>
+AF_CONSTEXPR __DH__ native_half_t float2half_impl(float value) {
+    return __float2half_rn(value);
+}
+
+template<std::float_round_style R = std::round_to_nearest>
+AF_CONSTEXPR __DH__ native_half_t float2half_impl(double value) {
+    return __float2half_rn(value);
 }
 
-AF_CONSTEXPR __DH__ inline float half2float(native_half_t value) noexcept {
+AF_CONSTEXPR __DH__ inline float half2float_impl(native_half_t value) noexcept {
     return __half2float(value);
 }
 
 template<typename T>
-AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept;
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept;
 
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(int value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(int value) noexcept {
     return __int2half_rn(value);
 }
 
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(unsigned value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(unsigned value) noexcept {
     return __uint2half_rn(value);
 }
 
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(long long value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(long long value) noexcept {
     return __ll2half_rn(value);
 }
 
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(unsigned long long value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t
+int2half_impl(unsigned long long value) noexcept {
     return __ull2half_rn(value);
 }
 
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(short value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(short value) noexcept {
     return __short2half_rn(value);
 }
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(unsigned short value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(unsigned short value) noexcept {
     return __ushort2half_rn(value);
 }
 
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(char value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(char value) noexcept {
     return __ull2half_rn(value);
 }
 template<>
-AF_CONSTEXPR __DH__ native_half_t int2half(unsigned char value) noexcept {
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(unsigned char value) noexcept {
     return __ull2half_rn(value);
 }
 
+#elif defined(AF_ONEAPI)
+
+template<std::float_round_style R = std::round_to_nearest>
+AF_CONSTEXPR native_half_t float2half_impl(float value) {
+    return static_cast<native_half_t>(value);
+}
+
+template<std::float_round_style R = std::round_to_nearest>
+AF_CONSTEXPR native_half_t float2half_impl(double value) {
+    return static_cast<native_half_t>(value);
+}
+
+AF_CONSTEXPR inline float half2float_impl(native_half_t value) noexcept {
+    return static_cast<float>(value);
+}
+
+template<typename T>
+AF_CONSTEXPR native_half_t int2half_impl(T value) noexcept;
+
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(int value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(unsigned value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(long long value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(unsigned long long value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(short value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(unsigned short value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(char value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+template<>
+AF_CONSTEXPR native_half_t int2half_impl(unsigned char value) noexcept {
+    return static_cast<native_half_t>(value);
+}
+
 #else
 
 /// Convert integer to half-precision floating point.
@@ -162,22 +227,6 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
     return bits;
 }
 
-template<typename T, std::float_round_style R = std::round_to_nearest,
-         typename std::enable_if_t<std::is_integral<T>::value &&
-                                   std::is_signed<T>::value>* = nullptr>
-AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
-    uint16_t out = (value < 0) ? int2half_impl<R, true, T>(value)
-                               : int2half_impl<R, false, T>(value);
-    return out;
-}
-
-template<typename T, std::float_round_style R = std::round_to_nearest,
-         typename std::enable_if_t<std::is_integral<T>::value &&
-                                   std::is_unsigned<T>::value>* = nullptr>
-AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
-    return int2half_impl<R, false, T>(value);
-}
-
 /// Convert IEEE single-precision to half-precision.
 /// Credit for this goes to [Jeroen van der
 /// Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf).
@@ -186,7 +235,7 @@ AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
 ///
 /// \param value single-precision value
 /// \return binary representation of half-precision value
-template<std::float_round_style R = std::round_indeterminate>
+template<std::float_round_style R = std::round_to_nearest>
 __DH__ native_half_t float2half_impl(float value) noexcept {
     uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
                         // //violating strict aliasing!
@@ -366,23 +415,7 @@ __DH__ native_half_t float2half_impl(double value) {
     return hbits;
 }
 
-template<typename T, std::float_round_style R = std::round_to_nearest>
-#ifdef __CUDA_ARCH__
-AF_CONSTEXPR
-#endif
-    __DH__ native_half_t
-    float2half(T val) {
-#ifdef __CUDA_ARCH__
-    return __float2half(val);
-#else
-    return float2half_impl<R>(val);
-#endif
-}
-
-__DH__ inline float half2float(native_half_t value) noexcept {
-#ifdef __CUDA_ARCH__
-    return __half2float(value);
-#else
+__DH__ inline float half2float_impl(native_half_t value) noexcept {
     // return _cvtsh_ss(data.data_);
     constexpr uint32_t mantissa_table[2048] = {
         0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34A00000,
@@ -749,12 +782,52 @@ __DH__ inline float half2float(native_half_t value) noexcept {
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024};
 
+    uint16_t value_bits = 0;
+    std::memcpy(&value_bits, &value, sizeof(uint16_t));
     uint32_t bits =
-        mantissa_table[offset_table[value >> 10] + (value & 0x3FF)] +
-        exponent_table[value >> 10];
+        mantissa_table[offset_table[value_bits >> 10] + (value_bits & 0x3FF)] +
+        exponent_table[value_bits >> 10];
     float out = 0.0f;
     std::memcpy(&out, &bits, sizeof(float));
     return out;
+}
+
+#endif  // __CUDACC_RTC__
+
+template<typename T, std::float_round_style R = std::round_to_nearest>
+#ifdef __CUDA_ARCH__
+AF_CONSTEXPR
+#endif
+    __DH__ native_half_t
+    float2half(T val) {
+    return float2half_impl<R>(val);
+}
+
+__DH__ inline float half2float(native_half_t value) noexcept {
+    return half2float_impl(value);
+}
+
+template<typename T, std::float_round_style R = std::round_to_nearest,
+         typename std::enable_if_t<std::is_integral<T>::value &&
+                                   std::is_signed<T>::value>* = nullptr>
+AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
+#if defined(__CUDACC_RTC__) || defined(AF_ONEAPI)
+    native_half_t out = int2half_impl(value);
+#else
+    uint16_t out = (value < 0) ? int2half_impl<R, true, T>(value)
+                               : int2half_impl<R, false, T>(value);
+#endif
+    return out;
+}
+
+template<typename T, std::float_round_style R = std::round_to_nearest,
+         typename std::enable_if_t<std::is_integral<T>::value &&
+                                   std::is_unsigned<T>::value>* = nullptr>
+AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
+#if defined(__CUDACC_RTC__) || defined(AF_ONEAPI)
+    return int2half_impl(value);
+#else
+    return int2half_impl<R, false, T>(value);
 #endif
 }
 
@@ -771,6 +844,24 @@ __DH__ inline float half2float(native_half_t value) noexcept {
 /// \param value The value to convert to integer
 template<std::float_round_style R, bool E, typename T>
 AF_CONSTEXPR T half2int(native_half_t value) {
+#ifdef __CUDA_ARCH__
+    if constexpr (std::is_same_v<T, short> || std::is_same_v<T, char> ||
+                  std::is_same_v<T, unsigned char>) {
+        return __half2short_rn(value);
+    } else if constexpr (std::is_same_v<T, unsigned short>) {
+        return __half2ushort_rn(value);
+    } else if constexpr (std::is_same_v<T, long long>) {
+        return __half2ll_rn(value);
+    } else if constexpr (std::is_same_v<T, unsigned long long>) {
+        return __half2ull_rn(value);
+    } else if constexpr (std::is_same_v<T, int>) {
+        return __half2int_rn(value);
+    } else if constexpr (std::is_same_v<T, unsigned>) {
+        return __half2uint_rn(value);
+    }
+#elif defined(AF_ONEAPI)
+    return static_cast<T>(value);
+#else
     static_assert(std::is_integral<T>::value,
                   "half to int conversion only supports builtin integer types");
     unsigned int e = value & 0x7FFF;
@@ -797,10 +888,9 @@ AF_CONSTEXPR T half2int(native_half_t value) {
     } else
         m <<= e - 25;
     return (value & 0x8000) ? -static_cast<T>(m) : static_cast<T>(m);
+#endif
 }
 
-#endif  // __CUDACC_RTC__
-
 namespace internal {
 /// Tag type for binary construction.
 struct binary_t {};
@@ -862,9 +952,6 @@ class alignas(2) half {
 #endif
     }
 
-#if defined(__CUDA_ARCH__)
-    AF_CONSTEXPR
-#endif
     __DH__ explicit half(double value) noexcept
         : data_(float2half<double>(value)) {}
 
@@ -876,23 +963,24 @@ class alignas(2) half {
 
     template<typename T>
     AF_CONSTEXPR __DH__ explicit half(T value) noexcept
-        : data_(int2half<T>(value)) {}
+        : data_(int2half(value)) {}
 
 #if defined(__CUDA_ARCH__)
     AF_CONSTEXPR
 #endif
     __DH__ half& operator=(const double& value) noexcept {
-        data_ = float2half<double>(value);
+        data_ = float2half(value);
         return *this;
     }
 
-#ifdef __CUDA_ARCH__
-    AF_CONSTEXPR __DH__ explicit half(__half value) noexcept : data_(value) {}
+#if defined(__CUDA_ARCH__) || defined(AF_ONEAPI)
+    AF_CONSTEXPR __DH__ explicit half(native_half_t value) noexcept
+        : data_(value) {}
 
-    AF_CONSTEXPR __DH__ half& operator=(__half value) noexcept {
-        // NOTE Assignment to ushort from __half only works with device code.
-        // using memcpy instead
-        data_ = *reinterpret_cast<native_half_t*>(&value);
+    AF_CONSTEXPR __DH__ half& operator=(native_half_t value) noexcept {
+        // NOTE Assignment to ushort from native_half_t only works with device
+        // code. using memcpy instead
+        data_ = value;
         return *this;
     }
 #endif
@@ -907,71 +995,41 @@ class alignas(2) half {
     }
 
     AF_CONSTEXPR __DH__ explicit operator short() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2short_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, short>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator long long() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2ll_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, long long int>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator int() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2int_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, int>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator unsigned() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2uint_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, unsigned>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator unsigned short() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2ushort_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, unsigned>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator unsigned long long() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2ull_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, unsigned>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator char() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2short_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, char>(data_);
-#endif
     }
 
     AF_CONSTEXPR __DH__ explicit operator unsigned char() const noexcept {
-#ifdef __CUDA_ARCH__
-        return __half2short_rn(data_);
-#else
         return half2int<std::round_indeterminate, true, unsigned char>(data_);
-#endif
     }
 
-#if defined(__CUDA_ARCH__)
-    AF_CONSTEXPR __DH__ operator __half() const noexcept { return data_; };
+#if defined(__CUDA_ARCH__) || defined(AF_ONEAPI)
+    AF_CONSTEXPR __DH__ operator native_half_t() const noexcept {
+        return data_;
+    };
 #endif
 
     friend AF_CONSTEXPR __DH__ bool operator==(half lhs, half rhs) noexcept;
@@ -988,6 +1046,8 @@ class alignas(2) half {
         return arrayfire::common::half(__hneg(data_));
 #elif defined(__CUDA_ARCH__)
         return arrayfire::common::half(-(__half2float(data_)));
+#elif defined(AF_ONEAPI)
+        return arrayfire::common::half(-data_);
 #else
         return arrayfire::common::half(internal::binary, data_ ^ 0x8000);
 #endif
@@ -1001,6 +1061,8 @@ class alignas(2) half {
         half out;
 #ifdef __CUDA_ARCH__
         out.data_ = __half_raw{0x7C00};
+#elif defined(AF_ONEAPI)
+        out.data_ = std::numeric_limits<sycl::half>::infinity();
 #else
         out.data_ = 0x7C00;
 #endif
@@ -1014,6 +1076,8 @@ AF_CONSTEXPR __DH__ static inline bool operator==(
     return __heq(lhs.data_, rhs.data_);
 #elif defined(__CUDA_ARCH__)
     return __half2float(lhs.data_) == __half2float(rhs.data_);
+#elif defined(AF_ONEAPI)
+    return lhs.data_ == rhs.data_;
 #else
     return (lhs.data_ == rhs.data_ || !((lhs.data_ | rhs.data_) & 0x7FFF)) &&
            !isnan(lhs);
@@ -1035,6 +1099,8 @@ __DH__ static inline bool operator<(arrayfire::common::half lhs,
     return __hlt(lhs.data_, rhs.data_);
 #elif defined(__CUDA_ARCH__)
     return __half2float(lhs.data_) < __half2float(rhs.data_);
+#elif defined(AF_ONEAPI)
+    return lhs.data_ < rhs.data_;
 #else
     int xabs = lhs.data_ & 0x7FFF, yabs = rhs.data_ & 0x7FFF;
     return xabs <= 0x7C00 && yabs <= 0x7C00 &&
@@ -1047,6 +1113,8 @@ __DH__ static inline bool operator<(arrayfire::common::half lhs,
                                     float rhs) noexcept {
 #if defined(__CUDA_ARCH__)
     return __half2float(lhs.data_) < rhs;
+#elif defined(AF_ONEAPI)
+    return lhs.data_ < rhs;
 #else
     return static_cast<float>(lhs) < rhs;
 #endif
@@ -1068,7 +1136,7 @@ static inline std::string to_string(const half&& val) {
 }  // namespace arrayfire
 
 #if !defined(__NVCC__) && !defined(__CUDACC_RTC__)
-//#endif
+// #endif
 /// Extensions to the C++ standard library.
 namespace std {
 /// Numeric limits for half-precision floats.
@@ -1230,6 +1298,8 @@ AF_CONSTEXPR __DH__ static inline bool isnan(half val) noexcept {
     return __hisnan(val.data_);
 #elif defined(__CUDA_ARCH__)
     return ::isnan(__half2float(val));
+#elif defined(AF_ONEAPI)
+    return std::isnan(val.data_);
 #else
     return (val.data_ & 0x7FFF) > 0x7C00;
 #endif
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index cca1d519f6..613e26bdb7 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -35,6 +35,12 @@ struct Param {
     // AF_DEPRECATED("Use Array<T>")
     Param(sycl::buffer<T>* data_, KParam info_) : data(data_), info(info_) {}
 
+    template<sycl::access::mode MODE>
+    sycl::accessor<data_t<T>, 1, MODE> get_accessor(sycl::handler& h) const {
+        auto o = data->template reinterpret<data_t<T>>();
+        return sycl::accessor<data_t<T>, 1, MODE>(o, h);
+    }
+
     ~Param() = default;
 };
 
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 14e5f757c5..bb1aa99d21 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -93,9 +93,9 @@ class reduceAllKernelSMEM {
         common::Binary<compute_t<To>, op> reduce;
         common::Transform<Ti, compute_t<To>, op> transform;
 
-        const data_t<Ti> *const iptr =
-            in_.get_pointer() + wid * iInfo_.strides[3] +
-            zid * iInfo_.strides[2] + yid * iInfo_.strides[1] + iInfo_.offset;
+        auto iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
+                    zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
+                    iInfo_.offset;
 
         bool cond = (yid < iInfo_.dims[1]) && (zid < iInfo_.dims[2]) &&
                     (wid < iInfo_.dims[3]);
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 99f0452785..22b9c0f8dc 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -43,14 +43,14 @@ using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
 template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
 class reduceDimKernelSMEM {
    public:
-    reduceDimKernelSMEM(write_accessor<To> out, KParam oInfo,
-                        read_accessor<Ti> in, KParam iInfo, uint groups_x,
+    reduceDimKernelSMEM(Param<To> out, Param<Ti> in, uint groups_x,
                         uint groups_y, uint offset_dim, bool change_nan,
-                        To nanval, local_accessor<compute_t<To>, 1> s_val)
-        : out_(out)
-        , oInfo_(oInfo)
-        , iInfo_(iInfo)
-        , in_(in)
+                        To nanval, local_accessor<compute_t<To>, 1> s_val,
+                        sycl::handler &h)
+        : out_(out.template get_accessor<sycl::access::mode::write>(h))
+        , in_(in.template get_accessor<sycl::access::mode::read>(h))
+        , oInfo_(out.info)
+        , iInfo_(in.info)
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , offset_dim_(offset_dim)
@@ -72,15 +72,16 @@ class reduceDimKernelSMEM {
         const uint yid       = groupId_y;
 
         uint ids[4] = {xid, yid, zid, wid};
+        using sycl::global_ptr;
 
-        data_t<To> *const optr =
+        global_ptr<data_t<To>> optr =
             out_.get_pointer() + ids[3] * oInfo_.strides[3] +
             ids[2] * oInfo_.strides[2] + ids[1] * oInfo_.strides[1] + ids[0];
 
         const uint groupIdx_dim = ids[dim];
         ids[dim]                = ids[dim] * g.get_local_range(1) + lidy;
 
-        const data_t<Ti> *iptr =
+        global_ptr<data_t<Ti>> iptr =
             in_.get_pointer() + ids[3] * iInfo_.strides[3] +
             ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] + ids[0];
 
@@ -91,15 +92,16 @@ class reduceDimKernelSMEM {
                         (ids[2] < iInfo_.dims[2]) && (ids[3] < iInfo_.dims[3]);
 
         common::Binary<compute_t<To>, op> reduce;
-        common::Transform<Ti, compute_t<To>, op> transform;
+        common::Transform<data_t<Ti>, compute_t<To>, op> transform;
 
         compute_t<To> out_val = common::Binary<compute_t<To>, op>::init();
         for (int id = id_dim_in; is_valid && (id < iInfo_.dims[dim]);
              id += offset_dim_ * g.get_local_range(1)) {
             compute_t<To> in_val = transform(*iptr);
-            if (change_nan_)
+            if (change_nan_) {
                 in_val = !IS_NAN(in_val) ? in_val
                                          : static_cast<compute_t<To>>(nanval_);
+            }
             out_val = reduce(in_val, out_val);
             iptr += offset_dim_ * g.get_local_range(1) * istride_dim;
         }
@@ -133,9 +135,9 @@ class reduceDimKernelSMEM {
     }
 
    protected:
-    write_accessor<To> out_;
+    write_accessor<data_t<To>> out_;
+    read_accessor<data_t<Ti>> in_;
     KParam oInfo_, iInfo_;
-    read_accessor<Ti> in_;
     uint groups_x_, groups_y_, offset_dim_;
     bool change_nan_;
     To nanval_;
@@ -152,9 +154,6 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                           blocks_dim[1] * blocks_dim[3] * local[1]);
 
     getQueue().submit([=](sycl::handler &h) {
-        write_accessor<To> out_acc{*out.data, h};
-        read_accessor<Ti> in_acc{*in.data, h};
-
         auto shrdMem =
             local_accessor<compute_t<To>, 1>(creduce::THREADS_X * threads_y, h);
 
@@ -163,33 +162,29 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
                     reduceDimKernelSMEM<Ti, To, op, dim, 8>(
-                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem));
+                        out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                        change_nan, scalar<To>(nanval), shrdMem, h));
                 break;
             case 4:
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
                     reduceDimKernelSMEM<Ti, To, op, dim, 4>(
-                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem));
+                        out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                        change_nan, scalar<To>(nanval), shrdMem, h));
                 break;
             case 2:
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
                     reduceDimKernelSMEM<Ti, To, op, dim, 2>(
-                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem));
+                        out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                        change_nan, scalar<To>(nanval), shrdMem, h));
                 break;
             case 1:
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
                     reduceDimKernelSMEM<Ti, To, op, dim, 1>(
-                        out_acc, out.info, in_acc, in.info, blocks_dim[0],
-                        blocks_dim[1], blocks_dim[dim], change_nan,
-                        scalar<To>(nanval), shrdMem));
+                        out, in, blocks_dim[0], blocks_dim[1], blocks_dim[dim],
+                        change_nan, scalar<To>(nanval), shrdMem, h));
                 break;
         }
     });
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index 342f6f2530..299919ae12 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -74,13 +74,12 @@ class reduceFirstKernelSMEM {
         common::Binary<compute_t<To>, op> reduce;
         common::Transform<Ti, compute_t<To>, op> transform;
 
-        const data_t<Ti> *const iptr =
-            in_.get_pointer() + wid * iInfo_.strides[3] +
-            zid * iInfo_.strides[2] + yid * iInfo_.strides[1] + iInfo_.offset;
+        Ti *const iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
+                         zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
+                         iInfo_.offset;
 
-        data_t<To> *const optr = out_.get_pointer() + wid * oInfo_.strides[3] +
-                                 zid * oInfo_.strides[2] +
-                                 yid * oInfo_.strides[1];
+        auto optr = out_.get_pointer() + wid * oInfo_.strides[3] +
+                    zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
 
         bool cond = (yid < iInfo_.dims[1]) && (zid < iInfo_.dims[2]) &&
                     (wid < iInfo_.dims[3]);
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
index 74d117a491..f4be516f3d 100644
--- a/src/backend/oneapi/types.hpp
+++ b/src/backend/oneapi/types.hpp
@@ -27,17 +27,18 @@ namespace common {
 /// are used
 template<>
 struct kernel_type<common::half> {
-    using data = common::half;
+    using data = sycl::half;
 
     // These are the types within a kernel
-    using native = float;
+    using native = sycl::half;
 
-    using compute = float;
+    using compute = sycl::half;
 };
 }  // namespace common
 }  // namespace arrayfire
 
 namespace arrayfire {
+
 namespace oneapi {
 using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
@@ -60,7 +61,6 @@ struct ToNumStr {
     std::string operator()(CONVERSION_TYPE val);
 };
 
-namespace {
 template<typename T>
 inline const char *shortname(bool caps = false) {
     return caps ? "X" : "x";
@@ -129,7 +129,6 @@ template<>
 inline const char *getFullName<cdouble>() {
     return "double2";
 }
-}  // namespace
 
 #if 0
 template<typename... ARGS>

From d2d2bc580313406c415e5cbafb8cb71048e5cb27 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 Mar 2023 19:13:24 -0500
Subject: [PATCH 2417/2677] More header cleanup

---
 src/api/c/handle.cpp                 | 1 +
 src/api/c/hist.cpp                   | 1 +
 src/api/c/image.cpp                  | 1 +
 src/api/c/surface.cpp                | 1 +
 src/backend/common/jit/Node.hpp      | 3 ++-
 src/backend/cuda/Array.hpp           | 1 +
 src/backend/oneapi/kernel/select.hpp | 1 -
 src/backend/oneapi/wrap.cpp          | 9 +++++----
 src/backend/opencl/types.hpp         | 1 +
 9 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
index a432d8a720..7a93847826 100644
--- a/src/api/c/handle.cpp
+++ b/src/api/c/handle.cpp
@@ -10,6 +10,7 @@
 #include <handle.hpp>
 
 #include <backend.hpp>
+#include <platform.hpp>
 #include <sparse_handle.hpp>
 
 #include <af/dim4.hpp>
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 350d97416d..f37ba5cea1 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -15,6 +15,7 @@
 #include <copy.hpp>
 #include <handle.hpp>
 #include <hist_graphics.hpp>
+#include <platform.hpp>
 #include <reduce.hpp>
 #include <af/graphics.h>
 
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 533612f45d..425530806c 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -21,6 +21,7 @@
 #include <handle.hpp>
 #include <image.hpp>
 #include <join.hpp>
+#include <platform.hpp>
 #include <reorder.hpp>
 #include <tile.hpp>
 
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index 62ef46e0e2..b2a6404a33 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -19,6 +19,7 @@
 #include <copy.hpp>
 #include <handle.hpp>
 #include <join.hpp>
+#include <platform.hpp>
 #include <reduce.hpp>
 #include <reorder.hpp>
 #include <surface.hpp>
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 9ed090fbaa..8a262e0734 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -11,7 +11,6 @@
 #include <backend.hpp>
 #include <common/defines.hpp>
 #include <optypes.hpp>
-#include <platform.hpp>
 #include <types.hpp>
 #include <af/defines.h>
 
@@ -38,6 +37,8 @@ class Node;
 }  // namespace arrayfire
 
 #ifdef AF_CPU
+#include <Param.hpp>
+
 namespace arrayfire {
 namespace cpu {
 namespace kernel {
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 6c00910c9d..d6774ded66 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -17,6 +17,7 @@
 #include <cuda_runtime_api.h>
 #include <jit/BufferNode.hpp>
 #include <memory.hpp>
+#include <platform.hpp>
 #include <traits.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
diff --git a/src/backend/oneapi/kernel/select.hpp b/src/backend/oneapi/kernel/select.hpp
index 618cea3437..7f63f2cbea 100644
--- a/src/backend/oneapi/kernel/select.hpp
+++ b/src/backend/oneapi/kernel/select.hpp
@@ -12,7 +12,6 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
-#include <debug_oneapi.hpp>
 #include <math.hpp>
 
 #include <string>
diff --git a/src/backend/oneapi/wrap.cpp b/src/backend/oneapi/wrap.cpp
index 1400db07f0..19e8c0260e 100644
--- a/src/backend/oneapi/wrap.cpp
+++ b/src/backend/oneapi/wrap.cpp
@@ -7,15 +7,16 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <wrap.hpp>
+
+#include <kernel/wrap.hpp>
+#include <kernel/wrap_dilated.hpp>
+
 #include <Array.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
-#include <kernel/wrap.hpp>
-#include <kernel/wrap_dilated.hpp>
 #include <math.hpp>
-#include <wrap.hpp>
-#include <stdexcept>
 
 using arrayfire::common::half;
 
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 2bc96996aa..620ab74ca9 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -13,6 +13,7 @@
 #include <common/kernel_type.hpp>
 #include <common/traits.hpp>
 #include <af/compilers.h>
+#include <af/traits.hpp>
 
 #include <algorithm>
 #include <array>

From 5544252d7eb1febf0a3e79f4056021132d92b71c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 Mar 2023 19:34:13 -0500
Subject: [PATCH 2418/2677] Expose base KernelInterface types to derived
 classes

---
 src/backend/common/KernelInterface.hpp | 13 ++++++++-----
 src/backend/opencl/Kernel.hpp          |  3 ---
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/backend/common/KernelInterface.hpp b/src/backend/common/KernelInterface.hpp
index 5eeb8710fd..0ead60a8cd 100644
--- a/src/backend/common/KernelInterface.hpp
+++ b/src/backend/common/KernelInterface.hpp
@@ -16,15 +16,18 @@ namespace arrayfire {
 namespace common {
 
 /// Kernel Interface that should be implemented by each backend
-template<typename ModuleType, typename KernelType, typename EnqueuerType,
-         typename DevPtrType>
+template<typename TModuleType, typename TKernelType, typename TEnqueuerType,
+         typename TDevPtrType>
 class KernelInterface {
-   private:
-    ModuleType mModuleHandle;
-    KernelType mKernelHandle;
+    TModuleType mModuleHandle;
+    TKernelType mKernelHandle;
     std::string mName;
 
    public:
+    using ModuleType   = TModuleType;
+    using KernelType   = TKernelType;
+    using EnqueuerType = TEnqueuerType;
+    using DevPtrType   = TDevPtrType;
     KernelInterface(std::string name, ModuleType mod, KernelType ker)
         : mModuleHandle(mod), mKernelHandle(ker), mName(name) {}
 
diff --git a/src/backend/opencl/Kernel.hpp b/src/backend/opencl/Kernel.hpp
index e3a05e7da8..c5582d8f1c 100644
--- a/src/backend/opencl/Kernel.hpp
+++ b/src/backend/opencl/Kernel.hpp
@@ -40,9 +40,6 @@ class Kernel
     : public common::KernelInterface<const cl::Program*, cl::Kernel, Enqueuer,
                                      cl::Buffer*> {
    public:
-    using ModuleType = const cl::Program*;
-    using KernelType = cl::Kernel;
-    using DevPtrType = cl::Buffer*;
     using BaseClass =
         common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType>;
 

From 5971cdcfde545b9e161cd892c6bae91f2c3df904 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 3 Mar 2023 22:26:57 -0500
Subject: [PATCH 2419/2677] Add flag to remove warnings on debug builds with
 oneAPI

---
 CMakeModules/InternalUtils.cmake | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 1d1c387245..863cbaed22 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -33,6 +33,8 @@ check_cxx_compiler_flag(-fno-signed-zeros  has_cxx_no_signed_zeros)
 check_cxx_compiler_flag(-mno-ieee-fp has_cxx_no_ieee_fp)
 check_cxx_compiler_flag(-Wno-unqualified-std-cast-call has_cxx_unqualified_std_cast_call)
 check_cxx_compiler_flag(-Werror=reorder-ctor has_cxx_error_reorder_ctor)
+check_cxx_compiler_flag(-Rno-debug-disables-optimization has_cxx_debug-disables-optimization)
+
 
 function(arrayfire_set_default_cxx_flags target)
   target_compile_options(${target}
@@ -75,7 +77,10 @@ function(arrayfire_set_default_cxx_flags target)
 
               $<$<NOT:$<BOOL:${AF_WITH_FAST_MATH}>>:
                     $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>>
-                      >
+
+              $<$<BOOL:${has_cxx_debug-disables-optimization}>:
+                  $<$<CONFIG:Debug>:-Rno-debug-disables-optimization>>
+          >
     )
 
   target_compile_definitions(${target}

From ebf754353327fd17cdaf588fcac4e00df3b72a24 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 6 Mar 2023 14:40:34 -0500
Subject: [PATCH 2420/2677] Drop 18.04 from GitHub workflows

---
 .github/workflows/unix_cpu_build.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 3c0e566d6f..01051f7e8f 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -25,7 +25,7 @@ jobs:
 
     documentation:
         name: Documentation
-        runs-on: ubuntu-18.04
+        runs-on: ubuntu-20.04
         env:
           DOXYGEN_VER: 1.8.18
         steps:
@@ -68,7 +68,7 @@ jobs:
             fail-fast: false
             matrix:
                 blas_backend: [Atlas, MKL, OpenBLAS]
-                os: [ubuntu-18.04, ubuntu-20.04, macos-latest]
+                os: [ubuntu-20.04, macos-latest]
                 compiler: [gcc, clang, icx]
                 exclude:
                     - os: macos-latest
@@ -128,7 +128,7 @@ jobs:
                   echo "CMAKE_PROGRAM=cmake" >> $GITHUB_ENV
 
             - name: Install Common Dependencies for Ubuntu
-              if: matrix.os == 'ubuntu-18.04' || matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-22.04'
+              if: matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-22.04'
               run: |
                   sudo add-apt-repository ppa:mhier/libboost-latest
                   sudo apt-get -qq update

From 7964d43b90a5a95d526c65c12efb429c8e354b66 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 6 Mar 2023 15:49:31 -0500
Subject: [PATCH 2421/2677] Remove constexpr from float2half because of the
 memcpy operation

---
 src/backend/common/half.hpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 0de986ceb5..f427d539cf 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -77,7 +77,8 @@ AF_CONSTEXPR __DH__ native_half_t float2half_impl(double value) {
     return __float2half_rn(value);
 }
 
-AF_CONSTEXPR __DH__ inline float half2float_impl(native_half_t value) noexcept {
+AF_CONSTEXPR
+__DH__ inline float half2float_impl(native_half_t value) noexcept {
     return __half2float(value);
 }
 
@@ -135,7 +136,7 @@ AF_CONSTEXPR native_half_t float2half_impl(double value) {
     return static_cast<native_half_t>(value);
 }
 
-AF_CONSTEXPR inline float half2float_impl(native_half_t value) noexcept {
+inline float half2float_impl(native_half_t value) noexcept {
     return static_cast<float>(value);
 }
 

From 05d8b9255defc51b893a87ea668837e46fcaff71 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Mar 2023 15:19:00 -0500
Subject: [PATCH 2422/2677] Fix half compilation on NVRTC based compilation

---
 src/backend/common/half.hpp         | 121 +++++++++++++++-------------
 src/backend/cuda/compile_module.cpp |   4 +
 2 files changed, 71 insertions(+), 54 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index f427d539cf..65a3930b15 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -39,6 +39,48 @@
 #include <backend.hpp>
 
 #ifdef __CUDACC_RTC__
+
+#if defined(__cpp_if_constexpr) || __cplusplus >= 201606L
+#define AF_IF_CONSTEXPR if constexpr
+#else
+#define AF_IF_CONSTEXPR if
+#endif
+
+namespace std {
+enum float_round_style {
+    round_indeterminate       = -1,
+    round_toward_zero         = 0,
+    round_to_nearest          = 1,
+    round_toward_infinity     = 2,
+    round_toward_neg_infinity = 3
+};
+
+template<bool B, class T = void>
+struct enable_if {};
+
+template<class T>
+struct enable_if<true, T> {
+    typedef T type;
+};
+
+template<bool B, class T = void>
+using enable_if_t = typename enable_if<B, T>::type;
+
+template<class T, class U>
+struct is_same {
+    static constexpr bool value = false;
+};
+
+template<class T>
+struct is_same<T, T> {
+    static constexpr bool value = true;
+};
+
+template<class T, class U>
+constexpr bool is_same_v = is_same<T, U>::value;
+
+}  //  namespace std
+
 using uint16_t = unsigned short;
 // we do not include the af/compilers header in nvrtc compilations so
 // we are defining the AF_CONSTEXPR expression here
@@ -140,44 +182,8 @@ inline float half2float_impl(native_half_t value) noexcept {
     return static_cast<float>(value);
 }
 
-template<typename T>
-AF_CONSTEXPR native_half_t int2half_impl(T value) noexcept;
-
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(int value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(unsigned value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(long long value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(unsigned long long value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(short value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(unsigned short value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(char value) noexcept {
-    return static_cast<native_half_t>(value);
-}
-template<>
-AF_CONSTEXPR native_half_t int2half_impl(unsigned char value) noexcept {
+template<std::float_round_style R, bool S, typename T>
+AF_CONSTEXPR native_half_t int2half_impl(T value) noexcept {
     return static_cast<native_half_t>(value);
 }
 
@@ -808,24 +814,26 @@ __DH__ inline float half2float(native_half_t value) noexcept {
     return half2float_impl(value);
 }
 
+#ifndef __CUDACC_RTC__
 template<typename T, std::float_round_style R = std::round_to_nearest,
          typename std::enable_if_t<std::is_integral<T>::value &&
                                    std::is_signed<T>::value>* = nullptr>
 AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
-#if defined(__CUDACC_RTC__) || defined(AF_ONEAPI)
-    native_half_t out = int2half_impl(value);
-#else
-    uint16_t out = (value < 0) ? int2half_impl<R, true, T>(value)
-                               : int2half_impl<R, false, T>(value);
-#endif
+    native_half_t out = (value < 0) ? int2half_impl<R, true, T>(value)
+                                    : int2half_impl<R, false, T>(value);
     return out;
 }
+#endif
 
-template<typename T, std::float_round_style R = std::round_to_nearest,
+template<typename T, std::float_round_style R = std::round_to_nearest
+#ifndef __CUDACC_RTC__
+         ,
          typename std::enable_if_t<std::is_integral<T>::value &&
-                                   std::is_unsigned<T>::value>* = nullptr>
+                                   std::is_unsigned<T>::value>* = nullptr
+#endif
+         >
 AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
-#if defined(__CUDACC_RTC__) || defined(AF_ONEAPI)
+#if defined(__CUDACC_RTC__)
     return int2half_impl(value);
 #else
     return int2half_impl<R, false, T>(value);
@@ -846,18 +854,23 @@ AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
 template<std::float_round_style R, bool E, typename T>
 AF_CONSTEXPR T half2int(native_half_t value) {
 #ifdef __CUDA_ARCH__
-    if constexpr (std::is_same_v<T, short> || std::is_same_v<T, char> ||
-                  std::is_same_v<T, unsigned char>) {
+    AF_IF_CONSTEXPR(std::is_same_v<T, short> || std::is_same_v<T, char> ||
+                    std::is_same_v<T, unsigned char>) {
         return __half2short_rn(value);
-    } else if constexpr (std::is_same_v<T, unsigned short>) {
+    }
+    else AF_IF_CONSTEXPR(std::is_same_v<T, unsigned short>) {
         return __half2ushort_rn(value);
-    } else if constexpr (std::is_same_v<T, long long>) {
+    }
+    else AF_IF_CONSTEXPR(std::is_same_v<T, long long>) {
         return __half2ll_rn(value);
-    } else if constexpr (std::is_same_v<T, unsigned long long>) {
+    }
+    else AF_IF_CONSTEXPR(std::is_same_v<T, unsigned long long>) {
         return __half2ull_rn(value);
-    } else if constexpr (std::is_same_v<T, int>) {
+    }
+    else AF_IF_CONSTEXPR(std::is_same_v<T, int>) {
         return __half2int_rn(value);
-    } else if constexpr (std::is_same_v<T, unsigned>) {
+    }
+    else AF_IF_CONSTEXPR(std::is_same_v<T, unsigned>) {
         return __half2uint_rn(value);
     }
 #elif defined(AF_ONEAPI)
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 3fddb93d95..36014049a8 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -266,7 +266,11 @@ Module compileModule(const string &moduleKey, span<const string> sources,
              computeFlag.first, computeFlag.second);
     vector<const char *> compiler_options = {
         arch.data(),
+#if CUDA_VERSION >= 11000
+        "--std=c++17",
+#else
         "--std=c++14",
+#endif
         "--device-as-default-execution-space",
 #ifdef AF_WITH_FAST_MATH
         "--use_fast_math",

From 6736e9384478099b84e1ac49dfb2bc32fb025553 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 8 Mar 2023 15:21:14 -0500
Subject: [PATCH 2423/2677] Move OpenCL error functions from global namespace
 to af::ocl ns

---
 src/backend/common/err_common.hpp     |  2 +-
 src/backend/opencl/compile_module.cpp | 49 ++++++++++++++-------------
 src/backend/opencl/err_opencl.hpp     | 14 ++++++++
 3 files changed, 40 insertions(+), 25 deletions(-)

diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 79c9d029d7..3936cee77c 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -214,5 +214,5 @@ namespace common {
 
 bool& is_stacktrace_enabled() noexcept;
 
-}
+}  // namespace common
 }  // namespace arrayfire
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 03fd41a196..832f5144a7 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -60,24 +60,6 @@ logger *getLogger() {
     return logger.get();
 }
 
-string getProgramBuildLog(const Program &prog) {
-    string build_error("");
-    try {
-        build_error.reserve(4096);
-        auto devices = prog.getInfo<CL_PROGRAM_DEVICES>();
-        for (auto &device : prog.getInfo<CL_PROGRAM_DEVICES>()) {
-            build_error +=
-                format("OpenCL Device: {}\n\tOptions: {}\n\tLog:\n{}\n",
-                       device.getInfo<CL_DEVICE_NAME>(),
-                       prog.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(device),
-                       prog.getBuildInfo<CL_PROGRAM_BUILD_LOG>(device));
-        }
-    } catch (const cl::Error &e) {
-        build_error = format("Failed to fetch build log: {}", e.what());
-    }
-    return build_error;
-}
-
 #define THROW_BUILD_LOG_EXCEPTION(PROG)                              \
     do {                                                             \
         string build_error = getProgramBuildLog(PROG);               \
@@ -129,8 +111,23 @@ Program buildProgram(span<const string> kernelSources,
     return retVal;
 }
 
-}  // namespace opencl
-}  // namespace arrayfire
+string getProgramBuildLog(const Program &prog) {
+    string build_error("");
+    try {
+        build_error.reserve(4096);
+        auto devices = prog.getInfo<CL_PROGRAM_DEVICES>();
+        for (auto &device : prog.getInfo<CL_PROGRAM_DEVICES>()) {
+            build_error +=
+                format("OpenCL Device: {}\n\tOptions: {}\n\tLog:\n{}\n",
+                       device.getInfo<CL_DEVICE_NAME>(),
+                       prog.getBuildInfo<CL_PROGRAM_BUILD_OPTIONS>(device),
+                       prog.getBuildInfo<CL_PROGRAM_BUILD_LOG>(device));
+        }
+    } catch (const cl::Error &e) {
+        build_error = format("Failed to fetch build log: {}", e.what());
+    }
+    return build_error;
+}
 
 string getKernelCacheFilename(const int device, const string &key) {
     auto &dev = arrayfire::opencl::getDevice(device);
@@ -147,6 +144,9 @@ string getKernelCacheFilename(const int device, const string &key) {
            to_string(AF_API_VERSION_CURRENT) + ".bin";
 }
 
+}  // namespace opencl
+}  // namespace arrayfire
+
 namespace arrayfire {
 namespace common {
 
@@ -164,8 +164,9 @@ Module compileModule(const string &moduleKey, span<const string> sources,
     const int device             = arrayfire::opencl::getActiveDeviceId();
     const string &cacheDirectory = getCacheDirectory();
     if (!cacheDirectory.empty()) {
-        const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
-                                 getKernelCacheFilename(device, moduleKey);
+        const string cacheFile =
+            cacheDirectory + AF_PATH_SEPARATOR +
+            opencl::getKernelCacheFilename(device, moduleKey);
         const string tempFile =
             cacheDirectory + AF_PATH_SEPARATOR + makeTempFilename();
         try {
@@ -223,7 +224,7 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
 
     auto &dev              = arrayfire::opencl::getDevice(device);
     const string cacheFile = cacheDirectory + AF_PATH_SEPARATOR +
-                             getKernelCacheFilename(device, moduleKey);
+                             opencl::getKernelCacheFilename(device, moduleKey);
     Program program;
     Module retVal{};
     try {
@@ -273,7 +274,7 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
             "{{{:<20} : Loading OpenCL binary({}) failed for {}; {}, Build "
             "Log: {}}}",
             moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>(), e.what(),
-            getProgramBuildLog(program));
+            opencl::getProgramBuildLog(program));
         removeFile(cacheFile);
     }
     return retVal;
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 845db9ee02..2c1187c569 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -11,6 +11,20 @@
 
 #include <common/err_common.hpp>
 
+#include <string>
+
+namespace cl {
+class Program;
+}
+
+namespace arrayfire {
+namespace opencl {
+
+std::string getProgramBuildLog(const cl::Program &prog);
+
+}  // namespace opencl
+}  // namespace arrayfire
+
 #define OPENCL_NOT_SUPPORTED(message)                                       \
     do {                                                                    \
         throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \

From f148b178b33282d8a6b73676ac294ceb403255b1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 9 Mar 2023 13:54:01 -0500
Subject: [PATCH 2424/2677] Update binary tests to use the new assert test
 functions

---
 test/binary.cpp | 102 +++++++++++++++++++++++-------------------------
 1 file changed, 48 insertions(+), 54 deletions(-)

diff --git a/test/binary.cpp b/test/binary.cpp
index f6f9a8928f..ab557f8c9a 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -44,60 +44,54 @@ af::array randgen(const int num, dtype ty) {
 
 #define MY_ASSERT_NEAR(aa, bb, cc) ASSERT_NEAR(abs(aa), abs(bb), (cc))
 
-#define BINARY_TESTS(Ta, Tb, Tc, func)                                \
-    TEST(BinaryTests, Test_##func##_##Ta##_##Tb) {                    \
-        SUPPORTED_TYPE_CHECK(Ta);                                     \
-        SUPPORTED_TYPE_CHECK(Tb);                                     \
-        SUPPORTED_TYPE_CHECK(Tc);                                     \
-                                                                      \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
-        af::array a = randgen(num, ta);                               \
-        af::array b = randgen(num, tb);                               \
-        af::array c = func(a, b);                                     \
-        Ta *h_a     = a.host<Ta>();                                   \
-        Tb *h_b     = b.host<Tb>();                                   \
-        Tc *h_c     = c.host<Tc>();                                   \
-        for (int i = 0; i < num; i++)                                 \
-            ASSERT_EQ(h_c[i], func(h_a[i], h_b[i]))                   \
-                << "for values: " << h_a[i] << "," << h_b[i] << endl; \
-        af_free_host(h_a);                                            \
-        af_free_host(h_b);                                            \
-        af_free_host(h_c);                                            \
-    }                                                                 \
-                                                                      \
-    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_left) {             \
-        SUPPORTED_TYPE_CHECK(Ta);                                     \
-        SUPPORTED_TYPE_CHECK(Tb);                                     \
-                                                                      \
-        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
-        af::array a = randgen(num, ta);                               \
-        Tb h_b      = 3.0;                                            \
-        af::array c = func(a, h_b);                                   \
-        Ta *h_a     = a.host<Ta>();                                   \
-        Ta *h_c     = c.host<Ta>();                                   \
-        for (int i = 0; i < num; i++)                                 \
-            ASSERT_EQ(h_c[i], func(h_a[i], h_b))                      \
-                << "for values: " << h_a[i] << "," << h_b << endl;    \
-        af_free_host(h_a);                                            \
-        af_free_host(h_c);                                            \
-    }                                                                 \
-                                                                      \
-    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_right) {            \
-        SUPPORTED_TYPE_CHECK(Ta);                                     \
-        SUPPORTED_TYPE_CHECK(Tb);                                     \
-                                                                      \
-        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
-        Ta h_a      = 5.0;                                            \
-        af::array b = randgen(num, tb);                               \
-        af::array c = func(h_a, b);                                   \
-        Tb *h_b     = b.host<Tb>();                                   \
-        Tb *h_c     = c.host<Tb>();                                   \
-        for (int i = 0; i < num; i++)                                 \
-            ASSERT_EQ(h_c[i], func(h_a, h_b[i]))                      \
-                << "for values: " << h_a << "," << h_b[i] << endl;    \
-        af_free_host(h_b);                                            \
-        af_free_host(h_c);                                            \
+#define BINARY_TESTS(Ta, Tb, Tc, func)                                    \
+    TEST(BinaryTests, Test_##func##_##Ta##_##Tb) {                        \
+        SUPPORTED_TYPE_CHECK(Ta);                                         \
+        SUPPORTED_TYPE_CHECK(Tb);                                         \
+        SUPPORTED_TYPE_CHECK(Tc);                                         \
+                                                                          \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;                \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;                \
+        af::array a = randgen(num, ta);                                   \
+        af::array b = randgen(num, tb);                                   \
+        af::array c = func(a, b);                                         \
+        Ta *h_a     = a.host<Ta>();                                       \
+        Tb *h_b     = b.host<Tb>();                                       \
+        vector<Tc> gold(num);                                             \
+        for (int i = 0; i < num; i++) { gold[i] = func(h_a[i], h_b[i]); } \
+        ASSERT_VEC_ARRAY_EQ(gold, dim4(num), c);                          \
+        af_free_host(h_a);                                                \
+        af_free_host(h_b);                                                \
+    }                                                                     \
+                                                                          \
+    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_left) {                 \
+        SUPPORTED_TYPE_CHECK(Ta);                                         \
+        SUPPORTED_TYPE_CHECK(Tb);                                         \
+                                                                          \
+        af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;                \
+        af::array a = randgen(num, ta);                                   \
+        Tb h_b      = 3.0;                                                \
+        af::array c = func(a, h_b);                                       \
+        Ta *h_a     = a.host<Ta>();                                       \
+        vector<Tc> gold(num);                                             \
+        for (int i = 0; i < num; i++) { gold[i] = func(h_a[i], h_b); }    \
+        ASSERT_VEC_ARRAY_EQ(gold, dim4(num), c);                          \
+        af_free_host(h_a);                                                \
+    }                                                                     \
+                                                                          \
+    TEST(BinaryTests, Test_##func##_##Ta##_##Tb##_right) {                \
+        SUPPORTED_TYPE_CHECK(Ta);                                         \
+        SUPPORTED_TYPE_CHECK(Tb);                                         \
+                                                                          \
+        af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;                \
+        Ta h_a      = 5.0;                                                \
+        af::array b = randgen(num, tb);                                   \
+        af::array c = func(h_a, b);                                       \
+        Tb *h_b     = b.host<Tb>();                                       \
+        vector<Tc> gold(num);                                             \
+        for (int i = 0; i < num; i++) { gold[i] = func(h_a, h_b[i]); }    \
+        ASSERT_VEC_ARRAY_EQ(gold, dim4(num), c);                          \
+        af_free_host(h_b);                                                \
     }
 
 #define BINARY_TESTS_NEAR_GENERAL(Ta, Tb, Tc, Td, Te, func, err)      \

From 214ab0a827009d1da8ac36ee56a153c0f5df6f0f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 9 Mar 2023 13:54:34 -0500
Subject: [PATCH 2425/2677] Initial implementation of OpenCL based JIT for the
 oneAPI backend

This is the initial implementation of jit based on OpenCL kernels
for the oneAPI backend. This is not a feature complete implementation
and some checks for shape size have been ignored because of the lack
of the memory manager. This implementation also does not fully support
4D tensors which should be implemented later. All jit operations should
be available with this change for most JIT based tests for other
functions.
---
 src/backend/oneapi/Array.cpp                 | 145 +++--
 src/backend/oneapi/Array.hpp                 |   5 +
 src/backend/oneapi/CMakeLists.txt            |  44 +-
 src/backend/oneapi/Kernel.hpp                |  81 +--
 src/backend/oneapi/Param.hpp                 |  83 +++
 src/backend/oneapi/arith.hpp                 |   3 -
 src/backend/oneapi/device_manager.cpp        |  16 +-
 src/backend/oneapi/err_oneapi.hpp            |  28 +
 src/backend/oneapi/histogram.cpp             |   4 +-
 src/backend/oneapi/index.cpp                 |   2 +-
 src/backend/oneapi/jit.cpp                   | 568 ++++++++++++++++++-
 src/backend/oneapi/jit/BufferNode.hpp        |   4 +-
 src/backend/oneapi/jit/kernel_generators.hpp |  25 +-
 src/backend/oneapi/kernel/KParam.hpp         |   8 +-
 src/backend/oneapi/kernel/histogram.hpp      |   9 -
 src/backend/oneapi/kernel/reduce_all.hpp     |   8 +-
 src/backend/oneapi/memory.cpp                |  12 +-
 17 files changed, 833 insertions(+), 212 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index a55915edb8..2db607c75c 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -214,9 +214,7 @@ void Array<T>::eval() {
 
     Param<T> res{data.get(), info};
 
-    // TODO: implement
-    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
-    // evalNodes(res, getNode().get());
+    evalNodes(res, getNode().get());
     node.reset();
 }
 
@@ -272,9 +270,7 @@ void evalMultiple(vector<Array<T> *> arrays) {
         nodes.push_back(array->getNode().get());
     }
 
-    // TODO: implement
-    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
-    // evalNodes(outputs, nodes);
+    evalNodes(outputs, nodes);
 
     for (Array<T> *array : output_arrays) { array->node.reset(); }
 }
@@ -283,10 +279,10 @@ template<typename T>
 Node_ptr Array<T>::getNode() {
     if (node) { return node; }
 
-    KParam kinfo   = *this;
+    AParam<T> info = *this;
     unsigned bytes = this->dims().elements() * sizeof(T);
     auto nn        = bufferNodePtr<T>();
-    nn->setData(kinfo, data, bytes, isLinear());
+    nn->setData(info, data, bytes, isLinear());
 
     return nn;
 }
@@ -318,78 +314,79 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
             return kJITHeuristics::TreeHeight;
         }
     }
-    ONEAPI_NOT_SUPPORTED("JIT NOT SUPPORTED");
 
-    // bool isBufferLimit = getMemoryPressure() >= getMemoryPressureThreshold();
+    // TODO(umar): add memory based checks for JIT kernel generation
+    bool isBufferLimit =
+        false;  // getMemoryPressure() >= getMemoryPressureThreshold();
     // auto platform      = getActivePlatform();
 
     // The Apple platform can have the nvidia card or the AMD card
     // bool isIntel = platform == AFCL_PLATFORM_INTEL;
 
-    // /// Intels param_size limit is much smaller than the other platforms
-    // /// so we need to start checking earlier with smaller trees
-    // int heightCheckLimit =
-    //     isIntel && getDeviceType() == CL_DEVICE_TYPE_GPU ? 3 : 6;
-
-    // // A lightweight check based on the height of the node. This is
-    // // an inexpensive operation and does not traverse the JIT tree.
-    // bool atHeightLimit =
-    //     std::any_of(std::begin(root_nodes), std::end(root_nodes),
-    //                 [heightCheckLimit](Node *n) {
-    //                     return (n->getHeight() + 1 >= heightCheckLimit);
-    //                 });
-
-    // if (atHeightLimit || isBufferLimit) {
-    //     // This is the base parameter size if the kernel had no
-    //     // arguments
-    //     size_t base_param_size =
-    //         (sizeof(T *) + sizeof(Param<T>)) * root_nodes.size() +
-    //         (3 * sizeof(uint));
-
-    //     const cl::Device &device = getDevice();
-    //     size_t max_param_size =
-    //     device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
-    //     // typical values:
-    //     //   NVIDIA     = 4096
-    //     //   AMD        = 3520  (AMD A10 iGPU = 1024)
-    //     //   Intel iGPU = 1024
-    //     max_param_size -= base_param_size;
-
-    //     struct tree_info {
-    //         size_t total_buffer_size;
-    //         size_t num_buffers;
-    //         size_t param_scalar_size;
-    //     };
-
-    //     tree_info info{0, 0, 0};
-    //     for (Node *n : root_nodes) {
-    //         NodeIterator<> it(n);
-    //         info = accumulate(
-    //             it, NodeIterator<>(), info, [](tree_info &prev, Node &n) {
-    //                 if (n.isBuffer()) {
-    //                     auto &buf_node = static_cast<BufferNode &>(n);
-    //                     // getBytes returns the size of the data Array.
-    //                     // Sub arrays will be represented by their parent
-    //                     // size.
-    //                     prev.total_buffer_size += buf_node.getBytes();
-    //                     prev.num_buffers++;
-    //                 } else {
-    //                     prev.param_scalar_size += n.getParamBytes();
-    //                 }
-    //                 return prev;
-    //             });
-    //     }
-    //     isBufferLimit = jitTreeExceedsMemoryPressure(info.total_buffer_size);
-
-    //     size_t param_size = (info.num_buffers * (sizeof(Param<T>) + sizeof(T
-    //     *)) +
-    //                          info.param_scalar_size);
-
-    //     bool isParamLimit = param_size >= max_param_size;
-
-    //     if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
-    //     if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
-    // }
+    /// Intels param_size limit is much smaller than the other platforms
+    /// so we need to start checking earlier with smaller trees
+    int heightCheckLimit = 3;
+
+    // A lightweight check based on the height of the node. This is
+    // an inexpensive operation and does not traverse the JIT tree.
+    bool atHeightLimit =
+        std::any_of(std::begin(root_nodes), std::end(root_nodes),
+                    [heightCheckLimit](Node *n) {
+                        return (n->getHeight() + 1 >= heightCheckLimit);
+                    });
+
+    if (atHeightLimit || isBufferLimit) {
+        // This is the base parameter size if the kernel had no
+        // arguments
+        size_t base_param_size =
+            (sizeof(T *) + sizeof(Param<T>)) * root_nodes.size() +
+            (3 * sizeof(uint));
+
+        const sycl::device &device = getDevice();
+        size_t max_param_size =
+            device.get_info<sycl::info::device::max_parameter_size>();
+        // typical values:
+        //   NVIDIA     = 4096
+        //   AMD        = 3520  (AMD A10 iGPU = 1024)
+        //   Intel iGPU = 1024
+        max_param_size -= base_param_size;
+
+        struct tree_info {
+            size_t total_buffer_size;
+            size_t num_buffers;
+            size_t param_scalar_size;
+        };
+
+        tree_info info{0, 0, 0};
+        for (Node *n : root_nodes) {
+            NodeIterator<> it(n);
+            info = accumulate(
+                it, NodeIterator<>(), info, [](tree_info &prev, Node &n) {
+                    if (n.isBuffer()) {
+                        auto &buf_node = static_cast<BufferNode<T> &>(n);
+                        // getBytes returns the size of the data Array.
+                        // Sub arrays will be represented by their parent
+                        // size.
+                        prev.total_buffer_size += buf_node.getBytes();
+                        prev.num_buffers++;
+                    } else {
+                        prev.param_scalar_size += n.getParamBytes();
+                    }
+                    return prev;
+                });
+        }
+        isBufferLimit = jitTreeExceedsMemoryPressure(info.total_buffer_size);
+
+        size_t param_size =
+            (info.num_buffers * (sizeof(Param<T>) + sizeof(T *)) +
+             info.param_scalar_size);
+
+        bool isParamLimit = param_size >= max_param_size;
+
+        if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
+        // TODO(umar): check buffer limit for JIT kernel generation
+        // if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
+    }
     return kJITHeuristics::Pass;
 }
 
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index d907cad92f..bc4e16c574 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -283,6 +283,11 @@ class Array {
         return out;
     }
 
+    operator AParam<T>() {
+        AParam<T> out(*getData(), dims().get(), strides().get(), getOffset());
+        return out;
+    }
+
     operator KParam() const {
         KParam kinfo = {
             {dims()[0], dims()[1], dims()[2], dims()[3]},
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 9abca35940..5b5684038d 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -7,6 +7,7 @@
 
 include(InternalUtils)
 include(build_cl2hpp)
+include(FileToString)
 
 add_library(afoneapi
   Array.cpp
@@ -93,6 +94,8 @@ add_library(afoneapi
   ireduce.cpp
   ireduce.hpp
   jit.cpp
+  jit/BufferNode.hpp
+  jit/kernel_generators.hpp
   join.cpp
   join.hpp
   logic.hpp
@@ -239,6 +242,24 @@ target_sources(afoneapi
     kernel/wrap_dilated.hpp
 )
 
+set(kernel_src
+  ${CMAKE_CURRENT_SOURCE_DIR}/../opencl/kernel/KParam.hpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/../opencl/kernel/jit.cl
+)
+
+set( kernel_headers_dir "kernel_headers")
+
+file_to_string(
+  SOURCES ${kernel_src}
+  VARNAME kernel_files
+  EXTENSION "hpp"
+  OUTPUT_DIR ${kernel_headers_dir}
+  TARGETS cl_kernel_targets
+  NAMESPACE "arrayfire oneapi opencl"
+)
+
+add_dependencies(afoneapi ${cl_kernel_targets})
+
 add_library(ArrayFire::afoneapi ALIAS afoneapi)
 
 arrayfire_set_default_cxx_flags(afoneapi)
@@ -254,23 +275,40 @@ target_include_directories(afoneapi
     $<INSTALL_INTERFACE:${AF_INSTALL_INC_DIR}>
   PRIVATE
     ${CMAKE_CURRENT_SOURCE_DIR}
-
+    ${CMAKE_CURRENT_BINARY_DIR}
   )
 
 target_compile_options(afoneapi
-  PRIVATE -fsycl)
+  PRIVATE
+    -fsycl
+    #-fsycl-targets=nvptx64-vidia-cuda
+    #-fsycl-force-target=nvptx64-nvidia-cuda-sm_86
+    #-Wno-unknown-cuda-version
+    -sycl-std=2020
+)
 
 target_compile_definitions(afoneapi
   PRIVATE
     AF_ONEAPI
+    CL_TARGET_OPENCL_VERSION=300
+    CL_HPP_TARGET_OPENCL_VERSION=300
+    CL_HPP_MINIMUM_OPENCL_VERSION=110
+    CL_HPP_ENABLE_EXCEPTIONS
   )
 
 target_link_libraries(afoneapi
   PRIVATE
+    -fsycl
+    -fno-lto
+    -fvisibility-inlines-hidden
+    #-fsycl-targets=nvptx64-nvidia-cuda-sm_86
+    #-fsycl-force-target=nvptx64-nvidia-cuda-sm_86
     c_api_interface
     cpp_api_interface
     afcommon_interface
-    -fsycl
+    OpenCL::OpenCL
+    OpenCL::cl2hpp
+    #-Wno-unknown-cuda-version
   )
 
 af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})
diff --git a/src/backend/oneapi/Kernel.hpp b/src/backend/oneapi/Kernel.hpp
index e36e202387..ee5a2fcd02 100644
--- a/src/backend/oneapi/Kernel.hpp
+++ b/src/backend/oneapi/Kernel.hpp
@@ -26,69 +26,40 @@ inline auto getLogger() -> spdlog::logger* {
 }  // namespace kernel_logger
 
 /*
+ */
 struct Enqueuer {
     template<typename... Args>
-    void operator()(std::string name, sycl::kernel ker,
-                    const cl::EnqueueArgs& qArgs, Args&&... args) {
-        auto launchOp = cl::KernelFunctor<Args...>(ker);
+    void operator()(std::string name, sycl::kernel ker, const Enqueuer& qArgs,
+                    Args&&... args) {
+        // auto launchOp = cl::KernelFunctor<Args...>(ker);
         using namespace kernel_logger;
         AF_TRACE("Launching {}", name);
-        launchOp(qArgs, std::forward<Args>(args)...);
+        // launchOp(qArgs, std::forward<Args>(args)...);
     }
 };
 
-class Kernel
-    : public common::KernelInterface<const cl::Program*, cl::Kernel, Enqueuer,
-                                     cl::Buffer*> {
-   public:
-    using ModuleType = const sycl::program*;
-    using KernelType = sycl::kernel;
-    using DevPtrType<T> = sycl::buffer<T>*;
-    using BaseClass =
-        common::KernelInterface<ModuleType, KernelType, Enqueuer,
-DevPtrType<T>>;
-
-    Kernel() : BaseClass("", nullptr, cl::Kernel{nullptr, false}) {}
-    Kernel(std::string name, ModuleType mod, KernelType ker)
-        : BaseClass(name, mod, ker) {}
-
-    // clang-format off
-    [[deprecated("OpenCL backend doesn't need Kernel::getDevPtr method")]]
-    DevPtrType<T> getDevPtr(const char* name) final;
-    // clang-format on
-
-    void copyToReadOnly(DevPtrType<T> dst, DevPtrType<T> src, size_t bytes)
-final;
-
-    void setFlag(DevPtrType<T> dst, int* scalarValPtr,
-                 const bool syncCopy = false) final;
-
-    int getFlag(DevPtrType<T> src) final;
-};
-*/
-
 class Kernel {
-   public:
-    using ModuleType =
-        const sycl::kernel_bundle<sycl::bundle_state::executable>*;
-    using KernelType = sycl::kernel;
-    template<typename T>
-    using DevPtrType = sycl::buffer<T>*;
-    // using BaseClass =
-    // common::KernelInterface<ModuleType, KernelType, Enqueuer, DevPtrType<T>>;
-
-    Kernel() {}
-    Kernel(std::string name, ModuleType mod, KernelType ker) {}
-
-    template<typename T>
-    void copyToReadOnly(DevPtrType<T> dst, DevPtrType<T> src, size_t bytes);
-
-    template<typename T>
-    void setFlag(DevPtrType<T> dst, int* scalarValPtr,
-                 const bool syncCopy = false);
-
-    template<typename T>
-    int getFlag(DevPtrType<T> src);
+    //   public:
+    //    using BaseClass =
+    //      common::KernelInterface<ModuleType, KernelType, Enqueuer,
+    //      sycl::buffer<float>*>;
+    //
+    //  Kernel() : {}
+    //    Kernel(std::string name, ModuleType mod, KernelType ker)
+    //        : BaseClass(name, mod, ker) {}
+    //
+    //    // clang-format off
+    //    [[deprecated("OpenCL backend doesn't need Kernel::getDevPtr method")]]
+    //    DevPtrType getDevPtr(const char* name) final;
+    //    // clang-format on
+    //
+    //    void copyToReadOnly(DevPtrType dst, DevPtrType src, size_t bytes)
+    // final;
+    //
+    //    void setFlag(DevPtrType dst, int* scalarValPtr,
+    //                 const bool syncCopy = false) final;
+    //
+    //    int getFlag(DevPtrType src) final;
 };
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 613e26bdb7..f6ca0ef8b1 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -14,10 +14,17 @@
 
 #include <af/dim4.hpp>
 
+/// The get_pointer function in the accessor class throws a few warnings in the
+/// 2023.0 release of the library. Review this warning in the future
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wsycl-strict"
 #include <sycl/accessor.hpp>
+#pragma clang diagnostic pop
 #include <sycl/buffer.hpp>
 #include <sycl/handler.hpp>
 
+#include <optional>
+
 namespace arrayfire {
 namespace oneapi {
 
@@ -44,9 +51,85 @@ struct Param {
     ~Param() = default;
 };
 
+template<typename T>
+struct AParam {
+    std::optional<sycl::accessor<T, 1>> data;
+    std::optional<sycl::accessor<T, 1, sycl::access::mode::read_write,
+                                 sycl::access::target::device,
+                                 sycl::access::placeholder::true_t>>
+        ph;
+    af::dim4 dims;
+    af::dim4 strides;
+    dim_t offset;
+    AParam& operator=(const AParam& other) = default;
+    AParam(const AParam& other)            = default;
+    AParam(AParam&& other)                 = default;
+
+    // AF_DEPRECATED("Use Array<T>")
+    AParam() : data(), ph(), dims{0, 0, 0, 0}, strides{0, 0, 0, 0}, offset(0) {}
+
+    AParam(sycl::buffer<T, 1>& data_, const dim_t dims_[4],
+           const dim_t strides_[4], dim_t offset_)
+        : data()
+        , ph(std::make_optional<
+              sycl::accessor<T, 1, sycl::access::mode::read_write,
+                             sycl::access::target::device,
+                             sycl::access::placeholder::true_t>>(data_))
+        , dims(4, dims_)
+        , strides(4, strides_)
+        , offset(offset_) {}
+    // AF_DEPRECATED("Use Array<T>")
+    AParam(sycl::handler& h, sycl::buffer<T, 1>& data_, const dim_t dims_[4],
+           const dim_t strides_[4], dim_t offset_)
+        : data{{data_, h}}
+        , ph(data_)
+        , dims(4, dims_)
+        , strides(4, strides_)
+        , offset(offset_) {}
+
+    template<sycl::access::mode MODE>
+    sycl::accessor<data_t<T>, 1, MODE> get_accessor(sycl::handler& h) const {
+        return *data;
+    }
+
+    void require(sycl::handler& h) {
+        if (!data) { h.require(ph.value()); }
+    }
+
+    operator KParam() const {
+        return KParam{{dims[0], dims[1], dims[2], dims[3]},
+                      {strides[0], strides[1], strides[2], strides[3]},
+                      offset};
+    }
+
+    ~AParam() = default;
+};
+
 // AF_DEPRECATED("Use Array<T>")
 template<typename T>
 Param<T> makeParam(sycl::buffer<T>& mem, int off, const int dims[4],
                    const int strides[4]);
+
+namespace opencl {
+
+template<typename T>
+struct Param {
+    cl_mem data;
+    KParam info;
+    Param& operator=(const Param& other) = default;
+    Param(const Param& other)            = default;
+    Param(Param&& other)                 = default;
+    Param(cl_mem data_, KParam info_) : data(data_), info(info_) {}
+
+    // AF_DEPRECATED("Use Array<T>")
+    Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
+
+    // AF_DEPRECATED("Use Array<T>")
+    Param(sycl::buffer<T>* data_, KParam info_) : data(data_), info(info_) {}
+
+    ~Param() = default;
+};
+}  // namespace opencl
+
 }  // namespace oneapi
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/arith.hpp b/src/backend/oneapi/arith.hpp
index 8f31a5383e..815df91b57 100644
--- a/src/backend/oneapi/arith.hpp
+++ b/src/backend/oneapi/arith.hpp
@@ -11,7 +11,6 @@
 
 #include <Array.hpp>
 #include <common/jit/BinaryNode.hpp>
-#include <err_oneapi.hpp>
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 
@@ -21,14 +20,12 @@ namespace oneapi {
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &&lhs, const Array<T> &&rhs,
                  const af::dim4 &odims) {
-    ONEAPI_NOT_SUPPORTED(__FUNCTION__);
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 
 template<typename T, af_op_t op>
 Array<T> arithOp(const Array<T> &lhs, const Array<T> &rhs,
                  const af::dim4 &odims) {
-    ONEAPI_NOT_SUPPORTED(__FUNCTION__);
     return common::createBinaryNode<T, T, op>(lhs, rhs, odims);
 }
 }  // namespace oneapi
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index aea4398c66..7134109146 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -64,6 +64,16 @@ static inline bool compare_default(const unique_ptr<sycl::device>& ldev,
     return l_mem > r_mem;
 }
 
+auto arrayfire_exception_handler(sycl::exception_list exceptions) {
+    for (std::exception_ptr const& e : exceptions) {
+        try {
+            std::rethrow_exception(e);
+        } catch (sycl::exception const& ex) {
+            AF_ERROR(ex.what(), AF_ERR_INTERNAL);
+        }
+    }
+}
+
 DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , mUserDeviceOffset(0)
@@ -115,12 +125,12 @@ DeviceManager::DeviceManager()
 
     // Create contexts and queues once the sort is done
     for (int i = 0; i < nDevices; i++) {
-        if (devices[i]->is_gpu() || devices[i]->is_cpu() ||
-            !devices[i]->is_accelerator()) {
+        if (devices[i]->is_gpu() || devices[i]->is_cpu()) {
             try {
                 mContexts.push_back(make_unique<sycl::context>(*devices[i]));
                 mQueues.push_back(
-                    make_unique<sycl::queue>(*mContexts.back(), *devices[i]));
+                    make_unique<sycl::queue>(*mContexts.back(), *devices[i],
+                                             arrayfire_exception_handler));
                 mIsGLSharingOn.push_back(false);
                 // TODO:
                 // mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
diff --git a/src/backend/oneapi/err_oneapi.hpp b/src/backend/oneapi/err_oneapi.hpp
index ff6c83d6ca..fad7d449c0 100644
--- a/src/backend/oneapi/err_oneapi.hpp
+++ b/src/backend/oneapi/err_oneapi.hpp
@@ -16,3 +16,31 @@
         throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
                            boost::stacktrace::stacktrace());                \
     } while (0)
+
+#define CL_CHECK(call)                                                      \
+    do {                                                                    \
+        if (cl_int err = (call)) {                                          \
+            char cl_err_msg[2048];                                          \
+            const char* cl_err_call = #call;                                \
+            snprintf(cl_err_msg, sizeof(cl_err_msg),                        \
+                     "CL Error %s(%d): %d = %s\n", __FILE__, __LINE__, err, \
+                     cl_err_call);                                          \
+            AF_ERROR(cl_err_msg, AF_ERR_INTERNAL);                          \
+        }                                                                   \
+    } while (0)
+
+#define CL_CHECK_BUILD(call)                                                  \
+    do {                                                                      \
+        if (cl_int err = (call)) {                                            \
+            char log[8192];                                                   \
+            char cl_err_msg[8192];                                            \
+            const char* cl_err_call = #call;                                  \
+            size_t log_ret;                                                   \
+            clGetProgramBuildInfo(prog, dev, CL_PROGRAM_BUILD_LOG, 8192, log, \
+                                  &log_ret);                                  \
+            snprintf(cl_err_msg, sizeof(cl_err_msg),                          \
+                     "OpenCL Error building %s(%d): %d = %s\nLog:\n%s",       \
+                     __FILE__, __LINE__, err, cl_err_call, log);              \
+            AF_ERROR(cl_err_msg, AF_ERR_INTERNAL);                            \
+        }                                                                     \
+    } while (0)
diff --git a/src/backend/oneapi/histogram.cpp b/src/backend/oneapi/histogram.cpp
index 4036a5229b..4dfece0640 100644
--- a/src/backend/oneapi/histogram.cpp
+++ b/src/backend/oneapi/histogram.cpp
@@ -26,9 +26,7 @@ Array<uint> histogram(const Array<T> &in, const unsigned &nbins,
                       const bool isLinear) {
     const dim4 &dims = in.dims();
     dim4 outDims     = dim4(nbins, 1, dims[2], dims[3]);
-    // Array<uint> out  = createValueArray<uint>(outDims, uint(0));
-    // \TODO revert createEmptyArray to createValueArray once JIT functions
-    Array<uint> out = createEmptyArray<uint>(outDims);
+    Array<uint> out  = createValueArray<uint>(outDims, uint(0));
     kernel::histogram<T>(out, in, nbins, minval, maxval, isLinear);
     return out;
 }
diff --git a/src/backend/oneapi/index.cpp b/src/backend/oneapi/index.cpp
index 03a6b74c56..f0eb5e1cc4 100644
--- a/src/backend/oneapi/index.cpp
+++ b/src/backend/oneapi/index.cpp
@@ -22,7 +22,7 @@ namespace oneapi {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
-    ONEAPI_NOT_SUPPORTED("");
+    ONEAPI_NOT_SUPPORTED("Indexing not supported");
     Array<T> out = createEmptyArray<T>(af::dim4(1));
     return out;
 }
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 3233f97430..519d4efeea 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -7,21 +7,32 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <CL/cl.h>
+#include <jit/kernel_generators.hpp>
+
+#include <kernel_headers/KParam.hpp>
+#include <kernel_headers/jit.hpp>
+
 #include <Array.hpp>
-#include <common/compile_module.hpp>
+#include <common/debug.hpp>
 #include <common/dispatch.hpp>
+#include <common/half.hpp>
 #include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
 #include <common/jit/NodeIterator.hpp>
-#include <common/kernel_cache.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <device_manager.hpp>
 #include <err_oneapi.hpp>
+#include <jit/BufferNode.hpp>
+#include <platform.hpp>
+#include <type_util.hpp>
 #include <af/dim4.hpp>
 
-//#include <jit/BufferNode.hpp>
+#include <sycl/backend.hpp>
+#include <sycl/interop_handle.hpp>
 
+#include <array>
 #include <cstdio>
 #include <functional>
 #include <sstream>
@@ -30,44 +41,553 @@
 #include <vector>
 
 using arrayfire::common::getFuncName;
+using arrayfire::common::half;
+using arrayfire::common::ModdimNode;
 using arrayfire::common::Node;
 using arrayfire::common::Node_ids;
 using arrayfire::common::Node_map_t;
+using arrayfire::common::Node_ptr;
+using arrayfire::common::NodeIterator;
+using arrayfire::oneapi::getActiveDeviceBaseBuildFlags;
+using arrayfire::oneapi::jit::BufferNode;
 
+using std::array;
+using std::find_if;
 using std::string;
 using std::stringstream;
 using std::to_string;
 using std::vector;
 
+using sycl::backend;
+
 namespace arrayfire {
-namespace oneapi {
 
-string getKernelString(const string &funcName, const vector<Node *> &full_nodes,
-                       const vector<Node_ids> &full_ids,
-                       const vector<int> &output_ids, bool is_linear) {
-    ONEAPI_NOT_SUPPORTED("");
-    return "";
-}
+namespace opencl {
+string getKernelString(const string& funcName, const vector<Node*>& full_nodes,
+                       const vector<Node_ids>& full_ids,
+                       const vector<int>& output_ids, const bool is_linear,
+                       const bool loop0, const bool loop1, const bool loop3) {
+    // Common OpenCL code
+    // This part of the code does not change with the kernel.
+
+    static const char* kernelVoid = R"JIT(
+__kernel void )JIT";
+    static const char* dimParams  = "KParam oInfo";
+    static const char* blockStart = "{";
+    static const char* blockEnd   = "\n}\n";
+
+    static const char* linearInit = R"JIT(
+   int idx = get_global_id(0);
+   const int idxEnd = oInfo.dims[0];
+   if (idx < idxEnd) {
+)JIT";
+    static const char* linearEnd  = R"JIT(
+   })JIT";
+
+    static const char* linearLoop0Start = R"JIT(
+        const int idxID0Inc = get_global_size(0);
+        do {)JIT";
+    static const char* linearLoop0End   = R"JIT(
+            idx += idxID0Inc;
+            if (idx >= idxEnd) break;
+        } while (true);)JIT";
+
+    // ///////////////////////////////////////////////
+    // oInfo = output optimized information (dims, strides, offset).
+    //         oInfo has removed dimensions, to optimized block scheduling
+    // iInfo = input internal information (dims, strides, offset)
+    //         iInfo has the original dimensions, auto generated code
+    //
+    // Loop3 is fastest and becomes inside loop, since
+    //      - #of loops is known upfront
+    // Loop1 is used for extra dynamic looping (writing into cache)
+    // All loops are conditional and idependent
+    // Format Loop1 & Loop3
+    // ////////////////////////////
+    //  *stridedLoopNInit               // Always
+    //  *stridedLoop1Init               // Conditional
+    //  *stridedLoop2Init               // Conditional
+    //  *stridedLoop3Init               // Conditional
+    //  *stridedLoop1Start              // Conditional
+    //      *stridedLoop3Start          // Conditional
+    //          auto generated code     // Always
+    //      *stridedLoop3End            // Conditional
+    //  *stridedLoop1End                // Conditional
+    //  *StridedEnd                     // Always
+    //
+    // format loop0 (Vector only)
+    // //////////////////////////
+    // *stridedLoop0Init                // Always
+    // *stridedLoop0Start               // Always
+    //      auto generated code         // Always
+    // *stridedLoop0End                 // Always
+    // *stridedEnd                      // Always
+
+    static const char* stridedLoop0Init  = R"JIT(
+    int id0 = get_global_id(0);
+    const int id0End = oInfo.dims[0];
+    if (id0 < id0End) {
+#define id1 0
+#define id2 0
+#define id3 0
+        const int ostrides0 = oInfo.strides[0];
+        int idx = ostrides0*id0;)JIT";
+    static const char* stridedLoop0Start = R"JIT(
+        const int id0Inc = get_global_size(0);
+        const int idxID0Inc = ostrides0*id0Inc;
+        do {)JIT";
+    static const char* stridedLoop0End   = R"JIT(
+            id0 += id0Inc;
+            if (id0 >= id0End) break;
+            idx += idxID0Inc;
+        } while (true);)JIT";
+
+    // -------------
+    static const char* stridedLoopNInit = R"JIT(
+    int id0 = get_global_id(0);
+    int id1 = get_global_id(1);
+    const int id0End = oInfo.dims[0];
+    const int id1End = oInfo.dims[1];
+    //printf("id0: %d  id1: %d id0End: %d, id1End: %d\n")
+    if ((id0 < id0End) & (id1 < id1End)) {
+        const int id2 = get_global_id(2);
+#define id3 0
+        const int ostrides1 = oInfo.strides[1];
+        int idx = (int)oInfo.strides[0]*id0 + ostrides1*id1 + (int)oInfo.strides[2]*id2;)JIT";
+    static const char* stridedEnd       = R"JIT(
+    })JIT";
+
+    static const char* stridedLoop3Init  = R"JIT(
+#undef id3
+        int id3 = 0;
+        const int id3End = oInfo.dims[3];
+        const int idxID3Inc = oInfo.strides[3];)JIT";
+    static const char* stridedLoop3Start = R"JIT(
+                const int idxBaseID3 = idx;
+                do {)JIT";
+    static const char* stridedLoop3End   = R"JIT(
+                    ++id3;
+                    if (id3 == id3End) break;
+                    idx += idxID3Inc;
+                } while (true);
+                id3 = 0;
+                idx = idxBaseID3;)JIT";
 
-/*
-cl::Kernel getKernel(const vector<Node *> &output_nodes,
-                     const vector<int> &output_ids,
-                     const vector<Node *> &full_nodes,
-                     const vector<Node_ids> &full_ids, const bool is_linear) {
-    ONEAPI_NOT_SUPPORTED("");
-    return common::getKernel("", "", true).get();
+    static const char* stridedLoop1Init  = R"JIT(
+        const int id1Inc = get_global_size(1);
+        const int idxID1Inc = id1Inc * ostrides1;)JIT";
+    static const char* stridedLoop1Start = R"JIT(
+        do {)JIT";
+    static const char* stridedLoop1End   = R"JIT(
+            id1 += id1Inc;
+            if (id1 >= id1End) break;
+            idx += idxID1Inc;
+        } while (true);)JIT";
+
+    // Reuse stringstreams, because they are very costly during initilization
+    thread_local stringstream inParamStream;
+    thread_local stringstream outParamStream;
+    thread_local stringstream outOffsetStream;
+    thread_local stringstream inOffsetsStream;
+    thread_local stringstream opsStream;
+
+    int oid{0};
+    for (size_t i{0}; i < full_nodes.size(); i++) {
+        const auto& node{full_nodes[i]};
+        const auto& ids_curr{full_ids[i]};
+        // Generate input parameters, only needs current id
+        node->genParams(inParamStream, ids_curr.id, is_linear);
+        // Generate input offsets, only needs current id
+        node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
+        // Generate the core function body, needs children ids as well
+        node->genFuncs(opsStream, ids_curr);
+        for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
+             (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
+            // Generate also output parameters
+            outParamStream << "__global "
+                           << full_nodes[ids_curr.id]->getTypeStr() << " *out"
+                           << oid << ", int offset" << oid << ",\n";
+            // Apply output offset
+            outOffsetStream << "\nout" << oid << " += offset" << oid << ';';
+            // Generate code to write the output
+            opsStream << "out" << oid << "[idx] = val" << ids_curr.id << ";\n";
+            ++oid;
+        }
+    }
+
+    thread_local stringstream kerStream;
+    kerStream << kernelVoid << funcName << "(\n"
+              << inParamStream.str() << outParamStream.str() << dimParams << ")"
+              << blockStart;
+    if (is_linear) {
+        kerStream << linearInit << inOffsetsStream.str()
+                  << outOffsetStream.str() << '\n';
+        if (loop0) kerStream << linearLoop0Start;
+        kerStream << "\n\n" << opsStream.str();
+        if (loop0) kerStream << linearLoop0End;
+        kerStream << linearEnd;
+    } else {
+        if (loop0) {
+            kerStream << stridedLoop0Init << outOffsetStream.str() << '\n'
+                      << stridedLoop0Start;
+        } else {
+            kerStream << stridedLoopNInit << outOffsetStream.str() << '\n';
+            if (loop3) kerStream << stridedLoop3Init;
+            if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
+            if (loop3) kerStream << stridedLoop3Start;
+        }
+        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+        if (loop3) kerStream << stridedLoop3End;
+        if (loop1) kerStream << stridedLoop1End;
+        if (loop0) kerStream << stridedLoop0End;
+        kerStream << stridedEnd;
+    }
+    kerStream << blockEnd;
+    const string ret{kerStream.str()};
+
+    // Prepare for next round, limit memory
+    inParamStream.str("");
+    outParamStream.str("");
+    inOffsetsStream.str("");
+    outOffsetStream.str("");
+    opsStream.str("");
+    kerStream.str("");
+
+    return ret;
 }
-*/
 
-/*
-void evalNodes(vector<Param> &outputs, const vector<Node *> &output_nodes) {
-    ONEAPI_NOT_SUPPORTED("");
+// cl::Kernel getKernel(const vector<Node*>& output_nodes,
+//                      const vector<int>& output_ids,
+//                      const vector<Node*>& full_nodes,
+//                      const vector<Node_ids>& full_ids, const bool is_linear)
+//                      {
+//     ONEAPI_NOT_SUPPORTED("");
+//     return common::getKernel("", "", true).get();
+// }
+
+}  // namespace opencl
+
+namespace oneapi {
+
+template<typename T>
+void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
+    if (outputs.empty()) return;
+    Node_map_t nodes;
+    vector<Node*> full_nodes;
+    vector<Node_ids> full_ids;
+    vector<int> output_ids;
+    vector<Node_ptr> node_clones;
+
+    bool is_linear{true};
+    dim_t numOutElems{1};
+    KParam& out_info{outputs[0].info};
+    dim_t* outDims{out_info.dims};
+    dim_t* outStrides{out_info.strides};
+
+    dim_t ndims{outDims[3] > 1   ? 4
+                : outDims[2] > 1 ? 3
+                : outDims[1] > 1 ? 2
+                : outDims[0] > 0 ? 1
+                                 : 0};
+    for (dim_t dim{0}; dim < ndims; ++dim) {
+        is_linear &= (numOutElems == outStrides[dim]);
+        numOutElems *= outDims[dim];
+    }
+    if (numOutElems == 0) { return; }
+
+    const af::dtype outputType{output_nodes[0]->getType()};
+    for (Node* node : output_nodes) {
+        assert(node->getType() == outputType);
+        const int id{node->getNodesMap(nodes, full_nodes, full_ids)};
+        output_ids.push_back(id);
+    }
+
+    bool moddimsFound{false};
+    for (const Node* node : full_nodes) {
+        is_linear &= node->isLinear(outDims);
+        moddimsFound |= (node->getOp() == af_moddims_t);
+    }
+
+    bool emptyColumnsFound{false};
+    if (is_linear) {
+        outDims[0]    = numOutElems;
+        outDims[1]    = 1;
+        outDims[2]    = 1;
+        outDims[3]    = 1;
+        outStrides[0] = 1;
+        outStrides[1] = numOutElems;
+        outStrides[2] = numOutElems;
+        outStrides[3] = numOutElems;
+        ndims         = 1;
+    } else {
+        emptyColumnsFound = ndims > (outDims[0] == 1   ? 1
+                                     : outDims[1] == 1 ? 2
+                                     : outDims[2] == 1 ? 3
+                                                       : 4);
+    }
+
+    // for (auto* node : full_nodes) SHOW(*node);
+    //  Keep in global scope, so that the nodes remain active for later
+    //  referral in case moddims operations or column elimination have to
+    //  take place
+    //  Avoid all cloning/copying when no moddims node is present (high
+    //  chance)
+    if (moddimsFound || emptyColumnsFound) {
+        node_clones.clear();
+        node_clones.reserve(full_nodes.size());
+        for (Node* node : full_nodes) {
+            node_clones.emplace_back(node->clone());
+        }
+
+        for (const Node_ids& ids : full_ids) {
+            auto& children{node_clones[ids.id]->m_children};
+            for (int i{0}; i < Node::kMaxChildren && children[i] != nullptr;
+                 i++) {
+                children[i] = node_clones[ids.child_ids[i]];
+            }
+        }
+
+        if (moddimsFound) {
+            const auto isModdim{[](const Node_ptr& ptr) {
+                return ptr->getOp() == af_moddims_t;
+            }};
+            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                 (nodeIt = find_if(nodeIt, endIt, isModdim)) != endIt;
+                 ++nodeIt) {
+                const ModdimNode* mn{static_cast<ModdimNode*>(nodeIt->get())};
+
+                const auto new_strides{calcStrides(mn->m_new_shape)};
+                const auto isBuffer{
+                    [](const Node& node) { return node.isBuffer(); }};
+                for (NodeIterator<> it{nodeIt->get()}, end{NodeIterator<>()};
+                     (it = find_if(it, end, isBuffer)) != end; ++it) {
+                    jit::BufferNode<T>* buf{
+                        static_cast<jit::BufferNode<T>*>(&(*it))};
+                    buf->m_param.dims[0]    = mn->m_new_shape[0];
+                    buf->m_param.dims[1]    = mn->m_new_shape[1];
+                    buf->m_param.dims[2]    = mn->m_new_shape[2];
+                    buf->m_param.dims[3]    = mn->m_new_shape[3];
+                    buf->m_param.strides[0] = new_strides[0];
+                    buf->m_param.strides[1] = new_strides[1];
+                    buf->m_param.strides[2] = new_strides[2];
+                    buf->m_param.strides[3] = new_strides[3];
+                }
+            }
+        }
+        if (emptyColumnsFound) {
+            const auto isBuffer{
+                [](const Node_ptr& ptr) { return ptr->isBuffer(); }};
+            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
+                 (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
+                 ++nodeIt) {
+                BufferNode<T>* buf{static_cast<BufferNode<T>*>(nodeIt->get())};
+                removeEmptyColumns(outDims, ndims, buf->m_param.dims.get(),
+                                   buf->m_param.strides.get());
+            }
+            for_each(++begin(outputs), end(outputs),
+                     [outDims, ndims](Param<T>& output) {
+                         removeEmptyColumns(outDims, ndims, output.info.dims,
+                                            output.info.strides);
+                     });
+            ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
+        }
+
+        full_nodes.clear();
+        for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
+    }
+
+    const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
+                                      is_linear, false, false, false,
+                                      outputs[0].info.dims[2] > 1)};
+
+    getQueue()
+        .submit([&](sycl::handler& h) {
+            for (Node* node : full_nodes) {
+                if (node->isBuffer()) {
+                    BufferNode<T>* n = static_cast<BufferNode<T>*>(node);
+                    n->m_param.require(h);
+                }
+            }
+            vector<AParam<T>> ap;
+            transform(begin(outputs), end(outputs), back_inserter(ap),
+                      [&](const Param<T>& p) {
+                          return AParam<T>(h, *p.data, p.info.dims,
+                                           p.info.strides, p.info.offset);
+                      });
+
+            h.host_task([ap, full_nodes, output_ids, full_ids, is_linear,
+                         funcName](sycl::interop_handle hh) {
+                switch (hh.get_backend()) {
+                    case backend::opencl: {
+                        string jitstr = arrayfire::opencl::getKernelString(
+                            funcName, full_nodes, full_ids, output_ids,
+                            is_linear, false, false, ap[0].dims[2] > 1);
+
+                        cl_command_queue q =
+                            hh.get_native_queue<backend::opencl>();
+                        cl_context ctx =
+                            hh.get_native_context<backend::opencl>();
+                        cl_device_id dev =
+                            hh.get_native_device<backend::opencl>();
+
+                        cl_int err;
+                        vector<const char*> jitsources = {
+                            {arrayfire::oneapi::opencl::KParam_hpp,
+                             arrayfire::oneapi::opencl::jit_cl,
+                             jitstr.c_str()}};
+                        vector<size_t> jitsizes = {
+                            arrayfire::oneapi::opencl::KParam_hpp_len,
+                            arrayfire::oneapi::opencl::jit_cl_len,
+                            jitstr.size()};
+
+                        cl_program prog = clCreateProgramWithSource(
+                            ctx, jitsources.size(), jitsources.data(),
+                            jitsizes.data(), &err);
+
+                        std::string options = getActiveDeviceBaseBuildFlags();
+
+                        CL_CHECK_BUILD(clBuildProgram(
+                            prog, 1, &dev, options.c_str(), nullptr, nullptr));
+
+                        vector<cl_kernel> kernels(10);
+                        cl_uint ret_kernels = 0;
+                        CL_CHECK(clCreateKernelsInProgram(
+                            prog, 1, kernels.data(), &ret_kernels));
+                        int nargs{0};
+                        for (Node* node : full_nodes) {
+                            if (node->isBuffer()) {
+                                nargs = node->setArgs(
+                                    nargs, is_linear,
+                                    [&kernels, &hh, &is_linear](
+                                        int id, const void* ptr,
+                                        size_t arg_size) {
+                                        AParam<T>* info =
+                                            static_cast<AParam<T>*>(
+                                                const_cast<void*>(ptr));
+                                        vector<cl_mem> mem =
+                                            hh.get_native_mem<backend::opencl>(
+                                                info->ph.value());
+                                        if (is_linear) {
+                                            CL_CHECK(clSetKernelArg(
+                                                kernels[0], id++,
+                                                sizeof(cl_mem), &mem[0]));
+                                            CL_CHECK(clSetKernelArg(
+                                                kernels[0], id++, sizeof(dim_t),
+                                                &info->offset));
+                                        } else {
+                                            CL_CHECK(clSetKernelArg(
+                                                kernels[0], id++,
+                                                sizeof(cl_mem), &mem[0]));
+                                            KParam ooo = *info;
+                                            CL_CHECK(clSetKernelArg(
+                                                kernels[0], id++,
+                                                sizeof(KParam), &ooo));
+                                        }
+                                    });
+                            } else {
+                                nargs = node->setArgs(
+                                    nargs, is_linear,
+                                    [&kernels](int id, const void* ptr,
+                                               size_t arg_size) {
+                                        CL_CHECK(clSetKernelArg(kernels[0], id,
+                                                                arg_size, ptr));
+                                    });
+                            }
+                        }
+
+                        // Set output parameters
+                        vector<cl_mem> mem;
+                        for (const auto& output : ap) {
+                            mem = hh.get_native_mem<backend::opencl>(
+                                output.data.value());
+                            cl_mem mmm = mem[0];
+                            CL_CHECK(clSetKernelArg(kernels[0], nargs++,
+                                                    sizeof(cl_mem), &mmm));
+                            int off = output.offset;
+                            CL_CHECK(clSetKernelArg(kernels[0], nargs++,
+                                                    sizeof(int), &off));
+                        }
+                        const KParam ooo = ap[0];
+                        CL_CHECK(clSetKernelArg(kernels[0], nargs++,
+                                                sizeof(KParam), &ooo));
+                        array<size_t, 3> offset{0, 0, 0};
+                        array<size_t, 3> global;
+                        int ndims = 0;
+                        if (is_linear) {
+                            global = {(size_t)ap[0].dims.elements(), 0, 0};
+                            ndims  = 1;
+                        } else {
+                            global = {(size_t)ap[0].dims[0],
+                                      (size_t)ap[0].dims[1],
+                                      (size_t)ap[0].dims[2]};
+                            ndims  = 3;
+                        }
+                        // SHOW(global);
+                        CL_CHECK(clEnqueueNDRangeKernel(
+                            q, kernels[0], ndims, offset.data(), global.data(),
+                            nullptr, 0, nullptr, nullptr));
+
+                        CL_CHECK(clReleaseKernel(kernels[0]));
+                        CL_CHECK(clReleaseProgram(prog));
+                        CL_CHECK(clReleaseDevice(dev));
+                        CL_CHECK(clReleaseContext(ctx));
+                        CL_CHECK(clReleaseCommandQueue(q));
+
+                    } break;
+                    default: ONEAPI_NOT_SUPPORTED("Backend not supported");
+                }
+            });
+        })
+        .wait();
 }
 
-void evalNodes(Param &out, Node *node) {
-    ONEAPI_NOT_SUPPORTED("");
+template<typename T>
+void evalNodes(Param<T>& out, Node* node) {
+    vector<Param<T>> outputs{out};
+    vector<Node*> nodes{node};
+    oneapi::evalNodes(outputs, nodes);
 }
-*/
+
+template void evalNodes<float>(Param<float>& out, Node* node);
+template void evalNodes<double>(Param<double>& out, Node* node);
+template void evalNodes<cfloat>(Param<cfloat>& out, Node* node);
+template void evalNodes<cdouble>(Param<cdouble>& out, Node* node);
+template void evalNodes<int>(Param<int>& out, Node* node);
+template void evalNodes<uint>(Param<uint>& out, Node* node);
+template void evalNodes<char>(Param<char>& out, Node* node);
+template void evalNodes<uchar>(Param<uchar>& out, Node* node);
+template void evalNodes<intl>(Param<intl>& out, Node* node);
+template void evalNodes<uintl>(Param<uintl>& out, Node* node);
+template void evalNodes<short>(Param<short>& out, Node* node);
+template void evalNodes<ushort>(Param<ushort>& out, Node* node);
+template void evalNodes<half>(Param<half>& out, Node* node);
+
+template void evalNodes<float>(vector<Param<float>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<double>(vector<Param<double>>& out,
+                                const vector<Node*>& node);
+template void evalNodes<cfloat>(vector<Param<cfloat>>& out,
+                                const vector<Node*>& node);
+template void evalNodes<cdouble>(vector<Param<cdouble>>& out,
+                                 const vector<Node*>& node);
+template void evalNodes<int>(vector<Param<int>>& out,
+                             const vector<Node*>& node);
+template void evalNodes<uint>(vector<Param<uint>>& out,
+                              const vector<Node*>& node);
+template void evalNodes<char>(vector<Param<char>>& out,
+                              const vector<Node*>& node);
+template void evalNodes<uchar>(vector<Param<uchar>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<intl>(vector<Param<intl>>& out,
+                              const vector<Node*>& node);
+template void evalNodes<uintl>(vector<Param<uintl>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<short>(vector<Param<short>>& out,
+                               const vector<Node*>& node);
+template void evalNodes<ushort>(vector<Param<ushort>>& out,
+                                const vector<Node*>& node);
+template void evalNodes<half>(vector<Param<half>>& out,
+                              const vector<Node*>& node);
 
 }  // namespace oneapi
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
index 5f8ead77e0..b6bedc5baf 100644
--- a/src/backend/oneapi/jit/BufferNode.hpp
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -8,7 +8,9 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
 #include <common/jit/BufferNodeBase.hpp>
+#include <jit/kernel_generators.hpp>
 
 #include <memory>
 
@@ -17,7 +19,7 @@ namespace oneapi {
 namespace jit {
 template<typename T>
 using BufferNode =
-    common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, KParam>;
+    common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, AParam<T>>;
 }
 }  // namespace oneapi
 
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index b3753955b9..3a15f78e8e 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -8,11 +8,16 @@
  ********************************************************/
 
 #pragma once
+#include <Param.hpp>
+#include <err_oneapi.hpp>
+
+#include <sycl/buffer.hpp>
+
+#include <functional>
+#include <memory>
 #include <sstream>
 #include <string>
 
-#include <err_oneapi.hpp>
-
 namespace arrayfire {
 namespace oneapi {
 
@@ -27,7 +32,7 @@ inline void generateParamDeclaration(std::stringstream& kerStream, int id,
                   << ", dim_t iInfo" << id << "_offset, \n";
     } else {
         kerStream << "__global " << m_type_str << " *in" << id
-                  << ", Param iInfo" << id << ", \n";
+                  << ", KParam iInfo" << id << ", \n";
     }
 }
 
@@ -36,18 +41,8 @@ template<typename T>
 inline int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-    const std::shared_ptr<sycl::buffer<T>>& ptr, const KParam& info) {
-    // TODO(oneapi)
-    ONEAPI_NOT_SUPPORTED("ERROR");
-    // setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
-    // sizeof(cl_mem));
-    if (is_linear) {
-        // setArg(start_id + 1, static_cast<const void*>(&info.offset),
-        // sizeof(dim_t));
-    } else {
-        // setArg(start_id + 1, static_cast<const void*>(&info),
-        // sizeof(KParam));
-    }
+    const std::shared_ptr<sycl::buffer<T>>& ptr, const AParam<T>& info) {
+    setArg(start_id + 0, static_cast<const void*>(&info), sizeof(Param<T>));
     return start_id + 2;
 }
 
diff --git a/src/backend/oneapi/kernel/KParam.hpp b/src/backend/oneapi/kernel/KParam.hpp
index b5bb98e850..c1cf30be4b 100644
--- a/src/backend/oneapi/kernel/KParam.hpp
+++ b/src/backend/oneapi/kernel/KParam.hpp
@@ -10,11 +10,11 @@
 #ifndef __KPARAM_H
 #define __KPARAM_H
 
-//#ifndef __OPENCL_VERSION__
-// Only define dim_t in host code. dim_t is defined when setting the program
-// options in program.cpp
+// #ifndef __OPENCL_VERSION__
+//  Only define dim_t in host code. dim_t is defined when setting the program
+//  options in program.cpp
 #include <af/defines.h>
-//#endif
+// #endif
 
 // Defines the size and shape of the data in the OpenCL buffer
 typedef struct {
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
index ea6c4c229a..3d53930bd4 100644
--- a/src/backend/oneapi/kernel/histogram.hpp
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -142,15 +142,6 @@ void histogram(Param<uint> out, const Param<T> in, int nbins, float minval,
     const size_t global1 = in.info.dims[3];
     auto global          = sycl::range{global0, global1};
 
-    // \TODO drop this first memset once createEmptyArray is reverted back to
-    //       createValueArray in ../histogram.cpp
-    getQueue()
-        .submit([&](sycl::handler &h) {
-            auto outAcc = out.data->get_access(h);
-            h.parallel_for(sycl::range<1>{(size_t)nbins},
-                           [=](sycl::id<1> idx) { outAcc[idx[0]] = 0; });
-        })
-        .wait();
     getQueue().submit([&](sycl::handler &h) {
         auto inAcc  = in.data->get_access(h);
         auto outAcc = out.data->get_access(h);
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index bb1aa99d21..ee27b706d9 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -262,14 +262,8 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
             AF_ERR_RUNTIME);
     }
     Array<To> tmp = createEmptyArray<To>(tmp_elements);
-    // TODO: JIT dependency
-    // Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
-    Array<unsigned> retirementCount = createEmptyArray<unsigned>(1);
-    getQueue().submit([=](sycl::handler &h) {
-        auto acc = retirementCount.getData()->get_access(h);
-        h.single_task([=] { acc[0] = 0; });
-    });
 
+    Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
     getQueue().submit([=](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
         auto retCount_acc = retirementCount.getData()->get_access(h);
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index ee47082295..aa620e8e2c 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -205,18 +205,10 @@ void Allocator::shutdown() {
     // }
 }
 
-int Allocator::getActiveDeviceId() {
-    ONEAPI_NOT_SUPPORTED("Allocator::getActiveDeviceId Not supported");
-
-    return 0;
-    // return opencl::getActiveDeviceId();
-}
+int Allocator::getActiveDeviceId() { return oneapi::getActiveDeviceId(); }
 
 size_t Allocator::getMaxMemorySize(int id) {
-    ONEAPI_NOT_SUPPORTED("Allocator::getMaxMemorySize Not supported");
-
-    return 0;
-    // return opencl::getDeviceMemorySize(id);
+    return oneapi::getDeviceMemorySize(id);
 }
 
 void *Allocator::nativeAlloc(const size_t bytes) {

From c167e9f16eb4b7bdff1018e7a0630dd722b3f57c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 7 Dec 2022 17:57:11 -0500
Subject: [PATCH 2426/2677] Add device-code-split flags to oneapi backend to
 avoid double failures

---
 src/backend/oneapi/CMakeLists.txt | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 5b5684038d..f7d1033b5a 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -281,9 +281,6 @@ target_include_directories(afoneapi
 target_compile_options(afoneapi
   PRIVATE
     -fsycl
-    #-fsycl-targets=nvptx64-vidia-cuda
-    #-fsycl-force-target=nvptx64-nvidia-cuda-sm_86
-    #-Wno-unknown-cuda-version
     -sycl-std=2020
 )
 
@@ -301,14 +298,14 @@ target_link_libraries(afoneapi
     -fsycl
     -fno-lto
     -fvisibility-inlines-hidden
-    #-fsycl-targets=nvptx64-nvidia-cuda-sm_86
-    #-fsycl-force-target=nvptx64-nvidia-cuda-sm_86
     c_api_interface
     cpp_api_interface
     afcommon_interface
     OpenCL::OpenCL
     OpenCL::cl2hpp
-    #-Wno-unknown-cuda-version
+    -fsycl
+    -fsycl-device-code-split=per_kernel
+    -fsycl-link-huge-device-code
   )
 
 af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})

From 1e4b1a02623d4240d1d7aaaaf14f5d9e467c199d Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Tue, 14 Mar 2023 19:26:16 +0100
Subject: [PATCH 2427/2677] multithreaded OPENBLAS & FFTW

---
 vcpkg.json | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/vcpkg.json b/vcpkg.json
index 72625d8fa9..5cf6972ce0 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -46,8 +46,14 @@
         "openblasfftw": {
             "description": "Build with OpenBLAS/FFTW",
             "dependencies": [
-                "fftw3",
-                "openblas",
+                {
+                    "name": "fftw3",
+                    "features": [ "threads" ]
+                },
+                {
+                    "name": "openblas",
+                    "features": [ "threads" ]
+                },
                 "lapack"
             ]
         },

From 97d4e61cc5941c0b63ca700b0398d19d42c162e4 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Fri, 17 Mar 2023 13:39:40 -0400
Subject: [PATCH 2428/2677] Implementation of FFT for oneAPI (#3379)

FFT implementation for the oneAPI backend

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt     |   3 +
 src/backend/oneapi/fft.cpp            | 184 ++++++++++++++++++--------
 src/backend/oneapi/kernel/memcopy.hpp |  41 +++---
 src/backend/oneapi/reshape.cpp        |  13 +-
 4 files changed, 162 insertions(+), 79 deletions(-)

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index f7d1033b5a..7e61118811 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -281,6 +281,7 @@ target_include_directories(afoneapi
 target_compile_options(afoneapi
   PRIVATE
     -fsycl
+    -qopenmp -qmkl=parallel
     -sycl-std=2020
 )
 
@@ -306,6 +307,8 @@ target_link_libraries(afoneapi
     -fsycl
     -fsycl-device-code-split=per_kernel
     -fsycl-link-huge-device-code
+    -qopenmp
+    -qmkl=parallel
   )
 
 af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})
diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index 9ccdcfcb86..8ac2cd410c 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2022, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <common/dispatch.hpp>
+
 #include <fft.hpp>
 
 #include <copy.hpp>
@@ -15,78 +17,156 @@
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
+#include <array>
+using std::array;
+
 using af::dim4;
 
+#include <oneapi/mkl/dfti.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 
 void setFFTPlanCacheSize(size_t numPlans) {}
 
-/*
-template<typename T>
-struct Precision;
-template<>
-struct Precision<cfloat> {
-    enum { type = CLFFT_SINGLE };
-};
-template<>
-struct Precision<cdouble> {
-    enum { type = CLFFT_DOUBLE };
-};
-*/
-
-void computeDims(size_t rdims[AF_MAX_DIMS], const dim4 &idims) {
-    for (int i = 0; i < AF_MAX_DIMS; i++) {
-        rdims[i] = static_cast<size_t>(idims[i]);
-    }
-}
-
-//(currently) true is in clFFT if length is a power of 2,3,5
-inline bool isSupLen(dim_t length) {
-    while (length > 1) {
-        if (length % 2 == 0) {
-            length /= 2;
-        } else if (length % 3 == 0) {
-            length /= 3;
-        } else if (length % 5 == 0) {
-            length /= 5;
-        } else if (length % 7 == 0) {
-            length /= 7;
-        } else if (length % 11 == 0) {
-            length /= 11;
-        } else if (length % 13 == 0) {
-            length /= 13;
-        } else {
-            return false;
-        }
-    }
-    return true;
-}
-
-void verifySupported(const int rank, const dim4 &dims) {
-    for (int i = 0; i < rank; i++) { ARG_ASSERT(1, isSupLen(dims[i])); }
+inline array<int, AF_MAX_DIMS> computeDims(const int rank, const dim4 &idims) {
+    array<int, AF_MAX_DIMS> retVal = {};
+    for (int i = 0; i < rank; i++) { retVal[i] = idims[(rank - 1) - i]; }
+    return retVal;
 }
 
 template<typename T>
 void fft_inplace(Array<T> &in, const int rank, const bool direction) {
-    ONEAPI_NOT_SUPPORTED("");
+    const dim4 idims    = in.dims();
+    const dim4 istrides = in.strides();
+
+    constexpr bool is_single = std::is_same_v<T, cfloat>;
+    constexpr auto precision = (is_single)
+                                   ? ::oneapi::mkl::dft::precision::SINGLE
+                                   : ::oneapi::mkl::dft::precision::DOUBLE;
+    using desc_ty =
+        ::oneapi::mkl::dft::descriptor<precision,
+                                       ::oneapi::mkl::dft::domain::COMPLEX>;
+
+    auto desc = [rank, &idims]() {
+        if (rank == 1) return desc_ty(idims[0]);
+        if (rank == 2) return desc_ty({idims[0], idims[1]});
+        if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
+        return desc_ty({idims[0], idims[1], idims[2], idims[3]});
+    }();
+
+    desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT, DFTI_INPLACE);
+
+    int batch = 1;
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+    desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
+                   (int64_t)batch);
+
+    desc.set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE,
+                   istrides[rank]);
+    desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
+                   istrides[rank]);
+
+    desc.commit(getQueue());
+    if (direction)
+        ::oneapi::mkl::dft::compute_forward(desc, *(in.getData()));
+    else
+        ::oneapi::mkl::dft::compute_backward(desc, *(in.getData()));
 }
 
 template<typename Tc, typename Tr>
 Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
-    ONEAPI_NOT_SUPPORTED("");
-    dim4 odims = in.dims();
-
-    odims[0] = odims[0] / 2 + 1;
+    const dim4 idims    = in.dims();
+    const dim4 istrides = in.strides();
+    Array<Tc> out       = createEmptyArray<Tc>(
+        dim4({idims[0] / 2 + 1, idims[1], idims[2], idims[3]}));
+    const dim4 ostrides = out.strides();
+
+    constexpr bool is_single = std::is_same_v<Tr, float>;
+    constexpr auto precision = (is_single)
+                                   ? ::oneapi::mkl::dft::precision::SINGLE
+                                   : ::oneapi::mkl::dft::precision::DOUBLE;
+    using desc_ty =
+        ::oneapi::mkl::dft::descriptor<precision,
+                                       ::oneapi::mkl::dft::domain::REAL>;
+
+    auto desc = [rank, &idims]() {
+        if (rank == 1) return desc_ty(idims[0]);
+        if (rank == 2) return desc_ty({idims[0], idims[1]});
+        if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
+        return desc_ty({idims[0], idims[1], idims[2], idims[3]});
+    }();
+
+    desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
+                   DFTI_NOT_INPLACE);
+
+    int batch = 1;
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+    desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
+                   (int64_t)batch);
+
+    desc.set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE,
+                   ostrides[rank]);
+    desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
+                   istrides[rank]);
+
+    const std::int64_t fft_output_strides[5] = {
+        0, ostrides[(rank == 2) ? 1 : 0], ostrides[(rank == 2) ? 0 : 1],
+        ostrides[2], ostrides[3]};
+    desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
+                   fft_output_strides, rank);
+
+    desc.commit(getQueue());
+    ::oneapi::mkl::dft::compute_forward(desc, *(in.getData()),
+                                        *(out.getData()));
 
-    Array<Tc> out = createEmptyArray<Tc>(odims);
     return out;
 }
 
 template<typename Tr, typename Tc>
 Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<Tr> out = createEmptyArray<Tr>(odims);
+    const dim4 idims    = in.dims();
+    const dim4 istrides = in.strides();
+    Array<Tr> out       = createEmptyArray<Tr>(odims);
+    const dim4 ostrides = out.strides();
+
+    constexpr bool is_single = std::is_same_v<Tr, float>;
+    constexpr auto precision = (is_single)
+                                   ? ::oneapi::mkl::dft::precision::SINGLE
+                                   : ::oneapi::mkl::dft::precision::DOUBLE;
+    using desc_ty =
+        ::oneapi::mkl::dft::descriptor<precision,
+                                       ::oneapi::mkl::dft::domain::REAL>;
+
+    auto desc = [rank, &odims]() {
+        if (rank == 1) return desc_ty(odims[0]);
+        if (rank == 2) return desc_ty({odims[0], odims[1]});
+        if (rank == 3) return desc_ty({odims[0], odims[1], odims[2]});
+        return desc_ty({odims[0], odims[1], odims[2], odims[3]});
+    }();
+
+    desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
+                   DFTI_NOT_INPLACE);
+
+    int batch = 1;
+    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+    desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
+                   (int64_t)batch);
+
+    desc.set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE,
+                   istrides[rank]);
+    desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
+                   ostrides[rank]);
+
+    const std::int64_t fft_output_strides[5] = {
+        0, ostrides[(rank == 2) ? 1 : 0], ostrides[(rank == 2) ? 0 : 1],
+        ostrides[2], ostrides[3]};
+    desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
+                   fft_output_strides, rank);
+
+    desc.commit(getQueue());
+    ::oneapi::mkl::dft::compute_backward(desc, *(in.getData()),
+                                         *(out.getData()));
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 294573b1bf..9d5f966dc2 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2022, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -9,6 +9,7 @@
 
 #pragma once
 
+#include <CL/sycl.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
@@ -131,54 +132,54 @@ cdouble scale<cdouble>(cdouble value, double factor) {
 }
 
 template<typename inType, typename outType>
-outType convertType(inType value) {
+static outType convertType(inType value) {
     return static_cast<outType>(value);
 }
 
 template<>
-char convertType<compute_t<arrayfire::common::half>, char>(
+static char convertType<compute_t<arrayfire::common::half>, char>(
     compute_t<arrayfire::common::half> value) {
     return (char)((short)value);
 }
 
 template<>
-compute_t<arrayfire::common::half>
-convertType<char, compute_t<arrayfire::common::half>>(char value) {
+compute_t<arrayfire::common::half> static convertType<
+    char, compute_t<arrayfire::common::half>>(char value) {
     return compute_t<arrayfire::common::half>(value);
 }
 
 template<>
-unsigned char convertType<compute_t<arrayfire::common::half>, unsigned char>(
+static unsigned char
+convertType<compute_t<arrayfire::common::half>, unsigned char>(
     compute_t<arrayfire::common::half> value) {
     return (unsigned char)((short)value);
 }
 
 template<>
-compute_t<arrayfire::common::half>
-convertType<unsigned char, compute_t<arrayfire::common::half>>(
-    unsigned char value) {
+compute_t<arrayfire::common::half> static convertType<
+    unsigned char, compute_t<arrayfire::common::half>>(unsigned char value) {
     return compute_t<arrayfire::common::half>(value);
 }
 
 template<>
-cdouble convertType<cfloat, cdouble>(cfloat value) {
+static cdouble convertType<cfloat, cdouble>(cfloat value) {
     return cdouble(value.real(), value.imag());
 }
 
 template<>
-cfloat convertType<cdouble, cfloat>(cdouble value) {
+static cfloat convertType<cdouble, cfloat>(cdouble value) {
     return cfloat(value.real(), value.imag());
 }
 
-#define OTHER_SPECIALIZATIONS(IN_T)                      \
-    template<>                                           \
-    cfloat convertType<IN_T, cfloat>(IN_T value) {       \
-        return cfloat(static_cast<float>(value), 0.0f);  \
-    }                                                    \
-                                                         \
-    template<>                                           \
-    cdouble convertType<IN_T, cdouble>(IN_T value) {     \
-        return cdouble(static_cast<double>(value), 0.0); \
+#define OTHER_SPECIALIZATIONS(IN_T)                         \
+    template<>                                              \
+    static cfloat convertType<IN_T, cfloat>(IN_T value) {   \
+        return cfloat(static_cast<float>(value), 0.0f);     \
+    }                                                       \
+                                                            \
+    template<>                                              \
+    static cdouble convertType<IN_T, cdouble>(IN_T value) { \
+        return cdouble(static_cast<double>(value), 0.0);    \
     }
 
 OTHER_SPECIALIZATIONS(float)
diff --git a/src/backend/oneapi/reshape.cpp b/src/backend/oneapi/reshape.cpp
index 768a167480..8f1b6f0ecb 100644
--- a/src/backend/oneapi/reshape.cpp
+++ b/src/backend/oneapi/reshape.cpp
@@ -1,6 +1,5 @@
-
 /*******************************************************
- * Copyright (c) 2020, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -12,7 +11,7 @@
 #include <err_oneapi.hpp>
 
 #include <common/half.hpp>
-// #include <kernel/memcopy.hpp>
+#include <kernel/memcopy.hpp>
 
 using arrayfire::common::half;
 
@@ -22,11 +21,11 @@ namespace oneapi {
 template<typename inType, typename outType>
 Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                        outType defaultValue, double scale) {
-    ONEAPI_NOT_SUPPORTED("reshape Not supported");
-
     Array<outType> out = createEmptyArray<outType>(outDims);
-    // kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale,
-    //                               in.dims() == outDims);
+    if (out.elements() > 0) {
+        kernel::copy<inType, outType>(out, in, in.ndims(), defaultValue, scale,
+                                      in.dims() == outDims);
+    }
     return out;
 }
 

From 8fc5ee650fdbeb2078746b5d6fd33789f55ab347 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 22 Mar 2023 15:04:58 -0400
Subject: [PATCH 2429/2677] Fix warnings in oneAPI backend and remove
 sycl::streams

---
 src/backend/oneapi/Kernel.hpp                 |  1 -
 src/backend/oneapi/Module.hpp                 |  3 +-
 src/backend/oneapi/compile_module.cpp         |  1 -
 src/backend/oneapi/copy.cpp                   | 10 ++--
 src/backend/oneapi/kernel/bilateral.hpp       |  4 +-
 src/backend/oneapi/kernel/convolve2.hpp       |  3 --
 src/backend/oneapi/kernel/convolve3.hpp       | 18 +++-----
 src/backend/oneapi/kernel/memcopy.hpp         | 38 ++++++---------
 src/backend/oneapi/kernel/random_engine.hpp   | 46 ++++++++-----------
 .../oneapi/kernel/random_engine_mersenne.hpp  | 28 ++++-------
 .../oneapi/kernel/random_engine_philox.hpp    | 18 ++------
 .../oneapi/kernel/random_engine_threefry.hpp  | 14 ++----
 .../oneapi/kernel/random_engine_write.hpp     |  3 +-
 src/backend/oneapi/kernel/range.hpp           | 33 ++++---------
 src/backend/oneapi/kernel/rotate.hpp          |  3 --
 src/backend/oneapi/kernel/transform.hpp       |  6 ---
 .../oneapi/kernel/transpose_inplace.hpp       | 25 ++++------
 17 files changed, 85 insertions(+), 169 deletions(-)

diff --git a/src/backend/oneapi/Kernel.hpp b/src/backend/oneapi/Kernel.hpp
index ee5a2fcd02..3fcf7b66b8 100644
--- a/src/backend/oneapi/Kernel.hpp
+++ b/src/backend/oneapi/Kernel.hpp
@@ -12,7 +12,6 @@
 #include <common/KernelInterface.hpp>
 #include <common/Logger.hpp>
 
-#include <CL/sycl.hpp>
 #include <backend.hpp>
 #include <string>
 
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
index c4de202761..cb4c4e130c 100644
--- a/src/backend/oneapi/Module.hpp
+++ b/src/backend/oneapi/Module.hpp
@@ -9,9 +9,10 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <common/ModuleInterface.hpp>
 
+#include <sycl/kernel_bundle.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index 39783a3c53..640fcc797c 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -10,7 +10,6 @@
 #include <common/compile_module.hpp>  //compileModule & loadModuleFromDisk
 #include <common/kernel_cache.hpp>    //getKernel(Module&, ...)
 
-#include <CL/sycl.hpp>
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/util.hpp>
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 23106f7dd1..4059bd27f0 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -219,10 +219,12 @@ T getScalar(const Array<T> &in) {
 
     getQueue()
         .submit([&](sycl::handler &h) {
-            auto acc_in = in.getData()->get_access(
-                h, sycl::range{1},
-                sycl::id{static_cast<uintl>(in.getOffset())});
-            auto acc_out = retBuffer.get_access();
+            auto acc_in =
+                in.get()->template get_access<sycl::access::mode::read>(
+                    h, sycl::range{1},
+                    sycl::id{static_cast<uintl>(in.getOffset())});
+            auto acc_out =
+                retBuffer.template get_access<sycl::access::mode::write>(h);
             h.copy(acc_in, acc_out);
         })
         .wait();
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
index cb3d323f07..c01ee4a4a5 100644
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -148,8 +148,8 @@ class bilateralKernel {
     void load2LocalMem(local_accessor<outType, 1> shrd, const inType* in,
                        int lx, int ly, int shrdStride, int dim0, int dim1,
                        int gx, int gy, int inStride1, int inStride0) const {
-        int gx_ = std::clamp(gx, 0, dim0 - 1);
-        int gy_ = std::clamp(gy, 0, dim1 - 1);
+        int gx_ = sycl::clamp(gx, 0, dim0 - 1);
+        int gy_ = sycl::clamp(gy, 0, dim1 - 1);
         shrd[lIdx(lx, ly, shrdStride, 1)] =
             (outType)in[lIdx(gx_, gy_, inStride1, inStride0)];
     }
diff --git a/src/backend/oneapi/kernel/convolve2.hpp b/src/backend/oneapi/kernel/convolve2.hpp
index 173405bdb8..5de34a2023 100644
--- a/src/backend/oneapi/kernel/convolve2.hpp
+++ b/src/backend/oneapi/kernel/convolve2.hpp
@@ -121,9 +121,6 @@ template<typename T, typename aT>
 void conv2Helper(const conv_kparam_t<aT> &param, Param<T> out,
                  const Param<T> signal, const Param<aT> filter,
                  const bool expand) {
-    constexpr bool IsComplex =
-        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
-
     const int f0 = filter.info.dims[0];
     const int f1 = filter.info.dims[1];
     const size_t LOC_SIZE =
diff --git a/src/backend/oneapi/kernel/convolve3.hpp b/src/backend/oneapi/kernel/convolve3.hpp
index 57f1538ddc..0e2dee72fe 100644
--- a/src/backend/oneapi/kernel/convolve3.hpp
+++ b/src/backend/oneapi/kernel/convolve3.hpp
@@ -55,18 +55,12 @@ class conv3HelperCreateKernel {
              sstep3_ *
                  sInfo_.strides[3]); /* activated with batched input filter */
 
-        int lx  = it.get_local_id(0);
-        int ly  = it.get_local_id(1);
-        int lz  = it.get_local_id(2);
-        int gx  = g.get_local_range(0) * (g.get_group_id(0) - b2 * nBBS0_) + lx;
-        int gy  = g.get_local_range(1) * g.get_group_id(1) + ly;
-        int gz  = g.get_local_range(2) * g.get_group_id(2) + lz;
-        int lx2 = lx + g.get_local_range(0);
-        int ly2 = ly + g.get_local_range(1);
-        int lz2 = lz + g.get_local_range(2);
-        int gx2 = gx + g.get_local_range(0);
-        int gy2 = gy + g.get_local_range(1);
-        int gz2 = gz + g.get_local_range(2);
+        int lx = it.get_local_id(0);
+        int ly = it.get_local_id(1);
+        int lz = it.get_local_id(2);
+        int gx = g.get_local_range(0) * (g.get_group_id(0) - b2 * nBBS0_) + lx;
+        int gy = g.get_local_range(1) * g.get_group_id(1) + ly;
+        int gz = g.get_local_range(2) * g.get_group_id(2) + lz;
 
         int s0 = sInfo_.strides[0];
         int s1 = sInfo_.strides[1];
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 9d5f966dc2..adabe3b29d 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -9,7 +9,6 @@
 
 #pragma once
 
-#include <CL/sycl.hpp>
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
@@ -116,69 +115,60 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
 }
 
 template<typename T>
-static T scale(T value, double factor) {
+inline T scale(T value, double factor) {
     return (T)(double(value) * factor);
 }
 
 template<>
-cfloat scale<cfloat>(cfloat value, double factor) {
+inline cfloat scale<cfloat>(cfloat value, double factor) {
     return cfloat{static_cast<float>(value.real() * factor),
                   static_cast<float>(value.imag() * factor)};
 }
 
 template<>
-cdouble scale<cdouble>(cdouble value, double factor) {
+inline cdouble scale<cdouble>(cdouble value, double factor) {
     return cdouble{value.real() * factor, value.imag() * factor};
 }
 
 template<typename inType, typename outType>
-static outType convertType(inType value) {
+inline outType convertType(inType value) {
     return static_cast<outType>(value);
 }
 
 template<>
-static char convertType<compute_t<arrayfire::common::half>, char>(
+inline char convertType<compute_t<arrayfire::common::half>, char>(
     compute_t<arrayfire::common::half> value) {
     return (char)((short)value);
 }
 
 template<>
-compute_t<arrayfire::common::half> static convertType<
-    char, compute_t<arrayfire::common::half>>(char value) {
+inline compute_t<arrayfire::common::half>
+convertType<char, compute_t<arrayfire::common::half>>(char value) {
     return compute_t<arrayfire::common::half>(value);
 }
 
 template<>
-static unsigned char
-convertType<compute_t<arrayfire::common::half>, unsigned char>(
+unsigned char inline convertType<compute_t<arrayfire::common::half>,
+                                 unsigned char>(
     compute_t<arrayfire::common::half> value) {
     return (unsigned char)((short)value);
 }
 
 template<>
-compute_t<arrayfire::common::half> static convertType<
-    unsigned char, compute_t<arrayfire::common::half>>(unsigned char value) {
+inline compute_t<arrayfire::common::half>
+convertType<unsigned char, compute_t<arrayfire::common::half>>(
+    unsigned char value) {
     return compute_t<arrayfire::common::half>(value);
 }
 
-template<>
-static cdouble convertType<cfloat, cdouble>(cfloat value) {
-    return cdouble(value.real(), value.imag());
-}
-
-template<>
-static cfloat convertType<cdouble, cfloat>(cdouble value) {
-    return cfloat(value.real(), value.imag());
-}
-
 #define OTHER_SPECIALIZATIONS(IN_T)                         \
     template<>                                              \
-    static cfloat convertType<IN_T, cfloat>(IN_T value) {   \
+    inline cfloat convertType<IN_T, cfloat>(IN_T value) {   \
         return cfloat(static_cast<float>(value), 0.0f);     \
     }                                                       \
                                                             \
     template<>                                              \
-    static cdouble convertType<IN_T, cdouble>(IN_T value) { \
+    inline cdouble convertType<IN_T, cdouble>(IN_T value) { \
         return cdouble(static_cast<double>(value), 0.0);    \
     }
 
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
index 66e286fea9..329387eef5 100644
--- a/src/backend/oneapi/kernel/random_engine.hpp
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -58,11 +58,9 @@ void uniformDistributionCBRNG(Param<T> out, const size_t elements,
             getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
-                sycl::stream debug_stream(2048, 128, h);
-                h.parallel_for(
-                    ndrange,
-                    uniformPhilox<T>(out_acc, hi, lo, hic, loc,
-                                     elementsPerBlock, elements, debug_stream));
+                h.parallel_for(ndrange,
+                               uniformPhilox<T>(out_acc, hi, lo, hic, loc,
+                                                elementsPerBlock, elements));
             });
             ONEAPI_DEBUG_FINISH(getQueue());
             break;
@@ -70,11 +68,9 @@ void uniformDistributionCBRNG(Param<T> out, const size_t elements,
             getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
-                sycl::stream debug_stream(2048, 128, h);
                 h.parallel_for(ndrange,
                                uniformThreefry<T>(out_acc, hi, lo, hic, loc,
-                                                  elementsPerBlock, elements,
-                                                  debug_stream));
+                                                  elementsPerBlock, elements));
             });
             ONEAPI_DEBUG_FINISH(getQueue());
             break;
@@ -102,22 +98,18 @@ void normalDistributionCBRNG(Param<T> out, const size_t elements,
             getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
-                sycl::stream debug_stream(2048, 128, h);
-                h.parallel_for(
-                    ndrange,
-                    normalPhilox<T>(out_acc, hi, lo, hic, loc, elementsPerBlock,
-                                    elements, debug_stream));
+                h.parallel_for(ndrange,
+                               normalPhilox<T>(out_acc, hi, lo, hic, loc,
+                                               elementsPerBlock, elements));
             });
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
             getQueue().submit([=](sycl::handler &h) {
                 auto out_acc = out.data->get_access(h);
 
-                sycl::stream debug_stream(2048, 128, h);
                 h.parallel_for(ndrange,
                                normalThreefry<T>(out_acc, hi, lo, hic, loc,
-                                                 elementsPerBlock, elements,
-                                                 debug_stream));
+                                                 elementsPerBlock, elements));
             });
             break;
         default:
@@ -154,12 +146,11 @@ void uniformDistributionMT(Param<T> out, const size_t elements,
         auto lrecursion_acc = local_accessor<uint, 1>(TABLE_SIZE, h);
         auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
 
-        sycl::stream debug_stream(2048, 128, h);
-        h.parallel_for(ndrange, uniformMersenne<T>(
-                                    out_acc, state_acc, pos_acc, sh1_acc,
-                                    sh2_acc, mask, recursion_acc, temper_acc,
-                                    lstate_acc, lrecursion_acc, ltemper_acc,
-                                    elementsPerBlock, elements, debug_stream));
+        h.parallel_for(
+            ndrange, uniformMersenne<T>(
+                         out_acc, state_acc, pos_acc, sh1_acc, sh2_acc, mask,
+                         recursion_acc, temper_acc, lstate_acc, lrecursion_acc,
+                         ltemper_acc, elementsPerBlock, elements));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
@@ -191,12 +182,11 @@ void normalDistributionMT(Param<T> out, const size_t elements,
         auto lrecursion_acc = local_accessor<uint, 1>(TABLE_SIZE, h);
         auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
 
-        sycl::stream debug_stream(2048, 128, h);
-        h.parallel_for(ndrange, normalMersenne<T>(
-                                    out_acc, state_acc, pos_acc, sh1_acc,
-                                    sh2_acc, mask, recursion_acc, temper_acc,
-                                    lstate_acc, lrecursion_acc, ltemper_acc,
-                                    elementsPerBlock, elements, debug_stream));
+        h.parallel_for(
+            ndrange, normalMersenne<T>(out_acc, state_acc, pos_acc, sh1_acc,
+                                       sh2_acc, mask, recursion_acc, temper_acc,
+                                       lstate_acc, lrecursion_acc, ltemper_acc,
+                                       elementsPerBlock, elements));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
index e0a0f57c8d..bbf5dae3e0 100644
--- a/src/backend/oneapi/kernel/random_engine_mersenne.hpp
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -107,13 +107,8 @@ static inline uint temper(const uint *const temper_table, const uint v,
 class initMersenneKernel {
    public:
     initMersenneKernel(sycl::accessor<uint> state, sycl::accessor<uint> tbl,
-                       local_accessor<uint, 1> lstate, uintl seed,
-                       sycl::stream debug_stream)
-        : state_(state)
-        , tbl_(tbl)
-        , lstate_(lstate)
-        , seed_(seed)
-        , debug_(debug_stream) {}
+                       local_accessor<uint, 1> lstate, uintl seed)
+        : state_(state), tbl_(tbl), lstate_(lstate), seed_(seed) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
@@ -147,7 +142,6 @@ class initMersenneKernel {
     sycl::accessor<uint> state_, tbl_;
     local_accessor<uint, 1> lstate_;
     uintl seed_;
-    sycl::stream debug_;
 };
 
 void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
@@ -157,10 +151,8 @@ void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
         auto tbl_acc    = tbl.data->get_access(h);
         auto lstate_acc = local_accessor<uint, 1>(N, h);
 
-        sycl::stream debug_stream(2048, 128, h);
-        h.parallel_for(ndrange,
-                       initMersenneKernel(state_acc, tbl_acc, lstate_acc, seed,
-                                          debug_stream));
+        h.parallel_for(
+            ndrange, initMersenneKernel(state_acc, tbl_acc, lstate_acc, seed));
     });
     // TODO: do we need to sync before using Mersenne generators?
     // force wait() here?
@@ -179,7 +171,7 @@ class uniformMersenne {
                     local_accessor<uint, 1> state,
                     local_accessor<uint, 1> recursion_table,
                     local_accessor<uint, 1> temper_table, uint elementsPerBlock,
-                    size_t elements, sycl::stream debug)
+                    size_t elements)
         : out_(out)
         , gState_(gState)
         , pos_tbl_(pos_tbl)
@@ -192,8 +184,7 @@ class uniformMersenne {
         , recursion_table_(recursion_table)
         , temper_table_(temper_table)
         , elementsPerBlock_(elementsPerBlock)
-        , elements_(elements)
-        , debug_(debug) {}
+        , elements_(elements) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
@@ -263,7 +254,6 @@ class uniformMersenne {
     local_accessor<uint, 1> state_, recursion_table_, temper_table_;
     uint elementsPerBlock_;
     size_t elements_;
-    sycl::stream debug_;
 };
 
 template<typename T>
@@ -278,7 +268,7 @@ class normalMersenne {
                    local_accessor<uint, 1> state,
                    local_accessor<uint, 1> recursion_table,
                    local_accessor<uint, 1> temper_table, uint elementsPerBlock,
-                   size_t elements, sycl::stream debug)
+                   size_t elements)
         : out_(out)
         , gState_(gState)
         , pos_tbl_(pos_tbl)
@@ -291,8 +281,7 @@ class normalMersenne {
         , recursion_table_(recursion_table)
         , temper_table_(temper_table)
         , elementsPerBlock_(elementsPerBlock)
-        , elements_(elements)
-        , debug_(debug) {}
+        , elements_(elements) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
@@ -363,7 +352,6 @@ class normalMersenne {
     local_accessor<uint, 1> state_, recursion_table_, temper_table_;
     uint elementsPerBlock_;
     size_t elements_;
-    sycl::stream debug_;
 };
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/random_engine_philox.hpp b/src/backend/oneapi/kernel/random_engine_philox.hpp
index b5887aa16e..3bfe44251d 100644
--- a/src/backend/oneapi/kernel/random_engine_philox.hpp
+++ b/src/backend/oneapi/kernel/random_engine_philox.hpp
@@ -107,22 +107,18 @@ template<typename T>
 class uniformPhilox {
    public:
     uniformPhilox(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
-                  uint elementsPerBlock, uint elements,
-                  sycl::stream debug_stream)
+                  uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
         , lo_(lo)
         , hic_(hic)
         , loc_(loc)
         , elementsPerBlock_(elementsPerBlock)
-        , elements_(elements)
-        , debug_(debug_stream) {}
+        , elements_(elements) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
 
-        // debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) <<
-        // "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
         uint index = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
         uint key[2] = {lo_, hi_};
         uint ctr[4] = {loc_, hic_, 0, 0};
@@ -145,28 +141,23 @@ class uniformPhilox {
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
-    sycl::stream debug_;
 };
 
 template<typename T>
 class normalPhilox {
    public:
     normalPhilox(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
-                 uint elementsPerBlock, uint elements,
-                 sycl::stream debug_stream)
+                 uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
         , lo_(lo)
         , hic_(hic)
         , loc_(loc)
         , elementsPerBlock_(elementsPerBlock)
-        , elements_(elements)
-        , debug_(debug_stream) {}
+        , elements_(elements) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
-        // debug_ << "<" << g.get_group_id(0)  << ":" << it.get_local_id(0) <<
-        // "/" << g.get_group_range(0) << sycl::stream_manipulator::endl;
 
         uint index = g.get_group_id(0) * elementsPerBlock_ + it.get_local_id(0);
         uint key[2] = {lo_, hi_};
@@ -192,7 +183,6 @@ class normalPhilox {
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
-    sycl::stream debug_;
 };
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/random_engine_threefry.hpp b/src/backend/oneapi/kernel/random_engine_threefry.hpp
index 2e8b6e0d16..919f04d010 100644
--- a/src/backend/oneapi/kernel/random_engine_threefry.hpp
+++ b/src/backend/oneapi/kernel/random_engine_threefry.hpp
@@ -162,16 +162,14 @@ template<typename T>
 class uniformThreefry {
    public:
     uniformThreefry(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
-                    uint elementsPerBlock, uint elements,
-                    sycl::stream debug_stream)
+                    uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
         , lo_(lo)
         , hic_(hic)
         , loc_(loc)
         , elementsPerBlock_(elementsPerBlock)
-        , elements_(elements)
-        , debug_(debug_stream) {}
+        , elements_(elements) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
@@ -203,23 +201,20 @@ class uniformThreefry {
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
-    sycl::stream debug_;
 };
 
 template<typename T>
 class normalThreefry {
    public:
     normalThreefry(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
-                   uint elementsPerBlock, uint elements,
-                   sycl::stream debug_stream)
+                   uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
         , lo_(lo)
         , hic_(hic)
         , loc_(loc)
         , elementsPerBlock_(elementsPerBlock)
-        , elements_(elements)
-        , debug_(debug_stream) {}
+        , elements_(elements) {}
 
     void operator()(sycl::nd_item<1> it) const {
         sycl::group g = it.get_group();
@@ -251,7 +246,6 @@ class normalThreefry {
     sycl::accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
-    sycl::stream debug_;
 };
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 9769285d2f..b3a4d60ed7 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -7,7 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-#include <CL/sycl.hpp>
+
+#include <sycl/builtins.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index cce47881f2..fb7e5ea449 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -29,20 +29,14 @@ template<typename T>
 class rangeOp {
    public:
     rangeOp(sycl::accessor<T> out, KParam oinfo, const int dim,
-            const int blocksPerMatX, const int blocksPerMatY,
-            sycl::stream debug)
+            const int blocksPerMatX, const int blocksPerMatY)
         : out_(out)
         , oinfo_(oinfo)
         , dim_(dim)
         , blocksPerMatX_(blocksPerMatX)
-        , blocksPerMatY_(blocksPerMatY)
-        , debug_(debug) {}
+        , blocksPerMatY_(blocksPerMatY) {}
 
     void operator()(sycl::nd_item<2> it) const {
-        // printf("[%d,%d]\n", it.get_global_id(0), it.get_global_id(1));
-        // debug_ << "[" << it.get_global_id(0) << "," << it.get_global_id(1) <<
-        // "]" << sycl::stream_manipulator::endl;
-
         const int mul0 = (dim_ == 0);
         const int mul1 = (dim_ == 1);
         const int mul2 = (dim_ == 2);
@@ -67,15 +61,15 @@ class rangeOp {
         const int incy = blocksPerMatY_ * g.get_local_range(1);
         const int incx = blocksPerMatX_ * g.get_local_range(0);
 
-        T valZW = (mul3 * ow) + (mul2 * oz);
+        compute_t<T> valZW = (mul3 * ow) + (mul2 * oz);
 
         T* optr = out_.get_pointer();
         for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
-            T valYZW = valZW + (mul1 * oy);
-            int oyzw = ozw + oy * oinfo_.strides[1];
+            compute_t<T> valYZW = valZW + (mul1 * oy);
+            int oyzw            = ozw + oy * oinfo_.strides[1];
             for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
-                int oidx = oyzw + ox;
-                T val    = valYZW + (mul0 * ox);
+                int oidx         = oyzw + ox;
+                compute_t<T> val = valYZW + (mul0 * ox);
 
                 optr[oidx] = val;
             }
@@ -87,7 +81,6 @@ class rangeOp {
     KParam oinfo_;
     int dim_;
     int blocksPerMatX_, blocksPerMatY_;
-    sycl::stream debug_;
 };
 
 template<typename T>
@@ -108,20 +101,12 @@ void range(Param<T> out, const int dim) {
     getQueue().submit([=](sycl::handler& h) {
         auto out_acc = out.data->get_access(h);
 
-        sycl::stream debug_stream(2048, 128, h);
-
-        h.parallel_for(ndrange,
-                       rangeOp<T>(out_acc, out.info, dim, blocksPerMatX,
-                                  blocksPerMatY, debug_stream));
+        h.parallel_for(ndrange, rangeOp<T>(out_acc, out.info, dim,
+                                           blocksPerMatX, blocksPerMatY));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
-template<>
-void range(Param<arrayfire::common::half> out, const int dim) {
-    ONEAPI_NOT_SUPPORTED("TODO: fix arrayfire::common::half support");
-}
-
 }  // namespace kernel
 }  // namespace oneapi
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/rotate.hpp b/src/backend/oneapi/kernel/rotate.hpp
index 61d736763a..b8c8357e79 100644
--- a/src/backend/oneapi/kernel/rotate.hpp
+++ b/src/backend/oneapi/kernel/rotate.hpp
@@ -136,9 +136,6 @@ void rotate(Param<T> out, const Param<T> in, const float theta,
 
     // Used for batching images
     constexpr int TI = 4;
-    constexpr bool isComplex =
-        static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-        static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
 
     const float c = cos(-theta), s = sin(-theta);
     float tx, ty;
diff --git a/src/backend/oneapi/kernel/transform.hpp b/src/backend/oneapi/kernel/transform.hpp
index b67a11c660..c18ac6c827 100644
--- a/src/backend/oneapi/kernel/transform.hpp
+++ b/src/backend/oneapi/kernel/transform.hpp
@@ -126,7 +126,6 @@ class transformCreateKernel {
 
         // Index of transform
         const int eTfs2 = sycl::max((nTfs2_ / nImg2_), 1);
-        const int eTfs3 = sycl::max((nTfs3_ / nImg3_), 1);
 
         int t_idx3        = -1;  // init
         int t_idx2        = -1;  // init
@@ -243,8 +242,6 @@ template<typename T>
 void transform(Param<T> out, const Param<T> in, const Param<float> tf,
                bool isInverse, bool isPerspective, af_interp_type method,
                int order) {
-    static int counter = 0;
-
     using std::string;
 
     using BT = typename dtype_traits<T>::base_type;
@@ -253,9 +250,6 @@ void transform(Param<T> out, const Param<T> in, const Param<float> tf,
     constexpr int TY = 16;
     // Used for batching images
     constexpr int TI = 4;
-    constexpr bool isComplex =
-        static_cast<af_dtype>(dtype_traits<T>::af_type) == c32 ||
-        static_cast<af_dtype>(dtype_traits<T>::af_type) == c64;
 
     const int nImg2 = in.info.dims[2];
     const int nImg3 = in.info.dims[3];
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
index d397436dfc..3dda946ced 100644
--- a/src/backend/oneapi/kernel/transpose_inplace.hpp
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -41,9 +41,9 @@ cdouble getConjugate(const cdouble &in) {
 
 #define doOp(v) (conjugate_ ? getConjugate((v)) : (v))
 
-constexpr int TILE_DIM  = 16;
-constexpr int THREADS_X = TILE_DIM;
-constexpr int THREADS_Y = 256 / TILE_DIM;
+constexpr dim_t TILE_DIM  = 16;
+constexpr dim_t THREADS_X = TILE_DIM;
+constexpr dim_t THREADS_Y = 256 / TILE_DIM;
 
 template<typename T, int dimensions>
 using local_accessor =
@@ -57,8 +57,7 @@ class transposeInPlaceKernel {
                            const int blocksPerMatX, const int blocksPerMatY,
                            const bool conjugate, const bool IS32MULTIPLE,
                            local_accessor<T, 1> shrdMem_s,
-                           local_accessor<T, 1> shrdMem_d,
-                           sycl::stream debugStream)
+                           local_accessor<T, 1> shrdMem_d)
         : iData_(iData)
         , in_(in)
         , blocksPerMatX_(blocksPerMatX)
@@ -66,8 +65,7 @@ class transposeInPlaceKernel {
         , conjugate_(conjugate)
         , IS32MULTIPLE_(IS32MULTIPLE)
         , shrdMem_s_(shrdMem_s)
-        , shrdMem_d_(shrdMem_d)
-        , debugStream_(debugStream) {}
+        , shrdMem_d_(shrdMem_d) {}
     void operator()(sycl::nd_item<2> it) const {
         const int shrdStride = TILE_DIM + 1;
 
@@ -165,7 +163,6 @@ class transposeInPlaceKernel {
     bool IS32MULTIPLE_;
     local_accessor<T, 1> shrdMem_s_;
     local_accessor<T, 1> shrdMem_d_;
-    sycl::stream debugStream_;
 };
 
 template<typename T>
@@ -180,16 +177,14 @@ void transpose_inplace(Param<T> in, const bool conjugate,
                               blk_y * local[1] * in.info.dims[3]};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto r = in.data->get_access(h);
-        sycl::stream debugStream(128, 128, h);
-
+        auto r         = in.data->get_access(h);
         auto shrdMem_s = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
         auto shrdMem_d = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 
-        h.parallel_for(sycl::nd_range{global, local},
-                       transposeInPlaceKernel<T>(
-                           r, in.info, blk_x, blk_y, conjugate, IS32MULTIPLE,
-                           shrdMem_s, shrdMem_d, debugStream));
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            transposeInPlaceKernel<T>(r, in.info, blk_x, blk_y, conjugate,
+                                      IS32MULTIPLE, shrdMem_s, shrdMem_d));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }

From 9ceaa06eeebc51ef9f8c0a9acd8e0fd0b0fe4864 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 23 Mar 2023 19:26:10 -0400
Subject: [PATCH 2430/2677] Remove getData usage in the oneAPI backend and make
 it private

The getData function should not be used to get the buffer pointer. The get
function should be called instead because the Array could be a node array and in
that case it would be a null pointer. The get function will evaluate the buffer
and then return the resulting array. This was causing crashes after the JIT
changes
---
 src/api/c/memory.cpp                     | 16 ++++------------
 src/backend/cuda/Array.hpp               |  3 ++-
 src/backend/oneapi/Array.hpp             | 13 +++----------
 src/backend/oneapi/fft.cpp               | 10 ++++------
 src/backend/oneapi/kernel/mean.hpp       | 10 ++++------
 src/backend/oneapi/kernel/reduce_all.hpp |  6 +++---
 src/backend/opencl/Array.hpp             |  4 ++--
 7 files changed, 22 insertions(+), 40 deletions(-)

diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index 17ea0a4d73..fbff61720e 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -144,10 +144,7 @@ af_err af_get_device_ptr(void **data, const af_array arr) {
 
 template<typename T>
 inline void lockArray(const af_array arr) {
-    // Ideally we need to use .get(false), i.e. get ptr without offset
-    // This is however not supported in opencl
-    // Use getData().get() as alternative
-    memLock(getArray<T>(arr).getData().get());
+    memLock(getArray<T>(arr).get());
 }
 
 af_err af_lock_device_ptr(const af_array arr) { return af_lock_array(arr); }
@@ -180,10 +177,8 @@ af_err af_lock_array(const af_array arr) {
 
 template<typename T>
 inline bool checkUserLock(const af_array arr) {
-    // Ideally we need to use .get(false), i.e. get ptr without offset
-    // This is however not supported in opencl
-    // Use getData().get() as alternative
-    return isLocked(static_cast<void *>(getArray<T>(arr).getData().get()));
+    detail::Array<T> &out = const_cast<detail::Array<T> &>(getArray<T>(arr));
+    return isLocked(static_cast<void *>(out.get()));
 }
 
 af_err af_is_locked_array(bool *res, const af_array arr) {
@@ -214,10 +209,7 @@ af_err af_is_locked_array(bool *res, const af_array arr) {
 
 template<typename T>
 inline void unlockArray(const af_array arr) {
-    // Ideally we need to use .get(false), i.e. get ptr without offset
-    // This is however not supported in opencl
-    // Use getData().get() as alternative
-    memUnlock(getArray<T>(arr).getData().get());
+    memUnlock(getArray<T>(arr).get());
 }
 
 af_err af_unlock_device_ptr(const af_array arr) { return af_unlock_array(arr); }
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index d6774ded66..7e1324d016 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -156,6 +156,8 @@ class Array {
     Array(Param<T> &tmp, bool owner);
     Array(const af::dim4 &dims, common::Node_ptr n);
 
+    std::shared_ptr<T> getData() const { return data; }
+
    public:
     Array(const Array<T> &other) = default;
 
@@ -227,7 +229,6 @@ class Array {
     void eval() const;
 
     dim_t getOffset() const { return info.getOffset(); }
-    std::shared_ptr<T> getData() const { return data; }
 
     dim4 getDataDims() const { return data_dims; }
 
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index bc4e16c574..9a4de1285c 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -177,6 +177,8 @@ class Array {
     explicit Array(const af::dim4 &dims, sycl::buffer<T> *const mem,
                    size_t offset, bool copy);
 
+    std::shared_ptr<sycl::buffer<T>> getData() const { return data; }
+
    public:
     Array(const Array<T> &other) = default;
 
@@ -250,14 +252,7 @@ class Array {
         return const_cast<Array<T> *>(this)->device();
     }
 
-    // FIXME: This should do a copy if it is not owner. You do not want to
-    // overwrite parents data
-    sycl::buffer<T> *get() {
-        if (!isReady()) eval();
-        return data.get();
-    }
-
-    const sycl::buffer<T> *get() const {
+    sycl::buffer<T> *get() const {
         if (!isReady()) eval();
         return data.get();
     }
@@ -266,8 +261,6 @@ class Array {
 
     dim_t getOffset() const { return info.getOffset(); }
 
-    std::shared_ptr<sycl::buffer<T>> getData() const { return data; }
-
     dim4 getDataDims() const { return data_dims; }
 
     void setDataDims(const dim4 &new_dims);
diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index 8ac2cd410c..eff8770bfc 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -69,9 +69,9 @@ void fft_inplace(Array<T> &in, const int rank, const bool direction) {
 
     desc.commit(getQueue());
     if (direction)
-        ::oneapi::mkl::dft::compute_forward(desc, *(in.getData()));
+        ::oneapi::mkl::dft::compute_forward(desc, *in.get());
     else
-        ::oneapi::mkl::dft::compute_backward(desc, *(in.getData()));
+        ::oneapi::mkl::dft::compute_backward(desc, *in.get());
 }
 
 template<typename Tc, typename Tr>
@@ -117,8 +117,7 @@ Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
                    fft_output_strides, rank);
 
     desc.commit(getQueue());
-    ::oneapi::mkl::dft::compute_forward(desc, *(in.getData()),
-                                        *(out.getData()));
+    ::oneapi::mkl::dft::compute_forward(desc, *in.get(), *out.get());
 
     return out;
 }
@@ -165,8 +164,7 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
                    fft_output_strides, rank);
 
     desc.commit(getQueue());
-    ::oneapi::mkl::dft::compute_backward(desc, *(in.getData()),
-                                         *(out.getData()));
+    ::oneapi::mkl::dft::compute_backward(desc, *in.get(), *out.get());
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 3f3dbc378b..4353bfff26 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -629,13 +629,12 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
 
         auto e1 = getQueue().submit([&](sycl::handler &h) {
             auto acc_in =
-                tmpOut.getData()->get_access(h, sycl::range{tmp_elements});
+                tmpOut.get()->get_access(h, sycl::range{tmp_elements});
             auto acc_out = hBuffer.get_access();
             h.copy(acc_in, acc_out);
         });
         auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                tmpWt.getData()->get_access(h, sycl::range{tmp_elements});
+            auto acc_in = tmpWt.get()->get_access(h, sycl::range{tmp_elements});
             auto acc_out = hwBuffer.get_access();
             h.copy(acc_in, acc_out);
         });
@@ -733,13 +732,12 @@ To mean_all(Param<Ti> in) {
 
         auto e1 = getQueue().submit([&](sycl::handler &h) {
             auto acc_in =
-                tmpOut.getData()->get_access(h, sycl::range{tmp_elements});
+                tmpOut.get()->get_access(h, sycl::range{tmp_elements});
             auto acc_out = hBuffer.get_access();
             h.copy(acc_in, acc_out);
         });
         auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                tmpCt.getData()->get_access(h, sycl::range{tmp_elements});
+            auto acc_in = tmpCt.get()->get_access(h, sycl::range{tmp_elements});
             auto acc_out = hcBuffer.get_access();
             h.copy(acc_in, acc_out);
         });
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index ee27b706d9..2089b60175 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -264,10 +264,10 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
     Array<To> tmp = createEmptyArray<To>(tmp_elements);
 
     Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
-        auto retCount_acc = retirementCount.getData()->get_access(h);
-        auto tmp_acc      = tmp.getData()->get_access(h);
+        auto retCount_acc = retirementCount.get()->get_access(h);
+        auto tmp_acc      = tmp.get()->get_access(h);
         read_accessor<Ti> in_acc{*in.data, h};
 
         auto shrdMem =
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 6951021f19..3a672d00f6 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -161,6 +161,8 @@ class Array {
     explicit Array(const af::dim4 &dims, const T *const in_data);
     explicit Array(const af::dim4 &dims, cl_mem mem, size_t offset, bool copy);
 
+    std::shared_ptr<cl::Buffer> getData() const { return data; }
+
    public:
     Array(const Array<T> &other) = default;
 
@@ -250,8 +252,6 @@ class Array {
 
     dim_t getOffset() const { return info.getOffset(); }
 
-    std::shared_ptr<cl::Buffer> getData() const { return data; }
-
     dim4 getDataDims() const { return data_dims; }
 
     void setDataDims(const dim4 &new_dims);

From e5d2dda8b91d0ae8ef1aabb5af590aa23f4deae9 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 24 Mar 2023 18:00:29 -0400
Subject: [PATCH 2431/2677] adds ireduce kernels

---
 src/backend/oneapi/CMakeLists.txt     |   4 +-
 src/backend/oneapi/ireduce.cpp        |   9 +-
 src/backend/oneapi/kernel/ireduce.hpp | 698 ++++++++++++++++++++++++++
 src/backend/oneapi/minmax_op.hpp      |  87 ++++
 4 files changed, 793 insertions(+), 5 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/ireduce.hpp
 create mode 100644 src/backend/oneapi/minmax_op.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 7e61118811..64a2b34715 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -117,6 +117,7 @@ add_library(afoneapi
   memory.cpp
   memory.hpp
   min.cpp
+  minmax_op.hpp
   moments.cpp
   moments.hpp
   morph.cpp
@@ -217,6 +218,7 @@ target_sources(afoneapi
     kernel/diff.hpp
     kernel/interp.hpp
     kernel/iota.hpp
+    kernel/ireduce.hpp
     kernel/histogram.hpp
     kernel/memcopy.hpp
     kernel/mean.hpp
@@ -281,7 +283,7 @@ target_include_directories(afoneapi
 target_compile_options(afoneapi
   PRIVATE
     -fsycl
-    -qopenmp -qmkl=parallel
+    -openmp -Qmkl=parallel
     -sycl-std=2020
 )
 
diff --git a/src/backend/oneapi/ireduce.cpp b/src/backend/oneapi/ireduce.cpp
index 6cca678b20..c7b4d263ab 100644
--- a/src/backend/oneapi/ireduce.cpp
+++ b/src/backend/oneapi/ireduce.cpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/ireduce.hpp>
 #include <optypes.hpp>
 #include <af/dim4.hpp>
 #include <complex>
@@ -24,19 +25,19 @@ namespace oneapi {
 template<af_op_t op, typename T>
 void ireduce(Array<T> &out, Array<uint> &loc, const Array<T> &in,
              const int dim) {
-    ONEAPI_NOT_SUPPORTED("");
+    Array<uint> rlen = createEmptyArray<uint>(af::dim4(0));
+    kernel::ireduce<T, op>(out, loc, in, dim, rlen);
 }
 
 template<af_op_t op, typename T>
 void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
              const Array<uint> &rlen) {
-    ONEAPI_NOT_SUPPORTED("");
+    kernel::ireduce<T, op>(out, loc, in, dim, rlen);
 }
 
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
-    return T(0);
+    return kernel::ireduce_all<T, op>(loc, in);
 }
 
 #define INSTANTIATE(ROp, T)                                           \
diff --git a/src/backend/oneapi/kernel/ireduce.hpp b/src/backend/oneapi/kernel/ireduce.hpp
new file mode 100644
index 0000000000..9e4e35c51d
--- /dev/null
+++ b/src/backend/oneapi/kernel/ireduce.hpp
@@ -0,0 +1,698 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/reduce_config.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <minmax_op.hpp>
+
+#include <sycl/sycl.hpp>  //TODO: exact headers
+
+#include <algorithm>
+#include <climits>
+#include <complex>
+#include <iostream>
+#include <memory>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T, int dimensions>
+using local_accessor =
+    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
+                   sycl::access::target::local>;
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
+class ireduceDimKernelSMEM {
+   public:
+    ireduceDimKernelSMEM(write_accessor<T> out, KParam oInfo,
+                         write_accessor<uint> oloc, KParam olocInfo,
+                         read_accessor<T> in, KParam iInfo,
+                         read_accessor<uint> iloc, KParam ilocInfo,
+                         uint groups_x, uint groups_y, uint groups_dim,
+                         read_accessor<uint> rlen, KParam rlenInfo,
+                         local_accessor<compute_t<T>, 1> s_val,
+                         local_accessor<uint, 1> s_idx)
+        : out_(out)
+        , oInfo_(oInfo)
+        , oloc_(oloc)
+        , olocInfo_(olocInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , iloc_(iloc)
+        , ilocInfo_(ilocInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , groups_dim_(groups_dim)
+        , rlen_(rlen)
+        , rlenInfo_(rlenInfo)
+        , s_val_(s_val)
+        , s_idx_(s_idx) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid       = groupId_x * g.get_local_range(0) + lidx;
+        const uint yid       = groupId_y;
+
+        uint ids[4] = {xid, yid, zid, wid};
+        T *optr     = out_.get_pointer() + ids[3] * oInfo_.strides[3] +
+                  ids[2] * oInfo_.strides[2] + ids[1] * oInfo_.strides[1] +
+                  ids[0] + oInfo_.offset;
+
+        uint *olptr = oloc_.get_pointer() + ids[3] * oInfo_.strides[3] +
+                      ids[2] * oInfo_.strides[2] + ids[1] * oInfo_.strides[1] +
+                      ids[0] + oInfo_.offset;
+
+        // There is only one element per block for out
+        // There are blockDim.y elements per block for in
+        // Hence increment ids[dim] just after offseting out and before
+        // offsetting in
+        const bool rlen_valid =
+            (ids[0] < rlenInfo_.dims[0]) && (ids[1] < rlenInfo_.dims[1]) &&
+            (ids[2] < rlenInfo_.dims[2]) && (ids[3] < rlenInfo_.dims[3]);
+        const bool rlen_nonnull = (rlenInfo_.dims[0] * rlenInfo_.dims[1] *
+                                   rlenInfo_.dims[2] * rlenInfo_.dims[3]) > 0;
+        uint *const rlenptr =
+            (rlen_nonnull && rlen_valid)
+                ? rlen_.get_pointer() + ids[3] * rlenInfo_.strides[3] +
+                      ids[2] * rlenInfo_.strides[2] +
+                      ids[1] * rlenInfo_.strides[1] + ids[0] + rlenInfo_.offset
+                : nullptr;
+
+        const uint groupIdx_dim = ids[dim];
+
+        // add thread offset for reduced dim for inputs
+        ids[dim] = ids[dim] * g.get_local_range(1) + lidy;
+
+        T *iptr = in_.get_pointer() + ids[3] * iInfo_.strides[3] +
+                  ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] +
+                  ids[0] + iInfo_.offset;
+        uint *ilptr;
+        if (!is_first) {
+            ilptr = iloc_.get_pointer() + ids[3] * iInfo_.strides[3] +
+                    ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] +
+                    ids[0] + iInfo_.offset;
+        }
+
+        const uint id_dim_in   = ids[dim];
+        const uint istride_dim = iInfo_.strides[dim];
+
+        size_t xlim   = iInfo_.dims[0];
+        size_t ylim   = iInfo_.dims[1];
+        size_t zlim   = iInfo_.dims[2];
+        size_t wlim   = iInfo_.dims[3];
+        bool is_valid = (ids[0] < xlim) && (ids[1] < ylim) && (ids[2] < zlim) &&
+                        (ids[3] < wlim);
+
+        compute_t<T> out_val = common::Binary<compute_t<T>, op>::init();
+        uint out_idx         = id_dim_in;
+
+        uint lim = rlenptr ? *rlenptr : iInfo_.dims[0];
+        lim      = is_first ? sycl::min((uint)iInfo_.dims[dim], lim) : lim;
+
+        bool within_ragged_bounds =
+            (is_first) ? (out_idx < lim)
+                       : ((rlenptr) ? ((is_valid) && (*ilptr < lim)) : true);
+        if (is_valid && id_dim_in < iInfo_.dims[dim] && within_ragged_bounds) {
+            out_val = *iptr;
+            if (!is_first) out_idx = *ilptr;
+        }
+
+        MinMaxOp<op, compute_t<T>> Op(out_val, out_idx);
+
+        const uint id_dim_in_start =
+            id_dim_in + groups_dim_ * g.get_local_range(1);
+        for (int id = id_dim_in_start; is_valid && (id < lim);
+             id += groups_dim_ * g.get_local_range(1)) {
+            iptr = iptr + groups_dim_ * g.get_local_range(1) * istride_dim;
+            if (!is_first) {
+                ilptr =
+                    ilptr + groups_dim_ * g.get_local_range(1) * istride_dim;
+                Op(*iptr, *ilptr);
+            } else {
+                Op(*iptr, id);
+            }
+        }
+
+        s_val_[lid] = Op.m_val;
+        s_idx_[lid] = Op.m_idx;
+        it.barrier();
+
+        compute_t<T> *s_vptr = s_val_.get_pointer() + lid;
+        uint *s_iptr         = s_idx_.get_pointer() + lid;
+
+        if (DIMY == 8) {
+            if (lidy < 4) {
+                Op(s_vptr[g.get_local_range(0) * 4],
+                   s_iptr[g.get_local_range(0) * 4]);
+                *s_vptr = Op.m_val;
+                *s_iptr = Op.m_idx;
+            }
+            it.barrier();
+        }
+        if (DIMY >= 4) {
+            if (lidy < 2) {
+                Op(s_vptr[g.get_local_range(0) * 2],
+                   s_iptr[g.get_local_range(0) * 2]);
+                *s_vptr = Op.m_val;
+                *s_iptr = Op.m_idx;
+            }
+            it.barrier();
+        }
+        if (DIMY >= 2) {
+            if (lidy < 1) {
+                Op(s_vptr[g.get_local_range(0) * 1],
+                   s_iptr[g.get_local_range(0) * 1]);
+                *s_vptr = Op.m_val;
+                *s_iptr = Op.m_idx;
+            }
+            it.barrier();
+        }
+        if (is_valid && lidy == 0 && (groupIdx_dim < oInfo_.dims[dim])) {
+            *optr  = data_t<T>(s_vptr[0]);
+            *olptr = s_iptr[0];
+        }
+    }
+
+   protected:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    write_accessor<uint> oloc_;
+    KParam olocInfo_;
+    read_accessor<T> in_;
+    KParam iInfo_;
+    read_accessor<uint> iloc_;
+    KParam ilocInfo_;
+    uint groups_x_, groups_y_, groups_dim_;
+    read_accessor<uint> rlen_;
+    KParam rlenInfo_;
+    local_accessor<compute_t<T>, 1> s_val_;
+    local_accessor<uint, 1> s_idx_;
+};
+
+template<typename T, af_op_t op, int dim, bool is_first>
+void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
+                          Param<uint> iloc, const uint threads_y,
+                          const dim_t groups_dim[4], Param<uint> rlen) {
+    sycl::range<2> local(creduce::THREADS_X, threads_y);
+    sycl::range<2> global(groups_dim[0] * groups_dim[2] * local[0],
+                          groups_dim[1] * groups_dim[3] * local[1]);
+
+    sycl::buffer<uint, 1> empty{sycl::range<1>(1)};
+    try {
+        getQueue().submit([&](sycl::handler &h) {
+            write_accessor<T> out_acc{*out.data, h};
+            write_accessor<uint> oloc_acc{*oloc.data, h};
+            read_accessor<T> in_acc{*in.data, h};
+
+            read_accessor<uint> iloc_acc{empty, h};
+            if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
+                    iloc.info.dims[3] >
+                0) {
+                iloc_acc = read_accessor<uint>{*iloc.data, h};
+            }
+
+            read_accessor<uint> rlen_acc{empty, h};
+            if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
+                    rlen.info.dims[3] >
+                0) {
+                rlen_acc = read_accessor<uint>{*rlen.data, h};
+            }
+
+            auto shrdVal =
+                local_accessor<compute_t<T>, 1>(creduce::THREADS_PER_BLOCK, h);
+            auto shrdLoc =
+                local_accessor<uint, 1>(creduce::THREADS_PER_BLOCK, h);
+
+            switch (threads_y) {
+                case 8:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_dim[0],
+                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
+                            shrdVal, shrdLoc));
+                    break;
+                case 4:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_dim[0],
+                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
+                            shrdVal, shrdLoc));
+                    break;
+                case 2:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_dim[0],
+                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
+                            shrdVal, shrdLoc));
+                    break;
+                case 1:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_dim[0],
+                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
+                            shrdVal, shrdLoc));
+                    break;
+            }
+        });
+        getQueue().wait_and_throw();
+        ONEAPI_DEBUG_FINISH(getQueue());
+    } catch (sycl::exception &e) { std::cout << e.what() << std::endl; }
+}
+
+template<typename T, af_op_t op, int dim>
+void ireduce_dim(Param<T> out, Param<uint> oloc, Param<T> in,
+                 Param<uint> rlen) {
+    uint threads_y = std::min(creduce::THREADS_Y, nextpow2(in.info.dims[dim]));
+    uint threads_x = creduce::THREADS_X;
+
+    dim_t blocks_dim[] = {divup(in.info.dims[0], threads_x), in.info.dims[1],
+                          in.info.dims[2], in.info.dims[3]};
+
+    blocks_dim[dim] = divup(in.info.dims[dim], threads_y * creduce::REPEAT);
+
+    Param<T> tmp      = out;
+    Param<uint> tlptr = oloc;
+    bufptr<T> tmp_alloc;
+    bufptr<uint> tlptr_alloc;
+
+    if (blocks_dim[dim] > 1) {
+        int tmp_elements   = 1;
+        tmp.info.dims[dim] = blocks_dim[dim];
+
+        for (int k = 0; k < 4; k++) tmp_elements *= tmp.info.dims[k];
+        tmp_alloc   = memAlloc<T>(tmp_elements);
+        tlptr_alloc = memAlloc<uint>(tmp_elements);
+        tmp.data    = tmp_alloc.get();
+        tlptr.data  = tlptr_alloc.get();
+
+        for (int k = dim + 1; k < 4; k++)
+            tmp.info.strides[k] *= blocks_dim[dim];
+    }
+
+    Param<uint> nullparam;
+    ireduce_dim_launcher<T, op, dim, true>(tmp, tlptr, in, nullparam, threads_y,
+                                           blocks_dim, rlen);
+
+    if (blocks_dim[dim] > 1) {
+        blocks_dim[dim] = 1;
+
+        ireduce_dim_launcher<T, op, dim, false>(out, oloc, tmp, tlptr,
+                                                threads_y, blocks_dim, rlen);
+    }
+}
+
+template<typename T, af_op_t op, bool is_first, uint DIMX>
+class ireduceFirstKernelSMEM {
+   public:
+    ireduceFirstKernelSMEM(write_accessor<T> out, KParam oInfo,
+                           write_accessor<uint> oloc, KParam olocInfo,
+                           read_accessor<T> in, KParam iInfo,
+                           read_accessor<uint> iloc, KParam ilocInfo,
+                           uint groups_x, uint groups_y, uint repeat,
+                           read_accessor<uint> rlen, KParam rlenInfo,
+                           local_accessor<compute_t<T>, 1> s_val,
+                           local_accessor<uint, 1> s_idx)
+        : out_(out)
+        , oInfo_(oInfo)
+        , oloc_(oloc)
+        , olocInfo_(olocInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , iloc_(iloc)
+        , ilocInfo_(ilocInfo)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y)
+        , repeat_(repeat)
+        , rlen_(rlen)
+        , rlenInfo_(rlenInfo)
+        , s_val_(s_val)
+        , s_idx_(s_idx) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g   = it.get_group();
+        const uint lidx = it.get_local_id(0);
+        const uint lidy = it.get_local_id(1);
+        const uint lid  = lidy * g.get_local_range(0) + lidx;
+
+        const uint zid       = g.get_group_id(0) / groups_x_;
+        const uint wid       = g.get_group_id(1) / groups_y_;
+        const uint groupId_x = g.get_group_id(0) - (groups_x_)*zid;
+        const uint groupId_y = g.get_group_id(1) - (groups_y_)*wid;
+        const uint xid = groupId_x * g.get_local_range(0) * repeat_ + lidx;
+        const uint yid = groupId_y * g.get_local_range(1) + lidy;
+
+        T *const iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
+                        zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
+                        iInfo_.offset;
+
+        T *optr = out_.get_pointer() + wid * oInfo_.strides[3] +
+                  zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+
+        const bool rlenvalid = (rlenInfo_.dims[0] * rlenInfo_.dims[1] *
+                                rlenInfo_.dims[2] * rlenInfo_.dims[3]) > 0;
+        uint *const rlenptr =
+            (rlenvalid)
+                ? rlen_.get_pointer() + wid * rlenInfo_.strides[3] +
+                      zid * rlenInfo_.strides[2] + yid * rlenInfo_.strides[1]
+                : nullptr;
+
+        uint *ilptr;
+        if (!is_first) {
+            ilptr = iloc_.get_pointer() + wid * iInfo_.strides[3] +
+                    zid * iInfo_.strides[2] + yid * iInfo_.strides[1];
+        }
+        uint *olptr = oloc_.get_pointer() + wid * oInfo_.strides[3] +
+                      zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+
+        size_t ylim   = iInfo_.dims[1];
+        size_t zlim   = iInfo_.dims[2];
+        size_t wlim   = iInfo_.dims[3];
+        bool is_valid = (yid < ylim) && (zid < zlim) && (wid < wlim);
+        // bool is_valid = (yid < iInfo_.dims[1]) && (zid < iInfo_.dims[2]) &&
+        //(wid < iInfo_.dims[3]);
+
+        int minlen = rlenptr ? sycl::min(*rlenptr, (uint)iInfo_.dims[0])
+                             : iInfo_.dims[0];
+        int lim    = sycl::min((int)(xid + repeat_ * DIMX), minlen);
+
+        compute_t<T> out_val = common::Binary<compute_t<T>, op>::init();
+        uint idx             = xid;
+
+        if (xid < lim) {
+            out_val = static_cast<compute_t<T>>(iptr[xid]);
+            if (!is_first) idx = ilptr[xid];
+        }
+
+        MinMaxOp<op, compute_t<T>> Op(out_val, idx);
+        for (int id = xid; is_valid && id < lim; id += DIMX) {
+            Op(static_cast<compute_t<T>>(iptr[id]),
+               (!is_first) ? ilptr[id] : id);
+        }
+
+        s_val_[lid] = Op.m_val;
+        s_idx_[lid] = Op.m_idx;
+        it.barrier();
+
+        compute_t<T> *s_vptr = s_val_.get_pointer() + lidy * DIMX;
+        uint *s_iptr         = s_idx_.get_pointer() + lidy * DIMX;
+
+        if (DIMX == 256) {
+            if (lidx < 128) {
+                Op(s_vptr[lidx + 128], s_iptr[lidx + 128]);
+                s_vptr[lidx] = Op.m_val;
+                s_iptr[lidx] = Op.m_idx;
+            }
+            it.barrier();
+        }
+
+        if (DIMX >= 128) {
+            if (lidx < 64) {
+                Op(s_vptr[lidx + 64], s_iptr[lidx + 64]);
+                s_vptr[lidx] = Op.m_val;
+                s_iptr[lidx] = Op.m_idx;
+            }
+            it.barrier();
+        }
+
+        if (DIMX >= 64) {
+            if (lidx < 32) {
+                Op(s_vptr[lidx + 32], s_iptr[lidx + 32]);
+                s_vptr[lidx] = Op.m_val;
+                s_iptr[lidx] = Op.m_idx;
+            }
+            it.barrier();
+        }
+
+        // TODO: replace with subgroup operations in optimized kernels
+        if (lidx < 16) {
+            Op(s_vptr[lidx + 16], s_iptr[lidx + 16]);
+            s_vptr[lidx] = Op.m_val;
+            s_iptr[lidx] = Op.m_idx;
+        }
+        it.barrier();
+
+        if (lidx < 8) {
+            Op(s_vptr[lidx + 8], s_iptr[lidx + 8]);
+            s_vptr[lidx] = Op.m_val;
+            s_iptr[lidx] = Op.m_idx;
+        }
+        it.barrier();
+
+        if (lidx < 4) {
+            Op(s_vptr[lidx + 4], s_iptr[lidx + 4]);
+            s_vptr[lidx] = Op.m_val;
+            s_iptr[lidx] = Op.m_idx;
+        }
+        it.barrier();
+
+        if (lidx < 2) {
+            Op(s_vptr[lidx + 2], s_iptr[lidx + 2]);
+            s_vptr[lidx] = Op.m_val;
+            s_iptr[lidx] = Op.m_idx;
+        }
+        it.barrier();
+
+        if (lidx < 1) {
+            Op(s_vptr[lidx + 1], s_iptr[lidx + 1]);
+            s_vptr[lidx] = Op.m_val;
+            s_iptr[lidx] = Op.m_idx;
+        }
+        it.barrier();
+
+        if (is_valid && lidx == 0) {
+            optr[groupId_x]  = data_t<T>(s_vptr[0]);
+            olptr[groupId_x] = s_iptr[0];
+        }
+    }
+
+   protected:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    write_accessor<uint> oloc_;
+    KParam olocInfo_;
+    read_accessor<T> in_;
+    KParam iInfo_;
+    read_accessor<uint> iloc_;
+    KParam ilocInfo_;
+    uint groups_x_, groups_y_, repeat_;
+    read_accessor<uint> rlen_;
+    KParam rlenInfo_;
+    local_accessor<compute_t<T>, 1> s_val_;
+    local_accessor<uint, 1> s_idx_;
+};
+
+template<typename T, af_op_t op, bool is_first>
+void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
+                            Param<uint> iloc, const uint groups_x,
+                            const uint groups_y, const uint threads_x,
+                            Param<uint> rlen) {
+    sycl::range<2> local(threads_x, creduce::THREADS_PER_BLOCK / threads_x);
+    sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
+                          groups_y * in.info.dims[3] * local[1]);
+
+    uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
+
+    sycl::buffer<uint, 1> empty{sycl::range<1>(1)};
+    try {
+        getQueue().submit([&](sycl::handler &h) {
+            write_accessor<T> out_acc{*out.data, h};
+            write_accessor<uint> oloc_acc{*oloc.data, h};
+            read_accessor<T> in_acc{*in.data, h};
+
+            read_accessor<uint> iloc_acc{empty, h};
+            if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
+                    iloc.info.dims[3] >
+                0) {
+                iloc_acc = read_accessor<uint>{*iloc.data, h};
+            }
+
+            read_accessor<uint> rlen_acc{empty, h};
+            if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
+                    rlen.info.dims[3] >
+                0) {
+                rlen_acc = read_accessor<uint>{*rlen.data, h};
+            }
+
+            auto shrdVal =
+                local_accessor<compute_t<T>, 1>(creduce::THREADS_PER_BLOCK, h);
+            auto shrdLoc =
+                local_accessor<uint, 1>(creduce::THREADS_PER_BLOCK, h);
+
+            switch (threads_x) {
+                case 32:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceFirstKernelSMEM<T, op, is_first, 32>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
+                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
+                    break;
+                case 64:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceFirstKernelSMEM<T, op, is_first, 64>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
+                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
+                    break;
+                case 128:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceFirstKernelSMEM<T, op, is_first, 128>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
+                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
+                    break;
+                case 256:
+                    h.parallel_for(
+                        sycl::nd_range<2>(global, local),
+                        ireduceFirstKernelSMEM<T, op, is_first, 256>(
+                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
+                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
+                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
+                    break;
+            }
+        });
+        getQueue().wait_and_throw();
+        ONEAPI_DEBUG_FINISH(getQueue());
+    } catch (sycl::exception &e) { std::cout << e.what() << std::endl; }
+}
+
+template<typename T, af_op_t op>
+void ireduce_first(Param<T> out, Param<uint> oloc, Param<T> in,
+                   Param<uint> rlen) {
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, creduce::THREADS_PER_BLOCK);
+    uint threads_y = creduce::THREADS_PER_BLOCK / threads_x;
+
+    uint blocks_x = divup(in.info.dims[0], threads_x * creduce::REPEAT);
+    uint blocks_y = divup(in.info.dims[1], threads_y);
+
+    Param<T> tmp      = out;
+    Param<uint> tlptr = oloc;
+    bufptr<T> tmp_alloc;
+    bufptr<uint> tlptr_alloc;
+    if (blocks_x > 1) {
+        auto elements =
+            blocks_x * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+        tmp_alloc   = memAlloc<T>(elements);
+        tlptr_alloc = memAlloc<uint>(elements);
+        tmp.data    = tmp_alloc.get();
+        tlptr.data  = tlptr_alloc.get();
+
+        tmp.info.dims[0] = blocks_x;
+        for (int k = 1; k < 4; k++) tmp.info.strides[k] *= blocks_x;
+    }
+
+    Param<uint> nullparam;
+    ireduce_first_launcher<T, op, true>(tmp, tlptr, in, nullparam, blocks_x,
+                                        blocks_y, threads_x, rlen);
+
+    if (blocks_x > 1) {
+        ireduce_first_launcher<T, op, false>(out, oloc, tmp, tlptr, 1, blocks_y,
+                                             threads_x, rlen);
+    }
+}
+
+template<typename T, af_op_t op>
+void ireduce(Param<T> out, Param<uint> oloc, Param<T> in, int dim,
+             Param<uint> rlen) {
+    switch (dim) {
+        case 0: return ireduce_first<T, op>(out, oloc, in, rlen);
+        case 1: return ireduce_dim<T, op, 1>(out, oloc, in, rlen);
+        case 2: return ireduce_dim<T, op, 2>(out, oloc, in, rlen);
+        case 3: return ireduce_dim<T, op, 3>(out, oloc, in, rlen);
+    }
+}
+
+template<typename T, af_op_t op>
+T ireduce_all(uint *idx, Param<T> in) {
+    int in_elements =
+        in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
+
+    bool is_linear = (in.info.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.info.strides[k] ==
+                      (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    }
+
+    if (is_linear) {
+        in.info.dims[0] = in_elements;
+        for (int k = 1; k < 4; k++) {
+            in.info.dims[k]    = 1;
+            in.info.strides[k] = in_elements;
+        }
+    }
+
+    uint threads_x = nextpow2(std::max(32u, (uint)in.info.dims[0]));
+    threads_x      = std::min(threads_x, creduce::THREADS_PER_BLOCK);
+    uint threads_y = creduce::THREADS_PER_BLOCK / threads_x;
+
+    // TODO: perf REPEAT, consider removing or runtime eval
+    // max problem size < SM resident threads, don't use REPEAT
+    uint groups_x = divup(in.info.dims[0], threads_x * creduce::REPEAT);
+    uint groups_y = divup(in.info.dims[1], threads_y);
+
+    Array<T> tmp = createEmptyArray<T>(
+        {groups_x, in.info.dims[1], in.info.dims[2], in.info.dims[3]});
+
+    int tmp_elements  = tmp.elements();
+    Array<uint> tlptr = createEmptyArray<uint>({tmp_elements, 1, 1, 1});
+
+    Param<uint> nullparam;
+    Array<uint> rlen = createEmptyArray<uint>(af::dim4(0));
+    ireduce_first_launcher<T, op, true>(tmp, tlptr, in, nullparam, groups_x,
+                                        groups_y, threads_x, rlen);
+
+    sycl::host_accessor h_ptr_raw{*tmp.get()};
+    sycl::host_accessor h_lptr_raw{*tlptr.get()};
+
+    MinMaxOp<op, T> Op(h_ptr_raw[0], h_lptr_raw[0]);
+
+    for (int i = 1; i < tmp_elements; i++) { Op(h_ptr_raw[i], h_lptr_raw[i]); }
+
+    *idx = Op.m_idx;
+    return Op.m_val;
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/minmax_op.hpp b/src/backend/oneapi/minmax_op.hpp
new file mode 100644
index 0000000000..f006ff419c
--- /dev/null
+++ b/src/backend/oneapi/minmax_op.hpp
@@ -0,0 +1,87 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <common/Binary.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+
+template<typename T>
+static double cabs(const T &in) {
+    return (double)in;
+}
+
+template<>
+double cabs<char>(const char &in) {
+    return (double)(in > 0);
+}
+
+template<>
+double cabs<cfloat>(const cfloat &in) {
+    return (double)abs(in);
+}
+
+template<>
+double cabs<cdouble>(const cdouble &in) {
+    return (double)abs(in);
+}
+
+template<typename T>
+static bool is_nan(const T &in) {
+    return in != in;
+}
+
+template<>
+bool is_nan<cfloat>(const cfloat &in) {
+    return in.real() != in.real() || in.imag() != in.imag();
+}
+
+template<>
+bool is_nan<cdouble>(const cdouble &in) {
+    return in.real() != in.real() || in.imag() != in.imag();
+}
+
+template<af_op_t op, typename T>
+struct MinMaxOp {
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = common::Binary<compute_t<T>, op>::init(); }
+    }
+
+    void operator()(T val, uint idx) {
+        if ((cabs(val) < cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx > m_idx))) {
+            m_val = val;
+            m_idx = idx;
+        }
+    }
+};
+
+template<typename T>
+struct MinMaxOp<af_max_t, T> {
+    T m_val;
+    uint m_idx;
+    MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        if (is_nan(val)) { m_val = common::Binary<T, af_max_t>::init(); }
+    }
+
+    void operator()(T val, uint idx) {
+        if ((cabs(val) > cabs(m_val) ||
+             (cabs(val) == cabs(m_val) && idx <= m_idx))) {
+            m_val = val;
+            m_idx = idx;
+        }
+    }
+};
+
+}  // namespace oneapi
+}  // namespace arrayfire

From 115a942544e3723bda4c650e051b364cb72e337d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 27 Mar 2023 10:51:24 -0400
Subject: [PATCH 2432/2677] Workaround compiler bug in range for oneAPI

---
 src/backend/oneapi/kernel/range.hpp | 33 ++++++++++++++++-------------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index fb7e5ea449..9cfea27964 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -52,26 +52,29 @@ class rangeOp {
         const int xx = it.get_local_id(0) + blockIdx_x * it.get_local_range(0);
         const int yy = it.get_local_id(1) + blockIdx_y * it.get_local_range(1);
 
-        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] ||
-            oz >= oinfo_.dims[2] || ow >= oinfo_.dims[3])
-            return;
+        const size_t odx = oinfo_.dims[0];
+        const size_t ody = oinfo_.dims[1];
+        const size_t odz = oinfo_.dims[2];
+        const size_t odw = oinfo_.dims[3];
 
-        const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
+        if (xx < odx && yy < ody && oz < odz && ow < odw) {
+            const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
 
-        const int incy = blocksPerMatY_ * g.get_local_range(1);
-        const int incx = blocksPerMatX_ * g.get_local_range(0);
+            const int incy = blocksPerMatY_ * g.get_local_range(1);
+            const int incx = blocksPerMatX_ * g.get_local_range(0);
 
-        compute_t<T> valZW = (mul3 * ow) + (mul2 * oz);
+            compute_t<T> valZW = (mul3 * ow) + (mul2 * oz);
 
-        T* optr = out_.get_pointer();
-        for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
-            compute_t<T> valYZW = valZW + (mul1 * oy);
-            int oyzw            = ozw + oy * oinfo_.strides[1];
-            for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
-                int oidx         = oyzw + ox;
-                compute_t<T> val = valYZW + (mul0 * ox);
+            T* optr = out_.get_pointer();
+            for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
+                compute_t<T> valYZW = valZW + (mul1 * oy);
+                int oyzw            = ozw + oy * oinfo_.strides[1];
+                for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
+                    int oidx         = oyzw + ox;
+                    compute_t<T> val = valYZW + (mul0 * ox);
 
-                optr[oidx] = val;
+                    optr[oidx] = val;
+                }
             }
         }
     }

From 40f9896c97b394150e03e63f8736cd394b4bd931 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 27 Mar 2023 10:52:03 -0400
Subject: [PATCH 2433/2677] Narrow access modes for transpose

---
 src/backend/oneapi/kernel/transpose.hpp | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index d22a6f4475..43b741ca32 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -50,11 +50,12 @@ using local_accessor =
 template<typename T>
 class transposeKernel {
    public:
-    transposeKernel(sycl::accessor<T> oData, const KParam out,
-                    const sycl::accessor<T> iData, const KParam in,
-                    const int blocksPerMatX, const int blocksPerMatY,
-                    const bool conjugate, const bool IS32MULTIPLE,
-                    local_accessor<T, 1> shrdMem)
+    transposeKernel(sycl::accessor<T, 1, sycl::access::mode::write> oData,
+                    const KParam out,
+                    const sycl::accessor<T, 1, sycl::access::mode::read> iData,
+                    const KParam in, const int blocksPerMatX,
+                    const int blocksPerMatY, const bool conjugate,
+                    const bool IS32MULTIPLE, local_accessor<T, 1> shrdMem)
         : oData_(oData)
         , out_(out)
         , iData_(iData)
@@ -124,9 +125,9 @@ class transposeKernel {
     }
 
    private:
-    sycl::accessor<T> oData_;
+    sycl::accessor<T, 1, sycl::access::mode::write> oData_;
     KParam out_;
-    sycl::accessor<T> iData_;
+    sycl::accessor<T, 1, sycl::access::mode::read> iData_;
     KParam in_;
     int blocksPerMatX_;
     int blocksPerMatY_;
@@ -147,8 +148,8 @@ void transpose(Param<T> out, const Param<T> in, const bool conjugate,
                               blk_y * local[1] * in.info.dims[3]};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto r = in.data->get_access(h);
-        auto q = out.data->get_access(h);
+        auto r = in.data->template get_access<sycl::access::mode::read>(h);
+        auto q = out.data->template get_access<sycl::access::mode::write>(h);
 
         auto shrdMem = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 

From 56be86b6a01c372a2da7e3f2e5ca608f3edaf154 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 27 Mar 2023 17:41:53 -0400
Subject: [PATCH 2434/2677] Pass values into JIT lambda by value

---
 src/backend/oneapi/jit.cpp              |  2 +-
 src/backend/oneapi/jit/BufferNode.hpp   |  2 +-
 src/backend/oneapi/kernel/bilateral.hpp |  2 --
 src/backend/oneapi/platform.cpp         | 12 ++++--------
 4 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 519d4efeea..bd4a5f2d43 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -401,7 +401,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                       outputs[0].info.dims[2] > 1)};
 
     getQueue()
-        .submit([&](sycl::handler& h) {
+        .submit([=](sycl::handler& h) {
             for (Node* node : full_nodes) {
                 if (node->isBuffer()) {
                     BufferNode<T>* n = static_cast<BufferNode<T>*>(node);
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
index b6bedc5baf..8c8d61abf2 100644
--- a/src/backend/oneapi/jit/BufferNode.hpp
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -20,7 +20,7 @@ namespace jit {
 template<typename T>
 using BufferNode =
     common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, AParam<T>>;
-}
+}  // namespace jit
 }  // namespace oneapi
 
 namespace common {
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
index c01ee4a4a5..2a5cf59fb1 100644
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -121,9 +121,7 @@ class bilateralKernel {
             int joff = (ly - radius) * shrdLen + (lx - radius);
             int goff = 0;
 
-#pragma unroll
             for (int wj = 0; wj < window_size; ++wj) {
-#pragma unroll
                 for (int wi = 0; wi < window_size; ++wi) {
                     outType tmp_color = localMem_[joff + wi];
                     const outType c   = center_color - tmp_color;
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index b95b5326bc..918b4666f4 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -320,14 +320,10 @@ const std::string& getActiveDeviceBaseBuildFlags() {
 size_t getDeviceMemorySize(int device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    sycl::device dev;
-    {
-        common::lock_guard_t lock(devMngr.deviceMutex);
-        // Assuming devices don't deallocate or are invalidated during execution
-        dev = *devMngr.mDevices[device];
-    }
-    size_t msize = dev.get_info<sycl::info::device::global_mem_size>();
-    return msize;
+    common::lock_guard_t lock(devMngr.deviceMutex);
+    // Assuming devices don't deallocate or are invalidated during execution
+    sycl::device& dev = *devMngr.mDevices[device];
+    return dev.get_info<sycl::info::device::global_mem_size>();
 }
 
 size_t getHostMemorySize() { return common::getHostMemorySize(); }

From 13cbcf1d15ccf7f5a4a5fafe1390a798d808b9d6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 27 Mar 2023 17:43:22 -0400
Subject: [PATCH 2435/2677] Use SKIP_TEST rather than returning for disabled
 feature checks

---
 test/anisotropic_diffusion.cpp |  2 +-
 test/arrayfire_test.cpp        | 12 ------------
 test/bilateral.cpp             |  2 +-
 test/canny.cpp                 |  4 ++--
 test/cholesky_dense.cpp        |  2 +-
 test/confidence_connected.cpp  |  2 +-
 test/fast.cpp                  |  4 ++--
 test/gloh.cpp                  |  4 ++--
 test/harris.cpp                |  4 ++--
 test/homography.cpp            |  4 ++--
 test/imageio.cpp               | 26 +++++++++++++-------------
 test/inverse_deconv.cpp        |  2 +-
 test/inverse_dense.cpp         |  2 +-
 test/iterative_deconv.cpp      |  2 +-
 test/lu_dense.cpp              | 14 +++++++-------
 test/meanshift.cpp             |  4 ++--
 test/medfilt.cpp               |  2 +-
 test/moments.cpp               |  2 +-
 test/morph.cpp                 |  4 ++--
 test/orb.cpp                   |  4 ++--
 test/qr_dense.cpp              |  6 +++---
 test/rank_dense.cpp            | 10 +++++-----
 test/sift.cpp                  |  4 ++--
 test/solve_common.hpp          |  6 +++---
 test/solve_dense.cpp           |  6 +++---
 test/susan.cpp                 |  2 +-
 test/svd_dense.cpp             |  6 +++---
 test/testHelpers.hpp           | 12 +++++++-----
 test/threading.cpp             |  2 +-
 test/transform.cpp             |  8 ++++----
 30 files changed, 77 insertions(+), 87 deletions(-)

diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index f4d78382f3..afeda45d52 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -50,7 +50,7 @@ void imageTest(string pTestFile, const float dt, const float K,
             OutType;
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     using af::dim4;
 
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 2128f7fbd3..4c6e966220 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -822,18 +822,6 @@ void cleanSlate() {
     ASSERT_EQ(af::getMemStepSize(), step_bytes);
 }
 
-bool noImageIOTests() {
-    bool ret = !af::isImageIOAvailable();
-    if (ret) printf("Image IO Not Configured. Test will exit\n");
-    return ret;
-}
-
-bool noLAPACKTests() {
-    bool ret = !af::isLAPACKAvailable();
-    if (ret) printf("LAPACK Not Configured. Test will exit\n");
-    return ret;
-}
-
 template<typename inType, typename outType>
 void readTestsFromFile(const std::string &FileName,
                        std::vector<af::dim4> &inputDims,
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index 8d83d2798b..f4ff949b55 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -25,7 +25,7 @@ using std::vector;
 template<typename T, bool isColor>
 void bilateralTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/canny.cpp b/test/canny.cpp
index b34a4923b4..7f2fa2918c 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -93,7 +93,7 @@ TEST(Canny, DISABLED_Exact) {
 template<typename T>
 void cannyImageOtsuTest(string pTestFile, bool isColor) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     using af::dim4;
 
@@ -220,7 +220,7 @@ TEST(CannyEdgeDetector, Sobel5x5_Invalid) {
 template<typename T>
 void cannyImageOtsuBatchTest(string pTestFile, const dim_t targetBatchCount) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     using af::array;
     using af::canny;
diff --git a/test/cholesky_dense.cpp b/test/cholesky_dense.cpp
index 0631ec2bad..dea036eca1 100644
--- a/test/cholesky_dense.cpp
+++ b/test/cholesky_dense.cpp
@@ -34,7 +34,7 @@ using std::vector;
 template<typename T>
 void choleskyTester(const int n, double eps, bool is_upper) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 9d081f068d..ac5b0bf2bc 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -58,7 +58,7 @@ void testImage(const std::string pTestFile, const size_t numSeeds,
                const int multiplier, const unsigned neighborhood_radius,
                const int iter) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<af::dim4> inDims;
     vector<string> inFiles;
diff --git a/test/fast.cpp b/test/fast.cpp
index 316fe57ad6..1d494641ff 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -69,7 +69,7 @@ TYPED_TEST_SUITE(FixedFAST, FixedTestTypes);
 template<typename T>
 void fastTest(string pTestFile, bool nonmax) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -180,7 +180,7 @@ using af::features;
 using af::loadImage;
 
 TEST(FloatFAST, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/gloh.cpp b/test/gloh.cpp
index e370984fbf..b360ac6a18 100644
--- a/test/gloh.cpp
+++ b/test/gloh.cpp
@@ -137,7 +137,7 @@ TYPED_TEST_SUITE(GLOH, TestTypes);
 template<typename T>
 void glohTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -261,7 +261,7 @@ GLOH_INIT(man, man);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(GLOH, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/harris.cpp b/test/harris.cpp
index ec6a1fa626..43c0bb6433 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -61,7 +61,7 @@ TYPED_TEST_SUITE(Harris, TestTypes);
 template<typename T>
 void harrisTest(string pTestFile, float sigma, unsigned block_size) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -167,7 +167,7 @@ using af::harris;
 using af::loadImage;
 
 TEST(FloatHarris, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/homography.cpp b/test/homography.cpp
index c6a6e43450..f4c1c75259 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -49,7 +49,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     using af::Pi;
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -220,7 +220,7 @@ using af::features;
 using af::loadImage;
 
 TEST(Homography, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 4869e50e15..00834fb693 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -36,7 +36,7 @@ typedef ::testing::Types<float> TestTypes;
 TYPED_TEST_SUITE(ImageIO, TestTypes);
 
 void loadImageTest(string pTestFile, string pImageFile, const bool isColor) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> numDims;
 
@@ -93,7 +93,7 @@ TYPED_TEST(ImageIO, ColorSeq) {
 }
 
 void loadimageArgsTest(string pImageFile, const bool isColor, af_err err) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     af_array imgArray = 0;
 
@@ -122,7 +122,7 @@ using af::saveImageMem;
 using af::span;
 
 TEST(ImageIO, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> numDims;
 
@@ -150,7 +150,7 @@ TEST(ImageIO, CPP) {
 }
 
 TEST(ImageIO, SavePNGCPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array input(10, 10, 3, f32);
 
@@ -170,7 +170,7 @@ TEST(ImageIO, SavePNGCPP) {
 }
 
 TEST(ImageIO, SaveBMPCPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array input(10, 10, 3, f32);
 
@@ -190,7 +190,7 @@ TEST(ImageIO, SaveBMPCPP) {
 }
 
 TEST(ImageMem, SaveMemPNG) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array img =
         loadImage(string(TEST_DIR "/imageio/color_seq.png").c_str(), true);
@@ -205,7 +205,7 @@ TEST(ImageMem, SaveMemPNG) {
 }
 
 TEST(ImageMem, SaveMemJPG1) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array img =
         loadImage(string(TEST_DIR "/imageio/color_seq.png").c_str(), false);
@@ -222,7 +222,7 @@ TEST(ImageMem, SaveMemJPG1) {
 }
 
 TEST(ImageMem, SaveMemJPG3) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array img =
         loadImage(string(TEST_DIR "/imageio/color_seq.png").c_str(), true);
@@ -239,7 +239,7 @@ TEST(ImageMem, SaveMemJPG3) {
 }
 
 TEST(ImageMem, SaveMemBMP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array img =
         loadImage(string(TEST_DIR "/imageio/color_rand.png").c_str(), true);
@@ -254,7 +254,7 @@ TEST(ImageMem, SaveMemBMP) {
 }
 
 TEST(ImageIO, LoadImage16CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> numDims;
 
@@ -284,7 +284,7 @@ TEST(ImageIO, LoadImage16CPP) {
 }
 
 TEST(ImageIO, SaveImage16CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     dim4 dims(16, 24, 3);
 
@@ -312,7 +312,7 @@ using af::saveImageNative;
 
 template<typename T>
 void loadImageNativeCPPTest(string pTestFile, string pImageFile) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> numDims;
 
@@ -362,7 +362,7 @@ TEST(ImageIONative, LoadImageNative16GrayCPP) {
 
 template<typename T>
 void saveLoadImageNativeCPPTest(dim4 dims) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     array input = randu(dims, (af_dtype)dtype_traits<T>::af_type);
 
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index 9cce59ea62..b6db793f4b 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -38,7 +38,7 @@ void invDeconvImageTest(string pTestFile, const float gamma,
             OutType;
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     using af::dim4;
 
diff --git a/test/inverse_dense.cpp b/test/inverse_dense.cpp
index a0bb6145d9..0d502389b8 100644
--- a/test/inverse_dense.cpp
+++ b/test/inverse_dense.cpp
@@ -34,7 +34,7 @@ using std::abs;
 template<typename T>
 void inverseTester(const int m, const int n, double eps) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 #if 1
     array A = cpu_randu<T>(dim4(m, n));
 #else
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 59e6b4598b..e59440b977 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -38,7 +38,7 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
             OutType;
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     using af::dim4;
 
diff --git a/test/lu_dense.cpp b/test/lu_dense.cpp
index ec69e1ccd9..35c925ab57 100644
--- a/test/lu_dense.cpp
+++ b/test/lu_dense.cpp
@@ -37,7 +37,7 @@ using std::string;
 using std::vector;
 
 TEST(LU, InPlaceSmall) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     int resultIdx = 0;
 
@@ -75,7 +75,7 @@ TEST(LU, InPlaceSmall) {
 }
 
 TEST(LU, SplitSmall) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     int resultIdx = 0;
 
@@ -128,7 +128,7 @@ TEST(LU, SplitSmall) {
 template<typename T>
 void luTester(const int m, const int n, double eps) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     array a_orig = cpu_randu<T>(dim4(m, n));
@@ -237,7 +237,7 @@ TYPED_TEST(LU, RectangularMultipleOfTwoLarge1) {
 }
 
 TEST(LU, NullLowerOutput) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
     dim4 dims(3, 3);
     af_array in = 0;
     ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
@@ -248,7 +248,7 @@ TEST(LU, NullLowerOutput) {
 }
 
 TEST(LU, NullUpperOutput) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
     dim4 dims(3, 3);
     af_array in = 0;
     ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
@@ -259,7 +259,7 @@ TEST(LU, NullUpperOutput) {
 }
 
 TEST(LU, NullPivotOutput) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
     dim4 dims(3, 3);
     af_array in = 0;
     ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
@@ -270,7 +270,7 @@ TEST(LU, NullPivotOutput) {
 }
 
 TEST(LU, InPlaceNullOutput) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
     dim4 dims(3, 3);
     af_array in = 0;
     ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 59f6bd2ee7..1f0aa697b3 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -54,7 +54,7 @@ TYPED_TEST(Meanshift, InvalidArgs) {
 template<typename T, bool isColor>
 void meanshiftTest(string pTestFile, const float ss) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -131,7 +131,7 @@ using af::seq;
 using af::span;
 
 TEST(Meanshift, Color_CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 2120da8e4c..1939379974 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -166,7 +166,7 @@ TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3) {
 template<typename T, bool isColor>
 void medfiltImageTest(string pTestFile, dim_t w_len, dim_t w_wid) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/moments.cpp b/test/moments.cpp
index d7a396ea95..6b02cb614a 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -98,7 +98,7 @@ void momentsTest(string pTestFile) {
 }
 
 void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
     vector<dim4> numDims;
 
     vector<vector<float>> in;
diff --git a/test/morph.cpp b/test/morph.cpp
index b24106b88b..9cc2255fb5 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -136,7 +136,7 @@ TYPED_TEST(Morph, Erode4x4x4) {
 template<typename T, bool isDilation, bool isColor>
 void morphImageTest(string pTestFile, dim_t seLen) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -390,7 +390,7 @@ using af::span;
 template<typename T, bool isDilation, bool isColor>
 void cppMorphImageTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/orb.cpp b/test/orb.cpp
index b29c7021ba..e519fd91dc 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -129,7 +129,7 @@ TYPED_TEST_SUITE(ORB, TestTypes);
 template<typename T>
 void orbTest(string pTestFile) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -246,7 +246,7 @@ TYPED_TEST(ORB, Lena) { orbTest<TypeParam>(string(TEST_DIR "/orb/lena.test")); }
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(ORB, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/qr_dense.cpp b/test/qr_dense.cpp
index 9d5f3f1c78..d87cb7b565 100644
--- a/test/qr_dense.cpp
+++ b/test/qr_dense.cpp
@@ -34,7 +34,7 @@ using std::vector;
 
 ///////////////////////////////// CPP ////////////////////////////////////
 TEST(QRFactorized, CPP) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     int resultIdx = 0;
 
@@ -90,7 +90,7 @@ template<typename T>
 void qrTester(const int m, const int n, double eps) {
     try {
         SUPPORTED_TYPE_CHECK(T);
-        if (noLAPACKTests()) return;
+        LAPACK_ENABLED_CHECK();
 
 #if 1
         array in = cpu_randu<T>(dim4(m, n));
@@ -181,7 +181,7 @@ TYPED_TEST(QR, RectangularMultipleOfTwoLarge1) {
 }
 
 TEST(QR, InPlaceNullOutput) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
     dim4 dims(3, 3);
     af_array in = 0;
     ASSERT_SUCCESS(af_randu(&in, dims.ndims(), dims.get(), f32));
diff --git a/test/rank_dense.cpp b/test/rank_dense.cpp
index bb838686f5..7625ab82d2 100644
--- a/test/rank_dense.cpp
+++ b/test/rank_dense.cpp
@@ -46,7 +46,7 @@ TYPED_TEST_SUITE(Det, TestTypes);
 template<typename T>
 void rankSmall() {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     T ha[] = {1, 4, 7, 2, 5, 8, 3, 6, 20};
     array a(3, 3, ha);
@@ -57,7 +57,7 @@ void rankSmall() {
 template<typename T>
 void rankBig(const int num) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
     array a  = randu(num, num, dt);
@@ -71,7 +71,7 @@ void rankBig(const int num) {
 template<typename T>
 void rankLow(const int num) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
 
@@ -93,7 +93,7 @@ TYPED_TEST(Rank, low) { rankBig<TypeParam>(512); }
 template<typename T>
 void detTest() {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype dt = (dtype)dtype_traits<T>::af_type;
 
@@ -114,7 +114,7 @@ void detTest() {
 TYPED_TEST(Det, Small) { detTest<TypeParam>(); }
 
 TEST(Rank, NullOutput) {
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
     dim4 dims(3, 3);
     af_array in = 0;
     af_randu(&in, dims.ndims(), dims.get(), f32);
diff --git a/test/sift.cpp b/test/sift.cpp
index 2410472b53..621659e259 100644
--- a/test/sift.cpp
+++ b/test/sift.cpp
@@ -138,7 +138,7 @@ template<typename T>
 void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
               float edgeThr, float initSigma, bool doubleInput) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
@@ -272,7 +272,7 @@ SIFT_INIT(Man_NoDoubleInput, man_nodoubleinput, 3, 0.04f, 10.0f, 1.6f, false);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(SIFT, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/solve_common.hpp b/test/solve_common.hpp
index c464bfdc47..0eee3d7029 100644
--- a/test/solve_common.hpp
+++ b/test/solve_common.hpp
@@ -35,7 +35,7 @@ void solveTester(const int m, const int n, const int k, double eps,
     af::deviceGC();
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(m, n));
@@ -65,7 +65,7 @@ void solveLUTester(const int n, const int k, double eps,
     af::deviceGC();
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(n, n));
@@ -95,7 +95,7 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps,
     af::deviceGC();
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     af::array A  = cpu_randu<T>(af::dim4(n, n));
diff --git a/test/solve_dense.cpp b/test/solve_dense.cpp
index b09c77645c..161aa7a212 100644
--- a/test/solve_dense.cpp
+++ b/test/solve_dense.cpp
@@ -51,7 +51,7 @@ void solveTester(const int m, const int n, const int k, const int b, double eps,
     deviceGC();
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     array A  = cpu_randu<T>(dim4(m, n, b));
@@ -88,7 +88,7 @@ void solveLUTester(const int n, const int k, double eps,
     deviceGC();
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     array A  = cpu_randu<T>(dim4(n, n));
@@ -125,7 +125,7 @@ void solveTriangleTester(const int n, const int k, bool is_upper, double eps,
     deviceGC();
 
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
 #if 1
     array A  = cpu_randu<T>(dim4(n, n));
diff --git a/test/susan.cpp b/test/susan.cpp
index 9bdc16d3d9..34929c22c0 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -67,7 +67,7 @@ TYPED_TEST_SUITE(Susan, TestTypes);
 template<typename T>
 void susanTest(string pTestFile, float t, float g) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;
diff --git a/test/svd_dense.cpp b/test/svd_dense.cpp
index e31603a84b..f0da346ce4 100644
--- a/test/svd_dense.cpp
+++ b/test/svd_dense.cpp
@@ -58,7 +58,7 @@ double get_val<cdouble>(cdouble val) {
 template<typename T>
 void svdTest(const int M, const int N) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
@@ -87,7 +87,7 @@ void svdTest(const int M, const int N) {
 template<typename T>
 void svdInPlaceTest(const int M, const int N) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
@@ -115,7 +115,7 @@ void svdInPlaceTest(const int M, const int N) {
 template<typename T>
 void checkInPlaceSameResults(const int M, const int N) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noLAPACKTests()) return;
+    LAPACK_ENABLED_CHECK();
 
     dtype ty = (dtype)dtype_traits<T>::af_type;
 
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 2382060ebf..3f1beb55bb 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -227,7 +227,13 @@ bool noHalfTests(af::dtype ty);
     if (noDoubleTests((af_dtype)af::dtype_traits<type>::af_type)) \
         GTEST_SKIP() << "Device doesn't support Doubles";         \
     if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type))   \
-        GTEST_SKIP() << "Device doesn't support Half";
+    GTEST_SKIP() << "Device doesn't support Half"
+
+#define LAPACK_ENABLED_CHECK() \
+    if (!af::isLAPACKAvailable()) GTEST_SKIP() << "LAPACK Not Configured."
+
+#define IMAGEIO_ENABLED_CHECK() \
+    if (!af::isImageIOAvailable()) GTEST_SKIP() << "Image IO Not Configured"
 
 #ifdef AF_WITH_FAST_MATH
 #define SKIP_IF_FAST_MATH_ENABLED() \
@@ -236,10 +242,6 @@ bool noHalfTests(af::dtype ty);
 #define SKIP_IF_FAST_MATH_ENABLED()
 #endif
 
-bool noImageIOTests();
-
-bool noLAPACKTests();
-
 template<typename TO, typename FROM>
 TO convert_to(FROM in) {
     return TO(in);
diff --git a/test/threading.cpp b/test/threading.cpp
index 96dd894e4f..41c4ebb723 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -141,7 +141,7 @@ void morphTest(const array input, const array mask, const bool isDilation,
 }
 
 TEST(Threading, SetPerThreadActiveDevice) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<bool> isDilationFlags;
     vector<bool> isColorFlags;
diff --git a/test/transform.cpp b/test/transform.cpp
index b7719d46fc..e3e0efe640 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -97,7 +97,7 @@ template<typename T>
 void transformTest(string pTestFile, string pHomographyFile,
                    const af_interp_type method, const bool invert) {
     SUPPORTED_TYPE_CHECK(T);
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     af_array sceneArray = 0;
     af_array goldArray  = 0;
@@ -304,7 +304,7 @@ class TransformV2 : public Transform<T> {
     }
 
     void setTestData(string pTestFile, string pHomographyFile) {
-        if (noImageIOTests()) return;
+        IMAGEIO_ENABLED_CHECK();
         releaseArrays();
 
         genTestData<T>(&gold, &in, &transform, &odim0, &odim1, pTestFile,
@@ -390,7 +390,7 @@ class TransformV2 : public Transform<T> {
 
     void testSpclOutArray(TestOutputArrayType out_array_type) {
         SUPPORTED_TYPE_CHECK(T);
-        if (noImageIOTests()) return;
+        IMAGEIO_ENABLED_CHECK();
 
         af_array out = 0;
         TestOutputArrayInfo metadata(out_array_type);
@@ -481,7 +481,7 @@ TEST_F(TransformNullArgs, V2NullTransformArray) {
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(Transform, CPP) {
-    if (noImageIOTests()) return;
+    IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
     vector<string> inFiles;

From be1ef85ea620219140c32a66575d5ba498f9f130 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Mar 2023 09:39:29 -0400
Subject: [PATCH 2436/2677] Add setting of default device using environment
 variable for oneAPI

---
 src/backend/oneapi/device_manager.cpp | 45 ++++++++++++++++++++++++---
 1 file changed, 41 insertions(+), 4 deletions(-)

diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 7134109146..13a314b2aa 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -163,13 +163,50 @@ DeviceManager::DeviceManager()
 
     bool default_device_set = false;
     string deviceENV        = getEnvVar("AF_ONEAPI_DEFAULT_DEVICE");
+
     if (!deviceENV.empty()) {
-        // TODO: handle default device from env variable
+        stringstream s(deviceENV);
+        int def_device = -1;
+        s >> def_device;
+        if (def_device >= static_cast<int>(mQueues.size()) ||
+            def_device >= static_cast<int>(DeviceManager::MAX_DEVICES)) {
+            AF_TRACE(
+                "AF_ONEAPI_DEFAULT_DEVICE ({}) \
+                   is out of range, Setting default device to 0",
+                def_device);
+            def_device = 0;
+        } else {
+            setActiveContext(def_device);
+            default_device_set = true;
+        }
     }
 
-    deviceENV = getEnvVar("AF_OPENCL_DEFAULT_DEVICE_TYPE");
+    deviceENV = getEnvVar("AF_ONEAPI_DEFAULT_DEVICE_TYPE");
     if (!default_device_set && !deviceENV.empty()) {
-        // TODO: handle default device by type env variable
+        sycl::info::device_type default_device_type =
+            sycl::info::device_type::gpu;
+        if (deviceENV == "CPU") {
+            default_device_type = sycl::info::device_type::cpu;
+        } else if (deviceENV == "ACC") {
+            default_device_type = sycl::info::device_type::accelerator;
+        }
+
+        bool default_device_set = false;
+        for (int i = 0; i < nDevices; i++) {
+            if (mDevices[i]->get_info<sycl::info::device::device_type>() ==
+                default_device_type) {
+                default_device_set = true;
+                AF_TRACE("Setting to first available {}({})", deviceENV, i);
+                setActiveContext(i);
+                break;
+            }
+        }
+        if (!default_device_set) {
+            AF_TRACE(
+                "AF_ONEAPI_DEFAULT_DEVICE_TYPE={} \
+                   is not available, Using default device as 0",
+                deviceENV);
+        }
     }
 
     // Define AF_DISABLE_GRAPHICS with any value to disable initialization
@@ -182,7 +219,7 @@ DeviceManager::DeviceManager()
 
     // TODO: init other needed libraries?
     // blas? program cache?
-    // AF_TRACE("Default device: {}", getActiveDeviceId());
+    AF_TRACE("Default device: {}", getActiveDeviceId());
 }
 
 spdlog::logger* DeviceManager::getLogger() { return logger.get(); }

From 40a71025c206a69f9637facda3c3966a2a55504d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Mar 2023 09:40:34 -0400
Subject: [PATCH 2437/2677] Updating device sorting compare function to prefer
 GPUS for oneAPI

---
 src/backend/oneapi/device_manager.cpp | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 13a314b2aa..05d6cb454d 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -57,11 +57,26 @@ namespace oneapi {
 
 static inline bool compare_default(const unique_ptr<sycl::device>& ldev,
                                    const unique_ptr<sycl::device>& rdev) {
-    // TODO: update sorting criteria
-    // select according to something applicable to oneapi backend
-    auto l_mem = ldev->get_info<sycl::info::device::global_mem_size>();
-    auto r_mem = rdev->get_info<sycl::info::device::global_mem_size>();
-    return l_mem > r_mem;
+    using sycl::info::device_type;
+
+    auto ldt = ldev->get_info<sycl::info::device::device_type>();
+    auto rdt = rdev->get_info<sycl::info::device::device_type>();
+
+    if (ldt == rdt) {
+        auto l_mem = ldev->get_info<sycl::info::device::global_mem_size>();
+        auto r_mem = rdev->get_info<sycl::info::device::global_mem_size>();
+        return l_mem > r_mem;
+    } else {
+        if (ldt == device_type::gpu)
+            return true;
+        else if (rdt == device_type::gpu)
+            return false;
+        else if (ldt == device_type::cpu)
+            return true;
+        else if (rdt == device_type::cpu)
+            return false;
+    }
+    return false;
 }
 
 auto arrayfire_exception_handler(sycl::exception_list exceptions) {

From c872f5686385f41e4e724a505058efe8caceb066 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Mar 2023 09:56:28 -0400
Subject: [PATCH 2438/2677] Update oneAPI af_info and print floating point
 aspects for devices

---
 src/backend/oneapi/platform.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index 918b4666f4..6d4c7df84c 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -41,6 +41,7 @@
 #include <utility>
 #include <vector>
 
+using sycl::aspect;
 using sycl::context;
 using sycl::device;
 using sycl::platform;
@@ -139,7 +140,7 @@ af_oneapi_platform getPlatformEnum(sycl::device dev) {
 
 string getDeviceInfo() noexcept {
     ostringstream info;
-    info << "ArrayFire v" << AF_VERSION << " (OpenCL, " << get_system()
+    info << "ArrayFire v" << AF_VERSION << " (oneAPI, " << get_system()
          << ", build " << AF_REVISION << ")\n";
 
     try {
@@ -156,11 +157,14 @@ string getDeviceInfo() noexcept {
 
             string id = (show_braces ? string("[") : "-") +
                         to_string(nDevices) + (show_braces ? string("]") : "-");
-
             size_t msize =
                 device->get_info<sycl::info::device::global_mem_size>();
             info << id << " " << getPlatformName(*device) << ": " << ltrim(dstr)
                  << ", " << msize / 1048576 << " MB";
+            info << " (";
+            if (device->has(aspect::fp64)) { info << "fp64 "; }
+            if (device->has(aspect::fp16)) { info << "fp16 "; }
+            info << "\b)";
 #ifndef NDEBUG
             info << " -- ";
             string devVersion = device->get_info<sycl::info::device::version>();
@@ -168,11 +172,7 @@ string getDeviceInfo() noexcept {
                 device->get_info<sycl::info::device::driver_version>();
             info << devVersion;
             info << " -- Device driver " << driVersion;
-            info << " -- FP64 Support: "
-                 << (device->get_info<sycl::info::device::
-                                          preferred_vector_width_double>() > 0
-                         ? "True"
-                         : "False");
+
             info << " -- Unified Memory ("
                  << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif

From 2b8e5eae49a0d3ce6a900ccce6b51c1d02ad719d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Mar 2023 09:57:27 -0400
Subject: [PATCH 2439/2677] Update info test print the default device before
 setting device

---
 test/info.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/test/info.cpp b/test/info.cpp
index f1519d3380..5cd82a6201 100644
--- a/test/info.cpp
+++ b/test/info.cpp
@@ -48,6 +48,7 @@ void infoTest() {
         testFunction<float>();
     } else {
         int oldDevice = getDevice();
+        testFunction<float>();
         for (int d = 0; d < nDevices; d++) {
             setDevice(d);
             testFunction<float>();

From be3334ef5ae9a401194c3f12b83b43e8e83f3dbd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 29 Mar 2023 12:28:01 -0400
Subject: [PATCH 2440/2677] Use Intel's MKLConfig.cmake instead of FindMKL for
 oneAPI.

---
 CMakeLists.txt                                | 27 +++++++++++++++++--
 .../{FindMKL.cmake => FindAF_MKL.cmake}       |  0
 src/backend/oneapi/CMakeLists.txt             |  5 +---
 3 files changed, 26 insertions(+), 6 deletions(-)
 rename CMakeModules/{FindMKL.cmake => FindAF_MKL.cmake} (100%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b049258552..708ef7f390 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -46,7 +46,15 @@ if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
 endif()
 
 #Set Intel OpenMP as default MKL thread layer
-set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
+if(CMAKE_CXX_COMPILER_ID STREQUAL "IntelLLVM" OR CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
+  set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
+  set(MKL_INTERFACE "ilp64")
+  set(MKL_INTERFACE_INTEGER_SIZE 8)
+else()
+  set(MKL_THREAD_LAYER "Intel OpenMP" CACHE STRING "The thread layer to choose for MKL")
+  set(MKL_INTERFACE "lp64")
+  set(MKL_INTERFACE_INTEGER_SIZE 4)
+endif()
 
 find_package(CUDA 10.2)
 find_package(cuDNN 4.0)
@@ -59,7 +67,7 @@ find_package(FFTW)
 find_package(CBLAS)
 find_package(LAPACKE)
 find_package(Doxygen)
-find_package(MKL)
+find_package(AF_MKL)
 find_package(spdlog QUIET ${AF_REQUIRED} NO_CMAKE_PACKAGE_REGISTRY)
 find_package(fmt QUIET ${AF_REQUIRED})
 find_package(span-lite QUIET)
@@ -103,6 +111,21 @@ if(MKL_FOUND)
   set(default_compute_library "Intel-MKL")
 endif()
 
+if(AF_WITH_STATIC_MKL)
+  set(MKL_LINK static)
+endif()
+if(MKL_THREAD_LAYER STREQUAL "Sequential")
+  set(MKL_THREADING "sequential")
+elseif(MKL_THREAD_LAYER STREQUAL "GNU OpenMP")
+  set(MKL_THREADING "gnu_thread")
+elseif(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
+  set(MKL_THREADING "intel_thread")
+elseif(MKL_THREAD_LAYER STREQUAL "TBB")
+  set(MKL_THREADING "tbb_thread")
+else()
+endif()
+find_package(MKL)
+
 af_multiple_option(NAME        AF_COMPUTE_LIBRARY
                    DEFAULT     ${default_compute_library}
                    DESCRIPTION "Compute library for signal processing and linear algebra routines"
diff --git a/CMakeModules/FindMKL.cmake b/CMakeModules/FindAF_MKL.cmake
similarity index 100%
rename from CMakeModules/FindMKL.cmake
rename to CMakeModules/FindAF_MKL.cmake
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 64a2b34715..c003f72152 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -283,7 +283,6 @@ target_include_directories(afoneapi
 target_compile_options(afoneapi
   PRIVATE
     -fsycl
-    -openmp -Qmkl=parallel
     -sycl-std=2020
 )
 
@@ -299,7 +298,6 @@ target_compile_definitions(afoneapi
 target_link_libraries(afoneapi
   PRIVATE
     -fsycl
-    -fno-lto
     -fvisibility-inlines-hidden
     c_api_interface
     cpp_api_interface
@@ -309,8 +307,7 @@ target_link_libraries(afoneapi
     -fsycl
     -fsycl-device-code-split=per_kernel
     -fsycl-link-huge-device-code
-    -qopenmp
-    -qmkl=parallel
+    MKL::MKL_DPCPP
   )
 
 af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})

From fe15ffeee5695fac7a735a50666e537119b5939e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Mar 2023 13:19:48 -0400
Subject: [PATCH 2441/2677] Fix the scalar funciton for the GPU in the oneAPI
 backend

---
 src/backend/oneapi/copy.cpp | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 4059bd27f0..d9d2fba2c5 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -214,18 +214,13 @@ template<typename T>
 T getScalar(const Array<T> &in) {
     T retVal{};
 
-    sycl::buffer retBuffer(&retVal, {1},
-                           {sycl::property::buffer::use_host_ptr()});
-
     getQueue()
         .submit([&](sycl::handler &h) {
             auto acc_in =
                 in.get()->template get_access<sycl::access::mode::read>(
                     h, sycl::range{1},
                     sycl::id{static_cast<uintl>(in.getOffset())});
-            auto acc_out =
-                retBuffer.template get_access<sycl::access::mode::write>(h);
-            h.copy(acc_in, acc_out);
+            h.copy(acc_in, &retVal);
         })
         .wait();
 

From 5bb5c167b7ab7732fbab9823ddf6778c97261cbd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Mar 2023 13:22:22 -0400
Subject: [PATCH 2442/2677] Refactor array test assert calls

---
 test/array.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/test/array.cpp b/test/array.cpp
index eeb7f2952b..5962797083 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -502,12 +502,12 @@ TEST(DeviceId, Different) {
 
 TEST(Device, empty) {
     array a = array();
-    ASSERT_EQ(a.device<float>() == NULL, 1);
+    ASSERT_EQ(a.device<float>(), nullptr);
 }
 
 TEST(Device, JIT) {
     array a = constant(1, 5, 5);
-    ASSERT_EQ(a.device<float>() != NULL, 1);
+    ASSERT_NE(a.device<float>(), nullptr);
 }
 
 TYPED_TEST(Array, Scalar) {
@@ -520,7 +520,7 @@ TYPED_TEST(Array, Scalar) {
 
     a.host((void *)gold.data());
 
-    EXPECT_EQ(true, gold[0] == a.scalar<TypeParam>());
+    EXPECT_EQ(gold[0], a.scalar<TypeParam>());
 }
 
 TEST(Array, ScalarTypeMismatch) {

From e6bc701a8b42658908c98df7feeab9051d0cb0d7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Mar 2023 13:23:03 -0400
Subject: [PATCH 2443/2677] Return a pointer to the buffer<T> object when
 calling device in oneAPI

---
 src/backend/oneapi/Array.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 2db607c75c..93c9e0df7e 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -397,10 +397,6 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const buffer<T> *buf = arr.device();
-    // if (!buf) { return NULL; }
-    // memLock(buf);
-    // cl_mem mem = (*buf)();
-    ONEAPI_NOT_SUPPORTED("pointer to sycl::buffer should be accessor");
     return (void *)buf;
 }
 

From 6cb7924c7026d9f9968b2cdaf864a4bf8052485a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 30 Mar 2023 14:19:08 -0400
Subject: [PATCH 2444/2677] Add the ability to run individual gtests separately
 in ctest

---
 CMakeLists.txt      |  1 +
 test/CMakeLists.txt | 37 +++++++++++++++++++++++++++++--------
 2 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 708ef7f390..29e2880949 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -101,6 +101,7 @@ option(AF_WITH_STATIC_CUDA_NUMERIC_LIBS "Link libafcuda with static numeric libr
 option(AF_WITH_SPDLOG_HEADER_ONLY "Build ArrayFire with header only version of spdlog" OFF)
 option(AF_WITH_FMT_HEADER_ONLY "Build ArrayFire with header only version of fmt" OFF)
 option(AF_WITH_FAST_MATH "Use lower precision but high performance numeric optimizations" OFF)
+option(AF_CTEST_SEPARATED "Run tests separately when called from ctest(increases test times)" OFF)
 
 if(AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   option(AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS "Prune CUDA static libraries to reduce binary size.(WARNING: May break some libs on older CUDA toolkits for some compute arch)" OFF)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index dbd81ea6e7..6f385f666a 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -11,6 +11,10 @@ set(AF_TEST_WITH_MTX_FILES
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
 
+if(AF_CTEST_SEPARATED)
+  include(GoogleTest)
+endif()
+
 if(AF_TEST_WITH_MTX_FILES)
   include(download_sparse_datasets)
 endif()
@@ -55,6 +59,28 @@ if(NOT TARGET mmio)
   add_subdirectory(mmio)
 endif()
 
+
+# Registers test with ctest
+#
+# Parameters
+#  target: The target associated with this test
+#  backend: The backend associated with this test
+#  is_serial: If true the test will be serialized
+function(af_add_test target backend is_serial)
+  if(AF_CTEST_SEPARATED)
+    gtest_discover_tests(${target}
+      TEST_PREFIX $<UPPER_CASE:${backend}>.
+      DISCOVERY_TIMEOUT 40)
+  else()
+    add_test(NAME ${target} COMMAND ${target})
+    if(${is_serial})
+      set_tests_properties(${target}
+        PROPERTIES
+        RUN_SERIAL ON)
+    endif(${is_serial})
+  endif()
+endfunction()
+
 # Reset the CXX flags for tests
 set(CMAKE_CXX_STANDARD 11)
 
@@ -238,12 +264,7 @@ function(make_test)
 
     # TODO(umar): Create this executable separately
     if(NOT ${backend} STREQUAL "unified" OR ${target} STREQUAL "backend_unified")
-      add_test(NAME ${target} COMMAND ${target})
-	    if(${mt_args_SERIAL})
-        set_tests_properties(${target}
-          PROPERTIES
-            RUN_SERIAL ON)
-	    endif(${mt_args_SERIAL})
+      af_add_test(${target} ${backend} ${mt_args_SERIAL})
     endif()
 
   endforeach()
@@ -387,7 +408,7 @@ if(CUDA_FOUND)
           OUTPUT_NAME "cuda_${backend}")
 
       if(NOT ${backend} STREQUAL "unified")
-        add_test(NAME ${target} COMMAND ${target})
+        af_add_test(${target} ${backend} ON)
       endif()
     endif()
   endforeach()
@@ -457,7 +478,7 @@ foreach(backend ${enabled_backends})
       PRIVATE
       ArrayFire::af${backend})
   endif()
-  add_test(NAME test_${target} COMMAND ${target})
+  af_add_test(${target} ${backend} ON)
 endforeach()
 
 if(AF_TEST_WITH_MTX_FILES)

From 7e7c250e5cc0373b57adc49aef29fc8296be62c4 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Fri, 31 Mar 2023 21:57:13 +0200
Subject: [PATCH 2445/2677] Corrected availability check of
 work_group_collection_functions for OCL2.X

---
 .../opencl/kernel/reduce_blocks_by_key_dim.cl | 29 +++++++++++--------
 .../kernel/reduce_blocks_by_key_first.cl      | 23 ++++++++++-----
 2 files changed, 32 insertions(+), 20 deletions(-)

diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
index 1fbd594e0a..66bbb3e6d2 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
@@ -9,7 +9,12 @@
 
 // Starting from OpenCL 2.0, core profile includes work group level
 // inclusive scan operations, hence skip defining custom one
-#if __OPENCL_VERSION__ < 200
+#if __OPENCL_C_VERSION__ == 200 || __OPENCL_C_VERSION__ == 210 || \
+    __OPENCL_C_VERSION__ == 220 || __opencl_c_work_group_collective_functions
+#define BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
+#endif
+
+#ifndef BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
 int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
     local int *active_buf;
 
@@ -29,15 +34,15 @@ int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
     int res = active_buf[lid];
     return res;
 }
-#endif  // __OPENCL_VERSION__ < 200
+#endif
 
 kernel void reduce_blocks_by_key_dim(global int *reduced_block_sizes,
-                                       global Tk *oKeys, KParam oKInfo,
-                                       global To *oVals, KParam oVInfo,
-                                       const global Tk *iKeys, KParam iKInfo,
-                                       const global Ti *iVals, KParam iVInfo,
-                                       int change_nan, To nanval, int n,
-                                       const int nBlocksZ) {
+                                     global Tk *oKeys, KParam oKInfo,
+                                     global To *oVals, KParam oVInfo,
+                                     const global Tk *iKeys, KParam iKInfo,
+                                     const global Ti *iVals, KParam iVInfo,
+                                     int change_nan, To nanval, int n,
+                                     const int nBlocksZ) {
     const uint lid  = get_local_id(0);
     const uint gidx = get_global_id(0);
 
@@ -50,7 +55,7 @@ kernel void reduce_blocks_by_key_dim(global int *reduced_block_sizes,
     local Tk reduced_keys[DIMX];
     local To reduced_vals[DIMX];
     local int unique_ids[DIMX];
-#if __OPENCL_VERSION__ < 200
+#ifndef BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
     local int wg_temp[DIMX];
     local int unique_flags[DIMX];
 #endif
@@ -98,11 +103,11 @@ kernel void reduce_blocks_by_key_dim(global int *reduced_block_sizes,
     int eq_check    = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
     int unique_flag = (eq_check || (lid == 0)) && (gidx < n);
 
-#if __OPENCL_VERSION__ < 200
+#ifdef BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
+    int unique_id = work_group_scan_inclusive_add(unique_flag);
+#else
     unique_flags[lid] = unique_flag;
     int unique_id     = work_group_scan_inclusive_add(wg_temp, unique_flags);
-#else
-    int unique_id = work_group_scan_inclusive_add(unique_flag);
 #endif
     unique_ids[lid] = unique_id;
 
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
index e473244152..f184e94818 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -9,7 +9,12 @@
 
 // Starting from OpenCL 2.0, core profile includes work group level
 // inclusive scan operations, hence skip defining custom one
-#if !__opencl_c_work_group_collective_functions
+#if __OPENCL_C_VERSION__ == 200 || __OPENCL_C_VERSION__ == 210 || \
+    __OPENCL_C_VERSION__ == 220 || __opencl_c_work_group_collective_functions
+#define BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
+#endif
+
+#ifndef BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
 int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
     local int *active_buf;
 
@@ -31,11 +36,13 @@ int work_group_scan_inclusive_add(local int *wg_temp, __local int *arr) {
 }
 #endif
 
-kernel void reduce_blocks_by_key_first(
-    global int *reduced_block_sizes, __global Tk *oKeys, KParam oKInfo,
-    global To *oVals, KParam oVInfo, const __global Tk *iKeys, KParam iKInfo,
-    const global Ti *iVals, KParam iVInfo, int change_nan, To nanval, int n,
-    const int nBlocksZ) {
+kernel void reduce_blocks_by_key_first(global int *reduced_block_sizes,
+                                       __global Tk *oKeys, KParam oKInfo,
+                                       global To *oVals, KParam oVInfo,
+                                       const __global Tk *iKeys, KParam iKInfo,
+                                       const global Ti *iVals, KParam iVInfo,
+                                       int change_nan, To nanval, int n,
+                                       const int nBlocksZ) {
     const uint lid = get_local_id(0);
     const uint gid = get_global_id(0);
 
@@ -48,7 +55,7 @@ kernel void reduce_blocks_by_key_first(
     local Tk reduced_keys[DIMX];
     local To reduced_vals[DIMX];
     local int unique_ids[DIMX];
-#if !__opencl_c_work_group_collective_functions
+#ifndef BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
     local int wg_temp[DIMX];
     local int unique_flags[DIMX];
 #endif
@@ -84,7 +91,7 @@ kernel void reduce_blocks_by_key_first(
     int eq_check    = (lid > 0) ? (k != reduced_keys[lid - 1]) : 0;
     int unique_flag = (eq_check || (lid == 0)) && (gid < n);
 
-#if __opencl_c_work_group_collective_functions
+#ifdef BUILTIN_WORK_GROUP_COLLECTIVE_FUNCTIONS
     int unique_id = work_group_scan_inclusive_add(unique_flag);
 #else
     unique_flags[lid] = unique_flag;

From afac0eaa884ff9524aa62d3cbfc52aa66a554323 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 31 Mar 2023 16:52:41 -0400
Subject: [PATCH 2446/2677] Fix oneAPI find_package command to look at the
 MKLROOT env var

---
 CMakeLists.txt | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 29e2880949..e7bf293ce4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -45,8 +45,8 @@ if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
   set(AF_REQUIRED REQUIRED)
 endif()
 
-#Set Intel OpenMP as default MKL thread layer
-if(CMAKE_CXX_COMPILER_ID STREQUAL "IntelLLVM" OR CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
+if(CXX_COMPILER_NAME STREQUAL "dpcpp" OR CXX_COMPILER_NAME STREQUAL "dpcpp.exe"
+   OR CXX_COMPILER_NAME STREQUAL "icpx" OR CXX_COMPILER_NAME STREQUAL "icx.exe")
   set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
   set(MKL_INTERFACE "ilp64")
   set(MKL_INTERFACE_INTEGER_SIZE 8)
@@ -125,7 +125,14 @@ elseif(MKL_THREAD_LAYER STREQUAL "TBB")
   set(MKL_THREADING "tbb_thread")
 else()
 endif()
-find_package(MKL)
+
+if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
+  # VCPKG overrides the find_package command and the PATH parameter is currently
+  # broken with the current version of VCPKG so we are setting the MKL_ROOT
+  # directory to the MKLROOT environment variable.
+  set(MKL_ROOT "$ENV{MKLROOT}")
+  find_package(MKL)
+endif()
 
 af_multiple_option(NAME        AF_COMPUTE_LIBRARY
                    DEFAULT     ${default_compute_library}
@@ -218,7 +225,7 @@ if(${AF_BUILD_CPU} OR ${AF_BUILD_OPENCL})
   if("${AF_COMPUTE_LIBRARY}" STREQUAL "Intel-MKL"
       OR "${AF_COMPUTE_LIBRARY}" STREQUAL "MKL")
     af_mkl_batch_check()
-    dependency_check(MKL_FOUND "Please ensure Intel-MKL / oneAPI-oneMKL is installed")
+    dependency_check(MKL_Shared_FOUND "Please ensure Intel-MKL / oneAPI-oneMKL is installed")
     set(BUILD_WITH_MKL ON)
   elseif("${AF_COMPUTE_LIBRARY}" STREQUAL "FFTW/LAPACK/BLAS")
     dependency_check(FFTW_FOUND "FFTW not found")

From 6503b97d686d0b158e684e400cb683c7aa3f3826 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 2 Apr 2023 08:39:03 -0400
Subject: [PATCH 2447/2677] Increase test timeout to avoid failures with
 jit_opencl on CI

---
 test/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 6f385f666a..5b7c869eba 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -76,7 +76,9 @@ function(af_add_test target backend is_serial)
     if(${is_serial})
       set_tests_properties(${target}
         PROPERTIES
-        RUN_SERIAL ON)
+          ENVIRONMENT AF_PRINT_ERRORS=1
+          TIMEOUT 900
+          RUN_SERIAL ON)
     endif(${is_serial})
   endif()
 endfunction()

From de0aba43640d6c6e39aa3d090aeb72c962b6cd7c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 2 Apr 2023 14:54:15 -0400
Subject: [PATCH 2448/2677] Remove exceptions thrown on OpenCL kernel cache
 miss.

---
 src/backend/opencl/compile_module.cpp | 21 +++++++++------------
 1 file changed, 9 insertions(+), 12 deletions(-)

diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 832f5144a7..89d382c9c0 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -230,7 +230,10 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
     try {
         std::ifstream in(cacheFile, std::ios::binary);
         if (!in.is_open()) {
-            AF_ERROR("Unable to open binary cache file", AF_ERR_INTERNAL);
+            AF_TRACE("{{{:<20} : Unable to open {} for {}}}", moduleKey,
+                     cacheFile, dev.getInfo<CL_DEVICE_NAME>());
+            removeFile(cacheFile);
+            return retVal;
         }
         in.exceptions(std::ios::failbit | std::ios::badbit);
 
@@ -247,7 +250,11 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
         const size_t recomputedHash =
             deterministicHash(clbin.data(), clbinSize);
         if (recomputedHash != clbinHash) {
-            AF_ERROR("Binary on disk seems to be corrupted", AF_ERR_LOAD_SYM);
+            AF_TRACE(
+                "{{{:<20} : Corrupt binary({}) found on disk for {}, removed}}",
+                moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>());
+            removeFile(cacheFile);
+            return retVal;
         }
         program = Program(arrayfire::opencl::getContext(), {dev}, {clbin});
         program.build();
@@ -255,16 +262,6 @@ Module loadModuleFromDisk(const int device, const string &moduleKey,
         AF_TRACE("{{{:<20} : loaded from {} for {} }}", moduleKey, cacheFile,
                  dev.getInfo<CL_DEVICE_NAME>());
         retVal.set(program);
-    } catch (const AfError &e) {
-        if (e.getError() == AF_ERR_LOAD_SYM) {
-            AF_TRACE(
-                "{{{:<20} : Corrupt binary({}) found on disk for {}, removed}}",
-                moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>());
-        } else {
-            AF_TRACE("{{{:<20} : Unable to open {} for {}}}", moduleKey,
-                     cacheFile, dev.getInfo<CL_DEVICE_NAME>());
-        }
-        removeFile(cacheFile);
     } catch (const std::ios_base::failure &e) {
         AF_TRACE("{{{:<20} : IO failure while loading {} for {}; {}}}",
                  moduleKey, cacheFile, dev.getInfo<CL_DEVICE_NAME>(), e.what());

From d70a59b4004e8afa4b2537d6fb065b14776d4a02 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sun, 2 Apr 2023 15:16:02 -0400
Subject: [PATCH 2449/2677] Limit the maximum kernel size to 5kb for OpenCL to
 manage compile times

---
 src/backend/opencl/Array.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 810666b9a6..311ec715b9 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -334,11 +334,15 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
             (3 * sizeof(uint));
 
         const cl::Device &device = getDevice();
-        size_t max_param_size = device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>();
         // typical values:
         //   NVIDIA     = 4096
         //   AMD        = 3520  (AMD A10 iGPU = 1024)
         //   Intel iGPU = 1024
+        //
+        // Setting the maximum to 5120 bytes to keep the compile times
+        // resonable. This still results in large kernels but its not excessive.
+        size_t max_param_size =
+            min(5120UL, device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>());
         max_param_size -= base_param_size;
 
         struct tree_info {

From aaca3e6f81669d033fafc5bec7be5e9f0a8e7f05 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 3 Apr 2023 15:22:23 -0400
Subject: [PATCH 2450/2677] set CXX_COMPILER_NAME based on the current compiler
 to detect sycl

---
 CMakeLists.txt | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e7bf293ce4..eed62e23a6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -45,6 +45,7 @@ if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
   set(AF_REQUIRED REQUIRED)
 endif()
 
+get_filename_component(CXX_COMPILER_NAME ${CMAKE_CXX_COMPILER} NAME)
 if(CXX_COMPILER_NAME STREQUAL "dpcpp" OR CXX_COMPILER_NAME STREQUAL "dpcpp.exe"
    OR CXX_COMPILER_NAME STREQUAL "icpx" OR CXX_COMPILER_NAME STREQUAL "icx.exe")
   set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
@@ -130,7 +131,9 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
   # VCPKG overrides the find_package command and the PATH parameter is currently
   # broken with the current version of VCPKG so we are setting the MKL_ROOT
   # directory to the MKLROOT environment variable.
-  set(MKL_ROOT "$ENV{MKLROOT}")
+  if(DEFINED ENV{MKLROOT} AND NOT DEFINED MKL_ROOT)
+    set(MKL_ROOT "$ENV{MKLROOT}")
+  endif()
   find_package(MKL)
 endif()
 

From 1e15e1f926a51468f094044b64f5d81f05912957 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 6 Apr 2023 15:22:16 -0400
Subject: [PATCH 2451/2677] Implement BLAS functions in oneAPI (#3396)

* initial blas function implementation
---
 src/backend/oneapi/Array.hpp          |   1 +
 src/backend/oneapi/Kernel.hpp         |   1 +
 src/backend/oneapi/blas.cpp           | 133 ++++++++++++++++++++++----
 src/backend/oneapi/blas.hpp           |   5 +-
 src/backend/oneapi/compile_module.cpp |   1 +
 src/backend/oneapi/jit.cpp            |   1 -
 src/backend/oneapi/kernel/memcopy.hpp |   1 +
 7 files changed, 121 insertions(+), 22 deletions(-)

diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index 9a4de1285c..d3173d7fb8 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 
+#include <Param.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
 #include <kernel/KParam.hpp>
diff --git a/src/backend/oneapi/Kernel.hpp b/src/backend/oneapi/Kernel.hpp
index 3fcf7b66b8..c0f15356f8 100644
--- a/src/backend/oneapi/Kernel.hpp
+++ b/src/backend/oneapi/Kernel.hpp
@@ -13,6 +13,7 @@
 #include <common/Logger.hpp>
 
 #include <backend.hpp>
+#include <sycl/sycl.hpp>
 #include <string>
 
 namespace arrayfire {
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 4a3b5e180d..964dcb6cde 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -12,53 +12,142 @@
 #include <Array.hpp>
 #include <arith.hpp>
 #include <common/half.hpp>
+#include <common/kernel_type.hpp>
 #include <common/traits.hpp>
 #include <complex.hpp>
+#include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
 #include <math.hpp>
+#include <platform.hpp>
 #include <reduce.hpp>
 #include <transpose.hpp>
+#include <types.hpp>
+
+#include <sycl/sycl.hpp>
+#include "oneapi/mkl/blas.hpp"
 
 #include <complex>
 #include <vector>
 
 using arrayfire::common::half;
 
-namespace arrayfire {
-namespace oneapi {
-
-void initBlas() { /*gpu_blas_init();*/
+// Converts an af_mat_prop options to a transpose type for mkl
+static oneapi::mkl::transpose toBlasTranspose(af_mat_prop opt) {
+    switch (opt) {
+        case AF_MAT_NONE: return oneapi::mkl::transpose::nontrans;
+        case AF_MAT_TRANS: return oneapi::mkl::transpose::trans;
+        case AF_MAT_CTRANS: return oneapi::mkl::transpose::conjtrans;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
 }
 
-void deInitBlas() { /*gpu_blas_deinit();*/
+template<typename T>
+static void gemvDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts, int M,
+                         int N, const T *alpha,
+                         const arrayfire::oneapi::Array<T> &lhs, dim_t lStride,
+                         const arrayfire::oneapi::Array<T> &x, dim_t incx,
+                         const T *beta, arrayfire::oneapi::Array<T> &out,
+                         dim_t oInc) {
+    using Dt                   = arrayfire::oneapi::data_t<T>;
+    sycl::buffer<Dt, 1> lhsBuf = lhs.get()->template reinterpret<Dt, 1>();
+    sycl::buffer<Dt, 1> xBuf   = x.get()->template reinterpret<Dt, 1>();
+    sycl::buffer<Dt, 1> outBuf = out.get()->template reinterpret<Dt, 1>();
+    ::oneapi::mkl::blas::gemv(queue, lOpts, (int64_t)M, (int64_t)N, (T)*alpha,
+                              lhsBuf, (int64_t)lStride, xBuf, (int64_t)incx,
+                              (T)*beta, outBuf, (int64_t)oInc);
 }
 
 template<typename T>
-void gemm_fallback(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-                   const T *alpha, const Array<T> &lhs, const Array<T> &rhs,
-                   const T *beta) {
-    ONEAPI_NOT_SUPPORTED("");
+static void gemmDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts,
+                         oneapi::mkl::transpose rOpts, int M, int N, int K,
+                         const T *alpha, const arrayfire::oneapi::Array<T> &lhs,
+                         dim_t lStride, const arrayfire::oneapi::Array<T> &rhs,
+                         dim_t rStride, const T *beta,
+                         arrayfire::oneapi::Array<T> &out, dim_t oleading) {
+    using Dt                   = arrayfire::oneapi::data_t<T>;
+    sycl::buffer<Dt, 1> lhsBuf = lhs.get()->template reinterpret<Dt, 1>();
+    sycl::buffer<Dt, 1> rhsBuf = rhs.get()->template reinterpret<Dt, 1>();
+    sycl::buffer<Dt, 1> outBuf = out.get()->template reinterpret<Dt, 1>();
+    ::oneapi::mkl::blas::gemm(queue, lOpts, rOpts, M, N, K, *alpha, lhsBuf,
+                              lStride, rhsBuf, rStride, *beta, outBuf,
+                              oleading);
 }
 
-template<>
-void gemm_fallback<half>(Array<half> & /*out*/, af_mat_prop /*optLhs*/,
-                         af_mat_prop /*optRhs*/, const half * /*alpha*/,
-                         const Array<half> & /*lhs*/,
-                         const Array<half> & /*rhs*/, const half * /*beta*/) {
-    ONEAPI_NOT_SUPPORTED("");
-    assert(false && "CPU fallback not implemented for f16");
+namespace arrayfire {
+namespace oneapi {
+
+void initBlas() { /*gpu_blas_init();*/
+}
+
+void deInitBlas() { /*gpu_blas_deinit();*/
 }
 
 template<typename T>
 void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
           const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
-    ONEAPI_NOT_SUPPORTED("");
+    const auto lOpts = toBlasTranspose(optLhs);
+    const auto rOpts = toBlasTranspose(optRhs);
+
+    const auto aRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+    const auto aColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+    const auto bColDim = (optRhs == AF_MAT_NONE) ? 1 : 0;
+
+    const dim4 &lDims = lhs.dims();
+    const dim4 &rDims = rhs.dims();
+    const int M       = lDims[aRowDim];
+    const int N       = rDims[bColDim];
+    const int K       = lDims[aColDim];
+    const dim4 oDims  = out.dims();
+
+    const dim4 &lStrides = lhs.strides();
+    const dim4 &rStrides = rhs.strides();
+    const dim4 oStrides  = out.strides();
+
+    if (oDims.ndims() <= 2) {  // if non-batched
+        if (rhs.dims()[bColDim] == 1) {
+            dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+            gemvDispatch<T>(getQueue(), lOpts, lDims[0], lDims[1], alpha, lhs,
+                            lStrides[1], rhs, incr, beta, out, oStrides[0]);
+        } else {
+            gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
+                            lStrides[1], rhs, rStrides[1], beta, out,
+                            oStrides[1]);
+        }
+    } else {  // if batched
+        using Dt = arrayfire::oneapi::data_t<T>;
+
+        sycl::buffer<Dt, 1> lhsBuf = lhs.get()->template reinterpret<Dt, 1>();
+        sycl::buffer<Dt, 1> rhsBuf = rhs.get()->template reinterpret<Dt, 1>();
+        sycl::buffer<Dt, 1> outBuf = out.get()->template reinterpret<Dt, 1>();
+
+        const int64_t lda = lStrides[1];
+        const int64_t ldb = rStrides[1];
+        const int64_t ldc = oStrides[1];
+
+        int64_t batchSize = static_cast<int64_t>(oDims[2] * oDims[3]);
+
+        const bool not_l_batched =
+            (oDims[2] != lDims[2] && oDims[3] != lDims[3]);
+        const bool not_r_batched =
+            (oDims[2] != rDims[2] && oDims[3] != rDims[3]);
+
+        ::oneapi::mkl::blas::gemm_batch(
+            getQueue(), lOpts, rOpts, M, N, K, *alpha, lhsBuf, lda,
+            not_l_batched ? 0 : lStrides[2], rhsBuf, ldb,
+            not_r_batched ? 0 : rStrides[2], *beta, outBuf, ldc, oStrides[2],
+            batchSize);
+    }
+
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
-    ONEAPI_NOT_SUPPORTED("");
+    auto lhs_ = (optLhs == AF_MAT_NONE ? lhs : conj<T>(lhs));
+    auto rhs_ = (optRhs == AF_MAT_NONE ? rhs : conj<T>(rhs));
+    auto temp = arithOp<T, af_mul_t>(lhs_, rhs_, lhs_.dims());
+    return reduce<af_add_t, T, T>(temp, 0, false, 0);
 }
 
 #define INSTANTIATE_GEMM(TYPE)                                               \
@@ -71,7 +160,13 @@ INSTANTIATE_GEMM(float)
 INSTANTIATE_GEMM(cfloat)
 INSTANTIATE_GEMM(double)
 INSTANTIATE_GEMM(cdouble)
-INSTANTIATE_GEMM(half)
+// INSTANTIATE_GEMM(half)
+template<>
+void gemm(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const half *alpha, const Array<half> &lhs, const Array<half> &rhs,
+          const half *beta) {
+    ONEAPI_NOT_SUPPORTED("");
+}
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
diff --git a/src/backend/oneapi/blas.hpp b/src/backend/oneapi/blas.hpp
index 605b3f6d6c..194fc4e6fb 100644
--- a/src/backend/oneapi/blas.hpp
+++ b/src/backend/oneapi/blas.hpp
@@ -30,8 +30,8 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     int Ndim     = optRhs == AF_MAT_NONE ? 1 : 0;
     Array<T> res = createEmptyArray<T>(
         dim4(lhs.dims()[Mdim], rhs.dims()[Ndim], lhs.dims()[2], lhs.dims()[3]));
-    static const T alpha = T(1.0);
-    static const T beta  = T(0.0);
+    static constexpr T alpha = 1.0;
+    static constexpr T beta  = 0.0;
     gemm(res, optLhs, optRhs, &alpha, lhs, rhs, &beta);
     return res;
 }
@@ -39,5 +39,6 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs);
+
 }  // namespace oneapi
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index 640fcc797c..4731d7dd87 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -13,6 +13,7 @@
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/util.hpp>
+#include <sycl/sycl.hpp>
 //#include <debug_opencl.hpp> TODO: remove?
 #include <err_oneapi.hpp>
 //#include <kernel_headers/KParam.hpp>
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index bd4a5f2d43..57c299a3f2 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -14,7 +14,6 @@
 #include <kernel_headers/jit.hpp>
 
 #include <Array.hpp>
-#include <common/debug.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/jit/ModdimNode.hpp>
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index adabe3b29d..59990dea39 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -14,6 +14,7 @@
 #include <common/half.hpp>
 #include <common/traits.hpp>
 #include <debug_oneapi.hpp>
+#include <sycl/sycl.hpp>
 #include <traits.hpp>
 
 #include <algorithm>

From ded9b338aa49e9ef79a5590fa0f5dcb548a37fdd Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 6 Apr 2023 16:01:54 -0400
Subject: [PATCH 2452/2677] Add linear algebra support to oneAPI (#3389)

This commit adds Linear Algebra support to oneAPI
---
 src/backend/oneapi/CMakeLists.txt             |   8 +-
 src/backend/oneapi/Module.hpp                 |   5 +-
 src/backend/oneapi/cholesky.cpp               |  46 +++-
 src/backend/oneapi/compile_module.cpp         |   9 +-
 src/backend/oneapi/copy.hpp                   |   2 +-
 src/backend/oneapi/identity.cpp               |   3 +-
 src/backend/oneapi/iir.cpp                    |   2 +-
 src/backend/oneapi/kernel/identity.hpp        |  86 +++++++
 src/backend/oneapi/kernel/lu_split.hpp        | 143 ++++++++++++
 .../oneapi/kernel/random_engine_write.hpp     |   3 +-
 src/backend/oneapi/lu.cpp                     |  67 +++++-
 src/backend/oneapi/platform.cpp               |   1 -
 src/backend/oneapi/reduce_impl.hpp            |   2 +-
 src/backend/oneapi/sort_by_key.cpp            |   2 +-
 src/backend/oneapi/sparse_blas.cpp            |  88 +++----
 src/backend/oneapi/svd.cpp                    | 221 +++---------------
 16 files changed, 416 insertions(+), 272 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/identity.hpp
 create mode 100644 src/backend/oneapi/kernel/lu_split.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index c003f72152..60c5aa9379 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -216,10 +216,12 @@ target_sources(afoneapi
     kernel/bilateral.hpp
     kernel/diagonal.hpp
     kernel/diff.hpp
+    kernel/histogram.hpp
+    kernel/identity.hpp
     kernel/interp.hpp
     kernel/iota.hpp
     kernel/ireduce.hpp
-    kernel/histogram.hpp
+    kernel/lu_split.hpp
     kernel/memcopy.hpp
     kernel/mean.hpp
     kernel/random_engine.hpp
@@ -268,7 +270,8 @@ arrayfire_set_default_cxx_flags(afoneapi)
 
 target_include_directories(afoneapi
   SYSTEM PRIVATE
-    ${SYCL_INCLUDE_DIR})
+    ${SYCL_INCLUDE_DIR}
+)
 
 target_include_directories(afoneapi
   PUBLIC
@@ -289,6 +292,7 @@ target_compile_options(afoneapi
 target_compile_definitions(afoneapi
   PRIVATE
     AF_ONEAPI
+    WITH_LINEAR_ALGEBRA
     CL_TARGET_OPENCL_VERSION=300
     CL_HPP_TARGET_OPENCL_VERSION=300
     CL_HPP_MINIMUM_OPENCL_VERSION=110
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
index cb4c4e130c..6a5ce71985 100644
--- a/src/backend/oneapi/Module.hpp
+++ b/src/backend/oneapi/Module.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/ModuleInterface.hpp>
+#include <sycl/sycl.hpp>
 
 #include <sycl/kernel_bundle.hpp>
 
@@ -19,9 +20,9 @@ namespace oneapi {
 /// oneapi backend wrapper for cl::Program object
 class Module
     : public common::ModuleInterface<
-          sycl::kernel_bundle<sycl::bundle_state::executable>*> {
+          sycl::kernel_bundle<sycl::bundle_state::executable> *> {
    public:
-    using ModuleType = sycl::kernel_bundle<sycl::bundle_state::executable>*;
+    using ModuleType = sycl::kernel_bundle<sycl::bundle_state::executable> *;
     using BaseClass  = common::ModuleInterface<ModuleType>;
 
     /// \brief Create an uninitialized Module
diff --git a/src/backend/oneapi/cholesky.cpp b/src/backend/oneapi/cholesky.cpp
index 905a3208c5..4fb0e08c58 100644
--- a/src/backend/oneapi/cholesky.cpp
+++ b/src/backend/oneapi/cholesky.cpp
@@ -11,23 +11,55 @@
 #include <cholesky.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
+#include <platform.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-//#include <triangle.hpp>
+#include <triangle.hpp>
+#include "oneapi/mkl/lapack.hpp"
 
 namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper) {
-    ONEAPI_NOT_SUPPORTED("");
+    dim4 iDims    = in.dims();
+    dim4 iStrides = in.strides();
+    int64_t N     = iDims[0];
+    int64_t LDA   = iStrides[1];
+
+    int64_t lwork = 0;
+
+    ::oneapi::mkl::uplo uplo = ::oneapi::mkl::uplo::lower;
+    if (is_upper) { uplo = ::oneapi::mkl::uplo::upper; }
+
+    lwork = ::oneapi::mkl::lapack::potrf_scratchpad_size<T>(getQueue(), uplo, N,
+                                                            LDA);
+
+    Array<T> workspace = createEmptyArray<T>(af::dim4(lwork));
+    Array<int> d_info  = createEmptyArray<int>(af::dim4(1));
+
+    try {
+        ::oneapi::mkl::lapack::potrf(getQueue(), uplo, N, *in.get(), LDA,
+                                     *workspace.get(), lwork);
+    } catch (::oneapi::mkl::lapack::exception const &e) {
+        AF_ERROR(
+            "Unexpected exception caught during synchronous\
+                call to LAPACK API",
+            AF_ERR_RUNTIME);
+        return e.info();
+    }
+
     return 0;
 }
 
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
+    Array<T> out = copyArray<T>(in);
+    *info        = cholesky_inplace(out, is_upper);
+
+    triangle<T>(out, out, is_upper, false);
+
+    return out;
 }
 
 #define INSTANTIATE_CH(T)                                                 \
@@ -50,12 +82,14 @@ namespace oneapi {
 
 template<typename T>
 Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper) {
-    AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend",
+             AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 int cholesky_inplace(Array<T> &in, const bool is_upper) {
-    AF_ERROR("Linear Algebra is disabled on OpenCL", AF_ERR_NOT_CONFIGURED);
+    AF_ERROR("Linear Algebra is disabled on OneAPI backend",
+             AF_ERR_NOT_CONFIGURED);
 }
 
 #define INSTANTIATE_CH(T)                                                 \
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index 4731d7dd87..7fce4b70c0 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -13,10 +13,9 @@
 #include <common/Logger.hpp>
 #include <common/defines.hpp>
 #include <common/util.hpp>
-#include <sycl/sycl.hpp>
-//#include <debug_opencl.hpp> TODO: remove?
 #include <err_oneapi.hpp>
-//#include <kernel_headers/KParam.hpp>
+#include <sycl/sycl.hpp>
+// #include <kernel_headers/KParam.hpp>
 #include <platform.hpp>
 #include <traits.hpp>
 
@@ -73,7 +72,7 @@ namespace arrayfire {
 namespace oneapi {
 
 // const static string DEFAULT_MACROS_STR(
-//"\n\
+// "\n\
                                            //#ifdef USE_DOUBLE\n\
                                            //#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
                                            //#endif\n                     \
@@ -85,7 +84,7 @@ namespace oneapi {
                                            //#ifndef M_PI\n               \
                                            //#define
 // M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n
-//\
+// \
                                            //#endif\n                     \
                                            //");
 
diff --git a/src/backend/oneapi/copy.hpp b/src/backend/oneapi/copy.hpp
index 048c89260a..4b05151dbd 100644
--- a/src/backend/oneapi/copy.hpp
+++ b/src/backend/oneapi/copy.hpp
@@ -9,7 +9,7 @@
 #pragma once
 
 #include <Array.hpp>
-//#include <kernel/pad_array_borders.hpp>
+// #include <kernel/pad_array_borders.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/identity.cpp b/src/backend/oneapi/identity.cpp
index c7db8e7d44..5a838a4cf0 100644
--- a/src/backend/oneapi/identity.cpp
+++ b/src/backend/oneapi/identity.cpp
@@ -11,6 +11,7 @@
 #include <Array.hpp>
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/identity.hpp>
 #include <af/dim4.hpp>
 
 using arrayfire::common::half;
@@ -19,8 +20,8 @@ namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> identity(const dim4& dims) {
-    ONEAPI_NOT_SUPPORTED("");
     Array<T> out = createEmptyArray<T>(dims);
+    kernel::identity<T>(out);
     return out;
 }
 
diff --git a/src/backend/oneapi/iir.cpp b/src/backend/oneapi/iir.cpp
index e0223ca6f1..e38a70294f 100644
--- a/src/backend/oneapi/iir.cpp
+++ b/src/backend/oneapi/iir.cpp
@@ -12,7 +12,7 @@
 #include <convolve.hpp>
 #include <err_oneapi.hpp>
 #include <iir.hpp>
-//#include <kernel/iir.hpp>
+// #include <kernel/iir.hpp>
 #include <math.hpp>
 #include <af/dim4.hpp>
 
diff --git a/src/backend/oneapi/kernel/identity.hpp b/src/backend/oneapi/kernel/identity.hpp
new file mode 100644
index 0000000000..20553a2149
--- /dev/null
+++ b/src/backend/oneapi/kernel/identity.hpp
@@ -0,0 +1,86 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <math.hpp>
+#include <types.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T>
+class identityKernel {
+   public:
+    identityKernel(write_accessor<T> out, KParam oInfo, const int groups_x,
+                   const int groups_y)
+        : out_(out), oInfo_(oInfo), groups_x_(groups_x), groups_y_(groups_y) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        size_t idz = g.get_group_id(0) / groups_x_;
+        size_t idw = g.get_group_id(1) / groups_y_;
+
+        size_t groupId_x = g.get_group_id(0) - idz * groups_x_;
+        size_t groupId_y = g.get_group_id(1) - idw * groups_y_;
+
+        size_t idx = it.get_local_id(0) + groupId_x * g.get_local_range(0);
+        size_t idy = it.get_local_id(1) + groupId_y * g.get_local_range(1);
+
+        size_t xlim = oInfo_.dims[0];
+        size_t ylim = oInfo_.dims[1];
+        size_t zlim = oInfo_.dims[2];
+        size_t wlim = oInfo_.dims[3];
+        if (idx < xlim && idy < ylim && idz < zlim && idw < wlim) {
+            const T one  = scalar<T>(1);
+            const T zero = scalar<T>(0);
+
+            T *ptr = out_.get_pointer() + idz * oInfo_.strides[2] +
+                     idw * oInfo_.strides[3];
+            T val                              = (idx == idy) ? one : zero;
+            ptr[idx + idy * oInfo_.strides[1]] = val;
+        }
+    }
+
+   protected:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    int groups_x_;
+    int groups_y_;
+};
+
+template<typename T>
+void identity(Param<T> out) {
+    sycl::range<2> local{32, 8};
+
+    int groups_x = divup(out.info.dims[0], local[0]);
+    int groups_y = divup(out.info.dims[1], local[1]);
+    sycl::range<2> global{groups_x * out.info.dims[2] * local[0],
+                          groups_y * out.info.dims[3] * local[1]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<T> oData{*out.data, h};
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       identityKernel<T>(oData, out.info, groups_x, groups_y));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/lu_split.hpp b/src/backend/oneapi/kernel/lu_split.hpp
new file mode 100644
index 0000000000..f42cf8644c
--- /dev/null
+++ b/src/backend/oneapi/kernel/lu_split.hpp
@@ -0,0 +1,143 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+
+#include <math.hpp>
+#include <array>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T, bool same_dims>
+class luSplitKernel {
+   public:
+    luSplitKernel(write_accessor<T> lower, KParam lInfo,
+                  write_accessor<T> upper, KParam uInfo, read_accessor<T> in,
+                  KParam iInfo, const int groupsPerMatX,
+                  const int groupsPerMatY)
+        : lower_(lower)
+        , lInfo_(lInfo)
+        , upper_(upper)
+        , uInfo_(uInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , groupsPerMatX_(groupsPerMatX)
+        , groupsPerMatY_(groupsPerMatY) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int oz  = g.get_group_id(0) / groupsPerMatX_;
+        const int ow  = g.get_group_id(1) / groupsPerMatY_;
+
+        const int blockIdx_x = g.get_group_id(0) - oz * groupsPerMatX_;
+        const int blockIdx_y = g.get_group_id(1) - ow * groupsPerMatY_;
+
+        const int xx = it.get_local_id(0) + blockIdx_x * g.get_local_range(0);
+        const int yy = it.get_local_id(1) + blockIdx_y * g.get_local_range(1);
+
+        const int incy = groupsPerMatY_ * g.get_local_range(1);
+        const int incx = groupsPerMatX_ * g.get_local_range(0);
+
+        T *d_l = lower_.get_pointer();
+        T *d_u = upper_.get_pointer();
+        T *d_i = in_.get_pointer();
+
+        if (oz < iInfo_.dims[2] && ow < iInfo_.dims[3]) {
+            d_i = d_i + oz * iInfo_.strides[2] + ow * iInfo_.strides[3];
+            d_l = d_l + oz * lInfo_.strides[2] + ow * lInfo_.strides[3];
+            d_u = d_u + oz * uInfo_.strides[2] + ow * uInfo_.strides[3];
+
+            for (int oy = yy; oy < iInfo_.dims[1]; oy += incy) {
+                T *Yd_i = d_i + oy * iInfo_.strides[1];
+                T *Yd_l = d_l + oy * lInfo_.strides[1];
+                T *Yd_u = d_u + oy * uInfo_.strides[1];
+                for (int ox = xx; ox < iInfo_.dims[0]; ox += incx) {
+                    if (ox > oy) {
+                        if (same_dims || oy < lInfo_.dims[1])
+                            Yd_l[ox] = Yd_i[ox];
+                        if (!same_dims || ox < uInfo_.dims[0])
+                            Yd_u[ox] = scalar<T>(0);
+                    } else if (oy > ox) {
+                        if (same_dims || oy < lInfo_.dims[1])
+                            Yd_l[ox] = scalar<T>(0);
+                        if (!same_dims || ox < uInfo_.dims[0])
+                            Yd_u[ox] = Yd_i[ox];
+                    } else if (ox == oy) {
+                        if (same_dims || oy < lInfo_.dims[1])
+                            Yd_l[ox] = scalar<T>(1.0);
+                        if (!same_dims || ox < uInfo_.dims[0])
+                            Yd_u[ox] = Yd_i[ox];
+                    }
+                }
+            }
+        }
+    }
+
+   protected:
+    write_accessor<T> lower_;
+    KParam lInfo_;
+    write_accessor<T> upper_;
+    KParam uInfo_;
+    read_accessor<T> in_;
+    KParam iInfo_;
+    int groupsPerMatX_;
+    int groupsPerMatY_;
+};
+
+template<typename T>
+void lu_split(Param<T> lower, Param<T> upper, Param<T> in) {
+    constexpr unsigned TX    = 32;
+    constexpr unsigned TY    = 8;
+    constexpr unsigned TILEX = 128;
+    constexpr unsigned TILEY = 32;
+
+    const bool sameDims = lower.info.dims[0] == in.info.dims[0] &&
+                          lower.info.dims[1] == in.info.dims[1];
+
+    sycl::range<2> local(TX, TY);
+
+    int groupsPerMatX = divup(in.info.dims[0], TILEX);
+    int groupsPerMatY = divup(in.info.dims[1], TILEY);
+    sycl::range<2> global(groupsPerMatX * in.info.dims[2] * local[0],
+                          groupsPerMatY * in.info.dims[3] * local[1]);
+
+    getQueue().submit([&](sycl::handler &h) {
+        read_accessor<T> iData{*in.data, h};
+        write_accessor<T> lData{*lower.data, h};
+        write_accessor<T> uData{*upper.data, h};
+
+        if (sameDims) {
+            h.parallel_for(sycl::nd_range{global, local},
+                           luSplitKernel<T, true>(
+                               lData, lower.info, uData, upper.info, iData,
+                               in.info, groupsPerMatX, groupsPerMatY));
+        } else {
+            h.parallel_for(sycl::nd_range{global, local},
+                           luSplitKernel<T, false>(
+                               lData, lower.info, uData, upper.info, iData,
+                               in.info, groupsPerMatX, groupsPerMatY));
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index b3a4d60ed7..dcd20dec13 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -7,8 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
-
-#include <sycl/builtins.hpp>
+#include <sycl/sycl.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
index b1d0b4b746..200b85d23b 100644
--- a/src/backend/oneapi/lu.cpp
+++ b/src/backend/oneapi/lu.cpp
@@ -13,28 +13,77 @@
 #if defined(WITH_LINEAR_ALGEBRA)
 #include <blas.hpp>
 #include <copy.hpp>
+#include <kernel/lu_split.hpp>
 #include <platform.hpp>
+#include "oneapi/mkl/lapack.hpp"
 
 namespace arrayfire {
 namespace oneapi {
 
-Array<int> convertPivot(int *ipiv, int in_sz, int out_sz) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<int> out = createEmptyArray<in_t>(af::dim4(1));
-    return out;
+Array<int> convertPivot(sycl::buffer<int64_t> &pivot, int out_sz,
+                        bool convert_pivot) {
+    dim_t d0 = pivot.get_range()[0];
+
+    std::vector<int> d_po(out_sz);
+    for (int i = 0; i < out_sz; i++) { d_po[i] = i; }
+
+    auto d_pi = pivot.get_host_access();
+
+    if (convert_pivot) {
+        for (int j = 0; j < d0; j++) {
+            // 1 indexed in pivot
+            std::swap(d_po[j], d_po[d_pi[j] - 1]);
+        }
+
+        Array<int> res = createHostDataArray(dim4(out_sz), &d_po[0]);
+        return res;
+    } else {
+        d_po.resize(d0);
+        for (int j = 0; j < d0; j++) { d_po[j] = static_cast<int>(d_pi[j]); }
+    }
+    Array<int> res = createHostDataArray(dim4(d0), &d_po[0]);
+    return res;
 }
 
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
         const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
+    dim4 iDims = in.dims();
+    int M      = iDims[0];
+    int N      = iDims[1];
+    int MN     = std::min(M, N);
+
+    Array<T> in_copy = copyArray<T>(in);
+    pivot            = lu_inplace(in_copy);
+
+    // SPLIT into lower and upper
+    dim4 ldims(M, MN);
+    dim4 udims(MN, N);
+    lower = createEmptyArray<T>(ldims);
+    upper = createEmptyArray<T>(udims);
+    kernel::lu_split<T>(lower, upper, in_copy);
 }
 
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<int> out = createEmptyArray<in_t>(af::dim4(1));
-    return out;
+    dim4 iDims    = in.dims();
+    dim4 iStrides = in.strides();
+    int64_t M     = iDims[0];
+    int64_t N     = iDims[1];
+    int64_t MN    = std::min(M, N);
+    int64_t LDA   = iStrides[1];
+
+    std::int64_t scratchpad_size =
+        ::oneapi::mkl::lapack::getrf_scratchpad_size<T>(getQueue(), M, N, LDA);
+
+    sycl::buffer<int64_t, 1> ipiv{sycl::range<1>(MN)};
+    Array<T> scratch = createEmptyArray<T>(af::dim4(scratchpad_size));
+
+    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, *in.get(), LDA, ipiv,
+                                 *scratch.get(), scratchpad_size);
+
+    Array<int> pivot = convertPivot(ipiv, M, convert_pivot);
+    return pivot;
 }
 
 bool isLAPACKAvailable() { return true; }
@@ -61,14 +110,12 @@ namespace oneapi {
 template<typename T>
 void lu(Array<T> &lower, Array<T> &upper, Array<int> &pivot,
         const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
     AF_ERROR("Linear Algebra is disabled on OneAPI backend",
              AF_ERR_NOT_CONFIGURED);
 }
 
 template<typename T>
 Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
-    ONEAPI_NOT_SUPPORTED("");
     AF_ERROR("Linear Algebra is disabled on OneAPI backend",
              AF_ERR_NOT_CONFIGURED);
 }
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index 6d4c7df84c..e0959a9390 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -172,7 +172,6 @@ string getDeviceInfo() noexcept {
                 device->get_info<sycl::info::device::driver_version>();
             info << devVersion;
             info << " -- Device driver " << driVersion;
-
             info << " -- Unified Memory ("
                  << (isHostUnifiedMemory(*device) ? "True" : "False") << ")";
 #endif
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index 898f77d006..14b5a9e269 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <err_oneapi.hpp>
 #include <kernel/reduce.hpp>
-//#include <kernel/reduce_by_key.hpp>
+// #include <kernel/reduce_by_key.hpp>
 #include <reduce.hpp>
 #include <af/dim4.hpp>
 #include <complex>
diff --git a/src/backend/oneapi/sort_by_key.cpp b/src/backend/oneapi/sort_by_key.cpp
index f7b5beca91..00a5bb55fa 100644
--- a/src/backend/oneapi/sort_by_key.cpp
+++ b/src/backend/oneapi/sort_by_key.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
-//#include <kernel/sort_by_key.hpp>
+// #include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <reorder.hpp>
 #include <sort_by_key.hpp>
diff --git a/src/backend/oneapi/sparse_blas.cpp b/src/backend/oneapi/sparse_blas.cpp
index 6d414c8ee0..67d7cb8352 100644
--- a/src/backend/oneapi/sparse_blas.cpp
+++ b/src/backend/oneapi/sparse_blas.cpp
@@ -27,7 +27,7 @@
 #include <af/dim4.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <cpu/cpu_sparse_blas.hpp>
+// #include <cpu/cpu_sparse_blas.hpp>
 #endif  // WITH_LINEAR_ALGEBRA
 
 namespace arrayfire {
@@ -39,55 +39,55 @@ template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
     ONEAPI_NOT_SUPPORTED("sparse matmul Not supported");
-    //#if defined(WITH_LINEAR_ALGEBRA)
-    //    if (OpenCLCPUOffload(
-    //            false)) {  // Do not force offload gemm on OSX Intel devices
-    //        return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
-    //    }
-    //#endif
+    // #if defined(WITH_LINEAR_ALGEBRA)
+    //     if (OpenCLCPUOffload(
+    //             false)) {  // Do not force offload gemm on OSX Intel devices
+    //         return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
+    //     }
+    // #endif
     //
-    //    int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
-    //    // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
-    //    static const int rColDim =
-    //        1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
+    //     int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+    //     // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
+    //     static const int rColDim =
+    //         1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
     //
-    //    dim4 lDims = lhs.dims();
-    //    dim4 rDims = rhsIn.dims();
-    //    int M      = lDims[lRowDim];
-    //    int N      = rDims[rColDim];
-    //    // int K = lDims[lColDim];
+    //     dim4 lDims = lhs.dims();
+    //     dim4 rDims = rhsIn.dims();
+    //     int M      = lDims[lRowDim];
+    //     int N      = rDims[rColDim];
+    //     // int K = lDims[lColDim];
     //
-    //    const Array<T> rhs =
-    //        (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) :
-    //        rhsIn;
-    //    Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    //     const Array<T> rhs =
+    //         (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) :
+    //         rhsIn;
+    //     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
     //
-    //    static const T alpha = scalar<T>(1.0);
-    //    static const T beta  = scalar<T>(0.0);
+    //     static const T alpha = scalar<T>(1.0);
+    //     static const T beta  = scalar<T>(0.0);
     //
-    //    const Array<T>& values   = lhs.getValues();
-    //    const Array<int>& rowIdx = lhs.getRowIdx();
-    //    const Array<int>& colIdx = lhs.getColIdx();
+    //     const Array<T>& values   = lhs.getValues();
+    //     const Array<int>& rowIdx = lhs.getRowIdx();
+    //     const Array<int>& colIdx = lhs.getColIdx();
     //
-    //    if (optLhs == AF_MAT_NONE) {
-    //        if (N == 1) {
-    //            kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
-    //        } else {
-    //            kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha,
-    //            beta);
-    //        }
-    //    } else {
-    //        // CSR transpose is a CSC matrix
-    //        if (N == 1) {
-    //            kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
-    //                          optLhs == AF_MAT_CTRANS);
-    //        } else {
-    //            kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha,
-    //            beta,
-    //                             optLhs == AF_MAT_CTRANS);
-    //        }
-    //    }
-    //    return out;
+    //     if (optLhs == AF_MAT_NONE) {
+    //         if (N == 1) {
+    //             kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
+    //         } else {
+    //             kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha,
+    //             beta);
+    //         }
+    //     } else {
+    //         // CSR transpose is a CSC matrix
+    //         if (N == 1) {
+    //             kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
+    //                           optLhs == AF_MAT_CTRANS);
+    //         } else {
+    //             kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha,
+    //             beta,
+    //                              optLhs == AF_MAT_CTRANS);
+    //         }
+    //     }
+    //     return out;
 }
 
 #define INSTANTIATE_SPARSE(T)                                            \
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
index fad4c2f35b..2c9b751d15 100644
--- a/src/backend/oneapi/svd.cpp
+++ b/src/backend/oneapi/svd.cpp
@@ -9,219 +9,50 @@
 
 #include <Array.hpp>
 #include <blas.hpp>
+#include <common/err_common.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>  // error check functions and Macros
 #include <math.hpp>
+#include <memory.hpp>
+#include <platform.hpp>
 #include <reduce.hpp>
 #include <svd.hpp>  // oneapi backend function header
 #include <transpose.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-
-#include <cpu/cpu_svd.hpp>
-#include <magma/magma.h>
-#include <magma/magma_cpu_lapack.h>
-#include <magma/magma_helper.h>
-#include <platform.hpp>
+#include "oneapi/mkl/lapack.hpp"
 
 namespace arrayfire {
 namespace oneapi {
 
-template<typename Tr>
-Tr calc_scale(Tr From, Tr To) {
-    // FIXME: I am not sure this is correct, removing this for now
-#if 0
-    //http://www.netlib.org/lapack/explore-3.1.1-html/dlascl.f.html
-    cpu_lapack_lamch_func<Tr> cpu_lapack_lamch;
-
-    Tr S = cpu_lapack_lamch('S');
-    Tr B = 1.0 / S;
-
-    Tr FromCopy = From, ToCopy = To;
-
-    Tr Mul = 1;
-
-    while (true) {
-        Tr From1 = FromCopy * S, To1 = ToCopy / B;
-        if (std::abs(From1) > std::abs(ToCopy) && ToCopy != 0) {
-            Mul *= S;
-            FromCopy = From1;
-        } else if (std::abs(To1) > std::abs(FromCopy)) {
-            Mul *= B;
-            ToCopy = To1;
-        } else {
-            Mul *= (ToCopy) / (FromCopy);
-            break;
-        }
-    }
-
-    return Mul;
-#else
-    return To / From;
-#endif
-}
-
-template<typename T, typename Tr>
-void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
-         bool want_vectors = true) {
-    ONEAPI_NOT_SUPPORTED("");
-    dim4 idims    = arrA.dims();
-    dim4 istrides = arrA.strides();
-
-    const int m      = static_cast<int>(idims[0]);
-    const int n      = static_cast<int>(idims[1]);
-    const int ldda   = static_cast<int>(istrides[1]);
-    const int lda    = m;
-    const int min_mn = std::min(m, n);
-    const int ldu    = m;
-    const int ldvt   = n;
-
-    const int nb    = magma_get_gebrd_nb<T>(n);
-    const int lwork = (m + n) * nb;
-
-    cpu_lapack_lacpy_func<T> cpu_lapack_lacpy;
-    cpu_lapack_bdsqr_work_func<T> cpu_lapack_bdsqr_work;
-    cpu_lapack_ungbr_work_func<T> cpu_lapack_ungbr_work;
-    cpu_lapack_lamch_func<Tr> cpu_lapack_lamch;
-
-    // Get machine constants
-    static const double eps    = cpu_lapack_lamch('P');
-    static const double smlnum = std::sqrt(cpu_lapack_lamch('S')) / eps;
-    static const double bignum = 1. / smlnum;
-
-    Tr anrm = abs(getScalar<T>(reduce_all<af_max_t, T, T>(arrA)));
-
-    T scale                = scalar<T>(1);
-    static const int ione  = 1;
-    static const int izero = 0;
-
-    bool iscl = false;
-    if (anrm > 0. && anrm < smlnum) {
-        iscl  = true;
-        scale = scalar<T>(calc_scale<Tr>(anrm, smlnum));
-    } else if (anrm > bignum) {
-        iscl  = true;
-        scale = scalar<T>(calc_scale<Tr>(anrm, bignum));
-    }
-
-    if (iscl == 1) { multiply_inplace(arrA, abs(scale)); }
-
-    int nru  = 0;
-    int ncvt = 0;
-
-    // Instead of copying U, S, VT, and A to the host and copying the results
-    // back to the device, create a pointer that's mapped to device memory where
-    // the computation can directly happen
-    T *mappedA = static_cast<T *>(getQueue().enqueueMapBuffer(
-        *arrA.get(), CL_FALSE, CL_MAP_READ, sizeof(T) * arrA.getOffset(),
-        sizeof(T) * arrA.elements()));
-    std::vector<T> tauq(min_mn), taup(min_mn);
-    std::vector<T> work(lwork);
-    Tr *mappedS0 = (Tr *)getQueue().enqueueMapBuffer(
-        *arrS.get(), CL_TRUE, CL_MAP_WRITE, sizeof(Tr) * arrS.getOffset(),
-        sizeof(Tr) * arrS.elements());
-    std::vector<Tr> s1(min_mn - 1);
-    std::vector<Tr> rwork(5 * min_mn);
-
-    int info = 0;
-
-    // Bidiagonalize A
-    // (CWorkspace: need 2*N + M, prefer 2*N + (M + N)*NB)
-    // (RWorkspace: need N)
-    magma_gebrd_hybrid<T>(m, n, mappedA, lda, (*arrA.get())(), arrA.getOffset(),
-                          ldda, (void *)mappedS0, static_cast<void *>(&s1[0]),
-                          &tauq[0], &taup[0], &work[0], lwork, getQueue()(),
-                          &info, false);
-
-    T *mappedU = nullptr, *mappedVT = nullptr;
-    std::vector<T> cdummy(1);
-
-    if (want_vectors) {
-        mappedU  = static_cast<T *>(getQueue().enqueueMapBuffer(
-             *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
-             sizeof(T) * arrU.elements()));
-        mappedVT = static_cast<T *>(getQueue().enqueueMapBuffer(
-            *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
-            sizeof(T) * arrVT.elements()));
-
-        // If left singular vectors desired in U, copy result to U
-        // and generate left bidiagonalizing vectors in U
-        // (CWorkspace: need 2*N + NCU, prefer 2*N + NCU*NB)
-        // (RWorkspace: 0)
-        LAPACKE_CHECK(cpu_lapack_lacpy('L', m, n, mappedA, lda, mappedU, ldu));
-
-        int ncu = m;
-        LAPACKE_CHECK(cpu_lapack_ungbr_work('Q', m, ncu, n, mappedU, ldu,
-                                            &tauq[0], &work[0], lwork));
-
-        // If right singular vectors desired in VT, copy result to
-        // VT and generate right bidiagonalizing vectors in VT
-        // (CWorkspace: need 3*N-1, prefer 2*N + (N-1)*NB)
-        // (RWorkspace: 0)
-        LAPACKE_CHECK(
-            cpu_lapack_lacpy('U', n, n, mappedA, lda, mappedVT, ldvt));
-        LAPACKE_CHECK(cpu_lapack_ungbr_work('P', n, n, n, mappedVT, ldvt,
-                                            &taup[0], &work[0], lwork));
-
-        nru  = m;
-        ncvt = n;
-    }
-    getQueue().enqueueUnmapMemObject(*arrA.get(), mappedA);
-
-    // Perform bidiagonal QR iteration, if desired, computing
-    // left singular vectors in U and computing right singular
-    // vectors in VT
-    // (CWorkspace: need 0)
-    // (RWorkspace: need BDSPAC)
-    LAPACKE_CHECK(cpu_lapack_bdsqr_work('U', n, ncvt, nru, izero, mappedS0,
-                                        &s1[0], mappedVT, ldvt, mappedU, ldu,
-                                        &cdummy[0], ione, &rwork[0]));
-
-    if (want_vectors) {
-        getQueue().enqueueUnmapMemObject(*arrU.get(), mappedU);
-        getQueue().enqueueUnmapMemObject(*arrVT.get(), mappedVT);
-    }
-
-    getQueue().enqueueUnmapMemObject(*arrS.get(), mappedS0);
-
-    if (iscl == 1) {
-        Tr rscale = scalar<Tr>(1);
-        if (anrm > bignum) {
-            rscale = calc_scale<Tr>(bignum, anrm);
-        } else if (anrm < smlnum) {
-            rscale = calc_scale<Tr>(smlnum, anrm);
-        }
-        multiply_inplace(arrS, rscale);
-    }
-}
-
 template<typename T, typename Tr>
 void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
-    // if (OpenCLCPUOffload()) { return cpu::svdInPlace(s, u, vt, in); }
-
-    // svd<T, Tr>(u, s, vt, in, true);
+    dim4 iDims = in.dims();
+    int64_t M  = iDims[0];
+    int64_t N  = iDims[1];
+
+    dim4 iStrides = in.strides();
+    dim4 uStrides = u.strides();
+    dim4 vStrides = vt.strides();
+    int64_t LDA   = iStrides[1];
+    int64_t LDU   = uStrides[1];
+    int64_t LDVt  = vStrides[1];
+
+    int64_t scratch_size = ::oneapi::mkl::lapack::gesvd_scratchpad_size<T>(
+        getQueue(), ::oneapi::mkl::jobsvd::vectors,
+        ::oneapi::mkl::jobsvd::vectors, M, N, LDA, LDU, LDVt);
+    Array<T> scratchpad = createEmptyArray<T>(af::dim4(scratch_size));
+
+    ::oneapi::mkl::lapack::gesvd(
+        getQueue(), ::oneapi::mkl::jobsvd::vectors,
+        ::oneapi::mkl::jobsvd::vectors, M, N, *in.get(), LDA, *s.get(),
+        *u.get(), LDU, *vt.get(), LDVt, *scratchpad.get(), scratch_size);
 }
 
 template<typename T, typename Tr>
 void svd(Array<Tr> &s, Array<T> &u, Array<T> &vt, const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
-
-    // if (OpenCLCPUOffload()) { return cpu::svd(s, u, vt, in); }
-
-    // dim4 iDims = in.dims();
-    // int M      = iDims[0];
-    // int N      = iDims[1];
-
-    // if (M >= N) {
-    //     Array<T> in_copy = copyArray(in);
-    //     svdInPlace(s, u, vt, in_copy);
-    // } else {
-    //     Array<T> in_trans = transpose(in, true);
-    //     svdInPlace(s, vt, u, in_trans);
-    //     transpose_inplace(u, true);
-    //     transpose_inplace(vt, true);
-    // }
+    Array<T> in_copy = copyArray<T>(in);
+    svdInPlace(s, u, vt, in_copy);
 }
 
 #define INSTANTIATE(T, Tr)                                               \

From 1ab90475d1cae36224c7c6fa3552279225ce0a52 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 4 Apr 2023 16:26:19 -0400
Subject: [PATCH 2453/2677] Updates to require oneAPI 2023.1

---
 CMakeLists.txt                                       |  2 +-
 src/backend/common/half.hpp                          |  2 +-
 src/backend/oneapi/Array.hpp                         |  2 +-
 src/backend/oneapi/Event.hpp                         |  4 +---
 src/backend/oneapi/Module.hpp                        |  3 +--
 src/backend/oneapi/Param.hpp                         | 10 +---------
 src/backend/oneapi/blas.cpp                          |  3 ++-
 src/backend/oneapi/compile_module.cpp                |  4 ++--
 src/backend/oneapi/device_manager.cpp                |  7 +------
 src/backend/oneapi/device_manager.hpp                |  4 +---
 src/backend/oneapi/jit.cpp                           |  2 +-
 src/backend/oneapi/jit/kernel_generators.hpp         |  2 +-
 src/backend/oneapi/kernel/approx1.hpp                |  5 +++--
 src/backend/oneapi/kernel/approx2.hpp                |  5 +++--
 src/backend/oneapi/kernel/assign.hpp                 |  2 ++
 src/backend/oneapi/kernel/bilateral.hpp              |  2 +-
 src/backend/oneapi/kernel/convolve.hpp               |  5 ++++-
 src/backend/oneapi/kernel/convolve1.hpp              | 10 ++++++++++
 src/backend/oneapi/kernel/diagonal.hpp               |  2 ++
 src/backend/oneapi/kernel/diff.hpp                   |  2 ++
 src/backend/oneapi/kernel/gradient.hpp               |  2 ++
 src/backend/oneapi/kernel/histogram.hpp              |  2 +-
 src/backend/oneapi/kernel/interp.hpp                 |  2 +-
 src/backend/oneapi/kernel/iota.hpp                   |  2 ++
 src/backend/oneapi/kernel/ireduce.hpp                |  2 +-
 src/backend/oneapi/kernel/lookup.hpp                 |  2 ++
 src/backend/oneapi/kernel/mean.hpp                   |  3 +--
 src/backend/oneapi/kernel/meanshift.hpp              |  2 ++
 src/backend/oneapi/kernel/memcopy.hpp                |  2 ++
 src/backend/oneapi/kernel/random_engine_mersenne.hpp |  2 ++
 src/backend/oneapi/kernel/range.hpp                  |  2 ++
 src/backend/oneapi/kernel/reduce_all.hpp             |  5 +----
 src/backend/oneapi/kernel/reduce_first.hpp           |  2 ++
 src/backend/oneapi/kernel/reorder.hpp                |  2 ++
 src/backend/oneapi/kernel/resize.hpp                 |  2 +-
 src/backend/oneapi/kernel/rotate.hpp                 |  2 ++
 src/backend/oneapi/kernel/scan_dim.hpp               |  3 +--
 src/backend/oneapi/kernel/scan_first.hpp             |  3 +--
 src/backend/oneapi/kernel/select.hpp                 |  2 ++
 src/backend/oneapi/kernel/tile.hpp                   |  2 ++
 src/backend/oneapi/kernel/transform.hpp              |  4 ++--
 src/backend/oneapi/kernel/transpose.hpp              |  2 ++
 src/backend/oneapi/kernel/transpose_inplace.hpp      |  2 ++
 src/backend/oneapi/kernel/triangle.hpp               |  2 ++
 src/backend/oneapi/kernel/unwrap.hpp                 |  2 ++
 src/backend/oneapi/kernel/where.hpp                  |  2 ++
 src/backend/oneapi/kernel/wrap.hpp                   |  2 ++
 src/backend/oneapi/memory.cpp                        |  3 +--
 src/backend/oneapi/memory.hpp                        |  2 +-
 src/backend/oneapi/platform.cpp                      |  2 +-
 src/backend/oneapi/platform.hpp                      |  4 +---
 src/backend/oneapi/types.hpp                         |  2 +-
 52 files changed, 92 insertions(+), 61 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index eed62e23a6..a4c3eef645 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -134,7 +134,7 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
   if(DEFINED ENV{MKLROOT} AND NOT DEFINED MKL_ROOT)
     set(MKL_ROOT "$ENV{MKLROOT}")
   endif()
-  find_package(MKL)
+  find_package(MKL 2023.1)
 endif()
 
 af_multiple_option(NAME        AF_COMPUTE_LIBRARY
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 65a3930b15..515c301079 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -33,7 +33,7 @@
 #endif
 
 #ifdef AF_ONEAPI
-#include <sycl/half_type.hpp>
+#include <sycl/sycl.hpp>
 #endif
 
 #include <backend.hpp>
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index d3173d7fb8..d3f81bff2c 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -17,7 +17,7 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
-#include <sycl/buffer.hpp>
+#include <sycl/sycl.hpp>
 
 #include <nonstd/span.hpp>
 #include <algorithm>
diff --git a/src/backend/oneapi/Event.hpp b/src/backend/oneapi/Event.hpp
index 90aaf1b2ca..ae7fdd8c29 100644
--- a/src/backend/oneapi/Event.hpp
+++ b/src/backend/oneapi/Event.hpp
@@ -9,11 +9,9 @@
 #pragma once
 
 #include <common/EventBase.hpp>
-
 #include <af/event.h>
 
-#include <sycl/event.hpp>
-#include <sycl/queue.hpp>
+#include <sycl/sycl.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/Module.hpp b/src/backend/oneapi/Module.hpp
index 6a5ce71985..dc2afe676d 100644
--- a/src/backend/oneapi/Module.hpp
+++ b/src/backend/oneapi/Module.hpp
@@ -10,9 +10,8 @@
 #pragma once
 
 #include <common/ModuleInterface.hpp>
-#include <sycl/sycl.hpp>
 
-#include <sycl/kernel_bundle.hpp>
+#include <sycl/sycl.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index f6ca0ef8b1..4a95dff6ec 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -11,17 +11,9 @@
 
 #include <kernel/KParam.hpp>
 #include <types.hpp>
-
 #include <af/dim4.hpp>
 
-/// The get_pointer function in the accessor class throws a few warnings in the
-/// 2023.0 release of the library. Review this warning in the future
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wsycl-strict"
-#include <sycl/accessor.hpp>
-#pragma clang diagnostic pop
-#include <sycl/buffer.hpp>
-#include <sycl/handler.hpp>
+#include <sycl/sycl.hpp>
 
 #include <optional>
 
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 964dcb6cde..73dbadfcfd 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -24,7 +24,8 @@
 #include <types.hpp>
 
 #include <sycl/sycl.hpp>
-#include "oneapi/mkl/blas.hpp"
+
+#include <oneapi/mkl/blas.hpp>
 
 #include <complex>
 #include <vector>
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index 7fce4b70c0..2737909208 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -14,11 +14,11 @@
 #include <common/defines.hpp>
 #include <common/util.hpp>
 #include <err_oneapi.hpp>
-#include <sycl/sycl.hpp>
-// #include <kernel_headers/KParam.hpp>
 #include <platform.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <algorithm>
 #include <cctype>
 #include <cstdio>
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index 05d6cb454d..ac06d5768c 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -23,12 +23,7 @@
 #include <af/oneapi.h>
 #include <af/version.h>
 
-#include <sycl/context.hpp>
-#include <sycl/device.hpp>
-#include <sycl/exception.hpp>
-#include <sycl/exception_list.hpp>
-#include <sycl/platform.hpp>
-#include <sycl/queue.hpp>
+#include <sycl/sycl.hpp>
 
 #include <algorithm>
 #include <iterator>
diff --git a/src/backend/oneapi/device_manager.hpp b/src/backend/oneapi/device_manager.hpp
index 198ddd07e0..28be51631b 100644
--- a/src/backend/oneapi/device_manager.hpp
+++ b/src/backend/oneapi/device_manager.hpp
@@ -9,9 +9,7 @@
 
 #pragma once
 
-#include <sycl/context.hpp>
-#include <sycl/device.hpp>
-#include <sycl/queue.hpp>
+#include <sycl/sycl.hpp>
 
 #include <memory>
 #include <mutex>
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 57c299a3f2..562a0ed1a2 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -29,7 +29,7 @@
 #include <af/dim4.hpp>
 
 #include <sycl/backend.hpp>
-#include <sycl/interop_handle.hpp>
+#include <sycl/sycl.hpp>
 
 #include <array>
 #include <cstdio>
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index 3a15f78e8e..a69553acd3 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -11,7 +11,7 @@
 #include <Param.hpp>
 #include <err_oneapi.hpp>
 
-#include <sycl/buffer.hpp>
+#include <sycl/sycl.hpp>
 
 #include <functional>
 #include <memory>
diff --git a/src/backend/oneapi/kernel/approx1.hpp b/src/backend/oneapi/kernel/approx1.hpp
index 4d9d039f1b..3f0e2cfbe5 100644
--- a/src/backend/oneapi/kernel/approx1.hpp
+++ b/src/backend/oneapi/kernel/approx1.hpp
@@ -14,9 +14,10 @@
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
 #include <kernel/interp.hpp>
-#include <af/constants.h>
-// #include <kernel/default_config.hpp>
 #include <traits.hpp>
+#include <af/constants.h>
+
+#include <sycl/sycl.hpp>
 
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/approx2.hpp b/src/backend/oneapi/kernel/approx2.hpp
index 5b7e509f9b..8713d87d20 100644
--- a/src/backend/oneapi/kernel/approx2.hpp
+++ b/src/backend/oneapi/kernel/approx2.hpp
@@ -14,9 +14,10 @@
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
 #include <kernel/interp.hpp>
-#include <af/constants.h>
-// #include <kernel/default_config.hpp>
 #include <traits.hpp>
+#include <af/constants.h>
+
+#include <sycl/sycl.hpp>
 
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 0876b9e16c..1ab8c42732 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -14,6 +14,8 @@
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
index 2a5cf59fb1..0fb213999a 100644
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -15,7 +15,7 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
-#include <sycl/builtins.hpp>
+#include <sycl/sycl.hpp>
 
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/convolve.hpp b/src/backend/oneapi/kernel/convolve.hpp
index 9f868ce729..ba1bda6b7c 100644
--- a/src/backend/oneapi/kernel/convolve.hpp
+++ b/src/backend/oneapi/kernel/convolve.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -9,11 +9,14 @@
 
 #pragma once
 #include <Param.hpp>
+#include <accessor.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
 #include <af/defines.h>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/convolve1.hpp b/src/backend/oneapi/kernel/convolve1.hpp
index 1d3df7ef3b..ca20b7a89e 100644
--- a/src/backend/oneapi/kernel/convolve1.hpp
+++ b/src/backend/oneapi/kernel/convolve1.hpp
@@ -1,3 +1,13 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
 template<typename T, typename aT>
 class conv1HelperCreateKernel {
    public:
diff --git a/src/backend/oneapi/kernel/diagonal.hpp b/src/backend/oneapi/kernel/diagonal.hpp
index c49d9871e3..8da78dba70 100644
--- a/src/backend/oneapi/kernel/diagonal.hpp
+++ b/src/backend/oneapi/kernel/diagonal.hpp
@@ -15,6 +15,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/diff.hpp b/src/backend/oneapi/kernel/diff.hpp
index f5a73c8c40..478da588c0 100644
--- a/src/backend/oneapi/kernel/diff.hpp
+++ b/src/backend/oneapi/kernel/diff.hpp
@@ -15,6 +15,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/gradient.hpp b/src/backend/oneapi/kernel/gradient.hpp
index fbaae20b51..7f29b4cec3 100644
--- a/src/backend/oneapi/kernel/gradient.hpp
+++ b/src/backend/oneapi/kernel/gradient.hpp
@@ -15,6 +15,8 @@
 #include <debug_oneapi.hpp>
 #include <kernel/default_config.hpp>
 
+#include <sycl/sycl.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
index 3d53930bd4..606bbebc35 100644
--- a/src/backend/oneapi/kernel/histogram.hpp
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -15,7 +15,7 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
-#include <sycl/atomic_ref.hpp>
+#include <sycl/sycl.hpp>
 
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
index d6bb62b177..f1e74d6c87 100644
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -12,7 +12,7 @@
 #include <types.hpp>
 #include <af/constants.h>
 
-#include <sycl/builtins.hpp>
+#include <sycl/sycl.hpp>
 
 #include <algorithm>
 
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index e326ff9416..8f102ed87f 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -16,6 +16,8 @@
 #include <traits.hpp>
 #include <af/dim4.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/ireduce.hpp b/src/backend/oneapi/kernel/ireduce.hpp
index 9e4e35c51d..e047826b08 100644
--- a/src/backend/oneapi/kernel/ireduce.hpp
+++ b/src/backend/oneapi/kernel/ireduce.hpp
@@ -19,7 +19,7 @@
 #include <memory.hpp>
 #include <minmax_op.hpp>
 
-#include <sycl/sycl.hpp>  //TODO: exact headers
+#include <sycl/sycl.hpp>
 
 #include <algorithm>
 #include <climits>
diff --git a/src/backend/oneapi/kernel/lookup.hpp b/src/backend/oneapi/kernel/lookup.hpp
index 8baf14ad21..a5d29fea09 100644
--- a/src/backend/oneapi/kernel/lookup.hpp
+++ b/src/backend/oneapi/kernel/lookup.hpp
@@ -14,6 +14,8 @@
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 4353bfff26..1d58458e46 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -20,8 +20,7 @@
 #include <math.hpp>
 #include <memory.hpp>
 
-#include <sycl/builtins.hpp>
-#include <sycl/group_algorithm.hpp>
+#include <sycl/sycl.hpp>
 
 #include <memory>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/meanshift.hpp b/src/backend/oneapi/kernel/meanshift.hpp
index 8dfb96a3b7..2211d81b73 100644
--- a/src/backend/oneapi/kernel/meanshift.hpp
+++ b/src/backend/oneapi/kernel/meanshift.hpp
@@ -14,6 +14,8 @@
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <algorithm>
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 59990dea39..87b46a4c22 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -17,6 +17,8 @@
 #include <sycl/sycl.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <algorithm>
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
index bbf5dae3e0..f78bc8d732 100644
--- a/src/backend/oneapi/kernel/random_engine_mersenne.hpp
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -44,6 +44,8 @@
 #pragma once
 #include <kernel/random_engine_write.hpp>
 
+#include <sycl/sycl.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index 9cfea27964..1c8512be0b 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -18,6 +18,8 @@
 #include <traits.hpp>
 #include <af/dim4.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 2089b60175..0878f33329 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -19,10 +19,7 @@
 #include <math.hpp>
 #include <memory.hpp>
 
-#include <sycl/atomic_fence.hpp>
-#include <sycl/atomic_ref.hpp>
-#include <sycl/builtins.hpp>
-#include <sycl/group_algorithm.hpp>
+#include <sycl/sycl.hpp>
 
 #include <algorithm>
 #include <climits>
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index 299919ae12..42ffb9199d 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -19,6 +19,8 @@
 #include <math.hpp>
 #include <memory.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <algorithm>
 #include <climits>
 #include <complex>
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
index b643bb6fc8..f3ee445fe7 100644
--- a/src/backend/oneapi/kernel/reorder.hpp
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -14,6 +14,8 @@
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/resize.hpp b/src/backend/oneapi/kernel/resize.hpp
index 5443815b75..b14ceafe14 100644
--- a/src/backend/oneapi/kernel/resize.hpp
+++ b/src/backend/oneapi/kernel/resize.hpp
@@ -15,7 +15,7 @@
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
 
-#include <sycl/builtins.hpp>
+#include <sycl/sycl.hpp>
 
 #include <string>
 #include <vector>
diff --git a/src/backend/oneapi/kernel/rotate.hpp b/src/backend/oneapi/kernel/rotate.hpp
index b8c8357e79..84641a3f76 100644
--- a/src/backend/oneapi/kernel/rotate.hpp
+++ b/src/backend/oneapi/kernel/rotate.hpp
@@ -16,6 +16,8 @@
 #include <math.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index b4a2678dac..a9ce3d7838 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -17,8 +17,7 @@
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
-#include <sycl/builtins.hpp>
-#include <sycl/group_algorithm.hpp>
+#include <sycl/sycl.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 777f8f205e..8660494657 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -17,8 +17,7 @@
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
-#include <sycl/builtins.hpp>
-#include <sycl/group_algorithm.hpp>
+#include <sycl/sycl.hpp>
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/kernel/select.hpp b/src/backend/oneapi/kernel/select.hpp
index 7f63f2cbea..abba384f80 100644
--- a/src/backend/oneapi/kernel/select.hpp
+++ b/src/backend/oneapi/kernel/select.hpp
@@ -14,6 +14,8 @@
 #include <common/kernel_cache.hpp>
 #include <math.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/tile.hpp b/src/backend/oneapi/kernel/tile.hpp
index 24112442a9..2c44594a34 100644
--- a/src/backend/oneapi/kernel/tile.hpp
+++ b/src/backend/oneapi/kernel/tile.hpp
@@ -14,6 +14,8 @@
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/transform.hpp b/src/backend/oneapi/kernel/transform.hpp
index c18ac6c827..6760e1a489 100644
--- a/src/backend/oneapi/kernel/transform.hpp
+++ b/src/backend/oneapi/kernel/transform.hpp
@@ -12,13 +12,13 @@
 #include <Param.hpp>
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
-// #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
-// #include <kernel/config.hpp>
 #include <kernel/interp.hpp>
 #include <math.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index 43b741ca32..eeb9387145 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -15,6 +15,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
index 3dda946ced..23f04c6559 100644
--- a/src/backend/oneapi/kernel/transpose_inplace.hpp
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -16,6 +16,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/triangle.hpp b/src/backend/oneapi/kernel/triangle.hpp
index 2f65abe20c..f4705035b3 100644
--- a/src/backend/oneapi/kernel/triangle.hpp
+++ b/src/backend/oneapi/kernel/triangle.hpp
@@ -15,6 +15,8 @@
 #include <err_oneapi.hpp>
 #include <traits.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/kernel/unwrap.hpp b/src/backend/oneapi/kernel/unwrap.hpp
index a6fa8ee64e..0c88bd4348 100644
--- a/src/backend/oneapi/kernel/unwrap.hpp
+++ b/src/backend/oneapi/kernel/unwrap.hpp
@@ -15,6 +15,8 @@
 #include <debug_oneapi.hpp>
 #include <kernel/default_config.hpp>
 
+#include <sycl/sycl.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index 3d8fe3324f..64b25ec211 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -16,6 +16,8 @@
 #include <kernel/scan_first.hpp>
 #include <memory.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <math.hpp>
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
index e574b4a127..ba503a1f56 100644
--- a/src/backend/oneapi/kernel/wrap.hpp
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -16,6 +16,8 @@
 #include <kernel/default_config.hpp>
 #include <math.hpp>
 
+#include <sycl/sycl.hpp>
+
 #include <string>
 #include <vector>
 
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index aa620e8e2c..971fa05b64 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -18,8 +18,7 @@
 #include <types.hpp>
 #include <af/dim4.hpp>
 
-#include <sycl/builtins.hpp>
-#include <sycl/usm.hpp>
+#include <sycl/sycl.hpp>
 
 #include <utility>
 
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index 462c1498f1..dea5e62f5a 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -10,7 +10,7 @@
 
 #include <common/AllocatorInterface.hpp>
 
-#include <sycl/buffer.hpp>
+#include <sycl/sycl.hpp>
 
 #include <cstdlib>
 #include <functional>
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index e0959a9390..edd62e0d6a 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -28,7 +28,7 @@
 #include <OpenGL/CGLCurrent.h>
 #endif
 
-#include <sycl/platform.hpp>
+#include <sycl/sycl.hpp>
 
 #include <cctype>
 #include <cstdlib>
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index de6ae498dc..86439a685c 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -11,9 +11,7 @@
 
 #include <af/oneapi.h>
 
-#include <sycl/context.hpp>
-#include <sycl/device.hpp>
-#include <sycl/queue.hpp>
+#include <sycl/sycl.hpp>
 
 #include <memory>
 #include <string>
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
index f4be516f3d..4537f27987 100644
--- a/src/backend/oneapi/types.hpp
+++ b/src/backend/oneapi/types.hpp
@@ -14,7 +14,7 @@
 #include <af/compilers.h>
 #include <af/traits.hpp>
 
-#include <sycl/aliases.hpp>
+#include <sycl/sycl.hpp>
 
 #include <algorithm>
 #include <array>

From e9fe5d3e2904e0a8e4202ef199c318c45c545669 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 5 Apr 2023 10:02:10 -0400
Subject: [PATCH 2454/2677] Use accessor directly in the AParam object.

---
 src/backend/oneapi/Param.hpp | 27 ++++++++++-----------------
 src/backend/oneapi/jit.cpp   |  6 +++---
 2 files changed, 13 insertions(+), 20 deletions(-)

diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 4a95dff6ec..447e8fb117 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -45,11 +45,9 @@ struct Param {
 
 template<typename T>
 struct AParam {
-    std::optional<sycl::accessor<T, 1>> data;
-    std::optional<sycl::accessor<T, 1, sycl::access::mode::read_write,
-                                 sycl::access::target::device,
-                                 sycl::access::placeholder::true_t>>
-        ph;
+    sycl::accessor<T, 1, sycl::access_mode::read_write, sycl::target::device,
+                   sycl::access::placeholder::true_t>
+        data;
     af::dim4 dims;
     af::dim4 strides;
     dim_t offset;
@@ -58,35 +56,30 @@ struct AParam {
     AParam(AParam&& other)                 = default;
 
     // AF_DEPRECATED("Use Array<T>")
-    AParam() : data(), ph(), dims{0, 0, 0, 0}, strides{0, 0, 0, 0}, offset(0) {}
+    AParam() : data(), dims{0, 0, 0, 0}, strides{0, 0, 0, 0}, offset(0) {}
 
     AParam(sycl::buffer<T, 1>& data_, const dim_t dims_[4],
            const dim_t strides_[4], dim_t offset_)
-        : data()
-        , ph(std::make_optional<
-              sycl::accessor<T, 1, sycl::access::mode::read_write,
-                             sycl::access::target::device,
-                             sycl::access::placeholder::true_t>>(data_))
+        : data(data_.get_access())
         , dims(4, dims_)
         , strides(4, strides_)
         , offset(offset_) {}
     // AF_DEPRECATED("Use Array<T>")
     AParam(sycl::handler& h, sycl::buffer<T, 1>& data_, const dim_t dims_[4],
            const dim_t strides_[4], dim_t offset_)
-        : data{{data_, h}}
-        , ph(data_)
+        : data(data_.get_access())
         , dims(4, dims_)
         , strides(4, strides_)
-        , offset(offset_) {}
+        , offset(offset_) {
+        require(h);
+    }
 
     template<sycl::access::mode MODE>
     sycl::accessor<data_t<T>, 1, MODE> get_accessor(sycl::handler& h) const {
         return *data;
     }
 
-    void require(sycl::handler& h) {
-        if (!data) { h.require(ph.value()); }
-    }
+    void require(sycl::handler& h) { h.require(data); }
 
     operator KParam() const {
         return KParam{{dims[0], dims[1], dims[2], dims[3]},
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 562a0ed1a2..2190dd8070 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -465,7 +465,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                                 const_cast<void*>(ptr));
                                         vector<cl_mem> mem =
                                             hh.get_native_mem<backend::opencl>(
-                                                info->ph.value());
+                                                info->data);
                                         if (is_linear) {
                                             CL_CHECK(clSetKernelArg(
                                                 kernels[0], id++,
@@ -497,8 +497,8 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                         // Set output parameters
                         vector<cl_mem> mem;
                         for (const auto& output : ap) {
-                            mem = hh.get_native_mem<backend::opencl>(
-                                output.data.value());
+                            mem =
+                                hh.get_native_mem<backend::opencl>(output.data);
                             cl_mem mmm = mem[0];
                             CL_CHECK(clSetKernelArg(kernels[0], nargs++,
                                                     sizeof(cl_mem), &mmm));

From 82cf75f1de7b3728b2c32c32096f22e5199a507f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Apr 2023 10:26:17 -0400
Subject: [PATCH 2455/2677] Workaround for the long long compiler bug for iota

---
 src/backend/oneapi/kernel/iota.hpp | 39 ++++++++++++++++--------------
 1 file changed, 21 insertions(+), 18 deletions(-)

diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 8f102ed87f..1ec05f31b0 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -51,24 +51,27 @@ class iotaKernel {
         const int xx = it.get_local_id(0) + blockIdx_x * gg.get_local_range(0);
         const int yy = it.get_local_id(1) + blockIdx_y * gg.get_local_range(1);
 
-        if (xx >= oinfo_.dims[0] || yy >= oinfo_.dims[1] ||
-            oz >= oinfo_.dims[2] || ow >= oinfo_.dims[3])
-            return;
-
-        const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
-
-        T val = static_cast<T>((ow % s3_) * s2_ * s1_ * s0_);
-        val += static_cast<T>((oz % s2_) * s1_ * s0_);
-
-        const int incy = blocksPerMatY_ * gg.get_local_range(1);
-        const int incx = blocksPerMatX_ * gg.get_local_range(0);
-
-        for (int oy = yy; oy < oinfo_.dims[1]; oy += incy) {
-            T valY   = val + (oy % s1_) * s0_;
-            int oyzw = ozw + oy * oinfo_.strides[1];
-            for (int ox = xx; ox < oinfo_.dims[0]; ox += incx) {
-                int oidx   = oyzw + ox;
-                out_[oidx] = valY + (ox % s0_);
+        size_t odims0 = oinfo_.dims[0];
+        size_t odims1 = oinfo_.dims[1];
+        size_t odims2 = oinfo_.dims[2];
+        size_t odims3 = oinfo_.dims[3];
+
+        if (xx < odims0 && yy < odims1 && oz < odims2 && ow < odims3) {
+            const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
+
+            T val = static_cast<T>((ow % s3_) * s2_ * s1_ * s0_);
+            val += static_cast<T>((oz % s2_) * s1_ * s0_);
+
+            const int incy = blocksPerMatY_ * gg.get_local_range(1);
+            const int incx = blocksPerMatX_ * gg.get_local_range(0);
+
+            for (int oy = yy; oy < odims1; oy += incy) {
+                T valY   = val + (oy % s1_) * s0_;
+                int oyzw = ozw + oy * oinfo_.strides[1];
+                for (int ox = xx; ox < odims0; ox += incx) {
+                    int oidx   = oyzw + ox;
+                    out_[oidx] = valY + (ox % s0_);
+                }
             }
         }
     }

From 4d139af32e2e2a4bff602a283d37d39301ab9845 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Apr 2023 10:26:46 -0400
Subject: [PATCH 2456/2677] Workaround for the long long compiler bug in
 memcopy

---
 src/backend/oneapi/kernel/memcopy.hpp | 30 ++++++++++++++++++++-------
 1 file changed, 22 insertions(+), 8 deletions(-)

diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 87b46a4c22..c3b317ef17 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -69,14 +69,19 @@ class memCopy {
                 id1 * istrides_.dim[1];
 
         int istride0 = istrides_.dim[0];
-        if (id0 < idims_.dim[0] && id1 < idims_.dim[1] && id2 < idims_.dim[2] &&
-            id3 < idims_.dim[3]) {
+        size_t idd0  = idims_.dim[0];
+        size_t idd1  = idims_.dim[1];
+        size_t idd2  = idims_.dim[2];
+        size_t idd3  = idims_.dim[3];
+
+        if (id0 < idd0 && id1 < idd1 && id2 < idd2 && id3 < idd3) {
             optr[id0] = iptr[id0 * istride0];
         }
     }
 
    protected:
-    sycl::accessor<T> out_, in_;
+    sycl::accessor<T> out_;
+    sycl::accessor<T> in_;
     dims_t ostrides_, idims_, istrides_;
     int offset_, groups_0_, groups_1_;
 };
@@ -228,13 +233,22 @@ class reshapeCopy {
         uint istride0 = iInfo_.strides[0];
         uint ostride0 = oInfo_.strides[0];
 
-        if (gy < oInfo_.dims[1] && gz < oInfo_.dims[2] && gw < oInfo_.dims[3]) {
+        size_t odims0 = oInfo_.dims[0];
+        size_t odims1 = oInfo_.dims[1];
+        size_t odims2 = oInfo_.dims[2];
+        size_t odims3 = oInfo_.dims[3];
+
+        size_t tdims0 = trgt_.dim[0];
+        size_t tdims1 = trgt_.dim[1];
+        size_t tdims2 = trgt_.dim[2];
+        size_t tdims3 = trgt_.dim[3];
+
+        if (gy < odims1 && gz < odims2 && gw < odims3) {
             int loop_offset = gg.get_local_range(0) * blk_x_;
-            bool cond =
-                gy < trgt_.dim[1] && gz < trgt_.dim[2] && gw < trgt_.dim[3];
-            for (int rep = gx; rep < oInfo_.dims[0]; rep += loop_offset) {
+            bool cond       = gy < tdims1 && gz < tdims2 && gw < tdims3;
+            for (int rep = gx; rep < odims0; rep += loop_offset) {
                 outType temp = default_value_;
-                if (SAMEDIMS || (rep < trgt_.dim[0] && cond)) {
+                if (SAMEDIMS || (rep < tdims0 && cond)) {
                     temp = convertType<inType, outType>(
                         scale<inType>(in[rep * istride0], factor_));
                 }

From 7ed1972285b4519d93141d0392504e29eed68aec Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 6 Apr 2023 10:27:37 -0400
Subject: [PATCH 2457/2677] Update common/debug.hpp to handle up to 6 variables

---
 src/backend/common/debug.hpp | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/backend/common/debug.hpp b/src/backend/common/debug.hpp
index e91c903d53..54e74a2953 100644
--- a/src/backend/common/debug.hpp
+++ b/src/backend/common/debug.hpp
@@ -43,15 +43,18 @@ void print(const char *F, const first &FF, ARGS... args) {
 #define SHOW5(val1, val2, val3, val4, val5)                              \
     debugging::print(#val1, val1, #val2, val2, #val3, val3, #val4, val4, \
                      #val5, val5)
+#define SHOW6(val1, val2, val3, val4, val5, val6)                        \
+    debugging::print(#val1, val1, #val2, val2, #val3, val3, #val4, val4, \
+                     #val5, val5, #val6, val6)
 
-#define GET_MACRO(_1, _2, _3, _4, _5, NAME, ...) NAME
+#define GET_MACRO(_1, _2, _3, _4, _5, _6, NAME, ...) NAME
 
-#define SHOW(...)                                                 \
-    do {                                                          \
-        fmt::print(std::cout, "{}:({}): ", __FILE__, __LINE__);   \
-        GET_MACRO(__VA_ARGS__, SHOW5, SHOW4, SHOW3, SHOW2, SHOW1) \
-        (__VA_ARGS__);                                            \
-        fmt::print(std::cout, "\n");                              \
+#define SHOW(...)                                                        \
+    do {                                                                 \
+        fmt::print(std::cout, "{}:({}): ", __FILE__, __LINE__);          \
+        GET_MACRO(__VA_ARGS__, SHOW6, SHOW5, SHOW4, SHOW3, SHOW2, SHOW1) \
+        (__VA_ARGS__);                                                   \
+        fmt::print(std::cout, "\n");                                     \
     } while (0)
 
 #define PRINTVEC(val)                                                        \

From 3e95f2bcd118597a42508fa232e6aef83c0988c8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Apr 2023 14:31:23 -0400
Subject: [PATCH 2458/2677] Add half support for iota in oneAPI

---
 src/backend/oneapi/iota.cpp        | 10 +---------
 src/backend/oneapi/kernel/iota.hpp | 10 ++++++----
 2 files changed, 7 insertions(+), 13 deletions(-)

diff --git a/src/backend/oneapi/iota.cpp b/src/backend/oneapi/iota.cpp
index 84bf693f1b..6d511df23f 100644
--- a/src/backend/oneapi/iota.cpp
+++ b/src/backend/oneapi/iota.cpp
@@ -29,15 +29,6 @@ Array<T> iota(const dim4 &dims, const dim4 &tile_dims) {
     return out;
 }
 
-template<>
-Array<half> iota(const dim4 &dims, const dim4 &tile_dims) {
-    ONEAPI_NOT_SUPPORTED("");
-    // dim4 outdims = dims * tile_dims;
-
-    // Array<half> out = createEmptyArray<half>(outdims);
-    // return out;
-}
-
 #define INSTANTIATE(T) \
     template Array<T> iota<T>(const af::dim4 &dims, const af::dim4 &tile_dims);
 
@@ -50,5 +41,6 @@ INSTANTIATE(uintl)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(half)
 }  // namespace oneapi
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 1ec05f31b0..87dbfc923c 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -14,6 +14,7 @@
 #include <common/half.hpp>
 #include <debug_oneapi.hpp>
 #include <traits.hpp>
+#include <types.hpp>
 #include <af/dim4.hpp>
 
 #include <sycl/sycl.hpp>
@@ -59,15 +60,16 @@ class iotaKernel {
         if (xx < odims0 && yy < odims1 && oz < odims2 && ow < odims3) {
             const int ozw = ow * oinfo_.strides[3] + oz * oinfo_.strides[2];
 
-            T val = static_cast<T>((ow % s3_) * s2_ * s1_ * s0_);
-            val += static_cast<T>((oz % s2_) * s1_ * s0_);
+            compute_t<T> val =
+                static_cast<compute_t<T>>((ow % s3_) * s2_ * s1_ * s0_);
+            val += static_cast<compute_t<T>>((oz % s2_) * s1_ * s0_);
 
             const int incy = blocksPerMatY_ * gg.get_local_range(1);
             const int incx = blocksPerMatX_ * gg.get_local_range(0);
 
             for (int oy = yy; oy < odims1; oy += incy) {
-                T valY   = val + (oy % s1_) * s0_;
-                int oyzw = ozw + oy * oinfo_.strides[1];
+                compute_t<T> valY = val + (oy % s1_) * s0_;
+                int oyzw          = ozw + oy * oinfo_.strides[1];
                 for (int ox = xx; ox < odims0; ox += incx) {
                     int oidx   = oyzw + ox;
                     out_[oidx] = valY + (ox % s0_);

From 31d5a368f00e14621125577fdaf0a384f43dd020 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Apr 2023 14:31:58 -0400
Subject: [PATCH 2459/2677] Fix CMake warning in FindAF_MKL

---
 CMakeModules/FindAF_MKL.cmake | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeModules/FindAF_MKL.cmake b/CMakeModules/FindAF_MKL.cmake
index 7c9baefecb..662f0046da 100644
--- a/CMakeModules/FindAF_MKL.cmake
+++ b/CMakeModules/FindAF_MKL.cmake
@@ -73,7 +73,6 @@
 
 include(CheckTypeSize)
 include(FindPackageHandleStandardArgs)
-find_package(OpenMP QUIET)
 
 check_type_size("int" INT_SIZE
   BUILTIN_TYPES_ONLY LANGUAGE C)

From ef1d3a51e99d155567f91c5d49bf77c065f9f71e Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 10 Apr 2023 19:09:45 -0400
Subject: [PATCH 2460/2677] Add sort and sort_by_key support for oneAPI (#3390)

Add sort and sort_by_key support in the oneAPI backend.

---------

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/backend/common/half.hpp                   |   3 +
 src/backend/oneapi/CMakeLists.txt             |   5 +
 src/backend/oneapi/kernel/bilateral.hpp       |  21 +-
 src/backend/oneapi/kernel/convolve.hpp        |   3 -
 src/backend/oneapi/kernel/convolve1.hpp       |   6 +-
 src/backend/oneapi/kernel/convolve2.hpp       |   6 +-
 src/backend/oneapi/kernel/convolve3.hpp       |   6 +-
 src/backend/oneapi/kernel/histogram.hpp       |   7 +-
 src/backend/oneapi/kernel/interp.hpp          |   6 +-
 src/backend/oneapi/kernel/reorder.hpp         |   6 +-
 src/backend/oneapi/kernel/sort.hpp            | 119 ++++++++++
 src/backend/oneapi/kernel/sort_by_key.hpp     |  29 +++
 .../oneapi/kernel/sort_by_key/CMakeLists.txt  |  53 +++++
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  20 ++
 .../oneapi/kernel/sort_by_key_impl.hpp        | 206 ++++++++++++++++++
 src/backend/oneapi/kernel/wrap.hpp            |   5 +-
 src/backend/oneapi/kernel/wrap_dilated.hpp    |   5 +-
 src/backend/oneapi/sort.cpp                   |  20 +-
 src/backend/oneapi/sort_by_key.cpp            |  32 ++-
 src/backend/oneapi/sort_index.cpp             |  24 +-
 src/backend/oneapi/topk.cpp                   | 128 +----------
 21 files changed, 531 insertions(+), 179 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/sort.hpp
 create mode 100644 src/backend/oneapi/kernel/sort_by_key.hpp
 create mode 100644 src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
 create mode 100644 src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp
 create mode 100644 src/backend/oneapi/kernel/sort_by_key_impl.hpp

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 515c301079..67bd47829f 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -919,10 +919,12 @@ AF_CONSTEXPR __DH__ static inline bool operator==(
     arrayfire::common::half lhs, arrayfire::common::half rhs) noexcept;
 AF_CONSTEXPR __DH__ static inline bool operator!=(
     arrayfire::common::half lhs, arrayfire::common::half rhs) noexcept;
+
 __DH__ static inline bool operator<(arrayfire::common::half lhs,
                                     arrayfire::common::half rhs) noexcept;
 __DH__ static inline bool operator<(arrayfire::common::half lhs,
                                     float rhs) noexcept;
+
 AF_CONSTEXPR __DH__ static inline bool isinf(half val) noexcept;
 
 /// Classification implementation.
@@ -1052,6 +1054,7 @@ class alignas(2) half {
                                  arrayfire::common::half rhs) noexcept;
     friend __DH__ bool operator<(arrayfire::common::half lhs,
                                  float rhs) noexcept;
+
     friend AF_CONSTEXPR __DH__ bool isinf(half val) noexcept;
     friend AF_CONSTEXPR __DH__ inline bool isnan(half val) noexcept;
 
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 60c5aa9379..7a58966711 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -237,6 +237,8 @@ target_sources(afoneapi
     kernel/reorder.hpp
     kernel/scan_first.hpp
     kernel/scan_dim.hpp
+    kernel/sort.hpp
+    kernel/sort_by_key.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
@@ -268,6 +270,8 @@ add_library(ArrayFire::afoneapi ALIAS afoneapi)
 
 arrayfire_set_default_cxx_flags(afoneapi)
 
+include("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/CMakeLists.txt")
+
 target_include_directories(afoneapi
   SYSTEM PRIVATE
     ${SYCL_INCLUDE_DIR}
@@ -305,6 +309,7 @@ target_link_libraries(afoneapi
     -fvisibility-inlines-hidden
     c_api_interface
     cpp_api_interface
+    oneapi_sort_by_key
     afcommon_interface
     OpenCL::OpenCL
     OpenCL::cl2hpp
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
index 0fb213999a..8c340ccb81 100644
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -57,14 +57,14 @@ class bilateralKernel {
         , nBBS0_(nBBS0)
         , nBBS1_(nBBS1) {}
     void operator()(sycl::nd_item<2> it) const {
-        sycl::group g                   = it.get_group();
-        const int radius                = fmax((int)(sigma_space_ * 1.5f), 1);
-        const int padding               = 2 * radius;
-        const int window_size           = padding + 1;
-        const int shrdLen               = g.get_local_range(0) + padding;
-        const float variance_range      = sigma_color_ * sigma_color_;
-        const float variance_space      = sigma_space_ * sigma_space_;
-        const float variance_space_neg2 = -2.0 * variance_space;
+        sycl::group g              = it.get_group();
+        const int radius           = sycl::max((int)(sigma_space_ * 1.5f), 1);
+        const int padding          = 2 * radius;
+        const int window_size      = padding + 1;
+        const int shrdLen          = g.get_local_range(0) + padding;
+        const float variance_range = sigma_color_ * sigma_color_;
+        const float variance_space = sigma_space_ * sigma_space_;
+        const float variance_space_neg2     = -2.0 * variance_space;
         const float inv_variance_range_neg2 = -0.5 / (variance_range);
 
         // gfor batch offsets
@@ -143,6 +143,11 @@ class bilateralKernel {
         return (y * stride1 + x * stride0);
     }
 
+    template<class T>
+    constexpr const T& clamp0(const T& v, const T& lo, const T& hi) const {
+        return (v < lo) ? lo : (hi < v) ? hi : v;
+    }
+
     void load2LocalMem(local_accessor<outType, 1> shrd, const inType* in,
                        int lx, int ly, int shrdStride, int dim0, int dim1,
                        int gx, int gy, int inStride1, int inStride0) const {
diff --git a/src/backend/oneapi/kernel/convolve.hpp b/src/backend/oneapi/kernel/convolve.hpp
index ba1bda6b7c..276c84c3af 100644
--- a/src/backend/oneapi/kernel/convolve.hpp
+++ b/src/backend/oneapi/kernel/convolve.hpp
@@ -109,9 +109,6 @@ void memcpyBuffer(sycl::buffer<T, 1> &dest, sycl::buffer<T, 1> &src,
     });
 }
 
-template<typename T>
-using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
-                                      sycl::access::target::local>;
 template<typename T>
 using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
 template<typename T>
diff --git a/src/backend/oneapi/kernel/convolve1.hpp b/src/backend/oneapi/kernel/convolve1.hpp
index ca20b7a89e..e156308b34 100644
--- a/src/backend/oneapi/kernel/convolve1.hpp
+++ b/src/backend/oneapi/kernel/convolve1.hpp
@@ -13,7 +13,7 @@ class conv1HelperCreateKernel {
    public:
     conv1HelperCreateKernel(write_accessor<T> out, KParam oInfo,
                             read_accessor<T> signal, KParam sInfo,
-                            local_accessor<aT> localMem,
+                            sycl::local_accessor<aT> localMem,
                             read_accessor<aT> impulse, KParam fInfo, int nBBS0,
                             int nBBS1, int ostep1, int ostep2, int ostep3,
                             int sstep1, int sstep2, int sstep3,
@@ -97,7 +97,7 @@ class conv1HelperCreateKernel {
     KParam oInfo_;
     read_accessor<T> signal_;
     KParam sInfo_;
-    local_accessor<aT> localMem_;
+    sycl::local_accessor<aT> localMem_;
     read_accessor<aT> impulse_;
     KParam fInfo_;
     int nBBS0_;
@@ -117,7 +117,7 @@ void conv1Helper(const conv_kparam_t<aT> &param, Param<T> &out,
                  const int rank, const bool expand) {
     auto Q = getQueue();
     Q.submit([&](auto &h) {
-        local_accessor<aT> localMem(param.loc_size, h);
+        sycl::local_accessor<aT> localMem(param.loc_size, h);
         write_accessor<T> outAcc{*out.data, h};
         read_accessor<T> signalAcc{*signal.data, h};
         read_accessor<aT> impulseAcc{*param.impulse, h};
diff --git a/src/backend/oneapi/kernel/convolve2.hpp b/src/backend/oneapi/kernel/convolve2.hpp
index 5de34a2023..fc5db9c06a 100644
--- a/src/backend/oneapi/kernel/convolve2.hpp
+++ b/src/backend/oneapi/kernel/convolve2.hpp
@@ -5,7 +5,7 @@ class conv2HelperCreateKernel {
                             read_accessor<T> signal, KParam sInfo,
                             read_accessor<aT> impulse, KParam fInfo, int nBBS0,
                             int nBBS1, int ostep2, int ostep3, int sstep2,
-                            int sstep3, local_accessor<aT> localMem,
+                            int sstep3, sycl::local_accessor<aT> localMem,
                             const int f0, const int f1, const bool expand)
         : out_(out)
         , oInfo_(oInfo)
@@ -111,7 +111,7 @@ class conv2HelperCreateKernel {
     int ostep3_;
     int sstep2_;
     int sstep3_;
-    local_accessor<aT> localMem_;
+    sycl::local_accessor<aT> localMem_;
     const int f0_;
     const int f1_;
     const bool expand_;
@@ -128,7 +128,7 @@ void conv2Helper(const conv_kparam_t<aT> &param, Param<T> out,
 
     auto Q = getQueue();
     Q.submit([&](auto &h) {
-        local_accessor<aT> localMem(LOC_SIZE, h);
+        sycl::local_accessor<aT> localMem(LOC_SIZE, h);
         write_accessor<T> outAcc{*out.data, h};
         read_accessor<T> signalAcc{*signal.data, h};
         read_accessor<aT> impulseAcc{*param.impulse, h};
diff --git a/src/backend/oneapi/kernel/convolve3.hpp b/src/backend/oneapi/kernel/convolve3.hpp
index 0e2dee72fe..30861a2a63 100644
--- a/src/backend/oneapi/kernel/convolve3.hpp
+++ b/src/backend/oneapi/kernel/convolve3.hpp
@@ -7,7 +7,7 @@ class conv3HelperCreateKernel {
    public:
     conv3HelperCreateKernel(write_accessor<T> out, KParam oInfo,
                             read_accessor<T> signal, KParam sInfo,
-                            local_accessor<aT> localMem,
+                            sycl::local_accessor<aT> localMem,
                             read_accessor<aT> impulse, KParam fInfo, int nBBS0,
                             int nBBS1, int ostep1, int ostep2, int ostep3,
                             int sstep1, int sstep2, int sstep3,
@@ -117,7 +117,7 @@ class conv3HelperCreateKernel {
     KParam oInfo_;
     read_accessor<T> signal_;
     KParam sInfo_;
-    local_accessor<aT> localMem_;
+    sycl::local_accessor<aT> localMem_;
     read_accessor<aT> impulse_;
     KParam fInfo_;
     int nBBS0_;
@@ -137,7 +137,7 @@ void conv3Helper(const conv_kparam_t<aT> &param, Param<T> &out,
                  const int rank, const bool EXPAND) {
     auto Q = getQueue();
     Q.submit([&](auto &h) {
-        local_accessor<aT> localMem(param.loc_size, h);
+        sycl::local_accessor<aT> localMem(param.loc_size, h);
         write_accessor<T> outAcc{*out.data, h};
         read_accessor<T> signalAcc{*signal.data, h};
         read_accessor<aT> impulseAcc{*param.impulse, h};
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
index 606bbebc35..35f21fc9b6 100644
--- a/src/backend/oneapi/kernel/histogram.hpp
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -71,7 +71,8 @@ class histogramKernel {
         int start     = (g.get_group_id(0) - b2 * nBBS_) * THRD_LOAD *
                         g.get_local_range(0) +
                     it.get_local_id(0);
-        int end = fmin((int)(start + THRD_LOAD * g.get_local_range(0)), len_);
+        int end =
+            sycl::min((int)(start + THRD_LOAD * g.get_local_range(0)), len_);
 
         // offset input and output to account for batch ops
         const T *in = d_src_.get_pointer() + b2 * iInfo_.strides[2] +
@@ -96,8 +97,8 @@ class histogramKernel {
             const int idx = isLinear_ ? row : i0 + i1 * iInfo_.strides[1];
 
             int bin = (int)(((float)in[idx] - minval_) / dx);
-            bin     = fmax(bin, 0);
-            bin     = fmin(bin, (int)nbins_ - 1);
+            bin     = sycl::max(bin, 0);
+            bin     = sycl::min(bin, (int)nbins_ - 1);
 
             if (use_global) {
                 global_atomic_ref<uint>(d_dst_[outOffset + bin])++;
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
index f1e74d6c87..516acea466 100644
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -115,7 +115,7 @@ struct Interp1<Ty, Tp, 1> {
 
         int xid = (method == AF_INTERP_LOWER ? sycl::floor(x) : sycl::round(x));
         bool cond = xid >= 0 && xid < x_lim;
-        if (clamp) xid = std::max((int)0, std::min(xid, x_lim));
+        if (clamp) xid = sycl::max((int)0, sycl::min(xid, x_lim));
 
         const int idx = ioff + xid * x_stride;
 
@@ -218,8 +218,8 @@ struct Interp2<Ty, Tp, 1> {
         const int y_stride = iInfo.strides[ydim];
 
         if (clamp) {
-            xid = std::max(0, std::min(xid, (int)iInfo.dims[xdim]));
-            yid = std::max(0, std::min(yid, (int)iInfo.dims[ydim]));
+            xid = sycl::max(0, sycl::min(xid, (int)iInfo.dims[xdim]));
+            yid = sycl::max(0, sycl::min(yid, (int)iInfo.dims[ydim]));
         }
 
         const int idx = ioff + yid * y_stride + xid * x_stride;
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
index f3ee445fe7..1064047f77 100644
--- a/src/backend/oneapi/kernel/reorder.hpp
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -63,9 +63,9 @@ class reorderCreateKernel {
         const int incy = blocksPerMatY_ * g.get_local_range(1);
         const int incx = blocksPerMatX_ * g.get_local_range(0);
 
-        const int o_off   = ow * op_.strides[3] + oz * op_.strides[2];
-        const int rdims[] = {d0_, d1_, d2_, d3_};
-        int ids[4]        = {0};
+        const int o_off    = ow * op_.strides[3] + oz * op_.strides[2];
+        const int rdims[4] = {d0_, d1_, d2_, d3_};
+        int ids[4]         = {0};
 
         ids[rdims[3]] = ow;
         ids[rdims[2]] = oz;
diff --git a/src/backend/oneapi/kernel/sort.hpp b/src/backend/oneapi/kernel/sort.hpp
new file mode 100644
index 0000000000..1789887b82
--- /dev/null
+++ b/src/backend/oneapi/kernel/sort.hpp
@@ -0,0 +1,119 @@
+/*******************************************************
+ * Copyright (c) 2022, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+// oneDPL headers should be included before standard headers
+#define ONEDPL_USE_PREDEFINED_POLICIES 0
+#include <oneapi/dpl/algorithm>
+#include <oneapi/dpl/execution>
+#include <oneapi/dpl/iterator>
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+#include <debug_oneapi.hpp>
+#include <iota.hpp>
+#include <traits.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+void sort0Iterative(Param<T> val, bool isAscending) {
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+    for (int w = 0; w < val.info.dims[3]; w++) {
+        int valW = w * val.info.strides[3];
+        for (int z = 0; z < val.info.dims[2]; z++) {
+            int valWZ = valW + z * val.info.strides[2];
+            for (int y = 0; y < val.info.dims[1]; y++) {
+                int valOffset = valWZ + y * val.info.strides[1];
+
+                auto buf_begin = ::oneapi::dpl::begin(*val.data) + valOffset;
+                auto buf_end   = buf_begin + val.info.dims[0];
+                if (isAscending) {
+                    std::sort(dpl_policy, buf_begin, buf_end,
+                              [](auto lhs, auto rhs) { return lhs < rhs; });
+                    // std::less<T>()); // mangled name errors in icx for now
+                } else {
+                    std::sort(dpl_policy, buf_begin, buf_end,
+                              [](auto lhs, auto rhs) { return lhs > rhs; });
+                    // std::greater<T>()); // mangled name errors in icx for now
+                }
+            }
+        }
+    }
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void sortBatched(Param<T> pVal, int dim, bool isAscending) {
+    af::dim4 inDims;
+    for (int i = 0; i < 4; i++) inDims[i] = pVal.info.dims[i];
+
+    // Sort dimension
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
+
+    // Create/call iota
+    Array<uint> pKey = iota<uint>(seqDims, tileDims);
+
+    pKey.setDataDims(inDims.elements());
+
+    // Flat
+    pVal.info.dims[0]    = inDims.elements();
+    pVal.info.strides[0] = 1;
+    for (int i = 1; i < 4; i++) {
+        pVal.info.dims[i]    = 1;
+        pVal.info.strides[i] = pVal.info.strides[i - 1] * pVal.info.dims[i - 1];
+    }
+
+    // Sort indices
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+
+    auto key_begin    = ::oneapi::dpl::begin(*pKey.get());
+    auto key_end      = ::oneapi::dpl::end(*pKey.get());
+    auto val_begin    = ::oneapi::dpl::begin(*pVal.data);
+    auto val_end      = ::oneapi::dpl::end(*pVal.data);
+    auto zipped_begin = dpl::make_zip_iterator(key_begin, val_begin);
+    auto zipped_end   = dpl::make_zip_iterator(key_end, val_end);
+
+    // sort values first
+    if (isAscending) {
+        std::sort(dpl_policy, zipped_begin, zipped_end, [](auto lhs, auto rhs) {
+            return std::get<1>(lhs) < std::get<1>(rhs);
+        });
+    } else {
+        std::sort(dpl_policy, zipped_begin, zipped_end, [](auto lhs, auto rhs) {
+            return std::get<1>(lhs) > std::get<1>(rhs);
+        });
+    }
+    // sort according to keys second
+    std::sort(dpl_policy, zipped_begin, zipped_end, [](auto lhs, auto rhs) {
+        return std::get<0>(lhs) < std::get<0>(rhs);
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+void sort0(Param<T> val, bool isAscending) {
+    int higherDims = val.info.dims[1] * val.info.dims[2] * val.info.dims[3];
+    // TODO Make a better heurisitic
+    if (higherDims > 10)
+        sortBatched<T>(val, 0, isAscending);
+    else
+        sort0Iterative<T>(val, isAscending);
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/sort_by_key.hpp b/src/backend/oneapi/kernel/sort_by_key.hpp
new file mode 100644
index 0000000000..3a1d7d38a8
--- /dev/null
+++ b/src/backend/oneapi/kernel/sort_by_key.hpp
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <Param.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Param<Tk> pKey, Param<Tv> pVal, bool isAscending);
+
+template<typename Tk, typename Tv>
+void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
+                      bool isAscending);
+
+template<typename Tk, typename Tv>
+void sort0ByKey(Param<Tk> pKey, Param<Tv> pVal, bool isAscending);
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt b/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
new file mode 100644
index 0000000000..ce184639eb
--- /dev/null
+++ b/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
@@ -0,0 +1,53 @@
+# Copyright (c) 2017, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp" FILESTRINGS)
+
+foreach(STR ${FILESTRINGS})
+    if(${STR} MATCHES "// SBK_TYPES")
+        string(REPLACE "// SBK_TYPES:" "" TEMP ${STR})
+        string(REPLACE " " ";" SBK_TYPES ${TEMP})
+    endif()
+endforeach()
+
+add_library(oneapi_sort_by_key INTERFACE)
+foreach(SBK_TYPE ${SBK_TYPES})
+  add_library(oneapi_sort_by_key_${SBK_TYPE} OBJECT
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
+        "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp"
+    )
+  set_target_properties(oneapi_sort_by_key_${SBK_TYPE}
+    PROPERTIES
+      COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL;$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>"
+      CXX_STANDARD 17
+      CXX_EXTENSIONS OFF
+      CXX_VISIBILITY_PRESET hidden
+      FOLDER "Generated Targets")
+
+  arrayfire_set_default_cxx_flags(oneapi_sort_by_key_${SBK_TYPE})
+
+  target_include_directories(oneapi_sort_by_key_${SBK_TYPE}
+    PUBLIC
+      .
+      ../../api/c
+      ${ArrayFire_SOURCE_DIR}/include
+      ${ArrayFire_BINARY_DIR}/include
+    PRIVATE
+      ../common
+      ..
+      )
+
+  target_include_directories(oneapi_sort_by_key_${SBK_TYPE}
+    SYSTEM PRIVATE
+      ${span-lite_SOURCE_DIR}/include
+      $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>)
+
+  target_compile_options(oneapi_sort_by_key_${SBK_TYPE} PUBLIC -fsycl)
+  set_target_properties(oneapi_sort_by_key_${SBK_TYPE} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+  target_sources(oneapi_sort_by_key
+    INTERFACE $<TARGET_OBJECTS:oneapi_sort_by_key_${SBK_TYPE}>)
+endforeach(SBK_TYPE ${SBK_TYPES})
diff --git a/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp
new file mode 100644
index 0000000000..9b04402904
--- /dev/null
+++ b/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/sort_by_key_impl.hpp>
+
+// SBK_TYPES:float double int uint intl uintl short ushort char uchar half
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+INSTANTIATE1(TYPE);
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/sort_by_key_impl.hpp b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
new file mode 100644
index 0000000000..c0c57d8eff
--- /dev/null
+++ b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
@@ -0,0 +1,206 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+// oneDPL headers should be included before standard headers
+#define ONEDPL_USE_PREDEFINED_POLICIES 0
+#include <oneapi/dpl/algorithm>
+#include <oneapi/dpl/execution>
+#include <oneapi/dpl/iterator>
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/half.hpp>
+#include <debug_oneapi.hpp>
+#include <iota.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <traits.hpp>
+#include <types.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+using arrayfire::common::half;
+
+template<typename Tk, typename Tv>
+void sort0ByKeyIterative(Param<Tk> pKey, Param<Tv> pVal, bool isAscending) {
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+
+    for (int w = 0; w < pKey.info.dims[3]; w++) {
+        int pKeyW = w * pKey.info.strides[3];
+        int pValW = w * pVal.info.strides[3];
+        for (int z = 0; z < pKey.info.dims[2]; z++) {
+            int pKeyWZ = pKeyW + z * pKey.info.strides[2];
+            int pValWZ = pValW + z * pVal.info.strides[2];
+            for (int y = 0; y < pKey.info.dims[1]; y++) {
+                int pKeyOffset = pKeyWZ + y * pKey.info.strides[1];
+                int pValOffset = pValWZ + y * pVal.info.strides[1];
+
+                auto key_begin =
+                    ::oneapi::dpl::begin(
+                        pKey.data->template reinterpret<compute_t<Tk>>()) +
+                    pKeyOffset;
+                auto key_end   = key_begin + pKey.info.dims[0];
+                auto val_begin = ::oneapi::dpl::begin(*pVal.data) + pValOffset;
+                auto val_end   = val_begin + pVal.info.dims[0];
+
+                auto zipped_begin =
+                    ::oneapi::dpl::make_zip_iterator(key_begin, val_begin);
+                auto zipped_end =
+                    ::oneapi::dpl::make_zip_iterator(key_end, val_end);
+
+                // sort by key
+                if (isAscending) {
+                    std::sort(dpl_policy, zipped_begin, zipped_end,
+                              [](auto lhs, auto rhs) {
+                                  return std::get<0>(lhs) < std::get<0>(rhs);
+                              });
+                } else {
+                    std::sort(dpl_policy, zipped_begin, zipped_end,
+                              [](auto lhs, auto rhs) {
+                                  return std::get<0>(lhs) > std::get<0>(rhs);
+                              });
+                }
+            }
+        }
+    }
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Tk, typename Tv>
+void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
+                      bool isAscending) {
+    af::dim4 inDims;
+    for (int i = 0; i < 4; i++) inDims[i] = pKey.info.dims[i];
+
+    const dim_t elements = inDims.elements();
+
+    // Sort dimension
+    // tileDims * seqDims = inDims
+    af::dim4 tileDims(1);
+    af::dim4 seqDims = inDims;
+    tileDims[dim]    = inDims[dim];
+    seqDims[dim]     = 1;
+
+    // Create/call iota
+    Array<uint> Seq = iota<uint>(seqDims, tileDims);
+
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+
+    // set up iterators for seq, key, val, and new cKey
+    auto seq_begin = ::oneapi::dpl::begin(*Seq.get());
+    auto seq_end   = ::oneapi::dpl::end(*Seq.get());
+    auto key_begin =
+        ::oneapi::dpl::begin(pKey.data->template reinterpret<compute_t<Tk>>());
+    auto key_end =
+        ::oneapi::dpl::end(pKey.data->template reinterpret<compute_t<Tk>>());
+    auto val_begin = ::oneapi::dpl::begin(*pVal.data);
+    auto val_end   = ::oneapi::dpl::end(*pVal.data);
+
+    auto cKey = memAlloc<Tk>(elements);
+    getQueue().submit([&](sycl::handler &h) {
+        h.copy(pKey.data->template reinterpret<compute_t<Tk>>().get_access(),
+               cKey.get()->template reinterpret<compute_t<Tk>>().get_access());
+    });
+    auto ckey_begin =
+        ::oneapi::dpl::begin(cKey.get()->template reinterpret<compute_t<Tk>>());
+    auto ckey_end =
+        ::oneapi::dpl::end(cKey.get()->template reinterpret<compute_t<Tk>>());
+
+    {
+        auto zipped_begin_KV  = dpl::make_zip_iterator(key_begin, val_begin);
+        auto zipped_end_KV    = dpl::make_zip_iterator(key_end, val_end);
+        auto zipped_begin_cKS = dpl::make_zip_iterator(ckey_begin, seq_begin);
+        auto zipped_end_cKS   = dpl::make_zip_iterator(ckey_end, seq_end);
+        if (isAscending) {
+            std::sort(dpl_policy, zipped_begin_KV, zipped_end_KV,
+                      [](auto lhs, auto rhs) {
+                          return std::get<0>(lhs) < std::get<0>(rhs);
+                      });
+            std::sort(dpl_policy, zipped_begin_cKS, zipped_end_cKS,
+                      [](auto lhs, auto rhs) {
+                          return std::get<0>(lhs) < std::get<0>(rhs);
+                      });
+        } else {
+            std::sort(dpl_policy, zipped_begin_KV, zipped_end_KV,
+                      [](auto lhs, auto rhs) {
+                          return std::get<0>(lhs) > std::get<0>(rhs);
+                      });
+            std::sort(dpl_policy, zipped_begin_cKS, zipped_end_cKS,
+                      [](auto lhs, auto rhs) {
+                          return std::get<0>(lhs) > std::get<0>(rhs);
+                      });
+        }
+    }
+
+    auto cSeq = memAlloc<uint>(elements);
+    getQueue().submit([&](sycl::handler &h) {
+        h.copy(Seq.get()->get_access(), cSeq.get()->get_access());
+    });
+    auto cseq_begin = ::oneapi::dpl::begin(*cSeq.get());
+    auto cseq_end   = ::oneapi::dpl::end(*cSeq.get());
+
+    {
+        auto zipped_begin_SV  = dpl::make_zip_iterator(seq_begin, val_begin);
+        auto zipped_end_SV    = dpl::make_zip_iterator(seq_end, val_end);
+        auto zipped_begin_cSK = dpl::make_zip_iterator(cseq_begin, key_begin);
+        auto zipped_end_cSK   = dpl::make_zip_iterator(cseq_end, key_end);
+        std::sort(dpl_policy, zipped_begin_SV, zipped_end_SV,
+                  [](auto lhs, auto rhs) {
+                      return std::get<0>(lhs) < std::get<0>(rhs);
+                  });
+        std::sort(dpl_policy, zipped_begin_cSK, zipped_end_cSK,
+                  [](auto lhs, auto rhs) {
+                      return std::get<0>(lhs) < std::get<0>(rhs);
+                  });
+    }
+}
+
+template<typename Tk, typename Tv>
+void sort0ByKey(Param<Tk> pKey, Param<Tv> pVal, bool isAscending) {
+    int higherDims = pKey.info.dims[1] * pKey.info.dims[2] * pKey.info.dims[3];
+    // Batched sort performs 4x sort by keys
+    // But this is only useful before GPU is saturated
+    // The GPU is saturated at around 1000,000 integers
+    // Call batched sort only if both conditions are met
+    if (higherDims > 4 && pKey.info.dims[0] < 1000000) {
+        kernel::sortByKeyBatched<Tk, Tv>(pKey, pVal, 0, isAscending);
+    } else {
+        kernel::sort0ByKeyIterative<Tk, Tv>(pKey, pVal, isAscending);
+    }
+}
+
+#define INSTANTIATE(Tk, Tv)                                                   \
+    template void sort0ByKey<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,          \
+                                     bool isAscending);                       \
+    template void sort0ByKeyIterative<Tk, Tv>(Param<Tk> okey, Param<Tv> oval, \
+                                              bool isAscending);              \
+    template void sortByKeyBatched<Tk, Tv>(Param<Tk> okey, Param<Tv> oval,    \
+                                           const int dim, bool isAscending);
+
+#define INSTANTIATE1(Tk)     \
+    INSTANTIATE(Tk, float)   \
+    INSTANTIATE(Tk, double)  \
+    INSTANTIATE(Tk, cfloat)  \
+    INSTANTIATE(Tk, cdouble) \
+    INSTANTIATE(Tk, int)     \
+    INSTANTIATE(Tk, uint)    \
+    INSTANTIATE(Tk, short)   \
+    INSTANTIATE(Tk, ushort)  \
+    INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, uchar)   \
+    INSTANTIATE(Tk, intl)    \
+    INSTANTIATE(Tk, uintl)
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
index ba503a1f56..5f2c92c641 100644
--- a/src/backend/oneapi/kernel/wrap.hpp
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -15,6 +15,7 @@
 #include <debug_oneapi.hpp>
 #include <kernel/default_config.hpp>
 #include <math.hpp>
+#include <sycl/sycl.hpp>
 
 #include <sycl/sycl.hpp>
 
@@ -82,8 +83,8 @@ class wrapCreateKernel {
         // / stride Each previous index has the value appear "stride" locations
         // earlier We work our way back from the last index
 
-        const int x_end = fmin(pidx0 / sx_, nx_ - 1);
-        const int y_end = fmin(pidx1 / sy_, ny_ - 1);
+        const int x_end = sycl::min(pidx0 / sx_, nx_ - 1);
+        const int y_end = sycl::min(pidx1 / sy_, ny_ - 1);
 
         const int x_off = pidx0 - sx_ * x_end;
         const int y_off = pidx1 - sy_ * y_end;
diff --git a/src/backend/oneapi/kernel/wrap_dilated.hpp b/src/backend/oneapi/kernel/wrap_dilated.hpp
index c479316968..dae994e371 100644
--- a/src/backend/oneapi/kernel/wrap_dilated.hpp
+++ b/src/backend/oneapi/kernel/wrap_dilated.hpp
@@ -16,6 +16,7 @@
 #include <kernel/default_config.hpp>
 #include <math.hpp>
 
+#include <sycl/sycl.hpp>
 #include <string>
 #include <vector>
 
@@ -87,10 +88,10 @@ class wrapDilatedCreateKernel {
         // earlier We work our way back from the last index
 
         const int y_start = (pidx1 < eff_wy) ? 0 : (pidx1 - eff_wy) / sy_ + 1;
-        const int y_end   = fmin(pidx1 / sy_ + 1, ny_);
+        const int y_end   = sycl::min(pidx1 / sy_ + 1, ny_);
 
         const int x_start = (pidx0 < eff_wx) ? 0 : (pidx0 - eff_wx) / sx_ + 1;
-        const int x_end   = fmin(pidx0 / sx_ + 1, nx_);
+        const int x_end   = sycl::min(pidx0 / sx_ + 1, nx_);
 
         T val   = (T)0;
         int idx = 1;
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
index 599d23c896..002385a320 100644
--- a/src/backend/oneapi/sort.cpp
+++ b/src/backend/oneapi/sort.cpp
@@ -7,10 +7,11 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#include <kernel/sort.hpp>
+
 #include <Array.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/sort.hpp>
 #include <math.hpp>
 #include <reorder.hpp>
 #include <sort.hpp>
@@ -18,19 +19,18 @@
 
 namespace arrayfire {
 namespace oneapi {
+
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
-    ONEAPI_NOT_SUPPORTED("sort Not supported");
-
     try {
         Array<T> out = copyArray<T>(in);
-        // switch (dim) {
-        //     case 0: kernel::sort0<T>(out, isAscending); break;
-        //     case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
-        //     case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
-        //     case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
-        //     default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        // }
+        switch (dim) {
+            case 0: kernel::sort0<T>(out, isAscending); break;
+            case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+            case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+            case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
 
         if (dim != 0) {
             af::dim4 preorderDims = out.dims();
diff --git a/src/backend/oneapi/sort_by_key.cpp b/src/backend/oneapi/sort_by_key.cpp
index 00a5bb55fa..9ec60130cd 100644
--- a/src/backend/oneapi/sort_by_key.cpp
+++ b/src/backend/oneapi/sort_by_key.cpp
@@ -10,7 +10,7 @@
 #include <Array.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/sort_by_key.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <reorder.hpp>
 #include <sort_by_key.hpp>
@@ -21,7 +21,35 @@ namespace oneapi {
 template<typename Tk, typename Tv>
 void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
                  const Array<Tv> &ival, const unsigned dim, bool isAscending) {
-    ONEAPI_NOT_SUPPORTED("");
+    okey = copyArray<Tk>(ikey);
+    oval = copyArray<Tv>(ival);
+
+    switch (dim) {
+        case 0: kernel::sort0ByKey<Tk, Tv>(okey, oval, isAscending); break;
+        case 1:
+        case 2:
+        case 3:
+            kernel::sortByKeyBatched<Tk, Tv>(okey, oval, dim, isAscending);
+            break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
+
+    if (dim != 0) {
+        af::dim4 preorderDims = okey.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0]  = okey.dims()[dim];
+        for (int i = 1; i <= (int)dim; i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i]    = okey.dims()[i - 1];
+        }
+
+        okey.setDataDims(preorderDims);
+        oval.setDataDims(preorderDims);
+
+        okey = reorder<Tk>(okey, reorderDims);
+        oval = reorder<Tv>(oval, reorderDims);
+    }
 }
 
 #define INSTANTIATE(Tk, Tv)                                        \
diff --git a/src/backend/oneapi/sort_index.cpp b/src/backend/oneapi/sort_index.cpp
index c0df0fb9de..17de33fbad 100644
--- a/src/backend/oneapi/sort_index.cpp
+++ b/src/backend/oneapi/sort_index.cpp
@@ -11,35 +11,33 @@
 #include <common/half.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
-// #include <kernel/sort_by_key.hpp>
+#include <kernel/sort_by_key.hpp>
 #include <math.hpp>
 #include <range.hpp>
 #include <reorder.hpp>
 #include <sort_index.hpp>
 #include <stdexcept>
 
-using arrayfire::common::half;
-
 namespace arrayfire {
 namespace oneapi {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
                 const uint dim, bool isAscending) {
-    ONEAPI_NOT_SUPPORTED("sort_index Not supported");
-
     try {
         // okey contains values, oval contains indices
         okey = copyArray<T>(in);
         oval = range<uint>(in.dims(), dim);
         oval.eval();
 
-        // switch (dim) {
-        //     case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending);
-        //     break; case 1: case 2: case 3:
-        //         kernel::sortByKeyBatched<T, uint>(okey, oval, dim,
-        //         isAscending); break;
-        //     default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        // }
+        switch (dim) {
+            case 0: kernel::sort0ByKey<T, uint>(okey, oval, isAscending); break;
+            case 1:
+            case 2:
+            case 3:
+                kernel::sortByKeyBatched<T, uint>(okey, oval, dim, isAscending);
+                break;
+            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+        }
 
         if (dim != 0) {
             af::dim4 preorderDims = okey.dims();
@@ -75,7 +73,7 @@ INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
-INSTANTIATE(half)
+INSTANTIATE(arrayfire::common::half)
 
 }  // namespace oneapi
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/topk.cpp b/src/backend/oneapi/topk.cpp
index 35c0b66975..17a14ce810 100644
--- a/src/backend/oneapi/topk.cpp
+++ b/src/backend/oneapi/topk.cpp
@@ -20,8 +20,6 @@
 #include <numeric>
 #include <vector>
 
-// using cl::Buffer;
-// using cl::Event;
 using arrayfire::common::half;
 
 using std::iota;
@@ -49,125 +47,12 @@ vector<af_index_t> indexForTopK(const int k) {
 template<typename T>
 void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
           const int k, const int dim, const af::topkFunction order) {
-    ONEAPI_NOT_SUPPORTED("topk Not supported");
-
-    // if (getDeviceType() == CL_DEVICE_TYPE_CPU) {
-    //     // This branch optimizes for CPU devices by first mapping the buffer
-    //     // and calling partial sort on the buffer
-
-    //     // TODO(umar): implement this in the kernel namespace
-
-    //     // The out_dims is of size k along the dimension of the topk
-    //     operation
-    //     // and the same as the input dimension otherwise.
-    //     dim4 out_dims(1);
-    //     int ndims = in.dims().ndims();
-    //     for (int i = 0; i < ndims; i++) {
-    //         if (i == dim) {
-    //             out_dims[i] = min(k, (int)in.dims()[i]);
-    //         } else {
-    //             out_dims[i] = in.dims()[i];
-    //         }
-    //     }
-
-    //     auto values          = createEmptyArray<T>(out_dims);
-    //     auto indices         = createEmptyArray<unsigned>(out_dims);
-    //     const Buffer* in_buf = in.get();
-    //     Buffer* ibuf         = indices.get();
-    //     Buffer* vbuf         = values.get();
-
-    //     cl::Event ev_in, ev_val, ev_ind;
-
-    //     T* ptr     = static_cast<T*>(getQueue().enqueueMapBuffer(
-    //         *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
-    //         nullptr, &ev_in));
-    //     uint* iptr = static_cast<uint*>(getQueue().enqueueMapBuffer(
-    //         *ibuf, CL_FALSE, CL_MAP_READ | CL_MAP_WRITE, 0, k * sizeof(uint),
-    //         nullptr, &ev_ind));
-    //     T* vptr    = static_cast<T*>(getQueue().enqueueMapBuffer(
-    //         *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr,
-    //         &ev_val));
-
-    //     vector<uint> idx(in.elements());
-
-    //     // Create a linear index
-    //     iota(begin(idx), end(idx), 0);
-    //     cl::Event::waitForEvents({ev_in, ev_ind});
-
-    //     int iter = in.dims()[1] * in.dims()[2] * in.dims()[3];
-    //     for (int i = 0; i < iter; i++) {
-    //         auto idx_itr = begin(idx) + i * in.strides()[1];
-    //         auto kiptr   = iptr + k * i;
-
-    //         if (order & AF_TOPK_MIN) {
-    //             if (order & AF_TOPK_STABLE) {
-    //                 partial_sort_copy(
-    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-    //                     [ptr](const uint lhs, const uint rhs) -> bool {
-    //                         return (compute_t<T>(ptr[lhs]) <
-    //                                 compute_t<T>(ptr[rhs]))
-    //                                    ? true
-    //                                : compute_t<T>(ptr[lhs]) ==
-    //                                        compute_t<T>(ptr[rhs])
-    //                                    ? (lhs < rhs)
-    //                                    : false;
-    //                     });
-    //             } else {
-    //                 // Sort the top k values in each column
-    //                 partial_sort_copy(
-    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-    //                     [ptr](const uint lhs, const uint rhs) -> bool {
-    //                         return compute_t<T>(ptr[lhs]) <
-    //                                compute_t<T>(ptr[rhs]);
-    //                     });
-    //             }
-    //         } else {
-    //             if (order & AF_TOPK_STABLE) {
-    //                 partial_sort_copy(
-    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-    //                     [ptr](const uint lhs, const uint rhs) -> bool {
-    //                         return (compute_t<T>(ptr[lhs]) >
-    //                                 compute_t<T>(ptr[rhs]))
-    //                                    ? true
-    //                                : compute_t<T>(ptr[lhs]) ==
-    //                                        compute_t<T>(ptr[rhs])
-    //                                    ? (lhs < rhs)
-    //                                    : false;
-    //                     });
-    //             } else {
-    //                 partial_sort_copy(
-    //                     idx_itr, idx_itr + in.strides()[1], kiptr, kiptr + k,
-    //                     [ptr](const uint lhs, const uint rhs) -> bool {
-    //                         return compute_t<T>(ptr[lhs]) >
-    //                                compute_t<T>(ptr[rhs]);
-    //                     });
-    //             }
-    //         }
-    //         ev_val.wait();
-
-    //         auto kvptr = vptr + k * i;
-    //         for (int j = 0; j < k; j++) {
-    //             // Update the value arrays with the original values
-    //             kvptr[j] = ptr[kiptr[j]];
-    //             // Convert linear indices back to column indices
-    //             kiptr[j] -= i * in.strides()[1];
-    //         }
-    //     }
-
-    //     getQueue().enqueueUnmapMemObject(*ibuf, iptr);
-    //     getQueue().enqueueUnmapMemObject(*vbuf, vptr);
-    //     getQueue().enqueueUnmapMemObject(*in_buf, ptr);
-
-    //     vals = values;
-    //     idxs = indices;
-    // } else {
-    //     auto values  = createEmptyArray<T>(in.dims());
-    //     auto indices = createEmptyArray<unsigned>(in.dims());
-    //     sort_index(values, indices, in, dim, order & AF_TOPK_MIN);
-    //     auto indVec = indexForTopK(k);
-    //     vals        = index<T>(values, indVec.data());
-    //     idxs        = index<unsigned>(indices, indVec.data());
-    // }
+    auto values  = createEmptyArray<T>(in.dims());
+    auto indices = createEmptyArray<unsigned>(in.dims());
+    sort_index(values, indices, in, dim, order & AF_TOPK_MIN);
+    auto indVec = indexForTopK(k);
+    vals        = index<T>(values, indVec.data());
+    idxs        = index<unsigned>(indices, indVec.data());
 }
 
 #define INSTANTIATE(T)                                                  \
@@ -181,5 +66,6 @@ INSTANTIATE(uint)
 INSTANTIATE(long long)
 INSTANTIATE(unsigned long long)
 INSTANTIATE(half)
+
 }  // namespace oneapi
 }  // namespace arrayfire

From 3d2ad9857083dee8af838dfcca41016215a1b6e5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 10 Apr 2023 20:21:23 -0400
Subject: [PATCH 2461/2677] adds iir, pad_array_borders kernels

---
 src/backend/oneapi/CMakeLists.txt             |   2 +
 src/backend/oneapi/copy.hpp                   |   4 +-
 src/backend/oneapi/iir.cpp                    |  27 ++-
 src/backend/oneapi/kernel/iir.hpp             | 150 ++++++++++++
 .../oneapi/kernel/pad_array_borders.hpp       | 216 ++++++++++++++++++
 5 files changed, 394 insertions(+), 5 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/iir.hpp
 create mode 100644 src/backend/oneapi/kernel/pad_array_borders.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 7a58966711..831234a5a8 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -217,6 +217,7 @@ target_sources(afoneapi
     kernel/diagonal.hpp
     kernel/diff.hpp
     kernel/histogram.hpp
+    kernel/iir.hpp
     kernel/identity.hpp
     kernel/interp.hpp
     kernel/iota.hpp
@@ -224,6 +225,7 @@ target_sources(afoneapi
     kernel/lu_split.hpp
     kernel/memcopy.hpp
     kernel/mean.hpp
+    kernel/pad_array_borders.hpp
     kernel/random_engine.hpp
     kernel/random_engine_write.hpp
     kernel/random_engine_mersenne.hpp
diff --git a/src/backend/oneapi/copy.hpp b/src/backend/oneapi/copy.hpp
index 4b05151dbd..85b3b861ea 100644
--- a/src/backend/oneapi/copy.hpp
+++ b/src/backend/oneapi/copy.hpp
@@ -9,7 +9,7 @@
 #pragma once
 
 #include <Array.hpp>
-// #include <kernel/pad_array_borders.hpp>
+#include <kernel/pad_array_borders.hpp>
 
 namespace arrayfire {
 namespace oneapi {
@@ -55,7 +55,7 @@ Array<T> padArrayBorders(Array<T> const &in, dim4 const &lowerBoundPadding,
 
     auto ret = createEmptyArray<T>(oDims);
 
-    // kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
+    kernel::padBorders<T>(ret, in, lowerBoundPadding, btype);
 
     return ret;
 }
diff --git a/src/backend/oneapi/iir.cpp b/src/backend/oneapi/iir.cpp
index e38a70294f..f60db52e8e 100644
--- a/src/backend/oneapi/iir.cpp
+++ b/src/backend/oneapi/iir.cpp
@@ -12,7 +12,7 @@
 #include <convolve.hpp>
 #include <err_oneapi.hpp>
 #include <iir.hpp>
-// #include <kernel/iir.hpp>
+#include <kernel/iir.hpp>
 #include <math.hpp>
 #include <af/dim4.hpp>
 
@@ -22,8 +22,29 @@ namespace arrayfire {
 namespace oneapi {
 template<typename T>
 Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> y = createEmptyArray<T>(dim4(1));
+    AF_BATCH_KIND type = x.ndims() == 1 ? AF_BATCH_NONE : AF_BATCH_SAME;
+    if (x.ndims() != b.ndims()) {
+        type = (x.ndims() < b.ndims()) ? AF_BATCH_RHS : AF_BATCH_LHS;
+    }
+
+    // Extract the first N elements
+    Array<T> c = convolve<T, T>(x, b, type, 1, true);
+    dim4 cdims = c.dims();
+    cdims[0]   = x.dims()[0];
+    c.resetDims(cdims);
+
+    int num_a = a.dims()[0];
+
+    if (num_a == 1) { return c; }
+
+    dim4 ydims = c.dims();
+    Array<T> y = createEmptyArray<T>(ydims);
+
+    if (a.ndims() > 1) {
+        kernel::iir<T, true>(y, c, a);
+    } else {
+        kernel::iir<T, false>(y, c, a);
+    }
     return y;
 }
 
diff --git a/src/backend/oneapi/kernel/iir.hpp b/src/backend/oneapi/kernel/iir.hpp
new file mode 100644
index 0000000000..ab00655fec
--- /dev/null
+++ b/src/backend/oneapi/kernel/iir.hpp
@@ -0,0 +1,150 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+
+#include <sycl/sycl.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+constexpr int MAX_A_SIZE = 1024;
+
+template<typename T, bool batch_a>
+class iirKernel {
+   public:
+    iirKernel(write_accessor<T> y, KParam yInfo, read_accessor<T> c,
+              KParam cInfo, read_accessor<T> a, KParam aInfo,
+              sycl::local_accessor<T> s_z, sycl::local_accessor<T> s_a,
+              sycl::local_accessor<T> s_y, int groups_y)
+        : y_(y)
+        , yInfo_(yInfo)
+        , c_(c)
+        , cInfo_(cInfo)
+        , a_(a)
+        , aInfo_(aInfo)
+        , s_z_(s_z)
+        , s_a_(s_a)
+        , s_y_(s_y)
+        , groups_y_(groups_y) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        const int idz = g.get_group_id(0);
+        const int idw = g.get_group_id(1) / groups_y_;
+        const int idy = g.get_group_id(1) - idw * groups_y_;
+
+        const int tx    = it.get_local_id(0);
+        const int num_a = aInfo_.dims[0];
+
+        int y_off = idw * yInfo_.strides[3] + idz * yInfo_.strides[2] +
+                    idy * yInfo_.strides[1];
+        int c_off = idw * cInfo_.strides[3] + idz * cInfo_.strides[2] +
+                    idy * cInfo_.strides[1];
+        int a_off = 0;
+
+        if (batch_a)
+            a_off = idw * aInfo_.strides[3] + idz * aInfo_.strides[2] +
+                    idy * aInfo_.strides[1];
+
+        T *d_y       = y_.get_pointer() + y_off;
+        const T *d_c = c_.get_pointer() + c_off;
+        const T *d_a = a_.get_pointer() + a_off;
+        const int repeat =
+            (num_a + g.get_local_range(0) - 1) / g.get_local_range(0);
+
+        for (int ii = 0; ii < MAX_A_SIZE / g.get_local_range(0); ii++) {
+            int id   = ii * g.get_local_range(0) + tx;
+            s_z_[id] = scalar<T>(0);
+            s_a_[id] = (id < num_a) ? d_a[id] : scalar<T>(0);
+        }
+        group_barrier(g);
+
+        for (int i = 0; i < yInfo_.dims[0]; i++) {
+            if (tx == 0) {
+                s_y_[0] = (d_c[i] + s_z_[0]) / s_a_[0];
+                d_y[i]  = s_y_[0];
+            }
+            group_barrier(g);
+
+#pragma unroll
+            for (int ii = 0; ii < repeat; ii++) {
+                int id = ii * g.get_local_range(0) + tx + 1;
+
+                T z = s_z_[id] - s_a_[id] * s_y_[0];
+                group_barrier(g);
+
+                s_z_[id - 1] = z;
+                group_barrier(g);
+            }
+        }
+    }
+
+   protected:
+    write_accessor<T> y_;
+    KParam yInfo_;
+    read_accessor<T> c_;
+    KParam cInfo_;
+    read_accessor<T> a_;
+    KParam aInfo_;
+    sycl::local_accessor<T> s_z_;
+    sycl::local_accessor<T> s_a_;
+    sycl::local_accessor<T> s_y_;
+    int groups_y_;
+};
+
+template<typename T, bool batch_a>
+void iir(Param<T> y, Param<T> c, Param<T> a) {
+    const int groups_y = y.info.dims[1];
+    const int groups_x = y.info.dims[2];
+
+    int threads = 256;
+    while (threads > y.info.dims[0] && threads > 32) threads /= 2;
+    sycl::range<2> local = sycl::range{threads, 1};
+
+    sycl::range<2> global =
+        sycl::range<2>{groups_x * local[0], groups_y * y.info.dims[3]};
+
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<T> yAcc{*y.data, h};
+        read_accessor<T> cAcc{*c.data, h};
+        read_accessor<T> aAcc{*a.data, h};
+
+        auto s_z = sycl::local_accessor<T>(MAX_A_SIZE, h);
+        auto s_a = sycl::local_accessor<T>(MAX_A_SIZE, h);
+        auto s_y = sycl::local_accessor<T>(1, h);
+
+        if (batch_a) {
+            h.parallel_for(sycl::nd_range{global, local},
+                           iirKernel<T, true>(yAcc, y.info, cAcc, c.info, aAcc,
+                                              a.info, s_z, s_a, s_y, groups_y));
+        } else {
+            h.parallel_for(
+                sycl::nd_range{global, local},
+                iirKernel<T, false>(yAcc, y.info, cAcc, c.info, aAcc, a.info,
+                                    s_z, s_a, s_y, groups_y));
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/pad_array_borders.hpp b/src/backend/oneapi/kernel/pad_array_borders.hpp
new file mode 100644
index 0000000000..620352f352
--- /dev/null
+++ b/src/backend/oneapi/kernel/pad_array_borders.hpp
@@ -0,0 +1,216 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <af/defines.h>
+
+#include <sycl/sycl.hpp>
+
+#include <array>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T, int BType>
+class padBordersKernel {
+   public:
+    padBordersKernel(write_accessor<T> out, KParam oInfo, read_accessor<T> in,
+                     KParam iInfo, const dim_t l0, const dim_t l1,
+                     const dim_t l2, const dim_t l3, const int groups_x,
+                     const int groups_y)
+        : out_(out)
+        , oInfo_(oInfo)
+        , in_(in)
+        , iInfo_(iInfo)
+        , l0_(l0)
+        , l1_(l1)
+        , l2_(l2)
+        , l3_(l3)
+        , groups_x_(groups_x)
+        , groups_y_(groups_y) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        const int lx  = it.get_local_id(0);
+        const int ly  = it.get_local_id(1);
+        const int k   = g.get_group_id(0) / groups_x_;
+        const int l   = g.get_group_id(1) / groups_y_;
+
+        const int blockIdx_x = g.get_group_id(0) - (groups_x_)*k;
+        const int blockIdx_y = g.get_group_id(1) - (groups_y_)*l;
+        const int i          = blockIdx_x * g.get_local_range(0) + lx;
+        const int j          = blockIdx_y * g.get_local_range(1) + ly;
+
+        const size_t d0 = iInfo_.dims[0];
+        const size_t d1 = iInfo_.dims[1];
+        const size_t d2 = iInfo_.dims[2];
+        const size_t d3 = iInfo_.dims[3];
+        const size_t s0 = iInfo_.strides[0];
+        const size_t s1 = iInfo_.strides[1];
+        const size_t s2 = iInfo_.strides[2];
+        const size_t s3 = iInfo_.strides[3];
+
+        const T* src = in_.get_pointer() + iInfo_.offset;
+        T* dst       = out_.get_pointer();
+
+        bool isNotPadding =
+            (l >= l3_ && l < (d3 + l3_)) && (k >= l2_ && k < (d2 + l2_)) &&
+            (j >= l1_ && j < (d1 + l1_)) && (i >= l0_ && i < (d0 + l0_));
+
+        T value = scalar<T>(0);
+        if (isNotPadding) {
+            unsigned iLOff = (l - l3_) * s3;
+            unsigned iKOff = (k - l2_) * s2;
+            unsigned iJOff = (j - l1_) * s1;
+            unsigned iIOff = (i - l0_) * s0;
+
+            value = src[iLOff + iKOff + iJOff + iIOff];
+        } else if (BType != AF_PAD_ZERO) {
+            unsigned iLOff =
+                padBordersKernel<T, BType>::idxByndEdge(l, l3_, d3) * s3;
+            unsigned iKOff =
+                padBordersKernel<T, BType>::idxByndEdge(k, l2_, d2) * s2;
+            unsigned iJOff =
+                padBordersKernel<T, BType>::idxByndEdge(j, l1_, d1) * s1;
+            unsigned iIOff =
+                padBordersKernel<T, BType>::idxByndEdge(i, l0_, d0) * s0;
+
+            value = src[iLOff + iKOff + iJOff + iIOff];
+        }
+
+        size_t xlim = oInfo_.dims[0];
+        size_t ylim = oInfo_.dims[1];
+        size_t zlim = oInfo_.dims[2];
+        size_t wlim = oInfo_.dims[3];
+
+        size_t woStrides = oInfo_.strides[3];
+        size_t zoStrides = oInfo_.strides[2];
+        size_t yoStrides = oInfo_.strides[1];
+        size_t xoStrides = oInfo_.strides[0];
+
+        if (i < xlim && j < ylim && k < zlim && l < wlim) {
+            unsigned off =
+                (l * woStrides + k * zoStrides + j * yoStrides + i * xoStrides);
+            dst[off] = value;
+        }
+    }
+
+    static int trimIndex(int idx, const int len) {
+        int ret_val = idx;
+        if (ret_val < 0) {
+            int offset = (abs(ret_val) - 1) % len;
+            ret_val    = offset;
+        } else if (ret_val >= len) {
+            int offset = abs(ret_val) % len;
+            ret_val    = len - offset - 1;
+        }
+        return ret_val;
+    }
+
+    static int idxByndEdge(const int i, const int lb, const int len) {
+        uint retVal;
+        switch (BType) {
+            case AF_PAD_SYM:
+                retVal = padBordersKernel<T, BType>::trimIndex(i - lb, len);
+                break;
+            case AF_PAD_CLAMP_TO_EDGE:
+                retVal = sycl::clamp(i - lb, 0, len - 1);
+                break;
+            case AF_PAD_PERIODIC: {
+                int rem   = (i - lb) % len;
+                bool cond = rem < 0;
+                retVal    = cond * (rem + len) + (1 - cond) * rem;
+            } break;
+            default: retVal = 0; break;  // AF_PAD_ZERO
+        }
+        return retVal;
+    }
+
+   protected:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    read_accessor<T> in_;
+    KParam iInfo_;
+    const dim_t l0_;
+    const dim_t l1_;
+    const dim_t l2_;
+    const dim_t l3_;
+    const int groups_x_;
+    const int groups_y_;
+};
+
+static const int PADB_THREADS_X = 32;
+static const int PADB_THREADS_Y = 8;
+
+template<typename T>
+void padBorders(Param<T> out, Param<T> in, dim4 const lBoundPadding,
+                const af::borderType btype) {
+    sycl::range<2> local(PADB_THREADS_X, PADB_THREADS_Y);
+
+    int groups_x = divup(out.info.dims[0], PADB_THREADS_X);
+    int groups_y = divup(out.info.dims[1], PADB_THREADS_Y);
+
+    sycl::range<2> global(groups_x * out.info.dims[2] * local[0],
+                          groups_y * out.info.dims[3] * local[1]);
+
+    getQueue().submit([&](sycl::handler& h) {
+        read_accessor<T> iData{*in.data, h};
+        write_accessor<T> oData{*out.data, h};
+
+        switch (btype) {
+            case AF_PAD_ZERO:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    padBordersKernel<T, AF_PAD_ZERO>(
+                        oData, out.info, iData, in.info, lBoundPadding[0],
+                        lBoundPadding[1], lBoundPadding[2], lBoundPadding[3],
+                        groups_x, groups_y));
+                break;
+            case AF_PAD_SYM:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    padBordersKernel<T, AF_PAD_SYM>(
+                        oData, out.info, iData, in.info, lBoundPadding[0],
+                        lBoundPadding[1], lBoundPadding[2], lBoundPadding[3],
+                        groups_x, groups_y));
+                break;
+            case AF_PAD_CLAMP_TO_EDGE:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    padBordersKernel<T, AF_PAD_CLAMP_TO_EDGE>(
+                        oData, out.info, iData, in.info, lBoundPadding[0],
+                        lBoundPadding[1], lBoundPadding[2], lBoundPadding[3],
+                        groups_x, groups_y));
+                break;
+            case AF_PAD_PERIODIC:
+                h.parallel_for(
+                    sycl::nd_range{global, local},
+                    padBordersKernel<T, AF_PAD_PERIODIC>(
+                        oData, out.info, iData, in.info, lBoundPadding[0],
+                        lBoundPadding[1], lBoundPadding[2], lBoundPadding[3],
+                        groups_x, groups_y));
+                break;
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire

From b1d7f6405466773bcf07aedf64d2f393e718432e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 16:46:23 -0400
Subject: [PATCH 2462/2677] Fix invalid offsets in the copy kernel invocation
 in oneAPI

---
 src/backend/oneapi/copy.cpp | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index d9d2fba2c5..cd7d38396e 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -18,6 +18,13 @@
 using arrayfire::common::half;
 using arrayfire::common::is_complex;
 
+using sycl::access_mode;
+using sycl::accessor;
+using sycl::buffer;
+using sycl::id;
+using sycl::range;
+using sycl::target;
+
 namespace arrayfire {
 namespace oneapi {
 
@@ -106,11 +113,11 @@ struct copyWrapper<T, T> {
     void operator()(Array<T> &out, Array<T> const &in) {
         if (out.isLinear() && in.isLinear() &&
             out.elements() == in.elements()) {
-            dim_t in_offset  = in.getOffset() * sizeof(T);
-            dim_t out_offset = out.getOffset() * sizeof(T);
+            dim_t in_offset  = in.getOffset();
+            dim_t out_offset = out.getOffset();
 
-            const sycl::buffer<T> *in_buf = in.get();
-            sycl::buffer<T> *out_buf      = out.get();
+            sycl::buffer<T> *in_buf  = in.get();
+            sycl::buffer<T> *out_buf = out.get();
 
             getQueue()
                 .submit([=](sycl::handler &h) {
@@ -119,10 +126,11 @@ struct copyWrapper<T, T> {
                     sycl::id out_offset_id(out_offset);
 
                     auto offset_acc_in =
-                        const_cast<sycl::buffer<T> *>(in_buf)->get_access(
+                        in_buf->template get_access<access_mode::read>(
                             h, rr, in_offset_id);
                     auto offset_acc_out =
-                        out_buf->get_access(h, rr, out_offset_id);
+                        out_buf->template get_access<access_mode::write>(
+                            h, rr, out_offset_id);
 
                     h.copy(offset_acc_in, offset_acc_out);
                 })

From cd14280a3fe941dbc8a30e09aac02f082bdab37a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 16:47:46 -0400
Subject: [PATCH 2463/2677] Address copy operation with unevaled arrays

---
 src/backend/oneapi/copy.cpp | 54 ++++++++++++++++++++++---------------
 1 file changed, 33 insertions(+), 21 deletions(-)

diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index cd7d38396e..e61dbbc8db 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -69,28 +69,40 @@ Array<T> copyArray(const Array<T> &A) {
     if (A.elements() == 0) { return out; }
 
     dim_t offset = A.getOffset();
-    if (A.isLinear()) {
-        // FIXME: Add checks
-
-        const sycl::buffer<T> *A_buf = A.get();
-        sycl::buffer<T> *out_buf     = out.get();
-
-        getQueue()
-            .submit([=](sycl::handler &h) {
-                sycl::range rr(A.elements());
-                sycl::id offset_id(offset);
-                auto offset_acc_A =
-                    const_cast<sycl::buffer<T> *>(A_buf)->get_access(h, rr,
-                                                                     offset_id);
-                auto acc_out = out_buf->get_access(h);
-
-                h.copy(offset_acc_A, acc_out);
-            })
-            .wait();
+    if (A.isReady()) {
+        if (A.isLinear()) {
+            // FIXME: Add checks
+
+            sycl::buffer<T> *A_buf   = A.get();
+            sycl::buffer<T> *out_buf = out.get();
+
+            size_t aelem = A.elements();
+            getQueue()
+                .submit([=](sycl::handler &h) {
+                    range rr(aelem);
+                    id offset_id(offset);
+                    accessor offset_acc_A =
+                        A_buf->template get_access<access_mode::read>(
+                            h, rr, offset_id);
+                    accessor acc_out =
+                        out_buf->template get_access<access_mode::write>(h);
+
+                    h.copy(offset_acc_A, acc_out);
+                })
+                .wait();
+        } else {
+            kernel::memcopy<T>(out.get(), out.strides().get(), A.get(),
+                               A.dims().get(), A.strides().get(), offset,
+                               (uint)A.ndims());
+        }
     } else {
-        kernel::memcopy<T>(out.get(), out.strides().get(), A.get(),
-                           A.dims().get(), A.strides().get(), offset,
-                           (uint)A.ndims());
+        Param info = {out.get(),
+                      {{A.dims().dims[0], A.dims().dims[1], A.dims().dims[2],
+                        A.dims().dims[3]},
+                       {out.strides().dims[0], out.strides().dims[1],
+                        out.strides().dims[2], out.strides().dims[3]},
+                       0}};
+        evalNodes(info, A.getNode().get());
     }
     return out;
 }

From 1a907a6c83a560d890569178ca292341b92c6ba1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 16:48:40 -0400
Subject: [PATCH 2464/2677] Workaround long long issue for the assign kernel in
 oneAPI

---
 src/backend/oneapi/kernel/assign.hpp | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 1ab8c42732..e37e95c7bd 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -76,8 +76,14 @@ class assignKernel {
         const int gy =
             g.get_local_range(1) * (g.get_group_id(1) - gw * nBBS1_) +
             it.get_local_id(1);
-        if (gx < iInfo_.dims[0] && gy < iInfo_.dims[1] && gz < iInfo_.dims[2] &&
-            gw < iInfo_.dims[3]) {
+
+        size_t idims0 = iInfo_.dims[0];
+        size_t idims1 = iInfo_.dims[1];
+        size_t idims2 = iInfo_.dims[2];
+        size_t idims3 = iInfo_.dims[3];
+
+        if (gx < idims0 && gy < idims1 && gz < idims2 &&
+            gw < idims3) {
             // calculate pointer offsets for input
             int i = p_.strds[0] *
                     trimIndex(s0 ? gx + p_.offs[0] : ptr0_[gx], oInfo_.dims[0]);

From 80d0eedcacd43047638a684f1f6f0e261c4d9713 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 16:49:51 -0400
Subject: [PATCH 2465/2677] Fix empty set issue error in the where function in
 oneAPI

---
 src/backend/oneapi/kernel/where.hpp | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index 64b25ec211..c5a0172134 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -148,19 +148,16 @@ static void where(Param<uint> &out, Param<T> in) {
 
     // Get output size and allocate output
     uint total;
-    sycl::buffer retBuffer(&total, {1},
-                           {sycl::property::buffer::use_host_ptr()});
 
     getQueue()
         .submit([&](sycl::handler &h) {
             auto acc_in  = rtmp.data->get_access(h, sycl::range{1},
                                                  sycl::id{rtmp_elements - 1});
-            auto acc_out = retBuffer.get_access();
-            h.copy(acc_in, acc_out);
+            h.copy(acc_in, &total);
         })
         .wait();
 
-    auto out_alloc = memAlloc<uint>(total);
+    auto out_alloc = memAlloc<uint>(std::max(1U,total));
     out.data       = out_alloc.get();
 
     out.info.dims[0]    = total;

From eecf0504b626dc4f08b13d72ae3fbd2451621ab9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 16:51:01 -0400
Subject: [PATCH 2466/2677] Update Index.Docs_Util_C_API test to return -1
 instead of throw

This is a C API example which shouldn't have throw calls. I wrapped this
funciton in a lambda so it is now returning a negative number on errors. The
lambda is not exposed in the example and it is only necessary for the return
functionallity.
---
 test/index.cpp | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/test/index.cpp b/test/index.cpp
index a593348773..c8e1a7ffb9 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -586,12 +586,13 @@ TYPED_TEST(Indexing, 3D_to_1D) {
 }
 
 TEST(Index, Docs_Util_C_API) {
+    // clang-format off
+    ASSERT_EQ(0, ([]() -> int {
     //![ex_index_util_0]
     af_index_t *indexers = 0;
-    af_err err           = af_create_indexers(
-                  &indexers);  // Memory is allocated on heap by the callee
-    // by default all the indexers span all the elements along the given
-    // dimension
+    af_err err = af_create_indexers(&indexers); // Memory is allocated on heap by the callee
+                                                // by default all the indexers span all the elements along
+                                                // the given dimension
 
     // Create array
     af_array a;
@@ -613,12 +614,11 @@ TEST(Index, Docs_Util_C_API) {
 
     // index with indexers
     af_array out;
-    af_index_gen(&out, a, 2,
-                 indexers);  // number of indexers should be two since
-                             // we have set only second af_index_t
+    err = af_index_gen(&out, a, 2, indexers);  // number of indexers should be two since
+                                               // we have set only second af_index_t
     if (err != AF_SUCCESS) {
         printf("Failed in af_index_gen: %d\n", err);
-        throw;
+        return 1;
     }
     af_print_array(out);
     af_release_array(out);
@@ -630,7 +630,7 @@ TEST(Index, Docs_Util_C_API) {
     err = af_index_gen(&out, a, 2, indexers);
     if (err != AF_SUCCESS) {
         printf("Failed in af_index_gen: %d\n", err);
-        throw;
+        return 1;
     }
     af_print_array(out);
 
@@ -638,7 +638,10 @@ TEST(Index, Docs_Util_C_API) {
     af_release_array(a);
     af_release_array(idx);
     af_release_array(out);
+    return 0;
     //![ex_index_util_0]
+    }()));
+    // clang-format on
 }
 
 //////////////////////////////// CPP ////////////////////////////////

From bacc43e147ddc038749fb874d0a8a7b8ec6259db Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 17:27:37 -0400
Subject: [PATCH 2467/2677] Implement generalized indexing in the oneAPI
 backend

---
 src/backend/oneapi/index.cpp                  |  39 ++++-
 src/backend/oneapi/kernel/assign.hpp          |   3 +-
 .../oneapi/kernel/assign_kernel_param.hpp     |  25 +++
 src/backend/oneapi/kernel/index.hpp           | 156 ++++++++++++++++++
 4 files changed, 219 insertions(+), 4 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/assign_kernel_param.hpp
 create mode 100644 src/backend/oneapi/kernel/index.hpp

diff --git a/src/backend/oneapi/index.cpp b/src/backend/oneapi/index.cpp
index f0eb5e1cc4..bec65902d8 100644
--- a/src/backend/oneapi/index.cpp
+++ b/src/backend/oneapi/index.cpp
@@ -12,18 +12,53 @@
 #include <Array.hpp>
 #include <err_oneapi.hpp>
 #include <handle.hpp>
+#include <kernel/assign_kernel_param.hpp>
+#include <kernel/index.hpp>
 #include <memory.hpp>
 #include <af/dim4.hpp>
 
 using arrayfire::common::half;
+using arrayfire::oneapi::IndexKernelParam;
 
 namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
 Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
-    ONEAPI_NOT_SUPPORTED("Indexing not supported");
-    Array<T> out = createEmptyArray<T>(af::dim4(1));
+    IndexKernelParam p;
+    std::vector<af_seq> seqs(4, af_span);
+    // create seq vector to retrieve output
+    // dimensions, offsets & offsets
+    for (dim_t x = 0; x < 4; ++x) {
+        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
+    }
+
+    // retrieve dimensions, strides and offsets
+    const dim4& iDims = in.dims();
+    dim4 dDims        = in.getDataDims();
+    dim4 oDims        = toDims(seqs, iDims);
+    dim4 iOffs        = toOffset(seqs, dDims);
+    dim4 iStrds       = in.strides();
+
+    for (dim_t i = 0; i < 4; ++i) {
+        p.isSeq[i] = idxrs[i].isSeq;
+        p.offs[i]  = iOffs[i];
+        p.strds[i] = iStrds[i];
+    }
+
+    std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4(1)));
+    // look through indexs to read af_array indexs
+    for (dim_t x = 0; x < 4; ++x) {
+        if (!p.isSeq[x]) {
+            idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
+            oDims[x]   = idxArrs[x].elements();
+        }
+    }
+
+    Array<T> out = createEmptyArray<T>(oDims);
+    if (oDims.elements() == 0) { return out; }
+    kernel::index<T>(out, in, p, idxArrs);
+
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index e37e95c7bd..27c4a58f1c 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -82,8 +82,7 @@ class assignKernel {
         size_t idims2 = iInfo_.dims[2];
         size_t idims3 = iInfo_.dims[3];
 
-        if (gx < idims0 && gy < idims1 && gz < idims2 &&
-            gw < idims3) {
+        if (gx < idims0 && gy < idims1 && gz < idims2 && gw < idims3) {
             // calculate pointer offsets for input
             int i = p_.strds[0] *
                     trimIndex(s0 ? gx + p_.offs[0] : ptr0_[gx], oInfo_.dims[0]);
diff --git a/src/backend/oneapi/kernel/assign_kernel_param.hpp b/src/backend/oneapi/kernel/assign_kernel_param.hpp
new file mode 100644
index 0000000000..e4c8a8c83a
--- /dev/null
+++ b/src/backend/oneapi/kernel/assign_kernel_param.hpp
@@ -0,0 +1,25 @@
+
+#include <sycl/sycl.hpp>
+
+#include <array>
+
+#pragma once
+
+namespace arrayfire {
+namespace oneapi {
+
+typedef struct {
+    int offs[4];
+    int strds[4];
+    bool isSeq[4];
+    std::array<sycl::accessor<unsigned int, 1, sycl::access::mode::read,
+                              sycl::access::target::device>,
+               4>
+        ptr;
+
+} AssignKernelParam;
+
+using IndexKernelParam = AssignKernelParam;
+
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/index.hpp b/src/backend/oneapi/kernel/index.hpp
new file mode 100644
index 0000000000..6e90d392ad
--- /dev/null
+++ b/src/backend/oneapi/kernel/index.hpp
@@ -0,0 +1,156 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/assign_kernel_param.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class indexKernel {
+    sycl::accessor<T, 1, sycl::access::mode::write> out;
+    KParam outp;
+    sycl::accessor<T, 1, sycl::access::mode::read> in;
+    KParam inp;
+    IndexKernelParam p;
+    int nBBS0;
+    int nBBS1;
+
+   public:
+    indexKernel(sycl::accessor<T, 1, sycl::access::mode::write> out_,
+                KParam outp_,
+                sycl::accessor<T, 1, sycl::access::mode::read> in_, KParam inp_,
+                const IndexKernelParam p_, const int nBBS0_, const int nBBS1_)
+        : out(out_)
+        , outp(outp_)
+        , in(in_)
+        , inp(inp_)
+        , p(p_)
+        , nBBS0(nBBS0_)
+        , nBBS1(nBBS1_) {}
+
+    int trimIndex(int idx, const int len) const {
+        int ret_val = idx;
+        if (ret_val < 0) {
+            int offset = (abs(ret_val) - 1) % len;
+            ret_val    = offset;
+        } else if (ret_val >= len) {
+            int offset = abs(ret_val) % len;
+            ret_val    = len - offset - 1;
+        }
+        return ret_val;
+    }
+
+    void operator()(sycl::nd_item<3> it) const {
+        // retrieve index pointers
+        // these can be 0 where af_array index is not used
+        sycl::group g    = it.get_group();
+        const uint* ptr0 = p.ptr[0].get_pointer();
+        const uint* ptr1 = p.ptr[1].get_pointer();
+        const uint* ptr2 = p.ptr[2].get_pointer();
+        const uint* ptr3 = p.ptr[3].get_pointer();
+        // retrive booleans that tell us which index to use
+        const bool s0 = p.isSeq[0];
+        const bool s1 = p.isSeq[1];
+        const bool s2 = p.isSeq[2];
+        const bool s3 = p.isSeq[3];
+
+        const int gz = g.get_group_id(0) / nBBS0;
+        const int gx = g.get_local_range(0) * (g.get_group_id(0) - gz * nBBS0) +
+                       it.get_local_id(0);
+
+        const int gw =
+            (g.get_group_id(1) + g.get_group_id(2) * g.get_group_range(1)) /
+            nBBS1;
+        const int gy =
+            g.get_local_range(1) * ((g.get_group_id(1) +
+                                     g.get_group_id(2) * g.get_group_range(1)) -
+                                    gw * nBBS1) +
+            it.get_local_id(1);
+
+        size_t odims0 = outp.dims[0];
+        size_t odims1 = outp.dims[1];
+        size_t odims2 = outp.dims[2];
+        size_t odims3 = outp.dims[3];
+
+        if (gx < odims0 && gy < odims1 && gz < odims2 && gw < odims3) {
+            // calculate pointer offsets for input
+            int i = p.strds[0] *
+                    trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], inp.dims[0]);
+            int j = p.strds[1] *
+                    trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], inp.dims[1]);
+            int k = p.strds[2] *
+                    trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], inp.dims[2]);
+            int l = p.strds[3] *
+                    trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], inp.dims[3]);
+            // offset input and output pointers
+            const T* src = (const T*)in.get_pointer() + (i + j + k + l);
+            T* dst       = (T*)out.get_pointer() +
+                     (gx * outp.strides[0] + gy * outp.strides[1] +
+                      gz * outp.strides[2] + gw * outp.strides[3]);
+            // set the output
+            dst[0] = src[0];
+        }
+    }
+};
+
+template<typename T>
+void index(Param<T> out, Param<T> in, IndexKernelParam& p,
+           std::vector<Array<uint>>& idxArrs) {
+    sycl::range<3> threads(0, 0, 1);
+    switch (out.info.dims[1]) {
+        case 1: threads[1] = 1; break;
+        case 2: threads[1] = 2; break;
+        case 3:
+        case 4: threads[1] = 4; break;
+        default: threads[1] = 8; break;
+    }
+    threads[0] = static_cast<unsigned>(256.f / threads[1]);
+
+    int blks_x = divup(out.info.dims[0], threads[0]);
+    int blks_y = divup(out.info.dims[1], threads[1]);
+
+    sycl::range<3> blocks(blks_x * out.info.dims[2], blks_y * out.info.dims[3],
+                          1);
+
+    const size_t maxBlocksY =
+        getDevice().get_info<sycl::info::device::max_work_item_sizes<3>>()[2];
+    blocks[2] = divup(blocks[1], maxBlocksY);
+    blocks[1] = divup(blocks[1], blocks[2]) * threads[1];
+    blocks[1] = blocks[1] * threads[1];
+    blocks[0] *= threads[0];
+
+    sycl::nd_range<3> marange(blocks, threads);
+    getQueue().submit([=](sycl::handler& h) {
+        auto pp = p;
+        for (dim_t x = 0; x < 4; ++x) {
+            pp.ptr[x] =
+                idxArrs[x].get()->get_access<sycl::access::mode::read>(h);
+        }
+
+        h.parallel_for(
+            marange,
+            indexKernel<T>(
+                out.data->template get_access<sycl::access::mode::write>(h),
+                out.info,
+                in.data->template get_access<sycl::access::mode::read>(h),
+                in.info, pp, blks_x, blks_y));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire

From bb4fdb6d3bffaddea115a285c71abc3a2504fedb Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 17:28:27 -0400
Subject: [PATCH 2468/2677] Update assign to use the same AssignKernelParam
 object as index

---
 src/backend/oneapi/assign.cpp        |  2 +-
 src/backend/oneapi/kernel/assign.hpp | 58 +++++++++++++---------------
 2 files changed, 27 insertions(+), 33 deletions(-)

diff --git a/src/backend/oneapi/assign.cpp b/src/backend/oneapi/assign.cpp
index 0f2b96e5d5..def9378d2d 100644
--- a/src/backend/oneapi/assign.cpp
+++ b/src/backend/oneapi/assign.cpp
@@ -25,7 +25,7 @@ namespace oneapi {
 
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
-    kernel::AssignKernelParam_t p;
+    AssignKernelParam p;
     std::vector<af_seq> seqs(4, af_span);
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 27c4a58f1c..2bddb4cccf 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/assign_kernel_param.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -23,12 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-typedef struct {
-    int offs[4];
-    int strds[4];
-    char isSeq[4];
-} AssignKernelParam_t;
-
 static int trimIndex(int idx, const int len) {
     int ret_val = idx;
     if (ret_val < 0) {
@@ -45,18 +40,13 @@ template<typename T>
 class assignKernel {
    public:
     assignKernel(sycl::accessor<T> out, KParam oInfo, sycl::accessor<T> in,
-                 KParam iInfo, AssignKernelParam_t p, sycl::accessor<uint> ptr0,
-                 sycl::accessor<uint> ptr1, sycl::accessor<uint> ptr2,
-                 sycl::accessor<uint> ptr3, const int nBBS0, const int nBBS1)
+                 KParam iInfo, AssignKernelParam p, const int nBBS0,
+                 const int nBBS1)
         : out_(out)
         , in_(in)
         , oInfo_(oInfo)
         , iInfo_(iInfo)
         , p_(p)
-        , ptr0_(ptr0)
-        , ptr1_(ptr1)
-        , ptr2_(ptr2)
-        , ptr3_(ptr3)
         , nBBS0_(nBBS0)
         , nBBS1_(nBBS1) {}
 
@@ -84,14 +74,18 @@ class assignKernel {
 
         if (gx < idims0 && gy < idims1 && gz < idims2 && gw < idims3) {
             // calculate pointer offsets for input
-            int i = p_.strds[0] *
-                    trimIndex(s0 ? gx + p_.offs[0] : ptr0_[gx], oInfo_.dims[0]);
-            int j = p_.strds[1] *
-                    trimIndex(s1 ? gy + p_.offs[1] : ptr1_[gy], oInfo_.dims[1]);
-            int k = p_.strds[2] *
-                    trimIndex(s2 ? gz + p_.offs[2] : ptr2_[gz], oInfo_.dims[2]);
-            int l = p_.strds[3] *
-                    trimIndex(s3 ? gw + p_.offs[3] : ptr3_[gw], oInfo_.dims[3]);
+            int i =
+                p_.strds[0] *
+                trimIndex(s0 ? gx + p_.offs[0] : p_.ptr[0][gx], oInfo_.dims[0]);
+            int j =
+                p_.strds[1] *
+                trimIndex(s1 ? gy + p_.offs[1] : p_.ptr[1][gy], oInfo_.dims[1]);
+            int k =
+                p_.strds[2] *
+                trimIndex(s2 ? gz + p_.offs[2] : p_.ptr[2][gz], oInfo_.dims[2]);
+            int l =
+                p_.strds[3] *
+                trimIndex(s3 ? gw + p_.offs[3] : p_.ptr[3][gw], oInfo_.dims[3]);
 
             T* iptr = in_.get_pointer();
             // offset input and output pointers
@@ -110,16 +104,16 @@ class assignKernel {
    protected:
     sycl::accessor<T> out_, in_;
     KParam oInfo_, iInfo_;
-    AssignKernelParam_t p_;
-    sycl::accessor<uint> ptr0_, ptr1_, ptr2_, ptr3_;
+    AssignKernelParam p_;
     const int nBBS0_, nBBS1_;
 };
 
 template<typename T>
-void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
+void assign(Param<T> out, const Param<T> in, const AssignKernelParam& p,
             sycl::buffer<uint>* bPtr[4]) {
     constexpr int THREADS_X = 32;
     constexpr int THREADS_Y = 8;
+    using sycl::access_mode;
 
     sycl::range<2> local(THREADS_X, THREADS_Y);
 
@@ -130,18 +124,18 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam_t& p,
                           blk_y * in.info.dims[3] * THREADS_Y);
 
     getQueue().submit([=](sycl::handler& h) {
+        auto pp      = p;
         auto out_acc = out.data->get_access(h);
         auto in_acc  = in.data->get_access(h);
 
-        auto bptr0 = bPtr[0]->get_access(h);
-        auto bptr1 = bPtr[1]->get_access(h);
-        auto bptr2 = bPtr[2]->get_access(h);
-        auto bptr3 = bPtr[3]->get_access(h);
+        pp.ptr[0] = bPtr[0]->template get_access<access_mode::read>(h);
+        pp.ptr[1] = bPtr[1]->template get_access<access_mode::read>(h);
+        pp.ptr[2] = bPtr[2]->template get_access<access_mode::read>(h);
+        pp.ptr[3] = bPtr[3]->template get_access<access_mode::read>(h);
 
-        h.parallel_for(
-            sycl::nd_range<2>(global, local),
-            assignKernel<T>(out_acc, out.info, in_acc, in.info, p, bptr0, bptr1,
-                            bptr2, bptr3, blk_x, blk_y));
+        h.parallel_for(sycl::nd_range<2>(global, local),
+                       assignKernel<T>(out_acc, out.info, in_acc, in.info, pp,
+                                       blk_x, blk_y));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }

From b3670f27ed0adff68a7459abeb283a95c4ba7dd1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 17:28:51 -0400
Subject: [PATCH 2469/2677] Workaround long long compiler error for where

---
 src/backend/oneapi/kernel/where.hpp | 33 ++++++++++++++++-------------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index c5a0172134..dd18189ae0 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -78,18 +78,21 @@ class whereKernel {
         iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
                 yid * iInfo_.strides[1];
 
-        bool cond = (yid < otInfo_.dims[1]) && (zid < otInfo_.dims[2]) &&
-                    (wid < otInfo_.dims[3]);
-        T zero = scalar<T>(0);
-
-        if (!cond) return;
-
-        uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
-
-        for (uint k = 0, id = xid; k < lim_ && id < otInfo_.dims[0];
-             k++, id += g.get_local_range(0)) {
-            uint idx = otptr[id] + accum;
-            if (iptr[id] != zero) out_acc_[idx - 1] = (off + id);
+        size_t odims0 = otInfo_.dims[0];
+        size_t odims1 = otInfo_.dims[1];
+        size_t odims2 = otInfo_.dims[2];
+        size_t odims3 = otInfo_.dims[3];
+        bool cond     = (yid < odims1) && (zid < odims2) && (wid < odims3);
+        T zero        = scalar<T>(0);
+
+        if (cond) {
+            uint accum = (bid == 0) ? 0 : rtptr[bid - 1];
+
+            for (uint k = 0, id = xid; k < lim_ && id < odims0;
+                 k++, id += g.get_local_range(0)) {
+                uint idx = otptr[id] + accum;
+                if (iptr[id] != zero) out_acc_[idx - 1] = (off + id);
+            }
         }
     }
 
@@ -151,13 +154,13 @@ static void where(Param<uint> &out, Param<T> in) {
 
     getQueue()
         .submit([&](sycl::handler &h) {
-            auto acc_in  = rtmp.data->get_access(h, sycl::range{1},
-                                                 sycl::id{rtmp_elements - 1});
+            auto acc_in = rtmp.data->get_access(h, sycl::range{1},
+                                                sycl::id{rtmp_elements - 1});
             h.copy(acc_in, &total);
         })
         .wait();
 
-    auto out_alloc = memAlloc<uint>(std::max(1U,total));
+    auto out_alloc = memAlloc<uint>(std::max(1U, total));
     out.data       = out_alloc.get();
 
     out.info.dims[0]    = total;

From 04d9f8418576461c76a5d8c89610fa8810d0f1ba Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 18:06:05 -0400
Subject: [PATCH 2470/2677] Catch sycl exceptions in the processException
 function

---
 src/backend/common/err_common.cpp | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index c7dc95b8fd..9e2b2e8a2f 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -24,6 +24,8 @@
 #ifdef AF_OPENCL
 #include <errorcodes.hpp>
 #include <platform.hpp>
+#elif defined(AF_ONEAPI)
+#include <sycl/sycl.hpp>
 #endif
 
 using boost::stacktrace::stacktrace;
@@ -161,6 +163,14 @@ af_err processException() {
         if (is_stacktrace_enabled()) { ss << ex.getStacktrace(); }
 
         err = set_global_error_string(ss.str(), ex.getError());
+#ifdef AF_ONEAPI
+    } catch (const sycl::exception &ex) {
+        char oneapi_err_msg[1024];
+        snprintf(oneapi_err_msg, sizeof(oneapi_err_msg),
+                 "oneAPI Error (%d): %s", ex.code().value(), ex.what());
+
+        err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
+#endif
 #ifdef AF_OPENCL
     } catch (const cl::Error &ex) {
         char opencl_err_msg[1024];

From aafb995546e96ff559977a67e4f5116ebafbbcf8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 11 Apr 2023 23:05:54 -0400
Subject: [PATCH 2471/2677] Fix iir errors and some other warnings in oneAPI

---
 src/backend/oneapi/jit.cpp                      | 2 --
 src/backend/oneapi/kernel/iir.hpp               | 7 ++++---
 src/backend/oneapi/kernel/pad_array_borders.hpp | 1 +
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 2190dd8070..4fc0e978ae 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -294,9 +294,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     }
     if (numOutElems == 0) { return; }
 
-    const af::dtype outputType{output_nodes[0]->getType()};
     for (Node* node : output_nodes) {
-        assert(node->getType() == outputType);
         const int id{node->getNodesMap(nodes, full_nodes, full_ids)};
         output_ids.push_back(id);
     }
diff --git a/src/backend/oneapi/kernel/iir.hpp b/src/backend/oneapi/kernel/iir.hpp
index ab00655fec..88b515fe86 100644
--- a/src/backend/oneapi/kernel/iir.hpp
+++ b/src/backend/oneapi/kernel/iir.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <math.hpp>
 
 #include <sycl/sycl.hpp>
 
@@ -112,10 +113,10 @@ class iirKernel {
 
 template<typename T, bool batch_a>
 void iir(Param<T> y, Param<T> c, Param<T> a) {
-    const int groups_y = y.info.dims[1];
-    const int groups_x = y.info.dims[2];
+    const size_t groups_y = y.info.dims[1];
+    const size_t groups_x = y.info.dims[2];
 
-    int threads = 256;
+    size_t threads = 256;
     while (threads > y.info.dims[0] && threads > 32) threads /= 2;
     sycl::range<2> local = sycl::range{threads, 1};
 
diff --git a/src/backend/oneapi/kernel/pad_array_borders.hpp b/src/backend/oneapi/kernel/pad_array_borders.hpp
index 620352f352..129f9bf381 100644
--- a/src/backend/oneapi/kernel/pad_array_borders.hpp
+++ b/src/backend/oneapi/kernel/pad_array_borders.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <math.hpp>
 #include <af/defines.h>
 
 #include <sycl/sycl.hpp>

From 488f4e5abeea0ff007e1494e5146403bae13c06a Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Sun, 9 Apr 2023 22:02:49 +0200
Subject: [PATCH 2472/2677] Corrects exceptions thrown in opencl tests
 qr_dense, rank_dense & solve_dense

---
 src/backend/opencl/kernel/laset.hpp    |  2 +-
 src/backend/opencl/kernel/swapdblk.hpp |  9 ++++-----
 src/backend/opencl/magma/magma_data.h  | 14 +++++++-------
 src/backend/opencl/magma/ungqr.cpp     |  7 ++++++-
 4 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/src/backend/opencl/kernel/laset.hpp b/src/backend/opencl/kernel/laset.hpp
index 63e9a66526..5e4588c41f 100644
--- a/src/backend/opencl/kernel/laset.hpp
+++ b/src/backend/opencl/kernel/laset.hpp
@@ -69,7 +69,7 @@ void laset(int m, int n, T offdiag, T diag, cl_mem dA, size_t dA_offset,
     // retain the cl_mem object during cl::Buffer creation
     cl::Buffer dAObj(dA, true);
 
-    cl::CommandQueue q(queue);
+    cl::CommandQueue q(queue, true);
     lasetOp(cl::EnqueueArgs(q, global, local), m, n, offdiag, diag, dAObj,
             dA_offset, ldda);
 }
diff --git a/src/backend/opencl/kernel/swapdblk.hpp b/src/backend/opencl/kernel/swapdblk.hpp
index 0b8b43fb72..a6c96ea940 100644
--- a/src/backend/opencl/kernel/swapdblk.hpp
+++ b/src/backend/opencl/kernel/swapdblk.hpp
@@ -34,6 +34,9 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     using std::string;
     using std::vector;
 
+    int nblocks = n / nb;
+    if (nblocks == 0) return;
+
     vector<TemplateArg> targs = {
         TemplateTypename<T>(),
     };
@@ -45,10 +48,6 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     auto swapdblk =
         common::getKernel("swapdblk", {{swapdblk_cl_src}}, targs, compileOpts);
 
-    int nblocks = n / nb;
-
-    if (nblocks == 0) return;
-
     int info = 0;
     if (n < 0) {
         info = -1;
@@ -75,7 +74,7 @@ void swapdblk(int n, int nb, cl_mem dA, size_t dA_offset, int ldda, int inca,
     Buffer dAObj(dA, true);
     Buffer dBObj(dB, true);
 
-    CommandQueue q(queue);
+    CommandQueue q(queue, true);
     swapdblk(EnqueueArgs(q, global, local), nb, dAObj, dA_offset, ldda, inca,
              dBObj, dB_offset, lddb, incb);
     CL_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 69bd5e36a8..6ee5ac053e 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -79,7 +79,7 @@ static magma_int_t magma_malloc(magma_ptr* ptrPtr, int num) {
 
 // --------------------
 // Free GPU memory allocated by magma_malloc.
-static inline magma_int_t magma_free(cl_mem ptr) {
+static inline magma_int_t magma_free(magma_ptr ptr) {
     cl_int err = clReleaseMemObject(ptr);
     if (err != CL_SUCCESS) { return MAGMA_ERR_INVALID_PTR; }
     return MAGMA_SUCCESS;
@@ -321,9 +321,9 @@ static void magma_setmatrix_async(magma_int_t m, magma_int_t n, T const* hA_src,
     size_t host_orig[3]     = {0, 0, 0};
     size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
     cl_int err              = clEnqueueWriteBufferRect(
-                     queue, dB_dst, CL_FALSE,  // non-blocking
-                     buffer_origin, host_orig, region, lddb * sizeof(T), 0, ldha * sizeof(T),
-                     0, hA_src, 0, NULL, event);
+        queue, dB_dst, CL_FALSE,  // non-blocking
+        buffer_origin, host_orig, region, lddb * sizeof(T), 0, ldha * sizeof(T),
+        0, hA_src, 0, NULL, event);
     clFlush(queue);
     check_error(err);
 }
@@ -357,9 +357,9 @@ static void magma_getmatrix_async(magma_int_t m, magma_int_t n, cl_mem dA_src,
     size_t host_orig[3]     = {0, 0, 0};
     size_t region[3]        = {m * sizeof(T), (size_t)n, 1};
     cl_int err              = clEnqueueReadBufferRect(
-                     queue, dA_src, CL_FALSE,  // non-blocking
-                     buffer_origin, host_orig, region, ldda * sizeof(T), 0, ldhb * sizeof(T),
-                     0, hB_dst, 0, NULL, event);
+        queue, dA_src, CL_FALSE,  // non-blocking
+        buffer_origin, host_orig, region, ldda * sizeof(T), 0, ldhb * sizeof(T),
+        0, hB_dst, 0, NULL, event);
     clFlush(queue);
     check_error(err);
 }
diff --git a/src/backend/opencl/magma/ungqr.cpp b/src/backend/opencl/magma/ungqr.cpp
index 8976758786..3f0ef001d2 100644
--- a/src/backend/opencl/magma/ungqr.cpp
+++ b/src/backend/opencl/magma/ungqr.cpp
@@ -129,7 +129,12 @@ magma_int_t magma_ungqr_gpu(magma_int_t m, magma_int_t n, magma_int_t k,
     // ((n+31)/32*32)*nb for dW larfb workspace.
     lddwork = std::min(m, n);
     cl_mem dW;
-    magma_malloc<Ty>(&dW, (((n + 31) / 32) * 32) * nb);
+    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dW, (((n + 31) / 32) * 32) * nb)) {
+        magma_free_cpu(work);
+        magma_free(dV);
+        *info = MAGMA_ERR_DEVICE_ALLOC;
+        return *info;
+    }
 
     cpu_lapack_ungqr_work_func<Ty> cpu_lapack_ungqr;
 

From 0398c55d4ebc603ba28c2fefeedba947f99814bd Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Apr 2023 10:07:25 -0400
Subject: [PATCH 2473/2677] Update clang-format version to 15 in GitHub actions

---
 .github/workflows/unix_cpu_build.yml      |  4 ++--
 src/api/unified/symbol_manager.hpp        |  2 +-
 src/backend/common/graphics_common.cpp    |  2 +-
 src/backend/cpu/convolve.cpp              |  4 ++--
 src/backend/cuda/convolveNN.cpp           |  4 ++--
 src/backend/cuda/kernel/random_engine.hpp |  4 ++--
 src/backend/oneapi/compile_module.cpp     | 17 -----------------
 src/backend/oneapi/exampleFunction.cpp    | 10 +++++-----
 src/backend/oneapi/jit.cpp                | 16 ++++++++++++++++
 src/backend/opencl/convolve.cpp           |  4 ++--
 src/backend/opencl/memory.cpp             |  4 ++--
 src/backend/opencl/svd.cpp                |  4 ++--
 src/backend/opencl/topk.cpp               |  6 +++---
 13 files changed, 40 insertions(+), 41 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 01051f7e8f..3146358772 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -17,11 +17,11 @@ jobs:
               uses: actions/checkout@master
 
             - name: Check Sources
-              uses: DoozyX/clang-format-lint-action@v0.14
+              uses: DoozyX/clang-format-lint-action@v0.15
               with:
                 source: './src ./test ./examples'
                 extensions: 'h,cpp,hpp'
-                clangFormatVersion: 14
+                clangFormatVersion: 15
 
     documentation:
         name: Documentation
diff --git a/src/api/unified/symbol_manager.hpp b/src/api/unified/symbol_manager.hpp
index df5d77705c..7f96f586e2 100644
--- a/src/api/unified/symbol_manager.hpp
+++ b/src/api/unified/symbol_manager.hpp
@@ -156,7 +156,7 @@ bool checkArrays(af_backend activeBackend, T a, Args... arg) {
         if (index_ != arrayfire::unified::getActiveBackend()) {                  \
             index_ = arrayfire::unified::getActiveBackend();                     \
             func   = (af_func)arrayfire::common::getFunctionPointer(             \
-                  arrayfire::unified::getActiveHandle(), __func__);              \
+                arrayfire::unified::getActiveHandle(), __func__);              \
         }                                                                        \
         return func(__VA_ARGS__);                                                \
     } else {                                                                     \
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 07084c43b2..217722eb36 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -260,7 +260,7 @@ fg_window ForgeManager::getMainWindow() {
             }
             fg_window w = nullptr;
             forgeError  = this->mPlugin->fg_create_window(
-                 &w, WIDTH, HEIGHT, "ArrayFire", NULL, true);
+                &w, WIDTH, HEIGHT, "ArrayFire", NULL, true);
             if (forgeError != FG_ERR_NONE) { return; }
             this->setWindowChartGrid(w, 1, 1);
             this->mPlugin->fg_make_window_current(w);
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index a57ace15f6..20138fd9e5 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -193,7 +193,7 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
@@ -232,7 +232,7 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
diff --git a/src/backend/cuda/convolveNN.cpp b/src/backend/cuda/convolveNN.cpp
index 1110d81506..d4be5d9616 100644
--- a/src/backend/cuda/convolveNN.cpp
+++ b/src/backend/cuda/convolveNN.cpp
@@ -260,7 +260,7 @@ Array<T> data_gradient_base(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
@@ -390,7 +390,7 @@ Array<T> filter_gradient_base(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     T alpha        = scalar<T>(1.0);
     T beta         = scalar<T>(0.0);
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 7fddcbfd20..07ba4163a2 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -60,9 +60,9 @@ static const int THREADS = 256;
 #define HALF_HALF_FACTOR __ushort_as_half(0x80)
 
 // Conversion to half adapted from Random123
-//#define SIGNED_HALF_FACTOR                                \
+// #define SIGNED_HALF_FACTOR                                \
     //((1.0f) / (std::numeric_limits<short>::max() + (1.0f)))
-//#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
+// #define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
 //
 // NOTE: The following constants for half were calculated using the formulas
 // above. This is done so that we can avoid unnecessary computations because the
diff --git a/src/backend/oneapi/compile_module.cpp b/src/backend/oneapi/compile_module.cpp
index 2737909208..016b2d7dcf 100644
--- a/src/backend/oneapi/compile_module.cpp
+++ b/src/backend/oneapi/compile_module.cpp
@@ -71,23 +71,6 @@ string getProgramBuildLog(const kernel_bundle<bundle_state::executable> &prog) {
 namespace arrayfire {
 namespace oneapi {
 
-// const static string DEFAULT_MACROS_STR(
-// "\n\
-                                           //#ifdef USE_DOUBLE\n\
-                                           //#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n\
-                                           //#endif\n                     \
-                                           //#ifdef USE_HALF\n\
-                                           //#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n\
-                                           //#else\n                     \
-                                           //#define half short\n          \
-                                           //#endif\n                      \
-                                           //#ifndef M_PI\n               \
-                                           //#define
-// M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n
-// \
-                                           //#endif\n                     \
-                                           //");
-
 /*
 get_kernel_bundle<>() needs sycl::context
 kernel_bundle<bundle_state::executable> buildProgram(const vector<string>
diff --git a/src/backend/oneapi/exampleFunction.cpp b/src/backend/oneapi/exampleFunction.cpp
index 9e6d81e9d5..6159d9d1d4 100644
--- a/src/backend/oneapi/exampleFunction.cpp
+++ b/src/backend/oneapi/exampleFunction.cpp
@@ -16,11 +16,11 @@
 #include <err_oneapi.hpp>  // error check functions and Macros
                            // specific to oneapi backend
 
-//#include <kernel/exampleFunction.hpp>  // this header under the folder
-// src/oneapi/kernel
-// defines the OneAPI kernel wrapper
-// function to which the main computation of your
-// algorithm should be relayed to
+// #include <kernel/exampleFunction.hpp>  // this header under the folder
+//  src/oneapi/kernel
+//  defines the OneAPI kernel wrapper
+//  function to which the main computation of your
+//  algorithm should be relayed to
 
 using af::dim4;
 
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 4fc0e978ae..0da9dfaf22 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -62,6 +62,22 @@ using sycl::backend;
 namespace arrayfire {
 
 namespace opencl {
+
+const static string DEFAULT_MACROS_STR(R"JIT(
+#ifdef USE_DOUBLE
+#pragma OPENCL EXTENSION cl_khr_fp64 : enable
+#endif
+#ifdef USE_HALF
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#else
+#define half short
+#endif
+#ifndef M_PI
+#define
+ M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164
+#endif
+)JIT");
+
 string getKernelString(const string& funcName, const vector<Node*>& full_nodes,
                        const vector<Node_ids>& full_ids,
                        const vector<int>& output_ids, const bool is_linear,
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index edc28e4e35..f826102caf 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -185,7 +185,7 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
@@ -224,7 +224,7 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
     Array<T> collapsed_gradient = incoming_gradient;
     collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
     collapsed_gradient          = modDims(
-                 collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
 
     Array<T> res =
         matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index 68ae43c5e8..d2e0190431 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -204,8 +204,8 @@ size_t Allocator::getMaxMemorySize(int id) {
 void *Allocator::nativeAlloc(const size_t bytes) {
     cl_int err = CL_SUCCESS;
     auto ptr   = static_cast<void *>(clCreateBuffer(
-          getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
-          bytes, nullptr, &err));
+        getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
+        bytes, nullptr, &err));
 
     if (err != CL_SUCCESS) {
         auto str = fmt::format("Failed to allocate device memory of size {}",
diff --git a/src/backend/opencl/svd.cpp b/src/backend/opencl/svd.cpp
index 7bda5306ca..b8bea727d0 100644
--- a/src/backend/opencl/svd.cpp
+++ b/src/backend/opencl/svd.cpp
@@ -137,8 +137,8 @@ void svd(Array<T> &arrU, Array<Tr> &arrS, Array<T> &arrVT, Array<T> &arrA,
 
     if (want_vectors) {
         mappedU  = static_cast<T *>(getQueue().enqueueMapBuffer(
-             *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
-             sizeof(T) * arrU.elements()));
+            *arrU.get(), CL_FALSE, CL_MAP_WRITE, sizeof(T) * arrU.getOffset(),
+            sizeof(T) * arrU.elements()));
         mappedVT = static_cast<T *>(getQueue().enqueueMapBuffer(
             *arrVT.get(), CL_TRUE, CL_MAP_WRITE, sizeof(T) * arrVT.getOffset(),
             sizeof(T) * arrVT.elements()));
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 9ff966ed65..18e03d2f0d 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -76,13 +76,13 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         cl::Event ev_in, ev_val, ev_ind;
 
         T* ptr     = static_cast<T*>(getQueue().enqueueMapBuffer(
-                *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
-                nullptr, &ev_in));
+            *in_buf, CL_FALSE, CL_MAP_READ, 0, in.elements() * sizeof(T),
+            nullptr, &ev_in));
         uint* iptr = static_cast<uint*>(getQueue().enqueueMapBuffer(
             *ibuf, CL_FALSE, CL_MAP_READ | CL_MAP_WRITE, 0, k * sizeof(uint),
             nullptr, &ev_ind));
         T* vptr    = static_cast<T*>(getQueue().enqueueMapBuffer(
-               *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
+            *vbuf, CL_FALSE, CL_MAP_WRITE, 0, k * sizeof(T), nullptr, &ev_val));
 
         vector<uint> idx(in.elements());
 

From 64586e04c8a5c7c3fe63b42c3a8514c810c18979 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 14 Apr 2023 10:48:16 -0400
Subject: [PATCH 2474/2677] Add macros to enable fp16 and fp64 in JIT kernels
 in oneAPI

---
 src/backend/oneapi/jit.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 0da9dfaf22..6c4d4c1828 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -231,7 +231,7 @@ __kernel void )JIT";
     }
 
     thread_local stringstream kerStream;
-    kerStream << kernelVoid << funcName << "(\n"
+    kerStream << DEFAULT_MACROS_STR << kernelVoid << funcName << "(\n"
               << inParamStream.str() << outParamStream.str() << dimParams << ")"
               << blockStart;
     if (is_linear) {

From febbe06f990abf58d7afe069cbb59cab6787da8f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Apr 2023 13:15:02 -0400
Subject: [PATCH 2475/2677] Implement memory manager for the oneAPI backend

---
 src/backend/oneapi/Array.cpp  |  12 +--
 src/backend/oneapi/memory.cpp | 135 ++++++++++++----------------------
 src/backend/oneapi/memory.hpp |   8 +-
 3 files changed, 55 insertions(+), 100 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 93c9e0df7e..4682df50f1 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -91,7 +91,7 @@ template<typename T>
 Array<T>::Array(const dim4 &dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(memAlloc<T>(info.elements()).release(), bufferFree<T>)
+    , data(memAlloc<T>(info.elements()).release(), memFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {}
@@ -112,7 +112,7 @@ template<typename T>
 Array<T>::Array(const dim4 &dims, const T *const in_data)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
-    , data(memAlloc<T>(info.elements()).release(), bufferFree<T>)
+    , data(memAlloc<T>(info.elements()).release(), memFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -138,7 +138,7 @@ Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(copy ? memAlloc<T>(info.elements()).release() : new buffer<T>(*mem),
-           bufferFree<T>)
+           memFree<T>)
     , data_dims(dims)
     , node()
     , owner(true) {
@@ -171,7 +171,7 @@ Array<T>::Array(Param<T> &tmp, bool owner_)
                 tmp.info.strides[3]),
            static_cast<af_dtype>(dtype_traits<T>::af_type))
     , data(
-          tmp.data, owner_ ? bufferFree<T> : [](buffer<T> * /*unused*/) {})
+          tmp.data, owner_ ? memFree<T> : [](sycl::buffer<T> * /*unused*/) {})
     , data_dims(dim4(tmp.info.dims[0], tmp.info.dims[1], tmp.info.dims[2],
                      tmp.info.dims[3]))
     , node()
@@ -205,7 +205,7 @@ void Array<T>::eval() {
 
     this->setId(getActiveDeviceId());
     data = std::shared_ptr<sycl::buffer<T>>(
-        memAlloc<T>(info.elements()).release(), bufferFree<T>);
+        memAlloc<T>(info.elements()).release(), memFree<T>);
 
     // Do not replace this with cast operator
     KParam info = {{dims()[0], dims()[1], dims()[2], dims()[3]},
@@ -256,7 +256,7 @@ void evalMultiple(vector<Array<T> *> arrays) {
 
         array->setId(getActiveDeviceId());
         array->data = std::shared_ptr<buffer<T>>(
-            memAlloc<T>(info.elements()).release(), bufferFree<T>);
+            memAlloc<T>(info.elements()).release(), memFree<T>);
 
         // Do not replace this with cast operator
         KParam kInfo = {
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 971fa05b64..2b383b9520 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -61,27 +61,47 @@ template<typename T>
 // unique_ptr<int, function<void(int *)>> memAlloc(
 std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>
 memAlloc(const size_t &elements) {
-    return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(
-        new sycl::buffer<T>(sycl::range(elements)), bufferFree<T>);
+    if (elements) {
+        dim4 dims(elements * sizeof(T));
+
+        // The alloc function returns a pointer to a buffer<std::byte> object.
+        // We need to reinterpret that object into buffer<T> while keeping the
+        // same pointer value for memory accounting purposes. We acheive this
+        // assigning the renterpreted buffer back into the original pointer.
+        // This would delete the buffer<std::byte> object and replace it with
+        // the buffer<T> object. We do the reverse in the memFree function
+        auto *ptr = static_cast<sycl::buffer<std::byte> *>(
+            memoryManager().alloc(false, 1, dims.get(), 1));
+        sycl::buffer<T> *optr = static_cast<sycl::buffer<T> *>((void *)ptr);
+        size_t bytes          = ptr->byte_size();
+
+        // TODO(umar): This could be a DANGEROUS function becasue we are calling
+        // delete on the reniterpreted buffer<T> instead of the orignal
+        // buffer<byte> object
+        *optr = ptr->template reinterpret<T>(sycl::range(bytes / sizeof(T)));
+        return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(
+            optr, memFree<T>);
+    } else {
+        return unique_ptr<sycl::buffer<T>, function<void(sycl::buffer<T> *)>>(
+            nullptr, memFree<T>);
+    }
 }
 
 void *memAllocUser(const size_t &bytes) {
-    ONEAPI_NOT_SUPPORTED("memAllocUser Not supported");
-    return nullptr;
-
-    // dim4 dims(bytes);
-    // void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
-    // auto buf  = static_cast<cl_mem>(ptr);
-    // return new cl::Buffer(buf, true);
+    dim4 dims(bytes);
+    void *ptr = memoryManager().alloc(true, 1, dims.get(), 1);
+    return ptr;
 }
 
-void memFree(void *ptr) {
-    ONEAPI_NOT_SUPPORTED("memFree Not supported");
-
-    // cl::Buffer *buf = reinterpret_cast<cl::Buffer *>(ptr);
-    // cl_mem mem      = static_cast<cl_mem>((*buf)());
-    // delete buf;
-    // return memoryManager().unlock(static_cast<void *>(mem), false);
+template<typename T>
+void memFree(sycl::buffer<T> *ptr) {
+    if (ptr) {
+        sycl::buffer<std::byte> *optr =
+            static_cast<sycl::buffer<std::byte> *>((void *)ptr);
+        size_t bytes = ptr->byte_size();
+        *optr        = ptr->template reinterpret<std::byte>(sycl::range(bytes));
+        memoryManager().unlock(optr, false);
+    }
 }
 
 void memFreeUser(void *ptr) {
@@ -90,49 +110,17 @@ void memFreeUser(void *ptr) {
     // cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
     // cl_mem mem      = (*buf)();
     // delete buf;
-    // memoryManager().unlock(mem, true);
-}
-
-template<typename T>
-sycl::buffer<T> *bufferAlloc(const size_t &bytes) {
-    ONEAPI_NOT_SUPPORTED("bufferAlloc Not supported");
-    return nullptr;
-
-    // dim4 dims(bytes);
-    // if (bytes) {
-    //     void *ptr       = memoryManager().alloc(false, 1, dims.get(), 1);
-    //     cl_mem mem      = static_cast<cl_mem>(ptr);
-    //     cl::Buffer *buf = new cl::Buffer(mem, true);
-    //     return buf;
-    // } else {
-    //     return nullptr;
-    // }
-}
-
-template<typename T>
-void bufferFree(sycl::buffer<T> *buf) {
-    if (buf) { delete buf; }
-    // if (buf) {
-    //     cl_mem mem = (*buf)();
-    //     delete buf;
-    //     memoryManager().unlock(static_cast<void *>(mem), false);
-    // }
+    memoryManager().unlock(ptr, true);
 }
 
 template<typename T>
 void memLock(const sycl::buffer<T> *ptr) {
-    ONEAPI_NOT_SUPPORTED("memLock Not supported");
-
-    // cl_mem mem = static_cast<cl_mem>((*ptr)());
-    // memoryManager().userLock(static_cast<void *>(mem));
+    memoryManager().userLock(static_cast<const void *>(ptr));
 }
 
 template<typename T>
 void memUnlock(const sycl::buffer<T> *ptr) {
-    ONEAPI_NOT_SUPPORTED("memUnlock Not supported");
-
-    // cl_mem mem = static_cast<cl_mem>((*ptr)());
-    // memoryManager().userUnlock(static_cast<void *>(mem));
+    memoryManager().userUnlock(static_cast<const void *>(ptr));
 }
 
 bool isLocked(const void *ptr) {
@@ -161,7 +149,6 @@ void pinnedFree(void *ptr) { pinnedMemoryManager().unlock(ptr, false); }
                              std::function<void(sycl::buffer<T> *)>> \
     memAlloc(const size_t &elements);                                \
     template T *pinnedAlloc(const size_t &elements);                 \
-    template void bufferFree(sycl::buffer<T> *buf);                  \
     template void memLock(const sycl::buffer<T> *buf);               \
     template void memUnlock(const sycl::buffer<T> *buf);
 
@@ -191,18 +178,7 @@ void *pinnedAlloc<void>(const size_t &elements) {
 
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
-void Allocator::shutdown() {
-    ONEAPI_NOT_SUPPORTED("Allocator::shutdown Not supported");
-
-    // for (int n = 0; n < opencl::getDeviceCount(); n++) {
-    //     try {
-    //         opencl::setDevice(n);
-    //         shutdownMemoryManager();
-    //     } catch (const AfError &err) {
-    //         continue;  // Do not throw any errors while shutting down
-    //     }
-    // }
-}
+void Allocator::shutdown() {}
 
 int Allocator::getActiveDeviceId() { return oneapi::getActiveDeviceId(); }
 
@@ -211,33 +187,16 @@ size_t Allocator::getMaxMemorySize(int id) {
 }
 
 void *Allocator::nativeAlloc(const size_t bytes) {
-    ONEAPI_NOT_SUPPORTED("Allocator::nativeAlloc Not supported");
-    return nullptr;
-
-    // cl_int err = CL_SUCCESS;
-    // auto ptr   = static_cast<void *>(clCreateBuffer(
-    //     getContext()(), CL_MEM_READ_WRITE,  // NOLINT(hicpp-signed-bitwise)
-    //     bytes, nullptr, &err));
-
-    // if (err != CL_SUCCESS) {
-    //     auto str = fmt::format("Failed to allocate device memory of size {}",
-    //                            bytesToString(bytes));
-    //     AF_ERROR(str, AF_ERR_NO_MEM);
-    // }
-
-    // AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes), ptr);
-    // return ptr;
+    auto *ptr = new sycl::buffer<std::byte>(sycl::range(bytes));
+    AF_TRACE("nativeAlloc: {} {}", bytesToString(bytes),
+             static_cast<void *>(ptr));
+    return ptr;
 }
 
 void Allocator::nativeFree(void *ptr) {
-    ONEAPI_NOT_SUPPORTED("Allocator::nativeFree Not supported");
-
-    // cl_mem buffer = static_cast<cl_mem>(ptr);
-    // AF_TRACE("nativeFree:          {}", ptr);
-    // cl_int err = clReleaseMemObject(buffer);
-    // if (err != CL_SUCCESS) {
-    //     AF_ERROR("Failed to release device memory.", AF_ERR_RUNTIME);
-    // }
+    auto *buf = static_cast<sycl::buffer<std::byte> *>(ptr);
+    AF_TRACE("nativeFree:          {}", ptr);
+    delete buf;
 }
 
 AllocatorPinned::AllocatorPinned() { logger = common::loggerFactory("mem"); }
diff --git a/src/backend/oneapi/memory.hpp b/src/backend/oneapi/memory.hpp
index dea5e62f5a..ebe5f2403b 100644
--- a/src/backend/oneapi/memory.hpp
+++ b/src/backend/oneapi/memory.hpp
@@ -20,11 +20,6 @@
 
 namespace arrayfire {
 namespace oneapi {
-template<typename T>
-sycl::buffer<T> *bufferAlloc(const size_t &bytes);
-
-template<typename T>
-void bufferFree(sycl::buffer<T> *buf);
 
 template<typename T>
 using bufptr =
@@ -37,7 +32,8 @@ void *memAllocUser(const size_t &bytes);
 // Need these as 2 separate function and not a default argument
 // This is because it is used as the deleter in shared pointer
 // which cannot support default arguments
-void memFree(void *ptr);
+template<typename T>
+void memFree(sycl::buffer<T> *ptr);
 void memFreeUser(void *ptr);
 
 template<typename T>

From a3344ee5e706d8a542e72ffcae39aadb18f7e43d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Apr 2023 13:38:50 -0400
Subject: [PATCH 2476/2677] Handle 0 element array in math and arith functions

---
 src/api/c/binary.cpp  | 22 +++++++++++++++++++++-
 src/api/c/complex.cpp |  9 ++++++++-
 src/api/c/unary.cpp   |  7 +++++++
 3 files changed, 36 insertions(+), 2 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index b9f9393421..566a4b22b5 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -128,6 +128,9 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
 
         if (batchMode || linfo.dims() == rinfo.dims()) {
             dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
+            if (odims.ndims() == 0) {
+                return af_create_handle(out, 0, nullptr, otype);
+            }
 
             switch (otype) {
                 case f32: res = arithOp<float, op>(lhs, rhs, odims); break;
@@ -146,6 +149,9 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
                 default: TYPE_ERROR(0, otype);
             }
         } else {
+            if (linfo.ndims() == 0 && rinfo.ndims() == 0) {
+                return af_create_handle(out, 0, nullptr, otype);
+            }
             switch (otype) {
                 case f32: res = arithOpBroadcast<float, op>(lhs, rhs); break;
                 case f64: res = arithOpBroadcast<double, op>(lhs, rhs); break;
@@ -178,8 +184,11 @@ static af_err af_arith_real(af_array *out, const af_array lhs,
         const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
-
         const af_dtype otype = implicit(linfo.getType(), rinfo.getType());
+        if (odims.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, otype);
+        }
+
         af_array res;
         switch (otype) {
             case f32: res = arithOp<float, op>(lhs, rhs, odims); break;
@@ -462,6 +471,9 @@ af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs,
         const ArrayInfo &rinfo = getInfo(rhs);
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
+        if (odims.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, type);
+        }
 
         af_array res;
         switch (type) {
@@ -491,6 +503,10 @@ af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs,
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        if (odims.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, type);
+        }
+
         af_array res;
         switch (type) {
             case f32: res = arithOp<float, af_hypot_t>(lhs, rhs, odims); break;
@@ -523,6 +539,10 @@ static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs,
 
         dim4 odims = getOutDims(linfo.dims(), rinfo.dims(), batchMode);
 
+        if (odims.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, type);
+        }
+
         af_array res;
         switch (type) {
             case f32: res = logicOp<float, op>(lhs, rhs, odims); break;
diff --git a/src/api/c/complex.cpp b/src/api/c/complex.cpp
index c7a4c4e2bc..afa24d8483 100644
--- a/src/api/c/complex.cpp
+++ b/src/api/c/complex.cpp
@@ -47,9 +47,11 @@ af_err af_cplx2(af_array *out, const af_array lhs, const af_array rhs,
         }
 
         if (type != f64) { type = f32; }
-
         dim4 odims =
             getOutDims(getInfo(lhs).dims(), getInfo(rhs).dims(), batchMode);
+        if (odims.ndims() == 0) {
+            return af_create_handle(out, 0, nullptr, type);
+        }
 
         af_array res;
         switch (type) {
@@ -72,6 +74,7 @@ af_err af_cplx(af_array *out, const af_array in) {
         if (type == c32 || type == c64) {
             AF_ERROR("Inputs to cplx2 can not be of complex type", AF_ERR_ARG);
         }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array tmp;
         AF_CHECK(af_constant(&tmp, 0, info.ndims(), info.dims().get(), type));
@@ -98,6 +101,7 @@ af_err af_real(af_array *out, const af_array in) {
         af_dtype type         = info.getType();
 
         if (type != c32 && type != c64) { return af_retain_array(out, in); }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array res;
         switch (type) {
@@ -125,6 +129,7 @@ af_err af_imag(af_array *out, const af_array in) {
         if (type != c32 && type != c64) {
             return af_constant(out, 0, info.ndims(), info.dims().get(), type);
         }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array res;
         switch (type) {
@@ -150,6 +155,7 @@ af_err af_conjg(af_array *out, const af_array in) {
         af_dtype type         = info.getType();
 
         if (type != c32 && type != c64) { return af_retain_array(out, in); }
+        if (info.ndims() == 0) { return af_retain_array(out, in); }
 
         af_array res;
         switch (type) {
@@ -178,6 +184,7 @@ af_err af_abs(af_array *out, const af_array in) {
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
         if (in_type == f16) { type = f16; }
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         switch (type) {
             // clang-format off
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index af18031eab..6d8b584ace 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -79,6 +79,7 @@ static af_err af_unary(af_array *out, const af_array in) {
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
         if (in_type == f16) { type = f16; }
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         switch (type) {
             case f16: res = unaryOp<half, op>(in); break;
@@ -104,6 +105,7 @@ static af_err af_unary_complex(af_array *out, const af_array in) {
         // Convert all inputs to floats / doubles
         af_dtype type = implicit(in_type, f32);
         if (in_type == f16) { type = f16; }
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         switch (type) {
             case f32: res = unaryOp<float, op>(in); break;
@@ -562,6 +564,7 @@ af_err af_not(af_array *out, const af_array in) {
     try {
         af_array tmp;
         const ArrayInfo &in_info = getInfo(in);
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         AF_CHECK(af_constant(&tmp, 0, in_info.ndims(), in_info.dims().get(),
                              in_info.getType()));
@@ -613,6 +616,7 @@ af_err af_bitnot(af_array *out, const af_array in) {
 af_err af_arg(af_array *out, const af_array in) {
     try {
         const ArrayInfo &in_info = getInfo(in);
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         if (!in_info.isComplex()) {
             return af_constant(out, 0, in_info.ndims(), in_info.dims().get(),
@@ -639,6 +643,7 @@ af_err af_pow2(af_array *out, const af_array in) {
     try {
         af_array two;
         const ArrayInfo &in_info = getInfo(in);
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         AF_CHECK(af_constant(&two, 2, in_info.ndims(), in_info.dims().get(),
                              in_info.getType()));
@@ -656,6 +661,7 @@ af_err af_factorial(af_array *out, const af_array in) {
     try {
         af_array one;
         const ArrayInfo &in_info = getInfo(in);
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         AF_CHECK(af_constant(&one, 1, in_info.ndims(), in_info.dims().get(),
                              in_info.getType()));
@@ -722,6 +728,7 @@ static af_err af_check(af_array *out, const af_array in) {
         // Convert all inputs to floats / doubles / complex
         af_dtype type = implicit(in_type, f32);
         if (in_type == f16) { type = f16; }
+        if (in_info.ndims() == 0) { return af_retain_array(out, in); }
 
         switch (type) {
             case f32: res = checkOp<float, op>(in); break;

From 48296c0a267609ce73146f093b213be2d3c9411f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 18 Apr 2023 13:39:33 -0400
Subject: [PATCH 2477/2677] Fix unroll warning in scan_first kernel

---
 src/backend/oneapi/kernel/scan_first.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 8660494657..3a5b113914 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -105,7 +105,6 @@ class scanFirstKernel {
             group_barrier(g);
 
             int start = 0;
-#pragma unroll
             for (int off = 1; off < DIMX_; off *= 2) {
                 if (lidx >= off) val = binop(val, sptr[(start - off) + lidx]);
                 start              = DIMX_ - start;

From 913ff69b2c8d2cd932639d923fd18204a3e3ef7f Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 18 Apr 2023 20:49:21 -0400
Subject: [PATCH 2478/2677] enables join in oneapi backend

---
 src/backend/oneapi/join.cpp           | 233 ++++++++++++++++++++++++--
 src/backend/oneapi/kernel/memcopy.hpp |  31 ++--
 2 files changed, 240 insertions(+), 24 deletions(-)

diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
index 9e8aa2f743..2633c43a62 100644
--- a/src/backend/oneapi/join.cpp
+++ b/src/backend/oneapi/join.cpp
@@ -11,13 +11,18 @@
 #include <common/half.hpp>
 #include <err_oneapi.hpp>
 #include <join.hpp>
+#include <kernel/memcopy.hpp>
+#include <platform.hpp>
 
 #include <algorithm>
+#include <map>
 #include <stdexcept>
 #include <vector>
 
 using af::dim4;
 using arrayfire::common::half;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
 using std::transform;
 using std::vector;
 
@@ -33,21 +38,229 @@ dim4 calcOffset(const dim4 &dims, int dim) {
 }
 
 template<typename T>
-Array<T> join(const int dim, const Array<T> &first, const Array<T> &second) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(af::dim4(1));
+Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
+    // All dimensions except join dimension must be equal
+    const dim4 &fdims{first.dims()};
+    const dim4 &sdims{second.dims()};
+
+    // Compute output dims
+    dim4 odims(fdims);
+    odims.dims[jdim] += sdims.dims[jdim];
+    Array<T> out = createEmptyArray<T>(odims);
+
+    // topspeed is achieved when byte size(in+out) ~= L2CacheSize
+    //
+    // 1 array: memcpy always copies 1 array.  topspeed
+    //      --> size(in) <= L2CacheSize/2
+    // 2 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/2
+    //          --> JIT can copy 2 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - size(in) < L2CacheSize/2
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called twice
+    //      - size(in) >= L2CacheSize/2
+    //          --> memcpy will achieve veryLargeArray speed.  The kernel
+    //              will be called twice
+    if (fdims.dims[jdim] == sdims.dims[jdim]) {
+        const size_t L2CacheSize{getL2CacheSize(oneapi::getDevice())};
+        if (!(first.isReady() || second.isReady()) ||
+            (fdims.elements() * sizeof(T) * 2 * 2 < L2CacheSize)) {
+            // Both arrays have same size & everything fits into the cache,
+            // so thread in 1 JIT kernel, iso individual copies which is
+            // always slower
+            const dim_t *outStrides{out.strides().dims};
+            vector<Param<T>> outputs{
+                {out.get(),
+                 {{fdims.dims[0], fdims.dims[1], fdims.dims[2], fdims.dims[3]},
+                  {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+                  0}},
+                {out.get(),
+                 {{sdims.dims[0], sdims.dims[1], sdims.dims[2], sdims.dims[3]},
+                  {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+                  fdims.dims[jdim] * outStrides[jdim]}}};
+            // Extend the life of the returned node, bij saving the
+            // corresponding shared_ptr
+            const Node_ptr fNode{first.getNode()};
+            const Node_ptr sNode{second.getNode()};
+            vector<Node *> nodes{fNode.get(), sNode.get()};
+            evalNodes(outputs, nodes);
+            return out;
+        }
+        // continue because individually processing is faster
+    }
+
+    // Handle each array individually
+    if (first.isReady()) {
+        if (1LL + jdim >= first.ndims() && first.isLinear()) {
+            // first & out are linear
+            getQueue()
+                .submit([=](sycl::handler &h) {
+                    sycl::range sz(first.elements());
+                    sycl::id src_offset(first.getOffset());
+                    sycl::accessor offset_acc_src =
+                        first.get()
+                            ->template get_access<sycl::access_mode::read>(
+                                h, sz, src_offset);
+                    sycl::id dst_offset(0);
+                    sycl::accessor offset_acc_dst =
+                        out.get()
+                            ->template get_access<sycl::access_mode::write>(
+                                h, sz, dst_offset);
+                    h.copy(offset_acc_src, offset_acc_dst);
+                })
+                .wait();
+        } else {
+            kernel::memcopy<T>(out.get(), out.strides().get(), first.get(),
+                               fdims.get(), first.strides().get(),
+                               first.getOffset(), first.ndims());
+        }
+    } else {
+        // Write the result directly in the out array
+        const dim_t *outStrides{out.strides().dims};
+        Param<T> output{
+            out.get(),
+            {{fdims.dims[0], fdims.dims[1], fdims.dims[2], fdims.dims[3]},
+             {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+             0}};
+        evalNodes(output, first.getNode().get());
+    }
+
+    if (second.isReady()) {
+        if (1LL + jdim >= second.ndims() && second.isLinear()) {
+            // second & out are linear
+            getQueue()
+                .submit([=](sycl::handler &h) {
+                    sycl::range sz(second.elements());
+                    sycl::id src_offset(second.getOffset());
+                    sycl::accessor offset_acc_src =
+                        second.get()
+                            ->template get_access<sycl::access_mode::read>(
+                                h, sz, src_offset);
+                    sycl::id dst_offset(fdims.dims[jdim] *
+                                        out.strides().dims[jdim]);
+                    sycl::accessor offset_acc_dst =
+                        out.get()
+                            ->template get_access<sycl::access_mode::write>(
+                                h, sz, dst_offset);
+                    h.copy(offset_acc_src, offset_acc_dst);
+                })
+                .wait();
+        } else {
+            kernel::memcopy<T>(out.get(), out.strides().get(), second.get(),
+                               sdims.get(), second.strides().get(),
+                               second.getOffset(), second.ndims(),
+                               fdims.dims[jdim] * out.strides().dims[jdim]);
+        }
+    } else {
+        // Write the result directly in the out array
+        const dim_t *outStrides{out.strides().dims};
+        Param<T> output{
+            out.get(),
+            {{sdims.dims[0], sdims.dims[1], sdims.dims[2], sdims.dims[3]},
+             {outStrides[0], outStrides[1], outStrides[2], outStrides[3]},
+             fdims.dims[jdim] * outStrides[jdim]}};
+        evalNodes(output, second.getNode().get());
+    }
     return out;
 }
 
 template<typename T>
-void join_wrapper(const int dim, Array<T> &out,
-                  const vector<Array<T>> &inputs) {
-    ONEAPI_NOT_SUPPORTED("");
-}
+void join(Array<T> &out, const int jdim, const vector<Array<T>> &inputs) {
+    class eval {
+       public:
+        vector<Param<T>> outputs;
+        vector<Node_ptr> nodePtrs;
+        vector<Node *> nodes;
+        vector<const Array<T> *> ins;
+    };
+    std::map<dim_t, eval> evals;
+    const dim_t *ostrides{out.strides().dims};
+    const size_t L2CacheSize{getL2CacheSize(oneapi::getDevice())};
 
-template<typename T>
-void join(Array<T> &out, const int dim, const vector<Array<T>> &inputs) {
-    ONEAPI_NOT_SUPPORTED("");
+    // topspeed is achieved when byte size(in+out) ~= L2CacheSize
+    //
+    // 1 array: memcpy always copies 1 array.  topspeed
+    //      --> size(in) <= L2CacheSize/2
+    // 2 arrays: topspeeds
+    //      - size(in) < L2CacheSize/2/2
+    //          --> JIT can copy 2 arrays in // and is fastest
+    //              (condition: array sizes have to be identical)
+    //      - size(in) < L2CacheSize/2
+    //          --> memcpy will achieve highest speed, although the kernel
+    //              has to be called twice
+    //      - size(in) >= L2CacheSize/2
+    //          --> memcpy will achieve veryLargeArray speed.  The kernel
+    //              will be called twice
+
+    // Group all arrays according to size
+    dim_t outOffset{0};
+    for (const Array<T> &iArray : inputs) {
+        const dim_t *idims{iArray.dims().dims};
+        eval &e{evals[idims[jdim]]};
+        const Param output{
+            out.get(),
+            {{idims[0], idims[1], idims[2], idims[3]},
+             {ostrides[0], ostrides[1], ostrides[2], ostrides[3]},
+             outOffset}};
+        e.outputs.push_back(output);
+        // Extend life of the returned node by saving the corresponding
+        // shared_ptr
+        e.nodePtrs.emplace_back(iArray.getNode());
+        e.nodes.push_back(e.nodePtrs.back().get());
+        e.ins.push_back(&iArray);
+        outOffset += idims[jdim] * ostrides[jdim];
+    }
+
+    for (auto &eval : evals) {
+        auto &s{eval.second};
+        if (s.ins.size() == 1 ||
+            s.ins[0]->elements() * sizeof(T) * 2 * 2 > L2CacheSize) {
+            // Process (evaluate arrays) individually for
+            //  - single small array
+            //  - very large arrays
+            auto nodeIt{begin(s.nodes)};
+            auto outputIt{begin(s.outputs)};
+            for (const Array<T> *in : s.ins) {
+                if (in->isReady()) {
+                    if (1LL + jdim >= in->ndims() && in->isLinear()) {
+                        getQueue()
+                            .submit([=](sycl::handler &h) {
+                                sycl::range sz(in->elements());
+                                sycl::id src_offset(in->getOffset());
+                                sycl::accessor offset_acc_src =
+                                    in->get()
+                                        ->template get_access<
+                                            sycl::access_mode::read>(
+                                            h, sz, src_offset);
+                                sycl::id dst_offset(outputIt->info.offset);
+                                sycl::accessor offset_acc_dst =
+                                    outputIt->data->template get_access<
+                                        sycl::access_mode::write>(h, sz,
+                                                                  dst_offset);
+                                h.copy(offset_acc_src, offset_acc_dst);
+                            })
+                            .wait();
+                    } else {
+                        kernel::memcopy<T>(
+                            outputIt->data,
+                            af::dim4(4, outputIt->info.strides).get(),
+                            in->get(), in->dims().get(), in->strides().get(),
+                            in->getOffset(), in->ndims(),
+                            outputIt->info.offset);
+                    }
+                    // eliminate this array from the list, so that it will
+                    // not be processed in bulk via JIT
+                    outputIt = s.outputs.erase(outputIt);
+                    nodeIt   = s.nodes.erase(nodeIt);
+                } else {
+                    ++outputIt;
+                    ++nodeIt;
+                }
+            }
+        }
+        evalNodes(s.outputs, s.nodes);
+    }
 }
 
 #define INSTANTIATE(T)                                              \
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index c3b317ef17..482c7cd366 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -34,15 +34,16 @@ typedef struct {
 template<typename T>
 class memCopy {
    public:
-    memCopy(sycl::accessor<T> out, dims_t ostrides, sycl::accessor<T> in,
-            dims_t idims, dims_t istrides, int offset, int groups_0,
-            int groups_1)
+    memCopy(sycl::accessor<T> out, dims_t ostrides, int ooffset,
+            sycl::accessor<T> in, dims_t idims, dims_t istrides, int ioffset,
+            int groups_0, int groups_1)
         : out_(out)
-        , in_(in)
         , ostrides_(ostrides)
+        , ooffset_(ooffset)
+        , in_(in)
         , idims_(idims)
         , istrides_(istrides)
-        , offset_(offset)
+        , ioffset_(ioffset)
         , groups_0_(groups_0)
         , groups_1_(groups_1) {}
 
@@ -59,14 +60,13 @@ class memCopy {
         const int id1        = group_id_1 * gg.get_local_range(1) + lid1;
 
         T *iptr = in_.get_pointer();
-        iptr += offset_;
         // FIXME: Do more work per work group
 
         T *optr = out_.get_pointer();
         optr += id3 * ostrides_.dim[3] + id2 * ostrides_.dim[2] +
-                id1 * ostrides_.dim[1];
+                id1 * ostrides_.dim[1] + ooffset_;
         iptr += id3 * istrides_.dim[3] + id2 * istrides_.dim[2] +
-                id1 * istrides_.dim[1];
+                id1 * istrides_.dim[1] + ioffset_;
 
         int istride0 = istrides_.dim[0];
         size_t idd0  = idims_.dim[0];
@@ -81,9 +81,11 @@ class memCopy {
 
    protected:
     sycl::accessor<T> out_;
+    dims_t ostrides_;
+    int ooffset_;
     sycl::accessor<T> in_;
-    dims_t ostrides_, idims_, istrides_;
-    int offset_, groups_0_, groups_1_;
+    dims_t idims_, istrides_;
+    int ioffset_, groups_0_, groups_1_;
 };
 
 constexpr uint DIM0 = 32;
@@ -92,13 +94,14 @@ constexpr uint DIM1 = 8;
 template<typename T>
 void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
              const sycl::buffer<T> *in, const dim_t *idims,
-             const dim_t *istrides, int offset, uint ndims) {
+             const dim_t *istrides, dim_t ioffset, uint indims,
+             dim_t ooffset = 0) {
     dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
     dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
     dims_t _idims    = {{idims[0], idims[1], idims[2], idims[3]}};
 
     size_t local_size[2] = {DIM0, DIM1};
-    if (ndims == 1) {
+    if (indims == 1) {
         local_size[0] *= local_size[1];
         local_size[1] = 1;
     }
@@ -116,8 +119,8 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
         auto in_acc  = const_cast<sycl::buffer<T> *>(in)->get_access(h);
 
         h.parallel_for(ndrange,
-                       memCopy<T>(out_acc, _ostrides, in_acc, _idims, _istrides,
-                                  offset, groups_0, groups_1));
+                       memCopy<T>(out_acc, _ostrides, ooffset, in_acc, _idims,
+                                  _istrides, ioffset, groups_0, groups_1));
     });
     ONEAPI_DEBUG_FINISH(getQueue());
 }

From 5042b885cc649c5e10f2d2222fc059465eef8564 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Apr 2023 14:52:06 -0400
Subject: [PATCH 2479/2677] Update ArrayFireConfig file to enable oneAPI if
 available

---
 CMakeModules/ArrayFireConfig.cmake.in | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/ArrayFireConfig.cmake.in b/CMakeModules/ArrayFireConfig.cmake.in
index 0d3cdda048..c258d19ed3 100644
--- a/CMakeModules/ArrayFireConfig.cmake.in
+++ b/CMakeModules/ArrayFireConfig.cmake.in
@@ -20,6 +20,8 @@
 #   Target for the ArrayFire CPU backend.
 # ``ArrayFire::afcuda``
 #   Target for the ArrayFire CUDA backend.
+# ``ArrayFire::afoneapi``
+#   Target for the ArrayFire oneAPI backend.
 # ``ArrayFire::afopencl``
 #   Target for the ArrayFire OpenCL backend.
 #
@@ -60,6 +62,11 @@
 # ``ArrayFire_CUDA_LIBRARIES``
 #   Location of ArrayFire's CUDA library, if found
 #
+# ``ArrayFire_oneAPI_FOUND``
+#   True of the ArrayFire oneAPI library has been found.
+# ``ArrayFire_oneAPI_LIBRARIES``
+#   Location of ArrayFire's oneAPI library, if found
+#
 # ``ArrayFire_OpenCL_FOUND``
 #   True of the ArrayFire OpenCL library has been found.
 # ``ArrayFire_OpenCL_LIBRARIES``
@@ -85,7 +92,7 @@
 
 set_and_check(ArrayFire_INCLUDE_DIRS @PACKAGE_INCLUDE_DIRS@)
 
-foreach(backend Unified CPU OpenCL CUDA)
+foreach(backend Unified CPU oneAPI OpenCL CUDA)
   if(backend STREQUAL "Unified")
     set(lowerbackend "")
   else()
@@ -140,4 +147,4 @@ foreach(_comp ${ArrayFire_FIND_COMPONENTS})
   endif()
 endforeach()
 
-check_required_components(CPU OpenCL CUDA Unified)
+check_required_components(CPU oneAPI OpenCL CUDA Unified)

From 58e4ff17e198a4fffca22a6a47a6314c39985145 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 19 Apr 2023 14:53:40 -0400
Subject: [PATCH 2480/2677] Enable the building of oneAPI examples

---
 examples/benchmarks/CMakeLists.txt       | 16 +++++++--
 examples/computer_vision/CMakeLists.txt  | 14 ++++++++
 examples/financial/CMakeLists.txt        | 11 ++++++
 examples/getting_started/CMakeLists.txt  | 14 ++++++++
 examples/graphics/CMakeLists.txt         | 30 ++++++++++++++++
 examples/helloworld/CMakeLists.txt       |  5 +++
 examples/image_processing/CMakeLists.txt | 44 ++++++++++++++++++++++++
 examples/lin_algebra/CMakeLists.txt      | 14 ++++++++
 examples/machine_learning/CMakeLists.txt | 32 +++++++++++++++++
 examples/pde/CMakeLists.txt              |  5 +++
 10 files changed, 183 insertions(+), 2 deletions(-)

diff --git a/examples/benchmarks/CMakeLists.txt b/examples/benchmarks/CMakeLists.txt
index c5b717f41a..d5ece4b562 100644
--- a/examples/benchmarks/CMakeLists.txt
+++ b/examples/benchmarks/CMakeLists.txt
@@ -26,7 +26,6 @@ if(ArrayFire_CPU_FOUND)
   target_link_libraries(pi_cpu ArrayFire::afcpu)
 endif()
 
-
 if(ArrayFire_CUDA_FOUND)
   add_executable(blas_cuda blas.cpp)
   target_link_libraries(blas_cuda ArrayFire::afcuda)
@@ -41,7 +40,6 @@ if(ArrayFire_CUDA_FOUND)
   target_link_libraries(pi_cuda ArrayFire::afcuda)
 endif()
 
-
 if(ArrayFire_OpenCL_FOUND)
   add_executable(blas_opencl blas.cpp)
   target_link_libraries(blas_opencl ArrayFire::afopencl)
@@ -55,3 +53,17 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(pi_opencl pi.cpp)
   target_link_libraries(pi_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(blas_oneapi blas.cpp)
+  target_link_libraries(blas_oneapi ArrayFire::afoneapi)
+
+  add_executable(cg_oneapi cg.cpp)
+  target_link_libraries(cg_oneapi ArrayFire::afoneapi)
+
+  add_executable(fft_oneapi fft.cpp)
+  target_link_libraries(fft_oneapi ArrayFire::afoneapi)
+
+  add_executable(pi_oneapi pi.cpp)
+  target_link_libraries(pi_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
index 521f7dc0a3..7314d29148 100644
--- a/examples/computer_vision/CMakeLists.txt
+++ b/examples/computer_vision/CMakeLists.txt
@@ -59,3 +59,17 @@ if (ArrayFire_OpenCL_FOUND)
   add_executable(susan_opencl susan.cpp)
   target_link_libraries(susan_opencl ArrayFire::afopencl)
 endif()
+
+if (ArrayFire_oneAPI_FOUND)
+  add_executable(fast_oneapi fast.cpp)
+  target_link_libraries(fast_oneapi ArrayFire::afoneapi)
+
+  add_executable(harris_oneapi harris.cpp)
+  target_link_libraries(harris_oneapi ArrayFire::afoneapi)
+
+  add_executable(matching_oneapi matching.cpp)
+  target_link_libraries(matching_oneapi ArrayFire::afoneapi)
+
+  add_executable(susan_oneapi susan.cpp)
+  target_link_libraries(susan_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/financial/CMakeLists.txt b/examples/financial/CMakeLists.txt
index 7c65c63595..9cc2435b25 100644
--- a/examples/financial/CMakeLists.txt
+++ b/examples/financial/CMakeLists.txt
@@ -47,3 +47,14 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(heston_model_opencl heston_model.cpp)
   target_link_libraries(heston_model_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(monte_carlo_options_oneapi monte_carlo_options.cpp)
+  target_link_libraries(monte_carlo_options_oneapi ArrayFire::afoneapi)
+
+  add_executable(black_scholes_options_oneapi black_scholes_options.cpp input.h)
+  target_link_libraries(black_scholes_options_oneapi ArrayFire::afoneapi)
+
+  add_executable(heston_model_oneapi heston_model.cpp)
+  target_link_libraries(heston_model_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/getting_started/CMakeLists.txt b/examples/getting_started/CMakeLists.txt
index 63bd043cd0..f0ee51249a 100644
--- a/examples/getting_started/CMakeLists.txt
+++ b/examples/getting_started/CMakeLists.txt
@@ -57,3 +57,17 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(vectorize_opencl vectorize.cpp)
   target_link_libraries(vectorize_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(convolve_oneapi convolve.cpp)
+  target_link_libraries(convolve_oneapi ArrayFire::afoneapi)
+
+  add_executable(integer_oneapi integer.cpp)
+  target_link_libraries(integer_oneapi ArrayFire::afoneapi)
+
+  add_executable(rainfall_oneapi rainfall.cpp)
+  target_link_libraries(rainfall_oneapi ArrayFire::afoneapi)
+
+  add_executable(vectorize_oneapi vectorize.cpp)
+  target_link_libraries(vectorize_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
index e7186cd1a7..d59a506278 100644
--- a/examples/graphics/CMakeLists.txt
+++ b/examples/graphics/CMakeLists.txt
@@ -111,3 +111,33 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(surface_opencl surface.cpp)
   target_link_libraries(surface_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(conway_oneapi conway.cpp)
+  target_link_libraries(conway_oneapi ArrayFire::afoneapi)
+
+  add_executable(conway_pretty_oneapi conway_pretty.cpp)
+  target_link_libraries(conway_pretty_oneapi ArrayFire::afoneapi)
+
+  add_executable(field_oneapi field.cpp)
+  target_link_libraries(field_oneapi ArrayFire::afoneapi)
+
+  add_executable(fractal_oneapi fractal.cpp)
+  target_link_libraries(fractal_oneapi ArrayFire::afoneapi)
+
+  add_executable(gravity_sim_oneapi gravity_sim.cpp gravity_sim_init.h)
+  target_link_libraries(gravity_sim_oneapi ArrayFire::afoneapi)
+
+  add_executable(histogram_oneapi histogram.cpp)
+  target_compile_definitions(histogram_oneapi PRIVATE "ASSETS_DIR=\"${ASSETS_DIR}\"")
+  target_link_libraries(histogram_oneapi ArrayFire::afoneapi)
+
+  add_executable(plot2d_oneapi plot2d.cpp)
+  target_link_libraries(plot2d_oneapi ArrayFire::afoneapi)
+
+  add_executable(plot3_oneapi plot3.cpp)
+  target_link_libraries(plot3_oneapi ArrayFire::afoneapi)
+
+  add_executable(surface_oneapi surface.cpp)
+  target_link_libraries(surface_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/helloworld/CMakeLists.txt b/examples/helloworld/CMakeLists.txt
index 64e9a6aa6a..3567873958 100644
--- a/examples/helloworld/CMakeLists.txt
+++ b/examples/helloworld/CMakeLists.txt
@@ -27,3 +27,8 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(helloworld_opencl helloworld.cpp)
   target_link_libraries(helloworld_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(helloworld_oneapi helloworld.cpp)
+  target_link_libraries(helloworld_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index ffffe17fa7..12307b679f 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -156,3 +156,47 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(deconvolution_opencl deconvolution.cpp)
   target_link_libraries(deconvolution_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(adaptive_thresholding_oneapi adaptive_thresholding.cpp)
+  target_link_libraries(adaptive_thresholding_oneapi ArrayFire::afoneapi)
+
+  add_executable(binary_thresholding_oneapi binary_thresholding.cpp)
+  target_link_libraries(binary_thresholding_oneapi ArrayFire::afoneapi)
+
+  add_executable(brain_segmentation_oneapi brain_segmentation.cpp)
+  target_link_libraries(brain_segmentation_oneapi ArrayFire::afoneapi)
+
+  add_executable(confidence_connected_components_oneapi
+      confidence_connected_components.cpp)
+  target_link_libraries(confidence_connected_components_oneapi ArrayFire::afoneapi)
+
+  add_executable(edge_oneapi edge.cpp)
+  target_link_libraries(edge_oneapi ArrayFire::afoneapi)
+
+  add_executable(filters_oneapi filters.cpp)
+  target_link_libraries(filters_oneapi ArrayFire::afoneapi)
+
+  add_executable(image_demo_oneapi image_demo.cpp)
+  target_link_libraries(image_demo_oneapi ArrayFire::afoneapi)
+
+  add_executable(image_editing_oneapi image_editing.cpp)
+  target_link_libraries(image_editing_oneapi ArrayFire::afoneapi)
+
+  add_executable(morphing_oneapi morphing.cpp)
+  target_link_libraries(morphing_oneapi ArrayFire::afoneapi)
+
+  add_executable(optical_flow_oneapi optical_flow.cpp)
+  target_link_libraries(optical_flow_oneapi ArrayFire::afoneapi)
+
+  add_executable(pyramids_oneapi pyramids.cpp)
+  target_link_libraries(pyramids_oneapi ArrayFire::afoneapi)
+
+  # Gradient anisotropic diffusion example
+  add_executable(gradient_diffusion_oneapi gradient_diffusion.cpp)
+  target_link_libraries(gradient_diffusion_oneapi ArrayFire::afoneapi)
+
+  #Image Deconvolution Example
+  add_executable(deconvolution_oneapi deconvolution.cpp)
+  target_link_libraries(deconvolution_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/lin_algebra/CMakeLists.txt b/examples/lin_algebra/CMakeLists.txt
index 59aa2cbcd9..baba1a4181 100644
--- a/examples/lin_algebra/CMakeLists.txt
+++ b/examples/lin_algebra/CMakeLists.txt
@@ -57,3 +57,17 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(svd_opencl svd.cpp)
   target_link_libraries(svd_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(cholesky_oneapi cholesky.cpp)
+  target_link_libraries(cholesky_oneapi ArrayFire::afoneapi)
+
+  add_executable(lu_oneapi lu.cpp)
+  target_link_libraries(lu_oneapi ArrayFire::afoneapi)
+
+  add_executable(qr_oneapi qr.cpp)
+  target_link_libraries(qr_oneapi ArrayFire::afoneapi)
+
+  add_executable(svd_oneapi svd.cpp)
+  target_link_libraries(svd_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/machine_learning/CMakeLists.txt b/examples/machine_learning/CMakeLists.txt
index 136e9338a0..9c2c3ade6c 100644
--- a/examples/machine_learning/CMakeLists.txt
+++ b/examples/machine_learning/CMakeLists.txt
@@ -119,3 +119,35 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(softmax_regression_opencl softmax_regression.cpp)
   target_link_libraries(softmax_regression_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(bagging_oneapi bagging.cpp)
+  target_link_libraries(bagging_oneapi ArrayFire::afoneapi)
+
+  add_executable(deep_belief_net_oneapi deep_belief_net.cpp)
+  target_link_libraries(deep_belief_net_oneapi ArrayFire::afoneapi)
+
+  add_executable(geneticalgorithm_oneapi geneticalgorithm.cpp)
+  target_link_libraries(geneticalgorithm_oneapi ArrayFire::afoneapi)
+
+  add_executable(kmeans_oneapi kmeans.cpp)
+  target_link_libraries(kmeans_oneapi ArrayFire::afoneapi)
+
+  add_executable(logistic_regression_oneapi logistic_regression.cpp)
+  target_link_libraries(logistic_regression_oneapi ArrayFire::afoneapi)
+
+  add_executable(naive_bayes_oneapi naive_bayes.cpp)
+  target_link_libraries(naive_bayes_oneapi ArrayFire::afoneapi)
+
+  add_executable(neural_network_oneapi neural_network.cpp)
+  target_link_libraries(neural_network_oneapi ArrayFire::afoneapi)
+
+  add_executable(perceptron_oneapi perceptron.cpp)
+  target_link_libraries(perceptron_oneapi ArrayFire::afoneapi)
+
+  add_executable(rbm_oneapi rbm.cpp)
+  target_link_libraries(rbm_oneapi ArrayFire::afoneapi)
+
+  add_executable(softmax_regression_oneapi softmax_regression.cpp)
+  target_link_libraries(softmax_regression_oneapi ArrayFire::afoneapi)
+endif()
diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index 345afeabfb..0b74e6165f 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -27,3 +27,8 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(swe_opencl swe.cpp)
   target_link_libraries(swe_opencl ArrayFire::afopencl)
 endif()
+
+if(ArrayFire_oneAPI_FOUND)
+  add_executable(swe_oneapi swe.cpp)
+  target_link_libraries(swe_oneapi ArrayFire::afoneapi)
+endif()

From e0f042342baa4abb84268951a9a526691253d266 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Mon, 24 Apr 2023 15:42:36 -0400
Subject: [PATCH 2481/2677] convolve2 and convolve_separable oneapi ports
 (#3409)

* convolve2 (not separable) stubs filled in. half is not supported

---------

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt             |   1 +
 src/backend/oneapi/convolve.cpp               | 115 +++++++++-
 src/backend/oneapi/convolve_separable.cpp     |  35 ++-
 src/backend/oneapi/kernel/convolve2.hpp       |  10 +
 src/backend/oneapi/kernel/convolve3.hpp       |  10 +
 .../oneapi/kernel/convolve_separable.cpp      | 217 ++++++++++++++++++
 .../oneapi/kernel/convolve_separable.hpp      |  29 +++
 7 files changed, 403 insertions(+), 14 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/convolve_separable.cpp
 create mode 100644 src/backend/oneapi/kernel/convolve_separable.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 831234a5a8..b1ab64d87e 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -214,6 +214,7 @@ target_sources(afoneapi
     kernel/approx2.hpp
     kernel/assign.hpp
     kernel/bilateral.hpp
+    kernel/convolve_separable.cpp
     kernel/diagonal.hpp
     kernel/diff.hpp
     kernel/histogram.hpp
diff --git a/src/backend/oneapi/convolve.cpp b/src/backend/oneapi/convolve.cpp
index ac940f501d..69c120569b 100644
--- a/src/backend/oneapi/convolve.cpp
+++ b/src/backend/oneapi/convolve.cpp
@@ -110,8 +110,38 @@ template<typename T>
 Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
                           const dim4 &stride, const dim4 &padding,
                           const dim4 &dilation) {
-    Array<T> out =
-        convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
+    dim4 sDims = signal.dims();
+    dim4 fDims = filter.dims();
+
+    dim_t outputWidth =
+        1 + (sDims[0] + 2 * padding[0] - (((fDims[0] - 1) * dilation[0]) + 1)) /
+                stride[0];
+    dim_t outputHeight =
+        1 + (sDims[1] + 2 * padding[1] - (((fDims[1] - 1) * dilation[1]) + 1)) /
+                stride[1];
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(signal, fDims[0], fDims[1], stride[0], stride[1], padding[0],
+               padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsedFilter = filter;
+
+    collapsedFilter = flip(collapsedFilter, {1, 1, 0, 0});
+    collapsedFilter = modDims(collapsedFilter,
+                              dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> res =
+        matmul(unwrapped, collapsedFilter, AF_MAT_TRANS, AF_MAT_NONE);
+    res = modDims(res, dim4(outputWidth, outputHeight, signal.dims()[3],
+                            collapsedFilter.dims()[1]));
+    Array<T> out = reorder(res, dim4(0, 1, 3, 2));
 
     return out;
 }
@@ -119,9 +149,15 @@ Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
 template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
                    const dim4 stride, const dim4 padding, const dim4 dilation) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
-    return out;
+    if constexpr (!std::is_same<T, half>::value) {
+        Array<T> out =
+            convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
+        return out;
+    } else {
+        ONEAPI_NOT_SUPPORTED("");
+        Array<T> out = createEmptyArray<T>(dim4(1));
+        return out;
+    }
 }
 
 #define INSTANTIATE(T)                                                        \
@@ -141,9 +177,39 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> & /*convolved_output*/,
                            af::dim4 stride, af::dim4 padding,
                            af::dim4 dilation) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
-    return out;
+    if constexpr (!std::is_same<T, half>::value) {
+        const dim4 &cDims = incoming_gradient.dims();
+        const dim4 &sDims = original_signal.dims();
+        const dim4 &fDims = original_filter.dims();
+
+        Array<T> collapsed_filter = original_filter;
+
+        collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
+        collapsed_filter = modDims(
+            collapsed_filter, dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+        Array<T> collapsed_gradient = incoming_gradient;
+        collapsed_gradient = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+        collapsed_gradient = modDims(
+            collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+        Array<T> res = matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE,
+                              AF_MAT_TRANS);
+        res          = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
+                                         fDims[0] * fDims[1], sDims[2]));
+        res          = reorder(res, dim4(0, 2, 3, 1));
+
+        const bool retCols = false;
+        res = wrap_dilated(res, sDims[0], sDims[1], fDims[0], fDims[1],
+                           stride[0], stride[1], padding[0], padding[1],
+                           dilation[0], dilation[1], retCols);
+
+        return res;
+    } else {
+        ONEAPI_NOT_SUPPORTED("");
+        Array<T> out = createEmptyArray<T>(dim4(1));
+        return out;
+    }
 }
 
 template<typename T>
@@ -153,9 +219,36 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> & /*convolved_output*/,
                              af::dim4 stride, af::dim4 padding,
                              af::dim4 dilation) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
-    return out;
+    if constexpr (!std::is_same<T, half>::value) {
+        const dim4 &cDims = incoming_gradient.dims();
+        const dim4 &fDims = original_filter.dims();
+
+        const bool retCols = false;
+        Array<T> unwrapped =
+            unwrap(original_signal, fDims[0], fDims[1], stride[0], stride[1],
+                   padding[0], padding[1], dilation[0], dilation[1], retCols);
+
+        unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+        dim4 uDims = unwrapped.dims();
+        unwrapped =
+            modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+        Array<T> collapsed_gradient = incoming_gradient;
+        collapsed_gradient = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+        collapsed_gradient = modDims(
+            collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+        Array<T> res =
+            matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
+        res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+
+        auto out = flip(res, {1, 1, 0, 0});
+        return out;
+    } else {
+        ONEAPI_NOT_SUPPORTED("");
+        Array<T> out = createEmptyArray<T>(dim4(1));
+        return out;
+    }
 }
 
 #define INSTANTIATE(T)                                                      \
diff --git a/src/backend/oneapi/convolve_separable.cpp b/src/backend/oneapi/convolve_separable.cpp
index 969aff66e2..fdf9fc952f 100644
--- a/src/backend/oneapi/convolve_separable.cpp
+++ b/src/backend/oneapi/convolve_separable.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2022, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -11,6 +11,7 @@
 
 #include <Array.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/convolve_separable.hpp>
 #include <af/dim4.hpp>
 
 using af::dim4;
@@ -21,8 +22,36 @@ namespace oneapi {
 template<typename T, typename accT>
 Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter,
                    Array<accT> const& r_filter, const bool expand) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> out = createEmptyArray<T>(dim4(1));
+    const auto cflen = c_filter.elements();
+    const auto rflen = r_filter.elements();
+
+    if ((cflen > kernel::MAX_SCONV_FILTER_LEN) ||
+        (rflen > kernel::MAX_SCONV_FILTER_LEN)) {
+        // TODO call upon fft
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\noneAPI Separable convolution doesn't support %llu(coloumn) "
+                 "%llu(row) filters\n",
+                 cflen, rflen);
+        ONEAPI_NOT_SUPPORTED(errMessage);
+    }
+
+    const dim4& sDims = signal.dims();
+    dim4 tDims        = sDims;
+    dim4 oDims        = sDims;
+
+    if (expand) {
+        tDims[0] += cflen - 1;
+        oDims[0] += cflen - 1;
+        oDims[1] += rflen - 1;
+    }
+
+    Array<T> temp = createEmptyArray<T>(tDims);
+    Array<T> out  = createEmptyArray<T>(oDims);
+
+    kernel::convSep<T, accT>(temp, signal, c_filter, 0, expand);
+    kernel::convSep<T, accT>(out, temp, r_filter, 1, expand);
+
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/convolve2.hpp b/src/backend/oneapi/kernel/convolve2.hpp
index fc5db9c06a..b216e50917 100644
--- a/src/backend/oneapi/kernel/convolve2.hpp
+++ b/src/backend/oneapi/kernel/convolve2.hpp
@@ -1,3 +1,13 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
 template<typename T, typename aT>
 class conv2HelperCreateKernel {
    public:
diff --git a/src/backend/oneapi/kernel/convolve3.hpp b/src/backend/oneapi/kernel/convolve3.hpp
index 30861a2a63..3ac4a50aa2 100644
--- a/src/backend/oneapi/kernel/convolve3.hpp
+++ b/src/backend/oneapi/kernel/convolve3.hpp
@@ -1,3 +1,13 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
 int index(int i, int j, int k, int jstride, int kstride) {
     return i + j * jstride + k * kstride;
 }
diff --git a/src/backend/oneapi/kernel/convolve_separable.cpp b/src/backend/oneapi/kernel/convolve_separable.cpp
new file mode 100644
index 0000000000..712570a558
--- /dev/null
+++ b/src/backend/oneapi/kernel/convolve_separable.cpp
@@ -0,0 +1,217 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
+
+template<typename T, typename accType>
+class convolveSeparableCreateKernel {
+   public:
+    convolveSeparableCreateKernel(write_accessor<T> out, KParam oInfo,
+                                  read_accessor<T> signal, KParam sInfo,
+                                  read_accessor<accType> impulse, int nBBS0,
+                                  int nBBS1, const int FLEN, const int CONV_DIM,
+                                  const bool EXPAND,
+                                  sycl::local_accessor<T> localMem)
+        : out_(out)
+        , oInfo_(oInfo)
+        , signal_(signal)
+        , sInfo_(sInfo)
+        , impulse_(impulse)
+        , nBBS0_(nBBS0)
+        , nBBS1_(nBBS1)
+        , FLEN_(FLEN)
+        , CONV_DIM_(CONV_DIM)
+        , EXPAND_(EXPAND)
+        , localMem_(localMem) {}
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        const int radius  = FLEN_ - 1;
+        const int padding = 2 * radius;
+        const int s0      = sInfo_.strides[0];
+        const int s1      = sInfo_.strides[1];
+        const int d0      = sInfo_.dims[0];
+        const int d1      = sInfo_.dims[1];
+        const int shrdLen =
+            g.get_local_range(0) + (CONV_DIM_ == 0 ? padding : 0);
+
+        unsigned b2 = g.get_group_id(0) / nBBS0_;
+        unsigned b3 = g.get_group_id(1) / nBBS1_;
+        T *dst      = out_.get_pointer() +
+                 (b2 * oInfo_.strides[2] + b3 * oInfo_.strides[3]);
+        const T *src = signal_.get_pointer() +
+                       (b2 * sInfo_.strides[2] + b3 * sInfo_.strides[3]) +
+                       sInfo_.offset;
+
+        int lx = it.get_local_id(0);
+        int ly = it.get_local_id(1);
+        int ox = g.get_local_range(0) * (g.get_group_id(0) - b2 * nBBS0_) + lx;
+        int oy = g.get_local_range(1) * (g.get_group_id(1) - b3 * nBBS1_) + ly;
+        int gx = ox;
+        int gy = oy;
+
+        // below if-else statement is based on MACRO value passed while kernel
+        // compilation
+        if (CONV_DIM_ == 0) {
+            gx += (EXPAND_ ? 0 : FLEN_ >> 1);
+            int endX = ((FLEN_ - 1) << 1) + g.get_local_range(0);
+#pragma unroll
+            for (int lx = it.get_local_id(0), glb_x = gx; lx < endX;
+                 lx += g.get_local_range(0), glb_x += g.get_local_range(0)) {
+                int i     = glb_x - radius;
+                int j     = gy;
+                bool is_i = i >= 0 && i < d0;
+                bool is_j = j >= 0 && j < d1;
+                localMem_[ly * shrdLen + lx] =
+                    (is_i && is_j ? src[i * s0 + j * s1] : (T)(0));
+            }
+
+        } else if (CONV_DIM_ == 1) {
+            gy += (EXPAND_ ? 0 : FLEN_ >> 1);
+            int endY = ((FLEN_ - 1) << 1) + g.get_local_range(1);
+#pragma unroll
+            for (int ly = it.get_local_id(1), glb_y = gy; ly < endY;
+                 ly += g.get_local_range(1), glb_y += g.get_local_range(1)) {
+                int i     = gx;
+                int j     = glb_y - radius;
+                bool is_i = i >= 0 && i < d0;
+                bool is_j = j >= 0 && j < d1;
+                localMem_[ly * shrdLen + lx] =
+                    (is_i && is_j ? src[i * s0 + j * s1] : (T)(0));
+            }
+        }
+        it.barrier();
+
+        if (ox < oInfo_.dims[0] && oy < oInfo_.dims[1]) {
+            // below conditional statement is based on MACRO value passed while
+            // kernel compilation
+            int i         = (CONV_DIM_ == 0 ? lx : ly) + radius;
+            accType accum = (accType)(0);
+#pragma unroll
+            for (int f = 0; f < FLEN_; ++f) {
+                accType f_val = impulse_[f];
+                // below conditional statement is based on MACRO value passed
+                // while kernel compilation
+                int s_idx = (CONV_DIM_ == 0 ? (ly * shrdLen + (i - f))
+                                            : ((i - f) * shrdLen + lx));
+                T s_val   = localMem_[s_idx];
+
+                // binOp omitted from OpenCL implementation (see
+                // convolve_separable.cl)
+                accum = accum + (accType)s_val * (accType)f_val;
+            }
+            dst[oy * oInfo_.strides[1] + ox] = (T)accum;
+        }
+    }
+
+   private:
+    write_accessor<T> out_;
+    KParam oInfo_;
+    read_accessor<T> signal_;
+    KParam sInfo_;
+    read_accessor<accType> impulse_;
+    int nBBS0_;
+    int nBBS1_;
+    const int FLEN_;
+    const int CONV_DIM_;
+    const bool EXPAND_;
+    sycl::local_accessor<T> localMem_;
+};
+
+template<typename T>
+void memcpyBuffer(sycl::buffer<T, 1> &dest, sycl::buffer<T, 1> &src,
+                  const size_t n, const size_t srcOffset) {
+    getQueue().submit([&](auto &h) {
+        sycl::accessor srcAcc{src, h, sycl::range{n}, sycl::id{srcOffset},
+                              sycl::read_only};
+        sycl::accessor destAcc{
+            dest,         h, sycl::range{n}, sycl::id{0}, sycl::write_only,
+            sycl::no_init};
+        h.copy(srcAcc, destAcc);
+    });
+}
+
+template<typename T, typename accType>
+void convSep(Param<T> out, const Param<T> signal, const Param<accType> filter,
+             const int conv_dim, const bool expand) {
+    if (!(conv_dim == 0 || conv_dim == 1)) {
+        AF_ERROR(
+            "Separable convolution accepts only 0 or 1 as convolution "
+            "dimension",
+            AF_ERR_NOT_SUPPORTED);
+    }
+    constexpr int THREADS_X = 16;
+    constexpr int THREADS_Y = 16;
+    constexpr bool IsComplex =
+        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
+
+    const int fLen       = filter.info.dims[0] * filter.info.dims[1];
+    const size_t C0_SIZE = (THREADS_X + 2 * (fLen - 1)) * THREADS_Y;
+    const size_t C1_SIZE = (THREADS_Y + 2 * (fLen - 1)) * THREADS_X;
+    size_t locSize       = (conv_dim == 0 ? C0_SIZE : C1_SIZE);
+
+    auto local = sycl::range(THREADS_X, THREADS_Y);
+
+    int blk_x = divup(out.info.dims[0], THREADS_X);
+    int blk_y = divup(out.info.dims[1], THREADS_Y);
+
+    auto global = sycl::range(blk_x * signal.info.dims[2] * THREADS_X,
+                              blk_y * signal.info.dims[3] * THREADS_Y);
+
+    sycl::buffer<accType> mBuff = {sycl::range(fLen * sizeof(accType))};
+    memcpyBuffer(mBuff, *filter.data, fLen, 0);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_signal{*signal.data, h, sycl::read_only};
+        sycl::accessor d_out{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor d_mBuff{mBuff, h, sycl::read_only};
+        sycl::local_accessor<T> localMem(locSize, h);
+        h.parallel_for(sycl::nd_range{global, local},
+                       convolveSeparableCreateKernel<T, accType>(
+                           d_out, out.info, d_signal, signal.info, d_mBuff,
+                           blk_x, blk_y, fLen, conv_dim, expand, localMem));
+    });
+}
+
+#define INSTANTIATE(T, accT)                                          \
+    template void convSep<T, accT>(Param<T>, const Param<T>,          \
+                                   const Param<accT> filt, const int, \
+                                   const bool);
+
+INSTANTIATE(cdouble, cdouble)
+INSTANTIATE(cfloat, cfloat)
+INSTANTIATE(double, double)
+INSTANTIATE(float, float)
+INSTANTIATE(uint, float)
+INSTANTIATE(int, float)
+INSTANTIATE(uchar, float)
+INSTANTIATE(char, float)
+INSTANTIATE(ushort, float)
+INSTANTIATE(short, float)
+INSTANTIATE(uintl, float)
+INSTANTIATE(intl, float)
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/convolve_separable.hpp b/src/backend/oneapi/kernel/convolve_separable.hpp
new file mode 100644
index 0000000000..0339c9c614
--- /dev/null
+++ b/src/backend/oneapi/kernel/convolve_separable.hpp
@@ -0,0 +1,29 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+// below shared MAX_*_LEN's are calculated based on
+// a maximum shared memory configuration of 48KB per block
+// considering complex types as well
+constexpr int MAX_SCONV_FILTER_LEN = 31;
+
+template<typename T, typename accT>
+void convSep(Param<T> out, const Param<T> sig, const Param<accT> filt,
+             const int cDim, const bool expand);
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire

From c6f5947213c0aec5ecb592271a40c6037ed31aa4 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 26 Apr 2023 02:07:52 -0400
Subject: [PATCH 2482/2677] corrects missing oneapi accessor semantics

---
 src/backend/oneapi/CMakeLists.txt             |   1 +
 src/backend/oneapi/join.cpp                   |  90 +++---
 src/backend/oneapi/kernel/accessors.hpp       |  17 ++
 src/backend/oneapi/kernel/approx1.hpp         |  12 +-
 src/backend/oneapi/kernel/approx2.hpp         |  12 +-
 src/backend/oneapi/kernel/assign.hpp          |  12 +-
 .../oneapi/kernel/assign_kernel_param.hpp     |  12 +-
 src/backend/oneapi/kernel/bilateral.hpp       |  33 +--
 src/backend/oneapi/kernel/convolve.hpp        |   7 +-
 src/backend/oneapi/kernel/diagonal.hpp        |  30 +-
 src/backend/oneapi/kernel/diff.hpp            |  16 +-
 src/backend/oneapi/kernel/gradient.hpp        |  15 +-
 src/backend/oneapi/kernel/histogram.hpp       |  27 +-
 src/backend/oneapi/kernel/identity.hpp        |   4 +-
 src/backend/oneapi/kernel/iir.hpp             |   8 +-
 src/backend/oneapi/kernel/index.hpp           |  12 +-
 src/backend/oneapi/kernel/interp.hpp          |   9 +-
 src/backend/oneapi/kernel/iota.hpp            |  34 +--
 src/backend/oneapi/kernel/ireduce.hpp         | 275 +++++++++---------
 src/backend/oneapi/kernel/lookup.hpp          |   6 +-
 src/backend/oneapi/kernel/lu_split.hpp        |   6 +-
 src/backend/oneapi/kernel/mean.hpp            |  44 ++-
 src/backend/oneapi/kernel/meanshift.hpp       |   6 +-
 src/backend/oneapi/kernel/memcopy.hpp         |  27 +-
 .../oneapi/kernel/pad_array_borders.hpp       |   6 +-
 src/backend/oneapi/kernel/random_engine.hpp   |  25 +-
 .../oneapi/kernel/random_engine_mersenne.hpp  |  49 ++--
 .../oneapi/kernel/random_engine_philox.hpp    |   9 +-
 .../oneapi/kernel/random_engine_threefry.hpp  |   9 +-
 src/backend/oneapi/kernel/range.hpp           |   7 +-
 src/backend/oneapi/kernel/reduce.hpp          |   1 +
 src/backend/oneapi/kernel/reduce_all.hpp      |  27 +-
 src/backend/oneapi/kernel/reduce_dim.hpp      |  21 +-
 src/backend/oneapi/kernel/reduce_first.hpp    |  22 +-
 src/backend/oneapi/kernel/reorder.hpp         |   6 +-
 src/backend/oneapi/kernel/resize.hpp          |   6 +-
 src/backend/oneapi/kernel/rotate.hpp          |   6 +-
 src/backend/oneapi/kernel/scan_dim.hpp        |  26 +-
 src/backend/oneapi/kernel/scan_first.hpp      |  23 +-
 src/backend/oneapi/kernel/select.hpp          |   6 +-
 src/backend/oneapi/kernel/tile.hpp            |   6 +-
 src/backend/oneapi/kernel/transform.hpp       |   6 +-
 src/backend/oneapi/kernel/transpose.hpp       |  12 +-
 .../oneapi/kernel/transpose_inplace.hpp       |  21 +-
 src/backend/oneapi/kernel/triangle.hpp        |  16 +-
 src/backend/oneapi/kernel/where.hpp           |   9 +-
 src/backend/oneapi/kernel/wrap.hpp            |  10 +-
 src/backend/oneapi/kernel/wrap_dilated.hpp    |   9 +-
 48 files changed, 439 insertions(+), 614 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/accessors.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index b1ab64d87e..8ea40564e9 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -210,6 +210,7 @@ add_library(afoneapi
 target_sources(afoneapi
   PRIVATE
     kernel/KParam.hpp
+    kernel/accessors.hpp
     kernel/approx1.hpp
     kernel/approx2.hpp
     kernel/assign.hpp
diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
index 2633c43a62..ecbcae0ba4 100644
--- a/src/backend/oneapi/join.cpp
+++ b/src/backend/oneapi/join.cpp
@@ -94,22 +94,18 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     if (first.isReady()) {
         if (1LL + jdim >= first.ndims() && first.isLinear()) {
             // first & out are linear
-            getQueue()
-                .submit([=](sycl::handler &h) {
-                    sycl::range sz(first.elements());
-                    sycl::id src_offset(first.getOffset());
-                    sycl::accessor offset_acc_src =
-                        first.get()
-                            ->template get_access<sycl::access_mode::read>(
-                                h, sz, src_offset);
-                    sycl::id dst_offset(0);
-                    sycl::accessor offset_acc_dst =
-                        out.get()
-                            ->template get_access<sycl::access_mode::write>(
-                                h, sz, dst_offset);
-                    h.copy(offset_acc_src, offset_acc_dst);
-                })
-                .wait();
+            getQueue().submit([=](sycl::handler &h) {
+                sycl::range sz(first.elements());
+                sycl::id src_offset(first.getOffset());
+                sycl::accessor offset_acc_src =
+                    first.get()->template get_access<sycl::access_mode::read>(
+                        h, sz, src_offset);
+                sycl::id dst_offset(0);
+                sycl::accessor offset_acc_dst =
+                    out.get()->template get_access<sycl::access_mode::write>(
+                        h, sz, dst_offset);
+                h.copy(offset_acc_src, offset_acc_dst);
+            });
         } else {
             kernel::memcopy<T>(out.get(), out.strides().get(), first.get(),
                                fdims.get(), first.strides().get(),
@@ -129,23 +125,19 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     if (second.isReady()) {
         if (1LL + jdim >= second.ndims() && second.isLinear()) {
             // second & out are linear
-            getQueue()
-                .submit([=](sycl::handler &h) {
-                    sycl::range sz(second.elements());
-                    sycl::id src_offset(second.getOffset());
-                    sycl::accessor offset_acc_src =
-                        second.get()
-                            ->template get_access<sycl::access_mode::read>(
-                                h, sz, src_offset);
-                    sycl::id dst_offset(fdims.dims[jdim] *
-                                        out.strides().dims[jdim]);
-                    sycl::accessor offset_acc_dst =
-                        out.get()
-                            ->template get_access<sycl::access_mode::write>(
-                                h, sz, dst_offset);
-                    h.copy(offset_acc_src, offset_acc_dst);
-                })
-                .wait();
+            getQueue().submit([=](sycl::handler &h) {
+                sycl::range sz(second.elements());
+                sycl::id src_offset(second.getOffset());
+                sycl::accessor offset_acc_src =
+                    second.get()->template get_access<sycl::access_mode::read>(
+                        h, sz, src_offset);
+                sycl::id dst_offset(fdims.dims[jdim] *
+                                    out.strides().dims[jdim]);
+                sycl::accessor offset_acc_dst =
+                    out.get()->template get_access<sycl::access_mode::write>(
+                        h, sz, dst_offset);
+                h.copy(offset_acc_src, offset_acc_dst);
+            });
         } else {
             kernel::memcopy<T>(out.get(), out.strides().get(), second.get(),
                                sdims.get(), second.strides().get(),
@@ -224,23 +216,21 @@ void join(Array<T> &out, const int jdim, const vector<Array<T>> &inputs) {
             for (const Array<T> *in : s.ins) {
                 if (in->isReady()) {
                     if (1LL + jdim >= in->ndims() && in->isLinear()) {
-                        getQueue()
-                            .submit([=](sycl::handler &h) {
-                                sycl::range sz(in->elements());
-                                sycl::id src_offset(in->getOffset());
-                                sycl::accessor offset_acc_src =
-                                    in->get()
-                                        ->template get_access<
-                                            sycl::access_mode::read>(
-                                            h, sz, src_offset);
-                                sycl::id dst_offset(outputIt->info.offset);
-                                sycl::accessor offset_acc_dst =
-                                    outputIt->data->template get_access<
-                                        sycl::access_mode::write>(h, sz,
-                                                                  dst_offset);
-                                h.copy(offset_acc_src, offset_acc_dst);
-                            })
-                            .wait();
+                        getQueue().submit([=](sycl::handler &h) {
+                            sycl::range sz(in->elements());
+                            sycl::id src_offset(in->getOffset());
+                            sycl::accessor offset_acc_src =
+                                in->get()
+                                    ->template get_access<
+                                        sycl::access_mode::read>(h, sz,
+                                                                 src_offset);
+                            sycl::id dst_offset(outputIt->info.offset);
+                            sycl::accessor offset_acc_dst =
+                                outputIt->data->template get_access<
+                                    sycl::access_mode::write>(h, sz,
+                                                              dst_offset);
+                            h.copy(offset_acc_src, offset_acc_dst);
+                        });
                     } else {
                         kernel::memcopy<T>(
                             outputIt->data,
diff --git a/src/backend/oneapi/kernel/accessors.hpp b/src/backend/oneapi/kernel/accessors.hpp
new file mode 100644
index 0000000000..902f48b0e0
--- /dev/null
+++ b/src/backend/oneapi/kernel/accessors.hpp
@@ -0,0 +1,17 @@
+/*******************************************************
+ * Copyright (c) 2022 ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+#include <sycl/sycl.hpp>
+
+template<typename T>
+using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
+
+template<typename T>
+using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
diff --git a/src/backend/oneapi/kernel/approx1.hpp b/src/backend/oneapi/kernel/approx1.hpp
index 3f0e2cfbe5..ed2290ffc9 100644
--- a/src/backend/oneapi/kernel/approx1.hpp
+++ b/src/backend/oneapi/kernel/approx1.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/interp.hpp>
 #include <traits.hpp>
 #include <af/constants.h>
@@ -30,17 +31,6 @@ constexpr int TILE_DIM  = 32;
 constexpr int THREADS_X = TILE_DIM;
 constexpr int THREADS_Y = 256 / TILE_DIM;
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ty, typename Tp, int order>
 class approx1Kernel {
    public:
diff --git a/src/backend/oneapi/kernel/approx2.hpp b/src/backend/oneapi/kernel/approx2.hpp
index 8713d87d20..c173b527b1 100644
--- a/src/backend/oneapi/kernel/approx2.hpp
+++ b/src/backend/oneapi/kernel/approx2.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/interp.hpp>
 #include <traits.hpp>
 #include <af/constants.h>
@@ -30,17 +31,6 @@ constexpr int TILE_DIM  = 32;
 constexpr int THREADS_X = TILE_DIM;
 constexpr int THREADS_Y = 256 / TILE_DIM;
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ty, typename Tp, int order>
 class approx2Kernel {
    public:
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 2bddb4cccf..6d553f18ad 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/assign_kernel_param.hpp>
 #include <traits.hpp>
 
@@ -39,7 +40,7 @@ static int trimIndex(int idx, const int len) {
 template<typename T>
 class assignKernel {
    public:
-    assignKernel(sycl::accessor<T> out, KParam oInfo, sycl::accessor<T> in,
+    assignKernel(write_accessor<T> out, KParam oInfo, read_accessor<T> in,
                  KParam iInfo, AssignKernelParam p, const int nBBS0,
                  const int nBBS1)
         : out_(out)
@@ -102,7 +103,8 @@ class assignKernel {
     }
 
    protected:
-    sycl::accessor<T> out_, in_;
+    write_accessor<T> out_;
+    read_accessor<T> in_;
     KParam oInfo_, iInfo_;
     AssignKernelParam p_;
     const int nBBS0_, nBBS1_;
@@ -124,9 +126,9 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam& p,
                           blk_y * in.info.dims[3] * THREADS_Y);
 
     getQueue().submit([=](sycl::handler& h) {
-        auto pp      = p;
-        auto out_acc = out.data->get_access(h);
-        auto in_acc  = in.data->get_access(h);
+        auto pp = p;
+        write_accessor<T> out_acc{*out.data, h};
+        read_accessor<T> in_acc{*in.data, h};
 
         pp.ptr[0] = bPtr[0]->template get_access<access_mode::read>(h);
         pp.ptr[1] = bPtr[1]->template get_access<access_mode::read>(h);
diff --git a/src/backend/oneapi/kernel/assign_kernel_param.hpp b/src/backend/oneapi/kernel/assign_kernel_param.hpp
index e4c8a8c83a..e2539ed2b3 100644
--- a/src/backend/oneapi/kernel/assign_kernel_param.hpp
+++ b/src/backend/oneapi/kernel/assign_kernel_param.hpp
@@ -1,10 +1,18 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
 
 #include <sycl/sycl.hpp>
 
 #include <array>
 
-#pragma once
-
 namespace arrayfire {
 namespace oneapi {
 
diff --git a/src/backend/oneapi/kernel/bilateral.hpp b/src/backend/oneapi/kernel/bilateral.hpp
index 8c340ccb81..210c92e911 100644
--- a/src/backend/oneapi/kernel/bilateral.hpp
+++ b/src/backend/oneapi/kernel/bilateral.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -24,11 +25,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename outType, bool USE_NATIVE_EXP>
 auto exp_native_nonnative(float in) {
     if constexpr (USE_NATIVE_EXP)
@@ -40,10 +36,10 @@ auto exp_native_nonnative(float in) {
 template<typename outType, typename inType, bool USE_NATIVE_EXP>
 class bilateralKernel {
    public:
-    bilateralKernel(sycl::accessor<outType> d_dst, KParam oInfo,
-                    sycl::accessor<inType> d_src, KParam iInfo,
-                    local_accessor<outType, 1> localMem,
-                    local_accessor<outType, 1> gauss2d, float sigma_space,
+    bilateralKernel(write_accessor<outType> d_dst, KParam oInfo,
+                    read_accessor<inType> d_src, KParam iInfo,
+                    sycl::local_accessor<outType, 1> localMem,
+                    sycl::local_accessor<outType, 1> gauss2d, float sigma_space,
                     float sigma_color, int gaussOff, int nBBS0, int nBBS1)
         : d_dst_(d_dst)
         , oInfo_(oInfo)
@@ -148,7 +144,7 @@ class bilateralKernel {
         return (v < lo) ? lo : (hi < v) ? hi : v;
     }
 
-    void load2LocalMem(local_accessor<outType, 1> shrd, const inType* in,
+    void load2LocalMem(sycl::local_accessor<outType, 1> shrd, const inType* in,
                        int lx, int ly, int shrdStride, int dim0, int dim1,
                        int gx, int gy, int inStride1, int inStride0) const {
         int gx_ = sycl::clamp(gx, 0, dim0 - 1);
@@ -158,12 +154,12 @@ class bilateralKernel {
     }
 
    private:
-    sycl::accessor<outType> d_dst_;
+    write_accessor<outType> d_dst_;
     KParam oInfo_;
-    sycl::accessor<inType> d_src_;
+    read_accessor<inType> d_src_;
     KParam iInfo_;
-    local_accessor<outType, 1> localMem_;
-    local_accessor<outType, 1> gauss2d_;
+    sycl::local_accessor<outType, 1> localMem_;
+    sycl::local_accessor<outType, 1> gauss2d_;
     float sigma_space_;
     float sigma_color_;
     int gaussOff_;
@@ -203,18 +199,17 @@ void bilateral(Param<outType> out, const Param<inType> in, const float s_sigma,
     }
 
     getQueue().submit([&](sycl::handler& h) {
-        auto inAcc  = in.data->get_access(h);
-        auto outAcc = out.data->get_access(h);
+        read_accessor<inType> inAcc{*in.data, h};
+        write_accessor<outType> outAcc{*out.data, h};
 
-        auto localMem = local_accessor<outType, 1>(num_shrd_elems, h);
-        auto gauss2d  = local_accessor<outType, 1>(num_shrd_elems, h);
+        auto localMem = sycl::local_accessor<outType, 1>(num_shrd_elems, h);
+        auto gauss2d  = sycl::local_accessor<outType, 1>(num_shrd_elems, h);
 
         h.parallel_for(sycl::nd_range{global, local},
                        bilateralKernel<outType, inType, UseNativeExp>(
                            outAcc, out.info, inAcc, in.info, localMem, gauss2d,
                            s_sigma, c_sigma, num_shrd_elems, blk_x, blk_y));
     });
-
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
diff --git a/src/backend/oneapi/kernel/convolve.hpp b/src/backend/oneapi/kernel/convolve.hpp
index 276c84c3af..ebec7dbe88 100644
--- a/src/backend/oneapi/kernel/convolve.hpp
+++ b/src/backend/oneapi/kernel/convolve.hpp
@@ -9,10 +9,10 @@
 
 #pragma once
 #include <Param.hpp>
-#include <accessor.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <af/defines.h>
 
 #include <sycl/sycl.hpp>
@@ -109,11 +109,6 @@ void memcpyBuffer(sycl::buffer<T, 1> &dest, sycl::buffer<T, 1> &src,
     });
 }
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 #include "convolve1.hpp"
 #include "convolve2.hpp"
 #include "convolve3.hpp"
diff --git a/src/backend/oneapi/kernel/diagonal.hpp b/src/backend/oneapi/kernel/diagonal.hpp
index 8da78dba70..91db3fbda1 100644
--- a/src/backend/oneapi/kernel/diagonal.hpp
+++ b/src/backend/oneapi/kernel/diagonal.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -24,16 +25,11 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 class diagCreateKernel {
    public:
-    diagCreateKernel(sycl::accessor<T> oData, KParam oInfo,
-                     sycl::accessor<T> iData, KParam iInfo, int num,
+    diagCreateKernel(write_accessor<T> oData, KParam oInfo,
+                     read_accessor<T> iData, KParam iInfo, int num,
                      int groups_x)
         : oData_(oData)
         , oInfo_(oInfo)
@@ -65,9 +61,9 @@ class diagCreateKernel {
     }
 
    private:
-    sycl::accessor<T> oData_;
+    write_accessor<T> oData_;
     KParam oInfo_;
-    sycl::accessor<T> iData_;
+    read_accessor<T> iData_;
     KParam iInfo_;
     int num_;
     int groups_x_;
@@ -82,8 +78,8 @@ static void diagCreate(Param<T> out, Param<T> in, int num) {
                               groups_y * local[1]};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto oData = out.data->get_access(h);
-        auto iData = in.data->get_access(h);
+        write_accessor<T> oData{*out.data, h};
+        read_accessor<T> iData{*in.data, h};
 
         h.parallel_for(sycl::nd_range{global, local},
                        diagCreateKernel<T>(oData, out.info, iData, in.info, num,
@@ -95,8 +91,8 @@ static void diagCreate(Param<T> out, Param<T> in, int num) {
 template<typename T>
 class diagExtractKernel {
    public:
-    diagExtractKernel(sycl::accessor<T> oData, KParam oInfo,
-                      sycl::accessor<T> iData, KParam iInfo, int num,
+    diagExtractKernel(write_accessor<T> oData, KParam oInfo,
+                      read_accessor<T> iData, KParam iInfo, int num,
                       int groups_z)
         : oData_(oData)
         , oInfo_(oInfo)
@@ -133,9 +129,9 @@ class diagExtractKernel {
     }
 
    private:
-    sycl::accessor<T> oData_;
+    write_accessor<T> oData_;
     KParam oInfo_;
-    sycl::accessor<T> iData_;
+    read_accessor<T> iData_;
     KParam iInfo_;
     int num_;
     int groups_z_;
@@ -150,8 +146,8 @@ static void diagExtract(Param<T> out, Param<T> in, int num) {
                               groups_z * local[1] * out.info.dims[3]};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto oData = out.data->get_access(h);
-        auto iData = in.data->get_access(h);
+        write_accessor<T> oData{*out.data, h};
+        read_accessor<T> iData{*in.data, h};
 
         h.parallel_for(sycl::nd_range{global, local},
                        diagExtractKernel<T>(oData, out.info, iData, in.info,
diff --git a/src/backend/oneapi/kernel/diff.hpp b/src/backend/oneapi/kernel/diff.hpp
index 478da588c0..5276786646 100644
--- a/src/backend/oneapi/kernel/diff.hpp
+++ b/src/backend/oneapi/kernel/diff.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -24,15 +25,10 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 class diffKernel {
    public:
-    diffKernel(sycl::accessor<T> outAcc, const sycl::accessor<T> inAcc,
+    diffKernel(write_accessor<T> outAcc, const read_accessor<T> inAcc,
                const KParam op, const KParam ip, const int oElem,
                const int blocksPerMatX, const int blocksPerMatY,
                const bool isDiff2, const unsigned DIM)
@@ -82,8 +78,8 @@ class diffKernel {
     }
 
    private:
-    sycl::accessor<T> outAcc_;
-    const sycl::accessor<T> inAcc_;
+    write_accessor<T> outAcc_;
+    const read_accessor<T> inAcc_;
     const KParam op_;
     const KParam ip_;
     const int oElem_;
@@ -111,8 +107,8 @@ void diff(Param<T> out, const Param<T> in, const unsigned indims,
                       out.info.dims[3];
 
     getQueue().submit([&](sycl::handler &h) {
-        auto inAcc  = in.data->get_access(h);
-        auto outAcc = out.data->get_access(h);
+        read_accessor<T> inAcc   = {*in.data, h};
+        write_accessor<T> outAcc = {*out.data, h};
 
         h.parallel_for(
             sycl::nd_range{global, local},
diff --git a/src/backend/oneapi/kernel/gradient.hpp b/src/backend/oneapi/kernel/gradient.hpp
index 7f29b4cec3..f8ae841444 100644
--- a/src/backend/oneapi/kernel/gradient.hpp
+++ b/src/backend/oneapi/kernel/gradient.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 
 #include <sycl/sycl.hpp>
@@ -21,14 +22,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
-                                      sycl::access::target::local>;
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 #define sidx(y, x) scratch_[((y + 1) * (TX + 2)) + (x + 1)]
 
 template<typename T, int TX, int TY>
@@ -38,7 +31,7 @@ class gradientCreateKernel {
                          write_accessor<T> d_grad1, const KParam grad1,
                          read_accessor<T> d_in, const KParam in,
                          const int blocksPerMatX, const int blocksPerMatY,
-                         local_accessor<T> scratch)
+                         sycl::local_accessor<T> scratch)
         : d_grad0_(d_grad0)
         , grad0_(grad0)
         , d_grad1_(d_grad1)
@@ -132,7 +125,7 @@ class gradientCreateKernel {
     const KParam in_;
     const int blocksPerMatX_;
     const int blocksPerMatY_;
-    local_accessor<T> scratch_;
+    sycl::local_accessor<T> scratch_;
 };
 
 template<typename T>
@@ -151,7 +144,7 @@ void gradient(Param<T> grad0, Param<T> grad1, const Param<T> in) {
         write_accessor<T> grad0Acc{*grad0.data, h};
         write_accessor<T> grad1Acc{*grad1.data, h};
         read_accessor<T> inAcc{*in.data, h};
-        auto scratch = local_accessor<T>((TY + 2) * (TX + 2), h);
+        auto scratch = sycl::local_accessor<T>((TY + 2) * (TX + 2), h);
         h.parallel_for(sycl::nd_range{global, local},
                        gradientCreateKernel<T, TX, TY>(
                            grad0Acc, grad0.info, grad1Acc, grad1.info, inAcc,
diff --git a/src/backend/oneapi/kernel/histogram.hpp b/src/backend/oneapi/kernel/histogram.hpp
index 35f21fc9b6..bd574c9e2d 100644
--- a/src/backend/oneapi/kernel/histogram.hpp
+++ b/src/backend/oneapi/kernel/histogram.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -42,18 +43,14 @@ using global_atomic_ref =
     sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
                      sycl::access::address_space::global_space>;
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 class histogramKernel {
    public:
-    histogramKernel(sycl::accessor<uint> d_dst, KParam oInfo,
-                    const sycl::accessor<T> d_src, KParam iInfo,
-                    local_accessor<uint, 1> localMemAcc, int len, int nbins,
-                    float minval, float maxval, int nBBS, const bool isLinear)
+    histogramKernel(write_accessor<uint> d_dst, KParam oInfo,
+                    const read_accessor<T> d_src, KParam iInfo,
+                    sycl::local_accessor<uint, 1> localMemAcc, int len,
+                    int nbins, float minval, float maxval, int nBBS,
+                    const bool isLinear)
         : d_dst_(d_dst)
         , oInfo_(oInfo)
         , d_src_(d_src)
@@ -118,11 +115,11 @@ class histogramKernel {
     }
 
    private:
-    sycl::accessor<uint> d_dst_;
+    write_accessor<uint> d_dst_;
     KParam oInfo_;
-    sycl::accessor<T> d_src_;
+    read_accessor<T> d_src_;
     KParam iInfo_;
-    local_accessor<uint, 1> localMemAcc_;
+    sycl::local_accessor<uint, 1> localMemAcc_;
     int len_;
     int nbins_;
     float minval_;
@@ -144,10 +141,10 @@ void histogram(Param<uint> out, const Param<T> in, int nbins, float minval,
     auto global          = sycl::range{global0, global1};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto inAcc  = in.data->get_access(h);
-        auto outAcc = out.data->get_access(h);
+        read_accessor<T> inAcc{*in.data, h};
+        write_accessor<uint> outAcc{*out.data, h};
 
-        auto localMem = local_accessor<uint, 1>(locSize, h);
+        auto localMem = sycl::local_accessor<uint, 1>(locSize, h);
 
         h.parallel_for(
             sycl::nd_range{global, local},
diff --git a/src/backend/oneapi/kernel/identity.hpp b/src/backend/oneapi/kernel/identity.hpp
index 20553a2149..0f6911606a 100644
--- a/src/backend/oneapi/kernel/identity.hpp
+++ b/src/backend/oneapi/kernel/identity.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <math.hpp>
 #include <types.hpp>
 
@@ -19,9 +20,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 class identityKernel {
    public:
diff --git a/src/backend/oneapi/kernel/iir.hpp b/src/backend/oneapi/kernel/iir.hpp
index 88b515fe86..38769ad46a 100644
--- a/src/backend/oneapi/kernel/iir.hpp
+++ b/src/backend/oneapi/kernel/iir.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2023, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <math.hpp>
 
 #include <sycl/sycl.hpp>
@@ -20,11 +21,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 constexpr int MAX_A_SIZE = 1024;
 
 template<typename T, bool batch_a>
diff --git a/src/backend/oneapi/kernel/index.hpp b/src/backend/oneapi/kernel/index.hpp
index 6e90d392ad..ef2b837b75 100644
--- a/src/backend/oneapi/kernel/index.hpp
+++ b/src/backend/oneapi/kernel/index.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/assign_kernel_param.hpp>
 
 namespace arrayfire {
@@ -20,19 +21,18 @@ namespace kernel {
 
 template<typename T>
 class indexKernel {
-    sycl::accessor<T, 1, sycl::access::mode::write> out;
+    write_accessor<T> out;
     KParam outp;
-    sycl::accessor<T, 1, sycl::access::mode::read> in;
+    read_accessor<T> in;
     KParam inp;
     IndexKernelParam p;
     int nBBS0;
     int nBBS1;
 
    public:
-    indexKernel(sycl::accessor<T, 1, sycl::access::mode::write> out_,
-                KParam outp_,
-                sycl::accessor<T, 1, sycl::access::mode::read> in_, KParam inp_,
-                const IndexKernelParam p_, const int nBBS0_, const int nBBS1_)
+    indexKernel(write_accessor<T> out_, KParam outp_, read_accessor<T> in_,
+                KParam inp_, const IndexKernelParam p_, const int nBBS0_,
+                const int nBBS1_)
         : out(out_)
         , outp(outp_)
         , in(in_)
diff --git a/src/backend/oneapi/kernel/interp.hpp b/src/backend/oneapi/kernel/interp.hpp
index 516acea466..bfc894dfdf 100644
--- a/src/backend/oneapi/kernel/interp.hpp
+++ b/src/backend/oneapi/kernel/interp.hpp
@@ -7,7 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
+#include <kernel/accessors.hpp>
 #include <math.hpp>
 #include <types.hpp>
 #include <af/constants.h>
@@ -19,12 +22,6 @@
 namespace arrayfire {
 namespace oneapi {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 struct itype_t {
     typedef float wtype;
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 87dbfc923c..97018b6a1d 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
@@ -29,7 +30,7 @@ namespace kernel {
 template<typename T>
 class iotaKernel {
    public:
-    iotaKernel(sycl::accessor<T> out, KParam oinfo, const int s0, const int s1,
+    iotaKernel(write_accessor<T> out, KParam oinfo, const int s0, const int s1,
                const int s2, const int s3, const int blocksPerMatX,
                const int blocksPerMatY)
         : out_(out)
@@ -79,7 +80,7 @@ class iotaKernel {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     KParam oinfo_;
     int s0_, s1_, s2_, s3_;
     int blocksPerMatX_, blocksPerMatY_;
@@ -100,24 +101,17 @@ void iota(Param<T> out, const af::dim4& sdims) {
                           local[1] * blocksPerMatY * out.info.dims[3]);
     sycl::nd_range<2> ndrange(global, local);
 
-    try {
-        getQueue()
-            .submit([=](sycl::handler& h) {
-                auto out_acc = out.data->get_access(h);
-
-                h.parallel_for(
-                    ndrange,
-                    iotaKernel<T>(out_acc, out.info, static_cast<int>(sdims[0]),
-                                  static_cast<int>(sdims[1]),
-                                  static_cast<int>(sdims[2]),
-                                  static_cast<int>(sdims[3]), blocksPerMatX,
-                                  blocksPerMatY));
-            })
-            .wait();
-        ONEAPI_DEBUG_FINISH(getQueue());
-    } catch (sycl::exception& e) {
-        std::cout << e.what() << std::endl;
-    } catch (std::exception& e) { std::cout << e.what() << std::endl; }
+    getQueue().submit([=](sycl::handler& h) {
+        write_accessor<T> out_acc{*out.data, h};
+
+        h.parallel_for(ndrange, iotaKernel<T>(out_acc, out.info,
+                                              static_cast<int>(sdims[0]),
+                                              static_cast<int>(sdims[1]),
+                                              static_cast<int>(sdims[2]),
+                                              static_cast<int>(sdims[3]),
+                                              blocksPerMatX, blocksPerMatY));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 }  // namespace kernel
diff --git a/src/backend/oneapi/kernel/ireduce.hpp b/src/backend/oneapi/kernel/ireduce.hpp
index e047826b08..0c6ae70383 100644
--- a/src/backend/oneapi/kernel/ireduce.hpp
+++ b/src/backend/oneapi/kernel/ireduce.hpp
@@ -8,12 +8,14 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/Binary.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/reduce_config.hpp>
 #include <math.hpp>
 #include <memory.hpp>
@@ -32,17 +34,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T, af_op_t op, uint dim, bool is_first, uint DIMY>
 class ireduceDimKernelSMEM {
    public:
@@ -52,8 +43,8 @@ class ireduceDimKernelSMEM {
                          read_accessor<uint> iloc, KParam ilocInfo,
                          uint groups_x, uint groups_y, uint groups_dim,
                          read_accessor<uint> rlen, KParam rlenInfo,
-                         local_accessor<compute_t<T>, 1> s_val,
-                         local_accessor<uint, 1> s_idx)
+                         sycl::local_accessor<compute_t<T>, 1> s_val,
+                         sycl::local_accessor<uint, 1> s_idx)
         : out_(out)
         , oInfo_(oInfo)
         , oloc_(oloc)
@@ -215,8 +206,8 @@ class ireduceDimKernelSMEM {
     uint groups_x_, groups_y_, groups_dim_;
     read_accessor<uint> rlen_;
     KParam rlenInfo_;
-    local_accessor<compute_t<T>, 1> s_val_;
-    local_accessor<uint, 1> s_idx_;
+    sycl::local_accessor<compute_t<T>, 1> s_val_;
+    sycl::local_accessor<uint, 1> s_idx_;
 };
 
 template<typename T, af_op_t op, int dim, bool is_first>
@@ -228,73 +219,70 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                           groups_dim[1] * groups_dim[3] * local[1]);
 
     sycl::buffer<uint, 1> empty{sycl::range<1>(1)};
-    try {
-        getQueue().submit([&](sycl::handler &h) {
-            write_accessor<T> out_acc{*out.data, h};
-            write_accessor<uint> oloc_acc{*oloc.data, h};
-            read_accessor<T> in_acc{*in.data, h};
-
-            read_accessor<uint> iloc_acc{empty, h};
-            if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
-                    iloc.info.dims[3] >
-                0) {
-                iloc_acc = read_accessor<uint>{*iloc.data, h};
-            }
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<T> out_acc{*out.data, h};
+        write_accessor<uint> oloc_acc{*oloc.data, h};
+        read_accessor<T> in_acc{*in.data, h};
+
+        read_accessor<uint> iloc_acc{empty, h};
+        if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
+                iloc.info.dims[3] >
+            0) {
+            iloc_acc = read_accessor<uint>{*iloc.data, h};
+        }
 
-            read_accessor<uint> rlen_acc{empty, h};
-            if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
-                    rlen.info.dims[3] >
-                0) {
-                rlen_acc = read_accessor<uint>{*rlen.data, h};
-            }
+        read_accessor<uint> rlen_acc{empty, h};
+        if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
+                rlen.info.dims[3] >
+            0) {
+            rlen_acc = read_accessor<uint>{*rlen.data, h};
+        }
 
-            auto shrdVal =
-                local_accessor<compute_t<T>, 1>(creduce::THREADS_PER_BLOCK, h);
-            auto shrdLoc =
-                local_accessor<uint, 1>(creduce::THREADS_PER_BLOCK, h);
-
-            switch (threads_y) {
-                case 8:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_dim[0],
-                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
-                            shrdVal, shrdLoc));
-                    break;
-                case 4:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_dim[0],
-                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
-                            shrdVal, shrdLoc));
-                    break;
-                case 2:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_dim[0],
-                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
-                            shrdVal, shrdLoc));
-                    break;
-                case 1:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_dim[0],
-                            groups_dim[1], groups_dim[dim], rlen_acc, rlen.info,
-                            shrdVal, shrdLoc));
-                    break;
-            }
-        });
-        getQueue().wait_and_throw();
-        ONEAPI_DEBUG_FINISH(getQueue());
-    } catch (sycl::exception &e) { std::cout << e.what() << std::endl; }
+        auto shrdVal = sycl::local_accessor<compute_t<T>, 1>(
+            creduce::THREADS_PER_BLOCK, h);
+        auto shrdLoc =
+            sycl::local_accessor<uint, 1>(creduce::THREADS_PER_BLOCK, h);
+
+        switch (threads_y) {
+            case 8:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
+                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
+                        shrdLoc));
+                break;
+            case 4:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
+                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
+                        shrdLoc));
+                break;
+            case 2:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
+                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
+                        shrdLoc));
+                break;
+            case 1:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
+                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
+                        shrdLoc));
+                break;
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op, int dim>
@@ -348,8 +336,8 @@ class ireduceFirstKernelSMEM {
                            read_accessor<uint> iloc, KParam ilocInfo,
                            uint groups_x, uint groups_y, uint repeat,
                            read_accessor<uint> rlen, KParam rlenInfo,
-                           local_accessor<compute_t<T>, 1> s_val,
-                           local_accessor<uint, 1> s_idx)
+                           sycl::local_accessor<compute_t<T>, 1> s_val,
+                           sycl::local_accessor<uint, 1> s_idx)
         : out_(out)
         , oInfo_(oInfo)
         , oloc_(oloc)
@@ -515,8 +503,8 @@ class ireduceFirstKernelSMEM {
     uint groups_x_, groups_y_, repeat_;
     read_accessor<uint> rlen_;
     KParam rlenInfo_;
-    local_accessor<compute_t<T>, 1> s_val_;
-    local_accessor<uint, 1> s_idx_;
+    sycl::local_accessor<compute_t<T>, 1> s_val_;
+    sycl::local_accessor<uint, 1> s_idx_;
 };
 
 template<typename T, af_op_t op, bool is_first>
@@ -531,69 +519,66 @@ void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
     uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
 
     sycl::buffer<uint, 1> empty{sycl::range<1>(1)};
-    try {
-        getQueue().submit([&](sycl::handler &h) {
-            write_accessor<T> out_acc{*out.data, h};
-            write_accessor<uint> oloc_acc{*oloc.data, h};
-            read_accessor<T> in_acc{*in.data, h};
-
-            read_accessor<uint> iloc_acc{empty, h};
-            if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
-                    iloc.info.dims[3] >
-                0) {
-                iloc_acc = read_accessor<uint>{*iloc.data, h};
-            }
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<T> out_acc{*out.data, h};
+        write_accessor<uint> oloc_acc{*oloc.data, h};
+        read_accessor<T> in_acc{*in.data, h};
+
+        read_accessor<uint> iloc_acc{empty, h};
+        if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
+                iloc.info.dims[3] >
+            0) {
+            iloc_acc = read_accessor<uint>{*iloc.data, h};
+        }
 
-            read_accessor<uint> rlen_acc{empty, h};
-            if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
-                    rlen.info.dims[3] >
-                0) {
-                rlen_acc = read_accessor<uint>{*rlen.data, h};
-            }
+        read_accessor<uint> rlen_acc{empty, h};
+        if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
+                rlen.info.dims[3] >
+            0) {
+            rlen_acc = read_accessor<uint>{*rlen.data, h};
+        }
 
-            auto shrdVal =
-                local_accessor<compute_t<T>, 1>(creduce::THREADS_PER_BLOCK, h);
-            auto shrdLoc =
-                local_accessor<uint, 1>(creduce::THREADS_PER_BLOCK, h);
-
-            switch (threads_x) {
-                case 32:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceFirstKernelSMEM<T, op, is_first, 32>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
-                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
-                    break;
-                case 64:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceFirstKernelSMEM<T, op, is_first, 64>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
-                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
-                    break;
-                case 128:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceFirstKernelSMEM<T, op, is_first, 128>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
-                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
-                    break;
-                case 256:
-                    h.parallel_for(
-                        sycl::nd_range<2>(global, local),
-                        ireduceFirstKernelSMEM<T, op, is_first, 256>(
-                            out_acc, out.info, oloc_acc, oloc.info, in_acc,
-                            in.info, iloc_acc, iloc.info, groups_x, groups_y,
-                            repeat, rlen_acc, rlen.info, shrdVal, shrdLoc));
-                    break;
-            }
-        });
-        getQueue().wait_and_throw();
-        ONEAPI_DEBUG_FINISH(getQueue());
-    } catch (sycl::exception &e) { std::cout << e.what() << std::endl; }
+        auto shrdVal = sycl::local_accessor<compute_t<T>, 1>(
+            creduce::THREADS_PER_BLOCK, h);
+        auto shrdLoc =
+            sycl::local_accessor<uint, 1>(creduce::THREADS_PER_BLOCK, h);
+
+        switch (threads_x) {
+            case 32:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceFirstKernelSMEM<T, op, is_first, 32>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_x, groups_y, repeat,
+                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                break;
+            case 64:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceFirstKernelSMEM<T, op, is_first, 64>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_x, groups_y, repeat,
+                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                break;
+            case 128:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceFirstKernelSMEM<T, op, is_first, 128>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_x, groups_y, repeat,
+                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                break;
+            case 256:
+                h.parallel_for(
+                    sycl::nd_range<2>(global, local),
+                    ireduceFirstKernelSMEM<T, op, is_first, 256>(
+                        out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
+                        iloc_acc, iloc.info, groups_x, groups_y, repeat,
+                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                break;
+        }
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<typename T, af_op_t op>
diff --git a/src/backend/oneapi/kernel/lookup.hpp b/src/backend/oneapi/kernel/lookup.hpp
index a5d29fea09..f3e2fcdcde 100644
--- a/src/backend/oneapi/kernel/lookup.hpp
+++ b/src/backend/oneapi/kernel/lookup.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 
 #include <sycl/sycl.hpp>
 
@@ -23,11 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 int trimIndex(int idx, const int len) {
     int ret_val = idx;
     if (ret_val < 0) {
diff --git a/src/backend/oneapi/kernel/lu_split.hpp b/src/backend/oneapi/kernel/lu_split.hpp
index f42cf8644c..fb69001ebc 100644
--- a/src/backend/oneapi/kernel/lu_split.hpp
+++ b/src/backend/oneapi/kernel/lu_split.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 
 #include <math.hpp>
 #include <array>
@@ -20,11 +21,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T, bool same_dims>
 class luSplitKernel {
    public:
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 1d58458e46..7d622e611c 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -15,6 +15,7 @@
 #include <common/half.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 #include <kernel/reduce_config.hpp>
 #include <math.hpp>
@@ -41,17 +42,6 @@ __device__ auto operator/(__half lhs, float rhs) -> __half {
 
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename To, typename Tw>
 void stable_mean(To *lhs, Tw *l_wt, To rhs, Tw r_wt) {
     if (((*l_wt) != (Tw)0) || (r_wt != (Tw)0)) {
@@ -71,9 +61,10 @@ class meanDimKernelSMEM {
                       write_accessor<Tw> owt, KParam owInfo,
                       read_accessor<Ti> in, KParam iInfo, read_accessor<Tw> iwt,
                       KParam iwInfo, uint groups_x, uint groups_y,
-                      uint offset_dim, local_accessor<compute_t<To>, 1> s_val,
-                      local_accessor<compute_t<Tw>, 1> s_idx, bool input_weight,
-                      bool output_weight)
+                      uint offset_dim,
+                      sycl::local_accessor<compute_t<To>, 1> s_val,
+                      sycl::local_accessor<compute_t<Tw>, 1> s_idx,
+                      bool input_weight, bool output_weight)
         : out_(out)
         , owt_(owt)
         , in_(in)
@@ -213,8 +204,8 @@ class meanDimKernelSMEM {
     read_accessor<Tw> iwt_;
     KParam oInfo_, owInfo_, iInfo_, iwInfo_;
     const uint groups_x_, groups_y_, offset_dim_;
-    local_accessor<compute_t<To>, 1> s_val_;
-    local_accessor<compute_t<Tw>, 1> s_idx_;
+    sycl::local_accessor<compute_t<To>, 1> s_val_;
+    sycl::local_accessor<compute_t<Tw>, 1> s_idx_;
     bool input_weight_, output_weight_;
 };
 
@@ -231,8 +222,10 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        auto s_val = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
-        auto s_idx = local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
+        auto s_val =
+            sycl::local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+        auto s_idx =
+            sycl::local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
 
         bool input_weight = ((iwt.info.dims[0] * iwt.info.dims[1] *
                               iwt.info.dims[2] * iwt.info.dims[3]) != 0);
@@ -327,8 +320,8 @@ class meanFirstKernelSMEM {
                         read_accessor<Tw> iwt, KParam iwInfo, const uint DIMX,
                         const uint groups_x, const uint groups_y,
                         const uint repeat,
-                        local_accessor<compute_t<To>, 1> s_val,
-                        local_accessor<compute_t<Tw>, 1> s_idx,
+                        sycl::local_accessor<compute_t<To>, 1> s_val,
+                        sycl::local_accessor<compute_t<Tw>, 1> s_idx,
                         bool input_weight, bool output_weight)
         : out_(out)
         , owt_(owt)
@@ -485,8 +478,8 @@ class meanFirstKernelSMEM {
     read_accessor<Tw> iwt_;
     KParam oInfo_, owInfo_, iInfo_, iwInfo_;
     const uint DIMX_, groups_x_, groups_y_, repeat_;
-    local_accessor<compute_t<To>, 1> s_val_;
-    local_accessor<compute_t<Tw>, 1> s_idx_;
+    sycl::local_accessor<compute_t<To>, 1> s_val_;
+    sycl::local_accessor<compute_t<Tw>, 1> s_idx_;
     bool input_weight_, output_weight_;
 };
 
@@ -505,8 +498,10 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        auto s_val = local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
-        auto s_idx = local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
+        auto s_val =
+            sycl::local_accessor<compute_t<To>, 1>(THREADS_PER_BLOCK, h);
+        auto s_idx =
+            sycl::local_accessor<compute_t<Tw>, 1>(THREADS_PER_BLOCK, h);
 
         bool input_weight = ((iwt.info.dims[0] * iwt.info.dims[1] *
                               iwt.info.dims[2] * iwt.info.dims[3]) != 0);
@@ -626,6 +621,7 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         sycl::buffer hwBuffer(h_wptr.data(), {tmp_elements},
                               {sycl::property::buffer::use_host_ptr()});
 
+        // TODO: fix when addressing other mean errors
         auto e1 = getQueue().submit([&](sycl::handler &h) {
             auto acc_in =
                 tmpOut.get()->get_access(h, sycl::range{tmp_elements});
diff --git a/src/backend/oneapi/kernel/meanshift.hpp b/src/backend/oneapi/kernel/meanshift.hpp
index 2211d81b73..ef28998d4d 100644
--- a/src/backend/oneapi/kernel/meanshift.hpp
+++ b/src/backend/oneapi/kernel/meanshift.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 
 #include <sycl/sycl.hpp>
 
@@ -24,11 +25,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 inline int convert_int_rtz(float number) { return ((int)(number)); }
 
 template<typename T, typename AccType, const int MAX_CHANNELS>
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 482c7cd366..33a53fc160 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -14,6 +14,7 @@
 #include <common/half.hpp>
 #include <common/traits.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <sycl/sycl.hpp>
 #include <traits.hpp>
 
@@ -34,8 +35,8 @@ typedef struct {
 template<typename T>
 class memCopy {
    public:
-    memCopy(sycl::accessor<T> out, dims_t ostrides, int ooffset,
-            sycl::accessor<T> in, dims_t idims, dims_t istrides, int ioffset,
+    memCopy(write_accessor<T> out, dims_t ostrides, int ooffset,
+            read_accessor<T> in, dims_t idims, dims_t istrides, int ioffset,
             int groups_0, int groups_1)
         : out_(out)
         , ostrides_(ostrides)
@@ -80,10 +81,10 @@ class memCopy {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     dims_t ostrides_;
     int ooffset_;
-    sycl::accessor<T> in_;
+    read_accessor<T> in_;
     dims_t idims_, istrides_;
     int ioffset_, groups_0_, groups_1_;
 };
@@ -115,8 +116,8 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
     sycl::nd_range<2> ndrange(global, local);
 
     getQueue().submit([=](sycl::handler &h) {
-        auto out_acc = out->get_access(h);
-        auto in_acc  = const_cast<sycl::buffer<T> *>(in)->get_access(h);
+        write_accessor<T> out_acc{*out, h};
+        read_accessor<T> in_acc{*const_cast<sycl::buffer<T> *>(in), h};
 
         h.parallel_for(ndrange,
                        memCopy<T>(out_acc, _ostrides, ooffset, in_acc, _idims,
@@ -198,8 +199,8 @@ OTHER_SPECIALIZATIONS(arrayfire::common::half)
 template<typename inType, typename outType, bool SAMEDIMS>
 class reshapeCopy {
    public:
-    reshapeCopy(sycl::accessor<outType> dst, KParam oInfo,
-                sycl::accessor<inType> src, KParam iInfo, outType default_value,
+    reshapeCopy(write_accessor<outType> dst, KParam oInfo,
+                read_accessor<inType> src, KParam iInfo, outType default_value,
                 float factor, dims_t trgt, int blk_x, int blk_y)
         : dst_(dst)
         , src_(src)
@@ -261,8 +262,8 @@ class reshapeCopy {
     }
 
    protected:
-    sycl::accessor<outType> dst_;
-    sycl::accessor<inType> src_;
+    write_accessor<outType> dst_;
+    read_accessor<inType> src_;
     KParam oInfo_, iInfo_;
     outType default_value_;
     float factor_;
@@ -303,9 +304,9 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
     }
 
     getQueue().submit([=](sycl::handler &h) {
-        auto dst_acc = dst.data->get_access(h);
-        auto src_acc =
-            const_cast<sycl::buffer<inType> *>(src.data)->get_access(h);
+        write_accessor<outType> dst_acc{*dst.data, h};
+        read_accessor<inType> src_acc{
+            *const_cast<sycl::buffer<inType> *>(src.data), h};
 
         if (same_dims) {
             h.parallel_for(ndrange, reshapeCopy<inType, outType, true>(
diff --git a/src/backend/oneapi/kernel/pad_array_borders.hpp b/src/backend/oneapi/kernel/pad_array_borders.hpp
index 129f9bf381..c5401a65c2 100644
--- a/src/backend/oneapi/kernel/pad_array_borders.hpp
+++ b/src/backend/oneapi/kernel/pad_array_borders.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <math.hpp>
 #include <af/defines.h>
 
@@ -23,11 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T, int BType>
 class padBordersKernel {
    public:
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
index 329387eef5..b416827a7d 100644
--- a/src/backend/oneapi/kernel/random_engine.hpp
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -12,6 +12,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/random_engine_mersenne.hpp>
 #include <kernel/random_engine_philox.hpp>
 #include <kernel/random_engine_threefry.hpp>
@@ -56,7 +57,7 @@ void uniformDistributionCBRNG(Param<T> out, const size_t elements,
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
             getQueue().submit([=](sycl::handler &h) {
-                auto out_acc = out.data->get_access(h);
+                write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
                                uniformPhilox<T>(out_acc, hi, lo, hic, loc,
@@ -66,7 +67,7 @@ void uniformDistributionCBRNG(Param<T> out, const size_t elements,
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
             getQueue().submit([=](sycl::handler &h) {
-                auto out_acc = out.data->get_access(h);
+                write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
                                uniformThreefry<T>(out_acc, hi, lo, hic, loc,
@@ -96,7 +97,7 @@ void normalDistributionCBRNG(Param<T> out, const size_t elements,
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
             getQueue().submit([=](sycl::handler &h) {
-                auto out_acc = out.data->get_access(h);
+                write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
                                normalPhilox<T>(out_acc, hi, lo, hic, loc,
@@ -105,7 +106,7 @@ void normalDistributionCBRNG(Param<T> out, const size_t elements,
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
             getQueue().submit([=](sycl::handler &h) {
-                auto out_acc = out.data->get_access(h);
+                write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
                                normalThreefry<T>(out_acc, hi, lo, hic, loc,
@@ -134,7 +135,7 @@ void uniformDistributionMT(Param<T> out, const size_t elements,
     sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
                               sycl::range<1>(threads));
     getQueue().submit([=](sycl::handler &h) {
-        auto out_acc       = out.data->get_access(h);
+        write_accessor<T> out_acc{*out.data, h};
         auto state_acc     = state.data->get_access(h);
         auto pos_acc       = pos.data->get_access(h);
         auto sh1_acc       = sh1.data->get_access(h);
@@ -142,9 +143,9 @@ void uniformDistributionMT(Param<T> out, const size_t elements,
         auto recursion_acc = sh2.data->get_access(h);
         auto temper_acc    = sh2.data->get_access(h);
 
-        auto lstate_acc     = local_accessor<uint, 1>(STATE_SIZE, h);
-        auto lrecursion_acc = local_accessor<uint, 1>(TABLE_SIZE, h);
-        auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
+        auto lstate_acc     = sycl::local_accessor<uint, 1>(STATE_SIZE, h);
+        auto lrecursion_acc = sycl::local_accessor<uint, 1>(TABLE_SIZE, h);
+        auto ltemper_acc    = sycl::local_accessor<uint, 1>(TABLE_SIZE, h);
 
         h.parallel_for(
             ndrange, uniformMersenne<T>(
@@ -170,7 +171,7 @@ void normalDistributionMT(Param<T> out, const size_t elements,
     sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
                               sycl::range<1>(threads));
     getQueue().submit([=](sycl::handler &h) {
-        auto out_acc       = out.data->get_access(h);
+        write_accessor<T> out_acc{*out.data, h};
         auto state_acc     = state.data->get_access(h);
         auto pos_acc       = pos.data->get_access(h);
         auto sh1_acc       = sh1.data->get_access(h);
@@ -178,9 +179,9 @@ void normalDistributionMT(Param<T> out, const size_t elements,
         auto recursion_acc = sh2.data->get_access(h);
         auto temper_acc    = sh2.data->get_access(h);
 
-        auto lstate_acc     = local_accessor<uint, 1>(STATE_SIZE, h);
-        auto lrecursion_acc = local_accessor<uint, 1>(TABLE_SIZE, h);
-        auto ltemper_acc    = local_accessor<uint, 1>(TABLE_SIZE, h);
+        auto lstate_acc     = sycl::local_accessor<uint, 1>(STATE_SIZE, h);
+        auto lrecursion_acc = sycl::local_accessor<uint, 1>(TABLE_SIZE, h);
+        auto ltemper_acc    = sycl::local_accessor<uint, 1>(TABLE_SIZE, h);
 
         h.parallel_for(
             ndrange, normalMersenne<T>(out_acc, state_acc, pos_acc, sh1_acc,
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
index f78bc8d732..f36b2b60d0 100644
--- a/src/backend/oneapi/kernel/random_engine_mersenne.hpp
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -42,6 +42,7 @@
  * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  *******************************************************/
 #pragma once
+#include <kernel/accessors.hpp>
 #include <kernel/random_engine_write.hpp>
 
 #include <sycl/sycl.hpp>
@@ -55,11 +56,6 @@ constexpr int BLOCKS     = 32;
 constexpr int STATE_SIZE = (256 * 3);
 constexpr int TABLE_SIZE = 16;
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 // Utils
 static inline void read_table(uint *const sharedTable, const uint *const table,
                               size_t groupId, size_t localId) {
@@ -108,8 +104,8 @@ static inline uint temper(const uint *const temper_table, const uint v,
 // Initialization
 class initMersenneKernel {
    public:
-    initMersenneKernel(sycl::accessor<uint> state, sycl::accessor<uint> tbl,
-                       local_accessor<uint, 1> lstate, uintl seed)
+    initMersenneKernel(write_accessor<uint> state, read_accessor<uint> tbl,
+                       sycl::local_accessor<uint, 1> lstate, uintl seed)
         : state_(state), tbl_(tbl), lstate_(lstate), seed_(seed) {}
 
     void operator()(sycl::nd_item<1> it) const {
@@ -141,17 +137,18 @@ class initMersenneKernel {
     }
 
    protected:
-    sycl::accessor<uint> state_, tbl_;
-    local_accessor<uint, 1> lstate_;
+    write_accessor<uint> state_;
+    read_accessor<uint> tbl_;
+    sycl::local_accessor<uint, 1> lstate_;
     uintl seed_;
 };
 
 void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
     sycl::nd_range<1> ndrange({BLOCKS * N}, {N});
     getQueue().submit([=](sycl::handler &h) {
-        auto state_acc  = state.data->get_access(h);
-        auto tbl_acc    = tbl.data->get_access(h);
-        auto lstate_acc = local_accessor<uint, 1>(N, h);
+        write_accessor<uint> state_acc{*state.data, h};
+        read_accessor<uint> tbl_acc{*tbl.data, h};
+        auto lstate_acc = sycl::local_accessor<uint, 1>(N, h);
 
         h.parallel_for(
             ndrange, initMersenneKernel(state_acc, tbl_acc, lstate_acc, seed));
@@ -164,16 +161,16 @@ void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
 template<typename T>
 class uniformMersenne {
    public:
-    uniformMersenne(sycl::accessor<T> out, sycl::accessor<uint> gState,
+    uniformMersenne(write_accessor<T> out, sycl::accessor<uint> gState,
                     sycl::accessor<uint> pos_tbl, sycl::accessor<uint> sh1_tbl,
                     sycl::accessor<uint> sh2_tbl, uint mask,
                     sycl::accessor<uint> g_recursion_table,
                     sycl::accessor<uint> g_temper_table,
                     // local memory caches of global state
-                    local_accessor<uint, 1> state,
-                    local_accessor<uint, 1> recursion_table,
-                    local_accessor<uint, 1> temper_table, uint elementsPerBlock,
-                    size_t elements)
+                    sycl::local_accessor<uint, 1> state,
+                    sycl::local_accessor<uint, 1> recursion_table,
+                    sycl::local_accessor<uint, 1> temper_table,
+                    uint elementsPerBlock, size_t elements)
         : out_(out)
         , gState_(gState)
         , pos_tbl_(pos_tbl)
@@ -248,12 +245,12 @@ class uniformMersenne {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     sycl::accessor<uint> gState_;
     sycl::accessor<uint> pos_tbl_, sh1_tbl_, sh2_tbl_;
     uint mask_;
     sycl::accessor<uint> g_recursion_table_, g_temper_table_;
-    local_accessor<uint, 1> state_, recursion_table_, temper_table_;
+    sycl::local_accessor<uint, 1> state_, recursion_table_, temper_table_;
     uint elementsPerBlock_;
     size_t elements_;
 };
@@ -261,16 +258,16 @@ class uniformMersenne {
 template<typename T>
 class normalMersenne {
    public:
-    normalMersenne(sycl::accessor<T> out, sycl::accessor<uint> gState,
+    normalMersenne(write_accessor<T> out, sycl::accessor<uint> gState,
                    sycl::accessor<uint> pos_tbl, sycl::accessor<uint> sh1_tbl,
                    sycl::accessor<uint> sh2_tbl, uint mask,
                    sycl::accessor<uint> g_recursion_table,
                    sycl::accessor<uint> g_temper_table,
                    // local memory caches of global state
-                   local_accessor<uint, 1> state,
-                   local_accessor<uint, 1> recursion_table,
-                   local_accessor<uint, 1> temper_table, uint elementsPerBlock,
-                   size_t elements)
+                   sycl::local_accessor<uint, 1> state,
+                   sycl::local_accessor<uint, 1> recursion_table,
+                   sycl::local_accessor<uint, 1> temper_table,
+                   uint elementsPerBlock, size_t elements)
         : out_(out)
         , gState_(gState)
         , pos_tbl_(pos_tbl)
@@ -346,12 +343,12 @@ class normalMersenne {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     sycl::accessor<uint> gState_;
     sycl::accessor<uint> pos_tbl_, sh1_tbl_, sh2_tbl_;
     uint mask_;
     sycl::accessor<uint> g_recursion_table_, g_temper_table_;
-    local_accessor<uint, 1> state_, recursion_table_, temper_table_;
+    sycl::local_accessor<uint, 1> state_, recursion_table_, temper_table_;
     uint elementsPerBlock_;
     size_t elements_;
 };
diff --git a/src/backend/oneapi/kernel/random_engine_philox.hpp b/src/backend/oneapi/kernel/random_engine_philox.hpp
index 3bfe44251d..afa29394e2 100644
--- a/src/backend/oneapi/kernel/random_engine_philox.hpp
+++ b/src/backend/oneapi/kernel/random_engine_philox.hpp
@@ -45,6 +45,7 @@
  *********************************************************/
 
 #pragma once
+#include <kernel/accessors.hpp>
 #include <kernel/random_engine_write.hpp>
 
 namespace arrayfire {
@@ -106,7 +107,7 @@ static inline void philox(uint key[2], uint ctr[4]) {
 template<typename T>
 class uniformPhilox {
    public:
-    uniformPhilox(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
+    uniformPhilox(write_accessor<T> out, uint hi, uint lo, uint hic, uint loc,
                   uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
@@ -138,7 +139,7 @@ class uniformPhilox {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
 };
@@ -146,7 +147,7 @@ class uniformPhilox {
 template<typename T>
 class normalPhilox {
    public:
-    normalPhilox(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
+    normalPhilox(write_accessor<T> out, uint hi, uint lo, uint hic, uint loc,
                  uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
@@ -180,7 +181,7 @@ class normalPhilox {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
 };
diff --git a/src/backend/oneapi/kernel/random_engine_threefry.hpp b/src/backend/oneapi/kernel/random_engine_threefry.hpp
index 919f04d010..1969bf3b69 100644
--- a/src/backend/oneapi/kernel/random_engine_threefry.hpp
+++ b/src/backend/oneapi/kernel/random_engine_threefry.hpp
@@ -45,6 +45,7 @@
  *********************************************************/
 
 #pragma once
+#include <kernel/accessors.hpp>
 #include <kernel/random_engine_write.hpp>
 
 namespace arrayfire {
@@ -161,7 +162,7 @@ void threefry(uint k[2], uint c[2], uint X[2]) {
 template<typename T>
 class uniformThreefry {
    public:
-    uniformThreefry(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
+    uniformThreefry(write_accessor<T> out, uint hi, uint lo, uint hic, uint loc,
                     uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
@@ -198,7 +199,7 @@ class uniformThreefry {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
 };
@@ -206,7 +207,7 @@ class uniformThreefry {
 template<typename T>
 class normalThreefry {
    public:
-    normalThreefry(sycl::accessor<T> out, uint hi, uint lo, uint hic, uint loc,
+    normalThreefry(write_accessor<T> out, uint hi, uint lo, uint hic, uint loc,
                    uint elementsPerBlock, uint elements)
         : out_(out)
         , hi_(hi)
@@ -243,7 +244,7 @@ class normalThreefry {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     uint hi_, lo_, hic_, loc_;
     uint elementsPerBlock_, elements_;
 };
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index 1c8512be0b..f052abb48c 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -15,6 +15,7 @@
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 #include <af/dim4.hpp>
 
@@ -30,7 +31,7 @@ namespace kernel {
 template<typename T>
 class rangeOp {
    public:
-    rangeOp(sycl::accessor<T> out, KParam oinfo, const int dim,
+    rangeOp(write_accessor<T> out, KParam oinfo, const int dim,
             const int blocksPerMatX, const int blocksPerMatY)
         : out_(out)
         , oinfo_(oinfo)
@@ -82,7 +83,7 @@ class rangeOp {
     }
 
    protected:
-    sycl::accessor<T> out_;
+    write_accessor<T> out_;
     KParam oinfo_;
     int dim_;
     int blocksPerMatX_, blocksPerMatY_;
@@ -104,7 +105,7 @@ void range(Param<T> out, const int dim) {
     sycl::nd_range<2> ndrange(global, local);
 
     getQueue().submit([=](sycl::handler& h) {
-        auto out_acc = out.data->get_access(h);
+        write_accessor<T> out_acc{*out.data, h};
 
         h.parallel_for(ndrange, rangeOp<T>(out_acc, out.info, dim,
                                            blocksPerMatX, blocksPerMatY));
diff --git a/src/backend/oneapi/kernel/reduce.hpp b/src/backend/oneapi/kernel/reduce.hpp
index 6807a68396..7089cb9b4e 100644
--- a/src/backend/oneapi/kernel/reduce.hpp
+++ b/src/backend/oneapi/kernel/reduce.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/Binary.hpp>
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 0878f33329..4bc3d5254d 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/Binary.hpp>
@@ -15,6 +16,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/reduce_config.hpp>
 #include <math.hpp>
 #include <memory.hpp>
@@ -31,22 +33,11 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 using global_atomic_ref =
     sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
                      sycl::access::address_space::global_space>;
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ti, typename To, af_op_t op>
 class reduceAllKernelSMEM {
    public:
@@ -56,8 +47,8 @@ class reduceAllKernelSMEM {
                         read_accessor<Ti> in, KParam iInfo, uint DIMX,
                         uint groups_x, uint groups_y, uint repeat,
                         bool change_nan, To nanval,
-                        local_accessor<compute_t<To>, 1> s_ptr,
-                        local_accessor<bool, 1> amLast)
+                        sycl::local_accessor<compute_t<To>, 1> s_ptr,
+                        sycl::local_accessor<bool, 1> amLast)
         : out_(out)
         , retCount_(retCount)
         , tmp_(tmp)
@@ -237,8 +228,8 @@ class reduceAllKernelSMEM {
     uint groups_x_, groups_y_;
     bool change_nan_;
     To nanval_;
-    local_accessor<compute_t<To>, 1> s_ptr_;
-    local_accessor<bool, 1> amLast_;
+    sycl::local_accessor<compute_t<To>, 1> s_ptr_;
+    sycl::local_accessor<bool, 1> amLast_;
 };
 
 template<typename Ti, typename To, af_op_t op>
@@ -267,9 +258,9 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
         auto tmp_acc      = tmp.get()->get_access(h);
         read_accessor<Ti> in_acc{*in.data, h};
 
-        auto shrdMem =
-            local_accessor<compute_t<To>, 1>(creduce::THREADS_PER_BLOCK, h);
-        auto amLast = local_accessor<bool, 1>(1, h);
+        auto shrdMem = sycl::local_accessor<compute_t<To>, 1>(
+            creduce::THREADS_PER_BLOCK, h);
+        auto amLast = sycl::local_accessor<bool, 1>(1, h);
         h.parallel_for(
             sycl::nd_range<2>(global, local),
             reduceAllKernelSMEM<Ti, To, op>(
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 22b9c0f8dc..926a7205e9 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/Binary.hpp>
@@ -15,6 +16,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/reduce_config.hpp>
 #include <math.hpp>
 #include <memory.hpp>
@@ -29,23 +31,12 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ti, typename To, af_op_t op, uint dim, uint DIMY>
 class reduceDimKernelSMEM {
    public:
     reduceDimKernelSMEM(Param<To> out, Param<Ti> in, uint groups_x,
                         uint groups_y, uint offset_dim, bool change_nan,
-                        To nanval, local_accessor<compute_t<To>, 1> s_val,
+                        To nanval, sycl::local_accessor<compute_t<To>, 1> s_val,
                         sycl::handler &h)
         : out_(out.template get_accessor<sycl::access::mode::write>(h))
         , in_(in.template get_accessor<sycl::access::mode::read>(h))
@@ -141,7 +132,7 @@ class reduceDimKernelSMEM {
     uint groups_x_, groups_y_, offset_dim_;
     bool change_nan_;
     To nanval_;
-    local_accessor<compute_t<To>, 1> s_val_;
+    sycl::local_accessor<compute_t<To>, 1> s_val_;
 };
 
 template<typename Ti, typename To, af_op_t op, uint dim>
@@ -154,8 +145,8 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
                           blocks_dim[1] * blocks_dim[3] * local[1]);
 
     getQueue().submit([=](sycl::handler &h) {
-        auto shrdMem =
-            local_accessor<compute_t<To>, 1>(creduce::THREADS_X * threads_y, h);
+        auto shrdMem = sycl::local_accessor<compute_t<To>, 1>(
+            creduce::THREADS_X * threads_y, h);
 
         switch (threads_y) {
             case 8:
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index 42ffb9199d..27143aa24b 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/Binary.hpp>
@@ -15,6 +16,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/reduce_config.hpp>
 #include <math.hpp>
 #include <memory.hpp>
@@ -31,24 +33,14 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ti, typename To, af_op_t op, uint DIMX>
 class reduceFirstKernelSMEM {
    public:
     reduceFirstKernelSMEM(write_accessor<To> out, KParam oInfo,
                           read_accessor<Ti> in, KParam iInfo, uint groups_x,
                           uint groups_y, uint repeat, bool change_nan,
-                          To nanval, local_accessor<compute_t<To>, 1> s_val)
+                          To nanval,
+                          sycl::local_accessor<compute_t<To>, 1> s_val)
         : out_(out)
         , oInfo_(oInfo)
         , iInfo_(iInfo)
@@ -145,7 +137,7 @@ class reduceFirstKernelSMEM {
     uint groups_x_, groups_y_, repeat_;
     bool change_nan_;
     To nanval_;
-    local_accessor<compute_t<To>, 1> s_val_;
+    sycl::local_accessor<compute_t<To>, 1> s_val_;
 };
 
 template<typename Ti, typename To, af_op_t op>
@@ -163,8 +155,8 @@ void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        auto shrdMem =
-            local_accessor<compute_t<To>, 1>(creduce::THREADS_PER_BLOCK, h);
+        auto shrdMem = sycl::local_accessor<compute_t<To>, 1>(
+            creduce::THREADS_PER_BLOCK, h);
 
         switch (threads_x) {
             case 32:
diff --git a/src/backend/oneapi/kernel/reorder.hpp b/src/backend/oneapi/kernel/reorder.hpp
index 1064047f77..adf1c8f57b 100644
--- a/src/backend/oneapi/kernel/reorder.hpp
+++ b/src/backend/oneapi/kernel/reorder.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -23,11 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 class reorderCreateKernel {
    public:
diff --git a/src/backend/oneapi/kernel/resize.hpp b/src/backend/oneapi/kernel/resize.hpp
index b14ceafe14..50cc041ab5 100644
--- a/src/backend/oneapi/kernel/resize.hpp
+++ b/src/backend/oneapi/kernel/resize.hpp
@@ -13,6 +13,7 @@
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -33,11 +34,6 @@ std::complex<double> mul(AT a, std::complex<double> b) {
     return std::complex<double>(a * b.real(), a * b.imag());
 }
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 using wtype_t = typename std::conditional<std::is_same<T, double>::value,
                                           double, float>::type;
diff --git a/src/backend/oneapi/kernel/rotate.hpp b/src/backend/oneapi/kernel/rotate.hpp
index 84641a3f76..a6d255d369 100644
--- a/src/backend/oneapi/kernel/rotate.hpp
+++ b/src/backend/oneapi/kernel/rotate.hpp
@@ -12,6 +12,7 @@
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/interp.hpp>
 #include <math.hpp>
 #include <traits.hpp>
@@ -22,11 +23,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 typedef struct {
     float tmat[6];
 } tmat_t;
diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index a9ce3d7838..eea34ffff7 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -14,6 +14,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
@@ -23,17 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ti, typename To, af_op_t op, int dim>
 class scanDimKernel {
    public:
@@ -42,8 +32,8 @@ class scanDimKernel {
                   read_accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
                   const uint groups_y, const uint blocks_dim, const uint lim,
                   const bool isFinalPass, const uint DIMY,
-                  const bool inclusive_scan, local_accessor<To, 1> s_val,
-                  local_accessor<To, 1> s_tmp)
+                  const bool inclusive_scan, sycl::local_accessor<To, 1> s_val,
+                  sycl::local_accessor<To, 1> s_tmp)
         : out_acc_(out_acc)
         , tmp_acc_(tmp_acc)
         , in_acc_(in_acc)
@@ -161,8 +151,8 @@ class scanDimKernel {
     KParam oInfo_, tInfo_, iInfo_;
     const uint groups_x_, groups_y_, blocks_dim_, lim_, DIMY_;
     const bool isFinalPass_, inclusive_scan_;
-    local_accessor<To, 1> s_val_;
-    local_accessor<To, 1> s_tmp_;
+    sycl::local_accessor<To, 1> s_val_;
+    sycl::local_accessor<To, 1> s_tmp_;
 };
 
 template<typename To, af_op_t op, int dim>
@@ -262,9 +252,9 @@ static void scan_dim_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
         write_accessor<To> tmp_acc{*tmp.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
-        auto s_val =
-            local_accessor<compute_t<To>, 1>(THREADS_X * threads_y * 2, h);
-        auto s_tmp = local_accessor<compute_t<To>, 1>(THREADS_X, h);
+        auto s_val = sycl::local_accessor<compute_t<To>, 1>(
+            THREADS_X * threads_y * 2, h);
+        auto s_tmp = sycl::local_accessor<compute_t<To>, 1>(THREADS_X, h);
 
         h.parallel_for(
             sycl::nd_range<2>(global, local),
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 3a5b113914..649e031b03 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -14,6 +14,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 #include <memory.hpp>
 
@@ -23,17 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename Ti, typename To, af_op_t op>
 class scanFirstKernel {
    public:
@@ -42,7 +32,8 @@ class scanFirstKernel {
                     read_accessor<Ti> in_acc, KParam iInfo, const uint groups_x,
                     const uint groups_y, const uint lim, const bool isFinalPass,
                     const uint DIMX, const bool inclusive_scan,
-                    local_accessor<To, 1> s_val, local_accessor<To, 1> s_tmp)
+                    sycl::local_accessor<To, 1> s_val,
+                    sycl::local_accessor<To, 1> s_tmp)
         : out_acc_(out_acc)
         , tmp_acc_(tmp_acc)
         , in_acc_(in_acc)
@@ -138,8 +129,8 @@ class scanFirstKernel {
     KParam oInfo_, tInfo_, iInfo_;
     const uint groups_x_, groups_y_, lim_, DIMX_;
     const bool isFinalPass_, inclusive_scan_;
-    local_accessor<To, 1> s_val_;
-    local_accessor<To, 1> s_tmp_;
+    sycl::local_accessor<To, 1> s_val_;
+    sycl::local_accessor<To, 1> s_tmp_;
 };
 
 template<typename To, af_op_t op>
@@ -220,8 +211,8 @@ static void scan_first_launcher(Param<To> out, Param<To> tmp, Param<Ti> in,
 
         const int DIMY            = THREADS_PER_BLOCK / threads_x;
         const int SHARED_MEM_SIZE = (2 * threads_x + 1) * (DIMY);
-        auto s_val = local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
-        auto s_tmp = local_accessor<compute_t<To>, 1>(DIMY, h);
+        auto s_val = sycl::local_accessor<compute_t<To>, 1>(SHARED_MEM_SIZE, h);
+        auto s_tmp = sycl::local_accessor<compute_t<To>, 1>(DIMY, h);
 
         // TODO threads_x as template arg for #pragma unroll?
         h.parallel_for(sycl::nd_range<2>(global, local),
diff --git a/src/backend/oneapi/kernel/select.hpp b/src/backend/oneapi/kernel/select.hpp
index abba384f80..b5a6ae5954 100644
--- a/src/backend/oneapi/kernel/select.hpp
+++ b/src/backend/oneapi/kernel/select.hpp
@@ -12,6 +12,7 @@
 #include <Param.hpp>
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
+#include <kernel/accessors.hpp>
 #include <math.hpp>
 
 #include <sycl/sycl.hpp>
@@ -23,11 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 constexpr uint DIMX  = 32;
 constexpr uint DIMY  = 8;
 constexpr int REPEAT = 64;
diff --git a/src/backend/oneapi/kernel/tile.hpp b/src/backend/oneapi/kernel/tile.hpp
index 2c44594a34..39cea65af3 100644
--- a/src/backend/oneapi/kernel/tile.hpp
+++ b/src/backend/oneapi/kernel/tile.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 
 #include <sycl/sycl.hpp>
 
@@ -23,11 +24,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 class tileCreateKernel {
    public:
diff --git a/src/backend/oneapi/kernel/transform.hpp b/src/backend/oneapi/kernel/transform.hpp
index 6760e1a489..07f70a3a62 100644
--- a/src/backend/oneapi/kernel/transform.hpp
+++ b/src/backend/oneapi/kernel/transform.hpp
@@ -13,6 +13,7 @@
 #include <common/complex.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/interp.hpp>
 #include <math.hpp>
 #include <traits.hpp>
@@ -26,11 +27,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 using wtype_t = typename std::conditional<std::is_same<T, double>::value,
                                           double, float>::type;
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index eeb9387145..bf7c7a874b 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -44,11 +45,6 @@ cdouble getConjugate(const cdouble &in) {
     return std::conj(in);
 }
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 class transposeKernel {
    public:
@@ -57,7 +53,7 @@ class transposeKernel {
                     const sycl::accessor<T, 1, sycl::access::mode::read> iData,
                     const KParam in, const int blocksPerMatX,
                     const int blocksPerMatY, const bool conjugate,
-                    const bool IS32MULTIPLE, local_accessor<T, 1> shrdMem)
+                    const bool IS32MULTIPLE, sycl::local_accessor<T, 1> shrdMem)
         : oData_(oData)
         , out_(out)
         , iData_(iData)
@@ -135,7 +131,7 @@ class transposeKernel {
     int blocksPerMatY_;
     bool conjugate_;
     bool IS32MULTIPLE_;
-    local_accessor<T, 1> shrdMem_;
+    sycl::local_accessor<T, 1> shrdMem_;
 };
 
 template<typename T>
@@ -153,7 +149,7 @@ void transpose(Param<T> out, const Param<T> in, const bool conjugate,
         auto r = in.data->template get_access<sycl::access::mode::read>(h);
         auto q = out.data->template get_access<sycl::access::mode::write>(h);
 
-        auto shrdMem = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
+        auto shrdMem = sycl::local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 
         h.parallel_for(sycl::nd_range{global, local},
                        transposeKernel<T>(q, out.info, r, in.info, blk_x, blk_y,
diff --git a/src/backend/oneapi/kernel/transpose_inplace.hpp b/src/backend/oneapi/kernel/transpose_inplace.hpp
index 23f04c6559..721a3befb9 100644
--- a/src/backend/oneapi/kernel/transpose_inplace.hpp
+++ b/src/backend/oneapi/kernel/transpose_inplace.hpp
@@ -47,19 +47,14 @@ constexpr dim_t TILE_DIM  = 16;
 constexpr dim_t THREADS_X = TILE_DIM;
 constexpr dim_t THREADS_Y = 256 / TILE_DIM;
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 class transposeInPlaceKernel {
    public:
     transposeInPlaceKernel(const sycl::accessor<T> iData, const KParam in,
                            const int blocksPerMatX, const int blocksPerMatY,
                            const bool conjugate, const bool IS32MULTIPLE,
-                           local_accessor<T, 1> shrdMem_s,
-                           local_accessor<T, 1> shrdMem_d)
+                           sycl::local_accessor<T, 1> shrdMem_s,
+                           sycl::local_accessor<T, 1> shrdMem_d)
         : iData_(iData)
         , in_(in)
         , blocksPerMatX_(blocksPerMatX)
@@ -163,8 +158,8 @@ class transposeInPlaceKernel {
     int blocksPerMatY_;
     bool conjugate_;
     bool IS32MULTIPLE_;
-    local_accessor<T, 1> shrdMem_s_;
-    local_accessor<T, 1> shrdMem_d_;
+    sycl::local_accessor<T, 1> shrdMem_s_;
+    sycl::local_accessor<T, 1> shrdMem_d_;
 };
 
 template<typename T>
@@ -179,9 +174,11 @@ void transpose_inplace(Param<T> in, const bool conjugate,
                               blk_y * local[1] * in.info.dims[3]};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto r         = in.data->get_access(h);
-        auto shrdMem_s = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
-        auto shrdMem_d = local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
+        auto r = in.data->get_access(h);
+        auto shrdMem_s =
+            sycl::local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
+        auto shrdMem_d =
+            sycl::local_accessor<T, 1>(TILE_DIM * (TILE_DIM + 1), h);
 
         h.parallel_for(
             sycl::nd_range{global, local},
diff --git a/src/backend/oneapi/kernel/triangle.hpp b/src/backend/oneapi/kernel/triangle.hpp
index f4705035b3..4634f69570 100644
--- a/src/backend/oneapi/kernel/triangle.hpp
+++ b/src/backend/oneapi/kernel/triangle.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <traits.hpp>
 
 #include <sycl/sycl.hpp>
@@ -24,15 +25,10 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T, int dimensions>
-using local_accessor =
-    sycl::accessor<T, dimensions, sycl::access::mode::read_write,
-                   sycl::access::target::local>;
-
 template<typename T>
 class triangleKernel {
    public:
-    triangleKernel(sycl::accessor<T> rAcc, KParam rinfo, sycl::accessor<T> iAcc,
+    triangleKernel(write_accessor<T> rAcc, KParam rinfo, read_accessor<T> iAcc,
                    KParam iinfo, const int groups_x, const int groups_y,
                    const bool is_upper, const bool is_unit_diag)
         : rAcc_(rAcc)
@@ -82,9 +78,9 @@ class triangleKernel {
     }
 
    private:
-    sycl::accessor<T> rAcc_;
+    write_accessor<T> rAcc_;
     KParam rinfo_;
-    sycl::accessor<T> iAcc_;
+    read_accessor<T> iAcc_;
     KParam iinfo_;
     const int groups_x_;
     const int groups_y_;
@@ -109,8 +105,8 @@ void triangle(Param<T> out, const Param<T> in, bool is_upper,
                               groups_y * out.info.dims[3] * local[1]};
 
     getQueue().submit([&](sycl::handler &h) {
-        auto iAcc = in.data->get_access(h);
-        auto rAcc = out.data->get_access(h);
+        read_accessor<T> iAcc{*in.data, h};
+        write_accessor<T> rAcc{*out.data, h};
 
         h.parallel_for(
             sycl::nd_range{global, local},
diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index dd18189ae0..b65e0d9333 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -7,11 +7,14 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Param.hpp>
 #include <backend.hpp>
 #include <common/dispatch.hpp>
 #include <debug_oneapi.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 #include <kernel/scan_first.hpp>
 #include <memory.hpp>
@@ -26,12 +29,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 class whereKernel {
    public:
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
index 5f2c92c641..ef8d2eba21 100644
--- a/src/backend/oneapi/kernel/wrap.hpp
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -13,9 +13,9 @@
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 #include <math.hpp>
-#include <sycl/sycl.hpp>
 
 #include <sycl/sycl.hpp>
 
@@ -26,14 +26,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
-                                      sycl::access::target::local>;
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 class wrapCreateKernel {
    public:
diff --git a/src/backend/oneapi/kernel/wrap_dilated.hpp b/src/backend/oneapi/kernel/wrap_dilated.hpp
index dae994e371..63bdf342a8 100644
--- a/src/backend/oneapi/kernel/wrap_dilated.hpp
+++ b/src/backend/oneapi/kernel/wrap_dilated.hpp
@@ -13,6 +13,7 @@
 #include <common/dispatch.hpp>
 #include <common/kernel_cache.hpp>
 #include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/default_config.hpp>
 #include <math.hpp>
 
@@ -24,14 +25,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-template<typename T>
-using local_accessor = sycl::accessor<T, 1, sycl::access::mode::read_write,
-                                      sycl::access::target::local>;
-template<typename T>
-using read_accessor = sycl::accessor<T, 1, sycl::access::mode::read>;
-template<typename T>
-using write_accessor = sycl::accessor<T, 1, sycl::access::mode::write>;
-
 template<typename T>
 class wrapDilatedCreateKernel {
    public:

From 7a393d7fa4b06b3088abf20e16d91d7e6f087732 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 25 Apr 2023 14:32:10 -0400
Subject: [PATCH 2483/2677] Add simple caching for oneAPI backend

---
 src/backend/oneapi/jit.cpp | 123 ++++++++++++++++++++++---------------
 1 file changed, 73 insertions(+), 50 deletions(-)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 6c4d4c1828..17b1a63c3f 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -37,6 +37,7 @@
 #include <sstream>
 #include <stdexcept>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 using arrayfire::common::getFuncName;
@@ -51,10 +52,14 @@ using arrayfire::oneapi::getActiveDeviceBaseBuildFlags;
 using arrayfire::oneapi::jit::BufferNode;
 
 using std::array;
+using std::begin;
+using std::end;
+using std::find;
 using std::find_if;
 using std::string;
 using std::stringstream;
 using std::to_string;
+using std::unordered_map;
 using std::vector;
 
 using sycl::backend;
@@ -78,10 +83,12 @@ const static string DEFAULT_MACROS_STR(R"JIT(
 #endif
 )JIT");
 
-string getKernelString(const string& funcName, const vector<Node*>& full_nodes,
-                       const vector<Node_ids>& full_ids,
-                       const vector<int>& output_ids, const bool is_linear,
-                       const bool loop0, const bool loop1, const bool loop3) {
+string getKernelString(const string& funcName,
+                       const nonstd::span<Node* const> full_nodes,
+                       nonstd::span<const Node_ids> full_ids,
+                       const nonstd::span<int const> output_ids,
+                       const bool is_linear, const bool loop0, const bool loop1,
+                       const bool loop3) {
     // Common OpenCL code
     // This part of the code does not change with the kernel.
 
@@ -163,7 +170,6 @@ __kernel void )JIT";
     int id1 = get_global_id(1);
     const int id0End = oInfo.dims[0];
     const int id1End = oInfo.dims[1];
-    //printf("id0: %d  id1: %d id0End: %d, id1End: %d\n")
     if ((id0 < id0End) & (id1 < id1End)) {
         const int id2 = get_global_id(2);
 #define id3 0
@@ -280,6 +286,48 @@ __kernel void )JIT";
 //     return common::getKernel("", "", true).get();
 // }
 
+template<typename T>
+cl_kernel getKernel(std::string funcName, cl_context ctx, cl_device_id dev,
+                    cl_command_queue q,
+                    const nonstd::span<Node* const> full_nodes,
+                    nonstd::span<Node_ids const> full_ids,
+                    nonstd::span<int const> output_ids,
+                    nonstd::span<oneapi::AParam<T> const> ap, bool is_linear) {
+    static unordered_map<std::string, cl_kernel> kernel_map;
+
+    vector<cl_kernel> kernels(10);
+    if (kernel_map.find(funcName) == end(kernel_map)) {
+        string jitstr = arrayfire::opencl::getKernelString(
+            funcName, full_nodes, full_ids, output_ids, is_linear, false, false,
+            ap[0].dims[2] > 1);
+
+        cl_int err;
+        vector<const char*> jitsources = {
+            {arrayfire::oneapi::opencl::KParam_hpp,
+             arrayfire::oneapi::opencl::jit_cl, jitstr.c_str()}};
+        vector<size_t> jitsizes = {arrayfire::oneapi::opencl::KParam_hpp_len,
+                                   arrayfire::oneapi::opencl::jit_cl_len,
+                                   jitstr.size()};
+
+        cl_program prog = clCreateProgramWithSource(
+            ctx, jitsources.size(), jitsources.data(), jitsizes.data(), &err);
+
+        std::string options = getActiveDeviceBaseBuildFlags();
+
+        CL_CHECK_BUILD(
+            clBuildProgram(prog, 1, &dev, options.c_str(), nullptr, nullptr));
+
+        cl_uint ret_kernels = 0;
+        CL_CHECK(
+            clCreateKernelsInProgram(prog, 1, kernels.data(), &ret_kernels));
+        kernel_map[funcName] = kernels[0];
+        CL_CHECK(clReleaseProgram(prog));
+    } else {
+        kernels[0] = kernel_map[funcName];
+    }
+    return kernels[0];
+}
+
 }  // namespace opencl
 
 namespace oneapi {
@@ -432,10 +480,6 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                          funcName](sycl::interop_handle hh) {
                 switch (hh.get_backend()) {
                     case backend::opencl: {
-                        string jitstr = arrayfire::opencl::getKernelString(
-                            funcName, full_nodes, full_ids, output_ids,
-                            is_linear, false, false, ap[0].dims[2] > 1);
-
                         cl_command_queue q =
                             hh.get_native_queue<backend::opencl>();
                         cl_context ctx =
@@ -443,35 +487,15 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                         cl_device_id dev =
                             hh.get_native_device<backend::opencl>();
 
-                        cl_int err;
-                        vector<const char*> jitsources = {
-                            {arrayfire::oneapi::opencl::KParam_hpp,
-                             arrayfire::oneapi::opencl::jit_cl,
-                             jitstr.c_str()}};
-                        vector<size_t> jitsizes = {
-                            arrayfire::oneapi::opencl::KParam_hpp_len,
-                            arrayfire::oneapi::opencl::jit_cl_len,
-                            jitstr.size()};
-
-                        cl_program prog = clCreateProgramWithSource(
-                            ctx, jitsources.size(), jitsources.data(),
-                            jitsizes.data(), &err);
-
-                        std::string options = getActiveDeviceBaseBuildFlags();
-
-                        CL_CHECK_BUILD(clBuildProgram(
-                            prog, 1, &dev, options.c_str(), nullptr, nullptr));
-
-                        vector<cl_kernel> kernels(10);
-                        cl_uint ret_kernels = 0;
-                        CL_CHECK(clCreateKernelsInProgram(
-                            prog, 1, kernels.data(), &ret_kernels));
+                        cl_kernel kernel = arrayfire::opencl::getKernel<T>(
+                            funcName, ctx, dev, q, full_nodes, full_ids,
+                            output_ids, ap, is_linear);
                         int nargs{0};
                         for (Node* node : full_nodes) {
                             if (node->isBuffer()) {
                                 nargs = node->setArgs(
                                     nargs, is_linear,
-                                    [&kernels, &hh, &is_linear](
+                                    [&kernel, &hh, &is_linear](
                                         int id, const void* ptr,
                                         size_t arg_size) {
                                         AParam<T>* info =
@@ -482,27 +506,27 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                                 info->data);
                                         if (is_linear) {
                                             CL_CHECK(clSetKernelArg(
-                                                kernels[0], id++,
-                                                sizeof(cl_mem), &mem[0]));
+                                                kernel, id++, sizeof(cl_mem),
+                                                &mem[0]));
                                             CL_CHECK(clSetKernelArg(
-                                                kernels[0], id++, sizeof(dim_t),
+                                                kernel, id++, sizeof(dim_t),
                                                 &info->offset));
                                         } else {
                                             CL_CHECK(clSetKernelArg(
-                                                kernels[0], id++,
-                                                sizeof(cl_mem), &mem[0]));
+                                                kernel, id++, sizeof(cl_mem),
+                                                &mem[0]));
                                             KParam ooo = *info;
                                             CL_CHECK(clSetKernelArg(
-                                                kernels[0], id++,
-                                                sizeof(KParam), &ooo));
+                                                kernel, id++, sizeof(KParam),
+                                                &ooo));
                                         }
                                     });
                             } else {
                                 nargs = node->setArgs(
                                     nargs, is_linear,
-                                    [&kernels](int id, const void* ptr,
-                                               size_t arg_size) {
-                                        CL_CHECK(clSetKernelArg(kernels[0], id,
+                                    [&kernel](int id, const void* ptr,
+                                              size_t arg_size) {
+                                        CL_CHECK(clSetKernelArg(kernel, id,
                                                                 arg_size, ptr));
                                     });
                             }
@@ -514,15 +538,15 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                             mem =
                                 hh.get_native_mem<backend::opencl>(output.data);
                             cl_mem mmm = mem[0];
-                            CL_CHECK(clSetKernelArg(kernels[0], nargs++,
+                            CL_CHECK(clSetKernelArg(kernel, nargs++,
                                                     sizeof(cl_mem), &mmm));
                             int off = output.offset;
-                            CL_CHECK(clSetKernelArg(kernels[0], nargs++,
+                            CL_CHECK(clSetKernelArg(kernel, nargs++,
                                                     sizeof(int), &off));
                         }
                         const KParam ooo = ap[0];
-                        CL_CHECK(clSetKernelArg(kernels[0], nargs++,
-                                                sizeof(KParam), &ooo));
+                        CL_CHECK(clSetKernelArg(kernel, nargs++, sizeof(KParam),
+                                                &ooo));
                         array<size_t, 3> offset{0, 0, 0};
                         array<size_t, 3> global;
                         int ndims = 0;
@@ -537,11 +561,10 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                         }
                         // SHOW(global);
                         CL_CHECK(clEnqueueNDRangeKernel(
-                            q, kernels[0], ndims, offset.data(), global.data(),
+                            q, kernel, ndims, offset.data(), global.data(),
                             nullptr, 0, nullptr, nullptr));
 
-                        CL_CHECK(clReleaseKernel(kernels[0]));
-                        CL_CHECK(clReleaseProgram(prog));
+                        // CL_CHECK(clReleaseKernel(kernel));
                         CL_CHECK(clReleaseDevice(dev));
                         CL_CHECK(clReleaseContext(ctx));
                         CL_CHECK(clReleaseCommandQueue(q));

From feb60fa536724104c62ef734b5d8a12807aba99a Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Sat, 29 Apr 2023 07:34:07 -0400
Subject: [PATCH 2484/2677] fix fft.cpp example wrong comment

---
 examples/benchmarks/fft.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/benchmarks/fft.cpp b/examples/benchmarks/fft.cpp
index 490a1fa18e..b28873f16a 100644
--- a/examples/benchmarks/fft.cpp
+++ b/examples/benchmarks/fft.cpp
@@ -17,7 +17,7 @@ using namespace af;
 // create a small wrapper to benchmark
 static array A;  // populated before each timing
 static void fn() {
-    array B = fft2(A);  // matrix multiply
+    array B = fft2(A);  // 2d fft
     B.eval();           // ensure evaluated
 }
 

From 4ec5f289d4e6628cfe2d33b57ae33e79949c8ed8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 3 May 2023 17:22:06 -0400
Subject: [PATCH 2485/2677] Fix synchronize issues related to OpenCL oneAPI JIT

---
 src/backend/oneapi/Array.cpp                 |   2 +-
 src/backend/oneapi/Array.hpp                 |  15 +-
 src/backend/oneapi/Param.hpp                 |  17 +-
 src/backend/oneapi/copy.cpp                  |  70 ++----
 src/backend/oneapi/jit.cpp                   | 249 +++++++++----------
 src/backend/oneapi/jit/BufferNode.hpp        |   4 +-
 src/backend/oneapi/jit/kernel_generators.hpp |   3 +-
 7 files changed, 170 insertions(+), 190 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 4682df50f1..3a9fbff3be 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -279,7 +279,7 @@ template<typename T>
 Node_ptr Array<T>::getNode() {
     if (node) { return node; }
 
-    AParam<T> info = *this;
+    AParam<T, sycl::access_mode::read> info = *this;
     unsigned bytes = this->dims().elements() * sizeof(T);
     auto nn        = bufferNodePtr<T>();
     nn->setData(info, data, bytes, isLinear());
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index d3f81bff2c..a6ca6c402c 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -41,7 +41,7 @@ namespace oneapi {
 
 template<typename T>
 struct Param;
-template<typename T>
+template<typename T, sycl::access_mode AM>
 struct AParam;
 
 template<typename T>
@@ -254,7 +254,7 @@ class Array {
     }
 
     sycl::buffer<T> *get() const {
-        if (!isReady()) eval();
+        if (!isReady()) { eval(); }
         return data.get();
     }
 
@@ -277,8 +277,15 @@ class Array {
         return out;
     }
 
-    operator AParam<T>() {
-        AParam<T> out(*getData(), dims().get(), strides().get(), getOffset());
+    operator AParam<T, sycl::access_mode::write>() {
+        AParam<T, sycl::access_mode::write> out(*getData(), dims().get(),
+                                                strides().get(), getOffset());
+        return out;
+    }
+
+    operator AParam<T, sycl::access_mode::read>() const {
+        AParam<T, sycl::access_mode::read> out(*getData(), dims().get(),
+                                               strides().get(), getOffset());
         return out;
     }
 
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 447e8fb117..7df0a73f85 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -8,13 +8,12 @@
  ********************************************************/
 
 #pragma once
+#include <sycl/sycl.hpp>
 
 #include <kernel/KParam.hpp>
 #include <types.hpp>
 #include <af/dim4.hpp>
 
-#include <sycl/sycl.hpp>
-
 #include <optional>
 
 namespace arrayfire {
@@ -43,9 +42,9 @@ struct Param {
     ~Param() = default;
 };
 
-template<typename T>
+template<typename T, sycl::access_mode AM>
 struct AParam {
-    sycl::accessor<T, 1, sycl::access_mode::read_write, sycl::target::device,
+    sycl::accessor<T, 1, AM, sycl::target::device,
                    sycl::access::placeholder::true_t>
         data;
     af::dim4 dims;
@@ -60,17 +59,11 @@ struct AParam {
 
     AParam(sycl::buffer<T, 1>& data_, const dim_t dims_[4],
            const dim_t strides_[4], dim_t offset_)
-        : data(data_.get_access())
-        , dims(4, dims_)
-        , strides(4, strides_)
-        , offset(offset_) {}
+        : data(data_), dims(4, dims_), strides(4, strides_), offset(offset_) {}
     // AF_DEPRECATED("Use Array<T>")
     AParam(sycl::handler& h, sycl::buffer<T, 1>& data_, const dim_t dims_[4],
            const dim_t strides_[4], dim_t offset_)
-        : data(data_.get_access())
-        , dims(4, dims_)
-        , strides(4, strides_)
-        , offset(offset_) {
+        : data(data_), dims(4, dims_), strides(4, strides_), offset(offset_) {
         require(h);
     }
 
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index e61dbbc8db..a70cc3a6f4 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -29,38 +29,22 @@ namespace arrayfire {
 namespace oneapi {
 
 template<typename T>
-void copyData(T *data, const Array<T> &A) {
-    if (A.elements() == 0) { return; }
-
-    // FIXME: Merge this with copyArray
-    A.eval();
-
-    dim_t offset = 0;
-    const sycl::buffer<T> *buf;
-    Array<T> out = A;
-
-    if (A.isLinear() ||  // No offsets, No strides
-        A.ndims() == 1   // Simple offset, no strides.
-    ) {
-        buf    = A.get();
-        offset = A.getOffset();
-    } else {
-        // FIXME: Think about implementing eval
-        out    = copyArray(A);
-        buf    = out.get();
-        offset = 0;
+void copyData(T *data, const Array<T> &src) {
+    if (src.elements() > 0) {
+        Array<T> lin = src.isReady() && src.isLinear() ? src : copyArray(src);
+        size_t elements = lin.elements();
+        Param<T> p      = lin;
+        getQueue()
+            .submit([&](sycl::handler &h) {
+                sycl::range rr(elements);
+                sycl::id offset_id(p.info.offset);
+                auto offset_acc =
+                    p.data->template get_access<sycl::access_mode::read_write>(
+                        h, rr, offset_id);
+                h.copy(offset_acc, data);
+            })
+            .wait();
     }
-
-    // FIXME: Add checks
-    getQueue()
-        .submit([=](sycl::handler &h) {
-            sycl::range rr(A.elements());
-            sycl::id offset_id(offset);
-            auto offset_acc = const_cast<sycl::buffer<T> *>(buf)->get_access(
-                h, rr, offset_id);
-            h.copy(offset_acc, data);
-        })
-        .wait();
 }
 
 template<typename T>
@@ -77,19 +61,17 @@ Array<T> copyArray(const Array<T> &A) {
             sycl::buffer<T> *out_buf = out.get();
 
             size_t aelem = A.elements();
-            getQueue()
-                .submit([=](sycl::handler &h) {
-                    range rr(aelem);
-                    id offset_id(offset);
-                    accessor offset_acc_A =
-                        A_buf->template get_access<access_mode::read>(
-                            h, rr, offset_id);
-                    accessor acc_out =
-                        out_buf->template get_access<access_mode::write>(h);
-
-                    h.copy(offset_acc_A, acc_out);
-                })
-                .wait();
+            getQueue().submit([=](sycl::handler &h) {
+                range rr(aelem);
+                id offset_id(offset);
+                accessor offset_acc_A =
+                    A_buf->template get_access<access_mode::read>(h, rr,
+                                                                  offset_id);
+                accessor acc_out =
+                    out_buf->template get_access<access_mode::write>(h);
+
+                h.copy(offset_acc_A, acc_out);
+            });
         } else {
             kernel::memcopy<T>(out.get(), out.strides().get(), A.get(),
                                A.dims().get(), A.strides().get(), offset,
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 17b1a63c3f..794bb7796f 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -287,12 +287,12 @@ __kernel void )JIT";
 // }
 
 template<typename T>
-cl_kernel getKernel(std::string funcName, cl_context ctx, cl_device_id dev,
-                    cl_command_queue q,
-                    const nonstd::span<Node* const> full_nodes,
-                    nonstd::span<Node_ids const> full_ids,
-                    nonstd::span<int const> output_ids,
-                    nonstd::span<oneapi::AParam<T> const> ap, bool is_linear) {
+cl_kernel getKernel(
+    std::string funcName, cl_context ctx, cl_device_id dev, cl_command_queue q,
+    const nonstd::span<Node* const> full_nodes,
+    nonstd::span<Node_ids const> full_ids, nonstd::span<int const> output_ids,
+    nonstd::span<oneapi::AParam<T, sycl::access_mode::write> const> ap,
+    bool is_linear) {
     static unordered_map<std::string, cl_kernel> kernel_map;
 
     vector<cl_kernel> kernels(10);
@@ -363,6 +363,10 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
         output_ids.push_back(id);
     }
 
+    node_clones.clear();
+    node_clones.reserve(full_nodes.size());
+    for (Node* node : full_nodes) { node_clones.emplace_back(node->clone()); }
+
     bool moddimsFound{false};
     for (const Node* node : full_nodes) {
         is_linear &= node->isLinear(outDims);
@@ -394,12 +398,6 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     //  Avoid all cloning/copying when no moddims node is present (high
     //  chance)
     if (moddimsFound || emptyColumnsFound) {
-        node_clones.clear();
-        node_clones.reserve(full_nodes.size());
-        for (Node* node : full_nodes) {
-            node_clones.emplace_back(node->clone());
-        }
-
         for (const Node_ids& ids : full_ids) {
             auto& children{node_clones[ids.id]->m_children};
             for (int i{0}; i < Node::kMaxChildren && children[i] != nullptr;
@@ -452,129 +450,128 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                      });
             ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
         }
-
-        full_nodes.clear();
-        for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
     }
 
+    full_nodes.clear();
+    for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
+
     const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
                                       is_linear, false, false, false,
                                       outputs[0].info.dims[2] > 1)};
 
-    getQueue()
-        .submit([=](sycl::handler& h) {
-            for (Node* node : full_nodes) {
-                if (node->isBuffer()) {
-                    BufferNode<T>* n = static_cast<BufferNode<T>*>(node);
-                    n->m_param.require(h);
-                }
+    getQueue().submit([&](sycl::handler& h) {
+        for (Node* node : full_nodes) {
+            if (node->isBuffer()) {
+                BufferNode<T>* n = static_cast<BufferNode<T>*>(node);
+                n->m_param.require(h);
             }
-            vector<AParam<T>> ap;
-            transform(begin(outputs), end(outputs), back_inserter(ap),
-                      [&](const Param<T>& p) {
-                          return AParam<T>(h, *p.data, p.info.dims,
-                                           p.info.strides, p.info.offset);
-                      });
-
-            h.host_task([ap, full_nodes, output_ids, full_ids, is_linear,
-                         funcName](sycl::interop_handle hh) {
-                switch (hh.get_backend()) {
-                    case backend::opencl: {
-                        cl_command_queue q =
-                            hh.get_native_queue<backend::opencl>();
-                        cl_context ctx =
-                            hh.get_native_context<backend::opencl>();
-                        cl_device_id dev =
-                            hh.get_native_device<backend::opencl>();
-
-                        cl_kernel kernel = arrayfire::opencl::getKernel<T>(
-                            funcName, ctx, dev, q, full_nodes, full_ids,
-                            output_ids, ap, is_linear);
-                        int nargs{0};
-                        for (Node* node : full_nodes) {
-                            if (node->isBuffer()) {
-                                nargs = node->setArgs(
-                                    nargs, is_linear,
-                                    [&kernel, &hh, &is_linear](
-                                        int id, const void* ptr,
-                                        size_t arg_size) {
-                                        AParam<T>* info =
-                                            static_cast<AParam<T>*>(
-                                                const_cast<void*>(ptr));
-                                        vector<cl_mem> mem =
-                                            hh.get_native_mem<backend::opencl>(
-                                                info->data);
-                                        if (is_linear) {
-                                            CL_CHECK(clSetKernelArg(
-                                                kernel, id++, sizeof(cl_mem),
-                                                &mem[0]));
-                                            CL_CHECK(clSetKernelArg(
-                                                kernel, id++, sizeof(dim_t),
-                                                &info->offset));
-                                        } else {
-                                            CL_CHECK(clSetKernelArg(
-                                                kernel, id++, sizeof(cl_mem),
-                                                &mem[0]));
-                                            KParam ooo = *info;
-                                            CL_CHECK(clSetKernelArg(
-                                                kernel, id++, sizeof(KParam),
-                                                &ooo));
-                                        }
-                                    });
-                            } else {
-                                nargs = node->setArgs(
-                                    nargs, is_linear,
-                                    [&kernel](int id, const void* ptr,
-                                              size_t arg_size) {
-                                        CL_CHECK(clSetKernelArg(kernel, id,
-                                                                arg_size, ptr));
-                                    });
-                            }
-                        }
-
-                        // Set output parameters
-                        vector<cl_mem> mem;
-                        for (const auto& output : ap) {
-                            mem =
-                                hh.get_native_mem<backend::opencl>(output.data);
-                            cl_mem mmm = mem[0];
-                            CL_CHECK(clSetKernelArg(kernel, nargs++,
-                                                    sizeof(cl_mem), &mmm));
-                            int off = output.offset;
-                            CL_CHECK(clSetKernelArg(kernel, nargs++,
-                                                    sizeof(int), &off));
-                        }
-                        const KParam ooo = ap[0];
-                        CL_CHECK(clSetKernelArg(kernel, nargs++, sizeof(KParam),
-                                                &ooo));
-                        array<size_t, 3> offset{0, 0, 0};
-                        array<size_t, 3> global;
-                        int ndims = 0;
-                        if (is_linear) {
-                            global = {(size_t)ap[0].dims.elements(), 0, 0};
-                            ndims  = 1;
+        }
+        vector<AParam<T, sycl::access_mode::write>> ap;
+        transform(begin(outputs), end(outputs), back_inserter(ap),
+                  [&](const Param<T>& p) {
+                      return AParam<T, sycl::access_mode::write>(
+                          h, *p.data, p.info.dims, p.info.strides,
+                          p.info.offset);
+                  });
+
+        h.host_task([ap, full_nodes, output_ids, full_ids, is_linear, funcName,
+                     node_clones, nodes, outputs](sycl::interop_handle hh) {
+            switch (hh.get_backend()) {
+                case backend::opencl: {
+                    auto ncc = node_clones;
+
+                    cl_command_queue q = hh.get_native_queue<backend::opencl>();
+                    cl_context ctx   = hh.get_native_context<backend::opencl>();
+                    cl_device_id dev = hh.get_native_device<backend::opencl>();
+
+                    cl_kernel kernel = arrayfire::opencl::getKernel<T>(
+                        funcName, ctx, dev, q, full_nodes, full_ids, output_ids,
+                        ap, is_linear);
+                    int nargs{0};
+                    for (Node* node : full_nodes) {
+                        if (node->isBuffer()) {
+                            nargs = node->setArgs(
+                                nargs, is_linear,
+                                [&kernel, &hh, &is_linear](
+                                    int id, const void* ptr, size_t arg_size) {
+                                    AParam<T, sycl::access_mode::read>* info =
+                                        static_cast<AParam<
+                                            T, sycl::access_mode::read>*>(
+                                            const_cast<void*>(ptr));
+                                    vector<cl_mem> mem =
+                                        hh.get_native_mem<backend::opencl>(
+                                            info->data);
+                                    if (is_linear) {
+                                        CL_CHECK(clSetKernelArg(kernel, id++,
+                                                                sizeof(cl_mem),
+                                                                &mem[0]));
+                                        CL_CHECK(clSetKernelArg(kernel, id++,
+                                                                sizeof(dim_t),
+                                                                &info->offset));
+                                    } else {
+                                        CL_CHECK(clSetKernelArg(kernel, id++,
+                                                                sizeof(cl_mem),
+                                                                &mem[0]));
+                                        KParam ooo = *info;
+                                        CL_CHECK(clSetKernelArg(kernel, id++,
+                                                                sizeof(KParam),
+                                                                &ooo));
+                                    }
+                                });
                         } else {
-                            global = {(size_t)ap[0].dims[0],
-                                      (size_t)ap[0].dims[1],
-                                      (size_t)ap[0].dims[2]};
-                            ndims  = 3;
+                            nargs = node->setArgs(
+                                nargs, is_linear,
+                                [&kernel](int id, const void* ptr,
+                                          size_t arg_size) {
+                                    CL_CHECK(clSetKernelArg(kernel, id,
+                                                            arg_size, ptr));
+                                });
                         }
-                        // SHOW(global);
-                        CL_CHECK(clEnqueueNDRangeKernel(
-                            q, kernel, ndims, offset.data(), global.data(),
-                            nullptr, 0, nullptr, nullptr));
-
-                        // CL_CHECK(clReleaseKernel(kernel));
-                        CL_CHECK(clReleaseDevice(dev));
-                        CL_CHECK(clReleaseContext(ctx));
-                        CL_CHECK(clReleaseCommandQueue(q));
-
-                    } break;
-                    default: ONEAPI_NOT_SUPPORTED("Backend not supported");
-                }
-            });
-        })
-        .wait();
+                    }
+
+                    // Set output parameters
+                    vector<cl_mem> mem;
+                    for (const auto& output : ap) {
+                        mem = hh.get_native_mem<backend::opencl>(output.data);
+                        cl_mem mmm = mem[0];
+                        CL_CHECK(clSetKernelArg(kernel, nargs++, sizeof(cl_mem),
+                                                &mmm));
+                        int off = output.offset;
+                        CL_CHECK(
+                            clSetKernelArg(kernel, nargs++, sizeof(int), &off));
+                    }
+                    const KParam ooo = ap[0];
+                    CL_CHECK(
+                        clSetKernelArg(kernel, nargs++, sizeof(KParam), &ooo));
+                    array<size_t, 3> offset{0, 0, 0};
+                    array<size_t, 3> global;
+                    int ndims = 0;
+                    if (is_linear) {
+                        global = {(size_t)ap[0].dims.elements(), 0, 0};
+                        ndims  = 1;
+                    } else {
+                        global = {(size_t)ap[0].dims[0], (size_t)ap[0].dims[1],
+                                  (size_t)ap[0].dims[2]};
+                        ndims  = 3;
+                    }
+                    // SHOW(global);
+                    cl_event kernel_event;
+                    CL_CHECK(clEnqueueNDRangeKernel(
+                        q, kernel, ndims, offset.data(), global.data(), nullptr,
+                        0, nullptr, &kernel_event));
+                    CL_CHECK(clEnqueueBarrierWithWaitList(q, 1, &kernel_event,
+                                                          nullptr));
+                    CL_CHECK(clReleaseEvent(kernel_event));
+
+                    CL_CHECK(clReleaseDevice(dev));
+                    CL_CHECK(clReleaseContext(ctx));
+                    CL_CHECK(clReleaseCommandQueue(q));
+
+                } break;
+                default: ONEAPI_NOT_SUPPORTED("Backend not supported");
+            }
+        });
+    });
 }
 
 template<typename T>
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
index 8c8d61abf2..94655f23e7 100644
--- a/src/backend/oneapi/jit/BufferNode.hpp
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -18,8 +18,8 @@ namespace arrayfire {
 namespace oneapi {
 namespace jit {
 template<typename T>
-using BufferNode =
-    common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>, AParam<T>>;
+using BufferNode = common::BufferNodeBase<std::shared_ptr<sycl::buffer<T>>,
+                                          AParam<T, sycl::access_mode::read>>;
 }  // namespace jit
 }  // namespace oneapi
 
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index a69553acd3..bc12929fe6 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -41,7 +41,8 @@ template<typename T>
 inline int setKernelArguments(
     int start_id, bool is_linear,
     std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
-    const std::shared_ptr<sycl::buffer<T>>& ptr, const AParam<T>& info) {
+    const std::shared_ptr<sycl::buffer<T>>& ptr,
+    const AParam<T, sycl::access_mode::read>& info) {
     setArg(start_id + 0, static_cast<const void*>(&info), sizeof(Param<T>));
     return start_id + 2;
 }

From 5a3ac34ef790f7b16a0004c0d9b6399e63f9648d Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 11 May 2023 22:39:39 +0200
Subject: [PATCH 2486/2677] Fixed compile error on MSVC 16.11.26

---
 src/backend/opencl/Array.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 311ec715b9..d479ac5752 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -342,7 +342,8 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
         // Setting the maximum to 5120 bytes to keep the compile times
         // resonable. This still results in large kernels but its not excessive.
         size_t max_param_size =
-            min(5120UL, device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>());
+            min(static_cast<cl::size_type>(5120),
+                device.getInfo<CL_DEVICE_MAX_PARAMETER_SIZE>());
         max_param_size -= base_param_size;
 
         struct tree_info {

From 8e3d1fa6cebd5df0bd62e15b80be5f430d9d8f88 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 11 May 2023 23:41:39 +0200
Subject: [PATCH 2487/2677] Fixed assignment with index after device change

---
 src/backend/opencl/assign.cpp | 31 +++++++++++++++++++++++++++----
 1 file changed, 27 insertions(+), 4 deletions(-)

diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 9e0f8074a3..57ceeaab2d 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -23,6 +23,11 @@ using arrayfire::common::half;
 namespace arrayfire {
 namespace opencl {
 
+static std::mutex mtx;
+static std::map<std::pair<const cl::Context*, const int /* deviceId */>,
+                cl::Buffer*>
+    cachedEmptyBuffers;
+
 template<typename T>
 void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
     kernel::AssignKernelParam_t p;
@@ -49,6 +54,27 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
     cl::Buffer* bPtrs[4];
 
     std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
+
+    // Prepare commonBuffer for empty indexes
+    // Buffer is dependent on the context.
+    // To avoid copying between devices, we add also deviceId as a dependency
+    cl::Buffer* emptyBuffer;
+    {
+        std::lock_guard<std::mutex> lck(mtx);
+        const auto dependent = std::make_pair<const cl::Context*, const int>(
+            &getContext(), getActiveDeviceId());
+        auto it = cachedEmptyBuffers.find(dependent);
+        if (it == cachedEmptyBuffers.end()) {
+            emptyBuffer = new cl::Buffer(
+                getContext(),
+                CL_MEM_READ_ONLY,  // NOLINT(hicpp-signed-bitwise)
+                sizeof(uint));
+            cachedEmptyBuffers[dependent] = emptyBuffer;
+        } else {
+            emptyBuffer = it->second;
+        }
+    }
+
     // look through indexs to read af_array indexs
     for (dim_t x = 0; x < 4; ++x) {
         // set index pointers were applicable
@@ -59,10 +85,7 @@ void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs) {
             // alloc an 1-element buffer to avoid OpenCL from failing using
             // direct buffer allocation as opposed to mem manager to avoid
             // reference count desprepancies between different backends
-            static auto* empty = new cl::Buffer(
-                getContext(), CL_MEM_READ_ONLY,  // NOLINT(hicpp-signed-bitwise)
-                sizeof(uint));
-            bPtrs[x] = empty;
+            bPtrs[x] = emptyBuffer;
         }
     }
 

From 11af3076d2439080e62ee9c21c772eaa1829afdf Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Fri, 12 May 2023 00:09:22 +0200
Subject: [PATCH 2488/2677] Random after device change

---
 src/api/c/random.cpp | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index f1a85b2891..915e733974 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -19,7 +19,9 @@
 #include <af/array.h>
 #include <af/data.h>
 #include <af/defines.h>
+#include <af/device.h>
 #include <af/dim4.hpp>
+#include <map>
 #include <memory>
 
 using af::dim4;
@@ -128,8 +130,20 @@ af_err af_get_default_random_engine(af_random_engine *r) {
     try {
         AF_CHECK(af_init());
 
-        thread_local auto *re = new RandomEngine;
-        *r                    = static_cast<af_random_engine>(re);
+        // RandomEngine contains device buffers which are dependent on
+        // context|stream/device. Since nor context or stream are available at
+        // this level, we will only use the deviceId.
+        thread_local std::map<int /*deviceId*/, RandomEngine *>
+            cachedDefaultRandomEngines;
+        const int dependent = af::getDevice();
+        auto it             = cachedDefaultRandomEngines.find(dependent);
+        if (it == cachedDefaultRandomEngines.end()) {
+            RandomEngine *defaultRandomEngine     = new RandomEngine;
+            cachedDefaultRandomEngines[dependent] = defaultRandomEngine;
+            *r = static_cast<af_random_engine>(defaultRandomEngine);
+        } else {
+            *r = static_cast<af_random_engine>(it->second);
+        }
         return AF_SUCCESS;
     }
     CATCHALL;

From 8889ee0c954e581ee7fdd20b60d76c42c6e07390 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 11 May 2023 23:39:22 +0200
Subject: [PATCH 2489/2677] Fixed initialization error on gebrd

---
 src/backend/opencl/magma/gebrd.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/backend/opencl/magma/gebrd.cpp b/src/backend/opencl/magma/gebrd.cpp
index 4e88a498ae..c63be4a5bb 100644
--- a/src/backend/opencl/magma/gebrd.cpp
+++ b/src/backend/opencl/magma/gebrd.cpp
@@ -239,11 +239,17 @@ magma_int_t magma_gebrd_hybrid(magma_int_t m, magma_int_t n, Ty *a,
         return *info;
     }
 
-    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dwork, (m + n) * nb)) {
+    const size_t size = (m + n) * nb;
+    if (MAGMA_SUCCESS != magma_malloc<Ty>(&dwork, size)) {
         *info = MAGMA_ERR_DEVICE_ALLOC;
         return *info;
     }
     size_t dwork_offset = 0;
+    // initialize dwork to 0.0
+    const float dfill = 0.0;
+    cl_int err = clEnqueueFillBuffer(queue, dwork, &dfill, sizeof(dfill), 0,
+                                     size * sizeof(Ty), 0, nullptr, nullptr);
+    check_error(err);
 
     cl_event event = 0;
 

From 852776d17769bcd55cce452a6637d666699e9a0f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 May 2023 18:54:48 -0400
Subject: [PATCH 2490/2677] Update accessor and fix segfault in mean

---
 src/backend/oneapi/kernel/mean.hpp | 109 +++++++++++++----------------
 1 file changed, 49 insertions(+), 60 deletions(-)

diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 7d622e611c..e7281f2e45 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -217,7 +217,8 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
     sycl::range<2> global(blocks_dim[0] * blocks_dim[2] * local[0],
                           blocks_dim[1] * blocks_dim[3] * local[1]);
 
-    sycl::buffer<Tw, 1> empty(sycl::range<1>{1});
+    auto empty  = memAlloc<Tw>(1);
+    auto oempty = memAlloc<Tw>(1);
     getQueue().submit([&](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
@@ -233,8 +234,8 @@ void mean_dim_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                                owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-        write_accessor<Tw> owt_acc{(output_weight) ? *owt.data : empty, h};
-        read_accessor<Tw> iwt_acc{(input_weight) ? *iwt.data : empty, h};
+        write_accessor<Tw> owt_acc{(output_weight) ? *owt.data : *oempty, h};
+        read_accessor<Tw> iwt_acc{(input_weight) ? *iwt.data : *empty, h};
 
         switch (threads_y) {
             case 8:
@@ -484,17 +485,18 @@ class meanFirstKernelSMEM {
 };
 
 template<typename Ti, typename Tw, typename To>
-void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
-                         Param<Tw> iwt, const uint groups_x,
-                         const uint groups_y, const uint threads_x) {
+sycl::event mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
+                                Param<Tw> iwt, const uint groups_x,
+                                const uint groups_y, const uint threads_x) {
     sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
     sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
                           groups_y * in.info.dims[3] * local[1]);
 
     uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
 
-    sycl::buffer<Tw, 1> empty(sycl::range<1>{1});
-    getQueue().submit([&](sycl::handler &h) {
+    auto empty  = memAlloc<Tw>(1);
+    auto oempty = memAlloc<Tw>(1);
+    return getQueue().submit([&](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
@@ -509,8 +511,8 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
         bool output_weight = ((owt.info.dims[0] * owt.info.dims[1] *
                                owt.info.dims[2] * owt.info.dims[3]) != 0);
 
-        write_accessor<Tw> owt_acc{(output_weight) ? *owt.data : empty, h};
-        read_accessor<Tw> iwt_acc{(input_weight) ? *iwt.data : empty, h};
+        write_accessor<Tw> owt_acc{(output_weight) ? *owt.data : *oempty, h};
+        read_accessor<Tw> iwt_acc{(input_weight) ? *iwt.data : *empty, h};
 
         h.parallel_for(
             sycl::nd_range<2>(global, local),
@@ -519,7 +521,6 @@ void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                 iwt.info, threads_x, groups_x, groups_y, repeat, s_val, s_idx,
                 input_weight, output_weight));
     });
-    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tw, typename To>
@@ -612,26 +613,24 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         uintl tmp_elements = tmpOut.elements();
 
         mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x,
-                                      blocks_y, threads_x);
+                                      blocks_y, threads_x)
+            .wait();
 
         std::vector<T> h_ptr(tmp_elements);
         std::vector<Tw> h_wptr(tmp_elements);
-        sycl::buffer hBuffer(h_ptr.data(), {tmp_elements},
-                             {sycl::property::buffer::use_host_ptr()});
-        sycl::buffer hwBuffer(h_wptr.data(), {tmp_elements},
-                              {sycl::property::buffer::use_host_ptr()});
 
         // TODO: fix when addressing other mean errors
         auto e1 = getQueue().submit([&](sycl::handler &h) {
             auto acc_in =
-                tmpOut.get()->get_access(h, sycl::range{tmp_elements});
-            auto acc_out = hBuffer.get_access();
-            h.copy(acc_in, acc_out);
+                tmpOut.get()->template get_access<sycl::access_mode::read>(
+                    h, sycl::range{tmp_elements});
+            h.copy(acc_in, h_ptr.data());
         });
         auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in = tmpWt.get()->get_access(h, sycl::range{tmp_elements});
-            auto acc_out = hwBuffer.get_access();
-            h.copy(acc_in, acc_out);
+            auto acc_in =
+                tmpWt.get()->template get_access<sycl::access_mode::read>(
+                    h, sycl::range{tmp_elements});
+            h.copy(acc_in, h_wptr.data());
         });
         e1.wait();
         e2.wait();
@@ -649,20 +648,16 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         std::vector<T> h_ptr(in_elements);
         std::vector<Tw> h_wptr(in_elements);
 
-        sycl::buffer hBuffer(h_ptr.data(), {in_elements},
-                             {sycl::property::buffer::use_host_ptr()});
-        sycl::buffer hwBuffer(h_wptr.data(), {in_elements},
-                              {sycl::property::buffer::use_host_ptr()});
-
         auto e1 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in  = in.data->get_access(h, sycl::range{in_elements});
-            auto acc_out = hBuffer.get_access();
-            h.copy(acc_in, acc_out);
+            auto acc_in = in.data->template get_access<sycl::access_mode::read>(
+                h, sycl::range{in_elements});
+            h.copy(acc_in, h_ptr.data());
         });
         auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in  = iwt.data->get_access(h, sycl::range{in_elements});
-            auto acc_out = hwBuffer.get_access();
-            h.copy(acc_in, acc_out);
+            auto acc_in =
+                iwt.data->template get_access<sycl::access_mode::read>(
+                    h, sycl::range{in_elements});
+            h.copy(acc_in, h_wptr.data());
         });
         e1.wait();
         e2.wait();
@@ -720,21 +715,17 @@ To mean_all(Param<Ti> in) {
         std::vector<To> h_ptr(tmp_elements);
         std::vector<Tw> h_cptr(tmp_elements);
 
-        sycl::buffer hBuffer(h_ptr.data(), {tmp_elements},
-                             {sycl::property::buffer::use_host_ptr()});
-        sycl::buffer hcBuffer(h_cptr.data(), {tmp_elements},
-                              {sycl::property::buffer::use_host_ptr()});
-
         auto e1 = getQueue().submit([&](sycl::handler &h) {
             auto acc_in =
-                tmpOut.get()->get_access(h, sycl::range{tmp_elements});
-            auto acc_out = hBuffer.get_access();
-            h.copy(acc_in, acc_out);
+                tmpOut.get()->template get_access<sycl::access_mode::read>(
+                    h, sycl::range{tmp_elements});
+            h.copy(acc_in, h_ptr.data());
         });
         auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in = tmpCt.get()->get_access(h, sycl::range{tmp_elements});
-            auto acc_out = hcBuffer.get_access();
-            h.copy(acc_in, acc_out);
+            auto acc_in =
+                tmpCt.get()->template get_access<sycl::access_mode::read>(
+                    h, sycl::range{tmp_elements});
+            h.copy(acc_in, h_cptr.data());
         });
         e1.wait();
         e2.wait();
@@ -749,27 +740,25 @@ To mean_all(Param<Ti> in) {
 
         return static_cast<To>(val);
     } else {
-        std::vector<Ti> h_ptr(in_elements);
-        sycl::buffer outBuffer(h_ptr.data(), {in_elements},
-                               {sycl::property::buffer::use_host_ptr()});
-
+        compute_t<To> val;
         getQueue()
             .submit([&](sycl::handler &h) {
-                auto acc_in  = in.data->get_access(h);
-                auto acc_out = outBuffer.get_access();
-                h.copy(acc_in, acc_out);
+                auto acc_in =
+                    in.data->template get_access<sycl::access_mode::read,
+                                                 sycl::target::host_buffer>(h);
+                h.host_task([&]() {
+                    common::Transform<Ti, compute_t<To>, af_add_t> transform;
+                    compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
+
+                    val                  = transform(acc_in[0]);
+                    compute_t<Tw> weight = count;
+                    for (int i = 1; i < in_elements; i++) {
+                        stable_mean(&val, &weight, transform(acc_in[i]), count);
+                    }
+                });
             })
             .wait();
 
-        common::Transform<Ti, compute_t<To>, af_add_t> transform;
-        compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
-
-        compute_t<To> val    = transform(h_ptr[0]);
-        compute_t<Tw> weight = count;
-        for (int i = 1; i < in_elements; i++) {
-            stable_mean(&val, &weight, transform(h_ptr[i]), count);
-        }
-
         return static_cast<To>(val);
     }
 }

From 67c013742c144cc553cb8eb835775011c416b59b Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 May 2023 18:57:27 -0400
Subject: [PATCH 2491/2677] Fix segfault in isHalfSupported function

---
 src/backend/oneapi/platform.cpp | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index edd62e0d6a..d9b6f1d832 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -384,12 +384,8 @@ bool isDoubleSupported(unsigned device) {
 bool isHalfSupported(unsigned device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
-    sycl::device dev;
-    {
-        common::lock_guard_t lock(devMngr.deviceMutex);
-        dev = *devMngr.mDevices[device];
-    }
-    return dev.has(sycl::aspect::fp16);
+    common::lock_guard_t lock(devMngr.deviceMutex);
+    return devMngr.mDevices[device]->has(sycl::aspect::fp16);
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {

From 5191729bfa9598978b9c19976b8f7b46697725c5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 May 2023 23:36:47 -0400
Subject: [PATCH 2492/2677] Update submit function lambdas to change to
 reference captures

---
 src/backend/oneapi/copy.cpp                          |  4 ++--
 src/backend/oneapi/join.cpp                          |  6 +++---
 src/backend/oneapi/kernel/assign.hpp                 |  2 +-
 src/backend/oneapi/kernel/index.hpp                  |  2 +-
 src/backend/oneapi/kernel/iota.hpp                   |  2 +-
 src/backend/oneapi/kernel/memcopy.hpp                |  4 ++--
 src/backend/oneapi/kernel/random_engine.hpp          | 12 ++++++------
 src/backend/oneapi/kernel/random_engine_mersenne.hpp |  2 +-
 src/backend/oneapi/kernel/range.hpp                  |  2 +-
 src/backend/oneapi/kernel/reduce_dim.hpp             |  2 +-
 src/backend/oneapi/kernel/reduce_first.hpp           |  2 +-
 11 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index a70cc3a6f4..f99f79854e 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -61,7 +61,7 @@ Array<T> copyArray(const Array<T> &A) {
             sycl::buffer<T> *out_buf = out.get();
 
             size_t aelem = A.elements();
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 range rr(aelem);
                 id offset_id(offset);
                 accessor offset_acc_A =
@@ -114,7 +114,7 @@ struct copyWrapper<T, T> {
             sycl::buffer<T> *out_buf = out.get();
 
             getQueue()
-                .submit([=](sycl::handler &h) {
+                .submit([&](sycl::handler &h) {
                     sycl::range rr(in.elements());
                     sycl::id in_offset_id(in_offset);
                     sycl::id out_offset_id(out_offset);
diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
index ecbcae0ba4..37c7c14fc9 100644
--- a/src/backend/oneapi/join.cpp
+++ b/src/backend/oneapi/join.cpp
@@ -94,7 +94,7 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     if (first.isReady()) {
         if (1LL + jdim >= first.ndims() && first.isLinear()) {
             // first & out are linear
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 sycl::range sz(first.elements());
                 sycl::id src_offset(first.getOffset());
                 sycl::accessor offset_acc_src =
@@ -125,7 +125,7 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     if (second.isReady()) {
         if (1LL + jdim >= second.ndims() && second.isLinear()) {
             // second & out are linear
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 sycl::range sz(second.elements());
                 sycl::id src_offset(second.getOffset());
                 sycl::accessor offset_acc_src =
@@ -216,7 +216,7 @@ void join(Array<T> &out, const int jdim, const vector<Array<T>> &inputs) {
             for (const Array<T> *in : s.ins) {
                 if (in->isReady()) {
                     if (1LL + jdim >= in->ndims() && in->isLinear()) {
-                        getQueue().submit([=](sycl::handler &h) {
+                        getQueue().submit([&](sycl::handler &h) {
                             sycl::range sz(in->elements());
                             sycl::id src_offset(in->getOffset());
                             sycl::accessor offset_acc_src =
diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 6d553f18ad..5e3ef6c666 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -125,7 +125,7 @@ void assign(Param<T> out, const Param<T> in, const AssignKernelParam& p,
     sycl::range<2> global(blk_x * in.info.dims[2] * THREADS_X,
                           blk_y * in.info.dims[3] * THREADS_Y);
 
-    getQueue().submit([=](sycl::handler& h) {
+    getQueue().submit([&](sycl::handler& h) {
         auto pp = p;
         write_accessor<T> out_acc{*out.data, h};
         read_accessor<T> in_acc{*in.data, h};
diff --git a/src/backend/oneapi/kernel/index.hpp b/src/backend/oneapi/kernel/index.hpp
index ef2b837b75..857b299aef 100644
--- a/src/backend/oneapi/kernel/index.hpp
+++ b/src/backend/oneapi/kernel/index.hpp
@@ -133,7 +133,7 @@ void index(Param<T> out, Param<T> in, IndexKernelParam& p,
     blocks[0] *= threads[0];
 
     sycl::nd_range<3> marange(blocks, threads);
-    getQueue().submit([=](sycl::handler& h) {
+    getQueue().submit([&](sycl::handler& h) {
         auto pp = p;
         for (dim_t x = 0; x < 4; ++x) {
             pp.ptr[x] =
diff --git a/src/backend/oneapi/kernel/iota.hpp b/src/backend/oneapi/kernel/iota.hpp
index 97018b6a1d..f334695ef5 100644
--- a/src/backend/oneapi/kernel/iota.hpp
+++ b/src/backend/oneapi/kernel/iota.hpp
@@ -101,7 +101,7 @@ void iota(Param<T> out, const af::dim4& sdims) {
                           local[1] * blocksPerMatY * out.info.dims[3]);
     sycl::nd_range<2> ndrange(global, local);
 
-    getQueue().submit([=](sycl::handler& h) {
+    getQueue().submit([&](sycl::handler& h) {
         write_accessor<T> out_acc{*out.data, h};
 
         h.parallel_for(ndrange, iotaKernel<T>(out_acc, out.info,
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index 33a53fc160..c6b8dbb04c 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -115,7 +115,7 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
                           groups_1 * idims[3] * local_size[1]);
     sycl::nd_range<2> ndrange(global, local);
 
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<T> out_acc{*out, h};
         read_accessor<T> in_acc{*const_cast<sycl::buffer<T> *>(in), h};
 
@@ -303,7 +303,7 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
         trgt_dims    = {{trgt_i, trgt_j, trgt_k, trgt_l}};
     }
 
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<outType> dst_acc{*dst.data, h};
         read_accessor<inType> src_acc{
             *const_cast<sycl::buffer<inType> *>(src.data), h};
diff --git a/src/backend/oneapi/kernel/random_engine.hpp b/src/backend/oneapi/kernel/random_engine.hpp
index b416827a7d..7e97a6fc59 100644
--- a/src/backend/oneapi/kernel/random_engine.hpp
+++ b/src/backend/oneapi/kernel/random_engine.hpp
@@ -56,7 +56,7 @@ void uniformDistributionCBRNG(Param<T> out, const size_t elements,
                               sycl::range<1>(threads));
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
@@ -66,7 +66,7 @@ void uniformDistributionCBRNG(Param<T> out, const size_t elements,
             ONEAPI_DEBUG_FINISH(getQueue());
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
@@ -96,7 +96,7 @@ void normalDistributionCBRNG(Param<T> out, const size_t elements,
                               sycl::range<1>(threads));
     switch (type) {
         case AF_RANDOM_ENGINE_PHILOX_4X32_10:
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
@@ -105,7 +105,7 @@ void normalDistributionCBRNG(Param<T> out, const size_t elements,
             });
             break;
         case AF_RANDOM_ENGINE_THREEFRY_2X32_16:
-            getQueue().submit([=](sycl::handler &h) {
+            getQueue().submit([&](sycl::handler &h) {
                 write_accessor<T> out_acc{*out.data, h};
 
                 h.parallel_for(ndrange,
@@ -134,7 +134,7 @@ void uniformDistributionMT(Param<T> out, const size_t elements,
 
     sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
                               sycl::range<1>(threads));
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<T> out_acc{*out.data, h};
         auto state_acc     = state.data->get_access(h);
         auto pos_acc       = pos.data->get_access(h);
@@ -170,7 +170,7 @@ void normalDistributionMT(Param<T> out, const size_t elements,
 
     sycl::nd_range<1> ndrange(sycl::range<1>(blocks * threads),
                               sycl::range<1>(threads));
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<T> out_acc{*out.data, h};
         auto state_acc     = state.data->get_access(h);
         auto pos_acc       = pos.data->get_access(h);
diff --git a/src/backend/oneapi/kernel/random_engine_mersenne.hpp b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
index f36b2b60d0..acb56f3c9f 100644
--- a/src/backend/oneapi/kernel/random_engine_mersenne.hpp
+++ b/src/backend/oneapi/kernel/random_engine_mersenne.hpp
@@ -145,7 +145,7 @@ class initMersenneKernel {
 
 void initMersenneState(Param<uint> state, const Param<uint> tbl, uintl seed) {
     sycl::nd_range<1> ndrange({BLOCKS * N}, {N});
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<uint> state_acc{*state.data, h};
         read_accessor<uint> tbl_acc{*tbl.data, h};
         auto lstate_acc = sycl::local_accessor<uint, 1>(N, h);
diff --git a/src/backend/oneapi/kernel/range.hpp b/src/backend/oneapi/kernel/range.hpp
index f052abb48c..b8678179c2 100644
--- a/src/backend/oneapi/kernel/range.hpp
+++ b/src/backend/oneapi/kernel/range.hpp
@@ -104,7 +104,7 @@ void range(Param<T> out, const int dim) {
                           local[1] * blocksPerMatY * out.info.dims[3]);
     sycl::nd_range<2> ndrange(global, local);
 
-    getQueue().submit([=](sycl::handler& h) {
+    getQueue().submit([&](sycl::handler& h) {
         write_accessor<T> out_acc{*out.data, h};
 
         h.parallel_for(ndrange, rangeOp<T>(out_acc, out.info, dim,
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 926a7205e9..6b51801fa7 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -144,7 +144,7 @@ void reduce_dim_launcher_default(Param<To> out, Param<Ti> in,
     sycl::range<2> global(blocks_dim[0] * blocks_dim[2] * local[0],
                           blocks_dim[1] * blocks_dim[3] * local[1]);
 
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         auto shrdMem = sycl::local_accessor<compute_t<To>, 1>(
             creduce::THREADS_X * threads_y, h);
 
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index 27143aa24b..f105d63671 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -151,7 +151,7 @@ void reduce_first_launcher_default(Param<To> out, Param<Ti> in,
 
     uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
 
-    getQueue().submit([=](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 

From efed24b9ce9773b9c1aa05751a058939a8755640 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 9 May 2023 23:38:57 -0400
Subject: [PATCH 2493/2677] Change basic_c's add_test function because its not
 a gtest test

The basic_c test binary is not a Google Test binary so the gtest
module in CMake fails when it tries to find tests.
---
 test/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5b7c869eba..0cb3cbfe51 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -480,7 +480,7 @@ foreach(backend ${enabled_backends})
       PRIVATE
       ArrayFire::af${backend})
   endif()
-  af_add_test(${target} ${backend} ON)
+  add_test(NAME ${target} COMMAND ${target})
 endforeach()
 
 if(AF_TEST_WITH_MTX_FILES)

From 15893ab197199009d18b6d8921b35e0d887de5e5 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 10 May 2023 20:04:51 -0400
Subject: [PATCH 2494/2677] Perform host operation in host_tasks for mean

---
 src/backend/oneapi/kernel/mean.hpp | 150 ++++++++++++++---------------
 1 file changed, 70 insertions(+), 80 deletions(-)

diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index e7281f2e45..ef98cb0954 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -613,62 +613,55 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         uintl tmp_elements = tmpOut.elements();
 
         mean_first_launcher<T, Tw, T>(tmpOut, tmpWt, in, iwt, blocks_x,
-                                      blocks_y, threads_x)
-            .wait();
-
-        std::vector<T> h_ptr(tmp_elements);
-        std::vector<Tw> h_wptr(tmp_elements);
-
-        // TODO: fix when addressing other mean errors
-        auto e1 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                tmpOut.get()->template get_access<sycl::access_mode::read>(
-                    h, sycl::range{tmp_elements});
-            h.copy(acc_in, h_ptr.data());
-        });
-        auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                tmpWt.get()->template get_access<sycl::access_mode::read>(
-                    h, sycl::range{tmp_elements});
-            h.copy(acc_in, h_wptr.data());
-        });
-        e1.wait();
-        e2.wait();
-
-        compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
-        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
-
-        for (int i = 1; i < tmp_elements; i++) {
-            stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
-                        compute_t<Tw>(h_wptr[i]));
-        }
+                                      blocks_y, threads_x);
 
+        compute_t<T> val;
+        getQueue()
+            .submit([&](sycl::handler &h) {
+                auto acc_in =
+                    tmpOut.get()
+                        ->template get_access<sycl::access_mode::read,
+                                              sycl::target::host_buffer>(h);
+                auto acc_wt =
+                    tmpWt.get()
+                        ->template get_access<sycl::access_mode::read,
+                                              sycl::target::host_buffer>(h);
+
+                h.host_task([acc_in, acc_wt, tmp_elements, &val] {
+                    val = static_cast<compute_t<T>>(acc_in[0]);
+                    compute_t<Tw> weight =
+                        static_cast<compute_t<Tw>>(acc_wt[0]);
+
+                    for (int i = 1; i < tmp_elements; i++) {
+                        stable_mean(&val, &weight, compute_t<T>(acc_in[i]),
+                                    compute_t<Tw>(acc_wt[i]));
+                    }
+                });
+            })
+            .wait();
         return static_cast<T>(val);
     } else {
-        std::vector<T> h_ptr(in_elements);
-        std::vector<Tw> h_wptr(in_elements);
-
-        auto e1 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in = in.data->template get_access<sycl::access_mode::read>(
-                h, sycl::range{in_elements});
-            h.copy(acc_in, h_ptr.data());
-        });
-        auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                iwt.data->template get_access<sycl::access_mode::read>(
-                    h, sycl::range{in_elements});
-            h.copy(acc_in, h_wptr.data());
-        });
-        e1.wait();
-        e2.wait();
-
-        compute_t<T> val     = static_cast<compute_t<T>>(h_ptr[0]);
-        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_wptr[0]);
-        for (int i = 1; i < in_elements; i++) {
-            stable_mean(&val, &weight, compute_t<T>(h_ptr[i]),
-                        compute_t<Tw>(h_wptr[i]));
-        }
-
+        compute_t<T> val;
+        getQueue()
+            .submit([&](sycl::handler &h) {
+                auto acc_in =
+                    in.data->template get_access<sycl::access_mode::read,
+                                                 sycl::target::host_buffer>(
+                        h, sycl::range{in_elements});
+                auto acc_wt =
+                    iwt.data->template get_access<sycl::access_mode::read>(
+                        h, sycl::range{in_elements});
+
+                h.host_task([acc_in, acc_wt, in_elements, &val]() {
+                    val                  = acc_in[0];
+                    compute_t<Tw> weight = acc_wt[0];
+                    for (int i = 1; i < in_elements; i++) {
+                        stable_mean(&val, &weight, compute_t<T>(acc_in[i]),
+                                    compute_t<Tw>(acc_wt[i]));
+                    }
+                });
+            })
+            .wait();
         return static_cast<T>(val);
     }
 }
@@ -712,32 +705,30 @@ To mean_all(Param<Ti> in) {
                                         blocks_y, threads_x);
 
         uintl tmp_elements = tmpOut.elements();
-        std::vector<To> h_ptr(tmp_elements);
-        std::vector<Tw> h_cptr(tmp_elements);
-
-        auto e1 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                tmpOut.get()->template get_access<sycl::access_mode::read>(
-                    h, sycl::range{tmp_elements});
-            h.copy(acc_in, h_ptr.data());
-        });
-        auto e2 = getQueue().submit([&](sycl::handler &h) {
-            auto acc_in =
-                tmpCt.get()->template get_access<sycl::access_mode::read>(
-                    h, sycl::range{tmp_elements});
-            h.copy(acc_in, h_cptr.data());
-        });
-        e1.wait();
-        e2.wait();
-
-        compute_t<To> val    = static_cast<compute_t<To>>(h_ptr[0]);
-        compute_t<Tw> weight = static_cast<compute_t<Tw>>(h_cptr[0]);
-
-        for (int i = 1; i < tmp_elements; i++) {
-            stable_mean(&val, &weight, compute_t<To>(h_ptr[i]),
-                        compute_t<Tw>(h_cptr[i]));
-        }
 
+        compute_t<To> val;
+        getQueue()
+            .submit([&](sycl::handler &h) {
+                auto out =
+                    tmpOut.get()
+                        ->template get_access<sycl::access_mode::read,
+                                              sycl::target::host_buffer>(h);
+                auto ct =
+                    tmpCt.get()
+                        ->template get_access<sycl::access_mode::read,
+                                              sycl::target::host_buffer>(h);
+
+                h.host_task([out, ct, tmp_elements, &val] {
+                    val                  = static_cast<compute_t<To>>(out[0]);
+                    compute_t<Tw> weight = static_cast<compute_t<Tw>>(ct[0]);
+
+                    for (int i = 1; i < tmp_elements; i++) {
+                        stable_mean(&val, &weight, compute_t<To>(out[i]),
+                                    compute_t<Tw>(ct[i]));
+                    }
+                });
+            })
+            .wait();
         return static_cast<To>(val);
     } else {
         compute_t<To> val;
@@ -746,7 +737,7 @@ To mean_all(Param<Ti> in) {
                 auto acc_in =
                     in.data->template get_access<sycl::access_mode::read,
                                                  sycl::target::host_buffer>(h);
-                h.host_task([&]() {
+                h.host_task([acc_in, in_elements, &val]() {
                     common::Transform<Ti, compute_t<To>, af_add_t> transform;
                     compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
 
@@ -758,7 +749,6 @@ To mean_all(Param<Ti> in) {
                 });
             })
             .wait();
-
         return static_cast<To>(val);
     }
 }

From e9432c2788ab6a4bbe4af036224d4572c15c1e77 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 10 May 2023 20:05:16 -0400
Subject: [PATCH 2495/2677] Add formatters for dim4 and complex

---
 src/backend/common/ArrayFireTypesIO.hpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/backend/common/ArrayFireTypesIO.hpp b/src/backend/common/ArrayFireTypesIO.hpp
index 8d36aa54c1..e7a2e085ee 100644
--- a/src/backend/common/ArrayFireTypesIO.hpp
+++ b/src/backend/common/ArrayFireTypesIO.hpp
@@ -10,7 +10,9 @@
 #pragma once
 #include <common/Version.hpp>
 #include <spdlog/fmt/ostr.h>
+#include <af/dim4.hpp>
 #include <af/seq.h>
+#include <complex>
 
 template<>
 struct fmt::formatter<af_seq> {
@@ -33,6 +35,15 @@ struct fmt::formatter<af_seq> {
     }
 };
 
+#if FMT_VERSION >= 90000
+template<>
+struct fmt::formatter<af::dim4> : ostream_formatter {};
+template<>
+struct fmt::formatter<std::complex<float>> : ostream_formatter {};
+template<>
+struct fmt::formatter<std::complex<double>> : ostream_formatter {};
+#endif
+
 template<>
 struct fmt::formatter<arrayfire::common::Version> {
     // show major version

From f3887ea091bf896bbccf83e350782b3baadfdfff Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 16 May 2023 12:26:44 -0400
Subject: [PATCH 2496/2677] Fix some maxDims tests due to launch dimensions
 exceeding int range

---
 src/backend/oneapi/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 8ea40564e9..f541bcb13b 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -294,6 +294,7 @@ target_include_directories(afoneapi
 target_compile_options(afoneapi
   PRIVATE
     -fsycl
+    -fno-sycl-id-queries-fit-in-int
     -sycl-std=2020
 )
 
@@ -317,9 +318,9 @@ target_link_libraries(afoneapi
     afcommon_interface
     OpenCL::OpenCL
     OpenCL::cl2hpp
-    -fsycl
     -fsycl-device-code-split=per_kernel
     -fsycl-link-huge-device-code
+    -fno-sycl-id-queries-fit-in-int
     MKL::MKL_DPCPP
   )
 

From 3fa27a58c63a91853c830d510fd5900cf7767965 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 16 May 2023 12:27:56 -0400
Subject: [PATCH 2497/2677] Improve oneAPI debug link times using -fno-sycl-rdc

---
 src/backend/oneapi/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index f541bcb13b..6de42d891c 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -296,6 +296,7 @@ target_compile_options(afoneapi
     -fsycl
     -fno-sycl-id-queries-fit-in-int
     -sycl-std=2020
+    -fno-sycl-rdc
 )
 
 target_compile_definitions(afoneapi
@@ -321,6 +322,7 @@ target_link_libraries(afoneapi
     -fsycl-device-code-split=per_kernel
     -fsycl-link-huge-device-code
     -fno-sycl-id-queries-fit-in-int
+    -fno-sycl-rdc
     MKL::MKL_DPCPP
   )
 

From 25d2b692f4f7374a7d93157da2611c2a91b2819a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 16 May 2023 17:00:57 -0400
Subject: [PATCH 2498/2677] Add -fsycl-max-parallel-link-jobs flag to improve
 link times

---
 src/backend/oneapi/CMakeLists.txt | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 6de42d891c..46e20c88d4 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -309,6 +309,9 @@ target_compile_definitions(afoneapi
     CL_HPP_ENABLE_EXCEPTIONS
   )
 
+cmake_host_system_information(RESULT NumberOfThreads
+  QUERY NUMBER_OF_LOGICAL_CORES)
+
 target_link_libraries(afoneapi
   PRIVATE
     -fsycl
@@ -319,10 +322,11 @@ target_link_libraries(afoneapi
     afcommon_interface
     OpenCL::OpenCL
     OpenCL::cl2hpp
-    -fsycl-device-code-split=per_kernel
-    -fsycl-link-huge-device-code
     -fno-sycl-id-queries-fit-in-int
     -fno-sycl-rdc
+    -fsycl-device-code-split=per_kernel
+    -fsycl-link-huge-device-code
+    -fsycl-max-parallel-link-jobs=${NumberOfThreads}
     MKL::MKL_DPCPP
   )
 

From 46d50bc6f70e75b957e174c72955247d792fb391 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 11 May 2023 23:55:33 +0200
Subject: [PATCH 2499/2677] Integrated magma memory allocations into arrayfire
 memory mgt

---
 src/backend/opencl/magma/magma_data.h | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/magma/magma_data.h b/src/backend/opencl/magma/magma_data.h
index 6ee5ac053e..04a1e5261c 100644
--- a/src/backend/opencl/magma/magma_data.h
+++ b/src/backend/opencl/magma/magma_data.h
@@ -55,6 +55,7 @@
 #ifndef MAGMA_DATA_H
 #define MAGMA_DATA_H
 
+#include <memory.hpp>
 #include <platform.hpp>
 #include "magma_types.h"
 
@@ -70,18 +71,18 @@ static magma_int_t magma_malloc(magma_ptr* ptrPtr, int num) {
     // malloc and free sometimes don't work for size=0, so allocate some minimal
     // size
     if (size == 0) size = sizeof(T);
-    cl_int err;
-    *ptrPtr = clCreateBuffer(arrayfire::opencl::getContext()(),
-                             CL_MEM_READ_WRITE, size, NULL, &err);
-    if (err != CL_SUCCESS) { return MAGMA_ERR_DEVICE_ALLOC; }
+    cl::Buffer* buf = arrayfire::opencl::bufferAlloc(size);
+    *ptrPtr         = static_cast<magma_ptr>(buf->get());
+    delete (buf);
+
+    if (ptrPtr == nullptr) { return MAGMA_ERR_DEVICE_ALLOC; };
     return MAGMA_SUCCESS;
 }
 
 // --------------------
 // Free GPU memory allocated by magma_malloc.
 static inline magma_int_t magma_free(magma_ptr ptr) {
-    cl_int err = clReleaseMemObject(ptr);
-    if (err != CL_SUCCESS) { return MAGMA_ERR_INVALID_PTR; }
+    arrayfire::opencl::memFree(ptr);
     return MAGMA_SUCCESS;
 }
 

From a5ad10b1219b34204d22c39da9ec9a1711081648 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 20 May 2023 15:37:45 -0400
Subject: [PATCH 2500/2677] Refactor isnan and is_nan functions to use standard
 isnan functions

---
 src/backend/common/complex.hpp             |  2 +-
 src/backend/common/half.hpp                |  1 +
 src/backend/cpu/kernel/ireduce.hpp         |  8 ++---
 src/backend/cpu/math.hpp                   | 30 ++++++++++++++++++
 src/backend/cuda/compile_module.cpp        |  2 +-
 src/backend/cuda/complex.hpp               |  2 ++
 src/backend/cuda/math.hpp                  | 36 ++++++++++++++++++++++
 src/backend/cuda/minmax_op.hpp             | 17 ++--------
 src/backend/oneapi/math.cpp                | 33 ++------------------
 src/backend/oneapi/math.hpp                | 32 +++++++++++++++++--
 src/backend/oneapi/minmax_op.hpp           | 16 +---------
 src/backend/opencl/kernel/sparse_arith.hpp |  1 +
 12 files changed, 111 insertions(+), 69 deletions(-)

diff --git a/src/backend/common/complex.hpp b/src/backend/common/complex.hpp
index b7663580dc..e6c5bb79ce 100644
--- a/src/backend/common/complex.hpp
+++ b/src/backend/common/complex.hpp
@@ -6,8 +6,8 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
-#include <Array.hpp>
 #include <backend.hpp>
 #include <types.hpp>
 
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 67bd47829f..ac03ea6d89 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -88,6 +88,7 @@ using uint16_t = unsigned short;
 #else
 #include <af/compilers.h>
 #include <cmath>
+#include <cstdint>
 #include <cstring>
 #include <ostream>
 #include <string>
diff --git a/src/backend/cpu/kernel/ireduce.hpp b/src/backend/cpu/kernel/ireduce.hpp
index 9c371498c7..9d2598af4b 100644
--- a/src/backend/cpu/kernel/ireduce.hpp
+++ b/src/backend/cpu/kernel/ireduce.hpp
@@ -10,7 +10,9 @@
 #pragma once
 #include <Param.hpp>
 #include <common/Binary.hpp>
+#include <common/half.hpp>
 #include <algorithm>
+#include <cmath>
 
 namespace arrayfire {
 namespace cpu {
@@ -23,16 +25,13 @@ double cabs(const T in) {
 static double cabs(const char in) { return (double)(in > 0); }
 static double cabs(const cfloat &in) { return (double)abs(in); }
 static double cabs(const cdouble &in) { return (double)abs(in); }
-template<typename T>
-static bool is_nan(T in) {
-    return in != in;
-}
 
 template<af_op_t op, typename T>
 struct MinMaxOp {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        using arrayfire::cpu::is_nan;
         if (is_nan(val)) { m_val = common::Binary<T, op>::init(); }
     }
 
@@ -50,6 +49,7 @@ struct MinMaxOp<af_max_t, T> {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        using arrayfire::cpu::is_nan;
         if (is_nan(val)) { m_val = common::Binary<T, af_max_t>::init(); }
     }
 
diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index d2735acd2a..16a4e2abbf 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -42,6 +42,36 @@ static inline T max(T lhs, T rhs) {
 cfloat max(cfloat lhs, cfloat rhs);
 cdouble max(cdouble lhs, cdouble rhs);
 
+template<typename T>
+static inline auto is_nan(const T &val) -> bool {
+    return false;
+}
+
+template<>
+inline auto is_nan<float>(const float &val) -> bool {
+    return std::isnan(val);
+}
+
+template<>
+inline auto is_nan<double>(const double &val) -> bool {
+    return std::isnan(val);
+}
+
+template<>
+inline auto is_nan<common::half>(const common::half &val) -> bool {
+    return isnan(val);
+}
+
+template<>
+inline auto is_nan<cfloat>(const cfloat &in) -> bool {
+    return std::isnan(real(in)) || std::isnan(imag(in));
+}
+
+template<>
+inline auto is_nan<cdouble>(const cdouble &in) -> bool {
+    return std::isnan(real(in)) || std::isnan(imag(in));
+}
+
 template<typename T>
 static inline T division(T lhs, double rhs) {
     return lhs / rhs;
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 36014049a8..06dfd0f377 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -176,7 +176,7 @@ Module compileModule(const string &moduleKey, span<const string> sources,
             "stdbool.h",       // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             "stdlib.h",        // DUMMY ENTRY TO SATISFY af/defines.h inclusion
             "vector_types.h",  // DUMMY ENTRY TO SATISFY cuComplex_h inclusion
-            "utility",         // DUMMY ENTRY TO SATISFY cuda_fp16.hpp inclusion
+            "utility",         // DUMMY ENTRY TO SATISFY utility inclusion
             "backend.hpp",
             "cuComplex.h",
             "jit.cuh",
diff --git a/src/backend/cuda/complex.hpp b/src/backend/cuda/complex.hpp
index d9d143ddbf..81f39dd785 100644
--- a/src/backend/cuda/complex.hpp
+++ b/src/backend/cuda/complex.hpp
@@ -7,6 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#pragma once
+
 #include <Array.hpp>
 #include <binary.hpp>
 #include <common/jit/BinaryNode.hpp>
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 3562565a86..f7b11347cc 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -260,6 +260,42 @@ __SDH__ double real(cdouble c) { return cuCreal(c); }
 __SDH__ float imag(cfloat c) { return cuCimagf(c); }
 __SDH__ double imag(cdouble c) { return cuCimag(c); }
 
+template<typename T>
+static inline __DH__ auto is_nan(const T &val) -> bool {
+    return false;
+}
+
+template<>
+inline __DH__ auto is_nan<float>(const float &val) -> bool {
+    return ::isnan(val);
+}
+
+template<>
+inline __DH__ auto is_nan<double>(const double &val) -> bool {
+    return ::isnan(val);
+}
+
+#ifdef __CUDA_ARCH__
+template<>
+inline __device__ auto is_nan<__half>(const __half &val) -> bool {
+#if __CUDA_ARCH__ >= 530
+    return __hisnan(val);
+#else
+    return ::isnan(__half2float(val));
+#endif
+}
+#endif
+
+template<>
+inline auto is_nan<cfloat>(const cfloat &in) -> bool {
+    return ::isnan(real(in)) || ::isnan(imag(in));
+}
+
+template<>
+inline auto is_nan<cdouble>(const cdouble &in) -> bool {
+    return ::isnan(real(in)) || ::isnan(imag(in));
+}
+
 template<typename T>
 T __SDH__ conj(T x) {
     return x;
diff --git a/src/backend/cuda/minmax_op.hpp b/src/backend/cuda/minmax_op.hpp
index 4fcc995c0b..a2b7149a07 100644
--- a/src/backend/cuda/minmax_op.hpp
+++ b/src/backend/cuda/minmax_op.hpp
@@ -34,26 +34,12 @@ double cabs<cdouble>(const cdouble &in) {
     return (double)abs(in);
 }
 
-template<typename T>
-static bool is_nan(const T &in) {
-    return in != in;
-}
-
-template<>
-bool is_nan<cfloat>(const cfloat &in) {
-    return in.x != in.x || in.y != in.y;
-}
-
-template<>
-bool is_nan<cdouble>(const cdouble &in) {
-    return in.x != in.x || in.y != in.y;
-}
-
 template<af_op_t op, typename T>
 struct MinMaxOp {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        using arrayfire::cuda::is_nan;
         if (is_nan(val)) { m_val = common::Binary<compute_t<T>, op>::init(); }
     }
 
@@ -71,6 +57,7 @@ struct MinMaxOp<af_max_t, T> {
     T m_val;
     uint m_idx;
     MinMaxOp(T val, uint idx) : m_val(val), m_idx(idx) {
+        using arrayfire::cuda::is_nan;
         if (is_nan(val)) { m_val = common::Binary<T, af_max_t>::init(); }
     }
 
diff --git a/src/backend/oneapi/math.cpp b/src/backend/oneapi/math.cpp
index a673f9293b..18bafd324b 100644
--- a/src/backend/oneapi/math.cpp
+++ b/src/backend/oneapi/math.cpp
@@ -12,43 +12,14 @@
 
 namespace arrayfire {
 namespace oneapi {
-cfloat operator+(cfloat lhs, cfloat rhs) {
-    // cfloat res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
-    cfloat res;
-    return res;
-}
-
-cdouble operator+(cdouble lhs, cdouble rhs) {
-    // cdouble res = {{lhs.s[0] + rhs.s[0], lhs.s[1] + rhs.s[1]}};
-    cdouble res;
-    return res;
-}
-
-cfloat operator*(cfloat lhs, cfloat rhs) {
-    cfloat out;
-    // out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
-    // out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
-    return out;
-}
-
-cdouble operator*(cdouble lhs, cdouble rhs) {
-    cdouble out;
-    // out.s[0] = lhs.s[0] * rhs.s[0] - lhs.s[1] * rhs.s[1];
-    // out.s[1] = lhs.s[0] * rhs.s[1] + lhs.s[1] * rhs.s[0];
-    return out;
-}
 
 cfloat division(cfloat lhs, double rhs) {
-    cfloat retVal;
-    // retVal.s[0] = real(lhs) / rhs;
-    // retVal.s[1] = imag(lhs) / rhs;
+    cfloat retVal(real(lhs) / rhs, imag(lhs) / rhs);
     return retVal;
 }
 
 cdouble division(cdouble lhs, double rhs) {
-    cdouble retVal;
-    // retVal.s[0] = real(lhs) / rhs;
-    // retVal.s[1] = imag(lhs) / rhs;
+    cdouble retVal(real(lhs) / rhs, imag(lhs) / rhs);
     return retVal;
 }
 }  // namespace oneapi
diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index 063d82f370..83973994c9 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -71,6 +71,36 @@ inline cdouble min<cdouble>(cdouble lhs, cdouble rhs) {
     return abs(lhs) < abs(rhs) ? lhs : rhs;
 }
 
+template<typename T>
+static inline auto is_nan(const T &val) -> bool {
+    return false;
+}
+
+template<>
+inline auto is_nan<sycl::half>(const sycl::half &val) -> bool {
+    return sycl::isnan(val);
+}
+
+template<>
+inline auto is_nan<float>(const float &val) -> bool {
+    return std::isnan(val);
+}
+
+template<>
+inline auto is_nan<double>(const double &val) -> bool {
+    return std::isnan(val);
+}
+
+template<>
+inline auto is_nan<cfloat>(const cfloat &in) -> bool {
+    return std::isnan(real(in)) || std::isnan(imag(in));
+}
+
+template<>
+inline auto is_nan<cdouble>(const cdouble &in) -> bool {
+    return std::isnan(real(in)) || std::isnan(imag(in));
+}
+
 template<typename T>
 static T scalar(double val) {
     return (T)(val);
@@ -79,8 +109,6 @@ static T scalar(double val) {
 template<>
 inline cfloat scalar<cfloat>(double val) {
     cfloat cval(static_cast<float>(val));
-    // cval.real() = (float)val;
-    // cval.imag() = 0;
     return cval;
 }
 
diff --git a/src/backend/oneapi/minmax_op.hpp b/src/backend/oneapi/minmax_op.hpp
index f006ff419c..40159d3ec9 100644
--- a/src/backend/oneapi/minmax_op.hpp
+++ b/src/backend/oneapi/minmax_op.hpp
@@ -10,6 +10,7 @@
 #pragma once
 
 #include <common/Binary.hpp>
+#include <math.hpp>
 
 namespace arrayfire {
 namespace oneapi {
@@ -34,21 +35,6 @@ double cabs<cdouble>(const cdouble &in) {
     return (double)abs(in);
 }
 
-template<typename T>
-static bool is_nan(const T &in) {
-    return in != in;
-}
-
-template<>
-bool is_nan<cfloat>(const cfloat &in) {
-    return in.real() != in.real() || in.imag() != in.imag();
-}
-
-template<>
-bool is_nan<cdouble>(const cdouble &in) {
-    return in.real() != in.real() || in.imag() != in.imag();
-}
-
 template<af_op_t op, typename T>
 struct MinMaxOp {
     T m_val;
diff --git a/src/backend/opencl/kernel/sparse_arith.hpp b/src/backend/opencl/kernel/sparse_arith.hpp
index 313fa902d2..17cd67ca8a 100644
--- a/src/backend/opencl/kernel/sparse_arith.hpp
+++ b/src/backend/opencl/kernel/sparse_arith.hpp
@@ -20,6 +20,7 @@
 #include <kernel_headers/sparse_arith_csr.hpp>
 #include <kernel_headers/ssarith_calc_out_nnz.hpp>
 #include <math.hpp>
+#include <memory.hpp>
 #include <traits.hpp>
 
 #include <string>

From 279087c5796698a57181f05aebbf1823756d3c2e Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 20 May 2023 17:12:12 -0400
Subject: [PATCH 2501/2677] Fix ragged reductions by passing a bool that checks
 if accessor valid

---
 src/backend/oneapi/kernel/ireduce.hpp | 102 +++++++++++++++-----------
 1 file changed, 59 insertions(+), 43 deletions(-)

diff --git a/src/backend/oneapi/kernel/ireduce.hpp b/src/backend/oneapi/kernel/ireduce.hpp
index 0c6ae70383..2366264ea4 100644
--- a/src/backend/oneapi/kernel/ireduce.hpp
+++ b/src/backend/oneapi/kernel/ireduce.hpp
@@ -42,7 +42,8 @@ class ireduceDimKernelSMEM {
                          read_accessor<T> in, KParam iInfo,
                          read_accessor<uint> iloc, KParam ilocInfo,
                          uint groups_x, uint groups_y, uint groups_dim,
-                         read_accessor<uint> rlen, KParam rlenInfo,
+                         bool rlenValid, read_accessor<uint> rlen,
+                         KParam rlenInfo,
                          sycl::local_accessor<compute_t<T>, 1> s_val,
                          sycl::local_accessor<uint, 1> s_idx)
         : out_(out)
@@ -56,6 +57,7 @@ class ireduceDimKernelSMEM {
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , groups_dim_(groups_dim)
+        , rlenValid_(rlenValid)
         , rlen_(rlen)
         , rlenInfo_(rlenInfo)
         , s_val_(s_val)
@@ -90,8 +92,7 @@ class ireduceDimKernelSMEM {
         const bool rlen_valid =
             (ids[0] < rlenInfo_.dims[0]) && (ids[1] < rlenInfo_.dims[1]) &&
             (ids[2] < rlenInfo_.dims[2]) && (ids[3] < rlenInfo_.dims[3]);
-        const bool rlen_nonnull = (rlenInfo_.dims[0] * rlenInfo_.dims[1] *
-                                   rlenInfo_.dims[2] * rlenInfo_.dims[3]) > 0;
+        const bool rlen_nonnull = rlenValid_;
         uint *const rlenptr =
             (rlen_nonnull && rlen_valid)
                 ? rlen_.get_pointer() + ids[3] * rlenInfo_.strides[3] +
@@ -204,6 +205,7 @@ class ireduceDimKernelSMEM {
     read_accessor<uint> iloc_;
     KParam ilocInfo_;
     uint groups_x_, groups_y_, groups_dim_;
+    bool rlenValid_;
     read_accessor<uint> rlen_;
     KParam rlenInfo_;
     sycl::local_accessor<compute_t<T>, 1> s_val_;
@@ -218,25 +220,25 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
     sycl::range<2> global(groups_dim[0] * groups_dim[2] * local[0],
                           groups_dim[1] * groups_dim[3] * local[1]);
 
-    sycl::buffer<uint, 1> empty{sycl::range<1>(1)};
+    auto iempty = memAlloc<uint>(1);
+    auto rempty = memAlloc<uint>(1);
     getQueue().submit([&](sycl::handler &h) {
         write_accessor<T> out_acc{*out.data, h};
         write_accessor<uint> oloc_acc{*oloc.data, h};
         read_accessor<T> in_acc{*in.data, h};
 
-        read_accessor<uint> iloc_acc{empty, h};
+        read_accessor<uint> iloc_acc{*iempty, h};
         if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
                 iloc.info.dims[3] >
             0) {
             iloc_acc = read_accessor<uint>{*iloc.data, h};
         }
 
-        read_accessor<uint> rlen_acc{empty, h};
-        if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
-                rlen.info.dims[3] >
-            0) {
-            rlen_acc = read_accessor<uint>{*rlen.data, h};
-        }
+        read_accessor<uint> rlen_acc{*rempty, h};
+        bool rlenValid = (rlen.info.dims[0] * rlen.info.dims[1] *
+                              rlen.info.dims[2] * rlen.info.dims[3] >
+                          0);
+        if (rlenValid) { rlen_acc = read_accessor<uint>{*rlen.data, h}; }
 
         auto shrdVal = sycl::local_accessor<compute_t<T>, 1>(
             creduce::THREADS_PER_BLOCK, h);
@@ -250,8 +252,8 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
-                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
-                        shrdLoc));
+                        groups_dim[dim], rlenValid, rlen_acc, rlen.info,
+                        shrdVal, shrdLoc));
                 break;
             case 4:
                 h.parallel_for(
@@ -259,8 +261,8 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
-                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
-                        shrdLoc));
+                        groups_dim[dim], rlenValid, rlen_acc, rlen.info,
+                        shrdVal, shrdLoc));
                 break;
             case 2:
                 h.parallel_for(
@@ -268,8 +270,8 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
-                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
-                        shrdLoc));
+                        groups_dim[dim], rlenValid, rlen_acc, rlen.info,
+                        shrdVal, shrdLoc));
                 break;
             case 1:
                 h.parallel_for(
@@ -277,8 +279,8 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
-                        groups_dim[dim], rlen_acc, rlen.info, shrdVal,
-                        shrdLoc));
+                        groups_dim[dim], rlenValid, rlen_acc, rlen.info,
+                        shrdVal, shrdLoc));
                 break;
         }
     });
@@ -335,7 +337,8 @@ class ireduceFirstKernelSMEM {
                            read_accessor<T> in, KParam iInfo,
                            read_accessor<uint> iloc, KParam ilocInfo,
                            uint groups_x, uint groups_y, uint repeat,
-                           read_accessor<uint> rlen, KParam rlenInfo,
+                           bool rlenValid, read_accessor<uint> rlen,
+                           KParam rlenInfo,
                            sycl::local_accessor<compute_t<T>, 1> s_val,
                            sycl::local_accessor<uint, 1> s_idx)
         : out_(out)
@@ -349,6 +352,7 @@ class ireduceFirstKernelSMEM {
         , groups_x_(groups_x)
         , groups_y_(groups_y)
         , repeat_(repeat)
+        , rlenValid_(rlenValid)
         , rlen_(rlen)
         , rlenInfo_(rlenInfo)
         , s_val_(s_val)
@@ -372,23 +376,24 @@ class ireduceFirstKernelSMEM {
                         iInfo_.offset;
 
         T *optr = out_.get_pointer() + wid * oInfo_.strides[3] +
-                  zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+                  zid * oInfo_.strides[2] + yid * oInfo_.strides[1] +
+                  oInfo_.offset;
 
-        const bool rlenvalid = (rlenInfo_.dims[0] * rlenInfo_.dims[1] *
-                                rlenInfo_.dims[2] * rlenInfo_.dims[3]) > 0;
-        uint *const rlenptr =
-            (rlenvalid)
-                ? rlen_.get_pointer() + wid * rlenInfo_.strides[3] +
-                      zid * rlenInfo_.strides[2] + yid * rlenInfo_.strides[1]
-                : nullptr;
+        const uint *rlenptr =
+            (rlenValid_) ? rlen_.get_pointer() + wid * rlenInfo_.strides[3] +
+                               zid * rlenInfo_.strides[2] +
+                               yid * rlenInfo_.strides[1] + rlenInfo_.offset
+                         : nullptr;
 
         uint *ilptr;
         if (!is_first) {
             ilptr = iloc_.get_pointer() + wid * iInfo_.strides[3] +
-                    zid * iInfo_.strides[2] + yid * iInfo_.strides[1];
+                    zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
+                    iInfo_.offset;
         }
         uint *olptr = oloc_.get_pointer() + wid * oInfo_.strides[3] +
-                      zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+                      zid * oInfo_.strides[2] + yid * oInfo_.strides[1] +
+                      oInfo_.offset;
 
         size_t ylim   = iInfo_.dims[1];
         size_t zlim   = iInfo_.dims[2];
@@ -404,7 +409,7 @@ class ireduceFirstKernelSMEM {
         compute_t<T> out_val = common::Binary<compute_t<T>, op>::init();
         uint idx             = xid;
 
-        if (xid < lim) {
+        if (xid < lim && is_valid) {
             out_val = static_cast<compute_t<T>>(iptr[xid]);
             if (!is_first) idx = ilptr[xid];
         }
@@ -501,6 +506,7 @@ class ireduceFirstKernelSMEM {
     read_accessor<uint> iloc_;
     KParam ilocInfo_;
     uint groups_x_, groups_y_, repeat_;
+    bool rlenValid_;
     read_accessor<uint> rlen_;
     KParam rlenInfo_;
     sycl::local_accessor<compute_t<T>, 1> s_val_;
@@ -518,25 +524,25 @@ void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
 
     uint repeat = divup(in.info.dims[0], (groups_x * threads_x));
 
-    sycl::buffer<uint, 1> empty{sycl::range<1>(1)};
+    auto iempty = memAlloc<uint>(1);
+    auto rempty = memAlloc<uint>(1);
     getQueue().submit([&](sycl::handler &h) {
         write_accessor<T> out_acc{*out.data, h};
         write_accessor<uint> oloc_acc{*oloc.data, h};
         read_accessor<T> in_acc{*in.data, h};
 
-        read_accessor<uint> iloc_acc{empty, h};
+        read_accessor<uint> iloc_acc{*iempty, h};
         if (iloc.info.dims[0] * iloc.info.dims[1] * iloc.info.dims[2] *
                 iloc.info.dims[3] >
             0) {
             iloc_acc = read_accessor<uint>{*iloc.data, h};
         }
 
-        read_accessor<uint> rlen_acc{empty, h};
-        if (rlen.info.dims[0] * rlen.info.dims[1] * rlen.info.dims[2] *
-                rlen.info.dims[3] >
-            0) {
-            rlen_acc = read_accessor<uint>{*rlen.data, h};
-        }
+        read_accessor<uint> rlen_acc{*rempty, h};
+        bool rlenValid = (rlen.info.dims[0] * rlen.info.dims[1] *
+                              rlen.info.dims[2] * rlen.info.dims[3] >
+                          0);
+        if (rlenValid) { rlen_acc = read_accessor<uint>{*rlen.data, h}; }
 
         auto shrdVal = sycl::local_accessor<compute_t<T>, 1>(
             creduce::THREADS_PER_BLOCK, h);
@@ -550,7 +556,7 @@ void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceFirstKernelSMEM<T, op, is_first, 32>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_x, groups_y, repeat,
-                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                        rlenValid, rlen_acc, rlen.info, shrdVal, shrdLoc));
                 break;
             case 64:
                 h.parallel_for(
@@ -558,7 +564,7 @@ void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceFirstKernelSMEM<T, op, is_first, 64>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_x, groups_y, repeat,
-                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                        rlenValid, rlen_acc, rlen.info, shrdVal, shrdLoc));
                 break;
             case 128:
                 h.parallel_for(
@@ -566,7 +572,7 @@ void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceFirstKernelSMEM<T, op, is_first, 128>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_x, groups_y, repeat,
-                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                        rlenValid, rlen_acc, rlen.info, shrdVal, shrdLoc));
                 break;
             case 256:
                 h.parallel_for(
@@ -574,7 +580,7 @@ void ireduce_first_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
                     ireduceFirstKernelSMEM<T, op, is_first, 256>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_x, groups_y, repeat,
-                        rlen_acc, rlen.info, shrdVal, shrdLoc));
+                        rlenValid, rlen_acc, rlen.info, shrdVal, shrdLoc));
                 break;
         }
     });
@@ -669,6 +675,16 @@ T ireduce_all(uint *idx, Param<T> in) {
 
     sycl::host_accessor h_ptr_raw{*tmp.get()};
     sycl::host_accessor h_lptr_raw{*tlptr.get()};
+    if (!is_linear) {
+        // Converting n-d index into a linear index
+        // in is of size   [   dims0, dims1, dims2, dims3]
+        // tidx is of size [blocks_x, dims1, dims2, dims3]
+        // i / blocks_x gives you the batch number "N"
+        // "N * dims0 + i" gives the linear index
+        for (int i = 0; i < tmp_elements; i++) {
+            h_lptr_raw[i] += (i / groups_x) * in.info.dims[0];
+        }
+    }
 
     MinMaxOp<op, T> Op(h_ptr_raw[0], h_lptr_raw[0]);
 

From 2f6bd933789f30fbce1e3ea9b0fdd144831d647d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 20 May 2023 17:13:49 -0400
Subject: [PATCH 2502/2677] Update half checks and add ASSERT_SUCCESS to
 reduction tests.

---
 test/reduce.cpp | 36 ++++++++++++++++++++++++++----------
 1 file changed, 26 insertions(+), 10 deletions(-)

diff --git a/test/reduce.cpp b/test/reduce.cpp
index fc16e60716..f01dafec45 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -408,7 +408,9 @@ class ReduceByKeyP : public ::testing::TestWithParam<reduce_by_key_params *> {
 
     void SetUp() {
         reduce_by_key_params *params = GetParam();
-        if (noHalfTests(params->vType_)) { return; }
+        if (noHalfTests(params->vType_)) {
+            GTEST_SKIP() << "Half not supported on this device";
+        }
 
         keys = ptrToArray(params->iSize, params->iKeys_, params->kType_);
         vals = ptrToArray(params->iSize, params->iVals_, params->vType_);
@@ -551,7 +553,12 @@ INSTANTIATE_TEST_SUITE_P(UniqueKeyTests, ReduceByKeyP,
                          testNameGenerator<ReduceByKeyP>);
 
 TEST_P(ReduceByKeyP, SumDim0) {
-    if (noHalfTests(GetParam()->vType_)) { return; }
+    if (noHalfTests(GetParam()->vType_)) {
+        GTEST_SKIP() << "Half not supported on this device";
+    }
+    if (noHalfTests(GetParam()->kType_)) {
+        GTEST_SKIP() << "Half not supported on this device";
+    }
     array keyRes, valsReduced;
     sumByKey(keyRes, valsReduced, keys, vals, 0, 0);
 
@@ -560,7 +567,12 @@ TEST_P(ReduceByKeyP, SumDim0) {
 }
 
 TEST_P(ReduceByKeyP, SumDim2) {
-    if (noHalfTests(GetParam()->vType_)) { return; }
+    if (noHalfTests(GetParam()->vType_)) {
+        GTEST_SKIP() << "Half not supported on this device";
+    }
+    if (noHalfTests(GetParam()->kType_)) {
+        GTEST_SKIP() << "Half not supported on this device";
+    }
     const int ntile = 2;
     vals            = tile(vals, 1, ntile, 1, 1);
     vals            = reorder(vals, 1, 2, 0, 3);
@@ -1946,7 +1958,9 @@ class RaggedReduceMaxRangeP : public ::testing::TestWithParam<ragged_params *> {
 
     void SetUp() {
         ragged_params *params = GetParam();
-        if (noHalfTests(params->vType_)) { return; }
+        if (noHalfTests(params->vType_)) {
+            GTEST_SKIP() << "Half not supported on this device";
+        }
 
         const size_t rdim_size = params->reduceDimLen_;
         const int dim          = params->reduceDim_;
@@ -2043,8 +2057,9 @@ INSTANTIATE_TEST_SUITE_P(RaggedReduceTests, RaggedReduceMaxRangeP,
                          testNameGeneratorRagged<RaggedReduceMaxRangeP>);
 
 TEST_P(RaggedReduceMaxRangeP, rangeMaxTest) {
-    if (noHalfTests(GetParam()->vType_)) { return; }
-
+    if (noHalfTests(GetParam()->vType_)) {
+        GTEST_SKIP() << "Half not supported on this device";
+    }
     array ragged_max, idx;
     const int dim = GetParam()->reduceDim_;
     max(ragged_max, idx, vals, ragged_lens, dim);
@@ -2308,20 +2323,21 @@ TEST(Reduce, nanval_issue_3255) {
     dim_t dims[1] = {8};
 
     int ikeys_src[8] = {0, 0, 1, 1, 1, 2, 2, 0};
-    af_create_array(&ikeys, ikeys_src, 1, dims, u32);
+    ASSERT_SUCCESS(af_create_array(&ikeys, ikeys_src, 1, dims, u32));
 
     int i;
     for (i = 0; i < 8; i++) {
         double ivals_src[8] = {1, 2, 3, 4, 5, 6, 7, 8};
         ivals_src[i]        = NAN;
-        af_create_array(&ivals, ivals_src, 1, dims, f64);
+        ASSERT_SUCCESS(af_create_array(&ivals, ivals_src, 1, dims, f64));
 
-        af_product_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);
+        ASSERT_SUCCESS(
+            af_product_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0));
         af::array ovals_cpp(ovals);
         ASSERT_FALSE(af::anyTrue<bool>(af::isNaN(ovals_cpp)));
         ASSERT_SUCCESS(af_release_array(okeys));
 
-        af_sum_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0);
+        ASSERT_SUCCESS(af_sum_by_key_nan(&okeys, &ovals, ikeys, ivals, 0, 1.0));
         ovals_cpp = af::array(ovals);
 
         ASSERT_FALSE(af::anyTrue<bool>(af::isNaN(ovals_cpp)));

From f14d57c3ba141a3a64e92efa33010725919a5530 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 20 May 2023 21:07:22 -0400
Subject: [PATCH 2503/2677] Remove operator+ for common::half in oneAPI
 backend. update wrap

---
 src/backend/oneapi/kernel/wrap_dilated.hpp | 37 ++++++++++++----------
 src/backend/oneapi/math.hpp                |  5 ---
 2 files changed, 20 insertions(+), 22 deletions(-)

diff --git a/src/backend/oneapi/kernel/wrap_dilated.hpp b/src/backend/oneapi/kernel/wrap_dilated.hpp
index 63bdf342a8..f8f9614d01 100644
--- a/src/backend/oneapi/kernel/wrap_dilated.hpp
+++ b/src/backend/oneapi/kernel/wrap_dilated.hpp
@@ -28,12 +28,13 @@ namespace kernel {
 template<typename T>
 class wrapDilatedCreateKernel {
    public:
-    wrapDilatedCreateKernel(write_accessor<T> optrAcc, KParam out,
-                            read_accessor<T> iptrAcc, KParam in, const int wx,
-                            const int wy, const int sx, const int sy,
-                            const int px, const int py, const int dx,
-                            const int dy, const int nx, const int ny,
-                            int groups_x, int groups_y, const bool is_column)
+    wrapDilatedCreateKernel(write_accessor<data_t<T>> optrAcc, KParam out,
+                            read_accessor<data_t<T>> iptrAcc, KParam in,
+                            const int wx, const int wy, const int sx,
+                            const int sy, const int px, const int py,
+                            const int dx, const int dy, const int nx,
+                            const int ny, int groups_x, int groups_y,
+                            const bool is_column)
         : optrAcc_(optrAcc)
         , out_(out)
         , iptrAcc_(iptrAcc)
@@ -63,10 +64,10 @@ class wrapDilatedCreateKernel {
         int oidx0 = it.get_local_id(0) + g.get_local_range(0) * groupId_x;
         int oidx1 = it.get_local_id(1) + g.get_local_range(1) * groupId_y;
 
-        T *optr = optrAcc_.get_pointer() + idx2 * out_.strides[2] +
-                  idx3 * out_.strides[3];
-        T *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
-                  idx3 * in_.strides[3] + in_.offset;
+        data_t<T> *optr = optrAcc_.get_pointer() + idx2 * out_.strides[2] +
+                          idx3 * out_.strides[3];
+        data_t<T> *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
+                          idx3 * in_.strides[3] + in_.offset;
 
         if (oidx0 >= out_.dims[0] || oidx1 >= out_.dims[1]) return;
 
@@ -86,7 +87,7 @@ class wrapDilatedCreateKernel {
         const int x_start = (pidx0 < eff_wx) ? 0 : (pidx0 - eff_wx) / sx_ + 1;
         const int x_end   = sycl::min(pidx0 / sx_ + 1, nx_);
 
-        T val   = (T)0;
+        compute_t<T> val(0);
         int idx = 1;
 
         for (int y = y_start; y < y_end; y++) {
@@ -111,8 +112,8 @@ class wrapDilatedCreateKernel {
                     idx = dim_end + win_end * in_.strides[1];
                 }
 
-                T ival;
-                ival = (yvalid && xvalid) ? iptr[idx] : (T)0;
+                compute_t<T> ival;
+                ival = (yvalid && xvalid) ? iptr[idx] : compute_t<T>(0);
                 val  = val + ival;
             }
         }
@@ -121,9 +122,9 @@ class wrapDilatedCreateKernel {
     }
 
    private:
-    write_accessor<T> optrAcc_;
+    write_accessor<data_t<T>> optrAcc_;
     KParam out_;
-    read_accessor<T> iptrAcc_;
+    read_accessor<data_t<T>> iptrAcc_;
     KParam in_;
     const int wx_;
     const int wy_;
@@ -158,8 +159,10 @@ void wrap_dilated(Param<T> out, const Param<T> in, const dim_t wx,
 
     auto Q = getQueue();
     Q.submit([&](sycl::handler &h) {
-        sycl::accessor outAcc{*out.data, h, sycl::write_only, sycl::no_init};
-        sycl::accessor inAcc{*in.data, h, sycl::read_only};
+        write_accessor<data_t<T>> outAcc =
+            out.template get_accessor<sycl::access_mode::write>(h);
+        read_accessor<data_t<T>> inAcc =
+            in.template get_accessor<sycl::access_mode::read>(h);
         h.parallel_for(sycl::nd_range{global, local},
                        wrapDilatedCreateKernel<T>(
                            outAcc, out.info, inAcc, in.info, wx, wy, sx, sy, px,
diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index 83973994c9..359b4ae9a3 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -170,11 +170,6 @@ static inline T imag(T in) {
     return std::imag(in);
 }
 
-inline arrayfire::common::half operator+(arrayfire::common::half lhs,
-                                         arrayfire::common::half rhs) noexcept {
-    return arrayfire::common::half(static_cast<float>(lhs) +
-                                   static_cast<float>(rhs));
-}
 }  // namespace oneapi
 }  // namespace arrayfire
 

From 435a55c7fb11872126b4ada425254b6d60963b87 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 19 May 2023 18:57:46 -0400
Subject: [PATCH 2504/2677] fix scale for non-double supported kernels in
 oneapi backend

---
 src/backend/oneapi/kernel/memcopy.hpp | 57 +++++++++++++++------------
 1 file changed, 31 insertions(+), 26 deletions(-)

diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index c6b8dbb04c..dea4fd000c 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -28,6 +28,27 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
+template<typename T>
+using factortypes = typename std::conditional<std::is_same_v<T, double> ||
+                                                  std::is_same_v<T, cdouble>,
+                                              double, float>::type;
+
+template<typename T, typename FACTORTYPE = factortypes<T>>
+inline T scale(T value, FACTORTYPE factor) {
+    return (T)(FACTORTYPE(value) * factor);
+}
+
+template<>
+inline cfloat scale<cfloat, float>(cfloat value, float factor) {
+    return cfloat{static_cast<float>(value.real() * factor),
+                  static_cast<float>(value.imag() * factor)};
+}
+
+template<>
+inline cdouble scale<cdouble, double>(cdouble value, double factor) {
+    return cdouble{value.real() * factor, value.imag() * factor};
+}
+
 typedef struct {
     dim_t dim[4];
 } dims_t;
@@ -126,22 +147,6 @@ void memcopy(sycl::buffer<T> *out, const dim_t *ostrides,
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
-template<typename T>
-inline T scale(T value, double factor) {
-    return (T)(double(value) * factor);
-}
-
-template<>
-inline cfloat scale<cfloat>(cfloat value, double factor) {
-    return cfloat{static_cast<float>(value.real() * factor),
-                  static_cast<float>(value.imag() * factor)};
-}
-
-template<>
-inline cdouble scale<cdouble>(cdouble value, double factor) {
-    return cdouble{value.real() * factor, value.imag() * factor};
-}
-
 template<typename inType, typename outType>
 inline outType convertType(inType value) {
     return static_cast<outType>(value);
@@ -201,7 +206,7 @@ class reshapeCopy {
    public:
     reshapeCopy(write_accessor<outType> dst, KParam oInfo,
                 read_accessor<inType> src, KParam iInfo, outType default_value,
-                float factor, dims_t trgt, int blk_x, int blk_y)
+                factortypes<inType> factor, dims_t trgt, int blk_x, int blk_y)
         : dst_(dst)
         , src_(src)
         , oInfo_(oInfo)
@@ -266,7 +271,7 @@ class reshapeCopy {
     read_accessor<inType> src_;
     KParam oInfo_, iInfo_;
     outType default_value_;
-    float factor_;
+    factortypes<inType> factor_;
     dims_t trgt_;
     int blk_x_, blk_y_;
 };
@@ -309,15 +314,15 @@ void copy(Param<outType> dst, const Param<inType> src, const int ndims,
             *const_cast<sycl::buffer<inType> *>(src.data), h};
 
         if (same_dims) {
-            h.parallel_for(ndrange, reshapeCopy<inType, outType, true>(
-                                        dst_acc, dst.info, src_acc, src.info,
-                                        default_value, (float)factor, trgt_dims,
-                                        blk_x, blk_y));
+            h.parallel_for(ndrange,
+                           reshapeCopy<inType, outType, true>(
+                               dst_acc, dst.info, src_acc, src.info,
+                               default_value, factor, trgt_dims, blk_x, blk_y));
         } else {
-            h.parallel_for(ndrange, reshapeCopy<inType, outType, false>(
-                                        dst_acc, dst.info, src_acc, src.info,
-                                        default_value, (float)factor, trgt_dims,
-                                        blk_x, blk_y));
+            h.parallel_for(ndrange,
+                           reshapeCopy<inType, outType, false>(
+                               dst_acc, dst.info, src_acc, src.info,
+                               default_value, factor, trgt_dims, blk_x, blk_y));
         }
     });
     ONEAPI_DEBUG_FINISH(getQueue());

From 5a42d39468b49c336523862fd13dc65a55deb3c3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 23 May 2023 10:18:43 -0400
Subject: [PATCH 2505/2677] Fix JIT failures due to reliance on stride[0]s

---
 src/backend/oneapi/jit/kernel_generators.hpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index bc12929fe6..5a3321d0a0 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -61,8 +61,9 @@ inline void generateBufferOffsets(std::stringstream& kerStream, int id,
                   << info_str << ".strides[3] * id3 + (id2 < " << info_str
                   << ".dims[2]) * " << info_str << ".strides[2] * id2 + (id1 < "
                   << info_str << ".dims[1]) * " << info_str
-                  << ".strides[1] * id1 + (id0 < " << info_str
-                  << ".dims[0]) * id0 + " << info_str << ".offset;\n";
+                  << ".strides[1] * id1 + (id0 < " << info_str << ".dims[0]) * "
+                  << info_str << ".strides[0]  * id0 + " << info_str
+                  << ".offset;\n";
     }
 }
 

From 40011556e423571b3b8bb40f3d5c317363a5cf2d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 23 May 2023 10:19:19 -0400
Subject: [PATCH 2506/2677] Fix wrap and unwrap failures due to invalid work
 group size

---
 src/backend/oneapi/kernel/unwrap.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/oneapi/kernel/unwrap.hpp b/src/backend/oneapi/kernel/unwrap.hpp
index 0c88bd4348..43301fd744 100644
--- a/src/backend/oneapi/kernel/unwrap.hpp
+++ b/src/backend/oneapi/kernel/unwrap.hpp
@@ -149,7 +149,7 @@ void unwrap(Param<T> out, const Param<T> in, const dim_t wx, const dim_t wy,
         reps = divup((wx * wy), TX);
     } else {
         TX   = THREADS_X;
-        TY   = THREADS_X;
+        TY   = THREADS_Y;
         BX   = divup(out.info.dims[0], TX);
         reps = divup((wx * wy), TY);
     }

From 2a2ca609708f6c44b72c508266890e57fe79ed7f Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 23 May 2023 14:07:56 -0400
Subject: [PATCH 2507/2677] fix multiblock offset in scanFirstBcastKernel

---
 src/backend/oneapi/kernel/scan_first.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index 649e031b03..dd483f069b 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -181,7 +181,7 @@ class scanFirstBcastKernel {
         // Shift broadcast one step to the right for exclusive scan (#2366)
         int offset = !inclusive_scan_;
         for (int k = 0, id = xid + offset; k < lim_ && id < oInfo_.dims[0];
-             k++, id += g.get_group_range(0)) {
+             k++, id += g.get_local_range(0)) {
             optr[id] = binop(accum, optr[id]);
         }
     }

From b7ce6153dd43c6c3e67b1c33d8748a2b4b0de8c7 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Wed, 24 May 2023 23:20:16 +0200
Subject: [PATCH 2508/2677] Fix cannyEdgeDetector for CUDA when compiled with
 AF_WITH_FAST_MATH option

---
 src/api/c/canny.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index ae1fa8add9..ef3ad029cd 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -93,7 +93,6 @@ Array<float> otsuThreshold(const Array<float>& in, const unsigned NUM_BINS,
     seqBegin[0] = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
     seqRest[0]  = af_make_seq(0, static_cast<double>(hDims[0] - 1), 1);
 
-    Array<float> TWOS   = createValueArray<float>(oDims, 2.0f);
     Array<float> UnitP  = createValueArray<float>(oDims, 1.0f);
     Array<float> histf  = cast<float, uint>(hist);
     Array<float> totals = createValueArray<float>(hDims, inDims[0] * inDims[1]);
@@ -126,7 +125,7 @@ Array<float> otsuThreshold(const Array<float>& in, const unsigned NUM_BINS,
         auto muL   = arithOp<float, af_div_t>(_muL, qL, oDims);
         auto muH   = arithOp<float, af_div_t>(_muH, qH, oDims);
         auto diff  = arithOp<float, af_sub_t>(muL, muH, oDims);
-        auto sqrd  = arithOp<float, af_pow_t>(diff, TWOS, oDims);
+        auto sqrd  = arithOp<float, af_mul_t>(diff, diff, oDims);
         auto op2   = createSubArray(qLqH, sliceIndex, false);
         auto sigma = arithOp<float, af_mul_t>(sqrd, op2, oDims);
 

From 24f426273c3e4b87ef5c3e05beefa945e78bc3dc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 24 May 2023 15:58:05 -0400
Subject: [PATCH 2509/2677] Fix const correctness in oneAPI kernels

---
 src/backend/oneapi/kernel/assign.hpp       |  2 +-
 src/backend/oneapi/kernel/ireduce.hpp      | 14 +++++++-------
 src/backend/oneapi/kernel/lu_split.hpp     | 12 ++++++------
 src/backend/oneapi/kernel/memcopy.hpp      |  2 +-
 src/backend/oneapi/kernel/reduce_dim.hpp   |  8 ++++----
 src/backend/oneapi/kernel/reduce_first.hpp |  2 +-
 src/backend/oneapi/kernel/select.hpp       | 15 ++++++++-------
 src/backend/oneapi/kernel/transpose.hpp    |  3 ++-
 src/backend/oneapi/kernel/wrap.hpp         |  4 ++--
 src/backend/oneapi/kernel/wrap_dilated.hpp |  4 ++--
 10 files changed, 34 insertions(+), 32 deletions(-)

diff --git a/src/backend/oneapi/kernel/assign.hpp b/src/backend/oneapi/kernel/assign.hpp
index 5e3ef6c666..1b69827d18 100644
--- a/src/backend/oneapi/kernel/assign.hpp
+++ b/src/backend/oneapi/kernel/assign.hpp
@@ -88,7 +88,7 @@ class assignKernel {
                 p_.strds[3] *
                 trimIndex(s3 ? gw + p_.offs[3] : p_.ptr[3][gw], oInfo_.dims[3]);
 
-            T* iptr = in_.get_pointer();
+            const T* iptr = in_.get_pointer();
             // offset input and output pointers
             const T* src =
                 iptr + (gx * iInfo_.strides[0] + gy * iInfo_.strides[1] +
diff --git a/src/backend/oneapi/kernel/ireduce.hpp b/src/backend/oneapi/kernel/ireduce.hpp
index 2366264ea4..5f8f96bfc8 100644
--- a/src/backend/oneapi/kernel/ireduce.hpp
+++ b/src/backend/oneapi/kernel/ireduce.hpp
@@ -93,7 +93,7 @@ class ireduceDimKernelSMEM {
             (ids[0] < rlenInfo_.dims[0]) && (ids[1] < rlenInfo_.dims[1]) &&
             (ids[2] < rlenInfo_.dims[2]) && (ids[3] < rlenInfo_.dims[3]);
         const bool rlen_nonnull = rlenValid_;
-        uint *const rlenptr =
+        const uint *rlenptr =
             (rlen_nonnull && rlen_valid)
                 ? rlen_.get_pointer() + ids[3] * rlenInfo_.strides[3] +
                       ids[2] * rlenInfo_.strides[2] +
@@ -105,10 +105,10 @@ class ireduceDimKernelSMEM {
         // add thread offset for reduced dim for inputs
         ids[dim] = ids[dim] * g.get_local_range(1) + lidy;
 
-        T *iptr = in_.get_pointer() + ids[3] * iInfo_.strides[3] +
-                  ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] +
-                  ids[0] + iInfo_.offset;
-        uint *ilptr;
+        const T *iptr = in_.get_pointer() + ids[3] * iInfo_.strides[3] +
+                        ids[2] * iInfo_.strides[2] +
+                        ids[1] * iInfo_.strides[1] + ids[0] + iInfo_.offset;
+        const uint *ilptr;
         if (!is_first) {
             ilptr = iloc_.get_pointer() + ids[3] * iInfo_.strides[3] +
                     ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] +
@@ -371,7 +371,7 @@ class ireduceFirstKernelSMEM {
         const uint xid = groupId_x * g.get_local_range(0) * repeat_ + lidx;
         const uint yid = groupId_y * g.get_local_range(1) + lidy;
 
-        T *const iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
+        const T *iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
                         zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
                         iInfo_.offset;
 
@@ -385,7 +385,7 @@ class ireduceFirstKernelSMEM {
                                yid * rlenInfo_.strides[1] + rlenInfo_.offset
                          : nullptr;
 
-        uint *ilptr;
+        const uint *ilptr;
         if (!is_first) {
             ilptr = iloc_.get_pointer() + wid * iInfo_.strides[3] +
                     zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
diff --git a/src/backend/oneapi/kernel/lu_split.hpp b/src/backend/oneapi/kernel/lu_split.hpp
index fb69001ebc..6d52fb3835 100644
--- a/src/backend/oneapi/kernel/lu_split.hpp
+++ b/src/backend/oneapi/kernel/lu_split.hpp
@@ -51,9 +51,9 @@ class luSplitKernel {
         const int incy = groupsPerMatY_ * g.get_local_range(1);
         const int incx = groupsPerMatX_ * g.get_local_range(0);
 
-        T *d_l = lower_.get_pointer();
-        T *d_u = upper_.get_pointer();
-        T *d_i = in_.get_pointer();
+        T *d_l       = lower_.get_pointer();
+        T *d_u       = upper_.get_pointer();
+        const T *d_i = in_.get_pointer();
 
         if (oz < iInfo_.dims[2] && ow < iInfo_.dims[3]) {
             d_i = d_i + oz * iInfo_.strides[2] + ow * iInfo_.strides[3];
@@ -61,9 +61,9 @@ class luSplitKernel {
             d_u = d_u + oz * uInfo_.strides[2] + ow * uInfo_.strides[3];
 
             for (int oy = yy; oy < iInfo_.dims[1]; oy += incy) {
-                T *Yd_i = d_i + oy * iInfo_.strides[1];
-                T *Yd_l = d_l + oy * lInfo_.strides[1];
-                T *Yd_u = d_u + oy * uInfo_.strides[1];
+                const T *Yd_i = d_i + oy * iInfo_.strides[1];
+                T *Yd_l       = d_l + oy * lInfo_.strides[1];
+                T *Yd_u       = d_u + oy * uInfo_.strides[1];
                 for (int ox = xx; ox < iInfo_.dims[0]; ox += incx) {
                     if (ox > oy) {
                         if (same_dims || oy < lInfo_.dims[1])
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index dea4fd000c..b400d04673 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -81,7 +81,7 @@ class memCopy {
         const int id0        = group_id_0 * gg.get_local_range(0) + lid0;
         const int id1        = group_id_1 * gg.get_local_range(1) + lid1;
 
-        T *iptr = in_.get_pointer();
+        const T *iptr = in_.get_pointer();
         // FIXME: Do more work per work group
 
         T *optr = out_.get_pointer();
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index 6b51801fa7..b1d3d81648 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -65,14 +65,14 @@ class reduceDimKernelSMEM {
         uint ids[4] = {xid, yid, zid, wid};
         using sycl::global_ptr;
 
-        global_ptr<data_t<To>> optr =
-            out_.get_pointer() + ids[3] * oInfo_.strides[3] +
-            ids[2] * oInfo_.strides[2] + ids[1] * oInfo_.strides[1] + ids[0];
+        data_t<To> *optr = out_.get_pointer() + ids[3] * oInfo_.strides[3] +
+                           ids[2] * oInfo_.strides[2] +
+                           ids[1] * oInfo_.strides[1] + ids[0];
 
         const uint groupIdx_dim = ids[dim];
         ids[dim]                = ids[dim] * g.get_local_range(1) + lidy;
 
-        global_ptr<data_t<Ti>> iptr =
+        const data_t<Ti> *iptr =
             in_.get_pointer() + ids[3] * iInfo_.strides[3] +
             ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] + ids[0];
 
diff --git a/src/backend/oneapi/kernel/reduce_first.hpp b/src/backend/oneapi/kernel/reduce_first.hpp
index f105d63671..152120648b 100644
--- a/src/backend/oneapi/kernel/reduce_first.hpp
+++ b/src/backend/oneapi/kernel/reduce_first.hpp
@@ -68,7 +68,7 @@ class reduceFirstKernelSMEM {
         common::Binary<compute_t<To>, op> reduce;
         common::Transform<Ti, compute_t<To>, op> transform;
 
-        Ti *const iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
+        const Ti *iptr = in_.get_pointer() + wid * iInfo_.strides[3] +
                          zid * iInfo_.strides[2] + yid * iInfo_.strides[1] +
                          iInfo_.offset;
 
diff --git a/src/backend/oneapi/kernel/select.hpp b/src/backend/oneapi/kernel/select.hpp
index b5a6ae5954..06db45ad79 100644
--- a/src/backend/oneapi/kernel/select.hpp
+++ b/src/backend/oneapi/kernel/select.hpp
@@ -59,9 +59,9 @@ class selectKernelCreateKernel {
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
 
-        char *cptr = cptr__.get_pointer() + cinfo_.offset;
-        T *aptr    = aptr__.get_pointer() + ainfo_.offset;
-        T *bptr    = bptr__.get_pointer() + binfo_.offset;
+        const char *cptr = cptr__.get_pointer() + cinfo_.offset;
+        const T *aptr    = aptr__.get_pointer() + ainfo_.offset;
+        const T *bptr    = bptr__.get_pointer() + binfo_.offset;
 
         const int idz = g.get_group_id(0) / groups_0_;
         const int idw = g.get_group_id(1) / groups_1_;
@@ -169,8 +169,8 @@ class selectScalarCreateKernel {
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
 
-        char *cptr = cptr__.get_pointer() + cinfo_.offset;
-        T *aptr    = aptr__.get_pointer() + ainfo_.offset;
+        const char *cptr = cptr__.get_pointer() + cinfo_.offset;
+        const T *aptr    = aptr__.get_pointer() + ainfo_.offset;
 
         const int idz = g.get_group_id(0) / groups_0_;
         const int idw = g.get_group_id(1) / groups_1_;
@@ -185,7 +185,8 @@ class selectScalarCreateKernel {
                         idy * oinfo_.strides[1];
 
         int ids[] = {idx0, idy, idz, idw};
-        optr_.get_pointer() += off;
+        T *optr   = optr_.get_pointer();
+        optr += off;
         aptr += getOffset(ainfo_.dims, ainfo_.strides, oinfo_.dims, ids);
         cptr += getOffset(cinfo_.dims, cinfo_.strides, oinfo_.dims, ids);
 
@@ -196,7 +197,7 @@ class selectScalarCreateKernel {
 
         for (int idx = idx0; idx < oinfo_.dims[0];
              idx += g.get_local_range(0) * groups_0_) {
-            optr_.get_pointer()[idx] = (cptr[idx] ^ flip_) ? aptr[idx] : b_;
+            optr[idx] = (cptr[idx] ^ flip_) ? aptr[idx] : b_;
         }
     }
 
diff --git a/src/backend/oneapi/kernel/transpose.hpp b/src/backend/oneapi/kernel/transpose.hpp
index bf7c7a874b..2752111534 100644
--- a/src/backend/oneapi/kernel/transpose.hpp
+++ b/src/backend/oneapi/kernel/transpose.hpp
@@ -95,7 +95,8 @@ class transposeKernel {
 
         // offset in_ and out_ based on batch id
         // also add the subBuffer offsets
-        T *iDataPtr = iData_.get_pointer(), *oDataPtr = oData_.get_pointer();
+        const T *iDataPtr = iData_.get_pointer();
+        T *oDataPtr       = oData_.get_pointer();
         iDataPtr += batchId_x * in_.strides[2] + batchId_y * in_.strides[3] +
                     in_.offset;
         oDataPtr += batchId_x * out_.strides[2] + batchId_y * out_.strides[3] +
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
index ef8d2eba21..b5e5226035 100644
--- a/src/backend/oneapi/kernel/wrap.hpp
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -63,8 +63,8 @@ class wrapCreateKernel {
 
         T *optr = optrAcc_.get_pointer() + idx2 * out_.strides[2] +
                   idx3 * out_.strides[3] + out_.offset;
-        T *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
-                  idx3 * in_.strides[3] + in_.offset;
+        const T *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
+                        idx3 * in_.strides[3] + in_.offset;
 
         if (oidx0 >= out_.dims[0] || oidx1 >= out_.dims[1]) return;
 
diff --git a/src/backend/oneapi/kernel/wrap_dilated.hpp b/src/backend/oneapi/kernel/wrap_dilated.hpp
index f8f9614d01..41112fbce4 100644
--- a/src/backend/oneapi/kernel/wrap_dilated.hpp
+++ b/src/backend/oneapi/kernel/wrap_dilated.hpp
@@ -66,8 +66,8 @@ class wrapDilatedCreateKernel {
 
         data_t<T> *optr = optrAcc_.get_pointer() + idx2 * out_.strides[2] +
                           idx3 * out_.strides[3];
-        data_t<T> *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
-                          idx3 * in_.strides[3] + in_.offset;
+        const data_t<T> *iptr = iptrAcc_.get_pointer() + idx2 * in_.strides[2] +
+                                idx3 * in_.strides[3] + in_.offset;
 
         if (oidx0 >= out_.dims[0] || oidx1 >= out_.dims[1]) return;
 

From 448a103d3d55ca4177162fffb27f75f0a3990bd1 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 24 May 2023 15:58:56 -0400
Subject: [PATCH 2510/2677] Add type checks in pinverse tests

---
 test/pinverse.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/test/pinverse.cpp b/test/pinverse.cpp
index 7258558bc2..13b2151836 100644
--- a/test/pinverse.cpp
+++ b/test/pinverse.cpp
@@ -124,6 +124,7 @@ TYPED_TEST_SUITE(Pinverse, TestTypes);
 // Test Moore-Penrose conditions in the following first 4 tests
 // See https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Definition
 TYPED_TEST(Pinverse, AApinvA_A) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array in = readTestInput<TypeParam>(
         string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
@@ -132,6 +133,7 @@ TYPED_TEST(Pinverse, AApinvA_A) {
 }
 
 TYPED_TEST(Pinverse, ApinvAApinv_Apinv) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array in = readTestInput<TypeParam>(
         string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
@@ -140,6 +142,7 @@ TYPED_TEST(Pinverse, ApinvAApinv_Apinv) {
 }
 
 TYPED_TEST(Pinverse, AApinv_IsHermitian) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array in = readTestInput<TypeParam>(
         string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
@@ -149,6 +152,7 @@ TYPED_TEST(Pinverse, AApinv_IsHermitian) {
 }
 
 TYPED_TEST(Pinverse, ApinvA_IsHermitian) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array in = readTestInput<TypeParam>(
         string(TEST_DIR "/pinverse/pinverse10x8.test"));
     array inpinv = pinverse(in);
@@ -158,6 +162,7 @@ TYPED_TEST(Pinverse, ApinvA_IsHermitian) {
 }
 
 TYPED_TEST(Pinverse, Large) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array in = readTestInput<TypeParam>(
         string(TEST_DIR "/pinverse/pinv_640x480_inputs.test"));
     array inpinv = pinverse(in);
@@ -166,6 +171,7 @@ TYPED_TEST(Pinverse, Large) {
 }
 
 TYPED_TEST(Pinverse, LargeTall) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array in = readTestInput<TypeParam>(
                    string(TEST_DIR "/pinverse/pinv_640x480_inputs.test"))
                    .T();
@@ -227,6 +233,7 @@ TEST(Pinverse, SmallSigValExistsFloat) {
 }
 
 TEST(Pinverse, SmallSigValExistsDouble) {
+    SUPPORTED_TYPE_CHECK(double);
     array in =
         readTestInput<double>(string(TEST_DIR "/pinverse/pinverse10x8.test"));
     const dim_t dim0 = in.dims(0);

From cc51889ca73d6350ff15fb530f9866d94545d20e Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Thu, 25 May 2023 20:53:33 +0200
Subject: [PATCH 2511/2677] speedup complexNorm when compiled with
 AF_WITH_FAST_MATH flag

---
 src/api/c/deconvolution.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index d5327d1efe..f579eeadf8 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -68,9 +68,8 @@ const dim_t GREATEST_PRIME_FACTOR = 7;
 
 template<typename T, typename CT>
 Array<T> complexNorm(const Array<CT>& input) {
-    auto mag  = detail::abs<T, CT>(input);
-    auto TWOS = createValueArray(input.dims(), scalar<T>(2));
-    return arithOp<T, af_pow_t>(mag, TWOS, input.dims());
+    auto mag = detail::abs<T, CT>(input);
+    return arithOp<T, af_mul_t>(mag, mag, input.dims());
 }
 
 std::vector<af_seq> calcPadInfo(dim4& inLPad, dim4& psfLPad, dim4& inUPad,

From b31d8c68537e0335386ae3838737296577fb9729 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 24 May 2023 23:36:18 -0400
Subject: [PATCH 2512/2677] fix offset stride in meanDimKernel

---
 src/backend/oneapi/kernel/mean.hpp | 54 +++++++++++++-----------------
 1 file changed, 23 insertions(+), 31 deletions(-)

diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index ef98cb0954..7c0f6f3243 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -29,17 +29,6 @@
 namespace arrayfire {
 namespace oneapi {
 
-/*
-TODO: port half
-__device__ auto operator*(float lhs, __half rhs) -> __half {
-    return __float2half(lhs * __half2float(rhs));
-}
-
-__device__ auto operator/(__half lhs, float rhs) -> __half {
-    return __float2half(__half2float(lhs) / rhs);
-}
-*/
-
 namespace kernel {
 
 template<typename To, typename Tw>
@@ -101,7 +90,7 @@ class meanDimKernelSMEM {
         To *optr       = out_.get_pointer();
 
         uint ooffset = ids[3] * oInfo_.strides[3] + ids[2] * oInfo_.strides[2] +
-                       ids[1] * oInfo_.strides[1] + ids[0];
+                       ids[1] * oInfo_.strides[1] + ids[0] + oInfo_.offset;
         // There is only one element per block for out
         // There are blockDim.y elements per block for in
         // Hence increment ids[dim] just after offseting out and before
@@ -112,11 +101,11 @@ class meanDimKernelSMEM {
         ids[dim]                = ids[dim] * g.get_local_range(1) + lidy;
 
         uint ioffset = ids[3] * iInfo_.strides[3] + ids[2] * iInfo_.strides[2] +
-                       ids[1] * iInfo_.strides[1] + ids[0];
+                       ids[1] * iInfo_.strides[1] + ids[0] + iInfo_.offset;
         iptr += ioffset;
 
-        const Tw *iwptr;
-        Tw *owptr;
+        const Tw *iwptr = nullptr;
+        Tw *owptr       = nullptr;
 
         if (output_weight_) owptr = owt_.get_pointer() + ooffset;
         if (input_weight_) iwptr = iwt_.get_pointer() + ioffset;
@@ -135,7 +124,7 @@ class meanDimKernelSMEM {
 
         if (is_valid && id_dim_in < iInfo_.dims[dim]) {
             val = transform(*iptr);
-            if (iwptr != NULL) {
+            if (iwptr) {
                 weight = *iwptr;
             } else {
                 weight = (Tw)1;
@@ -143,14 +132,14 @@ class meanDimKernelSMEM {
         }
 
         const uint id_dim_in_start =
-            id_dim_in + offset_dim_ * g.get_local_range(0);
+            id_dim_in + offset_dim_ * g.get_local_range(1);
 
         for (int id = id_dim_in_start; is_valid && (id < iInfo_.dims[dim]);
-             id += offset_dim_ * g.get_local_range(0)) {
-            iptr = iptr + offset_dim_ * g.get_local_range(0) * istride_dim;
+             id += offset_dim_ * g.get_local_range(1)) {
+            iptr = iptr + offset_dim_ * g.get_local_range(1) * istride_dim;
             if (input_weight_) {
                 iwptr =
-                    iwptr + offset_dim_ * g.get_local_range(0) * istride_dim;
+                    iwptr + offset_dim_ * g.get_local_range(1) * istride_dim;
                 stable_mean(&val, &weight, transform(*iptr),
                             compute_t<Tw>(*iwptr));
             } else {
@@ -358,19 +347,21 @@ class meanFirstKernelSMEM {
         To *optr       = out_.get_pointer();
 
         iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
-                yid * iInfo_.strides[1];
+                yid * iInfo_.strides[1] + iInfo_.offset;
         optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] +
-                yid * oInfo_.strides[1];
+                yid * oInfo_.strides[1] + oInfo_.offset;
 
-        const Tw *iwptr;
-        Tw *owptr;
+        const Tw *iwptr = nullptr;
+        Tw *owptr       = nullptr;
         if (input_weight_)
             iwptr = iwt_.get_pointer() + wid * iwInfo_.strides[3] +
-                    zid * iwInfo_.strides[2] + yid * iwInfo_.strides[1];
+                    zid * iwInfo_.strides[2] + yid * iwInfo_.strides[1] +
+                    iwInfo_.offset;
 
         if (output_weight_)
-            owptr = owt_.get_pointer() + wid * oInfo_.strides[3] +
-                    zid * oInfo_.strides[2] + yid * oInfo_.strides[1];
+            owptr = owt_.get_pointer() + wid * owInfo_.strides[3] +
+                    zid * owInfo_.strides[2] + yid * owInfo_.strides[1] +
+                    owInfo_.offset;
 
         bool cond = (yid < iInfo_.dims[1] && zid < iInfo_.dims[2] &&
                      wid < iInfo_.dims[3]);
@@ -485,9 +476,9 @@ class meanFirstKernelSMEM {
 };
 
 template<typename Ti, typename Tw, typename To>
-sycl::event mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
-                                Param<Tw> iwt, const uint groups_x,
-                                const uint groups_y, const uint threads_x) {
+void mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
+                         Param<Tw> iwt, const uint groups_x,
+                         const uint groups_y, const uint threads_x) {
     sycl::range<2> local(threads_x, THREADS_PER_BLOCK / threads_x);
     sycl::range<2> global(groups_x * in.info.dims[2] * local[0],
                           groups_y * in.info.dims[3] * local[1]);
@@ -496,7 +487,7 @@ sycl::event mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
 
     auto empty  = memAlloc<Tw>(1);
     auto oempty = memAlloc<Tw>(1);
-    return getQueue().submit([&](sycl::handler &h) {
+    getQueue().submit([&](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
         read_accessor<Ti> in_acc{*in.data, h};
 
@@ -521,6 +512,7 @@ sycl::event mean_first_launcher(Param<To> out, Param<Tw> owt, Param<Ti> in,
                 iwt.info, threads_x, groups_x, groups_y, repeat, s_val, s_idx,
                 input_weight, output_weight));
     });
+    ONEAPI_DEBUG_FINISH(getQueue());
 }
 
 template<typename Ti, typename Tw, typename To>

From d734fd1f7c420c9f2281daf69b9fab5d03e6ee52 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Wed, 31 May 2023 15:32:15 -0400
Subject: [PATCH 2513/2677] fftconvolve oneapi port (includes fft fix) (#3426)

* fftconvolve oneapi port
* fix fftconvolve reorder

---------

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/CMakeLists.txt             |   5 +
 src/backend/oneapi/fft.cpp                    |  12 +-
 src/backend/oneapi/fftconvolve.cpp            |  77 ++++++-
 .../oneapi/kernel/fftconvolve_common.hpp      |  74 +++++++
 .../oneapi/kernel/fftconvolve_multiply.hpp    | 155 ++++++++++++++
 .../oneapi/kernel/fftconvolve_pack.hpp        | 146 +++++++++++++
 src/backend/oneapi/kernel/fftconvolve_pad.hpp | 129 ++++++++++++
 .../oneapi/kernel/fftconvolve_reorder.hpp     | 193 ++++++++++++++++++
 8 files changed, 784 insertions(+), 7 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/fftconvolve_common.hpp
 create mode 100644 src/backend/oneapi/kernel/fftconvolve_multiply.hpp
 create mode 100644 src/backend/oneapi/kernel/fftconvolve_pack.hpp
 create mode 100644 src/backend/oneapi/kernel/fftconvolve_pad.hpp
 create mode 100644 src/backend/oneapi/kernel/fftconvolve_reorder.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 46e20c88d4..b13de94f95 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -218,6 +218,11 @@ target_sources(afoneapi
     kernel/convolve_separable.cpp
     kernel/diagonal.hpp
     kernel/diff.hpp
+    kernel/fftconvolve_common.hpp
+    kernel/fftconvolve_multiply.hpp
+    kernel/fftconvolve_pack.hpp
+    kernel/fftconvolve_pad.hpp
+    kernel/fftconvolve_reorder.hpp
     kernel/histogram.hpp
     kernel/iir.hpp
     kernel/identity.hpp
diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index eff8770bfc..b32c801423 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -50,9 +50,9 @@ void fft_inplace(Array<T> &in, const int rank, const bool direction) {
 
     auto desc = [rank, &idims]() {
         if (rank == 1) return desc_ty(idims[0]);
-        if (rank == 2) return desc_ty({idims[0], idims[1]});
-        if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
-        return desc_ty({idims[0], idims[1], idims[2], idims[3]});
+        if (rank == 2) return desc_ty({idims[1], idims[0]});
+        if (rank == 3) return desc_ty({idims[2], idims[1], idims[0]});
+        return desc_ty({idims[3], idims[2], idims[1], idims[0]});
     }();
 
     desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT, DFTI_INPLACE);
@@ -139,9 +139,9 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
 
     auto desc = [rank, &odims]() {
         if (rank == 1) return desc_ty(odims[0]);
-        if (rank == 2) return desc_ty({odims[0], odims[1]});
-        if (rank == 3) return desc_ty({odims[0], odims[1], odims[2]});
-        return desc_ty({odims[0], odims[1], odims[2], odims[3]});
+        if (rank == 2) return desc_ty({odims[1], odims[0]});
+        if (rank == 3) return desc_ty({odims[2], odims[1], odims[0]});
+        return desc_ty({odims[3], odims[2], odims[1], odims[0]});
     }();
 
     desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
diff --git a/src/backend/oneapi/fftconvolve.cpp b/src/backend/oneapi/fftconvolve.cpp
index c4aea5689c..de96d94c99 100644
--- a/src/backend/oneapi/fftconvolve.cpp
+++ b/src/backend/oneapi/fftconvolve.cpp
@@ -15,6 +15,12 @@
 #include <fft.hpp>
 #include <af/dim4.hpp>
 
+#include <kernel/fftconvolve_common.hpp>
+#include <kernel/fftconvolve_multiply.hpp>
+#include <kernel/fftconvolve_pack.hpp>
+#include <kernel/fftconvolve_pad.hpp>
+#include <kernel/fftconvolve_reorder.hpp>
+
 #include <cmath>
 #include <type_traits>
 #include <vector>
@@ -59,9 +65,78 @@ dim4 calcPackedSize(Array<T> const& i1, Array<T> const& i2, const dim_t rank) {
 template<typename T>
 Array<T> fftconvolve(Array<T> const& signal, Array<T> const& filter,
                      const bool expand, AF_BATCH_KIND kind, const int rank) {
-    ONEAPI_NOT_SUPPORTED("");
+    using convT = typename conditional<is_integral<T>::value ||
+                                           is_same<T, float>::value ||
+                                           is_same<T, cfloat>::value,
+                                       float, double>::type;
+    using cT    = typename conditional<is_same<convT, float>::value, cfloat,
+                                    cdouble>::type;
+
+    const dim4& sDims = signal.dims();
+    const dim4& fDims = filter.dims();
+
     dim4 oDims(1);
+    if (expand) {
+        for (int d = 0; d < AF_MAX_DIMS; ++d) {
+            if (kind == AF_BATCH_NONE || kind == AF_BATCH_RHS) {
+                oDims[d] = sDims[d] + fDims[d] - 1;
+            } else {
+                oDims[d] = (d < rank ? sDims[d] + fDims[d] - 1 : sDims[d]);
+            }
+        }
+    } else {
+        oDims = sDims;
+        if (kind == AF_BATCH_RHS) {
+            for (int i = rank; i < AF_MAX_DIMS; ++i) { oDims[i] = fDims[i]; }
+        }
+    }
+
+    const dim4 pDims = calcPackedSize<T>(signal, filter, rank);
+    Array<cT> packed = createEmptyArray<cT>(pDims);
+
+    kernel::packDataHelper<cT, T>(packed, signal, filter, rank, kind);
+    kernel::padDataHelper<cT, T>(packed, signal, filter, rank, kind);
+
+    fft_inplace<cT>(packed, rank, true);
+
+    kernel::complexMultiplyHelper<cT, T>(packed, signal, filter, rank, kind);
+
+    // Compute inverse FFT only on complex-multiplied data
+    if (kind == AF_BATCH_RHS) {
+        vector<af_seq> seqs;
+        for (int k = 0; k < AF_MAX_DIMS; k++) {
+            if (k < rank) {
+                seqs.push_back({0., static_cast<double>(pDims[k] - 1), 1.});
+            } else if (k == rank) {
+                seqs.push_back({1., static_cast<double>(pDims[k] - 1), 1.});
+            } else {
+                seqs.push_back({0., 0., 1.});
+            }
+        }
+
+        Array<cT> subPacked = createSubArray<cT>(packed, seqs);
+        fft_inplace<cT>(subPacked, rank, false);
+    } else {
+        vector<af_seq> seqs;
+        for (int k = 0; k < AF_MAX_DIMS; k++) {
+            if (k < rank) {
+                seqs.push_back({0., static_cast<double>(pDims[k]) - 1, 1.});
+            } else if (k == rank) {
+                seqs.push_back({0., static_cast<double>(pDims[k] - 2), 1.});
+            } else {
+                seqs.push_back({0., 0., 1.});
+            }
+        }
+
+        Array<cT> subPacked = createSubArray<cT>(packed, seqs);
+        fft_inplace<cT>(subPacked, rank, false);
+    }
+
     Array<T> out = createEmptyArray<T>(oDims);
+
+    kernel::reorderOutputHelper<T, cT>(out, packed, signal, filter, rank, kind,
+                                       expand);
+
     return out;
 }
 
diff --git a/src/backend/oneapi/kernel/fftconvolve_common.hpp b/src/backend/oneapi/kernel/fftconvolve_common.hpp
new file mode 100644
index 0000000000..6caf9923d2
--- /dev/null
+++ b/src/backend/oneapi/kernel/fftconvolve_common.hpp
@@ -0,0 +1,74 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
+#include <af/defines.h>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+constexpr int THREADS = 256;
+
+template<typename T, typename convT>
+void calcParamSizes(Param<T>& sig_tmp, Param<T>& filter_tmp,
+                    Param<convT>& packed, Param<T>& sig, Param<T>& filter,
+                    const int rank, AF_BATCH_KIND kind) {
+    sig_tmp.info.dims[0] = filter_tmp.info.dims[0] = packed.info.dims[0];
+    sig_tmp.info.strides[0] = filter_tmp.info.strides[0] = 1;
+
+    for (int k = 1; k < 4; k++) {
+        if (k < rank) {
+            sig_tmp.info.dims[k]    = packed.info.dims[k];
+            filter_tmp.info.dims[k] = packed.info.dims[k];
+        } else {
+            sig_tmp.info.dims[k]    = sig.info.dims[k];
+            filter_tmp.info.dims[k] = filter.info.dims[k];
+        }
+
+        sig_tmp.info.strides[k] =
+            sig_tmp.info.strides[k - 1] * sig_tmp.info.dims[k - 1];
+        filter_tmp.info.strides[k] =
+            filter_tmp.info.strides[k - 1] * filter_tmp.info.dims[k - 1];
+    }
+
+    // NOTE: The OpenCL implementation on which this oneAPI port is
+    // based treated the incoming `packed` buffer as a string of real
+    // scalars instead of complex numbers. OpenCL accomplished this
+    // with the hack depicted in the trailing two lines. This note
+    // remains here in an explanation of SYCL buffer reinterpret's in
+    // fftconvolve kernel invocations.
+
+    // sig_tmp.data    = packed.data;
+    // filter_tmp.data = packed.data;
+
+    // Calculate memory offsets for packed signal and filter
+    if (kind == AF_BATCH_RHS) {
+        filter_tmp.info.offset = 0;
+        sig_tmp.info.offset =
+            filter_tmp.info.strides[3] * filter_tmp.info.dims[3] * 2;
+    } else {
+        sig_tmp.info.offset = 0;
+        filter_tmp.info.offset =
+            sig_tmp.info.strides[3] * sig_tmp.info.dims[3] * 2;
+    }
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/fftconvolve_multiply.hpp b/src/backend/oneapi/kernel/fftconvolve_multiply.hpp
new file mode 100644
index 0000000000..e8968f6d0d
--- /dev/null
+++ b/src/backend/oneapi/kernel/fftconvolve_multiply.hpp
@@ -0,0 +1,155 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <af/defines.h>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class fftconvolve_multiplyCreateKernel {
+   public:
+    fftconvolve_multiplyCreateKernel(write_accessor<T> d_out, KParam oInfo,
+                                     read_accessor<T> d_in1, KParam i1Info,
+                                     read_accessor<T> d_in2, KParam i2Info,
+                                     const int nelem, const int kind)
+        : d_out_(d_out)
+        , oInfo_(oInfo)
+        , d_in1_(d_in1)
+        , i1Info_(i1Info)
+        , d_in2_(d_in2)
+        , i2Info_(i2Info)
+        , nelem_(nelem)
+        , kind_(kind) {}
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        const int t = it.get_global_id(0);
+
+        if (t >= nelem_) return;
+
+        if (kind_ == AF_BATCH_NONE || kind_ == AF_BATCH_SAME) {
+            // Complex multiply each signal to equivalent filter
+            const int ridx = t * 2;
+            const int iidx = t * 2 + 1;
+
+            T a = d_in1_[i1Info_.offset + ridx];
+            T b = d_in1_[i1Info_.offset + iidx];
+            T c = d_in2_[i2Info_.offset + ridx];
+            T d = d_in2_[i2Info_.offset + iidx];
+
+            d_out_[oInfo_.offset + ridx] = a * c - b * d;
+            d_out_[oInfo_.offset + iidx] = a * d + b * c;
+        } else if (kind_ == AF_BATCH_LHS) {
+            // Complex multiply all signals to filter
+            const int ridx1 = t * 2;
+            const int iidx1 = t * 2 + 1;
+
+            // Treating complex output array as real-only array,
+            // thus, multiply strides by 2
+            const int ridx2 =
+                ridx1 % (i2Info_.strides[3] * i2Info_.dims[3] * 2);
+            const int iidx2 =
+                iidx1 % (i2Info_.strides[3] * i2Info_.dims[3] * 2);
+
+            T a = d_in1_[i1Info_.offset + ridx1];
+            T b = d_in1_[i1Info_.offset + iidx1];
+            T c = d_in2_[i2Info_.offset + ridx2];
+            T d = d_in2_[i2Info_.offset + iidx2];
+
+            d_out_[oInfo_.offset + ridx1] = a * c - b * d;
+            d_out_[oInfo_.offset + iidx1] = a * d + b * c;
+        } else if (kind_ == AF_BATCH_RHS) {
+            // Complex multiply signal to all filters
+            const int ridx2 = t * 2;
+            const int iidx2 = t * 2 + 1;
+
+            // Treating complex output array as real-only array,
+            // thus, multiply strides by 2
+            const int ridx1 =
+                ridx2 % (i1Info_.strides[3] * i1Info_.dims[3] * 2);
+            const int iidx1 =
+                iidx2 % (i1Info_.strides[3] * i1Info_.dims[3] * 2);
+
+            T a = d_in1_[i1Info_.offset + ridx1];
+            T b = d_in1_[i1Info_.offset + iidx1];
+            T c = d_in2_[i2Info_.offset + ridx2];
+            T d = d_in2_[i2Info_.offset + iidx2];
+
+            d_out_[oInfo_.offset + ridx2] = a * c - b * d;
+            d_out_[oInfo_.offset + iidx2] = a * d + b * c;
+        }
+    }
+
+   private:
+    write_accessor<T> d_out_;
+    KParam oInfo_;
+    read_accessor<T> d_in1_;
+    KParam i1Info_;
+    read_accessor<T> d_in2_;
+    KParam i2Info_;
+    const int nelem_;
+    const int kind_;
+};
+
+template<typename convT, typename T>
+void complexMultiplyHelper(Param<convT> packed, Param<T> sig, Param<T> filter,
+                           const int rank, AF_BATCH_KIND kind) {
+    Param<T> sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
+
+    int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
+    int filter_packed_elem =
+        filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+    int mul_elem = (sig_packed_elem < filter_packed_elem) ? filter_packed_elem
+                                                          : sig_packed_elem;
+    int blocks   = divup(mul_elem, THREADS);
+
+    auto local  = sycl::range(THREADS);
+    auto global = sycl::range(blocks * THREADS);
+
+    // Treat complex output as an array of scalars
+    using convScalarT      = typename convT::value_type;
+    auto packed_num_elem   = (*packed.data).get_range().size();
+    auto packed_tmp_buffer = (*packed.data)
+                                 .template reinterpret<convScalarT>(
+                                     sycl::range<1>{packed_num_elem * 2});
+    auto sig_tmp_buffer = (*packed.data)
+                              .template reinterpret<convScalarT>(
+                                  sycl::range<1>{packed_num_elem * 2});
+    auto filter_tmp_buffer = (*packed.data)
+                                 .template reinterpret<convScalarT>(
+                                     sycl::range<1>{packed_num_elem * 2});
+
+    getQueue().submit([&](auto &h) {
+        write_accessor<convScalarT> d_packed    = {packed_tmp_buffer, h};
+        read_accessor<convScalarT> d_sig_tmp    = {sig_tmp_buffer, h};
+        read_accessor<convScalarT> d_filter_tmp = {filter_tmp_buffer, h};
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            fftconvolve_multiplyCreateKernel<typename convT::value_type>(
+                d_packed, packed.info, d_sig_tmp, sig_tmp.info, d_filter_tmp,
+                filter_tmp.info, mul_elem, (int)kind));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/fftconvolve_pack.hpp b/src/backend/oneapi/kernel/fftconvolve_pack.hpp
new file mode 100644
index 0000000000..c6b04d5a43
--- /dev/null
+++ b/src/backend/oneapi/kernel/fftconvolve_pack.hpp
@@ -0,0 +1,146 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <af/defines.h>
+
+#include <string>
+#include <vector>
+
+#include <iostream>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename inputType, typename outputType>
+class fftconvolve_packCreateKernel {
+   public:
+    fftconvolve_packCreateKernel(write_accessor<outputType> d_out, KParam oInfo,
+                                 read_accessor<inputType> d_in, KParam iInfo,
+                                 const int di0_half, const int odd_di0)
+        : d_out_(d_out)
+        , oInfo_(oInfo)
+        , d_in_(d_in)
+        , iInfo_(iInfo)
+        , di0_half_(di0_half)
+        , odd_di0_(odd_di0) {}
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        const int t = it.get_global_id(0);
+
+        const int tMax = oInfo_.strides[3] * oInfo_.dims[3];
+
+        if (t >= tMax) return;
+
+        const int do0 = oInfo_.dims[0];
+        const int do1 = oInfo_.dims[1];
+        const int do2 = oInfo_.dims[2];
+
+        const int so1 = oInfo_.strides[1];
+        const int so2 = oInfo_.strides[2];
+        const int so3 = oInfo_.strides[3];
+
+        const int to0 = t % so1;
+        const int to1 = (t / so1) % do1;
+        const int to2 = (t / so2) % do2;
+        const int to3 = t / so3;
+
+        const int di0 = iInfo_.dims[0];
+        const int di1 = iInfo_.dims[1];
+        const int di2 = iInfo_.dims[2];
+
+        const int si1 = iInfo_.strides[1];
+        const int si2 = iInfo_.strides[2];
+        const int si3 = iInfo_.strides[3];
+
+        const int ti0 = to0;
+        const int ti1 = to1 * si1;
+        const int ti2 = to2 * si2;
+        const int ti3 = to3 * si3;
+
+        const int iidx1 = iInfo_.offset + ti3 + ti2 + ti1 + ti0;
+        const int iidx2 = iidx1 + di0_half_;
+
+        // Treating complex output array as real-only array,
+        // thus, multiply strides by 2
+        const int oidx1 = oInfo_.offset + to3 * so3 * 2 + to2 * so2 * 2 +
+                          to1 * so1 * 2 + to0 * 2;
+        const int oidx2 = oidx1 + 1;
+
+        if (to0 < di0_half_ && to1 < di1 && to2 < di2) {
+            d_out_[oidx1] = (outputType)d_in_[iidx1];
+            if (ti0 == di0_half_ - 1 && odd_di0_ == 1)
+                d_out_[oidx2] = (outputType)0;
+            else
+                d_out_[oidx2] = (outputType)d_in_[iidx2];
+        } else {
+            // Pad remaining elements with 0s
+            d_out_[oidx1] = (outputType)0;
+            d_out_[oidx2] = (outputType)0;
+        }
+    }
+
+   private:
+    write_accessor<outputType> d_out_;
+    KParam oInfo_;
+    read_accessor<inputType> d_in_;
+    KParam iInfo_;
+    const int di0_half_;
+    const int odd_di0_;
+};
+
+template<typename convT, typename T>
+void packDataHelper(Param<convT> packed, Param<T> sig, Param<T> filter,
+                    const int rank, AF_BATCH_KIND kind) {
+    Param<T> sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
+
+    int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
+    int filter_packed_elem =
+        filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+
+    // Number of packed complex elements in dimension 0
+    int sig_half_d0     = divup(sig.info.dims[0], 2);
+    int sig_half_d0_odd = sig.info.dims[0] % 2;
+
+    int blocks = divup(sig_packed_elem, THREADS);
+
+    // Locate features kernel sizes
+    auto local  = sycl::range(THREADS);
+    auto global = sycl::range(blocks * THREADS);
+
+    // Treat complex output as an array of scalars
+    using convScalarT    = typename convT::value_type;
+    auto packed_num_elem = (*packed.data).get_range().size();
+    auto sig_tmp_buffer  = (*packed.data)
+                              .template reinterpret<convScalarT>(
+                                  sycl::range<1>{packed_num_elem * 2});
+
+    getQueue().submit([&](auto &h) {
+        read_accessor<T> d_sig                = {*sig.data, h};
+        write_accessor<convScalarT> d_sig_tmp = {sig_tmp_buffer, h};
+        h.parallel_for(sycl::nd_range{global, local},
+                       fftconvolve_packCreateKernel<T, convScalarT>(
+                           d_sig_tmp, sig_tmp.info, d_sig, sig.info,
+                           sig_half_d0, sig_half_d0_odd));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/fftconvolve_pad.hpp b/src/backend/oneapi/kernel/fftconvolve_pad.hpp
new file mode 100644
index 0000000000..6276b1da72
--- /dev/null
+++ b/src/backend/oneapi/kernel/fftconvolve_pad.hpp
@@ -0,0 +1,129 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <af/defines.h>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename inputType, typename outputType>
+class fftconvolve_padCreateKernel {
+   public:
+    fftconvolve_padCreateKernel(write_accessor<outputType> d_out, KParam oInfo,
+                                read_accessor<inputType> d_in, KParam iInfo)
+        : d_out_(d_out), oInfo_(oInfo), d_in_(d_in), iInfo_(iInfo) {}
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        const int t = it.get_global_id(0);
+
+        const int tMax = oInfo_.strides[3] * oInfo_.dims[3];
+
+        if (t >= tMax) return;
+
+        const int do0 = oInfo_.dims[0];
+        const int do1 = oInfo_.dims[1];
+        const int do2 = oInfo_.dims[2];
+
+        const int so1 = oInfo_.strides[1];
+        const int so2 = oInfo_.strides[2];
+        const int so3 = oInfo_.strides[3];
+
+        const int to0 = t % so1;
+        const int to1 = (t / so1) % do1;
+        const int to2 = (t / so2) % do2;
+        const int to3 = (t / so3);
+
+        const int di0 = iInfo_.dims[0];
+        const int di1 = iInfo_.dims[1];
+        const int di2 = iInfo_.dims[2];
+        const int di3 = iInfo_.dims[3];
+
+        const int si1 = iInfo_.strides[1];
+        const int si2 = iInfo_.strides[2];
+        const int si3 = iInfo_.strides[3];
+
+        const int ti0 = to0;
+        const int ti1 = to1 * si1;
+        const int ti2 = to2 * si2;
+        const int ti3 = to3 * si3;
+
+        const int iidx = iInfo_.offset + ti3 + ti2 + ti1 + ti0;
+
+        const int oidx = oInfo_.offset + t * 2;
+
+        if (to0 < di0 && to1 < di1 && to2 < di2 && to3 < di3) {
+            // Copy input elements to real elements, set imaginary elements to 0
+            d_out_[oidx]     = (outputType)d_in_[iidx];
+            d_out_[oidx + 1] = (outputType)0;
+        } else {
+            // Pad remaining of the matrix to 0s
+            d_out_[oidx]     = (outputType)0;
+            d_out_[oidx + 1] = (outputType)0;
+        }
+    }
+
+   private:
+    write_accessor<outputType> d_out_;
+    KParam oInfo_;
+    read_accessor<inputType> d_in_;
+    KParam iInfo_;
+};
+
+template<typename convT, typename T>
+void padDataHelper(Param<convT> packed, Param<T> sig, Param<T> filter,
+                   const int rank, AF_BATCH_KIND kind) {
+    Param<T> sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
+
+    int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
+    int filter_packed_elem =
+        filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
+
+    // Number of packed complex elements in dimension 0
+    int sig_half_d0     = divup(sig.info.dims[0], 2);
+    int sig_half_d0_odd = sig.info.dims[0] % 2;
+
+    int blocks = divup(filter_packed_elem, THREADS);
+
+    // Locate features kernel sizes
+    auto local  = sycl::range(THREADS);
+    auto global = sycl::range(blocks * THREADS);
+
+    // Treat complex output as an array of scalars
+    using convScalarT      = typename convT::value_type;
+    auto packed_num_elem   = (*packed.data).get_range().size();
+    auto filter_tmp_buffer = (*packed.data)
+                                 .template reinterpret<convScalarT>(
+                                     sycl::range<1>{packed_num_elem * 2});
+
+    getQueue().submit([&](auto &h) {
+        read_accessor<T> d_filter = {*filter.data, h, sycl::read_only};
+        write_accessor<convScalarT> d_filter_tmp = {filter_tmp_buffer, h};
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            fftconvolve_padCreateKernel<T, convScalarT>(
+                d_filter_tmp, filter_tmp.info, d_filter, filter.info));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/fftconvolve_reorder.hpp b/src/backend/oneapi/kernel/fftconvolve_reorder.hpp
new file mode 100644
index 0000000000..ec71b43bae
--- /dev/null
+++ b/src/backend/oneapi/kernel/fftconvolve_reorder.hpp
@@ -0,0 +1,193 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <af/defines.h>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T, typename convScalarT>
+class fftconvolve_reorderCreateKernel {
+   public:
+    fftconvolve_reorderCreateKernel(write_accessor<T> d_out, KParam oInfo,
+                                    read_accessor<convScalarT> d_in,
+                                    KParam iInfo, KParam fInfo,
+                                    const int half_di0, const int baseDim,
+                                    const int fftScale, const bool EXPAND,
+                                    const bool ROUND_OUT)
+        : d_out_(d_out)
+        , oInfo_(oInfo)
+        , d_in_(d_in)
+        , iInfo_(iInfo)
+        , fInfo_(fInfo)
+        , half_di0_(half_di0)
+        , baseDim_(baseDim)
+        , fftScale_(fftScale)
+        , EXPAND_(EXPAND)
+        , ROUND_OUT_(ROUND_OUT) {}
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        const int t = it.get_global_id(0);
+
+        const int tMax = oInfo_.strides[3] * oInfo_.dims[3];
+
+        if (t >= tMax) return;
+
+        const int do0 = oInfo_.dims[0];
+        const int do1 = oInfo_.dims[1];
+        const int do2 = oInfo_.dims[2];
+
+        const int so1 = oInfo_.strides[1];
+        const int so2 = oInfo_.strides[2];
+        const int so3 = oInfo_.strides[3];
+
+        // Treating complex input array as real-only array,
+        // thus, multiply dimension 0 and strides by 2
+        const int di0 = iInfo_.dims[0] * 2;
+        const int di1 = iInfo_.dims[1];
+        const int di2 = iInfo_.dims[2];
+
+        const int si1 = iInfo_.strides[1] * 2;
+        const int si2 = iInfo_.strides[2] * 2;
+        const int si3 = iInfo_.strides[3] * 2;
+
+        const int to0 = t % so1;
+        const int to1 = (t / so1) % do1;
+        const int to2 = (t / so2) % do2;
+        const int to3 = (t / so3);
+
+        int oidx = to3 * so3 + to2 * so2 + to1 * so1 + to0;
+
+        int ti0, ti1, ti2, ti3;
+        if (EXPAND_) {
+            ti0 = to0;
+            ti1 = to1 * si1;
+            ti2 = to2 * si2;
+            ti3 = to3 * si3;
+        } else {
+            ti0 = to0 + fInfo_.dims[0] / 2;
+            ti1 = (to1 + (baseDim_ > 1) * (fInfo_.dims[1] / 2)) * si1;
+            ti2 = (to2 + (baseDim_ > 2) * (fInfo_.dims[2] / 2)) * si2;
+            ti3 = to3 * si3;
+        }
+
+        // Divide output elements to cuFFT resulting scale, round result if
+        // output type is single or double precision floating-point
+        if (ti0 < half_di0_) {
+            // Copy top elements
+            int iidx = iInfo_.offset + ti3 + ti2 + ti1 + ti0 * 2;
+            if (ROUND_OUT_)
+                d_out_[oidx] = (T)round(d_in_[iidx] / fftScale_);
+            else
+                d_out_[oidx] = (T)(d_in_[iidx] / fftScale_);
+        } else if (ti0 < half_di0_ + fInfo_.dims[0] - 1) {
+            // Add central elements
+            int iidx1 = iInfo_.offset + ti3 + ti2 + ti1 + ti0 * 2;
+            int iidx2 =
+                iInfo_.offset + ti3 + ti2 + ti1 + (ti0 - half_di0_) * 2 + 1;
+            if (ROUND_OUT_)
+                d_out_[oidx] =
+                    (T)round((d_in_[iidx1] + d_in_[iidx2]) / fftScale_);
+            else
+                d_out_[oidx] = (T)((d_in_[iidx1] + d_in_[iidx2]) / fftScale_);
+        } else {
+            // Copy bottom elements
+            const int iidx =
+                iInfo_.offset + ti3 + ti2 + ti1 + (ti0 - half_di0_) * 2 + 1;
+            if (ROUND_OUT_)
+                d_out_[oidx] = (T)round(d_in_[iidx] / fftScale_);
+            else
+                d_out_[oidx] = (T)(d_in_[iidx] / fftScale_);
+        }
+    }
+
+   private:
+    write_accessor<T> d_out_;
+    KParam oInfo_;
+    read_accessor<convScalarT> d_in_;
+    KParam iInfo_;
+    KParam fInfo_;
+    const int half_di0_;
+    const int baseDim_;
+    const int fftScale_;
+    const bool EXPAND_;
+    const bool ROUND_OUT_;
+};
+
+template<typename T, typename convT>
+void reorderOutputHelper(Param<T> out, Param<convT> packed, Param<T> sig,
+                         Param<T> filter, const int rank, AF_BATCH_KIND kind,
+                         bool expand) {
+    int fftScale = 1;
+
+    // Calculate the scale by which to divide clFFT results
+    for (int k = 0; k < rank; k++) fftScale *= packed.info.dims[k];
+
+    Param<T> sig_tmp, filter_tmp;
+    calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
+
+    // Number of packed complex elements in dimension 0
+    int sig_half_d0 = divup(sig.info.dims[0], 2);
+
+    int blocks = divup(out.info.strides[3] * out.info.dims[3], THREADS);
+
+    constexpr bool round_out = std::is_integral<T>::value;
+
+    auto local  = sycl::range(THREADS);
+    auto global = sycl::range(blocks * THREADS);
+
+    using convScalarT = typename convT::value_type;
+
+    if (kind == AF_BATCH_RHS) {
+        auto packed_num_elem   = (*packed.data).get_range().size();
+        auto filter_tmp_buffer = (*packed.data)
+                                     .template reinterpret<convScalarT>(
+                                         sycl::range<1>{packed_num_elem * 2});
+        getQueue().submit([&](auto &h) {
+            read_accessor<convScalarT> d_filter_tmp = {filter_tmp_buffer, h};
+            write_accessor<T> d_out = {*out.data, h, sycl::write_only};
+            h.parallel_for(
+                sycl::nd_range{global, local},
+                fftconvolve_reorderCreateKernel<T, convScalarT>(
+                    d_out, out.info, d_filter_tmp, filter_tmp.info, filter.info,
+                    sig_half_d0, rank, fftScale, expand, round_out));
+        });
+    } else {
+        auto packed_num_elem = (*packed.data).get_range().size();
+        auto sig_tmp_buffer  = (*packed.data)
+                                  .template reinterpret<convScalarT>(
+                                      sycl::range<1>{packed_num_elem * 2});
+        getQueue().submit([&](auto &h) {
+            read_accessor<convScalarT> d_sig_tmp = {sig_tmp_buffer, h,
+                                                    sycl::read_only};
+            write_accessor<T> d_out              = {*out.data, h};
+            h.parallel_for(
+                sycl::nd_range{global, local},
+                fftconvolve_reorderCreateKernel<T, convScalarT>(
+                    d_out, out.info, d_sig_tmp, sig_tmp.info, filter.info,
+                    sig_half_d0, rank, fftScale, expand, round_out));
+        });
+    }
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire

From 8d84b47c0275757ea2da4ccb7dbf78a9717f6649 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 1 Jun 2023 14:43:36 -0400
Subject: [PATCH 2514/2677] reduce local memory usage in iir (#3440)

* reduces local memory requirements for oneapi iir

* move local memory error handling to right place
---
 src/backend/oneapi/iir.cpp        | 13 +++++++++++++
 src/backend/oneapi/kernel/iir.hpp | 26 +++++++++++++++-----------
 2 files changed, 28 insertions(+), 11 deletions(-)

diff --git a/src/backend/oneapi/iir.cpp b/src/backend/oneapi/iir.cpp
index f60db52e8e..4a7654bd38 100644
--- a/src/backend/oneapi/iir.cpp
+++ b/src/backend/oneapi/iir.cpp
@@ -37,6 +37,19 @@ Array<T> iir(const Array<T> &b, const Array<T> &a, const Array<T> &x) {
 
     if (num_a == 1) { return c; }
 
+    size_t local_bytes_req = (num_a * 2 + 1) * sizeof(T);
+    if (local_bytes_req >
+        getDevice().get_info<sycl::info::device::local_mem_size>()) {
+        char errMessage[256];
+        snprintf(errMessage, sizeof(errMessage),
+                 "\ncurrent OneAPI device does not have sufficient local "
+                 "memory,\n"
+                 "for iir kernel, %zu(required) > %zu(available)\n",
+                 local_bytes_req,
+                 getDevice().get_info<sycl::info::device::local_mem_size>());
+        AF_ERROR(errMessage, AF_ERR_RUNTIME);
+    }
+
     dim4 ydims = c.dims();
     Array<T> y = createEmptyArray<T>(ydims);
 
diff --git a/src/backend/oneapi/kernel/iir.hpp b/src/backend/oneapi/kernel/iir.hpp
index 38769ad46a..938202f32f 100644
--- a/src/backend/oneapi/kernel/iir.hpp
+++ b/src/backend/oneapi/kernel/iir.hpp
@@ -21,8 +21,6 @@ namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-constexpr int MAX_A_SIZE = 1024;
-
 template<typename T, bool batch_a>
 class iirKernel {
    public:
@@ -67,10 +65,9 @@ class iirKernel {
         const int repeat =
             (num_a + g.get_local_range(0) - 1) / g.get_local_range(0);
 
-        for (int ii = 0; ii < MAX_A_SIZE / g.get_local_range(0); ii++) {
-            int id   = ii * g.get_local_range(0) + tx;
-            s_z_[id] = scalar<T>(0);
-            s_a_[id] = (id < num_a) ? d_a[id] : scalar<T>(0);
+        for (int ii = tx; ii < num_a; ii += g.get_local_range(0)) {
+            s_z_[ii] = scalar<T>(0);
+            s_a_[ii] = (ii < num_a) ? d_a[ii] : scalar<T>(0);
         }
         group_barrier(g);
 
@@ -81,14 +78,19 @@ class iirKernel {
             }
             group_barrier(g);
 
-#pragma unroll
             for (int ii = 0; ii < repeat; ii++) {
                 int id = ii * g.get_local_range(0) + tx + 1;
 
-                T z = s_z_[id] - s_a_[id] * s_y_[0];
+                T z;
+
+                if (id < num_a) {
+                    z = s_z_[id] - s_a_[id] * s_y_[0];
+                } else {
+                    z = scalar<T>(0);
+                }
                 group_barrier(g);
 
-                s_z_[id - 1] = z;
+                if ((id - 1) < num_a) { s_z_[id - 1] = z; }
                 group_barrier(g);
             }
         }
@@ -124,8 +126,10 @@ void iir(Param<T> y, Param<T> c, Param<T> a) {
         read_accessor<T> cAcc{*c.data, h};
         read_accessor<T> aAcc{*a.data, h};
 
-        auto s_z = sycl::local_accessor<T>(MAX_A_SIZE, h);
-        auto s_a = sycl::local_accessor<T>(MAX_A_SIZE, h);
+        unsigned num_a = a.info.dims[0];
+
+        auto s_z = sycl::local_accessor<T>(num_a, h);
+        auto s_a = sycl::local_accessor<T>(num_a, h);
         auto s_y = sycl::local_accessor<T>(1, h);
 
         if (batch_a) {

From 4f291f3f28f7aca0359469e370538b1bf54b6be0 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Sat, 6 May 2023 18:01:53 -0400
Subject: [PATCH 2515/2677] new method to get array with offset

---
 src/backend/oneapi/Array.hpp | 26 +++++++++++
 src/backend/oneapi/blas.cpp  | 89 +++++++++++++++++++++++++-----------
 2 files changed, 88 insertions(+), 27 deletions(-)

diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index a6ca6c402c..249192db14 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -23,6 +23,7 @@
 #include <algorithm>
 #include <cstdlib>
 #include <memory>
+#include <type_traits>
 #include <vector>
 
 enum class kJITHeuristics;
@@ -258,6 +259,31 @@ class Array {
         return data.get();
     }
 
+    template<typename outT>
+    sycl::buffer<outT> getBufferWithOffset() const {
+        dim_t sz_remaining = data_dims.elements() - getOffset();
+        printf("dd%d, elements %d offset %d\n",data_dims.elements(), elements(), getOffset());
+        if constexpr(std::is_same_v<outT, T>) {
+            if(getOffset() == 0) {
+                printf("off0--noreint\n");
+                *data.get();
+            }
+            return sycl::buffer<outT, 1>(
+                *data.get(),
+                sycl::id<1>(getOffset()),
+                sycl::range<1>(sz_remaining));
+        } else {
+            if(getOffset() == 0) {
+                printf("off0--reint\n");
+                data.get()->template reinterpret<outT, 1>();
+            }
+            return sycl::buffer<T, 1>(
+                *data.get(),
+                sycl::id<1>(getOffset()),
+                sycl::range<1>(sz_remaining)).template reinterpret<outT, 1>();
+        }
+    }
+
     int useCount() const { return data.use_count(); }
 
     dim_t getOffset() const { return info.getOffset(); }
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 73dbadfcfd..0579e1421f 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -43,19 +43,26 @@ static oneapi::mkl::transpose toBlasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-static void gemvDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts, int M,
-                         int N, const T *alpha,
+static void gemvDispatch(sycl::queue queue,
+                         oneapi::mkl::transpose lOpts,
+                         oneapi::mkl::transpose rOpts,
+                         int M, int N, const T *alpha,
                          const arrayfire::oneapi::Array<T> &lhs, dim_t lStride,
                          const arrayfire::oneapi::Array<T> &x, dim_t incx,
                          const T *beta, arrayfire::oneapi::Array<T> &out,
                          dim_t oInc) {
     using Dt                   = arrayfire::oneapi::data_t<T>;
-    sycl::buffer<Dt, 1> lhsBuf = lhs.get()->template reinterpret<Dt, 1>();
-    sycl::buffer<Dt, 1> xBuf   = x.get()->template reinterpret<Dt, 1>();
-    sycl::buffer<Dt, 1> outBuf = out.get()->template reinterpret<Dt, 1>();
-    ::oneapi::mkl::blas::gemv(queue, lOpts, (int64_t)M, (int64_t)N, (T)*alpha,
-                              lhsBuf, (int64_t)lStride, xBuf, (int64_t)incx,
-                              (T)*beta, outBuf, (int64_t)oInc);
+    const af::dim4 lStrides = lhs.strides();
+    const af::dim4 xStrides = x.strides();
+    const af::dim4 oStrides = out.strides();
+    sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
+    sycl::buffer<Dt, 1> xBuf   =   x.template getBufferWithOffset<Dt>();
+    sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
+    if constexpr(!std::is_same_v<T, arrayfire::common::half>) {
+        ::oneapi::mkl::blas::gemv(queue, lOpts, (int64_t)M, (int64_t)N, (T)*alpha,
+                                lhsBuf, (int64_t)lStride, xBuf, (int64_t)incx,
+                                (T)*beta, outBuf, (int64_t)oInc);
+    }
 }
 
 template<typename T>
@@ -65,13 +72,21 @@ static void gemmDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts,
                          dim_t lStride, const arrayfire::oneapi::Array<T> &rhs,
                          dim_t rStride, const T *beta,
                          arrayfire::oneapi::Array<T> &out, dim_t oleading) {
-    using Dt                   = arrayfire::oneapi::data_t<T>;
-    sycl::buffer<Dt, 1> lhsBuf = lhs.get()->template reinterpret<Dt, 1>();
-    sycl::buffer<Dt, 1> rhsBuf = rhs.get()->template reinterpret<Dt, 1>();
-    sycl::buffer<Dt, 1> outBuf = out.get()->template reinterpret<Dt, 1>();
+    using Dt                = arrayfire::oneapi::data_t<T>;
+    const af::dim4 lStrides = lhs.strides();
+    const af::dim4 rStrides = rhs.strides();
+    const af::dim4 oStrides = out.strides();
+    sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
+    sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
+    sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
+    try {
     ::oneapi::mkl::blas::gemm(queue, lOpts, rOpts, M, N, K, *alpha, lhsBuf,
                               lStride, rhsBuf, rStride, *beta, outBuf,
                               oleading);
+    queue.wait_and_throw();
+    } catch(sycl::exception &e)  {
+        std::cout << e.what() << std::endl;
+    }
 }
 
 namespace arrayfire {
@@ -103,13 +118,21 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     const dim4 &lStrides = lhs.strides();
     const dim4 &rStrides = rhs.strides();
     const dim4 oStrides  = out.strides();
+    try{
 
     if (oDims.ndims() <= 2) {  // if non-batched
         if (rhs.dims()[bColDim] == 1) {
-            dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-            gemvDispatch<T>(getQueue(), lOpts, lDims[0], lDims[1], alpha, lhs,
-                            lStrides[1], rhs, incr, beta, out, oStrides[0]);
+            if constexpr(!std::is_same_v<T, arrayfire::common::half>) {
+                dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                gemvDispatch<T>(getQueue(), lOpts, rOpts, lDims[0], lDims[1], alpha, lhs,
+                                lStrides[1], rhs, incr, beta, out, oStrides[0]);
+            } else {
+                gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
+                                lStrides[1], rhs, rStrides[1], beta, out,
+                                oStrides[1]);
+            }
         } else {
+            printf("%d %d %d, l%d R%d o%d\n",  M, N, K, lStrides[1], rStrides[1], oStrides[1]);
             gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
                             lStrides[1], rhs, rStrides[1], beta, out,
                             oStrides[1]);
@@ -117,9 +140,9 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     } else {  // if batched
         using Dt = arrayfire::oneapi::data_t<T>;
 
-        sycl::buffer<Dt, 1> lhsBuf = lhs.get()->template reinterpret<Dt, 1>();
-        sycl::buffer<Dt, 1> rhsBuf = rhs.get()->template reinterpret<Dt, 1>();
-        sycl::buffer<Dt, 1> outBuf = out.get()->template reinterpret<Dt, 1>();
+        sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
+        sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
+        sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
 
         const int64_t lda = lStrides[1];
         const int64_t ldb = rStrides[1];
@@ -127,18 +150,36 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
 
         int64_t batchSize = static_cast<int64_t>(oDims[2] * oDims[3]);
 
+        bool is_l_d2_batched = (oDims[2] == lDims[2]) && lDims[2] != 1;
+        bool is_l_d3_batched = (oDims[3] == lDims[3]) && lDims[3] != 1;
+        bool is_r_d2_batched = (oDims[2] == rDims[2]) && rDims[2] != 1;
+        bool is_r_d3_batched = (oDims[3] == rDims[3]) && rDims[3] != 1;
+
+        std::cout << lStrides << std::endl;
+        std::cout << rStrides << std::endl;
+
         const bool not_l_batched =
             (oDims[2] != lDims[2] && oDims[3] != lDims[3]);
         const bool not_r_batched =
             (oDims[2] != rDims[2] && oDims[3] != rDims[3]);
 
+        //dim_t lstride = !not_l_batched ? 0 : (is_l_d2_batched) ? lStrides[2] : lStrides[3];
+        //dim_t rstride = !not_r_batched ? 0 : (is_r_d2_batched) ? rStrides[2] : rStrides[3];
+        dim_t lstride = (is_l_d2_batched) ? lStrides[2] : is_l_d3_batched ? lStrides[3] : 0;
+        dim_t rstride = (is_r_d2_batched) ? rStrides[2] : is_r_d3_batched ? rStrides[3] : 0;
+
         ::oneapi::mkl::blas::gemm_batch(
             getQueue(), lOpts, rOpts, M, N, K, *alpha, lhsBuf, lda,
-            not_l_batched ? 0 : lStrides[2], rhsBuf, ldb,
-            not_r_batched ? 0 : rStrides[2], *beta, outBuf, ldc, oStrides[2],
+            lstride, rhsBuf, ldb,
+            rstride, *beta, outBuf, ldc, oStrides[2],
             batchSize);
     }
 
+        getQueue().wait_and_throw();
+    } catch(sycl::exception &e)  {
+        std::cout << e.what() << std::endl;
+    }
+
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
@@ -161,13 +202,7 @@ INSTANTIATE_GEMM(float)
 INSTANTIATE_GEMM(cfloat)
 INSTANTIATE_GEMM(double)
 INSTANTIATE_GEMM(cdouble)
-// INSTANTIATE_GEMM(half)
-template<>
-void gemm(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
-          const half *alpha, const Array<half> &lhs, const Array<half> &rhs,
-          const half *beta) {
-    ONEAPI_NOT_SUPPORTED("");
-}
+INSTANTIATE_GEMM(half)
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \

From 50fef60640488fb964f96a40758429bc8c2ca830 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 30 May 2023 21:27:05 -0400
Subject: [PATCH 2516/2677] corrects batching for reordered output

---
 src/backend/oneapi/Array.hpp | 24 +++++-----
 src/backend/oneapi/blas.cpp  | 88 +++++++++++++++++++++---------------
 2 files changed, 62 insertions(+), 50 deletions(-)

diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index 249192db14..a47e32dbee 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -260,26 +260,24 @@ class Array {
     }
 
     template<typename outT>
-    sycl::buffer<outT> getBufferWithOffset() const {
-        dim_t sz_remaining = data_dims.elements() - getOffset();
-        printf("dd%d, elements %d offset %d\n",data_dims.elements(), elements(), getOffset());
+    sycl::buffer<outT> getBufferWithOffset(dim_t offset=-1) const {
+        offset = (offset == -1) ? getOffset() : offset;
+        dim_t sz_remaining = data_dims.elements() - offset;
         if constexpr(std::is_same_v<outT, T>) {
-            if(getOffset() == 0) {
-                printf("off0--noreint\n");
-                *data.get();
+            if(offset == 0) {
+                return *get();
             }
             return sycl::buffer<outT, 1>(
-                *data.get(),
-                sycl::id<1>(getOffset()),
+                *get(),
+                sycl::id<1>(offset),
                 sycl::range<1>(sz_remaining));
         } else {
-            if(getOffset() == 0) {
-                printf("off0--reint\n");
-                data.get()->template reinterpret<outT, 1>();
+            if(offset == 0) {
+                return get()->template reinterpret<outT, 1>();
             }
             return sycl::buffer<T, 1>(
-                *data.get(),
-                sycl::id<1>(getOffset()),
+                *get(),
+                sycl::id<1>(offset),
                 sycl::range<1>(sz_remaining)).template reinterpret<outT, 1>();
         }
     }
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 0579e1421f..9df9dd05f3 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -74,19 +74,15 @@ static void gemmDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts,
                          arrayfire::oneapi::Array<T> &out, dim_t oleading) {
     using Dt                = arrayfire::oneapi::data_t<T>;
     const af::dim4 lStrides = lhs.strides();
+
     const af::dim4 rStrides = rhs.strides();
     const af::dim4 oStrides = out.strides();
     sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
     sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
     sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
-    try {
     ::oneapi::mkl::blas::gemm(queue, lOpts, rOpts, M, N, K, *alpha, lhsBuf,
-                              lStride, rhsBuf, rStride, *beta, outBuf,
-                              oleading);
-    queue.wait_and_throw();
-    } catch(sycl::exception &e)  {
-        std::cout << e.what() << std::endl;
-    }
+                            lStride, rhsBuf, rStride, *beta, outBuf,
+                            oleading);
 }
 
 namespace arrayfire {
@@ -98,6 +94,10 @@ void initBlas() { /*gpu_blas_init();*/
 void deInitBlas() { /*gpu_blas_deinit();*/
 }
 
+bool checkMonotonicDim4(const af::dim4 &dim) {
+    return (dim[0] <= dim[1]) && (dim[1] <= dim[2]) && (dim[2] <= dim[3]);
+}
+
 template<typename T>
 void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
           const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
@@ -118,7 +118,6 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     const dim4 &lStrides = lhs.strides();
     const dim4 &rStrides = rhs.strides();
     const dim4 oStrides  = out.strides();
-    try{
 
     if (oDims.ndims() <= 2) {  // if non-batched
         if (rhs.dims()[bColDim] == 1) {
@@ -132,7 +131,6 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
                                 oStrides[1]);
             }
         } else {
-            printf("%d %d %d, l%d R%d o%d\n",  M, N, K, lStrides[1], rStrides[1], oStrides[1]);
             gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
                             lStrides[1], rhs, rStrides[1], beta, out,
                             oStrides[1]);
@@ -140,14 +138,6 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     } else {  // if batched
         using Dt = arrayfire::oneapi::data_t<T>;
 
-        sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
-        sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
-        sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
-
-        const int64_t lda = lStrides[1];
-        const int64_t ldb = rStrides[1];
-        const int64_t ldc = oStrides[1];
-
         int64_t batchSize = static_cast<int64_t>(oDims[2] * oDims[3]);
 
         bool is_l_d2_batched = (oDims[2] == lDims[2]) && lDims[2] != 1;
@@ -155,31 +145,55 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         bool is_r_d2_batched = (oDims[2] == rDims[2]) && rDims[2] != 1;
         bool is_r_d3_batched = (oDims[3] == rDims[3]) && rDims[3] != 1;
 
-        std::cout << lStrides << std::endl;
-        std::cout << rStrides << std::endl;
+        bool canBatchMKL = checkMonotonicDim4(oStrides);
+        if(canBatchMKL) {
+            sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
+            sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
+            sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
 
-        const bool not_l_batched =
-            (oDims[2] != lDims[2] && oDims[3] != lDims[3]);
-        const bool not_r_batched =
-            (oDims[2] != rDims[2] && oDims[3] != rDims[3]);
+            const int64_t lda = lStrides[1];
+            const int64_t ldb = rStrides[1];
+            const int64_t ldc = oStrides[1];
 
-        //dim_t lstride = !not_l_batched ? 0 : (is_l_d2_batched) ? lStrides[2] : lStrides[3];
-        //dim_t rstride = !not_r_batched ? 0 : (is_r_d2_batched) ? rStrides[2] : rStrides[3];
-        dim_t lstride = (is_l_d2_batched) ? lStrides[2] : is_l_d3_batched ? lStrides[3] : 0;
-        dim_t rstride = (is_r_d2_batched) ? rStrides[2] : is_r_d3_batched ? rStrides[3] : 0;
+            dim_t lstride = (is_l_d2_batched) ? lStrides[2] : is_l_d3_batched ? lStrides[3] : 0;
+            dim_t rstride = (is_r_d2_batched) ? rStrides[2] : is_r_d3_batched ? rStrides[3] : 0;
 
-        ::oneapi::mkl::blas::gemm_batch(
-            getQueue(), lOpts, rOpts, M, N, K, *alpha, lhsBuf, lda,
-            lstride, rhsBuf, ldb,
-            rstride, *beta, outBuf, ldc, oStrides[2],
-            batchSize);
-    }
+            ::oneapi::mkl::blas::gemm_batch(
+                getQueue(), lOpts, rOpts, M, N, K, *alpha, lhsBuf, lda,
+                lstride, rhsBuf, ldb,
+                rstride, *beta, outBuf, ldc, oStrides[2],
+                batchSize);
+        } else {
+            std::vector<sycl::buffer<Dt>> lptrs;
+            std::vector<sycl::buffer<Dt>> rptrs;
+            std::vector<sycl::buffer<Dt>> optrs;
+
+            lptrs.reserve(batchSize);
+            rptrs.reserve(batchSize);
+            optrs.reserve(batchSize);
+
+            for (int n = 0; n < batchSize; n++) {
+                ptrdiff_t w = n / oDims[2];
+                ptrdiff_t z = n - w * oDims[2];
+
+                ptrdiff_t loff = z * (is_l_d2_batched * lStrides[2]) +
+                                 w * (is_l_d3_batched * lStrides[3]);
+                ptrdiff_t roff = z * (is_r_d2_batched * rStrides[2]) +
+                                 w * (is_r_d3_batched * rStrides[3]);
+                ptrdiff_t zoff = z * oStrides[2] + w * oStrides[3];
+
+                lptrs.emplace_back(lhs.template getBufferWithOffset<Dt>(loff));
+                rptrs.emplace_back(rhs.template getBufferWithOffset<Dt>(roff));
+                optrs.emplace_back(out.template getBufferWithOffset<Dt>(zoff));
+            }
 
-        getQueue().wait_and_throw();
-    } catch(sycl::exception &e)  {
-        std::cout << e.what() << std::endl;
+            for (int n = 0; n < batchSize; n++) {
+                ::oneapi::mkl::blas::gemm(getQueue(), lOpts, rOpts, M, N, K,
+                    *alpha, lptrs[n], lStrides[1], rptrs[n], rStrides[1], *beta,
+                    optrs[n], oStrides[1]);
+            }
+        }
     }
-
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 

From f505951aa3dd7e2d00776e55e549ea8fdb1a56c2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 30 May 2023 21:27:55 -0400
Subject: [PATCH 2517/2677] adds mkl exceptions to err_common

---
 src/backend/common/err_common.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 9e2b2e8a2f..92df5beb27 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -26,6 +26,7 @@
 #include <platform.hpp>
 #elif defined(AF_ONEAPI)
 #include <sycl/sycl.hpp>
+#include <oneapi/mkl/exceptions.hpp>
 #endif
 
 using boost::stacktrace::stacktrace;
@@ -169,6 +170,12 @@ af_err processException() {
         snprintf(oneapi_err_msg, sizeof(oneapi_err_msg),
                  "oneAPI Error (%d): %s", ex.code().value(), ex.what());
 
+        err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
+    } catch (const oneapi::mkl::exception &ex) {
+        char oneapi_err_msg[1024];
+        snprintf(oneapi_err_msg, sizeof(oneapi_err_msg),
+                 "MKL Error: %s", ex.what());
+
         err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
 #endif
 #ifdef AF_OPENCL
@@ -184,6 +191,7 @@ af_err processException() {
             err = set_global_error_string(opencl_err_msg, AF_ERR_INTERNAL);
         }
 #endif
+    } catch (const std::exception &ex) { err = set_global_error_string(ex.what(), AF_ERR_UNKNOWN);
     } catch (...) { err = set_global_error_string(ss.str(), AF_ERR_UNKNOWN); }
 
     return err;

From 37eb8acea82e317aa0e9e39ba6c3f6ec44198686 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 30 May 2023 21:33:08 -0400
Subject: [PATCH 2518/2677] blas reorder clang-format

---
 src/backend/common/err_common.cpp |  9 ++--
 src/backend/oneapi/Array.hpp      | 27 ++++------
 src/backend/oneapi/blas.cpp       | 85 +++++++++++++++++--------------
 3 files changed, 62 insertions(+), 59 deletions(-)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 92df5beb27..885aa8d5f5 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -25,8 +25,8 @@
 #include <errorcodes.hpp>
 #include <platform.hpp>
 #elif defined(AF_ONEAPI)
-#include <sycl/sycl.hpp>
 #include <oneapi/mkl/exceptions.hpp>
+#include <sycl/sycl.hpp>
 #endif
 
 using boost::stacktrace::stacktrace;
@@ -173,8 +173,8 @@ af_err processException() {
         err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
     } catch (const oneapi::mkl::exception &ex) {
         char oneapi_err_msg[1024];
-        snprintf(oneapi_err_msg, sizeof(oneapi_err_msg),
-                 "MKL Error: %s", ex.what());
+        snprintf(oneapi_err_msg, sizeof(oneapi_err_msg), "MKL Error: %s",
+                 ex.what());
 
         err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
 #endif
@@ -191,7 +191,8 @@ af_err processException() {
             err = set_global_error_string(opencl_err_msg, AF_ERR_INTERNAL);
         }
 #endif
-    } catch (const std::exception &ex) { err = set_global_error_string(ex.what(), AF_ERR_UNKNOWN);
+    } catch (const std::exception &ex) {
+        err = set_global_error_string(ex.what(), AF_ERR_UNKNOWN);
     } catch (...) { err = set_global_error_string(ss.str(), AF_ERR_UNKNOWN); }
 
     return err;
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index a47e32dbee..e0b0962222 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -260,25 +260,18 @@ class Array {
     }
 
     template<typename outT>
-    sycl::buffer<outT> getBufferWithOffset(dim_t offset=-1) const {
-        offset = (offset == -1) ? getOffset() : offset;
+    sycl::buffer<outT> getBufferWithOffset(dim_t offset = -1) const {
+        offset             = (offset == -1) ? getOffset() : offset;
         dim_t sz_remaining = data_dims.elements() - offset;
-        if constexpr(std::is_same_v<outT, T>) {
-            if(offset == 0) {
-                return *get();
-            }
-            return sycl::buffer<outT, 1>(
-                *get(),
-                sycl::id<1>(offset),
-                sycl::range<1>(sz_remaining));
+        if constexpr (std::is_same_v<outT, T>) {
+            if (offset == 0) { return *get(); }
+            return sycl::buffer<outT, 1>(*get(), sycl::id<1>(offset),
+                                         sycl::range<1>(sz_remaining));
         } else {
-            if(offset == 0) {
-                return get()->template reinterpret<outT, 1>();
-            }
-            return sycl::buffer<T, 1>(
-                *get(),
-                sycl::id<1>(offset),
-                sycl::range<1>(sz_remaining)).template reinterpret<outT, 1>();
+            if (offset == 0) { return get()->template reinterpret<outT, 1>(); }
+            return sycl::buffer<T, 1>(*get(), sycl::id<1>(offset),
+                                      sycl::range<1>(sz_remaining))
+                .template reinterpret<outT, 1>();
         }
     }
 
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 9df9dd05f3..37495957e9 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -43,25 +43,24 @@ static oneapi::mkl::transpose toBlasTranspose(af_mat_prop opt) {
 }
 
 template<typename T>
-static void gemvDispatch(sycl::queue queue,
-                         oneapi::mkl::transpose lOpts,
-                         oneapi::mkl::transpose rOpts,
-                         int M, int N, const T *alpha,
-                         const arrayfire::oneapi::Array<T> &lhs, dim_t lStride,
-                         const arrayfire::oneapi::Array<T> &x, dim_t incx,
-                         const T *beta, arrayfire::oneapi::Array<T> &out,
-                         dim_t oInc) {
+static void gemvDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts,
+                         oneapi::mkl::transpose rOpts, int M, int N,
+                         const T *alpha, const arrayfire::oneapi::Array<T> &lhs,
+                         dim_t lStride, const arrayfire::oneapi::Array<T> &x,
+                         dim_t incx, const T *beta,
+                         arrayfire::oneapi::Array<T> &out, dim_t oInc) {
     using Dt                   = arrayfire::oneapi::data_t<T>;
-    const af::dim4 lStrides = lhs.strides();
-    const af::dim4 xStrides = x.strides();
-    const af::dim4 oStrides = out.strides();
+    const af::dim4 lStrides    = lhs.strides();
+    const af::dim4 xStrides    = x.strides();
+    const af::dim4 oStrides    = out.strides();
     sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
-    sycl::buffer<Dt, 1> xBuf   =   x.template getBufferWithOffset<Dt>();
+    sycl::buffer<Dt, 1> xBuf   = x.template getBufferWithOffset<Dt>();
     sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
-    if constexpr(!std::is_same_v<T, arrayfire::common::half>) {
-        ::oneapi::mkl::blas::gemv(queue, lOpts, (int64_t)M, (int64_t)N, (T)*alpha,
-                                lhsBuf, (int64_t)lStride, xBuf, (int64_t)incx,
-                                (T)*beta, outBuf, (int64_t)oInc);
+    if constexpr (!std::is_same_v<T, arrayfire::common::half>) {
+        ::oneapi::mkl::blas::gemv(queue, lOpts, (int64_t)M, (int64_t)N,
+                                  (T)*alpha, lhsBuf, (int64_t)lStride, xBuf,
+                                  (int64_t)incx, (T)*beta, outBuf,
+                                  (int64_t)oInc);
     }
 }
 
@@ -75,14 +74,14 @@ static void gemmDispatch(sycl::queue queue, oneapi::mkl::transpose lOpts,
     using Dt                = arrayfire::oneapi::data_t<T>;
     const af::dim4 lStrides = lhs.strides();
 
-    const af::dim4 rStrides = rhs.strides();
-    const af::dim4 oStrides = out.strides();
+    const af::dim4 rStrides    = rhs.strides();
+    const af::dim4 oStrides    = out.strides();
     sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
     sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
     sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
     ::oneapi::mkl::blas::gemm(queue, lOpts, rOpts, M, N, K, *alpha, lhsBuf,
-                            lStride, rhsBuf, rStride, *beta, outBuf,
-                            oleading);
+                              lStride, rhsBuf, rStride, *beta, outBuf,
+                              oleading);
 }
 
 namespace arrayfire {
@@ -94,7 +93,7 @@ void initBlas() { /*gpu_blas_init();*/
 void deInitBlas() { /*gpu_blas_deinit();*/
 }
 
-bool checkMonotonicDim4(const af::dim4 &dim) {
+bool isStrideMonotonic(const af::dim4 &dim) {
     return (dim[0] <= dim[1]) && (dim[1] <= dim[2]) && (dim[2] <= dim[3]);
 }
 
@@ -121,14 +120,17 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
 
     if (oDims.ndims() <= 2) {  // if non-batched
         if (rhs.dims()[bColDim] == 1) {
-            if constexpr(!std::is_same_v<T, arrayfire::common::half>) {
-                dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                gemvDispatch<T>(getQueue(), lOpts, rOpts, lDims[0], lDims[1], alpha, lhs,
-                                lStrides[1], rhs, incr, beta, out, oStrides[0]);
-            } else {
+            if constexpr (std::is_same_v<T, arrayfire::common::half>) {
+                // currently no half support for gemv, use gemm instead
                 gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
                                 lStrides[1], rhs, rStrides[1], beta, out,
                                 oStrides[1]);
+            } else {
+                dim_t incr =
+                    (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
+                gemvDispatch<T>(getQueue(), lOpts, rOpts, lDims[0], lDims[1],
+                                alpha, lhs, lStrides[1], rhs, incr, beta, out,
+                                oStrides[0]);
             }
         } else {
             gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
@@ -145,8 +147,11 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         bool is_r_d2_batched = (oDims[2] == rDims[2]) && rDims[2] != 1;
         bool is_r_d3_batched = (oDims[3] == rDims[3]) && rDims[3] != 1;
 
-        bool canBatchMKL = checkMonotonicDim4(oStrides);
-        if(canBatchMKL) {
+        // MKL requires stridec >= ldc * n, which may not be true with reordered
+        // outputs if the stride is monotonic, then MKL requirements for
+        // batching can be met
+        bool canBatchMKL = isStrideMonotonic(oStrides);
+        if (canBatchMKL) {
             sycl::buffer<Dt, 1> lhsBuf = lhs.template getBufferWithOffset<Dt>();
             sycl::buffer<Dt, 1> rhsBuf = rhs.template getBufferWithOffset<Dt>();
             sycl::buffer<Dt, 1> outBuf = out.template getBufferWithOffset<Dt>();
@@ -155,14 +160,17 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
             const int64_t ldb = rStrides[1];
             const int64_t ldc = oStrides[1];
 
-            dim_t lstride = (is_l_d2_batched) ? lStrides[2] : is_l_d3_batched ? lStrides[3] : 0;
-            dim_t rstride = (is_r_d2_batched) ? rStrides[2] : is_r_d3_batched ? rStrides[3] : 0;
-
-            ::oneapi::mkl::blas::gemm_batch(
-                getQueue(), lOpts, rOpts, M, N, K, *alpha, lhsBuf, lda,
-                lstride, rhsBuf, ldb,
-                rstride, *beta, outBuf, ldc, oStrides[2],
-                batchSize);
+            dim_t lstride = (is_l_d2_batched) ? lStrides[2]
+                            : is_l_d3_batched ? lStrides[3]
+                                              : 0;
+            dim_t rstride = (is_r_d2_batched) ? rStrides[2]
+                            : is_r_d3_batched ? rStrides[3]
+                                              : 0;
+
+            ::oneapi::mkl::blas::gemm_batch(getQueue(), lOpts, rOpts, M, N, K,
+                                            *alpha, lhsBuf, lda, lstride,
+                                            rhsBuf, ldb, rstride, *beta, outBuf,
+                                            ldc, oStrides[2], batchSize);
         } else {
             std::vector<sycl::buffer<Dt>> lptrs;
             std::vector<sycl::buffer<Dt>> rptrs;
@@ -189,8 +197,9 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
 
             for (int n = 0; n < batchSize; n++) {
                 ::oneapi::mkl::blas::gemm(getQueue(), lOpts, rOpts, M, N, K,
-                    *alpha, lptrs[n], lStrides[1], rptrs[n], rStrides[1], *beta,
-                    optrs[n], oStrides[1]);
+                                          *alpha, lptrs[n], lStrides[1],
+                                          rptrs[n], rStrides[1], *beta,
+                                          optrs[n], oStrides[1]);
             }
         }
     }

From 57dcc4f038b7f15938ef06247edda944b6fff41d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 1 Jun 2023 20:38:02 -0400
Subject: [PATCH 2519/2677] fix mismatching number of elements in copies from
 managed buffers

---
 src/backend/oneapi/Array.cpp                   | 17 ++++++++++-------
 src/backend/oneapi/kernel/sort_by_key_impl.hpp |  9 ++++++---
 2 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 3a9fbff3be..6f506ec2ba 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -125,10 +125,10 @@ Array<T>::Array(const dim4 &dims, const T *const in_data)
     static_assert(
         offsetof(Array<T>, info) == 0,
         "Array<T>::info must be the first member variable of Array<T>");
-    // getQueue().enqueueWriteBuffer(*data.get(), CL_TRUE, 0,
-    // sizeof(T) * info.elements(), in_data);
     getQueue()
-        .submit([&](sycl::handler &h) { h.copy(in_data, data->get_access(h)); })
+        .submit([&](sycl::handler &h) {
+            h.copy(in_data, data->get_access(h, sycl::range(info.elements())));
+        })
         .wait();
 }
 
@@ -145,7 +145,8 @@ Array<T>::Array(const af::dim4 &dims, buffer<T> *const mem, size_t offset,
     if (copy) {
         getQueue()
             .submit([&](sycl::handler &h) {
-                h.copy(mem->get_access(h), data->get_access(h));
+                h.copy(mem->get_access(h, sycl::range(info.elements())),
+                       data->get_access(h));
             })
             .wait();
     }
@@ -193,8 +194,10 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
     } else {
         data = memAlloc<T>(info.elements());
         getQueue()
-            .submit(
-                [&](sycl::handler &h) { h.copy(in_data, data->get_access(h)); })
+            .submit([&](sycl::handler &h) {
+                h.copy(in_data,
+                       data->get_access(h, sycl::range(info.elements())));
+            })
             .wait();
     }
 }
@@ -486,7 +489,7 @@ void writeHostDataArray(Array<T> &arr, const T *const data,
             buffer<T> &buf = *arr.get();
             // auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
             // TODO: offset accessor
-            auto offset_acc = buf.get_access(h);
+            auto offset_acc = buf.get_access(h, sycl::range(arr.elements()));
             h.copy(data, offset_acc);
         })
         .wait();
diff --git a/src/backend/oneapi/kernel/sort_by_key_impl.hpp b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
index c0c57d8eff..ad3b3c8a80 100644
--- a/src/backend/oneapi/kernel/sort_by_key_impl.hpp
+++ b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
@@ -108,8 +108,10 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
 
     auto cKey = memAlloc<Tk>(elements);
     getQueue().submit([&](sycl::handler &h) {
-        h.copy(pKey.data->template reinterpret<compute_t<Tk>>().get_access(),
-               cKey.get()->template reinterpret<compute_t<Tk>>().get_access());
+        h.copy(pKey.data->template reinterpret<compute_t<Tk>>().get_access(
+                   h, elements),
+               cKey.get()->template reinterpret<compute_t<Tk>>().get_access(
+                   h, elements));
     });
     auto ckey_begin =
         ::oneapi::dpl::begin(cKey.get()->template reinterpret<compute_t<Tk>>());
@@ -144,7 +146,8 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
 
     auto cSeq = memAlloc<uint>(elements);
     getQueue().submit([&](sycl::handler &h) {
-        h.copy(Seq.get()->get_access(), cSeq.get()->get_access());
+        h.copy(Seq.get()->get_access(h, elements),
+               cSeq.get()->get_access(h, elements));
     });
     auto cseq_begin = ::oneapi::dpl::begin(*cSeq.get());
     auto cseq_end   = ::oneapi::dpl::end(*cSeq.get());

From 50ca60117e11024fd5c79521df7b67dff003c171 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 1 Jun 2023 23:27:23 -0400
Subject: [PATCH 2520/2677] use exactly sized subbuffers for scratch space

---
 src/backend/oneapi/cholesky.cpp | 11 ++++++-----
 src/backend/oneapi/lu.cpp       | 14 +++++++++-----
 src/backend/oneapi/memory.cpp   |  1 +
 src/backend/oneapi/svd.cpp      | 16 ++++++++++++----
 4 files changed, 28 insertions(+), 14 deletions(-)

diff --git a/src/backend/oneapi/cholesky.cpp b/src/backend/oneapi/cholesky.cpp
index 4fb0e08c58..1b81be7f03 100644
--- a/src/backend/oneapi/cholesky.cpp
+++ b/src/backend/oneapi/cholesky.cpp
@@ -14,8 +14,9 @@
 #include <platform.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <memory.hpp>
+#include <oneapi/mkl/lapack.hpp>
 #include <triangle.hpp>
-#include "oneapi/mkl/lapack.hpp"
 
 namespace arrayfire {
 namespace oneapi {
@@ -35,12 +36,12 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     lwork = ::oneapi::mkl::lapack::potrf_scratchpad_size<T>(getQueue(), uplo, N,
                                                             LDA);
 
-    Array<T> workspace = createEmptyArray<T>(af::dim4(lwork));
-    Array<int> d_info  = createEmptyArray<int>(af::dim4(1));
+    auto workspaceMem  = memAlloc<compute_t<T>>(lwork);
+    sycl::buffer<compute_t<T>> in_buffer = in.template getBufferWithOffset<compute_t<T>>();
 
     try {
-        ::oneapi::mkl::lapack::potrf(getQueue(), uplo, N, *in.get(), LDA,
-                                     *workspace.get(), lwork);
+        ::oneapi::mkl::lapack::potrf(getQueue(), uplo, N, in_buffer, LDA,
+                                     *workspaceMem, lwork);
     } catch (::oneapi::mkl::lapack::exception const &e) {
         AF_ERROR(
             "Unexpected exception caught during synchronous\
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
index 200b85d23b..20c44f2529 100644
--- a/src/backend/oneapi/lu.cpp
+++ b/src/backend/oneapi/lu.cpp
@@ -11,11 +11,12 @@
 #include <lu.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
+#include <oneapi/mkl/lapack.hpp>
 #include <blas.hpp>
 #include <copy.hpp>
 #include <kernel/lu_split.hpp>
+#include <memory.hpp>
 #include <platform.hpp>
-#include "oneapi/mkl/lapack.hpp"
 
 namespace arrayfire {
 namespace oneapi {
@@ -76,11 +77,14 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     std::int64_t scratchpad_size =
         ::oneapi::mkl::lapack::getrf_scratchpad_size<T>(getQueue(), M, N, LDA);
 
-    sycl::buffer<int64_t, 1> ipiv{sycl::range<1>(MN)};
-    Array<T> scratch = createEmptyArray<T>(af::dim4(scratchpad_size));
+    auto ipivMem    = memAlloc<int64_t>(MN);
+    auto scratchMem = memAlloc<compute_t<T>>(scratchpad_size);
+    sycl::buffer<int64_t> ipiv(*ipivMem, 0, MN);
+    sycl::buffer<compute_t<T>> scratchpad(*scratchMem, 0, scratchpad_size);
 
-    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, *in.get(), LDA, ipiv,
-                                 *scratch.get(), scratchpad_size);
+    sycl::buffer<compute_t<T>> in_buffer = in.template getBufferWithOffset<compute_t<T>>();
+    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, in_buffer,  LDA, ipiv,
+                                 scratchpad, scratchpad_size);
 
     Array<int> pivot = convertPivot(ipiv, M, convert_pivot);
     return pivot;
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index 2b383b9520..f2cbab094c 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -165,6 +165,7 @@ INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
 INSTANTIATE(arrayfire::common::half)
+INSTANTIATE(int64_t)
 
 template<>
 void *pinnedAlloc<void>(const size_t &elements) {
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
index 2c9b751d15..ccea706ceb 100644
--- a/src/backend/oneapi/svd.cpp
+++ b/src/backend/oneapi/svd.cpp
@@ -38,15 +38,23 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     int64_t LDU   = uStrides[1];
     int64_t LDVt  = vStrides[1];
 
-    int64_t scratch_size = ::oneapi::mkl::lapack::gesvd_scratchpad_size<T>(
+    int64_t scratch_size = ::oneapi::mkl::lapack::gesvd_scratchpad_size<compute_t<T>>(
         getQueue(), ::oneapi::mkl::jobsvd::vectors,
         ::oneapi::mkl::jobsvd::vectors, M, N, LDA, LDU, LDVt);
-    Array<T> scratchpad = createEmptyArray<T>(af::dim4(scratch_size));
+
+    auto scratchpadMem  = memAlloc<compute_t<T>>(scratch_size);
+    sycl::buffer<compute_t<T>> scratchpad(*scratchpadMem, 0, scratch_size);
+
+    sycl::buffer<compute_t<T>> in_buffer = in.template getBufferWithOffset<compute_t<T>>();
+
+    sycl::buffer<compute_t<Tr>> sBuf  = s.template getBufferWithOffset<compute_t<Tr>>();
+    sycl::buffer<compute_t<T>> uBuf  = u.template getBufferWithOffset<compute_t<T>>();
+    sycl::buffer<compute_t<T>> vtBuf = vt.template getBufferWithOffset<compute_t<T>>();
 
     ::oneapi::mkl::lapack::gesvd(
         getQueue(), ::oneapi::mkl::jobsvd::vectors,
-        ::oneapi::mkl::jobsvd::vectors, M, N, *in.get(), LDA, *s.get(),
-        *u.get(), LDU, *vt.get(), LDVt, *scratchpad.get(), scratch_size);
+        ::oneapi::mkl::jobsvd::vectors, M, N, in_buffer, LDA, sBuf,
+        uBuf, LDU, vtBuf, LDVt, scratchpad, scratch_size);
 }
 
 template<typename T, typename Tr>

From 6602a4b85f5a04748f82845eca9de30a9d3e5402 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 2 Jun 2023 07:31:33 -0400
Subject: [PATCH 2521/2677] change oneMKL scratch size to exact sycl::buffer
 instead of memAlloc

---
 src/backend/oneapi/cholesky.cpp | 11 ++++++---
 src/backend/oneapi/lu.cpp       | 17 +++++++------
 src/backend/oneapi/svd.cpp      | 42 ++++++++++++++++++++-------------
 3 files changed, 43 insertions(+), 27 deletions(-)

diff --git a/src/backend/oneapi/cholesky.cpp b/src/backend/oneapi/cholesky.cpp
index 1b81be7f03..882f915d15 100644
--- a/src/backend/oneapi/cholesky.cpp
+++ b/src/backend/oneapi/cholesky.cpp
@@ -36,12 +36,17 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     lwork = ::oneapi::mkl::lapack::potrf_scratchpad_size<T>(getQueue(), uplo, N,
                                                             LDA);
 
-    auto workspaceMem  = memAlloc<compute_t<T>>(lwork);
-    sycl::buffer<compute_t<T>> in_buffer = in.template getBufferWithOffset<compute_t<T>>();
+    // MKL is finicky about exact scratch space size so we'll need to
+    // create sycl::buffer of exact size. if we use memAlloc, this might
+    // require a sub-buffer of a sub-buffer returned by memAlloc which is
+    // currently illegal in sycl
+    sycl::buffer<compute_t<T>> workspace(lwork);
+    sycl::buffer<compute_t<T>> in_buffer =
+        in.template getBufferWithOffset<compute_t<T>>();
 
     try {
         ::oneapi::mkl::lapack::potrf(getQueue(), uplo, N, in_buffer, LDA,
-                                     *workspaceMem, lwork);
+                                     workspace, lwork);
     } catch (::oneapi::mkl::lapack::exception const &e) {
         AF_ERROR(
             "Unexpected exception caught during synchronous\
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
index 20c44f2529..1ae473650b 100644
--- a/src/backend/oneapi/lu.cpp
+++ b/src/backend/oneapi/lu.cpp
@@ -11,11 +11,11 @@
 #include <lu.hpp>
 
 #if defined(WITH_LINEAR_ALGEBRA)
-#include <oneapi/mkl/lapack.hpp>
 #include <blas.hpp>
 #include <copy.hpp>
 #include <kernel/lu_split.hpp>
 #include <memory.hpp>
+#include <oneapi/mkl/lapack.hpp>
 #include <platform.hpp>
 
 namespace arrayfire {
@@ -77,13 +77,16 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     std::int64_t scratchpad_size =
         ::oneapi::mkl::lapack::getrf_scratchpad_size<T>(getQueue(), M, N, LDA);
 
-    auto ipivMem    = memAlloc<int64_t>(MN);
-    auto scratchMem = memAlloc<compute_t<T>>(scratchpad_size);
-    sycl::buffer<int64_t> ipiv(*ipivMem, 0, MN);
-    sycl::buffer<compute_t<T>> scratchpad(*scratchMem, 0, scratchpad_size);
+    // MKL is finicky about exact scratch space size so we'll need to
+    // create sycl::buffer of exact size. if we use memAlloc, this might
+    // require a sub-buffer of a sub-buffer returned by memAlloc which is
+    // currently illegal in sycl
+    sycl::buffer<int64_t> ipiv(MN);
+    sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
 
-    sycl::buffer<compute_t<T>> in_buffer = in.template getBufferWithOffset<compute_t<T>>();
-    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, in_buffer,  LDA, ipiv,
+    sycl::buffer<compute_t<T>> in_buffer =
+        in.template getBufferWithOffset<compute_t<T>>();
+    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, in_buffer, LDA, ipiv,
                                  scratchpad, scratchpad_size);
 
     Array<int> pivot = convertPivot(ipiv, M, convert_pivot);
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
index ccea706ceb..97b5f3a468 100644
--- a/src/backend/oneapi/svd.cpp
+++ b/src/backend/oneapi/svd.cpp
@@ -38,23 +38,31 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     int64_t LDU   = uStrides[1];
     int64_t LDVt  = vStrides[1];
 
-    int64_t scratch_size = ::oneapi::mkl::lapack::gesvd_scratchpad_size<compute_t<T>>(
-        getQueue(), ::oneapi::mkl::jobsvd::vectors,
-        ::oneapi::mkl::jobsvd::vectors, M, N, LDA, LDU, LDVt);
-
-    auto scratchpadMem  = memAlloc<compute_t<T>>(scratch_size);
-    sycl::buffer<compute_t<T>> scratchpad(*scratchpadMem, 0, scratch_size);
-
-    sycl::buffer<compute_t<T>> in_buffer = in.template getBufferWithOffset<compute_t<T>>();
-
-    sycl::buffer<compute_t<Tr>> sBuf  = s.template getBufferWithOffset<compute_t<Tr>>();
-    sycl::buffer<compute_t<T>> uBuf  = u.template getBufferWithOffset<compute_t<T>>();
-    sycl::buffer<compute_t<T>> vtBuf = vt.template getBufferWithOffset<compute_t<T>>();
-
-    ::oneapi::mkl::lapack::gesvd(
-        getQueue(), ::oneapi::mkl::jobsvd::vectors,
-        ::oneapi::mkl::jobsvd::vectors, M, N, in_buffer, LDA, sBuf,
-        uBuf, LDU, vtBuf, LDVt, scratchpad, scratch_size);
+    // MKL is finicky about exact scratch space size so we'll need to
+    // create sycl::buffer of exact size. if we use memAlloc, this might
+    // require a sub-buffer of a sub-buffer returned by memAlloc which is
+    // currently illegal in sycl
+    int64_t scratch_size =
+        ::oneapi::mkl::lapack::gesvd_scratchpad_size<compute_t<T>>(
+            getQueue(), ::oneapi::mkl::jobsvd::vectors,
+            ::oneapi::mkl::jobsvd::vectors, M, N, LDA, LDU, LDVt);
+
+    sycl::buffer<compute_t<T>> scratchpad(scratch_size);
+
+    sycl::buffer<compute_t<T>> in_buffer =
+        in.template getBufferWithOffset<compute_t<T>>();
+
+    sycl::buffer<compute_t<Tr>> sBuf =
+        s.template getBufferWithOffset<compute_t<Tr>>();
+    sycl::buffer<compute_t<T>> uBuf =
+        u.template getBufferWithOffset<compute_t<T>>();
+    sycl::buffer<compute_t<T>> vtBuf =
+        vt.template getBufferWithOffset<compute_t<T>>();
+
+    ::oneapi::mkl::lapack::gesvd(getQueue(), ::oneapi::mkl::jobsvd::vectors,
+                                 ::oneapi::mkl::jobsvd::vectors, M, N,
+                                 in_buffer, LDA, sBuf, uBuf, LDU, vtBuf, LDVt,
+                                 scratchpad, scratch_size);
 }
 
 template<typename T, typename Tr>

From 2f6e5e3e877cba145f99bc7550e10f4dbd35cfb5 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 2 Jun 2023 07:32:02 -0400
Subject: [PATCH 2522/2677] implement solve, pinverse in oneapi backend

---
 src/backend/oneapi/inverse.cpp |   3 +-
 src/backend/oneapi/solve.cpp   | 453 ++++++++++++++++++---------------
 2 files changed, 247 insertions(+), 209 deletions(-)

diff --git a/src/backend/oneapi/inverse.cpp b/src/backend/oneapi/inverse.cpp
index 97d91f4db4..2779393906 100644
--- a/src/backend/oneapi/inverse.cpp
+++ b/src/backend/oneapi/inverse.cpp
@@ -19,9 +19,8 @@ namespace oneapi {
 
 template<typename T>
 Array<T> inverse(const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("");
     Array<T> I = identity<T>(in.dims());
-    return I;
+    return solve<T>(in, I);
 }
 
 #define INSTANTIATE(T) template Array<T> inverse<T>(const Array<T> &in);
diff --git a/src/backend/oneapi/solve.cpp b/src/backend/oneapi/solve.cpp
index a4082c0d1f..07c4f3b171 100644
--- a/src/backend/oneapi/solve.cpp
+++ b/src/backend/oneapi/solve.cpp
@@ -11,111 +11,152 @@
 
 #include <err_oneapi.hpp>
 
-#if defined(WITH_LINEAR_ALGEBRA) && !defined(AF_ONEAPI)
+#if defined(WITH_LINEAR_ALGEBRA)
+#include <Array.hpp>
 #include <blas.hpp>
+#include <common/cast.hpp>
 #include <copy.hpp>
-#include <cpu/cpu_solve.hpp>
 #include <lu.hpp>
-#include <magma/magma.h>
-#include <magma/magma_blas.h>
-#include <magma/magma_data.h>
-#include <magma/magma_helper.h>
 #include <math.hpp>
+#include <memory.hpp>
+#include <oneapi/mkl/blas.hpp>
+#include <oneapi/mkl/lapack.hpp>
 #include <platform.hpp>
 #include <transpose.hpp>
-#include <af/opencl.h>
 
 #include <algorithm>
 #include <vector>
 
-using cl::Buffer;
+using arrayfire::common::cast;
 using std::min;
 using std::vector;
 
 namespace arrayfire {
 namespace oneapi {
 
+static ::oneapi::mkl::transpose toMKLTranspose(af_mat_prop opt) {
+    switch (opt) {
+        case AF_MAT_NONE: return ::oneapi::mkl::transpose::nontrans;
+        case AF_MAT_TRANS: return ::oneapi::mkl::transpose::trans;
+        case AF_MAT_CTRANS: return ::oneapi::mkl::transpose::conjtrans;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+}
+
 template<typename T>
 Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
                  const af_mat_prop options) {
-    ONEAPI_NOT_SUPPORTED("solveLU Not supported");
+    const int64_t N    = A.dims()[0];
+    const int64_t NRHS = b.dims()[1];
+    const int64_t LDA  = A.strides()[1];
+    const int64_t LDB  = b.strides()[1];
+
+    ::oneapi::mkl::transpose opts = toMKLTranspose(options);
+    // see comments in core lapack functions about MKL scratch space
+    // avoiding memAlloc, since this may require a sub-buffer of a sub-buffer
+    // returned by memAlloc which is currently illegal in sycl
+    std::int64_t scratchpad_size =
+        ::oneapi::mkl::lapack::getrs_scratchpad_size<compute_t<T>>(
+            getQueue(), opts, N, NRHS, LDA, LDB);
+
+    // TODO: which one?
+    Array<intl> ipiv              = cast<intl, int>(pivot);
+    sycl::buffer<int64_t> ipivBuf = ipiv.get()->reinterpret<int64_t, 1>();
+    sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+
+    /*
+    sycl::buffer<int64_t> ipivBuf(pivot.elements());
+    getQueue()
+        .submit([&](sycl::handler &h) {
+            auto ipivIn =
+                pivot.get()->template get_access<sycl::access_mode::read>(h);
+            auto ipivOut =
+                ipivBuf.get_access<sycl::access_mode::write>(h);
+            h.parallel_for(pivot.elements(),
+                    [=](sycl::id<1> i) {
+                        ipivOut[i] = static_cast<int64_t>(ipivIn[i]);
+                    });
+
+        });
+    */
+
+    Array<compute_t<T>> B = copyArray<compute_t<T>>(b);
+    sycl::buffer<compute_t<T>> aBuf =
+        A.template getBufferWithOffset<compute_t<T>>();
+    sycl::buffer<compute_t<T>> bBuf =
+        B.template getBufferWithOffset<compute_t<T>>();
+
+    ::oneapi::mkl::lapack::getrs(getQueue(), opts, N, NRHS, aBuf, LDA, ipivBuf,
+                                 bBuf, LDB, scratchpad, scratchpad_size);
+    return B;
+}
+
+template<typename T>
+Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
+    int batches = a.dims()[2] * a.dims()[3];
+
+    dim4 aDims = a.dims();
+    dim4 bDims = b.dims();
+    int M      = aDims[0];
+    int N      = aDims[1];
+    int K      = bDims[1];
+    int MN     = std::min(M, N);
+
+    int lda     = a.strides()[1];
+    int astride = a.strides()[2];
 
-    if (OpenCLCPUOffload()) { return cpu::solveLU(A, pivot, b, options); }
+    sycl::buffer<int64_t> ipiv(MN * batches);
+    int ipivstride = MN;
 
-    int N    = A.dims()[0];
-    int NRHS = b.dims()[1];
+    int ldb     = b.strides()[1];
+    int bstride = b.strides()[2];
 
-    vector<int> ipiv(N);
-    copyData(&ipiv[0], pivot);
+    vector<int> info(batches, 0);
 
+    Array<T> A = copyArray<T>(a);
     Array<T> B = copyArray<T>(b);
 
-    const Buffer *A_buf = A.get();
-    Buffer *B_buf       = B.get();
+    std::int64_t scratchpad_size =
+        ::oneapi::mkl::lapack::getrf_batch_scratchpad_size<compute_t<T>>(
+            getQueue(), M, N, lda, astride, ipivstride, batches);
 
-    int info = 0;
-    magma_getrs_gpu<T>(MagmaNoTrans, N, NRHS, (*A_buf)(), A.getOffset(),
-                       A.strides()[1], &ipiv[0], (*B_buf)(), B.getOffset(),
-                       B.strides()[1], getQueue()(), &info);
-    return B;
-}
+    sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
 
-template<typename T>
-Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
-    ONEAPI_NOT_SUPPORTED("generalSolve Not supported");
+    sycl::buffer<compute_t<T>> aBuf =
+        A.template getBufferWithOffset<compute_t<T>>();
+    sycl::buffer<compute_t<T>> bBuf =
+        B.template getBufferWithOffset<compute_t<T>>();
+    ::oneapi::mkl::lapack::getrf_batch(getQueue(), M, N, aBuf, lda, astride,
+                                       ipiv, ipivstride, batches, scratchpad,
+                                       scratchpad_size);
 
-    // dim4 aDims = a.dims();
-    // int batchz = aDims[2];
-    // int batchw = aDims[3];
+    scratchpad_size =
+        ::oneapi::mkl::lapack::getrs_batch_scratchpad_size<compute_t<T>>(
+            getQueue(), ::oneapi::mkl::transpose::nontrans, N, K, lda, astride,
+            ipivstride, ldb, bstride, batches);
 
-    // Array<T> A = copyArray<T>(a);
-    Array<T> B = copyArray<T>(b);
+    // TODO: reuse? or single large scratchpad?
+    sycl::buffer<compute_t<T>> scratchpad_rs(scratchpad_size);
+
+    ::oneapi::mkl::lapack::getrs_batch(
+        getQueue(), ::oneapi::mkl::transpose::nontrans, N, K, aBuf, lda,
+        astride, ipiv, ipivstride, bBuf, ldb, bstride, batches, scratchpad_rs,
+        scratchpad_size);
 
-    // for (int i = 0; i < batchw; i++) {
-    //     for (int j = 0; j < batchz; j++) {
-    //         int M  = aDims[0];
-    //         int N  = aDims[1];
-    //         int MN = min(M, N);
-    //         vector<int> ipiv(MN);
-
-    //         Buffer *A_buf      = A.get();
-    //         int info           = 0;
-    //         cl_command_queue q = getQueue()();
-    //         auto aoffset =
-    //             A.getOffset() + j * A.strides()[2] + i * A.strides()[3];
-    //         magma_getrf_gpu<T>(M, N, (*A_buf)(), aoffset, A.strides()[1],
-    //                            &ipiv[0], q, &info);
-
-    //         Buffer *B_buf = B.get();
-    //         int K         = B.dims()[1];
-
-    //         auto boffset =
-    //             B.getOffset() + j * B.strides()[2] + i * B.strides()[3];
-    //         magma_getrs_gpu<T>(MagmaNoTrans, M, K, (*A_buf)(), aoffset,
-    //                            A.strides()[1], &ipiv[0], (*B_buf)(), boffset,
-    //                            B.strides()[1], q, &info);
-    //     }
-    // }
     return B;
 }
 
 template<typename T>
 Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
-    ONEAPI_NOT_SUPPORTED("leastSquares Not supported");
-
-    int M  = a.dims()[0];
-    int N  = a.dims()[1];
-    int K  = b.dims()[1];
-    int MN = min(M, N);
+    int64_t M  = a.dims()[0];
+    int64_t N  = a.dims()[1];
+    int64_t K  = b.dims()[1];
+    int64_t MN = min(M, N);
 
     Array<T> B = createEmptyArray<T>(dim4());
-    gpu_blas_trsm_func<T> gpu_blas_trsm;
-
-    cl_event event;
-    cl_command_queue queue = getQueue()();
 
     if (M < N) {
-#define UNMQR 0  // FIXME: UNMQR == 1 should be faster but does not work
+        const dim4 NullShape(0, 0, 0, 0);
 
         // Least squres for this case is solved using the following
         // solve(A, B) == matmul(Q, Xpad);
@@ -127,71 +168,81 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
 
         // QR is performed on the transpose of A
         Array<T> A = transpose<T>(a, true);
-
-#if UNMQR
-        const dim4 NullShape(0, 0, 0, 0);
         dim4 endPadding(N - b.dims()[0], K - b.dims()[1], 0, 0);
         B = (endPadding == NullShape
                  ? copyArray(b)
                  : padArrayBorders(b, NullShape, endPadding, AF_PAD_ZERO));
-        B.resetDims(dim4(M, K));
-#else
-        B = copyArray<T>(b);
-#endif
-
-        int NB       = magma_get_geqrf_nb<T>(A.dims()[1]);
-        int NUM      = (2 * MN + ((M + 31) / 32) * 32) * NB;
-        Array<T> tmp = createEmptyArray<T>(dim4(NUM));
 
-        vector<T> h_tau(MN);
+        // Get workspace needed for QR
+        std::int64_t scratchpad_size =
+            ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
+                getQueue(), A.dims()[0], A.dims()[1], A.strides()[1]);
 
-        int info   = 0;
-        Buffer *dA = A.get();
-        Buffer *dT = tmp.get();
-        Buffer *dB = B.get();
+        sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+        Array<compute_t<T>> t = createEmptyArray<T>(af::dim4(MN, 1, 1, 1));
 
-        magma_geqrf3_gpu<T>(A.dims()[0], A.dims()[1], (*dA)(), A.getOffset(),
-                            A.strides()[1], &h_tau[0], (*dT)(), tmp.getOffset(),
-                            getQueue()(), &info);
+        sycl::buffer<compute_t<T>> aBuf =
+            A.template getBufferWithOffset<compute_t<T>>();
+        sycl::buffer<compute_t<T>> tBuf =
+            t.template getBufferWithOffset<compute_t<T>>();
+        // In place Perform in place QR
+        ::oneapi::mkl::lapack::geqrf(getQueue(), A.dims()[0], A.dims()[1], aBuf,
+                                     A.strides()[1], tBuf, scratchpad,
+                                     scratchpad_size);
 
+        // R1 = R(seq(M), seq(M));
         A.resetDims(dim4(M, M));
 
-        magmablas_swapdblk<T>(MN - 1, NB, (*dA)(), A.getOffset(),
-                              A.strides()[1], 1, (*dT)(),
-                              tmp.getOffset() + MN * NB, NB, 0, queue);
+        // Bt = tri_solve(R1, B);
+        B.resetDims(dim4(M, K));
 
-        OPENCL_BLAS_CHECK(
-            gpu_blas_trsm(OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
-                          OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL,
-                          B.dims()[0], B.dims()[1], scalar<T>(1), (*dA)(),
-                          A.getOffset(), A.strides()[1], (*dB)(), B.getOffset(),
-                          B.strides()[1], 1, &queue, 0, nullptr, &event));
+        sycl::buffer<compute_t<T>> bBuf =
+            B.template getBufferWithOffset<compute_t<T>>();
+        // TODO: move to helper? trsm<T>(A, B, AF_MAT_CTRANS, true, true,
+        // false);
+        compute_t<T> alpha = scalar<compute_t<T>>(1);
+        ::oneapi::mkl::blas::trsm(
+            getQueue(), ::oneapi::mkl::side::left, ::oneapi::mkl::uplo::upper,
+            ::oneapi::mkl::transpose::conjtrans, ::oneapi::mkl::diag::nonunit,
+            B.dims()[0], B.dims()[1], alpha, aBuf, A.strides()[1], bBuf,
+            B.strides()[1]);
+
+        // Bpad = pad(Bt, ..)
+        B.resetDims(dim4(N, K));
 
-        magmablas_swapdblk<T>(MN - 1, NB, (*dT)(), tmp.getOffset() + MN * NB,
-                              NB, 0, (*dA)(), A.getOffset(), A.strides()[1], 1,
-                              queue);
+        // matmul(Q, Bpad)
+        if constexpr (std::is_same_v<compute_t<T>, float> ||
+                      std::is_same_v<compute_t<T>, double>) {
+            std::int64_t scratchpad_size =
+                ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
+                    getQueue(), ::oneapi::mkl::side::left,
+                    ::oneapi::mkl::transpose::nontrans, B.dims()[0],
+                    B.dims()[1], A.dims()[0], A.strides()[1], B.strides()[1]);
+
+            sycl::buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
+            ::oneapi::mkl::lapack::ormqr(
+                getQueue(), ::oneapi::mkl::side::left,
+                ::oneapi::mkl::transpose::nontrans, B.dims()[0], B.dims()[1],
+                A.dims()[0], aBuf, A.strides()[1], tBuf, bBuf, B.strides()[1],
+                scratchpad_ormqr, scratchpad_size);
+        } else if constexpr (std::is_same_v<compute_t<T>,
+                                            std::complex<float>> ||
+                             std::is_same_v<compute_t<T>,
+                                            std::complex<double>>) {
+            std::int64_t scratchpad_size =
+                ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
+                    getQueue(), ::oneapi::mkl::side::left,
+                    ::oneapi::mkl::transpose::nontrans, B.dims()[0],
+                    B.dims()[1], A.dims()[0], A.strides()[1], B.strides()[1]);
+
+            sycl::buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
+            ::oneapi::mkl::lapack::unmqr(
+                getQueue(), ::oneapi::mkl::side::left,
+                ::oneapi::mkl::transpose::nontrans, B.dims()[0], B.dims()[1],
+                A.dims()[0], aBuf, A.strides()[1], tBuf, bBuf, B.strides()[1],
+                scratchpad_unmqr, scratchpad_size);
+        }
 
-#if UNMQR
-        int lwork = (B.dims()[0] - A.dims()[0] + NB) * (B.dims()[1] + 2 * NB);
-        vector<T> h_work(lwork);
-        B.resetDims(dim4(N, K));
-        magma_unmqr_gpu<T>(MagmaLeft, MagmaNoTrans, B.dims()[0], B.dims()[1],
-                           A.dims()[0], (*dA)(), A.getOffset(), A.strides()[1],
-                           &h_tau[0], (*dB)(), B.getOffset(), B.strides()[1],
-                           &h_work[0], lwork, (*dT)(), tmp.getOffset(), NB,
-                           queue, &info);
-#else
-        A.resetDims(dim4(N, M));
-        magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], min(M, N), (*dA)(),
-                           A.getOffset(), A.strides()[1], &h_tau[0], (*dT)(),
-                           tmp.getOffset(), NB, queue, &info);
-
-        Array<T> B_new = createEmptyArray<T>(dim4(A.dims()[0], B.dims()[1]));
-        T alpha        = scalar<T>(1.0);
-        T beta         = scalar<T>(0.0);
-        gemm<T>(B_new, AF_MAT_NONE, AF_MAT_NONE, &alpha, A, B, &beta);
-        B = B_new;
-#endif
     } else if (M > N) {
         // Least squres for this case is solved using the following
         // solve(A, B) == tri_solve(R1, Bt);
@@ -204,56 +255,65 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         Array<T> A = copyArray<T>(a);
         B          = copyArray(b);
 
-        int MN = min(M, N);
-        int NB = magma_get_geqrf_nb<T>(M);
-
-        int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
-        Array<T> tmp = createEmptyArray<T>(dim4(NUM));
-
-        vector<T> h_tau(NUM);
-
-        int info      = 0;
-        Buffer *A_buf = A.get();
-        Buffer *B_buf = B.get();
-        Buffer *dT    = tmp.get();
-
-        magma_geqrf3_gpu<T>(M, N, (*A_buf)(), A.getOffset(), A.strides()[1],
-                            &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
-                            &info);
-
-        int NRHS   = B.dims()[1];
-        int lhwork = (M - N + NB) * (NRHS + NB) + NRHS * NB;
-
-        vector<T> h_work(lhwork);
-        h_work[0] = scalar<T>(lhwork);
-
-        magma_unmqr_gpu<T>(MagmaLeft, MagmaConjTrans, M, NRHS, N, (*A_buf)(),
-                           A.getOffset(), A.strides()[1], &h_tau[0], (*B_buf)(),
-                           B.getOffset(), B.strides()[1], &h_work[0], lhwork,
-                           (*dT)(), tmp.getOffset(), NB, queue, &info);
-
-        magmablas_swapdblk<T>(MN - 1, NB, (*A_buf)(), A.getOffset(),
-                              A.strides()[1], 1, (*dT)(),
-                              tmp.getOffset() + NB * MN, NB, 0, queue);
-
-        if (getActivePlatform() == AFCL_PLATFORM_NVIDIA) {
-            Array<T> AT    = transpose<T>(A, true);
-            Buffer *AT_buf = AT.get();
-            OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
-                OPENCL_BLAS_CONJ_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
-                scalar<T>(1), (*AT_buf)(), AT.getOffset(), AT.strides()[1],
-                (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0,
-                nullptr, &event));
-        } else {
-            OPENCL_BLAS_CHECK(gpu_blas_trsm(
-                OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_UPPER,
-                OPENCL_BLAS_NO_TRANS, OPENCL_BLAS_NON_UNIT_DIAGONAL, N, NRHS,
-                scalar<T>(1), (*A_buf)(), A.getOffset(), A.strides()[1],
-                (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0,
-                nullptr, &event));
+        // Get workspace needed for QR
+        std::int64_t scratchpad_size =
+            ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
+                getQueue(), M, N, A.strides()[1]);
+
+        sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+        Array<compute_t<T>> t = createEmptyArray<T>(af::dim4(MN, 1, 1, 1));
+
+        sycl::buffer<compute_t<T>> aBuf =
+            A.template getBufferWithOffset<compute_t<T>>();
+        sycl::buffer<compute_t<T>> tBuf =
+            t.template getBufferWithOffset<compute_t<T>>();
+        // In place Perform in place QR
+        ::oneapi::mkl::lapack::geqrf(getQueue(), M, N, aBuf, A.strides()[1],
+                                     tBuf, scratchpad, scratchpad_size);
+
+        // matmul(Q1, B)
+        sycl::buffer<compute_t<T>> bBuf =
+            B.template getBufferWithOffset<compute_t<T>>();
+        if constexpr (std::is_same_v<compute_t<T>, float> ||
+                      std::is_same_v<compute_t<T>, double>) {
+            std::int64_t scratchpad_size =
+                ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
+                    getQueue(), ::oneapi::mkl::side::left,
+                    ::oneapi::mkl::transpose::trans, M, K, N, A.strides()[1],
+                    b.strides()[1]);
+
+            sycl::buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
+            ::oneapi::mkl::lapack::ormqr(
+                getQueue(), ::oneapi::mkl::side::left,
+                ::oneapi::mkl::transpose::trans, M, K, N, aBuf, A.strides()[1],
+                tBuf, bBuf, b.strides()[1], scratchpad_ormqr, scratchpad_size);
+        } else if constexpr (std::is_same_v<compute_t<T>,
+                                            std::complex<float>> ||
+                             std::is_same_v<compute_t<T>,
+                                            std::complex<double>>) {
+            std::int64_t scratchpad_size =
+                ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
+                    getQueue(), ::oneapi::mkl::side::left,
+                    ::oneapi::mkl::transpose::conjtrans, M, K, N,
+                    A.strides()[1], b.strides()[1]);
+
+            sycl::buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
+            ::oneapi::mkl::lapack::unmqr(getQueue(), ::oneapi::mkl::side::left,
+                                         ::oneapi::mkl::transpose::conjtrans, M,
+                                         K, N, aBuf, A.strides()[1], tBuf, bBuf,
+                                         b.strides()[1], scratchpad_unmqr,
+                                         scratchpad_size);
         }
+
+        // tri_solve(R1, Bt)
+        A.resetDims(dim4(N, N));
         B.resetDims(dim4(N, K));
+
+        compute_t<T> alpha = scalar<compute_t<T>>(1);
+        ::oneapi::mkl::blas::trsm(
+            getQueue(), ::oneapi::mkl::side::left, ::oneapi::mkl::uplo::upper,
+            ::oneapi::mkl::transpose::nontrans, ::oneapi::mkl::diag::nonunit, N,
+            K, alpha, aBuf, A.strides()[1], bBuf, B.strides()[1]);
     }
 
     return B;
@@ -262,53 +322,32 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
 template<typename T>
 Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
                        const af_mat_prop options) {
-    gpu_blas_trsm_func<T> gpu_blas_trsm;
-
-    Array<T> B = copyArray<T>(b);
-
-    int N    = B.dims()[0];
-    int NRHS = B.dims()[1];
-
-    const Buffer *A_buf = A.get();
-    Buffer *B_buf       = B.get();
-
-    cl_event event         = 0;
-    cl_command_queue queue = getQueue()();
-
-    if (getActivePlatform() == AFCL_PLATFORM_NVIDIA &&
-        (options & AF_MAT_UPPER)) {
-        Array<T> AT = transpose<T>(A, true);
-
-        cl::Buffer *AT_buf = AT.get();
-        OPENCL_BLAS_CHECK(gpu_blas_trsm(
-            OPENCL_BLAS_SIDE_LEFT, OPENCL_BLAS_TRIANGLE_LOWER,
-            OPENCL_BLAS_CONJ_TRANS,
-            options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL
-                                       : OPENCL_BLAS_NON_UNIT_DIAGONAL,
-            N, NRHS, scalar<T>(1), (*AT_buf)(), AT.getOffset(), AT.strides()[1],
-            (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0, nullptr,
-            &event));
-    } else {
-        OPENCL_BLAS_CHECK(gpu_blas_trsm(
-            OPENCL_BLAS_SIDE_LEFT,
-            options & AF_MAT_LOWER ? OPENCL_BLAS_TRIANGLE_LOWER
-                                   : OPENCL_BLAS_TRIANGLE_UPPER,
-            OPENCL_BLAS_NO_TRANS,
-            options & AF_MAT_DIAG_UNIT ? OPENCL_BLAS_UNIT_DIAGONAL
-                                       : OPENCL_BLAS_NON_UNIT_DIAGONAL,
-            N, NRHS, scalar<T>(1), (*A_buf)(), A.getOffset(), A.strides()[1],
-            (*B_buf)(), B.getOffset(), B.strides()[1], 1, &queue, 0, nullptr,
-            &event));
-    }
-
+    Array<compute_t<T>> B = copyArray<T>(b);
+
+    compute_t<T> alpha       = scalar<compute_t<T>>(1);
+    ::oneapi::mkl::uplo uplo = (options & AF_MAT_UPPER)
+                                   ? ::oneapi::mkl::uplo::upper
+                                   : ::oneapi::mkl::uplo::lower;
+
+    ::oneapi::mkl::diag unitdiag = (options & AF_MAT_DIAG_UNIT)
+                                       ? ::oneapi::mkl::diag::unit
+                                       : ::oneapi::mkl::diag::nonunit;
+
+    sycl::buffer<compute_t<T>> aBuf =
+        A.template getBufferWithOffset<compute_t<T>>();
+    sycl::buffer<compute_t<T>> bBuf =
+        B.template getBufferWithOffset<compute_t<T>>();
+
+    ::oneapi::mkl::blas::trsm(getQueue(), ::oneapi::mkl::side::left, uplo,
+                              ::oneapi::mkl::transpose::nontrans, unitdiag,
+                              B.dims()[0], B.dims()[1], alpha, aBuf,
+                              A.strides()[1], bBuf, B.strides()[1]);
     return B;
 }
 
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options) {
-    if (OpenCLCPUOffload()) { return cpu::solve(a, b, options); }
-
     if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
         return triangleSolve<T>(a, b, options);
     }

From 05303ad8b34593997a9ef681b96ecd9c7e0824f3 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 2 Jun 2023 17:00:48 -0400
Subject: [PATCH 2523/2677] minor cleanup, replaces repeated std::is_same_v
 with is_any_of

---
 src/backend/oneapi/solve.cpp | 105 +++++++++++++----------------------
 src/backend/oneapi/solve.hpp |  10 ++++
 2 files changed, 48 insertions(+), 67 deletions(-)

diff --git a/src/backend/oneapi/solve.cpp b/src/backend/oneapi/solve.cpp
index 07c4f3b171..d234b5920c 100644
--- a/src/backend/oneapi/solve.cpp
+++ b/src/backend/oneapi/solve.cpp
@@ -30,6 +30,7 @@
 using arrayfire::common::cast;
 using std::min;
 using std::vector;
+using sycl::buffer;
 
 namespace arrayfire {
 namespace oneapi {
@@ -59,32 +60,13 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
         ::oneapi::mkl::lapack::getrs_scratchpad_size<compute_t<T>>(
             getQueue(), opts, N, NRHS, LDA, LDB);
 
-    // TODO: which one?
-    Array<intl> ipiv              = cast<intl, int>(pivot);
-    sycl::buffer<int64_t> ipivBuf = ipiv.get()->reinterpret<int64_t, 1>();
-    sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
-
-    /*
-    sycl::buffer<int64_t> ipivBuf(pivot.elements());
-    getQueue()
-        .submit([&](sycl::handler &h) {
-            auto ipivIn =
-                pivot.get()->template get_access<sycl::access_mode::read>(h);
-            auto ipivOut =
-                ipivBuf.get_access<sycl::access_mode::write>(h);
-            h.parallel_for(pivot.elements(),
-                    [=](sycl::id<1> i) {
-                        ipivOut[i] = static_cast<int64_t>(ipivIn[i]);
-                    });
-
-        });
-    */
-
-    Array<compute_t<T>> B = copyArray<compute_t<T>>(b);
-    sycl::buffer<compute_t<T>> aBuf =
-        A.template getBufferWithOffset<compute_t<T>>();
-    sycl::buffer<compute_t<T>> bBuf =
-        B.template getBufferWithOffset<compute_t<T>>();
+    Array<intl> ipiv        = cast<intl, int>(pivot);
+    buffer<int64_t> ipivBuf = ipiv.get()->reinterpret<int64_t, 1>();
+    buffer<compute_t<T>> scratchpad(scratchpad_size);
+
+    Array<compute_t<T>> B     = copyArray<compute_t<T>>(b);
+    buffer<compute_t<T>> aBuf = A.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> bBuf = B.template getBufferWithOffset<compute_t<T>>();
 
     ::oneapi::mkl::lapack::getrs(getQueue(), opts, N, NRHS, aBuf, LDA, ipivBuf,
                                  bBuf, LDB, scratchpad, scratchpad_size);
@@ -102,10 +84,10 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
     int K      = bDims[1];
     int MN     = std::min(M, N);
 
-    int lda     = a.strides()[1];
-    int astride = a.strides()[2];
-
-    sycl::buffer<int64_t> ipiv(MN * batches);
+    int lda      = a.strides()[1];
+    int astride  = a.strides()[2];
+    auto ipivMem = memAlloc<int64_t>(MN * batches);
+    buffer<int64_t> ipiv(*ipivMem, 0, MN * batches);
     int ipivstride = MN;
 
     int ldb     = b.strides()[1];
@@ -113,19 +95,17 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
 
     vector<int> info(batches, 0);
 
-    Array<T> A = copyArray<T>(a);
-    Array<T> B = copyArray<T>(b);
+    Array<T> A = copyArray<T>(a);  // A will be overwritten by L,U
+    Array<T> B = copyArray<T>(b);  // will be overwritten with solution
 
     std::int64_t scratchpad_size =
         ::oneapi::mkl::lapack::getrf_batch_scratchpad_size<compute_t<T>>(
             getQueue(), M, N, lda, astride, ipivstride, batches);
 
-    sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+    buffer<compute_t<T>> scratchpad(scratchpad_size);
 
-    sycl::buffer<compute_t<T>> aBuf =
-        A.template getBufferWithOffset<compute_t<T>>();
-    sycl::buffer<compute_t<T>> bBuf =
-        B.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> aBuf = A.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> bBuf = B.template getBufferWithOffset<compute_t<T>>();
     ::oneapi::mkl::lapack::getrf_batch(getQueue(), M, N, aBuf, lda, astride,
                                        ipiv, ipivstride, batches, scratchpad,
                                        scratchpad_size);
@@ -135,8 +115,7 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
             getQueue(), ::oneapi::mkl::transpose::nontrans, N, K, lda, astride,
             ipivstride, ldb, bstride, batches);
 
-    // TODO: reuse? or single large scratchpad?
-    sycl::buffer<compute_t<T>> scratchpad_rs(scratchpad_size);
+    buffer<compute_t<T>> scratchpad_rs(scratchpad_size);
 
     ::oneapi::mkl::lapack::getrs_batch(
         getQueue(), ::oneapi::mkl::transpose::nontrans, N, K, aBuf, lda,
@@ -178,12 +157,12 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
             ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
                 getQueue(), A.dims()[0], A.dims()[1], A.strides()[1]);
 
-        sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+        buffer<compute_t<T>> scratchpad(scratchpad_size);
         Array<compute_t<T>> t = createEmptyArray<T>(af::dim4(MN, 1, 1, 1));
 
-        sycl::buffer<compute_t<T>> aBuf =
+        buffer<compute_t<T>> aBuf =
             A.template getBufferWithOffset<compute_t<T>>();
-        sycl::buffer<compute_t<T>> tBuf =
+        buffer<compute_t<T>> tBuf =
             t.template getBufferWithOffset<compute_t<T>>();
         // In place Perform in place QR
         ::oneapi::mkl::lapack::geqrf(getQueue(), A.dims()[0], A.dims()[1], aBuf,
@@ -196,7 +175,7 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         // Bt = tri_solve(R1, B);
         B.resetDims(dim4(M, K));
 
-        sycl::buffer<compute_t<T>> bBuf =
+        buffer<compute_t<T>> bBuf =
             B.template getBufferWithOffset<compute_t<T>>();
         // TODO: move to helper? trsm<T>(A, B, AF_MAT_CTRANS, true, true,
         // false);
@@ -211,31 +190,28 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
-        if constexpr (std::is_same_v<compute_t<T>, float> ||
-                      std::is_same_v<compute_t<T>, double>) {
+        if constexpr (is_any_of<compute_t<T>, float, double>()) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::nontrans, B.dims()[0],
                     B.dims()[1], A.dims()[0], A.strides()[1], B.strides()[1]);
 
-            sycl::buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
+            buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
             ::oneapi::mkl::lapack::ormqr(
                 getQueue(), ::oneapi::mkl::side::left,
                 ::oneapi::mkl::transpose::nontrans, B.dims()[0], B.dims()[1],
                 A.dims()[0], aBuf, A.strides()[1], tBuf, bBuf, B.strides()[1],
                 scratchpad_ormqr, scratchpad_size);
-        } else if constexpr (std::is_same_v<compute_t<T>,
-                                            std::complex<float>> ||
-                             std::is_same_v<compute_t<T>,
-                                            std::complex<double>>) {
+        } else if constexpr (is_any_of<compute_t<T>, std::complex<float>,
+                                       std::complex<double>>()) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::nontrans, B.dims()[0],
                     B.dims()[1], A.dims()[0], A.strides()[1], B.strides()[1]);
 
-            sycl::buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
+            buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
             ::oneapi::mkl::lapack::unmqr(
                 getQueue(), ::oneapi::mkl::side::left,
                 ::oneapi::mkl::transpose::nontrans, B.dims()[0], B.dims()[1],
@@ -260,44 +236,41 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
             ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
                 getQueue(), M, N, A.strides()[1]);
 
-        sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+        buffer<compute_t<T>> scratchpad(scratchpad_size);
         Array<compute_t<T>> t = createEmptyArray<T>(af::dim4(MN, 1, 1, 1));
 
-        sycl::buffer<compute_t<T>> aBuf =
+        buffer<compute_t<T>> aBuf =
             A.template getBufferWithOffset<compute_t<T>>();
-        sycl::buffer<compute_t<T>> tBuf =
+        buffer<compute_t<T>> tBuf =
             t.template getBufferWithOffset<compute_t<T>>();
         // In place Perform in place QR
         ::oneapi::mkl::lapack::geqrf(getQueue(), M, N, aBuf, A.strides()[1],
                                      tBuf, scratchpad, scratchpad_size);
 
         // matmul(Q1, B)
-        sycl::buffer<compute_t<T>> bBuf =
+        buffer<compute_t<T>> bBuf =
             B.template getBufferWithOffset<compute_t<T>>();
-        if constexpr (std::is_same_v<compute_t<T>, float> ||
-                      std::is_same_v<compute_t<T>, double>) {
+        if constexpr (is_any_of<compute_t<T>, float, double>()) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::trans, M, K, N, A.strides()[1],
                     b.strides()[1]);
 
-            sycl::buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
+            buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
             ::oneapi::mkl::lapack::ormqr(
                 getQueue(), ::oneapi::mkl::side::left,
                 ::oneapi::mkl::transpose::trans, M, K, N, aBuf, A.strides()[1],
                 tBuf, bBuf, b.strides()[1], scratchpad_ormqr, scratchpad_size);
-        } else if constexpr (std::is_same_v<compute_t<T>,
-                                            std::complex<float>> ||
-                             std::is_same_v<compute_t<T>,
-                                            std::complex<double>>) {
+        } else if constexpr (is_any_of<compute_t<T>, std::complex<float>,
+                                       std::complex<double>>()) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::conjtrans, M, K, N,
                     A.strides()[1], b.strides()[1]);
 
-            sycl::buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
+            buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
             ::oneapi::mkl::lapack::unmqr(getQueue(), ::oneapi::mkl::side::left,
                                          ::oneapi::mkl::transpose::conjtrans, M,
                                          K, N, aBuf, A.strides()[1], tBuf, bBuf,
@@ -333,10 +306,8 @@ Array<T> triangleSolve(const Array<T> &A, const Array<T> &b,
                                        ? ::oneapi::mkl::diag::unit
                                        : ::oneapi::mkl::diag::nonunit;
 
-    sycl::buffer<compute_t<T>> aBuf =
-        A.template getBufferWithOffset<compute_t<T>>();
-    sycl::buffer<compute_t<T>> bBuf =
-        B.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> aBuf = A.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> bBuf = B.template getBufferWithOffset<compute_t<T>>();
 
     ::oneapi::mkl::blas::trsm(getQueue(), ::oneapi::mkl::side::left, uplo,
                               ::oneapi::mkl::transpose::nontrans, unitdiag,
diff --git a/src/backend/oneapi/solve.hpp b/src/backend/oneapi/solve.hpp
index acea9327b4..819c0ced35 100644
--- a/src/backend/oneapi/solve.hpp
+++ b/src/backend/oneapi/solve.hpp
@@ -11,6 +11,16 @@
 
 namespace arrayfire {
 namespace oneapi {
+
+template<typename T, typename U, typename... Args>
+static inline constexpr bool is_any_of() {
+    if constexpr (!sizeof...(Args)) {
+        return std::is_same_v<T, U>;
+    } else {
+        return std::is_same_v<T, U> || is_any_of<T, Args...>();
+    }
+}
+
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options = AF_MAT_NONE);

From dbe4ee16594f862fcf30e0a7af1427b823b11ec2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Tue, 6 Jun 2023 19:42:00 -0400
Subject: [PATCH 2524/2677] match workspace size to actual size of workspace
 buffer

---
 src/api/c/solve.cpp             |  8 ++-
 src/backend/common/traits.hpp   |  9 ++++
 src/backend/oneapi/cholesky.cpp | 13 ++---
 src/backend/oneapi/lu.cpp       | 26 ++++------
 src/backend/oneapi/solve.cpp    | 91 ++++++++++++++++-----------------
 src/backend/oneapi/solve.hpp    |  9 ----
 src/backend/oneapi/svd.cpp      |  8 +--
 7 files changed, 75 insertions(+), 89 deletions(-)

diff --git a/src/api/c/solve.cpp b/src/api/c/solve.cpp
index ec17aafaba..31c1489484 100644
--- a/src/api/c/solve.cpp
+++ b/src/api/c/solve.cpp
@@ -95,8 +95,9 @@ static inline af_array solve_lu(const af_array a, const af_array pivot,
 af_err af_solve_lu(af_array* out, const af_array a, const af_array piv,
                    const af_array b, const af_mat_prop options) {
     try {
-        const ArrayInfo& a_info = getInfo(a);
-        const ArrayInfo& b_info = getInfo(b);
+        const ArrayInfo& a_info   = getInfo(a);
+        const ArrayInfo& b_info   = getInfo(b);
+        const ArrayInfo& piv_info = getInfo(piv);
 
         if (a_info.ndims() > 2 || b_info.ndims() > 2) {
             AF_ERROR("solveLU can not be used in batch mode", AF_ERR_BATCH);
@@ -116,6 +117,9 @@ af_err af_solve_lu(af_array* out, const af_array a, const af_array piv,
 
         TYPE_ASSERT(a_type == b_type);
 
+        af_dtype piv_type = piv_info.getType();
+        TYPE_ASSERT(piv_type == s32);  // TODO: add support for 64 bit types
+
         DIM_ASSERT(1, adims[0] == adims[1]);
         DIM_ASSERT(1, bdims[0] == adims[0]);
         DIM_ASSERT(1, bdims[2] == adims[2]);
diff --git a/src/backend/common/traits.hpp b/src/backend/common/traits.hpp
index 2b9090727c..7798c070c2 100644
--- a/src/backend/common/traits.hpp
+++ b/src/backend/common/traits.hpp
@@ -68,6 +68,15 @@ constexpr bool isFloating(af::dtype type) {
     return (!isInteger(type) && !isBool(type));
 }
 
+template<typename T, typename U, typename... Args>
+constexpr bool is_any_of() {
+    if constexpr (!sizeof...(Args)) {
+        return std::is_same_v<T, U>;
+    } else {
+        return std::is_same_v<T, U> || is_any_of<T, Args...>();
+    }
+}
+
 }  // namespace
 }  // namespace common
 }  // namespace arrayfire
diff --git a/src/backend/oneapi/cholesky.cpp b/src/backend/oneapi/cholesky.cpp
index 882f915d15..d399034383 100644
--- a/src/backend/oneapi/cholesky.cpp
+++ b/src/backend/oneapi/cholesky.cpp
@@ -17,6 +17,7 @@
 #include <memory.hpp>
 #include <oneapi/mkl/lapack.hpp>
 #include <triangle.hpp>
+#include <algorithm>
 
 namespace arrayfire {
 namespace oneapi {
@@ -33,20 +34,16 @@ int cholesky_inplace(Array<T> &in, const bool is_upper) {
     ::oneapi::mkl::uplo uplo = ::oneapi::mkl::uplo::lower;
     if (is_upper) { uplo = ::oneapi::mkl::uplo::upper; }
 
-    lwork = ::oneapi::mkl::lapack::potrf_scratchpad_size<T>(getQueue(), uplo, N,
-                                                            LDA);
+    lwork = ::oneapi::mkl::lapack::potrf_scratchpad_size<compute_t<T>>(
+        getQueue(), uplo, N, LDA);
 
-    // MKL is finicky about exact scratch space size so we'll need to
-    // create sycl::buffer of exact size. if we use memAlloc, this might
-    // require a sub-buffer of a sub-buffer returned by memAlloc which is
-    // currently illegal in sycl
-    sycl::buffer<compute_t<T>> workspace(lwork);
+    auto workspace = memAlloc<compute_t<T>>(std::max<int64_t>(lwork, 1));
     sycl::buffer<compute_t<T>> in_buffer =
         in.template getBufferWithOffset<compute_t<T>>();
 
     try {
         ::oneapi::mkl::lapack::potrf(getQueue(), uplo, N, in_buffer, LDA,
-                                     workspace, lwork);
+                                     *workspace, workspace->size());
     } catch (::oneapi::mkl::lapack::exception const &e) {
         AF_ERROR(
             "Unexpected exception caught during synchronous\
diff --git a/src/backend/oneapi/lu.cpp b/src/backend/oneapi/lu.cpp
index 1ae473650b..27e6bd4bf3 100644
--- a/src/backend/oneapi/lu.cpp
+++ b/src/backend/oneapi/lu.cpp
@@ -21,17 +21,15 @@
 namespace arrayfire {
 namespace oneapi {
 
-Array<int> convertPivot(sycl::buffer<int64_t> &pivot, int out_sz,
+Array<int> convertPivot(sycl::buffer<int64_t> &pivot, int in_sz, int out_sz,
                         bool convert_pivot) {
-    dim_t d0 = pivot.get_range()[0];
-
     std::vector<int> d_po(out_sz);
     for (int i = 0; i < out_sz; i++) { d_po[i] = i; }
 
     auto d_pi = pivot.get_host_access();
 
     if (convert_pivot) {
-        for (int j = 0; j < d0; j++) {
+        for (int j = 0; j < in_sz; j++) {
             // 1 indexed in pivot
             std::swap(d_po[j], d_po[d_pi[j] - 1]);
         }
@@ -39,10 +37,10 @@ Array<int> convertPivot(sycl::buffer<int64_t> &pivot, int out_sz,
         Array<int> res = createHostDataArray(dim4(out_sz), &d_po[0]);
         return res;
     } else {
-        d_po.resize(d0);
-        for (int j = 0; j < d0; j++) { d_po[j] = static_cast<int>(d_pi[j]); }
+        d_po.resize(in_sz);
+        for (int j = 0; j < in_sz; j++) { d_po[j] = static_cast<int>(d_pi[j]); }
     }
-    Array<int> res = createHostDataArray(dim4(d0), &d_po[0]);
+    Array<int> res = createHostDataArray(dim4(in_sz), &d_po[0]);
     return res;
 }
 
@@ -77,19 +75,15 @@ Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
     std::int64_t scratchpad_size =
         ::oneapi::mkl::lapack::getrf_scratchpad_size<T>(getQueue(), M, N, LDA);
 
-    // MKL is finicky about exact scratch space size so we'll need to
-    // create sycl::buffer of exact size. if we use memAlloc, this might
-    // require a sub-buffer of a sub-buffer returned by memAlloc which is
-    // currently illegal in sycl
-    sycl::buffer<int64_t> ipiv(MN);
-    sycl::buffer<compute_t<T>> scratchpad(scratchpad_size);
+    auto ipiv       = memAlloc<int64_t>(MN);
+    auto scratchpad = memAlloc<compute_t<T>>(scratchpad_size);
 
     sycl::buffer<compute_t<T>> in_buffer =
         in.template getBufferWithOffset<compute_t<T>>();
-    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, in_buffer, LDA, ipiv,
-                                 scratchpad, scratchpad_size);
+    ::oneapi::mkl::lapack::getrf(getQueue(), M, N, in_buffer, LDA, *ipiv,
+                                 *scratchpad, scratchpad->size());
 
-    Array<int> pivot = convertPivot(ipiv, M, convert_pivot);
+    Array<int> pivot = convertPivot(*ipiv, MN, M, convert_pivot);
     return pivot;
 }
 
diff --git a/src/backend/oneapi/solve.cpp b/src/backend/oneapi/solve.cpp
index d234b5920c..4d213d25ae 100644
--- a/src/backend/oneapi/solve.cpp
+++ b/src/backend/oneapi/solve.cpp
@@ -24,7 +24,9 @@
 #include <platform.hpp>
 #include <transpose.hpp>
 
+#include <common/traits.hpp>
 #include <algorithm>
+#include <type_traits>
 #include <vector>
 
 using arrayfire::common::cast;
@@ -53,23 +55,20 @@ Array<T> solveLU(const Array<T> &A, const Array<int> &pivot, const Array<T> &b,
     const int64_t LDB  = b.strides()[1];
 
     ::oneapi::mkl::transpose opts = toMKLTranspose(options);
-    // see comments in core lapack functions about MKL scratch space
-    // avoiding memAlloc, since this may require a sub-buffer of a sub-buffer
-    // returned by memAlloc which is currently illegal in sycl
     std::int64_t scratchpad_size =
         ::oneapi::mkl::lapack::getrs_scratchpad_size<compute_t<T>>(
             getQueue(), opts, N, NRHS, LDA, LDB);
 
     Array<intl> ipiv        = cast<intl, int>(pivot);
     buffer<int64_t> ipivBuf = ipiv.get()->reinterpret<int64_t, 1>();
-    buffer<compute_t<T>> scratchpad(scratchpad_size);
+    auto scratchpad         = memAlloc<compute_t<T>>(scratchpad_size);
 
     Array<compute_t<T>> B     = copyArray<compute_t<T>>(b);
     buffer<compute_t<T>> aBuf = A.template getBufferWithOffset<compute_t<T>>();
     buffer<compute_t<T>> bBuf = B.template getBufferWithOffset<compute_t<T>>();
 
     ::oneapi::mkl::lapack::getrs(getQueue(), opts, N, NRHS, aBuf, LDA, ipivBuf,
-                                 bBuf, LDB, scratchpad, scratchpad_size);
+                                 bBuf, LDB, *scratchpad, scratchpad->size());
     return B;
 }
 
@@ -84,10 +83,9 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
     int K      = bDims[1];
     int MN     = std::min(M, N);
 
-    int lda      = a.strides()[1];
-    int astride  = a.strides()[2];
-    auto ipivMem = memAlloc<int64_t>(MN * batches);
-    buffer<int64_t> ipiv(*ipivMem, 0, MN * batches);
+    int lda        = a.strides()[1];
+    int astride    = a.strides()[2];
+    auto ipiv      = memAlloc<int64_t>(MN * batches);
     int ipivstride = MN;
 
     int ldb     = b.strides()[1];
@@ -102,25 +100,25 @@ Array<T> generalSolve(const Array<T> &a, const Array<T> &b) {
         ::oneapi::mkl::lapack::getrf_batch_scratchpad_size<compute_t<T>>(
             getQueue(), M, N, lda, astride, ipivstride, batches);
 
-    buffer<compute_t<T>> scratchpad(scratchpad_size);
+    auto scratchpad = memAlloc<compute_t<T>>(scratchpad_size);
 
     buffer<compute_t<T>> aBuf = A.template getBufferWithOffset<compute_t<T>>();
     buffer<compute_t<T>> bBuf = B.template getBufferWithOffset<compute_t<T>>();
     ::oneapi::mkl::lapack::getrf_batch(getQueue(), M, N, aBuf, lda, astride,
-                                       ipiv, ipivstride, batches, scratchpad,
-                                       scratchpad_size);
+                                       *ipiv, ipivstride, batches, *scratchpad,
+                                       scratchpad->size());
 
     scratchpad_size =
         ::oneapi::mkl::lapack::getrs_batch_scratchpad_size<compute_t<T>>(
             getQueue(), ::oneapi::mkl::transpose::nontrans, N, K, lda, astride,
             ipivstride, ldb, bstride, batches);
 
-    buffer<compute_t<T>> scratchpad_rs(scratchpad_size);
+    auto scratchpad_rs = memAlloc<compute_t<T>>(scratchpad_size);
 
     ::oneapi::mkl::lapack::getrs_batch(
         getQueue(), ::oneapi::mkl::transpose::nontrans, N, K, aBuf, lda,
-        astride, ipiv, ipivstride, bBuf, ldb, bstride, batches, scratchpad_rs,
-        scratchpad_size);
+        astride, *ipiv, ipivstride, bBuf, ldb, bstride, batches, *scratchpad_rs,
+        scratchpad_rs->size());
 
     return B;
 }
@@ -157,17 +155,15 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
             ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
                 getQueue(), A.dims()[0], A.dims()[1], A.strides()[1]);
 
-        buffer<compute_t<T>> scratchpad(scratchpad_size);
-        Array<compute_t<T>> t = createEmptyArray<T>(af::dim4(MN, 1, 1, 1));
+        auto scratchpad = memAlloc<compute_t<T>>(scratchpad_size);
+        auto t          = memAlloc<compute_t<T>>(MN);
 
         buffer<compute_t<T>> aBuf =
             A.template getBufferWithOffset<compute_t<T>>();
-        buffer<compute_t<T>> tBuf =
-            t.template getBufferWithOffset<compute_t<T>>();
         // In place Perform in place QR
         ::oneapi::mkl::lapack::geqrf(getQueue(), A.dims()[0], A.dims()[1], aBuf,
-                                     A.strides()[1], tBuf, scratchpad,
-                                     scratchpad_size);
+                                     A.strides()[1], *t, *scratchpad,
+                                     scratchpad->size());
 
         // R1 = R(seq(M), seq(M));
         A.resetDims(dim4(M, M));
@@ -190,33 +186,33 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
-        if constexpr (is_any_of<compute_t<T>, float, double>()) {
+        if constexpr (std::is_floating_point<compute_t<T>>()) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::nontrans, B.dims()[0],
                     B.dims()[1], A.dims()[0], A.strides()[1], B.strides()[1]);
 
-            buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
+            auto scratchpad_ormqr = memAlloc<compute_t<T>>(scratchpad_size);
             ::oneapi::mkl::lapack::ormqr(
                 getQueue(), ::oneapi::mkl::side::left,
                 ::oneapi::mkl::transpose::nontrans, B.dims()[0], B.dims()[1],
-                A.dims()[0], aBuf, A.strides()[1], tBuf, bBuf, B.strides()[1],
-                scratchpad_ormqr, scratchpad_size);
-        } else if constexpr (is_any_of<compute_t<T>, std::complex<float>,
-                                       std::complex<double>>()) {
+                A.dims()[0], aBuf, A.strides()[1], *t, bBuf, B.strides()[1],
+                *scratchpad_ormqr, scratchpad_ormqr->size());
+        } else if constexpr (common::isComplex(static_cast<af::dtype>(
+                                 dtype_traits<compute_t<T>>::af_type))) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::nontrans, B.dims()[0],
                     B.dims()[1], A.dims()[0], A.strides()[1], B.strides()[1]);
 
-            buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
+            auto scratchpad_unmqr = memAlloc<compute_t<T>>(scratchpad_size);
             ::oneapi::mkl::lapack::unmqr(
                 getQueue(), ::oneapi::mkl::side::left,
                 ::oneapi::mkl::transpose::nontrans, B.dims()[0], B.dims()[1],
-                A.dims()[0], aBuf, A.strides()[1], tBuf, bBuf, B.strides()[1],
-                scratchpad_unmqr, scratchpad_size);
+                A.dims()[0], aBuf, A.strides()[1], *t, bBuf, B.strides()[1],
+                *scratchpad_unmqr, scratchpad_unmqr->size());
         }
 
     } else if (M > N) {
@@ -236,46 +232,45 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
             ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
                 getQueue(), M, N, A.strides()[1]);
 
-        buffer<compute_t<T>> scratchpad(scratchpad_size);
-        Array<compute_t<T>> t = createEmptyArray<T>(af::dim4(MN, 1, 1, 1));
+        auto scratchpad = memAlloc<compute_t<T>>(scratchpad_size);
+        auto t          = memAlloc<compute_t<T>>(MN);
 
         buffer<compute_t<T>> aBuf =
             A.template getBufferWithOffset<compute_t<T>>();
-        buffer<compute_t<T>> tBuf =
-            t.template getBufferWithOffset<compute_t<T>>();
         // In place Perform in place QR
-        ::oneapi::mkl::lapack::geqrf(getQueue(), M, N, aBuf, A.strides()[1],
-                                     tBuf, scratchpad, scratchpad_size);
+        ::oneapi::mkl::lapack::geqrf(getQueue(), M, N, aBuf, A.strides()[1], *t,
+                                     *scratchpad, scratchpad->size());
 
         // matmul(Q1, B)
         buffer<compute_t<T>> bBuf =
             B.template getBufferWithOffset<compute_t<T>>();
-        if constexpr (is_any_of<compute_t<T>, float, double>()) {
+        if constexpr (std::is_floating_point<compute_t<T>>()) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::trans, M, K, N, A.strides()[1],
                     b.strides()[1]);
 
-            buffer<compute_t<T>> scratchpad_ormqr(scratchpad_size);
-            ::oneapi::mkl::lapack::ormqr(
-                getQueue(), ::oneapi::mkl::side::left,
-                ::oneapi::mkl::transpose::trans, M, K, N, aBuf, A.strides()[1],
-                tBuf, bBuf, b.strides()[1], scratchpad_ormqr, scratchpad_size);
-        } else if constexpr (is_any_of<compute_t<T>, std::complex<float>,
-                                       std::complex<double>>()) {
+            auto scratchpad_ormqr = memAlloc<compute_t<T>>(scratchpad_size);
+            ::oneapi::mkl::lapack::ormqr(getQueue(), ::oneapi::mkl::side::left,
+                                         ::oneapi::mkl::transpose::trans, M, K,
+                                         N, aBuf, A.strides()[1], *t, bBuf,
+                                         b.strides()[1], *scratchpad_ormqr,
+                                         scratchpad_ormqr->size());
+        } else if constexpr (common::isComplex(static_cast<af::dtype>(
+                                 dtype_traits<compute_t<T>>::af_type))) {
             std::int64_t scratchpad_size =
                 ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
                     getQueue(), ::oneapi::mkl::side::left,
                     ::oneapi::mkl::transpose::conjtrans, M, K, N,
                     A.strides()[1], b.strides()[1]);
 
-            buffer<compute_t<T>> scratchpad_unmqr(scratchpad_size);
+            auto scratchpad_unmqr = memAlloc<compute_t<T>>(scratchpad_size);
             ::oneapi::mkl::lapack::unmqr(getQueue(), ::oneapi::mkl::side::left,
                                          ::oneapi::mkl::transpose::conjtrans, M,
-                                         K, N, aBuf, A.strides()[1], tBuf, bBuf,
-                                         b.strides()[1], scratchpad_unmqr,
-                                         scratchpad_size);
+                                         K, N, aBuf, A.strides()[1], *t, bBuf,
+                                         b.strides()[1], *scratchpad_unmqr,
+                                         scratchpad_unmqr->size());
         }
 
         // tri_solve(R1, Bt)
diff --git a/src/backend/oneapi/solve.hpp b/src/backend/oneapi/solve.hpp
index 819c0ced35..a0c8924fa9 100644
--- a/src/backend/oneapi/solve.hpp
+++ b/src/backend/oneapi/solve.hpp
@@ -12,15 +12,6 @@
 namespace arrayfire {
 namespace oneapi {
 
-template<typename T, typename U, typename... Args>
-static inline constexpr bool is_any_of() {
-    if constexpr (!sizeof...(Args)) {
-        return std::is_same_v<T, U>;
-    } else {
-        return std::is_same_v<T, U> || is_any_of<T, Args...>();
-    }
-}
-
 template<typename T>
 Array<T> solve(const Array<T> &a, const Array<T> &b,
                const af_mat_prop options = AF_MAT_NONE);
diff --git a/src/backend/oneapi/svd.cpp b/src/backend/oneapi/svd.cpp
index 97b5f3a468..7255226e1b 100644
--- a/src/backend/oneapi/svd.cpp
+++ b/src/backend/oneapi/svd.cpp
@@ -38,16 +38,12 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     int64_t LDU   = uStrides[1];
     int64_t LDVt  = vStrides[1];
 
-    // MKL is finicky about exact scratch space size so we'll need to
-    // create sycl::buffer of exact size. if we use memAlloc, this might
-    // require a sub-buffer of a sub-buffer returned by memAlloc which is
-    // currently illegal in sycl
     int64_t scratch_size =
         ::oneapi::mkl::lapack::gesvd_scratchpad_size<compute_t<T>>(
             getQueue(), ::oneapi::mkl::jobsvd::vectors,
             ::oneapi::mkl::jobsvd::vectors, M, N, LDA, LDU, LDVt);
 
-    sycl::buffer<compute_t<T>> scratchpad(scratch_size);
+    auto scratchpad = memAlloc<compute_t<T>>(scratch_size);
 
     sycl::buffer<compute_t<T>> in_buffer =
         in.template getBufferWithOffset<compute_t<T>>();
@@ -62,7 +58,7 @@ void svdInPlace(Array<Tr> &s, Array<T> &u, Array<T> &vt, Array<T> &in) {
     ::oneapi::mkl::lapack::gesvd(getQueue(), ::oneapi::mkl::jobsvd::vectors,
                                  ::oneapi::mkl::jobsvd::vectors, M, N,
                                  in_buffer, LDA, sBuf, uBuf, LDU, vtBuf, LDVt,
-                                 scratchpad, scratch_size);
+                                 *scratchpad, scratchpad->size());
 }
 
 template<typename T, typename Tr>

From a644b1048ba04cbd5e6e56ca6b6891d847418024 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 15 Jun 2023 16:28:52 -0400
Subject: [PATCH 2525/2677] Hash using kernel name and device name for oneAPI
 JIT (#3444)

Hash using kernel name and device name for oneAPI JIT
---
 src/backend/oneapi/jit.cpp | 42 +++++++++++++++++++++++++++++++++-----
 1 file changed, 37 insertions(+), 5 deletions(-)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 794bb7796f..546ca233b6 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -34,6 +34,7 @@
 #include <array>
 #include <cstdio>
 #include <functional>
+#include <mutex>
 #include <sstream>
 #include <stdexcept>
 #include <string>
@@ -286,6 +287,11 @@ __kernel void )JIT";
 //     return common::getKernel("", "", true).get();
 // }
 
+static unordered_map<cl_device_id, std::string> device_name_map;
+static std::mutex device_name_map_mutex;
+static unordered_map<std::string, cl_kernel> kernel_map;
+static std::mutex kernel_map_mutex;
+
 template<typename T>
 cl_kernel getKernel(
     std::string funcName, cl_context ctx, cl_device_id dev, cl_command_queue q,
@@ -293,10 +299,36 @@ cl_kernel getKernel(
     nonstd::span<Node_ids const> full_ids, nonstd::span<int const> output_ids,
     nonstd::span<oneapi::AParam<T, sycl::access_mode::write> const> ap,
     bool is_linear) {
-    static unordered_map<std::string, cl_kernel> kernel_map;
+    std::string devName;
+    {
+        std::lock_guard<std::mutex> lock(device_name_map_mutex);
+
+        auto devNameIt = device_name_map.find(dev);
+        if (devNameIt == device_name_map.end()) {
+            size_t devNameSz;
+            CL_CHECK(
+                clGetDeviceInfo(dev, CL_DEVICE_NAME, 0, nullptr, &devNameSz));
+            string newDevName(devNameSz, '\0');
+            CL_CHECK(clGetDeviceInfo(dev, CL_DEVICE_NAME, devNameSz,
+                                     newDevName.data(), nullptr));
+            device_name_map[dev] = newDevName;
+            devName              = newDevName;
+        } else {
+            devName = devNameIt->second;
+        }
+    }
 
     vector<cl_kernel> kernels(10);
-    if (kernel_map.find(funcName) == end(kernel_map)) {
+    bool kernel_found;
+    string kernelHash = funcName + devName;
+    {
+        std::lock_guard<std::mutex> lock(kernel_map_mutex);
+        kernel_found = !(kernel_map.find(kernelHash) == end(kernel_map));
+    }
+    if (kernel_found) {
+        std::lock_guard<std::mutex> lock(kernel_map_mutex);
+        kernels[0] = kernel_map[kernelHash];
+    } else {
         string jitstr = arrayfire::opencl::getKernelString(
             funcName, full_nodes, full_ids, output_ids, is_linear, false, false,
             ap[0].dims[2] > 1);
@@ -320,10 +352,10 @@ cl_kernel getKernel(
         cl_uint ret_kernels = 0;
         CL_CHECK(
             clCreateKernelsInProgram(prog, 1, kernels.data(), &ret_kernels));
-        kernel_map[funcName] = kernels[0];
+
+        std::lock_guard<std::mutex> lock(kernel_map_mutex);
+        kernel_map[kernelHash] = kernels[0];
         CL_CHECK(clReleaseProgram(prog));
-    } else {
-        kernels[0] = kernel_map[funcName];
     }
     return kernels[0];
 }

From a8a0f83f0213a25e6df99e69d5b829ea6e5bf6f2 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 14 Jun 2023 21:53:14 -0400
Subject: [PATCH 2526/2677] fixes out of bounds iterators for sorts

---
 src/backend/oneapi/kernel/sort.hpp            |  4 +-
 .../oneapi/kernel/sort_by_key_impl.hpp        | 13 +++---
 src/backend/oneapi/sort.cpp                   | 44 +++++++++----------
 3 files changed, 29 insertions(+), 32 deletions(-)

diff --git a/src/backend/oneapi/kernel/sort.hpp b/src/backend/oneapi/kernel/sort.hpp
index 1789887b82..71bedd1f50 100644
--- a/src/backend/oneapi/kernel/sort.hpp
+++ b/src/backend/oneapi/kernel/sort.hpp
@@ -80,9 +80,9 @@ void sortBatched(Param<T> pVal, int dim, bool isAscending) {
     auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
 
     auto key_begin    = ::oneapi::dpl::begin(*pKey.get());
-    auto key_end      = ::oneapi::dpl::end(*pKey.get());
+    auto key_end      = key_begin + pKey.dims()[0];
     auto val_begin    = ::oneapi::dpl::begin(*pVal.data);
-    auto val_end      = ::oneapi::dpl::end(*pVal.data);
+    auto val_end      = val_begin + pVal.info.dims[0];
     auto zipped_begin = dpl::make_zip_iterator(key_begin, val_begin);
     auto zipped_end   = dpl::make_zip_iterator(key_end, val_end);
 
diff --git a/src/backend/oneapi/kernel/sort_by_key_impl.hpp b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
index ad3b3c8a80..9a6348a3ad 100644
--- a/src/backend/oneapi/kernel/sort_by_key_impl.hpp
+++ b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
@@ -98,13 +98,13 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
 
     // set up iterators for seq, key, val, and new cKey
     auto seq_begin = ::oneapi::dpl::begin(*Seq.get());
-    auto seq_end   = ::oneapi::dpl::end(*Seq.get());
+    auto seq_end   = seq_begin + elements;
     auto key_begin =
         ::oneapi::dpl::begin(pKey.data->template reinterpret<compute_t<Tk>>());
-    auto key_end =
-        ::oneapi::dpl::end(pKey.data->template reinterpret<compute_t<Tk>>());
+    auto key_end = key_begin + elements;
+
     auto val_begin = ::oneapi::dpl::begin(*pVal.data);
-    auto val_end   = ::oneapi::dpl::end(*pVal.data);
+    auto val_end   = val_begin + elements;
 
     auto cKey = memAlloc<Tk>(elements);
     getQueue().submit([&](sycl::handler &h) {
@@ -115,8 +115,7 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
     });
     auto ckey_begin =
         ::oneapi::dpl::begin(cKey.get()->template reinterpret<compute_t<Tk>>());
-    auto ckey_end =
-        ::oneapi::dpl::end(cKey.get()->template reinterpret<compute_t<Tk>>());
+    auto ckey_end = ckey_begin + elements;
 
     {
         auto zipped_begin_KV  = dpl::make_zip_iterator(key_begin, val_begin);
@@ -150,7 +149,7 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
                cSeq.get()->get_access(h, elements));
     });
     auto cseq_begin = ::oneapi::dpl::begin(*cSeq.get());
-    auto cseq_end   = ::oneapi::dpl::end(*cSeq.get());
+    auto cseq_end   = cseq_begin + elements;
 
     {
         auto zipped_begin_SV  = dpl::make_zip_iterator(seq_begin, val_begin);
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
index 002385a320..a16ccadc55 100644
--- a/src/backend/oneapi/sort.cpp
+++ b/src/backend/oneapi/sort.cpp
@@ -22,31 +22,29 @@ namespace oneapi {
 
 template<typename T>
 Array<T> sort(const Array<T> &in, const unsigned dim, bool isAscending) {
-    try {
-        Array<T> out = copyArray<T>(in);
-        switch (dim) {
-            case 0: kernel::sort0<T>(out, isAscending); break;
-            case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
-            case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
-            case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
-            default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
-        }
-
-        if (dim != 0) {
-            af::dim4 preorderDims = out.dims();
-            af::dim4 reorderDims(0, 1, 2, 3);
-            reorderDims[dim] = 0;
-            preorderDims[0]  = out.dims()[dim];
-            for (int i = 1; i <= static_cast<int>(dim); i++) {
-                reorderDims[i - 1] = i;
-                preorderDims[i]    = out.dims()[i - 1];
-            }
+    Array<T> out = copyArray<T>(in);
+    switch (dim) {
+        case 0: kernel::sort0<T>(out, isAscending); break;
+        case 1: kernel::sortBatched<T>(out, 1, isAscending); break;
+        case 2: kernel::sortBatched<T>(out, 2, isAscending); break;
+        case 3: kernel::sortBatched<T>(out, 3, isAscending); break;
+        default: AF_ERROR("Not Supported", AF_ERR_NOT_SUPPORTED);
+    }
 
-            out.setDataDims(preorderDims);
-            out = reorder<T>(out, reorderDims);
+    if (dim != 0) {
+        af::dim4 preorderDims = out.dims();
+        af::dim4 reorderDims(0, 1, 2, 3);
+        reorderDims[dim] = 0;
+        preorderDims[0]  = out.dims()[dim];
+        for (int i = 1; i <= static_cast<int>(dim); i++) {
+            reorderDims[i - 1] = i;
+            preorderDims[i]    = out.dims()[i - 1];
         }
-        return out;
-    } catch (std::exception &ex) { AF_ERROR(ex.what(), AF_ERR_INTERNAL); }
+
+        out.setDataDims(preorderDims);
+        out = reorder<T>(out, reorderDims);
+    }
+    return out;
 }
 
 #define INSTANTIATE(T)                                                \

From f103b8abff4158b048291c8a69d5b5462ef333b0 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 16 Jun 2023 03:54:57 -0400
Subject: [PATCH 2527/2677] adds reduce by key to oneapi backend

---
 src/backend/oneapi/CMakeLists.txt           |  11 +-
 src/backend/oneapi/kernel/reduce_by_key.hpp | 704 ++++++++++++++++++++
 src/backend/oneapi/reduce_impl.hpp          | 559 +++++++++++++++-
 3 files changed, 1267 insertions(+), 7 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/reduce_by_key.hpp

diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index b13de94f95..1c8f789806 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -1,9 +1,9 @@
-# Copyright (c) 2022, ArrayFire
-# All rights reserved.
+#Copyright(c) 2022, ArrayFire
+#All rights reserved.
 #
-# This file is distributed under 3-clause BSD license.
-# The complete license agreement can be obtained at:
-# http://arrayfire.com/licenses/BSD-3-Clause
+#This file is distributed under 3 - clause BSD license.
+#The complete license agreement can be obtained at:
+#http:  // arrayfire.com/licenses/BSD-3-Clause
 
 include(InternalUtils)
 include(build_cl2hpp)
@@ -241,6 +241,7 @@ target_sources(afoneapi
     kernel/range.hpp
     kernel/reduce.hpp
     kernel/reduce_all.hpp
+    kernel/reduce_by_key.hpp
     kernel/reduce_first.hpp
     kernel/reduce_dim.hpp
     kernel/reorder.hpp
diff --git a/src/backend/oneapi/kernel/reduce_by_key.hpp b/src/backend/oneapi/kernel/reduce_by_key.hpp
new file mode 100644
index 0000000000..1da17ca5cc
--- /dev/null
+++ b/src/backend/oneapi/kernel/reduce_by_key.hpp
@@ -0,0 +1,704 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <Param.hpp>
+#include <backend.hpp>
+#include <common/Binary.hpp>
+#include <common/Transform.hpp>
+#include <common/dispatch.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
+#include <kernel/reduce_config.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <type_traits>
+
+using std::unique_ptr;
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+// Reduces keys across block boundaries
+template<typename Tk, typename To, af_op_t op>
+class finalBoundaryReduceKernel {
+   public:
+    finalBoundaryReduceKernel(write_accessor<int> reduced_block_sizes,
+                              read_accessor<Tk> iKeys, KParam iKInfo,
+                              sycl::accessor<To> oVals, KParam oVInfo,
+                              const int n)
+        : reduced_block_sizes_(reduced_block_sizes)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , oVals_(oVals)
+        , oVInfo_(oVInfo)
+        , n_(n) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g  = it.get_group();
+        const uint lid = it.get_local_id(0);
+        const uint gid = it.get_global_id(0);
+        const uint bid = g.get_group_id(0);
+
+        common::Binary<compute_t<To>, op> binOp;
+        if (gid == ((bid + 1) * it.get_local_range(0)) - 1 &&
+            bid < g.get_group_range(0) - 1) {
+            Tk k0 = iKeys_[gid];
+            Tk k1 = iKeys_[gid + 1];
+
+            if (k0 == k1) {
+                compute_t<To> v0          = compute_t<To>(oVals_[gid]);
+                compute_t<To> v1          = compute_t<To>(oVals_[gid + 1]);
+                oVals_[gid + 1]           = binOp(v0, v1);
+                reduced_block_sizes_[bid] = it.get_local_range(0) - 1;
+            } else {
+                reduced_block_sizes_[bid] = it.get_local_range(0);
+            }
+        }
+
+        // if last block, set block size to difference between n and block
+        // boundary
+        if (lid == 0 && bid == g.get_group_range(0) - 1) {
+            reduced_block_sizes_[bid] = n_ - (bid * it.get_local_range(0));
+        }
+    }
+
+   protected:
+    write_accessor<int> reduced_block_sizes_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    sycl::accessor<To> oVals_;
+    KParam oVInfo_;
+    int n_;
+};
+
+template<typename Tk, typename To, af_op_t op>
+class finalBoundaryReduceDimKernel {
+   public:
+    finalBoundaryReduceDimKernel(write_accessor<int> reduced_block_sizes,
+                                 read_accessor<Tk> iKeys, KParam iKInfo,
+                                 sycl::accessor<To> oVals, KParam oVInfo,
+                                 const int n, const int nGroupsZ)
+        : reduced_block_sizes_(reduced_block_sizes)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , oVals_(oVals)
+        , oVInfo_(oVInfo)
+        , n_(n)
+        , nGroupsZ_(nGroupsZ) {}
+
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g  = it.get_group();
+        const uint lid = it.get_local_id(0);
+        const uint gid = it.get_global_id(0);
+        const uint bid = g.get_group_id(0);
+
+        const int bidy = g.get_group_id(1);
+        const int bidz = g.get_group_id(2) % nGroupsZ_;
+        const int bidw = g.get_group_id(2) / nGroupsZ_;
+
+        common::Binary<compute_t<To>, op> binOp;
+        if (gid == ((bid + 1) * it.get_local_range(0)) - 1 &&
+            bid < g.get_group_range(0) - 1) {
+            Tk k0 = iKeys_[gid];
+            Tk k1 = iKeys_[gid + 1];
+
+            if (k0 == k1) {
+                compute_t<To> v0          = compute_t<To>(oVals_[gid]);
+                compute_t<To> v1          = compute_t<To>(oVals_[gid + 1]);
+                oVals_[gid + 1]           = binOp(v0, v1);
+                reduced_block_sizes_[bid] = it.get_local_range(0) - 1;
+            } else {
+                reduced_block_sizes_[bid] = it.get_local_range(0);
+            }
+        }
+
+        // if last block, set block size to difference between n and block
+        // boundary
+        if (lid == 0 && bid == g.get_group_range(0) - 1) {
+            reduced_block_sizes_[bid] = n_ - (bid * it.get_local_range(0));
+        }
+    }
+
+   protected:
+    write_accessor<int> reduced_block_sizes_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    sycl::accessor<To> oVals_;
+    KParam oVInfo_;
+    int n_;
+    int nGroupsZ_;
+};
+
+template<typename T>
+using global_atomic_ref =
+    sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
+                     sycl::access::address_space::global_space>;
+
+// Tests if data needs further reduction, including across block boundaries
+template<typename Tk>
+class testNeedsReductionKernel {
+   public:
+    testNeedsReductionKernel(sycl::accessor<int> needs_another_reduction,
+                             sycl::accessor<int> needs_block_boundary_reduced,
+                             read_accessor<Tk> iKeys, KParam iKInfo,
+                             const int n, const int DIMX,
+                             sycl::local_accessor<Tk> l_keys)
+        : needs_another_reduction_(needs_another_reduction)
+        , needs_block_boundary_reduced_(needs_block_boundary_reduced)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , n_(n)
+        , DIMX_(DIMX)
+        , l_keys_(l_keys) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g  = it.get_group();
+        const uint lid = it.get_local_id(0);
+        const uint gid = it.get_global_id(0);
+        const uint bid = g.get_group_id(0);
+
+        Tk k;
+        if (gid < n_) { k = iKeys_[gid]; }
+
+        l_keys_[lid] = k;
+        it.barrier();
+
+        int update_key =
+            (lid < DIMX_ - 2) && (k == l_keys_[lid + 1]) && (gid < (n_ - 1));
+
+        if (update_key) {
+            global_atomic_ref<int>(needs_another_reduction_[0]) |= update_key;
+        }
+
+        it.barrier();
+
+        // last thread in each block checks if any inter-block keys need further
+        // reduction
+        if (gid == ((bid + 1) * DIMX_) - 1 &&
+            bid < (g.get_group_range(0) - 1)) {
+            int k0 = iKeys_[gid];
+            int k1 = iKeys_[gid + 1];
+            if (k0 == k1) {
+                global_atomic_ref<int>(needs_block_boundary_reduced_[0]) |= 1;
+            }
+        }
+    }
+
+   protected:
+    sycl::accessor<int> needs_another_reduction_;
+    sycl::accessor<int> needs_block_boundary_reduced_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    int n_;
+    int DIMX_;
+    sycl::local_accessor<Tk> l_keys_;
+};
+
+// Compacts "incomplete" block-sized chunks of data in global memory
+template<typename Tk, typename To>
+class compactKernel {
+   public:
+    compactKernel(read_accessor<int> reduced_block_sizes,
+                  write_accessor<Tk> oKeys, KParam oKInfo,
+                  write_accessor<To> oVals, KParam oVInfo,
+                  read_accessor<Tk> iKeys, KParam iKInfo,
+                  read_accessor<To> iVals, KParam iVInfo, int nGroupsZ)
+        : reduced_block_sizes_(reduced_block_sizes)
+        , oKeys_(oKeys)
+        , oKInfo_(oKInfo)
+        , oVals_(oVals)
+        , oVInfo_(oVInfo)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , iVals_(iVals)
+        , iVInfo_(iVInfo)
+        , nGroupsZ_(nGroupsZ) {}
+
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g  = it.get_group();
+        const uint lid = it.get_local_id(0);
+        const uint bid = g.get_group_id(0);
+        const uint gid = it.get_global_id(0);
+
+        const int bidy = g.get_group_id(1);
+        const int bidz = g.get_group_id(2) % nGroupsZ_;
+        const int bidw = g.get_group_id(2) / nGroupsZ_;
+
+        Tk k;
+        To v;
+
+        const int bOffset = bidw * oVInfo_.strides[3] +
+                            bidz * oVInfo_.strides[2] +
+                            bidy * oVInfo_.strides[1];
+
+        // reduced_block_sizes should have inclusive sum of block sizes
+        int nwrite =
+            (bid == 0)
+                ? reduced_block_sizes_[0]
+                : (reduced_block_sizes_[bid] - reduced_block_sizes_[bid - 1]);
+        int writeloc = (bid == 0) ? 0 : reduced_block_sizes_[bid - 1];
+
+        k = iKeys_[gid];
+        v = iVals_[bOffset + gid];
+
+        if (lid < nwrite) {
+            oKeys_[writeloc + lid]           = k;
+            oVals_[bOffset + writeloc + lid] = v;
+        }
+    }
+
+   protected:
+    read_accessor<int> reduced_block_sizes_;
+    write_accessor<Tk> oKeys_;
+    KParam oKInfo_;
+    write_accessor<To> oVals_;
+    KParam oVInfo_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    read_accessor<To> iVals_;
+    KParam iVInfo_;
+    int nGroupsZ_;
+};
+
+// Compacts "incomplete" block-sized chunks of data in global memory
+template<typename Tk, typename To>
+class compactDimKernel {
+   public:
+    compactDimKernel(read_accessor<int> reduced_block_sizes,
+                     write_accessor<Tk> oKeys, KParam oKInfo,
+                     write_accessor<To> oVals, KParam oVInfo,
+                     read_accessor<Tk> iKeys, KParam iKInfo,
+                     read_accessor<To> iVals, KParam iVInfo, int nGroupsZ,
+                     int DIM)
+        : reduced_block_sizes_(reduced_block_sizes)
+        , oKeys_(oKeys)
+        , oKInfo_(oKInfo)
+        , oVals_(oVals)
+        , oVInfo_(oVInfo)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , iVals_(iVals)
+        , iVInfo_(iVInfo)
+        , nGroupsZ_(nGroupsZ)
+        , DIM_(DIM) {}
+
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g = it.get_group();
+
+        const uint lid  = it.get_local_id(0);
+        const uint gidx = it.get_global_id(0);
+        const uint bid  = g.get_group_id(0);
+
+        const int bidy = g.get_group_id(1);
+        const int bidz = g.get_group_id(2) % nGroupsZ_;
+        const int bidw = g.get_group_id(2) / nGroupsZ_;
+
+        int dims_ordering[4];
+        dims_ordering[0] = DIM_;
+        int d            = 1;
+        for (int i = 0; i < 4; ++i) {
+            if (i != DIM_) dims_ordering[d++] = i;
+        }
+
+        Tk k;
+        To v;
+
+        // reduced_block_sizes should have inclusive sum of block sizes
+        int nwrite =
+            (bid == 0)
+                ? reduced_block_sizes_[0]
+                : (reduced_block_sizes_[bid] - reduced_block_sizes_[bid - 1]);
+        int writeloc = (bid == 0) ? 0 : reduced_block_sizes_[bid - 1];
+
+        const int tid = bidw * iVInfo_.strides[dims_ordering[3]] +
+                        bidz * iVInfo_.strides[dims_ordering[2]] +
+                        bidy * iVInfo_.strides[dims_ordering[1]] +
+                        gidx * iVInfo_.strides[DIM_];
+        k = iKeys_[gidx];
+        v = iVals_[tid];
+
+        if (lid < nwrite) {
+            oKeys_[writeloc + lid] = k;
+            const int bOffset      = bidw * oVInfo_.strides[dims_ordering[3]] +
+                                bidz * oVInfo_.strides[dims_ordering[2]] +
+                                bidy * oVInfo_.strides[dims_ordering[1]];
+            oVals_[bOffset + (writeloc + lid) * oVInfo_.strides[DIM_]] = v;
+        }
+    }
+
+   protected:
+    read_accessor<int> reduced_block_sizes_;
+    write_accessor<Tk> oKeys_;
+    KParam oKInfo_;
+    write_accessor<To> oVals_;
+    KParam oVInfo_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    read_accessor<To> iVals_;
+    KParam iVInfo_;
+    int nGroupsZ_;
+    int DIM_;
+};
+
+// Reduces each block by key
+template<typename Ti, typename Tk, typename To, af_op_t op>
+class reduceBlocksByKeyKernel {
+   public:
+    reduceBlocksByKeyKernel(sycl::accessor<int> reduced_block_sizes,
+                            write_accessor<Tk> oKeys, KParam oKInfo,
+                            write_accessor<To> oVals, KParam oVInfo,
+                            read_accessor<Tk> iKeys, KParam iKInfo,
+                            read_accessor<Ti> iVals, KParam iVInfo,
+                            int change_nan, To nanval, int n, int nGroupsZ,
+                            int DIMX, sycl::local_accessor<Tk> l_keys,
+                            sycl::local_accessor<compute_t<To>> l_vals,
+                            sycl::local_accessor<Tk> l_reduced_keys,
+                            sycl::local_accessor<compute_t<To>> l_reduced_vals,
+                            sycl::local_accessor<int> l_unique_ids,
+                            sycl::local_accessor<int> l_wg_temp,
+                            sycl::local_accessor<int> l_unique_flags,
+                            sycl::local_accessor<int> l_reduced_block_size)
+        : reduced_block_sizes_(reduced_block_sizes)
+        , oKeys_(oKeys)
+        , oKInfo_(oKInfo)
+        , oVals_(oVals)
+        , oVInfo_(oVInfo)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , iVals_(iVals)
+        , iVInfo_(iVInfo)
+        , change_nan_(change_nan)
+        , nanval_(nanval)
+        , n_(n)
+        , nGroupsZ_(nGroupsZ)
+        , DIMX_(DIMX)
+        , l_keys_(l_keys)
+        , l_vals_(l_vals)
+        , l_reduced_keys_(l_reduced_keys)
+        , l_reduced_vals_(l_reduced_vals)
+        , l_unique_ids_(l_unique_ids)
+        , l_wg_temp_(l_wg_temp)
+        , l_unique_flags_(l_unique_flags)
+        , l_reduced_block_size_(l_reduced_block_size) {}
+
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g  = it.get_group();
+        const uint lid = it.get_local_id(0);
+        const uint gid = it.get_global_id(0);
+
+        const int bidy = g.get_group_id(1);
+        const int bidz = g.get_group_id(2) % nGroupsZ_;
+        const int bidw = g.get_group_id(2) / nGroupsZ_;
+
+        const compute_t<To> init_val =
+            common::Binary<compute_t<To>, op>::init();
+        common::Binary<compute_t<To>, op> binOp;
+        common::Transform<Ti, compute_t<To>, op> transform;
+
+        if (lid == 0) { l_reduced_block_size_[0] = 0; }
+
+        // load keys and values to threads
+        Tk k;
+        compute_t<To> v;
+        if (gid < n_) {
+            k                 = iKeys_[gid];
+            const int bOffset = bidw * iVInfo_.strides[3] +
+                                bidz * iVInfo_.strides[2] +
+                                bidy * iVInfo_.strides[1];
+            v = transform(iVals_[bOffset + gid]);
+            if (change_nan_) v = IS_NAN(v) ? nanval_ : v;
+        } else {
+            v = init_val;
+        }
+
+        l_keys_[lid] = k;
+        l_vals_[lid] = v;
+
+        l_reduced_keys_[lid] = k;
+        it.barrier();
+
+        // mark threads containing unique keys
+        int eq_check    = (lid > 0) ? (k != l_reduced_keys_[lid - 1]) : 0;
+        int unique_flag = (eq_check || (lid == 0)) && (gid < n_);
+
+        l_unique_flags_[lid] = unique_flag;
+        int unique_id =
+            work_group_scan_inclusive_add(it, l_wg_temp_, l_unique_flags_);
+
+        l_unique_ids_[lid] = unique_id;
+
+        if (lid == DIMX_ - 1) l_reduced_block_size_[0] = unique_id;
+
+        for (int off = 1; off < DIMX_; off *= 2) {
+            it.barrier();
+            int test_unique_id =
+                (lid + off < DIMX_) ? l_unique_ids_[lid + off] : ~unique_id;
+            eq_check = (unique_id == test_unique_id);
+            int update_key =
+                eq_check && (lid < (DIMX_ - off)) &&
+                ((gid + off) <
+                 n_);  // checks if this thread should perform a reduction
+            compute_t<To> uval = (update_key) ? l_vals_[lid + off] : init_val;
+            it.barrier();
+            l_vals_[lid] =
+                binOp(l_vals_[lid], uval);  // update if thread requires it
+        }
+
+        if (unique_flag) {
+            l_reduced_keys_[unique_id - 1] = k;
+            l_reduced_vals_[unique_id - 1] = l_vals_[lid];
+        }
+        it.barrier();
+
+        const int bid = g.get_group_id(0);
+        if (lid < l_reduced_block_size_[0]) {
+            const int bOffset = bidw * oVInfo_.strides[3] +
+                                bidz * oVInfo_.strides[2] +
+                                bidy * oVInfo_.strides[1];
+            oKeys_[bid * DIMX_ + lid]               = l_reduced_keys_[lid];
+            oVals_[bOffset + ((bid * DIMX_) + lid)] = l_reduced_vals_[lid];
+        }
+
+        reduced_block_sizes_[bid] = l_reduced_block_size_[0];
+    }
+
+    int work_group_scan_inclusive_add(sycl::nd_item<3> it,
+                                      sycl::local_accessor<int> wg_temp,
+                                      sycl::local_accessor<int> arr) const {
+        const uint lid = it.get_local_id(0);
+        int *active_buf;
+
+        int val    = arr[lid];
+        active_buf = arr.get_pointer();
+
+        bool swap_buffer = false;
+        for (int off = 1; off <= DIMX_; off *= 2) {
+            it.barrier();
+            if (lid >= off) { val = val + active_buf[lid - off]; }
+            swap_buffer = !swap_buffer;
+            active_buf =
+                swap_buffer ? wg_temp.get_pointer() : arr.get_pointer();
+            active_buf[lid] = val;
+        }
+
+        int res = active_buf[lid];
+        return res;
+    }
+
+   protected:
+    sycl::accessor<int> reduced_block_sizes_;
+    write_accessor<Tk> oKeys_;
+    KParam oKInfo_;
+    write_accessor<To> oVals_;
+    KParam oVInfo_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    read_accessor<Ti> iVals_;
+    KParam iVInfo_;
+    int change_nan_;
+    To nanval_;
+    int n_;
+    int nGroupsZ_;
+    int DIMX_;
+    sycl::local_accessor<Tk> l_keys_;
+    sycl::local_accessor<compute_t<To>> l_vals_;
+    sycl::local_accessor<Tk> l_reduced_keys_;
+    sycl::local_accessor<compute_t<To>> l_reduced_vals_;
+    sycl::local_accessor<int> l_unique_ids_;
+    sycl::local_accessor<int> l_wg_temp_;
+    sycl::local_accessor<int> l_unique_flags_;
+    sycl::local_accessor<int> l_reduced_block_size_;
+};
+
+// Reduces each block by key
+template<typename Ti, typename Tk, typename To, af_op_t op>
+class reduceBlocksByKeyDimKernel {
+   public:
+    reduceBlocksByKeyDimKernel(
+        sycl::accessor<int> reduced_block_sizes, write_accessor<Tk> oKeys,
+        KParam oKInfo, write_accessor<To> oVals, KParam oVInfo,
+        read_accessor<Tk> iKeys, KParam iKInfo, read_accessor<Ti> iVals,
+        KParam iVInfo, int change_nan, To nanval, int n, int nGroupsZ, int DIMX,
+        int DIM, sycl::local_accessor<Tk> l_keys,
+        sycl::local_accessor<compute_t<To>> l_vals,
+        sycl::local_accessor<Tk> l_reduced_keys,
+        sycl::local_accessor<compute_t<To>> l_reduced_vals,
+        sycl::local_accessor<int> l_unique_ids,
+        sycl::local_accessor<int> l_wg_temp,
+        sycl::local_accessor<int> l_unique_flags,
+        sycl::local_accessor<int> l_reduced_block_size)
+        : reduced_block_sizes_(reduced_block_sizes)
+        , oKeys_(oKeys)
+        , oKInfo_(oKInfo)
+        , oVals_(oVals)
+        , oVInfo_(oVInfo)
+        , iKeys_(iKeys)
+        , iKInfo_(iKInfo)
+        , iVals_(iVals)
+        , iVInfo_(iVInfo)
+        , change_nan_(change_nan)
+        , nanval_(nanval)
+        , n_(n)
+        , nGroupsZ_(nGroupsZ)
+        , DIMX_(DIMX)
+        , DIM_(DIM)
+        , l_keys_(l_keys)
+        , l_vals_(l_vals)
+        , l_reduced_keys_(l_reduced_keys)
+        , l_reduced_vals_(l_reduced_vals)
+        , l_unique_ids_(l_unique_ids)
+        , l_wg_temp_(l_wg_temp)
+        , l_unique_flags_(l_unique_flags)
+        , l_reduced_block_size_(l_reduced_block_size) {}
+
+    void operator()(sycl::nd_item<3> it) const {
+        sycl::group g  = it.get_group();
+        const uint lid = it.get_local_id(0);
+        const uint gid = it.get_global_id(0);
+
+        const int bidy = g.get_group_id(1);
+        const int bidz = g.get_group_id(2) % nGroupsZ_;
+        const int bidw = g.get_group_id(2) / nGroupsZ_;
+
+        const compute_t<To> init_val =
+            common::Binary<compute_t<To>, op>::init();
+        common::Binary<compute_t<To>, op> binOp;
+        common::Transform<Ti, compute_t<To>, op> transform;
+
+        if (lid == 0) { l_reduced_block_size_[0] = 0; }
+
+        int dims_ordering[4];
+        dims_ordering[0] = DIM_;
+        int d            = 1;
+        for (int i = 0; i < 4; ++i) {
+            if (i != DIM_) dims_ordering[d++] = i;
+        }
+        it.barrier();
+
+        // load keys and values to threads
+        Tk k;
+        compute_t<To> v;
+        if (gid < n_) {
+            k                 = iKeys_[gid];
+            const int bOffset = bidw * iVInfo_.strides[dims_ordering[3]] +
+                                bidz * iVInfo_.strides[dims_ordering[2]] +
+                                bidy * iVInfo_.strides[dims_ordering[1]];
+            v = transform(iVals_[bOffset + gid * iVInfo_.strides[DIM_]]);
+            if (change_nan_) v = IS_NAN(v) ? nanval_ : v;
+        } else {
+            v = init_val;
+        }
+
+        l_keys_[lid] = k;
+        l_vals_[lid] = v;
+
+        l_reduced_keys_[lid] = k;
+        it.barrier();
+
+        // mark threads containing unique keys
+        int eq_check    = (lid > 0) ? (k != l_reduced_keys_[lid - 1]) : 0;
+        int unique_flag = (eq_check || (lid == 0)) && (gid < n_);
+
+        l_unique_flags_[lid] = unique_flag;
+        int unique_id =
+            work_group_scan_inclusive_add(it, l_wg_temp_, l_unique_flags_);
+
+        l_unique_ids_[lid] = unique_id;
+
+        if (lid == DIMX_ - 1) l_reduced_block_size_[0] = unique_id;
+
+        for (int off = 1; off < DIMX_; off *= 2) {
+            it.barrier();
+            int test_unique_id =
+                (lid + off < DIMX_) ? l_unique_ids_[lid + off] : ~unique_id;
+            eq_check = (unique_id == test_unique_id);
+            int update_key =
+                eq_check && (lid < (DIMX_ - off)) &&
+                ((gid + off) <
+                 n_);  // checks if this thread should perform a reduction
+            compute_t<To> uval = (update_key) ? l_vals_[lid + off] : init_val;
+            it.barrier();
+            l_vals_[lid] =
+                binOp(l_vals_[lid], uval);  // update if thread requires it
+        }
+
+        if (unique_flag) {
+            l_reduced_keys_[unique_id - 1] = k;
+            l_reduced_vals_[unique_id - 1] = l_vals_[lid];
+        }
+        it.barrier();
+
+        const int bid = g.get_group_id(0);
+        if (lid < l_reduced_block_size_[0]) {
+            const int bOffset = bidw * oVInfo_.strides[dims_ordering[3]] +
+                                bidz * oVInfo_.strides[dims_ordering[2]] +
+                                bidy * oVInfo_.strides[dims_ordering[1]];
+            oKeys_[gid] = l_reduced_keys_[lid];
+            oVals_[bOffset + (gid)*oVInfo_.strides[DIM_]] =
+                l_reduced_vals_[lid];
+        }
+
+        reduced_block_sizes_[bid] = l_reduced_block_size_[0];
+    }
+
+    int work_group_scan_inclusive_add(sycl::nd_item<3> it,
+                                      sycl::local_accessor<int> wg_temp,
+                                      sycl::local_accessor<int> arr) const {
+        const uint lid = it.get_local_id(0);
+        int *active_buf;
+
+        int val    = arr[lid];
+        active_buf = arr.get_pointer();
+
+        bool swap_buffer = false;
+        for (int off = 1; off <= DIMX_; off *= 2) {
+            it.barrier();
+            if (lid >= off) { val = val + active_buf[lid - off]; }
+            swap_buffer = !swap_buffer;
+            active_buf =
+                swap_buffer ? wg_temp.get_pointer() : arr.get_pointer();
+            active_buf[lid] = val;
+        }
+
+        int res = active_buf[lid];
+        return res;
+    }
+
+   protected:
+    sycl::accessor<int> reduced_block_sizes_;
+    write_accessor<Tk> oKeys_;
+    KParam oKInfo_;
+    write_accessor<To> oVals_;
+    KParam oVInfo_;
+    read_accessor<Tk> iKeys_;
+    KParam iKInfo_;
+    read_accessor<Ti> iVals_;
+    KParam iVInfo_;
+    int change_nan_;
+    To nanval_;
+    int n_;
+    int nGroupsZ_;
+    int DIMX_;
+    int DIM_;
+    sycl::local_accessor<Tk> l_keys_;
+    sycl::local_accessor<compute_t<To>> l_vals_;
+    sycl::local_accessor<Tk> l_reduced_keys_;
+    sycl::local_accessor<compute_t<To>> l_reduced_vals_;
+    sycl::local_accessor<int> l_unique_ids_;
+    sycl::local_accessor<int> l_wg_temp_;
+    sycl::local_accessor<int> l_unique_flags_;
+    sycl::local_accessor<int> l_reduced_block_size_;
+};
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index 14b5a9e269..efada203e1 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -6,11 +6,19 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
+
+// oneDPL headers should be included before standard headers
+#define ONEDPL_USE_PREDEFINED_POLICIES 0
+#include <oneapi/dpl/execution>
+#include <oneapi/dpl/iterator>
+#include <oneapi/dpl/numeric>
 
 #include <Array.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
 #include <kernel/reduce.hpp>
-// #include <kernel/reduce_by_key.hpp>
+#include <kernel/reduce_by_key.hpp>
 #include <reduce.hpp>
 #include <af/dim4.hpp>
 #include <complex>
@@ -31,11 +39,558 @@ Array<To> reduce(const Array<Ti> &in, const int dim, bool change_nan,
     return out;
 }
 
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void reduceBlocksByKey(sycl::buffer<int> &reduced_block_sizes,
+                       Array<Tk> keys_out, Array<To> vals_out,
+                       const Array<Tk> keys, const Array<Ti> vals,
+                       int change_nan, double nanval, const int n,
+                       const int threads_x) {
+    int numBlocks = divup(n, threads_x);
+
+    sycl::range<3> local(threads_x, 1, 1);
+    sycl::range<3> global(local[0] * numBlocks, vals_out.dims()[1],
+                          vals_out.dims()[2] * vals_out.dims()[3]);
+
+    getQueue().submit([&](sycl::handler &h) {
+        sycl::accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
+        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
+        write_accessor<To> vals_out_acc{*vals_out.get(), h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        read_accessor<Ti> vals_acc{*vals.get(), h};
+
+        auto l_keys         = sycl::local_accessor<Tk>(threads_x, h);
+        auto l_vals         = sycl::local_accessor<compute_t<To>>(threads_x, h);
+        auto l_reduced_keys = sycl::local_accessor<Tk>(threads_x, h);
+        auto l_reduced_vals = sycl::local_accessor<compute_t<To>>(threads_x, h);
+        auto l_unique_ids   = sycl::local_accessor<int>(threads_x, h);
+        auto l_wq_temp      = sycl::local_accessor<int>(threads_x, h);
+        auto l_unique_flags = sycl::local_accessor<int>(threads_x, h);
+        auto l_reduced_block_size = sycl::local_accessor<int>(1, h);
+
+        h.parallel_for(
+            sycl::nd_range<3>(global, local),
+            kernel::reduceBlocksByKeyKernel<Ti, Tk, To, op>(
+                reduced_block_sizes_acc, keys_out_acc, keys_out, vals_out_acc,
+                vals_out, keys_acc, keys, vals_acc, vals, change_nan,
+                scalar<To>(nanval), n, static_cast<int>(vals_out.dims()[2]),
+                threads_x, l_keys, l_vals, l_reduced_keys, l_reduced_vals,
+                l_unique_ids, l_wq_temp, l_unique_flags, l_reduced_block_size));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Ti, typename Tk, typename To, af_op_t op>
+void reduceBlocksByKeyDim(sycl::buffer<int> &reduced_block_sizes,
+                          Array<Tk> keys_out, Array<To> vals_out,
+                          const Array<Tk> keys, const Array<Ti> vals,
+                          int change_nan, double nanval, const int n,
+                          const int threads_x, const int dim,
+                          std::vector<int> dim_ordering) {
+    int numBlocks = divup(n, threads_x);
+
+    sycl::range<3> local(threads_x, 1, 1);
+    sycl::range<3> global(
+        local[0] * numBlocks, vals_out.dims()[dim_ordering[1]],
+        vals_out.dims()[dim_ordering[2]] * vals_out.dims()[dim_ordering[3]]);
+
+    getQueue().submit([&](sycl::handler &h) {
+        sycl::accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
+        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
+        write_accessor<To> vals_out_acc{*vals_out.get(), h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        read_accessor<Ti> vals_acc{*vals.get(), h};
+
+        auto l_keys         = sycl::local_accessor<Tk>(threads_x, h);
+        auto l_vals         = sycl::local_accessor<compute_t<To>>(threads_x, h);
+        auto l_reduced_keys = sycl::local_accessor<Tk>(threads_x, h);
+        auto l_reduced_vals = sycl::local_accessor<compute_t<To>>(threads_x, h);
+        auto l_unique_ids   = sycl::local_accessor<int>(threads_x, h);
+        auto l_wq_temp      = sycl::local_accessor<int>(threads_x, h);
+        auto l_unique_flags = sycl::local_accessor<int>(threads_x, h);
+        auto l_reduced_block_size = sycl::local_accessor<int>(1, h);
+
+        h.parallel_for(
+            sycl::nd_range<3>(global, local),
+            kernel::reduceBlocksByKeyDimKernel<Ti, Tk, To, op>(
+                reduced_block_sizes_acc, keys_out_acc, keys_out, vals_out_acc,
+                vals_out, keys_acc, keys, vals_acc, vals, change_nan,
+                scalar<To>(nanval), n, static_cast<int>(vals_out.dims()[2]),
+                threads_x, dim, l_keys, l_vals, l_reduced_keys, l_reduced_vals,
+                l_unique_ids, l_wq_temp, l_unique_flags, l_reduced_block_size));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Tk, typename To, af_op_t op>
+void finalBoundaryReduce(sycl::buffer<int> &reduced_block_sizes, Array<Tk> keys,
+                         Array<To> vals_out, const int n, const int numBlocks,
+                         const int threads_x) {
+    sycl::range<1> local(threads_x);
+    sycl::range<1> global(local[0] * numBlocks);
+
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        sycl::accessor<To> vals_out_acc{*vals_out.get(), h};
+
+        h.parallel_for(sycl::nd_range<1>(global, local),
+                       kernel::finalBoundaryReduceKernel<Tk, To, op>(
+                           reduced_block_sizes_acc, keys_acc, keys,
+                           vals_out_acc, vals_out, n));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Tk, typename To, af_op_t op>
+void finalBoundaryReduceDim(sycl::buffer<int> &reduced_block_sizes,
+                            Array<Tk> keys, Array<To> vals_out, const int n,
+                            const int numBlocks, const int threads_x,
+                            const int dim, std::vector<int> dim_ordering) {
+    sycl::range<3> local(threads_x, 1, 1);
+    sycl::range<3> global(
+        local[0] * numBlocks, vals_out.dims()[dim_ordering[1]],
+        vals_out.dims()[dim_ordering[2]] * vals_out.dims()[dim_ordering[3]]);
+
+    getQueue().submit([&](sycl::handler &h) {
+        write_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        sycl::accessor<To> vals_out_acc{*vals_out.get(), h};
+
+        // TODO: fold 3,4 dimensions
+        h.parallel_for(
+            sycl::nd_range<3>(global, local),
+            kernel::finalBoundaryReduceDimKernel<Tk, To, op>(
+                reduced_block_sizes_acc, keys_acc, keys, vals_out_acc, vals_out,
+                n, vals_out.dims()[dim_ordering[2]]));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Tk, typename To>
+void compact(sycl::buffer<int> reduced_block_sizes, Array<Tk> &keys_out,
+             Array<To> &vals_out, const Array<Tk> &keys, const Array<To> &vals,
+             const int numBlocks, const int threads_x) {
+    sycl::range<3> local(threads_x, 1, 1);
+    sycl::range<3> global(local[0] * numBlocks, vals_out.dims()[1],
+                          vals_out.dims()[2] * vals_out.dims()[3]);
+
+    getQueue().submit([&](sycl::handler &h) {
+        read_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
+        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
+        write_accessor<To> vals_out_acc{*vals_out.get(), h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        read_accessor<To> vals_acc{*vals.get(), h};
+
+        h.parallel_for(sycl::nd_range<3>(global, local),
+                       kernel::compactKernel<Tk, To>(
+                           reduced_block_sizes_acc, keys_out_acc, keys_out,
+                           vals_out_acc, vals_out, keys_acc, keys, vals_acc,
+                           vals, static_cast<int>(vals_out.dims()[2])));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Tk, typename To>
+void compactDim(sycl::buffer<int> &reduced_block_sizes, Array<Tk> &keys_out,
+                Array<To> &vals_out, const Array<Tk> &keys,
+                const Array<To> &vals, const int numBlocks, const int threads_x,
+                const int dim, std::vector<int> dim_ordering) {
+    sycl::range<3> local(threads_x, 1, 1);
+    sycl::range<3> global(
+        local[0] * numBlocks, vals_out.dims()[dim_ordering[1]],
+        vals_out.dims()[dim_ordering[2]] * vals_out.dims()[dim_ordering[3]]);
+
+    getQueue().submit([&](sycl::handler &h) {
+        read_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
+        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
+        write_accessor<To> vals_out_acc{*vals_out.get(), h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        read_accessor<To> vals_acc{*vals.get(), h};
+
+        h.parallel_for(
+            sycl::nd_range<3>(global, local),
+            kernel::compactDimKernel<Tk, To>(
+                reduced_block_sizes_acc, keys_out_acc, keys_out, vals_out_acc,
+                vals_out, keys_acc, keys, vals_acc, vals,
+                static_cast<int>(vals_out.dims()[dim_ordering[2]]), dim));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename Tk>
+void testNeedsReduction(sycl::buffer<int> needs_reduction,
+                        sycl::buffer<int> needs_boundary, const Array<Tk> &keys,
+                        const int n, const int numBlocks, const int threads_x) {
+    sycl::range<1> local(threads_x);
+    sycl::range<1> global(local[0] * numBlocks);
+
+    getQueue().submit([&](sycl::handler &h) {
+        sycl::accessor<int> needs_reduction_acc{needs_reduction, h};
+        sycl::accessor<int> needs_boundary_acc{needs_boundary, h};
+        read_accessor<Tk> keys_acc{*keys.get(), h};
+        auto l_keys = sycl::local_accessor<Tk>(threads_x, h);
+
+        h.parallel_for(sycl::nd_range<1>(global, local),
+                       kernel::testNeedsReductionKernel<Tk>(
+                           needs_reduction_acc, needs_boundary_acc, keys_acc,
+                           keys, n, threads_x, l_keys));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+int reduce_by_key_first(Array<Tk> &keys_out, Array<To> &vals_out,
+                        const Array<Tk> &keys, const Array<Ti> &vals,
+                        bool change_nan, double nanval) {
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+
+    dim4 kdims = keys.dims();
+    dim4 odims = vals.dims();
+
+    Array<Tk> reduced_keys   = createEmptyArray<Tk>(kdims);
+    Array<To> reduced_vals   = createEmptyArray<To>(odims);
+    Array<Tk> t_reduced_keys = createEmptyArray<Tk>(kdims);
+    Array<To> t_reduced_vals = createEmptyArray<To>(odims);
+
+    // flags determining more reduction is necessary
+    auto needs_another_reduction        = memAlloc<int>(1);
+    auto needs_block_boundary_reduction = memAlloc<int>(1);
+
+    // reset flags
+    getQueue().submit([&](sycl::handler &h) {
+        auto wacc =
+            needs_another_reduction->get_access<sycl::access_mode::write>(h);
+        h.fill(wacc, 0);
+    });
+    getQueue().submit([&](sycl::handler &h) {
+        auto wacc = needs_block_boundary_reduction
+                        ->get_access<sycl::access_mode::write>(h);
+        h.fill(wacc, 0);
+    });
+
+    size_t nelems = kdims[0];
+
+    const unsigned int numThreads = 128;
+    int numBlocksD0               = divup(nelems, numThreads);
+    auto reduced_block_sizes      = memAlloc<int>(numBlocksD0);
+
+    int n_reduced_host = nelems;
+
+    int needs_another_reduction_host        = 0;
+    int needs_block_boundary_reduction_host = 0;
+
+    bool first_pass = true;
+    do {
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        if (first_pass) {
+            reduceBlocksByKey<Ti, Tk, To, op>(
+                *reduced_block_sizes.get(), reduced_keys, reduced_vals, keys,
+                vals, change_nan, nanval, n_reduced_host, numThreads);
+            first_pass = false;
+        } else {
+            constexpr af_op_t op2 = (op == af_notzero_t) ? af_add_t : op;
+            reduceBlocksByKey<To, Tk, To, op2>(
+                *reduced_block_sizes.get(), reduced_keys, reduced_vals,
+                t_reduced_keys, t_reduced_vals, change_nan, nanval,
+                n_reduced_host, numThreads);
+        }
+
+        auto val_buf_begin = ::oneapi::dpl::begin(*reduced_block_sizes.get());
+        auto val_buf_end   = val_buf_begin + numBlocksD0;
+        std::inclusive_scan(dpl_policy, val_buf_begin, val_buf_end,
+                            val_buf_begin);
+
+        compact<Tk, To>(*reduced_block_sizes.get(), t_reduced_keys,
+                        t_reduced_vals, reduced_keys, reduced_vals, numBlocksD0,
+                        numThreads);
+
+        sycl::event reduce_host_event =
+            getQueue().submit([&](sycl::handler &h) {
+                sycl::range rr(1);
+                sycl::id offset_id(numBlocksD0 - 1);
+                auto offset_acc =
+                    reduced_block_sizes
+                        ->template get_access<sycl::access_mode::read>(
+                            h, rr, offset_id);
+                h.copy(offset_acc, &n_reduced_host);
+            });
+
+        // reset flags
+        getQueue().submit([&](sycl::handler &h) {
+            auto wacc =
+                needs_another_reduction->get_access<sycl::access_mode::write>(
+                    h);
+            h.fill(wacc, 0);
+        });
+        getQueue().submit([&](sycl::handler &h) {
+            auto wacc = needs_block_boundary_reduction
+                            ->get_access<sycl::access_mode::write>(h);
+            h.fill(wacc, 0);
+        });
+
+        reduce_host_event.wait();
+
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        testNeedsReduction<Tk>(*needs_another_reduction.get(),
+                               *needs_block_boundary_reduction.get(),
+                               t_reduced_keys, n_reduced_host, numBlocksD0,
+                               numThreads);
+
+        sycl::event host_flag0_event = getQueue().submit([&](sycl::handler &h) {
+            sycl::range rr(1);
+            auto acc =
+                needs_another_reduction
+                    ->template get_access<sycl::access_mode::read>(h, rr);
+            h.copy(acc, &needs_another_reduction_host);
+        });
+        sycl::event host_flag1_event = getQueue().submit([&](sycl::handler &h) {
+            sycl::range rr(1);
+            auto acc =
+                needs_block_boundary_reduction
+                    ->template get_access<sycl::access_mode::read>(h, rr);
+            h.copy(acc, &needs_block_boundary_reduction_host);
+        });
+
+        host_flag1_event.wait();
+        host_flag0_event.wait();
+
+        if (needs_block_boundary_reduction_host &&
+            !needs_another_reduction_host) {
+            finalBoundaryReduce<Tk, To, op>(
+                *reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
+                n_reduced_host, numBlocksD0, numThreads);
+
+            auto val_buf_begin =
+                ::oneapi::dpl::begin(*reduced_block_sizes.get());
+            auto val_buf_end = val_buf_begin + numBlocksD0;
+            std::inclusive_scan(dpl_policy, val_buf_begin, val_buf_end,
+                                val_buf_begin);
+
+            sycl::event reduce_host_event =
+                getQueue().submit([&](sycl::handler &h) {
+                    sycl::range rr(1);
+                    sycl::id offset_id(numBlocksD0 - 1);
+                    auto offset_acc =
+                        reduced_block_sizes
+                            ->template get_access<sycl::access_mode::read>(
+                                h, rr, offset_id);
+                    h.copy(offset_acc, &n_reduced_host);
+                });
+
+            compact<Tk, To>(*reduced_block_sizes.get(), reduced_keys,
+                            reduced_vals, t_reduced_keys, t_reduced_vals,
+                            numBlocksD0, numThreads);
+
+            std::swap(t_reduced_keys, reduced_keys);
+            std::swap(t_reduced_vals, reduced_vals);
+            reduce_host_event.wait();
+        }
+    } while (needs_another_reduction_host ||
+             needs_block_boundary_reduction_host);
+
+    keys_out = t_reduced_keys;
+    vals_out = t_reduced_vals;
+    return n_reduced_host;
+}
+
+template<af_op_t op, typename Ti, typename Tk, typename To>
+int reduce_by_key_dim(Array<Tk> &keys_out, Array<To> &vals_out,
+                      const Array<Tk> &keys, const Array<Ti> &vals,
+                      bool change_nan, double nanval, const int dim) {
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+
+    std::vector<int> dim_ordering = {dim};
+    for (int i = 0; i < 4; ++i) {
+        if (i != dim) { dim_ordering.push_back(i); }
+    }
+
+    dim4 kdims = keys.dims();
+    dim4 odims = vals.dims();
+
+    Array<Tk> reduced_keys   = createEmptyArray<Tk>(kdims);
+    Array<To> reduced_vals   = createEmptyArray<To>(odims);
+    Array<Tk> t_reduced_keys = createEmptyArray<Tk>(kdims);
+    Array<To> t_reduced_vals = createEmptyArray<To>(odims);
+
+    // flags determining more reduction is necessary
+    auto needs_another_reduction        = memAlloc<int>(1);
+    auto needs_block_boundary_reduction = memAlloc<int>(1);
+
+    // reset flags
+    getQueue().submit([&](sycl::handler &h) {
+        auto wacc =
+            needs_another_reduction->get_access<sycl::access_mode::write>(h);
+        h.fill(wacc, 0);
+    });
+    getQueue().submit([&](sycl::handler &h) {
+        auto wacc = needs_block_boundary_reduction
+                        ->get_access<sycl::access_mode::write>(h);
+        h.fill(wacc, 0);
+    });
+
+    int nelems = kdims[0];
+
+    const unsigned int numThreads = 128;
+    int numBlocksD0               = divup(nelems, numThreads);
+    auto reduced_block_sizes      = memAlloc<int>(numBlocksD0);
+
+    int n_reduced_host = nelems;
+
+    int needs_another_reduction_host        = 0;
+    int needs_block_boundary_reduction_host = 0;
+
+    bool first_pass = true;
+    do {
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        if (first_pass) {
+            reduceBlocksByKeyDim<Ti, Tk, To, op>(
+                *reduced_block_sizes.get(), reduced_keys, reduced_vals, keys,
+                vals, change_nan, nanval, n_reduced_host, numThreads, dim,
+                dim_ordering);
+            first_pass = false;
+        } else {
+            constexpr af_op_t op2 = op == af_notzero_t ? af_add_t : op;
+            reduceBlocksByKeyDim<To, Tk, To, op2>(
+                *reduced_block_sizes.get(), reduced_keys, reduced_vals,
+                t_reduced_keys, t_reduced_vals, change_nan, nanval,
+                n_reduced_host, numThreads, dim, dim_ordering);
+        }
+
+        auto val_buf_begin = ::oneapi::dpl::begin(*reduced_block_sizes.get());
+        auto val_buf_end   = val_buf_begin + numBlocksD0;
+        std::inclusive_scan(dpl_policy, val_buf_begin, val_buf_end,
+                            val_buf_begin);
+
+        compactDim<Tk, To>(*reduced_block_sizes.get(), t_reduced_keys,
+                           t_reduced_vals, reduced_keys, reduced_vals,
+                           numBlocksD0, numThreads, dim, dim_ordering);
+
+        sycl::event reduce_host_event =
+            getQueue().submit([&](sycl::handler &h) {
+                sycl::range rr(1);
+                sycl::id offset_id(numBlocksD0 - 1);
+                auto offset_acc =
+                    reduced_block_sizes
+                        ->template get_access<sycl::access_mode::read>(
+                            h, rr, offset_id);
+                h.copy(offset_acc, &n_reduced_host);
+            });
+
+        // reset flags
+        getQueue().submit([&](sycl::handler &h) {
+            auto wacc =
+                needs_another_reduction->get_access<sycl::access_mode::write>(
+                    h);
+            h.fill(wacc, 0);
+        });
+        getQueue().submit([&](sycl::handler &h) {
+            auto wacc = needs_block_boundary_reduction
+                            ->get_access<sycl::access_mode::write>(h);
+            h.fill(wacc, 0);
+        });
+
+        reduce_host_event.wait();
+
+        numBlocksD0 = divup(n_reduced_host, numThreads);
+
+        testNeedsReduction<Tk>(*needs_another_reduction.get(),
+                               *needs_block_boundary_reduction.get(),
+                               t_reduced_keys, n_reduced_host, numBlocksD0,
+                               numThreads);
+
+        sycl::event host_flag0_event = getQueue().submit([&](sycl::handler &h) {
+            sycl::range rr(1);
+            auto acc =
+                needs_another_reduction
+                    ->template get_access<sycl::access_mode::read>(h, rr);
+            h.copy(acc, &needs_another_reduction_host);
+        });
+        sycl::event host_flag1_event = getQueue().submit([&](sycl::handler &h) {
+            sycl::range rr(1);
+            auto acc =
+                needs_block_boundary_reduction
+                    ->template get_access<sycl::access_mode::read>(h, rr);
+            h.copy(acc, &needs_block_boundary_reduction_host);
+        });
+
+        host_flag1_event.wait();
+        host_flag0_event.wait();
+
+        if (needs_block_boundary_reduction_host &&
+            !needs_another_reduction_host) {
+            finalBoundaryReduceDim<Tk, To, op>(
+                *reduced_block_sizes.get(), t_reduced_keys, t_reduced_vals,
+                n_reduced_host, numBlocksD0, numThreads, dim, dim_ordering);
+
+            auto val_buf_begin =
+                ::oneapi::dpl::begin(*reduced_block_sizes.get());
+            auto val_buf_end = val_buf_begin + numBlocksD0;
+            std::inclusive_scan(dpl_policy, val_buf_begin, val_buf_end,
+                                val_buf_begin);
+
+            sycl::event reduce_host_event =
+                getQueue().submit([&](sycl::handler &h) {
+                    sycl::range rr(1);
+                    sycl::id offset_id(numBlocksD0 - 1);
+                    auto offset_acc =
+                        reduced_block_sizes
+                            ->template get_access<sycl::access_mode::read>(
+                                h, rr, offset_id);
+                    h.copy(offset_acc, &n_reduced_host);
+                });
+
+            compactDim<Tk, To>(*reduced_block_sizes.get(), reduced_keys,
+                               reduced_vals, t_reduced_keys, t_reduced_vals,
+                               numBlocksD0, numThreads, dim, dim_ordering);
+
+            std::swap(t_reduced_keys, reduced_keys);
+            std::swap(t_reduced_vals, reduced_vals);
+            reduce_host_event.wait();
+        }
+    } while (needs_another_reduction_host ||
+             needs_block_boundary_reduction_host);
+
+    keys_out = t_reduced_keys;
+    vals_out = t_reduced_vals;
+
+    return n_reduced_host;
+}
+
 template<af_op_t op, typename Ti, typename Tk, typename To>
 void reduce_by_key(Array<Tk> &keys_out, Array<To> &vals_out,
                    const Array<Tk> &keys, const Array<Ti> &vals, const int dim,
                    bool change_nan, double nanval) {
-    ONEAPI_NOT_SUPPORTED("");
+    dim4 kdims = keys.dims();
+    dim4 odims = vals.dims();
+
+    // prepare output arrays
+    Array<Tk> reduced_keys = createEmptyArray<Tk>(dim4());
+    Array<To> reduced_vals = createEmptyArray<To>(dim4());
+
+    size_t n_reduced = 0;
+    if (dim == 0) {
+        n_reduced = reduce_by_key_first<op, Ti, Tk, To>(
+            reduced_keys, reduced_vals, keys, vals, change_nan, nanval);
+    } else {
+        n_reduced = reduce_by_key_dim<op, Ti, Tk, To>(
+            reduced_keys, reduced_vals, keys, vals, change_nan, nanval, dim);
+    }
+
+    kdims[0]   = n_reduced;
+    odims[dim] = n_reduced;
+    std::vector<af_seq> kindex, vindex;
+    for (int i = 0; i < odims.ndims(); ++i) {
+        af_seq sk = {0.0, (double)kdims[i] - 1, 1.0};
+        af_seq sv = {0.0, (double)odims[i] - 1, 1.0};
+        kindex.push_back(sk);
+        vindex.push_back(sv);
+    }
+
+    keys_out = createSubArray<Tk>(reduced_keys, kindex, true);
+    vals_out = createSubArray<To>(reduced_vals, vindex, true);
 }
 
 template<af_op_t op, typename Ti, typename To>

From b9cdc1941ed4a0991082fe8dbec4e425fc8277e3 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Tue, 20 Jun 2023 16:57:03 -0400
Subject: [PATCH 2528/2677] Fix FFT errors in oneAPI backend because of
 descriptor parameters (#3449)

---------

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
Co-authored-by: syurkevi <stefan@arrayfire.com>
Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/backend/oneapi/fft.cpp | 75 ++++++++++++++++++++++++++++----------
 1 file changed, 56 insertions(+), 19 deletions(-)

diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index b32c801423..5c3621c5e1 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -50,11 +50,22 @@ void fft_inplace(Array<T> &in, const int rank, const bool direction) {
 
     auto desc = [rank, &idims]() {
         if (rank == 1) return desc_ty(idims[0]);
-        if (rank == 2) return desc_ty({idims[1], idims[0]});
-        if (rank == 3) return desc_ty({idims[2], idims[1], idims[0]});
-        return desc_ty({idims[3], idims[2], idims[1], idims[0]});
+        if (rank == 2) return desc_ty({idims[0], idims[1]});
+        if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
+        return desc_ty({idims[0], idims[1], idims[2], idims[3]});
     }();
 
+    if (rank > 1) {
+        std::int64_t fft_input_strides[5];
+        fft_input_strides[0] = in.getOffset();
+        fft_input_strides[1] = istrides[0];
+        fft_input_strides[2] = istrides[1];
+        fft_input_strides[3] = istrides[2];
+        fft_input_strides[4] = istrides[3];
+        desc.set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
+                       fft_input_strides);
+    }
+
     desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT, DFTI_INPLACE);
 
     int batch = 1;
@@ -96,6 +107,25 @@ Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
         if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
         return desc_ty({idims[0], idims[1], idims[2], idims[3]});
     }();
+    if (rank > 1) {
+        std::int64_t fft_input_strides[5];
+        fft_input_strides[0] = in.getOffset();
+        fft_input_strides[1] = istrides[0];
+        fft_input_strides[2] = istrides[1];
+        fft_input_strides[3] = istrides[2];
+        fft_input_strides[4] = istrides[3];
+        desc.set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
+                       fft_input_strides);
+
+        std::int64_t fft_output_strides[5];
+        fft_output_strides[0] = out.getOffset();
+        fft_output_strides[1] = ostrides[0];
+        fft_output_strides[2] = ostrides[1];
+        fft_output_strides[3] = ostrides[2];
+        fft_output_strides[4] = ostrides[3];
+        desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
+                       fft_output_strides);
+    }
 
     desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
                    DFTI_NOT_INPLACE);
@@ -110,12 +140,6 @@ Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
     desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
                    istrides[rank]);
 
-    const std::int64_t fft_output_strides[5] = {
-        0, ostrides[(rank == 2) ? 1 : 0], ostrides[(rank == 2) ? 0 : 1],
-        ostrides[2], ostrides[3]};
-    desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
-                   fft_output_strides, rank);
-
     desc.commit(getQueue());
     ::oneapi::mkl::dft::compute_forward(desc, *in.get(), *out.get());
 
@@ -139,16 +163,35 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
 
     auto desc = [rank, &odims]() {
         if (rank == 1) return desc_ty(odims[0]);
-        if (rank == 2) return desc_ty({odims[1], odims[0]});
-        if (rank == 3) return desc_ty({odims[2], odims[1], odims[0]});
-        return desc_ty({odims[3], odims[2], odims[1], odims[0]});
+        if (rank == 2) return desc_ty({odims[0], odims[1]});
+        if (rank == 3) return desc_ty({odims[0], odims[1], odims[2]});
+        return desc_ty({odims[0], odims[1], odims[2], odims[3]});
     }();
+    if (rank > 1) {
+        std::int64_t fft_input_strides[5];
+        fft_input_strides[0] = in.getOffset();
+        fft_input_strides[1] = istrides[0];
+        fft_input_strides[2] = istrides[1];
+        fft_input_strides[3] = istrides[2];
+        fft_input_strides[4] = istrides[3];
+        desc.set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
+                       fft_input_strides);
+
+        std::int64_t fft_output_strides[5];
+        fft_output_strides[0] = out.getOffset();
+        fft_output_strides[1] = ostrides[0];
+        fft_output_strides[2] = ostrides[1];
+        fft_output_strides[3] = ostrides[2];
+        fft_output_strides[4] = ostrides[3];
+        desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
+                       fft_output_strides);
+    }
 
     desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
                    DFTI_NOT_INPLACE);
 
     int batch = 1;
-    for (int i = rank; i < 4; i++) { batch *= idims[i]; }
+    for (int i = rank; i < 4; i++) { batch *= odims[i]; }
     desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
                    (int64_t)batch);
 
@@ -157,12 +200,6 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
     desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
                    ostrides[rank]);
 
-    const std::int64_t fft_output_strides[5] = {
-        0, ostrides[(rank == 2) ? 1 : 0], ostrides[(rank == 2) ? 0 : 1],
-        ostrides[2], ostrides[3]};
-    desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
-                   fft_output_strides, rank);
-
     desc.commit(getQueue());
     ::oneapi::mkl::dft::compute_backward(desc, *in.get(), *out.get());
     return out;

From 159b744ee041a415685ce00f1c8dd25add023481 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 21 Jun 2023 17:26:54 -0400
Subject: [PATCH 2529/2677] Update processException to return NO_MEM for out of
 memory exceptions

---
 src/backend/common/err_common.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 885aa8d5f5..60fc207a63 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -170,7 +170,11 @@ af_err processException() {
         snprintf(oneapi_err_msg, sizeof(oneapi_err_msg),
                  "oneAPI Error (%d): %s", ex.code().value(), ex.what());
 
-        err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
+        if (ex.code() == sycl::errc::memory_allocation) {
+            err = set_global_error_string(oneapi_err_msg, AF_ERR_NO_MEM);
+        } else {
+            err = set_global_error_string(oneapi_err_msg, AF_ERR_INTERNAL);
+        }
     } catch (const oneapi::mkl::exception &ex) {
         char oneapi_err_msg[1024];
         snprintf(oneapi_err_msg, sizeof(oneapi_err_msg), "MKL Error: %s",

From 4e5cc2ef717119b81b5371f2f3b795c10f0236c4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 21 Jun 2023 17:31:03 -0400
Subject: [PATCH 2530/2677] Fix all memAlloc and memorymanager tests

---
 src/backend/oneapi/memory.cpp   | 25 ++++++++-----------------
 src/backend/oneapi/platform.cpp |  2 +-
 2 files changed, 9 insertions(+), 18 deletions(-)

diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index f2cbab094c..f94b6df5a4 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -62,7 +62,7 @@ template<typename T>
 std::unique_ptr<sycl::buffer<T>, std::function<void(sycl::buffer<T> *)>>
 memAlloc(const size_t &elements) {
     if (elements) {
-        dim4 dims(elements * sizeof(T));
+        dim4 dims(elements);
 
         // The alloc function returns a pointer to a buffer<std::byte> object.
         // We need to reinterpret that object into buffer<T> while keeping the
@@ -71,7 +71,7 @@ memAlloc(const size_t &elements) {
         // This would delete the buffer<std::byte> object and replace it with
         // the buffer<T> object. We do the reverse in the memFree function
         auto *ptr = static_cast<sycl::buffer<std::byte> *>(
-            memoryManager().alloc(false, 1, dims.get(), 1));
+            memoryManager().alloc(false, 1, dims.get(), sizeof(T)));
         sycl::buffer<T> *optr = static_cast<sycl::buffer<T> *>((void *)ptr);
         size_t bytes          = ptr->byte_size();
 
@@ -104,14 +104,7 @@ void memFree(sycl::buffer<T> *ptr) {
     }
 }
 
-void memFreeUser(void *ptr) {
-    ONEAPI_NOT_SUPPORTED("memFreeUser Not supported");
-
-    // cl::Buffer *buf = static_cast<cl::Buffer *>(ptr);
-    // cl_mem mem      = (*buf)();
-    // delete buf;
-    memoryManager().unlock(ptr, true);
-}
+void memFreeUser(void *ptr) { memoryManager().unlock(ptr, true); }
 
 template<typename T>
 void memLock(const sycl::buffer<T> *ptr) {
@@ -169,17 +162,15 @@ INSTANTIATE(int64_t)
 
 template<>
 void *pinnedAlloc<void>(const size_t &elements) {
-    ONEAPI_NOT_SUPPORTED("pinnedAlloc Not supported");
-
-    // // TODO: make pinnedAlloc aware of array shapes
-    // dim4 dims(elements);
-    // void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), sizeof(T));
-    return static_cast<void *>(nullptr);
+    // TODO: make pinnedAlloc aware of array shapes
+    dim4 dims(elements);
+    void *ptr = pinnedMemoryManager().alloc(false, 1, dims.get(), 1);
+    return ptr;
 }
 
 Allocator::Allocator() { logger = common::loggerFactory("mem"); }
 
-void Allocator::shutdown() {}
+void Allocator::shutdown() { shutdownMemoryManager(); }
 
 int Allocator::getActiveDeviceId() { return oneapi::getActiveDeviceId(); }
 
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index d9b6f1d832..a3f6a490e8 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -605,7 +605,7 @@ void setMemoryManager(unique_ptr<MemoryManagerBase> mgr) {
 }
 
 void resetMemoryManager() {
-    return DeviceManager::getInstance().resetMemoryManagerPinned();
+    return DeviceManager::getInstance().resetMemoryManager();
 }
 
 void setMemoryManagerPinned(unique_ptr<MemoryManagerBase> mgr) {

From c56ec51a7cde38cbd8a683a90aa5124388982ff1 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 7 Jun 2023 18:23:04 -0400
Subject: [PATCH 2531/2677] adds write functions to oneapi backend

---
 src/backend/oneapi/Array.cpp | 32 +++++++++++++-------------------
 1 file changed, 13 insertions(+), 19 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 6f506ec2ba..5845d95ecc 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -393,10 +393,6 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     return kJITHeuristics::Pass;
 }
 
-// Doesn't make sense with sycl::buffer
-// TODO: accessors? or return sycl::buffer?
-// TODO: return accessor.get_pointer() for access::target::global_buffer or
-// (host_buffer?)
 template<typename T>
 void *getDevicePtr(const Array<T> &arr) {
     const buffer<T> *buf = arr.device();
@@ -486,15 +482,12 @@ void writeHostDataArray(Array<T> &arr, const T *const data,
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
     getQueue()
         .submit([&](sycl::handler &h) {
-            buffer<T> &buf = *arr.get();
-            // auto offset_acc = buf.get_access(h, sycl::range, sycl::id<>)
-            // TODO: offset accessor
-            auto offset_acc = buf.get_access(h, sycl::range(arr.elements()));
-            h.copy(data, offset_acc);
+            auto host_acc =
+                arr.get()->template get_access<sycl::access_mode::write>(
+                    h, sycl::range(bytes / sizeof(T)), arr.getOffset());
+            h.copy(data, host_acc);
         })
         .wait();
-    // getQueue().enqueueWriteBuffer(*arr.get(), CL_TRUE, arr.getOffset(),
-    // bytes, data);
 }
 
 template<typename T>
@@ -502,14 +495,15 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
                           const size_t bytes) {
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
 
-    // clRetainMemObject(
-    //    reinterpret_cast<buffer<T> *>(const_cast<void *>(data)));
-    // buffer<T> data_buf =
-    //  buffer<T>(reinterpret_cast<buffer<T>*>(const_cast<void *>(data)));
-
-    ONEAPI_NOT_SUPPORTED("writeDeviceDataArray not supported");
-    // getQueue().enqueueCopyBuffer(data_buf, buf, 0,
-    // static_cast<size_t>(arr.getOffset()), bytes);
+    sycl::buffer<T> *dataptr =
+        static_cast<sycl::buffer<T> *>(const_cast<void *>(data));
+    getQueue().submit([&](sycl::handler &h) {
+        auto src_acc = dataptr->template get_access<sycl::access_mode::read>(
+            h, sycl::range(bytes / sizeof(T)));
+        auto dst_acc = arr.get()->template get_access<sycl::access_mode::write>(
+            h, sycl::range(bytes / sizeof(T)), arr.getOffset());
+        h.copy(src_acc, dst_acc);
+    });
 }
 
 template<typename T>

From aca7f01ecc195ea8f14bd3547a2f169ded4113c8 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Jun 2023 18:31:38 -0400
Subject: [PATCH 2532/2677] Introduce kNodeType enum to differentiate nodes

---
 src/backend/common/jit/BufferNodeBase.hpp |  6 +++---
 src/backend/common/jit/NaryNode.hpp       |  3 ++-
 src/backend/common/jit/Node.hpp           | 24 +++++++++++++++++++----
 src/backend/common/jit/ScalarNode.hpp     |  6 ++----
 src/backend/common/jit/ShiftNodeBase.hpp  |  4 +++-
 src/backend/cpu/jit/BinaryNode.hpp        |  2 +-
 src/backend/cpu/jit/BufferNode.hpp        |  4 +---
 src/backend/cpu/jit/Node.hpp              |  5 +++--
 src/backend/cpu/jit/ScalarNode.hpp        |  4 +---
 src/backend/cpu/jit/UnaryNode.hpp         |  3 ++-
 src/backend/oneapi/jit.cpp                |  3 +++
 11 files changed, 41 insertions(+), 23 deletions(-)

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 061aa37a8c..2e6d29c6d1 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -30,9 +30,9 @@ class BufferNodeBase : public common::Node {
    public:
     ParamType m_param;
     BufferNodeBase(af::dtype type)
-        : Node(type, 0, {}), m_bytes(0), m_linear_buffer(true) {}
-
-    bool isBuffer() const final { return true; }
+        : Node(type, 0, {}, kNodeType::Buffer)
+        , m_bytes(0)
+        , m_linear_buffer(true) {}
 
     std::unique_ptr<Node> clone() final {
         return std::make_unique<BufferNodeBase>(*this);
diff --git a/src/backend/common/jit/NaryNode.hpp b/src/backend/common/jit/NaryNode.hpp
index 0d78b9e86c..5f1e91a570 100644
--- a/src/backend/common/jit/NaryNode.hpp
+++ b/src/backend/common/jit/NaryNode.hpp
@@ -40,7 +40,8 @@ class NaryNode : public Node {
               type, height,
               std::forward<
                   const std::array<common::Node_ptr, Node::kMaxChildren>>(
-                  children))
+                  children),
+              kNodeType::Nary)
         , m_num_children(num_children)
         , m_op_str(op_str)
         , m_op(op) {
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 8a262e0734..3106172dae 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -32,6 +32,15 @@ enum class kJITHeuristics {
 
 namespace arrayfire {
 namespace common {
+
+enum class kNodeType {
+    Generic = 0,
+    Scalar  = 1,
+    Buffer  = 2,
+    Nary    = 3,
+    Shift   = 4,
+};
+
 class Node;
 }  // namespace common
 }  // namespace arrayfire
@@ -122,13 +131,17 @@ class Node {
     std::array<Node_ptr, kMaxChildren> m_children;
     af::dtype m_type;
     int m_height;
+    kNodeType m_node_type = kNodeType::Generic;
 
     template<typename T>
     friend class NodeIterator;
     Node() = default;
     Node(const af::dtype type, const int height,
-         const std::array<Node_ptr, kMaxChildren> children)
-        : m_children(children), m_type(type), m_height(height) {
+         const std::array<Node_ptr, kMaxChildren> children, kNodeType node_type)
+        : m_children(children)
+        , m_type(type)
+        , m_height(height)
+        , m_node_type(node_type) {
         static_assert(std::is_nothrow_move_assignable<Node>::value,
                       "Node is not move assignable");
     }
@@ -249,14 +262,17 @@ class Node {
     virtual size_t getBytes() const { return 0; }
 
     // Returns true if this node is a Buffer
-    virtual bool isBuffer() const { return false; }
+    bool isBuffer() const { return m_node_type == kNodeType::Buffer; }
 
     // Returns true if this node is a Scalar
-    virtual bool isScalar() const { return false; }
+    bool isScalar() const { return m_node_type == kNodeType::Scalar; }
 
     /// Returns true if the buffer is linear
     virtual bool isLinear(const dim_t dims[4]) const;
 
+    /// Returns the node type
+    kNodeType getNodeType() const { return m_node_type; }
+
     /// Returns the type
     af::dtype getType() const { return m_type; }
 
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 3a530a6911..3dbc98df5d 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -26,7 +26,8 @@ class ScalarNode : public common::Node {
 
    public:
     ScalarNode(T val)
-        : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), 0, {})
+        : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), 0, {},
+               kNodeType::Scalar)
         , m_val(val) {
         static_assert(std::is_nothrow_move_assignable<ScalarNode>::value,
                       "ScalarNode is not move assignable");
@@ -85,9 +86,6 @@ class ScalarNode : public common::Node {
                   << ";\n";
     }
 
-    // Returns true if this node is a Buffer
-    virtual bool isScalar() const { return false; }
-
     std::string getNameStr() const final { return detail::shortname<T>(false); }
 
     // Return the info for the params and the size of the buffers
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index bbc0f5863f..13cd8cb0ac 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -32,7 +32,9 @@ class ShiftNodeBase : public Node {
    public:
     ShiftNodeBase(const af::dtype type, std::shared_ptr<BufferNode> buffer_node,
                   const std::array<int, 4> shifts)
-        : Node(type, 0, {}), m_buffer_node(buffer_node), m_shifts(shifts) {
+        : Node(type, 0, {}, kNodeType::Shift)
+        , m_buffer_node(buffer_node)
+        , m_shifts(shifts) {
         static_assert(std::is_nothrow_move_assignable<ShiftNodeBase>::value,
                       "ShiftNode is not move assignable");
         static_assert(std::is_nothrow_move_constructible<ShiftNodeBase>::value,
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index 8c1cc39d68..d715d15f44 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -32,7 +32,7 @@ class BinaryNode : public TNode<compute_t<To>> {
     BinaryNode(common::Node_ptr lhs, common::Node_ptr rhs)
         : TNode<compute_t<To>>(compute_t<To>(0),
                                std::max(lhs->getHeight(), rhs->getHeight()) + 1,
-                               {{lhs, rhs}}) {}
+                               {{lhs, rhs}}, common::kNodeType::Nary) {}
 
     std::unique_ptr<common::Node> clone() final {
         return std::make_unique<BinaryNode>(*this);
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index e6be492b7f..2d53a52486 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -35,7 +35,7 @@ class BufferNode : public TNode<T> {
 
    public:
     BufferNode()
-        : TNode<T>(T(0), 0, {})
+        : TNode<T>(T(0), 0, {}, common::kNodeType::Buffer)
         , m_bytes(0)
         , m_strides{0, 0, 0, 0}
         , m_dims{0, 0, 0, 0}
@@ -145,8 +145,6 @@ class BufferNode : public TNode<T> {
                dims[3] == m_dims[3];
     }
 
-    bool isBuffer() const final { return true; }
-
     size_t getHash() const noexcept final {
         std::hash<const void *> ptr_hash;
         std::hash<af::dtype> aftype_hash;
diff --git a/src/backend/cpu/jit/Node.hpp b/src/backend/cpu/jit/Node.hpp
index b3914cbc70..c40b0adf92 100644
--- a/src/backend/cpu/jit/Node.hpp
+++ b/src/backend/cpu/jit/Node.hpp
@@ -43,9 +43,10 @@ class TNode : public common::Node {
 
    public:
     TNode(T val, const int height,
-          const std::array<common::Node_ptr, kMaxChildren> &&children)
+          const std::array<common::Node_ptr, kMaxChildren> &&children,
+          common::kNodeType node_type)
         : Node(static_cast<af::dtype>(af::dtype_traits<T>::af_type), height,
-               move(children)) {
+               move(children), node_type) {
         using namespace common;
         m_val.fill(static_cast<compute_t<T>>(val));
     }
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index a6d7eff5df..05c13cd386 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -20,7 +20,7 @@ namespace jit {
 template<typename T>
 class ScalarNode : public TNode<T> {
    public:
-    ScalarNode(T val) : TNode<T>(val, 0, {}) {}
+    ScalarNode(T val) : TNode<T>(val, 0, {}, common::kNodeType::Scalar) {}
 
     std::unique_ptr<common::Node> clone() final {
         return std::make_unique<ScalarNode>(*this);
@@ -59,8 +59,6 @@ class ScalarNode : public TNode<T> {
         UNUSED(kerStream);
         UNUSED(ids);
     }
-
-    bool isScalar() const final { return true; }
 };
 }  // namespace jit
 }  // namespace cpu
diff --git a/src/backend/cpu/jit/UnaryNode.hpp b/src/backend/cpu/jit/UnaryNode.hpp
index 9ae8e0aa94..5ca37ca8f4 100644
--- a/src/backend/cpu/jit/UnaryNode.hpp
+++ b/src/backend/cpu/jit/UnaryNode.hpp
@@ -34,7 +34,8 @@ class UnaryNode : public TNode<To> {
 
    public:
     UnaryNode(common::Node_ptr child)
-        : TNode<To>(To(0), child->getHeight() + 1, {{child}}) {}
+        : TNode<To>(To(0), child->getHeight() + 1, {{child}},
+                    common::kNodeType::Nary) {}
 
     std::unique_ptr<common::Node> clone() final {
         return std::make_unique<UnaryNode>(*this);
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 546ca233b6..b6a1a5c6d2 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -19,6 +19,7 @@
 #include <common/jit/ModdimNode.hpp>
 #include <common/jit/Node.hpp>
 #include <common/jit/NodeIterator.hpp>
+#include <common/jit/ShiftNodeBase.hpp>
 #include <common/util.hpp>
 #include <copy.hpp>
 #include <device_manager.hpp>
@@ -43,12 +44,14 @@
 
 using arrayfire::common::getFuncName;
 using arrayfire::common::half;
+using arrayfire::common::kNodeType;
 using arrayfire::common::ModdimNode;
 using arrayfire::common::Node;
 using arrayfire::common::Node_ids;
 using arrayfire::common::Node_map_t;
 using arrayfire::common::Node_ptr;
 using arrayfire::common::NodeIterator;
+using arrayfire::common::ShiftNodeBase;
 using arrayfire::oneapi::getActiveDeviceBaseBuildFlags;
 using arrayfire::oneapi::jit::BufferNode;
 

From d50195f7aee92ad2ea3f0908593b0ad89e59a2e6 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 24 Jun 2023 18:35:00 -0400
Subject: [PATCH 2533/2677] Add is_buffer paramenter to the setArgs functor

---
 src/backend/common/jit/BufferNodeBase.hpp    |  7 ++--
 src/backend/common/jit/Node.hpp              |  8 ++---
 src/backend/common/jit/ScalarNode.hpp        |  5 +--
 src/backend/common/jit/ShiftNodeBase.hpp     |  6 ++--
 src/backend/cpu/jit/BinaryNode.hpp           |  3 +-
 src/backend/cpu/jit/BufferNode.hpp           |  3 +-
 src/backend/cpu/jit/ScalarNode.hpp           |  3 +-
 src/backend/cuda/jit.cpp                     |  3 +-
 src/backend/cuda/jit/kernel_generators.hpp   | 10 +++---
 src/backend/oneapi/jit.cpp                   | 37 ++++++++------------
 src/backend/oneapi/jit/kernel_generators.hpp |  8 +++--
 src/backend/opencl/jit.cpp                   |  9 ++---
 src/backend/opencl/jit/kernel_generators.hpp | 12 ++++---
 13 files changed, 61 insertions(+), 53 deletions(-)

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index 2e6d29c6d1..fd63e89932 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -71,10 +71,11 @@ class BufferNodeBase : public common::Node {
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void *ptr, size_t arg_size)>
+                std::function<void(int id, const void *ptr, size_t arg_size,
+                                   bool is_buffer)>
                     setArg) const override {
-        return detail::setKernelArguments(start_id, is_linear, setArg, m_data,
-                                          m_param);
+        return detail::setBufferKernelArguments(start_id, is_linear, setArg,
+                                                m_data, m_param);
     }
 
     void genOffsets(std::stringstream &kerStream, int id,
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 3106172dae..42da5a09d3 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -238,10 +238,10 @@ class Node {
     ///
     /// \returns the next index that will need to be set in the kernl. This
     ///          is usually start_id + the number of times setArg is called
-    virtual int setArgs(
-        int start_id, bool is_linear,
-        std::function<void(int id, const void *ptr, size_t arg_size)> setArg)
-        const {
+    virtual int setArgs(int start_id, bool is_linear,
+                        std::function<void(int id, const void *ptr,
+                                           size_t arg_size, bool is_buffer)>
+                            setArg) const {
         UNUSED(is_linear);
         UNUSED(setArg);
         return start_id;
diff --git a/src/backend/common/jit/ScalarNode.hpp b/src/backend/common/jit/ScalarNode.hpp
index 3dbc98df5d..4236ec4725 100644
--- a/src/backend/common/jit/ScalarNode.hpp
+++ b/src/backend/common/jit/ScalarNode.hpp
@@ -73,10 +73,11 @@ class ScalarNode : public common::Node {
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void* ptr, size_t arg_size)>
+                std::function<void(int id, const void* ptr, size_t arg_size,
+                                   bool is_buffer)>
                     setArg) const final {
         UNUSED(is_linear);
-        setArg(start_id, static_cast<const void*>(&m_val), sizeof(T));
+        setArg(start_id, static_cast<const void*>(&m_val), sizeof(T), false);
         return start_id + 1;
     }
 
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 13cd8cb0ac..9f03e2a5ad 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -87,12 +87,14 @@ class ShiftNodeBase : public Node {
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void *ptr, size_t arg_size)>
+                std::function<void(int id, const void *ptr, size_t arg_size,
+                                   bool is_buffer)>
                     setArg) const {
         int curr_id = m_buffer_node->setArgs(start_id, is_linear, setArg);
         for (int i = 0; i < 4; i++) {
             const int &d = m_shifts[i];
-            setArg(curr_id + i, static_cast<const void *>(&d), sizeof(int));
+            setArg(curr_id + i, static_cast<const void *>(&d), sizeof(int),
+                   false);
         }
         return curr_id + 4;
     }
diff --git a/src/backend/cpu/jit/BinaryNode.hpp b/src/backend/cpu/jit/BinaryNode.hpp
index d715d15f44..424e37a63f 100644
--- a/src/backend/cpu/jit/BinaryNode.hpp
+++ b/src/backend/cpu/jit/BinaryNode.hpp
@@ -71,7 +71,8 @@ class BinaryNode : public TNode<compute_t<To>> {
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void *ptr, size_t arg_size)>
+                std::function<void(int id, const void *ptr, size_t arg_size,
+                                   bool is_buffer)>
                     setArg) const override {
         UNUSED(is_linear);
         UNUSED(setArg);
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index 2d53a52486..32a94b2a74 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -119,7 +119,8 @@ class BufferNode : public TNode<T> {
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void *ptr, size_t arg_size)>
+                std::function<void(int id, const void *ptr, size_t arg_size,
+                                   bool is_buffer)>
                     setArg) const override {
         UNUSED(is_linear);
         UNUSED(setArg);
diff --git a/src/backend/cpu/jit/ScalarNode.hpp b/src/backend/cpu/jit/ScalarNode.hpp
index 05c13cd386..0b119deb82 100644
--- a/src/backend/cpu/jit/ScalarNode.hpp
+++ b/src/backend/cpu/jit/ScalarNode.hpp
@@ -40,7 +40,8 @@ class ScalarNode : public TNode<T> {
     }
 
     int setArgs(int start_id, bool is_linear,
-                std::function<void(int id, const void *ptr, size_t arg_size)>
+                std::function<void(int id, const void *ptr, size_t arg_size,
+                                   bool is_buffer)>
                     setArg) const override {
         UNUSED(is_linear);
         UNUSED(setArg);
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 33a80adb50..903c47fe9f 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -508,7 +508,8 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
         vector<void*> args;
         for (const Node* node : full_nodes) {
             node->setArgs(0, is_linear,
-                          [&](int /*id*/, const void* ptr, size_t /*size*/) {
+                          [&](int /*id*/, const void* ptr, size_t /*size*/,
+                              bool /*is_buffer*/) {
                               args.push_back(const_cast<void*>(ptr));
                           });
         }
diff --git a/src/backend/cuda/jit/kernel_generators.hpp b/src/backend/cuda/jit/kernel_generators.hpp
index f675faf4b4..02f58f432d 100644
--- a/src/backend/cuda/jit/kernel_generators.hpp
+++ b/src/backend/cuda/jit/kernel_generators.hpp
@@ -33,15 +33,17 @@ void generateParamDeclaration(std::stringstream& kerStream, int id,
 
 /// Calls the setArg function to set the arguments for a kernel call
 template<typename T>
-int setKernelArguments(
+int setBufferKernelArguments(
     int start_id, bool is_linear,
-    std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+    std::function<void(int id, const void* ptr, size_t arg_size,
+                       bool is_buffer)>& setArg,
     const std::shared_ptr<T>& ptr, const Param<T>& info) {
     UNUSED(ptr);
     if (is_linear) {
-        setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*));
+        setArg(start_id, static_cast<const void*>(&info.ptr), sizeof(T*), true);
     } else {
-        setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>));
+        setArg(start_id, static_cast<const void*>(&info), sizeof(Param<T>),
+               true);
     }
     return start_id + 1;
 }
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index b6a1a5c6d2..a0793ff6d3 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -426,12 +426,10 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                                        : 4);
     }
 
-    // for (auto* node : full_nodes) SHOW(*node);
     //  Keep in global scope, so that the nodes remain active for later
     //  referral in case moddims operations or column elimination have to
-    //  take place
-    //  Avoid all cloning/copying when no moddims node is present (high
-    //  chance)
+    //  take place Avoid all cloning/copying when no moddims node is present
+    //  (high chance)
     if (moddimsFound || emptyColumnsFound) {
         for (const Node_ids& ids : full_ids) {
             auto& children{node_clones[ids.id]->m_children};
@@ -524,15 +522,15 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                         ap, is_linear);
                     int nargs{0};
                     for (Node* node : full_nodes) {
-                        if (node->isBuffer()) {
-                            nargs = node->setArgs(
-                                nargs, is_linear,
-                                [&kernel, &hh, &is_linear](
-                                    int id, const void* ptr, size_t arg_size) {
-                                    AParam<T, sycl::access_mode::read>* info =
-                                        static_cast<AParam<
-                                            T, sycl::access_mode::read>*>(
-                                            const_cast<void*>(ptr));
+                        nargs = node->setArgs(
+                            nargs, is_linear,
+                            [&kernel, &hh, &is_linear](int id, const void* ptr,
+                                                       size_t arg_size,
+                                                       bool is_buffer) {
+                                if (is_buffer) {
+                                    auto* info = static_cast<
+                                        AParam<T, sycl::access_mode::read>*>(
+                                        const_cast<void*>(ptr));
                                     vector<cl_mem> mem =
                                         hh.get_native_mem<backend::opencl>(
                                             info->data);
@@ -552,16 +550,12 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                                                 sizeof(KParam),
                                                                 &ooo));
                                     }
-                                });
-                        } else {
-                            nargs = node->setArgs(
-                                nargs, is_linear,
-                                [&kernel](int id, const void* ptr,
-                                          size_t arg_size) {
+
+                                } else {
                                     CL_CHECK(clSetKernelArg(kernel, id,
                                                             arg_size, ptr));
-                                });
-                        }
+                                }
+                            });
                     }
 
                     // Set output parameters
@@ -589,7 +583,6 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                   (size_t)ap[0].dims[2]};
                         ndims  = 3;
                     }
-                    // SHOW(global);
                     cl_event kernel_event;
                     CL_CHECK(clEnqueueNDRangeKernel(
                         q, kernel, ndims, offset.data(), global.data(), nullptr,
diff --git a/src/backend/oneapi/jit/kernel_generators.hpp b/src/backend/oneapi/jit/kernel_generators.hpp
index 5a3321d0a0..9ca9cd984e 100644
--- a/src/backend/oneapi/jit/kernel_generators.hpp
+++ b/src/backend/oneapi/jit/kernel_generators.hpp
@@ -38,12 +38,14 @@ inline void generateParamDeclaration(std::stringstream& kerStream, int id,
 
 /// Calls the setArg function to set the arguments for a kernel call
 template<typename T>
-inline int setKernelArguments(
+inline int setBufferKernelArguments(
     int start_id, bool is_linear,
-    std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+    std::function<void(int id, const void* ptr, size_t arg_size,
+                       bool is_buffer)>& setArg,
     const std::shared_ptr<sycl::buffer<T>>& ptr,
     const AParam<T, sycl::access_mode::read>& info) {
-    setArg(start_id + 0, static_cast<const void*>(&info), sizeof(Param<T>));
+    setArg(start_id + 0, static_cast<const void*>(&info),
+           sizeof(AParam<T, sycl::access_mode::read>), true);
     return start_id + 2;
 }
 
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index f7ba973032..727724cc85 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -448,10 +448,11 @@ void evalNodes(vector<Param>& outputs, const vector<Node*>& output_nodes) {
 
     int nargs{0};
     for (const Node* node : full_nodes) {
-        nargs = node->setArgs(nargs, is_linear,
-                              [&ker](int id, const void* ptr, size_t arg_size) {
-                                  ker.setArg(id, arg_size, ptr);
-                              });
+        nargs = node->setArgs(
+            nargs, is_linear,
+            [&ker](int id, const void* ptr, size_t arg_size, bool is_buffer) {
+                ker.setArg(id, arg_size, ptr);
+            });
     }
 
     // Set output parameters
diff --git a/src/backend/opencl/jit/kernel_generators.hpp b/src/backend/opencl/jit/kernel_generators.hpp
index d4700260c4..0228e7173f 100644
--- a/src/backend/opencl/jit/kernel_generators.hpp
+++ b/src/backend/opencl/jit/kernel_generators.hpp
@@ -30,17 +30,19 @@ inline void generateParamDeclaration(std::stringstream& kerStream, int id,
 }
 
 /// Calls the setArg function to set the arguments for a kernel call
-inline int setKernelArguments(
+inline int setBufferKernelArguments(
     int start_id, bool is_linear,
-    std::function<void(int id, const void* ptr, size_t arg_size)>& setArg,
+    std::function<void(int id, const void* ptr, size_t arg_size,
+                       bool is_buffer)>& setArg,
     const std::shared_ptr<cl::Buffer>& ptr, const KParam& info) {
     setArg(start_id + 0, static_cast<const void*>(&ptr.get()->operator()()),
-           sizeof(cl_mem));
+           sizeof(cl_mem), true);
     if (is_linear) {
         setArg(start_id + 1, static_cast<const void*>(&info.offset),
-               sizeof(dim_t));
+               sizeof(dim_t), true);
     } else {
-        setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam));
+        setArg(start_id + 1, static_cast<const void*>(&info), sizeof(KParam),
+               true);
     }
     return start_id + 2;
 }

From 86e28ae5f09471b845a02ad9ad59e7e822297522 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 22 Jun 2023 14:25:46 -0400
Subject: [PATCH 2534/2677] Add support for shift kernel.

---
 src/backend/common/jit/ShiftNodeBase.hpp |  2 ++
 src/backend/oneapi/Param.hpp             |  2 +-
 src/backend/oneapi/jit.cpp               | 14 +++++++++++---
 src/backend/oneapi/shift.cpp             | 11 ++++-------
 4 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 9f03e2a5ad..106040f693 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -65,6 +65,8 @@ class ShiftNodeBase : public Node {
         swap(m_shifts, other.m_shifts);
     }
 
+    const BufferNode &getBufferNode() const { return *m_buffer_node; }
+
     bool isLinear(const dim_t dims[4]) const final {
         UNUSED(dims);
         return false;
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 7df0a73f85..752a6f7039 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -72,7 +72,7 @@ struct AParam {
         return *data;
     }
 
-    void require(sycl::handler& h) { h.require(data); }
+    void require(sycl::handler& h) const { h.require(data); }
 
     operator KParam() const {
         return KParam{{dims[0], dims[1], dims[2], dims[3]},
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index a0793ff6d3..31c2a0b881 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -494,9 +494,17 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
 
     getQueue().submit([&](sycl::handler& h) {
         for (Node* node : full_nodes) {
-            if (node->isBuffer()) {
-                BufferNode<T>* n = static_cast<BufferNode<T>*>(node);
-                n->m_param.require(h);
+            switch (node->getNodeType()) {
+                case kNodeType::Buffer: {
+                    BufferNode<T>* n = static_cast<BufferNode<T>*>(node);
+                    n->m_param.require(h);
+                } break;
+                case kNodeType::Shift: {
+                    ShiftNodeBase<jit::BufferNode<T>>* sn =
+                        static_cast<ShiftNodeBase<jit::BufferNode<T>>*>(node);
+                    sn->getBufferNode().m_param.require(h);
+                } break;
+                default: break;
             }
         }
         vector<AParam<T, sycl::access_mode::write>> ap;
diff --git a/src/backend/oneapi/shift.cpp b/src/backend/oneapi/shift.cpp
index d72477c770..8a12eb81a8 100644
--- a/src/backend/oneapi/shift.cpp
+++ b/src/backend/oneapi/shift.cpp
@@ -23,13 +23,11 @@ using std::string;
 
 namespace arrayfire {
 namespace oneapi {
+template<typename T>
+using ShiftNode = ShiftNodeBase<jit::BufferNode<T>>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
-    ONEAPI_NOT_SUPPORTED("");
-    Array<T> o = createEmptyArray<T>(dim4(1));
-    return o;
-    /*
     // Shift should only be the first node in the JIT tree.
     // Force input to be evaluated so that in is always a buffer.
     in.eval();
@@ -49,11 +47,10 @@ Array<T> shift(const Array<T> &in, const int sdims[4]) {
         assert(shifts[i] >= 0 && shifts[i] <= oDims[i]);
     }
 
-    auto node = make_shared<ShiftNode>(
+    auto node = make_shared<ShiftNode<T>>(
         static_cast<af::dtype>(dtype_traits<T>::af_type),
-        static_pointer_cast<BufferNode>(in.getNode()), shifts);
+        static_pointer_cast<jit::BufferNode<T>>(in.getNode()), shifts);
     return createNodeArray<T>(oDims, common::Node_ptr(node));
-    */
 }
 
 #define INSTANTIATE(T) \

From 787d92780d30a4b8ccccb4d7bfdc6bd312899d5f Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Mon, 26 Jun 2023 13:03:46 -0400
Subject: [PATCH 2535/2677] fix: rotate did not pass INTERP_ORDER to Interp2
 class (#3452)

* fix: rotate did not pass INTERP_ORDER to Interp2 class

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/backend/oneapi/kernel/rotate.hpp | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/src/backend/oneapi/kernel/rotate.hpp b/src/backend/oneapi/kernel/rotate.hpp
index a6d255d369..2bb945f9a2 100644
--- a/src/backend/oneapi/kernel/rotate.hpp
+++ b/src/backend/oneapi/kernel/rotate.hpp
@@ -53,8 +53,7 @@ class rotateCreateKernel {
         , batches_(batches)
         , blocksXPerImage_(blocksXPerImage)
         , blocksYPerImage_(blocksYPerImage)
-        , method_(method)
-        , INTERP_ORDER_(INTERP_ORDER) {}
+        , method_(method) {}
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
 
@@ -72,7 +71,8 @@ class rotateCreateKernel {
         const int limages =
             std::min((int)out_.dims[2] - setId * nimages_, nimages_);
 
-        if (xido >= out_.dims[0] || yido >= out_.dims[1]) return;
+        if (xido >= (unsigned)out_.dims[0] || yido >= (unsigned)out_.dims[1])
+            return;
 
         InterpPosTy xidi = xido * t_.tmat[0] + yido * t_.tmat[1] + t_.tmat[2];
         InterpPosTy yidi = xido * t_.tmat[3] + yido * t_.tmat[4] + t_.tmat[5];
@@ -85,7 +85,7 @@ class rotateCreateKernel {
         const int loco = outoff + (yido * out_.strides[1] + xido);
 
         InterpInTy zero = (InterpInTy)0;
-        if (INTERP_ORDER_ > 1) {
+        if constexpr (INTERP_ORDER > 1) {
             // Special conditions to deal with boundaries for bilinear and
             // bicubic
             // FIXME: Ideally this condition should be removed or be present for
@@ -102,8 +102,8 @@ class rotateCreateKernel {
 
         // FIXME: Nearest and lower do not do clamping, but other methods do
         // Make it consistent
-        const bool doclamp = INTERP_ORDER_ != 1;
-        Interp2<T, InterpPosTy, 1> interp2;  // INTERP_ORDER> interp2;
+        constexpr bool doclamp = INTERP_ORDER != 1;
+        Interp2<T, InterpPosTy, INTERP_ORDER> interp2;
         interp2(d_out_, out_, loco, d_in_, in_, inoff, xidi, yidi, 0, 1,
                 method_, limages, doclamp, 2);
     }
@@ -119,7 +119,6 @@ class rotateCreateKernel {
     const int blocksXPerImage_;
     const int blocksYPerImage_;
     af::interpType method_;
-    const int INTERP_ORDER_;
 };
 
 template<typename T>

From 6a5ff1f1021330d5ec751c28b3ce3fe26ab6ca01 Mon Sep 17 00:00:00 2001
From: Mike Mullen <96440448+mfzmullen@users.noreply.github.com>
Date: Tue, 4 Jul 2023 19:41:16 -0500
Subject: [PATCH 2536/2677] Fix cuda_fp16 not finding vector_functions.h
 (#3461)

* fix cuda_fp16 not finding vector_functions.h

---------

Co-authored-by: Michael Mullen <michael@adialante.com>
---
 src/backend/cuda/CMakeLists.txt     | 1 +
 src/backend/cuda/compile_module.cpp | 5 ++++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 0dc208fd8b..b0b0841b54 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -142,6 +142,7 @@ set(nvrtc_src
   ${CUDA_INCLUDE_DIRS}/cuda_fp16.hpp
   ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
   ${CUDA_TOOLKIT_ROOT_DIR}/include/math_constants.h
+  ${CUDA_TOOLKIT_ROOT_DIR}/include/vector_functions.h
 
   ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
   ${PROJECT_SOURCE_DIR}/include/af/defines.h
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index 06dfd0f377..d1d988e66f 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -39,6 +39,7 @@
 #include <nvrtc_kernel_headers/traits_hpp.hpp>
 #include <nvrtc_kernel_headers/types_hpp.hpp>
 #include <nvrtc_kernel_headers/utility_hpp.hpp>
+#include <nvrtc_kernel_headers/vector_functions_h.hpp>
 #include <nvrtc_kernel_headers/version_h.hpp>
 #include <optypes.hpp>
 #include <platform.hpp>
@@ -201,6 +202,7 @@ Module compileModule(const string &moduleKey, span<const string> sources,
             "dims_param.hpp",
             "common/internal_enums.hpp",
             "minmax_op.hpp",
+            "vector_functions.h",
         };
 
         constexpr size_t numHeaders = extent<decltype(includeNames)>::value;
@@ -234,6 +236,7 @@ Module compileModule(const string &moduleKey, span<const string> sources,
             string(dims_param_hpp, dims_param_hpp_len),
             string(internal_enums_hpp, internal_enums_hpp_len),
             string(minmax_op_hpp, minmax_op_hpp_len),
+            string(vector_functions_h, vector_functions_h_len),
         }};
 
         static const char *headers[] = {
@@ -251,7 +254,7 @@ Module compileModule(const string &moduleKey, span<const string> sources,
             sourceStrings[22].c_str(), sourceStrings[23].c_str(),
             sourceStrings[24].c_str(), sourceStrings[25].c_str(),
             sourceStrings[26].c_str(), sourceStrings[27].c_str(),
-            sourceStrings[28].c_str()};
+            sourceStrings[28].c_str(), sourceStrings[29].c_str()};
         static_assert(extent<decltype(headers)>::value == numHeaders,
                       "headers array contains fewer sources than includeNames");
         NVRTC_CHECK(nvrtcCreateProgram(&prog, sources[0].c_str(),

From 66d858e37110413b38dc5855de16f72a5b86e951 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 3 Jul 2023 20:17:19 -0400
Subject: [PATCH 2537/2677] adds fft plan caching, corrects descriptor strides

---
 src/backend/oneapi/fft.cpp      | 284 +++++++++++++++++++-------------
 src/backend/oneapi/fft.hpp      |   1 +
 src/backend/oneapi/onefft.hpp   |  39 +++++
 src/backend/oneapi/platform.cpp |  11 ++
 src/backend/oneapi/platform.hpp |   2 +
 5 files changed, 227 insertions(+), 110 deletions(-)
 create mode 100644 src/backend/oneapi/onefft.hpp

diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index 5c3621c5e1..3bf15acf0a 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -7,32 +7,164 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-#include <common/dispatch.hpp>
-
 #include <fft.hpp>
 
+#include <common/dispatch.hpp>
 #include <copy.hpp>
 #include <err_oneapi.hpp>
 #include <math.hpp>
 #include <memory.hpp>
+#include <onefft.hpp>
+#include <platform.hpp>
 #include <af/dim4.hpp>
 
-#include <array>
-using std::array;
+#include <oneapi/mkl/dfti.hpp>
+#include <oneapi/mkl/exceptions.hpp>
 
-using af::dim4;
+#include <cstdint>
+#include <memory>
 
-#include <oneapi/mkl/dfti.hpp>
+using std::make_shared;
+
+using af::dim4;
 
 namespace arrayfire {
 namespace oneapi {
 
 void setFFTPlanCacheSize(size_t numPlans) {}
 
-inline array<int, AF_MAX_DIMS> computeDims(const int rank, const dim4 &idims) {
-    array<int, AF_MAX_DIMS> retVal = {};
-    for (int i = 0; i < rank; i++) { retVal[i] = idims[(rank - 1) - i]; }
-    return retVal;
+std::string genPlanHashStr(int rank, ::oneapi::mkl::dft::precision precision,
+                           ::oneapi::mkl::dft::domain domain,
+                           const bool isInPlace, const dim_t *n,
+                           std::int64_t *istrides, int ibatch,
+                           std::int64_t *ostrides, int obatch, int nbatch) {
+    // create the key string
+    char key_str_temp[64];
+    sprintf(key_str_temp, "%d:", rank);
+
+    std::string key_string(key_str_temp);
+
+    if (precision == ::oneapi::mkl::dft::precision::SINGLE) {
+        key_string.append("S:");
+    } else if (precision == ::oneapi::mkl::dft::precision::DOUBLE) {
+        key_string.append("D:");
+    }
+    if (domain == ::oneapi::mkl::dft::domain::REAL) {
+        key_string.append("R:");
+    } else if (domain == ::oneapi::mkl::dft::domain::COMPLEX) {
+        key_string.append("C:");
+    }
+    if (isInPlace) {
+        key_string.append("IIP:");
+    } else {
+        key_string.append("OOP:");
+    }
+
+    for (int r = 0; r < rank; ++r) {
+        sprintf(key_str_temp, "%lld:", n[r]);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    if (istrides != nullptr) {
+        for (int r = 0; r < rank + 1; ++r) {
+            sprintf(key_str_temp, "%ld:", istrides[r]);
+            key_string.append(std::string(key_str_temp));
+        }
+        sprintf(key_str_temp, "%d:", ibatch);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    if (ostrides != nullptr) {
+        for (int r = 0; r < rank + 1; ++r) {
+            sprintf(key_str_temp, "%ld:", ostrides[r]);
+            key_string.append(std::string(key_str_temp));
+        }
+        sprintf(key_str_temp, "%d:", obatch);
+        key_string.append(std::string(key_str_temp));
+    }
+
+    sprintf(key_str_temp, "%d", nbatch);
+    key_string.append(std::string(key_str_temp));
+
+    return key_string;
+}
+
+std::vector<std::int64_t> computeStrides(const int rank, const dim4 istrides,
+                                         const dim_t offset) {
+    if (rank == 2) return {offset, istrides[1], istrides[0]};
+    if (rank == 3) return {offset, istrides[2], istrides[1], istrides[0]};
+    if (rank == 4)
+        return {offset, istrides[3], istrides[2], istrides[1], istrides[0]};
+    return {offset};
+}
+
+template<::oneapi::mkl::dft::precision precision,
+         ::oneapi::mkl::dft::domain domain>
+PlanType findPlan(int rank, const bool isInPlace, const dim_t *idims,
+                  std::int64_t *istrides, int ibatch, std::int64_t *ostrides,
+                  int obatch, int nbatch) {
+    using desc_ty = ::oneapi::mkl::dft::descriptor<precision, domain>;
+
+    std::string key_string =
+        genPlanHashStr(rank, precision, domain, isInPlace, idims, istrides,
+                       ibatch, ostrides, obatch, nbatch);
+
+    PlanCache &planner               = arrayfire::oneapi::fftManager();
+    std::shared_ptr<PlanType> retVal = (planner.find(key_string));
+    if (retVal) { return *retVal; }
+
+    desc_ty *desc = [rank, &idims]() {
+        if (rank == 1) return new desc_ty(static_cast<int64_t>(idims[0]));
+        if (rank == 2) return new desc_ty({idims[1], idims[0]});
+        if (rank == 3) return new desc_ty({idims[2], idims[1], idims[0]});
+        return new desc_ty({idims[3], idims[2], idims[1], idims[0]});
+    }();
+
+    if (rank > 1) {
+        desc->set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
+                        istrides);
+        desc->set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
+                        ostrides);
+    }
+
+    if (isInPlace) {
+        desc->set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
+                        DFTI_INPLACE);
+    } else {
+        desc->set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
+                        DFTI_NOT_INPLACE);
+    }
+
+    desc->set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
+                    (int64_t)nbatch);
+
+    desc->set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE, ibatch);
+    desc->set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE, obatch);
+
+    if constexpr (domain == ::oneapi::mkl::dft::domain::COMPLEX) {
+        desc->set_value(::oneapi::mkl::dft::config_param::COMPLEX_STORAGE,
+                        DFTI_COMPLEX_COMPLEX);
+    } else {
+        desc->set_value(
+            ::oneapi::mkl::dft::config_param::CONJUGATE_EVEN_STORAGE,
+            DFTI_COMPLEX_COMPLEX);
+        desc->set_value(::oneapi::mkl::dft::config_param::PACKED_FORMAT,
+                        DFTI_CCE_FORMAT);
+    }
+
+    try {
+        desc->commit(getQueue());
+    } catch (::oneapi::mkl::device_bad_alloc &e) {
+        // If plan creation fails, clean up the memory we hold on to and try
+        // again
+        arrayfire::oneapi::signalMemoryCleanup();
+        desc->commit(getQueue());
+    }
+
+    // push the plan into plan cache
+    std::shared_ptr<void> ptr(desc);
+    planner.push(key_string, make_shared<PlanType>(ptr));
+    return ptr;
 }
 
 template<typename T>
@@ -48,41 +180,23 @@ void fft_inplace(Array<T> &in, const int rank, const bool direction) {
         ::oneapi::mkl::dft::descriptor<precision,
                                        ::oneapi::mkl::dft::domain::COMPLEX>;
 
-    auto desc = [rank, &idims]() {
-        if (rank == 1) return desc_ty(idims[0]);
-        if (rank == 2) return desc_ty({idims[0], idims[1]});
-        if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
-        return desc_ty({idims[0], idims[1], idims[2], idims[3]});
-    }();
-
-    if (rank > 1) {
-        std::int64_t fft_input_strides[5];
-        fft_input_strides[0] = in.getOffset();
-        fft_input_strides[1] = istrides[0];
-        fft_input_strides[2] = istrides[1];
-        fft_input_strides[3] = istrides[2];
-        fft_input_strides[4] = istrides[3];
-        desc.set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
-                       fft_input_strides);
-    }
-
-    desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT, DFTI_INPLACE);
+    std::vector<std::int64_t> fft_input_strides =
+        computeStrides(rank, istrides, in.getOffset());
 
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= idims[i]; }
-    desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
-                   (int64_t)batch);
 
-    desc.set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE,
-                   istrides[rank]);
-    desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
-                   istrides[rank]);
+    const bool isInPlace = true;
+    PlanType descP = findPlan<precision, ::oneapi::mkl::dft::domain::COMPLEX>(
+        rank, isInPlace, idims.get(), fft_input_strides.data(), istrides[rank],
+        fft_input_strides.data(), istrides[rank], batch);
+
+    desc_ty *desc = (desc_ty *)descP.get();
 
-    desc.commit(getQueue());
     if (direction)
-        ::oneapi::mkl::dft::compute_forward(desc, *in.get());
+        ::oneapi::mkl::dft::compute_forward(*desc, *in.get());
     else
-        ::oneapi::mkl::dft::compute_backward(desc, *in.get());
+        ::oneapi::mkl::dft::compute_backward(*desc, *in.get());
 }
 
 template<typename Tc, typename Tr>
@@ -101,47 +215,22 @@ Array<Tc> fft_r2c(const Array<Tr> &in, const int rank) {
         ::oneapi::mkl::dft::descriptor<precision,
                                        ::oneapi::mkl::dft::domain::REAL>;
 
-    auto desc = [rank, &idims]() {
-        if (rank == 1) return desc_ty(idims[0]);
-        if (rank == 2) return desc_ty({idims[0], idims[1]});
-        if (rank == 3) return desc_ty({idims[0], idims[1], idims[2]});
-        return desc_ty({idims[0], idims[1], idims[2], idims[3]});
-    }();
-    if (rank > 1) {
-        std::int64_t fft_input_strides[5];
-        fft_input_strides[0] = in.getOffset();
-        fft_input_strides[1] = istrides[0];
-        fft_input_strides[2] = istrides[1];
-        fft_input_strides[3] = istrides[2];
-        fft_input_strides[4] = istrides[3];
-        desc.set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
-                       fft_input_strides);
-
-        std::int64_t fft_output_strides[5];
-        fft_output_strides[0] = out.getOffset();
-        fft_output_strides[1] = ostrides[0];
-        fft_output_strides[2] = ostrides[1];
-        fft_output_strides[3] = ostrides[2];
-        fft_output_strides[4] = ostrides[3];
-        desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
-                       fft_output_strides);
-    }
-
-    desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
-                   DFTI_NOT_INPLACE);
+    std::vector<std::int64_t> fft_input_strides =
+        computeStrides(rank, istrides, in.getOffset());
+    std::vector<std::int64_t> fft_output_strides =
+        computeStrides(rank, ostrides, out.getOffset());
 
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= idims[i]; }
-    desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
-                   (int64_t)batch);
 
-    desc.set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE,
-                   ostrides[rank]);
-    desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
-                   istrides[rank]);
+    const bool isInPlace = false;
+    PlanType descP = findPlan<precision, ::oneapi::mkl::dft::domain::REAL>(
+        rank, isInPlace, idims.get(), fft_input_strides.data(), istrides[rank],
+        fft_output_strides.data(), ostrides[rank], batch);
 
-    desc.commit(getQueue());
-    ::oneapi::mkl::dft::compute_forward(desc, *in.get(), *out.get());
+    desc_ty *desc = (desc_ty *)descP.get();
+
+    ::oneapi::mkl::dft::compute_forward(*desc, *in.get(), *out.get());
 
     return out;
 }
@@ -161,47 +250,22 @@ Array<Tr> fft_c2r(const Array<Tc> &in, const dim4 &odims, const int rank) {
         ::oneapi::mkl::dft::descriptor<precision,
                                        ::oneapi::mkl::dft::domain::REAL>;
 
-    auto desc = [rank, &odims]() {
-        if (rank == 1) return desc_ty(odims[0]);
-        if (rank == 2) return desc_ty({odims[0], odims[1]});
-        if (rank == 3) return desc_ty({odims[0], odims[1], odims[2]});
-        return desc_ty({odims[0], odims[1], odims[2], odims[3]});
-    }();
-    if (rank > 1) {
-        std::int64_t fft_input_strides[5];
-        fft_input_strides[0] = in.getOffset();
-        fft_input_strides[1] = istrides[0];
-        fft_input_strides[2] = istrides[1];
-        fft_input_strides[3] = istrides[2];
-        fft_input_strides[4] = istrides[3];
-        desc.set_value(::oneapi::mkl::dft::config_param::INPUT_STRIDES,
-                       fft_input_strides);
-
-        std::int64_t fft_output_strides[5];
-        fft_output_strides[0] = out.getOffset();
-        fft_output_strides[1] = ostrides[0];
-        fft_output_strides[2] = ostrides[1];
-        fft_output_strides[3] = ostrides[2];
-        fft_output_strides[4] = ostrides[3];
-        desc.set_value(::oneapi::mkl::dft::config_param::OUTPUT_STRIDES,
-                       fft_output_strides);
-    }
-
-    desc.set_value(::oneapi::mkl::dft::config_param::PLACEMENT,
-                   DFTI_NOT_INPLACE);
+    std::vector<std::int64_t> fft_input_strides =
+        computeStrides(rank, istrides, in.getOffset());
+    std::vector<std::int64_t> fft_output_strides =
+        computeStrides(rank, ostrides, out.getOffset());
 
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= odims[i]; }
-    desc.set_value(::oneapi::mkl::dft::config_param::NUMBER_OF_TRANSFORMS,
-                   (int64_t)batch);
 
-    desc.set_value(::oneapi::mkl::dft::config_param::BWD_DISTANCE,
-                   istrides[rank]);
-    desc.set_value(::oneapi::mkl::dft::config_param::FWD_DISTANCE,
-                   ostrides[rank]);
+    const bool isInPlace = false;
+    PlanType descP = findPlan<precision, ::oneapi::mkl::dft::domain::REAL>(
+        rank, isInPlace, odims.get(), fft_input_strides.data(), ostrides[rank],
+        fft_output_strides.data(), istrides[rank], batch);
+
+    desc_ty *desc = (desc_ty *)descP.get();
 
-    desc.commit(getQueue());
-    ::oneapi::mkl::dft::compute_backward(desc, *in.get(), *out.get());
+    ::oneapi::mkl::dft::compute_backward(*desc, *in.get(), *out.get());
     return out;
 }
 
diff --git a/src/backend/oneapi/fft.hpp b/src/backend/oneapi/fft.hpp
index 0138970ba9..ca82f06118 100644
--- a/src/backend/oneapi/fft.hpp
+++ b/src/backend/oneapi/fft.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <Array.hpp>
 
diff --git a/src/backend/oneapi/onefft.hpp b/src/backend/oneapi/onefft.hpp
new file mode 100644
index 0000000000..a31a91d1e1
--- /dev/null
+++ b/src/backend/oneapi/onefft.hpp
@@ -0,0 +1,39 @@
+/*******************************************************
+ * Copyright (c) 2016, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <common/FFTPlanCache.hpp>
+#include <memory.hpp>
+#include <oneapi/mkl/dfti.hpp>
+
+#include <cstdint>
+
+namespace arrayfire {
+namespace oneapi {
+
+using ::oneapi::mkl::dft::domain;
+using ::oneapi::mkl::dft::precision;
+
+using PlanType   = std::shared_ptr<void>;
+using SharedPlan = std::shared_ptr<PlanType>;
+
+template<precision p, domain d>
+PlanType findPlan(int rank, const bool isInPlace, int *n,
+                  std::int64_t *istrides, int ibatch, std::int64_t *ostrides,
+                  int obatch, int nbatch);
+
+class PlanCache : public common::FFTPlanCache<PlanCache, PlanType> {
+    template<precision p, domain d>
+    friend PlanType findPlan(int rank, const bool isInPlace, int *n,
+                             std::int64_t *istrides, int ibatch,
+                             std::int64_t *ostrides, int obatch, int nbatch);
+};
+
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index a3f6a490e8..91e307d56c 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -21,6 +21,7 @@
 #include <err_oneapi.hpp>
 #include <errorcodes.hpp>
 #include <memory.hpp>
+#include <onefft.hpp>
 #include <af/oneapi.h>
 #include <af/version.h>
 
@@ -634,6 +635,16 @@ GraphicsResourceManager& interopManager() {
     return *(inst.gfxManagers[id].get());
 }
 
+unique_ptr<PlanCache>& oneFFTManager(const int deviceId) {
+    thread_local unique_ptr<PlanCache> caches[DeviceManager::MAX_DEVICES];
+    thread_local once_flag initFlags[DeviceManager::MAX_DEVICES];
+    call_once(initFlags[deviceId],
+              [&] { caches[deviceId] = make_unique<PlanCache>(); });
+    return caches[deviceId];
+}
+
+PlanCache& fftManager() { return *oneFFTManager(getActiveDeviceId()); }
+
 }  // namespace oneapi
 }  // namespace arrayfire
 
diff --git a/src/backend/oneapi/platform.hpp b/src/backend/oneapi/platform.hpp
index 86439a685c..bceb1e5db6 100644
--- a/src/backend/oneapi/platform.hpp
+++ b/src/backend/oneapi/platform.hpp
@@ -131,6 +131,8 @@ arrayfire::common::ForgeManager& forgeManager();
 
 GraphicsResourceManager& interopManager();
 
+PlanCache& fftManager();
+
 // afcl::platform getPlatformEnum(cl::Device dev);
 
 void setActiveContext(int device);

From d29ed794442ff190d057f087f59cd182f1b02a90 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 4 Jul 2023 16:40:09 -0400
Subject: [PATCH 2538/2677] Fix missing try/catch in C API layers

---
 src/api/c/binary.cpp      | 140 +++++++++++++++++++++-----------------
 src/api/c/fftconvolve.cpp |  22 +++---
 src/api/c/plot.cpp        |  34 ++++++---
 src/api/c/type_util.cpp   |   7 +-
 4 files changed, 119 insertions(+), 84 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 566a4b22b5..dc5eddf4bc 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -277,87 +277,101 @@ static af_err af_arith_sparse_dense(af_array *out, const af_array lhs,
 
 af_err af_add(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
-    // Check if inputs are sparse
-    const ArrayInfo &linfo = getInfo(lhs, false, true);
-    const ArrayInfo &rinfo = getInfo(rhs, false, true);
+    try {
+        // Check if inputs are sparse
+        const ArrayInfo &linfo = getInfo(lhs, false, true);
+        const ArrayInfo &rinfo = getInfo(rhs, false, true);
 
-    if (linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse<af_add_t>(out, lhs, rhs);
-    }
-    if (linfo.isSparse() && !rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_add_t>(out, lhs, rhs);
-    }
-    if (!linfo.isSparse() && rinfo.isSparse()) {
-        // second operand(Array) of af_arith call should be dense
-        return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true);
+        if (linfo.isSparse() && rinfo.isSparse()) {
+            return af_arith_sparse<af_add_t>(out, lhs, rhs);
+        }
+        if (linfo.isSparse() && !rinfo.isSparse()) {
+            return af_arith_sparse_dense<af_add_t>(out, lhs, rhs);
+        }
+        if (!linfo.isSparse() && rinfo.isSparse()) {
+            // second operand(Array) of af_arith call should be dense
+            return af_arith_sparse_dense<af_add_t>(out, rhs, lhs, true);
+        }
+        return af_arith<af_add_t>(out, lhs, rhs, batchMode);
     }
-    return af_arith<af_add_t>(out, lhs, rhs, batchMode);
+    CATCHALL;
 }
 
 af_err af_mul(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
-    // Check if inputs are sparse
-    const ArrayInfo &linfo = getInfo(lhs, false, true);
-    const ArrayInfo &rinfo = getInfo(rhs, false, true);
-
-    if (linfo.isSparse() && rinfo.isSparse()) {
-        // return af_arith_sparse<af_mul_t>(out, lhs, rhs);
-        // MKL doesn't have mul or div support yet, hence
-        // this is commented out although alternative cpu code exists
-        return AF_ERR_NOT_SUPPORTED;
-    }
-    if (linfo.isSparse() && !rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_mul_t>(out, lhs, rhs);
-    }
-    if (!linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_mul_t>(out, rhs, lhs,
-                                               true);  // dense should be rhs
+    try {
+        // Check if inputs are sparse
+        const ArrayInfo &linfo = getInfo(lhs, false, true);
+        const ArrayInfo &rinfo = getInfo(rhs, false, true);
+
+        if (linfo.isSparse() && rinfo.isSparse()) {
+            // return af_arith_sparse<af_mul_t>(out, lhs, rhs);
+            // MKL doesn't have mul or div support yet, hence
+            // this is commented out although alternative cpu code exists
+            return AF_ERR_NOT_SUPPORTED;
+        }
+        if (linfo.isSparse() && !rinfo.isSparse()) {
+            return af_arith_sparse_dense<af_mul_t>(out, lhs, rhs);
+        }
+        if (!linfo.isSparse() && rinfo.isSparse()) {
+            return af_arith_sparse_dense<af_mul_t>(
+                out, rhs, lhs,
+                true);  // dense should be rhs
+        }
+        return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
     }
-    return af_arith<af_mul_t>(out, lhs, rhs, batchMode);
+    CATCHALL;
 }
 
 af_err af_sub(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
-    // Check if inputs are sparse
-    const ArrayInfo &linfo = getInfo(lhs, false, true);
-    const ArrayInfo &rinfo = getInfo(rhs, false, true);
+    try {
+        // Check if inputs are sparse
+        const ArrayInfo &linfo = getInfo(lhs, false, true);
+        const ArrayInfo &rinfo = getInfo(rhs, false, true);
 
-    if (linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse<af_sub_t>(out, lhs, rhs);
-    }
-    if (linfo.isSparse() && !rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_sub_t>(out, lhs, rhs);
-    }
-    if (!linfo.isSparse() && rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_sub_t>(out, rhs, lhs,
-                                               true);  // dense should be rhs
+        if (linfo.isSparse() && rinfo.isSparse()) {
+            return af_arith_sparse<af_sub_t>(out, lhs, rhs);
+        }
+        if (linfo.isSparse() && !rinfo.isSparse()) {
+            return af_arith_sparse_dense<af_sub_t>(out, lhs, rhs);
+        }
+        if (!linfo.isSparse() && rinfo.isSparse()) {
+            return af_arith_sparse_dense<af_sub_t>(
+                out, rhs, lhs,
+                true);  // dense should be rhs
+        }
+        return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
     }
-    return af_arith<af_sub_t>(out, lhs, rhs, batchMode);
+    CATCHALL;
 }
 
 af_err af_div(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
-    // Check if inputs are sparse
-    const ArrayInfo &linfo = getInfo(lhs, false, true);
-    const ArrayInfo &rinfo = getInfo(rhs, false, true);
-
-    if (linfo.isSparse() && rinfo.isSparse()) {
-        // return af_arith_sparse<af_div_t>(out, lhs, rhs);
-        // MKL doesn't have mul or div support yet, hence
-        // this is commented out although alternative cpu code exists
-        return AF_ERR_NOT_SUPPORTED;
-    }
-    if (linfo.isSparse() && !rinfo.isSparse()) {
-        return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
-    }
-    if (!linfo.isSparse() && rinfo.isSparse()) {
-        // Division by sparse is currently not allowed - for convinence of
-        // dealing with division by 0
-        // return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); // dense
-        // should be rhs
-        return AF_ERR_NOT_SUPPORTED;
+    try {
+        // Check if inputs are sparse
+        const ArrayInfo &linfo = getInfo(lhs, false, true);
+        const ArrayInfo &rinfo = getInfo(rhs, false, true);
+
+        if (linfo.isSparse() && rinfo.isSparse()) {
+            // return af_arith_sparse<af_div_t>(out, lhs, rhs);
+            // MKL doesn't have mul or div support yet, hence
+            // this is commented out although alternative cpu code exists
+            return AF_ERR_NOT_SUPPORTED;
+        }
+        if (linfo.isSparse() && !rinfo.isSparse()) {
+            return af_arith_sparse_dense<af_div_t>(out, lhs, rhs);
+        }
+        if (!linfo.isSparse() && rinfo.isSparse()) {
+            // Division by sparse is currently not allowed - for convinence of
+            // dealing with division by 0
+            // return af_arith_sparse_dense<af_div_t>(out, rhs, lhs, true); //
+            // dense should be rhs
+            return AF_ERR_NOT_SUPPORTED;
+        }
+        return af_arith<af_div_t>(out, lhs, rhs, batchMode);
     }
-    return af_arith<af_div_t>(out, lhs, rhs, batchMode);
+    CATCHALL;
 }
 
 af_err af_maxof(af_array *out, const af_array lhs, const af_array rhs,
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index f92a3fc655..5e69d5d0ce 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -239,18 +239,24 @@ af_err af_fft_convolve1(af_array *out, const af_array signal,
 
 af_err af_fft_convolve2(af_array *out, const af_array signal,
                         const af_array filter, const af_conv_mode mode) {
-    if (getInfo(signal).dims().ndims() < 2 &&
-        getInfo(filter).dims().ndims() < 2) {
-        return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 1);
+    try {
+        if (getInfo(signal).dims().ndims() < 2 &&
+            getInfo(filter).dims().ndims() < 2) {
+            return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 1);
+        }
+        return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
     }
-    return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
+    CATCHALL;
 }
 
 af_err af_fft_convolve3(af_array *out, const af_array signal,
                         const af_array filter, const af_conv_mode mode) {
-    if (getInfo(signal).dims().ndims() < 3 &&
-        getInfo(filter).dims().ndims() < 3) {
-        return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
+    try {
+        if (getInfo(signal).dims().ndims() < 3 &&
+            getInfo(filter).dims().ndims() < 3) {
+            return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 2);
+        }
+        return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 3);
     }
-    return fft_convolve(out, signal, filter, mode == AF_CONV_EXPAND, 3);
+    CATCHALL;
 }
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index 3cf03d05cf..c2d954d481 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -385,40 +385,52 @@ af_err af_draw_plot3(const af_window wind, const af_array P,
 af_err af_draw_scatter_nd(const af_window wind, const af_array in,
                           const af_marker_type af_marker,
                           const af_cell* const props) {
-    fg_marker_type fg_marker = getFGMarker(af_marker);
-    return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
+    try {
+        fg_marker_type fg_marker = getFGMarker(af_marker);
+        return plotWrapper(wind, in, 1, props, FG_PLOT_SCATTER, fg_marker);
+    }
+    CATCHALL;
 }
 
 af_err af_draw_scatter_2d(const af_window wind, const af_array X,
                           const af_array Y, const af_marker_type af_marker,
                           const af_cell* const props) {
-    fg_marker_type fg_marker = getFGMarker(af_marker);
-    return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
+    try {
+        fg_marker_type fg_marker = getFGMarker(af_marker);
+        return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
+    }
+    CATCHALL;
 }
 
 af_err af_draw_scatter_3d(const af_window wind, const af_array X,
                           const af_array Y, const af_array Z,
                           const af_marker_type af_marker,
                           const af_cell* const props) {
-    fg_marker_type fg_marker = getFGMarker(af_marker);
-    return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
+    try {
+        fg_marker_type fg_marker = getFGMarker(af_marker);
+        return plotWrapper(wind, X, Y, Z, props, FG_PLOT_SCATTER, fg_marker);
+    }
+    CATCHALL;
 }
 
 // Deprecated Scatter API
 af_err af_draw_scatter(const af_window wind, const af_array X, const af_array Y,
                        const af_marker_type af_marker,
                        const af_cell* const props) {
-    fg_marker_type fg_marker = getFGMarker(af_marker);
-    return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
+    try {
+        fg_marker_type fg_marker = getFGMarker(af_marker);
+        return plotWrapper(wind, X, Y, props, FG_PLOT_SCATTER, fg_marker);
+    }
+    CATCHALL;
 }
 
 af_err af_draw_scatter3(const af_window wind, const af_array P,
                         const af_marker_type af_marker,
                         const af_cell* const props) {
-    fg_marker_type fg_marker = getFGMarker(af_marker);
     try {
-        const ArrayInfo& info = getInfo(P);
-        af::dim4 dims         = info.dims();
+        fg_marker_type fg_marker = getFGMarker(af_marker);
+        const ArrayInfo& info    = getInfo(P);
+        af::dim4 dims            = info.dims();
 
         if (dims.ndims() == 2 && dims[1] == 3) {
             return plotWrapper(wind, P, 1, props, FG_PLOT_SCATTER, fg_marker);
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index 4b70df3295..c78b85b1da 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -38,6 +38,9 @@ size_t size_of(af_dtype type) {
 }
 
 af_err af_get_size_of(size_t *size, af_dtype type) {
-    *size = size_of(type);
-    return AF_SUCCESS;
+    try {
+        *size = size_of(type);
+        return AF_SUCCESS;
+    }
+    CATCHALL;
 }

From e02bb301579760ad1f954f7944307d8e8a9694e4 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 28 Jun 2023 14:36:15 -0400
Subject: [PATCH 2539/2677] adds qr to oneapi backend

---
 src/backend/oneapi/qr.cpp | 156 +++++++++++++++++++++-----------------
 1 file changed, 86 insertions(+), 70 deletions(-)

diff --git a/src/backend/oneapi/qr.cpp b/src/backend/oneapi/qr.cpp
index 32bf559f4c..64884e4c24 100644
--- a/src/backend/oneapi/qr.cpp
+++ b/src/backend/oneapi/qr.cpp
@@ -11,94 +11,110 @@
 
 #include <err_oneapi.hpp>
 
-#if defined(WITH_LINEAR_ALGEBRA) && !defined(AF_ONEAPI)
+#if defined(WITH_LINEAR_ALGEBRA)
 
 #include <blas.hpp>
 #include <copy.hpp>
-#include <cpu/cpu_qr.hpp>
 #include <identity.hpp>
-// #include <kernel/triangle.hpp>
-#include <magma/magma.h>
-#include <magma/magma_data.h>
-#include <magma/magma_helper.h>
+#include <kernel/triangle.hpp>
+#include <memory.hpp>
+#include <oneapi/mkl/lapack.hpp>
 #include <platform.hpp>
 
 namespace arrayfire {
 namespace oneapi {
 
-template<typename T>
-void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &orig) {
-    if (OpenCLCPUOffload()) { return cpu::qr(q, r, t, orig); }
-
-    const dim4 NullShape(0, 0, 0, 0);
+using sycl::buffer;
 
-    dim4 iDims = orig.dims();
+template<typename T>
+void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
+    dim4 iDims = in.dims();
     int M      = iDims[0];
     int N      = iDims[1];
 
-    dim4 endPadding(M - iDims[0], max(M, N) - iDims[1], 0, 0);
-    Array<T> in =
-        (endPadding == NullShape
-             ? copyArray(orig)
-             : padArrayBorders(orig, NullShape, endPadding, AF_PAD_ZERO));
-    in.resetDims(iDims);
-
-    int MN = std::min(M, N);
-    int NB = magma_get_geqrf_nb<T>(M);
-
-    int NUM      = (2 * MN + ((N + 31) / 32) * 32) * NB;
-    Array<T> tmp = createEmptyArray<T>(dim4(NUM));
-
-    std::vector<T> h_tau(MN);
-
-    int info           = 0;
-    cl::Buffer *in_buf = in.get();
-    cl::Buffer *dT     = tmp.get();
-
-    magma_geqrf3_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
-                        &h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(),
-                        &info);
-
-    r = createEmptyArray<T>(in.dims());
-    kernel::triangle<T>(r, in, true, false);
-
-    cl::Buffer *r_buf = r.get();
-    magmablas_swapdblk<T>(MN - 1, NB, (*r_buf)(), r.getOffset(), r.strides()[1],
-                          1, (*dT)(), tmp.getOffset() + MN * NB, NB, 0,
-                          getQueue()());
-
-    q = in;  // No need to copy
+    Array<T> in_copy = copyArray<T>(in);
+
+    // Get workspace needed for QR
+    std::int64_t scratchpad_size =
+        ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
+            getQueue(), iDims[0], iDims[1], in_copy.strides()[1]);
+
+    auto scratchpad = memAlloc<compute_t<T>>(scratchpad_size);
+
+    t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
+
+    buffer<compute_t<T>> iBuf =
+        in_copy.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> tBuf = t.template getBufferWithOffset<compute_t<T>>();
+    ::oneapi::mkl::lapack::geqrf(getQueue(), M, N, iBuf, in_copy.strides()[1],
+                                 tBuf, *scratchpad, scratchpad->size());
+    // SPLIT into q and r
+    dim4 rdims(M, N);
+    r = createEmptyArray<T>(rdims);
+
+    constexpr bool is_upper     = true;
+    constexpr bool is_unit_diag = false;
+    kernel::triangle<T>(r, in_copy, is_upper, is_unit_diag);
+
+    int mn = max(M, N);
+    dim4 qdims(M, mn);
+    q = identity<T>(qdims);
+
+    buffer<compute_t<T>> qBuf = q.template getBufferWithOffset<compute_t<T>>();
+    if constexpr (std::is_floating_point<compute_t<T>>()) {
+        std::int64_t scratchpad_size =
+            ::oneapi::mkl::lapack::ormqr_scratchpad_size<compute_t<T>>(
+                getQueue(), ::oneapi::mkl::side::left,
+                ::oneapi::mkl::transpose::nontrans, q.dims()[0], q.dims()[1],
+                min(M, N), in_copy.strides()[1], q.strides()[1]);
+
+        auto scratchpad_ormqr = memAlloc<compute_t<T>>(scratchpad_size);
+        ::oneapi::mkl::lapack::ormqr(
+            getQueue(), ::oneapi::mkl::side::left,
+            ::oneapi::mkl::transpose::nontrans, q.dims()[0], q.dims()[1],
+            min(M, N), iBuf, in_copy.strides()[1], tBuf, qBuf, q.strides()[1],
+            *scratchpad_ormqr, scratchpad_ormqr->size());
+
+    } else if constexpr (common::isComplex(static_cast<af::dtype>(
+                             dtype_traits<compute_t<T>>::af_type))) {
+        std::int64_t scratchpad_size =
+            ::oneapi::mkl::lapack::unmqr_scratchpad_size<compute_t<T>>(
+                getQueue(), ::oneapi::mkl::side::left,
+                ::oneapi::mkl::transpose::nontrans, q.dims()[0], q.dims()[1],
+                min(M, N), in_copy.strides()[1], q.strides()[1]);
+
+        auto scratchpad_ormqr = memAlloc<compute_t<T>>(scratchpad_size);
+        ::oneapi::mkl::lapack::unmqr(
+            getQueue(), ::oneapi::mkl::side::left,
+            ::oneapi::mkl::transpose::nontrans, q.dims()[0], q.dims()[1],
+            min(M, N), iBuf, in_copy.strides()[1], tBuf, qBuf, q.strides()[1],
+            *scratchpad_ormqr, scratchpad_ormqr->size());
+    }
     q.resetDims(dim4(M, M));
-    cl::Buffer *q_buf = q.get();
-
-    magma_ungqr_gpu<T>(q.dims()[0], q.dims()[1], std::min(M, N), (*q_buf)(),
-                       q.getOffset(), q.strides()[1], &h_tau[0], (*dT)(),
-                       tmp.getOffset(), NB, getQueue()(), &info);
-
-    t = createHostDataArray(dim4(MN), &h_tau[0]);
 }
 
 template<typename T>
 Array<T> qr_inplace(Array<T> &in) {
-    if (OpenCLCPUOffload()) { return cpu::qr_inplace(in); }
-
-    dim4 iDims = in.dims();
-    int M      = iDims[0];
-    int N      = iDims[1];
-    int MN     = std::min(M, N);
-
-    getQueue().finish();  // FIXME: Does this need to be here?
-    cl::CommandQueue Queue2(getContext(), getDevice());
-    cl_command_queue queues[] = {getQueue()(), Queue2()};
-
-    std::vector<T> h_tau(MN);
-    cl::Buffer *in_buf = in.get();
-
-    int info = 0;
-    magma_geqrf2_gpu<T>(M, N, (*in_buf)(), in.getOffset(), in.strides()[1],
-                        &h_tau[0], queues, &info);
-
-    Array<T> t = createHostDataArray(dim4(MN), &h_tau[0]);
+    dim4 iDims    = in.dims();
+    dim4 iStrides = in.strides();
+    int M         = iDims[0];
+    int N         = iDims[1];
+
+    Array<T> t = createEmptyArray<T>(af::dim4(min(M, N), 1, 1, 1));
+
+    // Get workspace needed for QR
+    std::int64_t scratchpad_size =
+        ::oneapi::mkl::lapack::geqrf_scratchpad_size<compute_t<T>>(
+            getQueue(), iDims[0], iDims[1], iStrides[1]);
+
+    auto scratchpad = memAlloc<compute_t<T>>(scratchpad_size);
+
+    buffer<compute_t<T>> iBuf = in.template getBufferWithOffset<compute_t<T>>();
+    buffer<compute_t<T>> tBuf = t.template getBufferWithOffset<compute_t<T>>();
+    // In place Perform in place QR
+    ::oneapi::mkl::lapack::geqrf(getQueue(), iDims[0], iDims[1], iBuf,
+                                 iStrides[1], tBuf, *scratchpad,
+                                 scratchpad->size());
     return t;
 }
 

From a4f9a8c95071c3250b9ef1da74082cc6e0af8411 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 5 Jul 2023 15:40:10 -0400
Subject: [PATCH 2540/2677] adds set functions to oneapi backend (#3457)

* adds set functions to oneapi backend
---
 src/backend/oneapi/set.cpp | 146 +++++++++++++++----------------------
 src/backend/oneapi/set.hpp |   1 +
 2 files changed, 61 insertions(+), 86 deletions(-)

diff --git a/src/backend/oneapi/set.cpp b/src/backend/oneapi/set.cpp
index a76363f10b..416efb4040 100644
--- a/src/backend/oneapi/set.cpp
+++ b/src/backend/oneapi/set.cpp
@@ -6,6 +6,11 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+// oneDPL headers should be included before standard headers
+#define ONEDPL_USE_PREDEFINED_POLICIES 0
+#include <oneapi/dpl/algorithm>
+#include <oneapi/dpl/execution>
+#include <oneapi/dpl/iterator>
 
 #include <Array.hpp>
 #include <common/deprecated.hpp>
@@ -30,115 +35,84 @@ using type_t =
 
 template<typename T>
 Array<T> setUnique(const Array<T> &in, const bool is_sorted) {
-    ONEAPI_NOT_SUPPORTED("setUnique Not supported");
-    return createEmptyArray<T>(dim4(1, 1, 1, 1));
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
 
-    // try {
-    //     Array<T> out = copyArray<T>(in);
+    Array<T> out = copyArray<T>(in);
 
-    //     compute::command_queue queue(getQueue()());
+    auto out_begin = ::oneapi::dpl::begin(*out.get());
+    auto out_end   = out_begin + out.elements();
 
-    //     compute::buffer out_data((*out.get())());
+    if (!is_sorted) {
+        std::sort(dpl_policy, out_begin, out_end,
+                  [](auto lhs, auto rhs) { return lhs < rhs; });
+    }
 
-    //     compute::buffer_iterator<type_t<T>> begin(out_data, 0);
-    //     compute::buffer_iterator<type_t<T>> end(out_data, out.elements());
+    out_end = std::unique(dpl_policy, out_begin, out_end);
 
-    //     if (!is_sorted) { compute::sort(begin, end, queue); }
+    out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
 
-    //     end = compute::unique(begin, end, queue);
-
-    //     out.resetDims(dim4(std::distance(begin, end), 1, 1, 1));
-
-    //     return out;
-    // } catch (const std::exception &ex) { AF_ERROR(ex.what(),
-    // AF_ERR_INTERNAL); }
+    return out;
 }
 
 template<typename T>
 Array<T> setUnion(const Array<T> &first, const Array<T> &second,
                   const bool is_unique) {
-    ONEAPI_NOT_SUPPORTED("setUnion Not supported");
-    return createEmptyArray<T>(dim4(1, 1, 1, 1));
-
-    // try {
-    //     Array<T> unique_first  = first;
-    //     Array<T> unique_second = second;
+    Array<T> unique_first  = first;
+    Array<T> unique_second = second;
 
-    //     if (!is_unique) {
-    //         unique_first  = setUnique(first, false);
-    //         unique_second = setUnique(second, false);
-    //     }
+    if (!is_unique) {
+        unique_first  = setUnique(first, false);
+        unique_second = setUnique(second, false);
+    }
 
-    //     size_t out_size = unique_first.elements() + unique_second.elements();
-    //     Array<T> out    = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
+    size_t out_size = unique_first.elements() + unique_second.elements();
+    Array<T> out    = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
 
-    //     compute::command_queue queue(getQueue()());
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
 
-    //     compute::buffer first_data((*unique_first.get())());
-    //     compute::buffer second_data((*unique_second.get())());
-    //     compute::buffer out_data((*out.get())());
+    auto first_begin = ::oneapi::dpl::begin(*unique_first.get());
+    auto first_end   = first_begin + unique_first.elements();
 
-    //     compute::buffer_iterator<type_t<T>> first_begin(first_data, 0);
-    //     compute::buffer_iterator<type_t<T>> first_end(first_data,
-    //                                                   unique_first.elements());
-    //     compute::buffer_iterator<type_t<T>> second_begin(second_data, 0);
-    //     compute::buffer_iterator<type_t<T>> second_end(
-    //         second_data, unique_second.elements());
-    //     compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
+    auto second_begin = ::oneapi::dpl::begin(*unique_second.get());
+    auto second_end   = second_begin + unique_second.elements();
 
-    //     compute::buffer_iterator<type_t<T>> out_end = compute::set_union(
-    //         first_begin, first_end, second_begin, second_end, out_begin,
-    //         queue);
+    auto out_begin = ::oneapi::dpl::begin(*out.get());
 
-    //     out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
-    //     return out;
-
-    // } catch (const std::exception &ex) { AF_ERROR(ex.what(),
-    // AF_ERR_INTERNAL); }
+    auto out_end = std::set_union(dpl_policy, first_begin, first_end,
+                                  second_begin, second_end, out_begin);
+    out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
+    return out;
 }
 
 template<typename T>
 Array<T> setIntersect(const Array<T> &first, const Array<T> &second,
                       const bool is_unique) {
-    ONEAPI_NOT_SUPPORTED("setIntersect Not supported");
-    return createEmptyArray<T>(dim4(1, 1, 1, 1));
-
-    // try {
-    //     Array<T> unique_first  = first;
-    //     Array<T> unique_second = second;
-
-    //     if (!is_unique) {
-    //         unique_first  = setUnique(first, false);
-    //         unique_second = setUnique(second, false);
-    //     }
-
-    //     size_t out_size =
-    //         std::max(unique_first.elements(), unique_second.elements());
-    //     Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
-
-    //     compute::command_queue queue(getQueue()());
-
-    //     compute::buffer first_data((*unique_first.get())());
-    //     compute::buffer second_data((*unique_second.get())());
-    //     compute::buffer out_data((*out.get())());
-
-    //     compute::buffer_iterator<type_t<T>> first_begin(first_data, 0);
-    //     compute::buffer_iterator<type_t<T>> first_end(first_data,
-    //                                                   unique_first.elements());
-    //     compute::buffer_iterator<type_t<T>> second_begin(second_data, 0);
-    //     compute::buffer_iterator<type_t<T>> second_end(
-    //         second_data, unique_second.elements());
-    //     compute::buffer_iterator<type_t<T>> out_begin(out_data, 0);
-
-    //     compute::buffer_iterator<type_t<T>> out_end =
-    //     compute::set_intersection(
-    //         first_begin, first_end, second_begin, second_end, out_begin,
-    //         queue);
-
-    //     out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
-    //     return out;
-    // } catch (const std::exception &ex) { AF_ERROR(ex.what(),
-    // AF_ERR_INTERNAL); }
+    Array<T> unique_first  = first;
+    Array<T> unique_second = second;
+
+    if (!is_unique) {
+        unique_first  = setUnique(first, false);
+        unique_second = setUnique(second, false);
+    }
+
+    size_t out_size =
+        std::max(unique_first.elements(), unique_second.elements());
+    Array<T> out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
+
+    auto dpl_policy = ::oneapi::dpl::execution::make_device_policy(getQueue());
+
+    auto first_begin = ::oneapi::dpl::begin(*unique_first.get());
+    auto first_end   = first_begin + unique_first.elements();
+
+    auto second_begin = ::oneapi::dpl::begin(*unique_second.get());
+    auto second_end   = second_begin + unique_second.elements();
+
+    auto out_begin = ::oneapi::dpl::begin(*out.get());
+
+    auto out_end = std::set_intersection(dpl_policy, first_begin, first_end,
+                                         second_begin, second_end, out_begin);
+    out.resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
+    return out;
 }
 
 #define INSTANTIATE(T)                                                        \
diff --git a/src/backend/oneapi/set.hpp b/src/backend/oneapi/set.hpp
index 85d3386489..beef4a44b4 100644
--- a/src/backend/oneapi/set.hpp
+++ b/src/backend/oneapi/set.hpp
@@ -6,6 +6,7 @@
  * The complete license agreement can be obtained at:
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
+#pragma once
 
 #include <Array.hpp>
 

From f0b8538b30678dd20be5e38aee344d8b33b1d554 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 5 Jul 2023 15:45:24 -0400
Subject: [PATCH 2541/2677] remove oneapi warnings (#3463)

* remove several oneapi warnings from fftconvolve, mean,  rbk, convolve_seperable
* temporarily suppress internal DPL warnings
---
 .../oneapi/kernel/convolve_separable.cpp      |  5 ---
 .../oneapi/kernel/fftconvolve_multiply.hpp    |  2 --
 .../oneapi/kernel/fftconvolve_pack.hpp        |  8 ++---
 src/backend/oneapi/kernel/fftconvolve_pad.hpp |  9 +-----
 .../oneapi/kernel/fftconvolve_reorder.hpp     |  8 +----
 src/backend/oneapi/kernel/mean.hpp            | 31 ++++++-------------
 src/backend/oneapi/kernel/reduce_by_key.hpp   | 25 +++++----------
 .../oneapi/kernel/sort_by_key_impl.hpp        | 12 +++++++
 src/backend/oneapi/reduce_impl.hpp            | 12 +++++++
 src/backend/oneapi/sort.cpp                   | 12 +++++++
 10 files changed, 57 insertions(+), 67 deletions(-)

diff --git a/src/backend/oneapi/kernel/convolve_separable.cpp b/src/backend/oneapi/kernel/convolve_separable.cpp
index 712570a558..45a86efb7a 100644
--- a/src/backend/oneapi/kernel/convolve_separable.cpp
+++ b/src/backend/oneapi/kernel/convolve_separable.cpp
@@ -76,7 +76,6 @@ class convolveSeparableCreateKernel {
         if (CONV_DIM_ == 0) {
             gx += (EXPAND_ ? 0 : FLEN_ >> 1);
             int endX = ((FLEN_ - 1) << 1) + g.get_local_range(0);
-#pragma unroll
             for (int lx = it.get_local_id(0), glb_x = gx; lx < endX;
                  lx += g.get_local_range(0), glb_x += g.get_local_range(0)) {
                 int i     = glb_x - radius;
@@ -90,7 +89,6 @@ class convolveSeparableCreateKernel {
         } else if (CONV_DIM_ == 1) {
             gy += (EXPAND_ ? 0 : FLEN_ >> 1);
             int endY = ((FLEN_ - 1) << 1) + g.get_local_range(1);
-#pragma unroll
             for (int ly = it.get_local_id(1), glb_y = gy; ly < endY;
                  ly += g.get_local_range(1), glb_y += g.get_local_range(1)) {
                 int i     = gx;
@@ -108,7 +106,6 @@ class convolveSeparableCreateKernel {
             // kernel compilation
             int i         = (CONV_DIM_ == 0 ? lx : ly) + radius;
             accType accum = (accType)(0);
-#pragma unroll
             for (int f = 0; f < FLEN_; ++f) {
                 accType f_val = impulse_[f];
                 // below conditional statement is based on MACRO value passed
@@ -163,8 +160,6 @@ void convSep(Param<T> out, const Param<T> signal, const Param<accType> filter,
     }
     constexpr int THREADS_X = 16;
     constexpr int THREADS_Y = 16;
-    constexpr bool IsComplex =
-        std::is_same<T, cfloat>::value || std::is_same<T, cdouble>::value;
 
     const int fLen       = filter.info.dims[0] * filter.info.dims[1];
     const size_t C0_SIZE = (THREADS_X + 2 * (fLen - 1)) * THREADS_Y;
diff --git a/src/backend/oneapi/kernel/fftconvolve_multiply.hpp b/src/backend/oneapi/kernel/fftconvolve_multiply.hpp
index e8968f6d0d..32516f4056 100644
--- a/src/backend/oneapi/kernel/fftconvolve_multiply.hpp
+++ b/src/backend/oneapi/kernel/fftconvolve_multiply.hpp
@@ -38,8 +38,6 @@ class fftconvolve_multiplyCreateKernel {
         , nelem_(nelem)
         , kind_(kind) {}
     void operator()(sycl::nd_item<1> it) const {
-        sycl::group g = it.get_group();
-
         const int t = it.get_global_id(0);
 
         if (t >= nelem_) return;
diff --git a/src/backend/oneapi/kernel/fftconvolve_pack.hpp b/src/backend/oneapi/kernel/fftconvolve_pack.hpp
index c6b04d5a43..5f8afc2b7a 100644
--- a/src/backend/oneapi/kernel/fftconvolve_pack.hpp
+++ b/src/backend/oneapi/kernel/fftconvolve_pack.hpp
@@ -37,15 +37,13 @@ class fftconvolve_packCreateKernel {
         , di0_half_(di0_half)
         , odd_di0_(odd_di0) {}
     void operator()(sycl::nd_item<1> it) const {
-        sycl::group g = it.get_group();
-
         const int t = it.get_global_id(0);
 
         const int tMax = oInfo_.strides[3] * oInfo_.dims[3];
 
         if (t >= tMax) return;
 
-        const int do0 = oInfo_.dims[0];
+        // const int do0 = oInfo_.dims[0];
         const int do1 = oInfo_.dims[1];
         const int do2 = oInfo_.dims[2];
 
@@ -58,7 +56,7 @@ class fftconvolve_packCreateKernel {
         const int to2 = (t / so2) % do2;
         const int to3 = t / so3;
 
-        const int di0 = iInfo_.dims[0];
+        // const int di0 = iInfo_.dims[0];
         const int di1 = iInfo_.dims[1];
         const int di2 = iInfo_.dims[2];
 
@@ -109,8 +107,6 @@ void packDataHelper(Param<convT> packed, Param<T> sig, Param<T> filter,
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
 
     int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
-    int filter_packed_elem =
-        filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
 
     // Number of packed complex elements in dimension 0
     int sig_half_d0     = divup(sig.info.dims[0], 2);
diff --git a/src/backend/oneapi/kernel/fftconvolve_pad.hpp b/src/backend/oneapi/kernel/fftconvolve_pad.hpp
index 6276b1da72..6d60506236 100644
--- a/src/backend/oneapi/kernel/fftconvolve_pad.hpp
+++ b/src/backend/oneapi/kernel/fftconvolve_pad.hpp
@@ -29,15 +29,13 @@ class fftconvolve_padCreateKernel {
                                 read_accessor<inputType> d_in, KParam iInfo)
         : d_out_(d_out), oInfo_(oInfo), d_in_(d_in), iInfo_(iInfo) {}
     void operator()(sycl::nd_item<1> it) const {
-        sycl::group g = it.get_group();
-
         const int t = it.get_global_id(0);
 
         const int tMax = oInfo_.strides[3] * oInfo_.dims[3];
 
         if (t >= tMax) return;
 
-        const int do0 = oInfo_.dims[0];
+        // const int do0 = oInfo_.dims[0];
         const int do1 = oInfo_.dims[1];
         const int do2 = oInfo_.dims[2];
 
@@ -92,14 +90,9 @@ void padDataHelper(Param<convT> packed, Param<T> sig, Param<T> filter,
     Param<T> sig_tmp, filter_tmp;
     calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, rank, kind);
 
-    int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
     int filter_packed_elem =
         filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
 
-    // Number of packed complex elements in dimension 0
-    int sig_half_d0     = divup(sig.info.dims[0], 2);
-    int sig_half_d0_odd = sig.info.dims[0] % 2;
-
     int blocks = divup(filter_packed_elem, THREADS);
 
     // Locate features kernel sizes
diff --git a/src/backend/oneapi/kernel/fftconvolve_reorder.hpp b/src/backend/oneapi/kernel/fftconvolve_reorder.hpp
index ec71b43bae..589242007a 100644
--- a/src/backend/oneapi/kernel/fftconvolve_reorder.hpp
+++ b/src/backend/oneapi/kernel/fftconvolve_reorder.hpp
@@ -42,15 +42,13 @@ class fftconvolve_reorderCreateKernel {
         , EXPAND_(EXPAND)
         , ROUND_OUT_(ROUND_OUT) {}
     void operator()(sycl::nd_item<1> it) const {
-        sycl::group g = it.get_group();
-
         const int t = it.get_global_id(0);
 
         const int tMax = oInfo_.strides[3] * oInfo_.dims[3];
 
         if (t >= tMax) return;
 
-        const int do0 = oInfo_.dims[0];
+        // const int do0 = oInfo_.dims[0];
         const int do1 = oInfo_.dims[1];
         const int do2 = oInfo_.dims[2];
 
@@ -60,10 +58,6 @@ class fftconvolve_reorderCreateKernel {
 
         // Treating complex input array as real-only array,
         // thus, multiply dimension 0 and strides by 2
-        const int di0 = iInfo_.dims[0] * 2;
-        const int di1 = iInfo_.dims[1];
-        const int di2 = iInfo_.dims[2];
-
         const int si1 = iInfo_.strides[1] * 2;
         const int si2 = iInfo_.strides[2] * 2;
         const int si3 = iInfo_.strides[3] * 2;
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 7c0f6f3243..695fb7b375 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -1,3 +1,4 @@
+
 /*******************************************************
  * Copyright (c) 2022, ArrayFire
  * All rights reserved.
@@ -611,13 +612,9 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto acc_in =
-                    tmpOut.get()
-                        ->template get_access<sycl::access_mode::read,
-                                              sycl::target::host_buffer>(h);
+                    tmpOut.get()->template get_host_access(h, sycl::read_only);
                 auto acc_wt =
-                    tmpWt.get()
-                        ->template get_access<sycl::access_mode::read,
-                                              sycl::target::host_buffer>(h);
+                    tmpWt.get()->template get_host_access(h, sycl::read_only);
 
                 h.host_task([acc_in, acc_wt, tmp_elements, &val] {
                     val = static_cast<compute_t<T>>(acc_in[0]);
@@ -636,13 +633,10 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         compute_t<T> val;
         getQueue()
             .submit([&](sycl::handler &h) {
-                auto acc_in =
-                    in.data->template get_access<sycl::access_mode::read,
-                                                 sycl::target::host_buffer>(
-                        h, sycl::range{in_elements});
-                auto acc_wt =
-                    iwt.data->template get_access<sycl::access_mode::read>(
-                        h, sycl::range{in_elements});
+                auto acc_in = in.data->template get_host_access(
+                    h, sycl::range{in_elements}, sycl::read_only);
+                auto acc_wt = iwt.data->template get_host_access(
+                    h, sycl::range{in_elements}, sycl::read_only);
 
                 h.host_task([acc_in, acc_wt, in_elements, &val]() {
                     val                  = acc_in[0];
@@ -702,13 +696,9 @@ To mean_all(Param<Ti> in) {
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto out =
-                    tmpOut.get()
-                        ->template get_access<sycl::access_mode::read,
-                                              sycl::target::host_buffer>(h);
+                    tmpOut.get()->template get_host_access(h, sycl::read_only);
                 auto ct =
-                    tmpCt.get()
-                        ->template get_access<sycl::access_mode::read,
-                                              sycl::target::host_buffer>(h);
+                    tmpCt.get()->template get_host_access(h, sycl::read_only);
 
                 h.host_task([out, ct, tmp_elements, &val] {
                     val                  = static_cast<compute_t<To>>(out[0]);
@@ -727,8 +717,7 @@ To mean_all(Param<Ti> in) {
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto acc_in =
-                    in.data->template get_access<sycl::access_mode::read,
-                                                 sycl::target::host_buffer>(h);
+                    in.data->template get_host_access(h, sycl::read_only);
                 h.host_task([acc_in, in_elements, &val]() {
                     common::Transform<Ti, compute_t<To>, af_add_t> transform;
                     compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
diff --git a/src/backend/oneapi/kernel/reduce_by_key.hpp b/src/backend/oneapi/kernel/reduce_by_key.hpp
index 1da17ca5cc..3b5058a6bf 100644
--- a/src/backend/oneapi/kernel/reduce_by_key.hpp
+++ b/src/backend/oneapi/kernel/reduce_by_key.hpp
@@ -101,10 +101,6 @@ class finalBoundaryReduceDimKernel {
         const uint gid = it.get_global_id(0);
         const uint bid = g.get_group_id(0);
 
-        const int bidy = g.get_group_id(1);
-        const int bidz = g.get_group_id(2) % nGroupsZ_;
-        const int bidw = g.get_group_id(2) / nGroupsZ_;
-
         common::Binary<compute_t<To>, op> binOp;
         if (gid == ((bid + 1) * it.get_local_range(0)) - 1 &&
             bid < g.get_group_range(0) - 1) {
@@ -166,7 +162,7 @@ class testNeedsReductionKernel {
         const uint gid = it.get_global_id(0);
         const uint bid = g.get_group_id(0);
 
-        Tk k;
+        Tk k = scalar<Tk>(0);
         if (gid < n_) { k = iKeys_[gid]; }
 
         l_keys_[lid] = k;
@@ -233,9 +229,6 @@ class compactKernel {
         const int bidz = g.get_group_id(2) % nGroupsZ_;
         const int bidw = g.get_group_id(2) / nGroupsZ_;
 
-        Tk k;
-        To v;
-
         const int bOffset = bidw * oVInfo_.strides[3] +
                             bidz * oVInfo_.strides[2] +
                             bidy * oVInfo_.strides[1];
@@ -247,8 +240,8 @@ class compactKernel {
                 : (reduced_block_sizes_[bid] - reduced_block_sizes_[bid - 1]);
         int writeloc = (bid == 0) ? 0 : reduced_block_sizes_[bid - 1];
 
-        k = iKeys_[gid];
-        v = iVals_[bOffset + gid];
+        Tk k = iKeys_[gid];
+        To v = iVals_[bOffset + gid];
 
         if (lid < nwrite) {
             oKeys_[writeloc + lid]           = k;
@@ -407,8 +400,8 @@ class reduceBlocksByKeyKernel {
         if (lid == 0) { l_reduced_block_size_[0] = 0; }
 
         // load keys and values to threads
-        Tk k;
-        compute_t<To> v;
+        Tk k            = scalar<Tk>(0);
+        compute_t<To> v = init_val;
         if (gid < n_) {
             k                 = iKeys_[gid];
             const int bOffset = bidw * iVInfo_.strides[3] +
@@ -416,8 +409,6 @@ class reduceBlocksByKeyKernel {
                                 bidy * iVInfo_.strides[1];
             v = transform(iVals_[bOffset + gid]);
             if (change_nan_) v = IS_NAN(v) ? nanval_ : v;
-        } else {
-            v = init_val;
         }
 
         l_keys_[lid] = k;
@@ -585,8 +576,8 @@ class reduceBlocksByKeyDimKernel {
         it.barrier();
 
         // load keys and values to threads
-        Tk k;
-        compute_t<To> v;
+        Tk k            = scalar<Tk>(0);
+        compute_t<To> v = init_val;
         if (gid < n_) {
             k                 = iKeys_[gid];
             const int bOffset = bidw * iVInfo_.strides[dims_ordering[3]] +
@@ -594,8 +585,6 @@ class reduceBlocksByKeyDimKernel {
                                 bidy * iVInfo_.strides[dims_ordering[1]];
             v = transform(iVals_[bOffset + gid * iVInfo_.strides[DIM_]]);
             if (change_nan_) v = IS_NAN(v) ? nanval_ : v;
-        } else {
-            v = init_val;
         }
 
         l_keys_[lid] = k;
diff --git a/src/backend/oneapi/kernel/sort_by_key_impl.hpp b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
index 9a6348a3ad..5a05eac58c 100644
--- a/src/backend/oneapi/kernel/sort_by_key_impl.hpp
+++ b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
@@ -8,6 +8,13 @@
  ********************************************************/
 #pragma once
 
+#if defined(__clang__)
+#pragma clang diagnostic push
+// temporary ignores for DPL internals
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wdeprecated-declarations"
+#endif
+
 // oneDPL headers should be included before standard headers
 #define ONEDPL_USE_PREDEFINED_POLICIES 0
 #include <oneapi/dpl/algorithm>
@@ -206,3 +213,8 @@ void sort0ByKey(Param<Tk> pKey, Param<Tv> pVal, bool isAscending) {
 }  // namespace kernel
 }  // namespace oneapi
 }  // namespace arrayfire
+
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic pop
+#endif
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index efada203e1..698f2f1831 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -8,6 +8,13 @@
  ********************************************************/
 #pragma once
 
+#if defined(__clang__)
+#pragma clang diagnostic push
+// temporary ignores for DPL internals
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wdeprecated-declarations"
+#endif
+
 // oneDPL headers should be included before standard headers
 #define ONEDPL_USE_PREDEFINED_POLICIES 0
 #include <oneapi/dpl/execution>
@@ -614,3 +621,8 @@ Array<To> reduce_all(const Array<Ti> &in, bool change_nan, double nanval) {
         const Array<Ti> &vals, const int dim, bool change_nan, double nanval); \
     template Array<To> reduce_all<Op, Ti, To>(const Array<Ti> &in,             \
                                               bool change_nan, double nanval);
+
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic pop
+#endif
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
index a16ccadc55..4dc65a621c 100644
--- a/src/backend/oneapi/sort.cpp
+++ b/src/backend/oneapi/sort.cpp
@@ -7,6 +7,13 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if defined(__clang__)
+#pragma clang diagnostic push
+// temporary ignores for DPL internals
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wdeprecated-declarations"
+#endif
+
 #include <kernel/sort.hpp>
 
 #include <Array.hpp>
@@ -64,3 +71,8 @@ INSTANTIATE(uintl)
 
 }  // namespace oneapi
 }  // namespace arrayfire
+
+#if defined(__clang__)
+/* Clang/LLVM */
+#pragma clang diagnostic pop
+#endif

From aea98356f2396b29fffbf2f7e848dd6668306d08 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 5 Jul 2023 23:12:23 -0400
Subject: [PATCH 2542/2677] correct -infinity for half datatype in oneapi
 (#3466)

---
 src/backend/oneapi/math.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index 359b4ae9a3..b6aba91663 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -156,8 +156,8 @@ inline double minval() {
     return -std::numeric_limits<double>::infinity();
 }
 template<>
-inline arrayfire::common::half minval() {
-    return -std::numeric_limits<arrayfire::common::half>::infinity();
+inline sycl::half minval() {
+    return -1 * std::numeric_limits<sycl::half>::infinity();
 }
 
 template<typename T>

From 1eb6bcaef3c7eb18e33744d93753f0afa2872bb6 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Wed, 5 Jul 2023 23:24:50 -0400
Subject: [PATCH 2543/2677] enable half for convolve strided oneapi (#3465)

---
 src/backend/oneapi/blas.hpp     |   5 +-
 src/backend/oneapi/convolve.cpp | 126 ++++++++++++++------------------
 2 files changed, 57 insertions(+), 74 deletions(-)

diff --git a/src/backend/oneapi/blas.hpp b/src/backend/oneapi/blas.hpp
index 194fc4e6fb..9e2381c336 100644
--- a/src/backend/oneapi/blas.hpp
+++ b/src/backend/oneapi/blas.hpp
@@ -9,6 +9,7 @@
 
 #pragma once
 #include <Array.hpp>
+#include <math.hpp>
 
 // This file contains the common interface for OneAPI BLAS
 // functions
@@ -30,8 +31,8 @@ Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
     int Ndim     = optRhs == AF_MAT_NONE ? 1 : 0;
     Array<T> res = createEmptyArray<T>(
         dim4(lhs.dims()[Mdim], rhs.dims()[Ndim], lhs.dims()[2], lhs.dims()[3]));
-    static constexpr T alpha = 1.0;
-    static constexpr T beta  = 0.0;
+    static const T alpha = scalar<T>(1.0);
+    static const T beta  = scalar<T>(0.0);
     gemm(res, optLhs, optRhs, &alpha, lhs, rhs, &beta);
     return res;
 }
diff --git a/src/backend/oneapi/convolve.cpp b/src/backend/oneapi/convolve.cpp
index 69c120569b..d2cc41c588 100644
--- a/src/backend/oneapi/convolve.cpp
+++ b/src/backend/oneapi/convolve.cpp
@@ -149,15 +149,9 @@ Array<T> convolve2_unwrap(const Array<T> &signal, const Array<T> &filter,
 template<typename T>
 Array<T> convolve2(Array<T> const &signal, Array<T> const &filter,
                    const dim4 stride, const dim4 padding, const dim4 dilation) {
-    if constexpr (!std::is_same<T, half>::value) {
-        Array<T> out =
-            convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
-        return out;
-    } else {
-        ONEAPI_NOT_SUPPORTED("");
-        Array<T> out = createEmptyArray<T>(dim4(1));
-        return out;
-    }
+    Array<T> out =
+        convolve2_unwrap<T>(signal, filter, stride, padding, dilation);
+    return out;
 }
 
 #define INSTANTIATE(T)                                                        \
@@ -177,39 +171,33 @@ Array<T> conv2DataGradient(const Array<T> &incoming_gradient,
                            const Array<T> & /*convolved_output*/,
                            af::dim4 stride, af::dim4 padding,
                            af::dim4 dilation) {
-    if constexpr (!std::is_same<T, half>::value) {
-        const dim4 &cDims = incoming_gradient.dims();
-        const dim4 &sDims = original_signal.dims();
-        const dim4 &fDims = original_filter.dims();
-
-        Array<T> collapsed_filter = original_filter;
-
-        collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
-        collapsed_filter = modDims(
-            collapsed_filter, dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
-
-        Array<T> collapsed_gradient = incoming_gradient;
-        collapsed_gradient = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-        collapsed_gradient = modDims(
-            collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
-
-        Array<T> res = matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE,
-                              AF_MAT_TRANS);
-        res          = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
-                                         fDims[0] * fDims[1], sDims[2]));
-        res          = reorder(res, dim4(0, 2, 3, 1));
-
-        const bool retCols = false;
-        res = wrap_dilated(res, sDims[0], sDims[1], fDims[0], fDims[1],
-                           stride[0], stride[1], padding[0], padding[1],
-                           dilation[0], dilation[1], retCols);
-
-        return res;
-    } else {
-        ONEAPI_NOT_SUPPORTED("");
-        Array<T> out = createEmptyArray<T>(dim4(1));
-        return out;
-    }
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &sDims = original_signal.dims();
+    const dim4 &fDims = original_filter.dims();
+
+    Array<T> collapsed_filter = original_filter;
+
+    collapsed_filter = flip(collapsed_filter, {1, 1, 0, 0});
+    collapsed_filter = modDims(collapsed_filter,
+                               dim4(fDims[0] * fDims[1] * fDims[2], fDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    Array<T> res =
+        matmul(collapsed_gradient, collapsed_filter, AF_MAT_NONE, AF_MAT_TRANS);
+    res = modDims(res, dim4(res.dims()[0] / sDims[3], sDims[3],
+                            fDims[0] * fDims[1], sDims[2]));
+    res = reorder(res, dim4(0, 2, 3, 1));
+
+    const bool retCols = false;
+    res = wrap_dilated(res, sDims[0], sDims[1], fDims[0], fDims[1], stride[0],
+                       stride[1], padding[0], padding[1], dilation[0],
+                       dilation[1], retCols);
+
+    return res;
 }
 
 template<typename T>
@@ -219,36 +207,30 @@ Array<T> conv2FilterGradient(const Array<T> &incoming_gradient,
                              const Array<T> & /*convolved_output*/,
                              af::dim4 stride, af::dim4 padding,
                              af::dim4 dilation) {
-    if constexpr (!std::is_same<T, half>::value) {
-        const dim4 &cDims = incoming_gradient.dims();
-        const dim4 &fDims = original_filter.dims();
-
-        const bool retCols = false;
-        Array<T> unwrapped =
-            unwrap(original_signal, fDims[0], fDims[1], stride[0], stride[1],
-                   padding[0], padding[1], dilation[0], dilation[1], retCols);
-
-        unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
-        dim4 uDims = unwrapped.dims();
-        unwrapped =
-            modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
-
-        Array<T> collapsed_gradient = incoming_gradient;
-        collapsed_gradient = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
-        collapsed_gradient = modDims(
-            collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
-
-        Array<T> res =
-            matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
-        res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
-
-        auto out = flip(res, {1, 1, 0, 0});
-        return out;
-    } else {
-        ONEAPI_NOT_SUPPORTED("");
-        Array<T> out = createEmptyArray<T>(dim4(1));
-        return out;
-    }
+    const dim4 &cDims = incoming_gradient.dims();
+    const dim4 &fDims = original_filter.dims();
+
+    const bool retCols = false;
+    Array<T> unwrapped =
+        unwrap(original_signal, fDims[0], fDims[1], stride[0], stride[1],
+               padding[0], padding[1], dilation[0], dilation[1], retCols);
+
+    unwrapped  = reorder(unwrapped, dim4(1, 2, 0, 3));
+    dim4 uDims = unwrapped.dims();
+    unwrapped =
+        modDims(unwrapped, dim4(uDims[0] * uDims[1], uDims[2] * uDims[3]));
+
+    Array<T> collapsed_gradient = incoming_gradient;
+    collapsed_gradient          = reorder(collapsed_gradient, dim4(0, 1, 3, 2));
+    collapsed_gradient          = modDims(
+        collapsed_gradient, dim4(cDims[0] * cDims[1] * cDims[3], cDims[2]));
+
+    Array<T> res =
+        matmul(unwrapped, collapsed_gradient, AF_MAT_NONE, AF_MAT_NONE);
+    res = modDims(res, dim4(fDims[0], fDims[1], fDims[2], fDims[3]));
+
+    auto out = flip(res, {1, 1, 0, 0});
+    return out;
 }
 
 #define INSTANTIATE(T)                                                      \

From fddead17e689b58fb81f0eeb7aea4f7ac02ec552 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Thu, 6 Jul 2023 21:08:44 -0400
Subject: [PATCH 2544/2677] fix fftconvolve one2many tests

---
 src/backend/oneapi/fft.cpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/backend/oneapi/fft.cpp b/src/backend/oneapi/fft.cpp
index 3bf15acf0a..03ae19efc6 100644
--- a/src/backend/oneapi/fft.cpp
+++ b/src/backend/oneapi/fft.cpp
@@ -95,7 +95,7 @@ std::vector<std::int64_t> computeStrides(const int rank, const dim4 istrides,
     if (rank == 3) return {offset, istrides[2], istrides[1], istrides[0]};
     if (rank == 4)
         return {offset, istrides[3], istrides[2], istrides[1], istrides[0]};
-    return {offset};
+    return {offset, istrides[0]};
 }
 
 template<::oneapi::mkl::dft::precision precision,
@@ -180,9 +180,14 @@ void fft_inplace(Array<T> &in, const int rank, const bool direction) {
         ::oneapi::mkl::dft::descriptor<precision,
                                        ::oneapi::mkl::dft::domain::COMPLEX>;
 
+    // TODO[STF]: WTF
+    // getOffset() for s0 throwing Invalid Descriptor when targeting gpu
+    // on CPU, results are wrong but does not throw
+    // strides not working? TODO: test standalone oneMKL
+    // perhaps in.getDataDims() needed instead of in.dims()?
     std::vector<std::int64_t> fft_input_strides =
-        computeStrides(rank, istrides, in.getOffset());
-
+        computeStrides(rank, istrides, 0);
+    // computeStrides(rank, istrides, in.getOffset()); //TODO[STF]: WTF,
     int batch = 1;
     for (int i = rank; i < 4; i++) { batch *= idims[i]; }
 

From 48cd41fdca8a10e48e0d44f3cad1dd1fab830ff0 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 17 Jun 2023 18:23:44 -0400
Subject: [PATCH 2545/2677] Add logging to jit heuristic checks

---
 src/backend/cuda/Array.cpp   | 11 +++++++++++
 src/backend/oneapi/Array.cpp | 20 +++++++++++++++++---
 src/backend/opencl/Array.cpp | 19 +++++++++++++++++--
 3 files changed, 45 insertions(+), 5 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index db03d1b3e5..eb71a9f7a2 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/Logger.hpp>
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <copy.hpp>
@@ -253,8 +254,13 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
+    static auto getLogger = [&] { return spdlog::get("jit"); };
     for (Node *n : root_nodes) {
         if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            AF_TRACE(
+                "JIT tree evaluated because of tree height exceeds limit: {} > "
+                "{}",
+                n->getHeight(), getMaxJitSize());
             return kJITHeuristics::TreeHeight;
         }
     }
@@ -313,9 +319,14 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
         // should be checking the amount of memory available to guard
         // this eval
         if (param_size >= max_param_size) {
+            AF_TRACE(
+                "JIT tree evaluated because of kernel parameter size: {} >= {}",
+                param_size, max_param_size);
             return kJITHeuristics::KernelParameterSize;
         }
         if (jitTreeExceedsMemoryPressure(info.total_buffer_size)) {
+            AF_TRACE("JIT tree evaluated because of memory pressure: {}",
+                     info.total_buffer_size);
             return kJITHeuristics::MemoryPressure;
         }
     }
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 5845d95ecc..f227f8def3 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -10,6 +10,7 @@
 #include <Array.hpp>
 
 #include <Param.hpp>
+#include <common/Logger.hpp>
 #include <common/MemoryManagerBase.hpp>
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
@@ -312,8 +313,13 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
+    static auto getLogger = [&] { return common::loggerFactory("jit"); };
     for (const Node *n : root_nodes) {
         if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            AF_TRACE(
+                "JIT tree evaluated because of tree height exceeds limit: {} > "
+                "{}",
+                n->getHeight(), getMaxJitSize());
             return kJITHeuristics::TreeHeight;
         }
     }
@@ -386,9 +392,17 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
 
         bool isParamLimit = param_size >= max_param_size;
 
-        if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
-        // TODO(umar): check buffer limit for JIT kernel generation
-        // if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
+        if (isParamLimit) {
+            AF_TRACE(
+                "JIT tree evaluated because of kernel parameter size: {} >= {}",
+                param_size, max_param_size);
+            return kJITHeuristics::KernelParameterSize;
+        }
+        if (isBufferLimit) {
+            AF_TRACE("JIT tree evaluated because of memory pressure: {}",
+                     info.total_buffer_size);
+            return kJITHeuristics::MemoryPressure;
+        }
     }
     return kJITHeuristics::Pass;
 }
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index d479ac5752..2d3bc40e0b 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -9,6 +9,7 @@
 
 #include <Array.hpp>
 
+#include <common/Logger.hpp>
 #include <common/half.hpp>
 #include <common/jit/NodeIterator.hpp>
 #include <common/jit/ScalarNode.hpp>
@@ -301,8 +302,13 @@ Node_ptr Array<T>::getNode() const {
 template<typename T>
 kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
     if (!evalFlag()) { return kJITHeuristics::Pass; }
+    static auto getLogger = [&] { return common::loggerFactory("jit"); };
     for (const Node *n : root_nodes) {
         if (n->getHeight() > static_cast<int>(getMaxJitSize())) {
+            AF_TRACE(
+                "JIT tree evaluated because of tree height exceeds limit: {} > "
+                "{}",
+                n->getHeight(), getMaxJitSize());
             return kJITHeuristics::TreeHeight;
         }
     }
@@ -377,8 +383,17 @@ kJITHeuristics passesJitHeuristics(span<Node *> root_nodes) {
 
         bool isParamLimit = param_size >= max_param_size;
 
-        if (isParamLimit) { return kJITHeuristics::KernelParameterSize; }
-        if (isBufferLimit) { return kJITHeuristics::MemoryPressure; }
+        if (isParamLimit) {
+            AF_TRACE(
+                "JIT tree evaluated because of kernel parameter size: {} >= {}",
+                param_size, max_param_size);
+            return kJITHeuristics::KernelParameterSize;
+        }
+        if (isBufferLimit) {
+            AF_TRACE("JIT tree evaluated because of memory pressure: {}",
+                     info.total_buffer_size);
+            return kJITHeuristics::MemoryPressure;
+        }
     }
     return kJITHeuristics::Pass;
 }

From c2e31fde950baff6c22dcf839a4cf7a843e60518 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 30 Jun 2023 18:23:51 -0400
Subject: [PATCH 2546/2677] Add logging to oneAPI JIT kernel launches

---
 src/backend/oneapi/jit.cpp | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 31c2a0b881..3e317b68e2 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -14,6 +14,7 @@
 #include <kernel_headers/jit.hpp>
 
 #include <Array.hpp>
+#include <Kernel.hpp>
 #include <common/dispatch.hpp>
 #include <common/half.hpp>
 #include <common/jit/ModdimNode.hpp>
@@ -591,6 +592,19 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                                   (size_t)ap[0].dims[2]};
                         ndims  = 3;
                     }
+
+                    {
+                        using namespace oneapi::kernel_logger;
+                        AF_TRACE(
+                            "Launching {}: Dims: [{},{},{},{}] Global: "
+                            "[{},{},{}] threads: {}",
+                            funcName, ap[0].dims[0], ap[0].dims[1],
+                            ap[0].dims[2], ap[0].dims[3], global[0], global[1],
+                            global[2],
+                            global[0] * std::max<size_t>(1, global[1]) *
+                                std::max<size_t>(1, global[2]));
+                    }
+
                     cl_event kernel_event;
                     CL_CHECK(clEnqueueNDRangeKernel(
                         q, kernel, ndims, offset.data(), global.data(), nullptr,

From e9132c237bc0020ac1275427c3b93355979b3bd2 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Jul 2023 16:53:09 -0400
Subject: [PATCH 2547/2677] Update JIT mem pressure heuristic for small number
 of buffers

---
 src/backend/common/DefaultMemoryManager.cpp | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/src/backend/common/DefaultMemoryManager.cpp b/src/backend/common/DefaultMemoryManager.cpp
index d4aae2138e..0e0694631d 100644
--- a/src/backend/common/DefaultMemoryManager.cpp
+++ b/src/backend/common/DefaultMemoryManager.cpp
@@ -140,10 +140,19 @@ float DefaultMemoryManager::getMemoryPressure() {
     }
 }
 
-bool DefaultMemoryManager::jitTreeExceedsMemoryPressure(size_t bytes) {
+bool DefaultMemoryManager::jitTreeExceedsMemoryPressure(
+    size_t jit_tree_buffer_bytes) {
     lock_guard_t lock(this->memory_mutex);
     memory_info &current = this->getCurrentMemoryInfo();
-    return 2 * bytes > current.lock_bytes;
+    if (current.lock_bytes > 0.25f * current.max_bytes) {
+        /// Evaluate JIT if half of all locked buffers are locked by this JIT
+        /// tree
+        return jit_tree_buffer_bytes > current.lock_bytes * 0.5f;
+    } else {
+        /// Evaluate if this JIT Tree accounts for 10% of total memory on the
+        /// device
+        return jit_tree_buffer_bytes > 0.10f * current.max_bytes;
+    }
 }
 
 void *DefaultMemoryManager::alloc(bool user_lock, const unsigned ndims,

From d74656f7a4257fb9bcde84638f01efe2223a76f0 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Sat, 8 Jul 2023 14:25:12 -0400
Subject: [PATCH 2548/2677] oneapi sparse (#3469)

* adds sparse_arith, sparse blas to oneapi backend
---
 src/backend/common/Binary.hpp              |  14 +
 src/backend/oneapi/CMakeLists.txt          |   2 +
 src/backend/oneapi/kernel/sparse.hpp       | 470 +++++++++++++++++
 src/backend/oneapi/kernel/sparse_arith.hpp | 569 +++++++++++++++++++++
 src/backend/oneapi/sparse.cpp              | 229 ++++-----
 src/backend/oneapi/sparse_arith.cpp        | 159 +++---
 src/backend/oneapi/sparse_blas.cpp         | 124 ++---
 7 files changed, 1308 insertions(+), 259 deletions(-)
 create mode 100644 src/backend/oneapi/kernel/sparse.hpp
 create mode 100644 src/backend/oneapi/kernel/sparse_arith.hpp

diff --git a/src/backend/common/Binary.hpp b/src/backend/common/Binary.hpp
index 6ad8654f83..128cf18988 100644
--- a/src/backend/common/Binary.hpp
+++ b/src/backend/common/Binary.hpp
@@ -40,6 +40,13 @@ struct Binary<T, af_add_t> {
     __DH__ T operator()(T lhs, T rhs) { return lhs + rhs; }
 };
 
+template<typename T>
+struct Binary<T, af_sub_t> {
+    static __DH__ T init() { return scalar<T>(0); }
+
+    __DH__ T operator()(T lhs, T rhs) { return lhs - rhs; }
+};
+
 template<typename T>
 struct Binary<T, af_mul_t> {
     static __DH__ T init() { return scalar<T>(1); }
@@ -47,6 +54,13 @@ struct Binary<T, af_mul_t> {
     __DH__ T operator()(T lhs, T rhs) { return lhs * rhs; }
 };
 
+template<typename T>
+struct Binary<T, af_div_t> {
+    static __DH__ T init() { return scalar<T>(1); }
+
+    __DH__ T operator()(T lhs, T rhs) { return lhs / rhs; }
+};
+
 template<typename T>
 struct Binary<T, af_or_t> {
     static __DH__ T init() { return scalar<T>(0); }
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 1c8f789806..d4c7245311 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -249,6 +249,8 @@ target_sources(afoneapi
     kernel/scan_dim.hpp
     kernel/sort.hpp
     kernel/sort_by_key.hpp
+    kernel/sparse.hpp
+    kernel/sparse_arith.hpp
     kernel/transpose.hpp
     kernel/transpose_inplace.hpp
     kernel/triangle.hpp
diff --git a/src/backend/oneapi/kernel/sparse.hpp b/src/backend/oneapi/kernel/sparse.hpp
new file mode 100644
index 0000000000..70bf051868
--- /dev/null
+++ b/src/backend/oneapi/kernel/sparse.hpp
@@ -0,0 +1,470 @@
+/*******************************************************
+ * Copyright (c) 2014, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#pragma once
+
+#include <Array.hpp>
+#include <Param.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <kernel/accessors.hpp>
+#include <kernel/reduce.hpp>
+#include <kernel/scan_dim.hpp>
+#include <kernel/scan_first.hpp>
+#include <kernel/sort_by_key.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+template<typename T>
+class coo2DenseCreateKernel {
+   public:
+    coo2DenseCreateKernel(write_accessor<T> oPtr, const KParam output,
+                          write_accessor<T> vPtr, const KParam values,
+                          read_accessor<int> rPtr, const KParam rowIdx,
+                          read_accessor<int> cPtr, const KParam colIdx)
+        : oPtr_(oPtr)
+        , output_(output)
+        , vPtr_(vPtr)
+        , values_(values)
+        , rPtr_(rPtr)
+        , rowIdx_(rowIdx)
+        , cPtr_(cPtr)
+        , colIdx_(colIdx) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        const int id = g.get_group_id(0) * g.get_local_range(0) * REPEAT +
+                       it.get_local_id(0);
+
+        if (id >= values_.dims[0]) return;
+
+        const int dimSize = g.get_local_range(0);
+
+        for (int i = it.get_local_id(0); i < REPEAT * dimSize; i += dimSize) {
+            if (i >= values_.dims[0]) return;
+
+            T v   = vPtr_[i];
+            int r = rPtr_[i];
+            int c = cPtr_[i];
+
+            int offset = r + c * output_.strides[1];
+
+            oPtr_[offset] = v;
+        }
+    }
+
+   private:
+    write_accessor<T> oPtr_;
+    const KParam output_;
+    write_accessor<T> vPtr_;
+    const KParam values_;
+    read_accessor<int> rPtr_;
+    const KParam rowIdx_;
+    read_accessor<int> cPtr_;
+    const KParam colIdx_;
+};
+
+template<typename T>
+void coo2dense(Param<T> out, const Param<T> values, const Param<int> rowIdx,
+               const Param<int> colIdx) {
+    auto local  = sycl::range(THREADS_PER_BLOCK, 1);
+    auto global = sycl::range(
+        divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_BLOCK, 1);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::read_only};
+        sycl::accessor d_out{*out.data, h, sycl::write_only, sycl::no_init};
+        sycl::accessor d_values{*values.data, h, sycl::write_only,
+                                sycl::no_init};
+        h.parallel_for(sycl::nd_range{global, local},
+                       coo2DenseCreateKernel<T>(
+                           d_out, out.info, d_values, values.info, d_rowIdx,
+                           rowIdx.info, d_colIdx, colIdx.info));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, int THREADS>
+class csr2DenseCreateKernel {
+   public:
+    csr2DenseCreateKernel(write_accessor<T> output, read_accessor<T> values,
+                          read_accessor<int> rowidx, read_accessor<int> colidx,
+                          const int M)
+        : output_(output)
+        , values_(values)
+        , rowidx_(rowidx)
+        , colidx_(colidx)
+        , M_(M) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        int lid = it.get_local_id(0);
+        for (int rowId = g.get_group_id(0); rowId < M_;
+             rowId += it.get_group_range(0)) {
+            int colStart = rowidx_[rowId];
+            int colEnd   = rowidx_[rowId + 1];
+            for (int colId = colStart + lid; colId < colEnd; colId += THREADS) {
+                output_[rowId + colidx_[colId] * M_] = values_[colId];
+            }
+        }
+    }
+
+   private:
+    write_accessor<T> output_;
+    read_accessor<T> values_;
+    read_accessor<int> rowidx_;
+    read_accessor<int> colidx_;
+    const int M_;
+};
+
+template<typename T>
+void csr2dense(Param<T> output, const Param<T> values, const Param<int> rowIdx,
+               const Param<int> colIdx) {
+    constexpr int MAX_GROUPS = 4096;
+    // FIXME: This needs to be based non nonzeros per row
+    constexpr int threads = 64;
+
+    const int M = rowIdx.info.dims[0] - 1;
+
+    auto local   = sycl::range(threads, 1);
+    int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+    auto global  = sycl::range(local[0] * groups_x, 1);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_values{*values.data, h, sycl::read_only};
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::read_only};
+        sycl::accessor d_output{*output.data, h, sycl::write_only,
+                                sycl::no_init};
+        h.parallel_for(sycl::nd_range{global, local},
+                       csr2DenseCreateKernel<T, threads>(
+                           d_output, d_values, d_rowIdx, d_colIdx, M));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+class dense2csrCreateKernel {
+   public:
+    dense2csrCreateKernel(write_accessor<T> svalptr,
+                          write_accessor<int> scolptr, read_accessor<T> dvalptr,
+                          const KParam valinfo, read_accessor<int> dcolptr,
+                          const KParam colinfo, read_accessor<int> rowptr)
+        : svalptr_(svalptr)
+        , scolptr_(scolptr)
+        , dvalptr_(dvalptr)
+        , valinfo_(valinfo)
+        , dcolptr_(dcolptr)
+        , colinfo_(colinfo)
+        , rowptr_(rowptr) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        // sycl::group g = it.get_group();
+
+        int gidx = it.get_global_id(0);
+        int gidy = it.get_global_id(1);
+
+        if (gidx >= (unsigned)valinfo_.dims[0]) return;
+        if (gidy >= (unsigned)valinfo_.dims[1]) return;
+
+        int rowoff       = rowptr_[gidx];
+        T *svalptr_ptr   = svalptr_.get_pointer();
+        int *scolptr_ptr = scolptr_.get_pointer();
+        svalptr_ptr += rowoff;
+        scolptr_ptr += rowoff;
+
+        T *dvalptr_ptr   = dvalptr_.get_pointer();
+        int *dcolptr_ptr = dcolptr_.get_pointer();
+        dvalptr_ptr += valinfo_.offset;
+        dcolptr_ptr += colinfo_.offset;
+
+        T val = dvalptr_ptr[gidx + gidy * (unsigned)valinfo_.strides[1]];
+
+        if constexpr (std::is_same_v<decltype(val), std::complex<float>> ||
+                      std::is_same_v<decltype(val), std::complex<double>>) {
+            if (val.real() == 0 && val.imag() == 0) return;
+        } else {
+            if (val == 0) return;
+        }
+
+        int oloc              = dcolptr_ptr[gidx + gidy * colinfo_.strides[1]];
+        svalptr_ptr[oloc - 1] = val;
+        scolptr_ptr[oloc - 1] = gidy;
+    }
+
+   private:
+    write_accessor<T> svalptr_;
+    write_accessor<int> scolptr_;
+    read_accessor<T> dvalptr_;
+    const KParam valinfo_;
+    read_accessor<int> dcolptr_;
+    const KParam colinfo_;
+    read_accessor<int> rowptr_;
+};
+
+template<typename T>
+void dense2csr(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+               const Param<T> dense) {
+    int num_rows = dense.info.dims[0];
+    int num_cols = dense.info.dims[1];
+
+    // sd1 contains output of scan along dim 1 of dense
+    Array<int> sd1 = createEmptyArray<int>(dim4(num_rows, num_cols));
+    // rd1 contains output of nonzero count along dim 1 along dense
+    Array<int> rd1 = createEmptyArray<int>(num_rows);
+
+    scan_dim<T, int, af_notzero_t, 1>(sd1, dense, true);
+    reduce_dim_default<T, int, af_notzero_t, 1>(rd1, dense, 0, 0);
+    scan_first<int, int, af_add_t>(rowIdx, rd1, false);
+
+    const int nnz = values.info.dims[0];
+
+    const sycl::id<1> fillOffset(rowIdx.info.offset +
+                                 (rowIdx.info.dims[0] - 1));
+    const sycl::range<1> fillRange(rowIdx.info.dims[0] - fillOffset[0]);
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_rowIdx{*rowIdx.data, h, fillRange, fillOffset};
+        h.fill(d_rowIdx, nnz);
+    });
+
+    auto local   = sycl::range(THREADS_X, THREADS_Y);
+    int groups_x = divup(dense.info.dims[0], local[0]);
+    int groups_y = divup(dense.info.dims[1], local[1]);
+    auto global  = sycl::range(groups_x * local[0], groups_y * local[1]);
+
+    const Param<int> sdParam = sd1;
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_dense{*dense.data, h, sycl::read_only};
+        sycl::accessor d_sdParam{*sdParam.data, h, sycl::read_only};
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_values{*values.data, h, sycl::write_only,
+                                sycl::no_init};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::write_only,
+                                sycl::no_init};
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            dense2csrCreateKernel<T>(d_values, d_colIdx, d_dense, dense.info,
+                                     d_sdParam, sdParam.info, d_rowIdx));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+class swapIndexCreateKernel {
+   public:
+    swapIndexCreateKernel(write_accessor<T> ovalues, write_accessor<int> oindex,
+                          read_accessor<T> ivalues, read_accessor<int> iindex,
+                          read_accessor<int> swapIdx, const int nNZ)
+        : ovalues_(ovalues)
+        , oindex_(oindex)
+        , ivalues_(ivalues)
+        , iindex_(iindex)
+        , swapIdx_(swapIdx)
+        , nNZ_(nNZ) {}
+
+    void operator()(sycl::item<1> it) const {
+        int id = it.get_id(0);
+        if (id < nNZ_) {
+            int idx = swapIdx_[id];
+
+            ovalues_[id] = ivalues_[idx];
+            oindex_[id]  = iindex_[idx];
+        }
+    }
+
+   private:
+    write_accessor<T> ovalues_;
+    write_accessor<int> oindex_;
+    read_accessor<T> ivalues_;
+    read_accessor<int> iindex_;
+    read_accessor<int> swapIdx_;
+    const int nNZ_;
+};
+
+template<typename T>
+void swapIndex(Param<T> ovalues, Param<int> oindex, const Param<T> ivalues,
+               sycl::buffer<int> iindex, const Param<int> swapIdx) {
+    auto global = sycl::range(ovalues.info.dims[0]);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_ivalues{*ivalues.data, h, sycl::read_only};
+        sycl::accessor d_iindex{iindex, h, sycl::read_only};
+        sycl::accessor d_swapIdx{*swapIdx.data, h, sycl::read_only};
+        sycl::accessor d_ovalues{*ovalues.data, h, sycl::write_only,
+                                 sycl::no_init};
+        sycl::accessor d_oindex{*oindex.data, h, sycl::write_only,
+                                sycl::no_init};
+
+        h.parallel_for(global,
+                       swapIndexCreateKernel<T>(
+                           d_ovalues, d_oindex, d_ivalues, d_iindex, d_swapIdx,
+                           static_cast<int>(ovalues.info.dims[0])));
+    });
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+class csr2CooCreateKernel {
+   public:
+    csr2CooCreateKernel(write_accessor<int> orowidx,
+                        write_accessor<int> ocolidx, read_accessor<int> irowidx,
+                        read_accessor<int> icolidx, const int M)
+        : orowidx_(orowidx)
+        , ocolidx_(ocolidx)
+        , irowidx_(irowidx)
+        , icolidx_(icolidx)
+        , M_(M) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+
+        int lid = it.get_local_id(0);
+        for (int rowId = g.get_group_id(0); rowId < M_;
+             rowId += it.get_group_range(0)) {
+            int colStart = irowidx_[rowId];
+            int colEnd   = irowidx_[rowId + 1];
+            for (int colId = colStart + lid; colId < colEnd;
+                 colId += g.get_local_range(0)) {
+                orowidx_[colId] = rowId;
+                ocolidx_[colId] = icolidx_[colId];
+            }
+        }
+    }
+
+   private:
+    write_accessor<int> orowidx_;
+    write_accessor<int> ocolidx_;
+    read_accessor<int> irowidx_;
+    read_accessor<int> icolidx_;
+    const int M_;
+};
+
+template<typename T>
+void csr2coo(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
+             const Param<T> ivalues, const Param<int> irowIdx,
+             const Param<int> icolIdx, Param<int> index) {
+    const int MAX_GROUPS = 4096;
+    int M                = irowIdx.info.dims[0] - 1;
+    // FIXME: This needs to be based non nonzeros per row
+    int threads = 64;
+
+    auto scratch = memAlloc<int>(orowIdx.info.dims[0]);
+
+    auto local   = sycl::range(threads, 1);
+    int groups_x = std::min((int)(divup(M, local[0])), MAX_GROUPS);
+    auto global  = sycl::range(local[0] * groups_x, 1);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_irowIdx{*irowIdx.data, h, sycl::read_only};
+        sycl::accessor d_icolIdx{*icolIdx.data, h, sycl::read_only};
+        sycl::accessor d_scratch{*scratch, h, sycl::write_only, sycl::no_init};
+        sycl::accessor d_ocolIdx{*ocolIdx.data, h, sycl::write_only,
+                                 sycl::no_init};
+        h.parallel_for(sycl::nd_range{global, local},
+                       csr2CooCreateKernel<T>(d_scratch, d_ocolIdx, d_irowIdx,
+                                              d_icolIdx, M));
+    });
+
+    // Now we need to sort this into column major
+    kernel::sort0ByKeyIterative<int, int>(ocolIdx, index, true);
+
+    // Now use index to sort values and rows
+    kernel::swapIndex<T>(ovalues, orowIdx, ivalues, *scratch, index);
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T>
+class csrReduceKernel {
+   public:
+    csrReduceKernel(write_accessor<int> orowidx, read_accessor<int> irowidx,
+                    const int M, const int nNZ)
+        : orowidx_(orowidx), irowidx_(irowidx), M_(M), nNZ_(nNZ) {}
+
+    void operator()(sycl::item<1> it) const {
+        int id = it.get_id(0);
+
+        if (id < nNZ_) {
+            // Read COO row indices
+            int iRId  = irowidx_[id];
+            int iRId1 = 0;
+            if (id > 0) iRId1 = irowidx_[id - 1];
+
+            // If id is 0, then mark the edge cases of csrRow[0] and csrRow[M]
+            if (id == 0) {
+                orowidx_[id] = 0;
+                orowidx_[M_] = nNZ_;
+            } else if (iRId1 != iRId) {
+                // If iRId1 and iRId are not same, that means the row has
+                // incremented For example, if iRId is 5 and iRId1 is 4, that
+                // means row 4 has ended and row 5 has begun at index id. We use
+                // the for-loop because there can be any number of empty rows
+                // between iRId1 and iRId, all of which should be marked by id
+                for (int i = iRId1 + 1; i <= iRId; i++) orowidx_[i] = id;
+            }
+
+            // The last X rows are corner cases if they dont have any values
+            if (id < M_) {
+                if (id > irowidx_[nNZ_ - 1] && orowidx_[id] == 0) {
+                    orowidx_[id] = nNZ_;
+                }
+            }
+        }
+    }
+
+   private:
+    write_accessor<int> orowidx_;
+    read_accessor<int> irowidx_;
+    const int M_;
+    const int nNZ_;
+};
+
+template<typename T>
+void coo2csr(Param<T> ovalues, Param<int> orowIdx, Param<int> ocolIdx,
+             const Param<T> ivalues, const Param<int> irowIdx,
+             const Param<int> icolIdx, Param<int> index, Param<int> rowCopy,
+             const int M) {
+    // Now we need to sort this into column major
+    kernel::sort0ByKeyIterative<int, int>(rowCopy, index, true);
+
+    // Now use index to sort values and rows
+    kernel::swapIndex<T>(ovalues, ocolIdx, ivalues, *icolIdx.data, index);
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+
+    auto global = sycl::range(irowIdx.info.dims[0]);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_orowIdx{*orowIdx.data, h, sycl::write_only};
+        sycl::accessor d_rowCopy{*rowCopy.data, h, sycl::read_only};
+        h.parallel_for(
+            sycl::range{global},
+            csrReduceKernel<T>(d_orowIdx, d_rowCopy, M,
+                               static_cast<int>(ovalues.info.dims[0])));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/kernel/sparse_arith.hpp b/src/backend/oneapi/kernel/sparse_arith.hpp
new file mode 100644
index 0000000000..819af6ffce
--- /dev/null
+++ b/src/backend/oneapi/kernel/sparse_arith.hpp
@@ -0,0 +1,569 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+#pragma once
+
+#include <Array.hpp>
+#include <Param.hpp>
+#include <common/Binary.hpp>
+#include <common/complex.hpp>
+#include <common/dispatch.hpp>
+#include <common/kernel_cache.hpp>
+#include <debug_oneapi.hpp>
+#include <err_oneapi.hpp>
+#include <kernel/accessors.hpp>
+#include <math.hpp>
+#include <memory.hpp>
+#include <traits.hpp>
+
+#include <string>
+#include <vector>
+
+namespace arrayfire {
+namespace oneapi {
+namespace kernel {
+
+constexpr unsigned TX      = 32;
+constexpr unsigned TY      = 8;
+constexpr unsigned THREADS = TX * TY;
+
+template<typename T>
+using global_atomic_ref =
+    sycl::atomic_ref<T, sycl::memory_order::relaxed, sycl::memory_scope::system,
+                     sycl::access::address_space::global_space>;
+
+template<typename T, af_op_t op>
+class sparseArithCSRKernel {
+   public:
+    sparseArithCSRKernel(write_accessor<T> oPtr, const KParam out,
+                         read_accessor<T> values, read_accessor<int> rowIdx,
+                         read_accessor<int> colIdx, const int nNZ,
+                         read_accessor<T> rPtr, const KParam rhs,
+                         const int reverse)
+        : oPtr_(oPtr)
+        , out_(out)
+        , values_(values)
+        , rowIdx_(rowIdx)
+        , colIdx_(colIdx)
+        , nNZ_(nNZ)
+        , rPtr_(rPtr)
+        , rhs_(rhs)
+        , reverse_(reverse) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        common::Binary<T, op> binOP;
+
+        const int row =
+            g.get_group_id(0) * g.get_local_range(1) + it.get_local_id(1);
+
+        if (row < out_.dims[0]) {
+            const int rowStartIdx = rowIdx_[row];
+            const int rowEndIdx   = rowIdx_[row + 1];
+
+            // Repeat loop until all values in the row are computed
+            for (int idx = rowStartIdx + it.get_local_id(0); idx < rowEndIdx;
+                 idx += g.get_local_range(0)) {
+                const int col = colIdx_[idx];
+
+                if (row >= out_.dims[0] || col >= out_.dims[1])
+                    continue;  // Bad indices
+
+                // Get Values
+                const T val  = values_[idx];
+                const T rval = rPtr_[col * rhs_.strides[1] + row];
+
+                const int offset = col * out_.strides[1] + row;
+                if (reverse_)
+                    oPtr_[offset] = binOP(rval, val);
+                else
+                    oPtr_[offset] = binOP(val, rval);
+            }
+        }
+    }
+
+   private:
+    write_accessor<T> oPtr_;
+    const KParam out_;
+    read_accessor<T> values_;
+    read_accessor<int> rowIdx_;
+    read_accessor<int> colIdx_;
+    const int nNZ_;
+    read_accessor<T> rPtr_;
+    const KParam rhs_;
+    const int reverse_;
+};
+
+template<typename T, af_op_t op>
+void sparseArithOpCSR(Param<T> out, const Param<T> values,
+                      const Param<int> rowIdx, const Param<int> colIdx,
+                      const Param<T> rhs, const bool reverse) {
+    auto local  = sycl::range(TX, TY);
+    auto global = sycl::range(divup(out.info.dims[0], TY) * TX, TY);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_out{*out.data, h, sycl::write_only};
+        sycl::accessor d_values{*values.data, h, sycl::read_only};
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::read_only};
+        sycl::accessor d_rhs{*rhs.data, h, sycl::read_only};
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       sparseArithCSRKernel<T, op>(
+                           d_out, out.info, d_values, d_rowIdx, d_colIdx,
+                           static_cast<int>(values.info.dims[0]), d_rhs,
+                           rhs.info, static_cast<int>(reverse)));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+class sparseArithCOOKernel {
+   public:
+    sparseArithCOOKernel(write_accessor<T> oPtr, const KParam out,
+                         read_accessor<T> values, read_accessor<int> rowIdx,
+                         read_accessor<int> colIdx, const int nNZ,
+                         read_accessor<T> rPtr, const KParam rhs,
+                         const int reverse)
+        : oPtr_(oPtr)
+        , out_(out)
+        , values_(values)
+        , rowIdx_(rowIdx)
+        , colIdx_(colIdx)
+        , nNZ_(nNZ)
+        , rPtr_(rPtr)
+        , rhs_(rhs)
+        , reverse_(reverse) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        common::Binary<T, op> binOP;
+
+        const int idx = it.get_global_id(0);
+
+        if (idx < nNZ_) {
+            const int row = rowIdx_[idx];
+            const int col = colIdx_[idx];
+
+            if (row >= out_.dims[0] || col >= out_.dims[1])
+                return;  // Bad indices
+
+            // Get Values
+            const T val  = values_[idx];
+            const T rval = rPtr_[col * rhs_.strides[1] + row];
+
+            const int offset = col * out_.strides[1] + row;
+            if (reverse_)
+                oPtr_[offset] = binOP(rval, val);
+            else
+                oPtr_[offset] = binOP(val, rval);
+        }
+    }
+
+   private:
+    write_accessor<T> oPtr_;
+    const KParam out_;
+    read_accessor<T> values_;
+    read_accessor<int> rowIdx_;
+    read_accessor<int> colIdx_;
+    const int nNZ_;
+    read_accessor<T> rPtr_;
+    const KParam rhs_;
+    const int reverse_;
+};
+
+template<typename T, af_op_t op>
+void sparseArithOpCOO(Param<T> out, const Param<T> values,
+                      const Param<int> rowIdx, const Param<int> colIdx,
+                      const Param<T> rhs, const bool reverse) {
+    auto local  = sycl::range(THREADS);
+    auto global = sycl::range(divup(values.info.dims[0], THREADS) * THREADS);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_out{*out.data, h, sycl::write_only};
+        sycl::accessor d_values{*values.data, h, sycl::read_only};
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::read_only};
+        sycl::accessor d_rhs{*rhs.data, h, sycl::read_only};
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       sparseArithCOOKernel<T, op>(
+                           d_out, out.info, d_values, d_rowIdx, d_colIdx,
+                           static_cast<int>(values.info.dims[0]), d_rhs,
+                           rhs.info, static_cast<int>(reverse)));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+class sparseArithCSR2Kernel {
+   public:
+    sparseArithCSR2Kernel(sycl::accessor<T> values, read_accessor<int> rowIdx,
+                          read_accessor<int> colIdx, const int nNZ,
+                          read_accessor<T> rPtr, const KParam rhs,
+                          const int reverse)
+        : values_(values)
+        , rowIdx_(rowIdx)
+        , colIdx_(colIdx)
+        , nNZ_(nNZ)
+        , rPtr_(rPtr)
+        , rhs_(rhs)
+        , reverse_(reverse) {}
+
+    void operator()(sycl::nd_item<2> it) const {
+        sycl::group g = it.get_group();
+        common::Binary<T, op> binOP;
+
+        const int row =
+            g.get_group_id(0) * g.get_local_range(1) + it.get_local_id(1);
+
+        if (row < rhs_.dims[0]) {
+            const int rowStartIdx = rowIdx_[row];
+            const int rowEndIdx   = rowIdx_[row + 1];
+
+            // Repeat loop until all values in the row are computed
+            for (int idx = rowStartIdx + it.get_local_id(0); idx < rowEndIdx;
+                 idx += g.get_local_range(0)) {
+                const int col = colIdx_[idx];
+
+                if (row >= rhs_.dims[0] || col >= rhs_.dims[1])
+                    continue;  // Bad indices
+
+                // Get Values
+                const T val  = values_[idx];
+                const T rval = rPtr_[col * rhs_.strides[1] + row];
+
+                if (reverse_)
+                    values_[idx] = binOP(rval, val);
+                else
+                    values_[idx] = binOP(val, rval);
+            }
+        }
+    }
+
+   private:
+    sycl::accessor<T> values_;
+    read_accessor<int> rowIdx_;
+    read_accessor<int> colIdx_;
+    const int nNZ_;
+    read_accessor<T> rPtr_;
+    const KParam rhs_;
+    const int reverse_;
+};
+
+template<typename T, af_op_t op>
+void sparseArithOpCSR(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                      const Param<T> rhs, const bool reverse) {
+    auto local  = sycl::range(TX, TY);
+    auto global = sycl::range(divup(values.info.dims[0], TY) * TX, TY);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_values{*values.data, h, sycl::read_write};
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::read_only};
+        sycl::accessor d_rhs{*rhs.data, h, sycl::read_only};
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       sparseArithCSR2Kernel<T, op>(
+                           d_values, d_rowIdx, d_colIdx,
+                           static_cast<int>(values.info.dims[0]), d_rhs,
+                           rhs.info, static_cast<int>(reverse)));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+class sparseArithCOO2Kernel {
+   public:
+    sparseArithCOO2Kernel(sycl::accessor<T> values, read_accessor<int> rowIdx,
+                          read_accessor<int> colIdx, const int nNZ,
+                          read_accessor<T> rPtr, const KParam rhs,
+                          const int reverse)
+        : values_(values)
+        , rowIdx_(rowIdx)
+        , colIdx_(colIdx)
+        , nNZ_(nNZ)
+        , rPtr_(rPtr)
+        , rhs_(rhs)
+        , reverse_(reverse) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        common::Binary<T, op> binOP;
+
+        const int idx = it.get_global_id(0);
+
+        if (idx < nNZ_) {
+            const int row = rowIdx_[idx];
+            const int col = colIdx_[idx];
+
+            if (row >= rhs_.dims[0] || col >= rhs_.dims[1])
+                return;  // Bad indices
+
+            // Get Values
+            const T val  = values_[idx];
+            const T rval = rPtr_[col * rhs_.strides[1] + row];
+
+            if (reverse_)
+                values_[idx] = binOP(rval, val);
+            else
+                values_[idx] = binOP(val, rval);
+        }
+    }
+
+   private:
+    sycl::accessor<T> values_;
+    read_accessor<int> rowIdx_;
+    read_accessor<int> colIdx_;
+    const int nNZ_;
+    read_accessor<T> rPtr_;
+    const KParam rhs_;
+    const int reverse_;
+};
+
+template<typename T, af_op_t op>
+void sparseArithOpCOO(Param<T> values, Param<int> rowIdx, Param<int> colIdx,
+                      const Param<T> rhs, const bool reverse) {
+    auto local  = sycl::range(THREADS);
+    auto global = sycl::range(divup(values.info.dims[0], THREADS) * THREADS);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_values{*values.data, h, sycl::read_write};
+        sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
+        sycl::accessor d_colIdx{*colIdx.data, h, sycl::read_only};
+        sycl::accessor d_rhs{*rhs.data, h, sycl::read_only};
+
+        h.parallel_for(sycl::nd_range{global, local},
+                       sparseArithCOO2Kernel<T, op>(
+                           d_values, d_rowIdx, d_colIdx,
+                           static_cast<int>(values.info.dims[0]), d_rhs,
+                           rhs.info, static_cast<int>(reverse)));
+    });
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+class csrCalcOutNNZKernel {
+   public:
+    csrCalcOutNNZKernel(write_accessor<unsigned> nnzc,
+                        write_accessor<int> oRowIdx, unsigned M,
+                        read_accessor<int> lRowIdx, read_accessor<int> lColIdx,
+                        read_accessor<int> rRowIdx, read_accessor<int> rColIdx,
+                        sycl::local_accessor<unsigned, 1> blkNNZ)
+        : nnzc_(nnzc)
+        , oRowIdx_(oRowIdx)
+        , M_(M)
+        , lRowIdx_(lRowIdx)
+        , lColIdx_(lColIdx)
+        , rRowIdx_(rRowIdx)
+        , rColIdx_(rColIdx)
+        , blkNNZ_(blkNNZ) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        sycl::group g = it.get_group();
+
+        const uint row = it.get_global_id(0);
+        const uint tid = it.get_local_id(0);
+
+        const bool valid = row < M_;
+
+        const uint lEnd = (valid ? lRowIdx_[row + 1] : 0);
+        const uint rEnd = (valid ? rRowIdx_[row + 1] : 0);
+
+        blkNNZ_[tid] = 0;
+        it.barrier();
+
+        uint l   = (valid ? lRowIdx_[row] : 0);
+        uint r   = (valid ? rRowIdx_[row] : 0);
+        uint nnz = 0;
+        while (l < lEnd && r < rEnd) {
+            uint lci = lColIdx_[l];
+            uint rci = rColIdx_[r];
+            l += (lci <= rci);
+            r += (lci >= rci);
+            nnz++;
+        }
+        nnz += (lEnd - l);
+        nnz += (rEnd - r);
+
+        blkNNZ_[tid] = nnz;
+        it.barrier();
+
+        if (valid) oRowIdx_[row + 1] = nnz;
+
+        for (uint s = g.get_local_range(0) / 2; s > 0; s >>= 1) {
+            if (tid < s) { blkNNZ_[tid] += blkNNZ_[tid + s]; }
+            it.barrier();
+        }
+
+        if (tid == 0) {
+            nnz = blkNNZ_[0];
+            global_atomic_ref<uint>(nnzc_[0]) += nnz;
+        }
+    }
+
+   private:
+    write_accessor<unsigned> nnzc_;
+    write_accessor<int> oRowIdx_;
+    unsigned M_;
+    read_accessor<int> lRowIdx_;
+    read_accessor<int> lColIdx_;
+    read_accessor<int> rRowIdx_;
+    read_accessor<int> rColIdx_;
+    sycl::local_accessor<unsigned, 1> blkNNZ_;
+};
+
+static void csrCalcOutNNZ(Param<int> outRowIdx, unsigned &nnzC, const uint M,
+                          const uint N, uint nnzA, const Param<int> lrowIdx,
+                          const Param<int> lcolIdx, uint nnzB,
+                          const Param<int> rrowIdx, const Param<int> rcolIdx) {
+    UNUSED(N);
+    UNUSED(nnzA);
+    UNUSED(nnzB);
+
+    auto local  = sycl::range(256);
+    auto global = sycl::range(divup(M, local[0]) * local[0]);
+
+    Array<unsigned> out = createValueArray<unsigned>(1, 0);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_out{*out.get(), h, sycl::write_only};
+        sycl::accessor d_outRowIdx{*outRowIdx.data, h, sycl::write_only};
+        sycl::accessor d_lRowIdx{*lrowIdx.data, h, sycl::read_only};
+        sycl::accessor d_lColIdx{*lcolIdx.data, h, sycl::read_only};
+        sycl::accessor d_rRowIdx{*rrowIdx.data, h, sycl::read_only};
+        sycl::accessor d_rColIdx{*rcolIdx.data, h, sycl::read_only};
+
+        auto blkNNZ = sycl::local_accessor<unsigned, 1>(local[0], h);
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            csrCalcOutNNZKernel(d_out, d_outRowIdx, M, d_lRowIdx, d_lColIdx,
+                                d_rRowIdx, d_rColIdx, blkNNZ));
+    });
+
+    {
+        sycl::host_accessor nnz_acc{*out.get(), sycl::read_only};
+        nnzC = nnz_acc[0];
+    }
+
+    ONEAPI_DEBUG_FINISH(getQueue());
+}
+
+template<typename T, af_op_t op>
+class ssarithCSRKernel {
+   public:
+    ssarithCSRKernel(write_accessor<T> oVals, write_accessor<int> oColIdx,
+                     read_accessor<int> oRowIdx, unsigned M, unsigned N,
+                     unsigned nnza, read_accessor<T> lVals,
+                     read_accessor<int> lRowIdx, read_accessor<int> lColIdx,
+                     unsigned nnzb, read_accessor<T> rVals,
+                     read_accessor<int> rRowIdx, read_accessor<int> rColIdx)
+        : oVals_(oVals)
+        , oColIdx_(oColIdx)
+        , oRowIdx_(oRowIdx)
+        , M_(M)
+        , N_(N)
+        , nnza_(nnza)
+        , lVals_(lVals)
+        , lRowIdx_(lRowIdx)
+        , lColIdx_(lColIdx)
+        , nnzb_(nnzb)
+        , rVals_(rVals)
+        , rRowIdx_(rRowIdx)
+        , rColIdx_(rColIdx) {}
+
+    void operator()(sycl::nd_item<1> it) const {
+        common::Binary<T, op> binOP;
+
+        const uint row = it.get_global_id(0);
+
+        const bool valid  = row < M_;
+        const uint lEnd   = (valid ? lRowIdx_[row + 1] : 0);
+        const uint rEnd   = (valid ? rRowIdx_[row + 1] : 0);
+        const uint offset = (valid ? oRowIdx_[row] : 0);
+
+        T *ovPtr   = oVals_.get_pointer() + offset;
+        int *ocPtr = oColIdx_.get_pointer() + offset;
+
+        uint l = (valid ? lRowIdx_[row] : 0);
+        uint r = (valid ? rRowIdx_[row] : 0);
+
+        uint nnz = 0;
+        while (l < lEnd && r < rEnd) {
+            uint lci = lColIdx_[l];
+            uint rci = rColIdx_[r];
+
+            T lhs = (lci <= rci ? lVals_[l] : common::Binary<T, op>::init());
+            T rhs = (lci >= rci ? rVals_[r] : common::Binary<T, op>::init());
+
+            ovPtr[nnz] = binOP(lhs, rhs);
+            ocPtr[nnz] = (lci <= rci) ? lci : rci;
+
+            l += (lci <= rci);
+            r += (lci >= rci);
+            nnz++;
+        }
+        while (l < lEnd) {
+            ovPtr[nnz] = binOP(lVals_[l], common::Binary<T, op>::init());
+            ocPtr[nnz] = lColIdx_[l];
+            l++;
+            nnz++;
+        }
+        while (r < rEnd) {
+            ovPtr[nnz] = binOP(common::Binary<T, op>::init(), rVals_[r]);
+            ocPtr[nnz] = rColIdx_[r];
+            r++;
+            nnz++;
+        }
+    }
+
+   private:
+    write_accessor<T> oVals_;
+    write_accessor<int> oColIdx_;
+    read_accessor<int> oRowIdx_;
+    unsigned M_, N_;
+    unsigned nnza_;
+    read_accessor<T> lVals_;
+    read_accessor<int> lRowIdx_;
+    read_accessor<int> lColIdx_;
+    unsigned nnzb_;
+    read_accessor<T> rVals_;
+    read_accessor<int> rRowIdx_;
+    read_accessor<int> rColIdx_;
+};
+
+template<typename T, af_op_t op>
+void ssArithCSR(Param<T> oVals, Param<int> oColIdx, const Param<int> oRowIdx,
+                const uint M, const uint N, unsigned nnzA, const Param<T> lVals,
+                const Param<int> lRowIdx, const Param<int> lColIdx,
+                unsigned nnzB, const Param<T> rVals, const Param<int> rRowIdx,
+                const Param<int> rColIdx) {
+    auto local  = sycl::range(256);
+    auto global = sycl::range(divup(M, local[0]) * local[0]);
+
+    getQueue().submit([&](auto &h) {
+        sycl::accessor d_oVals{*oVals.data, h, sycl::write_only};
+        sycl::accessor d_oColIdx{*oColIdx.data, h, sycl::write_only};
+        sycl::accessor d_oRowIdx{*oRowIdx.data, h, sycl::read_only};
+
+        sycl::accessor d_lVals{*lVals.data, h, sycl::read_only};
+        sycl::accessor d_lRowIdx{*lRowIdx.data, h, sycl::read_only};
+        sycl::accessor d_lColIdx{*lColIdx.data, h, sycl::read_only};
+
+        sycl::accessor d_rVals{*rVals.data, h, sycl::read_only};
+        sycl::accessor d_rRowIdx{*rRowIdx.data, h, sycl::read_only};
+        sycl::accessor d_rColIdx{*rColIdx.data, h, sycl::read_only};
+
+        h.parallel_for(
+            sycl::nd_range{global, local},
+            ssarithCSRKernel<T, op>(d_oVals, d_oColIdx, d_oRowIdx, M, N, nnzA,
+                                    d_lVals, d_lRowIdx, d_lColIdx, nnzB,
+                                    d_rVals, d_rRowIdx, d_rColIdx));
+    });
+}
+
+}  // namespace kernel
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/oneapi/sparse.cpp b/src/backend/oneapi/sparse.cpp
index 37e5826430..2e9a67213f 100644
--- a/src/backend/oneapi/sparse.cpp
+++ b/src/backend/oneapi/sparse.cpp
@@ -7,7 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-// #include <kernel/sparse.hpp>
+#include <kernel/sparse.hpp>
 #include <sparse.hpp>
 
 #include <arith.hpp>
@@ -26,154 +26,151 @@
 #include <stdexcept>
 #include <string>
 
+#include <handle.hpp>
+
 namespace arrayfire {
 namespace oneapi {
 
 using namespace common;
 
+#define P(exp) af_print_array_gen(#exp, getHandle(exp), 2)
+
 // Partial template specialization of sparseConvertDenseToStorage for COO
 // However, template specialization is not allowed
 template<typename T>
 SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in) {
-    ONEAPI_NOT_SUPPORTED("sparseConvertDenseToCOO Not supported");
-    // in.eval();
+    in.eval();
 
-    // Array<uint> nonZeroIdx_ = where<T>(in);
-    // Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
+    Array<uint> nonZeroIdx_ = where<T>(in);
+    Array<int> nonZeroIdx   = cast<int, uint>(nonZeroIdx_);
+    nonZeroIdx.eval();
 
-    // dim_t nNZ = nonZeroIdx.elements();
+    dim_t nNZ = nonZeroIdx.elements();
 
-    // Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
-    // constDim.eval();
+    Array<int> constDim = createValueArray<int>(dim4(nNZ), in.dims()[0]);
+    constDim.eval();
 
-    // Array<int> rowIdx =
-    //     arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
-    // Array<int> colIdx =
-    //     arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> rowIdx =
+        arithOp<int, af_mod_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
+    Array<int> colIdx =
+        arithOp<int, af_div_t>(nonZeroIdx, constDim, nonZeroIdx.dims());
 
-    // Array<T> values = copyArray<T>(in);
-    // values          = modDims(values, dim4(values.elements()));
-    // values          = lookup<T, int>(values, nonZeroIdx, 0);
+    Array<T> values = copyArray<T>(in);
+    values          = modDims(values, dim4(values.elements()));
+    values          = lookup<T, int>(values, nonZeroIdx, 0);
 
-    // return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
-    //                                      AF_STORAGE_COO);
+    return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx,
+                                         AF_STORAGE_COO);
 }
 
 template<typename T, af_storage stype>
 SparseArray<T> sparseConvertDenseToStorage(const Array<T> &in_) {
-    ONEAPI_NOT_SUPPORTED("sparseConvertDenseToStorage Not supported");
-    //     in_.eval();
-    //
-    //     uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in_));
-    //
-    //     SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ,
-    //     stype); sparse_.eval();
-    //
-    //     Array<T> &values   = sparse_.getValues();
-    //     Array<int> &rowIdx = sparse_.getRowIdx();
-    //     Array<int> &colIdx = sparse_.getColIdx();
-
-    // kernel::dense2csr<T>(values, rowIdx, colIdx, in_);
-
-    // return sparse_;
+    in_.eval();
+
+    uint nNZ = getScalar<uint>(reduce_all<af_notzero_t, T, uint>(in_));
+
+    SparseArray<T> sparse_ = createEmptySparseArray<T>(in_.dims(), nNZ, stype);
+    sparse_.eval();
+
+    Array<T> &values   = sparse_.getValues();
+    Array<int> &rowIdx = sparse_.getRowIdx();
+    Array<int> &colIdx = sparse_.getColIdx();
+
+    kernel::dense2csr<T>(values, rowIdx, colIdx, in_);
+
+    return sparse_;
 }
 
 // Partial template specialization of sparseConvertStorageToDense for COO
 // However, template specialization is not allowed
 template<typename T>
 Array<T> sparseConvertCOOToDense(const SparseArray<T> &in) {
-    ONEAPI_NOT_SUPPORTED("sparseConvertCOOToDense Not supported");
-    //    in.eval();
-    //
-    //    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
-    //    dense.eval();
-    //
-    //    const Array<T> values   = in.getValues();
-    //    const Array<int> rowIdx = in.getRowIdx();
-    //    const Array<int> colIdx = in.getColIdx();
-
-    // kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
-
-    // return dense;
+    in.eval();
+
+    Array<T> dense = createValueArray<T>(in.dims(), scalar<T>(0));
+    dense.eval();
+
+    const Array<T> values   = in.getValues();
+    const Array<int> rowIdx = in.getRowIdx();
+    const Array<int> colIdx = in.getColIdx();
+
+    kernel::coo2dense<T>(dense, values, rowIdx, colIdx);
+
+    return dense;
 }
 
 template<typename T, af_storage stype>
 Array<T> sparseConvertStorageToDense(const SparseArray<T> &in_) {
-    ONEAPI_NOT_SUPPORTED("sparseConvertStorageToDense Not supported");
-    //
-    //    if (stype != AF_STORAGE_CSR) {
-    //        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
-    //                 AF_ERR_NOT_SUPPORTED);
-    //    }
-    //
-    //    in_.eval();
-    //
-    //    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
-    //    dense_.eval();
-    //
-    //    const Array<T> &values   = in_.getValues();
-    //    const Array<int> &rowIdx = in_.getRowIdx();
-    //    const Array<int> &colIdx = in_.getColIdx();
-    //
-    //    if (stype == AF_STORAGE_CSR) {
-    //        // kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
-    //    } else {
-    //        AF_ERROR("OpenCL Backend only supports CSR or COO to Dense",
-    //                 AF_ERR_NOT_SUPPORTED);
-    //    }
-    //
-    //    return dense_;
+    if (stype != AF_STORAGE_CSR) {
+        AF_ERROR("oneAPI Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
+    }
+
+    in_.eval();
+
+    Array<T> dense_ = createValueArray<T>(in_.dims(), scalar<T>(0));
+    dense_.eval();
+
+    const Array<T> &values   = in_.getValues();
+    const Array<int> &rowIdx = in_.getRowIdx();
+    const Array<int> &colIdx = in_.getColIdx();
+
+    if (stype == AF_STORAGE_CSR) {
+        kernel::csr2dense<T>(dense_, values, rowIdx, colIdx);
+    } else {
+        AF_ERROR("oneAPI Backend only supports CSR or COO to Dense",
+                 AF_ERR_NOT_SUPPORTED);
+    }
+
+    return dense_;
 }
 
 template<typename T, af_storage dest, af_storage src>
 SparseArray<T> sparseConvertStorageToStorage(const SparseArray<T> &in) {
-    ONEAPI_NOT_SUPPORTED("sparseConvertStorageToStorage Not supported");
-    // in.eval();
-
-    // SparseArray<T> converted = createEmptySparseArray<T>(
-    //     in.dims(), static_cast<int>(in.getNNZ()), dest);
-    // converted.eval();
-
-    // if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
-    //     Array<int> index = range<int>(in.getNNZ(), 0);
-    //     index.eval();
-
-    //    Array<T> &ovalues         = converted.getValues();
-    //    Array<int> &orowIdx       = converted.getRowIdx();
-    //    Array<int> &ocolIdx       = converted.getColIdx();
-    //    const Array<T> &ivalues   = in.getValues();
-    //    const Array<int> &irowIdx = in.getRowIdx();
-    //    const Array<int> &icolIdx = in.getColIdx();
-
-    //    // kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
-    //    // icolIdx,
-    //    //                    index);
-
-    //} else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
-    //    Array<int> index = range<int>(in.getNNZ(), 0);
-    //    index.eval();
-
-    //    Array<T> &ovalues         = converted.getValues();
-    //    Array<int> &orowIdx       = converted.getRowIdx();
-    //    Array<int> &ocolIdx       = converted.getColIdx();
-    //    const Array<T> &ivalues   = in.getValues();
-    //    const Array<int> &irowIdx = in.getRowIdx();
-    //    const Array<int> &icolIdx = in.getColIdx();
-
-    //    Array<int> rowCopy = copyArray<int>(irowIdx);
-    //    rowCopy.eval();
-
-    //    kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx,
-    //    icolIdx,
-    //                       index, rowCopy, in.dims()[0]);
-
-    //} else {
-    //    // Should never come here
-    //    AF_ERROR("OpenCL Backend invalid conversion combination",
-    //             AF_ERR_NOT_SUPPORTED);
-    //}
-
-    // return converted;
+    in.eval();
+
+    SparseArray<T> converted = createEmptySparseArray<T>(
+        in.dims(), static_cast<int>(in.getNNZ()), dest);
+    converted.eval();
+
+    if (src == AF_STORAGE_CSR && dest == AF_STORAGE_COO) {
+        Array<int> index = range<int>(in.getNNZ(), 0);
+        index.eval();
+
+        Array<T> &ovalues         = converted.getValues();
+        Array<int> &orowIdx       = converted.getRowIdx();
+        Array<int> &ocolIdx       = converted.getColIdx();
+        const Array<T> &ivalues   = in.getValues();
+        const Array<int> &irowIdx = in.getRowIdx();
+        const Array<int> &icolIdx = in.getColIdx();
+
+        kernel::csr2coo<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+                           index);
+
+    } else if (src == AF_STORAGE_COO && dest == AF_STORAGE_CSR) {
+        Array<int> index = range<int>(in.getNNZ(), 0);
+        index.eval();
+
+        Array<T> &ovalues         = converted.getValues();
+        Array<int> &orowIdx       = converted.getRowIdx();
+        Array<int> &ocolIdx       = converted.getColIdx();
+        const Array<T> &ivalues   = in.getValues();
+        const Array<int> &irowIdx = in.getRowIdx();
+        const Array<int> &icolIdx = in.getColIdx();
+
+        Array<int> rowCopy = copyArray<int>(irowIdx);
+        rowCopy.eval();
+
+        kernel::coo2csr<T>(ovalues, orowIdx, ocolIdx, ivalues, irowIdx, icolIdx,
+                           index, rowCopy, in.dims()[0]);
+
+    } else {
+        // Should never come here
+        AF_ERROR("oneAPI Backend invalid conversion combination",
+                 AF_ERR_NOT_SUPPORTED);
+    }
+
+    return converted;
 }
 
 #define INSTANTIATE_TO_STORAGE(T, S)                     \
diff --git a/src/backend/oneapi/sparse_arith.cpp b/src/backend/oneapi/sparse_arith.cpp
index 856d300553..4b3e7301c4 100644
--- a/src/backend/oneapi/sparse_arith.cpp
+++ b/src/backend/oneapi/sparse_arith.cpp
@@ -7,8 +7,8 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-// #include <kernel/sparse_arith.hpp>
 #include <err_oneapi.hpp>
+#include <kernel/sparse_arith.hpp>
 #include <sparse.hpp>
 
 #include <stdexcept>
@@ -51,104 +51,101 @@ cdouble getInf() {
 template<typename T, af_op_t op>
 Array<T> arithOpD(const SparseArray<T> &lhs, const Array<T> &rhs,
                   const bool reverse) {
-    ONEAPI_NOT_SUPPORTED("arithOpD Not supported");
-    // lhs.eval();
-    // rhs.eval();
-
-    // Array<T> out  = createEmptyArray<T>(dim4(0));
-    // Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
-    // switch (op) {
-    //     case af_add_t: out = copyArray<T>(rhs); break;
-    //     case af_sub_t:
-    //         out = reverse ? copyArray<T>(rhs)
-    //                       : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
-    //         break;
-    //     default: out = copyArray<T>(rhs);
-    // }
-    // out.eval();
-    // switch (lhs.getStorage()) {
-    //     case AF_STORAGE_CSR:
-    //         kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
-    //                                         lhs.getRowIdx(), lhs.getColIdx(),
-    //                                         rhs, reverse);
-    //         break;
-    //     case AF_STORAGE_COO:
-    //         kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
-    //                                         lhs.getRowIdx(), lhs.getColIdx(),
-    //                                         rhs, reverse);
-    //         break;
-    //     default:
-    //         AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
-    //                  AF_ERR_NOT_SUPPORTED);
-    // }
-
-    // return out;
+    lhs.eval();
+    rhs.eval();
+
+    Array<T> out  = createEmptyArray<T>(dim4(0));
+    Array<T> zero = createValueArray<T>(rhs.dims(), scalar<T>(0));
+    switch (op) {
+        case af_add_t: out = copyArray<T>(rhs); break;
+        case af_sub_t:
+            out = reverse ? copyArray<T>(rhs)
+                          : arithOp<T, af_sub_t>(zero, rhs, rhs.dims());
+            break;
+        default: out = copyArray<T>(rhs);
+    }
+    out.eval();
+    switch (lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            kernel::sparseArithOpCSR<T, op>(out, lhs.getValues(),
+                                            lhs.getRowIdx(), lhs.getColIdx(),
+                                            rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            kernel::sparseArithOpCOO<T, op>(out, lhs.getValues(),
+                                            lhs.getRowIdx(), lhs.getColIdx(),
+                                            rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
 }
 
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const Array<T> &rhs,
                        const bool reverse) {
-    ONEAPI_NOT_SUPPORTED("arithOp Not supported");
-    // lhs.eval();
-    // rhs.eval();
-
-    // SparseArray<T> out = createArrayDataSparseArray<T>(
-    //     lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
-    //     lhs.getStorage(), true);
-    // out.eval();
-    // switch (lhs.getStorage()) {
-    //     case AF_STORAGE_CSR:
-    //         kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
-    //                                         out.getColIdx(), rhs, reverse);
-    //         break;
-    //     case AF_STORAGE_COO:
-    //         kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
-    //                                         out.getColIdx(), rhs, reverse);
-    //         break;
-    //     default:
-    //         AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
-    //                  AF_ERR_NOT_SUPPORTED);
-    // }
-
-    // return out;
+    lhs.eval();
+    rhs.eval();
+
+    SparseArray<T> out = createArrayDataSparseArray<T>(
+        lhs.dims(), lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+        lhs.getStorage(), true);
+    out.eval();
+    switch (lhs.getStorage()) {
+        case AF_STORAGE_CSR:
+            kernel::sparseArithOpCSR<T, op>(out.getValues(), out.getRowIdx(),
+                                            out.getColIdx(), rhs, reverse);
+            break;
+        case AF_STORAGE_COO:
+            kernel::sparseArithOpCOO<T, op>(out.getValues(), out.getRowIdx(),
+                                            out.getColIdx(), rhs, reverse);
+            break;
+        default:
+            AF_ERROR("Sparse Arithmetic only supported for CSR or COO",
+                     AF_ERR_NOT_SUPPORTED);
+    }
+
+    return out;
 }
 
 template<typename T, af_op_t op>
 SparseArray<T> arithOp(const SparseArray<T> &lhs, const SparseArray<T> &rhs) {
-    ONEAPI_NOT_SUPPORTED("arithOp Not supported");
-    // lhs.eval();
-    // rhs.eval();
-    // af::storage sfmt = lhs.getStorage();
+    lhs.eval();
+    rhs.eval();
+    af::storage sfmt = lhs.getStorage();
 
-    // const dim4 &ldims = lhs.dims();
+    const dim4 &ldims = lhs.dims();
 
-    // const uint M = ldims[0];
-    // const uint N = ldims[1];
+    const uint M = ldims[0];
+    const uint N = ldims[1];
 
-    // const dim_t nnzA = lhs.getNNZ();
-    // const dim_t nnzB = rhs.getNNZ();
+    const dim_t nnzA = lhs.getNNZ();
+    const dim_t nnzB = rhs.getNNZ();
 
-    // auto temp = createValueArray<int>(dim4(M + 1), scalar<int>(0));
-    // temp.eval();
+    auto temp = createValueArray<int>(dim4(M + 1), scalar<int>(0));
+    temp.eval();
 
-    // unsigned nnzC = 0;
-    // kernel::csrCalcOutNNZ(temp, nnzC, M, N, nnzA, lhs.getRowIdx(),
-    //                       lhs.getColIdx(), nnzB, rhs.getRowIdx(),
-    //                       rhs.getColIdx());
+    unsigned nnzC = 0;
+    kernel::csrCalcOutNNZ(temp, nnzC, M, N, nnzA, lhs.getRowIdx(),
+                          lhs.getColIdx(), nnzB, rhs.getRowIdx(),
+                          rhs.getColIdx());
 
-    // auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
+    auto outRowIdx = scan<af_add_t, int, int>(temp, 0);
 
-    // auto outColIdx = createEmptyArray<int>(dim4(nnzC));
-    // auto outValues = createEmptyArray<T>(dim4(nnzC));
+    auto outColIdx = createEmptyArray<int>(dim4(nnzC));
+    auto outValues = createEmptyArray<T>(dim4(nnzC));
 
-    // kernel::ssArithCSR<T, op>(outValues, outColIdx, outRowIdx, M, N, nnzA,
-    //                           lhs.getValues(), lhs.getRowIdx(),
-    //                           lhs.getColIdx(), nnzB, rhs.getValues(),
-    //                           rhs.getRowIdx(), rhs.getColIdx());
+    kernel::ssArithCSR<T, op>(outValues, outColIdx, outRowIdx, M, N, nnzA,
+                              lhs.getValues(), lhs.getRowIdx(), lhs.getColIdx(),
+                              nnzB, rhs.getValues(), rhs.getRowIdx(),
+                              rhs.getColIdx());
 
-    // SparseArray<T> retVal = createArrayDataSparseArray(
-    //     ldims, outValues, outRowIdx, outColIdx, sfmt);
-    // return retVal;
+    SparseArray<T> retVal = createArrayDataSparseArray(
+        ldims, outValues, outRowIdx, outColIdx, sfmt);
+    return retVal;
 }
 
 #define INSTANTIATE(T)                                                         \
diff --git a/src/backend/oneapi/sparse_blas.cpp b/src/backend/oneapi/sparse_blas.cpp
index 67d7cb8352..0494a5806e 100644
--- a/src/backend/oneapi/sparse_blas.cpp
+++ b/src/backend/oneapi/sparse_blas.cpp
@@ -9,15 +9,6 @@
 
 #include <sparse_blas.hpp>
 
-// #include <kernel/cscmm.hpp>
-// #include <kernel/cscmv.hpp>
-// #include <kernel/csrmm.hpp>
-// #include <kernel/csrmv.hpp>
-
-#include <cassert>
-#include <stdexcept>
-#include <string>
-
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <err_oneapi.hpp>
@@ -26,68 +17,77 @@
 #include <transpose.hpp>
 #include <af/dim4.hpp>
 
-#if defined(WITH_LINEAR_ALGEBRA)
-// #include <cpu/cpu_sparse_blas.hpp>
-#endif  // WITH_LINEAR_ALGEBRA
+#include <oneapi/mkl/spblas.hpp>
+
+#include <sycl/sycl.hpp>
+
+#include <cassert>
+#include <stdexcept>
+#include <string>
 
 namespace arrayfire {
 namespace oneapi {
 
 using namespace common;
 
+// Converts an af_mat_prop options to a transpose type for mkl
+static ::oneapi::mkl::transpose toBlasTranspose(af_mat_prop opt) {
+    switch (opt) {
+        case AF_MAT_NONE: return ::oneapi::mkl::transpose::nontrans;
+        case AF_MAT_TRANS: return ::oneapi::mkl::transpose::trans;
+        case AF_MAT_CTRANS: return ::oneapi::mkl::transpose::conjtrans;
+        default: AF_ERROR("INVALID af_mat_prop", AF_ERR_ARG);
+    }
+}
+
 template<typename T>
 Array<T> matmul(const common::SparseArray<T>& lhs, const Array<T>& rhsIn,
                 af_mat_prop optLhs, af_mat_prop optRhs) {
-    ONEAPI_NOT_SUPPORTED("sparse matmul Not supported");
-    // #if defined(WITH_LINEAR_ALGEBRA)
-    //     if (OpenCLCPUOffload(
-    //             false)) {  // Do not force offload gemm on OSX Intel devices
-    //         return cpu::matmul(lhs, rhsIn, optLhs, optRhs);
-    //     }
-    // #endif
-    //
-    //     int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
-    //     // int lColDim = (optLhs == AF_MAT_NONE) ? 1 : 0;
-    //     static const int rColDim =
-    //         1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
-    //
-    //     dim4 lDims = lhs.dims();
-    //     dim4 rDims = rhsIn.dims();
-    //     int M      = lDims[lRowDim];
-    //     int N      = rDims[rColDim];
-    //     // int K = lDims[lColDim];
-    //
-    //     const Array<T> rhs =
-    //         (N != 1 && optLhs == AF_MAT_NONE) ? transpose(rhsIn, false) :
-    //         rhsIn;
-    //     Array<T> out = createEmptyArray<T>(af::dim4(M, N, 1, 1));
-    //
-    //     static const T alpha = scalar<T>(1.0);
-    //     static const T beta  = scalar<T>(0.0);
-    //
-    //     const Array<T>& values   = lhs.getValues();
-    //     const Array<int>& rowIdx = lhs.getRowIdx();
-    //     const Array<int>& colIdx = lhs.getColIdx();
-    //
-    //     if (optLhs == AF_MAT_NONE) {
-    //         if (N == 1) {
-    //             kernel::csrmv(out, values, rowIdx, colIdx, rhs, alpha, beta);
-    //         } else {
-    //             kernel::csrmm_nt(out, values, rowIdx, colIdx, rhs, alpha,
-    //             beta);
-    //         }
-    //     } else {
-    //         // CSR transpose is a CSC matrix
-    //         if (N == 1) {
-    //             kernel::cscmv(out, values, rowIdx, colIdx, rhs, alpha, beta,
-    //                           optLhs == AF_MAT_CTRANS);
-    //         } else {
-    //             kernel::cscmm_nn(out, values, rowIdx, colIdx, rhs, alpha,
-    //             beta,
-    //                              optLhs == AF_MAT_CTRANS);
-    //         }
-    //     }
-    //     return out;
+    int lRowDim = (optLhs == AF_MAT_NONE) ? 0 : 1;
+    static const int rColDim =
+        1;  // Unsupported : (optRhs == AF_MAT_NONE) ? 1 : 0;
+
+    dim4 lDims    = lhs.dims();
+    dim4 rDims    = rhsIn.dims();
+    dim4 rStrides = rhsIn.strides();
+    int M         = lDims[lRowDim];
+    int N         = rDims[rColDim];
+
+    Array<T> out  = createEmptyArray<T>(af::dim4(M, N, 1, 1));
+    dim4 oStrides = out.strides();
+
+    static const T alpha = scalar<T>(1.0);
+    static const T beta  = scalar<T>(0.0);
+
+    const Array<T>& values      = lhs.getValues();
+    const Array<int>& rowIdx    = lhs.getRowIdx();
+    const Array<int>& colIdx    = lhs.getColIdx();
+    sycl::buffer<T, 1> valBuf   = values.template getBufferWithOffset<T>();
+    sycl::buffer<int, 1> rowBuf = rowIdx.template getBufferWithOffset<int>();
+    sycl::buffer<int, 1> colBuf = colIdx.template getBufferWithOffset<int>();
+
+    const auto lOpts = toBlasTranspose(optLhs);
+    const auto rOpts = toBlasTranspose(optRhs);
+
+    sycl::buffer<T, 1> rhsBuf = rhsIn.template getBufferWithOffset<T>();
+    sycl::buffer<T, 1> outBuf = out.template getBufferWithOffset<T>();
+
+    ::oneapi::mkl::sparse::matrix_handle_t CSRHandle = nullptr;
+    ::oneapi::mkl::sparse::init_matrix_handle(&CSRHandle);
+    ::oneapi::mkl::sparse::set_csr_data(
+        getQueue(), CSRHandle, lDims[0], lDims[1],
+        ::oneapi::mkl::index_base::zero, rowBuf, colBuf, valBuf);
+
+    if (N == 1) {
+        ::oneapi::mkl::sparse::gemv(getQueue(), lOpts, alpha, CSRHandle, rhsBuf,
+                                    beta, outBuf);
+    } else {
+        ::oneapi::mkl::sparse::gemm(
+            getQueue(), ::oneapi::mkl::layout::col_major, lOpts, rOpts, alpha,
+            CSRHandle, rhsBuf, N, rStrides[1], beta, outBuf, oStrides[1]);
+    }
+    ::oneapi::mkl::sparse::release_matrix_handle(getQueue(), &CSRHandle);
+    return out;
 }
 
 #define INSTANTIATE_SPARSE(T)                                            \

From 886db208836b6d5ffba3442f36025cc3dd2bfca1 Mon Sep 17 00:00:00 2001
From: Mike Mullen <96440448+mfzmullen@users.noreply.github.com>
Date: Thu, 27 Jul 2023 17:56:02 -0500
Subject: [PATCH 2549/2677] fix finding vector_types and vector_functions
 (#3471)

* add to sourceIsJit

---------

Co-authored-by: Umar Arshad <umar@arrayfire.com>
---
 src/backend/cuda/CMakeLists.txt     |  1 +
 src/backend/cuda/compile_module.cpp | 10 ++++------
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index b0b0841b54..1f6e819b2f 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -142,6 +142,7 @@ set(nvrtc_src
   ${CUDA_INCLUDE_DIRS}/cuda_fp16.hpp
   ${CUDA_TOOLKIT_ROOT_DIR}/include/cuComplex.h
   ${CUDA_TOOLKIT_ROOT_DIR}/include/math_constants.h
+  ${CUDA_TOOLKIT_ROOT_DIR}/include/vector_types.h
   ${CUDA_TOOLKIT_ROOT_DIR}/include/vector_functions.h
 
   ${PROJECT_SOURCE_DIR}/src/api/c/optypes.hpp
diff --git a/src/backend/cuda/compile_module.cpp b/src/backend/cuda/compile_module.cpp
index d1d988e66f..d7ee8182bc 100644
--- a/src/backend/cuda/compile_module.cpp
+++ b/src/backend/cuda/compile_module.cpp
@@ -40,6 +40,7 @@
 #include <nvrtc_kernel_headers/types_hpp.hpp>
 #include <nvrtc_kernel_headers/utility_hpp.hpp>
 #include <nvrtc_kernel_headers/vector_functions_h.hpp>
+#include <nvrtc_kernel_headers/vector_types_h.hpp>
 #include <nvrtc_kernel_headers/version_h.hpp>
 #include <optypes.hpp>
 #include <platform.hpp>
@@ -156,15 +157,12 @@ Module compileModule(const string &moduleKey, span<const string> sources,
     using namespace arrayfire::cuda;
     if (sourceIsJIT) {
         constexpr const char *header_names[] = {
-            "utility",
-            "cuda_fp16.hpp",
-            "cuda_fp16.h",
+            "utility",        "cuda_fp16.hpp",      "cuda_fp16.h",
+            "vector_types.h", "vector_functions.h",
         };
         constexpr size_t numHeaders = extent<decltype(header_names)>::value;
         array<const char *, numHeaders> headers = {
-            "",
-            cuda_fp16_hpp,
-            cuda_fp16_h,
+            "", cuda_fp16_hpp, cuda_fp16_h, vector_types_h, vector_functions_h,
         };
         static_assert(headers.size() == numHeaders,
                       "headers array contains fewer sources than header_names");

From bcf0e54c51d5727cf602b9fa8af29da83fbf13a4 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 27 Jul 2023 18:58:54 -0400
Subject: [PATCH 2550/2677] Add minimum driver versions for cuda 12.2

---
 src/backend/cuda/device_manager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 8000f2f635..c60bf35437 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12020, 9, 0, 0},
     {12010, 9, 0, 0},
     {12000, 9, 0, 0},
     {11080, 9, 0, 0},
@@ -139,6 +140,7 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {12020, 525.60f, 527.41f},
         {12010, 525.60f, 527.41f},
         {12000, 525.60f, 527.41f},
         {11080, 450.80f, 452.39f},

From 0c16f7e586d1fab3c4a1562d07fc48cedfc12e46 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 27 Jul 2023 19:02:00 -0400
Subject: [PATCH 2551/2677] Use ::value functions and AF_IF_CONSTEXPR for cuda
 10.2 support

---
 src/backend/common/half.hpp   | 17 ++++++++---------
 src/backend/common/traits.hpp |  7 ++-----
 2 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index ac03ea6d89..b6585dc905 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -855,25 +855,24 @@ AF_CONSTEXPR __DH__ native_half_t int2half(T value) noexcept {
 template<std::float_round_style R, bool E, typename T>
 AF_CONSTEXPR T half2int(native_half_t value) {
 #ifdef __CUDA_ARCH__
-    AF_IF_CONSTEXPR(std::is_same_v<T, short> || std::is_same_v<T, char> ||
-                    std::is_same_v<T, unsigned char>) {
+    AF_IF_CONSTEXPR(std::is_same<T, short>::value ||
+                    std::is_same<T, char>::value ||
+                    std::is_same<T, unsigned char>::value) {
         return __half2short_rn(value);
     }
-    else AF_IF_CONSTEXPR(std::is_same_v<T, unsigned short>) {
+    else AF_IF_CONSTEXPR(std::is_same<T, unsigned short>::value) {
         return __half2ushort_rn(value);
     }
-    else AF_IF_CONSTEXPR(std::is_same_v<T, long long>) {
+    else AF_IF_CONSTEXPR(std::is_same<T, long long>::value) {
         return __half2ll_rn(value);
     }
-    else AF_IF_CONSTEXPR(std::is_same_v<T, unsigned long long>) {
+    else AF_IF_CONSTEXPR(std::is_same<T, unsigned long long>::value) {
         return __half2ull_rn(value);
     }
-    else AF_IF_CONSTEXPR(std::is_same_v<T, int>) {
+    else AF_IF_CONSTEXPR(std::is_same<T, int>::value) {
         return __half2int_rn(value);
     }
-    else AF_IF_CONSTEXPR(std::is_same_v<T, unsigned>) {
-        return __half2uint_rn(value);
-    }
+    else { return __half2uint_rn(value); }
 #elif defined(AF_ONEAPI)
     return static_cast<T>(value);
 #else
diff --git a/src/backend/common/traits.hpp b/src/backend/common/traits.hpp
index 7798c070c2..3036d91dd0 100644
--- a/src/backend/common/traits.hpp
+++ b/src/backend/common/traits.hpp
@@ -70,11 +70,8 @@ constexpr bool isFloating(af::dtype type) {
 
 template<typename T, typename U, typename... Args>
 constexpr bool is_any_of() {
-    if constexpr (!sizeof...(Args)) {
-        return std::is_same_v<T, U>;
-    } else {
-        return std::is_same_v<T, U> || is_any_of<T, Args...>();
-    }
+    AF_IF_CONSTEXPR(!sizeof...(Args)) { return std::is_same<T, U>::value; }
+    else { return std::is_same<T, U>::value || is_any_of<T, Args...>(); }
 }
 
 }  // namespace

From 5d469b8e5c9a7eed475b76285919db2d1a0c6a70 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 27 Jul 2023 20:56:53 -0400
Subject: [PATCH 2552/2677] Fix tests that fail on devices that do not support
 double

---
 test/binary.cpp | 7 ++++++-
 test/memory.cpp | 2 ++
 test/reduce.cpp | 6 ++++++
 3 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/test/binary.cpp b/test/binary.cpp
index ab557f8c9a..dafc3b8bff 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -373,7 +373,12 @@ class PowPrecisionTest : public ::testing::TestWithParam<T> {
         vector<T> hres(1, 0);                                              \
         B.host(&hres[0]);                                                  \
         std::fesetround(FE_TONEAREST);                                     \
-        T gold = (T)std::rint(std::pow((double)param, 2.0));               \
+        T gold;                                                            \
+        if (!af::isDoubleAvailable(af::getDevice())) {                     \
+            gold = (T)std::rint(std::pow((float)param, 2.0f));             \
+        } else {                                                           \
+            gold = (T)std::rint(std::pow((double)param, 2.0));             \
+        }                                                                  \
         ASSERT_EQ(hres[0], gold);                                          \
     }
 
diff --git a/test/memory.cpp b/test/memory.cpp
index 37a1de87b1..991756ca0b 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -917,6 +917,7 @@ TEST_F(MemoryManagerApi, E2ETest4D) {
 }
 
 TEST_F(MemoryManagerApi, E2ETest4DComplexDouble) {
+    SUPPORTED_TYPE_CHECK(double);
     size_t aSize = 8;
 
     af::array a = af::array(aSize, aSize, aSize, aSize, af::dtype::c64);
@@ -932,6 +933,7 @@ TEST_F(MemoryManagerApi, E2ETest4DComplexDouble) {
 }
 
 TEST_F(MemoryManagerApi, E2ETestMultipleAllocations) {
+    SUPPORTED_TYPE_CHECK(double);
     size_t aSize = 8;
 
     af::array a = af::array(aSize, af::dtype::c64);
diff --git a/test/reduce.cpp b/test/reduce.cpp
index f01dafec45..0726a11791 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -559,6 +559,9 @@ TEST_P(ReduceByKeyP, SumDim0) {
     if (noHalfTests(GetParam()->kType_)) {
         GTEST_SKIP() << "Half not supported on this device";
     }
+    if (noDoubleTests(GetParam()->vType_)) {
+        GTEST_SKIP() << "Double not supported on this device";
+    }
     array keyRes, valsReduced;
     sumByKey(keyRes, valsReduced, keys, vals, 0, 0);
 
@@ -573,6 +576,9 @@ TEST_P(ReduceByKeyP, SumDim2) {
     if (noHalfTests(GetParam()->kType_)) {
         GTEST_SKIP() << "Half not supported on this device";
     }
+    if (noDoubleTests(GetParam()->vType_)) {
+        GTEST_SKIP() << "Double not supported on this device";
+    }
     const int ntile = 2;
     vals            = tile(vals, 1, ntile, 1, 1);
     vals            = reorder(vals, 1, 2, 0, 3);

From d2a66367d859cdb554f2374e29d39c88d5fff978 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 28 Jul 2023 12:49:03 -0400
Subject: [PATCH 2553/2677] Update vcpkg baseline

---
 .github/workflows/win_cpu_build.yml | 2 +-
 vcpkg.json                          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/win_cpu_build.yml b/.github/workflows/win_cpu_build.yml
index 8564bd03b8..d42450f103 100644
--- a/.github/workflows/win_cpu_build.yml
+++ b/.github/workflows/win_cpu_build.yml
@@ -13,7 +13,7 @@ jobs:
         name: CPU (fftw, OpenBLAS, windows-latest)
         runs-on: windows-latest
         env:
-          VCPKG_HASH: f14984af3738e69f197bf0e647a8dca12de92996
+          VCPKG_HASH: 9d47b24eacbd1cd94f139457ef6cd35e5d92cc84
           VCPKG_DEFAULT_TRIPLET: x64-windows
         steps:
             - name: Checkout Repository
diff --git a/vcpkg.json b/vcpkg.json
index 5cf6972ce0..db3318eb47 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -83,5 +83,5 @@
             ]
         }
     },
-    "builtin-baseline": "f14984af3738e69f197bf0e647a8dca12de92996"
+    "builtin-baseline": "9d47b24eacbd1cd94f139457ef6cd35e5d92cc84"
 }

From 171d12d73ec30536f8055ca8b1079e808d23190a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 2 Aug 2023 21:31:08 -0400
Subject: [PATCH 2554/2677] Add CMake files to add support for the SYCL
 language

---
 CMakeModules/CMakeDetermineSYCLCompiler.cmake | 237 ++++++++++++++
 CMakeModules/CMakeSYCLCompiler.cmake.in       |  83 +++++
 CMakeModules/CMakeSYCLCompilerABI.cpp         |  31 ++
 CMakeModules/CMakeSYCLInformation.cmake       | 296 ++++++++++++++++++
 CMakeModules/CMakeTestSYCLCompiler.cmake      |  89 ++++++
 5 files changed, 736 insertions(+)
 create mode 100644 CMakeModules/CMakeDetermineSYCLCompiler.cmake
 create mode 100644 CMakeModules/CMakeSYCLCompiler.cmake.in
 create mode 100644 CMakeModules/CMakeSYCLCompilerABI.cpp
 create mode 100644 CMakeModules/CMakeSYCLInformation.cmake
 create mode 100644 CMakeModules/CMakeTestSYCLCompiler.cmake

diff --git a/CMakeModules/CMakeDetermineSYCLCompiler.cmake b/CMakeModules/CMakeDetermineSYCLCompiler.cmake
new file mode 100644
index 0000000000..c4ddf75e3f
--- /dev/null
+++ b/CMakeModules/CMakeDetermineSYCLCompiler.cmake
@@ -0,0 +1,237 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+
+# determine the compiler to use for C++ programs
+# NOTE, a generator may set CMAKE_CXX_COMPILER before
+# loading this file to force a compiler.
+# use environment variable CXX first if defined by user, next use
+# the cmake variable CMAKE_GENERATOR_CXX which can be defined by a generator
+# as a default compiler
+# If the internal cmake variable _CMAKE_TOOLCHAIN_PREFIX is set, this is used
+# as prefix for the tools (e.g. arm-elf-g++, arm-elf-ar etc.)
+#
+# Sets the following variables:
+#   CMAKE_CXX_COMPILER
+#   CMAKE_COMPILER_IS_GNUCXX
+#   CMAKE_AR
+#   CMAKE_RANLIB
+#
+# If not already set before, it also sets
+#   _CMAKE_TOOLCHAIN_PREFIX
+
+include(${CMAKE_ROOT}/Modules/CMakeDetermineCompiler.cmake)
+
+# Load system-specific compiler preferences for this language.
+include(Platform/${CMAKE_SYSTEM_NAME}-Determine-CXX OPTIONAL)
+include(Platform/${CMAKE_SYSTEM_NAME}-CXX OPTIONAL)
+if(NOT CMAKE_CXX_COMPILER_NAMES)
+  set(CMAKE_CXX_COMPILER_NAMES CC)
+endif()
+
+if(${CMAKE_GENERATOR} MATCHES "Visual Studio")
+elseif("${CMAKE_GENERATOR}" MATCHES "Green Hills MULTI")
+elseif("${CMAKE_GENERATOR}" MATCHES "Xcode")
+  set(CMAKE_CXX_COMPILER_XCODE_TYPE sourcecode.cpp.cpp)
+  _cmake_find_compiler_path(CXX)
+else()
+  if(NOT CMAKE_CXX_COMPILER)
+    set(CMAKE_CXX_COMPILER_INIT NOTFOUND)
+
+    # prefer the environment variable CXX
+    if(NOT $ENV{CXX} STREQUAL "")
+      get_filename_component(CMAKE_CXX_COMPILER_INIT $ENV{CXX} PROGRAM PROGRAM_ARGS CMAKE_CXX_FLAGS_ENV_INIT)
+      if(CMAKE_CXX_FLAGS_ENV_INIT)
+        set(CMAKE_CXX_COMPILER_ARG1 "${CMAKE_CXX_FLAGS_ENV_INIT}" CACHE STRING "Arguments to CXX compiler")
+      endif()
+      if(NOT EXISTS ${CMAKE_CXX_COMPILER_INIT})
+        message(FATAL_ERROR "Could not find compiler set in environment variable CXX:\n$ENV{CXX}.\n${CMAKE_CXX_COMPILER_INIT}")
+      endif()
+    endif()
+
+    # next prefer the generator specified compiler
+    if(CMAKE_GENERATOR_CXX)
+      if(NOT CMAKE_CXX_COMPILER_INIT)
+        set(CMAKE_CXX_COMPILER_INIT ${CMAKE_GENERATOR_CXX})
+      endif()
+    endif()
+
+    # finally list compilers to try
+    if(NOT CMAKE_CXX_COMPILER_INIT)
+      set(CMAKE_CXX_COMPILER_LIST CC ${_CMAKE_TOOLCHAIN_PREFIX}c++ ${_CMAKE_TOOLCHAIN_PREFIX}g++ aCC cl bcc xlC)
+      if(NOT CMAKE_HOST_WIN32)
+        # FIXME(#24314): Add support for the GNU-like icpx compiler driver
+        # on Windows, first introduced by Intel oneAPI 2023.0.
+        list(APPEND CMAKE_CXX_COMPILER_LIST icpx)
+      endif()
+      list(APPEND CMAKE_CXX_COMPILER_LIST icx clang++)
+    endif()
+
+    _cmake_find_compiler(CXX)
+  else()
+    _cmake_find_compiler_path(CXX)
+  endif()
+  mark_as_advanced(CMAKE_CXX_COMPILER)
+
+  # Each entry in this list is a set of extra flags to try
+  # adding to the compile line to see if it helps produce
+  # a valid identification file.
+  set(CMAKE_CXX_COMPILER_ID_TEST_FLAGS_FIRST)
+  set(CMAKE_CXX_COMPILER_ID_TEST_FLAGS
+    # Try compiling to an object file only.
+    "-c"
+    # IAR does not detect language automatically
+    "--c++"
+    "--ec++"
+
+    # ARMClang need target options
+    "--target=arm-arm-none-eabi -mcpu=cortex-m3"
+
+    # MSVC needs at least one include directory for __has_include to function,
+    # but custom toolchains may run MSVC with no INCLUDE env var and no -I flags.
+    # Also avoid linking so this works with no LIB env var.
+    "-c -I__does_not_exist__"
+    )
+endif()
+
+if(CMAKE_CXX_COMPILER_TARGET)
+  set(CMAKE_CXX_COMPILER_ID_TEST_FLAGS_FIRST "-c --target=${CMAKE_CXX_COMPILER_TARGET}")
+endif()
+
+# Build a small source file to identify the compiler.
+if(NOT CMAKE_CXX_COMPILER_ID_RUN)
+  set(CMAKE_CXX_COMPILER_ID_RUN 1)
+
+  # Try to identify the compiler.
+  set(CMAKE_CXX_COMPILER_ID)
+  set(CMAKE_CXX_PLATFORM_ID)
+  file(READ ${CMAKE_ROOT}/Modules/CMakePlatformId.h.in
+    CMAKE_CXX_COMPILER_ID_PLATFORM_CONTENT)
+
+  # The IAR compiler produces weird output.
+  # See https://gitlab.kitware.com/cmake/cmake/-/issues/10176#note_153591
+  list(APPEND CMAKE_CXX_COMPILER_ID_VENDORS IAR)
+  set(CMAKE_CXX_COMPILER_ID_VENDOR_FLAGS_IAR )
+  set(CMAKE_CXX_COMPILER_ID_VENDOR_REGEX_IAR "IAR .+ Compiler")
+
+  # Match the link line from xcodebuild output of the form
+  #  Ld ...
+  #      ...
+  #      /path/to/cc ...CompilerIdCXX/...
+  # to extract the compiler front-end for the language.
+  set(CMAKE_CXX_COMPILER_ID_TOOL_MATCH_REGEX "\nLd[^\n]*(\n[ \t]+[^\n]*)*\n[ \t]+([^ \t\r\n]+)[^\r\n]*-o[^\r\n]*CompilerIdCXX/(\\./)?(CompilerIdCXX.(framework|xctest|build/[^ \t\r\n]+)/)?CompilerIdCXX[ \t\n\\\"]")
+  set(CMAKE_CXX_COMPILER_ID_TOOL_MATCH_INDEX 2)
+
+  include(${CMAKE_ROOT}/Modules/CMakeDetermineCompilerId.cmake)
+  CMAKE_DETERMINE_COMPILER_ID(CXX CXXFLAGS CMakeCXXCompilerId.cpp)
+
+  _cmake_find_compiler_sysroot(CXX)
+
+  # Set old compiler and platform id variables.
+  if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+    set(CMAKE_COMPILER_IS_GNUCXX 1)
+  endif()
+else()
+  if(NOT DEFINED CMAKE_CXX_COMPILER_FRONTEND_VARIANT)
+    # Some toolchain files set our internal CMAKE_CXX_COMPILER_ID_RUN
+    # variable but are not aware of CMAKE_CXX_COMPILER_FRONTEND_VARIANT.
+    # They pre-date our support for the GNU-like variant targeting the
+    # MSVC ABI so we do not consider that here.
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang"
+      OR "x${CMAKE_CXX_COMPILER_ID}" STREQUAL "xIntelLLVM")
+      if("x${CMAKE_CXX_SIMULATE_ID}" STREQUAL "xMSVC")
+        set(CMAKE_CXX_COMPILER_FRONTEND_VARIANT "MSVC")
+      else()
+        set(CMAKE_CXX_COMPILER_FRONTEND_VARIANT "GNU")
+      endif()
+    else()
+      set(CMAKE_CXX_COMPILER_FRONTEND_VARIANT "")
+    endif()
+  endif()
+endif()
+
+if (NOT _CMAKE_TOOLCHAIN_LOCATION)
+  get_filename_component(_CMAKE_TOOLCHAIN_LOCATION "${CMAKE_CXX_COMPILER}" PATH)
+endif ()
+
+# if we have a g++ cross compiler, they have usually some prefix, like
+# e.g. powerpc-linux-g++, arm-elf-g++ or i586-mingw32msvc-g++ , optionally
+# with a 3-component version number at the end (e.g. arm-eabi-gcc-4.5.2).
+# The other tools of the toolchain usually have the same prefix
+# NAME_WE cannot be used since then this test will fail for names like
+# "arm-unknown-nto-qnx6.3.0-gcc.exe", where BASENAME would be
+# "arm-unknown-nto-qnx6" instead of the correct "arm-unknown-nto-qnx6.3.0-"
+
+
+if (NOT _CMAKE_TOOLCHAIN_PREFIX)
+
+  if("${CMAKE_CXX_COMPILER_ID}" MATCHES "GNU|Clang|QCC|LCC")
+    get_filename_component(COMPILER_BASENAME "${CMAKE_CXX_COMPILER}" NAME)
+    if (COMPILER_BASENAME MATCHES "^(.+-)?(clang\\+\\+|[gc]\\+\\+|clang-cl)(-[0-9]+(\\.[0-9]+)*)?(-[^.]+)?(\\.exe)?$")
+      set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_MATCH_1})
+      set(_CMAKE_TOOLCHAIN_SUFFIX ${CMAKE_MATCH_3})
+      set(_CMAKE_COMPILER_SUFFIX ${CMAKE_MATCH_5})
+    elseif("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
+      if(CMAKE_CXX_COMPILER_TARGET)
+        set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_CXX_COMPILER_TARGET}-)
+      endif()
+    elseif(COMPILER_BASENAME MATCHES "QCC(\\.exe)?$")
+      if(CMAKE_CXX_COMPILER_TARGET MATCHES "gcc_nto([a-z0-9]+_[0-9]+|[^_le]+)(le)")
+        set(_CMAKE_TOOLCHAIN_PREFIX nto${CMAKE_MATCH_1}-)
+      endif()
+    endif ()
+
+    # if "llvm-" is part of the prefix, remove it, since llvm doesn't have its own binutils
+    # but uses the regular ar, objcopy, etc. (instead of llvm-objcopy etc.)
+    if ("${_CMAKE_TOOLCHAIN_PREFIX}" MATCHES "(.+-)?llvm-$")
+      set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_MATCH_1})
+    endif ()
+  elseif("${CMAKE_CXX_COMPILER_ID}" MATCHES "TI")
+    # TI compilers are named e.g. cl6x, cl470 or armcl.exe
+    get_filename_component(COMPILER_BASENAME "${CMAKE_CXX_COMPILER}" NAME)
+    if (COMPILER_BASENAME MATCHES "^(.+)?cl([^.]+)?(\\.exe)?$")
+      set(_CMAKE_TOOLCHAIN_PREFIX "${CMAKE_MATCH_1}")
+      set(_CMAKE_TOOLCHAIN_SUFFIX "${CMAKE_MATCH_2}")
+    endif ()
+
+  endif()
+
+endif ()
+
+set(_CMAKE_PROCESSING_LANGUAGE "CXX")
+include(CMakeFindBinUtils)
+include(Compiler/${CMAKE_CXX_COMPILER_ID}-FindBinUtils OPTIONAL)
+unset(_CMAKE_PROCESSING_LANGUAGE)
+
+if(CMAKE_CXX_COMPILER_SYSROOT)
+  string(CONCAT _SET_CMAKE_CXX_COMPILER_SYSROOT
+    "set(CMAKE_CXX_COMPILER_SYSROOT \"${CMAKE_CXX_COMPILER_SYSROOT}\")\n"
+    "set(CMAKE_COMPILER_SYSROOT \"${CMAKE_CXX_COMPILER_SYSROOT}\")")
+else()
+  set(_SET_CMAKE_CXX_COMPILER_SYSROOT "")
+endif()
+
+if(CMAKE_CXX_COMPILER_ARCHITECTURE_ID)
+  set(_SET_CMAKE_CXX_COMPILER_ARCHITECTURE_ID
+    "set(CMAKE_CXX_COMPILER_ARCHITECTURE_ID ${CMAKE_CXX_COMPILER_ARCHITECTURE_ID})")
+else()
+  set(_SET_CMAKE_CXX_COMPILER_ARCHITECTURE_ID "")
+endif()
+
+if(MSVC_CXX_ARCHITECTURE_ID)
+  set(SET_MSVC_CXX_ARCHITECTURE_ID
+    "set(MSVC_CXX_ARCHITECTURE_ID ${MSVC_CXX_ARCHITECTURE_ID})")
+endif()
+
+if(CMAKE_CXX_XCODE_ARCHS)
+  set(SET_CMAKE_XCODE_ARCHS
+    "set(CMAKE_XCODE_ARCHS \"${CMAKE_CXX_XCODE_ARCHS}\")")
+endif()
+
+# configure all variables set in this file
+configure_file(${CMAKE_ROOT}/Modules/CMakeCXXCompiler.cmake.in
+  ${CMAKE_PLATFORM_INFO_DIR}/CMakeCXXCompiler.cmake
+  @ONLY
+  )
+
+set(CMAKE_CXX_COMPILER_ENV_VAR "CXX")
diff --git a/CMakeModules/CMakeSYCLCompiler.cmake.in b/CMakeModules/CMakeSYCLCompiler.cmake.in
new file mode 100644
index 0000000000..50edc9e474
--- /dev/null
+++ b/CMakeModules/CMakeSYCLCompiler.cmake.in
@@ -0,0 +1,83 @@
+set(CMAKE_SYCL_COMPILER "@CMAKE_SYCL_COMPILER@")
+set(CMAKE_SYCL_COMPILER_ARG1 "@CMAKE_SYCL_COMPILER_ARG1@")
+set(CMAKE_SYCL_COMPILER_ID "@CMAKE_SYCL_COMPILER_ID@")
+set(CMAKE_SYCL_COMPILER_VERSION "@CMAKE_SYCL_COMPILER_VERSION@")
+set(CMAKE_SYCL_COMPILER_VERSION_INTERNAL "@CMAKE_SYCL_COMPILER_VERSION_INTERNAL@")
+set(CMAKE_SYCL_COMPILER_WRAPPER "@CMAKE_SYCL_COMPILER_WRAPPER@")
+set(CMAKE_SYCL_STANDARD_COMPUTED_DEFAULT "@CMAKE_SYCL_STANDARD_COMPUTED_DEFAULT@")
+set(CMAKE_SYCL_EXTENSIONS_COMPUTED_DEFAULT "@CMAKE_SYCL_EXTENSIONS_COMPUTED_DEFAULT@")
+set(CMAKE_SYCL_COMPILE_FEATURES "@CMAKE_SYCL_COMPILE_FEATURES@")
+set(CMAKE_SYCL98_COMPILE_FEATURES "@CMAKE_SYCL98_COMPILE_FEATURES@")
+set(CMAKE_SYCL11_COMPILE_FEATURES "@CMAKE_SYCL11_COMPILE_FEATURES@")
+set(CMAKE_SYCL14_COMPILE_FEATURES "@CMAKE_SYCL14_COMPILE_FEATURES@")
+set(CMAKE_SYCL17_COMPILE_FEATURES "@CMAKE_SYCL17_COMPILE_FEATURES@")
+set(CMAKE_SYCL20_COMPILE_FEATURES "@CMAKE_SYCL20_COMPILE_FEATURES@")
+set(CMAKE_SYCL23_COMPILE_FEATURES "@CMAKE_SYCL23_COMPILE_FEATURES@")
+
+set(CMAKE_SYCL_PLATFORM_ID "@CMAKE_SYCL_PLATFORM_ID@")
+set(CMAKE_SYCL_SIMULATE_ID "@CMAKE_SYCL_SIMULATE_ID@")
+set(CMAKE_SYCL_COMPILER_FRONTEND_VARIANT "@CMAKE_SYCL_COMPILER_FRONTEND_VARIANT@")
+set(CMAKE_SYCL_SIMULATE_VERSION "@CMAKE_SYCL_SIMULATE_VERSION@")
+@_SET_CMAKE_SYCL_COMPILER_ARCHITECTURE_ID@
+@_SET_CMAKE_SYCL_COMPILER_SYSROOT@
+@SET_MSVC_SYCL_ARCHITECTURE_ID@
+@SET_CMAKE_XCODE_ARCHS@
+set(CMAKE_AR "@CMAKE_AR@")
+set(CMAKE_SYCL_COMPILER_AR "@CMAKE_SYCL_COMPILER_AR@")
+set(CMAKE_RANLIB "@CMAKE_RANLIB@")
+set(CMAKE_SYCL_COMPILER_RANLIB "@CMAKE_SYCL_COMPILER_RANLIB@")
+set(CMAKE_LINKER "@CMAKE_LINKER@")
+set(CMAKE_MT "@CMAKE_MT@")
+set(CMAKE_COMPILER_IS_GNUSYCL @CMAKE_COMPILER_IS_GNUSYCL@)
+set(CMAKE_SYCL_COMPILER_LOADED 1)
+set(CMAKE_SYCL_COMPILER_WORKS @CMAKE_SYCL_COMPILER_WORKS@)
+set(CMAKE_SYCL_ABI_COMPILED @CMAKE_SYCL_ABI_COMPILED@)
+
+set(CMAKE_SYCL_COMPILER_ENV_VAR "SYCL")
+
+set(CMAKE_SYCL_COMPILER_ID_RUN 1)
+set(CMAKE_SYCL_SOURCE_FILE_EXTENSIONS C;M;c++;cc;cpp;cxx;m;mm;mpp;CPP;ixx;cppm)
+set(CMAKE_SYCL_IGNORE_EXTENSIONS inl;h;hpp;HPP;H;o;O;obj;OBJ;def;DEF;rc;RC)
+
+foreach (lang C OBJC OBJSYCL)
+  if (CMAKE_${lang}_COMPILER_ID_RUN)
+    foreach(extension IN LISTS CMAKE_${lang}_SOURCE_FILE_EXTENSIONS)
+      list(REMOVE_ITEM CMAKE_SYCL_SOURCE_FILE_EXTENSIONS ${extension})
+    endforeach()
+  endif()
+endforeach()
+
+set(CMAKE_SYCL_LINKER_PREFERENCE 30)
+set(CMAKE_SYCL_LINKER_PREFERENCE_PROPAGATES 1)
+
+# Save compiler ABI information.
+set(CMAKE_SYCL_SIZEOF_DATA_PTR "@CMAKE_SYCL_SIZEOF_DATA_PTR@")
+set(CMAKE_SYCL_COMPILER_ABI "@CMAKE_SYCL_COMPILER_ABI@")
+set(CMAKE_SYCL_BYTE_ORDER "@CMAKE_SYCL_BYTE_ORDER@")
+set(CMAKE_SYCL_LIBRARY_ARCHITECTURE "@CMAKE_SYCL_LIBRARY_ARCHITECTURE@")
+
+if(CMAKE_SYCL_SIZEOF_DATA_PTR)
+  set(CMAKE_SIZEOF_VOID_P "${CMAKE_SYCL_SIZEOF_DATA_PTR}")
+endif()
+
+if(CMAKE_SYCL_COMPILER_ABI)
+  set(CMAKE_INTERNAL_PLATFORM_ABI "${CMAKE_SYCL_COMPILER_ABI}")
+endif()
+
+if(CMAKE_SYCL_LIBRARY_ARCHITECTURE)
+  set(CMAKE_LIBRARY_ARCHITECTURE "@CMAKE_SYCL_LIBRARY_ARCHITECTURE@")
+endif()
+
+set(CMAKE_SYCL_CL_SHOWINCLUDES_PREFIX "@CMAKE_SYCL_CL_SHOWINCLUDES_PREFIX@")
+if(CMAKE_SYCL_CL_SHOWINCLUDES_PREFIX)
+  set(CMAKE_CL_SHOWINCLUDES_PREFIX "${CMAKE_SYCL_CL_SHOWINCLUDES_PREFIX}")
+endif()
+
+@CMAKE_SYCL_COMPILER_CUSTOM_CODE@
+@CMAKE_SYCL_SYSROOT_FLAG_CODE@
+@CMAKE_SYCL_OSX_DEPLOYMENT_TARGET_FLAG_CODE@
+
+set(CMAKE_SYCL_IMPLICIT_INCLUDE_DIRECTORIES "@CMAKE_SYCL_IMPLICIT_INCLUDE_DIRECTORIES@")
+set(CMAKE_SYCL_IMPLICIT_LINK_LIBRARIES "@CMAKE_SYCL_IMPLICIT_LINK_LIBRARIES@")
+set(CMAKE_SYCL_IMPLICIT_LINK_DIRECTORIES "@CMAKE_SYCL_IMPLICIT_LINK_DIRECTORIES@")
+set(CMAKE_SYCL_IMPLICIT_LINK_FRAMEWORK_DIRECTORIES "@CMAKE_SYCL_IMPLICIT_LINK_FRAMEWORK_DIRECTORIES@")
diff --git a/CMakeModules/CMakeSYCLCompilerABI.cpp b/CMakeModules/CMakeSYCLCompilerABI.cpp
new file mode 100644
index 0000000000..fe7c926993
--- /dev/null
+++ b/CMakeModules/CMakeSYCLCompilerABI.cpp
@@ -0,0 +1,31 @@
+#ifndef __cplusplus
+#  error "A C compiler has been selected for C++."
+#endif
+
+#include "CMakeCompilerABI.h"
+#include <sycl/sycl.hpp>
+
+int main(int argc, char* argv[])
+{
+  int require = 0;
+  require += info_sizeof_dptr[argc];
+  require += info_byte_order_big_endian[argc];
+  require += info_byte_order_little_endian[argc];
+#if defined(ABI_ID)
+  require += info_abi[argc];
+#endif
+  static_cast<void>(argv);
+
+  int count = 0;
+  auto platforms = sycl::platform::get_platforms();
+  for(sycl::platform &platform : platforms) {
+    count += platform.get_devices().size();
+  }
+
+  if(count == 0) {
+    std::fprintf(stderr, "No SYCL devices found.\n");
+    return -1;
+  }
+
+  return require;
+}
diff --git a/CMakeModules/CMakeSYCLInformation.cmake b/CMakeModules/CMakeSYCLInformation.cmake
new file mode 100644
index 0000000000..53abf378d5
--- /dev/null
+++ b/CMakeModules/CMakeSYCLInformation.cmake
@@ -0,0 +1,296 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+
+# This file sets the basic flags for the C++ language in CMake.
+# It also loads the available platform file for the system-compiler
+# if it exists.
+# It also loads a system - compiler - processor (or target hardware)
+# specific file, which is mainly useful for crosscompiling and embedded systems.
+
+include(CMakeLanguageInformation)
+
+# some compilers use different extensions (e.g. sdcc uses .rel)
+# so set the extension here first so it can be overridden by the compiler specific file
+if(UNIX)
+  set(CMAKE_CXX_OUTPUT_EXTENSION .o)
+else()
+  set(CMAKE_CXX_OUTPUT_EXTENSION .obj)
+endif()
+
+set(_INCLUDED_FILE 0)
+
+# Load compiler-specific information.
+if(CMAKE_CXX_COMPILER_ID)
+  include(Compiler/${CMAKE_CXX_COMPILER_ID}-CXX OPTIONAL)
+endif()
+
+set(CMAKE_BASE_NAME)
+get_filename_component(CMAKE_BASE_NAME "${CMAKE_CXX_COMPILER}" NAME_WE)
+# since the gnu compiler has several names force g++
+if(CMAKE_COMPILER_IS_GNUCXX)
+  set(CMAKE_BASE_NAME g++)
+endif()
+
+
+# load a hardware specific file, mostly useful for embedded compilers
+if(CMAKE_SYSTEM_PROCESSOR)
+  if(CMAKE_CXX_COMPILER_ID)
+    include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_CXX_COMPILER_ID}-CXX-${CMAKE_SYSTEM_PROCESSOR} OPTIONAL RESULT_VARIABLE _INCLUDED_FILE)
+  endif()
+  if (NOT _INCLUDED_FILE)
+    include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_BASE_NAME}-${CMAKE_SYSTEM_PROCESSOR} OPTIONAL)
+  endif ()
+endif()
+
+# load the system- and compiler specific files
+if(CMAKE_CXX_COMPILER_ID)
+  include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_CXX_COMPILER_ID}-CXX OPTIONAL RESULT_VARIABLE _INCLUDED_FILE)
+endif()
+if (NOT _INCLUDED_FILE)
+  include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_BASE_NAME} OPTIONAL
+          RESULT_VARIABLE _INCLUDED_FILE)
+endif ()
+
+# load any compiler-wrapper specific information
+if (CMAKE_CXX_COMPILER_WRAPPER)
+  __cmake_include_compiler_wrapper(CXX)
+endif ()
+
+# We specify the compiler information in the system file for some
+# platforms, but this language may not have been enabled when the file
+# was first included.  Include it again to get the language info.
+# Remove this when all compiler info is removed from system files.
+if (NOT _INCLUDED_FILE)
+  include(Platform/${CMAKE_SYSTEM_NAME} OPTIONAL)
+endif ()
+
+if(CMAKE_CXX_SIZEOF_DATA_PTR)
+  foreach(f ${CMAKE_CXX_ABI_FILES})
+    include(${f})
+  endforeach()
+  unset(CMAKE_CXX_ABI_FILES)
+endif()
+
+# This should be included before the _INIT variables are
+# used to initialize the cache.  Since the rule variables
+# have if blocks on them, users can still define them here.
+# But, it should still be after the platform file so changes can
+# be made to those values.
+
+if(CMAKE_USER_MAKE_RULES_OVERRIDE)
+  # Save the full path of the file so try_compile can use it.
+  include(${CMAKE_USER_MAKE_RULES_OVERRIDE} RESULT_VARIABLE _override)
+  set(CMAKE_USER_MAKE_RULES_OVERRIDE "${_override}")
+endif()
+
+if(CMAKE_USER_MAKE_RULES_OVERRIDE_CXX)
+  # Save the full path of the file so try_compile can use it.
+  include(${CMAKE_USER_MAKE_RULES_OVERRIDE_CXX} RESULT_VARIABLE _override)
+  set(CMAKE_USER_MAKE_RULES_OVERRIDE_CXX "${_override}")
+endif()
+
+
+# Create a set of shared library variable specific to C++
+# For 90% of the systems, these are the same flags as the C versions
+# so if these are not set just copy the flags from the c version
+if(NOT CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS)
+  set(CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS})
+endif()
+
+if(NOT CMAKE_CXX_COMPILE_OPTIONS_PIC)
+  set(CMAKE_CXX_COMPILE_OPTIONS_PIC ${CMAKE_C_COMPILE_OPTIONS_PIC})
+endif()
+
+if(NOT CMAKE_CXX_COMPILE_OPTIONS_PIE)
+  set(CMAKE_CXX_COMPILE_OPTIONS_PIE ${CMAKE_C_COMPILE_OPTIONS_PIE})
+endif()
+if(NOT CMAKE_CXX_LINK_OPTIONS_PIE)
+  set(CMAKE_CXX_LINK_OPTIONS_PIE ${CMAKE_C_LINK_OPTIONS_PIE})
+endif()
+if(NOT CMAKE_CXX_LINK_OPTIONS_NO_PIE)
+  set(CMAKE_CXX_LINK_OPTIONS_NO_PIE ${CMAKE_C_LINK_OPTIONS_NO_PIE})
+endif()
+
+if(NOT CMAKE_CXX_COMPILE_OPTIONS_DLL)
+  set(CMAKE_CXX_COMPILE_OPTIONS_DLL ${CMAKE_C_COMPILE_OPTIONS_DLL})
+endif()
+
+if(NOT CMAKE_SHARED_LIBRARY_CXX_FLAGS)
+  set(CMAKE_SHARED_LIBRARY_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_C_FLAGS})
+endif()
+
+if(NOT DEFINED CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS)
+  set(CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_LINK_C_FLAGS})
+endif()
+
+if(NOT CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG)
+  set(CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG})
+endif()
+
+if(NOT CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP)
+  set(CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP ${CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG_SEP})
+endif()
+
+if(NOT CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG)
+  set(CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RPATH_LINK_C_FLAG})
+endif()
+
+if(NOT DEFINED CMAKE_EXE_EXPORTS_CXX_FLAG)
+  set(CMAKE_EXE_EXPORTS_CXX_FLAG ${CMAKE_EXE_EXPORTS_C_FLAG})
+endif()
+
+if(NOT DEFINED CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG)
+  set(CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG ${CMAKE_SHARED_LIBRARY_SONAME_C_FLAG})
+endif()
+
+if(NOT CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG)
+  set(CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG})
+endif()
+
+if(NOT CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG_SEP)
+  set(CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG_SEP ${CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP})
+endif()
+
+if(NOT CMAKE_EXECUTABLE_RPATH_LINK_CXX_FLAG)
+  set(CMAKE_EXECUTABLE_RPATH_LINK_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG})
+endif()
+
+if(NOT DEFINED CMAKE_SHARED_LIBRARY_LINK_CXX_WITH_RUNTIME_PATH)
+  set(CMAKE_SHARED_LIBRARY_LINK_CXX_WITH_RUNTIME_PATH ${CMAKE_SHARED_LIBRARY_LINK_C_WITH_RUNTIME_PATH})
+endif()
+
+if(NOT CMAKE_INCLUDE_FLAG_CXX)
+  set(CMAKE_INCLUDE_FLAG_CXX ${CMAKE_INCLUDE_FLAG_C})
+endif()
+
+# for most systems a module is the same as a shared library
+# so unless the variable CMAKE_MODULE_EXISTS is set just
+# copy the values from the LIBRARY variables
+if(NOT CMAKE_MODULE_EXISTS)
+  set(CMAKE_SHARED_MODULE_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_CXX_FLAGS})
+  set(CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS})
+endif()
+
+# repeat for modules
+if(NOT CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS)
+  set(CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS ${CMAKE_SHARED_MODULE_CREATE_C_FLAGS})
+endif()
+
+if(NOT CMAKE_SHARED_MODULE_CXX_FLAGS)
+  set(CMAKE_SHARED_MODULE_CXX_FLAGS ${CMAKE_SHARED_MODULE_C_FLAGS})
+endif()
+
+# Initialize CXX link type selection flags from C versions.
+foreach(type SHARED_LIBRARY SHARED_MODULE EXE)
+  if(NOT CMAKE_${type}_LINK_STATIC_CXX_FLAGS)
+    set(CMAKE_${type}_LINK_STATIC_CXX_FLAGS
+      ${CMAKE_${type}_LINK_STATIC_C_FLAGS})
+  endif()
+  if(NOT CMAKE_${type}_LINK_DYNAMIC_CXX_FLAGS)
+    set(CMAKE_${type}_LINK_DYNAMIC_CXX_FLAGS
+      ${CMAKE_${type}_LINK_DYNAMIC_C_FLAGS})
+  endif()
+endforeach()
+
+if(CMAKE_EXECUTABLE_FORMAT STREQUAL "ELF")
+  if(NOT DEFINED CMAKE_CXX_LINK_WHAT_YOU_USE_FLAG)
+    set(CMAKE_CXX_LINK_WHAT_YOU_USE_FLAG "LINKER:--no-as-needed")
+  endif()
+  if(NOT DEFINED CMAKE_LINK_WHAT_YOU_USE_CHECK)
+    set(CMAKE_LINK_WHAT_YOU_USE_CHECK ldd -u -r)
+  endif()
+endif()
+
+# add the flags to the cache based
+# on the initial values computed in the platform/*.cmake files
+# use _INIT variables so that this only happens the first time
+# and you can set these flags in the cmake cache
+set(CMAKE_CXX_FLAGS_INIT "$ENV{CXXFLAGS} ${CMAKE_CXX_FLAGS_INIT}")
+
+cmake_initialize_per_config_variable(CMAKE_CXX_FLAGS "Flags used by the CXX compiler")
+
+if(CMAKE_CXX_STANDARD_LIBRARIES_INIT)
+  set(CMAKE_CXX_STANDARD_LIBRARIES "${CMAKE_CXX_STANDARD_LIBRARIES_INIT}"
+    CACHE STRING "Libraries linked by default with all C++ applications.")
+  mark_as_advanced(CMAKE_CXX_STANDARD_LIBRARIES)
+endif()
+
+if(NOT CMAKE_CXX_COMPILER_LAUNCHER AND DEFINED ENV{CMAKE_CXX_COMPILER_LAUNCHER})
+  set(CMAKE_CXX_COMPILER_LAUNCHER "$ENV{CMAKE_CXX_COMPILER_LAUNCHER}"
+    CACHE STRING "Compiler launcher for CXX.")
+endif()
+
+if(NOT CMAKE_CXX_LINKER_LAUNCHER AND DEFINED ENV{CMAKE_CXX_LINKER_LAUNCHER})
+  set(CMAKE_CXX_LINKER_LAUNCHER "$ENV{CMAKE_CXX_LINKER_LAUNCHER}"
+    CACHE STRING "Linker launcher for CXX.")
+endif()
+
+include(CMakeCommonLanguageInclude)
+
+# now define the following rules:
+# CMAKE_CXX_CREATE_SHARED_LIBRARY
+# CMAKE_CXX_CREATE_SHARED_MODULE
+# CMAKE_CXX_COMPILE_OBJECT
+# CMAKE_CXX_LINK_EXECUTABLE
+
+# variables supplied by the generator at use time
+# <TARGET>
+# <TARGET_BASE> the target without the suffix
+# <OBJECTS>
+# <OBJECT>
+# <LINK_LIBRARIES>
+# <FLAGS>
+# <LINK_FLAGS>
+
+# CXX compiler information
+# <CMAKE_CXX_COMPILER>
+# <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS>
+# <CMAKE_CXX_SHARED_MODULE_CREATE_FLAGS>
+# <CMAKE_CXX_LINK_FLAGS>
+
+# Static library tools
+# <CMAKE_AR>
+# <CMAKE_RANLIB>
+
+
+# create a shared C++ library
+if(NOT CMAKE_CXX_CREATE_SHARED_LIBRARY)
+  set(CMAKE_CXX_CREATE_SHARED_LIBRARY
+      "<CMAKE_CXX_COMPILER> <CMAKE_SHARED_LIBRARY_CXX_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS> <SONAME_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>")
+endif()
+
+# create a c++ shared module copy the shared library rule by default
+if(NOT CMAKE_CXX_CREATE_SHARED_MODULE)
+  set(CMAKE_CXX_CREATE_SHARED_MODULE ${CMAKE_CXX_CREATE_SHARED_LIBRARY})
+endif()
+
+
+# Create a static archive incrementally for large object file counts.
+# If CMAKE_CXX_CREATE_STATIC_LIBRARY is set it will override these.
+if(NOT DEFINED CMAKE_CXX_ARCHIVE_CREATE)
+  set(CMAKE_CXX_ARCHIVE_CREATE "<CMAKE_AR> qc <TARGET> <LINK_FLAGS> <OBJECTS>")
+endif()
+if(NOT DEFINED CMAKE_CXX_ARCHIVE_APPEND)
+  set(CMAKE_CXX_ARCHIVE_APPEND "<CMAKE_AR> q <TARGET> <LINK_FLAGS> <OBJECTS>")
+endif()
+if(NOT DEFINED CMAKE_CXX_ARCHIVE_FINISH)
+  set(CMAKE_CXX_ARCHIVE_FINISH "<CMAKE_RANLIB> <TARGET>")
+endif()
+
+# compile a C++ file into an object file
+if(NOT CMAKE_CXX_COMPILE_OBJECT)
+  set(CMAKE_CXX_COMPILE_OBJECT
+    "<CMAKE_CXX_COMPILER> <DEFINES> <INCLUDES> <FLAGS> -o <OBJECT> -c <SOURCE>")
+endif()
+
+if(NOT CMAKE_CXX_LINK_EXECUTABLE)
+  set(CMAKE_CXX_LINK_EXECUTABLE
+    "<CMAKE_CXX_COMPILER> <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
+endif()
+
+mark_as_advanced(
+CMAKE_VERBOSE_MAKEFILE
+)
+
+set(CMAKE_CXX_INFORMATION_LOADED 1)
diff --git a/CMakeModules/CMakeTestSYCLCompiler.cmake b/CMakeModules/CMakeTestSYCLCompiler.cmake
new file mode 100644
index 0000000000..e640ff9b30
--- /dev/null
+++ b/CMakeModules/CMakeTestSYCLCompiler.cmake
@@ -0,0 +1,89 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+
+if(CMAKE_CXX_COMPILER_FORCED)
+  # The compiler configuration was forced by the user.
+  # Assume the user has configured all compiler information.
+  set(CMAKE_CXX_COMPILER_WORKS TRUE)
+  return()
+endif()
+
+include(CMakeTestCompilerCommon)
+
+# work around enforced code signing and / or missing executable target type
+set(__CMAKE_SAVED_TRY_COMPILE_TARGET_TYPE ${CMAKE_TRY_COMPILE_TARGET_TYPE})
+if(_CMAKE_FEATURE_DETECTION_TARGET_TYPE)
+  set(CMAKE_TRY_COMPILE_TARGET_TYPE ${_CMAKE_FEATURE_DETECTION_TARGET_TYPE})
+endif()
+
+# Remove any cached result from an older CMake version.
+# We now store this in CMakeCXXCompiler.cmake.
+unset(CMAKE_CXX_COMPILER_WORKS CACHE)
+
+# Try to identify the ABI and configure it into CMakeCXXCompiler.cmake
+include(${CMAKE_ROOT}/Modules/CMakeDetermineCompilerABI.cmake)
+CMAKE_DETERMINE_COMPILER_ABI(CXX ${CMAKE_ROOT}/Modules/CMakeCXXCompilerABI.cpp)
+if(CMAKE_CXX_ABI_COMPILED)
+  # The compiler worked so skip dedicated test below.
+  set(CMAKE_CXX_COMPILER_WORKS TRUE)
+  message(STATUS "Check for working CXX compiler: ${CMAKE_CXX_COMPILER} - skipped")
+endif()
+
+# This file is used by EnableLanguage in cmGlobalGenerator to
+# determine that the selected C++ compiler can actually compile
+# and link the most basic of programs.   If not, a fatal error
+# is set and cmake stops processing commands and will not generate
+# any makefiles or projects.
+if(NOT CMAKE_CXX_COMPILER_WORKS)
+  PrintTestCompilerStatus("CXX")
+  __TestCompiler_setTryCompileTargetType()
+  string(CONCAT __TestCompiler_testCXXCompilerSource
+    "#ifndef __cplusplus\n"
+    "# error \"The CMAKE_CXX_COMPILER is set to a C compiler\"\n"
+    "#endif\n"
+    "int main(){return 0;}\n")
+  # Clear result from normal variable.
+  unset(CMAKE_CXX_COMPILER_WORKS)
+  # Puts test result in cache variable.
+  try_compile(CMAKE_CXX_COMPILER_WORKS
+    SOURCE_FROM_VAR testCXXCompiler.cxx __TestCompiler_testCXXCompilerSource
+    OUTPUT_VARIABLE __CMAKE_CXX_COMPILER_OUTPUT)
+  unset(__TestCompiler_testCXXCompilerSource)
+  # Move result from cache to normal variable.
+  set(CMAKE_CXX_COMPILER_WORKS ${CMAKE_CXX_COMPILER_WORKS})
+  unset(CMAKE_CXX_COMPILER_WORKS CACHE)
+  __TestCompiler_restoreTryCompileTargetType()
+  if(NOT CMAKE_CXX_COMPILER_WORKS)
+    PrintTestCompilerResult(CHECK_FAIL "broken")
+    string(REPLACE "\n" "\n  " _output "${__CMAKE_CXX_COMPILER_OUTPUT}")
+    message(FATAL_ERROR "The C++ compiler\n  \"${CMAKE_CXX_COMPILER}\"\n"
+      "is not able to compile a simple test program.\nIt fails "
+      "with the following output:\n  ${_output}\n\n"
+      "CMake will not be able to correctly generate this project.")
+  endif()
+  PrintTestCompilerResult(CHECK_PASS "works")
+endif()
+
+# Try to identify the compiler features
+include(${CMAKE_ROOT}/Modules/CMakeDetermineCompileFeatures.cmake)
+CMAKE_DETERMINE_COMPILE_FEATURES(CXX)
+
+# Re-configure to save learned information.
+configure_file(
+  ${CMAKE_ROOT}/Modules/CMakeCXXCompiler.cmake.in
+  ${CMAKE_PLATFORM_INFO_DIR}/CMakeCXXCompiler.cmake
+  @ONLY
+  )
+include(${CMAKE_PLATFORM_INFO_DIR}/CMakeCXXCompiler.cmake)
+
+if(CMAKE_CXX_SIZEOF_DATA_PTR)
+  foreach(f ${CMAKE_CXX_ABI_FILES})
+    include(${f})
+  endforeach()
+  unset(CMAKE_CXX_ABI_FILES)
+endif()
+
+set(CMAKE_TRY_COMPILE_TARGET_TYPE ${__CMAKE_SAVED_TRY_COMPILE_TARGET_TYPE})
+unset(__CMAKE_SAVED_TRY_COMPILE_TARGET_TYPE)
+unset(__CMAKE_CXX_COMPILER_OUTPUT)

From b7a6074748d570c2a52f1cfd18e1ef90208063ab Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Wed, 2 Aug 2023 21:32:18 -0400
Subject: [PATCH 2555/2677] Update CMake language files from CXX to SYCL

---
 CMakeLists.txt                                |  15 +-
 CMakeModules/CMakeCompilerABI.h               |  45 +++
 CMakeModules/CMakeDetermineSYCLCompiler.cmake | 178 +++++-----
 CMakeModules/CMakeSYCLCompiler.cmake.in       |   2 +-
 CMakeModules/CMakeSYCLCompilerABI.cpp         |  12 -
 CMakeModules/CMakeSYCLCompilerId.cpp.in       | 105 ++++++
 CMakeModules/CMakeSYCLInformation.cmake       | 307 +++++++++++-------
 CMakeModules/CMakeTestSYCLCompiler.cmake      |  69 ++--
 CMakeModules/InternalUtils.cmake              |  32 ++
 src/backend/common/Logger.hpp                 |   1 +
 src/backend/oneapi/CMakeLists.txt             |  45 ++-
 src/backend/oneapi/device_manager.cpp         |   8 +-
 .../oneapi/kernel/sort_by_key/CMakeLists.txt  |  11 +-
 test/testHelpers.hpp                          |  15 +
 14 files changed, 567 insertions(+), 278 deletions(-)
 create mode 100644 CMakeModules/CMakeCompilerABI.h
 create mode 100644 CMakeModules/CMakeSYCLCompilerId.cpp.in

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a4c3eef645..e4cc17916f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,9 +10,8 @@ include(CheckLanguage)
 
 include(CMakeModules/AF_vcpkg_options.cmake)
 
-project(ArrayFire VERSION 3.9.0 LANGUAGES C CXX)
-
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
+project(ArrayFire VERSION 3.9.0 LANGUAGES C CXX)
 
 include(AFconfigure_deps_vars)
 include(AFBuildConfigurations)
@@ -44,10 +43,11 @@ option(AF_WITH_EXTERNAL_PACKAGES_ONLY "Build ArrayFire with External packages on
 if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
   set(AF_REQUIRED REQUIRED)
 endif()
-
-get_filename_component(CXX_COMPILER_NAME ${CMAKE_CXX_COMPILER} NAME)
-if(CXX_COMPILER_NAME STREQUAL "dpcpp" OR CXX_COMPILER_NAME STREQUAL "dpcpp.exe"
-   OR CXX_COMPILER_NAME STREQUAL "icpx" OR CXX_COMPILER_NAME STREQUAL "icx.exe")
+if(CMAKE_SYCL_COMPILER)
+  get_filename_component(SYCL_COMPILER_NAME ${CMAKE_SYCL_COMPILER} NAME)
+endif()
+if(SYCL_COMPILER_NAME STREQUAL "dpcpp" OR SYCL_COMPILER_NAME STREQUAL "dpcpp.exe"
+   OR SYCL_COMPILER_NAME STREQUAL "icpx" OR SYCL_COMPILER_NAME STREQUAL "icx.exe")
   set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
   set(MKL_INTERFACE "ilp64")
   set(MKL_INTERFACE_INTEGER_SIZE 8)
@@ -134,6 +134,9 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
   if(DEFINED ENV{MKLROOT} AND NOT DEFINED MKL_ROOT)
     set(MKL_ROOT "$ENV{MKLROOT}")
   endif()
+  set(DPCPP_COMPILER ON)
+  set(MKL_THREADING "tbb_thread")
+  set(MKL_INTERFACE "ilp64")
   find_package(MKL 2023.1)
 endif()
 
diff --git a/CMakeModules/CMakeCompilerABI.h b/CMakeModules/CMakeCompilerABI.h
new file mode 100644
index 0000000000..c5ce4dd9ab
--- /dev/null
+++ b/CMakeModules/CMakeCompilerABI.h
@@ -0,0 +1,45 @@
+
+/* Size of a pointer-to-data in bytes.  */
+#define SIZEOF_DPTR (sizeof(void*))
+const char info_sizeof_dptr[] = {
+  /* clang-format off */
+  'I', 'N', 'F', 'O', ':', 's', 'i', 'z', 'e', 'o', 'f', '_', 'd', 'p', 't',
+  'r', '[', ('0' + ((SIZEOF_DPTR / 10) % 10)), ('0' + (SIZEOF_DPTR % 10)), ']',
+  '\0'
+  /* clang-format on */
+};
+
+/* Byte order.  Only one of these will have bytes in the right order.  */
+static unsigned short const info_byte_order_big_endian[] = {
+  /* INFO:byte_order string for BIG_ENDIAN */
+  0x494E, 0x464F, 0x3A62, 0x7974, 0x655F, 0x6F72, 0x6465, 0x725B,
+  0x4249, 0x475F, 0x454E, 0x4449, 0x414E, 0x5D00, 0x0000
+};
+static unsigned short const info_byte_order_little_endian[] = {
+  /* INFO:byte_order string for LITTLE_ENDIAN */
+  0x4E49, 0x4F46, 0x623A, 0x7479, 0x5F65, 0x726F, 0x6564, 0x5B72,
+  0x494C, 0x5454, 0x454C, 0x455F, 0x444E, 0x4149, 0x5D4E, 0x0000
+};
+
+/* Application Binary Interface.  */
+
+/* Check for (some) ARM ABIs.
+ * See e.g. http://wiki.debian.org/ArmEabiPort for some information on this. */
+#if defined(__GNU__) && defined(__ELF__) && defined(__ARM_EABI__)
+#  define ABI_ID "ELF ARMEABI"
+#elif defined(__GNU__) && defined(__ELF__) && defined(__ARMEB__)
+#  define ABI_ID "ELF ARM"
+#elif defined(__GNU__) && defined(__ELF__) && defined(__ARMEL__)
+#  define ABI_ID "ELF ARM"
+
+#elif defined(__linux__) && defined(__ELF__) && defined(__amd64__) &&         \
+  defined(__ILP32__)
+#  define ABI_ID "ELF X32"
+
+#elif defined(__ELF__)
+#  define ABI_ID "ELF"
+#endif
+
+#if defined(ABI_ID)
+static char const info_abi[] = "INFO:abi[" ABI_ID "]";
+#endif
diff --git a/CMakeModules/CMakeDetermineSYCLCompiler.cmake b/CMakeModules/CMakeDetermineSYCLCompiler.cmake
index c4ddf75e3f..669e8a79e3 100644
--- a/CMakeModules/CMakeDetermineSYCLCompiler.cmake
+++ b/CMakeModules/CMakeDetermineSYCLCompiler.cmake
@@ -3,81 +3,82 @@
 
 
 # determine the compiler to use for C++ programs
-# NOTE, a generator may set CMAKE_CXX_COMPILER before
+# NOTE, a generator may set CMAKE_SYCL_COMPILER before
 # loading this file to force a compiler.
-# use environment variable CXX first if defined by user, next use
-# the cmake variable CMAKE_GENERATOR_CXX which can be defined by a generator
+# use environment variable SYCL first if defined by user, next use
+# the cmake variable CMAKE_GENERATOR_SYCL which can be defined by a generator
 # as a default compiler
 # If the internal cmake variable _CMAKE_TOOLCHAIN_PREFIX is set, this is used
 # as prefix for the tools (e.g. arm-elf-g++, arm-elf-ar etc.)
 #
 # Sets the following variables:
-#   CMAKE_CXX_COMPILER
-#   CMAKE_COMPILER_IS_GNUCXX
+#   CMAKE_SYCL_COMPILER
+#   CMAKE_COMPILER_IS_GNUSYCL
 #   CMAKE_AR
 #   CMAKE_RANLIB
 #
 # If not already set before, it also sets
 #   _CMAKE_TOOLCHAIN_PREFIX
 
-include(${CMAKE_ROOT}/Modules/CMakeDetermineCompiler.cmake)
+#list(APPEND CMAKE_MODULE_PATH ${CMAKE_ROOT})
+include(CMakeDetermineCompiler)
 
 # Load system-specific compiler preferences for this language.
-include(Platform/${CMAKE_SYSTEM_NAME}-Determine-CXX OPTIONAL)
-include(Platform/${CMAKE_SYSTEM_NAME}-CXX OPTIONAL)
-if(NOT CMAKE_CXX_COMPILER_NAMES)
-  set(CMAKE_CXX_COMPILER_NAMES CC)
+#include(Platform/${CMAKE_SYSTEM_NAME}-Determine-SYCL OPTIONAL)
+#include(Platform/${CMAKE_SYSTEM_NAME}-SYCL OPTIONAL)
+if(NOT CMAKE_SYCL_COMPILER_NAMES)
+  set(CMAKE_SYCL_COMPILER_NAMES icpx)
 endif()
 
 if(${CMAKE_GENERATOR} MATCHES "Visual Studio")
 elseif("${CMAKE_GENERATOR}" MATCHES "Green Hills MULTI")
 elseif("${CMAKE_GENERATOR}" MATCHES "Xcode")
-  set(CMAKE_CXX_COMPILER_XCODE_TYPE sourcecode.cpp.cpp)
-  _cmake_find_compiler_path(CXX)
+  set(CMAKE_SYCL_COMPILER_XCODE_TYPE sourcecode.cpp.cpp)
+  _cmake_find_compiler_path(SYCL)
 else()
-  if(NOT CMAKE_CXX_COMPILER)
-    set(CMAKE_CXX_COMPILER_INIT NOTFOUND)
-
-    # prefer the environment variable CXX
-    if(NOT $ENV{CXX} STREQUAL "")
-      get_filename_component(CMAKE_CXX_COMPILER_INIT $ENV{CXX} PROGRAM PROGRAM_ARGS CMAKE_CXX_FLAGS_ENV_INIT)
-      if(CMAKE_CXX_FLAGS_ENV_INIT)
-        set(CMAKE_CXX_COMPILER_ARG1 "${CMAKE_CXX_FLAGS_ENV_INIT}" CACHE STRING "Arguments to CXX compiler")
+  if(NOT CMAKE_SYCL_COMPILER)
+    set(CMAKE_SYCL_COMPILER_INIT NOTFOUND)
+
+    # prefer the environment variable SYCL
+    if(NOT $ENV{SYCL} STREQUAL "")
+      get_filename_component(CMAKE_SYCL_COMPILER_INIT $ENV{SYCL} PROGRAM PROGRAM_ARGS CMAKE_SYCL_FLAGS_ENV_INIT)
+      if(CMAKE_SYCL_FLAGS_ENV_INIT)
+        set(CMAKE_SYCL_COMPILER_ARG1 "${CMAKE_SYCL_FLAGS_ENV_INIT}" CACHE STRING "Arguments to SYCL compiler")
       endif()
-      if(NOT EXISTS ${CMAKE_CXX_COMPILER_INIT})
-        message(FATAL_ERROR "Could not find compiler set in environment variable CXX:\n$ENV{CXX}.\n${CMAKE_CXX_COMPILER_INIT}")
+      if(NOT EXISTS ${CMAKE_SYCL_COMPILER_INIT})
+        message(FATAL_ERROR "Could not find compiler set in environment variable SYCL:\n$ENV{SYCL}.\n${CMAKE_SYCL_COMPILER_INIT}")
       endif()
     endif()
 
     # next prefer the generator specified compiler
-    if(CMAKE_GENERATOR_CXX)
-      if(NOT CMAKE_CXX_COMPILER_INIT)
-        set(CMAKE_CXX_COMPILER_INIT ${CMAKE_GENERATOR_CXX})
+    if(CMAKE_GENERATOR_SYCL)
+      if(NOT CMAKE_SYCL_COMPILER_INIT)
+        set(CMAKE_SYCL_COMPILER_INIT ${CMAKE_GENERATOR_SYCL})
       endif()
     endif()
 
     # finally list compilers to try
-    if(NOT CMAKE_CXX_COMPILER_INIT)
-      set(CMAKE_CXX_COMPILER_LIST CC ${_CMAKE_TOOLCHAIN_PREFIX}c++ ${_CMAKE_TOOLCHAIN_PREFIX}g++ aCC cl bcc xlC)
+    if(NOT CMAKE_SYCL_COMPILER_INIT)
+      set(CMAKE_SYCL_COMPILER_LIST icpx icx)
       if(NOT CMAKE_HOST_WIN32)
         # FIXME(#24314): Add support for the GNU-like icpx compiler driver
         # on Windows, first introduced by Intel oneAPI 2023.0.
-        list(APPEND CMAKE_CXX_COMPILER_LIST icpx)
+        list(APPEND CMAKE_SYCL_COMPILER_LIST icpx)
       endif()
-      list(APPEND CMAKE_CXX_COMPILER_LIST icx clang++)
     endif()
 
-    _cmake_find_compiler(CXX)
+    _cmake_find_compiler(SYCL)
   else()
-    _cmake_find_compiler_path(CXX)
+    _cmake_find_compiler_path(SYCL)
   endif()
-  mark_as_advanced(CMAKE_CXX_COMPILER)
+  mark_as_advanced(CMAKE_SYCL_COMPILER)
 
   # Each entry in this list is a set of extra flags to try
   # adding to the compile line to see if it helps produce
   # a valid identification file.
-  set(CMAKE_CXX_COMPILER_ID_TEST_FLAGS_FIRST)
-  set(CMAKE_CXX_COMPILER_ID_TEST_FLAGS
+  set(CMAKE_SYCL_COMPILER_ID_TEST_FLAGS_FIRST)
+  set(CMAKE_SYCL_COMPILER_ID_TEST_FLAGS
+    "-fsycl"
     # Try compiling to an object file only.
     "-c"
     # IAR does not detect language automatically
@@ -94,64 +95,65 @@ else()
     )
 endif()
 
-if(CMAKE_CXX_COMPILER_TARGET)
-  set(CMAKE_CXX_COMPILER_ID_TEST_FLAGS_FIRST "-c --target=${CMAKE_CXX_COMPILER_TARGET}")
+if(CMAKE_SYCL_COMPILER_TARGET)
+  set(CMAKE_SYCL_COMPILER_ID_TEST_FLAGS_FIRST "-c --target=${CMAKE_SYCL_COMPILER_TARGET}")
 endif()
 
 # Build a small source file to identify the compiler.
-if(NOT CMAKE_CXX_COMPILER_ID_RUN)
-  set(CMAKE_CXX_COMPILER_ID_RUN 1)
+if(NOT CMAKE_SYCL_COMPILER_ID_RUN)
+  set(CMAKE_SYCL_COMPILER_ID_RUN 1)
 
   # Try to identify the compiler.
-  set(CMAKE_CXX_COMPILER_ID)
-  set(CMAKE_CXX_PLATFORM_ID)
+  set(CMAKE_SYCL_COMPILER_ID)
+  set(CMAKE_SYCL_PLATFORM_ID)
   file(READ ${CMAKE_ROOT}/Modules/CMakePlatformId.h.in
-    CMAKE_CXX_COMPILER_ID_PLATFORM_CONTENT)
+    CMAKE_SYCL_COMPILER_ID_PLATFORM_CONTENT)
 
   # The IAR compiler produces weird output.
   # See https://gitlab.kitware.com/cmake/cmake/-/issues/10176#note_153591
-  list(APPEND CMAKE_CXX_COMPILER_ID_VENDORS IAR)
-  set(CMAKE_CXX_COMPILER_ID_VENDOR_FLAGS_IAR )
-  set(CMAKE_CXX_COMPILER_ID_VENDOR_REGEX_IAR "IAR .+ Compiler")
+  list(APPEND CMAKE_SYCL_COMPILER_ID_VENDORS IAR)
+  set(CMAKE_SYCL_COMPILER_ID_VENDOR_FLAGS_IAR )
+  set(CMAKE_SYCL_COMPILER_ID_VENDOR_REGEX_IAR "IAR .+ Compiler")
 
   # Match the link line from xcodebuild output of the form
   #  Ld ...
   #      ...
-  #      /path/to/cc ...CompilerIdCXX/...
+  #      /path/to/cc ...CompilerIdSYCL/...
   # to extract the compiler front-end for the language.
-  set(CMAKE_CXX_COMPILER_ID_TOOL_MATCH_REGEX "\nLd[^\n]*(\n[ \t]+[^\n]*)*\n[ \t]+([^ \t\r\n]+)[^\r\n]*-o[^\r\n]*CompilerIdCXX/(\\./)?(CompilerIdCXX.(framework|xctest|build/[^ \t\r\n]+)/)?CompilerIdCXX[ \t\n\\\"]")
-  set(CMAKE_CXX_COMPILER_ID_TOOL_MATCH_INDEX 2)
+  set(CMAKE_SYCL_COMPILER_ID_TOOL_MATCH_REGEX "\nLd[^\n]*(\n[ \t]+[^\n]*)*\n[ \t]+([^ \t\r\n]+)[^\r\n]*-o[^\r\n]*CompilerIdSYCL/(\\./)?(CompilerIdSYCL.(framework|xctest|build/[^ \t\r\n]+)/)?CompilerIdSYCL[ \t\n\\\"]")
+  set(CMAKE_SYCL_COMPILER_ID_TOOL_MATCH_INDEX 2)
 
   include(${CMAKE_ROOT}/Modules/CMakeDetermineCompilerId.cmake)
-  CMAKE_DETERMINE_COMPILER_ID(CXX CXXFLAGS CMakeCXXCompilerId.cpp)
+  set(SYCLFLAGS "-fsycl -Werror")
+  CMAKE_DETERMINE_COMPILER_ID(SYCL SYCLFLAGS CMakeSYCLCompilerId.cpp)
 
-  _cmake_find_compiler_sysroot(CXX)
+  _cmake_find_compiler_sysroot(SYCL)
 
   # Set old compiler and platform id variables.
-  if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-    set(CMAKE_COMPILER_IS_GNUCXX 1)
+  if(CMAKE_SYCL_COMPILER_ID STREQUAL "GNU")
+    set(CMAKE_COMPILER_IS_GNUSYCL 1)
   endif()
 else()
-  if(NOT DEFINED CMAKE_CXX_COMPILER_FRONTEND_VARIANT)
-    # Some toolchain files set our internal CMAKE_CXX_COMPILER_ID_RUN
-    # variable but are not aware of CMAKE_CXX_COMPILER_FRONTEND_VARIANT.
+  if(NOT DEFINED CMAKE_SYCL_COMPILER_FRONTEND_VARIANT)
+    # Some toolchain files set our internal CMAKE_SYCL_COMPILER_ID_RUN
+    # variable but are not aware of CMAKE_SYCL_COMPILER_FRONTEND_VARIANT.
     # They pre-date our support for the GNU-like variant targeting the
     # MSVC ABI so we do not consider that here.
-    if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang"
-      OR "x${CMAKE_CXX_COMPILER_ID}" STREQUAL "xIntelLLVM")
-      if("x${CMAKE_CXX_SIMULATE_ID}" STREQUAL "xMSVC")
-        set(CMAKE_CXX_COMPILER_FRONTEND_VARIANT "MSVC")
+    if(CMAKE_SYCL_COMPILER_ID STREQUAL "Clang"
+      OR "x${CMAKE_SYCL_COMPILER_ID}" STREQUAL "xIntelLLVM")
+      if("x${CMAKE_SYCL_SIMULATE_ID}" STREQUAL "xMSVC")
+        set(CMAKE_SYCL_COMPILER_FRONTEND_VARIANT "MSVC")
       else()
-        set(CMAKE_CXX_COMPILER_FRONTEND_VARIANT "GNU")
+        set(CMAKE_SYCL_COMPILER_FRONTEND_VARIANT "GNU")
       endif()
     else()
-      set(CMAKE_CXX_COMPILER_FRONTEND_VARIANT "")
+      set(CMAKE_SYCL_COMPILER_FRONTEND_VARIANT "")
     endif()
   endif()
 endif()
 
 if (NOT _CMAKE_TOOLCHAIN_LOCATION)
-  get_filename_component(_CMAKE_TOOLCHAIN_LOCATION "${CMAKE_CXX_COMPILER}" PATH)
+  get_filename_component(_CMAKE_TOOLCHAIN_LOCATION "${CMAKE_SYCL_COMPILER}" PATH)
 endif ()
 
 # if we have a g++ cross compiler, they have usually some prefix, like
@@ -165,18 +167,18 @@ endif ()
 
 if (NOT _CMAKE_TOOLCHAIN_PREFIX)
 
-  if("${CMAKE_CXX_COMPILER_ID}" MATCHES "GNU|Clang|QCC|LCC")
-    get_filename_component(COMPILER_BASENAME "${CMAKE_CXX_COMPILER}" NAME)
+  if("${CMAKE_SYCL_COMPILER_ID}" MATCHES "GNU|Clang|QCC|LCC")
+    get_filename_component(COMPILER_BASENAME "${CMAKE_SYCL_COMPILER}" NAME)
     if (COMPILER_BASENAME MATCHES "^(.+-)?(clang\\+\\+|[gc]\\+\\+|clang-cl)(-[0-9]+(\\.[0-9]+)*)?(-[^.]+)?(\\.exe)?$")
       set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_MATCH_1})
       set(_CMAKE_TOOLCHAIN_SUFFIX ${CMAKE_MATCH_3})
       set(_CMAKE_COMPILER_SUFFIX ${CMAKE_MATCH_5})
-    elseif("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
-      if(CMAKE_CXX_COMPILER_TARGET)
-        set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_CXX_COMPILER_TARGET}-)
+    elseif("${CMAKE_SYCL_COMPILER_ID}" MATCHES "Clang")
+      if(CMAKE_SYCL_COMPILER_TARGET)
+        set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_SYCL_COMPILER_TARGET}-)
       endif()
     elseif(COMPILER_BASENAME MATCHES "QCC(\\.exe)?$")
-      if(CMAKE_CXX_COMPILER_TARGET MATCHES "gcc_nto([a-z0-9]+_[0-9]+|[^_le]+)(le)")
+      if(CMAKE_SYCL_COMPILER_TARGET MATCHES "gcc_nto([a-z0-9]+_[0-9]+|[^_le]+)(le)")
         set(_CMAKE_TOOLCHAIN_PREFIX nto${CMAKE_MATCH_1}-)
       endif()
     endif ()
@@ -186,9 +188,9 @@ if (NOT _CMAKE_TOOLCHAIN_PREFIX)
     if ("${_CMAKE_TOOLCHAIN_PREFIX}" MATCHES "(.+-)?llvm-$")
       set(_CMAKE_TOOLCHAIN_PREFIX ${CMAKE_MATCH_1})
     endif ()
-  elseif("${CMAKE_CXX_COMPILER_ID}" MATCHES "TI")
+  elseif("${CMAKE_SYCL_COMPILER_ID}" MATCHES "TI")
     # TI compilers are named e.g. cl6x, cl470 or armcl.exe
-    get_filename_component(COMPILER_BASENAME "${CMAKE_CXX_COMPILER}" NAME)
+    get_filename_component(COMPILER_BASENAME "${CMAKE_SYCL_COMPILER}" NAME)
     if (COMPILER_BASENAME MATCHES "^(.+)?cl([^.]+)?(\\.exe)?$")
       set(_CMAKE_TOOLCHAIN_PREFIX "${CMAKE_MATCH_1}")
       set(_CMAKE_TOOLCHAIN_SUFFIX "${CMAKE_MATCH_2}")
@@ -198,40 +200,40 @@ if (NOT _CMAKE_TOOLCHAIN_PREFIX)
 
 endif ()
 
-set(_CMAKE_PROCESSING_LANGUAGE "CXX")
+set(_CMAKE_PROCESSING_LANGUAGE "SYCL")
 include(CMakeFindBinUtils)
-include(Compiler/${CMAKE_CXX_COMPILER_ID}-FindBinUtils OPTIONAL)
+include(Compiler/${CMAKE_SYCL_COMPILER_ID}-FindBinUtils OPTIONAL)
 unset(_CMAKE_PROCESSING_LANGUAGE)
 
-if(CMAKE_CXX_COMPILER_SYSROOT)
-  string(CONCAT _SET_CMAKE_CXX_COMPILER_SYSROOT
-    "set(CMAKE_CXX_COMPILER_SYSROOT \"${CMAKE_CXX_COMPILER_SYSROOT}\")\n"
-    "set(CMAKE_COMPILER_SYSROOT \"${CMAKE_CXX_COMPILER_SYSROOT}\")")
+if(CMAKE_SYCL_COMPILER_SYSROOT)
+  string(CONCAT _SET_CMAKE_SYCL_COMPILER_SYSROOT
+    "set(CMAKE_SYCL_COMPILER_SYSROOT \"${CMAKE_SYCL_COMPILER_SYSROOT}\")\n"
+    "set(CMAKE_COMPILER_SYSROOT \"${CMAKE_SYCL_COMPILER_SYSROOT}\")")
 else()
-  set(_SET_CMAKE_CXX_COMPILER_SYSROOT "")
+  set(_SET_CMAKE_SYCL_COMPILER_SYSROOT "")
 endif()
 
-if(CMAKE_CXX_COMPILER_ARCHITECTURE_ID)
-  set(_SET_CMAKE_CXX_COMPILER_ARCHITECTURE_ID
-    "set(CMAKE_CXX_COMPILER_ARCHITECTURE_ID ${CMAKE_CXX_COMPILER_ARCHITECTURE_ID})")
+if(CMAKE_SYCL_COMPILER_ARCHITECTURE_ID)
+  set(_SET_CMAKE_SYCL_COMPILER_ARCHITECTURE_ID
+    "set(CMAKE_SYCL_COMPILER_ARCHITECTURE_ID ${CMAKE_SYCL_COMPILER_ARCHITECTURE_ID})")
 else()
-  set(_SET_CMAKE_CXX_COMPILER_ARCHITECTURE_ID "")
+  set(_SET_CMAKE_SYCL_COMPILER_ARCHITECTURE_ID "")
 endif()
 
-if(MSVC_CXX_ARCHITECTURE_ID)
-  set(SET_MSVC_CXX_ARCHITECTURE_ID
-    "set(MSVC_CXX_ARCHITECTURE_ID ${MSVC_CXX_ARCHITECTURE_ID})")
+if(MSVC_SYCL_ARCHITECTURE_ID)
+  set(SET_MSVC_SYCL_ARCHITECTURE_ID
+    "set(MSVC_SYCL_ARCHITECTURE_ID ${MSVC_SYCL_ARCHITECTURE_ID})")
 endif()
 
-if(CMAKE_CXX_XCODE_ARCHS)
+if(CMAKE_SYCL_XCODE_ARCHS)
   set(SET_CMAKE_XCODE_ARCHS
-    "set(CMAKE_XCODE_ARCHS \"${CMAKE_CXX_XCODE_ARCHS}\")")
+    "set(CMAKE_XCODE_ARCHS \"${CMAKE_SYCL_XCODE_ARCHS}\")")
 endif()
 
 # configure all variables set in this file
-configure_file(${CMAKE_ROOT}/Modules/CMakeCXXCompiler.cmake.in
-  ${CMAKE_PLATFORM_INFO_DIR}/CMakeCXXCompiler.cmake
+configure_file(${ArrayFire_SOURCE_DIR}/CMakeModules/CMakeSYCLCompiler.cmake.in
+  ${CMAKE_PLATFORM_INFO_DIR}/CMakeSYCLCompiler.cmake
   @ONLY
   )
 
-set(CMAKE_CXX_COMPILER_ENV_VAR "CXX")
+set(CMAKE_SYCL_COMPILER_ENV_VAR "SYCL")
diff --git a/CMakeModules/CMakeSYCLCompiler.cmake.in b/CMakeModules/CMakeSYCLCompiler.cmake.in
index 50edc9e474..e0193afb13 100644
--- a/CMakeModules/CMakeSYCLCompiler.cmake.in
+++ b/CMakeModules/CMakeSYCLCompiler.cmake.in
@@ -39,7 +39,7 @@ set(CMAKE_SYCL_COMPILER_ID_RUN 1)
 set(CMAKE_SYCL_SOURCE_FILE_EXTENSIONS C;M;c++;cc;cpp;cxx;m;mm;mpp;CPP;ixx;cppm)
 set(CMAKE_SYCL_IGNORE_EXTENSIONS inl;h;hpp;HPP;H;o;O;obj;OBJ;def;DEF;rc;RC)
 
-foreach (lang C OBJC OBJSYCL)
+foreach (lang SYCL)
   if (CMAKE_${lang}_COMPILER_ID_RUN)
     foreach(extension IN LISTS CMAKE_${lang}_SOURCE_FILE_EXTENSIONS)
       list(REMOVE_ITEM CMAKE_SYCL_SOURCE_FILE_EXTENSIONS ${extension})
diff --git a/CMakeModules/CMakeSYCLCompilerABI.cpp b/CMakeModules/CMakeSYCLCompilerABI.cpp
index fe7c926993..cac613b114 100644
--- a/CMakeModules/CMakeSYCLCompilerABI.cpp
+++ b/CMakeModules/CMakeSYCLCompilerABI.cpp
@@ -3,7 +3,6 @@
 #endif
 
 #include "CMakeCompilerABI.h"
-#include <sycl/sycl.hpp>
 
 int main(int argc, char* argv[])
 {
@@ -16,16 +15,5 @@ int main(int argc, char* argv[])
 #endif
   static_cast<void>(argv);
 
-  int count = 0;
-  auto platforms = sycl::platform::get_platforms();
-  for(sycl::platform &platform : platforms) {
-    count += platform.get_devices().size();
-  }
-
-  if(count == 0) {
-    std::fprintf(stderr, "No SYCL devices found.\n");
-    return -1;
-  }
-
   return require;
 }
diff --git a/CMakeModules/CMakeSYCLCompilerId.cpp.in b/CMakeModules/CMakeSYCLCompilerId.cpp.in
new file mode 100644
index 0000000000..913dbc7932
--- /dev/null
+++ b/CMakeModules/CMakeSYCLCompilerId.cpp.in
@@ -0,0 +1,105 @@
+/* This source file must have a .cpp extension so that all C++ compilers
+   recognize the extension without flags.  Borland does not know .cxx for
+   example.  */
+#ifndef __cplusplus
+# error "A C compiler has been selected for C++."
+#endif
+
+#if !defined(__has_include)
+/* If the compiler does not have __has_include, pretend the answer is
+   always no.  */
+#  define __has_include(x) 0
+#endif
+
+@CMAKE_SYCL_COMPILER_ID_CONTENT@
+
+/* Construct the string literal in pieces to prevent the source from
+   getting matched.  Store it in a pointer rather than an array
+   because some compilers will just produce instructions to fill the
+   array rather than assigning a pointer to a static array.  */
+char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]";
+#ifdef SIMULATE_ID
+char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]";
+#endif
+
+#ifdef __QNXNTO__
+char const* qnxnto = "INFO" ":" "qnxnto[]";
+#endif
+
+#if defined(__CRAYXT_COMPUTE_LINUX_TARGET)
+char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]";
+#endif
+
+@CMAKE_SYCL_COMPILER_ID_PLATFORM_CONTENT@
+@CMAKE_SYCL_COMPILER_ID_ERROR_FOR_TEST@
+
+#if defined(__INTEL_COMPILER) && defined(_MSVC_LANG) && _MSVC_LANG < 201403L
+#  if defined(__INTEL_CXX11_MODE__)
+#    if defined(__cpp_aggregate_nsdmi)
+#      define CXX_STD 201402L
+#    else
+#      define CXX_STD 201103L
+#    endif
+#  else
+#    define CXX_STD 199711L
+#  endif
+#elif defined(_MSC_VER) && defined(_MSVC_LANG)
+#  define CXX_STD _MSVC_LANG
+#else
+#  define CXX_STD __cplusplus
+#endif
+
+const char* info_language_standard_default = "INFO" ":" "standard_default["
+#if CXX_STD > 202002L
+  "23"
+#elif CXX_STD > 201703L
+  "20"
+#elif CXX_STD >= 201703L
+  "17"
+#elif CXX_STD >= 201402L
+  "14"
+#elif CXX_STD >= 201103L
+  "11"
+#else
+  "98"
+#endif
+"]";
+
+const char* info_language_extensions_default = "INFO" ":" "extensions_default["
+#if (defined(__clang__) || defined(__GNUC__) || defined(__xlC__) ||           \
+     defined(__TI_COMPILER_VERSION__)) &&                                     \
+  !defined(__STRICT_ANSI__)
+  "ON"
+#else
+  "OFF"
+#endif
+"]";
+
+/*--------------------------------------------------------------------------*/
+
+int main(int argc, char* argv[])
+{
+  int require = 0;
+  require += info_compiler[argc];
+  require += info_platform[argc];
+  require += info_arch[argc];
+#ifdef COMPILER_VERSION_MAJOR
+  require += info_version[argc];
+#endif
+#ifdef COMPILER_VERSION_INTERNAL
+  require += info_version_internal[argc];
+#endif
+#ifdef SIMULATE_ID
+  require += info_simulate[argc];
+#endif
+#ifdef SIMULATE_VERSION_MAJOR
+  require += info_simulate_version[argc];
+#endif
+#if defined(__CRAYXT_COMPUTE_LINUX_TARGET)
+  require += info_cray[argc];
+#endif
+  require += info_language_standard_default[argc];
+  require += info_language_extensions_default[argc];
+  (void)argv;
+  return require;
+}
diff --git a/CMakeModules/CMakeSYCLInformation.cmake b/CMakeModules/CMakeSYCLInformation.cmake
index 53abf378d5..5e9714327a 100644
--- a/CMakeModules/CMakeSYCLInformation.cmake
+++ b/CMakeModules/CMakeSYCLInformation.cmake
@@ -1,6 +1,11 @@
 # Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
 # file Copyright.txt or https://cmake.org/licensing for details.
 
+# make sure default modules are accesible
+list(APPEND CMAKE_MODULE_PATH ${CMAKE_ROOT}/Modules)
+message(${CMAKE_MODULE_PATH})
+
+set(CMAKE_SYCL_COMPILER_ID IntelLLVM)
 
 # This file sets the basic flags for the C++ language in CMake.
 # It also loads the available platform file for the system-compiler
@@ -13,49 +18,109 @@ include(CMakeLanguageInformation)
 # some compilers use different extensions (e.g. sdcc uses .rel)
 # so set the extension here first so it can be overridden by the compiler specific file
 if(UNIX)
-  set(CMAKE_CXX_OUTPUT_EXTENSION .o)
+  set(CMAKE_SYCL_OUTPUT_EXTENSION .o)
 else()
-  set(CMAKE_CXX_OUTPUT_EXTENSION .obj)
+  set(CMAKE_SYCL_OUTPUT_EXTENSION .obj)
 endif()
 
 set(_INCLUDED_FILE 0)
 
 # Load compiler-specific information.
-if(CMAKE_CXX_COMPILER_ID)
-  include(Compiler/${CMAKE_CXX_COMPILER_ID}-CXX OPTIONAL)
+if(CMAKE_SYCL_COMPILER_ID)
+  #include(Compiler/${CMAKE_SYCL_COMPILER_ID}-CXX OPTIONAL)
 endif()
 
 set(CMAKE_BASE_NAME)
-get_filename_component(CMAKE_BASE_NAME "${CMAKE_CXX_COMPILER}" NAME_WE)
+get_filename_component(CMAKE_BASE_NAME "${CMAKE_SYCL_COMPILER}" NAME_WE)
 # since the gnu compiler has several names force g++
-if(CMAKE_COMPILER_IS_GNUCXX)
+if(CMAKE_COMPILER_IS_GNUSYCL)
   set(CMAKE_BASE_NAME g++)
 endif()
 
+include(Compiler/${CMAKE_SYCL_COMPILER_ID} OPTIONAL)
+__compiler_intel_llvm(SYCL)
 
-# load a hardware specific file, mostly useful for embedded compilers
-if(CMAKE_SYSTEM_PROCESSOR)
-  if(CMAKE_CXX_COMPILER_ID)
-    include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_CXX_COMPILER_ID}-CXX-${CMAKE_SYSTEM_PROCESSOR} OPTIONAL RESULT_VARIABLE _INCLUDED_FILE)
+if("x${CMAKE_CXX_COMPILER_FRONTEND_VARIANT}" STREQUAL "xMSVC")
+  set(CMAKE_SYCL_COMPILE_OPTIONS_EXPLICIT_LANGUAGE -TP)
+  set(CMAKE_SYCL_CLANG_TIDY_DRIVER_MODE "cl")
+  set(CMAKE_SYCL_INCLUDE_WHAT_YOU_USE_DRIVER_MODE "cl")
+  if((NOT DEFINED CMAKE_DEPENDS_USE_COMPILER OR CMAKE_DEPENDS_USE_COMPILER)
+      AND CMAKE_GENERATOR MATCHES "Makefiles|WMake"
+      AND CMAKE_DEPFILE_FLAGS_SYCL)
+    set(CMAKE_SYCL_DEPENDS_USE_COMPILER TRUE)
+  endif()
+else()
+  set(CMAKE_SYCL_COMPILE_OPTIONS_EXPLICIT_LANGUAGE -x c++)
+  if((NOT DEFINED CMAKE_DEPENDS_USE_COMPILER OR CMAKE_DEPENDS_USE_COMPILER)
+      AND CMAKE_GENERATOR MATCHES "Makefiles|WMake"
+      AND CMAKE_DEPFILE_FLAGS_SYCL)
+    # dependencies are computed by the compiler itself
+    set(CMAKE_SYCL_DEPFILE_FORMAT gcc)
+    set(CMAKE_SYCL_DEPENDS_USE_COMPILER TRUE)
   endif()
-  if (NOT _INCLUDED_FILE)
-    include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_BASE_NAME}-${CMAKE_SYSTEM_PROCESSOR} OPTIONAL)
-  endif ()
+
+  set(CMAKE_SYCL_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
+
+  string(APPEND CMAKE_SYCL_FLAGS_MINSIZEREL_INIT " -DNDEBUG")
+  string(APPEND CMAKE_SYCL_FLAGS_RELEASE_INIT " -DNDEBUG")
+  string(APPEND CMAKE_SYCL_FLAGS_RELWITHDEBINFO_INIT " -DNDEBUG")
 endif()
 
-# load the system- and compiler specific files
-if(CMAKE_CXX_COMPILER_ID)
-  include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_CXX_COMPILER_ID}-CXX OPTIONAL RESULT_VARIABLE _INCLUDED_FILE)
+set(CMAKE_SYCL98_STANDARD__HAS_FULL_SUPPORT ON)
+set(CMAKE_SYCL11_STANDARD__HAS_FULL_SUPPORT ON)
+set(CMAKE_SYCL14_STANDARD__HAS_FULL_SUPPORT ON)
+
+if(NOT "x${CMAKE_SYCL_SIMULATE_ID}" STREQUAL "xMSVC")
+  set(CMAKE_SYCL98_STANDARD_COMPILE_OPTION  "-std=c++98")
+  set(CMAKE_SYCL98_EXTENSION_COMPILE_OPTION "-std=gnu++98")
+
+  set(CMAKE_SYCL11_STANDARD_COMPILE_OPTION  "-std=c++11")
+  set(CMAKE_SYCL11_EXTENSION_COMPILE_OPTION "-std=gnu++11")
+
+  set(CMAKE_SYCL14_STANDARD_COMPILE_OPTION  "-std=c++14")
+  set(CMAKE_SYCL14_EXTENSION_COMPILE_OPTION "-std=gnu++14")
+
+  set(CMAKE_SYCL17_STANDARD_COMPILE_OPTION  "-std=c++17")
+  set(CMAKE_SYCL17_EXTENSION_COMPILE_OPTION "-std=gnu++17")
+
+  set(CMAKE_SYCL20_STANDARD_COMPILE_OPTION  "-std=c++20")
+  set(CMAKE_SYCL20_EXTENSION_COMPILE_OPTION "-std=gnu++20")
+
+  set(CMAKE_SYCL23_STANDARD_COMPILE_OPTION  "-std=c++2b")
+  set(CMAKE_SYCL23_EXTENSION_COMPILE_OPTION "-std=gnu++2b")
+else()
+  set(CMAKE_SYCL98_STANDARD_COMPILE_OPTION  "")
+  set(CMAKE_SYCL98_EXTENSION_COMPILE_OPTION "")
+
+  set(CMAKE_SYCL11_STANDARD_COMPILE_OPTION  "")
+  set(CMAKE_SYCL11_EXTENSION_COMPILE_OPTION "")
+
+  set(CMAKE_SYCL14_STANDARD_COMPILE_OPTION  "-Qstd:c++14")
+  set(CMAKE_SYCL14_EXTENSION_COMPILE_OPTION "-Qstd:c++14")
+
+  set(CMAKE_SYCL17_STANDARD_COMPILE_OPTION  "-Qstd:c++17")
+  set(CMAKE_SYCL17_EXTENSION_COMPILE_OPTION "-Qstd:c++17")
+
+  set(CMAKE_SYCL20_STANDARD_COMPILE_OPTION  "-Qstd:c++20")
+  set(CMAKE_SYCL20_EXTENSION_COMPILE_OPTION "-Qstd:c++20")
+
+  set(CMAKE_SYCL23_STANDARD_COMPILE_OPTION  "-Qstd:c++2b")
+  set(CMAKE_SYCL23_EXTENSION_COMPILE_OPTION "-Qstd:c++2b")
 endif()
-if (NOT _INCLUDED_FILE)
-  include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_BASE_NAME} OPTIONAL
-          RESULT_VARIABLE _INCLUDED_FILE)
-endif ()
 
-# load any compiler-wrapper specific information
-if (CMAKE_CXX_COMPILER_WRAPPER)
-  __cmake_include_compiler_wrapper(CXX)
-endif ()
+include(Platform/${CMAKE_EFFECTIVE_SYSTEM_NAME}-${CMAKE_SYCL_COMPILER_ID} OPTIONAL RESULT_VARIABLE _INCLUDED_FILE)
+
+if(WIN32)
+  set(_COMPILE_CXX " /TP")
+  __windows_compiler_intel(SYCL)
+elseif(UNIX AND NOT APPLE)
+  __linux_compiler_intel_llvm(SYCL)
+  # This should be -isystem but icpx throws an error on Ubuntu
+  # when you include /usr/include as a system header
+  set(CMAKE_INCLUDE_SYSTEM_FLAG_SYCL "-I ")
+else()
+  __apple_compiler_intel_llvm(SYCL)
+endif()
 
 # We specify the compiler information in the system file for some
 # platforms, but this language may not have been enabled when the file
@@ -65,11 +130,11 @@ if (NOT _INCLUDED_FILE)
   include(Platform/${CMAKE_SYSTEM_NAME} OPTIONAL)
 endif ()
 
-if(CMAKE_CXX_SIZEOF_DATA_PTR)
-  foreach(f ${CMAKE_CXX_ABI_FILES})
+if(CMAKE_SYCL_SIZEOF_DATA_PTR)
+  foreach(f ${CMAKE_SYCL_ABI_FILES})
     include(${f})
   endforeach()
-  unset(CMAKE_CXX_ABI_FILES)
+  unset(CMAKE_SYCL_ABI_FILES)
 endif()
 
 # This should be included before the _INIT variables are
@@ -84,118 +149,118 @@ if(CMAKE_USER_MAKE_RULES_OVERRIDE)
   set(CMAKE_USER_MAKE_RULES_OVERRIDE "${_override}")
 endif()
 
-if(CMAKE_USER_MAKE_RULES_OVERRIDE_CXX)
+if(CMAKE_USER_MAKE_RULES_OVERRIDE_SYCL)
   # Save the full path of the file so try_compile can use it.
-  include(${CMAKE_USER_MAKE_RULES_OVERRIDE_CXX} RESULT_VARIABLE _override)
-  set(CMAKE_USER_MAKE_RULES_OVERRIDE_CXX "${_override}")
+  include(${CMAKE_USER_MAKE_RULES_OVERRIDE_SYCL} RESULT_VARIABLE _override)
+  set(CMAKE_USER_MAKE_RULES_OVERRIDE_SYCL "${_override}")
 endif()
 
 
 # Create a set of shared library variable specific to C++
 # For 90% of the systems, these are the same flags as the C versions
 # so if these are not set just copy the flags from the c version
-if(NOT CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS)
-  set(CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS})
+if(NOT CMAKE_SHARED_LIBRARY_CREATE_SYCL_FLAGS)
+  set(CMAKE_SHARED_LIBRARY_CREATE_SYCL_FLAGS ${CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS})
 endif()
 
-if(NOT CMAKE_CXX_COMPILE_OPTIONS_PIC)
-  set(CMAKE_CXX_COMPILE_OPTIONS_PIC ${CMAKE_C_COMPILE_OPTIONS_PIC})
+if(NOT CMAKE_SYCL_COMPILE_OPTIONS_PIC)
+  set(CMAKE_SYCL_COMPILE_OPTIONS_PIC ${CMAKE_CXX_COMPILE_OPTIONS_PIC})
 endif()
 
-if(NOT CMAKE_CXX_COMPILE_OPTIONS_PIE)
-  set(CMAKE_CXX_COMPILE_OPTIONS_PIE ${CMAKE_C_COMPILE_OPTIONS_PIE})
+if(NOT CMAKE_SYCL_COMPILE_OPTIONS_PIE)
+  set(CMAKE_SYCL_COMPILE_OPTIONS_PIE ${CMAKE_CXX_COMPILE_OPTIONS_PIE})
 endif()
-if(NOT CMAKE_CXX_LINK_OPTIONS_PIE)
-  set(CMAKE_CXX_LINK_OPTIONS_PIE ${CMAKE_C_LINK_OPTIONS_PIE})
+if(NOT CMAKE_SYCL_LINK_OPTIONS_PIE)
+  set(CMAKE_SYCL_LINK_OPTIONS_PIE ${CMAKE_CXX_LINK_OPTIONS_PIE})
 endif()
-if(NOT CMAKE_CXX_LINK_OPTIONS_NO_PIE)
-  set(CMAKE_CXX_LINK_OPTIONS_NO_PIE ${CMAKE_C_LINK_OPTIONS_NO_PIE})
+if(NOT CMAKE_SYCL_LINK_OPTIONS_NO_PIE)
+  set(CMAKE_SYCL_LINK_OPTIONS_NO_PIE ${CMAKE_CXX_LINK_OPTIONS_NO_PIE})
 endif()
 
-if(NOT CMAKE_CXX_COMPILE_OPTIONS_DLL)
-  set(CMAKE_CXX_COMPILE_OPTIONS_DLL ${CMAKE_C_COMPILE_OPTIONS_DLL})
+if(NOT CMAKE_SYCL_COMPILE_OPTIONS_DLL)
+  set(CMAKE_SYCL_COMPILE_OPTIONS_DLL ${CMAKE_CXX_COMPILE_OPTIONS_DLL})
 endif()
 
-if(NOT CMAKE_SHARED_LIBRARY_CXX_FLAGS)
-  set(CMAKE_SHARED_LIBRARY_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_C_FLAGS})
+if(NOT CMAKE_SHARED_LIBRARY_SYCL_FLAGS)
+  set(CMAKE_SHARED_LIBRARY_SYCL_FLAGS ${CMAKE_SHARED_LIBRARY_CXX_FLAGS})
 endif()
 
-if(NOT DEFINED CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS)
-  set(CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_LINK_C_FLAGS})
+if(NOT DEFINED CMAKE_SHARED_LIBRARY_LINK_SYCL_FLAGS)
+  set(CMAKE_SHARED_LIBRARY_LINK_SYCL_FLAGS ${CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS})
 endif()
 
-if(NOT CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG)
-  set(CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG})
+if(NOT CMAKE_SHARED_LIBRARY_RUNTIME_SYCL_FLAG)
+  set(CMAKE_SHARED_LIBRARY_RUNTIME_SYCL_FLAG ${CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG})
 endif()
 
-if(NOT CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP)
-  set(CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP ${CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG_SEP})
+if(NOT CMAKE_SHARED_LIBRARY_RUNTIME_SYCL_FLAG_SEP)
+  set(CMAKE_SHARED_LIBRARY_RUNTIME_SYCL_FLAG_SEP ${CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP})
 endif()
 
-if(NOT CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG)
-  set(CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RPATH_LINK_C_FLAG})
+if(NOT CMAKE_SHARED_LIBRARY_RPATH_LINK_SYCL_FLAG)
+  set(CMAKE_SHARED_LIBRARY_RPATH_LINK_SYCL_FLAG ${CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG})
 endif()
 
-if(NOT DEFINED CMAKE_EXE_EXPORTS_CXX_FLAG)
-  set(CMAKE_EXE_EXPORTS_CXX_FLAG ${CMAKE_EXE_EXPORTS_C_FLAG})
+if(NOT DEFINED CMAKE_EXE_EXPORTS_SYCL_FLAG)
+  set(CMAKE_EXE_EXPORTS_SYCL_FLAG ${CMAKE_EXE_EXPORTS_CXX_FLAG})
 endif()
 
-if(NOT DEFINED CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG)
-  set(CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG ${CMAKE_SHARED_LIBRARY_SONAME_C_FLAG})
+if(NOT DEFINED CMAKE_SHARED_LIBRARY_SONAME_SYCL_FLAG)
+  set(CMAKE_SHARED_LIBRARY_SONAME_SYCL_FLAG ${CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG})
 endif()
 
-if(NOT CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG)
-  set(CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG})
+if(NOT CMAKE_EXECUTABLE_RUNTIME_SYCL_FLAG)
+  set(CMAKE_EXECUTABLE_RUNTIME_SYCL_FLAG ${CMAKE_SHARED_LIBRARY_RUNTIME_SYCL_FLAG})
 endif()
 
-if(NOT CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG_SEP)
-  set(CMAKE_EXECUTABLE_RUNTIME_CXX_FLAG_SEP ${CMAKE_SHARED_LIBRARY_RUNTIME_CXX_FLAG_SEP})
+if(NOT CMAKE_EXECUTABLE_RUNTIME_SYCL_FLAG_SEP)
+  set(CMAKE_EXECUTABLE_RUNTIME_SYCL_FLAG_SEP ${CMAKE_SHARED_LIBRARY_RUNTIME_SYCL_FLAG_SEP})
 endif()
 
-if(NOT CMAKE_EXECUTABLE_RPATH_LINK_CXX_FLAG)
-  set(CMAKE_EXECUTABLE_RPATH_LINK_CXX_FLAG ${CMAKE_SHARED_LIBRARY_RPATH_LINK_CXX_FLAG})
+if(NOT CMAKE_EXECUTABLE_RPATH_LINK_SYCL_FLAG)
+  set(CMAKE_EXECUTABLE_RPATH_LINK_SYCL_FLAG ${CMAKE_SHARED_LIBRARY_RPATH_LINK_SYCL_FLAG})
 endif()
 
-if(NOT DEFINED CMAKE_SHARED_LIBRARY_LINK_CXX_WITH_RUNTIME_PATH)
-  set(CMAKE_SHARED_LIBRARY_LINK_CXX_WITH_RUNTIME_PATH ${CMAKE_SHARED_LIBRARY_LINK_C_WITH_RUNTIME_PATH})
+if(NOT DEFINED CMAKE_SHARED_LIBRARY_LINK_SYCL_WITH_RUNTIME_PATH)
+  set(CMAKE_SHARED_LIBRARY_LINK_SYCL_WITH_RUNTIME_PATH ${CMAKE_SHARED_LIBRARY_LINK_CXX_WITH_RUNTIME_PATH})
 endif()
 
-if(NOT CMAKE_INCLUDE_FLAG_CXX)
-  set(CMAKE_INCLUDE_FLAG_CXX ${CMAKE_INCLUDE_FLAG_C})
+if(NOT CMAKE_INCLUDE_FLAG_SYCL)
+  set(CMAKE_INCLUDE_FLAG_SYCL ${CMAKE_INCLUDE_FLAG_C})
 endif()
 
 # for most systems a module is the same as a shared library
 # so unless the variable CMAKE_MODULE_EXISTS is set just
 # copy the values from the LIBRARY variables
 if(NOT CMAKE_MODULE_EXISTS)
-  set(CMAKE_SHARED_MODULE_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_CXX_FLAGS})
-  set(CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS ${CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS})
+  set(CMAKE_SHARED_MODULE_SYCL_FLAGS ${CMAKE_SHARED_LIBRARY_SYCL_FLAGS})
+  set(CMAKE_SHARED_MODULE_CREATE_SYCL_FLAGS ${CMAKE_SHARED_LIBRARY_CREATE_SYCL_FLAGS})
 endif()
 
 # repeat for modules
-if(NOT CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS)
-  set(CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS ${CMAKE_SHARED_MODULE_CREATE_C_FLAGS})
+if(NOT CMAKE_SHARED_MODULE_CREATE_SYCL_FLAGS)
+  set(CMAKE_SHARED_MODULE_CREATE_SYCL_FLAGS ${CMAKE_SHARED_MODULE_CREATE_CXX_FLAGS})
 endif()
 
-if(NOT CMAKE_SHARED_MODULE_CXX_FLAGS)
-  set(CMAKE_SHARED_MODULE_CXX_FLAGS ${CMAKE_SHARED_MODULE_C_FLAGS})
+if(NOT CMAKE_SHARED_MODULE_SYCL_FLAGS)
+  set(CMAKE_SHARED_MODULE_SYCL_FLAGS ${CMAKE_SHARED_MODULE_CXX_FLAGS})
 endif()
 
-# Initialize CXX link type selection flags from C versions.
+# Initialize SYCL link type selection flags from C versions.
 foreach(type SHARED_LIBRARY SHARED_MODULE EXE)
-  if(NOT CMAKE_${type}_LINK_STATIC_CXX_FLAGS)
-    set(CMAKE_${type}_LINK_STATIC_CXX_FLAGS
-      ${CMAKE_${type}_LINK_STATIC_C_FLAGS})
+  if(NOT CMAKE_${type}_LINK_STATIC_SYCL_FLAGS)
+    set(CMAKE_${type}_LINK_STATIC_SYCL_FLAGS
+      ${CMAKE_${type}_LINK_STATIC_CXX_FLAGS})
   endif()
-  if(NOT CMAKE_${type}_LINK_DYNAMIC_CXX_FLAGS)
-    set(CMAKE_${type}_LINK_DYNAMIC_CXX_FLAGS
-      ${CMAKE_${type}_LINK_DYNAMIC_C_FLAGS})
+  if(NOT CMAKE_${type}_LINK_DYNAMIC_SYCL_FLAGS)
+    set(CMAKE_${type}_LINK_DYNAMIC_SYCL_FLAGS
+      ${CMAKE_${type}_LINK_DYNAMIC_CXX_FLAGS})
   endif()
 endforeach()
 
 if(CMAKE_EXECUTABLE_FORMAT STREQUAL "ELF")
-  if(NOT DEFINED CMAKE_CXX_LINK_WHAT_YOU_USE_FLAG)
-    set(CMAKE_CXX_LINK_WHAT_YOU_USE_FLAG "LINKER:--no-as-needed")
+  if(NOT DEFINED CMAKE_SYCL_LINK_WHAT_YOU_USE_FLAG)
+    set(CMAKE_SYCL_LINK_WHAT_YOU_USE_FLAG "LINKER:--no-as-needed")
   endif()
   if(NOT DEFINED CMAKE_LINK_WHAT_YOU_USE_CHECK)
     set(CMAKE_LINK_WHAT_YOU_USE_CHECK ldd -u -r)
@@ -206,33 +271,33 @@ endif()
 # on the initial values computed in the platform/*.cmake files
 # use _INIT variables so that this only happens the first time
 # and you can set these flags in the cmake cache
-set(CMAKE_CXX_FLAGS_INIT "$ENV{CXXFLAGS} ${CMAKE_CXX_FLAGS_INIT}")
+set(CMAKE_SYCL_FLAGS_INIT "-fsycl $ENV{SYCLFLAGS} ${CMAKE_SYCL_FLAGS_INIT}")
 
-cmake_initialize_per_config_variable(CMAKE_CXX_FLAGS "Flags used by the CXX compiler")
+cmake_initialize_per_config_variable(CMAKE_SYCL_FLAGS "Flags used by the SYCL compiler")
 
-if(CMAKE_CXX_STANDARD_LIBRARIES_INIT)
-  set(CMAKE_CXX_STANDARD_LIBRARIES "${CMAKE_CXX_STANDARD_LIBRARIES_INIT}"
+if(CMAKE_SYCL_STANDARD_LIBRARIES_INIT)
+  set(CMAKE_SYCL_STANDARD_LIBRARIES "${CMAKE_CXX_STANDARD_LIBRARIES_INIT}"
     CACHE STRING "Libraries linked by default with all C++ applications.")
-  mark_as_advanced(CMAKE_CXX_STANDARD_LIBRARIES)
+  mark_as_advanced(CMAKE_SYCL_STANDARD_LIBRARIES)
 endif()
 
-if(NOT CMAKE_CXX_COMPILER_LAUNCHER AND DEFINED ENV{CMAKE_CXX_COMPILER_LAUNCHER})
-  set(CMAKE_CXX_COMPILER_LAUNCHER "$ENV{CMAKE_CXX_COMPILER_LAUNCHER}"
-    CACHE STRING "Compiler launcher for CXX.")
+if(NOT CMAKE_SYCL_COMPILER_LAUNCHER AND DEFINED ENV{CMAKE_SYCL_COMPILER_LAUNCHER})
+  set(CMAKE_SYCL_COMPILER_LAUNCHER "$ENV{CMAKE_SYCL_COMPILER_LAUNCHER}"
+    CACHE STRING "Compiler launcher for SYCL.")
 endif()
 
-if(NOT CMAKE_CXX_LINKER_LAUNCHER AND DEFINED ENV{CMAKE_CXX_LINKER_LAUNCHER})
-  set(CMAKE_CXX_LINKER_LAUNCHER "$ENV{CMAKE_CXX_LINKER_LAUNCHER}"
-    CACHE STRING "Linker launcher for CXX.")
+if(NOT CMAKE_SYCL_LINKER_LAUNCHER AND DEFINED ENV{CMAKE_SYCL_LINKER_LAUNCHER})
+  set(CMAKE_SYCL_LINKER_LAUNCHER "$ENV{CMAKE_SYCL_LINKER_LAUNCHER}"
+    CACHE STRING "Linker launcher for SYCL.")
 endif()
 
 include(CMakeCommonLanguageInclude)
 
 # now define the following rules:
-# CMAKE_CXX_CREATE_SHARED_LIBRARY
-# CMAKE_CXX_CREATE_SHARED_MODULE
-# CMAKE_CXX_COMPILE_OBJECT
-# CMAKE_CXX_LINK_EXECUTABLE
+# CMAKE_SYCL_CREATE_SHARED_LIBRARY
+# CMAKE_SYCL_CREATE_SHARED_MODULE
+# CMAKE_SYCL_COMPILE_OBJECT
+# CMAKE_SYCL_LINK_EXECUTABLE
 
 # variables supplied by the generator at use time
 # <TARGET>
@@ -243,54 +308,54 @@ include(CMakeCommonLanguageInclude)
 # <FLAGS>
 # <LINK_FLAGS>
 
-# CXX compiler information
-# <CMAKE_CXX_COMPILER>
-# <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS>
-# <CMAKE_CXX_SHARED_MODULE_CREATE_FLAGS>
-# <CMAKE_CXX_LINK_FLAGS>
+# SYCL compiler information
+# <CMAKE_SYCL_COMPILER>
+# <CMAKE_SHARED_LIBRARY_CREATE_SYCL_FLAGS>
+# <CMAKE_SYCL_SHARED_MODULE_CREATE_FLAGS>
+# <CMAKE_SYCL_LINK_FLAGS>
 
 # Static library tools
 # <CMAKE_AR>
 # <CMAKE_RANLIB>
 
-
 # create a shared C++ library
-if(NOT CMAKE_CXX_CREATE_SHARED_LIBRARY)
-  set(CMAKE_CXX_CREATE_SHARED_LIBRARY
-      "<CMAKE_CXX_COMPILER> <CMAKE_SHARED_LIBRARY_CXX_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS> <SONAME_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>")
+if(NOT CMAKE_SYCL_CREATE_SHARED_LIBRARY)
+  set(CMAKE_SYCL_CREATE_SHARED_LIBRARY
+      "<CMAKE_SYCL_COMPILER> <CMAKE_SHARED_LIBRARY_SYCL_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_SYCL_FLAGS> <SONAME_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>")
 endif()
 
 # create a c++ shared module copy the shared library rule by default
-if(NOT CMAKE_CXX_CREATE_SHARED_MODULE)
-  set(CMAKE_CXX_CREATE_SHARED_MODULE ${CMAKE_CXX_CREATE_SHARED_LIBRARY})
+if(NOT CMAKE_SYCL_CREATE_SHARED_MODULE)
+  set(CMAKE_SYCL_CREATE_SHARED_MODULE ${CMAKE_SYCL_CREATE_SHARED_LIBRARY})
 endif()
 
 
 # Create a static archive incrementally for large object file counts.
-# If CMAKE_CXX_CREATE_STATIC_LIBRARY is set it will override these.
-if(NOT DEFINED CMAKE_CXX_ARCHIVE_CREATE)
-  set(CMAKE_CXX_ARCHIVE_CREATE "<CMAKE_AR> qc <TARGET> <LINK_FLAGS> <OBJECTS>")
+# If CMAKE_SYCL_CREATE_STATIC_LIBRARY is set it will override these.
+if(NOT DEFINED CMAKE_SYCL_ARCHIVE_CREATE)
+  set(CMAKE_SYCL_ARCHIVE_CREATE "<CMAKE_AR> qc <TARGET> <LINK_FLAGS> <OBJECTS>")
 endif()
-if(NOT DEFINED CMAKE_CXX_ARCHIVE_APPEND)
-  set(CMAKE_CXX_ARCHIVE_APPEND "<CMAKE_AR> q <TARGET> <LINK_FLAGS> <OBJECTS>")
+if(NOT DEFINED CMAKE_SYCL_ARCHIVE_APPEND)
+  set(CMAKE_SYCL_ARCHIVE_APPEND "<CMAKE_AR> q <TARGET> <LINK_FLAGS> <OBJECTS>")
 endif()
-if(NOT DEFINED CMAKE_CXX_ARCHIVE_FINISH)
-  set(CMAKE_CXX_ARCHIVE_FINISH "<CMAKE_RANLIB> <TARGET>")
+if(NOT DEFINED CMAKE_SYCL_ARCHIVE_FINISH)
+  set(CMAKE_SYCL_ARCHIVE_FINISH "<CMAKE_RANLIB> <TARGET>")
 endif()
 
 # compile a C++ file into an object file
-if(NOT CMAKE_CXX_COMPILE_OBJECT)
-  set(CMAKE_CXX_COMPILE_OBJECT
-    "<CMAKE_CXX_COMPILER> <DEFINES> <INCLUDES> <FLAGS> -o <OBJECT> -c <SOURCE>")
+if(NOT CMAKE_SYCL_COMPILE_OBJECT)
+  set(CMAKE_SYCL_COMPILE_OBJECT
+    "<CMAKE_SYCL_COMPILER> <DEFINES> <INCLUDES> <FLAGS> -o <OBJECT> -c <SOURCE>")
 endif()
 
-if(NOT CMAKE_CXX_LINK_EXECUTABLE)
-  set(CMAKE_CXX_LINK_EXECUTABLE
-    "<CMAKE_CXX_COMPILER> <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
+if(NOT CMAKE_SYCL_LINK_EXECUTABLE)
+  set(CMAKE_SYCL_LINK_EXECUTABLE
+    "<CMAKE_SYCL_COMPILER> <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
 endif()
 
+
 mark_as_advanced(
 CMAKE_VERBOSE_MAKEFILE
 )
 
-set(CMAKE_CXX_INFORMATION_LOADED 1)
+set(CMAKE_SYCL_INFORMATION_LOADED 1)
diff --git a/CMakeModules/CMakeTestSYCLCompiler.cmake b/CMakeModules/CMakeTestSYCLCompiler.cmake
index e640ff9b30..e2f37a2da0 100644
--- a/CMakeModules/CMakeTestSYCLCompiler.cmake
+++ b/CMakeModules/CMakeTestSYCLCompiler.cmake
@@ -2,10 +2,10 @@
 # file Copyright.txt or https://cmake.org/licensing for details.
 
 
-if(CMAKE_CXX_COMPILER_FORCED)
+if(CMAKE_SYCL_COMPILER_FORCED)
   # The compiler configuration was forced by the user.
   # Assume the user has configured all compiler information.
-  set(CMAKE_CXX_COMPILER_WORKS TRUE)
+  set(CMAKE_SYCL_COMPILER_WORKS TRUE)
   return()
 endif()
 
@@ -18,16 +18,16 @@ if(_CMAKE_FEATURE_DETECTION_TARGET_TYPE)
 endif()
 
 # Remove any cached result from an older CMake version.
-# We now store this in CMakeCXXCompiler.cmake.
-unset(CMAKE_CXX_COMPILER_WORKS CACHE)
+# We now store this in CMakeSYCLCompiler.cmake.
+unset(CMAKE_SYCL_COMPILER_WORKS CACHE)
 
-# Try to identify the ABI and configure it into CMakeCXXCompiler.cmake
-include(${CMAKE_ROOT}/Modules/CMakeDetermineCompilerABI.cmake)
-CMAKE_DETERMINE_COMPILER_ABI(CXX ${CMAKE_ROOT}/Modules/CMakeCXXCompilerABI.cpp)
-if(CMAKE_CXX_ABI_COMPILED)
+# Try to identify the ABI and configure it into CMakeSYCLCompiler.cmake
+include(CMakeDetermineCompilerABI)
+CMAKE_DETERMINE_COMPILER_ABI(SYCL ${ArrayFire_SOURCE_DIR}/CMakeModules/CMakeSYCLCompilerABI.cpp)
+if(CMAKE_SYCL_ABI_COMPILED)
   # The compiler worked so skip dedicated test below.
-  set(CMAKE_CXX_COMPILER_WORKS TRUE)
-  message(STATUS "Check for working CXX compiler: ${CMAKE_CXX_COMPILER} - skipped")
+  set(CMAKE_SYCL_COMPILER_WORKS TRUE)
+  message(STATUS "Check for working SYCL compiler: ${CMAKE_SYCL_COMPILER} - skipped")
 endif()
 
 # This file is used by EnableLanguage in cmGlobalGenerator to
@@ -35,29 +35,29 @@ endif()
 # and link the most basic of programs.   If not, a fatal error
 # is set and cmake stops processing commands and will not generate
 # any makefiles or projects.
-if(NOT CMAKE_CXX_COMPILER_WORKS)
-  PrintTestCompilerStatus("CXX")
+if(NOT CMAKE_SYCL_COMPILER_WORKS)
+  PrintTestCompilerStatus("SYCL")
   __TestCompiler_setTryCompileTargetType()
-  string(CONCAT __TestCompiler_testCXXCompilerSource
+  file(WRITE ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeTmp/testSYCLCompiler.cxx
     "#ifndef __cplusplus\n"
-    "# error \"The CMAKE_CXX_COMPILER is set to a C compiler\"\n"
+    "# error \"The CMAKE_SYCL_COMPILER is set to a C compiler\"\n"
     "#endif\n"
     "int main(){return 0;}\n")
   # Clear result from normal variable.
-  unset(CMAKE_CXX_COMPILER_WORKS)
+  unset(CMAKE_SYCL_COMPILER_WORKS)
   # Puts test result in cache variable.
-  try_compile(CMAKE_CXX_COMPILER_WORKS
-    SOURCE_FROM_VAR testCXXCompiler.cxx __TestCompiler_testCXXCompilerSource
-    OUTPUT_VARIABLE __CMAKE_CXX_COMPILER_OUTPUT)
-  unset(__TestCompiler_testCXXCompilerSource)
+  try_compile(CMAKE_SYCL_COMPILER_WORKS ${CMAKE_BINARY_DIR}
+    ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeTmp/testSYCLCompiler.cxx
+    OUTPUT_VARIABLE __CMAKE_SYCL_COMPILER_OUTPUT)
+  unset(__TestCompiler_testSYCLCompilerSource)
   # Move result from cache to normal variable.
-  set(CMAKE_CXX_COMPILER_WORKS ${CMAKE_CXX_COMPILER_WORKS})
-  unset(CMAKE_CXX_COMPILER_WORKS CACHE)
+  set(CMAKE_SYCL_COMPILER_WORKS ${CMAKE_SYCL_COMPILER_WORKS})
+  unset(CMAKE_SYCL_COMPILER_WORKS CACHE)
   __TestCompiler_restoreTryCompileTargetType()
-  if(NOT CMAKE_CXX_COMPILER_WORKS)
+  if(NOT CMAKE_SYCL_COMPILER_WORKS)
     PrintTestCompilerResult(CHECK_FAIL "broken")
-    string(REPLACE "\n" "\n  " _output "${__CMAKE_CXX_COMPILER_OUTPUT}")
-    message(FATAL_ERROR "The C++ compiler\n  \"${CMAKE_CXX_COMPILER}\"\n"
+    string(REPLACE "\n" "\n  " _output "${__CMAKE_SYCL_COMPILER_OUTPUT}")
+    message(FATAL_ERROR "The C++ compiler\n  \"${CMAKE_SYCL_COMPILER}\"\n"
       "is not able to compile a simple test program.\nIt fails "
       "with the following output:\n  ${_output}\n\n"
       "CMake will not be able to correctly generate this project.")
@@ -66,24 +66,25 @@ if(NOT CMAKE_CXX_COMPILER_WORKS)
 endif()
 
 # Try to identify the compiler features
-include(${CMAKE_ROOT}/Modules/CMakeDetermineCompileFeatures.cmake)
-CMAKE_DETERMINE_COMPILE_FEATURES(CXX)
+include(CMakeDetermineCompileFeatures)
+CMAKE_DETERMINE_COMPILE_FEATURES(SYCL)
 
+set(CMAKE_TRY_COMPILE_CONFIGURATION "")
 # Re-configure to save learned information.
 configure_file(
-  ${CMAKE_ROOT}/Modules/CMakeCXXCompiler.cmake.in
-  ${CMAKE_PLATFORM_INFO_DIR}/CMakeCXXCompiler.cmake
+  ${ArrayFire_SOURCE_DIR}/CMakeModules/CMakeSYCLCompiler.cmake.in
+  ${CMAKE_PLATFORM_INFO_DIR}/CMakeSYCLCompiler.cmake
   @ONLY
-  )
-include(${CMAKE_PLATFORM_INFO_DIR}/CMakeCXXCompiler.cmake)
+)
+include(${CMAKE_PLATFORM_INFO_DIR}/CMakeSYCLCompiler.cmake)
 
-if(CMAKE_CXX_SIZEOF_DATA_PTR)
-  foreach(f ${CMAKE_CXX_ABI_FILES})
+if(CMAKE_SYCL_SIZEOF_DATA_PTR)
+  foreach(f ${CMAKE_SYCL_ABI_FILES})
     include(${f})
   endforeach()
-  unset(CMAKE_CXX_ABI_FILES)
+  unset(CMAKE_SYCL_ABI_FILES)
 endif()
 
 set(CMAKE_TRY_COMPILE_TARGET_TYPE ${__CMAKE_SAVED_TRY_COMPILE_TARGET_TYPE})
 unset(__CMAKE_SAVED_TRY_COMPILE_TARGET_TYPE)
-unset(__CMAKE_CXX_COMPILER_OUTPUT)
+unset(__CMAKE_SYCL_COMPILER_OUTPUT)
diff --git a/CMakeModules/InternalUtils.cmake b/CMakeModules/InternalUtils.cmake
index 863cbaed22..8d29718365 100644
--- a/CMakeModules/InternalUtils.cmake
+++ b/CMakeModules/InternalUtils.cmake
@@ -39,6 +39,37 @@ check_cxx_compiler_flag(-Rno-debug-disables-optimization has_cxx_debug-disables-
 function(arrayfire_set_default_cxx_flags target)
   target_compile_options(${target}
     PRIVATE
+
+      $<$<BOOL:${CMAKE_SYCL_COMPILER}>:
+        $<$<COMPILE_LANGUAGE:SYCL>:
+                # OpenCL targets need this flag to avoid
+                # ignored attribute warnings in the OpenCL
+                # headers
+                -Wno-ignored-attributes
+                -Wall
+                -Wno-unqualified-std-cast-call
+                -Werror=reorder-ctor
+                #-fp-model precise
+                $<$<BOOL:${AF_WITH_FAST_MATH}>: -ffast-math -fno-errno-math -fno-trapping-math -fno-signed-zeros -mno-ieee-fp>
+                $<$<NOT:$<BOOL:${AF_WITH_FAST_MATH}>>: $<IF:$<PLATFORM_ID:Windows>,/fp=precise,-fp-model=precise>>
+                $<$<CONFIG:Debug>:-Rno-debug-disables-optimization>
+
+                $<$<PLATFORM_ID:Windows>: /wd4251
+                                          /wd4068
+                                          /wd4275
+                                          /wd4668
+                                          /wd4710
+                                          /wd4505
+                                          /we5038
+                                          /bigobj
+                                          /EHsc
+                                          /nologo
+                                          # MSVC incorrectly sets the cplusplus to 199711L even if the compiler supports
+                                          # c++11 features. This flag sets it to the correct standard supported by the
+                                          # compiler
+                                          $<$<BOOL:${cplusplus_define}>:/Zc:__cplusplus>
+                                          $<$<BOOL:${cxx_compliance}>:/permissive-> >
+            >>
       $<$<COMPILE_LANGUAGE:CXX>:
               # C4068: Warnings about unknown pragmas
               # C4668: Warnings about unknown defintions
@@ -53,6 +84,7 @@ function(arrayfire_set_default_cxx_flags target)
                                           /we5038
                                           /bigobj
                                           /EHsc
+                                          /nologo
                                           # MSVC incorrectly sets the cplusplus to 199711L even if the compiler supports
                                           # c++11 features. This flag sets it to the correct standard supported by the
                                           # compiler
diff --git a/src/backend/common/Logger.hpp b/src/backend/common/Logger.hpp
index a004e773fb..a9a8feaa0b 100644
--- a/src/backend/common/Logger.hpp
+++ b/src/backend/common/Logger.hpp
@@ -22,6 +22,7 @@
 /* Intel ICC/ICPC */
 // Fix the warning code here, if any
 #elif defined(__GNUC__) || defined(__GNUG__)
+#pragma GCC diagnostic push
 /* GNU GCC/G++ */
 #elif defined(_MSC_VER)
 /* Microsoft Visual Studio */
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index d4c7245311..4ecb470ef9 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -5,6 +5,10 @@
 #The complete license agreement can be obtained at:
 #http:  // arrayfire.com/licenses/BSD-3-Clause
 
+if(AF_BUILD_ONEAPI)
+    enable_language(SYCL)
+endif()
+
 include(InternalUtils)
 include(build_cl2hpp)
 include(FileToString)
@@ -260,6 +264,26 @@ target_sources(afoneapi
     kernel/wrap_dilated.hpp
 )
 
+function(set_sycl_language)
+  foreach(target ${ARGV})
+    set_target_properties(${target}
+      PROPERTIES
+        LINKER_LANGUAGE SYCL)
+
+    get_target_property(TGT_SOURCES ${target} SOURCES)
+    if(NOT TGT_SOURCES)
+      get_target_property(TGT_SOURCES ${target} INTERFACE_SOURCES)
+    endif()
+
+    foreach(FILE ${TGT_SOURCES})
+      get_filename_component(FILE_EXTENSION ${FILE} EXT)
+      if(FILE_EXTENSION STREQUAL ".cpp")
+        set_source_files_properties(${FILE} PROPERTIES LANGUAGE SYCL)
+      endif()
+    endforeach()
+  endforeach()
+endfunction()
+
 set(kernel_src
   ${CMAKE_CURRENT_SOURCE_DIR}/../opencl/kernel/KParam.hpp
   ${CMAKE_CURRENT_SOURCE_DIR}/../opencl/kernel/jit.cl
@@ -301,10 +325,11 @@ target_include_directories(afoneapi
 
 target_compile_options(afoneapi
   PRIVATE
-    -fsycl
+  $<$<COMPILE_LANGUAGE:SYCL>:
     -fno-sycl-id-queries-fit-in-int
     -sycl-std=2020
-    -fno-sycl-rdc
+    $<$<PLATFORM_ID:Linux>: -fno-sycl-rdc>
+    >
 )
 
 target_compile_definitions(afoneapi
@@ -322,8 +347,6 @@ cmake_host_system_information(RESULT NumberOfThreads
 
 target_link_libraries(afoneapi
   PRIVATE
-    -fsycl
-    -fvisibility-inlines-hidden
     c_api_interface
     cpp_api_interface
     oneapi_sort_by_key
@@ -331,14 +354,20 @@ target_link_libraries(afoneapi
     OpenCL::OpenCL
     OpenCL::cl2hpp
     -fno-sycl-id-queries-fit-in-int
-    -fno-sycl-rdc
-    -fsycl-device-code-split=per_kernel
-    -fsycl-link-huge-device-code
+    $<$<PLATFORM_ID:Linux>:-fsycl-link-huge-device-code>
+    $<$<PLATFORM_ID:Linux>:-fvisibility-inlines-hidden>
+    $<$<PLATFORM_ID:Linux>:-fno-sycl-rdc>
     -fsycl-max-parallel-link-jobs=${NumberOfThreads}
     MKL::MKL_DPCPP
   )
+  set_sycl_language(afcommon_interface
+    oneapi_sort_by_key
+    c_api_interface
+    cpp_api_interface
+    afoneapi)
+
 
-af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})
+#af_split_debug_info(afoneapi ${AF_INSTALL_LIB_DIR})
 
 install(TARGETS afoneapi
   EXPORT ArrayFireoneAPITargets
diff --git a/src/backend/oneapi/device_manager.cpp b/src/backend/oneapi/device_manager.cpp
index ac06d5768c..56125382a0 100644
--- a/src/backend/oneapi/device_manager.cpp
+++ b/src/backend/oneapi/device_manager.cpp
@@ -104,13 +104,7 @@ DeviceManager::DeviceManager()
     // Iterate through platforms, get all available devices and store them
     for (auto& platform : platforms) {
         vector<sycl::device> current_devices;
-        try {
-            current_devices = platform.get_devices();
-        } catch (sycl::exception& err) {
-            printf("DeviceManager::DeviceManager() exception: %s\n",
-                   err.what());
-            throw;
-        }
+        current_devices = platform.get_devices();
         AF_TRACE("Found {} devices on platform {}", current_devices.size(),
                  platform.get_info<sycl::info::platform::name>());
 
diff --git a/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt b/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
index ce184639eb..394d593d6e 100644
--- a/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
@@ -20,6 +20,10 @@ foreach(SBK_TYPE ${SBK_TYPES})
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
         "${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key_impl.hpp"
     )
+
+  set_source_files_properties("${CMAKE_CURRENT_SOURCE_DIR}/kernel/sort_by_key/sort_by_key_impl.cpp"
+    PROPERTIES
+      LANGUAGE SYCL)
   set_target_properties(oneapi_sort_by_key_${SBK_TYPE}
     PROPERTIES
       COMPILE_DEFINITIONS "TYPE=${SBK_TYPE};AFDLL;$<TARGET_PROPERTY:Boost::boost,INTERFACE_COMPILE_DEFINITIONS>"
@@ -41,12 +45,17 @@ foreach(SBK_TYPE ${SBK_TYPES})
       ..
       )
 
+  target_compile_options(oneapi_sort_by_key_${SBK_TYPE}
+    PRIVATE
+      $<$<COMPILE_LANGUAGE:SYCL>: -fno-sycl-id-queries-fit-in-int
+                                  -sycl-std=2020
+                                  $<$<PLATFORM_ID:Linux>: -fno-sycl-rdc>>)
+
   target_include_directories(oneapi_sort_by_key_${SBK_TYPE}
     SYSTEM PRIVATE
       ${span-lite_SOURCE_DIR}/include
       $<TARGET_PROPERTY:Boost::boost,INTERFACE_INCLUDE_DIRECTORIES>)
 
-  target_compile_options(oneapi_sort_by_key_${SBK_TYPE} PUBLIC -fsycl)
   set_target_properties(oneapi_sort_by_key_${SBK_TYPE} PROPERTIES POSITION_INDEPENDENT_CODE ON)
   target_sources(oneapi_sort_by_key
     INTERFACE $<TARGET_OBJECTS:oneapi_sort_by_key_${SBK_TYPE}>)
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 3f1beb55bb..84ac83839f 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -7,13 +7,17 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 #pragma once
+#ifdef __GNUC__
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wparentheses"
+#endif
 #include <half.hpp>
+#ifdef __GNUC__
 #pragma GCC diagnostic pop
+#endif
 #include <af/array.h>
 #include <af/defines.h>
 #include <af/dim4.hpp>
@@ -49,11 +53,20 @@ std::ostream &operator<<(std::ostream &os, const af_half &val);
     do { (void)(expr); } while (0)
 
 namespace aft {
+#ifdef __GNUC__
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#elif defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable : 4996)
+#endif
 typedef intl intl;
 typedef uintl uintl;
+#ifdef __GNUC__
 #pragma GCC diagnostic pop
+#elif defined(_MSC_VER)
+#pragma warning(pop)
+#endif
 }  // namespace aft
 
 using aft::intl;
@@ -630,4 +643,6 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          const af_array a, const af_array b,
                                          TestOutputArrayInfo *metadata);
 
+#ifdef __GNUC__
 #pragma GCC diagnostic pop
+#endif

From 26486330f581be8550a13420724e64f481c977d3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 14 Aug 2023 16:48:01 -0400
Subject: [PATCH 2556/2677] Set cmake_minimum_version for oneAPI. Fix compiler
 id

---
 .github/workflows/unix_cpu_build.yml | 4 ++--
 CMakeLists.txt                       | 6 +++++-
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 3146358772..460aaa9d34 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -63,7 +63,7 @@ jobs:
         needs: [clang-format, documentation]
         env:
           NINJA_VER: 1.10.2
-          CMAKE_VER: 3.10.2
+          CMAKE_VER: 3.16.3
         strategy:
             fail-fast: false
             matrix:
@@ -93,7 +93,7 @@ jobs:
                   chmod +x ninja
                   ${GITHUB_WORKSPACE}/ninja --version
 
-            - name: Download CMake 3.10.2 for Linux
+            - name: Download CMake 3.16.3 for Linux
               if: matrix.os != 'macos-latest'
               env:
                   OS_NAME: ${{ matrix.os }}
diff --git a/CMakeLists.txt b/CMakeLists.txt
index e4cc17916f..deafa7a759 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -5,7 +5,11 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.10.2)
+if(AF_BUILD_ONEAPI)
+  cmake_minimum_required(VERSION 3.20)
+else()
+  cmake_minimum_required(VERSION 3.16.3)
+endif()
 include(CheckLanguage)
 
 include(CMakeModules/AF_vcpkg_options.cmake)

From 0514a5da43736d22a39bce4b56e61e7e0fc464d3 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 15 Aug 2023 12:06:30 -0400
Subject: [PATCH 2557/2677] Source tbb because of the new default threading
 backend for CPU

---
 .github/workflows/unix_cpu_build.yml | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/unix_cpu_build.yml b/.github/workflows/unix_cpu_build.yml
index 460aaa9d34..07ffba36f7 100644
--- a/.github/workflows/unix_cpu_build.yml
+++ b/.github/workflows/unix_cpu_build.yml
@@ -151,7 +151,7 @@ jobs:
                   sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
                   sudo sh -c 'echo deb https://apt.repos.intel.com/oneapi all main > /etc/apt/sources.list.d/oneAPI.list'
                   sudo apt-get -qq update
-                  sudo apt-get install -y intel-oneapi-mkl-devel
+                  sudo apt-get install -y intel-oneapi-mkl-devel intel-oneapi-tbb-devel
                   if [ "$CC" == 'icx' ]; then sudo apt-get install -y intel-oneapi-compiler-dpcpp-cpp; fi
                   echo "MKLROOT=/opt/intel/oneapi/mkl/latest" >> ${GITHUB_ENV}
 
@@ -171,10 +171,10 @@ jobs:
                   branch=$(git rev-parse --abbrev-ref HEAD)
                   buildname=$(if [ -z "$prnum" ]; then echo "$branch"; else echo "PR-$prnum"; fi)
                   dashboard=$(if [ -z "$prnum" ]; then echo "Continuous"; else echo "Experimental"; fi)
-                  backend=$(if [ "$USE_MKL" == 1 ]; then echo "Intel-MKL"; else echo "FFTW/LAPACK/BLAS"; fi)
+                  backend=$(if [ "$USE_MKL" == true ]; then echo "Intel-MKL"; else echo "FFTW/LAPACK/BLAS"; fi)
                   buildname="$buildname-cpu-$BLAS_BACKEND"
                   cmake_rpath=$(if [ $OS_NAME == 'macos-latest' ]; then echo "-DCMAKE_INSTALL_RPATH=/opt/arrayfire/lib"; fi)
-                  if [ "$CC" == 'icx' ]; then source /opt/intel/oneapi/setvars.sh intel64; fi
+                  if [ "$CC" == 'icx' ] || [ "$USE_MKL" == true ]; then source /opt/intel/oneapi/setvars.sh; fi
                   mkdir build && cd build && unset VCPKG_ROOT
                   ${CMAKE_PROGRAM} -G Ninja \
                       -DCMAKE_MAKE_PROGRAM:FILEPATH=${GITHUB_WORKSPACE}/ninja \
@@ -189,7 +189,8 @@ jobs:
             - name: Build and Test
               env:
                   CC: ${{ matrix.compiler }}
+                  USE_MKL: ${{ matrix.blas_backend == 'MKL' }}
               run: |
                   cd ${GITHUB_WORKSPACE}/build
-                  if [ "$CC" == 'icx' ]; then source /opt/intel/oneapi/setvars.sh intel64; fi
+                  if [ "$CC" == 'icx' ] || [ "$USE_MKL" == true ]; then source /opt/intel/oneapi/setvars.sh; fi
                   ctest -D Experimental --track ${CTEST_DASHBOARD} -T Test -T Submit -R cpu -j2

From bda893a1d280dcbf3284f536408edee3c95e4c0d Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 7 Aug 2023 14:07:59 -0700
Subject: [PATCH 2558/2677] fix wrong number of elements in createStrided for
 oneapi

---
 src/backend/oneapi/Array.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index f227f8def3..8ff64d78ec 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -196,8 +196,7 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
         data = memAlloc<T>(info.elements());
         getQueue()
             .submit([&](sycl::handler &h) {
-                h.copy(in_data,
-                       data->get_access(h, sycl::range(info.elements())));
+                h.copy(in_data, data->get_access(h, sycl::range(info.total())));
             })
             .wait();
     }

From 8aabf16f74396494459e3db1abef62be37366d79 Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Mon, 7 Aug 2023 17:12:24 -0700
Subject: [PATCH 2559/2677] implements events in oneapi backend

---
 src/backend/oneapi/Event.cpp | 64 ++++++++++++++++--------------------
 src/backend/oneapi/Event.hpp | 13 ++++----
 2 files changed, 35 insertions(+), 42 deletions(-)

diff --git a/src/backend/oneapi/Event.cpp b/src/backend/oneapi/Event.cpp
index 056c6cf950..60bc8bcb77 100644
--- a/src/backend/oneapi/Event.cpp
+++ b/src/backend/oneapi/Event.cpp
@@ -24,56 +24,50 @@ namespace arrayfire {
 namespace oneapi {
 /// \brief Creates a new event and marks it in the queue
 Event makeEvent(sycl::queue& queue) {
-    ONEAPI_NOT_SUPPORTED("makeEvent");
-    return Event();
+    Event e;
+    if (e.create() == 0) { e.mark(queue); }
+    return e;
 }
 
 af_event createEvent() {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
-    // auto e = make_unique<Event>();
-    // // Ensure the default CL command queue is initialized
-    // getQueue();
-    // if (e->create() != CL_SUCCESS) {
-    //     AF_ERROR("Could not create event", AF_ERR_RUNTIME);
-    // }
-    // Event& ref = *e.release();
-    // return getHandle(ref);
+    auto e = make_unique<Event>();
+    // Ensure the default CL command queue is initialized
+    getQueue();
+    if (e->create() != 0) {
+        AF_ERROR("Could not create event", AF_ERR_RUNTIME);
+    }
+    Event& ref = *e.release();
+    return getHandle(ref);
 }
 
 void markEventOnActiveQueue(af_event eventHandle) {
-    ONEAPI_NOT_SUPPORTED("");
-    // Event& event = getEvent(eventHandle);
-    //// Use the currently-active stream
-    // if (event.mark(getQueue()()) != CL_SUCCESS) {
-    //    AF_ERROR("Could not mark event on active queue", AF_ERR_RUNTIME);
-    //}
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active stream
+    if (event.mark(getQueue()) != 0) {
+        AF_ERROR("Could not mark event on active queue", AF_ERR_RUNTIME);
+    }
 }
 
 void enqueueWaitOnActiveQueue(af_event eventHandle) {
-    ONEAPI_NOT_SUPPORTED("");
-    // Event& event = getEvent(eventHandle);
-    //// Use the currently-active stream
-    // if (event.enqueueWait(getQueue()()) != CL_SUCCESS) {
-    //    AF_ERROR("Could not enqueue wait on active queue for event",
-    //             AF_ERR_RUNTIME);
-    //}
+    Event& event = getEvent(eventHandle);
+    // Use the currently-active stream
+    if (event.enqueueWait(getQueue()) != 0) {
+        AF_ERROR("Could not enqueue wait on active queue for event",
+                 AF_ERR_RUNTIME);
+    }
 }
 
 void block(af_event eventHandle) {
-    ONEAPI_NOT_SUPPORTED("");
-    // Event& event = getEvent(eventHandle);
-    // if (event.block() != CL_SUCCESS) {
-    //    AF_ERROR("Could not block on active queue for event", AF_ERR_RUNTIME);
-    //}
+    Event& event = getEvent(eventHandle);
+    if (event.block() != 0) {
+        AF_ERROR("Could not block on active queue for event", AF_ERR_RUNTIME);
+    }
 }
 
 af_event createAndMarkEvent() {
-    ONEAPI_NOT_SUPPORTED("");
-    return 0;
-    // af_event handle = createEvent();
-    // markEventOnActiveQueue(handle);
-    // return handle;
+    af_event handle = createEvent();
+    markEventOnActiveQueue(handle);
+    return handle;
 }
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/Event.hpp b/src/backend/oneapi/Event.hpp
index ae7fdd8c29..44af139cda 100644
--- a/src/backend/oneapi/Event.hpp
+++ b/src/backend/oneapi/Event.hpp
@@ -17,33 +17,32 @@ namespace arrayfire {
 namespace oneapi {
 class OneAPIEventPolicy {
    public:
-    using EventType = sycl::event;
+    using EventType = sycl::event *;
     using QueueType = sycl::queue;
-    // using ErrorType = sycl::exception; //does this make sense
     using ErrorType = int;
 
     static ErrorType createAndMarkEvent(EventType *e) noexcept {
-        // Events are created when you mark them
+        *e = new sycl::event;
         return 0;
     }
 
     static ErrorType markEvent(EventType *e, QueueType stream) noexcept {
-        // return clEnqueueMarkerWithWaitList(stream, 0, nullptr, e);
+        **e = stream.ext_oneapi_submit_barrier();
         return 0;
     }
 
     static ErrorType waitForEvent(EventType *e, QueueType stream) noexcept {
-        // return clEnqueueMarkerWithWaitList(stream, 1, e, nullptr);
+        stream.ext_oneapi_submit_barrier({**e});
         return 0;
     }
 
     static ErrorType syncForEvent(EventType *e) noexcept {
-        // return clWaitForEvents(1, e);
+        (*e)->wait();
         return 0;
     }
 
     static ErrorType destroyEvent(EventType *e) noexcept {
-        // return clReleaseEvent(*e);
+        delete *e;
         return 0;
     }
 };

From b1f2f86361924f7cb1160120a1e8cb62510c649e Mon Sep 17 00:00:00 2001
From: syurkevi <stefan@arrayfire.com>
Date: Fri, 4 Aug 2023 11:12:23 -0700
Subject: [PATCH 2560/2677] corrects double checks for reduce tests

---
 test/reduce.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/test/reduce.cpp b/test/reduce.cpp
index 0726a11791..0b8317a960 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -411,6 +411,9 @@ class ReduceByKeyP : public ::testing::TestWithParam<reduce_by_key_params *> {
         if (noHalfTests(params->vType_)) {
             GTEST_SKIP() << "Half not supported on this device";
         }
+        if (noDoubleTests(GetParam()->vType_)) {
+            GTEST_SKIP() << "Double not supported on this device";
+        }
 
         keys = ptrToArray(params->iSize, params->iKeys_, params->kType_);
         vals = ptrToArray(params->iSize, params->iVals_, params->vType_);
@@ -1967,6 +1970,9 @@ class RaggedReduceMaxRangeP : public ::testing::TestWithParam<ragged_params *> {
         if (noHalfTests(params->vType_)) {
             GTEST_SKIP() << "Half not supported on this device";
         }
+        if (noDoubleTests(GetParam()->vType_)) {
+            GTEST_SKIP() << "Double not supported on this device";
+        }
 
         const size_t rdim_size = params->reduceDimLen_;
         const int dim          = params->reduceDim_;
@@ -2324,6 +2330,7 @@ TEST(Reduce, Test_Sum_Global_Array_nanval) {
 
 TEST(Reduce, nanval_issue_3255) {
     SKIP_IF_FAST_MATH_ENABLED();
+    SUPPORTED_TYPE_CHECK(double);
     char *info_str;
     af_array ikeys, ivals, okeys, ovals;
     dim_t dims[1] = {8};

From b803eb802fffc72bab3a628d5ea122a19b1e090d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 13 Jun 2023 18:11:26 -0400
Subject: [PATCH 2561/2677] Move device checks out of getInfo into getArray

---
 src/api/c/array.cpp                | 55 +++++++++++++++---------------
 src/api/c/binary.cpp               | 16 ++++-----
 src/api/c/blas.cpp                 |  8 ++---
 src/api/c/cast.cpp                 |  4 +--
 src/api/c/device.cpp               |  4 +--
 src/api/c/handle.cpp               |  2 +-
 src/api/c/handle.hpp               | 16 +++++++--
 src/api/c/sparse.cpp               |  2 +-
 src/api/c/sparse_handle.hpp        |  4 ++-
 src/backend/common/ArrayInfo.cpp   |  8 +----
 src/backend/common/SparseArray.cpp | 10 +++++-
 src/backend/common/SparseArray.hpp |  7 ++++
 src/backend/cpu/Array.cpp          |  8 ++++-
 src/backend/cpu/Array.hpp          |  7 ++++
 src/backend/cuda/Array.cpp         | 10 +++++-
 src/backend/cuda/Array.hpp         |  7 ++++
 src/backend/oneapi/Array.cpp       | 10 +++++-
 src/backend/oneapi/Array.hpp       |  7 ++++
 src/backend/opencl/Array.cpp       | 10 +++++-
 src/backend/opencl/Array.hpp       |  7 ++++
 test/array.cpp                     |  2 +-
 21 files changed, 143 insertions(+), 61 deletions(-)

diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 173c52171c..4e1877e364 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -20,6 +20,7 @@ using af::dim4;
 using arrayfire::copyData;
 using arrayfire::copySparseArray;
 using arrayfire::getSparseArrayBase;
+using arrayfire::getUseCount;
 using arrayfire::releaseHandle;
 using arrayfire::releaseSparseHandle;
 using arrayfire::retainSparseHandle;
@@ -192,24 +193,24 @@ af_err af_copy_array(af_array *out, const af_array in) {
 // Strong Exception Guarantee
 af_err af_get_data_ref_count(int *use_count, const af_array in) {
     try {
-        const ArrayInfo &info = getInfo(in, false, false);
+        const ArrayInfo &info = getInfo(in, false);
         const af_dtype type   = info.getType();
 
         int res;
         switch (type) {
-            case f32: res = getArray<float>(in).useCount(); break;
-            case c32: res = getArray<cfloat>(in).useCount(); break;
-            case f64: res = getArray<double>(in).useCount(); break;
-            case c64: res = getArray<cdouble>(in).useCount(); break;
-            case b8: res = getArray<char>(in).useCount(); break;
-            case s32: res = getArray<int>(in).useCount(); break;
-            case u32: res = getArray<uint>(in).useCount(); break;
-            case u8: res = getArray<uchar>(in).useCount(); break;
-            case s64: res = getArray<intl>(in).useCount(); break;
-            case u64: res = getArray<uintl>(in).useCount(); break;
-            case s16: res = getArray<short>(in).useCount(); break;
-            case u16: res = getArray<ushort>(in).useCount(); break;
-            case f16: res = getArray<half>(in).useCount(); break;
+            case f32: res = getUseCount<float>(in); break;
+            case c32: res = getUseCount<cfloat>(in); break;
+            case f64: res = getUseCount<double>(in); break;
+            case c64: res = getUseCount<cdouble>(in); break;
+            case b8: res = getUseCount<char>(in); break;
+            case s32: res = getUseCount<int>(in); break;
+            case u32: res = getUseCount<uint>(in); break;
+            case u8: res = getUseCount<uchar>(in); break;
+            case s64: res = getUseCount<intl>(in); break;
+            case u64: res = getUseCount<uintl>(in); break;
+            case s16: res = getUseCount<short>(in); break;
+            case u16: res = getUseCount<ushort>(in); break;
+            case f16: res = getUseCount<half>(in); break;
             default: TYPE_ERROR(1, type);
         }
         std::swap(*use_count, res);
@@ -221,7 +222,7 @@ af_err af_get_data_ref_count(int *use_count, const af_array in) {
 af_err af_release_array(af_array arr) {
     try {
         if (arr == 0) { return AF_SUCCESS; }
-        const ArrayInfo &info = getInfo(arr, false, false);
+        const ArrayInfo &info = getInfo(arr, false);
         af_dtype type         = info.getType();
 
         if (info.isSparse()) {
@@ -335,7 +336,7 @@ af_err af_write_array(af_array arr, const void *data, const size_t bytes,
 af_err af_get_elements(dim_t *elems, const af_array arr) {
     try {
         // Do not check for device mismatch
-        *elems = getInfo(arr, false, false).elements();
+        *elems = getInfo(arr, false).elements();
     }
     CATCHALL
     return AF_SUCCESS;
@@ -344,7 +345,7 @@ af_err af_get_elements(dim_t *elems, const af_array arr) {
 af_err af_get_type(af_dtype *type, const af_array arr) {
     try {
         // Do not check for device mismatch
-        *type = getInfo(arr, false, false).getType();
+        *type = getInfo(arr, false).getType();
     }
     CATCHALL
     return AF_SUCCESS;
@@ -354,7 +355,7 @@ af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
                    const af_array in) {
     try {
         // Do not check for device mismatch
-        const ArrayInfo &info = getInfo(in, false, false);
+        const ArrayInfo &info = getInfo(in, false);
         *d0                   = info.dims()[0];
         *d1                   = info.dims()[1];
         *d2                   = info.dims()[2];
@@ -367,7 +368,7 @@ af_err af_get_dims(dim_t *d0, dim_t *d1, dim_t *d2, dim_t *d3,
 af_err af_get_numdims(unsigned *nd, const af_array in) {
     try {
         // Do not check for device mismatch
-        const ArrayInfo &info = getInfo(in, false, false);
+        const ArrayInfo &info = getInfo(in, false);
         *nd                   = info.ndims();
     }
     CATCHALL
@@ -375,14 +376,14 @@ af_err af_get_numdims(unsigned *nd, const af_array in) {
 }
 
 #undef INSTANTIATE
-#define INSTANTIATE(fn1, fn2)                                  \
-    af_err fn1(bool *result, const af_array in) {              \
-        try {                                                  \
-            const ArrayInfo &info = getInfo(in, false, false); \
-            *result               = info.fn2();                \
-        }                                                      \
-        CATCHALL                                               \
-        return AF_SUCCESS;                                     \
+#define INSTANTIATE(fn1, fn2)                           \
+    af_err fn1(bool *result, const af_array in) {       \
+        try {                                           \
+            const ArrayInfo &info = getInfo(in, false); \
+            *result               = info.fn2();         \
+        }                                               \
+        CATCHALL                                        \
+        return AF_SUCCESS;                              \
     }
 
 INSTANTIATE(af_is_empty, isEmpty)
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index dc5eddf4bc..50590568f8 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -279,8 +279,8 @@ af_err af_add(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     try {
         // Check if inputs are sparse
-        const ArrayInfo &linfo = getInfo(lhs, false, true);
-        const ArrayInfo &rinfo = getInfo(rhs, false, true);
+        const ArrayInfo &linfo = getInfo(lhs, false);
+        const ArrayInfo &rinfo = getInfo(rhs, false);
 
         if (linfo.isSparse() && rinfo.isSparse()) {
             return af_arith_sparse<af_add_t>(out, lhs, rhs);
@@ -301,8 +301,8 @@ af_err af_mul(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     try {
         // Check if inputs are sparse
-        const ArrayInfo &linfo = getInfo(lhs, false, true);
-        const ArrayInfo &rinfo = getInfo(rhs, false, true);
+        const ArrayInfo &linfo = getInfo(lhs, false);
+        const ArrayInfo &rinfo = getInfo(rhs, false);
 
         if (linfo.isSparse() && rinfo.isSparse()) {
             // return af_arith_sparse<af_mul_t>(out, lhs, rhs);
@@ -327,8 +327,8 @@ af_err af_sub(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     try {
         // Check if inputs are sparse
-        const ArrayInfo &linfo = getInfo(lhs, false, true);
-        const ArrayInfo &rinfo = getInfo(rhs, false, true);
+        const ArrayInfo &linfo = getInfo(lhs, false);
+        const ArrayInfo &rinfo = getInfo(rhs, false);
 
         if (linfo.isSparse() && rinfo.isSparse()) {
             return af_arith_sparse<af_sub_t>(out, lhs, rhs);
@@ -350,8 +350,8 @@ af_err af_div(af_array *out, const af_array lhs, const af_array rhs,
               const bool batchMode) {
     try {
         // Check if inputs are sparse
-        const ArrayInfo &linfo = getInfo(lhs, false, true);
-        const ArrayInfo &rinfo = getInfo(rhs, false, true);
+        const ArrayInfo &linfo = getInfo(lhs, false);
+        const ArrayInfo &rinfo = getInfo(rhs, false);
 
         if (linfo.isSparse() && rinfo.isSparse()) {
             // return af_arith_sparse<af_div_t>(out, lhs, rhs);
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 0946d42083..0cd8fddd8d 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -134,8 +134,8 @@ af_err af_gemm(af_array *out, const af_mat_prop optLhs,
                const af_mat_prop optRhs, const void *alpha, const af_array lhs,
                const af_array rhs, const void *beta) {
     try {
-        const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
-        const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
+        const ArrayInfo &lhsInfo = getInfo(lhs, false);
+        const ArrayInfo &rhsInfo = getInfo(rhs, true);
 
         af_dtype lhs_type = lhsInfo.getType();
         af_dtype rhs_type = rhsInfo.getType();
@@ -227,8 +227,8 @@ af_err af_gemm(af_array *out, const af_mat_prop optLhs,
 af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                  const af_mat_prop optLhs, const af_mat_prop optRhs) {
     try {
-        const ArrayInfo &lhsInfo = getInfo(lhs, false, true);
-        const ArrayInfo &rhsInfo = getInfo(rhs, true, true);
+        const ArrayInfo &lhsInfo = getInfo(lhs, false);
+        const ArrayInfo &rhsInfo = getInfo(rhs, true);
 
         if (lhsInfo.isSparse()) {
             return af_sparse_matmul(out, lhs, rhs, optLhs, optRhs);
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 20e47a1a2d..328c81ca65 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -34,7 +34,7 @@ using detail::uintl;
 using detail::ushort;
 
 static af_array cast(const af_array in, const af_dtype type) {
-    const ArrayInfo& info = getInfo(in, false, true);
+    const ArrayInfo& info = getInfo(in, false);
 
     if (info.getType() == type) { return retain(in); }
 
@@ -68,7 +68,7 @@ static af_array cast(const af_array in, const af_dtype type) {
 
 af_err af_cast(af_array* out, const af_array in, const af_dtype type) {
     try {
-        const ArrayInfo& info = getInfo(in, false, true);
+        const ArrayInfo& info = getInfo(in, false);
 
         af_dtype inType = info.getType();
         if ((inType == c32 || inType == c64) &&
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index 1b6ef9fb93..ef37888523 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -80,7 +80,7 @@ af_err af_get_available_backends(int* result) {
 af_err af_get_backend_id(af_backend* result, const af_array in) {
     try {
         if (in) {
-            const ArrayInfo& info = getInfo(in, false, false);
+            const ArrayInfo& info = getInfo(in, false);
             *result               = info.getBackendId();
         } else {
             return AF_ERR_ARG;
@@ -93,7 +93,7 @@ af_err af_get_backend_id(af_backend* result, const af_array in) {
 af_err af_get_device_id(int* device, const af_array in) {
     try {
         if (in) {
-            const ArrayInfo& info = getInfo(in, false, false);
+            const ArrayInfo& info = getInfo(in, false);
             *device               = static_cast<int>(info.getDevId());
         } else {
             return AF_ERR_ARG;
diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
index 7a93847826..243bfdba63 100644
--- a/src/api/c/handle.cpp
+++ b/src/api/c/handle.cpp
@@ -29,7 +29,7 @@ using detail::ushort;
 namespace arrayfire {
 
 af_array retain(const af_array in) {
-    const ArrayInfo &info = getInfo(in, false, false);
+    const ArrayInfo &info = getInfo(in, false);
     af_dtype ty           = info.getType();
 
     if (info.isSparse()) {
diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index 97243ac353..add7a7c612 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -40,8 +40,7 @@ af_array createHandleFromDeviceData(const af::dim4 &d, af_dtype dtype,
                                     void *data);
 
 namespace common {
-const ArrayInfo &getInfo(const af_array arr, bool sparse_check = true,
-                         bool device_check = true);
+const ArrayInfo &getInfo(const af_array arr, bool sparse_check = true);
 
 template<typename To>
 detail::Array<To> castArray(const af_array &in);
@@ -53,6 +52,7 @@ const detail::Array<T> &getArray(const af_array &arr) {
     const detail::Array<T> *A = static_cast<const detail::Array<T> *>(arr);
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+    checkAndMigrate(*A);
     return *A;
 }
 
@@ -61,9 +61,21 @@ detail::Array<T> &getArray(af_array &arr) {
     detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
+    checkAndMigrate(*A);
     return *A;
 }
 
+/// Returns the use count
+///
+/// \note This function is called separately because we cannot call getArray in
+/// case the data was built on a different context. so we are avoiding the check
+/// and migrate function
+template<typename T>
+int getUseCount(const af_array &arr) {
+    detail::Array<T> *A = static_cast<detail::Array<T> *>(arr);
+    return A->useCount();
+}
+
 template<typename T>
 af_array getHandle(const detail::Array<T> &A) {
     detail::Array<T> *ret = new detail::Array<T>(A);
diff --git a/src/api/c/sparse.cpp b/src/api/c/sparse.cpp
index 917864dcaf..db57b0077b 100644
--- a/src/api/c/sparse.cpp
+++ b/src/api/c/sparse.cpp
@@ -347,7 +347,7 @@ af_err af_sparse_convert_to(af_array *out, const af_array in,
                             const af_storage destStorage) {
     try {
         // Handle dense case
-        const ArrayInfo &info = getInfo(in, false, true);
+        const ArrayInfo &info = getInfo(in, false);
         if (!info.isSparse()) {  // If input is dense
             return af_create_sparse_array_from_dense(out, in, destStorage);
         }
diff --git a/src/api/c/sparse_handle.hpp b/src/api/c/sparse_handle.hpp
index e99bbb36e5..62c5289ebc 100644
--- a/src/api/c/sparse_handle.hpp
+++ b/src/api/c/sparse_handle.hpp
@@ -30,6 +30,7 @@ const common::SparseArray<T> &getSparseArray(const af_array &arr) {
     const common::SparseArray<T> *A =
         static_cast<const common::SparseArray<T> *>(arr);
     ARG_ASSERT(0, A->isSparse() == true);
+    checkAndMigrate(*A);
     return *A;
 }
 
@@ -37,6 +38,7 @@ template<typename T>
 common::SparseArray<T> &getSparseArray(af_array &arr) {
     common::SparseArray<T> *A = static_cast<common::SparseArray<T> *>(arr);
     ARG_ASSERT(0, A->isSparse() == true);
+    checkAndMigrate(*A);
     return *A;
 }
 
@@ -62,7 +64,7 @@ af_array retainSparseHandle(const af_array in) {
 // based on castArray in handle.hpp
 template<typename To>
 common::SparseArray<To> castSparse(const af_array &in) {
-    const ArrayInfo &info = getInfo(in, false, true);
+    const ArrayInfo &info = getInfo(in, false);
     using namespace common;
 
 #define CAST_SPARSE(Ti)                                                          \
diff --git a/src/backend/common/ArrayInfo.cpp b/src/backend/common/ArrayInfo.cpp
index d919c942f8..60c55c3e52 100644
--- a/src/backend/common/ArrayInfo.cpp
+++ b/src/backend/common/ArrayInfo.cpp
@@ -221,8 +221,7 @@ dim4 toStride(const vector<af_seq> &seqs, const af::dim4 &parentDims) {
 namespace arrayfire {
 namespace common {
 
-const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
-                         bool device_check) {
+const ArrayInfo &getInfo(const af_array arr, bool sparse_check) {
     const ArrayInfo *info = nullptr;
     memcpy(&info, &arr, sizeof(af_array));
 
@@ -230,11 +229,6 @@ const ArrayInfo &getInfo(const af_array arr, bool sparse_check,
     // are accepted Otherwise only regular Array<T> is accepted
     if (sparse_check) { ARG_ASSERT(0, info->isSparse() == false); }
 
-    if (device_check && info->getDevId() != static_cast<unsigned>(
-                                                detail::getActiveDeviceId())) {
-        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
-    }
-
     return *info;
 }
 
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index ac91a29f31..ed9680c6a5 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -171,6 +171,13 @@ void destroySparseArray(SparseArray<T> *sparse) {
     delete sparse;
 }
 
+template<typename T>
+void checkAndMigrate(const SparseArray<T> &arr) {
+    checkAndMigrate(arr.getColIdx());
+    checkAndMigrate(arr.getRowIdx());
+    checkAndMigrate(arr.getValues());
+}
+
 ////////////////////////////////////////////////////////////////////////////
 // Sparse Array Class Implementations
 ////////////////////////////////////////////////////////////////////////////
@@ -250,7 +257,8 @@ SparseArray<T>::SparseArray(const SparseArray<T> &other, bool copy)
     template SparseArray<T>::SparseArray(                                    \
         const af::dim4 &_dims, const Array<T> &_values,                      \
         const Array<int> &_rowIdx, const Array<int> &_colIdx,                \
-        const af::storage _storage, bool _copy)
+        const af::storage _storage, bool _copy);                             \
+    template void checkAndMigrate(const SparseArray<T> &arr)
 
 // Instantiate only floating types
 INSTANTIATE(float);
diff --git a/src/backend/common/SparseArray.hpp b/src/backend/common/SparseArray.hpp
index 860f7814ac..046a92fbe7 100644
--- a/src/backend/common/SparseArray.hpp
+++ b/src/backend/common/SparseArray.hpp
@@ -248,5 +248,12 @@ class SparseArray {
     friend void destroySparseArray<T>(SparseArray<T> *sparse);
 };
 
+/// Checks if the Array object can be migrated to the current device and if not,
+/// an error is thrown
+///
+/// \param[in] arr The Array that will be checked.
+template<typename T>
+void checkAndMigrate(const SparseArray<T> &arr);
+
 }  // namespace common
 }  // namespace arrayfire
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index 88f4bcabee..dc0b5d5dad 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -134,6 +134,11 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
     }
 }
 
+template<typename T>
+void checkAndMigrate(const Array<T> &arr) {
+    return;
+}
+
 template<typename T>
 void Array<T>::eval() {
     evalMultiple<T>({this});
@@ -353,7 +358,8 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(vector<Array<T> *> arrays);                 \
     template kJITHeuristics passesJitHeuristics<T>(span<Node *> n);           \
-    template void Array<T>::setDataDims(const dim4 &new_dims);
+    template void Array<T>::setDataDims(const dim4 &new_dims);                \
+    template void checkAndMigrate<T>(const Array<T> &arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cpu/Array.hpp b/src/backend/cpu/Array.hpp
index 3c7b54c5ec..7afed3501e 100644
--- a/src/backend/cpu/Array.hpp
+++ b/src/backend/cpu/Array.hpp
@@ -127,6 +127,13 @@ void *getRawPtr(const Array<T> &arr) {
     return (void *)(arr.get(false));
 }
 
+/// Checks if the Array object can be migrated to the current device and if not,
+/// an error is thrown
+///
+/// \param[in] arr The Array that will be checked.
+template<typename T>
+void checkAndMigrate(const Array<T> &arr);
+
 // Array Array Implementation
 template<typename T>
 class Array {
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index eb71a9f7a2..9af853cb22 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -57,6 +57,13 @@ std::shared_ptr<BufferNode<T>> bufferNodePtr() {
         static_cast<af::dtype>(dtype_traits<T>::af_type));
 }
 
+template<typename T>
+void checkAndMigrate(const Array<T> &arr) {
+    if (arr.getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    }
+}
+
 template<typename T>
 Array<T>::Array(const af::dim4 &dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
@@ -468,7 +475,8 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
         Array<T> & arr, const void *const data, const size_t bytes);          \
     template void evalMultiple<T>(std::vector<Array<T> *> arrays);            \
     template kJITHeuristics passesJitHeuristics<T>(span<Node *> n);           \
-    template void Array<T>::setDataDims(const dim4 &new_dims);
+    template void Array<T>::setDataDims(const dim4 &new_dims);                \
+    template void checkAndMigrate<T>(const Array<T> &arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index 7e1324d016..caf1a90357 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -34,6 +34,13 @@ using af::dim4;
 template<typename T>
 class Array;
 
+/// Checks if the Array object can be migrated to the current device and if not,
+/// an error is thrown
+///
+/// \param[in] arr The Array that will be checked.
+template<typename T>
+void checkAndMigrate(const Array<T> &arr);
+
 template<typename T>
 void evalNodes(Param<T> out, common::Node *node);
 
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 8ff64d78ec..f2ef09c044 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -88,6 +88,13 @@ void verifyTypeSupport<arrayfire::common::half>() {
 }
 }  // namespace
 
+template<typename T>
+void checkAndMigrate(const Array<T> &arr) {
+    if (arr.getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    }
+}
+
 template<typename T>
 Array<T>::Array(const dim4 &dims)
     : info(getActiveDeviceId(), dims, 0, calcStrides(dims),
@@ -564,7 +571,8 @@ size_t Array<T>::getAllocatedBytes() const {
     template kJITHeuristics passesJitHeuristics<T>(span<Node *> node);       \
     template void *getDevicePtr<T>(const Array<T> &arr);                     \
     template void Array<T>::setDataDims(const dim4 &new_dims);               \
-    template size_t Array<T>::getAllocatedBytes() const;
+    template size_t Array<T>::getAllocatedBytes() const;                     \
+    template void checkAndMigrate<T>(const Array<T> &arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/oneapi/Array.hpp b/src/backend/oneapi/Array.hpp
index e0b0962222..5e7ec490f1 100644
--- a/src/backend/oneapi/Array.hpp
+++ b/src/backend/oneapi/Array.hpp
@@ -51,6 +51,13 @@ using af::dim4;
 template<typename T>
 class Array;
 
+/// Checks if the Array object can be migrated to the current device and if not,
+/// an error is thrown
+///
+/// \param[in] arr The Array that will be checked.
+template<typename T>
+void checkAndMigrate(const Array<T> &arr);
+
 template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays);
 
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 2d3bc40e0b..c54476d38d 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -192,6 +192,13 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
     }
 }
 
+template<typename T>
+void checkAndMigrate(const Array<T> &arr) {
+    if (arr.getDevId() != detail::getActiveDeviceId()) {
+        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    }
+}
+
 template<typename T>
 void Array<T>::eval() {
     if (isReady()) { return; }
@@ -552,7 +559,8 @@ size_t Array<T>::getAllocatedBytes() const {
     template kJITHeuristics passesJitHeuristics<T>(span<Node *> node);        \
     template void *getDevicePtr<T>(const Array<T> &arr);                      \
     template void Array<T>::setDataDims(const dim4 &new_dims);                \
-    template size_t Array<T>::getAllocatedBytes() const;
+    template size_t Array<T>::getAllocatedBytes() const;                      \
+    template void checkAndMigrate<T>(const Array<T> & arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 3a672d00f6..5bd6d422c4 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -41,6 +41,13 @@ using af::dim4;
 template<typename T>
 class Array;
 
+/// Checks if the Array object can be migrated to the current device and if not,
+/// an error is thrown
+///
+/// \param[in] arr The Array that will be checked.
+template<typename T>
+void checkAndMigrate(const Array<T> &arr);
+
 template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays);
 
diff --git a/test/array.cpp b/test/array.cpp
index 5962797083..4ba6452b2c 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -473,7 +473,7 @@ TEST(DeviceId, Same) {
 
 TEST(DeviceId, Different) {
     int ndevices = getDeviceCount();
-    if (ndevices < 2) return;
+    if (ndevices < 2) GTEST_SKIP() << "Skipping mult-GPU test";
     int id0 = getDevice();
     int id1 = (id0 + 1) % ndevices;
 

From bf7d1c80d55f79980cc9c9b5f6e003e17e5e8159 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 15 Jun 2023 22:00:15 -0400
Subject: [PATCH 2562/2677] Allow access to buffers on other devices with the
 same context

---
 src/backend/cuda/Array.cpp            |  4 ++-
 src/backend/cuda/device_manager.cpp   | 23 +++++++++++++++++
 src/backend/cuda/device_manager.hpp   |  9 +++++++
 src/backend/cuda/platform.cpp         |  6 +++++
 src/backend/cuda/platform.hpp         |  7 ++++++
 src/backend/opencl/Array.cpp          | 11 +++++---
 src/backend/opencl/device_manager.cpp | 28 +++++++++++++++------
 src/backend/opencl/device_manager.hpp |  2 ++
 src/backend/opencl/platform.cpp       | 36 ++++++++++++++++++---------
 src/backend/opencl/platform.hpp       |  7 ++++++
 test/array.cpp                        | 21 +++++++++++++++-
 11 files changed, 129 insertions(+), 25 deletions(-)

diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 9af853cb22..12a66f1293 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -59,7 +59,9 @@ std::shared_ptr<BufferNode<T>> bufferNodePtr() {
 
 template<typename T>
 void checkAndMigrate(const Array<T> &arr) {
-    if (arr.getDevId() != detail::getActiveDeviceId()) {
+    int arr_id = arr.getDevId();
+    int cur_id = detail::getActiveDeviceId();
+    if (!isDeviceBufferAccessible(arr_id, cur_id)) {
         AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
     }
 }
diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index c60bf35437..9e7cc2d68b 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -613,6 +613,29 @@ DeviceManager::DeviceManager()
 
     sortDevices();
 
+    // Set all default peer access to false
+    for (auto &dev_map : device_peer_access_map)
+        for (auto &dev_access : dev_map) { dev_access = false; }
+
+    // Enable peer 2 peer access to device memory if available
+    for (int i = 0; i < nDevices; i++) {
+        for (int j = 0; j < nDevices; j++) {
+            if (i != j) {
+                int can_access_peer;
+                CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, i, j));
+                if (can_access_peer) {
+                    CUDA_CHECK(cudaSetDevice(i));
+                    AF_TRACE("Peer access enabled for {}({}) and {}({})", i,
+                             cuDevices[i].prop.name, j, cuDevices[j].prop.name);
+                    CUDA_CHECK(cudaDeviceEnablePeerAccess(j, 0));
+                    device_peer_access_map[i][j] = true;
+                }
+            } else {
+                device_peer_access_map[i][j] = true;
+            }
+        }
+    }
+
     // Initialize all streams to 0.
     // Streams will be created in setActiveDevice()
     for (int i = 0; i < MAX_DEVICES; i++) {
diff --git a/src/backend/cuda/device_manager.hpp b/src/backend/cuda/device_manager.hpp
index 9275386011..ca43efaf1f 100644
--- a/src/backend/cuda/device_manager.hpp
+++ b/src/backend/cuda/device_manager.hpp
@@ -11,6 +11,7 @@
 
 #include <platform.hpp>
 
+#include <array>
 #include <memory>
 #include <mutex>
 #include <string>
@@ -95,6 +96,8 @@ class DeviceManager {
 
     friend std::pair<int, int> getComputeCapability(const int device);
 
+    friend bool isDeviceBufferAccessible(int buf_device_id, int execution_id);
+
    private:
     DeviceManager();
 
@@ -117,6 +120,12 @@ class DeviceManager {
 
     std::shared_ptr<spdlog::logger> logger;
 
+    /// A matrix of booleans where true indicates that the corresponding
+    /// corrdinate devices can access each other buffers. False indicates
+    /// buffers need to be copied over to the other device
+    std::array<std::array<bool, MAX_DEVICES>, MAX_DEVICES>
+        device_peer_access_map;
+
     std::vector<cudaDevice_t> cuDevices;
     std::vector<std::pair<int, int>> devJitComputes;
 
diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 3fab99bb7f..52a22cdbaf 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -208,6 +208,12 @@ DeviceManager::~DeviceManager() {
     }
 }
 
+bool isDeviceBufferAccessible(int buf_device_id, int execution_id) {
+    DeviceManager &mngr = DeviceManager::getInstance();
+    return buf_device_id == execution_id ||
+           mngr.device_peer_access_map[buf_device_id][execution_id];
+}
+
 int getBackend() { return AF_BACKEND_CUDA; }
 
 string getDeviceInfo(int device) noexcept {
diff --git a/src/backend/cuda/platform.hpp b/src/backend/cuda/platform.hpp
index cac1281b59..be9f0b9996 100644
--- a/src/backend/cuda/platform.hpp
+++ b/src/backend/cuda/platform.hpp
@@ -88,6 +88,13 @@ cudaStream_t getStream(int device);
 
 cudaStream_t getActiveStream();
 
+/// Returns true if the buffer on device buf_device_id can be accessed by
+/// kernels on device execution_id
+///
+/// \param[in] buf_device_id The device id of the buffer
+/// \param[in] execution_id The device where the buffer will be accessed.
+bool isDeviceBufferAccessible(int buf_device_id, int execution_id);
+
 /// Return a handle to the stream for the device.
 ///
 /// \param[in] device The device of the returned stream
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index c54476d38d..b4e66373a5 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -194,8 +194,13 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
 
 template<typename T>
 void checkAndMigrate(const Array<T> &arr) {
-    if (arr.getDevId() != detail::getActiveDeviceId()) {
-        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+    int arr_id = arr.getDevId();
+    int cur_id = detail::getActiveDeviceId();
+    if (!isDeviceBufferAccessible(arr_id, cur_id)) {
+        AF_ERROR(
+            "The array's device context does not match the current device's "
+            "context",
+            AF_ERR_DEVICE);
     }
 }
 
@@ -560,7 +565,7 @@ size_t Array<T>::getAllocatedBytes() const {
     template void *getDevicePtr<T>(const Array<T> &arr);                      \
     template void Array<T>::setDataDims(const dim4 &new_dims);                \
     template size_t Array<T>::getAllocatedBytes() const;                      \
-    template void checkAndMigrate<T>(const Array<T> & arr);
+    template void checkAndMigrate<T>(const Array<T> &arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index a8ca6e96c9..1e628af521 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -171,6 +171,14 @@ static inline bool compare_default(const unique_ptr<Device>& ldev,
     return l_mem > r_mem;
 }
 
+/// Class to compare two devices for sorting in a map
+class deviceLess {
+   public:
+    bool operator()(const cl::Device& lhs, const cl::Device& rhs) const {
+        return lhs() < rhs();
+    }
+};
+
 DeviceManager::DeviceManager()
     : logger(common::loggerFactory("platform"))
     , mUserDeviceOffset(0)
@@ -216,6 +224,7 @@ DeviceManager::DeviceManager()
 
     AF_TRACE("Found {} OpenCL platforms", platforms.size());
 
+    std::map<cl::Device, cl::Context, deviceLess> mDeviceContextMap;
     // Iterate through platforms, get all available devices and store them
     for (auto& platform : platforms) {
         vector<Device> current_devices;
@@ -227,11 +236,15 @@ DeviceManager::DeviceManager()
         }
         AF_TRACE("Found {} devices on platform {}", current_devices.size(),
                  platform.getInfo<CL_PLATFORM_NAME>());
-        for (auto& dev : current_devices) {
-            mDevices.emplace_back(make_unique<Device>(dev));
-            AF_TRACE("Found device {} on platform {}",
-                     dev.getInfo<CL_DEVICE_NAME>(),
-                     platform.getInfo<CL_PLATFORM_NAME>());
+        if (!current_devices.empty()) {
+            cl::Context ctx(current_devices);
+            for (auto& dev : current_devices) {
+                mDeviceContextMap[dev] = ctx;
+                mDevices.emplace_back(make_unique<Device>(dev));
+                AF_TRACE("Found device {} on platform {}",
+                         dev.getInfo<CL_DEVICE_NAME>(),
+                         platform.getInfo<CL_PLATFORM_NAME>());
+            }
         }
     }
 
@@ -250,10 +263,9 @@ DeviceManager::DeviceManager()
     for (int i = 0; i < nDevices; i++) {
         cl_platform_id device_platform =
             devices[i]->getInfo<CL_DEVICE_PLATFORM>();
-        cl_context_properties cps[3] = {
-            CL_CONTEXT_PLATFORM, (cl_context_properties)(device_platform), 0};
         try {
-            mContexts.push_back(make_unique<Context>(*devices[i], cps));
+            mContexts.emplace_back(
+                make_unique<cl::Context>(mDeviceContextMap[*devices[i]]));
             mQueues.push_back(make_unique<CommandQueue>(
                 *mContexts.back(), *devices[i], cl::QueueProperties::None));
             mIsGLSharingOn.push_back(false);
diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 432758bd87..69ddd80d2d 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -139,6 +139,8 @@ class DeviceManager {
 
     friend afcl::platform getActivePlatformVendor();
 
+    friend bool isDeviceBufferAccessible(int buf_device_id, int execution_id);
+
    public:
     static const int MAX_DEVICES = 32;
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index 165eded95f..ac07c3b818 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -278,6 +278,16 @@ afcl::platform getActivePlatformVendor() {
     return devMngr.mPlatforms[get<1>(devId)].second;
 }
 
+bool isDeviceBufferAccessible(int buf_device_id, int execution_id) {
+    DeviceManager& devMngr = DeviceManager::getInstance();
+
+    common::lock_guard_t lock(devMngr.deviceMutex);
+
+    return buf_device_id == execution_id ||
+           *devMngr.mContexts[buf_device_id] ==
+               *devMngr.mContexts[execution_id];
+}
+
 const Context& getContext() {
     device_id_t& devId = tlocalActiveDeviceId();
 
@@ -330,9 +340,9 @@ vector<Version> getOpenCLCDeviceVersion(const Device& device) {
     auto platform_version = device_platform.getInfo<CL_PLATFORM_VERSION>();
     vector<Version> out;
 
-    /// The ifdef allows us to support BUILDING ArrayFire with older versions of
-    /// OpenCL where as the if condition in the ifdef allows us to support older
-    /// versions of OpenCL at runtime
+    /// The ifdef allows us to support BUILDING ArrayFire with older
+    /// versions of OpenCL where as the if condition in the ifdef allows us
+    /// to support older versions of OpenCL at runtime
 #ifdef CL_DEVICE_OPENCL_C_ALL_VERSIONS
     if (platform_version.substr(7).c_str()[0] >= '3') {
         vector<cl_name_version> device_versions =
@@ -519,24 +529,25 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
     {
         common::lock_guard_t lock(devMngr.deviceMutex);
 
-        auto tDevice  = make_unique<cl::Device>(dev, true);
-        auto tContext = make_unique<cl::Context>(ctx, true);
+        cl::Device tDevice(dev, true);
+        cl::Context tContext(ctx, true);
         auto tQueue =
-            (que == NULL ? make_unique<cl::CommandQueue>(*tContext, *tDevice)
+            (que == NULL ? make_unique<cl::CommandQueue>(tContext, tDevice)
                          : make_unique<cl::CommandQueue>(que, true));
         // FIXME: add OpenGL Interop for user provided contexts later
         devMngr.mIsGLSharingOn.push_back(false);
         devMngr.mDeviceTypes.push_back(
-            static_cast<int>(tDevice->getInfo<CL_DEVICE_TYPE>()));
+            static_cast<int>(tDevice.getInfo<CL_DEVICE_TYPE>()));
 
-        auto device_platform = tDevice->getInfo<CL_DEVICE_PLATFORM>();
+        auto device_platform = tDevice.getInfo<CL_DEVICE_PLATFORM>();
         devMngr.mPlatforms.push_back(
             std::make_pair<std::unique_ptr<cl::Platform>, afcl_platform>(
                 make_unique<cl::Platform>(device_platform, true),
-                getPlatformEnum(*tDevice)));
+                getPlatformEnum(tDevice)));
 
-        devMngr.mDevices.push_back(move(tDevice));
-        devMngr.mContexts.push_back(move(tContext));
+        devMngr.mDevices.emplace_back(make_unique<cl::Device>(move(tDevice)));
+        devMngr.mContexts.emplace_back(
+            make_unique<cl::Context>(move(tContext)));
         devMngr.mQueues.push_back(move(tQueue));
         nDevices = static_cast<int>(devMngr.mDevices.size()) - 1;
 
@@ -594,7 +605,8 @@ void removeDeviceContext(cl_device_id dev, cl_context ctx) {
         common::lock_guard_t lock(devMngr.deviceMutex);
 
         const int dCount = static_cast<int>(devMngr.mDevices.size());
-        for (int i = 0; i < dCount; ++i) {
+        for (int i = static_cast<int>(devMngr.mUserDeviceOffset); i < dCount;
+             ++i) {
             if (devMngr.mDevices[i]->operator()() == dev &&
                 devMngr.mContexts[i]->operator()() == ctx) {
                 deleteIdx = i;
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index 94ab6dff52..c14c25f399 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -187,5 +187,12 @@ afcl::platform getPlatformEnum(cl::Device dev);
 
 void setActiveContext(int device);
 
+/// Returns true if the buffer on device buf_device_id can be accessed by
+/// kernels on device execution_id
+///
+/// \param[in] buf_device_id The device id of the buffer
+/// \param[in] execution_id The device where the buffer will be accessed.
+bool isDeviceBufferAccessible(int buf_device_id, int execution_id);
+
 }  // namespace opencl
 }  // namespace arrayfire
diff --git a/test/array.cpp b/test/array.cpp
index 4ba6452b2c..bcf6fa997e 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -491,7 +491,8 @@ TEST(DeviceId, Different) {
 
         af_array c;
         af_err err = af_matmul(&c, a.get(), b.get(), AF_MAT_NONE, AF_MAT_NONE);
-        ASSERT_EQ(err, AF_ERR_DEVICE);
+        af::sync();
+        ASSERT_EQ(err, AF_SUCCESS);
     }
 
     setDevice(id1);
@@ -657,3 +658,21 @@ TEST(Array, InitializerListFixDim4) {
     af::array b{dim4(3, 3), data.data()};
     ASSERT_ARRAYS_EQ(constant(3.14, 3, 3), b);
 }
+
+TEST(Array, OtherDevice) {
+    if (af::getDeviceCount() == 1) GTEST_SKIP() << "Single device. Skipping";
+    af::setDevice(0);
+    af::info();
+    af::array a = constant(3, 5, 5);
+    a.eval();
+    af::setDevice(1);
+    af::info();
+    af::array b = constant(2, 5, 5);
+    b.eval();
+
+    af::array c = a + b;
+    af::eval(c);
+    af::sync();
+    af::setDevice(0);
+    ASSERT_ARRAYS_EQ(constant(5, 5, 5), c);
+}

From 21b5a169cc76307904da2426d452f2c45c37b8cc Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 17 Jun 2023 16:51:16 -0400
Subject: [PATCH 2563/2677] Update OpenCL getQueue to accept the device id

---
 src/backend/opencl/device_manager.hpp | 2 +-
 src/backend/opencl/platform.cpp       | 6 +++---
 src/backend/opencl/platform.hpp       | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/device_manager.hpp b/src/backend/opencl/device_manager.hpp
index 69ddd80d2d..4b27a8f885 100644
--- a/src/backend/opencl/device_manager.hpp
+++ b/src/backend/opencl/device_manager.hpp
@@ -105,7 +105,7 @@ class DeviceManager {
 
     friend const cl::Context& getContext();
 
-    friend cl::CommandQueue& getQueue();
+    friend cl::CommandQueue& getQueue(int device_id);
 
     friend cl_command_queue getQueueHandle(int device_id);
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index ac07c3b818..eb9bc320e4 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -306,9 +306,9 @@ cl_command_queue getQueueHandle(int device_id) {
     return (*(devMngr.mQueues[device_id]))();
 }
 
-CommandQueue& getQueue() {
-    device_id_t& devId = tlocalActiveDeviceId();
-
+CommandQueue& getQueue(int device_id) {
+    device_id_t devId     = (device_id = -1) ? tlocalActiveDeviceId()
+      : make_pair<unsigned, unsigned>(device_id, device_id);
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
diff --git a/src/backend/opencl/platform.hpp b/src/backend/opencl/platform.hpp
index c14c25f399..30124d9aa2 100644
--- a/src/backend/opencl/platform.hpp
+++ b/src/backend/opencl/platform.hpp
@@ -65,7 +65,7 @@ int& getMaxJitSize();
 
 const cl::Context& getContext();
 
-cl::CommandQueue& getQueue();
+cl::CommandQueue& getQueue(int device_id = -1);
 
 /// Return a cl_command_queue handle to the queue for the device.
 ///

From 5c32bb11dcdc2325a8420764694dd01639b1825f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 17 Jun 2023 17:10:24 -0400
Subject: [PATCH 2564/2677] Use getInfo instead of getArray in releaseHandle to
 get device id

---
 src/api/c/handle.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
index 243bfdba63..9c980af9f0 100644
--- a/src/api/c/handle.cpp
+++ b/src/api/c/handle.cpp
@@ -139,9 +139,9 @@ dim4 verifyDims(const unsigned ndims, const dim_t *const dims) {
 
 template<typename T>
 void releaseHandle(const af_array arr) {
-    auto &Arr      = getArray<T>(arr);
+    auto &info     = getInfo(arr);
     int old_device = detail::getActiveDeviceId();
-    int array_id   = Arr.getDevId();
+    int array_id   = info.getDevId();
     if (array_id != old_device) {
         detail::setDevice(array_id);
         detail::destroyArray(static_cast<detail::Array<T> *>(arr));

From 97ccdc08136157ef055fb4595f86d425dfdcdaca Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Sat, 17 Jun 2023 18:16:54 -0400
Subject: [PATCH 2565/2677] Allow access to arrays on devices from other
 contexts and non-peer devices

---
 src/api/c/handle.hpp               |  2 +-
 src/backend/common/SparseArray.cpp |  6 +++---
 src/backend/cuda/Array.cpp         | 13 ++++++++++---
 src/backend/cuda/Array.hpp         |  3 ++-
 src/backend/opencl/Array.cpp       | 22 ++++++++++++++++------
 src/backend/opencl/Array.hpp       |  3 ++-
 src/backend/opencl/platform.cpp    |  5 +++--
 7 files changed, 37 insertions(+), 17 deletions(-)

diff --git a/src/api/c/handle.hpp b/src/api/c/handle.hpp
index add7a7c612..b2e3df97cc 100644
--- a/src/api/c/handle.hpp
+++ b/src/api/c/handle.hpp
@@ -52,7 +52,7 @@ const detail::Array<T> &getArray(const af_array &arr) {
     const detail::Array<T> *A = static_cast<const detail::Array<T> *>(arr);
     if ((af_dtype)af::dtype_traits<T>::af_type != A->getType())
         AF_ERROR("Invalid type for input array.", AF_ERR_INTERNAL);
-    checkAndMigrate(*A);
+    checkAndMigrate(*const_cast<detail::Array<T> *>(A));
     return *A;
 }
 
diff --git a/src/backend/common/SparseArray.cpp b/src/backend/common/SparseArray.cpp
index ed9680c6a5..052dc97e86 100644
--- a/src/backend/common/SparseArray.cpp
+++ b/src/backend/common/SparseArray.cpp
@@ -173,9 +173,9 @@ void destroySparseArray(SparseArray<T> *sparse) {
 
 template<typename T>
 void checkAndMigrate(const SparseArray<T> &arr) {
-    checkAndMigrate(arr.getColIdx());
-    checkAndMigrate(arr.getRowIdx());
-    checkAndMigrate(arr.getValues());
+    checkAndMigrate(const_cast<Array<int> &>(arr.getColIdx()));
+    checkAndMigrate(const_cast<Array<int> &>(arr.getRowIdx()));
+    checkAndMigrate(const_cast<Array<T> &>(arr.getValues()));
 }
 
 ////////////////////////////////////////////////////////////////////////////
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 12a66f1293..9193f329de 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -58,11 +58,18 @@ std::shared_ptr<BufferNode<T>> bufferNodePtr() {
 }
 
 template<typename T>
-void checkAndMigrate(const Array<T> &arr) {
+void checkAndMigrate(Array<T> &arr) {
     int arr_id = arr.getDevId();
     int cur_id = detail::getActiveDeviceId();
     if (!isDeviceBufferAccessible(arr_id, cur_id)) {
-        AF_ERROR("Input Array not created on current device", AF_ERR_DEVICE);
+        static auto getLogger = [&] { return spdlog::get("platform"); };
+        AF_TRACE("Migrating array from {} to {}.", arr_id, cur_id);
+        auto migrated_data = memAlloc<T>(arr.elements());
+        CUDA_CHECK(
+            cudaMemcpyPeerAsync(migrated_data.get(), getDeviceNativeId(cur_id),
+                                arr.get(), getDeviceNativeId(arr_id),
+                                arr.elements() * sizeof(T), getActiveStream()));
+        arr.data.reset(migrated_data.release(), memFree);
     }
 }
 
@@ -478,7 +485,7 @@ void Array<T>::setDataDims(const dim4 &new_dims) {
     template void evalMultiple<T>(std::vector<Array<T> *> arrays);            \
     template kJITHeuristics passesJitHeuristics<T>(span<Node *> n);           \
     template void Array<T>::setDataDims(const dim4 &new_dims);                \
-    template void checkAndMigrate<T>(const Array<T> &arr);
+    template void checkAndMigrate<T>(Array<T> & arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/cuda/Array.hpp b/src/backend/cuda/Array.hpp
index caf1a90357..82e8bb9583 100644
--- a/src/backend/cuda/Array.hpp
+++ b/src/backend/cuda/Array.hpp
@@ -39,7 +39,7 @@ class Array;
 ///
 /// \param[in] arr The Array that will be checked.
 template<typename T>
-void checkAndMigrate(const Array<T> &arr);
+void checkAndMigrate(Array<T> &arr);
 
 template<typename T>
 void evalNodes(Param<T> out, common::Node *node);
@@ -305,6 +305,7 @@ class Array {
     friend void destroyArray<T>(Array<T> *arr);
     friend void *getDevicePtr<T>(const Array<T> &arr);
     friend void *getRawPtr<T>(const Array<T> &arr);
+    friend void checkAndMigrate<T>(Array<T> &arr);
 };
 
 }  // namespace cuda
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index b4e66373a5..21dec5166c 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -193,14 +193,24 @@ Array<T>::Array(const dim4 &dims, const dim4 &strides, dim_t offset_,
 }
 
 template<typename T>
-void checkAndMigrate(const Array<T> &arr) {
+void checkAndMigrate(Array<T> &arr) {
     int arr_id = arr.getDevId();
     int cur_id = detail::getActiveDeviceId();
     if (!isDeviceBufferAccessible(arr_id, cur_id)) {
-        AF_ERROR(
-            "The array's device context does not match the current device's "
-            "context",
-            AF_ERR_DEVICE);
+        auto getLogger = [&] { return spdlog::get("platform"); };
+        AF_TRACE("Migrating array from {} to {}.", arr_id, cur_id);
+        auto migrated_data           = memAlloc<T>(arr.elements());
+        void *mapped_migrated_buffer = getQueue().enqueueMapBuffer(
+            *migrated_data, CL_TRUE, CL_MAP_READ, 0, arr.elements());
+        setDevice(arr_id);
+        Buffer &buf = *arr.get();
+        getQueue().enqueueReadBuffer(buf, CL_TRUE, 0, arr.elements(),
+                                     mapped_migrated_buffer);
+        setDevice(cur_id);
+        getQueue().enqueueUnmapMemObject(*migrated_data,
+                                         mapped_migrated_buffer);
+        arr.data.reset(migrated_data.release(), bufferFree);
+        arr.setId(cur_id);
     }
 }
 
@@ -565,7 +575,7 @@ size_t Array<T>::getAllocatedBytes() const {
     template void *getDevicePtr<T>(const Array<T> &arr);                      \
     template void Array<T>::setDataDims(const dim4 &new_dims);                \
     template size_t Array<T>::getAllocatedBytes() const;                      \
-    template void checkAndMigrate<T>(const Array<T> &arr);
+    template void checkAndMigrate<T>(Array<T> & arr);
 
 INSTANTIATE(float)
 INSTANTIATE(double)
diff --git a/src/backend/opencl/Array.hpp b/src/backend/opencl/Array.hpp
index 5bd6d422c4..05b0468333 100644
--- a/src/backend/opencl/Array.hpp
+++ b/src/backend/opencl/Array.hpp
@@ -46,7 +46,7 @@ class Array;
 ///
 /// \param[in] arr The Array that will be checked.
 template<typename T>
-void checkAndMigrate(const Array<T> &arr);
+void checkAndMigrate(Array<T> &arr);
 
 template<typename T>
 void evalMultiple(std::vector<Array<T> *> arrays);
@@ -330,6 +330,7 @@ class Array {
     friend void destroyArray<T>(Array<T> *arr);
     friend void *getDevicePtr<T>(const Array<T> &arr);
     friend void *getRawPtr<T>(const Array<T> &arr);
+    friend void checkAndMigrate<T>(Array<T> &arr);
 };
 
 }  // namespace opencl
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index eb9bc320e4..d6406a32e1 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -307,8 +307,9 @@ cl_command_queue getQueueHandle(int device_id) {
 }
 
 CommandQueue& getQueue(int device_id) {
-    device_id_t devId     = (device_id = -1) ? tlocalActiveDeviceId()
-      : make_pair<unsigned, unsigned>(device_id, device_id);
+    device_id_t devId =
+        (device_id = -1) ? tlocalActiveDeviceId()
+                         : make_pair<unsigned, unsigned>(device_id, device_id);
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);

From e43ecf8bf68c8907be718d362e9d8d4d3423435c Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 17 Aug 2023 13:34:41 -0400
Subject: [PATCH 2566/2677] Update minimum cmake version for examples to 3.5 to
 avoid warnings

---
 examples/CMakeLists.txt                  | 2 +-
 examples/benchmarks/CMakeLists.txt       | 2 +-
 examples/computer_vision/CMakeLists.txt  | 2 +-
 examples/financial/CMakeLists.txt        | 2 +-
 examples/getting_started/CMakeLists.txt  | 2 +-
 examples/graphics/CMakeLists.txt         | 2 +-
 examples/helloworld/CMakeLists.txt       | 2 +-
 examples/image_processing/CMakeLists.txt | 2 +-
 examples/lin_algebra/CMakeLists.txt      | 2 +-
 examples/machine_learning/CMakeLists.txt | 2 +-
 examples/pde/CMakeLists.txt              | 2 +-
 examples/unified/CMakeLists.txt          | 2 +-
 12 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index f69eff6e1f..91280e485e 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 cmake_policy(VERSION 3.5)
 project(ArrayFire-Examples
   VERSION 3.7.0
diff --git a/examples/benchmarks/CMakeLists.txt b/examples/benchmarks/CMakeLists.txt
index d5ece4b562..9cf8197317 100644
--- a/examples/benchmarks/CMakeLists.txt
+++ b/examples/benchmarks/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Benchmarks
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
index 7314d29148..7113816566 100644
--- a/examples/computer_vision/CMakeLists.txt
+++ b/examples/computer_vision/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Computer-Vision
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/financial/CMakeLists.txt b/examples/financial/CMakeLists.txt
index 9cc2435b25..f2b82d4de8 100644
--- a/examples/financial/CMakeLists.txt
+++ b/examples/financial/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Financial
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/getting_started/CMakeLists.txt b/examples/getting_started/CMakeLists.txt
index f0ee51249a..790afd3d1f 100644
--- a/examples/getting_started/CMakeLists.txt
+++ b/examples/getting_started/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Getting-Started
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
index d59a506278..dd2918b641 100644
--- a/examples/graphics/CMakeLists.txt
+++ b/examples/graphics/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Graphics
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/helloworld/CMakeLists.txt b/examples/helloworld/CMakeLists.txt
index 3567873958..0aa58ca2c9 100644
--- a/examples/helloworld/CMakeLists.txt
+++ b/examples/helloworld/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-HelloWorld
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index 12307b679f..cfcd109922 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Image-Processing
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/lin_algebra/CMakeLists.txt b/examples/lin_algebra/CMakeLists.txt
index baba1a4181..b08aceeeee 100644
--- a/examples/lin_algebra/CMakeLists.txt
+++ b/examples/lin_algebra/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Linear-Algebra
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/machine_learning/CMakeLists.txt b/examples/machine_learning/CMakeLists.txt
index 9c2c3ade6c..d1cbcc9541 100644
--- a/examples/machine_learning/CMakeLists.txt
+++ b/examples/machine_learning/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Linear-Algebra
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index 0b74e6165f..23a89ace31 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-PDE
   VERSION 3.5.0
   LANGUAGES CXX)
diff --git a/examples/unified/CMakeLists.txt b/examples/unified/CMakeLists.txt
index 330a9c4af7..42ab6432f0 100644
--- a/examples/unified/CMakeLists.txt
+++ b/examples/unified/CMakeLists.txt
@@ -5,7 +5,7 @@
 # The complete license agreement can be obtained at:
 # http://arrayfire.com/licenses/BSD-3-Clause
 
-cmake_minimum_required(VERSION 3.0)
+cmake_minimum_required(VERSION 3.5)
 project(ArrayFire-Example-Unified
   VERSION 3.5.0
   LANGUAGES CXX)

From 0ea179f9ee7a03fc550a57680d5252c82d5272b9 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 17 Aug 2023 13:35:27 -0400
Subject: [PATCH 2567/2677] Update CMakeSYCLInformation linker flags for
 executables

---
 CMakeModules/CMakeSYCLInformation.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/CMakeSYCLInformation.cmake b/CMakeModules/CMakeSYCLInformation.cmake
index 5e9714327a..df850959f1 100644
--- a/CMakeModules/CMakeSYCLInformation.cmake
+++ b/CMakeModules/CMakeSYCLInformation.cmake
@@ -350,7 +350,7 @@ endif()
 
 if(NOT CMAKE_SYCL_LINK_EXECUTABLE)
   set(CMAKE_SYCL_LINK_EXECUTABLE
-    "<CMAKE_SYCL_COMPILER> <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
+    "<CMAKE_SYCL_COMPILER> <FLAGS> <CMAKE_SYCL_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
 endif()
 
 

From 09fab2fdbae3c23226816e47c405bb0a3e1fae43 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 17 Aug 2023 13:36:04 -0400
Subject: [PATCH 2568/2677] Separate system includes in cuda_unified and
 cuda_cuda tests

---
 test/CMakeLists.txt | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 0cb3cbfe51..5f606e14f8 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -379,8 +379,9 @@ if(CUDA_FOUND)
       add_executable(${target} cuda.cu)
       target_include_directories(${target}
         PRIVATE
-          ${CMAKE_SOURCE_DIR}
-          ${CMAKE_CURRENT_SOURCE_DIR}
+        ${CMAKE_SOURCE_DIR}
+        ${CMAKE_CURRENT_SOURCE_DIR})
+      target_include_directories(${target}
         SYSTEM PRIVATE
           ${ArrayFire_SOURCE_DIR}/extern/half/include)
       if(${backend} STREQUAL "unified")

From 541687a0276a2019dd0be4cfdb339be514f28758 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Thu, 17 Aug 2023 13:36:28 -0400
Subject: [PATCH 2569/2677] Add guard around GTest::gtest alias

---
 test/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 5f606e14f8..cf7e66255f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -40,7 +40,9 @@ elseif(NOT TARGET GTest::gtest)
   target_compile_options(gtest
     PRIVATE
       $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>)
-  add_library(GTest::gtest ALIAS gtest)
+  if(NOT TARGET GTest::gtest)
+    add_library(GTest::gtest ALIAS gtest)
+  endif()
   # Hide gtest project variables
   mark_as_advanced(
     BUILD_SHARED_LIBS

From 30d9f0ba86e690f74e7126df2ed714d1493947f5 Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Thu, 17 Aug 2023 20:14:55 -0700
Subject: [PATCH 2570/2677] updates arith, data, lapack, blas documentation
 (#3485)

* updates algorithm, arith, data, lapack, blas documentation

---------

Co-authored-by: syurkevi <stefan@arrayfire.com>
---
 docs/details/algorithm.dox |  367 +++++-----
 docs/details/arith.dox     |   33 +-
 docs/details/blas.dox      |   40 +-
 docs/details/data.dox      |  100 +--
 docs/details/lapack.dox    |  229 +++---
 docs/details/random.dox    |    8 +-
 include/af/algorithm.h     | 1352 +++++++++++++++++++-----------------
 include/af/arith.h         |  828 ++++++++++++----------
 include/af/blas.h          |  276 ++++----
 include/af/data.h          | 1253 +++++++++++++++++----------------
 include/af/lapack.h        |  491 +++++++------
 include/af/random.h        |  345 ++++-----
 12 files changed, 2878 insertions(+), 2444 deletions(-)

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index 38b3c26d5a..055750098c 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -1,74 +1,76 @@
 /*!
 \page batch_detail_algo algorithm
-
-This function performs the operation across all batches present in the input simultaneously.
-
+This function runs across all batches in the input simultaneously.
 */
 
 
+
 /**
 \addtogroup arrayfire_func
 @{
-\defgroup reduce_func_sum sum
 
+
+
+\defgroup reduce_func_sum sum
 \ingroup reduce_mat
 
-Find the sum of values in the input
+Sum array elements over a given dimension.
 
-This table defines the return value types for the corresponding input types
+This table defines output types for corresponding input types:
 
 Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
-s32, u32, s64, u64  | same as input
+s32, s64, u32, u64  | same as input
 s16                 | s32
 u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_sum_by_key sumByKey
 
+
+\defgroup reduce_func_sum_by_key sumByKey
 \ingroup reduce_mat
 
-Finds the sum of an input array according to an array of keys.
+Sum array elements over a given dimension, according to an array of keys.
+
 The values corresponding to each group of consecutive equal keys will be summed
-together.  Keys can repeat, however only consecutive key values will be
+together. Keys can repeat; however, only consecutive key values will be
 considered for each reduction. If a key value is repeated somewhere else in the
-keys array it will be considered the start of a new reduction.  There are two
+keys array it will be considered the start of a new reduction. There are two
 outputs: the reduced set of consecutive keys and the corresponding final
-reduced values.  An example demonstrating the reduction behavior can be seen in
-the following snippet.
+set of reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_sum_by_key
 
-The keys input type must be an integer type(s32 or u32).
-This table defines the return types for the corresponding values type
+The keys' input type must be integer (s32 or u32).
+
+This table defines output types for corresponding input types:
 
 Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
-s32, u32, s64, u64  | same as input
+s32, s64, u32, u64  | same as input
 s16                 | s32
 u16, u8, b8         | u32
 f16                 | f32
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The keys array must be 1-dimensional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
 \snippet test/reduce.cpp ex_reduce_sum_by_key_dim
 
 
 
-
 \defgroup reduce_func_product product
-
 \ingroup reduce_mat
 
-Find the product of values in the input
+Multiply array elements over a given dimension.
 
-This table defines the return value types for the corresponding input types
+This table defines output types for corresponding input types:
 
 Input Type          | Output Type
 --------------------|---------------------
@@ -79,23 +81,28 @@ u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_product_by_key productByKey
 
+
+\defgroup reduce_func_product_by_key productByKey
 \ingroup reduce_mat
 
-Finds the product of an input array according to an array of keys.
+Multiply array elements over a given dimension, according to an array of keys.
+
 The values corresponding to each group of consecutive equal keys will be
-multiplied together.  Keys can repeat, however only consecutive key values will
+multiplied together. Keys can repeat; however, only consecutive key values will
 be considered for each reduction. If a key value is repeated somewhere else in
-the keys array it will be considered the start of a new reduction.  There are
+the keys array it will be considered the start of a new reduction. There are
 two outputs: the reduced set of consecutive keys and the corresponding final
-reduced values.  An example demonstrating the reduction behavior can be seen in
-the following snippet.
+set of reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_product_by_key
 
-The keys input type must be an integer type(s32 or u32).
-This table defines the return types for the corresponding values type
+The keys' input type must be integer (s32 or u32).
+
+This table defines output types for corresponding input types:
 
 Input Type          | Output Type
 --------------------|---------------------
@@ -105,208 +112,210 @@ s16                 | s32
 u16, u8, b8         | u32
 f16                 | f32
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The keys array must be 1-dimenstional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
 \snippet test/reduce.cpp ex_reduce_product_by_key_dim
 
 
 
-
 \defgroup reduce_func_min min
-
 \ingroup reduce_mat
 
-Find the minimum values and their locations
+Return the minimum along a given dimension.
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_min_by_key minByKey
 
+
+\defgroup reduce_func_min_by_key minByKey
 \ingroup reduce_mat
 
-Finds the min of an input array according to an array of keys.  The minimum
-will be found of all values corresponding to each group of consecutive equal
-keys.  Keys can repeat, however only consecutive key values will be considered
-for each reduction. If a key value is repeated somewhere else in the keys array
-it will be considered the start of a new reduction.  There are two outputs:
-the reduced set of consecutive keys and the corresponding final reduced
-values.  An example demonstrating the reduction behavior can be seen in the
-following snippet.
+Return the minimum along a given dimension, according to an array of keys.
+
+The minimum is returned from the values corresponding to each group of 
+consecutive equal keys. Keys can repeat; however, only consecutive key values
+will be considered for each reduction. If a key value is repeated somewhere
+else in the keys array it will be considered the start of a new reduction.
+There are two outputs: the reduced set of consecutive keys and the
+corresponding final set of reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_min_by_key
 
-The keys input type must be an integer type(s32 or u32).
-The values return type will be the same as the values input type.
+The keys' input type must be integer (s32 or u32).
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The output type is the same as input type.
+
+The keys array must be 1-dimenstional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
 \snippet test/reduce.cpp ex_reduce_min_by_key_dim
 
 
-\defgroup reduce_func_max max
 
+\defgroup reduce_func_max max
 \ingroup reduce_mat
 
-Find the maximum values and their locations
+Return the maximum along a given dimension.
 
 \copydoc batch_detail_algo
 
 
-\defgroup reduce_func_max_by_key maxByKey
 
+\defgroup reduce_func_max_by_key maxByKey
 \ingroup reduce_mat
 
-Finds the max of an input array according to an array of keys.  The maximum
-will be found of all values corresponding to each group of consecutive equal
-keys.  Keys can repeat, however only consecutive key values will be considered
-for each reduction. If a key value is repeated somewhere else in the keys array
-it will be considered the start of a new reduction.  There are two outputs:
-the reduced set of consecutive keys and the corresponding final reduced
-values.  An example demonstrating the reduction behavior can be seen in the
-following snippet.
+Return the maximum along a given dimension, according to an array of keys.
+
+The maximum is returned from the values corresponding to each group of 
+consecutive equal keys. Keys can repeat; however, only consecutive key values
+will be considered for each reduction. If a key value is repeated somewhere
+else in the keys array it will be considered the start of a new reduction.
+There are two outputs: the reduced set of consecutive keys and the
+corresponding final set of reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_max_by_key
 
-The keys input type must be an integer type(s32 or u32).
-The values return type will be the same as the values input type.
+The keys' input type must be integer (s32 or u32).
+
+The output type is the same as input type.
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The keys array must be 1-dimenstional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
 \snippet test/reduce.cpp ex_reduce_max_by_key_dim
 
 
 
 \defgroup reduce_func_all_true allTrue
-\brief Test if all values in an array are true
-
 \ingroup reduce_mat
 
-Find if of all of the values in input are true
+Check if all values along a given dimension are true.
 
-Return type is b8 for all input types
+Return type is `b8` for all input types.
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_all_true_by_key allTrueByKey
-\brief Calculate if all values that share the same consecutive keys are true
 
+
+\defgroup reduce_func_all_true_by_key allTrueByKey
 \ingroup reduce_mat
 
-Finds if all of the values of an input array are true according to an array of
-keys.  All values corresponding to each group of consecutive equal keys will be
-tested to make sure all are true.  Keys can repeat, however only consecutive
-key values will be considered for each reduction. If a key value is repeated
+Check if all values along a given dimension are true, according to an array of
+keys.
+
+All values corresponding to each group of consecutive equal keys will be tested
+to make sure all are true. Keys can repeat; however, only consecutive key
+values will be considered for each reduction. If a key value is repeated
 somewhere else in the keys array it will be considered the start of a new
-reduction.  There are two outputs: the reduced set of consecutive keys and the
-corresponding final reduced values.  An example demonstrating the reduction
-behavior can be seen in the following snippet.
+reduction. There are two outputs: the reduced set of consecutive keys and the
+corresponding final set of reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_alltrue_by_key
 
-The keys input type must be an integer type(s32 or u32).
-The values return type will be of type b8.
+The keys' input type must be integer (s32 or u32).
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The output type is `b8`.
 
-\snippet test/reduce.cpp ex_reduce_alltrue_by_key_dim
+The keys array must be 1-dimenstional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
+\snippet test/reduce.cpp ex_reduce_alltrue_by_key_dim
 
 
 
 \defgroup reduce_func_any_true anytrue
-\brief Calculate if any values in an array are true
-
 \ingroup reduce_mat
 
-Find if of any of the values in input are true
+Check if any values along a given dimension are true.
 
-Return type is b8 for all input types
+The output type is `b8`.
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_anytrue_by_key anyTrueByKey
-\brief Calculate if any values that share the same consecutive keys are true
 
+
+\defgroup reduce_func_anytrue_by_key anyTrueByKey
 \ingroup reduce_mat
 
-Finds if any of the values of an input array are true according to an array of
-keys.  All values corresponding to each group of consecutive equal keys will be
-tested to make sure any are true.  Keys can repeat, however only consecutive
-key values will be considered for each reduction. If a key value is repeated
+Check if any values along a given dimension are true, according to an array of
+keys.
+
+Values corresponding to each group of consecutive equal keys will be tested to
+check if any are true. Keys can repeat; however, only consecutive key
+values will be considered for each reduction. If a key value is repeated
 somewhere else in the keys array it will be considered the start of a new
-reduction.  There are two outputs: the reduced set of consecutive keys and the
-corresponding final reduced values.  An example demonstrating the reduction
-behavior can be seen in the following snippet.
+reduction. There are two outputs: the reduced set of consecutive keys and the
+corresponding final set of reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_anytrue_by_key
 
-The keys input type must be an integer type(s32 or u32).
-The values return type will be of type u8.
+The keys' input type must be integer (s32 or u32).
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The output type is `b8`.
+
+The keys array must be 1-dimenstional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
 \snippet test/reduce.cpp ex_reduce_anytrue_by_key_dim
 
 
-\defgroup reduce_func_count count
 
+\defgroup reduce_func_count count
 \ingroup reduce_mat
 
-Count the number of non-zero elements in the input
+Count non-zero values in an array along a given dimension.
 
-Return type is u32 for all input types
+The output type is `u32`.
 
 \copydoc batch_detail_algo
 
-\defgroup reduce_func_count_by_key countByKey
 
+
+\defgroup reduce_func_count_by_key countByKey
 \ingroup reduce_mat
 
-Counts the non-zero values of an input array according to an array of keys.
+Count non-zero values in an array, according to an array of keys.
+
 All non-zero values corresponding to each group of consecutive equal keys will
-be counted.  Keys can repeat, however only consecutive key values will be
+be counted. Keys can repeat; however, only consecutive key values will be
 considered for each reduction. If a key value is repeated somewhere else in the
-keys array it will be considered the start of a new reduction.  There are two
-outputs: the reduced set of consecutive keys and the corresponding final
-reduced values.  An example demonstrating the reduction behavior can be seen in
-the following snippet.
+keys array it will be considered the start of a new reduction. There are two
+outputs: the reduced set of consecutive keys and the corresponding final set of
+reduced values.
+
+An example demonstrating the reduction behavior can be seen in the following
+snippet.
 
 \snippet test/reduce.cpp ex_reduce_count_by_key
 
-The keys input type must be an integer type(s32 or u32).
-The values return type will be of type u32.
+The keys' input type must be integer (s32 or u32).
 
-The input keys must be a 1-D vector matching the size of the reduced dimension.
-In the case of multiple dimensions in the input values array, the dim parameter
-specifies which dimension to reduce along.  An example of multi-dimensional
-reduce by key can be seen below:
+The output type is `u32`.
 
-\snippet test/reduce.cpp ex_reduce_count_by_key_dim
+The keys array must be 1-dimenstional matching the size of the reduced
+dimension. An example of multi-dimensional reduce-by-key can be seen below:
 
+\snippet test/reduce.cpp ex_reduce_count_by_key_dim
 
 
 
 \defgroup scan_func_accum accum
-\brief Cumulative sum (inclusive). Also known as a scan
-
 \ingroup scan_mat
 
-Calculate the cumulative sum (inclusive) along the specified dimension
+Evaluate the cumulative sum (inclusive) along a given dimension.
 
 For a 1D array \f$X\f$, the inclusive cumulative sum calculates \f$x_i =
 \sum_{p=0}^{i}x_p\f$ for every \f$x \in X\f$. Here is a simple example for the
@@ -314,7 +323,7 @@ For a 1D array \f$X\f$, the inclusive cumulative sum calculates \f$x_i =
 
 \snippet test/scan.cpp ex_accum_1D
 
-For 2D arrays (and higher dimensions), you can specify the dimension along which
+For 2D arrays and higher dimensions, you can specify the dimension along which
 the cumulative sum will be calculated. Thus, the formula above will be
 calculated for all array slices along the specified dimension (in the 2D case
 for example, this looks like \f$x_{i,j} = \sum_{p=0}^{j}x_{i,p}\f$ if the second
@@ -325,12 +334,12 @@ required to be specified in the C API):
 \snippet test/scan.cpp ex_accum_2D
 
 The output array type may be different from the input array type. The following
-table defines the corresponding output types for each input type:
+table defines corresponding output types for each input type:
 
 Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
-s32, u32, s64, u64  | same as input
+s32, s64, u32, u64  | same as input
 s16                 | s32
 u16, u8, b8         | u32
 
@@ -338,151 +347,147 @@ u16, u8, b8         | u32
 
 
 
-\defgroup scan_func_where where
-
+\defgroup scan_func_scan scan
 \ingroup scan_mat
 
-Locate the indices of non-zero elements
-
-Return type is u32 for all input types
+Scan an array (generalized) over a given dimension.
 
-The locations are provided by flattening the input into a linear array.
+Perform inclusive or exclusive scan using a given binary operation along a
+given dimension.
 
+Binary operations can be [add](\ref AF_BINARY_ADD), [mul](\ref AF_BINARY_MUL),
+[min](\ref AF_BINARY_MIN), [max](\ref AF_BINARY_MAX) as defined by \ref
+af_binary_op.
 
 
-\defgroup scan_func_scan scan
 
+\defgroup scan_func_scanbykey scanByKey
 \ingroup scan_mat
 
-Inclusive or exclusive scan of an array
+Scan an array (generalized) over a given dimension, according to an array of
+keys.
 
 Perform inclusive or exclusive scan using a given binary operation along a
-given dimension.
+given dimension using a key.
 
 Binary operations can be [add](\ref AF_BINARY_ADD), [mul](\ref AF_BINARY_MUL),
-[min](\ref AF_BINARY_MIN), [max](\ref AF_BINARY_MAX) as defined by \ref af_binary_op.
-
+[min](\ref AF_BINARY_MIN), [max](\ref AF_BINARY_MAX) as defined by \ref
+af_binary_op.
 
 
-\defgroup scan_func_scanbykey scanByKey
 
+\defgroup scan_func_where where
 \ingroup scan_mat
 
-Inclusive or exclusive scan of an array by key
+Locate the indices of the non-zero values in an array.
 
-Perform inclusive or exclusive scan using a given binary operation along a
-given dimension using a key.
+Output type is `u32`.
 
-Binary operations can be [add](\ref AF_BINARY_ADD), [mul](\ref AF_BINARY_MUL),
-[min](\ref AF_BINARY_MIN), [max](\ref AF_BINARY_MAX) as defined by \ref af_binary_op.
+The locations are provided by flattening the input into a linear array.
 
 
 
 \defgroup calc_func_diff1 diff1
-
 \ingroup calc_mat
 
-First order numerical difference along specified dimension
+Calculate the first order difference in an array over a given dimension.
 
 \copydoc batch_detail_algo
 
 
 
 \defgroup calc_func_diff2 diff2
-
 \ingroup calc_mat
 
-Second order numerical difference along specified dimension
+Calculate the second order difference in an array over a given dimension.
 
 \copydoc batch_detail_algo
 
 
 
 \defgroup sort_func_sort sort
-
 \ingroup sort_mat
 
-Sort input arrays
-
-Sort an multi dimensional array
+Sort an array over a given dimension.
 
 
 
 \defgroup sort_func_sort_index sortIndex
-
 \ingroup sort_mat
 
-Sort input arrays get the sorted indices
+Sort an array over a given dimension and return the original indices.
 
-Sort a multi dimensional array and return sorted indices. Index array is of
-type u32.
+Output type is `u32`.
 
 
 
 \defgroup sort_func_sort_keys sortByKey
-
 \ingroup sort_mat
 
-Sort input arrays based on keys
-
-Sort a multi dimensional array based on keys
+Sort an array over a given dimension, according to an array of keys.
 
 
 
 \defgroup set_func_unique setunique
-
 \ingroup set_mat
 
-Finds unique values from an input set. The input must be a one-dimensional array. Batching is not currently supported.
+Return the unique values in an array.
+
+The input must be a one-dimensional array. Batching is not currently supported.
 
-A simple example of finding the unique values of a set using setUnique() can be seen below:
+An example, unsorted:
 
 \snippet test/set.cpp ex_set_unique_simple
 
 The function can be sped up if it is known that the inputs are sorted.
 
+An example, sorted (ascending):
+
 \snippet test/set.cpp ex_set_unique_sorted
 
 The inputs can be sorted in ascending or descending order.
 
-\snippet test/set.cpp ex_set_unique_desc
-
-
+An example, sorted (descending):
 
+\snippet test/set.cpp ex_set_unique_desc
 
 
 
 \defgroup set_func_union setunion
-
 \ingroup set_mat
 
-Find the union of two sets. The inputs must be one-dimensional arrays. Batching is not currently supported.
+Evaluate the union of two arrays.
+
+The inputs must be one-dimensional arrays. Batching is not currently supported.
 
-A simple example of finding the union of two sets using setUnion() can be seen below:
+An example:
 
 \snippet test/set.cpp ex_set_union_simple
 
-The function can be sped up if it is known that each input is sorted in increasing order and its values are unique.
+The function can be sped up if the input is sorted in increasing order and its
+values are unique.
 
 \snippet test/set.cpp ex_set_union
 
 
 
-
 \defgroup set_func_intersect setintersect
-
 \ingroup set_mat
 
-Find the intersection of two sets. The inputs must be one-dimensional arrays. Batching is not currently supported.
+Evaluate the intersection of two arrays.
+
+The inputs must be one-dimensional arrays. Batching is not currently supported.
 
-A simple example of finding the intersection of two sets using setIntersect() can be seen below:
+An example:
 
 \snippet test/set.cpp ex_set_intersect_simple
 
-The function can be sped up if it is known that each input is sorted in increasing order and its values are unique.
+The function can be sped up if the input is sorted in increasing order and its
+values are unique.
 
 \snippet test/set.cpp ex_set_intersect
 
 
+
 @}
 */
diff --git a/docs/details/arith.dox b/docs/details/arith.dox
index 2e123f7ba8..3a118bc890 100644
--- a/docs/details/arith.dox
+++ b/docs/details/arith.dox
@@ -1,6 +1,7 @@
 /*!
 \page arith_real_only arith_real
-\note This function supports real inputs only. Complex inputs are not yet supported.
+\note This function only supports real inputs; complex inputs are not yet
+supported.
 */
 
 /*!
@@ -19,28 +20,28 @@
 \defgroup arith_func_add add
 \ingroup arith_mat
 
-Elementwise addition
+Elementwise addition.
 
 
 
 \defgroup arith_func_sub sub
 \ingroup arith_mat
 
-Elementwise subtraction
+Elementwise subtraction.
 
 
 
 \defgroup arith_func_mul mul
 \ingroup arith_mat
 
-Elementwise multiply
+Elementwise multiply.
 
 
 
 \defgroup arith_func_div div
 \ingroup arith_mat
 
-Elementwise division
+Elementwise division.
 
 
 
@@ -67,7 +68,8 @@ Check if the elements of one array are greater than those of another array.
 
 Less than or equal to, an elementwise comparison of two arrays.
 
-Check if the elements of one array are less than or equal to those of another array.
+Check if the elements of one array are less than or equal to those of another
+array.
 
 
 \defgroup arith_func_ge ge
@@ -75,14 +77,15 @@ Check if the elements of one array are less than or equal to those of another ar
 
 Greater than or equal to, an elementwise comparison of two arrays.
 
-Check if the elements of one array are greater than or equal to those of another array.
+Check if the elements of one array are greater than or equal to those of
+another array.
 
 
 
 \defgroup arith_func_eq eq
 \ingroup logic_mat
 
-\brief Equal to, an elementwise comparison of two arrays.
+Equal to, an elementwise comparison of two arrays.
 
 Check if the elements of one array are equal to those of another array.
 
@@ -91,7 +94,7 @@ Check if the elements of one array are equal to those of another array.
 \defgroup arith_func_neq neq
 \ingroup logic_mat
 
-\brief Not equal to, an elementwise comparison of two arrays.
+Not equal to, an elementwise comparison of two arrays.
 
 Check if the elements of one array are not equal to those of another array.
 
@@ -384,10 +387,14 @@ Create complex arrays.
 
 Complex arrays are created from any of the following four inputs:
 
-1. a single real array, returning zeros for the imaginary component. See `array b` in the example.
-2. two real arrays, one for the real component and one for the imaginary component. See `array c` in the example.
-3. a single real array for the real component and a single scalar for each imaginary component. See `array d` in the example.
-4. a single scalar for each real component and a single real array for the imaginary component. See `array e` in the example.
+1. a single real array, returning zeros for the imaginary component. See
+   `array b` in the example.
+2. two real arrays, one for the real component and one for the imaginary
+   component. See `array c` in the example.
+3. a single real array for the real component and a single scalar for each
+   imaginary component. See `array d` in the example.
+4. a single scalar for each real component and a single real array for the
+   imaginary component. See `array e` in the example.
 
 __Examples:__
 
diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index b8757d81fb..943e77a502 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -1,29 +1,18 @@
 /**
 \addtogroup arrayfire_func
 @{
-\defgroup blas_func_dot dot
-
-\ingroup blas_mat
-
-\brief Calculate the dot product of a vector
-
-Scalar dot product between two vectors.  Also referred to as the inner
-product.
-
-=======================================================================
 
 \defgroup blas_func_matmul matmul
-\ingroup blas_mat
 
-\brief Matrix multiplication using array
+Matrix multiplication.
 
 Performs a matrix multiplication on the two input arrays after performing the
 operations specified in the options. The operations are done while reading the
 data from memory. This results in no additional memory being used for temporary
 buffers.
 
-Batched matrix multiplications are supported. Given below are the supported
-types of batch operations for any given set of two matrices A and B.
+Batched matrix multiplications are supported. The supported types of batch
+operations for any given set of two matrices A and B are given below,
 
 | Size of Input Matrix A     | Size of Input Matrix B     | Output Matrix Size          |
 |:--------------------------:|:--------------------------:|:---------------------------:|
@@ -32,8 +21,8 @@ types of batch operations for any given set of two matrices A and B.
 | \f$ \{ M, K,  1,  1 \} \f$ | \f$ \{ K, N, b2, b3 \} \f$ |  \f$ \{ M, N, b2, b3 \} \f$ |
 | \f$ \{ M, K, b2, b3 \} \f$ | \f$ \{ K, N,  1,  1 \} \f$ |  \f$ \{ M, N, b2, b3 \} \f$ |
 
-where M, K, N are dimensions of the matrix and b2, b3 indicate batch size along the
-respective dimension.
+where `M`, `K`, `N` are dimensions of the matrix and `b2`, `b3` indicate batch
+size along the respective dimension.
 
 For the last two entries in the above table, the 2D matrix is broadcasted to
 match the dimensions of 3D/4D array. This broadcast doesn't involve any additional
@@ -43,14 +32,24 @@ memory allocations either on host or device.
 for Sparse-Dense matrix multiplication. See the notes of the function for usage
 and restrictions.
 
+\ingroup blas_mat
 
 =======================================================================
 
-\defgroup blas_func_transpose transpose
+\defgroup blas_func_dot dot
+
+Compute the dot product.
+
+Scalar dot product between two vectors, also referred to as the inner
+product.
+
 \ingroup blas_mat
-\ingroup manip_mat
 
-\brief Transpose a matrix.
+=======================================================================
+
+\defgroup blas_func_transpose transpose
+
+Transpose a matrix.
 
 Reverse or permute the dimensions of an array; returns the modified array.
 For an array a with two dimensions, `transpose(a)` gives the matrix transpose.
@@ -70,6 +69,9 @@ __Examples:__
 
 \snippet test/transpose.cpp ex_blas_func_transpose
 
+\ingroup blas_mat
+\ingroup manip_mat
+
 =======================================================================
 
 @}
diff --git a/docs/details/data.dox b/docs/details/data.dox
index 99a94f1202..bb96a4c61f 100644
--- a/docs/details/data.dox
+++ b/docs/details/data.dox
@@ -4,20 +4,9 @@
 
 \defgroup data_func_constant constant
 
-\brief Create a array from a scalar input value
+Create an array from a scalar input value.
 
-The array created has the same value at all locations
-
-\ingroup data_mat
-\ingroup arrayfire_func
-
-=======================================================================
-
-\defgroup data_func_pad pad
-
-\brief Pad an array
-
-Pad the input array using a constant or values from input along border
+Generate an array with elements set to a specified value.
 
 \ingroup data_mat
 \ingroup arrayfire_func
@@ -26,7 +15,7 @@ Pad the input array using a constant or values from input along border
 
 \defgroup data_func_identity identity
 
-\brief Create an identity array with diagonal values 1
+Generate an identity matrix.
 
 \code
 array a = identity(5, 3);
@@ -45,7 +34,8 @@ array a = identity(5, 3);
 
 \defgroup data_func_range range
 
-\brief Create an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions.
+Generate an array with `[0, n-1]` values along the a specified dimension and
+tiled across other dimensions.
 
 __Examples:__
 
@@ -58,7 +48,8 @@ __Examples:__
 
 \defgroup data_func_iota iota
 
-\brief Create an sequence [0, dims.elements() - 1] and modify to specified dimensions dims and then tile it according to tile_dims
+Generate an array with `[0, n-1]` values modified to specified dimensions and
+tiling.
 
 \code
 // Generate [0, 5x3 - 1] in dimensions 5, 3
@@ -87,7 +78,12 @@ array b = iota(dim4(5, 3), dim4(1, 2))
 =======================================================================
 
 \defgroup data_func_diag diag
-\brief Extract diagonal from a matrix when \p extract is set to true. Create a diagonal matrix from input array when \p extract is set to false
+
+Extract the diagonal from an array.
+
+If `extract` is true, an array is extracted containing diagonal of the matrix,
+while a false condition returns a diagonal matrix.
+
 
 \code
 // Extraction
@@ -140,9 +136,10 @@ array b = diag(a, -1, false);
 
 \defgroup manip_func_join join
 
-\brief Join up to 4 arrays along specified dimension.
+Join up to 4 arrays along specified dimension.
 
-Requires that all dimensions except the join dimension must be the same for all arrays.
+Requires that all dimensions except the join dimension must be the same for all
+arrays.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
@@ -151,13 +148,14 @@ Requires that all dimensions except the join dimension must be the same for all
 
 \defgroup manip_func_tile tile
 
-\brief Repeat the contents of the input array along the specified dimensions
+Generate a tiled array by repeating an array's contents along a specified
+dimension.
 
 Creates copies of the input array and concatenates them with each other, such
 that the output array will have as many copies of the input array as the user
-specifies, along each dimension. In this sense, the output array is essentially
-a set of "tiles", where each copy of the input array (including the original) is
-a "tile" (hence the name of this function).
+specifies along each dimension. In this sense, the output array is a set of
+"tiles" where each copy of the input array, including the original, is
+a "tile".
 
 Given below are some examples. The input array looks like this:
 
@@ -184,7 +182,7 @@ dimension:
 
 \defgroup manip_func_reorder reorder
 
-\brief Reorder an array according to the specified dimensions.
+Reorder an array.
 
 Exchanges data of an array such that the requested change in dimension
 is satisfied. The linear ordering of data within the array is preserved.
@@ -201,7 +199,7 @@ a [2 2 3 1]
     2.0000     4.0000
 
 
-reorder(a, 1, 0, 2) [2 2 3 1]  //equivalent to a transpose
+reorder(a, 1, 0, 2) [2 2 3 1]  // equivalent to a transpose
     1.0000     2.0000
     3.0000     4.0000
 
@@ -229,9 +227,9 @@ reorder(a, 2, 0, 1) [3 2 2 1]
 
 \defgroup manip_func_shift shift
 
-\brief Circular shift slong specified dimensions
+Shift an array.
 
-Shifts the values in a circular fashion along the specified dimesion.
+Circular shift array values along a specified dimesion.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
@@ -240,9 +238,10 @@ Shifts the values in a circular fashion along the specified dimesion.
 
 \defgroup manip_func_moddims moddims
 
-\brief Modify the dimensions of an array without changing the order of its elements.
+Modify the dimensions of an array without changing the order of its elements.
 
-This function only modifies array metadata and requires no computation. It is a NOOP.
+This function only modifies array metadata and requires no computation. It is a
+NOOP.
 
 __Examples:__
 
@@ -255,9 +254,9 @@ __Examples:__
 
 \defgroup manip_func_flat flat
 
-\brief Flatten the input to a single dimension
+Flatten an array.
 
-Simply returns the array as a vector. This is a noop.
+Simply returns the array as a vector. This is a NOOP.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
@@ -266,9 +265,9 @@ Simply returns the array as a vector. This is a noop.
 
 \defgroup manip_func_flip flip
 
-\brief Flip the input along specified dimension
+Flip the input along a specified dimension.
 
-Mirrors the array along the specified dimensions.
+Mirrors the array along the specified dimension.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
@@ -277,7 +276,7 @@ Mirrors the array along the specified dimensions.
 
 \defgroup data_func_lower lower
 
-\brief Create a lower triangular matrix from input array
+Return the lower triangular matrix from an input array.
 
 \ingroup data_mat
 \ingroup arrayfire_func
@@ -286,7 +285,7 @@ Mirrors the array along the specified dimensions.
 
 \defgroup data_func_upper upper
 
-\brief Create a upper triangular matrix from input array
+Return the upper triangular matrix from an input array.
 
 \ingroup data_mat
 \ingroup arrayfire_func
@@ -295,13 +294,12 @@ Mirrors the array along the specified dimensions.
 
 \defgroup data_func_select select
 
-\brief Selects elements from two arrays based on the values of a binary
-       conditional array.
+Select elements based on a conditional array.
 
-Creates a new array that is composed of values either from array \p a or array
-\p b, based on a third conditional array. For all non-zero elements in the
-conditional array, the output array will contain values from \p a. Otherwise the
-output will contain values from \p b.
+Creates a new array that is composed of values either from array `a` or array
+`b`, based on a third conditional array. For all non-zero elements in the
+conditional array, the output array will contain values from `a`. Otherwise the
+output will contain values from `b`.
 
 \snippet test/select.cpp ex_data_select
 
@@ -309,7 +307,7 @@ is equivalent to:
 
 \snippet test/select.cpp ex_data_select_c
 
-The conditional array must be a b8 typed array.
+The conditional array must be a \ref b8 typed array.
 
 The select function can perform batched operations based on the size of each of
 the inputs. The following table describes the input and output sizes for
@@ -330,15 +328,27 @@ supported batched configurations.
 
 \defgroup data_func_replace replace
 
-\brief Replace elements of an array based on a conditional array
+Replace elements of an array with elements of another array.
 
-- Input values are retained when corresponding elements from condition array are true.
-- Input values are replaced when corresponding elements from condition array are false.
+Input values are retained when corresponding elements from the conditional
+array are true. Input values are replaced when corresponding elements from the
+conditional array are false.
 
 \ingroup manip_mat
 \ingroup arrayfire_func
 
 =======================================================================
 
+\defgroup data_func_pad pad
+
+Pad an array.
+
+Pad the input array using a constant or values from input along the border.
+
+\ingroup data_mat
+\ingroup arrayfire_func
+
+=======================================================================
+
 @}
 */
diff --git a/docs/details/lapack.dox b/docs/details/lapack.dox
index bf977b0c0c..995d47129b 100644
--- a/docs/details/lapack.dox
+++ b/docs/details/lapack.dox
@@ -1,25 +1,47 @@
 /**
 \addtogroup arrayfire_func
 @{
-\defgroup lapack_factor_func_lu lu
+
+\defgroup lapack_factor_func_svd svd
+
+Perform singular value decomposition.
+
+This function factorizes a matrix \f$A\f$ into two unitary matrices, \f$U\f$
+and \f$V^T\f$, and a diagonal matrix \f$S\f$, such that \f$A = USV^T\f$. If
+\f$A\f$ has \f$M\f$ rows and \f$N\f$ columns (\f$M \times N\f$), then \f$U\f$
+will be \f$M \times M\f$, \f$V\f$ will be \f$N \times N\f$, and \f$S\f$ will be
+\f$M \times N\f$. However, for \f$S\f$, this function only returns the non-zero
+diagonal elements as a sorted (in descending order) 1D array.
+
+To reconstruct the original matrix \f$A\f$ from the individual factors, the
+following code snippet can be used:
+
+\snippet test/svd_dense.cpp ex_svd_reg
+
+When memory is a concern, and \f$A\f$ is dispensable, \ref af::svdInPlace() can
+be used. However, this in-place version is currently limited to input arrays
+where \f$M \geq N\f$.
 
 \ingroup lapack_factor_mat
 
-\brief Perform LU decomposition
+===============================================================================
 
-This function decomposes input matrix **A** into a lower triangle **L**, an upper triangle **U** such that
+\defgroup lapack_factor_func_lu lu
 
-    \f$A = L * U\f$
+Perform LU decomposition.
 
-For stability, a permutation array **P** is also used to modify the formula in the following manner.
+This function decomposes input matrix \f$A\f$ into a lower triangle \f$L\f$, an
+upper triangle \f$U\f$ such that \f$A = L * U\f$.
 
-    \f$A(P, span) = L * U\f$
+For stability, a permutation array \f$P\f$ is also used to modify the formula
+in the following manner, \f$A(P, span) = L * U\f$.
 
-This operation can be performed in ArrayFire using the following code snippet.
+This operation can be performed in ArrayFire, using the following code snippet.
 
 \snippet test/lu_dense.cpp ex_lu_unpacked
 
-The permuted version of the original matrix can be reconstructed using the following snippet.
+The permuted version of the original matrix can be reconstructed, using the
+following snippet.
 
 \snippet test/lu_dense.cpp ex_lu_recon
 
@@ -57,115 +79,98 @@ a_perm [3 3 1 1]
     1.0000     4.0000     7.0000
 \endcode
 
-When memory is a concern, users can perform the LU decomposition in place as shown below.
+When memory is a concern, users can perform the LU decomposition in place as
+shown below.
 
 \snippet test/lu_dense.cpp ex_lu_packed
 
-The lower and upper triangle matrices can be obtained if necessary in the following manner.
+The lower and upper triangle matrices can be obtained if necessary in the
+following manner.
 
 \snippet test/lu_dense.cpp ex_lu_extract
 
-LU decompositions has many applications including <a href="http://en.wikipedia.org/wiki/LU_decomposition#Solving_linear_equations">solving a system of linear equations</a>. Check \ref af::solveLU fore more information.
-
-=======================================================================
-
-\defgroup lapack_factor_func_qr qr
+LU decompositions have many applications including
+<a href="http://en.wikipedia.org/wiki/LU_decomposition#Solving_linear_equations">
+solving a system of linear equations</a>. Check \ref af::solveLU for more
+information.
 
 \ingroup lapack_factor_mat
 
-\brief Perform QR decomposition
-
-This function decomposes input matrix **A** into an orthogonal matrix **Q** and an upper triangular matrix **R** such that
+===============================================================================
 
-     \f$A = Q * R\f$
+\defgroup lapack_factor_func_qr qr
 
-     \f$Q * Q^T = I\f$
+Perform QR decomposition.
 
-Where **I** is an identity matrix. The matrix **Q** is a square matrix of size **max(M, N)** where **M** and **N** are rows and columns of **A** respectively. The matrix **R** is the same size as **A*.
+This function decomposes input matrix \f$A\f$ into an orthogonal matrix \f$Q\f$
+and an upper triangular matrix \f$R\f$ such that, \f$A = Q * R\f$ and
+\f$Q * Q^T = I\f$, where \f$I\f$ is an identity matrix. The matrix \f$Q\f$ is a
+square matrix of size \f$max(M, N)\f$ where \f$M\f$ and \f$N\f$ are rows and
+columns of \f$A\f$ respectively. The matrix \f$R\f$ is the same size as
+\f$A\f$.
 
 This operation can be performed in ArrayFire using the following code snippet.
 
 \snippet test/qr_dense.cpp ex_qr_unpacked
 
-The additional parameter **Tau** can be used to speed up solving over and under determined system of equations.
+The additional parameter `tau` can be used to speed up solving over- and
+under-determined systems of equations.
 
 The original matrix can be reconstructed using the following code snippet.
 
 \snippet test/qr_dense.cpp ex_qr_recon
 
-When memory is a concern, users can perform QR decomposition in place as shown below.
+When memory is a concern, users can perform QR decomposition in place as shown
+below.
 
 \snippet test/qr_dense.cpp ex_qr_packed
 
-=======================================================================
-
-\defgroup lapack_factor_func_cholesky cholesky
-
 \ingroup lapack_factor_mat
 
-\brief Perform Cholesky decomposition
+===============================================================================
 
-This function decomposes a <a href="http://en.wikipedia.org/wiki/Positive-definite_matrix">positive definite</a> matrix **A** into two triangular matrices such that
+\defgroup lapack_factor_func_cholesky cholesky
 
-     \f$A = L * U\f$
+Perform Cholesky decomposition.
 
-     \f$L = U^T\f$
+This function decomposes a
+<a href="http://en.wikipedia.org/wiki/Positive-definite_matrix">positive
+definite</a> matrix \f$A\f$ into two triangular matrices such that,
+\f$A = L * U\f$ and \f$L = U^T\f$.
 
-Only one of **L** and **U** is stored to conserve space when solving linear equations.
+Only one of \f$L\f$ and \f$U\f$ is stored to conserve space when solving linear
+equations.
 
 This operation can be performed in ArrayFire using the following code snippet.
 
 \snippet test/cholesky_dense.cpp ex_chol_reg
 
-When memory is a concern, users can perform Cholesky decomposition in place as shown below.
+When memory is a concern, users can perform Cholesky decomposition in place as
+shown below.
 
 \snippet test/cholesky_dense.cpp ex_chol_inplace
 
-=======================================================================
-
-\defgroup lapack_factor_func_svd svd
-
 \ingroup lapack_factor_mat
 
-\brief Computes the singular value decomposition of a matrix
-
-This function factorizes a matrix \f$A\f$ into two unitary matrices, \f$U\f$ and
-\f$V^T\f$, and a diagonal matrix \f$S\f$, such that \f$A = USV^T\f$. If \f$A\f$
-has \f$M\f$ rows and \f$N\f$ columns (\f$M \times N\f$), then \f$U\f$ will be
-\f$M \times M\f$, \f$V\f$ will be \f$N \times N\f$, and \f$S\f$ will be
-\f$M \times N\f$. However, for \f$S\f$, this function only returns the non-zero
-diagonal elements as a sorted (in descending order) 1D array.
-
-To reconstruct the original matrix \f$A\f$ from the individual factors, the
-following code snippet can be used:
-
-\snippet test/svd_dense.cpp ex_svd_reg
-
-When memory is a concern, and \f$A\f$ is dispensable, \ref af::svdInPlace() can be
-used. However, this in-place version is currently limited to input arrays where
-\f$M \geq N\f$.
-
-=======================================================================
+===============================================================================
 
 \defgroup lapack_solve_func_gen solve
 
-\ingroup lapack_solve_mat
-
-\brief Solve a system of equations
+Solve a system of equations.
 
-This function takes a co-efficient matrix **A** and an output matrix **B**  as inputs to solve the following equation for **X**
-
-     \f$A * X = B\f$
+This function takes a co-efficient matrix \f$A\f$ and an output matrix \f$B\f$
+as inputs to solve the following equation for \f$X\f$, \f$A * X = B\f$.
 
 This operation can be done in ArrayFire using the following code snippet.
 
 \snippet test/solve_common.hpp ex_solve
 
-The results can be verified by reconstructing the output matrix using \ref af::matmul in the following manner.
+The results can be verified by reconstructing the output matrix using \ref
+af::matmul in the following manner,
 
 \snippet test/solve_common.hpp ex_solve_recon
 
-The sample output can be seen below
+The sample output can be seen below.
 
 \code
 A [3 3 1 1]
@@ -189,52 +194,57 @@ B1 [3 1 1 1]
    39.0000
 \endcode
 
-If the coefficient matrix is known to be a triangular matrix, \ref AF_MAT_LOWER or \ref AF_MAT_UPPER can be passed to make solve faster.
+If the coefficient matrix is known to be a triangular matrix, \ref AF_MAT_LOWER
+or \ref AF_MAT_UPPER can be passed to make solve faster.
 
-The sample code snippets for solving a lower triangular matrix can be seen below.
+The sample code snippets for solving a lower triangular matrix can be seen
+below.
 
 \snippet test/solve_common.hpp ex_solve_lower
 
-Similarily, the code snippet for solving an upper triangular matrix can be seen below.
+Similarily, the code snippet for solving an upper triangular matrix can be seen
+below.
 
 \snippet test/solve_common.hpp ex_solve_upper
 
 See also: \ref af::solveLU
 
-=======================================================================
-
-\defgroup lapack_solve_lu_func_gen solveLU
-
 \ingroup lapack_solve_mat
 
-\brief Solve a system of equations
+===============================================================================
+
+\defgroup lapack_solve_lu_func_gen solveLU
 
-This function takes a co-efficient matrix **A** and an output matrix **B**  as inputs to solve the following equation for **X**
+Solve a system of equations.
 
-     \f$A * X = B\f$
+This function takes a co-efficient matrix \f$A\f$ and an output matrix \f$B\f$
+as inputs to solve the following equation for \f$X\f$, \f$A * X = B\f$.
 
 This operation can be done in ArrayFire using the following code snippet.
 
 \snippet test/solve_common.hpp ex_solve_lu
 
-This function along with \ref af::lu split up the task af::solve performs for square matrices.
+This function, along with \ref af::lu, split up the task af::solve performs for
+square matrices.
 
-\note This function is beneficial over \ref af::solve only in long running application where the coefficient matrix **A** stays the same, but the observed variables keep changing.
+This function is beneficial over \ref af::solve only in long running
+application where the coefficient matrix \f$A\f$ stays the same, but the
+observed variables keep changing.
 
+\ingroup lapack_solve_mat
 
-=======================================================================
+===============================================================================
 
 \defgroup lapack_ops_func_inv inverse
 
-\ingroup lapack_ops_mat
-
-\brief Invert a matrix
+Invert a matrix.
 
-This function inverts a square matrix **A**. The code snippet to demonstrate this can be seen below.
+This function inverts a square matrix \f$A\f$. The code snippet to demonstrate
+this can be seen below.
 
 \snippet test/inverse_dense.cpp ex_inverse
 
-The sample output can be seen below
+The sample output can be seen below.
 
 \code
 A [3 3 1 1]
@@ -254,71 +264,74 @@ I [3 3 1 1]
 
 \endcode
 
-=======================================================================
+\ingroup lapack_ops_mat
 
-\defgroup lapack_ops_func_pinv pinverse
+===============================================================================
 
-\ingroup lapack_ops_mat
+\defgroup lapack_ops_func_pinv pinverse
 
-\brief Pseudo-invert a matrix
+Pseudo-invert (Moore-Penrose) a matrix.
 
 This function calculates the Moore-Penrose pseudoinverse of a matrix \f$A\f$,
-using \ref af::svd at its core. If \f$A\f$ is of size \f$M \times N\f$, then its
-pseudoinverse \f$A^+\f$ will be of size \f$N \times M\f$.
+using \ref af::svd at its core. If \f$A\f$ is of size \f$M \times N\f$, then
+its pseudoinverse \f$A^+\f$ will be of size \f$N \times M\f$.
 
 This calculation can be batched if the input array is three or four-dimensional
 \f$(M \times N \times P \times Q\f$, with \f$Q=1\f$ for only three dimensions
-\f$)\f$. Each \f$M \times N\f$ slice along the third dimension will have its own
-pseudoinverse, for a total of \f$P \times Q\f$ pseudoinverses in the output array
-\f$(N \times M \times P \times Q)\f$.
+\f$)\f$. Each \f$M \times N\f$ slice along the third dimension will have its
+own pseudoinverse, for a total of \f$P \times Q\f$ pseudoinverses in the output
+array \f$(N \times M \times P \times Q)\f$.
 
-Here's an example snippet of its usage. In this example, we have a matrix \f$A\f$
-and we compute its pseudoinverse \f$A^+\f$. This condition must hold:
+Below is an example snippet of its usage. In this example, we have a matrix
+\f$A\f$ and compute its pseudoinverse \f$A^+\f$. This condition must hold:
 \f$AA^+A=A\f$, given that the two matrices are pseudoinverses of each other (in
 fact, this is one of the Moore-Penrose conditions):
 
 \snippet test/pinverse.cpp ex_pinverse
 
-==================================================================================
+\ingroup lapack_ops_mat
+
+===============================================================================
 
 \defgroup lapack_ops_func_rank rank
 
-\ingroup lapack_ops_mat
+Find the rank of a matrix.
 
-\brief Find the rank of the input matrix.
+This function uses \ref af::qr to find the rank of the input matrix within the
+given tolerance.
 
-This function uses \ref af::qr to find the rank of the input matrix within the given tolerance.
+\ingroup lapack_ops_mat
 
-=====================================================================================
+===============================================================================
 
 \defgroup lapack_ops_func_det det
 
-\ingroup lapack_ops_mat
+Find the determinant of a matrix.
 
-\brief Find the determinant of the input matrix.
+This function requires scratch space equal to the input array.
 
-
-\note This function requires scratch space equal to the input array
+\ingroup lapack_ops_mat
 
 ===============================================================================
 
 \defgroup lapack_ops_func_norm norm
 
-\ingroup lapack_ops_mat
+Find the norm of a matrix
 
-\brief Find the norm of the input matrix
+This function can return the norm using various metrics based on the `type`
+parameter.
 
-This function can return the norm using various metrics based on the type paramter.
+\ref AF_NORM_MATRIX_2 is currently not supported.
 
-\note \ref AF_NORM_MATRIX_2 is currently not supported.
+\ingroup lapack_ops_mat
 
 ===============================================================================
 
 \defgroup lapack_helper_func_available isLAPACKAvailable
 
-\ingroup lapack_helper
+\brief Returns true if ArrayFire is compiled with LAPACK support
 
-\brief Returns true is ArrayFire is compiled with LAPACK support
+\ingroup lapack_helper
 
 ===============================================================================
 
diff --git a/docs/details/random.dox b/docs/details/random.dox
index 63ca846106..d2400fcbbe 100644
--- a/docs/details/random.dox
+++ b/docs/details/random.dox
@@ -5,7 +5,7 @@
 
 \brief Random Number Generation Functions
 
-Functions to generate and manage random numbers and random number engines
+Functions to generate and manage random numbers and random number engines.
 
 \ingroup data_mat
 
@@ -16,7 +16,7 @@ Functions to generate and manage random numbers and random number engines
 
 \defgroup random_func_random_engine randomEngine
 
-\brief Functions to create, modify, use, and destroy randomEngine objects
+\brief Functions to create, modify, use, and destroy randomEngine objects.
 
 A \ref af::randomEngine object can be used to generate psuedo random numbers
 using various types of random number generation algorithms defined by \ref
@@ -76,7 +76,7 @@ returned by \ref af_get_default_random_engine.
 
 \defgroup random_func_set_seed setSeed
 
-\brief Set the seed for random number generation
+\brief Set the seed for random number generation.
 
 Sets the seed for the current default random engine.
 
@@ -86,7 +86,7 @@ Sets the seed for the current default random engine.
 
 \defgroup random_func_get_seed getSeed
 
-\brief Returns the seed for random number generation
+\brief Returns the seed for random number generation.
 
 Returns the seed for the current default random engine.
 
diff --git a/include/af/algorithm.h b/include/af/algorithm.h
index 801792a32a..4949d0894d 100644
--- a/include/af/algorithm.h
+++ b/include/af/algorithm.h
@@ -16,62 +16,60 @@ namespace af
     class array;
 
     /**
-       C++ Interface for sum of elements in an array
+       C++ Interface to sum array elements over a given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the add operation occurs
-       \return    result of sum all values along dimension \p dim
+       \param[in] in  input array
+       \param[in] dim dimension along which the summation occurs, -1 denotes
+                      the first non-singleton dimension
+       \return        sum
 
        \ingroup reduce_func_sum
-
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
     */
     AFAPI array sum(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for sum of elements in an array while replacing nan values
+       C++ Interface to sum array elements over a given dimension, replacing
+       any NaNs with a specified value.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the add operation occurs
-       \param[in]  nanval   The value that will replace the NaNs in \p in
-       \return    result of sum all values along dimension \p dim
+       \param[in] in     input array
+       \param[in] dim    dimension along which the summation occurs
+       \param[in] nanval value that replaces NaNs
+       \return           sum
 
        \ingroup reduce_func_sum
-
     */
     AFAPI array sum(const array &in, const int dim, const double nanval);
 #endif
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for sum of elements along given dimension by key
+       C++ Interface to sum array elements over a given dimension, according to
+       an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the sum of all values in \p vals along
-                            \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the add operation occurs
+       \param[out] keys_out reduced keys
+       \param[out] vals_out sum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the summation occurs, -1
+                            denotes the first non-singleton dimension
 
        \ingroup reduce_func_sum_by_key
-
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
     */
     AFAPI void sumByKey(array &keys_out, array &vals_out,
                         const array &keys, const array &vals,
-                        const int dim=-1);
+                        const int dim = -1);
 
     /**
-       C++ Interface for sum of elements along given dimension by key while replacing nan values
+       C++ Interface to sum array elements over a given dimension, replacing
+       any NaNs with a specified value, according to an array of keys.
 
-       \param[out] keys_out Will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out Will contain the sum of all values in \p vals along
-                            \p dim according to \p keys
-       \param[in]  keys     Is the key array
-       \param[in]  vals     Is the array containing the values to be reduced
-       \param[in]  dim      The dimension along which the add operation occurs
-       \param[in]  nanval   The value that will replace the NaNs in \p vals
+       \param[out] keys_out reduced keys
+       \param[out] vals_out sum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the summation occurs
+       \param[in]  nanval   value that replaces NaNs
 
        \ingroup reduce_func_sum_by_key
     */
@@ -81,27 +79,26 @@ namespace af
 #endif
 
     /**
-       C++ Interface for product of elements in an array
+       C++ Interface to multiply array elements over a given dimension.
 
-       \param[in] in     The input array
-       \param[in] dim    The dimension along which the multiply operation occurs
-       \return    result of product all values along dimension \p dim
+       \param[in] in  input array
+       \param[in] dim dimension along which the product occurs, -1 denotes the
+                      first non-singleton dimension
+       \return        product
 
        \ingroup reduce_func_product
-
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
     */
     AFAPI array product(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for product of elements in an array while replacing nan
-       values
+       C++ Interface to multiply array elements over a given dimension,
+       replacing any NaNs with a specified value.
 
-       \param[in] in      The input array
-       \param[in] dim     The dimension along which the multiply operation occurs
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return    result of product all values along dimension \p dim
+       \param[in] in     input array
+       \param[in] dim    dimension along which the product occurs
+       \param[in] nanval value that replaces NaNs
+       \return           product
 
        \ingroup reduce_func_product
     */
@@ -110,35 +107,33 @@ namespace af
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for product of elements in an array according to a key
+       C++ Interface to multiply array elements over a given dimension,
+       according to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the product of all values in \p vals
-                            along \p dim according to \p keys
-       \param[in]  keys     The key array
-       \param[in]  vals     The array containing the values to be reduced
-       \param[in]  dim      The dimension along which the product operation occurs
+       \param[out] keys_out reduced keys
+       \param[out] vals_out product
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the product occurs, -1
+                            denotes the first non-singleton dimension
 
        \ingroup reduce_func_product_by_key
-
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
     */
     AFAPI void productByKey(array &keys_out, array &vals_out,
                             const array &keys, const array &vals,
                             const int dim = -1);
 
     /**
-       C++ Interface for product of elements in an array according to a key
-       while replacing nan values
+       C++ Interface to multiply array elements over a given dimension,
+       replacing any NaNs with a specified value, according to an array of
+       keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p
-                            dim
-       \param[out] vals_out will contain the product of all values in \p
-                            vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the product operation occurs
-       \param[in] nanval  The value that will replace the NaNs in \p vals
+       \param[out] keys_out reduced keys
+       \param[out] vals_out product
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the product occurs
+       \param[in]  nanval   value that replaces NaNs
 
        \ingroup reduce_func_product_by_key
 
@@ -149,33 +144,34 @@ namespace af
 #endif
 
     /**
-       C++ Interface for minimum values in an array
+       C++ Interface to return the minimum along a given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the minimum value needs to be extracted
-       \return    result of minimum all values along dimension \p dim
+       NaN values are ignored.
 
-       \ingroup reduce_func_min
+       \param[in] in  input array
+       \param[in] dim dimension along which the minimum is found, -1 denotes
+                      the first non-singleton dimension
+       \return        minimum
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_min
     */
     AFAPI array min(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for minimum values in an array according to a key
+       C++ Interface to return the minimum along a given dimension, according
+       to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the minimum of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the min operation occurs
+       NaN values are ignored.
 
-       \ingroup reduce_func_min_by_key
+       \param[out] keys_out reduced keys
+       \param[out] vals_out minimum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the minimum is found, -1
+                            denotes the first non-singleton dimension
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_min_by_key
     */
     AFAPI void minByKey(array &keys_out, array &vals_out,
                         const array &keys, const array &vals,
@@ -183,33 +179,34 @@ namespace af
 #endif
 
     /**
-       C++ Interface for maximum values in an array
+       C++ Interface to return the maximum along a given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the maximum value needs to be extracted
-       \return    result of maximum all values along dimension \p dim
+       NaN values are ignored.
 
-       \ingroup reduce_func_max
+       \param[in] in  input array
+       \param[in] dim dimension along which the maximum is found, -1 denotes
+                      the first non-singleton dimension
+       \return        maximum
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_max
     */
     AFAPI array max(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for maximum values in an array according to a key
+       C++ Interface to return the maximum along a given dimension, according
+       to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the maximum of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the max operation occurs
+       NaN values are ignored.
 
-       \ingroup reduce_func_max_by_key
+       \param[out] keys_out reduced keys
+       \param[out] vals_out maximum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the maximum is found, -1
+                            denotes the first non-singleton dimension
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_max_by_key
     */
     AFAPI void maxByKey(array &keys_out, array &vals_out,
                         const array &keys, const array &vals,
@@ -218,50 +215,51 @@ namespace af
 
 #if AF_API_VERSION >= 38
     /**
-       C++ Interface for ragged max values in an array
-       Uses an additional input array to determine the number of elements to use along the reduction axis.
+       C++ Interface to return the ragged maximum along a given dimension.
 
-       \param[out] val will contain the maximum ragged values in \p in along \p dim according to \p ragged_len
-       \param[out] idx will contain the locations of the maximum ragged values in \p in along \p dim according to \p ragged_len
-       \param[in] in contains the input values to be reduced
-       \param[in] ragged_len array containing number of elements to use when reducing along \p dim
-       \param[in] dim The dimension along which the max operation occurs
+       Input parameter `ragged_len` sets the number of elements to consider.
 
-       \ingroup reduce_func_max
+       NaN values are ignored.
+
+       \param[out] val        ragged maximum
+       \param[out] idx        locations of the maximum ragged values
+       \param[in]  in         input array
+       \param[in]  ragged_len array containing the number of elements to use
+       \param[in]  dim        dimension along which the maximum is found
 
-       \note NaN values are ignored
+       \ingroup reduce_func_max
     */
     AFAPI void max(array &val, array &idx, const array &in, const array &ragged_len, const int dim);
 #endif
 
     /**
-       C++ Interface for checking all true values in an array
+       C++ Interface to check if all values along a given dimension are true.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the values are checked to be all true
-       \return    result of checking if values along dimension \p dim are all true
+       NaN values are ignored.
 
-       \ingroup reduce_func_all_true
+       \param[in] in  input array
+       \param[in] dim dimension along which the check occurs, -1 denotes the
+                      first non-singleton dimension
+       \return        array containing 1's if all true; 0's otherwise
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_all_true
     */
     AFAPI array allTrue(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for checking all true values in an array according to a key
+       C++ Interface to check if all values along a given dimension are true,
+       according to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the reduced and of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the all true operation occurs
+       NaN values are ignored.
 
-       \ingroup reduce_func_alltrue_by_key
+       \param[out] keys_out reduced keys
+       \param[out] vals_out array containing 1's if all true; 0's otherwise
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the check occurs
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_alltrue_by_key
     */
     AFAPI void allTrueByKey(array &keys_out, array &vals_out,
                             const array &keys, const array &vals,
@@ -269,33 +267,33 @@ namespace af
 #endif
 
     /**
-       C++ Interface for checking any true values in an array
+       C++ Interface to check if any values along a given dimension are true.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the values are checked to be any true
-       \return    result of checking if values along dimension \p dim are any true
+       NaN values are ignored.
 
-       \ingroup reduce_func_any_true
+       \param[in] in  input array
+       \param[in] dim dimension along which the check occurs, -1 denotes the
+                      first non-singleton dimension
+       \return        array containing 1's if any true; 0's otherwise
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_any_true
     */
     AFAPI array anyTrue(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for checking any true values in an array according to a key
+       C++ Interface to check if any values along a given dimension are true,
+       according to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the reduced or of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the any true operation occurs
+       NaN values are ignored.
 
-       \ingroup reduce_func_anytrue_by_key
+       \param[out] keys_out reduced keys
+       \param[out] vals_out array containing 1's if any true; 0's otherwise
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the check occurs
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are ignored
+       \ingroup reduce_func_anytrue_by_key
     */
     AFAPI void anyTrueByKey(array &keys_out, array &vals_out,
                             const array &keys, const array &vals,
@@ -303,33 +301,35 @@ namespace af
 #endif
 
     /**
-       C++ Interface for counting non-zero values in an array
+       C++ Interface to count non-zero values in an array along a given
+       dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the the number of non-zero values are counted
-       \return    the number of non-zero values along dimension \p dim
+       NaN values are treated as non-zero.
 
-       \ingroup reduce_func_count
+       \param[in] in  input array
+       \param[in] dim dimension along which the count occurs, -1 denotes the
+                      first non-singleton dimension
+       \return        count
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are treated as non zero.
+       \ingroup reduce_func_count
     */
     AFAPI array count(const array &in, const int dim = -1);
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for counting non-zero values in an array according to a key
+       C++ Interface to count non-zero values in an array, according to an
+       array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the count of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the count operation occurs
+       NaN values are treated as non-zero.
 
-       \ingroup reduce_func_count_by_key
+       \param[out] keys_out reduced keys
+       \param[out] vals_out count
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the count occurs, -1 denotes
+                            the first non-singleton dimension
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
-       \note NaN values are treated as non zero.
+       \ingroup reduce_func_count_by_key
     */
     AFAPI void countByKey(array &keys_out, array &vals_out,
                           const array &keys, const array &vals,
@@ -337,10 +337,13 @@ namespace af
 #endif
 
     /**
-       C++ Interface for sum of all elements in an array
+       C++ Interface to sum array elements over all dimensions.
 
-       \param[in] in is the input array
-       \return    the sum of all values of \p in
+       Results in a single value as an output, which may be a single element
+       `af::array`.
+
+       \param[in] in  input array
+       \return        sum
 
        \ingroup reduce_func_sum
     */
@@ -348,12 +351,15 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for sum of all elements in an array while replacing nan
-       values
+       C++ Interface to sum array elements over all dimensions, replacing any
+       NaNs with a specified value.
+
+       Results in a single value as an output, which may be a single element
+       `af::array`.
 
-       \param[in] in is the input array
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return    the sum of all values of \p in
+       \param[in] in     input array
+       \param[in] nanval value that replaces NaNs
+       \return           sum
 
        \ingroup reduce_func_sum
     */
@@ -361,10 +367,11 @@ namespace af
 #endif
 
     /**
-       C++ Interface for product of all elements in an array
+       C++ Interface to multiply array elements over the first non-singleton
+       dimension.
 
-       \param[in] in is the input array
-       \return    the product of all values of \p in
+       \param[in] in input array
+       \return       product
 
        \ingroup reduce_func_product
     */
@@ -372,143 +379,155 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for product of all elements in an array while replacing nan
-       values
+       C++ Interface to multiply array elements over the first non-singleton
+       dimension, replacing any NaNs with a specified value.
 
-       \param[in] in is the input array
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return    the product of all values of \p in
+       \param[in] in     input array
+       \param[in] nanval value that replaces NaNs
+       \return           product
 
        \ingroup reduce_func_product
     */
     template<typename T> T product(const array &in, double nanval);
 #endif
 
-
     /**
-       C++ Interface for getting minimum value of an array
+       C++ Interface to return the minimum along the first non-singleton
+       dimension.
 
-       \param[in] in is the input array
-       \return    the minimum of all values of \p in
+       NaN values are ignored.
 
-       \ingroup reduce_func_min
+       \param[in] in input array
+       \return       minimum
 
-       \note NaN values are ignored
+       \ingroup reduce_func_min
     */
     template<typename T> T min(const array &in);
 
     /**
-       C++ Interface for getting maximum value of an array
+       C++ Interface to return the maximum along the first non-singleton
+       dimension.
 
-       \param[in] in is the input array
-       \return    the maximum of all values of \p in
+       NaN values are ignored.
 
-       \ingroup reduce_func_max
+       \param[in] in input array
+       \return       maximum
 
-       \note NaN values are ignored
+       \ingroup reduce_func_max
     */
     template<typename T> T max(const array &in);
 
     /**
-       C++ Interface for checking if all values in an array are true
+       C++ Interface to check if all values along the first non-singleton
+       dimension are true.
 
-       \param[in] in is the input array
-       \return    true if all values of \p in are true, false otherwise
+       NaN values are ignored.
 
-       \ingroup reduce_func_all_true
+       \param[in] in input array
+       \return       array containing 1's if all true; 0's otherwise
 
-       \note NaN values are ignored
+       \ingroup reduce_func_all_true
     */
     template<typename T> T allTrue(const array &in);
 
     /**
-       C++ Interface for checking if any values in an array are true
+       C++ Interface to check if any values along the first non-singleton
+       dimension are true.
 
-       \param[in] in is the input array
-       \return    true if any values of \p in are true, false otherwise
+       NaN values are ignored.
 
-       \ingroup reduce_func_any_true
+       \param[in] in input array
+       \return       array containing 1's if any true; 0's otherwise
 
-       \note NaN values are ignored
+       \ingroup reduce_func_any_true
     */
     template<typename T> T anyTrue(const array &in);
 
     /**
-       C++ Interface for counting total number of non-zero values in an array
+       C++ Interface to count non-zero values along the first non-singleton
+       dimension.
 
-       \param[in] in is the input array
-       \return    the number of non-zero values in \p in
+       NaN values are treated as non-zero.
 
-       \ingroup reduce_func_count
+       \param[in] in input array
+       \return       count
 
-       \note NaN values are treated as non zero
+       \ingroup reduce_func_count
     */
     template<typename T> T count(const array &in);
 
     /**
-       C++ Interface for getting minimum values and their locations in an array
-
-       \param[out] val will contain the minimum values along dimension \p dim
-       \param[out] idx will contain the locations of minimum all values along dimension \p dim
-       \param[in]  in is the input array
-       \param[in]  dim The dimension along which the minimum value needs to be extracted
+       C++ Interface to return the minimum and its location along a given
+       dimension.
 
-       \ingroup reduce_func_min
+       NaN values are ignored.
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \param[out] val minimum
+       \param[out] idx location
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the minimum is found, -1 denotes
+                       the first non-singleton dimension
 
-       \note NaN values are ignored
+       \ingroup reduce_func_min
     */
     AFAPI void min(array &val, array &idx, const array &in, const int dim = -1);
 
     /**
-       C++ Interface for getting maximum values and their locations in an array
-
-       \param[out] val will contain the maximum values along dimension \p dim
-       \param[out] idx will contain the locations of maximum all values along dimension \p dim
-       \param[in]  in is the input array
-       \param[in]  dim The dimension along which the maximum value needs to be extracted
+       C++ Interface to return the maximum and its location along a given
+       dimension.
 
-       \ingroup reduce_func_max
+       NaN values are ignored.
 
-       \note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
+       \param[out] val maximum
+       \param[out] idx location
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the maximum is found, -1 denotes
+                       the first non-singleton dimension
 
-       \note NaN values are ignored
+       \ingroup reduce_func_max
     */
     AFAPI void max(array &val, array &idx, const array &in, const int dim = -1);
 
     /**
-       C++ Interface for getting minimum value and its location from the entire array
+       C++ Interface to return the minimum and its location over all
+       dimensions.
 
-       \param[out] val will contain the minimum values in the input
-       \param[out] idx will contain the locations of minimum all values in the input
-       \param[in]  in is the input array
+       NaN values are ignored.
 
-       \ingroup reduce_func_min
+       Often used to return values directly to the host.
+
+       \param[out] val minimum
+       \param[out] idx location
+       \param[in]  in  input array
 
-       \note NaN values are ignored
+       \ingroup reduce_func_min
     */
     template<typename T> void min(T *val, unsigned *idx, const array &in);
 
     /**
-       C++ Interface for getting maximum value and its location from the entire array
+       C++ Interface to return the maximum and its location over all
+       dimensions.
 
-       \param[out] val contains the maximum values in the input
-       \param[out] idx contains the locations of maximum all values in the input
-       \param[in]  in is the input array
+       NaN values are ignored.
 
-       \ingroup reduce_func_max
+       Often used to return values directly to the host.
 
-       \note NaN values are ignored
+       \param[out] val maximum
+       \param[out] idx location
+       \param[in]  in  input array
+
+       \ingroup reduce_func_max
     */
     template<typename T> void max(T *val, unsigned *idx, const array &in);
 
     /**
-       C++ Interface for computing the cumulative sum (inclusive) of an array
+       C++ Interface to evaluate the cumulative sum (inclusive) along a given
+       dimension.
 
-       \param[in] in is the input array
-       \param[in] dim is the dimension along which the inclusive sum is calculated
-       \return the output containing inclusive sums of the input
+       \param[in] in  input array
+       \param[in] dim dimension along which the sum is accumulated, 0 denotes
+                      the first non-singleton dimension
+       \return        cumulative sum
 
        \ingroup scan_func_accum
     */
@@ -516,13 +535,14 @@ namespace af
 
 #if AF_API_VERSION >=34
     /**
-       C++ Interface generalized scan of an array
+       C++ Interface to scan an array (generalized) over a given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which scan is performed
-       \param[in] op is the type of binary operation used
-       \param[in] inclusive_scan is flag specifying whether scan is inclusive
-       \return the output containing scan of the input
+       \param[in] in             input array
+       \param[in] dim            dimension along which the scan occurs, 0
+                                 denotes the first non-singleton dimension
+       \param[in] op             type of binary operation used
+       \param[in] inclusive_scan flag specifying whether the scan is inclusive
+       \return                   scan
 
        \ingroup scan_func_scan
     */
@@ -530,14 +550,16 @@ namespace af
                      binaryOp op = AF_BINARY_ADD, bool inclusive_scan = true);
 
     /**
-       C++ Interface generalized scan by key of an array
+       C++ Interface to scan an array (generalized) over a given dimension,
+       according to an array of keys.
 
-       \param[in] key is the key array
-       \param[in] in is the input array
-       \param[in] dim The dimension along which scan is performed
-       \param[in] op is the type of binary operations used
-       \param[in] inclusive_scan is flag specifying whether scan is inclusive
-       \return the output containing scan of the input
+       \param[in] key            keys array
+       \param[in] in             input array
+       \param[in] dim            dimension along which the scan occurs, 0
+                                 denotes the first non-singleton dimension
+       \param[in] op             type of binary operation used
+       \param[in] inclusive_scan flag specifying whether the scan is inclusive
+       \return                   scan
 
        \ingroup scan_func_scanbykey
     */
@@ -546,44 +568,49 @@ namespace af
 #endif
 
     /**
-       C++ Interface for finding the locations of non-zero values in an array
+       C++ Interface to locate the indices of the non-zero values in an array.
 
-       \param[in] in is the input array.
-       \return linear indices where \p in is non-zero
+       \param[in] in input array
+       \return       linear indices where `in` is non-zero
 
        \ingroup scan_func_where
     */
     AFAPI array where(const array &in);
 
     /**
-       C++ Interface for calculating first order differences in an array
+       C++ Interface to calculate the first order difference in an array over a
+       given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \return array of first order numerical difference
+       \param[in] in  input array
+       \param[in] dim dimension along which the difference occurs, 0
+                      denotes the first non-singleton dimension
+       \return        first order numerical difference
 
        \ingroup calc_func_diff1
     */
     AFAPI array diff1(const array &in, const int dim = 0);
 
     /**
-       C++ Interface for calculating second order differences in an array
+       C++ Interface to calculate the second order difference in an array over
+       a given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \return array of second order numerical difference
+       \param[in] in  input array
+       \param[in] dim dimension along which the difference occurs, 0
+                      denotes the first non-singleton dimension
+       \return        second order numerical difference
 
        \ingroup calc_func_diff2
     */
     AFAPI array diff2(const array &in, const int dim = 0);
 
     /**
-       C++ Interface for sorting an array
+       C++ Interface to sort an array over a given dimension.
 
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
+       \param[in] in          input array
+       \param[in] dim         dimension along which the sort occurs, 0 denotes
+                              the first non-singleton dimension
        \param[in] isAscending specifies the sorting order
-       \return the sorted output
+       \return                sorted output
 
        \ingroup sort_func_sort
     */
@@ -591,27 +618,32 @@ namespace af
                      const bool isAscending = true);
 
     /**
-       C++ Interface for sorting an array and getting original indices
+       C++ Interface to sort an array over a given dimension and to return the
+       original indices.
 
-       \param[out] out will contain the sorted output
-       \param[out] indices will contain the indices in the original input
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \param[in] isAscending specifies the sorting order
+       \param[out] out         sorted output
+       \param[out] indices     indices from the input
+       \param[in]  in          input array
+       \param[in]  dim         dimension along which the sort occurs, 0 denotes
+                               the first non-singleton dimension
+       \param[in]  isAscending specifies the sorting order
 
        \ingroup sort_func_sort_index
     */
     AFAPI void  sort(array &out, array &indices, const array &in, const unsigned dim = 0,
                      const bool isAscending = true);
+
     /**
-       C++ Interface for sorting an array based on keys
+       C++ Interface to sort an array over a given dimension, according to an
+       array of keys.
 
-       \param[out] out_keys will contain the keys based on sorted values
-       \param[out] out_values will contain the sorted values
-       \param[in] keys is the input array
-       \param[in] values The dimension along which numerical difference is performed
-       \param[in] dim The dimension along which numerical difference is performed
-       \param[in] isAscending specifies the sorting order
+       \param[out] out_keys    sorted keys
+       \param[out] out_values  sorted output
+       \param[in]  keys        keys array
+       \param[in]  values      input array
+       \param[in]  dim         dimension along which the sort occurs, 0 denotes
+                               the first non-singleton dimension
+       \param[in]  isAscending specifies the sorting order
 
        \ingroup sort_func_sort_keys
     */
@@ -620,23 +652,23 @@ namespace af
                      const bool isAscending = true);
 
     /**
-       C++ Interface for getting unique values
+       C++ Interface to return the unique values in an array.
 
-       \param[in] in is the input array
-       \param[in] is_sorted if true, skips the sorting steps internally
-       \return the unique values from \p in
+       \param[in] in        input array
+       \param[in] is_sorted if true, skip the sorting steps internally
+       \return              unique values
 
        \ingroup set_func_unique
     */
     AFAPI array setUnique(const array &in, const bool is_sorted=false);
 
     /**
-       C++ Interface for finding the union of two arrays
+       C++ Interface to evaluate the union of two arrays.
 
-       \param[in] first is the first input array
-       \param[in] second is the second input array
-       \param[in] is_unique if true, skips calling unique internally
-       \return all unique values present in \p first and \p second (union) in increasing order
+       \param[in] first     input array
+       \param[in] second    input array
+       \param[in] is_unique if true, skip calling setUnique internally
+       \return              union, values in increasing order
 
        \ingroup set_func_union
     */
@@ -644,12 +676,12 @@ namespace af
                          const bool is_unique=false);
 
     /**
-       C++ Interface for finding the intersection of two arrays
+       C++ Interface to evaluate the intersection of two arrays.
 
-       \param[in] first is the first input array
-       \param[in] second is the second input array
-       \param[in] is_unique if true, skips calling unique internally
-       \return unique values that are present in both \p first and \p second(intersection) in increasing order
+       \param[in] first     input array
+       \param[in] second    input array
+       \param[in] is_unique if true, skip calling setUnique internally
+       \return              intersection, values in increasing order
 
        \ingroup set_func_intersect
     */
@@ -663,12 +695,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for sum of elements in an array
+       C Interface to sum array elements over a given dimension.
 
-       \param[out] out will contain the sum of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the add operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out sum
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the summation occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum
     */
@@ -676,11 +709,14 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for sum of all elements in an array, resulting in an array
+       C Interface to sum array elements over all dimensions.
 
-       \param[out] out will contain the sum of all values in \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       Results in a single element `af::array`.
+
+       \param[out] out sum
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum
     */
@@ -689,13 +725,15 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for sum of elements in an array while replacing nans
+       C Interface to sum array elements over a given dimension, replacing any
+       NaNs with a specified value.
 
-       \param[out] out will contain the sum of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the add operation occurs
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    sum
+       \param[in]  in     input array
+       \param[in]  dim    dimension along which the summation occurs
+       \param[in]  nanval value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum
     */
@@ -705,13 +743,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for sum of all elements in an array, resulting in an array with
-       nan substitution
+       C Interface to sum array elements over all dimensions, replacing any
+       NaNs with a specified value.
+
+       Results in a single element `af::array`.
 
-       \param[out] out will contain the sum of all values in \p in
-       \param[in] in is the input array
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    sum
+       \param[in]  in     input array
+       \param[in]  nanval value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum
     */
@@ -720,14 +761,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for sum of elements in an array according to key
+       C Interface to sum array elements over a given dimension, according to
+       an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the sum of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the add operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out sum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the summation occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum_by_key
     */
@@ -735,20 +778,17 @@ extern "C" {
                                const af_array keys, const af_array vals, const int dim);
 
     /**
-       C Interface for sum of elements in an array according to key while
-       replacing nans
-
-       \param[out] keys_out will contain the reduced keys in \p vals along \p
-                            dim
-       \param[out] vals_out will contain the sum of all values in \p vals
-                            along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the add operation occurs
-       \param[in] nanval  The value that will replace the NaNs in \p vals
+       C Interface to sum array elements over a given dimension, replacing any
+       NaNs with a specified value, according to an array of keys.
 
-
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out sum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the summation occurs
+       \param[in]  nanval   value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum_by_key
     */
@@ -758,12 +798,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for product of elements in an array
+       C Interface to multiply array elements over a given dimension.
 
-       \param[out] out will contain the product of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the multiply operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out product
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the product occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product
     */
@@ -771,11 +812,14 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for product of elements in an array, resulting in an array
+       C Interface to multiply array elements over all dimensions.
+
+       Results in a single element `af::array`.
 
-       \param[out] out will contain the product of all values in \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out product
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product
     */
@@ -784,14 +828,15 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for product of elements in an array while replacing nans
+       C Interface to multiply array elements over a given dimension, replacing
+       any NaNs with a specified value.
 
-       \param[out] out will contain the product of all values in \p in along \p
-                       dim
-       \param[in] in   is the input array
-       \param[in] dim  The dimension along which the product operation occurs
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    product
+       \param[in]  in     input array
+       \param[in]  dim    dimension along with the product occurs
+       \param[in]  nanval value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product
     */
@@ -800,13 +845,14 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for product of elements in an array, resulting in an array
-       while replacing nans
+       C Interface to multiply array elements over all dimensions, replacing
+       any NaNs with a specified value.
 
-       \param[out] out will contain the product of all values in \p in
-       \param[in] in   is the input array
-       \param[in] nanval  The value that will replace the NaNs in \p in
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    product
+       \param[in]  in     input array
+       \param[in]  nanval value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product
     */
@@ -815,14 +861,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for product of elements in an array according to key
+       C Interface to multiply array elements over a given dimension, according
+       to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the product of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the product operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out product
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the product occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product_by_key
     */
@@ -830,18 +878,17 @@ extern "C" {
                                    const af_array keys, const af_array vals, const int dim);
 
     /**
-       C Interface for product of elements in an array according to key while
-       replacing nans
+       C Interface to multiply array elements over a given dimension, replacing
+       any NaNs with a specified value, according to an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p
-                            dim
-       \param[out] vals_out will contain the product of all values in \p
-                            vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the product operation occurs
-       \param[in] nanval  The value that will replace the NaNs in \p vals
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out product
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the product occurs
+       \param[in]  nanval   value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product_by_key
     */
@@ -851,12 +898,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for minimum values in an array
+       C Interface to return the minimum along a given dimension.
 
-       \param[out] out will contain the minimum of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the minimum value is extracted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out minimum
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the minimum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_min
     */
@@ -864,14 +912,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for minimum values in an array according to key
+       C Interface to return the minimum along a given dimension, according to
+       an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the minimum of all values in \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the minimum value is extracted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out minimum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the minimum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_min_by_key
     */
@@ -881,12 +931,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for maximum values in an array
+       C Interface to return the maximum along a given dimension.
 
-       \param[out] out will contain the maximum of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the maximum value is extracted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out  maximum
+       \param[in]  in   input array
+       \param[in]  dim dimension along which the maximum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_max
     */
@@ -894,16 +945,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for maximum values in an array according to key
+       C Interface to return the maximum along a given dimension, according to
+       an array of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p
-                            dim
-       \param[out] vals_out will contain the maximum of all values in \p
-                            vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the maximum value is extracted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out maximum
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the maximum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_max_by_key
     */
@@ -914,30 +965,35 @@ extern "C" {
 
 #if AF_API_VERSION >= 38
     /**
-       C Interface for finding ragged max values in an array
-       Uses an additional input array to determine the number of elements to use along the reduction axis.
+       C Interface to return the ragged maximum over a given dimension.
 
-       \param[out] val will contain the maximum ragged values in \p in along \p dim according to \p ragged_len
-       \param[out] idx will contain the locations of the maximum ragged values in \p in along \p dim according to \p ragged_len
-       \param[in] in contains the input values to be reduced
-       \param[in] ragged_len array containing number of elements to use when reducing along \p dim
-       \param[in] dim The dimension along which the max operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       Input parameter `ragged_len` sets the number of elements to consider.
 
-       \ingroup reduce_func_max
+       NaN values are ignored.
+
+       \param[out] val        ragged maximum
+       \param[out] idx        locations of the maximum ragged values
+       \param[in]  in         input array
+       \param[in]  ragged_len array containing the number of elements to use
+       \param[in]  dim        dimension along which the maximum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-       \note NaN values are ignored
+       \ingroup reduce_func_max
     */
     AFAPI af_err af_max_ragged(af_array *val, af_array *idx, const af_array in, const af_array ragged_len, const int dim);
 #endif
 
     /**
-       C Interface for checking all true values in an array
+       C Interface  to check if all values along a given dimension are true.
 
-       \param[out] out will contain the result of "and" operation all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the "and" operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       NaN values are ignored.
+
+       \param[out] out array containing 1's if all true; 0's otherwise
+       \param[in]  in  input array
+       \param[in]  dim dimention along which the check occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_all_true
     */
@@ -945,15 +1001,18 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for checking all true values in an array according to key
+       C Interface to check if all values along a given dimension are true,
+       according to an array of keys.
+
+       NaN values are ignored.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the the reduced and of all values in
-                            \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the "and" operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out array containing 1's if all true; 0's otherwise
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the check occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_alltrue_by_key
     */
@@ -963,12 +1022,15 @@ extern "C" {
 #endif
 
     /**
-       C Interface for checking any true values in an array
+       C Interface to check if any values along a given dimension are true.
 
-       \param[out] out will contain the result of "or" operation all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the "or" operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       NaN values are ignored.
+
+       \param[out] out array containing 1's if any true; 0's otherwise
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the check occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_any_true
     */
@@ -976,15 +1038,17 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for checking any true values in an array according to key
+       C Interface to check if any values along a given dimension are true.
+
+       NaN values are ignored.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the reduced or of all values in
-                            \p vals along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the "or" operation occurs
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] keys_out reduced keys
+       \param[out] vals_out array containing 1's if any true; 0's otherwise
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimensions along which the check occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_anytrue_by_key
     */
@@ -994,12 +1058,16 @@ extern "C" {
 #endif
 
     /**
-       C Interface for counting non-zero values in an array
+       C Interface to count non-zero values in an array along a given
+       dimension.
+
+       NaN values are treated as non-zero.
 
-       \param[out] out will contain the number of non-zero values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the non-zero values are counted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out count
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the count occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_count
     */
@@ -1007,15 +1075,18 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for counting non-zero values in an array according to key
+       C Interface to count non-zero values in an array, according to an array
+       of keys.
 
-       \param[out] keys_out will contain the reduced keys in \p vals along \p dim
-       \param[out] vals_out will contain the count of all values in \p vals
-                            along \p dim according to \p keys
-       \param[in] keys is the key array
-       \param[in] vals is the array containing the values to be reduced
-       \param[in] dim The dimension along which the non-zero values are counted
-       \return \ref AF_SUCCESS if the execution completes properly
+       NaN values are treated as non-zero.
+
+       \param[out] keys_out reduced keys
+       \param[out] vals_out count
+       \param[in]  keys     keys array
+       \param[in]  vals     input array
+       \param[in]  dim      dimension along which the count occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_count_by_key
     */
@@ -1025,16 +1096,15 @@ extern "C" {
 #endif
 
     /**
-       C Interface for sum of all elements in an array
+       C Interface to sum array elements over all dimensions.
 
-       \param[out] real will contain the real part of adding all elements in
-                        input \p in
-       \param[out] imag will contain the imaginary part of adding all elements
-                        in input \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       If `in` is real, `imag` will be set to zeros.
 
-       \note \p imag is always set to 0 when \p in is real
+       \param[out] real sum of all real components
+       \param[out] imag sum of all imaginary components
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum
     */
@@ -1042,17 +1112,17 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for sum of all elements in an array while replacing nans
+       C Interface to sum array elements over all dimensions, replacing any
+       NaNs with a specified value.
 
-       \param[out] real will contain the real part of adding all elements in
-                        input \p in
-       \param[out] imag will contain the imaginary part of adding all elements
-                        in input \p in
-       \param[in] in is the input array
-       \param[in] nanval is the value which replaces nan
-       \return \ref AF_SUCCESS if the execution completes properly
+       If `in` is real, `imag` will be set to zeros.
 
-       \note \p imag is always set to 0 when \p in is real
+       \param[out] real   sum of all real components
+       \param[out] imag   sum of all imaginary components
+       \param[in]  in     input array
+       \param[in]  nanval value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_sum
     */
@@ -1061,14 +1131,15 @@ extern "C" {
 #endif
 
     /**
-       C Interface for product of all elements in an array
+       C Interface to multiply array elements over all dimensions.
 
-       \param[out] real will contain the real part of multiplying all elements in input \p in
-       \param[out] imag will contain the imaginary part of multiplying all elements in input \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       If `in` is real, `imag` will be set to zeros.
 
-       \note \p imag is always set to 0 when \p in is real
+       \param[out] real product of all real components
+       \param[out] imag product of all imaginary components
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product
     */
@@ -1076,17 +1147,17 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for product of all elements in an array while replacing nans
+       C Interface to multiply array elements over all dimensions, replacing
+       any NaNs with a specified value.
 
-       \param[out] real   will contain the real part of multiplication of all
-                          elements in input \p in
-       \param[out] imag   will contain the imaginary part of multiplication of
-                          all elements in input \p in
-       \param[in]  in     is the input array
-       \param[in]  nanval is the value which replaces nan
-       \return \ref AF_SUCCESS if the execution completes properly
+       If `in` is real, `imag` will be set to zeros.
 
-       \note \p imag is always set to 0 when \p in is real
+       \param[out] real   product of all real components
+       \param[out] imag   product of all imaginary components
+       \param[in]  in     input array
+       \param[in]  nanval value that replaces NaNs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_product
     */
@@ -1095,14 +1166,15 @@ extern "C" {
 #endif
 
     /**
-       C Interface for getting minimum value of an array
+       C Interface to return the minimum over all dimensions.
 
-       \param[out] real will contain the real part of minimum value of all elements in input \p in
-       \param[out] imag will contain the imaginary part of minimum value of all elements in input \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       If `in` is real, `imag` will be set to zeros.
 
-       \note \p imag is always set to 0 when \p in is real.
+       \param[out] real real component of the minimum
+       \param[out] imag imaginary component of the minimum
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_min
     */
@@ -1110,11 +1182,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for minimum values in an array, returning an array
+       C Interface to return the minimum over all dimensions.
 
-       \param[out] out will contain the minimum of all values in \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out minimum
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_min
     */
@@ -1122,14 +1195,15 @@ extern "C" {
 #endif
 
     /**
-       C Interface for getting maximum value of an array
+       C Interface to return the maximum over all dimensions.
 
-       \param[out] real will contain the real part of maximum value of all elements in input \p in
-       \param[out] imag will contain the imaginary part of maximum value of all elements in input \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       If `in` is real, `imag` will be set to zeros.
 
-       \note \p imag is always set to 0 when \p in is real.
+       \param[out] real real component of the maximum
+       \param[out] imag imaginary component of the maximum
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_max
     */
@@ -1137,13 +1211,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for getting maximum value of an array, returning an array
-
-       \param[out] out will contain the maximum of all values in \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       C Interface to return the maximum over all dimensions.
 
-       \note \p imag is always set to 0 when \p in is real.
+       \param[out] out maximum
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_max
     */
@@ -1151,14 +1224,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for checking if all values in an array are true
-
-       \param[out] real is 1 if all values of input \p in are true, 0 otherwise.
-       \param[out] imag is always set to 0.
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
-
-       \note \p imag is always set to 0.
+       C Interface to check if all values over all dimensions are true.
+ 
+       \param[out] real 1 if all true; 0 otherwise
+       \param[out] imag 0
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_all_true
     */
@@ -1166,14 +1238,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for checking if all values in an array are true,
-       while returning an af_array
-
-       \param[out] out will contain 1 if all values of input \p in are true, 0 otherwise
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
-
-       \note \p imag is always set to 0.
+       C Interface to check if all values over all dimensions are true.
+ 
+       \param[out] out 1 if all true; 0 otherwise
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_all_true
     */
@@ -1181,14 +1251,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for checking if any values in an array are true
-
-       \param[out] real is 1 if any value of input \p in is true, 0 otherwise.
-       \param[out] imag is always set to 0.
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       C Interface to check if any values over all dimensions are true.
 
-       \note \p imag is always set to 0.
+       \param[out] real 1 if any true; 0 otherwise
+       \param[out] imag 0
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_any_true
     */
@@ -1196,14 +1265,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for checking if any values in an array are true,
-       while returning an af_array
+       C Interface to check if any values over all dimensions are true.
 
-       \param[out] out will contain 1 if any value of input \p in is true, 0 otherwise
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
-
-       \note \p imag is always set to 0.
+       \param[out] out 1 if any true; 0 otherwise
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_any_true
     */
@@ -1211,14 +1278,13 @@ extern "C" {
 #endif
 
     /**
-       C Interface for counting total number of non-zero values in an array
-
-       \param[out] real will contain the number of non-zero values in \p in.
-       \param[out] imag is always set to 0.
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       C Interface to count non-zero values over all dimensions.
 
-       \note \p imag is always set to 0.
+       \param[out] real count
+       \param[out] imag 0
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_count
     */
@@ -1226,12 +1292,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       C Interface for counting total number of non-zero values in an array,
-       while returning an af_array
+       C Interface to count non-zero values over all dimensions.
 
-       \param[out] out contain the number of non-zero values in \p in.
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out count
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_count
     */
@@ -1239,13 +1305,15 @@ extern "C" {
 #endif
 
     /**
-       C Interface for getting minimum values and their locations in an array
+       C Interface to return the minimum and its location along a given
+       dimension.
 
-       \param[out] out will contain the minimum of all values in \p in along \p dim
-       \param[out] idx will contain the location of minimum of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the minimum value is extracted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out minimum
+       \param[out] idx location
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the minimum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_min
     */
@@ -1253,13 +1321,15 @@ extern "C" {
                          const int dim);
 
     /**
-       C Interface for getting maximum values and their locations in an array
+       C Interface to return the maximum and its location along a given
+       dimension.
 
-       \param[out] out will contain the maximum of all values in \p in along \p dim
-       \param[out] idx will contain the location of maximum of all values in \p in along \p dim
-       \param[in] in is the input array
-       \param[in] dim The dimension along which the maximum value is extracted
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out maximum
+       \param[out] idx location
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the maximum is found
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_max
     */
@@ -1267,15 +1337,16 @@ extern "C" {
                          const int dim);
 
     /**
-       C Interface for getting minimum value and its location from the entire array
+       C Interface to return the minimum and its location over all dimensions.
 
-       \param[out] real will contain the real part of minimum value of all elements in input \p in
-       \param[out] imag will contain the imaginary part of minimum value of all elements in input \p in
-       \param[out] idx will contain the location of minimum of all values in \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       NaN values are ignored.
 
-       \note \p imag is always set to 0 when \p in is real.
+       \param[out] real real component of the minimum
+       \param[out] imag imaginary component of the minimum; 0 if `idx` is real
+       \param[out] idx  location
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_min
     */
@@ -1283,27 +1354,30 @@ extern "C" {
                              const af_array in);
 
     /**
-       C Interface for getting maximum value and it's location from the entire array
+       C Interface to return the maximum and its location over all dimensions.
 
-       \param[out] real will contain the real part of maximum value of all elements in input \p in
-       \param[out] imag will contain the imaginary part of maximum value of all elements in input \p in
-       \param[out] idx will contain the location of maximum of all values in \p in
-       \param[in] in is the input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       NaN values are ignored.
 
-       \note \p imag is always set to 0 when \p in is real.
+       \param[out] real real component of the maximum
+       \param[out] imag imaginary component of the maximum; 0 if `idx` is real
+       \param[out] idx  location
+       \param[in]  in   input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup reduce_func_max
     */
     AFAPI af_err af_imax_all(double *real, double *imag, unsigned *idx, const af_array in);
 
     /**
-       C Interface for computing the cumulative sum (inclusive) of an array
+       C Interface to evaluate the cumulative sum (inclusive) along a given
+       dimension.
 
-       \param[out] out will contain inclusive sums of the input
-       \param[in] in is the input array
-       \param[in] dim is the dimension along which the inclusive sum is calculated
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out cumulative sum
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the sum is accumulated
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup scan_func_accum
     */
@@ -1311,14 +1385,15 @@ extern "C" {
 
 #if AF_API_VERSION >=34
     /**
-       C Interface generalized scan of an array
+       C Interface to scan an array (generalized) over a given dimension.
 
-       \param[out] out will contain scan of the input
-       \param[in] in is the input array
-       \param[in] dim The dimension along which scan is performed
-       \param[in] op is the type of binary operations used
-       \param[in] inclusive_scan is flag specifying whether scan is inclusive
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out            scan
+       \param[in]  in             input array
+       \param[in]  dim            dimension along which the scan occurs
+       \param[in]  op             type of binary operation used
+       \param[in]  inclusive_scan flag specifying whether the scan is inclusive
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup scan_func_scan
     */
@@ -1326,15 +1401,17 @@ extern "C" {
                          af_binary_op op, bool inclusive_scan);
 
     /**
-       C Interface generalized scan by key of an array
+       C Interface to scan an array (generalized) over a given dimension,
+       according to an array of keys.
 
-       \param[out] out will contain scan of the input
-       \param[in] key is the key array
-       \param[in] in is the input array
-       \param[in] dim The dimension along which scan is performed
-       \param[in] op is the type of binary operations used
-       \param[in] inclusive_scan is flag specifying whether scan is inclusive
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out            scan
+       \param[in]  key            keys array
+       \param[in]  in             input array
+       \param[in]  dim            dimension along which the scan occurs
+       \param[in]  op             type of binary operation used
+       \param[in]  inclusive_scan flag specifying whether the scan is inclusive
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup scan_func_scanbykey
     */
@@ -1345,48 +1422,54 @@ extern "C" {
 #endif
 
     /**
-       C Interface for finding the locations of non-zero values in an array
+       C Interface to locate the indices of the non-zero values in an array.
 
-       \param[out] idx will contain indices where \p in is non-zero
-       \param[in] in is the input array.
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] idx linear indices where `in` is non-zero
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup scan_func_where
     */
     AFAPI af_err af_where(af_array *idx, const af_array in);
 
     /**
-       C Interface for calculating first order differences in an array
+       C Interface to calculate the first order difference in an array over a
+       given dimension.
 
-       \param[out] out will contain the first order numerical differences of \p in
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out first order numerical difference
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the difference occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup calc_func_diff1
     */
     AFAPI af_err af_diff1(af_array *out, const af_array in, const int dim);
 
     /**
-       C Interface for calculating second order differences in an array
+       C Interface to calculate the second order difference in an array over a
+       given dimension.
 
-       \param[out] out will contain the second order numerical differences of \p in
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out second order numerical difference
+       \param[in]  in  input array
+       \param[in]  dim dimension along which the difference occurs
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup calc_func_diff2
     */
     AFAPI af_err af_diff2(af_array *out, const af_array in, const int dim);
 
     /**
-       C Interface for sorting an array
+       C Interface to sort an array over a given dimension.
 
-       \param[out] out will contain the sorted output
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \param[in] isAscending specifies the sorting order
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out         sorted output
+       \param[in]  in          input array
+       \param[in]  dim         dimension along which the sort occurs
+       \param[in]  isAscending specifies the sorting order
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup sort_func_sort
     */
@@ -1394,29 +1477,33 @@ extern "C" {
                          const bool isAscending);
 
     /**
-       C Interface for sorting an array and getting original indices
+       C Interface to sort an array over a given dimension and to return the
+       original indices.
 
-       \param[out] out will contain the sorted output
-       \param[out] indices will contain the indices in the original input
-       \param[in] in is the input array
-       \param[in] dim The dimension along which numerical difference is performed
-       \param[in] isAscending specifies the sorting order
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out         sorted output
+       \param[out] indices     indices from the input
+       \param[in]  in          input array
+       \param[in]  dim         dimension along which the sort occurs
+       \param[in]  isAscending specifies the sorting order
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup sort_func_sort_index
     */
     AFAPI af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
                                const unsigned dim, const bool isAscending);
     /**
-       C Interface for sorting an array based on keys
+       C Interface to sort an array over a given dimension, according to an
+       array of keys.
 
-       \param[out] out_keys will contain the keys based on sorted values
-       \param[out] out_values will contain the sorted values
-       \param[in] keys is the input array
-       \param[in] values The dimension along which numerical difference is performed
-       \param[in] dim The dimension along which numerical difference is performed
-       \param[in] isAscending specifies the sorting order
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out_keys    sorted keys
+       \param[out] out_values  sorted output
+       \param[in]  keys        keys array
+       \param[in]  values      input array
+       \param[in]  dim         dimension along which the sort occurs
+       \param[in]  isAscending specifies the sorting order
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup sort_func_sort_keys
     */
@@ -1425,25 +1512,27 @@ extern "C" {
                                 const unsigned dim, const bool isAscending);
 
     /**
-       C Interface for getting unique values
+       C Interface to return the unique values in an array.
 
-       \param[out] out will contain the unique values from \p in
-       \param[in] in is the input array
-       \param[in] is_sorted if true, skips the sorting steps internally
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out       unique values
+       \param[in]  in        input array
+       \param[in]  is_sorted if true, skip the sorting steps internally
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup set_func_unique
     */
     AFAPI af_err af_set_unique(af_array *out, const af_array in, const bool is_sorted);
 
     /**
-       C Interface for finding the union of two arrays
+       C Interface to evaluate the union of two arrays.
 
-       \param[out] out will contain the union of \p first and \p second
-       \param[in] first is the first input array
-       \param[in] second is the second input array
-       \param[in] is_unique if true, skips calling unique internally
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out       union, values in increasing order
+       \param[in]  first     input array
+       \param[in]  second    input array
+       \param[in]  is_unique if true, skip calling unique internally
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup set_func_union
     */
@@ -1451,13 +1540,14 @@ extern "C" {
                               const af_array second, const bool is_unique);
 
     /**
-       C Interface for finding the intersection of two arrays
+       C Interface to evaluate the intersection of two arrays.
 
-       \param[out] out will contain the intersection of \p first and \p second
-       \param[in] first is the first input array
-       \param[in] second is the second input array
-       \param[in] is_unique if true, skips calling unique internally
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out       intersection, values in increasing order
+       \param[in]  first     input array
+       \param[in]  second    input array
+       \param[in]  is_unique if true, skip calling unique internally
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup set_func_intersect
     */
diff --git a/include/af/arith.h b/include/af/arith.h
index ea9be6c328..9b02e668b6 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -18,25 +18,27 @@ namespace af
     ///
     /// \param[in] lhs input array
     /// \param[in] rhs input array
-    /// \return minimum of \p lhs and \p rhs
+    /// \return        minimum
     ///
     /// \ingroup arith_func_min
     AFAPI array min    (const array &lhs, const array &rhs);
 
-    /// C++ Interface to find the elementwise minimum between an array and a scalar value.
+    /// C++ Interface to find the elementwise minimum between an array and a
+    /// scalar value.
     ///
     /// \param[in] lhs input array
     /// \param[in] rhs scalar value
-    /// \return minimum of \p lhs and \p rhs
+    /// \return        minimum
     ///
     /// \ingroup arith_func_min
     AFAPI array min    (const array &lhs, const double rhs);
 
-    /// C++ Interface to find the elementwise minimum between an array and a scalar value.
+    /// C++ Interface to find the elementwise minimum between an array and a
+    /// scalar value.
     ///
     /// \param[in] lhs scalar value
     /// \param[in] rhs input array
-    /// \return minimum of \p lhs and \p rhs
+    /// \return        minimum
     ///
     /// \ingroup arith_func_min
     AFAPI array min    (const double lhs, const array &rhs);
@@ -45,25 +47,27 @@ namespace af
     ///
     /// \param[in] lhs input array
     /// \param[in] rhs input array
-    /// \return maximum of \p lhs and \p rhs
+    /// \return        maximum
     ///
     /// \ingroup arith_func_max
     AFAPI array max    (const array &lhs, const array &rhs);
 
-    /// C++ Interface to find the elementwise maximum between an array and a scalar value.
+    /// C++ Interface to find the elementwise maximum between an array and a
+    /// scalar value.
     ///
     /// \param[in] lhs input array
     /// \param[in] rhs scalar value
-    /// \return maximum of \p lhs and \p rhs
+    /// \return        maximum
     ///
     /// \ingroup arith_func_max
     AFAPI array max    (const array &lhs, const double rhs);
 
-    /// C++ Interface to find the elementwise maximum between an array and a scalar value.
+    /// C++ Interface to find the elementwise maximum between an array and a
+    /// scalar value.
     ///
     /// \param[in] lhs input array
     /// \param[in] rhs scalar value
-    /// \return maximum of \p lhs and \p rhs
+    /// \return        maximum
     ///
     /// \ingroup arith_func_max
     AFAPI array max    (const double lhs, const array &rhs);
@@ -75,7 +79,7 @@ namespace af
     /// \param[in] in input array
     /// \param[in] lo lower limit; can be an array or a scalar
     /// \param[in] hi upper limit; can be an array or a scalar
-    /// \return array containing values from \p in clamped between \p lo and \p hi
+    /// \return       clamped array
     /// 
     /// \ingroup arith_func_clamp
     AFAPI array clamp(const array &in, const array &lo, const array &hi);
@@ -102,7 +106,7 @@ namespace af
     ///
     /// \param[in] lhs numerator; can be an array or a scalar
     /// \param[in] rhs denominator; can be an array or a scalar
-    /// \return remainder of \p lhs divided by \p rhs
+    /// \return        remainder
     /// 
     /// \ingroup arith_func_rem
     AFAPI array rem    (const array &lhs, const array &rhs);
@@ -119,7 +123,7 @@ namespace af
     ///
     /// \param[in] lhs dividend; can be an array or a scalar
     /// \param[in] rhs divisor; can be an array or a scalar
-    /// \return \p lhs modulo \p rhs
+    /// \return        modulus
     /// 
     /// \ingroup arith_func_mod
     AFAPI array mod    (const array &lhs, const array &rhs);
@@ -134,15 +138,16 @@ namespace af
     /// C++ Interface to calculate the absolute value.
     ///
     /// \param[in] in input array
-    /// \return absolute value
+    /// \return       absolute value
     ///
     /// \ingroup arith_func_abs
     AFAPI array abs    (const array &in);
 
-    /// C++ Interface to calculate the phase angle (in radians) of a complex array.
+    /// C++ Interface to calculate the phase angle (in radians) of a complex
+    /// array.
     ///
     /// \param[in] in input array, typically complex
-    /// \return phase angle (in radians)
+    /// \return       phase angle (in radians)
     /// 
     /// \ingroup arith_func_arg
     AFAPI array arg    (const array &in);
@@ -150,7 +155,7 @@ namespace af
     /// C++ Interface to return the sign of elements in an array.
     ///
     /// \param[in] in input array
-    /// \return array containing 1's for negative values; 0's otherwise
+    /// \return       array containing 1's for negative values; 0's otherwise
     /// 
     /// \ingroup arith_func_sign
     AFAPI array sign  (const array &in);
@@ -158,7 +163,7 @@ namespace af
     /// C++ Interface to round numbers.
     ///
     /// \param[in] in input array
-    /// \return numbers rounded to nearest integer
+    /// \return       nearest integer
     ///
     /// \ingroup arith_func_round
     AFAPI array round  (const array &in);
@@ -166,7 +171,7 @@ namespace af
     /// C++ Interface to truncate numbers.
     ///
     /// \param[in] in input array
-    /// \return nearest integer not greater in magnitude than \p in
+    /// \return       nearest integer not greater in magnitude than `in`
     /// 
     /// \ingroup arith_func_trunc
     AFAPI array trunc  (const array &in);
@@ -174,7 +179,7 @@ namespace af
     /// C++ Interface to floor numbers.
     ///
     /// \param[in] in input array
-    /// \return values rounded to nearest integer less than or equal to current value
+    /// \return       nearest integer less than or equal to `in`
     ///
     /// \ingroup arith_func_floor
     AFAPI array floor  (const array &in);
@@ -182,7 +187,7 @@ namespace af
     /// C++ Interface to ceil numbers.
     ///
     /// \param[in] in input array
-    /// \return values rounded to nearest integer greater than or equal to current value
+    /// \return       nearest integer greater than or equal to `in`
     ///
     /// \ingroup arith_func_ceil
     AFAPI array ceil   (const array &in);
@@ -192,11 +197,11 @@ namespace af
     /// C++ Interface to calculate the length of the hypotenuse of two inputs.
     ///
     /// Calculates the hypotenuse of two inputs. The inputs can be both arrays
-    /// or an array and a scalar.
+    /// or can be an array and a scalar.
     ///
     /// \param[in] lhs length of first side
     /// \param[in] rhs length of second side
-    /// \return length of the hypotenuse
+    /// \return        length of the hypotenuse
     AFAPI array hypot  (const array &lhs, const array &rhs);
 
     /// \copydoc hypot(const array&, const array&)
@@ -209,7 +214,7 @@ namespace af
     /// C++ Interface to evaluate the sine function.
     ///
     /// \param[in] in input array
-    /// \return sine
+    /// \return       sine
     ///
     /// \ingroup arith_func_sin
     AFAPI array sin    (const array &in);
@@ -217,7 +222,7 @@ namespace af
     /// C++ Interface to evaluate the cosine function.
     ///
     /// \param[in] in input array
-    /// \return cosine
+    /// \return       cosine
     ///
     /// \ingroup arith_func_cos
     AFAPI array cos    (const array &in);
@@ -225,7 +230,7 @@ namespace af
     /// C++ Interface to evaluate the tangent function.
     ///
     /// \param[in] in input array
-    /// \return tangent
+    /// \return       tangent
     ///
     /// \ingroup arith_func_tan
     AFAPI array tan    (const array &in);
@@ -233,7 +238,7 @@ namespace af
     /// C++ Interface to evaluate the inverse sine function.
     ///
     /// \param[in] in input array
-    /// \return inverse sine
+    /// \return       inverse sine
     ///
     /// \ingroup arith_func_asin
     AFAPI array asin   (const array &in);
@@ -241,7 +246,7 @@ namespace af
     /// C++ Interface to evaluate the inverse cosine function.
     ///
     /// \param[in] in input array
-    /// \return inverse cosine
+    /// \return       inverse cosine
     ///
     /// \ingroup arith_func_acos
     AFAPI array acos   (const array &in);
@@ -249,7 +254,7 @@ namespace af
     /// C++ Interface to evaluate the inverse tangent function.
     ///
     /// \param[in] in input array
-    /// \return inverse tangent
+    /// \return       inverse tangent
     ///
     /// \ingroup arith_func_atan
     AFAPI array atan   (const array &in);
@@ -260,7 +265,7 @@ namespace af
     ///
     /// \param[in] lhs value of numerator
     /// \param[in] rhs value of denominator
-    /// \return inverse tangent of the inputs
+    /// \return        inverse tangent of the inputs
     AFAPI array atan2  (const array &lhs, const array &rhs);
 
     /// \copydoc atan2(const array&, const array&)
@@ -273,7 +278,7 @@ namespace af
     /// C++ Interface to evaluate the hyperbolic sine function.
     ///
     /// \param[in] in input array
-    /// \return hyperbolic sine
+    /// \return       hyperbolic sine
     ///
     /// \ingroup arith_func_sinh
     AFAPI array sinh(const array& in);
@@ -281,7 +286,7 @@ namespace af
     /// C++ Interface to evaluate the hyperbolic cosine function.
     ///
     /// \param[in] in input array
-    /// \return hyperbolic cosine
+    /// \return       hyperbolic cosine
     ///
     /// \ingroup arith_func_cosh
     AFAPI array cosh(const array& in);
@@ -289,7 +294,7 @@ namespace af
     /// C++ Interface to evaluate the hyperbolic tangent function.
     ///
     /// \param[in] in input array
-    /// \return hyperbolic tangent
+    /// \return       hyperbolic tangent
     ///
     /// \ingroup arith_func_tanh
     AFAPI array tanh(const array& in);
@@ -297,7 +302,7 @@ namespace af
     /// C++ Interface to evaluate the inverse hyperbolic sine function.
     ///
     /// \param[in] in input array
-    /// \return inverse hyperbolic sine
+    /// \return       inverse hyperbolic sine
     ///
     /// \ingroup arith_func_asinh
     AFAPI array asinh(const array& in);
@@ -305,7 +310,7 @@ namespace af
     /// C++ Interface to evaluate the inverse hyperbolic cosine function.
     ///
     /// \param[in] in input array
-    /// \return inverse hyperbolic cosine
+    /// \return       inverse hyperbolic cosine
     ///
     /// \ingroup arith_func_acosh
     AFAPI array acosh(const array& in);
@@ -313,7 +318,7 @@ namespace af
     /// C++ Interface to evaluate the inverse hyperbolic tangent function.
     ///
     /// \param[in] in input array
-    /// \return inverse hyperbolic tangent
+    /// \return       inverse hyperbolic tangent
     ///
     /// \ingroup arith_func_atanh
     AFAPI array atanh(const array& in);
@@ -322,36 +327,44 @@ namespace af
     /// @{
     /// C++ Interface to create a complex array from a single real array.
     ///
-    /// \param[in] in a real array
-    /// \return the returned complex array
+    /// \param[in] in input array
+    /// \return       complex array
     AFAPI array complex(const array& in);
  
     /// C++ Interface to create a complex array from two real arrays.
     ///
-    /// \param[in] real_ a real array to be assigned as the real component of the returned complex array
-    /// \param[in] imag_ a real array to be assigned as the imaginary component of the returned complex array
-    /// \return the returned complex array
+    /// \param[in] real_ input array to be assigned as the real component of
+    ///                  the returned complex array
+    /// \param[in] imag_ input array to be assigned as the imaginary component
+    ///                  of the returned complex array
+    /// \return          complex array
     AFAPI array complex(const array &real_, const array &imag_);
 
-    /// C++ Interface to create a complex array from a single real array for the real component and a single scalar for each imaginary component.
+    /// C++ Interface to create a complex array from a single real array for
+    /// the real component and a single scalar for each imaginary component.
     ///
-    /// \param[in] real_ a real array to be assigned as the real component of the returned complex array
-    /// \param[in] imag_ a single scalar to be assigned as the imaginary component of each value of the returned complex array
-    /// \return the returned complex array
+    /// \param[in] real_ input array to be assigned as the real component of
+    ///                  the returned complex array
+    /// \param[in] imag_ single scalar to be assigned as the imaginary
+    ///                  component of each value of the returned complex array
+    /// \return          complex array
     AFAPI array complex(const array &real_, const double imag_);
 
-    /// C++ Interface to create a complex array from a single scalar for each real component and a single real array for the imaginary component.
+    /// C++ Interface to create a complex array from a single scalar for each
+    /// real component and a single real array for the imaginary component.
     ///
-    /// \param[in] real_ a single scalar to be assigned as the real component of each value of the returned complex array
-    /// \param[in] imag_ a real array to be assigned as the imaginary component of the returned complex array
-    /// \return the returned complex array
+    /// \param[in] real_ single scalar to be assigned as the real component of
+    ///                  each value of the returned complex array
+    /// \param[in] imag_ input array to be assigned as the imaginary component
+    ///                  of the returned complex array
+    /// \return          complex array
     AFAPI array complex(const double real_, const array &imag_);
     /// @}
 
     /// C++ Interface to return the real part of a complex array.
     ///
     /// \param[in] in input complex array
-    /// \return real part
+    /// \return       real part
     ///
     /// \ingroup arith_func_real
     AFAPI array real   (const array &in);
@@ -359,7 +372,7 @@ namespace af
     /// C++ Interface to return the imaginary part of a complex array.
     ///
     /// \param[in] in input complex array
-    /// \return imaginary part
+    /// \return       imaginary part
     ///
     /// \ingroup arith_func_imag
     AFAPI array imag   (const array &in);
@@ -367,7 +380,7 @@ namespace af
     /// C++ Interface to calculate the complex conjugate of an input array.
     ///
     /// \param[in] in input complex array
-    /// \return complex conjugate
+    /// \return       complex conjugate
     ///
     /// \ingroup arith_func_conjg
     AFAPI array conjg  (const array &in);
@@ -375,8 +388,8 @@ namespace af
     /// C++ Interface to evaluate the nth root.
     ///
     /// \param[in] nth_root nth root
-    /// \param[in] value value
-    /// \return \p nth_root th root of \p value
+    /// \param[in] value    value
+    /// \return             `nth_root` th root of `value`
     ///
     /// \ingroup arith_func_root
     AFAPI array root    (const array &nth_root, const array &value);
@@ -384,8 +397,8 @@ namespace af
     /// C++ Interface to evaluate the nth root.
     ///
     /// \param[in] nth_root nth root
-    /// \param[in] value value
-    /// \return \p nth_root th root of \p value
+    /// \param[in] value    value
+    /// \return             `nth_root` th root of `value`
     ///
     /// \ingroup arith_func_root
     AFAPI array root    (const array &nth_root, const double value);
@@ -393,8 +406,8 @@ namespace af
     /// C++ Interface to evaluate the nth root.
     ///
     /// \param[in] nth_root nth root
-    /// \param[in] value value
-    /// \return \p nth_root th root of \p value
+    /// \param[in] value    value
+    /// \return             `nth_root` th root of `value`
     ///
     /// \ingroup arith_func_root
     AFAPI array root    (const double nth_root, const array &value);
@@ -404,11 +417,12 @@ namespace af
     /// @{
     /// C++ Interface to raise a base to a power (or exponent).
     ///
-    /// Computes the value of \p base raised to the power of \p exponent. The inputs can be two arrays or an array and a scalar.
+    /// Computes the value of `base` raised to the power of `exponent`. The
+    /// inputs can be two arrays or an array and a scalar.
     ///
-    /// \param[in] base base
+    /// \param[in] base     base
     /// \param[in] exponent exponent
-    /// \return \p base raised to the power of \p exponent
+    /// \return             `base` raised to the power of `exponent`
     AFAPI array pow    (const array &base, const array &exponent);
 
     /// \copydoc pow(const array&, const array&)
@@ -419,8 +433,8 @@ namespace af
 
     /// C++ Interface to raise 2 to a power (or exponent).
     ///
-    /// \param[in] in exponent
-    /// \return 2 raised to the power
+    /// \param[in] in power
+    /// \return       2 raised to the power
     ///
     AFAPI array pow2    (const array &in);
     /// @}
@@ -428,10 +442,10 @@ namespace af
 #if AF_API_VERSION >= 31
     /// C++ Interface to evaluate the logistical sigmoid function.
     ///
-    /// \param[in] in input
-    /// \return sigmoid
+    /// Computes \f$\frac{1}{1+e^{-x}}\f$.
     /// 
-    /// \note Computes `1/(1+e^-x)`.
+    /// \param[in] in input
+    /// \return       sigmoid
     ///
     /// \ingroup arith_func_sigmoid
     AFAPI array sigmoid (const array &in);
@@ -440,57 +454,61 @@ namespace af
     /// C++ Interface to evaluate the exponential.
     ///
     /// \param[in] in exponent
-    /// \return exponential
+    /// \return       exponential
     ///
     /// \ingroup arith_func_exp
     AFAPI array exp    (const array &in);
 
-    /// C++ Interface to evaluate the exponential of an array minus 1, `exp(in) - 1`.
+    /// C++ Interface to evaluate the exponential of an array minus 1,
+    /// `exp(in) - 1`.
     ///
+    /// This function is useful when `in` is small.
+    /// 
     /// \param[in] in exponent
-    /// \return the exponential minus 1
+    /// \return       exponential minus 1
     ///
-    /// \note This function is useful when \p in is small
     /// \ingroup arith_func_expm1
     AFAPI array expm1  (const array &in);
 
     /// C++ Interface to evaluate the error function.
     ///
-    /// \param[in] in input
-    /// \return error function
+    /// \param[in] in input array
+    /// \return       error function
     ///
     /// \ingroup arith_func_erf
     AFAPI array erf    (const array &in);
 
     /// C++ Interface to evaluate the complementary error function.
     ///
-    /// \param[in] in input
-    /// \return complementary error function
+    /// \param[in] in input array
+    /// \return       complementary error function
     ///
     /// \ingroup arith_func_erfc
     AFAPI array erfc   (const array &in);
 
     /// C++ Interface to evaluate the natural logarithm.
     ///
-    /// \param[in] in input
-    /// \return natural logarithm
+    /// \param[in] in input array
+    /// \return       natural logarithm
     ///
     /// \ingroup arith_func_log
     AFAPI array log    (const array &in);
 
-    /// C++ Interface to evaluate the natural logarithm of 1 + input, `ln(1+in)`.
-    ///
+    /// C++ Interface to evaluate the natural logarithm of 1 + input,
+    /// `ln(1+in)`.
+    /// 
+    /// This function is useful when `in` is small.
+    /// 
     /// \param[in] in input
     /// \return natural logarithm of `1 + input`
     ///
-    /// \note This function is useful when \p in is small
     /// \ingroup arith_func_log1p
     AFAPI array log1p  (const array &in);
 
     /// C++ Interface to evaluate the base 10 logarithm.
     ///
     /// \param[in] in input
-    /// \return base 10 logarithm
+    /// \return       base 10 logarithm
     ///
     /// \ingroup arith_func_log10
     AFAPI array log10  (const array &in);
@@ -498,7 +516,7 @@ namespace af
     /// C++ Interface to evaluate the base 2 logarithm.
     ///
     /// \param[in] in input
-    /// \return base 2 logarithm
+    /// \return       base 2 logarithm
     ///
     /// \ingroup explog_func_log2
     AFAPI array log2   (const array &in);
@@ -506,7 +524,7 @@ namespace af
     /// C++ Interface to evaluate the square root.
     ///
     /// \param[in] in input
-    /// \return square root
+    /// \return       square root
     ///
     /// \ingroup arith_func_sqrt
     AFAPI array sqrt   (const array &in);
@@ -515,7 +533,7 @@ namespace af
     /// C++ Interface to evaluate the reciprocal square root.
     ///
     /// \param[in] in input
-    /// \return reciprocal square root
+    /// \return       reciprocal square root
     ///
     /// \ingroup arith_func_rsqrt
     AFAPI array rsqrt   (const array &in);
@@ -524,7 +542,7 @@ namespace af
     /// C++ Interface to evaluate the cube root.
     ///
     /// \param[in] in input
-    /// \return cube root
+    /// \return       cube root
     ///
     /// \ingroup arith_func_cbrt
     AFAPI array cbrt   (const array &in);
@@ -532,7 +550,7 @@ namespace af
     /// C++ Interface to calculate the factorial.
     ///
     /// \param[in] in input
-    /// \return the factorial function
+    /// \return       factorial
     ///
     /// \ingroup arith_func_factorial
     AFAPI array factorial (const array &in);
@@ -540,15 +558,16 @@ namespace af
     /// C++ Interface to evaluate the gamma function.
     ///
     /// \param[in] in input
-    /// \return gamma function
+    /// \return       gamma function
     ///
     /// \ingroup arith_func_tgamma
     AFAPI array tgamma (const array &in);
 
-    /// C++ Interface to evaluate the logarithm of the absolute value of the gamma function.
+    /// C++ Interface to evaluate the logarithm of the absolute value of the
+    /// gamma function.
     ///
     /// \param[in] in input
-    /// \return logarithm of the absolute value of the gamma function
+    /// \return       logarithm of the absolute value of the gamma function
     ///
     /// \ingroup arith_func_lgamma
     AFAPI array lgamma (const array &in);
@@ -556,7 +575,7 @@ namespace af
     /// C++ Interface to check which values are zero.
     ///
     /// \param[in] in input
-    /// \return array containing 1's where input is 0; 0's otherwise
+    /// \return       array containing 1's where input is 0; 0's otherwise
     ///
     /// \ingroup arith_func_iszero
     AFAPI array iszero (const array &in);
@@ -564,7 +583,8 @@ namespace af
     /// C++ Interface to check if values are infinite.
     ///
     /// \param[in] in input
-    /// \return array containing 1's where input is Inf or -Inf; 0's otherwise
+    /// \return       array containing 1's where input is Inf or -Inf; 0's
+    ///               otherwise
     ///
     /// \ingroup arith_func_isinf
     AFAPI array isInf  (const array &in);
@@ -572,7 +592,7 @@ namespace af
     /// C++ Interface to check if values are NaN.
     ///
     /// \param[in] in input
-    /// \return array containing 1's where input is NaN; 0's otherwise
+    /// \return       array containing 1's where input is NaN; 0's otherwise
     ///
     /// \ingroup arith_func_isnan
     AFAPI array isNaN  (const array &in);
@@ -586,11 +606,12 @@ extern "C" {
     /**
        C Interface to add two arrays.
 
-       \param[out] out sum of \p lhs and \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   +
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_add
     */
@@ -599,11 +620,12 @@ extern "C" {
     /**
        C Interface to subtract one array from another array.
 
-       \param[out] out subtraction of \p lhs - \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   -
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_sub
     */
@@ -612,11 +634,12 @@ extern "C" {
     /**
        C Interface to multiply two arrays.
 
-       \param[out] out product of \p lhs and \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   *
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_mul
     */
@@ -625,89 +648,113 @@ extern "C" {
     /**
        C Interface to divide one array by another array.
 
-       \param[out] out result of \p lhs / \p rhs.
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   \
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_div
     */
     AFAPI af_err af_div   (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to perform a less-than comparison between corresponding elements of two arrays.
+       C Interface to perform a less-than comparison between corresponding
+       elements of two arrays.
+
+       Output type is b8.
 
-       \param[out] out result of \p lhs < \p rhs; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   1's where `lhs < rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup logic_func_lt
     */
     AFAPI af_err af_lt    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to perform a greater-than comparison between corresponding elements of two arrays.
+       C Interface to perform a greater-than comparison between corresponding
+       elements of two arrays.
 
-       \param[out] out result of \p lhs > \p rhs; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       Output type is b8.
+
+       \param[out] out   1's where `lhs > rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_gt
     */
     AFAPI af_err af_gt    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to perform a less-than-or-equal comparison between corresponding elements of two arrays.
+       C Interface to perform a less-than-or-equal comparison between
+       corresponding elements of two arrays.
+
+       Output type is b8.
 
-       \param[out] out result of \p lhs <= \p rhs; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   1's where `lhs <= rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_le
     */
     AFAPI af_err af_le    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to perform a greater-than-or-equal comparison between corresponding elements of two arrays.
+       C Interface to perform a greater-than-or-equal comparison between
+       corresponding elements of two arrays.
 
-       \param[out] out result of \p lhs >= \p rhs; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       Output type is b8.
+
+       \param[out] out   1's where `lhs >= rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_ge
     */
     AFAPI af_err af_ge    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if corresponding elements of two arrays are equal
+       C Interface to check if corresponding elements of two arrays are equal.
+
+       Output type is b8.
 
-       \param[out] out result of `lhs == rhs`; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   1's where `lhs == rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_eq
     */
     AFAPI af_err af_eq    (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to check if corresponding elements of two arrays are not equal
+       C Interface to check if corresponding elements of two arrays are not
+       equal.
+
+       Output type is b8.
 
-       \param[out] out result of `lhs != rhs`; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   1's where `lhs != rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_neq
     */
@@ -716,11 +763,14 @@ extern "C" {
     /**
        C Interface to evaluate the logical AND of two arrays.
 
-       \param[out] out result of \p lhs && \p rhs; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       Output type is b8.
+
+       \param[out] out   1's where `lhs && rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_and
     */
@@ -729,11 +779,14 @@ extern "C" {
     /**
        C Interface the evaluate the logical OR of two arrays.
 
-       \param[out] out result of \p lhs || \p rhs; type is b8
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       Output type is b8.
+
+       \param[out] out   1's where `lhs || rhs`, else 0's
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_or
     */
@@ -742,9 +795,12 @@ extern "C" {
     /**
        C Interface to evaluate the logical NOT of an array.
 
-       \param[out] out result of logical NOT; type is b8
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       Output type is b8.
+
+       \param[out] out !, logical NOT
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_not
     */
@@ -754,9 +810,10 @@ extern "C" {
     /**
        C Interface to evaluate the bitwise NOT of an array.
 
-       \param[out] out result of bitwise NOT
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out ~, bitwise NOT
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_bitnot
     */
@@ -766,11 +823,12 @@ extern "C" {
     /**
        C Interface to evaluate the bitwise AND of two arrays.
 
-       \param[out] out result of \p lhs & \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   &, bitwise AND
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_bitand
     */
@@ -779,11 +837,12 @@ extern "C" {
     /**
        C Interface to evaluate the bitwise OR of two arrays.
 
-       \param[out] out result of \p lhs | \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   |, bitwise OR
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_bitor
     */
@@ -792,11 +851,12 @@ extern "C" {
     /**
        C Interface to evaluate the bitwise XOR of two arrays.
 
-       \param[out] out result of \p lhs ^ \p rhs
-       \param[in] lhs first input
-       \param[in] rhs second input
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   ^, bitwise XOR
+       \param[in]  lhs   first input
+       \param[in]  rhs   second input
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_bitxor
     */
@@ -805,11 +865,12 @@ extern "C" {
     /**
        C Interface to shift the bits of integer arrays left.
 
-       \param[out] out result of the left shift
-       \param[in] lhs values to shift
-       \param[in] rhs n bits to shift
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   left shift
+       \param[in]  lhs   values to shift
+       \param[in]  rhs   n bits to shift
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_shiftl
     */
@@ -818,11 +879,12 @@ extern "C" {
     /**
        C Interface to shift the bits of integer arrays right.
 
-       \param[out] out result of the right shift
-       \param[in] lhs values to shift
-       \param[in] rhs n bits to shift
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   right shift
+       \param[in]  lhs   values to shift
+       \param[in]  rhs   n bits to shift
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_shiftr
     */
@@ -832,15 +894,16 @@ extern "C" {
        C Interface to cast an array from one type to another.
 
        This function casts an af_array object from one type to another. If the
-       type of the original array is the same as \p type then the same array is
+       type of the original array is the same as `type` then the same array is
        returned.
 
-       \note Consecitive casting operations may be may be optimized out if the
+       Consecutive casting operations may be may be optimized out if the
        original type of the af_array is the same as the final type. For example
-       if the original type is f64 which is then cast to f32 and then back to
-       f64, then the cast to f32 will be skipped and that operation will *NOT*
+       if the original type is f64, which is cast to f32 and then back to
+       f64, then the cast to f32 is skipped and that operation will *NOT*
        be performed by ArrayFire. The following table shows which casts will
        be optimized out. outer -> inner -> outer
+
        | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
        |---------|-----|-----|-----|-----|-----|-----|----|----|-----|-----|-----|-----|-----|
        | f32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
@@ -856,14 +919,16 @@ extern "C" {
        | s16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
        | u16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
        | f16     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-       If you want to avoid this behavior use af_eval after the first cast
+
+       If you want to avoid this behavior use, af_eval after the first cast
        operation. This will ensure that the cast operation is performed on the
        af_array.
 
-       \param[out] out values in the specified type
-       \param[in] in input
-       \param[in] type target data type \ref af_dtype
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out  values in the specified type
+       \param[in]  in   input
+       \param[in]  type target data type \ref af_dtype
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_cast
     */
@@ -872,24 +937,27 @@ extern "C" {
     /**
        C Interface to find the elementwise minimum between two arrays.
 
-       \param[out] out minimum of \p lhs and \p rhs
-       \param[in] lhs input array
-       \param[in] rhs input array
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   minimum
+       \param[in]  lhs   input array
+       \param[in]  rhs   input array
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_min
     */
     AFAPI af_err af_minof (af_array *out, const af_array lhs, const af_array rhs, const bool batch);
 
     /**
-       C Interface to find the elementwise minimum between an array and a scalar value.
+       C Interface to find the elementwise minimum between an array and a
+       scalar value.
 
-       \param[out] out maximum of \p lhs and \p rhs
-       \param[in] lhs input array
-       \param[in] rhs input array
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   maximum
+       \param[in]  lhs   input array
+       \param[in]  rhs   input array
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_max
     */
@@ -899,12 +967,13 @@ extern "C" {
     /**
        C Interface to clamp an array between an upper and a lower limit.
 
-       \param[out] out array containing values from \p in clamped between \p lo and \p hi
-       \param[in] in input array
-       \param[in] lo lower limit array
-       \param[in] hi upper limit array
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   clamped array
+       \param[in]  in    input array
+       \param[in]  lo    lower limit array
+       \param[in]  hi    upper limit array
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_clamp
     */
@@ -915,11 +984,12 @@ extern "C" {
     /**
        C Interface to calculate the remainder.
 
-       \param[out] out remainder of \p lhs divided by \p rhs
-       \param[in] lhs numerator
-       \param[in] rhs denominator
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   remainder
+       \param[in]  lhs   numerator
+       \param[in]  rhs   denominator
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_rem
     */
@@ -928,11 +998,12 @@ extern "C" {
     /**
        C Interface to calculate the modulus.
 
-       \param[out] out \p lhs modulo \p rhs
-       \param[in] lhs dividend
-       \param[in] rhs divisor
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   modulus
+       \param[in]  lhs   dividend
+       \param[in]  rhs   divisor
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_mod
     */
@@ -942,19 +1013,22 @@ extern "C" {
        C Interface to calculate the absolute value.
 
        \param[out] out absolute value
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_abs
     */
     AFAPI af_err af_abs     (af_array *out, const af_array in);
 
     /**
-       C Interface to calculate the phase angle (in radians) of a complex array.
+       C Interface to calculate the phase angle (in radians) of a complex
+       array.
 
        \param[out] out phase angle (in radians)
-       \param[in] in input array, typically complex
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array, typically complex
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_arg
     */
@@ -964,8 +1038,9 @@ extern "C" {
        C Interface to calculate the sign of elements in an array.
 
        \param[out] out array containing 1's for negative values; 0's otherwise
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_sign
     */
@@ -974,9 +1049,10 @@ extern "C" {
     /**
        C Interface to round numbers.
 
-       \param[out] out values rounded to nearest integer
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out nearest integer
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_round
     */
@@ -985,9 +1061,10 @@ extern "C" {
     /**
        C Interface to truncate numbers.
 
-       \param[out] out nearest integer not greater in magnitude than \p in
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out nearest integer not greater in magnitude than `in`
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_trunc
     */
@@ -996,9 +1073,10 @@ extern "C" {
     /**
        C Interface to floor numbers.
 
-       \param[out] out values rounded to nearest integer less than or equal to \p in
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out nearest integer less than or equal to `in`
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_floor
     */
@@ -1007,9 +1085,10 @@ extern "C" {
     /**
        C Interface to ceil numbers.
 
-       \param[out] out values rounded to nearest integer greater than or equal to \p in
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out nearest integer greater than or equal to `in`
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_ceil
     */
@@ -1018,11 +1097,12 @@ extern "C" {
     /**
        C Interface to calculate the length of the hypotenuse of two inputs.
 
-       \param[out] out length of the hypotenuse
-       \param[in] lhs length of first side
-       \param[in] rhs length of second side
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   length of the hypotenuse
+       \param[in]  lhs   length of first side
+       \param[in]  rhs   length of second side
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_floor
     */
@@ -1032,8 +1112,9 @@ extern "C" {
        C Interface to evaluate the sine function.
 
        \param[out] out sine
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_sin
     */
@@ -1043,8 +1124,9 @@ extern "C" {
        C Interface to evaluate the cosine function.
 
        \param[out] out cosine
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_cos
     */
@@ -1054,8 +1136,9 @@ extern "C" {
        C Interface to evaluate the tangent function.
 
        \param[out] out tangent
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_tan
     */
@@ -1065,8 +1148,9 @@ extern "C" {
        C Interface to evaluate the inverse sine function.
 
        \param[out] out inverse sine
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_asin
     */
@@ -1076,8 +1160,9 @@ extern "C" {
        C Interface to evaluate the inverse cosine function.
 
        \param[out] out inverse cos
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_acos
     */
@@ -1087,8 +1172,9 @@ extern "C" {
        C Interface to evaluate the inverse tangent function.
 
        \param[out] out inverse tangent
-       \param[in] in input array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_atan
     */
@@ -1097,11 +1183,12 @@ extern "C" {
     /**
        C Interface to evaluate the inverse tangent of two arrays.
 
-       \param[out] out inverse tangent of two arrays
-       \param[in] lhs numerator
-       \param[in] rhs denominator
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   inverse tangent of two arrays
+       \param[in]  lhs   numerator
+       \param[in]  rhs   denominator
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_atan
     */
@@ -1111,8 +1198,9 @@ extern "C" {
        C Interface to evaluate the hyperbolic sine function.
 
        \param[out] out hyperbolic sine
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_sinh
     */
@@ -1122,8 +1210,9 @@ extern "C" {
        C Interface to evaluate the hyperbolic cosine function.
 
        \param[out] out hyperbolic cosine
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_cosh
     */
@@ -1133,8 +1222,9 @@ extern "C" {
        C Interface to evaluate the hyperbolic tangent function.
 
        \param[out] out hyperbolic tangent
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_tanh
     */
@@ -1144,8 +1234,9 @@ extern "C" {
        C Interface to evaluate the inverse hyperbolic sine function.
 
        \param[out] out inverse hyperbolic sine
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_asinh
     */
@@ -1155,8 +1246,9 @@ extern "C" {
        C Interface to evaluate the inverse hyperbolic cosine function.
 
        \param[out] out inverse hyperbolic cosine
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_acosh
     */
@@ -1166,8 +1258,9 @@ extern "C" {
        C Interface to evaluate the inverse hyperbolic tangent function.
 
        \param[out] out inverse hyperbolic tangent
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_atanh
     */
@@ -1177,8 +1270,9 @@ extern "C" {
        C Interface to create a complex array from a single real array.
 
        \param[out] out complex array
-       \param[in] in real array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  real array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_cplx
     */
@@ -1187,11 +1281,14 @@ extern "C" {
     /**
        C Interface to create a complex array from two real arrays.
 
-       \param[out] out complex array
-       \param[in] real real array to be assigned as the real component of the returned complex array
-       \param[in] imag real array to be assigned as the imaginary component of the returned complex array
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   complex array
+       \param[in]  real  real array to be assigned as the real component of the
+                         returned complex array
+       \param[in]  imag  real array to be assigned as the imaginary component
+                         of the returned complex array
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_cplx
     */
@@ -1201,8 +1298,9 @@ extern "C" {
        C Interface to return the real part of a complex array.
 
        \param[out] out real part
-       \param[in] in complex array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  complex array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_real
     */
@@ -1212,8 +1310,9 @@ extern "C" {
        C Interface to return the imaginary part of a complex array.
 
        \param[out] out imaginary part
-       \param[in] in complex array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  complex array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_imag
     */
@@ -1223,8 +1322,9 @@ extern "C" {
        C Interface to evaluate the complex conjugate of an input array.
 
        \param[out] out complex conjugate
-       \param[in] in complex array
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  complex array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_conjg
     */
@@ -1233,11 +1333,12 @@ extern "C" {
     /**
        C Interface to evaluate the nth root.
 
-       \param[out] out \p lhs th root of \p rhs
-       \param[in] lhs nth root
-       \param[in] rhs value
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   `lhs` th root of `rhs`
+       \param[in]  lhs   nth root
+       \param[in]  rhs   value
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_root
     */
@@ -1247,11 +1348,12 @@ extern "C" {
     /**
        C Interface to raise a base to a power (or exponent).
 
-       \param[out] out \p lhs raised to the power of \p rhs
-       \param[in] lhs base
-       \param[in] rhs exponent
-       \param[in] batch specifies if operations need to be performed in batch mode
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out   `lhs` raised to the power of `rhs`
+       \param[in]  lhs   base
+       \param[in]  rhs   exponent
+       \param[in]  batch batch mode
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_pow
     */
@@ -1260,9 +1362,10 @@ extern "C" {
     /**
        C Interface to raise 2 to a power (or exponent).
 
-       \param[out] out 2 raised to the power of \p in
-       \param[in] in exponent
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out 2 raised to the power of `in`
+       \param[in]  in  exponent
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_pow2
     */
@@ -1272,11 +1375,12 @@ extern "C" {
     /**
        C Interface to evaluate the logistical sigmoid function.
 
-       Computes `1/(1+e^-x)`.
+       Computes \f$\frac{1}{1+e^{-x}}\f$.
 
        \param[out] out output of the logistic sigmoid function
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_sigmoid
     */
@@ -1286,20 +1390,23 @@ extern "C" {
     /**
        C Interface to evaluate the exponential.
 
-       \param[out] out e raised to the power of \p in
-       \param[in] in exponent
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out e raised to the power of `in`
+       \param[in]  in  exponent
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_exp
     */
     AFAPI af_err af_exp     (af_array *out, const af_array in);
 
     /**
-       C Interface to evaluate the exponential of an array minus 1, `exp(in) - 1`.
+       C Interface to evaluate the exponential of an array minus 1,
+       `exp(in) - 1`.
 
        \param[out] out exponential of `in - 1`
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_expm1
     */
@@ -1309,8 +1416,9 @@ extern "C" {
        C Interface to evaluate the error function.
 
        \param[out] out error function value
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_erf
     */
@@ -1320,8 +1428,9 @@ extern "C" {
        C Interface to evaluate the complementary error function.
 
        \param[out] out complementary error function
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_erfc
     */
@@ -1331,8 +1440,9 @@ extern "C" {
        C Interface to evaluate the natural logarithm.
 
        \param[out] out natural logarithm
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_log
     */
@@ -1342,8 +1452,9 @@ extern "C" {
        C Interface to evaluate the natural logarithm of 1 + input, `ln(1+in)`.
 
        \param[out] out logarithm of `in + 1`
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_log1p
     */
@@ -1353,8 +1464,9 @@ extern "C" {
        C Interface to evaluate the base 10 logarithm.
 
        \param[out] out base 10 logarithm
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_log10
     */
@@ -1364,8 +1476,9 @@ extern "C" {
        C Interface to evaluate the base 2 logarithm.
 
        \param[out] out base 2 logarithm
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup explog_func_log2
     */
@@ -1375,8 +1488,9 @@ extern "C" {
        C Interface to evaluate the square root.
 
        \param[out] out square root
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_sqrt
     */
@@ -1387,8 +1501,9 @@ extern "C" {
       C Interface to evaluate the reciprocal square root.
 
       \param[out] out reciprocal square root
-      \param[in] in input
-      \return \ref AF_SUCCESS if the execution completes properly
+      \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
       \ingroup arith_func_rsqrt
     */
@@ -1398,8 +1513,9 @@ extern "C" {
        C Interface to evaluate the cube root.
 
        \param[out] out cube root
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_cbrt
     */
@@ -1409,8 +1525,9 @@ extern "C" {
        C Interface to calculate the factorial.
 
        \param[out] out factorial
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_factorial
     */
@@ -1420,54 +1537,61 @@ extern "C" {
        C Interface to evaluate the gamma function.
 
        \param[out] out gamma function
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_tgamma
     */
     AFAPI af_err af_tgamma   (af_array *out, const af_array in);
 
     /**
-       C Interface to evaluate the logarithm of the absolute value of the gamma function.
+       C Interface to evaluate the logarithm of the absolute value of the
+       gamma function.
 
        \param[out] out logarithm of the absolute value of the gamma function
-       \param[in] in input
-       \return \ref AF_SUCCESS if the execution completes properly
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup arith_func_lgamma
     */
     AFAPI af_err af_lgamma   (af_array *out, const af_array in);
 
     /**
-        C Interface to check if values are zero.
+       C Interface to check if values are zero.
 
-        \param[out] out array containing 1's where input is 0; 0's otherwise
-        \param[in] in input
-        \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out array containing 1's where input is 0; 0's otherwise
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup arith_func_iszero
+       \ingroup arith_func_iszero
     */
     AFAPI af_err af_iszero  (af_array *out, const af_array in);
 
     /**
-        C Interface to check if values are infinite.
+       C Interface to check if values are infinite.
 
-        \param[out] out array containing 1's where input is Inf or -Inf; 0's otherwise
-        \param[in] in input
-        \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out array containing 1's where input is Inf or -Inf; 0's
+                       otherwise
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup arith_func_isinf
+       \ingroup arith_func_isinf
     */
     AFAPI af_err af_isinf   (af_array *out, const af_array in);
 
     /**
-        C Interface to check if values are NaN.
+       C Interface to check if values are NaN.
 
-        \param[out] out array containing 1's where input is NaN; 0's otherwise
-        \param[in] in input
-        \return \ref AF_SUCCESS if the execution completes properly
+       \param[out] out array containing 1's where input is NaN; 0's otherwise
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup arith_func_isnan
+       \ingroup arith_func_isnan
     */
     AFAPI af_err af_isnan   (af_array *out, const af_array in);
 
diff --git a/include/af/blas.h b/include/af/blas.h
index d20986b215..4580ea2112 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -1,4 +1,4 @@
-/*******************************************************
+/********************************************************
  * Copyright (c) 2014, ArrayFire
  * All rights reserved.
  *
@@ -7,15 +7,7 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
-/** \file blas.h
- *
- * Contains BLAS related functions
- *
- * Contains functions for basic BLAS functionallity
- */
-
 #pragma once
-
 #include <af/defines.h>
 
 #ifdef __cplusplus
@@ -23,93 +15,95 @@ namespace af
 {
     class array;
     /**
-        \brief Matrix multiply of two arrays
+       C++ Interface to multiply two matrices.
 
-        \copydetails blas_func_matmul
+       \copydetails blas_func_matmul
 
-        \param[in] lhs The array object on the left hand side
-        \param[in] rhs The array object on the right hand side
-        \param[in] optLhs Transpose left hand side before the function is performed
-        \param[in] optRhs Transpose right hand side before the function is performed
-        \return The result of the matrix multiplication of lhs, rhs
+       `optLhs` and `optRhs` can only be one of \ref AF_MAT_NONE,
+       \ref AF_MAT_TRANS, \ref AF_MAT_CTRANS.
 
-        \note optLhs and optRhs can only be one of \ref AF_MAT_NONE, \ref
-              AF_MAT_TRANS, \ref AF_MAT_CTRANS \note This function is not supported
-              in GFOR
+       This function is not supported in GFOR.
 
-        \note <b> The following applies for Sparse-Dense matrix multiplication.</b>
-        \note This function can be used with one sparse input. The sparse input
-              must always be the \p lhs and the dense matrix must be \p rhs.
-        \note The sparse array can only be of \ref AF_STORAGE_CSR format.
-        \note The returned array is always dense.
-        \note \p optLhs an only be one of \ref AF_MAT_NONE, \ref AF_MAT_TRANS,
-              \ref AF_MAT_CTRANS.
-        \note \p optRhs can only be \ref AF_MAT_NONE.
+       \note <b>The following applies for Sparse-Dense matrix multiplication.</b>
+       \note This function can be used with one sparse input. The sparse input
+             must always be the \p lhs and the dense matrix must be \p rhs.
+       \note The sparse array can only be of \ref AF_STORAGE_CSR format.
+       \note The returned array is always dense.
+       \note \p optLhs an only be one of \ref AF_MAT_NONE, \ref AF_MAT_TRANS,
+             \ref AF_MAT_CTRANS.
+       \note \p optRhs can only be \ref AF_MAT_NONE.
 
-        \ingroup blas_func_matmul
+       \param[in] lhs    input array on the left-hand side
+       \param[in] rhs    input array on the right-hand side
+       \param[in] optLhs transpose the left-hand side prior to multiplication
+       \param[in] optRhs transpose the right-hand side prior to multiplication
+       \return    `lhs` * `rhs`
 
-     */
+       \ingroup blas_func_matmul
+    */
     AFAPI array matmul(const array &lhs, const array &rhs,
                        const matProp optLhs = AF_MAT_NONE,
                        const matProp optRhs = AF_MAT_NONE);
 
     /**
-       \brief Matrix multiply of two arrays
+       C++ Interface to multiply two matrices.
+       The second matrix will be transposed.
 
        \copydetails blas_func_matmul
 
-       \param[in] lhs The array object on the left hand side
-       \param[in] rhs The array object on the right hand side
-       \return The result of the matrix multiplication of \p lhs, transpose(\p rhs)
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[in] lhs input array on the left-hand side
+       \param[in] rhs input array on the right-hand side
+       \return    `lhs` * transpose(`rhs`)
 
        \ingroup blas_func_matmul
     */
     AFAPI array matmulNT(const array &lhs, const array &rhs);
 
     /**
-       \brief Matrix multiply of two arrays
+       C++ Interface to multiply two matrices.
+       The first matrix will be transposed.
 
        \copydetails blas_func_matmul
 
-       \param[in] lhs The array object on the left hand side
-       \param[in] rhs The array object on the right hand side
-       \return The result of the matrix multiplication of transpose(\p lhs), \p rhs
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[in] lhs input array on the left-hand side
+       \param[in] rhs input array on the right-hand side
+       \return    transpose(`lhs`) * `rhs`
 
        \ingroup blas_func_matmul
     */
     AFAPI array matmulTN(const array &lhs, const array &rhs);
 
     /**
-       \brief Matrix multiply of two arrays
+       C++ Interface to multiply two matrices.
+       Both matrices will be transposed.
 
        \copydetails blas_func_matmul
 
-       \param[in] lhs The array object on the left hand side
-       \param[in] rhs The array object on the right hand side
-       \return The result of the matrix multiplication of transpose(\p lhs), transpose(\p rhs)
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[in] lhs input array on the left-hand side
+       \param[in] rhs input array on the right-hand side
+       \return    transpose(`lhs`) * transpose(`rhs`)
 
        \ingroup blas_func_matmul
     */
     AFAPI array matmulTT(const array &lhs, const array &rhs);
 
     /**
-       \brief Chain 2 matrix multiplications
+       C++ Interface to chain multiply three matrices.
 
-       The matrix multiplications are done in a way to reduce temporary memory
+       The matrix multiplications are done in a way to reduce temporary memory.
+
+       This function is not supported in GFOR.
 
        \param[in] a The first array
        \param[in] b The second array
        \param[in] c The third array
-
-       \returns out = a x b x c
-
-       \note This function is not supported in GFOR
+       \return    a x b x c
 
        \ingroup blas_func_matmul
     */
@@ -117,18 +111,17 @@ namespace af
 
 
     /**
-       \brief Chain 3 matrix multiplications
+       C++ Interface to chain multiply three matrices.
 
-       The matrix multiplications are done in a way to reduce temporary memory
+       The matrix multiplications are done in a way to reduce temporary memory.
+
+       This function is not supported in GFOR.
 
        \param[in] a The first array
        \param[in] b The second array
        \param[in] c The third array
        \param[in] d The fourth array
-
-       \returns out = a x b x c x d
-
-       \note This function is not supported in GFOR
+       \returns   a x b x c x d
 
        \ingroup blas_func_matmul
     */
@@ -136,36 +129,34 @@ namespace af
 
 #if AF_API_VERSION >= 35
     /**
-        \brief Dot Product
+        C++ Interface to compute the dot product.
 
-        Scalar dot product between two vectors. Also referred to as the inner
+        Scalar dot product between two vectors, also referred to as the inner
         product.
 
         \code
           // compute scalar dot product
-          array x = randu(100),
-          y = randu(100);
+          array x = randu(100), y = randu(100);
 
           af_print(dot(x, y));
           // OR
           printf("%f\n", dot<float>(x, y));
-
         \endcode
 
-        \tparam T The type of the output
-        \param[in] lhs The array object on the left hand side
-        \param[in] rhs The array object on the right hand side
-        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
-                  AF_MAT_CONJ are supported.
-        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and
-        AF_MAT_CONJ are supported \return The result of the dot product of lhs,
-        rhs
-
-        \note optLhs and optRhs can only be one of \ref AF_MAT_NONE or \ref
-              AF_MAT_CONJ
-        \note optLhs = AF_MAT_CONJ and optRhs = AF_MAT_NONE will run
-              conjugate dot operation.
-        \note This function is not supported in GFOR
+       Parameters `optLhs` and `optRhs` can only be one of \ref AF_MAT_NONE or
+       \ref AF_MAT_CONJ. The conjugate dot product can be computed by setting
+       `optLhs = AF_MAT_CONJ` and `optRhs = AF_MAT_NONE`.
+
+       This function is not supported in GFOR.
+
+        \tparam    T      type of the output
+        \param[in] lhs    input array on the left-hand side
+        \param[in] rhs    input array on the right-hand side
+        \param[in] optLhs `lhs` options, only \ref AF_MAT_NONE and \ref
+                          AF_MAT_CONJ are supported
+        \param[in] optRhs `rhs` options, only \ref AF_MAT_NONE and \ref
+                          AF_MAT_CONJ are supported
+        \return    dot product of `lhs` and `rhs`
 
         \ingroup blas_func_dot
     */
@@ -181,20 +172,21 @@ namespace af
                     const matProp optRhs = AF_MAT_NONE);
 
     /**
-        \brief C++ Interface for transposing a matrix
+        C++ Interface to transpose a matrix.
+
+        \param[in] in        input array
+        \param[in] conjugate if true, conjugate transposition is performed
+        \return    transpose
 
-        \param[in] in an input matrix
-        \param[in] conjugate if true, a conjugate transposition is performed
-        \return the transposed matrix
         \ingroup blas_func_transpose
     */
     AFAPI array transpose(const array &in, const bool conjugate = false);
 
     /**
-        \brief C++ Interface for transposing a matrix in-place
+        C++ Interface to transpose a matrix in-place.
 
-        \param[in,out] in the matrix to be transposed in-place
-        \param[in] conjugate if true, a conjugate transposition is performed
+        \param[in,out] in        input array to be transposed in-place
+        \param[in]     conjugate if true, conjugate transposition is performed
 
         \ingroup blas_func_transpose
     */
@@ -208,11 +200,10 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-        \brief BLAS general matrix multiply (GEMM) of two \ref af_array objects
+        C Interface to multiply two matrices.
 
-        \details
-        This provides a general interface to the BLAS level 3 general matrix
-        multiply (GEMM), which is generally defined as:
+        This provides an interface to the BLAS level 3 general matrix multiply
+        (GEMM) of two \ref af_array objects, which is generally defined as:
 
         \f[
         C = \alpha * opA(A)opB(B) + \beta * C
@@ -251,23 +242,15 @@ extern "C" {
 
         \snippet test/blas.cpp ex_af_gemm_overwrite
 
-        \param[in,out] C     Pointer to the output \ref af_array
-
-        \param[in]     opA   Operation to perform on A before the multiplication
-
-        \param[in]     opB   Operation to perform on B before the multiplication
-
-        \param[in]     alpha The alpha value; must be the same type as \p lhs
-                            and \p rhs
-
-        \param[in]     A     Left-hand side operand
-
-        \param[in]     B     Right-hand side operand
-
-        \param[in]     beta  The beta value; must be the same type as \p lhs
-                            and \p rhs
-
-        \return AF_SUCCESS if the operation is successful.
+        \param[in,out] C     `A` * `B` = `C`
+        \param[in]     opA   operation to perform on A before the multiplication
+        \param[in]     opB   operation to perform on B before the multiplication
+        \param[in]     alpha alpha value; must be the same type as `A` and `B`
+        \param[in]     A     input array on the left-hand side
+        \param[in]     B     input array on the right-hand side
+        \param[in]     beta  beta value; must be the same type as `A` and `B`
+        \return        \ref AF_SUCCESS, if function returns successfully, else
+                       an \ref af_err code is given
 
         \ingroup blas_func_matmul
     */
@@ -277,17 +260,9 @@ extern "C" {
 #endif
 
     /**
-        \brief Matrix multiply of two \ref af_array
-
-        \details Performs a matrix multiplication on two arrays (lhs, rhs).
+        C Interface to multiply two matrices.
 
-        \param[out] out Pointer to the output \ref af_array
-        \param[in] lhs A 2D matrix \ref af_array object
-        \param[in] rhs A 2D matrix \ref af_array object
-        \param[in] optLhs Transpose left hand side before the function is performed
-        \param[in] optRhs Transpose right hand side before the function is performed
-
-        \return AF_SUCCESS if the process is successful.
+        Performs matrix multiplication on two arrays.
 
         \note <b> The following applies for Sparse-Dense matrix multiplication.</b>
         \note This function can be used with one sparse input. The sparse input
@@ -298,30 +273,41 @@ extern "C" {
               \ref AF_MAT_CTRANS.
         \note \p optRhs can only be \ref AF_MAT_NONE.
 
+        \param[out] out    `lhs` * `rhs` = `out`
+        \param[in]  lhs    input array on the left-hand side
+        \param[in]  rhs    input array on the right-hand side
+        \param[in]  optLhs transpose `lhs` before the function is performed
+        \param[in]  optRhs transpose `rhs` before the function is performed
+        \return     \ref AF_SUCCESS, if function returns successfully, else
+                    an \ref af_err code is given
+
         \ingroup blas_func_matmul
      */
     AFAPI af_err af_matmul( af_array *out ,
                             const af_array lhs, const af_array rhs,
                             const af_mat_prop optLhs, const af_mat_prop optRhs);
 
-
     /**
-        Scalar dot product between two vectors.  Also referred to as the inner
+        C Interface to compute the dot product.
+
+        Scalar dot product between two vectors, also referred to as the inner
         product.
 
         \code
-        // compute scalar dot product
-        array x = randu(100), y = randu(100);
-        print(dot<float>(x,y));
+          // compute scalar dot product
+          array x = randu(100), y = randu(100);
+          print(dot<float>(x,y));
         \endcode
 
-        \param[out] out The array object with the result of the dot operation
-        \param[in] lhs The array object on the left hand side
-        \param[in] rhs The array object on the right hand side
-        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
-                   AF_MAT_CONJ are supported.
-        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
-        \return AF_SUCCESS if the process is successful.
+        \param[out] out    dot product of `lhs` and `rhs`
+        \param[in]  lhs    input array on the left-hand side
+        \param[in]  rhs    input array on the right-hand side
+        \param[in]  optLhs `lhs` options, only \ref AF_MAT_NONE and \ref
+                           AF_MAT_CONJ are supported
+        \param[in]  optRhs `rhs` options, only \ref AF_MAT_NONE and \ref
+                           AF_MAT_CONJ are supported
+        \return     \ref AF_SUCCESS, if function returns successfully, else
+                    an \ref af_err code is given
 
         \ingroup blas_func_dot
     */
@@ -331,18 +317,21 @@ extern "C" {
 
 #if AF_API_VERSION >= 35
     /**
+        C Interface to compute the dot product, scalar result returned on host.
+
         Scalar dot product between two vectors. Also referred to as the inner
         product. Returns the result as a host scalar.
 
-        \param[out] real is the real component of the result of dot operation
-        \param[out] imag is the imaginary component of the result of dot operation
-        \param[in] lhs The array object on the left hand side
-        \param[in] rhs The array object on the right hand side
-        \param[in] optLhs Options for lhs. Currently only \ref AF_MAT_NONE and
-                   AF_MAT_CONJ are supported.
-        \param[in] optRhs Options for rhs. Currently only \ref AF_MAT_NONE and AF_MAT_CONJ are supported
-
-        \return AF_SUCCESS if the process is successful.
+        \param[out] real   real component of the dot product
+        \param[out] imag   imaginary component of the dot product
+        \param[in]  lhs    input array on the left-hand side
+        \param[in]  rhs    input array on the right-hand side
+        \param[in]  optLhs `lhs` options, only \ref AF_MAT_NONE and \ref
+                           AF_MAT_CONJ are supported
+        \param[in]  optRhs `rhs` options, only \ref AF_MAT_NONE and \ref
+                           AF_MAT_CONJ are supported
+        \return     \ref AF_SUCCESS, if function returns successfully, else
+                    an \ref af_err code is given
 
         \ingroup blas_func_dot
     */
@@ -352,22 +341,25 @@ extern "C" {
 #endif
 
     /**
-        \brief C Interface for transposing a matrix
+        C Interface to transpose a matrix.
 
-        \param[out] out the transposed matrix
-        \param[in] in an input matrix
-        \param[in] conjugate if true, a conjugate transposition is performed
+        \param[out] out       transpose
+        \param[in]  in        input array
+        \param[in]  conjugate if true, conjugate transposition is performed
+        \return     \ref AF_SUCCESS, if function returns successfully, else
+                    an \ref af_err code is given
 
-        \return AF_SUCCESS if the process is successful.
         \ingroup blas_func_transpose
     */
     AFAPI af_err af_transpose(af_array *out, af_array in, const bool conjugate);
 
     /**
-        \brief C Interface for transposing a matrix in-place
+        C Interface to transpose a matrix in-place.
 
-        \param[in,out] in is the matrix to be transposed in place
-        \param[in] conjugate if true, a conjugate transposition is performed
+        \param[in,out] in        input array to be transposed in-place
+        \param[in]     conjugate if true, conjugate transposition is performed
+        \return        \ref AF_SUCCESS, if function returns successfully, else
+                       an \ref af_err code is given
 
         \ingroup blas_func_transpose
     */
diff --git a/include/af/data.h b/include/af/data.h
index 1559ea204f..22e1874439 100644
--- a/include/af/data.h
+++ b/include/af/data.h
@@ -17,509 +17,479 @@ namespace af
 {
     class array;
 
-    /**
-        \param[in] val is the value of each element of the array be genrated
-        \param[in] dims is the dimensions of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p dims
-
-        \ingroup data_func_constant
-    */
-
+    /// C++ Interface to generate an array with elements set to a specified
+    /// value.
+    ///
+    /// \param[in] val  constant value
+    /// \param[in] dims dimensions of the array to be generated
+    /// \param[in] ty   type
+    /// \return         constant array
+    ///
+    /// \ingroup data_func_constant
     template<typename T>
     array constant(T val, const dim4 &dims, const dtype ty=(af_dtype)dtype_traits<T>::ctype);
 
-    /**
-        \param[in] val is the value of each element of the array to be generated
-        \param[in] d0 is the size of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0
-
-        \ingroup data_func_constant
-    */
-
+    /// C++ Interface to generate a 1-D array with elements set to a specified
+    /// value.
+    ///
+    /// \param[in] val constant value
+    /// \param[in] d0  size of the first dimension
+    /// \param[in] ty  type
+    /// \return        constant 1-D array
+    ///
+    /// \ingroup data_func_constant
     template<typename T>
     array constant(T val, const dim_t d0, const af_dtype ty=(af_dtype)dtype_traits<T>::ctype);
 
-    /**
-        \param[in] val is the value of each element of the array to be generated
-        \param[in] d0 is the number of rows of the array to be generated
-        \param[in] d1 is the number of columns of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x d1
-
-        \ingroup data_func_constant
-    */
+    /// C++ Interface to generate a 2-D array with elements set to a specified
+    /// value.
+    ///
+    /// \param[in] val constant value
+    /// \param[in] d0  size of the first dimension
+    /// \param[in] d1  size of the second dimension
+    /// \param[in] ty  type
+    /// \return        constant 2-D array
+    ///
+    /// \ingroup data_func_constant
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const af_dtype ty=(af_dtype)dtype_traits<T>::ctype);
 
-    /**
-        \param[in] val is the value of each element of the array to be generated
-        \param[in] d0 is the size of the 1st dimension of the array to be generated
-        \param[in] d1 is the size of the 2nd dimension of the array to be generated
-        \param[in] d2 is the size of the 3rd dimension of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x d1 x d2
-
-        \ingroup data_func_constant
-    */
+    /// C++ Interface to generate a 3-D array with elements set to a specified
+    /// value.
+    ///
+    /// \param[in] val constant value
+    /// \param[in] d0  size of the first dimension
+    /// \param[in] d1  size of the second dimension
+    /// \param[in] d2  size of the third dimension
+    /// \param[in] ty  type
+    /// \return        constant 3-D array
+    ///
+    /// \ingroup data_func_constant
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const af_dtype ty=(af_dtype)dtype_traits<T>::ctype);
 
-    /**
-        \param[in] val is the value of each element of the array to be generated
-        \param[in] d0 is the size of the 1st dimension of the array to be generated
-        \param[in] d1 is the size of the 2nd dimension of the array to be generated
-        \param[in] d2 is the size of the 3rd dimension of the array to be generated
-        \param[in] d3 is the size of the 4rd dimension of the array to be generated
-        \param[in] ty is the type of the array
-
-        \return array of size \p d0 x d1 x d2 x d3
-
-        \ingroup data_func_constant
-    */
+    /// C++ Interface to generate a 4-D array with elements set to a specified
+    /// value.
+    ///
+    /// \param[in] val constant value
+    /// \param[in] d0  size of the first dimension
+    /// \param[in] d1  size of the second dimension
+    /// \param[in] d2  size of the third dimension
+    /// \param[in] d3  size of the fourth dimension
+    /// \param[in] ty  type
+    /// \return        constant 4-D array
+    ///
+    /// \ingroup data_func_constant
     template<typename T>
     array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3, const af_dtype ty=(af_dtype)dtype_traits<T>::ctype);
 
-    /**
-        \param[in] dims is dim4 for size of all dimensions
-        \param[in] ty is the type of array to generate
-
-        \returns an identity array of specified dimension and type
-
-        \ingroup data_func_identity
-    */
+    /// C++ Interface to generate an identity array.
+    ///
+    /// \param[in] dims size
+    /// \param[in] ty   type
+    /// \return         identity array
+    ///
+    /// \ingroup data_func_identity
     AFAPI array identity(const dim4 &dims, const dtype ty=f32);
 
-    /**
-        \param[in] d0 is size of first dimension
-        \param[in] ty is the type of array to generate
-
-        \returns an identity array of specified dimension and type
-
-        \ingroup data_func_identity
-    */
+    /// C++ Interface to generate a 1-D identity array.
+    ///
+    /// \param[in] d0 size of the first dimension
+    /// \param[in] ty type
+    /// \return       identity array
+    ///
+    /// \ingroup data_func_identity
     AFAPI array identity(const dim_t d0, const dtype ty=f32);
 
-    /**
-        \param[in] d0 is size of first dimension
-        \param[in] d1 is size of second dimension
-        \param[in] ty is the type of array to generate
-
-        \returns an identity array of specified dimension and type
-
-        \ingroup data_func_identity
-    */
+    /// C++ Interface to generate a 2-D identity array.
+    ///
+    /// \param[in] d0 size of the first dimension
+    /// \param[in] d1 size of the second dimension
+    /// \param[in] ty type
+    /// \return       identity array
+    ///
+    /// \ingroup data_func_identity
     AFAPI array identity(const dim_t d0, const dim_t d1, const dtype ty=f32);
 
-    /**
-        \param[in] d0 is size of first dimension
-        \param[in] d1 is size of second dimension
-        \param[in] d2 is size of third dimension
-        \param[in] ty is the type of array to generate
-
-        \returns an identity array of specified dimension and type
-
-        \ingroup data_func_identity
-    */
+    /// C++ Interface to generate a 3-D identity array.
+    ///
+    /// \param[in] d0 size of the first dimension
+    /// \param[in] d1 size of the second dimension
+    /// \param[in] d2 size of the third dimension
+    /// \param[in] ty type
+    /// \return       identity array
+    ///
+    /// \ingroup data_func_identity
     AFAPI array identity(const dim_t d0, const dim_t d1,
                          const dim_t d2, const dtype ty=f32);
 
-    /**
-        \param[in] d0 is size of first dimension
-        \param[in] d1 is size of second dimension
-        \param[in] d2 is size of third dimension
-        \param[in] d3 is size of fourth dimension
-        \param[in] ty is the type of array to generate
-
-        \returns an identity array of specified dimension and type
-
-        \ingroup data_func_identity
-    */
+    /// C++ Interface to generate a 4-D identity array.
+    ///
+    /// \param[in] d0 size of the first dimension
+    /// \param[in] d1 size of the second dimension
+    /// \param[in] d2 size of the third dimension
+    /// \param[in] d3 size of the fourth dimension
+    /// \param[in] ty type
+    /// \return       identity array
+    ///
+    /// \ingroup data_func_identity
     AFAPI array identity(const dim_t d0, const dim_t d1,
                          const dim_t d2, const dim_t d3, const dtype ty=f32);
 
-    /**
-    *  C++ Interface for creating an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions of shape `dim4`.
-    *
-        \param[in] dims the `dim4` object describing the shape of the generated array
-        \param[in] seq_dim the dimesion along which `[0, dim[seq_dim] - 1]` is created
-        \param[in] ty the type of the generated array
-
-        \returns the generated array
-
-        \ingroup data_func_range
-    */
+    /// C++ Interface to generate an array with `[0, n-1]` values along the
+    /// `seq_dim` dimension and tiled across other dimensions of shape `dim4`.
+    ///
+    /// \param[in] dims    size
+    /// \param[in] seq_dim dimesion along which the range is created
+    /// \param[in] ty      type
+    /// \return            range array
+    ///
+    /// \ingroup data_func_range
     AFAPI array range(const dim4 &dims, const int seq_dim = -1, const dtype ty=f32);
 
-    /**
-    *  C++ Interface for creating an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions described by dimension parameters.
-    *
-        \param[in] d0 the size of first dimension
-        \param[in] d1 the size of second dimension
-        \param[in] d2 the size of third dimension
-        \param[in] d3 the size of fourth dimension
-        \param[in] seq_dim the dimesion along which `[0, dim[seq_dim] - 1]` is created
-        \param[in] ty the type of the generated array
-
-        \returns the generated array
-
-        \ingroup data_func_range
-    */
+    /// C++ Interface to generate an array with `[0, n-1]` values along the
+    /// `seq_dim` dimension and tiled across other dimensions described by
+    /// dimension parameters.
+    ///
+    /// \param[in] d0      size of the first dimension
+    /// \param[in] d1      size of the second dimension
+    /// \param[in] d2      size of the third dimension
+    /// \param[in] d3      size of the fourth dimension
+    /// \param[in] seq_dim dimesion along which the range is created
+    /// \param[in] ty      type
+    /// \return            range array
+    ///
+    /// \ingroup data_func_range
     AFAPI array range(const dim_t d0, const dim_t d1 = 1, const dim_t d2 = 1,
                       const dim_t d3 = 1, const int seq_dim = -1, const dtype ty=f32);
 
-    /**
-        \param[in] dims is dim4 for unit dimensions of the sequence to be generated
-        \param[in] tile_dims is dim4 for the number of repetitions of the unit dimensions
-        \param[in] ty is the type of array to generate
-
-        \returns an array of integral range specified dimension and type
-
-        \ingroup data_func_iota
-    */
+    /// C++ Interface to generate an array with `[0, n-1]` values modified to
+    /// specified dimensions and tiling.
+    ///
+    /// \param[in] dims      size
+    /// \param[in] tile_dims number of tiled repetitions in each dimension
+    /// \param[in] ty        type
+    /// \return              iota array
+    ///
+    /// \ingroup data_func_iota
     AFAPI array iota(const dim4 &dims, const dim4 &tile_dims = dim4(1), const dtype ty=f32);
 
-    /**
-        \param[in] in is the input array
-        \param[in] num is the diagonal index
-        \param[in] extract when true returns an array containing diagonal of tha matrix
-        and when false returns a matrix with \p in as diagonal
-
-        \returns an array with either the diagonal or the matrix based on \p extract
-
-        \ingroup data_func_diag
-    */
+    /// C++ Interface to extract the diagonal from an array.
+    ///
+    /// \param[in] in      input array
+    /// \param[in] num     diagonal index
+    /// \param[in] extract if true, returns an array containing diagonal of the
+    ///                    matrix; if false, returns a diagonal matrix
+    /// \return            diagonal array (or matrix)
+    ///
+    /// \ingroup data_func_diag
     AFAPI array diag(const array &in, const int num = 0, const bool extract = true);
 
-    /**
-        \brief Join 2 arrays along \p dim
-
-        \param[in] dim is the dimension along which join occurs
-        \param[in] first is the first input array
-        \param[in] second is the second input array
-        \return the array that joins input arrays along the given dimension
-
-        \note empty arrays will be ignored
-
-        \ingroup manip_func_join
-    */
+    /// C++ Interface to join 2 arrays along a dimension.
+    ///
+    /// Empty arrays are ignored.
+    ///
+    /// \param[in] dim    dimension along which the join occurs
+    /// \param[in] first  input array
+    /// \param[in] second input array
+    /// \return           joined array
+    ///
+    /// \ingroup manip_func_join
     AFAPI array join(const int dim, const array &first, const array &second);
 
-    /**
-        \brief Join 3 arrays along \p dim
-
-        \param[in] dim is the dimension along which join occurs
-        \param[in] first is the first input array
-        \param[in] second is the second input array
-        \param[in] third is the third input array
-        \return the array that joins input arrays along the given dimension
-
-        \note empty arrays will be ignored
-
-        \ingroup manip_func_join
-    */
+    /// C++ Interface to join 3 arrays along a dimension.
+    ///
+    /// Empty arrays are ignored.
+    ///
+    /// \param[in] dim    dimension along which the join occurs
+    /// \param[in] first  input array
+    /// \param[in] second input array
+    /// \param[in] third  input array
+    /// \return           joined array
+    ///
+    /// \ingroup manip_func_join
     AFAPI array join(const int dim, const array &first, const array &second, const array &third);
 
-    /**
-        \brief Join 4 arrays along \p dim
-
-        \param[in] dim is the dimension along which join occurs
-        \param[in] first is the first input array
-        \param[in] second is the second input array
-        \param[in] third is the third input array
-        \param[in] fourth is the fourth input array
-        \return the array that joins input arrays along the given dimension
-
-        \note empty arrays will be ignored
-
-        \ingroup manip_func_join
-    */
+    /// C++ Interface to join 4 arrays along a dimension.
+    ///
+    /// Empty arrays are ignored.
+    ///
+    /// \param[in] dim    dimension along which the join occurs
+    /// \param[in] first  input array
+    /// \param[in] second input array
+    /// \param[in] third  input array
+    /// \param[in] fourth input array
+    /// \return           joined array
+    ///
+    /// \ingroup manip_func_join
     AFAPI array join(const int dim, const array &first, const array &second,
                      const array &third, const array &fourth);
 
-    /**
-        \param[in] in is the input array
-        \param[in] x is the number of times \p in is copied along the first dimension
-        \param[in] y is the number of times \p in is copied along the the second dimension
-        \param[in] z is the number of times \p in is copied along the third dimension
-        \param[in] w is the number of times \p in is copied along the fourth dimension
-        \return The tiled version of the input array
-
-        \note \p x, \p y, \p z, and \p w includes the original in the count as
-              well. Thus, if no duplicates are needed in a certain dimension,
-              leave it as 1 (the default value for just one copy)
-
-        \ingroup manip_func_tile
-    */
+    /// C++ Interface to generate a tiled array.
+    ///
+    /// Note, `x`, `y`, `z`, and `w` include the original in the count.
+    ///
+    /// \param[in] in input array
+    /// \param[in] x  number tiles along the first dimension
+    /// \param[in] y  number tiles along the second dimension
+    /// \param[in] z  number tiles along the third dimension
+    /// \param[in] w  number tiles along the fourth dimension
+    /// \return       tiled array
+    ///
+    /// \ingroup manip_func_tile
     AFAPI array tile(const array &in, const unsigned x, const unsigned y=1,
                      const unsigned z=1, const unsigned w=1);
 
-    /**
-        \param[in] in is the input array
-        \param[in] dims specifies the number of times \p in is copied along each dimension
-        \return The tiled version of the input array
-
-        \note Each component of \p dims includes the original in the count as
-              well. Thus, if no duplicates are needed in a certain dimension,
-              leave it as 1 (the default value for just one copy)
-
-        \ingroup manip_func_tile
-    */
+    /// C++ Interface to generate a tiled array.
+    ///
+    /// Each component of `dims` includes the original in the count. Thus, if
+    /// no duplicates are needed in a certain dimension, it is left as 1, the
+    /// default value for just one copy.
+    ///
+    /// \param[in] in   input array
+    /// \param[in] dims number of times `in` is copied along each dimension
+    /// \return         tiled array
+    ///
+    /// \ingroup manip_func_tile
     AFAPI array tile(const array &in, const dim4 &dims);
 
-    /**
-        \param[in] in is the input array
-        \param[in] x specifies which dimension should be first
-        \param[in] y specifies which dimension should be second
-        \param[in] z specifies which dimension should be third
-        \param[in] w specifies which dimension should be fourth
-        \return the reordered output
-
-        \ingroup manip_func_reorder
-    */
+    /// C++ Interface to reorder an array. 
+    ///
+    /// \param[in] in input array
+    /// \param[in] x  specifies which dimension should be first
+    /// \param[in] y  specifies which dimension should be second
+    /// \param[in] z  specifies which dimension should be third
+    /// \param[in] w  specifies which dimension should be fourth
+    /// \return       reordered array
+    ///
+    /// \ingroup manip_func_reorder
     AFAPI array reorder(const array& in, const unsigned x,
                         const unsigned y=1, const unsigned z=2, const unsigned w=3);
 
-    /**
-        \param[in] in is the input array
-        \param[in] x specifies the shift along first dimension
-        \param[in] y specifies the shift along second dimension
-        \param[in] z specifies the shift along third dimension
-        \param[in] w specifies the shift along fourth dimension
-
-        \return the shifted output
-
-        \ingroup manip_func_shift
-    */
+    /// C++ Interface to shift an array.
+    ///
+    /// \param[in] in input array
+    /// \param[in] x  specifies the shift along the first dimension
+    /// \param[in] y  specifies the shift along the second dimension
+    /// \param[in] z  specifies the shift along the third dimension
+    /// \param[in] w  specifies the shift along the fourth dimension
+    /// \return       shifted array
+    ///
+    /// \ingroup manip_func_shift
     AFAPI array shift(const array& in, const int x, const int y=0, const int z=0, const int w=0);
 
-    /**
-    * C++ Interface for modifying the dimensions of an input array to the shape specified by a `dim4` object
-    *
-        \param[in] in the input array
-        \param[in] dims the array of new dimension sizes
-        \return the modded output
-
-        \ingroup manip_func_moddims
-    */
+    /// C++ Interface to modify the dimensions of an input array to a specified
+    /// shape.
+    ///
+    /// \param[in] in   input array
+    /// \param[in] dims new dimension sizes
+    /// \return         modded output
+    ///
+    /// \ingroup manip_func_moddims
     AFAPI array moddims(const array& in, const dim4& dims);
 
-    /**
-    * C++ Interface for modifying the dimensions of an input array to the shape specified by dimension length parameters
-    *
-        \param[in] in the input array
-        \param[in] d0 the new size of the first dimension
-        \param[in] d1 the new size of the second dimension (optional)
-        \param[in] d2 the new size of the third dimension (optional)
-        \param[in] d3 the new size of the fourth dimension (optional)
-        \return the modded output
-
-        \ingroup manip_func_moddims
-    */
+    /// C++ Interface to modify the dimensions of an input array to a specified
+    /// shape.
+    ///
+    /// \param[in] in input array
+    /// \param[in] d0 new size of the first dimension
+    /// \param[in] d1 new size of the second dimension (optional)
+    /// \param[in] d2 new size of the third dimension (optional)
+    /// \param[in] d3 new size of the fourth dimension (optional)
+    /// \return       modded output
+    ///
+    /// \ingroup manip_func_moddims
     AFAPI array moddims(const array& in, const dim_t d0, const dim_t d1=1, const dim_t d2=1, const dim_t d3=1);
 
-    /**
-    * C++ Interface for modifying the dimensions of an input array to the shape specified by an array of `ndims` dimensions
-    *
-        \param[in] in the input array
-        \param[in] ndims the number of dimensions
-        \param[in] dims the array of new dimension sizes
-        \return the modded output
-
-        \ingroup manip_func_moddims
-    */
+    /// C++ Interface to modify the dimensions of an input array to a specified
+    /// shape.
+    ///
+    /// \param[in] in    input array
+    /// \param[in] ndims number of dimensions
+    /// \param[in] dims  new dimension sizes
+    /// \return          modded output
+    ///
+    /// \ingroup manip_func_moddims
     AFAPI array moddims(const array& in, const unsigned ndims, const dim_t* const dims);
 
-    /**
-        \param[in] in is the input array
-        \return the flat array
-
-        \ingroup manip_func_flat
-    */
+    /// C++ Interface to flatten an array.
+    ///
+    /// \param[in] in input array
+    /// \return       flat array
+    ///
+    /// \ingroup manip_func_flat
     AFAPI array flat(const array &in);
 
-    /**
-        \param[in] in is the input array
-        \param[in] dim is the dimensions to flip the array
-        \return the flipped array
-
-        \ingroup manip_func_flip
-    */
+    /// C++ Interface to flip an array.
+    ///
+    /// \param[in] in  input array
+    /// \param[in] dim dimension to flip
+    /// \return        flipped array
+    ///
+    /// \ingroup manip_func_flip
     AFAPI array flip(const array &in, const unsigned dim);
 
-    /**
-        \param[in] in is the input matrix
-        \param[in] is_unit_diag is a boolean parameter specifying if the diagonal elements should be 1
-        \return the lower triangle array
-
-        \ingroup data_func_lower
-    */
+    /// C++ Interface to return the lower triangle array.
+    ///
+    /// \param[in] in           input array
+    /// \param[in] is_unit_diag boolean specifying if diagonal elements are 1's
+    /// \return                 lower triangle array
+    ///
+    /// \ingroup data_func_lower
     AFAPI array lower(const array &in, bool is_unit_diag=false);
 
-    /**
-        \param[in] in is the input matrix
-        \param[in] is_unit_diag is a boolean parameter specifying if the diagonal elements should be 1
-        \return the upper triangle matrix
-
-        \ingroup data_func_upper
-    */
+    /// C++ Interface to return the upper triangle array.
+    ///
+    /// \param[in] in           input array
+    /// \param[in] is_unit_diag boolean specifying if diagonal elements are 1's
+    /// \return                 upper triangle matrix
+    ///
+    /// \ingroup data_func_upper
     AFAPI array upper(const array &in, bool is_unit_diag=false);
 
 #if AF_API_VERSION >= 31
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the condition
-       \param[in]  b is the array containing elements from the false part of the condition
-       \return  the output containing elements of \p a when \p cond is true else elements from \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select array element
+    /// \param[in] b    when false, select array element
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const array  &a, const array  &b);
 #endif
 
 #if AF_API_VERSION >= 31
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the condition
-       \param[in]  b is a scalar assigned to \p out when \p cond is false
-       \return  the output containing elements of \p a when \p cond is true else the value \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select array element
+    /// \param[in] b    when false, select scalar value
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const array  &a, const double &b);
 #endif
 
 #if AF_API_VERSION >= 31
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is a scalar assigned to \p out when \p cond is true
-       \param[in]  b is the array containing elements from the false part of the condition
-       \return  the output containing the value \p a when \p cond is true else elements from \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select scalar value
+    /// \param[in] b    when false, select array element
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const double &a, const array  &b);
 #endif
 
 #if AF_API_VERSION >= 31
-    /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement array.
-
-       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
-
-       \ingroup data_func_replace
-    */
+    /// C++ Interface to replace elements of an array with elements of another
+    /// array.
+    ///
+    /// Elements of `a` are replaced with corresponding elements of `b` when
+    /// `cond` is false.
+    ///
+    /// \param[inout] a    input array
+    /// \param[in]    cond conditional array
+    /// \param[in]    b    replacement array
+    ///
+    /// \ingroup data_func_replace
     AFAPI void replace(array &a, const array  &cond, const array  &b);
 #endif
 
 #if AF_API_VERSION >= 31
-    /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement value.
-
-       \note Values of \p a are replaced with value \p b, when \p cond is false.
-
-       \ingroup data_func_replace
-    */
+    /// C++ Interface to replace elements of an array with a scalar value.
+    ///
+    /// Elements of `a` are replaced with a scalar value when `cond` is false.
+    ///
+    /// \param[inout] a    input array
+    /// \param[in]    cond conditional array
+    /// \param[in]    b    replacement scalar value
+    ///
+    /// \ingroup data_func_replace
     AFAPI void replace(array &a, const array  &cond, const double &b);
 #endif
 
 #if AF_API_VERSION >= 37
-    /**
-       \param[in] in is the input array to be padded
-       \param[in] beginPadding informs the number of elements to be
-                  padded at beginning of each dimension
-       \param[in] endPadding informs the number of elements to be
-                  padded at end of each dimension
-       \param[in] padFillType is indicates what values should fill padded region
-
-       \return the padded array
-
-       \ingroup data_func_pad
-    */
+    /// C++ Interface to pad an array.
+    ///
+    /// \param[in] in           input array
+    /// \param[in] beginPadding number of elements to be padded at the start of
+    ///                         each dimension
+    /// \param[in] endPadding   number of elements to be padded at the end of
+    ///                         each dimension
+    /// \param[in] padFillType  values to fill into the padded region
+    /// \return                 padded array
+    ///
+    /// \ingroup data_func_pad
     AFAPI array pad(const array &in, const dim4 &beginPadding,
                     const dim4 &endPadding, const borderType padFillType);
 #endif
 
 #if AF_API_VERSION >= 39
-    /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement scalar value.
-
-       \note Values of \p a are replaced with value \p b, when \p cond is false.
-
-       \ingroup data_func_replace
-    */
+    /// C++ Interface to replace elements of an array with a scalar value.
+    ///
+    /// Elements of `a` are replaced with a scalar value when `cond` is false.
+    ///
+    /// \param[inout] a    input array
+    /// \param[in]    cond conditional array
+    /// \param[in]    b    replacement scalar value
+    ///
+    /// \ingroup data_func_replace
     AFAPI void replace(array &a, const array &cond, const long long b);
 
-    /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement scalar value.
-
-       \note Values of \p a are replaced with value \p b, when \p cond is false.
-
-       \ingroup data_func_replace
-    */
+    /// C++ Interface to replace elements of an array with a scalar value.
+    ///
+    /// Elements of `a` are replaced with a scalar value when `cond` is false.
+    ///
+    /// \param[inout] a    input array
+    /// \param[in]    cond conditional array
+    /// \param[in]    b    replacement scalar value
+    ///
+    /// \ingroup data_func_replace
     AFAPI void replace(array &a, const array &cond,
                        const unsigned long long b);
 
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the
-                   condition
-       \param[in]  b is a scalar assigned to \p out when \p cond is false
-       \return  the output containing elements of \p a when \p cond is true
-                else the value \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select array element
+    /// \param[in] b    when false, select scalar value
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const array &a, const long long b);
 
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the
-                   condition
-       \param[in]  b is a scalar assigned to \p out when \p cond is false
-       \return  the output containing elements of \p a when \p cond is true
-                else the value \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select array element
+    /// \param[in] b    when false, select scalar value
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const array &a,
                        const unsigned long long b);
 
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is a scalar assigned to \p out when \p cond is true
-       \param[in]  b is the array containing elements from the false part of the
-                   condition
-       \return  the output containing the value \p a when \p cond is true else
-                elements from \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select scalar value
+    /// \param[in] b    when false, select array element
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const long long a, const array &b);
 
-    /**
-       \param[in]  cond is the conditional array
-       \param[in]  a is a scalar assigned to \p out when \p cond is true
-       \param[in]  b is the array containing elements from the false part of the
-                   condition
-       \return  the output containing the value \p a when \p cond is true else
-                elements from \p b
-
-       \ingroup data_func_select
-    */
+    /// C++ Interface to select elements based on a conditional array.
+    ///
+    /// \param[in] cond conditional array
+    /// \param[in] a    when true, select scalar value
+    /// \param[in] b    when false, select array element
+    /// \return         `a` when `cond` is true, else `b`
+    ///
+    /// \ingroup data_func_select
     AFAPI array select(const array &cond, const unsigned long long a,
                        const array &b);
 #endif
@@ -530,46 +500,65 @@ namespace af
 extern "C" {
 #endif
     /**
-        \param[out] arr is the generated array of given type
-        \param[in] val is the value of each element in the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
+       C Interface to generate an array with elements set to a specified value.
+
+       \param[out] arr   constant array
+       \param[in]  val   constant value
+       \param[in]  ndims size of the dimension array
+       \param[in]  dims  dimensions of the array to be generated
+       \param[in]  type  type
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_constant
     */
     AFAPI af_err af_constant(af_array *arr, const double val, const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
-        \param[out] arr is the generated array of type \ref c32 or \ref c64
-        \param[in] real is the real value of each element in the generated array
-        \param[in] imag is the imaginary value of each element in the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
+       C Interface to generate a complex array with elements set to a specified
+       value.
+
+       \param[out] arr   constant complex array
+       \param[in]  real  real constant value
+       \param[in]  imag  imaginary constant value
+       \param[in]  ndims size of the dimension array
+       \param[in]  dims  dimensions of the array to be generated
+       \param[in]  type  type, \ref c32 or \ref c64
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_constant
     */
-
     AFAPI af_err af_constant_complex(af_array *arr, const double real, const double imag,
                                      const unsigned ndims, const dim_t * const dims, const af_dtype type);
 
     /**
-        \param[out] arr is the generated array of type \ref s64
-        \param[in] val is a complex value of each element in the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
+       C Interface to generate an array with elements set to a specified value.
+
+       Output type is \ref s64.
+
+       \param[out] arr   constant array
+       \param[in]  val   constant value
+       \param[in]  ndims size of the dimension array
+       \param[in]  dims  dimensions of the array to be generated
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_constant
     */
-
     AFAPI af_err af_constant_long (af_array *arr, const long long val, const unsigned ndims, const dim_t * const dims);
 
     /**
-        \param[out] arr is the generated array of type \ref u64
-        \param[in] val is a complex value of each element in the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
+       C Interface to generate an array with elements set to a specified value.
+
+       Output type is \ref u64.
+
+       \param[out] arr   constant array
+       \param[in]  val   constant value
+       \param[in]  ndims size of the dimension array
+       \param[in]  dims  dimensions of the array to be generated
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_constant
     */
@@ -577,186 +566,246 @@ extern "C" {
     AFAPI af_err af_constant_ulong(af_array *arr, const unsigned long long val, const unsigned ndims, const dim_t * const dims);
 
     /**
-    * C Interface for creating an array with `[0, n-1]` values along the `seq_dim` dimension and tiled across other dimensions specified by an array of `ndims` dimensions.
-    *
-        \param[out] out the generated array
-        \param[in] ndims the size of dimension array `dims`
-        \param[in] dims the array containing the dimension sizes
-        \param[in] seq_dim the dimension along which `[0, dim[seq_dim] - 1]` is created
-        \param[in] type the type of the generated array
-
-        \ingroup data_func_range
+       C Interface to generate an identity array.
+
+       \param[out] out   identity array
+       \param[in]  ndims number of dimensions
+       \param[in]  dims  size
+       \param[in]  type  type
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup data_func_identity
+    */
+    AFAPI af_err af_identity(af_array* out, const unsigned ndims, const dim_t* const dims, const af_dtype type);
+
+    /**
+       C Interface to generate an array with `[0, n-1]` values along the
+       `seq_dim` dimension and tiled across other dimensions of shape `dim4`.
+
+       \param[out] out     range array
+       \param[in]  ndims   number of dimensions, specified by the size of `dims`
+       \param[in]  dims    size
+       \param[in]  seq_dim dimension along which the range is created
+       \param[in]  type    type
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup data_func_range
     */
     AFAPI af_err af_range(af_array *out, const unsigned ndims, const dim_t * const dims,
                           const int seq_dim, const af_dtype type);
 
     /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] t_ndims is size of tile array \p tdims
-        \param[in] tdims is array containing the number of repetitions of the unit dimensions
-        \param[in] type is the type of array to generate
-
-        \ingroup data_func_iota
+       C Interface to generate an array with `[0, n-1]` values modified to
+       specified dimensions and tiling.
+
+       \param[out] out     iota array
+       \param[in]  ndims   number of dimensions
+       \param[in]  dims    size
+       \param[in]  t_ndims number of dimensions of tiled array
+       \param[in]  tdims   number of tiled repetitions in each dimension
+       \param[in]  type    type
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup data_func_iota
     */
     AFAPI af_err af_iota(af_array *out, const unsigned ndims, const dim_t * const dims,
                          const unsigned t_ndims, const dim_t * const tdims, const af_dtype type);
 
-
     /**
-        \param[out] out is the generated array
-        \param[in] ndims is size of dimension array \p dims
-        \param[in] dims is the array containing sizes of the dimension
-        \param[in] type is the type of array to generate
+       C Interface to create a diagonal matrix from an extracted diagonal
+       array.
 
-        \ingroup data_func_identity
-    */
-    AFAPI af_err af_identity(af_array *out, const unsigned ndims, const dim_t * const dims, const af_dtype type);
+       See also, \ref af_diag_extract.
 
-    /**
-        \param[out] out is the array created from the input array \p in
-        \param[in] in is the input array which is the diagonal
-        \param[in] num is the diagonal index
+       \param[out] out diagonal matrix
+       \param[in]  in  diagonal array
+       \param[in]  num diagonal index
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup data_func_diag
+       \ingroup data_func_diag
     */
     AFAPI af_err af_diag_create(af_array *out, const af_array in, const int num);
 
     /**
-        \param[out] out is the \p num -th diagonal of \p in
-        \param[in] in is the input matrix
-        \param[in] num is the diagonal index
+       C Interface to extract the diagonal from an array.
 
-        \ingroup data_func_diag
+       See also, \ref af_diag_create.
+
+       \param[out] out `num`-th diagonal array
+       \param[in]  in  input array
+       \param[in]  num diagonal index
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup data_func_diag
     */
     AFAPI af_err af_diag_extract(af_array *out, const af_array in, const int num);
 
     /**
-        \brief Join 2 arrays along \p dim
+       C Interface to join 2 arrays along a dimension.
 
-        \param[out] out is the generated array
-        \param[in] dim is the dimension along which join occurs
-        \param[in] first is the first input array
-        \param[in] second is the second input array
+       Empty arrays are ignored.
 
-        \note empty arrays will be ignored
+       \param[out] out    joined array
+       \param[in]  dim    dimension along which the join occurs
+       \param[in]  first  input array
+       \param[in]  second input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup manip_func_join
+       \ingroup manip_func_join
     */
     AFAPI af_err af_join(af_array *out, const int dim, const af_array first, const af_array second);
 
     /**
-        \brief Join many arrays along \p dim
-
-        Current limit is set to 10 arrays.
+       C Interface to join many arrays along a dimension.
 
-        \param[out] out is the generated array
-        \param[in] dim is the dimension along which join occurs
-        \param[in] n_arrays number of arrays to join
-        \param[in] inputs is an array of af_arrays containing handles to the arrays to be joined
+       Limited to 10 arrays. Empty arrays are ignored.
 
-        \note empty arrays will be ignored
+       \param[out] out      joined array
+       \param[in]  dim      dimension along which the join occurs
+       \param[in]  n_arrays number of arrays to join
+       \param[in]  inputs   array of af_arrays containing handles to the
+                             arrays to be joined
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup manip_func_join
+       \ingroup manip_func_join
     */
     AFAPI af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays, const af_array *inputs);
 
     /**
-        \param[out] out is the tiled version of the input array
-        \param[in] in is the input matrix
-        \param[in] x is the number of times \p in is copied along the first dimension
-        \param[in] y is the number of times \p in is copied along the the second dimension
-        \param[in] z is the number of times \p in is copied along the third dimension
-        \param[in] w is the number of times \p in is copied along the fourth dimension
-
-        \note \p x, \p y, \p z, and \p w includes the original in the count as
-              well. Thus, if no duplicates are needed in a certain dimension,
-              leave it as 1 (the default value for just one copy)
-
-        \ingroup manip_func_tile
+       C Interface to generate a tiled array.
+
+       Note, `x`, `y`, `z`, and `w` include the original in the count.
+
+       \param[out] out tiled array
+       \param[in]  in  input array
+       \param[in]  x   number of tiles along the first dimension
+       \param[in]  y   number of tiles along the second dimension
+       \param[in]  z   number of tiles along the third dimension
+       \param[in]  w   number of tiles along the fourth dimension
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup manip_func_tile
     */
     AFAPI af_err af_tile(af_array *out, const af_array in,
                          const unsigned x, const unsigned y, const unsigned z, const unsigned w);
 
     /**
-        \param[out] out is the reordered array
-        \param[in] in is the input matrix
-        \param[in] x specifies which dimension should be first
-        \param[in] y specifies which dimension should be second
-        \param[in] z specifies which dimension should be third
-        \param[in] w specifies which dimension should be fourth
-
-        \ingroup manip_func_reorder
+       C Interface to reorder an array.
+
+       \param[out] out reordered array
+       \param[in]  in  input array
+       \param[in]  x   specifies which dimension should be first
+       \param[in]  y   specifies which dimension should be second
+       \param[in]  z   specifies which dimension should be third
+       \param[in]  w   specifies which dimension should be fourth
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup manip_func_reorder
     */
     AFAPI af_err af_reorder(af_array *out, const af_array in,
                             const unsigned x, const unsigned y, const unsigned z, const unsigned w);
 
     /**
-        \param[in] out is the shifted array
-        \param[in] in is the input array
-        \param[in] x specifies the shift along first dimension
-        \param[in] y specifies the shift along second dimension
-        \param[in] z specifies the shift along third dimension
-        \param[in] w specifies the shift along fourth dimension
-
-        \ingroup manip_func_shift
+       C Interface to shift an array.
+
+       \param[out] out shifted array
+       \param[in]  in  input array
+       \param[in]  x   specifies the shift along first dimension
+       \param[in]  y   specifies the shift along second dimension
+       \param[in]  z   specifies the shift along third dimension
+       \param[in]  w   specifies the shift along fourth dimension
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup manip_func_shift
     */
     AFAPI af_err af_shift(af_array *out, const af_array in, const int x, const int y, const int z, const int w);
 
     /**
-    * C Interface for modifying the dimensions of an input array to the shape specified by an array of `ndims` dimensions
-    *
-        \param[out] out the modded output
-        \param[in] in the input array
-        \param[in] ndims the number of dimensions
-        \param[in] dims the array of new dimension sizes
-
-        \ingroup manip_func_moddims
+       C Interface to modify the dimensions of an input array to a specified
+       shape.
+
+       \param[out] out   modded output
+       \param[in]  in    input array
+       \param[in]  ndims number of dimensions
+       \param[in]  dims  new dimension sizes
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup manip_func_moddims
     */
     AFAPI af_err af_moddims(af_array *out, const af_array in, const unsigned ndims, const dim_t * const dims);
 
     /**
-        \param[out] out is the flat array
-        \param[in] in is the input array
+       C Interface to flatten an array.
+
+       \param[out] out flat array
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup manip_func_flat
+       \ingroup manip_func_flat
     */
     AFAPI af_err af_flat(af_array *out, const af_array in);
 
     /**
-        \param[out] out is the flipped array
-        \param[in] in is the input array
-        \param[in] dim is the dimensions to flip the array
+       C Interface to flip an array.
 
-        \ingroup manip_func_flip
+       \param[out] out flipped array
+       \param[in]  in  input array
+       \param[in]  dim dimension to flip
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup manip_func_flip
     */
     AFAPI af_err af_flip(af_array *out, const af_array in, const unsigned dim);
 
     /**
-        \param[out] out is the lower traingle matrix
-        \param[in] in is the input matrix
-        \param[in] is_unit_diag is a boolean parameter specifying if the diagonal elements should be 1
+       C Interface to return the lower triangle array.
+
+       \param[out] out          lower traingle array
+       \param[in]  in           input array
+       \param[in]  is_unit_diag boolean specifying if diagonal elements are 1's
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup data_func_lower
+       \ingroup data_func_lower
     */
     AFAPI af_err af_lower(af_array *out, const af_array in, bool is_unit_diag);
 
     /**
-        \param[out] out is the upper triangle matrix
-        \param[in] in is the input matrix
-        \param[in] is_unit_diag is a boolean parameter specifying if the diagonal elements should be 1
+       C Interface to return the upper triangle array.
+
+       \param[out] out          upper triangle array
+       \param[in]  in           input array
+       \param[in]  is_unit_diag boolean specifying if diagonal elements are 1's
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-        \ingroup data_func_upper
+       \ingroup data_func_upper
     */
     AFAPI af_err af_upper(af_array *out, const af_array in, bool is_unit_diag);
 
 #if AF_API_VERSION >= 31
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the condition
-       \param[in]  b is the array containing elements from the false part of the condition
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select array element
+       \param[in]  b    when false, select array element
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
@@ -765,10 +814,14 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the condition
-       \param[in]  b is a scalar assigned to \p out when \p cond is false
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select array element
+       \param[in]  b    when false, select scalar value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
@@ -777,10 +830,14 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is a scalar assigned to \p out when \p cond is true
-       \param[in]  b is the array containing elements from the false part of the condition
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select scalar value
+       \param[in]  b    when false, select array element
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
@@ -789,11 +846,17 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement array.
+       C Interface to replace elements of an array with elements of another
+       array.
 
-       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
+       Elements of `a` are replaced with corresponding elements of `b` when
+       `cond` is false.
+
+       \param[inout]  a    input array
+       \param[in]     cond conditional array
+       \param[in]     b    replacement array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_replace
     */
@@ -802,11 +865,15 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement array.
+       C Interface to replace elements of an array with a scalar value.
+
+       Elements of `a` are replaced with a scalar value when `cond` is false.
 
-       \note Values of \p a are replaced with corresponding values of \p b, when \p cond is false.
+       \param[inout] a    input array
+       \param[in]    cond conditional array
+       \param[in]    b    replacement scalar value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_replace
     */
@@ -815,15 +882,19 @@ extern "C" {
 
 #if AF_API_VERSION >= 37
     /**
-       \param[out] out is the padded array
-       \param[in] in is the input array to be padded
-       \param[in] begin_ndims is size of \p l_dims array
-       \param[in] begin_dims array contains padding size at beginning of each
-                  dimension
-       \param[in] end_ndims is size of \p u_dims array
-       \param[in] end_dims array contains padding sizes at end of each dimension
-       \param[in] pad_fill_type is indicates what values should fill
-                  padded region
+       C Interface to pad an array.
+
+       \param[out] out           padded array
+       \param[in]  in            input array
+       \param[in]  begin_ndims   number of dimensions for start padding
+       \param[in]  begin_dims    number of elements to be padded at the start
+                                 of each dimension
+       \param[in]  end_ndims     number of dimensions for end padding
+       \param[in]  end_dims      number of elements to be padded at the end of
+                                 each dimension
+       \param[in]  pad_fill_type values to fill into the padded region
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_pad
     */
@@ -836,12 +907,15 @@ extern "C" {
 
 #if AF_API_VERSION >= 39
     /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement array.
+       C Interface to replace elements of an array with a scalar value.
 
-       \note Values of \p a are replaced with corresponding values of \p b, when
-       \p cond is false.
+       Elements of `a` are replaced with a scalar value when `cond` is false.
+
+       \param[inout] a    input array
+       \param[in]    cond conditional array
+       \param[in]    b    replacement scalar value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_replace
     */
@@ -849,12 +923,15 @@ extern "C" {
                                         const long long b);
 
     /**
-       \param[inout]  a is the input array
-       \param[in]  cond is the conditional array.
-       \param[in]  b is the replacement array.
+       C Interface to replace elements of an array with a scalar value.
+
+       Elements of `a` are replaced with a scalar value when `cond` is false.
 
-       \note Values of \p a are replaced with corresponding values of \p b, when
-       \p cond is false.
+       \param[inout] a    input array
+       \param[in]    cond conditional array
+       \param[in]    b    replacement scalar value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_replace
     */
@@ -862,13 +939,14 @@ extern "C" {
                                          const unsigned long long b);
 
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is
-       true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the
-       condition
-       \param[in]  b is a scalar assigned to \p out when \p cond is
-       false
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select array element
+       \param[in]  b    when false, select scalar value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
@@ -876,13 +954,14 @@ extern "C" {
                                          const af_array a, const long long b);
 
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is
-       true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is the array containing elements from the true part of the
-       condition
-       \param[in]  b is a scalar assigned to \p out when \p cond is
-       false
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select array element
+       \param[in]  b    when false, select scalar value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
@@ -891,12 +970,14 @@ extern "C" {
                                           const unsigned long long b);
 
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is
-       true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is a scalar assigned to \p out when \p cond is true
-       \param[in]  b is the array containing elements from the false part of the
-       condition
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select scalar value
+       \param[in]  b    when false, select array element
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
@@ -904,12 +985,14 @@ extern "C" {
                                          const long long a, const af_array b);
 
     /**
-       \param[out] out is the output containing elements of \p a when \p cond is
-       true else elements from \p b
-       \param[in]  cond is the conditional array
-       \param[in]  a is a scalar assigned to \p out when \p cond is true
-       \param[in]  b is the array containing elements from the false part of the
-       condition
+       C Interface to select elements based on a conditional array.
+
+       \param[out] out  `a` when `cond` is true, else `b`
+       \param[in]  cond conditional array
+       \param[in]  a    when true, select scalar value
+       \param[in]  b    when false, select array element
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup data_func_select
     */
diff --git a/include/af/lapack.h b/include/af/lapack.h
index 271d99cf4c..be30cd5900 100644
--- a/include/af/lapack.h
+++ b/include/af/lapack.h
@@ -16,12 +16,13 @@ namespace af
 {
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for SVD decomposition
+       C++ Interface to perform singular value decomposition.
 
-       \param[out] u is the output array containing U
-       \param[out] s is the output array containing the diagonal values of sigma, (singular values of the input matrix))
-       \param[out] vt is the output array containing V^H
-       \param[in] in is the input matrix
+       \param[out] u  U
+       \param[out] s  diagonal values of sigma (singular values of the input 
+                      matrix)
+       \param[out] vt V^H
+       \param[in]  in input array
 
        \ingroup lapack_factor_func_svd
     */
@@ -30,18 +31,16 @@ namespace af
 
 #if AF_API_VERSION >= 31
     /**
-       C++ Interface for SVD decomposition (in-place)
+       C++ Interface to perform in-place singular value decomposition.
 
-       \param[out]    u is the output array containing U
-       \param[out]    s is the output array containing the diagonal values of sigma,
-                        (singular values of the input matrix))
-       \param[out]    vt is the output array containing V^H
-       \param[in,out] in is the input matrix and will contain random data after
-                         this operation
+       This function minimizes memory usage if `in` is dispensable. Input array
+       `in` is limited to arrays where `dim0` \f$\geq\f$ `dim1`.
 
-       \note Currently, \p in is limited to arrays where `dim0` \f$\geq\f$ `dim1`
-       \note This is best used when minimizing memory usage and \p in is
-             dispensable
+       \param[out]   u  U
+       \param[out]   s  diagonal values of sigma (singular values of the input
+                        matrix)
+       \param[out]   vt V^H
+       \param[inout] in input array; contains random data after the operation                       this operation
 
        \ingroup lapack_factor_func_svd
     */
@@ -49,158 +48,176 @@ namespace af
 #endif
 
     /**
-       C++ Interface for LU decomposition in packed format
+       C++ Interface to perform LU decomposition in packed format.
 
-       \param[out] out is the output array containing the packed LU decomposition
-       \param[out] pivot will contain the permutation indices to map the input to the decomposition
-       \param[in] in is the input matrix
-       \param[in] is_lapack_piv specifies if the pivot is returned in original LAPACK compliant format
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[out] out           packed LU decomposition
+       \param[out] pivot         permutation indices mapping the input to the
+                                 decomposition
+       \param[in]  in            input array
+       \param[in]  is_lapack_piv specifies if the pivot is returned in original
+                                 LAPACK compliant format
 
        \ingroup lapack_factor_func_lu
     */
     AFAPI void lu(array &out, array &pivot, const array &in, const bool is_lapack_piv=true);
 
     /**
-       C++ Interface for LU decomposition
+       C++ Interface to perform LU decomposition.
 
-       \param[out] lower will contain the lower triangular matrix of the LU decomposition
-       \param[out] upper will contain the upper triangular matrix of the LU decomposition
-       \param[out] pivot will contain the permutation indices to map the input to the decomposition
-       \param[in] in is the input matrix
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[out] lower lower triangular matrix of the LU decomposition
+       \param[out] upper upper triangular matrix of the LU decomposition
+       \param[out] pivot permutation indices mapping the input to the
+                         decomposition
+       \param[in]  in    input array
 
        \ingroup lapack_factor_func_lu
     */
     AFAPI void lu(array &lower, array &upper, array &pivot, const array &in);
 
     /**
-      C++ Interface for in place LU decomposition
+       C++ Interface to perform in-place LU decomposition.
 
-      \param[out] pivot will contain the permutation indices to map the input to the decomposition
-      \param[inout] in contains the input on entry, the packed LU decomposition on exit
-      \param[in] is_lapack_piv specifies if the pivot is returned in original LAPACK compliant format
+       This function is not supported in GFOR.
 
-      \note This function is not supported in GFOR
+       \param[out]   pivot         permutation indices mapping the input to the
+                                   decomposition
+       \param[inout] in            input array on entry; packed LU
+                                   decomposition on exit
+       \param[in]    is_lapack_piv specifies if the pivot is returned in
+                                   original LAPACK-compliant format
 
-      \ingroup lapack_factor_func_lu
+       \ingroup lapack_factor_func_lu
     */
     AFAPI void luInPlace(array &pivot, array &in, const bool is_lapack_piv=true);
 
     /**
-       C++ Interface for QR decomposition in packed format
+       C++ Interface to perform QR decomposition in packed format.
 
-       \param[out] out is the output array containing the packed QR decomposition
-       \param[out] tau will contain additional information needed for unpacking the data
-       \param[in] in is the input matrix
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[out] out packed QR decomposition
+       \param[out] tau additional information needed for unpacking the data
+       \param[in]  in  input array
 
        \ingroup lapack_factor_func_qr
     */
     AFAPI void qr(array &out, array &tau, const array &in);
 
     /**
-       C++ Interface for QR decomposition
+       C++ Interface to perform QR decomposition.
 
-       \param[out] q is the orthogonal matrix from QR decomposition
-       \param[out] r is the upper triangular matrix from QR decomposition
-       \param[out] tau will contain additional information needed for solving a least squares problem using \p q and \p r
-       \param[in] in is the input matrix
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[out] q   orthogonal matrix from QR decomposition
+       \param[out] r   upper triangular matrix from QR decomposition
+       \param[out] tau additional information needed for solving a
+                       least-squares problem using `q` and `r`
+       \param[in]  in  input array
 
        \ingroup lapack_factor_func_qr
     */
     AFAPI void qr(array &q, array &r, array &tau, const array &in);
 
     /**
-       C++ Interface for QR decomposition
+       C++ Interface to perform QR decomposition.
 
-       \param[out] tau will contain additional information needed for unpacking the data
-       \param[inout] in is the input matrix on entry. It contains packed QR decomposition on exit
+       This function is not supported in GFOR.
 
-       \note This function is not supported in GFOR
+       \param[out]   tau additional information needed for unpacking the data
+       \param[inout] in  input array on entry; packed QR decomposition on exit
 
        \ingroup lapack_factor_func_qr
     */
     AFAPI void qrInPlace(array &tau, array &in);
 
     /**
-       C++ Interface for cholesky decomposition
-
-       \param[out] out contains the triangular matrix. Multiply \p out with its conjugate transpose reproduces the input \p in.
-       \param[in] in is the input matrix
-       \param[in] is_upper a boolean determining if \p out is upper or lower triangular
+       C++ Interface to perform Cholesky decomposition.
 
-       \returns \p 0 if cholesky decomposition passes, if not it returns the rank at which the decomposition failed.
+       Multiplying `out` with its conjugate transpose reproduces the input
+       `in`.
+       
+       The input must be positive definite.
+       
+       This function is not supported in GFOR.
 
-       \note The input matrix \b has to be a positive definite matrix, if it is not zero, the cholesky decomposition functions return a non-zero output.
-       \note This function is not supported in GFOR
+       \param[out] out      triangular matrix; 
+       \param[in]  in       input matrix
+       \param[in]  is_upper boolean determining if `out` is upper or lower
+                            triangular
+       \returns    `0` if cholesky decomposition passes; if not, it returns the
+                   rank at which the decomposition fails
 
        \ingroup lapack_factor_func_cholesky
     */
     AFAPI int cholesky(array &out, const array &in, const bool is_upper = true);
 
     /**
-       C++ Interface for in place cholesky decomposition
+       C++ Interface to perform in-place Cholesky decomposition.
 
-       \param[inout] in is the input matrix on entry. It contains the triangular matrix on exit.
-       \param[in] is_upper a boolean determining if \p in is upper or lower triangular
+       The input must be positive definite.
 
-       \returns \p 0 if cholesky decomposition passes, if not it returns the rank at which the decomposition failed.
+       This function is not supported in GFOR.
 
-       \note The input matrix \b has to be a positive definite matrix, if it is not zero, the cholesky decomposition functions return a non-zero output.
-       \note This function is not supported in GFOR
+       \param[inout] in       input matrix on entry; triangular matrix on exit
+       \param[in]    is_upper boolean determining if `in` is upper or lower
+                              triangular
+       \returns      `0` if cholesky decomposition passes; if not, it returns
+                     the rank at which the decomposition fails
 
        \ingroup lapack_factor_func_cholesky
     */
     AFAPI int choleskyInPlace(array &in, const bool is_upper = true);
 
     /**
-       C++ Interface for solving a system of equations
+       C++ Interface to solve a system of equations.
 
-       \param[in] a is the coefficient matrix
-       \param[in] b is the measured values
-       \param[in] options determining various properties of matrix \p a
-       \returns \p x, the matrix of unknown variables
+       The `options` parameter must be one of \ref AF_MAT_NONE,
+       \ref AF_MAT_LOWER or \ref AF_MAT_UPPER.
 
-       \note \p options needs to be one of \ref AF_MAT_NONE, \ref AF_MAT_LOWER or \ref AF_MAT_UPPER
-       \note This function is not supported in GFOR
+       This function is not supported in GFOR.
+
+       \param[in] a       coefficient matrix
+       \param[in] b       measured values
+       \param[in] options determines various properties of matrix `a`
+       \returns   `x`, the matrix of unknown variables
 
        \ingroup lapack_solve_func_gen
     */
     AFAPI array solve(const array &a, const array &b, const matProp options = AF_MAT_NONE);
 
-
     /**
-       C++ Interface for solving a system of equations
+       C++ Interface to solve a system of equations.
 
-       \param[in] a is the output matrix from packed LU decomposition of the coefficient matrix
-       \param[in] piv is the pivot array from packed LU decomposition of the coefficient matrix
-       \param[in] b is the matrix of measured values
-       \param[in] options determining various properties of matrix \p a
-       \returns \p x, the matrix of unknown variables
+       The `options` parameter currently must be \ref AF_MAT_NONE.
 
-       \ingroup lapack_solve_lu_func_gen
+       This function is not supported in GFOR.
+
+       \param[in] a       packed LU decomposition of the coefficient matrix
+       \param[in] piv     pivot array from the packed LU decomposition of the
+                          coefficient matrix
+       \param[in] b       measured values
+       \param[in] options determines various properties of matrix `a`
+       \returns   `x`, the matrix of unknown variables
 
-       \note \p options currently needs to be \ref AF_MAT_NONE
-       \note This function is not supported in GFOR
+       \ingroup lapack_solve_lu_func_gen
     */
     AFAPI array solveLU(const array &a, const array &piv,
                         const array &b, const matProp options = AF_MAT_NONE);
 
     /**
-       C++ Interface for inverting a matrix
+       C++ Interface to invert a matrix.
+
+       The `options` parameter currently must be \ref AF_MAT_NONE.
 
-       \param[in] in is input matrix
-       \param[in] options determining various properties of matrix \p in
-       \returns \p x, the inverse of the input matrix
+       This function is not supported in GFOR.
 
-       \note \p options currently needs to be \ref AF_MAT_NONE
-       \note This function is not supported in GFOR
+       \param[in] in      input matrix
+       \param[in] options determines various properties of matrix `in`
+       \returns   inverse matrix
 
        \ingroup lapack_ops_func_inv
     */
@@ -208,19 +225,22 @@ namespace af
 
 #if AF_API_VERSION >= 37
     /**
-       C++ Interface for pseudo-inverting (Moore-Penrose) a matrix.
+       C++ Interface to pseudo-invert (Moore-Penrose) a matrix.
+
        Currently uses the SVD-based approach.
 
-       \param[in] in is the input matrix
-       \param[in] tol defines the lower threshold for singular values from SVD
-       \param[in] options must be AF_MAT_NONE (more options might be supported
-                  in the future)
-       \returns the pseudo-inverse of the input matrix
+       Parameter `tol` is not the actual lower threshold, but it is passed in
+       as a parameter to the calculation of the actual threshold relative to
+       the shape and contents of `in`.
+       
+       This function is not supported in GFOR.
 
-       \note \p tol is not the actual lower threshold, but it is passed in as
-             a parameter to the calculation of the actual threshold relative to
-             the shape and contents of \p in.
-       \note This function is not supported in GFOR
+       \param[in] in      input matrix
+       \param[in] tol     defines the lower threshold for singular values from
+                          SVD
+       \param[in] options must be AF_MAT_NONE (more options might be supported
+                          in the future)
+       \returns   pseudo-inverse matrix
 
        \ingroup lapack_ops_func_pinv
     */
@@ -229,37 +249,36 @@ namespace af
 #endif
 
     /**
-       C++ Interface for finding the rank of a matrix
-
-       \param[in] in is input matrix
-       \param[in] tol is the tolerance value
+       C++ Interface to find the rank of a matrix.
 
-       \returns the rank of the matrix
+       \param[in] in  input matrix
+       \param[in] tol tolerance value
+       \returns   rank
 
        \ingroup lapack_ops_func_rank
     */
     AFAPI unsigned rank(const array &in, const double tol=1E-5);
 
     /**
-       C++ Interface for finding the determinant of a matrix
+       C++ Interface to find the determinant of a matrix.
 
-       \param[in] in is input matrix
-
-       \returns the determinant of the matrix
+       \param[in] in input matrix
+       \returns   determinant
 
        \ingroup lapack_ops_func_det
     */
     template<typename T> T det(const array &in);
 
     /**
-       C++ Interface for norm of a matrix
-
-       \param[in] in is the input matrix
-       \param[in] type specifies the \ref af::normType. Default: \ref AF_NORM_VECTOR_1
-       \param[in] p specifies the value of P when \p type is one of \ref AF_NORM_VECTOR_P, AF_NORM_MATRIX_L_PQ is used. It is ignored for other values of \p type
-       \param[in] q specifies the value of Q when \p type is AF_NORM_MATRIX_L_PQ. This parameter is ignored if \p type is anything else
+       C++ Interface to find the norm of a matrix.
 
-       \returns the norm of \p inbased on \p type
+       \param[in] in   input matrix
+       \param[in] type \ref af::normType. Default: \ref AF_NORM_VECTOR_1
+       \param[in] p    value of P when `type` is \ref AF_NORM_VECTOR_P or
+                       \ref AF_NORM_MATRIX_L_PQ, else ignored
+       \param[in] q    value of Q when `type` is \ref AF_NORM_MATRIX_L_PQ, else
+                       ignored
+       \returns   norm
 
        \ingroup lapack_ops_func_norm
     */
@@ -268,9 +287,9 @@ namespace af
 
 #if AF_API_VERSION >= 33
     /**
-       Returns true is ArrayFire is compiled with LAPACK support
+       Returns true if ArrayFire is compiled with LAPACK support.
 
-       \returns true is LAPACK support is available, false otherwise
+       \returns true if LAPACK support is available; false otherwise
 
        \ingroup lapack_helper_func_available
     */
@@ -286,12 +305,15 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for SVD decomposition
+       C Interface to perform singular value decomposition.
 
-       \param[out] u is the output array containing U
-       \param[out] s is the output array containing the diagonal values of sigma, (singular values of the input matrix))
-       \param[out] vt is the output array containing V^H
-       \param[in] in is the input matrix
+       \param[out] u  U
+       \param[out] s  diagonal values of sigma (singular values of the input
+                      matrix)
+       \param[out] vt V^H
+       \param[in]  in input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_factor_func_svd
     */
@@ -300,18 +322,18 @@ extern "C" {
 
 #if AF_API_VERSION >= 31
     /**
-       C Interface for SVD decomposition (in-place)
+       C Interface to perform in-place singular value decomposition.
 
-       \param[out]    u  is the output array containing U
-       \param[out]    s  is the output array containing the diagonal values of
-                         sigma, (singular values of the input matrix))
-       \param[out]    vt is the output array containing V^H
-       \param[in,out] in is the input matrix that will contain random data after
-                         this operation
+       This function minimizes memory usage if `in` is dispensable. Input array
+       `in` is limited to arrays where `dim0` \f$\geq\f$ `dim1`.
 
-       \note Currently, \p in is limited to arrays where `dim0` \f$\geq\f$ `dim1`
-       \note This is best used when minimizing memory usage and \p in is
-             dispensable
+       \param[out]   u  U
+       \param[out]   s  diagonal values of sigma (singular values of the input
+                        matrix)
+       \param[out]   vt V^H
+       \param[inout] in input array; contains random data after the operation                       this operation
+       \return       \ref AF_SUCCESS, if function returns successfully, else
+                     an \ref af_err code is given
 
        \ingroup lapack_factor_func_svd
     */
@@ -319,139 +341,182 @@ extern "C" {
 #endif
 
     /**
-       C Interface for LU decomposition
+       C Interface to perform LU decomposition.
 
-       \param[out] lower will contain the lower triangular matrix of the LU decomposition
-       \param[out] upper will contain the upper triangular matrix of the LU decomposition
-       \param[out] pivot will contain the permutation indices to map the input to the decomposition
-       \param[in] in is the input matrix
+       \param[out] lower lower triangular matrix of the LU decomposition
+       \param[out] upper upper triangular matrix of the LU decomposition
+       \param[out] pivot permutation indices mapping the input to the
+                         decomposition
+       \param[in]  in    input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_factor_func_lu
     */
     AFAPI af_err af_lu(af_array *lower, af_array *upper, af_array *pivot, const af_array in);
 
     /**
-       C Interface for in place LU decomposition
+       C Interface to perform in-place LU decomposition.
+
+       This function is not supported in GFOR.
 
-       \param[out] pivot will contain the permutation indices to map the input to the decomposition
-       \param[inout] in contains the input on entry, the packed LU decomposition on exit
-       \param[in] is_lapack_piv specifies if the pivot is returned in original LAPACK compliant format
+       \param[out]   pivot         permutation indices mapping the input to the
+                                   decomposition
+       \param[inout] in            input array on entry; packed LU
+                                   decomposition on exit
+       \param[in]    is_lapack_piv specifies if the pivot is returned in
+                                   original LAPACK-compliant format
+       \return       \ref AF_SUCCESS, if function returns successfully, else
+                     an \ref af_err code is given
 
        \ingroup lapack_factor_func_lu
     */
     AFAPI af_err af_lu_inplace(af_array *pivot, af_array in, const bool is_lapack_piv);
 
     /**
-       C Interface for QR decomposition
+       C Interface to perform QR decomposition.
 
-       \param[out] q is the orthogonal matrix from QR decomposition
-       \param[out] r is the upper triangular matrix from QR decomposition
-       \param[out] tau will contain additional information needed for solving a least squares problem using \p q and \p r
-       \param[in] in is the input matrix
+       This function is not supported in GFOR.
+
+       \param[out] q   orthogonal matrix from QR decomposition
+       \param[out] r   upper triangular matrix from QR decomposition
+       \param[out] tau additional information needed for solving a
+                       least-squares problem using `q` and `r`
+       \param[in]  in  input array
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_factor_func_qr
     */
     AFAPI af_err af_qr(af_array *q, af_array *r, af_array *tau, const af_array in);
 
     /**
-       C Interface for QR decomposition
+       C Interface to perform QR decomposition.
+
+       This function is not supported in GFOR.
 
-       \param[out] tau will contain additional information needed for unpacking the data
-       \param[inout] in is the input matrix on entry. It contains packed QR decomposition on exit
+       \param[out]   tau additional information needed for unpacking the data
+       \param[inout] in  input array on entry; packed QR decomposition on exit
+       \return       \ref AF_SUCCESS, if function returns successfully, else
+                     an \ref af_err code is given
 
        \ingroup lapack_factor_func_qr
     */
     AFAPI af_err af_qr_inplace(af_array *tau, af_array in);
 
     /**
-       C++ Interface for cholesky decomposition
+       C Interface to perform Cholesky decomposition.
 
-       \param[out] out contains the triangular matrix. Multiply \p out with it conjugate transpose reproduces the input \p in.
-       \param[out] info is \p 0 if cholesky decomposition passes, if not it returns the rank at which the decomposition failed.
-       \param[in] in is the input matrix
-       \param[in] is_upper a boolean determining if \p out is upper or lower triangular
+       Multiplying `out` with its conjugate transpose reproduces the input
+       `in`.
 
-       \note The input matrix \b has to be a positive definite matrix, if it is not zero, the cholesky decomposition functions return a non zero output.
+       The input must be positive definite.
+
+       \param[out] out      triangular matrix;
+       \param[out] info     `0` if cholesky decomposition passes; if not, it
+                            returns the rank at which the decomposition fails
+       \param[in]  in       input matrix
+       \param[in]  is_upper boolean determining if `out` is upper or lower
+                            triangular
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_factor_func_cholesky
     */
     AFAPI af_err af_cholesky(af_array *out, int *info, const af_array in, const bool is_upper);
 
     /**
-       C Interface for in place cholesky decomposition
+       C Interface to perform in-place Cholesky decomposition.
 
-       \param[out] info is \p 0 if cholesky decomposition passes, if not it returns the rank at which the decomposition failed.
-       \param[inout] in is the input matrix on entry. It contains the triangular matrix on exit.
-       \param[in] is_upper a boolean determining if \p in is upper or lower triangular
+       The input must be positive definite.
 
-       \note The input matrix \b has to be a positive definite matrix, if it is not zero, the cholesky decomposition functions return a non zero output.
+       \param[out]   info     `0` if cholesky decomposition passes; if not, it
+                              returns the rank at which the decomposition fails
+       \param[inout] in       input matrix on entry; triangular matrix on exit
+       \param[in]    is_upper boolean determining if `in` is upper or lower
+                              triangular
+       \return       \ref AF_SUCCESS, if function returns successfully, else
+                     an \ref af_err code is given
 
        \ingroup lapack_factor_func_cholesky
     */
     AFAPI af_err af_cholesky_inplace(int *info, af_array in, const bool is_upper);
 
     /**
-       C Interface for solving a system of equations
+       C Interface to solve a system of equations.
 
-       \param[out] x is the matrix of unknown variables
-       \param[in] a is the coefficient matrix
-       \param[in] b is the measured values
-       \param[in] options determining various properties of matrix \p a
+       The `options` parameter must be one of \ref AF_MAT_NONE,
+       \ref AF_MAT_LOWER or \ref AF_MAT_UPPER.
 
-       \ingroup lapack_solve_func_gen
+       This function is not supported in GFOR.
 
-       \note \p options needs to be one of \ref AF_MAT_NONE, \ref AF_MAT_LOWER or \ref AF_MAT_UPPER
+       \param[out] x       matrix of unknown variables
+       \param[in]  a       coefficient matrix
+       \param[in]  b       measured values
+       \param[in]  options determines various properties of matrix `a`
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
+
+       \ingroup lapack_solve_func_gen
     */
     AFAPI af_err af_solve(af_array *x, const af_array a, const af_array b,
                           const af_mat_prop options);
 
     /**
-       C Interface for solving a system of equations
+       C Interface to solve a system of equations.
 
-       \param[out] x will contain the matrix of unknown variables
-       \param[in] a is the output matrix from packed LU decomposition of the coefficient matrix
-       \param[in] piv is the pivot array from packed LU decomposition of the coefficient matrix
-       \param[in] b is the matrix of measured values
-       \param[in] options determining various properties of matrix \p a
+       The `options` parameter currently must be \ref AF_MAT_NONE.
 
-       \ingroup lapack_solve_lu_func_gen
+       \param[out] x       matrix of unknown variables
+       \param[in]  a       packed LU decomposition of the coefficient matrix
+       \param[in]  piv     pivot array from the packed LU decomposition of the
+                           coefficient matrix
+       \param[in]  b       measured values
+       \param[in]  options determines various properties of matrix `a`
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-       \note \p options currently needs to be \ref AF_MAT_NONE
-       \note This function is not supported in GFOR
+       \ingroup lapack_solve_lu_func_gen
     */
     AFAPI af_err af_solve_lu(af_array *x, const af_array a, const af_array piv,
                              const af_array b, const af_mat_prop options);
 
     /**
-       C Interface for inverting a matrix
+       C Interface to invert a matrix.
 
-       \param[out] out will contain the inverse of matrix \p in
-       \param[in] in is input matrix
-       \param[in] options determining various properties of matrix \p in
+       The `options` parameter currently must be \ref AF_MAT_NONE.
 
-       \ingroup lapack_ops_func_inv
+       \param[out] out     inverse matrix
+       \param[in]  in      input matrix
+       \param[in]  options determines various properties of matrix `in`
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
-       \note currently options needs to be \ref AF_MAT_NONE
+       \ingroup lapack_ops_func_inv
     */
     AFAPI af_err af_inverse(af_array *out, const af_array in, const af_mat_prop options);
 
 #if AF_API_VERSION >= 37
     /**
-       C Interface for pseudo-inverting (Moore-Penrose) a matrix.
+       C Interface to pseudo-invert (Moore-Penrose) a matrix.
+
        Currently uses the SVD-based approach.
 
-       \param[out] out will contain the pseudo-inverse of matrix \p in
-       \param[in] in is the input matrix
-       \param[in] tol defines the lower threshold for singular values from SVD
-       \param[in] options must be AF_MAT_NONE (more options might be supported
-       in the future)
+       Parameter `tol` is not the actual lower threshold, but it is passed in
+       as a parameter to the calculation of the actual threshold relative to
+       the shape and contents of `in`.
 
-       \note \p tol is not the actual lower threshold, but it is passed in as a
-             parameter to the calculation of the actual threshold relative to the
-             shape and contents of \p in.
-       \note At first, try setting \p tol to 1e-6 for single precision and 1e-12
-             for double.
-       \note This function is not supported in GFOR
+       Suggested parameters for `tol`:  1e-6 for single precision and 1e-12 for
+       double precision.
+
+       \param[out] out     pseudo-inverse matrix
+       \param[in]  in      input matrix
+       \param[in]  tol     defines the lower threshold for singular values from
+                           SVD
+       \param[in]  options must be AF_MAT_NONE (more options might be supported
+                           in the future)
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_ops_func_pinv
     */
@@ -460,36 +525,43 @@ extern "C" {
 #endif
 
     /**
-       C Interface for finding the rank of a matrix
+       C Interface to find the rank of a matrix.
 
-       \param[out] rank will contain the rank of \p in
-       \param[in] in is input matrix
-       \param[in] tol is the tolerance value
+       \param[out] rank rank
+       \param[in]  in   input matrix
+       \param[in]  tol  tolerance value
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_ops_func_rank
     */
     AFAPI af_err af_rank(unsigned *rank, const af_array in, const double tol);
 
     /**
-       C Interface for finding the determinant of a matrix
+       C Interface to find the determinant of a matrix.
 
-       \param[out] det_real will contain the real part of the determinant of \p in
-       \param[out] det_imag will contain the imaginary part of the determinant of \p in
-       \param[in] in is input matrix
+       \param[out] det_real real part of the determinant
+       \param[out] det_imag imaginary part of the determinant
+       \param[in]  in       input matrix
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_ops_func_det
     */
     AFAPI af_err af_det(double *det_real, double *det_imag, const af_array in);
 
     /**
-       C Interface for norm of a matrix
-
-       \param[out] out will contain the norm of \p in
-       \param[in] in is the input matrix
-       \param[in] type specifies the \ref af::normType. Default: \ref AF_NORM_VECTOR_1
-       \param[in] p specifies the value of P when \p type is one of \ref AF_NORM_VECTOR_P,  AF_NORM_MATRIX_L_PQ is used. It is ignored for other values of \p type
-       \param[in] q specifies the value of Q when \p type is AF_NORM_MATRIX_L_PQ. This parameter is ignored if \p type is anything else
+       C Interface to find the norm of a matrix.
 
+       \param[out] out  norm
+       \param[in]  in   input matrix
+       \param[in]  type \ref af::normType. Default: \ref AF_NORM_VECTOR_1
+       \param[in]  p    value of P when `type` is \ref AF_NORM_VECTOR_P or
+                        \ref AF_NORM_MATRIX_L_PQ, else ignored
+       \param[in]  q    value of Q when `type` is \ref AF_NORM_MATRIX_L_PQ, else
+                        ignored
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup lapack_ops_func_norm
     */
@@ -497,11 +569,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 33
     /**
-       Returns true is ArrayFire is compiled with LAPACK support
-
-       \param[out] out is true if LAPACK support is available, false otherwise
+       Returns true if ArrayFire is compiled with LAPACK support.
 
-       \returns AF_SUCCESS if successful (does not depend on the value of out)
+       \param[out] out true if LAPACK support is available; false otherwise
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given; does not depend on the value
+                   of `out`
 
        \ingroup lapack_helper_func_available
     */
diff --git a/include/af/random.h b/include/af/random.h
index bf81e9218e..53939be226 100644
--- a/include/af/random.h
+++ b/include/af/random.h
@@ -11,7 +11,7 @@
 #include <af/defines.h>
 
 ///
-/// \brief Handle for random engine
+/// \brief Handle for a random engine object.
 ///
 /// This handle is used to reference the internal random engine object.
 ///
@@ -24,7 +24,7 @@ namespace af
     class array;
     class dim4;
 #if AF_API_VERSION >= 34
-    /// \brief Random Number Generation Engine Class
+    /// C++ Interface - Random Number Generation Engine Class
     ///
     /// The \ref af::randomEngine class is used to set the type and seed of
     /// random number generation engine based on \ref af::randomEngineType.
@@ -39,79 +39,79 @@ namespace af
 
     public:
       /**
-          This function creates a \ref af::randomEngine object with a
-          \ref af::randomEngineType and a seed.
+          C++ Interface to create a \ref af::randomEngine object with a \ref
+          af::randomEngineType and a seed.
 
           \code
-          // creates random engine of default type with seed = 1
-          randomEngine r(AF_RANDOM_ENGINE_DEFAULT, 1);
-         \endcode
+            // create a random engine of default type with seed = 1
+            randomEngine r(AF_RANDOM_ENGINE_DEFAULT, 1);
+          \endcode
       */
       explicit randomEngine(randomEngineType typeIn = AF_RANDOM_ENGINE_DEFAULT,
                             unsigned long long seedIn = 0);
 
       /**
-          Copy constructor for \ref af::randomEngine.
+          C++ Interface copy constructor for a \ref af::randomEngine.
 
-          \param[in] other The input random engine object
+          \param[in] other input random engine object
       */
       randomEngine(const randomEngine &other);
 
       /**
-          Creates a copy of the random engine object from a \ref
-          af_random_engine handle.
+          C++ Interface to create a copy of the random engine object from a
+          \ref af_random_engine handle.
 
           \param[in] engine The input random engine object
       */
       randomEngine(af_random_engine engine);
 
       /**
-          \brief Destructor for \ref af::randomEngine
+          C++ Interface destructor for a \ref af::randomEngine.
       */
       ~randomEngine();
 
       /**
-          \brief Assigns the internal state of randome engine
+          C++ Interface to assign the internal state of randome engine.
 
-          \param[in] other The object to be assigned to the random engine
+          \param[in] other object to be assigned to the random engine
 
-          \returns the reference to this
+          \return the reference to this
       */
       randomEngine &operator=(const randomEngine &other);
 
       /**
-          \brief Sets the random type of the random engine
+          C++ Interface to set the random type of the random engine.
 
-          \param[in] type The type of the random number generator
+          \param[in] type type of the random number generator
       */
       void setType(const randomEngineType type);
 
       /**
-          \brief Return the random type of the random engine
+          C++ Interface to get the random type of the random engine.
 
-          \returns the \ref af::randomEngineType associated with random engine
+          \return \ref af::randomEngineType associated with random engine
       */
       randomEngineType getType(void);
 
       /**
-          \brief Sets the seed of the random engine
+          C++ Interface to set the seed of the random engine.
 
-          \param[in] seed The initializing seed of the random number generator
+          \param[in] seed initializing seed of the random number generator
       */
       void setSeed(const unsigned long long seed);
 
       /**
-          \brief Returns the seed of the random engine
+          C++ Interface to return the seed of the random engine.
 
-          \returns the seed associated with random engine
+          \return seed associated with random engine
       */
       unsigned long long getSeed(void) const;
 
       /**
-          \brief Returns the af_random_engine handle of this object
+          C++ Interface to return the af_random_engine handle of this object.
 
-          \returns the handle to the af_random_engine associated with this
-                   random engine
+          \return handle to the af_random_engine associated with this random
+                  engine
       */
       af_random_engine get(void) const;
     };
@@ -119,11 +119,13 @@ namespace af
 
 #if AF_API_VERSION >= 34
     /**
-        \param[in] dims The dimensions of the array to be generated
-        \param[in] ty The type of the array
-        \param[in] r The random engine object
+        C++ Interface to create an array of random numbers uniformly
+        distributed.
 
-        \return array of size \p dims
+        \param[in] dims dimensions of the array to be generated
+        \param[in] ty   type of the array
+        \param[in] r    random engine object
+        \return    random number array of size `dims`
 
         \ingroup random_func_randu
     */
@@ -132,11 +134,13 @@ namespace af
 
 #if AF_API_VERSION >= 34
     /**
-        \param[in] dims The dimensions of the array to be generated
-        \param[in] ty The type of the array
-        \param[in] r The random engine object
+        C++ Interface to create an array of random numbers normally
+        distributed.
 
-        \return array of size \p dims
+        \param[in] dims dimensions of the array to be generated
+        \param[in] ty   type of the array
+        \param[in] r    random engine object
+        \return    random number array of size `dims`
 
         \ingroup random_func_randn
     */
@@ -144,31 +148,36 @@ namespace af
 #endif
 
     /**
-        \param[in] dims The dimensions of the array to be generated
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers uniformly
+        distributed.
 
-        \return array of size \p dims
+        \param[in] dims dimensions of the array to be generated
+        \param[in] ty   type of the array
 
         \ingroup random_func_randu
     */
     AFAPI array randu(const dim4 &dims, const dtype ty=f32);
 
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers uniformly
+        distributed.
 
-        \return array of size \p d0
+        \param[in] d0 size of the first dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0`
 
         \ingroup random_func_randu
     */
     AFAPI array randu(const dim_t d0, const dtype ty=f32);
 
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] d1 The size of the second dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers uniformly
+        distributed.
 
-        \return array of size \p d0 x \p d1
+        \param[in] d0 size of the first dimension
+        \param[in] d1 size of the second dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0` x `d1`
 
         \ingroup random_func_randu
     */
@@ -176,12 +185,14 @@ namespace af
                       const dim_t d1, const dtype ty=f32);
 
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] d1 The size of the second dimension
-        \param[in] d2 The size of the third dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers uniformly
+        distributed.
 
-        \return array of size \p d0 x \p d1 x \p d2
+        \param[in] d0 size of the first dimension
+        \param[in] d1 size of the second dimension
+        \param[in] d2 size of the third dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0` x `d1` x `d2`
 
         \ingroup random_func_randu
     */
@@ -189,13 +200,15 @@ namespace af
                       const dim_t d1, const dim_t d2, const dtype ty=f32);
 
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] d1 The size of the second dimension
-        \param[in] d2 The size of the third dimension
-        \param[in] d3 The size of the fourth dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers uniformly
+        distributed.
 
-        \return array of size \p d0 x \p d1 x \p d2 x \p d3
+        \param[in] d0 size of the first dimension
+        \param[in] d1 size of the second dimension
+        \param[in] d2 size of the third dimension
+        \param[in] d3 size of the fourth dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0` x `d1` x `d2` x `d3`
 
         \ingroup random_func_randu
     */
@@ -204,42 +217,50 @@ namespace af
                       const dim_t d3, const dtype ty=f32);
 
     /**
-        \param[in] dims The dimensions of the array to be generated
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers normally
+        distributed.
 
-        \return array of size \p dims
+        \param[in] dims dimensions of the array to be generated
+        \param[in] ty   type of the array
+        \return    random number array of size `dims`
 
         \ingroup random_func_randn
     */
     AFAPI array randn(const dim4 &dims, const dtype ty=f32);
 
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers normally
+        distributed.
 
-        \return array of size \p d0
+        \param[in] d0 size of the first dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0`
 
         \ingroup random_func_randn
     */
     AFAPI array randn(const dim_t d0, const dtype ty=f32);
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] d1 The size of the second dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers normally
+        distributed.
 
-        \return array of size \p d0 x \p d1
+        \param[in] d0 size of the first dimension
+        \param[in] d1 size of the second dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0` x `d1`
 
         \ingroup random_func_randn
     */
     AFAPI array randn(const dim_t d0,
                       const dim_t d1, const dtype ty=f32);
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] d1 The size of the second dimension
-        \param[in] d2 The size of the third dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers normally
+        distributed.
 
-        \return array of size \p d0 x \p d1 x \p d2
+        \param[in] d0 size of the first dimension
+        \param[in] d1 size of the second dimension
+        \param[in] d2 size of the third dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0` x `d1` x `d2`
 
         \ingroup random_func_randn
     */
@@ -247,13 +268,15 @@ namespace af
                       const dim_t d1, const dim_t d2, const dtype ty=f32);
 
     /**
-        \param[in] d0 The size of the first dimension
-        \param[in] d1 The size of the second dimension
-        \param[in] d2 The size of the third dimension
-        \param[in] d3 The size of the fourth dimension
-        \param[in] ty The type of the array
+        C++ Interface to create an array of random numbers normally
+        distributed.
 
-        \return array of size \p d0 x \p d1 x \p d2 x \p d3
+        \param[in] d0 size of the first dimension
+        \param[in] d1 size of the second dimension
+        \param[in] d2 size of the third dimension
+        \param[in] d3 size of the fourth dimension
+        \param[in] ty type of the array
+        \return    random number array of size `d0` x `d1` x `d2` x `d3`
 
         \ingroup random_func_randn
     */
@@ -263,7 +286,9 @@ namespace af
 
 #if AF_API_VERSION >= 34
     /**
-        \param[in] rtype The type of the random number generator
+        C++ Interface to set the default random engine type.
+
+        \param[in] rtype type of the random number generator
 
         \ingroup random_func_set_default_engine
     */
@@ -272,7 +297,9 @@ namespace af
 
 #if AF_API_VERSION >= 34
     /**
-        \returns the \ref af::randomEngine object for the default random engine
+        C++ Interface to get the default random engine type.
+
+        \return \ref af::randomEngine object for the default random engine
 
         \ingroup random_func_get_default_engine
     */
@@ -280,17 +307,19 @@ namespace af
 #endif
 
     /**
-        \brief Sets the seed of the default random number generator
+        C++ Interface to set the seed of the default random number generator.
+
+        \param[in] seed 64-bit unsigned integer
 
-        \param[in] seed A 64 bit unsigned integer
         \ingroup random_func_set_seed
     */
     AFAPI void setSeed(const unsigned long long seed);
 
     /**
-        \brief Gets the seed of the default random number generator
+        C++ Interface to get the seed of the default random number generator.
+
+        \return seed 64-bit unsigned integer
 
-        \returns seed A 64 bit unsigned integer
         \ingroup random_func_get_seed
     */
     AFAPI unsigned long long getSeed();
@@ -304,13 +333,13 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for creating random engine
+       C Interface to create a random engine.
 
-       \param[out]  engine The pointer to the returned random engine object
-       \param[in]   rtype The type of the random number generator
-       \param[in]   seed The initializing seed of the random number generator
-
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] engine pointer to the returned random engine object
+       \param[in]  rtype  type of the random number generator
+       \param[in]  seed   initializing seed of the random number generator
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -321,12 +350,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for retaining random engine
-
-       \param[out]  out The pointer to the returned random engine object
-       \param[in]   engine The random engine object
+       C Interface to retain a random engine.
 
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    pointer to the returned random engine object
+       \param[in]  engine random engine object
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -336,12 +365,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for changing random engine type
-
-       \param[in]   engine The random engine object
-       \param[in]   rtype The type of the random number generator
+       C Interface to change random engine type.
 
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[in]  engine random engine object
+       \param[in]  rtype  type of the random number generator
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -351,12 +380,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for getting random engine type
+       C Interface to get random engine type.
 
-       \param[out]  rtype The type of the random number generator
-       \param[in]   engine The random engine object
-
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] rtype  type of the random number generator
+       \param[in]  engine random engine object
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -366,18 +395,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for creating an array of uniform numbers using a random
-       engine
-
-       \param[out]  out The pointer to the returned object.
-       \param[in]   ndims The number of dimensions read from the \p dims
-                    parameter
-       \param[in]   dims A C pointer with \p ndims elements. Each value
-                    represents the size of that dimension
-       \param[in]   type The type of the \ref af_array object
-       \param[in]   engine The random engine object
+       C Interface to create an array of uniform numbers using a random engine.
 
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    pointer to the returned object
+       \param[in]  ndims  number of dimensions
+       \param[in]  dims   C pointer with `ndims` elements; each value
+                          represents the size of that dimension
+       \param[in]  type   type of the \ref af_array object
+       \param[in]  engine random engine object
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_randu
     */
@@ -388,17 +415,16 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for creating an array of normal numbers using a random engine
+       C Interface to create an array of normal numbers using a random engine.
 
-       \param[out]  out The pointer to the returned object.
-       \param[in]   ndims The number of dimensions read from the \p dims
-                    parameter
-       \param[in]   dims A C pointer with \p ndims elements. Each value
-                    represents the size of that dimension
-       \param[in]   type The type of the \ref af_array object
-       \param[in]   engine The random engine object
-
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] out    pointer to the returned object
+       \param[in]  ndims  number of dimensions
+       \param[in]  dims   C pointer with `ndims` elements; each value
+                          represents the size of that dimension
+       \param[in]  type   type of the \ref af_array object
+       \param[in]  engine random engine object
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_randn
     */
@@ -409,12 +435,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for setting the seed of a random engine
-
-       \param[out]  engine The pointer to the returned random engine object
-       \param[in]   seed The initializing seed of the random number generator
+       C Interface to set the seed of a random engine.
 
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] engine pointer to the returned random engine object
+       \param[in]  seed   initializing seed of the random number generator
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -424,11 +450,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for getting the default random engine
+       C Interface to get the default random engine.
 
-       \param[out]  engine The pointer to returned default random engine object
-
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] engine pointer to the returned default random engine object
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_get_default_engine
     */
@@ -437,11 +463,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for setting the type of the default random engine
-
-       \param[in]   rtype The type of the random number generator
+       C Interface to set the type of the default random engine.
 
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[in]  rtype type of the random number generator
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_set_default_engine
     */
@@ -450,12 +476,12 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for getting the seed of a random engine
-
-       \param[out]  seed The pointer to the returned seed.
-       \param[in]   engine The random engine object
+       C Interface to get the seed of a random engine.
 
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[out] seed   pointer to the returned seed
+       \param[in]  engine random engine object
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -465,10 +491,11 @@ extern "C" {
 
 #if AF_API_VERSION >= 34
     /**
-       C Interface for releasing random engine
+       C Interface to release a random engine.
 
-       \param[in] engine The random engine object
-       \returns \ref AF_SUCCESS if the execution completes properly
+       \param[in] engine random engine object
+       \return    \ref AF_SUCCESS, if function returns successfully, else
+                  an \ref af_err code is given
 
        \ingroup random_func_random_engine
     */
@@ -476,10 +503,12 @@ extern "C" {
 #endif
 
     /**
-        \param[out] out The generated array
-        \param[in] ndims Size of dimension array \p dims
-        \param[in] dims The array containing sizes of the dimension
-        \param[in] type The type of array to generate
+       \param[out] out   generated array
+       \param[in]  ndims number of dimensions
+       \param[in]  dims  array containing sizes of the dimension
+       \param[in]  type  type of array to generate
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_randu
     */
@@ -487,10 +516,12 @@ extern "C" {
                           const dim_t * const dims, const af_dtype type);
 
     /**
-        \param[out] out The generated array
-        \param[in] ndims Size of dimension array \p dims
-        \param[in] dims The array containing sizes of the dimension
-        \param[in] type The type of array to generate
+       \param[out] out   generated array
+       \param[in]  ndims number of dimensions
+       \param[in]  dims  array containing sizes of the dimension
+       \param[in]  type  type of array to generate
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
        \ingroup random_func_randn
     */
@@ -498,14 +529,18 @@ extern "C" {
                           const dim_t * const dims, const af_dtype type);
 
     /**
-        \param[in] seed A 64 bit unsigned integer
+       \param[in] seed a 64-bit unsigned integer
+       \return    \ref AF_SUCCESS, if function returns successfully, else
+                  an \ref af_err code is given
 
         \ingroup random_func_set_seed
     */
     AFAPI af_err af_set_seed(const unsigned long long seed);
 
     /**
-        \param[out] seed A 64 bit unsigned integer
+       \param[out] seed a 64-bit unsigned integer
+       \return     \ref AF_SUCCESS, if function returns successfully, else
+                   an \ref af_err code is given
 
         \ingroup random_func_get_seed
     */

From 4e4a4145e5a6305366835700fef44dd9b3cceab1 Mon Sep 17 00:00:00 2001
From: pv-pterab-s <75991366+pv-pterab-s@users.noreply.github.com>
Date: Thu, 17 Aug 2023 23:54:01 -0400
Subject: [PATCH 2571/2677] unified: backend id fix (#3424)

* fix: incorrect backend id bitshift. incorrect number of backends
* fix unified: convert backend_id to index with backend_index()


---------

Co-authored-by: Gallagher Donovan Pryor <gallagher@arrayfire.com>
---
 src/api/unified/symbol_manager.cpp | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/src/api/unified/symbol_manager.cpp b/src/api/unified/symbol_manager.cpp
index d3aed5f498..93ca06938f 100644
--- a/src/api/unified/symbol_manager.cpp
+++ b/src/api/unified/symbol_manager.cpp
@@ -193,16 +193,15 @@ AFSymbolManager::AFSymbolManager()
     // In order of priority.
     static const af_backend order[] = {AF_BACKEND_CUDA, AF_BACKEND_ONEAPI,
                                        AF_BACKEND_OPENCL, AF_BACKEND_CPU};
-
-    LibHandle handle    = nullptr;
-    af::Backend backend = AF_BACKEND_DEFAULT;
+    LibHandle handle                = nullptr;
+    af::Backend backend             = AF_BACKEND_DEFAULT;
     // Decremeting loop. The last successful backend loaded will be the most
     // prefered one.
     for (int i = NUM_BACKENDS - 1; i >= 0; i--) {
-        int backend_index          = order[i] >> 1U;  // 2 4 1 -> 1 2 0
-        bkndHandles[backend_index] = openDynLibrary(order[i]);
-        if (bkndHandles[backend_index]) {
-            handle  = bkndHandles[backend_index];
+        int bknd_idx          = backend_index(order[i]);
+        bkndHandles[bknd_idx] = openDynLibrary(order[i]);
+        if (bkndHandles[bknd_idx]) {
+            handle  = bkndHandles[bknd_idx];
             backend = order[i];
             numBackends++;
             backendsAvailable += order[i];
@@ -242,7 +241,7 @@ af_err setBackend(af::Backend bknd) {
             UNIFIED_ERROR_LOAD_LIB();
         }
     }
-    int idx = bknd >> 1U;  // Convert 1, 2, 4 -> 0, 1, 2
+    int idx = backend_index(bknd);
     if (instance.getHandle(idx)) {
         getActiveHandle()  = instance.getHandle(idx);
         getActiveBackend() = bknd;

From b2f18400bc0510ed43af4da8e4bb1370ef57809f Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 18 Aug 2023 20:30:05 -0400
Subject: [PATCH 2572/2677] Fix bug in Shift JIT kernels because of empty
 dimensions in Arrays

---
 src/backend/common/jit/Node.cpp          |  6 ++
 src/backend/common/jit/Node.hpp          | 71 ++++++++++++++++++++++++
 src/backend/common/jit/ShiftNodeBase.hpp |  3 +
 src/backend/cuda/CMakeLists.txt          |  1 +
 src/backend/cuda/Param.hpp               |  3 +
 src/backend/cuda/jit.cpp                 | 25 +++------
 src/backend/cuda/jit/ShiftNode.hpp       | 22 ++++++++
 src/backend/cuda/shift.cpp               |  6 +-
 src/backend/oneapi/CMakeLists.txt        |  1 +
 src/backend/oneapi/Param.hpp             |  6 ++
 src/backend/oneapi/jit.cpp               | 19 ++-----
 src/backend/oneapi/jit/ShiftNode.hpp     | 22 ++++++++
 src/backend/opencl/CMakeLists.txt        |  1 +
 src/backend/opencl/Param.hpp             |  3 +
 src/backend/opencl/jit.cpp               | 19 ++-----
 src/backend/opencl/jit/ShiftNode.hpp     | 21 +++++++
 src/backend/opencl/kernel/KParam.hpp     |  6 ++
 src/backend/opencl/shift.cpp             |  4 +-
 test/shift.cpp                           |  9 +++
 19 files changed, 195 insertions(+), 53 deletions(-)
 create mode 100644 src/backend/cuda/jit/ShiftNode.hpp
 create mode 100644 src/backend/oneapi/jit/ShiftNode.hpp
 create mode 100644 src/backend/opencl/jit/ShiftNode.hpp

diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index 0e67228f91..f77d68e260 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -76,6 +76,12 @@ auto isScalar(const Node &ptr) -> bool { return ptr.isScalar(); }
 
 bool Node::isLinear(const dim_t dims[4]) const { return true; }
 
+/// This function returns true if the \p node is a Shift node or a Buffer node
+auto isBufferOrShift(const Node_ptr &node) -> bool {
+    return node->getNodeType() == kNodeType::Buffer ||
+           node->getNodeType() == kNodeType::Shift;
+}
+
 }  // namespace common
 }  // namespace arrayfire
 
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 42da5a09d3..8f2e0183b6 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -14,6 +14,7 @@
 #include <types.hpp>
 #include <af/defines.h>
 
+#include <nonstd/span.hpp>
 #include <algorithm>
 #include <array>
 #include <functional>
@@ -330,9 +331,79 @@ std::string getFuncName(const std::vector<Node *> &output_nodes,
                         const bool is_linear, const bool loop0,
                         const bool loop1, const bool loop2, const bool loop3);
 
+/// Returns true if the \p ptr is a Buffer Node
 auto isBuffer(const Node &ptr) -> bool;
 
+/// Returns true if the \p ptr is a Scalar Node
 auto isScalar(const Node &ptr) -> bool;
 
+/// Returns true if \p node is a Buffer or a Shift node
+auto isBufferOrShift(const Node_ptr &node) -> bool;
+
+template<typename T>
+inline void applyShifts(std::array<int, 4> &shifts, nonstd::span<T> dims) {
+    std::array<T, 4> out;
+    for (size_t i = 0; i < shifts.size(); i++) { out[i] = dims[shifts[i]]; }
+    std::copy(begin(out), std::end(out), std::begin(dims));
+}
+
+template<typename ArrayT>
+inline std::array<int, 4> compressArray(ArrayT dims) {
+    std::array<int, 4> shifts{0, 1, 2, 3};
+    bool changed;
+    do {
+        changed = false;
+        for (int i = 0; i < AF_MAX_DIMS - 1; i++) {
+            if (dims[i] == 1 && dims[i + 1] != 1) {
+                std::swap(dims[i], dims[i + 1]);
+                std::swap(shifts[i], shifts[i + 1]);
+                changed = true;
+            }
+        }
+    } while (changed);
+    return shifts;
+}
+
+/// Removes empty columns from output and the other node pointers in \p nodes
+template<typename ParamT, typename BufferNodeT, typename ShiftNodeT>
+void removeEmptyDimensions(nonstd::span<ParamT> outputs,
+                           nonstd::span<Node_ptr> nodes) {
+    dim_t *outDims{outputs[0].dims_ptr()};
+    dim_t *outStrides{outputs[0].strides_ptr()};
+    auto shifts = compressArray(outDims);
+    applyShifts<dim_t>(shifts, {outStrides, AF_MAX_DIMS});
+    for (auto nodeIt{begin(nodes)}, endIt{end(nodes)};
+         (nodeIt = find_if(nodeIt, endIt, isBufferOrShift)) != endIt;
+         ++nodeIt) {
+        switch ((*nodeIt)->getNodeType()) {
+            case kNodeType::Buffer: {
+                BufferNodeT *buf{static_cast<BufferNodeT *>(nodeIt->get())};
+                applyShifts<dim_t>(shifts,
+                                   {buf->m_param.dims_ptr(), AF_MAX_DIMS});
+                applyShifts<dim_t>(shifts,
+                                   {buf->m_param.strides_ptr(), AF_MAX_DIMS});
+            } break;
+            case kNodeType::Shift: {
+                ShiftNodeT &shiftNode{
+                    *static_cast<ShiftNodeT *>(nodeIt->get())};
+                BufferNodeT &buf{shiftNode.getBufferNode()};
+                applyShifts<dim_t>(shifts,
+                                   {buf.m_param.dims_ptr(), AF_MAX_DIMS});
+                applyShifts<dim_t>(shifts,
+                                   {buf.m_param.strides_ptr(), AF_MAX_DIMS});
+
+                auto &node_shifts = shiftNode.getShifts();
+                applyShifts<int>(shifts, node_shifts);
+            } break;
+            default: break;
+        }
+    }
+    std::for_each(
+        std::begin(outputs) + 1, std::end(outputs), [&shifts](ParamT &output) {
+            applyShifts<dim_t>(shifts, {output.dims_ptr(), AF_MAX_DIMS});
+            applyShifts<dim_t>(shifts, {output.strides_ptr(), AF_MAX_DIMS});
+        });
+}
+
 }  // namespace common
 }  // namespace arrayfire
diff --git a/src/backend/common/jit/ShiftNodeBase.hpp b/src/backend/common/jit/ShiftNodeBase.hpp
index 106040f693..553f4a16a1 100644
--- a/src/backend/common/jit/ShiftNodeBase.hpp
+++ b/src/backend/common/jit/ShiftNodeBase.hpp
@@ -53,6 +53,8 @@ class ShiftNodeBase : public Node {
         return *this;
     }
 
+    std::array<int, 4> &getShifts() { return m_shifts; }
+
     std::unique_ptr<Node> clone() final {
         return std::make_unique<ShiftNodeBase>(*this);
     }
@@ -65,6 +67,7 @@ class ShiftNodeBase : public Node {
         swap(m_shifts, other.m_shifts);
     }
 
+    BufferNode &getBufferNode() { return *m_buffer_node; }
     const BufferNode &getBufferNode() const { return *m_buffer_node; }
 
     bool isLinear(const dim_t dims[4]) const final {
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 1f6e819b2f..5ffb28dafd 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -553,6 +553,7 @@ add_library(afcuda
     wrap.hpp
 
     jit/BufferNode.hpp
+    jit/ShiftNode.hpp
     jit/kernel_generators.hpp
 
     ${scan_by_key_sources}
diff --git a/src/backend/cuda/Param.hpp b/src/backend/cuda/Param.hpp
index 817d601eaa..496d4eea68 100644
--- a/src/backend/cuda/Param.hpp
+++ b/src/backend/cuda/Param.hpp
@@ -35,6 +35,9 @@ class Param {
         return dims[0] * dims[1] * dims[2] * dims[3];
     }
 
+    dim_t *dims_ptr() { return dims; }
+    dim_t *strides_ptr() { return strides; }
+
     Param(const Param<T> &other) noexcept               = default;
     Param(Param<T> &&other) noexcept                    = default;
     Param<T> &operator=(const Param<T> &other) noexcept = default;
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 903c47fe9f..146cb07db2 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -20,6 +20,7 @@
 #include <debug_cuda.hpp>
 #include <device_manager.hpp>
 #include <err_cuda.hpp>
+#include <jit/ShiftNode.hpp>
 #include <kernel_headers/jit_cuh.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -38,6 +39,8 @@ using arrayfire::common::findModule;
 using arrayfire::common::getEnvVar;
 using arrayfire::common::getFuncName;
 using arrayfire::common::half;
+using arrayfire::common::isBufferOrShift;
+using arrayfire::common::kNodeType;
 using arrayfire::common::ModdimNode;
 using arrayfire::common::Node;
 using arrayfire::common::Node_ids;
@@ -45,6 +48,8 @@ using arrayfire::common::Node_map_t;
 using arrayfire::common::Node_ptr;
 using arrayfire::common::NodeIterator;
 using arrayfire::common::saveKernel;
+using arrayfire::cuda::jit::BufferNode;
+using arrayfire::cuda::jit::ShiftNode;
 
 using std::array;
 using std::equal;
@@ -58,7 +63,6 @@ using std::vector;
 
 namespace arrayfire {
 namespace cuda {
-using jit::BufferNode;
 
 static string getKernelString(const string& funcName,
                               const vector<Node*>& full_nodes,
@@ -474,22 +478,9 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
                 }
             }
             if (emptyColumnsFound) {
-                const auto isBuffer{
-                    [](const Node_ptr& node) { return node->isBuffer(); }};
-                for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
-                     (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
-                     ++nodeIt) {
-                    BufferNode<T>* buf{
-                        static_cast<BufferNode<T>*>(nodeIt->get())};
-                    removeEmptyColumns(outDims, ndims, buf->m_param.dims,
-                                       buf->m_param.strides);
-                }
-                for_each(++begin(outputs), end(outputs),
-                         [outDims, ndims](Param<T>& output) {
-                             removeEmptyColumns(outDims, ndims, output.dims,
-                                                output.strides);
-                         });
-                ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
+                common::removeEmptyDimensions<Param<T>, BufferNode<T>,
+                                              ShiftNode<T>>(outputs,
+                                                            node_clones);
             }
 
             full_nodes.clear();
diff --git a/src/backend/cuda/jit/ShiftNode.hpp b/src/backend/cuda/jit/ShiftNode.hpp
new file mode 100644
index 0000000000..16bdf5d0f9
--- /dev/null
+++ b/src/backend/cuda/jit/ShiftNode.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/jit/ShiftNodeBase.hpp>
+#include <jit/BufferNode.hpp>
+
+namespace arrayfire {
+namespace cuda {
+namespace jit {
+
+template<typename T>
+using ShiftNode = common::ShiftNodeBase<BufferNode<T>>;
+
+}  // namespace jit
+}  // namespace cuda
+}  // namespace arrayfire
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index 82aab5e1fe..6f88a38472 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -11,6 +11,7 @@
 #include <common/jit/ShiftNodeBase.hpp>
 #include <err_cuda.hpp>
 #include <jit/BufferNode.hpp>
+#include <jit/ShiftNode.hpp>
 #include <shift.hpp>
 
 #include <memory>
@@ -18,9 +19,8 @@
 using af::dim4;
 
 using arrayfire::common::Node_ptr;
-using arrayfire::common::ShiftNodeBase;
-
 using arrayfire::cuda::jit::BufferNode;
+using arrayfire::cuda::jit::ShiftNode;
 
 using std::array;
 using std::make_shared;
@@ -29,8 +29,6 @@ using std::string;
 
 namespace arrayfire {
 namespace cuda {
-template<typename T>
-using ShiftNode = ShiftNodeBase<BufferNode<T>>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 4ecb470ef9..9bd7e0850a 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -99,6 +99,7 @@ add_library(afoneapi
   ireduce.hpp
   jit.cpp
   jit/BufferNode.hpp
+  jit/ShiftNode.hpp
   jit/kernel_generators.hpp
   join.cpp
   join.hpp
diff --git a/src/backend/oneapi/Param.hpp b/src/backend/oneapi/Param.hpp
index 752a6f7039..4a935c5e2c 100644
--- a/src/backend/oneapi/Param.hpp
+++ b/src/backend/oneapi/Param.hpp
@@ -27,6 +27,9 @@ struct Param {
     Param(const Param& other)            = default;
     Param(Param&& other)                 = default;
 
+    dim_t* dims_ptr() { return info.dims; }
+    dim_t* strides_ptr() { return info.strides; }
+
     // AF_DEPRECATED("Use Array<T>")
     Param() : data(nullptr), info{{0, 0, 0, 0}, {0, 0, 0, 0}, 0} {}
 
@@ -54,6 +57,9 @@ struct AParam {
     AParam(const AParam& other)            = default;
     AParam(AParam&& other)                 = default;
 
+    dim_t* dims_ptr() { return dims.get(); }
+    dim_t* strides_ptr() { return strides.get(); }
+
     // AF_DEPRECATED("Use Array<T>")
     AParam() : data(), dims{0, 0, 0, 0}, strides{0, 0, 0, 0}, offset(0) {}
 
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 3e317b68e2..ecd5bc04b9 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <CL/cl.h>
+#include <jit/ShiftNode.hpp>
 #include <jit/kernel_generators.hpp>
 
 #include <kernel_headers/KParam.hpp>
@@ -55,6 +56,7 @@ using arrayfire::common::NodeIterator;
 using arrayfire::common::ShiftNodeBase;
 using arrayfire::oneapi::getActiveDeviceBaseBuildFlags;
 using arrayfire::oneapi::jit::BufferNode;
+using arrayfire::oneapi::jit::ShiftNode;
 
 using std::array;
 using std::begin;
@@ -468,21 +470,8 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
             }
         }
         if (emptyColumnsFound) {
-            const auto isBuffer{
-                [](const Node_ptr& ptr) { return ptr->isBuffer(); }};
-            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
-                 (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
-                 ++nodeIt) {
-                BufferNode<T>* buf{static_cast<BufferNode<T>*>(nodeIt->get())};
-                removeEmptyColumns(outDims, ndims, buf->m_param.dims.get(),
-                                   buf->m_param.strides.get());
-            }
-            for_each(++begin(outputs), end(outputs),
-                     [outDims, ndims](Param<T>& output) {
-                         removeEmptyColumns(outDims, ndims, output.info.dims,
-                                            output.info.strides);
-                     });
-            ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
+            common::removeEmptyDimensions<Param<T>, BufferNode<T>,
+                                          ShiftNode<T>>(outputs, node_clones);
         }
     }
 
diff --git a/src/backend/oneapi/jit/ShiftNode.hpp b/src/backend/oneapi/jit/ShiftNode.hpp
new file mode 100644
index 0000000000..6a87b28729
--- /dev/null
+++ b/src/backend/oneapi/jit/ShiftNode.hpp
@@ -0,0 +1,22 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/jit/ShiftNodeBase.hpp>
+#include <jit/BufferNode.hpp>
+
+namespace arrayfire {
+namespace oneapi {
+namespace jit {
+
+template<typename T>
+using ShiftNode = common::ShiftNodeBase<BufferNode<T>>;
+
+}  // namespace jit
+}  // namespace oneapi
+}  // namespace arrayfire
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 8a0e55d2e4..5c920f44f8 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -468,6 +468,7 @@ target_sources(afopencl
 target_sources(afopencl
   PRIVATE
     jit/BufferNode.hpp
+    jit/ShiftNode.hpp
     jit/kernel_generators.hpp
   )
 
diff --git a/src/backend/opencl/Param.hpp b/src/backend/opencl/Param.hpp
index aaf19dea62..879c92c677 100644
--- a/src/backend/opencl/Param.hpp
+++ b/src/backend/opencl/Param.hpp
@@ -22,6 +22,9 @@ struct Param {
     Param(const Param& other)            = default;
     Param(Param&& other)                 = default;
 
+    dim_t* dims_ptr() { return info.dims; }
+    dim_t* strides_ptr() { return info.strides; }
+
     // AF_DEPRECATED("Use Array<T>")
     Param();
     // AF_DEPRECATED("Use Array<T>")
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 727724cc85..7ace33cd96 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -19,6 +19,7 @@
 #include <device_manager.hpp>
 #include <err_opencl.hpp>
 #include <jit/BufferNode.hpp>
+#include <jit/ShiftNode.hpp>
 #include <kernel_headers/jit.hpp>
 #include <threadsMgt.hpp>
 #include <type_util.hpp>
@@ -42,6 +43,7 @@ using arrayfire::common::Node_map_t;
 using arrayfire::common::Node_ptr;
 using arrayfire::common::NodeIterator;
 using arrayfire::common::saveKernel;
+using arrayfire::opencl::jit::ShiftNode;
 
 using cl::Kernel;
 using cl::NDRange;
@@ -418,21 +420,8 @@ void evalNodes(vector<Param>& outputs, const vector<Node*>& output_nodes) {
             }
         }
         if (emptyColumnsFound) {
-            const auto isBuffer{
-                [](const Node_ptr& ptr) { return ptr->isBuffer(); }};
-            for (auto nodeIt{begin(node_clones)}, endIt{end(node_clones)};
-                 (nodeIt = find_if(nodeIt, endIt, isBuffer)) != endIt;
-                 ++nodeIt) {
-                BufferNode* buf{static_cast<BufferNode*>(nodeIt->get())};
-                removeEmptyColumns(outDims, ndims, buf->m_param.dims,
-                                   buf->m_param.strides);
-            }
-            for_each(++begin(outputs), end(outputs),
-                     [outDims, ndims](Param& output) {
-                         removeEmptyColumns(outDims, ndims, output.info.dims,
-                                            output.info.strides);
-                     });
-            ndims = removeEmptyColumns(outDims, ndims, outDims, outStrides);
+            common::removeEmptyDimensions<Param, BufferNode, ShiftNode>(
+                outputs, node_clones);
         }
 
         full_nodes.clear();
diff --git a/src/backend/opencl/jit/ShiftNode.hpp b/src/backend/opencl/jit/ShiftNode.hpp
new file mode 100644
index 0000000000..8132105faf
--- /dev/null
+++ b/src/backend/opencl/jit/ShiftNode.hpp
@@ -0,0 +1,21 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <common/jit/ShiftNodeBase.hpp>
+#include <jit/BufferNode.hpp>
+
+namespace arrayfire {
+namespace opencl {
+namespace jit {
+
+using ShiftNode = common::ShiftNodeBase<BufferNode>;
+
+}  // namespace jit
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/KParam.hpp b/src/backend/opencl/kernel/KParam.hpp
index 38a3752760..165bec9b02 100644
--- a/src/backend/opencl/kernel/KParam.hpp
+++ b/src/backend/opencl/kernel/KParam.hpp
@@ -21,6 +21,12 @@ typedef struct {
     dim_t dims[4];
     dim_t strides[4];
     dim_t offset;
+
+#ifndef __OPENCL_VERSION__
+    dim_t *dims_ptr() { return dims; }
+    dim_t *strides_ptr() { return strides; }
+#endif
+
 } KParam;
 
 #endif
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index 512c113ed1..8b257f2c97 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -9,14 +9,15 @@
 
 #include <shift.hpp>
 
-#include <common/jit/ShiftNodeBase.hpp>
 #include <err_opencl.hpp>
+#include <jit/ShiftNode.hpp>
 #include <traits.hpp>
 
 using af::dim4;
 using arrayfire::common::Node_ptr;
 using arrayfire::common::ShiftNodeBase;
 using arrayfire::opencl::jit::BufferNode;
+using arrayfire::opencl::jit::ShiftNode;
 using std::array;
 using std::make_shared;
 using std::static_pointer_cast;
@@ -24,7 +25,6 @@ using std::string;
 
 namespace arrayfire {
 namespace opencl {
-using ShiftNode = ShiftNodeBase<BufferNode>;
 
 template<typename T>
 Array<T> shift(const Array<T> &in, const int sdims[4]) {
diff --git a/test/shift.cpp b/test/shift.cpp
index b37385a6f8..2de341b3bc 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -146,3 +146,12 @@ TEST(Shift, MaxDim) {
     output = abs(input - output);
     ASSERT_EQ(1.f, product<float>(output));
 }
+
+TEST(Shift, RowVector) {
+    const unsigned shift_x = 1;
+    const unsigned shift_y = 1;
+    array input            = iota(dim4(1, 4));
+    array output           = shift(input, shift_x, shift_y);
+    vector<float> gold{3.f, 0.f, 1.f, 2.f};
+    EXPECT_VEC_ARRAY_EQ(gold, dim4(1, 4), output);
+}

From 23ee0650e034e33a70015d98f71deb350238189d Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 18 Aug 2023 20:34:31 -0400
Subject: [PATCH 2573/2677] Fix reorder to avoid eval on copied array instead
 of input array

The reorder funciton was copying the Array object internally and
then the other operations were performed on the copy. This causes
the eval to be performed on the copied array instead of the input
array.
---
 src/api/c/reorder.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index b283c800bf..556e1f0e20 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -33,12 +33,14 @@ using std::swap;
 
 template<typename T>
 static inline af_array reorder(const af_array in, const af::dim4 &rdims0) {
-    Array<T> In = getArray<T>(in);
+    Array<T> In = detail::createEmptyArray<T>(af::dim4(0));
     dim4 rdims  = rdims0;
 
     if (rdims[0] == 1 && rdims[1] == 0) {
-        In = transpose(In, false);
+        In = transpose(getArray<T>(in), false);
         std::swap(rdims[0], rdims[1]);
+    } else {
+        In = getArray<T>(in);
     }
     const dim4 idims    = In.dims();
     const dim4 istrides = In.strides();
@@ -48,8 +50,7 @@ static inline af_array reorder(const af_array in, const af::dim4 &rdims0) {
 
     af_array out;
     if (rdims[0] == 0 && rdims[1] == 1 && rdims[2] == 2 && rdims[3] == 3) {
-        const Array<T> &Out = In;
-        out                 = getHandle(Out);
+        out = getHandle(In);
     } else if (rdims[0] == 0) {
         dim4 odims    = dim4(1, 1, 1, 1);
         dim4 ostrides = dim4(1, 1, 1, 1);

From 5583b899d2d402a25afe7008809615abd0cca0e9 Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Mon, 28 Aug 2023 17:35:19 -0400
Subject: [PATCH 2574/2677] updated the README, timing, install, gfor, and
 added the jit pages (#3490)

* updated the README, timing, install, gfor, and added the jit pages
* minor example code tweaks, adds jit to tutorials page

---------

Co-authored-by: syurkevi <stefan@arrayfire.com>
---
 docs/pages/README.md    | 101 ++++++++++++----------
 docs/pages/gfor.md      |  61 +++++++------
 docs/pages/install.md   |  55 ++++++------
 docs/pages/jit.md       | 102 ++++++++++++++++++++++
 docs/pages/timing.md    | 185 +++++++++++++++++++++++++++++-----------
 docs/pages/tutorials.md |   1 +
 6 files changed, 351 insertions(+), 154 deletions(-)
 create mode 100644 docs/pages/jit.md

diff --git a/docs/pages/README.md b/docs/pages/README.md
index d20dc6b246..08cc17578d 100644
--- a/docs/pages/README.md
+++ b/docs/pages/README.md
@@ -5,12 +5,14 @@ Overview {#mainpage}
 
 ## About ArrayFire
 
-ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming more accessible.
+ArrayFire is a high performance software library for parallel computing with
+an easy-to-use API. Its array based function set makes parallel programming
+more accessible.
 
 ## Installing ArrayFire
 
-You can install ArrayFire using either a binary installer for Windows, OSX,
-or Linux or download it from source:
+Install ArrayFire using either a binary installer for Windows, OSX, or Linux
+or download it from source:
 
 * [Binary installers for Windows, OSX, and Linux](\ref installing)
 * [Build from source](https://github.com/arrayfire/arrayfire)
@@ -20,18 +22,18 @@ or Linux or download it from source:
 The [array](\ref af::array) object is beautifully simple.
 
 Array-based notation effectively expresses computational algorithms in
-readable math-resembling notation. You _do not_ need expertise in
-parallel programming to use ArrayFire.
+readable math-resembling notation. Expertise in parallel programming _is not_
+required to use ArrayFire.
 
-A few lines of ArrayFire code
-accomplishes what can take 100s of complicated lines in CUDA or OpenCL
-kernels.
+A few lines of ArrayFire code accomplishes what can take 100s of complicated
+lines in CUDA, oneAPI, or OpenCL kernels.
 
 ## ArrayFire is extensive!
 
 #### Support for multiple domains
 
-ArrayFire contains [hundreds of functions](\ref arrayfire_func) across various domains including:
+ArrayFire contains [hundreds of functions](\ref arrayfire_func) across various
+domains including:
 - [Vector Algorithms](\ref vector_mat)
 - [Image Processing](\ref image_mat)
 - [Computer Vision](\ref cv_mat)
@@ -40,61 +42,67 @@ ArrayFire contains [hundreds of functions](\ref arrayfire_func) across various d
 - [Statistics](\ref stats_mat)
 - and more.
 
-Each function is hand-tuned by ArrayFire
-developers with all possible low-level optimizations.
+Each function is hand-tuned by ArrayFire developers with all possible
+low-level optimizations.
 
 #### Support for various data types and sizes
 
-ArrayFire operates on common [data shapes and sizes](\ref indexing),
-including vectors, matrices, volumes, and
+ArrayFire operates on common [data shapes and sizes](\ref indexing), including
+vectors, matrices, volumes, and
 
-It supports common [data types](\ref gettingstarted_datatypes),
-including single and double precision floating
-point values, complex numbers, booleans, and 32-bit signed and
-unsigned integers.
+It supports common [data types](\ref gettingstarted_datatypes), including
+single and double precision floating point values, complex numbers, booleans,
+and 32-bit signed and unsigned integers.
 
 #### Extending ArrayFire
 
-ArrayFire can be used as a stand-alone application or integrated with
-existing CUDA or OpenCL code. All ArrayFire `arrays` can be
-interchanged with other CUDA or OpenCL data structures.
+ArrayFire can be used as a stand-alone application or integrated with existing
+CUDA, oneAPI, or OpenCL code. All ArrayFire `arrays` can be interchanged with
+other CUDA, oneAPI, or OpenCL data structures.
 
 ## Code once, run anywhere!
 
-With support for x86, ARM, CUDA, and OpenCL devices, ArrayFire supports for a comprehensive list of devices.
+With support for x86, ARM, CUDA, oneAPI, and OpenCL devices, ArrayFire
+supports for a comprehensive list of devices.
 
 Each ArrayFire installation comes with:
- - a CUDA version (named 'libafcuda') for [NVIDIA
- GPUs](https://developer.nvidia.com/cuda-gpus),
- - an OpenCL version (named 'libafopencl') for [OpenCL devices](http://www.khronos.org/conformance/adopters/conformant-products#opencl)
- - a CPU version (named 'libafcpu') to fall back to when CUDA or OpenCL devices are not available.
+- a CUDA backend (named 'libafcuda') for [NVIDIA
+  GPUs](https://developer.nvidia.com/cuda-gpus),
+- a oneAPI backend (named 'libafoneapi') for [oneAPI
+  devices](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html),
+- an OpenCL backend (named 'libafopencl') for [OpenCL
+  devices](http://www.khronos.org/conformance/adopters/conformant-products#opencl),
+- a CPU backend (named 'libafcpu') to fall back to when CUDA, oneAPI, or
+  OpenCL devices are unavailable.
 
 ## ArrayFire is highly efficient
 
 #### Vectorized and Batched Operations
 
-ArrayFire supports batched operations on N-dimensional arrays.
-Batch operations in ArrayFire are run in parallel ensuring an optimal usage of your CUDA or OpenCL device.
+ArrayFire supports batched operations on N-dimensional arrays. Batch
+operations in ArrayFire are run in parallel ensuring an optimal usage of CUDA,
+oneAPI, or OpenCL devices.
 
-You can get the best performance out of ArrayFire using [vectorization techniques](\ref vectorization).
+Best performance with ArrayFire is achieved using
+[vectorization techniques](\ref vectorization).
 
 ArrayFire can also execute loop iterations in parallel with
 [the gfor function](\ref gfor).
 
 #### Just in Time compilation
 
-ArrayFire performs run-time analysis of your code to increase
-arithmetic intensity and memory throughput, while avoiding unnecessary
-temporary allocations. It has an awesome internal JIT compiler to make
-optimizations for you.
+ArrayFire performs run-time analysis of code to increase arithmetic intensity
+and memory throughput, while avoiding unnecessary temporary allocations. It
+has an awesome internal JIT compiler to make important optimizations.
 
-Read more about how [ArrayFire JIT](http://arrayfire.com/performance-of-arrayfire-jit-code-generation/) can improve the performance in your application.
+Read more about how [ArrayFire JIT](\ref jit).  can improve the performance in
+your application.
 
 ## Simple Example
 
-Here's a live example to let you see ArrayFire code. You create [arrays](\ref af::array)
-which reside on CUDA or OpenCL devices. Then you can use
-[ArrayFire functions](modules.htm) on those [arrays](\ref af::array).
+Here is an example of ArrayFire code. First, [arrays](\ref af::array) are
+created which reside on CUDA, oneAPI, or OpenCL devices. Then
+[ArrayFire functions](modules.htm) are used on those [arrays](\ref af::array).
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 // sample 40 million points on the GPU
@@ -111,17 +119,19 @@ af_print(pi);
 
 #### Free Community Options
 
-* [ArrayFire mailing list](https://groups.google.com/forum/#!forum/arrayfire-users) (recommended)
+* [ArrayFire mailing
+  list](https://groups.google.com/forum/#!forum/arrayfire-users) (recommended)
 * [StackOverflow](http://stackoverflow.com/questions/tagged/arrayfire)
 
 #### Premium Support
 
-* Phone Support - available for purchase ([request a quote](mailto:sales@arrayfire.com))
+* Phone Support - available for purchase ([request a
+  quote](mailto:sales@arrayfire.com))
 
 #### Contact Us
 
-* If you need to contact us, visit our
-[contact us page](http://arrayfire.com/company/#contact).
+* If you need to contact us, visit our [contact us
+  page](http://arrayfire.com/company/#contact).
 
 #### Email
 
@@ -130,9 +140,10 @@ af_print(pi);
 
 ## Citations and Acknowledgements
 
-If you redistribute ArrayFire, please follow the terms established in <a href="https://github.com/arrayfire/arrayfire/blob/master/LICENSE">the license</a>.
-If you wish to cite ArrayFire in an academic publication, please use the
-following reference:
+If you redistribute ArrayFire, please follow the terms established in <a
+href="https://github.com/arrayfire/arrayfire/blob/master/LICENSE">the
+license</a>. If you wish to cite ArrayFire in an academic publication, please
+use the following reference:
 
 Formatted:
 
@@ -153,4 +164,6 @@ BibTeX:
     year = {2015}
     }
 
-ArrayFire development is funded by ArrayFire LLC and several third parties, please see the list of <a href="https://github.com/arrayfire/arrayfire/blob/master/ACKNOWLEDGEMENTS.md">acknowledgements</a>.
+ArrayFire development is funded by AccelerEyes LLC (dba ArrayFire) and several
+third parties, please see the list of <a
+href="https://github.com/arrayfire/arrayfire/blob/master/ACKNOWLEDGEMENTS.md">acknowledgements</a>.
diff --git a/docs/pages/gfor.md b/docs/pages/gfor.md
index e6886b5bb4..bbced5d14b 100644
--- a/docs/pages/gfor.md
+++ b/docs/pages/gfor.md
@@ -8,18 +8,17 @@ Run many independent loops simultaneously on the GPU or device.
 Introduction {#gfor_intro}
 ============
 
-The gfor-loop construct may be used to simultaneously launch all of
-the iterations of a for-loop on the GPU or device, as long as the
-iterations are independent. While the standard for-loop performs each
-iteration sequentially, ArrayFire's gfor-loop performs each iteration
-at the same time (in parallel). ArrayFire does this by tiling out the
-values of all loop iterations and then performing computation on those
-tiles in one pass.
-
-You can think of `gfor` as performing auto-vectorization of your
-code, e.g. you write a gfor-loop that increments every element of a
-vector but behind the scenes ArrayFire rewrites it to operate on
-the entire vector in parallel.
+The gfor-loop construct may be used to simultaneously launch all of the
+iterations of a for-loop on the GPU or device, as long as the iterations are
+independent. While the standard for-loop performs each iteration sequentially,
+ArrayFire's gfor-loop performs each iteration at the same time (in
+parallel). ArrayFire does this by tiling out the values of all loop iterations
+and then performing computation on those tiles in one pass.
+
+You can think of `gfor` as performing auto-vectorization of your code,
+e.g. you write a gfor-loop that increments every element of a vector but
+behind the scenes ArrayFire rewrites it to operate on the entire vector in
+parallel.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 for (int i = 0; i < n; ++i)
@@ -29,19 +28,19 @@ gfor (seq i, n)
    A(i) = A(i) + 1;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Behind the scenes, ArrayFire rewrites your code into this
-equivalent and faster version:
+Behind the scenes, ArrayFire rewrites your code into this equivalent and
+faster version:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 A = A + 1;
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-It is best to vectorize computation as much as possible to avoid
-the overhead in both for-loops and gfor-loops.
+It is best to vectorize computation as much as possible to avoid the overhead
+in both for-loops and gfor-loops.
 
-To see another example, you could run an FFT on every 2D slice of a
-volume in a for-loop, or you could "vectorize" and simply do it all
-in one gfor-loop operation:
+To see another example, you could run an FFT on every 2D slice of a volume in
+a for-loop, or you could "vectorize" and simply do it all in one gfor-loop
+operation:
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 for (int i = 0; i < N; ++i)
@@ -89,11 +88,11 @@ User Functions called within GFOR {#gfor_user_functions}
 ---------------------------------
 
 If you have defined a function that you want to call within a GFOR loop, then
-that function has to meet all the conditions described in this page in
-order to be able to work as expected.
+that function has to meet all the conditions described in this page in order
+to be able to work as expected.
 
-Consider the (trivial) example below. The function compute() has to satisfy all
-requirements for GFOR Usage, so you cannot use if-else conditions inside
+Consider the (trivial) example below. The function compute() has to satisfy
+all requirements for GFOR Usage, so you cannot use if-else conditions inside
 it.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
@@ -384,7 +383,8 @@ gfor (seq i, n) {
 }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The problem is that every GFOR tile has a different number of elements, something which GFOR cannot yet handle.
+The problem is that every GFOR tile has a different number of elements,
+something which GFOR cannot yet handle.
 
 Similar to the workaround for conditional statements, it might work to use
 masked arithmetic:
@@ -410,14 +410,13 @@ gfor (seq i, n) {
 Memory considerations {#gfor_memory}
 =====================
 
-Since each computation is done in parallel for all iterator values,
-you need to have enough card memory available to do all iterations
-simultaneously. If the problem exceeds memory, it will trigger "out of
-memory" errors.
+Since each computation is done in parallel for all iterator values, you need
+to have enough card memory available to do all iterations simultaneously. If
+the problem exceeds memory, it will trigger "out of memory" errors.
 
-You can work around the memory limitations of your GPU or device by
-breaking the GFOR loop up into segments; however, you might want to
-consider using a larger memory GPU or device.
+You can work around the memory limitations of your GPU or device by breaking
+the GFOR loop up into segments; however, you might want to consider using a
+larger memory GPU or device.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 // BEFORE
diff --git a/docs/pages/install.md b/docs/pages/install.md
index 7a78b95f71..a0b3af61b3 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -1,24 +1,15 @@
 # ArrayFire Installer {#installing}
 
 Installing ArrayFire couldn't be easier. Navigate to
-https://arrayfire.com/download and download the installer for your architecture
-and operating system. Although you could [build ArrayFire from
-source](https://github.com/arrayfire/arrayfire), we recommend using our
-installers as we have packaged together all of the necessary dependencies to
-give you the best performance.
-
-We provide installers for Windows, Linux, and macOS. There are two installers
-for each operating system: one with graphics support and the other without
-graphics support. Download the installer with graphics support if you would like
-to be able to do high performance visualizations using our
-[Forge](https://github.com/arrayfire/forge) library. Otherwise, download the
-installer without graphics support.
-
-Make sure you have the latest device drivers installed on your system before
-using ArrayFire. If you are going to be targeting the CPU using ArrayFire’s
-OpenCL backend, you will need to have the OpenCL **runtime** installed on your
-system. Drivers and runtimes should be downloaded and installed from your device
-vendor’s website.
+https://arrayfire.com/download and download the appropriate installer for the
+target architecture and operating system. Although ArrayFire can be [built
+from source](https://github.com/arrayfire/arrayfire), the installers
+conveniently package necessary dependencies.
+
+Install the latest device drivers before using ArrayFire. If you are going to
+target the CPU using ArrayFire’s OpenCL backend, install the OpenCL
+runtime. Drivers and runtimes should be downloaded and installed from the
+device vendor’s website.
 
 # Install Instructions {#InstallInstructions}
 
@@ -29,14 +20,14 @@ vendor’s website.
 ## Windows {#Windows}
 
 Prior to installing ArrayFire on Windows,
-[download](https://www.microsoft.com/en-in/download/details.aspx?id=48145)
+[download](https://www.microsoft.com/en-in/download/details.aspx?id=48145) and
 install the Visual Studio 2015 (x64) runtime libraries.
 
-Once you have downloaded the ArrayFire installer, execute the installer as you
-normally would on Windows. If you choose not to modify the path during the
-installation procedure, you'll need to manually add ArrayFire to the path for
-all users. Simply append `%%AF_PATH%/lib` to the PATH variable so that the loader
-can find ArrayFire DLLs.
+Once the ArrayFire installer has been downloaded, run the installer. If you
+choose not to modify the path during the installation procedure, you'll need
+to manually add ArrayFire to the path for all users. Simply append
+`%%AF_PATH%/lib` to the PATH variable so that the loader can find ArrayFire
+DLLs.
 
 For more information on using ArrayFire on Windows, visit the following
 [page](http://arrayfire.org/docs/using_on_windows.htm).
@@ -47,13 +38,14 @@ There are two ways to install ArrayFire on Linux.
 1. Package Manager
 2. Using ArrayFire Linux Installer
 
-As of today, approach (1) is only supported for Ubuntu 18.04 and 20.04. Please go
-through [our GitHub wiki page](https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers)
+As of today, approach (1) is only supported for Ubuntu 18.04 and 20.04. Please
+go through [our GitHub wiki
+page](https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers)
 for the detailed instructions.
 
-For approach (2), once you have downloaded the ArrayFire installer, execute the
-installer from the terminal as shown below. Set the `--prefix` argument to the
-directory you would like to install ArrayFire to - we recommend `/opt`.
+For approach (2), once you have downloaded the ArrayFire installer, execute
+the installer from the terminal as shown below. Set the `--prefix` argument to
+the directory you would like to install ArrayFire to - we recommend `/opt`.
 
     ./Arrayfire_*_Linux_x86_64.sh --include-subdir --prefix=/opt
 
@@ -131,8 +123,9 @@ On Unix-like systems:
     ./helloworld/helloworld_{cpu,cuda,opencl}
 
 On Windows, open the CMakeLists.txt file from CMake-GUI and set `ASSETS_DIR`
-variable to the parent folder of examples folder. Once the project is configured
-and generated, you can build and run the examples from Visual Studio.
+variable to the parent folder of examples folder. Once the project is
+configured and generated, you can build and run the examples from Visual
+Studio.
 
 ## <a name="GettingHelp"></a> Getting help
 
diff --git a/docs/pages/jit.md b/docs/pages/jit.md
new file mode 100644
index 0000000000..8b5c783755
--- /dev/null
+++ b/docs/pages/jit.md
@@ -0,0 +1,102 @@
+ArrayFire JIT Code Generation {#jit}
+================
+
+The ArrayFire library offers JIT (Just In Time) compiling for elementwise
+arithmetic operations. This includes trigonometric functions, comparisons, and
+element-wise operations.
+
+At runtime, ArrayFire aggregates these function calls using an Abstract Syntax
+Tree (AST) data structure such that whenever a JIT-supported function is
+called, it is added into the AST for a given variable instance. The AST of the
+variable is computed if one of the following conditions is met:
+
+* an explication evaluation is required by the programmer using the
+  [eval](\ref af::eval) function, or
+* the variable is required to compute a different variable that is not
+  JIT-supported.
+
+When the above occurs, and the variable needs to be evaluated, the functions
+and variables in the AST data structure are used to create a single
+kernel. This is done by creating a customized kernel on-the-fly that is made
+up of all the functions in the AST. The customized function is then executed.
+
+This JIT compilation technique has multiple benefits:
+
+* A reduced number of kernel calls – a kernel call can be a significant
+  overhead for small data sets.
+* Better cache performance – there are many instances in which the memory
+  required by a single element in the array can be reused multiple times, or
+  the temporary value of a computation can be stored in the cache and reused
+  by future computations.
+* Temporary memory allocation and write-back can be reduced – when multiple
+  expressions are evaluated and stored into temporary arrays, these arrays
+  need to be allocated and the results written back to main memory.
+* Avoid computing elements that are not used – there are cases in which the
+  AST is created for a variable; however, the expression is not used later in
+  the computation. Thus, its evaluation can be avoided.
+* Better performance – all the above can help reduce the total execution time.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+// As JIT is automatically enabled in ArrayFire, this version of the function
+// forces each expression to be evaluated. If the eval() function calls are
+// removed, then the execution of this code would be equivalent to the
+// following function.
+
+static double pi_no_jit(array x, array y, array temp, int samples) {
+        temp = x * x;
+        temp.eval();
+        temp += y * y;
+        temp.eval();
+        temp = sqrt(temp);
+        temp.eval();
+        temp = temp < 1;
+        temp.eval();
+        return 4.0 sum(temp)/samples;
+}
+
+static double pi_jit(array x, array y, array temp,int samples){
+        temp = sqrt(x*x + y*y) < 1;
+        temp.eval();
+        return 4.0 * sum(temp) / samples;
+}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The above code computes the value of π using a Monte-Carlo simulation where
+points are randomly generated within the unit square. Each point is tested to
+see if it is within the unit circle. The ratio of points within the circle and
+square approximate the value π. The accuracy of π improves as the number of
+samples is increased, which motivates using additional samples.
+
+There are two implementations above:
+1. an implementation that does not benefit from the JIT (pi\_no\_jit), and
+2. an implementation that takes advantage of the JIT feature (pi\_jit).
+
+Specifically, as JIT is an integral feature of the ArrayFire library, it
+cannot simply be turned on and off. The only way for a programmer to sidestep
+the JIT operations is to manually force the evaluation of expressions. This is
+done in the non-JIT-supported implementation.
+
+Timing these two implementations results in the following performance
+benchmark:
+
+<img src="jit_cuda1.webp" alt="Performance of JIT and Non-JIT implementations"
+width="100%" />
+
+
+The above figure depicts the execution time (abscissa) as a function of the
+number of samples (ordinate) for the two implementations discussed above.
+
+When the number of samples is small, the execution time of pi\_no\_jit is
+dominated by the launch of multiple kernels and the execution time pi\_jit is
+dominated by on-the-fly compilation of the JIT code required to launch a
+single kernel. Even with this JIT compilation time, pi\_jit outperforms
+pi_no_jit by 1.4-2.0X for smaller sample sizes.
+
+When the number of samples is large, both the kernel launch overhead and the
+JIT code creation are no longer the limiting factors – the kernel’s
+computational load dominates the execution time. Here, the pi\_jit outperforms
+pi\_no\_jit by 2.0-2.7X.
+
+The number of applications that benefit from the JIT code generation is
+significant. The actual performance benefits are also application-dependent.
+
diff --git a/docs/pages/timing.md b/docs/pages/timing.md
index fc9b1a725f..8c43808a5c 100644
--- a/docs/pages/timing.md
+++ b/docs/pages/timing.md
@@ -1,64 +1,153 @@
-Timing Your Code {#timing}
+Timing ArrayFire Code {#timing}
 ================
 
-timer() : A platform-independent timer with microsecond accuracy:
-* [timer::start()](\ref af::timer::start) starts a timer
+In performance-sensitive applications, it is vital to profile and measure the
+execution time of operations. ArrayFire provides mechanisms to achieve this.
 
-* [timer::start()](\ref af::timer::stop) seconds since last \ref af::timer::start "start"
+ArrayFire employs an asynchronous evaluation model for all of its
+functions. This means that operations are queued to execute but do not
+necessarily complete prior to function return. Hence, directly measuring the
+time taken for an ArrayFire function could be misleading. To accurately
+measure time, one must ensure the operations are evaluated and synchronize the
+ArrayFire stream.
 
-* \ref af::timer::stop(af::timer start) "timer::stop(timer start)" seconds since 'start'
+ArrayFire also employs a lazy evaluation model for its elementwise arithmetic
+operations. This means operations are not queued for execution until the
+result is needed by downstream operations blocking until the operations are
+complete.
 
-Example: single timer
+The following describes how to time ArrayFire code using the eval and sync
+functions along with the timer and timeit functions. A final note on kernel
+caching also provides helpful details about ArrayFire runtimes.
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-   // start timer
-   timer::start();
-   // run your code
-   printf("elapsed seconds: %g\n", timer::stop());
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Using ArrayFire eval and sync functions
 
-Example: multiple timers
+ArrayFire provides functions to force the evaluation of lazy functions and to
+block until all asynchoronous operations complete.
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-   // start timers
-   timer start1 = timer::start();
-   timer start2 = timer::start();
-   // run some code
-   printf("elapsed seconds: %g\n", timer::stop(start1));
-   // run more code
-   printf("elapsed seconds: %g\n", timer::stop(start2));
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+1. The [eval](\ref af::eval) function:
 
-Accurate and reliable measurement of performance involves several factors:
-* Executing enough iterations to achieve peak performance.
-* Executing enough repetitions to amortize any overhead from system timers.
+   Forces the evaluation of an ArrayFire array. It ensures the execution of
+   operations queued up for a specific array.
 
-To take care of much of this boilerplate, [timeit](\ref af::timeit) provides
-accurate and reliable estimates of both CPU or GPU code.
+   It is only required for timing purposes if elementwise arithmetic functions
+   are called on the array, since these are handled by the ArrayFire JIT.
 
-Here`s a stripped down example of
-[Monte-Carlo estimation of PI](\ref benchmarks/pi.cpp) making use
-of [timeit](\ref af::timeit).  Notice how it expects a `void` function pointer.
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+   af::array A = af::randu(1000, 1000);
+   af::array B = A + A;                 // Elementwise arithmetic operation.
+   B.eval();                            // Forces evaluation of B.
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
-#include <stdio.h>
-#include <arrayfire.h>
-using namespace af;
+   The function initializes the evaluation of the JIT-tree for that array and
+   may return prior to the completion of those operations. To ensure proper
+   timing, combine with a [sync](\ref af::sync) function.
 
-void pi_function() {
-  int n = 20e6; // 20 million random samples
-  array x = randu(n,f32), y = randu(n,f32);
-  // how many fell inside unit circle?
-  float pi = 4.0 * sum<float>(sqrt(x*x + y*y)) < 1) / n;
-}
+2. The [sync](\ref af::sync) function:
 
-int main() {
-  printf("pi_function took %g seconds\n", timeit(pi_function));
-  return 0;
-}
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+   Synchronizes the ArrayFire stream. It waits for all the previous operations
+   in the stream to finish. It is often used after [eval](\ref af::eval) to
+   ensure that operations have indeed been completed.
 
-This produces:
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+   af::sync();  // Waits for all previous operations to complete.
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-    pi_function took 0.007252 seconds
-    (test machine: Core i7 920 @ 2.67GHz with a Tesla C2070)
+## Using ArrayFire timer and timeit functions
+
+ArrayFire provides a simple timer functions that returns the current time in
+seconds.
+
+1. The [timer](\ref af::timer) function:
+
+   timer() : A platform-independent timer with microsecond accuracy:
+   * [timer::start()](\ref af::timer::start) starts a timer
+
+   * [timer::start()](\ref af::timer::stop) seconds since last \ref
+     af::timer::start "start"
+
+   * \ref af::timer::stop(af::timer start) "timer::stop(timer start)" seconds
+     since 'start'
+
+   Example: single timer
+
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+       // start timer
+       // - be sure to use the eval and sync functions so that previous code
+       //   does not get timed as part of the execution segment being measured
+       timer::start();
+       // run a code segment
+       // - be sure to use the eval and sync functions to ensure the code
+       //   segment operations have been completed
+       // stop timer
+       printf("elapsed seconds: %g\n", timer::stop());
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+   Example: multiple timers
+
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+       // start timers
+       // - be sure to use the eval and sync functions so that previous code
+       //   does not get timed as part of the execution segment being measured
+       timer start1 = timer::start();
+       timer start2 = timer::start();
+       // run a code segment
+       // - be sure to use the eval and sync functions to ensure the code
+       //   segment operations have been completed
+       // stop timer1
+       printf("elapsed seconds: %g\n", timer::stop(start1));
+       // run another code segment
+       // - be sure to use the eval and sync functions to ensure the code
+       //   segment operations have been completed
+       // stop timer2
+       printf("elapsed seconds: %g\n", timer::stop(start2));
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+   Accurate and reliable measurement of performance involves several factors:
+   * Executing enough iterations to achieve peak performance.
+   * Executing enough repetitions to amortize any overhead from system timers.
+
+2. The [timeit](\ref af::timeit) function:
+
+   To take care of much of this boilerplate, [timeit](\ref af::timeit) provides
+   accurate and reliable estimates of both CPU or GPU code.
+
+   Here is a stripped down example of [Monte-Carlo estimation of PI](\ref
+   benchmarks/pi.cpp) making use of [timeit](\ref af::timeit). Notice how it
+   expects a `void` function pointer.
+
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
+   #include <stdio.h>
+   #include <arrayfire.h>
+   using namespace af;
+
+   void pi_function() {
+     int n = 20e6; // 20 million random samples
+     array x = randu(n, f32), y = randu(n, f32);
+     // how many fell inside unit circle?
+     float pi = 4.0 * sum<float>(sqrt(x*x + y*y)) < 1) / n;
+   }
+
+   int main() {
+     printf("pi_function took %g seconds\n", timeit(pi_function));
+     return 0;
+   }
+   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+   This produces:
+
+       pi_function took 0.007252 seconds
+       (test machine: Core i7 920 @ 2.67GHz with a Tesla C2070)
+
+
+## A note on kernel caching
+
+The first run of ArrayFire code exercises any JIT compilation in the
+application, automatically saving a cache of the compilation to
+disk. Subsequent runs load the cache from disk, executing without
+compilation. Therefore, it is typically best to "warm up" the code with one
+run to initiate the application's kernel cache. Afterwards, subsequent runs do
+not include the compile time and are tend to be faster than the first run.
+
+Averaging the time taken is always the best approach and one reason why the
+[timeit](\ref af::timeit) function is helpful.
diff --git a/docs/pages/tutorials.md b/docs/pages/tutorials.md
index f6056b8e19..34b65be12c 100644
--- a/docs/pages/tutorials.md
+++ b/docs/pages/tutorials.md
@@ -15,4 +15,5 @@
 * [Timing ArrayFire](\ref timing)
 * [Configuring ArrayFire Environment](\ref configuring_environment)
 * [Debugging ArrayFire Code](\ref debugging)
+* [ArrayFire JIT Code Generation](\ref jit)
 * [GFOR Usage](\ref page_gfor)

From 02ce5cb169762effe6aa793441227503167b3f61 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Aug 2023 19:36:57 -0400
Subject: [PATCH 2575/2677] Update release notes for v3.9 v3.8.3 and update
 docs to include oneAPI

---
 README.md                                     |  3 +-
 docs/pages/README.md                          |  8 +-
 .../configuring_arrayfire_environment.md      | 10 +++
 docs/pages/getting_started.md                 | 13 ++--
 docs/pages/install.md                         |  2 +-
 docs/pages/release_notes.md                   | 69 ++++++++++++++++++
 docs/pages/unified_backend.md                 | 73 ++++++++++++-------
 docs/pages/using_on_linux.md                  |  7 +-
 docs/pages/using_on_windows.md                |  1 +
 9 files changed, 145 insertions(+), 41 deletions(-)

diff --git a/README.md b/README.md
index c56f29623f..fed0820455 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,8 @@ Several of ArrayFire's benefits include:
 * [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable,
   [well-documented](http://arrayfire.org/docs) API
 * Rigorous benchmarks and tests ensuring top performance and numerical accuracy
-* Cross-platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux
+* Cross-platform compatibility with support for CUDA, oneAPI, OpenCL, and
+   native CPU on Windows, Mac, and Linux
 * Built-in visualization functions through [Forge](https://github.com/arrayfire/forge)
 * Commercially friendly open-source licensing
 * Enterprise support from [ArrayFire](http://arrayfire.com)
diff --git a/docs/pages/README.md b/docs/pages/README.md
index 08cc17578d..7c22adf87c 100644
--- a/docs/pages/README.md
+++ b/docs/pages/README.md
@@ -57,8 +57,7 @@ and 32-bit signed and unsigned integers.
 #### Extending ArrayFire
 
 ArrayFire can be used as a stand-alone application or integrated with existing
-CUDA, oneAPI, or OpenCL code. All ArrayFire `arrays` can be interchanged with
-other CUDA, oneAPI, or OpenCL data structures.
+CUDA, oneAPI, or OpenCL code.
 
 ## Code once, run anywhere!
 
@@ -100,9 +99,8 @@ your application.
 
 ## Simple Example
 
-Here is an example of ArrayFire code. First, [arrays](\ref af::array) are
-created which reside on CUDA, oneAPI, or OpenCL devices. Then
-[ArrayFire functions](modules.htm) are used on those [arrays](\ref af::array).
+Here is an example of ArrayFire code that performs a Monte Carlo estimation of
+PI.
 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cpp}
 // sample 40 million points on the GPU
diff --git a/docs/pages/configuring_arrayfire_environment.md b/docs/pages/configuring_arrayfire_environment.md
index fd11628105..7b20be9b4a 100644
--- a/docs/pages/configuring_arrayfire_environment.md
+++ b/docs/pages/configuring_arrayfire_environment.md
@@ -38,6 +38,16 @@ variable are the device identifiers shown when af::info is run.
 AF_CUDA_DEFAULT_DEVICE=1 ./myprogram_cuda
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+AF_ONEAPI_DEFAULT_DEVICE {#af_oneapi_default_device}
+-------------------------------------------------------------------------------
+
+Use this variable to set the default oneAPI device. Valid values for this
+variable are the device identifiers shown when af::info is run.
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AF_ONEAPI_DEFAULT_DEVICE=1 ./myprogram_oneapi
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 Note: af::setDevice call in the source code will take precedence over this
 variable.
 
diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index d958892c2e..19660f8cc8 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -24,6 +24,7 @@ can represent one of many different [basic data types](\ref af_dtype):
 * [c32](\ref c32) complex single-precision (`cfloat`)
 * [f64](\ref f64) real double-precision (`double`)
 * [c64](\ref c64) complex double-precision (`cdouble`)
+* [f16](\ref f16) real half-precision (`half_float::half`)
 * [b8](\ref b8) 8-bit boolean values (`bool`)
 * [s32](\ref s32) 32-bit signed integer (`int`)
 * [u32](\ref u32) 32-bit unsigned integer (`unsigned`)
@@ -153,11 +154,11 @@ using the `af::` namespace.
 
 # Indexing {#getting_started_indexing}
 
-Like all functions in ArrayFire, indexing is also executed in parallel on
-the OpenCL/CUDA device.
-Because of this, indexing becomes part of a JIT operation and is accomplished
-using parentheses instead of square brackets (i.e. as `A(0)` instead of `A[0]`).
-To index `af::array`s you may use one or a combination of the following functions:
+Like all functions in ArrayFire, indexing is also executed in parallel on the
+OpenCL/CUDA devices. Because of this, indexing becomes part of a JIT operation
+and is accomplished using parentheses instead of square brackets (i.e. as `A(0)`
+instead of `A[0]`). To index `af::array`s you may use one or a combination of
+the following functions:
 
 * integer scalars
 * [seq()](\ref af::seq) representing a linear sequence
@@ -223,7 +224,7 @@ simply include the `arrayfire.h` header file and start coding!
         double result;
         af_sum_all(&result, 0, a);
         printf("sum: %g\n", result);
-        
+
         return 0;
     }
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/pages/install.md b/docs/pages/install.md
index a0b3af61b3..555e702a1b 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -20,7 +20,7 @@ device vendor’s website.
 ## Windows {#Windows}
 
 Prior to installing ArrayFire on Windows,
-[download](https://www.microsoft.com/en-in/download/details.aspx?id=48145) and
+[download](https://www.microsoft.com/download/details.aspx?id=48145)
 install the Visual Studio 2015 (x64) runtime libraries.
 
 Once the ArrayFire installer has been downloaded, run the installer. If you
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index bc40f2a7b7..464eba664d 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,75 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.9.0
+======
+
+## Improvements
+- Add oneAPI backend \PR{3296}
+- Add support to directly access arrays on other devices \PR{3447}
+- Add broadcast support \PR{2871}
+- Improve OpenCL CPU JIT performance \PR{3257} \PR{3392}
+- Optimize thread/block calculations of several kernels \PR{3144}
+- Add support for fast math compiliation when building ArrayFire \PR{3334 \PR{3337}
+- Optimize performance of fftconvolve when using floats \PR{3338}
+- Add support for CUDA 12.1 and 12.2
+- Better handling of empty arrays \PR{3398}
+- Better handling of memory in linear algebra functions in OpenCL \PR{3423}
+- Better logging with JIT kernels \PR{3468}
+- Optimize memory manager/JIT interactions for small number of buffers \PR{3468}
+- Documentation improvements \PR{3485}
+- Optimize reorder function \PR{3488}
+
+## Fixes
+- Improve Errors when creating OpenCL contexts from devices \PR{3257}
+- Improvements to vcpkg builds \PR{3376 \PR{3476}
+- Fix reduce by key when nan's are present \PR{3261}
+- Fix error in convolve where the ndims parameter was forced to be equal to 2  \PR{3277}
+- Make constructors that accept dim_t to be explicit to avoid invalid conversions  \PR{3259} 
+- Fix error in randu when compiling against clang 14 \PR{3333} 
+- Fix bug in OpenCL linear algebra functions  \PR{3398}
+- Fix bug with thread local variables when device was changed \PR{3420} \PR{3421} 
+- Fix bug in qr related to uninitialized memory \PR{3422} 
+- Fix bug in shift where the array had an empty middle dimension \PR{3488}
+
+
+## Contributions
+
+Special thanks to our contributors:
+[Willy Born](https://github.com/willyborn)
+[Mike Mullen](https://github.com/mfzmullen)
+
+v3.8.3
+======
+
+## Improvements
+
+- Add support for CUDA 12 \PR{3352}
+- Modernize documentation style and content \PR{3351}
+- memcpy performance improvements \PR{3144}
+- JIT performance improvements \PR{3144}
+- join performance improvements \PR{3144}
+- Improve support for Intel and newer Clang compilers \PR{3334}
+- CCache support on Windows \PR{3257}
+
+## Fixes
+
+- Fix issue with some locales with OpenCL kernel generation \PR{3294}
+- Internal improvements
+- Fix leak in clfft on exit.
+- Fix some cases where ndims was incorrectly used ot calculate shape \PR{3277}
+- Fix issue when setDevice was not called in new threads \PR{3269}
+- Restrict initializer list to just fundamental types \PR{3264}
+
+## Contributions
+
+Special thanks to our contributors:
+[Carlo Cabrera](https://github.com/carlocab)
+[Guillaume Schmid](https://github.com/GuillaumeSchmid)
+[Willy Born](https://github.com/willyborn)
+[ktdq](https://github.com/ktdq)
+
+
 v3.8.2
 ======
 
diff --git a/docs/pages/unified_backend.md b/docs/pages/unified_backend.md
index 6924f92707..5a99bff8f4 100644
--- a/docs/pages/unified_backend.md
+++ b/docs/pages/unified_backend.md
@@ -7,7 +7,7 @@ Unified Backend {#unifiedbackend}
 
 The Unified backend was introduced in ArrayFire with version 3.2.
 While this is not an independent backend, it allows the user to switch between
-the different ArrayFire backends (CPU, CUDA and OpenCL) at runtime.
+the different ArrayFire backends (CPU, CUDA, oneAPI and OpenCL) at runtime.
 
 # Compiling with Unified
 
@@ -24,7 +24,7 @@ To use with CMake, use the __ArrayFire_Unified_LIBRARIES__ variable.
 # Using the Unified Backend
 
 The Unified backend will try to dynamically load the backend libraries. The
-priority of backends is __CUDA -> OpenCL -> CPU__
+priority of backends is __CUDA -> oneAPI -> OpenCL -> CPU__
 
 The most important aspect to note here is that all the libraries the ArrayFire
 libs depend on need to be in the environment paths
@@ -78,6 +78,15 @@ int main()
         fprintf(stderr, "%s\n", e.what());
     }
 
+    try {
+        printf("Trying oneAPI Backend\n");
+        af::setBackend(AF_BACKEND_ONEAPI);
+        testBackend();
+    } catch (af::exception& e) {
+        printf("Caught exception when trying oneAPI backend\n");
+        fprintf(stderr, "%s\n", e.what());
+    }
+
     try {
         printf("Trying CUDA Backend\n");
         af::setBackend(AF_BACKEND_CUDA);
@@ -103,39 +112,53 @@ int main()
 This output would be:
 
     Trying CPU Backend
-    ArrayFire v3.2.0 (CPU, 64-bit Linux, build fc7630f)
-    [0] Intel: Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz Max threads(8)
+    ArrayFire v3.9.0 (CPU, 64-bit Linux, build 23ee0650e)
+    [0] AMD: AMD Ryzen Threadripper PRO 3955WX 16-Cores     af::randu(5, 4)
+    [5 4 1 1]
+        0.6010     0.5497     0.1583     0.3636
+        0.0278     0.2864     0.3712     0.4165
+        0.9806     0.3410     0.3543     0.5814
+        0.2126     0.7509     0.6450     0.8962
+        0.0655     0.4105     0.9675     0.3712
+
+    Trying oneAPI Backend
+    ArrayFire v3.9.0 (oneAPI, 64-bit Linux, build 23ee0650e)
+    [0] Intel(R) OpenCL: AMD Ryzen Threadripper PRO 3955WX 16-Cores     , 128650 MB (fp64)
     af::randu(5, 4)
     [5 4 1 1]
-        0.0000     0.2190     0.3835     0.5297
-        0.1315     0.0470     0.5194     0.6711
-        0.7556     0.6789     0.8310     0.0077
-        0.4587     0.6793     0.0346     0.3834
-        0.5328     0.9347     0.0535     0.0668
+        0.6010     0.5497     0.1583     0.3636
+        0.0278     0.2864     0.3712     0.4165
+        0.9806     0.3410     0.3543     0.5814
+        0.2126     0.7509     0.6450     0.8962
+        0.0655     0.4105     0.9675     0.3712
 
     Trying CUDA Backend
-    ArrayFire v3.2.0 (CUDA, 64-bit Linux, build fc7630f)
-    Platform: CUDA Toolkit 7.5, Driver: 355.11
-    [0] Quadro K5000, 4093 MB, CUDA Compute 3.0
+    ArrayFire v3.9.0 (CUDA, 64-bit Linux, build 23ee0650e)
+    Platform: CUDA Runtime 12.2, Driver: 535.104.05
+    [0] NVIDIA RTX A5500, 22721 MB, CUDA Compute 8.6
+    -1- NVIDIA RTX A5500, 22719 MB, CUDA Compute 8.6
     af::randu(5, 4)
     [5 4 1 1]
-        0.7402     0.4464     0.7762     0.2920
-        0.9210     0.6673     0.2948     0.3194
-        0.0390     0.1099     0.7140     0.8109
-        0.9690     0.4702     0.3585     0.1541
-        0.9251     0.5132     0.6814     0.4452
+        0.6010     0.5497     0.1583     0.3636
+        0.0278     0.2864     0.3712     0.4165
+        0.9806     0.3410     0.3543     0.5814
+        0.2126     0.7509     0.6450     0.8962
+        0.0655     0.4105     0.9675     0.3712
 
     Trying OpenCL Backend
-    ArrayFire v3.2.0 (OpenCL, 64-bit Linux, build fc7630f)
-    [0] NVIDIA  : Quadro K5000
-    -1- INTEL   : Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz
+    ArrayFire v3.9.0 (OpenCL, 64-bit Linux, build 23ee0650e)
+    [0] NVIDIA: NVIDIA RTX A5500, 22720 MB
+    -1- NVIDIA: NVIDIA RTX A5500, 22718 MB
+    -2- Intel(R) FPGA Emulation Platform for OpenCL(TM): Intel(R) FPGA Emulation Device, 128650 MB
+    -3- INTEL: AMD Ryzen Threadripper PRO 3955WX 16-Cores     , 128650 MB
     af::randu(5, 4)
     [5 4 1 1]
-        0.4107     0.0081     0.6600     0.1046
-        0.8224     0.3775     0.0764     0.8827
-        0.9518     0.3027     0.0901     0.1647
-        0.1794     0.6456     0.5933     0.8060
-        0.4198     0.5591     0.1098     0.5938
+        0.6010     0.5497     0.1583     0.3636
+        0.0278     0.2864     0.3712     0.4165
+        0.9806     0.3410     0.3543     0.5814
+        0.2126     0.7509     0.6450     0.8962
+        0.0655     0.4105     0.9675     0.3712
+
 
 # Dos and Don'ts
 
diff --git a/docs/pages/using_on_linux.md b/docs/pages/using_on_linux.md
index 0fcd23bba1..7dbff74d2a 100644
--- a/docs/pages/using_on_linux.md
+++ b/docs/pages/using_on_linux.md
@@ -15,7 +15,7 @@ installer will populate files in the following sub-directories:
 
     include/arrayfire.h         - Primary ArrayFire include file
     include/af/*.h              - Additional include files
-    lib/libaf*                  - CPU, CUDA, and OpenCL libraries (.a, .so)
+    lib/libaf*                  - CPU, CUDA, oneAPI and OpenCL libraries (.a, .so)
     lib/libforge*               - Visualization library
     lib/libcu*                  - CUDA backend dependencies
     lib/libOpenCL.so            - OpenCL ICD Loader library
@@ -81,6 +81,7 @@ how to use CMake. To link with a specific backend directly, replace the
 
 * `ArrayFire::afcpu` for CPU backend.
 * `ArrayFire::afcuda` for CUDA backend.
+* `ArrayFire::afoneapi` for oneAPI backend.
 * `ArrayFire::afopencl` for OpenCL backend.
 
 Next we need to instruct CMake to create build instructions and then compile. We
@@ -116,8 +117,8 @@ directory containing `arrayfire.h` file. This should be `-I
 Similarly, you will need to specify the path to the ArrayFire library using the
 `-L` option (e.g. `-L/opt/arrayfire/lib`) followed by the specific ArrayFire
 library you wish to use using the `-l` option (for example `-lafcpu`,
-`-lafopencl`, `-lafcuda`, or `-laf` for the CPU, OpenCL, CUDA, and unified
-backends, respectively.
+`-lafopencl`, `-lafoneapi`, `-lafcuda`, or `-laf` for the CPU, OpenCL, oneAPI
+and CUDA, and unified backends, respectively.
 
 Here is a minimal example Makefile which uses ArrayFire's CPU backend:
 
diff --git a/docs/pages/using_on_windows.md b/docs/pages/using_on_windows.md
index b178ad9c86..072445a4ae 100644
--- a/docs/pages/using_on_windows.md
+++ b/docs/pages/using_on_windows.md
@@ -141,6 +141,7 @@ how to use CMake. To link with a specific backend directly, replace the
 
 * `ArrayFire::afcpu` for CPU backend.
 * `ArrayFire::afcuda` for CUDA backend.
+* `ArrayFire::afoneapi` for oneAPI backend.
 * `ArrayFire::afopencl` for OpenCL backend.
 
 Next we need to instruct CMake to create build instructions and then compile. We

From 9b9acea3aee7273e1bcde53fcb3da20fe55933f7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Aug 2023 19:37:38 -0400
Subject: [PATCH 2576/2677] Add FMT_HEADER_ONLY definition when SPDLOG is set
 to header only

---
 CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index deafa7a759..12d6e557c9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -279,6 +279,7 @@ else()
   if(AF_WITH_SPDLOG_HEADER_ONLY)
     set_target_properties(af_spdlog
       PROPERTIES
+        INTERFACE_COMPILE_DEFINITIONS "FMT_HEADER_ONLY=1"
         INTERFACE_LINK_LIBRARIES "spdlog_header_only")
   else()
     target_compile_options(spdlog

From 67bd7499d6ba9eadaa7c95d139d6f05bed7d5430 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Aug 2023 19:39:43 -0400
Subject: [PATCH 2577/2677] Fix namespace for isnan in oneAPI math header

---
 src/backend/oneapi/math.hpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index b6aba91663..4a3c8b41b2 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -83,22 +83,22 @@ inline auto is_nan<sycl::half>(const sycl::half &val) -> bool {
 
 template<>
 inline auto is_nan<float>(const float &val) -> bool {
-    return std::isnan(val);
+    return sycl::isnan(val);
 }
 
 template<>
 inline auto is_nan<double>(const double &val) -> bool {
-    return std::isnan(val);
+    return sycl::isnan(val);
 }
 
 template<>
 inline auto is_nan<cfloat>(const cfloat &in) -> bool {
-    return std::isnan(real(in)) || std::isnan(imag(in));
+    return sycl::isnan(real(in)) || sycl::isnan(imag(in));
 }
 
 template<>
 inline auto is_nan<cdouble>(const cdouble &in) -> bool {
-    return std::isnan(real(in)) || std::isnan(imag(in));
+    return sycl::isnan(real(in)) || sycl::isnan(imag(in));
 }
 
 template<typename T>

From bada0467a3158f50b8941f0845e690b0262e9469 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Aug 2023 19:43:07 -0400
Subject: [PATCH 2578/2677] Fix LIBRARY_SUFFIXES in FindAF_MKL to find MKL
 kernel libraries

---
 CMakeModules/FindAF_MKL.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/FindAF_MKL.cmake b/CMakeModules/FindAF_MKL.cmake
index 662f0046da..a58809d495 100644
--- a/CMakeModules/FindAF_MKL.cmake
+++ b/CMakeModules/FindAF_MKL.cmake
@@ -221,7 +221,7 @@ function(find_mkl_library)
   add_library(MKL::${mkl_args_NAME}_STATIC STATIC IMPORTED)
 
   if(NOT (WIN32 AND mkl_args_DLL_ONLY))
-    list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES ".so.1")
+    list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES ".so.1;.so.2;.so.3;.so.4;.so.12")
     find_library(MKL_${mkl_args_NAME}_LINK_LIBRARY
       NAMES
         ${mkl_args_LIBRARY_NAME}${shared_suffix}

From b59a1ae535da369db86451e5b28a7bc0eaf3e84a Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Mon, 28 Aug 2023 19:49:55 -0400
Subject: [PATCH 2579/2677] Pass OPENCL_LIBRARIES to CLBlast ExternalProject
 command

---
 CMakeModules/build_CLBlast.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 0f67d3fdee..933531cdf2 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -75,6 +75,7 @@ else()
         -DCMAKE_C_COMPILER:FILEPATH=${CMAKE_C_COMPILER}
         "-DCMAKE_C_FLAGS:STRING=${CMAKE_C_FLAGS}"
         -DCMAKE_POSITION_INDEPENDENT_CODE=ON
+        -DOPENCL_LIBRARIES="${OPENCL_LIBRARIES}"
         ${extproj_build_type_option}
         -DCMAKE_INSTALL_PREFIX:PATH=<INSTALL_DIR>
         -DCMAKE_INSTALL_LIBDIR:PATH=lib

From 4061db86e66306995175a14cf906c55c35373918 Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Wed, 4 Oct 2023 16:25:56 -0400
Subject: [PATCH 2580/2677] Update README.md to add Intel to GPUs and to fix
 word wrapping

---
 README.md | 54 +++++++++++++++++++++++++++++++-----------------------
 1 file changed, 31 insertions(+), 23 deletions(-)

diff --git a/README.md b/README.md
index fed0820455..eb6dc6a5f6 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,15 @@
-
 <p align="center"><a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="800"></a></p>
 
-ArrayFire is a general-purpose tensor library that simplifies the process of
-software development for the parallel architectures found in CPUs, GPUs, and
-other hardware acceleration devices. The library serves users in every technical
-computing market.
+ArrayFire is a general-purpose tensor library that simplifies the software
+development process for the parallel architectures found in CPUs, GPUs, and
+other hardware acceleration devices. The library serves users in every
+technical computing market.
 
 Several of ArrayFire's benefits include:
 
-* Hundreds of accelerated [tensor computing functions](https://arrayfire.org/docs/group__arrayfire__func.htm), in the following areas:
+* Hundreds of accelerated [tensor computing
+  functions](https://arrayfire.org/docs/group__arrayfire__func.htm), in the
+  following areas:
     * Array handling
     * Computer vision
     * Image processing
@@ -22,8 +23,9 @@ Several of ArrayFire's benefits include:
   [well-documented](http://arrayfire.org/docs) API
 * Rigorous benchmarks and tests ensuring top performance and numerical accuracy
 * Cross-platform compatibility with support for CUDA, oneAPI, OpenCL, and
-   native CPU on Windows, Mac, and Linux
-* Built-in visualization functions through [Forge](https://github.com/arrayfire/forge)
+  native CPU on Windows, Mac, and Linux
+* Built-in visualization functions through
+  [Forge](https://github.com/arrayfire/forge)
 * Commercially friendly open-source licensing
 * Enterprise support from [ArrayFire](http://arrayfire.com)
 
@@ -34,19 +36,22 @@ translated into near-optimal kernels that execute on the computational device.
 
 ArrayFire runs on devices ranging from low-power mobile phones to high-power
 GPU-enabled supercomputers. ArrayFire runs on CPUs from all major vendors
-(Intel, AMD, ARM), GPUs from the prominent manufacturers (NVIDIA, AMD, and
-Qualcomm), as well as a variety of other accelerator devices on Windows, Mac,
-and Linux.
+(Intel, AMD, ARM), GPUs from the prominent manufacturers (AMD, Intel, NVIDIA,
+and Qualcomm), as well as a variety of other accelerator devices on Windows,
+Mac, and Linux.
 
 # Getting ArrayFire
 
-Instructions to [install][32] or to build ArrayFire from source can be found on the [wiki][1].
+Instructions to [install][32] or to build ArrayFire from source can be found on
+the [wiki][1].
 
 ### Conway's Game of Life Using ArrayFire
 
 Visit the [Wikipedia page][2] for a description of Conway's Game of Life.
 
-<img align="left" src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif" alt="Conway's Game of Life" height="256" width="256">
+<img align="left"
+src="https://github.com/arrayfire/assets/blob/master/gifs/conway.gif"
+alt="Conway's Game of Life" height="256" width="256">
 
 ```cpp
 static const float h_kernel[] = { 1, 1, 1, 1, 0, 1, 1, 1, 1 };
@@ -66,7 +71,9 @@ The complete source code can be found [here][3].
 
 ### Perceptron
 
-<img align="left" src="https://github.com/arrayfire/assets/blob/imgs_readme_improv/gifs/perceptron.gif" alt="Perceptron" height="400" width="300">
+<img align="left"
+src="https://github.com/arrayfire/assets/blob/imgs_readme_improv/gifs/perceptron.gif"
+alt="Perceptron" height="400" width="300">
 
 ```cpp
 array predict(const array &X, const array &W) {
@@ -132,9 +139,10 @@ Mission](https://github.com/arrayfire/arrayfire/wiki/The-ArrayFire-Mission-State
 for fast scientific computing for all.
 
 Contributions of any kind are welcome! Please refer to [the
-wiki](https://github.com/arrayfire/arrayfire/wiki) and our [Code of Conduct](33)
-to learn more about how you can get involved with the ArrayFire Community
-through [Sponsorship](https://github.com/arrayfire/arrayfire/wiki/Sponsorship),
+wiki](https://github.com/arrayfire/arrayfire/wiki) and our [Code of
+Conduct](33) to learn more about how you can get involved with the ArrayFire
+Community through
+[Sponsorship](https://github.com/arrayfire/arrayfire/wiki/Sponsorship),
 [Developer
 Commits](https://github.com/arrayfire/arrayfire/wiki/Contributing-Code-to-ArrayFire),
 or [Governance](https://github.com/arrayfire/arrayfire/wiki/Governance).
@@ -146,8 +154,8 @@ license](LICENSE). If you wish to cite ArrayFire in an academic publication,
 please use the following [citation document](.github/CITATION.md).
 
 ArrayFire development is funded by AccelerEyes LLC and several third parties,
-please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for an expression
-of our gratitude.
+please see the list of [acknowledgements](ACKNOWLEDGEMENTS.md) for an
+expression of our gratitude.
 
 # Support and Contact Info
 
@@ -157,10 +165,10 @@ of our gratitude.
 
 # Trademark Policy
 
-The literal mark "ArrayFire" and ArrayFire logos are trademarks of
-AccelerEyes LLC (dba ArrayFire).
-If you wish to use either of these marks in your own project, please consult
-[ArrayFire's Trademark Policy](http://arrayfire.com/trademark-policy/)
+The literal mark "ArrayFire" and ArrayFire logos are trademarks of AccelerEyes
+LLC (dba ArrayFire). If you wish to use either of these marks in your own
+project, please consult [ArrayFire's Trademark
+Policy](http://arrayfire.com/trademark-policy/)
 
 [1]: https://github.com/arrayfire/arrayfire/wiki
 [2]: https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life

From f4db00f2cc57272f16f0e7e8534f65c2a895cfda Mon Sep 17 00:00:00 2001
From: Filip Matzner <floop@floop.cz>
Date: Thu, 7 Mar 2024 13:54:29 +0100
Subject: [PATCH 2581/2677] Update toolkit driver versions for CUDA 12.4

---
 src/backend/cuda/device_manager.cpp | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 9e7cc2d68b..e3faf0376d 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,8 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12040, 9, 0, 0},
+    {12030, 9, 0, 0},
     {12020, 9, 0, 0},
     {12010, 9, 0, 0},
     {12000, 9, 0, 0},
@@ -140,9 +142,11 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
-        {12020, 525.60f, 527.41f},
-        {12010, 525.60f, 527.41f},
-        {12000, 525.60f, 527.41f},
+        {12040, 525.60f, 528.33f},
+        {12030, 525.60f, 528.33f},
+        {12020, 525.60f, 528.33f},
+        {12010, 525.60f, 528.33f},
+        {12000, 525.60f, 528.33f},
         {11080, 450.80f, 452.39f},
         {11070, 450.80f, 452.39f},
         {11060, 450.80f, 452.39f},

From 48a97be35f3892044094172d8fe9db586ccb601a Mon Sep 17 00:00:00 2001
From: Edwin <edwinsolisf12@gmail.com>
Date: Wed, 12 Jun 2024 15:55:58 -0500
Subject: [PATCH 2582/2677] Fixed incompatibility issues with newer opencl hpp
 headers

---
 src/backend/opencl/device_manager.cpp |  9 ++++++---
 src/backend/opencl/platform.cpp       | 13 ++++++++++---
 2 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/device_manager.cpp b/src/backend/opencl/device_manager.cpp
index 1e628af521..62c06a21a5 100644
--- a/src/backend/opencl/device_manager.cpp
+++ b/src/backend/opencl/device_manager.cpp
@@ -261,8 +261,11 @@ DeviceManager::DeviceManager()
 
     // Create contexts and queues once the sort is done
     for (int i = 0; i < nDevices; i++) {
-        cl_platform_id device_platform =
-            devices[i]->getInfo<CL_DEVICE_PLATFORM>();
+        // For OpenCL-HPP >= v2023.12.14 type is cl::Platform instead of
+        // cl_platform_id
+        cl::Platform device_platform;
+        device_platform = devices[i]->getInfo<CL_DEVICE_PLATFORM>();
+
         try {
             mContexts.emplace_back(
                 make_unique<cl::Context>(mDeviceContextMap[*devices[i]]));
@@ -272,7 +275,7 @@ DeviceManager::DeviceManager()
             mDeviceTypes.push_back(getDeviceTypeEnum(*devices[i]));
             mPlatforms.push_back(
                 std::make_pair<std::unique_ptr<cl::Platform>, afcl_platform>(
-                    make_unique<cl::Platform>(device_platform, true),
+                    make_unique<cl::Platform>(device_platform(), true),
                     getPlatformEnum(*devices[i])));
             mDevices.emplace_back(std::move(devices[i]));
 
diff --git a/src/backend/opencl/platform.cpp b/src/backend/opencl/platform.cpp
index d6406a32e1..b6886c97bb 100644
--- a/src/backend/opencl/platform.cpp
+++ b/src/backend/opencl/platform.cpp
@@ -337,7 +337,11 @@ const std::string& getActiveDeviceBaseBuildFlags() {
 }
 
 vector<Version> getOpenCLCDeviceVersion(const Device& device) {
-    Platform device_platform(device.getInfo<CL_DEVICE_PLATFORM>(), false);
+    // For OpenCL-HPP >= v2023.12.14 type is cl::Platform instead of
+    // cl_platform_id
+    Platform device_platform;
+    device_platform = device.getInfo<CL_DEVICE_PLATFORM>();
+
     auto platform_version = device_platform.getInfo<CL_PLATFORM_VERSION>();
     vector<Version> out;
 
@@ -540,10 +544,13 @@ void addDeviceContext(cl_device_id dev, cl_context ctx, cl_command_queue que) {
         devMngr.mDeviceTypes.push_back(
             static_cast<int>(tDevice.getInfo<CL_DEVICE_TYPE>()));
 
-        auto device_platform = tDevice.getInfo<CL_DEVICE_PLATFORM>();
+        // For OpenCL-HPP >= v2023.12.14 type is cl::Platform instead of
+        // cl_platform_id
+        cl::Platform device_platform;
+        device_platform = tDevice.getInfo<CL_DEVICE_PLATFORM>();
         devMngr.mPlatforms.push_back(
             std::make_pair<std::unique_ptr<cl::Platform>, afcl_platform>(
-                make_unique<cl::Platform>(device_platform, true),
+                make_unique<cl::Platform>(device_platform(), true),
                 getPlatformEnum(tDevice)));
 
         devMngr.mDevices.emplace_back(make_unique<cl::Device>(move(tDevice)));

From cb09bfc5457489d6da434a7841b9098dded58cc0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Thu, 29 Aug 2024 19:29:59 -0500
Subject: [PATCH 2583/2677] Fix issue 3543: Explicitly added <climits> for C
 limits constants (#3592)

---
 src/backend/cpu/math.hpp    | 1 +
 src/backend/cuda/math.hpp   | 1 +
 src/backend/oneapi/math.hpp | 1 +
 src/backend/opencl/math.hpp | 1 +
 4 files changed, 4 insertions(+)

diff --git a/src/backend/cpu/math.hpp b/src/backend/cpu/math.hpp
index 16a4e2abbf..06c1027edf 100644
--- a/src/backend/cpu/math.hpp
+++ b/src/backend/cpu/math.hpp
@@ -15,6 +15,7 @@
 #include <af/defines.h>
 
 #include <algorithm>
+#include <climits>
 #include <limits>
 #include <numeric>
 
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index f7b11347cc..6986bcb445 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -18,6 +18,7 @@
 #endif  //__CUDACC__
 
 #include <algorithm>
+#include <climits>
 #include <limits>
 
 #endif  //__CUDACC_RTC__
diff --git a/src/backend/oneapi/math.hpp b/src/backend/oneapi/math.hpp
index 4a3c8b41b2..7362874442 100644
--- a/src/backend/oneapi/math.hpp
+++ b/src/backend/oneapi/math.hpp
@@ -18,6 +18,7 @@
 
 #include <algorithm>
 #include <complex>
+#include <climits>
 #include <limits>
 
 #if defined(__GNUC__) || defined(__GNUG__)
diff --git a/src/backend/opencl/math.hpp b/src/backend/opencl/math.hpp
index e4745d9e92..f164c3002c 100644
--- a/src/backend/opencl/math.hpp
+++ b/src/backend/opencl/math.hpp
@@ -18,6 +18,7 @@
 
 #include <algorithm>
 #include <complex>
+#include <climits>
 #include <limits>
 
 #if defined(__GNUC__) || defined(__GNUG__)

From bf233f381b46183c51dce66af8917f6ae70d7330 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Thu, 29 Aug 2024 19:31:25 -0500
Subject: [PATCH 2584/2677] Specified version for jasper, a subdependecy of
 freeimage, due to upstream build error (#3591)

---
 vcpkg.json | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/vcpkg.json b/vcpkg.json
index db3318eb47..6ca4ec32be 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -19,6 +19,10 @@
         {
             "name": "spdlog",
             "version": "1.9.2"
+        },
+        {
+            "name": "jasper",
+            "version": "4.2.0"
         }
     ],
     "features": {

From d3a6e2afcbbb26c23062531517e95d52eb7b7d84 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Thu, 29 Aug 2024 19:34:06 -0500
Subject: [PATCH 2585/2677] Update toolkit driver version for cuda 12.6 (#3586)

Note that this will be superseded by #3588
---
 src/backend/cuda/device_manager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index e3faf0376d..80f00f614a 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12060, 9, 0, 0},
     {12040, 9, 0, 0},
     {12030, 9, 0, 0},
     {12020, 9, 0, 0},
@@ -142,6 +143,7 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {12060, 525.60f, 528.33f},
         {12040, 525.60f, 528.33f},
         {12030, 525.60f, 528.33f},
         {12020, 525.60f, 528.33f},

From eefbb7c8c3df9e1ad11a55989d3a6a1e63113336 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 30 Aug 2024 10:07:43 -0500
Subject: [PATCH 2586/2677] Fix issue 3563: added message to dependency_check
 (#3564)

---
 test/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index cf7e66255f..92c4d90acd 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -20,7 +20,7 @@ if(AF_TEST_WITH_MTX_FILES)
 endif()
 
 if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
-    dependency_check(GTest_FOUND)
+  dependency_check(GTest_FOUND "Google Tests not found.")
 elseif(NOT TARGET GTest::gtest)
   af_dep_check_and_populate(${gtest_prefix}
     URI https://github.com/google/googletest.git

From 231a300eb11d770151ffdda3a6aa1d91d342cbe0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 30 Aug 2024 10:10:13 -0500
Subject: [PATCH 2587/2677] Fix issue 3556: cassert not being included for
 assert macro (#3557)

---
 src/backend/common/err_common.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index 3936cee77c..e1e4a6d118 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -17,6 +17,7 @@
 #include <common/defines.hpp>
 #include <af/defines.h>
 
+#include <cassert>
 #include <sstream>
 #include <stdexcept>
 #include <string>

From c9476633e82423645a83f6e1bca6027b356680a7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 30 Aug 2024 14:23:38 -0500
Subject: [PATCH 2588/2677] Fix for issue 3551: Implemented event_impl class
 for AF_DISABLE_CPU_ASYNC (#3555)

* Fix issue 3551: implemented empty event_impl class

* Applied clang format
---
 src/backend/cpu/queue.hpp | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/src/backend/cpu/queue.hpp b/src/backend/cpu/queue.hpp
index 594396a78e..cdcfb8092f 100644
--- a/src/backend/cpu/queue.hpp
+++ b/src/backend/cpu/queue.hpp
@@ -38,6 +38,42 @@ class queue_impl {
     }
 };
 
+class event_impl {
+   public:
+    event_impl() noexcept                              = default;
+    ~event_impl() noexcept                             = default;
+    explicit event_impl(const event_impl &other)       = default;
+    event_impl(event_impl &&other) noexcept            = default;
+    event_impl &operator=(event_impl &&other) noexcept = default;
+    event_impl &operator=(event_impl &other) noexcept  = default;
+
+    explicit event_impl(const int val) {}
+
+    event_impl &operator=(int val) noexcept { return *this; }
+
+    int create() {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+        return 0;
+    }
+
+    int mark(queue_impl &queue) {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+        return 0;
+    }
+
+    int wait(queue_impl &queue) const {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+        return 0;
+    }
+
+    int sync() const noexcept {
+        AF_ERROR("Incorrectly configured", AF_ERR_INTERNAL);
+        return 0;
+    }
+
+    operator bool() const noexcept { return false; }
+};
+
 #else
 
 #include <threads/async_queue.hpp>

From 773c96b18726a323c34617dbd2f29683f230157c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 30 Aug 2024 15:08:39 -0500
Subject: [PATCH 2589/2677] Fix for issue 3528: cmake generator expression
 space removed (#3554)

---
 src/api/unified/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/api/unified/CMakeLists.txt b/src/api/unified/CMakeLists.txt
index ca6805c7a4..bd373acab8 100644
--- a/src/api/unified/CMakeLists.txt
+++ b/src/api/unified/CMakeLists.txt
@@ -82,8 +82,8 @@ target_include_directories(af
 target_include_directories(af
   SYSTEM PRIVATE
     $<TARGET_PROPERTY:afcommon_interface,INTERFACE_INCLUDE_DIRECTORIES>
-    $<$<BOOL:${OpenCL_FOUND}>: $<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>>
-    $<$<BOOL:${CUDA_FOUND}>:  ${CUDA_INCLUDE_DIRS}>
+    $<$<BOOL:${OpenCL_FOUND}>:$<TARGET_PROPERTY:OpenCL::OpenCL,INTERFACE_INCLUDE_DIRECTORIES>>
+    $<$<BOOL:${CUDA_FOUND}>:${CUDA_INCLUDE_DIRS}>
   )
 
 target_link_libraries(af

From 41771248d2739a8388219fffc826b3f07104ca23 Mon Sep 17 00:00:00 2001
From: errata-c <77643526+errata-c@users.noreply.github.com>
Date: Fri, 30 Aug 2024 16:28:28 -0400
Subject: [PATCH 2590/2677] Implement simple fix for AF_JIT_KERNEL_TRACE on
 windows (#3517)

* Implement simple fix for AF_JIT_KERNEL_TRACE on windows

* Replaced tabs with spaces for consistency

---------

Co-authored-by: errata-c <erratachem@gmail.com>
---
 src/backend/common/util.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index 2d4a8e5ea0..f0b24bba65 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -125,7 +125,13 @@ void saveKernel(const string& funcName, const string& jit_ker,
     // Path to a folder
     const string ffp =
         string(jitKernelsOutput) + AF_PATH_SEPARATOR + funcName + ext;
+
+#if defined(OS_WIN)
+    FILE* f = fopen(ffp.c_str(), "w");
+#else
     FILE* f = fopen(ffp.c_str(), "we");
+#endif
+
     if (!f) {
         fprintf(stderr, "Cannot open file %s\n", ffp.c_str());
         return;

From 7978352aa858605ec7ebe333426475d130445d3d Mon Sep 17 00:00:00 2001
From: errata-c <77643526+errata-c@users.noreply.github.com>
Date: Fri, 30 Aug 2024 16:52:28 -0400
Subject: [PATCH 2591/2677] Fix build failure of cuda backend when cudnn is
 used. (#3521)

* Fixes formatting issue with cudnnStatus_t

* Fixed call to dependency_check with too few arguments

* Reformat according to the repository clang-format

---------

Co-authored-by: errata-c <erratachem@gmail.com>
Co-authored-by: Filip Matzner <floop@floop.cz>
Co-authored-by: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
---
 src/backend/cuda/platform.cpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/backend/cuda/platform.cpp b/src/backend/cuda/platform.cpp
index 52a22cdbaf..0de2451c4d 100644
--- a/src/backend/cuda/platform.cpp
+++ b/src/backend/cuda/platform.cpp
@@ -49,6 +49,7 @@
 #include <stdexcept>
 #include <string>
 #include <thread>
+#include <type_traits>
 
 using std::call_once;
 using std::make_unique;
@@ -123,8 +124,10 @@ unique_handle<cudnnHandle_t> *nnManager(const int deviceId) {
         if (!(*handle)) { getLogger()->error("Error initalizing cuDNN"); }
     });
     if (error) {
-        string error_msg = fmt::format("Error initializing cuDNN({}): {}.",
-                                       error, errorString(error));
+        string error_msg = fmt::format(
+            "Error initializing cuDNN({}): {}.",
+            static_cast<std::underlying_type<cudnnStatus_t>::type>(error),
+            errorString(error));
         AF_ERROR(error_msg, AF_ERR_RUNTIME);
     }
     CUDNN_CHECK(getCudnnPlugin().cudnnSetStream(cudnnHandles[deviceId],

From ebb13ff40edc173fe4d09742a315c32c3a9f050c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 30 Aug 2024 20:11:27 -0500
Subject: [PATCH 2592/2677] Fix missing installation of spdlog library (#3567)

---
 CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 12d6e557c9..f3a1484a72 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -285,6 +285,9 @@ else()
     target_compile_options(spdlog
       PRIVATE
         $<$<BOOL:${has_cxx_fp_model}>:-fp-model precise>)
+    install(TARGETS spdlog
+      COMPONENT common_backend_dependencies
+      DESTINATION ${AF_INSTALL_BIN_DIR})
     set_target_properties(af_spdlog
       PROPERTIES
         INTERFACE_LINK_LIBRARIES "spdlog")

From f4157374e73a2140293e68de7a00174fa675da6a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Tue, 3 Sep 2024 14:39:21 -0500
Subject: [PATCH 2593/2677] Fix for issue 3349: added cmake cuda version check
 (#3552)

---
 src/backend/cuda/CMakeLists.txt | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 5ffb28dafd..0c4563ed40 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -99,7 +99,12 @@ if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
     # contains GPU accelerated stedc and bdsqr. The user has to link
     # libcusolver_static.a with liblapack_static.a in order to build
     # successfully.
-    af_find_static_cuda_libs(lapack_static)
+    # Cuda Versions >= 12.0 changed lib name to libcusolver_lapack_static.a
+    if (CUDA_VERSION VERSION_GREATER_EQUAL 12.0)
+      af_find_static_cuda_libs(cusolver_lapack_static)
+    else()
+      af_find_static_cuda_libs(lapack_static)
+    endif()
 
     set(af_cuda_static_flags "${af_cuda_static_flags};-lcusolver_static")
   else()

From ec66afdf79fb10fc8c103ea2e1296d795f1f14a8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 30 Sep 2024 10:32:30 -0700
Subject: [PATCH 2594/2677] Fix issue 3378: Added back classify in naive bayes
 example (#3577)

---
 examples/machine_learning/mnist_common.h  | 2 +-
 examples/machine_learning/naive_bayes.cpp | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/machine_learning/mnist_common.h b/examples/machine_learning/mnist_common.h
index a32d21932c..11531f3ffb 100644
--- a/examples/machine_learning/mnist_common.h
+++ b/examples/machine_learning/mnist_common.h
@@ -145,7 +145,7 @@ static void display_results(const af::array &test_images,
             (test_images(span, span, i) > 0.1f).as(u8).host<unsigned char>();
         for (int j = 0; j < 28; j++) {
             for (int k = 0; k < 28; k++) {
-                std::cout << (img[j * 28 + k] ? "\u2588" : " ") << " ";
+                std::cout << (img[k * 28 + j] ? "\u2588" : " ") << " ";
             }
             std::cout << std::endl;
         }
diff --git a/examples/machine_learning/naive_bayes.cpp b/examples/machine_learning/naive_bayes.cpp
index 9fe6456f0e..aadca32bc0 100644
--- a/examples/machine_learning/naive_bayes.cpp
+++ b/examples/machine_learning/naive_bayes.cpp
@@ -135,8 +135,8 @@ void naive_bayes_demo(bool console, int perc) {
     if (!console) {
         test_images = test_images.T();
         test_labels = test_labels.T();
-        // FIXME: Crashing in mnist_common.h::classify
-        // display_results<false>(test_images, res_labels, test_labels , 20);
+
+        display_results<false>(test_images, res_labels, test_labels, 20);
     }
 }
 

From 90b27acf30d84437bd3672528d69fde092aa5519 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 30 Sep 2024 17:21:48 -0700
Subject: [PATCH 2595/2677] Fixed issue 3578: added correct compilation define
 for coo2dense kernel (#3579)

---
 src/backend/opencl/kernel/sparse.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index e1b29c986c..9005265710 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -39,7 +39,7 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
     };
     std::vector<std::string> compileOpts = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
-        DefineKeyValue(resp, REPEAT),
+        DefineKeyValue(reps, REPEAT),
     };
     compileOpts.emplace_back(getTypeBuildDefinition<T>());
 

From 989b71b0e88a59bd57a24d19d3fe981aa780c866 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 30 Sep 2024 17:22:02 -0700
Subject: [PATCH 2596/2677] Fix issue 3542: fixed opencl, oneapi, and cuda
 sparse coo2dense launch dimensions (#3583)

---
 src/backend/cuda/kernel/sparse.cuh     | 14 ++++++--------
 src/backend/cuda/kernel/sparse.hpp     |  2 +-
 src/backend/oneapi/kernel/sparse.hpp   | 17 +++++++----------
 src/backend/opencl/kernel/coo2dense.cl | 13 +++++--------
 src/backend/opencl/kernel/sparse.hpp   |  3 ++-
 5 files changed, 21 insertions(+), 28 deletions(-)

diff --git a/src/backend/cuda/kernel/sparse.cuh b/src/backend/cuda/kernel/sparse.cuh
index bdf0e20884..84825bdd24 100644
--- a/src/backend/cuda/kernel/sparse.cuh
+++ b/src/backend/cuda/kernel/sparse.cuh
@@ -17,15 +17,13 @@ namespace cuda {
 template<typename T>
 __global__ void coo2Dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
                           CParam<int> colIdx) {
-    int id = blockIdx.x * blockDim.x * reps + threadIdx.x;
-    if (id >= values.dims[0]) return;
+    for (int i = threadIdx.x; i < reps * blockDim.x; i += blockDim.x) {
+        int id = i + blockIdx.x * blockDim.x * reps;
+        if (id >= values.dims[0]) return;
 
-    for (int i = threadIdx.x; i <= reps * blockDim.x; i += blockDim.x) {
-        if (i >= values.dims[0]) return;
-
-        T v   = values.ptr[i];
-        int r = rowIdx.ptr[i];
-        int c = colIdx.ptr[i];
+        T v   = values.ptr[id];
+        int r = rowIdx.ptr[id];
+        int c = colIdx.ptr[id];
 
         int offset = r + c * output.strides[1];
 
diff --git a/src/backend/cuda/kernel/sparse.hpp b/src/backend/cuda/kernel/sparse.hpp
index 6629d0fec6..60068d3e20 100644
--- a/src/backend/cuda/kernel/sparse.hpp
+++ b/src/backend/cuda/kernel/sparse.hpp
@@ -30,7 +30,7 @@ void coo2dense(Param<T> output, CParam<T> values, CParam<int> rowIdx,
 
     dim3 threads(256, 1, 1);
 
-    dim3 blocks(divup(output.dims[0], threads.x * reps), 1, 1);
+    dim3 blocks(divup(values.dims[0], threads.x * reps), 1, 1);
 
     EnqueueArgs qArgs(blocks, threads, getActiveStream());
 
diff --git a/src/backend/oneapi/kernel/sparse.hpp b/src/backend/oneapi/kernel/sparse.hpp
index 70bf051868..8cc7f99fcc 100644
--- a/src/backend/oneapi/kernel/sparse.hpp
+++ b/src/backend/oneapi/kernel/sparse.hpp
@@ -47,19 +47,16 @@ class coo2DenseCreateKernel {
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
 
-        const int id = g.get_group_id(0) * g.get_local_range(0) * REPEAT +
-                       it.get_local_id(0);
-
-        if (id >= values_.dims[0]) return;
-
         const int dimSize = g.get_local_range(0);
 
         for (int i = it.get_local_id(0); i < REPEAT * dimSize; i += dimSize) {
-            if (i >= values_.dims[0]) return;
+            const int id =
+                g.get_group_id(0) * g.get_local_range(0) * REPEAT + i;
+            if (id >= values_.dims[0]) return;
 
-            T v   = vPtr_[i];
-            int r = rPtr_[i];
-            int c = cPtr_[i];
+            T v   = vPtr_[id];
+            int r = rPtr_[id];
+            int c = cPtr_[id];
 
             int offset = r + c * output_.strides[1];
 
@@ -83,7 +80,7 @@ void coo2dense(Param<T> out, const Param<T> values, const Param<int> rowIdx,
                const Param<int> colIdx) {
     auto local  = sycl::range(THREADS_PER_BLOCK, 1);
     auto global = sycl::range(
-        divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_BLOCK, 1);
+        divup(values.info.dims[0], local[0] * REPEAT) * THREADS_PER_BLOCK, 1);
 
     getQueue().submit([&](auto &h) {
         sycl::accessor d_rowIdx{*rowIdx.data, h, sycl::read_only};
diff --git a/src/backend/opencl/kernel/coo2dense.cl b/src/backend/opencl/kernel/coo2dense.cl
index f86c073621..539c98ada1 100644
--- a/src/backend/opencl/kernel/coo2dense.cl
+++ b/src/backend/opencl/kernel/coo2dense.cl
@@ -11,18 +11,15 @@ kernel void coo2Dense(global T *oPtr, const KParam output, global const T *vPtr,
                       const KParam values, global const int *rPtr,
                       const KParam rowIdx, global const int *cPtr,
                       const KParam colIdx) {
-    const int id = get_group_id(0) * get_local_size(0) * reps + get_local_id(0);
-
-    if (id >= values.dims[0]) return;
-
     const int dimSize = get_local_size(0);
 
     for (int i = get_local_id(0); i < reps * dimSize; i += dimSize) {
-        if (i >= values.dims[0]) return;
+        const int id = i + get_group_id(0) * dimSize * reps;
+        if (id >= values.dims[0]) return;
 
-        T v   = vPtr[i];
-        int r = rPtr[i];
-        int c = cPtr[i];
+        T v   = vPtr[id];
+        int r = rPtr[id];
+        int c = cPtr[id];
 
         int offset = r + c * output.strides[1];
 
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 9005265710..13a4a9c5fb 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -49,7 +49,8 @@ void coo2dense(Param out, const Param values, const Param rowIdx,
     cl::NDRange local(THREADS_PER_GROUP, 1, 1);
 
     cl::NDRange global(
-        divup(out.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1, 1);
+        divup(values.info.dims[0], local[0] * REPEAT) * THREADS_PER_GROUP, 1,
+        1);
 
     coo2dense(cl::EnqueueArgs(getQueue(), global, local), *out.data, out.info,
               *values.data, values.info, *rowIdx.data, rowIdx.info,

From 01c89484256b1bdc07c645e7e2e6c382d95e8aa5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 30 Sep 2024 17:40:25 -0700
Subject: [PATCH 2597/2677] Added sparse dense conversion test (#3589)

---
 test/sparse.cpp | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/test/sparse.cpp b/test/sparse.cpp
index a130a6bb58..3142a3735a 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -416,3 +416,24 @@ TEST(Sparse, CPPDenseToSparseToDenseUsage) {
     ASSERT_ARRAYS_EQ(in, gold);
     ASSERT_ARRAYS_EQ(dense, gold);
 }
+
+TEST(Sparse, CPPDenseToSparseConversions) {
+    array in      = af::randu(200, 200);
+    in(in < 0.75) = 0;
+
+    array coo_sparse_arr = af::sparse(in, AF_STORAGE_COO);
+    array csr_sparse_arr = af::sparse(in, AF_STORAGE_CSR);
+
+    array coo_dense_arr = af::dense(coo_sparse_arr);
+    array csr_dense_arr = af::dense(csr_sparse_arr);
+
+    ASSERT_ARRAYS_EQ(in, coo_dense_arr);
+    ASSERT_ARRAYS_EQ(in, csr_dense_arr);
+
+    array non_zero   = af::flat(in)(af::where(in));
+    array non_zero_T = af::flat(in.T())(af::where(in.T()));
+    ASSERT_ARRAYS_EQ(non_zero, af::sparseGetValues(coo_sparse_arr));
+    ASSERT_ARRAYS_EQ(
+        non_zero_T,
+        af::sparseGetValues(csr_sparse_arr));  // csr values are transposed
+}
\ No newline at end of file

From a4420e1a8a480323a07761e79bf4e1c8f7951bbf Mon Sep 17 00:00:00 2001
From: Tyler Hilbert <tylerjoshuahilbert@gmail.com>
Date: Tue, 1 Oct 2024 13:56:31 -0400
Subject: [PATCH 2598/2677] Fixed N and D comment for KMeans example (#3584)

---
 examples/machine_learning/kmeans.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/machine_learning/kmeans.cpp b/examples/machine_learning/kmeans.cpp
index e40cc34368..963d6a609f 100644
--- a/examples/machine_learning/kmeans.cpp
+++ b/examples/machine_learning/kmeans.cpp
@@ -17,7 +17,7 @@
 using namespace af;
 
 array distance(array data, array means) {
-    int n = data.dims(0);   // Number of features
+    int n = data.dims(0);   // Number of data points
     int k = means.dims(1);  // Number of means
 
     array data2  = tile(data, 1, k, 1);
@@ -60,8 +60,8 @@ array new_means(array data, array clusters, int k) {
 // means: output, vector of means
 void kmeans(array &means, array &clusters, const array in, int k,
             int iter = 100) {
-    unsigned n = in.dims(0);  // Num features
-    unsigned d = in.dims(2);  // feature length
+    unsigned n = in.dims(0);  // Num of data points
+    unsigned d = in.dims(2);  // Num of features (will only be 1 in spider image example)
 
     // reshape input
     array data = in * 0;

From f7e965183c35f6be21b2dce224e5587c91e832b7 Mon Sep 17 00:00:00 2001
From: j-bo <j-bo@users.noreply.github.com>
Date: Tue, 1 Oct 2024 20:41:28 +0200
Subject: [PATCH 2599/2677] Fix interop_cuda.md issue related to
 afcu::getStream (#3572)

afcu::getStream() documentation specify that the required id for it's call is the ArrayFire device id. There is not need to retrieve the cuda native id in the example as it may lead to sync issues when using a device with multiple GPUs
---
 docs/pages/interop_cuda.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/pages/interop_cuda.md b/docs/pages/interop_cuda.md
index dae46ae027..2132dfcb2c 100644
--- a/docs/pages/interop_cuda.md
+++ b/docs/pages/interop_cuda.md
@@ -80,8 +80,7 @@ int main() {
 
     // 5. Determine ArrayFire's CUDA stream
     int af_id = af::getDevice();
-    int cuda_id = afcu::getNativeId(af_id);
-    cudaStream_t af_cuda_stream = afcu::getStream(cuda_id);
+    cudaStream_t af_cuda_stream = afcu::getStream(af_id);
 
     // 6. Set arguments and run your kernel in ArrayFire's stream
     //    Here launch with 1 block of 10 threads

From a8576577af19a9b20d634080ed1cf4ea0b300f13 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 1 Oct 2024 19:08:30 -0400
Subject: [PATCH 2600/2677] Loosen indexing assertions for af_assign_gen
 (#3514)

* Loosen indexing assertions for af_assign_gen

* Add tests for assignment argument loosening
---
 include/af/index.h   |  2 +-
 src/api/c/assign.cpp |  3 ---
 test/gen_assign.cpp  | 43 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 44 insertions(+), 4 deletions(-)

diff --git a/include/af/index.h b/include/af/index.h
index 3bceb96cbf..8eaaeaa0a5 100644
--- a/include/af/index.h
+++ b/include/af/index.h
@@ -274,7 +274,7 @@ extern "C" {
     ///                     the sequences
     /// \param[in] lhs      is the input array
     /// \param[in] ndims    is the number of \ref af_index_t provided
-    /// \param[in] indices  is an af_array of \ref af_index_t objects
+    /// \param[in] indices  is a C array of \ref af_index_t objects
     /// \param[in] rhs      is the array whose values will be assigned to \p lhs
     ///
     /// \ingroup index_func_assign
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index e53b43a6c5..22f11255e9 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -260,8 +260,6 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
             return af_create_handle(out, 0, nullptr, lhsType);
         }
 
-        ARG_ASSERT(2, (ndims == 1) || (ndims == (dim_t)lInfo.ndims()));
-
         if (ndims == 1 && ndims != static_cast<dim_t>(lInfo.ndims())) {
             af_array tmp_in  = 0;
             af_array tmp_out = 0;
@@ -279,7 +277,6 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
 
         ARG_ASSERT(1, (lhsType == rhsType));
         ARG_ASSERT(1, (lhsDims.ndims() >= rhsDims.ndims()));
-        ARG_ASSERT(2, (lhsDims.ndims() >= ndims));
 
         af_array output = 0;
         if (*out != lhs) {
diff --git a/test/gen_assign.cpp b/test/gen_assign.cpp
index 7cfd78ae62..07685108c4 100644
--- a/test/gen_assign.cpp
+++ b/test/gen_assign.cpp
@@ -455,3 +455,46 @@ TEST(GeneralAssign, CPP_AANN) {
     freeHost(hIdx0);
     freeHost(hIdx1);
 }
+
+TEST(GeneralAssign, NDimsDoesNotMatchLDims) {
+    af_err err;
+    af_array zeros, l1, l2, sevens;
+    dim_t sevens_size[3] = {5, 1, 1};
+    short hsevens[5]     = {7, 7, 7, 7, 7};
+
+    dim_t zeros_size[3] = {5, 6, 1};
+    short hzeros[5 * 6] = {0};
+
+    dim_t hone[1] = {1};
+
+    ASSERT_SUCCESS(af_create_array(&zeros, hzeros, 3, zeros_size, s16));
+    ASSERT_SUCCESS(af_create_array(&sevens, hsevens, 3, sevens_size, s16));
+    ASSERT_SUCCESS(af_create_array(&l2, hone, 1, hone, s64));
+
+    af_index_t *ix;
+    ASSERT_SUCCESS(af_create_indexers(&ix));
+    ASSERT_SUCCESS(af_set_array_indexer(ix, l2, 1));
+
+    // clang-format off
+    vector<short> gold = {
+            0, 0, 0, 0, 0,
+            7, 7, 7, 7, 7,
+            0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0,
+        };
+    // clang-format on
+    for (int number_of_indices = 2; number_of_indices < 4;
+         number_of_indices++) {
+        af_array result = 0;
+        ASSERT_SUCCESS(
+            af_assign_gen(&result, zeros, number_of_indices, ix, sevens));
+
+        ASSERT_VEC_ARRAY_EQ(gold, dim4(3, zeros_size), af::array(result));
+    }
+    ASSERT_SUCCESS(af_release_array(zeros));
+    ASSERT_SUCCESS(af_release_array(sevens));
+    ASSERT_SUCCESS(af_release_array(l2));
+    ASSERT_SUCCESS(af_release_indexers(ix));
+}

From cc996ad36341ad191bd58e2c1327b6913322cf66 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 1 Oct 2024 19:44:17 -0400
Subject: [PATCH 2601/2677] Fix OpenCL memory migration on devices with
 different contexts (#3510)

---
 src/backend/opencl/Array.cpp |  6 ++++--
 test/array.cpp               | 23 +++++++++++++++++++++++
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index 21dec5166c..b4b6bcd5a9 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -201,10 +201,12 @@ void checkAndMigrate(Array<T> &arr) {
         AF_TRACE("Migrating array from {} to {}.", arr_id, cur_id);
         auto migrated_data           = memAlloc<T>(arr.elements());
         void *mapped_migrated_buffer = getQueue().enqueueMapBuffer(
-            *migrated_data, CL_TRUE, CL_MAP_READ, 0, arr.elements());
+            *migrated_data, CL_TRUE, CL_MAP_WRITE_INVALIDATE_REGION, 0,
+            sizeof(T) * arr.elements());
         setDevice(arr_id);
         Buffer &buf = *arr.get();
-        getQueue().enqueueReadBuffer(buf, CL_TRUE, 0, arr.elements(),
+        getQueue().enqueueReadBuffer(buf, CL_TRUE, 0,
+                                     sizeof(T) * arr.elements(),
                                      mapped_migrated_buffer);
         setDevice(cur_id);
         getQueue().enqueueUnmapMemObject(*migrated_data,
diff --git a/test/array.cpp b/test/array.cpp
index bcf6fa997e..b68f06820a 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -501,6 +501,29 @@ TEST(DeviceId, Different) {
     deviceGC();
 }
 
+TEST(Device, MigrateAllDevicesToAllDevices) {
+    int ndevices = getDeviceCount();
+    if (ndevices < 2) GTEST_SKIP() << "Skipping mult-GPU test";
+
+    for (int i = 0; i < ndevices; i++) {
+        for (int j = 0; j < ndevices; j++) {
+            setDevice(i);
+            array a = constant(i * 255, 10, 10);
+            a.eval();
+
+            setDevice(j);
+            array b = constant(j * 256, 10, 10);
+            b.eval();
+
+            array c = a + b;
+
+            std::vector<float> gold(10 * 10, i * 255 + j * 256);
+
+            ASSERT_VEC_ARRAY_EQ(gold, dim4(10, 10), c);
+        }
+    }
+}
+
 TEST(Device, empty) {
     array a = array();
     ASSERT_EQ(a.device<float>(), nullptr);

From a6f18278c39f4466a341846043c4773c3ee09471 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Tue, 1 Oct 2024 19:47:53 -0400
Subject: [PATCH 2602/2677] Fix source tarball GitHub workflow (#3498)

---
 .github/workflows/release_src_artifact.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/release_src_artifact.yml b/.github/workflows/release_src_artifact.yml
index c616c8db5b..41b01d4f72 100644
--- a/.github/workflows/release_src_artifact.yml
+++ b/.github/workflows/release_src_artifact.yml
@@ -9,7 +9,7 @@ name: ci
 jobs:
     upload_src_tarball:
         name: Upload release source tarball
-        runs-on: ubuntu-18.04
+        runs-on: ubuntu-latest
         steps:
             - name: Fetch Repo Info
               run: |
@@ -40,7 +40,7 @@ jobs:
                                           libopenblas-dev \
                                           ocl-icd-opencl-dev \
                                           nvidia-cuda-toolkit \
-                                          libboost1.68-dev
+                                          libboost-dev
 
             - name: CMake Configure
               run: |

From 4d5954d455a9aed0da58627740e7c27e2aaea464 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 23 Oct 2024 01:12:55 +0200
Subject: [PATCH 2603/2677] unified reports filled af::exception errors.
 (#3617)

---
 src/api/unified/error.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/unified/error.cpp b/src/api/unified/error.cpp
index 9fd89c0166..24a2dbfac9 100644
--- a/src/api/unified/error.cpp
+++ b/src/api/unified/error.cpp
@@ -42,7 +42,7 @@ void af_get_last_error(char **str, dim_t *len) {
         typedef void (*af_func)(char **, dim_t *);
         void *vfn    = LOAD_SYMBOL();
         af_func func = nullptr;
-        memcpy(&func, vfn, sizeof(void *));
+        memcpy(&func, &vfn, sizeof(void *));
         func(str, len);
     }
 }

From bdda3b3ea454030099871319c724eec4ee205bed Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 23 Oct 2024 02:34:11 +0200
Subject: [PATCH 2604/2677] Changed compare function to Strict Weak Ordering
 criteria. (#3612)

---
 examples/machine_learning/mnist_common.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/machine_learning/mnist_common.h b/examples/machine_learning/mnist_common.h
index 11531f3ffb..8d079df75a 100644
--- a/examples/machine_learning/mnist_common.h
+++ b/examples/machine_learning/mnist_common.h
@@ -13,7 +13,7 @@
 #include "../common/idxio.h"
 
 bool compare(const std::pair<float, int> l, const std::pair<float, int> r) {
-    return l.first >= r.first;
+    return l.first > r.first;
 }
 
 typedef std::pair<float, int> sort_type;

From 5c18fb3d73f3fe898b97bd2e26b34eba6df50aa6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Tue, 5 Nov 2024 11:59:13 -0800
Subject: [PATCH 2605/2677] Workaround fix for issue with inline namespace
 thrust (#3566)

* Fix issue with inline namespace thrust

* applied clang format

* Update ThrustArrayFirePolicy.hpp

Hi Edwin, there seems to be an issue caused by the clang format apply. I propose these changes.

---------

Co-authored-by: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
---
 src/backend/cuda/ThrustArrayFirePolicy.hpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/backend/cuda/ThrustArrayFirePolicy.hpp b/src/backend/cuda/ThrustArrayFirePolicy.hpp
index 189ee558b3..339d3ea088 100644
--- a/src/backend/cuda/ThrustArrayFirePolicy.hpp
+++ b/src/backend/cuda/ThrustArrayFirePolicy.hpp
@@ -37,7 +37,11 @@ inline void return_temporary_buffer(ThrustArrayFirePolicy, Pointer p) {
 }  // namespace cuda
 }  // namespace arrayfire
 
+#if defined(_WIN32)
+THRUST_NAMESPACE_BEGIN
+#else
 namespace thrust {
+#endif
 namespace cuda_cub {
 template<>
 __DH__ inline cudaStream_t get_stream<arrayfire::cuda::ThrustArrayFirePolicy>(
@@ -60,4 +64,8 @@ inline cudaError_t synchronize_stream(
 }
 
 }  // namespace cuda_cub
+#if defined(_WIN32)
+THRUST_NAMESPACE_END
+#else
 }  // namespace thrust
+#endif

From efec9b0822c47ab3c50b3610486ce094ed6011be Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 6 Nov 2024 21:27:19 +0100
Subject: [PATCH 2606/2677] Solves C7626 error when compiling with MSVC 2019
 16.6+ (#3512)

---
 src/backend/opencl/kernel/KParam.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/opencl/kernel/KParam.hpp b/src/backend/opencl/kernel/KParam.hpp
index 165bec9b02..1f4f1d5ba4 100644
--- a/src/backend/opencl/kernel/KParam.hpp
+++ b/src/backend/opencl/kernel/KParam.hpp
@@ -17,7 +17,7 @@
 #endif
 
 // Defines the size and shape of the data in the OpenCL buffer
-typedef struct {
+typedef struct KParam_t {
     dim_t dims[4];
     dim_t strides[4];
     dim_t offset;

From 77cd027cf8a4360530cf5b762fd88dd2dd7f9604 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Thu, 12 Dec 2024 11:32:29 -0800
Subject: [PATCH 2607/2677] Override boost build helper version to 1.84.0#3
 which fixes a bug that prevents building in Visual Studio 2022 (#3626)

---
 vcpkg.json | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/vcpkg.json b/vcpkg.json
index 6ca4ec32be..fe16a0aa6d 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -23,6 +23,10 @@
         {
             "name": "jasper",
             "version": "4.2.0"
+        },
+        {
+            "name": "boost-modular-build-helper",
+            "version": "1.84.0#3"
         }
     ],
     "features": {

From 41b67015641388ce00878d54e9d248fab8a75171 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <christophe@arrayfire.com>
Date: Wed, 21 Aug 2024 14:53:00 -0700
Subject: [PATCH 2608/2677] Fix for incorrect x axis values for histogram.
 Rounding was being applied to x axis min and max values but this should not
 be done for a histogram where the values are in fact bin labels.

---
 src/api/c/hist.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index f37ba5cea1..1e250b5df4 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -68,19 +68,21 @@ fg_chart setup_histogram(fg_window const window, const af_array in,
         T freqMax =
             getScalar<T>(detail::reduce_all<af_max_t, T, T>(histogramInput));
 
+	// For histogram, xMin and xMax should always be the first
+	// and last bin respectively and should not be rounded
         if (xMin == 0 && xMax == 0 && yMin == 0 && yMax == 0) {
             // No previous limits. Set without checking
-            xMin = static_cast<float>(step_round(minval, false));
-            xMax = static_cast<float>(step_round(maxval, true));
+            xMin = static_cast<float>(minval);
+            xMax = static_cast<float>(maxval);
             yMax = static_cast<float>(step_round(freqMax, true));
             // For histogram, always set yMin to 0.
             yMin = 0;
         } else {
             if (xMin > minval) {
-                xMin = static_cast<float>(step_round(minval, false));
+                xMin = static_cast<float>(minval);
             }
             if (xMax < maxval) {
-                xMax = static_cast<float>(step_round(maxval, true));
+                xMax = static_cast<float>(maxval);
             }
             if (yMax < freqMax) {
                 yMax = static_cast<float>(step_round(freqMax, true));

From d8a176f95007c2fab2b83f56b6f3839ae9e2e10b Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 8 Jan 2025 00:39:15 +0100
Subject: [PATCH 2609/2677] Corrected field example (#3369) (#3375)

* Extended field example to include 2D points and 2D coordinates

* Fix buffer overflow in vector_field #3369
---
 examples/graphics/field.cpp | 13 +++++++++++--
 src/api/c/vector_field.cpp  | 27 ++++++++++++++-------------
 2 files changed, 25 insertions(+), 15 deletions(-)

diff --git a/examples/graphics/field.cpp b/examples/graphics/field.cpp
index a723791fc8..f493c7ecd6 100644
--- a/examples/graphics/field.cpp
+++ b/examples/graphics/field.cpp
@@ -22,7 +22,7 @@ int main(int, char**) {
         af::info();
         af::Window myWindow(1024, 1024, "2D Vector Field example: ArrayFire");
 
-        myWindow.grid(1, 2);
+        myWindow.grid(2, 2);
 
         array dataRange = seq(MINIMUM, MAXIMUM, STEP);
 
@@ -38,12 +38,21 @@ int main(int, char**) {
             array saddle = join(1, flat(x), -1.0f * flat(y));
 
             array bvals = sin(scale * (x * x + y * y));
-            array hbowl = join(1, constant(1, x.elements()), flat(bvals));
+            array hbowl = join(1, constant(1., x.elements()), flat(bvals));
             hbowl.eval();
 
+            // 2D points
             myWindow(0, 0).vectorField(points, saddle, "Saddle point");
             myWindow(0, 1).vectorField(
                 points, hbowl, "hilly bowl (in a loop with varying amplitude)");
+
+            // 2D coordinates
+            myWindow(1, 0).vectorField(2.0 * flat(x), flat(y), flat(x),
+                                       -flat(y), "Saddle point");
+            myWindow(1, 1).vectorField(
+                2.0 * flat(x), flat(y), constant(1., x.elements()), flat(bvals),
+                "hilly bowl (in a loop with varying amplitude)");
+
             myWindow.show();
 
             scale -= 0.0010f;
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index a46d1eed47..701db6fc12 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -50,20 +50,21 @@ fg_chart setup_vector_field(fg_window window, const vector<af_array>& points,
     vector<Array<T>> pnts;
     vector<Array<T>> dirs;
 
-    for (unsigned i = 0; i < points.size(); ++i) {
-        pnts.push_back(getArray<T>(points[i]));
-        dirs.push_back(getArray<T>(directions[i]));
-    }
-
-    // Join for set up vector
-    dim4 odims(3, points.size());
-    Array<T> out_pnts = createEmptyArray<T>(odims);
-    Array<T> out_dirs = createEmptyArray<T>(odims);
-    detail::join(out_pnts, 1, pnts);
-    detail::join(out_dirs, 1, dirs);
-    Array<T> pIn = out_pnts;
-    Array<T> dIn = out_dirs;
+    Array<T> pIn = getArray<T>(points[0]);
+    Array<T> dIn = getArray<T>(directions[0]);
+    if (points.size() > 1) {
+        for (unsigned i = 0; i < points.size(); ++i) {
+            pnts.push_back(getArray<T>(points[i]));
+            dirs.push_back(getArray<T>(directions[i]));
+        }
 
+        // Join for set up vector
+        const dim4 odims(pIn.dims()[0], points.size());
+        pIn = createEmptyArray<T>(odims);
+        dIn = createEmptyArray<T>(odims);
+        detail::join<T>(pIn, 1, pnts);
+        detail::join<T>(dIn, 1, dirs);
+    }
     // do transpose if required
     if (transpose_) {
         pIn = transpose<T>(pIn, false);

From ab6978c54e975e039763b8c641b9594af49dd146 Mon Sep 17 00:00:00 2001
From: Tyler Hilbert <tylerjoshuahilbert@gmail.com>
Date: Tue, 7 Jan 2025 15:44:33 -0800
Subject: [PATCH 2610/2677] Added REQUIRED to CMake find_package for easier
 build debugging (#3581)

---
 examples/benchmarks/CMakeLists.txt       | 2 +-
 examples/computer_vision/CMakeLists.txt  | 2 +-
 examples/financial/CMakeLists.txt        | 2 +-
 examples/getting_started/CMakeLists.txt  | 2 +-
 examples/graphics/CMakeLists.txt         | 2 +-
 examples/helloworld/CMakeLists.txt       | 2 +-
 examples/image_processing/CMakeLists.txt | 2 +-
 examples/lin_algebra/CMakeLists.txt      | 2 +-
 examples/machine_learning/CMakeLists.txt | 2 +-
 examples/pde/CMakeLists.txt              | 2 +-
 examples/unified/CMakeLists.txt          | 2 +-
 11 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/examples/benchmarks/CMakeLists.txt b/examples/benchmarks/CMakeLists.txt
index 9cf8197317..4fd0853e58 100644
--- a/examples/benchmarks/CMakeLists.txt
+++ b/examples/benchmarks/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Benchmarks
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_CPU_FOUND)
   add_executable(blas_cpu blas.cpp)
diff --git a/examples/computer_vision/CMakeLists.txt b/examples/computer_vision/CMakeLists.txt
index 7113816566..2683eb1931 100644
--- a/examples/computer_vision/CMakeLists.txt
+++ b/examples/computer_vision/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Computer-Vision
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
diff --git a/examples/financial/CMakeLists.txt b/examples/financial/CMakeLists.txt
index f2b82d4de8..f365f88b47 100644
--- a/examples/financial/CMakeLists.txt
+++ b/examples/financial/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Financial
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_CPU_FOUND)
   # Black-Scholes Options
diff --git a/examples/getting_started/CMakeLists.txt b/examples/getting_started/CMakeLists.txt
index 790afd3d1f..a9d1ce4bcb 100644
--- a/examples/getting_started/CMakeLists.txt
+++ b/examples/getting_started/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Getting-Started
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_CPU_FOUND)
   # Convolve examples
diff --git a/examples/graphics/CMakeLists.txt b/examples/graphics/CMakeLists.txt
index dd2918b641..6140142343 100644
--- a/examples/graphics/CMakeLists.txt
+++ b/examples/graphics/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Graphics
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
diff --git a/examples/helloworld/CMakeLists.txt b/examples/helloworld/CMakeLists.txt
index 0aa58ca2c9..b3a02e9fc6 100644
--- a/examples/helloworld/CMakeLists.txt
+++ b/examples/helloworld/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-HelloWorld
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_CPU_FOUND)
   # Hello World example
diff --git a/examples/image_processing/CMakeLists.txt b/examples/image_processing/CMakeLists.txt
index cfcd109922..e4ab1d3d8a 100644
--- a/examples/image_processing/CMakeLists.txt
+++ b/examples/image_processing/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Image-Processing
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
diff --git a/examples/lin_algebra/CMakeLists.txt b/examples/lin_algebra/CMakeLists.txt
index b08aceeeee..89b9c89600 100644
--- a/examples/lin_algebra/CMakeLists.txt
+++ b/examples/lin_algebra/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Linear-Algebra
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_CPU_FOUND)
   # Cholesky example
diff --git a/examples/machine_learning/CMakeLists.txt b/examples/machine_learning/CMakeLists.txt
index d1cbcc9541..480f3f7f12 100644
--- a/examples/machine_learning/CMakeLists.txt
+++ b/examples/machine_learning/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Linear-Algebra
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
 
diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index 23a89ace31..bceb38665a 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-PDE
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_CPU_FOUND)
   # Shallow Water simulation example
diff --git a/examples/unified/CMakeLists.txt b/examples/unified/CMakeLists.txt
index 42ab6432f0..a399f58c00 100644
--- a/examples/unified/CMakeLists.txt
+++ b/examples/unified/CMakeLists.txt
@@ -10,7 +10,7 @@ project(ArrayFire-Example-Unified
   VERSION 3.5.0
   LANGUAGES CXX)
 
-find_package(ArrayFire)
+find_package(ArrayFire REQUIRED)
 
 if(ArrayFire_Unified_FOUND)
   # Simple unified backend example

From 279d0ea683bb928104041761d2a6a749ae231dd3 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 7 Jan 2025 16:25:39 -0800
Subject: [PATCH 2611/2677] 1918 sparse matrix not updated in for loop in
 opencl (#3602)

* Fix for issue in the opencl backend where array offsets for the values and/or row/cols arrays are not accounted for when converting a sparse array to a dense one. This can happen when a sparse matrix is constructed using values and/or row/cols arrays that have been indexed using the seq method.

* Add test case to verify fix for sparse to dense conversion bug in the opencl backend.

* Fix uninitialized array in test reference.
---
 src/backend/opencl/kernel/csr2dense.cl | 11 +++++++----
 src/backend/opencl/kernel/sparse.hpp   |  5 ++++-
 test/sparse.cpp                        | 21 +++++++++++++++++++++
 3 files changed, 32 insertions(+), 5 deletions(-)

diff --git a/src/backend/opencl/kernel/csr2dense.cl b/src/backend/opencl/kernel/csr2dense.cl
index 15a7c0c60d..e15ef014f3 100644
--- a/src/backend/opencl/kernel/csr2dense.cl
+++ b/src/backend/opencl/kernel/csr2dense.cl
@@ -9,13 +9,16 @@
 
 kernel void csr2Dense(global T *output, global const T *values,
                       global const int *rowidx, global const int *colidx,
-                      const int M) {
+                      const int M, const int v_off, const int r_off, const int c_off) {
+    T *v = values + v_off;
+    int *r = rowidx + r_off;
+    int *c = colidx + c_off;
     int lid = get_local_id(0);
     for (int rowId = get_group_id(0); rowId < M; rowId += get_num_groups(0)) {
-        int colStart = rowidx[rowId];
-        int colEnd   = rowidx[rowId + 1];
+        int colStart = r[rowId];
+        int colEnd   = r[rowId + 1];
         for (int colId = colStart + lid; colId < colEnd; colId += THREADS) {
-            output[rowId + colidx[colId] * M] = values[colId];
+            output[rowId + c[colId] * M] = v[colId];
         }
     }
 }
diff --git a/src/backend/opencl/kernel/sparse.hpp b/src/backend/opencl/kernel/sparse.hpp
index 13a4a9c5fb..4d3a33d14a 100644
--- a/src/backend/opencl/kernel/sparse.hpp
+++ b/src/backend/opencl/kernel/sparse.hpp
@@ -85,7 +85,10 @@ void csr2dense(Param output, const Param values, const Param rowIdx,
     cl::NDRange global(local[0] * groups_x, 1);
 
     csr2dense(cl::EnqueueArgs(getQueue(), global, local), *output.data,
-              *values.data, *rowIdx.data, *colIdx.data, M);
+              *values.data, *rowIdx.data, *colIdx.data, M,
+	      static_cast<int>(values.info.offset),
+	      static_cast<int>(rowIdx.info.offset),
+	      static_cast<int>(colIdx.info.offset));
     CL_DEBUG_FINISH(getQueue());
 }
 
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 3142a3735a..9e3f29ae35 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -19,6 +19,7 @@ using af::dtype_traits;
 using af::identity;
 using af::randu;
 using af::span;
+using af::seq;
 
 #define SPARSE_TESTS(T, eps)                                                \
     TEST(Sparse, T##Square) { sparseTester<T>(1000, 1000, 100, 5, eps); }   \
@@ -109,6 +110,26 @@ TEST(Sparse, ISSUE_1745) {
                               row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
 }
 
+TEST(Sparse, ISSUE_1918) {
+    array reference(2,2);
+    reference(0, span) = 0;
+    reference(1, span) = 2;
+    array output;
+    float value[] = { 1, 1, 2, 2 };
+    int index[] = { -1, 1, 2 };
+    int row[] = { 0, 2, 2, 0, 0, 2 };
+    int col[] = { 0, 1, 0, 1 };
+    array values(4, 1, value, afHost);
+    array rows(6, 1, row, afHost);
+    array cols(4, 1, col, afHost);
+    array S;
+  
+    S = sparse(2, 2, values(seq(2, 3)), rows(seq(3, 5)), cols(seq(2, 3)));
+    output = dense(S);
+
+    ASSERT_ARRAYS_EQ(reference, output);
+}
+
 TEST(Sparse, ISSUE_2134_COO) {
     int rows[]     = {0, 0, 0, 1, 1, 2, 2};
     int cols[]     = {0, 1, 2, 0, 1, 0, 2};

From 374bf9761cb3e5f1d6ad327249d009f2a37c4d0e Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 7 Jan 2025 17:19:54 -0800
Subject: [PATCH 2612/2677] Add note to write() array class method
 documentation about copy on write behavior. (#3613)

---
 include/af/array.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/af/array.h b/include/af/array.h
index 0edb9558e1..4186b95d08 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -655,6 +655,7 @@ namespace af
 
         /**
            Perform deep copy from host/device pointer to an existing array
+           \note Unlike all other assignment operations, this does NOT result in a copy on write.
         */
         template<typename T> void write(const T *ptr, const size_t bytes, af::source src = afHost);
 

From b25ff740afbf7e70e5f659d0fb29b172658c46ca Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 7 Jan 2025 17:21:08 -0800
Subject: [PATCH 2613/2677] Add condition so that cuda_* tests (including
 cuda_unified) are only built if AF_BUILD_CUDA is true (#3598)

---
 test/CMakeLists.txt | 88 +++++++++++++++++++++++----------------------
 1 file changed, 45 insertions(+), 43 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 92c4d90acd..95bab411bc 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -372,51 +372,53 @@ if(OpenCL_FOUND)
             CXX11)
 endif()
 
-if(CUDA_FOUND)
-  include(AFcuda_helpers)
-  foreach(backend ${enabled_backends})
-    set(cuda_test_backends "cuda" "unified")
-    if(${backend} IN_LIST cuda_test_backends)
-      set(target test_cuda_${backend})
-      add_executable(${target} cuda.cu)
-      target_include_directories(${target}
-        PRIVATE
-        ${CMAKE_SOURCE_DIR}
-        ${CMAKE_CURRENT_SOURCE_DIR})
-      target_include_directories(${target}
-        SYSTEM PRIVATE
-          ${ArrayFire_SOURCE_DIR}/extern/half/include)
-      if(${backend} STREQUAL "unified")
-        target_link_libraries(${target}
-          ArrayFire::af)
-      else()
+if(AF_BUILD_CUDA)
+  if(CUDA_FOUND)
+    include(AFcuda_helpers)
+    foreach(backend ${enabled_backends})
+      set(cuda_test_backends "cuda" "unified")
+      if(${backend} IN_LIST cuda_test_backends)
+        set(target test_cuda_${backend})
+        add_executable(${target} cuda.cu)
+        target_include_directories(${target}
+          PRIVATE
+          ${CMAKE_SOURCE_DIR}
+          ${CMAKE_CURRENT_SOURCE_DIR})
+        target_include_directories(${target}
+          SYSTEM PRIVATE
+            ${ArrayFire_SOURCE_DIR}/extern/half/include)
+        if(${backend} STREQUAL "unified")
+          target_link_libraries(${target}
+            ArrayFire::af)
+        else()
+          target_link_libraries(${target}
+            ArrayFire::af${backend})
+        endif()
         target_link_libraries(${target}
-          ArrayFire::af${backend})
-      endif()
-      target_link_libraries(${target}
-        mmio
-        arrayfire_test)
-
-      # Couldn't get Threads::Threads to work with this cuda binary. The import
-      # target would not add the -pthread flag which is required for this
-      # executable (on Ubuntu 18.04 anyway)
-      check_cxx_compiler_flag(-pthread pthread_flag)
-      if(pthread_flag)
-        target_link_libraries(${target} -pthread)
+          mmio
+          arrayfire_test)
+  
+        # Couldn't get Threads::Threads to work with this cuda binary. The import
+        # target would not add the -pthread flag which is required for this
+        # executable (on Ubuntu 18.04 anyway)
+        check_cxx_compiler_flag(-pthread pthread_flag)
+        if(pthread_flag)
+          target_link_libraries(${target} -pthread)
+        endif()
+  
+        af_detect_and_set_cuda_architectures(${target})
+  
+        set_target_properties(${target}
+          PROPERTIES
+            FOLDER "Tests"
+            OUTPUT_NAME "cuda_${backend}")
+  
+        if(NOT ${backend} STREQUAL "unified")
+          af_add_test(${target} ${backend} ON)
+        endif()
       endif()
-
-      af_detect_and_set_cuda_architectures(${target})
-
-      set_target_properties(${target}
-        PROPERTIES
-          FOLDER "Tests"
-          OUTPUT_NAME "cuda_${backend}")
-
-      if(NOT ${backend} STREQUAL "unified")
-        af_add_test(${target} ${backend} ON)
-      endif()
-    endif()
-  endforeach()
+    endforeach()
+  endif()
 endif()
 
 

From 23a990a40d3f43035bcbed0805987f8f40a664e9 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Wed, 8 Jan 2025 09:59:43 -0800
Subject: [PATCH 2614/2677] The shfl_instrinsics header file contains wrapper
 routines for the warp primitives and calls the new primitives for CUDA
 versions greater than 9 and the old ones for older CUDA versions. The new
 primitives have an additional argument which is a mask of the warp threads
 that are participating in the operation. The old primitives always involve
 all the threads in a warp. The wrapper routines originally allowed you to
 specify the mask which was ignored for the old primitives but this has now
 been removed. This is because if an old version of CUDA is being used then
 all threads must enter the wrapper routine and if a new version of CUDA is
 being used only the threads corresponding to the mask must enter. If threads
 outside the mask enter the routine then the behavior is undefined. In CUDA
 versions <=12.2 the primitive executes without any errors given however in
 later versions of CUDA a warp illegal instruction exception will be thrown.
 In order to preserve the same behavior of these wrapper functions for old and
 new versions of CUDA, the mask is always set to all threads in a warp for the
 new primitives. The specific new primitive can always be called with a custom
 mask which is already done elsewhere in the reduce_by_key routine. (#3576)

---
 src/backend/cuda/kernel/reduce_by_key.hpp   | 70 ++++++++-------------
 src/backend/cuda/kernel/shfl_intrinsics.hpp | 46 +++++++-------
 2 files changed, 49 insertions(+), 67 deletions(-)

diff --git a/src/backend/cuda/kernel/reduce_by_key.hpp b/src/backend/cuda/kernel/reduce_by_key.hpp
index ea015aaff2..1e04a123ec 100644
--- a/src/backend/cuda/kernel/reduce_by_key.hpp
+++ b/src/backend/cuda/kernel/reduce_by_key.hpp
@@ -25,8 +25,6 @@
 
 using std::unique_ptr;
 
-const static unsigned int FULL_MASK = 0xFFFFFFFF;
-
 namespace arrayfire {
 namespace cuda {
 namespace kernel {
@@ -68,9 +66,9 @@ __global__ void test_needs_reduction(int *needs_another_reduction,
 
     if (tid < n) { k = keys_in.ptr[tid]; }
 
-    int update_key = (k == shfl_down_sync(FULL_MASK, k, 1)) &&
+    int update_key = (k == shfl_down_sync(k, 1)) &&
                      (tid < (n - 1)) && ((threadIdx.x % 32) < 31);
-    int remaining_updates = any_sync(FULL_MASK, update_key);
+    int remaining_updates = any_sync(update_key);
 
     __syncthreads();
 
@@ -83,7 +81,7 @@ __global__ void test_needs_reduction(int *needs_another_reduction,
          && (threadIdx.x < (blockDim.x - 1))  // not last thread in block
          // next value valid and equal
          && ((tid + 1) < n) && (k == keys_in.ptr[tid + 1]));
-    remaining_updates = any_sync(FULL_MASK, update_key);
+    remaining_updates = any_sync(update_key);
 
     // TODO: single per warp? change to assignment rather than atomicOr
     if (remaining_updates) atomicOr(needs_another_reduction, remaining_updates);
@@ -243,7 +241,7 @@ __global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
         v = common::Binary<compute_t<To>, op>::init();
     }
 
-    compute_t<Tk> eq_check = (k != shfl_up_sync(FULL_MASK, k, 1));
+    compute_t<Tk> eq_check = (k != shfl_up_sync(k, 1));
     // mark threads containing unique keys
     char unique_flag = (eq_check || (laneid == 0)) && (tidx < n);
 
@@ -251,42 +249,33 @@ __global__ static void reduce_blocks_by_key(int *reduced_block_sizes,
     char unique_id = unique_flag;
 #pragma unroll
     for (int offset = 1; offset < 32; offset <<= 1) {
-        char y = shfl_up_sync(FULL_MASK, unique_id, offset);
+        char y = shfl_up_sync(unique_id, offset);
         if (laneid >= offset) unique_id += y;
     }
 
     //
     // Reduce each warp by key
-    char all_eq = (k == shfl_down_sync(FULL_MASK, k, 1));
-    if (all_sync(FULL_MASK,
-                 all_eq)) {  // check special case of single key per warp
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 1));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 2));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 4));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 8));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 16));
+    char all_eq = (k == shfl_down_sync(k, 1));
+    if (all_sync(all_eq)) {  // check special case of single key per warp
+        v = reduce(v, shfl_down_sync(v, 1));
+        v = reduce(v, shfl_down_sync(v, 2));
+        v = reduce(v, shfl_down_sync(v, 4));
+        v = reduce(v, shfl_down_sync(v, 8));
+        v = reduce(v, shfl_down_sync(v, 16));
     } else {
         compute_t<To> init = common::Binary<compute_t<To>, op>::init();
         int eq_check, update_key;
-        unsigned shflmask;
 #pragma unroll
         for (int delta = 1; delta < 32; delta <<= 1) {
             eq_check =
-                (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
+                (unique_id == shfl_down_sync(unique_id, delta));
 
             // checks if this thread should perform a reduction
             update_key =
                 eq_check && (laneid < (32 - delta)) && ((tidx + delta) < n);
 
-            // obtains mask of all threads that should be reduced
-            shflmask = ballot_sync(FULL_MASK, update_key);
-
-            // shifts mask to include source threads that should participate in
-            // _shfl
-            shflmask |= (shflmask << delta);
-
             // shfls data from neighboring threads
-            compute_t<To> uval = shfl_down_sync(shflmask, v, delta);
+            compute_t<To> uval = shfl_down_sync(v, delta);
 
             // update if thread requires it
             v = reduce(v, (update_key ? uval : init));
@@ -479,7 +468,7 @@ __global__ static void reduce_blocks_dim_by_key(
         v = init;
     }
 
-    Tk eq_check = (k != shfl_up_sync(FULL_MASK, k, 1));
+    Tk eq_check = (k != shfl_up_sync(k, 1));
     // mark threads containing unique keys
     char unique_flag = (eq_check || (laneid == 0)) && (tidx < n);
 
@@ -487,42 +476,33 @@ __global__ static void reduce_blocks_dim_by_key(
     char unique_id = unique_flag;
 #pragma unroll
     for (int offset = 1; offset < 32; offset <<= 1) {
-        char y = shfl_up_sync(FULL_MASK, unique_id, offset);
+        char y = shfl_up_sync(unique_id, offset);
         if (laneid >= offset) unique_id += y;
     }
 
     //
     // Reduce each warp by key
-    char all_eq = (k == shfl_down_sync(FULL_MASK, k, 1));
-    if (all_sync(FULL_MASK,
-                 all_eq)) {  // check special case of single key per warp
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 1));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 2));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 4));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 8));
-        v = reduce(v, shfl_down_sync(FULL_MASK, v, 16));
+    char all_eq = (k == shfl_down_sync(k, 1));
+    if (all_sync(all_eq)) {  // check special case of single key per warp
+        v = reduce(v, shfl_down_sync(v, 1));
+        v = reduce(v, shfl_down_sync(v, 2));
+        v = reduce(v, shfl_down_sync(v, 4));
+        v = reduce(v, shfl_down_sync(v, 8));
+        v = reduce(v, shfl_down_sync(v, 16));
     } else {
         compute_t<To> init = common::Binary<compute_t<To>, op>::init();
         int eq_check, update_key;
-        unsigned shflmask;
 #pragma unroll
         for (int delta = 1; delta < 32; delta <<= 1) {
             eq_check =
-                (unique_id == shfl_down_sync(FULL_MASK, unique_id, delta));
+                (unique_id == shfl_down_sync(unique_id, delta));
 
             // checks if this thread should perform a reduction
             update_key =
                 eq_check && (laneid < (32 - delta)) && ((tidx + delta) < n);
 
-            // obtains mask of all threads that should be reduced
-            shflmask = ballot_sync(FULL_MASK, update_key);
-
-            // shifts mask to include source threads that should participate in
-            // _shfl
-            shflmask |= (shflmask << delta);
-
             // shfls data from neighboring threads
-            compute_t<To> uval = shfl_down_sync(shflmask, v, delta);
+            compute_t<To> uval = shfl_down_sync(v, delta);
 
             // update if thread requires it
             v = reduce(v, (update_key ? uval : init));
diff --git a/src/backend/cuda/kernel/shfl_intrinsics.hpp b/src/backend/cuda/kernel/shfl_intrinsics.hpp
index 687abf5144..a91dc74148 100644
--- a/src/backend/cuda/kernel/shfl_intrinsics.hpp
+++ b/src/backend/cuda/kernel/shfl_intrinsics.hpp
@@ -11,11 +11,13 @@ namespace arrayfire {
 namespace cuda {
 namespace kernel {
 
+constexpr unsigned int FULL_MASK = 0xffffffff;
+
 //__all_sync wrapper
 template<typename T>
-__device__ T all_sync(unsigned mask, T var) {
+__device__ T all_sync(T var) {
 #if (CUDA_VERSION >= 9000)
-    return __all_sync(mask, var);
+    return __all_sync(FULL_MASK, var);
 #else
     return __all(var);
 #endif
@@ -23,9 +25,9 @@ __device__ T all_sync(unsigned mask, T var) {
 
 //__all_sync wrapper
 template<typename T>
-__device__ T any_sync(unsigned mask, T var) {
+__device__ T any_sync(T var) {
 #if (CUDA_VERSION >= 9000)
-    return __any_sync(mask, var);
+    return __any_sync(FULL_MASK, var);
 #else
     return __any(var);
 #endif
@@ -33,9 +35,9 @@ __device__ T any_sync(unsigned mask, T var) {
 
 //__shfl_down_sync wrapper
 template<typename T>
-__device__ T ballot_sync(unsigned mask, T var) {
+__device__ T ballot_sync(T var) {
 #if (CUDA_VERSION >= 9000)
-    return __ballot_sync(mask, var);
+    return __ballot_sync(FULL_MASK, var);
 #else
     return __ballot(var);
 #endif
@@ -43,19 +45,19 @@ __device__ T ballot_sync(unsigned mask, T var) {
 
 //__shfl_down_sync wrapper
 template<typename T>
-__device__ T shfl_down_sync(unsigned mask, T var, int delta) {
+__device__ T shfl_down_sync(T var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    return __shfl_down_sync(mask, var, delta);
+    return __shfl_down_sync(FULL_MASK, var, delta);
 #else
     return __shfl_down(var, delta);
 #endif
 }
 // specialization for cfloat
 template<>
-inline __device__ cfloat shfl_down_sync(unsigned mask, cfloat var, int delta) {
+inline __device__ cfloat shfl_down_sync(cfloat var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    cfloat res = {__shfl_down_sync(mask, var.x, delta),
-                  __shfl_down_sync(mask, var.y, delta)};
+    cfloat res = {__shfl_down_sync(FULL_MASK, var.x, delta),
+                  __shfl_down_sync(FULL_MASK, var.y, delta)};
 #else
     cfloat res  = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
 #endif
@@ -63,11 +65,11 @@ inline __device__ cfloat shfl_down_sync(unsigned mask, cfloat var, int delta) {
 }
 // specialization for cdouble
 template<>
-inline __device__ cdouble shfl_down_sync(unsigned mask, cdouble var,
+inline __device__ cdouble shfl_down_sync(cdouble var,
                                          int delta) {
 #if (CUDA_VERSION >= 9000)
-    cdouble res = {__shfl_down_sync(mask, var.x, delta),
-                   __shfl_down_sync(mask, var.y, delta)};
+    cdouble res = {__shfl_down_sync(FULL_MASK, var.x, delta),
+                   __shfl_down_sync(FULL_MASK, var.y, delta)};
 #else
     cdouble res = {__shfl_down(var.x, delta), __shfl_down(var.y, delta)};
 #endif
@@ -76,19 +78,19 @@ inline __device__ cdouble shfl_down_sync(unsigned mask, cdouble var,
 
 //__shfl_up_sync wrapper
 template<typename T>
-__device__ T shfl_up_sync(unsigned mask, T var, int delta) {
+__device__ T shfl_up_sync(T var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    return __shfl_up_sync(mask, var, delta);
+    return __shfl_up_sync(FULL_MASK, var, delta);
 #else
     return __shfl_up(var, delta);
 #endif
 }
 // specialization for cfloat
 template<>
-inline __device__ cfloat shfl_up_sync(unsigned mask, cfloat var, int delta) {
+inline __device__ cfloat shfl_up_sync(cfloat var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    cfloat res = {__shfl_up_sync(mask, var.x, delta),
-                  __shfl_up_sync(mask, var.y, delta)};
+    cfloat res = {__shfl_up_sync(FULL_MASK, var.x, delta),
+                  __shfl_up_sync(FULL_MASK, var.y, delta)};
 #else
     cfloat res  = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
 #endif
@@ -96,10 +98,10 @@ inline __device__ cfloat shfl_up_sync(unsigned mask, cfloat var, int delta) {
 }
 // specialization for cdouble
 template<>
-inline __device__ cdouble shfl_up_sync(unsigned mask, cdouble var, int delta) {
+inline __device__ cdouble shfl_up_sync(cdouble var, int delta) {
 #if (CUDA_VERSION >= 9000)
-    cdouble res = {__shfl_up_sync(mask, var.x, delta),
-                   __shfl_up_sync(mask, var.y, delta)};
+    cdouble res = {__shfl_up_sync(FULL_MASK, var.x, delta),
+                   __shfl_up_sync(FULL_MASK, var.y, delta)};
 #else
     cdouble res = {__shfl_up(var.x, delta), __shfl_up(var.y, delta)};
 #endif

From 424f1d6dd2f08dafb36e6a72653c150537de0d21 Mon Sep 17 00:00:00 2001
From: errata-c <77643526+errata-c@users.noreply.github.com>
Date: Wed, 8 Jan 2025 17:57:38 -0500
Subject: [PATCH 2615/2677] Added CMakeUserPresets.json to .gitignore, allow
 for local cmake configuration (#3520)

Co-authored-by: errata-c <erratachem@gmail.com>
---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index d56dd8ccf0..933736dba0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 #CMakeCache.txt
 #./CMakeFiles/
+CMakeUserPresets.json
 build*/
 Release/
 #Makefile

From f6559a5c4db0d9e486347c6d004b8d4032186564 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Wed, 8 Jan 2025 15:16:59 -0800
Subject: [PATCH 2616/2677] 3545 bug fp16 types not allowed for atan2 method
 (#3559)

* Add cases for float16 arguments to atan2 and hypot functions

* Added test cases for half precision atan2 and hypot functions
---
 src/api/c/binary.cpp |  6 ++++--
 test/binary.cpp      | 13 +++++++++++--
 2 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index 50590568f8..ee727c264a 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -476,7 +476,7 @@ af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs,
     try {
         const af_dtype type = implicit(lhs, rhs);
 
-        if (type != f32 && type != f64) {
+        if (type != f16 && type != f32 && type != f64) {
             AF_ERROR("Only floating point arrays are supported for atan2 ",
                      AF_ERR_NOT_SUPPORTED);
         }
@@ -491,6 +491,7 @@ af_err af_atan2(af_array *out, const af_array lhs, const af_array rhs,
 
         af_array res;
         switch (type) {
+            case f16: res = arithOp<half, af_atan2_t>(lhs, rhs, odims); break;
             case f32: res = arithOp<float, af_atan2_t>(lhs, rhs, odims); break;
             case f64: res = arithOp<double, af_atan2_t>(lhs, rhs, odims); break;
             default: TYPE_ERROR(0, type);
@@ -507,7 +508,7 @@ af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs,
     try {
         const af_dtype type = implicit(lhs, rhs);
 
-        if (type != f32 && type != f64) {
+        if (type != f16 && type != f32 && type != f64) {
             AF_ERROR("Only floating point arrays are supported for hypot ",
                      AF_ERR_NOT_SUPPORTED);
         }
@@ -523,6 +524,7 @@ af_err af_hypot(af_array *out, const af_array lhs, const af_array rhs,
 
         af_array res;
         switch (type) {
+            case f16: res = arithOp<half, af_hypot_t>(lhs, rhs, odims); break;
             case f32: res = arithOp<float, af_hypot_t>(lhs, rhs, odims); break;
             case f64: res = arithOp<double, af_hypot_t>(lhs, rhs, odims); break;
             default: TYPE_ERROR(0, type);
diff --git a/test/binary.cpp b/test/binary.cpp
index dafc3b8bff..c029a19da5 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -14,6 +14,8 @@
 #include <af/data.h>
 #include <af/device.h>
 #include <af/random.h>
+#include <af/half.h>
+#include "half.hpp"  //note: NOT common. From extern/half/include/half.hpp
 
 #include <cfenv>
 #include <cmath>
@@ -21,6 +23,8 @@
 using namespace std;
 using namespace af;
 
+using half_float_half = half_float::half;
+
 const int num = 10000;
 
 #define add(left, right) (left) + (right)
@@ -122,7 +126,7 @@ af::array randgen(const int num, dtype ty) {
                                                                       \
         af_dtype ta = (af_dtype)dtype_traits<Ta>::af_type;            \
         af::array a = randgen(num, ta);                               \
-        Tb h_b      = 0.3;                                            \
+        Tb h_b      = (Tb)0.3;                                            \
         af::array c = func(a, h_b);                                   \
         Ta *h_a     = a.host<Ta>();                                   \
         Td *h_d     = c.host<Td>();                                   \
@@ -139,7 +143,7 @@ af::array randgen(const int num, dtype ty) {
         SUPPORTED_TYPE_CHECK(Tc);                                     \
                                                                       \
         af_dtype tb = (af_dtype)dtype_traits<Tb>::af_type;            \
-        Ta h_a      = 0.3;                                            \
+        Ta h_a      = (Ta)0.3;                                            \
         af::array b = randgen(num, tb);                               \
         af::array c = func(h_a, b);                                   \
         Tb *h_b     = b.host<Tb>();                                   \
@@ -163,6 +167,8 @@ af::array randgen(const int num, dtype ty) {
 #define BINARY_TESTS_UINT(func) BINARY_TESTS(uint, uint, uint, func)
 #define BINARY_TESTS_INTL(func) BINARY_TESTS(intl, intl, intl, func)
 #define BINARY_TESTS_UINTL(func) BINARY_TESTS(uintl, uintl, uintl, func)
+#define BINARY_TESTS_NEAR_HALF(func) \
+    BINARY_TESTS_NEAR(half_float_half, half_float_half, half_float_half, func, 1e-3)
 #define BINARY_TESTS_NEAR_FLOAT(func) \
     BINARY_TESTS_NEAR(float, float, float, func, 1e-5)
 #define BINARY_TESTS_NEAR_DOUBLE(func) \
@@ -188,6 +194,9 @@ BINARY_TESTS_NEAR_FLOAT(atan2)
 BINARY_TESTS_NEAR_FLOAT(pow)
 BINARY_TESTS_NEAR_FLOAT(hypot)
 
+BINARY_TESTS_NEAR_HALF(atan2)
+BINARY_TESTS_NEAR_HALF(hypot)
+
 BINARY_TESTS_NEAR_DOUBLE(atan2)
 BINARY_TESTS_NEAR_DOUBLE(pow)
 BINARY_TESTS_NEAR_DOUBLE(hypot)

From f4edcf2685067e6e29889da0a0f400b76dc33196 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Thu, 9 Jan 2025 10:15:05 -0800
Subject: [PATCH 2617/2677] 3580 bug investigate test failures when running
 with cuda 126 (#3588)

* Update CUDA device manager structs for new versions of CUDA and drivers up to 12.6

* The shfl_instrinsics header file contains wrapper routines for the warp primitives and calls the new primitives for CUDA versions greater than 9 and the old ones for older CUDA versions. The new primitives have an additional argument which is a mask of the warp threads that are participating in the operation. The old primitives always involve all the threads in a warp. The wrapper routines originally allowed you to specify the mask which was ignored for the old primitives but this has now been removed. This is because if an old version of CUDA is being used then all threads must enter the wrapper routine and if a new version of CUDA is being used only the threads corresponding to the mask must enter. If threads outside the mask enter the routine then the behavior is undefined. In CUDA versions <=12.2 the primitive executes without any errors given however in later versions of CUDA a warp illegal instruction exception will be thrown. In order to preserve the same behavior of these wrapper functions for old and new versions of CUDA, the mask is always set to all threads in a warp for the new primitives. The specific new primitive can always be called with a custom mask which is already done elsewhere in the reduce_by_key routine.

* Fix for bug where new workspace size was not being calculated for the cusolver ormqr routine call which was causing memory errors.

* Fix for similar bug in the least squares solve routine where the new workspace size was not being calculated for the cusolver ormqr routine.

* Loosened tolerance for convolution filter tests for the floating point type to ensure all tests pass.

* Update src/backend/cuda/device_manager.cpp

Update driver versions to minimum required.

Co-authored-by: Filip Matzner <FloopCZ@users.noreply.github.com>

---------

Co-authored-by: Filip Matzner <FloopCZ@users.noreply.github.com>
---
 src/backend/cuda/device_manager.cpp |  2 ++
 src/backend/cuda/qr.cpp             | 33 ++++++++++++++++--
 src/backend/cuda/solve.cu           | 54 +++++++++++++++++++++++------
 test/convolve.cpp                   |  2 +-
 4 files changed, 76 insertions(+), 15 deletions(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 80f00f614a..05f775a821 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -102,6 +102,7 @@ static const int jetsonComputeCapabilities[] = {
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
     {12060, 9, 0, 0},
+    {12050, 9, 0, 0},
     {12040, 9, 0, 0},
     {12030, 9, 0, 0},
     {12020, 9, 0, 0},
@@ -144,6 +145,7 @@ struct ComputeCapabilityToStreamingProcessors {
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
         {12060, 525.60f, 528.33f},
+        {12050, 525.60f, 528.33f},
         {12040, 525.60f, 528.33f},
         {12030, 525.60f, 528.33f},
         {12020, 525.60f, 528.33f},
diff --git a/src/backend/cuda/qr.cpp b/src/backend/cuda/qr.cpp
index c28a41523f..f388944127 100644
--- a/src/backend/cuda/qr.cpp
+++ b/src/backend/cuda/qr.cpp
@@ -67,6 +67,16 @@ struct mqr_func_def_t {
                                               int, T *, int, int *);
 };
 
+template<typename T>
+struct mqr_buf_func_def_t {
+    using mqr_buf_func_def = cusolverStatus_t (*)(cusolverDnHandle_t,
+                                                  cublasSideMode_t,
+                                                  cublasOperation_t, int, int, int,
+                                                  const T *, int, const T *, T *,
+                                                  int, int *);
+};
+
+
 #define QR_FUNC_DEF(FUNC)                                         \
     template<typename T>                                          \
     typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func(); \
@@ -94,15 +104,25 @@ QR_FUNC(geqrf, double, D)
 QR_FUNC(geqrf, cfloat, C)
 QR_FUNC(geqrf, cdouble, Z)
 
-#define MQR_FUNC_DEF(FUNC) \
-    template<typename T>   \
-    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func();
+#define MQR_FUNC_DEF(FUNC)                                        \
+    template<typename T>                                          \
+    typename FUNC##_func_def_t<T>::FUNC##_func_def FUNC##_func(); \
+                                                                  \
+    template<typename T>                                          \
+    typename FUNC##_buf_func_def_t<T>::FUNC##_buf_func_def FUNC##_buf_func();
 
 #define MQR_FUNC(FUNC, TYPE, PREFIX)                                        \
     template<>                                                              \
     typename FUNC##_func_def_t<TYPE>::FUNC##_func_def FUNC##_func<TYPE>() { \
         return (FUNC##_func_def_t<TYPE>::FUNC##_func_def) &                 \
                cusolverDn##PREFIX;                                          \
+    }                                                                       \
+                                                                            \
+    template<>                                                              \
+    typename FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def               \
+        FUNC##_buf_func<TYPE>() {                                           \
+        return (FUNC##_buf_func_def_t<TYPE>::FUNC##_buf_func_def) &         \
+               cusolverDn##PREFIX##_bufferSize;                             \
     }
 
 MQR_FUNC_DEF(mqr)
@@ -143,6 +163,13 @@ void qr(Array<T> &q, Array<T> &r, Array<T> &t, const Array<T> &in) {
     dim4 qdims(M, mn);
     q = identity<T>(qdims);
 
+    CUSOLVER_CHECK(mqr_buf_func<T>()(
+        solverDnHandle(), CUBLAS_SIDE_LEFT, CUBLAS_OP_N, q.dims()[0],
+	q.dims()[1], min(M, N), in_copy.get(), in_copy.strides()[1], t.get(),
+        q.get(), q.strides()[1], &lwork));
+
+    workspace = memAlloc<T>(lwork);
+
     CUSOLVER_CHECK(mqr_func<T>()(
         solverDnHandle(), CUBLAS_SIDE_LEFT, CUBLAS_OP_N, q.dims()[0],
         q.dims()[1], min(M, N), in_copy.get(), in_copy.strides()[1], t.get(),
diff --git a/src/backend/cuda/solve.cu b/src/backend/cuda/solve.cu
index 884d7735b1..568e44b136 100644
--- a/src/backend/cuda/solve.cu
+++ b/src/backend/cuda/solve.cu
@@ -164,6 +164,13 @@ struct mqr_solve_func_def_t {
         const T *, int, const T *, T *, int, T *, int, int *);
 };
 
+template<typename T>
+struct mqr_solve_buf_func_def_t {
+    typedef cusolverStatus_t (*mqr_solve_buf_func_def)(
+	cusolverDnHandle_t, cublasSideMode_t, cublasOperation_t, int, int, int,
+        const T *, int, const T *, T *, int, int *);
+};
+
 #define QR_FUNC_DEF(FUNC)                                                     \
     template<typename T>                                                      \
     static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def         \
@@ -195,17 +202,28 @@ QR_FUNC(geqrf, double, D)
 QR_FUNC(geqrf, cfloat, C)
 QR_FUNC(geqrf, cdouble, Z)
 
-#define MQR_FUNC_DEF(FUNC)                                            \
-    template<typename T>                                              \
-    static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def \
-        FUNC##_solve_func();
-
-#define MQR_FUNC(FUNC, TYPE, PREFIX)                                    \
-    template<>                                                          \
-    typename FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def       \
-        FUNC##_solve_func<TYPE>() {                                     \
-        return (FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def) & \
-               cusolverDn##PREFIX;                                      \
+#define MQR_FUNC_DEF(FUNC)                                                    \
+    template<typename T>                                                      \
+    static typename FUNC##_solve_func_def_t<T>::FUNC##_solve_func_def         \
+        FUNC##_solve_func();                                                  \
+	                                                                      \
+    template<typename T>                                                      \
+    static typename FUNC##_solve_buf_func_def_t<T>::FUNC##_solve_buf_func_def \
+       	FUNC##_solve_buf_func();
+
+#define MQR_FUNC(FUNC, TYPE, PREFIX)                                            \
+    template<>                                                                  \
+    typename FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def               \
+        FUNC##_solve_func<TYPE>() {                                             \
+        return (FUNC##_solve_func_def_t<TYPE>::FUNC##_solve_func_def) &         \
+               cusolverDn##PREFIX;                                              \
+    }                                                                           \
+                                                                                \
+    template<>                                                                  \
+    typename FUNC##_solve_buf_func_def_t<TYPE>::FUNC##_solve_buf_func_def       \
+        FUNC##_solve_buf_func<TYPE>() {                                         \
+        return (FUNC##_solve_buf_func_def_t<TYPE>::FUNC##_solve_buf_func_def) & \
+               cusolverDn##PREFIX##_bufferSize;                                 \
     }
 
 MQR_FUNC_DEF(mqr)
@@ -393,6 +411,13 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
         B.resetDims(dim4(N, K));
 
         // matmul(Q, Bpad)
+        CUSOLVER_CHECK(mqr_solve_buf_func<T>()(
+            solverDnHandle(), CUBLAS_SIDE_LEFT, CUBLAS_OP_N, B.dims()[0],
+    	    B.dims()[1], A.dims()[0], A.get(), A.strides()[1], t.get(), B.get(),
+	    B.strides()[1], &lwork));
+    
+        workspace = memAlloc<T>(lwork);
+
         CUSOLVER_CHECK(mqr_solve_func<T>()(
             solverDnHandle(), CUBLAS_SIDE_LEFT, CUBLAS_OP_N, B.dims()[0],
             B.dims()[1], A.dims()[0], A.get(), A.strides()[1], t.get(), B.get(),
@@ -427,10 +452,17 @@ Array<T> leastSquares(const Array<T> &a, const Array<T> &b) {
             t.get(), workspace.get(), lwork, info.get()));
 
         // matmul(Q1, B)
+        CUSOLVER_CHECK(mqr_solve_buf_func<T>()(
+            solverDnHandle(), CUBLAS_SIDE_LEFT, trans<T>(), M, K, N, A.get(),
+	    A.strides()[1], t.get(), B.get(), B.strides()[1], &lwork));
+    
+        workspace = memAlloc<T>(lwork);
+
         CUSOLVER_CHECK(mqr_solve_func<T>()(
             solverDnHandle(), CUBLAS_SIDE_LEFT, trans<T>(), M, K, N, A.get(),
             A.strides()[1], t.get(), B.get(), B.strides()[1], workspace.get(),
             lwork, info.get()));
+
         // tri_solve(R1, Bt)
         A.resetDims(dim4(N, N));
         B.resetDims(dim4(N, K));
diff --git a/test/convolve.cpp b/test/convolve.cpp
index 8adeb40fd8..39daff3373 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -898,7 +898,7 @@ float tolerance();
 
 template<>
 float tolerance<float>() {
-    return 2e-3;
+    return 4e-3;
 }
 
 template<>

From e770c8875f9d9cb23b76c98a58c1c86dd3a931be Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Mon, 13 Jan 2025 10:14:39 -0800
Subject: [PATCH 2618/2677] Alternative OpenCL kernel for performing the CSC
 matrix vector multiply using atomic operations. Benchmarking so far has shown
 it to be on par with the CUDA backend on my Nvidia RTX 4060 GPU. Note that
 support has been included for the BLAS style matrix vector multiply with
 alpha and beta parameters however it appears that this is not supported
 elsewhere in the code for sparse matrices so it has not been tested. Existing
 sparse matrix vector multiply tests are all passing for single and double
 precision as well as complex. (#3608)

---
 src/backend/opencl/kernel/cscmv.cl  | 148 ++++++++++++----------------
 src/backend/opencl/kernel/cscmv.hpp |  51 +++++++---
 src/backend/opencl/traits.hpp       |  30 ++++++
 3 files changed, 129 insertions(+), 100 deletions(-)

diff --git a/src/backend/opencl/kernel/cscmv.cl b/src/backend/opencl/kernel/cscmv.cl
index fab18301a1..bc56f57e46 100644
--- a/src/backend/opencl/kernel/cscmv.cl
+++ b/src/backend/opencl/kernel/cscmv.cl
@@ -7,6 +7,10 @@
  * http://arrayfire.com/licenses/BSD-3-Clause
  ********************************************************/
 
+#if IS_DBL || IS_LONG
+#pragma OPENCL EXTENSION cl_khr_int64_base_atomics : enable
+#endif
+
 #if IS_CPLX
 T __cmul(T lhs, T rhs) {
     T out;
@@ -35,100 +39,70 @@ T __ccmul(T lhs, T rhs) {
 #define CMUL(a, b) (a) * (b)
 #endif
 
-int binary_search(global const int *ptr, int len, int val) {
-    int start = 0;
-    int end   = len;
-    while (end > start) {
-        int mid = start + (end - start) / 2;
-        if (val < ptr[mid]) {
-            end = mid;
-        } else if (val > ptr[mid]) {
-            start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    return start;
+#if IS_DBL || IS_LONG
+#define U ulong
+#define ATOMIC_FN atom_cmpxchg
+#else
+#define U unsigned
+#define ATOMIC_FN atomic_cmpxchg
+#endif
+
+#if IS_CPLX
+inline void atomicAdd(volatile __global T *ptr, T val) {
+    union {
+        U u[2];
+        T t;
+    } next, expected, current;
+    current.t = *ptr;
+
+    do {
+        expected.t.x = current.t.x;
+        next.t.x = expected.t.x + val.x;
+        current.u[0] = ATOMIC_FN((volatile __global U *) ptr, expected.u[0], next.u[0]);
+    } while(current.u[0] != expected.u[0]);
+    do {
+        expected.t.y = current.t.y;
+        next.t.y = expected.t.y + val.y;
+        current.u[1] = ATOMIC_FN(((volatile __global U *) ptr) + 1, expected.u[1], next.u[1]);
+    } while(current.u[1] != expected.u[1]);
+}
+#else
+inline void atomicAdd(volatile __global T *ptr, T val) {
+    union {
+        U u;
+        T t;
+    } next, expected, current;
+    current.t = *ptr;
+
+    do {
+        expected.t = current.t;
+        next.t = expected.t + val;
+        current.u = ATOMIC_FN((volatile __global U *) ptr, expected.u, next.u);
+    } while(current.u != expected.u);
+}
+#endif
+
+kernel void cscmv_beta(global T *output, const int M, const T beta) {
+    for(unsigned j = get_global_id(0); j < M; j += THREADS * get_num_groups(0))
+        output[j] *= beta;
 }
 
-// Each thread performs Matrix Vector multiplications for ROWS_PER_GROUP rows
-// and (K / THREAD) columns. This generates a local output buffer of size
-// ROWS_PER_THREAD for each thread. The outputs from each thread are added up to
-// generate the final result.
-kernel void cscmv_block(
-    global T *output, __global const T *values,
-    global const int *colidx,  // rowidx from csr is colidx in csc
-    global const int *rowidx,  // colidx from csr is rowidx in csc
-    const int M,                 // K from csr is M in csc
+kernel void cscmv_atomic(
+    global T *output, __global T *values,
+    global int *colidx,  // rowidx from csr is colidx in csc
+    global int *rowidx,  // colidx from csr is rowidx in csc
     const int K,                 // M from csr is K in csc
-    global const T *rhs, const KParam rinfo, const T alpha, const T beta) {
-    int lid = get_local_id(0);
+    global const T *rhs, const KParam rinfo, const T alpha) {
 
-    // Get the row offset for the current group in the uncompressed matrix
-    int rowOff = get_group_id(0) * ROWS_PER_GROUP;
-    int rowLim = min(ROWS_PER_GROUP, M - rowOff);
     rhs += rinfo.offset;
 
-    T l_outvals[ROWS_PER_GROUP];
-    for (int i = 0; i < rowLim; i++) { l_outvals[i] = 0; }
-
-    for (int colId = lid; colId < K; colId += THREADS) {
-        int rowStart     = colidx[colId];
-        int rowEnd       = colidx[colId + 1];
-        int nonZeroCount = rowEnd - rowStart;
-
-        // Find the location of the next non zero element after rowOff
-        int rowPos = binary_search(rowidx + rowStart, nonZeroCount, rowOff);
-        T rhsval   = rhs[colId];
-
-        // Traversing through nonzero elements in the current chunk
-        for (int id = rowPos + rowStart; id < rowEnd; id++) {
-            int rowId = rowidx[id];
-
-            // Exit if moving past current chunk
-            if (rowId >= rowOff + ROWS_PER_GROUP) break;
-
-            l_outvals[rowId - rowOff] += CMUL(values[id], rhsval);
-        }
-    }
-
-    // s_outvals is used for reduction
-    local T s_outvals[THREADS];
-
-    // s_output is used to store the final output into local memory
-    local T s_output[ROWS_PER_GROUP];
-
-    // For each row of output, copy registers to local memory, add results,
-    // write to output.
-    for (int i = 0; i < rowLim; i++) {
-        // Copying to local memory
-        s_outvals[lid] = l_outvals[i];
-        barrier(CLK_LOCAL_MEM_FENCE);
-
-        // Adding the results through reduction
-        for (int n = THREADS / 2; n > 0; n /= 2) {
-            if (lid < n) s_outvals[lid] += s_outvals[lid + n];
-            barrier(CLK_LOCAL_MEM_FENCE);
-        }
-
-        // Store to another local buffer so it can be written in a coalesced
-        // manner later
-        if (lid == 0) { s_output[i] = s_outvals[0]; }
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    // For each row in output, write output in coalesced manner
-    for (int i = lid; i < ROWS_PER_GROUP; i += THREADS) {
-        T outval = s_output[i];
-
+    for(unsigned j = get_group_id(0); j < K; j += get_num_groups(0)) {
+        for(unsigned i = get_local_id(0) + colidx[j]; i < colidx[j + 1]; i += THREADS) {
+            T outval = CMUL(values[i], rhs[j]);
 #if USE_ALPHA
-        outval = MUL(alpha, outval);
-#endif
-
-#if USE_BETA
-        output[rowOff + i] = outval + MUL(beta, output[j * M + rowOff + i]);
-#else
-        output[rowOff + i] = outval;
+            outval = MUL(alpha, outval);
 #endif
+            atomicAdd(output + rowidx[i], outval);
+        }
     }
 }
diff --git a/src/backend/opencl/kernel/cscmv.hpp b/src/backend/opencl/kernel/cscmv.hpp
index 88008480f8..2ab88b202c 100644
--- a/src/backend/opencl/kernel/cscmv.hpp
+++ b/src/backend/opencl/kernel/cscmv.hpp
@@ -32,39 +32,64 @@ void cscmv(Param out, const Param &values, const Param &colIdx,
            bool is_conj) {
     // TODO: rows_per_group limited by register pressure. Find better way to
     // handle this.
+    constexpr int threads_per_g = 64;
     constexpr int rows_per_group = 64;
 
     const bool use_alpha = (alpha != scalar<T>(1.0));
     const bool use_beta  = (beta != scalar<T>(0.0));
 
-    cl::NDRange local(THREADS_PER_GROUP);
+    cl::NDRange local(threads_per_g);
 
-    std::array<TemplateArg, 6> targs = {
+    int K        = colIdx.info.dims[0] - 1;
+    int M        = out.info.dims[0];
+
+    std::array<TemplateArg, 5> targs = {
         TemplateTypename<T>(),       TemplateArg(use_alpha),
-        TemplateArg(use_beta),       TemplateArg(is_conj),
-        TemplateArg(rows_per_group), TemplateArg(local[0]),
+        TemplateArg(is_conj), TemplateArg(rows_per_group),
+        TemplateArg(local[0]),
     };
-    std::array<std::string, 8> options = {
+    std::array<std::string, 9> options = {
         DefineKeyValue(T, dtype_traits<T>::getName()),
         DefineKeyValue(USE_ALPHA, use_alpha),
-        DefineKeyValue(USE_BETA, use_beta),
         DefineKeyValue(IS_CONJ, is_conj),
         DefineKeyValue(THREADS, local[0]),
         DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
         DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
+        DefineKeyValue(IS_DBL, (isdbl<T>() ? 1 : 0)),
+        DefineKeyValue(IS_LONG, (islong<T>() ? 1 : 0)),
         getTypeBuildDefinition<T>()};
 
-    auto cscmvBlock =
-        common::getKernel("cscmv_block", {{cscmv_cl_src}}, targs, options);
+    if(use_beta) {
+        std::array<TemplateArg, 4> targs_beta = {
+            TemplateTypename<T>(), TemplateArg(is_conj),
+            TemplateArg(rows_per_group), TemplateArg(local[0])};
+        std::array<std::string, 8> options_beta = {
+            DefineKeyValue(T, dtype_traits<T>::getName()),
+            DefineKeyValue(IS_CONJ, is_conj),
+            DefineKeyValue(THREADS, local[0]),
+            DefineKeyValue(ROWS_PER_GROUP, rows_per_group),
+            DefineKeyValue(IS_CPLX, (iscplx<T>() ? 1 : 0)),
+            DefineKeyValue(IS_DBL, (isdbl<T>() ? 1 : 0)),
+            DefineKeyValue(IS_LONG, (islong<T>() ? 1 : 0)),
+            getTypeBuildDefinition<T>()};
+
+        int groups_x = divup(M, rows_per_group * threads_per_g);
+        cl::NDRange global(local[0] * groups_x, 1);
+        auto cscmvBeta = common::getKernel("cscmv_beta", {{cscmv_cl_src}}, targs_beta, options_beta);
+        cscmvBeta(cl::EnqueueArgs(getQueue(), global, local), *out.data, M, beta);
+
+    } else {
+        getQueue().enqueueFillBuffer(*out.data, 0, 0, M * sizeof(T));
+    }
 
-    int K        = colIdx.info.dims[0] - 1;
-    int M        = out.info.dims[0];
     int groups_x = divup(M, rows_per_group);
     cl::NDRange global(local[0] * groups_x, 1);
 
-    cscmvBlock(cl::EnqueueArgs(getQueue(), global, local), *out.data,
-               *values.data, *colIdx.data, *rowIdx.data, M, K, *rhs.data,
-               rhs.info, alpha, beta);
+    auto cscmvAtomic =
+        common::getKernel("cscmv_atomic", {{cscmv_cl_src}}, targs, options);
+    cscmvAtomic(cl::EnqueueArgs(getQueue(), global, local), *out.data,
+                *values.data, *colIdx.data, *rowIdx.data, K, *rhs.data,
+                rhs.info, alpha);
     CL_DEBUG_FINISH(getQueue());
 }
 }  // namespace kernel
diff --git a/src/backend/opencl/traits.hpp b/src/backend/opencl/traits.hpp
index 00af1d17b0..2af7257b76 100644
--- a/src/backend/opencl/traits.hpp
+++ b/src/backend/opencl/traits.hpp
@@ -49,6 +49,36 @@ inline bool iscplx<cdouble>() {
     return true;
 }
 
+template<typename T>
+static bool isdbl() {
+    return false;
+}
+
+template<>
+inline bool isdbl<double>() {
+    return true;
+}
+
+template<>
+inline bool isdbl<cdouble>() {
+    return true;
+}
+
+template<typename T>
+static bool islong() {
+    return false;
+}
+
+template<>
+inline bool islong<long>() {
+    return true;
+}
+
+template<>
+inline bool islong<unsigned long>() {
+    return true;
+}
+
 template<typename T>
 inline std::string scalar_to_option(const T &val) {
     using namespace arrayfire::common;

From 7127a0babfa3a05ab2d166a2708d9bd6533569db Mon Sep 17 00:00:00 2001
From: errata-c <77643526+errata-c@users.noreply.github.com>
Date: Mon, 13 Jan 2025 15:26:22 -0500
Subject: [PATCH 2619/2677] Fixed padding comparison in convolve2GradientNN
 (#3519)

Co-authored-by: errata-c <erratachem@gmail.com>
---
 src/api/c/convolve.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index abbcd2f71b..61af7b1b16 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -437,7 +437,7 @@ af_err af_convolve2_gradient_nn(
         size_t padding_ndims  = padding.ndims();
         size_t dilation_ndims = dilation.ndims();
         ARG_ASSERT(3, stride_ndims > 0 && stride_ndims <= 2);
-        ARG_ASSERT(5, padding_ndims > 0 && padding_ndims <= 2);
+        ARG_ASSERT(5, padding_ndims >= 0 && padding_ndims <= 2);
         ARG_ASSERT(7, dilation_ndims > 0 && dilation_ndims <= 2);
 
         af_dtype type = oinfo.getType();

From 5c2ea2998573ddbbb9da3885e385faa40552567e Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 14 Jan 2025 10:20:35 -0800
Subject: [PATCH 2620/2677] Extend test for convolve2GradientNN function to
 verify zero padding fix in PR 3519 (#3631)

---
 test/convolve.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/test/convolve.cpp b/test/convolve.cpp
index 39daff3373..ac731ef31c 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -1176,4 +1176,10 @@ TEST(ConvolveNN, ZeroPadding_Issue2817) {
     array convolved = convolve2NN(signal, filter, strides, padding, dilation);
     ASSERT_EQ(sum<float>(abs(signal(seq(1, 3), seq(1, 3)) - convolved)) < 1E-5,
               true);
+
+    array incoming_gradient = constant(1 / 9.f, 3, 3);
+    array convolved_grad = convolve2GradientNN(incoming_gradient, signal, filter,
+                                               convolved, strides, padding, dilation,
+                                               AF_CONV_GRADIENT_FILTER);
+    ASSERT_EQ(sum<float>(abs(convolved - convolved_grad)) < 1E-5, true);
 }

From eef57732c94b29c5afc834eb111b5924dc232adb Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Thu, 16 Jan 2025 02:47:15 +0100
Subject: [PATCH 2621/2677] Correct the conversion from float/double to half on
 CUDA (#3627)

---
 src/backend/common/half.hpp | 44 +++++++++++++++++++++----------------
 1 file changed, 25 insertions(+), 19 deletions(-)

diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index b6585dc905..3f966c6f81 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -87,6 +87,7 @@ using uint16_t = unsigned short;
 #define AF_CONSTEXPR constexpr
 #else
 #include <af/compilers.h>
+#include <algorithm>
 #include <cmath>
 #include <cstdint>
 #include <cstring>
@@ -245,9 +246,9 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(T value) noexcept {
 /// \return binary representation of half-precision value
 template<std::float_round_style R = std::round_to_nearest>
 __DH__ native_half_t float2half_impl(float value) noexcept {
-    uint32_t bits = 0;  // = *reinterpret_cast<uint32*>(&value);
-                        // //violating strict aliasing!
-    std::memcpy(&bits, &value, sizeof(float));
+    alignas(std::max(alignof(uint32_t), alignof(float))) float _value = value;
+    uint32_t bits = *reinterpret_cast<uint32_t*>(&_value);
+
     constexpr uint16_t base_table[512] = {
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
         0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000,
@@ -337,9 +338,10 @@ __DH__ native_half_t float2half_impl(float value) noexcept {
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
         24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
         24, 24, 24, 24, 24, 24, 24, 13};
-    uint16_t hbits =
-        base_table[bits >> 23] +
-        static_cast<uint16_t>((bits & 0x7FFFFF) >> shift_table[bits >> 23]);
+    alignas(std::max(alignof(uint16_t), alignof(native_half_t)))
+        uint16_t hbits =
+            base_table[bits >> 23] +
+            static_cast<uint16_t>((bits & 0x7FFFFF) >> shift_table[bits >> 23]);
     AF_IF_CONSTEXPR(R == std::round_to_nearest)
     hbits +=
         (((bits & 0x7FFFFF) >> (shift_table[bits >> 23] - 1)) |
@@ -367,7 +369,8 @@ __DH__ native_half_t float2half_impl(float value) noexcept {
           (((bits >> 23) <= 358) & ((bits >> 23) != 256))) &
          (hbits < 0xFC00) & (hbits >> 15)) -
         ((hbits == 0x7C00) & ((bits >> 23) != 255));
-    return hbits;
+
+    return *reinterpret_cast<native_half_t*>(&hbits);
 }
 
 /// Convert IEEE double-precision to half-precision.
@@ -379,11 +382,11 @@ __DH__ native_half_t float2half_impl(float value) noexcept {
 /// \return binary representation of half-precision value
 template<std::float_round_style R>
 __DH__ native_half_t float2half_impl(double value) {
-    uint64_t bits{0};  // = *reinterpret_cast<uint64*>(&value);		//violating
-                       // strict aliasing!
-    std::memcpy(&bits, &value, sizeof(double));
+    alignas(std::max(alignof(uint64_t), alignof(double))) double _value = value;
+    uint64_t bits = *reinterpret_cast<uint64_t*>(&_value);
     uint32_t hi = bits >> 32, lo = bits & 0xFFFFFFFF;
-    uint16_t hbits = (hi >> 16) & 0x8000;
+    alignas(std::max(alignof(uint16_t), alignof(native_half_t)))
+        uint16_t hbits = (hi >> 16) & 0x8000;
     hi &= 0x7FFFFFFF;
     int exp = hi >> 20;
     if (exp == 2047)
@@ -420,7 +423,8 @@ __DH__ native_half_t float2half_impl(double value) {
         ~(hbits >> 15) & (s | g);
     else AF_IF_CONSTEXPR(R == std::round_toward_neg_infinity) hbits +=
         (hbits >> 15) & (g | s);
-    return hbits;
+
+    return *reinterpret_cast<native_half_t*>(&hbits);
 }
 
 __DH__ inline float half2float_impl(native_half_t value) noexcept {
@@ -790,14 +794,14 @@ __DH__ inline float half2float_impl(native_half_t value) noexcept {
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024,
         1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024};
 
-    uint16_t value_bits = 0;
-    std::memcpy(&value_bits, &value, sizeof(uint16_t));
-    uint32_t bits =
+    alignas(std::max(alignof(uint16_t), alignof(native_half_t)))
+        native_half_t _value = value;
+    uint16_t value_bits      = *reinterpret_cast<uint16_t*>(&_value);
+
+    alignas(std::max(alignof(uint32_t), alignof(float))) uint32_t bits =
         mantissa_table[offset_table[value_bits >> 10] + (value_bits & 0x3FF)] +
         exponent_table[value_bits >> 10];
-    float out = 0.0f;
-    std::memcpy(&out, &bits, sizeof(float));
-    return out;
+    return *reinterpret_cast<float*>(&bits);
 }
 
 #endif  // __CUDACC_RTC__
@@ -872,7 +876,9 @@ AF_CONSTEXPR T half2int(native_half_t value) {
     else AF_IF_CONSTEXPR(std::is_same<T, int>::value) {
         return __half2int_rn(value);
     }
-    else { return __half2uint_rn(value); }
+    else {
+        return __half2uint_rn(value);
+    }
 #elif defined(AF_ONEAPI)
     return static_cast<T>(value);
 #else

From 6e5dca46957e3c894c807f269d8f90aa28614160 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Wed, 15 Jan 2025 17:54:05 -0800
Subject: [PATCH 2622/2677] Reverted an error in the interop_cuda example code
 where the cuda stream id was being used instead of the arrayfire stream id.
 (#3594)


From ffda1b6e241ceb4d6a1d8718afd8be6a10ad7e26 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Thu, 30 Jan 2025 10:31:38 -0800
Subject: [PATCH 2623/2677] 3560 bug incorrect results when using the pow
 function with float16 arguments with cuda backend (#3561)

* Add cases for float16 arguments to atan2 and hypot functions

* Added test cases for half precision atan2 and hypot functions

* Fix for incorrect result when using the pow function with float16 arguments with the CUDA backend. Since the half precision CUDA library doesn't have a pow function, the default pow function is used, casting the arguments to double precision.
---
 src/backend/cuda/kernel/jit.cuh | 5 -----
 test/binary.cpp                 | 1 +
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index cfb5837719..76fd344010 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -59,14 +59,9 @@ typedef cuDoubleComplex cdouble;
 #define __rem(lhs, rhs) ((lhs) % (rhs))
 #define __mod(lhs, rhs) ((lhs) % (rhs))
 
-#ifdef AF_WITH_FAST_MATH
 #define __pow(lhs, rhs)  \
     static_cast<double>( \
         pow(static_cast<double>(lhs), static_cast<double>(rhs)));
-#else
-#define __pow(lhs, rhs) \
-    __float2int_rn(powf(__int2float_rn((int)lhs), __int2float_rn((int)rhs)))
-#endif
 #define __powll(lhs, rhs) \
     __double2ll_rn(pow(__ll2double_rn(lhs), __ll2double_rn(rhs)))
 #define __powul(lhs, rhs) \
diff --git a/test/binary.cpp b/test/binary.cpp
index c029a19da5..a274c11346 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -195,6 +195,7 @@ BINARY_TESTS_NEAR_FLOAT(pow)
 BINARY_TESTS_NEAR_FLOAT(hypot)
 
 BINARY_TESTS_NEAR_HALF(atan2)
+BINARY_TESTS_NEAR_HALF(pow)
 BINARY_TESTS_NEAR_HALF(hypot)
 
 BINARY_TESTS_NEAR_DOUBLE(atan2)

From c644e4f5a375e41608b36339f444d49ab2967287 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 10 Feb 2025 18:37:42 -0800
Subject: [PATCH 2624/2677] Added testing for sequence indexing with
 non-unitary steps mixed in with array indexing (#3587)

---
 test/gen_index.cpp | 50 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)

diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index e65d4e48e5..0716751fa0 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -253,6 +253,56 @@ TEST(GeneralIndex, AASS) {
     ASSERT_SUCCESS(af_release_array(outArray));
 }
 
+TEST(GeneralIndex, SSAS_LinearSteps) {
+    vector<dim4> numDims;
+    vector<vector<float>> in;
+    vector<vector<float>> tests;  // Read tests from file
+
+    readTestsFromFile<float, float>(
+        TEST_DIR "/gen_index/s29_9__3s0_9_2as0_n.test", numDims, in, tests);
+
+    af_array outArray  = 0;
+    af_array inArray   = 0;
+    af_array idxArray0 = 0;
+    dim4 dims0         = numDims[0];
+    dim4 dims1         = numDims[1];
+
+    ASSERT_SUCCESS(af_create_array(&inArray, &(in[0].front()), dims0.ndims(),
+                                   dims0.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+
+    ASSERT_SUCCESS(af_create_array(&idxArray0, &(in[1].front()), dims1.ndims(),
+                                   dims1.get(),
+                                   (af_dtype)dtype_traits<float>::af_type));
+
+    af_index_t indexs[4];
+    indexs[0].idx.seq = af_make_seq(29, 9, -3);
+    indexs[1].idx.seq = af_make_seq(0, 9, 2);
+    indexs[2].idx.arr = idxArray0;
+    indexs[3].idx.seq = af_span;
+
+    indexs[0].isSeq = true;
+    indexs[1].isSeq = true;
+    indexs[2].isSeq = false;
+    indexs[3].isSeq = true;
+
+    ASSERT_SUCCESS(af_index_gen(&outArray, inArray, 4, indexs));
+
+    vector<float> currGoldBar = tests[0];
+    size_t nElems             = currGoldBar.size();
+    vector<float> outData(nElems);
+
+    ASSERT_SUCCESS(af_get_data_ptr((void *)outData.data(), outArray));
+
+    for (size_t elIter = 0; elIter < nElems; ++elIter) {
+        ASSERT_EQ(currGoldBar[elIter], outData[elIter])
+            << "at: " << elIter << endl;
+    }
+
+    ASSERT_SUCCESS(af_release_array(inArray));
+    ASSERT_SUCCESS(af_release_array(outArray));
+}
+
 using af::array;
 using af::freeHost;
 using af::randu;

From 18028c090be521414a9e0c4495c2c29b1d4436f1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 10 Feb 2025 18:38:21 -0800
Subject: [PATCH 2625/2677] Fix issue 3525: correct handling of array indexing
 with sequence (#3585)

* Fix issue 3525: implement correct handling of steps in array indexing with sequence

* Implement handling of af_span sequence
---
 src/backend/cpu/index.cpp                     | 11 +++++++++-
 src/backend/cpu/kernel/index.hpp              | 22 ++++++++++---------
 src/backend/cuda/assign_kernel_param.hpp      |  1 +
 src/backend/cuda/index.cpp                    | 10 +++++++++
 src/backend/cuda/kernel/index.cuh             | 12 ++++++----
 src/backend/oneapi/index.cpp                  | 10 +++++++++
 .../oneapi/kernel/assign_kernel_param.hpp     |  1 +
 src/backend/oneapi/kernel/index.hpp           | 12 ++++++----
 src/backend/opencl/index.cpp                  | 10 +++++++++
 src/backend/opencl/kernel/index.cl            | 21 +++++++++++-------
 src/backend/opencl/kernel/index.hpp           |  1 +
 11 files changed, 84 insertions(+), 27 deletions(-)

diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 315406b46d..850239acfe 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -35,7 +35,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
     // create seq vector to retrieve output
     // dimensions, offsets & offsets
     for (unsigned x = 0; x < isSeq.size(); ++x) {
-        if (idxrs[x].isSeq) { seqs[x] = idxrs[x].idx.seq; }
+        if (idxrs[x].isSeq) {
+            af_seq seq = idxrs[x].idx.seq;
+            // Handle af_span as a sequence that covers the complete axis
+            if (seq.begin == af_span.begin && seq.end == af_span.end &&
+                seq.step == af_span.step) {
+                seqs[x] = af_seq{0, (double)(in.dims()[x] - 1), 1};
+            } else {
+                seqs[x] = seq;
+            }
+        }
         isSeq[x] = idxrs[x].isSeq;
     }
 
diff --git a/src/backend/cpu/kernel/index.hpp b/src/backend/cpu/kernel/index.hpp
index 2a6a6d9bc4..962b0713dc 100644
--- a/src/backend/cpu/kernel/index.hpp
+++ b/src/backend/cpu/kernel/index.hpp
@@ -34,25 +34,27 @@ void index(Param<T> out, CParam<T> in, const af::dim4 dDims,
 
     for (dim_t l = 0; l < oDims[3]; ++l) {
         dim_t lOff   = l * oStrides[3];
-        dim_t inIdx3 = trimIndex(isSeq[3] ? l + iOffs[3] : ptr3[l], iDims[3]);
+        dim_t inIdx3 = trimIndex(
+            isSeq[3] ? l * seqs[3].step + iOffs[3] : ptr3[l], iDims[3]);
         dim_t inOff3 = inIdx3 * iStrds[3];
 
         for (dim_t k = 0; k < oDims[2]; ++k) {
-            dim_t kOff = k * oStrides[2];
-            dim_t inIdx2 =
-                trimIndex(isSeq[2] ? k + iOffs[2] : ptr2[k], iDims[2]);
+            dim_t kOff   = k * oStrides[2];
+            dim_t inIdx2 = trimIndex(
+                isSeq[2] ? k * seqs[2].step + iOffs[2] : ptr2[k], iDims[2]);
             dim_t inOff2 = inIdx2 * iStrds[2];
 
             for (dim_t j = 0; j < oDims[1]; ++j) {
-                dim_t jOff = j * oStrides[1];
-                dim_t inIdx1 =
-                    trimIndex(isSeq[1] ? j + iOffs[1] : ptr1[j], iDims[1]);
+                dim_t jOff   = j * oStrides[1];
+                dim_t inIdx1 = trimIndex(
+                    isSeq[1] ? j * seqs[1].step + iOffs[1] : ptr1[j], iDims[1]);
                 dim_t inOff1 = inIdx1 * iStrds[1];
 
                 for (dim_t i = 0; i < oDims[0]; ++i) {
-                    dim_t iOff = i * oStrides[0];
-                    dim_t inIdx0 =
-                        trimIndex(isSeq[0] ? i + iOffs[0] : ptr0[i], iDims[0]);
+                    dim_t iOff   = i * oStrides[0];
+                    dim_t inIdx0 = trimIndex(
+                        isSeq[0] ? i * seqs[0].step + iOffs[0] : ptr0[i],
+                        iDims[0]);
                     dim_t inOff0 = inIdx0 * iStrds[0];
 
                     dst[lOff + kOff + jOff + iOff] =
diff --git a/src/backend/cuda/assign_kernel_param.hpp b/src/backend/cuda/assign_kernel_param.hpp
index 0591ca80ad..350893f911 100644
--- a/src/backend/cuda/assign_kernel_param.hpp
+++ b/src/backend/cuda/assign_kernel_param.hpp
@@ -15,6 +15,7 @@ namespace cuda {
 typedef struct {
     int offs[4];
     int strds[4];
+    int steps[4];
     bool isSeq[4];
     unsigned int* ptr[4];
 } AssignKernelParam;
diff --git a/src/backend/cuda/index.cpp b/src/backend/cuda/index.cpp
index 88a95da73b..d8acf90c12 100644
--- a/src/backend/cuda/index.cpp
+++ b/src/backend/cuda/index.cpp
@@ -44,6 +44,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
         p.isSeq[i] = idxrs[i].isSeq;
         p.offs[i]  = iOffs[i];
         p.strds[i] = iStrds[i];
+        p.steps[i] = 0;
+        if (idxrs[i].isSeq) {
+            af_seq seq = idxrs[i].idx.seq;
+            // The step for af_span used in the kernel must be 1
+            if (seq.begin == af_span.begin && seq.end == af_span.end &&
+                seq.step == af_span.step)
+                p.steps[i] = 1;
+            else
+                p.steps[i] = seq.step;
+        }
     }
 
     std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4()));
diff --git a/src/backend/cuda/kernel/index.cuh b/src/backend/cuda/kernel/index.cuh
index 37b6b63d46..968e9ae0c6 100644
--- a/src/backend/cuda/kernel/index.cuh
+++ b/src/backend/cuda/kernel/index.cuh
@@ -43,13 +43,17 @@ __global__ void index(Param<T> out, CParam<T> in, const IndexKernelParam p,
         gw < out.dims[3]) {
         // calculate pointer offsets for input
         int i =
-            p.strds[0] * trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], in.dims[0]);
+            p.strds[0] *
+            trimIndex(s0 ? gx * p.steps[0] + p.offs[0] : ptr0[gx], in.dims[0]);
         int j =
-            p.strds[1] * trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], in.dims[1]);
+            p.strds[1] *
+            trimIndex(s1 ? gy * p.steps[1] + p.offs[1] : ptr1[gy], in.dims[1]);
         int k =
-            p.strds[2] * trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], in.dims[2]);
+            p.strds[2] *
+            trimIndex(s2 ? gz * p.steps[2] + p.offs[2] : ptr2[gz], in.dims[2]);
         int l =
-            p.strds[3] * trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], in.dims[3]);
+            p.strds[3] *
+            trimIndex(s3 ? gw * p.steps[3] + p.offs[3] : ptr3[gw], in.dims[3]);
         // offset input and output pointers
         const T* src = (const T*)in.ptr + (i + j + k + l);
         T* dst = (T*)out.ptr + (gx * out.strides[0] + gy * out.strides[1] +
diff --git a/src/backend/oneapi/index.cpp b/src/backend/oneapi/index.cpp
index bec65902d8..2548df2011 100644
--- a/src/backend/oneapi/index.cpp
+++ b/src/backend/oneapi/index.cpp
@@ -44,6 +44,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
         p.isSeq[i] = idxrs[i].isSeq;
         p.offs[i]  = iOffs[i];
         p.strds[i] = iStrds[i];
+        p.steps[i] = 0;
+        if (idxrs[i].isSeq) {
+            af_seq seq = idxrs[i].idx.seq;
+            // The step for af_span used in the kernel must be 1
+            if (seq.begin == af_span.begin && seq.end == af_span.end &&
+                seq.step == af_span.step)
+                p.steps[i] = 1;
+            else
+                p.steps[i] = seq.step;
+        }
     }
 
     std::vector<Array<uint>> idxArrs(4, createEmptyArray<uint>(dim4(1)));
diff --git a/src/backend/oneapi/kernel/assign_kernel_param.hpp b/src/backend/oneapi/kernel/assign_kernel_param.hpp
index e2539ed2b3..e2eec56d18 100644
--- a/src/backend/oneapi/kernel/assign_kernel_param.hpp
+++ b/src/backend/oneapi/kernel/assign_kernel_param.hpp
@@ -19,6 +19,7 @@ namespace oneapi {
 typedef struct {
     int offs[4];
     int strds[4];
+    int steps[4];
     bool isSeq[4];
     std::array<sycl::accessor<unsigned int, 1, sycl::access::mode::read,
                               sycl::access::target::device>,
diff --git a/src/backend/oneapi/kernel/index.hpp b/src/backend/oneapi/kernel/index.hpp
index 857b299aef..c7bb591953 100644
--- a/src/backend/oneapi/kernel/index.hpp
+++ b/src/backend/oneapi/kernel/index.hpp
@@ -88,13 +88,17 @@ class indexKernel {
         if (gx < odims0 && gy < odims1 && gz < odims2 && gw < odims3) {
             // calculate pointer offsets for input
             int i = p.strds[0] *
-                    trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], inp.dims[0]);
+                    trimIndex(s0 ? gx * p.steps[0] + p.offs[0] : ptr0[gx],
+                              inp.dims[0]);
             int j = p.strds[1] *
-                    trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], inp.dims[1]);
+                    trimIndex(s1 ? gy * p.steps[1] + p.offs[1] : ptr1[gy],
+                              inp.dims[1]);
             int k = p.strds[2] *
-                    trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], inp.dims[2]);
+                    trimIndex(s2 ? gz * p.steps[2] + p.offs[2] : ptr2[gz],
+                              inp.dims[2]);
             int l = p.strds[3] *
-                    trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], inp.dims[3]);
+                    trimIndex(s3 ? gw * p.steps[3] + p.offs[3] : ptr3[gw],
+                              inp.dims[3]);
             // offset input and output pointers
             const T* src = (const T*)in.get_pointer() + (i + j + k + l);
             T* dst       = (T*)out.get_pointer() +
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index 0911229936..d2864e6a81 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -42,6 +42,16 @@ Array<T> index(const Array<T>& in, const af_index_t idxrs[]) {
         p.isSeq[i] = idxrs[i].isSeq ? 1 : 0;
         p.offs[i]  = iOffs[i];
         p.strds[i] = iStrds[i];
+        p.steps[i] = 0;
+        if (idxrs[i].isSeq) {
+            af_seq seq = idxrs[i].idx.seq;
+            // The step for af_span used in the kernel must be 1
+            if (seq.begin == af_span.begin && seq.end == af_span.end &&
+                seq.step == af_span.step)
+                p.steps[i] = 1;
+            else
+                p.steps[i] = seq.step;
+        }
     }
 
     cl::Buffer* bPtrs[4];
diff --git a/src/backend/opencl/kernel/index.cl b/src/backend/opencl/kernel/index.cl
index 85e6e10cc0..2cc3cb57fe 100644
--- a/src/backend/opencl/kernel/index.cl
+++ b/src/backend/opencl/kernel/index.cl
@@ -10,6 +10,7 @@
 typedef struct {
     int offs[4];
     int strds[4];
+    int steps[4];
     char isSeq[4];
 } IndexKernelParam_t;
 
@@ -47,14 +48,18 @@ kernel void indexKernel(global T* optr, KParam oInfo, global const T* iptr,
     if (gx < oInfo.dims[0] && gy < oInfo.dims[1] && gz < oInfo.dims[2] &&
         gw < oInfo.dims[3]) {
         // calculate pointer offsets for input
-        int i = p.strds[0] *
-                trimIndex(s0 ? gx + p.offs[0] : ptr0[gx], iInfo.dims[0]);
-        int j = p.strds[1] *
-                trimIndex(s1 ? gy + p.offs[1] : ptr1[gy], iInfo.dims[1]);
-        int k = p.strds[2] *
-                trimIndex(s2 ? gz + p.offs[2] : ptr2[gz], iInfo.dims[2]);
-        int l = p.strds[3] *
-                trimIndex(s3 ? gw + p.offs[3] : ptr3[gw], iInfo.dims[3]);
+        int i =
+            p.strds[0] * trimIndex(s0 ? gx * p.steps[0] + p.offs[0] : ptr0[gx],
+                                   iInfo.dims[0]);
+        int j =
+            p.strds[1] * trimIndex(s1 ? gy * p.steps[1] + p.offs[1] : ptr1[gy],
+                                   iInfo.dims[1]);
+        int k =
+            p.strds[2] * trimIndex(s2 ? gz * p.steps[2] + p.offs[2] : ptr2[gz],
+                                   iInfo.dims[2]);
+        int l =
+            p.strds[3] * trimIndex(s3 ? gw * p.steps[3] + p.offs[3] : ptr3[gw],
+                                   iInfo.dims[3]);
         // offset input and output pointers
         global const T* src = iptr + (i + j + k + l) + iInfo.offset;
         global T* dst = optr + (gx * oInfo.strides[0] + gy * oInfo.strides[1] +
diff --git a/src/backend/opencl/kernel/index.hpp b/src/backend/opencl/kernel/index.hpp
index 9433893b96..5362a8e78b 100644
--- a/src/backend/opencl/kernel/index.hpp
+++ b/src/backend/opencl/kernel/index.hpp
@@ -26,6 +26,7 @@ namespace kernel {
 typedef struct {
     int offs[4];
     int strds[4];
+    int steps[4];
     char isSeq[4];
 } IndexKernelParam_t;
 

From b1e85d3d59a97def95ab705598e78f890ec4b295 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Wed, 12 Feb 2025 20:32:06 -0500
Subject: [PATCH 2626/2677] 3539 build oneapi version 2024 incompatible for mkl
 (#3573)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Updated cmake files to support oneAPI version 2024.1. Currently doesn't support compiling on Windows, this will be added later.

* Some tests were showing oneAPI errors due to nested calls of submit to the SYCL queue which is not supported. This has been fixed by moving calls to get() out of the submit calls. If a get call is made to a node that has not yet been evaluated, it will need to submit work to the SYCL queue.

* Modify test cases to check if library functions are supported on the current backend. If a function is not supported the test is skipped. This works with both the C API which returns an error flag and the C++ API which throws an exception.

* Modified half support check to check the native vector width for half precision as well as the fp16 aspect for the oneAPI backend. Some devices advertise the fp16 aspect but their native vector width for half precision is zero which results in errors when calling OpenCL routines with half precision arguments.

* Fix for bug in index function introduced when implementing a fix for nested oneAPI queue submissions.

* Fixed bug in irreduce_dim_launcher where incorrect templated calls were made to ireduceDimKernelSMEM for 1, 2 and 4 y threads

* Fix bug in wrap function where one dimension of the output array was missing from the global problem size.

* Check for failure of asserts in apiWrapper and return from test to prevent segfaults in subsequent asserts.

* Modified ASSERT_SUCCESS macro to skip unsupported tests rather than failing when AF_SKIP_UNSUPPORTED_TESTS CMake option is enabled

* Fixed cmake policy issue for oneapi fix (#3569)

* Cmake function CMakeDetermineCompileFeatures has been changed to CMakeDetermineCompilerSupport in version 3.30. Added support for this.

* Removed unsupported compute capabilites from all architectures list for CUDA 12

* Rename variables for input and output arrays of join method

* Fix issue where NOT_SUPPORTED errors were storing the error message as the back end name

* Added macro for asserting success of C++ API functions that throw exceptions. If the not supported exception is thrown, the test can be skipped if the AF_SKIP_UNSUPPORTED_TESTS variable is on.

* Remove macros that check for unsupported exceptions and skip tests. A SKIP_BACKEND macro will be made instead that will need to be explicitly added to each test that calls a function unsupported for a given backend.

* The UNSUPPORTED_BACKEND macro has now been added to all tests that are not supported by the oneAPI back end. If AF_SKIP_UNSUPPORTED_TESTS is set to ON then all tests with this macro will be skipped. These will need to be removed as oneAPI support is added for each feature.

* Update getBackendName test helper function to support all back ends. It is now used for the UNSUPPORTED_BACKEND macro.

---------

Co-authored-by: Edwin Lester Solís Fuentes <68087165+edwinsolisf@users.noreply.github.com>
---
 CMakeLists.txt                                |   9 +-
 CMakeModules/CMakeTestSYCLCompiler.cmake      |   9 +-
 CMakeModules/CPackProjectConfig.cmake         |  44 ++++++
 CMakeModules/FindAF_MKL.cmake                 |   8 ++
 CMakeModules/nsis/NSIS.definitions.nsh.in     |   2 +-
 CMakeModules/nsis/NSIS.template.in            |   2 +-
 CMakeModules/select_compute_arch.cmake        |  24 +++-
 LICENSE                                       |   2 +-
 docs/doxygen.mk                               | 129 +++++++++++-------
 src/backend/common/err_common.cpp             |   4 +-
 src/backend/common/err_common.hpp             |   2 +-
 src/backend/cpu/err_cpu.hpp                   |   4 +-
 src/backend/cuda/CMakeLists.txt               |   3 +
 src/backend/cuda/err_cuda.hpp                 |   4 +-
 src/backend/oneapi/Array.cpp                  |   6 +-
 src/backend/oneapi/CMakeLists.txt             |   2 +-
 src/backend/oneapi/copy.cpp                   |   3 +-
 src/backend/oneapi/err_oneapi.hpp             |   4 +-
 src/backend/oneapi/join.cpp                   |  15 +-
 src/backend/oneapi/kernel/index.hpp           |   5 +-
 src/backend/oneapi/kernel/ireduce.hpp         |   6 +-
 src/backend/oneapi/kernel/mean.hpp            |  12 +-
 src/backend/oneapi/kernel/reduce_all.hpp      |  10 +-
 .../oneapi/kernel/sort_by_key_impl.hpp        |   9 +-
 src/backend/oneapi/kernel/sparse_arith.hpp    |   3 +-
 src/backend/oneapi/kernel/wrap.hpp            |   2 +-
 src/backend/oneapi/platform.cpp               |   7 +-
 src/backend/oneapi/reduce_impl.hpp            |  63 ++++++---
 src/backend/opencl/err_opencl.hpp             |   4 +-
 test/CMakeLists.txt                           |   5 +
 test/anisotropic_diffusion.cpp                |  12 +-
 test/arrayfire_test.cpp                       |  20 ++-
 test/canny.cpp                                |  13 +-
 test/confidence_connected.cpp                 |  29 ++--
 test/fast.cpp                                 |   3 +
 test/gloh.cpp                                 |   7 +-
 test/hamming.cpp                              |   6 +
 test/harris.cpp                               |   2 +
 test/homography.cpp                           |  23 ++--
 test/hsv_rgb.cpp                              |   4 +
 test/imageio.cpp                              |   8 +-
 test/match_template.cpp                       |   3 +
 test/medfilt.cpp                              |  13 ++
 test/moments.cpp                              |   5 +
 test/morph.cpp                                |  39 ++++--
 test/nearest_neighbour.cpp                    |  16 +++
 test/orb.cpp                                  |   7 +-
 test/regions.cpp                              |   8 +-
 test/scan_by_key.cpp                          |   4 +
 test/sift.cpp                                 |   8 +-
 test/sobel.cpp                                |   2 +
 test/susan.cpp                                |   1 +
 test/testHelpers.hpp                          |  11 +-
 test/threading.cpp                            |   1 +
 54 files changed, 474 insertions(+), 173 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f3a1484a72..8e0c37c19f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -41,6 +41,9 @@ set_policies(
            CMP0074
            CMP0077
            CMP0079)
+if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.27")
+  cmake_policy(SET CMP0146 OLD)
+endif()
 arrayfire_set_cmake_default_variables()
 
 option(AF_WITH_EXTERNAL_PACKAGES_ONLY "Build ArrayFire with External packages only" OFF)
@@ -107,6 +110,7 @@ option(AF_WITH_SPDLOG_HEADER_ONLY "Build ArrayFire with header only version of s
 option(AF_WITH_FMT_HEADER_ONLY "Build ArrayFire with header only version of fmt" OFF)
 option(AF_WITH_FAST_MATH "Use lower precision but high performance numeric optimizations" OFF)
 option(AF_CTEST_SEPARATED "Run tests separately when called from ctest(increases test times)" OFF)
+option(AF_SKIP_UNSUPPORTED_TESTS "Skip tests where functions are unsupported by the backend instead of failing" OFF)
 
 if(AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   option(AF_WITH_PRUNE_STATIC_CUDA_NUMERIC_LIBS "Prune CUDA static libraries to reduce binary size.(WARNING: May break some libs on older CUDA toolkits for some compute arch)" OFF)
@@ -138,10 +142,10 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
   if(DEFINED ENV{MKLROOT} AND NOT DEFINED MKL_ROOT)
     set(MKL_ROOT "$ENV{MKLROOT}")
   endif()
-  set(DPCPP_COMPILER ON)
+  set(SYCL_COMPILER ON)
   set(MKL_THREADING "tbb_thread")
   set(MKL_INTERFACE "ilp64")
-  find_package(MKL 2023.1)
+  find_package(MKL 2024.1)
 endif()
 
 af_multiple_option(NAME        AF_COMPUTE_LIBRARY
@@ -554,6 +558,7 @@ if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
     get_filename_component(mkl_shd ${MKL_Core_LINK_LIBRARY} REALPATH)
     get_filename_component(mkl_tly ${MKL_ThreadLayer_LINK_LIBRARY} REALPATH)
     install(FILES
+      ${mkl_sycl}
       ${mkl_rnt}
       ${mkl_shd}
       ${mkl_tly}
diff --git a/CMakeModules/CMakeTestSYCLCompiler.cmake b/CMakeModules/CMakeTestSYCLCompiler.cmake
index e2f37a2da0..ef38081b37 100644
--- a/CMakeModules/CMakeTestSYCLCompiler.cmake
+++ b/CMakeModules/CMakeTestSYCLCompiler.cmake
@@ -66,8 +66,13 @@ if(NOT CMAKE_SYCL_COMPILER_WORKS)
 endif()
 
 # Try to identify the compiler features
-include(CMakeDetermineCompileFeatures)
-CMAKE_DETERMINE_COMPILE_FEATURES(SYCL)
+if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.30.0)
+    include(CMakeDetermineCompilerSupport)
+    CMAKE_DETERMINE_COMPILER_SUPPORT(CXX)
+else()
+    include(CMakeDetermineCompileFeatures)
+    CMAKE_DETERMINE_COMPILE_FEATURES(CXX)
+endif()
 
 set(CMAKE_TRY_COMPILE_CONFIGURATION "")
 # Re-configure to save learned information.
diff --git a/CMakeModules/CPackProjectConfig.cmake b/CMakeModules/CPackProjectConfig.cmake
index 6cd6e20088..f85dcaa556 100644
--- a/CMakeModules/CPackProjectConfig.cmake
+++ b/CMakeModules/CPackProjectConfig.cmake
@@ -343,6 +343,42 @@ af_component(
   DEB_OPTIONAL "cmake (>= 3.0)"
 )
 
+af_component(
+  COMPONENT oneapi
+  DISPLAY_NAME "oneAPI Runtime"
+  SUMMARY "ArrayFire oneAPI backend shared libraries"
+  DESCRIPTION "ArrayFire oneAPI backend shared libraries"
+  REQUIRES ${oneapi_deps_comps} licenses
+  OPTIONAL forge
+  GROUP afruntime
+  INSTALL_TYPES All Runtime
+
+  DEB_PACKAGE_NAME ${deb_oneapi_runtime_package_name}
+  DEB_PROVIDES "arrayfire-oneapi (= ${CPACK_PACKAGE_VERSION}), arrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION}), libarrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-oneapi (<< ${CPACK_PACKAGE_VERSION}), arrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES ${deb_oneapi_runtime_requirements}
+  DEB_USE_SHLIBDEPS
+  DEB_ADD_POSTINST
+  DEB_OPTIONAL forge libfreeimage3
+)
+
+af_component(
+  COMPONENT oneapi_dev
+  DISPLAY_NAME "oneAPI Dev"
+  SUMMARY  "ArrayFire oneAPI backend development files"
+  DESCRIPTION  "ArrayFire oneAPI backend development files"
+  REQUIRES oneapi headers cmake
+  GROUP afdevelopment
+  INSTALL_TYPES All Development
+
+  DEB_PACKAGE_NAME arrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR}-dev
+  DEB_PROVIDES "arrayfire-oneapi-dev (= ${CPACK_PACKAGE_VERSION}), arrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR}-dev (= ${CPACK_PACKAGE_VERSION}), libarrayfire-oneapi-dev (= ${CPACK_PACKAGE_VERSION})"
+  DEB_REPLACES "arrayfire-oneapi-dev (<< ${CPACK_PACKAGE_VERSION}), arrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR}-dev (<< ${CPACK_PACKAGE_VERSION}), libarrayfire-oneapi-dev (<< ${CPACK_PACKAGE_VERSION})"
+  DEB_REQUIRES "arrayfire-oneapi${CPACK_PACKAGE_VERSION_MAJOR} (>= ${CPACK_PACKAGE_VERSION}), arrayfire-headers (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_RECOMMENDS "arrayfire-cmake (>= ${CPACK_PACKAGE_VERSION})"
+  DEB_OPTIONAL "cmake (>= 3.0)"
+)
+
 af_component(
   COMPONENT unified
   DISPLAY_NAME "Unified Runtime"
@@ -437,6 +473,14 @@ endif()
 # Debug symbols in debian installers are created using the DEBINFO property
 if(NOT APPLE AND
    NOT CPACK_GENERATOR MATCHES "DEB")
+  af_component(
+    COMPONENT afoneapi_debug_symbols
+    DISPLAY_NAME "oneAPI Debug Symbols"
+    DESCRIPTION "Debug symbols for the oneAPI backend."
+    GROUP debug
+    DISABLED
+    INSTALL_TYPES Development)
+
   af_component(
     COMPONENT afopencl_debug_symbols
     DISPLAY_NAME "OpenCL Debug Symbols"
diff --git a/CMakeModules/FindAF_MKL.cmake b/CMakeModules/FindAF_MKL.cmake
index a58809d495..88037c4519 100644
--- a/CMakeModules/FindAF_MKL.cmake
+++ b/CMakeModules/FindAF_MKL.cmake
@@ -321,6 +321,14 @@ endfunction()
 find_mkl_library(NAME Core LIBRARY_NAME mkl_core SEARCH_STATIC)
 find_mkl_library(NAME RT LIBRARY_NAME mkl_rt)
 
+if(AF_BUILD_ONEAPI)
+    find_mkl_library(NAME Sycl LIBRARY_NAME sycl DLL_ONLY)
+	find_mkl_library(NAME SyclLapack LIBRARY_NAME sycl_lapack DLL_ONLY)
+	find_mkl_library(NAME SyclDft LIBRARY_NAME sycl_dft DLL_ONLY)
+	find_mkl_library(NAME SyclBlas LIBRARY_NAME sycl_blas DLL_ONLY)
+	find_mkl_library(NAME SyclSparse LIBRARY_NAME sycl_sparse DLL_ONLY)
+endif()
+
 # MKL can link against Intel OpenMP, GNU OpenMP, TBB, and Sequential
 if(MKL_THREAD_LAYER STREQUAL "Intel OpenMP")
   find_mkl_library(NAME ThreadLayer LIBRARY_NAME mkl_intel_thread SEARCH_STATIC)
diff --git a/CMakeModules/nsis/NSIS.definitions.nsh.in b/CMakeModules/nsis/NSIS.definitions.nsh.in
index 4c6e8998b7..feedbd7c8d 100644
--- a/CMakeModules/nsis/NSIS.definitions.nsh.in
+++ b/CMakeModules/nsis/NSIS.definitions.nsh.in
@@ -3,7 +3,7 @@
 !define MUI_WELCOMEPAGE_TEXT    \
 "ArrayFire is a high performance software library for parallel computing with an easy-to-use API.\r\n\r\n\
 Its array based function set makes parallel programming simple.\r\n\r\n\
-ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable.\r\n\r\n\
+ArrayFire's multiple backends (CUDA, OneAPI, OpenCL, and native CPU) make it platform independent and highly portable.\r\n\r\n\
 A few lines of code in ArrayFire can replace dozens of lines of parallel compute code, \
 saving you valuable time and lowering development costs.\r\n\r\n\
 Follow these steps to install the ArrayFire libraries."
diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
index bc3a44f233..971eea59bf 100644
--- a/CMakeModules/nsis/NSIS.template.in
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -714,7 +714,7 @@ Section "-Core installation"
 
   ; make sure windows knows about the change
   SendMessage ${HWND_BROADCAST} ${WM_WININICHANGE} 0 "STR:Environment" /TIMEOUT=5000
-  MessageBox MB_OK "Added AF_PATH environment variable for all users.$\n$\nIf you chose not to modify PATH in the installer, please manually add $\"%AF_PATH%\lib$\" to the user or system PATH variable for running applications using ArrayFire."
+  MessageBox MB_OK "Added AF_PATH environment variable for all users.$\n$\nIf you chose not to modify PATH in the installer, please manually add $\"%AF_PATH%\lib$\" to the user or system PATH variable for running applications using ArrayFire." /SD IDOK
 
 
   ; Write special uninstall registry entries
diff --git a/CMakeModules/select_compute_arch.cmake b/CMakeModules/select_compute_arch.cmake
index 16abb8e6cd..e09490a7e5 100644
--- a/CMakeModules/select_compute_arch.cmake
+++ b/CMakeModules/select_compute_arch.cmake
@@ -7,7 +7,7 @@
 #      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
 #      NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing Ampere
 #      NUM: Any number. Only those pairs are currently accepted by NVCC though:
-#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0 8.6
+#            2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0 8.6 9.0
 #      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
 #      Additionally, sets ${out_variable}_readable to the resulting numeric list
 #      Example:
@@ -92,6 +92,25 @@ if(CUDA_VERSION VERSION_GREATER_EQUAL "11.1")
   set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
 endif()
 
+if(CUDA_VERSION VERSION_GREATER_EQUAL "11.8")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.9")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.9")
+
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "8.9+PTX")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
+endif()
+
+if(CUDA_VERSION VERSION_GREATER_EQUAL "12.0")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Hopper")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "9.0")
+
+  set(_CUDA_MAX_COMMON_ARCHITECTURE "9.0+PTX")
+  set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
+
+  list(REMOVE_ITEM CUDA_ALL_GPU_ARCHITECTURES "3.5" "3.7")
+endif()
+
 list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "${_CUDA_MAX_COMMON_ARCHITECTURE}")
 
 # Check with: cmake -DCUDA_VERSION=7.0 -P select_compute_arch.cmake
@@ -246,6 +265,9 @@ function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
       elseif(${arch_name} STREQUAL "Ampere")
         set(arch_bin 8.0)
         set(arch_ptx 8.0)
+      elseif(${arch_name} STREQUAL "Hopper")
+        set(arch_bin 9.0)
+        set(arch_ptx 9.0)
       else()
         message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS")
       endif()
diff --git a/LICENSE b/LICENSE
index 8f4c645ca1..3d960db185 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,4 +1,4 @@
-Copyright (c) 2014-2022, ArrayFire
+Copyright (c) 2014-2024, ArrayFire
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
diff --git a/docs/doxygen.mk b/docs/doxygen.mk
index 914ebb35b4..9f46a1e37b 100644
--- a/docs/doxygen.mk
+++ b/docs/doxygen.mk
@@ -1,4 +1,4 @@
-# Doxyfile 1.9.6
+# Doxyfile 1.9.7
 
 # This file describes the settings to be used by the documentation system
 # doxygen (www.doxygen.org) for a project.
@@ -377,6 +377,17 @@ MARKDOWN_SUPPORT       = YES
 
 TOC_INCLUDE_HEADINGS   = 0
 
+# The MARKDOWN_ID_STYLE tag can be used to specify the algorithm used to
+# generate identifiers for the Markdown headings. Note: Every identifier is
+# unique.
+# Possible values are: DOXYGEN Use a fixed 'autotoc_md' string followed by a
+# sequence number starting at 0. and GITHUB Use the lower case version of title
+# with any whitespace replaced by '-' and punctations characters removed..
+# The default value is: DOXYGEN.
+# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
+
+MARKDOWN_ID_STYLE      = DOXYGEN
+
 # When enabled doxygen tries to link words that correspond to documented
 # classes, or namespaces to their corresponding documentation. Such a link can
 # be prevented in individual cases by putting a % sign in front of the word or
@@ -501,6 +512,14 @@ LOOKUP_CACHE_SIZE      = 0
 
 NUM_PROC_THREADS       = 0
 
+# If the TIMESTAMP tag is set different from NO then each generated page will
+# contain the date or date and time when the page was generated. Setting this to
+# NO can help when comparing the output of multiple runs.
+# Possible values are: YES, NO, DATETIME and DATE.
+# The default value is: NO.
+
+TIMESTAMP              = YES
+
 #---------------------------------------------------------------------------
 # Build related configuration options
 #---------------------------------------------------------------------------
@@ -886,7 +905,14 @@ WARN_IF_UNDOC_ENUM_VAL = NO
 # a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS
 # then doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but
 # at the end of the doxygen process doxygen will return with a non-zero status.
-# Possible values are: NO, YES and FAIL_ON_WARNINGS.
+# If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS_PRINT then doxygen behaves
+# like FAIL_ON_WARNINGS but in case no WARN_LOGFILE is defined doxygen will not
+# write the warning messages in between other messages but write them at the end
+# of a run, in case a WARN_LOGFILE is defined the warning messages will be
+# besides being in the defined file also be shown at the end of a run, unless
+# the WARN_LOGFILE is defined as - i.e. standard output (stdout) in that case
+# the behavior will remain as with the setting FAIL_ON_WARNINGS.
+# Possible values are: NO, YES, FAIL_ON_WARNINGS and FAIL_ON_WARNINGS_PRINT.
 # The default value is: NO.
 
 WARN_AS_ERROR          = NO
@@ -1012,9 +1038,6 @@ EXCLUDE_PATTERNS       = *.cpp
 # output. The symbol name can be a fully qualified name, a word, or if the
 # wildcard * is used, a substring. Examples: ANamespace, AClass,
 # ANamespace::AClass, ANamespace::*Test
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories use the pattern */test/*
 
 EXCLUDE_SYMBOLS        =
 
@@ -1405,15 +1428,6 @@ HTML_COLORSTYLE_SAT    = 219
 
 HTML_COLORSTYLE_GAMMA  = 70
 
-# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting this
-# to YES can help to show when doxygen was last run and thus if the
-# documentation is up to date.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_TIMESTAMP         = YES
-
 # If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
 # documentation will contain a main index with vertical navigation menus that
 # are dynamically created via JavaScript. If disabled, the navigation index will
@@ -1563,6 +1577,16 @@ BINARY_TOC             = NO
 
 TOC_EXPAND             = NO
 
+# The SITEMAP_URL tag is used to specify the full URL of the place where the
+# generated documentation will be placed on the server by the user during the
+# deployment of the documentation. The generated sitemap is called sitemap.xml
+# and placed on the directory specified by HTML_OUTPUT. In case no SITEMAP_URL
+# is specified no sitemap is generated. For information about the sitemap
+# protocol see https://www.sitemaps.org
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SITEMAP_URL            =
+
 # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
 # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
 # can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
@@ -2051,9 +2075,16 @@ PDF_HYPERLINKS         = YES
 
 USE_PDFLATEX           = YES
 
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
-# command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help.
+# The LATEX_BATCHMODE tag ignals the behavior of LaTeX in case of an error.
+# Possible values are: NO same as ERROR_STOP, YES same as BATCH, BATCH In batch
+# mode nothing is printed on the terminal, errors are scrolled as if <return> is
+# hit at every error; missing files that TeX tries to input or request from
+# keyboard input (\read on a not open input stream) cause the job to abort,
+# NON_STOP In nonstop mode the diagnostic message will appear on the terminal,
+# but there is no possibility of user interaction just like in batch mode,
+# SCROLL In scroll mode, TeX will stop only for missing files to input or if
+# keyboard input is necessary and ERROR_STOP In errorstop mode, TeX will stop at
+# each error, asking for user intervention.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
@@ -2074,14 +2105,6 @@ LATEX_HIDE_INDICES     = NO
 
 LATEX_BIB_STYLE        = plain
 
-# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
-# page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_TIMESTAMP        = NO
-
 # The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
 # path from which the emoji images will be read. If a relative path is entered,
 # it will be relative to the LATEX_OUTPUT directory. If left blank the
@@ -2247,7 +2270,7 @@ DOCBOOK_OUTPUT         = docbook
 #---------------------------------------------------------------------------
 
 # If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
-# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# AutoGen Definitions (see https://autogen.sourceforge.net/) file that captures
 # the structure of the code including all documentation. Note that this feature
 # is still experimental and incomplete at the moment.
 # The default value is: NO.
@@ -2422,16 +2445,9 @@ EXTERNAL_GROUPS        = YES
 EXTERNAL_PAGES         = YES
 
 #---------------------------------------------------------------------------
-# Configuration options related to the dot tool
+# Configuration options related to diagram generator tools
 #---------------------------------------------------------------------------
 
-# You can include diagrams made with dia in doxygen documentation. Doxygen will
-# then run dia to produce the diagram and insert it in the documentation. The
-# DIA_PATH tag allows you to specify the directory where the dia binary resides.
-# If left empty dia is assumed to be found in the default search path.
-
-DIA_PATH               =
-
 # If set to YES the inheritance and collaboration graphs will hide inheritance
 # and usage relations if the target is undocumented or is not a class.
 # The default value is: YES.
@@ -2440,7 +2456,7 @@ HIDE_UNDOC_RELATIONS   = YES
 
 # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
 # available from the path. This tool is part of Graphviz (see:
-# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# https://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
 # Bell Labs. The other options in this section have no effect if this option is
 # set to NO
 # The default value is: NO.
@@ -2493,13 +2509,15 @@ DOT_NODE_ATTR          = "shape=box,height=0.2,width=0.4"
 
 DOT_FONTPATH           =
 
-# If the CLASS_GRAPH tag is set to YES (or GRAPH) then doxygen will generate a
-# graph for each documented class showing the direct and indirect inheritance
-# relations. In case HAVE_DOT is set as well dot will be used to draw the graph,
-# otherwise the built-in generator will be used. If the CLASS_GRAPH tag is set
-# to TEXT the direct and indirect inheritance relations will be shown as texts /
-# links.
-# Possible values are: NO, YES, TEXT and GRAPH.
+# If the CLASS_GRAPH tag is set to YES or GRAPH or BUILTIN then doxygen will
+# generate a graph for each documented class showing the direct and indirect
+# inheritance relations. In case the CLASS_GRAPH tag is set to YES or GRAPH and
+# HAVE_DOT is enabled as well, then dot will be used to draw the graph. In case
+# the CLASS_GRAPH tag is set to YES and HAVE_DOT is disabled or if the
+# CLASS_GRAPH tag is set to BUILTIN, then the built-in generator will be used.
+# If the CLASS_GRAPH tag is set to TEXT the direct and indirect inheritance
+# relations will be shown as texts / links.
+# Possible values are: NO, YES, TEXT, GRAPH and BUILTIN.
 # The default value is: YES.
 
 CLASS_GRAPH            = YES
@@ -2640,7 +2658,7 @@ DIR_GRAPH_MAX_DEPTH    = 1
 # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
 # generated by dot. For an explanation of the image formats see the section
 # output formats in the documentation of the dot tool (Graphviz (see:
-# http://www.graphviz.org/)).
+# https://www.graphviz.org/)).
 # Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
 # to make the SVG files visible in IE 9+ (other browsers do not have this
 # requirement).
@@ -2677,11 +2695,12 @@ DOT_PATH               =
 
 DOTFILE_DIRS           =
 
-# The MSCFILE_DIRS tag can be used to specify one or more directories that
-# contain msc files that are included in the documentation (see the \mscfile
-# command).
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
 
-MSCFILE_DIRS           =
+DIA_PATH               =
 
 # The DIAFILE_DIRS tag can be used to specify one or more directories that
 # contain dia files that are included in the documentation (see the \diafile
@@ -2758,3 +2777,19 @@ GENERATE_LEGEND        = YES
 # The default value is: YES.
 
 DOT_CLEANUP            = YES
+
+# You can define message sequence charts within doxygen comments using the \msc
+# command. If the MSCGEN_TOOL tag is left empty (the default), then doxygen will
+# use a built-in version of mscgen tool to produce the charts. Alternatively,
+# the MSCGEN_TOOL tag can also specify the name an external tool. For instance,
+# specifying prog as the value, doxygen will call the tool as prog -T
+# <outfile_format> -o <outputfile> <inputfile>. The external tool should support
+# output file formats "png", "eps", "svg", and "ismap".
+
+MSCGEN_TOOL            =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
+
+MSCFILE_DIRS           =
diff --git a/src/backend/common/err_common.cpp b/src/backend/common/err_common.cpp
index 60fc207a63..672afe6da0 100644
--- a/src/backend/common/err_common.cpp
+++ b/src/backend/common/err_common.cpp
@@ -92,8 +92,8 @@ int ArgumentError::getArgIndex() const noexcept { return argIndex; }
 
 SupportError::SupportError(const char *const func, const char *const file,
                            const int line, const char *const back,
-                           stacktrace st)
-    : AfError(func, file, line, "Unsupported Error", AF_ERR_NOT_SUPPORTED,
+                           const char *const message, stacktrace st)
+    : AfError(func, file, line, message, AF_ERR_NOT_SUPPORTED,
               std::move(st))
     , backend(back) {}
 
diff --git a/src/backend/common/err_common.hpp b/src/backend/common/err_common.hpp
index e1e4a6d118..846f4b516f 100644
--- a/src/backend/common/err_common.hpp
+++ b/src/backend/common/err_common.hpp
@@ -113,7 +113,7 @@ class SupportError : public AfError {
 
    public:
     SupportError(const char* const func, const char* const file, const int line,
-                 const char* const back,
+                 const char* const back, const char* const message,
                  const boost::stacktrace::stacktrace st);
     SupportError(SupportError&& other) noexcept = default;
 
diff --git a/src/backend/cpu/err_cpu.hpp b/src/backend/cpu/err_cpu.hpp
index d618cecb1e..58c7b59aab 100644
--- a/src/backend/cpu/err_cpu.hpp
+++ b/src/backend/cpu/err_cpu.hpp
@@ -11,6 +11,6 @@
 
 #define CPU_NOT_SUPPORTED(message)                                          \
     do {                                                                    \
-        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
-                           boost::stacktrace::stacktrace());                \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, "CPU",   \
+                           message, boost::stacktrace::stacktrace());       \
     } while (0)
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 0c4563ed40..6d8731e1e1 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -860,6 +860,9 @@ if(AF_INSTALL_STANDALONE)
     endif()
     afcu_collect_libs(cusolver)
     afcu_collect_libs(cusparse)
+    if(CUDA_VERSION VERSION_GREATER 12.0)
+      afcu_collect_libs(nvJitLink)
+    endif()
   elseif(NOT ${use_static_cuda_lapack})
     afcu_collect_libs(cusolver)
   endif()
diff --git a/src/backend/cuda/err_cuda.hpp b/src/backend/cuda/err_cuda.hpp
index 77926cdd79..f6db7e6822 100644
--- a/src/backend/cuda/err_cuda.hpp
+++ b/src/backend/cuda/err_cuda.hpp
@@ -14,8 +14,8 @@
 
 #define CUDA_NOT_SUPPORTED(message)                                         \
     do {                                                                    \
-        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
-                           boost::stacktrace::stacktrace());                \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, "CUDA",  \
+                           message, boost::stacktrace::stacktrace());       \
     } while (0)
 
 #define CU_CHECK(fn)                                                          \
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index f2ef09c044..8165e6fb08 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -500,10 +500,11 @@ template<typename T>
 void writeHostDataArray(Array<T> &arr, const T *const data,
                         const size_t bytes) {
     if (!arr.isOwner()) { arr = copyArray<T>(arr); }
+    auto arr_get = arr.get();
     getQueue()
         .submit([&](sycl::handler &h) {
             auto host_acc =
-                arr.get()->template get_access<sycl::access_mode::write>(
+                arr_get->template get_access<sycl::access_mode::write>(
                     h, sycl::range(bytes / sizeof(T)), arr.getOffset());
             h.copy(data, host_acc);
         })
@@ -517,10 +518,11 @@ void writeDeviceDataArray(Array<T> &arr, const void *const data,
 
     sycl::buffer<T> *dataptr =
         static_cast<sycl::buffer<T> *>(const_cast<void *>(data));
+    auto arr_get = arr.get();
     getQueue().submit([&](sycl::handler &h) {
         auto src_acc = dataptr->template get_access<sycl::access_mode::read>(
             h, sycl::range(bytes / sizeof(T)));
-        auto dst_acc = arr.get()->template get_access<sycl::access_mode::write>(
+        auto dst_acc = arr_get->template get_access<sycl::access_mode::write>(
             h, sycl::range(bytes / sizeof(T)), arr.getOffset());
         h.copy(src_acc, dst_acc);
     });
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 9bd7e0850a..054681d812 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -359,7 +359,7 @@ target_link_libraries(afoneapi
     $<$<PLATFORM_ID:Linux>:-fvisibility-inlines-hidden>
     $<$<PLATFORM_ID:Linux>:-fno-sycl-rdc>
     -fsycl-max-parallel-link-jobs=${NumberOfThreads}
-    MKL::MKL_DPCPP
+    MKL::MKL_SYCL
   )
   set_sycl_language(afcommon_interface
     oneapi_sort_by_key
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index f99f79854e..506206b11e 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -216,10 +216,11 @@ template<typename T>
 T getScalar(const Array<T> &in) {
     T retVal{};
 
+    auto in_get = in.get();
     getQueue()
         .submit([&](sycl::handler &h) {
             auto acc_in =
-                in.get()->template get_access<sycl::access::mode::read>(
+                in_get->template get_access<sycl::access::mode::read>(
                     h, sycl::range{1},
                     sycl::id{static_cast<uintl>(in.getOffset())});
             h.copy(acc_in, &retVal);
diff --git a/src/backend/oneapi/err_oneapi.hpp b/src/backend/oneapi/err_oneapi.hpp
index fad7d449c0..4f187b6273 100644
--- a/src/backend/oneapi/err_oneapi.hpp
+++ b/src/backend/oneapi/err_oneapi.hpp
@@ -13,8 +13,8 @@
 
 #define ONEAPI_NOT_SUPPORTED(message)                                       \
     do {                                                                    \
-        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
-                           boost::stacktrace::stacktrace());                \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, "oneAPI",\
+                           message, boost::stacktrace::stacktrace());       \
     } while (0)
 
 #define CL_CHECK(call)                                                      \
diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
index 37c7c14fc9..e95b63c392 100644
--- a/src/backend/oneapi/join.cpp
+++ b/src/backend/oneapi/join.cpp
@@ -94,15 +94,17 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     if (first.isReady()) {
         if (1LL + jdim >= first.ndims() && first.isLinear()) {
             // first & out are linear
+            auto first_array = first.get();
+            auto out_array = out.get();
             getQueue().submit([&](sycl::handler &h) {
                 sycl::range sz(first.elements());
                 sycl::id src_offset(first.getOffset());
                 sycl::accessor offset_acc_src =
-                    first.get()->template get_access<sycl::access_mode::read>(
+                    first_array->template get_access<sycl::access_mode::read>(
                         h, sz, src_offset);
                 sycl::id dst_offset(0);
                 sycl::accessor offset_acc_dst =
-                    out.get()->template get_access<sycl::access_mode::write>(
+                    out_array->template get_access<sycl::access_mode::write>(
                         h, sz, dst_offset);
                 h.copy(offset_acc_src, offset_acc_dst);
             });
@@ -125,16 +127,18 @@ Array<T> join(const int jdim, const Array<T> &first, const Array<T> &second) {
     if (second.isReady()) {
         if (1LL + jdim >= second.ndims() && second.isLinear()) {
             // second & out are linear
+            auto second_array = second.get();
+            auto out_array = out.get();
             getQueue().submit([&](sycl::handler &h) {
                 sycl::range sz(second.elements());
                 sycl::id src_offset(second.getOffset());
                 sycl::accessor offset_acc_src =
-                    second.get()->template get_access<sycl::access_mode::read>(
+                    second_array->template get_access<sycl::access_mode::read>(
                         h, sz, src_offset);
                 sycl::id dst_offset(fdims.dims[jdim] *
                                     out.strides().dims[jdim]);
                 sycl::accessor offset_acc_dst =
-                    out.get()->template get_access<sycl::access_mode::write>(
+                    out_array->template get_access<sycl::access_mode::write>(
                         h, sz, dst_offset);
                 h.copy(offset_acc_src, offset_acc_dst);
             });
@@ -216,11 +220,12 @@ void join(Array<T> &out, const int jdim, const vector<Array<T>> &inputs) {
             for (const Array<T> *in : s.ins) {
                 if (in->isReady()) {
                     if (1LL + jdim >= in->ndims() && in->isLinear()) {
+                        auto in_array = in->get();
                         getQueue().submit([&](sycl::handler &h) {
                             sycl::range sz(in->elements());
                             sycl::id src_offset(in->getOffset());
                             sycl::accessor offset_acc_src =
-                                in->get()
+                                in_array
                                     ->template get_access<
                                         sycl::access_mode::read>(h, sz,
                                                                  src_offset);
diff --git a/src/backend/oneapi/kernel/index.hpp b/src/backend/oneapi/kernel/index.hpp
index c7bb591953..e86c0bd808 100644
--- a/src/backend/oneapi/kernel/index.hpp
+++ b/src/backend/oneapi/kernel/index.hpp
@@ -137,11 +137,14 @@ void index(Param<T> out, Param<T> in, IndexKernelParam& p,
     blocks[0] *= threads[0];
 
     sycl::nd_range<3> marange(blocks, threads);
+    sycl::buffer<uint> *idxArrs_get[4];
+    for (dim_t x = 0; x < 4; ++x)
+        idxArrs_get[x] = idxArrs[x].get();
     getQueue().submit([&](sycl::handler& h) {
         auto pp = p;
         for (dim_t x = 0; x < 4; ++x) {
             pp.ptr[x] =
-                idxArrs[x].get()->get_access<sycl::access::mode::read>(h);
+                idxArrs_get[x]->get_access<sycl::access::mode::read>(h);
         }
 
         h.parallel_for(
diff --git a/src/backend/oneapi/kernel/ireduce.hpp b/src/backend/oneapi/kernel/ireduce.hpp
index 5f8f96bfc8..9ba79ed61b 100644
--- a/src/backend/oneapi/kernel/ireduce.hpp
+++ b/src/backend/oneapi/kernel/ireduce.hpp
@@ -258,7 +258,7 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
             case 4:
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
-                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 4>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
                         groups_dim[dim], rlenValid, rlen_acc, rlen.info,
@@ -267,7 +267,7 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
             case 2:
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
-                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 2>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
                         groups_dim[dim], rlenValid, rlen_acc, rlen.info,
@@ -276,7 +276,7 @@ void ireduce_dim_launcher(Param<T> out, Param<uint> oloc, Param<T> in,
             case 1:
                 h.parallel_for(
                     sycl::nd_range<2>(global, local),
-                    ireduceDimKernelSMEM<T, op, dim, is_first, 8>(
+                    ireduceDimKernelSMEM<T, op, dim, is_first, 1>(
                         out_acc, out.info, oloc_acc, oloc.info, in_acc, in.info,
                         iloc_acc, iloc.info, groups_dim[0], groups_dim[1],
                         groups_dim[dim], rlenValid, rlen_acc, rlen.info,
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index 695fb7b375..d6f33209a9 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -609,12 +609,14 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
                                       blocks_y, threads_x);
 
         compute_t<T> val;
+        auto tmpOut_get = tmpOut.get();
+        auto tmpWt_get = tmpWt.get();
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto acc_in =
-                    tmpOut.get()->template get_host_access(h, sycl::read_only);
+                    tmpOut_get->template get_host_access(h, sycl::read_only);
                 auto acc_wt =
-                    tmpWt.get()->template get_host_access(h, sycl::read_only);
+                    tmpWt_get->template get_host_access(h, sycl::read_only);
 
                 h.host_task([acc_in, acc_wt, tmp_elements, &val] {
                     val = static_cast<compute_t<T>>(acc_in[0]);
@@ -693,12 +695,14 @@ To mean_all(Param<Ti> in) {
         uintl tmp_elements = tmpOut.elements();
 
         compute_t<To> val;
+        auto tmpOut_get = tmpOut.get();
+        auto tmpCt_get = tmpCt.get();
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto out =
-                    tmpOut.get()->template get_host_access(h, sycl::read_only);
+                    tmpOut_get->template get_host_access(h, sycl::read_only);
                 auto ct =
-                    tmpCt.get()->template get_host_access(h, sycl::read_only);
+                    tmpCt_get->template get_host_access(h, sycl::read_only);
 
                 h.host_task([out, ct, tmp_elements, &val] {
                     val                  = static_cast<compute_t<To>>(out[0]);
diff --git a/src/backend/oneapi/kernel/reduce_all.hpp b/src/backend/oneapi/kernel/reduce_all.hpp
index 4bc3d5254d..7a1e842425 100644
--- a/src/backend/oneapi/kernel/reduce_all.hpp
+++ b/src/backend/oneapi/kernel/reduce_all.hpp
@@ -249,13 +249,17 @@ void reduce_all_launcher_default(Param<To> out, Param<Ti> in,
             "Too many blocks requested (typeof(retirementCount) == unsigned)",
             AF_ERR_RUNTIME);
     }
-    Array<To> tmp = createEmptyArray<To>(tmp_elements);
 
+    Array<To> tmp = createEmptyArray<To>(tmp_elements);
+    auto tmp_get = tmp.get();
+    
     Array<unsigned> retirementCount = createValueArray<unsigned>(1, 0);
+    auto ret_get = retirementCount.get();
+
     getQueue().submit([&](sycl::handler &h) {
         write_accessor<To> out_acc{*out.data, h};
-        auto retCount_acc = retirementCount.get()->get_access(h);
-        auto tmp_acc      = tmp.get()->get_access(h);
+        auto retCount_acc = ret_get->get_access(h);
+        auto tmp_acc      = tmp_get->get_access(h);
         read_accessor<Ti> in_acc{*in.data, h};
 
         auto shrdMem = sycl::local_accessor<compute_t<To>, 1>(
diff --git a/src/backend/oneapi/kernel/sort_by_key_impl.hpp b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
index 5a05eac58c..6e3a0bd655 100644
--- a/src/backend/oneapi/kernel/sort_by_key_impl.hpp
+++ b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
@@ -114,10 +114,11 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
     auto val_end   = val_begin + elements;
 
     auto cKey = memAlloc<Tk>(elements);
+    auto cKey_get = cKey.get();
     getQueue().submit([&](sycl::handler &h) {
         h.copy(pKey.data->template reinterpret<compute_t<Tk>>().get_access(
                    h, elements),
-               cKey.get()->template reinterpret<compute_t<Tk>>().get_access(
+               cKey_get->template reinterpret<compute_t<Tk>>().get_access(
                    h, elements));
     });
     auto ckey_begin =
@@ -150,10 +151,12 @@ void sortByKeyBatched(Param<Tk> pKey, Param<Tv> pVal, const int dim,
         }
     }
 
+    auto Seq_get = Seq.get();
     auto cSeq = memAlloc<uint>(elements);
+    auto cSeq_get = cSeq.get();
     getQueue().submit([&](sycl::handler &h) {
-        h.copy(Seq.get()->get_access(h, elements),
-               cSeq.get()->get_access(h, elements));
+        h.copy(Seq_get->get_access(h, elements),
+               cSeq_get->get_access(h, elements));
     });
     auto cseq_begin = ::oneapi::dpl::begin(*cSeq.get());
     auto cseq_end   = cseq_begin + elements;
diff --git a/src/backend/oneapi/kernel/sparse_arith.hpp b/src/backend/oneapi/kernel/sparse_arith.hpp
index 819af6ffce..b46baa69df 100644
--- a/src/backend/oneapi/kernel/sparse_arith.hpp
+++ b/src/backend/oneapi/kernel/sparse_arith.hpp
@@ -427,9 +427,10 @@ static void csrCalcOutNNZ(Param<int> outRowIdx, unsigned &nnzC, const uint M,
     auto global = sycl::range(divup(M, local[0]) * local[0]);
 
     Array<unsigned> out = createValueArray<unsigned>(1, 0);
+    auto out_get = out.get();
 
     getQueue().submit([&](auto &h) {
-        sycl::accessor d_out{*out.get(), h, sycl::write_only};
+        sycl::accessor d_out{*out_get, h, sycl::write_only};
         sycl::accessor d_outRowIdx{*outRowIdx.data, h, sycl::write_only};
         sycl::accessor d_lRowIdx{*lrowIdx.data, h, sycl::read_only};
         sycl::accessor d_lColIdx{*lcolIdx.data, h, sycl::read_only};
diff --git a/src/backend/oneapi/kernel/wrap.hpp b/src/backend/oneapi/kernel/wrap.hpp
index b5e5226035..e29403b604 100644
--- a/src/backend/oneapi/kernel/wrap.hpp
+++ b/src/backend/oneapi/kernel/wrap.hpp
@@ -140,7 +140,7 @@ void wrap(Param<T> out, const Param<T> in, const dim_t wx, const dim_t wy,
     dim_t groups_y = divup(out.info.dims[1], local[1]);
 
     auto global = sycl::range{groups_x * local[0] * out.info.dims[2],
-                              groups_y * local[1]};
+                              groups_y * local[1] * out.info.dims[3]};
 
     auto Q = getQueue();
     Q.submit([&](sycl::handler &h) {
diff --git a/src/backend/oneapi/platform.cpp b/src/backend/oneapi/platform.cpp
index 91e307d56c..3994a907a5 100644
--- a/src/backend/oneapi/platform.cpp
+++ b/src/backend/oneapi/platform.cpp
@@ -164,7 +164,9 @@ string getDeviceInfo() noexcept {
                  << ", " << msize / 1048576 << " MB";
             info << " (";
             if (device->has(aspect::fp64)) { info << "fp64 "; }
-            if (device->has(aspect::fp16)) { info << "fp16 "; }
+            if (device->has(aspect::fp16) &&
+                device->get_info<sycl::info::device::native_vector_width_half>() != 0)
+                { info << "fp16 "; }
             info << "\b)";
 #ifndef NDEBUG
             info << " -- ";
@@ -386,7 +388,8 @@ bool isHalfSupported(unsigned device) {
     DeviceManager& devMngr = DeviceManager::getInstance();
 
     common::lock_guard_t lock(devMngr.deviceMutex);
-    return devMngr.mDevices[device]->has(sycl::aspect::fp16);
+    return devMngr.mDevices[device]->has(sycl::aspect::fp16) &&
+           devMngr.mDevices[device]->get_info<sycl::info::device::native_vector_width_half>() != 0;
 }
 
 void devprop(char* d_name, char* d_platform, char* d_toolkit, char* d_compute) {
diff --git a/src/backend/oneapi/reduce_impl.hpp b/src/backend/oneapi/reduce_impl.hpp
index 698f2f1831..b2c478c71f 100644
--- a/src/backend/oneapi/reduce_impl.hpp
+++ b/src/backend/oneapi/reduce_impl.hpp
@@ -58,12 +58,16 @@ void reduceBlocksByKey(sycl::buffer<int> &reduced_block_sizes,
     sycl::range<3> global(local[0] * numBlocks, vals_out.dims()[1],
                           vals_out.dims()[2] * vals_out.dims()[3]);
 
+    auto keys_out_get = keys_out.get();
+    auto vals_out_get = vals_out.get();
+    auto keys_get = keys.get();
+    auto vals_get = vals.get();
     getQueue().submit([&](sycl::handler &h) {
         sycl::accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
-        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
-        write_accessor<To> vals_out_acc{*vals_out.get(), h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
-        read_accessor<Ti> vals_acc{*vals.get(), h};
+        write_accessor<Tk> keys_out_acc{*keys_out_get, h};
+        write_accessor<To> vals_out_acc{*vals_out_get, h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
+        read_accessor<Ti> vals_acc{*vals_get, h};
 
         auto l_keys         = sycl::local_accessor<Tk>(threads_x, h);
         auto l_vals         = sycl::local_accessor<compute_t<To>>(threads_x, h);
@@ -100,12 +104,16 @@ void reduceBlocksByKeyDim(sycl::buffer<int> &reduced_block_sizes,
         local[0] * numBlocks, vals_out.dims()[dim_ordering[1]],
         vals_out.dims()[dim_ordering[2]] * vals_out.dims()[dim_ordering[3]]);
 
+    auto keys_out_get = keys_out.get();
+    auto vals_out_get = vals_out.get();
+    auto keys_get = keys.get();
+    auto vals_get = vals.get();
     getQueue().submit([&](sycl::handler &h) {
         sycl::accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
-        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
-        write_accessor<To> vals_out_acc{*vals_out.get(), h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
-        read_accessor<Ti> vals_acc{*vals.get(), h};
+        write_accessor<Tk> keys_out_acc{*keys_out_get, h};
+        write_accessor<To> vals_out_acc{*vals_out_get, h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
+        read_accessor<Ti> vals_acc{*vals_get, h};
 
         auto l_keys         = sycl::local_accessor<Tk>(threads_x, h);
         auto l_vals         = sycl::local_accessor<compute_t<To>>(threads_x, h);
@@ -135,10 +143,12 @@ void finalBoundaryReduce(sycl::buffer<int> &reduced_block_sizes, Array<Tk> keys,
     sycl::range<1> local(threads_x);
     sycl::range<1> global(local[0] * numBlocks);
 
+    auto vals_out_get = vals_out.get();
+    auto keys_get = keys.get();
     getQueue().submit([&](sycl::handler &h) {
         write_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
-        sycl::accessor<To> vals_out_acc{*vals_out.get(), h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
+        sycl::accessor<To> vals_out_acc{*vals_out_get, h};
 
         h.parallel_for(sycl::nd_range<1>(global, local),
                        kernel::finalBoundaryReduceKernel<Tk, To, op>(
@@ -158,10 +168,12 @@ void finalBoundaryReduceDim(sycl::buffer<int> &reduced_block_sizes,
         local[0] * numBlocks, vals_out.dims()[dim_ordering[1]],
         vals_out.dims()[dim_ordering[2]] * vals_out.dims()[dim_ordering[3]]);
 
+    auto vals_out_get = vals_out.get();
+    auto keys_get = keys.get();
     getQueue().submit([&](sycl::handler &h) {
         write_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
-        sycl::accessor<To> vals_out_acc{*vals_out.get(), h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
+        sycl::accessor<To> vals_out_acc{*vals_out_get, h};
 
         // TODO: fold 3,4 dimensions
         h.parallel_for(
@@ -181,12 +193,16 @@ void compact(sycl::buffer<int> reduced_block_sizes, Array<Tk> &keys_out,
     sycl::range<3> global(local[0] * numBlocks, vals_out.dims()[1],
                           vals_out.dims()[2] * vals_out.dims()[3]);
 
+    auto keys_out_get = keys_out.get();
+    auto vals_out_get = vals_out.get();
+    auto keys_get = keys.get();
+    auto vals_get = vals.get();
     getQueue().submit([&](sycl::handler &h) {
         read_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
-        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
-        write_accessor<To> vals_out_acc{*vals_out.get(), h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
-        read_accessor<To> vals_acc{*vals.get(), h};
+        write_accessor<Tk> keys_out_acc{*keys_out_get, h};
+        write_accessor<To> vals_out_acc{*vals_out_get, h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
+        read_accessor<To> vals_acc{*vals_get, h};
 
         h.parallel_for(sycl::nd_range<3>(global, local),
                        kernel::compactKernel<Tk, To>(
@@ -207,12 +223,16 @@ void compactDim(sycl::buffer<int> &reduced_block_sizes, Array<Tk> &keys_out,
         local[0] * numBlocks, vals_out.dims()[dim_ordering[1]],
         vals_out.dims()[dim_ordering[2]] * vals_out.dims()[dim_ordering[3]]);
 
+    auto keys_out_get = keys_out.get();
+    auto vals_out_get = vals_out.get();
+    auto keys_get = keys.get();
+    auto vals_get = vals.get();
     getQueue().submit([&](sycl::handler &h) {
         read_accessor<int> reduced_block_sizes_acc{reduced_block_sizes, h};
-        write_accessor<Tk> keys_out_acc{*keys_out.get(), h};
-        write_accessor<To> vals_out_acc{*vals_out.get(), h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
-        read_accessor<To> vals_acc{*vals.get(), h};
+        write_accessor<Tk> keys_out_acc{*keys_out_get, h};
+        write_accessor<To> vals_out_acc{*vals_out_get, h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
+        read_accessor<To> vals_acc{*vals_get, h};
 
         h.parallel_for(
             sycl::nd_range<3>(global, local),
@@ -231,10 +251,11 @@ void testNeedsReduction(sycl::buffer<int> needs_reduction,
     sycl::range<1> local(threads_x);
     sycl::range<1> global(local[0] * numBlocks);
 
+    auto keys_get = keys.get();
     getQueue().submit([&](sycl::handler &h) {
         sycl::accessor<int> needs_reduction_acc{needs_reduction, h};
         sycl::accessor<int> needs_boundary_acc{needs_boundary, h};
-        read_accessor<Tk> keys_acc{*keys.get(), h};
+        read_accessor<Tk> keys_acc{*keys_get, h};
         auto l_keys = sycl::local_accessor<Tk>(threads_x, h);
 
         h.parallel_for(sycl::nd_range<1>(global, local),
diff --git a/src/backend/opencl/err_opencl.hpp b/src/backend/opencl/err_opencl.hpp
index 2c1187c569..9a24bc2789 100644
--- a/src/backend/opencl/err_opencl.hpp
+++ b/src/backend/opencl/err_opencl.hpp
@@ -27,6 +27,6 @@ std::string getProgramBuildLog(const cl::Program &prog);
 
 #define OPENCL_NOT_SUPPORTED(message)                                       \
     do {                                                                    \
-        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, message, \
-                           boost::stacktrace::stacktrace());                \
+        throw SupportError(__AF_FUNC__, __AF_FILENAME__, __LINE__, "OpenCL",\
+                           message, boost::stacktrace::stacktrace());       \
     } while (0)
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 95bab411bc..3fae5d68ec 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -259,6 +259,11 @@ function(make_test)
         MTX_TEST_DIR="${ArrayFire_BINARY_DIR}/extern/matrixmarket/"
         )
     endif()
+    if(AF_SKIP_UNSUPPORTED_TESTS)
+      target_compile_definitions(${target}
+        PRIVATE
+          SKIP_UNSUPPORTED_TESTS)
+    endif()
     if(WIN32)
       target_compile_definitions(${target}
         PRIVATE
diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index afeda45d52..60e3c75324 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -98,12 +98,12 @@ void imageTest(string pTestFile, const float dt, const float K,
 
         if (isCurvatureDiffusion) {
             ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K,
-                                                    iters, fluxKind,
-                                                    AF_DIFFUSION_MCDE));
+                                                                iters, fluxKind,
+                                                                AF_DIFFUSION_MCDE));
         } else {
             ASSERT_SUCCESS(af_anisotropic_diffusion(&_outArray, inArray, dt, K,
-                                                    iters, fluxKind,
-                                                    AF_DIFFUSION_GRAD));
+                                                                iters, fluxKind,
+                                                                AF_DIFFUSION_GRAD));
         }
 
         double maxima, minima, imag;
@@ -142,6 +142,7 @@ void imageTest(string pTestFile, const float dt, const float K,
 }
 
 TYPED_TEST(AnisotropicDiffusion, GradientGrayscale) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     // Numeric values separated by underscore are arguments to fn being tested.
     // Divide first value by 1000 to get time step `dt`
     // Divide second value by 100 to get time step `K`
@@ -153,6 +154,7 @@ TYPED_TEST(AnisotropicDiffusion, GradientGrayscale) {
 }
 
 TYPED_TEST(AnisotropicDiffusion, GradientColorImage) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     imageTest<TypeParam, true>(
         string(TEST_DIR "/gradient_diffusion/color_00125_100_2_exp.test"),
         0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL);
@@ -166,6 +168,7 @@ TEST(AnisotropicDiffusion, GradientInvalidInputArray) {
 }
 
 TYPED_TEST(AnisotropicDiffusion, CurvatureGrayscale) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     // Numeric values separated by underscore are arguments to fn being tested.
     // Divide first value by 1000 to get time step `dt`
     // Divide second value by 100 to get time step `K`
@@ -177,6 +180,7 @@ TYPED_TEST(AnisotropicDiffusion, CurvatureGrayscale) {
 }
 
 TYPED_TEST(AnisotropicDiffusion, CurvatureColorImage) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     imageTest<TypeParam, true>(
         string(TEST_DIR "/curvature_diffusion/color_00125_100_2_mcde.test"),
         0.125f, 1.0, 2, AF_FLUX_EXPONENTIAL, true);
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 4c6e966220..db1f67a341 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -102,14 +102,20 @@ std::string readNextNonEmptyLine(std::ifstream &file) {
     return result;
 }
 
-std::string getBackendName() {
+std::string getBackendName(bool lower) {
     af::Backend backend = af::getActiveBackend();
-    if (backend == AF_BACKEND_OPENCL)
-        return std::string("opencl");
-    else if (backend == AF_BACKEND_CUDA)
-        return std::string("cuda");
-
-    return std::string("cpu");
+    switch(backend) {
+    case AF_BACKEND_CPU:
+        return lower ? std::string("cpu") : std::string("CPU");
+    case AF_BACKEND_CUDA:
+        return lower ? std::string("cuda") : std::string("CUDA");
+    case AF_BACKEND_OPENCL:
+        return lower ? std::string("opencl") : std::string("OpenCL");
+    case AF_BACKEND_ONEAPI:
+        return lower ? std::string("oneapi") : std::string("oneAPI");
+    default:
+        return lower ? std::string("unknown") : std::string("Unknown");
+    }
 }
 
 std::string getTestName() {
diff --git a/test/canny.cpp b/test/canny.cpp
index 7f2fa2918c..a12ac73965 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -53,7 +53,7 @@ void cannyTest(string pTestFile) {
                                    (af_dtype)dtype_traits<T>::af_type));
 
     ASSERT_SUCCESS(af_canny(&outArray, sArray, AF_CANNY_THRESHOLD_MANUAL,
-                            0.4147f, 0.8454f, 3, true));
+                                        0.4147f, 0.8454f, 3, true));
 
     vector<char> outData(sDims.elements());
 
@@ -72,10 +72,12 @@ void cannyTest(string pTestFile) {
 }
 
 TYPED_TEST(CannyEdgeDetector, ArraySizeLessThanBlockSize10x10) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast10x10.test"));
 }
 
 TYPED_TEST(CannyEdgeDetector, ArraySizeEqualBlockSize16x16) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     cannyTest<TypeParam>(string(TEST_DIR "/CannyEdgeDetector/fast16x16.test"));
 }
 
@@ -129,8 +131,9 @@ void cannyImageOtsuTest(string pTestFile, bool isColor) {
             af_load_image_native(&goldArray, outFiles[testId].c_str()));
 
         ASSERT_SUCCESS(af_canny(&_outArray, inArray,
-                                AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3,
-                                false));
+                                            AF_CANNY_THRESHOLD_AUTO_OTSU,
+                                            0.08, 0.32, 3, false));
+
         unsigned ndims = 0;
         dim_t dims[4];
 
@@ -156,6 +159,7 @@ void cannyImageOtsuTest(string pTestFile, bool isColor) {
 }
 
 TEST(CannyEdgeDetector, OtsuThreshold) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     cannyImageOtsuTest<float>(string(TEST_DIR "/CannyEdgeDetector/gray.test"),
                               false);
 }
@@ -248,7 +252,7 @@ void cannyImageOtsuBatchTest(string pTestFile, const dim_t targetBatchCount) {
         array inputIm  = tile(readImg, 1, 1, targetBatchCount);
 
         array outIm =
-            canny(inputIm, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false);
+              canny(inputIm, AF_CANNY_THRESHOLD_AUTO_OTSU, 0.08, 0.32, 3, false);
         outIm *= 255.0;
 
         ASSERT_IMAGES_NEAR(goldIm, outIm.as(u8), 1.0e-3);
@@ -256,6 +260,7 @@ void cannyImageOtsuBatchTest(string pTestFile, const dim_t targetBatchCount) {
 }
 
 TEST(CannyEdgeDetector, BatchofImagesUsingCPPAPI) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     // DO NOT INCREASE BATCH COUNT BEYOND 4
     // This is a limitation on the test assert macro that is saving
     // images to disk which can't handle a batch of images.
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index ac5b0bf2bc..22254e5532 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -41,17 +41,6 @@ struct CCCTestParams {
     double replace;
 };
 
-void apiWrapper(af_array *out, const af_array in, const af_array seedx,
-                const af_array seedy, const CCCTestParams params) {
-    ASSERT_SUCCESS(af_confidence_cc(out, in, seedx, seedy, params.radius,
-                                    params.multiplier, params.iterations,
-                                    params.replace));
-
-    int device = 0;
-    ASSERT_SUCCESS(af_get_device(&device));
-    ASSERT_SUCCESS(af_sync(device));
-}
-
 template<typename T>
 void testImage(const std::string pTestFile, const size_t numSeeds,
                const unsigned *seedx, const unsigned *seedy,
@@ -103,7 +92,12 @@ void testImage(const std::string pTestFile, const size_t numSeeds,
         params.iterations = iter;
         params.replace    = 255.0;
 
-        apiWrapper(&outArray, inArray, seedxArr, seedyArr, params);
+        ASSERT_SUCCESS(af_confidence_cc(&outArray, inArray, seedxArr, seedyArr, params.radius,
+                                        params.multiplier, params.iterations,
+                                        params.replace));
+        int device = 0;
+        ASSERT_SUCCESS(af_get_device(&device));
+        ASSERT_SUCCESS(af_sync(device));
 
         ASSERT_ARRAYS_EQ(outArray, goldArray);
 
@@ -147,7 +141,12 @@ void testData(CCCTestParams params) {
                                    (af_dtype)af::dtype_traits<T>::af_type));
 
     af_array outArray = 0;
-    apiWrapper(&outArray, inArray, seedxArr, seedyArr, params);
+    ASSERT_SUCCESS(af_confidence_cc(&outArray, inArray, seedxArr, seedyArr, params.radius,
+                                    params.multiplier, params.iterations,
+                                    params.replace));
+    int device = 0;
+    ASSERT_SUCCESS(af_get_device(&device));
+    ASSERT_SUCCESS(af_sync(device));
 
     ASSERT_VEC_ARRAY_EQ(tests[0], dims, outArray);
 
@@ -161,6 +160,7 @@ class ConfidenceConnectedDataTest
     : public testing::TestWithParam<CCCTestParams> {};
 
 TYPED_TEST(ConfidenceConnectedImageTest, DonutBackgroundExtraction) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const unsigned seedx = 10;
     const unsigned seedy = 10;
     testImage<TypeParam>(std::string("donut_background.test"), 1, &seedx,
@@ -168,6 +168,7 @@ TYPED_TEST(ConfidenceConnectedImageTest, DonutBackgroundExtraction) {
 }
 
 TYPED_TEST(ConfidenceConnectedImageTest, DonutRingExtraction) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const unsigned seedx = 132;
     const unsigned seedy = 132;
     testImage<TypeParam>(std::string("donut_ring.test"), 1, &seedx, &seedy, 3,
@@ -175,6 +176,7 @@ TYPED_TEST(ConfidenceConnectedImageTest, DonutRingExtraction) {
 }
 
 TYPED_TEST(ConfidenceConnectedImageTest, DonutKernelExtraction) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const unsigned seedx = 150;
     const unsigned seedy = 150;
     testImage<TypeParam>(std::string("donut_core.test"), 1, &seedx, &seedy, 3,
@@ -182,6 +184,7 @@ TYPED_TEST(ConfidenceConnectedImageTest, DonutKernelExtraction) {
 }
 
 TEST_P(ConfidenceConnectedDataTest, SegmentARegion) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     testData<unsigned char>(GetParam());
 }
 
diff --git a/test/fast.cpp b/test/fast.cpp
index 1d494641ff..693c80db67 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -158,12 +158,14 @@ void fastTest(string pTestFile, bool nonmax) {
 
 #define FLOAT_FAST_INIT(desc, image, nonmax)                                \
     TYPED_TEST(FloatFAST, desc) {                                           \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                             \
         fastTest<TypeParam>(string(TEST_DIR "/fast/" #image "_float.test"), \
                             nonmax);                                        \
     }
 
 #define FIXED_FAST_INIT(desc, image, nonmax)                                \
     TYPED_TEST(FixedFAST, desc) {                                           \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                             \
         fastTest<TypeParam>(string(TEST_DIR "/fast/" #image "_fixed.test"), \
                             nonmax);                                        \
     }
@@ -180,6 +182,7 @@ using af::features;
 using af::loadImage;
 
 TEST(FloatFAST, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
diff --git a/test/gloh.cpp b/test/gloh.cpp
index b360ac6a18..4ce2fa547b 100644
--- a/test/gloh.cpp
+++ b/test/gloh.cpp
@@ -161,8 +161,9 @@ void glohTest(string pTestFile) {
             af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_gloh(&feat, &desc, inArray, 3, 0.04f, 10.0f, 1.6f,
-                               true, 1.f / 256.f, 0.05f));
+        ASSERT_SUCCESS(af_gloh(&feat, &desc, inArray, 3,
+                                           0.04f, 10.0f, 1.6f,
+                                           true, 1.f / 256.f, 0.05f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
@@ -253,6 +254,7 @@ void glohTest(string pTestFile) {
 
 #define GLOH_INIT(desc, image)                                         \
     TYPED_TEST(GLOH, desc) {                                           \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                        \
         glohTest<TypeParam>(string(TEST_DIR "/gloh/" #image ".test")); \
     }
 
@@ -261,6 +263,7 @@ GLOH_INIT(man, man);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(GLOH, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
diff --git a/test/hamming.cpp b/test/hamming.cpp
index b14a33db0a..b8394e36b5 100644
--- a/test/hamming.cpp
+++ b/test/hamming.cpp
@@ -95,21 +95,25 @@ void hammingMatcherTest(string pTestFile, int feat_dim) {
 }
 
 TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     hammingMatcherTest<TypeParam>(
         string(TEST_DIR "/hamming/hamming_500_5000_dim0_u8.test"), 0);
 }
 
 TYPED_TEST(HammingMatcher8, Hamming_500_5000_Dim1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     hammingMatcherTest<TypeParam>(
         string(TEST_DIR "/hamming/hamming_500_5000_dim1_u8.test"), 1);
 }
 
 TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     hammingMatcherTest<TypeParam>(
         string(TEST_DIR "/hamming/hamming_500_5000_dim0_u32.test"), 0);
 }
 
 TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     hammingMatcherTest<TypeParam>(
         string(TEST_DIR "/hamming/hamming_500_5000_dim1_u32.test"), 1);
 }
@@ -117,6 +121,7 @@ TYPED_TEST(HammingMatcher32, Hamming_500_5000_Dim1) {
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(HammingMatcher, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     using af::array;
     using af::dim4;
 
@@ -155,6 +160,7 @@ TEST(HammingMatcher, CPP) {
 }
 
 TEST(HammingMatcher64bit, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     using af::array;
     using af::dim4;
 
diff --git a/test/harris.cpp b/test/harris.cpp
index 43c0bb6433..f2fd27d47a 100644
--- a/test/harris.cpp
+++ b/test/harris.cpp
@@ -145,6 +145,7 @@ void harrisTest(string pTestFile, float sigma, unsigned block_size) {
 
 #define HARRIS_INIT(desc, image, sigma, block_size)                        \
     TYPED_TEST(Harris, desc) {                                             \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                            \
         harrisTest<TypeParam>(string(TEST_DIR "/harris/" #image "_" #sigma \
                                               "_" #block_size ".test"),    \
                               sigma, block_size);                          \
@@ -167,6 +168,7 @@ using af::harris;
 using af::loadImage;
 
 TEST(FloatHarris, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
diff --git a/test/homography.cpp b/test/homography.cpp
index f4c1c75259..bd4809d428 100644
--- a/test/homography.cpp
+++ b/test/homography.cpp
@@ -69,8 +69,8 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     ASSERT_SUCCESS(af_load_image(&trainArray_f32, inFiles[0].c_str(), false));
     ASSERT_SUCCESS(conv_image<T>(&trainArray, trainArray_f32));
 
-    ASSERT_SUCCESS(af_orb(&train_feat, &train_desc, trainArray, 20.0f, 2000,
-                          1.2f, 8, true));
+    ASSERT_SUCCESS(af_orb(&train_feat, &train_desc, trainArray,
+                               20.0f, 2000, 1.2f, 8, true));
 
     ASSERT_SUCCESS(af_get_features_xpos(&train_feat_x, train_feat));
     ASSERT_SUCCESS(af_get_features_ypos(&train_feat_y, train_feat));
@@ -96,15 +96,16 @@ void homographyTest(string pTestFile, const af_homography_type htype,
     const dim_t test_d0 = inDims[0][0] * size_ratio;
     const dim_t test_d1 = inDims[0][1] * size_ratio;
     const dim_t tDims[] = {test_d0, test_d1};
-    if (rotate)
+    if (rotate) {
         ASSERT_SUCCESS(af_rotate(&queryArray, trainArray, theta, false,
                                  AF_INTERP_NEAREST));
-    else
+    } else {
         ASSERT_SUCCESS(af_resize(&queryArray, trainArray, test_d0, test_d1,
                                  AF_INTERP_BILINEAR));
+    }
 
-    ASSERT_SUCCESS(af_orb(&query_feat, &query_desc, queryArray, 20.0f, 2000,
-                          1.2f, 8, true));
+    ASSERT_SUCCESS(af_orb(&query_feat, &query_desc, queryArray,
+                                      20.0f, 2000, 1.2f, 8, true));
 
     ASSERT_SUCCESS(
         af_hamming_matcher(&idx, &dist, train_desc, query_desc, 0, 1));
@@ -144,9 +145,9 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 
     int inliers = 0;
     ASSERT_SUCCESS(af_homography(&H, &inliers, train_feat_x_idx,
-                                 train_feat_y_idx, query_feat_x_idx,
-                                 query_feat_y_idx, htype, 3.0f, 1000,
-                                 (af_dtype)dtype_traits<T>::af_type));
+                                             train_feat_y_idx, query_feat_x_idx,
+                                             query_feat_y_idx, htype, 3.0f, 1000,
+                                             (af_dtype)dtype_traits<T>::af_type));
 
     array HH(H);
 
@@ -201,6 +202,7 @@ void homographyTest(string pTestFile, const af_homography_type htype,
 
 #define HOMOGRAPHY_INIT(desc, image, htype, rotate, size_ratio)            \
     TYPED_TEST(Homography, desc) {                                         \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                            \
         homographyTest<TypeParam>(                                         \
             string(TEST_DIR "/homography/" #image ".test"), htype, rotate, \
             size_ratio);                                                   \
@@ -220,6 +222,7 @@ using af::features;
 using af::loadImage;
 
 TEST(Homography, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
@@ -262,7 +265,7 @@ TEST(Homography, CPP) {
     array H;
     int inliers = 0;
     homography(H, inliers, feat_train_x, feat_train_y, feat_query_x,
-               feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
+                                  feat_query_y, AF_HOMOGRAPHY_RANSAC, 3.0f, 1000, f32);
 
     float* gold_t = new float[8];
     for (int i = 0; i < 8; i++) gold_t[i] = 0.f;
diff --git a/test/hsv_rgb.cpp b/test/hsv_rgb.cpp
index 423fc5fad5..134e56c6c3 100644
--- a/test/hsv_rgb.cpp
+++ b/test/hsv_rgb.cpp
@@ -38,6 +38,7 @@ TEST(hsv_rgb, InvalidArray) {
 }
 
 TEST(hsv2rgb, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<float>> in;
     vector<vector<float>> tests;
@@ -54,6 +55,7 @@ TEST(hsv2rgb, CPP) {
 }
 
 TEST(rgb2hsv, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<float>> in;
     vector<vector<float>> tests;
@@ -70,6 +72,7 @@ TEST(rgb2hsv, CPP) {
 }
 
 TEST(rgb2hsv, MaxDim) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<float>> in;
     vector<vector<float>> tests;
@@ -108,6 +111,7 @@ TEST(rgb2hsv, MaxDim) {
 }
 
 TEST(hsv2rgb, MaxDim) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<float>> in;
     vector<vector<float>> tests;
diff --git a/test/imageio.cpp b/test/imageio.cpp
index 00834fb693..16cead852c 100644
--- a/test/imageio.cpp
+++ b/test/imageio.cpp
@@ -160,7 +160,7 @@ TEST(ImageIO, SavePNGCPP) {
     input(9, 0, 2)          = 255;
     input(9, 9, span)       = 255;
 
-    std::string testname  = getTestName() + "_" + getBackendName();
+    std::string testname  = getTestName() + "_" + getBackendName(true);
     std::string imagename = "SaveCPP_" + testname + ".png";
 
     saveImage(imagename.c_str(), input);
@@ -180,7 +180,7 @@ TEST(ImageIO, SaveBMPCPP) {
     input(9, 0, 2)          = 255;
     input(9, 9, span)       = 255;
 
-    std::string testname  = getTestName() + "_" + getBackendName();
+    std::string testname  = getTestName() + "_" + getBackendName(true);
     std::string imagename = "SaveCPP_" + testname + ".bmp";
 
     saveImage(imagename.c_str(), input);
@@ -291,7 +291,7 @@ TEST(ImageIO, SaveImage16CPP) {
     array input     = randu(dims, u16);
     array input_255 = floor(input.as(f32) / 257);
 
-    std::string testname  = getTestName() + "_" + getBackendName();
+    std::string testname  = getTestName() + "_" + getBackendName(true);
     std::string imagename = "saveImage16CPP_" + testname + ".png";
 
     saveImage(imagename.c_str(), input);
@@ -366,7 +366,7 @@ void saveLoadImageNativeCPPTest(dim4 dims) {
 
     array input = randu(dims, (af_dtype)dtype_traits<T>::af_type);
 
-    std::string imagename = getTestName() + "_" + getBackendName() + ".png";
+    std::string imagename = getTestName() + "_" + getBackendName(true) + ".png";
 
     saveImageNative(imagename.c_str(), input);
 
diff --git a/test/match_template.cpp b/test/match_template.cpp
index 33b6096815..4ee8fc7e2d 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -84,16 +84,19 @@ void matchTemplateTest(string pTestFile, af_match_type pMatchType) {
 }
 
 TYPED_TEST(MatchTemplate, Matrix_SAD) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     matchTemplateTest<TypeParam>(
         string(TEST_DIR "/MatchTemplate/matrix_sad.test"), AF_SAD);
 }
 
 TYPED_TEST(MatchTemplate, Matrix_SSD) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     matchTemplateTest<TypeParam>(
         string(TEST_DIR "/MatchTemplate/matrix_ssd.test"), AF_SSD);
 }
 
 TYPED_TEST(MatchTemplate, MatrixBatch_SAD) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     matchTemplateTest<TypeParam>(
         string(TEST_DIR "/MatchTemplate/matrix_sad_batch.test"), AF_SAD);
 }
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 1939379974..2d874cb3ae 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -80,24 +80,28 @@ void medfiltTest(string pTestFile, dim_t w_len, dim_t w_wid,
 }
 
 TYPED_TEST(MedianFilter, ZERO_PAD_3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfiltTest<TypeParam>(
         string(TEST_DIR "/medianfilter/zero_pad_3x3_window.test"), 3, 3,
         AF_PAD_ZERO);
 }
 
 TYPED_TEST(MedianFilter, SYMMETRIC_PAD_3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfiltTest<TypeParam>(
         string(TEST_DIR "/medianfilter/symmetric_pad_3x3_window.test"), 3, 3,
         AF_PAD_SYM);
 }
 
 TYPED_TEST(MedianFilter, BATCH_ZERO_PAD_3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfiltTest<TypeParam>(
         string(TEST_DIR "/medianfilter/batch_zero_pad_3x3_window.test"), 3, 3,
         AF_PAD_ZERO);
 }
 
 TYPED_TEST(MedianFilter, BATCH_SYMMETRIC_PAD_3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfiltTest<TypeParam>(
         string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x3_window.test"), 3,
         3, AF_PAD_SYM);
@@ -140,24 +144,28 @@ void medfilt1_Test(string pTestFile, dim_t w_wid, af_border_type pad) {
 }
 
 TYPED_TEST(MedianFilter1d, ZERO_PAD_3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfilt1_Test<TypeParam>(
         string(TEST_DIR "/medianfilter/zero_pad_3x1_window.test"), 3,
         AF_PAD_ZERO);
 }
 
 TYPED_TEST(MedianFilter1d, SYMMETRIC_PAD_3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfilt1_Test<TypeParam>(
         string(TEST_DIR "/medianfilter/symmetric_pad_3x1_window.test"), 3,
         AF_PAD_SYM);
 }
 
 TYPED_TEST(MedianFilter1d, BATCH_ZERO_PAD_3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfilt1_Test<TypeParam>(
         string(TEST_DIR "/medianfilter/batch_zero_pad_3x1_window.test"), 3,
         AF_PAD_ZERO);
 }
 
 TYPED_TEST(MedianFilter1d, BATCH_SYMMETRIC_PAD_3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     medfilt1_Test<TypeParam>(
         string(TEST_DIR "/medianfilter/batch_symmetric_pad_3x1_window.test"), 3,
         AF_PAD_SYM);
@@ -338,6 +346,7 @@ TYPED_TEST(MedianFilter1d, InvalidPadType) { medfilt1d_PadTest<TypeParam>(); }
 using af::array;
 
 TEST(MedianFilter, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const dim_t w_len = 3;
     const dim_t w_wid = 3;
 
@@ -365,6 +374,7 @@ TEST(MedianFilter, CPP) {
 }
 
 TEST(MedianFilter1d, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const dim_t w_wid = 3;
 
     vector<dim4> numDims;
@@ -391,6 +401,7 @@ TEST(MedianFilter1d, CPP) {
 }
 
 TEST(MedianFilter, Docs) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     float input[] = {1.0000,  2.0000,  3.0000,  4.0000,  5.0000,  6.0000,
                      7.0000,  8.0000,  9.0000,  10.0000, 11.0000, 12.0000,
                      13.0000, 14.0000, 15.0000, 16.0000};
@@ -431,6 +442,7 @@ using af::seq;
 using af::span;
 
 TEST(MedianFilter, GFOR) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     dim4 dims = dim4(10, 10, 3);
     array A   = iota(dims);
     array B   = constant(0, dims);
@@ -445,6 +457,7 @@ TEST(MedianFilter, GFOR) {
 }
 
 TEST(MedianFilter1d, GFOR) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     dim4 dims = dim4(10, 10, 3);
     array A   = iota(dims);
     array B   = constant(0, dims);
diff --git a/test/moments.cpp b/test/moments.cpp
index 6b02cb614a..bec90e5b5d 100644
--- a/test/moments.cpp
+++ b/test/moments.cpp
@@ -158,25 +158,30 @@ void momentsOnImageTest(string pTestFile, string pImageFile, bool isColor) {
 }
 
 TEST(IMAGE, MomentsImage) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     momentsOnImageTest(string(TEST_DIR "/moments/gray_seq_16_moments.test"),
                        string(TEST_DIR "/imageio/gray_seq_16.png"), false);
 }
 
 TEST(Image, MomentsImageBatch) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     momentsTest<float>(
         string(TEST_DIR "/moments/simple_mat_batch_moments.test"));
 }
 
 TEST(Image, MomentsBatch2D) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     momentsOnImageTest(string(TEST_DIR "/moments/color_seq_16_moments.test"),
                        string(TEST_DIR "/imageio/color_seq_16.png"), true);
 }
 
 TYPED_TEST(Image, MomentsSynthTypes) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     momentsTest<TypeParam>(string(TEST_DIR "/moments/simple_mat_moments.test"));
 }
 
 TEST(Image, Moment_Issue1957) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     array A = identity(3, 3, b8);
 
     double m00;
diff --git a/test/morph.cpp b/test/morph.cpp
index 9cc2255fb5..ad62ded8f3 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -59,16 +59,19 @@ void morphTest(string pTestFile) {
                                    maskDims.ndims(), maskDims.get(),
                                    (af_dtype)dtype_traits<inType>::af_type));
 
+    af_err af_stat;
     if (isDilation) {
-        if (isVolume)
+        if (isVolume) {
             ASSERT_SUCCESS(af_dilate3(&outArray, inArray, maskArray));
-        else
+        } else {
             ASSERT_SUCCESS(af_dilate(&outArray, inArray, maskArray));
+        }
     } else {
-        if (isVolume)
+        if (isVolume) {
             ASSERT_SUCCESS(af_erode3(&outArray, inArray, maskArray));
-        else
+        } else {
             ASSERT_SUCCESS(af_erode(&outArray, inArray, maskArray));
+        }
     }
 
     for (size_t testIter = 0; testIter < tests.size(); ++testIter) {
@@ -83,52 +86,63 @@ void morphTest(string pTestFile) {
 }
 
 TYPED_TEST(Morph, Dilate3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, true, false>(string(TEST_DIR "/morph/dilate3x3.test"));
 }
 
 TYPED_TEST(Morph, Erode3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, false, false>(string(TEST_DIR "/morph/erode3x3.test"));
 }
 
 TYPED_TEST(Morph, Dilate4x4) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, true, false>(string(TEST_DIR "/morph/dilate4x4.test"));
 }
 
 TYPED_TEST(Morph, Dilate12x12) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, true, false>(
         string(TEST_DIR "/morph/dilate12x12.test"));
 }
 
 TYPED_TEST(Morph, Erode4x4) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, false, false>(string(TEST_DIR "/morph/erode4x4.test"));
 }
 
 TYPED_TEST(Morph, Dilate3x3_Batch) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, true, false>(
         string(TEST_DIR "/morph/dilate3x3_batch.test"));
 }
 
 TYPED_TEST(Morph, Erode3x3_Batch) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, false, false>(
         string(TEST_DIR "/morph/erode3x3_batch.test"));
 }
 
 TYPED_TEST(Morph, Dilate3x3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, true, true>(
         string(TEST_DIR "/morph/dilate3x3x3.test"));
 }
 
 TYPED_TEST(Morph, Erode3x3x3) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, false, true>(
         string(TEST_DIR "/morph/erode3x3x3.test"));
 }
 
 TYPED_TEST(Morph, Dilate4x4x4) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, true, true>(
         string(TEST_DIR "/morph/dilate4x4x4.test"));
 }
 
 TYPED_TEST(Morph, Erode4x4x4) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphTest<TypeParam, false, true>(
         string(TEST_DIR "/morph/erode4x4x4.test"));
 }
@@ -186,10 +200,10 @@ void morphImageTest(string pTestFile, dim_t seLen) {
         ASSERT_SUCCESS(error_code);
         ASSERT_IMAGES_NEAR(goldArray, outArray, 0.018f);
 #else
-        ASSERT_EQ(error_code,
-                  (targetType != b8 && seLen > 19 ? AF_ERR_NOT_SUPPORTED
-                                                  : AF_SUCCESS));
-        if (!(targetType != b8 && seLen > 19)) {
+        if (targetType != b8 && seLen > 19) {
+            ASSERT_EQ(error_code, AF_ERR_NOT_SUPPORTED);
+        } else {
+            ASSERT_SUCCESS(error_code);
             ASSERT_IMAGES_NEAR(goldArray, outArray, 0.018f);
         }
 #endif
@@ -204,10 +218,12 @@ void morphImageTest(string pTestFile, dim_t seLen) {
 }
 
 TEST(Morph, GrayscaleDilation3x3StructuringElement) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphImageTest<float, true, false>(string(TEST_DIR "/morph/gray.test"), 3);
 }
 
 TEST(Morph, ColorImageErosion3x3StructuringElement) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     morphImageTest<float, false, true>(string(TEST_DIR "/morph/color.test"), 3);
 }
 
@@ -428,14 +444,17 @@ void cppMorphImageTest(string pTestFile) {
 }
 
 TEST(Morph, Grayscale_CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     cppMorphImageTest<float, true, false>(string(TEST_DIR "/morph/gray.test"));
 }
 
 TEST(Morph, ColorImage_CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     cppMorphImageTest<float, false, true>(string(TEST_DIR "/morph/color.test"));
 }
 
 TEST(Morph, GFOR) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     dim4 dims  = dim4(10, 10, 3);
     array A    = iota(dims);
     array B    = constant(0, dims);
@@ -451,6 +470,7 @@ TEST(Morph, GFOR) {
 }
 
 TEST(Morph, EdgeIssue1564) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     int inputData[10 * 10] = {0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                               0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                               0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
@@ -466,12 +486,13 @@ TEST(Morph, EdgeIssue1564) {
     array input(10, 10, inputData);
     int maskData[3 * 3] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
     array mask(3, 3, maskData);
+    
     array dilated = dilate(input.as(b8), mask.as(b8));
 
     size_t nElems = dilated.elements();
     vector<char> outData(nElems);
     dilated.host((void*)outData.data());
-
+    
     for (size_t i = 0; i < nElems; ++i) {
         ASSERT_EQ((int)outData[i], goldData[i]);
     }
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 01847aea65..2db885f566 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -117,24 +117,28 @@ void nearestNeighbourTest(string pTestFile, int feat_dim,
 // SSD
 /////////////////////////////////////////////////
 TYPED_TEST(NearestNeighbour, NN_SSD_100_1000_Dim0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/ssd_100_1000_dim0.test"), 0,
         AF_SSD);
 }
 
 TYPED_TEST(NearestNeighbour, NN_SSD_100_1000_Dim1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/ssd_100_1000_dim1.test"), 1,
         AF_SSD);
 }
 
 TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/ssd_500_5000_dim0.test"), 0,
         AF_SSD);
 }
 
 TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/ssd_500_5000_dim1.test"), 1,
         AF_SSD);
@@ -144,24 +148,28 @@ TYPED_TEST(NearestNeighbour, NN_SSD_500_5000_Dim1) {
 // SAD
 /////////////////////////////////////////////////
 TYPED_TEST(NearestNeighbour, NN_SAD_100_1000_Dim0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/sad_100_1000_dim0.test"), 0,
         AF_SAD);
 }
 
 TYPED_TEST(NearestNeighbour, NN_SAD_100_1000_Dim1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/sad_100_1000_dim1.test"), 1,
         AF_SAD);
 }
 
 TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/sad_500_5000_dim0.test"), 0,
         AF_SAD);
 }
 
 TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearestNeighbourTest<TypeParam>(
         string(TEST_DIR "/nearest_neighbour/sad_500_5000_dim1.test"), 1,
         AF_SAD);
@@ -170,6 +178,7 @@ TYPED_TEST(NearestNeighbour, NN_SAD_500_5000_Dim1) {
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(NearestNeighbourSSD, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<uint>> in;
     vector<vector<uint>> tests;
@@ -206,6 +215,7 @@ TEST(NearestNeighbourSSD, CPP) {
 }
 
 TEST(NearestNeighbourSAD, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<uint>> in;
     vector<vector<uint>> tests;
@@ -242,6 +252,7 @@ TEST(NearestNeighbourSAD, CPP) {
 }
 
 TEST(NearestNeighbourSSD, small) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const int ntrain            = 1;
     const int nquery            = 5;
     const int nfeat             = 2;
@@ -272,6 +283,7 @@ TEST(NearestNeighbourSSD, small) {
 }
 
 TEST(KNearestNeighbourSSD, small) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const int ntrain = 5;
     const int nquery = 3;
     const int nfeat  = 2;
@@ -435,6 +447,7 @@ INSTANTIATE_TEST_SUITE_P(KNearestNeighborsSSD, KNearestNeighborsTest,
                          testNameGenerator<KNearestNeighborsTest>);
 
 TEST_P(NearestNeighborsTest, SingleQTests) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearest_neighbors_params params = GetParam();
     array query = array(params.qdims_, params.query_.data());
     array train = array(params.tdims_, params.train_.data());
@@ -454,6 +467,7 @@ TEST_P(NearestNeighborsTest, SingleQTests) {
 }
 
 TEST_P(KNearestNeighborsTest, SingleQTests) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     nearest_neighbors_params params = GetParam();
 
     array query = array(params.qdims_, params.query_.data());
@@ -504,6 +518,7 @@ TEST(KNearestNeighbours, InvalidLargeK) {
 }
 
 TEST(NearestNeighbour, DocSnippet1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     //! [ex_nearest_1]
     float h_pts[6] = {1.f, 2.f, 3.f, 8.f, 9.f, 10.f};
     array pts(dim4(1, 6), h_pts);
@@ -537,6 +552,7 @@ TEST(NearestNeighbour, DocSnippet1) {
 }
 
 TEST(NearestNeighbour, DocSnippet2) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     //! [ex_nearest_2]
     float h_pts[18] = {0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f, 0.f,
                        8.f, 9.f, 1.f, 9.f, 8.f, 1.f, 9.f, 9.f, 1.f};
diff --git a/test/orb.cpp b/test/orb.cpp
index e519fd91dc..3ace1f4b05 100644
--- a/test/orb.cpp
+++ b/test/orb.cpp
@@ -238,14 +238,19 @@ void orbTest(string pTestFile) {
 }
 
 TYPED_TEST(ORB, Square) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     orbTest<TypeParam>(string(TEST_DIR "/orb/square.test"));
 }
 
-TYPED_TEST(ORB, Lena) { orbTest<TypeParam>(string(TEST_DIR "/orb/lena.test")); }
+TYPED_TEST(ORB, Lena) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
+    orbTest<TypeParam>(string(TEST_DIR "/orb/lena.test"));
+}
 
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(ORB, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
diff --git a/test/regions.cpp b/test/regions.cpp
index 182a22e9b5..a6f14ede81 100644
--- a/test/regions.cpp
+++ b/test/regions.cpp
@@ -71,7 +71,7 @@ void regionsTest(string pTestFile, af_connectivity connectivity,
     }
 
     ASSERT_SUCCESS(af_regions(&outArray, inArray, connectivity,
-                              (af_dtype)dtype_traits<T>::af_type));
+                               (af_dtype)dtype_traits<T>::af_type));
 
     // Get result
     T* outData = new T[idims.elements()];
@@ -97,6 +97,7 @@ void regionsTest(string pTestFile, af_connectivity connectivity,
 
 #define REGIONS_INIT(desc, file, conn, conn_type)                             \
     TYPED_TEST(Regions, desc) {                                               \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                               \
         regionsTest<TypeParam>(                                               \
             string(TEST_DIR "/regions/" #file "_" #conn ".test"), conn_type); \
     }
@@ -109,6 +110,7 @@ REGIONS_INIT(Regions3, regions_128x128, 8, AF_CONNECTIVITY_8);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(Regions, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     vector<dim4> numDims;
     vector<vector<float>> in;
     vector<vector<float>> tests;
@@ -139,6 +141,7 @@ TEST(Regions, CPP) {
 
 ///////////////////////////////// Documentation Examples ///////////////////
 TEST(Regions, Docs_8) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     // input data
     uchar input[64] = {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
                        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
@@ -185,6 +188,7 @@ TEST(Regions, Docs_8) {
 }
 
 TEST(Regions, Docs_4) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     // input data
     uchar input[64] = {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
                        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
@@ -236,6 +240,7 @@ TEST(Regions, Docs_4) {
 }
 
 TEST(Regions, WholeImageComponent) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const int dim = 101;
     const int sz  = dim * dim;
     vector<char> input(sz, 1);
@@ -252,6 +257,7 @@ TEST(Regions, WholeImageComponent) {
 }
 
 TEST(Regions, NoComponentImage) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const int dim = 101;
     const int sz  = dim * dim;
     vector<char> input(sz, 0);
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
index fe4d61d095..0ea1dd8ecb 100644
--- a/test/scan_by_key.cpp
+++ b/test/scan_by_key.cpp
@@ -127,6 +127,7 @@ void scanByKeyTest(dim4 dims, int scanDim, vector<int> nodeLengths,
 
 #define SCAN_BY_KEY_TEST(FN, X, Y, Z, W, Ti, To, INC, DIM, DSTART, DEND, EPS) \
     TEST(ScanByKey, Test_Scan_By_Key_##FN##_##Ti##_##INC##_##DIM) {           \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                               \
         dim4 dims(X, Y, Z, W);                                                \
         int scanDim = DIM;                                                    \
         int nodel[] = {37, 256};                                              \
@@ -194,6 +195,7 @@ SCAN_BY_KEY_TEST(AF_BINARY_MAX, 4 * 1024, 512, 1, 1, float, float, false, 1, -5,
                  5, 1e-3);
 
 TEST(ScanByKey, Test_Scan_By_key_Simple_0) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     dim4 dims(16, 8, 2, 1);
     int scanDim = 0;
     int nodel[] = {4, 8};
@@ -207,6 +209,7 @@ TEST(ScanByKey, Test_Scan_By_key_Simple_0) {
 }
 
 TEST(ScanByKey, Test_Scan_By_key_Simple_1) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     dim4 dims(8, 256 + 128, 1, 1);
     int scanDim = 1;
     int nodel[] = {4, 8};
@@ -220,6 +223,7 @@ TEST(ScanByKey, Test_Scan_By_key_Simple_1) {
 }
 
 TEST(ScanByKey, FixOverflowWrite) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     const int SIZE = 41000;
     vector<int> keys(SIZE, 0);
     vector<float> vals(SIZE, 1.0f);
diff --git a/test/sift.cpp b/test/sift.cpp
index 621659e259..b96325d672 100644
--- a/test/sift.cpp
+++ b/test/sift.cpp
@@ -162,9 +162,9 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
             af_load_image(&inArray_f32, inFiles[testId].c_str(), false));
         ASSERT_SUCCESS(conv_image<T>(&inArray, inArray_f32));
 
-        ASSERT_SUCCESS(af_sift(&feat, &desc, inArray, nLayers, contrastThr,
-                               edgeThr, initSigma, doubleInput, 1.f / 256.f,
-                               0.05f));
+        ASSERT_SUCCESS(af_sift(&feat, &desc, inArray, nLayers,
+                                           contrastThr, edgeThr, initSigma,
+                                           doubleInput, 1.f / 256.f, 0.05f));
 
         dim_t n = 0;
         af_array x, y, score, orientation, size;
@@ -256,6 +256,7 @@ void siftTest(string pTestFile, unsigned nLayers, float contrastThr,
 #define SIFT_INIT(desc, image, nLayers, contrastThr, edgeThr, initSigma,  \
                   doubleInput)                                            \
     TYPED_TEST(SIFT, desc) {                                              \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                           \
         for (int i = 0; i < 1; i++)                                       \
             siftTest<TypeParam>(string(TEST_DIR "/sift/" #image ".test"), \
                                 nLayers, contrastThr, edgeThr, initSigma, \
@@ -272,6 +273,7 @@ SIFT_INIT(Man_NoDoubleInput, man_nodoubleinput, 3, 0.04f, 10.0f, 1.6f, false);
 ///////////////////////////////////// CPP ////////////////////////////////
 //
 TEST(SIFT, CPP) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     IMAGEIO_ENABLED_CHECK();
 
     vector<dim4> inDims;
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 298d36d299..84fae1d34c 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -79,11 +79,13 @@ void testSobelDerivatives(string pTestFile) {
 // border type is set to cv.BORDER_REFLECT_101 in opencv
 
 TYPED_TEST(Sobel, Rectangle) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     testSobelDerivatives<TypeParam, TypeParam>(
         string(TEST_DIR "/sobel/rectangle.test"));
 }
 
 TYPED_TEST(Sobel_Integer, Rectangle) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     testSobelDerivatives<TypeParam, int>(
         string(TEST_DIR "/sobel/rectangle.test"));
 }
diff --git a/test/susan.cpp b/test/susan.cpp
index 34929c22c0..3741dd2653 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -125,6 +125,7 @@ void susanTest(string pTestFile, float t, float g) {
 
 #define SUSAN_TEST(image, tval, gval)                                         \
     TYPED_TEST(Susan, image) {                                                \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                               \
         susanTest<TypeParam>(string(TEST_DIR "/susan/" #image ".test"), tval, \
                              gval);                                           \
     }
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 84ac83839f..4e9496f601 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -92,7 +92,7 @@ typedef unsigned char uchar;
 typedef unsigned int uint;
 typedef unsigned short ushort;
 
-std::string getBackendName();
+std::string getBackendName(bool lower = false);
 std::string getTestName();
 
 std::string readNextNonEmptyLine(std::ifstream &file);
@@ -242,6 +242,15 @@ bool noHalfTests(af::dtype ty);
     if (noHalfTests((af_dtype)af::dtype_traits<type>::af_type))   \
     GTEST_SKIP() << "Device doesn't support Half"
 
+#ifdef SKIP_UNSUPPORTED_TESTS
+#define UNSUPPORTED_BACKEND(backend)                        \
+    if(backend == af::getActiveBackend())                   \
+        GTEST_SKIP() << "Skipping unsupported function on " \
+                        + getBackendName() + " backend"
+#else
+#define UNSUPPORTED_BACKEND(backend)
+#endif
+
 #define LAPACK_ENABLED_CHECK() \
     if (!af::isLAPACKAvailable()) GTEST_SKIP() << "LAPACK Not Configured."
 
diff --git a/test/threading.cpp b/test/threading.cpp
index 41c4ebb723..1b71411f0e 100644
--- a/test/threading.cpp
+++ b/test/threading.cpp
@@ -130,6 +130,7 @@ int nextTargetDeviceId() {
 
 void morphTest(const array input, const array mask, const bool isDilation,
                const array gold, int targetDevice) {
+    UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);
     setDevice(targetDevice);
 
     array out;

From 553c38d4c2cb6e6943dfa053168085b57f106346 Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Mon, 17 Feb 2025 15:48:16 -0500
Subject: [PATCH 2627/2677] fix fallthrough in reduce_by_key_common for u8
 (#3503)

This fixes minByKey/maxByKey for u8.
---
 src/api/c/reduce.cpp |   1 +
 test/reduce.cpp      | 163 +++++++++++++++++++++++++++----------------
 2 files changed, 103 insertions(+), 61 deletions(-)

diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 8e1e670506..15be8b39e8 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -280,6 +280,7 @@ static af_err reduce_by_key_common(af_array *keys_out, af_array *vals_out,
             case u8:
                 reduce_key<op, uchar, uchar>(keys_out, vals_out, keys, vals,
                                              dim);
+                break;
             case f16:
                 reduce_key<op, half, half>(keys_out, vals_out, keys, vals, dim);
                 break;
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 0b8317a960..0a36431a54 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -36,10 +36,14 @@ using std::vector;
 template<typename T>
 class Reduce : public ::testing::Test {};
 
+template<typename T>
+class ReduceByKey : public ::testing::Test {};
+
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
                          uchar, short, ushort>
     TestTypes;
 TYPED_TEST_SUITE(Reduce, TestTypes);
+TYPED_TEST_SUITE(ReduceByKey, TestTypes);
 
 typedef af_err (*reduceFunc)(af_array *, const af_array, const int);
 
@@ -154,6 +158,16 @@ struct promote_type<ushort, af_product> {
     typedef uint type;
 };
 
+// float16 is promoted to float32 for sum and product
+template<>
+struct promote_type<half_float::half, af_sum> {
+    typedef float type;
+};
+template<>
+struct promote_type<half_float::half, af_product> {
+    typedef float type;
+};
+
 #define REDUCE_TESTS(FN)                                                       \
     TYPED_TEST(Reduce, Test_##FN) {                                            \
         reduceTest<TypeParam, typename promote_type<TypeParam, af_##FN>::type, \
@@ -598,12 +612,16 @@ TEST_P(ReduceByKeyP, SumDim2) {
     ASSERT_ARRAYS_NEAR(valsReducedGold, valsReduced, 1e-5);
 }
 
-TEST(ReduceByKey, MultiBlockReduceSingleval) {
+TYPED_TEST(ReduceByKey, MultiBlockReduceSingleval) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
     array keys = constant(0, 1024 * 1024, s32);
-    array vals = constant(1, 1024 * 1024, f32);
+    array vals = constant(1, 1024 * 1024,
+            (af_dtype)af::dtype_traits<TypeParam>::af_type);
 
     array keyResGold      = constant(0, 1);
-    array valsReducedGold = constant(1024 * 1024, 1, f32);
+    using promoted_t = typename promote_type<TypeParam, af_sum>::type;
+    array valsReducedGold = constant(1024 * 1024, 1,
+            (af_dtype)af::dtype_traits<promoted_t>::af_type);
 
     array keyRes, valsReduced;
     sumByKey(keyRes, valsReduced, keys, vals);
@@ -701,10 +719,11 @@ TEST(ReduceByKey, MultiBlockReduceByKeyRandom500) {
     reduce_by_key_test(string(TEST_DIR "/reduce/test_random500_by_key.test"));
 }
 
-TEST(ReduceByKey, productReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, productReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -713,15 +732,17 @@ TEST(ReduceByKey, productReduceByKey) {
     productByKey(reduced_keys, reduced_vals, keys, vals, 0, 1);
 
     const int goldSz = 5;
-    const vector<float> gold_reduce{0, 7, 6, 30, 4};
+    using promoted_t = typename promote_type<TypeParam, af_product>::type;
+    const vector<promoted_t> gold_reduce{0, 7, 6, 30, 4};
 
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, minReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, minReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -730,14 +751,15 @@ TEST(ReduceByKey, minReduceByKey) {
     minByKey(reduced_keys, reduced_vals, keys, vals);
 
     const int goldSz = 5;
-    const vector<float> gold_reduce{0, 1, 6, 2, 4};
+    const vector<TypeParam> gold_reduce{0, 1, 6, 2, 4};
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, maxReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, maxReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -746,14 +768,15 @@ TEST(ReduceByKey, maxReduceByKey) {
     maxByKey(reduced_keys, reduced_vals, keys, vals);
 
     const int goldSz = 5;
-    const vector<float> gold_reduce{0, 7, 6, 5, 4};
+    const vector<TypeParam> gold_reduce{0, 7, 6, 5, 4};
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, allTrueReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 1, 1, 1, 0, 1, 1, 1};
+TYPED_TEST(ReduceByKey, allTrueReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 1, 1, 1, 0, 1, 1, 1};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -766,10 +789,11 @@ TEST(ReduceByKey, allTrueReduceByKey) {
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, anyTrueReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 8, 8};
-    const float testVals[testSz] = {0, 1, 1, 1, 0, 1, 0, 0};
+TYPED_TEST(ReduceByKey, anyTrueReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 8, 8};
+    const TypeParam testVals[testSz] = {0, 1, 1, 1, 0, 1, 0, 0};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -783,10 +807,11 @@ TEST(ReduceByKey, anyTrueReduceByKey) {
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, countReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 5};
-    const float testVals[testSz] = {0, 1, 1, 1, 0, 1, 1, 1};
+TYPED_TEST(ReduceByKey, countReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 5};
+    const TypeParam testVals[testSz] = {0, 1, 1, 1, 0, 1, 1, 1};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -799,11 +824,18 @@ TEST(ReduceByKey, countReduceByKey) {
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, ReduceByKeyNans) {
+TYPED_TEST(ReduceByKey, ReduceByKeyNans) {
+    if (!IsFloatingPoint<TypeParam>::value) {
+        SUCCEED() << "Not a floating point type.";
+        return;
+    }
+
     SKIP_IF_FAST_MATH_ENABLED();
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, NAN, 6, 2, 5, 3, 4};
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam nan = std::numeric_limits<TypeParam>::quiet_NaN();
+    const TypeParam testVals[testSz] = {0, 7, nan, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -812,14 +844,16 @@ TEST(ReduceByKey, ReduceByKeyNans) {
     productByKey(reduced_keys, reduced_vals, keys, vals, 0, 1);
 
     const int goldSz = 5;
-    const vector<float> gold_reduce{0, 7, 6, 30, 4};
+    using promoted_t = typename promote_type<TypeParam, af_product>::type;
+    const vector<promoted_t> gold_reduce{0, 7, 6, 30, 4};
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, nDim0ReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, nDim0ReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -833,20 +867,22 @@ TEST(ReduceByKey, nDim0ReduceByKey) {
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
     const dim4 goldSz(5, 2, 2, 2);
-    const vector<float> gold_reduce{0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
+    using promoted_t = typename promote_type<TypeParam, af_sum>::type;
+    const vector<promoted_t> gold_reduce{0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
 
-                                    0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
+                                         0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
 
-                                    0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
+                                         0, 8, 6, 10, 4, 0, 8, 6, 10, 4,
 
-                                    0, 8, 6, 10, 4, 0, 8, 6, 10, 4};
+                                         0, 8, 6, 10, 4, 0, 8, 6, 10, 4};
     ASSERT_VEC_ARRAY_EQ(gold_reduce, goldSz, reduced_vals);
 }
 
-TEST(ReduceByKey, nDim1ReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, nDim1ReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -861,8 +897,9 @@ TEST(ReduceByKey, nDim1ReduceByKey) {
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
     const int goldSz                = 5;
-    const float gold_reduce[goldSz] = {0, 8, 6, 10, 4};
-    vector<float> hreduce(reduced_vals.elements());
+    using promoted_t = typename promote_type<TypeParam, af_sum>::type;
+    const promoted_t gold_reduce[goldSz] = {0, 8, 6, 10, 4};
+    vector<promoted_t> hreduce(reduced_vals.elements());
     reduced_vals.host(hreduce.data());
 
     for (int i = 0; i < goldSz * ntile; i++) {
@@ -870,10 +907,11 @@ TEST(ReduceByKey, nDim1ReduceByKey) {
     }
 }
 
-TEST(ReduceByKey, nDim2ReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, nDim2ReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -888,8 +926,9 @@ TEST(ReduceByKey, nDim2ReduceByKey) {
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
     const int goldSz                = 5;
-    const float gold_reduce[goldSz] = {0, 8, 6, 10, 4};
-    vector<float> h_a(reduced_vals.elements());
+    using promoted_t = typename promote_type<TypeParam, af_sum>::type;
+    const promoted_t gold_reduce[goldSz] = {0, 8, 6, 10, 4};
+    vector<promoted_t> h_a(reduced_vals.elements());
     reduced_vals.host(h_a.data());
 
     for (int i = 0; i < goldSz * ntile; i++) {
@@ -897,10 +936,11 @@ TEST(ReduceByKey, nDim2ReduceByKey) {
     }
 }
 
-TEST(ReduceByKey, nDim3ReduceByKey) {
-    const static int testSz      = 8;
-    const int testKeys[testSz]   = {0, 2, 2, 9, 5, 5, 5, 8};
-    const float testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
+TYPED_TEST(ReduceByKey, nDim3ReduceByKey) {
+    SUPPORTED_TYPE_CHECK(TypeParam);
+    const static int testSz          = 8;
+    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam testVals[testSz] = {0, 7, 1, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
     array vals(testSz, testVals);
@@ -915,8 +955,9 @@ TEST(ReduceByKey, nDim3ReduceByKey) {
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
     const int goldSz                = 5;
-    const float gold_reduce[goldSz] = {0, 8, 6, 10, 4};
-    vector<float> h_a(reduced_vals.elements());
+    using promoted_t = typename promote_type<TypeParam, af_sum>::type;
+    const promoted_t gold_reduce[goldSz] = {0, 8, 6, 10, 4};
+    vector<promoted_t> h_a(reduced_vals.elements());
     reduced_vals.host(h_a.data());
 
     for (int i = 0; i < goldSz * ntile; i++) {

From ccabfe60f584bc5e3a5435559822d991c7c0cded Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Thu, 20 Feb 2025 15:02:39 -0500
Subject: [PATCH 2628/2677] The test cmake file needs to be updated with the
 commit hash which includes the new test data needed for the test added in
 pull requests #3585 and #3587. (#3635)

---
 test/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 3fae5d68ec..4d53f4d4db 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -99,8 +99,8 @@ if(${AF_USE_RELATIVE_TEST_DIR})
 else(${AF_USE_RELATIVE_TEST_DIR})
   af_dep_check_and_populate(${testdata_prefix}
     URI https://github.com/arrayfire/arrayfire-data.git
-    #pinv large data set update change
-    REF 0144a599f913cc67c76c9227031b4100156abc25
+    #Add test file for SSAS_LinearSteps
+    REF 05703a4897c8b89b7a0ece1dbe21ede33d226f44
   )
   set(TESTDATA_SOURCE_DIR "${${testdata_prefix}_SOURCE_DIR}")
 endif(${AF_USE_RELATIVE_TEST_DIR})

From 6cea4d361b67d5f26e90ef15af0e6a1ce686a911 Mon Sep 17 00:00:00 2001
From: Fraser Cormack <frasercrmck@gmail.com>
Date: Thu, 20 Feb 2025 21:48:23 +0000
Subject: [PATCH 2629/2677] Fix race condition in OpenCL kernel (#3535)

Without the barrier at the end of barrierOR, it is possible for
work-item 0 to start the next loop iteration and update predicates[0]
while other work-items are still inside barrierOR reading `predicates`,
meaning they read the next loop iteration's exit condition. This results
in a divergent loop, where not all work-items reach the same barriers.

A previous fix identified this as a problem only on NVIDIA platforms,
but strictly speaking a barrier is required in all cases to avoid a spec
violation and undefined behaviour.
---
 src/backend/opencl/kernel/flood_fill.cl  | 6 ------
 src/backend/opencl/kernel/flood_fill.hpp | 2 --
 2 files changed, 8 deletions(-)

diff --git a/src/backend/opencl/kernel/flood_fill.cl b/src/backend/opencl/kernel/flood_fill.cl
index 0a7916fd49..ba8f8e109a 100644
--- a/src/backend/opencl/kernel/flood_fill.cl
+++ b/src/backend/opencl/kernel/flood_fill.cl
@@ -42,13 +42,7 @@ int barrierOR(local int *predicates) {
         barrier(CLK_LOCAL_MEM_FENCE);
     }
     int retVal = predicates[0];
-#if AF_IS_PLATFORM_NVIDIA
-    // Without the extra barrier sync after reading the reduction result,
-    // the caller's loop is going into infinite loop occasionally which is
-    // in turn randoms hangs. This doesn't seem to be an issue on non-nvidia
-    // hardware. Hence, the check.
     barrier(CLK_LOCAL_MEM_FENCE);
-#endif
     return retVal;
 }
 
diff --git a/src/backend/opencl/kernel/flood_fill.hpp b/src/backend/opencl/kernel/flood_fill.hpp
index 793ae5adcd..8035a61fd6 100644
--- a/src/backend/opencl/kernel/flood_fill.hpp
+++ b/src/backend/opencl/kernel/flood_fill.hpp
@@ -84,8 +84,6 @@ void floodFill(Param out, const Param image, const Param seedsx,
         DefineKeyValue(LMEM_WIDTH, (THREADS_X + 2 * RADIUS)),
         DefineKeyValue(LMEM_HEIGHT, (THREADS_Y + 2 * RADIUS)),
         DefineKeyValue(GROUP_SIZE, (THREADS_Y * THREADS_X)),
-        DefineKeyValue(AF_IS_PLATFORM_NVIDIA, (int)(AFCL_PLATFORM_NVIDIA ==
-                                                    getActivePlatformVendor())),
         getTypeBuildDefinition<T>()};
 
     auto floodStep =

From d63c391b5d21c3dda30f79ad98f15809c6d4a6e8 Mon Sep 17 00:00:00 2001
From: Filip Matzner <FloopCZ@users.noreply.github.com>
Date: Fri, 21 Feb 2025 01:03:41 +0100
Subject: [PATCH 2630/2677] Add support for CUDA 12.7 and 12.8 (#3636)

---
 src/backend/cuda/device_manager.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 05f775a821..c445d5784b 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,8 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12080, 9, 0, 0},
+    {12070, 9, 0, 0},
     {12060, 9, 0, 0},
     {12050, 9, 0, 0},
     {12040, 9, 0, 0},
@@ -144,6 +146,8 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {12080, 525.60f, 528.33f},
+        {12070, 525.60f, 528.33f},
         {12060, 525.60f, 528.33f},
         {12050, 525.60f, 528.33f},
         {12040, 525.60f, 528.33f},

From 48b7a9e4e173bdeb8c0896f1962f497a742d166f Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 26 Feb 2025 01:16:24 +0100
Subject: [PATCH 2631/2677] Join does not always respect the order of provided
 parameters (#3511) (#3513)

* Issue3511.  Join does not always respect order of provided parameters.

* Make test name more descriptive

---------

Co-authored-by: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
---
 src/backend/common/jit/Node.cpp |   6 ++
 src/backend/common/jit/Node.hpp |   1 +
 src/backend/cuda/jit.cpp        |  27 ++++----
 src/backend/oneapi/jit.cpp      |   4 +-
 src/backend/opencl/jit.cpp      | 118 ++++++++++++++++++--------------
 test/join.cpp                   |  46 +++++++++++++
 6 files changed, 137 insertions(+), 65 deletions(-)

diff --git a/src/backend/common/jit/Node.cpp b/src/backend/common/jit/Node.cpp
index f77d68e260..09c001a724 100644
--- a/src/backend/common/jit/Node.cpp
+++ b/src/backend/common/jit/Node.cpp
@@ -42,6 +42,7 @@ int Node::getNodesMap(Node_map_t &node_map, vector<Node *> &full_nodes,
 }
 
 std::string getFuncName(const vector<Node *> &output_nodes,
+                        const vector<int> &output_ids,
                         const vector<Node *> &full_nodes,
                         const vector<Node_ids> &full_ids, const bool is_linear,
                         const bool loop0, const bool loop1, const bool loop2,
@@ -59,6 +60,11 @@ std::string getFuncName(const vector<Node *> &output_nodes,
         funcName += node->getNameStr();
     }
 
+    for (const int id : output_ids) {
+        funcName += '-';
+        funcName += std::to_string(id);
+    }
+
     for (int i = 0; i < static_cast<int>(full_nodes.size()); i++) {
         full_nodes[i]->genKerName(funcName, full_ids[i]);
     }
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 8f2e0183b6..2cc3164fb5 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -326,6 +326,7 @@ struct Node_ids {
 };
 
 std::string getFuncName(const std::vector<Node *> &output_nodes,
+                        const std::vector<int> &output_ids,
                         const std::vector<Node *> &full_nodes,
                         const std::vector<Node_ids> &full_ids,
                         const bool is_linear, const bool loop0,
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 146cb07db2..9346491145 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -244,16 +244,18 @@ struct Param {
             node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
             // Generate the core function body, needs children ids as well
             node->genFuncs(opsStream, ids_curr);
-            for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
-                 (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
-                // Generate also output parameters
-                outParamStream << (oid == 0 ? "" : ",\n") << "Param<"
-                               << full_nodes[ids_curr.id]->getTypeStr()
-                               << "> out" << oid;
-                // Generate code to write the output (offset already in ptr)
-                opsStream << "out" << oid << ".ptr[idx] = val" << ids_curr.id
-                          << ";\n";
-                ++oid;
+            for (size_t output_idx{0}; output_idx < output_ids.size();
+                 ++output_idx) {
+                if (output_ids[output_idx] == ids_curr.id) {
+                    // Generate also output parameters
+                    outParamStream << (oid == 0 ? "" : ",\n") << "Param<"
+                                   << full_nodes[ids_curr.id]->getTypeStr()
+                                   << "> out" << oid;
+                    // Generate code to write the output (offset already in ptr)
+                    opsStream << "out" << output_idx << ".ptr[idx] = val"
+                              << ids_curr.id << ";\n";
+                    ++oid;
+                }
             }
         }
 
@@ -322,8 +324,9 @@ static CUfunction getKernel(const vector<Node*>& output_nodes,
                             const bool is_linear, const bool loop0,
                             const bool loop1, const bool loop2,
                             const bool loop3) {
-    const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
-                                      is_linear, loop0, loop1, loop2, loop3)};
+    const string funcName{getFuncName(output_nodes, output_ids, full_nodes,
+                                      full_ids, is_linear, loop0, loop1, loop2,
+                                      loop3)};
     // A forward lookup in module cache helps avoid recompiling
     // the JIT source generated from identical JIT-trees.
     const auto entry{
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index ecd5bc04b9..a112e99436 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -478,8 +478,8 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     full_nodes.clear();
     for (Node_ptr& node : node_clones) { full_nodes.push_back(node.get()); }
 
-    const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
-                                      is_linear, false, false, false,
+    const string funcName{getFuncName(output_nodes, output_ids, full_nodes,
+                                      full_ids, is_linear, false, false, false,
                                       outputs[0].info.dims[2] > 1)};
 
     getQueue().submit([&](sycl::handler& h) {
diff --git a/src/backend/opencl/jit.cpp b/src/backend/opencl/jit.cpp
index 7ace33cd96..c0858c3cc5 100644
--- a/src/backend/opencl/jit.cpp
+++ b/src/backend/opencl/jit.cpp
@@ -188,62 +188,77 @@ __kernel void )JIT";
     thread_local stringstream outOffsetStream;
     thread_local stringstream inOffsetsStream;
     thread_local stringstream opsStream;
+    thread_local stringstream kerStream;
 
-    int oid{0};
-    for (size_t i{0}; i < full_nodes.size(); i++) {
-        const auto& node{full_nodes[i]};
-        const auto& ids_curr{full_ids[i]};
-        // Generate input parameters, only needs current id
-        node->genParams(inParamStream, ids_curr.id, is_linear);
-        // Generate input offsets, only needs current id
-        node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
-        // Generate the core function body, needs children ids as well
-        node->genFuncs(opsStream, ids_curr);
-        for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
-             (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
-            // Generate also output parameters
-            outParamStream << "__global "
-                           << full_nodes[ids_curr.id]->getTypeStr() << " *out"
-                           << oid << ", int offset" << oid << ",\n";
-            // Apply output offset
-            outOffsetStream << "\nout" << oid << " += offset" << oid << ';';
-            // Generate code to write the output
-            opsStream << "out" << oid << "[idx] = val" << ids_curr.id << ";\n";
-            ++oid;
+    string ret;
+    try {
+        int oid{0};
+        for (size_t i{0}; i < full_nodes.size(); i++) {
+            const auto& node{full_nodes[i]};
+            const auto& ids_curr{full_ids[i]};
+            // Generate input parameters, only needs current id
+            node->genParams(inParamStream, ids_curr.id, is_linear);
+            // Generate input offsets, only needs current id
+            node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
+            // Generate the core function body, needs children ids as well
+            node->genFuncs(opsStream, ids_curr);
+            for (size_t output_idx{0}; output_idx < output_ids.size();
+                 ++output_idx) {
+                if (output_ids[output_idx] == ids_curr.id) {
+                    outParamStream
+                        << "__global " << full_nodes[ids_curr.id]->getTypeStr()
+                        << " *out" << oid << ", int offset" << oid << ",\n";
+                    // Apply output offset
+                    outOffsetStream << "\nout" << oid << " += offset" << oid
+                                    << ';';
+                    // Generate code to write the output
+                    opsStream << "out" << output_idx << "[idx] = val"
+                              << ids_curr.id << ";\n";
+                    ++oid;
+                }
+            }
         }
-    }
 
-    thread_local stringstream kerStream;
-    kerStream << kernelVoid << funcName << "(\n"
-              << inParamStream.str() << outParamStream.str() << dimParams << ")"
-              << blockStart;
-    if (is_linear) {
-        kerStream << linearInit << inOffsetsStream.str()
-                  << outOffsetStream.str() << '\n';
-        if (loop0) kerStream << linearLoop0Start;
-        kerStream << "\n\n" << opsStream.str();
-        if (loop0) kerStream << linearLoop0End;
-        kerStream << linearEnd;
-    } else {
-        if (loop0) {
-            kerStream << stridedLoop0Init << outOffsetStream.str() << '\n'
-                      << stridedLoop0Start;
+        kerStream << kernelVoid << funcName << "(\n"
+                  << inParamStream.str() << outParamStream.str() << dimParams
+                  << ")" << blockStart;
+        if (is_linear) {
+            kerStream << linearInit << inOffsetsStream.str()
+                      << outOffsetStream.str() << '\n';
+            if (loop0) kerStream << linearLoop0Start;
+            kerStream << "\n\n" << opsStream.str();
+            if (loop0) kerStream << linearLoop0End;
+            kerStream << linearEnd;
         } else {
-            kerStream << stridedLoopNInit << outOffsetStream.str() << '\n';
-            if (loop3) kerStream << stridedLoop3Init;
-            if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
-            if (loop3) kerStream << stridedLoop3Start;
+            if (loop0) {
+                kerStream << stridedLoop0Init << outOffsetStream.str() << '\n'
+                          << stridedLoop0Start;
+            } else {
+                kerStream << stridedLoopNInit << outOffsetStream.str() << '\n';
+                if (loop3) kerStream << stridedLoop3Init;
+                if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
+                if (loop3) kerStream << stridedLoop3Start;
+            }
+            kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+            if (loop3) kerStream << stridedLoop3End;
+            if (loop1) kerStream << stridedLoop1End;
+            if (loop0) kerStream << stridedLoop0End;
+            kerStream << stridedEnd;
         }
-        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
-        if (loop3) kerStream << stridedLoop3End;
-        if (loop1) kerStream << stridedLoop1End;
-        if (loop0) kerStream << stridedLoop0End;
-        kerStream << stridedEnd;
+        kerStream << blockEnd;
+        ret = kerStream.str();
+    } catch (...) {
+        // Prepare for next round
+        inParamStream.str("");
+        outParamStream.str("");
+        inOffsetsStream.str("");
+        outOffsetStream.str("");
+        opsStream.str("");
+        kerStream.str("");
+        throw;
     }
-    kerStream << blockEnd;
-    const string ret{kerStream.str()};
 
-    // Prepare for next round, limit memory
+    // Prepare for next round
     inParamStream.str("");
     outParamStream.str("");
     inOffsetsStream.str("");
@@ -259,8 +274,9 @@ cl::Kernel getKernel(const vector<Node*>& output_nodes,
                      const vector<Node*>& full_nodes,
                      const vector<Node_ids>& full_ids, const bool is_linear,
                      const bool loop0, const bool loop1, const bool loop3) {
-    const string funcName{getFuncName(output_nodes, full_nodes, full_ids,
-                                      is_linear, loop0, loop1, false, loop3)};
+    const string funcName{getFuncName(output_nodes, output_ids, full_nodes,
+                                      full_ids, is_linear, loop0, loop1, false,
+                                      loop3)};
     // A forward lookup in module cache helps avoid recompiling the JIT
     // source generated from identical JIT-trees.
     const auto entry{
diff --git a/test/join.cpp b/test/join.cpp
index cf33fccb67..4d25e8a6ae 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -15,6 +15,7 @@
 #include <af/index.h>
 #include <af/traits.hpp>
 
+#include <array>
 #include <complex>
 #include <iostream>
 #include <numeric>
@@ -266,3 +267,48 @@ TEST(Join, ManyEmpty) {
     ASSERT_ARRAYS_EQ(gold, eace);
     ASSERT_ARRAYS_EQ(gold, acee);
 }
+
+TEST(Join, respect_parameters_order_ISSUE3511) {
+    const float column_host1[] = {1., 2., 3.};
+    const float column_host2[] = {4., 5., 6.};
+    const af::array buf1(3, 1, column_host1);
+    const af::array buf2(3, 1, column_host2);
+
+    // We need to avoid that JIT arrays are evaluated during whatever call,
+    // so we will have to work with copies for single use
+    const af::array jit1{buf1 + 1.0};
+    const af::array jit2{buf2 + 2.0};
+    const std::array<af::array, 8> cases{jit1,  -jit1,       jit1 + 1.0, jit2,
+                                         -jit2, jit1 + jit2, buf1,       buf2};
+    const std::array<char*, 8> cases_name{"JIT1", "-JIT1", "JIT1+1.0",
+                                          "JIT2", "-JIT2", "JIT1+JIT2",
+                                          "BUF1", "BUF2"};
+    assert(cases.size() == cases_name.size());
+    for (size_t cl0{0}; cl0 < cases.size(); ++cl0) {
+        for (size_t cl1{0}; cl1 < cases.size(); ++cl1) {
+            printf("Testing: af::join(1,%s,%s)\n", cases_name[cl0],
+                   cases_name[cl1]);
+            const array col0{cases[cl0]};
+            const array col1{cases[cl1]};
+            const array result{af::join(1, col0, col1)};
+            ASSERT_ARRAYS_EQ(result(af::span, 0), col0);
+            ASSERT_ARRAYS_EQ(result(af::span, 1), col1);
+        }
+    }
+    // Join of 3 arrays
+    for (size_t cl0{0}; cl0 < cases.size(); ++cl0) {
+        for (size_t cl1{0}; cl1 < cases.size(); ++cl1) {
+            for (size_t cl2{0}; cl2 < cases.size(); ++cl2) {
+                printf("Testing: af::join(1,%s,%s,%s)\n", cases_name[cl0],
+                       cases_name[cl1], cases_name[cl2]);
+                const array col0{cases[cl0]};
+                const array col1{cases[cl1]};
+                const array col2{cases[cl2]};
+                const array result{af::join(1, col0, col1, col2)};
+                ASSERT_ARRAYS_EQ(result(af::span, 0), col0);
+                ASSERT_ARRAYS_EQ(result(af::span, 1), col1);
+                ASSERT_ARRAYS_EQ(result(af::span, 2), col2);
+            }
+        }
+    }
+}

From 408b504c4259ec2cb28c625c0906ab12fd1019d7 Mon Sep 17 00:00:00 2001
From: Umar Arshad <umar@arrayfire.com>
Date: Fri, 7 Mar 2025 12:09:45 -0500
Subject: [PATCH 2632/2677] Add f16 support for modulus and norm (#3258)

* Add support for f16 for modulus operations

* Update float math functions to use f<op>f functions to maintain types

float math functions in CUDA have the format f<op>f

* Add additional binary tests for integer types

* Add tests for norm

* Add support for half for norm

* Added tests for norm and modulus. Made consistent modulus for ints in cpu backend according to other backends

* Added more mod tests

* Updated documentation to reflect status quo of the mod and rem expected outputs

* Update copyright

---------

Co-authored-by: Edwin <edwinsolisf12@gmail.com>
Co-authored-by: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Co-authored-by: Christophe Murphy <christophe.murphy@outlook.com>
---
 include/af/arith.h              |  14 +-
 src/api/c/norm.cpp              |  67 ++++----
 src/backend/cpu/binary.hpp      |   5 +-
 src/backend/cuda/binary.hpp     |   9 +-
 src/backend/cuda/kernel/jit.cuh |  12 +-
 src/backend/opencl/binary.hpp   |   7 +-
 test/CMakeLists.txt             |   3 +-
 test/binary.cpp                 |  17 +-
 test/math.cpp                   |  50 +++++-
 test/norm.cpp                   | 285 ++++++++++++++++++++++++++++++++
 10 files changed, 423 insertions(+), 46 deletions(-)
 create mode 100644 test/norm.cpp

diff --git a/include/af/arith.h b/include/af/arith.h
index 9b02e668b6..5e470f448b 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -104,6 +104,9 @@ namespace af
     /// @{
     /// C++ Interface to calculate the remainder.
     ///
+    /// For integers, it returns the same output as modulus (% operator)
+    /// For floating point numbers, it returns the same as std::remainder from <cmath>
+    /// 
     /// \param[in] lhs numerator; can be an array or a scalar
     /// \param[in] rhs denominator; can be an array or a scalar
     /// \return        remainder
@@ -121,6 +124,9 @@ namespace af
     /// @{
     /// C++ Interface to calculate the modulus.
     ///
+    /// For integers, it returns the same output as modulus (% operator)
+    /// For floating point numbers, it returns the same as std::fmod from <cmath>
+    ///
     /// \param[in] lhs dividend; can be an array or a scalar
     /// \param[in] rhs divisor; can be an array or a scalar
     /// \return        modulus
@@ -984,6 +990,9 @@ extern "C" {
     /**
        C Interface to calculate the remainder.
 
+       For integers, it returns the same output as modulus (% operator)
+       For floating point numbers, it returns the same as `remainder` from <math.h>
+
        \param[out] out   remainder
        \param[in]  lhs   numerator
        \param[in]  rhs   denominator
@@ -998,6 +1007,9 @@ extern "C" {
     /**
        C Interface to calculate the modulus.
 
+       For integers, it returns the same output as modulus (% operator)
+       For floating point numbers, it returns the same as `fmod` from <math.h>
+
        \param[out] out   modulus
        \param[in]  lhs   dividend
        \param[in]  rhs   divisor
diff --git a/src/api/c/norm.cpp b/src/api/c/norm.cpp
index 84444eed58..7eef41afcc 100644
--- a/src/api/c/norm.cpp
+++ b/src/api/c/norm.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -10,6 +10,7 @@
 #include <arith.hpp>
 #include <backend.hpp>
 #include <common/ArrayInfo.hpp>
+#include <common/cast.hpp>
 #include <common/err_common.hpp>
 #include <complex.hpp>
 #include <copy.hpp>
@@ -24,6 +25,7 @@
 #include <af/traits.hpp>
 
 using af::dim4;
+using arrayfire::common::cast;
 using detail::arithOp;
 using detail::Array;
 using detail::cdouble;
@@ -35,15 +37,21 @@ using detail::reduce;
 using detail::reduce_all;
 using detail::scalar;
 
+template<typename T>
+using normReductionResult =
+    typename std::conditional<std::is_same<T, arrayfire::common::half>::value, float,
+                              T>::type;
+
 template<typename T>
 double matrixNorm(const Array<T> &A, double p) {
+    using RT = normReductionResult<T>;
     if (p == 1) {
-        Array<T> colSum = reduce<af_add_t, T, T>(A, 0);
-        return getScalar<T>(reduce_all<af_max_t, T, T>(colSum));
+        Array<RT> colSum = reduce<af_add_t, T, normReductionResult<T>>(A, 0);
+        return getScalar<RT>(reduce_all<af_max_t, RT, RT>(colSum));
     }
     if (p == af::Inf) {
-        Array<T> rowSum = reduce<af_add_t, T, T>(A, 1);
-        return getScalar<T>(reduce_all<af_max_t, T, T>(rowSum));
+        Array<RT> rowSum = reduce<af_add_t, T, RT>(A, 1);
+        return getScalar<RT>(reduce_all<af_max_t, RT, RT>(rowSum));
     }
 
     AF_ERROR("This type of norm is not supported in ArrayFire\n",
@@ -52,41 +60,45 @@ double matrixNorm(const Array<T> &A, double p) {
 
 template<typename T>
 double vectorNorm(const Array<T> &A, double p) {
-    if (p == 1) { return getScalar<T>(reduce_all<af_add_t, T, T>(A)); }
+    using RT = normReductionResult<T>;
+    if (p == 1) { return getScalar<RT>(reduce_all<af_add_t, T, RT>(A)); }
     if (p == af::Inf) {
-        return getScalar<T>(reduce_all<af_max_t, T, T>(A));
+        return getScalar<RT>(reduce_all<af_max_t, RT, RT>(cast<RT>(A)));
     } else if (p == 2) {
         Array<T> A_sq = arithOp<T, af_mul_t>(A, A, A.dims());
-        return std::sqrt(getScalar<T>(reduce_all<af_add_t, T, T>(A_sq)));
+        return std::sqrt(getScalar<RT>(reduce_all<af_add_t, T, RT>(A_sq)));
     }
 
     Array<T> P   = createValueArray<T>(A.dims(), scalar<T>(p));
     Array<T> A_p = arithOp<T, af_pow_t>(A, P, A.dims());
-    return std::pow(getScalar<T>(reduce_all<af_add_t, T, T>(A_p)), T(1.0 / p));
+    return std::pow(getScalar<RT>(reduce_all<af_add_t, T, RT>(A_p)), (1.0 / p));
 }
 
 template<typename T>
 double LPQNorm(const Array<T> &A, double p, double q) {
-    Array<T> A_p_norm = createEmptyArray<T>(dim4());
+    using RT           = normReductionResult<T>;
+    Array<RT> A_p_norm = createEmptyArray<RT>(dim4());
 
     if (p == 1) {
-        A_p_norm = reduce<af_add_t, T, T>(A, 0);
+        A_p_norm = reduce<af_add_t, T, RT>(A, 0);
     } else {
-        Array<T> P    = createValueArray<T>(A.dims(), scalar<T>(p));
-        Array<T> invP = createValueArray<T>(A.dims(), scalar<T>(1.0 / p));
+        Array<T> P     = createValueArray<T>(A.dims(), scalar<T>(p));
+        Array<RT> invP = createValueArray<RT>(A.dims(), scalar<RT>(1.0 / p));
 
-        Array<T> A_p     = arithOp<T, af_pow_t>(A, P, A.dims());
-        Array<T> A_p_sum = reduce<af_add_t, T, T>(A_p, 0);
-        A_p_norm         = arithOp<T, af_pow_t>(A_p_sum, invP, invP.dims());
+        Array<T> A_p      = arithOp<T, af_pow_t>(A, P, A.dims());
+        Array<RT> A_p_sum = reduce<af_add_t, T, RT>(A_p, 0);
+        A_p_norm          = arithOp<RT, af_pow_t>(A_p_sum, invP, invP.dims());
     }
 
-    if (q == 1) { return getScalar<T>(reduce_all<af_add_t, T, T>(A_p_norm)); }
+    if (q == 1) {
+        return getScalar<RT>(reduce_all<af_add_t, RT, RT>(A_p_norm));
+    }
 
-    Array<T> Q          = createValueArray<T>(A_p_norm.dims(), scalar<T>(q));
-    Array<T> A_p_norm_q = arithOp<T, af_pow_t>(A_p_norm, Q, Q.dims());
+    Array<RT> Q          = createValueArray<RT>(A_p_norm.dims(), scalar<RT>(q));
+    Array<RT> A_p_norm_q = arithOp<RT, af_pow_t>(A_p_norm, Q, Q.dims());
 
-    return std::pow(getScalar<T>(reduce_all<af_add_t, T, T>(A_p_norm_q)),
-                    T(1.0 / q));
+    return std::pow(getScalar<RT>(reduce_all<af_add_t, RT, RT>(A_p_norm_q)),
+                    (1.0 / q));
 }
 
 template<typename T>
@@ -98,21 +110,13 @@ double norm(const af_array a, const af_norm_type type, const double p,
 
     switch (type) {
         case AF_NORM_EUCLID: return vectorNorm(A, 2);
-
         case AF_NORM_VECTOR_1: return vectorNorm(A, 1);
-
         case AF_NORM_VECTOR_INF: return vectorNorm(A, af::Inf);
-
         case AF_NORM_VECTOR_P: return vectorNorm(A, p);
-
         case AF_NORM_MATRIX_1: return matrixNorm(A, 1);
-
         case AF_NORM_MATRIX_INF: return matrixNorm(A, af::Inf);
-
         case AF_NORM_MATRIX_2: return matrixNorm(A, 2);
-
         case AF_NORM_MATRIX_L_PQ: return LPQNorm(A, p, q);
-
         default:
             AF_ERROR("This type of norm is not supported in ArrayFire\n",
                      AF_ERR_NOT_SUPPORTED);
@@ -123,17 +127,13 @@ af_err af_norm(double *out, const af_array in, const af_norm_type type,
                const double p, const double q) {
     try {
         const ArrayInfo &i_info = getInfo(in);
-
         if (i_info.ndims() > 2) {
             AF_ERROR("solve can not be used in batch mode", AF_ERR_BATCH);
         }
 
         af_dtype i_type = i_info.getType();
-
         ARG_ASSERT(1, i_info.isFloating());  // Only floating and complex types
-
         *out = 0;
-
         if (i_info.ndims() == 0) { return AF_SUCCESS; }
 
         switch (i_type) {
@@ -141,6 +141,7 @@ af_err af_norm(double *out, const af_array in, const af_norm_type type,
             case f64: *out = norm<double>(in, type, p, q); break;
             case c32: *out = norm<cfloat>(in, type, p, q); break;
             case c64: *out = norm<cdouble>(in, type, p, q); break;
+            case f16: *out = norm<arrayfire::common::half>(in, type, p, q); break;
             default: TYPE_ERROR(1, i_type);
         }
     }
diff --git a/src/backend/cpu/binary.hpp b/src/backend/cpu/binary.hpp
index 3d130ba520..8d28501053 100644
--- a/src/backend/cpu/binary.hpp
+++ b/src/backend/cpu/binary.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2021, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -89,8 +89,7 @@ LOGIC_CPLX_FN(double, af_or_t, ||)
 
 template<typename T>
 static T __mod(T lhs, T rhs) {
-    T res = lhs % rhs;
-    return (res < 0) ? abs(rhs - res) : res;
+    return lhs % rhs; // Same as other backends
 }
 
 template<typename T>
diff --git a/src/backend/cuda/binary.hpp b/src/backend/cuda/binary.hpp
index 20f2bea9a6..ca707f30be 100644
--- a/src/backend/cuda/binary.hpp
+++ b/src/backend/cuda/binary.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -60,7 +60,7 @@ BINARY_TYPE_1(bitshiftr)
     };                                               \
     template<typename To>                            \
     struct BinOp<To, float, af_##fn##_t> {           \
-        const char *name() { return "f" #fn; }       \
+        const char *name() { return "f" #fn "f"; }   \
     };                                               \
     template<typename To>                            \
     struct BinOp<To, double, af_##fn##_t> {          \
@@ -80,6 +80,11 @@ BINARY_TYPE_2(max)
 BINARY_TYPE_2(rem)
 BINARY_TYPE_2(mod)
 
+template<>
+struct BinOp<common::half, common::half, af_mod_t> {
+    const char *name() { return "hmod"; }
+};
+
 template<typename To, typename Ti>
 struct BinOp<To, Ti, af_pow_t> {
     const char *name() { return "__pow"; }
diff --git a/src/backend/cuda/kernel/jit.cuh b/src/backend/cuda/kernel/jit.cuh
index 76fd344010..879d46f3c2 100644
--- a/src/backend/cuda/kernel/jit.cuh
+++ b/src/backend/cuda/kernel/jit.cuh
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -73,6 +73,7 @@ typedef cuDoubleComplex cdouble;
 
 #define __convert_char(val) (char)((val) != 0)
 #define frem(lhs, rhs) remainder((lhs), (rhs))
+#define fremf(lhs, rhs) remainderf((lhs), (rhs))
 
 // ----------------------------------------------
 // COMPLEX FLOAT OPERATIONS
@@ -214,6 +215,15 @@ __device__ __inline__ int __isinf<__half>(const __half in) {
 #endif
 }
 
+__device__ __inline__
+__half hmod(const __half lhs, const __half rhs) {
+#if __CUDA_ARCH__ >= 530
+    return __hsub(lhs, __hmul(htrunc(__hdiv(lhs, rhs)), rhs));
+#else
+    return __float2half(fmodf(__half2float(lhs), __half2float(rhs)));
+#endif
+}
+
 template<typename T>
 static __device__ __inline__ int __isnan(const T in) {
     return isnan(in);
diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 02291d566a..39f340942a 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -80,6 +80,11 @@ BINARY_TYPE_2(max)
 BINARY_TYPE_2(rem)
 BINARY_TYPE_2(mod)
 
+template<>
+struct BinOp<common::half, common::half, af_mod_t> {
+    const char *name() { return "fmod"; }
+};
+
 template<typename To, typename Ti>
 struct BinOp<To, Ti, af_pow_t> {
     const char *name() { return "__pow"; }
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 4d53f4d4db..8107f3c063 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -1,4 +1,4 @@
-# Copyright (c) 2020, ArrayFire
+# Copyright (c) 2025, ArrayFire
 # All rights reserved.
 #
 # This file is distributed under 3-clause BSD license.
@@ -359,6 +359,7 @@ make_test(SRC moments.cpp)
 make_test(SRC morph.cpp)
 make_test(SRC nearest_neighbour.cpp CXX11)
 make_test(SRC nodevice.cpp CXX11)
+make_test(SRC norm.cpp CXX11)
 
 if(OpenCL_FOUND)
   make_test(SRC ocl_ext_context.cpp
diff --git a/test/binary.cpp b/test/binary.cpp
index a274c11346..ed5b2c0869 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -40,6 +40,11 @@ T mod(T a, T b) {
     return std::fmod(a, b);
 }
 
+template<typename T>
+T rem(T x, T y) {
+    return remainder(x, y);
+}
+
 af::array randgen(const int num, dtype ty) {
     af::array tmp = round(1 + 2 * af::randu(num, f32)).as(ty);
     tmp.eval();
@@ -181,6 +186,7 @@ BINARY_TESTS_NEAR(float, float, float, div, 1e-3)  // FIXME
 BINARY_TESTS_FLOAT(min)
 BINARY_TESTS_FLOAT(max)
 BINARY_TESTS_NEAR(float, float, float, mod, 1e-5)  // FIXME
+BINARY_TESTS_FLOAT(rem)
 
 BINARY_TESTS_DOUBLE(add)
 BINARY_TESTS_DOUBLE(sub)
@@ -189,6 +195,7 @@ BINARY_TESTS_DOUBLE(div)
 BINARY_TESTS_DOUBLE(min)
 BINARY_TESTS_DOUBLE(max)
 BINARY_TESTS_DOUBLE(mod)
+BINARY_TESTS_DOUBLE(rem)
 
 BINARY_TESTS_NEAR_FLOAT(atan2)
 BINARY_TESTS_NEAR_FLOAT(pow)
@@ -205,18 +212,26 @@ BINARY_TESTS_NEAR_DOUBLE(hypot)
 BINARY_TESTS_INT(add)
 BINARY_TESTS_INT(sub)
 BINARY_TESTS_INT(mul)
+BINARY_TESTS_INT(div)
+BINARY_TESTS_INT(pow)
 
 BINARY_TESTS_UINT(add)
 BINARY_TESTS_UINT(sub)
 BINARY_TESTS_UINT(mul)
+BINARY_TESTS_UINT(div)
+BINARY_TESTS_UINT(pow)
 
 BINARY_TESTS_INTL(add)
 BINARY_TESTS_INTL(sub)
 BINARY_TESTS_INTL(mul)
+BINARY_TESTS_INTL(div)
+BINARY_TESTS_INTL(pow)
 
 BINARY_TESTS_UINTL(add)
 BINARY_TESTS_UINTL(sub)
 BINARY_TESTS_UINTL(mul)
+BINARY_TESTS_UINTL(div)
+BINARY_TESTS_UINTL(pow)
 
 BINARY_TESTS_CFLOAT(add)
 BINARY_TESTS_CFLOAT(sub)
diff --git a/test/math.cpp b/test/math.cpp
index 8e2243e13c..ee42a11423 100644
--- a/test/math.cpp
+++ b/test/math.cpp
@@ -1,5 +1,5 @@
 /*******************************************************
- * Copyright (c) 2014, ArrayFire
+ * Copyright (c) 2025, ArrayFire
  * All rights reserved.
  *
  * This file is distributed under 3-clause BSD license.
@@ -46,7 +46,7 @@ T rsqrt(T in) {
 }
 
 #define MATH_TEST(T, func, err, lo, hi)                                        \
-    TEST(MathTests, Test_##func##_##T) {                                       \
+    TEST(Math, func##_##T) {                                                   \
         try {                                                                  \
             SUPPORTED_TYPE_CHECK(T);                                           \
             af_dtype ty = (af_dtype)dtype_traits<T>::af_type;                  \
@@ -135,7 +135,7 @@ MATH_TESTS_REAL(erf)
 MATH_TESTS_REAL(erfc)
 #endif
 
-TEST(MathTests, Not) {
+TEST(Math, Not) {
     array a  = randu(5, 5, b8);
     array b  = !a;
     char *ha = a.host<char>();
@@ -146,3 +146,47 @@ TEST(MathTests, Not) {
     af_free_host(ha);
     af_free_host(hb);
 }
+
+TEST(Math, Modulus) {
+    af::dim4 shape(2, 2);
+    std::vector<long long> aData{3, 3, 3, 3};
+    std::vector<long long> bData{2, 2, 2, 2};
+
+    auto a    = af::array(shape, aData.data(), afHost);
+    auto b    = af::array(shape, bData.data(), afHost);
+    auto rem  = a % b;
+    auto neg_rem = -a % b;
+
+    ASSERT_ARRAYS_EQ(af::constant(1, shape, s64), rem);
+    ASSERT_ARRAYS_EQ(af::constant(-1, shape, s64), neg_rem);
+}
+
+TEST(Math, ModulusFloat) {
+    SUPPORTED_TYPE_CHECK(half_float::half);
+    af::dim4 shape(2, 2);
+
+    auto a     = af::constant(3, shape, af::dtype::f16);
+    auto b     = af::constant(2, shape, af::dtype::f16);
+    auto a32   = af::constant(3, shape, af::dtype::f32);
+    auto b32   = af::constant(2, shape, af::dtype::f32);
+    auto a64   = af::constant(3, shape, af::dtype::f64);
+    auto b64   = af::constant(2, shape, af::dtype::f64);
+
+    auto rem   = a % b;
+    auto rem32 = a32 % b32;
+    auto rem64 = a64 % b64;
+
+    auto neg_rem = -a % b;
+    auto neg_rem32 = -a32 % b32;
+    auto neg_rem64 = -a64 % b64;
+    
+    ASSERT_ARRAYS_EQ(af::constant(1, shape, af::dtype::f16), rem);
+    ASSERT_ARRAYS_EQ(af::constant(1, shape, af::dtype::f32), rem32);
+    ASSERT_ARRAYS_EQ(af::constant(1, shape, af::dtype::f64), rem64);
+
+    ASSERT_ARRAYS_EQ(af::constant(-1, shape, af::dtype::f16), neg_rem);
+    ASSERT_ARRAYS_EQ(af::constant(-1, shape, af::dtype::f32), neg_rem32);
+    ASSERT_ARRAYS_EQ(af::constant(-1, shape, af::dtype::f64), neg_rem64);
+
+    ASSERT_ARRAYS_EQ(rem32.as(f16), rem);
+}
diff --git a/test/norm.cpp b/test/norm.cpp
new file mode 100644
index 0000000000..c795c112c3
--- /dev/null
+++ b/test/norm.cpp
@@ -0,0 +1,285 @@
+/*******************************************************
+ * Copyright (c) 2025, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <arrayfire.h>
+#include <gtest/gtest.h>
+#include <testHelpers.hpp>
+#include <sstream>
+
+using af::array;
+using af::constant;
+using af::dim4;
+using std::complex;
+using std::stringstream;
+using std::vector;
+
+std::ostream &operator<<(std::ostream &os, af::normType nt) {
+    switch (nt) {
+        case AF_NORM_VECTOR_1: os << "AF_NORM_VECTOR_1"; break;
+        case AF_NORM_VECTOR_INF: os << "AF_NORM_VECTOR_INF"; break;
+        case AF_NORM_VECTOR_2: os << "AF_NORM_VECTOR_2"; break;
+        case AF_NORM_VECTOR_P: os << "AF_NORM_VECTOR_P"; break;
+        case AF_NORM_MATRIX_1: os << "AF_NORM_MATRIX_1"; break;
+        case AF_NORM_MATRIX_INF: os << "AF_NORM_MATRIX_INF"; break;
+        case AF_NORM_MATRIX_2: os << "AF_NORM_MATRIX_2"; break;
+        case AF_NORM_MATRIX_L_PQ: os << "AF_NORM_MATRIX_L_PQ"; break;
+    }
+    return os;
+}
+
+template<typename T>
+double cpu_norm1_impl(af::dim4 &dims, std::vector<T> &value) {
+    int M = dims[0];
+    int N = dims[1];
+
+    double norm1 = std::numeric_limits<double>::lowest();
+    for (int n = 0; n < N; n++) {
+        T *columnN = value.data() + n * M;
+        double sum = 0;
+        for (int m = 0; m < M; m++) { sum += abs(columnN[m]); }
+        norm1 = std::max(norm1, sum);
+    }
+    return norm1;
+}
+
+template<typename T>
+double cpu_norm_pq_impl(af::dim4 &dims, std::vector<T> &value, double p, double q) {
+    int N = dims[0];
+    int M = dims[1];
+
+    double norm = 0;
+    for (int n = 0; n < N; n++) {
+        T *columnN = value.data() + n * M;
+        double sum = 0;
+        
+        for (int m = 0; m < M; m++) { sum += std::pow(std::abs(columnN[m]), p); }
+
+        norm += std::pow(sum, q / p);
+    }
+    norm = std::pow(norm, 1.0 / q);
+
+    return norm;
+}
+
+double cpu_norm1(af::array &value) {
+    double norm1;
+    af::dim4 dims = value.dims();
+    if (value.type() == f16) {
+        vector<half_float::half> values(value.elements());
+        value.host(values.data());
+        norm1 = cpu_norm1_impl<half_float::half>(dims, values);
+    } else if (value.type() == c32 || value.type() == c64) {
+        vector<complex<double> > values(value.elements());
+        value.as(c64).host(values.data());
+        norm1 = cpu_norm1_impl<complex<double> >(dims, values);
+    } else {
+        vector<double> values(value.elements());
+        value.as(f64).host(values.data());
+        norm1 = cpu_norm1_impl<double>(dims, values);
+    }
+    return norm1;
+}
+
+double cpu_norm_pq(af::array &value, double p, double q) {
+    double norm2;
+    af::dim4 dims = value.dims();
+    if (value.type() == f16) {
+        vector<half_float::half> values(value.elements());
+        value.host(values.data());
+        norm2 = cpu_norm_pq_impl<half_float::half>(dims, values, p, q);
+    } else if (value.type() == c32 || value.type() == c64) {
+        vector<complex<double> > values(value.elements());
+        value.as(c64).host(values.data());
+        norm2 = cpu_norm_pq_impl<complex<double> >(dims, values, p, q);
+    } else {
+        vector<double> values(value.elements());
+        value.as(f64).host(values.data());
+        norm2 = cpu_norm_pq_impl<double>(dims, values, p, q);
+    }
+    return norm2;
+}
+
+template<typename T>
+double cpu_norm_inf_impl(af::dim4 &dims, std::vector<T> &value) {
+    int M = dims[0];
+    int N = dims[1];
+
+    double norm_inf = std::numeric_limits<double>::lowest();
+    for (int m = 0; m < M; m++) {
+        T *rowM    = value.data() + m;
+        double sum = 0;
+        for (int n = 0; n < N; n++) { sum += abs(rowM[n * M]); }
+        norm_inf = std::max(norm_inf, sum);
+    }
+    return norm_inf;
+}
+
+double cpu_norm_inf(af::array &value) {
+    double norm_inf;
+    af::dim4 dims = value.dims();
+    if (value.type() == c32 || value.type() == c64) {
+        vector<complex<double> > values(value.elements());
+        value.as(c64).host(values.data());
+        norm_inf = cpu_norm_inf_impl<complex<double> >(dims, values);
+    } else {
+        vector<double> values(value.elements());
+        value.as(f64).host(values.data());
+        norm_inf = cpu_norm_inf_impl<double>(dims, values);
+    }
+    return norm_inf;
+}
+
+using norm_params = std::tuple<af::dim4, af::dtype>;
+class Norm
+    : public ::testing::TestWithParam<std::tuple<af::dim4, af::dtype> > {};
+
+INSTANTIATE_TEST_CASE_P(
+    Norm, Norm,
+    ::testing::Combine(::testing::Values(dim4(3, 3), dim4(32, 32), dim4(33, 33),
+                                         dim4(64, 64), dim4(128, 128),
+                                         dim4(129, 129), dim4(256, 256),
+                                         dim4(257, 257)),
+                       ::testing::Values(f32, f64, c32, c64, f16)),
+    [](const ::testing::TestParamInfo<Norm::ParamType> info) {
+        stringstream ss;
+        using std::get;
+        ss << "dims_" << get<0>(info.param)[0] << "_" << get<0>(info.param)[1]
+           << "_dtype_" << get<1>(info.param);
+        return ss.str();
+    });
+
+TEST_P(Norm, Identity_AF_NORM_MATRIX_1) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+
+    array identity = af::identity(get<0>(param), get<1>(param));
+    double result  = norm(identity, AF_NORM_MATRIX_1);
+    double norm1   = cpu_norm1(identity);
+
+    ASSERT_DOUBLE_EQ(norm1, result);
+}
+
+TEST_P(Norm, Random_AF_NORM_MATRIX_1) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+
+    array in      = af::randu(get<0>(param), get<1>(param)) - 0.5f;
+    double result = norm(in, AF_NORM_MATRIX_1);
+    double norm1  = cpu_norm1(in);
+
+    ASSERT_NEAR(norm1, result, 2e-4);
+}
+
+TEST_P(Norm, Random_AF_NORM_VECTOR_1) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+
+    af::dim4 dims = get<0>(param);
+    dims[1] = 1; // Test a vector
+
+    array in      = af::randu(dims, get<1>(param)) - 0.5f;
+    double result = norm(in, AF_NORM_VECTOR_1);
+    double norm1  = cpu_norm_pq(in, 1, 1);
+
+    ASSERT_NEAR(norm1, result, 2e-4);
+}
+
+TEST_P(Norm, Random_AF_NORM_VECTOR_INF) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+
+    af::dim4 dims = get<0>(param);
+    dims[1] = 1; // Test a vector
+
+    array in      = af::randu(dims, get<1>(param)) - 0.5f;
+    double result = norm(in, AF_NORM_VECTOR_INF);
+    double norm_inf  = cpu_norm_inf(in);
+
+    ASSERT_NEAR(norm_inf, result, 2e-4);
+}
+
+TEST_P(Norm, Random_AF_NORM_VECTOR_2) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+
+    af::dim4 dims = get<0>(param);
+    dims[1] = 1; // Test a vector
+
+    array in      = af::randu(dims, get<1>(param)) - 0.5f;
+    double result = norm(in, AF_NORM_VECTOR_2);
+    double norm2  = cpu_norm_pq(in, 1, 2); // vectors lie in first dims so swap p and q
+
+    ASSERT_NEAR(norm2, result, 3e-4);
+}
+
+TEST_P(Norm, Random_AF_NORM_VECTOR_P_P_EQUAL_3_POINT_5) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+
+    af::dim4 dims = get<0>(param);
+    dims[1] = 1; // Test a vector
+
+    array in      = af::randu(dims, get<1>(param)) - 0.5f;
+    double result = norm(in, AF_NORM_VECTOR_P, 3.5);
+    double normp  = cpu_norm_pq(in, 1, 3.5); // vectors lie in first dims so swap p and q
+
+    ASSERT_NEAR(normp, result, 3e-4);
+}
+
+TEST_P(Norm, Identity_AF_NORM_MATRIX_2_NOT_SUPPORTED) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+    try {
+        double result =
+            norm(af::identity(get<0>(param), get<1>(param)), AF_NORM_MATRIX_2);
+        FAIL();
+    } catch (af::exception &ex) {
+        ASSERT_EQ(AF_ERR_NOT_SUPPORTED, ex.err());
+        return;
+    }
+    FAIL();
+}
+
+TEST_P(Norm, Identity_AF_NORM_MATRIX_INF) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+    array in        = af::identity(get<0>(param), get<1>(param));
+    double result   = norm(in, AF_NORM_MATRIX_INF);
+    double norm_inf = cpu_norm_inf(in);
+
+    ASSERT_DOUBLE_EQ(norm_inf, result);
+}
+
+TEST_P(Norm, Random_AF_NORM_MATRIX_INF) {
+    using std::get;
+    norm_params param = GetParam();
+    if (get<1>(param) == f16) SUPPORTED_TYPE_CHECK(half_float::half);
+    if (get<1>(param) == f64) SUPPORTED_TYPE_CHECK(double);
+    array in        = af::randu(get<0>(param), get<1>(param));
+    double result   = norm(in, AF_NORM_MATRIX_INF);
+    double norm_inf = cpu_norm_inf(in);
+
+    ASSERT_NEAR(norm_inf, result, 2e-4);
+}

From e073df6b6a5a21b20cf7bc99b5e046c1c1be1569 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 7 Mar 2025 18:14:22 -0500
Subject: [PATCH 2633/2677] Use offsets for CSR/COO to dense conversion in
 OpenCL and oneAPI (#3633)

* Offset values for sparse arrays are now taken into account when converting from sparse CSR/COO to dense for the OpenCL and oneAPI back ends. Test has been updated to also confirm fix for COO sparse format.

* Separate CSR and COO sparse to dense with offset tests and give them a more descriptive name.
---
 src/backend/oneapi/kernel/sparse.hpp   | 27 ++++++++++++-------
 src/backend/opencl/kernel/coo2dense.cl |  6 ++---
 test/sparse.cpp                        | 36 +++++++++++++++++++-------
 3 files changed, 47 insertions(+), 22 deletions(-)

diff --git a/src/backend/oneapi/kernel/sparse.hpp b/src/backend/oneapi/kernel/sparse.hpp
index 8cc7f99fcc..b7bc316267 100644
--- a/src/backend/oneapi/kernel/sparse.hpp
+++ b/src/backend/oneapi/kernel/sparse.hpp
@@ -54,9 +54,9 @@ class coo2DenseCreateKernel {
                 g.get_group_id(0) * g.get_local_range(0) * REPEAT + i;
             if (id >= values_.dims[0]) return;
 
-            T v   = vPtr_[id];
-            int r = rPtr_[id];
-            int c = cPtr_[id];
+            T v   = vPtr_[id + values_.offset];
+            int r = rPtr_[id + rowIdx_.offset];
+            int c = cPtr_[id + colIdx_.offset];
 
             int offset = r + c * output_.strides[1];
 
@@ -101,12 +101,15 @@ class csr2DenseCreateKernel {
    public:
     csr2DenseCreateKernel(write_accessor<T> output, read_accessor<T> values,
                           read_accessor<int> rowidx, read_accessor<int> colidx,
-                          const int M)
+                          const int M, const int v_off, const int r_off, const int c_off)
         : output_(output)
         , values_(values)
         , rowidx_(rowidx)
         , colidx_(colidx)
-        , M_(M) {}
+        , M_(M)
+        , v_off_(v_off)
+        , r_off_(r_off)
+        , c_off_(c_off) {}
 
     void operator()(sycl::nd_item<2> it) const {
         sycl::group g = it.get_group();
@@ -114,10 +117,10 @@ class csr2DenseCreateKernel {
         int lid = it.get_local_id(0);
         for (int rowId = g.get_group_id(0); rowId < M_;
              rowId += it.get_group_range(0)) {
-            int colStart = rowidx_[rowId];
-            int colEnd   = rowidx_[rowId + 1];
+            int colStart = rowidx_[rowId + r_off_];
+            int colEnd   = rowidx_[rowId + r_off_ + 1];
             for (int colId = colStart + lid; colId < colEnd; colId += THREADS) {
-                output_[rowId + colidx_[colId] * M_] = values_[colId];
+                output_[rowId + colidx_[colId + c_off_] * M_] = values_[colId + v_off_];
             }
         }
     }
@@ -128,6 +131,9 @@ class csr2DenseCreateKernel {
     read_accessor<int> rowidx_;
     read_accessor<int> colidx_;
     const int M_;
+    const int v_off_;
+    const int r_off_;
+    const int c_off_;
 };
 
 template<typename T>
@@ -151,7 +157,10 @@ void csr2dense(Param<T> output, const Param<T> values, const Param<int> rowIdx,
                                 sycl::no_init};
         h.parallel_for(sycl::nd_range{global, local},
                        csr2DenseCreateKernel<T, threads>(
-                           d_output, d_values, d_rowIdx, d_colIdx, M));
+                           d_output, d_values, d_rowIdx, d_colIdx, M,
+                           static_cast<int>(values.info.offset),
+                           static_cast<int>(rowIdx.info.offset),
+                           static_cast<int>(colIdx.info.offset)));
     });
 
     ONEAPI_DEBUG_FINISH(getQueue());
diff --git a/src/backend/opencl/kernel/coo2dense.cl b/src/backend/opencl/kernel/coo2dense.cl
index 539c98ada1..85afbfcd4b 100644
--- a/src/backend/opencl/kernel/coo2dense.cl
+++ b/src/backend/opencl/kernel/coo2dense.cl
@@ -17,9 +17,9 @@ kernel void coo2Dense(global T *oPtr, const KParam output, global const T *vPtr,
         const int id = i + get_group_id(0) * dimSize * reps;
         if (id >= values.dims[0]) return;
 
-        T v   = vPtr[id];
-        int r = rPtr[id];
-        int c = cPtr[id];
+        T v   = vPtr[id + values.offset];
+        int r = rPtr[id + rowIdx.offset];
+        int c = cPtr[id + colIdx.offset];
 
         int offset = r + c * output.strides[1];
 
diff --git a/test/sparse.cpp b/test/sparse.cpp
index 9e3f29ae35..f1e1b67d72 100644
--- a/test/sparse.cpp
+++ b/test/sparse.cpp
@@ -110,24 +110,40 @@ TEST(Sparse, ISSUE_1745) {
                               row_idx.get(), col_idx.get(), AF_STORAGE_CSR));
 }
 
-TEST(Sparse, ISSUE_1918) {
+TEST(Sparse, offsets_work_csr_to_dense_ISSUE_1918) {
     array reference(2,2);
     reference(0, span) = 0;
     reference(1, span) = 2;
-    array output;
     float value[] = { 1, 1, 2, 2 };
-    int index[] = { -1, 1, 2 };
-    int row[] = { 0, 2, 2, 0, 0, 2 };
+    int row_csr[] = { 0, 2, 2, 0, 0, 2 };
     int col[] = { 0, 1, 0, 1 };
     array values(4, 1, value, afHost);
-    array rows(6, 1, row, afHost);
+    array rows_csr(6, 1, row_csr, afHost);
     array cols(4, 1, col, afHost);
-    array S;
+    array S_csr;
   
-    S = sparse(2, 2, values(seq(2, 3)), rows(seq(3, 5)), cols(seq(2, 3)));
-    output = dense(S);
+    S_csr = sparse(2, 2, values(seq(2, 3)), rows_csr(seq(3, 5)), cols(seq(2, 3)));
+    array output_csr = dense(S_csr);
 
-    ASSERT_ARRAYS_EQ(reference, output);
+    EXPECT_ARRAYS_EQ(reference, output_csr);
+}
+
+TEST(Sparse, offsets_work_coo_to_dense_ISSUE_1918) {
+    array reference(2,2);
+    reference(0, span) = 0;
+    reference(1, span) = 2;
+    float value[] = { 1, 1, 2, 2 };
+    int row_coo[] = { 0, 0, 1, 1 };
+    int col[] = { 0, 1, 0, 1 };
+    array values(4, 1, value, afHost);
+    array rows_coo(4, 1, row_coo, afHost);
+    array cols(4, 1, col, afHost);
+    array S_coo;
+  
+    S_coo = sparse(2, 2, values(seq(2, 3)), rows_coo(seq(2, 3)), cols(seq(2, 3)), AF_STORAGE_COO);
+    array output_coo = dense(S_coo);
+
+    EXPECT_ARRAYS_EQ(reference, output_coo);
 }
 
 TEST(Sparse, ISSUE_2134_COO) {
@@ -457,4 +473,4 @@ TEST(Sparse, CPPDenseToSparseConversions) {
     ASSERT_ARRAYS_EQ(
         non_zero_T,
         af::sparseGetValues(csr_sparse_arr));  // csr values are transposed
-}
\ No newline at end of file
+}

From cdbbc75fc4f8d89a3c93369694cc38dd396c334d Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Wed, 4 Oct 2023 04:14:35 -0400
Subject: [PATCH 2634/2677] signed 8-bit integer support

---
 include/af/arith.h                            | 31 ++++++------
 include/af/array.h                            | 43 +++++++++-------
 include/af/defines.h                          |  1 +
 include/af/traits.hpp                         | 10 ++++
 src/api/c/anisotropic_diffusion.cpp           |  1 +
 src/api/c/array.cpp                           | 14 ++++++
 src/api/c/assign.cpp                          |  4 ++
 src/api/c/bilateral.cpp                       |  2 +
 src/api/c/binary.cpp                          |  6 +++
 src/api/c/canny.cpp                           |  5 ++
 src/api/c/cast.cpp                            |  2 +
 src/api/c/clamp.cpp                           |  2 +
 src/api/c/convolve.cpp                        |  9 ++++
 src/api/c/corrcoef.cpp                        |  2 +
 src/api/c/covariance.cpp                      |  2 +
 src/api/c/data.cpp                            | 10 ++++
 src/api/c/deconvolution.cpp                   |  3 ++
 src/api/c/device.cpp                          |  3 ++
 src/api/c/diff.cpp                            |  3 ++
 src/api/c/dog.cpp                             |  2 +
 src/api/c/exampleFunction.cpp                 |  1 +
 src/api/c/fast.cpp                            |  5 ++
 src/api/c/fftconvolve.cpp                     |  5 ++
 src/api/c/filters.cpp                         |  5 ++
 src/api/c/flip.cpp                            |  2 +
 src/api/c/handle.cpp                          |  6 +++
 src/api/c/hist.cpp                            |  5 ++
 src/api/c/histeq.cpp                          |  2 +
 src/api/c/histogram.cpp                       |  5 ++
 src/api/c/image.cpp                           |  2 +
 src/api/c/imageio.cpp                         |  4 +-
 src/api/c/imageio2.cpp                        |  5 +-
 src/api/c/implicit.cpp                        |  3 +-
 src/api/c/index.cpp                           |  5 ++
 src/api/c/internal.cpp                        |  9 ++++
 src/api/c/join.cpp                            |  3 ++
 src/api/c/match_template.cpp                  |  5 ++
 src/api/c/mean.cpp                            |  5 ++
 src/api/c/meanshift.cpp                       |  5 ++
 src/api/c/median.cpp                          |  3 ++
 src/api/c/memory.cpp                          |  8 +++
 src/api/c/moddims.cpp                         |  3 ++
 src/api/c/morph.cpp                           |  3 ++
 src/api/c/nearest_neighbour.cpp               |  5 ++
 src/api/c/plot.cpp                            | 11 ++++
 src/api/c/print.cpp                           |  6 +++
 src/api/c/random.cpp                          |  3 ++
 src/api/c/reduce.cpp                          | 28 +++++++++++
 src/api/c/reorder.cpp                         |  2 +
 src/api/c/replace.cpp                         |  3 ++
 src/api/c/resize.cpp                          |  2 +
 src/api/c/rgb_gray.cpp                        |  4 ++
 src/api/c/rotate.cpp                          |  2 +
 src/api/c/sat.cpp                             |  2 +
 src/api/c/scan.cpp                            |  6 +++
 src/api/c/select.cpp                          |  6 +++
 src/api/c/set.cpp                             |  4 ++
 src/api/c/shift.cpp                           |  2 +
 src/api/c/sobel.cpp                           |  4 ++
 src/api/c/sort.cpp                            | 10 ++++
 src/api/c/stdev.cpp                           |  3 ++
 src/api/c/stream.cpp                          |  3 ++
 src/api/c/surface.cpp                         |  4 ++
 src/api/c/susan.cpp                           |  5 ++
 src/api/c/tile.cpp                            |  2 +
 src/api/c/transform.cpp                       |  2 +
 src/api/c/transpose.cpp                       |  3 ++
 src/api/c/type_util.cpp                       |  1 +
 src/api/c/type_util.hpp                       |  5 ++
 src/api/c/unary.cpp                           |  2 +
 src/api/c/unwrap.cpp                          |  4 ++
 src/api/c/var.cpp                             | 14 ++++++
 src/api/c/vector_field.cpp                    | 12 +++++
 src/api/c/where.cpp                           |  2 +
 src/api/c/wrap.cpp                            |  2 +
 src/api/cpp/array.cpp                         |  7 +++
 src/api/cpp/corrcoef.cpp                      |  1 +
 src/api/cpp/data.cpp                          |  1 +
 src/api/cpp/device.cpp                        |  1 +
 src/api/cpp/mean.cpp                          |  1 +
 src/api/cpp/median.cpp                        |  1 +
 src/api/cpp/reduce.cpp                        |  3 ++
 src/api/cpp/stdev.cpp                         |  1 +
 src/api/cpp/var.cpp                           |  1 +
 src/backend/common/TemplateTypename.hpp       |  1 +
 src/backend/common/cast.cpp                   |  3 ++
 src/backend/common/cast.hpp                   | 29 +++++------
 src/backend/common/graphics_common.cpp        |  1 +
 src/backend/common/half.hpp                   |  9 ++++
 src/backend/common/jit/BinaryNode.cpp         |  4 ++
 src/backend/common/jit/Node.hpp               |  2 +
 src/backend/common/moddims.cpp                |  1 +
 src/backend/common/traits.hpp                 |  3 +-
 src/backend/common/util.cpp                   |  2 +
 src/backend/cpu/Array.cpp                     |  1 +
 src/backend/cpu/assign.cpp                    |  1 +
 src/backend/cpu/bilateral.cpp                 |  1 +
 src/backend/cpu/cast.hpp                      |  1 +
 src/backend/cpu/convolve.cpp                  |  1 +
 src/backend/cpu/copy.cpp                      |  5 ++
 src/backend/cpu/diagonal.cpp                  |  1 +
 src/backend/cpu/diff.cpp                      |  1 +
 src/backend/cpu/exampleFunction.cpp           |  1 +
 src/backend/cpu/fast.cpp                      |  1 +
 src/backend/cpu/fftconvolve.cpp               |  1 +
 src/backend/cpu/hist_graphics.cpp             |  1 +
 src/backend/cpu/histogram.cpp                 |  1 +
 src/backend/cpu/identity.cpp                  |  1 +
 src/backend/cpu/image.cpp                     |  1 +
 src/backend/cpu/index.cpp                     |  1 +
 src/backend/cpu/iota.cpp                      |  1 +
 src/backend/cpu/ireduce.cpp                   |  2 +
 src/backend/cpu/join.cpp                      |  2 +
 src/backend/cpu/kernel/random_engine.hpp      |  5 ++
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  2 +-
 src/backend/cpu/kernel/sort_by_key_impl.hpp   |  1 +
 src/backend/cpu/lookup.cpp                    |  3 ++
 src/backend/cpu/match_template.cpp            |  1 +
 src/backend/cpu/mean.cpp                      |  1 +
 src/backend/cpu/meanshift.cpp                 |  1 +
 src/backend/cpu/medfilt.cpp                   |  1 +
 src/backend/cpu/memory.cpp                    |  1 +
 src/backend/cpu/moments.cpp                   |  1 +
 src/backend/cpu/morph.cpp                     |  1 +
 src/backend/cpu/nearest_neighbour.cpp         |  1 +
 src/backend/cpu/plot.cpp                      |  1 +
 src/backend/cpu/random_engine.cpp             |  1 +
 src/backend/cpu/range.cpp                     |  1 +
 src/backend/cpu/reduce.cpp                    |  8 +++
 src/backend/cpu/reorder.cpp                   |  1 +
 src/backend/cpu/reshape.cpp                   |  4 ++
 src/backend/cpu/resize.cpp                    |  1 +
 src/backend/cpu/rotate.cpp                    |  1 +
 src/backend/cpu/scan.cpp                      |  1 +
 src/backend/cpu/select.cpp                    |  1 +
 src/backend/cpu/set.cpp                       |  1 +
 src/backend/cpu/shift.cpp                     |  1 +
 src/backend/cpu/sobel.cpp                     |  1 +
 src/backend/cpu/sort.cpp                      |  1 +
 src/backend/cpu/sort_by_key.cpp               |  2 +
 src/backend/cpu/sort_index.cpp                |  1 +
 src/backend/cpu/surface.cpp                   |  1 +
 src/backend/cpu/susan.cpp                     |  1 +
 src/backend/cpu/tile.cpp                      |  1 +
 src/backend/cpu/transform.cpp                 |  1 +
 src/backend/cpu/transpose.cpp                 |  1 +
 src/backend/cpu/triangle.cpp                  |  1 +
 src/backend/cpu/types.hpp                     |  1 +
 src/backend/cpu/unwrap.cpp                    |  1 +
 src/backend/cpu/vector_field.cpp              |  1 +
 src/backend/cpu/where.cpp                     |  1 +
 src/backend/cpu/wrap.cpp                      |  1 +
 src/backend/cuda/Array.cpp                    |  1 +
 src/backend/cuda/all.cu                       |  1 +
 src/backend/cuda/any.cu                       |  1 +
 src/backend/cuda/assign.cpp                   |  1 +
 src/backend/cuda/bilateral.cpp                |  1 +
 src/backend/cuda/cast.hpp                     |  1 +
 src/backend/cuda/convolve.cpp                 |  1 +
 src/backend/cuda/copy.cpp                     |  5 ++
 src/backend/cuda/count.cu                     |  1 +
 src/backend/cuda/cudaDataType.hpp             | 16 ++++++
 src/backend/cuda/cudnn.cpp                    |  6 +++
 src/backend/cuda/diagonal.cpp                 |  1 +
 src/backend/cuda/diff.cpp                     |  1 +
 src/backend/cuda/exampleFunction.cpp          |  1 +
 src/backend/cuda/fast.cu                      |  1 +
 src/backend/cuda/fast_pyramid.cpp             |  1 +
 src/backend/cuda/fftconvolve.cpp              |  1 +
 src/backend/cuda/hist_graphics.cpp            |  1 +
 src/backend/cuda/histogram.cpp                |  1 +
 src/backend/cuda/identity.cpp                 |  1 +
 src/backend/cuda/image.cpp                    |  1 +
 src/backend/cuda/index.cpp                    |  1 +
 src/backend/cuda/iota.cpp                     |  1 +
 src/backend/cuda/ireduce.cpp                  |  2 +
 src/backend/cuda/jit.cpp                      |  3 ++
 src/backend/cuda/join.cpp                     |  2 +
 .../cuda/kernel/convolve_separable.cpp        |  1 +
 src/backend/cuda/kernel/copy.cuh              | 13 +++++
 src/backend/cuda/kernel/random_engine.hpp     | 13 +++++
 src/backend/cuda/kernel/shared.hpp            |  1 +
 .../thrust_sort_by_key_impl.cu                |  2 +-
 .../cuda/kernel/thrust_sort_by_key_impl.hpp   |  1 +
 src/backend/cuda/lookup.cpp                   |  3 ++
 src/backend/cuda/match_template.cpp           |  1 +
 src/backend/cuda/math.hpp                     |  8 +++
 src/backend/cuda/max.cu                       |  1 +
 src/backend/cuda/mean.cu                      |  1 +
 src/backend/cuda/meanshift.cpp                |  1 +
 src/backend/cuda/medfilt.cpp                  |  1 +
 src/backend/cuda/memory.cpp                   |  1 +
 src/backend/cuda/min.cu                       |  1 +
 src/backend/cuda/moments.cpp                  |  1 +
 src/backend/cuda/morph.cpp                    |  1 +
 src/backend/cuda/nearest_neighbour.cu         |  1 +
 src/backend/cuda/pad_array_borders.cpp        |  1 +
 src/backend/cuda/plot.cpp                     |  1 +
 src/backend/cuda/product.cu                   |  1 +
 src/backend/cuda/random_engine.cu             |  1 +
 src/backend/cuda/range.cpp                    |  1 +
 src/backend/cuda/reorder.cpp                  |  1 +
 src/backend/cuda/reshape.cpp                  |  3 ++
 src/backend/cuda/resize.cpp                   |  1 +
 src/backend/cuda/rotate.cpp                   |  1 +
 src/backend/cuda/scan.cpp                     |  1 +
 src/backend/cuda/select.cpp                   |  1 +
 src/backend/cuda/set.cu                       |  1 +
 src/backend/cuda/shift.cpp                    |  1 +
 src/backend/cuda/sobel.cpp                    |  1 +
 src/backend/cuda/sort.cu                      |  1 +
 src/backend/cuda/sort_by_key.cu               |  2 +
 src/backend/cuda/sort_index.cu                |  1 +
 src/backend/cuda/sum.cu                       |  2 +
 src/backend/cuda/surface.cpp                  |  1 +
 src/backend/cuda/susan.cpp                    |  1 +
 src/backend/cuda/tile.cpp                     |  1 +
 src/backend/cuda/transform.cpp                |  1 +
 src/backend/cuda/transpose.cpp                |  1 +
 src/backend/cuda/transpose_inplace.cpp        |  1 +
 src/backend/cuda/triangle.cpp                 |  1 +
 src/backend/cuda/types.hpp                    |  6 +++
 src/backend/cuda/unwrap.cpp                   |  1 +
 src/backend/cuda/vector_field.cpp             |  1 +
 src/backend/cuda/where.cpp                    |  1 +
 src/backend/cuda/wrap.cpp                     |  1 +
 src/backend/oneapi/Array.cpp                  |  1 +
 src/backend/oneapi/all.cpp                    |  1 +
 src/backend/oneapi/any.cpp                    |  1 +
 src/backend/oneapi/assign.cpp                 |  1 +
 src/backend/oneapi/bilateral.cpp              |  1 +
 src/backend/oneapi/cast.hpp                   |  1 +
 src/backend/oneapi/convolve.cpp               |  1 +
 src/backend/oneapi/convolve_separable.cpp     |  1 +
 src/backend/oneapi/copy.cpp                   |  5 ++
 src/backend/oneapi/count.cpp                  |  1 +
 src/backend/oneapi/diagonal.cpp               |  1 +
 src/backend/oneapi/diff.cpp                   |  1 +
 src/backend/oneapi/exampleFunction.cpp        |  1 +
 src/backend/oneapi/fast.cpp                   |  1 +
 src/backend/oneapi/fftconvolve.cpp            |  1 +
 src/backend/oneapi/hist_graphics.cpp          |  1 +
 src/backend/oneapi/histogram.cpp              |  1 +
 src/backend/oneapi/identity.cpp               |  1 +
 src/backend/oneapi/image.cpp                  |  1 +
 src/backend/oneapi/index.cpp                  |  1 +
 src/backend/oneapi/iota.cpp                   |  1 +
 src/backend/oneapi/ireduce.cpp                |  2 +
 src/backend/oneapi/jit.cpp                    |  3 ++
 src/backend/oneapi/join.cpp                   |  2 +
 src/backend/oneapi/kernel/convolve1.hpp       |  1 +
 src/backend/oneapi/kernel/convolve2.hpp       |  1 +
 src/backend/oneapi/kernel/convolve3.hpp       |  1 +
 .../oneapi/kernel/convolve_separable.cpp      |  1 +
 src/backend/oneapi/kernel/memcopy.hpp         | 14 ++++++
 .../oneapi/kernel/random_engine_write.hpp     | 14 ++++++
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  2 +-
 .../oneapi/kernel/sort_by_key_impl.hpp        |  1 +
 src/backend/oneapi/lookup.cpp                 |  3 ++
 src/backend/oneapi/match_template.cpp         |  1 +
 src/backend/oneapi/max.cpp                    |  1 +
 src/backend/oneapi/mean.cpp                   |  1 +
 src/backend/oneapi/meanshift.cpp              |  1 +
 src/backend/oneapi/medfilt.cpp                |  1 +
 src/backend/oneapi/memory.cpp                 |  1 +
 src/backend/oneapi/min.cpp                    |  1 +
 src/backend/oneapi/moments.cpp                |  1 +
 src/backend/oneapi/morph.cpp                  |  1 +
 src/backend/oneapi/nearest_neighbour.cpp      |  1 +
 src/backend/oneapi/plot.cpp                   |  1 +
 src/backend/oneapi/product.cpp                |  1 +
 src/backend/oneapi/random_engine.cpp          |  1 +
 src/backend/oneapi/range.cpp                  |  1 +
 src/backend/oneapi/reorder.cpp                |  1 +
 src/backend/oneapi/reshape.cpp                |  3 ++
 src/backend/oneapi/resize.cpp                 |  1 +
 src/backend/oneapi/rotate.cpp                 |  1 +
 src/backend/oneapi/scan.cpp                   |  1 +
 src/backend/oneapi/select.cpp                 |  1 +
 src/backend/oneapi/set.cpp                    |  1 +
 src/backend/oneapi/shift.cpp                  |  1 +
 src/backend/oneapi/sobel.cpp                  |  1 +
 src/backend/oneapi/sort.cpp                   |  1 +
 src/backend/oneapi/sort_by_key.cpp            |  2 +
 src/backend/oneapi/sort_index.cpp             |  1 +
 src/backend/oneapi/sum.cpp                    |  2 +
 src/backend/oneapi/surface.cpp                |  1 +
 src/backend/oneapi/susan.cpp                  |  1 +
 src/backend/oneapi/tile.cpp                   |  1 +
 src/backend/oneapi/transform.cpp              |  1 +
 src/backend/oneapi/transpose.cpp              |  1 +
 src/backend/oneapi/transpose_inplace.cpp      |  1 +
 src/backend/oneapi/triangle.cpp               |  1 +
 src/backend/oneapi/types.hpp                  | 10 ++++
 src/backend/oneapi/unwrap.cpp                 |  1 +
 src/backend/oneapi/vector_field.cpp           |  1 +
 src/backend/oneapi/where.cpp                  |  1 +
 src/backend/oneapi/wrap.cpp                   |  1 +
 src/backend/opencl/Array.cpp                  |  1 +
 src/backend/opencl/CMakeLists.txt             |  1 +
 src/backend/opencl/all.cpp                    |  1 +
 src/backend/opencl/any.cpp                    |  1 +
 src/backend/opencl/assign.cpp                 |  1 +
 src/backend/opencl/bilateral.cpp              |  1 +
 src/backend/opencl/cast.hpp                   |  5 ++
 src/backend/opencl/compile_module.cpp         |  3 ++
 src/backend/opencl/convolve.cpp               |  1 +
 src/backend/opencl/convolve_separable.cpp     |  1 +
 src/backend/opencl/copy.cpp                   |  5 ++
 src/backend/opencl/count.cpp                  |  1 +
 src/backend/opencl/diagonal.cpp               |  1 +
 src/backend/opencl/diff.cpp                   |  1 +
 src/backend/opencl/exampleFunction.cpp        |  1 +
 src/backend/opencl/fast.cpp                   |  1 +
 src/backend/opencl/fftconvolve.cpp            |  1 +
 src/backend/opencl/flood_fill.cpp             |  1 +
 src/backend/opencl/hist_graphics.cpp          |  1 +
 src/backend/opencl/histogram.cpp              |  1 +
 src/backend/opencl/identity.cpp               |  1 +
 src/backend/opencl/image.cpp                  |  1 +
 src/backend/opencl/index.cpp                  |  1 +
 src/backend/opencl/iota.cpp                   |  1 +
 src/backend/opencl/ireduce.cpp                |  2 +
 src/backend/opencl/join.cpp                   |  2 +
 src/backend/opencl/kernel/convolve/conv1.cpp  |  1 +
 .../opencl/kernel/convolve/conv2_s8.cpp       | 20 ++++++++
 src/backend/opencl/kernel/convolve/conv3.cpp  |  1 +
 .../opencl/kernel/convolve_separable.cpp      |  1 +
 .../opencl/kernel/random_engine_write.cl      | 50 +++++++++++++++++++
 .../kernel/sort_by_key/sort_by_key_impl.cpp   |  2 +-
 .../opencl/kernel/sort_by_key_impl.hpp        |  1 +
 src/backend/opencl/lookup.cpp                 |  3 ++
 src/backend/opencl/match_template.cpp         |  1 +
 src/backend/opencl/max.cpp                    |  1 +
 src/backend/opencl/mean.cpp                   |  1 +
 src/backend/opencl/meanshift.cpp              |  1 +
 src/backend/opencl/medfilt.cpp                |  1 +
 src/backend/opencl/memory.cpp                 |  1 +
 src/backend/opencl/min.cpp                    |  1 +
 src/backend/opencl/moments.cpp                |  1 +
 src/backend/opencl/morph.cpp                  |  1 +
 src/backend/opencl/nearest_neighbour.cpp      |  1 +
 src/backend/opencl/plot.cpp                   |  1 +
 src/backend/opencl/product.cpp                |  1 +
 src/backend/opencl/random_engine.cpp          |  1 +
 src/backend/opencl/range.cpp                  |  1 +
 src/backend/opencl/reorder.cpp                |  1 +
 src/backend/opencl/resize.cpp                 |  1 +
 src/backend/opencl/rotate.cpp                 |  1 +
 src/backend/opencl/scan.cpp                   |  1 +
 src/backend/opencl/select.cpp                 |  1 +
 src/backend/opencl/set.cpp                    |  1 +
 src/backend/opencl/shift.cpp                  |  1 +
 src/backend/opencl/sobel.cpp                  |  1 +
 src/backend/opencl/sort.cpp                   |  1 +
 src/backend/opencl/sort_by_key.cpp            |  2 +
 src/backend/opencl/sort_index.cpp             |  1 +
 src/backend/opencl/sum.cpp                    |  2 +
 src/backend/opencl/surface.cpp                |  1 +
 src/backend/opencl/susan.cpp                  |  1 +
 src/backend/opencl/tile.cpp                   |  1 +
 src/backend/opencl/transform.cpp              |  1 +
 src/backend/opencl/transpose.cpp              |  1 +
 src/backend/opencl/transpose_inplace.cpp      |  1 +
 src/backend/opencl/triangle.cpp               |  1 +
 src/backend/opencl/types.cpp                  |  1 +
 src/backend/opencl/types.hpp                  | 10 ++++
 src/backend/opencl/unwrap.cpp                 |  1 +
 src/backend/opencl/vector_field.cpp           |  1 +
 src/backend/opencl/where.cpp                  |  1 +
 src/backend/opencl/wrap.cpp                   |  1 +
 test/anisotropic_diffusion.cpp                |  2 +-
 test/array.cpp                                | 15 +++++-
 test/arrayfire_test.cpp                       | 23 +++++++++
 test/arrayio.cpp                              |  4 +-
 test/assign.cpp                               |  4 +-
 test/bilateral.cpp                            |  3 +-
 test/binary.cpp                               |  6 ++-
 test/canny.cpp                                |  2 +-
 test/cast.cpp                                 |  2 +
 test/clamp.cpp                                |  1 +
 test/compare.cpp                              |  4 +-
 test/constant.cpp                             |  3 +-
 test/convolve.cpp                             |  4 +-
 test/corrcoef.cpp                             |  3 +-
 test/covariance.cpp                           | 10 ++--
 test/diagonal.cpp                             |  4 +-
 test/diff1.cpp                                |  2 +-
 test/diff2.cpp                                |  2 +-
 test/dog.cpp                                  |  3 +-
 test/fast.cpp                                 |  2 +-
 test/fftconvolve.cpp                          |  4 +-
 test/gen_index.cpp                            |  5 +-
 test/half.cpp                                 |  2 +
 test/histogram.cpp                            |  2 +-
 test/index.cpp                                |  7 +--
 test/inverse_deconv.cpp                       |  2 +-
 test/iota.cpp                                 |  3 +-
 test/iterative_deconv.cpp                     |  2 +-
 test/join.cpp                                 |  4 +-
 test/match_template.cpp                       |  3 +-
 test/mean.cpp                                 | 10 ++--
 test/meanshift.cpp                            |  4 +-
 test/medfilt.cpp                              |  3 +-
 test/memory.cpp                               |  3 +-
 test/moddims.cpp                              |  4 +-
 test/morph.cpp                                |  3 +-
 test/nearest_neighbour.cpp                    |  9 +++-
 test/pad_borders.cpp                          |  4 +-
 test/random.cpp                               | 12 ++---
 test/range.cpp                                |  5 +-
 test/reduce.cpp                               | 12 ++++-
 test/reorder.cpp                              |  2 +-
 test/replace.cpp                              |  2 +-
 test/resize.cpp                               |  4 +-
 test/rotate.cpp                               |  3 +-
 test/rotate_linear.cpp                        |  3 +-
 test/sat.cpp                                  |  4 +-
 test/select.cpp                               |  2 +-
 test/shift.cpp                                |  3 +-
 test/sobel.cpp                                |  3 +-
 test/sort.cpp                                 |  4 +-
 test/sort_by_key.cpp                          |  4 +-
 test/sort_index.cpp                           |  4 +-
 test/stdev.cpp                                |  9 ++--
 test/susan.cpp                                |  3 +-
 test/testHelpers.hpp                          |  1 +
 test/tile.cpp                                 |  4 +-
 test/transform.cpp                            |  2 +-
 test/translate.cpp                            |  2 +-
 test/transpose.cpp                            |  4 +-
 test/transpose_inplace.cpp                    |  4 +-
 test/triangle.cpp                             |  3 +-
 test/unwrap.cpp                               |  3 +-
 test/var.cpp                                  |  6 +--
 test/where.cpp                                |  2 +-
 test/wrap.cpp                                 |  3 +-
 test/write.cpp                                |  2 +-
 438 files changed, 1135 insertions(+), 159 deletions(-)
 create mode 100644 src/backend/opencl/kernel/convolve/conv2_s8.cpp

diff --git a/include/af/arith.h b/include/af/arith.h
index 5e470f448b..0dd2eb2c1f 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -910,21 +910,22 @@ extern "C" {
        be performed by ArrayFire. The following table shows which casts will
        be optimized out. outer -> inner -> outer
 
-       | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
-       |---------|-----|-----|-----|-----|-----|-----|----|----|-----|-----|-----|-----|-----|
-       | f32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-       | f64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-       | c32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-       | c64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-       | s32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
-       | u32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
-       | u8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
-       | b8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
-       | s64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
-       | u64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
-       | s16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
-       | u16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
-       | f16     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+       | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | s8 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
+       |---------|-----|-----|-----|-----|-----|-----|----|----|----|-----|-----|-----|-----|-----|
+       | f32     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+       | f64     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+       | c32     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+       | c64     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+       | s32     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   |     |     | x   |
+       | u32     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   |     |     | x   |
+       | s8      | x   | x   | x   | x   | x   | x   | x  | x  | x  | x   | x   | x   | x   | x   |
+       | u8      | x   | x   | x   | x   | x   | x   | x  | x  | x  | x   | x   | x   | x   | x   |
+       | b8      | x   | x   | x   | x   | x   | x   | x  | x  | x  | x   | x   | x   | x   | x   |
+       | s64     | x   | x   | x   | x   |     |     |    |    |    | x   | x   |     |     | x   |
+       | u64     | x   | x   | x   | x   |     |     |    |    |    | x   | x   |     |     | x   |
+       | s16     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   | x   | x   | x   |
+       | u16     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   | x   | x   | x   |
+       | f16     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
 
        If you want to avoid this behavior use, af_eval after the first cast
        operation. This will ensure that the cast operation is performed on the
diff --git a/include/af/array.h b/include/af/array.h
index 4186b95d08..a442147565 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -82,6 +82,7 @@ namespace af
             array_proxy& operator OP(const unsigned &a);            \
             array_proxy& operator OP(const bool &a);                \
             array_proxy& operator OP(const char &a);                \
+            array_proxy& operator OP(const signed char &a);         \
             array_proxy& operator OP(const unsigned char &a);       \
             array_proxy& operator OP(const long  &a);               \
             array_proxy& operator OP(const unsigned long &a);       \
@@ -762,8 +763,8 @@ namespace af
         bool isfloating() const;
 
         /**
-           \brief Returns true if the array type is \ref u8, \ref b8, \ref s32
-                  \ref u32, \ref s64, \ref u64, \ref s16, \ref u16
+           \brief Returns true if the array type is \ref s8, \ref u8, \ref b8,
+                  \ref s32, \ref u32, \ref s64, \ref u64, \ref s16, \ref u16
         */
         bool isinteger() const;
 
@@ -953,21 +954,22 @@ namespace af
         /// and then back to f64, then the cast to f32 will be skipped and that
         /// operation will *NOT* be performed by ArrayFire. The following table
         /// shows which casts will be optimized out. outer -> inner -> outer
-        /// | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
-        /// |---------|-----|-----|-----|-----|-----|-----|----|----|-----|-----|-----|-----|-----|
-        /// | f32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-        /// | f64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-        /// | c32     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-        /// | c64     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
-        /// | s32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
-        /// | u32     | x   | x   | x   | x   | x   | x   |    |    | x   | x   |     |     | x   |
-        /// | u8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
-        /// | b8      | x   | x   | x   | x   | x   | x   | x  | x  | x   | x   | x   | x   | x   |
-        /// | s64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
-        /// | u64     | x   | x   | x   | x   |     |     |    |    | x   | x   |     |     | x   |
-        /// | s16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
-        /// | u16     | x   | x   | x   | x   | x   | x   |    |    | x   | x   | x   | x   | x   |
-        /// | f16     | x   | x   | x   | x   |     |     |    |    |     |     |     |     | x   |
+        /// | inner-> | f32 | f64 | c32 | c64 | s32 | u32 | s8 | u8 | b8 | s64 | u64 | s16 | u16 | f16 |
+        /// |---------|-----|-----|-----|-----|-----|-----|----|----|----|-----|-----|-----|-----|-----|
+        /// | f32     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+        /// | f64     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+        /// | c32     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+        /// | c64     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
+        /// | s32     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   |     |     | x   |
+        /// | u32     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   |     |     | x   |
+        /// | s8      | x   | x   | x   | x   | x   | x   | x  | x  | x  | x   | x   | x   | x   | x   |
+        /// | u8      | x   | x   | x   | x   | x   | x   | x  | x  | x  | x   | x   | x   | x   | x   |
+        /// | b8      | x   | x   | x   | x   | x   | x   | x  | x  | x  | x   | x   | x   | x   | x   |
+        /// | s64     | x   | x   | x   | x   |     |     |    |    |    | x   | x   |     |     | x   |
+        /// | u64     | x   | x   | x   | x   |     |     |    |    |    | x   | x   |     |     | x   |
+        /// | s16     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   | x   | x   | x   |
+        /// | u16     | x   | x   | x   | x   | x   | x   |    |    |    | x   | x   | x   | x   | x   |
+        /// | f16     | x   | x   | x   | x   |     |     |    |    |    |     |     |     |     | x   |
         /// If you want to avoid this behavior use af_eval after the first cast
         /// operation. This will ensure that the cast operation is performed on
         /// the af::array
@@ -998,6 +1000,7 @@ namespace af
         array& OP2(const unsigned &val);            /**< \copydoc OP2##(const array &) */ \
         array& OP2(const bool &val);                /**< \copydoc OP2##(const array &) */ \
         array& OP2(const char &val);                /**< \copydoc OP2##(const array &) */ \
+        array& OP2(const signed char &val);         /**< \copydoc OP2##(const array &) */ \
         array& OP2(const unsigned char &val);       /**< \copydoc OP2##(const array &) */ \
         array& OP2(const long  &val);               /**< \copydoc OP2##(const array &) */ \
         array& OP2(const unsigned long &val);       /**< \copydoc OP2##(const array &) */ \
@@ -1144,6 +1147,7 @@ namespace af
     AFAPI array OP (const int& lhs, const array& rhs);                /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const unsigned& lhs, const array& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const char& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const signed char& lhs, const array& rhs);        /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const unsigned char& lhs, const array& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const long& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const unsigned long& lhs, const array& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
@@ -1157,6 +1161,7 @@ namespace af
     AFAPI array OP (const array& lhs, const int& rhs);                /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const unsigned& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const char& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const signed char& rhs);        /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const unsigned char& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const long& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const unsigned long& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
@@ -1394,6 +1399,7 @@ namespace af
     AFAPI array operator&(const array& lhs, const long long& rhs);
     AFAPI array operator&(const array& lhs, const long& rhs);
     AFAPI array operator&(const array& lhs, const short& rhs);
+    AFAPI array operator&(const array& lhs, const signed char& rhs);
     AFAPI array operator&(const array& lhs, const unsigned char& rhs);
     AFAPI array operator&(const array& lhs, const unsigned long long& rhs);
     AFAPI array operator&(const array& lhs, const unsigned long& rhs);
@@ -1409,6 +1415,7 @@ namespace af
     AFAPI array operator&(const long long& lhs, const array& rhs);
     AFAPI array operator&(const long& lhs, const array& rhs);
     AFAPI array operator&(const short& lhs, const array& rhs);
+    AFAPI array operator&(const signed char& lhs, const array& rhs);
     AFAPI array operator&(const unsigned char& lhs, const array& rhs);
     AFAPI array operator&(const unsigned long long& lhs, const array& rhs);
     AFAPI array operator&(const unsigned long& lhs, const array& rhs);
@@ -1437,6 +1444,7 @@ namespace af
     AFAPI array operator&&(const array& lhs, const long long& rhs);
     AFAPI array operator&&(const array& lhs, const long& rhs);
     AFAPI array operator&&(const array& lhs, const short& rhs);
+    AFAPI array operator&&(const array& lhs, const signed char& rhs);
     AFAPI array operator&&(const array& lhs, const unsigned char& rhs);
     AFAPI array operator&&(const array& lhs, const unsigned long long& rhs);
     AFAPI array operator&&(const array& lhs, const unsigned long& rhs);
@@ -1452,6 +1460,7 @@ namespace af
     AFAPI array operator&&(const long long& lhs, const array& rhs);
     AFAPI array operator&&(const long& lhs, const array& rhs);
     AFAPI array operator&&(const short& lhs, const array& rhs);
+    AFAPI array operator&&(const signed char& lhs, const array& rhs);
     AFAPI array operator&&(const unsigned char& lhs, const array& rhs);
     AFAPI array operator&&(const unsigned long long& lhs, const array& rhs);
     AFAPI array operator&&(const unsigned long& lhs, const array& rhs);
diff --git a/include/af/defines.h b/include/af/defines.h
index da6c5591de..4be88f97bd 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -227,6 +227,7 @@ typedef enum {
 #if AF_API_VERSION >= 37
     , f16    ///< 16-bit floating point value
 #endif
+    , s8     ///< 8-bit signed integral value /// TODO AF_API_VERSION
 } af_dtype;
 
 typedef enum {
diff --git a/include/af/traits.hpp b/include/af/traits.hpp
index 6c7d1bf5fa..330435a929 100644
--- a/include/af/traits.hpp
+++ b/include/af/traits.hpp
@@ -175,6 +175,16 @@ struct dtype_traits<half> {
     static const char* getName() { return "half"; }
 };
 #endif
+
+template<>
+struct dtype_traits<signed char> {
+    enum {
+        af_type = s8 ,
+        ctype = f32
+    };
+    typedef signed char base_type;
+    static const char* getName() { return "schar"; }
+};
 }
 
 #endif
diff --git a/src/api/c/anisotropic_diffusion.cpp b/src/api/c/anisotropic_diffusion.cpp
index 3c77f8644c..6268accb3b 100644
--- a/src/api/c/anisotropic_diffusion.cpp
+++ b/src/api/c/anisotropic_diffusion.cpp
@@ -90,6 +90,7 @@ af_err af_anisotropic_diffusion(af_array* out, const af_array in,
             case u32:
             case s16:
             case u16:
+            case s8:
             case u8:
                 output = diffusion<float>(input, dt, K, iterations, F, eq);
                 break;
diff --git a/src/api/c/array.cpp b/src/api/c/array.cpp
index 4e1877e364..d164faabdb 100644
--- a/src/api/c/array.cpp
+++ b/src/api/c/array.cpp
@@ -30,6 +30,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createDeviceDataArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -47,6 +48,7 @@ af_err af_get_data_ptr(void *data, const af_array arr) {
             case b8:  copyData(static_cast<char*    >(data), arr); break;
             case s32: copyData(static_cast<int*     >(data), arr); break;
             case u32: copyData(static_cast<unsigned*>(data), arr); break;
+            case s8:  copyData(static_cast<schar*   >(data), arr); break;
             case u8:  copyData(static_cast<uchar*   >(data), arr); break;
             case s64: copyData(static_cast<intl*    >(data), arr); break;
             case u64: copyData(static_cast<uintl*   >(data), arr); break;
@@ -96,6 +98,9 @@ af_err af_create_array(af_array *result, const void *const data,
             case u32:
                 out = createHandleFromData(d, static_cast<const uint *>(data));
                 break;
+            case s8:
+                out = createHandleFromData(d, static_cast<const schar *>(data));
+                break;
             case u8:
                 out = createHandleFromData(d, static_cast<const uchar *>(data));
                 break;
@@ -175,6 +180,7 @@ af_err af_copy_array(af_array *out, const af_array in) {
                 case b8: res = copyArray<char>(in); break;
                 case s32: res = copyArray<int>(in); break;
                 case u32: res = copyArray<uint>(in); break;
+                case s8: res = copyArray<schar>(in); break;
                 case u8: res = copyArray<uchar>(in); break;
                 case s64: res = copyArray<intl>(in); break;
                 case u64: res = copyArray<uintl>(in); break;
@@ -205,6 +211,7 @@ af_err af_get_data_ref_count(int *use_count, const af_array in) {
             case b8: res = getUseCount<char>(in); break;
             case s32: res = getUseCount<int>(in); break;
             case u32: res = getUseCount<uint>(in); break;
+            case s8: res = getUseCount<schar>(in); break;
             case u8: res = getUseCount<uchar>(in); break;
             case s64: res = getUseCount<intl>(in); break;
             case u64: res = getUseCount<uintl>(in); break;
@@ -242,6 +249,7 @@ af_err af_release_array(af_array arr) {
                 case b8: releaseHandle<char>(arr); break;
                 case s32: releaseHandle<int>(arr); break;
                 case u32: releaseHandle<uint>(arr); break;
+                case s8: releaseHandle<schar>(arr); break;
                 case u8: releaseHandle<uchar>(arr); break;
                 case s64: releaseHandle<intl>(arr); break;
                 case u64: releaseHandle<uintl>(arr); break;
@@ -308,6 +316,9 @@ af_err af_write_array(af_array arr, const void *data, const size_t bytes,
             case u32:
                 write_array(arr, static_cast<const uint *>(data), bytes, src);
                 break;
+            case s8:
+                write_array(arr, static_cast<const schar *>(data), bytes, src);
+                break;
             case u8:
                 write_array(arr, static_cast<const uchar *>(data), bytes, src);
                 break;
@@ -433,6 +444,9 @@ af_err af_get_scalar(void *output_value, const af_array arr) {
             case u32:
                 getScalar<uint>(reinterpret_cast<uint *>(output_value), arr);
                 break;
+            case s8:
+                getScalar<schar>(reinterpret_cast<schar *>(output_value), arr);
+                break;
             case u8:
                 getScalar<uchar>(reinterpret_cast<uchar *>(output_value), arr);
                 break;
diff --git a/src/api/c/assign.cpp b/src/api/c/assign.cpp
index 22f11255e9..bdf505048d 100644
--- a/src/api/c/assign.cpp
+++ b/src/api/c/assign.cpp
@@ -42,6 +42,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createSubArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -122,6 +123,7 @@ static if_real<T> assign(Array<T>& out, const vector<af_seq> iv,
         case u64: assign<T, uintl>(out, iv, getArray<uintl>(in)); break;
         case s16: assign<T, short>(out, iv, getArray<short>(in)); break;
         case u16: assign<T, ushort>(out, iv, getArray<ushort>(in)); break;
+        case s8: assign<T, schar>(out, iv, getArray<schar>(in)); break;
         case u8: assign<T, uchar>(out, iv, getArray<uchar>(in)); break;
         case b8: assign<T, char>(out, iv, getArray<char>(in)); break;
         case f16: assign<T, half>(out, iv, getArray<half>(in)); break;
@@ -201,6 +203,7 @@ af_err af_assign_seq(af_array* out, const af_array lhs, const unsigned ndims,
                     case u64: assign(getArray<uintl>(res), inSeqs, rhs); break;
                     case s16: assign(getArray<short>(res), inSeqs, rhs); break;
                     case u16: assign(getArray<ushort>(res), inSeqs, rhs); break;
+                    case s8: assign(getArray<schar>(res), inSeqs, rhs); break;
                     case u8: assign(getArray<uchar>(res), inSeqs, rhs); break;
                     case b8: assign(getArray<char>(res), inSeqs, rhs); break;
                     case f16: assign(getArray<half>(res), inSeqs, rhs); break;
@@ -382,6 +385,7 @@ af_err af_assign_gen(af_array* out, const af_array lhs, const dim_t ndims,
                 case s32: genAssign<int>(output, ptr, rhs); break;
                 case s16: genAssign<short>(output, ptr, rhs); break;
                 case u16: genAssign<ushort>(output, ptr, rhs); break;
+                case s8: genAssign<schar>(output, ptr, rhs); break;
                 case u8: genAssign<uchar>(output, ptr, rhs); break;
                 case b8: genAssign<char>(output, ptr, rhs); break;
                 case f16: genAssign<half>(output, ptr, rhs); break;
diff --git a/src/api/c/bilateral.cpp b/src/api/c/bilateral.cpp
index 44e15c725c..aeec279ea5 100644
--- a/src/api/c/bilateral.cpp
+++ b/src/api/c/bilateral.cpp
@@ -19,6 +19,7 @@
 
 using af::dim4;
 using detail::bilateral;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -50,6 +51,7 @@ af_err af_bilateral(af_array *out, const af_array in, const float ssigma,
             case b8: output = bilateral<char>(in, ssigma, csigma); break;
             case s32: output = bilateral<int>(in, ssigma, csigma); break;
             case u32: output = bilateral<uint>(in, ssigma, csigma); break;
+            case s8: output = bilateral<schar>(in, ssigma, csigma); break;
             case u8: output = bilateral<uchar>(in, ssigma, csigma); break;
             case s16: output = bilateral<short>(in, ssigma, csigma); break;
             case u16: output = bilateral<ushort>(in, ssigma, csigma); break;
diff --git a/src/api/c/binary.cpp b/src/api/c/binary.cpp
index ee727c264a..eebe62bdbb 100644
--- a/src/api/c/binary.cpp
+++ b/src/api/c/binary.cpp
@@ -43,6 +43,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -139,6 +140,7 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
                 case c64: res = arithOp<cdouble, op>(lhs, rhs, odims); break;
                 case s32: res = arithOp<int, op>(lhs, rhs, odims); break;
                 case u32: res = arithOp<uint, op>(lhs, rhs, odims); break;
+                case s8: res = arithOp<schar, op>(lhs, rhs, odims); break;
                 case u8: res = arithOp<uchar, op>(lhs, rhs, odims); break;
                 case b8: res = arithOp<char, op>(lhs, rhs, odims); break;
                 case s64: res = arithOp<intl, op>(lhs, rhs, odims); break;
@@ -159,6 +161,7 @@ static af_err af_arith(af_array *out, const af_array lhs, const af_array rhs,
                 case c64: res = arithOpBroadcast<cdouble, op>(lhs, rhs); break;
                 case s32: res = arithOpBroadcast<int, op>(lhs, rhs); break;
                 case u32: res = arithOpBroadcast<uint, op>(lhs, rhs); break;
+                case s8: res = arithOpBroadcast<schar, op>(lhs, rhs); break;
                 case u8: res = arithOpBroadcast<uchar, op>(lhs, rhs); break;
                 case b8: res = arithOpBroadcast<char, op>(lhs, rhs); break;
                 case s64: res = arithOpBroadcast<intl, op>(lhs, rhs); break;
@@ -195,6 +198,7 @@ static af_err af_arith_real(af_array *out, const af_array lhs,
             case f64: res = arithOp<double, op>(lhs, rhs, odims); break;
             case s32: res = arithOp<int, op>(lhs, rhs, odims); break;
             case u32: res = arithOp<uint, op>(lhs, rhs, odims); break;
+            case s8: res = arithOp<schar, op>(lhs, rhs, odims); break;
             case u8: res = arithOp<uchar, op>(lhs, rhs, odims); break;
             case b8: res = arithOp<char, op>(lhs, rhs, odims); break;
             case s64: res = arithOp<intl, op>(lhs, rhs, odims); break;
@@ -567,6 +571,7 @@ static af_err af_logic(af_array *out, const af_array lhs, const af_array rhs,
             case c64: res = logicOp<cdouble, op>(lhs, rhs, odims); break;
             case s32: res = logicOp<int, op>(lhs, rhs, odims); break;
             case u32: res = logicOp<uint, op>(lhs, rhs, odims); break;
+            case s8: res = logicOp<schar, op>(lhs, rhs, odims); break;
             case u8: res = logicOp<uchar, op>(lhs, rhs, odims); break;
             case b8: res = logicOp<char, op>(lhs, rhs, odims); break;
             case s64: res = logicOp<intl, op>(lhs, rhs, odims); break;
@@ -650,6 +655,7 @@ static af_err af_bitwise(af_array *out, const af_array lhs, const af_array rhs,
         switch (type) {
             case s32: res = bitOp<int, op>(lhs, rhs, odims); break;
             case u32: res = bitOp<uint, op>(lhs, rhs, odims); break;
+            case s8: res = bitOp<schar, op>(lhs, rhs, odims); break;
             case u8: res = bitOp<uchar, op>(lhs, rhs, odims); break;
             case b8: res = bitOp<char, op>(lhs, rhs, odims); break;
             case s64: res = bitOp<intl, op>(lhs, rhs, odims); break;
diff --git a/src/api/c/canny.cpp b/src/api/c/canny.cpp
index ef3ad029cd..b68b8d4ed0 100644
--- a/src/api/c/canny.cpp
+++ b/src/api/c/canny.cpp
@@ -53,6 +53,7 @@ using detail::logicOp;
 using detail::reduce;
 using detail::reduce_all;
 using detail::scan;
+using detail::schar;
 using detail::sobelDerivatives;
 using detail::uchar;
 using detail::uint;
@@ -265,6 +266,10 @@ af_err af_canny(af_array* out, const af_array in, const af_canny_threshold ct,
                 output = cannyHelper<ushort>(getArray<ushort>(in), t1, ct, t2,
                                              sw, isf);
                 break;
+            case s8:
+                output = cannyHelper<schar>(getArray<schar>(in), t1, ct, t2, sw,
+                                            isf);
+                break;
             case u8:
                 output = cannyHelper<uchar>(getArray<uchar>(in), t1, ct, t2, sw,
                                             isf);
diff --git a/src/api/c/cast.cpp b/src/api/c/cast.cpp
index 328c81ca65..7b421d28bb 100644
--- a/src/api/c/cast.cpp
+++ b/src/api/c/cast.cpp
@@ -28,6 +28,7 @@ using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -54,6 +55,7 @@ static af_array cast(const af_array in, const af_dtype type) {
             case c64: return getHandle(castArray<cdouble>(in));
             case s32: return getHandle(castArray<int>(in));
             case u32: return getHandle(castArray<uint>(in));
+            case s8: return getHandle(castArray<schar>(in));
             case u8: return getHandle(castArray<uchar>(in));
             case b8: return getHandle(castArray<char>(in));
             case s64: return getHandle(castArray<intl>(in));
diff --git a/src/api/c/clamp.cpp b/src/api/c/clamp.cpp
index fb821d3bf3..8c31469e55 100644
--- a/src/api/c/clamp.cpp
+++ b/src/api/c/clamp.cpp
@@ -28,6 +28,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -64,6 +65,7 @@ af_err af_clamp(af_array* out, const af_array in, const af_array lo,
             case c64: res = clampOp<cdouble>(in, lo, hi, odims); break;
             case s32: res = clampOp<int>(in, lo, hi, odims); break;
             case u32: res = clampOp<uint>(in, lo, hi, odims); break;
+            case s8: res = clampOp<schar>(in, lo, hi, odims); break;
             case u8: res = clampOp<uchar>(in, lo, hi, odims); break;
             case b8: res = clampOp<char>(in, lo, hi, odims); break;
             case s64: res = clampOp<intl>(in, lo, hi, odims); break;
diff --git a/src/api/c/convolve.cpp b/src/api/c/convolve.cpp
index 61af7b1b16..8d37c5d285 100644
--- a/src/api/c/convolve.cpp
+++ b/src/api/c/convolve.cpp
@@ -33,6 +33,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::convolve;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -196,6 +197,10 @@ af_err convolve(af_array *out, const af_array signal, const af_array filter,
                 output = convolve<uchar, float>(signal, filter, convBT, rank,
                                                 expand);
                 break;
+            case s8:
+                output = convolve<schar, float>(signal, filter, convBT, rank,
+                                                expand);
+                break;
             case b8:
                 output =
                     convolve<char, float>(signal, filter, convBT, rank, expand);
@@ -311,6 +316,10 @@ af_err af_convolve2_sep(af_array *out, const af_array col_filter,
                 output = convolve2<uchar, float>(signal, col_filter, row_filter,
                                                  expand);
                 break;
+            case s8:
+                output = convolve2<schar, float>(signal, col_filter, row_filter,
+                                                 expand);
+                break;
             case b8:
                 output = convolve2<char, float>(signal, col_filter, row_filter,
                                                 expand);
diff --git a/src/api/c/corrcoef.cpp b/src/api/c/corrcoef.cpp
index fd767fb0ba..fde3788dac 100644
--- a/src/api/c/corrcoef.cpp
+++ b/src/api/c/corrcoef.cpp
@@ -30,6 +30,7 @@ using detail::Array;
 using detail::getScalar;
 using detail::intl;
 using detail::reduce_all;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -89,6 +90,7 @@ af_err af_corrcoef(double* realVal, double* imagVal, const af_array X,
             case u64: *realVal = corrcoef<uintl, double>(X, Y); break;
             case s16: *realVal = corrcoef<short, float>(X, Y); break;
             case u16: *realVal = corrcoef<ushort, float>(X, Y); break;
+            case s8: *realVal = corrcoef<schar, float>(X, Y); break;
             case u8: *realVal = corrcoef<uchar, float>(X, Y); break;
             case b8: *realVal = corrcoef<char, float>(X, Y); break;
             default: TYPE_ERROR(1, xType);
diff --git a/src/api/c/covariance.cpp b/src/api/c/covariance.cpp
index f364558b11..a4241a8f0a 100644
--- a/src/api/c/covariance.cpp
+++ b/src/api/c/covariance.cpp
@@ -31,6 +31,7 @@ using detail::intl;
 using detail::mean;
 using detail::reduce;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -97,6 +98,7 @@ af_err af_cov_v2(af_array* out, const af_array X, const af_array Y,
             case u64: output = cov<uintl, double>(X, Y, bias); break;
             case s16: output = cov<short, float>(X, Y, bias); break;
             case u16: output = cov<ushort, float>(X, Y, bias); break;
+            case s8: output = cov<schar, float>(X, Y, bias); break;
             case u8: output = cov<uchar, float>(X, Y, bias); break;
             default: TYPE_ERROR(1, xType);
         }
diff --git a/src/api/c/data.cpp b/src/api/c/data.cpp
index 60ede3d4f6..324936e76e 100644
--- a/src/api/c/data.cpp
+++ b/src/api/c/data.cpp
@@ -35,6 +35,7 @@ using detail::iota;
 using detail::padArrayBorders;
 using detail::range;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -58,6 +59,7 @@ af_err af_constant(af_array *result, const double value, const unsigned ndims,
             case b8: out = createHandleFromValue<char>(d, value); break;
             case s32: out = createHandleFromValue<int>(d, value); break;
             case u32: out = createHandleFromValue<uint>(d, value); break;
+            case s8: out = createHandleFromValue<schar>(d, value); break;
             case u8: out = createHandleFromValue<uchar>(d, value); break;
             case s64: out = createHandleFromValue<intl>(d, value); break;
             case u64: out = createHandleFromValue<uintl>(d, value); break;
@@ -159,6 +161,7 @@ af_err af_identity(af_array *out, const unsigned ndims, const dim_t *const dims,
             case c64: result = identity_<cdouble>(d); break;
             case s32: result = identity_<int>(d); break;
             case u32: result = identity_<uint>(d); break;
+            case s8: result = identity_<schar>(d); break;
             case u8: result = identity_<uchar>(d); break;
             case u64: result = identity_<uintl>(d); break;
             case s64: result = identity_<intl>(d); break;
@@ -202,6 +205,7 @@ af_err af_range(af_array *result, const unsigned ndims, const dim_t *const dims,
             case u64: out = range_<uintl>(d, seq_dim); break;
             case s16: out = range_<short>(d, seq_dim); break;
             case u16: out = range_<ushort>(d, seq_dim); break;
+            case s8: out = range_<schar>(d, seq_dim); break;
             case u8: out = range_<uchar>(d, seq_dim); break;
             case f16: out = range_<half>(d, seq_dim); break;
             default: TYPE_ERROR(4, type);
@@ -242,6 +246,7 @@ af_err af_iota(af_array *result, const unsigned ndims, const dim_t *const dims,
             case u64: out = iota_<uintl>(d, t); break;
             case s16: out = iota_<short>(d, t); break;
             case u16: out = iota_<ushort>(d, t); break;
+            case s8: out = iota_<schar>(d, t); break;
             case u8: out = iota_<uchar>(d, t); break;
             case f16: out = iota_<half>(d, t); break;
             default: TYPE_ERROR(4, type);
@@ -285,6 +290,7 @@ af_err af_diag_create(af_array *out, const af_array in, const int num) {
             case u64: result = diagCreate<uintl>(in, num); break;
             case s16: result = diagCreate<short>(in, num); break;
             case u16: result = diagCreate<ushort>(in, num); break;
+            case s8: result = diagCreate<schar>(in, num); break;
             case u8:
                 result = diagCreate<uchar>(in, num);
                 break;
@@ -324,6 +330,7 @@ af_err af_diag_extract(af_array *out, const af_array in, const int num) {
             case u64: result = diagExtract<uintl>(in, num); break;
             case s16: result = diagExtract<short>(in, num); break;
             case u16: result = diagExtract<ushort>(in, num); break;
+            case s8: result = diagExtract<schar>(in, num); break;
             case u8:
                 result = diagExtract<uchar>(in, num);
                 break;
@@ -366,6 +373,7 @@ af_err af_lower(af_array *out, const af_array in, bool is_unit_diag) {
             case u64: res = triangle<uintl>(in, false, is_unit_diag); break;
             case s16: res = triangle<short>(in, false, is_unit_diag); break;
             case u16: res = triangle<ushort>(in, false, is_unit_diag); break;
+            case s8: res = triangle<schar>(in, false, is_unit_diag); break;
             case u8: res = triangle<uchar>(in, false, is_unit_diag); break;
             case b8: res = triangle<char>(in, false, is_unit_diag); break;
             case f16: res = triangle<half>(in, false, is_unit_diag); break;
@@ -395,6 +403,7 @@ af_err af_upper(af_array *out, const af_array in, bool is_unit_diag) {
             case u64: res = triangle<uintl>(in, true, is_unit_diag); break;
             case s16: res = triangle<short>(in, true, is_unit_diag); break;
             case u16: res = triangle<ushort>(in, true, is_unit_diag); break;
+            case s8: res = triangle<schar>(in, true, is_unit_diag); break;
             case u8: res = triangle<uchar>(in, true, is_unit_diag); break;
             case b8: res = triangle<char>(in, true, is_unit_diag); break;
             case f16: res = triangle<half>(in, true, is_unit_diag); break;
@@ -449,6 +458,7 @@ af_err af_pad(af_array *out, const af_array in, const unsigned begin_ndims,
             case u64: res = pad<uintl>(in, lPad, uPad, pad_type); break;
             case s16: res = pad<short>(in, lPad, uPad, pad_type); break;
             case u16: res = pad<ushort>(in, lPad, uPad, pad_type); break;
+            case s8: res = pad<schar>(in, lPad, uPad, pad_type); break;
             case u8: res = pad<uchar>(in, lPad, uPad, pad_type); break;
             case b8: res = pad<char>(in, lPad, uPad, pad_type); break;
             case f16: res = pad<half>(in, lPad, uPad, pad_type); break;
diff --git a/src/api/c/deconvolution.cpp b/src/api/c/deconvolution.cpp
index f579eeadf8..19ad89e5db 100644
--- a/src/api/c/deconvolution.cpp
+++ b/src/api/c/deconvolution.cpp
@@ -43,6 +43,7 @@ using detail::createValueArray;
 using detail::logicOp;
 using detail::padArrayBorders;
 using detail::scalar;
+using detail::schar;
 using detail::select_scalar;
 using detail::shift;
 using detail::uchar;
@@ -226,6 +227,7 @@ af_err af_iterative_deconv(af_array* out, const af_array in, const af_array ker,
             case u16:
                 res = iterDeconv<ushort>(in, ker, iters, rfac, algo);
                 break;
+            case s8: res = iterDeconv<schar>(in, ker, iters, rfac, algo); break;
             case u8: res = iterDeconv<uchar>(in, ker, iters, rfac, algo); break;
             default: TYPE_ERROR(1, inputType);
         }
@@ -323,6 +325,7 @@ af_err af_inverse_deconv(af_array* out, const af_array in, const af_array psf,
             case f32: res = invDeconv<float>(in, psf, gamma, algo); break;
             case s16: res = invDeconv<short>(in, psf, gamma, algo); break;
             case u16: res = invDeconv<ushort>(in, psf, gamma, algo); break;
+            case s8: res = invDeconv<schar>(in, psf, gamma, algo); break;
             case u8: res = invDeconv<uchar>(in, psf, gamma, algo); break;
             default: TYPE_ERROR(1, inputType);
         }
diff --git a/src/api/c/device.cpp b/src/api/c/device.cpp
index ef37888523..7427a1a4e5 100644
--- a/src/api/c/device.cpp
+++ b/src/api/c/device.cpp
@@ -47,6 +47,7 @@ using detail::init;
 using detail::intl;
 using detail::isDoubleSupported;
 using detail::isHalfSupported;
+using detail::schar;
 using detail::setDevice;
 using detail::uchar;
 using detail::uint;
@@ -290,6 +291,7 @@ af_err af_eval(af_array arr) {
                 case c64: eval<cdouble>(arr); break;
                 case s32: eval<int>(arr); break;
                 case u32: eval<uint>(arr); break;
+                case s8: eval<schar>(arr); break;
                 case u8: eval<uchar>(arr); break;
                 case b8: eval<char>(arr); break;
                 case s64: eval<intl>(arr); break;
@@ -344,6 +346,7 @@ af_err af_eval_multiple(int num, af_array* arrays) {
             case c64: evalMultiple<cdouble>(num, arrays); break;
             case s32: evalMultiple<int>(num, arrays); break;
             case u32: evalMultiple<uint>(num, arrays); break;
+            case s8: evalMultiple<schar>(num, arrays); break;
             case u8: evalMultiple<uchar>(num, arrays); break;
             case b8: evalMultiple<char>(num, arrays); break;
             case s64: evalMultiple<intl>(num, arrays); break;
diff --git a/src/api/c/diff.cpp b/src/api/c/diff.cpp
index c579f0b53e..f75d5c1ab1 100644
--- a/src/api/c/diff.cpp
+++ b/src/api/c/diff.cpp
@@ -21,6 +21,7 @@ using arrayfire::getHandle;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -64,6 +65,7 @@ af_err af_diff1(af_array* out, const af_array in, const int dim) {
             case u64: output = diff1<uintl>(in, dim); break;
             case s16: output = diff1<short>(in, dim); break;
             case u16: output = diff1<ushort>(in, dim); break;
+            case s8: output = diff1<schar>(in, dim); break;
             case u8: output = diff1<uchar>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
@@ -101,6 +103,7 @@ af_err af_diff2(af_array* out, const af_array in, const int dim) {
             case u64: output = diff2<uintl>(in, dim); break;
             case s16: output = diff2<short>(in, dim); break;
             case u16: output = diff2<ushort>(in, dim); break;
+            case s8: output = diff2<schar>(in, dim); break;
             case u8: output = diff2<uchar>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/dog.cpp b/src/api/c/dog.cpp
index fbbe94d211..848262daab 100644
--- a/src/api/c/dog.cpp
+++ b/src/api/c/dog.cpp
@@ -22,6 +22,7 @@ using af::dim4;
 using detail::arithOp;
 using detail::Array;
 using detail::convolve;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -70,6 +71,7 @@ af_err af_dog(af_array* out, const af_array in, const int radius1,
             case u32: output = dog<uint, float>(in, radius1, radius2); break;
             case s16: output = dog<short, float>(in, radius1, radius2); break;
             case u16: output = dog<ushort, float>(in, radius1, radius2); break;
+            case s8: output = dog<schar, float>(in, radius1, radius2); break;
             case u8: output = dog<uchar, float>(in, radius1, radius2); break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/exampleFunction.cpp b/src/api/c/exampleFunction.cpp
index 4a7a52f6bd..a58336f90c 100644
--- a/src/api/c/exampleFunction.cpp
+++ b/src/api/c/exampleFunction.cpp
@@ -76,6 +76,7 @@ af_err af_example_function(af_array* out, const af_array a,
             case f32: output = example<float>(a, a, param); break;
             case s32: output = example<int>(a, a, param); break;
             case u32: output = example<uint>(a, a, param); break;
+            case s8: output = example<schar>(a, a, param); break;
             case u8: output = example<uchar>(a, a, param); break;
             case b8: output = example<char>(a, a, param); break;
             case c32: output = example<cfloat>(a, a, param); break;
diff --git a/src/api/c/fast.cpp b/src/api/c/fast.cpp
index ed8822c402..08834ce4f4 100644
--- a/src/api/c/fast.cpp
+++ b/src/api/c/fast.cpp
@@ -22,6 +22,7 @@ using af::dim4;
 using detail::Array;
 using detail::createEmptyArray;
 using detail::createValueArray;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -96,6 +97,10 @@ af_err af_fast(af_features *out, const af_array in, const float thr,
                 *out = fast<ushort>(in, thr, arc_length, non_max, feature_ratio,
                                     edge);
                 break;
+            case s8:
+                *out = fast<schar>(in, thr, arc_length, non_max, feature_ratio,
+                                   edge);
+                break;
             case u8:
                 *out = fast<uchar>(in, thr, arc_length, non_max, feature_ratio,
                                    edge);
diff --git a/src/api/c/fftconvolve.cpp b/src/api/c/fftconvolve.cpp
index 5e69d5d0ce..ead2247c51 100644
--- a/src/api/c/fftconvolve.cpp
+++ b/src/api/c/fftconvolve.cpp
@@ -35,6 +35,7 @@ using detail::createSubArray;
 using detail::fftconvolve;
 using detail::intl;
 using detail::real;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -211,6 +212,10 @@ af_err fft_convolve(af_array *out, const af_array signal, const af_array filter,
                 output =
                     fftconvolve<uchar>(signal, filter, expand, convBT, baseDim);
                 break;
+            case s8:
+                output =
+                    fftconvolve<schar>(signal, filter, expand, convBT, baseDim);
+                break;
             case b8:
                 output =
                     fftconvolve<char>(signal, filter, expand, convBT, baseDim);
diff --git a/src/api/c/filters.cpp b/src/api/c/filters.cpp
index dc0067f257..4c154c16fb 100644
--- a/src/api/c/filters.cpp
+++ b/src/api/c/filters.cpp
@@ -18,6 +18,7 @@
 #include <af/signal.h>
 
 using af::dim4;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -64,6 +65,7 @@ af_err af_medfilt1(af_array *out, const af_array in, const dim_t wind_width,
             case u16:
                 output = medfilt1<ushort>(in, wind_width, edge_pad);
                 break;
+            case s8: output = medfilt1<schar>(in, wind_width, edge_pad); break;
             case u8: output = medfilt1<uchar>(in, wind_width, edge_pad); break;
             default: TYPE_ERROR(1, type);
         }
@@ -129,6 +131,9 @@ af_err af_medfilt2(af_array *out, const af_array in, const dim_t wind_length,
                 output =
                     medfilt2<ushort>(in, wind_length, wind_width, edge_pad);
                 break;
+            case s8:
+                output = medfilt2<schar>(in, wind_length, wind_width, edge_pad);
+                break;
             case u8:
                 output = medfilt2<uchar>(in, wind_length, wind_width, edge_pad);
                 break;
diff --git a/src/api/c/flip.cpp b/src/api/c/flip.cpp
index 080af47aac..4aea98ec73 100644
--- a/src/api/c/flip.cpp
+++ b/src/api/c/flip.cpp
@@ -25,6 +25,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uintl;
 using detail::ushort;
@@ -61,6 +62,7 @@ af_err af_flip(af_array *result, const af_array in, const unsigned dim) {
             case u64: out = flip<uintl>(in, dim); break;
             case s16: out = flip<short>(in, dim); break;
             case u16: out = flip<ushort>(in, dim); break;
+            case s8: out = flip<schar>(in, dim); break;
             case u8: out = flip<uchar>(in, dim); break;
             default: TYPE_ERROR(1, in_type);
         }
diff --git a/src/api/c/handle.cpp b/src/api/c/handle.cpp
index 9c980af9f0..d67f4ae9a1 100644
--- a/src/api/c/handle.cpp
+++ b/src/api/c/handle.cpp
@@ -21,6 +21,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createDeviceDataArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -46,6 +47,7 @@ af_array retain(const af_array in) {
             case f64: return retainHandle<double>(in);
             case s32: return retainHandle<int>(in);
             case u32: return retainHandle<uint>(in);
+            case s8: return retainHandle<schar>(in);
             case u8: return retainHandle<uchar>(in);
             case c32: return retainHandle<detail::cfloat>(in);
             case c64: return retainHandle<detail::cdouble>(in);
@@ -70,6 +72,7 @@ af_array createHandle(const dim4 &d, af_dtype dtype) {
         case b8:  return createHandle<char   >(d);
         case s32: return createHandle<int    >(d);
         case u32: return createHandle<uint   >(d);
+        case s8:  return createHandle<schar  >(d);
         case u8:  return createHandle<uchar  >(d);
         case s64: return createHandle<intl   >(d);
         case u64: return createHandle<uintl  >(d);
@@ -91,6 +94,7 @@ af_array createHandleFromValue(const dim4 &d, double val, af_dtype dtype) {
         case b8:  return createHandleFromValue<char   >(d, val);
         case s32: return createHandleFromValue<int    >(d, val);
         case u32: return createHandleFromValue<uint   >(d, val);
+        case s8:  return createHandleFromValue<schar  >(d, val);
         case u8:  return createHandleFromValue<uchar  >(d, val);
         case s64: return createHandleFromValue<intl   >(d, val);
         case u64: return createHandleFromValue<uintl  >(d, val);
@@ -113,6 +117,7 @@ af_array createHandleFromDeviceData(const af::dim4 &d, af_dtype dtype,
         case b8:  return getHandle(createDeviceDataArray<char   >(d, data, false));
         case s32: return getHandle(createDeviceDataArray<int    >(d, data, false));
         case u32: return getHandle(createDeviceDataArray<uint   >(d, data, false));
+        case s8:  return getHandle(createDeviceDataArray<schar  >(d, data, false));
         case u8:  return getHandle(createDeviceDataArray<uchar  >(d, data, false));
         case s64: return getHandle(createDeviceDataArray<intl   >(d, data, false));
         case u64: return getHandle(createDeviceDataArray<uintl  >(d, data, false));
@@ -182,5 +187,6 @@ INSTANTIATE(char);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
 INSTANTIATE(half);
+INSTANTIATE(schar);
 
 }  // namespace arrayfire
diff --git a/src/api/c/hist.cpp b/src/api/c/hist.cpp
index 1e250b5df4..0d8f9bfe6b 100644
--- a/src/api/c/hist.cpp
+++ b/src/api/c/hist.cpp
@@ -29,6 +29,7 @@ using detail::Array;
 using detail::copy_histogram;
 using detail::forgeManager;
 using detail::getScalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -133,6 +134,10 @@ af_err af_draw_hist(const af_window window, const af_array X,
                 chart =
                     setup_histogram<ushort>(window, X, minval, maxval, props);
                 break;
+            case s8:
+                chart =
+                    setup_histogram<schar>(window, X, minval, maxval, props);
+                break;
             case u8:
                 chart =
                     setup_histogram<uchar>(window, X, minval, maxval, props);
diff --git a/src/api/c/histeq.cpp b/src/api/c/histeq.cpp
index da2a7579d8..faed6a238c 100644
--- a/src/api/c/histeq.cpp
+++ b/src/api/c/histeq.cpp
@@ -33,6 +33,7 @@ using detail::intl;
 using detail::lookup;
 using detail::reduce_all;
 using detail::scan;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -95,6 +96,7 @@ af_err af_hist_equal(af_array* out, const af_array in, const af_array hist) {
             case u16: output = hist_equal<ushort, uint>(in, hist); break;
             case s64: output = hist_equal<intl, uint>(in, hist); break;
             case u64: output = hist_equal<uintl, uint>(in, hist); break;
+            case s8: output = hist_equal<schar, uint>(in, hist); break;
             case u8: output = hist_equal<uchar, uint>(in, hist); break;
             default: TYPE_ERROR(1, dataType);
         }
diff --git a/src/api/c/histogram.cpp b/src/api/c/histogram.cpp
index aa2744bb6c..69c6d71de5 100644
--- a/src/api/c/histogram.cpp
+++ b/src/api/c/histogram.cpp
@@ -15,6 +15,7 @@
 #include <af/image.h>
 
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -74,6 +75,10 @@ af_err af_histogram(af_array *out, const af_array in, const unsigned nbins,
                 output = histogram<uintl>(in, nbins, minval, maxval,
                                           info.isLinear());
                 break;
+            case s8:
+                output = histogram<schar>(in, nbins, minval, maxval,
+                                          info.isLinear());
+                break;
             case u8:
                 output = histogram<uchar>(in, nbins, minval, maxval,
                                           info.isLinear());
diff --git a/src/api/c/image.cpp b/src/api/c/image.cpp
index 425530806c..4650c0ec3d 100644
--- a/src/api/c/image.cpp
+++ b/src/api/c/image.cpp
@@ -39,6 +39,7 @@ using detail::Array;
 using detail::copy_image;
 using detail::createValueArray;
 using detail::forgeManager;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -102,6 +103,7 @@ af_err af_draw_image(const af_window window, const af_array in,
             case u32: image = convert_and_copy_image<uint>(in); break;
             case s16: image = convert_and_copy_image<short>(in); break;
             case u16: image = convert_and_copy_image<ushort>(in); break;
+            case s8: image = convert_and_copy_image<schar>(in); break;
             case u8: image = convert_and_copy_image<uchar>(in); break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index be5f528922..0f87e4df17 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -75,7 +75,7 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
             if (fo_color == 1) {
                 pDst0[indx] = static_cast<T>(*(src + (x * step)));
             } else if (fo_color >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
                     pDst0[indx] =
                         static_cast<float>(*(src + (x * step + FI_RGBA_RED)));
                     pDst1[indx] =
@@ -201,7 +201,7 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
             if (fo_color == 1) {
                 pDst[indx] = static_cast<T>(*(src + (x * step)));
             } else if (fo_color >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
                     r = *(src + (x * step + FI_RGBA_RED));
                     g = *(src + (x * step + FI_RGBA_GREEN));
                     b = *(src + (x * step + FI_RGBA_BLUE));
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 7130202397..4a00212207 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -71,7 +71,7 @@ static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
             if (fi_color == 1) {
                 pDst0[indx] = *(src + (x * step));
             } else if (fi_color >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
                     pDst0[indx] = *(src + (x * step + FI_RGBA_RED));
                     pDst1[indx] = *(src + (x * step + FI_RGBA_GREEN));
                     pDst2[indx] = *(src + (x * step + FI_RGBA_BLUE));
@@ -102,6 +102,7 @@ static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
 }
 
 FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type) {
+    // FIXME s8?
     if (channels == AFFI_GRAY) {
         if (type == u8) { return FIT_BITMAP; }
         if (type == u16) {
@@ -364,7 +365,7 @@ static void save_t(T* pDstLine, const af_array in, const dim4& dims,
             if (channels == 1) {
                 *(pDstLine + x * step) = pSrc0[indx];  // r -> 0
             } else if (channels >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
                     *(pDstLine + x * step + FI_RGBA_RED) =
                         pSrc0[indx];  // r -> 0
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
diff --git a/src/api/c/implicit.cpp b/src/api/c/implicit.cpp
index f30afda7eb..d045769cbd 100644
--- a/src/api/c/implicit.cpp
+++ b/src/api/c/implicit.cpp
@@ -14,7 +14,7 @@ Implicit type mimics C/C++ behavior.
 
 Order of precedence:
 - complex > real
-- double > float > uintl > intl > uint > int > uchar > char
+- double > float > uintl > intl > uint > int > uchar > schar > char
 */
 
 af_dtype implicit(const af_dtype lty, const af_dtype rty) {
@@ -38,6 +38,7 @@ af_dtype implicit(const af_dtype lty, const af_dtype rty) {
     if ((lty == u16) || (rty == u16)) { return u16; }
     if ((lty == s16) || (rty == s16)) { return s16; }
     if ((lty == u8) || (rty == u8)) { return u8; }
+    if ((lty == s8) || (rty == s8)) { return s8; }
     if ((lty == b8) && (rty == b8)) { return b8; }
 
     return f32;
diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index 1c7484f2bf..a697f8457c 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -40,6 +40,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::index;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -115,6 +116,7 @@ af_err af_index(af_array* result, const af_array in, const unsigned ndims,
             case u16: out = indexBySeqs<ushort>(in, indices_); break;
             case s64: out = indexBySeqs<intl>(in, indices_); break;
             case u64: out = indexBySeqs<uintl>(in, indices_); break;
+            case s8: out = indexBySeqs<schar>(in, indices_); break;
             case u8: out = indexBySeqs<uchar>(in, indices_); break;
             case f16: out = indexBySeqs<half>(in, indices_); break;
             default: TYPE_ERROR(1, type);
@@ -148,6 +150,7 @@ static af_array lookup(const af_array& in, const af_array& idx,
         case u64: return lookup<uintl, idx_t>(in, idx, dim);
         case s16: return lookup<short, idx_t>(in, idx, dim);
         case u16: return lookup<ushort, idx_t>(in, idx, dim);
+        case s8: return lookup<schar, idx_t>(in, idx, dim);
         case u8: return lookup<uchar, idx_t>(in, idx, dim);
         case b8: return lookup<char, idx_t>(in, idx, dim);
         case f16: return lookup<half, idx_t>(in, idx, dim);
@@ -185,6 +188,7 @@ af_err af_lookup(af_array* out, const af_array in, const af_array indices,
             case u16: output = lookup<ushort>(in, indices, dim); break;
             case s64: output = lookup<intl>(in, indices, dim); break;
             case u64: output = lookup<uintl>(in, indices, dim); break;
+            case s8: output = lookup<schar>(in, indices, dim); break;
             case u8: output = lookup<uchar>(in, indices, dim); break;
             case f16: output = lookup<half>(in, indices, dim); break;
             default: TYPE_ERROR(1, idxType);
@@ -289,6 +293,7 @@ af_err af_index_gen(af_array* out, const af_array in, const dim_t ndims,
             case s32: output = genIndex<int>(in, ptr); break;
             case u16: output = genIndex<ushort>(in, ptr); break;
             case s16: output = genIndex<short>(in, ptr); break;
+            case s8: output = genIndex<schar>(in, ptr); break;
             case u8: output = genIndex<uchar>(in, ptr); break;
             case b8: output = genIndex<char>(in, ptr); break;
             case f16: output = genIndex<half>(in, ptr); break;
diff --git a/src/api/c/internal.cpp b/src/api/c/internal.cpp
index 38c0c96dfe..c0314981cb 100644
--- a/src/api/c/internal.cpp
+++ b/src/api/c/internal.cpp
@@ -25,6 +25,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createStridedArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -120,6 +121,11 @@ af_err af_create_strided_array(af_array *arr, const void *data,
                     dims, strides, offset, static_cast<uchar *>(in_data),
                     isdev));
                 break;
+            case s8:
+                res = getHandle(createStridedArray<schar>(
+                    dims, strides, offset, static_cast<schar *>(in_data),
+                    isdev));
+                break;
             case f16:
                 res = getHandle(createStridedArray<half>(
                     dims, strides, offset, static_cast<half *>(in_data),
@@ -175,6 +181,7 @@ af_err af_get_raw_ptr(void **ptr, const af_array arr) {
             case s16: res = getRawPtr(getArray<short>(arr)); break;
             case b8: res = getRawPtr(getArray<char>(arr)); break;
             case u8: res = getRawPtr(getArray<uchar>(arr)); break;
+            case s8: res = getRawPtr(getArray<schar>(arr)); break;
             case f16: res = getRawPtr(getArray<half>(arr)); break;
             default: TYPE_ERROR(6, ty);
         }
@@ -212,6 +219,7 @@ af_err af_is_owner(bool *result, const af_array arr) {
             case s16: res = getArray<short>(arr).isOwner(); break;
             case b8: res = getArray<char>(arr).isOwner(); break;
             case u8: res = getArray<uchar>(arr).isOwner(); break;
+            case s8: res = getArray<schar>(arr).isOwner(); break;
             case f16: res = getArray<half>(arr).isOwner(); break;
             default: TYPE_ERROR(6, ty);
         }
@@ -241,6 +249,7 @@ af_err af_get_allocated_bytes(size_t *bytes, const af_array arr) {
             case s16: res = getArray<short>(arr).getAllocatedBytes(); break;
             case b8: res = getArray<char>(arr).getAllocatedBytes(); break;
             case u8: res = getArray<uchar>(arr).getAllocatedBytes(); break;
+            case s8: res = getArray<schar>(arr).getAllocatedBytes(); break;
             case f16: res = getArray<half>(arr).getAllocatedBytes(); break;
             default: TYPE_ERROR(6, ty);
         }
diff --git a/src/api/c/join.cpp b/src/api/c/join.cpp
index 4c47fbe495..d3e9cda6b5 100644
--- a/src/api/c/join.cpp
+++ b/src/api/c/join.cpp
@@ -26,6 +26,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -98,6 +99,7 @@ af_err af_join(af_array *out, const int dim, const af_array first,
             case u64: output = join<uintl>(dim, first, second); break;
             case s16: output = join<short>(dim, first, second); break;
             case u16: output = join<ushort>(dim, first, second); break;
+            case s8: output = join<schar>(dim, first, second); break;
             case u8: output = join<uchar>(dim, first, second); break;
             case f16: output = join<half>(dim, first, second); break;
             default: TYPE_ERROR(1, finfo.getType());
@@ -169,6 +171,7 @@ af_err af_join_many(af_array *out, const int dim, const unsigned n_arrays,
             case u64: output = join_many<uintl>(dim, n_arrays, inputs); break;
             case s16: output = join_many<short>(dim, n_arrays, inputs); break;
             case u16: output = join_many<ushort>(dim, n_arrays, inputs); break;
+            case s8: output = join_many<schar>(dim, n_arrays, inputs); break;
             case u8: output = join_many<uchar>(dim, n_arrays, inputs); break;
             case f16: output = join_many<half>(dim, n_arrays, inputs); break;
             default: TYPE_ERROR(1, assertType);
diff --git a/src/api/c/match_template.cpp b/src/api/c/match_template.cpp
index 6882711a7f..91d81c383c 100644
--- a/src/api/c/match_template.cpp
+++ b/src/api/c/match_template.cpp
@@ -19,6 +19,7 @@
 
 using af::dim4;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -82,6 +83,10 @@ af_err af_match_template(af_array* out, const af_array search_img,
             case b8:
                 output = match_template<char>(search_img, template_img, m_type);
                 break;
+            case s8:
+                output =
+                    match_template<schar>(search_img, template_img, m_type);
+                break;
             case u8:
                 output =
                     match_template<uchar>(search_img, template_img, m_type);
diff --git a/src/api/c/mean.cpp b/src/api/c/mean.cpp
index af9021983e..65fe057155 100644
--- a/src/api/c/mean.cpp
+++ b/src/api/c/mean.cpp
@@ -31,6 +31,7 @@ using detail::imag;
 using detail::intl;
 using detail::mean;
 using detail::real;
+using detail::schar;
 using detail::uchar;
 using detail::uintl;
 using detail::ushort;
@@ -77,6 +78,7 @@ af_err af_mean(af_array *out, const af_array in, const dim_t dim) {
             case u64: output = mean<uintl, double>(in, dim); break;
             case s16: output = mean<short, float>(in, dim); break;
             case u16: output = mean<ushort, float>(in, dim); break;
+            case s8: output = mean<schar, float>(in, dim); break;
             case u8: output = mean<uchar, float>(in, dim); break;
             case b8: output = mean<char, float>(in, dim); break;
             case c32: output = mean<cfloat, cfloat>(in, dim); break;
@@ -127,6 +129,7 @@ af_err af_mean_weighted(af_array *out, const af_array in,
             case u32:
             case s16:
             case u16:
+            case s8:
             case u8:
             case b8: output = mean<float>(in, w, dim); break;
             case f64:
@@ -158,6 +161,7 @@ af_err af_mean_all(double *realVal, double *imagVal, const af_array in) {
             case u64: *realVal = mean<uintl, double>(in); break;
             case s16: *realVal = mean<short, float>(in); break;
             case u16: *realVal = mean<ushort, float>(in); break;
+            case s8: *realVal = mean<schar, float>(in); break;
             case u8: *realVal = mean<uchar, float>(in); break;
             case b8: *realVal = mean<char, float>(in); break;
             case f16:
@@ -200,6 +204,7 @@ af_err af_mean_all_weighted(double *realVal, double *imagVal, const af_array in,
             case u32:
             case s16:
             case u16:
+            case s8:
             case u8:
             case b8:
             case f16: *realVal = mean<float>(in, weights); break;
diff --git a/src/api/c/meanshift.cpp b/src/api/c/meanshift.cpp
index 0c8322cafe..bf09bc4d2a 100644
--- a/src/api/c/meanshift.cpp
+++ b/src/api/c/meanshift.cpp
@@ -18,6 +18,7 @@
 using af::dim4;
 using detail::intl;
 using detail::meanshift;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -84,6 +85,10 @@ af_err af_mean_shift(af_array *out, const af_array in,
                 output = mean_shift<uintl>(in, spatial_sigma, chromatic_sigma,
                                            num_iterations, is_color);
                 break;
+            case s8:
+                output = mean_shift<schar>(in, spatial_sigma, chromatic_sigma,
+                                           num_iterations, is_color);
+                break;
             case u8:
                 output = mean_shift<uchar>(in, spatial_sigma, chromatic_sigma,
                                            num_iterations, is_color);
diff --git a/src/api/c/median.cpp b/src/api/c/median.cpp
index 5e22c1c36a..2fd0de18d8 100644
--- a/src/api/c/median.cpp
+++ b/src/api/c/median.cpp
@@ -23,6 +23,7 @@
 using af::dim4;
 using detail::Array;
 using detail::division;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -169,6 +170,7 @@ af_err af_median_all(double* realVal, double* imagVal,  // NOLINT
             case u32: *realVal = median<uint>(in); break;
             case s16: *realVal = median<short>(in); break;
             case u16: *realVal = median<ushort>(in); break;
+            case s8: *realVal = median<schar>(in); break;
             case u8: *realVal = median<uchar>(in); break;
             default: TYPE_ERROR(1, type);
         }
@@ -193,6 +195,7 @@ af_err af_median(af_array* out, const af_array in, const dim_t dim) {
             case u32: output = median<uint>(in, dim); break;
             case s16: output = median<short>(in, dim); break;
             case u16: output = median<ushort>(in, dim); break;
+            case s8: output = median<schar>(in, dim); break;
             case u8: output = median<uchar>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/memory.cpp b/src/api/c/memory.cpp
index fbff61720e..665a51ac9c 100644
--- a/src/api/c/memory.cpp
+++ b/src/api/c/memory.cpp
@@ -42,6 +42,7 @@ using detail::memUnlock;
 using detail::pinnedAlloc;
 using detail::pinnedFree;
 using detail::printMemInfo;
+using detail::schar;
 using detail::signalMemoryCleanup;
 using detail::uchar;
 using detail::uint;
@@ -95,6 +96,9 @@ af_err af_device_array(af_array *arr, void *data, const unsigned ndims,
             case u16:
                 res = getHandle(createDeviceDataArray<ushort>(d, data));
                 break;
+            case s8:
+                res = getHandle(createDeviceDataArray<schar>(d, data));
+                break;
             case u8:
                 res = getHandle(createDeviceDataArray<uchar>(d, data));
                 break;
@@ -130,6 +134,7 @@ af_err af_get_device_ptr(void **data, const af_array arr) {
             case u64: *data = getDevicePtr(getArray<uintl>(arr)); break;
             case s16: *data = getDevicePtr(getArray<short>(arr)); break;
             case u16: *data = getDevicePtr(getArray<ushort>(arr)); break;
+            case s8: *data = getDevicePtr(getArray<schar>(arr)); break;
             case u8: *data = getDevicePtr(getArray<uchar>(arr)); break;
             case b8: *data = getDevicePtr(getArray<char>(arr)); break;
             case f16: *data = getDevicePtr(getArray<half>(arr)); break;
@@ -164,6 +169,7 @@ af_err af_lock_array(const af_array arr) {
             case u64: lockArray<uintl>(arr); break;
             case s16: lockArray<short>(arr); break;
             case u16: lockArray<ushort>(arr); break;
+            case s8: lockArray<schar>(arr); break;
             case u8: lockArray<uchar>(arr); break;
             case b8: lockArray<char>(arr); break;
             case f16: lockArray<half>(arr); break;
@@ -196,6 +202,7 @@ af_err af_is_locked_array(bool *res, const af_array arr) {
             case u64: *res = checkUserLock<uintl>(arr); break;
             case s16: *res = checkUserLock<short>(arr); break;
             case u16: *res = checkUserLock<ushort>(arr); break;
+            case s8: *res = checkUserLock<schar>(arr); break;
             case u8: *res = checkUserLock<uchar>(arr); break;
             case b8: *res = checkUserLock<char>(arr); break;
             case f16: *res = checkUserLock<half>(arr); break;
@@ -229,6 +236,7 @@ af_err af_unlock_array(const af_array arr) {
             case u64: unlockArray<uintl>(arr); break;
             case s16: unlockArray<short>(arr); break;
             case u16: unlockArray<ushort>(arr); break;
+            case s8: unlockArray<schar>(arr); break;
             case u8: unlockArray<uchar>(arr); break;
             case b8: unlockArray<char>(arr); break;
             case f16: unlockArray<half>(arr); break;
diff --git a/src/api/c/moddims.cpp b/src/api/c/moddims.cpp
index 4f6f0f310d..f419a2fb04 100644
--- a/src/api/c/moddims.cpp
+++ b/src/api/c/moddims.cpp
@@ -22,6 +22,7 @@ using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -66,6 +67,7 @@ af_err af_moddims(af_array* out, const af_array in, const unsigned ndims,
             case b8: output = modDims<char>(in, newDims); break;
             case s32: output = modDims<int>(in, newDims); break;
             case u32: output = modDims<uint>(in, newDims); break;
+            case s8: output = modDims<schar>(in, newDims); break;
             case u8: output = modDims<uchar>(in, newDims); break;
             case s64: output = modDims<intl>(in, newDims); break;
             case u64: output = modDims<uintl>(in, newDims); break;
@@ -99,6 +101,7 @@ af_err af_flat(af_array* out, const af_array in) {
                 case b8: output = flat<char>(in); break;
                 case s32: output = flat<int>(in); break;
                 case u32: output = flat<uint>(in); break;
+                case s8: output = flat<schar>(in); break;
                 case u8: output = flat<uchar>(in); break;
                 case s64: output = flat<intl>(in); break;
                 case u64: output = flat<uintl>(in); break;
diff --git a/src/api/c/morph.cpp b/src/api/c/morph.cpp
index efaf6cc53a..418b84e8a9 100644
--- a/src/api/c/morph.cpp
+++ b/src/api/c/morph.cpp
@@ -34,6 +34,7 @@ using detail::createEmptyArray;
 using detail::createValueArray;
 using detail::logicOp;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::unaryOp;
@@ -137,6 +138,7 @@ af_err morph(af_array *out, const af_array &in, const af_array &mask,
             case u32: output = morph<uint>(in, mask, isDilation); break;
             case s16: output = morph<short>(in, mask, isDilation); break;
             case u16: output = morph<ushort>(in, mask, isDilation); break;
+            case s8: output = morph<schar>(in, mask, isDilation); break;
             case u8: output = morph<uchar>(in, mask, isDilation); break;
             default: TYPE_ERROR(1, type);
         }
@@ -170,6 +172,7 @@ af_err morph3d(af_array *out, const af_array &in, const af_array &mask,
             case u32: output = morph3d<uint>(in, mask, isDilation); break;
             case s16: output = morph3d<short>(in, mask, isDilation); break;
             case u16: output = morph3d<ushort>(in, mask, isDilation); break;
+            case s8: output = morph3d<schar>(in, mask, isDilation); break;
             case u8: output = morph3d<uchar>(in, mask, isDilation); break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/nearest_neighbour.cpp b/src/api/c/nearest_neighbour.cpp
index abc2a7b65b..10543649d9 100644
--- a/src/api/c/nearest_neighbour.cpp
+++ b/src/api/c/nearest_neighbour.cpp
@@ -21,6 +21,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -128,6 +129,10 @@ af_err af_nearest_neighbour(af_array* idx, af_array* dist, const af_array query,
                                                     dist_dim, n_dist,
                                                     dist_type);
                     break;
+                case s8:
+                    nearest_neighbour<schar, int>(&oIdx, &oDist, query, train,
+                                                  dist_dim, n_dist, dist_type);
+                    break;
                 case u8:
                     nearest_neighbour<uchar, uint>(&oIdx, &oDist, query, train,
                                                    dist_dim, n_dist, dist_type);
diff --git a/src/api/c/plot.cpp b/src/api/c/plot.cpp
index c2d954d481..be5aab06b1 100644
--- a/src/api/c/plot.cpp
+++ b/src/api/c/plot.cpp
@@ -35,6 +35,7 @@ using detail::Array;
 using detail::copy_plot;
 using detail::forgeManager;
 using detail::reduce;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -166,6 +167,10 @@ af_err plotWrapper(const af_window window, const af_array in,
                 chart = setup_plot<ushort>(window, in, dims[order_dim], props,
                                            ptype, marker);
                 break;
+            case s8:
+                chart = setup_plot<schar>(window, in, dims[order_dim], props,
+                                          ptype, marker);
+                break;
             case u8:
                 chart = setup_plot<uchar>(window, in, dims[order_dim], props,
                                           ptype, marker);
@@ -240,6 +245,9 @@ af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
             case u16:
                 chart = setup_plot<ushort>(window, in, 3, props, ptype, marker);
                 break;
+            case s8:
+                chart = setup_plot<schar>(window, in, 3, props, ptype, marker);
+                break;
             case u8:
                 chart = setup_plot<uchar>(window, in, 3, props, ptype, marker);
                 break;
@@ -307,6 +315,9 @@ af_err plotWrapper(const af_window window, const af_array X, const af_array Y,
             case u16:
                 chart = setup_plot<ushort>(window, in, 2, props, ptype, marker);
                 break;
+            case s8:
+                chart = setup_plot<schar>(window, in, 2, props, ptype, marker);
+                break;
             case u8:
                 chart = setup_plot<uchar>(window, in, 2, props, ptype, marker);
                 break;
diff --git a/src/api/c/print.cpp b/src/api/c/print.cpp
index 48fea73b48..2f1ae15c8d 100644
--- a/src/api/c/print.cpp
+++ b/src/api/c/print.cpp
@@ -36,6 +36,7 @@ using arrayfire::common::SparseArray;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -162,6 +163,7 @@ af_err af_print_array(af_array arr) {
                 case b8: print<char>(NULL, arr, 4); break;
                 case s32: print<int>(NULL, arr, 4); break;
                 case u32: print<unsigned>(NULL, arr, 4); break;
+                case s8: print<schar>(NULL, arr, 4); break;
                 case u8: print<uchar>(NULL, arr, 4); break;
                 case s64: print<intl>(NULL, arr, 4); break;
                 case u64: print<uintl>(NULL, arr, 4); break;
@@ -201,6 +203,7 @@ af_err af_print_array_gen(const char *exp, const af_array arr,
                 case b8: print<char>(exp, arr, precision); break;
                 case s32: print<int>(exp, arr, precision); break;
                 case u32: print<unsigned>(exp, arr, precision); break;
+                case s8: print<schar>(exp, arr, precision); break;
                 case u8: print<uchar>(exp, arr, precision); break;
                 case s64: print<intl>(exp, arr, precision); break;
                 case u64: print<uintl>(exp, arr, precision); break;
@@ -259,6 +262,9 @@ af_err af_array_to_string(char **output, const char *exp, const af_array arr,
                 case u32:
                     print<unsigned>(exp, arr, precision, ss, transpose);
                     break;
+                case s8:
+                    print<schar>(exp, arr, precision, ss, transpose);
+                    break;
                 case u8:
                     print<uchar>(exp, arr, precision, ss, transpose);
                     break;
diff --git a/src/api/c/random.cpp b/src/api/c/random.cpp
index 915e733974..6508786f53 100644
--- a/src/api/c/random.cpp
+++ b/src/api/c/random.cpp
@@ -42,6 +42,7 @@ using detail::createEmptyArray;
 using detail::createHostDataArray;
 using detail::intl;
 using detail::normalDistribution;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -296,6 +297,7 @@ af_err af_random_uniform(af_array *out, const unsigned ndims,
             case u64: result = uniformDistribution_<uintl>(d, e); break;
             case s16: result = uniformDistribution_<short>(d, e); break;
             case u16: result = uniformDistribution_<ushort>(d, e); break;
+            case s8: result = uniformDistribution_<schar>(d, e); break;
             case u8: result = uniformDistribution_<uchar>(d, e); break;
             case b8: result = uniformDistribution_<char>(d, e); break;
             case f16: result = uniformDistribution_<half>(d, e); break;
@@ -362,6 +364,7 @@ af_err af_randu(af_array *out, const unsigned ndims, const dim_t *const dims,
             case u64: result = uniformDistribution_<uintl>(d, e); break;
             case s16: result = uniformDistribution_<short>(d, e); break;
             case u16: result = uniformDistribution_<ushort>(d, e); break;
+            case s8: result = uniformDistribution_<schar>(d, e); break;
             case u8: result = uniformDistribution_<uchar>(d, e); break;
             case b8: result = uniformDistribution_<char>(d, e); break;
             case f16: result = uniformDistribution_<half>(d, e); break;
diff --git a/src/api/c/reduce.cpp b/src/api/c/reduce.cpp
index 15be8b39e8..65d3f85209 100644
--- a/src/api/c/reduce.cpp
+++ b/src/api/c/reduce.cpp
@@ -30,6 +30,7 @@ using detail::getScalar;
 using detail::imag;
 using detail::intl;
 using detail::real;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -107,6 +108,7 @@ static af_err reduce_type(af_array *out, const af_array in, const int dim) {
             case s16: res = reduce<op, short, To>(in, dim); break;
             case b8: res = reduce<op, char, To>(in, dim); break;
             case u8: res = reduce<op, uchar, To>(in, dim); break;
+            case s8: res = reduce<op, schar, To>(in, dim); break;
             case f16: res = reduce<op, half, To>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
@@ -171,6 +173,9 @@ static af_err reduce_by_key_type(af_array *keys_out, af_array *vals_out,
             case u8:
                 reduce_key<op, uchar, To>(keys_out, vals_out, keys, vals, dim);
                 break;
+            case s8:
+                reduce_key<op, schar, To>(keys_out, vals_out, keys, vals, dim);
+                break;
             case f16:
                 reduce_key<op, half, To>(keys_out, vals_out, keys, vals, dim);
                 break;
@@ -210,6 +215,7 @@ static af_err reduce_common(af_array *out, const af_array in, const int dim) {
             case s16: res = reduce<op, short, short>(in, dim); break;
             case b8: res = reduce<op, char, char>(in, dim); break;
             case u8: res = reduce<op, uchar, uchar>(in, dim); break;
+            case s8: res = reduce<op, schar, schar>(in, dim); break;
             case f16: res = reduce<op, half, half>(in, dim); break;
             default: TYPE_ERROR(1, type);
         }
@@ -281,6 +287,10 @@ static af_err reduce_by_key_common(af_array *keys_out, af_array *vals_out,
                 reduce_key<op, uchar, uchar>(keys_out, vals_out, keys, vals,
                                              dim);
                 break;
+            case s8:
+                reduce_key<op, schar, schar>(keys_out, vals_out, keys, vals,
+                                             dim);
+                break;
             case f16:
                 reduce_key<op, half, half>(keys_out, vals_out, keys, vals, dim);
                 break;
@@ -343,6 +353,9 @@ static af_err reduce_promote(af_array *out, const af_array in, const int dim,
             case u8:
                 res = reduce<op, uchar, uint>(in, dim, change_nan, nanval);
                 break;
+            case s8:
+                res = reduce<op, schar, int>(in, dim, change_nan, nanval);
+                break;
             case b8: {
                 if (op == af_mul_t) {
                     res = reduce<af_and_t, char, char>(in, dim, change_nan,
@@ -425,6 +438,10 @@ static af_err reduce_promote_by_key(af_array *keys_out, af_array *vals_out,
                 reduce_key<op, uchar, uint>(keys_out, vals_out, keys, vals, dim,
                                             change_nan, nanval);
                 break;
+            case s8:
+                reduce_key<op, schar, int>(keys_out, vals_out, keys, vals, dim,
+                                           change_nan, nanval);
+                break;
             case b8:
                 reduce_key<af_notzero_t, char, uint>(
                     keys_out, vals_out, keys, vals, dim, change_nan, nanval);
@@ -575,6 +592,7 @@ static af_err reduce_all_type(double *real, double *imag, const af_array in) {
             case s16: *real = reduce_all<op, short,   To>(in); break;
             case b8:  *real = reduce_all<op, char,    To>(in); break;
             case u8:  *real = reduce_all<op, uchar,   To>(in); break;
+            case s8:  *real = reduce_all<op, schar,   To>(in); break;
             case f16: *real = reduce_all<op, half,    To>(in); break;
             // clang-format on
             default: TYPE_ERROR(1, type);
@@ -606,6 +624,7 @@ static af_err reduce_all_type_array(af_array *out, const af_array in) {
             case s16: res = reduce_all_array<op, short,   To>(in); break;
             case b8:  res = reduce_all_array<op, char,    To>(in); break;
             case u8:  res = reduce_all_array<op, uchar,   To>(in); break;
+            case s8:  res = reduce_all_array<op, schar,   To>(in); break;
             case f16: res = reduce_all_array<op, half,    To>(in); break;
             // clang-format on
             default: TYPE_ERROR(1, type);
@@ -644,6 +663,7 @@ static af_err reduce_all_common(double *real_val, double *imag_val,
             case s16: *real_val = reduce_all<op, short,  short>(in); break;
             case b8:  *real_val = reduce_all<op, char,   char>(in); break;
             case u8:  *real_val = reduce_all<op, uchar,  uchar>(in); break;
+            case s8:  *real_val = reduce_all<op, schar,  schar>(in); break;
             case f16: *real_val = reduce_all<op, half,   half>(in); break;
             // clang-format on
             case c32:
@@ -689,6 +709,7 @@ static af_err reduce_all_common_array(af_array *out, const af_array in) {
             case s16: res = reduce_all_array<op, short,  short>(in); break;
             case b8:  res = reduce_all_array<op, char,   char>(in); break;
             case u8:  res = reduce_all_array<op, uchar,  uchar>(in); break;
+            case s8:  res = reduce_all_array<op, schar,  schar>(in); break;
             case f16: res = reduce_all_array<op, half,   half>(in); break;
             // clang-format on
             case c32: res = reduce_all_array<op, cfloat, cfloat>(in); break;
@@ -728,6 +749,7 @@ static af_err reduce_all_promote(double *real_val, double *imag_val,
             case u16: *real_val = reduce_all<op, ushort,   uint>(in, change_nan, nanval); break;
             case s16: *real_val = reduce_all<op, short,     int>(in, change_nan, nanval); break;
             case u8:  *real_val = reduce_all<op, uchar,    uint>(in, change_nan, nanval); break;
+            case s8:  *real_val = reduce_all<op, schar,     int>(in, change_nan, nanval); break;
             // clang-format on
             case b8: {
                 if (op == af_mul_t) {
@@ -813,6 +835,9 @@ static af_err reduce_all_promote_array(af_array *out, const af_array in,
             case u8:
                 res = reduce_all_array<op, uchar, uint>(in, change_nan, nanval);
                 break;
+            case s8:
+                res = reduce_all_array<op, schar, int>(in, change_nan, nanval);
+                break;
             case b8: {
                 if (op == af_mul_t) {
                     res = reduce_all_array<af_and_t, char, char>(in, change_nan,
@@ -953,6 +978,7 @@ static af_err ireduce_common(af_array *val, af_array *idx, const af_array in,
             case s16: ireduce<op, short>(&res, &loc, in, dim); break;
             case b8: ireduce<op, char>(&res, &loc, in, dim); break;
             case u8: ireduce<op, uchar>(&res, &loc, in, dim); break;
+            case s8: ireduce<op, schar>(&res, &loc, in, dim); break;
             case f16: ireduce<op, half>(&res, &loc, in, dim); break;
             default: TYPE_ERROR(1, type);
         }
@@ -1028,6 +1054,7 @@ static af_err rreduce_common(af_array *val, af_array *idx, const af_array in,
                 break;
             case b8: rreduce<op, char>(&res, &loc, in, dim, ragged_len); break;
             case u8: rreduce<op, uchar>(&res, &loc, in, dim, ragged_len); break;
+            case s8: rreduce<op, schar>(&res, &loc, in, dim, ragged_len); break;
             case f16: rreduce<op, half>(&res, &loc, in, dim, ragged_len); break;
             default: TYPE_ERROR(2, type);
         }
@@ -1086,6 +1113,7 @@ static af_err ireduce_all_common(double *real_val, double *imag_val,
                 break;
             case b8: *real_val = ireduce_all<op, char, double>(loc, in); break;
             case u8: *real_val = ireduce_all<op, uchar, double>(loc, in); break;
+            case s8: *real_val = ireduce_all<op, schar, double>(loc, in); break;
 
             case c32:
                 cfval = ireduce_all<op, cfloat>(loc, in);
diff --git a/src/api/c/reorder.cpp b/src/api/c/reorder.cpp
index 556e1f0e20..e29fb621c0 100644
--- a/src/api/c/reorder.cpp
+++ b/src/api/c/reorder.cpp
@@ -25,6 +25,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -108,6 +109,7 @@ af_err af_reorder(af_array *out, const af_array in, const af::dim4 &rdims) {
             case b8: output = reorder<char>(in, rdims); break;
             case s32: output = reorder<int>(in, rdims); break;
             case u32: output = reorder<uint>(in, rdims); break;
+            case s8: output = reorder<schar>(in, rdims); break;
             case u8: output = reorder<uchar>(in, rdims); break;
             case s64: output = reorder<intl>(in, rdims); break;
             case u64: output = reorder<uintl>(in, rdims); break;
diff --git a/src/api/c/replace.cpp b/src/api/c/replace.cpp
index b8fdd75e02..7bf66cc439 100644
--- a/src/api/c/replace.cpp
+++ b/src/api/c/replace.cpp
@@ -27,6 +27,7 @@ using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::select_scalar;
 using detail::uchar;
 using detail::uint;
@@ -74,6 +75,7 @@ af_err af_replace(af_array a, const af_array cond, const af_array b) {
             case u64: replace<uintl>(a, cond, b); break;
             case s16: replace<short>(a, cond, b); break;
             case u16: replace<ushort>(a, cond, b); break;
+            case s8: replace<schar>(a, cond, b); break;
             case u8: replace<uchar>(a, cond, b); break;
             case b8: replace<char>(a, cond, b); break;
             default: TYPE_ERROR(2, ainfo.getType());
@@ -116,6 +118,7 @@ af_err replaceScalar(af_array a, const af_array cond, const ScalarType b) {
             case u64: replace_scalar<uintl>(a, cond, b); break;
             case s16: replace_scalar<short>(a, cond, b); break;
             case u16: replace_scalar<ushort>(a, cond, b); break;
+            case s8: replace_scalar<schar>(a, cond, b); break;
             case u8: replace_scalar<uchar>(a, cond, b); break;
             case b8: replace_scalar<char>(a, cond, b); break;
             default: TYPE_ERROR(2, ainfo.getType());
diff --git a/src/api/c/resize.cpp b/src/api/c/resize.cpp
index 8b6df743da..814d4df0c8 100644
--- a/src/api/c/resize.cpp
+++ b/src/api/c/resize.cpp
@@ -19,6 +19,7 @@
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -68,6 +69,7 @@ af_err af_resize(af_array* out, const af_array in, const dim_t odim0,
             case u64: output = resize<uintl>(in, odim0, odim1, method); break;
             case s16: output = resize<short>(in, odim0, odim1, method); break;
             case u16: output = resize<ushort>(in, odim0, odim1, method); break;
+            case s8: output = resize<schar>(in, odim0, odim1, method); break;
             case u8: output = resize<uchar>(in, odim0, odim1, method); break;
             case b8: output = resize<char>(in, odim0, odim1, method); break;
             default: TYPE_ERROR(1, type);
diff --git a/src/api/c/rgb_gray.cpp b/src/api/c/rgb_gray.cpp
index 3bea06e855..c7abe042bc 100644
--- a/src/api/c/rgb_gray.cpp
+++ b/src/api/c/rgb_gray.cpp
@@ -30,6 +30,7 @@ using detail::createEmptyArray;
 using detail::createValueArray;
 using detail::join;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -157,6 +158,9 @@ af_err convert(af_array* out, const af_array in, const float r, const float g,
             case u8:
                 output = convert<uchar, float, isRGB2GRAY>(in, r, g, b);
                 break;
+            case s8:
+                output = convert<schar, float, isRGB2GRAY>(in, r, g, b);
+                break;
             default: TYPE_ERROR(1, iType); break;
         }
         std::swap(*out, output);
diff --git a/src/api/c/rotate.cpp b/src/api/c/rotate.cpp
index 762f77d7f4..50397a310a 100644
--- a/src/api/c/rotate.cpp
+++ b/src/api/c/rotate.cpp
@@ -19,6 +19,7 @@ using af::dim4;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -76,6 +77,7 @@ af_err af_rotate(af_array *out, const af_array in, const float theta,
             case u64: output = rotate<uintl>(in, theta, odims, method); break;
             case s16: output = rotate<short>(in, theta, odims, method); break;
             case u16: output = rotate<ushort>(in, theta, odims, method); break;
+            case s8: output = rotate<schar>(in, theta, odims, method); break;
             case u8:
             case b8: output = rotate<uchar>(in, theta, odims, method); break;
             default: TYPE_ERROR(1, itype);
diff --git a/src/api/c/sat.cpp b/src/api/c/sat.cpp
index 3ff72abacc..8715f4865c 100644
--- a/src/api/c/sat.cpp
+++ b/src/api/c/sat.cpp
@@ -18,6 +18,7 @@ using arrayfire::common::integralImage;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -44,6 +45,7 @@ af_err af_sat(af_array* out, const af_array in) {
             case s32: output = sat<int, int>(in); break;
             case u32: output = sat<uint, uint>(in); break;
             case b8: output = sat<int, char>(in); break;
+            case s8: output = sat<int, schar>(in); break;
             case u8: output = sat<uint, uchar>(in); break;
             case s64: output = sat<intl, intl>(in); break;
             case u64: output = sat<uintl, uintl>(in); break;
diff --git a/src/api/c/scan.cpp b/src/api/c/scan.cpp
index d8a3a7a95d..cac89d6c01 100644
--- a/src/api/c/scan.cpp
+++ b/src/api/c/scan.cpp
@@ -21,6 +21,7 @@
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -141,6 +142,7 @@ af_err af_accum(af_array* out, const af_array in, const int dim) {
             case u16: res = scan<af_add_t, ushort, uint>(in, dim); break;
             case s16: res = scan<af_add_t, short, int>(in, dim); break;
             case u8: res = scan<af_add_t, uchar, uint>(in, dim); break;
+            case s8: res = scan<af_add_t, schar, int>(in, dim); break;
             // Make sure you are adding only "1" for every non zero value, even
             // if op == af_add_t
             case b8: res = scan<af_notzero_t, char, uint>(in, dim); break;
@@ -204,6 +206,9 @@ af_err af_scan(af_array* out, const af_array in, const int dim, af_binary_op op,
             case u8:
                 res = scan_op<uchar, uint>(in, dim, op, inclusive_scan);
                 break;
+            case s8:
+                res = scan_op<schar, int>(in, dim, op, inclusive_scan);
+                break;
             case b8:
                 res = scan_op<char, uint>(in, dim, op, inclusive_scan);
                 break;
@@ -252,6 +257,7 @@ af_err af_scan_by_key(af_array* out, const af_array key, const af_array in,
                 break;
             case s16:
             case s32:
+            case s8:
                 res = scan_op<int, int>(key, in, dim, op, inclusive_scan);
                 break;
             case u64:
diff --git a/src/api/c/select.cpp b/src/api/c/select.cpp
index dec47166e7..c161aa5e9b 100644
--- a/src/api/c/select.cpp
+++ b/src/api/c/select.cpp
@@ -26,6 +26,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createSelectNode;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -76,6 +77,7 @@ af_err af_select(af_array* out, const af_array cond, const af_array a,
             case u64: res = select<uintl>(cond, a, b, odims); break;
             case s16: res = select<short>(cond, a, b, odims); break;
             case u16: res = select<ushort>(cond, a, b, odims); break;
+            case s8: res = select<schar>(cond, a, b, odims); break;
             case u8: res = select<uchar>(cond, a, b, odims); break;
             case b8: res = select<char>(cond, a, b, odims); break;
             case f16: res = select<half>(cond, a, b, odims); break;
@@ -163,6 +165,10 @@ af_err selectScalar(af_array* out, const af_array cond, const af_array e,
                 res = select_scalar<uintl, ScalarType, IsScalarTrueOutput>(
                     cond, e, c, odims);
                 break;
+            case s8:
+                res = select_scalar<schar, ScalarType, IsScalarTrueOutput>(
+                    cond, e, c, odims);
+                break;
             case u8:
                 res = select_scalar<uchar, ScalarType, IsScalarTrueOutput>(
                     cond, e, c, odims);
diff --git a/src/api/c/set.cpp b/src/api/c/set.cpp
index bf8b66e3c8..3353d7c5ee 100644
--- a/src/api/c/set.cpp
+++ b/src/api/c/set.cpp
@@ -18,6 +18,7 @@
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -51,6 +52,7 @@ af_err af_set_unique(af_array* out, const af_array in, const bool is_sorted) {
             case s64: res = setUnique<intl>(in, is_sorted); break;
             case u64: res = setUnique<uintl>(in, is_sorted); break;
             case b8: res = setUnique<char>(in, is_sorted); break;
+            case s8: res = setUnique<schar>(in, is_sorted); break;
             case u8: res = setUnique<uchar>(in, is_sorted); break;
             default: TYPE_ERROR(1, type);
         }
@@ -98,6 +100,7 @@ af_err af_set_union(af_array* out, const af_array first, const af_array second,
             case s64: res = setUnion<intl>(first, second, is_unique); break;
             case u64: res = setUnion<uintl>(first, second, is_unique); break;
             case b8: res = setUnion<char>(first, second, is_unique); break;
+            case s8: res = setUnion<schar>(first, second, is_unique); break;
             case u8: res = setUnion<uchar>(first, second, is_unique); break;
             default: TYPE_ERROR(1, first_type);
         }
@@ -156,6 +159,7 @@ af_err af_set_intersect(af_array* out, const af_array first,
                 res = setIntersect<uintl>(first, second, is_unique);
                 break;
             case b8: res = setIntersect<char>(first, second, is_unique); break;
+            case s8: res = setIntersect<schar>(first, second, is_unique); break;
             case u8: res = setIntersect<uchar>(first, second, is_unique); break;
             default: TYPE_ERROR(1, first_type);
         }
diff --git a/src/api/c/shift.cpp b/src/api/c/shift.cpp
index 42052fbfbc..cf195d2026 100644
--- a/src/api/c/shift.cpp
+++ b/src/api/c/shift.cpp
@@ -17,6 +17,7 @@
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -49,6 +50,7 @@ af_err af_shift(af_array *out, const af_array in, const int sdims[4]) {
             case u64: output = shift<uintl>(in, sdims); break;
             case s16: output = shift<short>(in, sdims); break;
             case u16: output = shift<ushort>(in, sdims); break;
+            case s8: output = shift<schar>(in, sdims); break;
             case u8: output = shift<uchar>(in, sdims); break;
             default: TYPE_ERROR(1, type);
         }
diff --git a/src/api/c/sobel.cpp b/src/api/c/sobel.cpp
index 6184d5502a..d466db1617 100644
--- a/src/api/c/sobel.cpp
+++ b/src/api/c/sobel.cpp
@@ -21,6 +21,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -66,6 +67,9 @@ af_err af_sobel_operator(af_array *dx, af_array *dy, const af_array img,
                 output = sobelDerivatives<ushort, int>(img, ker_size);
                 break;
             case b8: output = sobelDerivatives<char, int>(img, ker_size); break;
+            case s8:
+                output = sobelDerivatives<schar, int>(img, ker_size);
+                break;
             case u8:
                 output = sobelDerivatives<uchar, int>(img, ker_size);
                 break;
diff --git a/src/api/c/sort.cpp b/src/api/c/sort.cpp
index 4ec1c0a466..b917b8b3c5 100644
--- a/src/api/c/sort.cpp
+++ b/src/api/c/sort.cpp
@@ -27,6 +27,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createEmptyArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -59,6 +60,7 @@ af_err af_sort(af_array *out, const af_array in, const unsigned dim,
             case u16: val = sort<ushort>(in, dim, isAscending); break;
             case s64: val = sort<intl>(in, dim, isAscending); break;
             case u64: val = sort<uintl>(in, dim, isAscending); break;
+            case s8: val = sort<schar>(in, dim, isAscending); break;
             case u8: val = sort<uchar>(in, dim, isAscending); break;
             case b8: val = sort<char>(in, dim, isAscending); break;
             default: TYPE_ERROR(1, type);
@@ -118,6 +120,7 @@ af_err af_sort_index(af_array *out, af_array *indices, const af_array in,
             case u64:
                 sort_index<uintl>(&val, &idx, in, dim, isAscending);
                 break;
+            case s8: sort_index<schar>(&val, &idx, in, dim, isAscending); break;
             case u8: sort_index<uchar>(&val, &idx, in, dim, isAscending); break;
             case b8: sort_index<char>(&val, &idx, in, dim, isAscending); break;
             default: TYPE_ERROR(1, type);
@@ -185,6 +188,9 @@ void sort_by_key_tmplt(af_array *okey, af_array *oval, const af_array ikey,
         case u64:
             sort_by_key<Tk, uintl>(okey, oval, ikey, ival, dim, isAscending);
             break;
+        case s8:
+            sort_by_key<Tk, schar>(okey, oval, ikey, ival, dim, isAscending);
+            break;
         case u8:
             sort_by_key<Tk, uchar>(okey, oval, ikey, ival, dim, isAscending);
             break;
@@ -249,6 +255,10 @@ af_err af_sort_by_key(af_array *out_keys, af_array *out_values,
                 sort_by_key_tmplt<uintl>(&oKey, &oVal, keys, values, dim,
                                          isAscending);
                 break;
+            case s8:
+                sort_by_key_tmplt<schar>(&oKey, &oVal, keys, values, dim,
+                                         isAscending);
+                break;
             case u8:
                 sort_by_key_tmplt<uchar>(&oKey, &oVal, keys, values, dim,
                                          isAscending);
diff --git a/src/api/c/stdev.cpp b/src/api/c/stdev.cpp
index 7f64bf3355..d5589f4d39 100644
--- a/src/api/c/stdev.cpp
+++ b/src/api/c/stdev.cpp
@@ -38,6 +38,7 @@ using detail::mean;
 using detail::reduce;
 using detail::reduce_all;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -111,6 +112,7 @@ af_err af_stdev_all_v2(double* realVal, double* imagVal, const af_array in,
             case u16: *realVal = stdev<ushort, float>(in, bias); break;
             case s64: *realVal = stdev<intl, double>(in, bias); break;
             case u64: *realVal = stdev<uintl, double>(in, bias); break;
+            case s8: *realVal = stdev<schar, float>(in, bias); break;
             case u8: *realVal = stdev<uchar, float>(in, bias); break;
             case b8: *realVal = stdev<char, float>(in, bias); break;
             // TODO(umar): FIXME: sqrt(complex) is not present in cuda/opencl
@@ -152,6 +154,7 @@ af_err af_stdev_v2(af_array* out, const af_array in, const af_var_bias bias,
             case u16: output = stdev<ushort, float>(in, dim, bias); break;
             case s64: output = stdev<intl, double>(in, dim, bias); break;
             case u64: output = stdev<uintl, double>(in, dim, bias); break;
+            case s8: output = stdev<schar, float>(in, dim, bias); break;
             case u8: output = stdev<uchar, float>(in, dim, bias); break;
             case b8: output = stdev<char, float>(in, dim, bias); break;
             // TODO(umar): FIXME: sqrt(complex) is not present in cuda/opencl
diff --git a/src/api/c/stream.cpp b/src/api/c/stream.cpp
index 1be207c66d..45265e69b5 100644
--- a/src/api/c/stream.cpp
+++ b/src/api/c/stream.cpp
@@ -28,6 +28,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::createHostDataArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -141,6 +142,7 @@ af_err af_save_array(int *index, const char *key, const af_array arr,
             case b8: id = save<char>(key, arr, filename, append); break;
             case s32: id = save<int>(key, arr, filename, append); break;
             case u32: id = save<unsigned>(key, arr, filename, append); break;
+            case s8: id = save<schar>(key, arr, filename, append); break;
             case u8: id = save<uchar>(key, arr, filename, append); break;
             case s64: id = save<intl>(key, arr, filename, append); break;
             case u64: id = save<uintl>(key, arr, filename, append); break;
@@ -240,6 +242,7 @@ static af_array readArrayV1(const char *filename, const unsigned index) {
         case b8: out = readDataToArray<char>(fs); break;
         case s32: out = readDataToArray<int>(fs); break;
         case u32: out = readDataToArray<uint>(fs); break;
+        case s8: out = readDataToArray<schar>(fs); break;
         case u8: out = readDataToArray<uchar>(fs); break;
         case s64: out = readDataToArray<intl>(fs); break;
         case u64: out = readDataToArray<uintl>(fs); break;
diff --git a/src/api/c/surface.cpp b/src/api/c/surface.cpp
index b2a6404a33..d748677269 100644
--- a/src/api/c/surface.cpp
+++ b/src/api/c/surface.cpp
@@ -38,6 +38,7 @@ using detail::createEmptyArray;
 using detail::forgeManager;
 using detail::getScalar;
 using detail::reduce_all;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -190,6 +191,9 @@ af_err af_draw_surface(const af_window window, const af_array xVals,
             case u16:
                 chart = setup_surface<ushort>(window, xVals, yVals, S, props);
                 break;
+            case s8:
+                chart = setup_surface<schar>(window, xVals, yVals, S, props);
+                break;
             case u8:
                 chart = setup_surface<uchar>(window, xVals, yVals, S, props);
                 break;
diff --git a/src/api/c/susan.cpp b/src/api/c/susan.cpp
index 0621f7eb16..8ea7dc8945 100644
--- a/src/api/c/susan.cpp
+++ b/src/api/c/susan.cpp
@@ -24,6 +24,7 @@ using detail::cfloat;
 using detail::createEmptyArray;
 using detail::createValueArray;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::ushort;
@@ -98,6 +99,10 @@ af_err af_susan(af_features* out, const af_array in, const unsigned radius,
                 *out = susan<ushort>(in, radius, diff_thr, geom_thr,
                                      feature_ratio, edge);
                 break;
+            case s8:
+                *out = susan<schar>(in, radius, diff_thr, geom_thr,
+                                    feature_ratio, edge);
+                break;
             case u8:
                 *out = susan<uchar>(in, radius, diff_thr, geom_thr,
                                     feature_ratio, edge);
diff --git a/src/api/c/tile.cpp b/src/api/c/tile.cpp
index ce512e9958..2a50f12c43 100644
--- a/src/api/c/tile.cpp
+++ b/src/api/c/tile.cpp
@@ -26,6 +26,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -60,6 +61,7 @@ af_err af_tile(af_array *out, const af_array in, const af::dim4 &tileDims) {
             case u64: output = tile<uintl>(in, tileDims); break;
             case s16: output = tile<short>(in, tileDims); break;
             case u16: output = tile<ushort>(in, tileDims); break;
+            case s8: output = tile<schar>(in, tileDims); break;
             case u8: output = tile<uchar>(in, tileDims); break;
             case f16: output = tile<half>(in, tileDims); break;
             default: TYPE_ERROR(1, type);
diff --git a/src/api/c/transform.cpp b/src/api/c/transform.cpp
index 9bdaceb149..259d13840e 100644
--- a/src/api/c/transform.cpp
+++ b/src/api/c/transform.cpp
@@ -19,6 +19,7 @@ using af::dim4;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -158,6 +159,7 @@ void af_transform_common(af_array *out, const af_array in, const af_array tf,
     case u64: transform<uintl  >(out, in, tf, method, inverse, perspective);  break;
     case s16: transform<short  >(out, in, tf, method, inverse, perspective);  break;
     case u16: transform<ushort >(out, in, tf, method, inverse, perspective);  break;
+    case s8:  transform<schar  >(out, in, tf, method, inverse, perspective);  break;
     case u8:  transform<uchar  >(out, in, tf, method, inverse, perspective);  break;
     case b8:  transform<char   >(out, in, tf, method, inverse, perspective);  break;
     default:  TYPE_ERROR(1, itype);
diff --git a/src/api/c/transpose.cpp b/src/api/c/transpose.cpp
index 82ae18fef2..9d2fd48cbd 100644
--- a/src/api/c/transpose.cpp
+++ b/src/api/c/transpose.cpp
@@ -24,6 +24,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -67,6 +68,7 @@ af_err af_transpose(af_array* out, af_array in, const bool conjugate) {
             case b8: output = trs<char>(in, conjugate); break;
             case s32: output = trs<int>(in, conjugate); break;
             case u32: output = trs<uint>(in, conjugate); break;
+            case s8: output = trs<schar>(in, conjugate); break;
             case u8: output = trs<uchar>(in, conjugate); break;
             case s64: output = trs<intl>(in, conjugate); break;
             case u64: output = trs<uintl>(in, conjugate); break;
@@ -107,6 +109,7 @@ af_err af_transpose_inplace(af_array in, const bool conjugate) {
             case b8: transpose_inplace<char>(in, conjugate); break;
             case s32: transpose_inplace<int>(in, conjugate); break;
             case u32: transpose_inplace<uint>(in, conjugate); break;
+            case s8: transpose_inplace<schar>(in, conjugate); break;
             case u8: transpose_inplace<uchar>(in, conjugate); break;
             case s64: transpose_inplace<intl>(in, conjugate); break;
             case u64: transpose_inplace<uintl>(in, conjugate); break;
diff --git a/src/api/c/type_util.cpp b/src/api/c/type_util.cpp
index c78b85b1da..d409c0d868 100644
--- a/src/api/c/type_util.cpp
+++ b/src/api/c/type_util.cpp
@@ -20,6 +20,7 @@ size_t size_of(af_dtype type) {
             case f64: return sizeof(double);
             case s32: return sizeof(int);
             case u32: return sizeof(unsigned);
+            case s8: return sizeof(signed char);
             case u8: return sizeof(unsigned char);
             case b8: return sizeof(unsigned char);
             case c32: return sizeof(float) * 2;
diff --git a/src/api/c/type_util.hpp b/src/api/c/type_util.hpp
index 4214882492..8e6a7ff9cf 100644
--- a/src/api/c/type_util.hpp
+++ b/src/api/c/type_util.hpp
@@ -16,6 +16,11 @@ struct ToNum {
     inline T operator()(T val) { return val; }
 };
 
+template<>
+struct ToNum<signed char> {
+    inline int operator()(signed char val) { return static_cast<int>(val); }
+};
+
 template<>
 struct ToNum<unsigned char> {
     inline int operator()(unsigned char val) { return static_cast<int>(val); }
diff --git a/src/api/c/unary.cpp b/src/api/c/unary.cpp
index 6d8b584ace..505c831e74 100644
--- a/src/api/c/unary.cpp
+++ b/src/api/c/unary.cpp
@@ -43,6 +43,7 @@ using detail::intl;
 using detail::logicOp;
 using detail::real;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -598,6 +599,7 @@ af_err af_bitnot(af_array *out, const af_array in) {
         switch (type) {
             case s32: res = bitOpNot<int>(in); break;
             case u32: res = bitOpNot<uint>(in); break;
+            case s8: res = bitOpNot<schar>(in); break;
             case u8: res = bitOpNot<uchar>(in); break;
             case b8: res = bitOpNot<char>(in); break;
             case s64: res = bitOpNot<intl>(in); break;
diff --git a/src/api/c/unwrap.cpp b/src/api/c/unwrap.cpp
index ee0ac2a16e..6f09a6b7eb 100644
--- a/src/api/c/unwrap.cpp
+++ b/src/api/c/unwrap.cpp
@@ -20,6 +20,7 @@ using detail::Array;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -81,6 +82,9 @@ af_err af_unwrap(af_array* out, const af_array in, const dim_t wx,
             case u16:
                 output = unwrap<ushort>(in, wx, wy, sx, sy, px, py, is_column);
                 break;
+            case s8:
+                output = unwrap<schar>(in, wx, wy, sx, sy, px, py, is_column);
+                break;
             case u8:
                 output = unwrap<uchar>(in, wx, wy, sx, sy, px, py, is_column);
                 break;
diff --git a/src/api/c/var.cpp b/src/api/c/var.cpp
index c82c1ca0cd..64a5d8f693 100644
--- a/src/api/c/var.cpp
+++ b/src/api/c/var.cpp
@@ -43,6 +43,7 @@ using detail::real;
 using detail::reduce;
 using detail::reduce_all;
 using detail::scalar;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -225,6 +226,9 @@ af_err af_var_v2(af_array* out, const af_array in, const af_var_bias bias,
             case u64:
                 output = var_<uintl, double>(in, no_weights, bias, dim);
                 break;
+            case s8:
+                output = var_<schar, float>(in, no_weights, bias, dim);
+                break;
             case u8:
                 output = var_<uchar, float>(in, no_weights, bias, dim);
                 break;
@@ -298,6 +302,10 @@ af_err af_var_weighted(af_array* out, const af_array in, const af_array weights,
                 output = var_<uintl, double>(in, weights,
                                              AF_VARIANCE_POPULATION, dim);
                 break;
+            case s8:
+                output = var_<schar, float>(in, weights, AF_VARIANCE_POPULATION,
+                                            dim);
+                break;
             case u8:
                 output = var_<uchar, float>(in, weights, AF_VARIANCE_POPULATION,
                                             dim);
@@ -347,6 +355,7 @@ af_err af_var_all_v2(double* realVal, double* imagVal, const af_array in,
             case u16: *realVal = varAll<ushort, float>(in, bias); break;
             case s64: *realVal = varAll<intl, double>(in, bias); break;
             case u64: *realVal = varAll<uintl, double>(in, bias); break;
+            case s8: *realVal = varAll<schar, float>(in, bias); break;
             case u8: *realVal = varAll<uchar, float>(in, bias); break;
             case b8: *realVal = varAll<char, float>(in, bias); break;
             case f16: *realVal = varAll<half, float>(in, bias); break;
@@ -390,6 +399,7 @@ af_err af_var_all_weighted(double* realVal, double* imagVal, const af_array in,
             case u16: *realVal = varAll<ushort, float>(in, weights); break;
             case s64: *realVal = varAll<intl, double>(in, weights); break;
             case u64: *realVal = varAll<uintl, double>(in, weights); break;
+            case s8: *realVal = varAll<schar, float>(in, weights); break;
             case u8: *realVal = varAll<uchar, float>(in, weights); break;
             case b8: *realVal = varAll<char, float>(in, weights); break;
             case f16: *realVal = varAll<half, float>(in, weights); break;
@@ -453,6 +463,10 @@ af_err af_meanvar(af_array* mean, af_array* var, const af_array in,
                 tie(*mean, *var) =
                     meanvar<uintl, double>(in, weights, bias, dim);
                 break;
+            case s8:
+                tie(*mean, *var) =
+                    meanvar<schar, float>(in, weights, bias, dim);
+                break;
             case u8:
                 tie(*mean, *var) =
                     meanvar<uchar, float>(in, weights, bias, dim);
diff --git a/src/api/c/vector_field.cpp b/src/api/c/vector_field.cpp
index 701db6fc12..9eba21811c 100644
--- a/src/api/c/vector_field.cpp
+++ b/src/api/c/vector_field.cpp
@@ -35,6 +35,7 @@ using detail::copy_vector_field;
 using detail::createEmptyArray;
 using detail::forgeManager;
 using detail::reduce;
+using detail::schar;
 using detail::transpose;
 using detail::uchar;
 using detail::uint;
@@ -183,6 +184,9 @@ af_err vectorFieldWrapper(const af_window window, const af_array points,
             case u16:
                 chart = setup_vector_field<ushort>(window, pnts, dirs, props);
                 break;
+            case s8:
+                chart = setup_vector_field<schar>(window, pnts, dirs, props);
+                break;
             case u8:
                 chart = setup_vector_field<uchar>(window, pnts, dirs, props);
                 break;
@@ -289,6 +293,10 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
                 chart = setup_vector_field<ushort>(window, points, directions,
                                                    props);
                 break;
+            case s8:
+                chart = setup_vector_field<schar>(window, points, directions,
+                                                  props);
+                break;
             case u8:
                 chart = setup_vector_field<uchar>(window, points, directions,
                                                   props);
@@ -383,6 +391,10 @@ af_err vectorFieldWrapper(const af_window window, const af_array xPoints,
                 chart = setup_vector_field<ushort>(window, points, directions,
                                                    props);
                 break;
+            case s8:
+                chart = setup_vector_field<schar>(window, points, directions,
+                                                  props);
+                break;
             case u8:
                 chart = setup_vector_field<uchar>(window, points, directions,
                                                   props);
diff --git a/src/api/c/where.cpp b/src/api/c/where.cpp
index 4aeb7b60ba..6f83aed17d 100644
--- a/src/api/c/where.cpp
+++ b/src/api/c/where.cpp
@@ -18,6 +18,7 @@
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -51,6 +52,7 @@ af_err af_where(af_array* idx, const af_array in) {
             case u64: res = where<uintl>(in); break;
             case s16: res = where<short>(in); break;
             case u16: res = where<ushort>(in); break;
+            case s8: res = where<schar>(in); break;
             case u8: res = where<uchar>(in); break;
             case b8: res = where<char>(in); break;
             default: TYPE_ERROR(1, type);
diff --git a/src/api/c/wrap.cpp b/src/api/c/wrap.cpp
index f436f37350..e3c06a4642 100644
--- a/src/api/c/wrap.cpp
+++ b/src/api/c/wrap.cpp
@@ -19,6 +19,7 @@ using af::dim4;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -75,6 +76,7 @@ void af_wrap_common(af_array* out, const af_array in, const dim_t ox,
         case u64: wrap<uintl  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
         case s16: wrap<short  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
         case u16: wrap<ushort >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
+        case s8:  wrap<schar  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
         case u8:  wrap<uchar  >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
         case b8:  wrap<char   >(out, in, wx, wy, sx, sy, px, py, is_column);  break;
         default:  TYPE_ERROR(1, in_type);
diff --git a/src/api/cpp/array.cpp b/src/api/cpp/array.cpp
index 1d61c63c2d..418d94c52b 100644
--- a/src/api/cpp/array.cpp
+++ b/src/api/cpp/array.cpp
@@ -236,6 +236,7 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 INSTANTIATE(unsigned)
 INSTANTIATE(int)
+INSTANTIATE(signed char)
 INSTANTIATE(unsigned char)
 INSTANTIATE(char)
 INSTANTIATE(long long)
@@ -701,6 +702,7 @@ MEM_FUNC(af_array, get)
     ASSIGN_TYPE(long long, OP)          \
     ASSIGN_TYPE(unsigned long long, OP) \
     ASSIGN_TYPE(char, OP)               \
+    ASSIGN_TYPE(signed char, OP)        \
     ASSIGN_TYPE(unsigned char, OP)      \
     ASSIGN_TYPE(bool, OP)               \
     ASSIGN_TYPE(short, OP)              \
@@ -828,6 +830,7 @@ array &array::operator=(const array &other) {
     ASSIGN_TYPE(long long, OP)                                    \
     ASSIGN_TYPE(unsigned long long, OP)                           \
     ASSIGN_TYPE(char, OP)                                         \
+    ASSIGN_TYPE(signed char, OP)                                  \
     ASSIGN_TYPE(unsigned char, OP)                                \
     ASSIGN_TYPE(bool, OP)                                         \
     ASSIGN_TYPE(short, OP)                                        \
@@ -863,6 +866,7 @@ ASSIGN_OP(/=, af_div)
     ASSIGN_TYPE(long long, OP)          \
     ASSIGN_TYPE(unsigned long long, OP) \
     ASSIGN_TYPE(char, OP)               \
+    ASSIGN_TYPE(signed char, OP)        \
     ASSIGN_TYPE(unsigned char, OP)      \
     ASSIGN_TYPE(bool, OP)               \
     ASSIGN_TYPE(short, OP)              \
@@ -939,6 +943,7 @@ af::dtype implicit_dtype(af::dtype scalar_type, af::dtype array_type) {
     BINARY_TYPE(long long, OP, release_func, s64)                      \
     BINARY_TYPE(unsigned long long, OP, release_func, u64)             \
     BINARY_TYPE(char, OP, release_func, b8)                            \
+    BINARY_TYPE(signed char, OP, release_func, s8)                     \
     BINARY_TYPE(unsigned char, OP, release_func, u8)                   \
     BINARY_TYPE(bool, OP, release_func, b8)                            \
     BINARY_TYPE(short, OP, release_func, s16)                          \
@@ -1038,6 +1043,7 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 INSTANTIATE(unsigned)
 INSTANTIATE(int)
+INSTANTIATE(signed char)
 INSTANTIATE(unsigned char)
 INSTANTIATE(char)
 INSTANTIATE(long long)
@@ -1080,6 +1086,7 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 INSTANTIATE(unsigned)
 INSTANTIATE(int)
+INSTANTIATE(signed char)
 INSTANTIATE(unsigned char)
 INSTANTIATE(char)
 INSTANTIATE(long long)
diff --git a/src/api/cpp/corrcoef.cpp b/src/api/cpp/corrcoef.cpp
index f90be68b5f..dbedad5aee 100644
--- a/src/api/cpp/corrcoef.cpp
+++ b/src/api/cpp/corrcoef.cpp
@@ -26,6 +26,7 @@ INSTANTIATE_CORRCOEF(double);
 INSTANTIATE_CORRCOEF(int);
 INSTANTIATE_CORRCOEF(unsigned int);
 INSTANTIATE_CORRCOEF(char);
+INSTANTIATE_CORRCOEF(signed char);
 INSTANTIATE_CORRCOEF(unsigned char);
 INSTANTIATE_CORRCOEF(long long);
 INSTANTIATE_CORRCOEF(unsigned long long);
diff --git a/src/api/cpp/data.cpp b/src/api/cpp/data.cpp
index 3f86520bd0..f5eb8c2544 100644
--- a/src/api/cpp/data.cpp
+++ b/src/api/cpp/data.cpp
@@ -130,6 +130,7 @@ CONSTANT(float);
 CONSTANT(int);
 CONSTANT(unsigned);
 CONSTANT(char);
+CONSTANT(signed char);
 CONSTANT(unsigned char);
 CONSTANT(cfloat);
 CONSTANT(cdouble);
diff --git a/src/api/cpp/device.cpp b/src/api/cpp/device.cpp
index 89aab84754..b62589097e 100644
--- a/src/api/cpp/device.cpp
+++ b/src/api/cpp/device.cpp
@@ -192,6 +192,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(unsigned)
+INSTANTIATE(signed char)
 INSTANTIATE(unsigned char)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/api/cpp/mean.cpp b/src/api/cpp/mean.cpp
index c03a83fa51..61693ca40d 100644
--- a/src/api/cpp/mean.cpp
+++ b/src/api/cpp/mean.cpp
@@ -81,6 +81,7 @@ INSTANTIATE_MEAN(double);
 INSTANTIATE_MEAN(int);
 INSTANTIATE_MEAN(unsigned int);
 INSTANTIATE_MEAN(char);
+INSTANTIATE_MEAN(signed char);
 INSTANTIATE_MEAN(unsigned char);
 INSTANTIATE_MEAN(long long);
 INSTANTIATE_MEAN(unsigned long long);
diff --git a/src/api/cpp/median.cpp b/src/api/cpp/median.cpp
index 5f4b88fb2a..b288df74a9 100644
--- a/src/api/cpp/median.cpp
+++ b/src/api/cpp/median.cpp
@@ -27,6 +27,7 @@ INSTANTIATE_MEDIAN(double);
 INSTANTIATE_MEDIAN(int);
 INSTANTIATE_MEDIAN(unsigned int);
 INSTANTIATE_MEDIAN(char);
+INSTANTIATE_MEDIAN(signed char);
 INSTANTIATE_MEDIAN(unsigned char);
 INSTANTIATE_MEDIAN(long long);
 INSTANTIATE_MEDIAN(unsigned long long);
diff --git a/src/api/cpp/reduce.cpp b/src/api/cpp/reduce.cpp
index cfdadf85ae..8dc47fcab9 100644
--- a/src/api/cpp/reduce.cpp
+++ b/src/api/cpp/reduce.cpp
@@ -191,6 +191,7 @@ void max(array &val, array &idx, const array &in, const int dim) {
     INSTANTIATE_REAL(fnC, fnCPP, short)              \
     INSTANTIATE_REAL(fnC, fnCPP, unsigned short)     \
     INSTANTIATE_REAL(fnC, fnCPP, char)               \
+    INSTANTIATE_REAL(fnC, fnCPP, signed char)        \
     INSTANTIATE_REAL(fnC, fnCPP, unsigned char)      \
     INSTANTIATE_CPLX(fnC, fnCPP, af_cfloat, float)   \
     INSTANTIATE_CPLX(fnC, fnCPP, af_cdouble, double)
@@ -294,6 +295,7 @@ INSTANTIATE(product_nan, product)
     INSTANTIATE_COMPAT(fnCPP, fnCompat, long long)          \
     INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned long long) \
     INSTANTIATE_COMPAT(fnCPP, fnCompat, char)               \
+    INSTANTIATE_COMPAT(fnCPP, fnCompat, signed char)        \
     INSTANTIATE_COMPAT(fnCPP, fnCompat, unsigned char)      \
     INSTANTIATE_COMPAT(fnCPP, fnCompat, af_cfloat)          \
     INSTANTIATE_COMPAT(fnCPP, fnCompat, af_cdouble)         \
@@ -332,6 +334,7 @@ INSTANTIATE_COMPAT(anyTrue, anytrue, bool)
     INSTANTIATE_REAL(fn, int)              \
     INSTANTIATE_REAL(fn, unsigned)         \
     INSTANTIATE_REAL(fn, char)             \
+    INSTANTIATE_REAL(fn, signed char)      \
     INSTANTIATE_REAL(fn, unsigned char)    \
     INSTANTIATE_REAL(fn, short)            \
     INSTANTIATE_REAL(fn, unsigned short)   \
diff --git a/src/api/cpp/stdev.cpp b/src/api/cpp/stdev.cpp
index a9e22d58f6..66edaf816a 100644
--- a/src/api/cpp/stdev.cpp
+++ b/src/api/cpp/stdev.cpp
@@ -60,6 +60,7 @@ INSTANTIATE_STDEV(unsigned long long);
 INSTANTIATE_STDEV(short);
 INSTANTIATE_STDEV(unsigned short);
 INSTANTIATE_STDEV(char);
+INSTANTIATE_STDEV(signed char);
 INSTANTIATE_STDEV(unsigned char);
 
 #undef INSTANTIATE_STDEV
diff --git a/src/api/cpp/var.cpp b/src/api/cpp/var.cpp
index 80cd6a63c5..66f2d76252 100644
--- a/src/api/cpp/var.cpp
+++ b/src/api/cpp/var.cpp
@@ -112,6 +112,7 @@ INSTANTIATE_VAR(unsigned long long);
 INSTANTIATE_VAR(short);
 INSTANTIATE_VAR(unsigned short);
 INSTANTIATE_VAR(char);
+INSTANTIATE_VAR(signed char);
 INSTANTIATE_VAR(unsigned char);
 INSTANTIATE_VAR(af_half);
 INSTANTIATE_VAR(half_float::half);
diff --git a/src/backend/common/TemplateTypename.hpp b/src/backend/common/TemplateTypename.hpp
index 47286af899..96dfb3c6fe 100644
--- a/src/backend/common/TemplateTypename.hpp
+++ b/src/backend/common/TemplateTypename.hpp
@@ -33,6 +33,7 @@ struct TemplateTypename {
         operator std::string() const noexcept { return #NAME; } \
     }
 
+SPECIALIZE(signed char, detail::schar);
 SPECIALIZE(unsigned char, detail::uchar);
 SPECIALIZE(unsigned int, detail::uint);
 SPECIALIZE(unsigned short, detail::ushort);
diff --git a/src/backend/common/cast.cpp b/src/backend/common/cast.cpp
index cc98f0504f..bcb2dfb519 100644
--- a/src/backend/common/cast.cpp
+++ b/src/backend/common/cast.cpp
@@ -14,6 +14,7 @@ using arrayfire::common::half;
 using detail::cdouble;
 using detail::cfloat;
 using detail::intl;
+using detail::schar;
 using detail::uchar;
 using detail::uint;
 using detail::uintl;
@@ -38,6 +39,7 @@ detail::Array<To> castArray(const af_array &in) {
         case c64: return common::cast<To, cdouble>(getArray<cdouble>(in));
         case s32: return common::cast<To, int>(getArray<int>(in));
         case u32: return common::cast<To, uint>(getArray<uint>(in));
+        case s8: return common::cast<To, schar>(getArray<schar>(in));
         case u8: return common::cast<To, uchar>(getArray<uchar>(in));
         case b8: return common::cast<To, char>(getArray<char>(in));
         case s64: return common::cast<To, intl>(getArray<intl>(in));
@@ -56,6 +58,7 @@ template detail::Array<cfloat> castArray(const af_array &in);
 template detail::Array<cdouble> castArray(const af_array &in);
 template detail::Array<int> castArray(const af_array &in);
 template detail::Array<uint> castArray(const af_array &in);
+template detail::Array<schar> castArray(const af_array &in);
 template detail::Array<uchar> castArray(const af_array &in);
 template detail::Array<char> castArray(const af_array &in);
 template detail::Array<intl> castArray(const af_array &in);
diff --git a/src/backend/common/cast.hpp b/src/backend/common/cast.hpp
index 4186a03914..c60614a8a9 100644
--- a/src/backend/common/cast.hpp
+++ b/src/backend/common/cast.hpp
@@ -31,20 +31,21 @@ namespace common {
 ///                        outer -> inner -> outer
 ///
 ///                                inner cast
-///           f32  f64  c32  c64  s32  u32   u8   b8  s64  u64  s16  u16  f16
-///     f32    x    x    x    x                                            x
-///     f64    x    x    x    x                                            x
-///  o  c32    x    x    x    x                                            x
-///  u  c64    x    x    x    x                                            x
-///  t  s32    x    x    x    x    x    x              x    x              x
-///  e  u32    x    x    x    x    x    x              x    x              x
-///  r   u8    x    x    x    x    x    x    x    x    x    x    x    x    x
-///      b8    x    x    x    x    x    x    x    x    x    x    x    x    x
-///  c  s64    x    x    x    x                        x    x              x
-///  a  u64    x    x    x    x                        x    x              x
-///  s  s16    x    x    x    x    x    x              x    x    x    x    x
-///  t  u16    x    x    x    x    x    x              x    x    x    x    x
-///     f16    x    x    x    x                                            x
+///           f32  f64  c32  c64  s32  u32   s8   u8   b8  s64  u64  s16  u16  f16
+///     f32    x    x    x    x                                                 x
+///     f64    x    x    x    x                                                 x
+///  o  c32    x    x    x    x                                                 x
+///  u  c64    x    x    x    x                                                 x
+///  t  s32    x    x    x    x    x    x                   x    x              x
+///  e  u32    x    x    x    x    x    x                   x    x              x
+///  r   s8    x    x    x    x    x    x    x    x    x    x    x    x    x    x
+///      u8    x    x    x    x    x    x    x    x    x    x    x    x    x    x
+///  c   b8    x    x    x    x    x    x    x    x    x    x    x    x    x    x
+///  a  s64    x    x    x    x                             x    x              x
+///  s  u64    x    x    x    x                             x    x              x
+///  t  s16    x    x    x    x    x    x                   x    x    x    x    x
+///     u16    x    x    x    x    x    x                   x    x    x    x    x
+///     f16    x    x    x    x                                                 x
 ///
 /// \param[in] outer The type of the second cast and the child of the
 ///            previous cast
diff --git a/src/backend/common/graphics_common.cpp b/src/backend/common/graphics_common.cpp
index 217722eb36..01f94078d4 100644
--- a/src/backend/common/graphics_common.cpp
+++ b/src/backend/common/graphics_common.cpp
@@ -139,6 +139,7 @@ INSTANTIATE_GET_FG_TYPE(float, FG_FLOAT32);
 INSTANTIATE_GET_FG_TYPE(int, FG_INT32);
 INSTANTIATE_GET_FG_TYPE(unsigned, FG_UINT32);
 INSTANTIATE_GET_FG_TYPE(char, FG_INT8);
+INSTANTIATE_GET_FG_TYPE(signed char, FG_INT8);
 INSTANTIATE_GET_FG_TYPE(unsigned char, FG_UINT8);
 INSTANTIATE_GET_FG_TYPE(unsigned short, FG_UINT16);
 INSTANTIATE_GET_FG_TYPE(short, FG_INT16);
diff --git a/src/backend/common/half.hpp b/src/backend/common/half.hpp
index 3f966c6f81..42d18be47b 100644
--- a/src/backend/common/half.hpp
+++ b/src/backend/common/half.hpp
@@ -164,6 +164,10 @@ AF_CONSTEXPR __DH__ native_half_t int2half_impl(char value) noexcept {
     return __ull2half_rn(value);
 }
 template<>
+AF_CONSTEXPR __DH__ native_half_t int2half_impl(signed char value) noexcept {
+    return __ull2half_rn(value);
+}
+template<>
 AF_CONSTEXPR __DH__ native_half_t int2half_impl(unsigned char value) noexcept {
     return __ull2half_rn(value);
 }
@@ -861,6 +865,7 @@ AF_CONSTEXPR T half2int(native_half_t value) {
 #ifdef __CUDA_ARCH__
     AF_IF_CONSTEXPR(std::is_same<T, short>::value ||
                     std::is_same<T, char>::value ||
+                    std::is_same<T, signed char>::value ||
                     std::is_same<T, unsigned char>::value) {
         return __half2short_rn(value);
     }
@@ -1044,6 +1049,10 @@ class alignas(2) half {
         return half2int<std::round_indeterminate, true, char>(data_);
     }
 
+    AF_CONSTEXPR __DH__ explicit operator signed char() const noexcept {
+        return half2int<std::round_indeterminate, true, signed char>(data_);
+    }
+
     AF_CONSTEXPR __DH__ explicit operator unsigned char() const noexcept {
         return half2int<std::round_indeterminate, true, unsigned char>(data_);
     }
diff --git a/src/backend/common/jit/BinaryNode.cpp b/src/backend/common/jit/BinaryNode.cpp
index 84c5597e31..b017394876 100644
--- a/src/backend/common/jit/BinaryNode.cpp
+++ b/src/backend/common/jit/BinaryNode.cpp
@@ -69,6 +69,7 @@ INSTANTIATE(cdouble, double, af_cplx2_t);
     INSTANTIATE(unsigned short, unsigned short, op);         \
     INSTANTIATE(unsigned long long, unsigned long long, op); \
     INSTANTIATE(long long, long long, op);                   \
+    INSTANTIATE(signed char, signed char, op);               \
     INSTANTIATE(unsigned char, unsigned char, op);           \
     INSTANTIATE(char, char, op);                             \
     INSTANTIATE(common::half, common::half, op);             \
@@ -91,6 +92,7 @@ INSTANTIATE_ARITH(af_max_t);
     INSTANTIATE(unsigned short, unsigned short, op);         \
     INSTANTIATE(unsigned long long, unsigned long long, op); \
     INSTANTIATE(long long, long long, op);                   \
+    INSTANTIATE(signed char, signed char, op);               \
     INSTANTIATE(unsigned char, unsigned char, op);           \
     INSTANTIATE(char, char, op);                             \
     INSTANTIATE(common::half, common::half, op);             \
@@ -114,6 +116,7 @@ INSTANTIATE_FLOATOPS(af_atan2_t);
     INSTANTIATE(unsigned short, unsigned short, op);         \
     INSTANTIATE(unsigned long long, unsigned long long, op); \
     INSTANTIATE(long long, long long, op);                   \
+    INSTANTIATE(signed char, signed char, op);               \
     INSTANTIATE(unsigned char, unsigned char, op);           \
     INSTANTIATE(char, char, op);                             \
     INSTANTIATE(int, int, op)
@@ -136,6 +139,7 @@ INSTANTIATE_BITOP(af_bitxor_t);
     INSTANTIATE(char, unsigned short, op);     \
     INSTANTIATE(char, unsigned long long, op); \
     INSTANTIATE(char, long long, op);          \
+    INSTANTIATE(char, signed char, op);        \
     INSTANTIATE(char, unsigned char, op);      \
     INSTANTIATE(char, char, op);               \
     INSTANTIATE(char, int, op)
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 2cc3164fb5..4641ff182c 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -98,6 +98,7 @@ static const char *getFullName(af::dtype type) {
         case u16: return detail::getFullName<unsigned short>();
         case s16: return detail::getFullName<short>();
         case b8: return detail::getFullName<char>();
+        case s8: return detail::getFullName<signed char>();
         case u8: return detail::getFullName<unsigned char>();
         case f16: return "half";
     }
@@ -117,6 +118,7 @@ static const char *getShortName(af::dtype type) {
         case u16: return detail::shortname<unsigned short>();
         case s16: return detail::shortname<short>();
         case b8: return detail::shortname<char>();
+        case s8: return detail::shortname<signed char>();
         case u8: return detail::shortname<unsigned char>();
         case f16: return "h";
     }
diff --git a/src/backend/common/moddims.cpp b/src/backend/common/moddims.cpp
index 6fbd99650e..cf9d8d6bb9 100644
--- a/src/backend/common/moddims.cpp
+++ b/src/backend/common/moddims.cpp
@@ -94,6 +94,7 @@ INSTANTIATE(double);
 INSTANTIATE(detail::cfloat);
 INSTANTIATE(detail::cdouble);
 INSTANTIATE(arrayfire::common::half);
+INSTANTIATE(signed char);
 INSTANTIATE(unsigned char);
 INSTANTIATE(char);
 INSTANTIATE(unsigned short);
diff --git a/src/backend/common/traits.hpp b/src/backend/common/traits.hpp
index 3036d91dd0..51a4b53899 100644
--- a/src/backend/common/traits.hpp
+++ b/src/backend/common/traits.hpp
@@ -24,6 +24,7 @@ namespace {
 
 inline size_t dtypeSize(af::dtype type) {
     switch (type) {
+        case s8:
         case u8:
         case b8: return 1;
         case s16:
@@ -59,7 +60,7 @@ constexpr bool isRealFloating(af::dtype type) {
 
 constexpr bool isInteger(af::dtype type) {
     return (type == s32 || type == u32 || type == s64 || type == u64 ||
-            type == s16 || type == u16 || type == u8);
+            type == s16 || type == u16 || type == s8 || type == u8);
 }
 
 constexpr bool isBool(af::dtype type) { return (type == b8); }
diff --git a/src/backend/common/util.cpp b/src/backend/common/util.cpp
index f0b24bba65..87be74fa83 100644
--- a/src/backend/common/util.cpp
+++ b/src/backend/common/util.cpp
@@ -103,6 +103,7 @@ const char* getName(af_dtype type) {
         case u64: return "unsigned long long";
         case s64: return "long long";
         case u8: return "unsigned char";
+        case s8: return "signed char";
         case b8: return "bool";
         default: return "unknown type";
     }
@@ -275,6 +276,7 @@ template string toString<int>(int);
 template string toString<unsigned short>(unsigned short);
 template string toString<short>(short);
 template string toString<unsigned char>(unsigned char);
+template string toString<signed char>(signed char);
 template string toString<char>(char);
 template string toString<long>(long);
 template string toString<long long>(long long);
diff --git a/src/backend/cpu/Array.cpp b/src/backend/cpu/Array.cpp
index dc0b5d5dad..276ea952b4 100644
--- a/src/backend/cpu/Array.cpp
+++ b/src/backend/cpu/Array.cpp
@@ -367,6 +367,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/cpu/assign.cpp b/src/backend/cpu/assign.cpp
index cfeb5e168e..32af00e487 100644
--- a/src/backend/cpu/assign.cpp
+++ b/src/backend/cpu/assign.cpp
@@ -66,6 +66,7 @@ INSTANTIATE(uintl)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(int)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/bilateral.cpp b/src/backend/cpu/bilateral.cpp
index 027afb2c3b..19af80f3cb 100644
--- a/src/backend/cpu/bilateral.cpp
+++ b/src/backend/cpu/bilateral.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/cpu/cast.hpp b/src/backend/cpu/cast.hpp
index dd756eb2b3..d51b7838b8 100644
--- a/src/backend/cpu/cast.hpp
+++ b/src/backend/cpu/cast.hpp
@@ -150,6 +150,7 @@ struct UnOp<std::complex<double>, std::complex<float>, af_cast_t> {
 CAST_B8(float)
 CAST_B8(double)
 CAST_B8(int)
+CAST_B8(schar)
 CAST_B8(uchar)
 CAST_B8(char)
 
diff --git a/src/backend/cpu/convolve.cpp b/src/backend/cpu/convolve.cpp
index 20138fd9e5..2fd0e3bce3 100644
--- a/src/backend/cpu/convolve.cpp
+++ b/src/backend/cpu/convolve.cpp
@@ -111,6 +111,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/cpu/copy.cpp b/src/backend/cpu/copy.cpp
index b1d0985680..ea98c0f613 100644
--- a/src/backend/cpu/copy.cpp
+++ b/src/backend/cpu/copy.cpp
@@ -72,6 +72,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
@@ -101,6 +102,8 @@ INSTANTIATE(half)
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
                                            Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, schar>(Array<schar> & dst,         \
+                                          Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, char>(Array<char> & dst,           \
@@ -114,6 +117,7 @@ INSTANTIATE_COPY_ARRAY(int)
 INSTANTIATE_COPY_ARRAY(uint)
 INSTANTIATE_COPY_ARRAY(intl)
 INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(schar)
 INSTANTIATE_COPY_ARRAY(uchar)
 INSTANTIATE_COPY_ARRAY(char)
 INSTANTIATE_COPY_ARRAY(ushort)
@@ -144,6 +148,7 @@ INSTANTIATE_GETSCALAR(cfloat)
 INSTANTIATE_GETSCALAR(cdouble)
 INSTANTIATE_GETSCALAR(int)
 INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(schar)
 INSTANTIATE_GETSCALAR(uchar)
 INSTANTIATE_GETSCALAR(char)
 INSTANTIATE_GETSCALAR(intl)
diff --git a/src/backend/cpu/diagonal.cpp b/src/backend/cpu/diagonal.cpp
index eddd8c0a49..1767096ed0 100644
--- a/src/backend/cpu/diagonal.cpp
+++ b/src/backend/cpu/diagonal.cpp
@@ -62,6 +62,7 @@ INSTANTIATE_DIAGONAL(uint)
 INSTANTIATE_DIAGONAL(intl)
 INSTANTIATE_DIAGONAL(uintl)
 INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(schar)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
diff --git a/src/backend/cpu/diff.cpp b/src/backend/cpu/diff.cpp
index 8e9c67cae1..f9ced50f52 100644
--- a/src/backend/cpu/diff.cpp
+++ b/src/backend/cpu/diff.cpp
@@ -56,6 +56,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/exampleFunction.cpp b/src/backend/cpu/exampleFunction.cpp
index ee7b847524..3f677bc24b 100644
--- a/src/backend/cpu/exampleFunction.cpp
+++ b/src/backend/cpu/exampleFunction.cpp
@@ -56,6 +56,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(cfloat)
diff --git a/src/backend/cpu/fast.cpp b/src/backend/cpu/fast.cpp
index b8ac38eeaf..ac93345797 100644
--- a/src/backend/cpu/fast.cpp
+++ b/src/backend/cpu/fast.cpp
@@ -120,6 +120,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/fftconvolve.cpp b/src/backend/cpu/fftconvolve.cpp
index 728238c1ef..ff2e5b68c4 100644
--- a/src/backend/cpu/fftconvolve.cpp
+++ b/src/backend/cpu/fftconvolve.cpp
@@ -207,6 +207,7 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 INSTANTIATE(uint)
 INSTANTIATE(int)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(uintl)
diff --git a/src/backend/cpu/hist_graphics.cpp b/src/backend/cpu/hist_graphics.cpp
index 7635004c91..a77e9fe77e 100644
--- a/src/backend/cpu/hist_graphics.cpp
+++ b/src/backend/cpu/hist_graphics.cpp
@@ -43,6 +43,7 @@ void copy_histogram(const Array<T> &data, fg_histogram hist) {
 INSTANTIATE(float)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/histogram.cpp b/src/backend/cpu/histogram.cpp
index e2f8e15433..9d9c6ba8fa 100644
--- a/src/backend/cpu/histogram.cpp
+++ b/src/backend/cpu/histogram.cpp
@@ -48,6 +48,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/identity.cpp b/src/backend/cpu/identity.cpp
index 05695d7629..ce7f35bdb0 100644
--- a/src/backend/cpu/identity.cpp
+++ b/src/backend/cpu/identity.cpp
@@ -42,6 +42,7 @@ INSTANTIATE_IDENTITY(uint)
 INSTANTIATE_IDENTITY(intl)
 INSTANTIATE_IDENTITY(uintl)
 INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(schar)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
diff --git a/src/backend/cpu/image.cpp b/src/backend/cpu/image.cpp
index f11a2db4ca..2e24dec9be 100644
--- a/src/backend/cpu/image.cpp
+++ b/src/backend/cpu/image.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/index.cpp b/src/backend/cpu/index.cpp
index 850239acfe..84cff747bd 100644
--- a/src/backend/cpu/index.cpp
+++ b/src/backend/cpu/index.cpp
@@ -81,6 +81,7 @@ INSTANTIATE(uintl)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(int)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/iota.cpp b/src/backend/cpu/iota.cpp
index 1e7155bcd9..fe50919783 100644
--- a/src/backend/cpu/iota.cpp
+++ b/src/backend/cpu/iota.cpp
@@ -41,6 +41,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index 435d6ea44d..a20df27c1a 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -105,6 +105,7 @@ INSTANTIATE(af_min_t, uint)
 INSTANTIATE(af_min_t, intl)
 INSTANTIATE(af_min_t, uintl)
 INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, schar)
 INSTANTIATE(af_min_t, uchar)
 INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
@@ -120,6 +121,7 @@ INSTANTIATE(af_max_t, uint)
 INSTANTIATE(af_max_t, intl)
 INSTANTIATE(af_max_t, uintl)
 INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, schar)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
diff --git a/src/backend/cpu/join.cpp b/src/backend/cpu/join.cpp
index e9fed65df1..602f2db7f9 100644
--- a/src/backend/cpu/join.cpp
+++ b/src/backend/cpu/join.cpp
@@ -70,6 +70,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
@@ -90,6 +91,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/kernel/random_engine.hpp b/src/backend/cpu/kernel/random_engine.hpp
index 09c2bff20c..0ab49f8a80 100644
--- a/src/backend/cpu/kernel/random_engine.hpp
+++ b/src/backend/cpu/kernel/random_engine.hpp
@@ -115,6 +115,11 @@ uchar transform<uchar>(uint *val, uint index) {
     return v;
 }
 
+template<>
+schar transform<schar>(uint *val, uint index) {
+    return transform<uchar>(val, index);
+}
+
 template<>
 ushort transform<ushort>(uint *val, uint index) {
     ushort v = val[index >> 1U] >> (16U * (index & 1U)) & 0x0000ffff;
diff --git a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
index 6ac6875f3e..5873e93117 100644
--- a/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/cpu/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -9,7 +9,7 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
-// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+// SBK_TYPES:float double int uint intl uintl short ushort char schar uchar
 
 namespace arrayfire {
 namespace cpu {
diff --git a/src/backend/cpu/kernel/sort_by_key_impl.hpp b/src/backend/cpu/kernel/sort_by_key_impl.hpp
index acd7524a9b..e77e868d78 100644
--- a/src/backend/cpu/kernel/sort_by_key_impl.hpp
+++ b/src/backend/cpu/kernel/sort_by_key_impl.hpp
@@ -169,6 +169,7 @@ void sort0ByKey(Param<Tk> okey, Param<Tv> oval, bool isAscending) {
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)
diff --git a/src/backend/cpu/lookup.cpp b/src/backend/cpu/lookup.cpp
index 8a5c40d55c..b8c56e297c 100644
--- a/src/backend/cpu/lookup.cpp
+++ b/src/backend/cpu/lookup.cpp
@@ -51,6 +51,8 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                                       const unsigned);                         \
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
+    template Array<T> lookup<T, schar>(const Array<T> &, const Array<schar> &, \
+                                       const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
@@ -64,6 +66,7 @@ INSTANTIATE(int);
 INSTANTIATE(unsigned);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(ushort);
diff --git a/src/backend/cpu/match_template.cpp b/src/backend/cpu/match_template.cpp
index d3cfb26b4a..6b4d0f1b91 100644
--- a/src/backend/cpu/match_template.cpp
+++ b/src/backend/cpu/match_template.cpp
@@ -51,6 +51,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/cpu/mean.cpp b/src/backend/cpu/mean.cpp
index 6a256113f7..2323442110 100644
--- a/src/backend/cpu/mean.cpp
+++ b/src/backend/cpu/mean.cpp
@@ -141,6 +141,7 @@ INSTANTIATE(intl, double, double);
 INSTANTIATE(uintl, double, double);
 INSTANTIATE(short, float, float);
 INSTANTIATE(ushort, float, float);
+INSTANTIATE(schar, float, float);
 INSTANTIATE(uchar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
diff --git a/src/backend/cpu/meanshift.cpp b/src/backend/cpu/meanshift.cpp
index d52b56a99e..878aa4cacb 100644
--- a/src/backend/cpu/meanshift.cpp
+++ b/src/backend/cpu/meanshift.cpp
@@ -50,6 +50,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/medfilt.cpp b/src/backend/cpu/medfilt.cpp
index 53497be8c9..4c952fc762 100644
--- a/src/backend/cpu/medfilt.cpp
+++ b/src/backend/cpu/medfilt.cpp
@@ -63,6 +63,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/memory.cpp b/src/backend/cpu/memory.cpp
index 9bbb41d458..0a32186f2e 100644
--- a/src/backend/cpu/memory.cpp
+++ b/src/backend/cpu/memory.cpp
@@ -106,6 +106,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/cpu/moments.cpp b/src/backend/cpu/moments.cpp
index bd5c520eac..09db606bd4 100644
--- a/src/backend/cpu/moments.cpp
+++ b/src/backend/cpu/moments.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/morph.cpp b/src/backend/cpu/morph.cpp
index add13de416..e526e7c066 100644
--- a/src/backend/cpu/morph.cpp
+++ b/src/backend/cpu/morph.cpp
@@ -67,6 +67,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/nearest_neighbour.cpp b/src/backend/cpu/nearest_neighbour.cpp
index 2979090dd9..0581e97ab6 100644
--- a/src/backend/cpu/nearest_neighbour.cpp
+++ b/src/backend/cpu/nearest_neighbour.cpp
@@ -67,6 +67,7 @@ INSTANTIATE(int, int)
 INSTANTIATE(uint, uint)
 INSTANTIATE(intl, intl)
 INSTANTIATE(uintl, uintl)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, uint)
 INSTANTIATE(ushort, uint)
 INSTANTIATE(short, int)
diff --git a/src/backend/cpu/plot.cpp b/src/backend/cpu/plot.cpp
index abf1a7b397..1ca6ae7882 100644
--- a/src/backend/cpu/plot.cpp
+++ b/src/backend/cpu/plot.cpp
@@ -46,6 +46,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/random_engine.cpp b/src/backend/cpu/random_engine.cpp
index 3e1c8745c8..d42a7bdae1 100644
--- a/src/backend/cpu/random_engine.cpp
+++ b/src/backend/cpu/random_engine.cpp
@@ -149,6 +149,7 @@ INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
 INSTANTIATE_UNIFORM(uintl)
 INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(schar)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
diff --git a/src/backend/cpu/range.cpp b/src/backend/cpu/range.cpp
index 3b782837e0..ad100da4d4 100644
--- a/src/backend/cpu/range.cpp
+++ b/src/backend/cpu/range.cpp
@@ -54,6 +54,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/reduce.cpp b/src/backend/cpu/reduce.cpp
index 6ce141b316..5b13d6f96f 100644
--- a/src/backend/cpu/reduce.cpp
+++ b/src/backend/cpu/reduce.cpp
@@ -145,6 +145,7 @@ INSTANTIATE(af_min_t, uint, uint)
 INSTANTIATE(af_min_t, intl, intl)
 INSTANTIATE(af_min_t, uintl, uintl)
 INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, schar, schar)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
@@ -160,6 +161,7 @@ INSTANTIATE(af_max_t, uint, uint)
 INSTANTIATE(af_max_t, intl, intl)
 INSTANTIATE(af_max_t, uintl, uintl)
 INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, schar, schar)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
@@ -180,6 +182,8 @@ INSTANTIATE(af_add_t, uintl, uintl)
 INSTANTIATE(af_add_t, uintl, double)
 INSTANTIATE(af_add_t, char, int)
 INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, schar, int)
+INSTANTIATE(af_add_t, schar, float)
 INSTANTIATE(af_add_t, uchar, uint)
 INSTANTIATE(af_add_t, uchar, float)
 INSTANTIATE(af_add_t, short, int)
@@ -199,6 +203,7 @@ INSTANTIATE(af_mul_t, uint, uint)
 INSTANTIATE(af_mul_t, intl, intl)
 INSTANTIATE(af_mul_t, uintl, uintl)
 INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, schar, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
@@ -214,6 +219,7 @@ INSTANTIATE(af_notzero_t, uint, uint)
 INSTANTIATE(af_notzero_t, intl, uint)
 INSTANTIATE(af_notzero_t, uintl, uint)
 INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, schar, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
@@ -229,6 +235,7 @@ INSTANTIATE(af_or_t, uint, char)
 INSTANTIATE(af_or_t, intl, char)
 INSTANTIATE(af_or_t, uintl, char)
 INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, schar, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
@@ -244,6 +251,7 @@ INSTANTIATE(af_and_t, uint, char)
 INSTANTIATE(af_and_t, intl, char)
 INSTANTIATE(af_and_t, uintl, char)
 INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, schar, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
diff --git a/src/backend/cpu/reorder.cpp b/src/backend/cpu/reorder.cpp
index 67233542bd..dd0a43ccac 100644
--- a/src/backend/cpu/reorder.cpp
+++ b/src/backend/cpu/reorder.cpp
@@ -39,6 +39,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/cpu/reshape.cpp b/src/backend/cpu/reshape.cpp
index b2d46eb066..31a0053684 100644
--- a/src/backend/cpu/reshape.cpp
+++ b/src/backend/cpu/reshape.cpp
@@ -40,6 +40,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
@@ -68,6 +69,8 @@ INSTANTIATE(ushort)
                                                 const dim4 &, short, double); \
     template Array<ushort> reshape<SRC_T, ushort>(                            \
         const Array<SRC_T> &, const dim4 &, ushort, double);                  \
+    template Array<schar> reshape<SRC_T, schar>(const Array<SRC_T> &,         \
+                                                const dim4 &, schar, double); \
     template Array<uchar> reshape<SRC_T, uchar>(const Array<SRC_T> &,         \
                                                 const dim4 &, uchar, double); \
     template Array<char> reshape<SRC_T, char>(const Array<SRC_T> &,           \
@@ -79,6 +82,7 @@ INSTANTIATE_PAD_ARRAY(int)
 INSTANTIATE_PAD_ARRAY(uint)
 INSTANTIATE_PAD_ARRAY(intl)
 INSTANTIATE_PAD_ARRAY(uintl)
+INSTANTIATE_PAD_ARRAY(schar)
 INSTANTIATE_PAD_ARRAY(uchar)
 INSTANTIATE_PAD_ARRAY(char)
 INSTANTIATE_PAD_ARRAY(ushort)
diff --git a/src/backend/cpu/resize.cpp b/src/backend/cpu/resize.cpp
index 4f899d89d8..ffc473fd4e 100644
--- a/src/backend/cpu/resize.cpp
+++ b/src/backend/cpu/resize.cpp
@@ -53,6 +53,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/rotate.cpp b/src/backend/cpu/rotate.cpp
index 0e9806a2af..bed34b7bf3 100644
--- a/src/backend/cpu/rotate.cpp
+++ b/src/backend/cpu/rotate.cpp
@@ -53,6 +53,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/scan.cpp b/src/backend/cpu/scan.cpp
index af5c4d9efe..7f6843f99a 100644
--- a/src/backend/cpu/scan.cpp
+++ b/src/backend/cpu/scan.cpp
@@ -84,6 +84,7 @@ Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
     INSTANTIATE_SCAN(ROp, uintl, uintl)     \
     INSTANTIATE_SCAN(ROp, char, int)        \
     INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, schar, int)       \
     INSTANTIATE_SCAN(ROp, uchar, uint)      \
     INSTANTIATE_SCAN(ROp, short, int)       \
     INSTANTIATE_SCAN(ROp, ushort, uint)
diff --git a/src/backend/cpu/select.cpp b/src/backend/cpu/select.cpp
index 96849cecd1..8258cae47a 100644
--- a/src/backend/cpu/select.cpp
+++ b/src/backend/cpu/select.cpp
@@ -51,6 +51,7 @@ INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/set.cpp b/src/backend/cpu/set.cpp
index 838ad7675e..6db13c8760 100644
--- a/src/backend/cpu/set.cpp
+++ b/src/backend/cpu/set.cpp
@@ -120,6 +120,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/shift.cpp b/src/backend/cpu/shift.cpp
index f8942f641f..d812cbde89 100644
--- a/src/backend/cpu/shift.cpp
+++ b/src/backend/cpu/shift.cpp
@@ -37,6 +37,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/sobel.cpp b/src/backend/cpu/sobel.cpp
index 68bddee784..5708348295 100644
--- a/src/backend/cpu/sobel.cpp
+++ b/src/backend/cpu/sobel.cpp
@@ -44,6 +44,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(int, int)
 INSTANTIATE(uint, int)
 INSTANTIATE(char, int)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, int)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
diff --git a/src/backend/cpu/sort.cpp b/src/backend/cpu/sort.cpp
index e5067a8dba..41c6b75147 100644
--- a/src/backend/cpu/sort.cpp
+++ b/src/backend/cpu/sort.cpp
@@ -98,6 +98,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/sort_by_key.cpp b/src/backend/cpu/sort_by_key.cpp
index 169b598558..efe8eba2f1 100644
--- a/src/backend/cpu/sort_by_key.cpp
+++ b/src/backend/cpu/sort_by_key.cpp
@@ -71,6 +71,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
     INSTANTIATE(Tk, int)     \
     INSTANTIATE(Tk, uint)    \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
@@ -82,6 +83,7 @@ INSTANTIATE1(double)
 INSTANTIATE1(int)
 INSTANTIATE1(uint)
 INSTANTIATE1(char)
+INSTANTIATE1(schar)
 INSTANTIATE1(uchar)
 INSTANTIATE1(short)
 INSTANTIATE1(ushort)
diff --git a/src/backend/cpu/sort_index.cpp b/src/backend/cpu/sort_index.cpp
index cec724c85d..8b1f4a1319 100644
--- a/src/backend/cpu/sort_index.cpp
+++ b/src/backend/cpu/sort_index.cpp
@@ -75,6 +75,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/surface.cpp b/src/backend/cpu/surface.cpp
index e861dbeac7..d86bd6f469 100644
--- a/src/backend/cpu/surface.cpp
+++ b/src/backend/cpu/surface.cpp
@@ -47,6 +47,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/susan.cpp b/src/backend/cpu/susan.cpp
index 6ab2bfba78..c5321deb16 100644
--- a/src/backend/cpu/susan.cpp
+++ b/src/backend/cpu/susan.cpp
@@ -73,6 +73,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/tile.cpp b/src/backend/cpu/tile.cpp
index d2a8d3ab7c..884bfed40d 100644
--- a/src/backend/cpu/tile.cpp
+++ b/src/backend/cpu/tile.cpp
@@ -47,6 +47,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index 9a57424250..bbcf689f25 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/transpose.cpp b/src/backend/cpu/transpose.cpp
index 7cd713afd6..a9f6f9d3d5 100644
--- a/src/backend/cpu/transpose.cpp
+++ b/src/backend/cpu/transpose.cpp
@@ -51,6 +51,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/cpu/triangle.cpp b/src/backend/cpu/triangle.cpp
index 8e3b0569b2..6c276ca4bd 100644
--- a/src/backend/cpu/triangle.cpp
+++ b/src/backend/cpu/triangle.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/types.hpp b/src/backend/cpu/types.hpp
index 27a678af82..f1f58e7006 100644
--- a/src/backend/cpu/types.hpp
+++ b/src/backend/cpu/types.hpp
@@ -31,6 +31,7 @@ using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
 using intl    = long long;
 using uint    = unsigned int;
+using schar   = signed char;
 using uchar   = unsigned char;
 using uintl   = unsigned long long;
 using ushort  = unsigned short;
diff --git a/src/backend/cpu/unwrap.cpp b/src/backend/cpu/unwrap.cpp
index 49086fad49..dca2433ff8 100644
--- a/src/backend/cpu/unwrap.cpp
+++ b/src/backend/cpu/unwrap.cpp
@@ -55,6 +55,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cpu/vector_field.cpp b/src/backend/cpu/vector_field.cpp
index 2a7549de81..efe207be09 100644
--- a/src/backend/cpu/vector_field.cpp
+++ b/src/backend/cpu/vector_field.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/where.cpp b/src/backend/cpu/where.cpp
index 3eb65015f0..30f70efcb0 100644
--- a/src/backend/cpu/where.cpp
+++ b/src/backend/cpu/where.cpp
@@ -73,6 +73,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cpu/wrap.cpp b/src/backend/cpu/wrap.cpp
index d502bc85ad..0c0d397e3f 100644
--- a/src/backend/cpu/wrap.cpp
+++ b/src/backend/cpu/wrap.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/Array.cpp b/src/backend/cuda/Array.cpp
index 9193f329de..e0d5f73f5a 100644
--- a/src/backend/cuda/Array.cpp
+++ b/src/backend/cuda/Array.cpp
@@ -493,6 +493,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/cuda/all.cu b/src/backend/cuda/all.cu
index 3ff42ad599..fa0681dbaf 100644
--- a/src/backend/cuda/all.cu
+++ b/src/backend/cuda/all.cu
@@ -24,6 +24,7 @@ INSTANTIATE(af_and_t, uint, char)
 INSTANTIATE(af_and_t, intl, char)
 INSTANTIATE(af_and_t, uintl, char)
 INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, schar, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
diff --git a/src/backend/cuda/any.cu b/src/backend/cuda/any.cu
index 34092c94d3..801dcb6c10 100644
--- a/src/backend/cuda/any.cu
+++ b/src/backend/cuda/any.cu
@@ -24,6 +24,7 @@ INSTANTIATE(af_or_t, uint, char)
 INSTANTIATE(af_or_t, intl, char)
 INSTANTIATE(af_or_t, uintl, char)
 INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, schar, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
diff --git a/src/backend/cuda/assign.cpp b/src/backend/cuda/assign.cpp
index 67bcbd1291..b65265dc8b 100644
--- a/src/backend/cuda/assign.cpp
+++ b/src/backend/cuda/assign.cpp
@@ -73,6 +73,7 @@ INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/bilateral.cpp b/src/backend/cuda/bilateral.cpp
index f9f828018d..6d56640fa8 100644
--- a/src/backend/cuda/bilateral.cpp
+++ b/src/backend/cuda/bilateral.cpp
@@ -34,6 +34,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/cuda/cast.hpp b/src/backend/cuda/cast.hpp
index 9328dd5052..214d24845a 100644
--- a/src/backend/cuda/cast.hpp
+++ b/src/backend/cuda/cast.hpp
@@ -34,6 +34,7 @@ struct CastOp {
 CAST_FN(int)
 CAST_FN(unsigned int)
 CAST_FN(unsigned char)
+CAST_FN(signed char)
 CAST_FN(unsigned short)
 CAST_FN(short)
 CAST_FN(float)
diff --git a/src/backend/cuda/convolve.cpp b/src/backend/cuda/convolve.cpp
index 3a33c6f64f..043bfdcc9e 100644
--- a/src/backend/cuda/convolve.cpp
+++ b/src/backend/cuda/convolve.cpp
@@ -95,6 +95,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/cuda/copy.cpp b/src/backend/cuda/copy.cpp
index f8472a7dfb..5d1701d965 100644
--- a/src/backend/cuda/copy.cpp
+++ b/src/backend/cuda/copy.cpp
@@ -113,6 +113,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
@@ -142,6 +143,8 @@ INSTANTIATE(half)
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
                                            Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, schar>(Array<schar> & dst,         \
+                                          Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, char>(Array<char> & dst,           \
@@ -157,6 +160,7 @@ INSTANTIATE_COPY_ARRAY(intl)
 INSTANTIATE_COPY_ARRAY(uintl)
 INSTANTIATE_COPY_ARRAY(short)
 INSTANTIATE_COPY_ARRAY(ushort)
+INSTANTIATE_COPY_ARRAY(schar)
 INSTANTIATE_COPY_ARRAY(uchar)
 INSTANTIATE_COPY_ARRAY(char)
 INSTANTIATE_COPY_ARRAY(half)
@@ -187,6 +191,7 @@ INSTANTIATE_GETSCALAR(cfloat)
 INSTANTIATE_GETSCALAR(cdouble)
 INSTANTIATE_GETSCALAR(int)
 INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(schar)
 INSTANTIATE_GETSCALAR(uchar)
 INSTANTIATE_GETSCALAR(char)
 INSTANTIATE_GETSCALAR(intl)
diff --git a/src/backend/cuda/count.cu b/src/backend/cuda/count.cu
index 373def999c..3cb5806a88 100644
--- a/src/backend/cuda/count.cu
+++ b/src/backend/cuda/count.cu
@@ -26,6 +26,7 @@ INSTANTIATE(af_notzero_t, uintl, uint)
 INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
 INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, schar, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, half, uint)
 }  // namespace cuda
diff --git a/src/backend/cuda/cudaDataType.hpp b/src/backend/cuda/cudaDataType.hpp
index 1da3429e60..3746d0b4b9 100644
--- a/src/backend/cuda/cudaDataType.hpp
+++ b/src/backend/cuda/cudaDataType.hpp
@@ -44,6 +44,22 @@ inline cudaDataType_t getType<common::half>() {
     return CUDA_R_16F;
 }
 
+template<>
+inline cudaDataType_t getType<uchar>() {
+    return CUDA_R_8I;
+}
+
+template<>
+inline cudaDataType_t getType<schar>() {
+    return CUDA_R_8I;
+}
+
+/* only supports LStride/RStride % 4 == 0 */
+template<>
+inline cudaDataType_t getType<int>() {
+    return CUDA_R_32I;
+}
+
 template<typename T>
 inline cudaDataType_t getComputeType() {
     return getType<T>();
diff --git a/src/backend/cuda/cudnn.cpp b/src/backend/cuda/cudnn.cpp
index 39ee3305e6..5b8a500d00 100644
--- a/src/backend/cuda/cudnn.cpp
+++ b/src/backend/cuda/cudnn.cpp
@@ -64,6 +64,12 @@ cudnnDataType_t getCudnnDataType<int>() {
 }
 
 #if CUDNN_VERSION >= 7100
+/// TODONT COMMIT
+template<>
+cudnnDataType_t getCudnnDataType<signed char>() {
+    return CUDNN_DATA_INT8;
+}
+
 template<>
 cudnnDataType_t getCudnnDataType<unsigned char>() {
     return CUDNN_DATA_UINT8;
diff --git a/src/backend/cuda/diagonal.cpp b/src/backend/cuda/diagonal.cpp
index cbf3180a70..b5dd2b5c0b 100644
--- a/src/backend/cuda/diagonal.cpp
+++ b/src/backend/cuda/diagonal.cpp
@@ -54,6 +54,7 @@ INSTANTIATE_DIAGONAL(uint)
 INSTANTIATE_DIAGONAL(intl)
 INSTANTIATE_DIAGONAL(uintl)
 INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(schar)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
diff --git a/src/backend/cuda/diff.cpp b/src/backend/cuda/diff.cpp
index 55bb68ece0..b21ab36b72 100644
--- a/src/backend/cuda/diff.cpp
+++ b/src/backend/cuda/diff.cpp
@@ -55,6 +55,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/exampleFunction.cpp b/src/backend/cuda/exampleFunction.cpp
index b94f9f8e54..12bf635785 100644
--- a/src/backend/cuda/exampleFunction.cpp
+++ b/src/backend/cuda/exampleFunction.cpp
@@ -60,6 +60,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(cfloat)
diff --git a/src/backend/cuda/fast.cu b/src/backend/cuda/fast.cu
index 7744d4b6d6..63e9a57cb4 100644
--- a/src/backend/cuda/fast.cu
+++ b/src/backend/cuda/fast.cu
@@ -62,6 +62,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/fast_pyramid.cpp b/src/backend/cuda/fast_pyramid.cpp
index 97228af248..ba0b6dfbf4 100644
--- a/src/backend/cuda/fast_pyramid.cpp
+++ b/src/backend/cuda/fast_pyramid.cpp
@@ -120,6 +120,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/fftconvolve.cpp b/src/backend/cuda/fftconvolve.cpp
index ed22d0ea85..cb8359423e 100644
--- a/src/backend/cuda/fftconvolve.cpp
+++ b/src/backend/cuda/fftconvolve.cpp
@@ -112,6 +112,7 @@ INSTANTIATE(float)
 INSTANTIATE(uint)
 INSTANTIATE(int)
 INSTANTIATE(uchar)
+INSTANTIATE(schar)
 INSTANTIATE(char)
 INSTANTIATE(uintl)
 INSTANTIATE(intl)
diff --git a/src/backend/cuda/hist_graphics.cpp b/src/backend/cuda/hist_graphics.cpp
index 6678281db6..cabadeb1ad 100644
--- a/src/backend/cuda/hist_graphics.cpp
+++ b/src/backend/cuda/hist_graphics.cpp
@@ -69,6 +69,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/histogram.cpp b/src/backend/cuda/histogram.cpp
index ca7e6ced86..f012d6e64b 100644
--- a/src/backend/cuda/histogram.cpp
+++ b/src/backend/cuda/histogram.cpp
@@ -41,6 +41,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/identity.cpp b/src/backend/cuda/identity.cpp
index 995b09a9d9..ee62dcf549 100644
--- a/src/backend/cuda/identity.cpp
+++ b/src/backend/cuda/identity.cpp
@@ -37,6 +37,7 @@ INSTANTIATE_IDENTITY(uint)
 INSTANTIATE_IDENTITY(intl)
 INSTANTIATE_IDENTITY(uintl)
 INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(schar)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
diff --git a/src/backend/cuda/image.cpp b/src/backend/cuda/image.cpp
index 810d36d968..23bccf616e 100644
--- a/src/backend/cuda/image.cpp
+++ b/src/backend/cuda/image.cpp
@@ -70,6 +70,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/index.cpp b/src/backend/cuda/index.cpp
index d8acf90c12..dbb7d1ad60 100644
--- a/src/backend/cuda/index.cpp
+++ b/src/backend/cuda/index.cpp
@@ -90,6 +90,7 @@ INSTANTIATE(int)
 INSTANTIATE(uintl)
 INSTANTIATE(intl)
 INSTANTIATE(uchar)
+INSTANTIATE(schar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/iota.cpp b/src/backend/cuda/iota.cpp
index d9afef41c5..0ac6dbee74 100644
--- a/src/backend/cuda/iota.cpp
+++ b/src/backend/cuda/iota.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/ireduce.cpp b/src/backend/cuda/ireduce.cpp
index 94cd340a66..a2236230d4 100644
--- a/src/backend/cuda/ireduce.cpp
+++ b/src/backend/cuda/ireduce.cpp
@@ -62,6 +62,7 @@ INSTANTIATE(af_min_t, uintl)
 INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
 INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, schar)
 INSTANTIATE(af_min_t, uchar)
 INSTANTIATE(af_min_t, half)
 
@@ -77,6 +78,7 @@ INSTANTIATE(af_max_t, uintl)
 INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
 INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, schar)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, half)
 }  // namespace cuda
diff --git a/src/backend/cuda/jit.cpp b/src/backend/cuda/jit.cpp
index 9346491145..171ec66f61 100644
--- a/src/backend/cuda/jit.cpp
+++ b/src/backend/cuda/jit.cpp
@@ -552,6 +552,7 @@ template void evalNodes<cdouble>(Param<cdouble> out, Node* node);
 template void evalNodes<int>(Param<int> out, Node* node);
 template void evalNodes<uint>(Param<uint> out, Node* node);
 template void evalNodes<char>(Param<char> out, Node* node);
+template void evalNodes<schar>(Param<schar> out, Node* node);
 template void evalNodes<uchar>(Param<uchar> out, Node* node);
 template void evalNodes<intl>(Param<intl> out, Node* node);
 template void evalNodes<uintl>(Param<uintl> out, Node* node);
@@ -573,6 +574,8 @@ template void evalNodes<uint>(vector<Param<uint>>& out,
                               const vector<Node*>& node);
 template void evalNodes<char>(vector<Param<char>>& out,
                               const vector<Node*>& node);
+template void evalNodes<schar>(vector<Param<schar>>& out,
+                               const vector<Node*>& node);
 template void evalNodes<uchar>(vector<Param<uchar>>& out,
                                const vector<Node*>& node);
 template void evalNodes<intl>(vector<Param<intl>>& out,
diff --git a/src/backend/cuda/join.cpp b/src/backend/cuda/join.cpp
index 3eed6f7fb5..5065412342 100644
--- a/src/backend/cuda/join.cpp
+++ b/src/backend/cuda/join.cpp
@@ -209,6 +209,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
@@ -229,6 +230,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
diff --git a/src/backend/cuda/kernel/convolve_separable.cpp b/src/backend/cuda/kernel/convolve_separable.cpp
index 3c18a02240..14a62d1f1e 100644
--- a/src/backend/cuda/kernel/convolve_separable.cpp
+++ b/src/backend/cuda/kernel/convolve_separable.cpp
@@ -22,6 +22,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uchar, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
 INSTANTIATE(short, float)
diff --git a/src/backend/cuda/kernel/copy.cuh b/src/backend/cuda/kernel/copy.cuh
index 9e771e8c52..20f6bfa021 100644
--- a/src/backend/cuda/kernel/copy.cuh
+++ b/src/backend/cuda/kernel/copy.cuh
@@ -49,6 +49,18 @@ convertType<char, compute_t<common::half>>(char value) {
     return compute_t<common::half>(value);
 }
 
+template<>
+__inline__ __device__ schar
+convertType<compute_t<common::half>, schar>(compute_t<common::half> value) {
+    return (schar)((short)value);
+}
+
+template<>
+__inline__ __device__ compute_t<common::half>
+convertType<schar, compute_t<common::half>>(schar value) {
+    return compute_t<common::half>(value);
+}
+
 template<>
 __inline__ __device__ uchar
 convertType<compute_t<common::half>, uchar>(compute_t<common::half> value) {
@@ -90,6 +102,7 @@ OTHER_SPECIALIZATIONS(intl)
 OTHER_SPECIALIZATIONS(uintl)
 OTHER_SPECIALIZATIONS(short)
 OTHER_SPECIALIZATIONS(ushort)
+OTHER_SPECIALIZATIONS(schar)
 OTHER_SPECIALIZATIONS(uchar)
 OTHER_SPECIALIZATIONS(char)
 OTHER_SPECIALIZATIONS(common::half)
diff --git a/src/backend/cuda/kernel/random_engine.hpp b/src/backend/cuda/kernel/random_engine.hpp
index 07ba4163a2..a5e2305885 100644
--- a/src/backend/cuda/kernel/random_engine.hpp
+++ b/src/backend/cuda/kernel/random_engine.hpp
@@ -312,6 +312,12 @@ __device__ static void writeOut128Bytes(uchar *out, const uint &index,
     out[index + 15 * blockDim.x] = r4 >> 24;
 }
 
+__device__ static void writeOut128Bytes(schar *out, const uint &index,
+                                        const uint &r1, const uint &r2,
+                                        const uint &r3, const uint &r4) {
+    writeOut128Bytes((uchar *)(out), index, r1, r2, r3, r4);
+}
+
 __device__ static void writeOut128Bytes(char *out, const uint &index,
                                         const uint &r1, const uint &r2,
                                         const uint &r3, const uint &r4) {
@@ -535,6 +541,13 @@ __device__ static void partialWriteOut128Bytes(uchar *out, const uint &index,
     }
 }
 
+__device__ static void partialWriteOut128Bytes(schar *out, const uint &index,
+                                               const uint &r1, const uint &r2,
+                                               const uint &r3, const uint &r4,
+                                               const uint &elements) {
+    partialWriteOut128Bytes((uchar *)(out), index, r1, r2, r3, r4, elements);
+}
+
 __device__ static void partialWriteOut128Bytes(char *out, const uint &index,
                                                const uint &r1, const uint &r2,
                                                const uint &r3, const uint &r4,
diff --git a/src/backend/cuda/kernel/shared.hpp b/src/backend/cuda/kernel/shared.hpp
index 55d9f70a64..d1f15653c3 100644
--- a/src/backend/cuda/kernel/shared.hpp
+++ b/src/backend/cuda/kernel/shared.hpp
@@ -53,6 +53,7 @@ SPECIALIZE(int)
 SPECIALIZE(uint)
 SPECIALIZE(short)
 SPECIALIZE(ushort)
+SPECIALIZE(schar)
 SPECIALIZE(uchar)
 SPECIALIZE(intl)
 SPECIALIZE(uintl)
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
index 19b291356c..7a7e3616c9 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
+++ b/src/backend/cuda/kernel/thrust_sort_by_key/thrust_sort_by_key_impl.cu
@@ -11,7 +11,7 @@
 
 // This file instantiates sort_by_key as separate object files from CMake
 // The 3 lines below are read by CMake to determenine the instantiations
-// SBK_TYPES:float double int uint intl uintl short ushort char uchar
+// SBK_TYPES:float double int uint intl uintl short ushort char schar uchar
 // SBK_INSTS:0 1
 
 namespace arrayfire {
diff --git a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
index e4695ac48e..e909a786de 100644
--- a/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
+++ b/src/backend/cuda/kernel/thrust_sort_by_key_impl.hpp
@@ -39,6 +39,7 @@ void thrustSortByKey(Tk *keyPtr, Tv *valPtr, int elements, bool isAscending) {
     INSTANTIATE(Tk, cfloat)  \
     INSTANTIATE(Tk, cdouble) \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)
 
 #define INSTANTIATE1(Tk)    \
diff --git a/src/backend/cuda/lookup.cpp b/src/backend/cuda/lookup.cpp
index 133db5ba26..ca5b8f79ed 100644
--- a/src/backend/cuda/lookup.cpp
+++ b/src/backend/cuda/lookup.cpp
@@ -54,6 +54,8 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                                       const unsigned);                         \
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
+    template Array<T> lookup<T, schar>(const Array<T> &, const Array<schar> &, \
+                                       const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
@@ -67,6 +69,7 @@ INSTANTIATE(int);
 INSTANTIATE(unsigned);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(short);
diff --git a/src/backend/cuda/match_template.cpp b/src/backend/cuda/match_template.cpp
index d82137bb5c..63b50435b7 100644
--- a/src/backend/cuda/match_template.cpp
+++ b/src/backend/cuda/match_template.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/cuda/math.hpp b/src/backend/cuda/math.hpp
index 6986bcb445..28574ac7e2 100644
--- a/src/backend/cuda/math.hpp
+++ b/src/backend/cuda/math.hpp
@@ -192,6 +192,14 @@ inline __device__ uintl maxval<uintl>() {
     return 1ULL << (8 * sizeof(uintl) - 1);
 }
 template<>
+inline __device__ schar maxval<schar>() {
+    return 0x7f;
+}
+template<>
+inline __device__ schar minval<schar>() {
+    return 0x80;
+}
+template<>
 inline __device__ char maxval<char>() {
     return 0x7f;
 }
diff --git a/src/backend/cuda/max.cu b/src/backend/cuda/max.cu
index 03f712b303..9fe7b92409 100644
--- a/src/backend/cuda/max.cu
+++ b/src/backend/cuda/max.cu
@@ -24,6 +24,7 @@ INSTANTIATE(af_max_t, uint, uint)
 INSTANTIATE(af_max_t, intl, intl)
 INSTANTIATE(af_max_t, uintl, uintl)
 INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, schar, schar)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
diff --git a/src/backend/cuda/mean.cu b/src/backend/cuda/mean.cu
index 9b1eea74e9..b4dab3b866 100644
--- a/src/backend/cuda/mean.cu
+++ b/src/backend/cuda/mean.cu
@@ -63,6 +63,7 @@ INSTANTIATE(uintl, double, double);
 INSTANTIATE(short, float, float);
 INSTANTIATE(ushort, float, float);
 INSTANTIATE(uchar, float, float);
+INSTANTIATE(schar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
 INSTANTIATE(cdouble, double, cdouble);
diff --git a/src/backend/cuda/meanshift.cpp b/src/backend/cuda/meanshift.cpp
index d72d1aa041..83d12cb3ef 100644
--- a/src/backend/cuda/meanshift.cpp
+++ b/src/backend/cuda/meanshift.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/medfilt.cpp b/src/backend/cuda/medfilt.cpp
index c80c95c21f..cca97dd644 100644
--- a/src/backend/cuda/medfilt.cpp
+++ b/src/backend/cuda/medfilt.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/memory.cpp b/src/backend/cuda/memory.cpp
index dafbef1ce8..616547d6af 100644
--- a/src/backend/cuda/memory.cpp
+++ b/src/backend/cuda/memory.cpp
@@ -117,6 +117,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/cuda/min.cu b/src/backend/cuda/min.cu
index 72a3f1beef..b0fad5733c 100644
--- a/src/backend/cuda/min.cu
+++ b/src/backend/cuda/min.cu
@@ -24,6 +24,7 @@ INSTANTIATE(af_min_t, uint, uint)
 INSTANTIATE(af_min_t, intl, intl)
 INSTANTIATE(af_min_t, uintl, uintl)
 INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, schar, schar)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
diff --git a/src/backend/cuda/moments.cpp b/src/backend/cuda/moments.cpp
index 34c8cf753f..fa37b033e1 100644
--- a/src/backend/cuda/moments.cpp
+++ b/src/backend/cuda/moments.cpp
@@ -51,6 +51,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/morph.cpp b/src/backend/cuda/morph.cpp
index a49fd5a40e..f09f20bded 100644
--- a/src/backend/cuda/morph.cpp
+++ b/src/backend/cuda/morph.cpp
@@ -53,6 +53,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/nearest_neighbour.cu b/src/backend/cuda/nearest_neighbour.cu
index ca6a11a1c6..dc10695f8a 100644
--- a/src/backend/cuda/nearest_neighbour.cu
+++ b/src/backend/cuda/nearest_neighbour.cu
@@ -67,6 +67,7 @@ INSTANTIATE(int, int)
 INSTANTIATE(uint, uint)
 INSTANTIATE(intl, intl)
 INSTANTIATE(uintl, uintl)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, uint)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, uint)
diff --git a/src/backend/cuda/pad_array_borders.cpp b/src/backend/cuda/pad_array_borders.cpp
index bf41b5f2e7..af563733d2 100644
--- a/src/backend/cuda/pad_array_borders.cpp
+++ b/src/backend/cuda/pad_array_borders.cpp
@@ -48,6 +48,7 @@ INSTANTIATE_PAD_ARRAY_BORDERS(int)
 INSTANTIATE_PAD_ARRAY_BORDERS(uint)
 INSTANTIATE_PAD_ARRAY_BORDERS(intl)
 INSTANTIATE_PAD_ARRAY_BORDERS(uintl)
+INSTANTIATE_PAD_ARRAY_BORDERS(schar)
 INSTANTIATE_PAD_ARRAY_BORDERS(uchar)
 INSTANTIATE_PAD_ARRAY_BORDERS(char)
 INSTANTIATE_PAD_ARRAY_BORDERS(ushort)
diff --git a/src/backend/cuda/plot.cpp b/src/backend/cuda/plot.cpp
index e012377305..e69b149790 100644
--- a/src/backend/cuda/plot.cpp
+++ b/src/backend/cuda/plot.cpp
@@ -70,6 +70,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/product.cu b/src/backend/cuda/product.cu
index c4fff43b93..fb26c95562 100644
--- a/src/backend/cuda/product.cu
+++ b/src/backend/cuda/product.cu
@@ -24,6 +24,7 @@ INSTANTIATE(af_mul_t, uint, uint)
 INSTANTIATE(af_mul_t, intl, intl)
 INSTANTIATE(af_mul_t, uintl, uintl)
 INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, schar, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
diff --git a/src/backend/cuda/random_engine.cu b/src/backend/cuda/random_engine.cu
index a63ead0bf8..26cdbdc23b 100644
--- a/src/backend/cuda/random_engine.cu
+++ b/src/backend/cuda/random_engine.cu
@@ -143,6 +143,7 @@ INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
 INSTANTIATE_UNIFORM(uintl)
 INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(schar)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
diff --git a/src/backend/cuda/range.cpp b/src/backend/cuda/range.cpp
index 55a2553649..f821f283f7 100644
--- a/src/backend/cuda/range.cpp
+++ b/src/backend/cuda/range.cpp
@@ -48,6 +48,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/reorder.cpp b/src/backend/cuda/reorder.cpp
index c81fd02f6a..286dcde6ad 100644
--- a/src/backend/cuda/reorder.cpp
+++ b/src/backend/cuda/reorder.cpp
@@ -43,6 +43,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/cuda/reshape.cpp b/src/backend/cuda/reshape.cpp
index 9d6e57549f..329b7883cb 100644
--- a/src/backend/cuda/reshape.cpp
+++ b/src/backend/cuda/reshape.cpp
@@ -49,6 +49,8 @@ Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                                                 dim4 const &, short, double); \
     template Array<ushort> reshape<SRC_T, ushort>(                            \
         Array<SRC_T> const &, dim4 const &, ushort, double);                  \
+    template Array<schar> reshape<SRC_T, schar>(Array<SRC_T> const &,         \
+                                                dim4 const &, schar, double); \
     template Array<uchar> reshape<SRC_T, uchar>(Array<SRC_T> const &,         \
                                                 dim4 const &, uchar, double); \
     template Array<char> reshape<SRC_T, char>(Array<SRC_T> const &,           \
@@ -64,6 +66,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
diff --git a/src/backend/cuda/resize.cpp b/src/backend/cuda/resize.cpp
index 97dc8a7da8..dec6f09d26 100644
--- a/src/backend/cuda/resize.cpp
+++ b/src/backend/cuda/resize.cpp
@@ -41,6 +41,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/rotate.cpp b/src/backend/cuda/rotate.cpp
index 2f46894aef..7edb0de7a6 100644
--- a/src/backend/cuda/rotate.cpp
+++ b/src/backend/cuda/rotate.cpp
@@ -36,6 +36,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/scan.cpp b/src/backend/cuda/scan.cpp
index 10002cbbad..cf3f2a0b70 100644
--- a/src/backend/cuda/scan.cpp
+++ b/src/backend/cuda/scan.cpp
@@ -47,6 +47,7 @@ Array<To> scan(const Array<Ti>& in, const int dim, bool inclusive_scan) {
     INSTANTIATE_SCAN(ROp, uintl, uintl)     \
     INSTANTIATE_SCAN(ROp, char, int)        \
     INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, schar, int)       \
     INSTANTIATE_SCAN(ROp, uchar, uint)      \
     INSTANTIATE_SCAN(ROp, short, int)       \
     INSTANTIATE_SCAN(ROp, ushort, uint)
diff --git a/src/backend/cuda/select.cpp b/src/backend/cuda/select.cpp
index b13df55bfe..0b78263efd 100644
--- a/src/backend/cuda/select.cpp
+++ b/src/backend/cuda/select.cpp
@@ -127,6 +127,7 @@ INSTANTIATE(uint);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
 INSTANTIATE(char);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
diff --git a/src/backend/cuda/set.cu b/src/backend/cuda/set.cu
index fbbbc28c0a..d558d6e938 100644
--- a/src/backend/cuda/set.cu
+++ b/src/backend/cuda/set.cu
@@ -122,6 +122,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/shift.cpp b/src/backend/cuda/shift.cpp
index 6f88a38472..f073d3c844 100644
--- a/src/backend/cuda/shift.cpp
+++ b/src/backend/cuda/shift.cpp
@@ -68,6 +68,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/sobel.cpp b/src/backend/cuda/sobel.cpp
index 5200f69a45..1861d0c76c 100644
--- a/src/backend/cuda/sobel.cpp
+++ b/src/backend/cuda/sobel.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(int, int)
 INSTANTIATE(uint, int)
 INSTANTIATE(char, int)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, int)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
diff --git a/src/backend/cuda/sort.cu b/src/backend/cuda/sort.cu
index 9970ddd8b2..d56899a87d 100644
--- a/src/backend/cuda/sort.cu
+++ b/src/backend/cuda/sort.cu
@@ -54,6 +54,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/sort_by_key.cu b/src/backend/cuda/sort_by_key.cu
index bd19d16240..21d9efc5b2 100644
--- a/src/backend/cuda/sort_by_key.cu
+++ b/src/backend/cuda/sort_by_key.cu
@@ -67,6 +67,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)
@@ -78,6 +79,7 @@ INSTANTIATE1(uint)
 INSTANTIATE1(short)
 INSTANTIATE1(ushort)
 INSTANTIATE1(char)
+INSTANTIATE1(schar)
 INSTANTIATE1(uchar)
 INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
diff --git a/src/backend/cuda/sort_index.cu b/src/backend/cuda/sort_index.cu
index 039e77a147..d923f7c6e9 100644
--- a/src/backend/cuda/sort_index.cu
+++ b/src/backend/cuda/sort_index.cu
@@ -63,6 +63,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/sum.cu b/src/backend/cuda/sum.cu
index 44cfec9449..6a52c2c369 100644
--- a/src/backend/cuda/sum.cu
+++ b/src/backend/cuda/sum.cu
@@ -29,6 +29,8 @@ INSTANTIATE(af_add_t, uintl, uintl)
 INSTANTIATE(af_add_t, uintl, double)
 INSTANTIATE(af_add_t, char, int)
 INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, schar, int)
+INSTANTIATE(af_add_t, schar, float)
 INSTANTIATE(af_add_t, uchar, uint)
 INSTANTIATE(af_add_t, uchar, float)
 INSTANTIATE(af_add_t, short, int)
diff --git a/src/backend/cuda/surface.cpp b/src/backend/cuda/surface.cpp
index bef751239b..61f3457036 100644
--- a/src/backend/cuda/surface.cpp
+++ b/src/backend/cuda/surface.cpp
@@ -71,6 +71,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/susan.cpp b/src/backend/cuda/susan.cpp
index 4d0fcc078c..5f1d07d913 100644
--- a/src/backend/cuda/susan.cpp
+++ b/src/backend/cuda/susan.cpp
@@ -74,6 +74,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/tile.cpp b/src/backend/cuda/tile.cpp
index f93982eb43..edd2a7b686 100644
--- a/src/backend/cuda/tile.cpp
+++ b/src/backend/cuda/tile.cpp
@@ -48,6 +48,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/transform.cpp b/src/backend/cuda/transform.cpp
index baba9b1a04..e0d0509c8d 100644
--- a/src/backend/cuda/transform.cpp
+++ b/src/backend/cuda/transform.cpp
@@ -37,6 +37,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/transpose.cpp b/src/backend/cuda/transpose.cpp
index faa4659b68..03d6f3b91d 100644
--- a/src/backend/cuda/transpose.cpp
+++ b/src/backend/cuda/transpose.cpp
@@ -45,6 +45,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/cuda/transpose_inplace.cpp b/src/backend/cuda/transpose_inplace.cpp
index ff89730d47..dcc8c5664b 100644
--- a/src/backend/cuda/transpose_inplace.cpp
+++ b/src/backend/cuda/transpose_inplace.cpp
@@ -37,6 +37,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/cuda/triangle.cpp b/src/backend/cuda/triangle.cpp
index 4ec0a04e6f..c32e984626 100644
--- a/src/backend/cuda/triangle.cpp
+++ b/src/backend/cuda/triangle.cpp
@@ -48,6 +48,7 @@ INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/types.hpp b/src/backend/cuda/types.hpp
index 34815cba66..2230948f3a 100644
--- a/src/backend/cuda/types.hpp
+++ b/src/backend/cuda/types.hpp
@@ -35,6 +35,7 @@ namespace cuda {
 using cdouble = cuDoubleComplex;
 using cfloat  = cuFloatComplex;
 using intl    = long long;
+using schar   = signed char;
 using uchar   = unsigned char;
 using uint    = unsigned int;
 using uintl   = unsigned long long;
@@ -82,6 +83,10 @@ inline const char *shortname<char>(bool caps) {
     return caps ? "J" : "j";
 }
 template<>
+inline const char *shortname<schar>(bool caps) {
+    return caps ? "A" : "a"; // TODO
+}
+template<>
 inline const char *shortname<uchar>(bool caps) {
     return caps ? "V" : "v";
 }
@@ -120,6 +125,7 @@ SPECIALIZE(double)
 SPECIALIZE(cfloat)
 SPECIALIZE(cdouble)
 SPECIALIZE(char)
+SPECIALIZE(signed char)
 SPECIALIZE(unsigned char)
 SPECIALIZE(short)
 SPECIALIZE(unsigned short)
diff --git a/src/backend/cuda/unwrap.cpp b/src/backend/cuda/unwrap.cpp
index 6eae7d428b..9d96aec1d9 100644
--- a/src/backend/cuda/unwrap.cpp
+++ b/src/backend/cuda/unwrap.cpp
@@ -55,6 +55,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/cuda/vector_field.cpp b/src/backend/cuda/vector_field.cpp
index 2868979772..a0528cddb1 100644
--- a/src/backend/cuda/vector_field.cpp
+++ b/src/backend/cuda/vector_field.cpp
@@ -105,6 +105,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace cuda
diff --git a/src/backend/cuda/where.cpp b/src/backend/cuda/where.cpp
index efd488d26e..862b25fa24 100644
--- a/src/backend/cuda/where.cpp
+++ b/src/backend/cuda/where.cpp
@@ -36,6 +36,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/cuda/wrap.cpp b/src/backend/cuda/wrap.cpp
index d8963cacd9..dd7901cc0e 100644
--- a/src/backend/cuda/wrap.cpp
+++ b/src/backend/cuda/wrap.cpp
@@ -44,6 +44,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/Array.cpp b/src/backend/oneapi/Array.cpp
index 8165e6fb08..57c8f111ee 100644
--- a/src/backend/oneapi/Array.cpp
+++ b/src/backend/oneapi/Array.cpp
@@ -582,6 +582,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/oneapi/all.cpp b/src/backend/oneapi/all.cpp
index ad09e4aff1..e4e86232d2 100644
--- a/src/backend/oneapi/all.cpp
+++ b/src/backend/oneapi/all.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_and_t, uint, char)
 INSTANTIATE(af_and_t, intl, char)
 INSTANTIATE(af_and_t, uintl, char)
 INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, schar, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
diff --git a/src/backend/oneapi/any.cpp b/src/backend/oneapi/any.cpp
index bdf600e9a9..82e242a989 100644
--- a/src/backend/oneapi/any.cpp
+++ b/src/backend/oneapi/any.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_or_t, uint, char)
 INSTANTIATE(af_or_t, intl, char)
 INSTANTIATE(af_or_t, uintl, char)
 INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, schar, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
diff --git a/src/backend/oneapi/assign.cpp b/src/backend/oneapi/assign.cpp
index def9378d2d..de436495db 100644
--- a/src/backend/oneapi/assign.cpp
+++ b/src/backend/oneapi/assign.cpp
@@ -80,6 +80,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/bilateral.cpp b/src/backend/oneapi/bilateral.cpp
index d7d5dd33b9..6520cf9ffa 100644
--- a/src/backend/oneapi/bilateral.cpp
+++ b/src/backend/oneapi/bilateral.cpp
@@ -35,6 +35,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/oneapi/cast.hpp b/src/backend/oneapi/cast.hpp
index c9b015c4f2..7d4e2be76f 100644
--- a/src/backend/oneapi/cast.hpp
+++ b/src/backend/oneapi/cast.hpp
@@ -34,6 +34,7 @@ struct CastOp {
 
 CAST_FN(int)
 CAST_FN(uint)
+CAST_FN(schar)
 CAST_FN(uchar)
 CAST_FN(float)
 CAST_FN(double)
diff --git a/src/backend/oneapi/convolve.cpp b/src/backend/oneapi/convolve.cpp
index d2cc41c588..0e443d7b77 100644
--- a/src/backend/oneapi/convolve.cpp
+++ b/src/backend/oneapi/convolve.cpp
@@ -98,6 +98,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/oneapi/convolve_separable.cpp b/src/backend/oneapi/convolve_separable.cpp
index fdf9fc952f..ddf5c27a7e 100644
--- a/src/backend/oneapi/convolve_separable.cpp
+++ b/src/backend/oneapi/convolve_separable.cpp
@@ -65,6 +65,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(short, float)
diff --git a/src/backend/oneapi/copy.cpp b/src/backend/oneapi/copy.cpp
index 506206b11e..a89023261e 100644
--- a/src/backend/oneapi/copy.cpp
+++ b/src/backend/oneapi/copy.cpp
@@ -155,6 +155,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
@@ -184,6 +185,8 @@ INSTANTIATE(half)
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
                                            Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, schar>(Array<schar> & dst,         \
+                                          Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, char>(Array<char> & dst,           \
@@ -197,6 +200,7 @@ INSTANTIATE_COPY_ARRAY(int)
 INSTANTIATE_COPY_ARRAY(uint)
 INSTANTIATE_COPY_ARRAY(intl)
 INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(schar)
 INSTANTIATE_COPY_ARRAY(uchar)
 INSTANTIATE_COPY_ARRAY(char)
 INSTANTIATE_COPY_ARRAY(short)
@@ -238,6 +242,7 @@ INSTANTIATE_GETSCALAR(cfloat)
 INSTANTIATE_GETSCALAR(cdouble)
 INSTANTIATE_GETSCALAR(int)
 INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(schar)
 INSTANTIATE_GETSCALAR(uchar)
 INSTANTIATE_GETSCALAR(char)
 INSTANTIATE_GETSCALAR(intl)
diff --git a/src/backend/oneapi/count.cpp b/src/backend/oneapi/count.cpp
index f8ef354169..4ed59eb3b9 100644
--- a/src/backend/oneapi/count.cpp
+++ b/src/backend/oneapi/count.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_notzero_t, uint, uint)
 INSTANTIATE(af_notzero_t, intl, uint)
 INSTANTIATE(af_notzero_t, uintl, uint)
 INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, schar, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
diff --git a/src/backend/oneapi/diagonal.cpp b/src/backend/oneapi/diagonal.cpp
index a18d024585..900f53ba3c 100644
--- a/src/backend/oneapi/diagonal.cpp
+++ b/src/backend/oneapi/diagonal.cpp
@@ -54,6 +54,7 @@ INSTANTIATE_DIAGONAL(uint)
 INSTANTIATE_DIAGONAL(intl)
 INSTANTIATE_DIAGONAL(uintl)
 INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(schar)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
diff --git a/src/backend/oneapi/diff.cpp b/src/backend/oneapi/diff.cpp
index a3c37f6a4a..01cd18e37e 100644
--- a/src/backend/oneapi/diff.cpp
+++ b/src/backend/oneapi/diff.cpp
@@ -50,6 +50,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/oneapi/exampleFunction.cpp b/src/backend/oneapi/exampleFunction.cpp
index 6159d9d1d4..9a006febff 100644
--- a/src/backend/oneapi/exampleFunction.cpp
+++ b/src/backend/oneapi/exampleFunction.cpp
@@ -59,6 +59,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(cfloat)
diff --git a/src/backend/oneapi/fast.cpp b/src/backend/oneapi/fast.cpp
index cb9ae28d4c..a5b0934f97 100644
--- a/src/backend/oneapi/fast.cpp
+++ b/src/backend/oneapi/fast.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/fftconvolve.cpp b/src/backend/oneapi/fftconvolve.cpp
index de96d94c99..85718f4f4f 100644
--- a/src/backend/oneapi/fftconvolve.cpp
+++ b/src/backend/oneapi/fftconvolve.cpp
@@ -148,6 +148,7 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 INSTANTIATE(uint)
 INSTANTIATE(int)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(uintl)
diff --git a/src/backend/oneapi/hist_graphics.cpp b/src/backend/oneapi/hist_graphics.cpp
index 3b280592b1..e016337a54 100644
--- a/src/backend/oneapi/hist_graphics.cpp
+++ b/src/backend/oneapi/hist_graphics.cpp
@@ -28,6 +28,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/histogram.cpp b/src/backend/oneapi/histogram.cpp
index 4dfece0640..872431f14c 100644
--- a/src/backend/oneapi/histogram.cpp
+++ b/src/backend/oneapi/histogram.cpp
@@ -41,6 +41,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/identity.cpp b/src/backend/oneapi/identity.cpp
index 5a838a4cf0..68a592ab88 100644
--- a/src/backend/oneapi/identity.cpp
+++ b/src/backend/oneapi/identity.cpp
@@ -37,6 +37,7 @@ INSTANTIATE_IDENTITY(uint)
 INSTANTIATE_IDENTITY(intl)
 INSTANTIATE_IDENTITY(uintl)
 INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(schar)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
diff --git a/src/backend/oneapi/image.cpp b/src/backend/oneapi/image.cpp
index 723c29fb8b..7aa8b4b667 100644
--- a/src/backend/oneapi/image.cpp
+++ b/src/backend/oneapi/image.cpp
@@ -29,6 +29,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/index.cpp b/src/backend/oneapi/index.cpp
index 2548df2011..af204b0820 100644
--- a/src/backend/oneapi/index.cpp
+++ b/src/backend/oneapi/index.cpp
@@ -83,6 +83,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/iota.cpp b/src/backend/oneapi/iota.cpp
index 6d511df23f..e775f0dde6 100644
--- a/src/backend/oneapi/iota.cpp
+++ b/src/backend/oneapi/iota.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/ireduce.cpp b/src/backend/oneapi/ireduce.cpp
index c7b4d263ab..c4bfc7604f 100644
--- a/src/backend/oneapi/ireduce.cpp
+++ b/src/backend/oneapi/ireduce.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(af_min_t, uint)
 INSTANTIATE(af_min_t, intl)
 INSTANTIATE(af_min_t, uintl)
 INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, schar)
 INSTANTIATE(af_min_t, uchar)
 INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
@@ -73,6 +74,7 @@ INSTANTIATE(af_max_t, uint)
 INSTANTIATE(af_max_t, intl)
 INSTANTIATE(af_max_t, uintl)
 INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, schar)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index a112e99436..2bd34a5dc4 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -627,6 +627,7 @@ template void evalNodes<cdouble>(Param<cdouble>& out, Node* node);
 template void evalNodes<int>(Param<int>& out, Node* node);
 template void evalNodes<uint>(Param<uint>& out, Node* node);
 template void evalNodes<char>(Param<char>& out, Node* node);
+template void evalNodes<schar>(Param<schar>& out, Node* node);
 template void evalNodes<uchar>(Param<uchar>& out, Node* node);
 template void evalNodes<intl>(Param<intl>& out, Node* node);
 template void evalNodes<uintl>(Param<uintl>& out, Node* node);
@@ -648,6 +649,8 @@ template void evalNodes<uint>(vector<Param<uint>>& out,
                               const vector<Node*>& node);
 template void evalNodes<char>(vector<Param<char>>& out,
                               const vector<Node*>& node);
+template void evalNodes<schar>(vector<Param<schar>>& out,
+                               const vector<Node*>& node);
 template void evalNodes<uchar>(vector<Param<uchar>>& out,
                                const vector<Node*>& node);
 template void evalNodes<intl>(vector<Param<intl>>& out,
diff --git a/src/backend/oneapi/join.cpp b/src/backend/oneapi/join.cpp
index e95b63c392..a64e6edb9d 100644
--- a/src/backend/oneapi/join.cpp
+++ b/src/backend/oneapi/join.cpp
@@ -272,6 +272,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
@@ -292,6 +293,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
diff --git a/src/backend/oneapi/kernel/convolve1.hpp b/src/backend/oneapi/kernel/convolve1.hpp
index e156308b34..41c6facae6 100644
--- a/src/backend/oneapi/kernel/convolve1.hpp
+++ b/src/backend/oneapi/kernel/convolve1.hpp
@@ -174,6 +174,7 @@ INSTANTIATE_CONV1(double, double)
 INSTANTIATE_CONV1(float, float)
 INSTANTIATE_CONV1(uint, float)
 INSTANTIATE_CONV1(int, float)
+INSTANTIATE_CONV1(schar, float)
 INSTANTIATE_CONV1(uchar, float)
 INSTANTIATE_CONV1(char, float)
 INSTANTIATE_CONV1(ushort, float)
diff --git a/src/backend/oneapi/kernel/convolve2.hpp b/src/backend/oneapi/kernel/convolve2.hpp
index b216e50917..45bfa6c108 100644
--- a/src/backend/oneapi/kernel/convolve2.hpp
+++ b/src/backend/oneapi/kernel/convolve2.hpp
@@ -195,4 +195,5 @@ INSTANTIATE_CONV2(intl, float)
 INSTANTIATE_CONV2(ushort, float)
 INSTANTIATE_CONV2(uint, float)
 INSTANTIATE_CONV2(uintl, float)
+INSTANTIATE_CONV2(schar, float)
 INSTANTIATE_CONV2(uchar, float)
diff --git a/src/backend/oneapi/kernel/convolve3.hpp b/src/backend/oneapi/kernel/convolve3.hpp
index 3ac4a50aa2..bdfcc4eb24 100644
--- a/src/backend/oneapi/kernel/convolve3.hpp
+++ b/src/backend/oneapi/kernel/convolve3.hpp
@@ -193,6 +193,7 @@ INSTANTIATE_CONV3(double, double)
 INSTANTIATE_CONV3(float, float)
 INSTANTIATE_CONV3(uint, float)
 INSTANTIATE_CONV3(int, float)
+INSTANTIATE_CONV3(schar, float)
 INSTANTIATE_CONV3(uchar, float)
 INSTANTIATE_CONV3(char, float)
 INSTANTIATE_CONV3(ushort, float)
diff --git a/src/backend/oneapi/kernel/convolve_separable.cpp b/src/backend/oneapi/kernel/convolve_separable.cpp
index 45a86efb7a..0f3dfacb30 100644
--- a/src/backend/oneapi/kernel/convolve_separable.cpp
+++ b/src/backend/oneapi/kernel/convolve_separable.cpp
@@ -200,6 +200,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/oneapi/kernel/memcopy.hpp b/src/backend/oneapi/kernel/memcopy.hpp
index b400d04673..64bd26ba1e 100644
--- a/src/backend/oneapi/kernel/memcopy.hpp
+++ b/src/backend/oneapi/kernel/memcopy.hpp
@@ -164,6 +164,19 @@ convertType<char, compute_t<arrayfire::common::half>>(char value) {
     return compute_t<arrayfire::common::half>(value);
 }
 
+template<>
+signed char inline convertType<compute_t<arrayfire::common::half>, signed char>(
+    compute_t<arrayfire::common::half> value) {
+    return (signed char)((short)value);
+}
+
+template<>
+inline compute_t<arrayfire::common::half>
+convertType<signed char, compute_t<arrayfire::common::half>>(
+    signed char value) {
+    return compute_t<arrayfire::common::half>(value);
+}
+
 template<>
 unsigned char inline convertType<compute_t<arrayfire::common::half>,
                                  unsigned char>(
@@ -197,6 +210,7 @@ OTHER_SPECIALIZATIONS(intl)
 OTHER_SPECIALIZATIONS(uintl)
 OTHER_SPECIALIZATIONS(short)
 OTHER_SPECIALIZATIONS(ushort)
+OTHER_SPECIALIZATIONS(schar)
 OTHER_SPECIALIZATIONS(uchar)
 OTHER_SPECIALIZATIONS(char)
 OTHER_SPECIALIZATIONS(arrayfire::common::half)
diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index dcd20dec13..3ebf0a113e 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -303,6 +303,12 @@ static void writeOut128Bytes(uchar *out, const uint &index, const uint groupSz,
     out[index + 15 * groupSz] = r4 >> 24;
 }
 
+static void writeOut128Bytes(schar *out, const uint &index, const uint groupSz,
+                             const uint &r1, const uint &r2, const uint &r3,
+                             const uint &r4) {
+    writeOut128Bytes((uchar *)(out), index, groupSz, r1, r2, r3, r4);
+}
+
 static void writeOut128Bytes(char *out, const uint &index, const uint groupSz,
                              const uint &r1, const uint &r2, const uint &r3,
                              const uint &r4) {
@@ -505,6 +511,14 @@ static void partialWriteOut128Bytes(uchar *out, const uint &index,
     }
 }
 
+static void partialWriteOut128Bytes(schar *out, const uint &index,
+                                    const uint groupSz, const uint &r1,
+                                    const uint &r2, const uint &r3,
+                                    const uint &r4, const uint &elements) {
+    partialWriteOut128Bytes((uchar *)(out), index, groupSz, r1, r2, r3, r4,
+                            elements);
+}
+
 static void partialWriteOut128Bytes(char *out, const uint &index,
                                     const uint groupSz, const uint &r1,
                                     const uint &r2, const uint &r3,
diff --git a/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp
index 9b04402904..0b0a8fb13f 100644
--- a/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/oneapi/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -9,7 +9,7 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
-// SBK_TYPES:float double int uint intl uintl short ushort char uchar half
+// SBK_TYPES:float double int uint intl uintl short ushort char schar uchar half
 
 namespace arrayfire {
 namespace oneapi {
diff --git a/src/backend/oneapi/kernel/sort_by_key_impl.hpp b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
index 6e3a0bd655..2e462db4b6 100644
--- a/src/backend/oneapi/kernel/sort_by_key_impl.hpp
+++ b/src/backend/oneapi/kernel/sort_by_key_impl.hpp
@@ -209,6 +209,7 @@ void sort0ByKey(Param<Tk> pKey, Param<Tv> pVal, bool isAscending) {
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)
diff --git a/src/backend/oneapi/lookup.cpp b/src/backend/oneapi/lookup.cpp
index 9c87003375..de0a017c55 100644
--- a/src/backend/oneapi/lookup.cpp
+++ b/src/backend/oneapi/lookup.cpp
@@ -53,6 +53,8 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                                       const unsigned);                         \
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
+    template Array<T> lookup<T, schar>(const Array<T> &, const Array<schar> &, \
+                                       const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
@@ -66,6 +68,7 @@ INSTANTIATE(int);
 INSTANTIATE(unsigned);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(ushort);
diff --git a/src/backend/oneapi/match_template.cpp b/src/backend/oneapi/match_template.cpp
index 28794ff2eb..10b84757ac 100644
--- a/src/backend/oneapi/match_template.cpp
+++ b/src/backend/oneapi/match_template.cpp
@@ -32,6 +32,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/oneapi/max.cpp b/src/backend/oneapi/max.cpp
index 8b6ef71a10..fa21d78c1c 100644
--- a/src/backend/oneapi/max.cpp
+++ b/src/backend/oneapi/max.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_max_t, uint, uint)
 INSTANTIATE(af_max_t, intl, intl)
 INSTANTIATE(af_max_t, uintl, uintl)
 INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, schar, schar)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
diff --git a/src/backend/oneapi/mean.cpp b/src/backend/oneapi/mean.cpp
index 09763bb739..2f94101f56 100644
--- a/src/backend/oneapi/mean.cpp
+++ b/src/backend/oneapi/mean.cpp
@@ -60,6 +60,7 @@ INSTANTIATE(intl, double, double);
 INSTANTIATE(uintl, double, double);
 INSTANTIATE(short, float, float);
 INSTANTIATE(ushort, float, float);
+INSTANTIATE(schar, float, float);
 INSTANTIATE(uchar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
diff --git a/src/backend/oneapi/meanshift.cpp b/src/backend/oneapi/meanshift.cpp
index 1017b9074b..825b26eb88 100644
--- a/src/backend/oneapi/meanshift.cpp
+++ b/src/backend/oneapi/meanshift.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/medfilt.cpp b/src/backend/oneapi/medfilt.cpp
index 3b1ff319c5..50c2cc3dd8 100644
--- a/src/backend/oneapi/medfilt.cpp
+++ b/src/backend/oneapi/medfilt.cpp
@@ -59,6 +59,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/memory.cpp b/src/backend/oneapi/memory.cpp
index f94b6df5a4..3482742b73 100644
--- a/src/backend/oneapi/memory.cpp
+++ b/src/backend/oneapi/memory.cpp
@@ -152,6 +152,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/oneapi/min.cpp b/src/backend/oneapi/min.cpp
index ea9900543c..fe1a5a3fa4 100644
--- a/src/backend/oneapi/min.cpp
+++ b/src/backend/oneapi/min.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_min_t, uint, uint)
 INSTANTIATE(af_min_t, intl, intl)
 INSTANTIATE(af_min_t, uintl, uintl)
 INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, schar, schar)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
diff --git a/src/backend/oneapi/moments.cpp b/src/backend/oneapi/moments.cpp
index 50efe4ccd5..76e385990b 100644
--- a/src/backend/oneapi/moments.cpp
+++ b/src/backend/oneapi/moments.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/morph.cpp b/src/backend/oneapi/morph.cpp
index 44fe6a6529..11f3d3df7a 100644
--- a/src/backend/oneapi/morph.cpp
+++ b/src/backend/oneapi/morph.cpp
@@ -62,6 +62,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/nearest_neighbour.cpp b/src/backend/oneapi/nearest_neighbour.cpp
index 7a34ba0fba..bec80b5cce 100644
--- a/src/backend/oneapi/nearest_neighbour.cpp
+++ b/src/backend/oneapi/nearest_neighbour.cpp
@@ -82,6 +82,7 @@ INSTANTIATE(intl, intl)
 INSTANTIATE(uintl, uintl)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, uint)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, uint)
 
 INSTANTIATE(uintl, uint)  // For Hamming
diff --git a/src/backend/oneapi/plot.cpp b/src/backend/oneapi/plot.cpp
index d2fa041291..3bd287fbd6 100644
--- a/src/backend/oneapi/plot.cpp
+++ b/src/backend/oneapi/plot.cpp
@@ -78,6 +78,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/product.cpp b/src/backend/oneapi/product.cpp
index bc3f9421ae..4aa9cb61dd 100644
--- a/src/backend/oneapi/product.cpp
+++ b/src/backend/oneapi/product.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_mul_t, uint, uint)
 INSTANTIATE(af_mul_t, intl, intl)
 INSTANTIATE(af_mul_t, uintl, uintl)
 INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, schar, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
diff --git a/src/backend/oneapi/random_engine.cpp b/src/backend/oneapi/random_engine.cpp
index 7045dcc8cc..e3eac5da0b 100644
--- a/src/backend/oneapi/random_engine.cpp
+++ b/src/backend/oneapi/random_engine.cpp
@@ -92,6 +92,7 @@ INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
 INSTANTIATE_UNIFORM(uintl)
 INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(schar)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
diff --git a/src/backend/oneapi/range.cpp b/src/backend/oneapi/range.cpp
index caa8ed48bc..c08a7bea91 100644
--- a/src/backend/oneapi/range.cpp
+++ b/src/backend/oneapi/range.cpp
@@ -48,6 +48,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/reorder.cpp b/src/backend/oneapi/reorder.cpp
index d62db984e9..d9e264f70c 100644
--- a/src/backend/oneapi/reorder.cpp
+++ b/src/backend/oneapi/reorder.cpp
@@ -40,6 +40,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/oneapi/reshape.cpp b/src/backend/oneapi/reshape.cpp
index 8f1b6f0ecb..2b15f686e9 100644
--- a/src/backend/oneapi/reshape.cpp
+++ b/src/backend/oneapi/reshape.cpp
@@ -50,6 +50,8 @@ Array<outType> reshape(const Array<inType> &in, const dim4 &outDims,
                                                 dim4 const &, short, double); \
     template Array<ushort> reshape<SRC_T, ushort>(                            \
         Array<SRC_T> const &, dim4 const &, ushort, double);                  \
+    template Array<schar> reshape<SRC_T, schar>(Array<SRC_T> const &,         \
+                                                dim4 const &, schar, double); \
     template Array<uchar> reshape<SRC_T, uchar>(Array<SRC_T> const &,         \
                                                 dim4 const &, uchar, double); \
     template Array<char> reshape<SRC_T, char>(Array<SRC_T> const &,           \
@@ -65,6 +67,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
diff --git a/src/backend/oneapi/resize.cpp b/src/backend/oneapi/resize.cpp
index 005faf6b2b..b73f42eabb 100644
--- a/src/backend/oneapi/resize.cpp
+++ b/src/backend/oneapi/resize.cpp
@@ -40,6 +40,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/rotate.cpp b/src/backend/oneapi/rotate.cpp
index 10f1f93480..bcd7b5810a 100644
--- a/src/backend/oneapi/rotate.cpp
+++ b/src/backend/oneapi/rotate.cpp
@@ -50,6 +50,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/scan.cpp b/src/backend/oneapi/scan.cpp
index f7151ce076..9aaae59b49 100644
--- a/src/backend/oneapi/scan.cpp
+++ b/src/backend/oneapi/scan.cpp
@@ -45,6 +45,7 @@ Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
     INSTANTIATE_SCAN(ROp, intl, intl)       \
     INSTANTIATE_SCAN(ROp, uintl, uintl)     \
     INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, schar, int)       \
     INSTANTIATE_SCAN(ROp, uchar, uint)      \
     INSTANTIATE_SCAN(ROp, short, int)       \
     INSTANTIATE_SCAN(ROp, ushort, uint)
diff --git a/src/backend/oneapi/select.cpp b/src/backend/oneapi/select.cpp
index 8cb80c919d..b24b1fa340 100644
--- a/src/backend/oneapi/select.cpp
+++ b/src/backend/oneapi/select.cpp
@@ -128,6 +128,7 @@ INSTANTIATE(uint);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
 INSTANTIATE(char);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
diff --git a/src/backend/oneapi/set.cpp b/src/backend/oneapi/set.cpp
index 416efb4040..4c4b68e4b0 100644
--- a/src/backend/oneapi/set.cpp
+++ b/src/backend/oneapi/set.cpp
@@ -127,6 +127,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/shift.cpp b/src/backend/oneapi/shift.cpp
index 8a12eb81a8..7e5e31bf37 100644
--- a/src/backend/oneapi/shift.cpp
+++ b/src/backend/oneapi/shift.cpp
@@ -64,6 +64,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/sobel.cpp b/src/backend/oneapi/sobel.cpp
index 54ba117be7..e919a37b77 100644
--- a/src/backend/oneapi/sobel.cpp
+++ b/src/backend/oneapi/sobel.cpp
@@ -42,6 +42,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(int, int)
 INSTANTIATE(uint, int)
 INSTANTIATE(char, int)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, int)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
diff --git a/src/backend/oneapi/sort.cpp b/src/backend/oneapi/sort.cpp
index 4dc65a621c..9bfbeb9094 100644
--- a/src/backend/oneapi/sort.cpp
+++ b/src/backend/oneapi/sort.cpp
@@ -63,6 +63,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/sort_by_key.cpp b/src/backend/oneapi/sort_by_key.cpp
index 9ec60130cd..ba24249955 100644
--- a/src/backend/oneapi/sort_by_key.cpp
+++ b/src/backend/oneapi/sort_by_key.cpp
@@ -67,6 +67,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)
@@ -78,6 +79,7 @@ INSTANTIATE1(uint)
 INSTANTIATE1(short)
 INSTANTIATE1(ushort)
 INSTANTIATE1(char)
+INSTANTIATE1(schar)
 INSTANTIATE1(uchar)
 INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
diff --git a/src/backend/oneapi/sort_index.cpp b/src/backend/oneapi/sort_index.cpp
index 17de33fbad..a8c547f8a1 100644
--- a/src/backend/oneapi/sort_index.cpp
+++ b/src/backend/oneapi/sort_index.cpp
@@ -68,6 +68,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/sum.cpp b/src/backend/oneapi/sum.cpp
index fb20ce6121..990979ba25 100644
--- a/src/backend/oneapi/sum.cpp
+++ b/src/backend/oneapi/sum.cpp
@@ -29,6 +29,8 @@ INSTANTIATE(af_add_t, uintl, uintl)
 INSTANTIATE(af_add_t, uintl, double)
 INSTANTIATE(af_add_t, char, int)
 INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, schar, int)
+INSTANTIATE(af_add_t, schar, float)
 INSTANTIATE(af_add_t, uchar, uint)
 INSTANTIATE(af_add_t, uchar, float)
 INSTANTIATE(af_add_t, short, int)
diff --git a/src/backend/oneapi/surface.cpp b/src/backend/oneapi/surface.cpp
index 2a8d604772..ac50627938 100644
--- a/src/backend/oneapi/surface.cpp
+++ b/src/backend/oneapi/surface.cpp
@@ -80,6 +80,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/susan.cpp b/src/backend/oneapi/susan.cpp
index 437259681c..b51acf13df 100644
--- a/src/backend/oneapi/susan.cpp
+++ b/src/backend/oneapi/susan.cpp
@@ -70,6 +70,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/tile.cpp b/src/backend/oneapi/tile.cpp
index aca96e4ec6..928d0e2b19 100644
--- a/src/backend/oneapi/tile.cpp
+++ b/src/backend/oneapi/tile.cpp
@@ -42,6 +42,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/transform.cpp b/src/backend/oneapi/transform.cpp
index 54b328f7fd..a277df9661 100644
--- a/src/backend/oneapi/transform.cpp
+++ b/src/backend/oneapi/transform.cpp
@@ -50,6 +50,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/transpose.cpp b/src/backend/oneapi/transpose.cpp
index 580573125f..1f41e96cde 100644
--- a/src/backend/oneapi/transpose.cpp
+++ b/src/backend/oneapi/transpose.cpp
@@ -43,6 +43,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/oneapi/transpose_inplace.cpp b/src/backend/oneapi/transpose_inplace.cpp
index ddbb14e419..013027f780 100644
--- a/src/backend/oneapi/transpose_inplace.cpp
+++ b/src/backend/oneapi/transpose_inplace.cpp
@@ -40,6 +40,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/oneapi/triangle.cpp b/src/backend/oneapi/triangle.cpp
index e418c15b93..c8ab5e2b16 100644
--- a/src/backend/oneapi/triangle.cpp
+++ b/src/backend/oneapi/triangle.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/types.hpp b/src/backend/oneapi/types.hpp
index 4537f27987..395687396c 100644
--- a/src/backend/oneapi/types.hpp
+++ b/src/backend/oneapi/types.hpp
@@ -43,6 +43,7 @@ namespace oneapi {
 using cdouble = std::complex<double>;
 using cfloat  = std::complex<float>;
 using intl    = long long;
+using schar   = signed char;
 using uchar   = unsigned char;
 using uint    = unsigned int;
 using uintl   = unsigned long long;
@@ -95,6 +96,10 @@ inline const char *shortname<char>(bool caps) {
     return caps ? "J" : "j";
 }
 template<>
+inline const char *shortname<schar>(bool caps) {
+    return caps ? "A" : "a"; // TODO
+}
+template<>
 inline const char *shortname<uchar>(bool caps) {
     return caps ? "V" : "v";
 }
@@ -120,6 +125,11 @@ inline const char *getFullName() {
     return af::dtype_traits<T>::getName();
 }
 
+template<>
+inline const char *getFullName<schar>() {
+    return "signed char";
+}
+
 template<>
 inline const char *getFullName<cfloat>() {
     return "float2";
diff --git a/src/backend/oneapi/unwrap.cpp b/src/backend/oneapi/unwrap.cpp
index 15d60afe5d..bfc95e0f18 100644
--- a/src/backend/oneapi/unwrap.cpp
+++ b/src/backend/oneapi/unwrap.cpp
@@ -53,6 +53,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/oneapi/vector_field.cpp b/src/backend/oneapi/vector_field.cpp
index 92f310698a..d67fa73c51 100644
--- a/src/backend/oneapi/vector_field.cpp
+++ b/src/backend/oneapi/vector_field.cpp
@@ -31,6 +31,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace oneapi
diff --git a/src/backend/oneapi/where.cpp b/src/backend/oneapi/where.cpp
index bc9e45a515..fd08b975b8 100644
--- a/src/backend/oneapi/where.cpp
+++ b/src/backend/oneapi/where.cpp
@@ -36,6 +36,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/oneapi/wrap.cpp b/src/backend/oneapi/wrap.cpp
index 19e8c0260e..21c47ac007 100644
--- a/src/backend/oneapi/wrap.cpp
+++ b/src/backend/oneapi/wrap.cpp
@@ -44,6 +44,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/Array.cpp b/src/backend/opencl/Array.cpp
index b4b6bcd5a9..38fbfc4d84 100644
--- a/src/backend/opencl/Array.cpp
+++ b/src/backend/opencl/Array.cpp
@@ -585,6 +585,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index 5c920f44f8..a02ae6781d 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -454,6 +454,7 @@ target_sources(afopencl
     kernel/convolve/conv2_f32.cpp
     kernel/convolve/conv2_f64.cpp
     kernel/convolve/conv2_impl.hpp
+    kernel/convolve/conv2_s8.cpp
     kernel/convolve/conv2_s16.cpp
     kernel/convolve/conv2_s32.cpp
     kernel/convolve/conv2_s64.cpp
diff --git a/src/backend/opencl/all.cpp b/src/backend/opencl/all.cpp
index 2d2a1d4717..d81d9def34 100644
--- a/src/backend/opencl/all.cpp
+++ b/src/backend/opencl/all.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_and_t, uint, char)
 INSTANTIATE(af_and_t, intl, char)
 INSTANTIATE(af_and_t, uintl, char)
 INSTANTIATE(af_and_t, char, char)
+INSTANTIATE(af_and_t, schar, char)
 INSTANTIATE(af_and_t, uchar, char)
 INSTANTIATE(af_and_t, short, char)
 INSTANTIATE(af_and_t, ushort, char)
diff --git a/src/backend/opencl/any.cpp b/src/backend/opencl/any.cpp
index ce36f8ed90..ee2d16ab63 100644
--- a/src/backend/opencl/any.cpp
+++ b/src/backend/opencl/any.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_or_t, uint, char)
 INSTANTIATE(af_or_t, intl, char)
 INSTANTIATE(af_or_t, uintl, char)
 INSTANTIATE(af_or_t, char, char)
+INSTANTIATE(af_or_t, schar, char)
 INSTANTIATE(af_or_t, uchar, char)
 INSTANTIATE(af_or_t, short, char)
 INSTANTIATE(af_or_t, ushort, char)
diff --git a/src/backend/opencl/assign.cpp b/src/backend/opencl/assign.cpp
index 57ceeaab2d..fbe0370dde 100644
--- a/src/backend/opencl/assign.cpp
+++ b/src/backend/opencl/assign.cpp
@@ -104,6 +104,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/bilateral.cpp b/src/backend/opencl/bilateral.cpp
index 21ec82e2b6..6475377e75 100644
--- a/src/backend/opencl/bilateral.cpp
+++ b/src/backend/opencl/bilateral.cpp
@@ -34,6 +34,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/opencl/cast.hpp b/src/backend/opencl/cast.hpp
index 999d6188d9..cef1d76c0e 100644
--- a/src/backend/opencl/cast.hpp
+++ b/src/backend/opencl/cast.hpp
@@ -38,6 +38,11 @@ CAST_FN(uchar)
 CAST_FN(float)
 CAST_FN(double)
 
+template<typename Ti>
+struct CastOp<schar, Ti> {
+    const char *name() { return "convert_char"; }
+};
+
 #define CAST_CFN(TYPE)                                    \
     template<typename Ti>                                 \
     struct CastOp<TYPE, Ti> {                             \
diff --git a/src/backend/opencl/compile_module.cpp b/src/backend/opencl/compile_module.cpp
index 89d382c9c0..f0244b3b0d 100644
--- a/src/backend/opencl/compile_module.cpp
+++ b/src/backend/opencl/compile_module.cpp
@@ -81,6 +81,9 @@ const static string DEFAULT_MACROS_STR(
                                            #else\n                     \
                                            #define half short\n          \
                                            #endif\n                      \
+                                           #ifndef schar\n              \
+                                           #define schar char\n         \
+                                           #endif\n                     \
                                            #ifndef M_PI\n               \
                                            #define M_PI 3.1415926535897932384626433832795028841971693993751058209749445923078164\n \
                                            #endif\n                     \
diff --git a/src/backend/opencl/convolve.cpp b/src/backend/opencl/convolve.cpp
index f826102caf..34aa93b642 100644
--- a/src/backend/opencl/convolve.cpp
+++ b/src/backend/opencl/convolve.cpp
@@ -98,6 +98,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/opencl/convolve_separable.cpp b/src/backend/opencl/convolve_separable.cpp
index 03da468ac4..41b88b6ba8 100644
--- a/src/backend/opencl/convolve_separable.cpp
+++ b/src/backend/opencl/convolve_separable.cpp
@@ -65,6 +65,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(short, float)
diff --git a/src/backend/opencl/copy.cpp b/src/backend/opencl/copy.cpp
index 970deae518..97d54d432c 100644
--- a/src/backend/opencl/copy.cpp
+++ b/src/backend/opencl/copy.cpp
@@ -128,6 +128,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
@@ -157,6 +158,8 @@ INSTANTIATE(half)
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, ushort>(Array<ushort> & dst,       \
                                            Array<SRC_T> const &src);  \
+    template void copyArray<SRC_T, schar>(Array<schar> & dst,         \
+                                          Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, uchar>(Array<uchar> & dst,         \
                                           Array<SRC_T> const &src);   \
     template void copyArray<SRC_T, char>(Array<char> & dst,           \
@@ -170,6 +173,7 @@ INSTANTIATE_COPY_ARRAY(int)
 INSTANTIATE_COPY_ARRAY(uint)
 INSTANTIATE_COPY_ARRAY(intl)
 INSTANTIATE_COPY_ARRAY(uintl)
+INSTANTIATE_COPY_ARRAY(schar)
 INSTANTIATE_COPY_ARRAY(uchar)
 INSTANTIATE_COPY_ARRAY(char)
 INSTANTIATE_COPY_ARRAY(short)
@@ -201,6 +205,7 @@ INSTANTIATE_GETSCALAR(cfloat)
 INSTANTIATE_GETSCALAR(cdouble)
 INSTANTIATE_GETSCALAR(int)
 INSTANTIATE_GETSCALAR(uint)
+INSTANTIATE_GETSCALAR(schar)
 INSTANTIATE_GETSCALAR(uchar)
 INSTANTIATE_GETSCALAR(char)
 INSTANTIATE_GETSCALAR(intl)
diff --git a/src/backend/opencl/count.cpp b/src/backend/opencl/count.cpp
index 80f12e68cd..fe1b588f89 100644
--- a/src/backend/opencl/count.cpp
+++ b/src/backend/opencl/count.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_notzero_t, uint, uint)
 INSTANTIATE(af_notzero_t, intl, uint)
 INSTANTIATE(af_notzero_t, uintl, uint)
 INSTANTIATE(af_notzero_t, char, uint)
+INSTANTIATE(af_notzero_t, schar, uint)
 INSTANTIATE(af_notzero_t, uchar, uint)
 INSTANTIATE(af_notzero_t, short, uint)
 INSTANTIATE(af_notzero_t, ushort, uint)
diff --git a/src/backend/opencl/diagonal.cpp b/src/backend/opencl/diagonal.cpp
index 094906a77a..2d21b5f461 100644
--- a/src/backend/opencl/diagonal.cpp
+++ b/src/backend/opencl/diagonal.cpp
@@ -54,6 +54,7 @@ INSTANTIATE_DIAGONAL(uint)
 INSTANTIATE_DIAGONAL(intl)
 INSTANTIATE_DIAGONAL(uintl)
 INSTANTIATE_DIAGONAL(char)
+INSTANTIATE_DIAGONAL(schar)
 INSTANTIATE_DIAGONAL(uchar)
 INSTANTIATE_DIAGONAL(short)
 INSTANTIATE_DIAGONAL(ushort)
diff --git a/src/backend/opencl/diff.cpp b/src/backend/opencl/diff.cpp
index 020365d24c..e152301f0d 100644
--- a/src/backend/opencl/diff.cpp
+++ b/src/backend/opencl/diff.cpp
@@ -50,6 +50,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/opencl/exampleFunction.cpp b/src/backend/opencl/exampleFunction.cpp
index 10af977382..87306e329c 100644
--- a/src/backend/opencl/exampleFunction.cpp
+++ b/src/backend/opencl/exampleFunction.cpp
@@ -57,6 +57,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(cfloat)
diff --git a/src/backend/opencl/fast.cpp b/src/backend/opencl/fast.cpp
index bfe6c84177..4198cf82ba 100644
--- a/src/backend/opencl/fast.cpp
+++ b/src/backend/opencl/fast.cpp
@@ -53,6 +53,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/fftconvolve.cpp b/src/backend/opencl/fftconvolve.cpp
index f6b243baac..f5a875f41c 100644
--- a/src/backend/opencl/fftconvolve.cpp
+++ b/src/backend/opencl/fftconvolve.cpp
@@ -137,6 +137,7 @@ INSTANTIATE(double)
 INSTANTIATE(float)
 INSTANTIATE(uint)
 INSTANTIATE(int)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(uintl)
diff --git a/src/backend/opencl/flood_fill.cpp b/src/backend/opencl/flood_fill.cpp
index b57de824bd..4a759e095d 100644
--- a/src/backend/opencl/flood_fill.cpp
+++ b/src/backend/opencl/flood_fill.cpp
@@ -34,6 +34,7 @@ Array<T> floodFill(const Array<T>& image, const Array<uint>& seedsX,
 INSTANTIATE(float)
 INSTANTIATE(uint)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/hist_graphics.cpp b/src/backend/opencl/hist_graphics.cpp
index 6c2a06e0b1..a20daeb700 100644
--- a/src/backend/opencl/hist_graphics.cpp
+++ b/src/backend/opencl/hist_graphics.cpp
@@ -74,6 +74,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/histogram.cpp b/src/backend/opencl/histogram.cpp
index 7c3d432228..bbf7e9082e 100644
--- a/src/backend/opencl/histogram.cpp
+++ b/src/backend/opencl/histogram.cpp
@@ -41,6 +41,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/identity.cpp b/src/backend/opencl/identity.cpp
index 9d9ae55718..9aa72fc433 100644
--- a/src/backend/opencl/identity.cpp
+++ b/src/backend/opencl/identity.cpp
@@ -37,6 +37,7 @@ INSTANTIATE_IDENTITY(uint)
 INSTANTIATE_IDENTITY(intl)
 INSTANTIATE_IDENTITY(uintl)
 INSTANTIATE_IDENTITY(char)
+INSTANTIATE_IDENTITY(schar)
 INSTANTIATE_IDENTITY(uchar)
 INSTANTIATE_IDENTITY(short)
 INSTANTIATE_IDENTITY(ushort)
diff --git a/src/backend/opencl/image.cpp b/src/backend/opencl/image.cpp
index cffc2b8194..663fc63c24 100644
--- a/src/backend/opencl/image.cpp
+++ b/src/backend/opencl/image.cpp
@@ -78,6 +78,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/index.cpp b/src/backend/opencl/index.cpp
index d2864e6a81..b1cb238968 100644
--- a/src/backend/opencl/index.cpp
+++ b/src/backend/opencl/index.cpp
@@ -91,6 +91,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/iota.cpp b/src/backend/opencl/iota.cpp
index de69ca6595..87c840b419 100644
--- a/src/backend/opencl/iota.cpp
+++ b/src/backend/opencl/iota.cpp
@@ -39,6 +39,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/ireduce.cpp b/src/backend/opencl/ireduce.cpp
index ca4c916f63..d4b080389c 100644
--- a/src/backend/opencl/ireduce.cpp
+++ b/src/backend/opencl/ireduce.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(af_min_t, uint)
 INSTANTIATE(af_min_t, intl)
 INSTANTIATE(af_min_t, uintl)
 INSTANTIATE(af_min_t, char)
+INSTANTIATE(af_min_t, schar)
 INSTANTIATE(af_min_t, uchar)
 INSTANTIATE(af_min_t, short)
 INSTANTIATE(af_min_t, ushort)
@@ -73,6 +74,7 @@ INSTANTIATE(af_max_t, uint)
 INSTANTIATE(af_max_t, intl)
 INSTANTIATE(af_max_t, uintl)
 INSTANTIATE(af_max_t, char)
+INSTANTIATE(af_max_t, schar)
 INSTANTIATE(af_max_t, uchar)
 INSTANTIATE(af_max_t, short)
 INSTANTIATE(af_max_t, ushort)
diff --git a/src/backend/opencl/join.cpp b/src/backend/opencl/join.cpp
index 22875d0e61..7975ecfb5a 100644
--- a/src/backend/opencl/join.cpp
+++ b/src/backend/opencl/join.cpp
@@ -227,6 +227,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
@@ -247,6 +248,7 @@ INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(half)
diff --git a/src/backend/opencl/kernel/convolve/conv1.cpp b/src/backend/opencl/kernel/convolve/conv1.cpp
index 10ae600888..5bfa9668d6 100644
--- a/src/backend/opencl/kernel/convolve/conv1.cpp
+++ b/src/backend/opencl/kernel/convolve/conv1.cpp
@@ -58,6 +58,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/opencl/kernel/convolve/conv2_s8.cpp b/src/backend/opencl/kernel/convolve/conv2_s8.cpp
new file mode 100644
index 0000000000..b4b39b3f28
--- /dev/null
+++ b/src/backend/opencl/kernel/convolve/conv2_s8.cpp
@@ -0,0 +1,20 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <kernel/convolve/conv2_impl.hpp>
+
+namespace arrayfire {
+namespace opencl {
+namespace kernel {
+
+INSTANTIATE(schar, float)
+
+}  // namespace kernel
+}  // namespace opencl
+}  // namespace arrayfire
diff --git a/src/backend/opencl/kernel/convolve/conv3.cpp b/src/backend/opencl/kernel/convolve/conv3.cpp
index 9a1baf9c6b..1383e8f443 100644
--- a/src/backend/opencl/kernel/convolve/conv3.cpp
+++ b/src/backend/opencl/kernel/convolve/conv3.cpp
@@ -45,6 +45,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/opencl/kernel/convolve_separable.cpp b/src/backend/opencl/kernel/convolve_separable.cpp
index 41bfa55dde..83a9116d72 100644
--- a/src/backend/opencl/kernel/convolve_separable.cpp
+++ b/src/backend/opencl/kernel/convolve_separable.cpp
@@ -95,6 +95,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(float, float)
 INSTANTIATE(uint, float)
 INSTANTIATE(int, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(char, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/opencl/kernel/random_engine_write.cl b/src/backend/opencl/kernel/random_engine_write.cl
index 8711987e44..c36c5f1d6d 100644
--- a/src/backend/opencl/kernel/random_engine_write.cl
+++ b/src/backend/opencl/kernel/random_engine_write.cl
@@ -27,6 +27,26 @@ float getFloatNegative11(uint num) {
 
 // Writes without boundary checking
 
+void writeOut128Bytes_schar(global char *out, uint index, uint r1, uint r2,
+                            uint r3, uint r4) {
+    out[index]                = r1;
+    out[index + THREADS]      = r1 >> 8;
+    out[index + 2 * THREADS]  = r1 >> 16;
+    out[index + 3 * THREADS]  = r1 >> 24;
+    out[index + 4 * THREADS]  = r2;
+    out[index + 5 * THREADS]  = r2 >> 8;
+    out[index + 6 * THREADS]  = r2 >> 16;
+    out[index + 7 * THREADS]  = r2 >> 24;
+    out[index + 8 * THREADS]  = r3;
+    out[index + 9 * THREADS]  = r3 >> 8;
+    out[index + 10 * THREADS] = r3 >> 16;
+    out[index + 11 * THREADS] = r3 >> 24;
+    out[index + 12 * THREADS] = r4;
+    out[index + 13 * THREADS] = r4 >> 8;
+    out[index + 14 * THREADS] = r4 >> 16;
+    out[index + 15 * THREADS] = r4 >> 24;
+}
+
 void writeOut128Bytes_uchar(global uchar *out, uint index, uint r1, uint r2,
                             uint r3, uint r4) {
     out[index]                = r1;
@@ -154,6 +174,36 @@ void boxMullerTransform(T *const out1, T *const out2, T r1, T r2) {
 
 // Writes with boundary checking
 
+void partialWriteOut128Bytes_schar(global char *out, uint index, uint r1,
+                                   uint r2, uint r3, uint r4, uint elements) {
+    if (index < elements) { out[index] = r1; }
+    if (index + THREADS < elements) { out[index + THREADS] = r1 >> 8; }
+    if (index + 2 * THREADS < elements) { out[index + 2 * THREADS] = r1 >> 16; }
+    if (index + 3 * THREADS < elements) { out[index + 3 * THREADS] = r1 >> 24; }
+    if (index + 4 * THREADS < elements) { out[index + 4 * THREADS] = r2; }
+    if (index + 5 * THREADS < elements) { out[index + 5 * THREADS] = r2 >> 8; }
+    if (index + 6 * THREADS < elements) { out[index + 6 * THREADS] = r2 >> 16; }
+    if (index + 7 * THREADS < elements) { out[index + 7 * THREADS] = r2 >> 24; }
+    if (index + 8 * THREADS < elements) { out[index + 8 * THREADS] = r3; }
+    if (index + 9 * THREADS < elements) { out[index + 9 * THREADS] = r3 >> 8; }
+    if (index + 10 * THREADS < elements) {
+        out[index + 10 * THREADS] = r3 >> 16;
+    }
+    if (index + 11 * THREADS < elements) {
+        out[index + 11 * THREADS] = r3 >> 24;
+    }
+    if (index + 12 * THREADS < elements) { out[index + 12 * THREADS] = r4; }
+    if (index + 13 * THREADS < elements) {
+        out[index + 13 * THREADS] = r4 >> 8;
+    }
+    if (index + 14 * THREADS < elements) {
+        out[index + 14 * THREADS] = r4 >> 16;
+    }
+    if (index + 15 * THREADS < elements) {
+        out[index + 15 * THREADS] = r4 >> 24;
+    }
+}
+
 void partialWriteOut128Bytes_uchar(global uchar *out, uint index, uint r1,
                                    uint r2, uint r3, uint r4, uint elements) {
     if (index < elements) { out[index] = r1; }
diff --git a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
index dd74cccc7e..dd14eee6c5 100644
--- a/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
+++ b/src/backend/opencl/kernel/sort_by_key/sort_by_key_impl.cpp
@@ -9,7 +9,7 @@
 
 #include <kernel/sort_by_key_impl.hpp>
 
-// SBK_TYPES:float double int uint intl uintl short ushort char uchar half
+// SBK_TYPES:float double int uint intl uintl short ushort char schar uchar half
 
 namespace arrayfire {
 namespace opencl {
diff --git a/src/backend/opencl/kernel/sort_by_key_impl.hpp b/src/backend/opencl/kernel/sort_by_key_impl.hpp
index a070a60c67..f03721d01e 100644
--- a/src/backend/opencl/kernel/sort_by_key_impl.hpp
+++ b/src/backend/opencl/kernel/sort_by_key_impl.hpp
@@ -248,6 +248,7 @@ void sort0ByKey(Param pKey, Param pVal, bool isAscending) {
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)   \
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 2fee6f6ae0..36b5929f1f 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -53,6 +53,8 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
                                       const unsigned);                         \
     template Array<T> lookup<T, uintl>(const Array<T> &, const Array<uintl> &, \
                                        const unsigned);                        \
+    template Array<T> lookup<T, schar>(const Array<T> &, const Array<schar> &, \
+                                       const unsigned);                        \
     template Array<T> lookup<T, uchar>(const Array<T> &, const Array<uchar> &, \
                                        const unsigned);                        \
     template Array<T> lookup<T, half>(const Array<T> &, const Array<half> &,   \
@@ -66,6 +68,7 @@ INSTANTIATE(int);
 INSTANTIATE(unsigned);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(ushort);
diff --git a/src/backend/opencl/match_template.cpp b/src/backend/opencl/match_template.cpp
index f97bc6d353..7f02d886b3 100644
--- a/src/backend/opencl/match_template.cpp
+++ b/src/backend/opencl/match_template.cpp
@@ -37,6 +37,7 @@ INSTANTIATE(float, float)
 INSTANTIATE(char, float)
 INSTANTIATE(int, float)
 INSTANTIATE(uint, float)
+INSTANTIATE(schar, float)
 INSTANTIATE(uchar, float)
 INSTANTIATE(short, float)
 INSTANTIATE(ushort, float)
diff --git a/src/backend/opencl/max.cpp b/src/backend/opencl/max.cpp
index b2a2cdfdf0..695415517d 100644
--- a/src/backend/opencl/max.cpp
+++ b/src/backend/opencl/max.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_max_t, uint, uint)
 INSTANTIATE(af_max_t, intl, intl)
 INSTANTIATE(af_max_t, uintl, uintl)
 INSTANTIATE(af_max_t, char, char)
+INSTANTIATE(af_max_t, schar, schar)
 INSTANTIATE(af_max_t, uchar, uchar)
 INSTANTIATE(af_max_t, short, short)
 INSTANTIATE(af_max_t, ushort, ushort)
diff --git a/src/backend/opencl/mean.cpp b/src/backend/opencl/mean.cpp
index 7bd586e587..428c2812c3 100644
--- a/src/backend/opencl/mean.cpp
+++ b/src/backend/opencl/mean.cpp
@@ -59,6 +59,7 @@ INSTANTIATE(intl, double, double);
 INSTANTIATE(uintl, double, double);
 INSTANTIATE(short, float, float);
 INSTANTIATE(ushort, float, float);
+INSTANTIATE(schar, float, float);
 INSTANTIATE(uchar, float, float);
 INSTANTIATE(char, float, float);
 INSTANTIATE(cfloat, float, cfloat);
diff --git a/src/backend/opencl/meanshift.cpp b/src/backend/opencl/meanshift.cpp
index 3c6f140c98..9eaec9db9d 100644
--- a/src/backend/opencl/meanshift.cpp
+++ b/src/backend/opencl/meanshift.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/medfilt.cpp b/src/backend/opencl/medfilt.cpp
index 66a4c6969e..d3025a50b9 100644
--- a/src/backend/opencl/medfilt.cpp
+++ b/src/backend/opencl/medfilt.cpp
@@ -55,6 +55,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/memory.cpp b/src/backend/opencl/memory.cpp
index d2e0190431..7c69b33e24 100644
--- a/src/backend/opencl/memory.cpp
+++ b/src/backend/opencl/memory.cpp
@@ -162,6 +162,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/opencl/min.cpp b/src/backend/opencl/min.cpp
index 9cc6a09272..75c117caa8 100644
--- a/src/backend/opencl/min.cpp
+++ b/src/backend/opencl/min.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_min_t, uint, uint)
 INSTANTIATE(af_min_t, intl, intl)
 INSTANTIATE(af_min_t, uintl, uintl)
 INSTANTIATE(af_min_t, char, char)
+INSTANTIATE(af_min_t, schar, schar)
 INSTANTIATE(af_min_t, uchar, uchar)
 INSTANTIATE(af_min_t, short, short)
 INSTANTIATE(af_min_t, ushort, ushort)
diff --git a/src/backend/opencl/moments.cpp b/src/backend/opencl/moments.cpp
index 0b03d203c9..80afc2ece1 100644
--- a/src/backend/opencl/moments.cpp
+++ b/src/backend/opencl/moments.cpp
@@ -47,6 +47,7 @@ INSTANTIATE(float)
 INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/morph.cpp b/src/backend/opencl/morph.cpp
index e77b7a063c..a1cb86aa03 100644
--- a/src/backend/opencl/morph.cpp
+++ b/src/backend/opencl/morph.cpp
@@ -57,6 +57,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/nearest_neighbour.cpp b/src/backend/opencl/nearest_neighbour.cpp
index 535be4083f..615165a8e5 100644
--- a/src/backend/opencl/nearest_neighbour.cpp
+++ b/src/backend/opencl/nearest_neighbour.cpp
@@ -80,6 +80,7 @@ INSTANTIATE(intl, intl)
 INSTANTIATE(uintl, uintl)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, uint)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, uint)
 
 INSTANTIATE(uintl, uint)  // For Hamming
diff --git a/src/backend/opencl/plot.cpp b/src/backend/opencl/plot.cpp
index cc7f93262e..5b7dfa69cb 100644
--- a/src/backend/opencl/plot.cpp
+++ b/src/backend/opencl/plot.cpp
@@ -75,6 +75,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/product.cpp b/src/backend/opencl/product.cpp
index f13a9b9ae3..a949f87345 100644
--- a/src/backend/opencl/product.cpp
+++ b/src/backend/opencl/product.cpp
@@ -24,6 +24,7 @@ INSTANTIATE(af_mul_t, uint, uint)
 INSTANTIATE(af_mul_t, intl, intl)
 INSTANTIATE(af_mul_t, uintl, uintl)
 INSTANTIATE(af_mul_t, char, int)
+INSTANTIATE(af_mul_t, schar, int)
 INSTANTIATE(af_mul_t, uchar, uint)
 INSTANTIATE(af_mul_t, short, int)
 INSTANTIATE(af_mul_t, ushort, uint)
diff --git a/src/backend/opencl/random_engine.cpp b/src/backend/opencl/random_engine.cpp
index f2110c8be0..d307e54c2b 100644
--- a/src/backend/opencl/random_engine.cpp
+++ b/src/backend/opencl/random_engine.cpp
@@ -138,6 +138,7 @@ INSTANTIATE_UNIFORM(uint)
 INSTANTIATE_UNIFORM(intl)
 INSTANTIATE_UNIFORM(uintl)
 INSTANTIATE_UNIFORM(char)
+INSTANTIATE_UNIFORM(schar)
 INSTANTIATE_UNIFORM(uchar)
 INSTANTIATE_UNIFORM(short)
 INSTANTIATE_UNIFORM(ushort)
diff --git a/src/backend/opencl/range.cpp b/src/backend/opencl/range.cpp
index 92340d34eb..a49ba931c8 100644
--- a/src/backend/opencl/range.cpp
+++ b/src/backend/opencl/range.cpp
@@ -47,6 +47,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/reorder.cpp b/src/backend/opencl/reorder.cpp
index da485911e6..ecacccd677 100644
--- a/src/backend/opencl/reorder.cpp
+++ b/src/backend/opencl/reorder.cpp
@@ -40,6 +40,7 @@ INSTANTIATE(cfloat)
 INSTANTIATE(cdouble)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(intl)
diff --git a/src/backend/opencl/resize.cpp b/src/backend/opencl/resize.cpp
index ee7776b82f..bf3a8497b2 100644
--- a/src/backend/opencl/resize.cpp
+++ b/src/backend/opencl/resize.cpp
@@ -38,6 +38,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/rotate.cpp b/src/backend/opencl/rotate.cpp
index 46caa65c88..eab0c1da26 100644
--- a/src/backend/opencl/rotate.cpp
+++ b/src/backend/opencl/rotate.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/scan.cpp b/src/backend/opencl/scan.cpp
index 0fc36366ef..649789ef91 100644
--- a/src/backend/opencl/scan.cpp
+++ b/src/backend/opencl/scan.cpp
@@ -43,6 +43,7 @@ Array<To> scan(const Array<Ti>& in, const int dim, bool inclusiveScan) {
     INSTANTIATE_SCAN(ROp, intl, intl)       \
     INSTANTIATE_SCAN(ROp, uintl, uintl)     \
     INSTANTIATE_SCAN(ROp, char, uint)       \
+    INSTANTIATE_SCAN(ROp, schar, int)       \
     INSTANTIATE_SCAN(ROp, uchar, uint)      \
     INSTANTIATE_SCAN(ROp, short, int)       \
     INSTANTIATE_SCAN(ROp, ushort, uint)
diff --git a/src/backend/opencl/select.cpp b/src/backend/opencl/select.cpp
index bbafbe989c..20c900007a 100644
--- a/src/backend/opencl/select.cpp
+++ b/src/backend/opencl/select.cpp
@@ -127,6 +127,7 @@ INSTANTIATE(uint);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
 INSTANTIATE(char);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(short);
 INSTANTIATE(ushort);
diff --git a/src/backend/opencl/set.cpp b/src/backend/opencl/set.cpp
index 195cf23047..1c1b74396c 100644
--- a/src/backend/opencl/set.cpp
+++ b/src/backend/opencl/set.cpp
@@ -147,6 +147,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/shift.cpp b/src/backend/opencl/shift.cpp
index 8b257f2c97..19e37286d3 100644
--- a/src/backend/opencl/shift.cpp
+++ b/src/backend/opencl/shift.cpp
@@ -64,6 +64,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/sobel.cpp b/src/backend/opencl/sobel.cpp
index e718021b42..a7651de07d 100644
--- a/src/backend/opencl/sobel.cpp
+++ b/src/backend/opencl/sobel.cpp
@@ -40,6 +40,7 @@ INSTANTIATE(double, double)
 INSTANTIATE(int, int)
 INSTANTIATE(uint, int)
 INSTANTIATE(char, int)
+INSTANTIATE(schar, int)
 INSTANTIATE(uchar, int)
 INSTANTIATE(short, int)
 INSTANTIATE(ushort, int)
diff --git a/src/backend/opencl/sort.cpp b/src/backend/opencl/sort.cpp
index 8b977316f1..e2bfcaa057 100644
--- a/src/backend/opencl/sort.cpp
+++ b/src/backend/opencl/sort.cpp
@@ -56,6 +56,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/sort_by_key.cpp b/src/backend/opencl/sort_by_key.cpp
index 2e4b2dd616..f1a89aef4d 100644
--- a/src/backend/opencl/sort_by_key.cpp
+++ b/src/backend/opencl/sort_by_key.cpp
@@ -69,6 +69,7 @@ void sort_by_key(Array<Tk> &okey, Array<Tv> &oval, const Array<Tk> &ikey,
     INSTANTIATE(Tk, short)   \
     INSTANTIATE(Tk, ushort)  \
     INSTANTIATE(Tk, char)    \
+    INSTANTIATE(Tk, schar)   \
     INSTANTIATE(Tk, uchar)   \
     INSTANTIATE(Tk, intl)    \
     INSTANTIATE(Tk, uintl)
@@ -80,6 +81,7 @@ INSTANTIATE1(uint)
 INSTANTIATE1(short)
 INSTANTIATE1(ushort)
 INSTANTIATE1(char)
+INSTANTIATE1(schar)
 INSTANTIATE1(uchar)
 INSTANTIATE1(intl)
 INSTANTIATE1(uintl)
diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index 9c92f8406c..4840c24277 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -70,6 +70,7 @@ INSTANTIATE(double)
 INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/sum.cpp b/src/backend/opencl/sum.cpp
index 890280ba92..1ef26bdb89 100644
--- a/src/backend/opencl/sum.cpp
+++ b/src/backend/opencl/sum.cpp
@@ -29,6 +29,8 @@ INSTANTIATE(af_add_t, uintl, uintl)
 INSTANTIATE(af_add_t, uintl, double)
 INSTANTIATE(af_add_t, char, int)
 INSTANTIATE(af_add_t, char, float)
+INSTANTIATE(af_add_t, schar, int)
+INSTANTIATE(af_add_t, schar, float)
 INSTANTIATE(af_add_t, uchar, uint)
 INSTANTIATE(af_add_t, uchar, float)
 INSTANTIATE(af_add_t, short, int)
diff --git a/src/backend/opencl/surface.cpp b/src/backend/opencl/surface.cpp
index a0de95fb19..7a2e15276b 100644
--- a/src/backend/opencl/surface.cpp
+++ b/src/backend/opencl/surface.cpp
@@ -78,6 +78,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/susan.cpp b/src/backend/opencl/susan.cpp
index 6bd78e2540..91b011120b 100644
--- a/src/backend/opencl/susan.cpp
+++ b/src/backend/opencl/susan.cpp
@@ -66,6 +66,7 @@ INSTANTIATE(double)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/tile.cpp b/src/backend/opencl/tile.cpp
index 14e2d5beac..98c7eb2bfb 100644
--- a/src/backend/opencl/tile.cpp
+++ b/src/backend/opencl/tile.cpp
@@ -41,6 +41,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 14ee03c962..78428ed3a7 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -49,6 +49,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/transpose.cpp b/src/backend/opencl/transpose.cpp
index a25fa9be28..248de43017 100644
--- a/src/backend/opencl/transpose.cpp
+++ b/src/backend/opencl/transpose.cpp
@@ -43,6 +43,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/opencl/transpose_inplace.cpp b/src/backend/opencl/transpose_inplace.cpp
index dc23873814..d6b783e5b2 100644
--- a/src/backend/opencl/transpose_inplace.cpp
+++ b/src/backend/opencl/transpose_inplace.cpp
@@ -39,6 +39,7 @@ INSTANTIATE(cdouble)
 INSTANTIATE(char)
 INSTANTIATE(int)
 INSTANTIATE(uint)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
diff --git a/src/backend/opencl/triangle.cpp b/src/backend/opencl/triangle.cpp
index cb781eeef4..346f8d1af7 100644
--- a/src/backend/opencl/triangle.cpp
+++ b/src/backend/opencl/triangle.cpp
@@ -47,6 +47,7 @@ INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
 INSTANTIATE(char)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/types.cpp b/src/backend/opencl/types.cpp
index 35c2b5745a..90393de3f9 100644
--- a/src/backend/opencl/types.cpp
+++ b/src/backend/opencl/types.cpp
@@ -95,6 +95,7 @@ INSTANTIATE(int);
 INSTANTIATE(uint);
 INSTANTIATE(intl);
 INSTANTIATE(uintl);
+INSTANTIATE(schar);
 INSTANTIATE(uchar);
 INSTANTIATE(char);
 INSTANTIATE(half);
diff --git a/src/backend/opencl/types.hpp b/src/backend/opencl/types.hpp
index 620ab74ca9..48985ab837 100644
--- a/src/backend/opencl/types.hpp
+++ b/src/backend/opencl/types.hpp
@@ -40,6 +40,7 @@ namespace opencl {
 using cdouble = cl_double2;
 using cfloat  = cl_float2;
 using intl    = long long;
+using schar   = cl_char;
 using uchar   = cl_uchar;
 using uint    = cl_uint;
 using uintl   = unsigned long long;
@@ -93,6 +94,10 @@ inline const char *shortname<char>(bool caps) {
     return caps ? "J" : "j";
 }
 template<>
+inline const char *shortname<schar>(bool caps) {
+    return caps ? "A" : "a"; // TODO
+}
+template<>
 inline const char *shortname<uchar>(bool caps) {
     return caps ? "V" : "v";
 }
@@ -118,6 +123,11 @@ inline const char *getFullName() {
     return af::dtype_traits<T>::getName();
 }
 
+template<>
+inline const char *getFullName<schar>() {
+    return "char";
+}
+
 template<>
 inline const char *getFullName<cfloat>() {
     return "float2";
diff --git a/src/backend/opencl/unwrap.cpp b/src/backend/opencl/unwrap.cpp
index c6c7a12d4f..3fb0d9a14c 100644
--- a/src/backend/opencl/unwrap.cpp
+++ b/src/backend/opencl/unwrap.cpp
@@ -53,6 +53,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/src/backend/opencl/vector_field.cpp b/src/backend/opencl/vector_field.cpp
index e470f73c9a..4d85032602 100644
--- a/src/backend/opencl/vector_field.cpp
+++ b/src/backend/opencl/vector_field.cpp
@@ -101,6 +101,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 
 }  // namespace opencl
diff --git a/src/backend/opencl/where.cpp b/src/backend/opencl/where.cpp
index c3ac797454..ae86cd8521 100644
--- a/src/backend/opencl/where.cpp
+++ b/src/backend/opencl/where.cpp
@@ -35,6 +35,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(short)
 INSTANTIATE(ushort)
diff --git a/src/backend/opencl/wrap.cpp b/src/backend/opencl/wrap.cpp
index 42d684857a..418dc9bc1f 100644
--- a/src/backend/opencl/wrap.cpp
+++ b/src/backend/opencl/wrap.cpp
@@ -42,6 +42,7 @@ INSTANTIATE(int)
 INSTANTIATE(uint)
 INSTANTIATE(intl)
 INSTANTIATE(uintl)
+INSTANTIATE(schar)
 INSTANTIATE(uchar)
 INSTANTIATE(char)
 INSTANTIATE(short)
diff --git a/test/anisotropic_diffusion.cpp b/test/anisotropic_diffusion.cpp
index 60e3c75324..a498d4cdd8 100644
--- a/test/anisotropic_diffusion.cpp
+++ b/test/anisotropic_diffusion.cpp
@@ -29,7 +29,7 @@ using std::vector;
 template<typename T>
 class AnisotropicDiffusion : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, schar, uchar, short, ushort>
     TestTypes;
 
 TYPED_TEST_SUITE(AnisotropicDiffusion, TestTypes);
diff --git a/test/array.cpp b/test/array.cpp
index b68f06820a..c5befe1fdb 100644
--- a/test/array.cpp
+++ b/test/array.cpp
@@ -21,8 +21,8 @@ using std::vector;
 template<typename T>
 class Array : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
-                         int, uint, intl, uintl, short, ushort,
+typedef ::testing::Types<float, double, cfloat, cdouble, char, signed char,
+                         unsigned char, int, uint, intl, uintl, short, ushort,
                          half_float::half>
     TestTypes;
 
@@ -302,6 +302,17 @@ TYPED_TEST(Array, TypeAttributes) {
             EXPECT_FALSE(one.isbool());
             EXPECT_FALSE(one.ishalf());
             break;
+        case s8:
+            EXPECT_FALSE(one.isfloating());
+            EXPECT_FALSE(one.isdouble());
+            EXPECT_FALSE(one.issingle());
+            EXPECT_FALSE(one.isrealfloating());
+            EXPECT_TRUE(one.isinteger());
+            EXPECT_TRUE(one.isreal());
+            EXPECT_FALSE(one.iscomplex());
+            EXPECT_FALSE(one.isbool());
+            EXPECT_FALSE(one.ishalf());
+            break;
         case u8:
             EXPECT_FALSE(one.isfloating());
             EXPECT_FALSE(one.isdouble());
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index db1f67a341..5b41f505d7 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -77,6 +77,7 @@ std::ostream &operator<<(std::ostream &os, af::dtype type) {
         case b8: name = "b8"; break;
         case s32: name = "s32"; break;
         case u32: name = "u32"; break;
+        case s8: name = "s8"; break;
         case u8: name = "u8"; break;
         case s64: name = "s64"; break;
         case u64: name = "u64"; break;
@@ -168,6 +169,9 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
         case u32:
             return elemWiseEq<uint>(aName, bName, a, b, maxAbsDiff);
             break;
+        case s8:
+            return elemWiseEq<schar>(aName, bName, a, b, maxAbsDiff);
+            break;
         case u8:
             return elemWiseEq<uchar>(aName, bName, a, b, maxAbsDiff);
             break;
@@ -264,6 +268,7 @@ ::testing::AssertionResult assertImageEq(std::string aName, std::string bName,
                << "Expected: " << aName << "([" << a.dims() << "])";
 
     switch (arrDtype) {
+        case s8: return imageEq<signed char>(aName, bName, a, b, maxAbsDiff);
         case u8: return imageEq<unsigned char>(aName, bName, a, b, maxAbsDiff);
         case b8: return imageEq<char>(aName, bName, a, b, maxAbsDiff);
         case s32: return imageEq<int>(aName, bName, a, b, maxAbsDiff);
@@ -350,6 +355,7 @@ INSTANTIATE(double, float, int);
 INSTANTIATE(int, float, int);
 INSTANTIATE(unsigned int, float, int);
 INSTANTIATE(char, float, int);
+INSTANTIATE(signed char, float, int);
 INSTANTIATE(unsigned char, float, int);
 INSTANTIATE(short, float, int);
 INSTANTIATE(unsigned short, float, int);
@@ -364,6 +370,7 @@ INSTANTIATE(unsigned int, unsigned int, unsigned int);
 INSTANTIATE(long long, long long, int);
 INSTANTIATE(unsigned long long, unsigned long long, int);
 INSTANTIATE(char, char, int);
+INSTANTIATE(signed char, signed char, int);
 INSTANTIATE(unsigned char, unsigned char, int);
 INSTANTIATE(short, short, int);
 INSTANTIATE(unsigned short, unsigned short, int);
@@ -372,12 +379,19 @@ INSTANTIATE(af_half, af_half, int);
 INSTANTIATE(float, int, int);
 INSTANTIATE(unsigned int, int, int);
 INSTANTIATE(char, int, int);
+INSTANTIATE(signed char, int, int);
 INSTANTIATE(unsigned char, int, int);
 INSTANTIATE(short, int, int);
 INSTANTIATE(unsigned short, int, int);
 
+INSTANTIATE(signed char, unsigned short, int);
+INSTANTIATE(signed char, short, int);
+INSTANTIATE(signed char, unsigned char, int);
+INSTANTIATE(signed char, double, int);
+
 INSTANTIATE(unsigned char, unsigned short, int);
 INSTANTIATE(unsigned char, short, int);
+INSTANTIATE(unsigned char, signed char, int);
 INSTANTIATE(unsigned char, double, int);
 
 INSTANTIATE(long long, unsigned int, unsigned int);
@@ -386,6 +400,7 @@ INSTANTIATE(int, unsigned int, unsigned int);
 INSTANTIATE(short, unsigned int, unsigned int);
 INSTANTIATE(unsigned short, unsigned int, unsigned int);
 INSTANTIATE(char, unsigned int, unsigned int);
+INSTANTIATE(signed char, unsigned int, unsigned int);
 INSTANTIATE(unsigned char, unsigned int, unsigned int);
 INSTANTIATE(float, unsigned int, unsigned int);
 INSTANTIATE(double, unsigned int, unsigned int);
@@ -396,12 +411,14 @@ INSTANTIATE(int, unsigned int, int);
 INSTANTIATE(long long, unsigned int, int);
 INSTANTIATE(unsigned long long, unsigned int, int);
 INSTANTIATE(char, unsigned int, int);
+INSTANTIATE(signed char, unsigned int, int);
 INSTANTIATE(unsigned char, unsigned int, int);
 INSTANTIATE(short, unsigned int, int);
 INSTANTIATE(unsigned short, unsigned int, int);
 
 INSTANTIATE(float, char, int);
 INSTANTIATE(double, char, int);
+INSTANTIATE(signed char, char, int);
 INSTANTIATE(unsigned char, char, int);
 INSTANTIATE(short, char, int);
 INSTANTIATE(unsigned short, char, int);
@@ -412,6 +429,7 @@ INSTANTIATE(char, float, float);
 INSTANTIATE(int, float, float);
 INSTANTIATE(unsigned int, float, float);
 INSTANTIATE(short, float, float);
+INSTANTIATE(signed char, float, float);
 INSTANTIATE(unsigned char, float, float);
 INSTANTIATE(unsigned short, float, float);
 INSTANTIATE(double, float, float);
@@ -432,6 +450,7 @@ INSTANTIATE(unsigned int, unsigned int, float);
 INSTANTIATE(long long, long long, float);
 INSTANTIATE(unsigned long long, unsigned long long, float);
 INSTANTIATE(char, char, float);
+INSTANTIATE(signed char, signed char, float);
 INSTANTIATE(unsigned char, unsigned char, float);
 INSTANTIATE(short, short, float);
 INSTANTIATE(unsigned short, unsigned short, float);
@@ -448,6 +467,7 @@ INSTANTIATE(unsigned int, float, double);
 INSTANTIATE(short, float, double);
 INSTANTIATE(unsigned short, float, double);
 INSTANTIATE(char, float, double);
+INSTANTIATE(signed char, float, double);
 INSTANTIATE(unsigned char, float, double);
 INSTANTIATE(long long, double, double);
 INSTANTIATE(unsigned long long, double, double);
@@ -1356,6 +1376,7 @@ af_err conv_image(af_array *out, af_array in) {
 
 INSTANTIATE(float);
 INSTANTIATE(double);
+INSTANTIATE(signed char);
 INSTANTIATE(unsigned char);
 INSTANTIATE(half_float::half);
 INSTANTIATE(unsigned int);
@@ -1393,6 +1414,7 @@ af::array cpu_randu(const af::dim4 dims) {
 #define INSTANTIATE(To) template af::array cpu_randu<To>(const af::dim4 dims)
 INSTANTIATE(float);
 INSTANTIATE(double);
+INSTANTIATE(signed char);
 INSTANTIATE(unsigned char);
 INSTANTIATE(half_float::half);
 INSTANTIATE(unsigned int);
@@ -2001,6 +2023,7 @@ ::testing::AssertionResult assertRefEq(std::string hA_name,
 
 INSTANTIATE(float);
 INSTANTIATE(double);
+INSTANTIATE(signed char);
 INSTANTIATE(unsigned char);
 INSTANTIATE(half_float::half);
 INSTANTIATE(unsigned int);
diff --git a/test/arrayio.cpp b/test/arrayio.cpp
index 00d907a568..ea15165ac4 100644
--- a/test/arrayio.cpp
+++ b/test/arrayio.cpp
@@ -51,7 +51,8 @@ INSTANTIATE_TEST_SUITE_P(
                       type_params("s32", s32, 11), type_params("u32", u32, 12),
                       type_params("u8", u8, 13), type_params("b8", b8, 1),
                       type_params("s64", s64, 15), type_params("u64", u64, 16),
-                      type_params("s16", s16, 17), type_params("u16", u16, 18)),
+                      type_params("s16", s16, 17), type_params("u16", u16, 18),
+                      type_params("s8", s8, 19)),
     getTypeName);
 
 TEST_P(ArrayIOType, ReadType) {
@@ -103,6 +104,7 @@ TEST_P(ArrayIOType, ReadContent) {
         case c64: checkVals<af::cdouble>(arr, p.real, p.imag, p.type); break;
         case s32: checkVals<int>(arr, p.real, p.imag, p.type); break;
         case u32: checkVals<unsigned>(arr, p.real, p.imag, p.type); break;
+        case s8: checkVals<signed char>(arr, p.real, p.imag, p.type); break;
         case u8: checkVals<unsigned char>(arr, p.real, p.imag, p.type); break;
         case b8: checkVals<char>(arr, p.real, p.imag, p.type); break;
         case s64: checkVals<long long>(arr, p.real, p.imag, p.type); break;
diff --git a/test/assign.cpp b/test/assign.cpp
index cbfe6359b1..7b94bfa608 100644
--- a/test/assign.cpp
+++ b/test/assign.cpp
@@ -94,8 +94,8 @@ class ArrayAssign : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, uchar,
-                         intl, uintl, short, ushort, half_float::half>
+typedef ::testing::Types<float, cdouble, cfloat, double, int, uint, char, schar,
+                         uchar, intl, uintl, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/bilateral.cpp b/test/bilateral.cpp
index f4ff949b55..12b27fc33f 100644
--- a/test/bilateral.cpp
+++ b/test/bilateral.cpp
@@ -73,7 +73,8 @@ TEST(BilateralOnImage, Color) {
 template<typename T>
 class BilateralOnData : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort>
     DataTestTypes;
 
 // register the type list
diff --git a/test/binary.cpp b/test/binary.cpp
index ed5b2c0869..3dbfa44bb9 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -496,6 +496,7 @@ INSTANTIATE_TEST_SUITE_P(
                       result_type_param(b8),
                       result_type_param(s32),
                       result_type_param(u32),
+                      result_type_param(s8),
                       result_type_param(u8),
                       result_type_param(s64),
                       result_type_param(u64),
@@ -515,6 +516,7 @@ INSTANTIATE_TEST_SUITE_P(
                       result_type_param(f32, b8, f32),
                       result_type_param(f32, s32, f32),
                       result_type_param(f32, u32, f32),
+                      result_type_param(f32, s8, f32),
                       result_type_param(f32, u8, f32),
                       result_type_param(f32, s64, f32),
                       result_type_param(f32, u64, f32),
@@ -535,6 +537,7 @@ INSTANTIATE_TEST_SUITE_P(
                       result_type_param(f64, b8,  f64),
                       result_type_param(f64, s32, f64),
                       result_type_param(f64, u32, f64),
+                      result_type_param(f64, s8,  f64),
                       result_type_param(f64, u8,  f64),
                       result_type_param(f64, s64, f64),
                       result_type_param(f64, u64, f64),
@@ -567,7 +570,8 @@ class ResultTypeScalar : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double, unsigned int, int, short,
-                         unsigned short, char, unsigned char, half_float::half>
+                         unsigned short, char, signed char, unsigned char,
+                         half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(ResultTypeScalar, TestTypes);
 
diff --git a/test/canny.cpp b/test/canny.cpp
index a12ac73965..0a0fdbc08c 100644
--- a/test/canny.cpp
+++ b/test/canny.cpp
@@ -28,7 +28,7 @@ class CannyEdgeDetector : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, int, uint, short, ushort, uchar, double>
+typedef ::testing::Types<float, int, uint, short, ushort, schar, uchar, double>
     TestTypes;
 
 // register the type list
diff --git a/test/cast.cpp b/test/cast.cpp
index cb1f4e3f42..d2b4f95250 100644
--- a/test/cast.cpp
+++ b/test/cast.cpp
@@ -52,6 +52,7 @@ void cast_test() {
     REAL_TO_TESTS(Ti, char);     \
     REAL_TO_TESTS(Ti, int);      \
     REAL_TO_TESTS(Ti, unsigned); \
+    REAL_TO_TESTS(Ti, schar);    \
     REAL_TO_TESTS(Ti, uchar);    \
     REAL_TO_TESTS(Ti, intl);     \
     REAL_TO_TESTS(Ti, uintl);    \
@@ -67,6 +68,7 @@ REAL_TEST_INVOKE(double)
 REAL_TEST_INVOKE(char)
 REAL_TEST_INVOKE(int)
 REAL_TEST_INVOKE(unsigned)
+REAL_TEST_INVOKE(schar)
 REAL_TEST_INVOKE(uchar)
 REAL_TEST_INVOKE(intl)
 REAL_TEST_INVOKE(uintl)
diff --git a/test/clamp.cpp b/test/clamp.cpp
index 1e0b04b7c2..c830b06b2b 100644
--- a/test/clamp.cpp
+++ b/test/clamp.cpp
@@ -125,6 +125,7 @@ INSTANTIATE_TEST_SUITE_P(
                       clamp_params(dim4(10), f16, f16, f16, f16),
                       clamp_params(dim4(10), s32, f32, f32, f32),
                       clamp_params(dim4(10), u32, f32, f32, f32),
+                      clamp_params(dim4(10), s8,  f32, f32, f32),
                       clamp_params(dim4(10), u8,  f32, f32, f32),
                       clamp_params(dim4(10), b8,  f32, f32, f32),
                       clamp_params(dim4(10), s64, f32, f32, f32),
diff --git a/test/compare.cpp b/test/compare.cpp
index 66d9778039..877c08275f 100644
--- a/test/compare.cpp
+++ b/test/compare.cpp
@@ -23,8 +23,8 @@ using std::vector;
 template<typename T>
 class Compare : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, uint, int, intl, uintl, uchar, short,
-                         ushort, half_float::half>
+typedef ::testing::Types<float, double, uint, int, intl, uintl, schar, uchar,
+                         short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(Compare, TestTypes);
 
diff --git a/test/constant.cpp b/test/constant.cpp
index 0a75e3d974..b1d3e0a5af 100644
--- a/test/constant.cpp
+++ b/test/constant.cpp
@@ -31,7 +31,8 @@ template<typename T>
 class Constant : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
-                         uchar, uintl, intl, short, ushort, half_float::half>
+                         schar, uchar, uintl, intl, short, ushort,
+                         half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(Constant, TestTypes);
 
diff --git a/test/convolve.cpp b/test/convolve.cpp
index ac731ef31c..5df8961e1b 100644
--- a/test/convolve.cpp
+++ b/test/convolve.cpp
@@ -33,8 +33,8 @@ class Convolve : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, char, uchar,
-                         short, ushort, intl, uintl>
+typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, char, schar,
+                         uchar, short, ushort, intl, uintl>
     TestTypes;
 
 // register the type list
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index 213a8de092..ffcecacd61 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -31,7 +31,8 @@ class CorrelationCoefficient : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar>
+typedef ::testing::Types<float, double, int, uint, intl, uintl, char, schar,
+                         uchar>
     TestTypes;
 
 // register the type list
diff --git a/test/covariance.cpp b/test/covariance.cpp
index 4d4e4877f1..f149fbd095 100644
--- a/test/covariance.cpp
+++ b/test/covariance.cpp
@@ -34,8 +34,8 @@ class Covariance : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short,
-                         ushort>
+typedef ::testing::Types<float, double, int, uint, intl, uintl, schar, uchar,
+                         short, ushort>
     TestTypes;
 
 // register the type list
@@ -65,9 +65,9 @@ template<typename T>
 struct covOutType {
     typedef typename cond_type<
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
-            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
-            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
-            is_same_type<T, char>::value,
+            is_same_type<T, uint>::value || is_same_type<T, schar>::value ||
+            is_same_type<T, uchar>::value || is_same_type<T, short>::value ||
+            is_same_type<T, ushort>::value || is_same_type<T, char>::value,
         float, typename elseType<T>::type>::type type;
 };
 
diff --git a/test/diagonal.cpp b/test/diagonal.cpp
index 1eecb883ae..e3031f731c 100644
--- a/test/diagonal.cpp
+++ b/test/diagonal.cpp
@@ -31,8 +31,8 @@ using std::vector;
 template<typename T>
 class Diagonal : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, int, uint, char, unsigned char,
-                         half_float::half>
+typedef ::testing::Types<float, double, int, uint, char, signed char,
+                         unsigned char, half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(Diagonal, TestTypes);
 
diff --git a/test/diff1.cpp b/test/diff1.cpp
index a7456fd0a2..9fdf11a91a 100644
--- a/test/diff1.cpp
+++ b/test/diff1.cpp
@@ -46,7 +46,7 @@ class Diff1 : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
-                         uintl, char, unsigned char, short, ushort>
+                         uintl, char, signed char, unsigned char, short, ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/diff2.cpp b/test/diff2.cpp
index c7c17f333f..cdc2b9909e 100644
--- a/test/diff2.cpp
+++ b/test/diff2.cpp
@@ -51,7 +51,7 @@ class Diff2 : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
-                         uintl, char, unsigned char, short, ushort>
+                         uintl, char, signed char, unsigned char, short, ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/dog.cpp b/test/dog.cpp
index 0b764f2c06..af76c23f59 100644
--- a/test/dog.cpp
+++ b/test/dog.cpp
@@ -33,7 +33,8 @@ class DOG : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/fast.cpp b/test/fast.cpp
index 693c80db67..c5e3225d0e 100644
--- a/test/fast.cpp
+++ b/test/fast.cpp
@@ -61,7 +61,7 @@ class FixedFAST : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double> FloatTestTypes;
-typedef ::testing::Types<int, unsigned, short, ushort> FixedTestTypes;
+typedef ::testing::Types<int, unsigned, short, ushort, schar> FixedTestTypes;
 
 TYPED_TEST_SUITE(FloatFAST, FloatTestTypes);
 TYPED_TEST_SUITE(FixedFAST, FixedTestTypes);
diff --git a/test/fftconvolve.cpp b/test/fftconvolve.cpp
index 57d9398a04..a8f63e2f45 100644
--- a/test/fftconvolve.cpp
+++ b/test/fftconvolve.cpp
@@ -39,8 +39,8 @@ class FFTConvolveLarge : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<cfloat, cdouble, float, double, int, uint, char, uchar,
-                         intl, uintl>
+typedef ::testing::Types<cfloat, cdouble, float, double, int, uint, char, schar,
+                         uchar, intl, uintl>
     TestTypes;
 typedef ::testing::Types<float, double> TestTypesLarge;
 
diff --git a/test/gen_index.cpp b/test/gen_index.cpp
index 0716751fa0..fe684ebd27 100644
--- a/test/gen_index.cpp
+++ b/test/gen_index.cpp
@@ -108,8 +108,9 @@ INSTANTIATE_TEST_SUITE_P(
     ::testing::Combine(
         ::testing::Values(index_test(
             string(TEST_DIR "/gen_index/s0_3s0_1s1_2a.test"), dim4(4, 2, 2))),
-        ::testing::Values(f32, f64, c32, c64, u64, s64, u16, s16, u8, b8, f16),
-        ::testing::Values(f32, f64, u64, s64, u16, s16, u8, f16)),
+        ::testing::Values(f32, f64, c32, c64, u64, s64, u16, s16, s8, u8, b8,
+                          f16),
+        ::testing::Values(f32, f64, u64, s64, u16, s16, s8, u8, f16)),
     testNameGenerator);
 
 TEST_P(IndexGeneralizedLegacy, SSSA) {
diff --git a/test/half.cpp b/test/half.cpp
index 7f85950170..8afb6d5f4d 100644
--- a/test/half.cpp
+++ b/test/half.cpp
@@ -41,6 +41,7 @@ INSTANTIATE_TEST_SUITE_P(ToF16, HalfConvert,
                                            convert_params(f64, f16, 10),
                                            convert_params(s32, f16, 10),
                                            convert_params(u32, f16, 10),
+                                           convert_params(s8, f16, 10),
                                            convert_params(u8, f16, 10),
                                            convert_params(s64, f16, 10),
                                            convert_params(u64, f16, 10),
@@ -53,6 +54,7 @@ INSTANTIATE_TEST_SUITE_P(FromF16, HalfConvert,
                                            convert_params(f16, f64, 10),
                                            convert_params(f16, s32, 10),
                                            convert_params(f16, u32, 10),
+                                           convert_params(f16, s8, 10),
                                            convert_params(f16, u8, 10),
                                            convert_params(f16, s64, 10),
                                            convert_params(f16, u64, 10),
diff --git a/test/histogram.cpp b/test/histogram.cpp
index ca3df72f74..ea9431485c 100644
--- a/test/histogram.cpp
+++ b/test/histogram.cpp
@@ -33,7 +33,7 @@ class Histogram : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<half_float::half, float, double, int, uint, char,
-                         uchar, short, ushort, intl, uintl>
+                         schar, uchar, short, ushort, intl, uintl>
     TestTypes;
 
 // register the type list
diff --git a/test/index.cpp b/test/index.cpp
index c8e1a7ffb9..39491453e7 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -138,7 +138,7 @@ class Indexing1D : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned,
-                         unsigned char, intl, uintl, short, ushort,
+                         signed char, unsigned char, intl, uintl, short, ushort,
                          half_float::half>
     AllTypes;
 TYPED_TEST_SUITE(Indexing1D, AllTypes);
@@ -710,8 +710,9 @@ class lookup : public ::testing::Test {
     virtual void SetUp() {}
 };
 
-typedef ::testing::Types<float, double, int, unsigned, unsigned char, short,
-                         ushort, intl, uintl, half_float::half>
+typedef ::testing::Types<float, double, int, unsigned, signed char,
+                         unsigned char, short, ushort, intl, uintl,
+                         half_float::half>
     ArrIdxTestTypes;
 TYPED_TEST_SUITE(lookup, ArrIdxTestTypes);
 
diff --git a/test/inverse_deconv.cpp b/test/inverse_deconv.cpp
index b6db793f4b..86ac2869ab 100644
--- a/test/inverse_deconv.cpp
+++ b/test/inverse_deconv.cpp
@@ -25,7 +25,7 @@ template<typename T>
 class InverseDeconvolution : public ::testing::Test {};
 
 // create a list of types to be tested
-typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, schar, uchar, short, ushort> TestTypes;
 
 // register the type list
 TYPED_TEST_SUITE(InverseDeconvolution, TestTypes);
diff --git a/test/iota.cpp b/test/iota.cpp
index c776d7628e..33ff36e3ba 100644
--- a/test/iota.cpp
+++ b/test/iota.cpp
@@ -39,7 +39,8 @@ class Iota : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
-                         unsigned char, short, ushort, half_float::half>
+                         signed char, unsigned char, short, ushort,
+                         half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index e59440b977..432c9ff533 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -25,7 +25,7 @@ template<typename T>
 class IterativeDeconvolution : public ::testing::Test {};
 
 // create a list of types to be tested
-typedef ::testing::Types<float, uchar, short, ushort> TestTypes;
+typedef ::testing::Types<float, schar, uchar, short, ushort> TestTypes;
 
 // register the type list
 TYPED_TEST_SUITE(IterativeDeconvolution, TestTypes);
diff --git a/test/join.cpp b/test/join.cpp
index 4d25e8a6ae..aef578bcf2 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -48,8 +48,8 @@ class Join : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort,
-                         half_float::half>
+                         intl, uintl, char, signed char, unsigned char, short,
+                         ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/match_template.cpp b/test/match_template.cpp
index 4ee8fc7e2d..f5f6eb4fc7 100644
--- a/test/match_template.cpp
+++ b/test/match_template.cpp
@@ -31,7 +31,8 @@ class MatchTemplate : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/mean.cpp b/test/mean.cpp
index c9c6eb567b..79dd76db2d 100644
--- a/test/mean.cpp
+++ b/test/mean.cpp
@@ -40,7 +40,7 @@ class Mean : public ::testing::Test {
 // This list does not allow to cleanly add the af_half/half_float type : at the
 // moment half tested in some special unittests
 typedef ::testing::Types<cdouble, cfloat, float, double, int, uint, intl, uintl,
-                         char, uchar, short, ushort, half_float::half>
+                         char, schar, uchar, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
@@ -70,9 +70,9 @@ template<typename T>
 struct meanOutType {
     typedef typename cond_type<
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
-            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
-            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
-            is_same_type<T, char>::value,
+            is_same_type<T, uint>::value || is_same_type<T, schar>::value ||
+            is_same_type<T, uchar>::value || is_same_type<T, short>::value ||
+            is_same_type<T, ushort>::value || is_same_type<T, char>::value,
         float, typename elseType<T>::type>::type type;
 };
 
@@ -228,7 +228,7 @@ TEST(MeanAll, s32) { meanAllTest<int>(2, dim4(5, 5, 2, 2)); }
 
 TEST(MeanAll, u32) { meanAllTest<unsigned>(2, dim4(100, 1, 1, 1)); }
 
-TEST(MeanAll, s8) { meanAllTest<char>(2, dim4(5, 5, 2, 2)); }
+TEST(MeanAll, s8) { meanAllTest<schar>(2, dim4(5, 5, 2, 2)); }
 
 TEST(MeanAll, u8) { meanAllTest<uchar>(2, dim4(100, 1, 1, 1)); }
 
diff --git a/test/meanshift.cpp b/test/meanshift.cpp
index 1f0aa697b3..d91648ae52 100644
--- a/test/meanshift.cpp
+++ b/test/meanshift.cpp
@@ -28,8 +28,8 @@ class Meanshift : public ::testing::Test {
     virtual void SetUp() {}
 };
 
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort,
-                         intl, uintl>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort, intl, uintl>
     TestTypes;
 
 TYPED_TEST_SUITE(Meanshift, TestTypes);
diff --git a/test/medfilt.cpp b/test/medfilt.cpp
index 2d874cb3ae..5ef951d5b1 100644
--- a/test/medfilt.cpp
+++ b/test/medfilt.cpp
@@ -35,7 +35,8 @@ class MedianFilter1d : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/memory.cpp b/test/memory.cpp
index 991756ca0b..9214ab472c 100644
--- a/test/memory.cpp
+++ b/test/memory.cpp
@@ -74,7 +74,8 @@ class MemAlloc : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort>
+                         intl, uintl, char, signed char, unsigned char, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/moddims.cpp b/test/moddims.cpp
index a7dea52a00..c8b98f05d1 100644
--- a/test/moddims.cpp
+++ b/test/moddims.cpp
@@ -36,8 +36,8 @@ class Moddims : public ::testing::Test {
 
 // create a list of types to be tested
 // TODO: complex types tests have to be added
-typedef ::testing::Types<float, double, int, unsigned, char, unsigned char,
-                         short, ushort, half_float::half>
+typedef ::testing::Types<float, double, int, unsigned, char, signed char,
+                         unsigned char, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/morph.cpp b/test/morph.cpp
index ad62ded8f3..b68d95076f 100644
--- a/test/morph.cpp
+++ b/test/morph.cpp
@@ -30,7 +30,8 @@ class Morph : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/nearest_neighbour.cpp b/test/nearest_neighbour.cpp
index 2db885f566..82551bc31b 100644
--- a/test/nearest_neighbour.cpp
+++ b/test/nearest_neighbour.cpp
@@ -34,8 +34,8 @@ class NearestNeighbour : public ::testing::Test {
 };
 
 // create lists of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, uchar, short,
-                         ushort>
+typedef ::testing::Types<float, double, int, uint, intl, uintl, schar, uchar,
+                         short, ushort>
     TestTypes;
 
 template<typename T>
@@ -53,6 +53,11 @@ struct otype_t<ushort> {
     typedef uint otype;
 };
 
+template<>
+struct otype_t<schar> {
+    typedef int otype;
+};
+
 template<>
 struct otype_t<uchar> {
     typedef uint otype;
diff --git a/test/pad_borders.cpp b/test/pad_borders.cpp
index 028c946719..2642ed83ca 100644
--- a/test/pad_borders.cpp
+++ b/test/pad_borders.cpp
@@ -24,8 +24,8 @@ using std::vector;
 template<typename T>
 class PadBorders : public ::testing::Test {};
 
-typedef ::testing::Types<float, double, cfloat, cdouble, char, unsigned char,
-                         int, uint, intl, uintl, short,
+typedef ::testing::Types<float, double, cfloat, cdouble, char, signed char,
+                         unsigned char, int, uint, intl, uintl, short,
                          ushort /*, half_float::half*/>
     TestTypes;
 
diff --git a/test/random.cpp b/test/random.cpp
index d0860b70f2..f6fd0dd45f 100644
--- a/test/random.cpp
+++ b/test/random.cpp
@@ -36,7 +36,7 @@ class Random : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, intl,
-                         uintl, unsigned char, char, af_half>
+                         uintl, signed char, unsigned char, char, af_half>
     TestTypes;
 
 // register the type list
@@ -258,15 +258,15 @@ void testSetSeed(const uintl seed0, const uintl seed1) {
         ASSERT_EQ(h_in0[i], h_in2[i]) << "at : " << i;
 
         // Verify different arrays created with different seeds differ
-        // b8 and u9 can clash because they generate a small set of values
-        if (ty != b8 && ty != u8) {
+        // b8, s8 and u8 can clash because they generate a small set of values
+        if (ty != b8 && ty != s8 && ty != u8) {
             ASSERT_NE(h_in0[i], h_in1[i]) << "at : " << i;
         }
 
         // Verify different arrays created one after the other with same seed
-        // differ b8 and u9 can clash because they generate a small set of
+        // differ b8, s8 and u8 can clash because they generate a small set of
         // values
-        if (ty != b8 && ty != u8) {
+        if (ty != b8 && ty != s8 && ty != u8) {
             ASSERT_NE(h_in2[i], h_in3[i]) << "at : " << i;
         }
     }
@@ -394,7 +394,7 @@ void testRandomEngineSeed(randomEngineType type) {
 
     for (int i = 0; i < elem; i++) {
         ASSERT_EQ(h1[i], h3[i]) << "at : " << i;
-        if (ty != b8 && ty != u8) {
+        if (ty != b8 && ty != s8 && ty != u8) {
             ASSERT_NE(h1[i], h2[i]) << "at : " << i;
             ASSERT_NE(h3[i], h4[i]) << "at : " << i;
         }
diff --git a/test/range.cpp b/test/range.cpp
index 35708bde09..0e708160c2 100644
--- a/test/range.cpp
+++ b/test/range.cpp
@@ -46,12 +46,13 @@ class RangeMax : public Range<T> {};
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
-                         unsigned char, short, ushort, half_float::half>
+                         signed char, unsigned char, short, ushort,
+                         half_float::half>
     AllTypes;
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, int, unsigned int, intl, uintl,
-                         unsigned char, short, ushort>
+                         signed char, unsigned char, short, ushort>
     RegularTypes;
 
 // register the type list
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 0a36431a54..0d4ab59225 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -40,7 +40,7 @@ template<typename T>
 class ReduceByKey : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
-                         uchar, short, ushort>
+                         schar, uchar, short, ushort>
     TestTypes;
 TYPED_TEST_SUITE(Reduce, TestTypes);
 TYPED_TEST_SUITE(ReduceByKey, TestTypes);
@@ -126,6 +126,10 @@ struct promote_type {
 
 // char and uchar are promoted to int for sum and product
 template<>
+struct promote_type<schar, af_sum> {
+    typedef int type;
+};
+template<>
 struct promote_type<uchar, af_sum> {
     typedef uint type;
 };
@@ -142,6 +146,10 @@ struct promote_type<ushort, af_sum> {
     typedef uint type;
 };
 template<>
+struct promote_type<schar, af_product> {
+    typedef int type;
+};
+template<>
 struct promote_type<uchar, af_product> {
     typedef uint type;
 };
@@ -389,6 +397,7 @@ array ptrToArray(size_t size, void *ptr, af_dtype type) {
         case u16: res = array(size, (unsigned short *)ptr); break;
         case s16: res = array(size, (short *)ptr); break;
         case b8: res = array(size, (char *)ptr); break;
+        case s8: res = array(size, (signed char *)ptr); break;
         case u8: res = array(size, (unsigned char *)ptr); break;
         case f16: res = array(size, (half_float::half *)ptr); break;
     }
@@ -409,6 +418,7 @@ array ptrToArray(af::dim4 size, void *ptr, af_dtype type) {
         case u16: res = array(size, (unsigned short *)ptr); break;
         case s16: res = array(size, (short *)ptr); break;
         case b8: res = array(size, (char *)ptr); break;
+        case s8: res = array(size, (signed char *)ptr); break;
         case u8: res = array(size, (unsigned char *)ptr); break;
         case f16: res = array(size, (half_float::half *)ptr); break;
     }
diff --git a/test/reorder.cpp b/test/reorder.cpp
index b06f72cdda..3109839786 100644
--- a/test/reorder.cpp
+++ b/test/reorder.cpp
@@ -44,7 +44,7 @@ class Reorder : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         char, unsigned char, short, ushort>
+                         char, signed char, unsigned char, short, ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/replace.cpp b/test/replace.cpp
index 6d72cf7fc9..1156731732 100644
--- a/test/replace.cpp
+++ b/test/replace.cpp
@@ -35,7 +35,7 @@ template<typename T>
 class Replace : public ::testing::Test {};
 
 typedef ::testing::Types<half_float::half, float, double, cfloat, cdouble, uint,
-                         int, intl, uintl, uchar, char, short, ushort>
+                         int, intl, uintl, schar, uchar, char, short, ushort>
     TestTypes;
 
 TYPED_TEST_SUITE(Replace, TestTypes);
diff --git a/test/resize.cpp b/test/resize.cpp
index 423bb55416..50c46730f9 100644
--- a/test/resize.cpp
+++ b/test/resize.cpp
@@ -55,8 +55,8 @@ class ResizeI : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypesF;
-typedef ::testing::Types<int, unsigned, intl, uintl, unsigned char, char, short,
-                         ushort>
+typedef ::testing::Types<int, unsigned, intl, uintl, signed char, unsigned char,
+                         char, short, ushort>
     TestTypesI;
 
 // register the type list
diff --git a/test/rotate.cpp b/test/rotate.cpp
index 01675fa1d7..986398f88f 100644
--- a/test/rotate.cpp
+++ b/test/rotate.cpp
@@ -34,7 +34,8 @@ class Rotate : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short>
+typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, schar,
+                         short>
     TestTypes;
 
 // register the type list
diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index ea19f217e7..84276a3755 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -39,7 +39,8 @@ class RotateLinear : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, char, short>
+typedef ::testing::Types<float, double, cfloat, cdouble, int, intl, schar, char,
+                         short>
     TestTypes;
 
 // register the type list
diff --git a/test/sat.cpp b/test/sat.cpp
index 892e2f8f4e..f87b356b85 100644
--- a/test/sat.cpp
+++ b/test/sat.cpp
@@ -31,8 +31,8 @@ class SAT : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, char, uchar, uintl, intl,
-                         short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, uintl,
+                         intl, short, ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/select.cpp b/test/select.cpp
index 0b6724d8fa..4b4c96dd21 100644
--- a/test/select.cpp
+++ b/test/select.cpp
@@ -42,7 +42,7 @@ template<typename T>
 class Select : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, intl, uintl,
-                         uchar, char, short, ushort, half_float::half>
+                         schar, uchar, char, short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(Select, TestTypes);
 
diff --git a/test/shift.cpp b/test/shift.cpp
index 2de341b3bc..c86c43c8e3 100644
--- a/test/shift.cpp
+++ b/test/shift.cpp
@@ -42,7 +42,8 @@ class Shift : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort>
+                         intl, uintl, char, signed char, unsigned char, short,
+                         ushort>
     TestTypes;
 // register the type list
 TYPED_TEST_SUITE(Shift, TestTypes);
diff --git a/test/sobel.cpp b/test/sobel.cpp
index 84fae1d34c..72a70ddde3 100644
--- a/test/sobel.cpp
+++ b/test/sobel.cpp
@@ -35,7 +35,8 @@ class Sobel_Integer : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double> TestTypes;
-typedef ::testing::Types<int, unsigned, char, unsigned char, short, ushort>
+typedef ::testing::Types<int, unsigned, char, signed char, unsigned char, short,
+                         ushort>
     TestTypesInt;
 
 // register the type list
diff --git a/test/sort.cpp b/test/sort.cpp
index c9da609f93..bd60edb5b5 100644
--- a/test/sort.cpp
+++ b/test/sort.cpp
@@ -40,8 +40,8 @@ class Sort : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
-                         uintl>
+typedef ::testing::Types<float, double, uint, int, schar, uchar, short, ushort,
+                         intl, uintl>
     TestTypes;
 
 // register the type list
diff --git a/test/sort_by_key.cpp b/test/sort_by_key.cpp
index afd7908660..265ee570b7 100644
--- a/test/sort_by_key.cpp
+++ b/test/sort_by_key.cpp
@@ -40,8 +40,8 @@ class SortByKey : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
-                         uintl>
+typedef ::testing::Types<float, double, uint, int, schar, uchar, short, ushort,
+                         intl, uintl>
     TestTypes;
 
 // register the type list
diff --git a/test/sort_index.cpp b/test/sort_index.cpp
index f3a10b9084..5e1b88a97d 100644
--- a/test/sort_index.cpp
+++ b/test/sort_index.cpp
@@ -40,8 +40,8 @@ class SortIndex : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, uint, int, uchar, short, ushort, intl,
-                         uintl>
+typedef ::testing::Types<float, double, uint, int, schar, uchar, short, ushort,
+                         intl, uintl>
     TestTypes;
 
 // register the type list
diff --git a/test/stdev.cpp b/test/stdev.cpp
index 4b93f5b220..bf95801fed 100644
--- a/test/stdev.cpp
+++ b/test/stdev.cpp
@@ -37,7 +37,8 @@ class StandardDev : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, double, int, uint, intl, uintl, char, uchar>
+typedef ::testing::Types<float, double, int, uint, intl, uintl, char, schar,
+                         uchar>
     TestTypes;
 
 // register the type list
@@ -67,9 +68,9 @@ template<typename T>
 struct sdOutType {
     typedef typename cond_type<
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
-            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
-            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
-            is_same_type<T, char>::value,
+            is_same_type<T, uint>::value || is_same_type<T, schar>::value ||
+            is_same_type<T, uchar>::value || is_same_type<T, short>::value ||
+            is_same_type<T, ushort>::value || is_same_type<T, char>::value,
         float, typename elseType<T>::type>::type type;
 };
 
diff --git a/test/susan.cpp b/test/susan.cpp
index 3741dd2653..c488bda775 100644
--- a/test/susan.cpp
+++ b/test/susan.cpp
@@ -59,7 +59,8 @@ class Susan : public ::testing::Test {
     virtual void SetUp() {}
 };
 
-typedef ::testing::Types<float, double, int, uint, char, uchar, short, ushort>
+typedef ::testing::Types<float, double, int, uint, char, schar, uchar, short,
+                         ushort>
     TestTypes;
 
 TYPED_TEST_SUITE(Susan, TestTypes);
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 4e9496f601..5f6b02b5a4 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -88,6 +88,7 @@ struct dtype_traits<half_float::half> {
 
 }  // namespace af
 
+typedef signed char schar;
 typedef unsigned char uchar;
 typedef unsigned int uint;
 typedef unsigned short ushort;
diff --git a/test/tile.cpp b/test/tile.cpp
index bc0cdddba7..3a608fa987 100644
--- a/test/tile.cpp
+++ b/test/tile.cpp
@@ -47,8 +47,8 @@ class Tile : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort,
-                         half_float::half>
+                         intl, uintl, char, signed char, unsigned char, short,
+                         ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/transform.cpp b/test/transform.cpp
index e3e0efe640..ef3b0dd4f9 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -38,7 +38,7 @@ class TransformInt : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double> TestTypes;
-typedef ::testing::Types<int, intl, uint, uintl, short, ushort, uchar>
+typedef ::testing::Types<int, intl, uint, uintl, short, ushort, schar, uchar>
     TestTypesInt;
 
 TYPED_TEST_SUITE(Transform, TestTypes);
diff --git a/test/translate.cpp b/test/translate.cpp
index 55fd570ffb..edbab15a2c 100644
--- a/test/translate.cpp
+++ b/test/translate.cpp
@@ -39,7 +39,7 @@ class TranslateInt : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble> TestTypes;
-typedef ::testing::Types<int, intl, char, short> TestTypesInt;
+typedef ::testing::Types<int, intl, char, schar, short> TestTypesInt;
 
 // register the type list
 TYPED_TEST_SUITE(Translate, TestTypes);
diff --git a/test/transpose.cpp b/test/transpose.cpp
index 72a32194fa..420f6d88e3 100644
--- a/test/transpose.cpp
+++ b/test/transpose.cpp
@@ -44,8 +44,8 @@ class Transpose : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
-                         short, ushort, half_float::half>
+typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, schar,
+                         uchar, short, ushort, half_float::half>
     TestTypes;
 
 // register the type list
diff --git a/test/transpose_inplace.cpp b/test/transpose_inplace.cpp
index 82b071488a..7e542fd34f 100644
--- a/test/transpose_inplace.cpp
+++ b/test/transpose_inplace.cpp
@@ -30,8 +30,8 @@ class Transpose : public ::testing::Test {
 };
 
 // create a list of types to be tested
-typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, uchar,
-                         short, ushort>
+typedef ::testing::Types<float, cfloat, double, cdouble, int, uint, char, schar,
+                         uchar, short, ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/triangle.cpp b/test/triangle.cpp
index 90b50bb6dc..a7d47832e5 100644
--- a/test/triangle.cpp
+++ b/test/triangle.cpp
@@ -35,7 +35,8 @@ template<typename T>
 class Triangle : public ::testing::Test {};
 
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
-                         uchar, uintl, intl, short, ushort, half_float::half>
+                         schar, uchar, uintl, intl, short, ushort,
+                         half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(Triangle, TestTypes);
 
diff --git a/test/unwrap.cpp b/test/unwrap.cpp
index f43b73e7f4..9b97059dac 100644
--- a/test/unwrap.cpp
+++ b/test/unwrap.cpp
@@ -37,7 +37,8 @@ class Unwrap : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort>
+                         intl, uintl, char, signed char, unsigned char, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/var.cpp b/test/var.cpp
index db846f5d57..b889413646 100644
--- a/test/var.cpp
+++ b/test/var.cpp
@@ -26,7 +26,7 @@ template<typename T>
 class Var : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble, uint, int, uintl, intl,
-                         char, uchar, short, ushort, half_float::half>
+                         char, schar, uchar, short, ushort, half_float::half>
     TestTypes;
 TYPED_TEST_SUITE(Var, TestTypes);
 
@@ -42,8 +42,8 @@ struct varOutType {
     typedef typename cond_type<
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
             is_same_type<T, uint>::value || is_same_type<T, short>::value ||
-            is_same_type<T, ushort>::value || is_same_type<T, uchar>::value ||
-            is_same_type<T, char>::value,
+            is_same_type<T, ushort>::value || is_same_type<T, schar>::value ||
+            is_same_type<T, uchar>::value || is_same_type<T, char>::value,
         float, typename elseType<T>::type>::type type;
 };
 
diff --git a/test/where.cpp b/test/where.cpp
index bb5375822c..265c0d4d7b 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -34,7 +34,7 @@ template<typename T>
 class Where : public ::testing::Test {};
 
 typedef ::testing::Types<float, double, cfloat, cdouble, int, uint, intl, uintl,
-                         char, uchar, short, ushort>
+                         char, schar, uchar, short, ushort>
     TestTypes;
 TYPED_TEST_SUITE(Where, TestTypes);
 
diff --git a/test/wrap.cpp b/test/wrap.cpp
index baff77c5b1..4f53d9fd34 100644
--- a/test/wrap.cpp
+++ b/test/wrap.cpp
@@ -42,7 +42,8 @@ class Wrap : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, double, cfloat, cdouble, int, unsigned int,
-                         intl, uintl, char, unsigned char, short, ushort>
+                         intl, uintl, char, signed char, unsigned char, short,
+                         ushort>
     TestTypes;
 
 // register the type list
diff --git a/test/write.cpp b/test/write.cpp
index 8f18f6e954..db751939ab 100644
--- a/test/write.cpp
+++ b/test/write.cpp
@@ -34,7 +34,7 @@ class Write : public ::testing::Test {
 
 // create a list of types to be tested
 typedef ::testing::Types<float, cfloat, double, cdouble, int, unsigned, char,
-                         unsigned char, short, ushort>
+                         signed char, unsigned char, short, ushort>
     TestTypes;
 
 // register the type list

From 1636b5ae1d559dff995bda7448b11782ae3df36b Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Fri, 6 Oct 2023 10:14:22 -0400
Subject: [PATCH 2635/2677] fix image loading for s8 tests

---
 src/api/c/imageio.cpp   |  4 ++--
 src/api/c/imageio2.cpp  |  5 ++---
 test/arrayfire_test.cpp | 13 ++++++++++---
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/src/api/c/imageio.cpp b/src/api/c/imageio.cpp
index 0f87e4df17..be5f528922 100644
--- a/src/api/c/imageio.cpp
+++ b/src/api/c/imageio.cpp
@@ -75,7 +75,7 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
             if (fo_color == 1) {
                 pDst0[indx] = static_cast<T>(*(src + (x * step)));
             } else if (fo_color >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
                     pDst0[indx] =
                         static_cast<float>(*(src + (x * step + FI_RGBA_RED)));
                     pDst1[indx] =
@@ -201,7 +201,7 @@ static af_err readImage(af_array* rImage, const uchar* pSrcLine,
             if (fo_color == 1) {
                 pDst[indx] = static_cast<T>(*(src + (x * step)));
             } else if (fo_color >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
                     r = *(src + (x * step + FI_RGBA_RED));
                     g = *(src + (x * step + FI_RGBA_GREEN));
                     b = *(src + (x * step + FI_RGBA_BLUE));
diff --git a/src/api/c/imageio2.cpp b/src/api/c/imageio2.cpp
index 4a00212207..7130202397 100644
--- a/src/api/c/imageio2.cpp
+++ b/src/api/c/imageio2.cpp
@@ -71,7 +71,7 @@ static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
             if (fi_color == 1) {
                 pDst0[indx] = *(src + (x * step));
             } else if (fi_color >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
                     pDst0[indx] = *(src + (x * step + FI_RGBA_RED));
                     pDst1[indx] = *(src + (x * step + FI_RGBA_GREEN));
                     pDst2[indx] = *(src + (x * step + FI_RGBA_BLUE));
@@ -102,7 +102,6 @@ static af_err readImage_t(af_array* rImage, const uchar* pSrcLine,
 }
 
 FREE_IMAGE_TYPE getFIT(FI_CHANNELS channels, af_dtype type) {
-    // FIXME s8?
     if (channels == AFFI_GRAY) {
         if (type == u8) { return FIT_BITMAP; }
         if (type == u16) {
@@ -365,7 +364,7 @@ static void save_t(T* pDstLine, const af_array in, const dim4& dims,
             if (channels == 1) {
                 *(pDstLine + x * step) = pSrc0[indx];  // r -> 0
             } else if (channels >= 3) {
-                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) { // FIXME s8?
+                if (static_cast<af_dtype>(af::dtype_traits<T>::af_type) == u8) {
                     *(pDstLine + x * step + FI_RGBA_RED) =
                         pSrc0[indx];  // r -> 0
                     *(pDstLine + x * step + FI_RGBA_GREEN) =
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 5b41f505d7..b1b82813de 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -1358,10 +1358,17 @@ af_err conv_image(af_array *out, af_array in) {
 
     T *out_data = new T[nElems];
 
-    for (int i = 0; i < (int)nElems; i++) out_data[i] = (T)in_data[i];
+    af_dtype out_type = (af_dtype)af::dtype_traits<T>::af_type
+    for (int i = 0; i < (int)nElems; i++) {
+        if (out_type == s8) {
+            // shift to avoid overflow
+            out_data[i] = (T)(std::trunc(in_data[i]) - 128.f);
+        } else {
+            out_data[i] = (T)in_data[i];
+        }
+    }
 
-    af_create_array(&outArray, out_data, idims.ndims(), idims.get(),
-                    (af_dtype)af::dtype_traits<T>::af_type);
+    af_create_array(&outArray, out_data, idims.ndims(), idims.get(), out_type);
 
     std::swap(*out, outArray);
 

From 1a0c305e1adb33d1e0120fb95cd7ece6c1fd2441 Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Sat, 14 Oct 2023 16:02:04 -0400
Subject: [PATCH 2636/2677] skip Richardson-Lucy test for s8

Image loading with a shift causes the test data to contain negative values.
This test passes when the data is limited to 0-127 instead.
---
 test/iterative_deconv.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/test/iterative_deconv.cpp b/test/iterative_deconv.cpp
index 432c9ff533..290b81f0d6 100644
--- a/test/iterative_deconv.cpp
+++ b/test/iterative_deconv.cpp
@@ -40,6 +40,11 @@ void iterDeconvImageTest(string pTestFile, const unsigned iters, const float rf,
     SUPPORTED_TYPE_CHECK(T);
     IMAGEIO_ENABLED_CHECK();
 
+    if (is_same_type<T, schar>::value &&
+        algo == AF_ITERATIVE_DECONV_RICHARDSONLUCY) {
+        GTEST_SKIP() << "Incompatible with signed values";
+    }
+
     using af::dim4;
 
     vector<dim4> inDims;

From 3eadfdc9aeb3be6ed66613162335393092420232 Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Sat, 14 Oct 2023 16:03:48 -0400
Subject: [PATCH 2637/2677] skip rotate_linear tests for s8

This test data cannot be trivially shifted since rotate sets out-of-bounds
values to 0, not -128. Limiting the range to 0-127 mostly works, but introduces
rounding errors between output and gold.
---
 test/rotate_linear.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/test/rotate_linear.cpp b/test/rotate_linear.cpp
index 84276a3755..1324a59a77 100644
--- a/test/rotate_linear.cpp
+++ b/test/rotate_linear.cpp
@@ -54,6 +54,10 @@ void rotateTest(string pTestFile, const unsigned resultIdx, const float angle,
                 const vector<af_seq>* seqv = NULL) {
     SUPPORTED_TYPE_CHECK(T);
 
+    if (is_same_type<T, schar>::value && (int)angle % 90 != 0) {
+        GTEST_SKIP() << "Incompatible test data for s8";
+    }
+
     vector<dim4> numDims;
     vector<vector<T>> in;
     vector<vector<T>> tests;

From 42dfafaa28f084bb4936270a38f49ec1deeff87d Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Sat, 14 Oct 2023 16:13:10 -0400
Subject: [PATCH 2638/2677] define s8 interface for AF_API_VERSION 310

This also slightly extends the interface macros.
---
 include/af/array.h    | 78 ++++++++++++++++++++++++++++++-------------
 include/af/defines.h  |  4 ++-
 include/af/traits.hpp |  2 ++
 3 files changed, 59 insertions(+), 25 deletions(-)

diff --git a/include/af/array.h b/include/af/array.h
index a442147565..672c2716eb 100644
--- a/include/af/array.h
+++ b/include/af/array.h
@@ -71,7 +71,7 @@ namespace af
             operator array() const;
             operator array();
 
-#define ASSIGN(OP)                                                  \
+#define ASSIGN_(OP)                                                 \
             array_proxy& operator OP(const array_proxy &a);         \
             array_proxy& operator OP(const array &a);               \
             array_proxy& operator OP(const double &a);              \
@@ -82,24 +82,31 @@ namespace af
             array_proxy& operator OP(const unsigned &a);            \
             array_proxy& operator OP(const bool &a);                \
             array_proxy& operator OP(const char &a);                \
-            array_proxy& operator OP(const signed char &a);         \
             array_proxy& operator OP(const unsigned char &a);       \
             array_proxy& operator OP(const long  &a);               \
             array_proxy& operator OP(const unsigned long &a);       \
             array_proxy& operator OP(const long long  &a);          \
             array_proxy& operator OP(const unsigned long long &a);
 
-            ASSIGN(=)
-            ASSIGN(+=)
-            ASSIGN(-=)
-            ASSIGN(*=)
-            ASSIGN(/=)
-#undef ASSIGN
-
 #if AF_API_VERSION >= 32
-#define ASSIGN(OP)                                                  \
+#define ASSIGN_32(OP)                                               \
             array_proxy& operator OP(const short &a);               \
             array_proxy& operator OP(const unsigned short &a);
+#else
+#define ASSIGN_32(OP)
+#endif
+
+#if AF_API_VERSION >= 310
+#define ASSIGN_310(OP)                                              \
+            array_proxy& operator OP(const signed char &a);
+#else
+#define ASSIGN_310(OP)
+#endif
+
+#define ASSIGN(OP)              \
+            ASSIGN_(OP)         \
+            ASSIGN_32(OP)       \
+            ASSIGN_310(OP)
 
             ASSIGN(=)
             ASSIGN(+=)
@@ -107,7 +114,9 @@ namespace af
             ASSIGN(*=)
             ASSIGN(/=)
 #undef ASSIGN
-#endif
+#undef ASSIGN_
+#undef ASSIGN_32
+#undef ASSIGN_310
 
             // af::array member functions. same behavior as those below
             af_array get();
@@ -948,7 +957,7 @@ namespace af
 
         /// \brief Casts the array into another data type
         ///
-        /// \note Consecitive casting operations may be may be optimized out if
+        /// \note Consecutive casting operations may be optimized out if
         /// the original type of the af::array is the same as the final type.
         /// For example if the original type is f64 which is then cast to f32
         /// and then back to f64, then the cast to f32 will be skipped and that
@@ -1000,24 +1009,31 @@ namespace af
         array& OP2(const unsigned &val);            /**< \copydoc OP2##(const array &) */ \
         array& OP2(const bool &val);                /**< \copydoc OP2##(const array &) */ \
         array& OP2(const char &val);                /**< \copydoc OP2##(const array &) */ \
-        array& OP2(const signed char &val);         /**< \copydoc OP2##(const array &) */ \
         array& OP2(const unsigned char &val);       /**< \copydoc OP2##(const array &) */ \
         array& OP2(const long  &val);               /**< \copydoc OP2##(const array &) */ \
         array& OP2(const unsigned long &val);       /**< \copydoc OP2##(const array &) */ \
         array& OP2(const long long  &val);          /**< \copydoc OP2##(const array &) */ \
         array& OP2(const unsigned long long &val);
 
-
 #if AF_API_VERSION >= 32
-#define ASSIGN(OP)                                                                        \
-        ASSIGN_(OP)                                                                       \
-          array& OP(const short  &val);              /**< \copydoc OP##(const array &) */ \
-          array& OP(const unsigned short &val);
+#define ASSIGN_32(OP)                                                                    \
+        array& OP(const short  &val);               /**< \copydoc OP##(const array &) */ \
+        array& OP(const unsigned short &val);
+#else
+#define ASSIGN_32(OP)
+#endif
 
+#if AF_API_VERSION >= 310
+#define ASSIGN_310(OP)                                                                   \
+        array& OP(const signed char &val);          /**< \copydoc OP##(const array &) */
 #else
-#define ASSIGN(OP) ASSIGN_(OP)
+#define ASSIGN_310(OP)
 #endif
 
+#define ASSIGN(OP)          \
+        ASSIGN_(OP)         \
+        ASSIGN_32(OP)       \
+        ASSIGN_310(OP)
 
         /// \ingroup array_mem_operator_eq
         /// @{
@@ -1083,6 +1099,8 @@ namespace af
 
 #undef ASSIGN
 #undef ASSIGN_
+#undef ASSIGN_32
+#undef ASSIGN_310
 
         ///
         /// \brief Negates the values of the array
@@ -1147,7 +1165,6 @@ namespace af
     AFAPI array OP (const int& lhs, const array& rhs);                /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const unsigned& lhs, const array& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const char& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
-    AFAPI array OP (const signed char& lhs, const array& rhs);        /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const unsigned char& lhs, const array& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const long& lhs, const array& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const unsigned long& lhs, const array& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
@@ -1161,7 +1178,6 @@ namespace af
     AFAPI array OP (const array& lhs, const int& rhs);                /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const unsigned& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const char& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
-    AFAPI array OP (const array& lhs, const signed char& rhs);        /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const unsigned char& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const long& rhs);               /**< \copydoc OP##(const array&, const array&) */ \
     AFAPI array OP (const array& lhs, const unsigned long& rhs);      /**< \copydoc OP##(const array&, const array&) */ \
@@ -1173,17 +1189,29 @@ namespace af
     AFAPI array OP (const array& lhs, const cdouble& rhs);
 
 #if AF_API_VERSION >= 32
-#define BIN_OP(OP)                                                                                                        \
-        BIN_OP_(OP)                                                                                                       \
+#define BIN_OP_32(OP)                                                                                                    \
         AFAPI array OP (const short& lhs, const array& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
         AFAPI array OP (const unsigned short& lhs, const array& rhs);  /**< \copydoc OP##(const array&, const array&) */ \
         AFAPI array OP (const array& lhs, const short& rhs);           /**< \copydoc OP##(const array&, const array&) */ \
         AFAPI array OP (const array& lhs, const unsigned short& rhs);
 
 #else
-#define BIN_OP(OP) BIN_OP_(OP)
+#define BIN_OP_32(OP)
+#endif
+
+#if AF_API_VERSION >= 310
+#define BIN_OP_310(OP)                                                                                                  \
+    AFAPI array OP (const signed char& lhs, const array& rhs);        /**< \copydoc OP##(const array&, const array&) */ \
+    AFAPI array OP (const array& lhs, const signed char& rhs);        /**< \copydoc OP##(const array&, const array&) */
+#else
+#define BIN_OP_310(OP)
 #endif
 
+#define BIN_OP(OP)          \
+        BIN_OP_(OP)         \
+        BIN_OP_32(OP)       \
+        BIN_OP_310(OP)
+
     /// \ingroup arith_func_add
     /// @{
     /// \brief Adds two arrays or an array and a value.
@@ -1377,6 +1405,8 @@ namespace af
 
 #undef BIN_OP
 #undef BIN_OP_
+#undef BIN_OP_32
+#undef BIN_OP_310
 
     /// \ingroup arith_func_bitand
     /// @{
diff --git a/include/af/defines.h b/include/af/defines.h
index 4be88f97bd..42f71024fa 100644
--- a/include/af/defines.h
+++ b/include/af/defines.h
@@ -227,7 +227,9 @@ typedef enum {
 #if AF_API_VERSION >= 37
     , f16    ///< 16-bit floating point value
 #endif
-    , s8     ///< 8-bit signed integral value /// TODO AF_API_VERSION
+#if AF_API_VERSION >= 310
+    , s8     ///< 8-bit signed integral values
+#endif
 } af_dtype;
 
 typedef enum {
diff --git a/include/af/traits.hpp b/include/af/traits.hpp
index 330435a929..4216c3f046 100644
--- a/include/af/traits.hpp
+++ b/include/af/traits.hpp
@@ -176,6 +176,7 @@ struct dtype_traits<half> {
 };
 #endif
 
+#if AF_API_VERSION >= 310
 template<>
 struct dtype_traits<signed char> {
     enum {
@@ -185,6 +186,7 @@ struct dtype_traits<signed char> {
     typedef signed char base_type;
     static const char* getName() { return "schar"; }
 };
+#endif
 }
 
 #endif

From 0e259ccb5c36707616bcd53908909bb83de7b696 Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Mon, 16 Oct 2023 14:30:49 -0400
Subject: [PATCH 2639/2677] add s8 to documentation

---
 docs/details/algorithm.dox    | 10 +++++-----
 docs/details/image.dox        |  2 ++
 docs/pages/README.md          |  2 +-
 docs/pages/getting_started.md |  3 ++-
 4 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/docs/details/algorithm.dox b/docs/details/algorithm.dox
index 055750098c..69633524e2 100644
--- a/docs/details/algorithm.dox
+++ b/docs/details/algorithm.dox
@@ -22,7 +22,7 @@ Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
 s32, s64, u32, u64  | same as input
-s16                 | s32
+s16, s8             | s32
 u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
@@ -54,7 +54,7 @@ Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
 s32, s64, u32, u64  | same as input
-s16                 | s32
+s16, s8             | s32
 u16, u8, b8         | u32
 f16                 | f32
 
@@ -76,7 +76,7 @@ Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
 s32, u32, s64, u64  | same as input
-s16                 | s32
+s16, s8             | s32
 u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
@@ -108,7 +108,7 @@ Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
 s32, u32, s64, u64  | same as input
-s16                 | s32
+s16, s8             | s32
 u16, u8, b8         | u32
 f16                 | f32
 
@@ -340,7 +340,7 @@ Input Type          | Output Type
 --------------------|---------------------
 f32, f64, c32, c64  | same as input
 s32, s64, u32, u64  | same as input
-s16                 | s32
+s16, s8             | s32
 u16, u8, b8         | u32
 
 \copydoc batch_detail_algo
diff --git a/docs/details/image.dox b/docs/details/image.dox
index a93f1ebaed..312b88c880 100644
--- a/docs/details/image.dox
+++ b/docs/details/image.dox
@@ -1007,6 +1007,7 @@ Iterative deconvolution function excepts \ref af::array of the following types o
     - \ref f32
     - \ref s16
     - \ref u16
+    - \ref s8
     - \ref u8
 
 \note The type of output \ref af::array from deconvolution will be double if
@@ -1044,6 +1045,7 @@ Inverse deconvolution function excepts \ref af::array of the following types onl
     - \ref f32
     - \ref s16
     - \ref u16
+    - \ref s8
     - \ref u8
 
 \note The type of output \ref af::array from deconvolution will be double
diff --git a/docs/pages/README.md b/docs/pages/README.md
index 7c22adf87c..6ecb68ce4e 100644
--- a/docs/pages/README.md
+++ b/docs/pages/README.md
@@ -52,7 +52,7 @@ vectors, matrices, volumes, and
 
 It supports common [data types](\ref gettingstarted_datatypes), including
 single and double precision floating point values, complex numbers, booleans,
-and 32-bit signed and unsigned integers.
+and 8/16/32-bit signed and unsigned integers.
 
 #### Extending ArrayFire
 
diff --git a/docs/pages/getting_started.md b/docs/pages/getting_started.md
index 19660f8cc8..2bd3b4d1f6 100644
--- a/docs/pages/getting_started.md
+++ b/docs/pages/getting_started.md
@@ -28,7 +28,8 @@ can represent one of many different [basic data types](\ref af_dtype):
 * [b8](\ref b8) 8-bit boolean values (`bool`)
 * [s32](\ref s32) 32-bit signed integer (`int`)
 * [u32](\ref u32) 32-bit unsigned integer (`unsigned`)
-* [u8](\ref u8) 8-bit unsigned values (`unsigned char`)
+* [s8](\ref s8) 8-bit signed integer (`signed char`)
+* [u8](\ref u8) 8-bit unsigned integer (`unsigned char`)
 * [s64](\ref s64) 64-bit signed integer (`intl`)
 * [u64](\ref u64) 64-bit unsigned integer (`uintl`)
 * [s16](\ref s16) 16-bit signed integer (`short`)

From b0be72de52961dd14781ed7069f0204bf611ca11 Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Mon, 16 Oct 2023 16:36:29 -0400
Subject: [PATCH 2640/2677] add missing s8 tests

---
 test/arrayfire_test.cpp | 2 +-
 test/binary.cpp         | 6 ++++++
 test/corrcoef.cpp       | 6 +++---
 test/ireduce.cpp        | 2 ++
 test/meanvar.cpp        | 5 +++--
 test/median.cpp         | 2 ++
 test/scan.cpp           | 1 +
 test/set.cpp            | 2 ++
 8 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index b1b82813de..dedaedbf75 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -1358,7 +1358,7 @@ af_err conv_image(af_array *out, af_array in) {
 
     T *out_data = new T[nElems];
 
-    af_dtype out_type = (af_dtype)af::dtype_traits<T>::af_type
+    af_dtype out_type = (af_dtype)af::dtype_traits<T>::af_type;
     for (int i = 0; i < (int)nElems; i++) {
         if (out_type == s8) {
             // shift to avoid overflow
diff --git a/test/binary.cpp b/test/binary.cpp
index 3dbfa44bb9..7fd47bcfbd 100644
--- a/test/binary.cpp
+++ b/test/binary.cpp
@@ -324,6 +324,7 @@ UBITOP(bitnot, int)
 UBITOP(bitnot, uint)
 UBITOP(bitnot, intl)
 UBITOP(bitnot, uintl)
+UBITOP(bitnot, schar)
 UBITOP(bitnot, uchar)
 UBITOP(bitnot, short)
 UBITOP(bitnot, ushort)
@@ -414,6 +415,7 @@ DEF_TEST(Int, int)
 DEF_TEST(UShort, unsigned short)
 DEF_TEST(Short, short)
 DEF_TEST(UChar, unsigned char)
+DEF_TEST(SChar, signed char)
 
 #undef DEF_TEST
 
@@ -431,6 +433,8 @@ INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestShort,
                          testing::Range<short>(1, 180, 50));
 INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestUChar,
                          testing::Range<unsigned char>(1, 12, 5));
+INSTANTIATE_TEST_SUITE_P(PositiveValues, PowPrecisionTestSChar,
+                         testing::Range<signed char>(1, 9, 3));
 
 INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestLong,
                          testing::Range<long long>(-1e7, 0, 1e6));
@@ -438,6 +442,8 @@ INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestInt,
                          testing::Range<int>(-46340, 0, 10e3));
 INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestShort,
                          testing::Range<short>(-180, 0, 50));
+INSTANTIATE_TEST_SUITE_P(NegativeValues, PowPrecisionTestSChar,
+                         testing::Range<signed char>(-9, 0, 3));
 
 struct result_type_param {
     af_dtype result_;
diff --git a/test/corrcoef.cpp b/test/corrcoef.cpp
index ffcecacd61..e9bc5a5616 100644
--- a/test/corrcoef.cpp
+++ b/test/corrcoef.cpp
@@ -62,9 +62,9 @@ template<typename T>
 struct ccOutType {
     typedef typename cond_type<
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
-            is_same_type<T, uint>::value || is_same_type<T, uchar>::value ||
-            is_same_type<T, short>::value || is_same_type<T, ushort>::value ||
-            is_same_type<T, char>::value,
+            is_same_type<T, uint>::value || is_same_type<T, schar>::value ||
+            is_same_type<T, uchar>::value || is_same_type<T, short>::value ||
+            is_same_type<T, ushort>::value || is_same_type<T, char>::value,
         float, typename elseType<T>::type>::type type;
 };
 
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index 2ebd951d46..e93a8267b4 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -103,6 +103,7 @@ MINMAXOP(min, double)
 MINMAXOP(min, int)
 MINMAXOP(min, uint)
 MINMAXOP(min, char)
+MINMAXOP(min, schar)
 MINMAXOP(min, uchar)
 
 MINMAXOP(max, float)
@@ -110,6 +111,7 @@ MINMAXOP(max, double)
 MINMAXOP(max, int)
 MINMAXOP(max, uint)
 MINMAXOP(max, char)
+MINMAXOP(max, schar)
 MINMAXOP(max, uchar)
 
 TEST(IndexedReduce, MaxIndexedSmall) {
diff --git a/test/meanvar.cpp b/test/meanvar.cpp
index 08e4702481..c7eba339a8 100644
--- a/test/meanvar.cpp
+++ b/test/meanvar.cpp
@@ -40,8 +40,8 @@ struct varOutType {
     typedef typename cond_type<
         is_same_type<T, float>::value || is_same_type<T, int>::value ||
             is_same_type<T, uint>::value || is_same_type<T, short>::value ||
-            is_same_type<T, ushort>::value || is_same_type<T, uchar>::value ||
-            is_same_type<T, char>::value,
+            is_same_type<T, ushort>::value || is_same_type<T, schar>::value ||
+            is_same_type<T, uchar>::value || is_same_type<T, char>::value,
         float, typename elseType<T>::type>::type type;
 };
 
@@ -377,5 +377,6 @@ TEST_P(MeanVarHalf, TestingCPP) {
     }
 
 // Only test small sizes because the range of the large arrays go out of bounds
+MEANVAR_TEST(SignedChar, signed char)
 MEANVAR_TEST(UnsignedChar, unsigned char)
 // MEANVAR_TEST(Bool, unsigned char) // TODO(umar): test this type
diff --git a/test/median.cpp b/test/median.cpp
index c55251e66c..4f64631c6f 100644
--- a/test/median.cpp
+++ b/test/median.cpp
@@ -119,6 +119,7 @@ void median_test(int nx, int ny = 1, int nz = 1, int nw = 1) {
 MEDIAN_FLAT(float, float)
 MEDIAN_FLAT(float, int)
 MEDIAN_FLAT(float, uint)
+MEDIAN_FLAT(float, schar)
 MEDIAN_FLAT(float, uchar)
 MEDIAN_FLAT(float, short)
 MEDIAN_FLAT(float, ushort)
@@ -151,6 +152,7 @@ MEDIAN_FLAT(double, double)
 MEDIAN(float, float)
 MEDIAN(float, int)
 MEDIAN(float, uint)
+MEDIAN(float, schar)
 MEDIAN(float, uchar)
 MEDIAN(float, short)
 MEDIAN(float, ushort)
diff --git a/test/scan.cpp b/test/scan.cpp
index a29c6e0e52..8bfbe0dd20 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -113,6 +113,7 @@ SCAN_TESTS(accum, cdouble, cdouble, cdouble);
 SCAN_TESTS(accum, unsigned, unsigned, unsigned);
 SCAN_TESTS(accum, intl, intl, intl);
 SCAN_TESTS(accum, uintl, uintl, uintl);
+SCAN_TESTS(accum, schar, schar, int);
 SCAN_TESTS(accum, uchar, uchar, unsigned);
 SCAN_TESTS(accum, short, short, int);
 SCAN_TESTS(accum, ushort, ushort, uint);
diff --git a/test/set.cpp b/test/set.cpp
index 97e05d484b..0e1ececadc 100644
--- a/test/set.cpp
+++ b/test/set.cpp
@@ -77,6 +77,7 @@ UNIQUE_TESTS(float)
 UNIQUE_TESTS(double)
 UNIQUE_TESTS(int)
 UNIQUE_TESTS(uint)
+UNIQUE_TESTS(schar)
 UNIQUE_TESTS(uchar)
 UNIQUE_TESTS(short)
 UNIQUE_TESTS(ushort)
@@ -149,6 +150,7 @@ SET_TESTS(float)
 SET_TESTS(double)
 SET_TESTS(int)
 SET_TESTS(uint)
+SET_TESTS(schar)
 SET_TESTS(uchar)
 SET_TESTS(short)
 SET_TESTS(ushort)

From 7b82364c8f49bdcb2819924b13b489f1009bfe95 Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Mon, 16 Oct 2023 16:38:02 -0400
Subject: [PATCH 2641/2677] bump version to 3.10

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8e0c37c19f..b299e6d72f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -15,7 +15,7 @@ include(CheckLanguage)
 include(CMakeModules/AF_vcpkg_options.cmake)
 
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules")
-project(ArrayFire VERSION 3.9.0 LANGUAGES C CXX)
+project(ArrayFire VERSION 3.10.0 LANGUAGES C CXX)
 
 include(AFconfigure_deps_vars)
 include(AFBuildConfigurations)

From b568f87b2df71d920af211f8ea11029cf5e37923 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 14 Mar 2025 15:06:05 -0400
Subject: [PATCH 2642/2677] Fixes for compiling with oneAPI 2025 on Linux
 (#3643)

Some bugfixes and updated flags to get the code to compile successfully on Linux with oneAPI 2025
---
 CMakeLists.txt                       |  2 +-
 src/backend/oneapi/CMakeLists.txt    |  2 +-
 src/backend/oneapi/kernel/mean.hpp   | 14 +++++++-------
 src/backend/oneapi/kernel/sparse.hpp | 20 ++++++++------------
 4 files changed, 17 insertions(+), 21 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b299e6d72f..ca942f301a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -145,7 +145,7 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
   set(SYCL_COMPILER ON)
   set(MKL_THREADING "tbb_thread")
   set(MKL_INTERFACE "ilp64")
-  find_package(MKL 2024.1)
+  find_package(MKL)
 endif()
 
 af_multiple_option(NAME        AF_COMPUTE_LIBRARY
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 054681d812..702abd3125 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -355,7 +355,7 @@ target_link_libraries(afoneapi
     OpenCL::OpenCL
     OpenCL::cl2hpp
     -fno-sycl-id-queries-fit-in-int
-    $<$<PLATFORM_ID:Linux>:-fsycl-link-huge-device-code>
+    $<$<PLATFORM_ID:Linux>:-flink-huge-device-code>
     $<$<PLATFORM_ID:Linux>:-fvisibility-inlines-hidden>
     $<$<PLATFORM_ID:Linux>:-fno-sycl-rdc>
     -fsycl-max-parallel-link-jobs=${NumberOfThreads}
diff --git a/src/backend/oneapi/kernel/mean.hpp b/src/backend/oneapi/kernel/mean.hpp
index d6f33209a9..4c8533b1ec 100644
--- a/src/backend/oneapi/kernel/mean.hpp
+++ b/src/backend/oneapi/kernel/mean.hpp
@@ -614,9 +614,9 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto acc_in =
-                    tmpOut_get->template get_host_access(h, sycl::read_only);
+                    tmpOut_get->get_host_access(h, sycl::read_only);
                 auto acc_wt =
-                    tmpWt_get->template get_host_access(h, sycl::read_only);
+                    tmpWt_get->get_host_access(h, sycl::read_only);
 
                 h.host_task([acc_in, acc_wt, tmp_elements, &val] {
                     val = static_cast<compute_t<T>>(acc_in[0]);
@@ -635,9 +635,9 @@ T mean_all_weighted(Param<T> in, Param<Tw> iwt) {
         compute_t<T> val;
         getQueue()
             .submit([&](sycl::handler &h) {
-                auto acc_in = in.data->template get_host_access(
+                auto acc_in = in.data->get_host_access(
                     h, sycl::range{in_elements}, sycl::read_only);
-                auto acc_wt = iwt.data->template get_host_access(
+                auto acc_wt = iwt.data->get_host_access(
                     h, sycl::range{in_elements}, sycl::read_only);
 
                 h.host_task([acc_in, acc_wt, in_elements, &val]() {
@@ -700,9 +700,9 @@ To mean_all(Param<Ti> in) {
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto out =
-                    tmpOut_get->template get_host_access(h, sycl::read_only);
+                    tmpOut_get->get_host_access(h, sycl::read_only);
                 auto ct =
-                    tmpCt_get->template get_host_access(h, sycl::read_only);
+                    tmpCt_get->get_host_access(h, sycl::read_only);
 
                 h.host_task([out, ct, tmp_elements, &val] {
                     val                  = static_cast<compute_t<To>>(out[0]);
@@ -721,7 +721,7 @@ To mean_all(Param<Ti> in) {
         getQueue()
             .submit([&](sycl::handler &h) {
                 auto acc_in =
-                    in.data->template get_host_access(h, sycl::read_only);
+                    in.data->get_host_access(h, sycl::read_only);
                 h.host_task([acc_in, in_elements, &val]() {
                     common::Transform<Ti, compute_t<To>, af_add_t> transform;
                     compute_t<Tw> count = static_cast<compute_t<Tw>>(1);
diff --git a/src/backend/oneapi/kernel/sparse.hpp b/src/backend/oneapi/kernel/sparse.hpp
index b7bc316267..24458ed77d 100644
--- a/src/backend/oneapi/kernel/sparse.hpp
+++ b/src/backend/oneapi/kernel/sparse.hpp
@@ -191,17 +191,13 @@ class dense2csrCreateKernel {
         if (gidy >= (unsigned)valinfo_.dims[1]) return;
 
         int rowoff       = rowptr_[gidx];
-        T *svalptr_ptr   = svalptr_.get_pointer();
-        int *scolptr_ptr = scolptr_.get_pointer();
-        svalptr_ptr += rowoff;
-        scolptr_ptr += rowoff;
+        auto svalptr_ptr   = svalptr_.get_pointer();
+        auto scolptr_ptr = scolptr_.get_pointer();
 
-        T *dvalptr_ptr   = dvalptr_.get_pointer();
-        int *dcolptr_ptr = dcolptr_.get_pointer();
-        dvalptr_ptr += valinfo_.offset;
-        dcolptr_ptr += colinfo_.offset;
+        auto dvalptr_ptr   = dvalptr_.get_pointer();
+        auto dcolptr_ptr = dcolptr_.get_pointer();
 
-        T val = dvalptr_ptr[gidx + gidy * (unsigned)valinfo_.strides[1]];
+        T val = dvalptr_ptr[gidx + gidy * (unsigned)valinfo_.strides[1] + valinfo_.offset];
 
         if constexpr (std::is_same_v<decltype(val), std::complex<float>> ||
                       std::is_same_v<decltype(val), std::complex<double>>) {
@@ -210,9 +206,9 @@ class dense2csrCreateKernel {
             if (val == 0) return;
         }
 
-        int oloc              = dcolptr_ptr[gidx + gidy * colinfo_.strides[1]];
-        svalptr_ptr[oloc - 1] = val;
-        scolptr_ptr[oloc - 1] = gidy;
+        int oloc              = dcolptr_ptr[gidx + gidy * colinfo_.strides[1] + colinfo_.offset];
+        svalptr_ptr[oloc + rowoff - 1] = val;
+        scolptr_ptr[oloc + rowoff - 1] = gidy;
     }
 
    private:

From 360fefb3551a7c9f91250b0ec894aad76ec6a022 Mon Sep 17 00:00:00 2001
From: John Melonakos <john.melonakos@intel.com>
Date: Fri, 14 Mar 2025 15:11:55 -0400
Subject: [PATCH 2643/2677] updated 3.9 install and getting started
 instructions (#3496)

* updated 3.9 install and getting started instructions

* more tweaks to the 3.9 using arrayfire instructions

* fixed release notes

* Put the sentence about OpenCL CPU drivers back in.
---
 docs/pages/install.md          |  90 ++++++++++++-------------
 docs/pages/release_notes.md    |  22 ++++---
 docs/pages/using_on_linux.md   |  54 +++++++--------
 docs/pages/using_on_windows.md | 116 ++++++++++++++++-----------------
 include/af/arith.h             |  24 ++++---
 5 files changed, 149 insertions(+), 157 deletions(-)

diff --git a/docs/pages/install.md b/docs/pages/install.md
index 555e702a1b..01b268af34 100644
--- a/docs/pages/install.md
+++ b/docs/pages/install.md
@@ -6,10 +6,9 @@ target architecture and operating system. Although ArrayFire can be [built
 from source](https://github.com/arrayfire/arrayfire), the installers
 conveniently package necessary dependencies.
 
-Install the latest device drivers before using ArrayFire. If you are going to
-target the CPU using ArrayFire’s OpenCL backend, install the OpenCL
-runtime. Drivers and runtimes should be downloaded and installed from the
-device vendor’s website.
+Install the latest device drivers before using ArrayFire. If you target the
+CPU using ArrayFire’s OpenCL backend, install the OpenCL runtime. Drivers and
+runtimes should be downloaded and installed from each device vendor's website.
 
 # Install Instructions {#InstallInstructions}
 
@@ -19,15 +18,11 @@ device vendor’s website.
 
 ## Windows {#Windows}
 
-Prior to installing ArrayFire on Windows,
-[download](https://www.microsoft.com/download/details.aspx?id=48145)
-install the Visual Studio 2015 (x64) runtime libraries.
+Once the ArrayFire has been downloaded, run the installer.
 
-Once the ArrayFire installer has been downloaded, run the installer. If you
-choose not to modify the path during the installation procedure, you'll need
-to manually add ArrayFire to the path for all users. Simply append
-`%%AF_PATH%/lib` to the PATH variable so that the loader can find ArrayFire
-DLLs.
+The installer offers the option to automatically add ArrayFire to the path for
+all users. If the installer did not do this, simply append `%%AF_PATH%/lib` to
+the PATH variable so that the loader can find ArrayFire DLLs.
 
 For more information on using ArrayFire on Windows, visit the following
 [page](http://arrayfire.org/docs/using_on_windows.htm).
@@ -36,42 +31,42 @@ For more information on using ArrayFire on Windows, visit the following
 
 There are two ways to install ArrayFire on Linux.
 1. Package Manager
-2. Using ArrayFire Linux Installer
+2. Using the ArrayFire Linux Installer
 
 As of today, approach (1) is only supported for Ubuntu 18.04 and 20.04. Please
-go through [our GitHub wiki
-page](https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers)
-for the detailed instructions.
+go through [the GitHub
+wiki[page](https://github.com/arrayfire/arrayfire/wiki/Install-ArrayFire-From-Linux-Package-Managers)
+for detailed instructions.
 
-For approach (2), once you have downloaded the ArrayFire installer, execute
-the installer from the terminal as shown below. Set the `--prefix` argument to
-the directory you would like to install ArrayFire to - we recommend `/opt`.
+For approach (2), once the ArrayFire installer is downloaded, execute the
+installer from the terminal as shown below. Set the `--prefix` argument to the
+target install directory; we recommend `/opt`.
 
-    ./Arrayfire_*_Linux_x86_64.sh --include-subdir --prefix=/opt
+    ./ArrayFire_*_Linux_x86_64.sh --include-subdir --prefix=/opt
 
-Given sudo permissions, you can add the ArrayFire libraries via `ldconfig` like
-so:
+Given sudo permissions, the ArrayFire libraries can be added to the path via
+`ldconfig` like so:
 
     echo /opt/arrayfire/lib64 > /etc/ld.so.conf.d/arrayfire.conf
     sudo ldconfig
 
-Otherwise, you will need to set the `LD_LIBRARY_PATH` environment variable in
-order to let your shared library loader find the ArrayFire libraries.
+Otherwise, the `LD_LIBRARY_PATH` environment variable can be set so that the
+shared library loader can find the ArrayFire libraries.
 
 For more information on using ArrayFire on Linux, visit the following
 [page](http://arrayfire.org/docs/using_on_linux.htm).
 
 ### Graphics support
 
-ArrayFire allows you to do high performance visualizations via our
+ArrayFire enables high-performance visualizations via the
 [Forge](https://github.com/arrayfire/forge) library. On Linux, there are a few
-dependencies you will need to install to enable graphics support:
+dependencies to install to enable graphics support:
 
-FreeImage
-Fontconfig
-GLU (OpenGL Utility Library)
+* FreeImage
+* Fontconfig
+* GLU (OpenGL Utility Library)
 
-We show how to install these dependencies on common Linux distributions:
+To install these dependencies on common Linux distributions:
 
 __Debian, Ubuntu (14.04 and above), and other Debian derivatives__
 
@@ -84,9 +79,9 @@ __Fedora, Redhat, CentOS__
 
 ## macOS {#macOS}
 
-Once you have downloaded the ArrayFire installer, execute the installer by
-either double clicking on the ArrayFire `pkg` file or running the following
-command from your terminal:
+Once the ArrayFire installer has been downloaded, execute the installer by
+either double-clicking on the ArrayFire `pkg` file or running the following
+command:
 
     sudo installer -pkg Arrayfire-*_OSX.pkg -target /
 
@@ -95,11 +90,10 @@ For more information on using ArrayFire on macOS, visit the following
 
 ## NVIDIA Tegra devices
 
-ArrayFire is capable of running on TX1 and TX2 devices. The TK1 is no longer
-supported.
+ArrayFire is capable of running TX2 devices.
 
-Prior to installing ArrayFire, make sure you have the latest version of JetPack
-(v2.3 and above) or L4T (v24.2 and above) on your device.
+Before installing ArrayFire, make sure the latest version of JetPack (v2.3 and
+above) or L4T (v24.2 and above) is installed.
 
 ### Tegra prerequisites
 
@@ -109,27 +103,25 @@ The following dependencies are required for Tegra devices:
 
 ## Testing installation
 
-After ArrayFire is finished installing, we recommend building and running a few
-of the provided examples to verify things are working as expected.
+After ArrayFire is finished installing, we recommend building and running a
+few of the provided examples to verify things are working as expected.
 
-On Unix-like systems:
+On Windows, open the CMakeLists.txt file from CMake-GUI. Once the project is
+configured and generated, build and run the examples from Visual Studio.
+
+On Linux, run the following commands:
 
     cp -r /opt/arrayfire/share/ArrayFire/examples /tmp/examples
     cd /tmp/examples
     mkdir build
     cd build
-    cmake -DASSETS_DIR:PATH=/tmp ..
+    cmake ..
     make
-    ./helloworld/helloworld_{cpu,cuda,opencl}
-
-On Windows, open the CMakeLists.txt file from CMake-GUI and set `ASSETS_DIR`
-variable to the parent folder of examples folder. Once the project is
-configured and generated, you can build and run the examples from Visual
-Studio.
+    ./helloworld/helloworld_{cpu,cuda,oneapi,opencl}
 
 ## <a name="GettingHelp"></a> Getting help
 
 * Google Groups: https://groups.google.com/forum/#!forum/arrayfire-users
-* ArrayFire Services:  [Consulting](https://arrayfire.com/consulting/)  |  [Support](https://arrayfire.com/support/)   |  [Training](https://arrayfire.com/training/)
+* ArrayFire Services:  [Consulting](https://arrayfire.com/consulting/)  |  [Training](https://arrayfire.com/training/)
 * ArrayFire Blogs: http://arrayfire.com/blog/
-* Email: <mailto:technical@arrayfire.com>
+* Email: <mailto:support@arrayfire.com>
diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 464eba664d..1b55fea448 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -10,13 +10,15 @@ v3.9.0
 - Add broadcast support \PR{2871}
 - Improve OpenCL CPU JIT performance \PR{3257} \PR{3392}
 - Optimize thread/block calculations of several kernels \PR{3144}
-- Add support for fast math compiliation when building ArrayFire \PR{3334 \PR{3337}
+- Add support for fast math compiliation when building ArrayFire \PR{3334}
+  \PR{3337}
 - Optimize performance of fftconvolve when using floats \PR{3338}
 - Add support for CUDA 12.1 and 12.2
 - Better handling of empty arrays \PR{3398}
 - Better handling of memory in linear algebra functions in OpenCL \PR{3423}
 - Better logging with JIT kernels \PR{3468}
-- Optimize memory manager/JIT interactions for small number of buffers \PR{3468}
+- Optimize memory manager/JIT interactions for small number of buffers
+  \PR{3468}
 - Documentation improvements \PR{3485}
 - Optimize reorder function \PR{3488}
 
@@ -24,21 +26,24 @@ v3.9.0
 - Improve Errors when creating OpenCL contexts from devices \PR{3257}
 - Improvements to vcpkg builds \PR{3376 \PR{3476}
 - Fix reduce by key when nan's are present \PR{3261}
-- Fix error in convolve where the ndims parameter was forced to be equal to 2  \PR{3277}
-- Make constructors that accept dim_t to be explicit to avoid invalid conversions  \PR{3259} 
-- Fix error in randu when compiling against clang 14 \PR{3333} 
+- Fix error in convolve where the ndims parameter was forced to be equal to 2
+  \PR{3277}
+- Make constructors that accept dim_t to be explicit to avoid invalid
+  conversions \PR{3259}
+- Fix error in randu when compiling against clang 14 \PR{3333}
 - Fix bug in OpenCL linear algebra functions  \PR{3398}
-- Fix bug with thread local variables when device was changed \PR{3420} \PR{3421} 
-- Fix bug in qr related to uninitialized memory \PR{3422} 
+- Fix bug with thread local variables when device was changed \PR{3420}
+  \PR{3421}
+- Fix bug in qr related to uninitialized memory \PR{3422}
 - Fix bug in shift where the array had an empty middle dimension \PR{3488}
 
-
 ## Contributions
 
 Special thanks to our contributors:
 [Willy Born](https://github.com/willyborn)
 [Mike Mullen](https://github.com/mfzmullen)
 
+
 v3.8.3
 ======
 
@@ -101,6 +106,7 @@ Special thanks to our contributors:
 [Jacob Kahn](https://github.com/jacobkahn)
 [Willy Born](https://github.com/willyborn)
 
+
 v3.8.1
 ======
 
diff --git a/docs/pages/using_on_linux.md b/docs/pages/using_on_linux.md
index 7dbff74d2a..91035426c5 100644
--- a/docs/pages/using_on_linux.md
+++ b/docs/pages/using_on_linux.md
@@ -4,9 +4,9 @@ Using ArrayFire on Linux {#using_on_linux}
 Once you have [installed](\ref installing) ArrayFire on your system, the next
 thing to do is set up your build system. On Linux, you can create ArrayFire
 projects using almost any editor, compiler, or build system. The only
-requirements are that you include the ArrayFire header directories and link with
-the ArrayFire library you intend to use i.e. CUDA, OpenCL, CPU, or Unified
-backends.
+requirements are that you include the ArrayFire header directories and link
+with the ArrayFire library you intend to use i.e. CUDA, OpenCL, oneAPI, CPU,
+or Unified backends.
 
 ## The big picture  {#big-picture-linux}
 
@@ -15,17 +15,18 @@ installer will populate files in the following sub-directories:
 
     include/arrayfire.h         - Primary ArrayFire include file
     include/af/*.h              - Additional include files
-    lib/libaf*                  - CPU, CUDA, oneAPI and OpenCL libraries (.a, .so)
+    lib/libaf*                  - CPU, CUDA, oneAPI, and OpenCL libraries (.a, .so)
     lib/libforge*               - Visualization library
     lib/libcu*                  - CUDA backend dependencies
     lib/libOpenCL.so            - OpenCL ICD Loader library
     share/ArrayFire/cmake/*     - CMake config (find) scripts
     share/ArrayFire/examples/*  - All ArrayFire examples
 
-Because ArrayFire follows standard installation practices, you can use basically
-any build system to create and compile projects that use ArrayFire. Among the
-many possible build systems on Linux we suggest using ArrayFire with either
-CMake or Makefiles with CMake being our preferred build system.
+Because ArrayFire follows standard installation practices, you can use
+basically any build system to create and compile projects that use
+ArrayFire. Among the many possible build systems on Linux we suggest using
+ArrayFire with either CMake or Makefiles with CMake being our preferred build
+system.
 
 ## Prerequisite software
 
@@ -57,8 +58,8 @@ apt install build-essential cmake cmake-curses-gui
 ## CMake
 
 We recommend that the CMake build system be used to create ArrayFire projects.
-As [discussed above](#big-picture-linux), ArrayFire ships with a series of CMake
-scripts to make finding and using our library easy.
+As [discussed above](#big-picture-linux), ArrayFire ships with a series of
+CMake scripts to make finding and using our library easy.
 
 First create a file called `CMakeLists.txt` in your project directory:
 
@@ -74,19 +75,19 @@ and populate it with the following code:
     # Unified backend lets you choose the backend at runtime
     target_link_libraries(<my_executable> ArrayFire::af)
 
-where `my_executable` is the name of the executable you wish to create. See the
-[CMake documentation](https://cmake.org/documentation/) for more information on
-how to use CMake. To link with a specific backend directly, replace the
-`ArrayFire::af` with the following for their respective backends.
+where `my_executable` is the name of the executable you wish to create. See
+the [CMake documentation](https://cmake.org/documentation/) for more
+information on how to use CMake. To link with a specific backend directly,
+replace the `ArrayFire::af` with the following for their respective backends.
 
 * `ArrayFire::afcpu` for CPU backend.
 * `ArrayFire::afcuda` for CUDA backend.
 * `ArrayFire::afoneapi` for oneAPI backend.
 * `ArrayFire::afopencl` for OpenCL backend.
 
-Next we need to instruct CMake to create build instructions and then compile. We
-suggest using CMake's out-of-source build functionality to keep your build and
-source files cleanly separated. To do this open the CMake GUI.
+Next we need to instruct CMake to create build instructions and then
+compile. We suggest using CMake's out-of-source build functionality to keep
+your build and source files cleanly separated. To do this open the CMake GUI.
 
     cd your-project-directory
     mkdir build
@@ -98,8 +99,9 @@ source files cleanly separated. To do this open the CMake GUI.
 still help you out. When you execute CMake specify the path to ArrayFire
 installation root as `ArrayFire_DIR` variable.
 
-For example, if ArrayFire were installed locally to `/home/user/ArrayFire` then
-you would modify the `cmake` command above to contain the following definition:
+For example, if ArrayFire were installed locally to `/home/user/ArrayFire`
+then you would modify the `cmake` command above to contain the following
+definition:
 
     cmake -DArrayFire_DIR=/home/user/ArrayFire ..
 
@@ -107,18 +109,18 @@ You can also specify this information in the `ccmake` command-line interface.
 
 ## Makefiles
 
-Building ArrayFire projects with Makefiles is fairly similar to CMake except you
-must specify all paths and libraries manually.
+Building ArrayFire projects with Makefiles is fairly similar to CMake except
+you must specify all paths and libraries manually.
 
 As with any `make` project, you need to specify the include path to the
 directory containing `arrayfire.h` file. This should be `-I
 /opt/arrayfire/include` if you followed our installation instructions.
 
-Similarly, you will need to specify the path to the ArrayFire library using the
-`-L` option (e.g. `-L/opt/arrayfire/lib`) followed by the specific ArrayFire
-library you wish to use using the `-l` option (for example `-lafcpu`,
-`-lafopencl`, `-lafoneapi`, `-lafcuda`, or `-laf` for the CPU, OpenCL, oneAPI
-and CUDA, and unified backends, respectively.
+Similarly, you will need to specify the path to the ArrayFire library using
+the `-L` option (e.g. `-L/opt/arrayfire/lib`) followed by the specific
+ArrayFire library you wish to use using the `-l` option (for example
+`-lafcpu`, `-lafopencl`, `-lafoneapi`, `-lafcuda`, or `-laf` for the CPU,
+OpenCL, oneAPI, and CUDA, and unified backends, respectively.
 
 Here is a minimal example Makefile which uses ArrayFire's CPU backend:
 
diff --git a/docs/pages/using_on_windows.md b/docs/pages/using_on_windows.md
index 072445a4ae..b9084723d1 100644
--- a/docs/pages/using_on_windows.md
+++ b/docs/pages/using_on_windows.md
@@ -2,7 +2,8 @@ Using ArrayFire with Microsoft Windows and Visual Studio {#using_on_windows}
 ============================================================================
 
 If you have not already done so, please make sure you have installed,
-configured, and tested ArrayFire following the [installation instructions](#installing).
+configured, and tested ArrayFire following the [installation
+instructions](#installing).
 
 # The big picture {#big-picture-windows}
 
@@ -10,70 +11,60 @@ The ArrayFire Windows installer creates the following:
 1. **AF_PATH** environment variable to point to the installation location. The
    default install location is `C:\Program Files\ArrayFire\v3`
 2. **AF_PATH/include** : Header files for ArrayFire (include directory)
-3. **AF_PATH/lib** : All ArrayFire backends libraries, dlls and dependency dlls
-   (library directory)
-4. **AF_PATH/examples** : Examples to get started.
+3. **AF_PATH/lib** : All ArrayFire backend libraries, dlls, and dependency
+   dlls (library directory)
+4. **AF_PATH/examples** : Examples to get started
 5. **AF_PATH/cmake** : CMake config files
 6. **AF_PATH/uninstall.exe** : Uninstaller
 
-The installer will prompt the user for following three options.
-* Do not add **%%AF_PATH%/lib** to PATH
-* Add **%%AF_PATH%/lib** to PATH environment variable of current user
-* Add **%%AF_PATH%/lib** to PATH environment variable for all users
-
-If you chose not to modify PATH during installation please make sure to do so
-manually so that all applications using ArrayFire libraries will be able to find
-the required DLLs.
-
 # Build and Run Helloworld {#section1}
 
 This can be done in two ways either by using CMake build tool or using Visual
 Studio directly.
 
 ##  Using CMake {#section1part1}
-1. Download and install [CMake](https://cmake.org/download/), preferrably the
+1. Download and install [CMake](https://cmake.org/download/), preferably the
    latest version.
 2. Open CMake-GUI and set the field __Where is the source code__ to the root
    directory of examples.
 3. Set the field __Where to build the binaries__ to
-   **path_to_examples_root_dir/build** and click the `Configure` button towards
-   the lower left bottom.
-4. CMake will prompt you asking if it has to create the `build` directory if
-   it's not already present. Click yes to create the build directory.
-5. Before the configuration begins, CMake will show you a list(drop-down menu)
-   of available Visual Studio versions on your system to chose from. Select one
-   and check the radio button that says **Use default native compilers** and
-   click finish button in the bottom right corner.
-6. CMake will show you errors in red text if any once configuration is finished.
-   Ideally, you wouldn't need to do anything and CMake should be able to find
-   ArrayFire automatically. Please let us know if it didn't on your machine.
+   **path_to_examples_root_dir/build** and click the `Configure` button.
+4. CMake will prompt you to create the `build` directory if not already
+   present. Click "yes" to create the build directory.
+5. Before the configuration begins, CMake will show you a list (drop-down
+   menu) of available Visual Studio versions. Select one and check the radio
+   button that says **Use default native compilers** and click finish.
+6. CMake will show you errors in red text, if any, once configuration is
+   finished. Sometimes a second configuration is necessary.
 7. Click **Generate** button to generate the Visual Studio solution files for
    the examples.
 8. Click **Open Project** button that is right next to **Generate** button to
    open the solution file.
-9. You will see a bunch of examples segregated into three sets named after the
-   compute backends of ArrayFire: cpu, cuda & opencl if you have installed all
-   backends. Select the helloworld project from any of the installed backends
-   and mark it as startup project and hit `F5`.
+9. You will see the examples segregated into four sets named after the compute
+   backends of ArrayFire: cpu, cuda, oneapi, & opencl, if you installed all
+   backends. Select the helloworld project from any of the installed backends,
+   mark it as startup project, and hit `F5`.
 10. Once the helloworld example builds, you will see a console window with the
     output from helloworld program.
 
 ## Using Visual Studio {#section1part2}
 
-1. Open Visual Studio of your choice and create an empty C++ project.
-2. Right click the project and add an existing source file
+1. Open Visual Studio and create an empty C++ project.
+2. Right-click the project and add an existing source file
    `examples/helloworld/helloworld.cpp` to this project.
 3. Add `"$(AF_PATH)/include;"` to _Project Properties -> C/C++ -> General ->
    Additional Include Directories_.
 4. Add `"$(AF_PATH)/lib;"` to _Project Properties -> Linker -> General ->
    Additional Library Directories_.
-5. Add `afcpu.lib` or `afcuda.lib` or `afopencl.lib` to _Project Properties ->
-   Linker -> Input -> Additional Dependencies_. based on your preferred backend.
-6. (Optional) You may choose to define `NOMINMAX`, `AF_<CPU/CUDA/OPENCL>` and/or
-   `AF_<DEBUG/RELEASE>` in your projects. This can be added to _Project
-   Properties -> C/C++ -> General -> Preprocessor-> Preprocessory definitions_.
-7. Build and run the project. You will see a console window with the output from
-   helloworld program.
+5. Add `afcpu.lib`, `afcuda.lib`, `afoneapi.lib`, or `afopencl.lib` to
+   _Project Properties -> Linker -> Input -> Additional Dependencies_. based
+   on your preferred backend.
+6. (Optional) You may choose to define `NOMINMAX`,
+   `AF_<CPU/CUDA/ONEAPI/OPENCL>`, or `AF_<DEBUG/RELEASE>` in your
+   projects. This can be added to _Project Properties -> C/C++ -> General ->
+   Preprocessor-> Preprocessory definitions_.
+7. Build and run the project. You will see a console window with the output
+   from helloworld program.
 
 # Using ArrayFire within Existing Visual Studio Projects {#section2}
 This is divided into three parts:
@@ -83,10 +74,10 @@ This is divided into three parts:
 
 ## Part A: Adding ArrayFire to an existing solution (Single Backend) {#section2partA}
 
-Note: If you plan on using Native CUDA code in the project, use the steps under
-[Part B](#section2partB).
+Note: If you plan on using Native CUDA code in the project, use the steps
+under [Part B](#section2partB).
 
-Adding a single backend to an existing project is quite simple.
+Adding a single backend to an existing project is quite simple:
 
 1. Add `"$(AF_PATH)/include;"` to _Project Properties -> C/C++ -> General ->
    Additional Include Directories_.
@@ -97,8 +88,9 @@ Adding a single backend to an existing project is quite simple.
    preferred backend.
 
 ## Part B: Adding ArrayFire CUDA to a new/existing CUDA project {#section2partB}
-Lastly, if your project contains custom CUDA code, the instructions are slightly
-different as it requires using a CUDA NVCC Project:
+
+Lastly, if your project contains custom CUDA code, the instructions are
+slightly different as it requires using a CUDA NVCC Project:
 
 1. Create a custom "CUDA NVCC project" in Visual Studio
 2. Add `"$(AF_PATH)/include;"` to _Project Properties -> CUDA C/C++ -> General
@@ -108,7 +100,8 @@ different as it requires using a CUDA NVCC Project:
 4. Add `afcpu.lib`, `afcuda.lib`, `afopencl.lib`, or `af.lib` to _Project Properties ->
    Linker -> Input -> Additional Dependencies_. based on your preferred backend.
 
-### Part C: Project with all ArrayFire backends {#section2partC}
+## Part C: Project with all ArrayFire backends {#section2partC}
+
 If you wish to create a project that allows you to use all the ArrayFire
 backends with ease, you should use `af.lib` in step 3 from [Part
 A](#section2partA).
@@ -116,11 +109,12 @@ A](#section2partA).
 You can alternately download the template project from [ArrayFire Template
 Projects](https://github.com/arrayfire/arrayfire-project-templates)
 
-# <a name="section3" />Using ArrayFire with CMake
-ArrayFire ships with a series of CMake scripts to make finding and using our
+# Using ArrayFire with CMake
+
+ArrayFire ships with a series of CMake scripts to make finding and using the
 library easy.
 
-First create a file called `CMakeLists.txt` in your project directory:
+First, create a file called `CMakeLists.txt` in your project directory:
 
     cd your-project-directory
     touch CMakeLists.txt
@@ -130,13 +124,13 @@ and populate it with the following code:
     find_package(ArrayFire)
     add_executable(<my_executable> [list your source files here])
 
-    # To use Unified backend, do the following.
-    # Unified backend lets you choose the backend at runtime
+    # The Unified backend lets you choose the backend at runtime.
+    # To use the Unified backend, do the following:
     target_link_libraries(<my_executable> ArrayFire::af)
 
-where `<my_executable>` is the name of the executable you wish to create. See the
-[CMake documentation](https://cmake.org/documentation/) for more information on
-how to use CMake. To link with a specific backend directly, replace the
+, where `<my_executable>` is the name of the executable to create. See the
+[CMake documentation](https://cmake.org/documentation/) for more information
+on how to use CMake. To link with a specific backend directly, replace the
 `ArrayFire::af` with the following for their respective backends.
 
 * `ArrayFire::afcpu` for CPU backend.
@@ -144,13 +138,13 @@ how to use CMake. To link with a specific backend directly, replace the
 * `ArrayFire::afoneapi` for oneAPI backend.
 * `ArrayFire::afopencl` for OpenCL backend.
 
-Next we need to instruct CMake to create build instructions and then compile. We
-suggest using CMake's out-of-source build functionality to keep your build and
-source files cleanly separated. To do this open the CMake GUI.
+Next, instruct CMake to create build instructions and compile them. We suggest
+using CMake's out-of-source build functionality to keep your build and source
+files cleanly separated. To do this, open the CMake GUI.
 
-* Under source directory, add the path to your project
-* Under build directory, add the path to your project and append /build
-* Click configure and choose a 64 bit Visual Studio generator.
-* If configuration was successful, click generate. This will create a
-  my-project.sln file under build. Click `Open Project` in CMake-GUI to open the
-  solution and compile the ALL_BUILD project.
+* Under "source directory", add the path to your project.
+* Under "build directory", add the path to your project and append /build.
+* Click "configure" and choose a 64-bit Visual Studio generator.
+* If the configuration was successful, click "generate". This will create a
+  my-project.sln file under build. Click `Open Project` in CMake-GUI to open
+  the solution and compile the ALL_BUILD project.
diff --git a/include/af/arith.h b/include/af/arith.h
index 0dd2eb2c1f..c75544a5ab 100644
--- a/include/af/arith.h
+++ b/include/af/arith.h
@@ -80,7 +80,7 @@ namespace af
     /// \param[in] lo lower limit; can be an array or a scalar
     /// \param[in] hi upper limit; can be an array or a scalar
     /// \return       clamped array
-    /// 
+    ///
     /// \ingroup arith_func_clamp
     AFAPI array clamp(const array &in, const array &lo, const array &hi);
 #endif
@@ -110,7 +110,7 @@ namespace af
     /// \param[in] lhs numerator; can be an array or a scalar
     /// \param[in] rhs denominator; can be an array or a scalar
     /// \return        remainder
-    /// 
+    ///
     /// \ingroup arith_func_rem
     AFAPI array rem    (const array &lhs, const array &rhs);
 
@@ -130,7 +130,7 @@ namespace af
     /// \param[in] lhs dividend; can be an array or a scalar
     /// \param[in] rhs divisor; can be an array or a scalar
     /// \return        modulus
-    /// 
+    ///
     /// \ingroup arith_func_mod
     AFAPI array mod    (const array &lhs, const array &rhs);
 
@@ -154,7 +154,7 @@ namespace af
     ///
     /// \param[in] in input array, typically complex
     /// \return       phase angle (in radians)
-    /// 
+    ///
     /// \ingroup arith_func_arg
     AFAPI array arg    (const array &in);
 
@@ -162,7 +162,7 @@ namespace af
     ///
     /// \param[in] in input array
     /// \return       array containing 1's for negative values; 0's otherwise
-    /// 
+    ///
     /// \ingroup arith_func_sign
     AFAPI array sign  (const array &in);
 
@@ -178,7 +178,7 @@ namespace af
     ///
     /// \param[in] in input array
     /// \return       nearest integer not greater in magnitude than `in`
-    /// 
+    ///
     /// \ingroup arith_func_trunc
     AFAPI array trunc  (const array &in);
 
@@ -336,7 +336,7 @@ namespace af
     /// \param[in] in input array
     /// \return       complex array
     AFAPI array complex(const array& in);
- 
+
     /// C++ Interface to create a complex array from two real arrays.
     ///
     /// \param[in] real_ input array to be assigned as the real component of
@@ -418,7 +418,6 @@ namespace af
     /// \ingroup arith_func_root
     AFAPI array root    (const double nth_root, const array &value);
 
-
     /// \ingroup arith_func_pow
     /// @{
     /// C++ Interface to raise a base to a power (or exponent).
@@ -441,7 +440,6 @@ namespace af
     ///
     /// \param[in] in power
     /// \return       2 raised to the power
-    ///
     AFAPI array pow2    (const array &in);
     /// @}
 
@@ -449,7 +447,7 @@ namespace af
     /// C++ Interface to evaluate the logistical sigmoid function.
     ///
     /// Computes \f$\frac{1}{1+e^{-x}}\f$.
-    /// 
+    ///
     /// \param[in] in input
     /// \return       sigmoid
     ///
@@ -469,7 +467,7 @@ namespace af
     /// `exp(in) - 1`.
     ///
     /// This function is useful when `in` is small.
-    /// 
+    ///
     /// \param[in] in exponent
     /// \return       exponential minus 1
     ///
@@ -502,9 +500,9 @@ namespace af
 
     /// C++ Interface to evaluate the natural logarithm of 1 + input,
     /// `ln(1+in)`.
-    /// 
+    ///
     /// This function is useful when `in` is small.
-    /// 
+    ///
     /// \param[in] in input
     /// \return natural logarithm of `1 + input`
     ///

From c6269a6bf529a13a69c18373f172f6c04415883c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 21 Mar 2025 15:24:16 -0700
Subject: [PATCH 2644/2677] Add Lattice Boltzmann Fluid Simulation Example
 (#3455)

* Added cfd simulation example

* Added cfd simulation code

* Applied clang-format

* Fixed ambiguous call error

* Fixed scaling and image search issues
---
 examples/pde/CMakeLists.txt    |  16 +-
 examples/pde/boltzmann_cfd.cpp | 570 +++++++++++++++++++++++++++++++++
 2 files changed, 585 insertions(+), 1 deletion(-)
 create mode 100644 examples/pde/boltzmann_cfd.cpp

diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index bceb38665a..4a20caf5f9 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -12,23 +12,37 @@ project(ArrayFire-Example-PDE
 
 find_package(ArrayFire REQUIRED)
 
+add_definitions("-DASSETS_DIR=\"${ASSETS_DIR}\"")
+
 if(ArrayFire_CPU_FOUND)
   # Shallow Water simulation example
   add_executable(swe_cpu swe.cpp)
   target_link_libraries(swe_cpu ArrayFire::afcpu)
+
+  add_executable(boltzmann_cfd_cpu boltzmann_cfd.cpp)
+  target_link_libraries(boltzmann_cfd_cpu ArrayFire::afcpu)
 endif()
 
 if(ArrayFire_CUDA_FOUND)
   add_executable(swe_cuda swe.cpp)
   target_link_libraries(swe_cuda ArrayFire::afcuda)
+
+  add_executable(boltzmann_cfd_cuda boltzmann_cfd.cpp)
+  target_link_libraries(boltzmann_cfd_cuda ArrayFire::afcuda)
 endif()
 
 if(ArrayFire_OpenCL_FOUND)
   add_executable(swe_opencl swe.cpp)
   target_link_libraries(swe_opencl ArrayFire::afopencl)
+
+  add_executable(boltzmann_cfd_opencl boltzmann_cfd.cpp)
+  target_link_libraries(boltzmann_cfd_opencl ArrayFire::afopencl)
 endif()
 
 if(ArrayFire_oneAPI_FOUND)
   add_executable(swe_oneapi swe.cpp)
   target_link_libraries(swe_oneapi ArrayFire::afoneapi)
-endif()
+
+  add_executable(boltzmann_cfd_oneapi boltzmann_cfd.cpp)
+  target_link_libraries(boltzmann_cfd_oneapi ArrayFire::afoneapi)
+endif()
\ No newline at end of file
diff --git a/examples/pde/boltzmann_cfd.cpp b/examples/pde/boltzmann_cfd.cpp
new file mode 100644
index 0000000000..38882f3c5c
--- /dev/null
+++ b/examples/pde/boltzmann_cfd.cpp
@@ -0,0 +1,570 @@
+/*******************************************************
+ * Copyright (c) 2023, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/*
+    This is a Computational Fluid Dynamics Simulation using the Lattice
+   Boltzmann Method For this simulation we are using D2N9 (2 dimensions, 9
+   neighbors) with bounce-back boundary conditions For more information on the
+   simulation equations, check out
+   https://en.wikipedia.org/wiki/Lattice_Boltzmann_methods#Mathematical_equations_for_simulations
+
+    The initial conditions of the fluid are obtained from three images that
+   specify their properties using the function read_initial_condition_arrays.
+   These images can be modified to simulate different cases
+*/
+
+#include <arrayfire.h>
+#include <chrono>
+#include <iostream>
+#include <thread>
+
+/*
+    Values of the D2N9 grid follow the following order structure:
+
+
+          -1      0       1
+      * ----------------------> x
+  -1   |   6      3       0
+       |
+   0   |   7      4       1
+       |
+   1   |   8      5       2
+       |
+       v
+       y
+
+    The (-1, 0, 1) refer to the x and y offsets with respect to a single cell
+    and the (0-8) refer to indices of each cell in the 3x3 grid
+
+    Eg. Element with index 4 is the center of the grid which has an x-offset =
+  ex_vals[4] = 0 and y-offset = ey_vals[4] = 0 with its quantities being
+  weighted with weight wt_vals[4] = 16/36
+*/
+
+static const float ex_vals[] = {1.0, 1.0, 1.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0};
+
+static const float ey_vals[] = {1.0, 0.0, -1.0, 1.0, 0.0, -1.0, 1.0, 0.0, -1.0};
+
+static const float wt_vals[] = {1.0f / 36.0f, 4.0f / 36.0f,  1.0f / 36.0f,
+                                4.0f / 36.0f, 16.0f / 36.0f, 4.0f / 36.0f,
+                                1.0f / 36.0f, 4.0f / 36.0f,  1.0f / 36.0f};
+
+static const int opposite_indices[] = {8, 7, 6, 5, 4, 3, 2, 1, 0};
+
+struct Simulation {
+    // Fluid quantities
+    af::array ux;
+    af::array uy;
+    af::array rho;
+    af::array sigma;
+    af::array f;
+    af::array feq;
+
+    // Constant velocity boundary conditions positions
+    af::array set_boundaries;
+
+    // Simulation Parameters
+    size_t grid_width;
+    size_t grid_height;
+    float density;
+    float velocity;
+    float reynolds;
+
+    // Helper arrays stored for computation
+    af::array ex;
+    af::array ey;
+    af::array wt;
+
+    af::array ex_T;
+    af::array ey_T;
+    af::array wt_T;
+
+    af::array ex_;
+    af::array ey_;
+};
+
+/**
+ * @brief Create a simulation object containing all the initial parameters and
+ * condition of the simulation
+ *
+ * @details
+ * For the ux, uy, and boundary images, we use RGB values for to define the
+ * specific quantites for each grid cell/pixel
+ *
+ * /// R & B for ux & uy
+ *
+ * For ux and uy, Red means positive value while Blue means negative value. The
+ * speed value for both ux and uy is computed as $(R - B) * velocity / 255$.
+ *
+ * For example, for the same pixel in the two images if we had ux = RGB(255,0,0)
+ * and uy = RGB(0,0,255) means that cell's fluid has an x-velocity of +v and
+ * y-velocity of -v where v is the velocity quantity pass to this function.
+ *
+ * Note that having the same value in the R and B components will cancel each
+ * other out, i.e., have the fluid has 0 velocity in that direction similar to
+ * having it be 0.
+ *
+ * /// G for ux & uy
+ *
+ * The G component is reserved for an object or obstacle. Any non-zero value for
+ * the green component represents a hard boundary in the simulation
+ *
+ * /// RGB for boundary
+ *
+ * Any non-zero value for any of the components in the RGB value of the pixel
+ * means that the initial values passed for ux and uy will remain constant
+ * throught the simulation
+ *
+ */
+Simulation create_simulation(uint32_t grid_width, uint32_t grid_height,
+                             float density, float velocity, float reynolds,
+                             const char* ux_image_filename,
+                             const char* uy_image_filename,
+                             const char* boundaries_filename) {
+    Simulation sim;
+
+    sim.grid_width  = grid_width;
+    sim.grid_height = grid_height;
+    sim.velocity    = velocity;
+    sim.density     = density;
+    sim.reynolds    = reynolds;
+
+    try {
+        sim.ux = af::loadImage(ux_image_filename, true);
+    } catch (const af::exception& e) {
+        std::cerr << e.what() << std::endl;
+        sim.ux = af::constant(0, grid_width, grid_height, 3);
+    }
+
+    auto ux_dim = sim.ux.dims();
+    if (ux_dim[0] != grid_width || ux_dim[1] != grid_height) {
+        std::cerr
+            << "Fluid flow ux image has dimensions different to the simulation"
+            << std::endl;
+        throw std::runtime_error{
+            "Fluid flow ux image has dimensions different to the simulation"};
+    }
+
+    try {
+        sim.uy = af::loadImage(uy_image_filename, true);
+    } catch (const af::exception& e) {
+        std::cerr << e.what() << std::endl;
+        sim.uy = af::constant(0, grid_width, grid_height, 3);
+    }
+
+    auto uy_dim = sim.uy.dims();
+    if (uy_dim[0] != grid_width || uy_dim[1] != grid_height) {
+        std::cerr
+            << "Fluid flow uy image has dimensions different to the simulation"
+            << std::endl;
+        throw std::runtime_error{
+            "Fluid flow uy image has dimensions different to the simulation"};
+    }
+
+    try {
+        sim.set_boundaries = af::loadImage(boundaries_filename, false);
+    } catch (const af::exception& e) {
+        std::cerr << e.what() << std::endl;
+        sim.set_boundaries = af::constant(0, grid_width, grid_height);
+    }
+
+    auto b_dim = sim.set_boundaries.dims();
+    if (b_dim[0] != grid_width || b_dim[1] != grid_height) {
+        std::cerr
+            << "Fluid boundary image has dimensions different to the simulation"
+            << std::endl;
+        throw std::runtime_error{
+            "Fluid boundary image has dimensions different to the simulation"};
+    }
+
+    sim.ux = (sim.ux(af::span, af::span, 0).T() -
+              sim.ux(af::span, af::span, 2).T()) *
+             velocity / 255.f;
+    sim.uy = (sim.uy(af::span, af::span, 0).T() -
+              sim.uy(af::span, af::span, 2).T()) *
+             velocity / 255.f;
+    sim.set_boundaries = sim.set_boundaries.T() > 0;
+
+    return sim;
+}
+
+/**
+ * @brief Initializes internal values used for computation
+ *
+ */
+void initialize(Simulation& sim) {
+    auto& ux    = sim.ux;
+    auto& uy    = sim.uy;
+    auto& rho   = sim.rho;
+    auto& sigma = sim.sigma;
+    auto& f     = sim.f;
+    auto& feq   = sim.feq;
+
+    auto& ex   = sim.ex;
+    auto& ey   = sim.ey;
+    auto& wt   = sim.wt;
+    auto& ex_  = sim.ex_;
+    auto& ey_  = sim.ey_;
+    auto& ex_T = sim.ex_T;
+    auto& ey_T = sim.ey_T;
+    auto& wt_T = sim.wt_T;
+
+    auto density  = sim.density;
+    auto velocity = sim.velocity;
+    auto xcount   = sim.grid_width;
+    auto ycount   = sim.grid_height;
+
+    ex = af::array(1, 1, 9, ex_vals);
+    ey = af::array(1, 1, 9, ey_vals);
+    wt = af::array(1, 1, 9, wt_vals);
+
+    ex_T = af::array(1, 9, ex_vals);
+    ey_T = af::array(1, 9, ey_vals);
+    wt_T = af::moddims(wt, af::dim4(1, 9));
+
+    rho   = af::constant(density, xcount, ycount, f32);
+    sigma = af::constant(0, xcount, ycount, f32);
+
+    f = af::constant(0, xcount, ycount, 9, f32);
+
+    ex_ = af::tile(ex, xcount, ycount, 1);
+    ey_ = af::tile(ey, xcount, ycount, 1);
+
+    // Initialization of the distribution function
+    auto edotu = ex_ * ux + ey_ * uy;
+    auto udotu = ux * ux + uy * uy;
+
+    feq = rho * wt *
+          ((edotu * edotu * 4.5f) - (udotu * 1.5f) + (edotu * 3.0f) + 1.0f);
+    f = feq;
+}
+
+/**
+ * @brief Updates the particle distribution functions for the new simulation
+ * frame
+ *
+ */
+void collide_stream(Simulation& sim) {
+    auto& ux             = sim.ux;
+    auto& uy             = sim.uy;
+    auto& rho            = sim.rho;
+    auto& sigma          = sim.sigma;
+    auto& f              = sim.f;
+    auto& feq            = sim.feq;
+    auto& set_boundaries = sim.set_boundaries;
+
+    auto& ex   = sim.ex;
+    auto& ey   = sim.ey;
+    auto& wt   = sim.wt;
+    auto& ex_  = sim.ex_;
+    auto& ey_  = sim.ey_;
+    auto& ex_T = sim.ex_T;
+    auto& ey_T = sim.ey_T;
+    auto& wt_T = sim.wt_T;
+
+    auto density  = sim.density;
+    auto velocity = sim.velocity;
+    auto reynolds = sim.reynolds;
+    auto xcount   = sim.grid_width;
+    auto ycount   = sim.grid_height;
+
+    const float viscosity =
+        velocity * std::sqrt(static_cast<float>(xcount * ycount)) / reynolds;
+    const float tau  = 0.5f + 3.0f * viscosity;
+    const float csky = 0.16f;
+
+    auto edotu = ex_ * ux + ey_ * uy;
+    auto udotu = ux * ux + uy * uy;
+
+    // Compute the new distribution function
+    feq =
+        rho * wt * (edotu * edotu * 4.5f - udotu * 1.5f + edotu * 3.0f + 1.0f);
+
+    auto taut =
+        af::sqrt(sigma * (csky * csky * 18.0f * 0.25f) + (tau * tau * 0.25f)) -
+        (tau * 0.5f);
+
+    // Compute the shifted distribution functions
+    auto fplus = f - (f - feq) / (taut + tau);
+
+    // Compute new particle distribution according to the corresponding D2N9
+    // weights
+    for (int i = 0; i < 9; ++i) {
+        int xshift = static_cast<int>(ex_vals[i]);
+        int yshift = static_cast<int>(ey_vals[i]);
+
+        fplus(af::span, af::span, i) =
+            af::shift(fplus(af::span, af::span, i), xshift, yshift);
+    }
+
+    // Keep the boundary conditions at the borders the same
+    af::replace(fplus, af::tile(!set_boundaries, af::dim4(1, 1, 9)), f);
+
+    // Update the particle distribution
+    f = fplus;
+
+    // Computing u dot e at the each of the boundaries
+    af::array ux_top = ux.rows(0, 2);
+    ux_top =
+        af::moddims(af::tile(ux_top, af::dim4(1, 3)).T(), af::dim4(ycount, 9));
+    af::array ux_bot = ux.rows(xcount - 3, xcount - 1);
+    ux_bot =
+        af::moddims(af::tile(ux_bot, af::dim4(1, 3)).T(), af::dim4(ycount, 9));
+
+    af::array uy_top = uy.rows(0, 2);
+    uy_top =
+        af::moddims(af::tile(uy_top, af::dim4(1, 3)).T(), af::dim4(ycount, 9));
+    af::array uy_bot = uy.rows(xcount - 3, xcount - 1);
+    uy_bot =
+        af::moddims(af::tile(uy_bot, af::dim4(1, 3)).T(), af::dim4(ycount, 9));
+
+    auto ux_lft = af::tile(ux.cols(0, 2), af::dim4(1, 3));
+    auto uy_lft = af::tile(uy.cols(0, 2), af::dim4(1, 3));
+    auto ux_rht = af::tile(ux.cols(ycount - 3, ycount - 1), af::dim4(1, 3));
+    auto uy_rht = af::tile(uy.cols(ycount - 3, ycount - 1), af::dim4(1, 3));
+
+    auto ubdoute_top = ux_top * ex_T + uy_top * ey_T;
+    auto ubdoute_bot = ux_bot * ex_T + uy_bot * ey_T;
+    auto ubdoute_lft = ux_lft * ex_T + uy_lft * ey_T;
+    auto ubdoute_rht = ux_rht * ex_T + uy_rht * ey_T;
+
+    // Computing bounce-back boundary conditions
+    auto fnew_top = af::moddims(fplus.row(1), af::dim4(ycount, 9)) -
+                    6.0 * density * wt_T * ubdoute_top;
+    auto fnew_bot = af::moddims(fplus.row(xcount - 2), af::dim4(ycount, 9)) -
+                    6.0 * density * wt_T * ubdoute_bot;
+    auto fnew_lft = af::moddims(fplus.col(1), af::dim4(xcount, 9)) -
+                    6.0 * density * wt_T * ubdoute_lft;
+    auto fnew_rht = af::moddims(fplus.col(ycount - 2), af::dim4(xcount, 9)) -
+                    6.0 * density * wt_T * ubdoute_rht;
+
+    // Update the values near the boundaries with the correct bounce-back
+    // boundary
+    for (int i = 0; i < 9; ++i) {
+        int xshift = static_cast<int>(ex_vals[i]);
+        int yshift = static_cast<int>(ey_vals[i]);
+        if (xshift == 1)
+            f(1, af::span, opposite_indices[i]) = fnew_top(af::span, i);
+        if (xshift == -1)
+            f(xcount - 2, af::span, opposite_indices[i]) =
+                fnew_bot(af::span, i);
+        if (yshift == 1)
+            f(af::span, 1, opposite_indices[i]) = fnew_lft(af::span, i);
+        if (yshift == -1)
+            f(af::span, ycount - 2, opposite_indices[i]) =
+                fnew_rht(af::span, i);
+    }
+}
+
+/**
+ * @brief Updates the velocity field, density and strain at each point in the
+ * grid
+ *
+ */
+void update(Simulation& sim) {
+    auto& ux    = sim.ux;
+    auto& uy    = sim.uy;
+    auto& rho   = sim.rho;
+    auto& sigma = sim.sigma;
+    auto& f     = sim.f;
+    auto& feq   = sim.feq;
+    auto& ex    = sim.ex;
+    auto& ey    = sim.ey;
+
+    auto e_tile = af::join(3, af::constant(1, 1, 1, 9), ex, ey);
+    auto result = af::sum(f * e_tile, 2);
+
+    rho = result(af::span, af::span, af::span, 0);
+    result /= rho;
+    ux = result(af::span, af::span, af::span, 1);
+    uy = result(af::span, af::span, af::span, 2);
+
+    // Above code equivalent to
+    // rho = af::sum(f, 2);
+    // ux = af::sum(f * ex, 2) / rho;
+    // uy = af::sum(f * ey, 2) / rho;
+
+    auto product   = f - feq;
+    auto e_product = af::join(3, ex * ex, ex * ey * std::sqrt(2), ey * ey);
+
+    sigma = af::sqrt(af::sum(af::pow(af::sum(product * e_product, 2), 2), 3));
+
+    // Above code equivalent to
+
+    // auto xx = af::sum(product * ex * ex, 2);
+    // auto xy = af::sum(product * ex * ey, 2);
+    // auto yy = af::sum(product * ey * ey, 2);
+
+    // sigma = af::sqrt(xx * xx + xy * xy * 2 + yy * yy);
+}
+
+af::array generate_image(size_t width, size_t height, const Simulation& sim) {
+    const auto& ux         = sim.ux;
+    const auto& uy         = sim.uy;
+    const auto& boundaries = sim.set_boundaries;
+    auto velocity          = sim.velocity;
+
+    float image_scale =
+        static_cast<float>(width) / static_cast<float>(sim.grid_width - 1);
+
+    // Relative Flow speed at each cell
+    auto val = af::sqrt(ux * ux + uy * uy) / velocity;
+
+    af::replace(val, val != 0 || !boundaries, -1.0);
+
+    // Scaling and interpolating flow speed to the window size
+    if (width != sim.grid_width || height != sim.grid_height)
+        val =
+            af::approx2(val, af::iota(width, af::dim4(1, height)) / image_scale,
+                        af::iota(height, af::dim4(1, width)).T() / image_scale);
+
+    // Flip image
+    val = val.T();
+
+    auto image  = af::constant(0, height, width, 3);
+    auto image2 = image;
+
+    // Add custom coloring
+    image(af::span, af::span, 0) = val * 2;
+    image(af::span, af::span, 1) = val * 2;
+    image(af::span, af::span, 2) = 1.0 - val * 2;
+
+    image2(af::span, af::span, 0) = 1;
+    image2(af::span, af::span, 1) = -2 * val + 2;
+    image2(af::span, af::span, 2) = 0;
+
+    auto tile_val = af::tile(val, 1, 1, 3);
+    af::replace(image, tile_val < 0.5, image2);
+    af::replace(image, tile_val >= 0, 0.0);
+
+    return image;
+}
+
+void lattice_boltzmann_cfd_demo() {
+    // Define the lattice for the simulation
+    const size_t len         = 128;
+    const size_t grid_width  = len;
+    const size_t grid_height = len;
+
+    // Specify the image scaling displayed
+    float scale = 4.0f;
+
+    // Forge window initialization
+    int height = static_cast<int>(grid_width * scale);
+    int width  = static_cast<int>(grid_height * scale);
+    af::Window window(height, width, "Driven Cavity Flow");
+
+    int frame_count       = 0;
+    int max_frames        = 20000;
+    int simulation_frames = 100;
+    float total_time      = 0;
+    float total_time2     = 0;
+
+    // CFD fluid parameters
+    const float density  = 2.7f;
+    const float velocity = 0.35f;
+    const float reynolds = 1e5f;
+
+    const char* ux_image = ASSETS_DIR "/examples/images/default_ux.bmp";
+    const char* uy_image = ASSETS_DIR "/examples/images/default_uy.bmp";
+    const char* set_boundary_image =
+        ASSETS_DIR "/examples/images/default_boundary.bmp";
+
+    // Tesla Valve Fluid Simulation - entering from constricted side
+    {
+        //           ux_image = ASSETS_DIR "/examples/images/left_tesla_ux.bmp";
+        //           uy_image = ASSETS_DIR "/examples/images/left_tesla_uy.bmp";
+        // set_boundary_image = ASSETS_DIR
+        // "/examples/images/left_tesla_boundary.bmp";
+    }
+
+    // Tesla Valve Fluid Simulation - entering from transfer side
+    {
+        //           ux_image = ASSETS_DIR
+        //           "/examples/images/right_tesla_ux.bmp"; uy_image =
+        //           ASSETS_DIR "/examples/images/right_tesla_uy.bmp";
+        // set_boundary_image = ASSETS_DIR
+        // "/examples/images/right_tesla_boundary.bmp";
+    }
+
+    // Reads the initial values of fluid quantites and simulation parameters
+    Simulation sim =
+        create_simulation(grid_width, grid_height, density, velocity, reynolds,
+                          ux_image, uy_image, set_boundary_image);
+
+    // Initializes the simulation quantites
+    initialize(sim);
+
+    while (!window.close() && frame_count != max_frames) {
+        af::sync();
+        auto begin = std::chrono::high_resolution_clock::now();
+
+        // Computes the new particle distribution functions for the new
+        // simulation frame
+        collide_stream(sim);
+
+        // Updates the velocity, density, and stress fields
+        update(sim);
+
+        af::sync();
+        auto end = std::chrono::high_resolution_clock::now();
+
+        // Calculate computation time of 1 simulation frame
+        auto duration =
+            std::chrono::duration_cast<std::chrono::microseconds>(end - begin)
+                .count();
+
+        // Used for computing the distribution of frame computation time
+        total_time += duration;
+        total_time2 += duration * duration;
+
+        // Every number of `simulation_frames` display the last computed frame
+        // to the screen
+        if (frame_count % simulation_frames == 0) {
+            auto image = generate_image(width, height, sim);
+
+            // Display colored image
+            window.image(image);
+
+            float avg_time  = total_time / (float)simulation_frames;
+            float stdv_time = std::sqrt(total_time2 * simulation_frames -
+                                        total_time * total_time) /
+                              (float)simulation_frames;
+
+            std::cout << "Average Simulation Step Time: (" << avg_time
+                      << " +/- " << stdv_time
+                      << ") us; Total simulation time: " << total_time
+                      << " us; Simulation Frames: " << simulation_frames
+                      << std::endl;
+
+            total_time  = 0;
+            total_time2 = 0;
+        }
+
+        frame_count++;
+    }
+}
+
+int main(int argc, char** argv) {
+    int device = argc > 1 ? std::atoi(argv[1]) : 0;
+
+    try {
+        af::setDevice(device);
+        af::info();
+
+        std::cout << "** ArrayFire CFD Simulation Demo\n\n";
+
+        lattice_boltzmann_cfd_demo();
+    } catch (const af::exception& e) {
+        std::cerr << e.what() << std::endl;
+        return -1;
+    }
+
+    return 0;
+}
\ No newline at end of file

From 651988abf69d2f17a69a9d0c3d6beb8b00df4683 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 28 Mar 2025 11:41:03 -0700
Subject: [PATCH 2645/2677] Added Black Hole Raytracing Example (#3530)

* Added Black Hole Raytracing Example

* Fixed clang format issues

* Fixed compilation error

* Implemented adaptive rk, improved runtime and memory footprint, fixed math errors

* Fixed compilation issues

* Improved sample parameters for example

* Removed structure binding to comply with c++14

* Fix merge

* Remove black hole raytracing examples from oneapi backend

---------

Co-authored-by: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
---
 examples/pde/CMakeLists.txt |   12 +-
 examples/pde/bhrt.cpp       | 1139 +++++++++++++++++++++++++++++++++++
 2 files changed, 1150 insertions(+), 1 deletion(-)
 create mode 100644 examples/pde/bhrt.cpp

diff --git a/examples/pde/CMakeLists.txt b/examples/pde/CMakeLists.txt
index 4a20caf5f9..57f689a9e9 100644
--- a/examples/pde/CMakeLists.txt
+++ b/examples/pde/CMakeLists.txt
@@ -19,6 +19,10 @@ if(ArrayFire_CPU_FOUND)
   add_executable(swe_cpu swe.cpp)
   target_link_libraries(swe_cpu ArrayFire::afcpu)
 
+  # Black Hole Raytracing example
+  add_executable(bhrt_cpu bhrt.cpp)
+  target_link_libraries(bhrt_cpu ArrayFire::afcpu)
+
   add_executable(boltzmann_cfd_cpu boltzmann_cfd.cpp)
   target_link_libraries(boltzmann_cfd_cpu ArrayFire::afcpu)
 endif()
@@ -27,6 +31,9 @@ if(ArrayFire_CUDA_FOUND)
   add_executable(swe_cuda swe.cpp)
   target_link_libraries(swe_cuda ArrayFire::afcuda)
 
+  add_executable(bhrt_cuda bhrt.cpp)
+  target_link_libraries(bhrt_cuda ArrayFire::afcuda)
+
   add_executable(boltzmann_cfd_cuda boltzmann_cfd.cpp)
   target_link_libraries(boltzmann_cfd_cuda ArrayFire::afcuda)
 endif()
@@ -35,6 +42,9 @@ if(ArrayFire_OpenCL_FOUND)
   add_executable(swe_opencl swe.cpp)
   target_link_libraries(swe_opencl ArrayFire::afopencl)
 
+  add_executable(bhrt_opencl bhrt.cpp)
+  target_link_libraries(bhrt_opencl ArrayFire::afopencl)
+
   add_executable(boltzmann_cfd_opencl boltzmann_cfd.cpp)
   target_link_libraries(boltzmann_cfd_opencl ArrayFire::afopencl)
 endif()
@@ -45,4 +55,4 @@ if(ArrayFire_oneAPI_FOUND)
 
   add_executable(boltzmann_cfd_oneapi boltzmann_cfd.cpp)
   target_link_libraries(boltzmann_cfd_oneapi ArrayFire::afoneapi)
-endif()
\ No newline at end of file
+endif()
diff --git a/examples/pde/bhrt.cpp b/examples/pde/bhrt.cpp
new file mode 100644
index 0000000000..55e116a330
--- /dev/null
+++ b/examples/pde/bhrt.cpp
@@ -0,0 +1,1139 @@
+/*******************************************************
+ * Copyright (c) 2024, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+/*
+    This is a Black Hole Raytracer.
+    For this raytracer we are using backwards path tracing to compute the
+   resulting image The path of the rays shot from the camera are simulated step
+   by step from the null geodesics light follows in spacetime. The geodesics are
+   computed from the spacetime metric of the space. This project has three
+   metrics that can be used: Schwarzchild, Kerr, and Ellis.
+
+    For more information on the black hole raytracing, check out
+    Riazuelo, A. (2015). Seeing relativity -- I. Ray tracing in a Schwarzschild
+   metric to explore the maximal analytic extension of the metric and making a
+   proper rendering of the stars. ArXiv.
+   https://doi.org/10.1142/S0218271819500421
+
+    For more information on raytracing, check out
+    Raytracing in a Weekend Series, https://raytracing.github.io/
+
+    Image being used for the background is Westerlund 2 from
+    NASA, ESA, the Hubble Heritage Team (STScI/AURA), A. Nota (ESA/STScI), and
+   the Westerlund 2 Science Team See
+   http://www.spacetelescope.org/images/heic1509a/ for details.
+
+    The default scene is the rotating black hole using the Kerr metric set by
+   the global variable 'scene' The parameters of the blackholes/wormholes may be
+   changed at the top with the simulation constants The parameters of the image
+   may be changed in the 'raytracing' function.
+*/
+#include <arrayfire.h>
+
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+
+enum class Scene { ROTATE_BH, STATIC_BH, WORMHOLE };
+
+// Scene being computed
+static constexpr Scene scene = Scene::ROTATE_BH;
+
+// **** Simulation Constants ****
+static constexpr double M = 0.5;    // Black Hole Mass
+static constexpr double J = 0.249;  // Black Hole Rotation (J < M^2)
+static constexpr double b = 3.0;    // Wormhole drainhole parameter
+
+/**
+ * @brief Generates a string progress bar
+ *
+ * @param current current job
+ * @param total total number of jobs
+ * @param start_info progress bar prior info
+ */
+void status_bar(int64_t current, int64_t total, const std::string& start_info) {
+    auto precision         = std::cout.precision();
+    static auto prev_time  = std::chrono::high_resolution_clock::now();
+    static auto prev       = current - 1;
+    static auto prev2      = prev;
+    static auto prev2_time = prev_time;
+
+    auto curr_time = std::chrono::high_resolution_clock::now();
+
+    double percent  = 100.0 * (double)(current + 1) / (double)total;
+    std::string str = "[";
+    for (int i = 0; i < 50; ++i) {
+        if (percent >= i * 2)
+            str += "=";
+        else
+            str += " ";
+    }
+    str += "]";
+
+    auto time =
+        current != prev
+            ? (total - current) * (curr_time - prev_time) / (current - prev)
+            : (total - current) * (curr_time - prev2_time) / (current - prev2);
+
+    if (current != prev && prev != prev2) {
+        prev2      = prev;
+        prev2_time = prev_time;
+    }
+    prev      = current;
+    prev_time = curr_time;
+
+    if (current != total) {
+        using namespace std::chrono_literals;
+        std::cout << start_info << " " << std::fixed << std::setprecision(1)
+                  << percent << "%  " << str << " Time Remaining: ";
+        if (std::chrono::duration_cast<std::chrono::seconds>(time).count() >
+            300)
+            std::cout << std::chrono::duration_cast<std::chrono::minutes>(time)
+                             .count()
+                      << " min";
+        else
+            std::cout << std::chrono::duration_cast<std::chrono::seconds>(time)
+                             .count()
+                      << " s";
+
+        std::cout << std::string(5, ' ') << '\r';
+    } else
+        std::cout << "\rDone!" << std::string(120, ' ') << std::endl;
+
+    std::cout << std::setprecision(precision) << std::defaultfloat;
+}
+
+/**
+ * @brief Returns the euclidean dot product for two cartesian vectors with 3
+ * coords
+ *
+ * @param lhs
+ * @param rhs
+ * @return af::array
+ */
+af::array dot3(const af::array& lhs, const af::array& rhs) {
+    return af::sum(lhs * rhs, 0);
+}
+
+/**
+ * @brief Returns the euclidean norm for a cartesian vector with 3 coords
+ *
+ * @param vector
+ * @return af::array
+ */
+af::array norm3(const af::array& vector) {
+    return af::sqrt(dot3(vector, vector));
+}
+
+/**
+ * @brief Returns the normalized vector for a cartesian vector with 3 coords
+ *
+ * @param vector
+ * @return af::array
+ */
+af::array normalize3(const af::array& vector) { return vector / norm3(vector); }
+
+af::exception make_error(const char* string) {
+    std::cout << string << std::endl;
+    return af::exception(string);
+}
+
+/**
+ * @brief Transforms degrees to radians
+ *
+ * @param degrees
+ * @return double
+ */
+double radians(double degrees) { return degrees * af::Pi / 180.0; }
+
+/**
+ * @brief Computes the cross_product of two euclidean vectors
+ *
+ * @param lhs
+ * @param rhs
+ * @return af::array
+ */
+af::array cross_product(const af::array& lhs, const af::array& rhs) {
+    if (lhs.dims() != rhs.dims())
+        throw make_error("Arrays must have the same dimensions");
+    else if (lhs.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    return af::join(
+        0,
+        lhs(1, af::span, af::span) * rhs(2, af::span, af::span) -
+            lhs(2, af::span, af::span) * rhs(1, af::span, af::span),
+        lhs(2, af::span, af::span) * rhs(0, af::span, af::span) -
+            lhs(0, af::span, af::span) * rhs(2, af::span, af::span),
+        lhs(0, af::span, af::span) * rhs(1, af::span, af::span) -
+            lhs(1, af::span, af::span) * rhs(0, af::span, af::span));
+}
+
+/**
+ * @brief Transform the position vectors from cartesian to spherical coordinates
+ *
+ * @param pos
+ * @return af::array
+ */
+af::array cart_to_sph_position(const af::array& pos) {
+    if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array x = pos(0, af::span);
+    af::array y = pos(1, af::span);
+    af::array z = pos(2, af::span);
+
+    af::array r = af::sqrt(x * x + y * y + z * z);
+    af::array o = af::acos(z / r);
+    af::array p = af::atan2(y, x);
+
+    af::array transformed_pos = af::join(0, r, o, p);
+
+    return transformed_pos;
+}
+
+/**
+ * @brief Transform the velocity vectors from cartesian to spherical coordinates
+ *
+ * @param vel
+ * @param pos
+ * @return af::array
+ */
+af::array cart_to_sph_velocity(const af::array& vel, const af::array& pos) {
+    if (vel.dims() != pos.dims())
+        throw make_error("Arrays must have the same dimensions");
+    else if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array x = pos(0, af::span);
+    af::array y = pos(1, af::span);
+    af::array z = pos(2, af::span);
+
+    af::array r = af::sqrt(x * x + y * y + z * z);
+    af::array o = af::acos(z / r);
+    af::array p = af::atan2(y, x);
+
+    af::array ux = vel(0, af::span);
+    af::array uy = vel(1, af::span);
+    af::array uz = vel(2, af::span);
+
+    af::array ur = (ux * x + uy * y + uz * z) / r;
+    af::array up = (uy * af::cos(p) - ux * af::sin(p)) / (r * af::sin(o));
+    af::array uo =
+        (af::cos(o) * (ux * af::cos(p) + uy * af::sin(p)) - uz * af::sin(o)) /
+        r;
+    af::array transformed_vel = af::join(0, ur, uo, up);
+
+    return transformed_vel;
+}
+
+/**
+ * @brief Transform the velocity vectors from cartesian to spherical coordinates
+ *
+ * @param vel
+ * @param pos
+ * @return af::array
+ */
+af::array sph_to_cart_velocity(const af::array& vel, const af::array& pos) {
+    if (vel.dims() != pos.dims())
+        throw make_error("Arrays must have the same dimensions");
+    else if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array r = pos(0, af::span);
+    af::array o = pos(1, af::span);
+    af::array p = pos(2, af::span);
+
+    af::array ur = vel(0, af::span);
+    af::array uo = vel(1, af::span);
+    af::array up = vel(2, af::span);
+
+    af::array ux = (ur * af::sin(o) + uo * r * af::cos(o)) * af::cos(p) -
+                   up * r * af::sin(o) * af::sin(p);
+    af::array uy = (ur * af::sin(o) + uo * r * af::cos(o)) * af::sin(p) +
+                   up * r * af::sin(o) * af::cos(p);
+    af::array uz              = ur * af::cos(o) - uo * r * af::sin(o);
+    af::array transformed_vel = af::join(0, ux, uy, uz);
+
+    return transformed_vel;
+}
+
+/**
+ * @brief Transform the position vectors from cartesian to oblate coordinates
+ *
+ * @param vel
+ * @param pos
+ * @return af::array
+ */
+af::array cart_to_oblate_position(const af::array& pos) {
+    if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array x = pos(0, af::span);
+    af::array y = pos(1, af::span);
+    af::array z = pos(2, af::span);
+    auto a      = J / M;
+    auto diff   = x * x + y * y + z * z - a * a;
+
+    af::array r =
+        af::sqrt((diff + af::sqrt(diff * diff + z * z * a * a * 4.0)) / 2.0);
+    af::array o = af::acos(z / r);
+    af::array p = af::atan2(y, x);
+
+    af::array transformed_pos = af::join(0, r, o, p);
+
+    return transformed_pos;
+}
+
+/**
+ * @brief Transform the position vectors from oblate to cartesian coordinates
+ *
+ * @param vel
+ * @param pos
+ * @return af::array
+ */
+af::array oblate_to_cart_position(const af::array& pos) {
+    if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array r = pos(0, af::span);
+    af::array o = pos(1, af::span);
+    af::array p = pos(2, af::span);
+    auto a      = J / M;
+    auto R      = af::sqrt(r * r + a * a);
+
+    af::array x = R * af::sin(o) * af::cos(p);
+    af::array y = R * af::sin(o) * af::sin(p);
+    af::array z = r * af::cos(o);
+
+    af::array transformed_pos = af::join(0, x, y, z);
+
+    return transformed_pos;
+}
+
+/**
+ * @brief Transform the velocity vectors from oblate to cartesian coordinates
+ *
+ * @param vel
+ * @param pos
+ * @return af::array
+ */
+af::array oblate_to_cart_velocity(const af::array& vel, const af::array& pos) {
+    if (vel.dims() != pos.dims())
+        throw make_error("Arrays must have the same dimensions");
+    else if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array r = pos(0, af::span);
+    af::array o = pos(1, af::span);
+    af::array p = pos(2, af::span);
+
+    af::array ur = vel(0, af::span);
+    af::array uo = vel(1, af::span);
+    af::array up = vel(2, af::span);
+
+    double a     = J / M;
+    af::array ra = af::sqrt(r * r + a * a);
+
+    af::array ux =
+        (ur * r * af::sin(o) / ra + uo * ra * af::cos(o)) * af::cos(p) -
+        up * r * af::sin(o) * af::sin(p);
+    af::array uy =
+        (ur * r * af::sin(o) / ra + uo * ra * af::cos(o)) * af::sin(p) +
+        up * r * af::sin(o) * af::cos(p);
+    af::array uz              = ur * af::cos(o) - uo * r * af::sin(o);
+    af::array transformed_vel = af::join(0, ux, uy, uz);
+
+    return transformed_vel;
+}
+
+/**
+ * @brief Transform the velocity vectors from cartesian to oblate coordinates
+ *
+ * @param vel
+ * @param pos
+ * @return af::array
+ */
+af::array cart_to_oblate_velocity(const af::array& vel, const af::array& pos) {
+    if (vel.dims() != pos.dims())
+        throw make_error("Arrays must have the same dimensions");
+    else if (pos.dims()[0] != 3)
+        throw make_error("Arrays must have 3 principal coordintes");
+
+    af::array x = pos(0, af::span);
+    af::array y = pos(1, af::span);
+    af::array z = pos(2, af::span);
+
+    auto a    = J / M;
+    auto diff = x * x + y * y + z * z - a * a;
+
+    af::array r =
+        af::sqrt((diff + af::sqrt(diff * diff + z * z * a * a * 4.0)) / 2.0);
+    af::array o = af::acos(z / r);
+    af::array p = af::atan2(y, x);
+
+    af::array ux = vel(0, af::span);
+    af::array uy = vel(1, af::span);
+    af::array uz = vel(2, af::span);
+
+    af::array ra = r * r + a * a;
+    af::array ur = ((ux * x + uy * y) * r + uz * ra * z / r) /
+                   (r * r + af::pow(a * af::cos(o), 2.0));
+    af::array up = (uy * x - ux * y) / (x * x + y * y);
+    af::array uo = ((ux * x + uy * y) / af::tan(o) - uz * z * af::tan(o)) /
+                   (r * r + af::pow(a * af::cos(o), 2.0));
+    af::array transformed_vel = af::join(0, ur, uo, up);
+
+    return transformed_vel;
+}
+
+/**
+ * @brief Transform the position vectors from spherical to cartesian coordinates
+ *
+ * @param pos
+ * @return af::array
+ */
+af::array sph_to_cart_position(const af::array& pos) {
+    af::array r = pos(0, af::span);
+    af::array o = pos(1, af::span);
+    af::array p = pos(2, af::span);
+
+    af::array x = r * af::sin(o) * af::cos(p);
+    af::array y = r * af::sin(o) * af::sin(p);
+    af::array z = r * af::cos(o);
+
+    af::array transformed_pos = af::join(0, x, y, z);
+
+    return transformed_pos;
+}
+
+/**
+ * @brief Computes the inverse of a 4x4 matrix with the layout
+ *          [ a 0 0 b ]
+ *          [ 0 c 0 0 ]
+ *          [ 0 0 d 0 ]
+ *          [ b 0 0 e ]
+ *
+ * @param metric af::array with the shape af::dims4(4, 4, M, N)
+ *
+ * @return af::array with the shape af::dims4(4, 4, M, N)
+ */
+af::array inv_metric(const af::array& metric) {
+    af::array a = metric(0, 0, af::span);
+    af::array b = metric(3, 0, af::span);
+    af::array c = metric(1, 1, af::span);
+    af::array d = metric(2, 2, af::span);
+    af::array e = metric(3, 3, af::span);
+
+    af::array det = b * b - a * e;
+
+    auto res = af::constant(0, 4, 4, metric.dims()[2], metric.dims()[3], f64);
+
+    res(0, 0, af::span) = -e / det;
+    res(0, 3, af::span) = b / det;
+    res(3, 0, af::span) = b / det;
+    res(1, 1, af::span) = 1.0 / c;
+    res(2, 2, af::span) = 1.0 / d;
+    res(3, 3, af::span) = -a / det;
+
+    return res;
+}
+
+/**
+ * @brief Computes the 4x4 metric matrix for the given 4-vector positions
+ *
+ * @param pos af::dim4(4, N)
+ * @return af::array af::dim4(4, 4, 1, N)
+ */
+af::array metric4(const af::array& pos) {
+    if (pos.dims()[0] != 4)
+        throw make_error("Arrays must have 4 principal coordinates");
+
+    auto dims = pos.dims();
+
+    af::array t = af::moddims(pos(0, af::span), 1, 1, dims[1]);
+    af::array r = af::moddims(pos(1, af::span), 1, 1, dims[1]);
+    af::array o = af::moddims(pos(2, af::span), 1, 1, dims[1]);
+    af::array p = af::moddims(pos(3, af::span), 1, 1, dims[1]);
+
+    af::array gtt, gtr, gto, gtp, grt, grr, gro, grp, got, gor, goo, gop, gpt,
+        gpr, gpo, gpp;
+
+    switch (scene) {
+        // ******* Kerr Black Hole Metric *******
+        case Scene::ROTATE_BH: {
+            auto rs    = 2.0 * M;
+            auto a     = J / M;
+            auto delta = (r - rs) * r + a * a;
+            auto sigma = r * r + af::pow(a * af::cos(o), 2);
+
+            gtt = 1.0 - r * rs / sigma;
+            gtr = af::constant(0.0, 1, 1, dims[1], f64);
+            gto = af::constant(0.0, 1, 1, dims[1], f64);
+            gtp = rs * r * a * af::pow(af::sin(o), 2.0) / sigma;
+            grr = -sigma / delta;
+            gro = af::constant(0.0, 1, 1, dims[1], f64);
+            grp = af::constant(0.0, 1, 1, dims[1], f64);
+            goo = -sigma;
+            gop = af::constant(0.0, 1, 1, dims[1], f64);
+            gpp =
+                -(r * r + a * a + rs * r * af::pow(a * af::sin(o), 2) / sigma) *
+                af::pow(af::sin(o), 2);
+
+            break;
+        }
+
+        // ******* Schwarzchild Black Hole Metric *******
+        case Scene::STATIC_BH: {
+            gtt = 1.0 - 2.0 * M / r;
+            gtr = af::constant(0.0, 1, 1, dims[1], f64);
+            gto = af::constant(0.0, 1, 1, dims[1], f64);
+            gtp = af::constant(0.0, 1, 1, dims[1], f64);
+            grr = -1.0 / (1.0 - 2.0 * M / r);
+            gro = af::constant(0.0, 1, 1, dims[1], f64);
+            grp = af::constant(0.0, 1, 1, dims[1], f64);
+            goo = -r * r;
+            gop = af::constant(0.0, 1, 1, dims[1], f64);
+            gpp = -af::pow(r * af::sin(o), 2);
+
+            break;
+        }
+
+        // ******* Ellis Wormhole Metric *******
+        case Scene::WORMHOLE: {
+            gtt = af::constant(1.0, 1, 1, dims[1], f64);
+            gtr = af::constant(0.0, 1, 1, dims[1], f64);
+            gto = af::constant(0.0, 1, 1, dims[1], f64);
+            gtp = af::constant(0.0, 1, 1, dims[1], f64);
+            grr = -af::constant(1.0, 1, 1, dims[1], f64);
+            gro = af::constant(0.0, 1, 1, dims[1], f64);
+            grp = af::constant(0.0, 1, 1, dims[1], f64);
+            goo = -(r * r + b * b);
+            gop = af::constant(0.0, 1, 1, dims[1], f64);
+            gpp = -(r * r + b * b) * af::pow(af::sin(o), 2);
+
+            break;
+        }
+
+        default: throw;
+    }
+
+    auto res = af::join(
+        0, af::join(1, gtt, gtr, gto, gtp), af::join(1, gtr, grr, gro, grp),
+        af::join(1, gto, gro, goo, gop), af::join(1, gtp, grp, gop, gpp));
+
+    return res;
+}
+
+/**
+ * @brief Computes the dot product as defined by a metric between two 4-vector
+ * velocities
+ *
+ * @param pos
+ * @param lhs
+ * @param rhs
+ * @return af::array
+ */
+af::array dot_product(const af::array& pos, const af::array& lhs,
+                      const af::array& rhs) {
+    if (pos.dims() != lhs.dims())
+        throw make_error(
+            "Position and lhs velocity must have the same dimensions");
+    else if (lhs.dims() != rhs.dims())
+        throw make_error(
+            "Position and rhs velocity must have the same dimensions");
+    else if (rhs.dims()[0] != 4)
+        throw make_error("Arrays must have 4 principal coordinates");
+
+    return af::matmul(af::moddims(lhs, 1, 4, lhs.dims()[1]), metric4(pos),
+                      af::moddims(rhs, 4, 1, rhs.dims()[1]));
+}
+
+af::array norm4(const af::array& pos, const af::array& vel) {
+    return dot_product(pos, vel, vel);
+}
+
+af::array partials(const af::array& pos4, uint32_t index, double rel_diff,
+                   double abs_diff) {
+    double arr[4] = {0.0};
+    arr[index]    = 1.0;
+
+    auto pos_diff = pos4 * rel_diff + abs_diff;
+    auto h4       = pos_diff * af::array(af::dim4(4, 1), arr);
+    af::array h =
+        af::moddims(pos_diff(index, af::span), af::dim4(1, 1, pos4.dims()[1]));
+
+    return (-metric4(pos4 + h4 * 2.0) + metric4(pos4 + h4) * 8.0 -
+            metric4(pos4 - h4) * 8.0 + metric4(pos4 - h4 * 2.0)) /
+           (h * 12.0);
+}
+
+/**
+ * @brief Computes the geodesics from the established metric, 4-vector positions
+ * and velocities
+ *
+ * @param pos4
+ * @param vel4
+ * @return af::array
+ */
+af::array geodesics(const af::array& pos4, const af::array& vel4) {
+    auto N = vel4.dims()[1];
+
+    af::array uu = af::matmul(af::moddims(vel4, af::dim4(4, 1, N)),
+                              af::moddims(vel4, af::dim4(1, 4, N)));
+    uu           = af::moddims(uu, af::dim4(1, 4, 4, N));
+
+    af::array metric    = metric4(pos4);
+    af::array invmetric = af::moddims(inv_metric(metric), af::dim4(4, 4, 1, N));
+
+    // Compute the partials of the metric with respect to coordinates indices
+    af::array dt = af::constant(0, 4, 4, 1, N, f64);
+
+    auto dr     = partials(pos4, 1, 1e-6, 1e-12);
+    auto dtheta = partials(pos4, 2, 1e-6, 1e-12);
+    auto dphi   = partials(pos4, 3, 1e-6, 1e-12);
+
+    dr     = af::moddims(dr, af::dim4(4, 4, 1, N));
+    dtheta = af::moddims(dtheta, af::dim4(4, 4, 1, N));
+    dphi   = af::moddims(dphi, af::dim4(4, 4, 1, N));
+
+    // Compute the einsum for each of the christoffel terms
+    af::array partials = af::join(2, dt, dr, dtheta, dphi);
+    af::array p1       = af::matmul(invmetric, partials);
+    af::array p2       = af::reorder(p1, 0, 2, 1, 3);
+    af::array p3 = af::matmul(invmetric, af::reorder(partials, 2, 0, 1, 3));
+
+    auto christoffels = -0.5 * (p1 + p2 - p3);
+
+    // Use the geodesics equation to find the 4-vector acceleration
+    return af::moddims(af::sum(af::sum(christoffels * uu, 1), 2),
+                       af::dim4(4, N));
+}
+
+/**
+ * @brief Camera struct
+ *
+ * Contains all the data pertaining to the parameters for the image as seen from
+ * the camera
+ *
+ */
+struct Camera {
+    af::array position;
+    af::array lookat;
+    double fov;
+    double focal_length;
+    uint32_t width;
+    uint32_t height;
+
+    af::array direction;
+    af::array vertical;
+    af::array horizontal;
+    double aspect_ratio;
+
+    Camera(const af::array& position_, const af::array& lookat_, double fov_,
+           double focal_length_, uint32_t viewport_width_,
+           uint32_t viewport_height_)
+        : position(position_)
+        , lookat(lookat_)
+        , fov(fov_)
+        , focal_length(focal_length_)
+        , width(viewport_width_)
+        , height(viewport_height_) {
+        auto global_vertical = af::array(3, {0.0, 0.0, 1.0});
+
+        // Compute the camera three main axes
+        direction  = normalize3(lookat - position);
+        horizontal = normalize3(cross_product(direction, global_vertical));
+        vertical   = normalize3(cross_product(direction, horizontal));
+
+        aspect_ratio = (double)width / (double)height;
+    }
+
+    /**
+     * @brief Generates the initial rays 4-vector position and velocities
+     * (direction) for the simulation
+     *
+     * @return std::pair<af::array, af::array> (pos4, vel4)
+     */
+    std::pair<af::array, af::array> generate_viewport_4rays() {
+        auto& camera_direction  = direction;
+        auto& camera_horizontal = horizontal;
+        auto& camera_vertical   = vertical;
+        auto& camera_position   = position;
+        auto vfov               = fov;
+
+        double viewport_height = 2.0 * focal_length * std::tan(vfov / 2.0);
+        double viewport_width  = aspect_ratio * viewport_height;
+
+        // Create rays in equally spaced directions of the viewport
+        af::array viewport_rays = af::constant(0, 3, width, height, f64);
+        viewport_rays +=
+            (af::iota(af::dim4(1, width, 1), af::dim4(1, 1, height), f64) /
+                 (width - 1) -
+             0.5) *
+            viewport_width * camera_horizontal;
+        viewport_rays +=
+            (af::iota(af::dim4(1, 1, height), af::dim4(1, width, 1), f64) /
+                 (height - 1) -
+             0.5) *
+            viewport_height * camera_vertical;
+        viewport_rays += focal_length * camera_direction;
+        viewport_rays = af::moddims(af::reorder(viewport_rays, 1, 2, 0),
+                                    af::dim4(width * height, 3))
+                            .T();
+
+        // Compute the initial position from which the rays are launched
+        af::array viewport_position = viewport_rays + camera_position;
+        af::array viewport_sph_pos;
+        if (scene != Scene::ROTATE_BH)
+            viewport_sph_pos = cart_to_sph_position(viewport_position);
+        else
+            viewport_sph_pos = cart_to_oblate_position(viewport_position);
+
+        // Normalize the ray directions
+        viewport_rays = normalize3(viewport_rays);
+
+        // Generate the position 4-vector
+        af::array camera_sph_pos;
+        if (scene != Scene::ROTATE_BH)
+            camera_sph_pos = cart_to_sph_position(camera_position);
+        else
+            camera_sph_pos = cart_to_oblate_position(camera_position);
+
+        af::array camera_pos4 =
+            af::join(0, af::constant(0.0, 1, f64), camera_sph_pos);
+        double camera_velocity =
+            1.0 /
+            af::sqrt(norm4(camera_pos4, af::array(4, {1.0, 0.0, 0.0, 0.0})))
+                .scalar<double>();
+        af::array camera_vel4 = af::array(4, {camera_velocity, 0.0, 0.0, 0.0});
+
+        af::array viewport_rays_pos4 = af::join(
+            0, af::constant(0.0, 1, width * height, f64), viewport_sph_pos);
+
+        // Generate the velocity 4-vector by setting the camera to be stationary
+        // with respect to an observer at infinity
+        af::array vv;
+        if (scene != Scene::ROTATE_BH)
+            vv = cart_to_sph_velocity(viewport_rays, viewport_position);
+        else
+            vv = cart_to_oblate_velocity(viewport_rays, viewport_position);
+
+        af::array vvr = vv(0, af::span);
+        af::array vvo = vv(1, af::span);
+        af::array vvp = vv(2, af::span);
+        auto viewport_sph_rays4 =
+            af::join(0, af::constant(1, 1, width * height, f64), vvr, vvo, vvp);
+
+        af::array dot = af::moddims(
+            af::matmul(metric4(viewport_rays_pos4),
+                       af::moddims(viewport_sph_rays4 * viewport_sph_rays4,
+                                   af::dim4(4, 1, width * height))),
+            af::dim4(4, width * height));
+
+        // Normalize the 4-velocity vectors
+        af::array viewport_vel =
+            af::sqrt(-af::array(dot(0, af::span)) /
+                     (dot(1, af::span) + dot(2, af::span) + dot(3, af::span)));
+        af::array viewport_rays_vel4 =
+            af::join(0, af::constant(camera_velocity, 1, width * height, f64),
+                     vv * viewport_vel * camera_velocity);
+
+        return {viewport_rays_pos4, viewport_rays_vel4};
+    }
+};
+
+/**
+ * @brief Object struct
+ *
+ * Contains the methods for testing if a ray has collided with the object
+ *
+ */
+struct Object {
+    using HasHit = af::array;
+    using HitPos = af::array;
+
+    /**
+     * @brief Gets the color of the pixel that correspond to the ray that has
+     * intersected with the object
+     *
+     * @param ray_begin begining
+     * @param ray_end
+     * @return af::array
+     */
+    virtual af::array get_color(const af::array& ray_begin,
+                                const af::array& ray_end) const = 0;
+
+    /**
+     * @brief Returns a bool array if the rays have hit the object and the
+     * correspoding position where the ray has hit
+     *
+     * @param ray_begin
+     * @param ray_end
+     * @return std::pair<HasHit, HitPos>
+     */
+    virtual std::pair<HasHit, HitPos> intersect(
+        const af::array& ray_begin, const af::array& ray_end) const = 0;
+};
+
+struct AccretionDisk : public Object {
+    af::array disk_color;
+    af::array center;
+    af::array normal;
+    double inner_radius;
+    double outter_radius;
+
+    AccretionDisk(const af::array& center, const af::array& normal,
+                  double inner_radius, double outter_radius)
+        : disk_color(af::array(3, {209.f, 77.f, 0.f}))
+        , center(center)
+        , normal(normal)
+        , inner_radius(inner_radius)
+        , outter_radius(outter_radius) {
+        // disk_color = af::array(3, {254.f, 168.f, 29.f});
+    }
+
+    std::pair<HasHit, HitPos> intersect(
+        const af::array& ray_begin, const af::array& ray_end) const override {
+        uint32_t count = ray_begin.dims()[1];
+
+        // Compute intersection of ray with a plane
+        af::array has_hit = af::constant(0, count).as(b8);
+        af::array hit_pos = ray_end;
+        af::array a       = dot3(normal, center - ray_begin);
+        af::array b       = dot3(normal, ray_end - ray_begin);
+        af::array t       = af::select(b != 0.0, a / b, (double)0.0);
+
+        af::array plane_intersect = (ray_end - ray_begin) * t + ray_begin;
+        af::array dist            = norm3(plane_intersect - center);
+
+        t = af::abs(t);
+
+        // Determine if the intersection falls inside the disk radius and occurs
+        // with the current ray segment
+        has_hit = af::moddims((dist < outter_radius) && (t <= 1.0) &&
+                                  (t > 0.0) && (dist > inner_radius),
+                              af::dim4(count));
+        hit_pos = plane_intersect;
+
+        return {has_hit, hit_pos};
+    }
+
+    af::array get_color(const af::array& ray_begin,
+                        const af::array& ray_end) const override {
+        auto pair = intersect(ray_begin, ray_end);
+        af::array hit = pair.first;
+        af::array pos = pair.second;
+
+        auto val = 1.f - (norm3(pos - center).T() - inner_radius) /
+                             (outter_radius - inner_radius);
+
+        af::array color =
+            disk_color.T() * 1.5f * (val * val * (val * -2.f + 3.f)).as(f32);
+
+        return af::select(af::tile(hit, af::dim4(1, 3)), color, 0.f);
+    }
+};
+/**
+ * @brief Background struct
+ *
+ * Contains the methods for getting the color of background image
+ *
+ */
+struct Background {
+    af::array image;
+
+    Background(const af::array& image_) { image = image_; }
+
+    af::array get_color(const af::array& ray_dir) const {
+        auto spherical_dir = cart_to_sph_position(ray_dir);
+
+        auto img_height = image.dims()[0];
+        auto img_width  = image.dims()[1];
+        auto count      = ray_dir.dims()[1];
+
+        // Spherical mapping of the direction to a pixel of the image
+        af::array o = spherical_dir(1, af::span);
+        af::array p = spherical_dir(2, af::span);
+
+        auto x = (p / af::Pi + 1.0) * img_width / 2.0;
+        auto y = (o / af::Pi) * img_height;
+
+        // Interpolate the colors of the image from the calculated pixel
+        // positions
+        af::array colors = af::approx2(image, af::moddims(y.as(f32), count),
+                                       af::moddims(x.as(f32), count),
+                                       af::interpType::AF_INTERP_CUBIC_SPLINE);
+
+        // Zero out the color of any null rays
+        colors = af::moddims(colors, af::dim4(count, 3));
+        af::replace(colors, !af::isNaN(colors), 0.f);
+
+        return colors;
+    }
+};
+
+/**
+ * @brief Transform the array of pixels to the correct image format to display
+ *
+ * @param image
+ * @param width
+ * @param height
+ * @return af::array
+ */
+af::array rearrange_image(const af::array& image, uint32_t width,
+                          uint32_t height) {
+    return af::clamp(af::moddims(image, af::dim4(width, height, 3)).T(), 0.0,
+                     255.0)
+               .as(f32) /
+           255.f;
+}
+
+/**
+ * @brief Returns an rgb image containing the raytraced black hole from the
+ * camera rays, spacetime metric, objects living in the space, and background
+ *
+ * @param initial_pos initial position from where the rays are launched
+ * @param initial_vel initial velocities (directions) the rays have
+ * @param objects the objects the rays can collide with
+ * @param background the background of the scene
+ * @param time how long are the rays traced through space
+ * @param steps how many steps should be taken to trace the rays path
+ * @param width width of the image the camera produces
+ * @param height height of the image the camera produces
+ * @param checks the intervals between steps to check if the rays have collided
+ * with an object
+ * @return af::array
+ */
+af::array generate_image(const af::array& initial_pos,
+                         const af::array& initial_vel,
+                         const std::vector<std::unique_ptr<Object> >& objects,
+                         const Background& background, uint32_t width,
+                         uint32_t height, double time, double tol,
+                         uint32_t checks = 10) {
+    uint32_t lines = initial_pos.dims()[1];
+
+    auto def_step = 0.5 * pow(tol, 0.25);
+    auto dt       = af::constant(def_step, 1, lines, f64);
+    auto t        = af::constant(0.0, 1, lines, f64);
+    auto index    = af::iota(lines);
+    auto selected = t < time;
+
+    auto result = af::constant(0, lines, 3, f32);
+
+    auto pos = initial_pos;
+    auto vel = initial_vel;
+
+    af::Window window{(int)width, (int)height, "Black Hole Raytracing"};
+
+    af::array bg_col = af::constant(0.f, lines, 3);
+    af::array begin_pos, end_pos;
+    af::array bh_nohit;
+
+    if (scene != Scene::ROTATE_BH)
+        begin_pos = sph_to_cart_position(pos(af::seq(1, 3), af::span));
+    else
+        begin_pos = oblate_to_cart_position(pos(af::seq(1, 3), af::span));
+    end_pos = begin_pos;
+
+    int i = 0;
+
+    while (t.dims()[1] != 0 && af::anyTrue<bool>(t < time) &&
+           af::anyTrue<bool>(dt != 0.0)) {
+        // Displays the current progress and approximate time needed to finish
+        // it
+        status_bar((lines - t.dims()[1]) * time +
+                       af::sum<double>(af::clamp(t, 0.0, time)),
+                   time * lines, "Progress:");
+
+        // RK34 method for second order differential equation
+        auto dt2 = dt * dt;
+        auto k1  = geodesics(pos, vel);
+        auto k2  = geodesics(pos + vel * dt / 4.0 + k1 * dt2 / 32.0,
+                             vel + k1 * dt / 4.0);
+        auto k3  = geodesics(pos + vel * dt / 2.0 + (k1 + k2) * dt2 / 16.0,
+                             vel + k2 * dt / 2.0);
+        auto k4  = geodesics(pos + vel * dt + (k1 - k2 + k3 * 2.0) * dt2 / 4.0,
+                             vel + (k1 - k2 * 2.0 + 2.0 * k3) * dt);
+
+        auto diff4 = (k1 + k2 * 8.0 + k3 * 2.0 + k4) / 24.0;
+        auto diff3 = (k2 * 8.0 + k4) / 18.0;
+
+        auto err    = (af::max)(af::abs(diff4 - diff3), 0) * dt2;
+        auto maxerr = tol * (1.0 + (af::max)(af::abs(pos), 0));
+
+        auto rdt = af::constant(0, 1, dt.dims()[1], f64);
+        af::replace(rdt, err > maxerr, dt);
+
+        auto rdt2 = rdt * rdt;
+
+        pos += vel * rdt + (k1 + k2 * 8.0 + k3 * 2.0 + k4) * rdt2 / 24.0;
+        vel += (k1 + k3 * 4.0 + k4) * rdt / 6.0;
+        t += rdt;
+
+        auto q = af::clamp(0.8 * af::pow(maxerr / err, 0.25), 0.0, 5.0);
+
+        // Select the next time step
+        dt = af::select(q * dt < (time - t), q * dt, af::abs(time - t));
+
+        // Update image
+        if (i % checks == (checks - 1)) {
+            af::array ray_dir;
+            if (scene != Scene::ROTATE_BH) {
+                end_pos(af::span, index) =
+                    sph_to_cart_position(pos(af::seq(1, 3), af::span));
+                ray_dir = sph_to_cart_velocity(vel(af::seq(1, 3), af::span),
+                                               pos(af::seq(1, 3), af::span));
+            } else {
+                end_pos(af::span, index) =
+                    oblate_to_cart_position(pos(af::seq(1, 3), af::span));
+                ray_dir = oblate_to_cart_velocity(vel(af::seq(1, 3), af::span),
+                                                  pos(af::seq(1, 3), af::span));
+            }
+
+            af::array s_begin_pos = begin_pos(af::span, index);
+            af::array s_end_pos   = end_pos(af::span, index);
+
+            // Check if light ray intersect an object
+            for (const auto& obj : objects) {
+                result(index, af::span) +=
+                    obj->get_color(s_begin_pos, s_end_pos);
+            }
+
+            // Update background colors from rays
+            bg_col(index, af::span) = background.get_color(ray_dir);
+
+            // Display image
+            window.image(rearrange_image(result + bg_col, width, height));
+
+            begin_pos = end_pos;
+        }
+
+        // Stop rays entering the event horizon
+        switch (scene) {
+            case Scene::ROTATE_BH: {
+                auto a = J / M;
+                bh_nohit =
+                    (pos(1, af::span) > 1.01 * (M + std::sqrt(M * M - a * a)));
+                selected = bh_nohit && (t < time);
+
+                break;
+            }
+
+            case Scene::STATIC_BH: {
+                bh_nohit = pos(1, af::span) > 2.0 * M * 1.01;
+                selected = bh_nohit && (t < time);
+
+                break;
+            }
+
+            case Scene::WORMHOLE: {
+                selected = (t < time);
+            }
+            default: break;
+        }
+
+        // Remove finished rays from computation
+        if (af::sum<float>(selected.as(f32)) / (float)index.dims()[0] < 0.75) {
+            if (scene == Scene::STATIC_BH || scene == Scene::ROTATE_BH)
+                bg_col(af::array(index(!bh_nohit)), af::span) = 0.f;
+
+            index = index(selected);
+            pos   = pos(af::span, selected);
+            vel   = vel(af::span, selected);
+            dt    = dt(af::span, selected);
+            t     = t(af::span, selected);
+
+            // Free finished rays memory
+            af::deviceGC();
+        }
+
+        ++i;
+    }
+
+    result += bg_col;
+
+    return rearrange_image(result, width, height);
+}
+
+void raytracing(uint32_t width, uint32_t height) {
+    // Set the parameters of the raytraced image
+    double vfov         = radians(90.0);
+    double focal_length = 0.01;
+
+    // Set the parameters of the camera
+    af::array global_vertical            = af::array(3, {0.0, 0.0, 1.0});
+    af::array camera_position            = af::array(3, {-7.0, 6.0, 2.0});
+    af::array camera_lookat              = af::array(3, {0.0, 0.0, 0.0});
+    double accretion_inner_radius        = M * 3.0;
+    double accretion_outter_radius       = M * 8.0;
+    double simulation_tolerance          = 1e-6;
+    double max_simulation_time           = 12.;
+    uint32_t num_steps_per_collide_check = 1;
+
+    // Set the background of the scene
+    auto bg_image =
+        af::loadimage(ASSETS_DIR "/examples/images/westerlund.jpg", true);
+    auto background = Background(bg_image);
+
+    // Set the objects living in the scene
+    std::vector<std::unique_ptr<Object> > objects;
+    if (scene != Scene::WORMHOLE)
+        objects.push_back(std::make_unique<AccretionDisk>(
+            af::array(3, {0.0, 0.0, 0.0}), af::array(3, {0.0, 0.0, 1.0}),
+            accretion_inner_radius, accretion_outter_radius));
+
+    // Generate rays from the camera
+    auto camera = Camera(camera_position, camera_lookat, vfov, focal_length,
+                         width, height);
+    auto pair   = camera.generate_viewport_4rays();
+
+    auto ray4_pos = pair.first;
+    auto ray4_vel = pair.second;
+
+    auto begin = std::chrono::high_resolution_clock::now();
+    // Generate raytraced image
+    auto image = generate_image(
+        ray4_pos, ray4_vel, objects, background, width, height,
+        max_simulation_time, simulation_tolerance, num_steps_per_collide_check);
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    std::cout
+        << "\nSimulation took: "
+        << std::chrono::duration_cast<std::chrono::seconds>(end - begin).count()
+        << " s" << std::endl;
+
+    // Save image
+    af::saveImage("result.png", image);
+}
+
+int main(int argc, char** argv) {
+    int device = argc > 1 ? std::atoi(argv[1]) : 0;
+
+    int width  = argc > 2 ? std::atoi(argv[2]) : 200;
+    int height = argc > 3 ? std::atoi(argv[3]) : 200;
+
+    try {
+        af::setDevice(device);
+        af::info();
+
+        std::cout << "** ArrayFire Black Hole Raytracing Demo\n\n";
+
+        raytracing(width, height);
+    } catch (const af::exception& e) {
+        std::cerr << e.what() << std::endl;
+        return -1;
+    }
+
+    return 0;
+}
\ No newline at end of file

From ccac73e86ac6f761770ed594255c04a01087732d Mon Sep 17 00:00:00 2001
From: verstatx <ayer.3d@gmail.com>
Date: Wed, 4 Oct 2023 05:50:04 -0400
Subject: [PATCH 2646/2677] Add int8 matmul support to the CUDA backend

changes to gemm account for differing input/output types
---
 docs/details/blas.dox       |   4 ++
 include/af/blas.h           |   8 +++
 src/api/c/blas.cpp          |  35 +++++++++----
 src/backend/cpu/blas.cpp    |  57 +++++++++++---------
 src/backend/cpu/blas.hpp    |   7 +--
 src/backend/cuda/blas.cu    | 102 ++++++++++++++++++++----------------
 src/backend/cuda/blas.hpp   |   7 +--
 src/backend/oneapi/blas.cpp |  37 ++++++++-----
 src/backend/oneapi/blas.hpp |   7 +--
 src/backend/opencl/blas.cpp |  21 +++++---
 src/backend/opencl/blas.hpp |   7 +--
 test/blas.cpp               |  30 +++++++++++
 12 files changed, 212 insertions(+), 110 deletions(-)

diff --git a/docs/details/blas.dox b/docs/details/blas.dox
index 943e77a502..ac0aa99673 100644
--- a/docs/details/blas.dox
+++ b/docs/details/blas.dox
@@ -32,6 +32,10 @@ memory allocations either on host or device.
 for Sparse-Dense matrix multiplication. See the notes of the function for usage
 and restrictions.
 
+\par
+\note Limited support for \ref s8 was added to the CUDA backend in ArrayFire
+v3.10.0. See \ref af_gemm "s8 Support" notes for details.
+
 \ingroup blas_mat
 
 =======================================================================
diff --git a/include/af/blas.h b/include/af/blas.h
index 4580ea2112..05434ee861 100644
--- a/include/af/blas.h
+++ b/include/af/blas.h
@@ -242,6 +242,14 @@ extern "C" {
 
         \snippet test/blas.cpp ex_af_gemm_overwrite
 
+        \note <b>s8 Support</b>
+        \note Starting with ArrayFire version v3.10.0, the CUDA backend supports
+        \p A, \p B input arrays of type \ref s8.
+        \note Scalars \p alpha, \p beta must be of type \ref f32.
+        \note Output array \p C will be of type \ref f32.
+        \note <br><b>Requires</b>
+        \note CUDA version >= 10 on devices with compute capability >= 5.0
+
         \param[in,out] C     `A` * `B` = `C`
         \param[in]     opA   operation to perform on A before the multiplication
         \param[in]     opB   operation to perform on B before the multiplication
diff --git a/src/api/c/blas.cpp b/src/api/c/blas.cpp
index 0cd8fddd8d..f42bc7d57c 100644
--- a/src/api/c/blas.cpp
+++ b/src/api/c/blas.cpp
@@ -33,6 +33,7 @@ using detail::cdouble;
 using detail::cfloat;
 using detail::gemm;
 using detail::matmul;
+using detail::schar;
 
 namespace {
 template<typename T>
@@ -42,12 +43,12 @@ static inline af_array sparseMatmul(const af_array lhs, const af_array rhs,
         matmul<T>(getSparseArray<T>(lhs), getArray<T>(rhs), optLhs, optRhs));
 }
 
-template<typename T>
+template<typename Ti, typename To = Ti>
 static inline void gemm(af_array *out, af_mat_prop optLhs, af_mat_prop optRhs,
-                        const T *alpha, const af_array lhs, const af_array rhs,
-                        const T *betas) {
-    gemm<T>(getArray<T>(*out), optLhs, optRhs, alpha, getArray<T>(lhs),
-            getArray<T>(rhs), betas);
+                        const To *alpha, const af_array lhs, const af_array rhs,
+                        const To *betas) {
+    gemm<Ti, To>(getArray<To>(*out), optLhs, optRhs, alpha, getArray<Ti>(lhs),
+                 getArray<Ti>(rhs), betas);
 }
 
 template<typename T>
@@ -178,6 +179,8 @@ af_err af_gemm(af_array *out, const af_mat_prop optLhs,
         if (*out) {
             output = *out;
         } else {
+            af_dtype out_type = (lhs_type != s8) ? lhs_type : f32;
+
             const int aRowDim    = (optLhs == AF_MAT_NONE) ? 0 : 1;
             const int bColDim    = (optRhs == AF_MAT_NONE) ? 1 : 0;
             const int M          = lDims[aRowDim];
@@ -186,7 +189,7 @@ af_err af_gemm(af_array *out, const af_mat_prop optLhs,
             const dim_t d3       = std::max(lDims[3], rDims[3]);
             const af::dim4 oDims = af::dim4(M, N, d2, d3);
             AF_CHECK(af_create_handle(&output, lhsInfo.ndims(), oDims.get(),
-                                      lhs_type));
+                                      out_type));
         }
 
         switch (lhs_type) {
@@ -215,6 +218,11 @@ af_err af_gemm(af_array *out, const af_mat_prop optLhs,
                            static_cast<const half *>(alpha), lhs, rhs,
                            static_cast<const half *>(beta));
                 break;
+            case s8:
+                gemm<schar, float>(&output, optLhs, optRhs,
+                                   static_cast<const float *>(alpha), lhs, rhs,
+                                   static_cast<const float *>(beta));
+                break;
             default: TYPE_ERROR(3, lhs_type);
         }
 
@@ -246,11 +254,13 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
         const dim_t d3       = std::max(lDims[3], rDims[3]);
         const af::dim4 oDims = af::dim4(M, N, d2, d3);
 
-        af_array gemm_out = 0;
+        af_dtype lhs_type = lhsInfo.getType();
+
+        af_array gemm_out      = 0;
+        af_dtype gemm_out_type = (lhs_type != s8) ? lhs_type : f32;
         AF_CHECK(af_create_handle(&gemm_out, oDims.ndims(), oDims.get(),
-                                  lhsInfo.getType()));
+                                  gemm_out_type));
 
-        af_dtype lhs_type = lhsInfo.getType();
         switch (lhs_type) {
             case f16: {
                 static const half alpha(1.0f);
@@ -288,6 +298,13 @@ af_err af_matmul(af_array *out, const af_array lhs, const af_array rhs,
                                  &beta));
                 break;
             }
+            case s8: {
+                float alpha = 1.0;
+                float beta  = 0.0;
+                AF_CHECK(af_gemm(&gemm_out, optLhs, optRhs, &alpha, lhs, rhs,
+                                 &beta));
+                break;
+            }
             default: TYPE_ERROR(1, lhs_type);
         }
 
diff --git a/src/backend/cpu/blas.cpp b/src/backend/cpu/blas.cpp
index b7d158eb21..60cd9be655 100644
--- a/src/backend/cpu/blas.cpp
+++ b/src/backend/cpu/blas.cpp
@@ -219,9 +219,10 @@ toCblasTranspose(af_mat_prop opt) {
     return out;
 }
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
+template<typename Ti, typename To>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta) {
     const CBLAS_TRANSPOSE lOpts = toCblasTranspose(optLhs);
     const CBLAS_TRANSPOSE rOpts = toCblasTranspose(optRhs);
 
@@ -236,17 +237,17 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     const int K       = lDims[aColDim];
     const dim4 oDims  = out.dims();
 
-    using BT  = typename blas_base<T>::type;
-    using CBT = const typename blas_base<T>::type;
+    using BT  = typename blas_base<Ti>::type;
+    using CBT = const typename blas_base<Ti>::type;
 
-    auto alpha_ = scale_type<T, false>(alpha);
-    auto beta_  = scale_type<T, false>(beta);
+    auto alpha_ = scale_type<Ti, false>(alpha);
+    auto beta_  = scale_type<Ti, false>(beta);
 #ifdef USE_MKL
-    auto alpha_batched = scale_type<T, true>(alpha);
-    auto beta_batched  = scale_type<T, true>(beta);
+    auto alpha_batched = scale_type<Ti, true>(alpha);
+    auto beta_batched  = scale_type<Ti, true>(beta);
 #endif
 
-    auto func = [=](Param<T> output, CParam<T> left, CParam<T> right) {
+    auto func = [=](Param<Ti> output, CParam<Ti> left, CParam<Ti> right) {
         dim4 lStrides = left.strides();
         dim4 rStrides = right.strides();
         dim4 oStrides = output.strides();
@@ -255,14 +256,14 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
             if (right.dims()[bColDim] == 1) {
                 dim_t incr =
                     (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                gemv_func<T>()(
+                gemv_func<Ti>()(
                     CblasColMajor, lOpts, lDims[0], lDims[1], alpha_.getScale(),
                     reinterpret_cast<CBT *>(left.get()), lStrides[1],
                     reinterpret_cast<CBT *>(right.get()), incr,
                     beta_.getScale(), reinterpret_cast<BT *>(output.get()),
                     oStrides[0]);
             } else {
-                gemm_func<T>()(
+                gemm_func<Ti>()(
                     CblasColMajor, lOpts, rOpts, M, N, K, alpha_.getScale(),
                     reinterpret_cast<CBT *>(left.get()), lStrides[1],
                     reinterpret_cast<CBT *>(right.get()), rStrides[1],
@@ -303,24 +304,24 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
             const MKL_INT ldb = rStrides[1];
             const MKL_INT ldc = oStrides[1];
 
-            gemm_batch_func<T>()(CblasColMajor, &lOpts, &rOpts, &M, &N, &K,
-                                 alpha_batched.getScale(), lptrs.data(), &lda,
-                                 rptrs.data(), &ldb, beta_batched.getScale(),
-                                 optrs.data(), &ldc, 1, &batchSize);
+            gemm_batch_func<Ti>()(CblasColMajor, &lOpts, &rOpts, &M, &N, &K,
+                                  alpha_batched.getScale(), lptrs.data(), &lda,
+                                  rptrs.data(), &ldb, beta_batched.getScale(),
+                                  optrs.data(), &ldc, 1, &batchSize);
 #else
             for (int n = 0; n < batchSize; n++) {
                 if (rDims[bColDim] == 1) {
                     dim_t incr =
                         (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                    gemv_func<T>()(CblasColMajor, lOpts, lDims[0], lDims[1],
-                                   alpha_.getScale(), lptrs[n], lStrides[1],
-                                   rptrs[n], incr, beta_.getScale(), optrs[n],
-                                   oStrides[0]);
+                    gemv_func<Ti>()(CblasColMajor, lOpts, lDims[0], lDims[1],
+                                    alpha_.getScale(), lptrs[n], lStrides[1],
+                                    rptrs[n], incr, beta_.getScale(), optrs[n],
+                                    oStrides[0]);
                 } else {
-                    gemm_func<T>()(CblasColMajor, lOpts, rOpts, M, N, K,
-                                   alpha_.getScale(), lptrs[n], lStrides[1],
-                                   rptrs[n], rStrides[1], beta_.getScale(),
-                                   optrs[n], oStrides[1]);
+                    gemm_func<Ti>()(CblasColMajor, lOpts, rOpts, M, N, K,
+                                    alpha_.getScale(), lptrs[n], lStrides[1],
+                                    rptrs[n], rStrides[1], beta_.getScale(),
+                                    optrs[n], oStrides[1]);
                 }
             }
 #endif
@@ -341,6 +342,14 @@ void gemm<half>(Array<half> &out, af_mat_prop optLhs, af_mat_prop optRhs,
     copyArray(out, outArr);
 }
 
+template<>
+void gemm<schar, float>(Array<float> &out, af_mat_prop optLhs,
+                        af_mat_prop optRhs, const float *alpha,
+                        const Array<schar> &lhs, const Array<schar> &rhs,
+                        const float *beta) {
+    TYPE_ERROR(3, af_dtype::s8);
+}
+
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
diff --git a/src/backend/cpu/blas.hpp b/src/backend/cpu/blas.hpp
index 1043a567e9..c16916dafb 100644
--- a/src/backend/cpu/blas.hpp
+++ b/src/backend/cpu/blas.hpp
@@ -13,9 +13,10 @@
 namespace arrayfire {
 namespace cpu {
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+template<typename Ti, typename To = Ti>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta);
 
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/cuda/blas.cu b/src/backend/cuda/blas.cu
index 6c88ea002a..08df398a8d 100644
--- a/src/backend/cuda/blas.cu
+++ b/src/backend/cuda/blas.cu
@@ -91,6 +91,17 @@ BLAS_FUNC(gemmBatched, double, D)
 BLAS_FUNC(gemmBatched, cdouble, Z)
 BLAS_FUNC(gemmBatched, __half, H)
 
+template<>
+gemm_func_def<schar> gemm_func<schar>() {
+    TYPE_ERROR(3, af_dtype::s8);
+    return gemm_func_def<schar>();
+}
+template<>
+gemmBatched_func_def<schar> gemmBatched_func<schar>() {
+    TYPE_ERROR(3, af_dtype::s8);
+    return gemmBatched_func_def<schar>();
+}
+
 BLAS_FUNC_DEF(trsm)
 BLAS_FUNC(trsm, float, S)
 BLAS_FUNC(trsm, cfloat, C)
@@ -161,20 +172,20 @@ cublasGemmAlgo_t selectGEMMAlgorithm<__half>() {
     return selectGEMMAlgorithm<common::half>();
 }
 
-template<typename T>
+template<typename Ti, typename To = Ti>
 cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
                             cublasOperation_t rOpts, int M, int N, int K,
-                            const T *alpha, const Array<T> &lhs, dim_t lStride,
-                            const Array<T> &rhs, dim_t rStride, const T *beta,
-                            Array<T> &out, dim_t oleading) {
+                            const To *alpha, const Array<Ti> &lhs, dim_t lStride,
+                            const Array<Ti> &rhs, dim_t rStride, const To *beta,
+                            Array<To> &out, dim_t oleading) {
     auto prop = getDeviceProp(getActiveDeviceId());
 #if __CUDACC_VER_MAJOR__ >= 10
     if (prop.major > 3 && __CUDACC_VER_MAJOR__ >= 10) {
         return cublasGemmEx(
-            blasHandle(), lOpts, rOpts, M, N, K, alpha, lhs.get(), getType<T>(),
-            lStride, rhs.get(), getType<T>(), rStride, beta, out.get(),
-            getType<T>(), out.strides()[1],
-            getComputeType<T>(),  // Compute type
+            blasHandle(), lOpts, rOpts, M, N, K, alpha, lhs.get(), getType<Ti>(),
+            lStride, rhs.get(), getType<Ti>(), rStride, beta, out.get(),
+            getType<To>(), out.strides()[1],
+            getComputeType<To>(),  // Compute type
 
             // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
             // for the cublasGemm*Ex functions, the performance of the
@@ -184,10 +195,10 @@ cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
             // this change. Does this imply that the TENSOR_OP function
             // performs the computation in fp16 bit even when the compute
             // type is CUDA_R_32F?
-            selectGEMMAlgorithm<T>());
+            selectGEMMAlgorithm<Ti>());
     } else {
 #endif
-        using Nt = typename common::kernel_type<T>::native;
+        using Nt = typename common::kernel_type<Ti>::native;
         return gemm_func<Nt>()(blasHandle(), lOpts, rOpts, M, N, K, (Nt *)alpha,
                                (Nt *)lhs.get(), lStride, (Nt *)rhs.get(),
                                rStride, (Nt *)beta, (Nt *)out.get(), oleading);
@@ -197,21 +208,21 @@ cublasStatus_t gemmDispatch(BlasHandle handle, cublasOperation_t lOpts,
 #endif
 }
 
-template<typename T>
+template<typename Ti, typename To = Ti>
 cublasStatus_t gemmBatchedDispatch(BlasHandle handle, cublasOperation_t lOpts,
                                    cublasOperation_t rOpts, int M, int N, int K,
-                                   const T *alpha, const T **lptrs,
-                                   int lStrides, const T **rptrs, int rStrides,
-                                   const T *beta, T **optrs, int oStrides,
+                                   const To *alpha, const Ti **lptrs,
+                                   int lStrides, const Ti **rptrs, int rStrides,
+                                   const To *beta, To **optrs, int oStrides,
                                    int batchSize) {
     auto prop = getDeviceProp(getActiveDeviceId());
 #if __CUDACC_VER_MAJOR__ >= 10
     if (prop.major > 3) {
         return cublasGemmBatchedEx(
             blasHandle(), lOpts, rOpts, M, N, K, alpha, (const void **)lptrs,
-            getType<T>(), lStrides, (const void **)rptrs, getType<T>(),
-            rStrides, beta, (void **)optrs, getType<T>(), oStrides, batchSize,
-            getComputeType<T>(),  // compute type
+            getType<Ti>(), lStrides, (const void **)rptrs, getType<Ti>(),
+            rStrides, beta, (void **)optrs, getType<Ti>(), oStrides, batchSize,
+            getComputeType<Ti>(),  // compute type
             // NOTE: When using the CUBLAS_GEMM_DEFAULT_TENSOR_OP algorithm
             // for the cublasGemm*Ex functions, the performance of the
             // fp32 numbers seem to increase dramatically. Their numerical
@@ -220,10 +231,10 @@ cublasStatus_t gemmBatchedDispatch(BlasHandle handle, cublasOperation_t lOpts,
             // this change. Does this imply that the TENSOR_OP function
             // performs the computation in fp16 bit even when the compute
             // type is CUDA_R_32F?
-            selectGEMMAlgorithm<T>());
+            selectGEMMAlgorithm<Ti>());
     } else {
 #endif
-        using Nt = typename common::kernel_type<T>::native;
+        using Nt = typename common::kernel_type<Ti>::native;
         return gemmBatched_func<Nt>()(
             blasHandle(), lOpts, rOpts, M, N, K, (const Nt *)alpha,
             (const Nt **)lptrs, lStrides, (const Nt **)rptrs, rStrides,
@@ -233,9 +244,9 @@ cublasStatus_t gemmBatchedDispatch(BlasHandle handle, cublasOperation_t lOpts,
 #endif
 }
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
+template<typename Ti, typename To>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs, const To *alpha,
+          const Array<Ti> &lhs, const Array<Ti> &rhs, const To *beta) {
     const cublasOperation_t lOpts = toCblasTranspose(optLhs);
     const cublasOperation_t rOpts = toCblasTranspose(optRhs);
 
@@ -255,14 +266,14 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     dim4 oStrides = out.strides();
 
     if (oDims.ndims() <= 2) {
-        CUBLAS_CHECK(gemmDispatch<T>(blasHandle(), lOpts, rOpts, M, N, K, alpha,
-                                     lhs, lStrides[1], rhs, rStrides[1], beta,
-                                     out, oStrides[1]));
+        CUBLAS_CHECK((gemmDispatch<Ti, To>(blasHandle(), lOpts, rOpts, M, N, K, alpha,
+                                           lhs, lStrides[1], rhs, rStrides[1], beta,
+                                           out, oStrides[1])));
     } else {
         int batchSize = oDims[2] * oDims[3];
-        vector<const T *> lptrs(batchSize);
-        vector<const T *> rptrs(batchSize);
-        vector<T *> optrs(batchSize);
+        vector<const Ti *> lptrs(batchSize);
+        vector<const Ti *> rptrs(batchSize);
+        vector<To *> optrs(batchSize);
 
         bool is_l_d2_batched = oDims[2] == lDims[2];
         bool is_l_d3_batched = oDims[3] == lDims[3];
@@ -270,9 +281,9 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         bool is_r_d2_batched = oDims[2] == rDims[2];
         bool is_r_d3_batched = oDims[3] == rDims[3];
 
-        const T *lptr = lhs.get();
-        const T *rptr = rhs.get();
-        T *optr       = out.get();
+        const Ti *lptr = lhs.get();
+        const Ti *rptr = rhs.get();
+        To *optr    = out.get();
 
         for (int n = 0; n < batchSize; n++) {
             int w    = n / oDims[2];
@@ -286,7 +297,7 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
             optrs[n] = optr + z * oStrides[2] + w * oStrides[3];
         }
 
-        size_t bytes = batchSize * sizeof(T **);
+        size_t bytes = batchSize * sizeof(Ti **);
         auto d_lptrs = memAlloc<uchar>(bytes);
         auto d_rptrs = memAlloc<uchar>(bytes);
         auto d_optrs = memAlloc<uchar>(bytes);
@@ -302,11 +313,11 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         // afterwards
         CUDA_CHECK(cudaStreamSynchronize(getActiveStream()));
 
-        using Nt = typename common::kernel_type<T>::native;
+        using Nt = typename common::kernel_type<Ti>::native;
         CUBLAS_CHECK(gemmBatchedDispatch(
             blasHandle(), lOpts, rOpts, M, N, K, alpha,
-            (const T **)d_lptrs.get(), lStrides[1], (const T **)d_rptrs.get(),
-            rStrides[1], beta, (T **)d_optrs.get(), oStrides[1], batchSize));
+            (const Ti **)d_lptrs.get(), lStrides[1], (const Ti **)d_rptrs.get(),
+            rStrides[1], beta, (To **)d_optrs.get(), oStrides[1], batchSize));
     }
 }
 
@@ -340,17 +351,18 @@ void trsm(const Array<T> &lhs, Array<T> &rhs, af_mat_prop trans, bool is_upper,
         lhs.get(), lStrides[1], rhs.get(), rStrides[1]));
 }
 
-#define INSTANTIATE_GEMM(TYPE)                                               \
-    template void gemm<TYPE>(Array<TYPE> & out, af_mat_prop optLhs,          \
-                             af_mat_prop optRhs, const TYPE *alpha,          \
+#define INSTANTIATE_GEMM(TYPE, OUTTYPE)                                      \
+    template void gemm<TYPE>(Array<OUTTYPE> & out, af_mat_prop optLhs,       \
+                             af_mat_prop optRhs, const OUTTYPE *alpha,       \
                              const Array<TYPE> &lhs, const Array<TYPE> &rhs, \
-                             const TYPE *beta);
-
-INSTANTIATE_GEMM(float)
-INSTANTIATE_GEMM(cfloat)
-INSTANTIATE_GEMM(double)
-INSTANTIATE_GEMM(cdouble)
-INSTANTIATE_GEMM(half)
+                             const OUTTYPE *beta);
+
+INSTANTIATE_GEMM(float, float)
+INSTANTIATE_GEMM(cfloat, cfloat)
+INSTANTIATE_GEMM(double, double)
+INSTANTIATE_GEMM(cdouble, cdouble)
+INSTANTIATE_GEMM(half, half)
+INSTANTIATE_GEMM(schar, float)
 
 #define INSTANTIATE_DOT(TYPE)                                                  \
     template Array<TYPE> dot<TYPE>(const Array<TYPE> &lhs,                     \
diff --git a/src/backend/cuda/blas.hpp b/src/backend/cuda/blas.hpp
index dc4382d013..37432911e2 100644
--- a/src/backend/cuda/blas.hpp
+++ b/src/backend/cuda/blas.hpp
@@ -11,9 +11,10 @@
 
 namespace arrayfire {
 namespace cuda {
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+template<typename Ti, typename To = Ti>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta);
 
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/oneapi/blas.cpp b/src/backend/oneapi/blas.cpp
index 37495957e9..93ae6559a4 100644
--- a/src/backend/oneapi/blas.cpp
+++ b/src/backend/oneapi/blas.cpp
@@ -97,9 +97,10 @@ bool isStrideMonotonic(const af::dim4 &dim) {
     return (dim[0] <= dim[1]) && (dim[1] <= dim[2]) && (dim[2] <= dim[3]);
 }
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
+template<typename Ti, typename To>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta) {
     const auto lOpts = toBlasTranspose(optLhs);
     const auto rOpts = toBlasTranspose(optRhs);
 
@@ -120,25 +121,25 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
 
     if (oDims.ndims() <= 2) {  // if non-batched
         if (rhs.dims()[bColDim] == 1) {
-            if constexpr (std::is_same_v<T, arrayfire::common::half>) {
+            if constexpr (std::is_same_v<Ti, arrayfire::common::half>) {
                 // currently no half support for gemv, use gemm instead
-                gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
-                                lStrides[1], rhs, rStrides[1], beta, out,
-                                oStrides[1]);
+                gemmDispatch<Ti>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
+                                 lStrides[1], rhs, rStrides[1], beta, out,
+                                 oStrides[1]);
             } else {
                 dim_t incr =
                     (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-                gemvDispatch<T>(getQueue(), lOpts, rOpts, lDims[0], lDims[1],
-                                alpha, lhs, lStrides[1], rhs, incr, beta, out,
-                                oStrides[0]);
+                gemvDispatch<Ti>(getQueue(), lOpts, rOpts, lDims[0], lDims[1],
+                                 alpha, lhs, lStrides[1], rhs, incr, beta, out,
+                                 oStrides[0]);
             }
         } else {
-            gemmDispatch<T>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
-                            lStrides[1], rhs, rStrides[1], beta, out,
-                            oStrides[1]);
+            gemmDispatch<Ti>(getQueue(), lOpts, rOpts, M, N, K, alpha, lhs,
+                             lStrides[1], rhs, rStrides[1], beta, out,
+                             oStrides[1]);
         }
     } else {  // if batched
-        using Dt = arrayfire::oneapi::data_t<T>;
+        using Dt = arrayfire::oneapi::data_t<Ti>;
 
         int64_t batchSize = static_cast<int64_t>(oDims[2] * oDims[3]);
 
@@ -206,6 +207,14 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     ONEAPI_DEBUG_FINISH(getQueue());
 }
 
+template<>
+void gemm<schar, float>(Array<float> &out, af_mat_prop optLhs,
+                        af_mat_prop optRhs, const float *alpha,
+                        const Array<schar> &lhs, const Array<schar> &rhs,
+                        const float *beta) {
+    TYPE_ERROR(3, af_dtype::s8);
+}
+
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
diff --git a/src/backend/oneapi/blas.hpp b/src/backend/oneapi/blas.hpp
index 9e2381c336..af65f56d12 100644
--- a/src/backend/oneapi/blas.hpp
+++ b/src/backend/oneapi/blas.hpp
@@ -20,9 +20,10 @@ namespace oneapi {
 void initBlas();
 void deInitBlas();
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+template<typename Ti, typename To = Ti>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta);
 
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/src/backend/opencl/blas.cpp b/src/backend/opencl/blas.cpp
index 45b4149599..8010fe555d 100644
--- a/src/backend/opencl/blas.cpp
+++ b/src/backend/opencl/blas.cpp
@@ -62,13 +62,14 @@ void gemm_fallback<half>(Array<half> & /*out*/, af_mat_prop /*optLhs*/,
     assert(false && "CPU fallback not implemented for f16");
 }
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta) {
+template<typename Ti, typename To>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta) {
 #if defined(WITH_LINEAR_ALGEBRA)
     // Do not force offload gemm on OSX Intel devices
     if (OpenCLCPUOffload(false) &&
-        static_cast<af_dtype>(dtype_traits<T>::af_type) != f16) {
+        static_cast<af_dtype>(dtype_traits<Ti>::af_type) != f16) {
         gemm_fallback(out, optLhs, optRhs, alpha, lhs, rhs, beta);
         return;
     }
@@ -114,14 +115,14 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
         cl::Event event;
         if (rDims[bColDim] == 1) {
             dim_t incr = (optRhs == AF_MAT_NONE) ? rStrides[0] : rStrides[1];
-            gpu_blas_gemv_func<T> gemv;
+            gpu_blas_gemv_func<Ti> gemv;
             OPENCL_BLAS_CHECK(gemv(lOpts, lDims[0], lDims[1], *alpha,
                                    (*lhs.get())(), lOffset, lStrides[1],
                                    (*rhs.get())(), rOffset, incr, *beta,
                                    (*out.get())(), oOffset, oStrides[0], 1,
                                    &getQueue()(), 0, nullptr, &event()));
         } else {
-            gpu_blas_gemm_func<T> gemm;
+            gpu_blas_gemm_func<Ti> gemm;
             OPENCL_BLAS_CHECK(gemm(lOpts, rOpts, M, N, K, *alpha,
                                    (*lhs.get())(), lOffset, lStrides[1],
                                    (*rhs.get())(), rOffset, rStrides[1], *beta,
@@ -131,6 +132,14 @@ void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
     }
 }
 
+template<>
+void gemm<schar, float>(Array<float> &out, af_mat_prop optLhs,
+                        af_mat_prop optRhs, const float *alpha,
+                        const Array<schar> &lhs, const Array<schar> &rhs,
+                        const float *beta) {
+    TYPE_ERROR(3, af_dtype::s8);
+}
+
 template<typename T>
 Array<T> dot(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
              af_mat_prop optRhs) {
diff --git a/src/backend/opencl/blas.hpp b/src/backend/opencl/blas.hpp
index 4416960f46..fc4571d4b5 100644
--- a/src/backend/opencl/blas.hpp
+++ b/src/backend/opencl/blas.hpp
@@ -20,9 +20,10 @@ namespace opencl {
 void initBlas();
 void deInitBlas();
 
-template<typename T>
-void gemm(Array<T> &out, af_mat_prop optLhs, af_mat_prop optRhs, const T *alpha,
-          const Array<T> &lhs, const Array<T> &rhs, const T *beta);
+template<typename Ti, typename To = Ti>
+void gemm(Array<To> &out, af_mat_prop optLhs, af_mat_prop optRhs,
+          const To *alpha, const Array<Ti> &lhs, const Array<Ti> &rhs,
+          const To *beta);
 
 template<typename T>
 Array<T> matmul(const Array<T> &lhs, const Array<T> &rhs, af_mat_prop optLhs,
diff --git a/test/blas.cpp b/test/blas.cpp
index 6b0590d73b..6f77c10160 100644
--- a/test/blas.cpp
+++ b/test/blas.cpp
@@ -492,6 +492,36 @@ TEST(MatrixMultiply, half) {
     }
 }
 
+TEST(MatrixMultiply, schar) {
+    array A8         = array(3, 3, h_lhs).as(s8);
+    array B8         = array(3, 3, h_rhs).as(s8);
+    array expected32 = array(3, 3, h_gold).as(f32);
+
+    {
+        af_array C32 = 0;
+        const float alpha32(1.0f);
+        const float beta32(0.0f);
+        af_backend backend;
+        af_get_active_backend(&backend);
+        if (backend == AF_BACKEND_CUDA) {
+            ASSERT_SUCCESS(af_gemm(&C32, AF_MAT_NONE, AF_MAT_NONE, &alpha32,
+                                   A8.get(), B8.get(), &beta32));
+        } else {
+            ASSERT_EQ(AF_ERR_TYPE,
+                      af_gemm(&C32, AF_MAT_NONE, AF_MAT_NONE, &alpha32,
+                              A8.get(), B8.get(), &beta32));
+            SUCCEED();
+            return;
+        }
+        af::array C(C32);
+        ASSERT_ARRAYS_NEAR(expected32, C, 0.00001);
+    }
+    {
+        array C32 = matmul(A8, B8);
+        ASSERT_ARRAYS_NEAR(expected32, C32, 0.00001);
+    }
+}
+
 struct test_params {
     af_mat_prop opt_lhs;
     af_mat_prop opt_rhs;

From 65ad9105b811b6011bfd3a9a9f69c6d7f515b3e6 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 28 Mar 2025 15:39:18 -0400
Subject: [PATCH 2647/2677] Update FindcuDNN for version 9 (#3641)

* cuDNN library naming has changed in cuDNN 9. Update FindcuDNN to match the new pattern.

* Update cuDNN dependency for v9
---
 CMakeModules/CPackProjectConfig.cmake | 2 +-
 CMakeModules/FindcuDNN.cmake          | 6 +++++-
 src/backend/cuda/CMakeLists.txt       | 6 +++++-
 3 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/CMakeModules/CPackProjectConfig.cmake b/CMakeModules/CPackProjectConfig.cmake
index f85dcaa556..ec5df2ee11 100644
--- a/CMakeModules/CPackProjectConfig.cmake
+++ b/CMakeModules/CPackProjectConfig.cmake
@@ -287,7 +287,7 @@ af_component(
   DEB_USE_SHLIBDEPS
   DEB_PROVIDES "arrayfire-cuda (= ${CPACK_PACKAGE_VERSION}), arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION}), libarrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (= ${CPACK_PACKAGE_VERSION})"
   DEB_REPLACES "arrayfire-cuda (<< ${CPACK_PACKAGE_VERSION}), arrayfire-cuda${CPACK_PACKAGE_VERSION_MAJOR} (<< ${CPACK_PACKAGE_VERSION})"
-  DEB_OPTIONAL libcudnn8 forge libfreeimage3
+  DEB_OPTIONAL cudnn9-cuda-${CPACK_CUDA_VERSION_MAJOR}-${CPACK_CUDA_VERSION_MINOR} forge libfreeimage3
 )
 
 af_component(
diff --git a/CMakeModules/FindcuDNN.cmake b/CMakeModules/FindcuDNN.cmake
index 4c28d3c854..98641f4198 100644
--- a/CMakeModules/FindcuDNN.cmake
+++ b/CMakeModules/FindcuDNN.cmake
@@ -169,13 +169,17 @@ if(cuDNN_INCLUDE_DIRS)
   endmacro()
 
   af_find_cudnn_libs("") # gets base cudnn shared library
-  if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
+  if(cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
     af_find_cudnn_libs("_adv_infer")
     af_find_cudnn_libs("_adv_train")
     af_find_cudnn_libs("_cnn_infer")
     af_find_cudnn_libs("_cnn_train")
     af_find_cudnn_libs("_ops_infer")
     af_find_cudnn_libs("_ops_train")
+  elseif(cuDNN_VERSION_MAJOR VERSION_GREATER_EQUAL 9)
+    af_find_cudnn_libs("_adv")
+    af_find_cudnn_libs("_cnn")
+    af_find_cudnn_libs("_ops")
   endif()
 endif()
 
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6d8731e1e1..6d023f3cb8 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -838,13 +838,17 @@ endfunction()
 if(AF_INSTALL_STANDALONE)
   if(AF_WITH_CUDNN)
     afcu_collect_cudnn_libs("")
-    if(cuDNN_VERSION_MAJOR VERSION_GREATER 8 OR cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
+    if(cuDNN_VERSION_MAJOR VERSION_EQUAL 8)
       # cudnn changed how dlls are shipped starting major version 8
       # except the main dll a lot of the other DLLs are loaded upon demand
       afcu_collect_cudnn_libs(cnn_infer)
       afcu_collect_cudnn_libs(cnn_train)
       afcu_collect_cudnn_libs(ops_infer)
       afcu_collect_cudnn_libs(ops_train)
+    elseif(cuDNN_VERSION_MAJOR VERSION_GREATER_EQUAL 9)
+      # infer and train libraries are now combined in version 9
+      afcu_collect_cudnn_libs(cnn)
+      afcu_collect_cudnn_libs(ops)
     endif()
   endif()
 

From 9ae75d768008c1372896387c1e7e4348724c4546 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 28 Mar 2025 15:39:44 -0400
Subject: [PATCH 2648/2677] Add clang flag to include build id in elf data
 (#3644)

* Add clang flag to include build id in elf data

The build id is not generated by default when using intel oneapi. A flag has been added to enable it. This is to satisfy cpack when it is generating the deb file for the oneapi backend.

* Update LICENSE Copyright
---
 LICENSE                           | 2 +-
 src/backend/oneapi/CMakeLists.txt | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/LICENSE b/LICENSE
index 3d960db185..d63051d62b 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,4 +1,4 @@
-Copyright (c) 2014-2024, ArrayFire
+Copyright (c) 2014-2025, ArrayFire
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 702abd3125..a8a1c3aca6 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -358,6 +358,7 @@ target_link_libraries(afoneapi
     $<$<PLATFORM_ID:Linux>:-flink-huge-device-code>
     $<$<PLATFORM_ID:Linux>:-fvisibility-inlines-hidden>
     $<$<PLATFORM_ID:Linux>:-fno-sycl-rdc>
+    $<$<PLATFORM_ID:Linux>:-Wl,--build-id>
     -fsycl-max-parallel-link-jobs=${NumberOfThreads}
     MKL::MKL_SYCL
   )

From a13dcb64287355ee100351827f069d4bbf5fe471 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 1 Apr 2025 19:21:43 -0400
Subject: [PATCH 2649/2677] Update version numbers for CUDA libraries to be
 collected (#3645)

* Update version numbers for CUDA libraries to be collected

* The nvrtc-builtins library needs to be included in the target link libraries so that the runpath is set.
---
 src/backend/cuda/CMakeLists.txt | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index 6d023f3cb8..a4783b4936 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -50,7 +50,7 @@ set(CUDA_architecture_build_targets "Auto" CACHE
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
-list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY})
+list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY} ${CUDA_nvrtc-builtins_LIBRARY})
 
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   # The libraries that may be staticly linked or may be loaded at runtime
@@ -853,7 +853,9 @@ if(AF_INSTALL_STANDALONE)
   endif()
 
   if(WIN32 OR NOT AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
-    if(CUDA_VERSION_MAJOR VERSION_EQUAL 11)
+    if(CUDA_VERSION_MAJOR VERSION_EQUAL 12)
+        afcu_collect_libs(cufft LIB_MAJOR 11 LIB_MINOR 3)
+    elseif(CUDA_VERSION_MAJOR VERSION_EQUAL 11)
         afcu_collect_libs(cufft LIB_MAJOR 10 LIB_MINOR 4)
     else()
         afcu_collect_libs(cufft)
@@ -862,17 +864,25 @@ if(AF_INSTALL_STANDALONE)
     if(CUDA_VERSION VERSION_GREATER 10.0)
       afcu_collect_libs(cublasLt)
     endif()
-    afcu_collect_libs(cusolver)
+    if(CUDA_VERSION_MAJOR VERSION_EQUAL 12)
+        afcu_collect_libs(cusolver LIB_MAJOR 11 LIB_MINOR 7)
+    else()
+        afcu_collect_libs(cusolver)
+    endif()
     afcu_collect_libs(cusparse)
     if(CUDA_VERSION VERSION_GREATER 12.0)
       afcu_collect_libs(nvJitLink)
     endif()
   elseif(NOT ${use_static_cuda_lapack})
-    afcu_collect_libs(cusolver)
+    if(CUDA_VERSION_MAJOR VERSION_EQUAL 12)
+        afcu_collect_libs(cusolver LIB_MAJOR 11 LIB_MINOR 7)
+    else()
+        afcu_collect_libs(cusolver)
+    endif()
   endif()
 
   if(WIN32 OR CUDA_VERSION VERSION_LESS 11.5 OR NOT AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
-    afcu_collect_libs(nvrtc FULL_VERSION)
+    afcu_collect_libs(nvrtc)
     if(CUDA_VERSION VERSION_GREATER 10.0)
       afcu_collect_libs(nvrtc-builtins FULL_VERSION)
     else()

From c2e76f14605f696c7171a1384b3a4886bbe7fb94 Mon Sep 17 00:00:00 2001
From: willyborn <sabine.willy.born@gmail.com>
Date: Tue, 1 Oct 2024 16:28:22 +0200
Subject: [PATCH 2650/2677] Fixed missing offset handling in lookup

---
 src/backend/opencl/kernel/lookup.cl | 2 +-
 src/backend/opencl/lookup.cpp       | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/opencl/kernel/lookup.cl b/src/backend/opencl/kernel/lookup.cl
index 622a47e8f6..7ed4bc1cfa 100644
--- a/src/backend/opencl/kernel/lookup.cl
+++ b/src/backend/opencl/kernel/lookup.cl
@@ -31,7 +31,7 @@ kernel void lookupND(global in_t *out, KParam oInfo, global const in_t *in,
     int gx = get_local_size(0) * (get_group_id(0) - gz * nBBS0) + lx;
     int gy = get_local_size(1) * (get_group_id(1) - gw * nBBS1) + ly;
 
-    global const idx_t *idxPtr = indices;
+    global const idx_t *idxPtr = indices + idxInfo.offset;
 
     int i = iInfo.strides[0] *
             (DIM == 0 ? trimIndex((int)idxPtr[gx], iInfo.dims[0]) : gx);
diff --git a/src/backend/opencl/lookup.cpp b/src/backend/opencl/lookup.cpp
index 36b5929f1f..83bca0ac44 100644
--- a/src/backend/opencl/lookup.cpp
+++ b/src/backend/opencl/lookup.cpp
@@ -25,8 +25,8 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
     const dim4 &iDims = input.dims();
 
     dim4 oDims(1);
-    for (int d = 0; d < 4; ++d) {
-        oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
+    for (dim_t d = 0; d < 4; ++d) {
+        oDims[d] = (d == dim ? indices.elements() : iDims[d]);
     }
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);

From 83f4bb64a813f19e2a7c233e9dcd03e054ea4fcb Mon Sep 17 00:00:00 2001
From: Edwin Solis <edwinsolisf12@gmail.com>
Date: Mon, 31 Mar 2025 16:14:42 -0700
Subject: [PATCH 2651/2677] Added tests for lookup with indices with offsets
 and lying in different dimensions (with fix)

Tests added were for lookup with indices being subarrays with non-zero offsets and with indices lying in second, third, and fourth dimension
A fix was added for supporting indices that do not lie in the first dimension by making a copy and flattening the indices
---
 src/api/c/index.cpp |  32 ++++++++----
 test/index.cpp      | 121 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 143 insertions(+), 10 deletions(-)

diff --git a/src/api/c/index.cpp b/src/api/c/index.cpp
index a697f8457c..792a5a5af7 100644
--- a/src/api/c/index.cpp
+++ b/src/api/c/index.cpp
@@ -178,22 +178,34 @@ af_err af_lookup(af_array* out, const af_array in, const af_array indices,
         ARG_ASSERT(2, (idxType != b8));
 
         af_array output = 0;
+        af_array idx = 0;
+
+        if (!idxInfo.isColumn()) {
+            // Force a deep copy to flatten the array and handle subarrays of not column vector arrays correctly
+            AF_CHECK(af_copy_array(&idx, indices)); 
+        } else {
+            idx = indices;
+        }
 
         switch (idxType) {
-            case f32: output = lookup<float>(in, indices, dim); break;
-            case f64: output = lookup<double>(in, indices, dim); break;
+            case f32: output = lookup<float>(in, idx, dim); break;
+            case f64: output = lookup<double>(in, idx, dim); break;
             case s32: output = lookup<int>(in, indices, dim); break;
-            case u32: output = lookup<unsigned>(in, indices, dim); break;
-            case s16: output = lookup<short>(in, indices, dim); break;
-            case u16: output = lookup<ushort>(in, indices, dim); break;
-            case s64: output = lookup<intl>(in, indices, dim); break;
-            case u64: output = lookup<uintl>(in, indices, dim); break;
-            case s8: output = lookup<schar>(in, indices, dim); break;
-            case u8: output = lookup<uchar>(in, indices, dim); break;
-            case f16: output = lookup<half>(in, indices, dim); break;
+            case u32: output = lookup<unsigned>(in, idx, dim); break;
+            case s16: output = lookup<short>(in, idx, dim); break;
+            case u16: output = lookup<ushort>(in, idx, dim); break;
+            case s64: output = lookup<intl>(in, idx, dim); break;
+            case u64: output = lookup<uintl>(in, idx, dim); break;
+            case s8: output = lookup<schar>(in, idx, dim); break;
+            case u8: output = lookup<uchar>(in, idx, dim); break;
+            case f16: output = lookup<half>(in, idx, dim); break;
             default: TYPE_ERROR(1, idxType);
         }
         std::swap(*out, output);
+
+        if (idx != indices) {
+            AF_CHECK(af_release_array(idx)); // Release indices array if a copy has been made
+        }
     }
     CATCHALL;
     return AF_SUCCESS;
diff --git a/test/index.cpp b/test/index.cpp
index 39491453e7..d5d010ffb1 100644
--- a/test/index.cpp
+++ b/test/index.cpp
@@ -809,6 +809,127 @@ TEST(lookup, Issue2009) {
     ASSERT_ARRAYS_EQ(a, b);
 }
 
+TEST(lookup, Issue3613_FirstDimLookupWithOffset) {
+    dim4 dims(1);
+    const int selected_dim = 0; // selected span dimension
+    dims[selected_dim] = 125; // input size
+
+    array a = iota(dims);
+    array idxs = iota(dim4(5, 4, 3, 2));
+    array selected_idx = idxs(af::span, 3, 2, 1); // Offsets in second, third, & fourth dimension
+
+    array expected_selected_idx = range(dim4(5)) * 1 + 3 * 5 + 2 * (5 * 4) + 1 * (5 * 4 * 3);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+
+    array b = af::lookup(a, selected_idx, selected_dim);
+    dim4 output_dims(1);
+    output_dims[selected_dim] = 5; // output size
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, output_dims), b); // lookup output should be the same as looked up indices
+}
+
+TEST(lookup, Issue3613_SecondDimLookupWithOffset) {
+    dim4 dims(1);
+    const int selected_dim = 1; // selected span dimension
+    dims[selected_dim] = 125; // input size
+
+    array a = iota(dims);
+    array idxs = iota(dim4(5, 4, 3, 2));
+    array selected_idx = idxs(af::span, 3, 2, 1); // Offsets in second, third, & fourth dimension
+
+    array expected_selected_idx = range(dim4(5)) * 1 + 3 * 5 + 2 * (5 * 4) + 1 * (5 * 4 * 3);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+
+    array b = af::lookup(a, selected_idx, selected_dim);
+    dim4 output_dims(1);
+    output_dims[selected_dim] = 5; // output size
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, output_dims), b); // lookup output should be the same as looked up indices
+}
+
+
+TEST(lookup, Issue3613_ThirdDimLookupWithOffset) {
+    dim4 dims(1);
+    const int selected_dim = 2; // selected span dimension
+    dims[selected_dim] = 125; // input size
+    
+    array a = iota(dims);
+    array idxs = iota(dim4(5, 4, 3, 2));
+    array selected_idx = idxs(af::span, 3, 2, 1); // Offsets in second, third, & fourth dimension
+    
+    array expected_selected_idx = range(dim4(5)) * 1 + 3 * 5 + 2 * (5 * 4) + 1 * (5 * 4 * 3);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+    
+    array b = af::lookup(a, selected_idx, selected_dim);
+    dim4 output_dims(1);
+    output_dims[selected_dim] = 5; // output size
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, output_dims), b); // lookup output should be the same as looked up indices
+}
+
+TEST(lookup, Issue3613_FourthDimLookupWithOffset) {
+    dim4 dims(1);
+    const int selected_dim = 3; // selected span dimension
+    dims[selected_dim] = 125; // input size
+    
+    array a = iota(dims);
+    array idxs = iota(dim4(5, 4, 3, 2));
+    array selected_idx = idxs(af::span, 3, 2, 1); // Offsets in second, third, & fourth dimension
+    
+    array expected_selected_idx = range(dim4(5)) * 1 + 3 * 5 + 2 * (5 * 4) + 1 * (5 * 4 * 3);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+    
+    array b = af::lookup(a, selected_idx, selected_dim);
+    dim4 output_dims(1);
+    output_dims[selected_dim] = 5; // output size
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, output_dims), b); // lookup output should be the same as looked up indices
+}
+
+TEST(lookup, IndicesInSecondDimension) {
+    const int selected_dim = 1; // selected span dimension
+    dim4 dims(1);
+    dims[selected_dim] = 3;
+
+    array a = iota(dim4(100));
+    array idxs = iota(dim4(3, 3, 3, 3));
+    array selected_idx = idxs(0, af::span, 0, 0); // Indices along the second dimension
+
+    array expected_selected_idx = iota(dims) * pow(3, selected_dim);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+
+    array b = af::lookup(a, selected_idx);
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, dim4(3)), b);
+}
+
+TEST(lookup, IndicesInThirdDimension) {
+    const int selected_dim = 2; // selected span dimension
+    dim4 dims(1);
+    dims[selected_dim] = 3;
+
+    array a = iota(dim4(100));
+    array idxs = iota(dim4(3, 3, 3, 3));
+    array selected_idx = idxs(0, 0, af::span, 0); // Indices along the third dimension
+
+    array expected_selected_idx = iota(dims) * pow(3, selected_dim);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+
+    array b = af::lookup(a, selected_idx);
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, dim4(3)), b);
+}
+
+TEST(lookup, IndicesInFourthDimension) {
+    const int selected_dim = 3; // selected span dimension
+    dim4 dims(1);
+    dims[selected_dim] = 3;
+
+    array a = iota(dim4(100));
+    array idxs = iota(dim4(3, 3, 3, 3));
+    array selected_idx = idxs(0, 0, 0, af::span); // Indices along the fourth dimension
+
+    array expected_selected_idx = iota(dims) * pow(3, selected_dim);
+    ASSERT_ARRAYS_EQ(expected_selected_idx, selected_idx);
+
+    array b = af::lookup(a, selected_idx);
+    ASSERT_ARRAYS_EQ(af::moddims(expected_selected_idx, dim4(3)), b);
+}
+
 TEST(lookup, SNIPPET_lookup1d) {
     //! [ex_index_lookup1d]
 

From 59791340b750dc7e32666f05b3866d5ee146bc65 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 4 Apr 2025 15:49:25 -0400
Subject: [PATCH 2652/2677] Add minimum driver version check to allow minor
 version compatibility. (#3648)

* Add minimum driver version check to allow minor version compatibility.

* Simpler method of checking for nvidia driver cuda minor version compatibility.
---
 src/backend/cuda/device_manager.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index c445d5784b..88cbe487a8 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -511,7 +511,10 @@ void DeviceManager::checkCudaVsDriverVersion() {
 
     debugRuntimeCheck(getLogger(), runtime, driver);
 
-    if (runtime > driver) {
+    int runtime_major = runtime / 1000;
+    int driver_major = driver / 1000;
+
+    if (runtime_major > driver_major) {
         string msg =
             "ArrayFire was built with CUDA {} which requires GPU driver "
             "version {} or later. Please download and install the latest "

From f50057bc3b61ea11390c353da663baf0fa1496bd Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 3 Jun 2025 17:38:26 -0400
Subject: [PATCH 2653/2677] Additional libraries for standalone installer and
 fix ilp consistency with find AF_MKL. (#3646)

* Additional libraries for standalone installer and fix ilp consistency with find AF_MKL.

* Yet More oneAPI libraries and name typo fixes

* Remove libraries already provided by runtime

* Move additional mkl libraries collection into block for non-static build.

* Modifications to CMake files to use LP64 interface for CPU and OpenCL back ends and ILP64 for oneAPI back end.

* Both ILP64 and LP64 MKL interface libraries are needed for the oneAPI and CPU & OpenCL back ends respectively. So both need to be installed.
---
 CMakeLists.txt                    | 33 ++++++++++++++++++++++++++++---
 CMakeModules/FindAF_MKL.cmake     | 23 +++++++++++++++------
 src/api/c/CMakeLists.txt          |  4 ----
 src/backend/cpu/CMakeLists.txt    |  5 +++++
 src/backend/oneapi/CMakeLists.txt |  4 ++++
 src/backend/opencl/CMakeLists.txt |  5 +++++
 6 files changed, 61 insertions(+), 13 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index ca942f301a..b1cd049b64 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -143,8 +143,6 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.13)
     set(MKL_ROOT "$ENV{MKLROOT}")
   endif()
   set(SYCL_COMPILER ON)
-  set(MKL_THREADING "tbb_thread")
-  set(MKL_INTERFACE "ilp64")
   find_package(MKL)
 endif()
 
@@ -552,13 +550,21 @@ if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
         ${mkl_int}
         DESTINATION ${AF_INSTALL_LIB_DIR}
         COMPONENT mkl_dependencies)
+
+      # LP64 library is required for the CPU and OpenCL back ends, so install it too
+      if(MKL_INTERFACE_INTEGER_SIZE EQUAL 8)
+        get_filename_component(mkl_int_lp ${MKL_InterfaceLP_LINK_LIBRARY} REALPATH)
+        install(FILES
+          ${mkl_int_lp}
+          DESTINATION ${AF_INSTALL_LIB_DIR}
+          COMPONENT mkl_dependencies)
+      endif()
     endif()
 
     get_filename_component(mkl_rnt ${MKL_RT_LINK_LIBRARY} REALPATH)
     get_filename_component(mkl_shd ${MKL_Core_LINK_LIBRARY} REALPATH)
     get_filename_component(mkl_tly ${MKL_ThreadLayer_LINK_LIBRARY} REALPATH)
     install(FILES
-      ${mkl_sycl}
       ${mkl_rnt}
       ${mkl_shd}
       ${mkl_tly}
@@ -573,6 +579,27 @@ if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
       ${AF_ADDITIONAL_MKL_LIBRARIES}
       DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT mkl_dependencies)
+    if(AF_BUILD_ONEAPI)
+      get_filename_component(mkl_sycl_lapack ${MKL_SyclLapack_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_dft ${MKL_SyclDft_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_blas ${MKL_SyclBlas_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_sparse ${MKL_SyclSparse_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_data ${MKL_SyclDataFitting_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_rng ${MKL_SyclRNG_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_stats ${MKL_SyclStats_LINK_LIBRARY} REALPATH)
+      get_filename_component(mkl_sycl_vm ${MKL_SyclVM_LINK_LIBRARY} REALPATH)
+      install(FILES
+        ${mkl_sycl_lapack}
+        ${mkl_sycl_dft}
+        ${mkl_sycl_blas}
+        ${mkl_sycl_sparse}
+        ${mkl_sycl_data}
+        ${mkl_sycl_rng}
+        ${mkl_sycl_stats}
+        ${mkl_sycl_vm}
+        DESTINATION ${AF_INSTALL_LIB_DIR}
+        COMPONENT mkl_dependencies)
+    endif()
   endif()
 endif()
 
diff --git a/CMakeModules/FindAF_MKL.cmake b/CMakeModules/FindAF_MKL.cmake
index 88037c4519..123b6bee61 100644
--- a/CMakeModules/FindAF_MKL.cmake
+++ b/CMakeModules/FindAF_MKL.cmake
@@ -74,8 +74,12 @@
 include(CheckTypeSize)
 include(FindPackageHandleStandardArgs)
 
-check_type_size("int" INT_SIZE
-  BUILTIN_TYPES_ONLY LANGUAGE C)
+if(DEFINED MKL_INTERFACE_INTEGER_SIZE)
+  set(INT_SIZE ${MKL_INTERFACE_INTEGER_SIZE})
+else()
+  check_type_size("int" INT_SIZE
+    BUILTIN_TYPES_ONLY LANGUAGE C)
+endif()
 
 set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
 set_property(CACHE MKL_THREAD_LAYER PROPERTY STRINGS "TBB" "GNU OpenMP" "Intel OpenMP" "Sequential")
@@ -323,10 +327,14 @@ find_mkl_library(NAME RT LIBRARY_NAME mkl_rt)
 
 if(AF_BUILD_ONEAPI)
     find_mkl_library(NAME Sycl LIBRARY_NAME sycl DLL_ONLY)
-	find_mkl_library(NAME SyclLapack LIBRARY_NAME sycl_lapack DLL_ONLY)
-	find_mkl_library(NAME SyclDft LIBRARY_NAME sycl_dft DLL_ONLY)
-	find_mkl_library(NAME SyclBlas LIBRARY_NAME sycl_blas DLL_ONLY)
-	find_mkl_library(NAME SyclSparse LIBRARY_NAME sycl_sparse DLL_ONLY)
+    find_mkl_library(NAME SyclLapack LIBRARY_NAME mkl_sycl_lapack DLL_ONLY)
+    find_mkl_library(NAME SyclDft LIBRARY_NAME mkl_sycl_dft DLL_ONLY)
+    find_mkl_library(NAME SyclBlas LIBRARY_NAME mkl_sycl_blas DLL_ONLY)
+    find_mkl_library(NAME SyclSparse LIBRARY_NAME mkl_sycl_sparse DLL_ONLY)
+    find_mkl_library(NAME SyclDataFitting LIBRARY_NAME mkl_sycl_data_fitting DLL_ONLY)
+    find_mkl_library(NAME SyclRNG LIBRARY_NAME mkl_sycl_rng DLL_ONLY)
+    find_mkl_library(NAME SyclStats LIBRARY_NAME mkl_sycl_stats DLL_ONLY)
+    find_mkl_library(NAME SyclVM LIBRARY_NAME mkl_sycl_vm DLL_ONLY)
 endif()
 
 # MKL can link against Intel OpenMP, GNU OpenMP, TBB, and Sequential
@@ -356,10 +364,13 @@ endif()
 
 if("${INT_SIZE}" EQUAL 4)
   set(MKL_INTERFACE_INTEGER_SIZE 4)
+  set(MKL_INTERFACE "lp64")
   find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_lp64 SEARCH_STATIC)
 else()
   set(MKL_INTERFACE_INTEGER_SIZE 8)
+  set(MKL_INTERFACE "ilp64")
   find_mkl_library(NAME Interface LIBRARY_NAME mkl_intel_ilp64 SEARCH_STATIC)
+  find_mkl_library(NAME InterfaceLP LIBRARY_NAME mkl_intel_lp64 SEARCH_STATIC)
 endif()
 
 set(MKL_KernelLibraries "mkl_def;mkl_mc;mkl_mc3;mkl_avx;mkl_avx2;mkl_avx512")
diff --git a/src/api/c/CMakeLists.txt b/src/api/c/CMakeLists.txt
index 870d687382..d374b9a669 100644
--- a/src/api/c/CMakeLists.txt
+++ b/src/api/c/CMakeLists.txt
@@ -186,10 +186,6 @@ if(FreeImage_FOUND AND AF_WITH_IMAGEIO)
 endif()
 
 if(BUILD_WITH_MKL)
-  target_compile_definitions(c_api_interface
-    INTERFACE
-      AF_MKL_INTERFACE_SIZE=${MKL_INTERFACE_INTEGER_SIZE}
-    )
   # Create mkl thread layer compile option based on cmake cache variable
   if(MKL_THREAD_LAYER STREQUAL "Sequential")
     target_compile_definitions(c_api_interface INTERFACE AF_MKL_THREAD_LAYER=0)
diff --git a/src/backend/cpu/CMakeLists.txt b/src/backend/cpu/CMakeLists.txt
index b8025d53a2..8a83a55894 100644
--- a/src/backend/cpu/CMakeLists.txt
+++ b/src/backend/cpu/CMakeLists.txt
@@ -15,6 +15,10 @@ generate_product_version(af_cpu_ver_res_file
 add_library(afcpu "")
 add_library(ArrayFire::afcpu ALIAS afcpu)
 
+# CPU back end needs to use MKL LP64 interface
+set(MKL_INTERFACE_INTEGER_SIZE 4)
+set(MKL_INTERFACE "lp64")
+
 # CPU backend source files
 target_sources(afcpu
   PRIVATE
@@ -313,6 +317,7 @@ target_link_libraries(afcpu
   )
 if(BUILD_WITH_MKL)
   target_compile_definitions(afcpu PRIVATE USE_MKL)
+  target_compile_definitions(afcpu PRIVATE AF_MKL_INTERFACE_SIZE=${MKL_INTERFACE_INTEGER_SIZE})
 
   if(MKL_BATCH)
     target_compile_definitions(afcpu PRIVATE AF_USE_MKL_BATCH)
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index a8a1c3aca6..210c8f59a9 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -341,7 +341,11 @@ target_compile_definitions(afoneapi
     CL_HPP_TARGET_OPENCL_VERSION=300
     CL_HPP_MINIMUM_OPENCL_VERSION=110
     CL_HPP_ENABLE_EXCEPTIONS
+    AF_MKL_INTERFACE_SIZE=${MKL_INTERFACE_INTEGER_SIZE}
   )
+if(MKL_INTERFACE_INTEGER_SIZE EQUAL 8)
+  target_compile_definitions(afoneapi PRIVATE MKL_ILP64)
+endif()
 
 cmake_host_system_information(RESULT NumberOfThreads
   QUERY NUMBER_OF_LOGICAL_CORES)
diff --git a/src/backend/opencl/CMakeLists.txt b/src/backend/opencl/CMakeLists.txt
index a02ae6781d..23bedeedab 100644
--- a/src/backend/opencl/CMakeLists.txt
+++ b/src/backend/opencl/CMakeLists.txt
@@ -7,6 +7,10 @@
 
 dependency_check(OpenCL_FOUND "OpenCL not found.")
 
+# OpenCL back end needs to use MKL LP64 interface
+set(MKL_INTERFACE_INTEGER_SIZE 4)
+set(MKL_INTERFACE "lp64")
+
 include(InternalUtils)
 include(build_cl2hpp)
 include(build_CLBlast)
@@ -578,6 +582,7 @@ if(LAPACK_FOUND OR BUILD_WITH_MKL)
 
   if(BUILD_WITH_MKL)
     target_compile_definitions(afopencl PRIVATE USE_MKL)
+    target_compile_definitions(afopencl PRIVATE AF_MKL_INTERFACE_SIZE=${MKL_INTERFACE_INTEGER_SIZE})
     if(MKL_BATCH)
       target_compile_definitions(afopencl PRIVATE AF_USE_MKL_BATCH)
     endif()

From 20b8f6720dde399f2a620a2cfa853bb1458fd31f Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 3 Jun 2025 17:40:43 -0400
Subject: [PATCH 2654/2677] Update dependencies built with ArrayFire (#3651)

* Update dependencies built with ArrayFire

Update dependencies cloned from git and built along with ArrayFire:
- CLBlast (note that the minimum supported version needs to be overriden to 3.5 to work with the latest version of Cmake (v4.0)
- CL2HPP
- GoogleTest

* Remove version override for Boost, use the newest one available. Update baseline to use a more recent version of VCPKG package list. Remove MKL VCPKG package because it is old and is conflicting with the oneAPI system installed version.
---
 CMakeModules/AF_vcpkg_options.cmake |  5 +----
 CMakeModules/build_CLBlast.cmake    |  3 ++-
 CMakeModules/build_cl2hpp.cmake     |  2 +-
 test/CMakeLists.txt                 |  2 +-
 vcpkg.json                          | 12 +-----------
 5 files changed, 6 insertions(+), 18 deletions(-)

diff --git a/CMakeModules/AF_vcpkg_options.cmake b/CMakeModules/AF_vcpkg_options.cmake
index 09701af274..c84adcee82 100644
--- a/CMakeModules/AF_vcpkg_options.cmake
+++ b/CMakeModules/AF_vcpkg_options.cmake
@@ -6,7 +6,6 @@
 # http://arrayfire.com/licenses/BSD-3-Clause
 
 set(ENV{VCPKG_FEATURE_FLAGS} "versions")
-set(ENV{VCPKG_KEEP_ENV_VARS} "MKLROOT")
 set(VCPKG_MANIFEST_NO_DEFAULT_FEATURES ON)
 
 set(VCPKG_OVERLAY_TRIPLETS ${CMAKE_CURRENT_SOURCE_DIR}/CMakeModules/vcpkg/vcpkg-triplets)
@@ -28,9 +27,7 @@ if(BUILD_TESTING)
   list(APPEND VCPKG_MANIFEST_FEATURES "tests")
 endif()
 
-if(AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
-  list(APPEND VCPKG_MANIFEST_FEATURES "mkl")
-else()
+if(NOT AF_COMPUTE_LIBRARY STREQUAL "Intel-MKL")
   list(APPEND VCPKG_MANIFEST_FEATURES "openblasfftw")
 endif()
 
diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index 933531cdf2..a0d9fab435 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -26,7 +26,7 @@ if(TARGET clblast OR AF_WITH_EXTERNAL_PACKAGES_ONLY)
 else()
   af_dep_check_and_populate(${clblast_prefix}
     URI https://github.com/cnugteren/CLBlast.git
-    REF 4500a03440e2cc54998c0edab366babf5e504d67
+    REF 1.6.3
   )
 
   include(ExternalProject)
@@ -69,6 +69,7 @@ else()
       BUILD_BYPRODUCTS ${CLBlast_location}
       CONFIGURE_COMMAND ${CMAKE_COMMAND} ${extproj_gen_opts}
         -Wno-dev <SOURCE_DIR>
+        -DCMAKE_POLICY_VERSION_MINIMUM=3.5
         -DCMAKE_CXX_COMPILER:FILEPATH=${CMAKE_CXX_COMPILER}
         "-DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS}"
         -DOVERRIDE_MSVC_FLAGS_TO_MT:BOOL=OFF
diff --git a/CMakeModules/build_cl2hpp.cmake b/CMakeModules/build_cl2hpp.cmake
index 0a3fef2de0..b38c4bc1d1 100644
--- a/CMakeModules/build_cl2hpp.cmake
+++ b/CMakeModules/build_cl2hpp.cmake
@@ -27,7 +27,7 @@ if(NOT TARGET OpenCL::cl2hpp)
   elseif (NOT TARGET OpenCL::cl2hpp OR NOT TARGET cl2hpp)
     af_dep_check_and_populate(${cl2hpp_prefix}
       URI https://github.com/KhronosGroup/OpenCL-CLHPP.git
-      REF v2022.09.30)
+      REF v2024.10.24)
 
     find_path(cl2hpp_var
       NAMES CL/cl2.hpp
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 8107f3c063..64e1feb777 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -24,7 +24,7 @@ if(AF_WITH_EXTERNAL_PACKAGES_ONLY)
 elseif(NOT TARGET GTest::gtest)
   af_dep_check_and_populate(${gtest_prefix}
     URI https://github.com/google/googletest.git
-    REF release-1.12.1
+    REF v1.16.0
   )
   if(WIN32)
     set(gtest_force_shared_crt ON
diff --git a/vcpkg.json b/vcpkg.json
index fe16a0aa6d..063e402a02 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -23,10 +23,6 @@
         {
             "name": "jasper",
             "version": "4.2.0"
-        },
-        {
-            "name": "boost-modular-build-helper",
-            "version": "1.84.0#3"
         }
     ],
     "features": {
@@ -78,12 +74,6 @@
                 "opencl"
             ]
         },
-        "mkl": {
-            "description": "Build with MKL",
-            "dependencies": [
-                "intel-mkl"
-            ]
-        },
         "cudnn": {
             "description": "Build CUDA with support for cuDNN",
             "dependencies": [
@@ -91,5 +81,5 @@
             ]
         }
     },
-    "builtin-baseline": "9d47b24eacbd1cd94f139457ef6cd35e5d92cc84"
+    "builtin-baseline": "b02e341c927f16d991edbd915d8ea43eac52096c"
 }

From e8e30bdbe568044c80e4e708ded3ea600c605ad2 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 3 Jun 2025 17:49:26 -0400
Subject: [PATCH 2655/2677] Windows installer fixes (#3655)

* Additional libraries for standalone installer and fix ilp consistency with find AF_MKL.

* Yet More oneAPI libraries and name typo fixes

* Remove libraries already provided by runtime

* Move additional mkl libraries collection into block for non-static build.

* Fixes to enable SYCL language to work with MSVC runtime library variables

* Fixes to find FreeImage for Windows build

* Fixes to allow Nvidia libraries to be found for Windows build

* Fixes to allow all oneAPI and MKL libraries to be found for Windows build

* Fixes for CPack scripts to work with NSIS installer on Windows

* Fixes to find tbb libraries.

* Add missing Debug/Release flags for SYCL compiler on Windows

* Add boost program-options dependency required for clFFT

* fix typos in CMakeSYCLInformation

* Add Windows defines

* Only attempt to include debug data files in the installer package for debug builds.

* Revert some changes that removed some library installs which are needed for building on Linux

* Install Visual C++ redistributable as part of the Windows installer.
---
 CMakeLists.txt                                | 42 +++++++--
 CMakeModules/CMakeSYCLInformation.cmake       | 20 +++++
 CMakeModules/CPackConfig.cmake                |  7 +-
 CMakeModules/CPackProjectConfig.cmake         | 90 ++++++++++---------
 CMakeModules/FindAF_MKL.cmake                 |  6 ++
 CMakeModules/FindFreeImage.cmake              |  2 +
 CMakeModules/nsis/NSIS.InstallOptions.ini.in  |  6 +-
 CMakeModules/nsis/NSIS.definitions.nsh.in     | 12 +--
 CMakeModules/nsis/NSIS.template.in            |  5 ++
 src/backend/cuda/CMakeLists.txt               |  7 +-
 src/backend/oneapi/CMakeLists.txt             |  5 ++
 .../oneapi/kernel/sort_by_key/CMakeLists.txt  |  1 +
 vcpkg.json                                    |  1 +
 13 files changed, 142 insertions(+), 62 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b1cd049b64..4ce33555e1 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -56,6 +56,7 @@ endif()
 if(SYCL_COMPILER_NAME STREQUAL "dpcpp" OR SYCL_COMPILER_NAME STREQUAL "dpcpp.exe"
    OR SYCL_COMPILER_NAME STREQUAL "icpx" OR SYCL_COMPILER_NAME STREQUAL "icx.exe")
   set(MKL_THREAD_LAYER "TBB" CACHE STRING "The thread layer to choose for MKL")
+  set(TBB_ROOT "$ENV{TBBROOT}")
   set(MKL_INTERFACE "ilp64")
   set(MKL_INTERFACE_INTEGER_SIZE 8)
 else()
@@ -532,6 +533,14 @@ install(FILES ${ArrayFire_BINARY_DIR}/cmake/install/ArrayFireConfig.cmake
               DESTINATION ${AF_INSTALL_CMAKE_DIR}
               COMPONENT cmake)
 
+if(WIN32 AND AF_INSTALL_STANDALONE)
+  find_program(MSVC_REDIST NAMES vc_redist.x64.exe
+          PATHS "$ENV{VCINSTALLDIR}Redist\\MSVC\\v${MSVC_TOOLSET_VERSION}")
+  get_filename_component(MSVC_REDIST_INSTALLER ${MSVC_REDIST} NAME)
+  install(PROGRAMS ${MSVC_REDIST} COMPONENT common_backend_dependencies
+          DESTINATION ${AF_INSTALL_BIN_DIR})
+endif()
+
 if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
   if(TARGET MKL::ThreadingLibrary)
     get_filename_component(mkl_tl ${MKL_ThreadingLibrary_LINK_LIBRARY} REALPATH)
@@ -561,6 +570,7 @@ if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
       endif()
     endif()
 
+  if(UNIX)
     get_filename_component(mkl_rnt ${MKL_RT_LINK_LIBRARY} REALPATH)
     get_filename_component(mkl_shd ${MKL_Core_LINK_LIBRARY} REALPATH)
     get_filename_component(mkl_tly ${MKL_ThreadLayer_LINK_LIBRARY} REALPATH)
@@ -568,6 +578,11 @@ if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
       ${mkl_rnt}
       ${mkl_shd}
       ${mkl_tly}
+      DESTINATION ${AF_INSTALL_LIB_DIR}
+      COMPONENT mkl_dependencies)
+  endif()
+
+    install(FILES
       $<TARGET_FILE:MKL::RT>
       $<TARGET_FILE:MKL::Shared>
       $<TARGET_FILE:MKL::ThreadLayer>
@@ -580,14 +595,25 @@ if(BUILD_WITH_MKL AND AF_INSTALL_STANDALONE)
       DESTINATION ${AF_INSTALL_LIB_DIR}
       COMPONENT mkl_dependencies)
     if(AF_BUILD_ONEAPI)
-      get_filename_component(mkl_sycl_lapack ${MKL_SyclLapack_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_dft ${MKL_SyclDft_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_blas ${MKL_SyclBlas_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_sparse ${MKL_SyclSparse_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_data ${MKL_SyclDataFitting_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_rng ${MKL_SyclRNG_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_stats ${MKL_SyclStats_LINK_LIBRARY} REALPATH)
-      get_filename_component(mkl_sycl_vm ${MKL_SyclVM_LINK_LIBRARY} REALPATH)
+      if(WIN32)
+        get_filename_component(mkl_sycl_lapack ${MKL_SyclLapack_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_dft ${MKL_SyclDft_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_blas ${MKL_SyclBlas_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_sparse ${MKL_SyclSparse_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_data ${MKL_SyclDataFitting_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_rng ${MKL_SyclRNG_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_stats ${MKL_SyclStats_DLL_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_vm ${MKL_SyclVM_DLL_LIBRARY} REALPATH)
+      else()
+        get_filename_component(mkl_sycl_lapack ${MKL_SyclLapack_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_dft ${MKL_SyclDft_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_blas ${MKL_SyclBlas_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_sparse ${MKL_SyclSparse_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_data ${MKL_SyclDataFitting_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_rng ${MKL_SyclRNG_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_stats ${MKL_SyclStats_LINK_LIBRARY} REALPATH)
+        get_filename_component(mkl_sycl_vm ${MKL_SyclVM_LINK_LIBRARY} REALPATH)
+      endif()
       install(FILES
         ${mkl_sycl_lapack}
         ${mkl_sycl_dft}
diff --git a/CMakeModules/CMakeSYCLInformation.cmake b/CMakeModules/CMakeSYCLInformation.cmake
index df850959f1..b5ec7876db 100644
--- a/CMakeModules/CMakeSYCLInformation.cmake
+++ b/CMakeModules/CMakeSYCLInformation.cmake
@@ -41,6 +41,11 @@ include(Compiler/${CMAKE_SYCL_COMPILER_ID} OPTIONAL)
 __compiler_intel_llvm(SYCL)
 
 if("x${CMAKE_CXX_COMPILER_FRONTEND_VARIANT}" STREQUAL "xMSVC")
+  string(APPEND CMAKE_SYCL_FLAGS_INIT " /DWIN32 /D_WINDOWS")
+  string(APPEND CMAKE_SYCL_FLAGS_DEBUG_INIT " /Zi /Ob0 /Od /RTC1")
+  string(APPEND CMAKE_SYCL_FLAGS_MINSIZEREL_INIT " /O1 /Ob1 /DNDEBUG")
+  string(APPEND CMAKE_SYCL_FLAGS_RELEASE_INIT " /O2 /Ob2 /DNDEBUG")
+  string(APPEND CMAKE_SYCL_FLAGS_RELWITHDEBINFO_INIT " /Zi /O2 /Ob1 /DNDEBUG")
   set(CMAKE_SYCL_COMPILE_OPTIONS_EXPLICIT_LANGUAGE -TP)
   set(CMAKE_SYCL_CLANG_TIDY_DRIVER_MODE "cl")
   set(CMAKE_SYCL_INCLUDE_WHAT_YOU_USE_DRIVER_MODE "cl")
@@ -353,6 +358,21 @@ if(NOT CMAKE_SYCL_LINK_EXECUTABLE)
     "<CMAKE_SYCL_COMPILER> <FLAGS> <CMAKE_SYCL_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
 endif()
 
+if(CMAKE_HOST_WIN32)
+  set(MSVC_RUNTIME "")
+  if("${CMAKE_MSVC_RUNTIME_LIBRARY}" STREQUAL "MultiThreaded")
+    set(MSVC_RUNTIME "-MT")
+  elseif("${CMAKE_MSVC_RUNTIME_LIBRARY}" STREQUAL "MultiThreadedDLL")
+    set(MSVC_RUNTIME "-MD")
+  elseif("${CMAKE_MSVC_RUNTIME_LIBRARY}" STREQUAL "MultiThreadedDebug")
+    set(MSVC_RUNTIME "-MTd")
+  elseif("${CMAKE_MSVC_RUNTIME_LIBRARY}" STREQUAL "MultiThreadedDebugDLL")
+    set(MSVC_RUNTIME "-MDd")
+  else()
+    set(MSVC_RUNTIME "-MD$<$<CONFIG:Debug>:d>")
+  endif()
+  set(CMAKE_MSVC_RUNTIME_LIBRARY "")
+endif()
 
 mark_as_advanced(
 CMAKE_VERBOSE_MAKEFILE
diff --git a/CMakeModules/CPackConfig.cmake b/CMakeModules/CPackConfig.cmake
index 6cd13a1d71..8cf0880faa 100644
--- a/CMakeModules/CPackConfig.cmake
+++ b/CMakeModules/CPackConfig.cmake
@@ -43,9 +43,9 @@ set(CPACK_PACKAGE_NAME "${LIBRARY_NAME}")
 set(CPACK_PACKAGE_VENDOR "${VENDOR_NAME}")
 set(CPACK_PACKAGE_INSTALL_REGISTRY_KEY ${LIBRARY_NAME})
 set(CPACK_PACKAGE_CONTACT "ArrayFire <technical@arrayfire.com>")
-set(MY_CPACK_PACKAGE_ICON "${CMAKE_SOURCE_DIR}/assets/${APP_LOW_NAME}.ico")
+set(MY_CPACK_PACKAGE_ICON "${ASSETS_DIR}/${APP_LOW_NAME}.ico")
 
-file(TO_NATIVE_PATH "${CMAKE_SOURCE_DIR}/assets/" NATIVE_ASSETS_PATH)
+file(TO_NATIVE_PATH "${ASSETS_DIR}/" NATIVE_ASSETS_PATH)
 string(REPLACE "\\" "\\\\" NATIVE_ASSETS_PATH  ${NATIVE_ASSETS_PATH})
 set(CPACK_AF_ASSETS_DIR "${NATIVE_ASSETS_PATH}")
 
@@ -137,6 +137,9 @@ elseif(WIN32)
   else (CMAKE_CL_64)
     set(CPACK_NSIS_INSTALL_ROOT "$PROGRAMFILES")
   endif (CMAKE_CL_64)
+  configure_file(
+      ${PROJECT_SOURCE_DIR}/CMakeModules/nsis/NSIS.definitions.nsh.in
+      ${CMAKE_CURRENT_BINARY_DIR}/NSIS.definitions.nsh)
 else()
   set(CPACK_RESOURCE_FILE_LICENSE "${ArrayFire_SOURCE_DIR}/LICENSE")
   set(CPACK_RESOURCE_FILE_README "${ArrayFire_SOURCE_DIR}/README.md")
diff --git a/CMakeModules/CPackProjectConfig.cmake b/CMakeModules/CPackProjectConfig.cmake
index ec5df2ee11..f75591f8bb 100644
--- a/CMakeModules/CPackProjectConfig.cmake
+++ b/CMakeModules/CPackProjectConfig.cmake
@@ -161,9 +161,12 @@ if(NOT CPACK_GENERATOR MATCHES "DEB")
     DESCRIPTION "ArrayFire development files including headers and configuration files"
     EXPANDED)
 
-  cpack_add_component_group(debug
-    DISPLAY_NAME "ArrayFire Debug Symbols"
-    DESCRIPTION "ArrayFire Debug symbols")
+  if(CMAKE_BUILD_TYPE STREQUAL "Debug" OR
+     CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
+    cpack_add_component_group(debug
+      DISPLAY_NAME "ArrayFire Debug Symbols"
+      DESCRIPTION "ArrayFire Debug symbols")
+  endif()
 endif()
 
 set(arrayfire_cuda_runtime_name "CUDA Runtime(${CPACK_CUDA_VERSION_MAJOR}.${CPACK_CUDA_VERSION_MINOR})")
@@ -473,45 +476,48 @@ endif()
 # Debug symbols in debian installers are created using the DEBINFO property
 if(NOT APPLE AND
    NOT CPACK_GENERATOR MATCHES "DEB")
-  af_component(
-    COMPONENT afoneapi_debug_symbols
-    DISPLAY_NAME "oneAPI Debug Symbols"
-    DESCRIPTION "Debug symbols for the oneAPI backend."
-    GROUP debug
-    DISABLED
-    INSTALL_TYPES Development)
-
-  af_component(
-    COMPONENT afopencl_debug_symbols
-    DISPLAY_NAME "OpenCL Debug Symbols"
-    DESCRIPTION "Debug symbols for the OpenCL backend."
-    GROUP debug
-    DISABLED
-    INSTALL_TYPES Development)
-
-  af_component(
-    COMPONENT afcuda_debug_symbols
-    DISPLAY_NAME "CUDA Debug Symbols"
-    DESCRIPTION "Debug symbols for CUDA backend backend."
-    GROUP debug
-    DISABLED
-    INSTALL_TYPES Development)
-
-  af_component(
-    COMPONENT afcpu_debug_symbols
-    DISPLAY_NAME "CPU Debug Symbols"
-    DESCRIPTION "Debug symbols for CPU backend backend."
-    GROUP debug
-    DISABLED
-    INSTALL_TYPES Development)
-
-  af_component(
-    COMPONENT af_debug_symbols
-    DISPLAY_NAME "Unified Debug Symbols"
-    DESCRIPTION "Debug symbols for the Unified backend."
-    GROUP debug
-    DISABLED
-    INSTALL_TYPES Development)
+  if(CMAKE_BUILD_TYPE STREQUAL "Debug" OR
+     CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
+    af_component(
+      COMPONENT afoneapi_debug_symbols
+      DISPLAY_NAME "oneAPI Debug Symbols"
+      DESCRIPTION "Debug symbols for the oneAPI backend."
+      GROUP debug
+      DISABLED
+      INSTALL_TYPES Development)
+  
+    af_component(
+      COMPONENT afopencl_debug_symbols
+      DISPLAY_NAME "OpenCL Debug Symbols"
+      DESCRIPTION "Debug symbols for the OpenCL backend."
+      GROUP debug
+      DISABLED
+      INSTALL_TYPES Development)
+  
+    af_component(
+      COMPONENT afcuda_debug_symbols
+      DISPLAY_NAME "CUDA Debug Symbols"
+      DESCRIPTION "Debug symbols for CUDA backend backend."
+      GROUP debug
+      DISABLED
+      INSTALL_TYPES Development)
+  
+    af_component(
+      COMPONENT afcpu_debug_symbols
+      DISPLAY_NAME "CPU Debug Symbols"
+      DESCRIPTION "Debug symbols for CPU backend backend."
+      GROUP debug
+      DISABLED
+      INSTALL_TYPES Development)
+  
+    af_component(
+      COMPONENT af_debug_symbols
+      DISPLAY_NAME "Unified Debug Symbols"
+      DESCRIPTION "Debug symbols for the Unified backend."
+      GROUP debug
+      DISABLED
+      INSTALL_TYPES Development)
+  endif()
 endif()
 
 # if (AF_INSTALL_FORGE_DEV)
diff --git a/CMakeModules/FindAF_MKL.cmake b/CMakeModules/FindAF_MKL.cmake
index 123b6bee61..18037ca4fc 100644
--- a/CMakeModules/FindAF_MKL.cmake
+++ b/CMakeModules/FindAF_MKL.cmake
@@ -303,9 +303,15 @@ function(find_mkl_library)
       NAMES
         ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX}
         ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
+        ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}.2${CMAKE_SHARED_LIBRARY_SUFFIX}
+        ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}.5${CMAKE_SHARED_LIBRARY_SUFFIX}
+        ${CMAKE_SHARED_LIBRARY_PREFIX}${mkl_args_LIBRARY_NAME}12${CMAKE_SHARED_LIBRARY_SUFFIX}
         lib${mkl_args_LIBRARY_NAME}${md_suffix}${CMAKE_SHARED_LIBRARY_SUFFIX}
         $ENV{LIB}
         $ENV{LIBRARY_PATH}
+      PATHS
+        ${MKL_ROOT}/bin
+        ${TBB_ROOT}/bin
       PATH_SUFFIXES
         IntelSWTools/compilers_and_libraries/windows/redist/intel64/mkl
         IntelSWTools/compilers_and_libraries/windows/redist/intel64/compiler
diff --git a/CMakeModules/FindFreeImage.cmake b/CMakeModules/FindFreeImage.cmake
index b049ec06a3..3b2d3fca29 100644
--- a/CMakeModules/FindFreeImage.cmake
+++ b/CMakeModules/FindFreeImage.cmake
@@ -75,12 +75,14 @@ find_library(FreeImage_STATIC_LIBRARY
   DOC "The FreeImage static library")
 
 if (WIN32)
+  get_filename_component(FreeImage_LIB_PATH ${FreeImage_LINK_LIBRARY} DIRECTORY)
   find_file(FreeImage_DLL_LIBRARY
     NAMES
       ${CMAKE_SHARED_LIBRARY_PREFIX}FreeImage${CMAKE_SHARED_LIBRARY_SUFFIX}
       ${CMAKE_SHARED_LIBRARY_PREFIX}freeimage${CMAKE_SHARED_LIBRARY_SUFFIX}
     PATHS
       ${FreeImage_ROOT}
+      ${FreeImage_LIB_PATH}/../bin
     DOC "The FreeImage dll")
 	mark_as_advanced(FreeImage_DLL_LIBRARY)
 endif ()
diff --git a/CMakeModules/nsis/NSIS.InstallOptions.ini.in b/CMakeModules/nsis/NSIS.InstallOptions.ini.in
index d92d77959c..cc17d8268a 100644
--- a/CMakeModules/nsis/NSIS.InstallOptions.ini.in
+++ b/CMakeModules/nsis/NSIS.InstallOptions.ini.in
@@ -3,7 +3,7 @@ NumFields=5
 
 [Field 1]
 Type=label
-Text=By default @CPACK_PACKAGE_INSTALL_DIRECTORY@ does not add its directory to the system PATH.
+Text=By default @CPACK_PACKAGE_INSTALL_DIRECTORY@ will add its directory to the system PATH. This will make the dynamic libraries available to all users and software on the system.
 Left=0
 Right=-1
 Top=0
@@ -16,7 +16,7 @@ Left=0
 Right=-1
 Top=30
 Bottom=40
-State=1
+State=0
 
 [Field 3]
 Type=radiobutton
@@ -25,7 +25,7 @@ Left=0
 Right=-1
 Top=40
 Bottom=50
-State=0
+State=1
 
 [Field 4]
 Type=radiobutton
diff --git a/CMakeModules/nsis/NSIS.definitions.nsh.in b/CMakeModules/nsis/NSIS.definitions.nsh.in
index feedbd7c8d..1062271940 100644
--- a/CMakeModules/nsis/NSIS.definitions.nsh.in
+++ b/CMakeModules/nsis/NSIS.definitions.nsh.in
@@ -8,18 +8,18 @@ A few lines of code in ArrayFire can replace dozens of lines of parallel compute
 saving you valuable time and lowering development costs.\r\n\r\n\
 Follow these steps to install the ArrayFire libraries."
 
-!define MUI_ICON "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@.ico"
-!define MUI_UNICON "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@.ico"
+!define MUI_ICON "@CPACK_AF_ASSETS_DIR@@APP_LOW_NAME@.ico"
+!define MUI_UNICON "@CPACK_AF_ASSETS_DIR@@APP_LOW_NAME@.ico"
 
-!define MUI_WELCOMEFINISHPAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_sym.bmp"
-!define MUI_UNWELCOMEFINISHPAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_sym.bmp"
+!define MUI_WELCOMEFINISHPAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@APP_LOW_NAME@_sym.bmp"
+!define MUI_UNWELCOMEFINISHPAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@APP_LOW_NAME@_sym.bmp"
 !define MUI_WELCOMEFINISHPAGE_UNBITMAP_NOSTRETCH
 !define MUI_UNWELCOMEFINISHPAGE_BITMAP_NOSTRETCH
 
 !define MUI_HEADERIMAGE
 !define MUI_HEADERIMAGE_RIGHT
-!define MUI_HEADERIMAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_logo.bmp"
-!define MUI_HEADERIMAGE_UNBITMAP "@CPACK_AF_ASSETS_DIR@@CPACK_PACKAGE_NAME@_logo.bmp"
+!define MUI_HEADERIMAGE_BITMAP "@CPACK_AF_ASSETS_DIR@@APP_LOW_NAME@_logo.bmp"
+!define MUI_HEADERIMAGE_UNBITMAP "@CPACK_AF_ASSETS_DIR@@APP_LOW_NAME@_logo.bmp"
 !define MUI_HEADERIMAGE_BITMAP_NOSTRETCH
 !define MUI_HEADERIMAGE_UNBITMAP_NOSTRETCH
 !define MUI_ABORTWARNING
diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
index 971eea59bf..3eaad1c383 100644
--- a/CMakeModules/nsis/NSIS.template.in
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -740,6 +740,11 @@ Section "-Core installation"
 
 SectionEnd
 
+Section "-Visual C++ installation"
+  ExecWait "$INSTDIR\lib\vc_redist.x64.exe /install /passive"
+  Delete "$INSTDIR\lib\vc_redist.x64.exe"
+SectionEnd
+
 Section "-Add to path"
   Push $INSTDIR\lib
   StrCmp "@CPACK_NSIS_MODIFY_PATH@" "ON" 0 doNotAddToPath
diff --git a/src/backend/cuda/CMakeLists.txt b/src/backend/cuda/CMakeLists.txt
index a4783b4936..5085c57717 100644
--- a/src/backend/cuda/CMakeLists.txt
+++ b/src/backend/cuda/CMakeLists.txt
@@ -50,7 +50,10 @@ set(CUDA_architecture_build_targets "Auto" CACHE
 
 find_cuda_helper_libs(nvrtc)
 find_cuda_helper_libs(nvrtc-builtins)
-list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY} ${CUDA_nvrtc-builtins_LIBRARY})
+list(APPEND nvrtc_libs ${CUDA_nvrtc_LIBRARY})
+if(UNIX)
+  list(APPEND nvrtc_libs ${CUDA_nvrtc-builtins_LIBRARY})
+endif()
 
 if(UNIX AND AF_WITH_STATIC_CUDA_NUMERIC_LIBS)
   # The libraries that may be staticly linked or may be loaded at runtime
@@ -789,7 +792,9 @@ function(afcu_collect_libs libname)
       NAMES
         "${PX}${libname}64_${lib_major}${SX}"
         "${PX}${libname}64_${lib_major}${lib_minor}${SX}"
+        "${PX}${libname}64_${lib_major}0_0${SX}"
         "${PX}${libname}64_${lib_major}${lib_minor}_0${SX}"
+        "${PX}${libname}_${lib_major}0_0${SX}"
       PATHS ${dlib_path_prefix}
     )
     mark_as_advanced(CUDA_${libname}_LIBRARY_DLL)
diff --git a/src/backend/oneapi/CMakeLists.txt b/src/backend/oneapi/CMakeLists.txt
index 210c8f59a9..a41d3fa3b7 100644
--- a/src/backend/oneapi/CMakeLists.txt
+++ b/src/backend/oneapi/CMakeLists.txt
@@ -271,6 +271,11 @@ function(set_sycl_language)
       PROPERTIES
         LINKER_LANGUAGE SYCL)
 
+    get_target_property(target_type ${target} TYPE)
+    if(NOT (${target_type} STREQUAL "INTERFACE_LIBRARY"))
+      target_compile_options(${target} PRIVATE ${MSVC_RUNTIME})
+    endif()
+
     get_target_property(TGT_SOURCES ${target} SOURCES)
     if(NOT TGT_SOURCES)
       get_target_property(TGT_SOURCES ${target} INTERFACE_SOURCES)
diff --git a/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt b/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
index 394d593d6e..08b1d35f73 100644
--- a/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
+++ b/src/backend/oneapi/kernel/sort_by_key/CMakeLists.txt
@@ -49,6 +49,7 @@ foreach(SBK_TYPE ${SBK_TYPES})
     PRIVATE
       $<$<COMPILE_LANGUAGE:SYCL>: -fno-sycl-id-queries-fit-in-int
                                   -sycl-std=2020
+                                  ${MSVC_RUNTIME}
                                   $<$<PLATFORM_ID:Linux>: -fno-sycl-rdc>>)
 
   target_include_directories(oneapi_sort_by_key_${SBK_TYPE}
diff --git a/vcpkg.json b/vcpkg.json
index 063e402a02..d811275a6f 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -71,6 +71,7 @@
             "description": "Build OpenCL backend",
             "dependencies": [
                 "boost-compute",
+                "boost-program-options",
                 "opencl"
             ]
         },

From 83feba1826ae3dcfdd7cfbafdb1abdf706fc6175 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Thu, 12 Jun 2025 23:19:05 -0400
Subject: [PATCH 2656/2677] Add support for CUDA 12.9 (#3657)

---
 src/backend/cuda/device_manager.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/backend/cuda/device_manager.cpp b/src/backend/cuda/device_manager.cpp
index 88cbe487a8..ee7ce76980 100644
--- a/src/backend/cuda/device_manager.cpp
+++ b/src/backend/cuda/device_manager.cpp
@@ -101,6 +101,7 @@ static const int jetsonComputeCapabilities[] = {
 
 // clang-format off
 static const cuNVRTCcompute Toolkit2MaxCompute[] = {
+    {12090, 9, 0, 0},
     {12080, 9, 0, 0},
     {12070, 9, 0, 0},
     {12060, 9, 0, 0},
@@ -146,6 +147,7 @@ struct ComputeCapabilityToStreamingProcessors {
 // clang-format off
 static const ToolkitDriverVersions
     CudaToDriverVersion[] = {
+        {12090, 525.60f, 528.33f},
         {12080, 525.60f, 528.33f},
         {12070, 525.60f, 528.33f},
         {12060, 525.60f, 528.33f},

From dd6b43d28a34e6b4cb554d897bd52acf181e952c Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Thu, 12 Jun 2025 23:19:52 -0400
Subject: [PATCH 2657/2677] Use correct offset for lookup on oneapi back end.
 (#3659)

This was fixed in the opencl back end in PR #3650 but the issue also existed in the oneapi back end and is fixed here.
---
 src/backend/oneapi/kernel/lookup.hpp | 2 +-
 src/backend/oneapi/lookup.cpp        | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/backend/oneapi/kernel/lookup.hpp b/src/backend/oneapi/kernel/lookup.hpp
index f3e2fcdcde..6bceca3e97 100644
--- a/src/backend/oneapi/kernel/lookup.hpp
+++ b/src/backend/oneapi/kernel/lookup.hpp
@@ -64,7 +64,7 @@ class lookupNDCreateKernel {
         int gx = g.get_local_range(0) * (g.get_group_id(0) - gz * nBBS0_) + lx;
         int gy = g.get_local_range(1) * (g.get_group_id(1) - gw * nBBS1_) + ly;
 
-        const idx_t *idxPtr = indices_.get_pointer();
+        const idx_t *idxPtr = indices_.get_pointer() + idxInfo_.offset;
 
         int i = iInfo_.strides[0] *
                 (DIM_ == 0 ? trimIndex((int)idxPtr[gx], iInfo_.dims[0]) : gx);
diff --git a/src/backend/oneapi/lookup.cpp b/src/backend/oneapi/lookup.cpp
index de0a017c55..da658e12aa 100644
--- a/src/backend/oneapi/lookup.cpp
+++ b/src/backend/oneapi/lookup.cpp
@@ -25,8 +25,8 @@ Array<in_t> lookup(const Array<in_t> &input, const Array<idx_t> &indices,
     const dim4 &iDims = input.dims();
 
     dim4 oDims(1);
-    for (int d = 0; d < 4; ++d) {
-        oDims[d] = (d == int(dim) ? indices.elements() : iDims[d]);
+    for (dim_t d = 0; d < 4; ++d) {
+        oDims[d] = (d == dim ? indices.elements() : iDims[d]);
     }
 
     Array<in_t> out = createEmptyArray<in_t>(oDims);

From 9cac22e7ec596fa8e17bef44a2c28a514dababc5 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 13 Jun 2025 17:48:46 -0400
Subject: [PATCH 2658/2677] Fix JIT source for casting to signed char. (#3661)

An incorrect function name for casting to a signed char was used when generating the source for oneAPI JIT kernels resulting in a compilation error. This has been fixed with a template specialization of CastOp.
---
 src/backend/oneapi/cast.hpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/oneapi/cast.hpp b/src/backend/oneapi/cast.hpp
index 7d4e2be76f..11b64c9631 100644
--- a/src/backend/oneapi/cast.hpp
+++ b/src/backend/oneapi/cast.hpp
@@ -34,11 +34,15 @@ struct CastOp {
 
 CAST_FN(int)
 CAST_FN(uint)
-CAST_FN(schar)
 CAST_FN(uchar)
 CAST_FN(float)
 CAST_FN(double)
 
+template<typename Ti>
+struct CastOp<schar, Ti> {
+    const char *name() { return "convert_char"; }
+};
+
 #define CAST_CFN(TYPE)                                    \
     template<typename Ti>                                 \
     struct CastOp<TYPE, Ti> {                             \

From d4e96e35c4c7cb5434315f0836b47b7d5d53c9e4 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 13 Jun 2025 17:53:02 -0400
Subject: [PATCH 2659/2677] Implement oneAPI half precision RNG (#3662)

The writeOut routines for the uniform and normal distribution RNG were not implemented for the half type on the oneAPI back end. This resulted in undefined behavior when using the randn and randu methods. They have now been implemented.
---
 .../oneapi/kernel/random_engine_write.hpp     | 303 +++++-------------
 1 file changed, 85 insertions(+), 218 deletions(-)

diff --git a/src/backend/oneapi/kernel/random_engine_write.hpp b/src/backend/oneapi/kernel/random_engine_write.hpp
index 3ebf0a113e..a96d7d07fe 100644
--- a/src/backend/oneapi/kernel/random_engine_write.hpp
+++ b/src/backend/oneapi/kernel/random_engine_write.hpp
@@ -8,71 +8,12 @@
  ********************************************************/
 #pragma once
 #include <sycl/sycl.hpp>
+#include <math.hpp>
 
 namespace arrayfire {
 namespace oneapi {
 namespace kernel {
 
-// TODO: !!!! half functions still need to be ported !!!!
-
-//// Conversion to half adapted from Random123
-//// #define HALF_FACTOR (1.0f) / (std::numeric_limits<ushort>::max() + (1.0f))
-//// #define HALF_HALF_FACTOR ((0.5f) * HALF_FACTOR)
-////
-//// NOTE: The following constants for half were calculated using the formulas
-//// above. This is done so that we can avoid unnecessary computations because
-/// the / __half datatype is not a constexprable type. This prevents the
-/// compiler from / peforming these operations at compile time.
-// #define HALF_FACTOR __ushort_as_half(0x100u)
-// #define HALF_HALF_FACTOR __ushort_as_half(0x80)
-//
-//// Conversion to half adapted from Random123
-////#define SIGNED_HALF_FACTOR                                \
-//    //((1.0f) / (std::numeric_limits<short>::max() + (1.0f)))
-////#define SIGNED_HALF_HALF_FACTOR ((0.5f) * SIGNED_HALF_FACTOR)
-////
-//// NOTE: The following constants for half were calculated using the formulas
-//// above. This is done so that we can avoid unnecessary computations because
-/// the / __half datatype is not a constexprable type. This prevents the
-/// compiler from / peforming these operations at compile time
-// #define SIGNED_HALF_FACTOR __ushort_as_half(0x200u)
-// #define SIGNED_HALF_HALF_FACTOR __ushort_as_half(0x100u)
-//
-///// This is the largest integer representable by fp16. We need to
-///// make sure that the value converted from ushort is smaller than this
-///// value to avoid generating infinity
-// constexpr ushort max_int_before_infinity = 65504;
-//
-//// Generates rationals in (0, 1]
-//__device__ static __half oneMinusGetHalf01(uint num) {
-//    // convert to ushort before the min operation
-//    ushort v = min(max_int_before_infinity, ushort(num));
-// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 530
-//    return (1.0f - __half2float(__hfma(__ushort2half_rn(v), HALF_FACTOR,
-//                                       HALF_HALF_FACTOR)));
-// #else
-//    __half out = __ushort_as_half(0x3c00u) /*1.0h*/ -
-//                 __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
-//    if (__hisinf(out)) printf("val: %d ushort: %d\n", num, v);
-//    return out;
-// #endif
-//}
-//
-//// Generates rationals in (0, 1]
-//__device__ static __half getHalf01(uint num) {
-//    // convert to ushort before the min operation
-//    ushort v = min(max_int_before_infinity, ushort(num));
-//    return __hfma(__ushort2half_rn(v), HALF_FACTOR, HALF_HALF_FACTOR);
-//}
-//
-//// Generates rationals in (-1, 1]
-//__device__ static __half getHalfNegative11(uint num) {
-//    // convert to ushort before the min operation
-//    ushort v = min(max_int_before_infinity, ushort(num));
-//    return __hfma(__ushort2half_rn(v), SIGNED_HALF_FACTOR,
-//                  SIGNED_HALF_HALF_FACTOR);
-//}
-//
 // Generates rationals in (0, 1]
 static float getFloat01(uint num) {
     // Conversion to floats adapted from Random123
@@ -126,94 +67,43 @@ static double getDoubleNegative11(uint num1, uint num2) {
     return sycl::fma(static_cast<double>(num), signed_factor, half_factor);
 }
 
+/// This is the largest integer representable by fp16. We need to
+/// make sure that the value converted from ushort is smaller than this
+/// value to avoid generating infinity
+#define MAX_INT_BEFORE_INFINITY (ushort)65504u
+
+// Generates rationals in (0, 1]
+sycl::half getHalf01(uint num, uint index) {
+    sycl::half v = static_cast<sycl::half>(min(MAX_INT_BEFORE_INFINITY,
+                       static_cast<ushort>(num >> (16U * (index & 1U)) & 0x0000ffff)));
+
+    const sycl::half half_factor{1.526e-5}; // (1 / (USHRT_MAX + 1))
+    const sycl::half half_half_factor{7.6e-6}; // (0.5 * half_factor)
+    return sycl::fma(v, half_factor, half_half_factor);
+}
+
+sycl::half oneMinusGetHalf01(uint num, uint index) {
+    return static_cast<sycl::half>(1.) - getHalf01(num, index);
+}
+
+// Generates rationals in (-1, 1]
+sycl::half getHalfNegative11(uint num, uint index) {
+    sycl::half v = static_cast<sycl::half>(min(MAX_INT_BEFORE_INFINITY,
+                       static_cast<ushort>(num >> (16U * (index & 1U)) & 0x0000ffff)));
+
+    const sycl::half signed_half_factor{3.05e-5}; // (1 / (SHRT_MAX + 1))
+    const sycl::half signed_half_half_factor{1.526e-5}; // (0.5 * signed_half_factor)
+    return sycl::fma(v, signed_half_factor, signed_half_half_factor);
+}
+
 namespace {
-//
-// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
-// #define HALF_MATH_FUNC(OP, HALF_OP)    \
-//    template<>                         \
-//    __device__ __half OP(__half val) { \
-//        return ::HALF_OP(val);         \
-//    }
-// #else
-// #define HALF_MATH_FUNC(OP, HALF_OP)     \
-//    template<>                          \
-//    __device__ __half OP(__half val) {  \
-//        float fval = __half2float(val); \
-//        return __float2half(OP(fval));  \
-//    }
-// #endif
-//
-// #define MATH_FUNC(OP, DOUBLE_OP, FLOAT_OP, HALF_OP) \
-//    template<typename T>                            \
-//    __device__ T OP(T val);                         \
-//    template<>                                      \
-//    __device__ double OP(double val) {              \
-//        return ::DOUBLE_OP(val);                    \
-//    }                                               \
-//    template<>                                      \
-//    __device__ float OP(float val) {                \
-//        return ::FLOAT_OP(val);                     \
-//    }                                               \
-//    HALF_MATH_FUNC(OP, HALF_OP)
-//
-// MATH_FUNC(log, log, logf, hlog)
-// MATH_FUNC(sqrt, sqrt, sqrtf, hsqrt)
-// MATH_FUNC(sin, sin, sinf, hsin)
-// MATH_FUNC(cos, cos, cosf, hcos)
-//
-// template<typename T>
-//__device__ void sincos(T val, T *sptr, T *cptr);
-//
-// template<>
-//__device__ void sincos(double val, double *sptr, double *cptr) {
-//    ::sincos(val, sptr, cptr);
-//}
-//
-// template<>
-//__device__ void sincos(float val, float *sptr, float *cptr) {
-//    sincosf(val, sptr, cptr);
-//}
-//
-// template<>
-//__device__ void sincos(__half val, __half *sptr, __half *cptr) {
-// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
-//    *sptr = sin(val);
-//    *cptr = cos(val);
-// #else
-//    float s, c;
-//    float fval = __half2float(val);
-//    sincos(fval, &s, &c);
-//    *sptr = __float2half(s);
-//    *cptr = __float2half(c);
-// #endif
-//}
-//
 template<typename T>
 void sincospi(T val, T *sptr, T *cptr) {
     *sptr = sycl::sinpi(val);
     *cptr = sycl::cospi(val);
 }
-
-// template<>
-//__device__ void sincospi(__half val, __half *sptr, __half *cptr) {
-//    // CUDA cannot make __half into a constexpr as of CUDA 11 so we are
-//    // converting this offline
-// #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
-//    const __half pi_val = __ushort_as_half(0x4248);  // 0x4248 == 3.14062h
-//    val *= pi_val;
-//    *sptr = sin(val);
-//    *cptr = cos(val);
-// #else
-//    float fval = __half2float(val);
-//    float s, c;
-//    sincospi(fval, &s, &c);
-//    *sptr = __float2half(s);
-//    *cptr = __float2half(c);
-// #endif
-//}
-//
 }  // namespace
-//
+
 template<typename T>
 constexpr T neg_two() {
     return -2.0;
@@ -273,13 +163,6 @@ static void boxMullerTransform(Td *const out1, Td *const out2, const Tc &r1,
     *out1 = static_cast<Td>(r * s);
     *out2 = static_cast<Td>(r * c);
 }
-// template<>
-//__device__ void boxMullerTransform<arrayfire::common::half, __half>(
-//    arrayfire::common::half *const out1, arrayfire::common::half *const out2,
-//    const __half &r1, const __half &r2) { float o1, o2; float fr1 =
-//    __half2float(r1); float fr2 = __half2float(r2); boxMullerTransform(&o1,
-//    &o2, fr1, fr2); *out1 = o1; *out2 = o2;
-//}
 
 // Writes without boundary checking
 static void writeOut128Bytes(uchar *out, const uint &index, const uint groupSz,
@@ -413,14 +296,14 @@ static void writeOut128Bytes(cdouble *out, const uint &index,
 static void writeOut128Bytes(arrayfire::common::half *out, const uint &index,
                              const uint groupSz, const uint &r1, const uint &r2,
                              const uint &r3, const uint &r4) {
-    // out[index]               = oneMinusGetHalf01(r1);
-    // out[index + groupSz]     = oneMinusGetHalf01(r1 >> 16);
-    // out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
-    // out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
-    // out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
-    // out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
-    // out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
-    // out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
+    out[index]               = oneMinusGetHalf01(r1, 0);
+    out[index + groupSz]     = oneMinusGetHalf01(r1, 1);
+    out[index + 2 * groupSz] = oneMinusGetHalf01(r2, 0);
+    out[index + 3 * groupSz] = oneMinusGetHalf01(r2, 1);
+    out[index + 4 * groupSz] = oneMinusGetHalf01(r3, 0);
+    out[index + 5 * groupSz] = oneMinusGetHalf01(r3, 1);
+    out[index + 6 * groupSz] = oneMinusGetHalf01(r4, 0);
+    out[index + 7 * groupSz] = oneMinusGetHalf01(r4, 1);
 }
 
 // Normalized writes without boundary checking
@@ -464,17 +347,14 @@ static void boxMullerWriteOut128Bytes(arrayfire::common::half *out,
                                       const uint &index, const uint groupSz,
                                       const uint &r1, const uint &r2,
                                       const uint &r3, const uint &r4) {
-    //   boxMullerTransform(&out[index], &out[index + groupSz],
-    //                      getHalfNegative11(r1), getHalf01(r1 >> 16));
-    //   boxMullerTransform(&out[index + 2 * groupSz],
-    //                      &out[index + 3 * groupSz], getHalfNegative11(r2),
-    //                      getHalf01(r2 >> 16));
-    //   boxMullerTransform(&out[index + 4 * groupSz],
-    //                      &out[index + 5 * groupSz], getHalfNegative11(r3),
-    //                      getHalf01(r3 >> 16));
-    //   boxMullerTransform(&out[index + 6 * groupSz],
-    //                      &out[index + 7 * groupSz], getHalfNegative11(r4),
-    //                      getHalf01(r4 >> 16));
+    boxMullerTransform(&out[index], &out[index + groupSz],
+                       getHalfNegative11(r1, 0), getHalf01(r1, 1));
+    boxMullerTransform(&out[index + 2 * groupSz], &out[index + 3 * groupSz],
+                       getHalfNegative11(r2, 0), getHalf01(r2, 1));
+    boxMullerTransform(&out[index + 4 * groupSz], &out[index + 5 * groupSz],
+                       getHalfNegative11(r3, 0), getHalf01(r3, 1));
+    boxMullerTransform(&out[index + 6 * groupSz], &out[index + 7 * groupSz],
+                       getHalfNegative11(r4, 0), getHalf01(r4, 1));
 }
 
 // Writes with boundary checking
@@ -727,28 +607,28 @@ static void partialWriteOut128Bytes(arrayfire::common::half *out,
                                     const uint &r1, const uint &r2,
                                     const uint &r3, const uint &r4,
                                     const uint &elements) {
-    //  if (index < elements) { out[index] = oneMinusGetHalf01(r1); }
-    //  if (index + groupSz < elements) {
-    //      out[index + groupSz] = oneMinusGetHalf01(r1 >> 16);
-    //  }
-    //  if (index + 2 * groupSz < elements) {
-    //      out[index + 2 * groupSz] = oneMinusGetHalf01(r2);
-    //  }
-    //  if (index + 3 * groupSz < elements) {
-    //      out[index + 3 * groupSz] = oneMinusGetHalf01(r2 >> 16);
-    //  }
-    //  if (index + 4 * groupSz < elements) {
-    //      out[index + 4 * groupSz] = oneMinusGetHalf01(r3);
-    //  }
-    //  if (index + 5 * groupSz < elements) {
-    //      out[index + 5 * groupSz] = oneMinusGetHalf01(r3 >> 16);
-    //  }
-    //  if (index + 6 * groupSz < elements) {
-    //      out[index + 6 * groupSz] = oneMinusGetHalf01(r4);
-    //  }
-    //  if (index + 7 * groupSz < elements) {
-    //      out[index + 7 * groupSz] = oneMinusGetHalf01(r4 >> 16);
-    //  }
+    if (index < elements) { out[index] = oneMinusGetHalf01(r1, 0); }
+    if (index + groupSz < elements) {
+        out[index + groupSz] = oneMinusGetHalf01(r1, 1);
+    }
+    if (index + 2 * groupSz < elements) {
+        out[index + 2 * groupSz] = oneMinusGetHalf01(r2, 0);
+    }
+    if (index + 3 * groupSz < elements) {
+        out[index + 3 * groupSz] = oneMinusGetHalf01(r2, 1);
+    }
+    if (index + 4 * groupSz < elements) {
+        out[index + 4 * groupSz] = oneMinusGetHalf01(r3, 0);
+    }
+    if (index + 5 * groupSz < elements) {
+        out[index + 5 * groupSz] = oneMinusGetHalf01(r3, 1);
+    }
+    if (index + 6 * groupSz < elements) {
+        out[index + 6 * groupSz] = oneMinusGetHalf01(r4, 0);
+    }
+    if (index + 7 * groupSz < elements) {
+        out[index + 7 * groupSz] = oneMinusGetHalf01(r4, 1);
+    }
 }
 
 // Normalized writes with boundary checking
@@ -758,35 +638,22 @@ static void partialBoxMullerWriteOut128Bytes(arrayfire::common::half *out,
                                              const uint &r2, const uint &r3,
                                              const uint &r4,
                                              const uint &elements) {
-    //    arrayfire::common::half n[8];
-    //    boxMullerTransform(n + 0, n + 1, getHalfNegative11(r1),
-    //                       getHalf01(r1 >> 16));
-    //    boxMullerTransform(n + 2, n + 3, getHalfNegative11(r2),
-    //                       getHalf01(r2 >> 16));
-    //    boxMullerTransform(n + 4, n + 5, getHalfNegative11(r3),
-    //                       getHalf01(r3 >> 16));
-    //    boxMullerTransform(n + 6, n + 7, getHalfNegative11(r4),
-    //                       getHalf01(r4 >> 16));
-    //    if (index < elements) { out[index] = n[0]; }
-    //    if (index + groupSz < elements) { out[index + groupSz] = n[1]; }
-    //    if (index + 2 * groupSz < elements) {
-    //        out[index + 2 * groupSz] = n[2];
-    //    }
-    //    if (index + 3 * groupSz < elements) {
-    //        out[index + 3 * groupSz] = n[3];
-    //    }
-    //    if (index + 4 * groupSz < elements) {
-    //        out[index + 4 * groupSz] = n[4];
-    //    }
-    //    if (index + 5 * groupSz < elements) {
-    //        out[index + 5 * groupSz] = n[5];
-    //    }
-    //    if (index + 6 * groupSz < elements) {
-    //        out[index + 6 * groupSz] = n[6];
-    //    }
-    //    if (index + 7 * groupSz < elements) {
-    //        out[index + 7 * groupSz] = n[7];
-    //    }
+    sycl::half n1, n2;
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r1, 0), getHalf01(r1, 1));
+    if (index < elements) { out[index] = n1; }
+    if (index + groupSz < elements) { out[index + groupSz] = n2; }
+
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r2, 0), getHalf01(r2, 1));
+    if (index + 2 * groupSz < elements) { out[index + 2 * groupSz] = n1; }
+    if (index + 3 * groupSz < elements) { out[index + 3 * groupSz] = n2; }
+
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r3, 0), getHalf01(r3, 1));
+    if (index + 4 * groupSz < elements) { out[index + 4 * groupSz] = n1; }
+    if (index + 5 * groupSz < elements) { out[index + 5 * groupSz] = n2; }
+
+    boxMullerTransform(&n1, &n2, getHalfNegative11(r4, 0), getHalf01(r4, 1));
+    if (index + 6 * groupSz < elements) { out[index + 6 * groupSz] = n1; }
+    if (index + 7 * groupSz < elements) { out[index + 7 * groupSz] = n2; }
 }
 
 }  // namespace kernel

From 492f808f7781dd849099ddbbc20d6946e7f841a9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Fri, 20 Jun 2025 12:15:07 -0700
Subject: [PATCH 2660/2677] Fixed span lite add_subdirectory command missing
 build directory (#3669)

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4ce33555e1..21bc48d39e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -316,7 +316,7 @@ if(NOT TARGET nonstd::span-lite)
     URI https://github.com/martinmoene/span-lite
     REF "ccf2351"
     )
-  add_subdirectory(${span-lite_SOURCE_DIR} EXCLUDE_FROM_ALL)
+  add_subdirectory(${span-lite_SOURCE_DIR} ${span-lite_BINARY_DIR} EXCLUDE_FROM_ALL)
   get_property(span_include_dir
     TARGET span-lite
     PROPERTY INTERFACE_INCLUDE_DIRECTORIES)

From 0e8a6900c338c72fa8b217eb13ed4c3b529475e3 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Wed, 25 Jun 2025 11:02:09 -0400
Subject: [PATCH 2661/2677] Fixes to the indexed reduce function for the cpu,
 opencl and cuda back ends (#3658)

* Fixes to the indexed reduce function for the cpu, opencl and cuda back ends.

These back ends were incorrectly assuming a linear array. In the case of the cuda and opencl back end this was just for the cpu-fallback methods which are used when the total number of elements in the array is less than or equal to 4096.

* Ensure array is evaluated before reducing

Added an eval() to the input array on the CPU back end for the ireduce method to ensure that the array has been evaluated before reducing.
---
 src/backend/cpu/ireduce.cpp           |   6 +-
 src/backend/cuda/kernel/ireduce.hpp   |  14 +--
 src/backend/opencl/kernel/ireduce.hpp |  13 +--
 test/ireduce.cpp                      | 133 ++++++++++++++++++++++++++
 4 files changed, 151 insertions(+), 15 deletions(-)

diff --git a/src/backend/cpu/ireduce.cpp b/src/backend/cpu/ireduce.cpp
index a20df27c1a..b87c12bc87 100644
--- a/src/backend/cpu/ireduce.cpp
+++ b/src/backend/cpu/ireduce.cpp
@@ -58,11 +58,13 @@ void rreduce(Array<T> &out, Array<uint> &loc, const Array<T> &in, const int dim,
 
 template<af_op_t op, typename T>
 T ireduce_all(unsigned *loc, const Array<T> &in) {
+    in.eval();
     getQueue().sync();
 
     af::dim4 dims    = in.dims();
     af::dim4 strides = in.strides();
     const T *inPtr   = in.get();
+    dim_t idx = 0;
 
     kernel::MinMaxOp<op, T> Op(inPtr[0], 0);
 
@@ -76,8 +78,8 @@ T ireduce_all(unsigned *loc, const Array<T> &in) {
                 dim_t off1 = j * strides[1];
 
                 for (dim_t i = 0; i < dims[0]; i++) {
-                    dim_t idx = i + off1 + off2 + off3;
-                    Op(inPtr[idx], idx);
+                    dim_t d_idx = i + off1 + off2 + off3;
+                    Op(inPtr[d_idx], idx++);
                 }
             }
         }
diff --git a/src/backend/cuda/kernel/ireduce.hpp b/src/backend/cuda/kernel/ireduce.hpp
index c394c01f83..992d0871c4 100644
--- a/src/backend/cuda/kernel/ireduce.hpp
+++ b/src/backend/cuda/kernel/ireduce.hpp
@@ -165,14 +165,14 @@ T ireduce_all(uint *idx, CParam<T> in) {
     using std::unique_ptr;
     int in_elements = in.dims[0] * in.dims[1] * in.dims[2] * in.dims[3];
 
-    // FIXME: Use better heuristics to get to the optimum number
-    if (in_elements > 4096) {
-        bool is_linear = (in.strides[0] == 1);
-        for (int k = 1; k < 4; k++) {
-            is_linear &=
-                (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
-        }
+    bool is_linear = (in.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &=
+            (in.strides[k] == (in.strides[k - 1] * in.dims[k - 1]));
+    }
 
+    // FIXME: Use better heuristics to get to the optimum number
+    if (!is_linear || in_elements > 4096) {
         if (is_linear) {
             in.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
diff --git a/src/backend/opencl/kernel/ireduce.hpp b/src/backend/opencl/kernel/ireduce.hpp
index 1bbcf08d2b..d056fb8fea 100644
--- a/src/backend/opencl/kernel/ireduce.hpp
+++ b/src/backend/opencl/kernel/ireduce.hpp
@@ -251,13 +251,14 @@ T ireduceAll(uint *loc, Param in) {
     int in_elements =
         in.info.dims[0] * in.info.dims[1] * in.info.dims[2] * in.info.dims[3];
 
+    bool is_linear = (in.info.strides[0] == 1);
+    for (int k = 1; k < 4; k++) {
+        is_linear &= (in.info.strides[k] ==
+                      (in.info.strides[k - 1] * in.info.dims[k - 1]));
+    }
+
     // FIXME: Use better heuristics to get to the optimum number
-    if (in_elements > 4096) {
-        bool is_linear = (in.info.strides[0] == 1);
-        for (int k = 1; k < 4; k++) {
-            is_linear &= (in.info.strides[k] ==
-                          (in.info.strides[k - 1] * in.info.dims[k - 1]));
-        }
+    if (!is_linear || in_elements > 4096) {
         if (is_linear) {
             in.info.dims[0] = in_elements;
             for (int k = 1; k < 4; k++) {
diff --git a/test/ireduce.cpp b/test/ireduce.cpp
index e93a8267b4..b155512e32 100644
--- a/test/ireduce.cpp
+++ b/test/ireduce.cpp
@@ -420,3 +420,136 @@ TEST(IndexedReduce, MaxCplxPreferSmallerIdxIfEqual) {
 
     ASSERT_EQ(h_max_idx[0], gold_max_idx);
 }
+
+#define SUBA_TEST_DATA                                              \
+    float test_data[25] = {0.0168, 0.0278, 0.0317, 0.0248, 0.0131,  \
+                           0.0197, 0.0321, 0.0362, 0.0279, 0.0141,  \
+                           0.0218, 0.0353, 0.0394, 0.0297, 0.0143,  \
+                           0.0224, 0.0363, 0.0104, 0.0302, 0.0142,  \
+                           0.0217, 0.0409, 0.0398, 0.0302, 0.0144}; \
+    array a(5, 5, test_data);                                       \
+    array a_sub = a(seq(1, 3), seq(2,4))
+
+TEST(IndexedReduce, max_subarray_all) {
+    SUBA_TEST_DATA;
+
+    float gold_max_val = 0.0409;
+    unsigned gold_max_idx   = 6;
+
+    float max_val;
+    unsigned max_idx;
+    max<float>(&max_val, &max_idx, a_sub);
+
+    ASSERT_FLOAT_EQ(max_val, gold_max_val);
+    ASSERT_EQ(max_idx, gold_max_idx);
+}
+
+TEST(IndexedReduce, min_subarray_all) {
+    SUBA_TEST_DATA;
+
+    float gold_min_val = 0.0104;
+    unsigned gold_min_idx   = 4;
+
+    float min_val;
+    unsigned min_idx;
+    min<float>(&min_val, &min_idx, a_sub);
+
+    ASSERT_FLOAT_EQ(min_val, gold_min_val);
+    ASSERT_EQ(min_idx, gold_min_idx);
+}
+
+TEST(IndexedReduce, max_subarray_0) {
+    SUBA_TEST_DATA;
+
+    float gold_val[3] = {0.0394, 0.0363, 0.0409};
+    unsigned gold_idx[3] = {1, 0, 0};
+
+    array val;
+    array idx;
+    float h_val[3];
+    unsigned h_idx[3];
+
+    max(val, idx, a_sub);
+    val.host(&h_val);
+    idx.host(&h_idx);
+
+    for(int i = 0; i < 3; ++i) {
+        ASSERT_FLOAT_EQ(h_val[i], gold_val[i]);
+        ASSERT_EQ(h_idx[i], gold_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, min_subarray_0) {
+    SUBA_TEST_DATA;
+
+    float gold_val[3] = {0.0297, 0.0104, 0.0302};
+    unsigned gold_idx[3] = {2, 1, 2};
+
+    array val;
+    array idx;
+    float h_val[3];
+    unsigned h_idx[3];
+
+    min(val, idx, a_sub);
+    val.host(&h_val);
+    idx.host(&h_idx);
+
+    for(int i = 0; i < 3; ++i) {
+        ASSERT_FLOAT_EQ(h_val[i], gold_val[i]);
+        ASSERT_EQ(h_idx[i], gold_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, max_subarray_1) {
+    SUBA_TEST_DATA;
+
+    float gold_val[3] = {0.0409, 0.0398, 0.0302};
+    unsigned gold_idx[3] = {2, 2, 1};
+
+    array val;
+    array idx;
+    float h_val[3];
+    unsigned h_idx[3];
+
+    max(val, idx, a_sub, 1);
+    val.host(&h_val);
+    idx.host(&h_idx);
+
+    for(int i = 0; i < 3; ++i) {
+        ASSERT_FLOAT_EQ(h_val[i], gold_val[i]);
+        ASSERT_EQ(h_idx[i], gold_idx[i]);
+    }
+}
+
+TEST(IndexedReduce, min_subarray_1) {
+    SUBA_TEST_DATA;
+
+    float gold_val[3] = {0.0353, 0.0104, 0.0297};
+    unsigned gold_idx[3] = {0, 1, 0};
+
+    array val;
+    array idx;
+    float h_val[3];
+    unsigned h_idx[3];
+
+    min(val, idx, a_sub, 1);
+    val.host(&h_val);
+    idx.host(&h_idx);
+
+    for(int i = 0; i < 3; ++i) {
+        ASSERT_FLOAT_EQ(h_val[i], gold_val[i]);
+        ASSERT_EQ(h_idx[i], gold_idx[i]);
+    }
+}
+
+//Ensure that array is evaluated before reducing
+TEST(IndexedReduce, reduce_jit_array) {
+    af::array jit(af::dim4(2),{1.0f, 2.0f});
+    jit += af::constant(1.0f, af::dim4(2));
+    float val; unsigned idx;
+    float gold_val = 2.0f;
+    unsigned gold_idx = 0;
+    af::min(&val, &idx, jit);
+    ASSERT_EQ(val, gold_val);
+    ASSERT_EQ(idx, gold_idx);
+}

From 700db10ccd5074d6ee4f32a5314fec6005e6d01f Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 25 Jun 2025 19:19:21 +0200
Subject: [PATCH 2662/2677] Join does not always respect the order of provided
 parameters (oneapi) (#3511)(#3513) (#3667)

* Adds test helpers for temporary array formats (JIT, SUB, ...)

* Join does not always respect the order of provided parameters (oneapi) (#3511)(#3513)
---
 src/backend/oneapi/jit.cpp | 115 ++++++++++++++----------
 test/arrayfire_test.cpp    | 178 ++++++++++++++++++++++++++++++++++---
 test/join.cpp              |  25 +++++-
 test/testHelpers.hpp       |  32 ++++++-
 4 files changed, 283 insertions(+), 67 deletions(-)

diff --git a/src/backend/oneapi/jit.cpp b/src/backend/oneapi/jit.cpp
index 2bd34a5dc4..bda9e43ccf 100644
--- a/src/backend/oneapi/jit.cpp
+++ b/src/backend/oneapi/jit.cpp
@@ -218,61 +218,75 @@ __kernel void )JIT";
     thread_local stringstream outOffsetStream;
     thread_local stringstream inOffsetsStream;
     thread_local stringstream opsStream;
+    thread_local stringstream kerStream;
 
-    int oid{0};
-    for (size_t i{0}; i < full_nodes.size(); i++) {
-        const auto& node{full_nodes[i]};
-        const auto& ids_curr{full_ids[i]};
-        // Generate input parameters, only needs current id
-        node->genParams(inParamStream, ids_curr.id, is_linear);
-        // Generate input offsets, only needs current id
-        node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
-        // Generate the core function body, needs children ids as well
-        node->genFuncs(opsStream, ids_curr);
-        for (auto outIt{begin(output_ids)}, endIt{end(output_ids)};
-             (outIt = find(outIt, endIt, ids_curr.id)) != endIt; ++outIt) {
-            // Generate also output parameters
-            outParamStream << "__global "
-                           << full_nodes[ids_curr.id]->getTypeStr() << " *out"
-                           << oid << ", int offset" << oid << ",\n";
-            // Apply output offset
-            outOffsetStream << "\nout" << oid << " += offset" << oid << ';';
-            // Generate code to write the output
-            opsStream << "out" << oid << "[idx] = val" << ids_curr.id << ";\n";
-            ++oid;
+    string ret;
+    try {
+        int oid{0};
+        for (size_t i{0}; i < full_nodes.size(); i++) {
+            const auto& node{full_nodes[i]};
+            const auto& ids_curr{full_ids[i]};
+            // Generate input parameters, only needs current id
+            node->genParams(inParamStream, ids_curr.id, is_linear);
+            // Generate input offsets, only needs current id
+            node->genOffsets(inOffsetsStream, ids_curr.id, is_linear);
+            // Generate the core function body, needs children ids as well
+            node->genFuncs(opsStream, ids_curr);
+            for (size_t output_idx{0}; output_idx < output_ids.size();
+                 ++output_idx) {
+                if (output_ids[output_idx] == ids_curr.id) {
+                    outParamStream
+                        << "__global " << full_nodes[ids_curr.id]->getTypeStr()
+                        << " *out" << oid << ", int offset" << oid << ",\n";
+                    // Apply output offset
+                    outOffsetStream << "\nout" << oid << " += offset" << oid
+                                    << ';';
+                    // Generate code to write the output
+                    opsStream << "out" << output_idx << "[idx] = val"
+                              << ids_curr.id << ";\n";
+                    ++oid;
+                }
+            }
         }
-    }
 
-    thread_local stringstream kerStream;
-    kerStream << DEFAULT_MACROS_STR << kernelVoid << funcName << "(\n"
-              << inParamStream.str() << outParamStream.str() << dimParams << ")"
-              << blockStart;
-    if (is_linear) {
-        kerStream << linearInit << inOffsetsStream.str()
-                  << outOffsetStream.str() << '\n';
-        if (loop0) kerStream << linearLoop0Start;
-        kerStream << "\n\n" << opsStream.str();
-        if (loop0) kerStream << linearLoop0End;
-        kerStream << linearEnd;
-    } else {
-        if (loop0) {
-            kerStream << stridedLoop0Init << outOffsetStream.str() << '\n'
-                      << stridedLoop0Start;
+        kerStream << DEFAULT_MACROS_STR << kernelVoid << funcName << "(\n"
+                  << inParamStream.str() << outParamStream.str() << dimParams
+                  << ")" << blockStart;
+        if (is_linear) {
+            kerStream << linearInit << inOffsetsStream.str()
+                      << outOffsetStream.str() << '\n';
+            if (loop0) kerStream << linearLoop0Start;
+            kerStream << "\n\n" << opsStream.str();
+            if (loop0) kerStream << linearLoop0End;
+            kerStream << linearEnd;
         } else {
-            kerStream << stridedLoopNInit << outOffsetStream.str() << '\n';
-            if (loop3) kerStream << stridedLoop3Init;
-            if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
-            if (loop3) kerStream << stridedLoop3Start;
+            if (loop0) {
+                kerStream << stridedLoop0Init << outOffsetStream.str() << '\n'
+                          << stridedLoop0Start;
+            } else {
+                kerStream << stridedLoopNInit << outOffsetStream.str() << '\n';
+                if (loop3) kerStream << stridedLoop3Init;
+                if (loop1) kerStream << stridedLoop1Init << stridedLoop1Start;
+                if (loop3) kerStream << stridedLoop3Start;
+            }
+            kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
+            if (loop3) kerStream << stridedLoop3End;
+            if (loop1) kerStream << stridedLoop1End;
+            if (loop0) kerStream << stridedLoop0End;
+            kerStream << stridedEnd;
         }
-        kerStream << "\n\n" << inOffsetsStream.str() << opsStream.str();
-        if (loop3) kerStream << stridedLoop3End;
-        if (loop1) kerStream << stridedLoop1End;
-        if (loop0) kerStream << stridedLoop0End;
-        kerStream << stridedEnd;
+        kerStream << blockEnd;
+        ret = kerStream.str();
+    } catch (...) {
+        // Prepare for next round, limit memory
+        inParamStream.str("");
+        outParamStream.str("");
+        inOffsetsStream.str("");
+        outOffsetStream.str("");
+        opsStream.str("");
+        kerStream.str("");
+        throw;
     }
-    kerStream << blockEnd;
-    const string ret{kerStream.str()};
-
     // Prepare for next round, limit memory
     inParamStream.str("");
     outParamStream.str("");
@@ -381,9 +395,11 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
 
     bool is_linear{true};
     dim_t numOutElems{1};
+    assert(outputs.size() == output_nodes.size());
     KParam& out_info{outputs[0].info};
     dim_t* outDims{out_info.dims};
     dim_t* outStrides{out_info.strides};
+    // unsigned nrInputs{0};
 
     dim_t ndims{outDims[3] > 1   ? 4
                 : outDims[2] > 1 ? 3
@@ -409,6 +425,7 @@ void evalNodes(vector<Param<T>>& outputs, const vector<Node*>& output_nodes) {
     for (const Node* node : full_nodes) {
         is_linear &= node->isLinear(outDims);
         moddimsFound |= (node->getOp() == af_moddims_t);
+        // if (node->isBuffer()) { ++nrInputs; }
     }
 
     bool emptyColumnsFound{false};
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index dedaedbf75..6803cc586d 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -105,17 +105,16 @@ std::string readNextNonEmptyLine(std::ifstream &file) {
 
 std::string getBackendName(bool lower) {
     af::Backend backend = af::getActiveBackend();
-    switch(backend) {
-    case AF_BACKEND_CPU:
-        return lower ? std::string("cpu") : std::string("CPU");
-    case AF_BACKEND_CUDA:
-        return lower ? std::string("cuda") : std::string("CUDA");
-    case AF_BACKEND_OPENCL:
-        return lower ? std::string("opencl") : std::string("OpenCL");
-    case AF_BACKEND_ONEAPI:
-        return lower ? std::string("oneapi") : std::string("oneAPI");
-    default:
-        return lower ? std::string("unknown") : std::string("Unknown");
+    switch (backend) {
+        case AF_BACKEND_CPU:
+            return lower ? std::string("cpu") : std::string("CPU");
+        case AF_BACKEND_CUDA:
+            return lower ? std::string("cuda") : std::string("CUDA");
+        case AF_BACKEND_OPENCL:
+            return lower ? std::string("opencl") : std::string("OpenCL");
+        case AF_BACKEND_ONEAPI:
+            return lower ? std::string("oneapi") : std::string("oneAPI");
+        default: return lower ? std::string("unknown") : std::string("Unknown");
     }
 }
 
@@ -2046,6 +2045,163 @@ INSTANTIATE(std::complex<float>);
 INSTANTIATE(std::complex<double>);
 #undef INSTANTIATE
 
+af::array toTempFormat(tempFormat form, const af::array &in) {
+    af::array ret;
+    const af::dim4 &dims = in.dims();
+    switch (form) {
+        case JIT_FORMAT:
+            switch (in.type()) {
+                case b8: ret = not(in); break;
+                default: ret = in * 2;
+            }
+            // Make sure that the base array is <> form original
+            ret.eval();
+            switch (in.type()) {
+                case b8: ret = not(ret); break;
+                default: ret /= 2;
+            }
+            break;
+        case SUB_FORMAT_dim0: {
+            af::dim4 pdims(dims);
+            pdims[0] += 2;
+            af::array parent = af::randu(pdims, in.type());
+            parent(af::seq(1, dims[0]), af::span, af::span, af::span) = in;
+            ret = parent(af::seq(1, dims[0]), af::span, af::span, af::span);
+        }; break;
+        case SUB_FORMAT_dim1: {
+            af::dim4 pdims(dims);
+            pdims[1] += 2;
+            af::array parent = af::randu(pdims, in.type());
+            parent(af::span, af::seq(1, dims[1]), af::span, af::span) = in;
+            ret = parent(af::span, af::seq(1, dims[1]), af::span, af::span);
+        }; break;
+        case SUB_FORMAT_dim2: {
+            af::dim4 pdims(dims);
+            pdims[2] += 2;
+            af::array parent = af::randu(pdims, in.type());
+            parent(af::span, af::span, af::seq(1, dims[2]), af::span) = in;
+            ret = parent(af::span, af::span, af::seq(1, dims[2]), af::span);
+        }; break;
+        case SUB_FORMAT_dim3: {
+            af::dim4 pdims(dims);
+            pdims[3] += 2;
+            af::array parent = af::randu(pdims, in.type());
+            parent(af::span, af::span, af::span, af::seq(1, dims[3])) = in;
+            ret = parent(af::span, af::span, af::span, af::seq(1, dims[3]));
+        }; break;
+        case REORDERED_FORMAT: {
+            const dim_t idxs[4] = {0, 3, 1, 2};
+            // idxs[0] has to be 0, to keep the same data in mem
+            dim_t rev_idxs[4];
+            for (dim_t i = 0; i < 4; ++i) { rev_idxs[idxs[i]] = i; };
+            ret = af::reorder(in, idxs[0], idxs[1], idxs[2], idxs[3]);
+            ret = ret.copy();  // make data linear
+            ret = af::reorder(ret, rev_idxs[0], rev_idxs[1], rev_idxs[2],
+                              rev_idxs[3]);
+            // ret has same content as in, although data is stored in
+            // different order
+        }; break;
+        case LINEAR_FORMAT:
+        default: ret = in.copy();
+    };
+    return ret;
+}
+
+void toTempFormat(tempFormat form, af_array *out, const af_array &in) {
+    dim_t dims[4];
+    af_get_dims(dims, dims + 1, dims + 2, dims + 3, in);
+    unsigned numdims;
+    af_get_numdims(&numdims, in);
+    af_dtype ty;
+    af_get_type(&ty, in);
+    switch (form) {
+        case JIT_FORMAT: {
+            // af_array one = nullptr, min_one = nullptr, res = nullptr;
+            af_array res = nullptr, two = nullptr;
+            ASSERT_SUCCESS(af_constant(&two, 2, numdims, dims, ty));
+            switch (ty) {
+                case b8: af_not(&res, in); break;
+                default:
+                    // ret = in + af::constant(1, dims, in.type());
+                    ASSERT_SUCCESS(af_mul(&res, in, two, false));
+            }
+            // Make sure that the base array is <> form original
+            ASSERT_SUCCESS(af_eval(res));
+            switch (ty) {
+                case b8: af_not(out, res); break;
+                default:
+                    ASSERT_SUCCESS(af_div(out, res, two, false));  // NO EVAL!!
+            }
+            ASSERT_SUCCESS(af_release_array(two));
+            two = nullptr;
+            ASSERT_SUCCESS(af_release_array(res));
+            res = nullptr;
+        }; break;
+        case SUB_FORMAT_dim0: {
+            const dim_t pdims[4] = {dims[0] + 2, dims[1], dims[2], dims[3]};
+            af_array parent      = nullptr;
+            ASSERT_SUCCESS(af_randu(&parent, std::max(1u, numdims), pdims, ty));
+            const af_seq idxs[4] = {af_make_seq(1, dims[0], 1), af_span,
+                                    af_span, af_span};
+
+            ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
+            ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
+            ASSERT_SUCCESS(af_release_array(parent));
+        }; break;
+        case SUB_FORMAT_dim1: {
+            const dim_t pdims[4] = {dims[0], dims[1] + 2, dims[2], dims[3]};
+            af_array parent      = nullptr;
+            ASSERT_SUCCESS(af_randu(&parent, std::max(2u, numdims), pdims, ty));
+            const af_seq idxs[4] = {af_span, af_make_seq(1, dims[1], 1),
+                                    af_span, af_span};
+            ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
+            ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
+            ASSERT_SUCCESS(af_release_array(parent));
+            parent = nullptr;
+        }; break;
+        case SUB_FORMAT_dim2: {
+            const dim_t pdims[4] = {dims[0], dims[1], dims[2] + 2, dims[3]};
+            af_array parent      = nullptr;
+            ASSERT_SUCCESS(af_randu(&parent, std::max(3u, numdims), pdims, ty));
+            const af_seq idxs[4] = {af_span, af_span,
+                                    af_make_seq(1, dims[2], 1), af_span};
+            ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
+            ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
+            ASSERT_SUCCESS(af_release_array(parent));
+            parent = nullptr;
+        }; break;
+        case SUB_FORMAT_dim3: {
+            const dim_t pdims[4] = {dims[0], dims[1], dims[2], dims[3] + 2};
+            af_array parent      = nullptr;
+            ASSERT_SUCCESS(af_randu(&parent, std::max(4u, numdims), pdims, ty));
+            const af_seq idxs[4] = {af_span, af_span, af_span,
+                                    af_make_seq(1, dims[3], 1)};
+            ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
+            ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
+            ASSERT_SUCCESS(af_release_array(parent));
+            parent = nullptr;
+        }; break;
+        case REORDERED_FORMAT: {
+            const unsigned idxs[4] = {0, 3, 1, 2};
+            // idxs[0] has to be 0, to keep the same data in mem
+            dim_t rev_idxs[4];
+            for (dim_t i = 0; i < 4; ++i) { rev_idxs[idxs[i]] = i; };
+            af_array rev = nullptr;
+            ASSERT_SUCCESS(
+                af_reorder(&rev, in, idxs[0], idxs[1], idxs[2], idxs[3]));
+            ASSERT_SUCCESS(af_copy_array(out, rev));
+            ASSERT_SUCCESS(af_reorder(out, rev, rev_idxs[0], rev_idxs[1],
+                                      rev_idxs[2], rev_idxs[3]));
+            // ret has same content as in, although data is stored in
+            // different order
+            ASSERT_SUCCESS(af_release_array(rev));
+            rev = nullptr;
+        }; break;
+        case LINEAR_FORMAT:
+        default: af_copy_array(out, in);
+    };
+}
+
 int main(int argc, char **argv) {
     ::testing::InitGoogleTest(&argc, argv);
     return RUN_ALL_TESTS();
diff --git a/test/join.cpp b/test/join.cpp
index aef578bcf2..5cd470780f 100644
--- a/test/join.cpp
+++ b/test/join.cpp
@@ -280,9 +280,9 @@ TEST(Join, respect_parameters_order_ISSUE3511) {
     const af::array jit2{buf2 + 2.0};
     const std::array<af::array, 8> cases{jit1,  -jit1,       jit1 + 1.0, jit2,
                                          -jit2, jit1 + jit2, buf1,       buf2};
-    const std::array<char*, 8> cases_name{"JIT1", "-JIT1", "JIT1+1.0",
-                                          "JIT2", "-JIT2", "JIT1+JIT2",
-                                          "BUF1", "BUF2"};
+    const std::array<const char*, 8> cases_name{"JIT1", "-JIT1", "JIT1+1.0",
+                                                "JIT2", "-JIT2", "JIT1+JIT2",
+                                                "BUF1", "BUF2"};
     assert(cases.size() == cases_name.size());
     for (size_t cl0{0}; cl0 < cases.size(); ++cl0) {
         for (size_t cl1{0}; cl1 < cases.size(); ++cl1) {
@@ -312,3 +312,22 @@ TEST(Join, respect_parameters_order_ISSUE3511) {
         }
     }
 }
+
+#define TEST_TEMP_FORMAT(form, d)                                           \
+    TEST(TEMP_FORMAT, form##_dim##d) {                                      \
+        const dim4 dims(2, 2, 2, 2);                                        \
+        const array a(randu(dims));                                         \
+        const array b(randu(dims));                                         \
+                                                                            \
+        array out  = join(d, toTempFormat(form, a), toTempFormat(form, b)); \
+        array gold = join(d, a, b);                                         \
+        EXPECT_ARRAYS_EQ(gold, out);                                        \
+    }
+
+#define TEST_TEMP_FORMATS(form) \
+    TEST_TEMP_FORMAT(form, 0)   \
+    TEST_TEMP_FORMAT(form, 1)   \
+    TEST_TEMP_FORMAT(form, 2)   \
+    TEST_TEMP_FORMAT(form, 3)
+
+FOREACH_TEMP_FORMAT(TEST_TEMP_FORMATS)
diff --git a/test/testHelpers.hpp b/test/testHelpers.hpp
index 5f6b02b5a4..405f23309d 100644
--- a/test/testHelpers.hpp
+++ b/test/testHelpers.hpp
@@ -244,10 +244,10 @@ bool noHalfTests(af::dtype ty);
     GTEST_SKIP() << "Device doesn't support Half"
 
 #ifdef SKIP_UNSUPPORTED_TESTS
-#define UNSUPPORTED_BACKEND(backend)                        \
-    if(backend == af::getActiveBackend())                   \
-        GTEST_SKIP() << "Skipping unsupported function on " \
-                        + getBackendName() + " backend"
+#define UNSUPPORTED_BACKEND(backend)                                         \
+    if (backend == af::getActiveBackend())                                   \
+    GTEST_SKIP() << "Skipping unsupported function on " + getBackendName() + \
+                        " backend"
 #else
 #define UNSUPPORTED_BACKEND(backend)
 #endif
@@ -653,6 +653,30 @@ ::testing::AssertionResult assertArrayEq(std::string aName, std::string bName,
                                          const af_array a, const af_array b,
                                          TestOutputArrayInfo *metadata);
 
+enum tempFormat {
+    LINEAR_FORMAT,    // Linear array (= default)
+    JIT_FORMAT,       // Array which has JIT operations outstanding
+    SUB_FORMAT_dim0,  // Array where only a subset is allocated for dim0
+    SUB_FORMAT_dim1,  // Array where only a subset is allocated for dim1
+    SUB_FORMAT_dim2,  // Array where only a subset is allocated for dim2
+    SUB_FORMAT_dim3,  // Array where only a subset is allocated for dim3
+    REORDERED_FORMAT  // Array where the dimensions are reordered
+};
+// Calls the function fn for all available formats
+#define FOREACH_TEMP_FORMAT(TESTS) \
+    TESTS(LINEAR_FORMAT)           \
+    TESTS(JIT_FORMAT)              \
+    TESTS(SUB_FORMAT_dim0)         \
+    TESTS(SUB_FORMAT_dim1)         \
+    TESTS(SUB_FORMAT_dim2)         \
+    TESTS(SUB_FORMAT_dim3)         \
+    TESTS(REORDERED_FORMAT)
+
+// formats the "in" array according to provided format.  The content remains
+// unchanged.
+af::array toTempFormat(tempFormat form, const af::array &in);
+void toTempFormat(tempFormat form, af_array *out, const af_array &in);
+
 #ifdef __GNUC__
 #pragma GCC diagnostic pop
 #endif

From 6034d5fc0e2212914caae2a2c692386f8571cf2f Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Wed, 25 Jun 2025 21:56:30 +0200
Subject: [PATCH 2663/2677] Fixes sub-array support for scan (oneapi) (#3663)

* Adds test helpers for temporary array formats (JIT, SUB, ...)

* Fixes sub-array support for scan (oneapi)
---
 src/backend/oneapi/kernel/scan_dim.hpp   |  2 +-
 src/backend/oneapi/kernel/scan_first.hpp |  2 +-
 test/scan.cpp                            | 19 +++++++++++++++++++
 3 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/src/backend/oneapi/kernel/scan_dim.hpp b/src/backend/oneapi/kernel/scan_dim.hpp
index eea34ffff7..52450f5c98 100644
--- a/src/backend/oneapi/kernel/scan_dim.hpp
+++ b/src/backend/oneapi/kernel/scan_dim.hpp
@@ -82,7 +82,7 @@ class scanDimKernel {
         optr += ids[3] * oInfo_.strides[3] + ids[2] * oInfo_.strides[2] +
                 ids[1] * oInfo_.strides[1] + ids[0];
         iptr += ids[3] * iInfo_.strides[3] + ids[2] * iInfo_.strides[2] +
-                ids[1] * iInfo_.strides[1] + ids[0];
+                ids[1] * iInfo_.strides[1] + ids[0] + iInfo_.offset;
         int id_dim        = ids[dim];
         const int out_dim = oInfo_.dims[dim];
 
diff --git a/src/backend/oneapi/kernel/scan_first.hpp b/src/backend/oneapi/kernel/scan_first.hpp
index dd483f069b..4aa7fc502e 100644
--- a/src/backend/oneapi/kernel/scan_first.hpp
+++ b/src/backend/oneapi/kernel/scan_first.hpp
@@ -71,7 +71,7 @@ class scanFirstKernel {
         To *tptr       = tmp_acc_.get_pointer();
 
         iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
-                yid * iInfo_.strides[1];
+                yid * iInfo_.strides[1] + iInfo_.offset;
         optr += wid * oInfo_.strides[3] + zid * oInfo_.strides[2] +
                 yid * oInfo_.strides[1];
         tptr += wid * tInfo_.strides[3] + zid * tInfo_.strides[2] +
diff --git a/test/scan.cpp b/test/scan.cpp
index 8bfbe0dd20..afb488278d 100644
--- a/test/scan.cpp
+++ b/test/scan.cpp
@@ -346,3 +346,22 @@ TEST(Scan, ExclusiveSum2D_Dim3) {
 
     ASSERT_ARRAYS_EQ(gold, out);
 }
+
+#define TEST_TEMP_FORMAT(form, dim)                                      \
+    TEST(TEMP_FORMAT, form##_Dim##dim) {                                 \
+        const dim4 dims(2, 2, 2, 2);                                     \
+        const array in(af::moddims(range(dim4(dims.elements())), dims)); \
+        in.eval();                                                       \
+        const array gold = scan(in, dim);                                \
+                                                                         \
+        array out = scan(toTempFormat(form, in), dim);                   \
+        ASSERT_ARRAYS_EQ(gold, out);                                     \
+    }
+
+#define TEST_TEMP_FORMATS(form) \
+    TEST_TEMP_FORMAT(form, 0)   \
+    TEST_TEMP_FORMAT(form, 1)   \
+    TEST_TEMP_FORMAT(form, 2)   \
+    TEST_TEMP_FORMAT(form, 3)
+
+FOREACH_TEMP_FORMAT(TEST_TEMP_FORMATS)
\ No newline at end of file

From 0e49da28d3948ff763bab004bac9ad270ffb4fd6 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Thu, 26 Jun 2025 00:34:30 +0200
Subject: [PATCH 2664/2677] Fixes sub-array (oneapi) support for where (#3666)

* Adds test helpers for temporary array formats (JIT, SUB, ...)

* Fixes sub-array (oneapi) support for where
---
 src/backend/oneapi/kernel/where.hpp |  2 +-
 test/where.cpp                      | 19 +++++++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/src/backend/oneapi/kernel/where.hpp b/src/backend/oneapi/kernel/where.hpp
index b65e0d9333..69f2f7719a 100644
--- a/src/backend/oneapi/kernel/where.hpp
+++ b/src/backend/oneapi/kernel/where.hpp
@@ -73,7 +73,7 @@ class whereKernel {
         otptr += wid * otInfo_.strides[3] + zid * otInfo_.strides[2] +
                  yid * otInfo_.strides[1];
         iptr += wid * iInfo_.strides[3] + zid * iInfo_.strides[2] +
-                yid * iInfo_.strides[1];
+                yid * iInfo_.strides[1] + iInfo_.offset;
 
         size_t odims0 = otInfo_.dims[0];
         size_t odims1 = otInfo_.dims[1];
diff --git a/test/where.cpp b/test/where.cpp
index 265c0d4d7b..a6c8dcde46 100644
--- a/test/where.cpp
+++ b/test/where.cpp
@@ -136,3 +136,22 @@ TEST(Where, ISSUE_1259) {
     array indices = where(a > 2);
     ASSERT_EQ(indices.elements(), 0);
 }
+
+#define TEST_TEMP_FORMAT(form, dim)                                      \
+    TEST(TEMP_FORMAT, form##_Dim##dim) {                                 \
+        const dim4 dims(2, 3, 4, 5);                                     \
+        const array in(af::moddims(range(dim4(dims.elements())), dims)); \
+        in.eval();                                                       \
+        const array gold = where(in > 3.0);                              \
+                                                                         \
+        array out = where(toTempFormat(form, in) > 3.0);                 \
+        ASSERT_ARRAYS_EQ(gold, out);                                     \
+    }
+
+#define TEST_TEMP_FORMATS(form) \
+    TEST_TEMP_FORMAT(form, 0)   \
+    TEST_TEMP_FORMAT(form, 1)   \
+    TEST_TEMP_FORMAT(form, 2)   \
+    TEST_TEMP_FORMAT(form, 3)
+
+FOREACH_TEMP_FORMAT(TEST_TEMP_FORMATS)
\ No newline at end of file

From 7185202b800afae802026731e9a68112224bdbb0 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Thu, 26 Jun 2025 01:33:17 +0200
Subject: [PATCH 2665/2677] Fixes sub-array support for scan by key (opencl)
 (#3664)

* Adds test helpers for temporary array formats (JIT, SUB, ...)

* Fixes sub-array support for scanByKey (opencl)
---
 src/backend/opencl/kernel/scan_dim_by_key.cl  | 12 ++++------
 .../opencl/kernel/scan_first_by_key.cl        | 14 +++++------
 test/scan_by_key.cpp                          | 23 +++++++++++++++++++
 3 files changed, 35 insertions(+), 14 deletions(-)

diff --git a/src/backend/opencl/kernel/scan_dim_by_key.cl b/src/backend/opencl/kernel/scan_dim_by_key.cl
index 5446b28e29..eacd7f9283 100644
--- a/src/backend/opencl/kernel/scan_dim_by_key.cl
+++ b/src/backend/opencl/kernel/scan_dim_by_key.cl
@@ -34,7 +34,7 @@ kernel void scanDimByKeyNonfinal(
     // Hence increment ids[kDim] just after offseting out and before offsetting
     // in
     tData += ids[3] * tInfo.strides[3] + ids[2] * tInfo.strides[2] +
-             ids[1] * tInfo.strides[1] + ids[0];
+             ids[1] * tInfo.strides[1] + ids[0] ;
     tfData += ids[3] * tfInfo.strides[3] + ids[2] * tfInfo.strides[2] +
               ids[1] * tfInfo.strides[1] + ids[0];
     tiData += ids[3] * tiInfo.strides[3] + ids[2] * tiInfo.strides[2] +
@@ -45,10 +45,9 @@ kernel void scanDimByKeyNonfinal(
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0];
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-             ids[1] * iInfo.strides[1] + ids[0];
+             ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
     kData += ids[3] * kInfo.strides[3] + ids[2] * kInfo.strides[2] +
-             ids[1] * kInfo.strides[1] + ids[0];
-    iData += iInfo.offset;
+             ids[1] * kInfo.strides[1] + ids[0] + kInfo.offset;
 
     int id_dim        = ids[kDim];
     const int out_dim = oInfo.dims[kDim];
@@ -192,10 +191,9 @@ kernel void scanDimByKeyFinal(global To *oData, KParam oInfo,
     oData += ids[3] * oInfo.strides[3] + ids[2] * oInfo.strides[2] +
              ids[1] * oInfo.strides[1] + ids[0];
     iData += ids[3] * iInfo.strides[3] + ids[2] * iInfo.strides[2] +
-             ids[1] * iInfo.strides[1] + ids[0];
+             ids[1] * iInfo.strides[1] + ids[0] + iInfo.offset;
     kData += ids[3] * kInfo.strides[3] + ids[2] * kInfo.strides[2] +
-             ids[1] * kInfo.strides[1] + ids[0];
-    iData += iInfo.offset;
+             ids[1] * kInfo.strides[1] + ids[0] + kInfo.offset;
 
     int id_dim        = ids[kDim];
     const int out_dim = oInfo.dims[kDim];
diff --git a/src/backend/opencl/kernel/scan_first_by_key.cl b/src/backend/opencl/kernel/scan_first_by_key.cl
index 54d572d965..1793f0b293 100644
--- a/src/backend/opencl/kernel/scan_first_by_key.cl
+++ b/src/backend/opencl/kernel/scan_first_by_key.cl
@@ -39,13 +39,13 @@ kernel void scanFirstByKeyNonfinal(global To *oData, KParam oInfo,
              yid * kInfo.strides[1] + kInfo.offset;
 
     tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
-             yid * tInfo.strides[1] + tInfo.offset;
+             yid * tInfo.strides[1];
 
     tfData += wid * tfInfo.strides[3] + zid * tfInfo.strides[2] +
-              yid * tfInfo.strides[1] + tfInfo.offset;
+              yid * tfInfo.strides[1];
 
     tiData += wid * tiInfo.strides[3] + zid * tiInfo.strides[2] +
-              yid * tiInfo.strides[1] + tiInfo.offset;
+              yid * tiInfo.strides[1];
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
              yid * oInfo.strides[1] + oInfo.offset;
@@ -179,7 +179,7 @@ kernel void scanFirstByKeyFinal(global To *oData, KParam oInfo,
              yid * kInfo.strides[1] + kInfo.offset;
 
     oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-             yid * oInfo.strides[1] + oInfo.offset;
+             yid * oInfo.strides[1];
 
     local To l_val0[SHARED_MEM_SIZE];
     local To l_val1[SHARED_MEM_SIZE];
@@ -283,13 +283,13 @@ kernel void bcastFirstByKey(global To *oData, KParam oInfo,
 
         if (cond) {
             tiData += wid * tiInfo.strides[3] + zid * tiInfo.strides[2] +
-                      yid * tiInfo.strides[1] + tiInfo.offset;
+                      yid * tiInfo.strides[1];
 
             tData += wid * tInfo.strides[3] + zid * tInfo.strides[2] +
-                     yid * tInfo.strides[1] + tInfo.offset;
+                     yid * tInfo.strides[1];
 
             oData += wid * oInfo.strides[3] + zid * oInfo.strides[2] +
-                     yid * oInfo.strides[1] + oInfo.offset;
+                     yid * oInfo.strides[1];
 
             int boundary = tiData[groupId_x];
             To accum     = tData[groupId_x - 1];
diff --git a/test/scan_by_key.cpp b/test/scan_by_key.cpp
index 0ea1dd8ecb..08928b5fdc 100644
--- a/test/scan_by_key.cpp
+++ b/test/scan_by_key.cpp
@@ -240,3 +240,26 @@ TEST(ScanByKey, FixOverflowWrite) {
 
     ASSERT_EQ(prior, valsAF(0).scalar<float>());
 }
+
+#define TEST_TEMP_FORMAT(form, dim)                                           \
+    TEST(TEMP_FORMAT, form##_Dim##dim) {                                      \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                               \
+        const dim4 dims(2, 2, 2, 2);                                          \
+        const array in(af::moddims(range(dim4(dims.elements())), dims));      \
+        in.eval();                                                            \
+        const array keys(af::constant(0, dims, u32));                         \
+        keys.eval();                                                          \
+        const array gold = scanByKey(keys, in, dim);                          \
+                                                                              \
+        array out =                                                           \
+            scanByKey(toTempFormat(form, keys), toTempFormat(form, in), dim); \
+        ASSERT_ARRAYS_EQ(gold, out);                                          \
+    }
+
+#define TEST_TEMP_FORMATS(form) \
+    TEST_TEMP_FORMAT(form, 0)   \
+    TEST_TEMP_FORMAT(form, 1)   \
+    TEST_TEMP_FORMAT(form, 2)   \
+    TEST_TEMP_FORMAT(form, 3)
+
+FOREACH_TEMP_FORMAT(TEST_TEMP_FORMATS)
\ No newline at end of file

From eaa49caced87c6eb21d612b1d546ab3061d30a73 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Fri, 27 Jun 2025 19:50:36 +0200
Subject: [PATCH 2666/2677] Fixes sub-array (opencl, oneapi) support for reduce
 by key (#3665)

* Adds test helpers for temporary array formats (JIT, SUB, ...)

* Fixes sub-array (opencl, oneapi) support  for reduce by key

* Update reduce.cpp

Revert line breaks. While these lines are quite long, introducing more breaks here makes it more difficult to read.

---------

Co-authored-by: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
---
 src/backend/oneapi/kernel/reduce_by_key.hpp   |  31 ++---
 src/backend/oneapi/kernel/reduce_dim.hpp      |   3 +-
 .../opencl/kernel/reduce_blocks_by_key_dim.cl |   4 +-
 .../kernel/reduce_blocks_by_key_first.cl      |   4 +-
 .../opencl/kernel/reduce_by_key_compact.cl    |   4 +-
 .../kernel/reduce_by_key_compact_dim.cl       |   4 +-
 .../kernel/reduce_by_key_needs_reduction.cl   |   4 +-
 test/reduce.cpp                               | 113 ++++++++++++++++--
 8 files changed, 128 insertions(+), 39 deletions(-)

diff --git a/src/backend/oneapi/kernel/reduce_by_key.hpp b/src/backend/oneapi/kernel/reduce_by_key.hpp
index 3b5058a6bf..329fd33109 100644
--- a/src/backend/oneapi/kernel/reduce_by_key.hpp
+++ b/src/backend/oneapi/kernel/reduce_by_key.hpp
@@ -51,8 +51,8 @@ class finalBoundaryReduceKernel {
         common::Binary<compute_t<To>, op> binOp;
         if (gid == ((bid + 1) * it.get_local_range(0)) - 1 &&
             bid < g.get_group_range(0) - 1) {
-            Tk k0 = iKeys_[gid];
-            Tk k1 = iKeys_[gid + 1];
+            Tk k0 = iKeys_[gid + iKInfo_.offset];
+            Tk k1 = iKeys_[gid + 1 + iKInfo_.offset];
 
             if (k0 == k1) {
                 compute_t<To> v0          = compute_t<To>(oVals_[gid]);
@@ -104,8 +104,8 @@ class finalBoundaryReduceDimKernel {
         common::Binary<compute_t<To>, op> binOp;
         if (gid == ((bid + 1) * it.get_local_range(0)) - 1 &&
             bid < g.get_group_range(0) - 1) {
-            Tk k0 = iKeys_[gid];
-            Tk k1 = iKeys_[gid + 1];
+            Tk k0 = iKeys_[gid + iKInfo_.offset];
+            Tk k1 = iKeys_[gid + 1 + iKInfo_.offset];
 
             if (k0 == k1) {
                 compute_t<To> v0          = compute_t<To>(oVals_[gid]);
@@ -163,7 +163,7 @@ class testNeedsReductionKernel {
         const uint bid = g.get_group_id(0);
 
         Tk k = scalar<Tk>(0);
-        if (gid < n_) { k = iKeys_[gid]; }
+        if (gid < n_) { k = iKeys_[gid + iKInfo_.offset]; }
 
         l_keys_[lid] = k;
         it.barrier();
@@ -181,8 +181,8 @@ class testNeedsReductionKernel {
         // reduction
         if (gid == ((bid + 1) * DIMX_) - 1 &&
             bid < (g.get_group_range(0) - 1)) {
-            int k0 = iKeys_[gid];
-            int k1 = iKeys_[gid + 1];
+            int k0 = iKeys_[gid + iKInfo_.offset];
+            int k1 = iKeys_[gid + 1 + iKInfo_.offset];
             if (k0 == k1) {
                 global_atomic_ref<int>(needs_block_boundary_reduced_[0]) |= 1;
             }
@@ -240,8 +240,8 @@ class compactKernel {
                 : (reduced_block_sizes_[bid] - reduced_block_sizes_[bid - 1]);
         int writeloc = (bid == 0) ? 0 : reduced_block_sizes_[bid - 1];
 
-        Tk k = iKeys_[gid];
-        To v = iVals_[bOffset + gid];
+        Tk k = iKeys_[gid + iKInfo_.offset];
+        To v = iVals_[bOffset + gid + iVInfo_.offset];
 
         if (lid < nwrite) {
             oKeys_[writeloc + lid]           = k;
@@ -316,8 +316,8 @@ class compactDimKernel {
                         bidz * iVInfo_.strides[dims_ordering[2]] +
                         bidy * iVInfo_.strides[dims_ordering[1]] +
                         gidx * iVInfo_.strides[DIM_];
-        k = iKeys_[gidx];
-        v = iVals_[tid];
+        k = iKeys_[gidx + iKInfo_.offset];
+        v = iVals_[tid + iVInfo_.offset];
 
         if (lid < nwrite) {
             oKeys_[writeloc + lid] = k;
@@ -403,11 +403,11 @@ class reduceBlocksByKeyKernel {
         Tk k            = scalar<Tk>(0);
         compute_t<To> v = init_val;
         if (gid < n_) {
-            k                 = iKeys_[gid];
+            k                 = iKeys_[gid + iKInfo_.offset];
             const int bOffset = bidw * iVInfo_.strides[3] +
                                 bidz * iVInfo_.strides[2] +
                                 bidy * iVInfo_.strides[1];
-            v = transform(iVals_[bOffset + gid]);
+            v = transform(iVals_[bOffset + gid + iVInfo_.offset]);
             if (change_nan_) v = IS_NAN(v) ? nanval_ : v;
         }
 
@@ -579,11 +579,12 @@ class reduceBlocksByKeyDimKernel {
         Tk k            = scalar<Tk>(0);
         compute_t<To> v = init_val;
         if (gid < n_) {
-            k                 = iKeys_[gid];
+            k                 = iKeys_[gid + iKInfo_.offset];
             const int bOffset = bidw * iVInfo_.strides[dims_ordering[3]] +
                                 bidz * iVInfo_.strides[dims_ordering[2]] +
                                 bidy * iVInfo_.strides[dims_ordering[1]];
-            v = transform(iVals_[bOffset + gid * iVInfo_.strides[DIM_]]);
+            v = transform(
+                iVals_[bOffset + gid * iVInfo_.strides[DIM_] + iVInfo_.offset]);
             if (change_nan_) v = IS_NAN(v) ? nanval_ : v;
         }
 
diff --git a/src/backend/oneapi/kernel/reduce_dim.hpp b/src/backend/oneapi/kernel/reduce_dim.hpp
index b1d3d81648..0cc7055f14 100644
--- a/src/backend/oneapi/kernel/reduce_dim.hpp
+++ b/src/backend/oneapi/kernel/reduce_dim.hpp
@@ -74,7 +74,8 @@ class reduceDimKernelSMEM {
 
         const data_t<Ti> *iptr =
             in_.get_pointer() + ids[3] * iInfo_.strides[3] +
-            ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] + ids[0];
+            ids[2] * iInfo_.strides[2] + ids[1] * iInfo_.strides[1] + ids[0] +
+            iInfo_.offset;
 
         const uint id_dim_in   = ids[dim];
         const uint istride_dim = iInfo_.strides[dim];
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
index 66bbb3e6d2..76941ebbd7 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_dim.cl
@@ -82,12 +82,12 @@ kernel void reduce_blocks_by_key_dim(global int *reduced_block_sizes,
     Tk k;
     To v;
     if (gidx < n) {
-        k             = iKeys[gidx];
+        k             = iKeys[gidx + iKInfo.offset];
         const int gid = bidw * iVInfo.strides[dims_ordering[3]] +
                         bidz * iVInfo.strides[dims_ordering[2]] +
                         bidy * iVInfo.strides[dims_ordering[1]] +
                         gidx * iVInfo.strides[DIM];
-        v = transform(iVals[gid]);
+        v = transform(iVals[gid + iVInfo.offset]);
         if (change_nan) v = IS_NAN(v) ? nanval : v;
     } else {
         v = init_val;
diff --git a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
index f184e94818..c01d3c250d 100644
--- a/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
+++ b/src/backend/opencl/kernel/reduce_blocks_by_key_first.cl
@@ -72,10 +72,10 @@ kernel void reduce_blocks_by_key_first(global int *reduced_block_sizes,
     Tk k;
     To v;
     if (gid < n) {
-        k                 = iKeys[gid];
+        k                 = iKeys[gid + iKInfo.offset];
         const int bOffset = bidw * iVInfo.strides[3] +
                             bidz * iVInfo.strides[2] + bidy * iVInfo.strides[1];
-        v = transform(iVals[bOffset + gid]);
+        v = transform(iVals[bOffset + gid + iVInfo.offset]);
         if (change_nan) v = IS_NAN(v) ? nanval : v;
     } else {
         v = init_val;
diff --git a/src/backend/opencl/kernel/reduce_by_key_compact.cl b/src/backend/opencl/kernel/reduce_by_key_compact.cl
index c8081e45e9..58b78cd894 100644
--- a/src/backend/opencl/kernel/reduce_by_key_compact.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_compact.cl
@@ -31,8 +31,8 @@ kernel void compact(global int *reduced_block_sizes, global Tk *oKeys,
                    : (reduced_block_sizes[bid] - reduced_block_sizes[bid - 1]);
     int writeloc = (bid == 0) ? 0 : reduced_block_sizes[bid - 1];
 
-    k = iKeys[gid];
-    v = iVals[bOffset + gid];
+    k = iKeys[gid + iKInfo.offset];
+    v = iVals[bOffset + gid + iVInfo.offset];
 
     if (lid < nwrite) {
         oKeys[writeloc + lid]           = k;
diff --git a/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl b/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
index 285d4cc20c..3d07a63eb7 100644
--- a/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_compact_dim.cl
@@ -43,8 +43,8 @@ kernel void compact_dim(global int *reduced_block_sizes, global Tk *oKeys,
                     bidz * iVInfo.strides[dim_ordering[2]] +
                     bidy * iVInfo.strides[dim_ordering[1]] +
                     gidx * iVInfo.strides[DIM];
-    k = iKeys[gidx];
-    v = iVals[tid];
+    k = iKeys[gidx + iKInfo.offset];
+    v = iVals[tid + iVInfo.offset];
 
     if (lid < nwrite) {
         oKeys[writeloc + lid] = k;
diff --git a/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl b/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
index 4b12830aaf..c505689bff 100644
--- a/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
+++ b/src/backend/opencl/kernel/reduce_by_key_needs_reduction.cl
@@ -32,8 +32,8 @@ kernel void test_needs_reduction(global int *needs_another_reduction,
     // last thread in each block checks if any inter-block keys need further
     // reduction
     if (gid == ((bid + 1) * DIMX) - 1 && bid < get_num_groups(0) - 1) {
-        int k0 = iKeys[gid];
-        int k1 = iKeys[gid + 1];
+        int k0 = iKeys[gid + iKInfo.offset];
+        int k1 = iKeys[gid + 1 + iKInfo.offset];
         if (k0 == k1) { atomic_or(needs_block_boundary_reduced, 1); }
     }
 }
diff --git a/test/reduce.cpp b/test/reduce.cpp
index 0d4ab59225..c50f95d924 100644
--- a/test/reduce.cpp
+++ b/test/reduce.cpp
@@ -454,7 +454,7 @@ template<typename T>
 struct generateConsq {
     T vals;
 
-    generateConsq(T v_i = 0) : vals(v_i){};
+    generateConsq(T v_i = 0) : vals(v_i) {};
 
     T operator()() { return vals++; }
 };
@@ -463,7 +463,7 @@ template<typename T>
 struct generateConst {
     T vals;
 
-    generateConst(T v_i) : vals(v_i){};
+    generateConst(T v_i) : vals(v_i) {};
 
     T operator()() { return vals; }
 };
@@ -626,12 +626,12 @@ TYPED_TEST(ReduceByKey, MultiBlockReduceSingleval) {
     SUPPORTED_TYPE_CHECK(TypeParam);
     array keys = constant(0, 1024 * 1024, s32);
     array vals = constant(1, 1024 * 1024,
-            (af_dtype)af::dtype_traits<TypeParam>::af_type);
+                          (af_dtype)af::dtype_traits<TypeParam>::af_type);
 
     array keyResGold      = constant(0, 1);
-    using promoted_t = typename promote_type<TypeParam, af_sum>::type;
-    array valsReducedGold = constant(1024 * 1024, 1,
-            (af_dtype)af::dtype_traits<promoted_t>::af_type);
+    using promoted_t      = typename promote_type<TypeParam, af_sum>::type;
+    array valsReducedGold = constant(
+        1024 * 1024, 1, (af_dtype)af::dtype_traits<promoted_t>::af_type);
 
     array keyRes, valsReduced;
     sumByKey(keyRes, valsReduced, keys, vals);
@@ -842,9 +842,9 @@ TYPED_TEST(ReduceByKey, ReduceByKeyNans) {
 
     SKIP_IF_FAST_MATH_ENABLED();
     SUPPORTED_TYPE_CHECK(TypeParam);
-    const static int testSz          = 8;
-    const int testKeys[testSz]       = {0, 2, 2, 9, 5, 5, 5, 8};
-    const TypeParam nan = std::numeric_limits<TypeParam>::quiet_NaN();
+    const static int testSz    = 8;
+    const int testKeys[testSz] = {0, 2, 2, 9, 5, 5, 5, 8};
+    const TypeParam nan        = std::numeric_limits<TypeParam>::quiet_NaN();
     const TypeParam testVals[testSz] = {0, 7, nan, 6, 2, 5, 3, 4};
 
     array keys(testSz, testKeys);
@@ -906,7 +906,7 @@ TYPED_TEST(ReduceByKey, nDim1ReduceByKey) {
     const double nanval = 0.0;
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
-    const int goldSz                = 5;
+    const int goldSz = 5;
     using promoted_t = typename promote_type<TypeParam, af_sum>::type;
     const promoted_t gold_reduce[goldSz] = {0, 8, 6, 10, 4};
     vector<promoted_t> hreduce(reduced_vals.elements());
@@ -935,7 +935,7 @@ TYPED_TEST(ReduceByKey, nDim2ReduceByKey) {
     const double nanval = 0.0;
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
-    const int goldSz                = 5;
+    const int goldSz = 5;
     using promoted_t = typename promote_type<TypeParam, af_sum>::type;
     const promoted_t gold_reduce[goldSz] = {0, 8, 6, 10, 4};
     vector<promoted_t> h_a(reduced_vals.elements());
@@ -964,7 +964,7 @@ TYPED_TEST(ReduceByKey, nDim3ReduceByKey) {
     const double nanval = 0.0;
     sumByKey(reduced_keys, reduced_vals, keys, vals, dim, nanval);
 
-    const int goldSz                = 5;
+    const int goldSz = 5;
     using promoted_t = typename promote_type<TypeParam, af_sum>::type;
     const promoted_t gold_reduce[goldSz] = {0, 8, 6, 10, 4};
     vector<promoted_t> h_a(reduced_vals.elements());
@@ -2420,7 +2420,7 @@ TEST(Reduce, SNIPPET_algorithm_func_sum) {
                                           //      1, 3, 5]
 
     // Create b by summing across the first dimension
-    array b = sum(a);        // sum across the first dimension, same as sum(a, 0)
+    array b = sum(a);        // sum across the first dimension, same as sum(a,0)
 
     // Create c by summing across the second dimension
     array c = sum(a, 1);     // sum across the second dimension
@@ -2448,3 +2448,90 @@ TEST(Reduce, SNIPPET_algorithm_func_sum) {
     ASSERT_VEC_ARRAY_EQ(gold_a, d.dims(), d);
     ASSERT_VEC_ARRAY_EQ(gold_a, e.dims(), e);
 }
+
+#define TEMP_FORMAT_TESTS_reduce(form, op)                    \
+    TEST(TEMP_FORMAT, form##_##op##_array) {                  \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});    \
+        const array gold = op(in, 3);                         \
+        array out        = op(toTempFormat(form, in), 3);     \
+        EXPECT_ARRAYS_EQ(out, gold);                          \
+    }                                                         \
+    TEST(TEMP_FORMAT, form##_##op##_value) {                  \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});    \
+        const float gold = op<float>(in);                     \
+        float out        = op<float>(toTempFormat(form, in)); \
+        EXPECT_EQ(out, gold);                                 \
+    }
+
+#define TEMP_FORMAT_TESTS_ragged(form, op)                                     \
+    TEST(TEMP_FORMAT, form##_##op##_ragged) {                                  \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});                     \
+        const array ragged_len(dim4(1), {(unsigned)in.elements()});            \
+        array gold_vals, gold_idxs;                                            \
+        op(gold_vals, gold_idxs, in, ragged_len, 3);                           \
+        array vals, idxs;                                                      \
+        op(vals, idxs, toTempFormat(form, in), toTempFormat(form, ragged_len), \
+           3);                                                                 \
+        EXPECT_ARRAYS_EQ(vals, gold_vals);                                     \
+        EXPECT_ARRAYS_EQ(idxs, gold_idxs);                                     \
+    }
+
+#define TEMP_FORMAT_TESTS_ByKey(form, op)                      \
+    TEST(TEMP_FORMAT, form##_##op) {                           \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});     \
+        const array keys(constant(0, in.dims().dims[3], u32)); \
+        keys.eval();                                           \
+        array gold_keys, gold_vals;                            \
+        op(gold_keys, gold_vals, keys, in, 3);                 \
+        array out_keys, out_vals;                              \
+        op(out_keys, out_vals, toTempFormat(form, keys),       \
+           toTempFormat(form, in), 3);                         \
+        EXPECT_ARRAYS_EQ(gold_vals, out_vals);                 \
+        EXPECT_ARRAYS_EQ(gold_keys, out_keys);                 \
+    }
+
+#define TEMP_FORMAT_TESTS_allTest(form, op)                         \
+    TEST(TEMP_FORMAT, form##_##op##_array) {                        \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});          \
+        const array gold = op(in > 2.0, 3);                         \
+        array out        = op(toTempFormat(form, in) > 2.0, 3);     \
+        EXPECT_ARRAYS_EQ(gold, out);                                \
+    }                                                               \
+    TEST(TEMP_FORMAT, form##_##op##_value) {                        \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});          \
+        const float gold = op<float>(in > 2.0);                     \
+        float out        = op<float>(toTempFormat(form, in) > 2.0); \
+        EXPECT_EQ(gold, out);                                       \
+    }
+
+#define TEMP_FORMAT_TESTS_allTestByKey(form, op)               \
+    TEST(TEMP_FORMAT, form##_##op) {                           \
+        const array in(dim4(1, 1, 1, 3), {1.f, 2.f, 3.f});     \
+        const array keys(constant(0, in.dims().dims[3], u32)); \
+        array gold_vals, gold_keys;                            \
+        op(gold_keys, gold_vals, keys, in > 2.0, 3);           \
+        array out_vals, out_keys;                              \
+        op(out_keys, out_vals, toTempFormat(form, keys),       \
+           toTempFormat(form, in) > 2.0, 3);                   \
+        EXPECT_ARRAYS_EQ(gold_vals, out_vals);                 \
+        EXPECT_ARRAYS_EQ(gold_keys, out_keys);                 \
+    }
+
+#define TEMP_FORMATS_TESTS(form)                        \
+    TEMP_FORMAT_TESTS_reduce(form, min);                \
+    TEMP_FORMAT_TESTS_reduce(form, max);                \
+    TEMP_FORMAT_TESTS_reduce(form, sum);                \
+    TEMP_FORMAT_TESTS_reduce(form, product);            \
+    TEMP_FORMAT_TESTS_reduce(form, count);              \
+    TEMP_FORMAT_TESTS_ragged(form, max);                \
+    TEMP_FORMAT_TESTS_ByKey(form, minByKey);            \
+    TEMP_FORMAT_TESTS_ByKey(form, maxByKey);            \
+    TEMP_FORMAT_TESTS_ByKey(form, sumByKey);            \
+    TEMP_FORMAT_TESTS_ByKey(form, productByKey);        \
+    TEMP_FORMAT_TESTS_ByKey(form, countByKey);          \
+    TEMP_FORMAT_TESTS_allTest(form, allTrue);           \
+    TEMP_FORMAT_TESTS_allTest(form, anyTrue);           \
+    TEMP_FORMAT_TESTS_allTestByKey(form, allTrueByKey); \
+    TEMP_FORMAT_TESTS_allTestByKey(form, anyTrueByKey);
+
+FOREACH_TEMP_FORMAT(TEMP_FORMATS_TESTS)

From f01e6fe9dcddd058c3bcc217b1e707d35500320a Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Mon, 30 Jun 2025 08:38:03 +0200
Subject: [PATCH 2667/2677] Fixes sub array (opencl) support for confidenceCC
 (#3668)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Adds test helpers for temporary array formats (JIT, SUB, ...)

* Fixes sub-array (opencl) support for confidenceCC

* Update flood_fill.cpp

Removed unnecessary #include

* Added TODO comment to change this lines after subarrays fix

---------

Co-authored-by: Edwin Lester Solís Fuentes <68087165+edwinsolisf@users.noreply.github.com>
---
 src/api/c/confidence_connected.cpp      | 11 ++++-
 src/backend/opencl/kernel/flood_fill.cl |  9 ++--
 test/confidence_connected.cpp           | 59 ++++++++++++++++++++++---
 3 files changed, 67 insertions(+), 12 deletions(-)

diff --git a/src/api/c/confidence_connected.cpp b/src/api/c/confidence_connected.cpp
index ceb8ca7b75..903c06f87b 100644
--- a/src/api/c/confidence_connected.cpp
+++ b/src/api/c/confidence_connected.cpp
@@ -45,8 +45,15 @@ using std::swap;
 template<typename T>
 Array<T> pointList(const Array<T>& in, const Array<uint>& x,
                    const Array<uint>& y) {
-    af_array xcoords                          = getHandle<uint>(x);
-    af_array ycoords                          = getHandle<uint>(y);
+
+    // TODO: Temporary Fix, must fix handling subarrays upstream
+    // Array<T> has to be a basic array, to be accepted as af_index
+    Array<uint> x_ = (x.getOffset() == 0 && x.isLinear()) ? x : copyArray(x);
+    Array<uint> y_ = (y.getOffset() == 0 && y.isLinear()) ? y : copyArray(y);
+
+    af_array xcoords = getHandle<uint>(x_);
+    af_array ycoords = getHandle<uint>(y_);
+
     std::array<af_index_t, AF_MAX_DIMS> idxrs = {{{{xcoords}, false, false},
                                                   {{ycoords}, false, false},
                                                   createSpanIndex(),
diff --git a/src/backend/opencl/kernel/flood_fill.cl b/src/backend/opencl/kernel/flood_fill.cl
index ba8f8e109a..58d03b52e8 100644
--- a/src/backend/opencl/kernel/flood_fill.cl
+++ b/src/backend/opencl/kernel/flood_fill.cl
@@ -23,8 +23,8 @@ kernel void init_seeds(global T *out, KParam oInfo, global const uint *seedsx,
                        KParam syInfo) {
     uint tid = get_global_id(0);
     if (tid < sxInfo.dims[0]) {
-        uint x                                             = seedsx[tid];
-        uint y                                             = seedsy[tid];
+        uint x                                             = seedsx[tid + sxInfo.offset];
+        uint y                                             = seedsy[tid + syInfo.offset];
         out[(x * oInfo.strides[0] + y * oInfo.strides[1])] = VALID;
     }
 }
@@ -76,14 +76,15 @@ kernel void flood_step(global T *out, KParam oInfo, global const T *img,
 
     T tImgVal =
         img[(clamp(gx, 0, (int)(iInfo.dims[0] - 1)) * iInfo.strides[0] +
-             clamp(gy, 0, (int)(iInfo.dims[1] - 1)) * iInfo.strides[1])];
+             clamp(gy, 0, (int)(iInfo.dims[1] - 1)) * iInfo.strides[1])+ 
+             iInfo.offset];
     const int isPxBtwnThresholds =
         (tImgVal >= lowValue && tImgVal <= highValue);
 
     int tid = lx + get_local_size(0) * ly;
 
     barrier(CLK_LOCAL_MEM_FENCE);
-
+    
     T origOutVal     = lmem[j][i];
     bool isBorderPxl = (lx == 0 || ly == 0 || lx == (get_local_size(0) - 1) ||
                         ly == (get_local_size(1) - 1));
diff --git a/test/confidence_connected.cpp b/test/confidence_connected.cpp
index 22254e5532..39c0f8f0ff 100644
--- a/test/confidence_connected.cpp
+++ b/test/confidence_connected.cpp
@@ -92,9 +92,9 @@ void testImage(const std::string pTestFile, const size_t numSeeds,
         params.iterations = iter;
         params.replace    = 255.0;
 
-        ASSERT_SUCCESS(af_confidence_cc(&outArray, inArray, seedxArr, seedyArr, params.radius,
-                                        params.multiplier, params.iterations,
-                                        params.replace));
+        ASSERT_SUCCESS(af_confidence_cc(&outArray, inArray, seedxArr, seedyArr,
+                                        params.radius, params.multiplier,
+                                        params.iterations, params.replace));
         int device = 0;
         ASSERT_SUCCESS(af_get_device(&device));
         ASSERT_SUCCESS(af_sync(device));
@@ -141,9 +141,9 @@ void testData(CCCTestParams params) {
                                    (af_dtype)af::dtype_traits<T>::af_type));
 
     af_array outArray = 0;
-    ASSERT_SUCCESS(af_confidence_cc(&outArray, inArray, seedxArr, seedyArr, params.radius,
-                                    params.multiplier, params.iterations,
-                                    params.replace));
+    ASSERT_SUCCESS(af_confidence_cc(&outArray, inArray, seedxArr, seedyArr,
+                                    params.radius, params.multiplier,
+                                    params.iterations, params.replace));
     int device = 0;
     ASSERT_SUCCESS(af_get_device(&device));
     ASSERT_SUCCESS(af_sync(device));
@@ -201,3 +201,50 @@ INSTANTIATE_TEST_SUITE_P(
            << info.param.iterations << "_replace_" << info.param.replace;
         return ss.str();
     });
+
+#define TEST_FORMATS(form)                                                     \
+    TEST(TEMP_FORMAT, form##_2Dseed) {                                         \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                                \
+        const string filename(string(TEST_DIR) + "/confidence_cc/donut.png");  \
+        const af::array image(af::loadImage(filename.c_str()));                \
+        const af::array seed(dim4(1, 2), {10u, 8u});                           \
+                                                                               \
+        const af::array out =                                                  \
+            af::confidenceCC(toTempFormat(form, image),                        \
+                             toTempFormat(form, seed), 3, 3, 25, 255.0);       \
+        const af::array gold = af::confidenceCC(image, seed, 3, 3, 25, 255.0); \
+                                                                               \
+        EXPECT_ARRAYS_EQ(out, gold);                                           \
+    }                                                                          \
+                                                                               \
+    TEST(TEMP_FORMAT, form##_2xSeed) {                                         \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                                \
+        const string filename(string(TEST_DIR) + "/confidence_cc/donut.png");  \
+        const af::array image(af::loadImage(filename.c_str()));                \
+        const af::array seedx({10u});                                          \
+        const af::array seedy({8u});                                           \
+                                                                               \
+        const af::array out = af::confidenceCC(                                \
+            toTempFormat(form, image), toTempFormat(form, seedx),              \
+            toTempFormat(form, seedy), 3, 3, 25, 255.0);                       \
+        const af::array gold =                                                 \
+            af::confidenceCC(image, seedx, seedy, 3, 3, 25, 255.0);            \
+                                                                               \
+        EXPECT_ARRAYS_EQ(out, gold);                                           \
+    }                                                                          \
+    TEST(TEMP_FORMAT, form##_vectSeed) {                                       \
+        UNSUPPORTED_BACKEND(AF_BACKEND_ONEAPI);                                \
+        const string filename(string(TEST_DIR) + "/confidence_cc/donut.png");  \
+        const af::array image(af::loadImage(filename.c_str()));                \
+        const unsigned seedx[1] = {10u};                                       \
+        const unsigned seedy[1] = {8u};                                        \
+                                                                               \
+        const af::array out = af::confidenceCC(toTempFormat(form, image), 1,   \
+                                               seedx, seedy, 3, 3, 25, 255.0); \
+        const af::array gold =                                                 \
+            af::confidenceCC(image, 1, seedx, seedy, 3, 3, 25, 255.0);         \
+                                                                               \
+        EXPECT_ARRAYS_EQ(out, gold);                                           \
+    }
+
+FOREACH_TEMP_FORMAT(TEST_FORMATS)

From 8da6800e048152e233be3ba180a2c87b871acf06 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Sun, 29 Jun 2025 23:47:34 -0700
Subject: [PATCH 2668/2677] Fix half precision pow function for openCL backend
 (#3676)

---
 src/backend/opencl/binary.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/opencl/binary.hpp b/src/backend/opencl/binary.hpp
index 39f340942a..546c5bc085 100644
--- a/src/backend/opencl/binary.hpp
+++ b/src/backend/opencl/binary.hpp
@@ -9,6 +9,9 @@
 
 #pragma once
 #include <optypes.hpp>
+#include <common/half.hpp>
+
+using arrayfire::common::half;
 
 namespace arrayfire {
 namespace opencl {
@@ -98,6 +101,7 @@ struct BinOp<To, Ti, af_pow_t> {
 
 POW_BINARY_OP(double, "pow")
 POW_BINARY_OP(float, "pow")
+POW_BINARY_OP(half, "pow")
 POW_BINARY_OP(intl, "__powll")
 POW_BINARY_OP(uintl, "__powul")
 POW_BINARY_OP(uint, "__powui")

From 95fc0994346d93a4296d91d7d64bced740d5493b Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Mon, 30 Jun 2025 19:51:47 +0100
Subject: [PATCH 2669/2677] Fix half precision pow function for oneAPI back end
 (#3672)

* Fix half precision pow function for oneAPI back end

An incorrect power function was being used for half precision variables when building the JIT kernel for the oneAPI back end which lead to a fallback to an integer power function causing the result to be rounded. This fixes that.

* Apply half precision pow function fix to the OpenCL back end as well.

* Revert "Apply half precision pow function fix to the OpenCL back end as well."

This reverts commit e3357218911e5b592684c4f13e8a62f39c95b441.
---
 src/backend/oneapi/binary.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/backend/oneapi/binary.hpp b/src/backend/oneapi/binary.hpp
index a9bc4900e8..8bd36aff7e 100644
--- a/src/backend/oneapi/binary.hpp
+++ b/src/backend/oneapi/binary.hpp
@@ -9,6 +9,9 @@
 
 #pragma once
 #include <optypes.hpp>
+#include <common/half.hpp>
+
+using arrayfire::common::half;
 
 namespace arrayfire {
 namespace oneapi {
@@ -93,6 +96,7 @@ struct BinOp<To, Ti, af_pow_t> {
 
 POW_BINARY_OP(double, "pow")
 POW_BINARY_OP(float, "pow")
+POW_BINARY_OP(half, "pow")
 POW_BINARY_OP(intl, "__powll")
 POW_BINARY_OP(uintl, "__powul")
 POW_BINARY_OP(uint, "__powui")

From e58f6df8894537e3742451a0e645fa2406a3f249 Mon Sep 17 00:00:00 2001
From: willy born <70607676+willyborn@users.noreply.github.com>
Date: Tue, 8 Jul 2025 22:12:43 +0200
Subject: [PATCH 2670/2677] Fixes sub array (cpu, cuda, oneapi, opencl) for
 transform (#3679)

* Increased difficulty of sub-array testing

* Fixes sub-array (cpu, cuda, oneapi, opencl) for transform

* Added TODO comments for linear checks. This will be fixed at a higher level later.

---------

Co-authored-by: Christophe Murphy <christophe.murphy@outlook.com>
---
 src/backend/cpu/transform.cpp           |  17 ++-
 src/backend/cuda/transform.cpp          |   7 +-
 src/backend/oneapi/kernel/transform.hpp |   3 +-
 src/backend/oneapi/transform.cpp        |  14 ++-
 src/backend/opencl/kernel/transform.cl  |   2 +-
 src/backend/opencl/transform.cpp        |  14 ++-
 test/arrayfire_test.cpp                 | 145 ++++++++++++++----------
 test/transform.cpp                      |  40 +++++++
 test/transform_coordinates.cpp          |  25 +++-
 9 files changed, 188 insertions(+), 79 deletions(-)

diff --git a/src/backend/cpu/transform.cpp b/src/backend/cpu/transform.cpp
index bbcf689f25..0fbe10ea5c 100644
--- a/src/backend/cpu/transform.cpp
+++ b/src/backend/cpu/transform.cpp
@@ -8,6 +8,7 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <copy.hpp>
 #include <kernel/transform.hpp>
 #include <math.hpp>
 #include <platform.hpp>
@@ -22,23 +23,27 @@ void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const bool perspective) {
     out.eval();
     in.eval();
+
+    // TODO: Temporary Fix, must fix handling subarrays upstream
+    // tf has to be linear, although offset is allowed
+    const Array<float> tf_Lin = tf.isLinear() ? tf : copyArray(tf);
     tf.eval();
 
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            getQueue().enqueue(kernel::transform<T, 1>, out, in, tf, inverse,
-                               perspective, method);
+            getQueue().enqueue(kernel::transform<T, 1>, out, in, tf_Lin,
+                               inverse, perspective, method);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            getQueue().enqueue(kernel::transform<T, 2>, out, in, tf, inverse,
-                               perspective, method);
+            getQueue().enqueue(kernel::transform<T, 2>, out, in, tf_Lin,
+                               inverse, perspective, method);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            getQueue().enqueue(kernel::transform<T, 3>, out, in, tf, inverse,
-                               perspective, method);
+            getQueue().enqueue(kernel::transform<T, 3>, out, in, tf_Lin,
+                               inverse, perspective, method);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG); break;
     }
diff --git a/src/backend/cuda/transform.cpp b/src/backend/cuda/transform.cpp
index e0d0509c8d..af8b561191 100644
--- a/src/backend/cuda/transform.cpp
+++ b/src/backend/cuda/transform.cpp
@@ -9,6 +9,7 @@
 
 #include <transform.hpp>
 
+#include <copy.hpp>
 #include <kernel/transform.hpp>
 #include <utility.hpp>
 
@@ -19,7 +20,11 @@ template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af::interpType method, const bool inverse,
                const bool perspective) {
-    kernel::transform<T>(out, in, tf, inverse, perspective, method,
+    // TODO: Temporary Fix, must fix handling subarrays upstream
+    // tf has to be linear, although offset is allowed.
+    const Array<float> tf_Lin = tf.isLinear() ? tf : copyArray(tf);
+
+    kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
                          interpOrder(method));
 }
 
diff --git a/src/backend/oneapi/kernel/transform.hpp b/src/backend/oneapi/kernel/transform.hpp
index 07f70a3a62..874e9638c7 100644
--- a/src/backend/oneapi/kernel/transform.hpp
+++ b/src/backend/oneapi/kernel/transform.hpp
@@ -178,7 +178,8 @@ class transformCreateKernel {
         using TMatTy =
             typename std::conditional<PERSPECTIVE, float[9], float[6]>::type;
         TMatTy tmat;
-        const float *tmat_ptr = c_tmat_.get_pointer() + t_idx * transf_len;
+        const float *tmat_ptr =
+            c_tmat_.get_pointer() + tf_.offset + t_idx * transf_len;
 
         // We expect a inverse transform matrix by default
         // If it is an forward transform, then we need its inverse
diff --git a/src/backend/oneapi/transform.cpp b/src/backend/oneapi/transform.cpp
index a277df9661..00edc15817 100644
--- a/src/backend/oneapi/transform.cpp
+++ b/src/backend/oneapi/transform.cpp
@@ -9,6 +9,7 @@
 
 #include <transform.hpp>
 
+#include <copy.hpp>
 #include <err_oneapi.hpp>
 #include <kernel/transform.hpp>
 
@@ -19,18 +20,25 @@ template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective) {
+    // TODO: Temporary Fix, must fix handling subarrays upstream
+    // tf has to be linear, although offset is allowed.
+    const Array<float> tf_Lin = tf.isLinear() ? tf : copyArray(tf);
+
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::transform<T>(out, in, tf, inverse, perspective, method, 1);
+            kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
+                                 1);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::transform<T>(out, in, tf, inverse, perspective, method, 2);
+            kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
+                                 2);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::transform<T>(out, in, tf, inverse, perspective, method, 3);
+            kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
+                                 3);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
diff --git a/src/backend/opencl/kernel/transform.cl b/src/backend/opencl/kernel/transform.cl
index 85c6a293ab..4fae1c05f8 100644
--- a/src/backend/opencl/kernel/transform.cl
+++ b/src/backend/opencl/kernel/transform.cl
@@ -133,7 +133,7 @@ kernel void transformKernel(global T *d_out, const KParam out,
     const int transf_len = 6;
     float tmat[6];
 #endif
-    global const float *tmat_ptr = c_tmat + t_idx * transf_len;
+    global const float *tmat_ptr = c_tmat + tf.offset + t_idx * transf_len;
 
     // We expect a inverse transform matrix by default
     // If it is an forward transform, then we need its inverse
diff --git a/src/backend/opencl/transform.cpp b/src/backend/opencl/transform.cpp
index 78428ed3a7..de99f48a60 100644
--- a/src/backend/opencl/transform.cpp
+++ b/src/backend/opencl/transform.cpp
@@ -9,6 +9,7 @@
 
 #include <transform.hpp>
 
+#include <copy.hpp>
 #include <kernel/transform.hpp>
 
 namespace arrayfire {
@@ -18,18 +19,25 @@ template<typename T>
 void transform(Array<T> &out, const Array<T> &in, const Array<float> &tf,
                const af_interp_type method, const bool inverse,
                const bool perspective) {
+    // TODO: Temporary Fix, must fix handling subarrays upstream
+    // tf has to be linear, although offset is allowed.
+    const Array<float> tf_Lin = tf.isLinear() ? tf : copyArray(tf);
+
     switch (method) {
         case AF_INTERP_NEAREST:
         case AF_INTERP_LOWER:
-            kernel::transform<T>(out, in, tf, inverse, perspective, method, 1);
+            kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
+                                 1);
             break;
         case AF_INTERP_BILINEAR:
         case AF_INTERP_BILINEAR_COSINE:
-            kernel::transform<T>(out, in, tf, inverse, perspective, method, 2);
+            kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
+                                 2);
             break;
         case AF_INTERP_BICUBIC:
         case AF_INTERP_BICUBIC_SPLINE:
-            kernel::transform<T>(out, in, tf, inverse, perspective, method, 3);
+            kernel::transform<T>(out, in, tf_Lin, inverse, perspective, method,
+                                 3);
             break;
         default: AF_ERROR("Unsupported interpolation type", AF_ERR_ARG);
     }
diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index 6803cc586d..eab07f5b41 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -229,9 +229,9 @@ ::testing::AssertionResult imageEq(std::string aName, std::string bName,
         af::saveImage(result_path.c_str(), b.as(f32));
         af::saveImage(diff_path.c_str(), abs(a.as(f32) - b.as(f32)));
 
-        std::cout
-            << "<DartMeasurementFile type=\"image/png\" name=\"ValidImage\">"
-            << valid_path << "</DartMeasurementFile>\n";
+        std::cout << "<DartMeasurementFile type=\"image/png\" "
+                     "name=\"ValidImage\">"
+                  << valid_path << "</DartMeasurementFile>\n";
         std::cout
             << "<DartMeasurementFile type=\"image/png\" name=\"TestImage\">"
             << result_path << "</DartMeasurementFile>\n";
@@ -526,7 +526,8 @@ dim_t ravelIdx(af::dim4 coords, af::dim4 strides) {
                               0LL);
 }
 
-// Calculate a linearized index's multi-dimensonal coordinates in an af::array,
+// Calculate a linearized index's multi-dimensonal coordinates in an
+// af::array,
 //  given its dimension sizes and strides
 af::dim4 unravelIdx(dim_t idx, af::dim4 dims, af::dim4 strides) {
     af::dim4 coords;
@@ -567,8 +568,9 @@ std::string minimalDim4(af::dim4 coords, af::dim4 dims) {
     return os.str();
 }
 
-// Generates a random array. testWriteToOutputArray expects that it will receive
-// the same af_array that this generates after the af_* function is called
+// Generates a random array. testWriteToOutputArray expects that it will
+// receive the same af_array that this generates after the af_* function is
+// called
 void genRegularArray(TestOutputArrayInfo *metadata, const unsigned ndims,
                      const dim_t *const dims, const af_dtype ty) {
     metadata->init(ndims, dims, ty);
@@ -581,9 +583,9 @@ void genRegularArray(TestOutputArrayInfo *metadata, double val,
 }
 
 // Generates a large, random array, and extracts a subarray for the af_*
-// function to use. testWriteToOutputArray expects that the large array that it
-// receives is equal to the same large array with the gold array injected on the
-// same subarray location
+// function to use. testWriteToOutputArray expects that the large array that
+// it receives is equal to the same large array with the gold array injected
+// on the same subarray location
 void genSubArray(TestOutputArrayInfo *metadata, const unsigned ndims,
                  const dim_t *const dims, const af_dtype ty) {
     const dim_t pad_size = 2;
@@ -596,8 +598,9 @@ void genSubArray(TestOutputArrayInfo *metadata, const unsigned ndims,
     }
 
     // Calculate index of sub-array. These will be used also by
-    // testWriteToOutputArray so that the gold sub array will be placed in the
-    // same location. Currently, this location is the center of the large array
+    // testWriteToOutputArray so that the gold sub array will be placed in
+    // the same location. Currently, this location is the center of the
+    // large array
     af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
     for (uint i = 0; i < ndims; ++i) {
         af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
@@ -620,8 +623,9 @@ void genSubArray(TestOutputArrayInfo *metadata, double val,
     }
 
     // Calculate index of sub-array. These will be used also by
-    // testWriteToOutputArray so that the gold sub array will be placed in the
-    // same location. Currently, this location is the center of the large array
+    // testWriteToOutputArray so that the gold sub array will be placed in
+    // the same location. Currently, this location is the center of the
+    // large array
     af_seq subarr_idxs[4] = {af_span, af_span, af_span, af_span};
     for (uint i = 0; i < ndims; ++i) {
         af_seq idx     = {pad_size, pad_size + dims[i] - 1.0, 1.0};
@@ -631,13 +635,14 @@ void genSubArray(TestOutputArrayInfo *metadata, double val,
     metadata->init(val, ndims, full_arr_dims, ty, &subarr_idxs[0]);
 }
 
-// Generates a reordered array. testWriteToOutputArray expects that this array
-// will still have the correct output values from the af_* function, even though
-// the array was initially reordered.
+// Generates a reordered array. testWriteToOutputArray expects that this
+// array will still have the correct output values from the af_* function,
+// even though the array was initially reordered.
 void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
                        const dim_t *const dims, const af_dtype ty) {
-    // The rest of this function assumes that dims has 4 elements. Just in case
-    // dims has < 4 elements, use another dims array that is filled with 1s
+    // The rest of this function assumes that dims has 4 elements. Just in
+    // case dims has < 4 elements, use another dims array that is filled
+    // with 1s
     dim_t all_dims[4] = {1, 1, 1, 1};
     for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
 
@@ -648,7 +653,8 @@ void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
     uint reorder_idxs[4] = {0, 2, 1, 3};
 
     // Shape the output array such that the reordered output array will have
-    // the correct dimensions that the test asks for (i.e. must match dims arg)
+    // the correct dimensions that the test asks for (i.e. must match dims
+    // arg)
     dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
     for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
     metadata->init(4, init_dims, ty);
@@ -663,8 +669,9 @@ void genReorderedArray(TestOutputArrayInfo *metadata, const unsigned ndims,
 void genReorderedArray(TestOutputArrayInfo *metadata, double val,
                        const unsigned ndims, const dim_t *const dims,
                        const af_dtype ty) {
-    // The rest of this function assumes that dims has 4 elements. Just in case
-    // dims has < 4 elements, use another dims array that is filled with 1s
+    // The rest of this function assumes that dims has 4 elements. Just in
+    // case dims has < 4 elements, use another dims array that is filled
+    // with 1s
     dim_t all_dims[4] = {1, 1, 1, 1};
     for (uint i = 0; i < ndims; ++i) { all_dims[i] = dims[i]; }
 
@@ -675,7 +682,8 @@ void genReorderedArray(TestOutputArrayInfo *metadata, double val,
     uint reorder_idxs[4] = {0, 2, 1, 3};
 
     // Shape the output array such that the reordered output array will have
-    // the correct dimensions that the test asks for (i.e. must match dims arg)
+    // the correct dimensions that the test asks for (i.e. must match dims
+    // arg)
     dim_t init_dims[4] = {all_dims[0], all_dims[1], all_dims[2], all_dims[3]};
     for (uint i = 0; i < 4; ++i) { init_dims[i] = all_dims[reorder_idxs[i]]; }
     metadata->init(val, 4, init_dims, ty);
@@ -745,8 +753,8 @@ ::testing::AssertionResult testWriteToOutputArray(
 
     if (metadata->getOutputArrayType() == SUB_ARRAY) {
         // There are two full arrays. One will be injected with the gold
-        // subarray, the other should have already been injected with the af_*
-        // function's output. Then we compare the two full arrays
+        // subarray, the other should have already been injected with the
+        // af_* function's output. Then we compare the two full arrays
         af_array gold_full_array = metadata->getFullOutputCopy();
         af_assign_seq(&gold_full_array, gold_full_array,
                       metadata->getSubArrayNumDims(),
@@ -1293,9 +1301,11 @@ ::testing::AssertionResult mtxReadSparseMatrix(af::array &out,
                 return ::testing::AssertionFailure()
                        << "\nEnd of file reached, expected more data, "
                        << "following are some reasons this happens.\n"
-                       << "\t - use of template type that doesn't match data "
+                       << "\t - use of template type that doesn't match "
+                          "data "
                           "type\n"
-                       << "\t - the mtx file itself doesn't have enough data\n";
+                       << "\t - the mtx file itself doesn't have enough "
+                          "data\n";
             }
             I[i] = r - 1;
             J[i] = c - 1;
@@ -1319,9 +1329,11 @@ ::testing::AssertionResult mtxReadSparseMatrix(af::array &out,
                 return ::testing::AssertionFailure()
                        << "\nEnd of file reached, expected more data, "
                        << "following are some reasons this happens.\n"
-                       << "\t - use of template type that doesn't match data "
+                       << "\t - use of template type that doesn't match "
+                          "data "
                           "type\n"
-                       << "\t - the mtx file itself doesn't have enough data\n";
+                       << "\t - the mtx file itself doesn't have enough "
+                          "data\n";
             }
             I[i] = r - 1;
             J[i] = c - 1;
@@ -1531,8 +1543,8 @@ vector<sparseCooValue<T>> toCooVector(const af::array &arr) {
         }
     }
 
-    // Remove zero elements from result to ensure that only non-zero elements
-    // are compared
+    // Remove zero elements from result to ensure that only non-zero
+    // elements are compared
     out.erase(std::remove_if(out.begin(), out.end(), isZero<T>), out.end());
     std::sort(begin(out), end(out));
     return out;
@@ -1584,8 +1596,8 @@ std::string printContext(const std::vector<T> &hGold, std::string goldName,
 
     // Get dim0 positions and out/reference values for the context window
     //
-    // Also get the max string length between the position and out/ref values
-    // per item so that it can be used later as the field width for
+    // Also get the max string length between the position and out/ref
+    // values per item so that it can be used later as the field width for
     // displaying each item in the context window
     for (dim_t i = 0; i < ctxElems; ++i) {
         std::ostringstream tmpOs;
@@ -2063,31 +2075,35 @@ af::array toTempFormat(tempFormat form, const af::array &in) {
             break;
         case SUB_FORMAT_dim0: {
             af::dim4 pdims(dims);
-            pdims[0] += 2;
-            af::array parent = af::randu(pdims, in.type());
-            parent(af::seq(1, dims[0]), af::span, af::span, af::span) = in;
-            ret = parent(af::seq(1, dims[0]), af::span, af::span, af::span);
+            pdims[0] *= 2;
+            af::array parent  = af::randu(pdims, in.type());
+            const af::seq dim = af::seq(dims[0]) + static_cast<double>(dims[0]);
+            parent(dim, af::span, af::span, af::span) = in;
+            ret = parent(dim, af::span, af::span, af::span);
         }; break;
         case SUB_FORMAT_dim1: {
             af::dim4 pdims(dims);
-            pdims[1] += 2;
-            af::array parent = af::randu(pdims, in.type());
-            parent(af::span, af::seq(1, dims[1]), af::span, af::span) = in;
-            ret = parent(af::span, af::seq(1, dims[1]), af::span, af::span);
+            pdims[1] *= 2;
+            const af::seq dim = af::seq(dims[1]) + static_cast<double>(dims[1]);
+            af::array parent  = af::randu(pdims, in.type());
+            parent(af::span, dim, af::span, af::span) = in;
+            ret = parent(af::span, dim, af::span, af::span);
         }; break;
         case SUB_FORMAT_dim2: {
             af::dim4 pdims(dims);
-            pdims[2] += 2;
-            af::array parent = af::randu(pdims, in.type());
-            parent(af::span, af::span, af::seq(1, dims[2]), af::span) = in;
-            ret = parent(af::span, af::span, af::seq(1, dims[2]), af::span);
+            pdims[2] *= 2;
+            const af::seq dim = af::seq(dims[2]) + static_cast<double>(dims[2]);
+            af::array parent  = af::randu(pdims, in.type());
+            parent(af::span, af::span, dim, af::span) = in;
+            ret = parent(af::span, af::span, dim, af::span);
         }; break;
         case SUB_FORMAT_dim3: {
             af::dim4 pdims(dims);
-            pdims[3] += 2;
-            af::array parent = af::randu(pdims, in.type());
-            parent(af::span, af::span, af::span, af::seq(1, dims[3])) = in;
-            ret = parent(af::span, af::span, af::span, af::seq(1, dims[3]));
+            pdims[3] *= 2;
+            const af::seq dim = af::seq(dims[3]) + static_cast<double>(dims[3]);
+            af::array parent  = af::randu(pdims, in.type());
+            parent(af::span, af::span, af::span, dim) = in;
+            ret = parent(af::span, af::span, af::span, dim);
         }; break;
         case REORDERED_FORMAT: {
             const dim_t idxs[4] = {0, 3, 1, 2};
@@ -2138,21 +2154,22 @@ void toTempFormat(tempFormat form, af_array *out, const af_array &in) {
             res = nullptr;
         }; break;
         case SUB_FORMAT_dim0: {
-            const dim_t pdims[4] = {dims[0] + 2, dims[1], dims[2], dims[3]};
+            const dim_t pdims[4] = {dims[0] * 2, dims[1], dims[2], dims[3]};
             af_array parent      = nullptr;
-            ASSERT_SUCCESS(af_randu(&parent, std::max(1u, numdims), pdims, ty));
-            const af_seq idxs[4] = {af_make_seq(1, dims[0], 1), af_span,
-                                    af_span, af_span};
-
+            ASSERT_SUCCESS(af_randu(&parent, 4, pdims, ty));
+            const af_seq idxs[4] = {af_make_seq(dims[0], 2. * dims[0] - 1., 1.),
+                                    af_span, af_span, af_span};
             ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
             ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
             ASSERT_SUCCESS(af_release_array(parent));
+            parent = nullptr;
         }; break;
         case SUB_FORMAT_dim1: {
-            const dim_t pdims[4] = {dims[0], dims[1] + 2, dims[2], dims[3]};
+            const dim_t pdims[4] = {dims[0], dims[1] * 2, dims[2], dims[3]};
             af_array parent      = nullptr;
-            ASSERT_SUCCESS(af_randu(&parent, std::max(2u, numdims), pdims, ty));
-            const af_seq idxs[4] = {af_span, af_make_seq(1, dims[1], 1),
+            ASSERT_SUCCESS(af_randu(&parent, 4, pdims, ty));
+            const af_seq idxs[4] = {af_span,
+                                    af_make_seq(dims[1], 2. * dims[1] - 1., 1.),
                                     af_span, af_span};
             ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
             ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
@@ -2160,22 +2177,24 @@ void toTempFormat(tempFormat form, af_array *out, const af_array &in) {
             parent = nullptr;
         }; break;
         case SUB_FORMAT_dim2: {
-            const dim_t pdims[4] = {dims[0], dims[1], dims[2] + 2, dims[3]};
+            const dim_t pdims[4] = {dims[0], dims[1], dims[2] * 2, dims[3]};
             af_array parent      = nullptr;
-            ASSERT_SUCCESS(af_randu(&parent, std::max(3u, numdims), pdims, ty));
+            ASSERT_SUCCESS(af_randu(&parent, 4, pdims, ty));
             const af_seq idxs[4] = {af_span, af_span,
-                                    af_make_seq(1, dims[2], 1), af_span};
+                                    af_make_seq(dims[2], 2. * dims[2] - 1., 1.),
+                                    af_span};
             ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
             ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
             ASSERT_SUCCESS(af_release_array(parent));
             parent = nullptr;
         }; break;
         case SUB_FORMAT_dim3: {
-            const dim_t pdims[4] = {dims[0], dims[1], dims[2], dims[3] + 2};
+            const dim_t pdims[4] = {dims[0], dims[1], dims[2], dims[3] * 2};
             af_array parent      = nullptr;
-            ASSERT_SUCCESS(af_randu(&parent, std::max(4u, numdims), pdims, ty));
-            const af_seq idxs[4] = {af_span, af_span, af_span,
-                                    af_make_seq(1, dims[3], 1)};
+            ASSERT_SUCCESS(af_randu(&parent, 4, pdims, ty));
+            const af_seq idxs[4] = {
+                af_span, af_span, af_span,
+                af_make_seq(dims[3], 2. * dims[3] - 1., 1.)};
             ASSERT_SUCCESS(af_assign_seq(out, parent, numdims, idxs, in));
             ASSERT_SUCCESS(af_index(out, parent, numdims, idxs));
             ASSERT_SUCCESS(af_release_array(parent));
diff --git a/test/transform.cpp b/test/transform.cpp
index ef3b0dd4f9..e6026576ba 100644
--- a/test/transform.cpp
+++ b/test/transform.cpp
@@ -620,3 +620,43 @@ TEST(TransformBatching, CPP) {
         }
     }
 }
+
+#define TEST_TEMP_FORMAT(form, interp)                                         \
+    TEST(TEMP_FORMAT, form##_##interp) {                                       \
+        IMAGEIO_ENABLED_CHECK();                                               \
+                                                                               \
+        vector<dim4> inDims;                                                   \
+        vector<string> inFiles;                                                \
+        vector<dim_t> goldDim;                                                 \
+        vector<string> goldFiles;                                              \
+                                                                               \
+        vector<dim4> HDims;                                                    \
+        vector<vector<float>> HIn;                                             \
+        vector<vector<float>> HTests;                                          \
+        readTests<float, float, float>(TEST_DIR "/transform/tux_tmat.test",    \
+                                       HDims, HIn, HTests);                    \
+                                                                               \
+        readImageTests(string(TEST_DIR "/transform/tux_nearest.test"), inDims, \
+                       inFiles, goldDim, goldFiles);                           \
+        inFiles[1].insert(0, string(TEST_DIR "/transform/"));                  \
+        const array IH = array(HDims[0][0], HDims[0][1], &(HIn[0].front()));   \
+        const array scene_img = loadImage(inFiles[1].c_str(), false);          \
+                                                                               \
+        const array out =                                                      \
+            transform(toTempFormat(form, scene_img), toTempFormat(form, IH),   \
+                      inDims[0][0], inDims[0][1], interp, false);              \
+        const array gold = transform(scene_img, IH, inDims[0][0],              \
+                                     inDims[0][1], interp, false);             \
+                                                                               \
+        EXPECT_ARRAYS_EQ(out, gold);                                           \
+    }
+
+#define TESTS_TEMP_FORMAT(form)                       \
+    TEST_TEMP_FORMAT(form, AF_INTERP_NEAREST)         \
+    TEST_TEMP_FORMAT(form, AF_INTERP_BILINEAR)        \
+    TEST_TEMP_FORMAT(form, AF_INTERP_BILINEAR_COSINE) \
+    TEST_TEMP_FORMAT(form, AF_INTERP_BICUBIC)         \
+    TEST_TEMP_FORMAT(form, AF_INTERP_BICUBIC_SPLINE)  \
+    TEST_TEMP_FORMAT(form, AF_INTERP_LOWER)
+
+FOREACH_TEMP_FORMAT(TESTS_TEMP_FORMAT)
\ No newline at end of file
diff --git a/test/transform_coordinates.cpp b/test/transform_coordinates.cpp
index 2875f18c1a..bc5dbed4e9 100644
--- a/test/transform_coordinates.cpp
+++ b/test/transform_coordinates.cpp
@@ -61,7 +61,7 @@ void transformCoordinatesTest(string pTestFile) {
         dim_t outEl = 0;
         ASSERT_SUCCESS(af_get_elements(&outEl, outArray));
         vector<T> outData(outEl);
-        ASSERT_SUCCESS(af_get_data_ptr((void*)&outData.front(), outArray));
+        ASSERT_SUCCESS(af_get_data_ptr((void *)&outData.front(), outArray));
 
         ASSERT_SUCCESS(af_release_array(outArray));
         const float thr = 1.f;
@@ -114,3 +114,26 @@ TEST(TransformCoordinates, CPP) {
             << "at: " << elIter << endl;
     }
 }
+
+#define TESTS_TEMP_FORMAT(form)                                                \
+    TEST(TEMP_FORMAT, form) {                                                  \
+        vector<dim4> inDims;                                                   \
+        vector<vector<float>> in;                                              \
+        vector<vector<float>> gold;                                            \
+                                                                               \
+        readTests<float, float, float>(TEST_DIR                                \
+                                       "/transformCoordinates/3d_matrix.test", \
+                                       inDims, in, gold);                      \
+                                                                               \
+        const array tf(inDims[0][0], inDims[0][1], &(in[0].front()));          \
+        const float d0 = in[1][0];                                             \
+        const float d1 = in[1][1];                                             \
+                                                                               \
+        const array out =                                                      \
+            transformCoordinates(toTempFormat(form, tf), d0, d1);              \
+        const array gout = transformCoordinates(tf, d0, d1);                   \
+                                                                               \
+        EXPECT_ARRAYS_EQ(out, gout);                                           \
+    }
+
+FOREACH_TEMP_FORMAT(TESTS_TEMP_FORMAT)
\ No newline at end of file

From 82ca3b39d20e74962fa1163c57466edd9eb31295 Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Tue, 8 Jul 2025 22:51:49 +0200
Subject: [PATCH 2671/2677] Revert clBlast version to original reference. Newer
 versions are causing some test failures in the Cholesky decomposition.
 (#3678)

---
 CMakeModules/build_CLBlast.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeModules/build_CLBlast.cmake b/CMakeModules/build_CLBlast.cmake
index a0d9fab435..7ea0b43256 100644
--- a/CMakeModules/build_CLBlast.cmake
+++ b/CMakeModules/build_CLBlast.cmake
@@ -24,9 +24,10 @@ if(TARGET clblast OR AF_WITH_EXTERNAL_PACKAGES_ONLY)
     message(ERROR "CLBlast now found")
   endif()
 else()
+  # This specific reference passes tests
   af_dep_check_and_populate(${clblast_prefix}
     URI https://github.com/cnugteren/CLBlast.git
-    REF 1.6.3
+    REF 4500a03440e2cc54998c0edab366babf5e504d67
   )
 
   include(ExternalProject)

From a699cb9eb2d7f7bfbeca9e00a2b53b26d90b2efb Mon Sep 17 00:00:00 2001
From: Edwin Solis <edwinsolisf12@gmail.com>
Date: Mon, 14 Jul 2025 00:15:53 -0700
Subject: [PATCH 2672/2677] Fixed topk for half, marked sort_index with half
 unsupported

---
 src/backend/opencl/sort_index.cpp |  6 +++++
 src/backend/opencl/topk.cpp       | 44 ++++++++++++++++++++++++++-----
 2 files changed, 44 insertions(+), 6 deletions(-)

diff --git a/src/backend/opencl/sort_index.cpp b/src/backend/opencl/sort_index.cpp
index 4840c24277..afd8bf8413 100644
--- a/src/backend/opencl/sort_index.cpp
+++ b/src/backend/opencl/sort_index.cpp
@@ -25,6 +25,12 @@ namespace opencl {
 template<typename T>
 void sort_index(Array<T> &okey, Array<uint> &oval, const Array<T> &in,
                 const uint dim, bool isAscending) {
+    
+    // TODO: fix half implementation of sort0bykey to support this
+    if (std::is_same_v<T, half>) {
+        OPENCL_NOT_SUPPORTED("sort_index with half");
+    }
+
     try {
         // okey contains values, oval contains indices
         okey = copyArray<T>(in);
diff --git a/src/backend/opencl/topk.cpp b/src/backend/opencl/topk.cpp
index 18e03d2f0d..201ec06197 100644
--- a/src/backend/opencl/topk.cpp
+++ b/src/backend/opencl/topk.cpp
@@ -8,12 +8,17 @@
  ********************************************************/
 
 #include <Array.hpp>
+#include <common/cast.hpp>
 #include <common/half.hpp>
+#include <common/moddims.hpp>
 #include <err_opencl.hpp>
 #include <index.hpp>
 #include <sort.hpp>
 #include <sort_index.hpp>
 #include <types.hpp>
+#include <handle.hpp>
+#include <arith.hpp>
+#include <range.hpp>
 
 #include <algorithm>
 #include <cmath>
@@ -157,12 +162,39 @@ void topk(Array<T>& vals, Array<unsigned>& idxs, const Array<T>& in,
         vals = values;
         idxs = indices;
     } else {
-        auto values  = createEmptyArray<T>(in.dims());
-        auto indices = createEmptyArray<unsigned>(in.dims());
-        sort_index(values, indices, in, dim, order & AF_TOPK_MIN);
-        auto indVec = indexForTopK(k);
-        vals        = index<T>(values, indVec.data());
-        idxs        = index<unsigned>(indices, indVec.data());
+        
+        if (!std::is_same_v<T, half>) {
+            auto values  = createEmptyArray<T>(in.dims());
+            auto indices = createEmptyArray<unsigned>(in.dims());
+            sort_index(values, indices, in, dim, order & AF_TOPK_MIN);
+            auto indVec = indexForTopK(k);
+            idxs        = index<unsigned>(indices, indVec.data());
+            vals        = index<T>(values, indVec.data());
+        } else {
+            // Temporary implementation for topk due half not being supported in sort_index
+            // TODO: Fix sort_index and remove this
+
+            auto values  = createEmptyArray<float>(in.dims());
+            auto indices = createEmptyArray<unsigned>(in.dims());
+            sort_index(values, indices, common::cast<float>(in), dim, order & AF_TOPK_MIN);
+
+            auto indVec = indexForTopK(k);
+            idxs        = index<unsigned>(indices, indVec.data());
+
+            // Index values from original array by using the indices from the previous resuult
+            auto len = in.elements() / in.dims()[dim];
+            auto index_dims = dim4(k, len);
+            auto new_indices = common::flat(arithOp<unsigned, af_add_t>(arithOp<unsigned, af_mul_t>(range<unsigned>(index_dims, 1), createValueArray<unsigned>(index_dims, in.dims()[dim]), index_dims), idxs, index_dims));
+            auto indVecVals = indexForTopK(k);
+            indVecVals[0].idx.arr = getHandle(new_indices);
+            indVecVals[0].isSeq = false;
+            indVecVals[0].isBatch = false;
+            
+            vals = common::modDims(index<T>(common::flat(in), indVecVals.data()), idxs.dims());
+            vals.eval();
+
+            releaseHandle<unsigned>(indVecVals[0].idx.arr);
+        }
     }
 }
 

From 3ae9f0460e1d3dfeb17e10099c3c08b67c46bd8c Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Fri, 18 Jul 2025 01:09:23 +0200
Subject: [PATCH 2673/2677] Fix for evaluation containing array and its
 inverse. (#3671)

A bug was found in the jit calculation tree where under certain circumstances the incorrect result would be found when both an array and a transpose of the same array are used in the same evaluation. When the transpose method is used on an array the treatment depends on the structure of the jit tree of that array. In the particular case that the array is linear (e.g. not a sub-array) and is not simply a buffer (i.e. a combination of buffers and/or scalars with some operators) a moddim node will be added to the root of the tree with the new dimensions. When the tree is evaluated the dimensions of the child buffer(s) of a moddim node are changed and the moddim node is deleted. If an evaluation happens to include both an array and the transpose of that same array then when the moddim node is applied it changes the dimensions of the children for the remaining lifetime of the evaluation including any subsequent use of these nodes. As a result if the transpose of an array is used in an expression followed by the array itself the second instance will also be transposed.

In this fix instead of creating a moddims node a deep copy of the calculation tree is made at that point with the transpose applied to the child buffer nodes. These buffer node copies will have the transposed dimensions and strides but will still point to the same memory location for the data so no copy of the underlying data needs to be made.
---
 src/backend/common/jit/BufferNodeBase.hpp | 12 +++++++
 src/backend/common/jit/Node.hpp           |  5 +++
 src/backend/common/moddims.cpp            | 29 +++++++++-------
 src/backend/cpu/jit/BufferNode.hpp        | 13 ++++++++
 src/backend/cuda/jit/BufferNode.hpp       | 11 ++++++-
 src/backend/oneapi/jit/BufferNode.hpp     | 11 ++++++-
 src/backend/opencl/jit/BufferNode.hpp     | 11 ++++++-
 test/jit.cpp                              | 40 +++++++++++++++++++++++
 8 files changed, 118 insertions(+), 14 deletions(-)

diff --git a/src/backend/common/jit/BufferNodeBase.hpp b/src/backend/common/jit/BufferNodeBase.hpp
index fd63e89932..85576304ad 100644
--- a/src/backend/common/jit/BufferNodeBase.hpp
+++ b/src/backend/common/jit/BufferNodeBase.hpp
@@ -119,6 +119,18 @@ class BufferNodeBase : public common::Node {
         }
         return false;
     }
+
+    virtual void modDims(const af::dim4 &newDim) override {
+        af::dim4 strides(1, 1, 1, 1);
+        for(dim_t i = 1; i < 4; ++i) {
+            strides[i] = strides[i - 1] * newDim[i - 1];
+        }
+
+        for(dim_t i = 0; i < 4; ++i) {
+            m_param.dims[i] = newDim[i];
+            m_param.strides[i] = strides[i];
+        }
+    }
 };
 
 }  // namespace common
diff --git a/src/backend/common/jit/Node.hpp b/src/backend/common/jit/Node.hpp
index 4641ff182c..794c10c14c 100644
--- a/src/backend/common/jit/Node.hpp
+++ b/src/backend/common/jit/Node.hpp
@@ -13,6 +13,7 @@
 #include <optypes.hpp>
 #include <types.hpp>
 #include <af/defines.h>
+#include <af/dim4.hpp>
 
 #include <nonstd/span.hpp>
 #include <algorithm>
@@ -311,6 +312,10 @@ class Node {
     }
     virtual std::unique_ptr<Node> clone() = 0;
 
+    virtual void modDims(const af::dim4 &newDim) {
+        UNUSED(newDim);
+    }
+
 #ifdef AF_CPU
     template<typename U>
     friend void arrayfire::cpu::kernel::evalMultiple(
diff --git a/src/backend/common/moddims.cpp b/src/backend/common/moddims.cpp
index cf9d8d6bb9..25edfa5b0a 100644
--- a/src/backend/common/moddims.cpp
+++ b/src/backend/common/moddims.cpp
@@ -20,21 +20,27 @@ using detail::createNodeArray;
 
 using std::make_shared;
 using std::shared_ptr;
+using std::array;
+using arrayfire::common::Node;
+using arrayfire::common::Node_ptr;
 using std::vector;
 
 namespace arrayfire {
 namespace common {
+
+Node_ptr copyModdims(const Node_ptr &in, const af::dim4 &newDim) {
+
+    Node_ptr out = in->clone();
+    for(int i = 0; i < in->kMaxChildren && in->m_children[i] != nullptr; ++i) {
+        out->m_children[i] = copyModdims(in->m_children[i], newDim);
+    }
+    if(out->isBuffer()) out->modDims(newDim);
+
+    return out;
+}
+
 template<typename T>
 Array<T> moddimOp(const Array<T> &in, af::dim4 outDim) {
-    using arrayfire::common::Node;
-    using arrayfire::common::Node_ptr;
-    using std::array;
-
-    auto createModdim = [outDim](array<Node_ptr, 1> &operands) {
-        return make_shared<ModdimNode>(
-            outDim, static_cast<af::dtype>(af::dtype_traits<T>::af_type),
-            operands[0]);
-    };
 
     const auto &node = in.getNode();
 
@@ -49,8 +55,9 @@ Array<T> moddimOp(const Array<T> &in, af::dim4 outDim) {
     }
     if (all_linear == false) in.eval();
 
-    Node_ptr out = createNaryNode<T, 1>(outDim, createModdim, {&in});
-    return createNodeArray<T>(outDim, out);
+    Array<T> out = createNodeArray<T>(outDim, copyModdims(in.getNode(), outDim));
+
+    return out;
 }
 
 template<typename T>
diff --git a/src/backend/cpu/jit/BufferNode.hpp b/src/backend/cpu/jit/BufferNode.hpp
index 32a94b2a74..ca3cfe7bb5 100644
--- a/src/backend/cpu/jit/BufferNode.hpp
+++ b/src/backend/cpu/jit/BufferNode.hpp
@@ -175,6 +175,19 @@ class BufferNode : public TNode<T> {
         }
         return false;
     }
+
+    virtual void modDims(const af::dim4 &newDim) override {
+        af::dim4 strides(1, 1, 1, 1);
+        for(dim_t i = 1; i < 4; ++i) {
+            strides[i] = strides[i - 1] * newDim[i - 1];
+        }
+
+        for(dim_t i = 0; i < 4; ++i) {
+            m_dims[i] = newDim[i];
+            m_strides[i] = strides[i];
+        }
+    }
+
 };
 
 }  // namespace jit
diff --git a/src/backend/cuda/jit/BufferNode.hpp b/src/backend/cuda/jit/BufferNode.hpp
index 195353fdd8..8692b72515 100644
--- a/src/backend/cuda/jit/BufferNode.hpp
+++ b/src/backend/cuda/jit/BufferNode.hpp
@@ -27,7 +27,16 @@ bool BufferNodeBase<DataType, ParamType>::operator==(
     // clang-format off
     return m_data.get() == other.m_data.get() &&
            m_bytes == other.m_bytes &&
-           m_param.ptr == other.m_param.ptr;
+           m_param.ptr == other.m_param.ptr &&
+           m_linear_buffer == other.m_linear_buffer &&
+           m_param.dims[0] == other.m_param.dims[0] &&
+           m_param.dims[1] == other.m_param.dims[1] &&
+           m_param.dims[2] == other.m_param.dims[2] &&
+           m_param.dims[3] == other.m_param.dims[3] &&
+           m_param.strides[0] == other.m_param.strides[0] &&
+           m_param.strides[1] == other.m_param.strides[1] &&
+           m_param.strides[2] == other.m_param.strides[2] &&
+           m_param.strides[3] == other.m_param.strides[3];
     // clang-format on
 }
 
diff --git a/src/backend/oneapi/jit/BufferNode.hpp b/src/backend/oneapi/jit/BufferNode.hpp
index 94655f23e7..d10ca24cc3 100644
--- a/src/backend/oneapi/jit/BufferNode.hpp
+++ b/src/backend/oneapi/jit/BufferNode.hpp
@@ -31,7 +31,16 @@ bool BufferNodeBase<DataType, ParamType>::operator==(
     // clang-format off
     return m_data.get() == other.m_data.get() &&
            m_bytes == other.m_bytes &&
-           m_param.offset == other.m_param.offset;
+           m_param.offset == other.m_param.offset &&
+           m_linear_buffer == other.m_linear_buffer &&
+           m_param.dims[0] == other.m_param.dims[0] &&
+           m_param.dims[1] == other.m_param.dims[1] &&
+           m_param.dims[2] == other.m_param.dims[2] &&
+           m_param.dims[3] == other.m_param.dims[3] &&
+           m_param.strides[0] == other.m_param.strides[0] &&
+           m_param.strides[1] == other.m_param.strides[1] &&
+           m_param.strides[2] == other.m_param.strides[2] &&
+           m_param.strides[3] == other.m_param.strides[3];
     // clang-format on
 }
 
diff --git a/src/backend/opencl/jit/BufferNode.hpp b/src/backend/opencl/jit/BufferNode.hpp
index e188fb429f..14521030f7 100644
--- a/src/backend/opencl/jit/BufferNode.hpp
+++ b/src/backend/opencl/jit/BufferNode.hpp
@@ -28,7 +28,16 @@ bool BufferNodeBase<DataType, ParamType>::operator==(
     // clang-format off
     return m_data.get() == other.m_data.get() &&
            m_bytes == other.m_bytes &&
-           m_param.offset == other.m_param.offset;
+           m_param.offset == other.m_param.offset &&
+           m_linear_buffer == other.m_linear_buffer &&
+           m_param.dims[0] == other.m_param.dims[0] &&
+           m_param.dims[1] == other.m_param.dims[1] &&
+           m_param.dims[2] == other.m_param.dims[2] &&
+           m_param.dims[3] == other.m_param.dims[3] &&
+           m_param.strides[0] == other.m_param.strides[0] &&
+           m_param.strides[1] == other.m_param.strides[1] &&
+           m_param.strides[2] == other.m_param.strides[2] &&
+           m_param.strides[3] == other.m_param.strides[3];
     // clang-format on
 }
 
diff --git a/test/jit.cpp b/test/jit.cpp
index 3848a22242..487fdcb6e2 100644
--- a/test/jit.cpp
+++ b/test/jit.cpp
@@ -814,3 +814,43 @@ TEST(JIT, setKernelCacheDirectory) {
   // Reset to the old path
   ASSERT_SUCCESS(af_set_kernel_cache_directory(old_path.c_str(), false));
 }
+
+// Ensure that a correct result is obtained when evaluating an expression
+// that contains both an array and its transpose - see ISSUE 3660
+TEST(JIT, evaluateBothArrayAndItsTranspose) {
+  float X2_ptr[25] = { -1.,  -1.,  -1.,  -1.,  -1.,
+                      -0.5, -0.5, -0.5, -0.5, -0.5,
+                        0.,   0.,   0.,   0.,   0.,
+                       0.5,  0.5,  0.5,  0.5,  0.5,
+                        1.,   1.,   1.,   1.,   1. };
+  array X2_gold(5, 5, X2_ptr);
+
+  float Y2_ptr[25] = { -1., -0.5,   0.,  0.5,   1.,
+                       -1., -0.5,   0.,  0.5,   1.,
+                       -1., -0.5,   0.,  0.5,   1.,
+                       -1., -0.5,   0.,  0.5,   1.,
+                       -1., -0.5,   0.,  0.5,   1. };
+  array Y2_gold(5, 5, Y2_ptr);
+
+  float X2Y2_ptr[25] = {  -2., -1.5,  -1., -0.5,  0.,
+                         -1.5,  -1., -0.5,   0., 0.5,
+                          -1., -0.5,   0.,  0.5,  1.,
+                         -0.5,   0.,  0.5,   1., 1.5,
+                           0.,  0.5,   1.,  1.5,  2. };
+  array X2Y2_gold(5, 5, X2Y2_ptr);
+
+  int n = 5;
+  int half = (n - 1) / 2;
+  double delta = 1.0 / half;
+
+  array coord = delta * (af::range(n) - half);
+
+  array X2 = tile(coord.T(), n, 1);
+  array Y2 = tile(coord, 1, n);
+
+  array X2Y2 = X2 + Y2;
+
+  ASSERT_ARRAYS_EQ(X2_gold, X2);
+  ASSERT_ARRAYS_EQ(Y2_gold, Y2);
+  ASSERT_ARRAYS_EQ(X2Y2_gold, X2Y2);
+}

From 3994d1ec8bccd8a01e2a68e1fc9864fa13479307 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Thu, 17 Jul 2025 16:19:26 -0700
Subject: [PATCH 2674/2677] Added release notes for v3.10 (#3681)

* Added release notes for v3.10

* Update ArrayFire version in vckpg json file.

---------

Co-authored-by: Christophe Murphy <christophe.murphy@outlook.com>
---
 docs/pages/release_notes.md | 47 ++++++++++++++++++++++++++++++++++++-
 vcpkg.json                  |  2 +-
 2 files changed, 47 insertions(+), 2 deletions(-)

diff --git a/docs/pages/release_notes.md b/docs/pages/release_notes.md
index 1b55fea448..525542246f 100644
--- a/docs/pages/release_notes.md
+++ b/docs/pages/release_notes.md
@@ -1,6 +1,51 @@
 Release Notes {#releasenotes}
 ==============
 
+v3.10.0
+======
+
+## Improvements
+- Added signed int8 support \PR{3661} \PR{3508} \PR{3507} \PR{3503}
+- Increased support for half (fp16) \PR{3680} \PR{3258} \PR{3561} \PR{3627} \PR{3561} \PR{3627} \PR{3559}
+- Updated oneAPI to use Intel oneAPI (R) 2025.1 \PR{3643} \PR{3573}
+- Updated cl2hpp dependency \PR{3651} \pr{3562}
+- Add support for CUDA 12.3, 12.4, 12.5, 12.6, 12.8, and 12.9 \PR{3657} \PR{3645} \PR{3641} \PR{3636} \PR{3588} \PR{3552} \PR{3586} \PR{3541} 
+- Added minimum driver version check for CUDA GPUs \PR{3648}
+- Add more examples \PR{3530} \PR{3455} \PR{3375} \PR{3612} \PR{3584} \PR{3577}
+- Updated documentation \PR{3496} \PR{3613}
+- Improved performance of matrix multiplication of sparse matrices on the OpenCL backend \PR{3608}
+- Improved cmake configure \PR{3581} \PR{3569} \PR{3567} \PR{3564} \PR{3554}
+- Loosen indexing assertions for assignments \PR{3514}
+
+## Fixes
+- Fix jit tree when doing operations containing moddims and original array \PR{3671} 
+- Fix incorrect behavior of sub-arrays with multiple functions \PR{3679} \PR{3668} \PR{3666} \PR{3665} \PR{3664} \PR{3663} \PR{3658} \PR{3659} \PR{3650} \PR{3611} \PR{3633} \PR{3602}
+- Fix half precision operations in multiple backends \PR{3676} \PR{3662}
+- Fix for join not always respecting the order of parameters \PR{3667} \PR{3513}
+- Fix for cmake building as an external project (needed by arrayfire python wheels) \PR{3669}
+- Fix for cmake build in Windows (including with vcpkg) \PR{3655} \PR{3646} \PR{3644} \PR{3512} \PR{3626} \PR{3566} \PR{3557} \pr{3591} \PR{3592}
+- Fix race condition in OpenCL flood fill \PR{3535}
+- Fix indexing array using sequences `af_seq` that have non-unit steps \PR{3587}
+- Fix padding issue convolve2GradientNN \PR{3519}
+- Fix incorrect axis values for histogram \PR{3590}
+- Fix unified exceptions errors \PR{3617}
+- Fix OpenCL memory migration on devices with different contexts \PR{3510}
+- Fix conversion of COO Sparse to Dense matrix \PR{3589} \PR{3579}
+- Fix `AF_JIT_KERNEL_TRACE` on Windows \PR{3517}
+- Fix cmake build with CUDNN \PR{3521}
+- Fix cmake build with `AF_DISABLE_CPU_ASYNC` \PR{3551}
+
+
+## Contributions
+
+Special thanks to our contributors:
+[Willy Born](https://github.com/willyborn)
+[verstatx](https://github.com/verstatx)
+[Filip Matzner](https://github.com/FloopCZ)
+[Fraser Cormack](https://github.com/frasercrmck)
+[errata-c](https://github.com/errata-c)
+[Tyler Hilbert](https://github.com/Tyler-Hilbert)
+
 v3.9.0
 ======
 
@@ -24,7 +69,7 @@ v3.9.0
 
 ## Fixes
 - Improve Errors when creating OpenCL contexts from devices \PR{3257}
-- Improvements to vcpkg builds \PR{3376 \PR{3476}
+- Improvements to vcpkg builds \PR{3376} \PR{3476}
 - Fix reduce by key when nan's are present \PR{3261}
 - Fix error in convolve where the ndims parameter was forced to be equal to 2
   \PR{3277}
diff --git a/vcpkg.json b/vcpkg.json
index d811275a6f..7b8d9bca2f 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
     "name": "arrayfire",
-    "version": "3.9.0",
+    "version": "3.10.0",
     "homepage": "https://github.com/arrayfire/arrayfire",
     "description": "ArrayFire is a HPC general-purpose library targeting parallel and massively-parallel architectures such as CPUs, GPUs, etc.",
     "supports": "x64",

From d12e298f8feaecd45ecb90135045951b0612992e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 28 Jul 2025 08:40:51 -0700
Subject: [PATCH 2675/2677] not keyword not recognized in msvc (#3684)

---
 test/arrayfire_test.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/arrayfire_test.cpp b/test/arrayfire_test.cpp
index eab07f5b41..687de09aab 100644
--- a/test/arrayfire_test.cpp
+++ b/test/arrayfire_test.cpp
@@ -2063,13 +2063,13 @@ af::array toTempFormat(tempFormat form, const af::array &in) {
     switch (form) {
         case JIT_FORMAT:
             switch (in.type()) {
-                case b8: ret = not(in); break;
+                case b8: ret = !(in); break;
                 default: ret = in * 2;
             }
             // Make sure that the base array is <> form original
             ret.eval();
             switch (in.type()) {
-                case b8: ret = not(ret); break;
+                case b8: ret = !(ret); break;
                 default: ret /= 2;
             }
             break;

From 3d50c357cbb386d3be255c8ef48d6c5656687792 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Edwin=20Lester=20Sol=C3=ADs=20Fuentes?=
 <68087165+edwinsolisf@users.noreply.github.com>
Date: Mon, 28 Jul 2025 08:43:32 -0700
Subject: [PATCH 2676/2677] Fixed dlls not found due to missing search paths
 (#3683)

---
 CMakeModules/FindAF_MKL.cmake | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CMakeModules/FindAF_MKL.cmake b/CMakeModules/FindAF_MKL.cmake
index 18037ca4fc..2da1ed4584 100644
--- a/CMakeModules/FindAF_MKL.cmake
+++ b/CMakeModules/FindAF_MKL.cmake
@@ -310,8 +310,9 @@ function(find_mkl_library)
         $ENV{LIB}
         $ENV{LIBRARY_PATH}
       PATHS
-        ${MKL_ROOT}/bin
-        ${TBB_ROOT}/bin
+        $ENV{MKLROOT}/bin
+        $ENV{TBBROOT}/bin
+        $ENV{ONEAPI_ROOT}/compiler/latest/bin
       PATH_SUFFIXES
         IntelSWTools/compilers_and_libraries/windows/redist/intel64/mkl
         IntelSWTools/compilers_and_libraries/windows/redist/intel64/compiler

From 492718b5a256d4a9d5198fdce89d8fd21772bfda Mon Sep 17 00:00:00 2001
From: Christophe Murphy <72265703+christophe-murphy@users.noreply.github.com>
Date: Mon, 28 Jul 2025 08:44:18 -0700
Subject: [PATCH 2677/2677] Don't restart automatically after installing VC
 redistributable (#3685)

Add flag to VC redistributable installer to prevent it from restarting the computer automatically without prompting the user.

Co-authored-by: Abc <xyz@abc.com>
---
 CMakeModules/nsis/NSIS.template.in | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeModules/nsis/NSIS.template.in b/CMakeModules/nsis/NSIS.template.in
index 3eaad1c383..c46274518c 100644
--- a/CMakeModules/nsis/NSIS.template.in
+++ b/CMakeModules/nsis/NSIS.template.in
@@ -741,7 +741,7 @@ Section "-Core installation"
 SectionEnd
 
 Section "-Visual C++ installation"
-  ExecWait "$INSTDIR\lib\vc_redist.x64.exe /install /passive"
+  ExecWait "$INSTDIR\lib\vc_redist.x64.exe /install /passive /norestart"
   Delete "$INSTDIR\lib\vc_redist.x64.exe"
 SectionEnd